From e379c40375b91caa35e00aa5fa12f29a16655e33 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Fri, 3 Sep 2021 13:04:19 -0400 Subject: [PATCH 001/300] Initial commit --- LICENSE | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) create mode 100644 LICENSE diff --git a/LICENSE b/LICENSE new file mode 100644 index 00000000..28da2627 --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2021 Kaze Wong + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. From 7933b0f32b506d3b16fc3d414eef027be81027a8 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Fri, 3 Sep 2021 13:20:07 -0400 Subject: [PATCH 002/300] add loading GWTC2 data part to PowerLawPlusPeak.py --- GaussianExample.py | 4 ++-- PowerLawPlusPeak.py | 29 +++++++++++++++++++++++++---- 2 files changed, 27 insertions(+), 6 deletions(-) diff --git a/GaussianExample.py b/GaussianExample.py index 965b0335..7d124816 100644 --- a/GaussianExample.py +++ b/GaussianExample.py @@ -22,8 +22,8 @@ def population_likelihood(params, data): return -jnp.inf true_param = [0,1] -data = random.normal(key,shape=(100,))*true_param[1]+true_param[0] -#data = jnp.append(data,10) +data = random.normal(key,shape=(1000,))*true_param[1]+true_param[0] +data = jnp.append(data,10) dLdlambda = grad(population_likelihood)([0.,1.],data) dLdtheta = grad(population_likelihood,argnums=1)([0.,1.],data) diff --git a/PowerLawPlusPeak.py b/PowerLawPlusPeak.py index d89655f0..87988689 100644 --- a/PowerLawPlusPeak.py +++ b/PowerLawPlusPeak.py @@ -10,6 +10,10 @@ key = random.PRNGKey(42) +######################################## +# Defining our model +######################################## + def truncated_power_law(x,alpha,xmin,xmax): norm = (xmax**(1-alpha)-xmin**(1-alpha))/(1-alpha) output = (x**-alpha)/norm @@ -21,8 +25,8 @@ def gaussian(x,mean,sigma): return (1./jnp.sqrt(2*jnp.pi)/sigma)*jnp.exp(-(((x-mean)/sigma)**2)/2) def power_law_plus_peak(x,params): -# Add smoothing later -# Since each component is normalized, the +# !!! Add smoothing later +# Since each component is normalized, the combine pdf should be normalized powerlaw = truncated_power_law(x,params['alpha'],params['xmin'],params['xmax']) peak = gaussian(x,params['mean'],params['sigma']) combine = (1-params['mixing'])*powerlaw+params['mixing']*peak @@ -55,6 +59,10 @@ def population_likelihood(params, data): else: return -jnp.inf +######################################## +# Generating mock data for pipeline testing +######################################## + true_param = {} true_param['alpha'] = 2. true_param['xmin'] = 2. @@ -71,9 +79,11 @@ def population_likelihood(params, data): m1_sample = random.uniform(subkeys[0],shape=(N_sample,1))*98+2 q_sample = random.uniform(subkeys[0],shape=(N_sample,1))*0.99+0.01 z_sample = random.uniform(subkeys[0],shape=(N_sample,1)) - data = jnp.concatenate((m1_sample, q_sample, z_sample), axis=1) +######################################## +# Defining function to compute the selection bias +######################################## O12 = h5py.File('./data/injections_O1O2an_spin.h5','r') O3 = h5py.File('./data/o3a_bbhpop_inj_info.hdf','r') O3_selection= (O3['injections/ifar_gstlal'][()]>1) | (O3['injections/ifar_pycbc_bbh'][()]>1) | (O3['injections/ifar_pycbc_full'][()]>1) @@ -81,10 +91,21 @@ def population_likelihood(params, data): m2 = np.append(O12['mass2_source'][()],O3['injections/mass2_source'][()][O3_selection]) z = np.append(O12['redshift'][()],O3['injections/redshift'][()][O3_selection]) pdraw = np.append(O12['sampling_pdf'][()],O3['injections/sampling_pdf'][()][O3_selection]) -# Maybe missing a jacobian due to m2->q +# !!! Remember to fix the Jacobian going from (m1,m2,z) -> (m1,q,z) samples = np.array([m1,m2/m1,z]).T Ndraw = O3.attrs['total_generated']+7.1*1e7 def evaluate_selection(params,data): likelihood = combine_pdf(params,data) return np.sum(likelihood/pdraw)/Ndraw + +######################################## +# loading GWTC2 data +######################################## + +data = np.load('./data/GWTC12_m1m2z_highsig.npz') +posterior = data['posterior_sample'] +posteiror[...,1] = posterior[...,1]/posterior[...,0] +prior = data['prior'][:,:,0] # !!! Remember to fix the Jacobian going from (m1,m2,z) -> (m1,q,z) +N_event = prior.shape[0] + From 495679a2025fac50be77046152c4d831b80c732e Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Fri, 3 Sep 2021 18:07:23 -0400 Subject: [PATCH 003/300] Full analysis --- PowerLawPlusPeak.py | 60 ++++++++++++++++++++++++++++++++++----------- 1 file changed, 46 insertions(+), 14 deletions(-) diff --git a/PowerLawPlusPeak.py b/PowerLawPlusPeak.py index 87988689..168386e0 100644 --- a/PowerLawPlusPeak.py +++ b/PowerLawPlusPeak.py @@ -17,9 +17,11 @@ def truncated_power_law(x,alpha,xmin,xmax): norm = (xmax**(1-alpha)-xmin**(1-alpha))/(1-alpha) output = (x**-alpha)/norm - output = index_update(output,((xxmax)),-jnp.inf) + output = index_update(output,((xxmax)),0) return output +truncated_power_law = jit(truncated_power_law) + @jit def gaussian(x,mean,sigma): return (1./jnp.sqrt(2*jnp.pi)/sigma)*jnp.exp(-(((x-mean)/sigma)**2)/2) @@ -37,6 +39,7 @@ def power_law_plus_peak(x,params): z_axis = jnp.linspace(z_range[0],z_range[1],10000) dVdz = jnp.array(Planck15.differential_comoving_volume(z_axis).value/1e9) +@jit def redshift_distribution(z,kappa): dVdz_local = jnp.interp(z,z_axis,dVdz) norm_z = jnp.trapz((1+z_axis)**(kappa-1)*jnp.array(dVdz)) @@ -51,9 +54,10 @@ def combine_pdf(params,data): p_z = redshift_distribution(z,params['kappa']) return p_m1*p_q*p_z -def population_likelihood(params, data): - combine_pdf = combine_pdf(params,data) - output = jnp.sum(jnp.log(combine_pdf)) +def population_likelihood(params, data, prior): + combine_pdf_local = combine_pdf(params,data) + selection_bias = evaluate_selection(params,selection_samples) + output = jnp.sum(jnp.log(jnp.mean(combine_pdf_local/prior/selection_bias,axis=1))) if jnp.isfinite(output): return output else: @@ -64,13 +68,13 @@ def population_likelihood(params, data): ######################################## true_param = {} -true_param['alpha'] = 2. -true_param['xmin'] = 2. -true_param['xmax'] = 100. -true_param['mean'] = 30. -true_param['sigma'] = 1. -true_param['mixing'] = 0.5 -true_param['beta'] = 2. +true_param['alpha'] = 2.63 +true_param['beta'] = 1.26 +true_param['xmin'] = 4.59 +true_param['xmax'] = 86.22 +true_param['mean'] = 33.07 +true_param['sigma'] = 5.69 +true_param['mixing'] = 0.1 true_param['kappa'] = 0. N_sample = 1000 @@ -91,13 +95,14 @@ def population_likelihood(params, data): m2 = np.append(O12['mass2_source'][()],O3['injections/mass2_source'][()][O3_selection]) z = np.append(O12['redshift'][()],O3['injections/redshift'][()][O3_selection]) pdraw = np.append(O12['sampling_pdf'][()],O3['injections/sampling_pdf'][()][O3_selection]) +pdraw = pdraw/m1 # !!! Remember to fix the Jacobian going from (m1,m2,z) -> (m1,q,z) -samples = np.array([m1,m2/m1,z]).T +selection_samples = np.array([m1,m2/m1,z]).T Ndraw = O3.attrs['total_generated']+7.1*1e7 def evaluate_selection(params,data): likelihood = combine_pdf(params,data) - return np.sum(likelihood/pdraw)/Ndraw + return jnp.sum(likelihood/pdraw)/Ndraw ######################################## # loading GWTC2 data @@ -105,7 +110,34 @@ def evaluate_selection(params,data): data = np.load('./data/GWTC12_m1m2z_highsig.npz') posterior = data['posterior_sample'] -posteiror[...,1] = posterior[...,1]/posterior[...,0] +posterior[...,1] = posterior[...,1]/posterior[...,0] prior = data['prior'][:,:,0] # !!! Remember to fix the Jacobian going from (m1,m2,z) -> (m1,q,z) +prior = prior/posterior[:,:,0] N_event = prior.shape[0] +######################################## +# Checking Gradient +######################################## + +def make_param(alpha=2.63,beta=1.26,xmin=3.59,xmax=86.22,mixing=0.1,mean=33.07,sigma=5.69,kappa=0.): + param = {} + param['alpha'] = alpha + param['xmin'] = xmin + param['xmax'] = xmax + param['mean'] = mean + param['sigma'] = sigma + param['mixing'] = mixing + param['beta'] = beta + param['kappa'] = kappa + return param + + + +def compute_dLdt(alpha,beta,xmin,xmax,mixing,mean,sigma,kappa=0.): + param = make_param(alpha,beta,xmin,xmax,mixing,mean,sigma,kappa) + + L = population_likelihood(param,posterior,prior) + dLdlambda = jnp.stack(list(grad(population_likelihood)(param,posterior,prior).values())) + dLdtheta = grad(population_likelihood,argnums=1)(param,posterior,prior) + return L, dLdtheta[None]/dLdlambda.reshape(-1,1,1,1) + From 5337bb849abf0ed6e6c52449247579d3c5b697d6 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Fri, 3 Sep 2021 20:13:09 -0400 Subject: [PATCH 004/300] Add error and joint optimization to Gaussian example --- GaussianExample.py | 54 +++++++++++++++++++++++++++++---------------- PowerLawPlusPeak.py | 2 +- 2 files changed, 36 insertions(+), 20 deletions(-) diff --git a/GaussianExample.py b/GaussianExample.py index 7d124816..6daf8adc 100644 --- a/GaussianExample.py +++ b/GaussianExample.py @@ -1,31 +1,47 @@ import numpy as np import jax.numpy as jnp -from jax import random, grad, jit, vmap +from jax import random, grad, jit, vmap, value_and_grad +from jax.experimental.optimizers import adam -key = random.PRNGKey(42) +key, *sub_keys = random.split(random.PRNGKey(32),num=3) +@jit def gaussian(x,mean,sigma): return (1./jnp.sqrt(2*jnp.pi)/sigma)*jnp.exp(-(((x-mean)/sigma)**2)/2) -def population_likelihood(params, data): - # Checkpoint 1, what are these lines doing - if (params[0]>10) or (params[0]<-10): - return -jnp.inf - if (params[1]>5) or (params[1]<0): - return -jnp.inf - # End of Checkpoint 1 - output = jnp.sum(jnp.log(gaussian(data,params[0],params[1]))) # Checkpoint 2, what is this line doing? How does it compared to the full form we have in the introduction? - if jnp.isfinite(output): - return output - else: - return -jnp.inf +@jit +def population_likelihood(params,data): + return -jnp.sum(jnp.log(gaussian(data,params[0],params[1]))) +@jit +def population_likelihood_event(point,params,obs_std,data): + return -jnp.sum(jnp.log(gaussian(point,data,obs_std)*gaussian(data,params[0],params[1]))) + + +N_obs = 2000 true_param = [0,1] -data = random.normal(key,shape=(1000,))*true_param[1]+true_param[0] -data = jnp.append(data,10) +obs_std = 0.2 +true_data = random.normal(sub_keys[0],shape=(N_obs,))*true_param[1]+true_param[0] +obs_data = true_data+random.normal(sub_keys[1],shape=(N_obs,))*obs_std +#obs_data = jnp.append(obs_data,10) + + +learning_rate = 1e-1 +opt_init, opt_update, get_params = adam(learning_rate) +opt_state = opt_init((obs_data,[jnp.array(1.),jnp.array(2.)])) + +def step(step, opt_state): + params = get_params(opt_state) + value, grads = value_and_grad(population_likelihood_event,argnums=(0,1))(params[0],params[1], obs_std, obs_data) + opt_state = opt_update(step, grads, opt_state) + return value, opt_state -dLdlambda = grad(population_likelihood)([0.,1.],data) -dLdtheta = grad(population_likelihood,argnums=1)([0.,1.],data) +for i in range(200): + value, opt_state = step(i, opt_state) + print(value,get_params(opt_state)[1]) -dlambdadtheta = (dLdtheta[None,:]/jnp.array(dLdlambda)[:,None]).mean(axis=0) +#dLdlambda = grad(population_likelihood)([0.,1.],data) +#dLdtheta = grad(population_likelihood,argnums=1)([0.,1.],data) +# +#dlambdadtheta = (dLdtheta[None,:]/jnp.array(dLdlambda)[:,None]).mean(axis=0) diff --git a/PowerLawPlusPeak.py b/PowerLawPlusPeak.py index 168386e0..2a10b4d7 100644 --- a/PowerLawPlusPeak.py +++ b/PowerLawPlusPeak.py @@ -20,7 +20,7 @@ def truncated_power_law(x,alpha,xmin,xmax): output = index_update(output,((xxmax)),0) return output -truncated_power_law = jit(truncated_power_law) +#truncated_power_law = jit(truncated_power_law) @jit def gaussian(x,mean,sigma): From a94d8970aafd887567ee2aaae9feacaddf2acfac Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Thu, 9 Sep 2021 10:36:04 -0400 Subject: [PATCH 005/300] Hessian of the likelihood seems be sensitive to potential subpopulation --- GaussianExample.py | 47 ++++++++++++++++++++++++++++++---------------- 1 file changed, 31 insertions(+), 16 deletions(-) diff --git a/GaussianExample.py b/GaussianExample.py index 6daf8adc..91485d95 100644 --- a/GaussianExample.py +++ b/GaussianExample.py @@ -1,14 +1,23 @@ import numpy as np import jax.numpy as jnp -from jax import random, grad, jit, vmap, value_and_grad +from jax import random, grad, jit, vmap, value_and_grad, jacfwd, jacrev, hessian from jax.experimental.optimizers import adam +import matplotlib.pyplot as plt -key, *sub_keys = random.split(random.PRNGKey(32),num=3) - +key, *sub_keys = random.split(random.PRNGKey(32),num=4) @jit def gaussian(x,mean,sigma): - return (1./jnp.sqrt(2*jnp.pi)/sigma)*jnp.exp(-(((x-mean)/sigma)**2)/2) + return (1./jnp.sqrt(2*jnp.pi)/sigma)*jnp.exp(-(((x-mean)/sigma)**2)/2) + +@jit +def log_gaussian(x,mean,sigma): + return jnp.log(gaussian(x,mean,sigma)) + + +@jit +def sum_log_gaussian(x,mean,sigma): + return jnp.sum(jnp.log(gaussian(x,mean,sigma))) @jit def population_likelihood(params,data): @@ -16,20 +25,21 @@ def population_likelihood(params,data): @jit def population_likelihood_event(point,params,obs_std,data): - return -jnp.sum(jnp.log(gaussian(point,data,obs_std)*gaussian(data,params[0],params[1]))) + return -jnp.sum(jnp.log(gaussian(point[:,None],data,obs_std)*gaussian(data,params[0],params[1]))) -N_obs = 2000 -true_param = [0,1] -obs_std = 0.2 -true_data = random.normal(sub_keys[0],shape=(N_obs,))*true_param[1]+true_param[0] -obs_data = true_data+random.normal(sub_keys[1],shape=(N_obs,))*obs_std -#obs_data = jnp.append(obs_data,10) +N_obs = 10000 +N_subpop = 0#1000 +true_param = jnp.array([0.,5]) +obs_std = 0.05 +true_data = (random.normal(sub_keys[0],shape=(N_obs,))*true_param[1]+true_param[0]) +true_data = jnp.append(true_data,(random.normal(sub_keys[1],shape=(N_subpop,))*0.1-5)) +obs_data = true_data[:,None]+random.normal(sub_keys[2],shape=(N_obs+N_subpop,100))*obs_std learning_rate = 1e-1 opt_init, opt_update, get_params = adam(learning_rate) -opt_state = opt_init((obs_data,[jnp.array(1.),jnp.array(2.)])) +opt_state = opt_init((true_data,[jnp.array(10.),jnp.array(10.)])) def step(step, opt_state): params = get_params(opt_state) @@ -41,7 +51,12 @@ def step(step, opt_state): value, opt_state = step(i, opt_state) print(value,get_params(opt_state)[1]) -#dLdlambda = grad(population_likelihood)([0.,1.],data) -#dLdtheta = grad(population_likelihood,argnums=1)([0.,1.],data) -# -#dlambdadtheta = (dLdtheta[None,:]/jnp.array(dLdlambda)[:,None]).mean(axis=0) +best_x, best_lambda = get_params(opt_state) + +dlambdadtheta = jacfwd(jacrev(population_likelihood_event),argnums=1)(best_x,best_lambda,obs_std,obs_data) +#dthetadlambda = jacfwd(jacrev(population_likelihood_event,argnums=1))(best_x,best_lambda,obs_std,obs_data) + +fig,ax = plt.subplots(1,2,figsize=(20,9)) +ax[0].plot(dlambdadtheta[0]) +ax[1].plot(dlambdadtheta[1]) +fig.show() From 8952862fccd2d885567f425e06d14abc508cb470 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Thu, 9 Sep 2021 10:45:15 -0400 Subject: [PATCH 006/300] Copy PowerLawPlusPeak model to GWTC2 analysis --- GWTC2.py | 143 +++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 143 insertions(+) create mode 100644 GWTC2.py diff --git a/GWTC2.py b/GWTC2.py new file mode 100644 index 00000000..2a10b4d7 --- /dev/null +++ b/GWTC2.py @@ -0,0 +1,143 @@ +import numpy as np +import jax.numpy as jnp +import copy +import astropy.units as u +import h5py +from jax import random, grad, jit, vmap +from jax.ops import index_update +from astropy.cosmology import Planck15 +from scipy.interpolate import interp1d + +key = random.PRNGKey(42) + +######################################## +# Defining our model +######################################## + +def truncated_power_law(x,alpha,xmin,xmax): + norm = (xmax**(1-alpha)-xmin**(1-alpha))/(1-alpha) + output = (x**-alpha)/norm + output = index_update(output,((xxmax)),0) + return output + +#truncated_power_law = jit(truncated_power_law) + +@jit +def gaussian(x,mean,sigma): + return (1./jnp.sqrt(2*jnp.pi)/sigma)*jnp.exp(-(((x-mean)/sigma)**2)/2) + +def power_law_plus_peak(x,params): +# !!! Add smoothing later +# Since each component is normalized, the combine pdf should be normalized + powerlaw = truncated_power_law(x,params['alpha'],params['xmin'],params['xmax']) + peak = gaussian(x,params['mean'],params['sigma']) + combine = (1-params['mixing'])*powerlaw+params['mixing']*peak + return combine + + +z_range = [0.,1] +z_axis = jnp.linspace(z_range[0],z_range[1],10000) +dVdz = jnp.array(Planck15.differential_comoving_volume(z_axis).value/1e9) + +@jit +def redshift_distribution(z,kappa): + dVdz_local = jnp.interp(z,z_axis,dVdz) + norm_z = jnp.trapz((1+z_axis)**(kappa-1)*jnp.array(dVdz)) + return (1+z)**kappa*dVdz_local/norm_z + +def combine_pdf(params,data): + m1 = data[..., 0] + q = data[..., 1] + z = data[..., 2] + p_m1 = power_law_plus_peak(m1,params) + p_q = truncated_power_law(q,params['beta'],0.01,1) + p_z = redshift_distribution(z,params['kappa']) + return p_m1*p_q*p_z + +def population_likelihood(params, data, prior): + combine_pdf_local = combine_pdf(params,data) + selection_bias = evaluate_selection(params,selection_samples) + output = jnp.sum(jnp.log(jnp.mean(combine_pdf_local/prior/selection_bias,axis=1))) + if jnp.isfinite(output): + return output + else: + return -jnp.inf + +######################################## +# Generating mock data for pipeline testing +######################################## + +true_param = {} +true_param['alpha'] = 2.63 +true_param['beta'] = 1.26 +true_param['xmin'] = 4.59 +true_param['xmax'] = 86.22 +true_param['mean'] = 33.07 +true_param['sigma'] = 5.69 +true_param['mixing'] = 0.1 +true_param['kappa'] = 0. + +N_sample = 1000 + +key, *subkeys = random.split(key,num=4) +m1_sample = random.uniform(subkeys[0],shape=(N_sample,1))*98+2 +q_sample = random.uniform(subkeys[0],shape=(N_sample,1))*0.99+0.01 +z_sample = random.uniform(subkeys[0],shape=(N_sample,1)) +data = jnp.concatenate((m1_sample, q_sample, z_sample), axis=1) + +######################################## +# Defining function to compute the selection bias +######################################## +O12 = h5py.File('./data/injections_O1O2an_spin.h5','r') +O3 = h5py.File('./data/o3a_bbhpop_inj_info.hdf','r') +O3_selection= (O3['injections/ifar_gstlal'][()]>1) | (O3['injections/ifar_pycbc_bbh'][()]>1) | (O3['injections/ifar_pycbc_full'][()]>1) +m1 = np.append(O12['mass1_source'][()],O3['injections/mass1_source'][()][O3_selection]) +m2 = np.append(O12['mass2_source'][()],O3['injections/mass2_source'][()][O3_selection]) +z = np.append(O12['redshift'][()],O3['injections/redshift'][()][O3_selection]) +pdraw = np.append(O12['sampling_pdf'][()],O3['injections/sampling_pdf'][()][O3_selection]) +pdraw = pdraw/m1 +# !!! Remember to fix the Jacobian going from (m1,m2,z) -> (m1,q,z) +selection_samples = np.array([m1,m2/m1,z]).T +Ndraw = O3.attrs['total_generated']+7.1*1e7 + +def evaluate_selection(params,data): + likelihood = combine_pdf(params,data) + return jnp.sum(likelihood/pdraw)/Ndraw + +######################################## +# loading GWTC2 data +######################################## + +data = np.load('./data/GWTC12_m1m2z_highsig.npz') +posterior = data['posterior_sample'] +posterior[...,1] = posterior[...,1]/posterior[...,0] +prior = data['prior'][:,:,0] # !!! Remember to fix the Jacobian going from (m1,m2,z) -> (m1,q,z) +prior = prior/posterior[:,:,0] +N_event = prior.shape[0] + +######################################## +# Checking Gradient +######################################## + +def make_param(alpha=2.63,beta=1.26,xmin=3.59,xmax=86.22,mixing=0.1,mean=33.07,sigma=5.69,kappa=0.): + param = {} + param['alpha'] = alpha + param['xmin'] = xmin + param['xmax'] = xmax + param['mean'] = mean + param['sigma'] = sigma + param['mixing'] = mixing + param['beta'] = beta + param['kappa'] = kappa + return param + + + +def compute_dLdt(alpha,beta,xmin,xmax,mixing,mean,sigma,kappa=0.): + param = make_param(alpha,beta,xmin,xmax,mixing,mean,sigma,kappa) + + L = population_likelihood(param,posterior,prior) + dLdlambda = jnp.stack(list(grad(population_likelihood)(param,posterior,prior).values())) + dLdtheta = grad(population_likelihood,argnums=1)(param,posterior,prior) + return L, dLdtheta[None]/dLdlambda.reshape(-1,1,1,1) + From b541b62f148a656730b093539d1337aac4dd1d46 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Thu, 9 Sep 2021 11:56:12 -0400 Subject: [PATCH 007/300] Bug fix in Gaussian example, hessian is a very strong indicator of outliers now. Add power law with smooth cutoff to PowerLawPlusPeak.py --- GaussianExample.py | 10 +- PowerLawPlusPeak.py | 225 ++++++++++++++++++++++++++------------------ 2 files changed, 139 insertions(+), 96 deletions(-) diff --git a/GaussianExample.py b/GaussianExample.py index 91485d95..fa616065 100644 --- a/GaussianExample.py +++ b/GaussianExample.py @@ -25,13 +25,13 @@ def population_likelihood(params,data): @jit def population_likelihood_event(point,params,obs_std,data): - return -jnp.sum(jnp.log(gaussian(point[:,None],data,obs_std)*gaussian(data,params[0],params[1]))) + return -jnp.sum(jnp.log(gaussian(data,point[:,None],obs_std)*gaussian(point[:,None],params[0],params[1]))) -N_obs = 10000 -N_subpop = 0#1000 -true_param = jnp.array([0.,5]) -obs_std = 0.05 +N_obs = 1000 +N_subpop = 1000 +true_param = jnp.array([0.,1]) +obs_std = 0.1 true_data = (random.normal(sub_keys[0],shape=(N_obs,))*true_param[1]+true_param[0]) true_data = jnp.append(true_data,(random.normal(sub_keys[1],shape=(N_subpop,))*0.1-5)) obs_data = true_data[:,None]+random.normal(sub_keys[2],shape=(N_obs+N_subpop,100))*obs_std diff --git a/PowerLawPlusPeak.py b/PowerLawPlusPeak.py index 2a10b4d7..e23a728a 100644 --- a/PowerLawPlusPeak.py +++ b/PowerLawPlusPeak.py @@ -3,7 +3,7 @@ import copy import astropy.units as u import h5py -from jax import random, grad, jit, vmap +from jax import random, grad, jit, vmap, jacfwd, jacrev from jax.ops import index_update from astropy.cosmology import Planck15 from scipy.interpolate import interp1d @@ -20,51 +20,35 @@ def truncated_power_law(x,alpha,xmin,xmax): output = index_update(output,((xxmax)),0) return output -#truncated_power_law = jit(truncated_power_law) + +# Since truncated power law is not differentiable, we choose tanh as a smoother cutoff +x_axis = jnp.linspace(1,150,100000) +@jit +def power_law_tanh(x,alpha,xmin,xmax): + lower_window = (jnp.tanh(x-xmin)+1)/2 + upper_window = -(jnp.tanh(x-xmax)-1)/2 + power_law = x**-alpha + output_unnorm = power_law*lower_window*upper_window + # This normalization factor is supposed to be a good approximation but not perfect + norm = jnp.trapz(x_axis**-alpha*(jnp.tanh(x_axis-xmin)+1)/2*(-(jnp.tanh(x_axis-xmax)-1)/2),x=x_axis) + output = output_unnorm/norm + return output @jit def gaussian(x,mean,sigma): return (1./jnp.sqrt(2*jnp.pi)/sigma)*jnp.exp(-(((x-mean)/sigma)**2)/2) +@jit def power_law_plus_peak(x,params): # !!! Add smoothing later # Since each component is normalized, the combine pdf should be normalized - powerlaw = truncated_power_law(x,params['alpha'],params['xmin'],params['xmax']) + powerlaw = power_law_tanh(x,params['alpha'],params['xmin'],params['xmax']) peak = gaussian(x,params['mean'],params['sigma']) combine = (1-params['mixing'])*powerlaw+params['mixing']*peak return combine - -z_range = [0.,1] -z_axis = jnp.linspace(z_range[0],z_range[1],10000) -dVdz = jnp.array(Planck15.differential_comoving_volume(z_axis).value/1e9) - -@jit -def redshift_distribution(z,kappa): - dVdz_local = jnp.interp(z,z_axis,dVdz) - norm_z = jnp.trapz((1+z_axis)**(kappa-1)*jnp.array(dVdz)) - return (1+z)**kappa*dVdz_local/norm_z - -def combine_pdf(params,data): - m1 = data[..., 0] - q = data[..., 1] - z = data[..., 2] - p_m1 = power_law_plus_peak(m1,params) - p_q = truncated_power_law(q,params['beta'],0.01,1) - p_z = redshift_distribution(z,params['kappa']) - return p_m1*p_q*p_z - -def population_likelihood(params, data, prior): - combine_pdf_local = combine_pdf(params,data) - selection_bias = evaluate_selection(params,selection_samples) - output = jnp.sum(jnp.log(jnp.mean(combine_pdf_local/prior/selection_bias,axis=1))) - if jnp.isfinite(output): - return output - else: - return -jnp.inf - ######################################## -# Generating mock data for pipeline testing +# Sampling data ######################################## true_param = {} @@ -75,69 +59,128 @@ def population_likelihood(params, data, prior): true_param['mean'] = 33.07 true_param['sigma'] = 5.69 true_param['mixing'] = 0.1 -true_param['kappa'] = 0. N_sample = 1000 -key, *subkeys = random.split(key,num=4) -m1_sample = random.uniform(subkeys[0],shape=(N_sample,1))*98+2 -q_sample = random.uniform(subkeys[0],shape=(N_sample,1))*0.99+0.01 -z_sample = random.uniform(subkeys[0],shape=(N_sample,1)) -data = jnp.concatenate((m1_sample, q_sample, z_sample), axis=1) - -######################################## -# Defining function to compute the selection bias -######################################## -O12 = h5py.File('./data/injections_O1O2an_spin.h5','r') -O3 = h5py.File('./data/o3a_bbhpop_inj_info.hdf','r') -O3_selection= (O3['injections/ifar_gstlal'][()]>1) | (O3['injections/ifar_pycbc_bbh'][()]>1) | (O3['injections/ifar_pycbc_full'][()]>1) -m1 = np.append(O12['mass1_source'][()],O3['injections/mass1_source'][()][O3_selection]) -m2 = np.append(O12['mass2_source'][()],O3['injections/mass2_source'][()][O3_selection]) -z = np.append(O12['redshift'][()],O3['injections/redshift'][()][O3_selection]) -pdraw = np.append(O12['sampling_pdf'][()],O3['injections/sampling_pdf'][()][O3_selection]) -pdraw = pdraw/m1 -# !!! Remember to fix the Jacobian going from (m1,m2,z) -> (m1,q,z) -selection_samples = np.array([m1,m2/m1,z]).T -Ndraw = O3.attrs['total_generated']+7.1*1e7 - -def evaluate_selection(params,data): - likelihood = combine_pdf(params,data) - return jnp.sum(likelihood/pdraw)/Ndraw - -######################################## -# loading GWTC2 data -######################################## - -data = np.load('./data/GWTC12_m1m2z_highsig.npz') -posterior = data['posterior_sample'] -posterior[...,1] = posterior[...,1]/posterior[...,0] -prior = data['prior'][:,:,0] # !!! Remember to fix the Jacobian going from (m1,m2,z) -> (m1,q,z) -prior = prior/posterior[:,:,0] -N_event = prior.shape[0] +m1_sample = jnp.empty(0) -######################################## -# Checking Gradient -######################################## - -def make_param(alpha=2.63,beta=1.26,xmin=3.59,xmax=86.22,mixing=0.1,mean=33.07,sigma=5.69,kappa=0.): - param = {} - param['alpha'] = alpha - param['xmin'] = xmin - param['xmax'] = xmax - param['mean'] = mean - param['sigma'] = sigma - param['mixing'] = mixing - param['beta'] = beta - param['kappa'] = kappa - return param +while m1_sample.shape[0]1) | (O3['injections/ifar_pycbc_bbh'][()]>1) | (O3['injections/ifar_pycbc_full'][()]>1) +#m1 = np.append(O12['mass1_source'][()],O3['injections/mass1_source'][()][O3_selection]) +#m2 = np.append(O12['mass2_source'][()],O3['injections/mass2_source'][()][O3_selection]) +#z = np.append(O12['redshift'][()],O3['injections/redshift'][()][O3_selection]) +#pdraw = np.append(O12['sampling_pdf'][()],O3['injections/sampling_pdf'][()][O3_selection]) +#pdraw = pdraw/m1 +## !!! Remember to fix the Jacobian going from (m1,m2,z) -> (m1,q,z) +#selection_samples = np.array([m1,m2/m1,z]).T +#Ndraw = O3.attrs['total_generated']+7.1*1e7 +# +#def evaluate_selection(params,data): +# likelihood = combine_pdf(params,data) +# return jnp.sum(likelihood/pdraw)/Ndraw +# +######################################### +## loading GWTC2 data +######################################### +# +#data = np.load('./data/GWTC12_m1m2z_highsig.npz') +#posterior = data['posterior_sample'] +#posterior[...,1] = posterior[...,1]/posterior[...,0] +#prior = data['prior'][:,:,0] # !!! Remember to fix the Jacobian going from (m1,m2,z) -> (m1,q,z) +#prior = prior/posterior[:,:,0] +#N_event = prior.shape[0] +# +######################################### +## Checking Gradient +######################################### +# +#def make_param(alpha=2.63,beta=1.26,xmin=3.59,xmax=86.22,mixing=0.1,mean=33.07,sigma=5.69,kappa=0.): +# param = {} +# param['alpha'] = alpha +# param['xmin'] = xmin +# param['xmax'] = xmax +# param['mean'] = mean +# param['sigma'] = sigma +# param['mixing'] = mixing +# param['beta'] = beta +# param['kappa'] = kappa +# return param +# +# +# +#def compute_dLdt(alpha,beta,xmin,xmax,mixing,mean,sigma,kappa=0.): +# param = make_param(alpha,beta,xmin,xmax,mixing,mean,sigma,kappa) +# +# L = population_likelihood(param,posterior,prior) +# dLdlambda = jnp.stack(list(grad(population_likelihood)(param,posterior,prior).values())) +# dLdtheta = grad(population_likelihood,argnums=1)(param,posterior,prior) +# return L, dLdtheta[None]/dLdlambda.reshape(-1,1,1,1) +# From 304b55792b28b51a1413f89389740482be95731e Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Tue, 21 Sep 2021 10:42:08 -0400 Subject: [PATCH 008/300] Working Gaussian and power law plus peak version --- GWTC2.py | 20 +++- GaussianExample.py | 51 ++++++++- PowerLawPlusPeak.py | 247 ++++++++++++++++++++++---------------------- 3 files changed, 187 insertions(+), 131 deletions(-) diff --git a/GWTC2.py b/GWTC2.py index 2a10b4d7..17f253e6 100644 --- a/GWTC2.py +++ b/GWTC2.py @@ -3,10 +3,11 @@ import copy import astropy.units as u import h5py -from jax import random, grad, jit, vmap +from jax import random, grad, jit, vmap, value_and_grad from jax.ops import index_update from astropy.cosmology import Planck15 from scipy.interpolate import interp1d +from jax.experimental.optimizers import adam key = random.PRNGKey(42) @@ -141,3 +142,20 @@ def compute_dLdt(alpha,beta,xmin,xmax,mixing,mean,sigma,kappa=0.): dLdtheta = grad(population_likelihood,argnums=1)(param,posterior,prior) return L, dLdtheta[None]/dLdlambda.reshape(-1,1,1,1) + +learning_rate = 1e-1 +opt_init, opt_update, get_params = adam(learning_rate) +opt_state = opt_init((true_param)) + +def step(step, opt_state): + params = get_params(opt_state) + value, grads = value_and_grad(population_likelihood)(params, posterior, prior) + opt_state = opt_update(step, grads, opt_state) + return value, opt_state + +for i in range(200): + value, opt_state = step(i, opt_state) + if jnp.isnan(value): + break + print(value,get_params(opt_state)) + diff --git a/GaussianExample.py b/GaussianExample.py index fa616065..05799d92 100644 --- a/GaussianExample.py +++ b/GaussianExample.py @@ -3,6 +3,29 @@ from jax import random, grad, jit, vmap, value_and_grad, jacfwd, jacrev, hessian from jax.experimental.optimizers import adam import matplotlib.pyplot as plt +import matplotlib as mpl +params = {'axes.labelsize': 32, + 'font.family': 'serif', + 'font.serif': 'Computer Modern Raman', + 'font.size': 32, + 'axes.linewidth': 2, + 'legend.fontsize': 28, + 'xtick.labelsize': 28, + 'xtick.top': True, + 'xtick.direction': "in", + 'ytick.labelsize': 20, + 'ytick.right': True, + 'ytick.direction': "in", + 'axes.grid' : False, + 'text.usetex': True, + 'savefig.dpi' : 100, + 'lines.markersize' : 14, +# 'axes.formatter.useoffset': False, + 'axes.formatter.limits' : (-3,3)} + +mpl.rcParams['text.latex.preamble'] = [r'\usepackage{amsmath}'] #for \text command + +mpl.rcParams.update(params) key, *sub_keys = random.split(random.PRNGKey(32),num=4) @@ -29,13 +52,16 @@ def population_likelihood_event(point,params,obs_std,data): N_obs = 1000 -N_subpop = 1000 +N_subpop = 100 true_param = jnp.array([0.,1]) obs_std = 0.1 true_data = (random.normal(sub_keys[0],shape=(N_obs,))*true_param[1]+true_param[0]) -true_data = jnp.append(true_data,(random.normal(sub_keys[1],shape=(N_subpop,))*0.1-5)) +true_data = jnp.append(true_data,(random.normal(sub_keys[1],shape=(N_subpop,))*0.1)) obs_data = true_data[:,None]+random.normal(sub_keys[2],shape=(N_obs+N_subpop,100))*obs_std +index = np.random.choice(np.arange(N_obs+N_subpop),replace=False,size=N_obs+N_subpop) +obs_data = obs_data[index] +true_data = true_data[index] learning_rate = 1e-1 opt_init, opt_update, get_params = adam(learning_rate) @@ -56,7 +82,22 @@ def step(step, opt_state): dlambdadtheta = jacfwd(jacrev(population_likelihood_event),argnums=1)(best_x,best_lambda,obs_std,obs_data) #dthetadlambda = jacfwd(jacrev(population_likelihood_event,argnums=1))(best_x,best_lambda,obs_std,obs_data) -fig,ax = plt.subplots(1,2,figsize=(20,9)) -ax[0].plot(dlambdadtheta[0]) -ax[1].plot(dlambdadtheta[1]) + + +fig,ax = plt.subplots(1,3,figsize=(30,9)) +ax[0].hist(true_data,bins=50,density=True,histtype='step',lw=3,label='Truth') +axis = np.linspace(ax[0].get_xlim()[0],ax[0].get_xlim()[1],1000) +ax[0].plot(axis,gaussian(axis,best_lambda[0],best_lambda[1]),label='Best fitted') +ax[0].set_ylabel(r'$p(x)$') +ax[0].set_xlabel(r'$x$') +ax[0].legend(loc='upper right') +ax[1].plot(dlambdadtheta[0],label='Raw') +ax[1].plot(dlambdadtheta[0][np.argsort(dlambdadtheta[0])],label='sorted',lw=5) +ax[1].legend(loc='upper left') +ax[1].set_xlabel('Event number') +ax[1].set_ylabel(r'$\frac{\partial^2\mathcal{L}}{\partial \theta \partial \mu}$') +ax[2].plot(dlambdadtheta[1]) +ax[2].plot(dlambdadtheta[1][np.argsort(dlambdadtheta[1])],lw=5) +ax[2].set_xlabel('Event number') +ax[2].set_ylabel(r'$\frac{\partial^2\mathcal{L}}{\partial \theta \partial \sigma}$') fig.show() diff --git a/PowerLawPlusPeak.py b/PowerLawPlusPeak.py index e23a728a..61d5687d 100644 --- a/PowerLawPlusPeak.py +++ b/PowerLawPlusPeak.py @@ -1,12 +1,34 @@ -import numpy as np import jax.numpy as jnp import copy -import astropy.units as u -import h5py -from jax import random, grad, jit, vmap, jacfwd, jacrev -from jax.ops import index_update -from astropy.cosmology import Planck15 -from scipy.interpolate import interp1d +from jax import random, grad, jit, vmap, jacfwd, jacrev, value_and_grad +from jax.experimental.optimizers import adam +import matplotlib.pyplot as plt +import matplotlib as mpl +params = {'axes.labelsize': 32, + 'font.family': 'serif', + 'font.serif': 'Computer Modern Raman', + 'font.size': 32, + 'axes.linewidth': 2, + 'legend.fontsize': 28, + 'xtick.labelsize': 28, + 'xtick.top': True, + 'xtick.direction': "in", + 'ytick.labelsize': 20, + 'ytick.right': True, + 'ytick.direction': "in", + 'axes.grid' : False, + 'text.usetex': True, + 'savefig.dpi' : 100, + 'lines.markersize' : 14, +# 'axes.formatter.useoffset': False, + 'axes.formatter.limits' : (-3,3)} + +mpl.rcParams['text.latex.preamble'] = [r'\usepackage{amsmath}'] #for \text command + +mpl.rcParams.update(params) + + + key = random.PRNGKey(42) @@ -24,9 +46,12 @@ def truncated_power_law(x,alpha,xmin,xmax): # Since truncated power law is not differentiable, we choose tanh as a smoother cutoff x_axis = jnp.linspace(1,150,100000) @jit -def power_law_tanh(x,alpha,xmin,xmax): - lower_window = (jnp.tanh(x-xmin)+1)/2 - upper_window = -(jnp.tanh(x-xmax)-1)/2 +def power_law_tanh(x,params): + alpha = params['alpha'] + xmin = params['xmin'] + xmax = params['xmax'] + lower_window = (jnp.tanh((x-xmin)*10)+1)/2 + upper_window = -(jnp.tanh((x-xmax)*10)-1)/2 power_law = x**-alpha output_unnorm = power_law*lower_window*upper_window # This normalization factor is supposed to be a good approximation but not perfect @@ -42,145 +67,117 @@ def gaussian(x,mean,sigma): def power_law_plus_peak(x,params): # !!! Add smoothing later # Since each component is normalized, the combine pdf should be normalized - powerlaw = power_law_tanh(x,params['alpha'],params['xmin'],params['xmax']) + powerlaw = power_law_tanh(x,params) peak = gaussian(x,params['mean'],params['sigma']) combine = (1-params['mixing'])*powerlaw+params['mixing']*peak return combine +@jit +def population_likelihood_powerlaw(point,params,obs_std,data): + return -jnp.sum(jnp.log(gaussian(data,point[:,None],obs_std)*power_law_tanh(point[:,None],params))) + +def population_likelihood_powerlaw_peak(point,params,obs_std,data): + if params['mixing'] < 0: + params['mixing'] = 0. + return -jnp.sum(jnp.log(gaussian(data,point[:,None],obs_std)*power_law_plus_peak(point[:,None],params))) + ######################################## -# Sampling data +# Power law Only ######################################## true_param = {} true_param['alpha'] = 2.63 -true_param['beta'] = 1.26 true_param['xmin'] = 4.59 true_param['xmax'] = 86.22 true_param['mean'] = 33.07 true_param['sigma'] = 5.69 -true_param['mixing'] = 0.1 +true_param['mixing'] = 0.3 N_sample = 1000 +obs_std = 0.1 m1_sample = jnp.empty(0) while m1_sample.shape[0]1) | (O3['injections/ifar_pycbc_bbh'][()]>1) | (O3['injections/ifar_pycbc_full'][()]>1) -#m1 = np.append(O12['mass1_source'][()],O3['injections/mass1_source'][()][O3_selection]) -#m2 = np.append(O12['mass2_source'][()],O3['injections/mass2_source'][()][O3_selection]) -#z = np.append(O12['redshift'][()],O3['injections/redshift'][()][O3_selection]) -#pdraw = np.append(O12['sampling_pdf'][()],O3['injections/sampling_pdf'][()][O3_selection]) -#pdraw = pdraw/m1 -## !!! Remember to fix the Jacobian going from (m1,m2,z) -> (m1,q,z) -#selection_samples = np.array([m1,m2/m1,z]).T -#Ndraw = O3.attrs['total_generated']+7.1*1e7 -# -#def evaluate_selection(params,data): -# likelihood = combine_pdf(params,data) -# return jnp.sum(likelihood/pdraw)/Ndraw -# -######################################### -## loading GWTC2 data -######################################### -# -#data = np.load('./data/GWTC12_m1m2z_highsig.npz') -#posterior = data['posterior_sample'] -#posterior[...,1] = posterior[...,1]/posterior[...,0] -#prior = data['prior'][:,:,0] # !!! Remember to fix the Jacobian going from (m1,m2,z) -> (m1,q,z) -#prior = prior/posterior[:,:,0] -#N_event = prior.shape[0] -# -######################################### -## Checking Gradient -######################################### -# -#def make_param(alpha=2.63,beta=1.26,xmin=3.59,xmax=86.22,mixing=0.1,mean=33.07,sigma=5.69,kappa=0.): -# param = {} -# param['alpha'] = alpha -# param['xmin'] = xmin -# param['xmax'] = xmax -# param['mean'] = mean -# param['sigma'] = sigma -# param['mixing'] = mixing -# param['beta'] = beta -# param['kappa'] = kappa -# return param -# -# -# -#def compute_dLdt(alpha,beta,xmin,xmax,mixing,mean,sigma,kappa=0.): -# param = make_param(alpha,beta,xmin,xmax,mixing,mean,sigma,kappa) -# -# L = population_likelihood(param,posterior,prior) -# dLdlambda = jnp.stack(list(grad(population_likelihood)(param,posterior,prior).values())) -# dLdtheta = grad(population_likelihood,argnums=1)(param,posterior,prior) -# return L, dLdtheta[None]/dLdlambda.reshape(-1,1,1,1) -# +key, *subkeys = random.split(key,num=2) +obs_m1 = m1_sample[:,None] + random.normal(subkeys[0],shape=(N_sample,100))*obs_std + + +guess_param = {} +guess_param['alpha'] = 2.2 +guess_param['xmin'] = 1. +guess_param['xmax'] = 90. +guess_param['mean'] = 35. +guess_param['sigma'] = 5.9 +guess_param['mixing'] = 0. + +learning_rate = 1e-1 +opt_init, opt_update, get_params = adam(learning_rate) +opt_state = opt_init((m1_sample,guess_param)) + +def step(step, opt_state): + params = get_params(opt_state) + value, grads = value_and_grad(population_likelihood_powerlaw,argnums=(0,1))(params[0],params[1], obs_std, obs_m1) + opt_state = opt_update(step, grads, opt_state) + return value, opt_state + +for i in range(500): + value, opt_state = step(i, opt_state) + if jnp.isnan(value): + break + print(value,get_params(opt_state)[1]) + +best_x_pl, best_lambda_pl = get_params(opt_state) + +dlambdadtheta_pl = jacfwd(jacrev(population_likelihood_powerlaw),argnums=1)(best_x_pl,best_lambda_pl,obs_std,obs_m1) + +learning_rate = 1e-2 +opt_init, opt_update, get_params = adam(learning_rate) +opt_state = opt_init((m1_sample,guess_param)) + +def step(step, opt_state): + params = get_params(opt_state) + value, grads = value_and_grad(population_likelihood_powerlaw_peak,argnums=(0,1))(params[0], params[1], obs_std, obs_m1) + opt_state = opt_update(step, grads, opt_state) + return value, opt_state + +for i in range(500): + value, opt_state = step(i, opt_state) + if jnp.isnan(value): + break + print(value,get_params(opt_state)[1]) + +best_x_plpk, best_lambda_plpk = get_params(opt_state) + +dlambdadtheta_plpk = jacfwd(jacrev(population_likelihood_powerlaw_peak),argnums=1)(best_x_plpk,best_lambda_plpk,obs_std,obs_m1) + + +fig,ax = plt.subplots(1,3,figsize=(30,9)) +ax[0].hist(m1_sample,bins=50,density=True,histtype='step',lw=3,label='Truth',color='C2') +axis = jnp.linspace(ax[0].get_xlim()[0],ax[0].get_xlim()[1],1000) +ax[0].plot(axis,power_law_plus_peak(axis,best_lambda_pl),label='Power law',c='C0') +ax[0].plot(axis,power_law_plus_peak(axis,best_lambda_plpk),label='Power law + peak',c='C1') +ax[0].set_ylabel(r'$p(x)$') +ax[0].set_xlabel(r'$x$') +ax[0].legend(loc='upper right',fontsize=20) +ax[1].plot(dlambdadtheta_pl['alpha'][jnp.argsort(dlambdadtheta_pl['alpha'])],label='Power law sorted',lw=3) +ax[1].plot(dlambdadtheta_plpk['alpha'][jnp.argsort(dlambdadtheta_plpk['alpha'])],label='Power law + peak sorted',lw=3) +ax[1].legend(loc='lower right',fontsize=20) +ax[1].set_xlabel('Event number') +ax[1].set_ylabel(r'$\frac{\partial^2\mathcal{L}}{\partial \theta \partial \alpha}$') +ax[2].plot(dlambdadtheta_pl['xmin'][jnp.argsort(dlambdadtheta_pl['xmin'])],label='Power law sorted',lw=3) +ax[2].plot(dlambdadtheta_plpk['xmin'][jnp.argsort(dlambdadtheta_plpk['xmin'])],label='Power law + peak sorted',lw=3) +ax[2].legend(loc='lower right',fontsize=20) +ax[2].set_xlabel('Event number') +ax[2].set_ylabel(r'$\frac{\partial^2\mathcal{L}}{\partial \theta \partial x_{min}}$') + +fig.show() From e568f351a94c7964795e6673776d0784aa37f72b Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Tue, 21 Sep 2021 15:50:41 -0400 Subject: [PATCH 009/300] Add detector projection code --- jaxgw/__init__.py | 0 jaxgw/__pycache__/__init__.cpython-37.pyc | Bin 0 -> 141 bytes jaxgw/likelihood/.detector_projection.py.swp | Bin 0 -> 20480 bytes jaxgw/likelihood/__init__.py | 0 .../__pycache__/__init__.cpython-37.pyc | Bin 0 -> 152 bytes .../detector_projection.cpython-37.pyc | Bin 0 -> 2575 bytes jaxgw/likelihood/detector_projection.py | 90 ++++++++++++++++++ jaxgw/pop/__init__.py | 0 test/__init__.py | 0 test/test_interferometer.py | 25 +++++ GWTC2.py => test/toy_example/GWTC2.py | 0 .../toy_example/GaussianExample.py | 0 test/toy_example/Gaussian_kde.py | 24 +++++ .../toy_example/PowerLawPlusPeak.py | 0 14 files changed, 139 insertions(+) create mode 100644 jaxgw/__init__.py create mode 100644 jaxgw/__pycache__/__init__.cpython-37.pyc create mode 100644 jaxgw/likelihood/.detector_projection.py.swp create mode 100644 jaxgw/likelihood/__init__.py create mode 100644 jaxgw/likelihood/__pycache__/__init__.cpython-37.pyc create mode 100644 jaxgw/likelihood/__pycache__/detector_projection.cpython-37.pyc create mode 100644 jaxgw/likelihood/detector_projection.py create mode 100644 jaxgw/pop/__init__.py create mode 100644 test/__init__.py create mode 100644 test/test_interferometer.py rename GWTC2.py => test/toy_example/GWTC2.py (100%) rename GaussianExample.py => test/toy_example/GaussianExample.py (100%) create mode 100644 test/toy_example/Gaussian_kde.py rename PowerLawPlusPeak.py => test/toy_example/PowerLawPlusPeak.py (100%) diff --git a/jaxgw/__init__.py b/jaxgw/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/jaxgw/__pycache__/__init__.cpython-37.pyc b/jaxgw/__pycache__/__init__.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..28c516055e0267bb602d3759199196d8c01bea04 GIT binary patch literal 141 zcmZ?b<>g`kf+HV16G8N25CH>>K!yVl7qb9~6oz01O-8?!3`HPe1o6vEKR2&LKO;Xk zRlmGEKQCS1Jv^W&KPxr4L_Z+EAU-oMJ}a?8ABfY-_2Yru%#!$cy@JYH95%W6DWy57 Lb|CXU12F>tk3S*q literal 0 HcmV?d00001 diff --git a/jaxgw/likelihood/.detector_projection.py.swp b/jaxgw/likelihood/.detector_projection.py.swp new file mode 100644 index 0000000000000000000000000000000000000000..524c8d44ad42608edbfb0cc940f26aa3923ac977 GIT binary patch literal 20480 zcmeI3Ta4UR8Gzk1DWs)M1wsgMJIzY$Y%)80X`=wrB0->0D$;~j4T$Y_a1u_m33H<3qDE9xxs-9xxs-9xxs-9xxs-9xxs-9xxs-9xxvG4|u??Th`Ct zW?7G^#GUv5N&f%h9?SX@JOz)yF}M@`AMApia4r1d9hUVl zT!5pnAO3cOW&IVNgJ)q4Z1@x$g5O_HzwjX31)qg^cxks~Jr8H$^RNeQf@jcO>F>fD z*z#-GgbkR55(xMZJb#^K{SYp})9@%f1T!FDC%nANvVH|Wg>S((;8FNG+z$)z1`8gq z!YlANd=1XSIaq~zVHdp0!pU#p1^6ypgiSaF_rg*547|<)%rD^yxC4&BHSiA>Vy?ht z_;Io*lZ_LhHm@a@ZLuI$-FBtsdqGfYm4!QHsj>-i$Zpm;>dOpwIj*@PeO08ICHpa( zOA&g)Uh|x~;Oh^>D>>WIqel>WdahVsk*;WYb=ee-D;BpZe5>{7WQ?j=zYWv3Aadwo zp|Ia&D^w{Q_Ozfp8waDRXX=isIv|9Gxk6pKyx9CvHcnMZs`{YcV}$l7y}GfQ?Q9zz zY^V9?HmXL~AFFQuDC@X%fETJ14o##Cx(SXSDWhDw)uE!~q%psZ&`a;T|5LZifkyW_ zV_?|lTLYc`s2>( zWLYq`wx-1X0i|v_^YSTUL(gHArnfHr(v(5-Q8vrGAuezZ+4&mz+uu`hZc@q$u2aFhva@u|RZ^(kGsm93nVphQx+7Pr@x*W#xf z+xP9w(&DrlI=an@Gga<(r;lcrBFA*{ZS-hUGO3hNFxVp)dOw%4?M=K*~esD{!FJip}IWz}_AsD@>M?+IAu<0-Dr z#I`q!Tbsvsy?&>rpU}JpokZX0im2l^BV4;A9(A*o1DIM;*Qs~#*?w!TEY#n@flBGT zt}{P1r9G7>ns_J`GOv81`xfz*1Qb>9elt`dBC9J=BxNh9u*_Da{ViC8J1xYj=`Pq0oVi2k?TJRUxqVq9~^@+d=U141^*(~{~mk^z5rLr>Hi7O z!X;37{}I>?KOwI_59dJT`IGPrx&5E}<@ecKKmDDI|84BIH{?}E!&{E7k-0zR$F8F{Ji+D;2oMo5C1*hR91*tew``Wh8&rtfo6wd;QCL>hbYCTLDqv~L* zbNUvwEcB2+%pbX;kJAfP3WvoO#p2>|wu3@h;jkzJn*bmtzbf+E}W?-pJIaRnZVt;s?gGaY0t$Fddhv=#dq4 z-+gQKJ;|6)We-tXjri1qIUBN4v%j-fY3v^IcLO}CH?0FXgw4PG)sw!Gy-v9^kzvJ+ zhFy%DRa@xO4(&?Am-lz1TifITk1TRAr&PW|%MMO0MRQD7v5^hLz6GI$>f5d#C1-ju zyP|To?AeB`{>iZvxj@-FJ-5m;cQ@DLpdRvPh>}~gBFbRKu>vE~X=MvG>&!!Lv}0q? zdI8m|%hIb!mlQ~+qt#%=X+#NdrfflUr;f$RxP?xBCzr^hdu$Iv+YfcQj*$uR3AZjt z!Chh3^^Z(nyhx zN~j3eJ#qN>-CAZ4i=%-O^id_J(!F|FGkkk;iR?R_6Bj2*%8RG+wno=TrR|ZQ@SwI` zpW$7Iy;*JeUMuk_Yf4eq-RqQwUNXpfjs`y<( zp>=yrs%MDY(nAmz6RZTdFM~EolqTz0 z5)o>jE!Ni|EzRnexYu@Bz;NwU?~<;Kky=6Z0;<2!l~ZyPS4Hc1k(&^C`ixE^)ksg* z9TJb|+f{jhxNNYfbqVZ9rYM!vr)wdrWrw9m79vye(fSMRdsl;^>)59NO_$d@EO7+- zrqrj#;Dsb@V2{^&)ETxArHoS>Q$Zlb2^Dpp_I+-JdrzMct``bBXN|rRx9m-xUUlF^ zrpaa?x44qZ(yV^#HFz~u7mrxL-5g^zb!*)XUBKPgYIm{jQJ0qTZNFvOWEICDUqG_T z|BsQ!e+AV0f6{BceV=^)5_m8RH^B9<8(vK6$92ulc))nTc))nTc))nTc))nTc))nT nc))nTc%ZKbRBk$Wix<|-S0K5MNYr-?`|}HGi3{J%S0MieCH1jw literal 0 HcmV?d00001 diff --git a/jaxgw/likelihood/__init__.py b/jaxgw/likelihood/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/jaxgw/likelihood/__pycache__/__init__.cpython-37.pyc b/jaxgw/likelihood/__pycache__/__init__.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..beec8ddd8e89790dd3965586bbc7e0bbd9f7c4b6 GIT binary patch literal 152 zcmZ?b<>g`kf(su!6G8N25CH>>K!yVl7qb9~6oz01O-8?!3`HPe1o6vJKR2&LKO;Xk zRlmGEKQCS1Jv^W&KPxr4L_Z+EAU-oMJ}a?8ABfY-^>Z?_Q*$yi^7B*l+5lqOgJ literal 0 HcmV?d00001 diff --git a/jaxgw/likelihood/__pycache__/detector_projection.cpython-37.pyc b/jaxgw/likelihood/__pycache__/detector_projection.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..51497c5e161e70b1e490afa332c98d4752188625 GIT binary patch literal 2575 zcmZuzTW=dh6yDj3?+x|h6Ck`cRy!; zTyvbiaByB+=zIj9-i3-goJ7u)^j+d`mwP`ueUJM*fY#>~UWGQ`HC~6d!pWi2ZoB|3 zXFv3@fl@8M$I$BMP#Ncpz~^Spk~DGTVULrMwds2a?K$ObIfBGBfM zLWg6_2!~ui{=BkJARpegvx%|s_D2AN^5=&qRoR)`eIzGsSD~T2B4n)7Y1{4l;K19q zzGrt@RJc<9%F25qVx1@;s-vwxL?L~{04gjk_*TCC4hhMeQ{ck{Sark6$hLEed3J4% zgEH5Zk_%o2J&^brNNnbPoM}3iAJtONN=vo^j=NS`%c^$HFF8`PD_9o6n)On8&MTCb z$Svozk@;uA(&65btDb|T!u_nC)omoP-^dym$wT0y#RY)-dxZm8*>9i@aCSwtkrs<2jL>7F)At*X`fwA5;5&84@pShKR($|A^CxR=+iTjZ~$w1HMuNbuOM zUE__jdgd?5Pvl3!ktfal7noFgkaB+(rFun|JP1{yb-(^p31-F;&PMC}Jjs|vIeC_o z#mS8yPHCJN%IGYKn35-O&XSl;6E5CtHwwnNGpRgW21%pvcFKb&nG2=rII)2fjjvgh ziYH1XDz6L^HD#vV%3Z~BkBcz(jhu?yn`xQ*XdAj?nFnSp49iKHljEFBa}wtP9Mch7 zr}NfGn89TS2L-KPho$u$s2tBHSKJ412;t|p$W4OZ6>`&SL5)8sy0 zZ@~aU4#zNqiz`Fg20(uI%;O|;CjKMM;a=&ZvN7L|uC4-tO+hHf8Et zdAzs#aQA+9_nmtWC|crTC}W9+HBE+g=^{3|NL~>e-L=NQOFQ(qL+9UvTQl%X#Kvko zVakl}4i%e<0ypi@xI=CD*RgYUEG|L!6C?J}h!Rw4*qB+&5-MYB_rkez-!8Nfv%b+H z1P2TqF+yqU%m=Gthl=gdVU#dqM;Le|M`MVS4&in~zJg)JxMXpm50qQifT;+0Z_}}c zMsBXdM}iq~ z@#j*(^MbxZ3gL;Fu~9(@+#Vj0JqpjvYQrxj7{&CC=#IK{2M_&Dhwj+ZzSAzk`IRuK zDq~TG(ps`_>;NCRJ^C@d|Li{S3trs|Y83G)N%dbo1No5XdL}|Sl!9AFizVUWnRFz) zRP9Q?I+~~OL~DE6>>Xlnihd2E3RzYQ`v$eExvxbuRGV<})N5E>$7&0!ZK(R!{^!WA zUkz7FF9+wR?mf`d_Ak_pSBbg}Bm64?&wDpcr|^IWwkf>AXWIHwc#7al_VJof_BpM+ W4ij', arm1, arm1) - jnp.einsum('i,j->ij', arm2, arm2)) + +########################################################## +# Construction of detector tensor +########################################################## + +def get_polarization_tensor(ra, dec, time, psi, mode): + + #gmst = fmod(greenwich_mean_sidereal_time(time), 2 * jnp.pi) + phi = ra #- gmst + theta = jnp.pi / 2 - dec + + u = jnp.array([jnp.cos(phi) * jnp.cos(theta), jnp.cos(theta) * jnp.sin(phi), -jnp.sin(theta)]) + v = jnp.array([-jnp.sin(phi), jnp.cos(phi), 0]) + m = -u * jnp.sin(psi) - v * jnp.cos(psi) + n = -u * jnp.cos(psi) + v * jnp.sin(psi) + + if mode.lower() == 'plus': + return jnp.einsum('i,j->ij', m, m) - jnp.einsum('i,j->ij', n, n) + elif mode.lower() == 'cross': + return jnp.einsum('i,j->ij', m, n) + jnp.einsum('i,j->ij', n, m) + elif mode.lower() == 'breathing': + return jnp.einsum('i,j->ij', m, m) + jnp.einsum('i,j->ij', n, n) + + # Calculating omega here to avoid calculation when model in [plus, cross, breathing] + omega = jnp.cross(m, n) + if mode.lower() == 'longitudinal': + return jnp.einsum('i,j->ij', omega, omega) + elif mode.lower() == 'x': + return jnp.einsum('i,j->ij', m, omega) + jnp.einsum('i,j->ij', omega, m) + elif mode.lower() == 'y': + return jnp.einsum('i,j->ij', n, omega) + jnp.einsum('i,j->ij', omega, n) + else: + raise ValueError("{} not a polarization mode!".format(mode)) + +def antenna_response(detector_tensor, ra, dec, time, psi, mode): + polarization_tensor = gwutils.get_polarization_tensor(ra, dec, time, psi, mode) + return jnp.einsum('ij,ij->', detector_tensor, polarization_tensor) + +def get_detector_response(self, waveform_polarizations, parameters): + signal = {} + for mode in waveform_polarizations.keys(): + det_response = self.antenna_response( + parameters['ra'], + parameters['dec'], + parameters['geocent_time'], + parameters['psi'], mode) + + signal[mode] = waveform_polarizations[mode] * det_response + signal_ifo = sum(signal.values()) + + signal_ifo *= self.strain_data.frequency_mask + + time_shift = self.time_delay_from_geocenter( + parameters['ra'], parameters['dec'], parameters['geocent_time']) + + # Be careful to first subtract the two GPS times which are ~1e9 sec. + # And then add the time_shift which varies at ~1e-5 sec + dt_geocent = parameters['geocent_time'] - self.strain_data.start_time + dt = dt_geocent + time_shift + + signal_ifo[self.strain_data.frequency_mask] = signal_ifo[self.strain_data.frequency_mask] * jnp.exp( + -1j * 2 * jnp.pi * dt * self.strain_data.frequency_array[self.strain_data.frequency_mask]) + + signal_ifo[self.strain_data.frequency_mask] *= self.calibration_model.get_calibration_factor( + self.strain_data.frequency_array[self.strain_data.frequency_mask], + prefix='recalib_{}_'.format(self.name), **parameters) + + return signal_ifo + + diff --git a/jaxgw/pop/__init__.py b/jaxgw/pop/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/test/__init__.py b/test/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/test/test_interferometer.py b/test/test_interferometer.py new file mode 100644 index 00000000..cb39bf67 --- /dev/null +++ b/test/test_interferometer.py @@ -0,0 +1,25 @@ +from jaxgw.likelihood.detector_projection import construct_arm, detector_tensor + + +H1_lat = 46 + 27. / 60 + 18.528 / 3600 +H1_long = -(119 + 24. / 60 + 27.5657 / 3600) +H1_xarm_azimuth = 125.9994 +H1_yarm_azimuth = 215.9994 +H1_xarm_tilt = -6.195e-4 +H1_yarm_tilt = 1.25e-5 + +L1_lat = 30 + 33. / 60 + 46.4196 / 3600 +L1_long = -(90 + 46. / 60 + 27.2654 / 3600) +L1_xarm_azimuth = 197.7165 +L1_yarm_azimuth = 287.7165 +L1_xarm_tilt = 0 +L1_yarm_tilt = 0 + +H1_arm1 = construct_arm(H1_long, H1_lat, H1_xarm_tilt, H1_xarm_azimuth) +H1_arm2 = construct_arm(H1_long, H1_lat, H1_yarm_tilt, H1_yarm_azimuth) + +L1_arm1 = construct_arm(L1_long, L1_lat, L1_xarm_tilt, L1_xarm_azimuth) +L1_arm2 = construct_arm(L1_long, L1_lat, L1_yarm_tilt, L1_yarm_azimuth) + +H1 = detector_tensor(H1_arm1, H1_arm2) +L1 = detector_tensor(L1_arm1, L1_arm2) diff --git a/GWTC2.py b/test/toy_example/GWTC2.py similarity index 100% rename from GWTC2.py rename to test/toy_example/GWTC2.py diff --git a/GaussianExample.py b/test/toy_example/GaussianExample.py similarity index 100% rename from GaussianExample.py rename to test/toy_example/GaussianExample.py diff --git a/test/toy_example/Gaussian_kde.py b/test/toy_example/Gaussian_kde.py new file mode 100644 index 00000000..a67c2694 --- /dev/null +++ b/test/toy_example/Gaussian_kde.py @@ -0,0 +1,24 @@ +import jax +import jax.numpy as jnp +from jax import jit,vmap + +@jit +def gaussian(x,mean,sigma): + return (1./jnp.sqrt(2*jnp.pi)/sigma)*jnp.exp(-(((x-mean)/sigma)**2)/2) + +@jit +def multivariate_gaussian(x,mean,covariance,dim=1): + numerator = jnp.exp(-1./2*(x-mean).T@jnp.linalg.inv(covariance)@(x-mean)) + denominator = jnp.sqrt((2*jnp.pi)**dim*jnp.linalg.det(covariance)) + return numerator/denominator + +batch_multivariate_gaussian = vmap(multivariate_gaussian, (None,0,None), 0) + +def gaussian_kde(datapoint,training_point): + n = datapoint.shape[0] + d = datapoint.shape[1] + bandwidth = n**(-1/(d+4)) + cov_matrix = jnp.eye(d) + return jnp.mean(batch_multivariate_gaussian(datapoint,training_point,cov_matrix,dim=d)) + + diff --git a/PowerLawPlusPeak.py b/test/toy_example/PowerLawPlusPeak.py similarity index 100% rename from PowerLawPlusPeak.py rename to test/toy_example/PowerLawPlusPeak.py From 87ada80d486075e8dfe13fbc94030584af9e2ec1 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Tue, 21 Sep 2021 16:57:33 -0400 Subject: [PATCH 010/300] Minor bug fix --- jaxgw/likelihood/.detector_projection.py.swp | Bin 20480 -> 0 bytes .../__pycache__/__init__.cpython-37.pyc | Bin 152 -> 0 bytes .../detector_projection.cpython-37.pyc | Bin 2575 -> 0 bytes jaxgw/likelihood/detector_projection.py | 2 +- test/test_interferometer.py | 4 +++- 5 files changed, 4 insertions(+), 2 deletions(-) delete mode 100644 jaxgw/likelihood/.detector_projection.py.swp delete mode 100644 jaxgw/likelihood/__pycache__/__init__.cpython-37.pyc delete mode 100644 jaxgw/likelihood/__pycache__/detector_projection.cpython-37.pyc diff --git a/jaxgw/likelihood/.detector_projection.py.swp b/jaxgw/likelihood/.detector_projection.py.swp deleted file mode 100644 index 524c8d44ad42608edbfb0cc940f26aa3923ac977..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 20480 zcmeI3Ta4UR8Gzk1DWs)M1wsgMJIzY$Y%)80X`=wrB0->0D$;~j4T$Y_a1u_m33H<3qDE9xxs-9xxs-9xxs-9xxs-9xxs-9xxs-9xxvG4|u??Th`Ct zW?7G^#GUv5N&f%h9?SX@JOz)yF}M@`AMApia4r1d9hUVl zT!5pnAO3cOW&IVNgJ)q4Z1@x$g5O_HzwjX31)qg^cxks~Jr8H$^RNeQf@jcO>F>fD z*z#-GgbkR55(xMZJb#^K{SYp})9@%f1T!FDC%nANvVH|Wg>S((;8FNG+z$)z1`8gq z!YlANd=1XSIaq~zVHdp0!pU#p1^6ypgiSaF_rg*547|<)%rD^yxC4&BHSiA>Vy?ht z_;Io*lZ_LhHm@a@ZLuI$-FBtsdqGfYm4!QHsj>-i$Zpm;>dOpwIj*@PeO08ICHpa( zOA&g)Uh|x~;Oh^>D>>WIqel>WdahVsk*;WYb=ee-D;BpZe5>{7WQ?j=zYWv3Aadwo zp|Ia&D^w{Q_Ozfp8waDRXX=isIv|9Gxk6pKyx9CvHcnMZs`{YcV}$l7y}GfQ?Q9zz zY^V9?HmXL~AFFQuDC@X%fETJ14o##Cx(SXSDWhDw)uE!~q%psZ&`a;T|5LZifkyW_ zV_?|lTLYc`s2>( zWLYq`wx-1X0i|v_^YSTUL(gHArnfHr(v(5-Q8vrGAuezZ+4&mz+uu`hZc@q$u2aFhva@u|RZ^(kGsm93nVphQx+7Pr@x*W#xf z+xP9w(&DrlI=an@Gga<(r;lcrBFA*{ZS-hUGO3hNFxVp)dOw%4?M=K*~esD{!FJip}IWz}_AsD@>M?+IAu<0-Dr z#I`q!Tbsvsy?&>rpU}JpokZX0im2l^BV4;A9(A*o1DIM;*Qs~#*?w!TEY#n@flBGT zt}{P1r9G7>ns_J`GOv81`xfz*1Qb>9elt`dBC9J=BxNh9u*_Da{ViC8J1xYj=`Pq0oVi2k?TJRUxqVq9~^@+d=U141^*(~{~mk^z5rLr>Hi7O z!X;37{}I>?KOwI_59dJT`IGPrx&5E}<@ecKKmDDI|84BIH{?}E!&{E7k-0zR$F8F{Ji+D;2oMo5C1*hR91*tew``Wh8&rtfo6wd;QCL>hbYCTLDqv~L* zbNUvwEcB2+%pbX;kJAfP3WvoO#p2>|wu3@h;jkzJn*bmtzbf+E}W?-pJIaRnZVt;s?gGaY0t$Fddhv=#dq4 z-+gQKJ;|6)We-tXjri1qIUBN4v%j-fY3v^IcLO}CH?0FXgw4PG)sw!Gy-v9^kzvJ+ zhFy%DRa@xO4(&?Am-lz1TifITk1TRAr&PW|%MMO0MRQD7v5^hLz6GI$>f5d#C1-ju zyP|To?AeB`{>iZvxj@-FJ-5m;cQ@DLpdRvPh>}~gBFbRKu>vE~X=MvG>&!!Lv}0q? zdI8m|%hIb!mlQ~+qt#%=X+#NdrfflUr;f$RxP?xBCzr^hdu$Iv+YfcQj*$uR3AZjt z!Chh3^^Z(nyhx zN~j3eJ#qN>-CAZ4i=%-O^id_J(!F|FGkkk;iR?R_6Bj2*%8RG+wno=TrR|ZQ@SwI` zpW$7Iy;*JeUMuk_Yf4eq-RqQwUNXpfjs`y<( zp>=yrs%MDY(nAmz6RZTdFM~EolqTz0 z5)o>jE!Ni|EzRnexYu@Bz;NwU?~<;Kky=6Z0;<2!l~ZyPS4Hc1k(&^C`ixE^)ksg* z9TJb|+f{jhxNNYfbqVZ9rYM!vr)wdrWrw9m79vye(fSMRdsl;^>)59NO_$d@EO7+- zrqrj#;Dsb@V2{^&)ETxArHoS>Q$Zlb2^Dpp_I+-JdrzMct``bBXN|rRx9m-xUUlF^ zrpaa?x44qZ(yV^#HFz~u7mrxL-5g^zb!*)XUBKPgYIm{jQJ0qTZNFvOWEICDUqG_T z|BsQ!e+AV0f6{BceV=^)5_m8RH^B9<8(vK6$92ulc))nTc))nTc))nTc))nTc))nT nc))nTc%ZKbRBk$Wix<|-S0K5MNYr-?`|}HGi3{J%S0MieCH1jw diff --git a/jaxgw/likelihood/__pycache__/__init__.cpython-37.pyc b/jaxgw/likelihood/__pycache__/__init__.cpython-37.pyc deleted file mode 100644 index beec8ddd8e89790dd3965586bbc7e0bbd9f7c4b6..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 152 zcmZ?b<>g`kf(su!6G8N25CH>>K!yVl7qb9~6oz01O-8?!3`HPe1o6vJKR2&LKO;Xk zRlmGEKQCS1Jv^W&KPxr4L_Z+EAU-oMJ}a?8ABfY-^>Z?_Q*$yi^7B*l+5lqOgJ diff --git a/jaxgw/likelihood/__pycache__/detector_projection.cpython-37.pyc b/jaxgw/likelihood/__pycache__/detector_projection.cpython-37.pyc deleted file mode 100644 index 51497c5e161e70b1e490afa332c98d4752188625..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 2575 zcmZuzTW=dh6yDj3?+x|h6Ck`cRy!; zTyvbiaByB+=zIj9-i3-goJ7u)^j+d`mwP`ueUJM*fY#>~UWGQ`HC~6d!pWi2ZoB|3 zXFv3@fl@8M$I$BMP#Ncpz~^Spk~DGTVULrMwds2a?K$ObIfBGBfM zLWg6_2!~ui{=BkJARpegvx%|s_D2AN^5=&qRoR)`eIzGsSD~T2B4n)7Y1{4l;K19q zzGrt@RJc<9%F25qVx1@;s-vwxL?L~{04gjk_*TCC4hhMeQ{ck{Sark6$hLEed3J4% zgEH5Zk_%o2J&^brNNnbPoM}3iAJtONN=vo^j=NS`%c^$HFF8`PD_9o6n)On8&MTCb z$Svozk@;uA(&65btDb|T!u_nC)omoP-^dym$wT0y#RY)-dxZm8*>9i@aCSwtkrs<2jL>7F)At*X`fwA5;5&84@pShKR($|A^CxR=+iTjZ~$w1HMuNbuOM zUE__jdgd?5Pvl3!ktfal7noFgkaB+(rFun|JP1{yb-(^p31-F;&PMC}Jjs|vIeC_o z#mS8yPHCJN%IGYKn35-O&XSl;6E5CtHwwnNGpRgW21%pvcFKb&nG2=rII)2fjjvgh ziYH1XDz6L^HD#vV%3Z~BkBcz(jhu?yn`xQ*XdAj?nFnSp49iKHljEFBa}wtP9Mch7 zr}NfGn89TS2L-KPho$u$s2tBHSKJ412;t|p$W4OZ6>`&SL5)8sy0 zZ@~aU4#zNqiz`Fg20(uI%;O|;CjKMM;a=&ZvN7L|uC4-tO+hHf8Et zdAzs#aQA+9_nmtWC|crTC}W9+HBE+g=^{3|NL~>e-L=NQOFQ(qL+9UvTQl%X#Kvko zVakl}4i%e<0ypi@xI=CD*RgYUEG|L!6C?J}h!Rw4*qB+&5-MYB_rkez-!8Nfv%b+H z1P2TqF+yqU%m=Gthl=gdVU#dqM;Le|M`MVS4&in~zJg)JxMXpm50qQifT;+0Z_}}c zMsBXdM}iq~ z@#j*(^MbxZ3gL;Fu~9(@+#Vj0JqpjvYQrxj7{&CC=#IK{2M_&Dhwj+ZzSAzk`IRuK zDq~TG(ps`_>;NCRJ^C@d|Li{S3trs|Y83G)N%dbo1No5XdL}|Sl!9AFizVUWnRFz) zRP9Q?I+~~OL~DE6>>Xlnihd2E3RzYQ`v$eExvxbuRGV<})N5E>$7&0!ZK(R!{^!WA zUkz7FF9+wR?mf`d_Ak_pSBbg}Bm64?&wDpcr|^IWwkf>AXWIHwc#7al_VJof_BpM+ W4', detector_tensor, polarization_tensor) def get_detector_response(self, waveform_polarizations, parameters): diff --git a/test/test_interferometer.py b/test/test_interferometer.py index cb39bf67..718c0d1e 100644 --- a/test/test_interferometer.py +++ b/test/test_interferometer.py @@ -1,4 +1,4 @@ -from jaxgw.likelihood.detector_projection import construct_arm, detector_tensor +from jaxgw.likelihood.detector_projection import construct_arm, detector_tensor, antenna_response H1_lat = 46 + 27. / 60 + 18.528 / 3600 @@ -23,3 +23,5 @@ H1 = detector_tensor(H1_arm1, H1_arm2) L1 = detector_tensor(L1_arm1, L1_arm2) + +H1_proj = antenna_response(H1, 1, 1, 0, 1,'plus') From 4fc363e36d99f8694f3fecfff4109024d76cf3ed Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Fri, 24 Sep 2021 16:28:20 -0400 Subject: [PATCH 011/300] Add likelihood example --- jaxgw/likelihood/detector_projection.py | 33 +++++---- jaxgw/likelihood/utils.py | 7 ++ test/test_integration.py | 3 + test/test_likelihood.py | 89 +++++++++++++++++++++++++ 4 files changed, 114 insertions(+), 18 deletions(-) create mode 100644 jaxgw/likelihood/utils.py create mode 100644 test/test_integration.py create mode 100644 test/test_likelihood.py diff --git a/jaxgw/likelihood/detector_projection.py b/jaxgw/likelihood/detector_projection.py index e59a0160..14614fe2 100644 --- a/jaxgw/likelihood/detector_projection.py +++ b/jaxgw/likelihood/detector_projection.py @@ -56,10 +56,11 @@ def antenna_response(detector_tensor, ra, dec, time, psi, mode): polarization_tensor = get_polarization_tensor(ra, dec, time, psi, mode) return jnp.einsum('ij,ij->', detector_tensor, polarization_tensor) -def get_detector_response(self, waveform_polarizations, parameters): +def get_detector_response(waveform_polarizations, parameters, detector_tensor): signal = {} for mode in waveform_polarizations.keys(): - det_response = self.antenna_response( + det_response = antenna_response( + detector_tensor, parameters['ra'], parameters['dec'], parameters['geocent_time'], @@ -68,22 +69,18 @@ def get_detector_response(self, waveform_polarizations, parameters): signal[mode] = waveform_polarizations[mode] * det_response signal_ifo = sum(signal.values()) - signal_ifo *= self.strain_data.frequency_mask - - time_shift = self.time_delay_from_geocenter( - parameters['ra'], parameters['dec'], parameters['geocent_time']) - - # Be careful to first subtract the two GPS times which are ~1e9 sec. - # And then add the time_shift which varies at ~1e-5 sec - dt_geocent = parameters['geocent_time'] - self.strain_data.start_time - dt = dt_geocent + time_shift - - signal_ifo[self.strain_data.frequency_mask] = signal_ifo[self.strain_data.frequency_mask] * jnp.exp( - -1j * 2 * jnp.pi * dt * self.strain_data.frequency_array[self.strain_data.frequency_mask]) - - signal_ifo[self.strain_data.frequency_mask] *= self.calibration_model.get_calibration_factor( - self.strain_data.frequency_array[self.strain_data.frequency_mask], - prefix='recalib_{}_'.format(self.name), **parameters) +# signal_ifo *= self.strain_data.frequency_mask +# +# time_shift = self.time_delay_from_geocenter( +# parameters['ra'], parameters['dec'], parameters['geocent_time']) +# +# # Be careful to first subtract the two GPS times which are ~1e9 sec. +# # And then add the time_shift which varies at ~1e-5 sec +# dt_geocent = parameters['geocent_time'] - self.strain_data.start_time +# dt = dt_geocent + time_shift +# +# signal_ifo[self.strain_data.frequency_mask] = signal_ifo[self.strain_data.frequency_mask] * jnp.exp( +# -1j * 2 * jnp.pi * dt * self.strain_data.frequency_array[self.strain_data.frequency_mask]) return signal_ifo diff --git a/jaxgw/likelihood/utils.py b/jaxgw/likelihood/utils.py new file mode 100644 index 00000000..af2baf6b --- /dev/null +++ b/jaxgw/likelihood/utils.py @@ -0,0 +1,7 @@ +import jax.numpy as jnp + +def inner_product(h1, h2, frequency, PSD, PSD_frequency): + psd_interp = jnp.interp(frequency, PSD_frequency, PSD) + df = frequency[1] - frequency[0] + integrand = jnp.conj(h1)* h2 / psd_interp + return 4. * jnp.real(jnp.sum(integrand)*df) diff --git a/test/test_integration.py b/test/test_integration.py new file mode 100644 index 00000000..aec10fb7 --- /dev/null +++ b/test/test_integration.py @@ -0,0 +1,3 @@ +from jaxgw.likelihood.utils.py import inner_product + + diff --git a/test/test_likelihood.py b/test/test_likelihood.py new file mode 100644 index 00000000..ce6e4164 --- /dev/null +++ b/test/test_likelihood.py @@ -0,0 +1,89 @@ +import numpy as np +import bilby +import jax.numpy as jnp + +from jax.config import config +from jax import grad +config.update("jax_enable_x64", True) + +# Set the duration and sampling frequency of the data segment that we're +# going to inject the signal into +duration = 4. +sampling_frequency = 2048. +minimum_frequency = 20 + +# Specify the output directory and the name of the simulation. +outdir = 'outdir' +label = 'fast_tutorial' +bilby.core.utils.setup_logger(outdir=outdir, label=label) + +# Set up a random seed for result reproducibility. This is optional! +np.random.seed(88170235) + +# We are going to inject a binary black hole waveform. We first establish a +# dictionary of parameters that includes all of the different waveform +# parameters, including masses of the two black holes (mass_1, mass_2), +# spins of both black holes (a, tilt, phi), etc. +injection_parameters = dict( + mass_1=36., mass_2=29., a_1=0.4, a_2=0.3, tilt_1=0.5, tilt_2=1.0, + phi_12=1.7, phi_jl=0.3, luminosity_distance=2000., theta_jn=0.4, psi=2.659, + phase=1.3, geocent_time=1126259642.413, ra=1.375, dec=-1.2108) + +# Fixed arguments passed into the source model +waveform_arguments = dict(waveform_approximant='IMRPhenomPv2', + reference_frequency=50., + minimum_frequency=minimum_frequency) + +# Create the waveform_generator using a LAL BinaryBlackHole source function +waveform_generator = bilby.gw.WaveformGenerator( + duration=duration, sampling_frequency=sampling_frequency, + frequency_domain_source_model=bilby.gw.source.lal_binary_black_hole, + parameter_conversion=bilby.gw.conversion.convert_to_lal_binary_black_hole_parameters, + waveform_arguments=waveform_arguments) + +# Set up interferometers. In this case we'll use two interferometers +# (LIGO-Hanford (H1), LIGO-Livingston (L1). These default to their design +# sensitivity +ifos = bilby.gw.detector.InterferometerList(['H1']) +ifos.set_strain_data_from_power_spectral_densities( + sampling_frequency=sampling_frequency, duration=duration, + start_time=injection_parameters['geocent_time'] - 3) +ifos.inject_signal(waveform_generator=waveform_generator, + parameters=injection_parameters) + +# Initialise the likelihood by passing in the interferometer data (ifos) and +# the waveform generator +likelihood = bilby.gw.GravitationalWaveTransient( + interferometers=ifos, waveform_generator=waveform_generator) + +likelihood.parameters = injection_parameters +snr_bilby = likelihood.calculate_snrs(waveform_generator.frequency_domain_strain(),ifos[0]).optimal_snr_squared + +############################################## +# Jax section +############################################## + + +waveform = waveform_generator.frequency_domain_strain() +waveform_frequency = waveform_generator.frequency_array +psd = ifos[0].power_spectral_density_array +psd_frequency = ifos[0].frequency_array + +from jaxgw.likelihood.detector_projection import construct_arm, detector_tensor, antenna_response, get_detector_response +from jaxgw.likelihood.utils import inner_product + +H1_lat = 46 + 27. / 60 + 18.528 / 3600 +H1_long = -(119 + 24. / 60 + 27.5657 / 3600) +H1_xarm_azimuth = 125.9994 +H1_yarm_azimuth = 215.9994 +H1_xarm_tilt = -6.195e-4 +H1_yarm_tilt = 1.25e-5 + +H1_arm1 = construct_arm(H1_long, H1_lat, H1_xarm_tilt, H1_xarm_azimuth) +H1_arm2 = construct_arm(H1_long, H1_lat, H1_yarm_tilt, H1_yarm_azimuth) + +H1 = detector_tensor(H1_arm1, H1_arm2) + +strain = get_detector_response(waveform,injection_parameters,H1)[jnp.isfinite(psd)] +jaxgw_snr = inner_product(strain, strain, waveform_frequency[jnp.isfinite(psd)], psd[jnp.isfinite(psd)], psd_frequency[jnp.isfinite(psd)]) +d_jaxgw_snr = grad(inner_product)(strain, strain, waveform_frequency[jnp.isfinite(psd)], psd[jnp.isfinite(psd)], psd_frequency[jnp.isfinite(psd)]) From a2decb7067bfe6c6374054d21dbca3e181b7074a Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Fri, 24 Sep 2021 21:01:54 -0400 Subject: [PATCH 012/300] Add TaylorF2 --- jaxgw/utils.py | 8 ++++ jaxgw/waveform/TaylorF2.py | 35 ++++++++++++++++++ .../__pycache__/TaylorF2.cpython-37.pyc | Bin 0 -> 1439 bytes test/test_likelihood.py | 21 +++++++++-- 4 files changed, 61 insertions(+), 3 deletions(-) create mode 100644 jaxgw/utils.py create mode 100644 jaxgw/waveform/TaylorF2.py create mode 100644 jaxgw/waveform/__pycache__/TaylorF2.cpython-37.pyc diff --git a/jaxgw/utils.py b/jaxgw/utils.py new file mode 100644 index 00000000..29584b1e --- /dev/null +++ b/jaxgw/utils.py @@ -0,0 +1,8 @@ +from astropy.constants import c,au,G,pc +from astropy.units import year as yr +from astropy.cosmology import WMAP9 as cosmo + +Msun = 4.9255e-6 +year = (1*yr).cgs.value +Mpc = 1e6*pc.value/c.value + diff --git a/jaxgw/waveform/TaylorF2.py b/jaxgw/waveform/TaylorF2.py new file mode 100644 index 00000000..f5f74085 --- /dev/null +++ b/jaxgw/waveform/TaylorF2.py @@ -0,0 +1,35 @@ +import jax.numpy as jnp +from jaxgw.utils import * + +def TaylorF2(f,params): + local_m1 = params['mass_1']*Msun + local_m2 = params['mass_2']*Msun + local_d = params['luminosity_distance']*Mpc + + + M_tot = local_m1+local_m2 + eta = local_m1*local_m2/(local_m1+local_m2)**2 + M_chirp = eta**(3./5)*M_tot + PNcoef = (jnp.pi*M_tot*f)**(1./3) + euler_gamma = 0.57721566490153286060 + + amplitude = M_chirp**(5./6)/local_d + + PN0 = 1. + PN1 = (20./9) * (743./336 + 11./4*eta) * PNcoef**2 + PN1d5 = -16*jnp.pi*PNcoef**3 + PN2 = 10 * ((3058673./1016064)+ 5429./1008 *eta + 617./144 * eta**2) * PNcoef**4 + PN2d5 = jnp.pi*(38645./756-65./9*eta)*(1 + 3*jnp.log(6**(3./2)*jnp.pi*M_tot*f)) * PNcoef**5 +# PN3 = 11583231236531./4694215680 - 640./3 *jnp.pi**2 - 6868./21*(euler_gamma+jnp.log(4) + + phase = 2*jnp.pi*f*params['geocent_time'] - params['phase'] - jnp.pi/4 + 3./(128*eta*PNcoef**5) * \ + (PN0+PN1+PN1d5)#+PN2+PN2d5) + + totalh = jnp.sqrt(5./96)/jnp.pi**(2./3)*amplitude*f**(-7./6)*jnp.exp(1j*phase) + hp = totalh * (1/2*(1+jnp.cos(params['theta_jn'])**2)*jnp.cos(2*params['psi'])) + hc = totalh * jnp.cos(params['theta_jn'])*jnp.sin(2*params['psi']) + + return {'plus':hp,'cross':hc} + +def flso(M): + return (6**3./2*jnp.pi*M)**-1 diff --git a/jaxgw/waveform/__pycache__/TaylorF2.cpython-37.pyc b/jaxgw/waveform/__pycache__/TaylorF2.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..98b21f538f105ad3c661b49ff36cdaddd39005a2 GIT binary patch literal 1439 zcmYjR&2Jk;6rb5IuVcH7Khne`gtiF?qY@`Ul?p=G0tv(+RwP;>EeC70Z?j%&*Sp2c+J5nGjBFyvZ_Lm?(&qIaU#?h&lSTT*=jCHqps8*lY$`ovpHo zksF}bS*|0mkSRlzP>$KasI!R)g-J0lfQbwZ*$Ysdlwbi$P|l48TSi`PQYMTpLxtj{ z=YsT9;36zSb;g7m?j)J>sJi4_pcrVt60ufj-%EhLZ!}n*XPeh^7f&*eT#=C%L7OwE z3J%TY%s$t|JThy26%nk$8s)}1W!x&uOL=*jU(2iKxsru{>^y5y&ue)d zHosEv(qHTeBec`BzcaVHq8^Mxe-x#DcH9R)&FqomPN^Tf*QA_uyjxNpb-d;;(SM&G zJ??nl-6MI@@sNk|wBsdf4{onL;9akLf4hADdjzv3qw7CCJw2VOIGs`tdrux7wJ$H3 zUGJxBfBbmuv!$;0`kj*-cQ&fsDRwsV{yzBa%jy^J&)p|$-6{4V^M3!Kvi=BO?M`)^ ziL&QLjyuZwnIF1Bj|X<@&UZAs(@!X1R^z?1^8Y%k`k!BqarvBJ^|RTZ9bskzH?#Z0 zkx=8*Zz)2HgK;W!CyCOuRkXCdbc}0zu_M&sC>BcW3pI#5p{2KzOsMWrEL0~-g_`=K zRzvUuVZ?S~hpA--kz)t_aOZ!tYZYcbu=Krt7Eyd=TZO&8Gw_qxGWL5;q!OAWEq&$4hBLQ#FjE}zGcZ3bpP9F zhoh`Lh(fo0co>bm_In@hC(+P#vi5!y_x(|SXdkt4c!%vn`(yVYO2YO{`#6Y_cXzkq zV_}|6=*Hx`!+4oK)#MGdHLfXTMdK@oHu)Rs4Zf&s@om1TBpVpv(2bIirCyWoO$`C? zj1953QnAdLv6l$!D6CDbiL z6!Ab?qj8A!>(a Date: Tue, 28 Sep 2021 11:32:48 -0400 Subject: [PATCH 013/300] Add IMRPhenomB and testing script. Note that there is some slight mismatch between the waveformed generated and bilby, mostly in the merger part and the overall amplitude --- jaxgw/waveform/IMRPhenomB.py | 83 +++++++++++++++++++++++++++++ jaxgw/waveform/IMRPhenomC.py | 82 ++++++++++++++++++++++++++++ test/waveform_test.py | 100 +++++++++++++++++++++++++++++++++++ 3 files changed, 265 insertions(+) create mode 100644 jaxgw/waveform/IMRPhenomB.py create mode 100644 jaxgw/waveform/IMRPhenomC.py create mode 100644 test/waveform_test.py diff --git a/jaxgw/waveform/IMRPhenomB.py b/jaxgw/waveform/IMRPhenomB.py new file mode 100644 index 00000000..f3168b3e --- /dev/null +++ b/jaxgw/waveform/IMRPhenomB.py @@ -0,0 +1,83 @@ +""" +Implementation of IMRPhenomB waveform following 0909.2867 +Note that there is some offset in amplitude that yet need to be fixed. +""" +import jax.numpy as jnp +from jax import jit +from jaxgw.utils import * + +@jit +def Lorentzian(x, x0, gamma): + return (gamma**2/((x-x0)**2+gamma**2/4)) + +@jit +def getPhenomCoef(M, eta, chi): + psi_coef = jnp.array([[3715/756, -920.9, 492.1, 135, 6742, -1053, -1.34*1e4], \ + [-16*jnp.pi + 113*chi/3, 1.702*1e4, -9566, -2182, -1.214*1e5, 2.075*1e4, 2.386*1e5], \ + [15293365/508032 - 405*chi**2/8, -1.254*1e5, 7.507*1e4, 1.338*1e4, 8.735*1e5, -1.657*1e5, -1.694*1e6], \ + [0, -8.898*1e5, 6.31*1e5, 5.068*1e4, 5.981*1e6, -1.415*1e6, -1.128*1e7], \ + [0, 8.696*1e5, -6.71*1e5, -3.008*1e4, -5.838*1e6, 1.514*1e6, 1.089*1e7]]) + + mu_coef = jnp.array([[1-4.455*(1-chi)**0.217+3.521*(1-chi)**0.26, 0.6437, 0.827, -0.2706, -0.05822, -3.935, -7.092], \ + [(1-0.63*(1-chi)**0.3)/2, 0.1469, -0.1228, -0.02609, -0.0249, 0.1701, 2.325], \ + [(1-0.63*(1-chi)**0.3)*((1-chi)**0.45)/4, -0.4098, -0.03523, 0.1008, 1.829, -0.02017, -2.87], \ + [0.3236 + 0.04894*chi + 0.01346*chi**2, -0.1331, -0.08172, 0.1451, -0.2714, 0.1279, 4.922]]) + psi = psi_coef[:,0] + eta * (psi_coef[:,1] + psi_coef[:,2]*chi + psi_coef[:,3]*chi**2)\ + + eta**2 * (psi_coef[:,4] + psi_coef[:,5]*chi)\ + + eta**3 * psi_coef[:,6] + f1, f2, sigma, f3 = (mu_coef[:,0] + eta * (mu_coef[:,1] + mu_coef[:,2]*chi + mu_coef[:,3]*chi**2)\ + + eta**2 * (mu_coef[:,4] + mu_coef[:,5]*chi)\ + + eta**3 * mu_coef[:,6]) / (jnp.pi * M) + + return psi, f1, f2, sigma, f3 + +@jit +def IMRPhenomB(f,params): + + + f = f[:,None] + + local_m1 = params['mass_1']*Msun + local_m2 = params['mass_2']*Msun + local_d = params['luminosity_distance']*Mpc + local_spin1 = params['a_1'] + local_spin2 = params['a_2'] + + M_tot = local_m1+local_m2 + eta = local_m1*local_m2/(local_m1+local_m2)**2 + chi_eff = (local_spin1*local_m1 + local_spin2*local_m2)/M_tot + M_chirp = eta**(3./5)*M_tot + PNcoef = (jnp.pi*M_tot*f)**(1./3) + + epsilon1 = 1.4547*chi_eff - 1.8897 + epsilon2 = -1.8153*chi_eff + 1.6557 + alpha2 = -323./224 + 451.*eta/168 + alpha3 = (27./8 - 11.*eta/6)*chi_eff + + psi, f1, f2, sigma, f3 = getPhenomCoef(M_tot, eta, chi_eff) + + Afactor_inspiral = (1 + alpha2*PNcoef**2+ alpha3*PNcoef**3) + Afactor_merger = (1 + epsilon1*PNcoef+ epsilon2*PNcoef**2) + omega_merger = Afactor_inspiral/Afactor_merger + omega_ringdown = Afactor_merger/Lorentzian(f2,f2,sigma) + + + phase = 2*jnp.pi*f*params['geocent_time'] - params['phase'] + phase += 3./(128*eta*PNcoef**5) * (1+ jnp.sum(psi*PNcoef**jnp.array([2,3,4,6,7]),axis=1)[:,None]) + + A_overall = M_chirp**(5./6)/local_d*f1**(-7./6) + A_inspiral = (f/f1)**(-7./6) * Afactor_inspiral + A_merger = omega_merger * (f/f1)**(-2./3) * Afactor_merger + A_ringdown = omega_ringdown * Lorentzian(f, f2, sigma) + + amplitude = A_overall * (A_inspiral * jnp.heaviside(f1-f,0) \ + + A_merger * jnp.heaviside(f-f1,1) * jnp.heaviside(f2-f,0) \ + + A_ringdown * jnp.heaviside(f-f2,1))# * jnp.heaviside(f3-f,0)) + + + + totalh = amplitude*jnp.exp(-1j*phase) + hp = totalh * (1/2*(1+jnp.cos(params['theta_jn'])**2)*jnp.cos(2*params['psi'])) + hc = totalh * jnp.cos(params['theta_jn'])*jnp.sin(2*params['psi']) + + return {'plus':hp,'cross':hc} diff --git a/jaxgw/waveform/IMRPhenomC.py b/jaxgw/waveform/IMRPhenomC.py new file mode 100644 index 00000000..1bb954b3 --- /dev/null +++ b/jaxgw/waveform/IMRPhenomC.py @@ -0,0 +1,82 @@ +""" +Implementation of IMRPhenomC waveform following 1005.3306 +""" +import jax.numpy as jnp +from jax import jit +from jaxgw.utils import * + +@jit +def Lorentzian(x, x0, gamma): + return (gamma**2/((x-x0)**2+gamma**2/4)) + +@jit +def getPhenomCoef(M, eta, chi): + psi_coef = jnp.array([[3715/756, -920.9, 492.1, 135, 6742, -1053, -1.34*1e4], \ + [-16*jnp.pi + 113*chi/3, 1.702*1e4, -9566, -2182, -1.214*1e5, 2.075*1e4, 2.386*1e5], \ + [15293365/508032 - 405*chi**2/8, -1.254*1e5, 7.507*1e4, 1.338*1e4, 8.735*1e5, -1.657*1e5, -1.694*1e6], \ + [0, -8.898*1e5, 6.31*1e5, 5.068*1e4, 5.981*1e6, -1.415*1e6, -1.128*1e7], \ + [0, 8.696*1e5, -6.71*1e5, -3.008*1e4, -5.838*1e6, 1.514*1e6, 1.089*1e7]]) + + mu_coef = jnp.array([[1-4.455*(1-chi)**0.217+3.521*(1-chi)**0.26, 0.6437, 0.827, -0.2706, -0.05822, -3.935, -7.092], \ + [(1-0.63*(1-chi)**0.3)/2, 0.1469, -0.1228, -0.02609, -0.0249, 0.1701, 2.325], \ + [(1-0.63*(1-chi)**0.3)*((1-chi)**0.45)/4, -0.4098, -0.03523, 0.1008, 1.829, -0.02017, -2.87], \ + [0.3236 + 0.04894*chi + 0.01346*chi**2, -0.1331, -0.08172, 0.1451, -0.2714, 0.1279, 4.922]]) + psi = psi_coef[:,0] + eta * (psi_coef[:,1] + psi_coef[:,2]*chi + psi_coef[:,3]*chi**2)\ + + eta**2 * (psi_coef[:,4] + psi_coef[:,5]*chi)\ + + eta**3 * psi_coef[:,6] + f1, f2, sigma, f3 = (mu_coef[:,0] + eta * (mu_coef[:,1] + mu_coef[:,2]*chi + mu_coef[:,3]*chi**2)\ + + eta**2 * (mu_coef[:,4] + mu_coef[:,5]*chi)\ + + eta**3 * mu_coef[:,6]) / (jnp.pi * M) + + return psi, f1, f2, sigma, f3 + +@jit +def IMRPhenomB(f,params): + + + f = f[:,None] + + local_m1 = params['mass_1']*Msun + local_m2 = params['mass_2']*Msun + local_d = params['luminosity_distance']*Mpc + local_spin1 = params['a_1'] + local_spin2 = params['a_2'] + + M_tot = local_m1+local_m2 + eta = local_m1*local_m2/(local_m1+local_m2)**2 + chi_eff = (local_spin1*local_m1 + local_spin2*local_m2)/M_tot + M_chirp = eta**(3./5)*M_tot + PNcoef = (jnp.pi*M_tot*f)**(1./3) + + epsilon1 = 1.4547*chi_eff - 1.8897 + epsilon2 = -1.8153*chi_eff + 1.6557 + alpha2 = -323./224 + 451.*eta/168 + alpha3 = (27./8 - 11.*eta/6)*chi_eff + + psi, f1, f2, sigma, f3 = getPhenomCoef(M_tot, eta, chi_eff) + + Afactor_inspiral = (1 + alpha2*PNcoef**2+ alpha3*PNcoef**3) + Afactor_merger = (1 + epsilon1*PNcoef+ epsilon2*PNcoef**2) + omega_merger = Afactor_inspiral/Afactor_merger + omega_ringdown = Afactor_merger/Lorentzian(f2,f2,sigma) + + + phase = 2*jnp.pi*f*params['geocent_time'] - params['phase'] + phase += 3./(128*eta*PNcoef**5) * (1+ jnp.sum(psi*PNcoef**jnp.array([2,3,4,6,7]),axis=1)[:,None]) + + A_overall = M_chirp**(5./6)/local_d*f1**(-7./6) + A_inspiral = (f/f1)**(-7./6) * Afactor_inspiral + A_merger = omega_merger * (f/f1)**(-2./3) * Afactor_merger + A_ringdown = omega_ringdown * Lorentzian(f, f2, sigma) + + amplitude = A_overall * (A_inspiral * jnp.heaviside(f1-f,0) \ + + A_merger * jnp.heaviside(f-f1,1) * jnp.heaviside(f2-f,0) \ + + A_ringdown * jnp.heaviside(f-f2,1))# * jnp.heaviside(f3-f,0)) + + + + totalh = amplitude*jnp.exp(-1j*phase) + hp = totalh * (1/2*(1+jnp.cos(params['theta_jn'])**2)*jnp.cos(2*params['psi'])) + hc = totalh * jnp.cos(params['theta_jn'])*jnp.sin(2*params['psi']) + + return {'plus':hp,'cross':hc} diff --git a/test/waveform_test.py b/test/waveform_test.py new file mode 100644 index 00000000..b20dc4b1 --- /dev/null +++ b/test/waveform_test.py @@ -0,0 +1,100 @@ +import numpy as np +import bilby +import jax.numpy as jnp + +from jax.config import config +from jax import grad +config.update("jax_enable_x64", True) + +# Set the duration and sampling frequency of the data segment that we're +# going to inject the signal into +duration = 4. +sampling_frequency = 2048. +minimum_frequency = 20 + +# Specify the output directory and the name of the simulation. +outdir = 'outdir' +label = 'fast_tutorial' +bilby.core.utils.setup_logger(outdir=outdir, label=label) + +# Set up a random seed for result reproducibility. This is optional! +np.random.seed(88170235) + +# We are going to inject a binary black hole waveform. We first establish a +# dictionary of parameters that includes all of the different waveform +# parameters, including masses of the two black holes (mass_1, mass_2), +# spins of both black holes (a, tilt, phi), etc. +injection_parameters = dict( + mass_1=36., mass_2=29., a_1=0.4, a_2=0.3, tilt_1=0., tilt_2=0., + phi_12=0., phi_jl=0., luminosity_distance=2000., theta_jn=0.4, psi=2.659, + phase=1.3, geocent_time=1126259642.413, ra=1.375, dec=-1.2108) + +# Fixed arguments passed into the source model +waveform_arguments = dict(waveform_approximant='IMRPhenomB', + reference_frequency=50., + minimum_frequency=minimum_frequency) + +# Create the waveform_generator using a LAL BinaryBlackHole source function +waveform_generator = bilby.gw.WaveformGenerator( + duration=duration, sampling_frequency=sampling_frequency, + frequency_domain_source_model=bilby.gw.source.lal_binary_black_hole, + parameter_conversion=bilby.gw.conversion.convert_to_lal_binary_black_hole_parameters, + waveform_arguments=waveform_arguments) + +# Set up interferometers. In this case we'll use two interferometers +# (LIGO-Hanford (H1), LIGO-Livingston (L1). These default to their design +# sensitivity +ifos = bilby.gw.detector.InterferometerList(['H1']) +ifos.set_strain_data_from_power_spectral_densities( + sampling_frequency=sampling_frequency, duration=duration, + start_time=injection_parameters['geocent_time'] - 3) +ifos.inject_signal(waveform_generator=waveform_generator, + parameters=injection_parameters) + +############################################## +# Jax section +############################################## + + +waveform = waveform_generator.frequency_domain_strain() +waveform_frequency = waveform_generator.frequency_array + +psd = ifos[0].power_spectral_density_array +psd_frequency = ifos[0].frequency_array + +waveform_frequency = waveform_frequency[jnp.isfinite(psd)] +psd_frequency = psd_frequency[jnp.isfinite(psd)] +psd = psd[jnp.isfinite(psd)] + +from jaxgw.likelihood.detector_projection import construct_arm, detector_tensor, antenna_response, get_detector_response +from jaxgw.likelihood.utils import inner_product +from jaxgw.waveform.TaylorF2 import TaylorF2 +from jaxgw.waveform.IMRPhenomB import IMRPhenomB, getPhenomCoef, Lorentzian + + +waveform = IMRPhenomB(waveform_frequency, injection_parameters) +H1_lat = 46 + 27. / 60 + 18.528 / 3600 +H1_long = -(119 + 24. / 60 + 27.5657 / 3600) +H1_xarm_azimuth = 125.9994 +H1_yarm_azimuth = 215.9994 +H1_xarm_tilt = -6.195e-4 +H1_yarm_tilt = 1.25e-5 + +H1_arm1 = construct_arm(H1_long, H1_lat, H1_xarm_tilt, H1_xarm_azimuth) +H1_arm2 = construct_arm(H1_long, H1_lat, H1_yarm_tilt, H1_yarm_azimuth) + +H1 = detector_tensor(H1_arm1, H1_arm2) + +strain = get_detector_response(waveform,injection_parameters,H1) +jaxgw_snr = inner_product(strain, strain, waveform_frequency, psd, psd_frequency) +d_jaxgw_snr = grad(inner_product)(strain, strain, waveform_frequency, psd, psd_frequency) + +def jax_likelihood(params, data, data_f, PSD, PSD_f): + waveform = IMRPhenomB(data_f, params) + waveform = get_detector_response(waveform, params, H1) + output = inner_product(waveform, data, data_f, PSD, PSD_f) + return output + +dlikelihood = grad(jax_likelihood)(injection_parameters, strain, waveform_frequency, psd, psd_frequency) + + From a4aa8ae2158c4a6feac03d6d2d5afcb028b4a207 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Tue, 28 Sep 2021 15:46:00 -0400 Subject: [PATCH 014/300] Add IMRphenomC file, TaylorF2 part almost complete --- jaxgw/waveform/IMRPhenomC.py | 202 +++++++++++++++++++++++++---------- 1 file changed, 148 insertions(+), 54 deletions(-) diff --git a/jaxgw/waveform/IMRPhenomC.py b/jaxgw/waveform/IMRPhenomC.py index 1bb954b3..2ffcc16c 100644 --- a/jaxgw/waveform/IMRPhenomC.py +++ b/jaxgw/waveform/IMRPhenomC.py @@ -5,78 +5,172 @@ from jax import jit from jaxgw.utils import * +euler_gamma = 0.577215664901532860606512090082 + @jit def Lorentzian(x, x0, gamma): return (gamma**2/((x-x0)**2+gamma**2/4)) -@jit -def getPhenomCoef(M, eta, chi): - psi_coef = jnp.array([[3715/756, -920.9, 492.1, 135, 6742, -1053, -1.34*1e4], \ - [-16*jnp.pi + 113*chi/3, 1.702*1e4, -9566, -2182, -1.214*1e5, 2.075*1e4, 2.386*1e5], \ - [15293365/508032 - 405*chi**2/8, -1.254*1e5, 7.507*1e4, 1.338*1e4, 8.735*1e5, -1.657*1e5, -1.694*1e6], \ - [0, -8.898*1e5, 6.31*1e5, 5.068*1e4, 5.981*1e6, -1.415*1e6, -1.128*1e7], \ - [0, 8.696*1e5, -6.71*1e5, -3.008*1e4, -5.838*1e6, 1.514*1e6, 1.089*1e7]]) - - mu_coef = jnp.array([[1-4.455*(1-chi)**0.217+3.521*(1-chi)**0.26, 0.6437, 0.827, -0.2706, -0.05822, -3.935, -7.092], \ - [(1-0.63*(1-chi)**0.3)/2, 0.1469, -0.1228, -0.02609, -0.0249, 0.1701, 2.325], \ - [(1-0.63*(1-chi)**0.3)*((1-chi)**0.45)/4, -0.4098, -0.03523, 0.1008, 1.829, -0.02017, -2.87], \ - [0.3236 + 0.04894*chi + 0.01346*chi**2, -0.1331, -0.08172, 0.1451, -0.2714, 0.1279, 4.922]]) - psi = psi_coef[:,0] + eta * (psi_coef[:,1] + psi_coef[:,2]*chi + psi_coef[:,3]*chi**2)\ - + eta**2 * (psi_coef[:,4] + psi_coef[:,5]*chi)\ - + eta**3 * psi_coef[:,6] - f1, f2, sigma, f3 = (mu_coef[:,0] + eta * (mu_coef[:,1] + mu_coef[:,2]*chi + mu_coef[:,3]*chi**2)\ - + eta**2 * (mu_coef[:,4] + mu_coef[:,5]*chi)\ - + eta**3 * mu_coef[:,6]) / (jnp.pi * M) - return psi, f1, f2, sigma, f3 +def PNAmplitudeAndPhasing(f,m1,m2,chi1,chi2): -@jit -def IMRPhenomB(f,params): + f = f[:,None] + x = (jnp.pi*f)**(2./3) # I assume in the m in 3.12 is the m from harmonics instead of mass + eta = m1*m2/(m1+m2)**2 + chi_eff = (m1*chi1+m2*chi2)/(m1+m2) - f = f[:,None] - local_m1 = params['mass_1']*Msun - local_m2 = params['mass_2']*Msun - local_d = params['luminosity_distance']*Mpc - local_spin1 = params['a_1'] - local_spin2 = params['a_2'] +# Taylor T4 expansion coefficient from A3 for 3.6, needed for amplitude in fourier space + T4_alpha = 64.*eta/5.* x**5*jnp.array([x**0, \ + + x * (-7.43/3.36 - 11.*eta/4.),\ + + x**(3./2)*(4.*jnp.pi - 11.3*chi_eff/1.2 + 19.*eta*(chi1+chi2)/6.),\ + + x**(2) * (3.4103/1.8144 + 5*chi_eff**2 + eta*(13.661/2.016 - chi1*chi2/8.) + 5.9*eta**2/1.8),\ + + x**(5./2) * (-jnp.pi*(41.59/6.72 + 189.*eta/8.) - chi_eff*(31.571/1.008 - 116.5*eta/2.4) +\ + (chi1+chi2)*(21.863*eta/1.008 - 79.*eta**2/6.) - 3*chi_eff**3/4. +\ + 9.*eta*chi_eff*chi1*chi2/4.),\ + + x**(3.) * (164.47322263/1.39708800 - 17.12*euler_gamma/1.05 +\ + 16.*jnp.pi**2/3 - 8.56*jnp.log(16.*x)/1.05 +\ + eta*(45.1*jnp.pi**2/4.8 - 561.98689/2.17728) +\ + 5.41*eta**2/8.96 - 5.605*eta**3/2.592 - 80.*jnp.pi*chi_eff/3. +\ + eta*(chi1+chi2)*(20.*jnp.pi/3. - 113.5*chi_eff/3.6) +\ + chi_eff**2*(64.153/1.008 - 45.7*eta/3.6) -\ + chi1*chi2*(7.87*eta/1.44 - 30.37*eta**2/1.44)),\ + + + x**(7./2)* (-jnp.pi*(4.415/4.032 - 358.675*eta/6.048 - 91.495*eta**2/1.512) -\ + chi_eff*(252.9407/2.7216 - 845.827*eta/6.048 + 415.51*eta**2/8.64) +\ + (chi1+chi2)*(158.0239*eta/5.4432 - 451.597*eta**2/6.048 + 20.45*eta**3/4.32 +\ + 107.*eta*chi_eff**2/6. - 5.*eta**2*chi1*chi2/24.) +\ + 12.*jnp.pi*chi_eff**2 - chi_eff**3*(150.5/2.4 + eta/8.) +\ + chi_eff*chi1*chi2*(10.1*eta/2.4 + 3.*eta**2/8.))]) + - M_tot = local_m1+local_m2 - eta = local_m1*local_m2/(local_m1+local_m2)**2 - chi_eff = (local_spin1*local_m1 + local_spin2*local_m2)/M_tot - M_chirp = eta**(3./5)*M_tot - PNcoef = (jnp.pi*M_tot*f)**(1./3) + T4_A = 8.*eta*jnp.sqrt(jnp.pi/5.)*x*jnp.array([x**0,\ - epsilon1 = 1.4547*chi_eff - 1.8897 - epsilon2 = -1.8153*chi_eff + 1.6557 - alpha2 = -323./224 + 451.*eta/168 - alpha3 = (27./8 - 11.*eta/6)*chi_eff + x * ((-107. + 55.*eta)/42.),\ - psi, f1, f2, sigma, f3 = getPhenomCoef(M_tot, eta, chi_eff) + x**(3./2)*(2.*jnp.pi - 4.*chi_eff/3. + 2.*eta*(chi1+chi2)/3.),\ - Afactor_inspiral = (1 + alpha2*PNcoef**2+ alpha3*PNcoef**3) - Afactor_merger = (1 + epsilon1*PNcoef+ epsilon2*PNcoef**2) - omega_merger = Afactor_inspiral/Afactor_merger - omega_ringdown = Afactor_merger/Lorentzian(f2,f2,sigma) + x**(2.)*(-2.173/1.512 - eta*(10.69/2.16 - 2.*chi1*chi2) + 2.047*eta**2/1.512),\ + x**(5./2)*(-10.7*jnp.pi/2.1 + eta*(3.4*jnp.pi/2.1-24.*1j)),\ - phase = 2*jnp.pi*f*params['geocent_time'] - params['phase'] - phase += 3./(128*eta*PNcoef**5) * (1+ jnp.sum(psi*PNcoef**jnp.array([2,3,4,6,7]),axis=1)[:,None]) + x**(3.)*(270.27409/6.46800 - 8.56*euler_gamma/1.05 +\ + 2.*jnp.pi**2/3. +\ + eta*(4.1*jnp.pi**2/9.6 - 27.8185/3.3264) -\ + 20.261*eta**2/2.772 + 11.4635*eta**3/9.9792 +\ + 4.28*(1j*jnp.pi-jnp.log(16.*x))/1.05)]) - A_overall = M_chirp**(5./6)/local_d*f1**(-7./6) - A_inspiral = (f/f1)**(-7./6) * Afactor_inspiral - A_merger = omega_merger * (f/f1)**(-2./3) * Afactor_merger - A_ringdown = omega_ringdown * Lorentzian(f, f2, sigma) +# Taylor F2 Phasing coefficient from A4 + F2_alpha = 3.0/(128.0 * eta)*(jnp.pi)**(-5./3)*jnp.array([f**0,\ - amplitude = A_overall * (A_inspiral * jnp.heaviside(f1-f,0) \ - + A_merger * jnp.heaviside(f-f1,1) * jnp.heaviside(f2-f,0) \ - + A_ringdown * jnp.heaviside(f-f2,1))# * jnp.heaviside(f3-f,0)) + (jnp.pi*f)**(2./3)*((3715./756.) + (55.*eta/9.0)),\ + (jnp.pi*f)**(3./3)*(-16.0*jnp.pi + (113./3.)*chi_eff - 38.*eta*(chi1+chi2)/3.),\ + (jnp.pi*f)**(4./3)*((152.93365/5.08032) - 50.*chi_eff**2 + eta*(271.45/5.04 + 1.25*chi1*chi2) + \ + 3085.*eta**2/72.),\ - totalh = amplitude*jnp.exp(-1j*phase) - hp = totalh * (1/2*(1+jnp.cos(params['theta_jn'])**2)*jnp.cos(2*params['psi'])) - hc = totalh * jnp.cos(params['theta_jn'])*jnp.sin(2*params['psi']) + (jnp.pi*f)**(5./3)*((1+ jnp.log(jnp.pi*f))*(jnp.pi*(386.45/7.56 - 65.*eta/9.) - \ + chi_eff*(735.505/2.268 + 130.*eta/9.) + (chi1+chi2)*(1285.0*eta/8.1 + 170.*eta**2/9.) -\ + 10.*chi_eff**3/3. + 10.*eta*chi_eff*(chi1*chi2))), \ + + (jnp.pi*f)**(6./3)*(11583.231236531/4.694215680 - 640.0*jnp.pi**2/3. - \ + 6848.0*euler_gamma/21. - 684.8*jnp.log(64.*jnp.pi*f)/6.3 + \ + eta*(2255.*jnp.pi**2/12. - 15737.765635/3.048192) + \ + 76.055*eta**2/1.728 - (127.825*eta**3/1.296) + \ + 2920.*jnp.pi*chi_eff/3. - (175. - 1490.*eta)*chi_eff**2/3. - \ + (1120.*jnp.pi/3. - 1085.*chi_eff/3.)*eta*(chi1+chi2) + \ + (269.45*eta/3.36 - 2365.*eta**2/6.)*chi1*chi2), \ + + (jnp.pi*f)**(7./3)*(jnp.pi*(770.96675/2.54016 + 378.515*eta/1.512 - 740.45*eta**2/7.56) - \ + chi_eff*(20373.952415/3.048192 + 1509.35*eta/2.24 - 5786.95*eta**2/4.32) + \ + (chi1+chi2)*(4862.041225*eta/1.524096 + 1189.775*eta**2/1.008 - \ + 717.05*eta**3/2.16 - 830.*eta*chi_eff**2/3. + 35.*eta**2*chi1*chi2/3.) - \ + 560.*jnp.pi*chi_eff**2 + 20.*jnp.pi*eta*chi1*chi2 + \ + chi_eff**3*(945.55/1.68 - 85.*eta) + chi_eff*chi1*chi2*(396.65*eta/1.68 + 255.*eta**2))]) + + + return T4_alpha, T4_A, F2_alpha + + + + +@jit +def getPhenomCoef(M, eta, chi): + + alpha_coef = jnp.array([[-2.417 * 1e-3, -1.093 * 1e-3, -1.917 * 1e-2, 7.267 * 1e-2, -2.504 * 1e-1],\ + [5.962 * 1e-1, -5.6 * 1e-2, 1.52 * 1e-1, -2.97, 1.312 * 1e1],\ + [-3.283 * 1e1, 8.859, 2.931 * 1e1, 7.954 * 1e1, -4.349 * 1e2],\ + [1.619 * 1e2, -4.702 * 1e1, -1.751 * 1e2, -3.225 * 1e2, 1.587 * 1e3],\ + [-6.32 * 1e2, 2.463 * 1e2, 1.048 * 1e3, 3.355 * 1e2, -5.115 * 1e3],\ + [-4.809 * 1e1, -3.643 * 1e2, -5.215 * 1e2, 1.87 * 1e3, 7.354 * 1e2]]) + + gamma_coef = jnp.array([4.149, -4.07, -8.752 * 1e1, -4.897 * 1e1, 6.665 * 1e2]) + + delta_coef = jnp.array([[-5.472 * 1e-2, 2.094 * 1e-2, 3.554 * 1e-1, 1.151 * 1e-1, 9.64 * 1e-1], \ + [-1.235, 3.423*1e-1, 6.062, 5.949, -1.069*1e1]]) + + + + return psi, f1, f2, sigma, f3 - return {'plus':hp,'cross':hc} +#return p; +# } +# +#@jit +#def IMRPhenomB(f,params): +# +# +# f = f[:,None] +# +# local_m1 = params['mass_1']*Msun +# local_m2 = params['mass_2']*Msun +# local_d = params['luminosity_distance']*Mpc +# local_spin1 = params['a_1'] +# local_spin2 = params['a_2'] +# +# M_tot = local_m1+local_m2 +# eta = local_m1*local_m2/(local_m1+local_m2)**2 +# chi_eff = (local_spin1*local_m1 + local_spin2*local_m2)/M_tot +# M_chirp = eta**(3./5)*M_tot +# PNcoef = (jnp.pi*M_tot*f)**(1./3) +# +# epsilon1 = 1.4547*chi_eff - 1.8897 +# epsilon2 = -1.8153*chi_eff + 1.6557 +# alpha2 = -323./224 + 451.*eta/168 +# alpha3 = (27./8 - 11.*eta/6)*chi_eff +# +# psi, f1, f2, sigma, f3 = getPhenomCoef(M_tot, eta, chi_eff) +# +# Afactor_inspiral = (1 + alpha2*PNcoef**2+ alpha3*PNcoef**3) +# Afactor_merger = (1 + epsilon1*PNcoef+ epsilon2*PNcoef**2) +# omega_merger = Afactor_inspiral/Afactor_merger +# omega_ringdown = Afactor_merger/Lorentzian(f2,f2,sigma) +# +# +# phase = 2*jnp.pi*f*params['geocent_time'] - params['phase'] +# phase += 3./(128*eta*PNcoef**5) * (1+ jnp.sum(psi*PNcoef**jnp.array([2,3,4,6,7]),axis=1)[:,None]) +# +# A_overall = M_chirp**(5./6)/local_d*f1**(-7./6) +# A_inspiral = (f/f1)**(-7./6) * Afactor_inspiral +# A_merger = omega_merger * (f/f1)**(-2./3) * Afactor_merger +# A_ringdown = omega_ringdown * Lorentzian(f, f2, sigma) +# +# amplitude = A_overall * (A_inspiral * jnp.heaviside(f1-f,0) \ +# + A_merger * jnp.heaviside(f-f1,1) * jnp.heaviside(f2-f,0) \ +# + A_ringdown * jnp.heaviside(f-f2,1))# * jnp.heaviside(f3-f,0)) +# +# +# +# totalh = amplitude*jnp.exp(-1j*phase) +# hp = totalh * (1/2*(1+jnp.cos(params['theta_jn'])**2)*jnp.cos(2*params['psi'])) +# hc = totalh * jnp.cos(params['theta_jn'])*jnp.sin(2*params['psi']) +# +# return {'plus':hp,'cross':hc} From 230cc553230cb3e024f6980d17137bf74fe6122d Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Tue, 28 Sep 2021 16:00:42 -0400 Subject: [PATCH 015/300] PhenomC TaylorT4 part tested against paper succesfully --- jaxgw/waveform/IMRPhenomC.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/jaxgw/waveform/IMRPhenomC.py b/jaxgw/waveform/IMRPhenomC.py index 2ffcc16c..79f9a2ea 100644 --- a/jaxgw/waveform/IMRPhenomC.py +++ b/jaxgw/waveform/IMRPhenomC.py @@ -11,7 +11,7 @@ def Lorentzian(x, x0, gamma): return (gamma**2/((x-x0)**2+gamma**2/4)) - +@jit def PNAmplitudeAndPhasing(f,m1,m2,chi1,chi2): f = f[:,None] From 3e9b6fa940d48a2458b3c6d716a65e2888004f96 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Tue, 28 Sep 2021 17:36:11 -0400 Subject: [PATCH 016/300] Functional form of the waveform completed. Still need final spin fit and tuning to match lal --- jaxgw/waveform/IMRPhenomC.py | 157 ++++++++++++++++++----------------- 1 file changed, 82 insertions(+), 75 deletions(-) diff --git a/jaxgw/waveform/IMRPhenomC.py b/jaxgw/waveform/IMRPhenomC.py index 79f9a2ea..b23ed3cb 100644 --- a/jaxgw/waveform/IMRPhenomC.py +++ b/jaxgw/waveform/IMRPhenomC.py @@ -12,9 +12,16 @@ def Lorentzian(x, x0, gamma): return (gamma**2/((x-x0)**2+gamma**2/4)) @jit -def PNAmplitudeAndPhasing(f,m1,m2,chi1,chi2): +def smoothing_plus(f,f0,d): + return (1+jnp.tanh(4*(f-f0)/d))/2 + +@jit +def smoothing_minus(f,f0,d): + return (1-jnp.tanh(4*(f-f0)/d))/2 + +@jit +def PNAmplitudeAndPhasing(f,m1,m2,chi1,chi2,t0,phase0): - f = f[:,None] x = (jnp.pi*f)**(2./3) # I assume in the m in 3.12 is the m from harmonics instead of mass eta = m1*m2/(m1+m2)**2 @@ -50,7 +57,7 @@ def PNAmplitudeAndPhasing(f,m1,m2,chi1,chi2): 12.*jnp.pi*chi_eff**2 - chi_eff**3*(150.5/2.4 + eta/8.) +\ chi_eff*chi1*chi2*(10.1*eta/2.4 + 3.*eta**2/8.))]) - +# Taylor T4 amplitude coefficient from A5 T4_A = 8.*eta*jnp.sqrt(jnp.pi/5.)*x*jnp.array([x**0,\ x * ((-107. + 55.*eta)/42.),\ @@ -97,80 +104,80 @@ def PNAmplitudeAndPhasing(f,m1,m2,chi1,chi2): chi_eff**3*(945.55/1.68 - 85.*eta) + chi_eff*chi1*chi2*(396.65*eta/1.68 + 255.*eta**2))]) - return T4_alpha, T4_A, F2_alpha + amplitude = jnp.sum(T4_A,axis=0)*jnp.sqrt(jnp.pi/jnp.sum(T4_alpha,axis=0)) + phase = 2*jnp.pi*f*t0 - phase0 - jnp.pi/4 + jnp.sum(F2_alpha,axis=0) + + return amplitude, phase + + +alpha_coef = jnp.array([[-2.417 * 1e-3, -1.093 * 1e-3, -1.917 * 1e-2, 7.267 * 1e-2, -2.504 * 1e-1],\ + [5.962 * 1e-1, -5.6 * 1e-2, 1.52 * 1e-1, -2.97, 1.312 * 1e1],\ + [-3.283 * 1e1, 8.859, 2.931 * 1e1, 7.954 * 1e1, -4.349 * 1e2],\ + [1.619 * 1e2, -4.702 * 1e1, -1.751 * 1e2, -3.225 * 1e2, 1.587 * 1e3],\ + [-6.32 * 1e2, 2.463 * 1e2, 1.048 * 1e3, 3.355 * 1e2, -5.115 * 1e3],\ + [-4.809 * 1e1, -3.643 * 1e2, -5.215 * 1e2, 1.87 * 1e3, 7.354 * 1e2]]) + +gamma_coef = jnp.array([4.149, -4.07, -8.752 * 1e1, -4.897 * 1e1, 6.665 * 1e2]) + +delta_coef = jnp.array([[-5.472 * 1e-2, 2.094 * 1e-2, 3.554 * 1e-1, 1.151 * 1e-1, 9.64 * 1e-1], \ + [-1.235, 3.423*1e-1, 6.062, 5.949, -1.069*1e1]]) - @jit -def getPhenomCoef(M, eta, chi): - - alpha_coef = jnp.array([[-2.417 * 1e-3, -1.093 * 1e-3, -1.917 * 1e-2, 7.267 * 1e-2, -2.504 * 1e-1],\ - [5.962 * 1e-1, -5.6 * 1e-2, 1.52 * 1e-1, -2.97, 1.312 * 1e1],\ - [-3.283 * 1e1, 8.859, 2.931 * 1e1, 7.954 * 1e1, -4.349 * 1e2],\ - [1.619 * 1e2, -4.702 * 1e1, -1.751 * 1e2, -3.225 * 1e2, 1.587 * 1e3],\ - [-6.32 * 1e2, 2.463 * 1e2, 1.048 * 1e3, 3.355 * 1e2, -5.115 * 1e3],\ - [-4.809 * 1e1, -3.643 * 1e2, -5.215 * 1e2, 1.87 * 1e3, 7.354 * 1e2]]) - - gamma_coef = jnp.array([4.149, -4.07, -8.752 * 1e1, -4.897 * 1e1, 6.665 * 1e2]) +def getPhenomCoef(eta, chi): + eta_chi = jnp.array([chi,chi**2,eta*chi,eta,eta**2]) + alpha = jnp.sum(alpha_coef*eta_chi,axis=1) + gamma = jnp.sum(gamma_coef*eta_chi) + delta = jnp.sum(delta_coef*eta_chi,axis=1) + return alpha, gamma, delta + +def getFinalSpin(m1,m2,a1,a2): + return 0.7 + +@jit +def IMRPhenomC(f,params): + + f = f[:,None] + + local_m1 = params['mass_1']*Msun + local_m2 = params['mass_2']*Msun + local_d = params['luminosity_distance']*Mpc + local_spin1 = params['a_1'] + local_spin2 = params['a_2'] + + M_tot = local_m1+local_m2 + eta = local_m1*local_m2/(local_m1+local_m2)**2 + chi_eff = (local_spin1*local_m1 + local_spin2*local_m2)/M_tot - delta_coef = jnp.array([[-5.472 * 1e-2, 2.094 * 1e-2, 3.554 * 1e-1, 1.151 * 1e-1, 9.64 * 1e-1], \ - [-1.235, 3.423*1e-1, 6.062, 5.949, -1.069*1e1]]) - - - - return psi, f1, f2, sigma, f3 - -#return p; -# } -# -#@jit -#def IMRPhenomB(f,params): -# -# -# f = f[:,None] -# -# local_m1 = params['mass_1']*Msun -# local_m2 = params['mass_2']*Msun -# local_d = params['luminosity_distance']*Mpc -# local_spin1 = params['a_1'] -# local_spin2 = params['a_2'] -# -# M_tot = local_m1+local_m2 -# eta = local_m1*local_m2/(local_m1+local_m2)**2 -# chi_eff = (local_spin1*local_m1 + local_spin2*local_m2)/M_tot -# M_chirp = eta**(3./5)*M_tot -# PNcoef = (jnp.pi*M_tot*f)**(1./3) -# -# epsilon1 = 1.4547*chi_eff - 1.8897 -# epsilon2 = -1.8153*chi_eff + 1.6557 -# alpha2 = -323./224 + 451.*eta/168 -# alpha3 = (27./8 - 11.*eta/6)*chi_eff -# -# psi, f1, f2, sigma, f3 = getPhenomCoef(M_tot, eta, chi_eff) -# -# Afactor_inspiral = (1 + alpha2*PNcoef**2+ alpha3*PNcoef**3) -# Afactor_merger = (1 + epsilon1*PNcoef+ epsilon2*PNcoef**2) -# omega_merger = Afactor_inspiral/Afactor_merger -# omega_ringdown = Afactor_merger/Lorentzian(f2,f2,sigma) -# -# -# phase = 2*jnp.pi*f*params['geocent_time'] - params['phase'] -# phase += 3./(128*eta*PNcoef**5) * (1+ jnp.sum(psi*PNcoef**jnp.array([2,3,4,6,7]),axis=1)[:,None]) -# -# A_overall = M_chirp**(5./6)/local_d*f1**(-7./6) -# A_inspiral = (f/f1)**(-7./6) * Afactor_inspiral -# A_merger = omega_merger * (f/f1)**(-2./3) * Afactor_merger -# A_ringdown = omega_ringdown * Lorentzian(f, f2, sigma) -# -# amplitude = A_overall * (A_inspiral * jnp.heaviside(f1-f,0) \ -# + A_merger * jnp.heaviside(f-f1,1) * jnp.heaviside(f2-f,0) \ -# + A_ringdown * jnp.heaviside(f-f2,1))# * jnp.heaviside(f3-f,0)) -# -# -# -# totalh = amplitude*jnp.exp(-1j*phase) -# hp = totalh * (1/2*(1+jnp.cos(params['theta_jn'])**2)*jnp.cos(2*params['psi'])) -# hc = totalh * jnp.cos(params['theta_jn'])*jnp.sin(2*params['psi']) -# -# return {'plus':hp,'cross':hc} + final_spin = getFinalSpin(local_m1, local_m2, local_spin1, local_spin2) + f_rd = 1.//(2*jnp.pi*(M_tot))*(1.5251 - 1.1568*(1-final_spin)**0.1292) + decay_time = 0.7 + 1.4187*(1-final_spin)**(-0.499) # Q in the paper + +# Constructing phase of the waveform + + A_PN, phase_PN = PNAmplitudeAndPhasing(f,local_m1,local_m2,local_spin1,local_spin2,params['geocent_time'],params['phase']) + alpha, gamma, delta = getPhenomCoef(eta,chi_eff) + + + phase_PM = 1./eta*jnp.sum(alpha*f**jnp.array([-5./3,-3./3,-1./3,0,2./3,1]),axis=1) + + beta_1 = 1./eta*jnp.sum(alpha*f_rd**jnp.array([-5./3,-3./3,-1./3,0,2./3,1])) + beta_2 = 1./eta*jnp.sum(jnp.array([-5./3,-3./3,-1./3,2./3,1])*alpha[jnp.array([0,1,2,4,5])]*f_rd**jnp.array([-8./3,-6./3,-4./3,-1./3,0])) + phase_RD = beta_1 + beta_2*f + + phase = phase_PN*smoothing_minus(f,0.1*f_rd,0.005) + phase_PN*smoothing_plus(f,0.1*f_rd,0.005)*smoothing_minus(f,f_rd,0.005) + phase_PN*smoothing_plus(f,f_rd,0.005) + +# Constructing amplitude of the waveform + + A_PM = A_PN + gamma*f**(5./6) + + A_RD = delta[1]*Lorentzian(f,f_rd,delta[2]*decay_time)*f**(-7./6) + + amplitude = A_PM *smoothing_minus(f,0.98*f_rd,0.015) + A_RD*smoothing_plus(f,0.98*f_rd,0.015) + + totalh = amplitude*jnp.exp(-1j*phase) + hp = totalh * (1/2*(1+jnp.cos(params['theta_jn'])**2)*jnp.cos(2*params['psi'])) + hc = totalh * jnp.cos(params['theta_jn'])*jnp.sin(2*params['psi']) + + return {'plus':hp,'cross':hc} From 358986abe91296fa9e6c645bdc28339322150654 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Tue, 28 Sep 2021 17:46:18 -0400 Subject: [PATCH 017/300] Fix unit problem in ringdown frequency --- jaxgw/waveform/IMRPhenomC.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/jaxgw/waveform/IMRPhenomC.py b/jaxgw/waveform/IMRPhenomC.py index b23ed3cb..4852538b 100644 --- a/jaxgw/waveform/IMRPhenomC.py +++ b/jaxgw/waveform/IMRPhenomC.py @@ -151,8 +151,9 @@ def IMRPhenomC(f,params): chi_eff = (local_spin1*local_m1 + local_spin2*local_m2)/M_tot final_spin = getFinalSpin(local_m1, local_m2, local_spin1, local_spin2) - f_rd = 1.//(2*jnp.pi*(M_tot))*(1.5251 - 1.1568*(1-final_spin)**0.1292) + f_rd = 1./(2*jnp.pi*(M_tot/Msun))*(1.5251 - 1.1568*(1-final_spin)**0.1292) decay_time = 0.7 + 1.4187*(1-final_spin)**(-0.499) # Q in the paper + print(f_rd) # Constructing phase of the waveform From e845ef361581c80d4e8dd46c35d727dc60bf2462 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Tue, 28 Sep 2021 20:45:17 -0400 Subject: [PATCH 018/300] Finally the amplitude start to work, still need to get the final spin and check phases --- jaxgw/waveform/IMRPhenomC.py | 19 +++++++++++-------- 1 file changed, 11 insertions(+), 8 deletions(-) diff --git a/jaxgw/waveform/IMRPhenomC.py b/jaxgw/waveform/IMRPhenomC.py index 4852538b..f6222543 100644 --- a/jaxgw/waveform/IMRPhenomC.py +++ b/jaxgw/waveform/IMRPhenomC.py @@ -104,7 +104,7 @@ def PNAmplitudeAndPhasing(f,m1,m2,chi1,chi2,t0,phase0): chi_eff**3*(945.55/1.68 - 85.*eta) + chi_eff*chi1*chi2*(396.65*eta/1.68 + 255.*eta**2))]) - amplitude = jnp.sum(T4_A,axis=0)*jnp.sqrt(jnp.pi/jnp.sum(T4_alpha,axis=0)) + amplitude = jnp.sum(T4_A,axis=0)*jnp.sqrt(jnp.pi/(3./2*jnp.sqrt(x)*jnp.sum(T4_alpha,axis=0))) phase = 2*jnp.pi*f*t0 - phase0 - jnp.pi/4 + jnp.sum(F2_alpha,axis=0) return amplitude, phase @@ -135,7 +135,6 @@ def getPhenomCoef(eta, chi): def getFinalSpin(m1,m2,a1,a2): return 0.7 -@jit def IMRPhenomC(f,params): f = f[:,None] @@ -146,13 +145,16 @@ def IMRPhenomC(f,params): local_spin1 = params['a_1'] local_spin2 = params['a_2'] + M_tot = local_m1+local_m2 eta = local_m1*local_m2/(local_m1+local_m2)**2 chi_eff = (local_spin1*local_m1 + local_spin2*local_m2)/M_tot - + f = f*M_tot + final_spin = getFinalSpin(local_m1, local_m2, local_spin1, local_spin2) - f_rd = 1./(2*jnp.pi*(M_tot/Msun))*(1.5251 - 1.1568*(1-final_spin)**0.1292) + f_rd = 1./(2*jnp.pi*(M_tot))*(1.5251 - 1.1568*(1-final_spin)**0.1292) decay_time = 0.7 + 1.4187*(1-final_spin)**(-0.499) # Q in the paper + print(f_rd) # Constructing phase of the waveform @@ -171,13 +173,14 @@ def IMRPhenomC(f,params): # Constructing amplitude of the waveform - A_PM = A_PN + gamma*f**(5./6) + A_PM = A_PN + gamma*(f)**(5./6) - A_RD = delta[1]*Lorentzian(f,f_rd,delta[2]*decay_time)*f**(-7./6) + A_RD = delta[0]*Lorentzian(f,f_rd*M_tot,delta[1]*f_rd*M_tot/decay_time)*f**(-7./6) - amplitude = A_PM *smoothing_minus(f,0.98*f_rd,0.015) + A_RD*smoothing_plus(f,0.98*f_rd,0.015) + amplitude = A_PM *smoothing_minus(f,0.98*f_rd*M_tot,0.015) + A_RD*smoothing_plus(f,0.98*f_rd*M_tot,0.015) + #amplitude = A_RD*smoothing_plus(f,0.98*f_rd,0.015) - totalh = amplitude*jnp.exp(-1j*phase) + totalh = amplitude*jnp.exp(-1j*phase)/local_d hp = totalh * (1/2*(1+jnp.cos(params['theta_jn'])**2)*jnp.cos(2*params['psi'])) hc = totalh * jnp.cos(params['theta_jn'])*jnp.sin(2*params['psi']) From 0cdb9b2fc382f79d0f26c7a15c5d8398f927a4dc Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Wed, 29 Sep 2021 09:31:35 -0400 Subject: [PATCH 019/300] IMRPhenomC phase could still be wrong, and amplitude normalization is also weird --- jaxgw/likelihood/utils.py | 15 ++++++++++----- jaxgw/waveform/IMRPhenomC.py | 16 ++++++++++------ 2 files changed, 20 insertions(+), 11 deletions(-) diff --git a/jaxgw/likelihood/utils.py b/jaxgw/likelihood/utils.py index af2baf6b..1f571059 100644 --- a/jaxgw/likelihood/utils.py +++ b/jaxgw/likelihood/utils.py @@ -1,7 +1,12 @@ import jax.numpy as jnp +from jax import jit -def inner_product(h1, h2, frequency, PSD, PSD_frequency): - psd_interp = jnp.interp(frequency, PSD_frequency, PSD) - df = frequency[1] - frequency[0] - integrand = jnp.conj(h1)* h2 / psd_interp - return 4. * jnp.real(jnp.sum(integrand)*df) +@jit +def inner_product(h1, h2, frequency, PSD): + """ + Do PSD interpolation outside the inner product loop to speed up the evaluation + """ + #psd_interp = jnp.interp(frequency, PSD_frequency, PSD) + df = frequency[1] - frequency[0] + integrand = jnp.conj(h1)* h2 / PSD + return 4. * jnp.real(jnp.sum(integrand)*df) diff --git a/jaxgw/waveform/IMRPhenomC.py b/jaxgw/waveform/IMRPhenomC.py index f6222543..822f72f1 100644 --- a/jaxgw/waveform/IMRPhenomC.py +++ b/jaxgw/waveform/IMRPhenomC.py @@ -132,9 +132,13 @@ def getPhenomCoef(eta, chi): delta = jnp.sum(delta_coef*eta_chi,axis=1) return alpha, gamma, delta -def getFinalSpin(m1,m2,a1,a2): - return 0.7 +def getFinalSpin(eta,chi_eff): + finspin = chi_eff - 0.129*chi_eff**2*eta -0.384*eta**2*chi_eff -2.686*eta*chi_eff \ + + 2.*jnp.sqrt(3.)*eta -3.454*eta**2 + 2.353*eta**3 + return finspin + +@jit def IMRPhenomC(f,params): f = f[:,None] @@ -148,15 +152,14 @@ def IMRPhenomC(f,params): M_tot = local_m1+local_m2 eta = local_m1*local_m2/(local_m1+local_m2)**2 + M_chirp = M_tot*eta**(3./5) chi_eff = (local_spin1*local_m1 + local_spin2*local_m2)/M_tot f = f*M_tot - final_spin = getFinalSpin(local_m1, local_m2, local_spin1, local_spin2) + final_spin = getFinalSpin(eta,chi_eff) f_rd = 1./(2*jnp.pi*(M_tot))*(1.5251 - 1.1568*(1-final_spin)**0.1292) decay_time = 0.7 + 1.4187*(1-final_spin)**(-0.499) # Q in the paper - print(f_rd) - # Constructing phase of the waveform A_PN, phase_PN = PNAmplitudeAndPhasing(f,local_m1,local_m2,local_spin1,local_spin2,params['geocent_time'],params['phase']) @@ -180,7 +183,8 @@ def IMRPhenomC(f,params): amplitude = A_PM *smoothing_minus(f,0.98*f_rd*M_tot,0.015) + A_RD*smoothing_plus(f,0.98*f_rd*M_tot,0.015) #amplitude = A_RD*smoothing_plus(f,0.98*f_rd,0.015) - totalh = amplitude*jnp.exp(-1j*phase)/local_d + amplitude_factor = 2. * jnp.sqrt(5. / (64.*jnp.pi)) * M_tot**2 / local_d + totalh = amplitude*jnp.exp(-1j*phase)* amplitude_factor hp = totalh * (1/2*(1+jnp.cos(params['theta_jn'])**2)*jnp.cos(2*params['psi'])) hc = totalh * jnp.cos(params['theta_jn'])*jnp.sin(2*params['psi']) From bd31bb768a3a93a356b9e48aadbeb68bbcce3bf0 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Wed, 29 Sep 2021 10:31:48 -0400 Subject: [PATCH 020/300] Move waveform generation routine to Mf space. Amplitude consistent with lalsuite (but not bilby?) --- jaxgw/waveform/IMRPhenomC.py | 16 ++++++++++------ 1 file changed, 10 insertions(+), 6 deletions(-) diff --git a/jaxgw/waveform/IMRPhenomC.py b/jaxgw/waveform/IMRPhenomC.py index 822f72f1..7ec63e99 100644 --- a/jaxgw/waveform/IMRPhenomC.py +++ b/jaxgw/waveform/IMRPhenomC.py @@ -6,6 +6,8 @@ from jaxgw.utils import * euler_gamma = 0.577215664901532860606512090082 +MR_sun = 1.476625061404649406193430731479084713e3 + @jit def Lorentzian(x, x0, gamma): @@ -140,6 +142,9 @@ def getFinalSpin(eta,chi_eff): @jit def IMRPhenomC(f,params): + """ + The amplitude and phase are generated first in unitless Mf space, then scaled with total mass and distance. + """ f = f[:,None] @@ -154,10 +159,10 @@ def IMRPhenomC(f,params): eta = local_m1*local_m2/(local_m1+local_m2)**2 M_chirp = M_tot*eta**(3./5) chi_eff = (local_spin1*local_m1 + local_spin2*local_m2)/M_tot - f = f*M_tot + f = f*Msun final_spin = getFinalSpin(eta,chi_eff) - f_rd = 1./(2*jnp.pi*(M_tot))*(1.5251 - 1.1568*(1-final_spin)**0.1292) + f_rd = 1./(2*jnp.pi*(Msun))*(1.5251 - 1.1568*(1-final_spin)**0.1292)*Msun decay_time = 0.7 + 1.4187*(1-final_spin)**(-0.499) # Q in the paper # Constructing phase of the waveform @@ -178,13 +183,12 @@ def IMRPhenomC(f,params): A_PM = A_PN + gamma*(f)**(5./6) - A_RD = delta[0]*Lorentzian(f,f_rd*M_tot,delta[1]*f_rd*M_tot/decay_time)*f**(-7./6) + A_RD = delta[0]*Lorentzian(f,f_rd,delta[1]*f_rd/decay_time)*f**(-7./6) - amplitude = A_PM *smoothing_minus(f,0.98*f_rd*M_tot,0.015) + A_RD*smoothing_plus(f,0.98*f_rd*M_tot,0.015) - #amplitude = A_RD*smoothing_plus(f,0.98*f_rd,0.015) + amplitude = A_PM *smoothing_minus(f,0.98*f_rd,0.015) + A_RD*smoothing_plus(f,0.98*f_rd,0.015) amplitude_factor = 2. * jnp.sqrt(5. / (64.*jnp.pi)) * M_tot**2 / local_d - totalh = amplitude*jnp.exp(-1j*phase)* amplitude_factor + totalh = amplitude*amplitude_factor#*jnp.exp(-1j*phase)#* amplitude_factor hp = totalh * (1/2*(1+jnp.cos(params['theta_jn'])**2)*jnp.cos(2*params['psi'])) hc = totalh * jnp.cos(params['theta_jn'])*jnp.sin(2*params['psi']) From 81a580dd2d425d09fc052f136f5e10cbc609d1df Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Wed, 29 Sep 2021 10:50:13 -0400 Subject: [PATCH 021/300] Move psd interpolation outside likelihood evaluation --- test/waveform_test.py | 26 +++++++++++++++----------- 1 file changed, 15 insertions(+), 11 deletions(-) diff --git a/test/waveform_test.py b/test/waveform_test.py index b20dc4b1..b423ce60 100644 --- a/test/waveform_test.py +++ b/test/waveform_test.py @@ -3,7 +3,7 @@ import jax.numpy as jnp from jax.config import config -from jax import grad +from jax import grad, jit config.update("jax_enable_x64", True) # Set the duration and sampling frequency of the data segment that we're @@ -25,12 +25,12 @@ # parameters, including masses of the two black holes (mass_1, mass_2), # spins of both black holes (a, tilt, phi), etc. injection_parameters = dict( - mass_1=36., mass_2=29., a_1=0.4, a_2=0.3, tilt_1=0., tilt_2=0., - phi_12=0., phi_jl=0., luminosity_distance=2000., theta_jn=0.4, psi=2.659, + mass_1=36., mass_2=29., a_1=0., a_2=0., tilt_1=0., tilt_2=0., + phi_12=0., phi_jl=0., luminosity_distance=410., theta_jn=0.4, psi=2.659, phase=1.3, geocent_time=1126259642.413, ra=1.375, dec=-1.2108) # Fixed arguments passed into the source model -waveform_arguments = dict(waveform_approximant='IMRPhenomB', +waveform_arguments = dict(waveform_approximant='IMRPhenomC', reference_frequency=50., minimum_frequency=minimum_frequency) @@ -70,9 +70,10 @@ from jaxgw.likelihood.utils import inner_product from jaxgw.waveform.TaylorF2 import TaylorF2 from jaxgw.waveform.IMRPhenomB import IMRPhenomB, getPhenomCoef, Lorentzian +from jaxgw.waveform.IMRPhenomC import IMRPhenomC -waveform = IMRPhenomB(waveform_frequency, injection_parameters) +waveform = IMRPhenomC(waveform_frequency, injection_parameters) H1_lat = 46 + 27. / 60 + 18.528 / 3600 H1_long = -(119 + 24. / 60 + 27.5657 / 3600) H1_xarm_azimuth = 125.9994 @@ -85,16 +86,19 @@ H1 = detector_tensor(H1_arm1, H1_arm2) +psd_interp = jnp.interp(waveform_frequency, psd_frequency, psd) strain = get_detector_response(waveform,injection_parameters,H1) -jaxgw_snr = inner_product(strain, strain, waveform_frequency, psd, psd_frequency) -d_jaxgw_snr = grad(inner_product)(strain, strain, waveform_frequency, psd, psd_frequency) +jaxgw_snr = inner_product(strain, strain, waveform_frequency, psd_interp) +d_jaxgw_snr = grad(inner_product)(strain, strain, waveform_frequency, psd_interp) -def jax_likelihood(params, data, data_f, PSD, PSD_f): - waveform = IMRPhenomB(data_f, params) +@jit +def jax_likelihood(params, data, data_f, PSD): + waveform = IMRPhenomC(data_f, params) waveform = get_detector_response(waveform, params, H1) - output = inner_product(waveform, data, data_f, PSD, PSD_f) + output = inner_product(waveform, data, data_f, PSD) return output -dlikelihood = grad(jax_likelihood)(injection_parameters, strain, waveform_frequency, psd, psd_frequency) + +dlikelihood = grad(jax_likelihood)(injection_parameters, strain, waveform_frequency, psd_interp) From a88261d43013b87079049b1bad4d343d6ef3ca6d Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Wed, 29 Sep 2021 18:19:54 -0400 Subject: [PATCH 022/300] Absorb mass dependency in frequency to waveform generation --- jaxgw/waveform/IMRPhenomC.py | 2 +- test/waveform_test.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/jaxgw/waveform/IMRPhenomC.py b/jaxgw/waveform/IMRPhenomC.py index 7ec63e99..77bbf75c 100644 --- a/jaxgw/waveform/IMRPhenomC.py +++ b/jaxgw/waveform/IMRPhenomC.py @@ -159,7 +159,7 @@ def IMRPhenomC(f,params): eta = local_m1*local_m2/(local_m1+local_m2)**2 M_chirp = M_tot*eta**(3./5) chi_eff = (local_spin1*local_m1 + local_spin2*local_m2)/M_tot - f = f*Msun + f = f*M_tot final_spin = getFinalSpin(eta,chi_eff) f_rd = 1./(2*jnp.pi*(Msun))*(1.5251 - 1.1568*(1-final_spin)**0.1292)*Msun diff --git a/test/waveform_test.py b/test/waveform_test.py index b423ce60..ea2f4442 100644 --- a/test/waveform_test.py +++ b/test/waveform_test.py @@ -87,14 +87,14 @@ H1 = detector_tensor(H1_arm1, H1_arm2) psd_interp = jnp.interp(waveform_frequency, psd_frequency, psd) -strain = get_detector_response(waveform,injection_parameters,H1) +strain = get_detector_response(waveform,injection_parameters,H1).T[0] jaxgw_snr = inner_product(strain, strain, waveform_frequency, psd_interp) d_jaxgw_snr = grad(inner_product)(strain, strain, waveform_frequency, psd_interp) @jit def jax_likelihood(params, data, data_f, PSD): waveform = IMRPhenomC(data_f, params) - waveform = get_detector_response(waveform, params, H1) + waveform = get_detector_response(waveform, params, H1).T[0] output = inner_product(waveform, data, data_f, PSD) return output From 728b948f954cd2c7bb7f23742f7817b6183efcb2 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 4 Oct 2021 17:35:04 -0400 Subject: [PATCH 023/300] Add blackjax HMC example; Move toy_example to example directory; Took away polarization project in TaylorF2 for blackjax HMC's sake; Fix minor bug in IMRPhenomC --- example/blackjax/HMC_injection.py | 107 ++++++++++++++++++ example/numpyro/HMC_injection.py | 65 +++++++++++ .../toy_pop_example}/GWTC2.py | 0 .../toy_pop_example}/GaussianExample.py | 0 .../toy_pop_example}/Gaussian_kde.py | 0 .../toy_pop_example}/PowerLawPlusPeak.py | 0 jaxgw/waveform/IMRPhenomC.py | 2 +- jaxgw/waveform/TaylorF2.py | 13 ++- test/test_integration.py | 3 - 9 files changed, 182 insertions(+), 8 deletions(-) create mode 100644 example/blackjax/HMC_injection.py create mode 100644 example/numpyro/HMC_injection.py rename {test/toy_example => example/toy_pop_example}/GWTC2.py (100%) rename {test/toy_example => example/toy_pop_example}/GaussianExample.py (100%) rename {test/toy_example => example/toy_pop_example}/Gaussian_kde.py (100%) rename {test/toy_example => example/toy_pop_example}/PowerLawPlusPeak.py (100%) delete mode 100644 test/test_integration.py diff --git a/example/blackjax/HMC_injection.py b/example/blackjax/HMC_injection.py new file mode 100644 index 00000000..4bae3890 --- /dev/null +++ b/example/blackjax/HMC_injection.py @@ -0,0 +1,107 @@ +import numpy as np +import bilby +import jax +import jax.numpy as jnp +import time + +from jax.config import config +config.update("jax_enable_x64", True) + +from jaxgw.likelihood.detector_projection import construct_arm, detector_tensor, antenna_response, get_detector_response + +from jaxgw.likelihood.utils import inner_product +from jaxgw.waveform.TaylorF2 import TaylorF2 +from jaxgw.waveform.IMRPhenomC import IMRPhenomC +from jax import random, grad, jit, vmap, jacfwd, jacrev, value_and_grad + +#injection_parameters = dict( +# mass_1=36., mass_2=29., luminosity_distance=410., theta_jn=0.4, psi=2.659, +# phase=1.3, geocent_time=1126259642.413, ra=1.375, dec=-1.2108) +# +#guess_parameters = dict( +# mass_1=40., mass_2=25., luminosity_distance=400., theta_jn=0.4, psi=2.659, +# phase=1.3, geocent_time=1126259642.413, ra=1.375, dec=-1.2108) + +injection_parameters = dict(mass_1=36., mass_2=29., luminosity_distance=410.) + +guess_parameters = dict(mass_1=40., mass_2=25., luminosity_distance=400.) + + + +# Set up interferometers. In this case we'll use two interferometers +# (LIGO-Hanford (H1), LIGO-Livingston (L1). These default to their design +# sensitivity +ifos = bilby.gw.detector.InterferometerList(['H1']) +ifos.set_strain_data_from_power_spectral_densities( + sampling_frequency=2048, duration=4, + start_time=- 3) + +psd = ifos[0].power_spectral_density_array +psd_frequency = ifos[0].frequency_array + +psd_frequency = psd_frequency[jnp.isfinite(psd)] +psd = psd[jnp.isfinite(psd)] + +waveform = TaylorF2(psd_frequency, injection_parameters) +#H1_lat = 46 + 27. / 60 + 18.528 / 3600 +#H1_long = -(119 + 24. / 60 + 27.5657 / 3600) +#H1_xarm_azimuth = 125.9994 +#H1_yarm_azimuth = 215.9994 +#H1_xarm_tilt = -6.195e-4 +#H1_yarm_tilt = 1.25e-5 +# +#H1_arm1 = construct_arm(H1_long, H1_lat, H1_xarm_tilt, H1_xarm_azimuth) +#H1_arm2 = construct_arm(H1_long, H1_lat, H1_yarm_tilt, H1_yarm_azimuth) +# +#H1 = detector_tensor(H1_arm1, H1_arm2) +strain = waveform#get_detector_response(waveform,injection_parameters,H1) + +@jit +def jax_likelihood(params, data, data_f, PSD): + waveform = TaylorF2(data_f, params) +# waveform = get_detector_response(waveform, params, H1) + match_filter_SNR = inner_product(waveform, data, data_f, PSD) + optimal_SNR = inner_product(waveform, waveform, data_f, PSD) + return -(2*match_filter_SNR - optimal_SNR)/2 + +#def logprob_wrap(mass_1, mass_2, luminosity_distance, theta_jn, psi, phase, geocent_time, ra, dec): +# params = dict(mass_1=mass_1, mass_2=mass_2, luminosity_distance=luminosity_distance, theta_jn=theta_jn, psi=psi, phase=phase, geocent_time=geocent_time, ra=ra, dec=dec) +# return jax_likelihood(params, strain, psd_frequency, psd) + +def logprob_wrap(mass_1, mass_2, luminosity_distance): + params = dict(mass_1=mass_1, mass_2=mass_2, luminosity_distance=luminosity_distance) + return jax_likelihood(params, strain, psd_frequency, psd) + + + +log_prob = lambda x: logprob_wrap(**x) + +import blackjax.hmc as hmc +import blackjax.nuts as nuts +import blackjax.stan_warmup as stan_warmup + +initial_state = hmc.new_state(guess_parameters, log_prob) +#inv_mass_matrix = np.array([0.05, 0.005, 0.5, 0.05, 0.0005, 0.005, 0.05, 0.05, 0.05])*0.1 +inv_mass_matrix = np.array([0.05, 0.005, 5])*10 +num_integration_steps = 60 +step_size = 1e-3 + +hmc_kernel = hmc.kernel(log_prob, step_size, inv_mass_matrix, num_integration_steps) +hmc_kernel = jit(hmc_kernel) + +def inference_loop(rng_key, kernel, initial_state, num_samples): + def one_step(state, rng_key): + state, _ = kernel(rng_key, state) + return state, state + + keys = jax.random.split(rng_key, num_samples) + _, states = jax.lax.scan(one_step, initial_state, keys) + + return states + +print("Start sampling") +rng_key = jax.random.PRNGKey(0) +time1 = time.time() +states = inference_loop(rng_key, hmc_kernel, initial_state, 5000) +print("Sampling takes: "+str(time.time()-time1)+" seconds") + diff --git a/example/numpyro/HMC_injection.py b/example/numpyro/HMC_injection.py new file mode 100644 index 00000000..81a53717 --- /dev/null +++ b/example/numpyro/HMC_injection.py @@ -0,0 +1,65 @@ +import numpy as np +import bilby +import jax.numpy as jnp + +from jax.config import config +from jax import grad, jit +config.update("jax_enable_x64", True) + +from jaxgw.likelihood.utils import inner_product +from jaxgw.waveform.TaylorF2 import TaylorF2 +from jaxgw.waveform.IMRPhenomC import IMRPhenomC +from jax import random, grad, jit, vmap, jacfwd, jacrev, value_and_grad + + + +injection_parameters = dict( + mass_1=36., mass_2=29., luminosity_distance=40., theta_jn=0.4, psi=2.659, + phase=1.3, geocent_time=1126259642.413, ra=1.375, dec=-1.2108) + +# Set up interferometers. In this case we'll use two interferometers +# (LIGO-Hanford (H1), LIGO-Livingston (L1). These default to their design +# sensitivity +ifos = bilby.gw.detector.InterferometerList(['H1']) +ifos.set_strain_data_from_power_spectral_densities( + sampling_frequency=2048, duration=4, + start_time=- 3) + +psd = ifos[0].power_spectral_density_array +psd_frequency = ifos[0].frequency_array + +psd_frequency = psd_frequency[jnp.isfinite(psd)] +psd = psd[jnp.isfinite(psd)] + +waveform = TaylorF2(psd_frequency, injection_parameters) +strain = waveform['plus']#get_detector_response(waveform,injection_parameters,H1).T[0] + +@jit +def jax_likelihood(params, data, data_f, PSD): + waveform = TaylorF2(data_f, params)['plus'] +# waveform = get_detector_response(waveform, params, H1).T[0] + match_filter_SNR = inner_product(waveform, data, data_f, PSD) + optimal_SNR = inner_product(waveform, waveform, data_f, PSD) + return -(2*match_filter_SNR - optimal_SNR)/2 + +def jax_posterior(params,data,data_f,PSD): + if params['mass_1'] < 0: + params['mass_1'] = 0 + if params['mass_2'] < 0: + params['mass_2'] = 0 + if (params['a_1'] < 0): + params['a_1'] = 0 + if (params['a_2'] < 0): + params['a_2'] = 0 + return jax_likelihood(params,data,data_f,PSD) + + +from numpyro.infer import MCMC, NUTS + +nuts_kernel = NUTS(jax_likelihood) + +mcmc = MCMC(nuts_kernel, num_warmup=5, num_samples=10) + +rng_key = random.PRNGKey(0) + +mcmc.run(rng_key, injection_parameters, strain, psd_frequency, psd, extra_fields=('potential_energy',)) diff --git a/test/toy_example/GWTC2.py b/example/toy_pop_example/GWTC2.py similarity index 100% rename from test/toy_example/GWTC2.py rename to example/toy_pop_example/GWTC2.py diff --git a/test/toy_example/GaussianExample.py b/example/toy_pop_example/GaussianExample.py similarity index 100% rename from test/toy_example/GaussianExample.py rename to example/toy_pop_example/GaussianExample.py diff --git a/test/toy_example/Gaussian_kde.py b/example/toy_pop_example/Gaussian_kde.py similarity index 100% rename from test/toy_example/Gaussian_kde.py rename to example/toy_pop_example/Gaussian_kde.py diff --git a/test/toy_example/PowerLawPlusPeak.py b/example/toy_pop_example/PowerLawPlusPeak.py similarity index 100% rename from test/toy_example/PowerLawPlusPeak.py rename to example/toy_pop_example/PowerLawPlusPeak.py diff --git a/jaxgw/waveform/IMRPhenomC.py b/jaxgw/waveform/IMRPhenomC.py index 77bbf75c..cf416c9a 100644 --- a/jaxgw/waveform/IMRPhenomC.py +++ b/jaxgw/waveform/IMRPhenomC.py @@ -188,7 +188,7 @@ def IMRPhenomC(f,params): amplitude = A_PM *smoothing_minus(f,0.98*f_rd,0.015) + A_RD*smoothing_plus(f,0.98*f_rd,0.015) amplitude_factor = 2. * jnp.sqrt(5. / (64.*jnp.pi)) * M_tot**2 / local_d - totalh = amplitude*amplitude_factor#*jnp.exp(-1j*phase)#* amplitude_factor + totalh = amplitude*amplitude_factor*jnp.exp(-1j*phase)#* amplitude_factor hp = totalh * (1/2*(1+jnp.cos(params['theta_jn'])**2)*jnp.cos(2*params['psi'])) hc = totalh * jnp.cos(params['theta_jn'])*jnp.sin(2*params['psi']) diff --git a/jaxgw/waveform/TaylorF2.py b/jaxgw/waveform/TaylorF2.py index f5f74085..d4f66ca1 100644 --- a/jaxgw/waveform/TaylorF2.py +++ b/jaxgw/waveform/TaylorF2.py @@ -22,14 +22,19 @@ def TaylorF2(f,params): PN2d5 = jnp.pi*(38645./756-65./9*eta)*(1 + 3*jnp.log(6**(3./2)*jnp.pi*M_tot*f)) * PNcoef**5 # PN3 = 11583231236531./4694215680 - 640./3 *jnp.pi**2 - 6868./21*(euler_gamma+jnp.log(4) - phase = 2*jnp.pi*f*params['geocent_time'] - params['phase'] - jnp.pi/4 + 3./(128*eta*PNcoef**5) * \ +# phase = 2*jnp.pi*f*params['geocent_time'] - params['phase'] - jnp.pi/4 + 3./(128*eta*PNcoef**5) * \ +# (PN0+PN1+PN1d5)#+PN2+PN2d5) + + phase = - jnp.pi/4 + 3./(128*eta*PNcoef**5) * \ (PN0+PN1+PN1d5)#+PN2+PN2d5) + + totalh = jnp.sqrt(5./96)/jnp.pi**(2./3)*amplitude*f**(-7./6)*jnp.exp(1j*phase) - hp = totalh * (1/2*(1+jnp.cos(params['theta_jn'])**2)*jnp.cos(2*params['psi'])) - hc = totalh * jnp.cos(params['theta_jn'])*jnp.sin(2*params['psi']) + #hp = totalh * (1/2*(1+jnp.cos(params['theta_jn'])**2)*jnp.cos(2*params['psi'])) + #hc = totalh * jnp.cos(params['theta_jn'])*jnp.sin(2*params['psi']) - return {'plus':hp,'cross':hc} + return totalh#{'plus':hp,'cross':hc} def flso(M): return (6**3./2*jnp.pi*M)**-1 diff --git a/test/test_integration.py b/test/test_integration.py deleted file mode 100644 index aec10fb7..00000000 --- a/test/test_integration.py +++ /dev/null @@ -1,3 +0,0 @@ -from jaxgw.likelihood.utils.py import inner_product - - From 70285cbed4472a2d9a33e5d8fb0b98ae1697ec1c Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 4 Oct 2021 17:36:11 -0400 Subject: [PATCH 024/300] Fixed integration method in inner product. Original summing method contribute huge error --- jaxgw/likelihood/utils.py | 2 +- .../waveform/__pycache__/TaylorF2.cpython-37.pyc | Bin 1439 -> 0 bytes 2 files changed, 1 insertion(+), 1 deletion(-) delete mode 100644 jaxgw/waveform/__pycache__/TaylorF2.cpython-37.pyc diff --git a/jaxgw/likelihood/utils.py b/jaxgw/likelihood/utils.py index 1f571059..7b8b5cde 100644 --- a/jaxgw/likelihood/utils.py +++ b/jaxgw/likelihood/utils.py @@ -9,4 +9,4 @@ def inner_product(h1, h2, frequency, PSD): #psd_interp = jnp.interp(frequency, PSD_frequency, PSD) df = frequency[1] - frequency[0] integrand = jnp.conj(h1)* h2 / PSD - return 4. * jnp.real(jnp.sum(integrand)*df) + return 4. * jnp.real(jnp.trapz(integrand,dx=df)) diff --git a/jaxgw/waveform/__pycache__/TaylorF2.cpython-37.pyc b/jaxgw/waveform/__pycache__/TaylorF2.cpython-37.pyc deleted file mode 100644 index 98b21f538f105ad3c661b49ff36cdaddd39005a2..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 1439 zcmYjR&2Jk;6rb5IuVcH7Khne`gtiF?qY@`Ul?p=G0tv(+RwP;>EeC70Z?j%&*Sp2c+J5nGjBFyvZ_Lm?(&qIaU#?h&lSTT*=jCHqps8*lY$`ovpHo zksF}bS*|0mkSRlzP>$KasI!R)g-J0lfQbwZ*$Ysdlwbi$P|l48TSi`PQYMTpLxtj{ z=YsT9;36zSb;g7m?j)J>sJi4_pcrVt60ufj-%EhLZ!}n*XPeh^7f&*eT#=C%L7OwE z3J%TY%s$t|JThy26%nk$8s)}1W!x&uOL=*jU(2iKxsru{>^y5y&ue)d zHosEv(qHTeBec`BzcaVHq8^Mxe-x#DcH9R)&FqomPN^Tf*QA_uyjxNpb-d;;(SM&G zJ??nl-6MI@@sNk|wBsdf4{onL;9akLf4hADdjzv3qw7CCJw2VOIGs`tdrux7wJ$H3 zUGJxBfBbmuv!$;0`kj*-cQ&fsDRwsV{yzBa%jy^J&)p|$-6{4V^M3!Kvi=BO?M`)^ ziL&QLjyuZwnIF1Bj|X<@&UZAs(@!X1R^z?1^8Y%k`k!BqarvBJ^|RTZ9bskzH?#Z0 zkx=8*Zz)2HgK;W!CyCOuRkXCdbc}0zu_M&sC>BcW3pI#5p{2KzOsMWrEL0~-g_`=K zRzvUuVZ?S~hpA--kz)t_aOZ!tYZYcbu=Krt7Eyd=TZO&8Gw_qxGWL5;q!OAWEq&$4hBLQ#FjE}zGcZ3bpP9F zhoh`Lh(fo0co>bm_In@hC(+P#vi5!y_x(|SXdkt4c!%vn`(yVYO2YO{`#6Y_cXzkq zV_}|6=*Hx`!+4oK)#MGdHLfXTMdK@oHu)Rs4Zf&s@om1TBpVpv(2bIirCyWoO$`C? zj1953QnAdLv6l$!D6CDbiL z6!Ab?qj8A!>(a Date: Mon, 4 Oct 2021 18:16:59 -0400 Subject: [PATCH 025/300] Add inverse mass matrix finder, there are some problem, but in general okay --- example/blackjax/HMC_injection.py | 79 ++++++++++++++++++------------- jaxgw/waveform/TaylorF2.py | 6 +-- 2 files changed, 48 insertions(+), 37 deletions(-) diff --git a/example/blackjax/HMC_injection.py b/example/blackjax/HMC_injection.py index 4bae3890..bb403d1b 100644 --- a/example/blackjax/HMC_injection.py +++ b/example/blackjax/HMC_injection.py @@ -14,17 +14,17 @@ from jaxgw.waveform.IMRPhenomC import IMRPhenomC from jax import random, grad, jit, vmap, jacfwd, jacrev, value_and_grad -#injection_parameters = dict( -# mass_1=36., mass_2=29., luminosity_distance=410., theta_jn=0.4, psi=2.659, -# phase=1.3, geocent_time=1126259642.413, ra=1.375, dec=-1.2108) -# -#guess_parameters = dict( -# mass_1=40., mass_2=25., luminosity_distance=400., theta_jn=0.4, psi=2.659, -# phase=1.3, geocent_time=1126259642.413, ra=1.375, dec=-1.2108) +injection_parameters = dict( + mass_1=36., mass_2=29., luminosity_distance=410., theta_jn=0.4, psi=2.659, + phase=1.3, geocent_time=1126259642.413, ra=1.375, dec=-1.2108) -injection_parameters = dict(mass_1=36., mass_2=29., luminosity_distance=410.) +guess_parameters = dict( + mass_1=40., mass_2=25., luminosity_distance=400., theta_jn=0.4, psi=2.659, + phase=1.3, geocent_time=1126259642.413, ra=1.375, dec=-1.2108) -guess_parameters = dict(mass_1=40., mass_2=25., luminosity_distance=400.) +#injection_parameters = dict(mass_1=36., mass_2=29., luminosity_distance=410.) +# +#guess_parameters = dict(mass_1=40., mass_2=25., luminosity_distance=400.) @@ -43,35 +43,35 @@ psd = psd[jnp.isfinite(psd)] waveform = TaylorF2(psd_frequency, injection_parameters) -#H1_lat = 46 + 27. / 60 + 18.528 / 3600 -#H1_long = -(119 + 24. / 60 + 27.5657 / 3600) -#H1_xarm_azimuth = 125.9994 -#H1_yarm_azimuth = 215.9994 -#H1_xarm_tilt = -6.195e-4 -#H1_yarm_tilt = 1.25e-5 -# -#H1_arm1 = construct_arm(H1_long, H1_lat, H1_xarm_tilt, H1_xarm_azimuth) -#H1_arm2 = construct_arm(H1_long, H1_lat, H1_yarm_tilt, H1_yarm_azimuth) -# -#H1 = detector_tensor(H1_arm1, H1_arm2) -strain = waveform#get_detector_response(waveform,injection_parameters,H1) +H1_lat = 46 + 27. / 60 + 18.528 / 3600 +H1_long = -(119 + 24. / 60 + 27.5657 / 3600) +H1_xarm_azimuth = 125.9994 +H1_yarm_azimuth = 215.9994 +H1_xarm_tilt = -6.195e-4 +H1_yarm_tilt = 1.25e-5 + +H1_arm1 = construct_arm(H1_long, H1_lat, H1_xarm_tilt, H1_xarm_azimuth) +H1_arm2 = construct_arm(H1_long, H1_lat, H1_yarm_tilt, H1_yarm_azimuth) + +H1 = detector_tensor(H1_arm1, H1_arm2) +strain = get_detector_response(waveform,injection_parameters,H1) @jit def jax_likelihood(params, data, data_f, PSD): waveform = TaylorF2(data_f, params) -# waveform = get_detector_response(waveform, params, H1) + waveform = get_detector_response(waveform, params, H1) match_filter_SNR = inner_product(waveform, data, data_f, PSD) optimal_SNR = inner_product(waveform, waveform, data_f, PSD) return -(2*match_filter_SNR - optimal_SNR)/2 -#def logprob_wrap(mass_1, mass_2, luminosity_distance, theta_jn, psi, phase, geocent_time, ra, dec): -# params = dict(mass_1=mass_1, mass_2=mass_2, luminosity_distance=luminosity_distance, theta_jn=theta_jn, psi=psi, phase=phase, geocent_time=geocent_time, ra=ra, dec=dec) -# return jax_likelihood(params, strain, psd_frequency, psd) - -def logprob_wrap(mass_1, mass_2, luminosity_distance): - params = dict(mass_1=mass_1, mass_2=mass_2, luminosity_distance=luminosity_distance) +def logprob_wrap(mass_1, mass_2, luminosity_distance, theta_jn, psi, phase, geocent_time, ra, dec): + params = dict(mass_1=mass_1, mass_2=mass_2, luminosity_distance=luminosity_distance, theta_jn=theta_jn, psi=psi, phase=phase, geocent_time=geocent_time, ra=ra, dec=dec) return jax_likelihood(params, strain, psd_frequency, psd) +#def logprob_wrap(mass_1, mass_2, luminosity_distance): +# params = dict(mass_1=mass_1, mass_2=mass_2, luminosity_distance=luminosity_distance) +# return jax_likelihood(params, strain, psd_frequency, psd) + log_prob = lambda x: logprob_wrap(**x) @@ -80,13 +80,25 @@ def logprob_wrap(mass_1, mass_2, luminosity_distance): import blackjax.nuts as nuts import blackjax.stan_warmup as stan_warmup +rng_key = jax.random.PRNGKey(0) +key, subkey = random.split(rng_key) + initial_state = hmc.new_state(guess_parameters, log_prob) -#inv_mass_matrix = np.array([0.05, 0.005, 0.5, 0.05, 0.0005, 0.005, 0.05, 0.05, 0.05])*0.1 -inv_mass_matrix = np.array([0.05, 0.005, 5])*10 +print('Finding step size and mass matrix') +kernel_generator = lambda step_size, inverse_mass_matrix: hmc.kernel( + log_prob, step_size, inverse_mass_matrix, 30 +) + +final_state, (step_size, inverse_mass_matrix), info = stan_warmup.run( + key, + kernel_generator, + initial_state, + 3000, +) + num_integration_steps = 60 -step_size = 1e-3 -hmc_kernel = hmc.kernel(log_prob, step_size, inv_mass_matrix, num_integration_steps) +hmc_kernel = hmc.kernel(log_prob, step_size, inverse_mass_matrix, num_integration_steps) hmc_kernel = jit(hmc_kernel) def inference_loop(rng_key, kernel, initial_state, num_samples): @@ -100,8 +112,7 @@ def one_step(state, rng_key): return states print("Start sampling") -rng_key = jax.random.PRNGKey(0) time1 = time.time() -states = inference_loop(rng_key, hmc_kernel, initial_state, 5000) +states = inference_loop(subkey, hmc_kernel, initial_state, 5000) print("Sampling takes: "+str(time.time()-time1)+" seconds") diff --git a/jaxgw/waveform/TaylorF2.py b/jaxgw/waveform/TaylorF2.py index d4f66ca1..68e615ea 100644 --- a/jaxgw/waveform/TaylorF2.py +++ b/jaxgw/waveform/TaylorF2.py @@ -31,10 +31,10 @@ def TaylorF2(f,params): totalh = jnp.sqrt(5./96)/jnp.pi**(2./3)*amplitude*f**(-7./6)*jnp.exp(1j*phase) - #hp = totalh * (1/2*(1+jnp.cos(params['theta_jn'])**2)*jnp.cos(2*params['psi'])) - #hc = totalh * jnp.cos(params['theta_jn'])*jnp.sin(2*params['psi']) + hp = totalh * (1/2*(1+jnp.cos(params['theta_jn'])**2)*jnp.cos(2*params['psi'])) + hc = totalh * jnp.cos(params['theta_jn'])*jnp.sin(2*params['psi']) - return totalh#{'plus':hp,'cross':hc} + return {'plus':hp,'cross':hc} def flso(M): return (6**3./2*jnp.pi*M)**-1 From c70ce1cf72e2fa6ab70ba2ec7d7dcc1e3bc1e547 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Fri, 8 Oct 2021 12:26:30 -0400 Subject: [PATCH 026/300] Experimenting with HMC --- example/blackjax/HMC_injection.py | 131 +++++++++++++++++------------- jaxgw/waveform/IMRPhenomC.py | 8 +- jaxgw/waveform/TaylorF2.py | 6 +- 3 files changed, 81 insertions(+), 64 deletions(-) diff --git a/example/blackjax/HMC_injection.py b/example/blackjax/HMC_injection.py index bb403d1b..aaf93bd5 100644 --- a/example/blackjax/HMC_injection.py +++ b/example/blackjax/HMC_injection.py @@ -15,12 +15,11 @@ from jax import random, grad, jit, vmap, jacfwd, jacrev, value_and_grad injection_parameters = dict( - mass_1=36., mass_2=29., luminosity_distance=410., theta_jn=0.4, psi=2.659, + mass_1=36., mass_2=29., a_1=0.0, a_2=0.0, luminosity_distance=410., theta_jn=0.4, psi=2.659, phase=1.3, geocent_time=1126259642.413, ra=1.375, dec=-1.2108) guess_parameters = dict( - mass_1=40., mass_2=25., luminosity_distance=400., theta_jn=0.4, psi=2.659, - phase=1.3, geocent_time=1126259642.413, ra=1.375, dec=-1.2108) + mass_1=20., mass_2=20.)#, luminosity_distance=400.)#, theta_jn=0.4, psi=2.659, ra=1.375, dec=-1.2108) #injection_parameters = dict(mass_1=36., mass_2=29., luminosity_distance=410.) # @@ -42,7 +41,7 @@ psd_frequency = psd_frequency[jnp.isfinite(psd)] psd = psd[jnp.isfinite(psd)] -waveform = TaylorF2(psd_frequency, injection_parameters) +waveform = IMRPhenomC(psd_frequency, injection_parameters) H1_lat = 46 + 27. / 60 + 18.528 / 3600 H1_long = -(119 + 24. / 60 + 27.5657 / 3600) H1_xarm_azimuth = 125.9994 @@ -54,65 +53,83 @@ H1_arm2 = construct_arm(H1_long, H1_lat, H1_yarm_tilt, H1_yarm_azimuth) H1 = detector_tensor(H1_arm1, H1_arm2) -strain = get_detector_response(waveform,injection_parameters,H1) +strain = waveform#get_detector_response(waveform,injection_parameters,H1) @jit def jax_likelihood(params, data, data_f, PSD): - waveform = TaylorF2(data_f, params) - waveform = get_detector_response(waveform, params, H1) + waveform = IMRPhenomC(data_f, params) +# waveform = get_detector_response(waveform, params, H1) match_filter_SNR = inner_product(waveform, data, data_f, PSD) optimal_SNR = inner_product(waveform, waveform, data_f, PSD) - return -(2*match_filter_SNR - optimal_SNR)/2 + return (2*match_filter_SNR - optimal_SNR)/2 -def logprob_wrap(mass_1, mass_2, luminosity_distance, theta_jn, psi, phase, geocent_time, ra, dec): - params = dict(mass_1=mass_1, mass_2=mass_2, luminosity_distance=luminosity_distance, theta_jn=theta_jn, psi=psi, phase=phase, geocent_time=geocent_time, ra=ra, dec=dec) +def logprob_wrap(mass_1, mass_2):#, luminosity_distance):#, theta_jn, psi, ra, dec): + params = dict(mass_1=mass_1, mass_2=mass_2, a_1=0, a_2=0, luminosity_distance=410, theta_jn=0.4, psi=2.659, phase=1.3, geocent_time=1126259642.413, ra=1.375, dec=-1.2108) return jax_likelihood(params, strain, psd_frequency, psd) -#def logprob_wrap(mass_1, mass_2, luminosity_distance): -# params = dict(mass_1=mass_1, mass_2=mass_2, luminosity_distance=luminosity_distance) -# return jax_likelihood(params, strain, psd_frequency, psd) - - - -log_prob = lambda x: logprob_wrap(**x) - -import blackjax.hmc as hmc -import blackjax.nuts as nuts -import blackjax.stan_warmup as stan_warmup - -rng_key = jax.random.PRNGKey(0) -key, subkey = random.split(rng_key) - -initial_state = hmc.new_state(guess_parameters, log_prob) -print('Finding step size and mass matrix') -kernel_generator = lambda step_size, inverse_mass_matrix: hmc.kernel( - log_prob, step_size, inverse_mass_matrix, 30 -) - -final_state, (step_size, inverse_mass_matrix), info = stan_warmup.run( - key, - kernel_generator, - initial_state, - 3000, -) - -num_integration_steps = 60 - -hmc_kernel = hmc.kernel(log_prob, step_size, inverse_mass_matrix, num_integration_steps) -hmc_kernel = jit(hmc_kernel) - -def inference_loop(rng_key, kernel, initial_state, num_samples): - def one_step(state, rng_key): - state, _ = kernel(rng_key, state) - return state, state - - keys = jax.random.split(rng_key, num_samples) - _, states = jax.lax.scan(one_step, initial_state, keys) - - return states - -print("Start sampling") -time1 = time.time() -states = inference_loop(subkey, hmc_kernel, initial_state, 5000) -print("Sampling takes: "+str(time.time()-time1)+" seconds") +#log_prob = lambda x: logprob_wrap(**x) +def log_prob(param): + if (param[0]<=0) or (param[1]<=0): + return -jnp.inf + params = dict(mass_1=param[0], mass_2=param[1], a_1=0, a_2=0, luminosity_distance=410, theta_jn=0.4, psi=2.659, phase=1.3, geocent_time=1126259642.413, ra=1.375, dec=-1.2108) + return jax_likelihood(params, strain, psd_frequency, psd) +################################################################ +# Test with Emcee to make sure likelihood looks fine +################################################################ + +import emcee + +nwalkers = 32 +ndim = 2 +p0 = np.random.rand(nwalkers, ndim) +sampler = emcee.EnsembleSampler(nwalkers, ndim, log_prob) +state = sampler.run_mcmc(p0, 100) +sampler.reset() +sampler.run_mcmc(state, 10000) + +#import blackjax.hmc as hmc +#import blackjax.nuts as nuts +#import blackjax.stan_warmup as stan_warmup +# +#rng_key = jax.random.PRNGKey(0) +#key, subkey = random.split(rng_key) +# +#initial_state = hmc.new_state(guess_parameters, log_prob) +#print('Finding step size and mass matrix') +# +#time1 = time.time() +#kernel_generator = lambda step_size, inverse_mass_matrix: hmc.kernel( +# log_prob, step_size, inverse_mass_matrix, 100 +#) +# +#final_state, (step_size, inverse_mass_matrix), info = stan_warmup.run( +# key, +# kernel_generator, +# initial_state, +# 1000, +#) +# +#print("Finding inverse mass matrix takes: "+str(time.time()-time1)+" seconds") +#num_integration_steps = 60 +# +#hmc_kernel = hmc.kernel(log_prob, step_size, inverse_mass_matrix, num_integration_steps) +#hmc_kernel = jit(hmc_kernel) +# +#test_likelihood = hmc_kernel(subkey, initial_state) +#print("Energy of the first step is: "+str(test_likelihood[1].energy)) +# +#def inference_loop(rng_key, kernel, initial_state, num_samples): +# def one_step(state, rng_key): +# state, _ = kernel(rng_key, state) +# return state, state +# +# keys = jax.random.split(rng_key, num_samples) +# _, states = jax.lax.scan(one_step, initial_state, keys) +# +# return states +# +#print("Start sampling") +#time1 = time.time() +#states = inference_loop(subkey, hmc_kernel, initial_state, 1000) +#print("Sampling takes: "+str(time.time()-time1)+" seconds") diff --git a/jaxgw/waveform/IMRPhenomC.py b/jaxgw/waveform/IMRPhenomC.py index cf416c9a..59acd125 100644 --- a/jaxgw/waveform/IMRPhenomC.py +++ b/jaxgw/waveform/IMRPhenomC.py @@ -188,8 +188,8 @@ def IMRPhenomC(f,params): amplitude = A_PM *smoothing_minus(f,0.98*f_rd,0.015) + A_RD*smoothing_plus(f,0.98*f_rd,0.015) amplitude_factor = 2. * jnp.sqrt(5. / (64.*jnp.pi)) * M_tot**2 / local_d - totalh = amplitude*amplitude_factor*jnp.exp(-1j*phase)#* amplitude_factor - hp = totalh * (1/2*(1+jnp.cos(params['theta_jn'])**2)*jnp.cos(2*params['psi'])) - hc = totalh * jnp.cos(params['theta_jn'])*jnp.sin(2*params['psi']) + totalh = (amplitude*amplitude_factor*jnp.exp(-1j*phase))[:,0]#* amplitude_factor +# hp = totalh * (1/2*(1+jnp.cos(params['theta_jn'])**2)*jnp.cos(2*params['psi'])) +# hc = totalh * jnp.cos(params['theta_jn'])*jnp.sin(2*params['psi']) - return {'plus':hp,'cross':hc} + return totalh#{'plus':hp.T[0],'cross':hc.T[0]} diff --git a/jaxgw/waveform/TaylorF2.py b/jaxgw/waveform/TaylorF2.py index 68e615ea..d4f66ca1 100644 --- a/jaxgw/waveform/TaylorF2.py +++ b/jaxgw/waveform/TaylorF2.py @@ -31,10 +31,10 @@ def TaylorF2(f,params): totalh = jnp.sqrt(5./96)/jnp.pi**(2./3)*amplitude*f**(-7./6)*jnp.exp(1j*phase) - hp = totalh * (1/2*(1+jnp.cos(params['theta_jn'])**2)*jnp.cos(2*params['psi'])) - hc = totalh * jnp.cos(params['theta_jn'])*jnp.sin(2*params['psi']) + #hp = totalh * (1/2*(1+jnp.cos(params['theta_jn'])**2)*jnp.cos(2*params['psi'])) + #hc = totalh * jnp.cos(params['theta_jn'])*jnp.sin(2*params['psi']) - return {'plus':hp,'cross':hc} + return totalh#{'plus':hp,'cross':hc} def flso(M): return (6**3./2*jnp.pi*M)**-1 From 297bf2df90a61944f4b4dfa46433a10bd8a87c2a Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Sat, 9 Oct 2021 13:45:58 -0400 Subject: [PATCH 027/300] TaylorF2 revert to full waveform without comparmise. HMC example works when start near the peak. --- example/blackjax/HMC_injection.py | 158 +++++++++++++++++------------- jaxgw/waveform/TaylorF2.py | 14 +-- 2 files changed, 97 insertions(+), 75 deletions(-) diff --git a/example/blackjax/HMC_injection.py b/example/blackjax/HMC_injection.py index aaf93bd5..481de180 100644 --- a/example/blackjax/HMC_injection.py +++ b/example/blackjax/HMC_injection.py @@ -14,12 +14,17 @@ from jaxgw.waveform.IMRPhenomC import IMRPhenomC from jax import random, grad, jit, vmap, jacfwd, jacrev, value_and_grad +true_m1 = 3. +true_m2 = 3. +true_ld = 320 + + injection_parameters = dict( - mass_1=36., mass_2=29., a_1=0.0, a_2=0.0, luminosity_distance=410., theta_jn=0.4, psi=2.659, + mass_1=true_m1, mass_2=true_m2, a_1=0.0, a_2=0.0, luminosity_distance=true_ld, theta_jn=0.4, psi=2.659, phase=1.3, geocent_time=1126259642.413, ra=1.375, dec=-1.2108) guess_parameters = dict( - mass_1=20., mass_2=20.)#, luminosity_distance=400.)#, theta_jn=0.4, psi=2.659, ra=1.375, dec=-1.2108) + mass_1=true_m1, mass_2=true_m2)#, luminosity_distance=400.)#, theta_jn=0.4, psi=2.659, ra=1.375, dec=-1.2108) #injection_parameters = dict(mass_1=36., mass_2=29., luminosity_distance=410.) # @@ -32,7 +37,7 @@ # sensitivity ifos = bilby.gw.detector.InterferometerList(['H1']) ifos.set_strain_data_from_power_spectral_densities( - sampling_frequency=2048, duration=4, + sampling_frequency=2048, duration=16, start_time=- 3) psd = ifos[0].power_spectral_density_array @@ -41,7 +46,8 @@ psd_frequency = psd_frequency[jnp.isfinite(psd)] psd = psd[jnp.isfinite(psd)] -waveform = IMRPhenomC(psd_frequency, injection_parameters) +#waveform = IMRPhenomC(psd_frequency, injection_parameters) +waveform = TaylorF2(psd_frequency, injection_parameters) H1_lat = 46 + 27. / 60 + 18.528 / 3600 H1_long = -(119 + 24. / 60 + 27.5657 / 3600) H1_xarm_azimuth = 125.9994 @@ -53,83 +59,99 @@ H1_arm2 = construct_arm(H1_long, H1_lat, H1_yarm_tilt, H1_yarm_azimuth) H1 = detector_tensor(H1_arm1, H1_arm2) -strain = waveform#get_detector_response(waveform,injection_parameters,H1) +strain = get_detector_response(waveform,injection_parameters,H1) @jit def jax_likelihood(params, data, data_f, PSD): - waveform = IMRPhenomC(data_f, params) -# waveform = get_detector_response(waveform, params, H1) +# waveform = IMRPhenomC(data_f, params) + waveform = TaylorF2(data_f, params) + waveform = get_detector_response(waveform, params, H1) match_filter_SNR = inner_product(waveform, data, data_f, PSD) optimal_SNR = inner_product(waveform, waveform, data_f, PSD) - return (2*match_filter_SNR - optimal_SNR)/2 + return (-2*match_filter_SNR + optimal_SNR)/2#, match_filter_SNR, optimal_SNR + +@jit +def Mq_to_m1m2(M_tot,q): + m1 = M_tot/(1+q) + m2 = m1*q + inv_1pq = 1./(1+q) + Jac_det = inv_1pq*(M*(inv_1pq-q/inv_1pq**2))+(q*inv_1pq**3)*M + return m1, m2, Jac_det def logprob_wrap(mass_1, mass_2):#, luminosity_distance):#, theta_jn, psi, ra, dec): - params = dict(mass_1=mass_1, mass_2=mass_2, a_1=0, a_2=0, luminosity_distance=410, theta_jn=0.4, psi=2.659, phase=1.3, geocent_time=1126259642.413, ra=1.375, dec=-1.2108) +# m1, m2, Jac_det = Mq_to_m1m2(M_tot, q) + + params = dict(mass_1=mass_1, mass_2=mass_2, a_1=0, a_2=0, luminosity_distance=true_ld, theta_jn=0.4, psi=2.659, phase=1.3, geocent_time=1126259642.413, ra=1.375, dec=-1.2108) return jax_likelihood(params, strain, psd_frequency, psd) -#log_prob = lambda x: logprob_wrap(**x) +log_prob = lambda x: logprob_wrap(**x) + +#def log_prob(param): +# if (param[0]<=0) or (param[1]<=0): +# return -jnp.inf +# params = dict(mass_1=param[0], mass_2=param[1], a_1=0, a_2=0, luminosity_distance=true_ld, theta_jn=0.4, psi=2.659, phase=1.3, geocent_time=1126259642.413, ra=1.375, dec=-1.2108) +# return jax_likelihood(params, strain, psd_frequency, psd) -def log_prob(param): - if (param[0]<=0) or (param[1]<=0): - return -jnp.inf - params = dict(mass_1=param[0], mass_2=param[1], a_1=0, a_2=0, luminosity_distance=410, theta_jn=0.4, psi=2.659, phase=1.3, geocent_time=1126259642.413, ra=1.375, dec=-1.2108) - return jax_likelihood(params, strain, psd_frequency, psd) ################################################################ # Test with Emcee to make sure likelihood looks fine ################################################################ import emcee -nwalkers = 32 -ndim = 2 -p0 = np.random.rand(nwalkers, ndim) -sampler = emcee.EnsembleSampler(nwalkers, ndim, log_prob) -state = sampler.run_mcmc(p0, 100) -sampler.reset() -sampler.run_mcmc(state, 10000) - -#import blackjax.hmc as hmc -#import blackjax.nuts as nuts -#import blackjax.stan_warmup as stan_warmup -# -#rng_key = jax.random.PRNGKey(0) -#key, subkey = random.split(rng_key) -# -#initial_state = hmc.new_state(guess_parameters, log_prob) -#print('Finding step size and mass matrix') -# -#time1 = time.time() -#kernel_generator = lambda step_size, inverse_mass_matrix: hmc.kernel( -# log_prob, step_size, inverse_mass_matrix, 100 -#) -# -#final_state, (step_size, inverse_mass_matrix), info = stan_warmup.run( -# key, -# kernel_generator, -# initial_state, -# 1000, -#) -# -#print("Finding inverse mass matrix takes: "+str(time.time()-time1)+" seconds") -#num_integration_steps = 60 -# -#hmc_kernel = hmc.kernel(log_prob, step_size, inverse_mass_matrix, num_integration_steps) -#hmc_kernel = jit(hmc_kernel) -# -#test_likelihood = hmc_kernel(subkey, initial_state) -#print("Energy of the first step is: "+str(test_likelihood[1].energy)) -# -#def inference_loop(rng_key, kernel, initial_state, num_samples): -# def one_step(state, rng_key): -# state, _ = kernel(rng_key, state) -# return state, state -# -# keys = jax.random.split(rng_key, num_samples) -# _, states = jax.lax.scan(one_step, initial_state, keys) -# -# return states -# -#print("Start sampling") -#time1 = time.time() -#states = inference_loop(subkey, hmc_kernel, initial_state, 1000) -#print("Sampling takes: "+str(time.time()-time1)+" seconds") +#nwalkers = 32 +#ndim = 2 +#p0 = np.random.rand(nwalkers, ndim) + [true_m1,true_m2] +#sampler = emcee.EnsembleSampler(nwalkers, ndim, log_prob) +#state = sampler.run_mcmc(p0, 100) +#sampler.reset() +#sampler.run_mcmc(state, 10000) + +################################################################ +# BlackJax section +################################################################ + +import blackjax.hmc as hmc +import blackjax.nuts as nuts +import blackjax.stan_warmup as stan_warmup + +rng_key = jax.random.PRNGKey(0) +key, subkey = random.split(rng_key) + +initial_state = hmc.new_state(guess_parameters, log_prob) +print('Finding step size and mass matrix') + +time1 = time.time() +kernel_generator = lambda step_size, inverse_mass_matrix: hmc.kernel( + log_prob, step_size, inverse_mass_matrix, 100 +) + +final_state, (step_size, inverse_mass_matrix), info = stan_warmup.run( + key, + kernel_generator, + initial_state, + 300, +) + +print("Finding inverse mass matrix takes: "+str(time.time()-time1)+" seconds") +num_integration_steps = 100 + +hmc_kernel = hmc.kernel(log_prob, step_size, inverse_mass_matrix, num_integration_steps) +hmc_kernel = jit(hmc_kernel) + +test_likelihood = hmc_kernel(subkey, initial_state) +print("Energy of the first step is: "+str(test_likelihood[1].energy)) + +def inference_loop(rng_key, kernel, initial_state, num_samples): + def one_step(state, rng_key): + state, _ = kernel(rng_key, state) + return state, state + + keys = jax.random.split(rng_key, num_samples) + _, states = jax.lax.scan(one_step, initial_state, keys) + + return states + +print("Start sampling") +time1 = time.time() +states = inference_loop(subkey, hmc_kernel, initial_state, 1000) +print("Sampling takes: "+str(time.time()-time1)+" seconds") diff --git a/jaxgw/waveform/TaylorF2.py b/jaxgw/waveform/TaylorF2.py index d4f66ca1..704144aa 100644 --- a/jaxgw/waveform/TaylorF2.py +++ b/jaxgw/waveform/TaylorF2.py @@ -22,19 +22,19 @@ def TaylorF2(f,params): PN2d5 = jnp.pi*(38645./756-65./9*eta)*(1 + 3*jnp.log(6**(3./2)*jnp.pi*M_tot*f)) * PNcoef**5 # PN3 = 11583231236531./4694215680 - 640./3 *jnp.pi**2 - 6868./21*(euler_gamma+jnp.log(4) -# phase = 2*jnp.pi*f*params['geocent_time'] - params['phase'] - jnp.pi/4 + 3./(128*eta*PNcoef**5) * \ -# (PN0+PN1+PN1d5)#+PN2+PN2d5) - - phase = - jnp.pi/4 + 3./(128*eta*PNcoef**5) * \ + phase = 2*jnp.pi*f*params['geocent_time'] - params['phase'] - jnp.pi/4 + 3./(128*eta*PNcoef**5) * \ (PN0+PN1+PN1d5)#+PN2+PN2d5) +# phase = - jnp.pi/4 + 3./(128*eta*PNcoef**5) * \ +# (PN0+PN1+PN1d5)#+PN2+PN2d5) + totalh = jnp.sqrt(5./96)/jnp.pi**(2./3)*amplitude*f**(-7./6)*jnp.exp(1j*phase) - #hp = totalh * (1/2*(1+jnp.cos(params['theta_jn'])**2)*jnp.cos(2*params['psi'])) - #hc = totalh * jnp.cos(params['theta_jn'])*jnp.sin(2*params['psi']) + hp = totalh * (1/2*(1+jnp.cos(params['theta_jn'])**2)*jnp.cos(2*params['psi'])) + hc = totalh * jnp.cos(params['theta_jn'])*jnp.sin(2*params['psi']) - return totalh#{'plus':hp,'cross':hc} + return {'plus':hp,'cross':hc} def flso(M): return (6**3./2*jnp.pi*M)**-1 From 4de7ee06773bf8a71ea26b46fea277dfa30e51fb Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Sat, 9 Oct 2021 16:13:40 -0400 Subject: [PATCH 028/300] Add option to change mass matrix estimation to non-diagonal --- example/blackjax/HMC_injection.py | 34 ++++++++++++++++++------------- 1 file changed, 20 insertions(+), 14 deletions(-) diff --git a/example/blackjax/HMC_injection.py b/example/blackjax/HMC_injection.py index 481de180..548a85fb 100644 --- a/example/blackjax/HMC_injection.py +++ b/example/blackjax/HMC_injection.py @@ -16,7 +16,7 @@ true_m1 = 3. true_m2 = 3. -true_ld = 320 +true_ld = 1000 injection_parameters = dict( @@ -46,8 +46,8 @@ psd_frequency = psd_frequency[jnp.isfinite(psd)] psd = psd[jnp.isfinite(psd)] -#waveform = IMRPhenomC(psd_frequency, injection_parameters) -waveform = TaylorF2(psd_frequency, injection_parameters) +waveform = IMRPhenomC(psd_frequency, injection_parameters) +#waveform = TaylorF2(psd_frequency, injection_parameters) H1_lat = 46 + 27. / 60 + 18.528 / 3600 H1_long = -(119 + 24. / 60 + 27.5657 / 3600) H1_xarm_azimuth = 125.9994 @@ -59,13 +59,16 @@ H1_arm2 = construct_arm(H1_long, H1_lat, H1_yarm_tilt, H1_yarm_azimuth) H1 = detector_tensor(H1_arm1, H1_arm2) -strain = get_detector_response(waveform,injection_parameters,H1) +strain = waveform#get_detector_response(waveform,injection_parameters,H1) +#strain = get_detector_response(waveform,injection_parameters,H1) + +print('SNR of the event: '+str(np.sqrt(inner_product(strain,strain,psd_frequency,psd)))) @jit def jax_likelihood(params, data, data_f, PSD): -# waveform = IMRPhenomC(data_f, params) - waveform = TaylorF2(data_f, params) - waveform = get_detector_response(waveform, params, H1) + waveform = IMRPhenomC(data_f, params) +# waveform = TaylorF2(data_f, params) +# waveform = get_detector_response(waveform, params, H1) match_filter_SNR = inner_product(waveform, data, data_f, PSD) optimal_SNR = inner_product(waveform, waveform, data_f, PSD) return (-2*match_filter_SNR + optimal_SNR)/2#, match_filter_SNR, optimal_SNR @@ -96,8 +99,8 @@ def logprob_wrap(mass_1, mass_2):#, luminosity_distance):#, theta_jn, psi, ra, d # Test with Emcee to make sure likelihood looks fine ################################################################ -import emcee - +#import emcee +# #nwalkers = 32 #ndim = 2 #p0 = np.random.rand(nwalkers, ndim) + [true_m1,true_m2] @@ -106,9 +109,9 @@ def logprob_wrap(mass_1, mass_2):#, luminosity_distance):#, theta_jn, psi, ra, d #sampler.reset() #sampler.run_mcmc(state, 10000) -################################################################ +############################################################### # BlackJax section -################################################################ +############################################################### import blackjax.hmc as hmc import blackjax.nuts as nuts @@ -122,18 +125,21 @@ def logprob_wrap(mass_1, mass_2):#, luminosity_distance):#, theta_jn, psi, ra, d time1 = time.time() kernel_generator = lambda step_size, inverse_mass_matrix: hmc.kernel( - log_prob, step_size, inverse_mass_matrix, 100 + log_prob, step_size, inverse_mass_matrix, 60 ) final_state, (step_size, inverse_mass_matrix), info = stan_warmup.run( key, kernel_generator, initial_state, - 300, + 1000, + is_mass_matrix_diagonal=False ) print("Finding inverse mass matrix takes: "+str(time.time()-time1)+" seconds") -num_integration_steps = 100 +print("Stepsize: "+str(step_size)) +print("Mass_matrix: "+str(inverse_mass_matrix)) +num_integration_steps = 60 hmc_kernel = hmc.kernel(log_prob, step_size, inverse_mass_matrix, num_integration_steps) hmc_kernel = jit(hmc_kernel) From 4167a342ec8ad250a327c18937f83310564620f4 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Fri, 15 Oct 2021 15:20:01 -0400 Subject: [PATCH 029/300] Fix IMRPhenomC phasing, m1-m2 injection test seems working --- jaxgw/waveform/IMRPhenomC.py | 95 +++++++++++++++++++++--------------- 1 file changed, 56 insertions(+), 39 deletions(-) diff --git a/jaxgw/waveform/IMRPhenomC.py b/jaxgw/waveform/IMRPhenomC.py index 59acd125..a36bd64f 100644 --- a/jaxgw/waveform/IMRPhenomC.py +++ b/jaxgw/waveform/IMRPhenomC.py @@ -8,16 +8,12 @@ euler_gamma = 0.577215664901532860606512090082 MR_sun = 1.476625061404649406193430731479084713e3 - -@jit def Lorentzian(x, x0, gamma): return (gamma**2/((x-x0)**2+gamma**2/4)) -@jit def smoothing_plus(f,f0,d): return (1+jnp.tanh(4*(f-f0)/d))/2 -@jit def smoothing_minus(f,f0,d): return (1-jnp.tanh(4*(f-f0)/d))/2 @@ -29,22 +25,24 @@ def PNAmplitudeAndPhasing(f,m1,m2,chi1,chi2,t0,phase0): eta = m1*m2/(m1+m2)**2 chi_eff = (m1*chi1+m2*chi2)/(m1+m2) - # Taylor T4 expansion coefficient from A3 for 3.6, needed for amplitude in fourier space - T4_alpha = 64.*eta/5.* x**5*jnp.array([x**0, \ - x * (-7.43/3.36 - 11.*eta/4.),\ + T4_alpha_power_index = x[:,None]**(jnp.array([0,2,3,4,5,6,7])/2) - x**(3./2)*(4.*jnp.pi - 11.3*chi_eff/1.2 + 19.*eta*(chi1+chi2)/6.),\ + T4_alpha_coeff = jnp.array([1, \ - x**(2) * (3.4103/1.8144 + 5*chi_eff**2 + eta*(13.661/2.016 - chi1*chi2/8.) + 5.9*eta**2/1.8),\ + (-7.43/3.36 - 11.*eta/4.),\ - x**(5./2) * (-jnp.pi*(41.59/6.72 + 189.*eta/8.) - chi_eff*(31.571/1.008 - 116.5*eta/2.4) +\ + (4.*jnp.pi - 11.3*chi_eff/1.2 + 19.*eta*(chi1+chi2)/6.),\ + + (3.4103/1.8144 + 5*chi_eff**2 + eta*(13.661/2.016 - chi1*chi2/8.) + 5.9*eta**2/1.8),\ + + (-jnp.pi*(41.59/6.72 + 189.*eta/8.) - chi_eff*(31.571/1.008 - 116.5*eta/2.4) +\ (chi1+chi2)*(21.863*eta/1.008 - 79.*eta**2/6.) - 3*chi_eff**3/4. +\ 9.*eta*chi_eff*chi1*chi2/4.),\ - x**(3.) * (164.47322263/1.39708800 - 17.12*euler_gamma/1.05 +\ - 16.*jnp.pi**2/3 - 8.56*jnp.log(16.*x)/1.05 +\ + (164.47322263/1.39708800 - 17.12*euler_gamma/1.05 +\ + 16.*jnp.pi**2/3 +\ eta*(45.1*jnp.pi**2/4.8 - 561.98689/2.17728) +\ 5.41*eta**2/8.96 - 5.605*eta**3/2.592 - 80.*jnp.pi*chi_eff/3. +\ eta*(chi1+chi2)*(20.*jnp.pi/3. - 113.5*chi_eff/3.6) +\ @@ -52,64 +50,82 @@ def PNAmplitudeAndPhasing(f,m1,m2,chi1,chi2,t0,phase0): chi1*chi2*(7.87*eta/1.44 - 30.37*eta**2/1.44)),\ - x**(7./2)* (-jnp.pi*(4.415/4.032 - 358.675*eta/6.048 - 91.495*eta**2/1.512) -\ + (-jnp.pi*(4.415/4.032 - 358.675*eta/6.048 - 91.495*eta**2/1.512) -\ chi_eff*(252.9407/2.7216 - 845.827*eta/6.048 + 415.51*eta**2/8.64) +\ (chi1+chi2)*(158.0239*eta/5.4432 - 451.597*eta**2/6.048 + 20.45*eta**3/4.32 +\ 107.*eta*chi_eff**2/6. - 5.*eta**2*chi1*chi2/24.) +\ 12.*jnp.pi*chi_eff**2 - chi_eff**3*(150.5/2.4 + eta/8.) +\ chi_eff*chi1*chi2*(10.1*eta/2.4 + 3.*eta**2/8.))]) + T4_alpha_log = - 8.56*jnp.log(16.*x)/1.05*x**3 + + T4_alpha = 64.*eta/5. *x**5* (jnp.sum(T4_alpha_power_index*T4_alpha_coeff,axis=1)+T4_alpha_log) + # Taylor T4 amplitude coefficient from A5 - T4_A = 8.*eta*jnp.sqrt(jnp.pi/5.)*x*jnp.array([x**0,\ - x * ((-107. + 55.*eta)/42.),\ + T4_A_power_index = x[:,None]**(jnp.array([0,2,3,4,5,6])/2) - x**(3./2)*(2.*jnp.pi - 4.*chi_eff/3. + 2.*eta*(chi1+chi2)/3.),\ + T4_A_coeff = jnp.array([1,\ - x**(2.)*(-2.173/1.512 - eta*(10.69/2.16 - 2.*chi1*chi2) + 2.047*eta**2/1.512),\ + ((-107. + 55.*eta)/42.),\ - x**(5./2)*(-10.7*jnp.pi/2.1 + eta*(3.4*jnp.pi/2.1-24.*1j)),\ + (2.*jnp.pi - 4.*chi_eff/3. + 2.*eta*(chi1+chi2)/3.),\ - x**(3.)*(270.27409/6.46800 - 8.56*euler_gamma/1.05 +\ + (-2.173/1.512 - eta*(10.69/2.16 - 2.*chi1*chi2) + 2.047*eta**2/1.512),\ + + (-10.7*jnp.pi/2.1 + eta*(3.4*jnp.pi/2.1-24.*1j)),\ + + (270.27409/6.46800 - 8.56*euler_gamma/1.05 +\ 2.*jnp.pi**2/3. +\ eta*(4.1*jnp.pi**2/9.6 - 27.8185/3.3264) -\ 20.261*eta**2/2.772 + 11.4635*eta**3/9.9792 +\ - 4.28*(1j*jnp.pi-jnp.log(16.*x))/1.05)]) + 4.28*(1j*jnp.pi)/1.05)]) + + T4_A_log = (4.28*(-jnp.log(16.*x))/1.05)*x**3 + + T4_A = 8.*eta*jnp.sqrt(jnp.pi/5.)*x*(jnp.sum(T4_A_power_index*T4_A_coeff,axis=1)+T4_A_log) # Taylor F2 Phasing coefficient from A4 - F2_alpha = 3.0/(128.0 * eta)*(jnp.pi)**(-5./3)*jnp.array([f**0,\ - (jnp.pi*f)**(2./3)*((3715./756.) + (55.*eta/9.0)),\ + F2_alpha_power_index = jnp.pi*f[:,None]**(jnp.array([0,2,3,4,5,6,7])/3.) + + F2_alpha_coeff = jnp.array([1,\ - (jnp.pi*f)**(3./3)*(-16.0*jnp.pi + (113./3.)*chi_eff - 38.*eta*(chi1+chi2)/3.),\ + ((3715./756.) + (55.*eta/9.0)),\ - (jnp.pi*f)**(4./3)*((152.93365/5.08032) - 50.*chi_eff**2 + eta*(271.45/5.04 + 1.25*chi1*chi2) + \ + (-16.0*jnp.pi + (113./3.)*chi_eff - 38.*eta*(chi1+chi2)/3.),\ + + ((152.93365/5.08032) - 50.*chi_eff**2 + eta*(271.45/5.04 + 1.25*chi1*chi2) + \ 3085.*eta**2/72.),\ - (jnp.pi*f)**(5./3)*((1+ jnp.log(jnp.pi*f))*(jnp.pi*(386.45/7.56 - 65.*eta/9.) - \ + ((jnp.pi*(386.45/7.56 - 65.*eta/9.) - \ chi_eff*(735.505/2.268 + 130.*eta/9.) + (chi1+chi2)*(1285.0*eta/8.1 + 170.*eta**2/9.) -\ 10.*chi_eff**3/3. + 10.*eta*chi_eff*(chi1*chi2))), \ - (jnp.pi*f)**(6./3)*(11583.231236531/4.694215680 - 640.0*jnp.pi**2/3. - \ - 6848.0*euler_gamma/21. - 684.8*jnp.log(64.*jnp.pi*f)/6.3 + \ + (11583.231236531/4.694215680 - 640.0*jnp.pi**2/3. - \ + 6848.0*euler_gamma/21. + \ eta*(2255.*jnp.pi**2/12. - 15737.765635/3.048192) + \ 76.055*eta**2/1.728 - (127.825*eta**3/1.296) + \ 2920.*jnp.pi*chi_eff/3. - (175. - 1490.*eta)*chi_eff**2/3. - \ (1120.*jnp.pi/3. - 1085.*chi_eff/3.)*eta*(chi1+chi2) + \ (269.45*eta/3.36 - 2365.*eta**2/6.)*chi1*chi2), \ - (jnp.pi*f)**(7./3)*(jnp.pi*(770.96675/2.54016 + 378.515*eta/1.512 - 740.45*eta**2/7.56) - \ + (jnp.pi*(770.96675/2.54016 + 378.515*eta/1.512 - 740.45*eta**2/7.56) - \ chi_eff*(20373.952415/3.048192 + 1509.35*eta/2.24 - 5786.95*eta**2/4.32) + \ (chi1+chi2)*(4862.041225*eta/1.524096 + 1189.775*eta**2/1.008 - \ 717.05*eta**3/2.16 - 830.*eta*chi_eff**2/3. + 35.*eta**2*chi1*chi2/3.) - \ 560.*jnp.pi*chi_eff**2 + 20.*jnp.pi*eta*chi1*chi2 + \ chi_eff**3*(945.55/1.68 - 85.*eta) + chi_eff*chi1*chi2*(396.65*eta/1.68 + 255.*eta**2))]) + F2_alpha_log = F2_alpha_coeff[4]*jnp.log(jnp.pi*f)*(jnp.pi*f)**(5./3) + (- 684.8*jnp.log(64.*jnp.pi*f)/6.3)*(jnp.pi*f)**2 + + F2_alpha = 3.0/(128.0 * eta)*(jnp.pi*f)**(-5./3)*(jnp.sum(F2_alpha_power_index*F2_alpha_coeff,axis=1)+F2_alpha_log) - amplitude = jnp.sum(T4_A,axis=0)*jnp.sqrt(jnp.pi/(3./2*jnp.sqrt(x)*jnp.sum(T4_alpha,axis=0))) - phase = 2*jnp.pi*f*t0 - phase0 - jnp.pi/4 + jnp.sum(F2_alpha,axis=0) + amplitude = T4_A*jnp.sqrt(jnp.pi/(3./2*jnp.sqrt(x)*T4_alpha)) + phase = 2*jnp.pi*f*t0 - phase0 - jnp.pi/4 + F2_alpha return amplitude, phase +# return T4_alpha,T4_A,F2_alpha#amplitude, phase alpha_coef = jnp.array([[-2.417 * 1e-3, -1.093 * 1e-3, -1.917 * 1e-2, 7.267 * 1e-2, -2.504 * 1e-1],\ @@ -124,6 +140,7 @@ def PNAmplitudeAndPhasing(f,m1,m2,chi1,chi2,t0,phase0): delta_coef = jnp.array([[-5.472 * 1e-2, 2.094 * 1e-2, 3.554 * 1e-1, 1.151 * 1e-1, 9.64 * 1e-1], \ [-1.235, 3.423*1e-1, 6.062, 5.949, -1.069*1e1]]) +phase_PM_order = jnp.array([-5./3,-3./3,-1./3,0,2./3,1]) @jit @@ -135,6 +152,7 @@ def getPhenomCoef(eta, chi): return alpha, gamma, delta +@jit def getFinalSpin(eta,chi_eff): finspin = chi_eff - 0.129*chi_eff**2*eta -0.384*eta**2*chi_eff -2.686*eta*chi_eff \ + 2.*jnp.sqrt(3.)*eta -3.454*eta**2 + 2.353*eta**3 @@ -146,8 +164,6 @@ def IMRPhenomC(f,params): The amplitude and phase are generated first in unitless Mf space, then scaled with total mass and distance. """ - f = f[:,None] - local_m1 = params['mass_1']*Msun local_m2 = params['mass_2']*Msun local_d = params['luminosity_distance']*Mpc @@ -171,13 +187,14 @@ def IMRPhenomC(f,params): alpha, gamma, delta = getPhenomCoef(eta,chi_eff) - phase_PM = 1./eta*jnp.sum(alpha*f**jnp.array([-5./3,-3./3,-1./3,0,2./3,1]),axis=1) + phase_PM = 1./eta*jnp.sum(alpha*f[:,None]**phase_PM_order,axis=1) - beta_1 = 1./eta*jnp.sum(alpha*f_rd**jnp.array([-5./3,-3./3,-1./3,0,2./3,1])) + beta_1 = 1./eta*jnp.sum(alpha*f_rd**phase_PM_order) beta_2 = 1./eta*jnp.sum(jnp.array([-5./3,-3./3,-1./3,2./3,1])*alpha[jnp.array([0,1,2,4,5])]*f_rd**jnp.array([-8./3,-6./3,-4./3,-1./3,0])) phase_RD = beta_1 + beta_2*f - phase = phase_PN*smoothing_minus(f,0.1*f_rd,0.005) + phase_PN*smoothing_plus(f,0.1*f_rd,0.005)*smoothing_minus(f,f_rd,0.005) + phase_PN*smoothing_plus(f,f_rd,0.005) + phase = phase_PN*smoothing_minus(f,0.1*f_rd,0.005) + phase_PM*smoothing_plus(f,0.1*f_rd,0.005)*smoothing_minus(f,f_rd,0.005) + phase_RD*smoothing_plus(f,f_rd,0.005) + #phase = phase_PM*smoothing_plus(f,0.1*f_rd,0.005)*smoothing_minus(f,f_rd,0.005) + phase_RD*smoothing_plus(f,f_rd,0.005) # Constructing amplitude of the waveform @@ -188,8 +205,8 @@ def IMRPhenomC(f,params): amplitude = A_PM *smoothing_minus(f,0.98*f_rd,0.015) + A_RD*smoothing_plus(f,0.98*f_rd,0.015) amplitude_factor = 2. * jnp.sqrt(5. / (64.*jnp.pi)) * M_tot**2 / local_d - totalh = (amplitude*amplitude_factor*jnp.exp(-1j*phase))[:,0]#* amplitude_factor -# hp = totalh * (1/2*(1+jnp.cos(params['theta_jn'])**2)*jnp.cos(2*params['psi'])) -# hc = totalh * jnp.cos(params['theta_jn'])*jnp.sin(2*params['psi']) + totalh = (amplitude*amplitude_factor*jnp.exp(1j*phase))#* amplitude_factor + hp = totalh * (1/2*(1+jnp.cos(params['theta_jn'])**2)*jnp.cos(2*params['psi'])) + hc = totalh * jnp.cos(params['theta_jn'])*jnp.sin(2*params['psi']) - return totalh#{'plus':hp.T[0],'cross':hc.T[0]} + return {'plus':hp,'cross':hc} From 9bb556614b804e6eda8f927dbaac533c96031e11 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Fri, 15 Oct 2021 15:20:16 -0400 Subject: [PATCH 030/300] Update HMC example --- example/blackjax/HMC_injection.py | 61 +++++++++++++++++++------------ 1 file changed, 37 insertions(+), 24 deletions(-) diff --git a/example/blackjax/HMC_injection.py b/example/blackjax/HMC_injection.py index 548a85fb..68369d8a 100644 --- a/example/blackjax/HMC_injection.py +++ b/example/blackjax/HMC_injection.py @@ -12,23 +12,24 @@ from jaxgw.likelihood.utils import inner_product from jaxgw.waveform.TaylorF2 import TaylorF2 from jaxgw.waveform.IMRPhenomC import IMRPhenomC -from jax import random, grad, jit, vmap, jacfwd, jacrev, value_and_grad +from jax import random, grad, jit, vmap, jacfwd, jacrev, value_and_grad, pmap true_m1 = 3. -true_m2 = 3. -true_ld = 1000 +true_m2 = 2.99 +true_ld = 300 + injection_parameters = dict( mass_1=true_m1, mass_2=true_m2, a_1=0.0, a_2=0.0, luminosity_distance=true_ld, theta_jn=0.4, psi=2.659, - phase=1.3, geocent_time=1126259642.413, ra=1.375, dec=-1.2108) + phase=0, geocent_time=0, ra=1.375, dec=-1.2108) + guess_parameters = dict( - mass_1=true_m1, mass_2=true_m2)#, luminosity_distance=400.)#, theta_jn=0.4, psi=2.659, ra=1.375, dec=-1.2108) + trans_M_tot=true_m1, trans_q=true_m2)#, luminosity_distance=400.)#, theta_jn=0.4, psi=2.659, ra=1.375, dec=-1.2108) -#injection_parameters = dict(mass_1=36., mass_2=29., luminosity_distance=410.) -# -#guess_parameters = dict(mass_1=40., mass_2=25., luminosity_distance=400.) +#guess_parameters = dict(trans_M_tot=jnp.log(true_m1+true_m2), trans_q=jnp.log(true_m2/true_m1)-jnp.log(1-true_m2/true_m1)) +guess_parameters = dict(m1=true_m1, m2=true_m2) @@ -37,7 +38,7 @@ # sensitivity ifos = bilby.gw.detector.InterferometerList(['H1']) ifos.set_strain_data_from_power_spectral_densities( - sampling_frequency=2048, duration=16, + sampling_frequency=2048, duration=4, start_time=- 3) psd = ifos[0].power_spectral_density_array @@ -59,8 +60,8 @@ H1_arm2 = construct_arm(H1_long, H1_lat, H1_yarm_tilt, H1_yarm_azimuth) H1 = detector_tensor(H1_arm1, H1_arm2) -strain = waveform#get_detector_response(waveform,injection_parameters,H1) -#strain = get_detector_response(waveform,injection_parameters,H1) +#strain = waveform#get_detector_response(waveform,injection_parameters,H1) +strain = get_detector_response(waveform,injection_parameters,H1) print('SNR of the event: '+str(np.sqrt(inner_product(strain,strain,psd_frequency,psd)))) @@ -68,26 +69,38 @@ def jax_likelihood(params, data, data_f, PSD): waveform = IMRPhenomC(data_f, params) # waveform = TaylorF2(data_f, params) -# waveform = get_detector_response(waveform, params, H1) + waveform = get_detector_response(waveform, params, H1) match_filter_SNR = inner_product(waveform, data, data_f, PSD) optimal_SNR = inner_product(waveform, waveform, data_f, PSD) return (-2*match_filter_SNR + optimal_SNR)/2#, match_filter_SNR, optimal_SNR @jit -def Mq_to_m1m2(M_tot,q): +def m1m2_to_Mq(m1,m2): + M_tot = jnp.log(m1+m2) + q = jnp.log(m2/m1)-jnp.log(1-m2/m1) + return M_tot, q + +@jit +def Mq_to_m1m2(trans_M_tot,trans_q): + M_tot = jnp.exp(trans_M_tot) + q = 1./(1+jnp.exp(-trans_q)) m1 = M_tot/(1+q) m2 = m1*q - inv_1pq = 1./(1+q) - Jac_det = inv_1pq*(M*(inv_1pq-q/inv_1pq**2))+(q*inv_1pq**3)*M - return m1, m2, Jac_det +# Jac_det = M_tot/(1+q)**2*jnp.exp(trans_M_tot-trans_q)/(1+jnp.exp(-trans_q))**2 + return m1, m2#, Jac_det -def logprob_wrap(mass_1, mass_2):#, luminosity_distance):#, theta_jn, psi, ra, dec): -# m1, m2, Jac_det = Mq_to_m1m2(M_tot, q) +#def logprob_wrap(trans_M_tot, trans_q):#, luminosity_distance):#, theta_jn, psi, ra, dec): +@jit +def logprob_wrap(m1, m2):#, luminosity_distance):#, theta_jn, psi, ra, dec): +# print(trans_M_tot,trans_q) +# m1, m2, Jac_det = Mq_to_m1m2(trans_M_tot, trans_q) +# m1, m2 = Mq_to_m1m2(trans_M_tot, trans_q) - params = dict(mass_1=mass_1, mass_2=mass_2, a_1=0, a_2=0, luminosity_distance=true_ld, theta_jn=0.4, psi=2.659, phase=1.3, geocent_time=1126259642.413, ra=1.375, dec=-1.2108) - return jax_likelihood(params, strain, psd_frequency, psd) + params = dict(mass_1=m1, mass_2=m2, a_1=0, a_2=0, luminosity_distance=true_ld, theta_jn=0.4, psi=2.659, phase=0, geocent_time=0, ra=1.375, dec=-1.2108) + return jax_likelihood(params, strain, psd_frequency, psd)#*Jac_det log_prob = lambda x: logprob_wrap(**x) +log_prob = jit(log_prob) #def log_prob(param): # if (param[0]<=0) or (param[1]<=0): @@ -125,20 +138,20 @@ def logprob_wrap(mass_1, mass_2):#, luminosity_distance):#, theta_jn, psi, ra, d time1 = time.time() kernel_generator = lambda step_size, inverse_mass_matrix: hmc.kernel( - log_prob, step_size, inverse_mass_matrix, 60 + log_prob, step_size, inverse_mass_matrix, 100 ) final_state, (step_size, inverse_mass_matrix), info = stan_warmup.run( key, kernel_generator, initial_state, - 1000, - is_mass_matrix_diagonal=False + 300, + #is_mass_matrix_diagonal=False ) print("Finding inverse mass matrix takes: "+str(time.time()-time1)+" seconds") print("Stepsize: "+str(step_size)) -print("Mass_matrix: "+str(inverse_mass_matrix)) +print("Inverse mass matrix: "+str(inverse_mass_matrix)) num_integration_steps = 60 hmc_kernel = hmc.kernel(log_prob, step_size, inverse_mass_matrix, num_integration_steps) From b84a73d82415687f9d5e18b239c7b26e3119db76 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Sat, 16 Oct 2021 13:04:29 -0400 Subject: [PATCH 031/300] Can produce samples for IMRPhenomD now., but it finish with 10000 samples in 2 hours. Most of the computational cost goes into gradient computation, need to optimize a bit --- example/blackjax/HMC_injection.py | 85 +++++++++++-------------------- 1 file changed, 30 insertions(+), 55 deletions(-) diff --git a/example/blackjax/HMC_injection.py b/example/blackjax/HMC_injection.py index 68369d8a..e6accc9b 100644 --- a/example/blackjax/HMC_injection.py +++ b/example/blackjax/HMC_injection.py @@ -14,23 +14,39 @@ from jaxgw.waveform.IMRPhenomC import IMRPhenomC from jax import random, grad, jit, vmap, jacfwd, jacrev, value_and_grad, pmap -true_m1 = 3. -true_m2 = 2.99 -true_ld = 300 +@jit +def m1m2_to_Mq(m1,m2): + M_tot = jnp.log(m1+m2) + q = jnp.log(m2/m1)-jnp.log(1-m2/m1) + return M_tot, q +@jit +def Mq_to_m1m2(trans_M_tot,trans_q): + M_tot = jnp.exp(trans_M_tot) + q = 1./(1+jnp.exp(-trans_q)) + m1 = M_tot/(1+q) + m2 = m1*q +# Jac_det = M_tot/(1+q)**2*jnp.exp(trans_M_tot-trans_q)/(1+jnp.exp(-trans_q))**2 + return m1, m2#, Jac_det -injection_parameters = dict( - mass_1=true_m1, mass_2=true_m2, a_1=0.0, a_2=0.0, luminosity_distance=true_ld, theta_jn=0.4, psi=2.659, - phase=0, geocent_time=0, ra=1.375, dec=-1.2108) -guess_parameters = dict( - trans_M_tot=true_m1, trans_q=true_m2)#, luminosity_distance=400.)#, theta_jn=0.4, psi=2.659, ra=1.375, dec=-1.2108) +true_m1 = 3. +true_m2 = 2.99 +true_ld = 600 +true_phase = 0. +true_gt = 0. +trans_M_tot, trans_q = m1m2_to_Mq(true_m1,true_m2) -#guess_parameters = dict(trans_M_tot=jnp.log(true_m1+true_m2), trans_q=jnp.log(true_m2/true_m1)-jnp.log(1-true_m2/true_m1)) -guess_parameters = dict(m1=true_m1, m2=true_m2) +injection_parameters = dict( + mass_1=true_m1, mass_2=true_m2, a_1=0.0, a_2=0.0, luminosity_distance=true_ld, theta_jn=0.4, psi=2.659, + phase=true_phase, geocent_time=true_gt, ra=1.375, dec=-1.2108) + +#guess_parameters = dict(trans_M_tot=trans_M_tot, trans_q=trans_q, phase=true_phase, geocent_time=true_gt,) +guess_parameters = dict(m1=true_m1, m2=true_m2, phase=true_phase, geocent_time=true_gt,) +#guess_parameters = dict(m1=true_m1, m2=true_m2) # Set up interferometers. In this case we'll use two interferometers @@ -48,7 +64,6 @@ psd = psd[jnp.isfinite(psd)] waveform = IMRPhenomC(psd_frequency, injection_parameters) -#waveform = TaylorF2(psd_frequency, injection_parameters) H1_lat = 46 + 27. / 60 + 18.528 / 3600 H1_long = -(119 + 24. / 60 + 27.5657 / 3600) H1_xarm_azimuth = 125.9994 @@ -60,7 +75,6 @@ H1_arm2 = construct_arm(H1_long, H1_lat, H1_yarm_tilt, H1_yarm_azimuth) H1 = detector_tensor(H1_arm1, H1_arm2) -#strain = waveform#get_detector_response(waveform,injection_parameters,H1) strain = get_detector_response(waveform,injection_parameters,H1) print('SNR of the event: '+str(np.sqrt(inner_product(strain,strain,psd_frequency,psd)))) @@ -73,55 +87,16 @@ def jax_likelihood(params, data, data_f, PSD): match_filter_SNR = inner_product(waveform, data, data_f, PSD) optimal_SNR = inner_product(waveform, waveform, data_f, PSD) return (-2*match_filter_SNR + optimal_SNR)/2#, match_filter_SNR, optimal_SNR - @jit -def m1m2_to_Mq(m1,m2): - M_tot = jnp.log(m1+m2) - q = jnp.log(m2/m1)-jnp.log(1-m2/m1) - return M_tot, q +#def logprob_wrap(trans_M_tot, trans_q, geocent_time, phase): +def logprob_wrap(m1, m2, geocent_time, phase): -@jit -def Mq_to_m1m2(trans_M_tot,trans_q): - M_tot = jnp.exp(trans_M_tot) - q = 1./(1+jnp.exp(-trans_q)) - m1 = M_tot/(1+q) - m2 = m1*q -# Jac_det = M_tot/(1+q)**2*jnp.exp(trans_M_tot-trans_q)/(1+jnp.exp(-trans_q))**2 - return m1, m2#, Jac_det - -#def logprob_wrap(trans_M_tot, trans_q):#, luminosity_distance):#, theta_jn, psi, ra, dec): -@jit -def logprob_wrap(m1, m2):#, luminosity_distance):#, theta_jn, psi, ra, dec): -# print(trans_M_tot,trans_q) -# m1, m2, Jac_det = Mq_to_m1m2(trans_M_tot, trans_q) -# m1, m2 = Mq_to_m1m2(trans_M_tot, trans_q) - - params = dict(mass_1=m1, mass_2=m2, a_1=0, a_2=0, luminosity_distance=true_ld, theta_jn=0.4, psi=2.659, phase=0, geocent_time=0, ra=1.375, dec=-1.2108) + params = dict(mass_1=m1, mass_2=m2, a_1=0, a_2=0, luminosity_distance=true_ld, theta_jn=0.4, psi=2.659, phase=phase, geocent_time=geocent_time, ra=1.375, dec=-1.2108) return jax_likelihood(params, strain, psd_frequency, psd)#*Jac_det log_prob = lambda x: logprob_wrap(**x) log_prob = jit(log_prob) -#def log_prob(param): -# if (param[0]<=0) or (param[1]<=0): -# return -jnp.inf -# params = dict(mass_1=param[0], mass_2=param[1], a_1=0, a_2=0, luminosity_distance=true_ld, theta_jn=0.4, psi=2.659, phase=1.3, geocent_time=1126259642.413, ra=1.375, dec=-1.2108) -# return jax_likelihood(params, strain, psd_frequency, psd) - -################################################################ -# Test with Emcee to make sure likelihood looks fine -################################################################ - -#import emcee -# -#nwalkers = 32 -#ndim = 2 -#p0 = np.random.rand(nwalkers, ndim) + [true_m1,true_m2] -#sampler = emcee.EnsembleSampler(nwalkers, ndim, log_prob) -#state = sampler.run_mcmc(p0, 100) -#sampler.reset() -#sampler.run_mcmc(state, 10000) - ############################################################### # BlackJax section ############################################################### @@ -172,5 +147,5 @@ def one_step(state, rng_key): print("Start sampling") time1 = time.time() -states = inference_loop(subkey, hmc_kernel, initial_state, 1000) +states = inference_loop(subkey, hmc_kernel, initial_state, 10000) print("Sampling takes: "+str(time.time()-time1)+" seconds") From c7c64897c793513a42059a6d2bbe9f7e9da05cab Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Tue, 19 Oct 2021 10:48:02 -0400 Subject: [PATCH 032/300] Add detector presets --- jaxgw/likelihood/detector_preset.py | 29 +++++++++++++++++++++++++++++ 1 file changed, 29 insertions(+) create mode 100644 jaxgw/likelihood/detector_preset.py diff --git a/jaxgw/likelihood/detector_preset.py b/jaxgw/likelihood/detector_preset.py new file mode 100644 index 00000000..4d258967 --- /dev/null +++ b/jaxgw/likelihood/detector_preset.py @@ -0,0 +1,29 @@ +from jaxgw.likelihood.detector_projection import construct_arm, detector_tensor, antenna_response, get_detector_response + +# See https://git.ligo.org/lscsoft/bilby/-/tree/master/bilby/gw/detector/detectors for detector parameters. + +def get_H1(): + H1_lat = 46 + 27. / 60 + 18.528 / 3600 + H1_long = -(119 + 24. / 60 + 27.5657 / 3600) + H1_xarm_azimuth = 125.9994 + H1_yarm_azimuth = 215.9994 + H1_xarm_tilt = -6.195e-4 + H1_yarm_tilt = 1.25e-5 + + H1_arm1 = construct_arm(H1_long, H1_lat, H1_xarm_tilt, H1_xarm_azimuth) + H1_arm2 = construct_arm(H1_long, H1_lat, H1_yarm_tilt, H1_yarm_azimuth) + + return detector_tensor(H1_arm1, H1_arm2) + +def get_L1(): + L1_lat = 30 + 33. / 60 + 46.4196 / 3600 + L1_long = -(90 + 46. / 60 + 27.2654 / 3600) + L1_xarm_azimuth = 197.7165 + L1_yarm_azimuth = 287.7165 + L1_xarm_tilt = 0 + L1_yarm_tilt = 0 + + L1_arm1 = construct_arm(L1_long, L1_lat, L1_xarm_tilt, L1_xarm_azimuth) + L1_arm2 = construct_arm(L1_long, L1_lat, L1_yarm_tilt, L1_yarm_azimuth) + + return detector_tensor(L1_arm1, L1_arm2) From d88bc613ae7cab93c6749e144f45abfa058d54b5 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Tue, 19 Oct 2021 13:16:06 -0400 Subject: [PATCH 033/300] Add population example with gravitational wave likelihood as single event likelihood. Note that even though the likelihood evaluation can be done, the gradient descent part still gives unresonable results. --- example/Population_injection.py | 210 ++++++++++++++++++++++++++++++++ 1 file changed, 210 insertions(+) create mode 100644 example/Population_injection.py diff --git a/example/Population_injection.py b/example/Population_injection.py new file mode 100644 index 00000000..17b1ff77 --- /dev/null +++ b/example/Population_injection.py @@ -0,0 +1,210 @@ +import jax.numpy as jnp +import numpy as np +import copy +import bilby +from jax import random, grad, jit, vmap, jacfwd, jacrev, value_and_grad +from jax.experimental.optimizers import adam +from jax.config import config +config.update("jax_enable_x64", True) + +from jaxgw.likelihood.utils import inner_product +from jaxgw.waveform.IMRPhenomC import IMRPhenomC +from jaxgw.likelihood.detector_preset import get_H1 +from jaxgw.likelihood.detector_projection import get_detector_response + + +import matplotlib.pyplot as plt +import matplotlib as mpl + +params = {'axes.labelsize': 32, + 'font.family': 'serif', + 'font.serif': 'Computer Modern Raman', + 'font.size': 32, + 'axes.linewidth': 2, + 'legend.fontsize': 28, + 'xtick.labelsize': 28, + 'xtick.top': True, + 'xtick.direction': "in", + 'ytick.labelsize': 20, + 'ytick.right': True, + 'ytick.direction': "in", + 'axes.grid' : False, + 'text.usetex': True, + 'savefig.dpi' : 100, + 'lines.markersize' : 14, +# 'axes.formatter.useoffset': False, + 'axes.formatter.limits' : (-3,3)} + +mpl.rcParams['text.latex.preamble'] = [r'\usepackage{amsmath}'] #for \text command + +mpl.rcParams.update(params) + +key = random.PRNGKey(42) + +######################################## +# Defining our model +######################################## + +# Since truncated power law is not differentiable, we choose tanh as a smoother cutoff +x_axis = jnp.linspace(1,150,100000) +@jit +def power_law_tanh(x,params): + alpha = params['alpha'] + xmin = params['xmin'] + xmax = params['xmax'] + lower_window = (jnp.tanh((x-xmin)*10)+1)/2 + upper_window = -(jnp.tanh((x-xmax)*10)-1)/2 + power_law = x**-alpha + output_unnorm = power_law*lower_window*upper_window + # This normalization factor is supposed to be a good approximation but not perfect + norm = jnp.trapz(x_axis**-alpha*(jnp.tanh(x_axis-xmin)+1)/2*(-(jnp.tanh(x_axis-xmax)-1)/2),x=x_axis) + output = output_unnorm/norm + return output + +@jit +def gaussian(x,mean,sigma): + return (1./jnp.sqrt(2*jnp.pi)/sigma)*jnp.exp(-(((x-mean)/sigma)**2)/2) + +@jit +def power_law_plus_peak(x,params): +# !!! Add smoothing later +# Since each component is normalized, the combine pdf should be normalized + powerlaw = power_law_tanh(x,params) + peak = gaussian(x,params['mean'],params['sigma']) + combine = (1-params['mixing'])*powerlaw+params['mixing']*peak + return combine + +true_ld = 600 +true_phase = 0. +true_gt = 0. + +# Set up interferometers. In this case we'll use two interferometers +# (LIGO-Hanford (H1), LIGO-Livingston (L1). These default to their design +# sensitivity +ifos = bilby.gw.detector.InterferometerList(['H1']) +ifos.set_strain_data_from_power_spectral_densities( + sampling_frequency=2048, duration=32, + start_time=- 3) + +psd = ifos[0].power_spectral_density_array +psd_frequency = ifos[0].frequency_array +psd_frequency = psd_frequency[jnp.isfinite(psd)] +psd = psd[jnp.isfinite(psd)] + +H1 = get_H1() + +def gen_params(m1): + params = dict(mass_1=m1, mass_2=m1, a_1=0, a_2=0, luminosity_distance=true_ld, phase=true_phase, geocent_time=true_gt, theta_jn=0.4, psi=2.659, ra=1.375, dec=-1.2108) + return params + +def gen_event(params): + waveform = IMRPhenomC(psd_frequency, params) + strain = get_detector_response(waveform, params, H1) + return strain + +@jit +def jax_likelihood(params, data, data_f, PSD): + waveform = IMRPhenomC(data_f, params) + waveform = get_detector_response(waveform, params, H1) + match_filter_SNR = inner_product(waveform, data, data_f, PSD) + optimal_SNR = inner_product(waveform, waveform, data_f, PSD) + return (-2*match_filter_SNR + optimal_SNR)/2#, match_filter_SNR, optimal_SNR + +@jit +def logprob_wrap(m1): + params = dict(mass_1=m1, mass_2=m1, a_1=0, a_2=0, luminosity_distance=true_ld, phase=true_phase, geocent_time=true_gt, theta_jn=0.4, psi=2.659, ra=1.375, dec=-1.2108) + return jax_likelihood(params, strain, psd_frequency, psd)#*Jac_det + +log_prob = lambda x: logprob_wrap(**x) +log_prob = jit(log_prob) + + + +######################################## +# Power law Only +######################################## + +true_param = {} +true_param['alpha'] = 2.63 +true_param['xmin'] = 4.59 +true_param['xmax'] = 86.22 +true_param['mean'] = 33.07 +true_param['sigma'] = 5.69 +true_param['mixing'] = 0.3 + +N_sample = 50 +obs_std = 0.1 + +m1_sample = jnp.empty(0) + + +while m1_sample.shape[0] Date: Thu, 21 Oct 2021 10:41:52 -0400 Subject: [PATCH 034/300] Update waveform test with match filter snr --- test/waveform_test.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/test/waveform_test.py b/test/waveform_test.py index ea2f4442..19b1272d 100644 --- a/test/waveform_test.py +++ b/test/waveform_test.py @@ -95,10 +95,12 @@ def jax_likelihood(params, data, data_f, PSD): waveform = IMRPhenomC(data_f, params) waveform = get_detector_response(waveform, params, H1).T[0] - output = inner_product(waveform, data, data_f, PSD) - return output + match_filter_SNR = inner_product(waveform, data, data_f, PSD) + optimal_SNR = inner_product(waveform, waveform, data_f, PSD) + return (2*match_filter_SNR - optimal_SNR)/2 +likelihood = jax_likelihood(injection_parameters, strain, waveform_frequency, psd_interp) dlikelihood = grad(jax_likelihood)(injection_parameters, strain, waveform_frequency, psd_interp) From 777fbc4ae1e1e70c6d9861534f78908511626e67 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Thu, 21 Oct 2021 10:42:48 -0400 Subject: [PATCH 035/300] Experimental update of HMC_injection example --- example/blackjax/HMC_injection.py | 33 +++++++++++++++++-------------- 1 file changed, 18 insertions(+), 15 deletions(-) diff --git a/example/blackjax/HMC_injection.py b/example/blackjax/HMC_injection.py index e6accc9b..bd7b37a8 100644 --- a/example/blackjax/HMC_injection.py +++ b/example/blackjax/HMC_injection.py @@ -33,8 +33,8 @@ def Mq_to_m1m2(trans_M_tot,trans_q): true_m1 = 3. -true_m2 = 2.99 -true_ld = 600 +true_m2 = 2. +true_ld = 150. true_phase = 0. true_gt = 0. trans_M_tot, trans_q = m1m2_to_Mq(true_m1,true_m2) @@ -43,10 +43,9 @@ def Mq_to_m1m2(trans_M_tot,trans_q): mass_1=true_m1, mass_2=true_m2, a_1=0.0, a_2=0.0, luminosity_distance=true_ld, theta_jn=0.4, psi=2.659, phase=true_phase, geocent_time=true_gt, ra=1.375, dec=-1.2108) -#guess_parameters = dict(trans_M_tot=trans_M_tot, trans_q=trans_q, phase=true_phase, geocent_time=true_gt,) -guess_parameters = dict(m1=true_m1, m2=true_m2, phase=true_phase, geocent_time=true_gt,) - -#guess_parameters = dict(m1=true_m1, m2=true_m2) +#guess_parameters = dict(m1=true_m1, m2=true_m2, luminosity_distance=true_ld, phase=true_phase, geocent_time=true_gt, theta_jn=0.4, psi=2.659, ra=1.375, dec=-1.2108) +# +guess_parameters = dict(m1=true_m1, m2=true_m2) # Set up interferometers. In this case we'll use two interferometers @@ -54,7 +53,7 @@ def Mq_to_m1m2(trans_M_tot,trans_q): # sensitivity ifos = bilby.gw.detector.InterferometerList(['H1']) ifos.set_strain_data_from_power_spectral_densities( - sampling_frequency=2048, duration=4, + sampling_frequency=2048, duration=32, start_time=- 3) psd = ifos[0].power_spectral_density_array @@ -87,11 +86,15 @@ def jax_likelihood(params, data, data_f, PSD): match_filter_SNR = inner_product(waveform, data, data_f, PSD) optimal_SNR = inner_product(waveform, waveform, data_f, PSD) return (-2*match_filter_SNR + optimal_SNR)/2#, match_filter_SNR, optimal_SNR -@jit -#def logprob_wrap(trans_M_tot, trans_q, geocent_time, phase): -def logprob_wrap(m1, m2, geocent_time, phase): +#@jit +##def logprob_wrap(trans_M_tot, trans_q, geocent_time, phase): +#def logprob_wrap(m1, m2, luminosity_distance, geocent_time, phase, theta_jn, psi, ra, dec): +# params = dict(mass_1=m1, mass_2=m2, a_1=0, a_2=0, luminosity_distance=luminosity_distance, theta_jn=theta_jn, psi=psi, phase=phase, geocent_time=geocent_time, ra=ra, dec=dec) +# return jax_likelihood(params, strain, psd_frequency, psd)#*Jac_det - params = dict(mass_1=m1, mass_2=m2, a_1=0, a_2=0, luminosity_distance=true_ld, theta_jn=0.4, psi=2.659, phase=phase, geocent_time=geocent_time, ra=1.375, dec=-1.2108) +@jit +def logprob_wrap(m1, m2): + params = dict(mass_1=m1, mass_2=m2, a_1=0, a_2=0, luminosity_distance=true_ld, phase=true_phase, geocent_time=true_gt, theta_jn=0.4, psi=2.659, ra=1.375, dec=-1.2108) return jax_likelihood(params, strain, psd_frequency, psd)#*Jac_det log_prob = lambda x: logprob_wrap(**x) @@ -113,21 +116,21 @@ def logprob_wrap(m1, m2, geocent_time, phase): time1 = time.time() kernel_generator = lambda step_size, inverse_mass_matrix: hmc.kernel( - log_prob, step_size, inverse_mass_matrix, 100 + log_prob, step_size, inverse_mass_matrix, 30 ) final_state, (step_size, inverse_mass_matrix), info = stan_warmup.run( key, kernel_generator, initial_state, - 300, + 500, #is_mass_matrix_diagonal=False ) print("Finding inverse mass matrix takes: "+str(time.time()-time1)+" seconds") print("Stepsize: "+str(step_size)) print("Inverse mass matrix: "+str(inverse_mass_matrix)) -num_integration_steps = 60 +num_integration_steps = 30 hmc_kernel = hmc.kernel(log_prob, step_size, inverse_mass_matrix, num_integration_steps) hmc_kernel = jit(hmc_kernel) @@ -147,5 +150,5 @@ def one_step(state, rng_key): print("Start sampling") time1 = time.time() -states = inference_loop(subkey, hmc_kernel, initial_state, 10000) +states = inference_loop(subkey, hmc_kernel, initial_state, 4000) print("Sampling takes: "+str(time.time()-time1)+" seconds") From 1fab75ac980412a2dfa0a13aca1752351843b099 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Thu, 21 Oct 2021 11:14:15 -0400 Subject: [PATCH 036/300] Add utility functions for converting individual masses to total mass and mass ratio, and vice versa. --- jaxgw/likelihood/utils.py | 40 ++++++++++++++++++++++++++++++++------- jaxgw/pop/__init__.py | 0 2 files changed, 33 insertions(+), 7 deletions(-) delete mode 100644 jaxgw/pop/__init__.py diff --git a/jaxgw/likelihood/utils.py b/jaxgw/likelihood/utils.py index 7b8b5cde..f35208d9 100644 --- a/jaxgw/likelihood/utils.py +++ b/jaxgw/likelihood/utils.py @@ -3,10 +3,36 @@ @jit def inner_product(h1, h2, frequency, PSD): - """ - Do PSD interpolation outside the inner product loop to speed up the evaluation - """ - #psd_interp = jnp.interp(frequency, PSD_frequency, PSD) - df = frequency[1] - frequency[0] - integrand = jnp.conj(h1)* h2 / PSD - return 4. * jnp.real(jnp.trapz(integrand,dx=df)) + """ + Do PSD interpolation outside the inner product loop to speed up the evaluation + """ + #psd_interp = jnp.interp(frequency, PSD_frequency, PSD) + df = frequency[1] - frequency[0] + integrand = jnp.conj(h1)* h2 / PSD + return 4. * jnp.real(jnp.trapz(integrand,dx=df)) + +@jit +def m1m2_to_Mq(m1,m2): + """ + Transforming the primary mass m1 and secondary mass m2 to the Total mass M + and mass ratio q. + + Args: + m1: Primary mass of the binary. + m2: Secondary mass of the binary. + + Returns: + A tuple containing both the total mass M and mass ratio q. + """ + M_tot = jnp.log(m1+m2) + q = jnp.log(m2/m1)-jnp.log(1-m2/m1) + return M_tot, q + +@jit +def Mq_to_m1m2(trans_M_tot,trans_q): + M_tot = jnp.exp(trans_M_tot) + q = 1./(1+jnp.exp(-trans_q)) + m1 = M_tot/(1+q) + m2 = m1*q + return m1, m2 + diff --git a/jaxgw/pop/__init__.py b/jaxgw/pop/__init__.py deleted file mode 100644 index e69de29b..00000000 From c8af9bc0e7a4a23a526fe0900d93acf833e545b8 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Thu, 21 Oct 2021 11:18:33 -0400 Subject: [PATCH 037/300] Rename utils.py to constant, move functions from HMC_injection to package. --- example/blackjax/HMC_injection.py | 63 +++++++++---------------------- jaxgw/{utils.py => constants.py} | 0 2 files changed, 18 insertions(+), 45 deletions(-) rename jaxgw/{utils.py => constants.py} (100%) diff --git a/example/blackjax/HMC_injection.py b/example/blackjax/HMC_injection.py index bd7b37a8..c84992d5 100644 --- a/example/blackjax/HMC_injection.py +++ b/example/blackjax/HMC_injection.py @@ -10,38 +10,21 @@ from jaxgw.likelihood.detector_projection import construct_arm, detector_tensor, antenna_response, get_detector_response from jaxgw.likelihood.utils import inner_product +from jaxgw.likelihood.detector_preset import get_H1 from jaxgw.waveform.TaylorF2 import TaylorF2 from jaxgw.waveform.IMRPhenomC import IMRPhenomC from jax import random, grad, jit, vmap, jacfwd, jacrev, value_and_grad, pmap -@jit -def m1m2_to_Mq(m1,m2): - M_tot = jnp.log(m1+m2) - q = jnp.log(m2/m1)-jnp.log(1-m2/m1) - return M_tot, q - -@jit -def Mq_to_m1m2(trans_M_tot,trans_q): - M_tot = jnp.exp(trans_M_tot) - q = 1./(1+jnp.exp(-trans_q)) - m1 = M_tot/(1+q) - m2 = m1*q -# Jac_det = M_tot/(1+q)**2*jnp.exp(trans_M_tot-trans_q)/(1+jnp.exp(-trans_q))**2 - return m1, m2#, Jac_det - - - true_m1 = 3. true_m2 = 2. true_ld = 150. true_phase = 0. true_gt = 0. -trans_M_tot, trans_q = m1m2_to_Mq(true_m1,true_m2) injection_parameters = dict( - mass_1=true_m1, mass_2=true_m2, a_1=0.0, a_2=0.0, luminosity_distance=true_ld, theta_jn=0.4, psi=2.659, - phase=true_phase, geocent_time=true_gt, ra=1.375, dec=-1.2108) + mass_1=true_m1, mass_2=true_m2, a_1=0.0, a_2=0.0, luminosity_distance=true_ld, theta_jn=0.4, psi=2.659, + phase=true_phase, geocent_time=true_gt, ra=1.375, dec=-1.2108) #guess_parameters = dict(m1=true_m1, m2=true_m2, luminosity_distance=true_ld, phase=true_phase, geocent_time=true_gt, theta_jn=0.4, psi=2.659, ra=1.375, dec=-1.2108) # @@ -53,8 +36,8 @@ def Mq_to_m1m2(trans_M_tot,trans_q): # sensitivity ifos = bilby.gw.detector.InterferometerList(['H1']) ifos.set_strain_data_from_power_spectral_densities( - sampling_frequency=2048, duration=32, - start_time=- 3) + sampling_frequency=2048, duration=32, + start_time=- 3) psd = ifos[0].power_spectral_density_array psd_frequency = ifos[0].frequency_array @@ -63,17 +46,7 @@ def Mq_to_m1m2(trans_M_tot,trans_q): psd = psd[jnp.isfinite(psd)] waveform = IMRPhenomC(psd_frequency, injection_parameters) -H1_lat = 46 + 27. / 60 + 18.528 / 3600 -H1_long = -(119 + 24. / 60 + 27.5657 / 3600) -H1_xarm_azimuth = 125.9994 -H1_yarm_azimuth = 215.9994 -H1_xarm_tilt = -6.195e-4 -H1_yarm_tilt = 1.25e-5 - -H1_arm1 = construct_arm(H1_long, H1_lat, H1_xarm_tilt, H1_xarm_azimuth) -H1_arm2 = construct_arm(H1_long, H1_lat, H1_yarm_tilt, H1_yarm_azimuth) - -H1 = detector_tensor(H1_arm1, H1_arm2) +H1 = get_H1() strain = get_detector_response(waveform,injection_parameters,H1) print('SNR of the event: '+str(np.sqrt(inner_product(strain,strain,psd_frequency,psd)))) @@ -116,15 +89,15 @@ def logprob_wrap(m1, m2): time1 = time.time() kernel_generator = lambda step_size, inverse_mass_matrix: hmc.kernel( - log_prob, step_size, inverse_mass_matrix, 30 + log_prob, step_size, inverse_mass_matrix, 30 ) final_state, (step_size, inverse_mass_matrix), info = stan_warmup.run( - key, - kernel_generator, - initial_state, - 500, - #is_mass_matrix_diagonal=False + key, + kernel_generator, + initial_state, + 500, + #is_mass_matrix_diagonal=False ) print("Finding inverse mass matrix takes: "+str(time.time()-time1)+" seconds") @@ -139,14 +112,14 @@ def logprob_wrap(m1, m2): print("Energy of the first step is: "+str(test_likelihood[1].energy)) def inference_loop(rng_key, kernel, initial_state, num_samples): - def one_step(state, rng_key): - state, _ = kernel(rng_key, state) - return state, state + def one_step(state, rng_key): + state, _ = kernel(rng_key, state) + return state, state - keys = jax.random.split(rng_key, num_samples) - _, states = jax.lax.scan(one_step, initial_state, keys) + keys = jax.random.split(rng_key, num_samples) + _, states = jax.lax.scan(one_step, initial_state, keys) - return states + return states print("Start sampling") time1 = time.time() diff --git a/jaxgw/utils.py b/jaxgw/constants.py similarity index 100% rename from jaxgw/utils.py rename to jaxgw/constants.py From 29fdc4f25baece1d2455ecb2442cc047e7d89b7e Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Thu, 21 Oct 2021 11:35:54 -0400 Subject: [PATCH 038/300] Remove numpyro directory --- example/numpyro/HMC_injection.py | 65 -------------------------------- 1 file changed, 65 deletions(-) delete mode 100644 example/numpyro/HMC_injection.py diff --git a/example/numpyro/HMC_injection.py b/example/numpyro/HMC_injection.py deleted file mode 100644 index 81a53717..00000000 --- a/example/numpyro/HMC_injection.py +++ /dev/null @@ -1,65 +0,0 @@ -import numpy as np -import bilby -import jax.numpy as jnp - -from jax.config import config -from jax import grad, jit -config.update("jax_enable_x64", True) - -from jaxgw.likelihood.utils import inner_product -from jaxgw.waveform.TaylorF2 import TaylorF2 -from jaxgw.waveform.IMRPhenomC import IMRPhenomC -from jax import random, grad, jit, vmap, jacfwd, jacrev, value_and_grad - - - -injection_parameters = dict( - mass_1=36., mass_2=29., luminosity_distance=40., theta_jn=0.4, psi=2.659, - phase=1.3, geocent_time=1126259642.413, ra=1.375, dec=-1.2108) - -# Set up interferometers. In this case we'll use two interferometers -# (LIGO-Hanford (H1), LIGO-Livingston (L1). These default to their design -# sensitivity -ifos = bilby.gw.detector.InterferometerList(['H1']) -ifos.set_strain_data_from_power_spectral_densities( - sampling_frequency=2048, duration=4, - start_time=- 3) - -psd = ifos[0].power_spectral_density_array -psd_frequency = ifos[0].frequency_array - -psd_frequency = psd_frequency[jnp.isfinite(psd)] -psd = psd[jnp.isfinite(psd)] - -waveform = TaylorF2(psd_frequency, injection_parameters) -strain = waveform['plus']#get_detector_response(waveform,injection_parameters,H1).T[0] - -@jit -def jax_likelihood(params, data, data_f, PSD): - waveform = TaylorF2(data_f, params)['plus'] -# waveform = get_detector_response(waveform, params, H1).T[0] - match_filter_SNR = inner_product(waveform, data, data_f, PSD) - optimal_SNR = inner_product(waveform, waveform, data_f, PSD) - return -(2*match_filter_SNR - optimal_SNR)/2 - -def jax_posterior(params,data,data_f,PSD): - if params['mass_1'] < 0: - params['mass_1'] = 0 - if params['mass_2'] < 0: - params['mass_2'] = 0 - if (params['a_1'] < 0): - params['a_1'] = 0 - if (params['a_2'] < 0): - params['a_2'] = 0 - return jax_likelihood(params,data,data_f,PSD) - - -from numpyro.infer import MCMC, NUTS - -nuts_kernel = NUTS(jax_likelihood) - -mcmc = MCMC(nuts_kernel, num_warmup=5, num_samples=10) - -rng_key = random.PRNGKey(0) - -mcmc.run(rng_key, injection_parameters, strain, psd_frequency, psd, extra_fields=('potential_energy',)) From e36ef6b82549d2daf953506f70511b3d99695121 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Thu, 21 Oct 2021 12:18:26 -0400 Subject: [PATCH 039/300] Collect constant into constant script --- jaxgw/constants.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/jaxgw/constants.py b/jaxgw/constants.py index 29584b1e..efe940d9 100644 --- a/jaxgw/constants.py +++ b/jaxgw/constants.py @@ -5,4 +5,7 @@ Msun = 4.9255e-6 year = (1*yr).cgs.value Mpc = 1e6*pc.value/c.value +euler_gamma = 0.577215664901532860606512090082 +MR_sun = 1.476625061404649406193430731479084713e3 + From 32dffe92247180e73ab8c250bf2a227c71a7b811 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Thu, 21 Oct 2021 12:20:27 -0400 Subject: [PATCH 040/300] Slightly remodel parameter parsing of waveform models. Add converter function in IMRPhenomC --- jaxgw/waveform/IMRPhenomC.py | 24 +++++++++++++++++------- jaxgw/waveform/TaylorF2.py | 5 ++--- 2 files changed, 19 insertions(+), 10 deletions(-) diff --git a/jaxgw/waveform/IMRPhenomC.py b/jaxgw/waveform/IMRPhenomC.py index a36bd64f..99ae5153 100644 --- a/jaxgw/waveform/IMRPhenomC.py +++ b/jaxgw/waveform/IMRPhenomC.py @@ -3,10 +3,7 @@ """ import jax.numpy as jnp from jax import jit -from jaxgw.utils import * - -euler_gamma = 0.577215664901532860606512090082 -MR_sun = 1.476625061404649406193430731479084713e3 +from jaxgw.constants import * def Lorentzian(x, x0, gamma): return (gamma**2/((x-x0)**2+gamma**2/4)) @@ -162,13 +159,18 @@ def getFinalSpin(eta,chi_eff): def IMRPhenomC(f,params): """ The amplitude and phase are generated first in unitless Mf space, then scaled with total mass and distance. + + Args + f: Frequency array where the waveform is generated + params: + """ local_m1 = params['mass_1']*Msun local_m2 = params['mass_2']*Msun local_d = params['luminosity_distance']*Mpc - local_spin1 = params['a_1'] - local_spin2 = params['a_2'] + local_spin1 = params['spin_1'] + local_spin2 = params['spin_2'] M_tot = local_m1+local_m2 @@ -183,7 +185,7 @@ def IMRPhenomC(f,params): # Constructing phase of the waveform - A_PN, phase_PN = PNAmplitudeAndPhasing(f,local_m1,local_m2,local_spin1,local_spin2,params['geocent_time'],params['phase']) + A_PN, phase_PN = PNAmplitudeAndPhasing(f,local_m1,local_m2,local_spin1,local_spin2,params['t_c'],params['phase_c']) alpha, gamma, delta = getPhenomCoef(eta,chi_eff) @@ -210,3 +212,11 @@ def IMRPhenomC(f,params): hc = totalh * jnp.cos(params['theta_jn'])*jnp.sin(2*params['psi']) return {'plus':hp,'cross':hc} + +def IMRPhenomC_dict2list(params): + """ + """ + + return jnp.array([params['mass_1'], params['mass_2'], params['spin_1'], params['spin_2'],\ + params['luminosity_distance'], params['phase_c'],params['t_c'],\ + params['theta_jn'], params['psi'], params['ra'], params['dec']]) diff --git a/jaxgw/waveform/TaylorF2.py b/jaxgw/waveform/TaylorF2.py index 704144aa..dff58875 100644 --- a/jaxgw/waveform/TaylorF2.py +++ b/jaxgw/waveform/TaylorF2.py @@ -1,5 +1,5 @@ import jax.numpy as jnp -from jaxgw.utils import * +from jaxgw.constants import * def TaylorF2(f,params): local_m1 = params['mass_1']*Msun @@ -11,7 +11,6 @@ def TaylorF2(f,params): eta = local_m1*local_m2/(local_m1+local_m2)**2 M_chirp = eta**(3./5)*M_tot PNcoef = (jnp.pi*M_tot*f)**(1./3) - euler_gamma = 0.57721566490153286060 amplitude = M_chirp**(5./6)/local_d @@ -22,7 +21,7 @@ def TaylorF2(f,params): PN2d5 = jnp.pi*(38645./756-65./9*eta)*(1 + 3*jnp.log(6**(3./2)*jnp.pi*M_tot*f)) * PNcoef**5 # PN3 = 11583231236531./4694215680 - 640./3 *jnp.pi**2 - 6868./21*(euler_gamma+jnp.log(4) - phase = 2*jnp.pi*f*params['geocent_time'] - params['phase'] - jnp.pi/4 + 3./(128*eta*PNcoef**5) * \ + phase = 2*jnp.pi*f*params['t_c'] - params['phase_c'] - jnp.pi/4 + 3./(128*eta*PNcoef**5) * \ (PN0+PN1+PN1d5)#+PN2+PN2d5) # phase = - jnp.pi/4 + 3./(128*eta*PNcoef**5) * \ From 571c4998a4fbba4b31efccd79c5c2309cc209b2b Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Thu, 21 Oct 2021 12:21:09 -0400 Subject: [PATCH 041/300] Update name convention in detector projection --- jaxgw/likelihood/detector_projection.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/jaxgw/likelihood/detector_projection.py b/jaxgw/likelihood/detector_projection.py index 14614fe2..cc8ed6a4 100644 --- a/jaxgw/likelihood/detector_projection.py +++ b/jaxgw/likelihood/detector_projection.py @@ -63,7 +63,7 @@ def get_detector_response(waveform_polarizations, parameters, detector_tensor): detector_tensor, parameters['ra'], parameters['dec'], - parameters['geocent_time'], + parameters['t_c'], parameters['psi'], mode) signal[mode] = waveform_polarizations[mode] * det_response From 7a52ca5848d6642ebe10c73fe0a5f12ebf18df86 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Thu, 21 Oct 2021 12:24:16 -0400 Subject: [PATCH 042/300] Add single detector derivative example --- example/blackjax/Single_event_derivatives.py | 68 ++++++++++++++++++++ 1 file changed, 68 insertions(+) create mode 100644 example/blackjax/Single_event_derivatives.py diff --git a/example/blackjax/Single_event_derivatives.py b/example/blackjax/Single_event_derivatives.py new file mode 100644 index 00000000..2315b3bf --- /dev/null +++ b/example/blackjax/Single_event_derivatives.py @@ -0,0 +1,68 @@ +import numpy as np +import bilby +import jax +import jax.numpy as jnp +import time + +from jax.config import config +config.update("jax_enable_x64", True) + +from jaxgw.likelihood.detector_projection import construct_arm, detector_tensor, antenna_response, get_detector_response + +from jaxgw.likelihood.utils import inner_product +from jaxgw.likelihood.detector_preset import get_H1 +from jaxgw.waveform.IMRPhenomC import IMRPhenomC, IMRPhenomC_dict2list +from jax import random, grad, jit, vmap, jacfwd, jacrev, value_and_grad, pmap + + +true_m1 = 3. +true_m2 = 2. +true_ld = 150. +true_phase = 0. +true_gt = 0. + +injection_parameters = dict(mass_1=true_m1, mass_2=true_m2, spin_1=0.0, spin_2=0.0, luminosity_distance=true_ld, theta_jn=0.4, psi=2.659, phase_c=true_phase, t_c=true_gt, ra=1.375, dec=-1.2108) + +guess_parameters = IMRPhenomC_dict2list(dict(mass_1=true_m1, mass_2=true_m2, spin_1=0.1, spin_2=0.0, luminosity_distance=true_ld, theta_jn=0.4, psi=2.659, phase_c=true_phase, t_c=true_gt, ra=1.375, dec=-1.2108)) + +#injection_parameters = IMRPhenomC_dict2list(injection_parameters) + + +# Set up interferometers. In this case we'll use two interferometers +# (LIGO-Hanford (H1), LIGO-Livingston (L1). These default to their design +# sensitivity +ifos = bilby.gw.detector.InterferometerList(['H1']) +ifos.set_strain_data_from_power_spectral_densities( + sampling_frequency=2048, duration=32, + start_time=- 3) + +psd = ifos[0].power_spectral_density_array +psd_frequency = ifos[0].frequency_array + +psd_frequency = psd_frequency[jnp.isfinite(psd)] +psd = psd[jnp.isfinite(psd)] + +waveform = IMRPhenomC(psd_frequency, injection_parameters) +H1 = get_H1() +strain = get_detector_response(waveform,injection_parameters,H1) + +print('SNR of the event: '+str(np.sqrt(inner_product(strain,strain,psd_frequency,psd)))) + +@jit +def jax_likelihood(params, data, data_f, PSD): + waveform = IMRPhenomC(data_f, params) + waveform = get_detector_response(waveform, params, H1) + match_filter_SNR = inner_product(waveform, data, data_f, PSD) + optimal_SNR = inner_product(waveform, waveform, data_f, PSD) + return (-2*match_filter_SNR + optimal_SNR)/2 + +@jit +def logprob_wrap(params): + parameters = dict(mass_1=params[0], mass_2=params[1], spin_1=params[2], spin_2=params[3], luminosity_distance=params[4], phase_c=params[5], t_c=params[6], theta_jn=params[7], psi=params[8], ra=params[9], dec=params[10]) + return jax_likelihood(parameters, strain, psd_frequency, psd) + +logL = logprob_wrap(guess_parameters) +logL_jacobian = jacfwd(logprob_wrap)(guess_parameters) +logL_hessian = jacfwd(jacrev(logprob_wrap))(guess_parameters) + + From d139ba5a834561b4ad555ecbe4df401ecaf51de7 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Thu, 21 Oct 2021 13:26:31 -0400 Subject: [PATCH 043/300] Put GW related modules into GW folder for potential extension on the sampler side --- example/blackjax/Single_event_derivatives.py | 8 ++++---- jaxgw/__init__.py | 1 + jaxgw/__pycache__/__init__.cpython-37.pyc | Bin 141 -> 0 bytes jaxgw/{ => gw}/constants.py | 0 jaxgw/{ => gw}/likelihood/detector_preset.py | 2 +- jaxgw/{ => gw}/likelihood/detector_projection.py | 0 jaxgw/gw/likelihood/single_event_likelihood.py | 13 +++++++++++++ jaxgw/{ => gw}/likelihood/utils.py | 0 jaxgw/{ => gw}/waveform/IMRPhenomB.py | 0 jaxgw/{ => gw}/waveform/IMRPhenomC.py | 2 +- jaxgw/{ => gw}/waveform/TaylorF2.py | 2 +- jaxgw/likelihood/__init__.py | 0 12 files changed, 21 insertions(+), 7 deletions(-) delete mode 100644 jaxgw/__pycache__/__init__.cpython-37.pyc rename jaxgw/{ => gw}/constants.py (100%) rename jaxgw/{ => gw}/likelihood/detector_preset.py (87%) rename jaxgw/{ => gw}/likelihood/detector_projection.py (100%) create mode 100644 jaxgw/gw/likelihood/single_event_likelihood.py rename jaxgw/{ => gw}/likelihood/utils.py (100%) rename jaxgw/{ => gw}/waveform/IMRPhenomB.py (100%) rename jaxgw/{ => gw}/waveform/IMRPhenomC.py (99%) rename jaxgw/{ => gw}/waveform/TaylorF2.py (97%) delete mode 100644 jaxgw/likelihood/__init__.py diff --git a/example/blackjax/Single_event_derivatives.py b/example/blackjax/Single_event_derivatives.py index 2315b3bf..41bcca5d 100644 --- a/example/blackjax/Single_event_derivatives.py +++ b/example/blackjax/Single_event_derivatives.py @@ -7,11 +7,11 @@ from jax.config import config config.update("jax_enable_x64", True) -from jaxgw.likelihood.detector_projection import construct_arm, detector_tensor, antenna_response, get_detector_response +from jaxgw.gw.likelihood.detector_projection import construct_arm, detector_tensor, antenna_response, get_detector_response -from jaxgw.likelihood.utils import inner_product -from jaxgw.likelihood.detector_preset import get_H1 -from jaxgw.waveform.IMRPhenomC import IMRPhenomC, IMRPhenomC_dict2list +from jaxgw.gw.likelihood.utils import inner_product +from jaxgw.gw.likelihood.detector_preset import get_H1 +from jaxgw.gw.waveform.IMRPhenomC import IMRPhenomC, IMRPhenomC_dict2list from jax import random, grad, jit, vmap, jacfwd, jacrev, value_and_grad, pmap diff --git a/jaxgw/__init__.py b/jaxgw/__init__.py index e69de29b..8b137891 100644 --- a/jaxgw/__init__.py +++ b/jaxgw/__init__.py @@ -0,0 +1 @@ + diff --git a/jaxgw/__pycache__/__init__.cpython-37.pyc b/jaxgw/__pycache__/__init__.cpython-37.pyc deleted file mode 100644 index 28c516055e0267bb602d3759199196d8c01bea04..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 141 zcmZ?b<>g`kf+HV16G8N25CH>>K!yVl7qb9~6oz01O-8?!3`HPe1o6vEKR2&LKO;Xk zRlmGEKQCS1Jv^W&KPxr4L_Z+EAU-oMJ}a?8ABfY-_2Yru%#!$cy@JYH95%W6DWy57 Lb|CXU12F>tk3S*q diff --git a/jaxgw/constants.py b/jaxgw/gw/constants.py similarity index 100% rename from jaxgw/constants.py rename to jaxgw/gw/constants.py diff --git a/jaxgw/likelihood/detector_preset.py b/jaxgw/gw/likelihood/detector_preset.py similarity index 87% rename from jaxgw/likelihood/detector_preset.py rename to jaxgw/gw/likelihood/detector_preset.py index 4d258967..e84c4363 100644 --- a/jaxgw/likelihood/detector_preset.py +++ b/jaxgw/gw/likelihood/detector_preset.py @@ -1,4 +1,4 @@ -from jaxgw.likelihood.detector_projection import construct_arm, detector_tensor, antenna_response, get_detector_response +from jaxgw.gw.likelihood.detector_projection import construct_arm, detector_tensor, antenna_response, get_detector_response # See https://git.ligo.org/lscsoft/bilby/-/tree/master/bilby/gw/detector/detectors for detector parameters. diff --git a/jaxgw/likelihood/detector_projection.py b/jaxgw/gw/likelihood/detector_projection.py similarity index 100% rename from jaxgw/likelihood/detector_projection.py rename to jaxgw/gw/likelihood/detector_projection.py diff --git a/jaxgw/gw/likelihood/single_event_likelihood.py b/jaxgw/gw/likelihood/single_event_likelihood.py new file mode 100644 index 00000000..0784b827 --- /dev/null +++ b/jaxgw/gw/likelihood/single_event_likelihood.py @@ -0,0 +1,13 @@ +from jax import jit +from jaxgw.likelihood.detector_projection import get_detector_response +from jaxgw.likelihood.utils import inner_product + +def single_detector_likelihood(waveform_model, params, data, data_f, PSD, detector): + waveform = waveform_model(data_f, params) + waveform = get_detector_response(waveform, params, detector) + match_filter_SNR = inner_product(waveform, data, data_f, PSD) + optimal_SNR = inner_product(waveform, waveform, data_f, PSD) + return (-2*match_filter_SNR + optimal_SNR)/2 + + + diff --git a/jaxgw/likelihood/utils.py b/jaxgw/gw/likelihood/utils.py similarity index 100% rename from jaxgw/likelihood/utils.py rename to jaxgw/gw/likelihood/utils.py diff --git a/jaxgw/waveform/IMRPhenomB.py b/jaxgw/gw/waveform/IMRPhenomB.py similarity index 100% rename from jaxgw/waveform/IMRPhenomB.py rename to jaxgw/gw/waveform/IMRPhenomB.py diff --git a/jaxgw/waveform/IMRPhenomC.py b/jaxgw/gw/waveform/IMRPhenomC.py similarity index 99% rename from jaxgw/waveform/IMRPhenomC.py rename to jaxgw/gw/waveform/IMRPhenomC.py index 99ae5153..5f0db313 100644 --- a/jaxgw/waveform/IMRPhenomC.py +++ b/jaxgw/gw/waveform/IMRPhenomC.py @@ -3,7 +3,7 @@ """ import jax.numpy as jnp from jax import jit -from jaxgw.constants import * +from jaxgw.gw.constants import * def Lorentzian(x, x0, gamma): return (gamma**2/((x-x0)**2+gamma**2/4)) diff --git a/jaxgw/waveform/TaylorF2.py b/jaxgw/gw/waveform/TaylorF2.py similarity index 97% rename from jaxgw/waveform/TaylorF2.py rename to jaxgw/gw/waveform/TaylorF2.py index dff58875..d92aeb48 100644 --- a/jaxgw/waveform/TaylorF2.py +++ b/jaxgw/gw/waveform/TaylorF2.py @@ -1,5 +1,5 @@ import jax.numpy as jnp -from jaxgw.constants import * +from jaxgw.gw.constants import * def TaylorF2(f,params): local_m1 = params['mass_1']*Msun diff --git a/jaxgw/likelihood/__init__.py b/jaxgw/likelihood/__init__.py deleted file mode 100644 index e69de29b..00000000 From 72f00df14494790354337145518f7ab453ab2f1d Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Thu, 21 Oct 2021 14:56:55 -0400 Subject: [PATCH 044/300] Update HMC injection convention --- example/blackjax/HMC_injection.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/example/blackjax/HMC_injection.py b/example/blackjax/HMC_injection.py index c84992d5..33980e0f 100644 --- a/example/blackjax/HMC_injection.py +++ b/example/blackjax/HMC_injection.py @@ -7,12 +7,12 @@ from jax.config import config config.update("jax_enable_x64", True) -from jaxgw.likelihood.detector_projection import construct_arm, detector_tensor, antenna_response, get_detector_response +from jaxgw.gw.likelihood.detector_projection import construct_arm, detector_tensor, antenna_response, get_detector_response -from jaxgw.likelihood.utils import inner_product -from jaxgw.likelihood.detector_preset import get_H1 -from jaxgw.waveform.TaylorF2 import TaylorF2 -from jaxgw.waveform.IMRPhenomC import IMRPhenomC +from jaxgw.gw.likelihood.utils import inner_product +from jaxgw.gw.likelihood.detector_preset import get_H1 +from jaxgw.gw.waveform.TaylorF2 import TaylorF2 +from jaxgw.gw.waveform.IMRPhenomC import IMRPhenomC from jax import random, grad, jit, vmap, jacfwd, jacrev, value_and_grad, pmap @@ -23,8 +23,8 @@ true_gt = 0. injection_parameters = dict( - mass_1=true_m1, mass_2=true_m2, a_1=0.0, a_2=0.0, luminosity_distance=true_ld, theta_jn=0.4, psi=2.659, - phase=true_phase, geocent_time=true_gt, ra=1.375, dec=-1.2108) + mass_1=true_m1, mass_2=true_m2, spin_1=0.0, spin_2=0.0, luminosity_distance=true_ld, theta_jn=0.4, psi=2.659, + phase_c=true_phase, t_c=true_gt, ra=1.375, dec=-1.2108) #guess_parameters = dict(m1=true_m1, m2=true_m2, luminosity_distance=true_ld, phase=true_phase, geocent_time=true_gt, theta_jn=0.4, psi=2.659, ra=1.375, dec=-1.2108) # @@ -67,7 +67,7 @@ def jax_likelihood(params, data, data_f, PSD): @jit def logprob_wrap(m1, m2): - params = dict(mass_1=m1, mass_2=m2, a_1=0, a_2=0, luminosity_distance=true_ld, phase=true_phase, geocent_time=true_gt, theta_jn=0.4, psi=2.659, ra=1.375, dec=-1.2108) + params = dict(mass_1=m1, mass_2=m2, spin_1=0, spin_2=0, luminosity_distance=true_ld, phase_c=true_phase, t_c=true_gt, theta_jn=0.4, psi=2.659, ra=1.375, dec=-1.2108) return jax_likelihood(params, strain, psd_frequency, psd)#*Jac_det log_prob = lambda x: logprob_wrap(**x) From 024928419f09973fa31f06e03e7d5a735ce88d1a Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Fri, 22 Oct 2021 16:22:47 -0400 Subject: [PATCH 045/300] Add time projection back into detector projection. --- jaxgw/gw/likelihood/detector_projection.py | 18 ++++++++++++++---- 1 file changed, 14 insertions(+), 4 deletions(-) diff --git a/jaxgw/gw/likelihood/detector_projection.py b/jaxgw/gw/likelihood/detector_projection.py index cc8ed6a4..9cfc3e9d 100644 --- a/jaxgw/gw/likelihood/detector_projection.py +++ b/jaxgw/gw/likelihood/detector_projection.py @@ -1,7 +1,7 @@ import jax.numpy as jnp ########################################################## -# Construction of detector tensor +# Construction of arms ########################################################## def construct_arm(longitude, latitude, arm_tilt, arm_azimuth): @@ -24,9 +24,19 @@ def detector_tensor(arm1, arm2): ########################################################## def get_polarization_tensor(ra, dec, time, psi, mode): - - #gmst = fmod(greenwich_mean_sidereal_time(time), 2 * jnp.pi) - phi = ra #- gmst + """ + + Args: + + ra: + dec: + time: time in greenwich_mean_sidereal_time + psi: + mode: + """ + + gmst = jnp.mod(time, 2 * jnp.pi) + phi = ra - gmst theta = jnp.pi / 2 - dec u = jnp.array([jnp.cos(phi) * jnp.cos(theta), jnp.cos(theta) * jnp.sin(phi), -jnp.sin(theta)]) From 6f3953a06e58f5bf7227f17897976c434a6fba23 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Fri, 22 Oct 2021 16:32:10 -0400 Subject: [PATCH 046/300] Add ra dec conversion function --- jaxgw/gw/likelihood/utils.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/jaxgw/gw/likelihood/utils.py b/jaxgw/gw/likelihood/utils.py index f35208d9..40a31a01 100644 --- a/jaxgw/gw/likelihood/utils.py +++ b/jaxgw/gw/likelihood/utils.py @@ -36,3 +36,8 @@ def Mq_to_m1m2(trans_M_tot,trans_q): m2 = m1*q return m1, m2 +def ra_dec_to_theta_phi(ra, dec, gmst): + phi = ra - gmst + theta = np.pi / 2 - dec + return theta, phi + From 99d5c0e48f4cc7e8b19d478e197929a4b1f9ccbb Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Fri, 22 Oct 2021 16:54:28 -0400 Subject: [PATCH 047/300] Add time delay related functions and fix preset angles unit (degree to radian) --- jaxgw/gw/likelihood/detector_preset.py | 29 ++++-- jaxgw/gw/likelihood/detector_projection.py | 109 ++++++++++++++++++--- 2 files changed, 113 insertions(+), 25 deletions(-) diff --git a/jaxgw/gw/likelihood/detector_preset.py b/jaxgw/gw/likelihood/detector_preset.py index e84c4363..b8e1abad 100644 --- a/jaxgw/gw/likelihood/detector_preset.py +++ b/jaxgw/gw/likelihood/detector_preset.py @@ -1,29 +1,38 @@ -from jaxgw.gw.likelihood.detector_projection import construct_arm, detector_tensor, antenna_response, get_detector_response +from jaxgw.gw.likelihood.detector_projection import construct_arm, detector_tensor, antenna_response, get_detector_response, get_vertex_position_geocentric +import jax.numpy as jnp # See https://git.ligo.org/lscsoft/bilby/-/tree/master/bilby/gw/detector/detectors for detector parameters. +degree_to_radian = jnp.pi/180 + def get_H1(): - H1_lat = 46 + 27. / 60 + 18.528 / 3600 - H1_long = -(119 + 24. / 60 + 27.5657 / 3600) - H1_xarm_azimuth = 125.9994 - H1_yarm_azimuth = 215.9994 + H1_lat = (46 + 27. / 60 + 18.528 / 3600) * degree_to_radian + H1_long = -(119 + 24. / 60 + 27.5657 / 3600) * degree_to_radian + H1_xarm_azimuth = 125.9994 * degree_to_radian + H1_yarm_azimuth = 215.9994 * degree_to_radian H1_xarm_tilt = -6.195e-4 H1_yarm_tilt = 1.25e-5 + H1_elevation = 142.554 H1_arm1 = construct_arm(H1_long, H1_lat, H1_xarm_tilt, H1_xarm_azimuth) H1_arm2 = construct_arm(H1_long, H1_lat, H1_yarm_tilt, H1_yarm_azimuth) + + H1_vertex = get_vertex_position_geocentric(H1_lat, H1_long, H1_elevation) - return detector_tensor(H1_arm1, H1_arm2) + return detector_tensor(H1_arm1, H1_arm2), H1_vertex def get_L1(): - L1_lat = 30 + 33. / 60 + 46.4196 / 3600 - L1_long = -(90 + 46. / 60 + 27.2654 / 3600) - L1_xarm_azimuth = 197.7165 - L1_yarm_azimuth = 287.7165 + L1_lat = 30 + 33. / 60 + 46.4196 / 3600 * degree_to_radian + L1_long = -(90 + 46. / 60 + 27.2654 / 3600) * degree_to_radian + L1_xarm_azimuth = 197.7165 * degree_to_radian + L1_yarm_azimuth = 287.7165 * degree_to_radian L1_xarm_tilt = 0 L1_yarm_tilt = 0 + L1_elevation = -6.574 L1_arm1 = construct_arm(L1_long, L1_lat, L1_xarm_tilt, L1_xarm_azimuth) L1_arm2 = construct_arm(L1_long, L1_lat, L1_yarm_tilt, L1_yarm_azimuth) + + L1_vertex = get_vertex_position_geocentric(L1_lat, L1_long, L1_elevation) return detector_tensor(L1_arm1, L1_arm2) diff --git a/jaxgw/gw/likelihood/detector_projection.py b/jaxgw/gw/likelihood/detector_projection.py index 9cfc3e9d..78c3d920 100644 --- a/jaxgw/gw/likelihood/detector_projection.py +++ b/jaxgw/gw/likelihood/detector_projection.py @@ -1,10 +1,23 @@ +# Credit some part of the source code from bilby + import jax.numpy as jnp ########################################################## # Construction of arms ########################################################## -def construct_arm(longitude, latitude, arm_tilt, arm_azimuth): +def construct_arm(latitude, longitude, arm_tilt, arm_azimuth): + """ + + Args: + + latitude: Latitude in radian + longitude: Longitude in radian + arm_tilt: Arm tilt in radian + arm_azimuth: Arm azimuth in radian + + """ + e_long = jnp.array([-jnp.sin(longitude), jnp.cos(longitude), 0]) e_lat = jnp.array([-jnp.sin(latitude) * jnp.cos(longitude), -jnp.sin(latitude) * jnp.sin(longitude), jnp.cos(latitude)]) @@ -30,7 +43,7 @@ def get_polarization_tensor(ra, dec, time, psi, mode): ra: dec: - time: time in greenwich_mean_sidereal_time + time: Greenwich Mean Sidereal Time in geocentric frame psi: mode: """ @@ -66,7 +79,80 @@ def antenna_response(detector_tensor, ra, dec, time, psi, mode): polarization_tensor = get_polarization_tensor(ra, dec, time, psi, mode) return jnp.einsum('ij,ij->', detector_tensor, polarization_tensor) -def get_detector_response(waveform_polarizations, parameters, detector_tensor): +def time_delay_geocentric(detector1, detector2, ra, dec, time): + """ + Calculate time delay between two detectors in geocentric coordinates based on XLALArrivaTimeDiff in TimeDelay.c + + Parameters + ========== + detector1: array_like + Cartesian coordinate vector for the first detector in the geocentric frame + generated by the Interferometer class as self.vertex. + detector2: array_like + Cartesian coordinate vector for the second detector in the geocentric frame. + To get time delay from Earth center, use detector2 = np.array([0,0,0]) + ra: float + Right ascension of the source in radians + dec: float + Declination of the source in radians + time: float + GPS time in the geocentric frame + + Returns + ======= + float: Time delay between the two detectors in the geocentric frame + + """ + gmst = jnp.mod(time, 2 * jnp.pi) + phi = ra - gmst + theta = np.pi / 2 - dec + omega = jnp.array([jnp.sin(theta) * jnp.cos(phi), jnp.sin(theta) * jnp.sin(phi), jnp.cos(theta)]) + delta_d = detector2 - detector1 + return jnnp.dot(omega, delta_d) / speed_of_light + +def get_vertex_position_geocentric(latitude, longitude, elevation): + """ + Calculate the position of the IFO vertex in geocentric coordinates in meters. + + Based on arXiv:gr-qc/0008066 Eqs. B11-B13 except for the typo in the definition of the local radius. + See Section 2.1 of LIGO-T980044-10 for the correct expression + + Parameters + ========== + latitude: float + Latitude in radians + longitude: + Longitude in radians + elevation: + Elevation in meters + + Returns + ======= + array_like: A 3D representation of the geocentric vertex position + + """ + semi_major_axis = 6378137 # for ellipsoid model of Earth, in m + semi_minor_axis = 6356752.314 # in m + radius = semi_major_axis**2 * (semi_major_axis**2 * jnp.cos(latitude)**2 + + semi_minor_axis**2 * jnp.sin(latitude)**2)**(-0.5) + x_comp = (radius + elevation) * jnp.cos(latitude) * jnp.cos(longitude) + y_comp = (radius + elevation) * jnp.cos(latitude) * jnp.sin(longitude) + z_comp = ((semi_minor_axis / semi_major_axis)**2 * radius + elevation) * jnp.sin(latitude) + return jnp.array([x_comp, y_comp, z_comp]) + + +def get_detector_response(frequency, waveform_polarizations, parameters, detector_tensor, detector_vertex): + """ + + Args: + + ra: Right Ascension in radian + dec:Right Ascension in radian + time: Greenwich Mean Sidereal Time in geocentric frame + psi: + mode: + + """ signal = {} for mode in waveform_polarizations.keys(): det_response = antenna_response( @@ -79,18 +165,11 @@ def get_detector_response(waveform_polarizations, parameters, detector_tensor): signal[mode] = waveform_polarizations[mode] * det_response signal_ifo = sum(signal.values()) -# signal_ifo *= self.strain_data.frequency_mask -# -# time_shift = self.time_delay_from_geocenter( -# parameters['ra'], parameters['dec'], parameters['geocent_time']) -# -# # Be careful to first subtract the two GPS times which are ~1e9 sec. -# # And then add the time_shift which varies at ~1e-5 sec -# dt_geocent = parameters['geocent_time'] - self.strain_data.start_time -# dt = dt_geocent + time_shift -# -# signal_ifo[self.strain_data.frequency_mask] = signal_ifo[self.strain_data.frequency_mask] * jnp.exp( -# -1j * 2 * jnp.pi * dt * self.strain_data.frequency_array[self.strain_data.frequency_mask]) + time_shift = time_delay_geocentric(detector_vertex, jnp.array([0.,0.,0.]),parameters['ra'], parameters['dec'], parameters['t_c']) + + dt = parameters['t_c'] + time_shift # Note that we always assume the start time of the strain to be 0 + + signal_ifo = signal_ifo * jnp.exp(-1j * 2 * jnp.pi * dt * frequency) return signal_ifo From f1acec7d5c0291cce304a11f70b3cdbc42ab7d2b Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Fri, 22 Oct 2021 17:57:34 -0400 Subject: [PATCH 048/300] Update HMC in black jax, and fix minor bugs in calling time delay --- example/blackjax/HMC_injection.py | 26 +++++++++++++--------- jaxgw/gw/constants.py | 2 +- jaxgw/gw/likelihood/detector_preset.py | 2 +- jaxgw/gw/likelihood/detector_projection.py | 5 +++-- 4 files changed, 20 insertions(+), 15 deletions(-) diff --git a/example/blackjax/HMC_injection.py b/example/blackjax/HMC_injection.py index 33980e0f..4b78ff4f 100644 --- a/example/blackjax/HMC_injection.py +++ b/example/blackjax/HMC_injection.py @@ -10,7 +10,7 @@ from jaxgw.gw.likelihood.detector_projection import construct_arm, detector_tensor, antenna_response, get_detector_response from jaxgw.gw.likelihood.utils import inner_product -from jaxgw.gw.likelihood.detector_preset import get_H1 +from jaxgw.gw.likelihood.detector_preset import get_H1, get_L1 from jaxgw.gw.waveform.TaylorF2 import TaylorF2 from jaxgw.gw.waveform.IMRPhenomC import IMRPhenomC from jax import random, grad, jit, vmap, jacfwd, jacrev, value_and_grad, pmap @@ -46,19 +46,23 @@ psd = psd[jnp.isfinite(psd)] waveform = IMRPhenomC(psd_frequency, injection_parameters) -H1 = get_H1() -strain = get_detector_response(waveform,injection_parameters,H1) +H1, H1_vertex = get_H1() +L1, L1_vertex = get_L1() +strain_H1 = get_detector_response(psd_frequency, waveform, injection_parameters, H1, H1_vertex) +strain_L1 = get_detector_response(psd_frequency, waveform, injection_parameters, L1, L1_vertex) -print('SNR of the event: '+str(np.sqrt(inner_product(strain,strain,psd_frequency,psd)))) +print('SNR of the event in H1: '+str(np.sqrt(inner_product(strain_H1,strain_H1,psd_frequency,psd)))) +print('SNR of the event in L1: '+str(np.sqrt(inner_product(strain_L1,strain_L1,psd_frequency,psd)))) @jit -def jax_likelihood(params, data, data_f, PSD): +def single_detector_likelihood(params, data, data_f, PSD, detector, detector_vertex): waveform = IMRPhenomC(data_f, params) # waveform = TaylorF2(data_f, params) - waveform = get_detector_response(waveform, params, H1) + waveform = get_detector_response(data_f, waveform, params, detector, detector_vertex) match_filter_SNR = inner_product(waveform, data, data_f, PSD) optimal_SNR = inner_product(waveform, waveform, data_f, PSD) return (-2*match_filter_SNR + optimal_SNR)/2#, match_filter_SNR, optimal_SNR + #@jit ##def logprob_wrap(trans_M_tot, trans_q, geocent_time, phase): #def logprob_wrap(m1, m2, luminosity_distance, geocent_time, phase, theta_jn, psi, ra, dec): @@ -68,14 +72,14 @@ def jax_likelihood(params, data, data_f, PSD): @jit def logprob_wrap(m1, m2): params = dict(mass_1=m1, mass_2=m2, spin_1=0, spin_2=0, luminosity_distance=true_ld, phase_c=true_phase, t_c=true_gt, theta_jn=0.4, psi=2.659, ra=1.375, dec=-1.2108) - return jax_likelihood(params, strain, psd_frequency, psd)#*Jac_det + return single_detector_likelihood(params, strain_H1, psd_frequency, psd, H1, H1_vertex)+single_detector_likelihood(params, strain_L1, psd_frequency, psd, L1, L1_vertex) log_prob = lambda x: logprob_wrap(**x) log_prob = jit(log_prob) -############################################################### -# BlackJax section -############################################################### +################################################################ +## BlackJax section +################################################################ import blackjax.hmc as hmc import blackjax.nuts as nuts @@ -123,5 +127,5 @@ def one_step(state, rng_key): print("Start sampling") time1 = time.time() -states = inference_loop(subkey, hmc_kernel, initial_state, 4000) +states = inference_loop(subkey, hmc_kernel, initial_state, 100) print("Sampling takes: "+str(time.time()-time1)+" seconds") diff --git a/jaxgw/gw/constants.py b/jaxgw/gw/constants.py index efe940d9..01f87e88 100644 --- a/jaxgw/gw/constants.py +++ b/jaxgw/gw/constants.py @@ -7,5 +7,5 @@ Mpc = 1e6*pc.value/c.value euler_gamma = 0.577215664901532860606512090082 MR_sun = 1.476625061404649406193430731479084713e3 - +speed_of_light = 299792458.0 diff --git a/jaxgw/gw/likelihood/detector_preset.py b/jaxgw/gw/likelihood/detector_preset.py index b8e1abad..b5e869bc 100644 --- a/jaxgw/gw/likelihood/detector_preset.py +++ b/jaxgw/gw/likelihood/detector_preset.py @@ -35,4 +35,4 @@ def get_L1(): L1_vertex = get_vertex_position_geocentric(L1_lat, L1_long, L1_elevation) - return detector_tensor(L1_arm1, L1_arm2) + return detector_tensor(L1_arm1, L1_arm2), L1_vertex diff --git a/jaxgw/gw/likelihood/detector_projection.py b/jaxgw/gw/likelihood/detector_projection.py index 78c3d920..71f561c2 100644 --- a/jaxgw/gw/likelihood/detector_projection.py +++ b/jaxgw/gw/likelihood/detector_projection.py @@ -1,6 +1,7 @@ # Credit some part of the source code from bilby import jax.numpy as jnp +from jaxgw.gw.constants import * ########################################################## # Construction of arms @@ -105,10 +106,10 @@ def time_delay_geocentric(detector1, detector2, ra, dec, time): """ gmst = jnp.mod(time, 2 * jnp.pi) phi = ra - gmst - theta = np.pi / 2 - dec + theta = jnp.pi / 2 - dec omega = jnp.array([jnp.sin(theta) * jnp.cos(phi), jnp.sin(theta) * jnp.sin(phi), jnp.cos(theta)]) delta_d = detector2 - detector1 - return jnnp.dot(omega, delta_d) / speed_of_light + return jnp.dot(omega, delta_d) / speed_of_light def get_vertex_position_geocentric(latitude, longitude, elevation): """ From 7e2aa75ed331bde1118bff035b11007a606e94ad Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 25 Oct 2021 13:25:13 -0400 Subject: [PATCH 049/300] Add comments to detector_preset and IMRPhenomC with github copilot --- jaxgw/gw/likelihood/detector_preset.py | 21 ++++ jaxgw/gw/waveform/IMRPhenomC.py | 134 ++++++++++++++++++++++++- 2 files changed, 152 insertions(+), 3 deletions(-) diff --git a/jaxgw/gw/likelihood/detector_preset.py b/jaxgw/gw/likelihood/detector_preset.py index b8e1abad..c5b0bf2b 100644 --- a/jaxgw/gw/likelihood/detector_preset.py +++ b/jaxgw/gw/likelihood/detector_preset.py @@ -6,6 +6,16 @@ degree_to_radian = jnp.pi/180 def get_H1(): + """ + Get the detector response matrix and the vertex position for H1. + + Returns + ------- + H1_detector_response : ndarray + The detector response matrix for H1. + H1_vertex : ndarray + The vertex position for H1. + """ H1_lat = (46 + 27. / 60 + 18.528 / 3600) * degree_to_radian H1_long = -(119 + 24. / 60 + 27.5657 / 3600) * degree_to_radian H1_xarm_azimuth = 125.9994 * degree_to_radian @@ -22,6 +32,17 @@ def get_H1(): return detector_tensor(H1_arm1, H1_arm2), H1_vertex def get_L1(): + """ + Get the detector response matrix and the vertex position for L1. + + Returns + ------- + L1_detector_response : ndarray + The detector response matrix for L1. + L1_vertex : ndarray + The vertex position for L1. + + """ L1_lat = 30 + 33. / 60 + 46.4196 / 3600 * degree_to_radian L1_long = -(90 + 46. / 60 + 27.2654 / 3600) * degree_to_radian L1_xarm_azimuth = 197.7165 * degree_to_radian diff --git a/jaxgw/gw/waveform/IMRPhenomC.py b/jaxgw/gw/waveform/IMRPhenomC.py index 5f0db313..d2250643 100644 --- a/jaxgw/gw/waveform/IMRPhenomC.py +++ b/jaxgw/gw/waveform/IMRPhenomC.py @@ -6,23 +6,103 @@ from jaxgw.gw.constants import * def Lorentzian(x, x0, gamma): + """ + Lorentzian function given by: + f(x) = gamma / (pi * (x - x0)**2 + gamma**2) + + Parameters + ---------- + x : float + Value to evaluate the function at. + x0 : float + Center of the Lorentzian function. + gamma : float + Width of the Lorentzian function. + + Returns + ------- + float + Value of the Lorentzian function at x. + """ return (gamma**2/((x-x0)**2+gamma**2/4)) def smoothing_plus(f,f0,d): + """ + A smoothing function that is 1 at f0 and 0 at f0+d with a slope of -1/2 at f0+d and 1/2 at f0. + + Parameters + ---------- + f : float + Value to evaluate the function at. + f0 : float + Center of the smoothing function. + d : float + Width of the smoothing function. + + Returns + ------- + float + Value of the smoothing function at f. + """ return (1+jnp.tanh(4*(f-f0)/d))/2 def smoothing_minus(f,f0,d): + """ + A smoothing function that is 1 at f0 and 0 at f0-d with a slope of 1/2 at f0-d and -1/2 at f0. + + Parameters + ---------- + f : float + Value to evaluate the function at. + f0 : float + Center of the smoothing function. + d : float + Width of the smoothing function. + + Returns + ------- + float + Value of the smoothing function at f. + """ return (1-jnp.tanh(4*(f-f0)/d))/2 @jit def PNAmplitudeAndPhasing(f,m1,m2,chi1,chi2,t0,phase0): + """ + Compute the amplitude and phase of the PN waveform using the TaylorF2 approximant. + + Parameters + ---------- + f : float array + Frequency at which to evaluate the waveform. + m1 : float + Mass of the first component in solar masses. + m2 : float + Mass of the second component in solar masses. + chi1 : float + Dimensionless spin of the first component. + chi2 : float + Dimensionless spin of the second component. + t0 : float + Time of the peak of the waveform in seconds. + phase0 : float + Initial phase of the waveform. + + Returns + ------- + float array + Amplitude of the waveform. + float array + Phase of the waveform. + """ x = (jnp.pi*f)**(2./3) # I assume in the m in 3.12 is the m from harmonics instead of mass eta = m1*m2/(m1+m2)**2 chi_eff = (m1*chi1+m2*chi2)/(m1+m2) -# Taylor T4 expansion coefficient from A3 for 3.6, needed for amplitude in fourier space + # Taylor T4 expansion coefficient from A3 for 3.6, needed for amplitude in fourier space + T4_alpha_power_index = x[:,None]**(jnp.array([0,2,3,4,5,6,7])/2) @@ -58,7 +138,7 @@ def PNAmplitudeAndPhasing(f,m1,m2,chi1,chi2,t0,phase0): T4_alpha = 64.*eta/5. *x**5* (jnp.sum(T4_alpha_power_index*T4_alpha_coeff,axis=1)+T4_alpha_log) -# Taylor T4 amplitude coefficient from A5 + # Taylor T4 amplitude coefficient from A5 T4_A_power_index = x[:,None]**(jnp.array([0,2,3,4,5,6])/2) @@ -82,7 +162,7 @@ def PNAmplitudeAndPhasing(f,m1,m2,chi1,chi2,t0,phase0): T4_A = 8.*eta*jnp.sqrt(jnp.pi/5.)*x*(jnp.sum(T4_A_power_index*T4_A_coeff,axis=1)+T4_A_log) -# Taylor F2 Phasing coefficient from A4 + # Taylor F2 Phasing coefficient from A4 F2_alpha_power_index = jnp.pi*f[:,None]**(jnp.array([0,2,3,4,5,6,7])/3.) @@ -142,6 +222,27 @@ def PNAmplitudeAndPhasing(f,m1,m2,chi1,chi2,t0,phase0): @jit def getPhenomCoef(eta, chi): + """ + Calculate the phenomenological coefficients for the IMRPhenomC model. + + Parameters + ---------- + eta : float + Symmetric mass ratio + chi : float + Reduced spin parameter + + Returns + ------- + alpha : float + The alpha coefficient + beta : float + The beta coefficient + gamma : float + The gamma coefficient + delta : float + The delta coefficient + """ eta_chi = jnp.array([chi,chi**2,eta*chi,eta,eta**2]) alpha = jnp.sum(alpha_coef*eta_chi,axis=1) gamma = jnp.sum(gamma_coef*eta_chi) @@ -151,6 +252,21 @@ def getPhenomCoef(eta, chi): @jit def getFinalSpin(eta,chi_eff): + """ + Calculate the final spin of the binary. + + Parameters + ---------- + eta : float + Symmetric mass ratio + chi_eff : float + Effective spin parameter + + Returns + ------- + chi_final : float + Final spin of the binary + """ finspin = chi_eff - 0.129*chi_eff**2*eta -0.384*eta**2*chi_eff -2.686*eta*chi_eff \ + 2.*jnp.sqrt(3.)*eta -3.454*eta**2 + 2.353*eta**3 return finspin @@ -158,6 +274,7 @@ def getFinalSpin(eta,chi_eff): @jit def IMRPhenomC(f,params): """ + The amplitude and phase are generated first in unitless Mf space, then scaled with total mass and distance. Args @@ -215,6 +332,17 @@ def IMRPhenomC(f,params): def IMRPhenomC_dict2list(params): """ + Convert a dictionary of parameters to a list of parameters. + + Parameters + ---------- + params : dict + Dictionary of parameters + + Returns + ------- + params_list : list + List of parameters """ return jnp.array([params['mass_1'], params['mass_2'], params['spin_1'], params['spin_2'],\ From a7720280c9b1f126b918238483a31dfd65eb1887 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Tue, 26 Oct 2021 11:31:58 -0400 Subject: [PATCH 050/300] Update .gitignore --- .gitignore | 129 +++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 129 insertions(+) create mode 100644 .gitignore diff --git a/.gitignore b/.gitignore new file mode 100644 index 00000000..b6e47617 --- /dev/null +++ b/.gitignore @@ -0,0 +1,129 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ From 9f74603e00d8123001255f4949e01fbb9924f585 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Tue, 26 Oct 2021 11:34:02 -0400 Subject: [PATCH 051/300] Update population injection script --- .gitignore | 2 + example/Population_injection.py | 70 ++++++++++++++-------------- jaxgw/gw/likelihood/time_and_date.py | 38 +++++++++++++++ 3 files changed, 76 insertions(+), 34 deletions(-) create mode 100644 jaxgw/gw/likelihood/time_and_date.py diff --git a/.gitignore b/.gitignore index b6e47617..99b15426 100644 --- a/.gitignore +++ b/.gitignore @@ -127,3 +127,5 @@ dmypy.json # Pyre type checker .pyre/ +.vscode/launch.json +. \ No newline at end of file diff --git a/example/Population_injection.py b/example/Population_injection.py index 17b1ff77..07e786e0 100644 --- a/example/Population_injection.py +++ b/example/Population_injection.py @@ -2,15 +2,16 @@ import numpy as np import copy import bilby +import jax from jax import random, grad, jit, vmap, jacfwd, jacrev, value_and_grad from jax.experimental.optimizers import adam from jax.config import config config.update("jax_enable_x64", True) -from jaxgw.likelihood.utils import inner_product -from jaxgw.waveform.IMRPhenomC import IMRPhenomC -from jaxgw.likelihood.detector_preset import get_H1 -from jaxgw.likelihood.detector_projection import get_detector_response +from jaxgw.gw.likelihood.utils import inner_product +from jaxgw.gw.waveform.IMRPhenomC import IMRPhenomC +from jaxgw.gw.likelihood.detector_preset import get_H1,get_L1 +from jaxgw.gw.likelihood.detector_projection import get_detector_response import matplotlib.pyplot as plt @@ -74,7 +75,7 @@ def power_law_plus_peak(x,params): combine = (1-params['mixing'])*powerlaw+params['mixing']*peak return combine -true_ld = 600 +true_ld = 600. true_phase = 0. true_gt = 0. @@ -91,34 +92,35 @@ def power_law_plus_peak(x,params): psd_frequency = psd_frequency[jnp.isfinite(psd)] psd = psd[jnp.isfinite(psd)] -H1 = get_H1() +H1, H1_vertex = get_H1() +L1, L1_vertex = get_L1() def gen_params(m1): - params = dict(mass_1=m1, mass_2=m1, a_1=0, a_2=0, luminosity_distance=true_ld, phase=true_phase, geocent_time=true_gt, theta_jn=0.4, psi=2.659, ra=1.375, dec=-1.2108) + params = dict(mass_1=m1, mass_2=m1, spin_1=0., spin_2=0., luminosity_distance=true_ld, phase_c=true_phase, t_c=true_gt, theta_jn=0.4, psi=2.659, ra=1.375, dec=-1.2108) return params -def gen_event(params): +def gen_event(params,detector, detector_vertex): waveform = IMRPhenomC(psd_frequency, params) - strain = get_detector_response(waveform, params, H1) - return strain + waveform = get_detector_response(psd_frequency, waveform, params, detector, detector_vertex) + return waveform @jit -def jax_likelihood(params, data, data_f, PSD): - waveform = IMRPhenomC(data_f, params) - waveform = get_detector_response(waveform, params, H1) - match_filter_SNR = inner_product(waveform, data, data_f, PSD) - optimal_SNR = inner_product(waveform, waveform, data_f, PSD) - return (-2*match_filter_SNR + optimal_SNR)/2#, match_filter_SNR, optimal_SNR +def single_detector_likelihood(params, data, data_f, PSD, detector, detector_vertex): + waveform = IMRPhenomC(data_f, params) + waveform = get_detector_response(data_f, waveform, params, detector, detector_vertex) + match_filter_SNR = inner_product(waveform, data, data_f, PSD) + optimal_SNR = inner_product(waveform, waveform, data_f, PSD) + return (-2*match_filter_SNR + optimal_SNR)/2 @jit -def logprob_wrap(m1): - params = dict(mass_1=m1, mass_2=m1, a_1=0, a_2=0, luminosity_distance=true_ld, phase=true_phase, geocent_time=true_gt, theta_jn=0.4, psi=2.659, ra=1.375, dec=-1.2108) - return jax_likelihood(params, strain, psd_frequency, psd)#*Jac_det - -log_prob = lambda x: logprob_wrap(**x) -log_prob = jit(log_prob) - +def log_prob(m1, strain_H1, strain_L1): + params = gen_params(m1) + return single_detector_likelihood(params, strain_H1, psd_frequency, psd, H1, H1_vertex)+single_detector_likelihood(params, strain_L1, psd_frequency, psd, L1, L1_vertex) +@jit +def log_prob_scan(args,index): + result = log_prob(args[0][index],args[1][index],args[2][index]) + return args, result ######################################## # Power law Only @@ -147,15 +149,15 @@ def logprob_wrap(m1): m1_sample = m1_sample[:N_sample] -data_array = vmap(gen_event)(vmap(gen_params)(m1_sample)) +H1_data_array = vmap(gen_event,(0, None, None))(vmap(gen_params)(m1_sample), H1, H1_vertex) +L1_data_array = vmap(gen_event,(0, None, None))(vmap(gen_params)(m1_sample), L1, L1_vertex) multi_event_gen_param = jit(vmap(gen_params)) multi_event_gen_event = jit(vmap(gen_event)) -multi_event_likelihood = jit(vmap(jax_likelihood,(0,0,None,None),0)) +multi_detector_likelihood = jit(vmap(log_prob,(0,0,0),0)) def population_likelihood_powerlaw_peak(point,params): - if params['mixing'] < 0: - params['mixing'] = 0. - return -jnp.sum(multi_event_likelihood(multi_event_gen_param(point),data_array,psd_frequency,psd)*power_law_plus_peak(point[:,None],params)) + _, single_event_likelihood = jax.lax.scan(log_prob_scan,[point,H1_data_array,L1_data_array],jnp.arange(N_sample)) + return -jnp.sum(single_event_likelihood*power_law_plus_peak(point[:,None],params)) @@ -177,15 +179,15 @@ def step(step, opt_state): opt_state = opt_update(step, grads, opt_state) return value, opt_state -for i in range(1000): - value, opt_state = step(i, opt_state) - if jnp.isnan(value): - break - print(value,get_params(opt_state)[1]) +# for i in range(1000): +# value, opt_state = step(i, opt_state) +# if jnp.isnan(value): +# break +# print(value,get_params(opt_state)[1]) best_x_plpk, best_lambda_plpk = get_params(opt_state) -dlambdadtheta_plpk = jacfwd(jacrev(population_likelihood_powerlaw_peak),argnums=1)(best_x_plpk,best_lambda_plpk) +dlambdadtheta_plpk = jacfwd(jacrev(population_likelihood_powerlaw_peak,argnums=0),argnums=1)(best_x_plpk,best_lambda_plpk) #fig,ax = plt.subplots(1,3,figsize=(30,9)) diff --git a/jaxgw/gw/likelihood/time_and_date.py b/jaxgw/gw/likelihood/time_and_date.py new file mode 100644 index 00000000..9e4f7794 --- /dev/null +++ b/jaxgw/gw/likelihood/time_and_date.py @@ -0,0 +1,38 @@ +import jax.numpy as np + + {2444239.5, -43200, 19}, /* 1980-Jan-01 */ + {2444786.5, 46828800, 20}, /* 1981-Jul-01 */ + {2445151.5, 78364801, 21}, /* 1982-Jul-01 */ + {2445516.5, 109900802, 22}, /* 1983-Jul-01 */ + {2446247.5, 173059203, 23}, /* 1985-Jul-01 */ +#if 0 + /* NOTE: IF THIS WERE A NEGATIVE LEAP SECOND, INSERT AS FOLLOWS */ + {2447161.5, 252028803, 22}, /* 1988-Jan-01 EXAMPLE ONLY! */ +#endif + {2447161.5, 252028804, 24}, /* 1988-Jan-01 */ + {2447892.5, 315187205, 25}, /* 1990-Jan-01 */ + {2448257.5, 346723206, 26}, /* 1991-Jan-01 */ + {2448804.5, 393984007, 27}, /* 1992-Jul-01 */ + {2449169.5, 425520008, 28}, /* 1993-Jul-01 */ + {2449534.5, 457056009, 29}, /* 1994-Jul-01 */ + {2450083.5, 504489610, 30}, /* 1996-Jan-01 */ + {2450630.5, 551750411, 31}, /* 1997-Jul-01 */ + {2451179.5, 599184012, 32}, /* 1999-Jan-01 */ + {2453736.5, 820108813, 33}, /* 2006-Jan-01 */ + {2454832.5, 914803214, 34}, /* 2009-Jan-01 */ + {2456109.5, 1025136015, 35}, /* 2012-Jul-01 */ + {2457204.5, 1119744016, 36}, /* 2015-Jul-01 */ + {2457754.5, 1167264017, 37}, /* 2017-Jan-01 */ + + +def gps_to_utc(gps_time): + +def greenwich_mean_sidereal_time(gps_time): + +def time_delay_geocentric(detector1, detector2, ra, dec, time): + gmst = fmod(greenwich_mean_sidereal_time(time), 2 * np.pi) + theta, phi = ra_dec_to_theta_phi(ra, dec, gmst) + omega = np.array([np.sin(theta) * np.cos(phi), np.sin(theta) * np.sin(phi), np.cos(theta)]) + delta_d = detector2 - detector1 + return np.dot(omega, delta_d) / speed_of_light + From c5f7d9584bf43a26035bdb1f896310d92cf6ac10 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Tue, 26 Oct 2021 11:37:06 -0400 Subject: [PATCH 052/300] Add data folder in ignore file --- .gitignore | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index 99b15426..7aade577 100644 --- a/.gitignore +++ b/.gitignore @@ -128,4 +128,5 @@ dmypy.json # Pyre type checker .pyre/ .vscode/launch.json -. \ No newline at end of file +. +data From 085feb3b569cd6fcaff99aecc7cd2b4142c5abde Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Tue, 26 Oct 2021 14:03:41 -0400 Subject: [PATCH 053/300] Computing the hessian for the entire catalogue dataset is too memory intensive and inefficient. Instead we should look over events since the event sector is block diagonal --- example/Population_injection.py | 13 ++++++++--- example/blackjax/HMC_injection.py | 37 +++++++++++++++++-------------- 2 files changed, 30 insertions(+), 20 deletions(-) diff --git a/example/Population_injection.py b/example/Population_injection.py index 07e786e0..bdddcf2d 100644 --- a/example/Population_injection.py +++ b/example/Population_injection.py @@ -113,8 +113,7 @@ def single_detector_likelihood(params, data, data_f, PSD, detector, detector_ver return (-2*match_filter_SNR + optimal_SNR)/2 @jit -def log_prob(m1, strain_H1, strain_L1): - params = gen_params(m1) +def log_prob(params, strain_H1, strain_L1): return single_detector_likelihood(params, strain_H1, psd_frequency, psd, H1, H1_vertex)+single_detector_likelihood(params, strain_L1, psd_frequency, psd, L1, L1_vertex) @jit @@ -155,6 +154,11 @@ def log_prob_scan(args,index): multi_event_gen_event = jit(vmap(gen_event)) multi_detector_likelihood = jit(vmap(log_prob,(0,0,0),0)) +def single_population_likelihood(event_params, pop_params, strain_H1, strain_L1): + event_likelihood = log_prob(event_params, strain_H1, strain_L1) + return log_prob(event_params, strain_H1, strain_L1) + jnp.log(power_law_plus_peak(jnp.array([event_params['mass_1']]),pop_params)[0]) + + def population_likelihood_powerlaw_peak(point,params): _, single_event_likelihood = jax.lax.scan(log_prob_scan,[point,H1_data_array,L1_data_array],jnp.arange(N_sample)) return -jnp.sum(single_event_likelihood*power_law_plus_peak(point[:,None],params)) @@ -187,7 +191,10 @@ def step(step, opt_state): best_x_plpk, best_lambda_plpk = get_params(opt_state) -dlambdadtheta_plpk = jacfwd(jacrev(population_likelihood_powerlaw_peak,argnums=0),argnums=1)(best_x_plpk,best_lambda_plpk) +event_fisher = jacfwd(jacrev(single_population_likelihood)) +event_hyper_fisher = jacfwd(jacrev(single_population_likelihood,argnums=1),argnums=0) +hyper_fisher = jacfwd(jacrev(population_likelihood_powerlaw_peak,argnums=1),argnums=1) +#dlambdadtheta_plpk = jacfwd(jacrev(population_likelihood_powerlaw_peak,argnums=0),argnums=1)(best_x_plpk,best_lambda_plpk) #fig,ax = plt.subplots(1,3,figsize=(30,9)) diff --git a/example/blackjax/HMC_injection.py b/example/blackjax/HMC_injection.py index 4b78ff4f..5e56a4d8 100644 --- a/example/blackjax/HMC_injection.py +++ b/example/blackjax/HMC_injection.py @@ -16,9 +16,9 @@ from jax import random, grad, jit, vmap, jacfwd, jacrev, value_and_grad, pmap -true_m1 = 3. -true_m2 = 2. -true_ld = 150. +true_m1 = 15. +true_m2 = 5. +true_ld = 600. true_phase = 0. true_gt = 0. @@ -26,9 +26,13 @@ mass_1=true_m1, mass_2=true_m2, spin_1=0.0, spin_2=0.0, luminosity_distance=true_ld, theta_jn=0.4, psi=2.659, phase_c=true_phase, t_c=true_gt, ra=1.375, dec=-1.2108) -#guess_parameters = dict(m1=true_m1, m2=true_m2, luminosity_distance=true_ld, phase=true_phase, geocent_time=true_gt, theta_jn=0.4, psi=2.659, ra=1.375, dec=-1.2108) -# -guess_parameters = dict(m1=true_m1, m2=true_m2) + +#guess_parameters = dict(m1=true_m1, m2=true_m2) + +guess_parameters = dict( + mass_1=true_m1, mass_2=true_m2, luminosity_distance=true_ld, theta_jn=0.4, psi=2.659, + phase_c=true_phase, t_c=true_gt, ra=1.375, dec=-1.2108) + # Set up interferometers. In this case we'll use two interferometers @@ -36,7 +40,7 @@ # sensitivity ifos = bilby.gw.detector.InterferometerList(['H1']) ifos.set_strain_data_from_power_spectral_densities( - sampling_frequency=2048, duration=32, + sampling_frequency=2048, duration=1, start_time=- 3) psd = ifos[0].power_spectral_density_array @@ -64,14 +68,13 @@ def single_detector_likelihood(params, data, data_f, PSD, detector, detector_ver return (-2*match_filter_SNR + optimal_SNR)/2#, match_filter_SNR, optimal_SNR #@jit -##def logprob_wrap(trans_M_tot, trans_q, geocent_time, phase): -#def logprob_wrap(m1, m2, luminosity_distance, geocent_time, phase, theta_jn, psi, ra, dec): -# params = dict(mass_1=m1, mass_2=m2, a_1=0, a_2=0, luminosity_distance=luminosity_distance, theta_jn=theta_jn, psi=psi, phase=phase, geocent_time=geocent_time, ra=ra, dec=dec) -# return jax_likelihood(params, strain, psd_frequency, psd)#*Jac_det - +#def logprob_wrap(m1, m2): +# params = dict(mass_1=m1, mass_2=m2, spin_1=0, spin_2=0, luminosity_distance=true_ld, phase_c=true_phase, t_c=true_gt, theta_jn=0.4, psi=2.659, ra=1.375, dec=-1.2108) +# return single_detector_likelihood(params, strain_H1, psd_frequency, psd, H1, H1_vertex)+single_detector_likelihood(params, strain_L1, psd_frequency, psd, L1, L1_vertex) +# @jit -def logprob_wrap(m1, m2): - params = dict(mass_1=m1, mass_2=m2, spin_1=0, spin_2=0, luminosity_distance=true_ld, phase_c=true_phase, t_c=true_gt, theta_jn=0.4, psi=2.659, ra=1.375, dec=-1.2108) +def logprob_wrap(mass_1, mass_2, luminosity_distance, phase_c, t_c, theta_jn, psi, ra, dec): + params = dict(mass_1=mass_1, mass_2=mass_2, spin_1=0, spin_2=0, luminosity_distance=luminosity_distance, phase_c=phase_c, t_c=t_c, theta_jn=theta_jn, psi=psi, ra=ra, dec=dec) return single_detector_likelihood(params, strain_H1, psd_frequency, psd, H1, H1_vertex)+single_detector_likelihood(params, strain_L1, psd_frequency, psd, L1, L1_vertex) log_prob = lambda x: logprob_wrap(**x) @@ -93,7 +96,7 @@ def logprob_wrap(m1, m2): time1 = time.time() kernel_generator = lambda step_size, inverse_mass_matrix: hmc.kernel( - log_prob, step_size, inverse_mass_matrix, 30 + log_prob, step_size, inverse_mass_matrix, 5 ) final_state, (step_size, inverse_mass_matrix), info = stan_warmup.run( @@ -107,7 +110,7 @@ def logprob_wrap(m1, m2): print("Finding inverse mass matrix takes: "+str(time.time()-time1)+" seconds") print("Stepsize: "+str(step_size)) print("Inverse mass matrix: "+str(inverse_mass_matrix)) -num_integration_steps = 30 +num_integration_steps = 20 hmc_kernel = hmc.kernel(log_prob, step_size, inverse_mass_matrix, num_integration_steps) hmc_kernel = jit(hmc_kernel) @@ -127,5 +130,5 @@ def one_step(state, rng_key): print("Start sampling") time1 = time.time() -states = inference_loop(subkey, hmc_kernel, initial_state, 100) +states = inference_loop(subkey, hmc_kernel, initial_state, 10000) print("Sampling takes: "+str(time.time()-time1)+" seconds") From f446cf2799f5bb5537db0c51fd1afec6897319ec Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Wed, 27 Oct 2021 13:30:35 -0400 Subject: [PATCH 054/300] Decide to loop over the fisher matrix element --- example/Population_injection.py | 25 ++++++++++++++++++++----- 1 file changed, 20 insertions(+), 5 deletions(-) diff --git a/example/Population_injection.py b/example/Population_injection.py index bdddcf2d..58e85b02 100644 --- a/example/Population_injection.py +++ b/example/Population_injection.py @@ -84,7 +84,7 @@ def power_law_plus_peak(x,params): # sensitivity ifos = bilby.gw.detector.InterferometerList(['H1']) ifos.set_strain_data_from_power_spectral_densities( - sampling_frequency=2048, duration=32, + sampling_frequency=2048, duration=128, start_time=- 3) psd = ifos[0].power_spectral_density_array @@ -133,7 +133,7 @@ def log_prob_scan(args,index): true_param['sigma'] = 5.69 true_param['mixing'] = 0.3 -N_sample = 50 +N_sample = 500 obs_std = 0.1 m1_sample = jnp.empty(0) @@ -159,9 +159,11 @@ def single_population_likelihood(event_params, pop_params, strain_H1, strain_L1) return log_prob(event_params, strain_H1, strain_L1) + jnp.log(power_law_plus_peak(jnp.array([event_params['mass_1']]),pop_params)[0]) +single_population_likelihood_ = vmap(single_population_likelihood,(0,None,0,0),0) + def population_likelihood_powerlaw_peak(point,params): - _, single_event_likelihood = jax.lax.scan(log_prob_scan,[point,H1_data_array,L1_data_array],jnp.arange(N_sample)) - return -jnp.sum(single_event_likelihood*power_law_plus_peak(point[:,None],params)) +# _, single_event_likelihood = jax.lax.scan(log_prob_scan,[point,H1_data_array,L1_data_array],jnp.arange(N_sample)) + return -jnp.sum(single_population_likelihood_(point,params,H1_data_array,L1_data_array)) @@ -192,10 +194,23 @@ def step(step, opt_state): best_x_plpk, best_lambda_plpk = get_params(opt_state) event_fisher = jacfwd(jacrev(single_population_likelihood)) +#event_fisher = vmap(event_fisher,(0,None,0,0),0) event_hyper_fisher = jacfwd(jacrev(single_population_likelihood,argnums=1),argnums=0) +#event_hyper_fisher = vmap(event_hyper_fisher,(0,None,0,0),0) hyper_fisher = jacfwd(jacrev(population_likelihood_powerlaw_peak,argnums=1),argnums=1) #dlambdadtheta_plpk = jacfwd(jacrev(population_likelihood_powerlaw_peak,argnums=0),argnums=1)(best_x_plpk,best_lambda_plpk) +# Loop over event_fisher and event_hyper_fisher over m1_sample + +event_fisher_array = [] +event_hyper_fisher_array = [] + +for i in range(N_sample): + print(i) + event_fisher_array.append(event_fisher(gen_params(m1_sample[i]),guess_param,H1_data_array[i],L1_data_array[i])) + event_hyper_fisher_array.append(event_hyper_fisher(gen_params(m1_sample[i]),guess_param,H1_data_array[i],L1_data_array[i])) + + #fig,ax = plt.subplots(1,3,figsize=(30,9)) #ax[0].hist(m1_sample,bins=50,density=True,histtype='step',lw=3,label='Truth',color='C2') @@ -214,6 +229,6 @@ def step(step, opt_state): #ax[2].plot(dlambdadtheta_plpk['xmin'][jnp.argsort(dlambdadtheta_plpk['xmin'])],label='Power law + peak sorted',lw=3) #ax[2].legend(loc='lower right',fontsize=20) #ax[2].set_xlabel('Event number') -#ax[2].set_ylabel(r'$\frac{\partial^2\mathcal{L}}{\partial \theta \partial x_{min}}$') +#ax[2].set_ylabel(r'$\frac{\partial^2\mathcal{L} }{\partial \theta \partial x_{min}}$') # #fig.show() From 1713b1920b856f10490dfe3d2b26350fb9d20dd0 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Wed, 27 Oct 2021 14:33:21 -0400 Subject: [PATCH 055/300] Delete vmap part since that does not fit into memory --- example/Population_injection.py | 20 -------------------- 1 file changed, 20 deletions(-) diff --git a/example/Population_injection.py b/example/Population_injection.py index 58e85b02..8e2c3808 100644 --- a/example/Population_injection.py +++ b/example/Population_injection.py @@ -175,28 +175,8 @@ def population_likelihood_powerlaw_peak(point,params): guess_param['sigma'] = 5.69 guess_param['mixing'] = 0.3 -learning_rate = 1e-2 -opt_init, opt_update, get_params = adam(learning_rate) -opt_state = opt_init((m1_sample,guess_param)) - -def step(step, opt_state): - params = get_params(opt_state) - value, grads = value_and_grad(population_likelihood_powerlaw_peak,argnums=(0,1))(params[0], params[1]) - opt_state = opt_update(step, grads, opt_state) - return value, opt_state - -# for i in range(1000): -# value, opt_state = step(i, opt_state) -# if jnp.isnan(value): -# break -# print(value,get_params(opt_state)[1]) - -best_x_plpk, best_lambda_plpk = get_params(opt_state) - event_fisher = jacfwd(jacrev(single_population_likelihood)) -#event_fisher = vmap(event_fisher,(0,None,0,0),0) event_hyper_fisher = jacfwd(jacrev(single_population_likelihood,argnums=1),argnums=0) -#event_hyper_fisher = vmap(event_hyper_fisher,(0,None,0,0),0) hyper_fisher = jacfwd(jacrev(population_likelihood_powerlaw_peak,argnums=1),argnums=1) #dlambdadtheta_plpk = jacfwd(jacrev(population_likelihood_powerlaw_peak,argnums=0),argnums=1)(best_x_plpk,best_lambda_plpk) From eec1fd9ad8926196da99a2487aed89e4c80e0e6f Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Sat, 13 Nov 2021 12:57:30 -0500 Subject: [PATCH 056/300] Add numpyro neutra HMC example --- example/numpyro/NeuTra_HMC.py | 89 +++++++++++++++++++++++++++++++++++ 1 file changed, 89 insertions(+) create mode 100644 example/numpyro/NeuTra_HMC.py diff --git a/example/numpyro/NeuTra_HMC.py b/example/numpyro/NeuTra_HMC.py new file mode 100644 index 00000000..0ec56dec --- /dev/null +++ b/example/numpyro/NeuTra_HMC.py @@ -0,0 +1,89 @@ +import numpy as np +import bilby +import jax +import jax.numpy as jnp +import time + +from jax.config import config +config.update("jax_enable_x64", True) + +from jaxgw.gw.likelihood.detector_projection import construct_arm, detector_tensor, antenna_response, get_detector_response + +from jaxgw.gw.likelihood.utils import inner_product +from jaxgw.gw.likelihood.detector_preset import get_H1, get_L1 +from jaxgw.gw.waveform.TaylorF2 import TaylorF2 +from jaxgw.gw.waveform.IMRPhenomC import IMRPhenomC +from jax import random, grad, jit, vmap, jacfwd, jacrev, value_and_grad, pmap + +import numpyro +from numpyro import optim +from numpyro.diagnostics import print_summary +import numpyro.distributions as dist +from numpyro.distributions import constraints +from numpyro.infer import MCMC, NUTS, SVI, Trace_ELBO +from numpyro.infer.autoguide import AutoBNAFNormal +from numpyro.infer.reparam import NeuTraReparam + +true_m1 = 15. +true_m2 = 5. +true_ld = 600. +true_phase = 0. +true_gt = 0. + +injection_parameters = dict( + mass_1=true_m1, mass_2=true_m2, spin_1=0.0, spin_2=0.0, luminosity_distance=true_ld, theta_jn=0.4, psi=2.659, + phase_c=true_phase, t_c=true_gt, ra=1.375, dec=-1.2108) + + +#guess_parameters = dict(m1=true_m1, m2=true_m2) + +guess_parameters = dict( + mass_1=true_m1, mass_2=true_m2, luminosity_distance=true_ld, theta_jn=0.4, psi=2.659, + phase_c=true_phase, t_c=true_gt, ra=1.375, dec=-1.2108) + + + +# Set up interferometers. In this case we'll use two interferometers +# (LIGO-Hanford (H1), LIGO-Livingston (L1). These default to their design +# sensitivity +ifos = bilby.gw.detector.InterferometerList(['H1']) +ifos.set_strain_data_from_power_spectral_densities( + sampling_frequency=2048, duration=1, + start_time=- 3) + +psd = ifos[0].power_spectral_density_array +psd_frequency = ifos[0].frequency_array + +psd_frequency = psd_frequency[jnp.isfinite(psd)] +psd = psd[jnp.isfinite(psd)] + +waveform = IMRPhenomC(psd_frequency, injection_parameters) +H1, H1_vertex = get_H1() +L1, L1_vertex = get_L1() +strain_H1 = get_detector_response(psd_frequency, waveform, injection_parameters, H1, H1_vertex) +strain_L1 = get_detector_response(psd_frequency, waveform, injection_parameters, L1, L1_vertex) + +print('SNR of the event in H1: '+str(np.sqrt(inner_product(strain_H1,strain_H1,psd_frequency,psd)))) +print('SNR of the event in L1: '+str(np.sqrt(inner_product(strain_L1,strain_L1,psd_frequency,psd)))) + +@jit +def single_detector_likelihood(params, data, data_f, PSD, detector, detector_vertex): + waveform = IMRPhenomC(data_f, params) +# waveform = TaylorF2(data_f, params) + waveform = get_detector_response(data_f, waveform, params, detector, detector_vertex) + match_filter_SNR = inner_product(waveform, data, data_f, PSD) + optimal_SNR = inner_product(waveform, waveform, data_f, PSD) + return (-2*match_filter_SNR + optimal_SNR)/2#, match_filter_SNR, optimal_SNR + +#@jit +#def logprob_wrap(m1, m2): +# params = dict(mass_1=m1, mass_2=m2, spin_1=0, spin_2=0, luminosity_distance=true_ld, phase_c=true_phase, t_c=true_gt, theta_jn=0.4, psi=2.659, ra=1.375, dec=-1.2108) +# return single_detector_likelihood(params, strain_H1, psd_frequency, psd, H1, H1_vertex)+single_detector_likelihood(params, strain_L1, psd_frequency, psd, L1, L1_vertex) +# +@jit +def logprob_wrap(mass_1, mass_2, luminosity_distance, phase_c, t_c, theta_jn, psi, ra, dec): + params = dict(mass_1=mass_1, mass_2=mass_2, spin_1=0, spin_2=0, luminosity_distance=luminosity_distance, phase_c=phase_c, t_c=t_c, theta_jn=theta_jn, psi=psi, ra=ra, dec=dec) + return single_detector_likelihood(params, strain_H1, psd_frequency, psd, H1, H1_vertex)+single_detector_likelihood(params, strain_L1, psd_frequency, psd, L1, L1_vertex) + +log_prob = lambda x: logprob_wrap(**x) +log_prob = jit(log_prob) From c730e7e84512fc9fab6c522fe83c80e041d078d1 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Wed, 17 Nov 2021 12:56:40 -0500 Subject: [PATCH 057/300] Add emcee_injection --- example/emcee_injection.py | 92 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 92 insertions(+) create mode 100644 example/emcee_injection.py diff --git a/example/emcee_injection.py b/example/emcee_injection.py new file mode 100644 index 00000000..235e6381 --- /dev/null +++ b/example/emcee_injection.py @@ -0,0 +1,92 @@ +import numpy as np +import bilby +import jax +import jax.numpy as jnp +import time + +from jax.config import config +config.update("jax_enable_x64", True) + +from jaxgw.gw.likelihood.detector_projection import construct_arm, detector_tensor, antenna_response, get_detector_response + +from jaxgw.gw.likelihood.utils import inner_product +from jaxgw.gw.likelihood.detector_preset import get_H1, get_L1 +from jaxgw.gw.waveform.TaylorF2 import TaylorF2 +from jaxgw.gw.waveform.IMRPhenomC import IMRPhenomC +from jax import random, grad, jit, vmap, jacfwd, jacrev, value_and_grad, pmap + + +true_m1 = 15. +true_m2 = 5. +true_ld = 600. +true_phase = 0. +true_gt = 0. + +injection_parameters = dict( + mass_1=true_m1, mass_2=true_m2, spin_1=0.0, spin_2=0.0, luminosity_distance=true_ld, theta_jn=0.4, psi=2.659, + phase_c=true_phase, t_c=true_gt, ra=1.375, dec=-1.2108) + + +#guess_parameters = dict(m1=true_m1, m2=true_m2) + +guess_parameters = dict( + mass_1=true_m1, mass_2=true_m2, luminosity_distance=true_ld, theta_jn=0.4, psi=2.659, + phase_c=true_phase, t_c=true_gt, ra=1.375, dec=-1.2108) + + + +# Set up interferometers. In this case we'll use two interferometers +# (LIGO-Hanford (H1), LIGO-Livingston (L1). These default to their design +# sensitivity +ifos = bilby.gw.detector.InterferometerList(['H1']) +ifos.set_strain_data_from_power_spectral_densities( + sampling_frequency=2048, duration=1, + start_time=- 3) + +psd = ifos[0].power_spectral_density_array +psd_frequency = ifos[0].frequency_array + +psd_frequency = psd_frequency[jnp.isfinite(psd)] +psd = psd[jnp.isfinite(psd)] + +waveform = IMRPhenomC(psd_frequency, injection_parameters) +H1, H1_vertex = get_H1() +L1, L1_vertex = get_L1() +strain_H1 = get_detector_response(psd_frequency, waveform, injection_parameters, H1, H1_vertex) +strain_L1 = get_detector_response(psd_frequency, waveform, injection_parameters, L1, L1_vertex) + +print('SNR of the event in H1: '+str(np.sqrt(inner_product(strain_H1,strain_H1,psd_frequency,psd)))) +print('SNR of the event in L1: '+str(np.sqrt(inner_product(strain_L1,strain_L1,psd_frequency,psd)))) + +def single_detector_likelihood(params, data, data_f, PSD, detector, detector_vertex): + waveform = IMRPhenomC(data_f, params) +# waveform = TaylorF2(data_f, params) + waveform = get_detector_response(data_f, waveform, params, detector, detector_vertex) + match_filter_SNR = inner_product(waveform, data, data_f, PSD) + optimal_SNR = inner_product(waveform, waveform, data_f, PSD) + return (-2*match_filter_SNR + optimal_SNR)/2#, match_filter_SNR, optimal_SNR + +#@jit +#def logprob_wrap(m1, m2): +# params = dict(mass_1=m1, mass_2=m2, spin_1=0, spin_2=0, luminosity_distance=true_ld, phase_c=true_phase, t_c=true_gt, theta_jn=0.4, psi=2.659, ra=1.375, dec=-1.2108) +# return single_detector_likelihood(params, strain_H1, psd_frequency, psd, H1, H1_vertex)+single_detector_likelihood(params, strain_L1, psd_frequency, psd, L1, L1_vertex) +# +def log_prob(params): + if (params[0]<=0) or (params[1]<=0): + return -jnp.inf + params = dict(mass_1=params[0], mass_2=params[1], spin_1=0, spin_2=0, luminosity_distance=params[2], phase_c=params[3], t_c=params[4], theta_jn=params[5], psi=params[6], ra=params[7], dec=params[8]) + return single_detector_likelihood(params, strain_H1, psd_frequency, psd, H1, H1_vertex)+single_detector_likelihood(params, strain_L1, psd_frequency, psd, L1, L1_vertex) + +################################################################ +## BlackJax section +################################################################ + +import emcee + +nwalkers = 32 +ndim = 9 +p0 = np.random.rand(nwalkers, ndim) + list(guess_parameters.values()) +sampler = emcee.EnsembleSampler(nwalkers, ndim, log_prob) +state = sampler.run_mcmc(p0, 100) +sampler.reset() +sampler.run_mcmc(state, 5000) From a85de078325d9c5198162780ed150d568ee2f938 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Wed, 15 Dec 2021 13:17:47 -0500 Subject: [PATCH 058/300] Start cleaning up commits. Adding TaylorF2 in non-debug mode --- jaxgw/gw/waveform/TaylorF2.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/jaxgw/gw/waveform/TaylorF2.py b/jaxgw/gw/waveform/TaylorF2.py index d92aeb48..0c0fdae0 100644 --- a/jaxgw/gw/waveform/TaylorF2.py +++ b/jaxgw/gw/waveform/TaylorF2.py @@ -22,7 +22,7 @@ def TaylorF2(f,params): # PN3 = 11583231236531./4694215680 - 640./3 *jnp.pi**2 - 6868./21*(euler_gamma+jnp.log(4) phase = 2*jnp.pi*f*params['t_c'] - params['phase_c'] - jnp.pi/4 + 3./(128*eta*PNcoef**5) * \ - (PN0+PN1+PN1d5)#+PN2+PN2d5) + (PN0+PN1+PN1d5+PN2+PN2d5) # phase = - jnp.pi/4 + 3./(128*eta*PNcoef**5) * \ # (PN0+PN1+PN1d5)#+PN2+PN2d5) From afb102533b7d05b9aab202b5bbf71f3cb352eb49 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Wed, 15 Dec 2021 13:18:24 -0500 Subject: [PATCH 059/300] Add NF random walk example --- example/NFRandomWalk.py | 203 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 203 insertions(+) create mode 100644 example/NFRandomWalk.py diff --git a/example/NFRandomWalk.py b/example/NFRandomWalk.py new file mode 100644 index 00000000..2e3ce25a --- /dev/null +++ b/example/NFRandomWalk.py @@ -0,0 +1,203 @@ +import numpy as np +import bilby +import jax +import jax.numpy as jnp + + +from jax.config import config +config.update("jax_enable_x64", True) + +from jaxgw.gw.likelihood.detector_projection import construct_arm, detector_tensor, antenna_response, get_detector_response + +from jaxgw.gw.likelihood.utils import inner_product +from jaxgw.gw.likelihood.detector_preset import get_H1, get_L1 +from jaxgw.gw.waveform.TaylorF2 import TaylorF2 +from jaxgw.gw.waveform.IMRPhenomC import IMRPhenomC +from jax import random, grad, jit, vmap, jacfwd, jacrev, value_and_grad, pmap + +from jaxgw.sampler.Gaussian_random_walk import rw_metropolis_sampler +from jaxgw.sampler.maf import MaskedAutoregressiveFlow +from jax.scipy.stats import multivariate_normal +from flax.training import train_state # Useful dataclass to keep train state +import optax # Optimizers + + +true_m1 = 30. +true_m2 = 5. +true_ld = 1000. +true_phase = 0. +true_gt = 0. + +injection_parameters = dict( + mass_1=true_m1, mass_2=true_m2, spin_1=0.0, spin_2=0.0, luminosity_distance=true_ld, theta_jn=0.4, psi=2.659, + phase_c=true_phase, t_c=true_gt, ra=1.375, dec=-1.2108) + + +#guess_parameters = dict(m1=true_m1, m2=true_m2) + +guess_parameters = dict( + mass_1=true_m1*0.99, mass_2=true_m2*1.01, luminosity_distance=true_ld, theta_jn=0.4, psi=2.659, + phase_c=true_phase, t_c=true_gt, ra=1.375, dec=-1.2108) + + + +# Set up interferometers. In this case we'll use two interferometers +# (LIGO-Hanford (H1), LIGO-Livingston (L1). These default to their design +# sensitivity +ifos = bilby.gw.detector.InterferometerList(['H1']) +ifos.set_strain_data_from_power_spectral_densities( + sampling_frequency=2048, duration=1, + start_time=- 3) + +psd = ifos[0].power_spectral_density_array +psd_frequency = ifos[0].frequency_array + +psd_frequency = psd_frequency[jnp.isfinite(psd)] +psd = psd[jnp.isfinite(psd)] + +#waveform = IMRPhenomC(psd_frequency, injection_parameters) +waveform = TaylorF2(psd_frequency, injection_parameters) +H1, H1_vertex = get_H1() +L1, L1_vertex = get_L1() +strain_H1 = get_detector_response(psd_frequency, waveform, injection_parameters, H1, H1_vertex) +strain_L1 = get_detector_response(psd_frequency, waveform, injection_parameters, L1, L1_vertex) + +print('SNR of the event in H1: '+str(np.sqrt(inner_product(strain_H1,strain_H1,psd_frequency,psd)))) +print('SNR of the event in L1: '+str(np.sqrt(inner_product(strain_L1,strain_L1,psd_frequency,psd)))) + +@jit +def single_detector_likelihood(params, data, data_f, PSD, detector, detector_vertex): +# waveform = IMRPhenomC(data_f, params) + waveform = TaylorF2(data_f, params) + waveform = get_detector_response(data_f, waveform, params, detector, detector_vertex) + match_filter_SNR = inner_product(waveform, data, data_f, PSD) + optimal_SNR = inner_product(waveform, waveform, data_f, PSD) + return -(-2*match_filter_SNR + optimal_SNR)/2#, match_filter_SNR, optimal_SNR + +#@jit +#def logprob_wrap(m1, m2): +# params = dict(mass_1=m1, mass_2=m2, spin_1=0, spin_2=0, luminosity_distance=true_ld, phase_c=true_phase, t_c=true_gt, theta_jn=0.4, psi=2.659, ra=1.375, dec=-1.2108) +# return single_detector_likelihood(params, strain_H1, psd_frequency, psd, H1, H1_vertex)+single_detector_likelihood(params, strain_L1, psd_frequency, psd, L1, L1_vertex) +# +@jit +def logprob_wrap(mass_1, mass_2, luminosity_distance, phase_c, t_c, theta_jn, psi, ra, dec): + params = dict(mass_1=mass_1, mass_2=mass_2, spin_1=0, spin_2=0, luminosity_distance=true_ld, phase_c=phase_c, t_c=t_c, theta_jn=theta_jn, psi=psi, ra=ra, dec=dec) +# params = dict(mass_1=mass_1, mass_2=mass_2, spin_1=0, spin_2=0, luminosity_distance=true_ld, theta_jn=0.4, psi=2.659, phase_c=true_phase, t_c=true_gt, ra=1.375, dec=-1.2108) + return single_detector_likelihood(params, strain_H1, psd_frequency, psd, H1, H1_vertex)+single_detector_likelihood(params, strain_L1, psd_frequency, psd, L1, L1_vertex) + +likelihood = lambda x: logprob_wrap(*x) +para_logp = jit(jax.vmap(likelihood)) + +#### Sampling #### + + +def train_flow(rng, model, state, data): + + @jax.jit + def train_step(state, batch): + def loss(params): + y, log_det = model.apply({'params': params},batch) + mean = jnp.zeros((batch.shape[0],model.n_dim)) + cov = jnp.repeat(jnp.eye(model.n_dim)[None,:],batch.shape[0],axis=0) + log_det = log_det + multivariate_normal.logpdf(y,mean,cov) + return -jnp.mean(log_det) + grad_fn = jax.value_and_grad(loss) + value, grad = grad_fn(state.params) + state = state.apply_gradients(grads=grad) + return value,state + + @jax.jit + def eval_step(params, batch): + y, log_det = model.apply({'params': params},batch) + mean = jnp.zeros((batch.shape[0],model.n_dim)) + cov = jnp.repeat(jnp.eye(model.n_dim)[None,:],batch.shape[0],axis=0) + log_det = log_det + multivariate_normal.logpdf(y,mean,cov) + return -jnp.mean(log_det) + + def train_epoch(state, train_ds, batch_size, epoch, rng): + """Train for a single epoch.""" + train_ds_size = len(train_ds) + steps_per_epoch = train_ds_size // batch_size + + perms = jax.random.permutation(rng, train_ds_size) + perms = perms[:steps_per_epoch * batch_size] # skip incomplete batch + perms = perms.reshape((steps_per_epoch, batch_size)) + for perm in perms: + batch = train_ds[perm, ...] + value, state = train_step(state, batch) + + return value, state + + for epoch in range(1, num_epochs + 1): + print('Epoch %d' % epoch) + # Use a separate PRNG key to permute image data during shuffling + rng, input_rng = jax.random.split(rng) + # Run an optimization step over a training batch + value, state = train_epoch(state, data, batch_size, epoch, input_rng) + print('Train loss: %.3f' % value) + + return rng, state + +def sample_nf(model, param, rng_key,n_sample): + rng_key, subkey = random.split(rng_key) + samples = model.apply({'params': param}, subkey, n_sample,param, method=model.sample) + samples = jnp.flip(samples[0],axis=1) + return rng_key,samples + +n_dim = 9 +n_samples = 100 +nf_samples = 100 +n_chains = 100 +learning_rate = 0.01 +momentum = 0.9 +num_epochs = 100 +batch_size = 10000 +precompiled = False + +print("Preparing RNG keys") +rng_key = jax.random.PRNGKey(42) +rng_key_mcmc, rng_key_nf = jax.random.split(rng_key,2) + +rng_keys_mcmc = jax.random.split(rng_key_mcmc, n_chains) # (nchains,) +rng_keys_nf, init_rng_keys_nf = jax.random.split(rng_key_nf,2) + +print("Initializing MCMC model and normalizing flow model.") + +initial_position = (jnp.zeros((9, n_chains)).T + jnp.array(list(guess_parameters.values()))).T #(n_dim, n_chains) + +model = MaskedAutoregressiveFlow(n_dim,64,4) +params = model.init(init_rng_keys_nf, jnp.ones((1,n_dim)))['params'] + +run_mcmc = jax.vmap(rw_metropolis_sampler, in_axes=(0, None, None, 1), + out_axes=0) + +tx = optax.adam(learning_rate, momentum) +state = train_state.TrainState.create(apply_fn=model.apply, params=params, tx=tx) + + +def sampling_loop(rng_keys_nf, rng_keys_mcmc, model, state, initial_position): + rng_keys_mcmc, positions, log_prob = run_mcmc(rng_keys_mcmc, n_samples, likelihood, initial_position) + flat_chain = positions.reshape(-1,n_dim) + + rng_keys_nf, state = train_flow(rng_key_nf, model, state, flat_chain) + + rng_keys_nf, samples = sample_nf(model, state.params, rng_keys_nf, n_chains*nf_samples) + rng_keys_nf, subkey = jax.random.split(rng_keys_nf) + log_pdf_nfsample = log_prob(samples).reshape(nf_samples,n_chains) + log_uniform = jnp.log(jax.random.uniform(subkey,(nf_samples,n_chains))) + do_accept = log_uniform < log_pdf_nfsample - log_prob + + accept_index = jnp.argmax(do_accept>0 , axis=0)*n_chains + jnp.arange(n_chains) + accept_nf_sample = samples[accept_index] + accept_nf_log_prob = log_pdf_nfsample.flatten()[accept_index] + return rng_keys_nf, rng_keys_mcmc, state, accept_nf_sample, accept_nf_log_prob, positions + +last_step = initial_position +chains = [] +for i in range(5): + rng_keys_nf, rng_keys_mcmc, state, last_step, accept_nf_log_prob, positions = sampling_loop(rng_keys_nf, rng_keys_mcmc, model, state, last_step) + last_step = last_step.T + chains.append(positions) + +chains = np.concatenate(chains,axis=1) +nf_samples = sample_nf(model, state.params, rng_keys_nf, 10000) From dcfbdb8219809e412938c4e51f44457b43ea311d Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Wed, 15 Dec 2021 13:19:23 -0500 Subject: [PATCH 060/300] Add maf model --- jaxgw/sampler/maf.py | 95 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 95 insertions(+) create mode 100644 jaxgw/sampler/maf.py diff --git a/jaxgw/sampler/maf.py b/jaxgw/sampler/maf.py new file mode 100644 index 00000000..0f0db007 --- /dev/null +++ b/jaxgw/sampler/maf.py @@ -0,0 +1,95 @@ +from typing import Callable +import jax +import jax.numpy as jnp +from flax import linen as nn + +def get_masks(input_dim, hidden_dim=64, num_hidden=1): + masks = [] + input_degrees = jnp.arange(input_dim) + degrees = [input_degrees] + + for n_h in range(num_hidden + 1): + degrees += [jnp.arange(hidden_dim) % (input_dim - 1)] + degrees += [input_degrees % input_dim - 1] + + for (d0, d1) in zip(degrees[:-1], degrees[1:]): + masks += [jnp.transpose(jnp.expand_dims(d1, -1) >= jnp.expand_dims(d0, 0)).astype(jnp.float32)] + return masks + +class MaskedDense(nn.Module): + n_dim: int + n_hidden: int + kernel_init: Callable = nn.initializers.lecun_normal() + bias_init: Callable = nn.initializers.zeros + + @nn.compact + def __call__(self, x, mask): + weight = self.param('weights', self.kernel_init, (self.n_dim, self.n_hidden)) + bias = self.param('bias', self.bias_init, (self.n_hidden,)) + return jnp.dot(x, weight * mask) + bias + +class MaskedAutoEncoder(nn.Module): + n_dim: int + n_hidden: int + + def setup(self): + self.mask = get_masks(self.n_dim, self.n_hidden) + self.up = MaskedDense(self.n_dim, self.n_hidden) + self.mid = MaskedDense(self.n_hidden, self.n_hidden) + self.down = MaskedDense(self.n_hidden, 2*self.n_dim) + + def __call__(self, inputs): + log_weight, bias = self.forward(inputs) + outputs = (inputs - bias)*jnp.exp(-log_weight) + log_jacobian = -jnp.sum(log_weight, axis=-1) + return outputs, log_jacobian + + def forward(self, inputs): + x = self.up(inputs, self.mask[0]) + x = nn.swish(x) + x = self.mid(x, self.mask[1]) + x = nn.swish(x) + log_weight, bias = self.down(x, self.mask[2].tile(2)).split(2, -1) + return log_weight, bias + + def inverse(self, inputs): + outputs = jnp.zeros_like(inputs) + for i_col in range(inputs.shape[1]): + log_weight, bias = self.forward(outputs) + outputs = jax.ops.index_update( + outputs, jax.ops.index[:, i_col], inputs[:, i_col] * jnp.exp(log_weight[:, i_col]) + bias[:, i_col] + ) + log_det_jacobian = -log_weight.sum(-1) + return outputs, log_det_jacobian + +class MaskedAutoregressiveFlow(nn.Module): + n_dim: int + n_hidden: int + n_layer: int + + def setup(self): + self.layers = [MaskedAutoEncoder(self.n_dim, self.n_hidden) for _ in range(self.n_layer)] + + def __call__(self, inputs): + log_jacobian = 0 + for layer in self.layers: + inputs, log_jacobian_ = layer(inputs) + inputs = inputs[:,::-1] + log_jacobian += log_jacobian_ + return inputs, log_jacobian + + def inverse(self, inputs): + # Be careful about flipping the inputs when inverting the flow. + log_jacobian = 0 + for layer in reversed(self.layers): + inputs, log_jacobian_ = layer.inverse(inputs) + inputs = inputs[:,::-1] + log_jacobian += log_jacobian_ + return inputs, log_jacobian + + def sample(self, rng_key, n_samples, params): + mean = jnp.zeros((n_samples,self.n_dim)) + cov = jnp.repeat(jnp.eye(self.n_dim)[None,:],n_samples,axis=0) + gaussian = jax.random.multivariate_normal(rng_key, mean, cov) + samples = self.apply({'params': params},gaussian,method=self.inverse) + return samples \ No newline at end of file From 2eb221734295940ca0da0c0ef4dbe976ac7050f8 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Wed, 15 Dec 2021 13:23:40 -0500 Subject: [PATCH 061/300] Add realNVP model --- jaxgw/sampler/realNVP.py | 94 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 94 insertions(+) create mode 100644 jaxgw/sampler/realNVP.py diff --git a/jaxgw/sampler/realNVP.py b/jaxgw/sampler/realNVP.py new file mode 100644 index 00000000..ad2c9ed4 --- /dev/null +++ b/jaxgw/sampler/realNVP.py @@ -0,0 +1,94 @@ +from typing import Sequence, Callable +import jax +import jax.numpy as jnp +from flax import linen as nn +import numpy as np + +class MLP(nn.Module): + features: Sequence[int] + activation: Callable = nn.relu + use_bias: bool = True + init_weight_scale: float = 1e-4 + kernel_i: Callable = jax.nn.initializers.variance_scaling + + def setup(self): + self.layers = [nn.Dense(feat, use_bias=self.use_bias, kernel_init=self.kernel_i(self.init_weight_scale, "fan_in", "normal")) for feat in self.features] + + def __call__(self, x): + for l, layer in enumerate(self.layers[:-1]): + x = self.activation(layer(x)) + x = self.layers[-1](x) + return x + + +class AffineCoupling(nn.Module): + + n_features: int + n_hidden: int + mask: jnp.array + dt: float = 1 + + def setup(self): + self.scale_MLP = MLP([self.n_features, self.n_hidden, self.n_features]) + self.translate_MLP = MLP([self.n_features, self.n_hidden, self.n_features]) + + def __call__(self, x): + s = self.mask * self.scale_MLP(x*(1-self.mask)) + s = jnp.tanh(s) + t = self.mask * self.translate_MLP(x*(1-self.mask)) + s = self.dt * s + t = self.dt * t + log_det = s.reshape(s.shape[0], -1).sum(axis=-1) + outputs = (x + t) * jnp.exp(s) + return outputs, log_det + + def inverse(self, x): + s = self.mask * self.scale_MLP(x*(1-self.mask)) + s = jnp.tanh(s) + t = self.mask * self.translate_MLP(x*(1-self.mask)) + s = self.dt * s + t = self.dt * t + log_det = -s.reshape(s.shape[0], -1).sum(axis=-1) + outputs = x * jnp.exp(-s) - t + return outputs, log_det + + + +class RealNVP(nn.Module): + + n_layer: int + n_features: int + n_hidden: int + dt: float = 1 + + def setup(self): + affine_coupling = [] + for i in range(self.n_layer): + mask = np.ones(self.n_features) + mask[int(self.n_features/2):] = 0 + if i % 2 == 0: + mask = 1 - mask + mask = jnp.array(mask) + affine_coupling.append(AffineCoupling(self.n_features, self.n_hidden, mask, dt=self.dt)) + self.affine_coupling = affine_coupling + + def __call__(self, x): + log_det = jnp.zeros(x.shape[0]) + for i in range(self.n_layer): + x, log_det_i = self.affine_coupling[i](x) + log_det += log_det_i + return x, log_det + + def inverse(self, x): + log_det = jnp.zeros(x.shape[0]) + for i in range(self.n_layer): + x, log_det_i = self.affine_coupling[self.n_layer-1-i].inverse(x) + log_det += log_det_i + return x, log_det + + def sample(self, rng_key, n_samples, params): + mean = jnp.zeros((n_samples,self.n_dim)) + cov = jnp.repeat(jnp.eye(self.n_dim)[None,:],n_samples,axis=0) + gaussian = jax.random.multivariate_normal(rng_key, mean, cov) + samples = self.apply({'params': params},gaussian,method=self.inverse) + return samples \ No newline at end of file From 3b8dab8fb4776811f3719726005b6b30c6182da1 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Wed, 15 Dec 2021 13:25:03 -0500 Subject: [PATCH 062/300] change n_dim to n_features --- jaxgw/sampler/realNVP.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/jaxgw/sampler/realNVP.py b/jaxgw/sampler/realNVP.py index ad2c9ed4..4ad31776 100644 --- a/jaxgw/sampler/realNVP.py +++ b/jaxgw/sampler/realNVP.py @@ -87,8 +87,8 @@ def inverse(self, x): return x, log_det def sample(self, rng_key, n_samples, params): - mean = jnp.zeros((n_samples,self.n_dim)) - cov = jnp.repeat(jnp.eye(self.n_dim)[None,:],n_samples,axis=0) + mean = jnp.zeros((n_samples,self.n_features)) + cov = jnp.repeat(jnp.eye(self.n_features)[None,:],n_samples,axis=0) gaussian = jax.random.multivariate_normal(rng_key, mean, cov) samples = self.apply({'params': params},gaussian,method=self.inverse) return samples \ No newline at end of file From 2caba056e539b75678be43d8d216fcb94fff2ca4 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Wed, 15 Dec 2021 14:08:23 -0500 Subject: [PATCH 063/300] Add Gaussian random walk kernel --- jaxgw/sampler/Gaussian_random_walk.py | 56 +++++++++++++++++++++++++++ 1 file changed, 56 insertions(+) create mode 100644 jaxgw/sampler/Gaussian_random_walk.py diff --git a/jaxgw/sampler/Gaussian_random_walk.py b/jaxgw/sampler/Gaussian_random_walk.py new file mode 100644 index 00000000..f2d1d47e --- /dev/null +++ b/jaxgw/sampler/Gaussian_random_walk.py @@ -0,0 +1,56 @@ +import jax +import jax.numpy as jnp +from functools import partial + +@partial(jax.jit, static_argnums=(1,)) +def rw_metropolis_kernel(rng_key, logpdf, position, log_prob): + """Moves the chains by one step using the Random Walk Metropolis algorithm. + Attributes + ---------- + rng_key: jax.random.PRNGKey + Key for the pseudo random number generator. + logpdf: function + Returns the log-probability of the model given a position. + position: jnp.ndarray, shape (n_dims,) + The starting position. + log_prob: float + The log probability at the starting position. + Returns + ------- + Tuple + The next positions of the chains along with their log probability. + """ + key1, key2 = jax.random.split(rng_key) + move_proposal = jax.random.normal(key1, shape=position.shape) * 0.1 + proposal = position + move_proposal + proposal_log_prob = logpdf(proposal) + + log_uniform = jnp.log(jax.random.uniform(key2)) + do_accept = log_uniform < proposal_log_prob - log_prob + + position = jnp.where(do_accept, proposal, position) + log_prob = jnp.where(do_accept, proposal_log_prob, log_prob) + return position, log_prob + + +@partial(jax.jit, static_argnums=(1, 2)) +def rw_metropolis_sampler(rng_key, n_samples, logpdf, initial_position): + + def mh_update_sol2(i, state): + key, positions, log_prob = state + _, key = jax.random.split(key) + new_position, new_log_prob = rw_metropolis_kernel(key, logpdf, positions[i-1], log_prob) + positions=positions.at[i].set(new_position) + return (key, positions, new_log_prob) + + + logp = logpdf(initial_position) + all_positions = jnp.zeros((n_samples,)+initial_position.shape) + initial_position + initial_state = (rng_key,all_positions, logp) + rng_key, all_positions, log_prob = jax.lax.fori_loop(1, n_samples, + mh_update_sol2, + initial_state) + + + return rng_key, all_positions, log_prob + From cf799777cdd290be724e9ec46126c03c484ae0c5 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Wed, 15 Dec 2021 18:07:58 -0500 Subject: [PATCH 064/300] Add NF_proposal --- jaxgw/sampler/NF_prposal.py | 48 +++++++++++++++++++++++++++++++++++++ 1 file changed, 48 insertions(+) create mode 100644 jaxgw/sampler/NF_prposal.py diff --git a/jaxgw/sampler/NF_prposal.py b/jaxgw/sampler/NF_prposal.py new file mode 100644 index 00000000..38ab5960 --- /dev/null +++ b/jaxgw/sampler/NF_prposal.py @@ -0,0 +1,48 @@ +import jax +import jax.numpy as jnp +from functools import partial +from jax import random, jit, vmap + + +def nf_metropolis_kernel(rng_key, proposal_position, initial_position, proposal_pdf, proposal_nf_pdf, initial_pdf, initial_nf_pdf): + + rng_key, subkeys = random.split(rng_key,2) + ratio = (proposal_pdf - initial_pdf) - (proposal_nf_pdf - initial_nf_pdf) + ratio = jnp.exp(ratio) + u = jax.random.uniform(subkeys, ratio.shape) + do_accept = u < ratio + position = jnp.where(do_accept, proposal_position, initial_position) + log_prob = jnp.where(do_accept, proposal_pdf, initial_pdf) + log_prob_nf = jnp.where(do_accept, proposal_nf_pdf, initial_nf_pdf) + return position, log_prob, log_prob_nf + +nf_metropolis_kernel = vmap(jit(nf_metropolis_kernel)) + +def nf_metropolis_sampler(rng_key, n_samples, nf_model, nf_param, target_pdf, initial_position): + + def mh_update_sol2(i, state): + key, positions, log_prob, log_prob_nf = state + key, *sub_key = jax.random.split(key, positions.shape[1]+1) + sub_key = jnp.array(sub_key) + new_position, new_log_prob, new_log_prob_nf = nf_metropolis_kernel(sub_key, positions[i], positions[i-1], log_pdf_proposal[i], log_pdf_nf_proposal[i], log_prob, log_prob_nf) + positions=positions.at[i].set(new_position) + return (key, positions, new_log_prob, new_log_prob_nf) + + rng_key, *subkeys = random.split(rng_key,3) + all_positions = jnp.zeros((n_samples,)+initial_position.shape) + initial_position + proposal_position = nf_model.apply({'params': nf_param}, subkeys[0], initial_position.shape[0]*n_samples, nf_param, method=nf_model.sample)[0] + + + log_pdf_nf_proposal = nf_model.apply({'params': nf_param}, proposal_position, method=nf_model.log_prob) + log_pdf_nf_initial = nf_model.apply({'params': nf_param}, initial_position, method=nf_model.log_prob) + log_pdf_proposal = target_pdf(proposal_position) + log_pdf_initial = target_pdf(initial_position) + + proposal_position = proposal_position.reshape(n_samples, initial_position.shape[0], initial_position.shape[1]) + log_pdf_nf_proposal = log_pdf_nf_proposal.reshape(n_samples, initial_position.shape[0]) + log_pdf_proposal = log_pdf_proposal.reshape(n_samples, initial_position.shape[0]) + initial_state = (subkeys[1], all_positions, log_pdf_initial, log_pdf_nf_initial) + rng_key, all_positions, log_prob, log_prob_nf = jax.lax.fori_loop(1, n_samples, + mh_update_sol2, + initial_state) + return rng_key, all_positions, log_prob, log_prob_nf From 0884a97de7a2499d0bd6f5bf75c1ebd5b441018c Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Thu, 16 Dec 2021 10:27:02 -0500 Subject: [PATCH 065/300] Add NF_sampler proposal --- example/NFRandomWalk.py | 80 ++++++++++++++++--------------------- jaxgw/sampler/NF_prposal.py | 1 + jaxgw/sampler/realNVP.py | 11 ++++- 3 files changed, 45 insertions(+), 47 deletions(-) diff --git a/example/NFRandomWalk.py b/example/NFRandomWalk.py index 2e3ce25a..52bb3544 100644 --- a/example/NFRandomWalk.py +++ b/example/NFRandomWalk.py @@ -5,6 +5,8 @@ from jax.config import config + +from jaxgw.sampler.NF_prposal import nf_metropolis_kernel, nf_metropolis_sampler config.update("jax_enable_x64", True) from jaxgw.gw.likelihood.detector_projection import construct_arm, detector_tensor, antenna_response, get_detector_response @@ -17,14 +19,15 @@ from jaxgw.sampler.Gaussian_random_walk import rw_metropolis_sampler from jaxgw.sampler.maf import MaskedAutoregressiveFlow +from jaxgw.sampler.realNVP import RealNVP from jax.scipy.stats import multivariate_normal from flax.training import train_state # Useful dataclass to keep train state import optax # Optimizers true_m1 = 30. -true_m2 = 5. -true_ld = 1000. +true_m2 = 30. +true_ld = 500. true_phase = 0. true_gt = 0. @@ -90,29 +93,21 @@ def logprob_wrap(mass_1, mass_2, luminosity_distance, phase_c, t_c, theta_jn, ps #### Sampling #### +def train_step(model, state, batch): + def loss(params): + y, log_det = model.apply({'params': params},batch) + mean = jnp.zeros((batch.shape[0],model.n_features)) + cov = jnp.repeat(jnp.eye(model.n_features)[None,:],batch.shape[0],axis=0) + log_det = log_det + multivariate_normal.logpdf(y,mean,cov) + return -jnp.mean(log_det) + grad_fn = jax.value_and_grad(loss) + value, grad = grad_fn(state.params) + state = state.apply_gradients(grads=grad) + return value,state -def train_flow(rng, model, state, data): +train_step = jax.jit(train_step,static_argnums=(0,)) - @jax.jit - def train_step(state, batch): - def loss(params): - y, log_det = model.apply({'params': params},batch) - mean = jnp.zeros((batch.shape[0],model.n_dim)) - cov = jnp.repeat(jnp.eye(model.n_dim)[None,:],batch.shape[0],axis=0) - log_det = log_det + multivariate_normal.logpdf(y,mean,cov) - return -jnp.mean(log_det) - grad_fn = jax.value_and_grad(loss) - value, grad = grad_fn(state.params) - state = state.apply_gradients(grads=grad) - return value,state - - @jax.jit - def eval_step(params, batch): - y, log_det = model.apply({'params': params},batch) - mean = jnp.zeros((batch.shape[0],model.n_dim)) - cov = jnp.repeat(jnp.eye(model.n_dim)[None,:],batch.shape[0],axis=0) - log_det = log_det + multivariate_normal.logpdf(y,mean,cov) - return -jnp.mean(log_det) +def train_flow(rng, model, state, data): def train_epoch(state, train_ds, batch_size, epoch, rng): """Train for a single epoch.""" @@ -124,7 +119,7 @@ def train_epoch(state, train_ds, batch_size, epoch, rng): perms = perms.reshape((steps_per_epoch, batch_size)) for perm in perms: batch = train_ds[perm, ...] - value, state = train_step(state, batch) + value, state = train_step(model, state, batch) return value, state @@ -145,12 +140,12 @@ def sample_nf(model, param, rng_key,n_sample): return rng_key,samples n_dim = 9 -n_samples = 100 -nf_samples = 100 +n_samples = 1000 +nf_samples = 10 n_chains = 100 learning_rate = 0.01 momentum = 0.9 -num_epochs = 100 +num_epochs = 500 batch_size = 10000 precompiled = False @@ -165,7 +160,8 @@ def sample_nf(model, param, rng_key,n_sample): initial_position = (jnp.zeros((9, n_chains)).T + jnp.array(list(guess_parameters.values()))).T #(n_dim, n_chains) -model = MaskedAutoregressiveFlow(n_dim,64,4) +#model = MaskedAutoregressiveFlow(n_dim,64,4) +model = RealNVP(10,n_dim,64, 1) params = model.init(init_rng_keys_nf, jnp.ones((1,n_dim)))['params'] run_mcmc = jax.vmap(rw_metropolis_sampler, in_axes=(0, None, None, 1), @@ -176,28 +172,22 @@ def sample_nf(model, param, rng_key,n_sample): def sampling_loop(rng_keys_nf, rng_keys_mcmc, model, state, initial_position): - rng_keys_mcmc, positions, log_prob = run_mcmc(rng_keys_mcmc, n_samples, likelihood, initial_position) - flat_chain = positions.reshape(-1,n_dim) - - rng_keys_nf, state = train_flow(rng_key_nf, model, state, flat_chain) - - rng_keys_nf, samples = sample_nf(model, state.params, rng_keys_nf, n_chains*nf_samples) - rng_keys_nf, subkey = jax.random.split(rng_keys_nf) - log_pdf_nfsample = log_prob(samples).reshape(nf_samples,n_chains) - log_uniform = jnp.log(jax.random.uniform(subkey,(nf_samples,n_chains))) - do_accept = log_uniform < log_pdf_nfsample - log_prob + rng_keys_mcmc, positions, log_prob = run_mcmc(rng_keys_mcmc, n_samples, likelihood, initial_position) + flat_chain = positions.reshape(-1,n_dim) + rng_keys_nf, state = train_flow(rng_key_nf, model, state, flat_chain) + rng_keys_nf, nf_chain, log_prob, log_prob_nf = nf_metropolis_sampler(rng_keys_nf, nf_samples, model, state.params , para_logp, positions[:,-1]) - accept_index = jnp.argmax(do_accept>0 , axis=0)*n_chains + jnp.arange(n_chains) - accept_nf_sample = samples[accept_index] - accept_nf_log_prob = log_pdf_nfsample.flatten()[accept_index] - return rng_keys_nf, rng_keys_mcmc, state, accept_nf_sample, accept_nf_log_prob, positions + positions = jnp.concatenate((positions,nf_chain),axis=1) + return rng_keys_nf, rng_keys_mcmc, state, positions last_step = initial_position chains = [] for i in range(5): - rng_keys_nf, rng_keys_mcmc, state, last_step, accept_nf_log_prob, positions = sampling_loop(rng_keys_nf, rng_keys_mcmc, model, state, last_step) - last_step = last_step.T - chains.append(positions) + rng_keys_nf, rng_keys_mcmc, state, positions = sampling_loop(rng_keys_nf, rng_keys_mcmc, model, state, last_step) + last_step = positions[:,-1].T + # rng_keys_mcmc, positions, log_prob = run_mcmc(rng_keys_mcmc, n_samples, likelihood, initial_position) + # last_step = last_step.T + chains.append(positions) chains = np.concatenate(chains,axis=1) nf_samples = sample_nf(model, state.params, rng_keys_nf, 10000) diff --git a/jaxgw/sampler/NF_prposal.py b/jaxgw/sampler/NF_prposal.py index 38ab5960..d6eda60f 100644 --- a/jaxgw/sampler/NF_prposal.py +++ b/jaxgw/sampler/NF_prposal.py @@ -45,4 +45,5 @@ def mh_update_sol2(i, state): rng_key, all_positions, log_prob, log_prob_nf = jax.lax.fori_loop(1, n_samples, mh_update_sol2, initial_state) + all_positions = all_positions.swapaxes(0,1) return rng_key, all_positions, log_prob, log_prob_nf diff --git a/jaxgw/sampler/realNVP.py b/jaxgw/sampler/realNVP.py index 4ad31776..eeb14d26 100644 --- a/jaxgw/sampler/realNVP.py +++ b/jaxgw/sampler/realNVP.py @@ -90,5 +90,12 @@ def sample(self, rng_key, n_samples, params): mean = jnp.zeros((n_samples,self.n_features)) cov = jnp.repeat(jnp.eye(self.n_features)[None,:],n_samples,axis=0) gaussian = jax.random.multivariate_normal(rng_key, mean, cov) - samples = self.apply({'params': params},gaussian,method=self.inverse) - return samples \ No newline at end of file + samples = self.inverse(gaussian) + return samples + + def log_prob(self, x): + y, log_det = self.__call__(x) + mean = jnp.zeros((x.shape[0],self.n_features)) + cov = jnp.repeat(jnp.eye(self.n_features)[None,:],x.shape[0],axis=0) + log_det = log_det + jax.scipy.stats.multivariate_normal.logpdf(y,mean,cov) + return log_det \ No newline at end of file From 4afd16bc70cdc29f3b0edc0f3a4bf723e7b05cc1 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Thu, 16 Dec 2021 10:28:21 -0500 Subject: [PATCH 066/300] Correct name of NF_proposal --- jaxgw/sampler/{NF_prposal.py => NF_proposal.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename jaxgw/sampler/{NF_prposal.py => NF_proposal.py} (100%) diff --git a/jaxgw/sampler/NF_prposal.py b/jaxgw/sampler/NF_proposal.py similarity index 100% rename from jaxgw/sampler/NF_prposal.py rename to jaxgw/sampler/NF_proposal.py From 41a90c3e5e32d6a4dd37b392853572cd3bec16be Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Thu, 16 Dec 2021 13:24:58 -0500 Subject: [PATCH 067/300] Working version of NF inference --- example/NFRandomWalk.py | 10 +++++----- jaxgw/sampler/NF_proposal.py | 2 +- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/example/NFRandomWalk.py b/example/NFRandomWalk.py index 52bb3544..dc387c8d 100644 --- a/example/NFRandomWalk.py +++ b/example/NFRandomWalk.py @@ -6,7 +6,7 @@ from jax.config import config -from jaxgw.sampler.NF_prposal import nf_metropolis_kernel, nf_metropolis_sampler +from jaxgw.sampler.NF_proposal import nf_metropolis_kernel, nf_metropolis_sampler config.update("jax_enable_x64", True) from jaxgw.gw.likelihood.detector_projection import construct_arm, detector_tensor, antenna_response, get_detector_response @@ -27,7 +27,7 @@ true_m1 = 30. true_m2 = 30. -true_ld = 500. +true_ld = 1000. true_phase = 0. true_gt = 0. @@ -141,11 +141,11 @@ def sample_nf(model, param, rng_key,n_sample): n_dim = 9 n_samples = 1000 -nf_samples = 10 -n_chains = 100 +nf_samples = 100 +n_chains = 200 learning_rate = 0.01 momentum = 0.9 -num_epochs = 500 +num_epochs = 300 batch_size = 10000 precompiled = False diff --git a/jaxgw/sampler/NF_proposal.py b/jaxgw/sampler/NF_proposal.py index d6eda60f..433f1b79 100644 --- a/jaxgw/sampler/NF_proposal.py +++ b/jaxgw/sampler/NF_proposal.py @@ -24,7 +24,7 @@ def mh_update_sol2(i, state): key, positions, log_prob, log_prob_nf = state key, *sub_key = jax.random.split(key, positions.shape[1]+1) sub_key = jnp.array(sub_key) - new_position, new_log_prob, new_log_prob_nf = nf_metropolis_kernel(sub_key, positions[i], positions[i-1], log_pdf_proposal[i], log_pdf_nf_proposal[i], log_prob, log_prob_nf) + new_position, new_log_prob, new_log_prob_nf = nf_metropolis_kernel(sub_key, proposal_position[i], positions[i-1], log_pdf_proposal[i], log_pdf_nf_proposal[i], log_prob, log_prob_nf) positions=positions.at[i].set(new_position) return (key, positions, new_log_prob, new_log_prob_nf) From af2644589c4c83c941444a9fe6b8abdfba53b056 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 20 Dec 2021 12:48:55 -0500 Subject: [PATCH 068/300] Dependencies fix in single_event_likelihood.py --- jaxgw/gw/likelihood/single_event_likelihood.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/jaxgw/gw/likelihood/single_event_likelihood.py b/jaxgw/gw/likelihood/single_event_likelihood.py index 0784b827..6b1d2844 100644 --- a/jaxgw/gw/likelihood/single_event_likelihood.py +++ b/jaxgw/gw/likelihood/single_event_likelihood.py @@ -1,6 +1,6 @@ from jax import jit -from jaxgw.likelihood.detector_projection import get_detector_response -from jaxgw.likelihood.utils import inner_product +from jaxgw.gw.likelihood.detector_projection import get_detector_response +from jaxgw.gw.likelihood.utils import inner_product def single_detector_likelihood(waveform_model, params, data, data_f, PSD, detector): waveform = waveform_model(data_f, params) From d3c64b3c2039647ad986a69b888c020dafd34cd9 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 20 Dec 2021 12:49:31 -0500 Subject: [PATCH 069/300] IMRPhenomC bug fix in beta --- jaxgw/gw/waveform/IMRPhenomC.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/jaxgw/gw/waveform/IMRPhenomC.py b/jaxgw/gw/waveform/IMRPhenomC.py index d2250643..58960ac6 100644 --- a/jaxgw/gw/waveform/IMRPhenomC.py +++ b/jaxgw/gw/waveform/IMRPhenomC.py @@ -308,8 +308,9 @@ def IMRPhenomC(f,params): phase_PM = 1./eta*jnp.sum(alpha*f[:,None]**phase_PM_order,axis=1) - beta_1 = 1./eta*jnp.sum(alpha*f_rd**phase_PM_order) + beta_2 = 1./eta*jnp.sum(jnp.array([-5./3,-3./3,-1./3,2./3,1])*alpha[jnp.array([0,1,2,4,5])]*f_rd**jnp.array([-8./3,-6./3,-4./3,-1./3,0])) + beta_1 = 1./eta*jnp.sum(alpha*f_rd**phase_PM_order) - beta_2*f_rd phase_RD = beta_1 + beta_2*f phase = phase_PN*smoothing_minus(f,0.1*f_rd,0.005) + phase_PM*smoothing_plus(f,0.1*f_rd,0.005)*smoothing_minus(f,f_rd,0.005) + phase_RD*smoothing_plus(f,f_rd,0.005) From aa728e9c1b067eb112b7e09f5d9cc4e0ea70b8b2 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 20 Dec 2021 12:50:13 -0500 Subject: [PATCH 070/300] Update NFRandomWalk.py --- example/NFRandomWalk.py | 29 ++++++------ example/numpyro/NeuTra_HMC.py | 89 ----------------------------------- 2 files changed, 15 insertions(+), 103 deletions(-) delete mode 100644 example/numpyro/NeuTra_HMC.py diff --git a/example/NFRandomWalk.py b/example/NFRandomWalk.py index dc387c8d..6a05a2fd 100644 --- a/example/NFRandomWalk.py +++ b/example/NFRandomWalk.py @@ -26,8 +26,8 @@ true_m1 = 30. -true_m2 = 30. -true_ld = 1000. +true_m2 = 20. +true_ld = 300. true_phase = 0. true_gt = 0. @@ -58,8 +58,8 @@ psd_frequency = psd_frequency[jnp.isfinite(psd)] psd = psd[jnp.isfinite(psd)] -#waveform = IMRPhenomC(psd_frequency, injection_parameters) -waveform = TaylorF2(psd_frequency, injection_parameters) +waveform = IMRPhenomC(psd_frequency, injection_parameters) +#waveform = TaylorF2(psd_frequency, injection_parameters) H1, H1_vertex = get_H1() L1, L1_vertex = get_L1() strain_H1 = get_detector_response(psd_frequency, waveform, injection_parameters, H1, H1_vertex) @@ -70,8 +70,8 @@ @jit def single_detector_likelihood(params, data, data_f, PSD, detector, detector_vertex): -# waveform = IMRPhenomC(data_f, params) - waveform = TaylorF2(data_f, params) + waveform = IMRPhenomC(data_f, params) +# waveform = TaylorF2(data_f, params) waveform = get_detector_response(data_f, waveform, params, detector, detector_vertex) match_filter_SNR = inner_product(waveform, data, data_f, PSD) optimal_SNR = inner_product(waveform, waveform, data_f, PSD) @@ -140,9 +140,9 @@ def sample_nf(model, param, rng_key,n_sample): return rng_key,samples n_dim = 9 -n_samples = 1000 +n_samples = 100000 nf_samples = 100 -n_chains = 200 +n_chains = 20 learning_rate = 0.01 momentum = 0.9 num_epochs = 300 @@ -174,7 +174,6 @@ def sample_nf(model, param, rng_key,n_sample): def sampling_loop(rng_keys_nf, rng_keys_mcmc, model, state, initial_position): rng_keys_mcmc, positions, log_prob = run_mcmc(rng_keys_mcmc, n_samples, likelihood, initial_position) flat_chain = positions.reshape(-1,n_dim) - rng_keys_nf, state = train_flow(rng_key_nf, model, state, flat_chain) rng_keys_nf, nf_chain, log_prob, log_prob_nf = nf_metropolis_sampler(rng_keys_nf, nf_samples, model, state.params , para_logp, positions[:,-1]) positions = jnp.concatenate((positions,nf_chain),axis=1) @@ -182,11 +181,13 @@ def sampling_loop(rng_keys_nf, rng_keys_mcmc, model, state, initial_position): last_step = initial_position chains = [] -for i in range(5): - rng_keys_nf, rng_keys_mcmc, state, positions = sampling_loop(rng_keys_nf, rng_keys_mcmc, model, state, last_step) - last_step = positions[:,-1].T - # rng_keys_mcmc, positions, log_prob = run_mcmc(rng_keys_mcmc, n_samples, likelihood, initial_position) - # last_step = last_step.T +for i in range(15): + # rng_keys_nf, rng_keys_mcmc, state, positions = sampling_loop(rng_keys_nf, rng_keys_mcmc, model, state, last_step) + # last_step = positions[:,-1].T + rng_keys_mcmc, positions, log_prob = run_mcmc(rng_keys_mcmc, n_samples, likelihood, initial_position) + last_step = last_step.T + # if i%5 == 0: + # rng_keys_nf, state = train_flow(rng_key_nf, model, state, positions.reshape(-1,n_dim)) chains.append(positions) chains = np.concatenate(chains,axis=1) diff --git a/example/numpyro/NeuTra_HMC.py b/example/numpyro/NeuTra_HMC.py deleted file mode 100644 index 0ec56dec..00000000 --- a/example/numpyro/NeuTra_HMC.py +++ /dev/null @@ -1,89 +0,0 @@ -import numpy as np -import bilby -import jax -import jax.numpy as jnp -import time - -from jax.config import config -config.update("jax_enable_x64", True) - -from jaxgw.gw.likelihood.detector_projection import construct_arm, detector_tensor, antenna_response, get_detector_response - -from jaxgw.gw.likelihood.utils import inner_product -from jaxgw.gw.likelihood.detector_preset import get_H1, get_L1 -from jaxgw.gw.waveform.TaylorF2 import TaylorF2 -from jaxgw.gw.waveform.IMRPhenomC import IMRPhenomC -from jax import random, grad, jit, vmap, jacfwd, jacrev, value_and_grad, pmap - -import numpyro -from numpyro import optim -from numpyro.diagnostics import print_summary -import numpyro.distributions as dist -from numpyro.distributions import constraints -from numpyro.infer import MCMC, NUTS, SVI, Trace_ELBO -from numpyro.infer.autoguide import AutoBNAFNormal -from numpyro.infer.reparam import NeuTraReparam - -true_m1 = 15. -true_m2 = 5. -true_ld = 600. -true_phase = 0. -true_gt = 0. - -injection_parameters = dict( - mass_1=true_m1, mass_2=true_m2, spin_1=0.0, spin_2=0.0, luminosity_distance=true_ld, theta_jn=0.4, psi=2.659, - phase_c=true_phase, t_c=true_gt, ra=1.375, dec=-1.2108) - - -#guess_parameters = dict(m1=true_m1, m2=true_m2) - -guess_parameters = dict( - mass_1=true_m1, mass_2=true_m2, luminosity_distance=true_ld, theta_jn=0.4, psi=2.659, - phase_c=true_phase, t_c=true_gt, ra=1.375, dec=-1.2108) - - - -# Set up interferometers. In this case we'll use two interferometers -# (LIGO-Hanford (H1), LIGO-Livingston (L1). These default to their design -# sensitivity -ifos = bilby.gw.detector.InterferometerList(['H1']) -ifos.set_strain_data_from_power_spectral_densities( - sampling_frequency=2048, duration=1, - start_time=- 3) - -psd = ifos[0].power_spectral_density_array -psd_frequency = ifos[0].frequency_array - -psd_frequency = psd_frequency[jnp.isfinite(psd)] -psd = psd[jnp.isfinite(psd)] - -waveform = IMRPhenomC(psd_frequency, injection_parameters) -H1, H1_vertex = get_H1() -L1, L1_vertex = get_L1() -strain_H1 = get_detector_response(psd_frequency, waveform, injection_parameters, H1, H1_vertex) -strain_L1 = get_detector_response(psd_frequency, waveform, injection_parameters, L1, L1_vertex) - -print('SNR of the event in H1: '+str(np.sqrt(inner_product(strain_H1,strain_H1,psd_frequency,psd)))) -print('SNR of the event in L1: '+str(np.sqrt(inner_product(strain_L1,strain_L1,psd_frequency,psd)))) - -@jit -def single_detector_likelihood(params, data, data_f, PSD, detector, detector_vertex): - waveform = IMRPhenomC(data_f, params) -# waveform = TaylorF2(data_f, params) - waveform = get_detector_response(data_f, waveform, params, detector, detector_vertex) - match_filter_SNR = inner_product(waveform, data, data_f, PSD) - optimal_SNR = inner_product(waveform, waveform, data_f, PSD) - return (-2*match_filter_SNR + optimal_SNR)/2#, match_filter_SNR, optimal_SNR - -#@jit -#def logprob_wrap(m1, m2): -# params = dict(mass_1=m1, mass_2=m2, spin_1=0, spin_2=0, luminosity_distance=true_ld, phase_c=true_phase, t_c=true_gt, theta_jn=0.4, psi=2.659, ra=1.375, dec=-1.2108) -# return single_detector_likelihood(params, strain_H1, psd_frequency, psd, H1, H1_vertex)+single_detector_likelihood(params, strain_L1, psd_frequency, psd, L1, L1_vertex) -# -@jit -def logprob_wrap(mass_1, mass_2, luminosity_distance, phase_c, t_c, theta_jn, psi, ra, dec): - params = dict(mass_1=mass_1, mass_2=mass_2, spin_1=0, spin_2=0, luminosity_distance=luminosity_distance, phase_c=phase_c, t_c=t_c, theta_jn=theta_jn, psi=psi, ra=ra, dec=dec) - return single_detector_likelihood(params, strain_H1, psd_frequency, psd, H1, H1_vertex)+single_detector_likelihood(params, strain_L1, psd_frequency, psd, L1, L1_vertex) - -log_prob = lambda x: logprob_wrap(**x) -log_prob = jit(log_prob) From 9c2fdd117aaaaadb18d52e394ea535e3e9dea0ea Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 20 Dec 2021 13:09:33 -0500 Subject: [PATCH 071/300] Relic commit in test --- test/optimize_single_event_likelihood.py | 137 +++++++++++++++++++++++ test/test_IMRPhenomC.py | 23 ++++ 2 files changed, 160 insertions(+) create mode 100644 test/optimize_single_event_likelihood.py create mode 100644 test/test_IMRPhenomC.py diff --git a/test/optimize_single_event_likelihood.py b/test/optimize_single_event_likelihood.py new file mode 100644 index 00000000..767c0dda --- /dev/null +++ b/test/optimize_single_event_likelihood.py @@ -0,0 +1,137 @@ +import numpy as np +import bilby +import jax.numpy as jnp + +from jax.config import config +from jax import grad, jit +config.update("jax_enable_x64", True) + +# Set the duration and sampling frequency of the data segment that we're +# going to inject the signal into +duration = 4. +sampling_frequency = 2048. +minimum_frequency = 20 + +# Specify the output directory and the name of the simulation. +outdir = 'outdir' +label = 'fast_tutorial' +bilby.core.utils.setup_logger(outdir=outdir, label=label) + +# Set up a random seed for result reproducibility. This is optional! +np.random.seed(88170235) + +# We are going to inject a binary black hole waveform. We first establish a +# dictionary of parameters that includes all of the different waveform +# parameters, including masses of the two black holes (mass_1, mass_2), +# spins of both black holes (a, tilt, phi), etc. +injection_parameters = dict( + mass_1=36., mass_2=29., a_1=0., a_2=0., tilt_1=0., tilt_2=0., + phi_12=0., phi_jl=0., luminosity_distance=40., theta_jn=0.4, psi=2.659, + phase=1.3, geocent_time=1126259642.413, ra=1.375, dec=-1.2108) + +# Fixed arguments passed into the source model +waveform_arguments = dict(waveform_approximant='IMRPhenomC', + reference_frequency=50., + minimum_frequency=minimum_frequency) + +# Create the waveform_generator using a LAL BinaryBlackHole source function +waveform_generator = bilby.gw.WaveformGenerator( + duration=duration, sampling_frequency=sampling_frequency, + frequency_domain_source_model=bilby.gw.source.lal_binary_black_hole, + parameter_conversion=bilby.gw.conversion.convert_to_lal_binary_black_hole_parameters, + waveform_arguments=waveform_arguments) + +# Set up interferometers. In this case we'll use two interferometers +# (LIGO-Hanford (H1), LIGO-Livingston (L1). These default to their design +# sensitivity +ifos = bilby.gw.detector.InterferometerList(['H1']) +ifos.set_strain_data_from_power_spectral_densities( + sampling_frequency=sampling_frequency, duration=duration, + start_time=injection_parameters['geocent_time'] - 3) +ifos.inject_signal(waveform_generator=waveform_generator, + parameters=injection_parameters) + +############################################## +# Jax section +############################################## + + +waveform = waveform_generator.frequency_domain_strain() +waveform_frequency = waveform_generator.frequency_array + +psd = ifos[0].power_spectral_density_array +psd_frequency = ifos[0].frequency_array + +waveform_frequency = waveform_frequency[jnp.isfinite(psd)] +psd_frequency = psd_frequency[jnp.isfinite(psd)] +psd = psd[jnp.isfinite(psd)] + +from jaxgw.likelihood.detector_projection import construct_arm, detector_tensor, antenna_response, get_detector_response +from jaxgw.likelihood.utils import inner_product +from jaxgw.waveform.TaylorF2 import TaylorF2 +from jaxgw.waveform.IMRPhenomB import IMRPhenomB, getPhenomCoef, Lorentzian +from jaxgw.waveform.IMRPhenomC import IMRPhenomC +from jax.experimental.optimizers import adam,sgd +from jax import random, grad, jit, vmap, jacfwd, jacrev, value_and_grad + + +waveform = TaylorF2(waveform_frequency, injection_parameters) +H1_lat = 46 + 27. / 60 + 18.528 / 3600 +H1_long = -(119 + 24. / 60 + 27.5657 / 3600) +H1_xarm_azimuth = 125.9994 +H1_yarm_azimuth = 215.9994 +H1_xarm_tilt = -6.195e-4 +H1_yarm_tilt = 1.25e-5 + +H1_arm1 = construct_arm(H1_long, H1_lat, H1_xarm_tilt, H1_xarm_azimuth) +H1_arm2 = construct_arm(H1_long, H1_lat, H1_yarm_tilt, H1_yarm_azimuth) + +H1 = detector_tensor(H1_arm1, H1_arm2) + +psd_interp = jnp.interp(waveform_frequency, psd_frequency, psd) +strain = waveform['plus']#get_detector_response(waveform,injection_parameters,H1).T[0] + +@jit +def jax_likelihood(params, data, data_f, PSD): + waveform = TaylorF2(data_f, params)['plus'] +# waveform = get_detector_response(waveform, params, H1).T[0] + match_filter_SNR = inner_product(waveform, data, data_f, PSD) + optimal_SNR = inner_product(waveform, waveform, data_f, PSD) + return -(2*match_filter_SNR - optimal_SNR)/2 + +def jax_posterior(params,data,data_f,PSD): + if params['mass_1'] < 0: + params['mass_1'] = 0 + if params['mass_2'] < 0: + params['mass_2'] = 0 + if (params['a_1'] < 0): + params['a_1'] = 0 + if (params['a_2'] < 0): + params['a_2'] = 0 + return jax_likelihood(params,data,data_f,PSD) + +guess_parameters = dict( + mass_1=36., mass_2=29.9, a_1=0., a_2=0., luminosity_distance=40., theta_jn=0.4, psi=2.659, + phase=1.3, geocent_time=1126259642.413, ra=1.375, dec=-1.2108) + +print("True likelihood"+str(jax_likelihood(injection_parameters,strain, waveform_frequency,psd_interp))) +print("Guess likelihood"+str(jax_likelihood(guess_parameters,strain, waveform_frequency,psd_interp))) + + +learning_rate = 1e-4 +opt_init, opt_update, get_params = adam(learning_rate) +opt_state = opt_init(guess_parameters) + +def step(step, opt_state): + params = get_params(opt_state) + value, grads = value_and_grad(jax_likelihood,argnums=(0))(params, strain, waveform_frequency, psd_interp) + opt_state = opt_update(step, grads, opt_state) + return value, opt_state + +for i in range(10000): + value, opt_state = step(i, opt_state) + if jnp.isnan(value): + break + if i%10 == 0: + print(value,get_params(opt_state)) + diff --git a/test/test_IMRPhenomC.py b/test/test_IMRPhenomC.py new file mode 100644 index 00000000..dc42d642 --- /dev/null +++ b/test/test_IMRPhenomC.py @@ -0,0 +1,23 @@ +from lal import MSUN_SI, PC_SI, MTSUN_SI +import lalsimulation as lalsim +import numpy as np + +from jaxgw.gw.waveform.IMRPhenomC import IMRPhenomC +from jaxgw.gw.waveform.TaylorF2 import TaylorF2 + +mass_1 = 30. +mass_2 = 30. +luminosity_distance = 410. +f0 = 20. +max_f = 2048 +delta_f = 1./8 +spin = 0. + +injection_parameters = dict( + mass_1=mass_1, mass_2=mass_2, spin_1=0.0, spin_2=0.0, luminosity_distance=luminosity_distance, phase_c=0, t_c=0, theta_jn=0.4, psi=2.659,) + + +waveform1 = lalsim.SimIMRPhenomCGenerateFD(0., delta_f, mass_1*MSUN_SI, mass_2* MSUN_SI, 0., f0, max_f,luminosity_distance* 1e6*PC_SI,{}) +frequency = waveform1.f0 + np.arange(len(waveform1.data.data)) * waveform1.deltaF +waveform2 = IMRPhenomC(frequency,injection_parameters) +waveform3 = TaylorF2(frequency,injection_parameters) \ No newline at end of file From 4055301dacbc6679d2992ddb975a8e6d374c65f9 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 20 Dec 2021 13:10:17 -0500 Subject: [PATCH 072/300] Update git ignore --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 7aade577..ca556eb9 100644 --- a/.gitignore +++ b/.gitignore @@ -130,3 +130,4 @@ dmypy.json .vscode/launch.json . data +.vscode/settings.json From fdde41d9fee4b6bf604930c3ae0058dbf9d8a935 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 20 Dec 2021 13:14:31 -0500 Subject: [PATCH 073/300] Bug fix in utils --- jaxgw/gw/likelihood/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/jaxgw/gw/likelihood/utils.py b/jaxgw/gw/likelihood/utils.py index 40a31a01..32badb4f 100644 --- a/jaxgw/gw/likelihood/utils.py +++ b/jaxgw/gw/likelihood/utils.py @@ -38,6 +38,6 @@ def Mq_to_m1m2(trans_M_tot,trans_q): def ra_dec_to_theta_phi(ra, dec, gmst): phi = ra - gmst - theta = np.pi / 2 - dec + theta = jnp.pi / 2 - dec return theta, phi From f8269e539f668325fb636d65b377c0f8187d1eb1 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 20 Dec 2021 15:04:07 -0500 Subject: [PATCH 074/300] update all example --- example/NFRandomWalk.py | 8 ++++---- example/Population_injection.py | 2 ++ example/blackjax/HMC_injection.py | 13 +++++++------ example/emcee_injection.py | 20 ++++++++++---------- example/toy_pop_example/PowerLawPlusPeak.py | 2 +- 5 files changed, 24 insertions(+), 21 deletions(-) diff --git a/example/NFRandomWalk.py b/example/NFRandomWalk.py index 6a05a2fd..e8845863 100644 --- a/example/NFRandomWalk.py +++ b/example/NFRandomWalk.py @@ -181,14 +181,14 @@ def sampling_loop(rng_keys_nf, rng_keys_mcmc, model, state, initial_position): last_step = initial_position chains = [] -for i in range(15): +#for i in range(15): # rng_keys_nf, rng_keys_mcmc, state, positions = sampling_loop(rng_keys_nf, rng_keys_mcmc, model, state, last_step) # last_step = positions[:,-1].T - rng_keys_mcmc, positions, log_prob = run_mcmc(rng_keys_mcmc, n_samples, likelihood, initial_position) - last_step = last_step.T +# rng_keys_mcmc, positions, log_prob = run_mcmc(rng_keys_mcmc, n_samples, likelihood, initial_position) +# last_step = last_step.T # if i%5 == 0: # rng_keys_nf, state = train_flow(rng_key_nf, model, state, positions.reshape(-1,n_dim)) - chains.append(positions) +# chains.append(positions) chains = np.concatenate(chains,axis=1) nf_samples = sample_nf(model, state.params, rng_keys_nf, 10000) diff --git a/example/Population_injection.py b/example/Population_injection.py index 8e2c3808..dfb32dac 100644 --- a/example/Population_injection.py +++ b/example/Population_injection.py @@ -148,6 +148,8 @@ def log_prob_scan(args,index): m1_sample = m1_sample[:N_sample] +m1_sample = jnp.append(m1_sample,jnp.ones(10)*100) + H1_data_array = vmap(gen_event,(0, None, None))(vmap(gen_params)(m1_sample), H1, H1_vertex) L1_data_array = vmap(gen_event,(0, None, None))(vmap(gen_params)(m1_sample), L1, L1_vertex) multi_event_gen_param = jit(vmap(gen_params)) diff --git a/example/blackjax/HMC_injection.py b/example/blackjax/HMC_injection.py index 5e56a4d8..bfa96c89 100644 --- a/example/blackjax/HMC_injection.py +++ b/example/blackjax/HMC_injection.py @@ -16,9 +16,9 @@ from jax import random, grad, jit, vmap, jacfwd, jacrev, value_and_grad, pmap -true_m1 = 15. +true_m1 = 5. true_m2 = 5. -true_ld = 600. +true_ld = 1000. true_phase = 0. true_gt = 0. @@ -30,7 +30,7 @@ #guess_parameters = dict(m1=true_m1, m2=true_m2) guess_parameters = dict( - mass_1=true_m1, mass_2=true_m2, luminosity_distance=true_ld, theta_jn=0.4, psi=2.659, + mass_1=true_m1*0.99, mass_2=true_m2*1.01, luminosity_distance=true_ld, theta_jn=0.4, psi=2.659, phase_c=true_phase, t_c=true_gt, ra=1.375, dec=-1.2108) @@ -50,6 +50,7 @@ psd = psd[jnp.isfinite(psd)] waveform = IMRPhenomC(psd_frequency, injection_parameters) +#waveform = TaylorF2(psd_frequency, injection_parameters) H1, H1_vertex = get_H1() L1, L1_vertex = get_L1() strain_H1 = get_detector_response(psd_frequency, waveform, injection_parameters, H1, H1_vertex) @@ -103,14 +104,14 @@ def logprob_wrap(mass_1, mass_2, luminosity_distance, phase_c, t_c, theta_jn, ps key, kernel_generator, initial_state, - 500, + 100, #is_mass_matrix_diagonal=False ) print("Finding inverse mass matrix takes: "+str(time.time()-time1)+" seconds") print("Stepsize: "+str(step_size)) print("Inverse mass matrix: "+str(inverse_mass_matrix)) -num_integration_steps = 20 +num_integration_steps = 5 hmc_kernel = hmc.kernel(log_prob, step_size, inverse_mass_matrix, num_integration_steps) hmc_kernel = jit(hmc_kernel) @@ -130,5 +131,5 @@ def one_step(state, rng_key): print("Start sampling") time1 = time.time() -states = inference_loop(subkey, hmc_kernel, initial_state, 10000) +states = inference_loop(subkey, hmc_kernel, initial_state, 1000) print("Sampling takes: "+str(time.time()-time1)+" seconds") diff --git a/example/emcee_injection.py b/example/emcee_injection.py index 235e6381..fb56c67c 100644 --- a/example/emcee_injection.py +++ b/example/emcee_injection.py @@ -40,7 +40,7 @@ # sensitivity ifos = bilby.gw.detector.InterferometerList(['H1']) ifos.set_strain_data_from_power_spectral_densities( - sampling_frequency=2048, duration=1, + sampling_frequency=2048, duration=30, start_time=- 3) psd = ifos[0].power_spectral_density_array @@ -81,12 +81,12 @@ def log_prob(params): ## BlackJax section ################################################################ -import emcee - -nwalkers = 32 -ndim = 9 -p0 = np.random.rand(nwalkers, ndim) + list(guess_parameters.values()) -sampler = emcee.EnsembleSampler(nwalkers, ndim, log_prob) -state = sampler.run_mcmc(p0, 100) -sampler.reset() -sampler.run_mcmc(state, 5000) +#import emcee +# +#nwalkers = 32 +#ndim = 9 +#p0 = np.random.rand(nwalkers, ndim) + list(guess_parameters.values()) +#sampler = emcee.EnsembleSampler(nwalkers, ndim, log_prob) +#state = sampler.run_mcmc(p0, 100) +#sampler.reset() +#sampler.run_mcmc(state, 5000) diff --git a/example/toy_pop_example/PowerLawPlusPeak.py b/example/toy_pop_example/PowerLawPlusPeak.py index 61d5687d..9530aef6 100644 --- a/example/toy_pop_example/PowerLawPlusPeak.py +++ b/example/toy_pop_example/PowerLawPlusPeak.py @@ -94,7 +94,7 @@ def population_likelihood_powerlaw_peak(point,params,obs_std,data): true_param['mixing'] = 0.3 N_sample = 1000 -obs_std = 0.1 +obs_std = 0.01 m1_sample = jnp.empty(0) From 9c7907de48c9bb5a9bd5bea92701e852cf3d5fd4 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 20 Dec 2021 15:04:43 -0500 Subject: [PATCH 075/300] Fix detector preset projection. It agrees with bilby and tested in test_likelihood_bilby.py now --- jaxgw/gw/likelihood/detector_preset.py | 10 +-- test/test_likelihood_bilby.py | 99 ++++++++++++++++++++++++++ 2 files changed, 104 insertions(+), 5 deletions(-) create mode 100644 test/test_likelihood_bilby.py diff --git a/jaxgw/gw/likelihood/detector_preset.py b/jaxgw/gw/likelihood/detector_preset.py index 359a78d9..9e920e49 100644 --- a/jaxgw/gw/likelihood/detector_preset.py +++ b/jaxgw/gw/likelihood/detector_preset.py @@ -24,8 +24,8 @@ def get_H1(): H1_yarm_tilt = 1.25e-5 H1_elevation = 142.554 - H1_arm1 = construct_arm(H1_long, H1_lat, H1_xarm_tilt, H1_xarm_azimuth) - H1_arm2 = construct_arm(H1_long, H1_lat, H1_yarm_tilt, H1_yarm_azimuth) + H1_arm1 = construct_arm(H1_lat, H1_long, H1_xarm_tilt, H1_xarm_azimuth) + H1_arm2 = construct_arm(H1_lat, H1_long, H1_yarm_tilt, H1_yarm_azimuth) H1_vertex = get_vertex_position_geocentric(H1_lat, H1_long, H1_elevation) @@ -43,7 +43,7 @@ def get_L1(): The vertex position for L1. """ - L1_lat = 30 + 33. / 60 + 46.4196 / 3600 * degree_to_radian + L1_lat = (30 + 33. / 60 + 46.4196 / 3600) * degree_to_radian L1_long = -(90 + 46. / 60 + 27.2654 / 3600) * degree_to_radian L1_xarm_azimuth = 197.7165 * degree_to_radian L1_yarm_azimuth = 287.7165 * degree_to_radian @@ -51,8 +51,8 @@ def get_L1(): L1_yarm_tilt = 0 L1_elevation = -6.574 - L1_arm1 = construct_arm(L1_long, L1_lat, L1_xarm_tilt, L1_xarm_azimuth) - L1_arm2 = construct_arm(L1_long, L1_lat, L1_yarm_tilt, L1_yarm_azimuth) + L1_arm1 = construct_arm(L1_lat, L1_long, L1_xarm_tilt, L1_xarm_azimuth) + L1_arm2 = construct_arm(L1_lat, L1_long, L1_yarm_tilt, L1_yarm_azimuth) L1_vertex = get_vertex_position_geocentric(L1_lat, L1_long, L1_elevation) diff --git a/test/test_likelihood_bilby.py b/test/test_likelihood_bilby.py new file mode 100644 index 00000000..cb892a9d --- /dev/null +++ b/test/test_likelihood_bilby.py @@ -0,0 +1,99 @@ +import bilby +from gwpy.timeseries import TimeSeries +from bilby.gw.utils import greenwich_mean_sidereal_time + +logger = bilby.core.utils.logger +outdir = 'outdir' +label = 'GW150914' + +# Data set up +trigger_time = 1126259462 + +roll_off = 0.4 # Roll off duration of tukey window in seconds, default is 0.4s +duration = 4 # Analysis segment duration +post_trigger_duration = 2 # Time between trigger time and end of segment +end_time = trigger_time + post_trigger_duration +start_time = end_time - duration + +psd_duration = 32 * duration +psd_start_time = start_time - psd_duration +psd_end_time = start_time + +# We now use gwpy to obtain analysis and psd data and create the ifo_list +ifo_list = bilby.gw.detector.InterferometerList([]) +for det in ["H1", "L1"]: + logger.info("Downloading analysis data for ifo {}".format(det)) + ifo = bilby.gw.detector.get_empty_interferometer(det) + data = TimeSeries.fetch_open_data(det, start_time, end_time) + ifo.strain_data.set_from_gwpy_timeseries(data) + + logger.info("Downloading psd data for ifo {}".format(det)) + psd_data = TimeSeries.fetch_open_data(det, psd_start_time, psd_end_time) + psd_alpha = 2 * roll_off / duration + psd = psd_data.psd( + fftlength=duration, + overlap=0, + window=("tukey", psd_alpha), + method="median" + ) + ifo.power_spectral_density = bilby.gw.detector.PowerSpectralDensity( + frequency_array=psd.frequencies.value, psd_array=psd.value) + ifo_list.append(ifo) + +logger.info("Saving data plots to {}".format(outdir)) +bilby.core.utils.check_directory_exists_and_if_not_mkdir(outdir) +ifo_list.plot_data(outdir=outdir, label=label) + +# We now define the prior. +# We have defined our prior distribution in a local file, GW150914.prior +# The prior is printed to the terminal at run-time. +# You can overwrite this using the syntax below in the file, +# or choose a fixed value by just providing a float value as the prior. +priors = bilby.gw.prior.BBHPriorDict(filename='GW150914.prior') + +# In this step we define a `waveform_generator`. This is the object which +# creates the frequency-domain strain. In this instance, we are using the +# `lal_binary_black_hole model` source model. We also pass other parameters: +# the waveform approximant and reference frequency and a parameter conversion +# which allows us to sample in chirp mass and ratio rather than component mass +waveform_generator = bilby.gw.WaveformGenerator( + frequency_domain_source_model=bilby.gw.source.lal_binary_black_hole, + parameter_conversion=bilby.gw.conversion.convert_to_lal_binary_black_hole_parameters, + waveform_arguments={'waveform_approximant': 'IMRPhenomPv2', + 'reference_frequency': 50}) + +# In this step, we define the likelihood. Here we use the standard likelihood +# function, passing it the data and the waveform generator. +likelihood = bilby.gw.likelihood.GravitationalWaveTransient( + ifo_list, waveform_generator, priors=priors, time_marginalization=False, + phase_marginalization=False, distance_marginalization=False) + +priors['geocent_time'] = float(likelihood.interferometers.start_time) + +likelihood.parameters = priors.sample() +likelihood.parameters['t_c'] = greenwich_mean_sidereal_time(likelihood.parameters['geocent_time']) +waveform = likelihood.waveform_generator.frequency_domain_strain(likelihood.parameters) +frequency_array = ifo_list[0].frequency_array + +print("Likelihood value from bilby: "+str(likelihood.log_likelihood_ratio())) + +import numpy as np +import bilby +import jax +import jax.numpy as jnp + +from jax.config import config +from jaxgw.sampler.NF_proposal import nf_metropolis_kernel, nf_metropolis_sampler +config.update("jax_enable_x64", True) +from jaxgw.gw.likelihood.detector_projection import construct_arm, detector_tensor, antenna_response, get_detector_response +from jaxgw.gw.likelihood.utils import inner_product +from jaxgw.gw.likelihood.detector_preset import get_H1, get_L1 +from jaxgw.gw.waveform.TaylorF2 import TaylorF2 +from jaxgw.gw.waveform.IMRPhenomC import IMRPhenomC +from jax import random, grad, jit, vmap, jacfwd, jacrev, value_and_grad, pmap + +H1, H1_vertex = get_H1() +L1, L1_vertex = get_L1() +strain_H1 = get_detector_response(frequency_array, waveform, likelihood.parameters, H1, H1_vertex) +strain_L1 = get_detector_response(frequency_array, waveform, likelihood.parameters, L1, L1_vertex) + From acb583b53ab1178c89c2d23e4fce8a2cc17471c8 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 20 Dec 2021 15:58:12 -0500 Subject: [PATCH 076/300] Add SNR check with bilby --- test/test_likelihood_bilby.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/test/test_likelihood_bilby.py b/test/test_likelihood_bilby.py index cb892a9d..31586903 100644 --- a/test/test_likelihood_bilby.py +++ b/test/test_likelihood_bilby.py @@ -1,6 +1,9 @@ import bilby from gwpy.timeseries import TimeSeries from bilby.gw.utils import greenwich_mean_sidereal_time +import numpy as np + +np.random.seed(1) logger = bilby.core.utils.logger outdir = 'outdir' @@ -97,3 +100,7 @@ strain_H1 = get_detector_response(frequency_array, waveform, likelihood.parameters, H1, H1_vertex) strain_L1 = get_detector_response(frequency_array, waveform, likelihood.parameters, L1, L1_vertex) +jaxgw_H1_SNR = inner_product(ifo_list[0].strain_data.frequency_domain_strain, strain_H1, frequency_array, ifo_list[0].power_spectral_density_array) +bilby_H1_SNR = ifo_list[0].inner_product(strain_H1) + +print(jaxgw_H1_SNR, bilby_H1_SNR) From 5177a94d3c7c2362cd3d217b0c23058de5679187 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 27 Dec 2021 14:23:35 -0500 Subject: [PATCH 077/300] Update TaylorF2 to follow lalsuite implementation. --- jaxgw/gw/waveform/TaylorF2.py | 49 ++++++++++++++++++++++++----------- test/test_TaylorF2.py | 21 +++++++++++++++ 2 files changed, 55 insertions(+), 15 deletions(-) create mode 100644 test/test_TaylorF2.py diff --git a/jaxgw/gw/waveform/TaylorF2.py b/jaxgw/gw/waveform/TaylorF2.py index 0c0fdae0..dca0e21e 100644 --- a/jaxgw/gw/waveform/TaylorF2.py +++ b/jaxgw/gw/waveform/TaylorF2.py @@ -5,35 +5,54 @@ def TaylorF2(f,params): local_m1 = params['mass_1']*Msun local_m2 = params['mass_2']*Msun local_d = params['luminosity_distance']*Mpc - + local_spin1 = params['spin_1'] + local_spin2 = params['spin_2'] M_tot = local_m1+local_m2 eta = local_m1*local_m2/(local_m1+local_m2)**2 M_chirp = eta**(3./5)*M_tot + chi_eff = (local_spin1*local_m1 + local_spin2*local_m2)/M_tot PNcoef = (jnp.pi*M_tot*f)**(1./3) - amplitude = M_chirp**(5./6)/local_d - - PN0 = 1. - PN1 = (20./9) * (743./336 + 11./4*eta) * PNcoef**2 - PN1d5 = -16*jnp.pi*PNcoef**3 - PN2 = 10 * ((3058673./1016064)+ 5429./1008 *eta + 617./144 * eta**2) * PNcoef**4 - PN2d5 = jnp.pi*(38645./756-65./9*eta)*(1 + 3*jnp.log(6**(3./2)*jnp.pi*M_tot*f)) * PNcoef**5 -# PN3 = 11583231236531./4694215680 - 640./3 *jnp.pi**2 - 6868./21*(euler_gamma+jnp.log(4) + # Flux coefficients + FT_PN0 = 32.0 * eta*eta / 5.0 + # FT_PN1 = -(12.47/3.36 + 3.5/1.2 * eta) + # FT_PN1d5 = 4 * jnp.pi + # FT_PN2 = -(44.711/9.072 - 92.71/5.04 * eta - 6.5/1.8 * eta*eta) + # FT_PN2d5 = -(81.91/6.72 + 58.3/2.4 * eta) * jnp.pi + # FT_PN3 = (664.3739519/6.9854400 + 16.0/3.0 * jnp.pi*jnp.pi - 17.12/1.05 * euler_gamma - 17.12/1.05*jnp.log(4.) + (4.1/4.8 * jnp.pi*jnp.pi - 134.543/7.776) * eta - 94.403/3.024 * eta*eta - 7.75/3.24 * eta*eta*eta) + # FT_PN3log = -17.12/1.05 + # FT_PN3d5 = -(162.85/5.04 - 214.745/1.728 * eta - 193.385/3.024 * eta*eta) * jnp.pi + + # Energy coefficients + E_PN0 = 2. * -eta / 2.0 + # E_PN1 = 2. * -(0.75 + eta/12.0) + # E_PN2 = 3. * -(27.0/8.0 - 19.0/8.0 * eta + 1./24.0 * eta*eta) + # E_PN3 = 4. * -(67.5/6.4 - (344.45/5.76 - 20.5/9.6 * jnp.pi*jnp.pi) * eta + 15.5/9.6 * eta*eta + 3.5/518.4 * eta*eta*eta) - phase = 2*jnp.pi*f*params['t_c'] - params['phase_c'] - jnp.pi/4 + 3./(128*eta*PNcoef**5) * \ - (PN0+PN1+PN1d5+PN2+PN2d5) + -# phase = - jnp.pi/4 + 3./(128*eta*PNcoef**5) * \ -# (PN0+PN1+PN1d5)#+PN2+PN2d5) + amplitude = (-4. * local_m1 * local_m2 / local_d* jnp.sqrt(jnp.pi/12.))* jnp.sqrt(-(E_PN0*PNcoef)/(FT_PN0 * PNcoef**10)) * PNcoef + Ph_PN0 = 1. + Ph_PN1 = (20./9) * (743./336 + 11./4*eta) * PNcoef**2 + Ph_PN1d5 = -16*jnp.pi*PNcoef**3 + Ph_PN2 = (5.*(3058.673/7.056 + 5429./7.*eta+617.*eta*eta)/72.) * PNcoef**4 + Ph_PN2d5 = 5./9.*(772.9/8.4-13.*eta)*jnp.pi * PNcoef**5 + Ph_PN2d5_log = (5./3.*(772.9/8.4-13.*eta)*jnp.pi) * PNcoef**5 * jnp.log(PNcoef) + Ph_PN3 = (11583.231236531/4.694215680 - 640./3.*jnp.pi*jnp.pi - 684.8/2.1*euler_gamma + eta*(-15737.765635/3.048192 + 225.5/1.2*jnp.pi*jnp.pi) + eta*eta*76.055/1.728 - eta*eta*eta*127.825/1.296) * PNcoef**6 + Ph_PN3_log = -684.8/2.1 * jnp.log(PNcoef) * PNcoef**6 + Ph_PN3d5 = jnp.pi*(770.96675/2.54016 + 378.515/1.512*eta - 740.45/7.56*eta*eta) * PNcoef**7 + phase = 2*jnp.pi*f*params['t_c'] - params['phase_c'] - jnp.pi/4 + 3./(128*eta*PNcoef**5) * \ + (Ph_PN0+Ph_PN1+Ph_PN1d5+Ph_PN2+Ph_PN2d5+Ph_PN2d5_log+Ph_PN3+Ph_PN3_log+Ph_PN3d5) + - totalh = jnp.sqrt(5./96)/jnp.pi**(2./3)*amplitude*f**(-7./6)*jnp.exp(1j*phase) + totalh = amplitude * jnp.cos(phase) - amplitude * jnp.sin(phase) * 1j hp = totalh * (1/2*(1+jnp.cos(params['theta_jn'])**2)*jnp.cos(2*params['psi'])) hc = totalh * jnp.cos(params['theta_jn'])*jnp.sin(2*params['psi']) - return {'plus':hp,'cross':hc} + return totalh#{'plus':hp,'cross':hc} def flso(M): return (6**3./2*jnp.pi*M)**-1 diff --git a/test/test_TaylorF2.py b/test/test_TaylorF2.py new file mode 100644 index 00000000..7e1353dd --- /dev/null +++ b/test/test_TaylorF2.py @@ -0,0 +1,21 @@ +from lal import MSUN_SI, PC_SI, MTSUN_SI +import lalsimulation as lalsim +import numpy as np + +from jaxgw.gw.waveform.TaylorF2 import TaylorF2 + +mass_1 = 30. +mass_2 = 30. +luminosity_distance = 410. +f0 = 20. +max_f = 2048 +delta_f = 1./8 +spin = 0. + +injection_parameters = dict( + mass_1=mass_1, mass_2=mass_2, spin_1=0.0, spin_2=0.0, luminosity_distance=luminosity_distance, phase_c=0, t_c=0, theta_jn=0.4, psi=2.659,) + + +waveform1 = lalsim.SimInspiralTaylorF2(0., delta_f, mass_1*MSUN_SI, mass_2* MSUN_SI, 0., 0., f0, max_f, 0.,luminosity_distance* 1e6*PC_SI,{}) +frequency = waveform1.f0 + np.arange(len(waveform1.data.data)) * waveform1.deltaF +waveform3 = TaylorF2(frequency,injection_parameters) \ No newline at end of file From ecc3a945c1c433ec66d4fc2f22da9cc3a7a1b5c2 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 27 Dec 2021 14:59:21 -0500 Subject: [PATCH 078/300] Update TaylorF2 related scripts. Add reference frequency --- jaxgw/gw/waveform/TaylorF2.py | 36 ++++++++++++++++++++--------------- test/test_TaylorF2.py | 2 +- test/test_likelihood_bilby.py | 22 ++++++++++++++++++++- 3 files changed, 43 insertions(+), 17 deletions(-) diff --git a/jaxgw/gw/waveform/TaylorF2.py b/jaxgw/gw/waveform/TaylorF2.py index dca0e21e..650a5b8f 100644 --- a/jaxgw/gw/waveform/TaylorF2.py +++ b/jaxgw/gw/waveform/TaylorF2.py @@ -7,14 +7,16 @@ def TaylorF2(f,params): local_d = params['luminosity_distance']*Mpc local_spin1 = params['spin_1'] local_spin2 = params['spin_2'] + f_ref = params['f_ref'] M_tot = local_m1+local_m2 eta = local_m1*local_m2/(local_m1+local_m2)**2 M_chirp = eta**(3./5)*M_tot chi_eff = (local_spin1*local_m1 + local_spin2*local_m2)/M_tot PNcoef = (jnp.pi*M_tot*f)**(1./3) + PNcoef_ref = (jnp.pi*M_tot*f_ref)**(1./3) - # Flux coefficients +# Flux coefficients FT_PN0 = 32.0 * eta*eta / 5.0 # FT_PN1 = -(12.47/3.36 + 3.5/1.2 * eta) # FT_PN1d5 = 4 * jnp.pi @@ -24,29 +26,33 @@ def TaylorF2(f,params): # FT_PN3log = -17.12/1.05 # FT_PN3d5 = -(162.85/5.04 - 214.745/1.728 * eta - 193.385/3.024 * eta*eta) * jnp.pi - # Energy coefficients +# Energy coefficients E_PN0 = 2. * -eta / 2.0 # E_PN1 = 2. * -(0.75 + eta/12.0) # E_PN2 = 3. * -(27.0/8.0 - 19.0/8.0 * eta + 1./24.0 * eta*eta) # E_PN3 = 4. * -(67.5/6.4 - (344.45/5.76 - 20.5/9.6 * jnp.pi*jnp.pi) * eta + 15.5/9.6 * eta*eta + 3.5/518.4 * eta*eta*eta) - - amplitude = (-4. * local_m1 * local_m2 / local_d* jnp.sqrt(jnp.pi/12.))* jnp.sqrt(-(E_PN0*PNcoef)/(FT_PN0 * PNcoef**10)) * PNcoef +# Phase coefficients Ph_PN0 = 1. - Ph_PN1 = (20./9) * (743./336 + 11./4*eta) * PNcoef**2 - Ph_PN1d5 = -16*jnp.pi*PNcoef**3 - Ph_PN2 = (5.*(3058.673/7.056 + 5429./7.*eta+617.*eta*eta)/72.) * PNcoef**4 - Ph_PN2d5 = 5./9.*(772.9/8.4-13.*eta)*jnp.pi * PNcoef**5 - Ph_PN2d5_log = (5./3.*(772.9/8.4-13.*eta)*jnp.pi) * PNcoef**5 * jnp.log(PNcoef) - Ph_PN3 = (11583.231236531/4.694215680 - 640./3.*jnp.pi*jnp.pi - 684.8/2.1*euler_gamma + eta*(-15737.765635/3.048192 + 225.5/1.2*jnp.pi*jnp.pi) + eta*eta*76.055/1.728 - eta*eta*eta*127.825/1.296) * PNcoef**6 - Ph_PN3_log = -684.8/2.1 * jnp.log(PNcoef) * PNcoef**6 - Ph_PN3d5 = jnp.pi*(770.96675/2.54016 + 378.515/1.512*eta - 740.45/7.56*eta*eta) * PNcoef**7 - - phase = 2*jnp.pi*f*params['t_c'] - params['phase_c'] - jnp.pi/4 + 3./(128*eta*PNcoef**5) * \ - (Ph_PN0+Ph_PN1+Ph_PN1d5+Ph_PN2+Ph_PN2d5+Ph_PN2d5_log+Ph_PN3+Ph_PN3_log+Ph_PN3d5) + Ph_PN1 = (20./9) * (743./336 + 11./4*eta) + Ph_PN1d5 = -16*jnp.pi + Ph_PN2 = (5.*(3058.673/7.056 + 5429./7.*eta+617.*eta*eta)/72.) + Ph_PN2d5 = 5./9.*(772.9/8.4-13.*eta)*jnp.pi + Ph_PN2d5_log = (5./3.*(772.9/8.4-13.*eta)*jnp.pi) + Ph_PN3 = (11583.231236531/4.694215680 - 640./3.*jnp.pi*jnp.pi - 684.8/2.1*euler_gamma + eta*(-15737.765635/3.048192 + 225.5/1.2*jnp.pi*jnp.pi) + eta*eta*76.055/1.728 - eta*eta*eta*127.825/1.296) + Ph_PN3_log = -684.8/2.1 + Ph_PN3d5 = jnp.pi*(770.96675/2.54016 + 378.515/1.512*eta - 740.45/7.56*eta*eta) + + PN_phasing = 3./(128*eta*PNcoef**5) * \ + (Ph_PN0 + Ph_PN1 * PNcoef**2 + Ph_PN1d5 * PNcoef**3 + Ph_PN2 * PNcoef**4 + Ph_PN2d5 * PNcoef**5 + Ph_PN2d5_log * PNcoef**5 * jnp.log(PNcoef) + Ph_PN3 * PNcoef**6 + Ph_PN3_log * PNcoef**6 * jnp.log(PNcoef) + Ph_PN3d5 * PNcoef**7) + + PN_phasing_ref = 3./(128*eta*PNcoef_ref**5) * \ + (Ph_PN0 + Ph_PN1 * PNcoef_ref**2 + Ph_PN1d5 * PNcoef_ref**3 + Ph_PN2 * PNcoef_ref**4 + Ph_PN2d5 * PNcoef_ref**5 + Ph_PN2d5_log * PNcoef_ref**5 * jnp.log(PNcoef_ref) + Ph_PN3 * PNcoef_ref**6 + Ph_PN3_log * PNcoef_ref**6 * jnp.log(PNcoef_ref) + Ph_PN3d5 * PNcoef_ref**7) + + phase = 2*jnp.pi*f*params['t_c'] - params['phase_c'] - jnp.pi/4 + PN_phasing - PN_phasing_ref totalh = amplitude * jnp.cos(phase) - amplitude * jnp.sin(phase) * 1j hp = totalh * (1/2*(1+jnp.cos(params['theta_jn'])**2)*jnp.cos(2*params['psi'])) diff --git a/test/test_TaylorF2.py b/test/test_TaylorF2.py index 7e1353dd..03f48c67 100644 --- a/test/test_TaylorF2.py +++ b/test/test_TaylorF2.py @@ -13,7 +13,7 @@ spin = 0. injection_parameters = dict( - mass_1=mass_1, mass_2=mass_2, spin_1=0.0, spin_2=0.0, luminosity_distance=luminosity_distance, phase_c=0, t_c=0, theta_jn=0.4, psi=2.659,) + mass_1=mass_1, mass_2=mass_2, spin_1=0.0, spin_2=0.0, luminosity_distance=luminosity_distance, phase_c=0, t_c=0, theta_jn=0.4, psi=2.659,f_ref = 0.00001) waveform1 = lalsim.SimInspiralTaylorF2(0., delta_f, mass_1*MSUN_SI, mass_2* MSUN_SI, 0., 0., f0, max_f, 0.,luminosity_distance* 1e6*PC_SI,{}) diff --git a/test/test_likelihood_bilby.py b/test/test_likelihood_bilby.py index 31586903..f9af9f35 100644 --- a/test/test_likelihood_bilby.py +++ b/test/test_likelihood_bilby.py @@ -62,7 +62,7 @@ waveform_generator = bilby.gw.WaveformGenerator( frequency_domain_source_model=bilby.gw.source.lal_binary_black_hole, parameter_conversion=bilby.gw.conversion.convert_to_lal_binary_black_hole_parameters, - waveform_arguments={'waveform_approximant': 'IMRPhenomPv2', + waveform_arguments={'waveform_approximant': 'IMRPhenomC', 'reference_frequency': 50}) # In this step, we define the likelihood. Here we use the standard likelihood @@ -75,6 +75,26 @@ likelihood.parameters = priors.sample() likelihood.parameters['t_c'] = greenwich_mean_sidereal_time(likelihood.parameters['geocent_time']) +likelihood.parameters['a_1'] = 0 +likelihood.parameters['a_2'] = 0 +likelihood.parameters['tilt_1'] = 0 +likelihood.parameters['tilt_2'] = 0 +likelihood.parameters['phi_12'] = 0 +likelihood.parameters['phi_jl'] = 0 +params = {} +q = likelihood.parameters['mass_ratio'] +params['mass_1'] = likelihood.parameters['chirp_mass']/((q/(1+q)**2)**(3./5)*(1+q)) +params['mass_2'] = q*params['mass_1'] +params['spin_1'] = likelihood.parameters['a_1'] +params['spin_2'] = likelihood.parameters['a_2'] +params['luminosity_distance'] = likelihood.parameters['luminosity_distance'] +params['theta_jn'] = likelihood.parameters['theta_jn'] +params['psi'] = likelihood.parameters['psi'] +params['ra'] = likelihood.parameters['ra'] +params['dec'] = likelihood.parameters['dec'] +params['phase_c'] = likelihood.parameters['phase'] +params['t_c'] = likelihood.parameters['t_c'] + waveform = likelihood.waveform_generator.frequency_domain_strain(likelihood.parameters) frequency_array = ifo_list[0].frequency_array From ac333dc1250317a5b741c591de71975f36c64dc1 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 27 Dec 2021 15:02:33 -0500 Subject: [PATCH 079/300] Undo debug options --- jaxgw/gw/waveform/TaylorF2.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/jaxgw/gw/waveform/TaylorF2.py b/jaxgw/gw/waveform/TaylorF2.py index 650a5b8f..b00facc5 100644 --- a/jaxgw/gw/waveform/TaylorF2.py +++ b/jaxgw/gw/waveform/TaylorF2.py @@ -48,7 +48,7 @@ def TaylorF2(f,params): PN_phasing = 3./(128*eta*PNcoef**5) * \ (Ph_PN0 + Ph_PN1 * PNcoef**2 + Ph_PN1d5 * PNcoef**3 + Ph_PN2 * PNcoef**4 + Ph_PN2d5 * PNcoef**5 + Ph_PN2d5_log * PNcoef**5 * jnp.log(PNcoef) + Ph_PN3 * PNcoef**6 + Ph_PN3_log * PNcoef**6 * jnp.log(PNcoef) + Ph_PN3d5 * PNcoef**7) - + PN_phasing_ref = 3./(128*eta*PNcoef_ref**5) * \ (Ph_PN0 + Ph_PN1 * PNcoef_ref**2 + Ph_PN1d5 * PNcoef_ref**3 + Ph_PN2 * PNcoef_ref**4 + Ph_PN2d5 * PNcoef_ref**5 + Ph_PN2d5_log * PNcoef_ref**5 * jnp.log(PNcoef_ref) + Ph_PN3 * PNcoef_ref**6 + Ph_PN3_log * PNcoef_ref**6 * jnp.log(PNcoef_ref) + Ph_PN3d5 * PNcoef_ref**7) @@ -58,7 +58,7 @@ def TaylorF2(f,params): hp = totalh * (1/2*(1+jnp.cos(params['theta_jn'])**2)*jnp.cos(2*params['psi'])) hc = totalh * jnp.cos(params['theta_jn'])*jnp.sin(2*params['psi']) - return totalh#{'plus':hp,'cross':hc} + return {'plus':hp,'cross':hc} def flso(M): return (6**3./2*jnp.pi*M)**-1 From 962fc037019abb26f80460bbc453fde641bd8fff Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Thu, 6 Jan 2022 15:41:31 -0500 Subject: [PATCH 080/300] Fix sampling bug --- example/NFRandomWalk.py | 96 ++++++++++++++++++++++++----------------- 1 file changed, 57 insertions(+), 39 deletions(-) diff --git a/example/NFRandomWalk.py b/example/NFRandomWalk.py index e8845863..2fee096f 100644 --- a/example/NFRandomWalk.py +++ b/example/NFRandomWalk.py @@ -25,22 +25,22 @@ import optax # Optimizers -true_m1 = 30. -true_m2 = 20. -true_ld = 300. +true_m1 = 10. +true_m2 = 5 +true_ld = 500. true_phase = 0. true_gt = 0. injection_parameters = dict( mass_1=true_m1, mass_2=true_m2, spin_1=0.0, spin_2=0.0, luminosity_distance=true_ld, theta_jn=0.4, psi=2.659, - phase_c=true_phase, t_c=true_gt, ra=1.375, dec=-1.2108) + phase_c=true_phase, t_c=true_gt, ra=1.375, dec=-1.2108, f_ref=50) #guess_parameters = dict(m1=true_m1, m2=true_m2) guess_parameters = dict( - mass_1=true_m1*0.99, mass_2=true_m2*1.01, luminosity_distance=true_ld, theta_jn=0.4, psi=2.659, - phase_c=true_phase, t_c=true_gt, ra=1.375, dec=-1.2108) + mass_1=true_m1*0.99, mass_2=true_m2*1.01, theta_jn=0.4, psi=2.659, + phase_c=true_phase, ra=1.375, dec=-1.2108) @@ -49,7 +49,7 @@ # sensitivity ifos = bilby.gw.detector.InterferometerList(['H1']) ifos.set_strain_data_from_power_spectral_densities( - sampling_frequency=2048, duration=1, + sampling_frequency=2048, duration=4, start_time=- 3) psd = ifos[0].power_spectral_density_array @@ -58,8 +58,8 @@ psd_frequency = psd_frequency[jnp.isfinite(psd)] psd = psd[jnp.isfinite(psd)] -waveform = IMRPhenomC(psd_frequency, injection_parameters) -#waveform = TaylorF2(psd_frequency, injection_parameters) +#waveform = IMRPhenomC(psd_frequency, injection_parameters) +waveform = TaylorF2(psd_frequency, injection_parameters) H1, H1_vertex = get_H1() L1, L1_vertex = get_L1() strain_H1 = get_detector_response(psd_frequency, waveform, injection_parameters, H1, H1_vertex) @@ -70,21 +70,16 @@ @jit def single_detector_likelihood(params, data, data_f, PSD, detector, detector_vertex): - waveform = IMRPhenomC(data_f, params) -# waveform = TaylorF2(data_f, params) +# waveform = IMRPhenomC(data_f, params) + waveform = TaylorF2(data_f, params) waveform = get_detector_response(data_f, waveform, params, detector, detector_vertex) match_filter_SNR = inner_product(waveform, data, data_f, PSD) optimal_SNR = inner_product(waveform, waveform, data_f, PSD) - return -(-2*match_filter_SNR + optimal_SNR)/2#, match_filter_SNR, optimal_SNR + return (-2*match_filter_SNR + optimal_SNR)/2#, match_filter_SNR, optimal_SNR -#@jit -#def logprob_wrap(m1, m2): -# params = dict(mass_1=m1, mass_2=m2, spin_1=0, spin_2=0, luminosity_distance=true_ld, phase_c=true_phase, t_c=true_gt, theta_jn=0.4, psi=2.659, ra=1.375, dec=-1.2108) -# return single_detector_likelihood(params, strain_H1, psd_frequency, psd, H1, H1_vertex)+single_detector_likelihood(params, strain_L1, psd_frequency, psd, L1, L1_vertex) -# @jit -def logprob_wrap(mass_1, mass_2, luminosity_distance, phase_c, t_c, theta_jn, psi, ra, dec): - params = dict(mass_1=mass_1, mass_2=mass_2, spin_1=0, spin_2=0, luminosity_distance=true_ld, phase_c=phase_c, t_c=t_c, theta_jn=theta_jn, psi=psi, ra=ra, dec=dec) +def logprob_wrap(mass_1, mass_2, phase_c, theta_jn, psi, ra, dec): + params = dict(mass_1=mass_1, mass_2=mass_2, spin_1=0, spin_2=0, luminosity_distance=true_ld, phase_c=phase_c, t_c=true_gt, theta_jn=theta_jn, psi=psi, ra=ra, dec=dec, f_ref=50) # params = dict(mass_1=mass_1, mass_2=mass_2, spin_1=0, spin_2=0, luminosity_distance=true_ld, theta_jn=0.4, psi=2.659, phase_c=true_phase, t_c=true_gt, ra=1.375, dec=-1.2108) return single_detector_likelihood(params, strain_H1, psd_frequency, psd, H1, H1_vertex)+single_detector_likelihood(params, strain_L1, psd_frequency, psd, L1, L1_vertex) @@ -136,29 +131,47 @@ def train_epoch(state, train_ds, batch_size, epoch, rng): def sample_nf(model, param, rng_key,n_sample): rng_key, subkey = random.split(rng_key) samples = model.apply({'params': param}, subkey, n_sample,param, method=model.sample) - samples = jnp.flip(samples[0],axis=1) return rng_key,samples -n_dim = 9 -n_samples = 100000 +n_dim = 7 +n_samples = 100 nf_samples = 100 -n_chains = 20 +n_chains = 100 learning_rate = 0.01 momentum = 0.9 num_epochs = 300 batch_size = 10000 +look_back_epoch = 10 +train_epoch = 25 +nf_sample_epoch = 25 +total_epoch = 1000 precompiled = False print("Preparing RNG keys") rng_key = jax.random.PRNGKey(42) -rng_key_mcmc, rng_key_nf = jax.random.split(rng_key,2) +rng_key_ic, rng_key_mcmc, rng_key_nf = jax.random.split(rng_key,3) rng_keys_mcmc = jax.random.split(rng_key_mcmc, n_chains) # (nchains,) rng_keys_nf, init_rng_keys_nf = jax.random.split(rng_key_nf,2) print("Initializing MCMC model and normalizing flow model.") -initial_position = (jnp.zeros((9, n_chains)).T + jnp.array(list(guess_parameters.values()))).T #(n_dim, n_chains) +# prior_range = [] +# prior_range.append([1.0,15.0]) +# prior_range.append([1.0,15.0]) +# prior_range.append([np.log10(0.1),np.log10(3000.0)]) +# prior_range.append([0.,2*jnp.pi]) +# prior_range.append([0.,jnp.pi]) +# prior_range.append([0.,jnp.pi]) +# prior_range.append([0.,2*jnp.pi]) +# prior_range.append([0.,jnp.pi]) +# prior_range = jnp.array(prior_range) + +# initial_position = jax.random.uniform(rng_key_ic,(n_chains,n_dim)) #(n_dim, n_chains) +# initial_position = (initial_position*(prior_range[:,1]-prior_range[:,0])+prior_range[:,0]).T + +initial_position = (jax.random.normal(rng_key_ic,(n_chains,n_dim))*0.05 + jnp.array(list(guess_parameters.values()))).T #(n_dim, n_chains) + #model = MaskedAutoregressiveFlow(n_dim,64,4) model = RealNVP(10,n_dim,64, 1) @@ -171,24 +184,29 @@ def sample_nf(model, param, rng_key,n_sample): state = train_state.TrainState.create(apply_fn=model.apply, params=params, tx=tx) -def sampling_loop(rng_keys_nf, rng_keys_mcmc, model, state, initial_position): - rng_keys_mcmc, positions, log_prob = run_mcmc(rng_keys_mcmc, n_samples, likelihood, initial_position) - flat_chain = positions.reshape(-1,n_dim) - rng_keys_nf, nf_chain, log_prob, log_prob_nf = nf_metropolis_sampler(rng_keys_nf, nf_samples, model, state.params , para_logp, positions[:,-1]) +def sample(rng_key, params): + return model.apply({'params': params}, rng_key, n_samples*n_chains, params, method=model.sample)[0] + +def log_prob_nf_function(params, location): + return model.apply({'params': params}, location, method=model.log_prob) - positions = jnp.concatenate((positions,nf_chain),axis=1) - return rng_keys_nf, rng_keys_mcmc, state, positions +sample = jax.jit(sample) +log_prob_nf_function = jax.jit(log_prob_nf_function) +trained = False last_step = initial_position chains = [] -#for i in range(15): - # rng_keys_nf, rng_keys_mcmc, state, positions = sampling_loop(rng_keys_nf, rng_keys_mcmc, model, state, last_step) - # last_step = positions[:,-1].T -# rng_keys_mcmc, positions, log_prob = run_mcmc(rng_keys_mcmc, n_samples, likelihood, initial_position) -# last_step = last_step.T - # if i%5 == 0: - # rng_keys_nf, state = train_flow(rng_key_nf, model, state, positions.reshape(-1,n_dim)) -# chains.append(positions) +for i in range(total_epoch): + rng_keys_mcmc, positions, log_prob = run_mcmc(rng_keys_mcmc, n_samples, likelihood, last_step) + last_step = positions[:,-1].T + # if i%train_epoch == train_epoch-1: + # train_sample = np.concatenate(chains[-look_back_epoch:],axis=1).reshape(-1,n_dim) + # rng_keys_nf, state = train_flow(rng_key_nf, model, state, train_sample) + # trained = True + # if i%nf_sample_epoch == 0 and trained == True: + # rng_keys_nf, nf_chain, log_prob, log_prob_nf = nf_metropolis_sampler(rng_keys_nf, sample, log_prob_nf_function, state.params , para_logp, positions[:,-1]) + # positions = jnp.concatenate((positions,nf_chain),axis=1) + chains.append(positions) chains = np.concatenate(chains,axis=1) nf_samples = sample_nf(model, state.params, rng_keys_nf, 10000) From ba9de9316c108ba789a9ea2c48271e04b8c7b98c Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Fri, 7 Jan 2022 11:32:01 -0500 Subject: [PATCH 081/300] Update for experiment. The likelihood seems odd. The optimalSNR grows faster than the match_filter_SNR decays. Could have problem in the way we generate the signal. --- example/NFRandomWalk.py | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/example/NFRandomWalk.py b/example/NFRandomWalk.py index 2fee096f..d29f3940 100644 --- a/example/NFRandomWalk.py +++ b/example/NFRandomWalk.py @@ -25,8 +25,8 @@ import optax # Optimizers -true_m1 = 10. -true_m2 = 5 +true_m1 = 5. +true_m2 = 5. true_ld = 500. true_phase = 0. true_gt = 0. @@ -144,7 +144,7 @@ def sample_nf(model, param, rng_key,n_sample): look_back_epoch = 10 train_epoch = 25 nf_sample_epoch = 25 -total_epoch = 1000 +total_epoch = 100 precompiled = False print("Preparing RNG keys") @@ -159,7 +159,7 @@ def sample_nf(model, param, rng_key,n_sample): # prior_range = [] # prior_range.append([1.0,15.0]) # prior_range.append([1.0,15.0]) -# prior_range.append([np.log10(0.1),np.log10(3000.0)]) +# #prior_range.append([np.log10(0.1),np.log10(3000.0)]) # prior_range.append([0.,2*jnp.pi]) # prior_range.append([0.,jnp.pi]) # prior_range.append([0.,jnp.pi]) @@ -170,7 +170,7 @@ def sample_nf(model, param, rng_key,n_sample): # initial_position = jax.random.uniform(rng_key_ic,(n_chains,n_dim)) #(n_dim, n_chains) # initial_position = (initial_position*(prior_range[:,1]-prior_range[:,0])+prior_range[:,0]).T -initial_position = (jax.random.normal(rng_key_ic,(n_chains,n_dim))*0.05 + jnp.array(list(guess_parameters.values()))).T #(n_dim, n_chains) +initial_position = (jax.random.normal(rng_key_ic,(n_chains,n_dim))*0.5 + jnp.array(list(guess_parameters.values()))).T #(n_dim, n_chains) #model = MaskedAutoregressiveFlow(n_dim,64,4) @@ -199,13 +199,13 @@ def log_prob_nf_function(params, location): for i in range(total_epoch): rng_keys_mcmc, positions, log_prob = run_mcmc(rng_keys_mcmc, n_samples, likelihood, last_step) last_step = positions[:,-1].T - # if i%train_epoch == train_epoch-1: - # train_sample = np.concatenate(chains[-look_back_epoch:],axis=1).reshape(-1,n_dim) - # rng_keys_nf, state = train_flow(rng_key_nf, model, state, train_sample) - # trained = True - # if i%nf_sample_epoch == 0 and trained == True: - # rng_keys_nf, nf_chain, log_prob, log_prob_nf = nf_metropolis_sampler(rng_keys_nf, sample, log_prob_nf_function, state.params , para_logp, positions[:,-1]) - # positions = jnp.concatenate((positions,nf_chain),axis=1) + if i%train_epoch == train_epoch-1: + train_sample = np.concatenate(chains[-look_back_epoch:],axis=1).reshape(-1,n_dim) + rng_keys_nf, state = train_flow(rng_key_nf, model, state, train_sample) + trained = True + if i%nf_sample_epoch == 0 and trained == True: + rng_keys_nf, nf_chain, log_prob, log_prob_nf = nf_metropolis_sampler(rng_keys_nf, sample, log_prob_nf_function, state.params , para_logp, positions[:,-1]) + positions = jnp.concatenate((positions,nf_chain),axis=1) chains.append(positions) chains = np.concatenate(chains,axis=1) From e21ca8bfa342c78ba242796c055a544104617d5f Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Fri, 7 Jan 2022 14:23:24 -0500 Subject: [PATCH 082/300] Add GW170817 example. Not working yet --- example/GW170817.py | 321 ++++++++++++++++++++++++++ example/NFRandomWalk.py | 4 +- jaxgw/sampler/Gaussian_random_walk.py | 8 +- 3 files changed, 327 insertions(+), 6 deletions(-) create mode 100644 example/GW170817.py diff --git a/example/GW170817.py b/example/GW170817.py new file mode 100644 index 00000000..65c917db --- /dev/null +++ b/example/GW170817.py @@ -0,0 +1,321 @@ +import numpy as np +import bilby +import jax +import jax.numpy as jnp + +from jax.config import config + +from jaxgw.sampler.NF_proposal import nf_metropolis_kernel, nf_metropolis_sampler +config.update("jax_enable_x64", True) + +from jaxgw.gw.likelihood.detector_projection import construct_arm, detector_tensor, antenna_response, get_detector_response + +from jaxgw.gw.likelihood.utils import inner_product +from jaxgw.gw.likelihood.detector_preset import get_H1, get_L1 +from jaxgw.gw.waveform.TaylorF2 import TaylorF2 +from jaxgw.gw.waveform.IMRPhenomC import IMRPhenomC +from jax import random, grad, jit, vmap, jacfwd, jacrev, value_and_grad, pmap + +from jaxgw.sampler.Gaussian_random_walk import rw_metropolis_sampler +from jaxgw.sampler.maf import MaskedAutoregressiveFlow +from jaxgw.sampler.realNVP import RealNVP +from jax.scipy.stats import multivariate_normal +from flax.training import train_state # Useful dataclass to keep train state +import optax # Optimizers + + +""" +This tutorial includes advanced specifications +for analysing binary neutron star event data. +Here GW170817 is used as an example. +""" +import bilby +from gwpy.timeseries import TimeSeries +from bilby.gw.utils import greenwich_mean_sidereal_time + +logger = bilby.core.utils.logger + + +outdir = 'outdir' +data_dir = '/mnt/home/wwong/ceph/GWProject/GWTC/individual_events/' +label = 'GW170817' +time_of_event = bilby.gw.utils.get_event_time(label) +bilby.core.utils.setup_logger(outdir=outdir, label=label) +# GET DATA FROM INTERFEROMETER +# include 'V1' for appropriate O2 events +interferometer_names = ['H1', 'L1', 'V1'] +duration = 32 +roll_off = 0.2 # how smooth is the transition from no signal +# to max signal in a Tukey Window. +psd_offset = -512 # PSD is estimated using data from +# `center_time+psd_offset` to `center_time+psd_offset + psd_duration` +# This determines the time window used to fetch open data. +psd_duration = 1024 +coherence_test = False # coherence between detectors +filter_freq = None # low pass filter frequency to cut signal content above +# Nyquist frequency. The condition is 2 * filter_freq >= sampling_frequency +end_time = time_of_event + duration/2 +start_time = end_time - duration + +psd_duration = 32 * duration +psd_start_time = start_time - psd_duration +psd_end_time = start_time + + +ifo_list = bilby.gw.detector.InterferometerList([]) +for det in ["H1", "L1", "V1"]: + try: + logger.info("Loading signal data for detector %s", det) + data = TimeSeries.read(data_dir+label+'/'+det+'_signal.hdf5') + except: + logger.info("Downloading signal data for ifo {}".format(det)) + data = TimeSeries.fetch_open_data(det, start_time, end_time) + data.write(data_dir+label+'/'+det+'_signal.hdf5') + + ifo = bilby.gw.detector.get_empty_interferometer(det) + ifo.strain_data.set_from_gwpy_timeseries(data) + + + try: + logger.info("Loading psd data for detector %s", det) + psd_data = TimeSeries.read(data_dir+label+'/'+det+'_psd.hdf5') + except: + logger.info("Downloading psd data for ifo {}".format(det)) + psd_data = TimeSeries.fetch_open_data(det, psd_start_time, psd_end_time) + psd_data.write(data_dir+label+'/'+det+'_psd.hdf5') + psd_alpha = 2 * roll_off / duration + psd = psd_data.psd( + fftlength=duration, + overlap=0, + window=("tukey", psd_alpha), + method="median" + ) + ifo.power_spectral_density = bilby.gw.detector.PowerSpectralDensity( + frequency_array=psd.frequencies.value, psd_array=psd.value) + ifo_list.append(ifo) + +logger.info("Saving data plots to {}".format(outdir)) +bilby.core.utils.check_directory_exists_and_if_not_mkdir(outdir) +ifo_list.plot_data(outdir=outdir, label=label) + +# CHOOSE PRIOR FILE +prior = bilby.gw.prior.BNSPriorDict(filename='GW170817.prior') +deltaT = 0.1 +prior['geocent_time'] = bilby.core.prior.Uniform( + minimum=time_of_event - deltaT / 2, + maximum=time_of_event + deltaT / 2, + name='geocent_time', + latex_label='$t_c$', + unit='$s$') +# GENERATE WAVEFORM +# OVERVIEW OF APPROXIMANTS: +# https://www.lsc-group.phys.uwm.edu/ligovirgo/cbcnote/Waveforms/Overview +duration = None # duration and sampling frequency will be overwritten +# to match the ones in interferometers. +sampling_frequency = 4096 +start_time = 0 # set the starting time of the time array +waveform_arguments = { + 'waveform_approximant': 'IMRPhenomPv2_NRTidal', 'reference_frequency': 20} + +source_model = bilby.gw.source.lal_binary_neutron_star +convert_bns = bilby.gw.conversion.convert_to_lal_binary_neutron_star_parameters +waveform_generator = bilby.gw.WaveformGenerator( + duration=duration, + sampling_frequency=sampling_frequency, + start_time=start_time, + frequency_domain_source_model=source_model, + parameter_conversion=convert_bns, + waveform_arguments=waveform_arguments,) + +# CHOOSE LIKELIHOOD FUNCTION +# Time marginalisation uses FFT. +# Distance marginalisation uses a look up table calculated at run time. +# Phase marginalisation is done analytically using a Bessel function. +likelihood = bilby.gw.likelihood.GravitationalWaveTransient( + ifo_list, + waveform_generator, + time_marginalization=False, + distance_marginalization=False, + phase_marginalization=False,) + +strain_H1 = ifo_list[0].frequency_domain_strain[1:] +strain_L1 = ifo_list[1].frequency_domain_strain[1:] +psd_frequency = ifo_list[0].frequency_array[1:] +psd_H1 = ifo_list[0].power_spectral_density_array[1:] +psd_L1 = ifo_list[1].power_spectral_density_array[1:] + +H1, H1_vertex = get_H1() +L1, L1_vertex = get_L1() + +duration = waveform_generator.duration + +print('SNR of the event in H1: '+str(np.sqrt(inner_product(strain_H1,strain_H1,psd_frequency,psd_H1)))) +print('SNR of the event in L1: '+str(np.sqrt(inner_product(strain_L1,strain_L1,psd_frequency,psd_L1)))) + +@jit +def single_detector_likelihood(params, data, data_f, PSD, detector, detector_vertex): +# waveform = IMRPhenomC(data_f, params) + waveform = TaylorF2(data_f, params) + waveform = get_detector_response(data_f, waveform, params, detector, detector_vertex) + match_filter_SNR = inner_product(waveform, data, data_f, PSD) + optimal_SNR = inner_product(waveform, waveform, data_f, PSD) + return (-2*match_filter_SNR + optimal_SNR)/2#, match_filter_SNR, optimal_SNR + +@jit +def single_detector_likelihood_bilby(params, data, data_f, PSD, detector, detector_vertex): +# waveform = IMRPhenomC(data_f, params) + waveform = TaylorF2(data_f, params) + waveform = get_detector_response(data_f, waveform, params, detector, detector_vertex) + log_l = -2 / duration * jnp.vdot(data-waveform, (data-waveform)/PSD) + return log_l.real + +@jit +def logprob_wrap(mass_1, mass_2, phase_c, theta_jn, psi, ra, dec): + params = dict(mass_1=mass_1, mass_2=mass_2, spin_1=0, spin_2=0, luminosity_distance=40., phase_c=phase_c, t_c=greenwich_mean_sidereal_time(time_of_event), theta_jn=theta_jn, psi=psi, ra=ra, dec=dec, f_ref=50) +# params = dict(mass_1=mass_1, mass_2=mass_2, spin_1=0, spin_2=0, luminosity_distance=true_ld, theta_jn=0.4, psi=2.659, phase_c=true_phase, t_c=true_gt, ra=1.375, dec=-1.2108) + return single_detector_likelihood_bilby(params, strain_H1, psd_frequency, psd_H1, H1, H1_vertex)+single_detector_likelihood_bilby(params, strain_L1, psd_frequency, psd_L1, L1, L1_vertex) + + + +likelihood = lambda x: logprob_wrap(*x) +para_logp = jit(jax.vmap(likelihood)) + +#### Sampling #### + +def train_step(model, state, batch): + def loss(params): + y, log_det = model.apply({'params': params},batch) + mean = jnp.zeros((batch.shape[0],model.n_features)) + cov = jnp.repeat(jnp.eye(model.n_features)[None,:],batch.shape[0],axis=0) + log_det = log_det + multivariate_normal.logpdf(y,mean,cov) + return -jnp.mean(log_det) + grad_fn = jax.value_and_grad(loss) + value, grad = grad_fn(state.params) + state = state.apply_gradients(grads=grad) + return value,state + +train_step = jax.jit(train_step,static_argnums=(0,)) + +def train_flow(rng, model, state, data): + + def train_epoch(state, train_ds, batch_size, epoch, rng): + """Train for a single epoch.""" + train_ds_size = len(train_ds) + steps_per_epoch = train_ds_size // batch_size + + perms = jax.random.permutation(rng, train_ds_size) + perms = perms[:steps_per_epoch * batch_size] # skip incomplete batch + perms = perms.reshape((steps_per_epoch, batch_size)) + for perm in perms: + batch = train_ds[perm, ...] + value, state = train_step(model, state, batch) + + return value, state + + for epoch in range(1, num_epochs + 1): + print('Epoch %d' % epoch) + # Use a separate PRNG key to permute image data during shuffling + rng, input_rng = jax.random.split(rng) + # Run an optimization step over a training batch + value, state = train_epoch(state, data, batch_size, epoch, input_rng) + print('Train loss: %.3f' % value) + + return rng, state + +def sample_nf(model, param, rng_key,n_sample): + rng_key, subkey = random.split(rng_key) + samples = model.apply({'params': param}, subkey, n_sample,param, method=model.sample) + return rng_key,samples + +n_dim = 7 +n_samples = 100 +nf_samples = 10 +n_chains = 100 +learning_rate = 0.01 +momentum = 0.9 +num_epochs = 300 +batch_size = 10000 +look_back_epoch = 10 +train_epoch = 25 +nf_sample_epoch = 25 +total_epoch = 100 +precompiled = False + +print("Preparing RNG keys") +rng_key = jax.random.PRNGKey(42) +rng_key_ic, rng_key_mcmc, rng_key_nf = jax.random.split(rng_key,3) + +rng_keys_mcmc = jax.random.split(rng_key_mcmc, n_chains) # (nchains,) +rng_keys_nf, init_rng_keys_nf = jax.random.split(rng_key_nf,2) + +print("Finding initial position for chains") + +prior_range = [] +prior_range.append([1.6093862655801942,1.6093862655801943 ]) +prior_range.append([1.1616754457131563,1.1616754457131564]) +#prior_range.append([np.log10(0.1),np.log10(3000.0)]) +prior_range.append([0.,2*jnp.pi]) +prior_range.append([0.,jnp.pi]) +prior_range.append([0.,jnp.pi]) +prior_range.append([0.,2*jnp.pi]) +prior_range.append([0.,jnp.pi]) +prior_range = jnp.array(prior_range) + +initial_guess = jax.random.uniform(rng_key_ic,(n_chains,n_dim)) #(n_dim, n_chains) +initial_guess = (initial_guess*(prior_range[:,1]-prior_range[:,0])+prior_range[:,0]) + +from scipy.optimize import minimize + +loss = lambda x: -likelihood(x) + +initial_position = [] +for i in range(n_chains): + res = minimize(loss,initial_guess[i,:],method='L-BFGS-B') + initial_position.append(res.x) + +initial_position = jnp.array(initial_position).T + + +#initial_position = (jax.random.normal(rng_key_ic,(n_chains,n_dim))*0.5 + jnp.array(list(guess_parameters.values()))).T #(n_dim, n_chains) + +print("Initializing MCMC model and normalizing flow model.") + +#model = MaskedAutoregressiveFlow(n_dim,64,4) +# model = RealNVP(10,n_dim,64, 1) +# params = model.init(init_rng_keys_nf, jnp.ones((1,n_dim)))['params'] + +# run_mcmc = jax.vmap(rw_metropolis_sampler, in_axes=(0, None, None, 1, None), +# out_axes=0) + +# tx = optax.adam(learning_rate, momentum) +# state = train_state.TrainState.create(apply_fn=model.apply, params=params, tx=tx) + + +# def sample(rng_key, params): +# return model.apply({'params': params}, rng_key, nf_samples*n_chains, params, method=model.sample)[0] + +# def log_prob_nf_function(params, location): +# return model.apply({'params': params}, location, method=model.log_prob) + +# sample = jax.jit(sample) +# log_prob_nf_function = jax.jit(log_prob_nf_function) + +# print("Starting sampling") + +# trained = False +# last_step = initial_position +# chains = [] +# for i in range(total_epoch): +# rng_keys_mcmc, positions, log_prob = run_mcmc(rng_keys_mcmc, n_samples, likelihood, last_step, 0.001) +# last_step = positions[:,-1].T +# # if i%train_epoch == train_epoch-1: +# # train_sample = np.concatenate(chains[-look_back_epoch:],axis=1).reshape(-1,n_dim) +# # rng_keys_nf, state = train_flow(rng_key_nf, model, state, train_sample) +# # trained = True +# # if i%nf_sample_epoch == 0 and trained == True: +# # rng_keys_nf, nf_chain, log_prob, log_prob_nf = nf_metropolis_sampler(rng_keys_nf, sample, log_prob_nf_function, state.params , para_logp, positions[:,-1]) +# # positions = jnp.concatenate((positions,nf_chain),axis=1) +# chains.append(positions) + +# chains = np.concatenate(chains,axis=1) +# nf_samples = sample_nf(model, state.params, rng_keys_nf, 10000) diff --git a/example/NFRandomWalk.py b/example/NFRandomWalk.py index d29f3940..fc2d644b 100644 --- a/example/NFRandomWalk.py +++ b/example/NFRandomWalk.py @@ -177,7 +177,7 @@ def sample_nf(model, param, rng_key,n_sample): model = RealNVP(10,n_dim,64, 1) params = model.init(init_rng_keys_nf, jnp.ones((1,n_dim)))['params'] -run_mcmc = jax.vmap(rw_metropolis_sampler, in_axes=(0, None, None, 1), +run_mcmc = jax.vmap(rw_metropolis_sampler, in_axes=(0, None, None, 1, None), out_axes=0) tx = optax.adam(learning_rate, momentum) @@ -197,7 +197,7 @@ def log_prob_nf_function(params, location): last_step = initial_position chains = [] for i in range(total_epoch): - rng_keys_mcmc, positions, log_prob = run_mcmc(rng_keys_mcmc, n_samples, likelihood, last_step) + rng_keys_mcmc, positions, log_prob = run_mcmc(rng_keys_mcmc, n_samples, likelihood, last_step,0.001) last_step = positions[:,-1].T if i%train_epoch == train_epoch-1: train_sample = np.concatenate(chains[-look_back_epoch:],axis=1).reshape(-1,n_dim) diff --git a/jaxgw/sampler/Gaussian_random_walk.py b/jaxgw/sampler/Gaussian_random_walk.py index f2d1d47e..67dde1a7 100644 --- a/jaxgw/sampler/Gaussian_random_walk.py +++ b/jaxgw/sampler/Gaussian_random_walk.py @@ -3,7 +3,7 @@ from functools import partial @partial(jax.jit, static_argnums=(1,)) -def rw_metropolis_kernel(rng_key, logpdf, position, log_prob): +def rw_metropolis_kernel(rng_key, logpdf, position, log_prob, kernal_size=0.1): """Moves the chains by one step using the Random Walk Metropolis algorithm. Attributes ---------- @@ -21,7 +21,7 @@ def rw_metropolis_kernel(rng_key, logpdf, position, log_prob): The next positions of the chains along with their log probability. """ key1, key2 = jax.random.split(rng_key) - move_proposal = jax.random.normal(key1, shape=position.shape) * 0.1 + move_proposal = jax.random.normal(key1, shape=position.shape) * kernal_size proposal = position + move_proposal proposal_log_prob = logpdf(proposal) @@ -34,12 +34,12 @@ def rw_metropolis_kernel(rng_key, logpdf, position, log_prob): @partial(jax.jit, static_argnums=(1, 2)) -def rw_metropolis_sampler(rng_key, n_samples, logpdf, initial_position): +def rw_metropolis_sampler(rng_key, n_samples, logpdf, initial_position, kernal_size=0.1): def mh_update_sol2(i, state): key, positions, log_prob = state _, key = jax.random.split(key) - new_position, new_log_prob = rw_metropolis_kernel(key, logpdf, positions[i-1], log_prob) + new_position, new_log_prob = rw_metropolis_kernel(key, logpdf, positions[i-1], log_prob, kernal_size) positions=positions.at[i].set(new_position) return (key, positions, new_log_prob) From caf830c0dab7a5ee0d1f9df93e7e2e8a104d696d Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Fri, 7 Jan 2022 15:08:06 -0500 Subject: [PATCH 083/300] Change NFproposal to use compile function for speed up --- jaxgw/sampler/NF_proposal.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/jaxgw/sampler/NF_proposal.py b/jaxgw/sampler/NF_proposal.py index 433f1b79..4622d2ab 100644 --- a/jaxgw/sampler/NF_proposal.py +++ b/jaxgw/sampler/NF_proposal.py @@ -18,7 +18,7 @@ def nf_metropolis_kernel(rng_key, proposal_position, initial_position, proposal_ nf_metropolis_kernel = vmap(jit(nf_metropolis_kernel)) -def nf_metropolis_sampler(rng_key, n_samples, nf_model, nf_param, target_pdf, initial_position): +def nf_metropolis_sampler(rng_key, sample_function, log_prob_function, params, target_pdf, initial_position): def mh_update_sol2(i, state): key, positions, log_prob, log_prob_nf = state @@ -29,12 +29,14 @@ def mh_update_sol2(i, state): return (key, positions, new_log_prob, new_log_prob_nf) rng_key, *subkeys = random.split(rng_key,3) + proposal_position = sample_function(subkeys[0], params) + n_samples = int(proposal_position.shape[0]/initial_position.shape[0]) all_positions = jnp.zeros((n_samples,)+initial_position.shape) + initial_position - proposal_position = nf_model.apply({'params': nf_param}, subkeys[0], initial_position.shape[0]*n_samples, nf_param, method=nf_model.sample)[0] + proposal_position = sample_function(subkeys[0], params)#nf_model.apply({'params': nf_param}, subkeys[0], initial_position.shape[0]*n_samples, nf_param, method=nf_model.sample)[0] - log_pdf_nf_proposal = nf_model.apply({'params': nf_param}, proposal_position, method=nf_model.log_prob) - log_pdf_nf_initial = nf_model.apply({'params': nf_param}, initial_position, method=nf_model.log_prob) + log_pdf_nf_proposal = log_prob_function(params, proposal_position)#nf_model.apply({'params': nf_param}, proposal_position, method=nf_model.log_prob) + log_pdf_nf_initial = log_prob_function(params, initial_position)#nf_model.apply({'params': nf_param}, initial_position, method=nf_model.log_prob) log_pdf_proposal = target_pdf(proposal_position) log_pdf_initial = target_pdf(initial_position) From e44eb37db5d2432fbe3f45e8244a5145d7f20303 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Fri, 7 Jan 2022 16:47:05 -0500 Subject: [PATCH 084/300] Add MALA sampler, not sure if it work yet. --- jaxgw/sampler/MALA.py | 45 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 45 insertions(+) create mode 100644 jaxgw/sampler/MALA.py diff --git a/jaxgw/sampler/MALA.py b/jaxgw/sampler/MALA.py new file mode 100644 index 00000000..8f7df9bf --- /dev/null +++ b/jaxgw/sampler/MALA.py @@ -0,0 +1,45 @@ +import jax +import jax.numpy as jnp +from jax import grad +from functools import partial + +@partial(jax.jit, static_argnums=(1, 2)) +def mala_kernel(rng_key, logpdf, d_logpdf, position, log_prob, kernal_size=0.1): + + key1, key2 = jax.random.split(rng_key) + proposal = position + kernal_size * d_logpdf(position) + proposal += kernal_size * jnp.sqrt(2/kernal_size) * jax.random.normal(key1, shape=position.shape) + ratio = logpdf(proposal) - logpdf(position) + ratio -= ((position - proposal - kernal_size * d_logpdf(proposal)) ** 2 / (4 * kernal_size)).sum() + ratio += ((proposal - position - kernal_size * d_logpdf(position)) ** 2 / (4 * kernal_size)).sum() + proposal_log_prob = logpdf(proposal) + + log_uniform = jnp.log(jax.random.uniform(key2)) + do_accept = log_uniform < ratio + + position = jnp.where(do_accept, proposal, position) + log_prob = jnp.where(do_accept, proposal_log_prob, log_prob) + return position, log_prob + + +@partial(jax.jit, static_argnums=(1, 2, 3)) +def mala_sampler(rng_key, n_samples, logpdf, d_logpdf, initial_position, kernal_size=0.1): + + def mh_update_sol2(i, state): + key, positions, log_prob = state + _, key = jax.random.split(key) + new_position, new_log_prob = mala_kernel(key, logpdf, d_logpdf, positions[i-1], log_prob, kernal_size) + positions=positions.at[i].set(new_position) + return (key, positions, new_log_prob) + + + logp = logpdf(initial_position) + all_positions = jnp.zeros((n_samples,)+initial_position.shape) + initial_position + initial_state = (rng_key,all_positions, logp) + rng_key, all_positions, log_prob = jax.lax.fori_loop(1, n_samples, + mh_update_sol2, + initial_state) + + + return rng_key, all_positions, log_prob + From 8a40b24ba1f7af1811179605fd244656b950c0b1 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Fri, 7 Jan 2022 18:49:47 -0500 Subject: [PATCH 085/300] GW170817 updated, starting to work --- example/GW170817.py | 73 +++++++++++++++++++----------------- jaxgw/gw/likelihood/utils.py | 8 ++++ 2 files changed, 46 insertions(+), 35 deletions(-) diff --git a/example/GW170817.py b/example/GW170817.py index 65c917db..23c54e21 100644 --- a/example/GW170817.py +++ b/example/GW170817.py @@ -131,7 +131,7 @@ # Time marginalisation uses FFT. # Distance marginalisation uses a look up table calculated at run time. # Phase marginalisation is done analytically using a Bessel function. -likelihood = bilby.gw.likelihood.GravitationalWaveTransient( +bilby_likelihood = bilby.gw.likelihood.GravitationalWaveTransient( ifo_list, waveform_generator, time_marginalization=False, @@ -170,14 +170,16 @@ def single_detector_likelihood_bilby(params, data, data_f, PSD, detector, detect return log_l.real @jit -def logprob_wrap(mass_1, mass_2, phase_c, theta_jn, psi, ra, dec): - params = dict(mass_1=mass_1, mass_2=mass_2, spin_1=0, spin_2=0, luminosity_distance=40., phase_c=phase_c, t_c=greenwich_mean_sidereal_time(time_of_event), theta_jn=theta_jn, psi=psi, ra=ra, dec=dec, f_ref=50) +def logprob_wrap(mass_1, mass_2, luminosity_distance, phase_c, t_c, theta_jn, psi, ra, dec): + params = dict(mass_1=mass_1, mass_2=mass_2, spin_1=0, spin_2=0, luminosity_distance=10**luminosity_distance, phase_c=phase_c, t_c=10**t_c, theta_jn=theta_jn, psi=psi, ra=ra, dec=dec, f_ref=50) # params = dict(mass_1=mass_1, mass_2=mass_2, spin_1=0, spin_2=0, luminosity_distance=true_ld, theta_jn=0.4, psi=2.659, phase_c=true_phase, t_c=true_gt, ra=1.375, dec=-1.2108) return single_detector_likelihood_bilby(params, strain_H1, psd_frequency, psd_H1, H1, H1_vertex)+single_detector_likelihood_bilby(params, strain_L1, psd_frequency, psd_L1, L1, L1_vertex) likelihood = lambda x: logprob_wrap(*x) +likelihood = jit(likelihood) +d_likelihood = jit(grad(likelihood)) para_logp = jit(jax.vmap(likelihood)) #### Sampling #### @@ -227,7 +229,7 @@ def sample_nf(model, param, rng_key,n_sample): samples = model.apply({'params': param}, subkey, n_sample,param, method=model.sample) return rng_key,samples -n_dim = 7 +n_dim = 9 n_samples = 100 nf_samples = 10 n_chains = 100 @@ -253,8 +255,9 @@ def sample_nf(model, param, rng_key,n_sample): prior_range = [] prior_range.append([1.6093862655801942,1.6093862655801943 ]) prior_range.append([1.1616754457131563,1.1616754457131564]) -#prior_range.append([np.log10(0.1),np.log10(3000.0)]) +prior_range.append([np.log10(0.1),np.log10(3000.0)]) prior_range.append([0.,2*jnp.pi]) +prior_range.append([np.log10(greenwich_mean_sidereal_time(time_of_event)),np.log10(greenwich_mean_sidereal_time(time_of_event)+1)]) prior_range.append([0.,jnp.pi]) prior_range.append([0.,jnp.pi]) prior_range.append([0.,2*jnp.pi]) @@ -270,7 +273,7 @@ def sample_nf(model, param, rng_key,n_sample): initial_position = [] for i in range(n_chains): - res = minimize(loss,initial_guess[i,:],method='L-BFGS-B') + res = minimize(loss,initial_guess[i,:],method='Nelder-Mead') initial_position.append(res.x) initial_position = jnp.array(initial_position).T @@ -281,41 +284,41 @@ def sample_nf(model, param, rng_key,n_sample): print("Initializing MCMC model and normalizing flow model.") #model = MaskedAutoregressiveFlow(n_dim,64,4) -# model = RealNVP(10,n_dim,64, 1) -# params = model.init(init_rng_keys_nf, jnp.ones((1,n_dim)))['params'] +model = RealNVP(10,n_dim,64, 1) +params = model.init(init_rng_keys_nf, jnp.ones((1,n_dim)))['params'] -# run_mcmc = jax.vmap(rw_metropolis_sampler, in_axes=(0, None, None, 1, None), -# out_axes=0) +run_mcmc = jax.vmap(rw_metropolis_sampler, in_axes=(0, None, None, 1, None), + out_axes=0) -# tx = optax.adam(learning_rate, momentum) -# state = train_state.TrainState.create(apply_fn=model.apply, params=params, tx=tx) +tx = optax.adam(learning_rate, momentum) +state = train_state.TrainState.create(apply_fn=model.apply, params=params, tx=tx) -# def sample(rng_key, params): -# return model.apply({'params': params}, rng_key, nf_samples*n_chains, params, method=model.sample)[0] +def sample(rng_key, params): + return model.apply({'params': params}, rng_key, nf_samples*n_chains, params, method=model.sample)[0] -# def log_prob_nf_function(params, location): -# return model.apply({'params': params}, location, method=model.log_prob) +def log_prob_nf_function(params, location): + return model.apply({'params': params}, location, method=model.log_prob) -# sample = jax.jit(sample) -# log_prob_nf_function = jax.jit(log_prob_nf_function) +sample = jax.jit(sample) +log_prob_nf_function = jax.jit(log_prob_nf_function) -# print("Starting sampling") +print("Starting sampling") -# trained = False -# last_step = initial_position -# chains = [] -# for i in range(total_epoch): -# rng_keys_mcmc, positions, log_prob = run_mcmc(rng_keys_mcmc, n_samples, likelihood, last_step, 0.001) -# last_step = positions[:,-1].T -# # if i%train_epoch == train_epoch-1: -# # train_sample = np.concatenate(chains[-look_back_epoch:],axis=1).reshape(-1,n_dim) -# # rng_keys_nf, state = train_flow(rng_key_nf, model, state, train_sample) -# # trained = True -# # if i%nf_sample_epoch == 0 and trained == True: -# # rng_keys_nf, nf_chain, log_prob, log_prob_nf = nf_metropolis_sampler(rng_keys_nf, sample, log_prob_nf_function, state.params , para_logp, positions[:,-1]) -# # positions = jnp.concatenate((positions,nf_chain),axis=1) -# chains.append(positions) +trained = False +last_step = initial_position +chains = [] +for i in range(total_epoch): + rng_keys_mcmc, positions, log_prob = run_mcmc(rng_keys_mcmc, n_samples, likelihood, last_step, 0.01) + last_step = positions[:,-1].T + if i%train_epoch == train_epoch-1: + train_sample = np.concatenate(chains[-look_back_epoch:],axis=1).reshape(-1,n_dim) + rng_keys_nf, state = train_flow(rng_key_nf, model, state, train_sample) + trained = True + if i%nf_sample_epoch == 0 and trained == True: + rng_keys_nf, nf_chain, log_prob, log_prob_nf = nf_metropolis_sampler(rng_keys_nf, sample, log_prob_nf_function, state.params , para_logp, positions[:,-1]) + positions = jnp.concatenate((positions,nf_chain),axis=1) + chains.append(positions) -# chains = np.concatenate(chains,axis=1) -# nf_samples = sample_nf(model, state.params, rng_keys_nf, 10000) +chains = np.concatenate(chains,axis=1) +nf_samples = sample_nf(model, state.params, rng_keys_nf, 10000) diff --git a/jaxgw/gw/likelihood/utils.py b/jaxgw/gw/likelihood/utils.py index 32badb4f..b521263c 100644 --- a/jaxgw/gw/likelihood/utils.py +++ b/jaxgw/gw/likelihood/utils.py @@ -36,6 +36,14 @@ def Mq_to_m1m2(trans_M_tot,trans_q): m2 = m1*q return m1, m2 +@jit +def Mc_q_to_m1m2(Mc,q): + eta = q/(1+q)**2 + M_tot = Mc/eta**(3./5) + m1 = M_tot/(1+q) + m2 = m1*q + return m1, m2 + def ra_dec_to_theta_phi(ra, dec, gmst): phi = ra - gmst theta = jnp.pi / 2 - dec From 12c2b1b845c7a8066fae0e5ca2ce4c078670ef7e Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 10 Jan 2022 11:08:57 -0500 Subject: [PATCH 086/300] Update detector reponse to align with bilby convention in dt. --- jaxgw/gw/likelihood/detector_projection.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/jaxgw/gw/likelihood/detector_projection.py b/jaxgw/gw/likelihood/detector_projection.py index 71f561c2..0477165d 100644 --- a/jaxgw/gw/likelihood/detector_projection.py +++ b/jaxgw/gw/likelihood/detector_projection.py @@ -168,7 +168,8 @@ def get_detector_response(frequency, waveform_polarizations, parameters, detecto time_shift = time_delay_geocentric(detector_vertex, jnp.array([0.,0.,0.]),parameters['ra'], parameters['dec'], parameters['t_c']) - dt = parameters['t_c'] + time_shift # Note that we always assume the start time of the strain to be 0 + dt = parameters['geocent_time'] - parameters['start_time'] + dt = dt + time_shift # Note that we always assume the start time of the strain to be 0 signal_ifo = signal_ifo * jnp.exp(-1j * 2 * jnp.pi * dt * frequency) From ea7259c55911790bade1872b3384053a06441508 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 10 Jan 2022 13:43:33 -0500 Subject: [PATCH 087/300] Fix TaylorF2 phasing. There were a missing term in 3 PN term --- jaxgw/gw/waveform/TaylorF2.py | 10 ++++++---- test/test_TaylorF2.py | 8 +++++--- 2 files changed, 11 insertions(+), 7 deletions(-) diff --git a/jaxgw/gw/waveform/TaylorF2.py b/jaxgw/gw/waveform/TaylorF2.py index b00facc5..8645d381 100644 --- a/jaxgw/gw/waveform/TaylorF2.py +++ b/jaxgw/gw/waveform/TaylorF2.py @@ -41,16 +41,16 @@ def TaylorF2(f,params): Ph_PN2 = (5.*(3058.673/7.056 + 5429./7.*eta+617.*eta*eta)/72.) Ph_PN2d5 = 5./9.*(772.9/8.4-13.*eta)*jnp.pi Ph_PN2d5_log = (5./3.*(772.9/8.4-13.*eta)*jnp.pi) - Ph_PN3 = (11583.231236531/4.694215680 - 640./3.*jnp.pi*jnp.pi - 684.8/2.1*euler_gamma + eta*(-15737.765635/3.048192 + 225.5/1.2*jnp.pi*jnp.pi) + eta*eta*76.055/1.728 - eta*eta*eta*127.825/1.296) + Ph_PN3 = (11583.231236531/4.694215680 - 640./3.*jnp.pi*jnp.pi - 684.8/2.1*euler_gamma + eta*(-15737.765635/3.048192 + 225.5/1.2*jnp.pi*jnp.pi) + eta*eta*76.055/1.728 - eta*eta*eta*127.825/1.296 -684.8/2.1 *jnp.log(4)) Ph_PN3_log = -684.8/2.1 Ph_PN3d5 = jnp.pi*(770.96675/2.54016 + 378.515/1.512*eta - 740.45/7.56*eta*eta) PN_phasing = 3./(128*eta*PNcoef**5) * \ - (Ph_PN0 + Ph_PN1 * PNcoef**2 + Ph_PN1d5 * PNcoef**3 + Ph_PN2 * PNcoef**4 + Ph_PN2d5 * PNcoef**5 + Ph_PN2d5_log * PNcoef**5 * jnp.log(PNcoef) + Ph_PN3 * PNcoef**6 + Ph_PN3_log * PNcoef**6 * jnp.log(PNcoef) + Ph_PN3d5 * PNcoef**7) + (Ph_PN0 + Ph_PN1 * PNcoef**2 + Ph_PN1d5 * PNcoef**3 + Ph_PN2 * PNcoef**4 + (Ph_PN2d5 + Ph_PN2d5_log * jnp.log(PNcoef)) * PNcoef**5 + (Ph_PN3 + Ph_PN3_log * jnp.log(PNcoef)) * PNcoef**6 + Ph_PN3d5 * PNcoef**7) PN_phasing_ref = 3./(128*eta*PNcoef_ref**5) * \ - (Ph_PN0 + Ph_PN1 * PNcoef_ref**2 + Ph_PN1d5 * PNcoef_ref**3 + Ph_PN2 * PNcoef_ref**4 + Ph_PN2d5 * PNcoef_ref**5 + Ph_PN2d5_log * PNcoef_ref**5 * jnp.log(PNcoef_ref) + Ph_PN3 * PNcoef_ref**6 + Ph_PN3_log * PNcoef_ref**6 * jnp.log(PNcoef_ref) + Ph_PN3d5 * PNcoef_ref**7) + (Ph_PN0 + Ph_PN1 * PNcoef_ref**2 + Ph_PN1d5 * PNcoef_ref**3 + Ph_PN2 * PNcoef_ref**4 + (Ph_PN2d5 + Ph_PN2d5_log * jnp.log(PNcoef_ref))* PNcoef_ref**5 + (Ph_PN3 + Ph_PN3_log * jnp.log(PNcoef_ref)) * PNcoef_ref**6 + Ph_PN3d5 * PNcoef_ref**7) phase = 2*jnp.pi*f*params['t_c'] - params['phase_c'] - jnp.pi/4 + PN_phasing - PN_phasing_ref @@ -58,7 +58,9 @@ def TaylorF2(f,params): hp = totalh * (1/2*(1+jnp.cos(params['theta_jn'])**2)*jnp.cos(2*params['psi'])) hc = totalh * jnp.cos(params['theta_jn'])*jnp.sin(2*params['psi']) - return {'plus':hp,'cross':hc} + + + return totalh#{'plus':hp,'cross':hc} def flso(M): return (6**3./2*jnp.pi*M)**-1 diff --git a/test/test_TaylorF2.py b/test/test_TaylorF2.py index 03f48c67..227ad6f6 100644 --- a/test/test_TaylorF2.py +++ b/test/test_TaylorF2.py @@ -3,6 +3,7 @@ import numpy as np from jaxgw.gw.waveform.TaylorF2 import TaylorF2 +from bilby.gw.utils import greenwich_mean_sidereal_time mass_1 = 30. mass_2 = 30. @@ -10,12 +11,13 @@ f0 = 20. max_f = 2048 delta_f = 1./8 -spin = 0. +spin = 0.02 injection_parameters = dict( - mass_1=mass_1, mass_2=mass_2, spin_1=0.0, spin_2=0.0, luminosity_distance=luminosity_distance, phase_c=0, t_c=0, theta_jn=0.4, psi=2.659,f_ref = 0.00001) + mass_1=mass_1, mass_2=mass_2, spin_1=spin, spin_2=spin, luminosity_distance=luminosity_distance, phase_c=0, t_c=0,\ + theta_jn=0.4, psi=2.659,f_ref = 50) -waveform1 = lalsim.SimInspiralTaylorF2(0., delta_f, mass_1*MSUN_SI, mass_2* MSUN_SI, 0., 0., f0, max_f, 0.,luminosity_distance* 1e6*PC_SI,{}) +waveform1 = lalsim.SimInspiralTaylorF2(0., delta_f, mass_1*MSUN_SI, mass_2* MSUN_SI, 0., 0., f0, max_f, 50,luminosity_distance* 1e6*PC_SI,{}) frequency = waveform1.f0 + np.arange(len(waveform1.data.data)) * waveform1.deltaF waveform3 = TaylorF2(frequency,injection_parameters) \ No newline at end of file From 31b2db0591cfb07cd8062ce54082ee56b36345e0 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Tue, 11 Jan 2022 10:50:09 -0500 Subject: [PATCH 088/300] TaylorF2 confirmed with lalsim. However, bilby seems to have more complicated input than lalsim --- jaxgw/gw/waveform/TaylorF2.py | 2 +- test/test_TaylorF2.py | 21 ++++++++++++++++++++- 2 files changed, 21 insertions(+), 2 deletions(-) diff --git a/jaxgw/gw/waveform/TaylorF2.py b/jaxgw/gw/waveform/TaylorF2.py index 8645d381..a260fa2e 100644 --- a/jaxgw/gw/waveform/TaylorF2.py +++ b/jaxgw/gw/waveform/TaylorF2.py @@ -60,7 +60,7 @@ def TaylorF2(f,params): - return totalh#{'plus':hp,'cross':hc} + return {'plus':hp,'cross':hc} def flso(M): return (6**3./2*jnp.pi*M)**-1 diff --git a/test/test_TaylorF2.py b/test/test_TaylorF2.py index 227ad6f6..cd0d371c 100644 --- a/test/test_TaylorF2.py +++ b/test/test_TaylorF2.py @@ -19,5 +19,24 @@ waveform1 = lalsim.SimInspiralTaylorF2(0., delta_f, mass_1*MSUN_SI, mass_2* MSUN_SI, 0., 0., f0, max_f, 50,luminosity_distance* 1e6*PC_SI,{}) +waveform2 = lalsim.SimInspiralChooseFDWaveform(mass_1*MSUN_SI, mass_2*MSUN_SI, 0, 0, 0, 0, 0, 0, luminosity_distance*1e6*PC_SI,0.4,0,0,0,0,1./8,40,2048,50,{},5) frequency = waveform1.f0 + np.arange(len(waveform1.data.data)) * waveform1.deltaF -waveform3 = TaylorF2(frequency,injection_parameters) \ No newline at end of file +waveform3 = TaylorF2(frequency,injection_parameters) + +import bilby + +duration = 32 +sampling_frequency = 2 * 1024 + +# Fixed arguments passed into the source model. The analysis starts at 40 Hz. +waveform_arguments = dict(waveform_approximant='TaylorF2', + reference_frequency=50., minimum_frequency=40.0) + +# Create the waveform_generator using a LAL Binary Neutron Star source function +waveform_generator = bilby.gw.WaveformGenerator( + duration=duration, sampling_frequency=sampling_frequency, + frequency_domain_source_model=bilby.gw.source.lal_binary_neutron_star, + parameter_conversion=bilby.gw.conversion.convert_to_lal_binary_neutron_star_parameters, + waveform_arguments=waveform_arguments) + +waveform4 = waveform_generator.frequency_domain_source_model(frequency, mass_1, mass_2, luminosity_distance, 0, 0, 0, 0, 0, 0, 0.4, 0, 0, 0) \ No newline at end of file From 06b7457ab004f9f9871767a9b9a6c81f7fbfdcd9 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Tue, 11 Jan 2022 18:08:29 -0500 Subject: [PATCH 089/300] Add bns_injection experiment --- example/bns_injection.py | 337 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 337 insertions(+) create mode 100644 example/bns_injection.py diff --git a/example/bns_injection.py b/example/bns_injection.py new file mode 100644 index 00000000..2cc03354 --- /dev/null +++ b/example/bns_injection.py @@ -0,0 +1,337 @@ +import numpy as np +import bilby +import jax +import jax.numpy as jnp + +from jax.config import config + +from jaxgw.sampler.NF_proposal import nf_metropolis_kernel, nf_metropolis_sampler +config.update("jax_enable_x64", True) + +from jaxgw.gw.likelihood.detector_projection import construct_arm, detector_tensor, antenna_response, get_detector_response + +from jaxgw.gw.likelihood.utils import Mc_q_to_m1m2, inner_product +from jaxgw.gw.likelihood.detector_preset import get_H1, get_L1 +from jaxgw.gw.waveform.TaylorF2 import TaylorF2 +from jaxgw.gw.waveform.IMRPhenomC import IMRPhenomC +from jax import random, grad, jit, vmap, jacfwd, jacrev, value_and_grad, pmap + +from jaxgw.sampler.Gaussian_random_walk import rw_metropolis_sampler +from jaxgw.sampler.maf import MaskedAutoregressiveFlow +from jaxgw.sampler.realNVP import RealNVP +from jax.scipy.stats import multivariate_normal +from flax.training import train_state # Useful dataclass to keep train state +import optax # Optimizers + + +""" +This tutorial includes advanced specifications +for analysing binary neutron star event data. +Here GW170817 is used as an example. +""" +import bilby +from gwpy.timeseries import TimeSeries +from bilby.gw.utils import greenwich_mean_sidereal_time + +logger = bilby.core.utils.logger + +outdir = 'outdir' +label = 'bns_example' +bilby.core.utils.setup_logger(outdir=outdir, label=label) + +# Set up a random seed for result reproducibility. This is optional! +np.random.seed(88170235) + +# We are going to inject a binary neutron star waveform. We first establish a +# dictionary of parameters that includes all of the different waveform +# parameters, including masses of the two black holes (mass_1, mass_2), +# aligned spins of both black holes (chi_1, chi_2), etc. +injection_parameters = dict( + mass_1=1.5, mass_2=1.3, chi_1=0.0, chi_2=0.0, luminosity_distance=200., + theta_jn=0.4, psi=2.659, phase=1.3, geocent_time=1126259642.413, + ra=1.375, dec=-1.2108, lambda_1=0, lambda_2=0, spin_1 = 0.0, spin_2 = 0.0, + f_ref=50., t_c = 0, phase_c = 1.3, + greenwich_mean_sidereal_time=greenwich_mean_sidereal_time(1126259642.413), + start_time=1126259642.413) + +# Set the duration and sampling frequency of the data segment that we're going +# to inject the signal into. For the +# TaylorF2 waveform, we cut the signal close to the isco frequency +duration = 4 +sampling_frequency = 2 * 1024 +start_time = injection_parameters['geocent_time'] + 2 - duration + +# Fixed arguments passed into the source model. The analysis starts at 40 Hz. +waveform_arguments = dict(waveform_approximant='TaylorF2', + reference_frequency=50., minimum_frequency=40.0) + +# Create the waveform_generator using a LAL Binary Neutron Star source function +waveform_generator = bilby.gw.WaveformGenerator( + duration=duration, sampling_frequency=sampling_frequency, + frequency_domain_source_model=bilby.gw.source.lal_binary_neutron_star, + parameter_conversion=bilby.gw.conversion.convert_to_lal_binary_neutron_star_parameters, + waveform_arguments=waveform_arguments) + +# Set up interferometers. In this case we'll use three interferometers +# (LIGO-Hanford (H1), LIGO-Livingston (L1), and Virgo (V1)). +# These default to their design sensitivity and start at 40 Hz. +interferometers = bilby.gw.detector.InterferometerList(['H1', 'L1']) +for interferometer in interferometers: + interferometer.minimum_frequency = 40 +interferometers.set_strain_data_from_power_spectral_densities( + sampling_frequency=sampling_frequency, duration=duration, + start_time=start_time) +# interferometers.inject_signal(parameters=injection_parameters, +# waveform_generator=waveform_generator) + +# Load the default prior for binary neutron stars. +# We're going to sample in chirp_mass, symmetric_mass_ratio, lambda_tilde, and +# delta_lambda rather than mass_1, mass_2, lambda_1, and lambda_2. +# BNS have aligned spins by default, if you want to allow precessing spins +# pass aligned_spin=False to the BNSPriorDict +priors = bilby.gw.prior.BNSPriorDict() +for key in ['psi', 'geocent_time', 'ra', 'dec', 'chi_1', 'chi_2', + 'theta_jn', 'luminosity_distance', 'phase']: + priors[key] = injection_parameters[key] +priors.pop('mass_ratio') +priors.pop('lambda_1') +priors.pop('lambda_2') +priors['chirp_mass'] = bilby.core.prior.Gaussian( + 1.215, 0.1, name='chirp_mass', unit='$M_{\\odot}$') +priors['symmetric_mass_ratio'] = bilby.core.prior.Uniform( + 0.1, 0.25, name='symmetric_mass_ratio') +priors['lambda_tilde'] = bilby.core.prior.Uniform(0, 5000, name='lambda_tilde') +priors['delta_lambda'] = bilby.core.prior.Uniform( + -5000, 5000, name='delta_lambda') + +# Initialise the likelihood by passing in the interferometer data (IFOs) +# and the waveform generator +bilby_likelihood = bilby.gw.GravitationalWaveTransient( + interferometers=interferometers, waveform_generator=waveform_generator, + time_marginalization=False, phase_marginalization=False, + distance_marginalization=False, priors=priors) + +psd_frequency = interferometers[0].frequency_array +true_signal = TaylorF2(psd_frequency,injection_parameters) +true_signal['plus'] = np.array(true_signal['plus']) +true_signal['cross'] = np.array(true_signal['cross']) +true_signal['plus'][0] = 0 +true_signal['cross'][0] = 0 +for interferometer in interferometers: + interferometer.inject_signal_from_waveform_polarizations(injection_parameters,true_signal) + +strain_H1 = interferometers[0].frequency_domain_strain[1:] +strain_L1 = interferometers[1].frequency_domain_strain[1:] + +psd_H1 = interferometers[0].power_spectral_density_array[1:] +psd_L1 = interferometers[1].power_spectral_density_array[1:] + +H1, H1_vertex = get_H1() +L1, L1_vertex = get_L1() + +duration = waveform_generator.duration + +print('SNR of the event in H1: '+str(np.sqrt(inner_product(strain_H1,strain_H1,psd_frequency,psd_H1)))) +print('SNR of the event in L1: '+str(np.sqrt(inner_product(strain_L1,strain_L1,psd_frequency,psd_L1)))) + +mask = np.ones(psd_frequency[1:].shape) +mask[psd_frequency[1:]<40] = 0 + +@jit +def single_detector_likelihood(params, data, data_f, PSD, detector, detector_vertex): +# waveform = IMRPhenomC(data_f, params) + waveform = TaylorF2(data_f, params) + waveform = get_detector_response(data_f, waveform, params, detector, detector_vertex) + waveform *= mask + match_filter_SNR = inner_product(waveform, data, data_f, PSD) + optimal_SNR = inner_product(waveform, waveform, data_f, PSD) + return match_filter_SNR, optimal_SNR + +@jit +def single_detector_likelihood_bilby(params, data, data_f, PSD, detector, detector_vertex): +# waveform = IMRPhenomC(data_f, params) + waveform = TaylorF2(data_f, params) + waveform = get_detector_response(data_f, waveform, params, detector, detector_vertex) + log_l = -2 / duration * jnp.vdot(data-waveform, (data-waveform)/PSD) + return log_l.real + + +def logprob_wrap(mass_1, mass_2, luminosity_distance, phase_c, t_c, theta_jn, psi, ra, dec): + params = dict(mass_1=mass_1, mass_2=mass_2, spin_1=0, spin_2=0, luminosity_distance=luminosity_distance, phase_c=phase_c%(2*jnp.pi), t_c=t_c,\ + theta_jn=theta_jn%(jnp.pi), psi=psi%(jnp.pi), ra=ra%(2*jnp.pi), dec=dec%(jnp.pi),\ + f_ref=50., geocent_time = interferometers[0].strain_data.start_time+t_c, start_time=interferometers[0].strain_data.start_time, + greenwich_mean_sidereal_time=greenwich_mean_sidereal_time(interferometers[0].strain_data.start_time)) + H1_SNR = single_detector_likelihood(params, strain_H1, psd_frequency[1:], psd_H1, H1, H1_vertex) + L1_SNR = single_detector_likelihood(params, strain_L1, psd_frequency[1:], psd_L1, L1, L1_vertex) + match_filter_SNR = H1_SNR[0] + L1_SNR[0] + optimal_SNR = H1_SNR[1] + L1_SNR[1] + return match_filter_SNR - optimal_SNR/2 + +# def logprob_wrap(Mc, q, luminosity_distance, phase_c, t_c, theta_jn, psi, ra, dec): +# mass_1, mass_2 = Mc_q_to_m1m2(Mc, q) +# params = dict(mass_1=mass_1, mass_2=mass_2, spin_1=0, spin_2=0, luminosity_distance=10**luminosity_distance, phase_c=phase_c%(2*jnp.pi), t_c=0,\ +# theta_jn=theta_jn%(jnp.pi), psi=psi%(jnp.pi), ra=ra%(2*jnp.pi), dec=dec%(jnp.pi),\ +# f_ref=50., geocent_time = interferometers[0].strain_data.start_time, start_time=interferometers[0].strain_data.start_time, +# greenwich_mean_sidereal_time=greenwich_mean_sidereal_time(interferometers[0].strain_data.start_time)) +# H1_SNR = single_detector_likelihood(params, strain_H1, psd_frequency[1:], psd_H1, H1, H1_vertex) +# L1_SNR = single_detector_likelihood(params, strain_L1, psd_frequency[1:], psd_L1, L1, L1_vertex) +# match_filter_SNR = H1_SNR[0] + L1_SNR[0] +# optimal_SNR = H1_SNR[1] + L1_SNR[1] +# return match_filter_SNR - optimal_SNR/2 + +likelihood = lambda x: logprob_wrap(*x) +likelihood = jit(likelihood) +d_likelihood = jit(grad(likelihood)) +para_logp = jit(jax.vmap(likelihood)) + +#### Sampling #### + +def train_step(model, state, batch): + def loss(params): + y, log_det = model.apply({'params': params},batch) + mean = jnp.zeros((batch.shape[0],model.n_features)) + cov = jnp.repeat(jnp.eye(model.n_features)[None,:],batch.shape[0],axis=0) + log_det = log_det + multivariate_normal.logpdf(y,mean,cov) + return -jnp.mean(log_det) + grad_fn = jax.value_and_grad(loss) + value, grad = grad_fn(state.params) + state = state.apply_gradients(grads=grad) + return value,state + +train_step = jax.jit(train_step,static_argnums=(0,)) + +def train_flow(rng, model, state, data): + + def train_epoch(state, train_ds, batch_size, epoch, rng): + """Train for a single epoch.""" + train_ds_size = len(train_ds) + steps_per_epoch = train_ds_size // batch_size + + perms = jax.random.permutation(rng, train_ds_size) + perms = perms[:steps_per_epoch * batch_size] # skip incomplete batch + perms = perms.reshape((steps_per_epoch, batch_size)) + for perm in perms: + batch = train_ds[perm, ...] + value, state = train_step(model, state, batch) + + return value, state + + for epoch in range(1, num_epochs + 1): + print('Epoch %d' % epoch) + # Use a separate PRNG key to permute image data during shuffling + rng, input_rng = jax.random.split(rng) + # Run an optimization step over a training batch + value, state = train_epoch(state, data, batch_size, epoch, input_rng) + print('Train loss: %.3f' % value) + + return rng, state + +def sample_nf(model, param, rng_key,n_sample): + rng_key, subkey = random.split(rng_key) + samples = model.apply({'params': param}, subkey, n_sample,param, method=model.sample) + return rng_key,samples + +n_dim = 9 +n_samples = 100 +nf_samples = 10 +n_chains = 20 +learning_rate = 0.01 +momentum = 0.9 +num_epochs = 300 +batch_size = 10000 +look_back_epoch = 10 +start_train_epoch = 100 +train_epoch = 100 +nf_sample_epoch = 25 +total_epoch = 100 +precompiled = False + +print("Preparing RNG keys") +rng_key = jax.random.PRNGKey(42) +rng_key_ic, rng_key_mcmc, rng_key_nf = jax.random.split(rng_key,3) + +rng_keys_mcmc = jax.random.split(rng_key_mcmc, n_chains) # (nchains,) +rng_keys_nf, init_rng_keys_nf = jax.random.split(rng_key_nf,2) + +print("Finding initial position for chains") + +prior_range = [] +prior_range.append([1.0,2.0]) +prior_range.append([1.0,2.0]) +prior_range.append([100.0,300.0]) +# prior_range.append([0.5,3.0]) +# prior_range.append([0.1,1.0]) +# prior_range.append([np.log10(100.0),np.log10(300.0)]) + +prior_range.append([0.,2*jnp.pi]) +prior_range.append([0,0.1]) +prior_range.append([0.,jnp.pi]) +prior_range.append([0.,jnp.pi]) +prior_range.append([0.,2*jnp.pi]) +prior_range.append([0.,jnp.pi]) +prior_range = jnp.array(prior_range) + +initial_guess = jax.random.uniform(rng_key_ic,(n_chains,n_dim)) #(n_dim, n_chains) +initial_guess = (initial_guess*(prior_range[:,1]-prior_range[:,0])+prior_range[:,0]) + +from scipy.optimize import minimize + +loss = lambda x: -likelihood(x) +loss = jit(loss) + +initial_position = [] +for i in range(n_chains): + res = minimize(loss,initial_guess[i,:],method='Nelder-Mead') + initial_position.append(res.x) + +initial_position = jnp.array(initial_position).T + + +#initial_position = (jax.random.normal(rng_key_ic,(n_chains,n_dim))*0.5 + jnp.array(list(guess_parameters.values()))).T #(n_dim, n_chains) + +print("Initializing MCMC model and normalizing flow model.") + +#model = MaskedAutoregressiveFlow(n_dim,64,4) +model = RealNVP(10,n_dim,64, 1) +params = model.init(init_rng_keys_nf, jnp.ones((1,n_dim)))['params'] + +run_mcmc = jax.vmap(rw_metropolis_sampler, in_axes=(0, None, None, 1, None), + out_axes=0) + +tx = optax.adam(learning_rate, momentum) +state = train_state.TrainState.create(apply_fn=model.apply, params=params, tx=tx) + + +def sample(rng_key, params): + return model.apply({'params': params}, rng_key, nf_samples*n_chains, params, method=model.sample)[0] + +def log_prob_nf_function(params, location): + return model.apply({'params': params}, location, method=model.log_prob) + +sample = jax.jit(sample) +log_prob_nf_function = jax.jit(log_prob_nf_function) + +print("Starting sampling") + +trained = False +last_step = initial_position +chains = [] +for i in range(total_epoch): + rng_keys_mcmc, positions, log_prob = run_mcmc(rng_keys_mcmc, n_samples, likelihood, last_step, 0.1) + positions = positions.at[:,:,3].set(positions[:,:,3]%(2*jnp.pi)) + positions = positions.at[:,:,5].set(positions[:,:,5]%(jnp.pi)) + positions = positions.at[:,:,6].set(positions[:,:,6]%(jnp.pi)) + positions = positions.at[:,:,7].set(positions[:,:,7]%(2*jnp.pi)) + positions = positions.at[:,:,8].set(positions[:,:,8]%(jnp.pi)) + last_step = positions[:,-1].T + # if (i > start_train_epoch) and (i%train_epoch == train_epoch-1): + # train_sample = np.concatenate(chains[-look_back_epoch:],axis=1).reshape(-1,n_dim) + # rng_keys_nf, state = train_flow(rng_key_nf, model, state, train_sample) + # trained = True + # if i%nf_sample_epoch == 0 and trained == True: + # rng_keys_nf, nf_chain, log_prob, log_prob_nf = nf_metropolis_sampler(rng_keys_nf, sample, log_prob_nf_function, state.params , para_logp, positions[:,-1]) + # positions = jnp.concatenate((positions,nf_chain),axis=1) + chains.append(positions) + +chains = np.concatenate(chains,axis=1) +nf_samples = sample_nf(model, state.params, rng_keys_nf, 10000) From 049b9df521fb96c9a3d3307d1070b29ac92a9e36 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Wed, 12 Jan 2022 12:31:06 -0500 Subject: [PATCH 090/300] Add old version of TaylorF2 back. Commit before revert and test --- example/NFRandomWalk.py | 39 +++++++++++++++++------------- example/bns_injection.py | 45 ++++++++++++++++++++++------------- jaxgw/gw/waveform/TaylorF2.py | 32 +++++++++++++++++++++++++ 3 files changed, 84 insertions(+), 32 deletions(-) diff --git a/example/NFRandomWalk.py b/example/NFRandomWalk.py index fc2d644b..708a022e 100644 --- a/example/NFRandomWalk.py +++ b/example/NFRandomWalk.py @@ -1,3 +1,4 @@ +from bilby.gw.utils import greenwich_mean_sidereal_time import numpy as np import bilby import jax @@ -13,11 +14,12 @@ from jaxgw.gw.likelihood.utils import inner_product from jaxgw.gw.likelihood.detector_preset import get_H1, get_L1 -from jaxgw.gw.waveform.TaylorF2 import TaylorF2 +from jaxgw.gw.waveform.TaylorF2 import TaylorF2,TaylorF2_old from jaxgw.gw.waveform.IMRPhenomC import IMRPhenomC from jax import random, grad, jit, vmap, jacfwd, jacrev, value_and_grad, pmap from jaxgw.sampler.Gaussian_random_walk import rw_metropolis_sampler +from jaxgw.sampler.MALA import mala_kernel, mala_sampler from jaxgw.sampler.maf import MaskedAutoregressiveFlow from jaxgw.sampler.realNVP import RealNVP from jax.scipy.stats import multivariate_normal @@ -27,19 +29,20 @@ true_m1 = 5. true_m2 = 5. -true_ld = 500. +true_ld = 300. true_phase = 0. true_gt = 0. injection_parameters = dict( mass_1=true_m1, mass_2=true_m2, spin_1=0.0, spin_2=0.0, luminosity_distance=true_ld, theta_jn=0.4, psi=2.659, - phase_c=true_phase, t_c=true_gt, ra=1.375, dec=-1.2108, f_ref=50) + phase_c=true_phase, t_c=true_gt, ra=1.375, dec=-1.2108, f_ref=50, + greenwich_mean_sidereal_time=greenwich_mean_sidereal_time(true_gt),start_time = true_gt, geocent_time = true_gt) #guess_parameters = dict(m1=true_m1, m2=true_m2) guess_parameters = dict( - mass_1=true_m1*0.99, mass_2=true_m2*1.01, theta_jn=0.4, psi=2.659, + mass_1=true_m1*0.99, mass_2=true_m2*1.01, t_c=true_gt, theta_jn=0.4, psi=2.659, phase_c=true_phase, ra=1.375, dec=-1.2108) @@ -59,7 +62,7 @@ psd = psd[jnp.isfinite(psd)] #waveform = IMRPhenomC(psd_frequency, injection_parameters) -waveform = TaylorF2(psd_frequency, injection_parameters) +waveform = TaylorF2_old(psd_frequency, injection_parameters) H1, H1_vertex = get_H1() L1, L1_vertex = get_L1() strain_H1 = get_detector_response(psd_frequency, waveform, injection_parameters, H1, H1_vertex) @@ -71,19 +74,23 @@ @jit def single_detector_likelihood(params, data, data_f, PSD, detector, detector_vertex): # waveform = IMRPhenomC(data_f, params) - waveform = TaylorF2(data_f, params) + waveform = TaylorF2_old(data_f, params) waveform = get_detector_response(data_f, waveform, params, detector, detector_vertex) match_filter_SNR = inner_product(waveform, data, data_f, PSD) optimal_SNR = inner_product(waveform, waveform, data_f, PSD) - return (-2*match_filter_SNR + optimal_SNR)/2#, match_filter_SNR, optimal_SNR + return -(-2*match_filter_SNR + optimal_SNR)/2#, match_filter_SNR, optimal_SNR @jit -def logprob_wrap(mass_1, mass_2, phase_c, theta_jn, psi, ra, dec): - params = dict(mass_1=mass_1, mass_2=mass_2, spin_1=0, spin_2=0, luminosity_distance=true_ld, phase_c=phase_c, t_c=true_gt, theta_jn=theta_jn, psi=psi, ra=ra, dec=dec, f_ref=50) -# params = dict(mass_1=mass_1, mass_2=mass_2, spin_1=0, spin_2=0, luminosity_distance=true_ld, theta_jn=0.4, psi=2.659, phase_c=true_phase, t_c=true_gt, ra=1.375, dec=-1.2108) +def logprob_wrap(mass_1, mass_2, t_c, phase_c, theta_jn, psi, ra, dec): + params = dict(mass_1=mass_1, mass_2=mass_2, spin_1=0, spin_2=0, luminosity_distance=true_ld,\ + phase_c=phase_c%(2*jnp.pi), t_c=t_c,\ + theta_jn=theta_jn%(jnp.pi), psi=psi%(jnp.pi), ra=ra%(2*jnp.pi), dec=dec%(jnp.pi),\ + f_ref=50,greenwich_mean_sidereal_time=greenwich_mean_sidereal_time(true_gt),start_time = true_gt, geocent_time = true_gt) return single_detector_likelihood(params, strain_H1, psd_frequency, psd, H1, H1_vertex)+single_detector_likelihood(params, strain_L1, psd_frequency, psd, L1, L1_vertex) likelihood = lambda x: logprob_wrap(*x) +likelihood = jit(likelihood) +d_likelihood = jit(grad(likelihood)) para_logp = jit(jax.vmap(likelihood)) #### Sampling #### @@ -133,7 +140,7 @@ def sample_nf(model, param, rng_key,n_sample): samples = model.apply({'params': param}, subkey, n_sample,param, method=model.sample) return rng_key,samples -n_dim = 7 +n_dim = 8 n_samples = 100 nf_samples = 100 n_chains = 100 @@ -141,9 +148,9 @@ def sample_nf(model, param, rng_key,n_sample): momentum = 0.9 num_epochs = 300 batch_size = 10000 -look_back_epoch = 10 -train_epoch = 25 -nf_sample_epoch = 25 +look_back_epoch = 50 +train_epoch = 50 +nf_sample_epoch = 50 total_epoch = 100 precompiled = False @@ -177,7 +184,7 @@ def sample_nf(model, param, rng_key,n_sample): model = RealNVP(10,n_dim,64, 1) params = model.init(init_rng_keys_nf, jnp.ones((1,n_dim)))['params'] -run_mcmc = jax.vmap(rw_metropolis_sampler, in_axes=(0, None, None, 1, None), +run_mcmc = jax.vmap(mala_sampler, in_axes=(0, None, None, None, 1, None), out_axes=0) tx = optax.adam(learning_rate, momentum) @@ -197,7 +204,7 @@ def log_prob_nf_function(params, location): last_step = initial_position chains = [] for i in range(total_epoch): - rng_keys_mcmc, positions, log_prob = run_mcmc(rng_keys_mcmc, n_samples, likelihood, last_step,0.001) + rng_keys_mcmc, positions, log_prob = run_mcmc(rng_keys_mcmc, n_samples, likelihood, d_likelihood, last_step,0.001) last_step = positions[:,-1].T if i%train_epoch == train_epoch-1: train_sample = np.concatenate(chains[-look_back_epoch:],axis=1).reshape(-1,n_dim) diff --git a/example/bns_injection.py b/example/bns_injection.py index 2cc03354..45a1ebe7 100644 --- a/example/bns_injection.py +++ b/example/bns_injection.py @@ -17,6 +17,7 @@ from jax import random, grad, jit, vmap, jacfwd, jacrev, value_and_grad, pmap from jaxgw.sampler.Gaussian_random_walk import rw_metropolis_sampler +from jaxgw.sampler.MALA import mala_sampler from jaxgw.sampler.maf import MaskedAutoregressiveFlow from jaxgw.sampler.realNVP import RealNVP from jax.scipy.stats import multivariate_normal @@ -47,13 +48,19 @@ # parameters, including masses of the two black holes (mass_1, mass_2), # aligned spins of both black holes (chi_1, chi_2), etc. injection_parameters = dict( - mass_1=1.5, mass_2=1.3, chi_1=0.0, chi_2=0.0, luminosity_distance=200., + mass_1=10, mass_2=10, chi_1=0.0, chi_2=0.0, luminosity_distance=200., theta_jn=0.4, psi=2.659, phase=1.3, geocent_time=1126259642.413, ra=1.375, dec=-1.2108, lambda_1=0, lambda_2=0, spin_1 = 0.0, spin_2 = 0.0, f_ref=50., t_c = 0, phase_c = 1.3, greenwich_mean_sidereal_time=greenwich_mean_sidereal_time(1126259642.413), start_time=1126259642.413) +guess_parameters = dict( + mass_1=10, mass_2=10, luminosity_distance=200., + phase_c=1.3, t_c = 0, theta_jn=0.4, psi=2.659, + ra=1.375, dec=-1.2108) + + # Set the duration and sampling frequency of the data segment that we're going # to inject the signal into. For the # TaylorF2 waveform, we cut the signal close to the isco frequency @@ -120,14 +127,15 @@ for interferometer in interferometers: interferometer.inject_signal_from_waveform_polarizations(injection_parameters,true_signal) -strain_H1 = interferometers[0].frequency_domain_strain[1:] -strain_L1 = interferometers[1].frequency_domain_strain[1:] +H1, H1_vertex = get_H1() +L1, L1_vertex = get_L1() + +strain_H1 = get_detector_response(psd_frequency[1:], TaylorF2(psd_frequency[1:], injection_parameters), injection_parameters, H1, H1_vertex)#interferometers[0].frequency_domain_strain[1:] +strain_L1 = get_detector_response(psd_frequency[1:], TaylorF2(psd_frequency[1:], injection_parameters), injection_parameters, L1, L1_vertex)#interferometers[1].frequency_domain_strain[1:] psd_H1 = interferometers[0].power_spectral_density_array[1:] psd_L1 = interferometers[1].power_spectral_density_array[1:] -H1, H1_vertex = get_H1() -L1, L1_vertex = get_L1() duration = waveform_generator.duration @@ -256,10 +264,10 @@ def sample_nf(model, param, rng_key,n_sample): print("Finding initial position for chains") prior_range = [] -prior_range.append([1.0,2.0]) -prior_range.append([1.0,2.0]) +prior_range.append([1.0,20.0]) +prior_range.append([1.0,20.0]) prior_range.append([100.0,300.0]) -# prior_range.append([0.5,3.0]) +# prior_range.append([0.5,20.0]) # prior_range.append([0.1,1.0]) # prior_range.append([np.log10(100.0),np.log10(300.0)]) @@ -279,15 +287,15 @@ def sample_nf(model, param, rng_key,n_sample): loss = lambda x: -likelihood(x) loss = jit(loss) -initial_position = [] -for i in range(n_chains): - res = minimize(loss,initial_guess[i,:],method='Nelder-Mead') - initial_position.append(res.x) +# initial_position = [] +# for i in range(n_chains): +# res = minimize(loss,initial_guess[i,:],method='Nelder-Mead') +# initial_position.append(res.x) -initial_position = jnp.array(initial_position).T +# initial_position = jnp.array(initial_position).T -#initial_position = (jax.random.normal(rng_key_ic,(n_chains,n_dim))*0.5 + jnp.array(list(guess_parameters.values()))).T #(n_dim, n_chains) +initial_position = (jax.random.normal(rng_key_ic,(n_chains,n_dim))*0.5 + jnp.array(list(guess_parameters.values()))).T #(n_dim, n_chains) print("Initializing MCMC model and normalizing flow model.") @@ -295,7 +303,7 @@ def sample_nf(model, param, rng_key,n_sample): model = RealNVP(10,n_dim,64, 1) params = model.init(init_rng_keys_nf, jnp.ones((1,n_dim)))['params'] -run_mcmc = jax.vmap(rw_metropolis_sampler, in_axes=(0, None, None, 1, None), +run_mcmc = jax.vmap(mala_sampler, in_axes=(0, None, None, None, 1, None), out_axes=0) tx = optax.adam(learning_rate, momentum) @@ -317,7 +325,7 @@ def log_prob_nf_function(params, location): last_step = initial_position chains = [] for i in range(total_epoch): - rng_keys_mcmc, positions, log_prob = run_mcmc(rng_keys_mcmc, n_samples, likelihood, last_step, 0.1) + rng_keys_mcmc, positions, log_prob = run_mcmc(rng_keys_mcmc, n_samples, likelihood, d_likelihood, last_step, 0.0001) positions = positions.at[:,:,3].set(positions[:,:,3]%(2*jnp.pi)) positions = positions.at[:,:,5].set(positions[:,:,5]%(jnp.pi)) positions = positions.at[:,:,6].set(positions[:,:,6]%(jnp.pi)) @@ -335,3 +343,8 @@ def log_prob_nf_function(params, location): chains = np.concatenate(chains,axis=1) nf_samples = sample_nf(model, state.params, rng_keys_nf, 10000) + +def chain_to_param(chain): + return dict(chirp_mass=chain[0], mass_ratio=chain[1], luminosity_distance=10**chain[2], + phase=chain[3], geocent_time=chain[4], theta_jn=chain[5], psi=chain[6], + ra=chain[7], dec=chain[8],a_1=0,a_2=0,tilt_1=0,tilt_2=0,phi_12=0,phi_jl=0) diff --git a/jaxgw/gw/waveform/TaylorF2.py b/jaxgw/gw/waveform/TaylorF2.py index a260fa2e..471bc0a4 100644 --- a/jaxgw/gw/waveform/TaylorF2.py +++ b/jaxgw/gw/waveform/TaylorF2.py @@ -58,8 +58,40 @@ def TaylorF2(f,params): hp = totalh * (1/2*(1+jnp.cos(params['theta_jn'])**2)*jnp.cos(2*params['psi'])) hc = totalh * jnp.cos(params['theta_jn'])*jnp.sin(2*params['psi']) + return {'plus':hp,'cross':hc} + +def TaylorF2_old(f,params): + local_m1 = params['mass_1']*Msun + local_m2 = params['mass_2']*Msun + local_d = params['luminosity_distance']*Mpc + + + M_tot = local_m1+local_m2 + eta = local_m1*local_m2/(local_m1+local_m2)**2 + M_chirp = eta**(3./5)*M_tot + PNcoef = (jnp.pi*M_tot*f)**(1./3) + + amplitude = M_chirp**(5./6)/local_d + + PN0 = 1. + PN1 = (20./9) * (743./336 + 11./4*eta) * PNcoef**2 + PN1d5 = -16*jnp.pi*PNcoef**3 + PN2 = 10 * ((3058673./1016064)+ 5429./1008 *eta + 617./144 * eta**2) * PNcoef**4 + PN2d5 = jnp.pi*(38645./756-65./9*eta)*(1 + 3*jnp.log(6**(3./2)*jnp.pi*M_tot*f)) * PNcoef**5 +# PN3 = 11583231236531./4694215680 - 640./3 *jnp.pi**2 - 6868./21*(euler_gamma+jnp.log(4) + + phase = 2*jnp.pi*f*params['t_c'] - params['phase_c'] - jnp.pi/4 + 3./(128*eta*PNcoef**5) * \ + (PN0+PN1+PN1d5+PN2+PN2d5) + +# phase = - jnp.pi/4 + 3./(128*eta*PNcoef**5) * \ +# (PN0+PN1+PN1d5)#+PN2+PN2d5) + + totalh = jnp.sqrt(5./96)/jnp.pi**(2./3)*amplitude*f**(-7./6)*jnp.exp(1j*phase) + hp = totalh * (1/2*(1+jnp.cos(params['theta_jn'])**2)*jnp.cos(2*params['psi'])) + hc = totalh * jnp.cos(params['theta_jn'])*jnp.sin(2*params['psi']) + return {'plus':hp,'cross':hc} def flso(M): From 9bf552ad5a069d76013c92dde2f37a4c5d7b70af Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Wed, 12 Jan 2022 12:32:30 -0500 Subject: [PATCH 091/300] Change detector project to match bilby. Probably a bad idea --- example/GW170817.py | 54 ++-- jaxgw/gw/likelihood/detector_projection.py | 4 +- test/test_likelihood_bilby.py | 303 +++++++++++++-------- 3 files changed, 227 insertions(+), 134 deletions(-) diff --git a/example/GW170817.py b/example/GW170817.py index 23c54e21..6775d3e6 100644 --- a/example/GW170817.py +++ b/example/GW170817.py @@ -159,7 +159,7 @@ def single_detector_likelihood(params, data, data_f, PSD, detector, detector_ver waveform = get_detector_response(data_f, waveform, params, detector, detector_vertex) match_filter_SNR = inner_product(waveform, data, data_f, PSD) optimal_SNR = inner_product(waveform, waveform, data_f, PSD) - return (-2*match_filter_SNR + optimal_SNR)/2#, match_filter_SNR, optimal_SNR + return -(-2*match_filter_SNR + optimal_SNR)/2#, match_filter_SNR, optimal_SNR @jit def single_detector_likelihood_bilby(params, data, data_f, PSD, detector, detector_vertex): @@ -171,9 +171,9 @@ def single_detector_likelihood_bilby(params, data, data_f, PSD, detector, detect @jit def logprob_wrap(mass_1, mass_2, luminosity_distance, phase_c, t_c, theta_jn, psi, ra, dec): - params = dict(mass_1=mass_1, mass_2=mass_2, spin_1=0, spin_2=0, luminosity_distance=10**luminosity_distance, phase_c=phase_c, t_c=10**t_c, theta_jn=theta_jn, psi=psi, ra=ra, dec=dec, f_ref=50) + params = dict(mass_1=mass_1, mass_2=mass_2, spin_1=0, spin_2=0, luminosity_distance=luminosity_distance, phase_c=phase_c%(2*jnp.pi), t_c=t_c+greenwich_mean_sidereal_time(time_of_event), theta_jn=theta_jn%(jnp.pi), psi=psi%(jnp.pi), ra=ra%(2*jnp.pi), dec=dec%(jnp.pi), f_ref=50) # params = dict(mass_1=mass_1, mass_2=mass_2, spin_1=0, spin_2=0, luminosity_distance=true_ld, theta_jn=0.4, psi=2.659, phase_c=true_phase, t_c=true_gt, ra=1.375, dec=-1.2108) - return single_detector_likelihood_bilby(params, strain_H1, psd_frequency, psd_H1, H1, H1_vertex)+single_detector_likelihood_bilby(params, strain_L1, psd_frequency, psd_L1, L1, L1_vertex) + return single_detector_likelihood(params, strain_H1, psd_frequency, psd_H1, H1, H1_vertex)+single_detector_likelihood(params, strain_L1, psd_frequency, psd_L1, L1, L1_vertex) @@ -238,9 +238,10 @@ def sample_nf(model, param, rng_key,n_sample): num_epochs = 300 batch_size = 10000 look_back_epoch = 10 -train_epoch = 25 +start_train_epoch = 100 +train_epoch = 200 nf_sample_epoch = 25 -total_epoch = 100 +total_epoch = 1000 precompiled = False print("Preparing RNG keys") @@ -255,9 +256,9 @@ def sample_nf(model, param, rng_key,n_sample): prior_range = [] prior_range.append([1.6093862655801942,1.6093862655801943 ]) prior_range.append([1.1616754457131563,1.1616754457131564]) -prior_range.append([np.log10(0.1),np.log10(3000.0)]) +prior_range.append([0.1,300.0]) prior_range.append([0.,2*jnp.pi]) -prior_range.append([np.log10(greenwich_mean_sidereal_time(time_of_event)),np.log10(greenwich_mean_sidereal_time(time_of_event)+1)]) +prior_range.append([0,0.1]) prior_range.append([0.,jnp.pi]) prior_range.append([0.,jnp.pi]) prior_range.append([0.,2*jnp.pi]) @@ -305,20 +306,25 @@ def log_prob_nf_function(params, location): print("Starting sampling") -trained = False -last_step = initial_position -chains = [] -for i in range(total_epoch): - rng_keys_mcmc, positions, log_prob = run_mcmc(rng_keys_mcmc, n_samples, likelihood, last_step, 0.01) - last_step = positions[:,-1].T - if i%train_epoch == train_epoch-1: - train_sample = np.concatenate(chains[-look_back_epoch:],axis=1).reshape(-1,n_dim) - rng_keys_nf, state = train_flow(rng_key_nf, model, state, train_sample) - trained = True - if i%nf_sample_epoch == 0 and trained == True: - rng_keys_nf, nf_chain, log_prob, log_prob_nf = nf_metropolis_sampler(rng_keys_nf, sample, log_prob_nf_function, state.params , para_logp, positions[:,-1]) - positions = jnp.concatenate((positions,nf_chain),axis=1) - chains.append(positions) - -chains = np.concatenate(chains,axis=1) -nf_samples = sample_nf(model, state.params, rng_keys_nf, 10000) +# trained = False +# last_step = initial_position +# chains = [] +# for i in range(total_epoch): +# rng_keys_mcmc, positions, log_prob = run_mcmc(rng_keys_mcmc, n_samples, likelihood, last_step, 0.1) +# positions = positions.at[:,:,3].set(positions[:,:,3]%(2*jnp.pi)) +# positions = positions.at[:,:,5].set(positions[:,:,5]%(jnp.pi)) +# positions = positions.at[:,:,6].set(positions[:,:,6]%(jnp.pi)) +# positions = positions.at[:,:,7].set(positions[:,:,7]%(2*jnp.pi)) +# positions = positions.at[:,:,8].set(positions[:,:,8]%(jnp.pi)) +# last_step = positions[:,-1].T +# if (i > start_train_epoch) and (i%train_epoch == train_epoch-1): +# train_sample = np.concatenate(chains[-look_back_epoch:],axis=1).reshape(-1,n_dim) +# rng_keys_nf, state = train_flow(rng_key_nf, model, state, train_sample) +# trained = True +# if i%nf_sample_epoch == 0 and trained == True: +# rng_keys_nf, nf_chain, log_prob, log_prob_nf = nf_metropolis_sampler(rng_keys_nf, sample, log_prob_nf_function, state.params , para_logp, positions[:,-1]) +# positions = jnp.concatenate((positions,nf_chain),axis=1) +# chains.append(positions) + +# chains = np.concatenate(chains,axis=1) +# nf_samples = sample_nf(model, state.params, rng_keys_nf, 10000) diff --git a/jaxgw/gw/likelihood/detector_projection.py b/jaxgw/gw/likelihood/detector_projection.py index 0477165d..5ffcc2d2 100644 --- a/jaxgw/gw/likelihood/detector_projection.py +++ b/jaxgw/gw/likelihood/detector_projection.py @@ -160,13 +160,13 @@ def get_detector_response(frequency, waveform_polarizations, parameters, detecto detector_tensor, parameters['ra'], parameters['dec'], - parameters['t_c'], + parameters['greenwich_mean_sidereal_time'], parameters['psi'], mode) signal[mode] = waveform_polarizations[mode] * det_response signal_ifo = sum(signal.values()) - time_shift = time_delay_geocentric(detector_vertex, jnp.array([0.,0.,0.]),parameters['ra'], parameters['dec'], parameters['t_c']) + time_shift = time_delay_geocentric(detector_vertex, jnp.array([0.,0.,0.]),parameters['ra'], parameters['dec'], parameters['greenwich_mean_sidereal_time']) dt = parameters['geocent_time'] - parameters['start_time'] dt = dt + time_shift # Note that we always assume the start time of the strain to be 0 diff --git a/test/test_likelihood_bilby.py b/test/test_likelihood_bilby.py index f9af9f35..efda70e8 100644 --- a/test/test_likelihood_bilby.py +++ b/test/test_likelihood_bilby.py @@ -1,126 +1,213 @@ -import bilby -from gwpy.timeseries import TimeSeries -from bilby.gw.utils import greenwich_mean_sidereal_time -import numpy as np - -np.random.seed(1) - -logger = bilby.core.utils.logger -outdir = 'outdir' -label = 'GW150914' - -# Data set up -trigger_time = 1126259462 - -roll_off = 0.4 # Roll off duration of tukey window in seconds, default is 0.4s -duration = 4 # Analysis segment duration -post_trigger_duration = 2 # Time between trigger time and end of segment -end_time = trigger_time + post_trigger_duration -start_time = end_time - duration - -psd_duration = 32 * duration -psd_start_time = start_time - psd_duration -psd_end_time = start_time - -# We now use gwpy to obtain analysis and psd data and create the ifo_list -ifo_list = bilby.gw.detector.InterferometerList([]) -for det in ["H1", "L1"]: - logger.info("Downloading analysis data for ifo {}".format(det)) - ifo = bilby.gw.detector.get_empty_interferometer(det) - data = TimeSeries.fetch_open_data(det, start_time, end_time) - ifo.strain_data.set_from_gwpy_timeseries(data) - - logger.info("Downloading psd data for ifo {}".format(det)) - psd_data = TimeSeries.fetch_open_data(det, psd_start_time, psd_end_time) - psd_alpha = 2 * roll_off / duration - psd = psd_data.psd( - fftlength=duration, - overlap=0, - window=("tukey", psd_alpha), - method="median" - ) - ifo.power_spectral_density = bilby.gw.detector.PowerSpectralDensity( - frequency_array=psd.frequencies.value, psd_array=psd.value) - ifo_list.append(ifo) - -logger.info("Saving data plots to {}".format(outdir)) -bilby.core.utils.check_directory_exists_and_if_not_mkdir(outdir) -ifo_list.plot_data(outdir=outdir, label=label) - -# We now define the prior. -# We have defined our prior distribution in a local file, GW150914.prior -# The prior is printed to the terminal at run-time. -# You can overwrite this using the syntax below in the file, -# or choose a fixed value by just providing a float value as the prior. -priors = bilby.gw.prior.BBHPriorDict(filename='GW150914.prior') - -# In this step we define a `waveform_generator`. This is the object which -# creates the frequency-domain strain. In this instance, we are using the -# `lal_binary_black_hole model` source model. We also pass other parameters: -# the waveform approximant and reference frequency and a parameter conversion -# which allows us to sample in chirp mass and ratio rather than component mass -waveform_generator = bilby.gw.WaveformGenerator( - frequency_domain_source_model=bilby.gw.source.lal_binary_black_hole, - parameter_conversion=bilby.gw.conversion.convert_to_lal_binary_black_hole_parameters, - waveform_arguments={'waveform_approximant': 'IMRPhenomC', - 'reference_frequency': 50}) - -# In this step, we define the likelihood. Here we use the standard likelihood -# function, passing it the data and the waveform generator. -likelihood = bilby.gw.likelihood.GravitationalWaveTransient( - ifo_list, waveform_generator, priors=priors, time_marginalization=False, - phase_marginalization=False, distance_marginalization=False) - -priors['geocent_time'] = float(likelihood.interferometers.start_time) - -likelihood.parameters = priors.sample() -likelihood.parameters['t_c'] = greenwich_mean_sidereal_time(likelihood.parameters['geocent_time']) -likelihood.parameters['a_1'] = 0 -likelihood.parameters['a_2'] = 0 -likelihood.parameters['tilt_1'] = 0 -likelihood.parameters['tilt_2'] = 0 -likelihood.parameters['phi_12'] = 0 -likelihood.parameters['phi_jl'] = 0 -params = {} -q = likelihood.parameters['mass_ratio'] -params['mass_1'] = likelihood.parameters['chirp_mass']/((q/(1+q)**2)**(3./5)*(1+q)) -params['mass_2'] = q*params['mass_1'] -params['spin_1'] = likelihood.parameters['a_1'] -params['spin_2'] = likelihood.parameters['a_2'] -params['luminosity_distance'] = likelihood.parameters['luminosity_distance'] -params['theta_jn'] = likelihood.parameters['theta_jn'] -params['psi'] = likelihood.parameters['psi'] -params['ra'] = likelihood.parameters['ra'] -params['dec'] = likelihood.parameters['dec'] -params['phase_c'] = likelihood.parameters['phase'] -params['t_c'] = likelihood.parameters['t_c'] - -waveform = likelihood.waveform_generator.frequency_domain_strain(likelihood.parameters) -frequency_array = ifo_list[0].frequency_array - -print("Likelihood value from bilby: "+str(likelihood.log_likelihood_ratio())) - +from bilby.gw.detector import psd import numpy as np import bilby import jax import jax.numpy as jnp from jax.config import config + from jaxgw.sampler.NF_proposal import nf_metropolis_kernel, nf_metropolis_sampler config.update("jax_enable_x64", True) + from jaxgw.gw.likelihood.detector_projection import construct_arm, detector_tensor, antenna_response, get_detector_response + from jaxgw.gw.likelihood.utils import inner_product from jaxgw.gw.likelihood.detector_preset import get_H1, get_L1 from jaxgw.gw.waveform.TaylorF2 import TaylorF2 from jaxgw.gw.waveform.IMRPhenomC import IMRPhenomC from jax import random, grad, jit, vmap, jacfwd, jacrev, value_and_grad, pmap +from jaxgw.sampler.Gaussian_random_walk import rw_metropolis_sampler +from jaxgw.sampler.maf import MaskedAutoregressiveFlow +from jaxgw.sampler.realNVP import RealNVP +from jax.scipy.stats import multivariate_normal +from flax.training import train_state # Useful dataclass to keep train state +import optax # Optimizers + + +""" +This tutorial includes advanced specifications +for analysing binary neutron star event data. +Here GW170817 is used as an example. +""" +import bilby +from gwpy.timeseries import TimeSeries +from bilby.gw.utils import greenwich_mean_sidereal_time +import lalsimulation as lalsim +from lal import MSUN_SI, PC_SI, MTSUN_SI + +logger = bilby.core.utils.logger + +outdir = 'outdir' +label = 'bns_example' +bilby.core.utils.setup_logger(outdir=outdir, label=label) + +# Set up a random seed for result reproducibility. This is optional! +np.random.seed(88170235) + +# We are going to inject a binary neutron star waveform. We first establish a +# dictionary of parameters that includes all of the different waveform +# parameters, including masses of the two black holes (mass_1, mass_2), +# aligned spins of both black holes (chi_1, chi_2), etc. +injection_parameters = dict( + mass_1=1.5, mass_2=1.3, chi_1=0.0, chi_2=0.0, luminosity_distance=50., + theta_jn=0.4, psi=2.659, phase=1.3, geocent_time=1126259642.413, + ra=1.375, dec=-1.2108, lambda_1=0, lambda_2=0) + + +# Set the duration and sampling frequency of the data segment that we're going +# to inject the signal into. For the +# TaylorF2 waveform, we cut the signal close to the isco frequency +duration = 32 +sampling_frequency = 2 * 1024 +start_time = injection_parameters['geocent_time'] + 2 - duration + +jaxgw_params = dict(mass_1=1.5, mass_2=1.3, spin_1=0.0, spin_2=0.0, luminosity_distance=50, phase_c=1.3, t_c=0,\ + theta_jn=0.4, psi=2.659, ra=1.375, dec=-1.2108,\ + f_ref=50., geocent_time = start_time, start_time=start_time, + greenwich_mean_sidereal_time=greenwich_mean_sidereal_time(start_time)) + + +# Fixed arguments passed into the source model. The analysis starts at 40 Hz. +waveform_arguments = dict(waveform_approximant='TaylorF2', + reference_frequency=50., minimum_frequency=40.0) + +# Create the waveform_generator using a LAL Binary Neutron Star source function +waveform_generator = bilby.gw.WaveformGenerator( + duration=duration, sampling_frequency=sampling_frequency, + frequency_domain_source_model=bilby.gw.source.lal_binary_neutron_star, + parameter_conversion=bilby.gw.conversion.convert_to_lal_binary_neutron_star_parameters, + waveform_arguments=waveform_arguments) + +# Set up interferometers. In this case we'll use three interferometers +# (LIGO-Hanford (H1), LIGO-Livingston (L1), and Virgo (V1)). +# These default to their design sensitivity and start at 40 Hz. +interferometers = bilby.gw.detector.InterferometerList(['H1', 'L1']) +for interferometer in interferometers: + interferometer.minimum_frequency = 40 +interferometers.set_strain_data_from_power_spectral_densities( + sampling_frequency=sampling_frequency, duration=duration, + start_time=start_time) +interferometers.inject_signal(parameters=injection_parameters, + waveform_generator=waveform_generator) + +# Load the default prior for binary neutron stars. +# We're going to sample in chirp_mass, symmetric_mass_ratio, lambda_tilde, and +# delta_lambda rather than mass_1, mass_2, lambda_1, and lambda_2. +# BNS have aligned spins by default, if you want to allow precessing spins +# pass aligned_spin=False to the BNSPriorDict +priors = bilby.gw.prior.BNSPriorDict() +for key in ['psi', 'geocent_time', 'ra', 'dec', 'chi_1', 'chi_2', + 'theta_jn', 'luminosity_distance', 'phase']: + priors[key] = injection_parameters[key] +priors.pop('mass_ratio') +priors.pop('lambda_1') +priors.pop('lambda_2') +priors['chirp_mass'] = bilby.core.prior.Gaussian( + 1.215, 0.1, name='chirp_mass', unit='$M_{\\odot}$') +priors['symmetric_mass_ratio'] = bilby.core.prior.Uniform( + 0.1, 0.25, name='symmetric_mass_ratio') +priors['lambda_tilde'] = bilby.core.prior.Uniform(0, 5000, name='lambda_tilde') +priors['delta_lambda'] = bilby.core.prior.Uniform( + -5000, 5000, name='delta_lambda') + +# Initialise the likelihood by passing in the interferometer data (IFOs) +# and the waveform generator +bilby_likelihood = bilby.gw.GravitationalWaveTransient( + interferometers=interferometers, waveform_generator=waveform_generator, + time_marginalization=False, phase_marginalization=False, + distance_marginalization=False, priors=priors) + +psd_frequency = interferometers[0].frequency_array[1:] H1, H1_vertex = get_H1() L1, L1_vertex = get_L1() -strain_H1 = get_detector_response(frequency_array, waveform, likelihood.parameters, H1, H1_vertex) -strain_L1 = get_detector_response(frequency_array, waveform, likelihood.parameters, L1, L1_vertex) -jaxgw_H1_SNR = inner_product(ifo_list[0].strain_data.frequency_domain_strain, strain_H1, frequency_array, ifo_list[0].power_spectral_density_array) -bilby_H1_SNR = ifo_list[0].inner_product(strain_H1) - -print(jaxgw_H1_SNR, bilby_H1_SNR) +strain_H1 = get_detector_response(psd_frequency,TaylorF2(psd_frequency,jaxgw_params), jaxgw_params,H1,H1_vertex)#interferometers[0].frequency_domain_strain[1:] +strain_L1 = get_detector_response(psd_frequency,TaylorF2(psd_frequency,jaxgw_params), jaxgw_params,L1,L1_vertex)#interferometers[1].frequency_domain_strain[1:] +psd_H1 = interferometers[0].power_spectral_density_array[1:] +psd_L1 = interferometers[1].power_spectral_density_array[1:] + +duration = waveform_generator.duration + +print('SNR of the event in H1: '+str(np.sqrt(inner_product(strain_H1,strain_H1,psd_frequency,psd_H1)))) +print('SNR of the event in L1: '+str(np.sqrt(inner_product(strain_L1,strain_L1,psd_frequency,psd_L1)))) + +@jit +def single_detector_likelihood(params, data, data_f, PSD, detector, detector_vertex): +# waveform = IMRPhenomC(data_f, params) + waveform = TaylorF2(data_f, params) + waveform = get_detector_response(data_f, waveform, params, detector, detector_vertex) +# waveform *= mask + match_filter_SNR = inner_product(waveform, data, data_f, PSD) + optimal_SNR = inner_product(waveform, waveform, data_f, PSD) + return match_filter_SNR, optimal_SNR + +@jit +def single_detector_likelihood_bilby(params, data, data_f, PSD, detector, detector_vertex): +# waveform = IMRPhenomC(data_f, params) + waveform = TaylorF2(data_f, params) + waveform = get_detector_response(data_f, waveform, params, detector, detector_vertex) + log_l = -2 / duration * jnp.vdot(data-waveform, (data-waveform)/PSD) + return log_l.real + +@jit +def logprob_wrap(mass_1, mass_2, luminosity_distance, phase_c, t_c, theta_jn, psi, ra, dec): + params = dict(mass_1=mass_1, mass_2=mass_2, spin_1=0, spin_2=0, luminosity_distance=luminosity_distance, phase_c=phase_c%(2*jnp.pi), t_c=t_c,\ + theta_jn=theta_jn%(jnp.pi), psi=psi%(jnp.pi), ra=ra%(2*jnp.pi), dec=dec%(jnp.pi),\ + f_ref=50., geocent_time = interferometers[0].strain_data.start_time+t_c, start_time=interferometers[0].strain_data.start_time, + greenwich_mean_sidereal_time=greenwich_mean_sidereal_time(interferometers[0].strain_data.start_time)) + H1_SNR = single_detector_likelihood(params, strain_H1, psd_frequency, psd_H1, H1, H1_vertex) + L1_SNR = single_detector_likelihood(params, strain_L1, psd_frequency, psd_L1, L1, L1_vertex) + match_filter_SNR = H1_SNR[0] + L1_SNR[0] + optimal_SNR = H1_SNR[1] + L1_SNR[1] + return match_filter_SNR - optimal_SNR/2 + + +likelihood = lambda x: logprob_wrap(*x) +likelihood = jit(likelihood) +d_likelihood = jit(grad(likelihood)) +para_logp = jit(jax.vmap(likelihood)) + +result = bilby.result.read_in_result(filename='/mnt/home/wwong/GWProject/Tutorial/bilby_tutorial/outdir/bns_example_result.json') + +for i in result.posterior.keys(): + bilby_likelihood.parameters[i] = result.posterior[i].values[-1] + +print("Section where we use bilby waveform generator.") + +waveform = bilby_likelihood.waveform_generator.frequency_domain_strain(bilby_likelihood.parameters) +params = {} +params['mass_1'] = bilby_likelihood.parameters['mass_1'] +params['mass_2'] = bilby_likelihood.parameters['mass_2'] +params['spin_1'] = 0.0#bilby_likelihood.parameters['a_1'] +params['spin_2'] = 0.0#bilby_likelihood.parameters['a_2'] +params['luminosity_distance'] = bilby_likelihood.parameters['luminosity_distance'] +params['phase_c'] = bilby_likelihood.parameters['phase'] +params['t_c'] = 0#bilby_likelihood.parameters['geocent_time'] +params['theta_jn'] = bilby_likelihood.parameters['theta_jn'] +params['psi'] = bilby_likelihood.parameters['psi'] +params['ra'] = bilby_likelihood.parameters['ra'] +params['dec'] = bilby_likelihood.parameters['dec'] +params['f_ref'] = bilby_likelihood.parameters['reference_frequency'] +params['start_time'] = interferometers[0].strain_data.start_time +params['geocent_time'] = bilby_likelihood.parameters['geocent_time'] +params['greenwich_mean_sidereal_time'] = greenwich_mean_sidereal_time(bilby_likelihood.parameters['geocent_time']) + +mask = np.ones(psd_frequency.shape) +mask[psd_frequency Date: Fri, 12 Aug 2022 13:25:30 -0400 Subject: [PATCH 092/300] Add heterodyne likelihood --- example/realtimePE/heterodyneLikelihood.py | 107 +++++++++++++++++++++ 1 file changed, 107 insertions(+) create mode 100644 example/realtimePE/heterodyneLikelihood.py diff --git a/example/realtimePE/heterodyneLikelihood.py b/example/realtimePE/heterodyneLikelihood.py new file mode 100644 index 00000000..6e04d8f6 --- /dev/null +++ b/example/realtimePE/heterodyneLikelihood.py @@ -0,0 +1,107 @@ +from cmath import phase +import numpy as np +import jax.numpy as jnp +import jax + +from ripple.waveforms import IMRPhenomD, IMRPhenomD_utils +import matplotlib.pyplot as plt +from ripple import ms_to_Mc_eta + +from scipy.interpolate import interp1d + +# Get a frequency domain waveform +# source parameters + +m1_msun = 20.0 # In solar masses +m2_msun = 19.0 +chi1 = 0.5 # Dimensionless spin +chi2 = -0.5 +tc = 0.0 # Time of coalescence in seconds +phic = 0.0 # Time of coalescence +dist_mpc = 440 # Distance to source in Mpc +inclination = 0.0 # Inclination Angle +polarization_angle = 0.2 # Polarization angle + +# The PhenomD waveform model is parameterized with the chirp mass and symmetric mass ratio +Mc, eta = ms_to_Mc_eta(jnp.array([m1_msun, m2_msun])) + +# These are the parametrs that go into the waveform generator +# Note that JAX does not give index errors, so if you pass in the +# the wrong array it will behave strangely +theta_ripple = jnp.array([Mc, eta, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle]) +theta_ripple_vec = np.array(jnp.repeat(theta_ripple[None,:],100000,axis=0)*np.random.normal(loc=1,scale=0.001,size=(100000,9))) +theta_ripple_vec[theta_ripple_vec[:,1]>0.25,1] = 0.25 + + +# Now we need to generate the frequency grid +f_l = 24 +f_u = 1024 +del_f = 10 +fs = jnp.arange(f_l, f_u, del_f) + +# And finally lets generate the waveform! +hp_ripple, hc_ripple = IMRPhenomD.gen_IMRPhenomD_polar(fs, theta_ripple) + +@jax.jit +def waveform_gen(theta): + return IMRPhenomD.gen_IMRPhenomD_polar(fs, theta) + +waveform_gen_vec = jax.vmap(waveform_gen) + +# Choosing binning scheme + +def max_phase_diff(f, f_low, f_high, chi=1): + gamma = np.arange(-5,6,1)/3. + f = np.repeat(f[:,None],len(gamma),axis=1) + f_star = np.repeat(f_low, len(gamma)) + f_star[gamma >= 0] = f_high + return 2*np.pi*chi*np.sum((f/f_star)**gamma*np.sign(gamma),axis=1) + +f_fine = np.linspace(f_l, f_u, 10000) +phase_diff_array = max_phase_diff(f_fine,f_l,f_u,chi=1) +bin_f = interp1d(phase_diff_array, f_fine) +n_bin = 1001 +f_bins = np.array([]) +for i in np.linspace(phase_diff_array[0], phase_diff_array[-1], n_bin): + f_bins = np.append(f_bins,bin_f(i)) +f_bins_center = (f_bins[:-1] + f_bins[1:])/2 + +# Compute coefficients from reference waveform + +# IMRPhenomD_jit = jax.vmap(jax.jit(IMRPhenomD.gen_IMRPhenomD_polar),(0,None),0) + +data = IMRPhenomD.gen_IMRPhenomD_polar(f_fine, theta_ripple)[0] +bin_coef = [] +theta_ref = jnp.array([Mc, 0.23, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle]) +h_ref = IMRPhenomD.gen_IMRPhenomD_polar(f_fine, theta_ref)[0] +h_ref_bin_center = IMRPhenomD.gen_IMRPhenomD_polar(f_bins_center, theta_ref)[0] +h_ref_bin_low = IMRPhenomD.gen_IMRPhenomD_polar(f_bins[:-1], theta_ref)[0] +A0_array = [] +A1_array = [] + +data_prod = np.array(data*h_ref.conj()) +for i in range(len(f_bins)-1): + print(i) + f_index = np.where((f_fine >= f_bins[i]) & (f_fine < f_bins[i+1]))[0] + A0_array.append(np.sum(data_prod[f_index])) + A1_array.append(np.sum(data_prod[f_index]*(f_fine[f_index]-f_bins_center[i]))) + +A0_array = jnp.array(A0_array) +A1_array = jnp.array(A1_array) + +# run time evaluation of inner product + +theta_test = jnp.array([Mc, 0.22, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle]) +h_test_fine = IMRPhenomD.gen_IMRPhenomD_polar(f_fine, theta_test)[0] +h_test_bin_center = IMRPhenomD.gen_IMRPhenomD_polar(f_bins_center, theta_test)[0] +h_test_bin_low = IMRPhenomD.gen_IMRPhenomD_polar(f_bins[:-1], theta_test)[0] +true_SNR = jnp.sum(data*h_test_fine.conj()) + +r0 = h_test_bin_center/h_ref_bin_center +r1 = (h_test_bin_low/h_ref_bin_low - r0)/(f_bins[:-1]-f_bins_center) + +bin_SNR = np.sum(A0_array*r0.conj() + A1_array*r1.conj()) + +print(bin_SNR, true_SNR) + + From cd1ea41d6685b3f56b00037ffb4a95d98e3f2a87 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Fri, 12 Aug 2022 13:38:22 -0400 Subject: [PATCH 093/300] Clean up repo and add heterodyne likelihood --- example/GW170817.py | 330 ---------------- example/NFRandomWalk.py | 219 ----------- .../heterodyneLikelihood.py | 0 example/Population_injection.py | 216 ----------- example/blackjax/HMC_injection.py | 135 ------- example/blackjax/Single_event_derivatives.py | 68 ---- example/bns_injection.py | 350 ----------------- example/emcee_injection.py | 92 ----- example/toy_pop_example/GWTC2.py | 161 -------- example/toy_pop_example/GaussianExample.py | 103 ----- example/toy_pop_example/Gaussian_kde.py | 24 -- example/toy_pop_example/PowerLawPlusPeak.py | 183 --------- jaxgw/PE/HeterodyneLikelihood.py | 0 jaxgw/gw/waveform/IMRPhenomB.py | 83 ----- jaxgw/gw/waveform/IMRPhenomC.py | 351 ------------------ jaxgw/gw/waveform/TaylorF2.py | 98 ----- jaxgw/sampler/Gaussian_random_walk.py | 56 --- jaxgw/sampler/MALA.py | 45 --- jaxgw/sampler/NF_proposal.py | 51 --- jaxgw/sampler/maf.py | 95 ----- jaxgw/sampler/realNVP.py | 101 ----- 21 files changed, 2761 deletions(-) delete mode 100644 example/GW170817.py delete mode 100644 example/NFRandomWalk.py rename example/{realtimePE => ParameterEstimation}/heterodyneLikelihood.py (100%) delete mode 100644 example/Population_injection.py delete mode 100644 example/blackjax/HMC_injection.py delete mode 100644 example/blackjax/Single_event_derivatives.py delete mode 100644 example/bns_injection.py delete mode 100644 example/emcee_injection.py delete mode 100644 example/toy_pop_example/GWTC2.py delete mode 100644 example/toy_pop_example/GaussianExample.py delete mode 100644 example/toy_pop_example/Gaussian_kde.py delete mode 100644 example/toy_pop_example/PowerLawPlusPeak.py create mode 100644 jaxgw/PE/HeterodyneLikelihood.py delete mode 100644 jaxgw/gw/waveform/IMRPhenomB.py delete mode 100644 jaxgw/gw/waveform/IMRPhenomC.py delete mode 100644 jaxgw/gw/waveform/TaylorF2.py delete mode 100644 jaxgw/sampler/Gaussian_random_walk.py delete mode 100644 jaxgw/sampler/MALA.py delete mode 100644 jaxgw/sampler/NF_proposal.py delete mode 100644 jaxgw/sampler/maf.py delete mode 100644 jaxgw/sampler/realNVP.py diff --git a/example/GW170817.py b/example/GW170817.py deleted file mode 100644 index 6775d3e6..00000000 --- a/example/GW170817.py +++ /dev/null @@ -1,330 +0,0 @@ -import numpy as np -import bilby -import jax -import jax.numpy as jnp - -from jax.config import config - -from jaxgw.sampler.NF_proposal import nf_metropolis_kernel, nf_metropolis_sampler -config.update("jax_enable_x64", True) - -from jaxgw.gw.likelihood.detector_projection import construct_arm, detector_tensor, antenna_response, get_detector_response - -from jaxgw.gw.likelihood.utils import inner_product -from jaxgw.gw.likelihood.detector_preset import get_H1, get_L1 -from jaxgw.gw.waveform.TaylorF2 import TaylorF2 -from jaxgw.gw.waveform.IMRPhenomC import IMRPhenomC -from jax import random, grad, jit, vmap, jacfwd, jacrev, value_and_grad, pmap - -from jaxgw.sampler.Gaussian_random_walk import rw_metropolis_sampler -from jaxgw.sampler.maf import MaskedAutoregressiveFlow -from jaxgw.sampler.realNVP import RealNVP -from jax.scipy.stats import multivariate_normal -from flax.training import train_state # Useful dataclass to keep train state -import optax # Optimizers - - -""" -This tutorial includes advanced specifications -for analysing binary neutron star event data. -Here GW170817 is used as an example. -""" -import bilby -from gwpy.timeseries import TimeSeries -from bilby.gw.utils import greenwich_mean_sidereal_time - -logger = bilby.core.utils.logger - - -outdir = 'outdir' -data_dir = '/mnt/home/wwong/ceph/GWProject/GWTC/individual_events/' -label = 'GW170817' -time_of_event = bilby.gw.utils.get_event_time(label) -bilby.core.utils.setup_logger(outdir=outdir, label=label) -# GET DATA FROM INTERFEROMETER -# include 'V1' for appropriate O2 events -interferometer_names = ['H1', 'L1', 'V1'] -duration = 32 -roll_off = 0.2 # how smooth is the transition from no signal -# to max signal in a Tukey Window. -psd_offset = -512 # PSD is estimated using data from -# `center_time+psd_offset` to `center_time+psd_offset + psd_duration` -# This determines the time window used to fetch open data. -psd_duration = 1024 -coherence_test = False # coherence between detectors -filter_freq = None # low pass filter frequency to cut signal content above -# Nyquist frequency. The condition is 2 * filter_freq >= sampling_frequency -end_time = time_of_event + duration/2 -start_time = end_time - duration - -psd_duration = 32 * duration -psd_start_time = start_time - psd_duration -psd_end_time = start_time - - -ifo_list = bilby.gw.detector.InterferometerList([]) -for det in ["H1", "L1", "V1"]: - try: - logger.info("Loading signal data for detector %s", det) - data = TimeSeries.read(data_dir+label+'/'+det+'_signal.hdf5') - except: - logger.info("Downloading signal data for ifo {}".format(det)) - data = TimeSeries.fetch_open_data(det, start_time, end_time) - data.write(data_dir+label+'/'+det+'_signal.hdf5') - - ifo = bilby.gw.detector.get_empty_interferometer(det) - ifo.strain_data.set_from_gwpy_timeseries(data) - - - try: - logger.info("Loading psd data for detector %s", det) - psd_data = TimeSeries.read(data_dir+label+'/'+det+'_psd.hdf5') - except: - logger.info("Downloading psd data for ifo {}".format(det)) - psd_data = TimeSeries.fetch_open_data(det, psd_start_time, psd_end_time) - psd_data.write(data_dir+label+'/'+det+'_psd.hdf5') - psd_alpha = 2 * roll_off / duration - psd = psd_data.psd( - fftlength=duration, - overlap=0, - window=("tukey", psd_alpha), - method="median" - ) - ifo.power_spectral_density = bilby.gw.detector.PowerSpectralDensity( - frequency_array=psd.frequencies.value, psd_array=psd.value) - ifo_list.append(ifo) - -logger.info("Saving data plots to {}".format(outdir)) -bilby.core.utils.check_directory_exists_and_if_not_mkdir(outdir) -ifo_list.plot_data(outdir=outdir, label=label) - -# CHOOSE PRIOR FILE -prior = bilby.gw.prior.BNSPriorDict(filename='GW170817.prior') -deltaT = 0.1 -prior['geocent_time'] = bilby.core.prior.Uniform( - minimum=time_of_event - deltaT / 2, - maximum=time_of_event + deltaT / 2, - name='geocent_time', - latex_label='$t_c$', - unit='$s$') -# GENERATE WAVEFORM -# OVERVIEW OF APPROXIMANTS: -# https://www.lsc-group.phys.uwm.edu/ligovirgo/cbcnote/Waveforms/Overview -duration = None # duration and sampling frequency will be overwritten -# to match the ones in interferometers. -sampling_frequency = 4096 -start_time = 0 # set the starting time of the time array -waveform_arguments = { - 'waveform_approximant': 'IMRPhenomPv2_NRTidal', 'reference_frequency': 20} - -source_model = bilby.gw.source.lal_binary_neutron_star -convert_bns = bilby.gw.conversion.convert_to_lal_binary_neutron_star_parameters -waveform_generator = bilby.gw.WaveformGenerator( - duration=duration, - sampling_frequency=sampling_frequency, - start_time=start_time, - frequency_domain_source_model=source_model, - parameter_conversion=convert_bns, - waveform_arguments=waveform_arguments,) - -# CHOOSE LIKELIHOOD FUNCTION -# Time marginalisation uses FFT. -# Distance marginalisation uses a look up table calculated at run time. -# Phase marginalisation is done analytically using a Bessel function. -bilby_likelihood = bilby.gw.likelihood.GravitationalWaveTransient( - ifo_list, - waveform_generator, - time_marginalization=False, - distance_marginalization=False, - phase_marginalization=False,) - -strain_H1 = ifo_list[0].frequency_domain_strain[1:] -strain_L1 = ifo_list[1].frequency_domain_strain[1:] -psd_frequency = ifo_list[0].frequency_array[1:] -psd_H1 = ifo_list[0].power_spectral_density_array[1:] -psd_L1 = ifo_list[1].power_spectral_density_array[1:] - -H1, H1_vertex = get_H1() -L1, L1_vertex = get_L1() - -duration = waveform_generator.duration - -print('SNR of the event in H1: '+str(np.sqrt(inner_product(strain_H1,strain_H1,psd_frequency,psd_H1)))) -print('SNR of the event in L1: '+str(np.sqrt(inner_product(strain_L1,strain_L1,psd_frequency,psd_L1)))) - -@jit -def single_detector_likelihood(params, data, data_f, PSD, detector, detector_vertex): -# waveform = IMRPhenomC(data_f, params) - waveform = TaylorF2(data_f, params) - waveform = get_detector_response(data_f, waveform, params, detector, detector_vertex) - match_filter_SNR = inner_product(waveform, data, data_f, PSD) - optimal_SNR = inner_product(waveform, waveform, data_f, PSD) - return -(-2*match_filter_SNR + optimal_SNR)/2#, match_filter_SNR, optimal_SNR - -@jit -def single_detector_likelihood_bilby(params, data, data_f, PSD, detector, detector_vertex): -# waveform = IMRPhenomC(data_f, params) - waveform = TaylorF2(data_f, params) - waveform = get_detector_response(data_f, waveform, params, detector, detector_vertex) - log_l = -2 / duration * jnp.vdot(data-waveform, (data-waveform)/PSD) - return log_l.real - -@jit -def logprob_wrap(mass_1, mass_2, luminosity_distance, phase_c, t_c, theta_jn, psi, ra, dec): - params = dict(mass_1=mass_1, mass_2=mass_2, spin_1=0, spin_2=0, luminosity_distance=luminosity_distance, phase_c=phase_c%(2*jnp.pi), t_c=t_c+greenwich_mean_sidereal_time(time_of_event), theta_jn=theta_jn%(jnp.pi), psi=psi%(jnp.pi), ra=ra%(2*jnp.pi), dec=dec%(jnp.pi), f_ref=50) -# params = dict(mass_1=mass_1, mass_2=mass_2, spin_1=0, spin_2=0, luminosity_distance=true_ld, theta_jn=0.4, psi=2.659, phase_c=true_phase, t_c=true_gt, ra=1.375, dec=-1.2108) - return single_detector_likelihood(params, strain_H1, psd_frequency, psd_H1, H1, H1_vertex)+single_detector_likelihood(params, strain_L1, psd_frequency, psd_L1, L1, L1_vertex) - - - -likelihood = lambda x: logprob_wrap(*x) -likelihood = jit(likelihood) -d_likelihood = jit(grad(likelihood)) -para_logp = jit(jax.vmap(likelihood)) - -#### Sampling #### - -def train_step(model, state, batch): - def loss(params): - y, log_det = model.apply({'params': params},batch) - mean = jnp.zeros((batch.shape[0],model.n_features)) - cov = jnp.repeat(jnp.eye(model.n_features)[None,:],batch.shape[0],axis=0) - log_det = log_det + multivariate_normal.logpdf(y,mean,cov) - return -jnp.mean(log_det) - grad_fn = jax.value_and_grad(loss) - value, grad = grad_fn(state.params) - state = state.apply_gradients(grads=grad) - return value,state - -train_step = jax.jit(train_step,static_argnums=(0,)) - -def train_flow(rng, model, state, data): - - def train_epoch(state, train_ds, batch_size, epoch, rng): - """Train for a single epoch.""" - train_ds_size = len(train_ds) - steps_per_epoch = train_ds_size // batch_size - - perms = jax.random.permutation(rng, train_ds_size) - perms = perms[:steps_per_epoch * batch_size] # skip incomplete batch - perms = perms.reshape((steps_per_epoch, batch_size)) - for perm in perms: - batch = train_ds[perm, ...] - value, state = train_step(model, state, batch) - - return value, state - - for epoch in range(1, num_epochs + 1): - print('Epoch %d' % epoch) - # Use a separate PRNG key to permute image data during shuffling - rng, input_rng = jax.random.split(rng) - # Run an optimization step over a training batch - value, state = train_epoch(state, data, batch_size, epoch, input_rng) - print('Train loss: %.3f' % value) - - return rng, state - -def sample_nf(model, param, rng_key,n_sample): - rng_key, subkey = random.split(rng_key) - samples = model.apply({'params': param}, subkey, n_sample,param, method=model.sample) - return rng_key,samples - -n_dim = 9 -n_samples = 100 -nf_samples = 10 -n_chains = 100 -learning_rate = 0.01 -momentum = 0.9 -num_epochs = 300 -batch_size = 10000 -look_back_epoch = 10 -start_train_epoch = 100 -train_epoch = 200 -nf_sample_epoch = 25 -total_epoch = 1000 -precompiled = False - -print("Preparing RNG keys") -rng_key = jax.random.PRNGKey(42) -rng_key_ic, rng_key_mcmc, rng_key_nf = jax.random.split(rng_key,3) - -rng_keys_mcmc = jax.random.split(rng_key_mcmc, n_chains) # (nchains,) -rng_keys_nf, init_rng_keys_nf = jax.random.split(rng_key_nf,2) - -print("Finding initial position for chains") - -prior_range = [] -prior_range.append([1.6093862655801942,1.6093862655801943 ]) -prior_range.append([1.1616754457131563,1.1616754457131564]) -prior_range.append([0.1,300.0]) -prior_range.append([0.,2*jnp.pi]) -prior_range.append([0,0.1]) -prior_range.append([0.,jnp.pi]) -prior_range.append([0.,jnp.pi]) -prior_range.append([0.,2*jnp.pi]) -prior_range.append([0.,jnp.pi]) -prior_range = jnp.array(prior_range) - -initial_guess = jax.random.uniform(rng_key_ic,(n_chains,n_dim)) #(n_dim, n_chains) -initial_guess = (initial_guess*(prior_range[:,1]-prior_range[:,0])+prior_range[:,0]) - -from scipy.optimize import minimize - -loss = lambda x: -likelihood(x) - -initial_position = [] -for i in range(n_chains): - res = minimize(loss,initial_guess[i,:],method='Nelder-Mead') - initial_position.append(res.x) - -initial_position = jnp.array(initial_position).T - - -#initial_position = (jax.random.normal(rng_key_ic,(n_chains,n_dim))*0.5 + jnp.array(list(guess_parameters.values()))).T #(n_dim, n_chains) - -print("Initializing MCMC model and normalizing flow model.") - -#model = MaskedAutoregressiveFlow(n_dim,64,4) -model = RealNVP(10,n_dim,64, 1) -params = model.init(init_rng_keys_nf, jnp.ones((1,n_dim)))['params'] - -run_mcmc = jax.vmap(rw_metropolis_sampler, in_axes=(0, None, None, 1, None), - out_axes=0) - -tx = optax.adam(learning_rate, momentum) -state = train_state.TrainState.create(apply_fn=model.apply, params=params, tx=tx) - - -def sample(rng_key, params): - return model.apply({'params': params}, rng_key, nf_samples*n_chains, params, method=model.sample)[0] - -def log_prob_nf_function(params, location): - return model.apply({'params': params}, location, method=model.log_prob) - -sample = jax.jit(sample) -log_prob_nf_function = jax.jit(log_prob_nf_function) - -print("Starting sampling") - -# trained = False -# last_step = initial_position -# chains = [] -# for i in range(total_epoch): -# rng_keys_mcmc, positions, log_prob = run_mcmc(rng_keys_mcmc, n_samples, likelihood, last_step, 0.1) -# positions = positions.at[:,:,3].set(positions[:,:,3]%(2*jnp.pi)) -# positions = positions.at[:,:,5].set(positions[:,:,5]%(jnp.pi)) -# positions = positions.at[:,:,6].set(positions[:,:,6]%(jnp.pi)) -# positions = positions.at[:,:,7].set(positions[:,:,7]%(2*jnp.pi)) -# positions = positions.at[:,:,8].set(positions[:,:,8]%(jnp.pi)) -# last_step = positions[:,-1].T -# if (i > start_train_epoch) and (i%train_epoch == train_epoch-1): -# train_sample = np.concatenate(chains[-look_back_epoch:],axis=1).reshape(-1,n_dim) -# rng_keys_nf, state = train_flow(rng_key_nf, model, state, train_sample) -# trained = True -# if i%nf_sample_epoch == 0 and trained == True: -# rng_keys_nf, nf_chain, log_prob, log_prob_nf = nf_metropolis_sampler(rng_keys_nf, sample, log_prob_nf_function, state.params , para_logp, positions[:,-1]) -# positions = jnp.concatenate((positions,nf_chain),axis=1) -# chains.append(positions) - -# chains = np.concatenate(chains,axis=1) -# nf_samples = sample_nf(model, state.params, rng_keys_nf, 10000) diff --git a/example/NFRandomWalk.py b/example/NFRandomWalk.py deleted file mode 100644 index 708a022e..00000000 --- a/example/NFRandomWalk.py +++ /dev/null @@ -1,219 +0,0 @@ -from bilby.gw.utils import greenwich_mean_sidereal_time -import numpy as np -import bilby -import jax -import jax.numpy as jnp - - -from jax.config import config - -from jaxgw.sampler.NF_proposal import nf_metropolis_kernel, nf_metropolis_sampler -config.update("jax_enable_x64", True) - -from jaxgw.gw.likelihood.detector_projection import construct_arm, detector_tensor, antenna_response, get_detector_response - -from jaxgw.gw.likelihood.utils import inner_product -from jaxgw.gw.likelihood.detector_preset import get_H1, get_L1 -from jaxgw.gw.waveform.TaylorF2 import TaylorF2,TaylorF2_old -from jaxgw.gw.waveform.IMRPhenomC import IMRPhenomC -from jax import random, grad, jit, vmap, jacfwd, jacrev, value_and_grad, pmap - -from jaxgw.sampler.Gaussian_random_walk import rw_metropolis_sampler -from jaxgw.sampler.MALA import mala_kernel, mala_sampler -from jaxgw.sampler.maf import MaskedAutoregressiveFlow -from jaxgw.sampler.realNVP import RealNVP -from jax.scipy.stats import multivariate_normal -from flax.training import train_state # Useful dataclass to keep train state -import optax # Optimizers - - -true_m1 = 5. -true_m2 = 5. -true_ld = 300. -true_phase = 0. -true_gt = 0. - -injection_parameters = dict( - mass_1=true_m1, mass_2=true_m2, spin_1=0.0, spin_2=0.0, luminosity_distance=true_ld, theta_jn=0.4, psi=2.659, - phase_c=true_phase, t_c=true_gt, ra=1.375, dec=-1.2108, f_ref=50, - greenwich_mean_sidereal_time=greenwich_mean_sidereal_time(true_gt),start_time = true_gt, geocent_time = true_gt) - - -#guess_parameters = dict(m1=true_m1, m2=true_m2) - -guess_parameters = dict( - mass_1=true_m1*0.99, mass_2=true_m2*1.01, t_c=true_gt, theta_jn=0.4, psi=2.659, - phase_c=true_phase, ra=1.375, dec=-1.2108) - - - -# Set up interferometers. In this case we'll use two interferometers -# (LIGO-Hanford (H1), LIGO-Livingston (L1). These default to their design -# sensitivity -ifos = bilby.gw.detector.InterferometerList(['H1']) -ifos.set_strain_data_from_power_spectral_densities( - sampling_frequency=2048, duration=4, - start_time=- 3) - -psd = ifos[0].power_spectral_density_array -psd_frequency = ifos[0].frequency_array - -psd_frequency = psd_frequency[jnp.isfinite(psd)] -psd = psd[jnp.isfinite(psd)] - -#waveform = IMRPhenomC(psd_frequency, injection_parameters) -waveform = TaylorF2_old(psd_frequency, injection_parameters) -H1, H1_vertex = get_H1() -L1, L1_vertex = get_L1() -strain_H1 = get_detector_response(psd_frequency, waveform, injection_parameters, H1, H1_vertex) -strain_L1 = get_detector_response(psd_frequency, waveform, injection_parameters, L1, L1_vertex) - -print('SNR of the event in H1: '+str(np.sqrt(inner_product(strain_H1,strain_H1,psd_frequency,psd)))) -print('SNR of the event in L1: '+str(np.sqrt(inner_product(strain_L1,strain_L1,psd_frequency,psd)))) - -@jit -def single_detector_likelihood(params, data, data_f, PSD, detector, detector_vertex): -# waveform = IMRPhenomC(data_f, params) - waveform = TaylorF2_old(data_f, params) - waveform = get_detector_response(data_f, waveform, params, detector, detector_vertex) - match_filter_SNR = inner_product(waveform, data, data_f, PSD) - optimal_SNR = inner_product(waveform, waveform, data_f, PSD) - return -(-2*match_filter_SNR + optimal_SNR)/2#, match_filter_SNR, optimal_SNR - -@jit -def logprob_wrap(mass_1, mass_2, t_c, phase_c, theta_jn, psi, ra, dec): - params = dict(mass_1=mass_1, mass_2=mass_2, spin_1=0, spin_2=0, luminosity_distance=true_ld,\ - phase_c=phase_c%(2*jnp.pi), t_c=t_c,\ - theta_jn=theta_jn%(jnp.pi), psi=psi%(jnp.pi), ra=ra%(2*jnp.pi), dec=dec%(jnp.pi),\ - f_ref=50,greenwich_mean_sidereal_time=greenwich_mean_sidereal_time(true_gt),start_time = true_gt, geocent_time = true_gt) - return single_detector_likelihood(params, strain_H1, psd_frequency, psd, H1, H1_vertex)+single_detector_likelihood(params, strain_L1, psd_frequency, psd, L1, L1_vertex) - -likelihood = lambda x: logprob_wrap(*x) -likelihood = jit(likelihood) -d_likelihood = jit(grad(likelihood)) -para_logp = jit(jax.vmap(likelihood)) - -#### Sampling #### - -def train_step(model, state, batch): - def loss(params): - y, log_det = model.apply({'params': params},batch) - mean = jnp.zeros((batch.shape[0],model.n_features)) - cov = jnp.repeat(jnp.eye(model.n_features)[None,:],batch.shape[0],axis=0) - log_det = log_det + multivariate_normal.logpdf(y,mean,cov) - return -jnp.mean(log_det) - grad_fn = jax.value_and_grad(loss) - value, grad = grad_fn(state.params) - state = state.apply_gradients(grads=grad) - return value,state - -train_step = jax.jit(train_step,static_argnums=(0,)) - -def train_flow(rng, model, state, data): - - def train_epoch(state, train_ds, batch_size, epoch, rng): - """Train for a single epoch.""" - train_ds_size = len(train_ds) - steps_per_epoch = train_ds_size // batch_size - - perms = jax.random.permutation(rng, train_ds_size) - perms = perms[:steps_per_epoch * batch_size] # skip incomplete batch - perms = perms.reshape((steps_per_epoch, batch_size)) - for perm in perms: - batch = train_ds[perm, ...] - value, state = train_step(model, state, batch) - - return value, state - - for epoch in range(1, num_epochs + 1): - print('Epoch %d' % epoch) - # Use a separate PRNG key to permute image data during shuffling - rng, input_rng = jax.random.split(rng) - # Run an optimization step over a training batch - value, state = train_epoch(state, data, batch_size, epoch, input_rng) - print('Train loss: %.3f' % value) - - return rng, state - -def sample_nf(model, param, rng_key,n_sample): - rng_key, subkey = random.split(rng_key) - samples = model.apply({'params': param}, subkey, n_sample,param, method=model.sample) - return rng_key,samples - -n_dim = 8 -n_samples = 100 -nf_samples = 100 -n_chains = 100 -learning_rate = 0.01 -momentum = 0.9 -num_epochs = 300 -batch_size = 10000 -look_back_epoch = 50 -train_epoch = 50 -nf_sample_epoch = 50 -total_epoch = 100 -precompiled = False - -print("Preparing RNG keys") -rng_key = jax.random.PRNGKey(42) -rng_key_ic, rng_key_mcmc, rng_key_nf = jax.random.split(rng_key,3) - -rng_keys_mcmc = jax.random.split(rng_key_mcmc, n_chains) # (nchains,) -rng_keys_nf, init_rng_keys_nf = jax.random.split(rng_key_nf,2) - -print("Initializing MCMC model and normalizing flow model.") - -# prior_range = [] -# prior_range.append([1.0,15.0]) -# prior_range.append([1.0,15.0]) -# #prior_range.append([np.log10(0.1),np.log10(3000.0)]) -# prior_range.append([0.,2*jnp.pi]) -# prior_range.append([0.,jnp.pi]) -# prior_range.append([0.,jnp.pi]) -# prior_range.append([0.,2*jnp.pi]) -# prior_range.append([0.,jnp.pi]) -# prior_range = jnp.array(prior_range) - -# initial_position = jax.random.uniform(rng_key_ic,(n_chains,n_dim)) #(n_dim, n_chains) -# initial_position = (initial_position*(prior_range[:,1]-prior_range[:,0])+prior_range[:,0]).T - -initial_position = (jax.random.normal(rng_key_ic,(n_chains,n_dim))*0.5 + jnp.array(list(guess_parameters.values()))).T #(n_dim, n_chains) - - -#model = MaskedAutoregressiveFlow(n_dim,64,4) -model = RealNVP(10,n_dim,64, 1) -params = model.init(init_rng_keys_nf, jnp.ones((1,n_dim)))['params'] - -run_mcmc = jax.vmap(mala_sampler, in_axes=(0, None, None, None, 1, None), - out_axes=0) - -tx = optax.adam(learning_rate, momentum) -state = train_state.TrainState.create(apply_fn=model.apply, params=params, tx=tx) - - -def sample(rng_key, params): - return model.apply({'params': params}, rng_key, n_samples*n_chains, params, method=model.sample)[0] - -def log_prob_nf_function(params, location): - return model.apply({'params': params}, location, method=model.log_prob) - -sample = jax.jit(sample) -log_prob_nf_function = jax.jit(log_prob_nf_function) - -trained = False -last_step = initial_position -chains = [] -for i in range(total_epoch): - rng_keys_mcmc, positions, log_prob = run_mcmc(rng_keys_mcmc, n_samples, likelihood, d_likelihood, last_step,0.001) - last_step = positions[:,-1].T - if i%train_epoch == train_epoch-1: - train_sample = np.concatenate(chains[-look_back_epoch:],axis=1).reshape(-1,n_dim) - rng_keys_nf, state = train_flow(rng_key_nf, model, state, train_sample) - trained = True - if i%nf_sample_epoch == 0 and trained == True: - rng_keys_nf, nf_chain, log_prob, log_prob_nf = nf_metropolis_sampler(rng_keys_nf, sample, log_prob_nf_function, state.params , para_logp, positions[:,-1]) - positions = jnp.concatenate((positions,nf_chain),axis=1) - chains.append(positions) - -chains = np.concatenate(chains,axis=1) -nf_samples = sample_nf(model, state.params, rng_keys_nf, 10000) diff --git a/example/realtimePE/heterodyneLikelihood.py b/example/ParameterEstimation/heterodyneLikelihood.py similarity index 100% rename from example/realtimePE/heterodyneLikelihood.py rename to example/ParameterEstimation/heterodyneLikelihood.py diff --git a/example/Population_injection.py b/example/Population_injection.py deleted file mode 100644 index dfb32dac..00000000 --- a/example/Population_injection.py +++ /dev/null @@ -1,216 +0,0 @@ -import jax.numpy as jnp -import numpy as np -import copy -import bilby -import jax -from jax import random, grad, jit, vmap, jacfwd, jacrev, value_and_grad -from jax.experimental.optimizers import adam -from jax.config import config -config.update("jax_enable_x64", True) - -from jaxgw.gw.likelihood.utils import inner_product -from jaxgw.gw.waveform.IMRPhenomC import IMRPhenomC -from jaxgw.gw.likelihood.detector_preset import get_H1,get_L1 -from jaxgw.gw.likelihood.detector_projection import get_detector_response - - -import matplotlib.pyplot as plt -import matplotlib as mpl - -params = {'axes.labelsize': 32, - 'font.family': 'serif', - 'font.serif': 'Computer Modern Raman', - 'font.size': 32, - 'axes.linewidth': 2, - 'legend.fontsize': 28, - 'xtick.labelsize': 28, - 'xtick.top': True, - 'xtick.direction': "in", - 'ytick.labelsize': 20, - 'ytick.right': True, - 'ytick.direction': "in", - 'axes.grid' : False, - 'text.usetex': True, - 'savefig.dpi' : 100, - 'lines.markersize' : 14, -# 'axes.formatter.useoffset': False, - 'axes.formatter.limits' : (-3,3)} - -mpl.rcParams['text.latex.preamble'] = [r'\usepackage{amsmath}'] #for \text command - -mpl.rcParams.update(params) - -key = random.PRNGKey(42) - -######################################## -# Defining our model -######################################## - -# Since truncated power law is not differentiable, we choose tanh as a smoother cutoff -x_axis = jnp.linspace(1,150,100000) -@jit -def power_law_tanh(x,params): - alpha = params['alpha'] - xmin = params['xmin'] - xmax = params['xmax'] - lower_window = (jnp.tanh((x-xmin)*10)+1)/2 - upper_window = -(jnp.tanh((x-xmax)*10)-1)/2 - power_law = x**-alpha - output_unnorm = power_law*lower_window*upper_window - # This normalization factor is supposed to be a good approximation but not perfect - norm = jnp.trapz(x_axis**-alpha*(jnp.tanh(x_axis-xmin)+1)/2*(-(jnp.tanh(x_axis-xmax)-1)/2),x=x_axis) - output = output_unnorm/norm - return output - -@jit -def gaussian(x,mean,sigma): - return (1./jnp.sqrt(2*jnp.pi)/sigma)*jnp.exp(-(((x-mean)/sigma)**2)/2) - -@jit -def power_law_plus_peak(x,params): -# !!! Add smoothing later -# Since each component is normalized, the combine pdf should be normalized - powerlaw = power_law_tanh(x,params) - peak = gaussian(x,params['mean'],params['sigma']) - combine = (1-params['mixing'])*powerlaw+params['mixing']*peak - return combine - -true_ld = 600. -true_phase = 0. -true_gt = 0. - -# Set up interferometers. In this case we'll use two interferometers -# (LIGO-Hanford (H1), LIGO-Livingston (L1). These default to their design -# sensitivity -ifos = bilby.gw.detector.InterferometerList(['H1']) -ifos.set_strain_data_from_power_spectral_densities( - sampling_frequency=2048, duration=128, - start_time=- 3) - -psd = ifos[0].power_spectral_density_array -psd_frequency = ifos[0].frequency_array -psd_frequency = psd_frequency[jnp.isfinite(psd)] -psd = psd[jnp.isfinite(psd)] - -H1, H1_vertex = get_H1() -L1, L1_vertex = get_L1() - -def gen_params(m1): - params = dict(mass_1=m1, mass_2=m1, spin_1=0., spin_2=0., luminosity_distance=true_ld, phase_c=true_phase, t_c=true_gt, theta_jn=0.4, psi=2.659, ra=1.375, dec=-1.2108) - return params - -def gen_event(params,detector, detector_vertex): - waveform = IMRPhenomC(psd_frequency, params) - waveform = get_detector_response(psd_frequency, waveform, params, detector, detector_vertex) - return waveform - -@jit -def single_detector_likelihood(params, data, data_f, PSD, detector, detector_vertex): - waveform = IMRPhenomC(data_f, params) - waveform = get_detector_response(data_f, waveform, params, detector, detector_vertex) - match_filter_SNR = inner_product(waveform, data, data_f, PSD) - optimal_SNR = inner_product(waveform, waveform, data_f, PSD) - return (-2*match_filter_SNR + optimal_SNR)/2 - -@jit -def log_prob(params, strain_H1, strain_L1): - return single_detector_likelihood(params, strain_H1, psd_frequency, psd, H1, H1_vertex)+single_detector_likelihood(params, strain_L1, psd_frequency, psd, L1, L1_vertex) - -@jit -def log_prob_scan(args,index): - result = log_prob(args[0][index],args[1][index],args[2][index]) - return args, result - -######################################## -# Power law Only -######################################## - -true_param = {} -true_param['alpha'] = 2.63 -true_param['xmin'] = 4.59 -true_param['xmax'] = 86.22 -true_param['mean'] = 33.07 -true_param['sigma'] = 5.69 -true_param['mixing'] = 0.3 - -N_sample = 500 -obs_std = 0.1 - -m1_sample = jnp.empty(0) - - -while m1_sample.shape[0] start_train_epoch) and (i%train_epoch == train_epoch-1): - # train_sample = np.concatenate(chains[-look_back_epoch:],axis=1).reshape(-1,n_dim) - # rng_keys_nf, state = train_flow(rng_key_nf, model, state, train_sample) - # trained = True - # if i%nf_sample_epoch == 0 and trained == True: - # rng_keys_nf, nf_chain, log_prob, log_prob_nf = nf_metropolis_sampler(rng_keys_nf, sample, log_prob_nf_function, state.params , para_logp, positions[:,-1]) - # positions = jnp.concatenate((positions,nf_chain),axis=1) - chains.append(positions) - -chains = np.concatenate(chains,axis=1) -nf_samples = sample_nf(model, state.params, rng_keys_nf, 10000) - -def chain_to_param(chain): - return dict(chirp_mass=chain[0], mass_ratio=chain[1], luminosity_distance=10**chain[2], - phase=chain[3], geocent_time=chain[4], theta_jn=chain[5], psi=chain[6], - ra=chain[7], dec=chain[8],a_1=0,a_2=0,tilt_1=0,tilt_2=0,phi_12=0,phi_jl=0) diff --git a/example/emcee_injection.py b/example/emcee_injection.py deleted file mode 100644 index fb56c67c..00000000 --- a/example/emcee_injection.py +++ /dev/null @@ -1,92 +0,0 @@ -import numpy as np -import bilby -import jax -import jax.numpy as jnp -import time - -from jax.config import config -config.update("jax_enable_x64", True) - -from jaxgw.gw.likelihood.detector_projection import construct_arm, detector_tensor, antenna_response, get_detector_response - -from jaxgw.gw.likelihood.utils import inner_product -from jaxgw.gw.likelihood.detector_preset import get_H1, get_L1 -from jaxgw.gw.waveform.TaylorF2 import TaylorF2 -from jaxgw.gw.waveform.IMRPhenomC import IMRPhenomC -from jax import random, grad, jit, vmap, jacfwd, jacrev, value_and_grad, pmap - - -true_m1 = 15. -true_m2 = 5. -true_ld = 600. -true_phase = 0. -true_gt = 0. - -injection_parameters = dict( - mass_1=true_m1, mass_2=true_m2, spin_1=0.0, spin_2=0.0, luminosity_distance=true_ld, theta_jn=0.4, psi=2.659, - phase_c=true_phase, t_c=true_gt, ra=1.375, dec=-1.2108) - - -#guess_parameters = dict(m1=true_m1, m2=true_m2) - -guess_parameters = dict( - mass_1=true_m1, mass_2=true_m2, luminosity_distance=true_ld, theta_jn=0.4, psi=2.659, - phase_c=true_phase, t_c=true_gt, ra=1.375, dec=-1.2108) - - - -# Set up interferometers. In this case we'll use two interferometers -# (LIGO-Hanford (H1), LIGO-Livingston (L1). These default to their design -# sensitivity -ifos = bilby.gw.detector.InterferometerList(['H1']) -ifos.set_strain_data_from_power_spectral_densities( - sampling_frequency=2048, duration=30, - start_time=- 3) - -psd = ifos[0].power_spectral_density_array -psd_frequency = ifos[0].frequency_array - -psd_frequency = psd_frequency[jnp.isfinite(psd)] -psd = psd[jnp.isfinite(psd)] - -waveform = IMRPhenomC(psd_frequency, injection_parameters) -H1, H1_vertex = get_H1() -L1, L1_vertex = get_L1() -strain_H1 = get_detector_response(psd_frequency, waveform, injection_parameters, H1, H1_vertex) -strain_L1 = get_detector_response(psd_frequency, waveform, injection_parameters, L1, L1_vertex) - -print('SNR of the event in H1: '+str(np.sqrt(inner_product(strain_H1,strain_H1,psd_frequency,psd)))) -print('SNR of the event in L1: '+str(np.sqrt(inner_product(strain_L1,strain_L1,psd_frequency,psd)))) - -def single_detector_likelihood(params, data, data_f, PSD, detector, detector_vertex): - waveform = IMRPhenomC(data_f, params) -# waveform = TaylorF2(data_f, params) - waveform = get_detector_response(data_f, waveform, params, detector, detector_vertex) - match_filter_SNR = inner_product(waveform, data, data_f, PSD) - optimal_SNR = inner_product(waveform, waveform, data_f, PSD) - return (-2*match_filter_SNR + optimal_SNR)/2#, match_filter_SNR, optimal_SNR - -#@jit -#def logprob_wrap(m1, m2): -# params = dict(mass_1=m1, mass_2=m2, spin_1=0, spin_2=0, luminosity_distance=true_ld, phase_c=true_phase, t_c=true_gt, theta_jn=0.4, psi=2.659, ra=1.375, dec=-1.2108) -# return single_detector_likelihood(params, strain_H1, psd_frequency, psd, H1, H1_vertex)+single_detector_likelihood(params, strain_L1, psd_frequency, psd, L1, L1_vertex) -# -def log_prob(params): - if (params[0]<=0) or (params[1]<=0): - return -jnp.inf - params = dict(mass_1=params[0], mass_2=params[1], spin_1=0, spin_2=0, luminosity_distance=params[2], phase_c=params[3], t_c=params[4], theta_jn=params[5], psi=params[6], ra=params[7], dec=params[8]) - return single_detector_likelihood(params, strain_H1, psd_frequency, psd, H1, H1_vertex)+single_detector_likelihood(params, strain_L1, psd_frequency, psd, L1, L1_vertex) - -################################################################ -## BlackJax section -################################################################ - -#import emcee -# -#nwalkers = 32 -#ndim = 9 -#p0 = np.random.rand(nwalkers, ndim) + list(guess_parameters.values()) -#sampler = emcee.EnsembleSampler(nwalkers, ndim, log_prob) -#state = sampler.run_mcmc(p0, 100) -#sampler.reset() -#sampler.run_mcmc(state, 5000) diff --git a/example/toy_pop_example/GWTC2.py b/example/toy_pop_example/GWTC2.py deleted file mode 100644 index 17f253e6..00000000 --- a/example/toy_pop_example/GWTC2.py +++ /dev/null @@ -1,161 +0,0 @@ -import numpy as np -import jax.numpy as jnp -import copy -import astropy.units as u -import h5py -from jax import random, grad, jit, vmap, value_and_grad -from jax.ops import index_update -from astropy.cosmology import Planck15 -from scipy.interpolate import interp1d -from jax.experimental.optimizers import adam - -key = random.PRNGKey(42) - -######################################## -# Defining our model -######################################## - -def truncated_power_law(x,alpha,xmin,xmax): - norm = (xmax**(1-alpha)-xmin**(1-alpha))/(1-alpha) - output = (x**-alpha)/norm - output = index_update(output,((xxmax)),0) - return output - -#truncated_power_law = jit(truncated_power_law) - -@jit -def gaussian(x,mean,sigma): - return (1./jnp.sqrt(2*jnp.pi)/sigma)*jnp.exp(-(((x-mean)/sigma)**2)/2) - -def power_law_plus_peak(x,params): -# !!! Add smoothing later -# Since each component is normalized, the combine pdf should be normalized - powerlaw = truncated_power_law(x,params['alpha'],params['xmin'],params['xmax']) - peak = gaussian(x,params['mean'],params['sigma']) - combine = (1-params['mixing'])*powerlaw+params['mixing']*peak - return combine - - -z_range = [0.,1] -z_axis = jnp.linspace(z_range[0],z_range[1],10000) -dVdz = jnp.array(Planck15.differential_comoving_volume(z_axis).value/1e9) - -@jit -def redshift_distribution(z,kappa): - dVdz_local = jnp.interp(z,z_axis,dVdz) - norm_z = jnp.trapz((1+z_axis)**(kappa-1)*jnp.array(dVdz)) - return (1+z)**kappa*dVdz_local/norm_z - -def combine_pdf(params,data): - m1 = data[..., 0] - q = data[..., 1] - z = data[..., 2] - p_m1 = power_law_plus_peak(m1,params) - p_q = truncated_power_law(q,params['beta'],0.01,1) - p_z = redshift_distribution(z,params['kappa']) - return p_m1*p_q*p_z - -def population_likelihood(params, data, prior): - combine_pdf_local = combine_pdf(params,data) - selection_bias = evaluate_selection(params,selection_samples) - output = jnp.sum(jnp.log(jnp.mean(combine_pdf_local/prior/selection_bias,axis=1))) - if jnp.isfinite(output): - return output - else: - return -jnp.inf - -######################################## -# Generating mock data for pipeline testing -######################################## - -true_param = {} -true_param['alpha'] = 2.63 -true_param['beta'] = 1.26 -true_param['xmin'] = 4.59 -true_param['xmax'] = 86.22 -true_param['mean'] = 33.07 -true_param['sigma'] = 5.69 -true_param['mixing'] = 0.1 -true_param['kappa'] = 0. - -N_sample = 1000 - -key, *subkeys = random.split(key,num=4) -m1_sample = random.uniform(subkeys[0],shape=(N_sample,1))*98+2 -q_sample = random.uniform(subkeys[0],shape=(N_sample,1))*0.99+0.01 -z_sample = random.uniform(subkeys[0],shape=(N_sample,1)) -data = jnp.concatenate((m1_sample, q_sample, z_sample), axis=1) - -######################################## -# Defining function to compute the selection bias -######################################## -O12 = h5py.File('./data/injections_O1O2an_spin.h5','r') -O3 = h5py.File('./data/o3a_bbhpop_inj_info.hdf','r') -O3_selection= (O3['injections/ifar_gstlal'][()]>1) | (O3['injections/ifar_pycbc_bbh'][()]>1) | (O3['injections/ifar_pycbc_full'][()]>1) -m1 = np.append(O12['mass1_source'][()],O3['injections/mass1_source'][()][O3_selection]) -m2 = np.append(O12['mass2_source'][()],O3['injections/mass2_source'][()][O3_selection]) -z = np.append(O12['redshift'][()],O3['injections/redshift'][()][O3_selection]) -pdraw = np.append(O12['sampling_pdf'][()],O3['injections/sampling_pdf'][()][O3_selection]) -pdraw = pdraw/m1 -# !!! Remember to fix the Jacobian going from (m1,m2,z) -> (m1,q,z) -selection_samples = np.array([m1,m2/m1,z]).T -Ndraw = O3.attrs['total_generated']+7.1*1e7 - -def evaluate_selection(params,data): - likelihood = combine_pdf(params,data) - return jnp.sum(likelihood/pdraw)/Ndraw - -######################################## -# loading GWTC2 data -######################################## - -data = np.load('./data/GWTC12_m1m2z_highsig.npz') -posterior = data['posterior_sample'] -posterior[...,1] = posterior[...,1]/posterior[...,0] -prior = data['prior'][:,:,0] # !!! Remember to fix the Jacobian going from (m1,m2,z) -> (m1,q,z) -prior = prior/posterior[:,:,0] -N_event = prior.shape[0] - -######################################## -# Checking Gradient -######################################## - -def make_param(alpha=2.63,beta=1.26,xmin=3.59,xmax=86.22,mixing=0.1,mean=33.07,sigma=5.69,kappa=0.): - param = {} - param['alpha'] = alpha - param['xmin'] = xmin - param['xmax'] = xmax - param['mean'] = mean - param['sigma'] = sigma - param['mixing'] = mixing - param['beta'] = beta - param['kappa'] = kappa - return param - - - -def compute_dLdt(alpha,beta,xmin,xmax,mixing,mean,sigma,kappa=0.): - param = make_param(alpha,beta,xmin,xmax,mixing,mean,sigma,kappa) - - L = population_likelihood(param,posterior,prior) - dLdlambda = jnp.stack(list(grad(population_likelihood)(param,posterior,prior).values())) - dLdtheta = grad(population_likelihood,argnums=1)(param,posterior,prior) - return L, dLdtheta[None]/dLdlambda.reshape(-1,1,1,1) - - -learning_rate = 1e-1 -opt_init, opt_update, get_params = adam(learning_rate) -opt_state = opt_init((true_param)) - -def step(step, opt_state): - params = get_params(opt_state) - value, grads = value_and_grad(population_likelihood)(params, posterior, prior) - opt_state = opt_update(step, grads, opt_state) - return value, opt_state - -for i in range(200): - value, opt_state = step(i, opt_state) - if jnp.isnan(value): - break - print(value,get_params(opt_state)) - diff --git a/example/toy_pop_example/GaussianExample.py b/example/toy_pop_example/GaussianExample.py deleted file mode 100644 index 05799d92..00000000 --- a/example/toy_pop_example/GaussianExample.py +++ /dev/null @@ -1,103 +0,0 @@ -import numpy as np -import jax.numpy as jnp -from jax import random, grad, jit, vmap, value_and_grad, jacfwd, jacrev, hessian -from jax.experimental.optimizers import adam -import matplotlib.pyplot as plt -import matplotlib as mpl -params = {'axes.labelsize': 32, - 'font.family': 'serif', - 'font.serif': 'Computer Modern Raman', - 'font.size': 32, - 'axes.linewidth': 2, - 'legend.fontsize': 28, - 'xtick.labelsize': 28, - 'xtick.top': True, - 'xtick.direction': "in", - 'ytick.labelsize': 20, - 'ytick.right': True, - 'ytick.direction': "in", - 'axes.grid' : False, - 'text.usetex': True, - 'savefig.dpi' : 100, - 'lines.markersize' : 14, -# 'axes.formatter.useoffset': False, - 'axes.formatter.limits' : (-3,3)} - -mpl.rcParams['text.latex.preamble'] = [r'\usepackage{amsmath}'] #for \text command - -mpl.rcParams.update(params) - -key, *sub_keys = random.split(random.PRNGKey(32),num=4) - -@jit -def gaussian(x,mean,sigma): - return (1./jnp.sqrt(2*jnp.pi)/sigma)*jnp.exp(-(((x-mean)/sigma)**2)/2) - -@jit -def log_gaussian(x,mean,sigma): - return jnp.log(gaussian(x,mean,sigma)) - - -@jit -def sum_log_gaussian(x,mean,sigma): - return jnp.sum(jnp.log(gaussian(x,mean,sigma))) - -@jit -def population_likelihood(params,data): - return -jnp.sum(jnp.log(gaussian(data,params[0],params[1]))) - -@jit -def population_likelihood_event(point,params,obs_std,data): - return -jnp.sum(jnp.log(gaussian(data,point[:,None],obs_std)*gaussian(point[:,None],params[0],params[1]))) - - -N_obs = 1000 -N_subpop = 100 -true_param = jnp.array([0.,1]) -obs_std = 0.1 -true_data = (random.normal(sub_keys[0],shape=(N_obs,))*true_param[1]+true_param[0]) -true_data = jnp.append(true_data,(random.normal(sub_keys[1],shape=(N_subpop,))*0.1)) -obs_data = true_data[:,None]+random.normal(sub_keys[2],shape=(N_obs+N_subpop,100))*obs_std - -index = np.random.choice(np.arange(N_obs+N_subpop),replace=False,size=N_obs+N_subpop) -obs_data = obs_data[index] -true_data = true_data[index] - -learning_rate = 1e-1 -opt_init, opt_update, get_params = adam(learning_rate) -opt_state = opt_init((true_data,[jnp.array(10.),jnp.array(10.)])) - -def step(step, opt_state): - params = get_params(opt_state) - value, grads = value_and_grad(population_likelihood_event,argnums=(0,1))(params[0],params[1], obs_std, obs_data) - opt_state = opt_update(step, grads, opt_state) - return value, opt_state - -for i in range(200): - value, opt_state = step(i, opt_state) - print(value,get_params(opt_state)[1]) - -best_x, best_lambda = get_params(opt_state) - -dlambdadtheta = jacfwd(jacrev(population_likelihood_event),argnums=1)(best_x,best_lambda,obs_std,obs_data) -#dthetadlambda = jacfwd(jacrev(population_likelihood_event,argnums=1))(best_x,best_lambda,obs_std,obs_data) - - - -fig,ax = plt.subplots(1,3,figsize=(30,9)) -ax[0].hist(true_data,bins=50,density=True,histtype='step',lw=3,label='Truth') -axis = np.linspace(ax[0].get_xlim()[0],ax[0].get_xlim()[1],1000) -ax[0].plot(axis,gaussian(axis,best_lambda[0],best_lambda[1]),label='Best fitted') -ax[0].set_ylabel(r'$p(x)$') -ax[0].set_xlabel(r'$x$') -ax[0].legend(loc='upper right') -ax[1].plot(dlambdadtheta[0],label='Raw') -ax[1].plot(dlambdadtheta[0][np.argsort(dlambdadtheta[0])],label='sorted',lw=5) -ax[1].legend(loc='upper left') -ax[1].set_xlabel('Event number') -ax[1].set_ylabel(r'$\frac{\partial^2\mathcal{L}}{\partial \theta \partial \mu}$') -ax[2].plot(dlambdadtheta[1]) -ax[2].plot(dlambdadtheta[1][np.argsort(dlambdadtheta[1])],lw=5) -ax[2].set_xlabel('Event number') -ax[2].set_ylabel(r'$\frac{\partial^2\mathcal{L}}{\partial \theta \partial \sigma}$') -fig.show() diff --git a/example/toy_pop_example/Gaussian_kde.py b/example/toy_pop_example/Gaussian_kde.py deleted file mode 100644 index a67c2694..00000000 --- a/example/toy_pop_example/Gaussian_kde.py +++ /dev/null @@ -1,24 +0,0 @@ -import jax -import jax.numpy as jnp -from jax import jit,vmap - -@jit -def gaussian(x,mean,sigma): - return (1./jnp.sqrt(2*jnp.pi)/sigma)*jnp.exp(-(((x-mean)/sigma)**2)/2) - -@jit -def multivariate_gaussian(x,mean,covariance,dim=1): - numerator = jnp.exp(-1./2*(x-mean).T@jnp.linalg.inv(covariance)@(x-mean)) - denominator = jnp.sqrt((2*jnp.pi)**dim*jnp.linalg.det(covariance)) - return numerator/denominator - -batch_multivariate_gaussian = vmap(multivariate_gaussian, (None,0,None), 0) - -def gaussian_kde(datapoint,training_point): - n = datapoint.shape[0] - d = datapoint.shape[1] - bandwidth = n**(-1/(d+4)) - cov_matrix = jnp.eye(d) - return jnp.mean(batch_multivariate_gaussian(datapoint,training_point,cov_matrix,dim=d)) - - diff --git a/example/toy_pop_example/PowerLawPlusPeak.py b/example/toy_pop_example/PowerLawPlusPeak.py deleted file mode 100644 index 9530aef6..00000000 --- a/example/toy_pop_example/PowerLawPlusPeak.py +++ /dev/null @@ -1,183 +0,0 @@ -import jax.numpy as jnp -import copy -from jax import random, grad, jit, vmap, jacfwd, jacrev, value_and_grad -from jax.experimental.optimizers import adam -import matplotlib.pyplot as plt -import matplotlib as mpl -params = {'axes.labelsize': 32, - 'font.family': 'serif', - 'font.serif': 'Computer Modern Raman', - 'font.size': 32, - 'axes.linewidth': 2, - 'legend.fontsize': 28, - 'xtick.labelsize': 28, - 'xtick.top': True, - 'xtick.direction': "in", - 'ytick.labelsize': 20, - 'ytick.right': True, - 'ytick.direction': "in", - 'axes.grid' : False, - 'text.usetex': True, - 'savefig.dpi' : 100, - 'lines.markersize' : 14, -# 'axes.formatter.useoffset': False, - 'axes.formatter.limits' : (-3,3)} - -mpl.rcParams['text.latex.preamble'] = [r'\usepackage{amsmath}'] #for \text command - -mpl.rcParams.update(params) - - - - -key = random.PRNGKey(42) - -######################################## -# Defining our model -######################################## - -def truncated_power_law(x,alpha,xmin,xmax): - norm = (xmax**(1-alpha)-xmin**(1-alpha))/(1-alpha) - output = (x**-alpha)/norm - output = index_update(output,((xxmax)),0) - return output - - -# Since truncated power law is not differentiable, we choose tanh as a smoother cutoff -x_axis = jnp.linspace(1,150,100000) -@jit -def power_law_tanh(x,params): - alpha = params['alpha'] - xmin = params['xmin'] - xmax = params['xmax'] - lower_window = (jnp.tanh((x-xmin)*10)+1)/2 - upper_window = -(jnp.tanh((x-xmax)*10)-1)/2 - power_law = x**-alpha - output_unnorm = power_law*lower_window*upper_window - # This normalization factor is supposed to be a good approximation but not perfect - norm = jnp.trapz(x_axis**-alpha*(jnp.tanh(x_axis-xmin)+1)/2*(-(jnp.tanh(x_axis-xmax)-1)/2),x=x_axis) - output = output_unnorm/norm - return output - -@jit -def gaussian(x,mean,sigma): - return (1./jnp.sqrt(2*jnp.pi)/sigma)*jnp.exp(-(((x-mean)/sigma)**2)/2) - -@jit -def power_law_plus_peak(x,params): -# !!! Add smoothing later -# Since each component is normalized, the combine pdf should be normalized - powerlaw = power_law_tanh(x,params) - peak = gaussian(x,params['mean'],params['sigma']) - combine = (1-params['mixing'])*powerlaw+params['mixing']*peak - return combine - -@jit -def population_likelihood_powerlaw(point,params,obs_std,data): - return -jnp.sum(jnp.log(gaussian(data,point[:,None],obs_std)*power_law_tanh(point[:,None],params))) - -def population_likelihood_powerlaw_peak(point,params,obs_std,data): - if params['mixing'] < 0: - params['mixing'] = 0. - return -jnp.sum(jnp.log(gaussian(data,point[:,None],obs_std)*power_law_plus_peak(point[:,None],params))) - -######################################## -# Power law Only -######################################## - -true_param = {} -true_param['alpha'] = 2.63 -true_param['xmin'] = 4.59 -true_param['xmax'] = 86.22 -true_param['mean'] = 33.07 -true_param['sigma'] = 5.69 -true_param['mixing'] = 0.3 - -N_sample = 1000 -obs_std = 0.01 - -m1_sample = jnp.empty(0) - - -while m1_sample.shape[0]= jnp.expand_dims(d0, 0)).astype(jnp.float32)] - return masks - -class MaskedDense(nn.Module): - n_dim: int - n_hidden: int - kernel_init: Callable = nn.initializers.lecun_normal() - bias_init: Callable = nn.initializers.zeros - - @nn.compact - def __call__(self, x, mask): - weight = self.param('weights', self.kernel_init, (self.n_dim, self.n_hidden)) - bias = self.param('bias', self.bias_init, (self.n_hidden,)) - return jnp.dot(x, weight * mask) + bias - -class MaskedAutoEncoder(nn.Module): - n_dim: int - n_hidden: int - - def setup(self): - self.mask = get_masks(self.n_dim, self.n_hidden) - self.up = MaskedDense(self.n_dim, self.n_hidden) - self.mid = MaskedDense(self.n_hidden, self.n_hidden) - self.down = MaskedDense(self.n_hidden, 2*self.n_dim) - - def __call__(self, inputs): - log_weight, bias = self.forward(inputs) - outputs = (inputs - bias)*jnp.exp(-log_weight) - log_jacobian = -jnp.sum(log_weight, axis=-1) - return outputs, log_jacobian - - def forward(self, inputs): - x = self.up(inputs, self.mask[0]) - x = nn.swish(x) - x = self.mid(x, self.mask[1]) - x = nn.swish(x) - log_weight, bias = self.down(x, self.mask[2].tile(2)).split(2, -1) - return log_weight, bias - - def inverse(self, inputs): - outputs = jnp.zeros_like(inputs) - for i_col in range(inputs.shape[1]): - log_weight, bias = self.forward(outputs) - outputs = jax.ops.index_update( - outputs, jax.ops.index[:, i_col], inputs[:, i_col] * jnp.exp(log_weight[:, i_col]) + bias[:, i_col] - ) - log_det_jacobian = -log_weight.sum(-1) - return outputs, log_det_jacobian - -class MaskedAutoregressiveFlow(nn.Module): - n_dim: int - n_hidden: int - n_layer: int - - def setup(self): - self.layers = [MaskedAutoEncoder(self.n_dim, self.n_hidden) for _ in range(self.n_layer)] - - def __call__(self, inputs): - log_jacobian = 0 - for layer in self.layers: - inputs, log_jacobian_ = layer(inputs) - inputs = inputs[:,::-1] - log_jacobian += log_jacobian_ - return inputs, log_jacobian - - def inverse(self, inputs): - # Be careful about flipping the inputs when inverting the flow. - log_jacobian = 0 - for layer in reversed(self.layers): - inputs, log_jacobian_ = layer.inverse(inputs) - inputs = inputs[:,::-1] - log_jacobian += log_jacobian_ - return inputs, log_jacobian - - def sample(self, rng_key, n_samples, params): - mean = jnp.zeros((n_samples,self.n_dim)) - cov = jnp.repeat(jnp.eye(self.n_dim)[None,:],n_samples,axis=0) - gaussian = jax.random.multivariate_normal(rng_key, mean, cov) - samples = self.apply({'params': params},gaussian,method=self.inverse) - return samples \ No newline at end of file diff --git a/jaxgw/sampler/realNVP.py b/jaxgw/sampler/realNVP.py deleted file mode 100644 index eeb14d26..00000000 --- a/jaxgw/sampler/realNVP.py +++ /dev/null @@ -1,101 +0,0 @@ -from typing import Sequence, Callable -import jax -import jax.numpy as jnp -from flax import linen as nn -import numpy as np - -class MLP(nn.Module): - features: Sequence[int] - activation: Callable = nn.relu - use_bias: bool = True - init_weight_scale: float = 1e-4 - kernel_i: Callable = jax.nn.initializers.variance_scaling - - def setup(self): - self.layers = [nn.Dense(feat, use_bias=self.use_bias, kernel_init=self.kernel_i(self.init_weight_scale, "fan_in", "normal")) for feat in self.features] - - def __call__(self, x): - for l, layer in enumerate(self.layers[:-1]): - x = self.activation(layer(x)) - x = self.layers[-1](x) - return x - - -class AffineCoupling(nn.Module): - - n_features: int - n_hidden: int - mask: jnp.array - dt: float = 1 - - def setup(self): - self.scale_MLP = MLP([self.n_features, self.n_hidden, self.n_features]) - self.translate_MLP = MLP([self.n_features, self.n_hidden, self.n_features]) - - def __call__(self, x): - s = self.mask * self.scale_MLP(x*(1-self.mask)) - s = jnp.tanh(s) - t = self.mask * self.translate_MLP(x*(1-self.mask)) - s = self.dt * s - t = self.dt * t - log_det = s.reshape(s.shape[0], -1).sum(axis=-1) - outputs = (x + t) * jnp.exp(s) - return outputs, log_det - - def inverse(self, x): - s = self.mask * self.scale_MLP(x*(1-self.mask)) - s = jnp.tanh(s) - t = self.mask * self.translate_MLP(x*(1-self.mask)) - s = self.dt * s - t = self.dt * t - log_det = -s.reshape(s.shape[0], -1).sum(axis=-1) - outputs = x * jnp.exp(-s) - t - return outputs, log_det - - - -class RealNVP(nn.Module): - - n_layer: int - n_features: int - n_hidden: int - dt: float = 1 - - def setup(self): - affine_coupling = [] - for i in range(self.n_layer): - mask = np.ones(self.n_features) - mask[int(self.n_features/2):] = 0 - if i % 2 == 0: - mask = 1 - mask - mask = jnp.array(mask) - affine_coupling.append(AffineCoupling(self.n_features, self.n_hidden, mask, dt=self.dt)) - self.affine_coupling = affine_coupling - - def __call__(self, x): - log_det = jnp.zeros(x.shape[0]) - for i in range(self.n_layer): - x, log_det_i = self.affine_coupling[i](x) - log_det += log_det_i - return x, log_det - - def inverse(self, x): - log_det = jnp.zeros(x.shape[0]) - for i in range(self.n_layer): - x, log_det_i = self.affine_coupling[self.n_layer-1-i].inverse(x) - log_det += log_det_i - return x, log_det - - def sample(self, rng_key, n_samples, params): - mean = jnp.zeros((n_samples,self.n_features)) - cov = jnp.repeat(jnp.eye(self.n_features)[None,:],n_samples,axis=0) - gaussian = jax.random.multivariate_normal(rng_key, mean, cov) - samples = self.inverse(gaussian) - return samples - - def log_prob(self, x): - y, log_det = self.__call__(x) - mean = jnp.zeros((x.shape[0],self.n_features)) - cov = jnp.repeat(jnp.eye(self.n_features)[None,:],x.shape[0],axis=0) - log_det = log_det + jax.scipy.stats.multivariate_normal.logpdf(y,mean,cov) - return log_det \ No newline at end of file From cd4704751eeaccbcc03502304cdfbeeb6c111185 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Fri, 12 Aug 2022 13:38:53 -0400 Subject: [PATCH 094/300] restructure PE branch --- jaxgw/{gw => PE}/constants.py | 0 jaxgw/{gw/likelihood => PE}/detector_preset.py | 0 jaxgw/{gw/likelihood => PE}/detector_projection.py | 0 jaxgw/{gw/likelihood => PE}/single_event_likelihood.py | 0 jaxgw/{gw/likelihood => PE}/time_and_date.py | 0 jaxgw/{gw/likelihood => PE}/utils.py | 0 6 files changed, 0 insertions(+), 0 deletions(-) rename jaxgw/{gw => PE}/constants.py (100%) rename jaxgw/{gw/likelihood => PE}/detector_preset.py (100%) rename jaxgw/{gw/likelihood => PE}/detector_projection.py (100%) rename jaxgw/{gw/likelihood => PE}/single_event_likelihood.py (100%) rename jaxgw/{gw/likelihood => PE}/time_and_date.py (100%) rename jaxgw/{gw/likelihood => PE}/utils.py (100%) diff --git a/jaxgw/gw/constants.py b/jaxgw/PE/constants.py similarity index 100% rename from jaxgw/gw/constants.py rename to jaxgw/PE/constants.py diff --git a/jaxgw/gw/likelihood/detector_preset.py b/jaxgw/PE/detector_preset.py similarity index 100% rename from jaxgw/gw/likelihood/detector_preset.py rename to jaxgw/PE/detector_preset.py diff --git a/jaxgw/gw/likelihood/detector_projection.py b/jaxgw/PE/detector_projection.py similarity index 100% rename from jaxgw/gw/likelihood/detector_projection.py rename to jaxgw/PE/detector_projection.py diff --git a/jaxgw/gw/likelihood/single_event_likelihood.py b/jaxgw/PE/single_event_likelihood.py similarity index 100% rename from jaxgw/gw/likelihood/single_event_likelihood.py rename to jaxgw/PE/single_event_likelihood.py diff --git a/jaxgw/gw/likelihood/time_and_date.py b/jaxgw/PE/time_and_date.py similarity index 100% rename from jaxgw/gw/likelihood/time_and_date.py rename to jaxgw/PE/time_and_date.py diff --git a/jaxgw/gw/likelihood/utils.py b/jaxgw/PE/utils.py similarity index 100% rename from jaxgw/gw/likelihood/utils.py rename to jaxgw/PE/utils.py From 0e8a9eb93ed6f6089f20e45ce796d42c931dda5e Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Sat, 13 Aug 2022 18:23:48 -0400 Subject: [PATCH 095/300] Repacakge --- jaxgw/PE/detector_preset.py | 2 +- jaxgw/PE/detector_projection.py | 2 +- jaxgw/PE/single_event_likelihood.py | 4 ++-- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/jaxgw/PE/detector_preset.py b/jaxgw/PE/detector_preset.py index 9e920e49..c474ec9f 100644 --- a/jaxgw/PE/detector_preset.py +++ b/jaxgw/PE/detector_preset.py @@ -1,4 +1,4 @@ -from jaxgw.gw.likelihood.detector_projection import construct_arm, detector_tensor, antenna_response, get_detector_response, get_vertex_position_geocentric +from jaxgw.PE.detector_projection import construct_arm, detector_tensor, antenna_response, get_detector_response, get_vertex_position_geocentric import jax.numpy as jnp # See https://git.ligo.org/lscsoft/bilby/-/tree/master/bilby/gw/detector/detectors for detector parameters. diff --git a/jaxgw/PE/detector_projection.py b/jaxgw/PE/detector_projection.py index 5ffcc2d2..e2d69535 100644 --- a/jaxgw/PE/detector_projection.py +++ b/jaxgw/PE/detector_projection.py @@ -1,7 +1,7 @@ # Credit some part of the source code from bilby import jax.numpy as jnp -from jaxgw.gw.constants import * +from jaxgw.PE.constants import * ########################################################## # Construction of arms diff --git a/jaxgw/PE/single_event_likelihood.py b/jaxgw/PE/single_event_likelihood.py index 6b1d2844..e0fc4c2a 100644 --- a/jaxgw/PE/single_event_likelihood.py +++ b/jaxgw/PE/single_event_likelihood.py @@ -1,6 +1,6 @@ from jax import jit -from jaxgw.gw.likelihood.detector_projection import get_detector_response -from jaxgw.gw.likelihood.utils import inner_product +from jaxgw.PE.detector_projection import get_detector_response +from jaxgw.PE.utils import inner_product def single_detector_likelihood(waveform_model, params, data, data_f, PSD, detector): waveform = waveform_model(data_f, params) From 9f9994039fef5f6fe064a8042e4ec530ae72c5cc Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 15 Aug 2022 15:55:06 -0400 Subject: [PATCH 096/300] heterodyne likelihood infrastructure is here. Need to figure out numerics --- example/ParameterEstimation/Injection_test.py | 124 ++++++++++++++++++ jaxgw/PE/HeterodyneLikelihood.py | 0 jaxgw/PE/heterodyneLikelihood.py | 66 ++++++++++ 3 files changed, 190 insertions(+) create mode 100644 example/ParameterEstimation/Injection_test.py delete mode 100644 jaxgw/PE/HeterodyneLikelihood.py create mode 100644 jaxgw/PE/heterodyneLikelihood.py diff --git a/example/ParameterEstimation/Injection_test.py b/example/ParameterEstimation/Injection_test.py new file mode 100644 index 00000000..ed2ea698 --- /dev/null +++ b/example/ParameterEstimation/Injection_test.py @@ -0,0 +1,124 @@ +# Import packages + +from xml.sax.handler import property_declaration_handler +import scipy.signal as ssig +import lalsimulation as lalsim +import numpy as np +import jax.numpy as jnp +import jax + +# from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar +from ripple import ms_to_Mc_eta +from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar +from jaxgw.PE.detector_preset import * +from jaxgw.PE.heterodyneLikelihood import make_heterodyne_likelihood + +import matplotlib.pyplot as plt + +psd_func_dict = { + 'H1': lalsim.SimNoisePSDaLIGOZeroDetHighPower, + 'L1': lalsim.SimNoisePSDaLIGOZeroDetHighPower, + 'V1': lalsim.SimNoisePSDAdvVirgo, +} +ifos = list(psd_func_dict.keys()) + +# define center of time array +tgps_geo = 1126259462.423 + +# define sampling rate and duration +fsamp = 8192 +duration = 4 + +delta_t = 1/fsamp +tlen = int(round(duration / delta_t)) + +freqs = np.fft.rfftfreq(tlen, delta_t) +delta_f = freqs[1] - freqs[0] + + + +# we will want to pad low frequencies; the function below applies a +# prescription to do so smoothly, but this is not really needed: you +# could just set all values below `fmin` to a constant. +fmin = 30 +def pad_low_freqs(f, psd_ref): + return psd_ref + psd_ref*(fmin-f)*np.exp(-(fmin-f))/3 + +psd_dict = {} +for ifo in ifos: + psd = np.zeros(len(freqs)) + for i,f in enumerate(freqs): + if f >= fmin: + psd[i] = psd_func_dict[ifo](f) + else: + psd[i] = pad_low_freqs(f, psd_func_dict[ifo](fmin)) + psd_dict[ifo] = psd + + + +rng = np.random.default_rng(12345) + +noise_fd_dict = {} +for ifo, psd in psd_dict.items(): + var = psd / (4.*delta_f) # this is the variance of LIGO noise given the definition of the likelihood function + noise_real = rng.normal(size=len(psd), loc=0, scale=np.sqrt(var)) + noise_imag = rng.normal(size=len(psd), loc=0, scale=np.sqrt(var)) + noise_fd_dict[ifo] = noise_real + 1j*noise_imag + + + +# These are the parameters of the injected signal +m1 = 50.0 +m2 = 10.0 +Mc, eta = ms_to_Mc_eta(jnp.array([m1, m2])) +chi1 = 0.4 +chi2 = -0.3 +dist_mpc = 400.0 +tc = 2.0 +phic = 0.0 +inclination = np.pi +polarization_angle = np.pi/2 +ra = 0.3 +dec = 0.5 + +detector_presets = {'H1': get_H1()} + +theta_ripple = jnp.array([Mc, eta, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle]) +theta_ripple_vec = np.array(jnp.repeat(theta_ripple[None,:],1000,axis=0)*np.random.normal(loc=1,scale=0.001,size=(1000,9))) +theta_ripple_vec[theta_ripple_vec[:,1]>0.25,1] = 0.25 + +f_list = freqs[freqs>fmin] +hp = gen_IMRPhenomD_polar(f_list, theta_ripple) +noise_psd = psd[freqs>fmin] +data = noise_psd + hp[0] + + +# plt.figure(figsize=(15,5)) +# plt.loglog(f_list, np.abs(data), label="Signal", alpha=0.3) +# plt.loglog(f_list, np.abs(noise_fd_dict["H1"][freqs>fmin]), label="H1 noise", alpha=0.3) +# plt.loglog(f_list, np.abs(hp[0]), label="H1 waveform", alpha=0.3) +# plt.ylim(1e-25, 1e-21) +# plt.legend() +# plt.show() + +@jax.jit +def LogLikelihood(theta): + h_test = gen_IMRPhenomD_polar(f_list, theta) + df = f_list[1] - f_list[0] + match_filter_SNR = 4*jnp.sum((jnp.conj(h_test[0])*data)/noise_psd*df).real + optimal_SNR = 4*jnp.sum((jnp.conj(h_test[0])*h_test[0])/noise_psd*df).real + return (-match_filter_SNR+optimal_SNR/2) + +theta_ref = jnp.array([Mc, 0.23, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle]) + +h_function = lambda f,theta:gen_IMRPhenomD_polar(f,theta)[0] + +logL = make_heterodyne_likelihood(data, h_function, theta_ref, noise_psd, f_list, 1001) + +L1 = jax.vmap(LogLikelihood)(theta_ripple_vec) +L2 = jax.vmap(jax.jit(logL))(theta_ripple_vec) +# Create data + +# Create likelihood object + +# Samples the likelihood with flowMC \ No newline at end of file diff --git a/jaxgw/PE/HeterodyneLikelihood.py b/jaxgw/PE/HeterodyneLikelihood.py deleted file mode 100644 index e69de29b..00000000 diff --git a/jaxgw/PE/heterodyneLikelihood.py b/jaxgw/PE/heterodyneLikelihood.py new file mode 100644 index 00000000..f9c15a19 --- /dev/null +++ b/jaxgw/PE/heterodyneLikelihood.py @@ -0,0 +1,66 @@ +import numpy as np +from scipy.interpolate import interp1d + +import jax.numpy as jnp + + +def max_phase_diff(f, f_low, f_high, chi=1): + gamma = np.arange(-5,6,1)/3. + f = np.repeat(f[:,None],len(gamma),axis=1) + f_star = np.repeat(f_low, len(gamma)) + f_star[gamma >= 0] = f_high + return 2*np.pi*chi*np.sum((f/f_star)**gamma*np.sign(gamma),axis=1) + + +def make_binning_scheme(freqs, n_bins, chi=1): + phase_diff_array = max_phase_diff(freqs,freqs[0],freqs[-1],chi=1) + bin_f = interp1d(phase_diff_array, freqs) + f_bins = np.array([]) + for i in np.linspace(phase_diff_array[0], phase_diff_array[-1], n_bins): + f_bins = np.append(f_bins,bin_f(i)) + f_bins_center = (f_bins[:-1] + f_bins[1:])/2 + return f_bins, f_bins_center + +def compute_coefficients(data, h_ref, psd, freqs, f_bins, f_bins_center): + A0_array = [] + A1_array = [] + B0_array = [] + B1_array = [] + + df = freqs[1] - freqs[0] + data_prod = np.array(data*h_ref.conj()) + self_prod = np.array(h_ref*h_ref.conj()) + for i in range(len(f_bins)-1): + f_index = np.where((freqs >= f_bins[i]) & (freqs < f_bins[i+1]))[0] + A0_array.append(4*np.sum(data_prod[f_index]/psd[f_index]*df)) + A1_array.append(4*np.sum(data_prod[f_index]/psd[f_index]*df*(freqs[f_index]-f_bins_center[i]))) + B0_array.append(4*np.sum(self_prod[f_index]/psd[f_index]*df)) + B1_array.append(4*np.sum(self_prod[f_index]/psd[f_index]*df*(freqs[f_index]-f_bins_center[i]))) + + A0_array = jnp.array(A0_array) + A1_array = jnp.array(A1_array) + B0_array = jnp.array(B0_array) + B1_array = jnp.array(B1_array) + return A0_array, A1_array, B0_array, B1_array + +def make_heterodyne_likelihood(data, h_function, ref_theta, psd, freqs, n_bins=101): + f_bins, f_bins_center = make_binning_scheme(freqs, n_bins) + h_ref = h_function(freqs, ref_theta) + h_ref_low = h_function(f_bins[:-1], ref_theta) + h_ref_bincenter = h_function(f_bins_center, ref_theta) + + A0, A1, B0, B1 = compute_coefficients(data, h_ref, psd, freqs, f_bins, f_bins_center) + + def heterodyne_likelihood(params): + waveform_low = h_function(f_bins[:-1], params) + waveform_center = h_function(f_bins_center, params) + + r0 = waveform_center/h_ref_bincenter + r1 = (waveform_low/h_ref_low - r0)/(f_bins[:-1]-f_bins_center) + + match_filter_SNR = jnp.sum(A0*r0.conj() + A1*r1.conj()) + optimal_SNR = jnp.sum(B0*jnp.abs(r0) + B1*(r0*r1.conj().real)) + + return (- match_filter_SNR + optimal_SNR/2).real + + return heterodyne_likelihood \ No newline at end of file From e65ca007724079eb28253172d2219bb83e5f9767 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 15 Aug 2022 16:00:45 -0400 Subject: [PATCH 097/300] Tiny bug fix. Not completely solved yet. --- example/ParameterEstimation/Injection_test.py | 2 +- jaxgw/PE/heterodyneLikelihood.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/example/ParameterEstimation/Injection_test.py b/example/ParameterEstimation/Injection_test.py index ed2ea698..305480df 100644 --- a/example/ParameterEstimation/Injection_test.py +++ b/example/ParameterEstimation/Injection_test.py @@ -113,7 +113,7 @@ def LogLikelihood(theta): h_function = lambda f,theta:gen_IMRPhenomD_polar(f,theta)[0] -logL = make_heterodyne_likelihood(data, h_function, theta_ref, noise_psd, f_list, 1001) +logL = make_heterodyne_likelihood(data, h_function, theta_ref, noise_psd, f_list, 101) L1 = jax.vmap(LogLikelihood)(theta_ripple_vec) L2 = jax.vmap(jax.jit(logL))(theta_ripple_vec) diff --git a/jaxgw/PE/heterodyneLikelihood.py b/jaxgw/PE/heterodyneLikelihood.py index f9c15a19..3371cc20 100644 --- a/jaxgw/PE/heterodyneLikelihood.py +++ b/jaxgw/PE/heterodyneLikelihood.py @@ -59,7 +59,7 @@ def heterodyne_likelihood(params): r1 = (waveform_low/h_ref_low - r0)/(f_bins[:-1]-f_bins_center) match_filter_SNR = jnp.sum(A0*r0.conj() + A1*r1.conj()) - optimal_SNR = jnp.sum(B0*jnp.abs(r0) + B1*(r0*r1.conj().real)) + optimal_SNR = jnp.sum(B0*jnp.abs(r0)**2 + 2*B1*(r0*r1.conj()).real) return (- match_filter_SNR + optimal_SNR/2).real From 0d6d291fde69c40b5841b8bee5589d6f7f2e0502 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 15 Aug 2022 17:46:52 -0400 Subject: [PATCH 098/300] Turns out it is the reference waveform being shit. Heterodyne_likelihood working now --- example/ParameterEstimation/Injection_test.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/example/ParameterEstimation/Injection_test.py b/example/ParameterEstimation/Injection_test.py index 305480df..15769c8f 100644 --- a/example/ParameterEstimation/Injection_test.py +++ b/example/ParameterEstimation/Injection_test.py @@ -84,7 +84,7 @@ def pad_low_freqs(f, psd_ref): detector_presets = {'H1': get_H1()} theta_ripple = jnp.array([Mc, eta, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle]) -theta_ripple_vec = np.array(jnp.repeat(theta_ripple[None,:],1000,axis=0)*np.random.normal(loc=1,scale=0.001,size=(1000,9))) +theta_ripple_vec = np.array(jnp.repeat(theta_ripple[None,:],1000,axis=0)*np.random.normal(loc=1,scale=0.0001,size=(1000,9))) theta_ripple_vec[theta_ripple_vec[:,1]>0.25,1] = 0.25 f_list = freqs[freqs>fmin] @@ -109,7 +109,7 @@ def LogLikelihood(theta): optimal_SNR = 4*jnp.sum((jnp.conj(h_test[0])*h_test[0])/noise_psd*df).real return (-match_filter_SNR+optimal_SNR/2) -theta_ref = jnp.array([Mc, 0.23, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle]) +theta_ref = jnp.array([Mc, 0.138, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle]) h_function = lambda f,theta:gen_IMRPhenomD_polar(f,theta)[0] From ba95ef659859199c0cba840f7f2d6fda89ac09f7 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Tue, 16 Aug 2022 22:05:02 -0400 Subject: [PATCH 099/300] add compiled version of logL and dlogL in --- example/ParameterEstimation/Injection_test.py | 52 ++++++++++++++++++- 1 file changed, 50 insertions(+), 2 deletions(-) diff --git a/example/ParameterEstimation/Injection_test.py b/example/ParameterEstimation/Injection_test.py index 15769c8f..e77b6f35 100644 --- a/example/ParameterEstimation/Injection_test.py +++ b/example/ParameterEstimation/Injection_test.py @@ -13,6 +13,13 @@ from jaxgw.PE.detector_preset import * from jaxgw.PE.heterodyneLikelihood import make_heterodyne_likelihood + +from flowMC.nfmodel.realNVP import RealNVP +from flowMC.sampler.MALA import mala_sampler +from flowMC.sampler.Sampler import Sampler +from flowMC.utils.PRNG_keys import initialize_rng_keys +from flowMC.nfmodel.utils import * + import matplotlib.pyplot as plt psd_func_dict = { @@ -84,7 +91,7 @@ def pad_low_freqs(f, psd_ref): detector_presets = {'H1': get_H1()} theta_ripple = jnp.array([Mc, eta, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle]) -theta_ripple_vec = np.array(jnp.repeat(theta_ripple[None,:],1000,axis=0)*np.random.normal(loc=1,scale=0.0001,size=(1000,9))) +theta_ripple_vec = np.array(jnp.repeat(theta_ripple[None,:],10000,axis=0)*np.random.normal(loc=1,scale=0.0001,size=(10000,9))) theta_ripple_vec[theta_ripple_vec[:,1]>0.25,1] = 0.25 f_list = freqs[freqs>fmin] @@ -121,4 +128,45 @@ def LogLikelihood(theta): # Create likelihood object -# Samples the likelihood with flowMC \ No newline at end of file +# Samples the likelihood with flowMC + +n_dim = 9 +n_chains = 10 +n_loop = 5 +n_local_steps = 100 +n_global_steps = 0 +stepsize = 0.01 + +print("Preparing RNG keys") +rng_key_set = initialize_rng_keys(n_chains, seed=42) + +print("Initializing MCMC model and normalizing flow model.") + +initial_position = jax.random.uniform(rng_key_set[0], shape=(n_chains, n_dim)) * 1 +initial_position = initial_position.at[:,0].set(initial_position[:,0]*20 + 10) +initial_position = initial_position.at[:,1].set(initial_position[:,1]*0.25) +initial_position = initial_position.at[:,2].set(initial_position[:,2]*2 - 1) +initial_position = initial_position.at[:,3].set(initial_position[:,3]*2 - 1) +initial_position = initial_position.at[:,4].set(initial_position[:,4]*2000) +initial_position = initial_position.at[:,5].set(initial_position[:,5]*10-5) +initial_position = initial_position.at[:,6].set(initial_position[:,6]*np.pi-np.pi/2) +initial_position = initial_position.at[:,7].set(initial_position[:,7]*np.pi-np.pi/2) +initial_position = initial_position.at[:,8].set(initial_position[:,8]*2*np.pi ) + +model = RealNVP(10, n_dim, 64, 1) +run_mcmc = jax.vmap(mala_sampler, in_axes=(0, None, None, None, 0, None), out_axes=0) + +print("Initializing sampler class") + +logL = jax.jit(logL) +dlogL = jax.jit(jax.grad(logL)) # compiling each of these function first should improve the performance by a lot + +nf_sampler = Sampler(n_dim, rng_key_set, model, run_mcmc, + logL, + d_likelihood=jax.grad(logL), + n_loop=n_loop, + n_local_steps=n_local_steps, + n_global_steps=n_global_steps, + n_chains=n_chains, + stepsize=stepsize, + use_global=False,) \ No newline at end of file From 533b8faa5448307da3e2d5f11807038865492432 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Wed, 17 Aug 2022 16:49:33 -0400 Subject: [PATCH 100/300] Update injection --- example/ParameterEstimation/Injection_test.py | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/example/ParameterEstimation/Injection_test.py b/example/ParameterEstimation/Injection_test.py index e77b6f35..ac7a89ca 100644 --- a/example/ParameterEstimation/Injection_test.py +++ b/example/ParameterEstimation/Injection_test.py @@ -15,7 +15,7 @@ from flowMC.nfmodel.realNVP import RealNVP -from flowMC.sampler.MALA import mala_sampler +from flowMC.sampler.MALA import make_mala_sampler from flowMC.sampler.Sampler import Sampler from flowMC.utils.PRNG_keys import initialize_rng_keys from flowMC.nfmodel.utils import * @@ -131,9 +131,9 @@ def LogLikelihood(theta): # Samples the likelihood with flowMC n_dim = 9 -n_chains = 10 +n_chains = 1000 n_loop = 5 -n_local_steps = 100 +n_local_steps = 1000 n_global_steps = 0 stepsize = 0.01 @@ -154,16 +154,18 @@ def LogLikelihood(theta): initial_position = initial_position.at[:,8].set(initial_position[:,8]*2*np.pi ) model = RealNVP(10, n_dim, 64, 1) -run_mcmc = jax.vmap(mala_sampler, in_axes=(0, None, None, None, 0, None), out_axes=0) +# run_mcmc = jax.vmap(mala_sampler, in_axes=(0, None, None, None, 0, None), out_axes=0) print("Initializing sampler class") logL = jax.jit(logL) dlogL = jax.jit(jax.grad(logL)) # compiling each of these function first should improve the performance by a lot -nf_sampler = Sampler(n_dim, rng_key_set, model, run_mcmc, +local_sampler = make_mala_sampler(n_local_steps,logL, dlogL, initial_position) + +nf_sampler = Sampler(n_dim, rng_key_set, model, local_sampler, logL, - d_likelihood=jax.grad(logL), + d_likelihood=dlogL, n_loop=n_loop, n_local_steps=n_local_steps, n_global_steps=n_global_steps, From 89a4513914c993962e8d7576822ad8ef5f05cc67 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Wed, 17 Aug 2022 17:44:47 -0400 Subject: [PATCH 101/300] Update injection test --- example/ParameterEstimation/Injection_test.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/example/ParameterEstimation/Injection_test.py b/example/ParameterEstimation/Injection_test.py index ac7a89ca..2c8a53dd 100644 --- a/example/ParameterEstimation/Injection_test.py +++ b/example/ParameterEstimation/Injection_test.py @@ -131,7 +131,7 @@ def LogLikelihood(theta): # Samples the likelihood with flowMC n_dim = 9 -n_chains = 1000 +n_chains = 100 n_loop = 5 n_local_steps = 1000 n_global_steps = 0 @@ -161,7 +161,7 @@ def LogLikelihood(theta): logL = jax.jit(logL) dlogL = jax.jit(jax.grad(logL)) # compiling each of these function first should improve the performance by a lot -local_sampler = make_mala_sampler(n_local_steps,logL, dlogL, initial_position) +local_sampler = make_mala_sampler(logL, dlogL,1e-4) nf_sampler = Sampler(n_dim, rng_key_set, model, local_sampler, logL, @@ -171,4 +171,6 @@ def LogLikelihood(theta): n_global_steps=n_global_steps, n_chains=n_chains, stepsize=stepsize, - use_global=False,) \ No newline at end of file + use_global=False,) + +local_sampler = make_mala_sampler(logL, dlogL) From 8c4d4910ce5bdc552ca89353f3fab2eef1e6fbb6 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Wed, 17 Aug 2022 17:49:04 -0400 Subject: [PATCH 102/300] Update injection intest --- example/ParameterEstimation/Injection_test.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/example/ParameterEstimation/Injection_test.py b/example/ParameterEstimation/Injection_test.py index 2c8a53dd..4403ab7d 100644 --- a/example/ParameterEstimation/Injection_test.py +++ b/example/ParameterEstimation/Injection_test.py @@ -131,7 +131,7 @@ def LogLikelihood(theta): # Samples the likelihood with flowMC n_dim = 9 -n_chains = 100 +n_chains = 1000 n_loop = 5 n_local_steps = 1000 n_global_steps = 0 @@ -173,4 +173,4 @@ def LogLikelihood(theta): stepsize=stepsize, use_global=False,) -local_sampler = make_mala_sampler(logL, dlogL) +local_sampler(rng_key_set[1], n_local_steps, logL, dlogL, initial_position) From f07b4e98cb4ad6f1411ed8f2b23acd458e0bd7cc Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Wed, 17 Aug 2022 22:53:21 -0400 Subject: [PATCH 103/300] Tiny bug fix for not outputing the state correctly --- example/ParameterEstimation/Injection_test.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/example/ParameterEstimation/Injection_test.py b/example/ParameterEstimation/Injection_test.py index 4403ab7d..865e8829 100644 --- a/example/ParameterEstimation/Injection_test.py +++ b/example/ParameterEstimation/Injection_test.py @@ -131,9 +131,9 @@ def LogLikelihood(theta): # Samples the likelihood with flowMC n_dim = 9 -n_chains = 1000 +n_chains = 100 n_loop = 5 -n_local_steps = 1000 +n_local_steps = 100 n_global_steps = 0 stepsize = 0.01 @@ -161,7 +161,7 @@ def LogLikelihood(theta): logL = jax.jit(logL) dlogL = jax.jit(jax.grad(logL)) # compiling each of these function first should improve the performance by a lot -local_sampler = make_mala_sampler(logL, dlogL,1e-4) +local_sampler = make_mala_sampler(logL, dlogL,1e-5) nf_sampler = Sampler(n_dim, rng_key_set, model, local_sampler, logL, From 58e80eaf311f5b7cfe3f8140c7710a35eebee45b Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Thu, 18 Aug 2022 22:13:58 -0400 Subject: [PATCH 104/300] Fix sign in heterodyne likelihood --- jaxgw/PE/heterodyneLikelihood.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/jaxgw/PE/heterodyneLikelihood.py b/jaxgw/PE/heterodyneLikelihood.py index 3371cc20..02e90e2a 100644 --- a/jaxgw/PE/heterodyneLikelihood.py +++ b/jaxgw/PE/heterodyneLikelihood.py @@ -61,6 +61,6 @@ def heterodyne_likelihood(params): match_filter_SNR = jnp.sum(A0*r0.conj() + A1*r1.conj()) optimal_SNR = jnp.sum(B0*jnp.abs(r0)**2 + 2*B1*(r0*r1.conj()).real) - return (- match_filter_SNR + optimal_SNR/2).real + return (match_filter_SNR - optimal_SNR/2).real return heterodyne_likelihood \ No newline at end of file From 65592ee29853963c6c5fe1e1fb334455d2d01f35 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Thu, 18 Aug 2022 22:14:12 -0400 Subject: [PATCH 105/300] Modified experiement in PE --- example/ParameterEstimation/Injection_test.py | 61 +++++++++++-------- 1 file changed, 37 insertions(+), 24 deletions(-) diff --git a/example/ParameterEstimation/Injection_test.py b/example/ParameterEstimation/Injection_test.py index 865e8829..6fc9b9de 100644 --- a/example/ParameterEstimation/Injection_test.py +++ b/example/ParameterEstimation/Injection_test.py @@ -80,7 +80,7 @@ def pad_low_freqs(f, psd_ref): Mc, eta = ms_to_Mc_eta(jnp.array([m1, m2])) chi1 = 0.4 chi2 = -0.3 -dist_mpc = 400.0 +dist_mpc = 1000.0 tc = 2.0 phic = 0.0 inclination = np.pi @@ -91,7 +91,7 @@ def pad_low_freqs(f, psd_ref): detector_presets = {'H1': get_H1()} theta_ripple = jnp.array([Mc, eta, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle]) -theta_ripple_vec = np.array(jnp.repeat(theta_ripple[None,:],10000,axis=0)*np.random.normal(loc=1,scale=0.0001,size=(10000,9))) +theta_ripple_vec = np.array(jnp.repeat(theta_ripple[None,:],100,axis=0)*np.random.normal(loc=1,scale=0.0001,size=(100,9))) theta_ripple_vec[theta_ripple_vec[:,1]>0.25,1] = 0.25 f_list = freqs[freqs>fmin] @@ -100,13 +100,12 @@ def pad_low_freqs(f, psd_ref): data = noise_psd + hp[0] -# plt.figure(figsize=(15,5)) -# plt.loglog(f_list, np.abs(data), label="Signal", alpha=0.3) -# plt.loglog(f_list, np.abs(noise_fd_dict["H1"][freqs>fmin]), label="H1 noise", alpha=0.3) -# plt.loglog(f_list, np.abs(hp[0]), label="H1 waveform", alpha=0.3) -# plt.ylim(1e-25, 1e-21) -# plt.legend() -# plt.show() +# def top_hat(x, low_lim, high_lim): +# return jnp.heaviside(x-low_lim,1)*(1-jnp.heaviside(x-high_lim,1)) + +# def LogPrior(theta): + + @jax.jit def LogLikelihood(theta): @@ -134,7 +133,12 @@ def LogLikelihood(theta): n_chains = 100 n_loop = 5 n_local_steps = 100 -n_global_steps = 0 +n_global_steps = 1000 +learning_rate = 0.1 +max_samples = 50000 +momentum = 0.9 +num_epochs = 100 +batch_size = 10000 stepsize = 0.01 print("Preparing RNG keys") @@ -142,16 +146,18 @@ def LogLikelihood(theta): print("Initializing MCMC model and normalizing flow model.") -initial_position = jax.random.uniform(rng_key_set[0], shape=(n_chains, n_dim)) * 1 -initial_position = initial_position.at[:,0].set(initial_position[:,0]*20 + 10) -initial_position = initial_position.at[:,1].set(initial_position[:,1]*0.25) -initial_position = initial_position.at[:,2].set(initial_position[:,2]*2 - 1) -initial_position = initial_position.at[:,3].set(initial_position[:,3]*2 - 1) -initial_position = initial_position.at[:,4].set(initial_position[:,4]*2000) -initial_position = initial_position.at[:,5].set(initial_position[:,5]*10-5) -initial_position = initial_position.at[:,6].set(initial_position[:,6]*np.pi-np.pi/2) -initial_position = initial_position.at[:,7].set(initial_position[:,7]*np.pi-np.pi/2) -initial_position = initial_position.at[:,8].set(initial_position[:,8]*2*np.pi ) +initial_position = theta_ripple_vec +# initial_position = jax.random.uniform(rng_key_set[0], shape=(n_chains, n_dim)) * 1 +# initial_position = initial_position.at[:,0].set(initial_position[:,0]*20 + 10) +# initial_position = initial_position.at[:,1].set(initial_position[:,1]*0.25) +# initial_position = initial_position.at[:,2].set(initial_position[:,2]*2 - 1) +# initial_position = initial_position.at[:,3].set(initial_position[:,3]*2 - 1) +# initial_position = initial_position.at[:,4].set(initial_position[:,4]*2000) +# initial_position = initial_position.at[:,5].set(initial_position[:,5]*10-5) +# initial_position = initial_position.at[:,6].set(initial_position[:,6]*np.pi-np.pi/2) +# initial_position = initial_position.at[:,7].set(initial_position[:,7]*np.pi-np.pi/2) +# initial_position = initial_position.at[:,8].set(initial_position[:,8]*2*np.pi ) + model = RealNVP(10, n_dim, 64, 1) # run_mcmc = jax.vmap(mala_sampler, in_axes=(0, None, None, None, 0, None), out_axes=0) @@ -161,7 +167,7 @@ def LogLikelihood(theta): logL = jax.jit(logL) dlogL = jax.jit(jax.grad(logL)) # compiling each of these function first should improve the performance by a lot -local_sampler = make_mala_sampler(logL, dlogL,1e-5) +local_sampler = make_mala_sampler(logL, dlogL,1e-7) nf_sampler = Sampler(n_dim, rng_key_set, model, local_sampler, logL, @@ -171,6 +177,13 @@ def LogLikelihood(theta): n_global_steps=n_global_steps, n_chains=n_chains, stepsize=stepsize, - use_global=False,) - -local_sampler(rng_key_set[1], n_local_steps, logL, dlogL, initial_position) + n_nf_samples=100, + learning_rate=learning_rate, + n_epochs= num_epochs, + max_samples = max_samples, + momentum=momentum, + batch_size=batch_size, + use_global=True,) + +nf_sampler.sample(initial_position) +# state = local_sampler(rng_key_set[1], n_local_steps, logL, dlogL, initial_position) From 64a29fb254e03dc0906bd01211a0c5b807808ef0 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Thu, 18 Aug 2022 23:34:07 -0400 Subject: [PATCH 106/300] This setup seems working? --- example/ParameterEstimation/Injection_test.py | 35 ++++++++----------- 1 file changed, 15 insertions(+), 20 deletions(-) diff --git a/example/ParameterEstimation/Injection_test.py b/example/ParameterEstimation/Injection_test.py index 6fc9b9de..c2f26230 100644 --- a/example/ParameterEstimation/Injection_test.py +++ b/example/ParameterEstimation/Injection_test.py @@ -88,10 +88,22 @@ def pad_low_freqs(f, psd_ref): ra = 0.3 dec = 0.5 +n_dim = 9 +n_chains = 1000 +n_loop = 3 +n_local_steps = 1000 +n_global_steps = 1000 +learning_rate = 0.01 +max_samples = 50000 +momentum = 0.9 +num_epochs = 1000 +batch_size = 10000 +stepsize = 0.01 + detector_presets = {'H1': get_H1()} theta_ripple = jnp.array([Mc, eta, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle]) -theta_ripple_vec = np.array(jnp.repeat(theta_ripple[None,:],100,axis=0)*np.random.normal(loc=1,scale=0.0001,size=(100,9))) +theta_ripple_vec = np.array(jnp.repeat(theta_ripple[None,:],n_chains,axis=0)*np.random.normal(loc=1,scale=0.01,size=(n_chains,9))) theta_ripple_vec[theta_ripple_vec[:,1]>0.25,1] = 0.25 f_list = freqs[freqs>fmin] @@ -100,13 +112,6 @@ def pad_low_freqs(f, psd_ref): data = noise_psd + hp[0] -# def top_hat(x, low_lim, high_lim): -# return jnp.heaviside(x-low_lim,1)*(1-jnp.heaviside(x-high_lim,1)) - -# def LogPrior(theta): - - - @jax.jit def LogLikelihood(theta): h_test = gen_IMRPhenomD_polar(f_list, theta) @@ -129,17 +134,7 @@ def LogLikelihood(theta): # Samples the likelihood with flowMC -n_dim = 9 -n_chains = 100 -n_loop = 5 -n_local_steps = 100 -n_global_steps = 1000 -learning_rate = 0.1 -max_samples = 50000 -momentum = 0.9 -num_epochs = 100 -batch_size = 10000 -stepsize = 0.01 + print("Preparing RNG keys") rng_key_set = initialize_rng_keys(n_chains, seed=42) @@ -167,7 +162,7 @@ def LogLikelihood(theta): logL = jax.jit(logL) dlogL = jax.jit(jax.grad(logL)) # compiling each of these function first should improve the performance by a lot -local_sampler = make_mala_sampler(logL, dlogL,1e-7) +local_sampler = make_mala_sampler(logL, dlogL,5e-7) nf_sampler = Sampler(n_dim, rng_key_set, model, local_sampler, logL, From 97a0281b361915a4feeadad93ed0b08c5431b6d0 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Fri, 19 Aug 2022 12:11:54 -0400 Subject: [PATCH 107/300] Add GPU profiling code that use scan to do its computation --- example/ParameterEstimation/GPUprofiling.py | 153 ++++++++++++++++++++ 1 file changed, 153 insertions(+) create mode 100644 example/ParameterEstimation/GPUprofiling.py diff --git a/example/ParameterEstimation/GPUprofiling.py b/example/ParameterEstimation/GPUprofiling.py new file mode 100644 index 00000000..a91ced00 --- /dev/null +++ b/example/ParameterEstimation/GPUprofiling.py @@ -0,0 +1,153 @@ +# Import packages + +from xml.sax.handler import property_declaration_handler +import scipy.signal as ssig +import lalsimulation as lalsim +import numpy as np +import jax.numpy as jnp +import jax + +# from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar +from ripple import ms_to_Mc_eta +from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar +from jaxgw.PE.detector_preset import * +from jaxgw.PE.heterodyneLikelihood import make_heterodyne_likelihood + + +from flowMC.nfmodel.realNVP import RealNVP +from flowMC.sampler.MALA import make_mala_sampler +from flowMC.sampler.Sampler import Sampler +from flowMC.utils.PRNG_keys import initialize_rng_keys +from flowMC.nfmodel.utils import * + +import matplotlib.pyplot as plt + +psd_func_dict = { + 'H1': lalsim.SimNoisePSDaLIGOZeroDetHighPower, + 'L1': lalsim.SimNoisePSDaLIGOZeroDetHighPower, + 'V1': lalsim.SimNoisePSDAdvVirgo, +} +ifos = list(psd_func_dict.keys()) + +# define center of time array +tgps_geo = 1126259462.423 + +# define sampling rate and duration +fsamp = 8192 +duration = 4 + +delta_t = 1/fsamp +tlen = int(round(duration / delta_t)) + +freqs = np.fft.rfftfreq(tlen, delta_t) +delta_f = freqs[1] - freqs[0] + + + +# we will want to pad low frequencies; the function below applies a +# prescription to do so smoothly, but this is not really needed: you +# could just set all values below `fmin` to a constant. +fmin = 30 +def pad_low_freqs(f, psd_ref): + return psd_ref + psd_ref*(fmin-f)*np.exp(-(fmin-f))/3 + +psd_dict = {} +for ifo in ifos: + psd = np.zeros(len(freqs)) + for i,f in enumerate(freqs): + if f >= fmin: + psd[i] = psd_func_dict[ifo](f) + else: + psd[i] = pad_low_freqs(f, psd_func_dict[ifo](fmin)) + psd_dict[ifo] = psd + + + +rng = np.random.default_rng(12345) + +noise_fd_dict = {} +for ifo, psd in psd_dict.items(): + var = psd / (4.*delta_f) # this is the variance of LIGO noise given the definition of the likelihood function + noise_real = rng.normal(size=len(psd), loc=0, scale=np.sqrt(var)) + noise_imag = rng.normal(size=len(psd), loc=0, scale=np.sqrt(var)) + noise_fd_dict[ifo] = noise_real + 1j*noise_imag + + + +# These are the parameters of the injected signal +m1 = 50.0 +m2 = 10.0 +Mc, eta = ms_to_Mc_eta(jnp.array([m1, m2])) +chi1 = 0.4 +chi2 = -0.3 +dist_mpc = 1000.0 +tc = 2.0 +phic = 0.0 +inclination = np.pi +polarization_angle = np.pi/2 +ra = 0.3 +dec = 0.5 + +n_chains = 100 + +detector_presets = {'H1': get_H1()} + +theta_ripple = jnp.array([Mc, eta, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle]) +theta_ripple_vec = np.array(jnp.repeat(theta_ripple[None,:],n_chains,axis=0)*np.random.normal(loc=1,scale=0.01,size=(n_chains,9))) +theta_ripple_vec[theta_ripple_vec[:,1]>0.25,1] = 0.25 + +f_list = freqs[freqs>fmin] +hp = gen_IMRPhenomD_polar(f_list, theta_ripple) +noise_psd = psd[freqs>fmin] +data = noise_psd + hp[0] + + +@jax.jit +def LogLikelihood(theta): + h_test = gen_IMRPhenomD_polar(f_list, theta) + df = f_list[1] - f_list[0] + match_filter_SNR = 4*jnp.sum((jnp.conj(h_test[0])*data)/noise_psd*df).real + optimal_SNR = 4*jnp.sum((jnp.conj(h_test[0])*h_test[0])/noise_psd*df).real + return (-match_filter_SNR+optimal_SNR/2) + +theta_ref = jnp.array([Mc, 0.138, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle]) + +h_function = lambda f,theta:gen_IMRPhenomD_polar(f,theta)[0] + +logpdf = jax.jit(make_heterodyne_likelihood(data, h_function, theta_ref, noise_psd, f_list, 101)) +d_logpdf = jax.jit(jax.grad(logpdf)) + +L1 = jax.vmap(LogLikelihood)(theta_ripple_vec) +L2 = jax.vmap(jax.jit(logpdf))(theta_ripple_vec) + + +#def mala_kernel(rng_key, position, log_prob, dt=0.1): + +dt = 1e-7 +def mala_kernel(carry, data): + rng_key, position, log_prob, do_accept = carry + rng_key, key1, key2 = jax.random.split(rng_key,3) + proposal = position + dt * d_logpdf(position) + proposal += dt * jnp.sqrt(2/dt) * jax.random.normal(key1, shape=position.shape) + ratio = logpdf(proposal) - logpdf(position) + ratio -= ((position - proposal - dt * d_logpdf(proposal)) ** 2 / (4 * dt)).sum() + ratio += ((proposal - position - dt * d_logpdf(position)) ** 2 / (4 * dt)).sum() + proposal_log_prob = logpdf(proposal) + + log_uniform = jnp.log(jax.random.uniform(key2)) + do_accept = log_uniform < ratio + + position = jax.lax.cond(do_accept, lambda: proposal, lambda: position) + log_prob = jax.lax.cond(do_accept, lambda: proposal_log_prob, lambda: log_prob) + return (rng_key, position, log_prob, do_accept), (position, log_prob, do_accept) + +mala_kernel = jax.jit(mala_kernel) +state = (jax.random.PRNGKey(1),theta_ripple, logpdf(theta_ripple), False) +# jax.lax.scan(mala_kernel, state, jax.random.split(jax.random.PRNGKey(1),10)) +def mala_update(rng_key, position, logpdf, n_steps=100): + carry = (rng_key, position, logpdf, False) + y = jax.lax.scan(mala_kernel, carry, jax.random.split(rng_key,n_steps)) + return y + +mala_update = jax.jit(jax.vmap(mala_update)) +result = mala_update(jax.random.split(jax.random.PRNGKey(1),100), theta_ripple_vec, jax.vmap(logpdf)(theta_ripple_vec)) \ No newline at end of file From 449ad254da8e026ae15cc4e5734b32ccae950f47 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Fri, 19 Aug 2022 13:02:52 -0400 Subject: [PATCH 108/300] update GPUprofiling --- example/ParameterEstimation/GPUprofiling.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/example/ParameterEstimation/GPUprofiling.py b/example/ParameterEstimation/GPUprofiling.py index a91ced00..dda32b26 100644 --- a/example/ParameterEstimation/GPUprofiling.py +++ b/example/ParameterEstimation/GPUprofiling.py @@ -149,5 +149,6 @@ def mala_update(rng_key, position, logpdf, n_steps=100): y = jax.lax.scan(mala_kernel, carry, jax.random.split(rng_key,n_steps)) return y -mala_update = jax.jit(jax.vmap(mala_update)) -result = mala_update(jax.random.split(jax.random.PRNGKey(1),100), theta_ripple_vec, jax.vmap(logpdf)(theta_ripple_vec)) \ No newline at end of file +with jax.profiler.trace("./", create_perfetto_link=True): + mala_update = jax.jit(jax.vmap(mala_update)) + result = mala_update(jax.random.split(jax.random.PRNGKey(1),100), theta_ripple_vec, jax.vmap(logpdf)(theta_ripple_vec)) \ No newline at end of file From 229f9d61dd3d05ea7e0077c376c91b0f4aff0b3e Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 29 Aug 2022 11:34:13 -0400 Subject: [PATCH 109/300] Remove hetrodynelikelihood.py from example. --- .../heterodyneLikelihood.py | 107 ------------------ 1 file changed, 107 deletions(-) delete mode 100644 example/ParameterEstimation/heterodyneLikelihood.py diff --git a/example/ParameterEstimation/heterodyneLikelihood.py b/example/ParameterEstimation/heterodyneLikelihood.py deleted file mode 100644 index 6e04d8f6..00000000 --- a/example/ParameterEstimation/heterodyneLikelihood.py +++ /dev/null @@ -1,107 +0,0 @@ -from cmath import phase -import numpy as np -import jax.numpy as jnp -import jax - -from ripple.waveforms import IMRPhenomD, IMRPhenomD_utils -import matplotlib.pyplot as plt -from ripple import ms_to_Mc_eta - -from scipy.interpolate import interp1d - -# Get a frequency domain waveform -# source parameters - -m1_msun = 20.0 # In solar masses -m2_msun = 19.0 -chi1 = 0.5 # Dimensionless spin -chi2 = -0.5 -tc = 0.0 # Time of coalescence in seconds -phic = 0.0 # Time of coalescence -dist_mpc = 440 # Distance to source in Mpc -inclination = 0.0 # Inclination Angle -polarization_angle = 0.2 # Polarization angle - -# The PhenomD waveform model is parameterized with the chirp mass and symmetric mass ratio -Mc, eta = ms_to_Mc_eta(jnp.array([m1_msun, m2_msun])) - -# These are the parametrs that go into the waveform generator -# Note that JAX does not give index errors, so if you pass in the -# the wrong array it will behave strangely -theta_ripple = jnp.array([Mc, eta, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle]) -theta_ripple_vec = np.array(jnp.repeat(theta_ripple[None,:],100000,axis=0)*np.random.normal(loc=1,scale=0.001,size=(100000,9))) -theta_ripple_vec[theta_ripple_vec[:,1]>0.25,1] = 0.25 - - -# Now we need to generate the frequency grid -f_l = 24 -f_u = 1024 -del_f = 10 -fs = jnp.arange(f_l, f_u, del_f) - -# And finally lets generate the waveform! -hp_ripple, hc_ripple = IMRPhenomD.gen_IMRPhenomD_polar(fs, theta_ripple) - -@jax.jit -def waveform_gen(theta): - return IMRPhenomD.gen_IMRPhenomD_polar(fs, theta) - -waveform_gen_vec = jax.vmap(waveform_gen) - -# Choosing binning scheme - -def max_phase_diff(f, f_low, f_high, chi=1): - gamma = np.arange(-5,6,1)/3. - f = np.repeat(f[:,None],len(gamma),axis=1) - f_star = np.repeat(f_low, len(gamma)) - f_star[gamma >= 0] = f_high - return 2*np.pi*chi*np.sum((f/f_star)**gamma*np.sign(gamma),axis=1) - -f_fine = np.linspace(f_l, f_u, 10000) -phase_diff_array = max_phase_diff(f_fine,f_l,f_u,chi=1) -bin_f = interp1d(phase_diff_array, f_fine) -n_bin = 1001 -f_bins = np.array([]) -for i in np.linspace(phase_diff_array[0], phase_diff_array[-1], n_bin): - f_bins = np.append(f_bins,bin_f(i)) -f_bins_center = (f_bins[:-1] + f_bins[1:])/2 - -# Compute coefficients from reference waveform - -# IMRPhenomD_jit = jax.vmap(jax.jit(IMRPhenomD.gen_IMRPhenomD_polar),(0,None),0) - -data = IMRPhenomD.gen_IMRPhenomD_polar(f_fine, theta_ripple)[0] -bin_coef = [] -theta_ref = jnp.array([Mc, 0.23, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle]) -h_ref = IMRPhenomD.gen_IMRPhenomD_polar(f_fine, theta_ref)[0] -h_ref_bin_center = IMRPhenomD.gen_IMRPhenomD_polar(f_bins_center, theta_ref)[0] -h_ref_bin_low = IMRPhenomD.gen_IMRPhenomD_polar(f_bins[:-1], theta_ref)[0] -A0_array = [] -A1_array = [] - -data_prod = np.array(data*h_ref.conj()) -for i in range(len(f_bins)-1): - print(i) - f_index = np.where((f_fine >= f_bins[i]) & (f_fine < f_bins[i+1]))[0] - A0_array.append(np.sum(data_prod[f_index])) - A1_array.append(np.sum(data_prod[f_index]*(f_fine[f_index]-f_bins_center[i]))) - -A0_array = jnp.array(A0_array) -A1_array = jnp.array(A1_array) - -# run time evaluation of inner product - -theta_test = jnp.array([Mc, 0.22, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle]) -h_test_fine = IMRPhenomD.gen_IMRPhenomD_polar(f_fine, theta_test)[0] -h_test_bin_center = IMRPhenomD.gen_IMRPhenomD_polar(f_bins_center, theta_test)[0] -h_test_bin_low = IMRPhenomD.gen_IMRPhenomD_polar(f_bins[:-1], theta_test)[0] -true_SNR = jnp.sum(data*h_test_fine.conj()) - -r0 = h_test_bin_center/h_ref_bin_center -r1 = (h_test_bin_low/h_ref_bin_low - r0)/(f_bins[:-1]-f_bins_center) - -bin_SNR = np.sum(A0_array*r0.conj() + A1_array*r1.conj()) - -print(bin_SNR, true_SNR) - - From 96cd5d192518276195bb5b05fbb5ce73263e9c8b Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Fri, 2 Sep 2022 10:42:34 -0400 Subject: [PATCH 110/300] Update load scripts --- .../ParameterEstimation/DetectorProjection.py | 47 +++++++++ example/ParameterEstimation/Injection_test.py | 99 +++++++++++++------ example/ParameterEstimation/PE_training.py | 79 +++++++++++++++ 3 files changed, 193 insertions(+), 32 deletions(-) create mode 100644 example/ParameterEstimation/DetectorProjection.py create mode 100644 example/ParameterEstimation/PE_training.py diff --git a/example/ParameterEstimation/DetectorProjection.py b/example/ParameterEstimation/DetectorProjection.py new file mode 100644 index 00000000..ac71f4c8 --- /dev/null +++ b/example/ParameterEstimation/DetectorProjection.py @@ -0,0 +1,47 @@ +from cmath import phase +from webbrowser import get +import numpy as np +import jax.numpy as jnp + +from ripple.waveforms import IMRPhenomD, IMRPhenomD_utils +import matplotlib.pyplot as plt +from ripple import ms_to_Mc_eta + +from jaxgw.PE.detector_preset import * +from jaxgw.PE.detector_projection import get_detector_response + + +# Get a frequency domain waveform +# source parameters + +m1_msun = 20.0 # In solar masses +m2_msun = 19.0 +chi1 = 0.5 # Dimensionless spin +chi2 = -0.5 +tc = 0.0 # Time of coalescence in seconds +phic = 0.0 # Time of coalescence +dist_mpc = 440 # Distance to source in Mpc +inclination = 0.0 # Inclination Angle +polarization_angle = 0.2 # Polarization angle + +# The PhenomD waveform model is parameterized with the chirp mass and symmetric mass ratio +Mc, eta = ms_to_Mc_eta(jnp.array([m1_msun, m2_msun])) + +# These are the parametrs that go into the waveform generator +# Note that JAX does not give index errors, so if you pass in the +# the wrong array it will behave strangely +theta_ripple = jnp.array([Mc, eta, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle]) + + +# Now we need to generate the frequency grid +f_l = 24 +f_u = 1024 +del_f = 10 +fs = jnp.arange(f_l, f_u, del_f) + +# And finally lets generate the waveform! +hp_ripple, hc_ripple = IMRPhenomD.gen_IMRPhenomD_polar(fs, theta_ripple) + +H1 = get_H1() + +get_detector_response(H1, hp_ripple, hc_ripple, fs) diff --git a/example/ParameterEstimation/Injection_test.py b/example/ParameterEstimation/Injection_test.py index c2f26230..09a37544 100644 --- a/example/ParameterEstimation/Injection_test.py +++ b/example/ParameterEstimation/Injection_test.py @@ -19,6 +19,7 @@ from flowMC.sampler.Sampler import Sampler from flowMC.utils.PRNG_keys import initialize_rng_keys from flowMC.nfmodel.utils import * +import time import matplotlib.pyplot as plt @@ -42,8 +43,6 @@ freqs = np.fft.rfftfreq(tlen, delta_t) delta_f = freqs[1] - freqs[0] - - # we will want to pad low frequencies; the function below applies a # prescription to do so smoothly, but this is not really needed: you # could just set all values below `fmin` to a constant. @@ -61,8 +60,6 @@ def pad_low_freqs(f, psd_ref): psd[i] = pad_low_freqs(f, psd_func_dict[ifo](fmin)) psd_dict[ifo] = psd - - rng = np.random.default_rng(12345) noise_fd_dict = {} @@ -72,39 +69,40 @@ def pad_low_freqs(f, psd_ref): noise_imag = rng.normal(size=len(psd), loc=0, scale=np.sqrt(var)) noise_fd_dict[ifo] = noise_real + 1j*noise_imag - - # These are the parameters of the injected signal -m1 = 50.0 -m2 = 10.0 +m1 = 35.0 +m2 = 30.0 Mc, eta = ms_to_Mc_eta(jnp.array([m1, m2])) chi1 = 0.4 chi2 = -0.3 dist_mpc = 1000.0 tc = 2.0 phic = 0.0 -inclination = np.pi +inclination = np.pi/2 polarization_angle = np.pi/2 ra = 0.3 dec = 0.5 n_dim = 9 n_chains = 1000 -n_loop = 3 -n_local_steps = 1000 +n_loop = 5 +n_local_steps = 2000 n_global_steps = 1000 learning_rate = 0.01 max_samples = 50000 momentum = 0.9 -num_epochs = 1000 -batch_size = 10000 +num_epochs = 300 +batch_size = 50000 stepsize = 0.01 detector_presets = {'H1': get_H1()} theta_ripple = jnp.array([Mc, eta, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle]) -theta_ripple_vec = np.array(jnp.repeat(theta_ripple[None,:],n_chains,axis=0)*np.random.normal(loc=1,scale=0.01,size=(n_chains,9))) + +theta_ripple_vec = np.array(jnp.repeat(theta_ripple[None,:],int(n_chains/2),axis=0)*np.random.normal(loc=1,scale=0.01,size=(int(n_chains/2),9))) theta_ripple_vec[theta_ripple_vec[:,1]>0.25,1] = 0.25 +theta_ripple_vec[:,6] = (theta_ripple_vec[:,6]+np.pi/2)%(np.pi)-np.pi/2 +theta_ripple_vec[:,7] = (theta_ripple_vec[:,7]+np.pi/2)%(np.pi)-np.pi/2 f_list = freqs[freqs>fmin] hp = gen_IMRPhenomD_polar(f_list, theta_ripple) @@ -118,21 +116,17 @@ def LogLikelihood(theta): df = f_list[1] - f_list[0] match_filter_SNR = 4*jnp.sum((jnp.conj(h_test[0])*data)/noise_psd*df).real optimal_SNR = 4*jnp.sum((jnp.conj(h_test[0])*h_test[0])/noise_psd*df).real - return (-match_filter_SNR+optimal_SNR/2) + return (match_filter_SNR-optimal_SNR/2) -theta_ref = jnp.array([Mc, 0.138, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle]) +theta_ref = jnp.array([Mc, eta, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle]) h_function = lambda f,theta:gen_IMRPhenomD_polar(f,theta)[0] logL = make_heterodyne_likelihood(data, h_function, theta_ref, noise_psd, f_list, 101) + L1 = jax.vmap(LogLikelihood)(theta_ripple_vec) L2 = jax.vmap(jax.jit(logL))(theta_ripple_vec) -# Create data - -# Create likelihood object - -# Samples the likelihood with flowMC @@ -141,9 +135,16 @@ def LogLikelihood(theta): print("Initializing MCMC model and normalizing flow model.") -initial_position = theta_ripple_vec -# initial_position = jax.random.uniform(rng_key_set[0], shape=(n_chains, n_dim)) * 1 -# initial_position = initial_position.at[:,0].set(initial_position[:,0]*20 + 10) +@jax.jit +def reparam_logL(theta): + theta = theta.at[0].set(jnp.exp(theta[0])) + theta = theta.at[4].set(jnp.exp(theta[4])) + return logL(theta) + + + +initial_position = jax.random.uniform(rng_key_set[0], shape=(int(n_chains/2), n_dim)) * 1 +# initial_position = initial_position.at[:,0].set(initial_position[:,0]*60 + 10) # initial_position = initial_position.at[:,1].set(initial_position[:,1]*0.25) # initial_position = initial_position.at[:,2].set(initial_position[:,2]*2 - 1) # initial_position = initial_position.at[:,3].set(initial_position[:,3]*2 - 1) @@ -151,22 +152,57 @@ def LogLikelihood(theta): # initial_position = initial_position.at[:,5].set(initial_position[:,5]*10-5) # initial_position = initial_position.at[:,6].set(initial_position[:,6]*np.pi-np.pi/2) # initial_position = initial_position.at[:,7].set(initial_position[:,7]*np.pi-np.pi/2) -# initial_position = initial_position.at[:,8].set(initial_position[:,8]*2*np.pi ) +# initial_position = initial_position.at[:,8].set(initial_position[:,8]*2*np.pi) + +initial_position = jnp.append(initial_position, theta_ripple_vec, axis=0) + +prior_range = jnp.array([[10,70],[0.0,0.25],[-1,1],[-1,1],[0,2000],[-5,5],[-np.pi/2,np.pi/2],[-np.pi/2,np.pi/2],[0,2*np.pi]]) model = RealNVP(10, n_dim, 64, 1) -# run_mcmc = jax.vmap(mala_sampler, in_axes=(0, None, None, None, 0, None), out_axes=0) print("Initializing sampler class") -logL = jax.jit(logL) -dlogL = jax.jit(jax.grad(logL)) # compiling each of these function first should improve the performance by a lot +# likelihood = jax.jit(reparam_logL) +# dlikelihood = jax.jit(jax.grad(reparam_logL)) # compiling each of these function first should improve the performance by a lot + +likelihood = logL + +def top_hat(x): + output = 0. + for i in range(n_dim): + output = jax.lax.cond(x[i]>=prior_range[i,0], lambda: output, lambda: -jnp.inf) + output = jax.lax.cond(x[i]<=prior_range[i,1], lambda: output, lambda: -jnp.inf) + return output + +def posterior(theta): + prior = top_hat(theta) + return likelihood(theta) + prior + +posterior = jax.jit(posterior) +dposterior = jax.jit(jax.grad(posterior)) + +mass_matrix = jnp.eye(n_dim) +mass_matrix = mass_matrix.at[1,1].set(1e-3) + +local_sampler,updater, kernel,logp,dlogp = make_mala_sampler(posterior, dposterior,1e-3, jit=True, M=mass_matrix) + +# print("Warming up kernels and likelihood functions") +# local_time = time.time() +# logp(initial_position) +# dlogp(initial_position) +# kernel(rng_key_set[1],initial_position,logp(initial_position)) +# acceptance = jnp.zeros((n_chains,2,)) +# all_positions = jnp.zeros((n_chains, 2,)+initial_position.shape[-1:]) + initial_position[:,None] +# all_logp = jnp.zeros((n_chains,2,)) +# state = (rng_key_set[1], all_positions, all_logp, acceptance) +# updater(1,state) -local_sampler = make_mala_sampler(logL, dlogL,5e-7) +# print("Warmup complete. Time taken: {}".format(time.time()-local_time)) nf_sampler = Sampler(n_dim, rng_key_set, model, local_sampler, - logL, - d_likelihood=dlogL, + posterior, + d_likelihood=dposterior, n_loop=n_loop, n_local_steps=n_local_steps, n_global_steps=n_global_steps, @@ -181,4 +217,3 @@ def LogLikelihood(theta): use_global=True,) nf_sampler.sample(initial_position) -# state = local_sampler(rng_key_set[1], n_local_steps, logL, dlogL, initial_position) diff --git a/example/ParameterEstimation/PE_training.py b/example/ParameterEstimation/PE_training.py new file mode 100644 index 00000000..7810b3f8 --- /dev/null +++ b/example/ParameterEstimation/PE_training.py @@ -0,0 +1,79 @@ +import numpy as np +from flowMC.nfmodel.realNVP import RealNVP +import jax +import optax +import flax + +from flowMC.nfmodel.utils import * +from flax import linen as nn # The Linen API +from flax.training import train_state # Useful dataclass to keep train state + +from tqdm import tqdm + +# data = np.load('./data/injection_posterior2.npz') +# chains = data['chains'] +# log_prob = data['log_prob'] +# data = jnp.array(chains).reshape(-1,9) +# data = data[::1000] + +from sklearn.datasets import make_moons + +data = make_moons(n_samples=10000, noise=0.05)[0] + +n_dim = 2 +num_epochs = 5000 +batch_size = 10000 +learning_rate = 0.01 +momentum = 0.9 +n_layers = 10 +n_hidden = 100 +dt = 1 / n_layers + +model = RealNVP(10, n_dim, 64, 1) + +key1, rng, init_rng = jax.random.split(jax.random.PRNGKey(0),3) + +def create_train_state(rng, learning_rate, momentum): + params = model.init(rng, jnp.ones((1,n_dim)))['params'] + tx = optax.adam(learning_rate, momentum) + return train_state.TrainState.create(apply_fn=model.apply, params=params, tx=tx) + +state = create_train_state(init_rng, learning_rate, momentum) + + +variables = model.init(rng, jnp.ones((1,n_dim)))['variables'] + + +rng, state, loss_values = train_flow(rng, model, state, data, num_epochs, batch_size, variables) +samples = sample_nf(model,state.params, rng,10000,variables)[1][0] + +# from flowMC.nfmodel.utils import train_step + +# @jax.jit +# def eval_step(params, batch): +# log_det = model.apply({'params': params,'variables': variables}, batch, method=model.log_prob) +# return -jnp.mean(log_det) + +# def train_epoch(state, train_ds, batch_size, epoch, rng): +# """Train for a single epoch.""" +# train_ds_size = len(train_ds) +# steps_per_epoch = train_ds_size // batch_size + +# perms = jax.random.permutation(rng, train_ds_size) +# perms = perms[:steps_per_epoch * batch_size] # skip incomplete batch +# perms = perms.reshape((steps_per_epoch, batch_size)) +# for perm in perms: +# batch = train_ds[perm, ...] +# value, state = train_step(model, batch, state, variables) + +# return state + +# for epoch in tqdm(range(1, num_epochs+1),desc='Training',miniters=int(num_epochs/10)): + +# # Use a separate PRNG key to permute image data during shuffling +# rng, input_rng = jax.random.split(rng) +# # Run an optimization step over a training batch +# state = train_epoch(state, data, batch_size, epoch, input_rng) +# if epoch % int(num_epochs/10) == 0: +# print('Epoch %d' % epoch, end=' ') +# print('Loss: %.3f' % eval_step(state.params, data)) From 9cdb54ecd77ce5bb271c965712695f3a01480e2c Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Fri, 2 Sep 2022 11:24:19 -0400 Subject: [PATCH 111/300] Compactify detector response code. --- .../ParameterEstimation/DetectorProjection.py | 12 +- jaxgw/PE/detector_projection.py | 128 +++++++++--------- 2 files changed, 70 insertions(+), 70 deletions(-) diff --git a/example/ParameterEstimation/DetectorProjection.py b/example/ParameterEstimation/DetectorProjection.py index ac71f4c8..9f98b14c 100644 --- a/example/ParameterEstimation/DetectorProjection.py +++ b/example/ParameterEstimation/DetectorProjection.py @@ -7,8 +7,7 @@ import matplotlib.pyplot as plt from ripple import ms_to_Mc_eta -from jaxgw.PE.detector_preset import * -from jaxgw.PE.detector_projection import get_detector_response + # Get a frequency domain waveform @@ -42,6 +41,13 @@ # And finally lets generate the waveform! hp_ripple, hc_ripple = IMRPhenomD.gen_IMRPhenomD_polar(fs, theta_ripple) + +from jaxgw.PE.detector_preset import * +from jaxgw.PE.detector_projection_new import make_detector_response + H1 = get_H1() +L1 = get_L1() +H1_response = make_detector_response(H1[0], H1[1]) +L1_response = make_detector_response(L1[0], L1[1]) +H1_response(fs,hp_ripple, hc_ripple, 0.2, 0.3, 0.,0.5) -get_detector_response(H1, hp_ripple, hc_ripple, fs) diff --git a/jaxgw/PE/detector_projection.py b/jaxgw/PE/detector_projection.py index e2d69535..e95bf3a3 100644 --- a/jaxgw/PE/detector_projection.py +++ b/jaxgw/PE/detector_projection.py @@ -2,7 +2,19 @@ import jax.numpy as jnp from jaxgw.PE.constants import * - +from jaxgw.PE.detector_projection import antenna_response + + +def make_detector_response(detector_tensor, detector_vertex): + antenna_response_plus = make_antenna_response(detector_tensor,'plus') + antenna_response_cross = make_antenna_response(detector_tensor, 'cross') + def detector_response(f, hp, hc, ra, dec, time, psi): + output = antenna_response_plus(ra, dec, time, psi)*hp + antenna_response_cross(ra, dec, time, psi)*hc + timeshift = time_delay_geocentric(detector_vertex, jnp.array([0.,0.,0.]), ra, dec, time) + output = output * jnp.exp(-1j * 2 * jnp.pi * f * timeshift) + return output + return detector_response + ########################################################## # Construction of arms ########################################################## @@ -37,48 +49,62 @@ def detector_tensor(arm1, arm2): # Construction of detector tensor ########################################################## -def get_polarization_tensor(ra, dec, time, psi, mode): - """ - - Args: +def make_get_polarization_tensor(mode): - ra: - dec: - time: Greenwich Mean Sidereal Time in geocentric frame - psi: - mode: """ - gmst = jnp.mod(time, 2 * jnp.pi) - phi = ra - gmst - theta = jnp.pi / 2 - dec + Since most of the application will only use specific modes, + this function hoist the if-else loop out from the actual kernel to save time from compiling the kernel. + + Args: + mode: string - u = jnp.array([jnp.cos(phi) * jnp.cos(theta), jnp.cos(theta) * jnp.sin(phi), -jnp.sin(theta)]) - v = jnp.array([-jnp.sin(phi), jnp.cos(phi), 0]) - m = -u * jnp.sin(psi) - v * jnp.cos(psi) - n = -u * jnp.cos(psi) + v * jnp.sin(psi) + """ if mode.lower() == 'plus': - return jnp.einsum('i,j->ij', m, m) - jnp.einsum('i,j->ij', n, n) + kernel = lambda m,n: jnp.einsum('i,j->ij', m, m) - jnp.einsum('i,j->ij', n, n) elif mode.lower() == 'cross': - return jnp.einsum('i,j->ij', m, n) + jnp.einsum('i,j->ij', n, m) + kernel = lambda m,n: jnp.einsum('i,j->ij', m, n) + jnp.einsum('i,j->ij', n, m) elif mode.lower() == 'breathing': - return jnp.einsum('i,j->ij', m, m) + jnp.einsum('i,j->ij', n, n) - - # Calculating omega here to avoid calculation when model in [plus, cross, breathing] - omega = jnp.cross(m, n) - if mode.lower() == 'longitudinal': - return jnp.einsum('i,j->ij', omega, omega) - elif mode.lower() == 'x': - return jnp.einsum('i,j->ij', m, omega) + jnp.einsum('i,j->ij', omega, m) - elif mode.lower() == 'y': - return jnp.einsum('i,j->ij', n, omega) + jnp.einsum('i,j->ij', omega, n) - else: - raise ValueError("{} not a polarization mode!".format(mode)) - -def antenna_response(detector_tensor, ra, dec, time, psi, mode): - polarization_tensor = get_polarization_tensor(ra, dec, time, psi, mode) - return jnp.einsum('ij,ij->', detector_tensor, polarization_tensor) + kernel = lambda m,n: jnp.einsum('i,j->ij', m, m) + jnp.einsum('i,j->ij', n, n) + + # Calculating omega here to avoid calculation when model in [plus, cross, breathing] + if mode.lower() == 'longitudinal': + def kernel(m,n): + omega = jnp.cross(m, n) + return jnp.einsum('i,j->ij', omega, omega) + elif mode.lower() == 'x': + def kernel(m,n): + omega = jnp.cross(m, n) + return jnp.einsum('i,j->ij', m, omega) + jnp.einsum('i,j->ij', omega, m) + elif mode.lower() == 'y': + def kernel(m,n): + omega = jnp.cross(m, n) + return jnp.einsum('i,j->ij', n, omega) + jnp.einsum('i,j->ij', omega, n) + else: + raise ValueError("{} not a polarization mode!".format(mode)) + + def get_polarization_tensor(ra, dec, time, psi): + gmst = jnp.mod(time, 2 * jnp.pi) + phi = ra - gmst + theta = jnp.pi / 2 - dec + + u = jnp.array([jnp.cos(phi) * jnp.cos(theta), jnp.cos(theta) * jnp.sin(phi), -jnp.sin(theta)]) + v = jnp.array([-jnp.sin(phi), jnp.cos(phi), 0]) + m = -u * jnp.sin(psi) - v * jnp.cos(psi) + n = -u * jnp.cos(psi) + v * jnp.sin(psi) + + return kernel(m, n) + + return get_polarization_tensor + + +def make_antenna_response(detector_tensor, mode): + kernel = make_get_polarization_tensor(mode) + def antenna_response(ra, dec, time, psi): + polarization_tensor = kernel(ra, dec, time, psi) + return jnp.einsum('ij,ij->', detector_tensor, polarization_tensor) + return antenna_response def time_delay_geocentric(detector1, detector2, ra, dec, time): """ @@ -142,37 +168,5 @@ def get_vertex_position_geocentric(latitude, longitude, elevation): return jnp.array([x_comp, y_comp, z_comp]) -def get_detector_response(frequency, waveform_polarizations, parameters, detector_tensor, detector_vertex): - """ - - Args: - - ra: Right Ascension in radian - dec:Right Ascension in radian - time: Greenwich Mean Sidereal Time in geocentric frame - psi: - mode: - - """ - signal = {} - for mode in waveform_polarizations.keys(): - det_response = antenna_response( - detector_tensor, - parameters['ra'], - parameters['dec'], - parameters['greenwich_mean_sidereal_time'], - parameters['psi'], mode) - - signal[mode] = waveform_polarizations[mode] * det_response - signal_ifo = sum(signal.values()) - - time_shift = time_delay_geocentric(detector_vertex, jnp.array([0.,0.,0.]),parameters['ra'], parameters['dec'], parameters['greenwich_mean_sidereal_time']) - - dt = parameters['geocent_time'] - parameters['start_time'] - dt = dt + time_shift # Note that we always assume the start time of the strain to be 0 - - signal_ifo = signal_ifo * jnp.exp(-1j * 2 * jnp.pi * dt * frequency) - - return signal_ifo From d2edf6d56b26c9abe4e2ca6ad7fd564fe6290b64 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Fri, 2 Sep 2022 11:47:20 -0400 Subject: [PATCH 112/300] Fix detector preset --- jaxgw/PE/detector_preset.py | 2 +- jaxgw/PE/detector_projection.py | 1 - 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/jaxgw/PE/detector_preset.py b/jaxgw/PE/detector_preset.py index c474ec9f..eeebbe5c 100644 --- a/jaxgw/PE/detector_preset.py +++ b/jaxgw/PE/detector_preset.py @@ -1,4 +1,4 @@ -from jaxgw.PE.detector_projection import construct_arm, detector_tensor, antenna_response, get_detector_response, get_vertex_position_geocentric +from jaxgw.PE.detector_projection import construct_arm, detector_tensor, get_vertex_position_geocentric import jax.numpy as jnp # See https://git.ligo.org/lscsoft/bilby/-/tree/master/bilby/gw/detector/detectors for detector parameters. diff --git a/jaxgw/PE/detector_projection.py b/jaxgw/PE/detector_projection.py index e95bf3a3..d15611ff 100644 --- a/jaxgw/PE/detector_projection.py +++ b/jaxgw/PE/detector_projection.py @@ -2,7 +2,6 @@ import jax.numpy as jnp from jaxgw.PE.constants import * -from jaxgw.PE.detector_projection import antenna_response def make_detector_response(detector_tensor, detector_vertex): From 161b422155ae88f189dd1759383ed650563b1f23 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Fri, 2 Sep 2022 11:49:45 -0400 Subject: [PATCH 113/300] Add detector projection to likelihood --- .../ParameterEstimation/DetectorProjection.py | 2 +- example/ParameterEstimation/Injection_test.py | 158 ++++++++---------- 2 files changed, 70 insertions(+), 90 deletions(-) diff --git a/example/ParameterEstimation/DetectorProjection.py b/example/ParameterEstimation/DetectorProjection.py index 9f98b14c..c35a2bd9 100644 --- a/example/ParameterEstimation/DetectorProjection.py +++ b/example/ParameterEstimation/DetectorProjection.py @@ -49,5 +49,5 @@ L1 = get_L1() H1_response = make_detector_response(H1[0], H1[1]) L1_response = make_detector_response(L1[0], L1[1]) -H1_response(fs,hp_ripple, hc_ripple, 0.2, 0.3, 0.,0.5) +H1_response(fs, hp_ripple, hc_ripple, 0.2, 0.3, 0.,0.5) diff --git a/example/ParameterEstimation/Injection_test.py b/example/ParameterEstimation/Injection_test.py index 09a37544..f6ee6d56 100644 --- a/example/ParameterEstimation/Injection_test.py +++ b/example/ParameterEstimation/Injection_test.py @@ -12,16 +12,14 @@ from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar from jaxgw.PE.detector_preset import * from jaxgw.PE.heterodyneLikelihood import make_heterodyne_likelihood - +from jaxgw.PE.detector_projection import make_detector_response from flowMC.nfmodel.realNVP import RealNVP from flowMC.sampler.MALA import make_mala_sampler from flowMC.sampler.Sampler import Sampler from flowMC.utils.PRNG_keys import initialize_rng_keys from flowMC.nfmodel.utils import * -import time -import matplotlib.pyplot as plt psd_func_dict = { 'H1': lalsim.SimNoisePSDaLIGOZeroDetHighPower, @@ -83,7 +81,7 @@ def pad_low_freqs(f, psd_ref): ra = 0.3 dec = 0.5 -n_dim = 9 +n_dim = 11 n_chains = 1000 n_loop = 5 n_local_steps = 2000 @@ -95,54 +93,52 @@ def pad_low_freqs(f, psd_ref): batch_size = 50000 stepsize = 0.01 -detector_presets = {'H1': get_H1()} +H1 = get_H1() +H1_response = make_detector_response(H1[0], H1[1]) + + +def gen_waveform(f, theta): + theta_waveform = theta[:9] + ra = theta[9] + dec = theta[10] + hp, hc = gen_IMRPhenomD_polar(f, theta_waveform) + return H1_response(f, hp, hc, ra, dec, theta[5], theta[8]) + -theta_ripple = jnp.array([Mc, eta, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle]) -theta_ripple_vec = np.array(jnp.repeat(theta_ripple[None,:],int(n_chains/2),axis=0)*np.random.normal(loc=1,scale=0.01,size=(int(n_chains/2),9))) -theta_ripple_vec[theta_ripple_vec[:,1]>0.25,1] = 0.25 -theta_ripple_vec[:,6] = (theta_ripple_vec[:,6]+np.pi/2)%(np.pi)-np.pi/2 -theta_ripple_vec[:,7] = (theta_ripple_vec[:,7]+np.pi/2)%(np.pi)-np.pi/2 +true_param = jnp.array([Mc, eta, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle, ra, dec]) + +guess_param = np.array(jnp.repeat(true_param[None,:],int(n_chains/2),axis=0)*np.random.normal(loc=1,scale=0.01,size=(int(n_chains/2),n_dim))) +guess_param[guess_param[:,1]>0.25,1] = 0.25 +guess_param[:,6] = (guess_param[:,6]+np.pi/2)%(np.pi)-np.pi/2 +guess_param[:,7] = (guess_param[:,7]+np.pi/2)%(np.pi)-np.pi/2 f_list = freqs[freqs>fmin] -hp = gen_IMRPhenomD_polar(f_list, theta_ripple) +signal = gen_waveform(f_list, true_param) noise_psd = psd[freqs>fmin] -data = noise_psd + hp[0] +data = noise_psd + signal @jax.jit def LogLikelihood(theta): - h_test = gen_IMRPhenomD_polar(f_list, theta) + h_test = gen_waveform(f_list,theta) df = f_list[1] - f_list[0] - match_filter_SNR = 4*jnp.sum((jnp.conj(h_test[0])*data)/noise_psd*df).real - optimal_SNR = 4*jnp.sum((jnp.conj(h_test[0])*h_test[0])/noise_psd*df).real + match_filter_SNR = 4*jnp.sum((jnp.conj(h_test)*data)/noise_psd*df).real + optimal_SNR = 4*jnp.sum((jnp.conj(h_test)*h_test)/noise_psd*df).real return (match_filter_SNR-optimal_SNR/2) -theta_ref = jnp.array([Mc, eta, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle]) - -h_function = lambda f,theta:gen_IMRPhenomD_polar(f,theta)[0] - -logL = make_heterodyne_likelihood(data, h_function, theta_ref, noise_psd, f_list, 101) - - -L1 = jax.vmap(LogLikelihood)(theta_ripple_vec) -L2 = jax.vmap(jax.jit(logL))(theta_ripple_vec) +ref_param = jnp.array([Mc, eta, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle, ra, dec]) +logL = make_heterodyne_likelihood(data, gen_waveform, ref_param, noise_psd, f_list, 101) +L1 = jax.vmap(LogLikelihood)(guess_param) +L2 = jax.vmap(jax.jit(logL))(guess_param) print("Preparing RNG keys") rng_key_set = initialize_rng_keys(n_chains, seed=42) print("Initializing MCMC model and normalizing flow model.") -@jax.jit -def reparam_logL(theta): - theta = theta.at[0].set(jnp.exp(theta[0])) - theta = theta.at[4].set(jnp.exp(theta[4])) - return logL(theta) - - - initial_position = jax.random.uniform(rng_key_set[0], shape=(int(n_chains/2), n_dim)) * 1 # initial_position = initial_position.at[:,0].set(initial_position[:,0]*60 + 10) # initial_position = initial_position.at[:,1].set(initial_position[:,1]*0.25) @@ -154,66 +150,50 @@ def reparam_logL(theta): # initial_position = initial_position.at[:,7].set(initial_position[:,7]*np.pi-np.pi/2) # initial_position = initial_position.at[:,8].set(initial_position[:,8]*2*np.pi) -initial_position = jnp.append(initial_position, theta_ripple_vec, axis=0) +initial_position = jnp.append(initial_position, guess_param, axis=0) prior_range = jnp.array([[10,70],[0.0,0.25],[-1,1],[-1,1],[0,2000],[-5,5],[-np.pi/2,np.pi/2],[-np.pi/2,np.pi/2],[0,2*np.pi]]) model = RealNVP(10, n_dim, 64, 1) -print("Initializing sampler class") - -# likelihood = jax.jit(reparam_logL) -# dlikelihood = jax.jit(jax.grad(reparam_logL)) # compiling each of these function first should improve the performance by a lot - -likelihood = logL - -def top_hat(x): - output = 0. - for i in range(n_dim): - output = jax.lax.cond(x[i]>=prior_range[i,0], lambda: output, lambda: -jnp.inf) - output = jax.lax.cond(x[i]<=prior_range[i,1], lambda: output, lambda: -jnp.inf) - return output - -def posterior(theta): - prior = top_hat(theta) - return likelihood(theta) + prior - -posterior = jax.jit(posterior) -dposterior = jax.jit(jax.grad(posterior)) - -mass_matrix = jnp.eye(n_dim) -mass_matrix = mass_matrix.at[1,1].set(1e-3) - -local_sampler,updater, kernel,logp,dlogp = make_mala_sampler(posterior, dposterior,1e-3, jit=True, M=mass_matrix) - -# print("Warming up kernels and likelihood functions") -# local_time = time.time() -# logp(initial_position) -# dlogp(initial_position) -# kernel(rng_key_set[1],initial_position,logp(initial_position)) -# acceptance = jnp.zeros((n_chains,2,)) -# all_positions = jnp.zeros((n_chains, 2,)+initial_position.shape[-1:]) + initial_position[:,None] -# all_logp = jnp.zeros((n_chains,2,)) -# state = (rng_key_set[1], all_positions, all_logp, acceptance) -# updater(1,state) - -# print("Warmup complete. Time taken: {}".format(time.time()-local_time)) - -nf_sampler = Sampler(n_dim, rng_key_set, model, local_sampler, - posterior, - d_likelihood=dposterior, - n_loop=n_loop, - n_local_steps=n_local_steps, - n_global_steps=n_global_steps, - n_chains=n_chains, - stepsize=stepsize, - n_nf_samples=100, - learning_rate=learning_rate, - n_epochs= num_epochs, - max_samples = max_samples, - momentum=momentum, - batch_size=batch_size, - use_global=True,) - -nf_sampler.sample(initial_position) +# print("Initializing sampler class") + +# likelihood = logL + +# def top_hat(x): +# output = 0. +# for i in range(n_dim): +# output = jax.lax.cond(x[i]>=prior_range[i,0], lambda: output, lambda: -jnp.inf) +# output = jax.lax.cond(x[i]<=prior_range[i,1], lambda: output, lambda: -jnp.inf) +# return output + +# def posterior(theta): +# prior = top_hat(theta) +# return likelihood(theta) + prior + +# posterior = jax.jit(posterior) +# dposterior = jax.jit(jax.grad(posterior)) + +# mass_matrix = jnp.eye(n_dim) +# mass_matrix = mass_matrix.at[1,1].set(1e-3) + +# local_sampler,updater, kernel,logp,dlogp = make_mala_sampler(posterior, dposterior,1e-3, jit=True, M=mass_matrix) + +# nf_sampler = Sampler(n_dim, rng_key_set, model, local_sampler, +# posterior, +# d_likelihood=dposterior, +# n_loop=n_loop, +# n_local_steps=n_local_steps, +# n_global_steps=n_global_steps, +# n_chains=n_chains, +# stepsize=stepsize, +# n_nf_samples=100, +# learning_rate=learning_rate, +# n_epochs= num_epochs, +# max_samples = max_samples, +# momentum=momentum, +# batch_size=batch_size, +# use_global=True,) + +# nf_sampler.sample(initial_position) From 26e6848c667635953cb284e87f096e04e903f27d Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Fri, 2 Sep 2022 15:21:12 -0400 Subject: [PATCH 114/300] Change sampling setting --- example/ParameterEstimation/Injection_test.py | 137 +++++++++--------- 1 file changed, 71 insertions(+), 66 deletions(-) diff --git a/example/ParameterEstimation/Injection_test.py b/example/ParameterEstimation/Injection_test.py index f6ee6d56..91d8ea46 100644 --- a/example/ParameterEstimation/Injection_test.py +++ b/example/ParameterEstimation/Injection_test.py @@ -32,7 +32,7 @@ tgps_geo = 1126259462.423 # define sampling rate and duration -fsamp = 8192 +fsamp = 1024 duration = 4 delta_t = 1/fsamp @@ -75,23 +75,13 @@ def pad_low_freqs(f, psd_ref): chi2 = -0.3 dist_mpc = 1000.0 tc = 2.0 -phic = 0.0 -inclination = np.pi/2 -polarization_angle = np.pi/2 +phic = np.pi/4 +inclination = 3.27*np.pi/4 +polarization_angle = 1.2*np.pi/8 ra = 0.3 dec = 0.5 -n_dim = 11 -n_chains = 1000 -n_loop = 5 -n_local_steps = 2000 -n_global_steps = 1000 -learning_rate = 0.01 -max_samples = 50000 -momentum = 0.9 -num_epochs = 300 -batch_size = 50000 -stepsize = 0.01 + H1 = get_H1() H1_response = make_detector_response(H1[0], H1[1]) @@ -108,10 +98,6 @@ def gen_waveform(f, theta): true_param = jnp.array([Mc, eta, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle, ra, dec]) -guess_param = np.array(jnp.repeat(true_param[None,:],int(n_chains/2),axis=0)*np.random.normal(loc=1,scale=0.01,size=(int(n_chains/2),n_dim))) -guess_param[guess_param[:,1]>0.25,1] = 0.25 -guess_param[:,6] = (guess_param[:,6]+np.pi/2)%(np.pi)-np.pi/2 -guess_param[:,7] = (guess_param[:,7]+np.pi/2)%(np.pi)-np.pi/2 f_list = freqs[freqs>fmin] signal = gen_waveform(f_list, true_param) @@ -131,15 +117,36 @@ def LogLikelihood(theta): logL = make_heterodyne_likelihood(data, gen_waveform, ref_param, noise_psd, f_list, 101) -L1 = jax.vmap(LogLikelihood)(guess_param) -L2 = jax.vmap(jax.jit(logL))(guess_param) +# L1 = jax.vmap(LogLikelihood)(guess_param) +# L2 = jax.vmap(jax.jit(logL))(guess_param) + + +n_dim = 11 +n_chains = 1000 +n_loop = 5 +n_local_steps = 2000 +n_global_steps = 1000 +learning_rate = 0.01 +max_samples = 50000 +momentum = 0.9 +num_epochs = 300 +batch_size = 50000 +stepsize = 0.01 + +guess_param = np.array(jnp.repeat(true_param[None,:],int(n_chains),axis=0)*np.random.normal(loc=1,scale=0.1,size=(int(n_chains),n_dim))) +guess_param[guess_param[:,1]>0.25,1] = 0.25 +guess_param[:,6] = (guess_param[:,6]+np.pi/2)%(np.pi)-np.pi/2 +guess_param[:,7] = (guess_param[:,7]+np.pi/2)%(np.pi)-np.pi/2 + print("Preparing RNG keys") rng_key_set = initialize_rng_keys(n_chains, seed=42) print("Initializing MCMC model and normalizing flow model.") -initial_position = jax.random.uniform(rng_key_set[0], shape=(int(n_chains/2), n_dim)) * 1 +prior_range = jnp.array([[10,70],[0.0,0.25],[-1,1],[-1,1],[0,2000],[-5,5],[-np.pi/2,np.pi/2],[-np.pi/2,np.pi/2],[0,2*np.pi]]) + +# initial_position = jax.random.uniform(rng_key_set[0], shape=(int(n_chains), n_dim)) * 1 # initial_position = initial_position.at[:,0].set(initial_position[:,0]*60 + 10) # initial_position = initial_position.at[:,1].set(initial_position[:,1]*0.25) # initial_position = initial_position.at[:,2].set(initial_position[:,2]*2 - 1) @@ -149,51 +156,49 @@ def LogLikelihood(theta): # initial_position = initial_position.at[:,6].set(initial_position[:,6]*np.pi-np.pi/2) # initial_position = initial_position.at[:,7].set(initial_position[:,7]*np.pi-np.pi/2) # initial_position = initial_position.at[:,8].set(initial_position[:,8]*2*np.pi) +# initial_position = jnp.append(initial_position, guess_param, axis=0) -initial_position = jnp.append(initial_position, guess_param, axis=0) - -prior_range = jnp.array([[10,70],[0.0,0.25],[-1,1],[-1,1],[0,2000],[-5,5],[-np.pi/2,np.pi/2],[-np.pi/2,np.pi/2],[0,2*np.pi]]) - +initial_position = guess_param model = RealNVP(10, n_dim, 64, 1) -# print("Initializing sampler class") - -# likelihood = logL - -# def top_hat(x): -# output = 0. -# for i in range(n_dim): -# output = jax.lax.cond(x[i]>=prior_range[i,0], lambda: output, lambda: -jnp.inf) -# output = jax.lax.cond(x[i]<=prior_range[i,1], lambda: output, lambda: -jnp.inf) -# return output - -# def posterior(theta): -# prior = top_hat(theta) -# return likelihood(theta) + prior - -# posterior = jax.jit(posterior) -# dposterior = jax.jit(jax.grad(posterior)) - -# mass_matrix = jnp.eye(n_dim) -# mass_matrix = mass_matrix.at[1,1].set(1e-3) - -# local_sampler,updater, kernel,logp,dlogp = make_mala_sampler(posterior, dposterior,1e-3, jit=True, M=mass_matrix) - -# nf_sampler = Sampler(n_dim, rng_key_set, model, local_sampler, -# posterior, -# d_likelihood=dposterior, -# n_loop=n_loop, -# n_local_steps=n_local_steps, -# n_global_steps=n_global_steps, -# n_chains=n_chains, -# stepsize=stepsize, -# n_nf_samples=100, -# learning_rate=learning_rate, -# n_epochs= num_epochs, -# max_samples = max_samples, -# momentum=momentum, -# batch_size=batch_size, -# use_global=True,) - -# nf_sampler.sample(initial_position) +print("Initializing sampler class") + +likelihood = logL + +def top_hat(x): + output = 0. + for i in range(n_dim): + output = jax.lax.cond(x[i]>=prior_range[i,0], lambda: output, lambda: -jnp.inf) + output = jax.lax.cond(x[i]<=prior_range[i,1], lambda: output, lambda: -jnp.inf) + return output + +def posterior(theta): + prior = top_hat(theta) + return likelihood(theta) + prior + +posterior = jax.jit(posterior) +dposterior = jax.jit(jax.grad(posterior)) + +mass_matrix = jnp.eye(n_dim) +mass_matrix = mass_matrix.at[1,1].set(1e-3) + +local_sampler,updater, kernel,logp,dlogp = make_mala_sampler(posterior, dposterior,1e-3, jit=True, M=mass_matrix) + +nf_sampler = Sampler(n_dim, rng_key_set, model, local_sampler, + posterior, + d_likelihood=dposterior, + n_loop=n_loop, + n_local_steps=n_local_steps, + n_global_steps=n_global_steps, + n_chains=n_chains, + stepsize=stepsize, + n_nf_samples=100, + learning_rate=learning_rate, + n_epochs= num_epochs, + max_samples = max_samples, + momentum=momentum, + batch_size=batch_size, + use_global=True,) + +nf_sampler.sample(initial_position) From 5871c3d1ca9ec50ea0c6ff72ffdd3fb20a003cd1 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Fri, 2 Sep 2022 15:34:08 -0400 Subject: [PATCH 115/300] LowerSNR seems to give interesting behaviour --- example/ParameterEstimation/Injection_test.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/example/ParameterEstimation/Injection_test.py b/example/ParameterEstimation/Injection_test.py index 91d8ea46..f4a5a7dc 100644 --- a/example/ParameterEstimation/Injection_test.py +++ b/example/ParameterEstimation/Injection_test.py @@ -32,7 +32,7 @@ tgps_geo = 1126259462.423 # define sampling rate and duration -fsamp = 1024 +fsamp = 2048 duration = 4 delta_t = 1/fsamp From fa3a399a355a9fbaf334cb0c6560e3e743ffe112 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Fri, 2 Sep 2022 16:15:30 -0400 Subject: [PATCH 116/300] Use more relaxed initialization --- example/ParameterEstimation/Injection_test.py | 36 +++++++++++-------- 1 file changed, 21 insertions(+), 15 deletions(-) diff --git a/example/ParameterEstimation/Injection_test.py b/example/ParameterEstimation/Injection_test.py index f4a5a7dc..70effa42 100644 --- a/example/ParameterEstimation/Injection_test.py +++ b/example/ParameterEstimation/Injection_test.py @@ -73,10 +73,10 @@ def pad_low_freqs(f, psd_ref): Mc, eta = ms_to_Mc_eta(jnp.array([m1, m2])) chi1 = 0.4 chi2 = -0.3 -dist_mpc = 1000.0 +dist_mpc = 300.0 tc = 2.0 phic = np.pi/4 -inclination = 3.27*np.pi/4 +inclination = 1.57*np.pi/4 polarization_angle = 1.2*np.pi/8 ra = 0.3 dec = 0.5 @@ -144,21 +144,27 @@ def LogLikelihood(theta): print("Initializing MCMC model and normalizing flow model.") -prior_range = jnp.array([[10,70],[0.0,0.25],[-1,1],[-1,1],[0,2000],[-5,5],[-np.pi/2,np.pi/2],[-np.pi/2,np.pi/2],[0,2*np.pi]]) - -# initial_position = jax.random.uniform(rng_key_set[0], shape=(int(n_chains), n_dim)) * 1 -# initial_position = initial_position.at[:,0].set(initial_position[:,0]*60 + 10) -# initial_position = initial_position.at[:,1].set(initial_position[:,1]*0.25) -# initial_position = initial_position.at[:,2].set(initial_position[:,2]*2 - 1) -# initial_position = initial_position.at[:,3].set(initial_position[:,3]*2 - 1) -# initial_position = initial_position.at[:,4].set(initial_position[:,4]*2000) -# initial_position = initial_position.at[:,5].set(initial_position[:,5]*10-5) -# initial_position = initial_position.at[:,6].set(initial_position[:,6]*np.pi-np.pi/2) -# initial_position = initial_position.at[:,7].set(initial_position[:,7]*np.pi-np.pi/2) -# initial_position = initial_position.at[:,8].set(initial_position[:,8]*2*np.pi) +prior_range = jnp.array([[10,70],[0.0,0.25],[-1,1],[-1,1],[0,2000],[-5,5],[-np.pi/2,np.pi/2],[-np.pi/2,np.pi/2],[0,2*np.pi],[0,2*np.pi],[0,np.pi]]) + +initial_position = jax.random.uniform(rng_key_set[0], shape=(int(n_chains), n_dim)) * 1 +initial_position = initial_position.at[:,0].set(initial_position[:,0]*60 + 10) +initial_position = initial_position.at[:,1].set(initial_position[:,1]*0.25) +initial_position = initial_position.at[:,2].set(initial_position[:,2]*2 - 1) +initial_position = initial_position.at[:,3].set(initial_position[:,3]*2 - 1) +initial_position = initial_position.at[:,4].set(initial_position[:,4]*2000) +initial_position = initial_position.at[:,5].set(initial_position[:,5]*10-5) +initial_position = initial_position.at[:,6].set(initial_position[:,6]*np.pi-np.pi/2) +initial_position = initial_position.at[:,7].set(initial_position[:,7]*np.pi-np.pi/2) +initial_position = initial_position.at[:,8].set(initial_position[:,8]*2*np.pi) +initial_position = initial_position.at[:,9].set(initial_position[:,9]*2*np.pi) +initial_position = initial_position.at[:,10].set(initial_position[:,10]*np.pi) # initial_position = jnp.append(initial_position, guess_param, axis=0) -initial_position = guess_param +initial_position = initial_position.at[:,0].set(guess_param[:,0]) +initial_position = initial_position.at[:,1].set(guess_param[:,1]) +initial_position = initial_position.at[:,2].set(guess_param[:,2]) +initial_position = initial_position.at[:,3].set(guess_param[:,3]) + model = RealNVP(10, n_dim, 64, 1) From 087481c8e359318f7a625a9296b8bfbd68fc1f43 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Sat, 3 Sep 2022 10:33:59 -0400 Subject: [PATCH 117/300] Use both H1 and L1 --- example/ParameterEstimation/Injection_test.py | 85 ++++++++++--------- 1 file changed, 44 insertions(+), 41 deletions(-) diff --git a/example/ParameterEstimation/Injection_test.py b/example/ParameterEstimation/Injection_test.py index 70effa42..eb7b8254 100644 --- a/example/ParameterEstimation/Injection_test.py +++ b/example/ParameterEstimation/Injection_test.py @@ -76,7 +76,7 @@ def pad_low_freqs(f, psd_ref): dist_mpc = 300.0 tc = 2.0 phic = np.pi/4 -inclination = 1.57*np.pi/4 +inclination = 1.57*np.pi/8 polarization_angle = 1.2*np.pi/8 ra = 0.3 dec = 0.5 @@ -85,46 +85,54 @@ def pad_low_freqs(f, psd_ref): H1 = get_H1() H1_response = make_detector_response(H1[0], H1[1]) +L1 = get_L1() +L1_response = make_detector_response(L1[0], L1[1]) -def gen_waveform(f, theta): +def gen_waveform_H1(f, theta): theta_waveform = theta[:9] ra = theta[9] dec = theta[10] hp, hc = gen_IMRPhenomD_polar(f, theta_waveform) return H1_response(f, hp, hc, ra, dec, theta[5], theta[8]) - +def gen_waveform_L1(f, theta): + theta_waveform = theta[:9] + ra = theta[9] + dec = theta[10] + hp, hc = gen_IMRPhenomD_polar(f, theta_waveform) + return L1_response(f, hp, hc, ra, dec, theta[5], theta[8]) true_param = jnp.array([Mc, eta, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle, ra, dec]) f_list = freqs[freqs>fmin] -signal = gen_waveform(f_list, true_param) -noise_psd = psd[freqs>fmin] -data = noise_psd + signal - +H1_signal = gen_waveform_H1(f_list, true_param) +H1_noise_psd = noise_fd_dict['H1'][freqs>fmin] +H1_data = H1_noise_psd + H1_signal -@jax.jit -def LogLikelihood(theta): - h_test = gen_waveform(f_list,theta) - df = f_list[1] - f_list[0] - match_filter_SNR = 4*jnp.sum((jnp.conj(h_test)*data)/noise_psd*df).real - optimal_SNR = 4*jnp.sum((jnp.conj(h_test)*h_test)/noise_psd*df).real - return (match_filter_SNR-optimal_SNR/2) +L1_signal = gen_waveform_L1(f_list, true_param) +L1_noise_psd = noise_fd_dict['L1'][freqs>fmin] +L1_data = L1_noise_psd + L1_signal -ref_param = jnp.array([Mc, eta, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle, ra, dec]) -logL = make_heterodyne_likelihood(data, gen_waveform, ref_param, noise_psd, f_list, 101) +# @jax.jit +# def LogLikelihood(theta): +# h_test = gen_waveform(f_list,theta) +# df = f_list[1] - f_list[0] +# match_filter_SNR = 4*jnp.sum((jnp.conj(h_test)*data)/noise_psd*df).real +# optimal_SNR = 4*jnp.sum((jnp.conj(h_test)*h_test)/noise_psd*df).real +# return (match_filter_SNR-optimal_SNR/2) -# L1 = jax.vmap(LogLikelihood)(guess_param) -# L2 = jax.vmap(jax.jit(logL))(guess_param) +ref_param = jnp.array([Mc, eta, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle, ra, dec]) +H1_logL = make_heterodyne_likelihood(H1_data, gen_waveform_H1, ref_param, psd_dict['H1'], f_list, 101) +L1_logL = make_heterodyne_likelihood(L1_data, gen_waveform_L1, ref_param, psd_dict['L1'], f_list, 101) n_dim = 11 -n_chains = 1000 +n_chains = 100 n_loop = 5 -n_local_steps = 2000 +n_local_steps = 1000 n_global_steps = 1000 learning_rate = 0.01 max_samples = 50000 @@ -147,31 +155,14 @@ def LogLikelihood(theta): prior_range = jnp.array([[10,70],[0.0,0.25],[-1,1],[-1,1],[0,2000],[-5,5],[-np.pi/2,np.pi/2],[-np.pi/2,np.pi/2],[0,2*np.pi],[0,2*np.pi],[0,np.pi]]) initial_position = jax.random.uniform(rng_key_set[0], shape=(int(n_chains), n_dim)) * 1 -initial_position = initial_position.at[:,0].set(initial_position[:,0]*60 + 10) -initial_position = initial_position.at[:,1].set(initial_position[:,1]*0.25) -initial_position = initial_position.at[:,2].set(initial_position[:,2]*2 - 1) -initial_position = initial_position.at[:,3].set(initial_position[:,3]*2 - 1) -initial_position = initial_position.at[:,4].set(initial_position[:,4]*2000) -initial_position = initial_position.at[:,5].set(initial_position[:,5]*10-5) -initial_position = initial_position.at[:,6].set(initial_position[:,6]*np.pi-np.pi/2) -initial_position = initial_position.at[:,7].set(initial_position[:,7]*np.pi-np.pi/2) -initial_position = initial_position.at[:,8].set(initial_position[:,8]*2*np.pi) -initial_position = initial_position.at[:,9].set(initial_position[:,9]*2*np.pi) -initial_position = initial_position.at[:,10].set(initial_position[:,10]*np.pi) -# initial_position = jnp.append(initial_position, guess_param, axis=0) +for i in range(n_dim): + initial_position = initial_position.at[:,i].set(initial_position[:,i]*(prior_range[i,1]-prior_range[i,0])+prior_range[i,0]) initial_position = initial_position.at[:,0].set(guess_param[:,0]) initial_position = initial_position.at[:,1].set(guess_param[:,1]) initial_position = initial_position.at[:,2].set(guess_param[:,2]) initial_position = initial_position.at[:,3].set(guess_param[:,3]) - -model = RealNVP(10, n_dim, 64, 1) - -print("Initializing sampler class") - -likelihood = logL - def top_hat(x): output = 0. for i in range(n_dim): @@ -181,7 +172,19 @@ def top_hat(x): def posterior(theta): prior = top_hat(theta) - return likelihood(theta) + prior + return jnp.sqrt(H1_logL(theta)**2 + L1_logL(theta)**2) + prior + + +# # L1 = jax.vmap(LogLikelihood)(guess_param) +# # L2 = jax.vmap(jax.jit(logL))(guess_param) + + + + + +model = RealNVP(10, n_dim, 64, 1) + +print("Initializing sampler class") posterior = jax.jit(posterior) dposterior = jax.jit(jax.grad(posterior)) @@ -205,6 +208,6 @@ def posterior(theta): max_samples = max_samples, momentum=momentum, batch_size=batch_size, - use_global=True,) + use_global=False,) nf_sampler.sample(initial_position) From 33ecf6335f7557ea139b5382e36053e852db6b6b Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Tue, 6 Sep 2022 13:49:34 -0400 Subject: [PATCH 118/300] Initialization matters a lot --- example/ParameterEstimation/Injection_test.py | 38 +++++++++++++------ 1 file changed, 26 insertions(+), 12 deletions(-) diff --git a/example/ParameterEstimation/Injection_test.py b/example/ParameterEstimation/Injection_test.py index eb7b8254..5a8bdc4b 100644 --- a/example/ParameterEstimation/Injection_test.py +++ b/example/ParameterEstimation/Injection_test.py @@ -73,7 +73,7 @@ def pad_low_freqs(f, psd_ref): Mc, eta = ms_to_Mc_eta(jnp.array([m1, m2])) chi1 = 0.4 chi2 = -0.3 -dist_mpc = 300.0 +dist_mpc = 1000.0 tc = 2.0 phic = np.pi/4 inclination = 1.57*np.pi/8 @@ -130,19 +130,19 @@ def gen_waveform_L1(f, theta): L1_logL = make_heterodyne_likelihood(L1_data, gen_waveform_L1, ref_param, psd_dict['L1'], f_list, 101) n_dim = 11 -n_chains = 100 +n_chains = 1000 n_loop = 5 n_local_steps = 1000 n_global_steps = 1000 learning_rate = 0.01 max_samples = 50000 momentum = 0.9 -num_epochs = 300 +num_epochs = 1000 batch_size = 50000 stepsize = 0.01 -guess_param = np.array(jnp.repeat(true_param[None,:],int(n_chains),axis=0)*np.random.normal(loc=1,scale=0.1,size=(int(n_chains),n_dim))) -guess_param[guess_param[:,1]>0.25,1] = 0.25 +guess_param = np.array(jnp.repeat(true_param[None,:],int(n_chains),axis=0)*np.random.normal(loc=1,scale=0.01,size=(int(n_chains),n_dim))) +guess_param[guess_param[:,1]>0.25,1] = 0.249 guess_param[:,6] = (guess_param[:,6]+np.pi/2)%(np.pi)-np.pi/2 guess_param[:,7] = (guess_param[:,7]+np.pi/2)%(np.pi)-np.pi/2 @@ -160,8 +160,10 @@ def gen_waveform_L1(f, theta): initial_position = initial_position.at[:,0].set(guess_param[:,0]) initial_position = initial_position.at[:,1].set(guess_param[:,1]) -initial_position = initial_position.at[:,2].set(guess_param[:,2]) -initial_position = initial_position.at[:,3].set(guess_param[:,3]) +for i in range(2,11): + initial_position = initial_position.at[:int(n_chains/10),i].set(guess_param[:int(n_chains/10),i]) + +initial_position = initial_position.at[:,5].set(guess_param[:,5]) def top_hat(x): output = 0. @@ -172,7 +174,7 @@ def top_hat(x): def posterior(theta): prior = top_hat(theta) - return jnp.sqrt(H1_logL(theta)**2 + L1_logL(theta)**2) + prior + return H1_logL(theta) + L1_logL(theta) + prior # # L1 = jax.vmap(LogLikelihood)(guess_param) @@ -186,13 +188,25 @@ def posterior(theta): print("Initializing sampler class") -posterior = jax.jit(posterior) -dposterior = jax.jit(jax.grad(posterior)) +posterior = posterior +dposterior = jax.grad(posterior) mass_matrix = jnp.eye(n_dim) mass_matrix = mass_matrix.at[1,1].set(1e-3) +mass_matrix = mass_matrix.at[5,5].set(1e-3) + +local_sampler,updater, kernel, logp, dlogp = make_mala_sampler(posterior, dposterior,5e-3, jit=True, M=mass_matrix) + +# print("Precompling") +# from time import time + +# current_time = time() +# logp(initial_position) +# dlogp(initial_position) +# kernel(rng_key_set[1], initial_position, logp(initial_position)) +# print("Precompling time: ", time()-current_time) -local_sampler,updater, kernel,logp,dlogp = make_mala_sampler(posterior, dposterior,1e-3, jit=True, M=mass_matrix) +print("Running sampler") nf_sampler = Sampler(n_dim, rng_key_set, model, local_sampler, posterior, @@ -208,6 +222,6 @@ def posterior(theta): max_samples = max_samples, momentum=momentum, batch_size=batch_size, - use_global=False,) + use_global=True,) nf_sampler.sample(initial_position) From bf1c997064ea2479639b84789418572686d67f72 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Sat, 17 Sep 2022 14:56:47 -0400 Subject: [PATCH 119/300] Batch script adding. Add V1 detector present --- example/ParameterEstimation/GW150914.py | 157 ++++++++++++++++++ example/ParameterEstimation/GW170817.py | 157 ++++++++++++++++++ example/ParameterEstimation/Injection_test.py | 32 +--- jaxgw/PE/detector_preset.py | 26 +++ 4 files changed, 348 insertions(+), 24 deletions(-) create mode 100644 example/ParameterEstimation/GW150914.py create mode 100644 example/ParameterEstimation/GW170817.py diff --git a/example/ParameterEstimation/GW150914.py b/example/ParameterEstimation/GW150914.py new file mode 100644 index 00000000..305666f6 --- /dev/null +++ b/example/ParameterEstimation/GW150914.py @@ -0,0 +1,157 @@ +import numpy as np +import jax.numpy as jnp +import jax + +from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar +from jaxgw.PE.detector_preset import * +from jaxgw.PE.heterodyneLikelihood import make_heterodyne_likelihood +from jaxgw.PE.detector_projection import make_detector_response + +from flowMC.nfmodel.rqSpline import RQSpline +from flowMC.sampler.MALA import make_mala_sampler +from flowMC.sampler.Sampler import Sampler +from flowMC.utils.PRNG_keys import initialize_rng_keys +from flowMC.nfmodel.utils import * + +data = np.load('./data/GW150914_data.npz',allow_pickle=True) + +minimum_frequency = data['minimum_frequency'] + +H1_frequency = data['frequency'].tolist()['H1'] +H1_data = data['data'].tolist()['H1'][H1_frequency>minimum_frequency] +H1_psd = data['psd'].tolist()['H1'][H1_frequency>minimum_frequency] +H1_frequency = H1_frequency[H1_frequency>minimum_frequency] + +L1_frequency = data['frequency'].tolist()['L1'] +L1_data = data['data'].tolist()['L1'][L1_frequency>minimum_frequency] +L1_psd = data['psd'].tolist()['L1'][L1_frequency>minimum_frequency] +L1_frequency = L1_frequency[L1_frequency>minimum_frequency] + +H1 = get_H1() +H1_response = make_detector_response(H1[0], H1[1]) +L1 = get_L1() +L1_response = make_detector_response(L1[0], L1[1]) + +def gen_waveform_H1(f, theta): + theta_waveform = theta[:9] + ra = theta[9] + dec = theta[10] + hp, hc = gen_IMRPhenomD_polar(f, theta_waveform) + return H1_response(f, hp, hc, ra, dec, theta[5], theta[8]) + +def gen_waveform_L1(f, theta): + theta_waveform = theta[:9] + ra = theta[9] + dec = theta[10] + hp, hc = gen_IMRPhenomD_polar(f, theta_waveform) + return L1_response(f, hp, hc, ra, dec, theta[5], theta[8]) + +def H1_LogLikelihood(theta): + h_test = gen_waveform_H1(H1_frequency,theta) + df = H1_frequency[1] - H1_frequency[0] + match_filter_SNR = 4*jnp.sum((jnp.conj(h_test)*H1_data)/H1_psd*df).real + optimal_SNR = 4*jnp.sum((jnp.conj(h_test)*h_test)/H1_psd*df).real + return (match_filter_SNR-optimal_SNR/2) + +def L1_LogLikelihood(theta): + h_test = gen_waveform_L1(L1_frequency,theta) + df = L1_frequency[1] - L1_frequency[0] + match_filter_SNR = 4*jnp.sum((jnp.conj(h_test)*L1_data)/L1_psd*df).real + optimal_SNR = 4*jnp.sum((jnp.conj(h_test)*h_test)/L1_psd*df).real + return (match_filter_SNR-optimal_SNR/2) + +ref_param = jnp.array([ 3.41096639e+01, 2.42240502e-01, 7.03845904e-02, + 1.45055597e-01, 4.00156164e+02, -1.97202379e+00, + 1.08177416e+00, -6.94499550e-02, 1.95503312e+00, + 8.60901399e-01, 2.89425087e+00]) + +ref_param = ref_param.at[-1].set(ref_param[-1]%(jnp.pi)) +ref_param = ref_param.at[6].set((ref_param[6]+jnp.pi/2)%(jnp.pi)-jnp.pi/2) + + +H1_logL = make_heterodyne_likelihood(H1_data, gen_waveform_H1, ref_param, H1_psd, H1_frequency, 101) +L1_logL = make_heterodyne_likelihood(L1_data, gen_waveform_L1, ref_param, L1_psd, L1_frequency, 101) + +# H1_logL = H1_LogLikelihood +# L1_logL = L1_LogLikelihood + + +n_dim = 11 +n_chains = 1000 +n_loop = 10 +n_local_steps = 1000 +n_global_steps = 1000 +learning_rate = 0.001 +max_samples = 50000 +momentum = 0.9 +num_epochs = 300 +batch_size = 50000 +stepsize = 0.01 + +guess_param = ref_param + +guess_param = np.array(jnp.repeat(guess_param[None,:],int(n_chains),axis=0)*np.random.normal(loc=1,scale=0.1,size=(int(n_chains),n_dim))) +guess_param[guess_param[:,1]>0.25,1] = 0.249 +guess_param[:,6] = (guess_param[:,6]+np.pi/2)%(np.pi)-np.pi/2 +guess_param[:,7] = (guess_param[:,7]+np.pi/2)%(np.pi)-np.pi/2 + + +print("Preparing RNG keys") +rng_key_set = initialize_rng_keys(n_chains, seed=42) + +print("Initializing MCMC model and normalizing flow model.") + +prior_range = jnp.array([[10,70],[0.0,0.25],[-1,1],[-1,1],[0,2000],[-5,5],[-np.pi/2,np.pi/2],[-np.pi/2,np.pi/2],[0,2*np.pi],[0,2*np.pi],[0,np.pi]]) + +initial_position = jax.random.uniform(rng_key_set[0], shape=(int(n_chains), n_dim)) * 1 +for i in range(n_dim): + initial_position = initial_position.at[:,i].set(initial_position[:,i]*(prior_range[i,1]-prior_range[i,0])+prior_range[i,0]) + +initial_position = initial_position.at[:,0].set(guess_param[:,0]) +initial_position = initial_position.at[:,1].set(guess_param[:,1]) +initial_position = initial_position.at[:,5].set(guess_param[:,5]) + +def top_hat(x): + output = 0. + for i in range(n_dim): + output = jax.lax.cond(x[i]>=prior_range[i,0], lambda: output, lambda: -jnp.inf) + output = jax.lax.cond(x[i]<=prior_range[i,1], lambda: output, lambda: -jnp.inf) + return output + +def posterior(theta): + prior = top_hat(theta) + return H1_logL(theta) + L1_logL(theta) + prior + +model = RQSpline(n_dim, 10, [128,128], 8) + +print("Initializing sampler class") + +posterior = posterior +dposterior = jax.grad(posterior) + +mass_matrix = jnp.eye(n_dim) +mass_matrix = mass_matrix.at[1,1].set(1e-3) +mass_matrix = mass_matrix.at[5,5].set(1e-2) + +local_sampler,updater, kernel, logp, dlogp = make_mala_sampler(posterior, dposterior,2e-3, jit=True, M=mass_matrix) + +print("Running sampler") + +nf_sampler = Sampler(n_dim, rng_key_set, model, local_sampler, + posterior, + d_likelihood=dposterior, + n_loop=n_loop, + n_local_steps=n_local_steps, + n_global_steps=n_global_steps, + n_chains=n_chains, + stepsize=stepsize, + n_nf_samples=100, + learning_rate=learning_rate, + n_epochs= num_epochs, + max_samples = max_samples, + momentum=momentum, + batch_size=batch_size, + use_global=True, + keep_quantile=0.) + +nf_sampler.sample(initial_position) diff --git a/example/ParameterEstimation/GW170817.py b/example/ParameterEstimation/GW170817.py new file mode 100644 index 00000000..5f52bcd9 --- /dev/null +++ b/example/ParameterEstimation/GW170817.py @@ -0,0 +1,157 @@ +import numpy as np +import jax.numpy as jnp +import jax + +from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar +from jaxgw.PE.detector_preset import * +from jaxgw.PE.heterodyneLikelihood import make_heterodyne_likelihood +from jaxgw.PE.detector_projection import make_detector_response + +from flowMC.nfmodel.rqSpline import RQSpline +from flowMC.sampler.MALA import make_mala_sampler +from flowMC.sampler.Sampler import Sampler +from flowMC.utils.PRNG_keys import initialize_rng_keys +from flowMC.nfmodel.utils import * + +data = np.load('./data/GW170817_data.npz',allow_pickle=True) + +minimum_frequency = data['minimum_frequency'] + +H1_frequency = data['frequency'].tolist()['H1'] +H1_data = data['data'].tolist()['H1'][H1_frequency>minimum_frequency] +H1_psd = data['psd'].tolist()['H1'][H1_frequency>minimum_frequency] +H1_frequency = H1_frequency[H1_frequency>minimum_frequency] + +L1_frequency = data['frequency'].tolist()['L1'] +L1_data = data['data'].tolist()['L1'][L1_frequency>minimum_frequency] +L1_psd = data['psd'].tolist()['L1'][L1_frequency>minimum_frequency] +L1_frequency = L1_frequency[L1_frequency>minimum_frequency] + +V1_frequency = data['frequency'].tolist()['V1'] +V1_data = data['data'].tolist()['V1'][V1_frequency>minimum_frequency] +V1_psd = data['psd'].tolist()['V1'][V1_frequency>minimum_frequency] +V1_frequency = V1_frequency[V1_frequency>minimum_frequency] + +H1 = get_H1() +H1_response = make_detector_response(H1[0], H1[1]) +L1 = get_L1() +L1_response = make_detector_response(L1[0], L1[1]) +V1 = get_V1() +V1_response = make_detector_response(V1[0], V1[1]) + +def gen_waveforms(f,theta): + theta_waveform = theta[:9] + ra = theta[9] + dec = theta[10] + hp, hc = gen_IMRPhenomD_polar(f, theta_waveform) + return H1_response(f, hp, hc, ra, dec, theta[5], theta[8]), L1_response(f, hp, hc, ra, dec, theta[5], theta[8]), V1_response(f, hp, hc, ra, dec, theta[5], theta[8]) + +def H1_LogLikelihood(theta): + h_test = gen_waveform_H1(H1_frequency,theta) + df = H1_frequency[1] - H1_frequency[0] + match_filter_SNR = 4*jnp.sum((jnp.conj(h_test)*H1_data)/H1_psd*df).real + optimal_SNR = 4*jnp.sum((jnp.conj(h_test)*h_test)/H1_psd*df).real + return (match_filter_SNR-optimal_SNR/2) + +def L1_LogLikelihood(theta): + h_test = gen_waveform_L1(L1_frequency,theta) + df = L1_frequency[1] - L1_frequency[0] + match_filter_SNR = 4*jnp.sum((jnp.conj(h_test)*L1_data)/L1_psd*df).real + optimal_SNR = 4*jnp.sum((jnp.conj(h_test)*h_test)/L1_psd*df).real + return (match_filter_SNR-optimal_SNR/2) + +ref_param = jnp.array([ 3.41096639e+01, 2.42240502e-01, 7.03845904e-02, + 1.45055597e-01, 4.00156164e+02, -1.97202379e+00, + 1.08177416e+00, -6.94499550e-02, 1.95503312e+00, + 8.60901399e-01, 2.89425087e+00]) + +ref_param = ref_param.at[-1].set(ref_param[-1]%(jnp.pi)) +ref_param = ref_param.at[6].set((ref_param[6]+jnp.pi/2)%(jnp.pi)-jnp.pi/2) + + +H1_logL = make_heterodyne_likelihood(H1_data, gen_waveform_H1, ref_param, H1_psd, H1_frequency, 101) +L1_logL = make_heterodyne_likelihood(L1_data, gen_waveform_L1, ref_param, L1_psd, L1_frequency, 101) + +# H1_logL = H1_LogLikelihood +# L1_logL = L1_LogLikelihood + + +n_dim = 11 +n_chains = 1000 +n_loop = 10 +n_local_steps = 1000 +n_global_steps = 1000 +learning_rate = 0.001 +max_samples = 50000 +momentum = 0.9 +num_epochs = 300 +batch_size = 50000 +stepsize = 0.01 + +guess_param = ref_param + +guess_param = np.array(jnp.repeat(guess_param[None,:],int(n_chains),axis=0)*np.random.normal(loc=1,scale=0.1,size=(int(n_chains),n_dim))) +guess_param[guess_param[:,1]>0.25,1] = 0.249 +guess_param[:,6] = (guess_param[:,6]+np.pi/2)%(np.pi)-np.pi/2 +guess_param[:,7] = (guess_param[:,7]+np.pi/2)%(np.pi)-np.pi/2 + + +print("Preparing RNG keys") +rng_key_set = initialize_rng_keys(n_chains, seed=42) + +print("Initializing MCMC model and normalizing flow model.") + +prior_range = jnp.array([[10,70],[0.0,0.25],[-1,1],[-1,1],[0,2000],[-5,5],[-np.pi/2,np.pi/2],[-np.pi/2,np.pi/2],[0,2*np.pi],[0,2*np.pi],[0,np.pi]]) + +initial_position = jax.random.uniform(rng_key_set[0], shape=(int(n_chains), n_dim)) * 1 +for i in range(n_dim): + initial_position = initial_position.at[:,i].set(initial_position[:,i]*(prior_range[i,1]-prior_range[i,0])+prior_range[i,0]) + +initial_position = initial_position.at[:,0].set(guess_param[:,0]) +initial_position = initial_position.at[:,1].set(guess_param[:,1]) +initial_position = initial_position.at[:,5].set(guess_param[:,5]) + +def top_hat(x): + output = 0. + for i in range(n_dim): + output = jax.lax.cond(x[i]>=prior_range[i,0], lambda: output, lambda: -jnp.inf) + output = jax.lax.cond(x[i]<=prior_range[i,1], lambda: output, lambda: -jnp.inf) + return output + +def posterior(theta): + prior = top_hat(theta) + return H1_logL(theta) + L1_logL(theta) + prior + +model = RQSpline(n_dim, 10, [128,128], 8) + +print("Initializing sampler class") + +posterior = posterior +dposterior = jax.grad(posterior) + +mass_matrix = jnp.eye(n_dim) +mass_matrix = mass_matrix.at[1,1].set(1e-3) +mass_matrix = mass_matrix.at[5,5].set(1e-2) + +local_sampler,updater, kernel, logp, dlogp = make_mala_sampler(posterior, dposterior,2e-3, jit=True, M=mass_matrix) + +print("Running sampler") + +nf_sampler = Sampler(n_dim, rng_key_set, model, local_sampler, + posterior, + d_likelihood=dposterior, + n_loop=n_loop, + n_local_steps=n_local_steps, + n_global_steps=n_global_steps, + n_chains=n_chains, + stepsize=stepsize, + n_nf_samples=100, + learning_rate=learning_rate, + n_epochs= num_epochs, + max_samples = max_samples, + momentum=momentum, + batch_size=batch_size, + use_global=True, + keep_quantile=0.) + +nf_sampler.sample(initial_position) diff --git a/example/ParameterEstimation/Injection_test.py b/example/ParameterEstimation/Injection_test.py index 5a8bdc4b..d5ac8144 100644 --- a/example/ParameterEstimation/Injection_test.py +++ b/example/ParameterEstimation/Injection_test.py @@ -14,7 +14,7 @@ from jaxgw.PE.heterodyneLikelihood import make_heterodyne_likelihood from jaxgw.PE.detector_projection import make_detector_response -from flowMC.nfmodel.realNVP import RealNVP +from flowMC.nfmodel.rqSpline import RQSpline from flowMC.sampler.MALA import make_mala_sampler from flowMC.sampler.Sampler import Sampler from flowMC.utils.PRNG_keys import initialize_rng_keys @@ -115,15 +115,6 @@ def gen_waveform_L1(f, theta): L1_noise_psd = noise_fd_dict['L1'][freqs>fmin] L1_data = L1_noise_psd + L1_signal - -# @jax.jit -# def LogLikelihood(theta): -# h_test = gen_waveform(f_list,theta) -# df = f_list[1] - f_list[0] -# match_filter_SNR = 4*jnp.sum((jnp.conj(h_test)*data)/noise_psd*df).real -# optimal_SNR = 4*jnp.sum((jnp.conj(h_test)*h_test)/noise_psd*df).real -# return (match_filter_SNR-optimal_SNR/2) - ref_param = jnp.array([Mc, eta, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle, ra, dec]) H1_logL = make_heterodyne_likelihood(H1_data, gen_waveform_H1, ref_param, psd_dict['H1'], f_list, 101) @@ -134,10 +125,10 @@ def gen_waveform_L1(f, theta): n_loop = 5 n_local_steps = 1000 n_global_steps = 1000 -learning_rate = 0.01 +learning_rate = 0.001 max_samples = 50000 momentum = 0.9 -num_epochs = 1000 +num_epochs = 300 batch_size = 50000 stepsize = 0.01 @@ -160,9 +151,6 @@ def gen_waveform_L1(f, theta): initial_position = initial_position.at[:,0].set(guess_param[:,0]) initial_position = initial_position.at[:,1].set(guess_param[:,1]) -for i in range(2,11): - initial_position = initial_position.at[:int(n_chains/10),i].set(guess_param[:int(n_chains/10),i]) - initial_position = initial_position.at[:,5].set(guess_param[:,5]) def top_hat(x): @@ -177,14 +165,9 @@ def posterior(theta): return H1_logL(theta) + L1_logL(theta) + prior -# # L1 = jax.vmap(LogLikelihood)(guess_param) -# # L2 = jax.vmap(jax.jit(logL))(guess_param) - - - - +# model = RealNVP(10, n_dim, 64, 1) +model = RQSpline(n_dim, 10, [128,128], 8) -model = RealNVP(10, n_dim, 64, 1) print("Initializing sampler class") @@ -195,7 +178,7 @@ def posterior(theta): mass_matrix = mass_matrix.at[1,1].set(1e-3) mass_matrix = mass_matrix.at[5,5].set(1e-3) -local_sampler,updater, kernel, logp, dlogp = make_mala_sampler(posterior, dposterior,5e-3, jit=True, M=mass_matrix) +local_sampler,updater, kernel, logp, dlogp = make_mala_sampler(posterior, dposterior,2e-3, jit=True, M=mass_matrix) # print("Precompling") # from time import time @@ -222,6 +205,7 @@ def posterior(theta): max_samples = max_samples, momentum=momentum, batch_size=batch_size, - use_global=True,) + use_global=True, + keep_quantile=0.5) nf_sampler.sample(initial_position) diff --git a/jaxgw/PE/detector_preset.py b/jaxgw/PE/detector_preset.py index eeebbe5c..fdd2ed5e 100644 --- a/jaxgw/PE/detector_preset.py +++ b/jaxgw/PE/detector_preset.py @@ -57,3 +57,29 @@ def get_L1(): L1_vertex = get_vertex_position_geocentric(L1_lat, L1_long, L1_elevation) return detector_tensor(L1_arm1, L1_arm2), L1_vertex + +def get_V1(): + """ + Get the detector response matrix and the vertex position for V1. + + Returns + ------- + V1_detector_response : ndarray + The detector response matrix for V1. + V1_vertex : ndarray + The vertex position for V1. + """ + V1_lat = (43 + 37. / 60 + 53.0921 / 3600) * degree_to_radian + V1_long = (10 + 30. / 60 + 16.1878 / 3600) * degree_to_radian + V1_xarm_azimuth = 70.5674 * degree_to_radian + V1_yarm_azimuth = 160.5674 * degree_to_radian + V1_xarm_tilt = 0 + V1_yarm_tilt = 0 + V1_elevation = 51.884 + + V1_arm1 = construct_arm(V1_lat, V1_long, V1_xarm_tilt, V1_xarm_azimuth) + V1_arm2 = construct_arm(V1_lat, V1_long, V1_yarm_tilt, V1_yarm_azimuth) + + V1_vertex = get_vertex_position_geocentric(V1_lat, V1_long, V1_elevation) + + return detector_tensor(V1_arm1, V1_arm2), V1_vertex From 395780fb584dcc260b527100b7281ac03c381002 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Sat, 17 Sep 2022 15:13:31 -0400 Subject: [PATCH 120/300] Add multiple detector heterodyne likelihood function. The compilation time should be shorter for this one. --- jaxgw/PE/heterodyneLikelihood.py | 50 +++++++++++++++++++++++++++++++- 1 file changed, 49 insertions(+), 1 deletion(-) diff --git a/jaxgw/PE/heterodyneLikelihood.py b/jaxgw/PE/heterodyneLikelihood.py index 02e90e2a..46395b56 100644 --- a/jaxgw/PE/heterodyneLikelihood.py +++ b/jaxgw/PE/heterodyneLikelihood.py @@ -1,3 +1,4 @@ +import wave import numpy as np from scipy.interpolate import interp1d @@ -63,4 +64,51 @@ def heterodyne_likelihood(params): return (match_filter_SNR - optimal_SNR/2).real - return heterodyne_likelihood \ No newline at end of file + return heterodyne_likelihood + +def make_heterodyne_likelihood_mutliple_detector(data_array, psd_list, respose_list, h_function, ref_theta, freqs, n_bins=101): + + num_detector = len(data_array) + + f_bins, f_bins_center = make_binning_scheme(freqs, n_bins) + raw_hp, raw_hc = h_function(freqs, ref_theta[:9]) + ra, dec = ref_theta[9], ref_theta[10] + h_ref = [] + h_ref_low = [] + h_ref_bincenter = [] + for i in range(num_detector): + h_ref.append(respose_list[i](freqs, raw_hp, raw_hc, ra, dec, ref_theta[5],ref_theta[8])) + h_ref_low.append(respose_list[i](f_bins[:-1], raw_hp, raw_hc, ra, dec, ref_theta[5],ref_theta[8])) + h_ref_bincenter.append(respose_list[i](f_bins_center, raw_hp, raw_hc, ra, dec, ref_theta[5],ref_theta[8])) + + A0_array = [] + A1_array = [] + B0_array = [] + B1_array = [] + + for i in range(num_detector): + A0, A1, B0, B1 = compute_coefficients(data_array[i], h_ref[i], psd_list[i], freqs, f_bins, f_bins_center) + A0_array.append(A0) + A1_array.append(A1) + B0_array.append(B0) + B1_array.append(B1) + + def hetrodyne_likelihood(params): + raw_hc, raw_hp = h_function(freqs, params[:9]) + ra, dec = params[9], params[10] + + output_SNR = 0 + + for i in range(num_detector): + waveform_low = respose_list[i](f_bins[:-1], raw_hp, raw_hc, ra, dec, params[5], params[8]) + waveform_center = respose_list[i](f_bins_center, raw_hp, raw_hc, ra, dec, params[5], params[8]) + + r0 = waveform_center/h_ref_bincenter[i] + r1 = (waveform_low/h_ref_low[i] - r0)/(f_bins[:-1]-f_bins_center) + + match_filter_SNR = jnp.sum(A0_array[i]*r0.conj() + A1_array[i]*r1.conj()) + optimal_SNR = jnp.sum(B0_array[i]*jnp.abs(r0)**2 + 2*B1_array[i]*(r0*r1.conj()).real) + + output_SNR += (match_filter_SNR - optimal_SNR/2).real + + return output_SNR \ No newline at end of file From 31733192f0a36bf9d5fe56ced9681b90c975ce6e Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Sat, 17 Sep 2022 15:52:18 -0400 Subject: [PATCH 121/300] Bug fix in multiple detector heterodyne likelihood --- example/ParameterEstimation/GW170817.py | 171 ++++++++++++------------ jaxgw/PE/heterodyneLikelihood.py | 25 ++-- 2 files changed, 98 insertions(+), 98 deletions(-) diff --git a/example/ParameterEstimation/GW170817.py b/example/ParameterEstimation/GW170817.py index 5f52bcd9..2b7e0c05 100644 --- a/example/ParameterEstimation/GW170817.py +++ b/example/ParameterEstimation/GW170817.py @@ -4,7 +4,7 @@ from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar from jaxgw.PE.detector_preset import * -from jaxgw.PE.heterodyneLikelihood import make_heterodyne_likelihood +from jaxgw.PE.heterodyneLikelihood import make_heterodyne_likelihood_mutliple_detector from jaxgw.PE.detector_projection import make_detector_response from flowMC.nfmodel.rqSpline import RQSpline @@ -17,17 +17,17 @@ minimum_frequency = data['minimum_frequency'] -H1_frequency = data['frequency'].tolist()['H1'] +H1_frequency = data['frequency'] H1_data = data['data'].tolist()['H1'][H1_frequency>minimum_frequency] H1_psd = data['psd'].tolist()['H1'][H1_frequency>minimum_frequency] H1_frequency = H1_frequency[H1_frequency>minimum_frequency] -L1_frequency = data['frequency'].tolist()['L1'] +L1_frequency = data['frequency'] L1_data = data['data'].tolist()['L1'][L1_frequency>minimum_frequency] L1_psd = data['psd'].tolist()['L1'][L1_frequency>minimum_frequency] L1_frequency = L1_frequency[L1_frequency>minimum_frequency] -V1_frequency = data['frequency'].tolist()['V1'] +V1_frequency = data['frequency'] V1_data = data['data'].tolist()['V1'][V1_frequency>minimum_frequency] V1_psd = data['psd'].tolist()['V1'][V1_frequency>minimum_frequency] V1_frequency = V1_frequency[V1_frequency>minimum_frequency] @@ -39,26 +39,22 @@ V1 = get_V1() V1_response = make_detector_response(V1[0], V1[1]) -def gen_waveforms(f,theta): - theta_waveform = theta[:9] +def LogLikelihood(theta): ra = theta[9] dec = theta[10] - hp, hc = gen_IMRPhenomD_polar(f, theta_waveform) - return H1_response(f, hp, hc, ra, dec, theta[5], theta[8]), L1_response(f, hp, hc, ra, dec, theta[5], theta[8]), V1_response(f, hp, hc, ra, dec, theta[5], theta[8]) - -def H1_LogLikelihood(theta): - h_test = gen_waveform_H1(H1_frequency,theta) - df = H1_frequency[1] - H1_frequency[0] - match_filter_SNR = 4*jnp.sum((jnp.conj(h_test)*H1_data)/H1_psd*df).real - optimal_SNR = 4*jnp.sum((jnp.conj(h_test)*h_test)/H1_psd*df).real - return (match_filter_SNR-optimal_SNR/2) - -def L1_LogLikelihood(theta): - h_test = gen_waveform_L1(L1_frequency,theta) - df = L1_frequency[1] - L1_frequency[0] - match_filter_SNR = 4*jnp.sum((jnp.conj(h_test)*L1_data)/L1_psd*df).real - optimal_SNR = 4*jnp.sum((jnp.conj(h_test)*h_test)/L1_psd*df).real - return (match_filter_SNR-optimal_SNR/2) + hp_test, hc_test = gen_IMRPhenomD_polar(H1_frequency, theta[:9]) + h_test_H1 = H1_response(H1_frequency, hp_test, hc_test, ra, dec, theta[5], theta[8]) + h_test_L1 = L1_response(L1_frequency, hp_test, hc_test, ra, dec, theta[5], theta[8]) + h_test_V1 = V1_response(V1_frequency, hp_test, hc_test, ra, dec, theta[5], theta[8]) + df = H1_frequency[1] - H1_frequency[0] + match_filter_SNR_H1 = 4*jnp.sum((jnp.conj(h_test_H1)*H1_data)/H1_psd*df).real + match_filter_SNR_L1 = 4*jnp.sum((jnp.conj(h_test_L1)*L1_data)/L1_psd*df).real + match_filter_SNR_V1 = 4*jnp.sum((jnp.conj(h_test_V1)*V1_data)/V1_psd*df).real + optimal_SNR_H1 = 4*jnp.sum((jnp.conj(h_test_H1)*h_test_H1)/H1_psd*df).real + optimal_SNR_L1 = 4*jnp.sum((jnp.conj(h_test_L1)*h_test_L1)/L1_psd*df).real + optimal_SNR_V1 = 4*jnp.sum((jnp.conj(h_test_V1)*h_test_V1)/V1_psd*df).real + + return (match_filter_SNR_H1-optimal_SNR_H1/2) + (match_filter_SNR_L1-optimal_SNR_L1/2) + (match_filter_SNR_V1-optimal_SNR_V1/2) ref_param = jnp.array([ 3.41096639e+01, 2.42240502e-01, 7.03845904e-02, 1.45055597e-01, 4.00156164e+02, -1.97202379e+00, @@ -68,90 +64,89 @@ def L1_LogLikelihood(theta): ref_param = ref_param.at[-1].set(ref_param[-1]%(jnp.pi)) ref_param = ref_param.at[6].set((ref_param[6]+jnp.pi/2)%(jnp.pi)-jnp.pi/2) +data_list = [H1_data, L1_data, V1_data] +psd_list = [H1_psd, L1_psd, V1_psd] +response_list = [H1_response, L1_response, V1_response] -H1_logL = make_heterodyne_likelihood(H1_data, gen_waveform_H1, ref_param, H1_psd, H1_frequency, 101) -L1_logL = make_heterodyne_likelihood(L1_data, gen_waveform_L1, ref_param, L1_psd, L1_frequency, 101) +logL = make_heterodyne_likelihood_mutliple_detector(data_list, psd_list, response_list, gen_IMRPhenomD_polar, ref_param, H1_frequency, 101) -# H1_logL = H1_LogLikelihood -# L1_logL = L1_LogLikelihood +# n_dim = 11 +# n_chains = 1000 +# n_loop = 10 +# n_local_steps = 1000 +# n_global_steps = 1000 +# learning_rate = 0.001 +# max_samples = 50000 +# momentum = 0.9 +# num_epochs = 300 +# batch_size = 50000 +# stepsize = 0.01 -n_dim = 11 -n_chains = 1000 -n_loop = 10 -n_local_steps = 1000 -n_global_steps = 1000 -learning_rate = 0.001 -max_samples = 50000 -momentum = 0.9 -num_epochs = 300 -batch_size = 50000 -stepsize = 0.01 +# guess_param = ref_param -guess_param = ref_param +# guess_param = np.array(jnp.repeat(guess_param[None,:],int(n_chains),axis=0)*np.random.normal(loc=1,scale=0.1,size=(int(n_chains),n_dim))) +# guess_param[guess_param[:,1]>0.25,1] = 0.249 +# guess_param[:,6] = (guess_param[:,6]+np.pi/2)%(np.pi)-np.pi/2 +# guess_param[:,7] = (guess_param[:,7]+np.pi/2)%(np.pi)-np.pi/2 -guess_param = np.array(jnp.repeat(guess_param[None,:],int(n_chains),axis=0)*np.random.normal(loc=1,scale=0.1,size=(int(n_chains),n_dim))) -guess_param[guess_param[:,1]>0.25,1] = 0.249 -guess_param[:,6] = (guess_param[:,6]+np.pi/2)%(np.pi)-np.pi/2 -guess_param[:,7] = (guess_param[:,7]+np.pi/2)%(np.pi)-np.pi/2 +# print("Preparing RNG keys") +# rng_key_set = initialize_rng_keys(n_chains, seed=42) -print("Preparing RNG keys") -rng_key_set = initialize_rng_keys(n_chains, seed=42) +# print("Initializing MCMC model and normalizing flow model.") -print("Initializing MCMC model and normalizing flow model.") +# prior_range = jnp.array([[10,70],[0.0,0.25],[-1,1],[-1,1],[0,2000],[-5,5],[-np.pi/2,np.pi/2],[-np.pi/2,np.pi/2],[0,2*np.pi],[0,2*np.pi],[0,np.pi]]) -prior_range = jnp.array([[10,70],[0.0,0.25],[-1,1],[-1,1],[0,2000],[-5,5],[-np.pi/2,np.pi/2],[-np.pi/2,np.pi/2],[0,2*np.pi],[0,2*np.pi],[0,np.pi]]) +# initial_position = jax.random.uniform(rng_key_set[0], shape=(int(n_chains), n_dim)) * 1 +# for i in range(n_dim): +# initial_position = initial_position.at[:,i].set(initial_position[:,i]*(prior_range[i,1]-prior_range[i,0])+prior_range[i,0]) -initial_position = jax.random.uniform(rng_key_set[0], shape=(int(n_chains), n_dim)) * 1 -for i in range(n_dim): - initial_position = initial_position.at[:,i].set(initial_position[:,i]*(prior_range[i,1]-prior_range[i,0])+prior_range[i,0]) +# initial_position = initial_position.at[:,0].set(guess_param[:,0]) +# initial_position = initial_position.at[:,1].set(guess_param[:,1]) +# initial_position = initial_position.at[:,5].set(guess_param[:,5]) -initial_position = initial_position.at[:,0].set(guess_param[:,0]) -initial_position = initial_position.at[:,1].set(guess_param[:,1]) -initial_position = initial_position.at[:,5].set(guess_param[:,5]) +# def top_hat(x): +# output = 0. +# for i in range(n_dim): +# output = jax.lax.cond(x[i]>=prior_range[i,0], lambda: output, lambda: -jnp.inf) +# output = jax.lax.cond(x[i]<=prior_range[i,1], lambda: output, lambda: -jnp.inf) +# return output -def top_hat(x): - output = 0. - for i in range(n_dim): - output = jax.lax.cond(x[i]>=prior_range[i,0], lambda: output, lambda: -jnp.inf) - output = jax.lax.cond(x[i]<=prior_range[i,1], lambda: output, lambda: -jnp.inf) - return output +# def posterior(theta): +# prior = top_hat(theta) +# return H1_logL(theta) + L1_logL(theta) + prior -def posterior(theta): - prior = top_hat(theta) - return H1_logL(theta) + L1_logL(theta) + prior +# model = RQSpline(n_dim, 10, [128,128], 8) -model = RQSpline(n_dim, 10, [128,128], 8) +# print("Initializing sampler class") -print("Initializing sampler class") +# posterior = posterior +# dposterior = jax.grad(posterior) -posterior = posterior -dposterior = jax.grad(posterior) +# mass_matrix = jnp.eye(n_dim) +# mass_matrix = mass_matrix.at[1,1].set(1e-3) +# mass_matrix = mass_matrix.at[5,5].set(1e-2) -mass_matrix = jnp.eye(n_dim) -mass_matrix = mass_matrix.at[1,1].set(1e-3) -mass_matrix = mass_matrix.at[5,5].set(1e-2) +# local_sampler,updater, kernel, logp, dlogp = make_mala_sampler(posterior, dposterior,2e-3, jit=True, M=mass_matrix) -local_sampler,updater, kernel, logp, dlogp = make_mala_sampler(posterior, dposterior,2e-3, jit=True, M=mass_matrix) +# print("Running sampler") -print("Running sampler") +# nf_sampler = Sampler(n_dim, rng_key_set, model, local_sampler, +# posterior, +# d_likelihood=dposterior, +# n_loop=n_loop, +# n_local_steps=n_local_steps, +# n_global_steps=n_global_steps, +# n_chains=n_chains, +# stepsize=stepsize, +# n_nf_samples=100, +# learning_rate=learning_rate, +# n_epochs= num_epochs, +# max_samples = max_samples, +# momentum=momentum, +# batch_size=batch_size, +# use_global=True, +# keep_quantile=0.) -nf_sampler = Sampler(n_dim, rng_key_set, model, local_sampler, - posterior, - d_likelihood=dposterior, - n_loop=n_loop, - n_local_steps=n_local_steps, - n_global_steps=n_global_steps, - n_chains=n_chains, - stepsize=stepsize, - n_nf_samples=100, - learning_rate=learning_rate, - n_epochs= num_epochs, - max_samples = max_samples, - momentum=momentum, - batch_size=batch_size, - use_global=True, - keep_quantile=0.) - -nf_sampler.sample(initial_position) +# nf_sampler.sample(initial_position) diff --git a/jaxgw/PE/heterodyneLikelihood.py b/jaxgw/PE/heterodyneLikelihood.py index 46395b56..13274a8a 100644 --- a/jaxgw/PE/heterodyneLikelihood.py +++ b/jaxgw/PE/heterodyneLikelihood.py @@ -66,19 +66,21 @@ def heterodyne_likelihood(params): return heterodyne_likelihood -def make_heterodyne_likelihood_mutliple_detector(data_array, psd_list, respose_list, h_function, ref_theta, freqs, n_bins=101): +def make_heterodyne_likelihood_mutliple_detector(data_list, psd_list, respose_list, h_function, ref_theta, freqs, n_bins=101): - num_detector = len(data_array) + num_detector = len(data_list) f_bins, f_bins_center = make_binning_scheme(freqs, n_bins) - raw_hp, raw_hc = h_function(freqs, ref_theta[:9]) ra, dec = ref_theta[9], ref_theta[10] h_ref = [] h_ref_low = [] h_ref_bincenter = [] for i in range(num_detector): + raw_hp, raw_hc = h_function(freqs, ref_theta[:9]) h_ref.append(respose_list[i](freqs, raw_hp, raw_hc, ra, dec, ref_theta[5],ref_theta[8])) - h_ref_low.append(respose_list[i](f_bins[:-1], raw_hp, raw_hc, ra, dec, ref_theta[5],ref_theta[8])) + raw_hp, raw_hc = h_function(f_bins[:-1], ref_theta[:9]) + h_ref_low.append(respose_list[i](f_bins[:-1], raw_hp, raw_hc, ra, dec, ref_theta[5], ref_theta[8])) + raw_hp, raw_hc = h_function(f_bins_center, ref_theta[:9]) h_ref_bincenter.append(respose_list[i](f_bins_center, raw_hp, raw_hc, ra, dec, ref_theta[5],ref_theta[8])) A0_array = [] @@ -87,21 +89,22 @@ def make_heterodyne_likelihood_mutliple_detector(data_array, psd_list, respose_l B1_array = [] for i in range(num_detector): - A0, A1, B0, B1 = compute_coefficients(data_array[i], h_ref[i], psd_list[i], freqs, f_bins, f_bins_center) + A0, A1, B0, B1 = compute_coefficients(data_list[i], h_ref[i], psd_list[i], freqs, f_bins, f_bins_center) A0_array.append(A0) A1_array.append(A1) B0_array.append(B0) B1_array.append(B1) def hetrodyne_likelihood(params): - raw_hc, raw_hp = h_function(freqs, params[:9]) ra, dec = params[9], params[10] - output_SNR = 0 + raw_hp_edge, raw_hc_edge = h_function(f_bins[:-1], params[:9]) + raw_hp_center, raw_hc_center = h_function(f_bins_center, params[:9]) + for i in range(num_detector): - waveform_low = respose_list[i](f_bins[:-1], raw_hp, raw_hc, ra, dec, params[5], params[8]) - waveform_center = respose_list[i](f_bins_center, raw_hp, raw_hc, ra, dec, params[5], params[8]) + waveform_low = respose_list[i](f_bins[:-1], raw_hp_edge, raw_hc_edge, ra, dec, params[5], params[8]) + waveform_center = respose_list[i](f_bins_center, raw_hp_center, raw_hc_center, ra, dec, params[5], params[8]) r0 = waveform_center/h_ref_bincenter[i] r1 = (waveform_low/h_ref_low[i] - r0)/(f_bins[:-1]-f_bins_center) @@ -111,4 +114,6 @@ def hetrodyne_likelihood(params): output_SNR += (match_filter_SNR - optimal_SNR/2).real - return output_SNR \ No newline at end of file + return output_SNR + + return hetrodyne_likelihood \ No newline at end of file From 5b7ca69e5188af2c6f058b10cff7c814585c88e0 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 19 Sep 2022 10:16:36 -0400 Subject: [PATCH 122/300] Update GW170817 --- example/ParameterEstimation/GW150914.py | 10 ++- example/ParameterEstimation/GW170817.py | 49 ++++++++------ example/ParameterEstimation/PE_training.py | 79 ---------------------- jaxgw/PE/heterodyneLikelihood.py | 1 + 4 files changed, 37 insertions(+), 102 deletions(-) delete mode 100644 example/ParameterEstimation/PE_training.py diff --git a/example/ParameterEstimation/GW150914.py b/example/ParameterEstimation/GW150914.py index 305666f6..c834c81e 100644 --- a/example/ParameterEstimation/GW150914.py +++ b/example/ParameterEstimation/GW150914.py @@ -68,12 +68,16 @@ def L1_LogLikelihood(theta): ref_param = ref_param.at[-1].set(ref_param[-1]%(jnp.pi)) ref_param = ref_param.at[6].set((ref_param[6]+jnp.pi/2)%(jnp.pi)-jnp.pi/2) - H1_logL = make_heterodyne_likelihood(H1_data, gen_waveform_H1, ref_param, H1_psd, H1_frequency, 101) L1_logL = make_heterodyne_likelihood(L1_data, gen_waveform_L1, ref_param, L1_psd, L1_frequency, 101) -# H1_logL = H1_LogLikelihood -# L1_logL = L1_LogLikelihood +from jaxgw.PE.heterodyneLikelihood import make_heterodyne_likelihood_mutliple_detector + +data_list = [H1_data, L1_data] +psd_list = [H1_psd, L1_psd] +response_list = [H1_response, L1_response] + +logL = make_heterodyne_likelihood_mutliple_detector(data_list, psd_list, response_list, gen_IMRPhenomD_polar, ref_param, H1_frequency, 101) n_dim = 11 diff --git a/example/ParameterEstimation/GW170817.py b/example/ParameterEstimation/GW170817.py index 2b7e0c05..be03d60e 100644 --- a/example/ParameterEstimation/GW170817.py +++ b/example/ParameterEstimation/GW170817.py @@ -39,6 +39,15 @@ V1 = get_V1() V1_response = make_detector_response(V1[0], V1[1]) +def gen_data(theta): + ra = theta[9] + dec = theta[10] + hp_test, hc_test = gen_IMRPhenomD_polar(H1_frequency, theta[:9]) + h_test_H1 = H1_response(H1_frequency, hp_test, hc_test, ra, dec, theta[5], theta[8]) + h_test_L1 = L1_response(L1_frequency, hp_test, hc_test, ra, dec, theta[5], theta[8]) + h_test_V1 = V1_response(V1_frequency, hp_test, hc_test, ra, dec, theta[5], theta[8]) + return h_test_H1, h_test_L1, h_test_V1 + def LogLikelihood(theta): ra = theta[9] dec = theta[10] @@ -71,32 +80,32 @@ def LogLikelihood(theta): logL = make_heterodyne_likelihood_mutliple_detector(data_list, psd_list, response_list, gen_IMRPhenomD_polar, ref_param, H1_frequency, 101) -# n_dim = 11 -# n_chains = 1000 -# n_loop = 10 -# n_local_steps = 1000 -# n_global_steps = 1000 -# learning_rate = 0.001 -# max_samples = 50000 -# momentum = 0.9 -# num_epochs = 300 -# batch_size = 50000 -# stepsize = 0.01 +n_dim = 11 +n_chains = 1000 +n_loop = 10 +n_local_steps = 1000 +n_global_steps = 1000 +learning_rate = 0.001 +max_samples = 50000 +momentum = 0.9 +num_epochs = 300 +batch_size = 50000 +stepsize = 0.01 -# guess_param = ref_param +guess_param = ref_param -# guess_param = np.array(jnp.repeat(guess_param[None,:],int(n_chains),axis=0)*np.random.normal(loc=1,scale=0.1,size=(int(n_chains),n_dim))) -# guess_param[guess_param[:,1]>0.25,1] = 0.249 -# guess_param[:,6] = (guess_param[:,6]+np.pi/2)%(np.pi)-np.pi/2 -# guess_param[:,7] = (guess_param[:,7]+np.pi/2)%(np.pi)-np.pi/2 +guess_param = np.array(jnp.repeat(guess_param[None,:],int(n_chains),axis=0)*np.random.normal(loc=1,scale=0.1,size=(int(n_chains),n_dim))) +guess_param[guess_param[:,1]>0.25,1] = 0.249 +guess_param[:,6] = (guess_param[:,6]+np.pi/2)%(np.pi)-np.pi/2 +guess_param[:,7] = (guess_param[:,7]+np.pi/2)%(np.pi)-np.pi/2 -# print("Preparing RNG keys") -# rng_key_set = initialize_rng_keys(n_chains, seed=42) +print("Preparing RNG keys") +rng_key_set = initialize_rng_keys(n_chains, seed=42) -# print("Initializing MCMC model and normalizing flow model.") +print("Initializing MCMC model and normalizing flow model.") -# prior_range = jnp.array([[10,70],[0.0,0.25],[-1,1],[-1,1],[0,2000],[-5,5],[-np.pi/2,np.pi/2],[-np.pi/2,np.pi/2],[0,2*np.pi],[0,2*np.pi],[0,np.pi]]) +prior_range = jnp.array([[1.2,2.5],[0.1,0.25],[-1,1],[-1,1],[0,200],[-60,60],[-np.pi/2,np.pi/2],[-np.pi/2,np.pi/2],[0,np.pi],[0,2*np.pi],[0,np.pi]]) # initial_position = jax.random.uniform(rng_key_set[0], shape=(int(n_chains), n_dim)) * 1 # for i in range(n_dim): diff --git a/example/ParameterEstimation/PE_training.py b/example/ParameterEstimation/PE_training.py deleted file mode 100644 index 7810b3f8..00000000 --- a/example/ParameterEstimation/PE_training.py +++ /dev/null @@ -1,79 +0,0 @@ -import numpy as np -from flowMC.nfmodel.realNVP import RealNVP -import jax -import optax -import flax - -from flowMC.nfmodel.utils import * -from flax import linen as nn # The Linen API -from flax.training import train_state # Useful dataclass to keep train state - -from tqdm import tqdm - -# data = np.load('./data/injection_posterior2.npz') -# chains = data['chains'] -# log_prob = data['log_prob'] -# data = jnp.array(chains).reshape(-1,9) -# data = data[::1000] - -from sklearn.datasets import make_moons - -data = make_moons(n_samples=10000, noise=0.05)[0] - -n_dim = 2 -num_epochs = 5000 -batch_size = 10000 -learning_rate = 0.01 -momentum = 0.9 -n_layers = 10 -n_hidden = 100 -dt = 1 / n_layers - -model = RealNVP(10, n_dim, 64, 1) - -key1, rng, init_rng = jax.random.split(jax.random.PRNGKey(0),3) - -def create_train_state(rng, learning_rate, momentum): - params = model.init(rng, jnp.ones((1,n_dim)))['params'] - tx = optax.adam(learning_rate, momentum) - return train_state.TrainState.create(apply_fn=model.apply, params=params, tx=tx) - -state = create_train_state(init_rng, learning_rate, momentum) - - -variables = model.init(rng, jnp.ones((1,n_dim)))['variables'] - - -rng, state, loss_values = train_flow(rng, model, state, data, num_epochs, batch_size, variables) -samples = sample_nf(model,state.params, rng,10000,variables)[1][0] - -# from flowMC.nfmodel.utils import train_step - -# @jax.jit -# def eval_step(params, batch): -# log_det = model.apply({'params': params,'variables': variables}, batch, method=model.log_prob) -# return -jnp.mean(log_det) - -# def train_epoch(state, train_ds, batch_size, epoch, rng): -# """Train for a single epoch.""" -# train_ds_size = len(train_ds) -# steps_per_epoch = train_ds_size // batch_size - -# perms = jax.random.permutation(rng, train_ds_size) -# perms = perms[:steps_per_epoch * batch_size] # skip incomplete batch -# perms = perms.reshape((steps_per_epoch, batch_size)) -# for perm in perms: -# batch = train_ds[perm, ...] -# value, state = train_step(model, batch, state, variables) - -# return state - -# for epoch in tqdm(range(1, num_epochs+1),desc='Training',miniters=int(num_epochs/10)): - -# # Use a separate PRNG key to permute image data during shuffling -# rng, input_rng = jax.random.split(rng) -# # Run an optimization step over a training batch -# state = train_epoch(state, data, batch_size, epoch, input_rng) -# if epoch % int(num_epochs/10) == 0: -# print('Epoch %d' % epoch, end=' ') -# print('Loss: %.3f' % eval_step(state.params, data)) diff --git a/jaxgw/PE/heterodyneLikelihood.py b/jaxgw/PE/heterodyneLikelihood.py index 13274a8a..cb3c3105 100644 --- a/jaxgw/PE/heterodyneLikelihood.py +++ b/jaxgw/PE/heterodyneLikelihood.py @@ -95,6 +95,7 @@ def make_heterodyne_likelihood_mutliple_detector(data_list, psd_list, respose_li B0_array.append(B0) B1_array.append(B1) + def hetrodyne_likelihood(params): ra, dec = params[9], params[10] output_SNR = 0 From 5e04cb5bbdd1c18882b13b5e0cdfa5126b1de668 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Wed, 21 Sep 2022 09:40:52 -0400 Subject: [PATCH 123/300] Pretty functional GW150914 --- example/JaxPjitExperiment.py | 24 +++++++++++++++++++ example/ParameterEstimation/GW150914.py | 12 +++++----- example/ParameterEstimation/GW170817.py | 9 ++++--- example/ParameterEstimation/Injection_test.py | 18 +++++++------- 4 files changed, 44 insertions(+), 19 deletions(-) create mode 100644 example/JaxPjitExperiment.py diff --git a/example/JaxPjitExperiment.py b/example/JaxPjitExperiment.py new file mode 100644 index 00000000..bb0ff210 --- /dev/null +++ b/example/JaxPjitExperiment.py @@ -0,0 +1,24 @@ +import jax +import jax.numpy as jnp +from jax.experimental import maps +from jax.experimental import PartitionSpec +from jax.experimental.pjit import pjit +from jax.scipy.special import logsumexp + +import numpy as np + +def dual_moon_pe(x): + """ + Term 2 and 3 separate the distribution and smear it along the first and second dimension + """ + term1 = 0.5 * ((jnp.linalg.norm(x) - 2) / 0.1) ** 2 + term2 = -0.5 * ((x[:1] + jnp.array([-3.0, 3.0])) / 0.8) ** 2 + term3 = -0.5 * ((x[1:2] + jnp.array([-3.0, 3.0])) / 0.6) ** 2 + return -(term1 - logsumexp(term2) - logsumexp(term3)) + +mesh_shape = (4, 1) +devices = np.asarray(jax.devices()).reshape(*mesh_shape) +# 'x', 'y' axis names are used here for simplicity +mesh = maps.Mesh(devices, ('x', 'y')) + +input_data = jnp.array(np.random.uniform(size=(100000,5))) diff --git a/example/ParameterEstimation/GW150914.py b/example/ParameterEstimation/GW150914.py index c834c81e..117899d9 100644 --- a/example/ParameterEstimation/GW150914.py +++ b/example/ParameterEstimation/GW150914.py @@ -81,14 +81,14 @@ def L1_LogLikelihood(theta): n_dim = 11 -n_chains = 1000 -n_loop = 10 -n_local_steps = 1000 -n_global_steps = 1000 +n_chains = 100 +n_loop = 100 +n_local_steps = 100 +n_global_steps = 100 learning_rate = 0.001 max_samples = 50000 momentum = 0.9 -num_epochs = 300 +num_epochs = 30 batch_size = 50000 stepsize = 0.01 @@ -105,7 +105,7 @@ def L1_LogLikelihood(theta): print("Initializing MCMC model and normalizing flow model.") -prior_range = jnp.array([[10,70],[0.0,0.25],[-1,1],[-1,1],[0,2000],[-5,5],[-np.pi/2,np.pi/2],[-np.pi/2,np.pi/2],[0,2*np.pi],[0,2*np.pi],[0,np.pi]]) +prior_range = jnp.array([[10,80],[0.0,0.25],[0,1],[0,1],[0,2000],[-5,5],[0,2*np.pi],[0,np.pi],[0,np.pi],[0,2*np.pi],[-np.pi/2,np.pi/2]]) initial_position = jax.random.uniform(rng_key_set[0], shape=(int(n_chains), n_dim)) * 1 for i in range(n_dim): diff --git a/example/ParameterEstimation/GW170817.py b/example/ParameterEstimation/GW170817.py index be03d60e..59e73bc6 100644 --- a/example/ParameterEstimation/GW170817.py +++ b/example/ParameterEstimation/GW170817.py @@ -65,10 +65,9 @@ def LogLikelihood(theta): return (match_filter_SNR_H1-optimal_SNR_H1/2) + (match_filter_SNR_L1-optimal_SNR_L1/2) + (match_filter_SNR_V1-optimal_SNR_V1/2) -ref_param = jnp.array([ 3.41096639e+01, 2.42240502e-01, 7.03845904e-02, - 1.45055597e-01, 4.00156164e+02, -1.97202379e+00, - 1.08177416e+00, -6.94499550e-02, 1.95503312e+00, - 8.60901399e-01, 2.89425087e+00]) +ref_param = jnp.array([ 1.18622027e+00, 1.43466815e-01, 2.36654514e-02, 2.95581081e-02, + 4.28198717e+01, -3.04432711e+01, 7.16390478e-01, 7.09306354e-01, + 1.53402146e+00, 5.39492767e+00, 6.05554691e-02]) ref_param = ref_param.at[-1].set(ref_param[-1]%(jnp.pi)) ref_param = ref_param.at[6].set((ref_param[6]+jnp.pi/2)%(jnp.pi)-jnp.pi/2) @@ -105,7 +104,7 @@ def LogLikelihood(theta): print("Initializing MCMC model and normalizing flow model.") -prior_range = jnp.array([[1.2,2.5],[0.1,0.25],[-1,1],[-1,1],[0,200],[-60,60],[-np.pi/2,np.pi/2],[-np.pi/2,np.pi/2],[0,np.pi],[0,2*np.pi],[0,np.pi]]) +prior_range = jnp.array([[1.17,1.20],[0.1,0.25],[0,0.85],[0,0.85],[0,200],[-60,60],[0,2*np.pi],[0,np.pi],[0,np.pi],[0,2*np.pi],[-np.pi/2,np.pi/2]]) # initial_position = jax.random.uniform(rng_key_set[0], shape=(int(n_chains), n_dim)) * 1 # for i in range(n_dim): diff --git a/example/ParameterEstimation/Injection_test.py b/example/ParameterEstimation/Injection_test.py index d5ac8144..75781601 100644 --- a/example/ParameterEstimation/Injection_test.py +++ b/example/ParameterEstimation/Injection_test.py @@ -28,9 +28,6 @@ } ifos = list(psd_func_dict.keys()) -# define center of time array -tgps_geo = 1126259462.423 - # define sampling rate and duration fsamp = 2048 duration = 4 @@ -120,15 +117,17 @@ def gen_waveform_L1(f, theta): H1_logL = make_heterodyne_likelihood(H1_data, gen_waveform_H1, ref_param, psd_dict['H1'], f_list, 101) L1_logL = make_heterodyne_likelihood(L1_data, gen_waveform_L1, ref_param, psd_dict['L1'], f_list, 101) + + n_dim = 11 -n_chains = 1000 -n_loop = 5 -n_local_steps = 1000 -n_global_steps = 1000 +n_chains = 100 +n_loop = 50 +n_local_steps = 100 +n_global_steps = 100 learning_rate = 0.001 max_samples = 50000 momentum = 0.9 -num_epochs = 300 +num_epochs = 30 batch_size = 50000 stepsize = 0.01 @@ -209,3 +208,6 @@ def posterior(theta): keep_quantile=0.5) nf_sampler.sample(initial_position) + +labels = ['Mc', 'eta', 'chi1', 'chi2', 'dist_mpc', 'tc', 'phic', 'inclination', 'polarization_angle', 'ra', 'dec'] +truths = true_param \ No newline at end of file From 89707001714d0d098e1a705dfde2bcb30e69ae66 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Wed, 21 Sep 2022 11:10:14 -0400 Subject: [PATCH 124/300] Add generate_noise function wrapper --- jaxgw/PE/generate_noise.py | 53 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 53 insertions(+) create mode 100644 jaxgw/PE/generate_noise.py diff --git a/jaxgw/PE/generate_noise.py b/jaxgw/PE/generate_noise.py new file mode 100644 index 00000000..39fc0297 --- /dev/null +++ b/jaxgw/PE/generate_noise.py @@ -0,0 +1,53 @@ +# Import packages +from typing import List, Tuple +import lalsimulation as lalsim +import jax.numpy as jnp +import jax +import numpy as np +jax.config.update('jax_enable_x64', True) + +psd_func_dict = { + 'H1': lalsim.SimNoisePSDaLIGOZeroDetHighPower, + 'L1': lalsim.SimNoisePSDaLIGOZeroDetHighPower, + 'V1': lalsim.SimNoisePSDAdvVirgo, +} + +def generate_noise(seed: int, f_sampling: int = 2048, duration: int = 4, f_min: float = 30., ifos: List = ['H1', 'L1']): + + + # define sampling rate and duration + + delta_t = 1/f_sampling + tlen = int(round(duration / delta_t)) + + freqs = np.fft.rfftfreq(tlen, delta_t) + delta_f = freqs[1] - freqs[0] + + # we will want to pad low frequencies; the function below applies a + # prescription to do so smoothly, but this is not really needed: you + # could just set all values below `fmin` to a constant. + def pad_low_freqs(f, psd_ref): + return psd_ref + psd_ref*(f_min-f)*jnp.exp(-(f_min-f))/3 + + psd_dict = {} + for ifo in ifos: + psd = np.zeros(len(freqs)) + for i,f in enumerate(freqs): + if f >= f_min: + psd[i] = psd_func_dict[ifo](f) + else: + psd[i] = pad_low_freqs(f, psd_func_dict[ifo](f_min)) + psd_dict[ifo] = jnp.array(psd,dtype=jnp.float64) + + rng_key = jax.random.PRNGKey(seed) + rng_keys = jax.random.split(rng_key) + + noise_fd_dict = {} + for ifo, psd in psd_dict.items(): + rng_keys = jax.random.split(rng_keys[0], 3) + var = psd / (4.*delta_f) # this is the variance of LIGO noise given the definition of the likelihood function + noise_real = jax.random.normal(rng_keys[1],shape=(len(psd),))*jnp.sqrt(var) + noise_imag = jax.random.normal(rng_keys[2],shape=(len(psd),))*jnp.sqrt(var) + noise_fd_dict[ifo] = noise_real + 1j*noise_imag + + return psd_dict, noise_fd_dict \ No newline at end of file From ad077ec2e2faf7ce8bec204aa12b11afad3990e9 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Wed, 21 Sep 2022 11:25:50 -0400 Subject: [PATCH 125/300] Add frequency to generate noise --- jaxgw/PE/generate_noise.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/jaxgw/PE/generate_noise.py b/jaxgw/PE/generate_noise.py index 39fc0297..02084c35 100644 --- a/jaxgw/PE/generate_noise.py +++ b/jaxgw/PE/generate_noise.py @@ -50,4 +50,4 @@ def pad_low_freqs(f, psd_ref): noise_imag = jax.random.normal(rng_keys[2],shape=(len(psd),))*jnp.sqrt(var) noise_fd_dict[ifo] = noise_real + 1j*noise_imag - return psd_dict, noise_fd_dict \ No newline at end of file + return freqs, psd_dict, noise_fd_dict \ No newline at end of file From aaa785b0d5240313f4a144961473cd2022e7092e Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Wed, 21 Sep 2022 11:40:15 -0400 Subject: [PATCH 126/300] Clean up useless files --- .../ParameterEstimation/DetectorProjection.py | 53 ------------------- 1 file changed, 53 deletions(-) delete mode 100644 example/ParameterEstimation/DetectorProjection.py diff --git a/example/ParameterEstimation/DetectorProjection.py b/example/ParameterEstimation/DetectorProjection.py deleted file mode 100644 index c35a2bd9..00000000 --- a/example/ParameterEstimation/DetectorProjection.py +++ /dev/null @@ -1,53 +0,0 @@ -from cmath import phase -from webbrowser import get -import numpy as np -import jax.numpy as jnp - -from ripple.waveforms import IMRPhenomD, IMRPhenomD_utils -import matplotlib.pyplot as plt -from ripple import ms_to_Mc_eta - - - - -# Get a frequency domain waveform -# source parameters - -m1_msun = 20.0 # In solar masses -m2_msun = 19.0 -chi1 = 0.5 # Dimensionless spin -chi2 = -0.5 -tc = 0.0 # Time of coalescence in seconds -phic = 0.0 # Time of coalescence -dist_mpc = 440 # Distance to source in Mpc -inclination = 0.0 # Inclination Angle -polarization_angle = 0.2 # Polarization angle - -# The PhenomD waveform model is parameterized with the chirp mass and symmetric mass ratio -Mc, eta = ms_to_Mc_eta(jnp.array([m1_msun, m2_msun])) - -# These are the parametrs that go into the waveform generator -# Note that JAX does not give index errors, so if you pass in the -# the wrong array it will behave strangely -theta_ripple = jnp.array([Mc, eta, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle]) - - -# Now we need to generate the frequency grid -f_l = 24 -f_u = 1024 -del_f = 10 -fs = jnp.arange(f_l, f_u, del_f) - -# And finally lets generate the waveform! -hp_ripple, hc_ripple = IMRPhenomD.gen_IMRPhenomD_polar(fs, theta_ripple) - - -from jaxgw.PE.detector_preset import * -from jaxgw.PE.detector_projection_new import make_detector_response - -H1 = get_H1() -L1 = get_L1() -H1_response = make_detector_response(H1[0], H1[1]) -L1_response = make_detector_response(L1[0], L1[1]) -H1_response(fs, hp_ripple, hc_ripple, 0.2, 0.3, 0.,0.5) - From 4bf78923327cc1a72118f838f1abfefc76eb4682 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Wed, 21 Sep 2022 11:46:00 -0400 Subject: [PATCH 127/300] Add injection_script with parser --- .../Injection_withParser.py | 234 ++++++++++++++++++ 1 file changed, 234 insertions(+) create mode 100644 example/ParameterEstimation/Injection_withParser.py diff --git a/example/ParameterEstimation/Injection_withParser.py b/example/ParameterEstimation/Injection_withParser.py new file mode 100644 index 00000000..8febd22f --- /dev/null +++ b/example/ParameterEstimation/Injection_withParser.py @@ -0,0 +1,234 @@ +# Import packages +import lalsimulation as lalsim +import numpy as np +import jax.numpy as jnp +import jax + +# from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar +from ripple import ms_to_Mc_eta +from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar +from jaxgw.PE.detector_preset import * +from jaxgw.PE.heterodyneLikelihood import make_heterodyne_likelihood_mutliple_detector +from jaxgw.PE.detector_projection import make_detector_response +from jaxgw.PE.generate_noise import generate_noise + +from flowMC.nfmodel.rqSpline import RQSpline +from flowMC.sampler.MALA import make_mala_sampler +from flowMC.sampler.Sampler import Sampler +from flowMC.utils.PRNG_keys import initialize_rng_keys +from flowMC.nfmodel.utils import * + +import argparse +import yaml + +parser = argparse.ArgumentParser(description='Injection test') + +parser.add_argument('--config', type=str, default='config.yaml', help='config file') + +# Add noise parameters to parser +parser.add_argument('--seed', type=int, default=0, help='seed for random number generator') +parser.add_argument('--f_sampling', type=int, default=2048, help='sampling frequency') +parser.add_argument('--duration', type=int, default=4, help='duration of the data') +parser.add_argument('--fmin', type=float, default=30., help='minimum frequency') +parser.add_argument('--ifos', nargs='+', default=['H1', 'L1'], help='list of detectors') + +# Add injection parameters to parser +parser.add_argument('--m1', type=float, default=None, help='mass of the first component') +parser.add_argument('--m2', type=float, default=None, help='mass of the second component') +parser.add_argument('--chi1', type=float, default=None, help='dimensionless spin of the first component') +parser.add_argument('--chi2', type=float, default=None, help='dimensionless spin of the second component') +parser.add_argument('--dist_mpc', type=float, default=None, help='distance in megaparsecs') +parser.add_argument('--tc', type=float, default=None, help='coalescence time') +parser.add_argument('--phic', type=float, default=None, help='phase of coalescence') +parser.add_argument('--inclination', type=float, default=None, help='inclination angle') +parser.add_argument('--polarization_angle', type=float, default=None, help='polarization angle') +parser.add_argument('--ra', type=float, default=None, help='right ascension') +parser.add_argument('--dec', type=float, default=None, help='declination') +parser.add_argument('--heterodyne_bins', type=int, default=101, help='number of bins for heterodyne likelihood') + +# Add sampler parameters to parser + +parser.add_argument('--n_dim', type=int, default=None, help='number of parameters') +parser.add_argument('--n_chains', type=int, default=None, help='number of chains') +parser.add_argument('--n_loop', type=int, default=None, help='number of loops') +parser.add_argument('--n_local_steps', type=int, default=None, help='number of local steps') +parser.add_argument('--n_global_steps', type=int, default=None, help='number of global steps') +parser.add_argument('--learning_rate', type=float, default=None, help='learning rate') +parser.add_argument('--max_samples', type=int, default=None, help='maximum number of samples') +parser.add_argument('--momentum', type=float, default=None, help='momentum during training') +parser.add_argument('--num_epochs', type=int, default=None, help='number of epochs') +parser.add_argument('--batch_size', type=int, default=None, help='batch size') +parser.add_argument('--stepsize', type=float, default=None, help='stepsize for Local sampler') + +# parser + +args = parser.parse_args() +opt = yaml.load(open(args.config,'r'), Loader=yaml.FullLoader) +args = opt + +# Fetch noise parameters + +print("Constructing detectors") +print("Making noises") + +seed = args.seed +f_sampling = args.f_sampling +duration = args.duration +fmin = args.fmin +ifos = args.ifos + +freqs, psd_dict, noise_dict = generate_noise(seed+1234, f_sampling, duration, fmin, ifos) + + +# Fetch injection parameters and inject signal + +print("Injection signals") + +m1 = args.m1 +m2 = args.m2 +Mc, eta = ms_to_Mc_eta(m1, m2) +chi1 = args.chi1 +chi2 = args.chi2 +dist_mpc = args.dist_mpc +tc = args.tc +phic = args.phic +inclination = args.inclination +polarization_angle = args.polarization_angle +ra = args.ra +dec = args.dec + +heterodyne_bins = args.heterodyne_bins + +H1 = get_H1() +H1_response = make_detector_response(H1[0], H1[1]) +L1 = get_L1() +L1_response = make_detector_response(L1[0], L1[1]) + + +def gen_waveform_H1(f, theta): + theta_waveform = theta[:9] + ra = theta[9] + dec = theta[10] + hp, hc = gen_IMRPhenomD_polar(f, theta_waveform) + return H1_response(f, hp, hc, ra, dec, theta[5], theta[8]) + +def gen_waveform_L1(f, theta): + theta_waveform = theta[:9] + ra = theta[9] + dec = theta[10] + hp, hc = gen_IMRPhenomD_polar(f, theta_waveform) + return L1_response(f, hp, hc, ra, dec, theta[5], theta[8]) + +true_param = jnp.array([Mc, eta, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle, ra, dec]) + + +f_list = freqs[freqs>fmin] +H1_signal = gen_waveform_H1(f_list, true_param) +H1_noise_psd = noise_dict['H1'][freqs>fmin] +H1_data = H1_noise_psd + H1_signal + +L1_signal = gen_waveform_L1(f_list, true_param) +L1_noise_psd = noise_dict['L1'][freqs>fmin] +L1_data = L1_noise_psd + L1_signal + +ref_param = jnp.array([Mc, eta, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle, ra, dec]) + +data_list = [H1_data, L1_data] +psd_list = [psd_dict['H1'], psd_dict['L1']] +response_list = [H1_response, L1_response] + +logL = make_heterodyne_likelihood_mutliple_detector(data_list, psd_list, response_list, gen_IMRPhenomD_polar, ref_param, f_list, heterodyne_bins) + +# Fetch sampler parameters, construct sampler and initial guess + +print("Making sampler") + +n_dim = args.n_dim +n_chains = args.n_chains +n_loop = args.n_loop +n_local_steps = args.n_local_steps +n_global_steps = args.n_global_steps +learning_rate = args.learning_rate +max_samples = args.max_samples +momentum = args.momentum +num_epochs = args.num_epochs +batch_size = args.batch_size +stepsize = args.stepsize + +guess_param = np.array(jnp.repeat(true_param[None,:],int(n_chains),axis=0)*np.random.normal(loc=1,scale=0.01,size=(int(n_chains),n_dim))) +guess_param[guess_param[:,1]>0.25,1] = 0.249 +guess_param[:,6] = (guess_param[:,6]+np.pi/2)%(np.pi)-np.pi/2 +guess_param[:,7] = (guess_param[:,7]+np.pi/2)%(np.pi)-np.pi/2 +guess_param[:,8] = (guess_param[:,8]%(2*np.pi)) +guess_param[:,9] = (guess_param[:,9]%(2*np.pi)) +guess_param[:,10] = (guess_param[:,10]%(np.pi)) + +print("Preparing RNG keys") +rng_key_set = initialize_rng_keys(n_chains, seed=seed) + +print("Initializing MCMC model and normalizing flow model.") + +prior_range = jnp.array([[10,70],[0.0,0.25],[-1,1],[-1,1],[0,2000],[-5,5],[-np.pi/2,np.pi/2],[-np.pi/2,np.pi/2],[0,2*np.pi],[0,2*np.pi],[0,np.pi]]) + +initial_position = jax.random.uniform(rng_key_set[0], shape=(int(n_chains), n_dim)) * 1 +for i in range(n_dim): + initial_position = initial_position.at[:,i].set(initial_position[:,i]*(prior_range[i,1]-prior_range[i,0])+prior_range[i,0]) + +initial_position = initial_position.at[:,0].set(guess_param[:,0]) +initial_position = initial_position.at[:,1].set(guess_param[:,1]) +initial_position = initial_position.at[:,5].set(guess_param[:,5]) + +def top_hat(x): + output = 0. + for i in range(n_dim): + output = jax.lax.cond(x[i]>=prior_range[i,0], lambda: output, lambda: -jnp.inf) + output = jax.lax.cond(x[i]<=prior_range[i,1], lambda: output, lambda: -jnp.inf) + return output + +def posterior(theta): + prior = top_hat(theta) + return logL(theta) + prior + + +model = RQSpline(n_dim, 10, [128,128], 8) + + +print("Initializing sampler class") + +posterior = posterior +dposterior = jax.grad(posterior) + +mass_matrix = jnp.eye(n_dim) +mass_matrix = mass_matrix.at[1,1].set(1e-3) +mass_matrix = mass_matrix.at[5,5].set(1e-3) + +local_sampler,updater, kernel, logp, dlogp = make_mala_sampler(posterior, dposterior,2e-3, jit=True, M=mass_matrix) + +print("Running sampler") + +nf_sampler = Sampler(n_dim, rng_key_set, model, local_sampler, + posterior, + d_likelihood=dposterior, + n_loop=n_loop, + n_local_steps=n_local_steps, + n_global_steps=n_global_steps, + n_chains=n_chains, + stepsize=stepsize, + n_nf_samples=100, + learning_rate=learning_rate, + n_epochs= num_epochs, + max_samples = max_samples, + momentum=momentum, + batch_size=batch_size, + use_global=True, + keep_quantile=0.5) + +# nf_sampler.sample(initial_position) + +# labels = ['Mc', 'eta', 'chi1', 'chi2', 'dist_mpc', 'tc', 'phic', 'inclination', 'polarization_angle', 'ra', 'dec'] + +# print("Saving to output") + +# chains, log_prob, local_accs, global_accs, loss_vals = nf_sampler.get_sampler_state() + +# np.savez(args.output, chains=chains, log_prob=log_prob, local_accs=local_accs, global_accs=global_accs, loss_vals=loss_vals, labels=labels) From f5716faf941dc9843f1c32c11be98e85dc7a88df Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Wed, 21 Sep 2022 13:49:27 -0400 Subject: [PATCH 128/300] Injection with parser seems pretty functional --- .../Injection_withParser.py | 93 +++++++++++-------- 1 file changed, 54 insertions(+), 39 deletions(-) diff --git a/example/ParameterEstimation/Injection_withParser.py b/example/ParameterEstimation/Injection_withParser.py index 8febd22f..f3cf6e93 100644 --- a/example/ParameterEstimation/Injection_withParser.py +++ b/example/ParameterEstimation/Injection_withParser.py @@ -26,11 +26,11 @@ parser.add_argument('--config', type=str, default='config.yaml', help='config file') # Add noise parameters to parser -parser.add_argument('--seed', type=int, default=0, help='seed for random number generator') -parser.add_argument('--f_sampling', type=int, default=2048, help='sampling frequency') -parser.add_argument('--duration', type=int, default=4, help='duration of the data') -parser.add_argument('--fmin', type=float, default=30., help='minimum frequency') -parser.add_argument('--ifos', nargs='+', default=['H1', 'L1'], help='list of detectors') +parser.add_argument('--seed', type=int, default=None, help='seed for random number generator') +parser.add_argument('--f_sampling', type=int, default=None, help='sampling frequency') +parser.add_argument('--duration', type=int, default=None, help='duration of the data') +parser.add_argument('--fmin', type=float, default=None, help='minimum frequency') +parser.add_argument('--ifos', nargs='+', default=None, help='list of detectors') # Add injection parameters to parser parser.add_argument('--m1', type=float, default=None, help='mass of the first component') @@ -60,10 +60,17 @@ parser.add_argument('--batch_size', type=int, default=None, help='batch size') parser.add_argument('--stepsize', type=float, default=None, help='stepsize for Local sampler') +# Add output parameters to parser + +parser.add_argument('--output_path', type=str, default=None, help='output file path') +parser.add_argument('--downsample_factor', type=int, default=1, help='downsample factor') + # parser args = parser.parse_args() -opt = yaml.load(open(args.config,'r'), Loader=yaml.FullLoader) +opt = vars(args) +args = yaml.load(open(opt['config'], 'r'), Loader=yaml.FullLoader) +opt.update(args) args = opt # Fetch noise parameters @@ -71,11 +78,12 @@ print("Constructing detectors") print("Making noises") -seed = args.seed -f_sampling = args.f_sampling -duration = args.duration -fmin = args.fmin -ifos = args.ifos +seed = args['seed'] +f_sampling = args['f_sampling'] +duration = args['duration'] +fmin = args['fmin'] +ifos = args['ifos'] + freqs, psd_dict, noise_dict = generate_noise(seed+1234, f_sampling, duration, fmin, ifos) @@ -84,20 +92,21 @@ print("Injection signals") -m1 = args.m1 -m2 = args.m2 -Mc, eta = ms_to_Mc_eta(m1, m2) -chi1 = args.chi1 -chi2 = args.chi2 -dist_mpc = args.dist_mpc -tc = args.tc -phic = args.phic -inclination = args.inclination -polarization_angle = args.polarization_angle -ra = args.ra -dec = args.dec - -heterodyne_bins = args.heterodyne_bins +m1 = args['m1'] +m2 = args['m2'] +chi1 = args['chi1'] +chi2 = args['chi2'] +dist_mpc = args['dist_mpc'] +tc = args['tc'] +phic = args['phic'] +inclination = args['inclination'] +polarization_angle = args['polarization_angle'] +ra = args['ra'] +dec = args['dec'] + +Mc, eta = ms_to_Mc_eta(jnp.array([m1, m2])) + +heterodyne_bins = args['heterodyne_bins'] H1 = get_H1() H1_response = make_detector_response(H1[0], H1[1]) @@ -143,19 +152,20 @@ def gen_waveform_L1(f, theta): print("Making sampler") -n_dim = args.n_dim -n_chains = args.n_chains -n_loop = args.n_loop -n_local_steps = args.n_local_steps -n_global_steps = args.n_global_steps -learning_rate = args.learning_rate -max_samples = args.max_samples -momentum = args.momentum -num_epochs = args.num_epochs -batch_size = args.batch_size -stepsize = args.stepsize - -guess_param = np.array(jnp.repeat(true_param[None,:],int(n_chains),axis=0)*np.random.normal(loc=1,scale=0.01,size=(int(n_chains),n_dim))) +n_dim = args['n_dim'] +n_chains = args['n_chains'] +n_loop = args['n_loop'] +n_local_steps = args['n_local_steps'] +n_global_steps = args['n_global_steps'] +learning_rate = args['learning_rate'] +max_samples = args['max_samples'] +momentum = args['momentum'] +num_epochs = args['num_epochs'] +batch_size = args['batch_size'] +stepsize = args['stepsize'] + + +guess_param = np.array(jnp.repeat(true_param[None,:],int(n_chains),axis=0)*(1+0.1*jax.random.normal(jax.random.PRNGKey(seed+98127),shape=(int(n_chains),n_dim)))) guess_param[guess_param[:,1]>0.25,1] = 0.249 guess_param[:,6] = (guess_param[:,6]+np.pi/2)%(np.pi)-np.pi/2 guess_param[:,7] = (guess_param[:,7]+np.pi/2)%(np.pi)-np.pi/2 @@ -231,4 +241,9 @@ def posterior(theta): # chains, log_prob, local_accs, global_accs, loss_vals = nf_sampler.get_sampler_state() -# np.savez(args.output, chains=chains, log_prob=log_prob, local_accs=local_accs, global_accs=global_accs, loss_vals=loss_vals, labels=labels) +# # Fetch output parameters + +# output_path = args['output_path'] +# downsample_factor = args['downsample_factor'] + +# np.savez(args['output_path'], chains=chains, log_prob=log_prob, local_accs=local_accs, global_accs=global_accs, loss_vals=loss_vals, labels=labels) From f19051fae7b77d92002a9d5b465dbd8308c493c2 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Wed, 21 Sep 2022 13:49:41 -0400 Subject: [PATCH 129/300] Add example injection yaml --- .../configs/injection_1.yaml | 43 +++++++++++++++++++ 1 file changed, 43 insertions(+) create mode 100644 example/ParameterEstimation/configs/injection_1.yaml diff --git a/example/ParameterEstimation/configs/injection_1.yaml b/example/ParameterEstimation/configs/injection_1.yaml new file mode 100644 index 00000000..a1e58dbb --- /dev/null +++ b/example/ParameterEstimation/configs/injection_1.yaml @@ -0,0 +1,43 @@ +# Book keeping parameters + +output_path: /mnt/home/wwong/ceph/GWProject/JaxGW/RealtimePE/ppPlots/injection_1 +downsample_factor: 10 + +# Noise parameters + +seed: 1234 +f_sampling: 2048 +duration: 4 +fmin: 30 +ifos: + - H1 + - L1 + +# Injection parameters + +m1: 35 +m2: 30 +chi1: 0.3 +chi2: -0.4 +dist_mpc: 400 +tc: 2 +phic: 0.1 +inclination: 0.5 +polarization_angle: 0.2 +ra: 1.2 +dec: 0.3 +heterodyne_bins: 101 + +# Sampler parameters + +n_dim: 11 +n_chains: 1000 +n_loop: 10 +n_local_steps: 1000 +n_global_steps: 1000 +learning_rate: 0.001 +max_samples: 50000 +momentum: 0.9 +num_epochs: 300 +batch_size: 50000 +stepsize: 0.01 \ No newline at end of file From 2a95f8094db87e4a6eb7dfc153308343f7c9e9df Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Wed, 21 Sep 2022 16:21:21 -0400 Subject: [PATCH 130/300] rename injection example config --- .../Injection_withParser.py | 16 ++--- ...njection_1.yaml => injection_example.yaml} | 0 .../gen_injection_config.py | 58 +++++++++++++++++++ 3 files changed, 66 insertions(+), 8 deletions(-) rename example/ParameterEstimation/configs/{injection_1.yaml => injection_example.yaml} (100%) create mode 100644 example/ParameterEstimation/gen_injection_config.py diff --git a/example/ParameterEstimation/Injection_withParser.py b/example/ParameterEstimation/Injection_withParser.py index f3cf6e93..a47b4d8a 100644 --- a/example/ParameterEstimation/Injection_withParser.py +++ b/example/ParameterEstimation/Injection_withParser.py @@ -233,17 +233,17 @@ def posterior(theta): use_global=True, keep_quantile=0.5) -# nf_sampler.sample(initial_position) +nf_sampler.sample(initial_position) -# labels = ['Mc', 'eta', 'chi1', 'chi2', 'dist_mpc', 'tc', 'phic', 'inclination', 'polarization_angle', 'ra', 'dec'] +labels = ['Mc', 'eta', 'chi1', 'chi2', 'dist_mpc', 'tc', 'phic', 'inclination', 'polarization_angle', 'ra', 'dec'] -# print("Saving to output") +print("Saving to output") -# chains, log_prob, local_accs, global_accs, loss_vals = nf_sampler.get_sampler_state() +chains, log_prob, local_accs, global_accs, loss_vals = nf_sampler.get_sampler_state() -# # Fetch output parameters +# Fetch output parameters -# output_path = args['output_path'] -# downsample_factor = args['downsample_factor'] +output_path = args['output_path'] +downsample_factor = args['downsample_factor'] -# np.savez(args['output_path'], chains=chains, log_prob=log_prob, local_accs=local_accs, global_accs=global_accs, loss_vals=loss_vals, labels=labels) +np.savez(args['output_path'], chains=chains, log_prob=log_prob, local_accs=local_accs, global_accs=global_accs, loss_vals=loss_vals, labels=labels) diff --git a/example/ParameterEstimation/configs/injection_1.yaml b/example/ParameterEstimation/configs/injection_example.yaml similarity index 100% rename from example/ParameterEstimation/configs/injection_1.yaml rename to example/ParameterEstimation/configs/injection_example.yaml diff --git a/example/ParameterEstimation/gen_injection_config.py b/example/ParameterEstimation/gen_injection_config.py new file mode 100644 index 00000000..7ec968d6 --- /dev/null +++ b/example/ParameterEstimation/gen_injection_config.py @@ -0,0 +1,58 @@ +import numpy as np + +prior_range = np.array([[20,50],[0.15,0.25],[-0.5,0.5],[-0.5,0.5],[400,1000],[-2,2],[-np.pi/2,np.pi/2],[-np.pi/2,np.pi/2],[0,2*np.pi],[0,2*np.pi],[0,np.pi]]) + +N_config = 10 + +m1 = np.random.uniform(prior_range[0,0],prior_range[0,1],N_config) +m2 = np.random.uniform(prior_range[1,0],prior_range[1,1],N_config) +chi1 = np.random.uniform(prior_range[2,0],prior_range[2,1],N_config) +chi2 = np.random.uniform(prior_range[3,0],prior_range[3,1],N_config) +dist_mpc = np.random.uniform(prior_range[4,0],prior_range[4,1],N_config) +tc = np.random.uniform(prior_range[5,0],prior_range[5,1],N_config) +phic = np.random.uniform(prior_range[6,0],prior_range[6,1],N_config) +inclination = np.random.uniform(prior_range[7,0],prior_range[7,1],N_config) +polarization_angle = np.random.uniform(prior_range[8,0],prior_range[8,1],N_config) +ra = np.random.uniform(prior_range[9,0],prior_range[9,1],N_config) +dec = np.random.uniform(prior_range[10,0],prior_range[10,1],N_config) + +directory = '/mnt/home/wwong/ceph/GWProject/JaxGW/RealtimePE/ppPlots/configs/' + +for i in range(N_config): + f = open(directory+"injection_config_"+str(i)+".yaml","w") + + f.write('output_path: /mnt/home/wwong/ceph/GWProject/JaxGW/RealtimePE/ppPlots/injection_'+str(i)+'\n') + f.write('downsample_factor: 10\n') + f.write('seed: '+str(np.random.randint(low=0,high=10000))+'\n') + f.write('f_sampling: 2048\n') + f.write('duration: 4\n') + f.write('fmin: 30\n') + f.write('ifos:\n') + f.write(' - H1\n') + f.write(' - L1\n') + + f.write("m1: "+str(m1[i])+"\n") + f.write("m2: "+str(m2[i])+"\n") + f.write("chi1: "+str(chi1[i])+"\n") + f.write("chi2: "+str(chi2[i])+"\n") + f.write("dist_mpc: "+str(dist_mpc[i])+"\n") + f.write("tc: "+str(tc[i])+"\n") + f.write("phic: "+str(phic[i])+"\n") + f.write("inclination: "+str(inclination[i])+"\n") + f.write("polarization_angle: "+str(polarization_angle[i])+"\n") + f.write("ra: "+str(ra[i])+"\n") + f.write("dec: "+str(dec[i])+"\n") + + f.write("n_dim: 11\n") + f.write("n_chains: 1000\n") + f.write("n_loop: 10\n") + f.write("n_local_steps: 1000\n") + f.write("n_global_steps: 1000\n") + f.write("learning_rate: 0.001\n") + f.write("max_samples: 50000\n") + f.write("momentum: 0.9\n") + f.write("num_epochs: 300\n") + f.write("batch_size: 50000\n") + f.write("stepsize: 0.01\n") + + f.close() \ No newline at end of file From f986c6fc2f226cc49b1fecc45c879d4fe01d30fa Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Thu, 22 Sep 2022 09:57:35 -0400 Subject: [PATCH 131/300] Update gitignore --- .gitignore | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.gitignore b/.gitignore index ca556eb9..9e6e173f 100644 --- a/.gitignore +++ b/.gitignore @@ -131,3 +131,6 @@ dmypy.json . data .vscode/settings.json + +slurm_script* +build* \ No newline at end of file From 1ed3c414ad825079158a445cc6b32a1c6f29c7ab Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Thu, 22 Sep 2022 09:57:45 -0400 Subject: [PATCH 132/300] Add setup tools --- pyproject.toml | 3 +++ setup.cfg | 22 ++++++++++++++++++++++ 2 files changed, 25 insertions(+) create mode 100644 pyproject.toml create mode 100644 setup.cfg diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 00000000..482af776 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,3 @@ +[build-system] +requires = ["setuptools","wheel"] +build-backend = "setuptools.build_meta" \ No newline at end of file diff --git a/setup.cfg b/setup.cfg new file mode 100644 index 00000000..5609afb4 --- /dev/null +++ b/setup.cfg @@ -0,0 +1,22 @@ +[metadata] +name = jaxGW +version = 0.0.1 +author = Kaze Wong +author_email = kazewong.physics@gmail.com +url = https://github.com/kazewong/JaxGW +description = Gravitatioanl wave data analysis tool in Jax +keywords = sampling, inference, machine learning, normalizing, autodiff, jax +license = MIT + +[options] +packages_dir= + =src +packages = find: +install_requires = + jax + jaxlib + flax +python_requires = >=3.7 + +[options.packages.find] +where=src From 9980ca5bd8bffc6499bb6d129f040c43f4cf0128 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Thu, 22 Sep 2022 11:02:40 -0400 Subject: [PATCH 133/300] Move Jaxgw into src --- .gitignore | 3 ++- jaxgw/__init__.py | 1 - {jaxgw => src/jaxgw}/PE/constants.py | 0 {jaxgw => src/jaxgw}/PE/detector_preset.py | 0 {jaxgw => src/jaxgw}/PE/detector_projection.py | 0 {jaxgw => src/jaxgw}/PE/generate_noise.py | 0 {jaxgw => src/jaxgw}/PE/heterodyneLikelihood.py | 0 {jaxgw => src/jaxgw}/PE/single_event_likelihood.py | 0 {jaxgw => src/jaxgw}/PE/time_and_date.py | 0 {jaxgw => src/jaxgw}/PE/utils.py | 0 10 files changed, 2 insertions(+), 2 deletions(-) delete mode 100644 jaxgw/__init__.py rename {jaxgw => src/jaxgw}/PE/constants.py (100%) rename {jaxgw => src/jaxgw}/PE/detector_preset.py (100%) rename {jaxgw => src/jaxgw}/PE/detector_projection.py (100%) rename {jaxgw => src/jaxgw}/PE/generate_noise.py (100%) rename {jaxgw => src/jaxgw}/PE/heterodyneLikelihood.py (100%) rename {jaxgw => src/jaxgw}/PE/single_event_likelihood.py (100%) rename {jaxgw => src/jaxgw}/PE/time_and_date.py (100%) rename {jaxgw => src/jaxgw}/PE/utils.py (100%) diff --git a/.gitignore b/.gitignore index 9e6e173f..c27d92b7 100644 --- a/.gitignore +++ b/.gitignore @@ -133,4 +133,5 @@ data .vscode/settings.json slurm_script* -build* \ No newline at end of file +build* + diff --git a/jaxgw/__init__.py b/jaxgw/__init__.py deleted file mode 100644 index 8b137891..00000000 --- a/jaxgw/__init__.py +++ /dev/null @@ -1 +0,0 @@ - diff --git a/jaxgw/PE/constants.py b/src/jaxgw/PE/constants.py similarity index 100% rename from jaxgw/PE/constants.py rename to src/jaxgw/PE/constants.py diff --git a/jaxgw/PE/detector_preset.py b/src/jaxgw/PE/detector_preset.py similarity index 100% rename from jaxgw/PE/detector_preset.py rename to src/jaxgw/PE/detector_preset.py diff --git a/jaxgw/PE/detector_projection.py b/src/jaxgw/PE/detector_projection.py similarity index 100% rename from jaxgw/PE/detector_projection.py rename to src/jaxgw/PE/detector_projection.py diff --git a/jaxgw/PE/generate_noise.py b/src/jaxgw/PE/generate_noise.py similarity index 100% rename from jaxgw/PE/generate_noise.py rename to src/jaxgw/PE/generate_noise.py diff --git a/jaxgw/PE/heterodyneLikelihood.py b/src/jaxgw/PE/heterodyneLikelihood.py similarity index 100% rename from jaxgw/PE/heterodyneLikelihood.py rename to src/jaxgw/PE/heterodyneLikelihood.py diff --git a/jaxgw/PE/single_event_likelihood.py b/src/jaxgw/PE/single_event_likelihood.py similarity index 100% rename from jaxgw/PE/single_event_likelihood.py rename to src/jaxgw/PE/single_event_likelihood.py diff --git a/jaxgw/PE/time_and_date.py b/src/jaxgw/PE/time_and_date.py similarity index 100% rename from jaxgw/PE/time_and_date.py rename to src/jaxgw/PE/time_and_date.py diff --git a/jaxgw/PE/utils.py b/src/jaxgw/PE/utils.py similarity index 100% rename from jaxgw/PE/utils.py rename to src/jaxgw/PE/utils.py From dfce520798edb6d5a1d7542b55387fb6e2a2038b Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Thu, 22 Sep 2022 15:12:41 -0400 Subject: [PATCH 134/300] Fix generation config script --- example/ParameterEstimation/Injection_withParser.py | 13 +++++++++++-- example/ParameterEstimation/gen_injection_config.py | 3 ++- 2 files changed, 13 insertions(+), 3 deletions(-) diff --git a/example/ParameterEstimation/Injection_withParser.py b/example/ParameterEstimation/Injection_withParser.py index a47b4d8a..8b402b31 100644 --- a/example/ParameterEstimation/Injection_withParser.py +++ b/example/ParameterEstimation/Injection_withParser.py @@ -21,6 +21,15 @@ import argparse import yaml +from tqdm import tqdm +from functools import partialmethod + +tqdm.__init__ = partialmethod(tqdm.__init__, disable=True) + + +import sys +sys.path.append('/mnt/home/wwong/GWProject/JaxGW') + parser = argparse.ArgumentParser(description='Injection test') parser.add_argument('--config', type=str, default='config.yaml', help='config file') @@ -231,7 +240,7 @@ def posterior(theta): momentum=momentum, batch_size=batch_size, use_global=True, - keep_quantile=0.5) + keep_quantile=0.) nf_sampler.sample(initial_position) @@ -246,4 +255,4 @@ def posterior(theta): output_path = args['output_path'] downsample_factor = args['downsample_factor'] -np.savez(args['output_path'], chains=chains, log_prob=log_prob, local_accs=local_accs, global_accs=global_accs, loss_vals=loss_vals, labels=labels) +np.savez(args['output_path'], chains=chains[:,::downsample_factor], log_prob=log_prob[:,::downsample_factor], local_accs=local_accs[:,::downsample_factor], global_accs=global_accs[:,::downsample_factor], loss_vals=loss_vals, labels=labels) diff --git a/example/ParameterEstimation/gen_injection_config.py b/example/ParameterEstimation/gen_injection_config.py index 7ec968d6..5f3fa98c 100644 --- a/example/ParameterEstimation/gen_injection_config.py +++ b/example/ParameterEstimation/gen_injection_config.py @@ -1,11 +1,12 @@ import numpy as np -prior_range = np.array([[20,50],[0.15,0.25],[-0.5,0.5],[-0.5,0.5],[400,1000],[-2,2],[-np.pi/2,np.pi/2],[-np.pi/2,np.pi/2],[0,2*np.pi],[0,2*np.pi],[0,np.pi]]) +prior_range = np.array([[20,50],[20,50],[-0.5,0.5],[-0.5,0.5],[400,1000],[-2,2],[-np.pi/2,np.pi/2],[-np.pi/2,np.pi/2],[0,2*np.pi],[0,2*np.pi],[0,np.pi]]) N_config = 10 m1 = np.random.uniform(prior_range[0,0],prior_range[0,1],N_config) m2 = np.random.uniform(prior_range[1,0],prior_range[1,1],N_config) +m2,m1 = np.sort([m1,m2],axis=0) chi1 = np.random.uniform(prior_range[2,0],prior_range[2,1],N_config) chi2 = np.random.uniform(prior_range[3,0],prior_range[3,1],N_config) dist_mpc = np.random.uniform(prior_range[4,0],prior_range[4,1],N_config) From 8a9eb0e8babbcddad4d9e08469b293ca408cdf5d Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Thu, 22 Sep 2022 15:13:41 -0400 Subject: [PATCH 135/300] Clean up injection_test.py --- .gitignore | 2 +- example/ParameterEstimation/Injection_test.py | 15 +-------------- 2 files changed, 2 insertions(+), 15 deletions(-) diff --git a/.gitignore b/.gitignore index c27d92b7..04b822a1 100644 --- a/.gitignore +++ b/.gitignore @@ -134,4 +134,4 @@ data slurm_script* build* - +log* \ No newline at end of file diff --git a/example/ParameterEstimation/Injection_test.py b/example/ParameterEstimation/Injection_test.py index 75781601..d2a6a62b 100644 --- a/example/ParameterEstimation/Injection_test.py +++ b/example/ParameterEstimation/Injection_test.py @@ -1,7 +1,4 @@ # Import packages - -from xml.sax.handler import property_declaration_handler -import scipy.signal as ssig import lalsimulation as lalsim import numpy as np import jax.numpy as jnp @@ -20,7 +17,6 @@ from flowMC.utils.PRNG_keys import initialize_rng_keys from flowMC.nfmodel.utils import * - psd_func_dict = { 'H1': lalsim.SimNoisePSDaLIGOZeroDetHighPower, 'L1': lalsim.SimNoisePSDaLIGOZeroDetHighPower, @@ -179,15 +175,6 @@ def posterior(theta): local_sampler,updater, kernel, logp, dlogp = make_mala_sampler(posterior, dposterior,2e-3, jit=True, M=mass_matrix) -# print("Precompling") -# from time import time - -# current_time = time() -# logp(initial_position) -# dlogp(initial_position) -# kernel(rng_key_set[1], initial_position, logp(initial_position)) -# print("Precompling time: ", time()-current_time) - print("Running sampler") nf_sampler = Sampler(n_dim, rng_key_set, model, local_sampler, @@ -210,4 +197,4 @@ def posterior(theta): nf_sampler.sample(initial_position) labels = ['Mc', 'eta', 'chi1', 'chi2', 'dist_mpc', 'tc', 'phic', 'inclination', 'polarization_angle', 'ra', 'dec'] -truths = true_param \ No newline at end of file +truths = true_param From 0d4da31e273992fa75edf7df414e6d0b594183fe Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Thu, 22 Sep 2022 15:24:11 -0400 Subject: [PATCH 136/300] Add true_params to injection_withParser.py --- example/ParameterEstimation/Injection_withParser.py | 2 +- example/ParameterEstimation/gen_injection_config.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/example/ParameterEstimation/Injection_withParser.py b/example/ParameterEstimation/Injection_withParser.py index 8b402b31..4d5edd52 100644 --- a/example/ParameterEstimation/Injection_withParser.py +++ b/example/ParameterEstimation/Injection_withParser.py @@ -255,4 +255,4 @@ def posterior(theta): output_path = args['output_path'] downsample_factor = args['downsample_factor'] -np.savez(args['output_path'], chains=chains[:,::downsample_factor], log_prob=log_prob[:,::downsample_factor], local_accs=local_accs[:,::downsample_factor], global_accs=global_accs[:,::downsample_factor], loss_vals=loss_vals, labels=labels) +np.savez(args['output_path'], chains=chains[:,::downsample_factor], log_prob=log_prob[:,::downsample_factor], local_accs=local_accs[:,::downsample_factor], global_accs=global_accs[:,::downsample_factor], loss_vals=loss_vals, labels=labels, true_param=true_param) diff --git a/example/ParameterEstimation/gen_injection_config.py b/example/ParameterEstimation/gen_injection_config.py index 5f3fa98c..8ed9c0b0 100644 --- a/example/ParameterEstimation/gen_injection_config.py +++ b/example/ParameterEstimation/gen_injection_config.py @@ -2,7 +2,7 @@ prior_range = np.array([[20,50],[20,50],[-0.5,0.5],[-0.5,0.5],[400,1000],[-2,2],[-np.pi/2,np.pi/2],[-np.pi/2,np.pi/2],[0,2*np.pi],[0,2*np.pi],[0,np.pi]]) -N_config = 10 +N_config = 96 m1 = np.random.uniform(prior_range[0,0],prior_range[0,1],N_config) m2 = np.random.uniform(prior_range[1,0],prior_range[1,1],N_config) From 9cb7f9cb4dd3d0b0ffd02bc79383ce97da5da8ac Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 26 Sep 2022 17:13:22 -0400 Subject: [PATCH 137/300] This is the working version for GW150914.py --- example/ParameterEstimation/GW150914.py | 53 +++++++++++++------------ 1 file changed, 28 insertions(+), 25 deletions(-) diff --git a/example/ParameterEstimation/GW150914.py b/example/ParameterEstimation/GW150914.py index 117899d9..7cff0bde 100644 --- a/example/ParameterEstimation/GW150914.py +++ b/example/ParameterEstimation/GW150914.py @@ -8,7 +8,7 @@ from jaxgw.PE.detector_projection import make_detector_response from flowMC.nfmodel.rqSpline import RQSpline -from flowMC.sampler.MALA import make_mala_sampler +from flowMC.sampler.MALA import make_mala_sampler, mala_sampler_autotune from flowMC.sampler.Sampler import Sampler from flowMC.utils.PRNG_keys import initialize_rng_keys from flowMC.nfmodel.utils import * @@ -81,16 +81,16 @@ def L1_LogLikelihood(theta): n_dim = 11 -n_chains = 100 -n_loop = 100 -n_local_steps = 100 -n_global_steps = 100 +n_chains = 1000 +n_loop_training = 10 +n_loop_production = 10 +n_local_steps = 1000 +n_global_steps = 1000 learning_rate = 0.001 max_samples = 50000 momentum = 0.9 -num_epochs = 30 +num_epochs = 300 batch_size = 50000 -stepsize = 0.01 guess_param = ref_param @@ -137,25 +137,28 @@ def posterior(theta): mass_matrix = mass_matrix.at[1,1].set(1e-3) mass_matrix = mass_matrix.at[5,5].set(1e-2) -local_sampler,updater, kernel, logp, dlogp = make_mala_sampler(posterior, dposterior,2e-3, jit=True, M=mass_matrix) - +local_sampler_caller = lambda x: make_mala_sampler(x, jit=True) +sampler_params = {'dt':2e-3} print("Running sampler") -nf_sampler = Sampler(n_dim, rng_key_set, model, local_sampler, - posterior, - d_likelihood=dposterior, - n_loop=n_loop, - n_local_steps=n_local_steps, - n_global_steps=n_global_steps, - n_chains=n_chains, - stepsize=stepsize, - n_nf_samples=100, - learning_rate=learning_rate, - n_epochs= num_epochs, - max_samples = max_samples, - momentum=momentum, - batch_size=batch_size, - use_global=True, - keep_quantile=0.) +nf_sampler = Sampler( + n_dim, + rng_key_set, + local_sampler_caller, + sampler_params, + posterior, + model, + n_loop_training=n_loop_training, + n_loop_production = n_loop_production, + n_local_steps=n_local_steps, + n_global_steps=n_global_steps, + n_chains=n_chains, + n_epochs=num_epochs, + learning_rate=learning_rate, + momentum=momentum, + batch_size=batch_size, + use_global=True, + keep_quantile=0., +) nf_sampler.sample(initial_position) From c0c45ec3f9d73ef2d2d537c025606fc5fbf950bc Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Wed, 28 Sep 2022 14:30:01 -0400 Subject: [PATCH 138/300] Use the multiple detector heterodyne likelihood for GW150914 --- example/ParameterEstimation/GW150914.py | 9 +- example/ParameterEstimation/GW170817.py | 109 ++++---- example/ParameterEstimation/Injection_test.py | 54 ++-- .../Injection_withParserBNS.py | 258 ++++++++++++++++++ 4 files changed, 350 insertions(+), 80 deletions(-) create mode 100644 example/ParameterEstimation/Injection_withParserBNS.py diff --git a/example/ParameterEstimation/GW150914.py b/example/ParameterEstimation/GW150914.py index 7cff0bde..6de523dd 100644 --- a/example/ParameterEstimation/GW150914.py +++ b/example/ParameterEstimation/GW150914.py @@ -82,14 +82,14 @@ def L1_LogLikelihood(theta): n_dim = 11 n_chains = 1000 -n_loop_training = 10 +n_loop_training = 20 n_loop_production = 10 n_local_steps = 1000 n_global_steps = 1000 learning_rate = 0.001 max_samples = 50000 momentum = 0.9 -num_epochs = 300 +num_epochs = 60 batch_size = 50000 guess_param = ref_param @@ -124,21 +124,20 @@ def top_hat(x): def posterior(theta): prior = top_hat(theta) - return H1_logL(theta) + L1_logL(theta) + prior + return logL(theta) + prior model = RQSpline(n_dim, 10, [128,128], 8) print("Initializing sampler class") posterior = posterior -dposterior = jax.grad(posterior) mass_matrix = jnp.eye(n_dim) mass_matrix = mass_matrix.at[1,1].set(1e-3) mass_matrix = mass_matrix.at[5,5].set(1e-2) local_sampler_caller = lambda x: make_mala_sampler(x, jit=True) -sampler_params = {'dt':2e-3} +sampler_params = {'dt':mass_matrix*3e-3} print("Running sampler") nf_sampler = Sampler( diff --git a/example/ParameterEstimation/GW170817.py b/example/ParameterEstimation/GW170817.py index 59e73bc6..535e4ca1 100644 --- a/example/ParameterEstimation/GW170817.py +++ b/example/ParameterEstimation/GW170817.py @@ -81,7 +81,8 @@ def LogLikelihood(theta): n_dim = 11 n_chains = 1000 -n_loop = 10 +n_loop_training = 10 +n_loop_production = 10 n_local_steps = 1000 n_global_steps = 1000 learning_rate = 0.001 @@ -106,55 +107,57 @@ def LogLikelihood(theta): prior_range = jnp.array([[1.17,1.20],[0.1,0.25],[0,0.85],[0,0.85],[0,200],[-60,60],[0,2*np.pi],[0,np.pi],[0,np.pi],[0,2*np.pi],[-np.pi/2,np.pi/2]]) -# initial_position = jax.random.uniform(rng_key_set[0], shape=(int(n_chains), n_dim)) * 1 -# for i in range(n_dim): -# initial_position = initial_position.at[:,i].set(initial_position[:,i]*(prior_range[i,1]-prior_range[i,0])+prior_range[i,0]) - -# initial_position = initial_position.at[:,0].set(guess_param[:,0]) -# initial_position = initial_position.at[:,1].set(guess_param[:,1]) -# initial_position = initial_position.at[:,5].set(guess_param[:,5]) - -# def top_hat(x): -# output = 0. -# for i in range(n_dim): -# output = jax.lax.cond(x[i]>=prior_range[i,0], lambda: output, lambda: -jnp.inf) -# output = jax.lax.cond(x[i]<=prior_range[i,1], lambda: output, lambda: -jnp.inf) -# return output - -# def posterior(theta): -# prior = top_hat(theta) -# return H1_logL(theta) + L1_logL(theta) + prior - -# model = RQSpline(n_dim, 10, [128,128], 8) - -# print("Initializing sampler class") - -# posterior = posterior -# dposterior = jax.grad(posterior) - -# mass_matrix = jnp.eye(n_dim) -# mass_matrix = mass_matrix.at[1,1].set(1e-3) -# mass_matrix = mass_matrix.at[5,5].set(1e-2) - -# local_sampler,updater, kernel, logp, dlogp = make_mala_sampler(posterior, dposterior,2e-3, jit=True, M=mass_matrix) - -# print("Running sampler") - -# nf_sampler = Sampler(n_dim, rng_key_set, model, local_sampler, -# posterior, -# d_likelihood=dposterior, -# n_loop=n_loop, -# n_local_steps=n_local_steps, -# n_global_steps=n_global_steps, -# n_chains=n_chains, -# stepsize=stepsize, -# n_nf_samples=100, -# learning_rate=learning_rate, -# n_epochs= num_epochs, -# max_samples = max_samples, -# momentum=momentum, -# batch_size=batch_size, -# use_global=True, -# keep_quantile=0.) - -# nf_sampler.sample(initial_position) +initial_position = jax.random.uniform(rng_key_set[0], shape=(int(n_chains), n_dim)) * 1 +for i in range(n_dim): + initial_position = initial_position.at[:,i].set(initial_position[:,i]*(prior_range[i,1]-prior_range[i,0])+prior_range[i,0]) + +initial_position = initial_position.at[:,0].set(guess_param[:,0]) +initial_position = initial_position.at[:,1].set(guess_param[:,1]) +initial_position = initial_position.at[:,5].set(guess_param[:,5]) + +def top_hat(x): + output = 0. + for i in range(n_dim): + output = jax.lax.cond(x[i]>=prior_range[i,0], lambda: output, lambda: -jnp.inf) + output = jax.lax.cond(x[i]<=prior_range[i,1], lambda: output, lambda: -jnp.inf) + return output + +def posterior(theta): + prior = top_hat(theta) + return logL(theta) + prior + +model = RQSpline(n_dim, 10, [128,128], 8) + +print("Initializing sampler class") + +posterior = posterior + +mass_matrix = jnp.eye(n_dim) +mass_matrix = mass_matrix.at[1,1].set(1e-3) +mass_matrix = mass_matrix.at[5,5].set(1e-2) + +local_sampler_caller = lambda x: make_mala_sampler(x, jit=True) +sampler_params = {'dt':mass_matrix*3e-3} +print("Running sampler") + +nf_sampler = Sampler( + n_dim, + rng_key_set, + local_sampler_caller, + sampler_params, + posterior, + model, + n_loop_training=n_loop_training, + n_loop_production = n_loop_production, + n_local_steps=n_local_steps, + n_global_steps=n_global_steps, + n_chains=n_chains, + n_epochs=num_epochs, + learning_rate=learning_rate, + momentum=momentum, + batch_size=batch_size, + use_global=True, + keep_quantile=0., +) + +nf_sampler.sample(initial_position) diff --git a/example/ParameterEstimation/Injection_test.py b/example/ParameterEstimation/Injection_test.py index d2a6a62b..4b629b59 100644 --- a/example/ParameterEstimation/Injection_test.py +++ b/example/ParameterEstimation/Injection_test.py @@ -1,4 +1,5 @@ # Import packages +from curses import KEY_REPLACE import lalsimulation as lalsim import numpy as np import jax.numpy as jnp @@ -12,7 +13,7 @@ from jaxgw.PE.detector_projection import make_detector_response from flowMC.nfmodel.rqSpline import RQSpline -from flowMC.sampler.MALA import make_mala_sampler +from flowMC.sampler.MALA import make_mala_sampler, mala_sampler_autotune from flowMC.sampler.Sampler import Sampler from flowMC.utils.PRNG_keys import initialize_rng_keys from flowMC.nfmodel.utils import * @@ -116,10 +117,11 @@ def gen_waveform_L1(f, theta): n_dim = 11 -n_chains = 100 -n_loop = 50 -n_local_steps = 100 -n_global_steps = 100 +n_chains = 1000 +n_loop_training = 10 +n_loop_production = 30 +n_local_steps = 1000 +n_global_steps = 1000 learning_rate = 0.001 max_samples = 50000 momentum = 0.9 @@ -173,26 +175,34 @@ def posterior(theta): mass_matrix = mass_matrix.at[1,1].set(1e-3) mass_matrix = mass_matrix.at[5,5].set(1e-3) -local_sampler,updater, kernel, logp, dlogp = make_mala_sampler(posterior, dposterior,2e-3, jit=True, M=mass_matrix) +local_sampler_caller = lambda x: make_mala_sampler(x, jit=True) print("Running sampler") -nf_sampler = Sampler(n_dim, rng_key_set, model, local_sampler, - posterior, - d_likelihood=dposterior, - n_loop=n_loop, - n_local_steps=n_local_steps, - n_global_steps=n_global_steps, - n_chains=n_chains, - stepsize=stepsize, - n_nf_samples=100, - learning_rate=learning_rate, - n_epochs= num_epochs, - max_samples = max_samples, - momentum=momentum, - batch_size=batch_size, - use_global=True, - keep_quantile=0.5) + + +nf_sampler = Sampler( + n_dim, + rng_key_set, + local_sampler_caller, + {'dt':2e-3}, + posterior, + model, + n_loop_training=n_loop_training, + n_loop_production = n_loop_production, + n_local_steps=n_local_steps, + n_global_steps=n_global_steps, + n_chains=n_chains, + n_epochs=num_epochs, + n_nf_samples=100, + learning_rate=learning_rate, + momentum=momentum, + batch_size=batch_size, + use_global=True, + local_autotune=mala_sampler_autotune, + keep_quantile=0.5, +) + nf_sampler.sample(initial_position) diff --git a/example/ParameterEstimation/Injection_withParserBNS.py b/example/ParameterEstimation/Injection_withParserBNS.py new file mode 100644 index 00000000..b3a92f4c --- /dev/null +++ b/example/ParameterEstimation/Injection_withParserBNS.py @@ -0,0 +1,258 @@ +# Import packages +import lalsimulation as lalsim +import numpy as np +import jax.numpy as jnp +import jax + +# from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar +from ripple import ms_to_Mc_eta +from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar +from jaxgw.PE.detector_preset import * +from jaxgw.PE.heterodyneLikelihood import make_heterodyne_likelihood_mutliple_detector +from jaxgw.PE.detector_projection import make_detector_response +from jaxgw.PE.generate_noise import generate_noise + +from flowMC.nfmodel.rqSpline import RQSpline +from flowMC.sampler.MALA import make_mala_sampler +from flowMC.sampler.Sampler import Sampler +from flowMC.utils.PRNG_keys import initialize_rng_keys +from flowMC.nfmodel.utils import * + +import argparse +import yaml + +from tqdm import tqdm +from functools import partialmethod + +tqdm.__init__ = partialmethod(tqdm.__init__, disable=True) + + +import sys +sys.path.append('/mnt/home/wwong/GWProject/JaxGW') + +parser = argparse.ArgumentParser(description='Injection test') + +parser.add_argument('--config', type=str, default='config.yaml', help='config file') + +# Add noise parameters to parser +parser.add_argument('--seed', type=int, default=None, help='seed for random number generator') +parser.add_argument('--f_sampling', type=int, default=None, help='sampling frequency') +parser.add_argument('--duration', type=int, default=None, help='duration of the data') +parser.add_argument('--fmin', type=float, default=None, help='minimum frequency') +parser.add_argument('--ifos', nargs='+', default=None, help='list of detectors') + +# Add injection parameters to parser +parser.add_argument('--m1', type=float, default=None, help='mass of the first component') +parser.add_argument('--m2', type=float, default=None, help='mass of the second component') +parser.add_argument('--chi1', type=float, default=None, help='dimensionless spin of the first component') +parser.add_argument('--chi2', type=float, default=None, help='dimensionless spin of the second component') +parser.add_argument('--dist_mpc', type=float, default=None, help='distance in megaparsecs') +parser.add_argument('--tc', type=float, default=None, help='coalescence time') +parser.add_argument('--phic', type=float, default=None, help='phase of coalescence') +parser.add_argument('--inclination', type=float, default=None, help='inclination angle') +parser.add_argument('--polarization_angle', type=float, default=None, help='polarization angle') +parser.add_argument('--ra', type=float, default=None, help='right ascension') +parser.add_argument('--dec', type=float, default=None, help='declination') +parser.add_argument('--heterodyne_bins', type=int, default=101, help='number of bins for heterodyne likelihood') + +# Add sampler parameters to parser + +parser.add_argument('--n_dim', type=int, default=None, help='number of parameters') +parser.add_argument('--n_chains', type=int, default=None, help='number of chains') +parser.add_argument('--n_loop', type=int, default=None, help='number of loops') +parser.add_argument('--n_local_steps', type=int, default=None, help='number of local steps') +parser.add_argument('--n_global_steps', type=int, default=None, help='number of global steps') +parser.add_argument('--learning_rate', type=float, default=None, help='learning rate') +parser.add_argument('--max_samples', type=int, default=None, help='maximum number of samples') +parser.add_argument('--momentum', type=float, default=None, help='momentum during training') +parser.add_argument('--num_epochs', type=int, default=None, help='number of epochs') +parser.add_argument('--batch_size', type=int, default=None, help='batch size') +parser.add_argument('--stepsize', type=float, default=None, help='stepsize for Local sampler') + +# Add output parameters to parser + +parser.add_argument('--output_path', type=str, default=None, help='output file path') +parser.add_argument('--downsample_factor', type=int, default=1, help='downsample factor') + +# parser + +args = parser.parse_args() +opt = vars(args) +args = yaml.load(open(opt['config'], 'r'), Loader=yaml.FullLoader) +opt.update(args) +args = opt + +# Fetch noise parameters + +print("Constructing detectors") +print("Making noises") + +seed = args['seed'] +f_sampling = args['f_sampling'] +duration = args['duration'] +fmin = args['fmin'] +ifos = args['ifos'] + + +freqs, psd_dict, noise_dict = generate_noise(seed+1234, f_sampling, duration, fmin, ifos) + + +# Fetch injection parameters and inject signal + +print("Injection signals") + +m1 = args['m1'] +m2 = args['m2'] +chi1 = args['chi1'] +chi2 = args['chi2'] +dist_mpc = args['dist_mpc'] +tc = args['tc'] +phic = args['phic'] +inclination = args['inclination'] +polarization_angle = args['polarization_angle'] +ra = args['ra'] +dec = args['dec'] + +Mc, eta = ms_to_Mc_eta(jnp.array([m1, m2])) + +heterodyne_bins = args['heterodyne_bins'] + +H1 = get_H1() +H1_response = make_detector_response(H1[0], H1[1]) +L1 = get_L1() +L1_response = make_detector_response(L1[0], L1[1]) + + +def gen_waveform_H1(f, theta): + theta_waveform = theta[:9] + ra = theta[9] + dec = theta[10] + hp, hc = gen_IMRPhenomD_polar(f, theta_waveform) + return H1_response(f, hp, hc, ra, dec, theta[5], theta[8]) + +def gen_waveform_L1(f, theta): + theta_waveform = theta[:9] + ra = theta[9] + dec = theta[10] + hp, hc = gen_IMRPhenomD_polar(f, theta_waveform) + return L1_response(f, hp, hc, ra, dec, theta[5], theta[8]) + +true_param = jnp.array([Mc, eta, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle, ra, dec]) + + +f_list = freqs[freqs>fmin] +H1_signal = gen_waveform_H1(f_list, true_param) +H1_noise_psd = noise_dict['H1'][freqs>fmin] +H1_data = H1_noise_psd + H1_signal + +L1_signal = gen_waveform_L1(f_list, true_param) +L1_noise_psd = noise_dict['L1'][freqs>fmin] +L1_data = L1_noise_psd + L1_signal + +ref_param = jnp.array([Mc, eta, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle, ra, dec]) + +data_list = [H1_data, L1_data] +psd_list = [psd_dict['H1'], psd_dict['L1']] +response_list = [H1_response, L1_response] + +logL = make_heterodyne_likelihood_mutliple_detector(data_list, psd_list, response_list, gen_IMRPhenomD_polar, ref_param, f_list, heterodyne_bins) + +# Fetch sampler parameters, construct sampler and initial guess + +print("Making sampler") + +n_dim = args['n_dim'] +n_chains = args['n_chains'] +n_loop = args['n_loop'] +n_local_steps = args['n_local_steps'] +n_global_steps = args['n_global_steps'] +learning_rate = args['learning_rate'] +max_samples = args['max_samples'] +momentum = args['momentum'] +num_epochs = args['num_epochs'] +batch_size = args['batch_size'] +stepsize = args['stepsize'] + + +guess_param = np.array(jnp.repeat(true_param[None,:],int(n_chains),axis=0)*(1+0.1*jax.random.normal(jax.random.PRNGKey(seed+98127),shape=(int(n_chains),n_dim)))) +guess_param[guess_param[:,1]>0.25,1] = 0.249 +guess_param[:,6] = (guess_param[:,6]+np.pi/2)%(np.pi)-np.pi/2 +guess_param[:,7] = (guess_param[:,7]+np.pi/2)%(np.pi)-np.pi/2 +guess_param[:,8] = (guess_param[:,8]%(2*np.pi)) +guess_param[:,9] = (guess_param[:,9]%(2*np.pi)) +guess_param[:,10] = (guess_param[:,10]%(np.pi)) + +print("Preparing RNG keys") +rng_key_set = initialize_rng_keys(n_chains, seed=seed) + +print("Initializing MCMC model and normalizing flow model.") + +prior_range = jnp.array([[1,2.5],[0.0,0.25],[-0.1,0.1],[-0.1,0.1],[0,400],[-60,60],[-np.pi/2,np.pi/2],[-np.pi/2,np.pi/2],[0,2*np.pi],[0,2*np.pi],[0,np.pi]]) + +initial_position = jax.random.uniform(rng_key_set[0], shape=(int(n_chains), n_dim)) * 1 +for i in range(n_dim): + initial_position = initial_position.at[:,i].set(initial_position[:,i]*(prior_range[i,1]-prior_range[i,0])+prior_range[i,0]) + +initial_position = initial_position.at[:,0].set(guess_param[:,0]) +initial_position = initial_position.at[:,1].set(guess_param[:,1]) +initial_position = initial_position.at[:,5].set(guess_param[:,5]) + +def top_hat(x): + output = 0. + for i in range(n_dim): + output = jax.lax.cond(x[i]>=prior_range[i,0], lambda: output, lambda: -jnp.inf) + output = jax.lax.cond(x[i]<=prior_range[i,1], lambda: output, lambda: -jnp.inf) + return output + +def posterior(theta): + prior = top_hat(theta) + return logL(theta) + prior + + +model = RQSpline(n_dim, 10, [128,128], 8) + + +print("Initializing sampler class") + +posterior = posterior +dposterior = jax.grad(posterior) + +mass_matrix = jnp.eye(n_dim) +mass_matrix = mass_matrix.at[1,1].set(1e-3) +mass_matrix = mass_matrix.at[5,5].set(1e-3) + +local_sampler,updater, kernel, logp, dlogp = make_mala_sampler(posterior, dposterior,2e-3, jit=True, M=mass_matrix) + +print("Running sampler") + +nf_sampler = Sampler(n_dim, rng_key_set, model, local_sampler, + posterior, + d_likelihood=dposterior, + n_loop=n_loop, + n_local_steps=n_local_steps, + n_global_steps=n_global_steps, + n_chains=n_chains, + stepsize=stepsize, + n_nf_samples=100, + learning_rate=learning_rate, + n_epochs= num_epochs, + max_samples = max_samples, + momentum=momentum, + batch_size=batch_size, + use_global=True, + keep_quantile=0.) + +nf_sampler.sample(initial_position) + +labels = ['Mc', 'eta', 'chi1', 'chi2', 'dist_mpc', 'tc', 'phic', 'inclination', 'polarization_angle', 'ra', 'dec'] + +print("Saving to output") + +chains, log_prob, local_accs, global_accs, loss_vals = nf_sampler.get_sampler_state() + +# Fetch output parameters + +output_path = args['output_path'] +downsample_factor = args['downsample_factor'] + +np.savez(args['output_path'], chains=chains[:,::downsample_factor], log_prob=log_prob[:,::downsample_factor], local_accs=local_accs[:,::downsample_factor], global_accs=global_accs[:,::downsample_factor], loss_vals=loss_vals, labels=labels, true_param=true_param) From 8065f343afb22291e23f5144103cfd5ddf4cf962 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Thu, 29 Sep 2022 17:33:53 -0400 Subject: [PATCH 139/300] tc passed to ripple should always be zeros --- src/jaxgw/PE/heterodyneLikelihood.py | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/src/jaxgw/PE/heterodyneLikelihood.py b/src/jaxgw/PE/heterodyneLikelihood.py index cb3c3105..deb624aa 100644 --- a/src/jaxgw/PE/heterodyneLikelihood.py +++ b/src/jaxgw/PE/heterodyneLikelihood.py @@ -71,16 +71,18 @@ def make_heterodyne_likelihood_mutliple_detector(data_list, psd_list, respose_li num_detector = len(data_list) f_bins, f_bins_center = make_binning_scheme(freqs, n_bins) + ripple_params = ref_theta[:9] + ripple_params = ripple_params.at[5].set(0) ra, dec = ref_theta[9], ref_theta[10] h_ref = [] h_ref_low = [] h_ref_bincenter = [] for i in range(num_detector): - raw_hp, raw_hc = h_function(freqs, ref_theta[:9]) + raw_hp, raw_hc = h_function(freqs, ripple_params) h_ref.append(respose_list[i](freqs, raw_hp, raw_hc, ra, dec, ref_theta[5],ref_theta[8])) - raw_hp, raw_hc = h_function(f_bins[:-1], ref_theta[:9]) + raw_hp, raw_hc = h_function(f_bins[:-1],ripple_params) h_ref_low.append(respose_list[i](f_bins[:-1], raw_hp, raw_hc, ra, dec, ref_theta[5], ref_theta[8])) - raw_hp, raw_hc = h_function(f_bins_center, ref_theta[:9]) + raw_hp, raw_hc = h_function(f_bins_center, ripple_params) h_ref_bincenter.append(respose_list[i](f_bins_center, raw_hp, raw_hc, ra, dec, ref_theta[5],ref_theta[8])) A0_array = [] @@ -98,10 +100,12 @@ def make_heterodyne_likelihood_mutliple_detector(data_list, psd_list, respose_li def hetrodyne_likelihood(params): ra, dec = params[9], params[10] + ripple_params = params[:9] + ripple_params = ripple_params.at[5].set(0) output_SNR = 0 - raw_hp_edge, raw_hc_edge = h_function(f_bins[:-1], params[:9]) - raw_hp_center, raw_hc_center = h_function(f_bins_center, params[:9]) + raw_hp_edge, raw_hc_edge = h_function(f_bins[:-1], ripple_params) + raw_hp_center, raw_hc_center = h_function(f_bins_center, ripple_params) for i in range(num_detector): waveform_low = respose_list[i](f_bins[:-1], raw_hp_edge, raw_hc_edge, ra, dec, params[5], params[8]) From 4da467b74587f070dbf73e8aa9bb0ae283d8359b Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Thu, 29 Sep 2022 17:34:18 -0400 Subject: [PATCH 140/300] batch uploade --- example/ParameterEstimation/GW150914.ipynb | 321 ++++++++++++++++++ example/ParameterEstimation/GW170817.py | 26 +- example/ParameterEstimation/Injection_test.py | 4 +- .../gen_injection_config.py | 2 +- example/ParameterEstimation/optimize_param.py | 112 ++++++ 5 files changed, 453 insertions(+), 12 deletions(-) create mode 100644 example/ParameterEstimation/GW150914.ipynb create mode 100644 example/ParameterEstimation/optimize_param.py diff --git a/example/ParameterEstimation/GW150914.ipynb b/example/ParameterEstimation/GW150914.ipynb new file mode 100644 index 00000000..2c4c264b --- /dev/null +++ b/example/ParameterEstimation/GW150914.ipynb @@ -0,0 +1,321 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [], + "source": [ + "import gwpy\n", + "from gwpy.timeseries import TimeSeries\n", + "from gwosc.datasets import event_gps\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "gps = event_gps(\"GW150914\")\n", + "H1data = TimeSeries.fetch_open_data('H1', gps-5, gps+5)\n", + "L1data = TimeSeries.fetch_open_data('L1', gps-5, gps+5)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(H1data.times,H1data.value)\n", + "plt.plot(L1data.times,L1data.value)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$[6.9624529 \\times 10^{-21},~2.2290922 \\times 10^{-20},~8.4740181 \\times 10^{-21},~\\dots,~1.3528194 \\times 10^{-25},~1.4836579 \\times 10^{-25},~5.0278456 \\times 10^{-26}] \\; \\mathrm{\\frac{1}{Hz^{1/2}}}$" + ], + "text/plain": [ + ",\n", + " df=,\n", + " epoch=
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.loglog(f,h1asd)\n", + "plt.loglog(f,jnp.abs(hp))\n", + "plt.ylim(1e-24,1e-20)" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/mnt/home/wwong/Environment/GW/lib/python3.10/site-packages/jax/_src/device_array.py:265: ComplexWarning: Casting complex values to real discards the imaginary part\n", + " return np.asarray(self._value, dtype=dtype)\n" + ] + }, + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(jnp.fft.ifft(hp))" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(40960,)" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.fft.fftfreq(H1data.times.value.shape[0],H1data.dt.value).shape" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$[6.9624529 \\times 10^{-21},~2.2290922 \\times 10^{-20},~8.4740181 \\times 10^{-21},~\\dots,~1.3528194 \\times 10^{-25},~1.4836579 \\times 10^{-25},~5.0278456 \\times 10^{-26}] \\; \\mathrm{\\frac{1}{Hz^{1/2}}}$" + ], + "text/plain": [ + ",\n", + " df=,\n", + " epoch=
  • *`uCxUva5~Haf$>WXN1DD_)~gc8ldzX9xv_$7stm-BY^kV(zbuUezMgI zK$Mu6xJ~#@C~>v{q=2Humz+X+d&&BONo05%x+D|NwLj=D(SV!BmIGNnZCQww=DFa9 zQB2B_Z+)1loL-sGAORkM z(G-M7GVd*9<@f(3aB^Zv1wO-;zx~?*O15S*y8}od6UCw4J&d^^A!|bDbjMCx%S zjyBo)PfM%7xqJnwdC<~V!?+o0%QDY{Q(9t?MQoM*5Uk=ed^VCIg7@Yu28d`T*y2>B@DbL0d`Ts#ysWIaJ3oeEnTG0EzT3co z0}U^3H#XX`b?f4d-53rwHhu3i6Cd=IMQ=KJ2X{WG<2XIwLUC-KTi}H2^A;@FihvVB zNOA*xxt=fmF90b;h3Q(l7&WpFhDFnD_H7p2Nf%@wyMGCf2_oJgGKkQMO)(Q)r`({m z$uFTGRBhMJ2XN2v>4x>dJ8mw>eLoW#)Lk3Ut?+5VXX5D$(m2t0D^gr-(A_!n;C-KS zjl4~RpKiX(3D}K@f?E6b)2D-9^dDhY_YM!16*VmP*=>KNt?`nql987pBi|DuML0#~ zm1d~=8A~fjCYgR=pdhK{hMw`)Gki#{{B*O!U%2(sXX9Xs)>A~VL`a>=R{bpx0OMsj zDmeSdJjI+~bB|S7aN#d5+SG-=r#~udd{DW_T)L>fU%eXn=!ce7vow zgAZ$KZqUx`RWQO(pWJqrUX&73#`Z^*4^nfyl}f=sp&w@z>#>t?Lz@W@+8mrPXu(5o{tuWU{*PT`$-N{7`zx)c_ijbG zJ{3`dG=sT0Iu?1h?Er?nY99KDYx7f%1@Jtor-trOy-YG!Ka+Df>h>f+9UoElCVl;9 zzY~0Q_NPxW`=~l{0#mtn$jeHDv#M<&>=t=6XW1fYK=z%0^7xC}vEVwY_kzza>yYUz zX9cDCtBkoU=4oikaPv@oJ%nh9WufObi(iy79uxKYDTTLjVpVM)B&p`kY8oulF zH=JS#v!dPrYV<5WSu?DsrYREj1 zUXjA?z(B-a9~Nj1iMtT~T}<+PRC~!HMcMT><3dL?IudUie+ii$f=w&KzB5$6`pnJe zLSLnVBW{I#IOO<;oEkhRgy;qjF+AqVC@o4717>z!v#0FGJOf0p8)f@aMUM1`d=>z;qx> zzP+D>tWmAV6GF`=AmFu@#ayiuC5t#8EA+oXzV}hqOV}maUj)WoigGI!o6J2qwlTDw z4_qFqhEohfNyOqiP+z|Z)NItc3xfbuzIyv!Hb5NhQt{n^nT-cD$He7?S+%#Ys^SNB z#-szY(YfwFgl`4`P#rw++;*x(r{VSD>gg1uha}Pmkq(leB5J@L9d%nGy%x;|!Q%lo zWeNrz%e2%Uw(6$Kml#LAi%w*6gzq&ren3vtOz!)?n?o8^v)QMPt${Lq3PcO>4yv*> z5v;j|Bo9L%;Z0#`Gt9iu6E4x$7l$9bixdpn-z-)V1da zI?5rXHj*U5A;o%n9Dg&(UngXG*F7RDgk1w*G>qH)lI_$XtH$*Q$&8&nL>SeCCr?zp zgX`>ldi()dhD+A)IPH7l=9u_4dpzq0uju?ypYxx)IgaO4Tv}SaAXCf0zyJ)f1L+Oo zkD-uj-QaEdSWV(%$fIJ&9t#|2rq}qS@S5UzB`m%7x>)7`dNN%r zUxpJT+cc8J!A?dFcs$>-?>1z*udyBgNBT_wa7PousUt|P?A^2Ixz&#MmWccSDTJ@s)G z=AMJ03`uxHA`hKt!haOa4+bn{6ATP_2T)Q{N%^X z!T_(wa{JqjT)X^exVffpPj#8;{CDr_VKmp|KiQ*lbd=jcDfAcwFtJDz*C1TE14ebx zugXyCZ)vXi<$r^>N4u6Plk`5eVGf%P|0xsijS1K9(B<^FQ@c;uU(Yz%4!Fw!<~(bSF?)>ZsL{bym2z zJQ>OAqmPJ8_=LBy?bNnQIK3#Q_EKPZiD8^~Xy<3}qpbpF^MviWJmOTtG|1cc@8h9w zbeRApv}ZUS>OP@4=kB|O*H`e2@X!psPo=|~Tn#Rf#Wlng#~STKPUYI_3LPj*uKuUN z-+yg>d)GFDQjPkR{l9q|-w+#yq130G z5N}E#Bnad_|K~k)mnvrUWox+1xTiK^! z7Gw9F0vLkP#db?ZC;`~wn9gp<%O;>VEUh)f$AQ|FpRA-^IP&$YS2=S!R!-ySH1#47 z?1gs={ef0QToJhJb;XB{aa#tStc4#THp=>0zP?nu%cd_N8u0eF@Z99!(uY5E@_14# zaQ+ZS))~H#PTzg1lC~q)4x}gGe{c`Xf?RY+360#mrG&_HG@MoX? zZQHh;ie`t8C3F3J$l-3)X9u@k4OtBvdIzrzFTaDAA%j=)5g)CFeZh^d1?o6Tx*qP` zYA(k(X?w>+eFWc9d%iZyx!M)YR0qcWqQkwDPlVx+Mb6Oh>6l zG4_a{emkkDrl zOWuEfzglb~AlyENB*L-UI5_walX(vd;1jUFkjXAZXo!y*T==6Riii1lKVgI*D>|q} zj|ZPzPL*jDz4kUCOl-SVRNwgxc4$crT{rb%pr6*Jrdchs;;)wV_B9HPf(vSfLaEMi zPF3TY>KON9G{XREcH|rd`>aNc>pv=XT&DVj#gUDkEU;RC-Xv3dRdrSMja7?i3C*N- z1g|duRU#7F3BJ%+8^bqh4j;+ukl`$}Y?h(ZGNZO}AI0;PuF~%aGzI%|g9Tqx$cj>! z=^I3ek?p5sDi06$?V!5UN@{d|Wgoy!L~-w)J$!s@6-grbNE}GTXCK%b~K<%*?{HGbSN)?qX_U zRvpWVUT3EIidm1;Pa}#BspZB{$Q=bm4?YB8qvLp*p}-YUf3APc7@>;H^2fUk za4)Y}Xm5YuZLNWWTW6$2p;1o`4k@WQ^k~JeJ+Fnwe|kOV5h7@<%^M2fDa0B zDn=w!qaPQtd4V}+nTZEK(f-wk8H>GZ6ui6;S#&vRZ&!l`=oqvhuXdOQ%a0yYT7T~P=_-Hv!tP3G&BsdMvz8Uth9Xj@_F!vyy&kX zg{MxQWNST^I~(b4KIUi@-L&r-6Y{*a4IU!X_nouTe^_ZZb*c(a{Vp@T5;@y0HS66f z%RQy4s{Ha94R^XX`y-nF^41_e7tI*Zy*|A;10kianGpECyWY0y0MepC`WqU0Ja1X7 zWPSA3WKb(3*yvr5XC*^C47O-w8)D=DdW{$0W*`WRb-ehS zkKqc5YmlSwpo$tVJ@Wg)$-LqgvJPT!VVBYq5Q~T+^UgLMwmvB#p@A1It%6mQ(Pn(m z(8jHIv*4G25oEa~=D|H>dDe z$pbH@c-A!8>qpgC2YqnK*elX%;2fZDe2n4)n!^yjRqSG9EE2RLaGf%)$GT3a=V6Lv zZ)<2@E#+TdVvq4fYHGAwiztN1^V)wA0=IAzhn1hhhuJ2!b zRyH~iCJhF6sl^OOC3K_9=P z3}rrNJEP$&cF6&aOHTqSeDUjDU)6e(=4YhxUVkuQA`fvOKZ1j7J?etFrl`4jA#$LYgbS-Fmj0zIuaH^? zO`r`;9eLKA*vEvT&=i4dN>~|m3Z0$x|9|r{Isu9kph3VukVn_#wz zeCB2#l0#_f1~M2u=ccQL^`xrcb!*ouQe)Q3__gY*$-(An?6yK|Xv9e$=KB4w&3EHw z(AS;GCK;}NcVzQYxT&eB6-CiBw3FE(1W7vSeYI;o07UD$2YSs!>EJ3>)e5n*28zUF zzN$C~hf2L72!oo=fRluf5ST1Cs)d=hNs#FZ0NVAHAN6I@oA}Nw7Kr#FdK2+-BZ01^ zE5sn^7X3fCh_dB^#*0iDP`*8;bVYdENLkrv`30PaP`et_u;UFi1n|*y*-j3?%4Qcy zDl_v4z4#TtpPQXZh#QNtVnzXWa!KA)s4~UFptk`Lm?LAnJd0otZM2=PH=|$DBU}=o zU)3Agxk8VkE6GngcgF6F^RxCNdKk!!;uRQRPs6!NynTBE)d=%>^#xt=Po?b)%X|O| zA2B(6&qTn0&h4;eG6P66t-h=cx<1PA*T7Jf_TB0aC-j{NBe#$XnZIZ&-w)d&>6ws3 z1W$p^utGXt6y(02??|VUBch5AIK#kxRLTlm4`nkXZA~QfcWC`Pupd=Sj`)1c)Wx9=yiI_XiEYTFgS+7oulcvP!YgsHA;U2?ov}XFBDdw)|#QkS;fCdiQUhdk8d2*PE7my+7gg?re8*Nf6I>WlGud;6pif+x9+F!xl8SgEVUQ`X z+s~hug%paF8%LBNFa0Q-h{Qgh`TtvRx0viNk1mk)^MvRe$}9VOdp7W-K745OpH*ft z272cq(YaBnNIe@g?^tHx)*s?atkO6sF?=zJuX$Oid0th0P4#2V?Y*8D&bU|=+g<&Ja*;0Mf*tW@_e>&2+ec)azM z#DIVyh^o9F4H`6%F$iwUyXaf#ee~gWy|yblRz3<&6^X10)ljF0syjV{`+? z9)vBgQYs^&7)+=1AY~kNSQOz=L-SjD)A&ZeK#dElG&HG^YAj>={i%*(s_MwR7 zH+JK*xauzWPuTJQ{>HZDEtWo$?&<3<13yhb4c+^n20%8f4?VeRw|)kFQPiMfHa2_F zo!Jbwb18uc>7l`2PI|A_b1p_ave(wDXOF$D__C-NMu|-xbrrgNLl%7Rno(YImv}@ zDFBm43~H0J#q`Gp`g!OZCKpEoo;kf!Q49-d&PEE%15VP$;ASnS6$*&)Klw?F^8$9IuPj-_Eo(LbLUDyyfk4v_25zgh`=7UM^ zaWYiTFI|KMFHl1+5g$by3$ds<2>k0gd^hJC{k}SHK*p#rBlt?+DY?B2_rR@9)$|=U zVgyu8QjdTe|F6mX_kVkt>Q+B|CM~P^-GT9j$9w(@M&{O$Rjo>&rkw4rQ?a4)c&ME#ET?(*;ahVG7JL>$8L_VA@Z&Klh?r@*w z8baLx8fK2?CJNC+QX;vX412#l(D<|4eCo4^3uPG`T~WKxB>#w`8e{BL^R3NTODI|$ zezHQ?Bglt)hqMA81y}J6F7Yd?JPBmQuKvIC{ zT~CZmg3ViY?hG_tBWfB-zJ*{1-0cDZ6lMtY?UMmZ@d*BR@*?CJ`Ok~awK5u-wQx1zBl$F4w!Nr*Z#B!HYZfp)$0|SE8d*ANnWLVQT)qx z$`X*I{v)k{8QfAOCid%6#7~g}vINi|lzpWiUk04fm4=~owTVm90L$X`7%-#UrGiF0 z_w_CvEaD0oO-X#bT82Ikn%esV4k8w;DCE-6Sr_v}mOb1CLd2F^iGmyerw^o);3?u% zdi;b<*&&3Xfrt#qZN5Vu|9WIn|14^)QrV6?3557-&b2|p!Ep5PtT;+5Qq%zOMTTg# zX*NzQ4C*y2`<0s6+nhyh1ses zw0wjP1ihaf>D*thS6pWuzua% z>e&&aQesDDi13M&Cu?V9#*_;#ziPyY5!A~y7H=oPaWFE$M~~OyBk6|L+kmLp< zOtZk+qQ)oGxK-aM-vt0>{D=G#BdbR~^4ICBfEnji)ngQk^x%iu3kYHSZH4nl8jWoUo;(;Lkv5Z=z_lXGz@XHXIKYfGYp1 zVVHw!0MlpMSZ1TVFG=?c4~(VdY8*Ja6H@|UKUq;h;b{NpXgkB3CEde+mY?4BpEWRD zQr0x~=I6CQVH7q%Q?w7X9_7awL^_d&yYla4{SldbSetNVmWL_!QuyE2QwTG0uZUtrPG zRbZZsZptu%e-qc*A35<&2=sUx*W|r|tCFscvXygVg$4yzJ)h=l2yelAhOJgYI8|#Jqa0Jl2s&BcfAXQ3bvG??BbRn)>#%MG@<6Zn|oSZsxa>_XAO9ey8D z>k08RHrO2GrON9%nSK>o{S0(5S#}z=Xh8jNUYe}dgMmfCCRCch(nE)49DSR2NG#Y< zK4axA{pm5u`Ir7jB1ls9Sn3+vEgUf`SZXXUSu7sN0zIjN#HNx5{yS47@`q?@JpG>S zq$kSaspgTE)lVAS$&t}HQGlb6HEX4^$Nd0aFB-*826IETJ8KP6S#SI%J^Bj%t6uu`+K+R^_4y_cj6vQ*}hT#1a{VD zc*QdY(W+>zpe61Okv5m<3{rZGZY_ygR;STQk_}lrVTZYYOsKjdwI-=<4-7xrkNu-| z9XZkgI$8v+(z%*E)rrP2Q7d~}jQVosiX!e^kJoX9KI39Yu3 zxf7lZ13_Oc7NLF-rMZyskN`=r7nhzzF&mE_i63A~f2?KmCrXIcgir;&NVijdJGHev zTJ|@Ec&Bfp6DUJ|JU&z(zrRR-5wi`pwjuj^dGi}luwKfXG+^dM1z|zGr`~W#lYlLA zY%|r^Zg1Qx*XQ&NXoY(?U#woNr^l#}oME5222n%)gtg1>*A1;=n^TX;Hs3zHk#$?IryDDbY z&io|axCk%cpo0N)yx+6rY3n6nQ+s>()#m^93jmA@Gbn1j0dTdSkjbQ+Vc_VSc{Q+k zsf-mM82y8n!V1!|69W!bwb81WL6N|r6f!Ab$3jMBC|p*oStCe$t(%)z4au@8irTi! zI3R%*Wf;wb@x5F)t!b#X0S_(WIYH1JK664da=`NsYST`A+7UPky(v1ts8-LOH4>;$eGXQl=m8#QL7*Mtuf zoHWL&)u#h_F*Gf6-oEuh+eEeUOmk5QNm1gULbQCC+zeW5h7IR3Px6mRH_Yr9lbE8VrRrpJ8u@ zFB#Wn_8;e&vLD{TY>^he^~9KXc}J>I_PQ70%8%d{&G;_wzm36ybl) z@fV$1*UgGlH|0;He=5r1lZSntM2?bU46{BXYf|}pi%?57FZz4vj2Y;XO@@6_^jlHx1*5j-5IQaR=8g`-h|s=h(B% zDJksS>W}^`*CcNC5-L>h;UUD7@K0nDnUa%InFvmQW7p7*7&ng=2SWrN(jKB8tauA^ ziD+=2l=aXCVU~W?N9dsb+j+&kd=P}#>y8&w?=dpKY3czbBnzNG)*|st&Sj=Mi2tB2 zno1Doy8SfRCY$%h197=^@u{hMZ0>H=xK^Jh8we|OGH*^>UFXw}?%K#m36a+;!q2!r ztYbbEsNj7n9{cL<^=HpUlKNsJBnCfdyXw`7y`eM1^)Ej|CnN#~q+Snr!YswGh|a#) zWfo%w=w(hrg78UXjscW?^yPzJH`HuS_0*G9MNGcCyL6!AMt`pK<^k(tR5>bU??3S= zTn~2_%Bu4ye_0*O^fhc(om+g{RfB5C;!5H~L6^0Gt>!WjSGzrYKE%(u!EZS@XN;v$ zrjuglj<07UJ;I03L}Ub^>8T|j6SWAq{%>P|k;bLUZM&87F<2a<9JjfC%O?Fad;z*5 z%%;GDysr;w567a@CNHw_PBzg7G%(94sVa9-h>DfoROHM2*^bKWf(SC5cFlP{hU1el zBqp?rT2SN>P`9k!N&ite%Tci6f`-pwa;w8CLy7o?Mv@G^41GGiwkqw zfpZlY6Cp>tIN;!4LwOJOvQEo*el%rjZR&|BOYm9Rz+sg&g>{$_`c>#yFN{9Vir#2ote;K`bcxs#x(Wf~PerjCpZ1#Awq)rwl@=H_U5Gm=a4 z=KdcqD=wX!Y9yb>3pxwxV-~7LnosnUF7r2lxbSwBGb%?>aKqWE{`T7+M7fVyv7XyA zx_Jyhnr=s+6Ga0uDte`*gQhr5eaNhyo@xYo^3f6m$a*r!3I5CZ@?|pEG}t&e<(m zgPwbP81jGedz)74RhijQEvHG`74|N{SkUZI5IZcssW{g0 z>!h%vGU?MkgztT;_FQYuzwmtMJ814?Q>=*!pAu-%xf2-tE3v=j*ci{rJPSEG0GB?N z+sbq!Awc=PN{qSNZt76MW|>L&LOvuo3W8*8LC^x`YHyi2B&o{X!AwjtS1dzgx1XK6 zZVrD%nS)X%o4C}ca}gI*&v@LSGHkR=%x>ph$@1vV_P>&u?%5Y%r%tam8MB{&oE9=e zOFk~6-AYV3dA~7$2oe>FDR>;Jv83XTX(WvmQ20C$Q(BlK0e16hH@ zs}yf!Lc7dcGlLvOg2}+(CiU{b?Ix({xKdaU|HraZtX+?{A0f?(2GeTdX*2cpYM{M!euJY;3h{p%$BBIpXqeP~`g`|MHU={oc43 zQ_YD6h~yv*Ced!NgO&8$i}c0k#us$O>eUMfaj?y^mpO6()D#@~UMOW2aiKK?M?Gh}huZ$wl8jvB74B|r)3 z1Uz@l>J}qZRtd4hJB4|x$bfch_oMx~{$f0#?|vhu7;~f`Md?6GyVQ3?F^Ih?P&WWu03~u8C zRbJ>CF71>RIb&hO4&_Ac14`UYO7bDM)8aeKa&0)P497+#v-t;97iT#}fVgd7c+prx z^9zN8#q=Fn_E5r5w29us^mL15CEi!*RU*a6hJyiHcEPy*W>oan9uZy?`!1ja;4mHkv;BgWJxNr3B^#mm&s5_=rv5gvz-FThu-B0yMuKoVejE8$L z?QYpp3O*PpB~JbD;M4!Tj&mm7(A?h?gPM$=tzvsD>1(nmbGl*Xr)I@VC}LwRrmBP9E`*y{wAVp)r;9R?6--Hu~XSzzPK9KeC>QT)|VZwrKvOv|wBYvqJOc&HI{O zSwjP8Dthc=BM)~q*s56ca;3+l&sp8HDMO4t4T}B9?Lp#88DxZ`DbV5+iX!3TxowQD zI+C{@GAckFH5<#CN?kklex$Fq-r9yf{5WZwzMhGK28OB`AjRXB@c?B#e9Mv^%WY1?~E^ICp~J z{C}ny#-Es)%9sn)^+eia@GNN+RWM!87LQmGcqlF}GCW+?;dC6adWW<)E3Dd84u6bg zPEcX_4{N9Tzl!>+G;3~*n!DKwqWB^w1cG`3^cxg73ep6Wp<3!cx&zn7D1_jmK6{Z% z|DhA=FRyx9S4rqT6pS*0!oeR$39@fgVA--kLxwEijzX2}B1QcT>Bo3pc%o}N+xSex zrH2sFI=+j!mQn;mq9!$u5EoFcGy1j%t**pAJ%d6X4-RqN0cv@EjN0de#9hkICtrTL znYWzO7^FfRlG9565O0S~=ut1)B{ru%-wt>u+@9d@(u{WKlv?&8bk4PwqW*X2?Ni1GH8gp!4eE+mzCIkahjfrG*(;&C%<1Y!8pFmS?zuI^)WHEm_ z$>GSvC7Ryrg9Ry2_;S>ZhJEhfab}MnWjFSLGFT0A{+5^AB+h(nd*;V!4af#vQBIHu zB*&pPG+p!9mHu1;1gflR7zM#C23E<1uq^(opH?ievpW_1 zhlAvp9fO3GRxaAI9gsCrFT(4~wQbn2;p#81f)DM<7oRWsXZs?Tp}(hooVav%ITBxc z&(Te~#&qGAgT)Mp{U}FX4PAyxz<1C8j;VLEaKGa9oKB&PfdT!+gx&fz;>@y}2fOV9 zxzwSjVKVTuBq0WuctTtl?qFh8)|fZz?J{$2dQ@AQC9_)zmk!Q>f}WDiC9Q!8Rvo?k zS{v)19t)BCoOTSPruc`DXfN_01Lh2Tw5Mxba6m)D)j=2F)5D?vV9LeG0Gh9?tPG^X z6qh|Mu_R?5h=V4Ev-JAm?FNso^wL7*PuY@17We_1ye0rdLS9iBKpA8^pYrA?(o7 z8ZGM>d$=QoOc2p*?Qu1f+X+AyKA1hLe7<=yN}~J zp8el9bzQ&T_q*1)&ULPHp%s#1gg<4T*;L2g32)x57P)TVok?rFcPHm z2&mDKFZyB$LQ#ods9xS}Ue&l>R2(phUq8KW0N}90D@NML zq&`>YINUcKGgE=|dx6$(T;`EcQBDMd&lfKaW*$gozLTS)86SiKLIv988X6ETX?103=!Qwd6Aihx zPdYgUcI@%Rr5&k?SI0aNo{D2g5Z>3j#QobRFM?PAr&v6zbPA2vtFGDwd0E&(q4CQZ z^E@m%C)^=VvCV*<+mBgl=C46j-`^|)L%Mc-xocNj%O=c0s>m7*LM^{&WSDE*2^&!` zQShwCXGK9ZrLgbPx80JU3s~J44_|$#Wd=Wl3Lz@5H&J2CYV$#k0((dv-RN_|I44T0 zRpL!@kDfiNn~j^{4@W3+72Q)0q|XSsUE_E6}SVXAAlnWQ6spd zPonQ3KCm;-UBldN~e>hFp6nZ>U&S6#32FlwfgZ9{NV61n z3ng+~*u8a>lVanuz`?xFO}smjHtjw zp}KP1@BEU71%f`n5s(pO5l&{8#?Am<5JIa%r*J+)N>c5MrUSz_=eZm}UEb>)LO2P) zAQ$=}t@-S+je(q;>ovO{o)_c2C(;^ai2`P{qMoG1l6G;&3=HL1$|?+eieVC+Ajjhq zkl}f915VNLo*lMp5JD|k9*_N(Ci5DgG7(&~-M&NqFS5j^i%E~76V7f-Pl8AjqdWz4 z63x5ri3LK^sy3KVx_sMdFtPZG5mfZqaZ4l$3;l4|iEBgM+t@SILw%BtWqA|g5p57- zjP+nZDYhE?H7Ya$I>HEGgZ<5J$EP!*scmJ*E+okvD05HO#wf)mSW zrW|@_oaaA#5xqsE32$fa7C1iCy3>SH9%MaL?169r0%aW|b?%LXe~cGXq&TPA!d&`<%=C|A{`PP>xYpp)Fgj7i1(lS}+A_ z`<)c`(r%Ddrj(SjLvZa_QvvC8&KrZ|KuCA3$I~CvX5L=q4u)zncSZ!9MSn}~3GCym z+zAqJ7|5ep4FDeD9LL?|t{x$+aQHTgH^ooOk|P&fSK12yj29c6vt`;UK~6Z((XAP7 z)v#M@HGBF6KR0YlhYU`sJ5t&iU`V{`1FX$o;c%TTY|zbJz9?GUoF@@N0go$bjnZHi zPc2X$8{5)M^1Z^#MVcJ`P$LlbS}b?@mTOUO(z+Mx=?+bb$2V-!=`3;2q;njF6bRVT*Nmwo4 zhMCC>s#h4idt4iAP;LUZMEJ`&v1j;9)*U~q{e}usB9PRZy6$*ba& zIBVIfjxoJ;@7~9KPtZIxLBg*9#|OOnl4;SYRdxNVXM~_88A8#<(`;m3>(Ge2ILzUI z7(!LkfbNd*!GSFu-C6*-Y2-HXppeu4`V1oUhb?h`xpt?kUN^Txcf#Rj`Q)Cqt-e5| zV~xR5D6hPHf><%S2GG_b7V$CZhq9|C?P6=)v8VIKg=L6AB~9%jxGRXG-Ei`N=q)Av zCX<{*nk>97+1GrI-JT9(+Hw!B@v$&quq-N>_PZcfAgJ*PSdW(e zxZb*ZSMS+{pT22rcyOHV)~MeZOA>CQyd3*!lv7xzZOb#{q@t!7!udvqV7{dPyg=J| z=e%7HLW#&Ym8x_7qM<6DvoF=1a{2xG`7Q;8<3dKY=8jyYj;Fu1W89A6-(8g|_@dz7 zh^{SfQX|Ba4K+2@r&-5WAgR)EwcQsl-D5t)PGYOfPol3}L#Kf#;gf!?D_cwMxJ8al z5(ygf83uvPnR(tj52Ry`;uwcN2UrhOXLDMVeH@@7^CENqI`2p7V<$`!MJ~8iCD!)! zq|sTnqvHuR{EaQnfy76CzUAUq{em85#8s3n-L@^^pmcc9FjC;QSKr)wO{9An)s|$Q z@Nse>@P|_St7oXY?<0>1gEH2_QE>RlDWh>0flnHWu9Z>nKVkC)*uzH8uIT#*yGv2s zi0GZr2=>54Tw4mCn>U9Q?>QNfI&g~5F{jX#v5lKFY5T3jTbRugU9&1_Ud0i1#FGqus?x&B@2<4TPKN&31B+csz=rvnXNtn-oVNW|lEv8NHt08|*oMG! z^(X;j_qk(;*(UrH7SrtteEyxw`6p3G zy26y?PHS&@c@3CKSHseHt}r(nqQ(>yn?1}g^!->d{`XFMa z1>@j}o{xt&C}@=SrRZ%Zo3WM+;3MBxcI+N^s$c0Uy>c#VGBcpmg>6gM&YfE=vP!jD zj?onWDuEu5L^95rQg9po3cwbd2``@Qs+x3U#niUI{bko!BZ>%AzT7Vo z^gf~ldax5qU+zkvCL|{gB(eSNAJUeY5Sw?a1sK7V0;N;nazI$aPiYOC<2A4AV^X1Y zy)duJSV_%%RC)>VJPH5FX|K789?O&>Mb(AeS|Q6UR#csmDdZQRV+JiEh*q&^<_Spi zluu|o#jz_}wISZ5M6#`xfFk?DhYyOo$|I*to-7uY!VoY2P7g|qiN&sH`SN4Ldtr_y zCV%V*v6(Tlx$eETtu*k^!Zk|Q>ICZLMMc@BC@6)H=xC9?!RcC<2HM`IDJA6t4FMTDU(`Ze zTcAT^Q;}zAol9Kh@WJs$huBDf&@YLRDW02B`iNwqI{EN(4KKZ1P>yHB12(t)TGd|ytiZt#?e>s86W`DUO$2r zVLi)!D0R_pgnyy-r1`nhji1O&`=L^h|gw z{>N8ni15@TMYL?!?wEG!=Oe&f>Dw?YZh@qU<&Qlmv5{Q10vrqhKpXi5Lm)J^ zm-cm^yE+AI>4;A_y?mjpY!96Ut1DJDRQMbjnc$&6A?jr^Ax~u^@tcJ7 z5*P*9ELaO5td0HBgtsLpIDW~q3tXA_lblJ}qK}9eT-OXgSEJ^2xeT&9>B0JV5p(kN zb{xj0;!N6>ii4C_7quhWoW1c9G<9Zv=>PUpN8?C@z3M&6Kx*+%19sePP0q&hP3a1c zzh<2}_a8q(?!**!GtbZmR0cN}%y;KhqBFcgfgKqYl>>K5l{0i!LD~*d=|?C%L6)$; zqgVIIZp7^L_*QK@eFp7>my8Luh61bS0nsKpNp4~XG_|OTglIv0kPg`g#7yD76BGZC zf@b2Q!zj9m#~X<>)8@~=!~aIbS&N7IVEtLfdFzySbu2ytn_1;9!+n$<;z^61Lundy zG-G}G&pzye_<){}1y?W-u!~C;_s>qAK7W2)k=kdUxv_rt7hmu+*3A82y!EIr52Uty zGPN7@KCz*l(-!)p_h>B+@ojONOSlrhJRYnZe?eX_g<>Ee<}+Lpx9h1x{rZZ(HJ=pM z0M^-BI>*uNA$W*wIu*Jz<0IU)FM%gN6y)R#M!gF2M|ZF%M`RtB9firx6Gir6k!WR5)ABd4G}W(gVz8iXi_ee;Uqi+qU;rXkKE*@JD!;f9eHTxa z%{%avqB_&=*It7z{x&8G>0dhX96-hCiK7$UN6ig$+F@~Ic2pB$H3wbk71sE?nBg zB<(r#xIM~-6&MDAm>f<~7IyN5C_$Y1$Vt_%45Q7a%)4=V_lZXoj4^7quk&5|> z!}iY;ojDU3*uO|yhujqayiiXKuZI)3thQ8Os@HqR_%?1j6rO}C8Sj8V3&CUZLD~B! z=X{CiQU+0Jm@o^lwz4zcxDuqEED}P1ABcy#2&1F`L74c0|H8UV$3eD{)YLLoN7ad% zZJ2Z1prC0ySTQr8)~ibYkB-nfE);)2@ir?ikYKI!{Qdi_moHy_wsqLylX5TMb4VkC zc%aji(0~c=OSn`J-MHq%czU0@#nJgfP$4Ob?7+GK&{9Xy(-S^|sDoDR8DGTMAbFQ}Cj2W1)kpAzNEcSm(Ko;D@*}jXy1CWtKvSWneIse0O)>w_XoEO)g zQc9G8qI3LSzEWf$u!SiUPMeeY$OiIxNx$bWT^fUi{Pu=!hzs!C7>H6B5ITz%+F^R1 ziOz3iJvLK%Pz0m64ELvQ{hBE32v|@jbS-vCp%7iMt459p2@Z@!^#~6pyC(jY5-Aw) z{}Og%2FIm_OqfcP~*qNU$Yg$#x4cmUvW2 z>m4pczA?4s0Y)~?fr{X=Ax_YsmOql=4LJ#fPnu0Hcw9WsO^{=+-}TsKO}ng${qa7A zRP!Vt5jx6IpqvjV&%{n}2L9UOM9DX0DdkM`)#iM+rD}D(XUv_ug||(g852r1`oYln zgB=|m_qUor|F1pV%-X>5gK9oq#kd_c-o5e`ViUiyX~(nGeuLEI790)sN%gF?p$abx zSy1ds{Wmwg?;35UTA;{iLe(O!d8!S@NgU!^{!rgDF2#&QZX|cZi;}l^HAd5S9-Qnb zuNj6&sviD+Itfr5-*^CH`Y(gUeZ(FVW^NAA|Ku9BB%_OlkO6&s+pi=i=WjY`*a%No zdSgnOeU~`uM&vy#j@m!;n>C795A7NWZq}kMLL(qvt5|y>+$qiI8L9MR<3sNeuo4Uc z{jfSgPDjKlB9F+#-cO&PJtN|)7)7nWbBL6fe8HX1*$mbhP1Wn%?$1|qjJrbXCtxo? zOS(3n4K4X6n}&s12=X>VERvUI!VO9OhId;CDI~KEG-V)d?ml*6Ns5@`N~yt+6IMd^ zhBdV;GBV1}_k&@<%>4!N1ny0yx4B+w<;hL5o^T#|WL3~l?t_R1Oqc`ouG75lcKXJ5 zA>2fm1a{47J{Wr=hR4LT85OvIta2k$%7O{_$jNYG!6HzI{0%J8u!J_U&>ZPmd6Ayr z-&W*3N_^-vkw0Cc=fMxjAmzcEzrt`&>mykAK~=fE@z&y!!Vt(BnW{K7ZNMYHv&|Xl z>Cwd#f0BF0M*8B6=bvHhaxykC7fGSZGqD4V@W#H@0j4qeHbN6XDgJ#g@% zn2?qq1gJ4ZwSLa5zP_JydbT3i4Iol~4m|&8 z#=LpA`8W++%E2QF+p!vD=R~JO9~I@j4Erev#M60zMwSgv6t>1Ko3WL>SMFh_Ae--G zx&TZlu!jHdNYdf}=0sG(A-(cycVQ&%T#;_frP|YA8}RVCv}H(A-oRd(wgCxClg&@9m!doxr9&+P%ff^rX+)BE1p%^UdP&J6GVY1(5iHlaPhCq0%YYq#ld z`3?#)Gq65Zps_G>{Z}D0IKNmCYRFh6O%u{*E7S0nV89ewYbZa(LyfuQ1K<@{o7a#6 z?rd@Nud*@IuxsWxNAtNc{LP%#uT>6@_^dZ&Q&;ySy;+0ut>)bQQkwp`bRV{h-dT)7^6*_@U3G8b_&Yxes;%r9xa!nCO9FP?@4M%RH-q$ ztmN+JQa_vGq;^5YA$*)gYnKp4#kUExBS!8rs|qc7W^c~T8x~o?^L7t@GHxDUrXsH} z@{H@jrJpaRA}#?0+mq{<@KXg}Fj3_NeS})wfnY~o(|%pp5_2emy=)kv;%>`X7sm-s ziokw;fx(Mb^8)i*Rye;HYGcd6u?ZjX$>rgf!k~9$8(9;Xmrsp{x4$ykgn3Wz;{CJB z%1up8#Y{A3&z?PzX!?Lp%-IRn+3TE%Z0Bd$yqSp%&SoC)TO>Oq8$YO*h|EMfghE82 zST!N@sMV=g@XdAn!_IBS6ganywi#ACs4Kmr#5;HD${^eA)L*0vHH$iy*{yae{rZ)u ztEYvA3*J_1*XGdfx08vi*8|$sqgMJW*0rgpN+U$j3bDe6hL;w{(a}TSPWM85p~L>h zMe`$zzlR{2kRS%M?G7QBj0%wNpziwwxxILCKq68EaK*Y)6SsVGZGj6K5n)NtNCpvr zLND*Py_dS8#p`(Y@Y@ddESvEXEfxJ4@!F+&?vkOE9rSLcgOk%u7(h|iGJ?;_q%h zFF@ZS9UH14D=ssRmtmzRr=pJ$G6mIy3<3hFj)KE&+`IfvfetFSaC_wOhy>OL6P4u$ z(2IwIoD4_wP-lP(^}np(Jov~s9jbeWF6 z#zzsU26%XTPyb(!J{=UVuXa`KvnIi=Q{2~(!eG%Ldy4SsBxeD{K)@gCpXX0%j2Ovs z+S~CpYk^cY6LK(APNBSzV<>7kz`tloCQcVo0}^`a9TFb5jz6(_>eQ*SrxQ9E1YYn1 z6uV9w_%`mD?i&sqnp1`T*guwbU=)M+tq-8Uq;j_!REvNi^xnduFdaq3^u0^Nw?qwI zt7Ky_fuVQIe?Te7*=Rz!_rpa~qq7L>u{jN%`XJKjVHeg(q|(uo9UQ`=q@0IOwgYWV zj#=Ty*;K=Zh$ky79k?_-rc1`09vr_ztiTafz6YsV*hdm_j!Pq{@2Q~_(}7!t+vm1jV|4bzvD`yG6-_#E?>Bq*^3_=S1lUHhk7dOZ|05=p@@NsX zc#ifU*}l!o;;s|H2)BX~_$}g8K-(kZi3_A4>N6SJsZyniUeBIKIqPVeqL5C>v^mLM z6m#fCU@_=mRzoAP^x{_jP#S78CMSBVk#SWP3WY}Xk?M~99dgF0c=nzRkUPq&k)2ht zEDyQQH1_!tJ%r9J9L5|D5wUWff1? zAfESjgnE2HcG;(bMnt;7w_)l-I#n^3FQcfbMX@IP(Pgm`L(0Nl0fk{1zjdfG;-WvZ z=fb%_ctXiYWz0OmJtlC~&+RX_dJ3lma+LJ|d_u}Pb4Kb0f=fj+s=&Gg-3Ls;VIz^G zW1+!%D0&Q!dc^+T;u8pmd;a`+@eKeN9xJ{e9xHJ4`uh5IbhGKD zH=>s!nytr2VO8X=B01-2*1UpZ?@;kC6}}N)Nr5qu7T+7p|7${nAq(K#IpZ$`FT**& zhJ3^k5<&in$3{P)H8Eua-B`K|88>C6|q%-gP>J)g$u6F@DzR{`}?o-2g)FY9wv^qWvXs9r9y=V7cujQt_>YV z-<1B>j(BLCBwBj%=I3-BR?~-SUqT|IsMq)dU*YseuQp$m4n|Q3E115B93spf6m8$; zCF2>Zxx$Q=sAPE0Qao`r?0A}RJ)_H4%Iq0xca?-Q!-n7DPOSL!sy^*q3Zf3C&dJG` zjM9l*ly|GKjCK{P+PH`dG!XI81?OXVtA~n<41a_7bcp$&^_w=yo=fse$hhz(qz2iT zxqib2F`kDa=#Wula$L9u&X#85#<-P*l1qMPgc7u615s=DHEzlWBer;gAYYfe^4HNSZ+L@Rwe4-gyig`HKD` zET6hl)mG|ie)LuDF(H}NlsE2A^`FXAZ!O3J-dF-%U?NdAUc+_eo{)ech4e)!$h3i! zbucodkhV^L@jcn}D6oQmvs_XJ-=u_V(54L=O#SnpAkO8~j#$!zkaX-5}V=w!12lh z+)%6^{&IS~*(mO}CjOlYf=lVh>LK#lflx|M&+Sp~Fbi2V*iv*z2xj;G`G~V5O;3td z^UHD3z^D#vRVEoP@aK*f~Ch zBp2|U?96}^m&L&Z*7!idO>53-P*E+8hyLE{Q9K`?=kos2g-AAMtBR$&a;8EuobEB_V8R%Z0&Y6<9U zk^aE_<lc7Ll*>u1UN!c64on=x&7R=*@xch{DLJ5i} zV@`N%tPx<$*d0)7jwaAD^*)?7pnrwow*iRsEWOR=v8S}m zhgrDddUg2%BJSYYq?3q#f3V-ZAxXO%7>FfV)?YJ`4cd*^}q%_s^dvj4pFkSxj(#e8ZQlElTYh6JkWS|rixQ1>7uoop_It!!4DPzx` ziDVv6ZT^xhp-r=%w)v0CuVhOY_0JLFtWem(VO%5JM}f@wg?__c;) z^atZ8wlf1lh}53#gAWBogB3;qVn@Z3AV{Gv?U|AsHj<75x2FNRU&=Lslq2aOB*5v4 z44rqbLs7;=h4ktzSEtE581dYC>g;TAQ8MKeP%VfO!sZMb4w0MwJx~%+aKt&H*?b?sq2R>Z-&a@jtGy^U< z&)jwEa*2{CU-)?A-c{13bSZhd94YXXm=g%Zs7VVhpCz1MO-|mVtfID%abC(ZQ=X4( zVW51HcP7rX;G>B#?oO87JNVs9O4`VsE9Ndc!;iu?4F-Nfbku7b-~~r0P?#R~7T72S z*+q*ss>bJ*-7Y(DDvUawgv6Hig#s{a3P3`Pucx5a&&fi=e&V~9KaiT+SMAid^E*Qt zsH-Q?qT(;sMn@$bayjNQuf>PA)6>NAwk3yN)?yN;>v>R?54G7sBg>wHB_RR^V`9t{ zv(Kjhwwu1qtrZv@4Vv^#Yt@$lb&tUB8a*Ox&+$FT=e zKuB~@ik~jedXbx}p7A2VamXP*kYd$B|HR-+d}5ZNEO~E0vcFjSuHYC}oR|O;cnuZ7b+lsC^Geh3oe?_l8otF`R*^zbGYOTy$_ zI|Nbo??0uh9&LDtrxSLoV*E*Nc6;?iPBaD{ae`ntk$l= zjqsoM9lKbzAUaWE4dIB&Is|D4;Y}|BJ>xM@w|r0eUojRGT8HP-In&+j+CW~4CyImf zzC(kjQfe|@QqsCVikwUWcnm>?(YZ_^Q0*Q7N=Ph~EO(BWV?08)8-<>d* z+sZP9z3%K6!SMA&Yv#~eK9w4kSSfb)vP_1Id*a*50`8S4fO(nwI(jI9imG~0^oTh) zsP#IJse{d#S2fB+NJu)U{wF|B<6S?8IUl)WvAe8yl2o5hN$lY|4%482|52H=UfUCV zaK|MoX__8x+Ah%fl~>QU4Nze@AjL## z5~^Ynu|1FS;*0i?78JBH{7PXWc7^N}R>w?q;pf?UnqsFOSN^d!u};5T82`)t{+3xB zHX%#MSx012!#1}3kji;D!5qFQ&n!9nkz47@M$eyqS|@t$hec+eqOd-9zIF?EiWj(_ z>66>6GbWEMrZW~>_P(DuBa)os|LUT>Xa?Oq#wl;@-yMK}lTMTwut1sy^69AmFh{#O zr?4UDBbX?)A3b%{*#h=0u)64!MIE*1K_^2=dSW2T|FjSp6{BJ0Y4iNsTHX*tM4M<` z>0|Qt%h^_~4H+>xQp7eO61nwgXsXKF=grB=3g{1|Q-~a80G1=n@v37~{-G+zesx5* zh1^Y1n^CUiUn-1^YQGV{BFiyk`jZd+m~hcy^x^09cgjDdk5r(v8JTkR-!RF)Xn1Z@ zjwC!!)6>-3Gh9Z>D6+8jd|Z-=uG7EM#BQ|Pqzm0fOWjKus>lJ_lFkiLI~ecTMf zElMA0Bu=E#I$x`-BBlV+^CTV%jDX^D`7|V!NCYdRz<=0J6e|}Bxt&p9O9o+YkBPT< zD--I)sbG;qoC*!gk-H3LkA^TYA@W^-Z6JV1k5Aid+w*`YGgACF2nL06?1?E84|8z% z6Q~gSEOw-I6eJ4~OrR^(JURRHf}b|qV%GoRWM&@8(>KK-o9L}vy*gB}EBaMNc3SaV=#4;u+Kc9Z*MCCF(iSD zkOi}hPX7@Tc0C`XEl5`A=DHc*dA)~lG@y4_8zxSacK|;zO20Msi(F;!z^SOH>Byip z0wZWjb+S1l-MK}`w*bcp96Su|w6LI(K}O({`xpJqp8x&h;-jTKuv2bZqnNO4E6nkp zUh&$nWk?$y$kp`p^q5Xe@=?BSAX~UvecsHMFGi3LH`8d&=Dtqd_Y4;uIp|P?6C7xz zn10I#AMLE>d8M)Glh{c6H zNAT%=Z2SPChtR&zTX{2>Xp)_qtkZZNkkb~DOYRfhh23FZGUVLS?duv^p~dm_j1IBgSy8xE_-_HaZ3XqgJp zh~ti2Nfd+fJV|vn^W3%4{H{(DzYA@It1@;(_Hq5PeVLtD2y+nfkRxI@pnWS>FCfKX zgqzG4n~q(;*|-WAi{zi)LBs8LLIfAR$>>O(nM`X*yt}x-@Cr|}4gZ!AK~J}kM@sbq z0q(wdAJsvXQ=Ef(XM1SY5o(44mxwT0#J9vL!CR>Grg7KNKb8lSJ%Zcei95)gBTH8a zyNi%tU=$OatEbe8)QQzZ3l9;3K53|rd7bj8{U_$pIN6?O(|^^=MNh{tE@931btv`+PPruCeo;pAp8QOqDnA?RD5;AedKlOtGHj-2yjwul1hpVxPN!s) zWjP!y*~uW=mjPhj2%qFDl8Xd>gRs^3ii`^1@(r=?w&%pRQ(xR(#s`-bH(ETk-SFMp zAm_HRijCHUcy*Hx@~Kw(%8VC#IHwVPwR7)Ds`#HipkZih@%z9l= zfALhxG2YW`Zi5$%S$FlRH9?0~;{BMpoOy=1_W+x^FMB&d zZabg2O<(oMU^15QE%3P4##Fwm#xP@3Ey-pEkKEJXi!Ml+R6boXO0eBvB{M%HA zZnEP}5STaw^vZG>3=tz+7pMc;5t;!|YxsdVg@vgifuz03@2||BuIlAQ7QaC!N;=C_ zY5Y2bH5wv>=7GkBq#}|Sg}N1%Kqkjg(T&-iVpkd7B9BxlAO5dY@P)XpD@3UqJV9p_ z)bLF5`;|+_O+LJjuDjB9H)$U0p0-54&kDYSEU{;F#`%mdAldsKFD6xg1CbK$B zmDv}r@h!fN05Y)vq2lv?H7dd4eO6p5>2l5TjQDJ9-ApSu9NP)Br#Wd{rg-XDspL5l zGbthPR>X&>p8OBUMC1t&D{|mTg1!l?m%c7wUmhS|PM=a3>6v$$qc*2g6hR_@<6w!p zk@*4!>ZYF$WT~Z?-2&wI6X-=^EPLxvFR`sY@#f8rx8`_o;I5#rqao#o+_si_`zxF~ zz->)Ni-S&jWSX>%keDV&IP740E zUOVl2F!`+IN`1p{Mv{zoi&h-yzKnOlPm;}DqEcr$W%}@qJ6dUIF&dAAKrBeR*v8)C zAdBRSmW3cIG-@(w%W0LpQ)KxWd>##-`lT1y`Q=BaxyoGv6q@6?Y0I+Q$~ueSpEM=2 z#K@ZO((skM2RdTN{U}F3vb}UQ{xnt!OFl@N6Trey{ru?T3jt?AkYwXt5Bn$g^Z(~M zsl8CK2j^)+B;{p6h29{1GczRXw2()SLS0K2R<5`|7wsb^b+k0iNSARTzA|mbaiQHL zI9w=Q@gB#%UD+8VCS0($*+4d=Q+pUzBR}RK z<`j(2ph}2^_w^dG#*WvIgNjHKWeX}ApkVS6@wCQ@@vjTWHqw^n@yd>Hx+KQnfjvE( zG=MEvE?HRaY`dwk&k$&c>K@Pvkf3(+Ws99S_l~XSeVb-oZCQ;7km%L55Ohj*%Yo_v z0=X@VLq&9z6GX_dXT6n-u|x4u)A8xlsiW(Ft9b|OnZTq`Z_g76yxbE0M7}GZ>jD#c zBI6|bODljA5kN3ceLp5vbcR~+_PoTb{+G|`z(R}6oLBRb#KLfLAg2N#kwBYS)@<=U zwqChz`pZkxzUcX3?jB024SVfDKdzc}s*@dNPTk2#|4v`cv##lhD{vzmv?_(izq-u7 zHkPItBLK%}{!610s3kl*wOVD`JV8^OAi*L$i+G1_EPoKEE-`rq-65aYO1S&6r_j*F z@nm~ke=ja<9&!EQHP|%rv*@#hOXTDVEkvx8eyB@jMG1bIO)OkP)Q)@(R6KkGBidT*T&9JTFmQKj2Bh1vG@ z^%<~LevU=*_u$xl>=@-k$d(kT8W1bMY7{f)1KuA(Z0UdjD23CHHQ;vR(#zC==7?q` zi%0=(qoY&03JPEo`lKq-o8XSlEjnxNuYHxd15oxT`oomRSLxvoCi}I~o&0ai1{PHO{LVjDJ|;7tit_$2Dxnr67ibtfTFeZi_c`{9zytL`n7$Ei zT}e6-6lca@%ua8>V9g|~5v(M`%J3IQP8J>TY_CkydzEy_tal>rnT%ja1KQWXytk!a1nq4?;o9u%38)J)u*;*>8GhW7l%5=aNSvq0= zmKe|z&H+0;A_qS5BnBRW@z=D|?_naQ%2yu$LyPl^O62ViLxmXYJyKQDcS>$$ba_LzlGw zvGMH)1eXHI#{tL!mox8pnz9N`%n=Ew;{3|9^Xt2G4v*~`L8bGEx@8ffH$!v)A90Nb zW5yRY+S>}&V*tGb!__UdY@?t}Wz6#V)BC;%ji>QO<-!5`6ovk-kYS1Fl2`Ct8ofJ4 zHp(;U%K2q>^n2P_T6g@`KLS$L!*_EozXiKm+|pwppN^r-{|OOEqgT`i9C$nxjJXMq ziO}c$d1~t>LRTRP`jgm4bg02U$Wjtef`!;bA1{r%!S_3{)KSZpqFo23085y<5KIj+ zUi<>cJk9DfAXJdkLBuTUDLpB_pXS$NVP$&95 zC7QTT5ub`~_sY79tN{@p&3?Z=`frH=)a%u1)Yw$hr3W**2;v)pqH%YFL}|!STriCv->BVYw#RLkf=6B51nhuKK*h96 z&| z(ye}l2;PTHZ5-}H5D+48AT;_xESY%?)ov^Pto&#qK@LOxJm9QhCN<5~o$#F`c-hg4 z5&;}{t_VQ>w<>>j(S%-_b!0sek)iMW<1c09@Ai+ zo>8BQ=_aBXCHTJt_~Xpz!EM|fPzU^w!heumxfC1Hp%Y@AtnE)kGxHgv-S;u~i%*`< z+sAWTeEe!R-RIkAPx*r2NBsKRUiQ#GGiVM8H3B%8bY=l<7dQ%2`tC9-PbkYJU0@?v z--9tBKXRv_MoPSCJ92isN`;nY##-uY=|Ev?1^3qJc-uTyn?MeV^pf8QeOjNc2z2)iDl6==;ZRJf-_ zPFi;)A|g7%$tuO6+lH0>9~jn!DJV{*o)rB+%fB`qDdVMdR#3#vlGWQX$i{=HqN=X0 z?DBP0WtOtOlW(%>kVE?S3(*8!aWse>-=Q!rb(qQ9lIg*3T<>NEFOIp)W4o1KH2q-; zApkK!O_yh%{2#cv9xD5MBQw=Ix-9C;SD*KVLwCOI6?`)Em+St~Zrn)KNt8wkFio3* zYnAh|Iv2(52+>|O%wJoDFf`8k}}t7(2fKe3#@-Oua@q!0)KSu;2&`DGmE|@hUyB9}fx?E7eJFSEr3yJE4wxw`!BESMvfnETSXQHuqo8 z6%3#z7&$+HyvuQsG694P-61AcRA)kWY3a=O3t55@871!>x>F+aMms}P)t@|e0P&pi zy6J8v6^N13X`RTxC2{0{Oz@VvjUwtwMCam(UgkrWNM|Ixw#@=WllsG~ju)gcHhSH4 zOI2AzNF9~Sd5>B3c{7b_m_K#)3xO5_w!*+vplRe>&?3 zCw96)6Dg_?NlUjvT<&=SrmtdkzFj1y@SlH7LR&TS@ie<%R8rE8)xnf4RH{agA3T0M zv0?B3!j@sHLAst5yXHHZo9r`~t%iav;m)0{ls#i;DyP1OY?BZvu^Ol>3-0JE4OTYk zs5U?z4Ykd-Rs*YmW2c?m?qG@R%bwqkLTCfrbk4hXmc$z&773=Mxo2raCo%g$z~HRf ze}0r(Aewe`>UFQsaTB`N@e*y)V(W4BCk~uT*Gl4$3^NITL0%GK9k%5t&K0%>@4^yX z5RqSj1)~^6*QP1?%A^l3+_F!Xe_!&)4JQt^YOD5`HiZKHA7#yZ?=mrMp%a(8L zXArDH9yjAi!SK^K{ylw@9`+3(D^_vLzbHD5xWh-2G(FD8GtQ^@jd?8J@^Y{zyIOG>Gkc%^sUJ#F676ZL~dTAFIctVUT z957V02$B`d*iwC!)Z9EydsWD!b6u##XOf3WBG1w0jWnt-fo_v7KOg-VJhyHC=o|t3GvYNn4RP6dH)n*9Okr04;pJBig|OIF9iRVZ+qbn1Vk==vLLPq*E)qf zILZqanhtYl5$uVzRL0kryKG!l@H%AH3ABw~|6x<`Jc3%G+(wa8p|~ z3oZdQ#S9KWIlb|`mqH(KT*NMpwzMBG?(JJG_v-VxrEp*)=)lX4cFIS;7VAuW2r!)1 zBL)9%&1Urwvsus@ZdIn5s+B27g&6gspN&P zV{)MRaZAkS;Av)&^C+(Cc#vQ|()ACZ-x*SKl6Iz;CUBXgN}@Zyj=cJuem3Hb^W+aa zp!jpj0VxR_X2eEmHOfS_P9rgxCv*Hv`?QT$r^#3l^(9*nJM$a|^X4E}YI2UedgdSZ z_8w-vsnxh;lvT2rc)4vS!L8!%1E?0>nelI-ubwTVBM?NLVH=Z$oSX?EYXv0CP@Zsa zm#=zUFIk>q`&Y!T#cM+U`UhK`g(8M2{*u-~yLt2GWRkj6Pg~Eb*iW50RhS`QhC+N$ z;2cZE-Nx!rQls(pVNA4{+rD2&fj!Qxh;l1nEAR??OqqEE)GmJ=;`8%Xk58G(#x67T?wQEGz%^gjy ze|lsW`KkAb1CH}w7pqNC%eD{j^*O|a+-UQ?HQJn9y#7FCeo!SGPE#4lUA(wf#+`c) zOQ_~3r(3KjdHjl@g;|myuzsU;Be1;Hm;E&!7#4PJp1*Uet&{sn@ArWXIzNsujQ=G^ zn4GLRQdRcd!kJV!?2~T-2KTJ{(pxkmoP>3}o%GN#?a&w{5UL%SgaV`-14G)ntdkYk z2$1^UKTV>uY4bK>C&z@6TD&+ojP%GGz?ecZsB%Y@Tk+}z%MUPDq0?1}>s>dF2(JGmfy1c&HUZA3|M^NR=+%<|eD z934&YkZ%p!*^SPFYxUpcUz<%J zt_fo!wy0=gFpT+=N>5@oQPp)(C0(B9-1j`$tDLn@&B`#4&ZSIW^HJYYHOX(F%MV{i zCd!5h26tHX8ZuqQr;Xo-^xoeV)02>^VAXu1N!6B3hf+Z3jl5aLsevF%0O|`cq1M#K zeRf>M@NSquGQu&Eyh2TB$R(`yC-_Ws9%vwYO%AmEH5_Z_d3{ihB$7LqeVHfSDY(4z zZWVpu}H3FI5WH>ppzaIw7lD*N-&N#q{{vH zJ6*1MUvzCKtGIX_3b-3`)pqGif#*1%8qrt1#?Ef$wH^oMT{tGH@>pd{iu5&sf18f0 z>)7skrMDF+wb-jGY2x9Erm#nZGuu`9P73JqA#*1G3$*wbwP>yKlVf2+WhDTSk^t4S zFud2E;YekuZ(7KQ=Tw}6b`%5hm|+H|BUkPkvrBiZuD`bVHIRw|SA$wh8{Bp1=2~<= zz`nVF7es=B^GUXQZ2K`q`=J(V5gF4(uGd7ohh(`bEar9M?V;X%OKm=L?m^^?rdy&E zeKskJ{vV7^3K@Mjere=e-?i<3djqFUEher5e#jnH{1@W+BVwlnf(yp^Mzrm;#!QB2 zYDhPX!ZsiFQhe{YM{r!qs=8O@(6gUX)*vaI7e9i=^@5k;UHG-Q6cfRpvueK9(=%ZZ zuZi@mBpG8wpy3}^M4h~6xK-o5E{TJV2FTo|h>M_635d!oN?vXNH&xlDXLqf=x79EC z$VAKb@D6KT-n&RO=}^leGr}ND_ASR9&CXI0$~XvRnYbR2`IMgNxHtAm8Z{4rs&LZE z%DZ;~R=0AeJ`j5f@=md^s*?uo`(kUi)+u)KDd4KB6EFe0*%Blxf>tQBBHV7L zQ8sY^?59s(N46)g1Cn~ z>|X6W53op5eCvVSG_+6di|05+PUa}dw4q#j_-sZ^J02hGAF&^c9C-Qe(9wq>{N%gC zd!)l`q9&_)h2_c^iOf|Ws;l*JE3O*k$H2Kc1qDsb)BgK}2~#apt4g+cv8koF>xf!T zTDM>~y2lH-3!~;i-$q79Gp)Xrv@Nd|qG#W8Aod{^Z~ta(U^{ihPKPkXw)lN+Gx}LwR{+zcMj-#&V+gjEo-EW8eF-@8H_w z$9>d-{|ka%2V%b|M>50$&)Q6mG=K_=*f!{#DWkB-NCo3D0C^Q=7;lCj>D$X*K`NMu z4&?2#%lp8Xvb;K^f3y026T~58yYARpbev%jFy~uty-EAyz}A7aO@f=YYu8Tth9Y!? z3fjoK;W({~&^_sCX8nIwk&PQQ+GV!+Ka`~XrSwgMgzRMBk$?;7)&TNqT%lLQ5k8HR zfb^SW@Abmx3QCt5yejyII(!LHJW88|5CGv7;UE7!K68il@U{VCOPmav46dNXu;uEs z6@^DbkMy!S^kzkPMT}YQsV@aHXS?s*e7soKVRH2!yBumeYt84(@@sup_P6PGq4n{k z1#^qy9;W;97NbKBL`S$k&=^}-d*|l&Wf^4;Hcc!mjfyIZ^ZiPvMS}kB;YT$~Vr7D%#Uc7Ua%NkaB+WR^w|0P#`*>Lr_8kn3|BfLvzC(0z)O} z0$g zS@`BVf|$|#^`8VY3)M2;Z8vJc7#-z0l6_QV!6^Na5acZidWm^ogvZ)DEp9P6Y9w%cJW-Gdv)RA1--&wb)!)~ZnL%a%;J)ge8kc@sK>T^=ujaH7xmhYVF&m! z5ek#_J<0QFJn5QG<9_{21hct^l_P!WQB;Lt-0S{~CN=j`E}HJ(vNuANqL~rV1I$Kg zVg+W@yX2xEPZGf-)E4}IEo4>l8399+x;px4z5-22*cWO8^H=h;))IFpJy}}maC`3# z+}9P;n8>76Ae1RF1kBKG&~(*bt5}+c^R~D8qQV7kx^MZ&G|~>Pm0H|15yEY5Q+9FB z@U}9uyktq8@ur8iSuCVX5mAKnKBzY6>?YCN4om2$Hl}f%>drF~udDiRDL^CxL3R5k z*=a!wUuqLS4d4HLBGVdWD4EP6x&yj}G3S9}v)_V7YyCt&*QazG6Tvb{?a@g$D(TJ+ zQmQfoULbK6Pl-!A}|iGCBD@sU{y z*+B!_J!`~Yh~SI>W}bI7e?7nayM0p~PuFWC!q;hsYVh&}CPDJ}6YxUn4)dVe*l@|0 z-(5sqAgi0deR(IaN9;dI$1i9HpB=PaOW&68!61y&;kOQN72`&LzDmBlGWNr?O?k_@x!3YaN>hd6hz8#U*tou`bg+i z{`S?YYk=!CWK}bQyj*s~mgY6(=nMtP3!sweW)k4+Rp#>RG-=KMf3cAtmyTIGK@(*Y z&}gEbW*w{Mn@tpiWN}WX<5DkXUv?LcfmO2|*B^GK%#!jLo}Ve12^W`y&Tl4lL4qpD zjwtZ_0HeI#?ht5Z+H(srpTPh!q3w z_Z`LwblP~#d4xwqVa&I}N0gh*P*&_7-d0$)pt6e0gR`8XKCOs5oB#OU*DXU1jV&qD zDYSbq?R7!Zr?%%_-Jv59og5KehN|l98@KSMckbnQ9$z%OgO9COd)6q>0%~%- zm$aUIW zmBKO6+$FBE9OTvi^~st|;EVkI*EIzH(fIXzqxau=&+Y!mWU1o6U+~Yrsp9ej_1%{v7R{r@h}OwC%b3-kG$eYHrnb34YVN)6l7FJz%U~C zcwETG9$R~@yQ-SEXlO~X&PCgi^X3M(9dC5}S=U2l1@*@Um-XI!{5!Fx^PE`*O~NnQ z4olowY;d;OCbQw}$|}415{gvx(Fz`noT5E;|DGbVPREq>|GqGOHFMv--2z&`z_;aO zBk}}K^bgrC#0#)c`%kbMX0>+r&$n@XJTpA?+}$fjo-Lj5vSZ{HHa75wTe==>iSUZS zst=z(C*HpOhr7G`zpsG5nbH2A2a?AspjQC$x=*62cxlV@`X{)(4 z8LIW-*dlGM2hVw$adB~DzkU4tIoiTV>*Hs}i<(H21tDc?NG+=4HGt7cGcI*~H|d|_ zabmEFy&W1DT0vrG3^*3WqKG@7_C-TH+1y?j_OBma1noqB-!rDQx64?eJVxf2X_xaa z+A;K*cq!P!Uzy|Dw@)8TnCC`+H_@KyM12AMFbj6RFnkiN2T^H*tpMVifx=dZ2tW{< zyuEJ@Ej#1z+ou{TpGvci0=8-cb;$=tVEpiD#6ti`EXrEj{n|b8%cD4+@qGXWAM{}- z(us@MkmP6f^=EbO=ehnNMMBM*HR;;bracn&={3gXxwY#@UPm1u)emE8ifQOhR;*qv z!$rW>1}EReo>}nEYl&>3<2ez7+})yrz(_{iWsrMdZlw>mqL_y6LjVv!3ANjN@3N%} z7hd7Hm9IvBwrtrda}&c&zkZ$x-rU^3gX4+!kR0NpO3$06f-xa=^Svu5NyRS&_W|)~ z2MCAK$Ux0aD?+<|=6Gtb-bl`50~ObS@8I8MN?-I5!>AX{c6NH)sChbHm^j^@DoZ6g z24q9~W4+UFk9OGd@b5#fZ}+dPc>3Uhh6ms5BWX4r-6bDr6O9G%pblS|?1!r73U8># zhE0Db6SGb-Kqs35;U9#}r0B?}g@v_qeLgfj%~^~5AME?6pq?=dg?OPM467#N zXCOySz8+k2B5{Ev;B)%xPo_ruBSu<_utyzfDDpw#F>1T#?OIasu}a$9wy*yLtQ71x zNJ<`*GyCGU&C=5xWZ7#ab{sSt2Y~`H(QJjRoF>NxCJl#J`Nu0Tp{;X;zcHwDAq%pZMA9=t1yu3uG}32{zqvI+^^qTX>&ZJrI&;h7DO|9zW z+s4V)*)PO_IR5%;^p_Rh#|wrni94C70U{W5)XMkdlBwoP^8Y8{(1t;#>we}oY4a5$ zis04NkK>cCrfi}0ng zlHMy5X%17*%D)&men_iGqEx!|uG8!`|3IYrBUS9LP+bsP>w!HW8MhG|MO-6;1Q(Zd zp!Fh#a4<14cE{nAA0eFzSTkw$%1k*3o1GMY=IZ^pYJQrx{5!OEb#-AsEP!GxDY6f#%F@p({ZD5s!s9R;PPt#~9a&?`4G?zqOc&!j9KR5I+o2|EsJx z_ua`w`ZQqRk~An2*HhYbqtF*`Iy5b}=ZP2+-WDU5@>%m{nK%6>YAVTRbLTpNxB$44ST381z_>jo^TzKbvHtMj? z-x0;r7|8@B$=VkV&ZnNGB5rD@(eF$e0|8(e*<E60EoDXmKFMZ`hN6K9gC(5AG|`TNKr&qNA%pIzWw9jS17*3A=$rrJ6CR5I&eG zs{S{!8~!|Op80Ryd`mGQ5g*)UF_Ap7HPhIz_2J{=%|1s|Hay)QvdHhjczOatq>wMg zKuK&wDUH`(aR28emwt2ktYWVq)az9?^KUk@+a3}HUI3B?6wc9lM19v`lm7-f3AF~P z1@U=IDWT&t9x`^)X>Xps330g7Ui)s)rV5(TMe7O}8do-kqichrnBp7YRTqT41~m@- zc8z)Mex3*|E!%oj@9WbX6Yx(EmXtBQ;>ENJ#XO#{A+tB?L)!oR(;was>ra*{?CcJW zPT(#(5liJA-f-vjMzst2*D2MSG+b@;g#o>Lf4#gE`J0faGOnr1pu+sWPMvpUUZ#_U zYDe4H{tEI*-LbAMo)hY6;K(H=t!RY0Zji?12lv$CUH^YUU(5O*Yee(OMRtc#rkHiT zdKl4?Y6P{0&gM2hr)9h<a5g{9dKr#5?*+oV*x={|MK06i=5TN_PY&&DP zNhv8Ry?UJ+dDa;N7f4t?^&Lxv&R=)cBQP*)b%D%igEpj}f+kW27CGoaC5T6^P;dqN z%%3)S5!;SA7s*tRI^AirqPn_Ou(59Q8{FmBL}<9I+R;zp!B!yELUq&4_Gawi2*?|0lwbH5LWW*bG^4NAPh@W@c&`$&EtCBxAy;pO{PuAEYcuFgE2$V zKoXMJWlDotC_+Sr423A9B2g-{$~+aCq>@Y-GL<4Tp+V{QT=urlx$p1&=kGke_jjMO z_g0_J`~7;Yb**b%>so?2F^eG{ia!hToP4b#uO}uv+4a5q@7H=is!qJVuU%6#5A8_` z(|KFcD5H@y6t(iN|HMy0rJ&TPQI)F@$581gQ@surq3Ht|{t zvb8Ng|9neuMBC9jC^8`c_X9B#mIfAbO7@}ZKw1%YcGJri#SswKnqGr|E6Fb}k~avg z#Cc8NavwCVBcGlI&1}}_z9f>!fF>E<$K4Uhfh#XW za5wN0mk2|yd^&0X(Ta-hkGxP>;KIMw9+osl#s_862SkTP=_b(U+UfuOkha#>{;b+} z-Xw69co;&|i!oNSV7Z10*Dh6bR&DjA{e(?dr^e0p`IV>B{^(q&cJ!6L=H{q_{r@~V z$XGYJ)Tnaf$il9N?Uz@j1-n=DzMN!H)X)Dz#KNQR=k!03*e7B@0Ao-&`muZHTesou zt(adS>^;fyT)}2Bmog~0+&3mg$)N9jvIFW2|IufBuuh}Zv?U=&+3^O35zGtM;)~G2 z@y&3RmJk`A>45&*bpGnGvZi{8D7;XrxU-*vluuvU_^?^Mr{jM12WZ1_%2))EJ%~O# z=lv>J*W}2uyjS1HF=r?GS$f$IWhP6C0e+LrCByDlTBLtNup^_lXdz9zpMY9L zWcvRi++hrZDs00Np2U`ohL8=+o%xB)&g76P6cF8)Z#|>bpn)?4Ix|9T2V4yK{3SId zWQhB)m>jGaj$)H+hMsQ$%GEn6vOgJ(R(;f z=Fa93di2I13-SuSM_gFBVw5A@4pADYd@Nh|w+^XlinB8@n~CB%=g$1No%p-em~x>} zOvrc7JC=*TzjF{<@W0uIW*79BE#>NNY_4^4m`-N|Ae-kWR;)+Ey`9Ei{VnMeoSn1w zo$In)!(g|yCbAwg^xD}`Xwxi$Q^FIsg?7TEJw3T_dD{Lt(P}8>-yn(%ctjD zGC4&jA_^Ktyzc1ex+UuM&w*8cS^M?oq{N$yACVsD{RGqTs;jf~YIIz!GSSzc#E_8h z;xeT#ON4*^{4u}Bq;sFHx*;# z)SEC(VCk39_MZ!ys&ZBXVa`r6?6RZ!V0@y@SAnQseWpQ^GBO4 zncI3;ZuPgZ-Y=RzDO`KK%`k%(UiF)eb>5=wel4%r9P7jFHOE9j^)vCcB4G<;5rfGn zl{~m=HgDw)(lPj!o}7O(sL$^xru_M9@39Td%S<%?#o|@F@K~o>t9eP;;K#ta z4Vx`c>nP9ufu__3tg`j{{EWqpkJz&C_kZknb^FZ%LN=xX8v$Y6MLi>4Je11$i7R$f z9qxWCvPWiu77(^_9{u;nh~LpkC7FzjbJDHWEdrZw^(U}#~HW$@n1|JNgN_?+f# z@XcsUexOcFnwwj0wU^DWXR%4wpu}sVc6{27enaB@y^k{l^NEcEG?Y`}tC(19MX*vw zu3X%Q%nG!8aq{ecVW|MOS#M*q#Y{r>wYwu||< zt5p9zsZIV^+xc*EGr#fHn$5vrow=J29(WHtJFd|3W#NC*B7gHEx*dl7{?+lTl~W=E zxlnSS4>mDVpYVeotR>Ul!}8`JM;Q4JpYrz~bWZMP#nz=7&Z6ChW}2z9Cg*D3j7$5? z>RQg-S0{cZErWhnE}B>y_3LNu-YXD|w}of@hy(NOhpEziVsiiKz){AUnwz_f$XPxo z!sq_e6A)qt!=s<21RQ=xhcCy`bzh#GlQ;XXF=15ciuxh$# zDAp10Xb-s6ESTeF)pUDK%lp56opOq?ZlC;vo3tZK`&B-gr=T-Lx7hz?p{0^@V3#xP z=i7B0wy5JUW#ioH>VaoAJ8tYyc>VtUK9ADAJNn{>x;P3zDto?tVkAKP4-#?YV z`bEEs?62VGV68d!)YB7Jr*6FeW;J49IY8a2m77a->wd(cf;T z`vy}dd&+P~aB5+p+wAJ!T)O^aqrZ82rDng+;70TI8XJe7V)i11bT1=(L}{U(h$H5n z_J2Pc<%W1>#bbN~smvn4mI#jwPv~GLfF4443apEQxvp`@Y?y*jJuK}R8(bi0Ol@K; z+f{XqeIPtHL^_=(--q{szcO^N(dzfNRWE?IkIYAa_8KQ*pkz<6Yi)zJQw*jdH!PeM zsi}QA4gD(0UvQjMzhWPT*x{Qo3JYa3T2kmj0??X}DT_fH#h#E5^a!?4X7A+_h3u+V zx30K@BQHw@HTFYG!wBI^UWNcb`dc!7CbL0!Mno3Gr8EGt{in*`dZzSO%ON|`%H`i9 zlN+j>Qs?<)?cUek(lK^A*1Ig|Qv~KpN zo`G5H@Ae=xshmEEeth}D*z z6n$1(3~VoRf=)iqs4IQ%Gew40pXeNdAT zg(ts$WB#*OO%*#2uw(V#x^=5pC^=T9NKuKgsOwhMK*~!)JMr4?LaJgT;1TQb`>|o{h5EkgG0nv|6bvEy57-ukc_ZqPQZFEsrLGyg3 zF-}W7X!M8Vyu4lfeAyr)vx=9FT(SkU%Zd&p*bM3U&bP=gdxNr>G8U*2JownPjy;bdgG1;0Nn z&o_5P9SU7G;aCj>V;lfg);w|#WP|}B0|Je_*+2f%hl}sS2|0k;5e@rpbXlc*UXF1I zb>fE)9|$%p7VM^*Vt^gpKT{>*A1gHSojawMeGxZ2jYB>s1s^XmJG1As8|E+~vWN5O z)2B2@np5qGL`qm?hWI?UJ0@B&A>cw`8F4zBQUa^B)2Z)CSE9nETGOClVCkAAc4r;JC)IkY5qY>#M0WY|tRmF=-_nR-IKuDcR14LS@FN{&=;0U;fyLe$tB< zLpZ(S$|0qe%#ty1{|nu`Kfnz!|C4<;#k$s`XZZa3fW0g4ul8zW%(Ex~Nluj%!`CJT&Ytzoz&x8}U z3Y*bpIJvkqf047n(r&kjPoj|#lTyp1?*&t^TU7{;Z_6os&~k~9J+CRGh9~28a2gN~ zf0>*;^;Pty417LW@m0+P-YBY37yeIXw;{l|#ib5+uKU}oF$zV@VY1PG%a+D6^Z>gT zac>w5D)s#z!Vu6Z5SMU@VX<1_Pp&1))v?f1K6x3%DPAG;Rl0aPQg*`xuY-$QfIA>f z*qQJlz90WGkKBr=iLcX=&dOFAT^tE5%z2&WEkTWfeLAUVG!3&~azj1smM`kO}`b9YZ<+`Tc#0Z7_~t2Ty!bU&tX9zY`u=GDOWU z`xrW2BuBf*82fD6BeH#gq@03oMo1`Phu)jasroi2r;o%lJ%=SC5*k8q@{l$enT&R< zG=orhW!?L3y0VD_mIVFf<`FlKdLK~B&Ey}+i{mN@`!2fo1Eazp&8{|Kt*N}5%CECf z+Gc|YXeSlXWBQnyiz5EW`@hT+^HD%4Sn}h1zxwbFS;BoC@>do~(SS>S_^=}CeRXxU z!+8%XJzbGbs?Nnyd^BiIx?D!H#EeD~T>jbc@D5-qQE^Ey<6jp?H=fQ=5R6Ia)ZeNQ zkW45DvRMIVT6B3ZOtpl7kMJy2Xw)80eGCa00!J$M2PQ=M!&39e=9G@4Heo#}=JXWcaH0qZY>ntV z>E3rSm|y@6Song3t~$4nX-%#Ugry7YAkVZZ(UZy2pQ%4u27bE8Vpe$CV%}!4UL%Ln zhPA-VeYG|e(WC-*cC_+-^9QB_w3^VS2fN zoJ9U%AvjHv7x4q}*UWn>#0QK^!mm{Yeo_E*0p<0s`H^Ijj-3jmga zWXPlW@v^;0#BnBDRQeHlrtD(kYt{oL3c4Wb@Q_kn4RZ-hSX}T$YIBXd(8?KaG^$y} zK#HyX1e4+h&SH+mcN233zFUv{YDWg}Xdnom^rz+qSb*J0Elx4W36<$IWfucw^nk_x z^H+ynlrraOGoT>jo1|7T57r2#HJV|EcWi&*@FZ7-o<=Dq zUoTaUwjRScz2rfV4U41xL^kQnuy^sHa_Os}iE#K^6^(@H*`;F~9G3OS8)mvl{QP`F zw{;sM)=sk6fSVVH$c8pQ$hIBmRg3cG#jnf_(G zM81;BW&DWoWQ=Iq?{oq?u42yi7CVcjEkXX5Pt~|>FByZUM~ETUC_PDSXey$)0$Rnv)*?cg9J1S#DbX?S#!l}Xv z1Ve}qn@$Xqa)ztVli9%kog`@5DCMF3h(X*eO*RUPTFR7R?0L^+)82 zP3EmAODL)q(zYhBT_d9y_ba*y2#{C)w^zmjG+H(-Rdl~$gh5uDdAw6;5N*B=$9Uf}uu{sodf-q@O}@)aBO5s3(7~=iCSS(rhRD z*yA!mn67zOuHigrWmQPCE<-<2b1^;J=nCyp)6*nIX{rbG1U>YVO0{nj$6Or9^;<+$<%XLIJNvOneBWV`!Sm(pGNP zI5%@+3D+cr7nfglUB%T5LI6>kjHQI|&;=<<%%ohExu=0%9_L~P5#>Ab^=DAC;zi@a zf;?JhB^=X(Z|8GW+TbVyB8jF90eY`--AKhf+0nWpgcLeJ_ zjBg?~B^2fy++49$``A$-pV% ziz@^d87t?~r{Z0Sphy9B%Zyh}c2?tw#UKq3sLE4d_f6^5Ik6h=-n}a?iH#8ZJX&?K z(n(z7xVE`W7?&f z#5c6!wdw#7WRw1+uBIvBio`?UR#tW5vTrvc%kf^EGiJaD$G&3hs&e{=P2H~XRTU@g zSSM}q-njS54J>)&Uc_OOOD~6Uj9Zh6^bUNHMcRzUeqCNoE?z}VCxg;>=dB74pL}pb zhU>EV4@t@b`(d#P`NZPa%apALMV4s9h?+8%$fJ-BCv{==q|2Sr$HBeDkt&7zAv%B( zd!{qD>>;#7rL{T09D9wZpP1OVThL@GxXyRmh|Y$2nd~h z@n_&$7bEo)OIz2c{^}M4CwyHtyTsjmTEY`z(Y1jc zVTU$VUZAb~=|kyQMzN(&&w&shdY0#Gql)2rwd%IN4OZhae*LEdf1_Q=p&j7tKsm;^=yH>OfZl@1ZMueeR9+g#YrjpYEX1 zNqvH+I3BGw%6$~kMR(dPAF_v}GzBDrYspBZ=uG%B>F{QuIEYAqfnE^0nybr5<(;h^ zMSt24P)DelK@1Qn0q1Bt6q`8UG465J^lLf)!W)S zHGMz7C1ztip=-CB!77`il`D>X`0F>~az^sKj*eO7o5Rax z`B@{w`|qU1AClJr*H84Lz8AEsaCWo7*h&Tj=|194U8=RcmRYx6RVQSAtFoHFQtRpD zR&tNQO|lk+=O0VOFAfUemh^mMYA+V<1oHzwG1yGf7z$k}ZB8+@xEwf{8c)C-);Kf+ z4p5nhcRYpa`p?$G&8gm1VaD~fyir*C0aj(3bJ?S|TZSTv&Kg@p^7kXUECj-$3A;f! zVC+?P1u@IK;!`n~a7776=|GZ65>ze!cpz?EhgSUdo`e6v> zN>Z`D6#gM%&xdkupw#!IGD2`N7$$M%q*#h=0K2eoAh%SZd8m34FXVhu3oPL*5fJOH zLcWVdU!l!gH#aw7fMfw5>g?w<8b$fVw}+5y!SYM`LIwO58FD9mo@AFESf};j2(ENI zW(y2;IZY}4_;EC`M@%BX1FoFYX2CCkP0BLnDK#3hn19$$G4UqJG-WUp7}0O}cU^hQ zgcW|l$g%s=JrG1W9sl#cRFNlLZ`}A(V^3rF4pLuIp7ed@&;K}j{Dk{@K4o;=jm2dRtl{_(<$qoqbNzqftYE?$%=Rqn>$nNi$x0ofR}Aj zrTzJ6L}k%`z+d7g~W~zBZ-nDVnJK- zf|sHfv0=^-1l$ppBJ@hmoowjV&x26G(V^&R_g$%LwB^6z7Gl%34j~G*d1ILezELG(-uH$y_rfRh8KfW zi(kfZqb+U)CW2W-v{vrDCp=t+ z!LzfwXUSw5*!EF3C3_K1YV28Om;1wJvsLBqA@Pc3+C#Dwsfr6Ips+mK%J40&4`S^P z0p2h#`|y=3S8Q4?#doLs-U1Cn$!s9cFSKc7JhSu*Df%{g-LSs%=jDDKtlAU%3T~8a zmw}%4(iacwLZ>kD~|2xW7aKm!-*qkz*Q zs1;#Sv77F`*Z92)NZ*nXZMePqj$U8Y2GN%FkN=%iF z(cqRgj5$c@BfK<&&Qj-*qM$x>@3sgM^BP_qt_u-FU;~C;<;N1NhtooHRZXz(YWQ@k z2=@3}wOSi{_XE;3KK+w$Q*7l(t=&g&E9Dn`d|8twvPlBa9c~O+dNecxpz+tRHcSFO z_Oc~Do?Bvb&r);+*mE7iNlHs+>y?YU?~$`-fVb5d+$FGzb*PX&FyT^I!FG3sU4y3R zarX>lP7FV#_>BpqIR1CqmqvGx{VJTYXu^nae008R>C%U}Ms0kG zInG;7Jy7=msXu2B@@cP3Rpn-W6aF4uRagaE5a#pmXpYi;q_Xs?!k{I@gMw(DM#2#& zjAFk4*dm%+z>UwCXa7!+tr)Ixe51?-Nbi;if~Wp9Ch>=JunzGxLVRBTnaME{LP&x7 z_2+BVkI=*Ns!_hl=L3wS^Mxd0kX2B!DT+Orw-rGQ zaocF|yVk&&85~FzE#*P9Yk-Z2=978tqoCMUFUe?1r6C8$ab?9i&emsIxrkHS2X1KL zvSMy~d|nDqR*L+Bdf^4%<<~k%u*h~gDr`cVb$V%(AkC6zNl9LBI`K#_BRphxNLlR4 zJ)iz$a$$c=L2VhzqArOAcklrdILwe0Y0%EN_BPpBwG+6+zLV7`Pvq^9tc7R*sF*u= z?D2k-o;HTFY`S8e^y_H={y6HtfGh~Klu0s$M0y=Irh>U5@#~V5X#Z#>0&7nkzA0SOwx@Xly$l zGCnn75rY+46m9YbxB)#~uQ!!YeDdT;udo-idd1Ur9 zap$?oEo2C+{H5Kl`(H?eM5n2VDu4X(M*)keI5ukD&kX#G^HL`(z1yb@x36zHP3n_q-gi$?Ue!Am%ag^9A2xD$wYVS(zMSF|dc5s#xu zpllu6$~Q6~a`J6WiGR|*WK2^0cK~Xys|TtyIOMDI8(J!N&>r%xIZLpzVH0F?A*+^l zcrEisC2)&`FFrm*FAif)tC6#M|L+;e?ms_;3|ihts!cHcB9^ZZD1vw}{J;*!SFT;N zMmkmGuDbj9Y>-bcw?F217&6dD&=-$oE|yqX6n_Jax=_Wxys>i=D8{$VTAG0(AE&V` z)fEl^9HxLWjm9xp_D{E1BVHhn!!0a#DJ+tmiU>J;-y~;R2ROvtBtmSeTrwj4&P%cK~@6D zkD`W@-V#NkEIz|-b2rCC9Ll)t%VnJnf%C{Tz|VKaM`;S*0?gsFZp|S;A*e|B78@(sHA&-6RB-HJoIk3vWyOwd zdYaxP%YG|11Di59?#pyJ$z>=PTl6dNZ?Xn7(p*VMXH+4w;10G-dcg3A&ri<#g0R{5 zustifTOZffIaWlsi4vyn(z5P9rHqMwCc%eBFabjW>=;X|QW@k(8k1pJ9Qf$ZX3>NY z!k9u=j7A(6xpar2KjrpV1=m^v$G6;z9_Rjzs`8tfc4W`4j$n2-dwE+Q;8dg$i z!&d|PoXHaLhnJaJdORThh=~vXtkQK_5Xb@FSIk6ugX?MdkMNr4RSZfHjSd8I$AIwcpINJXJY1NcG6K~ihZJdAyOIJ>>t&0vF0;N7aYhV zn^r{5N-T++dR3A6zijR^g`LV|Rouh?QSxu>k5Q97qd@ZFL(K^hqd}kLq$Ig1L_hI3 zA|Pxa+4ZD+vQe5Z-wxUaY#gTZaOH98(@&HXWyZU&UVQlikJC*wMhuABh&N>Q0FFu) zzT=fgFeA#*0rrO;`9ou5nB7N<8ncME&YBz)INkzTN5D_sXb*P8gk18I4%QG{4wwGbGhD{L@)FM8y%f4pV|t@E+RC+e1BT|UOWu=X(BQM@J5Qpiv9^?vD1thWmBeYL!l%_#j9-k4zf7< z9?T@$-Czo8OOKNWS(!U)s@c39?JycjljV@?WY~mWid$G2d03CC!i6TA$V>>GKBp=r z)6QaCj&^}2Xh4`j&cGdhmT+;yp=l;F7kd4tRG1J2 z_i>EHev-FHBjtwv_GC^u%#Ft1_IeH)0WfME2QH;HjjC|ka$rr3<_0kp;>Y+Jx!vPg z*SgMwI#>i}DYNDP9QWd^Hi(5xf%pp865iJOAxTd#2Ut#yB%+twAkPKuLqRd~KiVki z@j<3yH6_ABlC>;vK~C`b?@|TIMbt{<*kl01swMZ5P!W#g)eFH-!%LPO_WJ8UH;4h& z(QdLB7YFMlKE8ajBC)d~ztaZAT|Y-(rvP;2QF2>@!;wk46Up+Y5BXrNKB8io@BI&B zDOw8zC!ark>V&ls5%0Qpc9TN|k7yvQWEg(INS{lzlm+dvJ`30`iv+|OX#RZFH%2q_ z&l%8jJ;2|9WT;2Lm07a-KXm8AN^rsiKSOHOzLo9m*OFxIw9@1a^V9^`fVZ&==ZSE8m917*Y|B1mUAENg6kxBPR>=$^nv1Rz17Oo^iVSKRKh!8l5B z0)x({dH%abk97J5=_MDJV~&hFZf4t5Xfi|U(fSqB&ocE=v;PSi8xA@VmyuldgKYu? zB%PII*_JE=Knzn%<5lJ_Ro|P?cP_82WKn`~n0Iq$I`vmB`tBP2Owpaxy^BI!zdz5^cB7d1;!Xeh0=o{ zZYGzxdPjp*w;&-Qg{aA`b4;vSn^%SuY9aD1k--!7jnJ(U0j@FZ1ivJmD7}EIu7>gK z3YJ0@*__!iYJ_B#p8y!wTeTmtcc2mHrI(5E*Pl(5%Y3#cgQS}m-wn`nOngcBX1|tY z4Y^>F1RxPQ5>ywe9njbr1Qbkz8IUOhN>)u^AjnZO>%ICiVcCUPB()H^WwEA|SO~q& z(;x#=h{Lu5vwPwI3(W9C3&2%z;TH?Rbmd|LJ9rt$u=kg(9GryPL9r;hO%auTO#Bf* z4@maf5Gw$O#T;`PSrCmpP>S26@}_hzWER@D{U{WW2Zk|Q=d-{O#5;{+EqW9FerRk9 zj#6JJzg}8zRkUqij@WB^k^quR9fy94h%$*L2JfUNRt#ABxs4jtFp$k#tmGKXV-;LP zLUkd~z3_iA7XJNejmyWQ$&oiY*3^WxX}>MVP~XPR&hFiZfYZy}{>qy$V7$qN<4-Q1 zd3$_v>j&ZcLZ-(5(WmRwRg2QAem-CFaaZf9_2-mb+Pk4%$npzB53f*0doh=tP?VOI=DTiP zJc34tQ#+Y)_J6J3wDuy8D6w#(@6hyv)0&)gxi1_(;H(t#INKd_-m$~7`HZ$(G&9|X znVZYDW~2{YF6MRJX(eQ~om~s*-H>ghG=(}bpu7vW*w4e+Ue_7s(5g>vtBxIkAS=${ z1~c+Q^?d`P(?3uC$E8ll^_?A+Osq$eKUvecUj(1SToLi-4+L|IP9OSt*v z#}~&gefk9RQN=-K^XAPD=$sKBv^sZQ&AUUuwI1FU7&UNZmHm;f25M?+pa`W0PlViI zeQtk4))vWjZH~Cg;DGttH*VV0MOSxoTwI*pTdU*Cw{=zNfyf0qEYL@`AJSLa?w5?( z=FXNib!0f6ts&y&0c?n#0HAiqO{spvhJ~PZal?=`5uAnX=iAS6LF*{O9|UT*7-fa1 z8ZLDht5sQ`CRTcF{6p$KYqD(FGXCIJ#*0_;1@WIYW6t*mpE)ie!5Nx^I&3pplycP+ z4qhSd)G2vnP-0!oS{ZK{n#>=vw6xqu0?GLPCv#mQ)+LG*J>z7(LXPQ60zma!2kjA?XqWO1*6*2mZMSBYaPRhF}}vuwT**#-*Edpu7$7+!h^6rm4SM!0qy;8h2v_kO z_SD(YX^rQJc`*aw|URcAg zD4{J+oYK@WSy^?RCskB)C(PU0@5qrORZHycTXXvI*)+*<)M#G&_1m|idFFC61JbEm zH-C}}W^mV#IJpK|-7dd~H0?99Kd4y7x}Bg{9#yS_A$V08&Hr07dl4ggCLfH^TX+0N zW>ROhZ*zj~d~~$7wmzJP9w-#ywk$qTfcGha_9IlOnf;|7Z5wLcDfqtn-WF|wm3@4C z*p<4Ki6ZW`!ohp9DL_WTA{1pD;|z;y1HajbekC9#iqz?GzyZ-y?iy~hle4qjfqW*ZV1 zo!P^>H?At80ckJNyBZ!4G16*6vr`ThdBg1{m+4FnD9vh6yRfkE^ps(Iw>HS$q&^^1 zio^whsF*&LtM~@!u%C04uV}AIM!G~lxB8=e+h}~!c4M13U3>~|(_$cBJ zGFkIBZTzgFBf%k5w+ib_hj@DrWTwpHt2;xJTn*{ zW@o0P_)-`|jX%O^u~={Vf}5q_=NxiCZ8i_LMT5G*!NDP6VahFArqVrY!?sJ}GLi_X z$9k4`0UbwTiy>=#jKzDC&n2W_TD^^YbVD^yc4^ zaePzyp@ze}K7A{|{!kIi;Q*zgq9P6w{32OyB6y77k92(2`uCb0*2Zi&^(?ljs-u7zDx;K%I=wn0^fWCx`=^rs0`BO&_j1m<9kmU+(S=nw2!w&HJg0Eck zgkihtZT~qk&+31m07BHKie`!i%Hd08YaH;QoD@DSFyy7Vv#V3eAB$;F+@cw3Yc5y8+VA7EOXteH^|hmxwf zib@*W>Q+-+3s} zhzSwWv&Cgga|??M_ao!SkN@(PmpgFT*ZQk?wK9Ed>|MSJAE35d*7Go*(;NWxczUR@ zldvy0q)XSX8woookvin(=L?nLWT#Y zTO@g^mR#zPMlPc?WIzL$#KRd9ZM)z0kQCTNt^*wC4Cm|}RvH|wC`f`!%gX`)gfapOD$BoU0$Wldg+jI?68qw1zx(Z!q2A@6R@1_yiER$IQXzg(mxuA{-U9srM>4cJ+zRl9OxGcc$qQc)aJ$v!hUqzO(E__pDch{o;iST?pn1%-Yy2 z9`j=oRkG>gP7NA2-sz}^2n@`JqoYi7W=86{H4ovJy#hl?V}Z8~8r< zv%20~DcLK@M^y-0VvH}5j0_ivJM(A6P~_!{vfW z)1CMPatboip4F=@Q-}Vxhjyek0q)VG8miK4wg?5ftKi)g3lgHR1@0PTn`cK92r@PW`Eu7nUY zO0qDDsXXShO%3~6@nb+>_KDu-b(aQCN(K+ZESuc+{AlS%<3f~e90^{&EjSmVQvGbp z)~z@43&#HybxN9Qxw*MS+0g-G@6YU&G0BR2zZIm~ZKr=oNNb{Jr-mtp{wJ*cLPKTM zqjcj3M}}0A`XLQ7O<))ch(pp%gHRDsL3rJvYxK+(Ejp$9^&&SpM zNAC4EdatgozJQ@cO0iW#qpng&N&uQUQ|4E_AT7BR1#rQrCDcN@B7F?nq3iJFwu-%RNnurjwh)iL}E*gBTL_jpDd_t z29_Ci$WAVyaMQ3qNc|+>PfGL-rP?ii(5E5LeEG1bTzqosjM_WUZ}sY0T&!EeAMDvK z!xmtw$lRH)sp@$fnYM#SkPHC3(*^6*wlSfN>bY*0E?vH2_o!-Smh4({#I74mzCQ*t z)NI?sTM(N9ST-R~lW+c|-(W#tBqK>zP;WD&;mP$CT{HwzGqrhF`>y4*f}fz8w2~9} zFH*;OJ1YMV$X`CHe00MUQ&ZCiXWZH$>XC(HqA3&ko3dYY(M@{4?f+IdXz`3Ik863! z23|(C=2rTC0mB4CdXx5q;+6TLMd|LSk_4IOH2g43L6?^_9aG z&;A0DibM{y=rnceU@T8X)gTf_hR;adYY_`N^A8Z3ja51;{S0CP3oZiBj#b_O%#?-0 z3Vz#na5!G^kOxOD-`+xLIslqzRKiL%Y2|Yg#ay#w-hAQ)Qk<>;ap!;9ZwN~S3{olV zeDUL(qew@YL9De3?I@?Rp0q0=rpN==9y|!)BRd2vy?f8|dREqEx~9@VrHP(HdApPY zDl9VYYKJBz^KC464+eevz9N_9v$xh^MbN|$t7y|hcA7tq*>o*uehmav$ZeGJn^ZcM zjt0f|%Ws}rrdr>C9#c5T?4+)%*ci#Uehr5i&PJ_UH>)Wzv9ZHI z6jH-e%HPxZN%y9A?%a{03F(}4RVdgktgRc-5pLt3LNl}+*bM(qG{FhC zX6G5TAJP%x7%GVU-LZhiW5$e;edlp;`h2<&ntQ?X(q^Pko1*sG@@_mSvj>oL9r200 zeJ5k2X**t28Ur-o6nD5iSLZ*6clr(?gQRh<(_~_q=D>k_Xrf0?;FKE-P8rd_V=shy z!&Mw}qltlk1w9Vo0`rYo+_}ZiPl*ObL$YN?aC%yr2SMXna&j%#zDfM}&p>uL09YBS z?b5T4>qM7yKR1iC*#O%=;ffFipjNi)CKVHz^bh3->u0>#!-b{rAPM~%O(nuo`ern5 z0blM6Ehi%dB0=J6%vicvr$vL6N`+ z-9A8V@uEd9dDSv>AoV&tA&mxd^-CZ>1jQIs0KywksBuN}mR0kQKq!8|FIkvEA{HGt zbR}#~2iD|8#>S@0rc5rGEJlH%4tkx=ha@0Z{9N;~O!B0iyvKorXrX4zrLy;JVaUOlh% zpDd$a;`=K~VHyO##(-J4&gF`J+q&CzBkr@>$5LutL@w$Mb-JqeUxxwTI+*tYL`A@& zo=*UEO~ve-wDP*7Bp2omW;N*D(y?7zP~+Ob_`$Gkp$>+;d`X=tB159rv^f(`nsOIG zXoKh%$UYoNvlIyu@A!fuSE3Lq-k`d2+q$-#foYgezz-@=yu!7tJAK+S=FzyWEf`yF z0Ih&?(M`$}hLFU6Qt-4W`rwI%2DHzB{r8wVaeGk9&W8toI{hGF+9vPI6Hs)v@*EMc zWy>^5>MsvI!Kpcs105=j-@JO2j)a~b>=h_6@c%T2*VAXuwnE=9^WNc>P;&B7DL)H~ zic*O%qI_xBXYRJ7`4jBz222sEs^=%YlT_*_tU*fMbcNDy z()$;%u4-5@I;TO~wrvwg_pUb^k+`%PtXm^pjplyHyft>4cjrwJ@Xexfr_P>T+5197 zmz3005pdP>nQNg?OF?0{`4Br(Gc##MgO&3p%;QFamKAk$bPzQDa40w=J{Ta}RVf>z zL^_C!)q3Q}lkX1)!G=@%W^$Wx*0DZw6n(Vh3>sFEcHKC?>9=nyj~Fo`D?2+rF7C>; zYl{32qkyi*`dJO%(Mc)4&YC^4?T(tOZnSL%4=!O+4mVH|mI7L(5ohNvU3yM8@eP@> zn|p|_dcU!U?9hR>B${p~AP18++l&A7)Q|Ha^!Nro_snrW$b)`%J=GKye34%4) zaaX36#y1DSiGT~200vUe>VE6o9m0&?6bt<$ zR!``*gxba+0cU}UzJ*0IrqkUxzMo#q~OIs#2`3aI0$hFaA3((E0O#>!sw`jl@m%J@oV}-(pnUdOzntY?U zIE&VhA3%q==_i>Gt7-MeOAcHR1kS0;K#LMPn+h=yKEgNG|ZvTX3HeF zBGwj{Pm$pPggLJ-`8XN*CxmI`%AuhQ&N&Ul)G{`nvhhsa|5>*xUY${7vZNkMNsqBK zq6O_$1s+}(y-i!U4vV%$lsr_yLH!I9l@E75=f}2g+t%65E!@Sur{i{TRNVbUj3!orR-qe5%Tu$;_k1@+ViWtOAiRrfW?$ z*nWP8+-IG);0H1b8@f*2@579D78MQ~n55BCpuJ#7KpYrAfzhDQLrd@N5WKi$s2NSm zo_iV%U}%C&jnt#x$T8yx&{DY*VI=bt-D~+p8Z2D6kWd&L({L0KL$;HmeoanJ?lSJ{ z98RkEc5Hi{c3=xpN~V{L5_9nSySJG4&M(a1gB1B$OdfOOp2&#dh$xN&4|&#Xp)pcR zsH}IM!_{XxJF9IvLN`fVkN=*67WZe)F#UH8+Cv~YY0UuYEcoTJl{cY(c6PRmCrkeo z-iD5$l;Xn-Or3XhRBv!Iq5t_gz;Igm=NTX3f;_x%e1u}B(-Xqq^>>~&Z5@>Zm8tpA zp^AQyLw8!OyL@>N+3h8HW6_8FHoQvlu3Nj-ENspDcT=65uJQzC=0?^<94`KNVv0q- zmFJQ#E>x zwQJWV-0CFtIIj$`y;n4-s0ilH-_2c;!hwb!cT~we=J`d`fpt+^HC}~yFbqk|^xpyhPWXlq@H}i_HeGwdsjkTMU;rS<0$mghl-8N9W>Ekg=rFXr`mQi{);9+IU61S#-gBB7Bof{)l8wo{9zQ!PDN!XRHq zgt8c`MIazpQP$U8z94f9wMcoB!}_LE0LXAIY?KUB7(LyJj0PpcC>o$L1||>E;>>@IwWtx}2t zFnq^6lW*U?iL`XU;&%$lbr(Il*&*nPm@=3T8y0oqM8v$y!vhNj%F1e2SJzoy(1#qN zXo~uSJWJ=68OL-kI==_)teWem*Eu(^j^$||++a0_fKWjgE&48QR6NOHr9m%bX|G;iKb&~_GZ)R!h0!$2e_6k1^} zD=v}?w!NMgdb{%y2rrrGrGhh{cz|g;`D6gD+07PGQCRsY=E zAtT9xmRtPUvw4+IV^eP3YO14iMDLc)|315Kx0fz4O&UYjAuJCC&?@T&uhOmc(b3M z6WS{wbAk1I*Hf-Hb&M?R?L)lR_M>y)aD15Z#+`V{y!9A6cC3T>S%9kU*)B~jh>V-as9Paz$2{tP<#qc4IL0;K6><4K9tO#iSY{vVv(|$bX_Sy zN%(g*yJc_WbrpiS>5CqmaQg6lLu6=IZr=`i{ra`_fYlBYC%R0VX5sB!<@40ST6xcb zMS(lor?hCWuIfCs)QuBb7RSbm{Q^H(oB{xre+d0SVj1%Gz(VR>p6=*x7hZM>9;+D7 z7oB#bW5y8O=)qanuh)mLs;JqaNEyCNybkF1h>8Z-tH~P$gJlH9J-qttnd9PH)?#r6 zk5f!3A^DX3+JZ&sKc0Eq!rSN{>AcclI3vc|CIr7_YP7!84#={g{{{3gQyMaMa<&Q_ zBix)A;|1$E?v1+ETU+t3$IJ5)rj4Lu&G2?gZmvQ4qZaiOJtHF{TN^n0>Nc6kP&-^r zmD)`w8Vo`ZB=nmLD~D*Ob9#!C^n6oq-P#7K{lbfGf26N=&|ux@wXN%~e(*}RTy5M~ zUqogXFD}1e!oQ^Ap7cM#!oTKYMxs1Ah%;4HV{f6hg<5myb7=VkPOHZ$RW1x{(`S;Vq*cDij7{%% zyZna zxW4PC(xF550lDC$p);<=(2F4=B7!u1m0?Cz;BZ&0P{QvI_uIR-BcIlVXEp2lxcxTQ zM_D7$&9bX*u+DVxl?(L2f-B4C5AkR^LT|dg@V$xvQedMA-t#!F)pKeLp)l z%YaD_!|k%QSKAZmw@v8uN5;Ag?Yhp?GH?iVl19TAcJAC+rof;Xq)ZahJ70?)p=45& zeN4wpJ6r@kD`TnfU1N{}k|jrh8eYzcdH$GW))(EfFAOyEdFB9%laphiVc{8Nvg`Hh znZZ3K-{62JJbO5Q$EQyV9O}eo$B&M-HDCr(fm0VfZmxFIs>kQ>ouJ1sl`sCK>;-}@ z@9&ET4H_gG4W2QOe}Mgj2?Z}-ra=69RCU%MX`|q&e}%zYk5luMGTdz*pBt*Yb3~F! z-je4v{W-x$X;kj>H*aoGC<(C#3V#;f&-!tlYAAY{sV1I@h3V-D*5I6@w)Ag!25M-=v>rRzu+Q-zDgZe*}(BKDZ1Z2 z;&6<={r0^-gM*Pkg+MPbU*U-++!RK1`}w!7=F`oMj52mDs9WAKx7hcq2E`4AFO+)2 zr8oPvWV^&mzf&><&RvuO9H4?TNvy+npbDtbiJ}eYOXeOKHf`eR+0l0vTYjGWNVe0W z{Ge~bz)#?GZzo&6aO~KNwskrWrfd}4DxLwtt=*W`OZ(OH=U1Tc$ln2@B4ZD{TKo0- z>xJ9PC;U(yOvCX~cF^Fgloa)cKRSKms^~dXO`x)Z?)p+g3vl?!aMV09{`TSN<;-oJ z&01r~fW)ih`^aSxVB}5f)>(v!i{!3(gKmG@$D1tif2MxBzvh8uaP<@!2WU8bF+?pS z3P>&zc|7B3EVY$_&3TV$Qxd&12-ES%8i~jxb(yt=Qla!2wSGnIm*-reMmg;2>?LIo zg!W~o>74_QB{#rK3`Om=lvx($vt7Z(_V)HRocZua_r6trK7C`wNDO@golF?d_dGnp zW9f2$5)Ck7Z(Hs#`k!s@^7oFPyVMHi>U1J8+7p@|_D8I!7@)J&T4Ff4!|O#I^1}PM zo5p%jN$#o{Woaqv;FvEB17V4`8&94MWNwE`OF7>tOk;u0837Lut&YreNp$a z(`oN7eAhEHWV||9pItPh!0P~F83E)L^j{I9o$EOeRYyxvIKv_o<&iRS;N%3X=+@u0 za)$5m!Xv=K1vL10dn3^Dggsm6>3M$S`NXf6^?LQPQwcOHa96+F_x4-3)Zw1qC11|3 z$To=lZ)u@K^75bMe+}5^wQQM;)oV51FeFmvrg)>G#U_r`wu-iWn>`NnF4IxO57BNf z(Q0x>kOD`f_EbdZ$S}HPCva<}2TX_a`f+=sRG|hSu@q}Jk7P+pI$ev3HPVfcgR#l@Nzd%v~<6|Aa>S^I!$NHk86+uQ0k z*S7E;-bM2p%_gPX1KamMs;{IJ=%UtLSC@sm1BtNGOO<}{(wo*gUkrQB?A6xmV#3=_ zo!|fUXwoRqrx|7PECxah-R*5DHD;k}`{klY(^NW;1aPRAy)lFB+64v%N=D-0VUwhf zXp9OX;q}CJd@N^vh>%IZ0%@#O3{r93O3e2l%vZdE<_k6cb2z`59K!wz9U6`@PsseG zMs{@r<(&6~q~{#Y{%50X4~ejjqD_>Gum=lZ3v|C#j9AFRHJKhqm&{=;M4wOuWw;L} zMpi{hsvtGUT-(Eu=eV}B?$4M$Jx%yU!VjD!?dNSo|JSz}N;JW{qMVJcZ${xw<{>2F8tINRd+ zp=dBGbV1Yql@sdMaa`kbdhk(>m{`+Lb!HamXM8&Vg#s;PxUkroYjVn8Q&(3PlUEJ+ z(V)7D&w5h!%g6QgtpgYwMm2f6J0W<20u*Oxk^N9NxzO+w+qZ2SOilW7>DHX#i|abG zTn+eIOGtrUvx1x|fOm{-Dv{zwz#fQ=8BeonZZpj_KB_!_I2(n*#^a$-ayZ#Ni&Q@G zNSlFGK|3s8TYO=f9^F|K3ct{G;VaEqRfWvjFIv3&x-JzcC!-#8tk59Zuavc&Zzm_4 zY1ND0L0wex?r`a`cs8>$+>uGo%MaG&*!b9zFPf8wY2?M7s^bg>0(s!WQCqsp5fLLt zj$}GnkrInbWk~e!3hkrr?yy)*)6C4w&(DuMcR<-`cCRq&_gX<0ttJ@Qqi-uoYi?HH z>PbI9VJYqi#GMpaT<#T0nON83OK0uRo!OdUVK{zbtu!aGT2Q(|^?DxCCG0J-fF0B| zPP1lZQ0%uJxHPWHxM4y7VSvN9%|}|WGCP5$f})qhI3#u%Z%h6+mKTGga&3sg1p6Jw zC*I`H+k-wimoAVu2~mf8h!D8%yv=mqIVi&GAUqDw%bCSbK)Ny|Y~IOjuiHa_c>KyC z_&v?=i`GR!WOnWxK2oO ztM=WRx(0J(fIb#nW-*c^Oga(LD4d7Qc=bAq4na0YU7EMzl?BsB#fQ9gEk!IJotmMo z(bIVosy-$EselT&&j^=GUoCZ<4Pn?iW{${AXt%5TfA^w(#oY^^PBh;*yzj*5c9*CW zMyWLKYEi~96HhB~FE$F&DylP?{sF4R1s8fueAXPX7}Bf_9L|iu@v}ak&rjwyF;d=u zYTAyHmaDrWaOuH!4N~Y2{5cqE;`W}Z(SEjvT1AWWM896XoU)FWbnd zD9=Y{tD&_@OSjj^z!|84&b6#uu|9jGApqm(#IsF6WZXNM=J!;Y2Etb4cp}&Es{DHB zrW+LVXKXf}Q@oKv&_9hEf3fBs=Sm;R?lzyFtX+86%uFqM>KNsX(R-S%OR0WGXNmig zapE!pV=z!u?o1(=5Sk=EdK3ng!aQXTDB|?@ksfDnP}ByW9qpmrvv1$gDz#9uOYZ`k zr&aB9Lz(tjhlGp(9!7(fw=*-)ngNq zETNse-vOgN8Ya@0la@NTxOvBpdylzQo)`=Ap^z6tb}G_th&oef+hMo_WJsegi#}%l zg}_9_T*%X#Vz&X2&Ap;a-=n1eAhaaH9laiY~l{hIM=NSi)o%oLyM~@@e&l927cqFG~pIbg%WB0Tw)E?S zOQch&g&>*VC24{9Yn)kjl6qt;uH@2YqB(2OyZ1EeDCB<(=zeKraW`Tuk&ai?k?QSs zOPd+%PPI=7GH%p)i-GU|>fv&4yp@c7Rz7Gobid8M+?lA<2S?imG7n+Zg8zRNFOIw< zZffG$2DY5ta(y4_k` zK`=063N3y3*Bw^TRZDK<@swgt>USET7!U7cv{*fA>2Nn(ciZ4|3-f->(We?XuEWq^ z=(`D>o6a=rKLr39uy^ki!zJAbUu4=;G_tq1BtF4?1#@)7gSR0HVCr{Fe+YJ=OSUdB zuo>4sYmpCLwz52eZUtplOq(U&KNk+8JG|QV&;gJ>NTJnED+~HBY;kiU_%M9d^~t$Y zM{VI1`C{o{_kL`;rvWX_MmHA>;@>7QxzljrX!f@#U`+yp<8BXEXroJ~+M!1Llr39W*MK+L+}G$3F?`*02T>=(X%M}xg1OO1D3RG- zlDDOa1B9M6`6D0%Q^t0p*@K6!?e&m*O6X6h+RJoHzwGZ^lx)gf>?BMmb6r4KqT6}+ z^mt7-ld4Z<37-!Pje5Zk$BCKx`A^C~gfabSIL2~*#oYjes^IryCz%TZv2)k1^$~!I zDuA$h+pUx)yf7FFGBAP7vGgtB@0sh5;a>JHbKGz9%ZIba(ksRrGri#alQq zdYS6<%)jI{cJJMlO#g(vx&t+U28|*?#N;qSfpm9aN>XTlii@$l7uXtyfckDhL1a75 z&$L%@S=MXr9~xVa?}-!+tDUo>-X7T8?MBJ#$oZ@J0?m;Ha$|GcZ+ng3ea>Fli%y)Q z%DX@6)ob3Og}PpC{XTufut;VH=<zHcsH{dBYz7@wW_0M{ zp5w3PXfG?Jnty1Vj_{&Es~(#X2(;bu2Ow3rGh`6BQ8{BYFd-^Qy|X8!yr>k&;lXvc z0C|H_y)Lw$NDzqPmh~GjV2Qb64$4uW*Di>W0CuBhB0T5Is+&$6VN&&hw9 zOoiP>(gA~5rxS{0Na2YpTT5u3no_(Z9(SEqv!hOeZB5e4+AVH>o{931-)p-n>}$Tp`N41A%+~pT(AUEkjy*Hu7#4ttxWUEq`dMW})ygQxDj^?^ zJ8Ns(>{AfAjd+SPXj;DH*`Cv^kyTzdp@b=)HqaygA6su8*5lf?e}@cZN@hZ6AQ>uC znUbN>L@X4E6f#AFu_T!yX;zdBg;2;G%21LBk%WXwB2!pI^!psv{ci8J?)!QEc(!}3 zrF_5Fb)LttAN#%^3Uq0aotC8J5Zl*|-aCdmz_#}Y)SbMh1q;Ns%$j3<2uA^`ez-x+ z(9rTW$=&|&ASC!slcfKOo+YZz1RqMED|pcflm24aHPVmE+8-GGoq zi1f2Phpnq^6M8ZqZw^_FMRHD>c=hhjj==RDfo$}u9OmzBL) zT>}LqR!J@}ETKe()HcUFoLNUkR+g-Wr(N4f;w1JuQ*9cq8V-|{@$!VAr_c$9A~k!k z@)sHmg@An*0(18&K(^vINj|R@JLNga_lxUYn7D@z^*dU1=MkN=gqPX4194*#(G>uJ@JE(ELl+SgadoHnbU~cqWou zMidvU#T8nKpb4q#WT%F0TerqR#~`?!p(}7cEsT`^ut@0=ZQicp!jv-+nmTRTXVSQ= zrJ+`hW-l_wsjdvfEHQexHe3)jRs?W3DM;=-{JT-!SWYaBg8t&CgJ}=e)28w}nSe{5 z(}KIr7>jf`7m&_q>$RIZ`rbjophv|?w=FR2^XD_G%J1~u?&`^UATi54iIku6oSZO+ zBC2ABue&NoLdGiOdHzp`Zd4+)#G&J!{so#v+(S2Lu`H=NM%oAhR+tc_i9eL2?1^r~ zG-D4~RR4vKf*Ii5qjVHxQ*JG@QE%y881N^eBjucQMbAhdaU5!f_={45vLt4|?jzhf zkE?7_a&JWxfnB|g-T(;mR@{i%lV`NI&4MIdV-;{sfoIk*c;T(cmS)!Rd_kMPQ$45m ziWs-DviqCH*4C3u&U6y#0vOaGc%-h`Ts zKaoXtkh};+Z*-x#((fmH+y-cuM7c)w1iJN#b40m5zxC7urPLwoH*J!vy!Ul4z!m5o z^#azl?-}~n6fPVt0Kv637V3XP59OWTyMJF6W1``C3Hgc3D}^x!b1tQ(xZLpdXES}L zlGe}PaS9+3CAbom{e?7Vk#C4>S}F?eWz>KLlQ+HK-_ymX?8)9keosTZCnqa|mC7P$2s6GGxdQf~$6% zFB{wSi^|WP8%$ffk;)R-f^t_pDtHCZEMzE6C(eNzLlR*a`hooR$LUIC^a}ogtwMtH1lg4BOxkKcLt>Q( zLKNsza;nZW(l-UVgE^oI>=6dT1&-du$yb4=*U4eUfKT5wt^tfF=Z9 z+HsjjPtp-}T z;PQhvM_Gt42AudjUm$4fu)M3F)#>Es9ybr)DM@DOKu{qkj1pn*>fis#f)f6@0fBSx=)`c!R@*?Yu{x@ zd!>v?IuUA1dk4JzZ$6ISbC0T`#u+$8R#WYXHyp&y5N*AL8@u75e1_{k2>{JD8x9Q< zdl!Vypx2Z2en2slijg-RbWRGvPBc+- z?bWpOR(zZ(_lTZ|KfKd=T~i;QUurGS`z$bfKT8g4>=lg`TRbnB2b4yBdS5gI4VAj- zSQ(yK-Lth02;*>3sfEXCBqb#!Vs%9`hM#PGu|L9@oR4tw=~ysC*;6g&P8lgeXfefbpmR!;jA^et6bCk1nkq#1CIa zG^u@7Z)EP?@jVXVz{||@c1Vvbnq?sp(1{&CapIo&)7Bk2+~g{jd7Mv3&?KBD5N4w1 z|Af+)(()OV$*D*JQP_srq63BXWPi30F&C~ZODKXL%pU;A@X+3o9*lNI*s{@Y7Fgds zVAWAbZ#-&{aPz-gs;R}iISuS*zycA^tV_17E~so)8s3&yUk>sq7DRhTZIWpnchKWv zC-eThTQB`qE=f_sB|0ID>^GLV6{TkKTqpEs#p>>8dLY-{JA#ANE`&-=;ckhm;RhCI+nRzMQC@mAa24@0mNt&c|kGG%V-eQ-* z+-AZc0ep8%{pr}MqN41SR)uXI=FZ||!0=%z3WG|mP&;0&5M=i7rxUeqao%!>_v3rC zlEiCNFbym~B!l>%uLa*DfLnW1aFn%1{M};ZN;4%j`bS80(hYL93z-RU?e-F9QNg!mZJA|b zH-H)i{jZ%5NLZJ?9GDY8J3zPb?cYtsK!7_5Zf1AMMEvaL@)D8RRa|VkE~dM7iDLfz)7DoT z!ZZO%I*-8vRTz1R_eaCtT7TH$Ri9sNQ#)9+-?TYne`K!vYf9j5Li*+p%E*at3ZO6& zSC5^|#)_5@(5`5iW<&L!`tL2og)+9Y=iS!Xi9Jq!NH-2{53Oe{v^i0(h$T6goMGqg zy_7Q21~fbo|0%TnHop%TK#_{k%@ZTV7>z>Vl(JiKx~~lb{Q0Mi{wh7A5mdlctMAm! z$@b$gVd4=)vG~e9ew6 z>Z*X=H)|dW7!M(o?e<2dv`rsP8e7jaFflN7V7^I=y!DG2=V=r)(<7duX$99v^ zR6jyJq9+l(kSw?=LNVmnUgbl7XOzkGs5vbdw6XxIA9>12zrqw-z(lF~c@JkkMt5Nm z_y5{Vz0WCf>4j>NhtTqs)Bob z51kPfJo+j3fb_7Efb62*;%sv62-oxB3C_G@d&ljtq_`VZwSWW^NIbowNlV9@hy4BX zlq_eGFv3-V>d^h#?*TNSj1GmRL4{XGrVdo*mVZA(1lG#Tvpzx3fH`SKe*S=nbA1wq zY(153@qV$Y>zMiIKGqT}#l;xYGT_l0OFAmVW+phkHTpc@m>YMp{li`Xs|-vER%FE| z2UXDLH&_2;l)?(g>gjj@tVGqPzBSgfwen(K8wD8E;aJ|-m@TdF9E_q|a$HERX}LG_rQ=-_(PZNY-ACVQXr)da17*;q*g)^F~A^;uK< z`;v8;=e9;xE7yc{)7sj)S7fDku>KbP-kwFx9JV)iIJP78)R6_d;{1;l{#~HH|MT{7 z2{I@Js}K5-h8+kTbOAj2cI!9P^gwK8BdwA|NE7-^oj7L9`8jE(4 zjnjut$CTP@Sf$Ku)AP++9x4&9&u#ZmkEyhN?TtJSsZ3N#+0ehI7hMyxo4xmqxXEN< zuEFm9^~20V-dMHw+uxX;={Yfk@}AN|+jQi(WNpXbn=EEXPW@rXDE3`l^LBTh1eraDXTsEf@? z?1@e?By+!QOy#%0R(d_vG8E%Fa_u2oP}+2Q_Usu+dF1bAu`Ru~wg}_TR{5J7zKe={ z_Q-x(Mm$4p?_*-sNtNa4>sdWq=ij~acYW{Wb@doJ$qB{=m5R;dofr3C-Z zV1gB0M`-~s?Z(Mk9HQ!IK)j;oqIDn;qck_^HigSxu#B4an|x| zSCx2i_b2xOmO{nyw|;Au^%o78fv04{l(!QnuQ#GqGu}5OJTWm*w}+XI%6 ziOcHuMcJCNU#gswe-(V&a;gZt%=*SBjNO@pu}6y$YP zOUw-1P0R$&As3JF^z=+!S4u3CW??|7yEp!&!(Tz&O8A=InwG0xTQYk^Q2=l zIvEEw4RJc5uJ;2^>ALXeg#G()Hlha#9NL)@zzK%@)(Z?FB7%zv!(%Edq4@6mNJ&&^ zsD2K4b?SNM7@sft+3-aS160f|YH4oU`>XGB;23^EozU}}uGA`JcrNXvucx<{NfT}e zCPCe93&lW`Z@FdfDl3HnM4JvtSvTjMPQUd-@qVe zlJ6-oGytLY8-4IR#ihoy;*JyE$N)$KZ=bV(G{KBjya73h@Rv8x%e$OUU#r&Lfl%%?zpi8O zXFT|1uRO$Qf5NLILOLoEhx3tV9D%p3dPjF=YD+j!VrqN!>Q((tZk;=J>@`FIyrZ8* z(23%@h&5%MrrE_;o6osl*%8mzWjDC9FhSPdQFOR}hi%^1^6|&waT`!^K)_me2H}6peE~XaHHy|+3a@lN$X`^?Py368NKp?fnQRlZEQRR`%- zeKW1#@bz=K1CU(iCB5Z|LpJEdl>hues9Lt00_N8py6jxaY}i(F2)@ZSUtz4LdsMslQ$a2Lo=q?Z5yY?1Vw?lq;yY9 zgK&rl&5?o@@L$93s1lEP;J@L zAIA!uu4jS3i6a74!;SYL6l1yJW(f#8Q%W{ru|j5hxY7X!DaLc4R$&fhGA*Fuvb8$O z=zuDz{$txd@;L1 z?#JIZk)5wxkc6+^y;QudvDIov{!}38#WU6}OrE%R{1RBVZyqLDn3**qvd!g~I+mrn zi{aq08BL7=9cdTL|GNI*LI3-?sd?WVXb>rd6*QJVhzLgQ*|@RTb}@g!V9$n1U}y8L z|C5|YT7M*_BA$|_3wgbQs@uhJE$zv2v*C(!P(%kzH@y9iTdY`h3Nip_%n`|(@^sy? zO&ZOFEf2&g)2@{J7IT6Z?|!SJV!-?`Zs)^S#!ibDOJVtkCJHot!!wSjAf-Bqp9eRQ z^vYDGch-Z1(bxi^ACdVsXdLe`caoV!?T7@L2z(0IBHk=juKe>Yjju!|%=!%TkqsL*1ccyL zU!(R$H2@l)%^85NFnaI!@u%ni#rS*-FWF^KcZcBVeE@uR%#YSk$w^jtNeCCi9q1ZD zH)Bylx2zVwr(mz&A-LVJ1S{`(vu2Hi_34*!$Z%2EK0{E%Rv=MMJWJU*Pc(wWAci{% z2uQl>>NcUdq5q9cOx5(8I%m2%nBX0JtYmd4$W^PJw(F@a?@?a#A2WxCE>2Qd@d$Z# z;kt_)6B!dhFMsnOO~W`Rx#e6^-MpgI0l-5UupvYa1pbS?9S8jYr%pJYAnPUNlIk*gw&SnPwl+MB} zGR#}f$>XLr&@j5^GRHe2oWBm^$qks?PyrO!kKKQ73C?jiKwLvTzJdV<%47Vc))8aw z0wgayALZjpEhnA=NQq_iECN$uuV2(s@$M;;D_s2HBJU;Mq>R_qhWt>0%Q_75*LzFk(gu%GB(jm`%*KD=7#e+Hg1pR+{Hz+?;Yq$e0}46 z{(Js~%?Q-$-G}Hu6=Yq4p%7!J?;5IV|4uNkDFpdA14$zFnBpgv1iAqn{B1;;|Psnc;Zs9HyohG z!2A0W2(^L^iHln9*SS;F4~*~84r>u07Xn*dXmI|MOz`cIu`C1x4cL~T*N}|X+a{86 zHi}4+`I?C0iW-`$M>6u8l%o+cf0?2aV(@rQB!)h+c?(!F?7+Mi#n-tZ3?8}?yGn`3 z3a-jax=!!}s~W}8;XmNfa`u; zv-i#i8#8sSbJsBF3;h=BqLcwS=o4RK`$g6Ju)~yyRy23e0Z{9;rF45i10MZmuC0dO zRO|D`BVV5Ak4iR~zLWFbo~D))C*20E#YC`_W1XA-?IvUXl);BN;#k&9fM*7J-IMGJ z<7OX0NH8lg5&}>a%*sCLI%|jF(5+jyPJdw*B1fK6-Q#%lLqhh5uNA2%R)K^}U8PPq z6pN>W0*Uyc&R8GT7*ZEHA#>-+?IY1ccoGLIs4+-w`#pK`NmKU9Abfp+-x0+PH(aKU zwG&%hU*}ejSqAS+f*xpwY}Cn`ZJ$4X0MB6#0T2*Qns-8KUZDCNS0o2pX#?Op#z(qx&B`7&;a~1Xash@nEZ$~}#i5gscwszlhYXS3 zVvYG2C$R$J=w@rtqV+D*_N-k{8b7#+{s;92wQUWeBaNG=YAe85vGMfmPh`8-p+37l zF&zpI@dN#)F6)%RfiwXV#&LWv=|YNMD;^WUZeZYTo#0I9ro~!nImm<%DeS! z)LktViyW)JR5zQK&(H2Xe(>i1E??H&33yiU-gu4_q|^hmaD$Wb7g%rtym@B4j8j0P zsUchjMSp0$!6f8G#`wIBcORSSH0uR@T18q>C$=z!@dBy8Xl%VeAWz7c3EsKNYqt65pdzK6p@qmM0!38scb zxg(3hWOzm9g&_KV($!5Y8i}_rAjb04#B&@6^e_c|nlFYzlqn3R-uLf* zJ#TC0twBMNI}XN#+iZ>Rvx+2B4#AH}80N5Hy{Yr2rhL^nxHJ6I>eMvfo|!YyXeq!b z*YcQdPN8__7@=-K(enF7Zd^M(6lKISeC+yAW#xumv$KEQAw6=k<*ad@MaT3<@kzq+ zrya0t4~VLN&kc&s#3X6M00Aoky131qXpOy590u%h1r)`k@baJj~rmj|#EK2;q zCAg1XyYCkK)j!(PGXIq8L{Uj%5xOxfEKK)7G;epZ-+_Ui!*ad6{C=nvY>XMpkZ_rS zEt#gbH9TNXDelgXjrPMSlMDDf_s? z_YnnYGI55BMW-p2qKDld=lY@%15ugrehQ4n(65m8e-WEnkh9^&$Vxo5%)>|O?L5=N zX_3aTNJ0yFZF{9<%$Ji7@~!kM*05kzyb00IhZNNt!afzEkyo4Z6*_Bf&r1)x`}Oh1 z)7KBCRt>m*FxvIowCgOzrN*p#oIU42f>;ZTb@+B9Fm1Q@k$CQ2v)omw-OW-Y4tonSYF^sugxQ1ux+|i?b(14Dgo8-tiy5f0|93 zJ%))s=k;#&tFP(X!e1U|;{Ue)gxD5XM2Be|zVD&pKe{|lX!j7`2+;R+=VE)p_<|rO zgKGkzbI!^`G-_zSHxl#33IP)4c2v5lZ=GAUmz#+JqYP~d#X%VN^|m-Q$e``?EehAt z>}1?XWmTT#+3x2%x%4-WY(X*;D&fJDROQ{a%sm=mVnq-Aj}s>r%juOr;(N~+SW$|0 z`r_ohhX<19vtar#5D|I?xusUSB#ncA;PdLpaOg;~zC!#s(Y{HG&t2v!XkJ_3v0}ww zj6*Pu8@)RwnIe@QQCxRitutiGOG!?_Ep0a%Jh2F;Tq3suG%IssujS@8W6(?tXOd7A z?cmwT$*-)NsS zhay=-9$fYm#C`A|1sob`t>V0!l8*)~G}B}5ChBwmX}QUW$Xq)Qf|7fM;SdvsrL?)C z<$HIg&YGG}rl4$De3g|HO%$j(OJao}*rCG~@By-**%@8zEh(-QWQX${opFXkfYDDf zeui`Bm$ATsI_Okn{=EL8cmQ-Ja`9b4eJR|uS6i7RO*TN`2o|so^R82yr>NghVkm0H z6G13>kf?09tn=LsqW9w&|3{uDy*JEAF~rSUza0KC6~{WJ>tcw=4|>_#s1Bd2aVYXf z^cN#trOR~7HnIz0smaaF+59GPFrtGg+ideM28X~+H(b-&`%R_urtXc}xf6%fwI+Mb zv{Wrt`LDn=QFu4B1^pO(3Dp*G7IMQCRvyuAcrRNzbzGe%XH9NS%_g{%Z64g@_XqRW zF+ow`xStH<7m}6RV3z`auY2toRwW51OfL$@--3d+1VtY6CTL$oB&&%}A;of!eRAO5 z+A$0v`dSwFjY*vqH`zN3BC~oot@?J_=*w}NX}XrS+SO|);HYdd2a^=pka*W5oN@2w zq_2%CLK~~oHfuCnuhslDr1v1a6k=!BJwZ1r<|FEQsVm44S2MddX7Glw+gd(_;3!l; zn!H++;`3Wi%0%UcUY%Kt8IMPSl6@aR(7s4blX=%Qdew7YZ)J_CD9!kZKWnc+GqEV% zhE+NkT?uzicuG{C>gRI`aC^8nuRjG*{K`uL`Ov2D$(`udiP2umk}VrE(FeH6f{peF z&uOeCqE)-k88mi4FFxhQ=3T5QA~5LhChBtXIW^6cBPVyM`w&AkK72lK;DDk2Mz1AH zF4J~XdEu)oyG~Et?=UKh z0gbShW%zaY7%HHbK*VyxAYi&&@J+=Z_zo=>q%O)!sR-a$IBQ=Gm(58;+PBlwnJiOU zNf%@IXn~))hfJEVF~p!$3?V3$s$Ui_O*(p1gvkWWxs1GpgATTr!edu6?9v&F7`9^U zp;sXscm=KNgO(xqd*_O(q7fC>n9jG6;*wiQjfD0n`{ua1dOg?KIy%Pj!91ASNO)It z`fp72`N@w~jYM2}hiARA=DVJ>DKx*Z7>wUKr;p}3^@KeHyYU5jC#mJ2Gk?VC23R2a zPTn0pplX*c+b>=EwY;lz*@0@8qFOqes<&y?Dt=-$k93~Tu>EFxAh1Xj;WV>awVeB# z9qEXAmBL^mXw0OyhbQSsN}-2flslW#D60i-XV=RfY3aVl{Crai0%)sWe0zTaPLT4*>`D*^M6a~Ce)6MGvR)HXBaz$T6O>d?A+ z0kQ`Om`TK8m`Kfge5xl8Ct`n!VtZ7xkJ5cv8_5#6JXyHHJbXb=%r_}4{sG=fIX2mmcGz-2V64tUL=2itnM5pGmXZR4#)Vp4 zLE2KgWtBNi_->+uLcF)vHc{|BUaLS+vZILvDGLqGUmUxC7S~Pzt&l%8CLn0vT3YLB zH`o`@Hn_4ce{}o)2Bt?Go$GAog*+p0-QPOD7^*?O?10!tpe6VV{RRw(*nZ@wC=;t| zgcdA?{AVy%n{8vm-nX;cPDVvM3baNyr3tvVPQ z8OaJ8X4qMjFL~SIe%RwI+&g;kQ8*2*sqdWYyIwVRKYzErLwBu{CPJD+8_6?5HmkX{ zOe6*r7a)QysPJe%z9W0AF0TD;3Rr8K@4~c_7)i~m^UpF7HuiQ&dN(f4hn@#U8_EI*WxDuhF}fIHK+A_kBp;&T*%=?*G`n>(Y16|h7W(m2*&xF7z9m2+cxR}U?c6pM zF^OOVTejTPTe@x_K58}qF%hra6Kuum9w*(Dk2!eL^VPIk`nzaV^-J)dI#v#V@jj17 zv_OYFhOGM{VikZSk!)UE^>qtyi!A2A2ae}iLbGA~a=?C25Y}XBINU2CQ9MH49K0g! z0}D)nCg-vZOB~i1<`@9zt*QOBTv)=QuEK-$`t`AGHX}IHElks}%A3DyXb>rn@s}{H zkRL&H-laBdvVc;EM*)3B94p8e{>hg4h>1V`^|AtH@p`&5h8fb?{E_8Ai!`nOrgjbz zSDhh2uZSR~^+%E5C~L*xN>D{mJyxvx|1qr$gp0!~Ko{o9UO|S`VgpD1w zmU#8izOkq|fNO#b&)@m&JdfrS>g~BDg6z_-&b{E6D$@iAPzryxsWPwzV=Mz(6I2W^ zy&vfL`5NihmM<4@i~n+P)p2-Z-XUumD@MP-fum~8MSSJB4nP0;xgSUo(iV9@_8^H; zL7*}ouxpU?mYq7qT3&9=|F6t0xuuE` zq%rxD-A&GsZ_T$7-0C=w3+%SY#yaa{C&rlsAx#ghUU0@3jL2Y)YcpZ@#j~tEBX=uQ zSnqDDGiftNyriGgEKzDv~#3kyXkDGOiOY)L5X4xl5(#dHZ& z8CG;K`n>~fbs~fZ{sPw#S$BTjOoh;*Au4#S&%_>$_N6Y zMO5POHzuMAXr1RI#pPMNJA+VGIvj_;>vE!~uCA*F?cQXO`O0+w9pvIn-2S1Q<8W32V zH_}B{!I=245qC*hmJ6IL>f&JjW_ZP$H{$^RgjNCOXnA^QMJ;zBv2$*trcE)xXo!|k zjCG4eY=WXMZly)qh~JA!Qr)5f!bxe^dpDyn*3{8?vtYTBnWyq{FE1BrBucp6)BX(R z)$iTz->blk48h)Jwl5X@VXjpz{}|pPC$HAFOK7+A3m5e7@Q*qLR!v>!1=IWfo4dz9 zReZ`<#J8<3EDgY1b{t7PBwHC7*ik-8%LgX*$CCng6>itf6Mw!xSPm0LgTif(@962h zfs=$Aj<_%LEHWQHoIIlqH4-z2C%2D|Ur4aXe%eq0_%o_34R8NAoFk-MW-ZNeHDBeY zD96^4;oe-#<-7X@cJF53&k+HVtdEN1&s0blc5VB_=s3Mc$3favHS^3eJNAK+l~htE ze#@Y7=KLitepBhjZF>)b1xcwc?sHs}r4;duBRq72EI;~|ep&kEv{0-W_)6H}FI-U6 z#=KEj%OLikg@RL_L%tQc}79{Ms4TFoJ^7!1-E+RT?))_B6?=+qfDeE zbbw@V0|5s=kLO_vvp^jhCKu!m#xF~gJ&%I+iIKPZgmaw#^^GmBlg1akqJk2O}Bf;XN?kEi`wLM;Yn@M zN5qDi73W{?+_md7rY&pU->y#nesIHp{ZAXla(ZPU4>CGHOby8W~DuFhSU z`>_RKi#^NZjV<4pl7t_zu2A0y*v|qGSy|JA1-s*-)l+z0w`dEdQhg;ZyT=igqGzr{& zVR88$svW9Pn(khv$|n16lmP>YZWRDu09Pjb^7gnPHd>xHlx^ehg7Dq%t!-sO!BDOhPWc3|jL}))3)4*n^ez7y$$_Jka<%5PWVquYh zM3=Pq+*{#fE6AXOXbAr&GOGi;jqsAV;SFtX&7|R-7*LS+l6n+`n|a?A=MNP-#KeR9tKV~&~dw27vaF?`)n9XTRIsrhm0oD~uEpnqp*{TS!wP(wc1yCj+myDI9 zUJ(0dS$Gh=V8JR*=TfLe|8adHzgTSibg*LEo%l7r*^U0w2UiIq{5r+UYc<^pUBSRT)MrO{!dFcwfXgC`JTEX*gbeZq45>TM+ljIM6z9r}0 zPaC7E<+HWfqR?HFG4IOzZtQ+FR~HULq(dU4(=ea(NVR)iCp2Ln!On#a*^4Ia>OR$W z(DVwA;F#+grW5}!9A~G0I}M|iP1^Nai%2+vOOg|&8M@UNy54tg6+lTnhTURKBEf`- z5dXo1Z!lst397~n_-$yJwCUJp2idQ9cq< zaWS2OZJ>4S&MRsc<9$=R?vEN`uKtQi)nY@QIx|ZV6hhtF!$_qfvaR#(J4@Ak7O}r4 zn+_3fe9*%(!(n?5QVinO3a&1{M`{<0AWO}P%=O@|5opoMyK~48v0kMmXXm=*zzGK? z>4=ph8+ahYNlk30F)#vyq9 z$JEpT@DyZwlmeZ8mfCjr+$NMH+D1k@VKe}375#sw{2wfB6q`5zw}l=#E&eg22;Fxr z#bg$4CH865y-*E%LG5F3vXt)(Tg#H~BV%PU92h7!O%$F09nznCudYrwe3lZ)uJc)o zx+h}1KaQNj1rlpUF~s0U2!#jQ>rG>vg>P;)RiKD&IU)O~f#nO$o?YVX!T><_HA0NT zp+)w0|1Mvj?fS`0t-$AqS^4H$qnG>kbRVa#<+I5bt`6;+T?Y%qV(>r4AXU*feNBog zoG|^#%8^qV>T$}uE!^P$Z?y_X{`++AuNU5tCFhXH;GDLGe9q_899x5lrhkumSr;PedZA)AK*aq}`+dW&1kgaig5*Q3Zi~ zC9LF|1}|2OU7y^{DY)GQ=f5~Z1l66e;>Xi6$=X+ zuW~bSxjpyLJx`;we9NO&HCVx5LP!LUU8!%dyx7-CHwp7AKx;`^@sYbC1 zaY&2Yu4bqoak;xTu(Z%8*1oTMg2P4$?T3JX%^lSXeR4JfuNSTy-&XI8@ertUS^%_I zY*G5KPVnZ#)zJ=D#M)k{GqSHoY+%owlWmraLcqnesWkrMxI_aKJ^vR@Mywv#gdC6f zeHE8G-sIez_T?5vn>90O#Q}0ECY_;!VNTIC|839AMHom^uup)k&aV_9J`ljIfcMIZ zMxPy*sPv*8prO2uuu-_2RJfJ}z%?jEG%(r&qqDml^WIr5YcFS1tTym=PkdAo_`h8y+l-m3@dSs>3AGcUtD!cu zA$ugEg8qk-$i`d%zs3yfWJNHYgaPc4zWRnccjsIN5TuZ62%pdb_>$oJ#1q$OfrgO?aNmj4t#3P zSI+|7AmeG`ol%vxt4->f#b~jrih;F-*-y7UfzcLJKCwE-DmJb%h6N^lZ9=^e+%jAd zfgI!qODZDD&3)GIcw9Gq2Q7T0LZ+v38N*i!jR@2F82_0?ju8*+lEIFh&t?ahYeTLoZbq+5k;O!q=FvFFVUCD|OvuQA5ue7tdYPO?o?d za`wCtMF2d|(b-vOt6gxo_OVw3)$R=q=BG2-p^6b^D7+*v=J-l)xx%nFYy&zW))4y; z=)J+m8mkiX^Ag=c!yH!$=FTN3dHXg8R8d?V$=2a_3c1S?t6-G?TbdFz%0ACWvm?s; zCr@_Koudn)_^;25>t3SFa`arW)@X=1rZjclL}guAj+~4o;ruE zae+e5A`8W%-+#qhj5w>Pfk+Cx^~yYG6kAe!^g=0jC3yw zGf89>%BCl`5b^}S166ba5oA81>#*rc{0TubbKK+NR}=_c1MNVPi^hbX3RHLjbn3+;V8d+8e6gB;~Wqp)JJtKFV}$L zNY&DEEuq~vKPtz!9{jYFtrgr_s7NJH6X=7!s$y!?*ZpwFH+$^RD$hrFyjSey|&c5zVF~OW@A3Jy9G#^m=75cD_ z%W)m#NhK};GeDi7`3M%kortH=sun z-)0d!b4;r%4#vGEm4Y5uA2sQY_Fqb9$MoTxhurb)qL{0I^D15jv_5@3DPhy8?Y>Ey z%^5<`XcwmYrsoC)JQj=%vlT431#%RJDSCzLuywh6U1_{Ql4}>NU$V}vi|f$n*k3<9 zLSRx?KMl=OGCD~xlC@dtpJw?V?K~T4?qwPTc1DQj{qf^_C;(g|r|3kayj?lYFY3!~ z?P0#-%HLIv{`5Z_l98*t(TkbIw4+Y!FVBe(%jH9U)~nMRv`cbC3~V%+o1 zRv=XG&(8`923@NnG1ij`+3BUto|aI;hVSr?nzWqv={Ne*rnorAptB)%0*-<5jPKEk z+cT<-=bT7hM!kUd^B%8{wG9l3iu(DRO{+3bHQuK_$R%*e~=agXWJJCQ^7rc<~Sa^xG8~u)D@}}OOkqLm6YiFGg%`JD2VsBqwbHNs|jZV z3vE`FD8R5=7&99;7h<}cC46SY*s}(oSwWk*qu{{TLE>bFIRA79C@9$5WN`L{2MQ)_ z-sbGj(}y^4d<1O-7W9;J<~y(^MZ3Ne_3A7@AU^R%{_DY&l@uj^mU$DUA=D;9P!p~A z`A(ciAu{0YC8KwMR6v7z0Y5YOdlkO3BU~uM&^qTbMlCEh_A_ocyu4n+!ZGB$ZPB-M zB)U)%0#NGP#e2>{g`ASp-B=Lvz_SDT_g6I24793H2V(8pIAi_))h5lmGwS;mDYGaz z`!RW9WGWXE)Foy1L~B`fO;NfXUk`cc;4`w8n#wSz*(^Y6*^I?un+Q*nB|GB;+yWLR zJ23Gv+dVI@prBUw*DdA$AlFPOW#3Pp^bkX~nT4(3Fn8rSrM$Li>uxI_@(%T>0{bFt z&gg1sHK2NM7Mm|?aGd8Saa~{S>U&c7G2H! z$N7(_^TAEYSQJ-OWaZ>N&Q3!XlR=n@O#Bgl;}U-jE^EoBZ-K=LIKtos1K3@{WLiBW zWhfTRX581vQ-3znDfngg**6g;)~o^c5;^xse)ewb_~C1%^H%d!}|sCc>t%238g zR~|fQWfnNTar5R`Y-Oe4VLgB_MQIvQ>?3^;NBZ;ewO1!Gg0-KWeZlMPMqmS-2xV>v)C)7gyBOfn5oRAxB2s6YU}>1k z1j2%N+#W&kvuDqs(QcQ)CDZ8tP{=Syy+8Nodln;^UA^H}R_9KbMI*YM+gP8efCLcxV)!R&vAz#F{<{dg zQak&@(QCVmY6p31C$9Fh-DAAB)$(?kE=_iHEq)PLr{h5He(jpHOKsX^_NYx4EV4en z=#!aLH~h?l4a%oA(ntIlGB+aLYFy*MOW(Z8*1cNcp1b;4%$?7}w?8k=T&=C*f2v^u zRr6ke%Iefu(DV~f_W%wc4>hIAb0&V;jdtQeownbGyoAC1LC_aLIHB*&L~#JhSD5N` z{2~@@~oJzb@+9;+G^jCc&AY_0$$N(jHY8^^^H)DcuBJ0KnhxpacOnh z_m_?RgA6YDoAR98)#~hSU*YHbW8bmi?^~5Eh`=%LoGTP#fA-YUaHL|P)k8T00MuBa7p#FN_PzpdH(KY zS=pHR^L6OHgjZGF=fN8KH~XYW!oL9`K*)20k(KZPUvaX>HqMCu+=sNWwcR8WqXMug zS!oOSyXqjjn2$&;rNC%p%CX~Cdv*bZz(eUdVc+^^D}e?LYI=Vi067}q?Ev_NU8L9! zQ-Dc748U}fCr%k{W@Y8m_o;=$^yzF6>p{IYR%tke^i3>oqlPS(wLW)OR$kxJtJy#e zjnU;7^U9GI_y}K@j;zkL`U5J?el46Erw3)EdeveIOJ>+rzYft2h!6@^7C;yamV=MY zJ8i#mk0sbj@uz36$Ewz;i;-T+|&MZwAKov5L>?fnl35$ujMG4@!MJ%@{ z8O0)11_UZ9>TSm`R4o2<@AY*cWF9>f)|)hmfo(RQ<_~e--Cta-&m^Yjh(m)ED-5#L z5<@!jEO{++z*^Ay2Qo|-9_h+e^o6oLj&@K~_YIO5Q0nX%j7>&oF^A#1vlFx4T zWdteU6axt797w9J6Z$sx*Fi?{5u*O$qRJQ4?U7`0waAV$H=$BZhYU&MYjf8vE@46&y;_%b@$E z-=squVtbXBXTjS$%cm8QYZhZx;yw$B% zbW@t8@#2tiup>jmO*G<={;%^*Ym%3nYUe5Jx%g)5IO`L6##?K&*g)}Y&YW3CJZ*y4 zOk+0woF(UI`(Rx7xQ$u;VJEdUT5YUVJc9f`OLQTs7U%8$Z^1%sGxsU8YKs+1$@JMu zZn$9f3Z$5OcHi4J`qDLaT4=ioS2=578ugZ-GTmR$5#27ef;fUWQ^p0V8Oo6MK1*3Z zVaAX?p7mPVuBN2YrP8ZU3pO>PJ6dZ!%Zf0x)_1h?x|%ZjSvR!>2?Qj+sTiVHIh}PM zZB&cY&slF1BoMWOx&iJ@uP22PYSNV9~W`u0*J7wQn?4BuYuAZRYFK9N3Dx%z=SBX!Z}!@p_`)R8+RMw)z<%C# z&y-P8EKxYzI@vC{@)1jUX|AxloS9?)|N3IVcB1FOeHo%{=zGf%z>q&dl2Xa}muU7M4vIaQ1ObyG0O0RJ?lsuZo9B z9T7~-bEniNO+M`@_sZ@u;ZV%Iu+{4joSSqn7g&LDfj zjRv5b8=Y*Mn6f!T#B76#y+>iU2EOk^HWVH^vF7ZhpXbM}>JdB5Zz>V8pPruVQXD(% zcWGFp+s}=5xXu<7&eKL8hL0AKKI3P{ z>w`PHWck%=*a+!5u1U-^L{L!udqI=XX;VP$i+9eO2DL#i4EKBuzbKYo-g`T#M=M(J zhGtvcEc*`40X1qWLnwOq1mYg&fTVg22LZu%eQQ2(PInf-RXD+VZ}rP46Y%X ze|&;IZoBi9i~8#I?)dRNWXS_jzG`iY*^DU|%$Y5Vz&O`XLC72B=H})hPp({iQ=+UG zEu~vJ#+C{~i#=m~Gn>3bJ4-sH4Px5>pmGkOiYd zvb>p!s8L69)5FE z)h}DWH>+Fgca)B?Wfwveofu3+?0@GjS{vB|R$A&TIe=;bnaFK@NBneZe3K@#o~{X0 z0K1IfipEb0XB`?mK)axi`SkG!OCi>|Pj`HE$8Jjyfa$UZSLUpmWy>`bZXBp8gUJz3 zzE)ftzdZV>QI|K}M7-7m{9t5I2r=8KI8r@H2%n(68>*q_FtUADGV(6%N z`S$ImOoNDC^vI&A7wd_|~bX5kl0q z8%$;`aW0_FSlj=_N%}q6@CD2A~Og{92(i z>>btc($5JP=llO{liHlZRaPuvU&CgW*+i5N8;+|V`(aUS&itvt@sEf|+Bbw(>d1I)M#m%dzoak{&^zHyrqtAM|oK2F}- zZ8`15rThGO^rir|rzb1*}HH?!5K^yZ^BoZUFX^n>k^s2%Cst9E|7RNUwE z2*3~!3I!l`Kp$7$Omd8N(|76ZR{gH{Q4_zyob}{LJ(M@R+~$yIFl2N4H!tTMGVbkB zn(`oyVC=oo)jnyUzp>?y&YBJNMvO@R_V@JvaTKau{JSMeGqr&Ll`<$K8_0zrZyf*( zv3s>+-+mlTaJ4j)Wi}va3^K3)8$)6I?8=1F&gLuURKv*8CAr%HaV>AF)Q~vnm=FEjx=FwG_|#{ zFc*NA8KL*w4dWA6^f~RiaZmnyC^1UJu2)1W1-7(Xe5m2vm5{Hlj;yT30N%apRGoQ0 ze*VM>_YNtqteUSa%4C=m%tSYG_HJ&-y(LuX@bFf2qJj|%g3XmE<;zi8brI29n`tha zCLf6?QES?w$t%s!dbXxmi{NNehOcTJ!=bxq9;+@zePw|{W3 z_JYEDes~s3CFhQkm;!b1e>Uw}VYb0YWhUd(H)B@qWU4H2ruau{ z4(#prpbDKbceoc4L=TX0F{CCo0qpM+k=QCljjCL`FR7EEOEMHfk^xJMR}K=J$UeC+t1T={rd3E)|29y zwu-IUhgHi{G(p?UTFx!|D}AE7%Iaw?!a^tRDQyzjxrJWO|0>@aJ*(g4qOm2bYvB)y zL$-K;3{!^JHd0dmnG!AGPmN7)NULop55T zI`sWk@;HjB?p?dqLAimD@blQ88sFWzD~u)2pBW~JmuM9S!JViLiR&t@V>R%01=}(1 z{Zy2FFm{%Bgu43|?Nt0Bn2WB8!GlHC%+J;dw7mEemP&$8foE!(UP}AG)YW;Vjl1cp zC*P)A@%0z{1`e$P{J~$j>-TN!_O-g|a1xV;;A1Dww$N+erj6FtOfSRH;tScYSd^CotgW^ z1U;A&QM8+)ULt}x*V)!5ukkYd#u<6r+tda-wW#mbC3~;-$(a@+ij$p1ScsPOP+3hu zMoH8#Z(rBb_PuEoN(o4Iit_;61uBKk|NryaA?C!QN)moDGlk+uAvfAlxYEqX{S0_O z$SmWBg|i1Lyo9o+Nr*Y^3w-?TlDwgBFO1zDf9^7Wv&b}j7hQ;RY{l@Fv~No*Q+)|0 zEXYy-LM6HssekyB(zZj}-?jJM2+%)tR)YqeZL+TqhbT4y&~p zN@eiTKu~*u*GX#=F)m`if{_7*SsAp_1~Y-KYd-ka=HxF`(w7A|0Re(#G2o4hA&j6#!+0|Vg& zT`U=PvwhFWg?FbuiV1#t4KtnMn*+4Q)J#sa9aFCN+FeDA$ti}(vD8f4+;6r1Z->FU zqgM2)djdSE>h-qfwQ*jNMbt=^zpiGXNw+#-m3oG9$1TZF>}F{ zPc~A86x=n-bexFm<;a7|;a_%SigS20u|T}>*t2~_T725C-IcYQ0&yH-BL=l6YjaKL zJ^_#UKtsjV9)n*c0|NtQkV5xY-~`=C>5Si_1Z9R$Xv~MuQkM~igl@?1Z`r;*mGAnR zW1c=2->oqJuI;2xY2dG5@7@<&x(x%DJ6^LcK!%VBVmd2XV5JG|IPK^AA>x{Y1Lc*s zaNB2MRZO{-!U7jO=Ei~KHC?8_5;^bR)#V`WAG5XN`Y{Na-f6pfFXDu3M`==nl=sI~9b6e|F<&M;a#%^{Y4Si4Kn#D7^XD;Db+LwDe zG;^Y@kl`f@itq{3{$CBR`kj?r60xuyY-E|A=n|wx10G0Gz~GC+D!cIHQ~=Ke>L`fv1o?`~=BD>E z(OM{YDmT<@@r0UQmOTla4swn^bjKRQM!%mBxP(pf|JZu-xSaF1{Xbi_kS!t#$sQ_8 z){=J1SO$@hijXBCkr0WL5-AF)EMsgTLS-r1v|&h;rBt?3(SrP*r}_T#nfv$0{kR{G z&&+gP*Zci?oy&0?=W%jhTKeBP*Kh3fiBq_7ejg4@^xc24x4ypB(SJb2C?)$`+c$lh zwl1^I7-Sw687r><(W!Sr!gv76)Hm-BJ~dl8?S^$oLE1bf>SAVxLZeLS}w)<0E2oAn81>a@L zAo4HblwlSW6~{_vFK|zSLt|7-5#5l^9F2Fy#k(kEsY}F1*mTkL+e4s8$dHHTPk;FE z;o7U+xvN_>ZZzjzEj%Yxzy{84@|XN}UAp}BXxr>gIb)NR zJB?X<>DskoR9*|2wU#8#3y~Wp2c<0G*Ghj+Cma9zwaE{E_8B&;qrr={jW_yLeHQ~m z>>t>{SP4>^D;AB+z_yBv?ftF^bq0g-JGgqV1GiCjRM^GU`8PgB5kBga<)`U&1@ZpF zSAV94cOGx_U+kn=#r_}NMt%QfgWXTr6 zf~^s@h`1F-`7Br8K2m8RVOv4mT#g?BMgV2XvNSrDdBKCnu*?eifp;;yK)> z9)J9JCa3T|WxM@sZFg!<(RYK{+Yfz1MZlTd{bOssa^$YB(*veZf86qKV$1tY*NGE< z8;nKi$YhNj@RbB?NGa-BbEZ=Q&v{KJ$3v~yFZTj&$}}B+{gVV z2UN(`7lY?a5Yu+8RPR#MY`aqO2-lVpc45fZ#I~6Lrm_e=s-4bvpDd5245{B`UUwPQz~ix zqDQ9RzwaeWuUQBqu@>L+F*PS>5f!TgqiPaw?KzY#h@-H7Jn+rzKIfIK`SQk?9ru-X zKXZ!1x{+k;;Z7>j=0q;9G^+j7!_@=UOob=Yhs6Ns4V;mSZHTkXt)RN+ZSMmZA}*u` zt{XLabXvkLL?UTOJboYUIxTvxC;IbmTv+0_@sMJd-Swyb>sy%ICO_4m{k)svTL00G z znxoo9l5EQB6|54W1R=l^YGu~?d8w`4BVr#_vbx*<@L>xAsY{CuEmQ_3(^u-R|Ja#c zk3r8@Z{HdrccUm~#KO6%WZK4h(*0Wg-sTMp+ui#0gK-YAf~0;1!pgn4s1YynpV0}L z+6Q*nY{0IdH8oq_$hjG6zJ1~19Zpm$2KCJoH9o0bfGZZ3n|mP!3_N_0 z%3ULzS5KZYMUEBh;}>@5%%FY_s|*Pa9)dNe^(xb5iH&P_lEvU`iZ5r5drN$f8ziGT zZrzp@^p*;lunRhmP%Y%cX@~j3(~Cz5K-N<;zew)i_t3o^ijkE;W_!WpG5Ip%(!+6?q@5%dwx z0Xuk-$>(iDWP>JL#xQ{bsUNnk+4;z$J;NN`x5W{dVoR0}oF4EjEp32lr*7>Sz zPsne?Nt068gewgcf?nY=+$2n=85nI$8hea_$$m!}y(p)#=S_hM82IHK+;ghVx~Eup zQ$&H!2@2)*REAK6@g<*#<1F29E@(O?pQ_6K{ z#wg-0xwrQ`VQn=Wybb=w(~-D;eAZc6)|(Uh0ClG{fTgTjW?m#ZFCF3MGd7NM4xi48 zbf!3_0Tgn;;BYfrE?{npfonx zBm=WalQd~2$;nvy2h|_Kj!W&{5mmfJ|Ni@LGav}?u9hu*2DW{T#2@Lx{?O1#;DLZU zy#&_i_bz9t9>1!w!G%98I;}F&2s%nXCi5Ir|G6mTx}N(0s7jP-r>ty119qLtwEL&1 zl}xqxtK(L8nfU`TTLyL)FL5G*e*HuzmZHoC$F1QL(Iylt?l~7n6m@0SH)rnCh+8EnBvf0DyRpi!4WxcPJxM;@E&n z9ALWEwZks~=|Mf9ghupVHgxRtTXq!p(H(D6FpjG&Y){q|TNHwu9BBRfk4q=CTNky# z+YR)G&P}IgWMsW>cqJz3R-sndUibPo%Rhuvk{$29srE^vdHb+W>%kjgK$~)saQLiD z>a?JoNUO`a6DF77D}!P(87b-tnWkYaWflEs8JrfL>PgKzZ@y z)C63qM6gZ9G&}!LHFJtCJRDd++!JB00&)9H^-7HuQiTMm?nSjaX*P_kOL46c+KQ5m~UZes20UFsF~UC|`a1+!bi z0&bAPOy_<4W-@ec_7{*Cw_;F&MmasT)_pw|74t_#HvMqVgtAw!6ukd`u8DfSMWP-3 z73Q7&QD~hYUy*Utx^)9x;v^B9NGvTl=|kOKGD9U|T{_E}sMIVC3 z=;$JS^C1q47H!+Lt0~p|kJyOpD^M_gWSa22LH9a&eNIX0|m7GuB0`nw(#Ntq@mcv7@7ihIA+HxAXJMF?Fbp zAnFgnH(DI^xlVG75p*lI?AR)-%zZ<|kjTEvR=l3sKi}@b%{pt*zWw^OOMQ!I*&%i% zX8{V^jo1|$zo7I6DFV&@zz3gb%@6UD@CZA6uF?`V2LClV{XAa*U6ALvqHm;03Mw9r z!JStT6uf}Gls32qPg2Q@+>rce*;Sc?0TAJ?irHgfSq~i@owRtgX1frKKt$A{K_}m2 ze^{SQ2m`IYM?YIzbZ7y~kxl%LH7{8|(_*l4;K>ipgC7~&EI8yfYFyES+A*xrsC|yH z{z5QjWb>Y|KtrKk;anUp-h51Y7vQ>=$zBZ{erYWpVN)$05yYh$EQo{}qkunjD0|se z5CqN$tKJQ~GI4Xdqv&*vpI~tKdJR?NA>*msc6+#4=g0Zv6Q#KY-1i2uNmLVKOX>%>fvY{nN2qA-KjqaI&ZlrqvVI29k^1OJ-Z8kLTd0ZI|`X_BXXr!OlIG z@n!QjhK2yhP@~SEic@<#sMWe4-Kx4Fk&|^5jlR2j*%aSrDfC;l3scX{N+TMP>P}Dy z;BR2IL@%Z=zxOo6o3f0J-zuAAZ-u3QIR(qvFga%x_UX-qgGKnbWBjzX&u zWw9!_@6^hws>V4xg3)6qB0Hns66vWR`#4YGSZoU)rFb>YEAgI1@oDTD*Mv=1AKgdi zSAB|Op8leeugjC&2aLBq(kiY^m*=N8&;B{lXna?reZ79xf>J)9w3Y^fbnpgHUd-*N zmn&I(n#Bm7TAra%W+S7_v#2FS^-DoTaW>HAR%cwyl3u1|ebYbnJ%D&CmR5}9qH1sk zRgM4oZC+f#wa3SRR1?$EEVulkk03yUG&c~tqcIOL4*}nXVZ#;}nNjMaD|4L`FRj4o z^Tjd$x?^nj{4Wqu*mTuH?d1?`A|6MhISrQv;AXMX;e^R}V|u8*?(7dNi!I_e3FoWr zX5J=ZQ&q_}*g#V5s6z+4|CM=om=DC1m~EbQuNg57c@nHa%i9bF6gCVVy16Rb^!E6q z3GQZ>y549JdgD4<|Agh{6uG^$!^kq_TEUhyY*H1_-LCSRHXhzWOEO6jc z{R=Ya6qxGg47^De83edL%T4-a8&7JSkI*H5IR=qEihzxt@=67y7 zw8peE6Q)xcQi`ek7sE?Dv|vjLa~lmtLUF`=xgvN@etvi8AJVZ)1`gYU_z7wJXjPo1 zL zDzd$SF**kbby7SK!leNe2{gJ(%|3l}k$nwJ`bbqOx} z0Duo|J|S!C7&i(@jCITr4t8mBt z6!~|dgM$fVpUQ^hs+km=J|hSA>$h825d;#H)Jsu(`{f0`$%(AdGuoKfHjwF>ofCSf zKZ00|_O1nh>~FeZ3vI-xq~YSWDKkk)05ufKGVG-BX;i3=y@g`+u@Qy^8oNVheR7`w9yT5Ii%b|5h??T^VVJQ0ZVDxUa8xtnf2#F zj>!xyEe->GDL_-XtP}AQl@Ku#K=2fh_SP+J&%(-l-+#k;_T$0=xmw(PEJog>-N+8TG!scN6#& z<_m7Ge%i$dvDhN+i^1W*NmK5QzOzf@alJ>kleN|$Z;wmXnK>Qdt{Ya49yLn%wOMB$ zzL^l%O=cpH>?n{9|6FzJI2G;uKuD~j;TmIs78>Rx9%W`lXiXeieR%?vkaZO>*s4Ll~jz*MNqL`G}s=nA^Vq)Ya7*>|Gmz?8x|&h5|QF<@32yi8g1=jWC)Ohif+7JGi^ zx9UEoGuSh^uoR~&T;Q??DCiEio!-tH_9N|LqYj;Y*}X$uD!iC{{?DozppN?(Prvg4 zz~&Umbn9zv*LHOEz{<@?5=^JwEiid}XloM1+ zQclSJYRtJbwYB|t1Syp!#>Od)v}jIbH3)T1`o(yV#Mr5XtkGPi_w19+(8?HTG0}zNm2F-nbza&jOrH0?D(M3nUGx?5BdsY zOsRQh*10bJ?|z7ry3zyfj#;`lADbCh^UO+opKNX~CU%r^)R=_oF_|y=HZJQU>&EGS zF{%+-ROW>1eign7N1h%Xs%A zQYkN6mJ0Ro3{L@S!Q)VeWQw&k8e3{!Hx+kn#PKwITI0rbM{MVL0RyHx8w%WaSwy|z z%_{0UmP{Bqvg83a*WfWh@L$KXmx{ES5_LrXlew@LL}%OO%hQl;ID!%?2yOhLp0gFe z^JRS%UkC~mw^D9J@}<>v*q+O}JBWThkl|=s1x8r6B@f z(0*pjgC5NjVE~lI&1z0v#^Y{z8|ERp*&>l$8h#8NH$gkj^O=}_@7%rt3Nn?XcfwOZBr_tgw*M3pV`bKm%Pbiega% z1r4;iOVE%m1g=aZTo}ga=X^zI-LuW=%+APxK~eY2ZY)6_us>F9-$rTq3hw9dsPl8CZ( z%e6hw(i=xJ5IU+@QlC&em?xqK+frY<#7Zq1oxV(&1I=?t{?Tt|!vZg!82Q8m9^Npb zsAG#JWY?(Q{m`cSVERR+H`JK9^8M(>F!ph^YXS2FoFr3AL5lJz^V+9o`Gd$6n~Avs z8w=M?w2OEfsWCaRZ^uxjb;_O|wp!S5ta$v5GQ8%E&tN9QU(pmrzxCCohh(>SAF6Mo z>=lF(gTw5^2yBm_nY+7_XCnM~Ht0L=v-!WQ-VY9u0t$kH)802pNgUiZTdi+;hlGSQ zn4od84bs>6y7vR>s}p`Tkg*caZkC#&FJrc_!F6=o%&2SNML>%xYbVv$I6G5pHpF-y z5&&rsGk+gC-jnD~OO)DHI9{D=r39~Edygm}TZ$;Y7(v1-?U(h-mKuX@u&$C) z{laW-e^GnpcDzbrr-QwB{FCVJ{-7bXTD`PcX;PaTiR!>XhkmX4yX9QeePVD1`ibHh zBMb{tkU)g~Dub*T`jSV+`z`vO%IQ$(%uoiXy7)k1+h)CG0Zm~=p}EGV8&8ZSbx~N- zX|sf67y10f@yrG4Iafm-Z4}@LL0>BWOx`&tholg3PJv+py<9po!KS=SO!!y?9ESRW zwU)K$F-3xVvn;-m;H}*L2dCFP&=Qxh=_5u!ol{6N5GTfj+rz~6v8F)*8+Uy2wc zyMAzT)XT%3HyDB>VEte<(Av>!{$}(*8UrfdFIXtRNCXy*WJCo7ra`iX${DGRilsUz7X4gNxfo6vwso9=ow(;ui0} z_K~1+P$5W4W|Cp)CMe57QEp8LF=D7%e$w0Qz}t`VyVS(mAb>Z*Bztwc=o`+J-_@_b{)*EwN$6O zxU@MwYQqONT&@&QGF5Dn*lpsxcCGHxpRwVLOgX0?cO6pq8b1WP*8*{_{bXCidttNL zP68PSf{v^HnXLzpd{V(67m~1nc;3flpQi;>aVhPu;-GS#l=^#5Ek{%QdASr$Gzj^Egwzo=b;uV{3Cw!y9bA18LH70vEQJsHTa4>o!H}g3dcQt{XzR{{K10+>-2?|R zHjc|azLVDtZP#Mw-B&U5mvzFIadFlUd1y-yg!XM^{JtrC>K~_`jbK4SeZnQdx@Ud< z9&-4w4g-#)9_{my@-B|Ng>)7NvPZOMm6)&8Pd@2KD|29J8c-J^-GNv~s~CKd@1`*H-J zwOShbGWP5~o-12UQn*$XE*|UJ{=3)+Wz*iSBeV;yo!UoJoV$DO)5>iSto%4OhB%K` z$T_NOR&7yw&DKpb;86V1`HpUNcV_OtccE|!EBl&tz?zN}ETRB1jqWW3Hb-8UargHu z>G8iuz1A$gXn;l1#@%G44|`W&y$(qxHC9}&ss?1)+oz{R)NqM^dy`W@gfMb|;z;~6 zSLDS^_FK9A%C0<-VbQJd{a_Jl-yK;s5;sU7QsThaOuw%MduN6o3Z0;2JX_Pm4bYHc zr8#0DTRMek_QPScbNw4%Z^=Z#RgM>$Q`lPKwxGImcrqB1ir9Q5AKRBm$XrK0p6v>eG2>;h;o<7Mrm=&hg1#2lH#p zs8Qo<-*u&((|0axhJiV3q0}JL8RB7tI3Oac+i}DW+(}%#(`fgFe&q&7G6hPyvvQq} z1(m+`-=u6j_YZMh1f!KN!INiNBK}=vKD|CXYryM#cRG=vU^R5cNdlt=&zkZs2Hpq3TT&cNyS_vgn8aWu$BQaYOAj z94`*TA|y6dKi{GvxIKPaaESR}y~%@Bba$Xn&=pk+)eT&qc$V{HqDwB5@#GI7 zyfEKUe&BOy4Tn0u@*F2r3Jr^#61oF9Nn-ELA17uSGny+RmT7sNetehOmC{UB&soQB z4w30$AQT;9DglS`Y}?0^%bGC_z{bJf@>_;3H9pzo@t7Il(J&ogF_==Y_80Xbw>^1j z_;r`TEME^ac3O3Wh!Ov*e$$ViKgF&R2~V=KT29&g0Rzqse55&)LFy33M3OGu*fN?* zF}dL-o_boDNSXgnyifGcZbHq7`RB=yyJ z!}#WjIi`gSatUpY$igLKKvMl6n_F|r>bj3{m9rWw}IN}IgTNWH4L0T|i zossn&Ic9(sIsA{rY{kfq&Cya6`YrBDp#9hKV$}hI6$oAs{jg`aCFykK%qvA6J8^Ab zgN#M(QE@o1dSS0bdqb^92fc5bfKsm=@PCLtG~125)W=$mY$H4T)vPVyk0aTHS4Z`rnnsemkyRY<`M zR7cV$Lu>ZNXHNI8zI6y0x$DjR4a<*)q&tarr3GWSAh&kvUk8E{xR?&&4om$q%i6jT~LM)AC$OqgV@LXKGq}nPB3IiqJYu12y*)4g7 z%4sOFMNZa9-TN<|^)+`lYj|nHlcyH{0+O^Bi&w461P9fuUh=4yv4KTv_fmk^kbOOH zX(3E8NOt1UwzkY7;J!@o(9gN6tU2vq)otre)KNj*IxtCp>{FeHJ!!)3 z;#46+Z}7|~Fh)~sIYInH_iA^zL668-9ju4ACg_aWdy7UGoRFhw-!^2JxPeL~F>l3G z(g@}pTfMJ-&tjj(LawF)^BH>q9X$_0#teWJxeY$c>+ah7@C~=G{r%r%^-g+NB&(ED zJ}*R^5_BPsy}^J!)D$5ZJ#Pc-G0Da}05*z~sokyEek#$Bb}yQtp2mY<*`<5wm9ch$ zb56hxWBs)ewd8~4!az=GvG#=DDEuS8_Tn?<#uRlX-S@i?G`5)U+0x* zJXyC9K#dd4)Bvg#tc2fyNcsER)XxU*XDX@Zv9}16L{G^AEj#61=AuxdJj=gJ$)IH7m@eK%9H-9c5wq`mQqCsHfL~xQHNk zRn46SAdv=VpG;?*Bd$7ZL`QGKxgH)^&dIJMZW>eB{@Ed>?<+sCC>8pPA^l!cyN+>P z9TbzEkuha`X`XO$--l2?B8@a#xbXP2>`wybd{4Qdw&5#7O6g=P%Dyu%M*Kt{p`&*B z)xA?>LF!NyHoe6T9?txyTExN`)2GkDM~Rzh2YrQh3@fNazVcoa-|zTS z*ks7scax-OlK(?t&+TkVaFE$d@XVBjmx8nz?BZIa{!}<~e9-ROk6K!gx9u*rw{}Ac z1?;*6=tOEs@?th{0nm6NeO}d`WlBj1sE=)_V^2vN$`Uw5W5pg*)`RJI?i*Lv47H5t zr19L0DgJI7RzWn>^M9jF?+k-Y+tCM8KfKL z2?U3=Vl~R|Ent&z386F=7D!M~!Q8M%k+4s{-88aQaMn6)9+pTTsdRZS(SLNV-`(X~ zRaN?{lHiTCRBjEk*ik`!tTav=&Cml&hfbCu@ntG(>seedU0uxCKIYC)2?g17xIPuQ zct3XYvJ;O&K~-<5GVjx#>KE6ydBvk>6;aeQhdD`Jqaj2SRa?`AL_5W@YF;2x@uYkA z?k#O_6dAWnjiBx4aqk8Y>vHwhs{#?D7mf?nX6sQJ0wwWt68RV?;*-%A=%#p7(vL$@ zS2DpNC>O_5w4?l;T5Y93*V(OYkrGPDP8Pu*Hv`RM)wd?d552ro(o5DV2fldbqQBI0 z<+f-48f92^`95#X|J1f3@m6|$PU)#!?tIs72s7o?QPux(cGe(53;~VBmTbZmXh^(z zP_#(Vj*^=V&oZkm3nL?Xe*5lSWiWQIP+7uSp}Wu*PO)a;o~B-EyDmLo2VbjtwY7J%%ARJ` zMpmwayO6Uk)_8pHlsBz*XJut6d9d*^gv<|3-3t6`aHp!?rw)9SikM`G`iL8l$gTdG z@c|dstTnQGfa5Qd0&q?ulI2XoCDWeuxh7OiM)cqG=$ud0e9N-#TH-OqTpp{J<3FA} zJ~i22)u>w{*s%1Zl(MW>lxjBOMWjyP)8e$7nzGk&x#8Tr@x&(@M%$_zIS(eW+BDg3 z3_myElzFuZpT;uG7PmqBacQ=Cp_k+H0Ly7BvOgzRmWbZFny~1emr|N}bs!QkS#wNhqCM30hdh$tFCy>UXYVAA+FJ{GhkkL%0@*A4oGWY(Q^9f?{Oz z24bs)S&|f~7!xy31ps}KDO>tw=G!rLKV%%~xN23K$k=O9``-Rz3OX)wPSUgpujvtH zmfiAFscF@*duKQI&d)OvTJGwyn|J-L@5PpF+MFWW@wn-J=UiRZk$KbCTz`G-c~~e> z;v6%ycBLED_%x2zL?gk2VFOTbmgX5pWWNG6U|tY0cXp{F8=0H{U3ZXyaV$f{)hExL zJLT?WPu(jHuQa@>iT0%l`_kXk^J`;|MOpfX`&6wo$9YMp9b^^*-|WRRv>e3ZGU*o{ zyO+-dsBqouhoUy9)fZrW41zO~7l_Eh%4_Y|%2&InK%@D0rFZwJzx8qAnan#Q8MNb8 zD3s#|+GX>&C7-9VI&q)kpi}#9sgx9&CVV#181d7Su3DzS37Wa&V6Wu^e=eCrwzqN( zQ!U-Fn4*Q|J1|opE*acaM2(EzAQSEU_<>HgYk?bYJo#x#g#(8Uf`atc$ zvsvfTW{O(SQv~2m4C*JfGVK4%yw_isWm}#Cvx6qx2c6qjSXiiJ+t^5u7fL}IFGloS zGFaMWP6I`AdPZ5sAcPEn3g((ag5Hm2az^uH7zUJ;Z zH-EL}^b~vi`mRoUC{p#j?X!58oRJ0#(Xp6x@{6>8`SPW&^VSp@9+7!>iUsYmUww{k zw!QdFuTSKg_?c!%+qxy9c%f%h zYqU{@BYW)j!5pODv8mkGl;O+z_3yt&f-{%h`R)ByamF@_dA^dkXqCIZI6t663qrpS zYEHTh@drZ-tX8(kU#rV}_T&n#wDv74&T|EQ?0e!3A_ zt-t6Wsm(b|;1FymL$1cHeuV&*j(;gx2@X+TI#*Z-7|LmH;uOziT3^eIQdo!WKoe0oDZtz62R4Dh0N*FRHGiib1~Ss~{XW|SA$uO!Qe%IbmxxjQ~V_Q(|ty`L2suEQ#9d&0jpX_W?0;&jJ z(1nN&6-7W-HC;On|IIe|s_CRvF`&N&r~yC%v!RC-$_t!FR+N`#4Y(B$I!qX1@JHH< z;|Q^Zk>Ur5aXutvKeRSdhJ!8(u5{&B`99BJW$&NYM6pDg5EB7T&ijrLW+yrkUE+G0 zxFeN_{bK?9yGJ}R$A)7;FLlc=_=kIx)=#^D7CiC57mH-b4?OH~a`NY~JyK?`Jdiw4E*rA*&t`1dd>0mvX6FQ)hrwnr#U*1bXJcyfCoLeOKr zcd0a=Jo#W)&u(gJ49e_(SxK5ItUp+IWgS%vXr=DoZxN{hD0dRy1Mi-?(+6s4n2vk@ zhImB?Y4EGcv{G1Mgh-%)O+Bg?)JI!C~{{kLFRdtdc$|Px}rJoh``&)`kIf1}`G7lK1yC^s@PsRJ( zf`p^QghoR^CgqyF)tRnta431$&)a@gpH>rR5qNWJ;u1dKc%eD_69>2^%wzEXGA#|a zy}DNv*U{Tv#oWxpgC18J_im%t%OB0nH2@O_aAlL?9t`{LwC%86?~XUiW`Aq3ls$vM zVZ2&gmwnw6!e8;=iE(xz^_&TG(S2{)OUOf4366JL#*^5`?q{oaraUAz_*iJ@z)p>j z1nuzgvGgh>;&Y`M{JOrQR4lP6JUXyWZAD8}P!IgRgeZy_Fw-qEGYWyS;={o;LssK% z1+D}YAoB#Xk838)F>h^oe>EB^2r$5qtkTo_JHbgXSJoF??C7bT<2aY7a(USgd)g#7 zH|nXyC_xG`#MZZgnhW~Qo$f!D%2K?^g$$udX09rvhgQ3s#swV5CeblPA)*gJ0LE@I zaZCmg+;*$2H)w)Q{Vz_#*NjfxyUXzH$Te>!dVN0yc!*bDH<~<3ahdU=Mmf;?aDQDg zCk31i%_EwAjMx|9E}$AZ?a``x$ai=u((6ccN1-Vr2%tS8q!pc<_7uxwJ{`{Vd0Fi& zTeM`Zod*P*W1pq7eJ{Hgr!iP7*$LE?sEQEX84|tw7}w*vUJpUC5q?qjEpf4qM!gA$ zK}Z$>D_k}!CxPy*Mh=Kw2-RSw^3`I3ZS#JLF=%2`pPL|EB)P}BemRjpP9_RSi1Zsq zOEzEJQP@4GzCO~j!gXhyc>;{@qrm}c(YcK(TTRZbh+ww)^M{_g$z zzh=x)>DaONHTUT^`{=vJEPtAD{AauW>S(sVD9>+}NSV$=&cP!;CE5M_Cn zqNXD5iLE1e{z683kS&BVOc!^s=|lAv;Gx{vE@nR0hPbE>NjFr|x#8_ZPQG`ArbK)w z@l%uTiisgP?g)|rQIQhKKo=*-VV>TB#Xfr`I=_3HTL&a2tdfp$oWVqUr1 z(+fz4SP4ec1_IZI5i^Vf+KV<20m*fiB+J9$Z2!F=p*xZ>()!^d*L6L*^=a1h(yyK< z>%Jr=(c=a<>;&J$&pndBd;Bl{;7TdSnv0~sz z)N%igVgpMb4qv#6iF%3Em{y>TAo4MF5vH!n3}U`q^#5nL!QlZ%H8sk4rP;JRWDx6$wJ0dNl{IhYll= zy&WjnyMtgJhc%)E%rB|A`l2HI`hh0cnYW!4^3uAh$L>Fwmui(i1=q|5Ivw7i5ulXs zfsX@$l@M!0c_0fPBs*hm&P*+ET2t;ls;~3;Uy;+RGMRVn+6J%f^*pd`SG;?X5(cJm z2%Bw{&iJ%h8@eNUCR`t(pCAdQQfl;Lc8W-R>NlIUO{4B27zyZOv~i-Kok@xd)SaPg{1jV+&)I*E^@5 zNw0{TO@81E;<%d(5XRoPB|s_);xXe{DH>|8aY4+pL+YzG4nuYlzmg1CvT9`C()T=J^5@6W7r>dc%h zS83>&n9lP$?d#>8$Y0m}E6$L%#Y93&B|>UofmNIZ>lLc3}ToWW=l3E1W1 zv!OmWr-|)5CECYvTD$FBWJegCNo!HuBCpl#*Uwyg?)w2(ELUppKR#fvOr45H4ucqc z%c7A9g{e8z;0>f@pA~WzqhnChaU$?-4am|9Msg-&oZ7mg~KT?qGM!Mb>_@ zxU4)Ue$fXXE$9Tj~8_%>JcSola}g^955GVrM&&b5$T zit0v8a&jH)$TPp;4Jb^xDC9>49kto(84frIIMkpV$y(Q2ZMJ_DDxJhoylkayN7f*d zrCQUhq`aC`{QC7asxc515076;pVl)#j+&nkYqjGHGPUeP$}upJ9vCBb(6$biv5$Mh z%L)=i$4Uh+AmqOkcP5`*IC3=r1U=(qdSpRfSYE(vY{?kK()do8>0Nt5$7JH6a$jft z$2nW-O8XE9nR7l#Ha5|ZB`x`Y#}08R`9q37W~9&vh+7$dxn0+;mgwWE@@FGs6^n?i zA7iwXBOlzqugFvjA|+vr`Nc)RMYQ_j#UXZwpDLivqf1JhtY^fAQz0Y0@CKRT^c-B3 z$0|$}f31muiMC!nn{NcyzFf7f_az&LFD_sQP;bf%Z;M%m)jz@k@Rh`-0&*Hrc=rDo8X$q&XtP7;Qi%y~Nph5-;x*6M!;vDxm37&f$Wf!aPT10X zxmcy)AS*=^t&0GN&V>(->`k0vVX;3 z0+XKYNE7;>`=hzko=LwqJ} z^X&b)XqVDX0Tkh^TDRFiPq{35QUIyS40Nv<{AHZA>BgNMhDKTL=&j$r$V7uZzo~$c zYg}AlAH9eoovTzC{aNCD^9L|v(emNfsGVhYUyASY>Y34Y=6GV*@4dI9N5e~y53-X> zLEb(xI>++5@tPIiN9_;&;Oui;(HqJ# zm=ggYXwhD4(kp%sf&d-(Rqph`#U8y_IY$j80_ZP{L0*KFIyz>f2qC%BJ1F z8;k9<0J+9Pd1dtp9g6ykaSoa^UbKv1B8i~ctWlb>1(8Jb$O?*iAs7gp)7Iav_?I=I zeQ(5N1w-%eQ~31UJX1S6yP&uJq1|CG3+Ke#>hSS{5iV;8qIns{^jog%<$^jHh6HTH zoMui?EdS5Kqcrc@VCERr}+~xT;{as8(7Dsl=8ipg z5X)FDWe5_4C_D9%!n!w!&5=vVxFCRQ#irOn4)`3fsNoJOASvU>-6NLjqs1g@G6Ce4 zG~$NYVCA&Wb6|e&5Tj7%ihzY?%=6lpaQ}j*0W(8`5{KMy#oK{?Y9c<4%;b;BTW=$q zMc_=YBcGr>VTKjXK18V!KfH!!?Y-?c#LENZAnS79_$L#vUXZW~;U~f)ti%}rZv^r3 zuijeY2B zUv5xu;@DYHuwe5S(NzGQtBG_XwF(QcsSlraGr8sy%}x>~RVehquY zMx|X{ zd&!5OW{%nK{I!&a-p{QxDmMD~G_E<~wRb#>F`H}rImz=?+*P+1ss$Z!{c>$*Ro8Ob z*V-H1%>7wHJ1imyOFRF((^UaQBA?#<&#=y?Io4vf$a@U?>EL+#fXfSEyKv~@u@s%1 zGv&Y8>&LZ?r`iVg>(>wUZR*{o=VtksL^Q}TsbjQhH++pQdxSq1B(#b2ef*Q8$aikT z`VPzZ&jo-bA?&>qy%ye^;qXqPp#xM4Mk?*O=WOm!!YWhRg|n%t_)QrRaha;TL>7p) zh&0YpeScfa*QK7Ipfs1I%+vi;mNDp~fQW0wi|cL8*8a2@T=i&YR8*Apkn|K|*=$T> z+QeY}v9u{O10jqFj&fe9e8uJ&wUo0<8&ADmqqd5gcG{4J_clI#I?bl%Zfo66Sy5~A zb941QkN8{?0W|*tBSx}uOy;#mulnN;e{26wc*hZ|Pf<)Lh_)4Wrt?)No(9uvxc=?W ztfY%g^BbBA5MUQ@YUt2F5{qH@9}+dirKl1l+n>IreY&0YhE}%f{^!fAz=WmZKP5zz}W-~=C}__bI-F+zYD zK6G90)GjE*xUZ-q#UT5S9+nj4Z)iHyV7>B9Or+ySC7HJlrt~>MeZ!f#_L$hH&l+d+AKhqeh}OgLf)vFqS1yvvIho}WxdV8VZw=z6I+9WzvUHk1#?gi=b8`bBSLpO^}!+W$~}xz zXwL=y5X;J|%MN}FTFtm@Y}T25GT{Kc2?EAA$KwwpgZA%l80i~R{q^fX)7`V_syycm z_*vYi+qH;@#X*@*vTV;;MA@RLVhbl;W|;&T21e%w)#`X8pdgaP7-l|YI#u6Ctp62R za`)EUg71k(tWCZ4KmEL;?w>yHQ@i+@s0?oqJue(_Q-*g^sI`TrW~YlH<3>3x$O830 zC(qccmaGu;x(wi9i4~CA&z+2gB!2}V-1H*B;}~~V!SQ@^WG#ppFH@W{Zs`*hWgqi- zI=H9Q+8_fCs}H2NrmY|~l7V|`Xc$}=32lgVglLMe*H%TR-fW(Kq-3psnwpw= zytbmQ&b@urd{a6A{T8mRcHg%w(>VFzdZR{-#8j3e8yTrdQPg{?kGHo5e~$qSbgDyi zbt#wx3C_A?uH8-l621rn%UI4R4hsNdLv?i{NC#Zrl)K;3rS)N=0QG`h_N*x=$Y#|i zaCT7-F%v!9$6tJn44%8YbA)B1CmMT%RI;Ly|0nd$sLV%v3z>6J5Uya?E>pjTWqe}W z_U&a=vg#<8rGB6O3O|v$VO>FLn?t$jB{%&bUcR3k5k{k?kkQcwvL3!Y3@fef4T9zl$bNc4(4W0J{B@@+Os~ekd%|JT}$qhRHuN^Hv+( z-u57NxOikmGkXfUF7mYyhEOOyb8gBK`c}_ZzFRtKw}=cjRyshhPhl`rY2{O1m+P^* z-F4@Cu@?31x*zB3E_tnd`p2T_HZQB$Ns%&k$c32YOE;}bHaGzfunon#O`3c`NzApxlR}L!(KN?4D{6Sp z%2&IoVn|STd5`c7o?{lC0so@PZx4=!(6vE>1|vt0euNnVJ*9Z%OKBWbV)5UyRKs;f z{u$5CVr9k{jw1C+(90Fb%6}+}kFv zy(mU3g9ggD7z#>p$Or$ilsDL^png!#BLtj=S?E(&%%3lKtNpg%9>Ott)6<8AkM zET(^8t?CI==&v$#Su;?<i}Sof+6# zcAqjGew;T!@9hs>!IyMiF(PW@WiX1)SNG*yUFGe42L1|>U$d=JKgo$D5MKgCtsZr> zv|y`dP)?sM`m0|l_`mBOWV5hSw{8z%u4Rx`FnsC{fi#F0Vvj`&i@C>0knfFK%6$F& z-XU0u9{Gv>DD~2u0WVDj?_1snfn)eJvkOm_i6Oyt`aA#}F;$Q($~e?CjPm0?{P=Jo zf&EGM8004G7GH0}oS_}5RspteWTx@8?H+V0MB|2?3+G(LNU{6)Euwtq^s@kWp}l<) zL9JC}(uPwT=DMt2ovcyk^z34Iwk>jm&u!Vn%2zb3?m=WB0QB;CNaapyXVRE)RM`r% zI_~6#Q>f_!$M4%j^*gIFHw0uG;V)?OTS<*F4XK80ea&7Wf`P5xH`!hvEVlrM*<8 z?c25uI9rhVU$)c0zkQjt#w>bfP)<|s4%c$Ox)PEOh6IU<5!^K(1Uy#<53U}*$CSSo z2})a00NA}ybQHluwt-w+21$?^B+>R#i_M8y5pt`nY&1x{v*8w|sT2oSQ}n|f(enhT zFI)0}l*Trz`(Sz;u<$3#WSq5&uhzV#I`hr4`}5x^`rf{F<%%7~xa?2TtA5Rw~4%xbRvzxhwN{0@1 z2g*&_c{fp1?4swvycjyyaq^~MYdEI5%yIJ{n_+YzBvxz~@E<41BorP>D zNWnW+UTkzkquT65a%f{)5k!Z57W&t4j?ANp!w)bmT=T=#>ar7uWD!nlX;NTH(z73G zOgdOQ#zT`v3J5RSm8a8~>X0gr{zuii;Uo23+StmTe)4{^Z@A}`^~+%AsX!GGXk!V1 zP9tba)65Z3oN7>wa>SnzJr6W#Xykvsk5RWl1s!wTwJt|2!dTs^%K%I#>X#!Wfhu$M z&_88n-LLysVy@K-)3Ia5Z1retE8y{me_CmHrk(h&pRaOZM-H*#A8haxe@4!u>R7ic zA4m31nl3U0wkE82IkaNW#~r3bC%CG}pK-QO|S(2IhAC;ND&x?gV2224~Y2}=|H8ja>zX5Xe zjFORuNbs`eh^h#8m5wy`BStQ3mZLTdL_sk!HmrH$RWmeB&g@^Bd%2IlR)s7ddAVE3 z*m)cpy1^iP4|T>486tB`h`QITTlcW%(JSmMyg*L$ zq<9;KIhMAZ|6W8?!Z`ZPIfC(>z+|XHg)v-edij@F-Ela$Pg9znM`9N~>nuyqPJGPb z_e1Mp>8?tFtnYV*qaZ3;z%N;|KyPfnW-{0t2q`32cXf4j&t4aESScVCs&oX<9fX=0 zC~iHChn7YRj=ObhZ~m6`vVj5au%YAKmG0BhLWce`R>q?6zGw-mM@jI_D2r z-`DpCtOM`HQOgm+n|qKsWIn13l+y6>8K9HDy`mh^qYag5^SOK30BkSw8hF^)jQHoH zn1X>&^Yf6c*xgBGu%zb$)03UN?fb;gONjO>{qV4~SEpZxCjYQ9r!^4o1o0_>kmUIx z4)32=(41caaB>;Jy=RgQx3|LmkL&CS=u-OJt)Pi5J9-pyo_94`JaVn4OTjI;KaQ=* zpyi7f8zB?grq^R0w~EB0f4^ZTcvgnX@i!23%k0?rKk@iZ z!mjEGoaAdO=Sz*mB&l%vVD_&;lo>s$0BU8JQmVvCr|&`>b0edspQ*faW!6~{2(iRx zx^|&?(h_l3aB{-vA@g9%CwW>ZIl+Y*2&ftn6h|ez)$sEI(Y4Zs$q*JZA{yt)m9nU< z)l99IAnHxs*_s)sh@dCCW_HlivY8H!$F!9IsDJ=6UBfFh8GQnD!5hVe=+G{#IkVlP z&Ggsf)^^owp2%oI$f>;LsI9Rk1sa!8Oj>V;nYX5b@7XWe!Mj|t`XBzTa35S35%qF6 z*R^QRz=`y~5O6AirC^H5J7eK-I^WtieUp<}qMd^b3{go*=p;Rfp9q$3&FBnJat5QV zBCH--ylnhLCI%-X87X{<;A*emTqL?pU0SZele?&MRMnd(d?WPhcwo_pb`|tbo@ak1 zu2ezvE;7@H#gI8)M=XV)NjU5BP5BVlm7Z*10 zpQgdCxbqw4lbA)1gfM*I8mHLTz~C|hB}me#WGnDl+>*BZ{EC@b9ljPZ`CnO{xk2a_ zN@Wp{$6g3)KfUp3WjXBT{vk)kW#vWkE2ZUOw&22V+~dy6m)%H3WJ0!iw7X>KDTB?n z{w1^C4?ug@+q2OLP34=cO8`|enA-uRl*}b^db`B!`{f{G6tn;_7edu`{g2I_2oW$2<#gKUHL(njZR!qc%=Y{ zIPj&G^SsjsKtDuzLd&n<=(p61vb>4FE3ylMb^*<}16NRSbZn$)|9@B(msVomL|tot zOT%{`Ec=%i85@paC9K8z{!*P}pKm3V&4xyf?%{I2Yt=RoX^ClUc5({>cGIZ^ycKz!9&kD&;G6N>CF`j*i#m@uV)Nq2{CPcICggZ@5BXA6Wyix-s3<##%rW=) z9E+6F3L}-SE%RH!m$4XGMuou^zO#{fFiASvY%>B1)Dy*dF}!ufAG$SnJEK0-{Sg+p zDR{@@ElWPPwEXE)<=zB~uhE~`=20Fm8zkVAF{oT0a{(GUwNaD*hURTwZq05ubgM8~ zjKhnQ5QVNRDWcU4RDbtw7?0@fkyY_`Qd9R}^`nD-`-(7}-xr-P-0qBtR|l}xKXc(^ynE7y7*2m2IkS%-T-<*4=HmN7b&I@dx)D! zY`a}|#%t^9cO25>S2whA9I`f1OSnr4829$i%sfOZFax;-36wFy)OPcNx&d3?O-pMD zl7UZ%?#NKo)?|X#k=^!beomjn-olTHSuCxL`?Lf-6W0q?E!D4O?iY$|x-?Vt0py7x zC0#~{unSGqwkyx3E`F)}YceykJ9vvSB!~#6(QVpx-%)LRI`r%LAOPXk$a{K_JxnQ! zGZwMF2)|fUToy`7`i{Pj!B5IcTYV5&_K2Py*fZ!wn$pWmAjxFo2jJf_;|qN*Y)u%aP|S?>BL%U2Sy{17_eu0) zRVzObBOm|+fX33My}2QxC;Z-d$Q8^1mWHwo<{$&L4W+j(?J!rdapPpaTQmyyaZE;) zgS~Kn({oF9xp<^s*fwPQFF~At4;E~l*7i-=c4EqOFbQVNQ$Vf*mJE#!>4&M@);mA< zRPhlGtM&CXw~V4`q5W-uxm})8^{kXZ=K#^71MTYEEz8c=gtzbB-EZzW2ViEX$zFL+ zQ7Bcu_|xh?WJAq`@_d#doO7KC@kUTGrsXaQ_`2~SIxP^GMi8N59ZhGn8c#YkW4ONU z@brh5TSv_9#m27(i_AKJ{`I3bX37|ldq zHbUI0y4^<((7bhP97AW?m}14*YQ0LfFu&k<&-K}^Kt9Co zCX~9!9KK)hPOHDxUPZ+V91VUJv`MDRs$07BxVnixI%o7g3hc!#22eFFn!9Y-U)dQ8 z{NFwQld_k;D>6!%5n|yuIxnvAvhsCjEH$*|7wiv|1vz3@wN39*GNsk!zfWxg>uj<|phMK2;TW|vL4n@L%yhF;n{yTgTTO8NHJ5CnR zu@FiM8qxlPl|5V-@Ci|B?0P0X?Vh+jmGrNKIr|_~B3$lbr6j?(X zLYC~5NLhwd))-NevbCwSKCg4k{k=WU^Zn!g%{@bXKJWK+oy&0?=W(v3S#PUbd>S>{ zFCEJg2&|qNGRYt&%(yyeV^PZDFTCevKmD)G1AZO0A-%g^x}oMJdZ}a1MYbja9KpNf>hv(3w>OsN z?U+9MDJ7D^zdqsR!wn77kA%)yw-BO<2^`OMF2A;L?haSqj^+i_wq;gl6Qo4eZ zF}Ph%@PtlE7E`dPc0E_`jz$5(clj!n}0|GA~!_-N8g z^#U8WIAqoIzQt?6%tRJ3#nNrxwAScY9+2{0vXZ^$`qg3Lu>iRvi@~tuy)j`~@E`W5 z_V2eJ@i02Wa9hX0`S|tsD(7IM$e(=7t0wKa`=ptXU{@tP-gd-J318x zO5_EAuwv#O`*6cKtAse2rb0<#FVI~5J=VHcpcp-e!R%&jAe;p0? z0uS!|vO1~-loadOej$aHy`Rc5iBfwOXU&g|DJ|Od>h43*M-lb}s(<_;(pD)Dfe(~A z1s)YM;B&rq%d&kpQoqT%5o!R|d+bkqh#?Q#j2Szf^U1TJm=v_1`ID&;ni&*)EQ)Ln z90MN9ylarz4sGBKU<+(g1JusDOQ*hLhDeiL?`)EJ2Z&&T-mJt>geOn{O!Ryl@+Ymd ztaO4cutunhMMvC%*OObNeQ1?mvIc1ox8T{}Kpj=O^$C|g7Z(@r`e%6hHphmq*lsAR z?u?C%$)uqaRSIH6Aqio~>e3Y}Mlz}P!UJ6?4U7!rQ*_s#QyxK7`SbcW87WQh^@Rjo zPu+*uJWqBM17PrqN!Q06+VAT?9H7`|)$72k-wzxH7rU3xnMxtdEZBjpO!uyQivs=p zKRXtys(%=WC3U^@$N4up&f57x_N0&lr2-_Z;aYRy%aeVrt(v=C)nG%qR@CGeQNod+ z2pyb=6Dk>~1asqkD69L8Wf%JYmjK%7@?pAeX&;%V7wIF#c72GdKDNNHWMbxVly7lL z+pz2q>BP47OY=W}-p1Dg7C~A1;i0kqA6wgR+qkLKHEaSX#+p++g0(`j0d+#lS(KRWt= z9j6?H-fdDn4F}A`HvVT;wKp`>> z0rM}<#^ICC!4M1gGWUwFo#Ij(3=<9SWFbS6%6f^?{m1#;hx4>^sq7`^QHCjq?-oLf zet#Tmrg1IbbE)TFf8`Oqr~;j8jU$c6Iy#2-d^4Ypc+cnY`hQndRb}8fH09|q1hhCo z^$ANM$oBK7dz&|Z}58F)aw&sPrqd7_?VW_ z)^*2b9DVxjdyB`9ZFQ#pF{yObp*t=A>K1?DdcW&mygsgqa{~H~9s3uUAp>Y3d7_n2 zT3~@@FZ@j_yt%03R`6le!*xv|A<1NJ+_Rn+ zolYA@ks{kYJ>NXLZmFABzYpJo7b$=Bew4a;s>-z3qYKtI^PaCVZ4vbTy~D$Y^1^}K zdM1}&uc)nd`?SLAXT12sgB(~QZxK$Cmp2ovN$GN{fUn+NG0QMbI`{O+**AHquk(6^ zG6(-FpYZL4M}O;$eps`MgBkOBvK3CAK9A}sr%$rt37^84s*0j6 zL?sfH9aM^+-(;5zNFs&Y4^zhO>SntG)hqG_y91+dLhht<=y2wh{d2^h2C53kK?j;YHE4>5v5wR*k548i`jul)r-LCBbcqkydhfNAhLE zCk^4(qmB>di;cKm4Rwubw>KpkWv1PF6@uv20h&iM@`Js*M~z>|nmtA*kH}0!GE#@YXxWP^VzHLV~Qc_B15{n*vG+r*OPK3Vr7~~*8 z=#X1muYpn_rybX^d*qU1(`@+5qxmnO>f$z+W+NWB*jUJv`&~Wrq=DSb4Y%oEG$IYl zVvmCe2-C#71TeQCk2G62xZMFJ0UNZ`NQ&2u@UUtq0v3=prCTie(cXBclzOp&<@~(7 z(r0(*z_d4}{hxazH%i;M+CU?Th%=IeCAKmAA2&u?u%znsU|HqP(2?PZj~+E+zYqvF zF#ZSX%{Mnz?FMve=e4me)d-hJtX9$F%Cu_HFaD`9>}`-@>u9AD1|;%x`Zf~+F^ zwCMy`c(hzH^MoVM_=StW>mqubn^}^dA$qXe{CFsjIJ-mjwd3z~8ZdWKaG!Sxfw0 zMqIb`rWkjUZbC*l>3!}K!w?TZy{mK4mAH1WLalTy)o?Z=@`zkRwdG5F#=nBkK{X!Y zi0XACQrX(V#LVm<`_9RYcV4~>U9Mk!>BCAEQUS|{ct zGnx$1Bm!C3LkoUp)e;rlKUwkk#$g%d4>XwNl zT(K5GBJ0MXW00<5c8mO&^M5{iP?VD*>l!4*0FGibb%3AmU1GRk{`|H6sY{6x)}e34 zhc!w%cKz0St_(sc)wPJQO9bM*-rn03Qp7E2;S1lcR?J`=JHE5~Z+FvbYOQ}0x5~wi zBFbPQoGKuR9qP$3I|sCFhF*=jn_j>cag6{o)Da)SQa(Hg`P^tx;$?nW3qE&!u~B)T zkx`UL8A+p}fXh&_WArnV>7a}THev&VZNstZ1YyBBfzNY|8EA$T?uQSxtmH=RCG8!= z2Aa-^SiUpGlEhWXZr{9w&0Jgt1=*t$M}Lj!58 zah+)}{RNE^lAk}HtlT1bg;#$T^8m3&jNZKo3q+Lrw-Lg?!$#QIr)OoEJTE(f{_}+9 z&x`tQygb;k<|J`hD^9O|Za}cb3@)aIg=5@0&QsKu_&3299cLD~p%jVFFWz6-*;$zN5ViZgsqJ{N8it4EPcg z%3k3O8M%5MC9@c$q6pE!HR?fL3JZRh(rbw2H^4|6j<}HR)TJo*ThNY>*0KHbi74;t zy?lCw@58}F7a)p|@~xqnw@yBr3#4JqJ!7hZ@-`iNBt+1{8j^6>8P+@bRul}J=P4)X zQf$}!b0_TZ7#Gikl-98kvv~1c{_!P9fd8x9Vv;mIb74H!QnO8)B|IdL9{H7(mGw2> zs7>)-Mi0wR+whknF@j<3!Q789`2or2$U|<$ACCXd8$7tle)b|(900uz$ z2KT9~Jx^8GG3E$-t9S(MZqT<3mk)o0`Ce}Nzmk$v_;!(t+1O6aKcoKVg?2+<$*gAw zT5l1J@p=sm4Yzu_-GBK~^N>vnv9XZZa7GSb_H_0ycJ%N#ZkHBG=Cuy(VLZU&$yz#O zS)<{EJJ8HA18ZikKsJp5{W#LSf#KH5^ca*#fF`C-V@{a-g^-o>beB-u<)3fJ+5jJ< zETozlcqQb+UJ5(haJA4kpp?o0WcACd!J;GL)*C(>b9K&f83nd|DQPK2^h3HR7S^qO zNnu4z7A=hT4qF+xhJ`~Su!%4i#6;mrXJ3l*S6pql^mmk;AAl7^eYo!%f0L?fBT-aV zc(ZK+np73+L6#jeptqjJVdACvudGT24;dnK^CW%yxL$u5N3*<_X<$|FHKV;>8;C1a z($x-MFTVt4P@V9z&~{Ocd~)DeStVQnMi)AGX_Q$Bw?B~Qy%gmHuuI#Vs);lLGDSjr z`JRL;E5fNBrAX)FB~$Q8-c3yGoSAdeCfXuAO`&BXE+2UwFJvqMwQKMZ2UxZ_Z*L4n zl0Kn+0QGVX-;WW=jWR0#{!SwynfW(^yhIHmTdrzdus1uj|^5D;)L)SwHUk-(fMy5{EO z_>s*iAn`FBv2PJQ2@|_bxmcK^9Et1Q-^gh4Ndx>L4#79=0=r$*F|1-pi1i3QLU7-g zdG_MTHiqJzr_hc5>A}~^LV;?6y*qO_MUzia@{Vn$mS1{cES{K^)rm3&JcmlrhH%P< z6n6>`$oEW9!2gS<5HVJVbXEE(%@30gSu+o!Z`jIv=+K&mnyP8FSdQt{JNNQ!3K^Ar z4G$?xws`@gsLsUh)oekVF~NFAZ%m%e7V@?A8XSuo{XB3fCq;f2P<%R(NH0U8){!Gx za5HA4&G>8Iz%nOLQi{r$svec`VKtj|buPnN{CyySU%MyC$s-3Z=^^cytZEx_Xa~s2L`0sT03GJ+N1G@e zsn)S)_FfNmcJ@Tv=w|`sdwK67;;V%Q)58eefCfA#u_c7T}h&mK$IvlebJ~jKP zelpO|MBd#uox8+1Y;}R7=z2&kbftlpZhk<8bcKSMSCr+ot8Z_h3Dz+i-D;s6UQ@u) z;i-*+s$wdg3fBFJ7j@mlld(1Q1@jqqOP&L%Y(sy}$iW?oV%~X6-X@`(9B3Bvj8$?M z^z2RP69}^Q3&q+q;Ir&Dp-Jk>TM~B)GP@lvW>zV+a*NQX2Ya;a4oA7!=!JP`Q=v>NJI*&~h7WMVQhN43ddn0~|spXisPv*^tNWj{5 z2&0)9djuYEKg=Y-bE-;{CbyAIolq=8A<6Mm2ri6-1x|N!%qp8yms#s%g}PSCW8w)u zOHFxWMtbd|2C~&xCeMHTaK~zl^f@qn4Q|5UH3u#{XxD5h*uw<#Ta?`=WC8muKa=Y} z@-rF{sib)##09T7LKtx;3o7_78)03P^YYcJ?nmo@0C&Pvi?caWHB^}eeWuxu<@bY; z5I`H<;$bD6sIOU_F!u#0bk5X4PXiWi3Ud<&lmnZ9QH2x7?gmUU9mC8zt>*U>Mv~tOjq1Xz@iLZZyV%Z9x^pb=D&K5gykw&BmNb z&C!{4(~%XScGvXxSasZ_sCCw7N6cwKpOdq)!sM^LzBDmBtuJ4ghh@n+F4N32|5)qd)Z-0+KcZEG<{5*JA@pzY(2t?S5>yh)vhppS=>pOysWqa9QWx*Eh z>crZ*pWj_FZ>|}rKu59t+<;vbC7eeheKM9GR$ObEeJnNF$|hfGQ2mT&u`ZqbJsP%g zK5%nU8pMgNhfGLJKIOQMBvMyX-ihp!gDD=san7etR-6>(;ezab_T)mK_S{ApAg)gQ zVR5SC4_iBG83FKfz#x`J3nFOBH4Is8?AMkx353q4 zR0Wi~qR^r0*o?ZEG)mP?H{<7YI*MdYLA{yA+<>=7L9&^DNj{T)pMJm%P?`!Q*k~<9 z%z#fzwm;?xhXPIUIH2c(`nVV?5SQ-q7pdyp5LcuZ^l~q_yE-s!`W9EF;&Fn{?zOTs-2TkeePlvHc6(9qQle+y+-}9o zos!@)9LN`PVLz8dMz*=@YI6kbB(smAy@Zu**Uo4@TkSTJ8+NfVoZi=z!`5Z^bLu(q zP(~S1zhy{YxNK|g)@b>^3R?hxot7Tyh z)msa``AncD$vrsDoL!VPx__qnZsa#O8NCp+o zracbQH|Hi|lhyv~pdawF4& z)~F>ieak-;Q#uX`92cE&Ze@|1;KY9w6h z|60hY-i&1u@Ixc*lefHAO*J$!qH%foz6`$`b833P>tt@$`k4+j_3qys?z> z;tfEWZI2vD8>>N6%asKb);E0XI%4la+V5*CTxe|mH#6+Pe7!cWX_H2ty$umoGz z2Q)phFoMpUz-tWJRtQr1%h+gf!23SlR~}_%4oQB##DI;1ZsZiZKCcBWHU?alGNQ@3p~){2TPK3)zDr5iAfOY4`c z{Tr;Mx}@lJ!=%z~oAG(&C|Q4Ms;&Q2q7#!wY=ghYO-CL+DbC))XJen+2`QKjg{YU4 zl3hVmil(2|C*R3M;z^66K3Cm_04S=~;5o7bh9aK&Zw8lCuB|wDkRphuBgwW18RcIh zZ&GO4tv&(MoS&)QQ#0x^l`2XExSFo?omp`Kpt>0t&AhAY6&zp6i3Gjwa!5xbdkiMX zGnhMJ-APAyUBm`bLh4P7Qeh+p{nIv%!eZpjE1UV}%FweUq=C9{JVUs>|)&0v65vW0{BATbOF zH04{O{oLuemsamK5mSyKgP&db&!`_FBag>9*IeV)jXL=U(&ZFTF zsxx|*j`;F{ba-c6f)_9pRA+PoQJG^DcJ z(V~GH6>ZSMgq20w0Ln7q%JE&)ar1ADvU$>n%T4?{U>8B&^(@LBVHaU#Ml2IKvYq`{ z+dB6ZJg)9eaeBbb$A~W0uOqXzfRWI7fUg?LDWT*}=Wi=cNhP|Ty>MYBHHtvDprzcu z10J>t;tBxwN9MZ!pYEIE@F!3I0YPySFMZ$U#LV>`?R$e)IGd7FR!q=i z-E|z?MGho7YQQm>`jqU3?J~Ls>+7RAL4LNwKHurgX-Fb;y!%@72B3m5HhcZ9-W7%G z7$jz&*753&2(qyHP}_5VU%zgksQ{E+Ghb8^%m8TtXFkSgJaO}^D0HPdu5TH)^{GiG z;2FEOewc3-J)66dsz$7)zP!F12tFXSH5%Jnyh&y=MUIx$Ki*_CZ&mwdSEgajv97?k zK&;&;YB$qPW^-A2(Xz9kQS5Rcb|$c6%|9tmIHICzAm(0W3z9T!PoD%FZLcT}qQ;myy2%XLkmR&mlxWMi&Y0gCJ%ZCD;Sx-NH zftekxZ0)t&G})?2fwYlrAR@^p*>Kxzf7TG+gz<86U~m#V?fF2L3fg2bM50t`$Q7vk zXq1AuQ&IuJ@BQb`5BV!6rd}-`xcNo4li8PtT2*;pb=9jz%>BLOb;($yuDTSwmWmoi zn*CO{d8$)xt{%WsjiP6nVD6@xHa7|uu|Id5T5%_2tVfR>H6B9_^|p)L@=3EYatYOj zP=DO1aC$%?s9}UA;_z>k(NIy*t7PR50x|imGsvj0HarCO2!sw=;uhfAg6>(S)1bFG zv|G~mpOfZv-V2+-@8@_d-^hEcR|uCu-ibP>hzPWv1TG52m#<%sa=z$6tw_J*ihm1f zRu(?QJZl>_9Z8>GR7RK=&#sRG@u%)gk7vGR^X$FaSvXJhzM2JZAj_k1hy!u5)uS>6 z`_JK~ATS6b^CevyW?_4)U1nJ+(OGHS3pj?{a^|AX6DFZSDS4!%vNqMdR%1AX$V#xW zY%_0cx0eYk+CQsuI=hf!8Bw3(s?>_}*?0E(JRd#W;?j#JDVBZU5iw+Uj81b{DTqH- z;bqMj2H~zmih{JG z?T^##!({=CmUiETpK8UISpgXraA!w!Dm4W&BWB}CLKDv%@}}Y;O#KA(f5d|v%%BT- zn%sk_e&gzd)oCx_zV_0XU!ZtU;LE4bAFuUqa*`t_UcB5O2&5mey;IrP4QDGY1$X+k zf6Fe~H#g^7JkBbaUUnV6H`5`o9sW@0=ay2PQt>78(go*`bH&Mi@Oebm3gipc#JkAbZm!&lvR$4WK+C6yYiKE~1&NZws zp8d%n^GW%>3Lg{0Wve!|Z}B0tWj_m(tag>F4NA||daa_k!l$AiC>DV3RjMGYzVg7F zq^Bc`VE^1aGShuN`UdGtxZ1wy^QP7LR1T<2`Od3At<^m_zcgjIy83Rb)QiSi zld=E%+0OR=3X>WKY58nh)Dd3Imar=G)S?C@fy~d0Q%0l5mQMfJ*hRtWZ7chTI6{0M zHEE(4N2ptn5Fl331Z`a!GEM?a#b(7RvZIWcF!u1UHYoWil@>2q^0Of1DTN>Xw&mb7 zdW5-!_v88;VqFh$>+I~*jy#3MI2dYattDR|uIb37-Gr-d@XyJy)0>$RtJEXZc8ho* zWbwAn-SRd*c^J}TZ~C?6^vb+V!540E$$UOOQTcQJeCEiP)6O>_W*AiHyv-x!O1Y-M zIP{M1okx;J|0c6205cqktG{g{6EgJ8AQ$UpPawYC=Yg+r1mvLvE}u>pnNU|dhB(PE zp7<&dLGE5V11y#f{z7c06LW=Q1msGY^EYT}J^zV4qLIq>W9b>a_XAE};}$Kt@yAFA zyTmpf)tR*WCUw>Xv6V)ZRE1jJ>FuN(rWG2p$Aa^Gx zP5S0cl$A|wBw)Vlvv=;YqMDG~vTfT3{Fo5awJd|o-hAj#2XH^ibYr3xlD$EEhQB&x zEg#Z>fA=SZiWiBP`i7;U72nTn_;HoeUq+F&vX_R&J2`@}+z;-)J6-LHG^3yEN>Vo! zt(yNVe;ES~K~S58gcMl;6dC!kyy9)E-R3%zfq>;pO1xurf?RC$D7dt=wYvV^y(xE2b5L#CxUnd3KRnzU z$iv(PGNEu{(QNErJu#*KwUCI_9zu4(E0lZ!|Q!rJ3m}}8*#w`Ut z;ocda;jY?9WF@!Bci;`5rz(%VRA?QfeRp7NKWI+~0s{b7xiex3NjloG_?_~YY`Do%UBj|Vx|rJTWOR_D_-pGJU7AE_H;>KG(5o8LxR zxR0@HIU?kJF~G?@X$rdci)BkCP|B1?! zdaYViWwCk8r`u0>Jb7G0(UeJ6;$4Y9GorFF1g7l%JLtX6qEe98$sffFyl~*6i99V- zP7R1I?jRaBysP$bguE|S=H!){y@=g7;fF?&aa~UU*8#j3;N@onFYjsfdqEaEx-|E^ z{5RMHzU`#jMwLe#z!cD3ipDW>)erk5oHMtvtQExJr-5JXXsigPz_js-sR!_*EmNH4 z0(Y|bufMwSc&(oWQ>pRTk607k_+&C*e^<0CEKgOR~`o9t|a4 zI6qB<@uY+BjC?&j1KJqQvf61-z;|pajm8D1v@J<7jP}T2|9>iNU_1J(9I_VIt#)>c z@0)+|@UHitl+Dc2#t@IUU9aU=^~lwsuA)zYU@9ssoWajT>rH!omtV(*ZVmLbV!i^o zJM6&?jx!?+b|>s~Q6%s%COY-L8$qfJXg^quQjc#Y@p?X+vp6LFcMOSfmb-pJ$5%45=c~b(!Gev662pe5Z4SeFjt=obNgx85QIwY zPuROdsjDVDyQ2_UFq42O4~Or;=B~C}5=x0zV6fEX5rnZxtSN;#7|*B_wr>&|1_N%z zoqhr8`u%FCnSWN0)9D=(H5UGAc^+{w2!JgJoJU|*aBJ8Fv(e_pL9&T{>oU?!^40BS zdnK2VJUJ(>aG8wI4Ydgwoe;hLC4aY`-sDrEaV{Fa9buhN%TB5nE?#s8k5TC`2n;Lg&IO;Z5$9zvk!&y$w3%fUx0uW2sWc!zZ(G^n^2G@Q_7q7*GAdMdAc}63VR-7!`$1hAfeE=1 zQL^k$=yatnYp88L2ef~@)cW2G6$6qi0FJ>BkV9gQ)hR6Q;fkuagVJ z2zCw>`+8q@E?Q2=#J9ovrwC_&7v6K;&OMIikFXYl)*oHd2E@mU(J|;wEz~_^vl(`e z+R2UiSAK9K#yQ2QV12`;9`t-S8nyr;Jhq_e>{+ucNdwGGO5fUZ>Xq@z#$NHMoMc_q zHevP7At#fVLxdJZ2!4ic@K(cC7Oo{N3#7^&9w`Wq^QBX2NwSBSH|hqaDLpM67DoBH zh%s(~R4|`tXC7&1HrBsFw$Ji!H1XRfOweT5kvj)nJlLKzL}$iP-h>3jr#w{1K2oQA zI%;$hs%Cq&jKHdS8cC)0R$LGuMPj*<)tb@2F~F9$txJ1#?bWLnW{&&qnwhx-b!V|I za&6$q;N7PxtN7-0ovg^vHj7EEK8d{S4#;^SwhGX2+@P+XDiLQS(u>QUYf#}?ggOFd z+4a5c2oA7`(!VP}9J_N?l2Z1#o%dGUwCCP)*_T4t0l$rbSaM`eh|4N7G@xtMUpjXUg(zBr>JFN6&ylKm#6Q-b zDQKVEoF8NEzi4;&iCyWt?;=(gZI&SJgs+~{t_5?!GN9zFGUoMfiw4t@zev+M1D1By z-=QsW&dg1D^X4E>A;j!KX;BJHpF3HTA09iQqp$BtT*nR@T)HM`SRItf5epd2&*g(|P_(P?G^O znLo~(D-3`r^(#7pN#t~)(NuYMk<&9!Vj{e=xD)DnFu%_<8CSDiM zA2(JxQ+MDZ89*fO6h&O|AJb;G{w=ioztp+5>?wukN?s@piG5~0C!F`5&FymWb3dpl zG>XJ{&VSS!A8<6$Y0_za;#D2Ybgk|!{UJG;Qd)Ea^cA`lL%@kY5mLDrA9(B@7c;-F z{=&x>xww3J1-Jx(3zjTwF^47L@JxiL&jSPRVnompMYJZ326^}~@kTxF?(T}4m}gMT ziR~oby=o9Lw|JoxK%Af^R59d$9i;yBnK38ed-V)d{SGH*tiMqkQSG z3G2z*h?^*uJ<=*tb7MH7Ck+fRf0uDFtQF)g^8plSU_hd*F<>to5zW|kX{vC*K=qJA#=11%J=?FTJZ;5?Fs zZd3n;g5OZ(2>~H355WR^O4)`Z7V0E9)G%qVIVi&0&e?}HfXmeG-2taDK98}9K5?U~ z%3ic$k#>Jm(EsG*<3+Eu`HN@X<3~hASx`!q(-kK)gkGLyU#5tt2oSe;{{Y0wd`t)) z99&}*UmfOT*`6?6gM34_CNpM65fW1T^9**YqG+bNr!PG|q%iR9R$#Ei+1e}d~a(@e<&dpzWW1$I%24V zFTID&nl_!uq2l8;@UQq=^>tAmZ6jGl(f@eTD&ivANMwGp4ML>s*OPnw-h7@LJ*nO~ z5PVtg0$sPNs;n3OaIC&)JaFJ;y>6C2P^$(THHQkP;tA$g_XXHCF>EhuIED)lLH?u# zU53~+@x?w9U!R`6dMP(<{EkKx7RNioQ*2#HovUgp2LR#s*ssfXy|1`#747z@W|)=Oid|ZZ@RO7D^owv4d+Expio^7wcdDc1Dh5sDJTRX0bYGiN&{zWi;4w8`8J4}tncLUpp5gAWf7>b zH>tGYh&jitJ3{7XhGx@+3;kfre!9Nau1!rz8FfPI65JZX?}byHdJwB7P)`cJEAavH z383da(eqZFpR3f=Wbwq?uJF==o2?~h3w8;>pTY&Y%5W*O-7+u^J4sHKx1u0|dm?~! z?m=jRLaZ*5KhN{TI9UO-eAwgGDqh{e28dIkXc)ME$yAUi4t8ABJ*}Eug-t;KBFsY2 z$uyjBNtW}+J5QBC%-++iDfNYWZQ#CU6oC^E_Fx!4hmsq-9YpP!b^-AW)ZZy(w^H))C>J4%<>DWWK3BQTp{d z!9ImRCA%VsH_F#+q`lVYk<4J>AP}m74U-O=`0hX}m*JC?RG@(XiUn~cxO7jPBBu40*TvCW zxBs5uYgVA`-nx5thV7h3*U11fF@{qD8j^E|y|z6DK%;Vh;4s-Nnca)aeMJ~+BWV5o|tzw zybGt)*K5$&Sh7bzhoO52T4!;}0xJ)F_qiup6qv+!jOig>>19)*#E>YUGH2I@nB&NF z1zn(W|McTT?priM$PYyhfWScCghJ5Y(`^MWNQ5uY5+W}FJMH$qZ_7o+d8zpGe57}l zDjuv>HpyV7_y|J=?~6G^dk}k6*_fGJxwqy_7I#@Z5`Sv03Kq9QPWDweknf68z6rE*PBeJ&Mb#Y|k?c zqpqR2)Ml)%|NNJQ(VK6(xw*TKU?x|%dDwlSX!)2k=gb*RV{&Yg<5dc?y6Tb^^x2A9 zJTu`=XRAz8Et*nV(s$Cj`VE73elM2|0JJz+H``nM!`LUt6v7Iw(PmG%pM>uyLKX7p z?M#hu+}a7R0!9QaJR&+n=s^DEeZts{%deh0yqEbSn11wq`)BLtqS}d)~ijyA5rhM_2^k7-HA7@* z>O_!9@mYIivE%&2W?(k*M`i8^AdvG>Q=mN5B4#2A4uj0sSQFiXgcHFz(UJSvE^Zi_ z0)c^uN}~~ch!Vd{amLD(R}vrg+b}ZxJ$DXzXCGQWL^?AM8d061jj0E(jdo7zEnq_K z>;)#LsNAU+`gz>kq_l9^kV9q}cQ0n{su++|wwfg=T9jbeM-;=-ihBkCu9(bF9t(6T z%dwHGxgVC3&iW$m^rqdrCkDiB<2lOQX<%TW-Nt+X2pMCevl3;KAn3q|;gG)+JDW(@ zJ3(8i;hHot^jH(#bo?g9thhEjO|&-pzsBm@GQGd;BBkd!M3y{5d5xKw`-$6<(-9;n563`{IX# zEu4O!WZnrx2N?jT^e+iGY?T_>8s(GC#wHrUsx!uZcXqkvQ**8Ox=r*asWFX{lb`I2IQl)I zXxh5I1~*z@hiLF%MS^dgaeAGJ@6DAfSN2^~HXq<2_)-uj1`WFARPP@pri@G0izAA!8eK}= zvuTrQvjykofBO93N$T<~@4esKs%52aKAO_QqNX5hjLJW6`;-TY+!bR8X;aAApc85{ zURI5Q;FCQNM5{=ON1;l<5Jn9>-T7;edJxAE)hPcLiG~J*j2tu@{mz4`m@QjIpL#f$ zO^s++D0M<8`@3sA28LBzFmI*B;TD7c{Eg`BoUNUvm?Y{`4hHJzaOiEag&G!loM<=z zHwZC~%|^hCCGbDgX&~pEJw>WW!Ww4EFH+wIxY*Fki#U={O258~Dop27C3$lz!39=S zfPYGl+?W!E+av@aM_G&BlGr!4ut1fI0m3iL5W3(aZ>Hn8hb%D=@sv30b9x2T0+=gi zq#i-deekK^e1@grh?lAu*XwLK6;e5e;V8;M(XavC6T5_oY~IOO#d3q^e`?*26)!q*b?+xrshb%wCEznc!#wni4TR{h4aSSMA1mZ7vI1d@j1GYfq3OVmR_6!WMyWXPgkzo4@;E;hh6 zqP->g@{K#V48Pyjflo_izWWzo({9njf37a=1~^z>`{ZHV8vDQ9)1kGDEK>!`94p`< zay7EO%uk%`uB$Eo4djE~!{X{!?mdDm8TzE(*-Vkgqw4BgTWu1T{SVJk@JZkzrn&|` zpEHg%g;bS-SHi$cjldMMVw=4-(K%Gm4UeF?7yEp(=Up~bEMe$G$YaWuZ2qHteYZ`X z%a`Yp!sxJN5?V%2JQ_GIaR*A_tvDhqQ6P>e`JPb{F}v!;o3jv<#IKqE8~oAO)t`Ff zAsTQVlE}S8Y7Sw`>Ve>$jwJ5&0FRVb=>xI;!2(WOG&O)=*ARIVC@LZi zUzC4)O;Urw-o*xv)uo@-QO80R%AO{2-cl$FznIZKD=SB~y&M$OQW}Z+Gb?9gasukf z5RaKgxUnd1#aIWxuKToW{Q)NG3Wm`iD>ZZ&QQekdG};zfog&69k9!5uGdv_3NfS-j zl$|Zg9S>7NG(qn*x%T_Vk0WXKRL%_h zYqoa3Nlr1OYT>xnKFU!wnT$M`A0_4u++Ku6;yXlpmrEsb5EO!pAbP^y({J3Pa6<;I z4ZcE%*ueVTAXqCajTSi*Nt0@~LBKS4RYo4h5S)B<^;u(ASC)&LVP*OEup^(<%&P|p z3(f-{)K+rIDDI(j49#AYuQEMFwPauuoslp5FsKB?K!T3B?VIyG&cHB;iwYS-uzdwQ zVFie#vt`<0;;9lc%5I*DrgLcHAk~A14%MS~Z#(2rb1ab2T+j@e%&OszN;At{Fu+=s z`8nB%NF72olbc0Mw5;^yR1<7TUhin$fHno{kkNtFtIt!2T7CWrZO7NNIhgW-f|aoq z5VVHkl2HD9g?25ZPZ2@|q*9Hz3>H92DoqsMqC*Atm8$(;UTGol0iTERyD=Sri0_p) zJE@53oaM0rc(_wgN`FS-DGM`EZ!CObNIK_Xi6@niE_?uSW%)cz#`tIaO{8SbnitSW zEBoehsE8#)w)FNnLv|BO4^%CuhXo2M1(<66>^1k1K_scrVQ6yclHou*;y@xOI9V9n zC*IEW6dw!BLzBe`v6nUvcnanvNHeg8NEU5MFcjUuxffp&^i@gxBO>YW;_J0#)ETNWW?Ith(#V=skW(i|TuL3%+8ZklXE zCa?f`cV9QMYB}M&i=Li8cu+PJ3K$vA5m)TaP5v$07KQ7aypT)P?uO2Ta;XDxS_Vj{ z(ed!M9;D6T2a&J&aB@=IhRzKFpq=R`fO!))$nJ>+gPxI2I#gRCRjJ@}B{2(gcc z_g31I@c^?cJ3ZuUO3_HT6w`duB;^W@c?SEs{A}%-6!imMYK=ggTa7jI_=Dvw@$#bX z79)l1$dlq@2i7oQVT&eBwt6~)Pljw&CP&LfhiTXC|B;Qnz*1&M>;~ z@_1>=$vfgd!MV+evtiQ;vVK(>qRixCL8-|c{=B3B-WQq!RJ8rq)bq%Ib$7`HJ2}>fYw0ckWII*x*LCs zf+lzSv{TJ!6dzyhOFgaJteI4nqMDV_W7Lw=`KSz~+#-V^uF5HGSR5v6H_*EKlM#^^ zw1HihJgh!z8V3Mxg+r7)v=b^^-|gT~bPRWihVU!0e2zdO{+^A2V#a@@W}MAwl5trQ zruW2lr-@}UGtEfM8O0_9i*eLP`P3PJa@)hUYiQTohg%6ABQo7a?V*Xthu^l{pz#q1 zC44f(<%loxFAD83d>`;U$$d1~Hzpt3Jk8%GCFvyp_`Gb00Jt>Gs|cfllgR+U;ukoZ z%24`FeS3Wh#FKAoDKFuKa+YZ?0v?6}a#Z%(flvXwMRQm}#VT|he8foNihw+#L~h`QD6BiRY8`%8!yB(MVBNijE(2?+(OVD7_{ns_fMS(yTs^S)88- zT;75IBK*g0Q*P11;kFynb~~TH zh*Ttt^amBK2hvAS*QypxMaHq8qPf0&KW6IyLoOzUIsa&FDKIjvFORUWs7MB6`Iys{ z1HM8XfWC=U3$E@twYQ+ zuo^cUKB}1k8d3S;&71ag*t&&fm}H?-N}^#5&OLx=e89ZP!r%gMciGsZsx-gLI-kxy z7LfW;Fsf9bauj8N15cbz^BPx;0fvj9+7L+-GDy*UazERDF6tRS4Go}ho#P<|=zZ=; zW4@;+-jz=)TyuUq^ydC9UA6F49Ln^iKMHnG^5c_ET6I2`YU@)*j3q6n$2jx>Y2A!D z7KN5uEbGUVF?V_Bjg5}-9ezwK(4M- zhSrOlXxqFwaNvOG35k%$CiOFY zmZEx*u`0s-meRv@t5!xq1InH|<^_eLf-Vr%H1~cc4ThlCy#EYH=t0|;QXohXf!?6F zqG+bpvD}z#+r~u9U0ylkCDi~rr|}E_TCpMu0vJid!y^SNSbjo}Mz50H-==7uP|!>V z1}z+Oz^E6|GlI+llH>a#Z!)a}up<*44an+@j4Y|HSR#-Y-8b8!qyUvR%D-`IYMb^{ zY$Uc#^sB#YMrPpQ1DXv`A7QwFcLf0I8=Pvz9Iwcjd8g-m>uRl@X*X@v(~71CAZy{l z9l2qb+m)4;PP#n<#R~DtLfMG7B7{7-(Siy+{^vP$^Xg2DRmKt{+fe3HQRGMq4the& zx%)2XBOxC(Mtzv}k9<-w=U~p~31^r1wf{)xc~k>{RtkcB7-_7q^!e}ZO; zCWezxkETqGvcsIZC3qFkjF_|FR%9$x7ISyfGf{I8XEq@|7HuW?qG;;5wEBV~XMg;d zOhQ>n`K!>T-=DHKLb#&m`lHRO5w89%(ilSWOavCNP+O43WPm#ZP$W?)t}Op{$j}?O zSDvc0bKthfMc+ZH@#PAOi)Cs^tX%OUrp5`Rzo)-Aw@QxS<$|;wfs3hDJC4?km&IrWWRkLRy#13Ne z8;Ed>CS)UF5m5=;wn)S&EE!k2`{KpGWOsfCDQYCp59soiNprrFJ*0`^O6j?-saY`q zo(>dV6f%q2Ug!Go50ji0u$3tM*<3U%=hS9h0+yy-$!c%h%3P--jWemDkY-P!tSps+ zVU|VP+@!vrY9^hYJTbw?TZBh+)UwbS;4qr1O(ypci@{Cp*UHJlg`R(Rmw^hR)jRed zaaTjWBJ=o>J7P?l3^(AX*C=4*{|fW$^5TpJZiu6fAsb?@Xgk1xCn;OTS-58Mi#Qu8 z4<6{J538IpS(pK4K;dJ}BJB{D@1J27#<7x>>5S!&qjY$*3HF1=+M;2@evN08%qR?T zR@Rw=oe%|8P(P-8ajT7(ps)3 z&9Ev&bwE&>ZC1k#6$>ecxEDK7fn)F;$fnnb-8Ot32H5pQGA6UPuex>U&;cJA38e~x z2GE}|m<|dmH${AK3Cwgg;TsYu6}Ox|y&xiY@>IkVwty;EUDrNA&=fI`I5hrTSnDmNJVp^>+X9YK-YX~^cEeY1LNd}REe1ld8i)N zoM;*Uq!s`NbQJPmqv{mVc^{+ za{B>5YCe=mTqyVzW-v|$VrK7Vmv1ii{&Z-284O8k+jb!85z0;|(g3sz{a{LI=7m?0A$SrE9&-KOCRdYA zq3Rl&Qn_MkpbHbDr0q0LX3gI|*G>8^Bf&C$s9)ve=*Xqe?QHNr(%OGe<(zzRLjzus zDSP^Ee&%M*rEKw`rV$A`&rT8%g^m&>g8SQ4zbWF&dPU3Fsnf{WU_-JzmT0J}Bf}yP zaHr!Q{oFHG?DvA4nm26JNaQlHGwUXUFhqb+1ue|TUncUibp|yF^c)}=*wD+;s6l^y zJ7^dpJYx?Vr}Lqr_TL84OP%*8Q9Y;Q_&#|K`%J#FId% z&VRM14dciLNGeZNdrhyg{r;aoj~1poE)6+II{Q-G2%4)rIVkV1@*ZDD5d*n|@*!EN zVna|816@uX0B9hRN~z2#5fkPOhw+M=`)n%-_bk{6FM0=fxnNZyjA8!{ZxIHh=2`h) zzs4kyvdRdPX;PIFKlNV`nbVgoNuM1u1CK#Ui`4h_?e!EI^(^N$ZF2xE+MW9c2KJ%R>in;m zVGy4)nCmY*G_<+Qy$gOqdG!I6M-+MU554j4uJ=}I+N_!TzX$41)8=p`>zQX(+eNJvb~ zWEgaPNqQiXN?ewxC?0D*sgy;VeUXYpkQ>DvhGtfp_k_CI zhrm;s?0RCEuxX^--N8CWcp)?H+9CfEs56|Z%8)h-8e2s7i->Gx`m4mW2!4$x|5ZRK z4vM{xkURjm!3AcN-Lv@^{n1#WiWpB7 zu;39@K(qaQy1E>;+J-E?rl3D}66Nl@?CkZx)S&q?wE#l582K7|Dm&;p?x!1+H43zx zftLr=%!0@UXgy(ocO$&dr#gSJsP+T z6)V;EEb3gw4-Nt*q`?45)krCXOi)(yGB*z2XfZvNeoCy(Mdm>25f2ghgIFyAkp1iM zCqu&;!zsxjRbL8ElZBi#4a-x%*9Vi!i*7x6apCCa0i{1?Tg9YqDC}Xsbo(3=KIRLD z^4+No49@*LpF4M~N;6%bugibb)~vzB+C|J);ru8#3Ah`0%GGwRRWA9n~2K~O<$|n!0oCDO}*Xc{V#ww4(jVO*Y_q2Fz^MCmx6Zue9%mC9IcQet|J7Q zWC9h*RIo|HyiA`Vflo|L?UQ&W{@Lc6B19A`7@61!kT^#(9!c7f4L<^YlJD>S98*X| zCHjZlI>I`=S`XuaIh)fkX83#GZh8}8_H_G}vq()2IX6Wv8=$nHS0H=h$HNrB2-MKw zUdqgyj6n9gfv82xEvOgguidc2y!z~df~_$z^SlHoLU8fDxPKjQhFB+)q{&Z?RotwZTa{K$ zVU0{mgE}0brHLu)Ab4G?gP-p%uU`ih;xYa<-_;lWF$I6G$`KcL7PO_LzF9AF?y z&mJ}pjRp+ZK4}88NiW|u>AWQ|uUCOo?9{V^5mSJPib_flM)*jy*qSop!JA|6k`ZR{2kPc-B643=)8Fh~G}UR7L(r+078~dOLd>y#S7u43)HssXgky#ZL74=L&1wV3sAAAQ- z(`Mq9xXSjXKA2e?I5JZ`;ZkT>7jLb}h-_`<sb~a2VPP;F7X}-3DSq(b8+=>B*R*UvsFIg4v4Yx$K_eTULEJKhpEQojQ4Fp@uWim2 zRW}Dr7KJv|tO$#E0(ecRi5^A36Go4LtSR?fD9HCFSR`j|vTIzl20`I5m_y`K?`O9d zQO(EF4F^*epAkSuaq9-+@cE=ksKeYP@Q>`DA(;hRl91O8Pj0mfNvW{Nv^aKn&Y_as zsijfu25aB$|HubB87H9!BO+q5E87Jm3M_hA?h}f zWun0mwFsC;Zg#A|ROl~OmwmZUT_d9wx)JKC-lcLR5-SO@+9R<5Vtmxx!U9vxqeA>Mo4_sU zUZOi|kV>t~-BV~?1g!#0;Ipm^39*mZ=}2?HY=Aommmu^UF5OR8d|H$4@!DL^=~|3< z3bZwm8!OW!)VK^N^`#+vI`>Cv$CIk&{$UYNGu8CskdCCtz>OR4rgq$=y2OYYBv)!m zu8xR!iSLruxjYx}IdtDo)t2++%UEh38Sut64`&sgT12OsuPm@SaV?^b@3E!yI`Twj za_pTdDk~~vlS1U9s=auc{S77=TvlM-xM=9Vwm=tVN7Lkpwhxioc@DeihdE(fjXRGX zZRfii_*Ab%!NUG1UGc?)BO`?#M1$F21|trLdeOc%Y}n)ve*@Ea*5e42#I`hoP*u=@ z9=~6Tpz6}Knx;&dykumD79gB@O_YrMa6N|?t5>d+mX4uzb+4Xm1!14VB-Mu?h+Mfp z0coZGq0*#NRMXY@6Y5kl0tF?#VDQ?P1I6l&`yumJazGffMBEh{HoPaH*<=d0H2B?E zctD?Gj|~+@gw(B14Q;=3I|SGzXf7dJN>Zhy6;FS{bCqk(7#cl5-)@`~DG*@*aH@FK z>5@<94Ha!@Vb8<`H&<4c^&4WQL_I6pXTCxE$*$VTH97m*D=YT7~H3S|fzsLe5N^{b<^sV4zO^09_tEED<;FgY#bRrp|@2<^JW(MnE`=x zpot?}i5?IXZYFwKK|+}bVcepff>7JgY@GZx>jQo{QdHM6kSvlgUe4AsAMcH?V<~`? z#?78pI;N&OAIe1hN0CDHgA~o7!wc@?a+r4^!?S)L6IPXUg@<+HzsdA8Z;oxRXC{sM ze~lr99J zMo5|`EJ0(Prno2xFM-}6n?NSNl*&r?p635($e=yo)iE3VpZ964>gPY%Pnt{w*2>ooLs2p2;z`H& z-(J2ydAfrh0u`DY5V4XKS+d56$vheT4mvw z;TNIiL<((177)=D&kRTi_XC6vUPftJM&TwJ7{)di+#rixi zDI40M+eZII2@h*qW_|BeRC7hMxOSeaYa$?~9?UsU!wobYAD}?uu$bC_(w-T6fXY`Y z6JQ#(Qsr?` zBVg56QY!m!jv$?!p8mP+R;$0i0@>5(v1T)sDa=igXKGHTyCsWwGAqR>(PADKpV|!i zK8dW!I*CX|C75k(`6Ax@pC92jfr~_CS++2%jzQS3^75vzV|=OVqSGUdjMzn@#8 zrrC`di-X~x5vjK*G(Z|%{y&fPyLrTcZ*NW$ds(yTMPL%XkQ;=*N$=a=uy~^e#Yhlo z&JgM7p^QQSu>?1Fg6{XG7ykZD|Bj^mS94=}N8N21GFvh4; zqS2}qQ>6WYVd$Qq0v2J0Orr7p*6E*T_S=t7?Dq?^Y1%cC;`q4|pKuhZ{r_=@pTpjq zz+c2GnI67`r~q|m!0msY%5R_5*DNo#k+0Yc61?;w={mM<-nMNcW3F{Qyi`E4bUoRL z8h&RXs(2zclgrx3;f&lr{`TSi`Ju&Ew|f3i$bmaHrJ3-j>^4kx$;cWs1@Rw1(Yb_N zD<=+u2)CW1534ioAd6uAkm~&9|0TYD`}v+83HHK*p`YTf%r@Ak6V_AV#P}_ME|QfP zkx8eW{@bv<)(KY<@7yakf9zOd-mPbRQfW!E>>X*R;-|G!g%s*D+pt52o3C~{B~&bC zK}2}=!Jlmt!cMuY(W;#LEof9ir+5c1qC`qnbNvwO(ube)+y^SJvDYXw+*Xu(Rwbvj z^z8P_MukNq{&h^(|Fo|d-@OwmvD=KV9A(TAWFnSwnDy&8bSm_j&c%xYw29_~lDBee zyWbXk{1aWw*8Q5V=tw9Q290ULMT{Mi|I9&vloy9=$9)_@IUsJ#K#|3?Lau$WQu^&N z{r5E-EAOu82r&``vKCsEIKPBJ73xgsgYf|qX?a<7RbDgwICQ7%Q3Nl4L3cCz`N-cw zzDn^7l)5s?h|E|#7P;)0Q4hazN1wK&pb_2yGIKy+`>?*If1O>k_B->lMq?u&rJk@t zaOfb-n#PF5gzIOiDJq@zw^x^4LtddlK7I%MW)JA!+tFkGu#ejfT z(>tgqlF%52LxtAM{i?jzh3<$5%&1+so6Yckx8`@@_%~+t!h?T3VqddP_vdrbcQY0O#=V&^8kb_i!x_qG981wS{Z ztqcv>nEtfTLipjj(S0u3=R&nE%>}9$Bs}6QfGV#U{m|+r`#b*io^sT8V?9h0UQjt7 zT(L=5&uznNuEhodDintU>q|vTC}rpiC{VeV*EIia>a=|)(wEeh5hH>dF<4Fb(#2*^ zy6K?4#nTQ#MG>w$czk$(gp}@K9oiV9*iCURYaydmTtRUYgm*;aMj4f}s$bX1BnQ)D z4}p9ocT3~;q3l2TIY zf&J(&YZk<#!r=co0~w#9bKj`ezfWtEVE}G=((M5tMyqrd_r)caKy5*(M`78Kxy9E! zHL&b!asH56^bf}EFxx9Ok)h=oOQUXkDO2{BOWD;V;&|#F=-%-WqdR+PY6RL)q1#ibOLseRu6eZ$#1)qvm zV#D}j;}oYW;82Q`Ko;2|jR|~CP)vn+5MWC?WY`JoZ^It^a~+8m?UiUFA`~hy&JmGPtYcg0GVqX4v@ zB%{#4!7UzbxH>Lys<*3s*v}U|mO_8nrTOHmK$KFMt5MPYDK;In!nR;*ai8qoJN|xy zMpMGA%uf(FdAYD|tx&v*Oez{A$K6_3;If>U`kVf}1i^DN03Q|g zUz3vIyn-jso=Mp#uBpgjW`@Mlb!2D&ka|njL37b&+9wCIqtpg*1pl8bl@METKE_!P z!p|3X7B;u#=wd_w*zES4U)jtRPP0q~32ADLB=0Cswi#K%M?%BEWZo8otH?izt#!Ij`73}W z7InQp_WyS5_3+e5IJ-2XM+aH^u3V!mDKTU6D^ieYYHoYg8=*hqHW&5!9QxyDrK{)n zC*fwT$wND$30@MHz?FTT@yWZ%Ov@ zPe9r(p(Sx@e4L_8EYg6-gjpP(gq4DmkP&E(rQOhp3E`v+1%LD}K1Ymj2Kduu?`xZ3 z02^JlCXi^}b617q+MhgY?%XZXD4+G5V}L|rc2p7VNfCO%nbw)GV$1xtDCwkT!pRm^ zZ|nji{tF+edSiFhzph{LKuZIc4H&HoM?{Mo4oqb&Ln*e15~*IzsSu?PKx9LysM~oe zwdrOouQ(#_g|VW~aDUUy|6V`Ojif=A3TYM^Y^=p%pRTB?!0P1WqzO?XRbbuW{q>1| zDKxOaigB9)C80~-K>F8IGYHIeOWQE?z?l>>G4BF!cMQAAk&ah9T)PK5F0O78dlYCl zA-er+QCePB)^gAfG(3z$&i?X^ra9BGhIMQj_hK+e6vilrS3lyL0^&C@+C$}+_IgFxk540L zNZ>cti=N&!C?)b`Cbg($jQ!uS&C~w>VchUvWktQ>+NSFqz}^u6Xf$wZJXTOrbFGpd ze_4O+{{1FQ_W3AoW+?g^OY9g|;5giZ?QH+@2m7V(!`IRiPUqW-meb9F^g{8l&0q~U z6P@x~BDI5kHve89F9LX`F*jx?5X}25{|L{{PK4yw1w4()Q8mtFJnuwN9z@rbzMhiL zJFk*=4ONQ!o11&Bn!0X5_FSl8g)xe*%sDv$X=Q77*EfAl-@7t72VL!me2qE8t?&b~ z+JuHfsPUA)t>N3Bu`u{^CxMJ}5rW0Bf^Y>?IedTi&%#p)B7o%PgmEx9cP$yJWMn`( z={IlIr0*q#!r6X*<03elV73Kx01~(^`ZAYn>(J0y`$BWC_KW$(72O>sAScj9?LB5e zPo`l|0K6wC1}HjmdUjl}UqeFb&+DG>cp3Rnf%54`o5S0`fQE{u-Flqgb=EI>FpRZ{ z;v4eq$)zPxnXWk#81sl}QKTR7vRHPeybca4Q4kbKer#!Q;-6$-9$w!UwUr+a{45#{ zNyUiRM96O;ofKKkMQ}dDGzHb{Cj38n7hoi_lav2z@8!D@%?^SO+%h~-D`CB_ z7rm7GuUH_bP?4ax`{4QW9_O-JX=q>!lMpv#P2_QYN{b;W4r}46Va6j-Q%FyQHwrM@ zb}BvUl**61+@nzmgO4PS`@1;$*+zTc+KI5T(!C}Q%m8%>VMV?$SMu|qtma7ytH+0} zYYZmv`kL|&Wz)`c4c(xM$YaIzny3>^&%DxX*|N-M_*ak6SeuFi@owWso@kN~G^E~l z*ilK^_G$ZQ_8pk44|8mk-n~;oAPfPoHVL3rTv}TFVZ(KzZr#@a!uzkhQ1mFEF8X1g z`*%ER=Q)cjS4U#2%%>1r;47WcRG<=2U@-5`aw&iu)goc+zv}*>74dXDkun@Y7;wg# zrRHqTGWEQ7)NvW>HvqEgk#}XDum<+Z##c_BBA`vhp%@iT;t1nA|J;z5aGV@!MYHE~ zN=5(~YEG}Ns)PoAFz38mHY~sBYq_{g=Y6}JJnJY7tVn*QNPxH;t36&3 zip6~!#%wr!d*Pr!Yqp;=_*CaIQepS+a;S1V2L2s-wc94lGajk=v+Qd%;ZYo9`Ho+( z1rewLVh622pi2IdmPX!=HUHFAzDDBGbm7zT{_O4+bVZ8nNXN`X#f!qds9Vmk2gj| zMP*i%!X^<}x|YaTG&UmilLKEr|EFAdEF>*&JsH&^ZKWG`?kERYHPhApv0~zJej?VF zJB7)B1%?(mgUlP)i#|_q&%ZI70=K*!mJp9z$$|0Dj*Rk6ym9~jnEGjn>|FU4T;Dec zSAQ7X9W+eFplIW5-vV_}cncqn<IE3XF((1tt5FrzgCPq4|i{?baIoE`J-7-AW_OK}fAtO%HxF&n_xCc6#Pai~UUl`ghz|X)!G-XF_m`A03KUmA9#ywyn_f z93k*3bHQ2ci-Egmi;)kiDxsJ1iYI&e{@*$<(uJ+4N5T`Tf|olDXygcZe1i6T#+%23!i?KKGUqLHggN9MyxEQHxq! zr33dbSZ?vbo)>0{ zc-#{o$(?JMbVg`w#K^5Q)0kXKmiePk%bCnY%p~?%#nYw@3Ddr%=d-okytmUG zhU$=(VO}-!gu$#7Pp5oCua186ulWDGRnd8CzxdL_4x=1bHTv%K!st=y_VQ&<|MeIv zk5=YS@M=9Ic;c;n6K~Z|sGgWtnV-CK@vT!1vkve?>lYBLr7K8G!*U`fw0E5*UQ_Mu z56>U>>k?;gG@U|=*zc6fumZAJ4*8l|?iD&-9g*;hxB&W~YwQk|50F=KKYeO5>mXw8 z9JI?|tK18E|A6CxMxS~QW_p!%NqF-dLLU%e9fFzaCYtL&YT4i;gfx?kLbRimjh=dU z8TW?BC!+=(#wnLKOZd38!THIR4}0E%{Rr~2WkHFuB6t;!A>hR&uZ^ZPg%ZB8@v zYWCN|iQ3w4$iE)W_|;p>mp%E{!@0iWDJB%ThyHpvUbp^yI05o-Tn{7+&FfWME|zlu zZC{U;6onSmMJx}1YZUXNGaGLzfFb~LD0GI!z0AmHT(|Bfoh-JitRr&iVMAXt*7%>g z_hZzf#f@Qc5D!D%Xyx1<{O5>1<>q#xf-Lvqsms(Mq@SE7sxnzyhQ5oBP@3_g);}q{ zV#n2z?VH=XX8-7;|F-ou9a{45F1|akq|w}|OVY#VrVprlXV2MVCtW8@(*MwAG9Q#Y8)ii*ruWvP1bVM6XU=6QC@46|ULwSFg`ktyfF&&!1FF@p^TS z>{~{a)iaraVK(J9@;6zqVilolfQdj7@t+m# z9@V&l(8!=7c6Gj4_~`S(4V+&TpXL2xU)wY`a@=ont~}xGlykq#2U;6VUiBt{ZTog!Xo*NxohE$Oly_-m1BO6`C)%ff99_- z&vaE*j}qYpkSH4Jo8C!~5uCxUG;j>7a1WiG20_Y3$R=D3WTP@4D*_|VkjL#1%vbq) z2$N1#J9+)u zRs8;9>Uk>1U-D^T3~?yaS+H9(2$5oBsQ}7TfCAq@_{0JzJ7(|SN8_&(^1uJ_#kAPI zzpv#~#x@Za6*FUrsjDdnLxwT=Chs_SV)4%#@G5jx`q#Jm|M`g58(&}6QKjZ`rY?C? z&SW)UIUWW?bQh-G&OvbFc?%bYS*b#>W5X*KKiyG`Mm z^=s*NC<`$DlIQEGt+IN6{DH&vd~?O?!T$80M^y_miM+HnkNFnm|;f?@7 z?PQ83ZjwVtN{R&wVt=04-%e%qW3g$*JW(xp3qLwy+rNJ3zkXMG#=RZ%n3pq<5xd}) zOL2$THl7E6L_Pfdx9XKWYIskIM^sdJD!iT2FV83`F|pQKl$g`!%-Pq@0~Z9J+iNO* zj;XrLVd612ghgQY!!f%a?b=;GRK`3Y8e%a#4g=aTt!}yS5(1B25Way{9XSxY5*ni- z1Xv0*6V3>L;#4?%*f3GkDX@%q8}s1EZe1_ThaDFf4!G^jsFqV2{q}UgxL7seiZSwR zJV9QhB#=BKc^!7$yH?)P*br8hzOc{DJ{@9Vxzj-GS%Wraeuchwo@jJ?cAFCnGao$= zrr(o;_1#nAj+_tf`XrivD1;tPkxk}qnwJ;n)Bw)bhCNgts9y{N}2av8$ z10;>3&aT0XY&zXT=_c6NWT>SjB_%~p&Jd9nP>ynLCV(MzH~i2(8U->+Lg|lwV#&Vw zg$NfY3}k|a8e98ejK<)mKc?I{&JCKy1FBQnff44hU4qhM0P-<#A3Wy~01R<-6Iw@j z1A6@s7FRvkdoNqniS;)Cb#^Z1`kZZW~V&h<<1X7#5GDR;}{XAcQA6^ zyl&voT4H7?yUZC#SD<=W7cl07X*ja9Ce4~9QM5x%+Wj%>=8YSoy%b>v(EMS{M!dp0 zR###m!jQ zV`Swz^{eY%dVx;)7e7zgwncyC<&D#-;~)Nf<^SVu$iQGqAKrmF=A`~}CYkPV$bA3s zfknaUY3vi0(Qe9hoWGg>8#i|BSOCYe&vy`93D*lG*4_Oazf6#-uyk{Fbf8{+S8%t@eYZ#eS>wW1sG*C~(6I|ID|rhu}y!7xoEVVrYO znDT%1o9%^vR>LkXGiC%h?*-1trdS@{b(rMo$&+yA4KnyIKp+q||3KW-C}Kt%rOi8rv#w)m2kkF(vd-n^vtxtX@6HD$=&7 zjFym_!fjlRx%xg1!y_K{aJ|&-JIe{_r;X83bFEr1RO`q0!itua`K#1@BiBDjV89MC zQTL$N;>C;I98hxw#2#aE;rp%&!{UaM3Va-XjL5Sqtm)du#nlVC=2d(vO--A5^Nz2+ z+bbd=a_QCb!0OuGd)9|lT147*EZ-J6_u%lu*J;4vLGx>6w~)DI^!C1b3_hPd{Mo^8 zt2%3I=MT=vRMN71?_(8tgSdNQ{IMkm_wU)0|H3rWFgVgPd~BDj7Z+@!wfpvM-l4-` z<)$082RmoRTV{8*ch2|7xTkO+>~UAq*zyD|75#JR5tTY8aziqImAV@Ba&H~*ZROEZ z3kq8I>C@-oD&@$+1lAg#inc7bvud$2K5D7a&XdJtFby}&e!Y9|JT*A;jQ`1*nQKOV z$jIEvAW^Lb2f2HUC%#LE`O=qtZ$V2kXD90ssdp%#y;1! zpKE8@rStqLakmt|_NiJDdil-sL7KA+HXf{7=Gm{{;BJ3^t)A82;<|1#-L>6(*R=91 zRbR_$tHk8w!mAfXTP5!IrHP&v`IqHFf^5D>T z7w9G1|i%J!9i{1@}Bu3mk4_S*mc@Bcv@e&NaJ)iEta$@uzb@@O)c<)o2g6G2&Zqws`Ywre$cw{&Yk1uBuBizIQ+0) z|Be|^FRrYxv)26CQMeHX1Pc4N;sCZhpk$*NuGz|ZPV%H<505PGmbFA&yWCJRxyN>o z>|0Qt?!Rip8> zq2c=F?3FO4072XtU$82d>uDjr6uDoakFJ?l%bZ z+N&;aTrsoUw%EKK!diua126adBbm(=7WQgi%D&GHiBVA2xZS!pVbhE`Y7Ge%CIuH{uxTvu%T zgA!t6W2vKQuGhMHV>=RXs?a*dNH%ScBl+Y($|NXxrLh8d?AP%NlP6DBtX^iLucvqY z(WB-TYN0F)sk0Vznfv;+{~UWMaJzJo<}$96nJ+G2P=^#DF)2w8-G*}G6k@1J*xCt(%26W%86y`61D$+;cW2I`$AYoYE~a(7ZWx&#fI0?NbG?7Y-nb=M+hMhnt{K*t1>79^2z&iUCG zAc&?-U;k`Ie53lcjG7{-5*a_c-SK!BwLU}t(_yfp4{oWh1? zSE%K80S5r%wTW|CP!Uw!bMV3_mqFbuteIJ6t%7WLL^?6P=wceprbt`Mv18SEYaCjC zHU(*YukUF4A(h(jcIj|L8@xd%rdx;~CA~AI)OW!P3;qUHPGXuh$sY3~=C)KdG*anN z9d$?;BHg7AyXxbU0@|Dbj8j1Po!N-7QwF3;8_+yiPW$m#F-f-m1(+ zh>VBP>-HTwfFi1w^jtF2(NWgiDR5WI!{aD}yc2_?b=!5{b=!(x-dntp4jjNt^DuK| zsm|eD)s)05hGsq+;d2mlQQdA}xS-SiH?7*V8ASt$LQ!!#$y5N|q*V+#TnAoaA}#hK zbv{LX8_J%$8;8CsUiT7DWCm=sIhmO4=_I)U0%ST%>^csO)_E%%CJ-=u{qn|C_6tBx zvqHGe@ZrP1eSDhrT44%v8vU6gT00p0q0bq?69og7;IoOqSfoV1=%6twBtBu3x*DqwfEbmp(MXv8#n7m0KtO<) z4RBS&!vnOQHw$5mn+kgxvJ4A{nVH=Kkuph;3LyqT`Dlt4TMxk?F1YtW;wwU-%j5~V z45MRLnRvQZ?N`kSd2vfx9PH+!VCXvjID?OolOF@66m+ns;}^Itc#eh@0EF_5opOh| zjZD{xFrEgw&761nbuy!8|!~MrxYhb^w4qE!(N^ z4K5b9ML-=8r`B{Zx2s`nT14Wrz;!_5-zUXY**9<0w^YyB`k8TaJa<;5eA09s*O5()GSwvwAHn# zwW})VU@*1GO$e6dbZ6NjBQkOc+^o2swCF$}m{4MP+|o~*DaDz`lQ%CibBPp(Jmd8nauO>-M$^IN^c(2M3=oM zPMm0MF$v!y5PKpR8^iPu9eVZZ zd2D*@*R|_}?ann)aK)FG7*b@pE}J`Y+PL z4Mti4MBKFPO5z_;*S)qQc~4}-@Psnl$12C(%WhLc_hyC|X89&xP^P`<_0J-walAV} zJ^yUI$(Db)m3u>>?3oQlu=RjwB8bIRl_3-tlV4M;8fHnObcWt%WTi-9d%}=*> zv9FU~$7Xvn#=e(xhLNF^Bj z@s*BBJ$F%haVc*;eyq;NmdSB0WTr<(#Xj*GVv0)<|Ad^LZm$6Qlx};V0K-P>aHNh5 zdTOjml|XS>+rr&qtd82xvNFZd=Vp(28eml~G5!Eklqrut(`aKdGvxTzZ&NHYKJDxp zGyd49aa-p(Irgvte`{K(>09nH zKo^6PRaI5_LLqYiWMZI1h)4V30}Fs9cpAZ2ht?HYG4;at_n}_m3yr3dof#q5%XM2P z#&^%2X;ijbNNn>a~ zo|kMZ15EH1zZR*(9Ra}ItGrNh5V(^$zL-`2v3;Nq^b}7#3Mm9E3A@+andnqn% zC*%nW%kFToFc^R3^6B{wYpcq9dTEB)`}gjBQ&157IPwJViKwvwrZvFdf9=q)L&$|? zIX!e0J^j3~L;Q7gp0b^se;}w$(+`hN$yQkR+iQ9NwiRBJU%oE03~53l?iO`*B#gB5z-$kKAtwZ#iW$++=(_Djn3!fS&07xWa`Y*TXYo>NkmCsbc zcs8N{H4C*{k$o0i0aXz>86-<{{GAT3URBR~ zVoOQ7X!sZa(#be<07oi3>ckThthbSSb&u$IZu0y%vvGn=(B2qkuPNO7$oAPETNibN z2w^rvhK0q@?|3>~5v#juXxr%RRilal7cyGYN@l$1P@_6oPq|lkaW!-cGQ43jgvjqh z2M`dl3A*_S_PLwOn`vtHyu8Wd)3au>*}K$^c=+dCJ;i!?iK~1j@oL8}uiiDzn4SB8 z2eS?2r5J~I-MVShrdMuogzf1vP&eIYOq%JL-Vt}Z1Sz4a4$=4#YM2^`B!cQe`r}x% z=+^zT%hAcnj9U?Zv^&Z0Jp*4AxcVBMFzy!VsXbu2CBh!9q$P)7$WU5*LELh3+SS{r zA>@Jcw1sb@`no-uT0Y@noy7>%DgwgxuT zrOJlR)b1SfI#hqCs=n{GG&Fg`50#e-@P_g17WF`_BKFS@><0X4+chH zRB@YXi6=P&C2OetgxRxoBqHzwK*3^E%cOJ22g5n%CoesBa&*|b)pvKdPKrNb{L$sJ zIpX`Azl5}u6VvC;?K*JNrOTJ;725DUWP%h{KgNHITa)s!BfH%{WKt8g#5Oef904ti zuFx)=L79WQOy$#0eR}tn-8=H+fmpe8rRfJ!GmLy3aX#X>L&8B)RSmxzrzpk?JOUdj zD+eYylpXjIQ9NS$^h1MoTQ|htQQ`$v0i6pe@j@SZ{!i9@Y+5fd^EIgp05Ff@5;J5faau%yf}c4<2&0vio|rg(I2rM|BMZ~MwYklnON4p8(MB2iap732Je&LlUwXlyok_R-jERPaW>GobsoCp7nKhVur>O?!`}C}LqW@WekMna zs($*AsY@uYInsdNNJhMAbF{T^f4dKx0m1cLOY|YwL(S(O!D!fjgS~5U^r0_yOyA4m zTyOyc)b`C+Fjm_7{A4;=8@9Kam|1CYn}N=?i4R7D{$oy0`eTGFwjpBcPbTOytJOY=~eZsGaQ^F&;DzS=#Dric%NA_aOwUdfuJee z3Ghh%{MSA%p}lCvC|Gq_8bbsAFd!N7DS#Q@6>D6xwPoYV#53)oCIapDZQ8Iw6ovde zadjYj=Rtg6{L+fAO$JP+CYQz5pwB4u4;759iy0Z0HEn)oaQa++yaN7k4bX@~+vvgv zzRGRJMjJ|Fx*3r)eTjg^RUx)F@0D~ZxZ1yua+~T|?$x~>6r|oOQ+~Q5aQjOyy_0GA z?~iVM^|{m_{gHWQ`8sVCs*qVRC-_j2p%6@ zdhGQfB2X2Zuj!;f;O4ZYr~AN%g{X9dPzfukX2Rzy=o2ZJ_}+;9>%%#;fE2Wq61DYg z$%-*0H~mYrB70X4GwQ8ono-jD*XnEk(=>ggGPI7GjkyP3 zeE>W2z|~C3j3`?C9T5WZzNuGN20>IZb&Oa@+`Z8IOsZrdV06+XhS?|)g{mwx;9Oy ztr2p;%FWrL%d`L~o)!4Uefq29;1YiLu=q^2L&pjPmlZQ1hhojZmXl~L@AHUXRl1$> zP?0r&q3#2{Elm=>UEE&3_x7SQnrlXJJ7k9sYKWUGo@>-;(xgeEfR{02vZC!LJGzH4 z#=>j6t$mx&9`j_5tIvfg*nR+flF*Y*ytpXrK%;IQ0$Mi%dykqp@rliV=YtQIjUie8 z%-vxmDgp(GLP*iSe%-UDg_z0lrXd5JNpEglQhE;i(7-aIk}9j@$EPWn!{uE&6gJc- z+FJK}dQ`d+pf6cI2L!$yu&<~^C2n}a+%SGQ@?hBo9f z@l1pj9`&xO28%b-Y2AVHRt}p+1Bmo0YH}81_3+9@&?#m=w5sD*6<^CbhvhR zuYPApJ3dkgnE&Y+YqvUMY?XnZfm34JcSpZ&G^Bz7)??b;*Sc*mM(4mU**?3)bE)@(n zH)rr^r2>aRYs+(~@Q8hV7dVyDaMos#h31nVuLHN%G!dY(1w5<-w+lgYkL`t)$Nfn-29 z@=cA}fCXN|`G_{=Kii?RuJmMS9BL39n|b+9&vxdrS}k71NSMf^IZFEALbbx~bWHfB zl+zvd8h@nPlM+-I9zVy+ymQ$V&8;rLbwR3R%i`7vCFoT z0mIhQjc~D$$_K7XmN#No`{k2!q?axS)q2WzhkA)rF*Oh6Dmd2zQ%y&R?e^?4gxeW; z)-G*26I%3_e-YABtXEi&XR)l!PUDfGt2P+>k0E9X6M#3dY+hy$E~w>eTF(S=&W-#GUu_ABo8-ll+uH zVYy@5Mc%`h)3Z7HX822?Z^=OI%+@&tEL-0o0I^^#H5}J_?G`F?z0eh_1cveEo zni1q7hV?Es1PX+6+7%2d6WKnyv|)ha6;;A9xXEPjcPmje1nHm8?_5*?X zYWlUmBP21mL(J7MlVDV82IC82^+f?CdPHU+*MTX-q!N52OBnb^BQD;hX(*dYMvqnz zRV@f`=$D=lA#l_ z9;Q>^k>amO!+>l`sUtQcY;`>}wu`ua;Hvf-!J+P>CQX~(w#_Vnr%T+-Ah2xVYwT$dT=6w(a&#oz7D!UFqO)}gEqy$NBB+OTx zE=u`5(^frJA=T1sRYyiIbsIn5p;1MrW`5r>+T-0WL5cgi+@`@!Rj_+|tlEc7*qL!% z#d{+7Gt2%I?~_j*oYbA{B8nu#=mg~%TqqdH=A30t?yN;0RO@3iTX#WL;h;_`EjF_) zifVybdW%NGW#EUWv-t^qj#sle?P**k4(6B@Vg^_vngoWi?(x-$! zGg?PYoQMd)GBzAC)+^KzR}kV}jVD)E_2c~A{<0Jh+(w%2F3VpUHKltbx+W%)L|^`^ zoJNhXw>*_z{rb5O_N0e+^Fk|J+(@~sW(~7e7^Ujlaa7p8w%VnqQ6pJKWWwW&e z7O2i11Mh!&ammj7-mYn~;sJeFjfsGWm<1KEqu~2?+9%h{<$^hVe<}~;&Mod72 z5oRRXa3>t+&7%k9GMf~G17G#WzDk+NOoC~!b*Vywc^5*wGt+KcTj;Ih7!=d6xc?u2 zFH7jY7WlCPJ96ltu*G*$qvI{9cj+L~5#zU5>idTTVxxrN>Rqq^PcNZj4HsD9B0Y?l zl2L9w8NhiL+;-zwAt(@rN0AQ+__YzbLd9Zp65Z|S+C=vNx@)pyRJ~=O)f3O!2 z_S+!E`y2F)?5<%h1sT{Re2nM;eRXqH?&`N$+ncN3w|}$4*A^ja7vmMac1LaP^?YO~ z^qw}#0WY>{vYQ$C8{0Riam|vwzuUp)z17Pm0?YgqCr023I#IT1T;~t7=Iv*}CsW@v zqk>TEr!<;D*bzHyA(^NUXL1tx=g^I9npQlC01DrkOIwB>Qm_AXR(9nq?P(vU9W}iz zJL(YEjXWQMO1Z9T?L{Kz;(ACc5h9V!Tr^t7SFc_%Mz4f$4bwQnjtd;0!JG&65U?TB z!td&|%Fy41s;lcl^MSe^K8dhwN;tg5(}q#n*Lo~csIP`}%A?aiR5amd)m8P$Drja^ zkP8*!w%Nzmw_cm%o#eHrCq6F8x}+OqMJ(2i+OQhNl3qrTokv6x5Lt{+6yP0?pw=r` zAj*<%YFmAMElSH2z1sL28Gg+V7H`mn6paZz}Z9&Hx zV!Ns^AZanTc=6JuV!S9lzk=u9Hp1=EAr$!z&7cq6cC}9I?UF8#M~&^}`=BfTs)9sPO{^~uU#g@$6AUjUeU)6;wgV&9BZWcTfxG=;hRv&f zz1LhZiBe=%a+WyFg71Bvy?OoG7~N`(i7`%o6>FG{J|_F)eIPQ(&&{D2YmQk~BBN-G zG4hE(S<_Kj3*hF>-^2HVoG!;qWc37|8*r2a&D*1Ti=H$LIl^R_3YJ`V}A4Q(Y ziV!hq*HKfF1#HX};$-Fmvt>4X_=n>-LrHrGSoN0QEvvyHkyiiEf5&<%Dmg{S7lECq z4-SnzQu)m6*u&fDGsKx=O04x#hn@bNEY@^UsW~LA;S~SA6dF884GVWAAeMjUjo4<2 zY6O(Wh^N@K7`vI@wGm~342RhT(9(y7oa0XiRa)?(lEgy0ci1F%cX1XHLVc^I(LvJL zE~JhKRAiMJ%hIp05gR7#EIwZ1_ex#C=tBEmz5IECK|o7Izt5MzCWpDM6^Iu_1R^Ij zYXFOvc+eP!hXKxIqx9X}2}}x6DS@+vDC9t~(nyAwX~(Do$YVxib3m}@aRLM(l@r5j z8c=I(qeUQq4a|p4UM`(}1KwW_cc|s4t+dxTp(I#Mna`;yJ6iZYq>Z6Vr}<|#9pqWD zhwfR`J$*0IOk-^&y&E(})gGcUD0wLH zi$bX1?4^G|##O|40TV;>w$I=OWEd~Fcj)?r#eG#PUf@0!!(_37IIckgDJ$6)m>L?b z`j#K?`N7GJd`;N^N{2?a3vf0>bwWtjz;aJc-|gEosa53Spuh$UqZkHYoLb`@^l{vPZ0&L;nIGxVEQFpZP8>=9nUT zo2j3P=rLwzmN@7gWtAsAgyDzpi#A3EC8MHTHutcgWqRxR^Hv*%njq|~msx4fBcHhR z;ws8{RAZYyj@;FfvdHbrQfg1b+(_~=6TVBSsMFzy8#ecA#`@B=*p%GP*3i(v*S4e9 z{YbQUQVnCHo=z6^Hj90{)jfp>`JQho&W=Jxf@SH5=-Av?v~;O6{d(y#@MB`QkYfv% zVB1f;?Oo|-1QgP#J_4e3`y&^+Ii-=VqW8645iVD#IA&R_v~&%( zB{DrZKUbYtBIeM-AQ3EO4S_fxP&JCejw-2LmoA&3mgpVb1XGIvuB?c}Kfyq!S;P>8 zMLx7r>7I&xbJu22@+I1Rmos*5-+`A+@=b z{>!VExVzU<%`%{NZLyX;b7Kn$u(JAriWv+Dhge(aD_}7OiW(ehx;O<$=4mWAk_Nac((*?noQ+81VLzoW%Ngn(Uo-!yM^CMJY8s zObUx{c7ycL!fznQ@#uGFC;SiPRkdnsyhqLWJ{0f*a;2Y1(cEduL4y{3DS?5Xi5wQ0 z4iW;27))g&n-4E6%&vNRW7|T3dB5P0)+QdBxzca*W=Bda$ukEv8v5y5^VMwtAs)^a zU4yJd6oN3UKSUvs@%po8WoMrkk!ZM&GEXa}Pxf(v@Xb+i)>KW>X(lh`GBhAs5e`J! zx}K)sZ*sY;uA!cm`CP%6t!Fo@7avFwd3gM>XxJ4JZFP8@ww}fCEjLk0bKclwzlZFY z(#U`ZDf9Eu)6{|Y;-W3nyUZ*&abjhy$imy>>(;F+29Vf=iX|*R%!Tj1vgV?KVQ$*x zQ)BC!+@@{YYAy8hk;1ZlZOO}b5Ipe73>Edb$&?{CDF}oR1xml8|p`AH!M8VE2_L%+U3+bgC zr*{-;;(5#bu(Zv!?!P=j#lUj{e#DS_!fI&Ild@7Jr|6KrEr#$}h3yw|FKghhiMWh;0&8u+vdTtSE$ zllu|dmr3!f2S6^rrT!AAJmc0b(6G@W@x$^k{OmMJUD-Vb|KEWYbE;=4k2?$3c@F31(9qBW zomMm5BfU4q`t|9zDv(2Dvl8{m$alx18q;Y<1A3c4A~HKF2m>khCOf9e0rO3j- zrU6Hm@Eeq~W+9pynyOQ*2kVMMTGh|o#=;=;6r(8y2q~Q@@mw!V`b~^&?0=@>fubG; z(Lm`@pzD;7fw3lqa%IW>Olz3nEV~7blW-}#(U@b&J5u-}-ihw}3KlQpPwPv=0*LI&8O99}jKqBesR0UkKq`xfwMHR3SQSAvS@3GSmF|R)0|%b4`-yz%1nCIB?y$5*rAQuRzX&bV6ttM4Nu=Sj zGOaoLhGEnfbBK77I$Mr&dAEmz*vCEZ*`!UIZMdwWiU$cap{GYZh0?kv*;=eq=+xFi zT9%WOL$x?GE`5rn)&tX}VwQ<7+tT}G;C2ZA>Tj&PsXNHW@ z`uU83T6sorZsYzKo9b`a(R-qq+4@&`@L~MK%+oVWWfK;^-zi`m*cGo3zdcH>rOr8qU-mMS!&i|<$KlShES!xkCQxTKAe=7D z4On;1xJV1&3x4GVJ2=JmLWH~`d~G{Lz52Wsf|3d0nC?~{xJ-037OlIs#0u0l8Wy!1 zssR~GXuc!TO!zA%$7nYR-HC`qj6}f(!rq856xETmF2C-1s^Z;3+i)F4(N^?3vQm{L zHzH<}-3%1IBKb>wBvb^%fa@cMZk-7yO8)a=Y*0)M@T}-h`pxBx$5?cWT>K;B27f2h z{kTu!dyUlM$-f1O*CT$q)^?=z;K-hriXYDxva)Z&shS+ehbzW(txTZ!f_s!rC+Nw2 zckHMRbE%Lm%!Fx$Uc`T&$Cn|^d8IoYBI4rWMEC{r+m-V%E5bs51m#5rrKZlLn78T~ z?N8vdBKkx?uMj1Kn!+`yd)3&KBEf`A&SDTzw!swW+i!P93o3>X0JSf4SY%RByrmfF z?lSF1gCPx|I>_w`vLtH7Fga@e3;EJs*w;`54CDCEc@CIHBAAm+Zd!Fj4WUEwHuadVp!Te}2*xbj?()Ijg z+Hdv6LfHLe5`ECXEK6K()4WEI-2>3^;YuU=HdrvLlF0{KayWSfb{FPN;?3QnOb}8P z=1Pl{@%3tZBKFD+eq|KH=199Z^n@uErZOxuIr);#RYZuJpahxT+KVd-^o$IKO956? zWWxiLmrN|337#%EX~eh)%`^qN>IhHNR%?Lw^C<5Hqm$_ntR5emF_5GqK>$S;9-2+0 zUyPyvpo67cI=#pi5R7y*WS3y>t%h8yPEkfd;$}ub;;iP6>O75m{y96rh6xMK7vA>W zO@H(Qz7gaXgL1X>?m?Q)!~(K?lx9y@8bCgRYbH^;Zs|6PEpaGB00~Ycl+cbUK4!s( zNgOgKPw|cI$~Ke2Hb$O!hm_G@=DaEM{Fr_vL`Z1Ebh@LCPRgOcWTJ13?;WlsO0+MO z*KF;&#m!$o-R_I23KC4f-&(U}pSG?{w;Z9>X$G1upqT5+xIUC1iB#^WF`Oq>L+7?r zAe~sAxx?j9__n!^@csN#lJbx_q}FoG{5dhyc%;840&qaVpW2Otv#fp~Mk8YlMNXyN zwEu|DGm!g=JEv%Hot)NRy?V7{h-Ei2qS#Whg%t1=hw&1=;SwYh?7PcA)tHddaPax1FkU-| zW?2-!pw`(qK+|as4N?7J{+_pf@$%)S&6)){PXe+5={8u5{P&H&u3nO*l!(Q?Ur31T z^xeHdou+vXPzvp#IGj5T^^WLyjXK|H%$|0RM~n87tc8?dvX$aMZ+3`)PMS}2zooE4 zTop$8`I@#OHCm@TX=x)-S|nf4p^&hZTO7WWq9lfkh4kHqsUCmbTL1OJ`SZ77??;yD z%q|m-J{8(N_xVh=tfOv`wH3Ul;Q~TwKQhLkqT9K4flSlG@wcJ4f`TeZ`c6@n(ld0| z5k&JG6)L-q4IB_ly!|#A{K=sOkgi81O<$g+nI8)j0jDN;h#fp3!qeB!G`yGmLmg9aVIoRoQ3MQtT3X%cdO6J~o!%}MQWc*2uP zV5|Ak?4Wo2;c3d$EEj;%YJOWJoe4Jr zOd|`mwiIJqVPHd$4v8R8XUx!Pod)S4Z-!{QK*fM323l(Ys)=N_il{MCmPoOcM#H;#0&!d&kott#-6OS&~rkwE>5- z8;_iuhdLqnPK|FP*wIr*Ivn^sAwke*nw^H8UsTk4b))A6H<{3ZSRBJzUhe)L)Ivbq zv@f43>Vb;6_Zyhm_@hS^|B?cc1J_Qa>A&Y#za&+hQ`F@#!x)0U;Cu%jdI;Bg@W8Ka+5SI>58;Er%B5=p!y(ZmwyR20fmD^rlE|e zKx8Oe`vl*wG1N3e-88Lo-pSb;+Xlmo`P`25s34fjRuBLDEBNDesvGhhyR26RvA>v2 z{Z(|%`9LUx6T+BrPVu4MhB_#rs;n!$e8W8G?0QJqiSrN3U@H$emunU0#S&h)F|4W{ ze0<^=#q*OxMyBli)_+Hvfz9dR!|ed-I>IT$Z*1W3WSOIQAAS1pG_?~V7Gl@ilMQ;{Ehpsy??VKc;i%Iti&uTkbG$?4jiZ&&cNYI8b z0KcqUmRTkw%O9HM#u0iU!a((JmoiZ(Jphp|qYD_g^y59t30>Po9zlI6YaQiqp-_`1 z+|$V8Tvu6n${o%5^l22|-u6XfaFqzU5J`z5o&&aWSOlk!iF{vB$B}%u7RaMG(!16; zz|%5egifxLus)DpNFJ|gO=w|S9vQaXmuXn#PYD|X%UiC=yl3cjVey%;Ty@ifM(W)s z*OHbFIzj92Ei~fnnXYSaqJ8`Jwyig5bZM*a9-bMMaaDv|Doc3P!47fmmOVg$J&Ryr zFS{o?pd${dMvHDJgA{P_F&%no6+Nejh6IVxnd-3F(lp)3G~@m^|N9;-pBX7%oo0D* zDn3}#X)P(pkTc>1#p?HpQAP2qFqDUd@@8Izr#cfzZk+Sef!T^eobR0;pLt+Aw9~LT zSy4=5+@{7KSy@!%SJ3pvgKxUQ-^=TKb{V zq36Lw6?_ijt@vD6g@`Dm8i24vAOQ9v?Aq;aGq&P^N0AHQ{pIUYv)`u`9B$j}PfKJr znBElYdf-LGnTIGQG>@zZq!B97f{)%|$dH4KBK)<3I!`^fx&ahXUG{u~UNHimxG#@= z(9&Rs9SuhYLw7Lu>`Pm1?auuy+!v1+F;yCZ5XfNd<(t>_rRw`t`y)VZ;pD!!jGct3{Gq2*{;SpTdKowt7nV_nfg|%NcfPZH0;G?HC6vs?RA0ZU~7{Qlb$}E z)M$M&Bi&>N{_gH0S|c=JNNTEve9lyQaVs%VfT08+c2ZMZS#@euzE3o4fX@G@FLOvy zy1V=6>CLG4S&T)A3S2iIh@iYG&S#3Hy+f_)WWwb{rVHhs!IFvP532wP^Dut(0630t zKB?=@NTk58&J^Dd<`#3DC&1(D&^& zpv)?KM149nP)9Ww4W1tJ9b%foRa%t7l|j5>_pB53C9up7Ek1;3E!E@%Sjh*^x5K<; zVM-PpoUl_fmvv+3up($EF590Z&3`!O! z1R$dG?3b2p65}`Kr>aQ318azlDu}MbQ@8Vz@6wjat43fwDCvC~{BX@h9Q8Ic_G@ns z${edy_e_S&*suCA4>2q~m7t^ExL_XNHbI-_@)h_FO_JnDBb-i3*cF8Fc@&W5q)ZQs`JbuyG+_=viZ~Cf zrIKT)F&{0V_~PIMC8NNAxnksZz2-kNfDK|5UKCmj>0|J6yb9mB)MF)5z{{IB{_b@C z1-mYm__VLYqnPnX=Z#nIEO>CV38gQc;usFi*t9a1%%E5W6v9|o=^0NW1wu5g9WXF9ViGQm@j-A?QaI7>2vbq}(L7mR>HRBuGAmV+8h`?92%)5yWA zVNBG2JLCe92=UJ*Mmsnre-6<8F~MyJvRmu(Q%MDU*!Mg_xijSE469kC9f7_gO+%L{ zj3E)`z~EJVlfI(AA|i;cSnGZe2H<@PlzLey>IFFVo<-Z2&^=2yQ%Ch>-e;GH< z+GY~HX*O+c!&inGaOTUW1ES%4X_%O??v%-O+36e*&;%?ngJe>t?gQ~#$2$iBO9U)q z!JK2S)ADh>f&ZZb9(UZj5pv&nLR`ZcpxN1X1;lZeNs$1ImE9v4qud$RbQcDO9N@p zQ|+gC7o)L*-!UcHwZPPy#NKJhkhcB$^<&P>mk&-wFcbLfHkhND#QIr^$X|v*fwV6f zz8141U3O?wN9Zj-UXEjxY=(qhK%&rB_9w9AkRHK(7C^T&nEmI{9x8E?oGgQcIg*XE zXZTVKoL+*rlL7Li4vN=H*Be;=!Lx9UQ;Jl#)M~D-9vq%k@_4x0Aos_iCqU8)!ujpgD+`V1DhbT>YSjP{q}ts zpPgy_{ay{c(faJ#`7*(7X!O`jMs+r;=?=v-g>o8agI&B7H)@%T5Bl_jQ2D?KL}zX1 z`-#scpMBW!<^2&MUVHw0;LQ#DxvOF5@MJod7)n_X9QM7z@6y3|X%@81s~huYD*LJ5#YYezO#fu(+iuJGn1O(krBcdfEAtGIE}7X`|Wrc`*E zqY<$jB6qyJq;A)M3Jh{0a3La z(z!zdh(T9}dF95Ab0+9s3;B;Uy9Ee`AR%4kIuE-J@QvIZ*9GK3JPP3#GTMo;10WY*F(XFI-`nKbMt!g8pf+^EOC`8rmEEQJZ@2=@Dw!*06tn z)|ALNEF-V4xdY;U%4v>ay&24}6-!X18!bi^pDZmLkJ3#lte0$S>Q>^PXeZ-zjOB{J z7x-@Q*bB1;$j1Qt_6$WL0&W4pC$(eSw$`W>DoUe<<`65z)eSeH6(~@w z%SJ#4d;ZE($2{;ZPiD-qhs_Gzqktbgl z6j+idCQ>iMRt0a+<$r|p0cg3lCc3Z}*#F2;Lx>CtgC!rI=t1A_cb>DXK6-ox38Boy z+djtbDFaBC;CY}DoaqQ)=#la|w=?#eRQCM=X%MI^`*vDhN&U%hRZ&+rH#1B0`Y^R? zJHHLzBIi7sbN=gB-oqQVnIh^!M1)AorszE1(udv=I>m-y`#Rc&8`u~q6RR*lZ&%$6 zHgu~Vi4yzl(od5WxD7UdHuC8W%!n8$CtRI5e?AHO@b{JJ(1NULz{77H0fE``YjnWZ zzE#vqb*)-*U`EziO*(Yp2oK!$eSaXAm3{<^0<4;Q$dY&}=LVn-$SgT!1nNj~f?;%G zQ&rXFG*yKd;+V*u21x(ZM$7rSTt1FPQ#7lb0C7;EpT8gUcp9yhaF%$gUytr)mji_& zEi4`{Hb}$S!XXlB5S*J)lZxf?6VZkkqz!q0!Tr*Li+63k)eKR0qXP3A}@gR)ko1QvkbI8XwRSOwoj$v1(!E%dnah@ z-9BmrzD}GwLWGP&K^5Kse(-RHf)N2h28Fv7wW4cZ?ls2Y=~Y1ixZ%LYk(z+H>!g0d)mbtPCj+yKq5GcitmT+(rBZseH!M$^8C%c@ax8A8 zrq*XOC36fSoxwa`XdsxwHQH8iW;s^NcUwc0%=_USfk(U#U~The8Cw zY8Ny*J|g!rv<|WFR0oxsZ}q|hyEIcx9uE%_MM;^OI2&~FhQeB!s(|d&WbujuwhIgr zCUr&Y7v%r8|A(tLf$KSM+x{zrY?UI!v?sEZELmHWHc8pZ(jpmKWSNjiw4sgcl3yi^ z-Pre5A%%&tlq?kn*(#*}`<&~#@8_QXyq@QEUo&Is_xpZ7pL03R<2cU2%fj#o0VU~I z8H%Ls7fw?5hEfV2Qa*C$7>3)7Ey(c@mh~G-Ar(=9Vvc`>5=}NTkSR&v>v`Kkyrc3E zP7iHNT$4E}S5M0jT!~)PW6AddCq6v`Jif40)?o6MD>?=0Z`k~$xvV=F0fmtBhRXX! z)lo_}(X^4-oc`Fz@Ic0}LF$d@ewqTbJR=YoWWNXyJeuBBm})}!wn zCfCn*CMVF0<4WNLd38*IiII^s-AZs!8}82x{`J(PI^*AF&s7$28GuB^Leqt(Pln65;VnA>$FZnw_ZIszsdraM<@e73^)E_OcyaP#D5T*2in)^43Q^-0WAuuuzR<}d zT@-0aM%TkaG2py1vB_Tyw~y0jQqm&I*TZ@ESumx}{r^ZJ1`fPDnv^r)a_A5+drIpr z;`70G7OrV@yB;n~sUWId!A_JR;%~b9iMnsbFb>+rYKOS42l~F~(o22CT=2&na42O; z;H>eFHX1FYd#@e;$Z@t)RgTQ_=4|`i#fE8!?}GfjW@$?s7r#gwm3yyv0NrCj&A0i_ zwth9l@j93LtSp{0qI4f%PP+w~?%l2z!gXmi5I8(I?jQ4`rT*DM?r0|`t*y6Bq7wP%dalCefU&Y=}Re8;069Dd>T--D}fL|%>Zr1 zf$XE}H(<*h`!9cb!$644udmlkL-bh_TL%(U7Cq>%0CEjtxgF;&|t2)bgUKrYU-g+q> zzQC;kN;Ll^_1oqe(;iF~?fKz2rU1JRx%DxjEnw8*OL;@ydf^cLb^;0LZp(w?Kllo% zn-+p|AsnWkQ(f+!##===Cp<8ZgZF=+qFfj^$QXjMJ1sMCX+opsW)dj4luS*g)4O~;w`RD8BbDZhLP~!)^Tm_@M07R6 z?K@JMT`yVGa@?lKqa;gVXHqraZ1K)Zb63dHb^oAeG?aI>LcmLp;bb6hh}M+o&BP;F zBnd!pp$+v*Isc}~C9_Y-Bm9S5e8#lNdgG!k!;81oIB)U?nt>K=!WfqMS$^rDfdg-p ztfyQ{JF|+UH|NPYZHR!P`v6h$iF@D~XCa#C|G-UT8D@mXDVTYv`@C+mL_qA#XU>%E zSmaap6Z4?4fUO8|0mCDVz9v-Y#uMWLH~Kd4Gx=ANNd7Ttj@h&+fUZ>L(44y&fq~8- z`40h>1Ja(uyJ7S6xGsxM1>SWuT@Z@?o;om}V(I0v2^Sz5H$CU}+VLY1tgOPBZ{+qjs6w>EXmj4oSw#3&Dz&kApr8J|ujoFXIRT;rv=AwWw0kwX%+)TadA@=<7OZ$7$yGg~( zm|Z$B{+ZAh#952_lj|)~BvGUBZpi|zXvikMm_Yc4<{V8e#%1**)bnx+zF_(tN8klg zvxX2K5HsM{d;67*)Up*p^B}Uma9$Ibwz%Dj(lh7J)6M2<4cpVlQS1LsQ{$?Cd{(h= za%(6o00f^ER{8e;Qmm7XAKk#yFH*MyDVz@z%LIsiw z+}m=Vh$=7NxcOcvJFNCOb}T5mM0Mp{We|Wq`&ZCK=kFTSpU9ZdsGmwLJh=Vie&7Bh zUbpX;Y9x4_pOsMWCTxyhvmi7yG;m-a{B^7+v^eKuVHT#6o{ClH^~h>Sc8F%5uLP^^8^9T1l+VdGQ!o&VU={5*k&GfttN0f|$} zF02YFwZ|$Vj9Lo=js}LO=EYS0w>p~I4x}@NBdpJ~ZMWRneb_q2+f}7~17}Q!b(<^&+ef`2cSl%Q=tUw%<^hQ>t zQAt1>vt(Wo{)mMR|Ez(gq2mYh=qe7*I|$vI!mq8a?x94Nub1sQE~;#s$=Cj^rO}3^ zcuErS@^#x7D}tb)?z&y$?47jM#(bsIyBSDVwnP+O#ztKU`E}fpPn;kbt)hgui!&Fk z%k5N$7!-!RY1cV8q#HBPYRlDoC_M=-`{-WjbT>13Yppxr3X!`{ux1nzUy7k%RK515 z2KXOaGE!s6oR6>v)j2I{>wMkdVX~Qy_@KhDM0#AP`isBHZ0BQ^f@1V_=ZXvm&|I^L z$BjO$I*ds~z!vYnhW1~a_V2;Ol?7{U-Zro=D~AMrHne^qk*8wQu%Bg62VMGhezYmp zGc)Vq!w$X8uk>tUd3l}9TY$%ff29y`ypXGE6&%3*)WPkaoyoo)9a9ueD#v)|X&nb& zWKk_Q05sg>dO*P*CZ6EXb=jwE^ooxAyDCws_%+p}BqjE~v}D@k2TSSWq4_I?Sw&!j zi8z9xQuWub{VLPWvoV97^JBqxzI+gpYBB?)H9>HgCO@y#dbY z4h)TqhL5-a(qd9Geq~jY#DpPQ)Bpv>^N^vrZ z>)1rtbfh|qk;xOHAVX9&>uTKp$S88;4APkrbnzc2B4)%@x}C%jfDsvkFKV`FUD`so zWVNsP`MQ3y??n>J#gkKSCD2n)?@SxF*(ZX%;~RG}Yd)knm1iW%69}TH8&{J$poC+k zd7N^WDC5XG-49D%!ZQ3skOMdcUC)13`MB&1U7Bi()Fx|=WRt^%Or#_c^4y-Y#hCGK z+j$^_;lQ-TlfOCLvTgaZFn^%Ac`O-Q841~8t?VJiUyK7+66h{n9yDM;UQO932S1g1 zBkf3M5YKg3lxW~#mti+@gW}x`=O*X&Brx%&#CG6aGwU}oFjzy#WOeAimd*{6g&PB| z|1Rm~I%LR&BvDe^v8#u-##Jf-mUqRe2V#;hWy8S4zJjJurQeWtg0)}UGmn1JPpeHT zo-p%9l5>|~F=Gyv{=z|e4hB8)i*KHv>c4EmO551!ZE4Igg^xVzKjRk@E@A08=V{tz zJ@)CV90Th^XN>R!&f4JXf2nI~UgIm#S!H0V5SU)j2tCy1h9ygvj;e`$1YCk~UKTx& z*RHy5XRqR7&}*#z?TVuTgog@HJ0)Thg-nZ>MyPKuDd|MY}7K zZ?8*Fa{yJ{pT!e3kvGg`iq$dNG|n&s}1grmY0|X;)J8o`f;# zs+EoR8?BzOY}pyxtC`b6>%=k)Z|&eI)dD{;M-d4s1roupPf+>rT~~Zv=&OR5@9Py+ zmjPPPl@7Y~MX7H0$G`^HkikR$wueD9Cw8XZ>VoiAXH* z%E7B?Q2n5<4Lr;_500+p6tS$k5=IUG?xQ8G2RXf|4aZ&_0|5y>K6tENGr76*y@b!( zlM=8q)H=6rIz9kMY=tWadg{ZO6z2ta{+GM5}nlZ2BMpE%(kX9rS;o}Y( zV@c`j`+UrT*WFDUFGE>Bf6pM->a-eNxk=*|!m zbgsy{o#*r(J>=RFp18}2ceRa$>j>nda)&sj)u@T)9<>gh*REb&4FgSFg;efP!C37Y z{?pZR(xI8NLJr2d9jxwQ@!5FQq}(({?!xcp?OVdbult_#SZipjwQ?XRhW9}mAN-po z87cabPXw^<-L1an2-Z%4YCBW*^7PCjOGJ$c~xOhp%5> zfrMJk{VNG<<2b!66MuN)ZY*C~GS{V5)llZIo#FExMGXU1BoB>-A%57ZQ1+xBxI2ER z!$On0v#bVIOe(ij>d!*`DlQw#V)bv?ETX~WH>Z=& z<+}j9i2M1)!)D??-&u3l%5Oy$FD`A~eQ$#joRWJQji?0@X}zjqdnBZfO_Gh9s1lxm zDTxCRk%(F+@zqykP%9Q(%kROc9G;woKgK%(`kw53cy{<6I#L z5VX+Wm%VzGzUY=%aB%L37o+K_#+>fdwym$n_m+#FSe<-aGjyu!{L&v&Ek5hstSq(P zS0oUnOo};+{v{X5(0e^IUo5HGF(qY_Vx?8zgpJxV#>NLudM&`VGFqvREA#GrA3eJy zTw(O2P5$qHvT%gwx640a87O%y0Zt_)xT^T$$_dlH8x@^SOiaAswtW^`?Yp1%v`r0v zQ1kTirG9}UC$(tPrUIysGMV=B@S-*hY`P2F$(T;2P1Lf|KKm^O)y{u6Lp%$B+&>O6 z=u65%j?h|UG@#eAJD3W=13M)KJ>drrc876wg`G(c&$QEwSPdVm3>K>&i@Djk%Ij_# zzB9n9t-gtI)$r(hy%p15LI3AvSitGw)>UyXIK^9XHF%XXxvyf~##rz5=lCS2fjIzw zL*ezPXujWqKJ4^4pc)*K*Q`g%_FWxGp{VJC{+^6a*G%MjwZ@o%8cAr_>Ul zkc=w5bT(uxdQAalrn=oYxmDFv?M|J#XB6N@Q00=?(gX7<=Gs`p355Wlzdg)B2145y zj9Ugh0Fm)(W$9{`%(PHcKcM;XcgxcjKUF08SY}o}$2B5Lxnf#K#bJJzvIlLSxzUX(0Pu&8G?gPmO zOB-m_&52j{cO-nFAEx$g)EjeYFg@MJ?%mE|hQCnl2Iam6zACtgy}jVym0t|q1KDgB z>IptVQuYk!hSICS+mux^Y(YO_*t*e+&^>=1i;0TTXIYe!R*g&GL4{VHXA11oPvrHM zLF%qdbXPM&l94reR_F^;+v)0>4@%$AWyrUKr)>3{q6kA!@FJOff?>;Wj?V(8OYLFZ zr^`M}KqO1@XCM##+%v~Zvn^T}gR#$?#pxs<{p9ZFMkQ&AD#NtzhmvlO>o}}M(_<&& zO$@;L#=n2@JIRRB)Ozvjq$yv$XfVU*EQx+qE*G0cm3$C#5gaE9{7CeasHRDUeel^3 z$_JMi*UEBaDZC%#IXEtm6 z7eE>_%ue_Jx^-OL6s{gwpCuL*PM2-mO6NTO482p*G^)*PG)~+U|4P?liY;k~LDJ4F zs%R|(w}30IPMM#;nA7_Y*=E%T1M(cz^WAi!ttS6LVKA)TEogNp4K}U0dB+Wo?vIk& zaRhj2V#%RrF?TGDP2y%(Kn@r{gtHl#Td;0dN*N0Il1`Q58AZcNBY%r8scdRGUFc+c zl&p%iXI~`<3Vt<53LO?EcJow3UadFIv@yR3lbiI}5o$F&N?H_*YCR7Y-~P6+cBA<5Pd-AkU15&(S*>?TA7Dluf8mkp%mo&&c0- zf?m*#cq}v%sw4MtB{V<&0fY1V3PVw+Q>RZbzB`{Dy7Hgv=gXj^Xh+ARJU%{@rk0lC z#4fV8GPCIXF{gJWjdJh-x|qp{gKF*?Y1k)@(y|Zz2PrB$Ou5m5;9w*AGtzWkrs|g& zG`<;&VKMKzdU${)mv07~6bawZ#l$*1CS9*uw-XDU`Kkh?;s78>Ii)3(h;lOa$KQV9 z2g~#l$)*EEAyhJS2=Xe?!nv9(xyK@He-ekdUSs-B{~(&KxJsP|$SGlVNAn~YK*UG* zI>$er@Aakz6>lsKz$lV$)O4>3(Qlqbo$KFzv%nEQD=I6uz6xE@>cWK!P!XlHT6kl^ z>at%AqoQ~8{ne2#JrNEMV4>JDvOz&|%)=8EtDXw33S*$<6>bII1wY1@rdug@>rs?U zU5yD)3b8aW`rPNVtx0Qp7oT!eY_-y@>fdd2DneWyO8lOc!ibooiy+S=rt-@W!w%8R zlR>QHI!W6@Em2p~qE)Mx+!k>Irh<~5&v8?iTeMQ}Lj%A=OWzLtWX%9*7x9LDST=Yh zO1$zuCOl7MPZWCav<LD&p!+QFbEF{p(Z0*=S~_z){vjTY^>nw zAqA({5<*Wg)V|Xe%uPBxfmrTtyaB?#>KHF3mU(F+x9-84e z+U9Rmac<&Mb#c9I`N~6d4r1yJawIkcyIzw2rS7C=k{K{VeeSD^$vsNnc5oR8`}uzZ zA0>K^!z(A<@as1%d)+LXRo{G&O;cgDt~?j%ta6Okn=46 z8zYTEF(!dCcD3ql<8LDAh5L_Fi}+ZEoU%p?i}AaRtHr94YUJ)x?E4*zj+UnYyk2c^9D zLmPo9r60?H$3|bXtNQtg?KjH3;Yi{&uVxe-Lf0#>wzysp^R`6%*y9r!{(%32p3>0D zYRtTYR(~dK+>Fsp#9kg{!ok8-&a};FQIw{0lpE^4HW?;+V94X*zzO5_78hH_;{ab! zEm#8R4_|fG(es~5QoyODTcyT^Fn0HEkEAZJhtN$c-Er=)O_RB=>IREeLF#Q)EUw2- zx$5Z}FksdlJz6GqQe_twjtY2~d}e)>A-oDP$VN3IE+@ixqzsjLBa?)L-1Q*WMAW;G z8p7{7gpQJYEw>T7_F1#`5B@C&Iv9;9pu>O_wMPd}%<_OZ-0O0ix@p>x{zl|aH?KKc z#&vYMTDUr?+^WavvqxSIF#UfXRcqe=!jizK_%;4!O+F0EM(-S26->=j_*Xj4Kr%~D znRYppd|z|obzKthCWKBd^e~|^Jb2(ibY-WlkdCMo2WFa##ih^w2gT9A#ZNB$mPkn> zWgv8%K8lrbTXSYHu%k4TUZ2R<=g_>X4PA)wr`4hMUFJ0|xM;?BjF49>*r z8XlIIcaZFS=gu7|$1FJb1znZ6&>VS_O!-5l?KN=Oo)%mcWBbFq>yE@va+P8eU_f2!c8G%D|>*2p+V-o zFJ!qpc1njQqiBPG!ok}-RbOGG3jBKwS2mjw`;Dy^Dn{s~kLgwg)e3L1qUH}=kZ-!b za}~9kxIl6x#kezLr&jO}E2oK!^+~ZJyECu7v))MkyH#0z=6*MqAz#eECTw9?;Sy7q z7D`yaVN1`zz|MVDU{_(NFisHdBf#YhgsDX1r%#`P4lsWv zlnAEs0-1mowz@*h5P2h3^yH<9H_JGF&{!b$(Ms(^c1pR0h1lAs{dNvrIce&jygV8%vSP?2fv0y*add?aTS;#u#%1rMS+P`%!=rwhXu8<9ZFoUQpj#zd% zk2^kVg3#GaT)SO^!GWT@m$Tk0;422Z-3{7S!qk6oB4CrP$}x$Kg|-qg-fwemQruyP zvYXKC3Exa1yk+Q1cRBIQ0)5V!ETX&qboq)FG$dkl4? zdAW9)vjzLxU0aR}>ZV)q|2PL_6%LZ<6jt_C+dB6=yN_6tQLuOTHB4p*B|zhS9^1Ok z7BL7lk5JCJ{{_8X4qH4s{;>r*b;XIB5R#mWs2E@w}5r4c4u-s8Ph-mQM(Is8#EoPbl2!y8yTR+n*>zXL}s;Qt%O= zPM0m|nV5B-n@bM!z0}L^m_zf4Pl6irbAc8T6ZS)Jp1buOp})= zCp7My&|9^4*S@Zvu!lS^tODs#a?*<4@ShC}jVjONp)yz3)omF!C;bxq9^yb#&0XC- zr$w&K3fq2izO%z$&UZbO^F61zz|m#>hT4zPi&Nkc)*Mj;Hg1&-7l?Uxo>{!&n|@Kf zbEHw9b|g!wW@wOTtXi*lvkwlhEPjcLOEK)|)~&Z&fp-Q8Nm>vt1Cz=a$9?f$J6&qR z6|23eIOrzDpB=1IIk-tIPie)E+IXnZo!48h^zDQXGZW)<1F+qV>A8&I5F>QQ)iGSQ9?YT!6tuYUNR_y88gEZtyLFYkb~px=$c8n(9%9A+uI8^}jO$YX%bY&Smr&H?f_vs)G$#Cu#_5_qbEt2oZmLd~sHh7)6va9(3Z(dD9GWdm;bYr+Gd@ z=Rf0b?Pj4%ie3Kymm<4za(|l-f5+*bGD>b{kKJ=Cp3pOJZz0U{!#Z4eN1yQF(?VX7S2sdMu z1WL20DWS&K$(o=1dT*3C0H%7QyT%nS7N=hTqj=ddh;8@>i%+a3)$F__;azD(8KiR} zHo#76R2U6C!!t_NoOPEF=7AU}WQsIid|^ui7lCr{#d1d;30|f5GHHwlil7TZF}TJ$ zdZtFp$hjrk?(YP~=jk{iQ974~tFPY8KVc2E-5tEYn0Rig? z!en#jKmMjS{>16vD<)X`IbZvo*=ffYkN0_W6-oioUab4@h8pR9#JmXd6p$}ZRzws` z6X=!!1e(ydH7=u3qfn7IeQ8CVU;8~to^IdRQK;DZ?c*XoOe-odEm#-)v4B}mRIX+K z_Q>*KCBd3s)ppa@SH?7bWJC;NaQRuH+#-Am_|C-ZIz^5sxVSRK8de#}u;HbN=RR28 zo9NQf5L=1~6_Yw^Yd0~99~KS%JRaHbKzZ-n@} zgg*>u^7FFQ**tbXf{u}6n4{s9WBi?+QFhYIrdfWe+sCa7JDl>Ew!6y?C*Ru7VpOCO zwX*(hlqQOUWM)ihRkfRgeG>mK(E1I7^*uUN1+QMs{4kLFDaLQ4-J@V9&^*)H4j9$S zEUbjq{D@`DpO6muhe7bbP?=sq1t56x>{A_GT?_nHuOO44IxJv5DMoS}o1|&)T7%p0 z9;M)7_-F&;`RPEU0fEKk8I$VRgK)Sm0`Qc zV}yKpdj6ibA)n0v{qe+D1F*e-AmXRejYBV03avX#FJrzdJ_m6ySVXHK<}lcSlF)xW zevbF^B5B4RRp$iNP6VuO85t{>FPYD)(=yYU1`q6^w|)=t52Iyb`XBPB)>_Or+C?^> z5n%8wYX%~&J7(m3@$IAY^yj@waO8{&Vq;xzWVE|G;3o1a@Uhk~&YYwr2_DScfZnH5rjYe0R-|IeusIKRIT%1<-GJCS~g-1egr-bL@ zna_MtZA>96HXVpu{DDlZ=0BY>|HC;F6QD*o@nT_Bu`xS0)*#g8AT!U+n>XEA8goXf zLNU9&%JYe5w;@!7)>poQ_4CNK-L&n~gA{lTe^f_qzNgmBW|pc|HLmy-rA4@f3>n$& zjGL>Ts)PVwubbpy5ko-(5PJrQqDjaqJQ&xBIFl(Fz0*F5T=7Y!O@ps2^f{1Ek#18J z(Ak@}bnUT!Ihqo^(T!)$n|Jtxo6Gu1NZzd;^^(|5c1bbR>@;Y}p}~JxSy=3)hoTUG z&(uF|z|W+dHJ&@5(5|I{^$K|1cI8u?sVM)HjzaUWcBo61y)9S)v@}Xz-|pRvHAiH* zia2D#X*xc^(pYpFJef!wNJ$q}n!W*6__NL6EwW*B6XD?b%YVGtgh(yfmrhi5*d`O> zp+l#>3rB#G@1cLvNWJ^COBZg}6rY;9u-WFuKHrMZH{2Y&qBf3v2OKPXUjk`j?ks2T z?q0I>Pq>v(F)JgpIM3NI{Lp5108*)Ydv7;#H#z7a0mMgPIsXSyc>DG(e-n541hvw# zL)+WID`R8IVDC3{kNF$-ngvlxfzo>|PF>f~HhtCWWu_aF?`m#c8BJ~0#AnY?S9pQm zbTjMDpFeMI<&I%y%>0CY-g>pG-tJmDL967^y7y1g4D}2iWFLMo`QC%&Z!iA(*5c2s zdG{vx^oq(ke5~f_^7pNfHXrKREYc~t_4)7h$4YmSYYb+6j9E0TWa^D~Z*RVRx6?Y{ z)ymtSh)e(T*N^#nEwV2849&J}nL@iWr}R>1a4(Xq@y^r5<=~oN&nOP;AcPDHT(#jU zV;mAP%Fo5*%m{U3iYL_;^Eoa7KuH+w1;H5AZ`>V?0T1^j)N=7^qbQlVe?fb8W?|-U zt`;Xb1S8Obll$UtmW<$Dh?z5(wLhYD(4)NR@iIV^F(?2AEVVK%_mP@_O@fB-u&39}S#o)h0e*5(YoBC!B5vcbrtB zJc~)#ISc08Lf3nL(7ePJ zSw^GXJx4Uo_wL@q)}7lY@?OTU2p{?&E*h9^Nl-V)`AEVd*s!Q^&e6Z235m9f(h`ES zR`+qSU=FXN77|~TkmT=NcAK1q)`sLK+aD-zD7!S^9{c$zYA1g?jRaE$6n8h8Tjm(9 zEUeOA-m*Ck?Kfwk6<3Qei1=_rJt5`+7jFQYLu9~6Q}~jxIjrW*&D)wZYibb%U%3pY zH-H=V8j$9I@Vl~lP0TFSy3zW4ZhYC9&sP3;dy!Mw3xi|GVS7_eXth=6WeLrh_jGsA zcyIi-!eGW=2^JAP;o~B0!vZY}nYRLV z>Ctyj>=qUi9j*EN!HbF^Ra1I3&r(DQ0k`1z-wo}>CNZV$gIhqNxBT~jbTSQCG^z>y z6EoLjd>4lnO;oohh)Yb8DutSMwB-3K&qpLd*}UjmHii6>`i@Xnt@ zTZB=cQ?`%>Uo;RfJBcovUw;Dv8ctmZ9A3t?&g?nq0&T6=hQoXJr(|Hn05n(`8S~gY8?E|&ok>$Nwj{@*1ZGH zz7X9bCu1}}mtIRw4~fU6dL`dw+>a%=W{M6pq=1zUvK`RIRM+ay4Hj_pC`~nVbRwA+ z5Vvyf-P3|SP8!NQJ!dwEOp{)|jeX$j@+Fjoq&9kht6buUlsE82DR&hU(BJQ-NR4_g zfH$~W&j{bd7kXR9=zXOfx2fqQ!a9nT6A%oA<4S-MAqGYjkZqdryF}H&S@aKj&^~KI zQ;Wbun=jN(QaHu8Y+g?p9|_|UsfI|-8Cy3xiq}MVg)eg-3bdnt{PAZ@*ve0@HjIv# z&b)+9M1XxJC4VlzRZ=oOvR^@xneXR-IG}R!t1u0?r{YgCb(EW%AHBK~(<8@bK4hx1 zEbvTeJ931hV|3xX`SXvQZsmNEMHKzWyx|yA(OBL1aLEMwJ)77;{x!X~^}0&3`0d+g zGjw}GL%aVH){GD=c)5i2@I9g5niC6U5Xa-1cz((6>TW;1#1H`QsAy-GghrzsBcq~Z z&9@R$4MW!>cM*^~xw#pUV$5=m<#*0Pr{7vHYnkOhBaQjR&MNC<>oJ)pgYifCialD+ zMl)I34Td9o@fJ*5i3#w!!_~&v6c^9{itjadL(oX3okf>dtrCxUvXGc=3 zB_7FW_`1`ZccRQw5upL{!>&}La7naF#Mk&LQdKZT5Yc0&Pvc^#{dWm_ukkhQ$unMR* zawQIlyVcW`F_|pg|95)(7`^zD6DIOC;P!0D%nVdZ)(ISU`s8Eo<=Lh0yHGP}tWmF8 z1a)$c)HM{9{qCopeX&MMN5={5dKn22kEZFIjgS-HV>fvB{#sKwE7pG8-0?mAI+`53 z+}`Z39t6}M)w#d?zy?f~0fT#vh6i|Tr&ahxvbu<{0UU}iuWHcxF2(>hruU{GvH;di zr;jk(GHBJ$3$?4L@|fzfy@X{=nKhI6-B}HNJGP=w&jmCAB(3^=)b?Es%##VXyv^bJ zijcJDQnjzGJj6X$g4xnKj6XM;(p0O|b>WOy=Az)NVTbcfqts)A?m2G~p(=`Rgum?c z=&%&3s{S@H?j8}?!MlV3>hL0LjMpOHR77sJdbRv^a<5;|z%s(H7Of^3N7fl) zDDhd%jxBdr>7ePFM9;fPqj zz9~OjE7zMIEBlxZG>GVsltt=^-`Zo>p@hLCi#uSSbcb_HXvtXW&UOrCegwf^{2IqR z87cE-YI-pbhCBBI%|q%)**VFvz~W014css1u}P+Xk7qQqHZr%Qr2U=d_KD}m>AVT^ zn{%ue!=#B9FCBVYYI$ycx6R$|*Es!M0rVA^Gt*hc1WA&!OZM3jiW$q(wcuQ<5$WcSfwZlgu+RWLqZV86jX_9lGY-V({}DAlOVwnH8MQdqzRex$+hn+8N&<=7pr8VbaFN=7 zryA+dZ;lR`Ofb&!8NR-W<)s7?&0l^Qg`ER6)$ap_#x9=bTu^Uj)?hqi(YUFpIR7tS)-Ytq{Yb|RA<*JXFB7(^VJ8J!T~-BAF^L@nfSIq@u+#l8b{391>;ead z{IP$9hUk31X!Ogp#mq%yaQyn783>$FXs>*Y-aXJ2*>YTk07ds2*z-H*|Keri_U~T! z+6CGr7|ond7$bZZpB>CcH!?AauKs}($15_p&zpWXF!&zsltohv(J#{QVh5Roo33VT&y6Tu=Rg>B@^#N#J;@y&sI zUC~ejw^o3^C{f(sPkA%8`1wSZz0%6c?1Z~w4``OP_y!+3_S@8isVx}O!o^Bou(NOQ zEx*VY;RxWB zhOBQz&6A~p?UoU|1B(^SmAHWSy5AAZLyR!qyxDPWhPgF27SqB6+GMb+o7}9sr$UqC zEw*4TEczO5CeY}~tEtnexG>LQ4C1QwvejrD0jQj~3bOn?aNc;JgVf>8q3mvdZOIW7 z`x4GxZ^k2II^Q&^NaHNT=p0qd>y@%-+!;hUDQ&fMuJhZ3p9S^y$A{}}953chpiUp| zw*k`Ymc!pyttPsQK^(h~W_jpt(coWh9lG*dq}dSASf)(Qqx0rc)H6s|DZ@PuZrm2J zk^$xulN(bvw;0eSjqck-Oz^o^A zQ(-*b!un}#Y0NQ=#7r!19B?s_tr)BrrPqWjZ%b_C(a@5_a*6S;HGBLHBxc*|b25+T z^)phHjo(BR&|NfcEKO8Fm`9~#p1QFH4H_5Q!Qzp}+SFk8-#yc!i}vKJBka?3_kl<% zKmizYi`-jePTNr)#;1oB{X|a{%0$0D1QTe58)#T8aJX#d-;C zs8$(yiJVx>v1rV!airnp%PwJA!3sj4Rw8>!3R0JEE4J_bV_m5;K3jV8|YVKRh+fXl}gc)RScZjzL zBzFaNL(Aw8YAL><+Vo`xNk-@5*Ipt?vxtMjHILeI!_!R-mN?rGhM7U{GU#V%24SF# zoKL}Z7eya{7UM@Zjm<63y`T8{I#Fize|B|&X8%)Cre?l?z2(@f2Of+5V~kSK8&S6i zbV-rK)xWD1p5cHeCS-@Mn{Sx64RB#F^N+HmeJ^*M0|32ZY{yRyTxdIHOw!Xcf5ttZ z7lN{ABR8B8?GA(Jv0%pk`s>vveUJ!G&mDC;H55MQ8Rk80re|Zi)^)C92x6W7D~K!*9sIU)JC4s|NFr)!~2UTPMQ>*y6)JC5--{OAOdh=MC7)|OiW+! zn?nkK&6_nudBUcFq$h~rI-`)r? zsxvlk16>}HY|f%E3#j=%r#=^H4Bu?{+fBniMmkqLNSbm|@%%3qU?CHsNzYpT3oiI} z=1=feCpsG&o_%G z_}%DQTxtO`d8PVg`OIw)=dJV1RDc2&?<3_|2v!uXRe8K7@>I@OmlGd7%5bZGbRSg{GhHWT+ZVW^Yv6wBvl^Igc?3G6FD0Wi0> z`#83inwY3%oK+O~4c9YqShJ+^j_s00|AH658S2*LX(UcF!ylVkc-U{9LHtER0m!gW zgv@=0rS14aylh*&Bp+KInZ*e6V*c0DXV0EhPY};>?kUTBf)*~k2BzAy>3Z|?+h)NU z;hYQpDkEMBUahVkN_U=|1V1;VN%o~2HRw6Yz``P)8zk12%#ua5Tcpv((z3%|VtFjI z5kj_*pCN%Kq}trUc|erq#8EtpolZUJ%OMov!0XpHaC!KgdN~#*!f}-Bz!5Q%e$N55 zAV<>icI4b-3$NmG=*0zHlOA8EjDUEU%M1@wdAecY(m3;Lybd&anwPs2_nu^kM7!C*=N!EN9XxT zCp~zqw;1Ytp3)h*p*ZM?Fc^5#ZqBSo>QEsMiewGvh^(a_;5lY=`c%vB2-9+3mGEFh zyU6=~Hw6e<|#v#aUD&-&%?Odw0Mcd9)} znhRFIgcfhQV#4J`g@1>rKUc>G$JJ?a`}(iW05Aqa6B9K<3l6t2}OE_ z{mSUbL5ffIv>bfly-zRYXV2fZDjMb%m$nB~NVXL!uKIJtM~W#0>O^`-yJf!32pn`Y z*MKc$0M5)jowtq^d^D(9^soJ^LT=IWQ@dqEUN7hdF;nPvf}1k5c3xV?rWbsKn^mS3 z_c55ITz@A2&Ye42&jZXXkML7ezt3~l+j8&fa%y&Qq#>3G3dF*!9eY=PJEDJ952s4Q z`~Ff!6D?!`3@>w$gmSfC--`QzeB8qr}KgiMhZ(0OsYYPBJpy(g(|#KI@q>?eW(rG8iP6VPMv)=!`5&F>VXs0G1GDJ3vj>@Btk_)h z_Cdw2bI83eOaw<*iU@7@ zK;QaK=Ka~&Id~0!<+Mip`WWvGSdykv4cP4Y*_A+)3#K8VcEO*n(TGa1i^zhzMYV}2 zLja-JZg!L#ns?eq@{`!k(t@bpFw>|yLCbq~q$0cI-daZl>>SX})Q_s5;Ea1S5m3uE zBrF1{N-SPcQ)+kH>{969w(#LVwis<6JsD1#@PbKSv{$mMd|3VGwy6*UEeP4XcDLtu1TmSBMRV??tlr(Hh*AdnkinKk zP&iZLY8mG%(F5)F-!sC|t2YFpU)?6iDV6XFa&d20jawvd~9($k^J* z%oKcCoQRPD+40rn3xTMRr{YDwS{Kkr>H->nkc{EY<`fH6fYzHHLpBY zeGsi9JO>8|S8Vu&>BSZYd6AqEm0+J^ireR=VU9iNjBC1yIw8#hAhc0%?qMr;Ps9`* ztt2i{wL?U9(r7c^$LAXUyJl17U|?tdEsD3NHX>gkntuphjHtM0S}Vl~+Us$9rx*>9h^kGN3++KDsN*zasMVb~55|qN7FmQt5Fa3^q zqVWHH7TPWZ5zV@|iIj={+^~8(cdw^U@dW+MH{O7p0Q}_HYs@k!2c-cTxqeEAZM}%F zZCJNJ>@a?^9u6!6l!nhe3;t&g`Op7dp?N&~!FZPtcdZ;x2Gqjl0YRdV3F$Lw<%$)& z3h_3%|8Oz3>$Q+vvofHkG)Y(F<{nlL$sT!-1* z&-RbTZ#&YKyCKLKt8~_G?Pd+`cn$Q3iyzfh+g?^~6PEwcE4^QTuOF`RIguKMYZJEt zU-4X?Ghj|_)~<8s9??P05lWsHHlCb98$?M6sFl{K*S4)&yQ*o@{S*W&c1rkbIpQ@w z?2KXZ8Aq5HEPl;`r8wP33J*rm%rh=%SAa%1&*+qQ2nudEa% zx>Va6O+7os?=*(iynzP?G(cO|VuZ$jX!pCn?_%U7pNs;OTu>D?0^l8bBs~(#&|K)n zpg_phDAkyPb(99zo^;Jz{*ZitNc+lPe+|6edbQ=W)|oatw|MPgn6h9>%N8x}w*AY+ zsK*_=6Y|Qw8Lc-D+(CP8#BvA$1n{bXNsBd5D@ux)EdI)L{5lhl4}e190ZRqFCOn*5 zY4NR3Bb*debhM3kjs|ZNyBXW|HvsOY<66W75A2lItB3itOS-j#+$U(N?AW%ARVzLp zE*NR&rTJqVDodi{{&=$?_GYr9<^{!)DYLCQK6`KyII5-fN(|l;2@#fX)EV#z!H?#v zycBU5>OTe=ExB4$hNGK}`e|mA;X&idp@cu%2J=8b1#In_a5Iu#oiQZY@wk+AV(Sub z%V7fk@qFKRvr7cp71ht=kshndZ5gvbT!84WChg&-U` znQX0PjK6zpKJZN0#f=0)g4#8LD-UHaM~@kIB%&<*&bFq$PdxkuxXgujuk=K*1zo|^ zGkVOzoChnK_g<$1EXgyM4dS9T8W3eXcWLpL__ZM0WHJ1@z%BNfsx!-!WdS_d;kwD} zHkO(fB%GnC3~5Qn*Pggf3YblU0Q=^*>ZKnq-J=kI-C@zs{dG5_xm#XY%N}r{7OL+ zvy#QoqJOKjHGZ?-Gsm$ewax&)<%7Sr?i=>bB#=m+w`bVpr`L;$ES@ww7yALJ6E73M z$E>`oP5XyRW--bMjT{_z_VM$x3?E~a@OX8C;n@ejEn5}`)AK;1c%yD+{NQypBIK(5 z{{1;vG4O=A;_#M18>j?Y{pj4mK-(NL{1>&p`Hzkm^=4Cu%jvm1uyj%UZI8mw7e2bq*`H!AoHj9=CL;@gd5%dXo;5qy_iD~>t{9Q*a^>O<> zR#R3BRglQFR>yqs;K$SB-S>Kw!9txGKN_S_w5U>4MXyT;4wrv2KGx1%4XjUXXB#Texyh*AH~Ga|k@}NXWSfc% z{AHR2<%z6m`12O*F0}|1pa`<5Y`Ug6AQNKtd9eLu)m7(HjW!M1lH-=&;@L!p*%b+} z&lh1^DLViN4f}Csg)Rn(T2{T*NDw49DWTD5+q{= z-Qk{-o$ZATnUyu1tsryn`K^Y4FD?aAEiy~Sw^L5M5byyKf}p$o$y&DA4U?fB!?lRE z+bg1Pv#Gxzk{1O%=@<*?$@7PBtQf~Ef>I~^I9MrJOp#{NmWJ_jfRMl6SIZ&;^VTa6 zje>dIo^G|28RHqyI=B@wNt<#pWbA;`_)$T@8p&hNWMpT%mvy#<^6vNrh=n%JrQqu-$&b8WHFIsWUgzWqCIi7GMq48V6_ME+c#qUN8(PY#}*DNDMUGPHrc&ST0 zO+jIra(hOC+Bpoa=C`+swucIl4+(~Y=}*bQd5JEG?Q9s@8rd{Xs^cr!;(?+K5Q=6@+`m+R}jvrPOw2u zb@dEFLs(Du6Fj9kj3WT-oAti~`Q;7(KCj5TIvSs&pl;N=a;iFr81F2rQ~(|c6<^AD+je@Yl{9HZ zp$JJuEeM4c94LqO3OU$PwWW#|qN;(YPnofSU7hOT51DO~4Qn2X!k(5aAE=An(7xM? z!atn0F+V%YDP$`6+4*?gcEbESw$I2exiRtR%VWBqxX+^RlZsp)L15PRZ_ z@z0IE-;3o=aHu}f!Lyb?|5AU^w**lLP`}DzR`5)f;ED(s7|Q2-E;0;Zv}l4h3uL*Y zuKkU?ABj#YNtEYp$4_)N_wo1t;#{6XGzRw&BPpf_G7wTt-qCx83NVoIz=7LoxMbS- zY_74$;rNHj9oj}33qyuSiz(Gq$BnvRQ3339aXqkccHox+LK=~`7%3xHU<8@*%UXwo zWv@5|5E!dAOip`g`Q}6YWp~OZ!C7;2M?}_m?ISO%9$DmRNIwGAK=}?_9wmMC?w$&# z=$F8P!#l<2tOZ2*#{m!VR2;@U8qYe?vOTWC0=-vbUurA`&W zo;cF<$p%IU%b9BVP%Rbt>(M<1&J!ec(1+@3xus(@{5|0=47WP+zr3G+{>RN#jV9`S z-E8nAD{;zb%WxV#lAhqIOdp>)ejatsUa@XWk}#y%+DdnY+-;PPk3mXIXNxh1l2YI6 zeTmFhX!u)N?vXy1+KRI6712ty$yhQ)IpkOIL?Vf&FB{UkxB4CJY|BMA|Cm*z!le|{ z4BZi}x+#Tvs!c7_J9vrU8dYk_iX~g(_vhiHR{r9}a9Unr(C9ccIyc}Jd9=|{1UVev zxu5$PDX%wIFX+f@&XGSQ&jIu{vuAs4?QDs#FnyUNOQNZ2mt?$p>t4G@+(U7HMany1 z9mulRnFTX&A2@^eBG6Xb)(K)C0|v!-0H8)ZNd~CWVr7BVF9S$ow3|V(P}Sx1*aKts zg5m`BY;^9AU+2%4#S{X)+>O%bSTo#E`et{OvWEm__DAOZ9SWpLxztPL^~ zc&n6@Zjm{jzh0;6VC*}haN9pbLzQEzuIm(mD>C2fu;Lb#G-cNb^g<+8d%mbdYMNms z*$zzp)@a-II>U2ip&IB`bo+Ef=F(!N$pD|Xx5c2RyJoW$2lkb4S+WOY5-T&KvykG@ zN0w82!jKv2+Smsq&y<@fgwTXe^A<`jC;bVxkCxZ@%a6D&9OZqCh-3`SQxrJ0dOKSw zVVf~##IR^A>cX(%AZdr`297L8X~tQF~>#P7wZa1 zX%;PIzh-MZZ}>M}k}SUyeRR{{zkg+Y$(eKK7ERtKJO!u>Vj)ciTANKz zzifpTOqojnFNc8vX`JU60DRC(36M4e)~V`|XLN~th}Im=e?X?73ETnz6WO`|Kbhw1 z3iS%=#FtV6mKrwCN^ti~c-+W;^@9dgq66xR6LTN+1Ij9%(vKAY4N1{jRsBZitF{Ms%f_>vU?UeNjwGj6co;1ITGu0+{?W zI`;5j#s$<#v zhhONrC>*N?>pf0ZytnvWXP|GyfSjHIZ*SdS<#O`tsi>E+^+OW64BO*^4Wl$>#KshR z55Qk>%OaGiH>p+hxoW(04VZvptA5X0Ym(#s8u{_z_2O2yK_Fz}14z=Qa!M0WXC<0jE2oOt{;R6#gDnL24-3q*3GGqqX?;h6!Y z(`fI$efvh2_w_Xl+OYU_t?8rDd?sk|1qq8gvqA%k2UT)w8|K5XB0{5IqP!NuD^-;G z;e|@1Hd$Sd?_-+?yBQuwO*@zHTH6&3LU1FP3a?b6f9U+{J8Dm*NwEgQ$4WmwQ1k5ZFDwhPS=zIKq9$VboR8JJHf`DzdFbTHld{=ZcknWGvI<&0 zvn`2@uVTfO`e05o-w4h8%bQ61dM|_K&6(l}hAG`Pbwv&%M{y4$hLY!T9*}k#Dx8=% z0ptcPS&~likvXsh=>5Jwel^eOQdnFQ*Cl6nMZ@3E2m0pD$oYD?;og8p51walEyO!b zICA3;Pq_+0O7;8t;!iVbyXA~eWWsT0fT8Do%80zat;m`NpO0N?QqDAnPq;H&)2XAh=5pP6oE6 zSl`D*FRxRm96NMoT<^6nZftLt^EAMz@Y$Ga51w0BojDADfVE4zX`4SWWO?1gw0T*d zb58(d7?HHB%!-9a51^%vORZ<75df&!(DT8q;&2zY$Vv1`rWu)Y$99~i&GpiF1*NV0 zDB!)w<*lUMXMoP}VP8k)()v@VGEjFweV+*mz(0=c`dl2=V9jVfx=6VaIJ{V#~z3H&T_9St51nvRW#^bnec=(VegKUCJBfIR4CwNi(Kb02+|#UY>A7nD|V`AZPu=rnX{ou zpBoSw7||QhObHwT-&DO*Cs$5)M5KvX@o(?En)o=22QCL#C4~vgwyAqqaci8lb|0e5 zH;E-uqX8&_L%*b83^~%59kL=DeLVym=hD|m=P>GEpV_mc8AlU7@)H)rFNkM2PC>dK z&%rSDdz2=nDGR2)K5bN#!^y1v{5cDrH=ji36#B3?ybSy4(^IB)LXxXqokJtvwym4c z-w2@&TK=>k1PZ&~$I0Gx@SMcge*l{0@`C_)`sg$q8h4O|YyQwzamt8bBmnV&zRZwE za&dJ1R;%hfk1WNWJ=3TR(_u}XOOIcj)wjHQ%tNMbU2Frdetty}YGFK8rSD&6av zyOS@ba&Ftf;#(s#%RXer8}GS65h40Dih17qrSISeO>0=jRCN7t;IA8Tuu>HGC|)HAs?pm4v9*a+CXRyijB^f-hdC|kPI$sNdx zAMRgtwZvTgpvT@d12WkALrXyLsyb&&kPJuJT9qBmD%B_oabM;-fInFShYT5lhj+*!!{GA0l{S> ziiL#+6(w_sGt{lzzqUPnX{X93)YR0BsP^78&cFA6w9CCb~lyGLlWS0x64RtU^56$+bzrJN*_cP|7*Ih5!6mEK^Xm zia$o8f5$7>+UM{AQx`{dk$oDG*>*ynm!84${@W^qPTRTbvR>Ur@c{}o+=BD4G1UXQZ1U3F0m z{LR}K&zkk^0wNKzw>Kz~DcOmcPHS%Kf1o8NeRz1#QD2o|RD^g2wb9JGwVjm9cJ#(nkkAvebEjA} z@>DVx%~#eQsH%g4=a84^e{7)r&82H1F1Ube=^&Dn_u`4Y zJZneEDYuIkE)b70+zbYv%ouia3j;n+)5WEO`$o;4O`)~)+dDOEaQe;o>#~mO!g=#@ z@>kWi2@VdnF^Lx+A0!;erdNx%O9f&kOCL@eF_g0`oyv9grYMhYrP48&Yw@7InyemA1*XLdzpwGt#PH7 zfdQKSZuqp3KIq}$vGczQc9Y8s6(xnFk4wlU=ga6IkL3 z?fOZPvVre5PlOd|rRkq8*;l5~2BOv^H4 zZM^|N5nWGtwRi7|YXu2hLuK87fb|VJWRwNBMb4`|*%ZwNg2&IiXGnq(Xf~)ky&!tgahh5^mu;OeHP#qz!AB<1 z%N=uk@@|e*SuYb%zS|e4IM9ZfP3vSZ@7DbrO3Ne zz;lKN9By+PBn>bGAlSZb+n938hJd-j1^#GH5+2wi3*%VUOAEdcnoklC4KM^i?11_4 z=kLQICXuhu8|;G4#et5CK%06i!JMX0Qwmdk+U2nB>|*Q-AFF`L?6K121clj#YQu{! zAJJHEYmjaurF6{-VZ{JhPGkcm}rxrk`7Tj z=2@v53u3iDtJgKEQ+DIbL#{_}aEF>%tLW}l>x@|!<2gbpD!`_o@ektSsLXdj*O`nv z5ijN~CqnF3K&S2f{)pVt;i5~=AvTrXE)nK%J`2{_Zi3mTS_}>e(I!{i+1TiJ_)FQ$ z$@`bp#$u5UyQHbBih*Z?zQy`4E@$#qzN;MPFB~^+T=0h2ST~gs8)P;M1_}jb_mE{6 zF31*hLIs~* zcUye|>1HI`lz-I$ddlZx8_D^6+dO-dcL-{F{Zbp=+`~2*D<8gQHr*MX-7BalsnGGE zB-T2;yuZB@UUpY0ALwL2$M4d%uwSt4klU#P&*sVv#Ijmk0Gd0-z9C3(UfHxJgKY3J zK8U*+O%H;4&%GTR7hKZGXT}UDg2oi`7G87N07fg77z7@p%T2q@z}NyL>j+lFn+fJ|5ahqS6RK}R6fi6i->Y|a0hkv848B-wchJ**pWKIe zRM^~?JO|=dv|ZxQ_u1#|?POP5O&z(D;6f|l75SWwhnP2^Je~G5t-lOGa?dR?mCUYR zoqBD(vL5B$G}y!Fo?~SmY@;^}e=kc#m**?q-qb|RDMPfN#(uwyb8>20qH2nP01*Rf z=3#_TX4YGI9o*|01OnD6&n9pB!;B zMw3?csGBxT;y{A;0=OSsV0Ksxm|=jv;VspG*trl+HX6>MX(9`e(Z8xI8R8OJIq;%? zwd+cE_nv5Cq-7$bsGK`$h7B4XSIE3GLV&J;@|#$0K3mn!5^f zb+P1S`nIEwk)AwXRozVMPy400S1Gf7-Of(Zj6IDrXoJG=(nxIw?%v)d^*Z}`TxU*+ z@`va@VJRgf;s_RvY~izOhK|08pahP=2aZ4(>Pf!($q&`l)K*g_0z})CZcn)F7#TdK zM-V=GT2Jr9X(aC@OgC+!VrHAu4Ls#=2{tt3J`(JLz%ys0;h39ozt2Az` zdT000fKGwLE)cc|DL`K278=Z-n`YfaQrAwSlf8c|U13gHJUzeV>oS?NAb;xpb}aU_ z4c|`Hho~rc;V+u-R%$)_FWFc!28|HGQMN#kN}SBi6(HGUJ+5_tb$(V;yWaM;9ktEd zH1LNh*YdvLg{Fd7(3NTe#G_^p8lBX zh}$uSYte$7Q9$T;bmbsBal!nKux8vzd@3EVL~jVEbe;-6J6=b#q^)KP1SHGL%t?@WrNGY zI*;x;{m2)-3Z_kQr#pGV?zU{HX|ynWYj#VjOMqAK*Ztr8ZA(Ywn{HioD@whoAxdF} z71l8shCU&I%eMUZP^8X*MBJ}MPD3LYEoT~PcK-JVcpV%MFhV-B<^nB;BECzP#drVS zPZ>vj%hU#K$fWZma~mxZP}W0(Ufj}N-AZ-B+#5ahkERl7(x?>pKE>NxH#?3p+~V%G zyPCbqEO$~JzNF*E6xR3bowl2wf(6738J!>*<9A8$Jcd_kpU|>;OXBiaK~L%>KTbF| zqF?(Ck4tkR>Y>v@Vb3gEUAfg3qq?1KD}ov>&`J1jBk<%7E)ymQ3W=^2IL47i zOtWMAsY%7R1ogodW$Iu{u=juJX&?h)!vcV~x7N-vTrZ9zi_5vbtLNJJ71o60O z$TgjWhlaQs`EA-r{*slc8NCEnSWHu=UPP%(^#ZBhINwwadD>@SH4$U$v$0yz_Sw6z zdA|~C*@A}31RLS*|0p{nI0GpZ4t@_hvMftmb?CjpuL4#dnKdHvvt5rs>~p^mu%XA$ zXj>n?r!qDjirU+z11+9xU?|g74MDZA&dP1hiyjnlm|DQDm>3(%r*+r%(`cO61o48Z z{}NP+L5)xFV3E71MC@b$rnqiQT z;UpYQLy!8Bn!0xnW>9V(cVD7=+z|htzpFp-UN=o1b5LV~O4_Oy7%s)z)R-d9k)4FH z)|U|Efus|TSCb|;oY^bRA%Ow_ndu%->)kiQMSb+2EViN(&TUql^v&yw#j+VTABN4i z17%{X)}^(&x>8NA9EEpS#}cl@-PvcmU=9|lf;k0kXsCw^C)omSY8@n>=jt#dRh>58)tS9!} zn;{Dwcn(a44q=f_6s^_;IJp~Mnt(tF=)ihj@e7{^ZH;t`wgJPPEx6A>Edm)E8x?6K zAz>Dq-h#Mc`wRMFXpP+eAf!~td``|Ay1uXGmaP&;E$}OK)&^n`+6tG}c$jzimr?dM5LI}GsX$=!T{nS#<(P^Y7Ia(<#-jXd8 zA|vG)j$D%NvVzKKr*pv+azGN(=3HJJ9qxeX;c8lE)n}r@x%SkLSm7AL3w(l}GJoo?65sHglI`df3Bcu5~6QelGG)^$>dLQMv8>KrG zLL(ksBDD_pHgd}rbDkg6Ml*tr(Ypj$j}vkwGG-Lxq?>~sn&BzQ#Jpl#jQha6Vifx3 zP2#y-I=>vsJ22kd>sQ0%x=0yu91sxQYXV^M>}QgE;$su4n3vu(Yfmbb{qGW{xzgRZOm#Jfe z)B*jZUm&|B2xY5&l_fbG?OC?eM8D8fhkcRi9c?S0#J;?IH=J5U`3j$VbmREMcYoyO zj$q{!SlRW+xM9#4GTBVw{!)C3HB{*TSD_Vyd(cLPEh8!9yU}>XQmBg%9{Hq42STPX zAG+zgU+ChimCUBjx3E~_T#Z@ID#S{%#RNjsq2NB*FtOCKgrOwJ9%8=88ZoSuom>0} zS)azU_a?-0%0=!CL)qe;hjWyZkHn>9mvww-QJXNa7{+gggp?@+Tnc~noU==7 zuNA@f;`igIV65nRF>&4tJ+BPPG4sg=z(4vjt%!I-92*JHck+KRjLJ&x^GoS<9S9!z zvje9Ga+5#XwD)K_Ax%@YZ9Kw3egw&N=IMoD3PCM66tvHXc_JGJ097yLPnUu|cMGZv^(~ zd*~=yzMXLM=nA6tJv0NwFg2(*=j^ZQ>iq@NnBlfp>!p3mX6WMgG0u*SNLRYBaR7<~ zZU>K#OVI}IyaGM|sOw&$(vlu~FRwmmbF zP7KLFX5&fZ8P0vC?}Q-%csA9_S~{{_1u18Y(?KRWhVij)@^jqSrL$*L*Ir~ZeDAWK zJehdss6)4zKigejRpy<)Fi^%rwTB;3?W&F|*!v|#l^!Z(|%(fAcY6*$ap`Fp~LgM2uI6zXl; z9xOfGARz7&HU;A0!ET$sSA0E5ZAsscICyYU`X4YAZ;6M(Imji&6q6Hq9egLq3@YIC z%m=I+&m|R6gY2cXs&DyA2GQ8wh>8+LSXB8C(&4=K4-FBQXVR}wfXI)TS7aZev3opS zG+-ouQnhwMgtnFF{uS6D7e21FUqm_VC!nX0Z{Vlb4Bx>GKt^#FAqfvdlg*!c0RqIf zr0<`@#?G*@_)cC4sqPG3kdlfnM3)Zs;RdQvRfP!t)sV?TRP!wCzrKWsPG6P#KNMyu z=B3GKx&iOlRqqytfT<}8Aew>V2*v2kcEXww>?)=gb~N_!(YQIvU9x zcBhGS_4)J8jT$vlJ07Kplr^(iA?J}HtM*94iC+!puJLIF0h9IkaeGvaqPO?%E%p7- zx|%5B9y^v~U31pWm#4R6s+=@`pZCS-%>Dp%{mCb3%aw&}L-OuG&8FO<#e;!Enb8we z2J1TlU|;z3z~#%AV@B5Le;AqQA``Zd3TrI(;ijUgG3g!+6^>j>4UJ3^V9~@R>-}nu zcJ$`$exx7d_^*Hc85NiEmSgF~b!Kjl!XpO;4_^5bS>=eRZ2NXSn?GV949Us*sOxAZ zAK9U^^T4#jlp#dq24p}G_&V3attz+KzW(&3^o|Uz0mjH3T@3PO6mHUL-mIB7c|l4x z5Gh4BLyHXweMJKgz+T=WKtVRKPS4`}?nG-ugk|K*V4WLBUf+-z0vbrRI?u`r})P8anR1_BKCvHZxd4 z>=zFHWLH{AT|)C1epMsfEZZ#DHhhsl111=>e+`m6xe7Xo^7{KTci<%~GjDv&xb2o4 zZK09Yzbc2jQ+XF}P*?FfEMU#?`4fNWA^@L4K7-0EoT4+!nw7-re85EbTTv!35Y*PM zWzgl0nM3~6`U{d^CGEka#7JMUV@>n^)= z-%S*)aMO43*}76FvGQg!8b=y7a>+C`ZK3MeE+hivskjts{u;tM1sStI{@G6~*I14s zi2lRkCTa6e`%OAS#40eR*1Kk2M_qS*ToGE^A@{X|OV?#;T~3KDPurD~)xG{;OiwfnnYLlPQ<1%KKW#5A z|6cg?BYc$0pmXdJq~}FPr30*fm+swj-UaVPgNUB6$A514$B88n(Iw_M#%)6WrVIG%LdVB`&KC}J9gat(MV>a0v+2*+Y_nH z>g43);Nwl$NJF36AehD zJm^9J+@|-Gz4Y@-uo3gc&Kj>OniCA z<4Bw01ABBay&veeZUm*ei^ErCini)2L^sfBKd2E%}-&$Dx^`SXNT`yj2XM}%tj z49+dDw052QBhGT)sD$ct@9#hFKKx!gx^#QVb$9gZc8Nw-gSMr-4tA>Q06mO9_S1vk zv$C=j^&f9fsn(lqg0aOd!{~h#?!#@4tySlla;H>e=`~rA6(YvPWd}vq7{Ew`%JxDK z-|Lc+`ZXUMcgnh8-#Z+*R!S-S4u97!+YLW-zgQ6x>hZqHYQdiVmH=CXBAp?Jya;u# zjAf#OlR%whMFJ8Tm7G%UNOE@On>%rF@b5MJr z7|7U@bjsj>VIWF?fK@z4Ms$9dZh*%`0tice0e?2KIaTL1+G?S{_as(hZ`a! z@PK@`q9~r~MnrzJCxm5Zs1g!=KmBGAWkN*=exc+3b1-H8dPY@feZh40$clOJu%fHy zrwz5+JMQ)V-{B>%Aq^f4GiVmMDHD&%t6<%%BhxVBS6M|EwLI$DewS|5TaoH?7JhqQ{n~mK!9w0jj-6w`P%xRRuCDGVutoW!@3r>$p zv_04ZA{qGnmd3L<@o=#sVIQPqM@dCT(9XI0rp=puDT0_XaoKCGND1z9`r$($D(_G+ zUwKPttNDr6FeaIGQ6aay3g7a#$n3))DIbCsuSYv7(m|swxqFF{;=}buvjFXyi47;`dDrH`SS{9 z`_BEh?I8btbGudRVBM?vVSR0N4!9(v1za1PqxAI1wCU@QqiH73WEj;wzX6YFWjH>~ zaJwcNCPGy`)q)l-9bBv%c)%i&-18y+xH{LNq+|QfY`yc3*{=Z7jNz)hTn%3an!aNc z(f-|>e5Up~zg+Xd-|y`cxpYyV}^zonTLLVYS!z^Hif~qw!xA3 zn8FKLrR1R-;N>@2J!Xc@WxSK`8DHouv!OhAx=$xy6EtqCkTw+k!aHZ5O>{~`KNIM9 zmw^&?~6HJGo&sm?V$lSkfo^(2>WRU8|NZ8nOd8kfXs~wPVEF5pg$p z(jrGcY6#W4H)#;z-v;u9!e==bW#fE_v-D2En)IgDb4d@(%VHzQeuhUFK$kG3D2rlL z)V2lbs2)b20UUs=*x#}B(5=$BWv+zan$5M&`sy;cRJ*G zHXc1hE~PZ(!MUGK5iAv7xDQSJ63~j^0{-y*%%Z1l9=p2#pOXIXfBCuyGs>S6@)Ov- z)oP<|-2__jrB9zcd3a`K8{l}}$(??2YyX`zNa%Brf~en(Nv}X<37piSyE=h!N^m|x?JGkgrLvAD4 zTYOp(3*F||rNco-idU?xxEY%RL>>wgFD}3LB28>St@rN(?8V)B+LQ50oT^E?1@l)L zHGW_MANBI!IO;kCB8^#&mORE|>eSV+smOS-eSA74;4AGmkvq5~mr%&$eqKc(E4#dj zqLPK^BfdHVsFuyxv^BnI_Zr$Ja_JKmZT~yOgH$@^kJ;mQ#BtzelU7-hkVWhss-JJ^AYb@DBYgCj{W9lt5m9aWtM_275V9Si zJ#o$#GZzpsszn7eHf$nWqp+tekh3G8hcZiMKq_agTzO$ylnf$W&j-Q}-MMKTT}Ns? z1=%jTJU%<2xknjh4?RP!%Yva5Bh_7PJa2#Nkg0%lU*QV;d*&BS%Abjr*}2UWo4~V(4?*Aad=DFPA2m&9$w31JqUY zbG~4d+;!3hki4ONbsx+}A)K#5+5?|)mFFy}8r_F-G=gRo(h2%z z9KtCmX%jQ+_F7t6f!qs36`7VwKUcV{F1fKsqijeP{pEGT{d{<@uNk-V0tQGaMhsLG zUDf{MnymdRdy;CrH+@J{b}UBdnZ?EJ5Wi^{-itJMSge{iUKS_AR#R!kL;W`F41U}P zM=Z8TJq~UEzZTQq|57zKswz39?zM~!q1PxROw!TRvGgcY-i$>Bpp%pqgeFJ~rl>D5 zpDSup{5A9xwVPp76;Dyblqi{-Ht-OvzMse=UB|f7gQ>o7!V_0!0$J2< zU9Xe77{7mKmF5$~NK-iV5#pzgP#T_hrRjMMwhfW04@&#k@cFQH2PPXhEC8%#lZ5fI zxqIl(O&A);&OJuPY`|_=3t+FPh7_f(Mukb=T)$K< zV)i<9Rc)GTiG6dA>89^dR%|4qd=;|?M4pWK8~t_l=i|W!OnT9n^!{0EWaM@WBqE{) zawJ0A(fHcP!ZWI)X>_|7r*|-Z_0c|J%a$v|ScM^OQw+ZEbo+4k0vbe+M0X5)$f&pc zqSGv08l@!Il;SOIC)2HgQLItjs+wgy`|!At$qcF#q_A$xh&|1dS3zS}7>3MXZ?2|ZKS*&=#* zIV57xD&50qK0N>#fxXv^${rADeMz^~8W4oEIO za@k%-Uaoc9-TBWRtzh$g7=>X7!(`VR=|m19o3H4jB2<6A^R1;eLJxH zbU5nNZ6PASv6gWJCXf!IS3nT!SrIU&y)9XOLWmM-e$4Srrj{2Ftdnj&X{A2Lw92fDW$2Z#VK4n3HblL+oQX-I@EBL+EvE9Y9=(Nnp!c15p zbzM7Voi@0QBvy({M`8fIt5@TiHEx`9a%MF{yJF5pI}M`1=B3JPUthiWdqh-wIBlfC zHrtwX9SIQFRgh5E0(~chooqPfRHn_}i3nCqjpIk!T~C1|c~GBXXSx3|jl(<8dAW7i zmL-W4J|*dw?6@IUY|EPZKa*KT?uPDX%x^MrPZ2Sd$s}0a0V{v@1z6S(ZMl)IWHf(@ zMTalzl+PWcHET>$fQn@VA5O#=SmgPF?q!~U%Qhd^{rT5~Mw;Hbg$eQ-M7)?AsAe3* zO+vwIoP&paIc1s(*TG+R;OS!qulNHNSNzUol^NF>{b;p8D|a@R0Y{o?`J4^tL2;E@)=v!cb zvinEd1M7T|8cQj`caq7)i`v5gQL?Uy#qE8%*(Q%Mnt9(qfw7u8H%kw+x39UvRIVGi zWxiFv>JB47j6r4L$y^MXC+Nk)>}+cdZDiNEJ57!+bwWt+Jv@cqAc7vGFTfsErQ=Tc zH8jdM?P?P~l>Fuc+n-Em+$XCc z8k^Q2JsI-{0UBMLoNr}JyHb2(P^rY~Rzz3%=|oIk;A?Qy^>FEl($}wtBTi#vHe>oM zdg!uwl%I(mwj(u}$Vup3K&vZXH_PAHg~Fc>%NufQ(E9Zr*hvG!MhCY(DHQ7HM7vh7 z{?c+_pp4p%U2{{+A0%BQhTZzk!A+>_%FWP1?o<8HduyE2x9Z+`e5b?Y&M*rpL0(+i!yO!Kw1 zVc1ST$T7BR_oZ$+5qC>NQ(aNeu^SFiU+{p}Fzw#O{f&_CfnvX~&`nKU-2%-{EN*Wc z5yzDG{8d$;9VkFp`)DwgBzr!_c5k)As3OH%odU^tQTZ4{O?nx9(ZPt>Ji{3rEaRo0 zSo=H=BRhcWS~RuPyx8Op3;k0}M1ayXsfBO#IQfcm{gCa?3IxrZ$U8Lj{`VixLGJ#U zUSJd1giHZCmB4Dz;mqO><8e{rDBW3>2SY9sTXW`wCnhF#_p9x*m*ov>Z5Bj^81ZH6 zC;0C->(_6Lr8`@lNBjTyLqv| zY7T?ra6Z9HXNXlu&phNrS05y<*;L}QZCApnhl*mj4c0&TgF`q87H>Y5K% zFMksdCuozkk14LVup~-;S@%zC=Xw2pOaCk5(KIS{FtqaO7)*Sj3uWe#a$jv|lI!7Q zMiAbuMP2rj;kb<Dq}K05#w4NjP#E3>0$iUYhgxY%{84<|hedi?^j|;8@4!HAk5t*4y-R?9?js zzrNX$Js`J%2N(p34*O0ElA73m1N@NC3Ns^R2xnaG837SOeicn9X=w1@gX|Jzv#0j@ zHhzXak*~K=q_H;VyhWw)I534vFPyLfHCS|cf3M|q z)<+HSpw=ETI%Oq)5F~~N|9EPXYma@?R%)&KWmfI z);i5K=Vr?8h}pKz*Wn$jOTL3+WMCEx0m_#V=n?S#LbAByQ2xl|F)mb#GSo)sl@~%| zN!D*MO$R4q&L8AswIsxX8bqgt=KwN0u z@{Ix|yo`4$in`RQ!!&x*Jh6Iy;d>%?9)cbmx*;jaH!UycN5B74?!PCj{H~Nj<_UiF zn8Jr9%@iWw-%Gg=I*<;2zTG*uv9j_$Y61o)f_S*zJwiS|4>dH71?@^$ffvj0ui(w` zr5RDJv|Tp>`{Ca|U$@{!%h(^uQre{&8kY0sop`noWjf3IUY3?#2X|7Gh><4W4$Y92 zxK{@0WQ#19PN=xs@qL9`y;j|)k}ep_nk4OH69XUNO50(#R+PT1TrdZz4taF6FXEFc z2hZx?awPViFHnkKShTNDO){I;-K3N8f6Eem63_em9U&aR&dc+jC(kqD6F4uPXr>W zM(H+JTaQVf7$1tQ0{$>1U?XWa@bFT2mt!8paxwq)N0i14&1_M1F;LB=MJXOA>n2myM^zMBZejeN>u}KucSc91|+q8FKrCs>}Q;x9!DynPtNOz|C zF+P8r0^}sKfO4O}Lsrq=%ORgSVz$oT^JZGN*no~S57jDP$F{1&P8;)=KEGjC`%x~_ zrZO2`@#w_VdVtu5eikV=Lk=o-Q60#3WEcl+>mQ@(vqV;SEa&`@#lqmmAV8*1er@|8 z-nM}U-!NnT-HZ&2iA(abmb~7g9W*p)ZA>PY2G#`s+6Yo^VsL+)fv(b&q`JE(e$Vh7 z6fm?s^s_H22w{wF4+3z|J>^EWB>a8Ujxy5$!c3unr?9*xJrN?ArkUTt;E{J`NP$Kr z^@tQI4Gy$xV~$PKq(4FJVuPy(sam$w@SHZCRmC(42Vt=nVMHa7pY5@m#Fk0T$ejyb zc&$zw1n0ESz&rGXwe{OL$!sK-0uc*sKXdY=`Wmw#ESkFVs4&112{Ov{wyiV^ zF&kvk(iFsYpg!z^k4TO=v-4S>#zETStv1!jy|Xw13|-ZM2n4}3blKo+YX^+a=i{HL zuz$BEFqQ$EHsOtVzIp0n0kS!=O`SKo0Od6kby}127-|!qp0`#*I0W6AjZSfQY3c$3 zm5GnI-V;~af@!Q0{)97Ix7T@jv)`?|weB${Smwo`3c#1&gDy}JXXoWL0p)TBp!Rbj zYok2gqx;X+P#q2!IB+z=H`HFE8{1J6AlvYylT`wp zdad^ks9k>F7L>LaKe6RvYW5H>GY+rwd2!_v2^O`77=g-GNBeWSQA6lY$LH^Q(%(8{ zJEnpym=n^V5EpEK;)2=vP1L0{ANGTyDOR@=wnN(9Al0__t0KT=Os}w~%(>19y#Z2} zF%;1Oa@3@d5T6O~Il2;058ml&`u;}7)YyQYZf{g@(w|Q%4CNCN2s{yoi7UR4 z(ml3ylI}iY8U?U|WAGt7|0vVGc|B`S*8b}`uNOx_q>~# z2J0@$+uAYHDEwQkzw*AX95zMf9~Y@yH$u-x^SH~CsG@7wwE>2Uc;!yTOUR^YOFlWm zR6R^xsE1L;3mQM5k+1M+Op)Abdc3GDX=mEqgKCiUrLj?tNx=6g@6pPbHGblS~qUjvu9XZa80+&i~-lGIsgE z_i>+f77^azqq8$}uRgb0X+F2xDc8gICVYhR!}KN?G0ptZHT(}bSOa=;0}rV!%r#uR zII)XKUnbUMfP!)oX$j-1E!*7^e>x}N1OaJ)sc9u*$1&Tt#;1yF9dSSNE0^7q z`NLN~2a#UVsv9M@xsFz37@?Q_b+X2sOFsUBE7^^;G)*Rn`8l;=`@Mm|=#~V^Ud|1e&XrB(A zFXnFV%Uy%m1UzPxIhvLB&`_aGCAUn*mAOj{7w{a_%uBncQsEPWo4@;&3?(T}*5;)r zJ)34*ziZd^bfc$LLrv)qYc!w2kKzq&jJeJk>5E4=66hJ9&qZr*)El`Yv(M$43~F@6 zNjvR}vvu^g@nI_&HIyxU^r?n%|!dyDWXLK)@^>{2ETzb@;BvPvVuojf-W z9qxawOX%Q_VXfM>O%KREn|GwslHZSQi{})*iYreF8{9l}a2-x)yfwKn3NnlUAI}EL z6aXUGL<)O`a%>h32BRiI;V^u_FK2;T3TM^J?iAoMR0cc5YlEIo(>)cTgO5+TylOXi zk@pDZ&<-&%<*KUJZ9leTSm~bKcRv_cJ+xYo8p8H*&$KsTZ7*qR{w%(!oc zyoCK740TOTeG|B@FlnV1xd<)eRrcL8uYd>SK1pCTG#|rh1NcUTx5Kvx98vP3PNMQfJ*6qNR=VNZt*{5Q7Q#150K7J*U{BG zHP%^l>7Y%fNE$nlgehGrB(q$hJIrF@urPo9IqDU?C%y3Aui3oo`tLSwmsXmBs+oTY z!o=j=ceeI!jMv)LZ^4)zt!+Jya*$-^m9{wlPy6hEo?F7#32*LtPTc+>T*c}OsVd#6 zw!IACGV2kk>iHQ?o^Gn|EsKn$$MY2&MU>DEvf5h_hxQe7lQ-OZx08c4U)#>!)v9Gn z>BciAtg&HXNzQ7%5xk32%~(wL2md$+Pv>L1x|9~R^cavveWs0tU_Z3A-T^Ah{@wA+ z;6r055JyKG(gwVgh0a8pWYoz09uxr8s74R`IM)TmTkp)>T(P$hm%LlUtOYc|&~Da8 zKeoSHPw1KT;v)r%KoTpOG}-!Xz*b;vB~G*mDP*>blA8NnggOD@gJjxMs>koO;=IX^ z!bYl)g<=%l+ukUdP2Yt%%|-{)9X1a(AoMXAps8<4YboQ6nxFRj)0sx zCr-dfB(yUz@N5ft{zNURFs6*|O+owZ_`*nM0}Z$3`>(VcBt(dx4zWNQZ&dy0jq6c` z%NF<|zpPqb0WDiD%<$lyRFBh6e2VN&*G2Xj(cWN$>IKVEp@$21HLgrX(*g%#Ni$4- z4@b@irJu*d-Jk=CXpEAZfb?XiDrqRBa)|t*bj=`WJ*djuE^T(-0SV7`gg5;mR=6=GMry8l)7DX@rC#!;;t*oM`01Rd@j&OVZgF42?eJf6%}vh;K~AYE~M0a zznvSkjIHZMX?cG2GwMda*Oi6C8Tx8fu5%q5Nx-Jiuq5};@t+W;V=S~6Lwf-B@bh*y z7N?G6fLZub=qF`M{Ii)Naw0qtyzr>QoUkSX&=@xP91b>^d?I6TW~2&SxjnJ^xBD9i zfQ*-O5SmaWxqbSc&|JZG^T6DQE2veV&B#iQKm$4f@zl_6<84;qb@bUgekU}Y*u&8$ z6mcSZ)Am0zfiH*r?T+m~P~~={zZ9kn2#EnxyliB4tlTLT584{VzW%@bOpQvKh|O#h zCFlx8?&ZCO2P1FS(X3i-u@)3Enog{Y8UYZEE;qdlfPZI(40Q)P}F5M*oMOW z3wNHIYt2`ckPZKszpx_WNmjzcZ>P6RTnoPXwK;8DPR>e4al2-?M`6-s4#{GLms&EC zM(C3ksq|_pe{&;?xKtU}uAy@iC6`h0;2vGE6q41d{A#nb*6lVSA96_ucFwb(AI-BN zg{r|``(#cT&t;lfxXh__u;zvmt!>T)m!acP$cZF|Th-35<#w4Th<}4nq5R@vzydAu zU|2zJLVVat6&znEc6okMO%uTPgM-Os#Ip?k1U9O6hoWxs?Wf~>#B z_p-9nrRA$*Nr<&=29Me=Qf=P;X`9a7PdsZ=GsBX9}mY6JD5v?J*&vxeK zfd!8C;4XB|Lffl-)t0FlUOXGqJ5U#j<_em%-I-_nrg_jYG$Wa`dg`n1^ZS8J@e%dR z_ZQ?}@?Symv&SPAw98T#yYg>B962krAGp|nvyO>OU+8v&B~_6fG9y(o;a)P~(3etQ zZrijNyR6|PD8;{fd`fikKFWv_sFIX$`&&xVy%~?r&Th{U*X7Mn=%1MX+FEpdj0Xmn z){}V$1P>Qh)ym8CnUQX!*%b!r%hJ=3+*4^>VF4}J_s-7PSVMR>+@-(dUJr+_Mc~o2 zqgga>9_7nH++-BKY`<`uwX>@U8?yL+;5ZA#6b#NE2cEClEOjDKn3SroL2X?G~-o| z=JYTCa)*^I(qVuK6UP8L+MCIoijq$DU!DN@(qHD?7?I>hP78VWCv$iJD3N|k$P_L9 z#}yzCh6r{cEyFf44~0}v;~s|*1zG6q&}?%vv~eav49kA*^k;((k8v6MxWvY@EFohu zQ-~M4nFjkScd4#3gC)zH?3+2v0J$L*oH2bKVd4M?uwTt&yp1d_lOnvMSL{9DaY8nU zRUcP)^tm~cQucr|f-i~D6AljVqK4W-OL3Img1We=z&T55uIYFR@x!#3T;vz2Vq#cY z2z~HilitXXl5Y1IJOnb6%xewic-ZRAV-iiIf|N&6MRHdp8VW>8O{D24pAy|EKTtKQ z^8ZnOdwKmfm3!)vT&*JeO{h&$(RcBjTUs3fJe^>ZD}lJG#m6_>t$IxCw%I~9AS(bf z5@FX~Ekh85r=G&3%8$=22?UC-J7yAJsy?QFQpjxf;wCggD|QVjUE!rMGkh!Z`;D3< zboVo2lO?C9o9f*f;&f}+P5Q2l2kUM~`Zg|gfvy4!P0P|8+E^h6U57_Zg_r`L%VtHgg%8G`hqjc-LFMed(75_C9Hv-1S=0Np2&mpZex>);Lls zWF(RP2a+CDUhcsIg;C}DviQZqvd7n|h-|mqb)zB~u0Dv~fSplIw!FFi9?=Y=y@P27 zpFe;81Y=C_idnKagEIx+t3Yp2mio$C3;JRr=Toarr!8~wXW{AY&hhnOuN%E!UCZ#J zuAV5oPERjV0L~`zE4W-1H8f^El{+XSaXc<%6T{?BH|$`FQWDZS8R_c6m&DK}T zr#v`T=lSW#wL>|bOn#?sZ$7O4gv^g~3eF5{oi%QtGu!Rco3u(dOvy68|IyK5mX&3F zjRuM!?AO12wl!&UUMnsA`QW(Hm(Cw|nHn#Qja}$-d&RRQ+dB+Q#h%IGd7IVdTB?of zEB*WbhW)C#1HZ*erZrp|ZV;ib1I^@Ru_b#xyGuVau-MSR(52tjKT<;ao%n?2>dvv> zdQ5Z7d2xK$*hyRBwP9n8XAgzLwgk;?WO$gD;OpD{l9;eIr}*~un|lEw(m289q3O%A?#kB$ zQju;p;d03Lg}u@$g<_pL9Nx(((KCZ=*-Z~=uQg^DIB8j4yn7vERO2v1$vvM*cCI8L`59R zTmUq20ufebR4@^iK0OH5XwmcfYXVf@#qiDYK#A)VUAP`jfFev6xt}c6@`-=Q(r zma%4_VQa@D<2xoP4^}W3xeU(mLX|tuP zJ{it&)~q9gB3`bDSE=jSmU^S5DJ|bCPz8(B&)LTx;!cZ0p=2h)lSKBe)}c~XiS7mE zck1(LUS2`0b=|w_tl0L@w?LC&;P&3jdP~q4ZZVyJZWVFXw1gNW@6xzPcg<5sziZC@ zPVHVmG?ti7CmtbIUs~Hx7rW!@?I}6&fNBllm(e2k|GN$UTnf$MCDUq$E?>T!VpMIb z-S@bbLAo+vE{Y=D}-t`X2%q;X$n+vSz~bDaLB* zYA(8u#gF@qB7ynqj71-X93`Y>J$x8Li$Zt89wd?=D+ey>pqw48*^>Sibr7O~@m>D_ zy~Y4L#nSdgA7>DR4sb>AChrKDhbYY&Hf#vVfQTf^;o==VYbU48m}vBcLWS0i!RmuE zw9Rv~E)(ZBK~wUmklNOK{Ls3mYg4V*Ye7gdFu_4;@dX+Dqn8fu?r{`=BE^KH6YW`@ z+Uy^7W=O1c2)4XK77<_NS|WdKLNY6*!Z?}libior92iv}jVd07xp)1i-#gTblQW22 z+#vI#;Qpqif{K>I0&(+bQR~PShnO^FhG|jx2$lME)(N)#Io1^3G9RBUJIP4h|OVyTeelq293?|ex0pbJd zesgK2Fg!zzs6XNCk(wjWZcgkEX4V1GOg+A!08oqAs! zt0^27&>zcyAS64~GKtU|$!9l3&U;VE8GE+#n!4s2Jwu22ce4GYbSbIRf0NwripGsn zEEx~(qH5l&KFRnE)bAkwUt@khDJWnymA_~+_$mGG%LE-04&og_l93&80YUcLzGb*4 zx|Eg76SyK6RzB*1kDD045WK>jSBH* z=H?D)%CKL*5>6aTLHpl(*a>U`=ompO2MdWQG&^O@IQk5raQr>D9_%s!P_$<1lV&A1 z+2&Ah_l7MF z48Nom2DYnN^vu3_ORgBG@#RF&N?^Wd3BW3uI{$tpdKj0w|%Na9e(X?4JQ9Z+wY+om1qV?~V zoM70zi+N`uaucD z$XW{1n8SqtA8HlkO~Pvu21tl{`#lM#%*?54>ZVhXc(>`9Th*h#v?(GY)OeHLfakzp zX}@D1*h1X!q^>esgWNS?<|NxAJ9en{HjE6kS6_tjP4vW$Oo0n`AGrSOUZ@*nE)%y~ z?8`6qZLM>i1M#n+F11@GHJU)8w#GgVA$NW;*%am3W;3?^9#2U-GLuZ1#|}Ju#%1ZR zZR@H|i@|&KeYhRDn=(oI{ZS8H0ajpZHqz*z0V9nspzs%Fm1FwP_OBg~$?Q}aN3Fa) ze!zPG8E)7OF_$3`B=hiKM{kdrKaLws%GF|r2R9m?*P0VjG|@>t*t=w)dd(8~*6fi0 zBg-=Rs#2bCyUP{7iqLNJ$kuJz{a;8~w|SmwOC4$M!cU895)Ed95u&#fh6in7CWTeh zMU9)pUM{jXbcwXZiKvBy`OE#o^(sR@lQa4?az*SUPXKIP)#c-4EF*ad-9sB zH+LFI<5>P1ofdLtB(YiJ+*EcneXL~(!gk*}GxYDhPEjU7;jO37$3=QzW)lmIEPZ+? zDiKILK09(ix5SpyN8Nq$WKe8hOO_6DdI^g1bOm{)?DMIyeA5iJ0R( z-=fJv_S5X(gmcFX;v@2lim*AI>=`&JHt8QX4f<%jRs{Zh`&mwIc z7O3x}Q~@_ct(y<+n~|TdO8SyfiJMamF4}0H028UVYj?=-bFb8{w)O6-bG1l`^H4*` z9f#^Mnxxe$-;s}qZ>Ju}{WT1*Y%HT?<;?aZ5{os@9z}V+o8?Y5=p=*<;+X;;Sis5+ zuQ_H=GBTzq3yx`5?mK?`j+k|zz+JT9nXQA%>T&#eD#7}9pI^U|z>J!hHp5PxSXH|m z`^~!1=lZDD6y+e901v!`Qv34N-A+~xkhWnVv$+C_i`v&3OEiJEyorGiRE6Zv-3<;G z>g>MT;Y#G3CchDVDSe5q??Is`qh_4VnVFwNd$psv2NTQ|fjb;QyqVajjzhM9u$-XS zRkRgw%(^`fA3yHI#GB}_xLo(k29e<9bCM4{y}UB{X;_FT-1WbKdLwtv0&>|@8bX@t z^C;JMV$BmKj3i@Av}kCCBq2U4$=lA>tq6~c08^mTe0WeQB+=RsO>hqAGx|;v$xY%u zF-X))TsE^NJvsc%8?TPh@+Y{_T<+|a(^F0g?I@!$Nnv3lPueW}pR9JcaU;XkuDEtL zyF#hiZ^&Gf5qlSZv3*~tJ$2Tsn*?UzZ=pxo>3cR?a{*hmRSyW;T0y*D_-H+`w_H7QdumGA-2-ThLo=~Uj z(q7}@$|@u9awZs&u6#oGS<8NX(`RSxRM4#OV+cq^Sp*Jj+_Wj+O_ACZ@hrWbh7@$Z zoLg+ZU%hytmA*^II_bOOrRv%f94Wd{#AMT4eChB8A-Z;6TO~#rb>zH`c?Pqb=t}v zKm$?01XHAMKvKW9^U&X!4<0vT>ePLww6qdqPAAxW2dxK7?aR21h}Il;4HyP$DOsSY z>36GX=KJrThL3f&qA`B*4peYS%mtR*O!4tKaO$Ad&T-xJBVS{3GG)h_divq}n0FB4 zQRW(i3*%rIGJ2M$+e+O=XOVtSqREcxM*7!lWb%se-Lz>_=_^4y-5b}5{x;M%+qA@W zR&><_6$hQORQ2?d>K*KwMFw1b`O}3ulZkTyV!0nj6b2sPF!1l~QZ|z8>fLpoj}3mi zyni|v9uzD8YL9r-b_5*}!NLKHS`B=S2#t4a>QWo7vu|V2rAtHjvpA{dRXlq1ND&#~ zp7$4QWr$t~MKC%5UsOsgutFf;Kq_!;?KOlzy`8uAsM|KUPl1-ni~&d_60Rt<;~~V` ztRH+q)-S)XxE>g$aoYsk6QXnCe3^ejq$A622w0J(7e&Lzv6;t48rT6_5~YIQ z;{HXt*_v8LMlMMU$}|IOlJ+xCKD#qAnX7OVG3eo?Fhv|k#Ao@%53dJTh;M$y2k;(t ze4{hfVpHp0Qut8*NfVe*T(unk%IjPXVlqH>5u=@~Yl*o3OZ}XhEiYi}@y;yhk-vbD zrue;n{n-x=>I|~olv$ewsy)dpuY%*ALAjv&GIms`tkaHtn2OzA|QU z+p%xj+3K`spB60|dgJ|vZvM@Gy6c_7DQ(luA>qIrh3O+4>>eL=J=#u+QsQmk_;1lm z<6P1^>sUXin7H%QK`sw1BOw~3$%$UK+^=>$gV*H?-PJcGQa-L*c|4T8uY^+?b_!vC zO)OuCch6_IYE#Dnj1LDEKQ-3YR$>+xU0_M|G&?23Nx$dReOst;y6)-eeTTKolJm~} zW47(Xfnoe)H6Tp{?_s~!Q}W0MBh>L>Ej^9$NE-dhzy9^twzxK&Mh2ta@}Z?ZX0Aq6 zRW%R@O>UA6$ozs}4_cs-@KMs3bsS;$$vHCer)eMTe*s_8mZI-Tft0|*lLay>d%yd& z>KZE|cvYupq?`9jID77#lWXs?gteJAN;lvSh{cQ4hnFvZ{vS$HvZPnjms;KOMeWv? zf(zuG?faC+*v11O#0y z-57jlsWqd?^W}qbqTIv<14wD!%dGHiQ&9&HP{mV@2XNQSd;yMFAod1VIQXN(hG-;& z>r8Yg-yy^OVqCgsPbV0kk!L59!8qB{Y2=0$K|lTfo3eQ|nkI8P(9aTEFe-B6r)&2V z2F|lni<+KlGl#Wp@Chsxk-ZR95%HihXsFe+P{bh^a|2qhcI%5zf_tKd2CSk=V1fFY znq@RB6u$?0jbD2=ZBC>7@-1Cb({;jP>qByHf6}ejI&2INz3AAJx6=SHatV2XoT5P9Isy?Q_I#2+?yfaLfL3zm1; zz941jPQ`i+r_7lnWzhf(Mjb0KK{*z+l5p$Q#hcxtE8Xw^QT7h~SN*$piXsOz{jL5} zR?flNz0}&a5#ZU9`%hFrJF>~(g?(s=OYDvbB}5f5TL$LAZpi?{vPZlWETYVDahN-I zGq;7xC1Tw-8orTr2V!j@H)sl7rw2tue@XwC<~wZ}5g$|L|Fz+Kn6;&4Uq(2|DwvqN zt&gvT0?~GwXJeK!{69Gn?*8)+t@<8HM`34jZg=G=7moXjWTnIvOC8fO9AL%^i|Xi1 z(9|DsrPkqeR`~ScAsGgv4}>~~JLvy^WSw_B*Ztf6KWHxvEvY0UEzzVwLm?q)DT%bS zMLSYg3Js$|OQEE$2GQVZBC9E-G?Ys&DAVl9Yzf#BellJ9& z+EdDM=9@yEjR=8u9)wSef!=8M~m!3ou9Vl+T#oPk7y~qWHSD|fh&vXbD#4jj)g>}GO zW9rkY=x{uXFgD>&2B$Y1G(Y&!J>#K2TK{)fQ%CvQ&f?gHY4T&p_V)XO-?JG4e2l8& z>YW0e;p$cFQdI!Fk1zfrDlHNdImPYumOMUVD;Yz+`s|t0j_O}i-@Mh-P4|5uk>wm6 z#*kmN)Z*e1r9ax8k|qldVe+Y}Qy&`DT*86w0DmP23FGPw@78jtw7ClRep)lB*XD!8 zbu}m8=q7v$l{}ldJgBOV>+?2;hqs&RnJ00mw<={(R}+)HG(HQq$FuoHrjn@nb zYFaNFQtL+iP>joO!qNEEX>Xp(3yA)0AU@m*2!k0^UL$yJpTB)8CH7&&X0_nG`Y z^(J*wtI#|+z{QQ|!PI3!bUqBUSPjyB%Ps=ytmYj%ZpS4A<9zwO%mmTh)6lvk*H5W< zb*smi!2lSOYW|}1S7e=fvQ5)?s|@2tG#~A;5<9hshhD6MbMw804$gaPD?iOe!tZnB zWETnTzq~{8gCDKJy(>OGG9WEvtlH2yyFiMvZ8|fxEK!&k-uM2;h}>^l4OiRlAEb1w zSl7$zv!y@4Iefc}E&!aWL$#X`MTj!82VF*|h}>fCNouvy4f&Inm7Y!|uf^^-!-eZ{R!S74VJ?Zn*)}wNm(TMrN(%LmP6fxb)%%GHWW~m={^hVMgl(>D<#+ zjT@mPW-)npMp<_4{q5^l*|?7T05Z#9vdNsE?SOh{T${)|3$9-8pPb(b!6%S8;Xx!2 z6c%_p4}Wb@v~4Nl?hIDq4>XhFps;miuc1CiUtiYxckS|XSyeyZM?C+Z6{UeyMaKHH z13FYtD2S^5bZQwh91*84`}vTTu4wV)>({)vQxIr(c{K{Y;Bmt~HCyMu#6^4qaiu(f>#44_ic zof;!^V@gSf40QC416s<8YswR(-Je!|2-GZ=h8{dq#*LpoefoyFA4*lgcw{?gSU(3! zxl2E41VD6SGOg%zfX@nh6<7saxnf!#*1+6-j8WGa9&};Qw^;Mv;xw0b8)TRAwj&$P zdA48@7um^&7Fd9MbuUEy9Gb>JN$eYMGd^@`Z*TrQzuQ02c}6pSX>Vddj}xDG?*d8q$Oym1$ehb-1I2VH48Jy2|h4ic7ty zfl&jzYfG$9HkiUv)3BM}qvZRX!SbWJtl8ZCD3 z^YuA2>YJ)tit{l(+Guj2kxnNaB!sDyBbv{Ozqs+!$ls?_fph^nz+iSi*#ful1)Lj{ zjPD=plumV;h6;x>j{;@dj^&AG&d3HL*b*j+G*z``*P*JC?U|&Fez)uMKRV$~M069~ zPRVzx0A=NGLkGvJJfkftt<~G^-jg-TFbM*_315ZE9HnsV7sV3ruWqasApq_&PX#a& zd<^rpFC39i>f0DjL_Zt>vU-+@UcT+t5r;KROdNM4+#*2*RIVjFPPNu+8q}y}7!@p- zVhq!XG9k#k*>1sE(URc9dyT82KQ$qy=ljfVg6s2eD`x&?{-E2WKifeg)bxJ&*C@n| zJ+~&FJ*yH_w_AkaqxHC;n)Pa9^Y)|GqG)hf@wY%`PUbvtvx}{V6;uLLcUxG$#^d8( zn3^L|Gc+0MT{qU3_f5 zOtd$xe>6d0S5GCbi?ssy zc{KmRqq6I`HTdvs@H=C`W@UO2dR>{jr!o;(iQaC)jDZ6Nv;itUXzJ7KSmgPbxoe1M z9Pm{{0+2<^SMQH+r&G5>EhFYT=&}3i6d4a?b~Y=t0pIKUvu_NtZAW2t#`){BDv zT}iQ_zxX1vXWUQy-nKpBhyPdeQLEv-SDopIQRl*J8$wf~bp9Fo-6E(B@qO%o363Mw zw^n`suuo&)(Z=@cW?6lnlw8hX6d@=55@cyzLcmKpY{H%pj!bCYK!;B)u7L(uF_VEq z$NImxnK&U_$G?8sv?p$-T9v)zP5h_!=%PH@7^uv<-&zqkk3UB ziY%KBOm=`iE*o85NW59fwjrk1#o>g(qagGC?GLk)O(<`Q8l6^8EN2^H12h`xQvH030gD}B8vh( z`}Dl+@K*xZiNKa9E_E-|1sJ=#SGNaJ^Wu^!2m=@I2K^3l@Ms*iFy%$TE_wkPlwcmg z+($1zQsa>og18PBPj?-smf56jzxjt|1Z)`FQ2*Sc=g()H7^L&rY~ASeJmqUwTEm1J z>#ku?Fv9T%s)yP4{2^tDhS9S!c2t~ooFfpRf{JCAyFhEKP+*eWBJAf;5FJ^aA*}$z zg;1+8@#{E=9P#m}N!(f;zU*Dy-PAU$$K+;amm?e1kH2Yt*e>&qiRFQEqP3Kfu#pwN zlrvprJLZDKSV%pwe+7&_92=X#eB7rKyWz~)Ra*8ny^#0Mm4l0RgNf@ zD?YK2HF)#Gke|QT&bxnfd(VEA@Wdrq%anaz6#60uWpaZCVT*7qKas&byRvq2-Ty1* z^EErv)fAO1E<*<=Mzn+$s4eTOU00u1#OQ>0e@UU+>2AGmfeRUH8|G7U6Paw{=!Ns5 zd5*yTM6A&C!mRh$ycM7icX$-TyWVi%JEL;v59$*WG=+l)PErWh?H4kp~Cm- zg0Vh?-ncf~!L!0aXtM5I#*FIc$5juwZL@%n$hz{LMJ<~bY{x&j2FgrSb47+*rIUuw zgS}C_L(zg!`^>D5pC`+R{S$En_4y`;JL%$S<*rvZZaPdb1cu0Ec#F%S><`oxgI;sg z#lfTfWzrCgaVB@+&nfX6`T10db-BplL)V_0G#&ufK)eA#=f#Gc?^j*kuIum{OvViw zJXn?l@sLt+aUex%gR|Y&gmXQ#bK;gf)LW zwMVqY#?F>Y53~%i%fS@l_1bXRK9<;QBNCXL&mn**mJhwHJ`G10x0#L?49qlSSHT}> z8f2v%Xcr2eX(d?;go@=2irf+&$8dhG_Qj#%eokH`UwlD608^etRJ;JXIsG(9od~pu zuF`tIPGxHqgJ9+|&Wlv(Ok(1$I~Nmmk6_h<=Ym)YahyxYMCu`T7D%u_he`G@&;@N+j*_kJl$Gc93aCUbT!^Z2OIv6PEj+OCUFWCu;`6M#A*^=R$QD)1ZT|t8$gL~ zGkEBoe8cQ`Gk-?X!+QR$N3r=95fS0CU_lPSj^2N2^5*W0sU#Fzg97QZTk%Fk%-KIC z6!YDf3}|k5CDQ$8P@*WTlb=m|30Cl%Pp?wY&j zJZU>(@JQF(uFbLC{dKCk4t)H)ds@WO8&F9sK9c-Ozs?S!OgL|f z^3Ww`qg_kea`JMR<6jl|>z zWm^2#i<2p|#n=~ihY+6Y_>nZu$!KJkvV~sd|58-kL`Knf)ldA7Ol95idnft-upUsN zVA!%{%fs&{Kn3LALG4ih?V#^xI?ZusK9jZBPqv`HfJc~$*dn?9t-7X>AVSap8lumD zE-1OSPq}WRnM;<~lFyP%(#^+=kyvqR#-#u<5PBav!uiwtGA|%TOPu?b&}LM@46^T; zW!HJgstwQzbYL+M0dWUGGuH*5KA%>UL=hXzaFMtP5Xt~lCPy!FUKRWLO&y`fxm zbER|a>fuu#oTu5mD*w#+^RYhfkv#D ztL0je5$w26c;(dz4++(!cRFflNC|`Z`!$J{HfmI~!JLH)t$B3%6#N|iV5ga^3>i)@ zy_&*TBx6rxbr`lzu?~^5LvVacr<)Pyh9^%3N2GWIl7neBLTN?)-Bx5XG!-bJWNw?B zMr&`?W?O50d+i4`B|u#zwUNMI2>zAqm^^|E78=m&`Hwv5sDP-^_b_O72alK?RBczP zzfU`wXVAQXcTZtM5z$WEa+|->rFpCWr@G(h)6le+s|Sx7cQ09?$J>+O%M8*BYBUMe z%w!_9j4aybeXVd)Nn*&LJ!55ywt`+U=7ddI`l``IL2^Vv&uI2vuWXo%22N(Vpxs#w zU7j-cOmcE^OJsrEHh{OiHt)pS5qIVW%fiiWfHEV3yw78X;t^v;&QsQ|E^Jo$$n0gq0WUaW!yQ-10vU zu-p64ioMnvDWZX6qF%&ovu8kAroG7vL|Q96xZ?6FhrMV z6(u#*EbMSsty;C$@Fp)-7i^Z&6%(oMsz!P@u3p{1Z_U1<+F`&pKogxFZQHf8;Oer} z@eoxcs(i-_5mmQn-Q~SfY1~)4{5pGgV+stu#%TCsaDpPqsVOPNMMc-8Z~o{*2A@qL zl~RAVl{w%wjFrFNpq~0j5{?2D3D`?~7X2%#XPn1m!87wZO94#Oo~3A?&*Qq!n?4O( z@3ObH((KW}X$t)hBbhPmIgA|OdHmz5kTX*fJ8Z1gf<~ZGY6(d#AL8d>wYVf53(C2IAgrQ&&J(<8_>NHB-jzyrim ziMG=G(9Ov7C|IKmOmg>Iv~9bEDR>B{Ih0><0jc2jtJq~uj+#Sh%o2vp>{K~JY$tQ4 z^8>+V50U7pYR*CpgR-^5Z9v#>@I9WqlsZ(Xa!@(*at?SSU*HB|&@$EZve;ypbNl_F zkAug4IQaEoDeB>hFU3DdCZHlB#p3l+*U1pK(UHaf<{*4OR`pxEmE)R#+Ct2kfDcO} z(pnNRWMt!kQupD%Eql4^85U^hnoZC=>d?J^RC6$(`p3HV4;ig3TXfPFJwRbY{KuML z3pv-OQmn$vI;J;}ZGa~Yrcc<{r3WNbIKztMRvm17srIzYmCPLPsVZpwf}DHr#CWIE zlsvyv!K(VzL%sVYH&dPsecAilk(K)zZ7zz??IToS&Q3n~*Z3`|#V zs=dok@7RMry{2;SzJ0O3&)aBxZpC&P45KONrTl;sV#;UKL&hKgj8$D*steG>-C^M712cj=pk%UHK+~;twOSW&{ z9*a3~>_y>=yD<*kCtT`0`_^9B2_V@Qq;d*NNGJ~A2t`#R9Xn&=l+iBqK3T5obY42R z);H7-XqNXQAIBOb%^CPb_M2{;=>Nw87(K-La2~Sxo1SW98`u6!Dv{@*zI&)<^9-iX zIQa9^o}WX`CnV&uNOs}W?Gjh_5LaatB!l#`$3FxOEHy&WuUb9v%i1Y=o$Hzb#43# z+0sCJrT4g`tF^4vrZ-pCD8SZnN%{bFlzLkL4lu@!fOXxOW2U&Z|dJCbRqot-_+Qii*m~J5GRh@>_(n0$s{p-x8Kc3#Ht|)CxR11qfq!asu@_W{j zC6Ptny%xpl1eeetig1vGAc`lRO&_!jzdjjU^08_R#&#h4ze<16TKD@E*L@cyq%3!0 z8H5E20)4bpy!SaYo5Vtc^m)413b%aSXtU;XM7#Orzj-KS$0w{Q`$@Oqc`5Jvk00V+ z(@ibF!8tEBoxC&%S(n7Osb_V^tqe~T_c>`XnKDRiKSndNn|+fEV!cw*$Y+q%S8{Fv z?bNXgyzkjsH~q$q3c~K0HQ(Gha<&Bi{%M0-LeX<<;~ABtg_7sXeqik6roLY9Eu@rI zJGKc26!mGS|1kaG!vPP9gGcpf@72Fm%sBE4Z%UQDJh-g2BIn|$VTKZucNfD+z^+TT z)s{Gi;|^VQ(Sk-0KbhA>79w5VYP%jfK*vllE5s>NJ%`Z+!FVJ!{i@gu5EIibGzI=o&wUvY z4?i*SgdZX+5z!%}O|1-d+&{(4Od*!Hd3Utt`{VJv4LN?*WE$fRRDAVoTCSS&2ZeFP zuP<{&9}h2M-)kDEA?tT!$#Wb@2-n%F?@yMP4BpCQOverAJY%L#^0-p-e6HCB(l>nu zJDpmSiSIh{?1!J2oEFo3pkY2N~d}KNWLT z(t(T>oS4*0@CL{pe)m6A^?1IR=YbAXN;IIkXio6K>Ynm=(bEg#-z^U+nYn)fL|qsOA4SXI@jpGO#cU76C8YF=N#Mf27<2e7HS{wYwZ@4 zr=^PtgUYdvGwfM-|G6}$t=KhUGE8A|n9iwPyLQd7B&9R)+L1EYqol#1PS)1l``z6Q z`JdI>4Ty8Mv)`Ouc&Sk3D3aDb^10lE78IeC5i1%2h+nf-YD zD`e1n;v7F5-3PVL66QeMNR2pGnm#t+%gy3I$$&pKo@nmr9IXH)?L99ApG(3`3`h^N z<2lFgg|rN*9O?c!q&y5PsP}@6+r(7`oL7w2_yusqY8#+sU$Qofc_E{C^O-X>IU{e% z%H|LHW5b$0UX;D;GjnA=4U$aUHBhi4Cp?b862;*ALYl;3;}d(}Yy;+Bc0XE;R{B8kobs$c}P;tHce zF=pk?;;_PW;+a_)&Cow-1)laQC8hr9l+wn|c~i2DjYub8mSf11GVV*~En}>2O(;wx zO5Po(sXK1*?-{fvzMPCQ5W1?IrGO5c7-+svU7Yb23E6%rk-kza-@7=#Yi(Dj#CsQotcz`ngJ%X45dSb{S zzV&rcqyIt2Bt_;`4K-e|(T8P))Md^I?JqDCK!FBgUPKYalifp^dzU3S*d>hyMdrXX zVh2bME|T7mA%Bm+fA_=^dGrA&Wlp#5lbuAh#~sbve%?ADDk_T6ttLQc;=M?9GxylR zG*YJ@<8Cs(O+RxbWtgv~KE=jds#}JhX`#*eW0sHR89yI0!lQq#c~FFd#yO}^(U+4o zQEkj3`FVm9KK|C@fhqf)WquoQ5^B+n|3Yi2lRmYIUW(J(8O8)Y>EwMge`7Dc(?4w2?TzdzS~0>x<4d=uvPg5<;(*id zO(AF2ao>FUehQq~VM7|?$%;Bq;Z-%6fI!VKmIq9MbM5hC<6D_|JS3U_mO_l#U!R&^ zbN$SXH{L{)bAU&T$HFx-un68LhGf8~{qZ|@?*hUsQG`L0 z@Be$@fuO?DVG7~C_-Q*3b>3&GMNVE+Pn|7;4Gc~<$vASPq|F!PAdiPGqz_g9kR$U7 ztg{gUhTfv$^$1Pf=4KpKkjAEz*y3KxnR~Nt{c(rxqjzYCjFWI2OC~ETSRpfHxa*K- z7!V@9>~P32WeA(Ehybv^YwiTCvvpj#k)Sgh84jBN)gVOc+e%{1(Js4MHFGs>W?8!F$}|kCbS>`tYGTRkwgExtaGSPI1&(%nKZ*?Z6GVYRf{~ z|00P+p{i2XRZJ}T1PNP*|8@%Ne9g3e)^OavHWE(e^$gWWqbMrFYfj!58AOJD$s6Tp zowpCKAQ!GaWET)&tzr8M3j*>5np%5zDs|3|bN zkh8|zUEGnZzQw%K9#2#2m~NvFEPDCU7gZ;b>j+(yTrwvw3&iglY1mpF>e6h%qcdHo zIa!%4i$^ib6PFXlXmc;zkdl&XHH$|iDF1&*Py~r)2?<=3(2fTPUYl9EYLkIg8P4HB z?#7n;wKdYbp`#X6s!rDQ;SD78buheATMaMbZ&s@qum3QjJk zjVf~f$!YF-0KqX`!V}J(jpk1nU7WDEFfsU1T{k*!V2HgWYn=u4TgjqnWPUkR7qeX) z9UOMOe;!5%1_V($_GBDTGyut=6^^hr*MT8dD23Mhq%Dz|i^1u3L7-dl8kB}9v4*%^ zQM7Dg-C00ogFeI`n&$X~gzGsuV`Hy*Ui%>io^)k8wG>&S7hL4W8LKDpsPW#OzGA_C zq=DCOf9J~lG8&*eRW1jkOf zqTLAzNL>lSM8je-M_J22%O87Vipo$av}oPh9OY@hIvOCsVI5~GiMF}cIUrn3+3=cA zd#XhqYiW2gSG5lX_ko8)xzl7gVC+bupWD1jcE7k_0H=^`4pY}Hca2$<1;lgo*SfYC z!DbBe**SJR0s+y;5_iU2UNW!ik(wRsXEZX(F86K_0*qF2Wtn@_YQ)60Z3ZFJ-W$3E z^i&pc%IQG;CuU`^YOUD0$e;!lSTS~T;?IZD^bA^ma@NL6m(~Y-^w4L~Zw~0jF(g`K zg<=je&4tW}GvE93TfVcB5IgjpwuT4enYfhAKpfrqUx&u{H}sE=)cG+K_cPW&;5;b{ zXGuY21+`yia%`#C9@CtN5n8=^xlCxdg%0_5YOE8Hsu~#Hlj)yEo0XA|5RG(e54K@& z#e8z}Yj?&3+{n$(xBXsW!u_Vt@`tty;jxgec==o9TmxNXF zfhFD1rP#r0a0DT3nA95bIj0(UR0MqrSogjfsa+2EaQI+=V$@fMwV~6AB8si6{=FOB z9~webz)=JiN}Es))fvtSzQ0ua_ohDWVt(yy(H_mF$Hk)y%qlywhgu^fPNN(g)PUP` z2(%9>*9aWu-{lSmu^4KX4gC7OIpAfjMx5ahe#Cpor)zwO#Z4NfP z+}Wwt)c^HlEh0#y=g zpXscO&$H-RC&j0B7&8AbpYuscM-b{%*!n8hWw3W7S6#qH7#^y8DK}_y$8b7XX0yr_AI~04*gBntkw? zOE(0jAq6%h=u3)1$R1H4{u*){IvU>f8WqR-_0?+iGoquS7@}xL$iQFq1&bocpO1im zaRd}1iU&I$Cfwmk2g~x+D0a8sIqj5E?^twN%hR1(wv6ghFX)f`8-`e3d-`ra-N zKb=SMT&R`BKuo52R-E4bbQ-9-kTHD6?7QEAVQ{Wm(4bi}*@modqG`40MDP4RDPkaN zunl2J(Lt9Vr(i;4+wg${P6^)D_A!^;b3fU-=UlpUsc)jH@&^Ihk&qC{$!`xGbur^0 z%;$XEPq8#^;w}xQ?GXQ9l+NHt5R)&mH4ng1+F^ufyPmzgJkY2gxrWw$`x^>&i7nkuUy4dc4(IEj>wZk@YD%?-T zl6_@T7;17E7BdKP0+9|83Q7rzMC9qc&x8G_MX$6k2AA33c%eU;`yYiP%vJiGJE~Ao zAVKwx)h}c}!ByIW`2~sk!sk#-@twEOu!{DQ(plGe6YZwMNyptX$AO-L=Sw-#jN2e= z3z0H9HdZt)iQWbwsZ;D3&`sr^T|jS7)sg<-!Ndg@!53b_)gv>P1{=ttY{j|eF+0ij znb-f~ihjmrlr$WAvtjPdn@#YrBBV8F)aW`lrj6r<-%o!QO-`I=0myHz1w`>rUEZ7%6i$wLOF2c?Fikw+bJ&lGTLGG ztkAN1b16JvpCt)n%*73rN`mQ8>+dxqP-b_c?FO;m46+A3V%gvR>)M~lDQWgg38_p{ zomuzGgEzhV&!^;Y=_?O;-!^FTz@&4w6{| zpu|+%N#F)SOge49mY?{D4JX7JY$@)Mwq)r0WyEXc;iF~ri%JK@!I%|RR*ks;3ge>Q z{rP^QSsaCezdzunfPfVVl>1+Pe_J4CzE3Znym@%NKc#wT*iMXCQjw=c z3|OR&wUCEeXERPG&eZGto;0!4e8`jy!0TA>EEJNtbVAJMwqOP4^^A-yylaXB!+`_m z0^YN}LY8nYl|bPvHf>&*m;d#?xjo`Qn)Z@RCJ9E5M}97Y+t)6GXv}Nkcmj>~1_PZ~ z20r~yoajMdm#u*N2d}i56=ZuI(3bOd@6uXCe-}scu^>~=H#ey@V>F_~x0cwSfW{rE z>=;_&(1yxF({RAKu~Cr8SDjBSFlG3#%jse(Bk%SlBT4a|<8!bzm+iymDK zhO74<+4XXMq{K7MD~q0#gRYEii1CS4SnpovTi8!u=6ZgdlU4+mb|O8VG?p@42bJTX}nnIZS*qA2cHcFSzXOxR5PHxz6=9TIW%4JA9Euv43seD#Br zBQa}E*S0S@1Hp7XHFY(l!AQR**O(&b*Gunla{XntXJc4)i|-T%6ASQT75|a^h0Vhl z+M@nZ4+x;QXdOf;Em#l#4MG(DawH}LB_7n|xa~Cq@}pqQ9eQ}oWMPKjU4H%k)`QnP zo60uIx%i)$*zjhf|cdPzng zPc?v)_#11!w_X)(4q6VJxo^WU$!R{mtfjv!!~mtG+(&zWuy|kdEe&hh2nt0A=0A#h z!Q^CUC*N>%!R9Vt2O*|Aw{3HSrc(d~Ydf9IBv8)Q%MwLA2~UyiSr!#~Vyb=2@Ev(| zTsa&<5{%INewOOP1=+J?E8_h9)6S+OPw76C_4ph=EBHZ;z(xjhr9|E_wfO0|xL3M9`_Y6Z}0bHTTD96afuh9(9V3q(x@A}cy(Svt$ikZjRL zj-bFrsxE4Dl5D?41%K~0h2-NS%6b;@@}hCe(meX!>?%wU)?)9*OnGiqA{(iMgDK-bUoj2*75NRNx2pL z_>6taUaiHEk1PU;9oL-C`5%(VrS+4vQq*ancE{B!=KK} ziGnhEN1(vQTxvCB6S3{4HQHO$=ZJkomu2BrN9gSX(en23u3A-3wUae?21Q8>y%&qe z4kBxmEx|Bfn}AL!+Q1=(I;OjbfIwqV5Z*pPY_0_g4|=QJL4S_%{Hhd=-}~>Y(Wx;T z*7R8A1OLtMr1sqW0ixI`<5UkS1Ma>CQ;>lE8BmZ{1|Oa9ucT-eWCQ|d5zL{efY!zU zR1DYr-bI*rb*BYHT&>e87KxhQ+ zjb@Y~$r-WV_(tZ;o6}7qxiU}N(4b}j%8}V)qDdx4r_NxsqlatDJi}0`JF@wh6|1F( zX0Q-tVJ?@~^V3So`FRD2aaEN#;En)#yYHl0RjRqiF|7n{l`24w!3-3MdLy5-cr%*e zX$;JxK+{FT%j_c}W_{<&Lqxv9XQI0rwL-`Jal(;@TMJ2zDn99kWgF4u10c%$Cl$2g z$$rx4*e@eBJAF~MO}jgb_g(n~*ipd;v>njgz-beRlg0f-OF&|n!QDrfN&wxfmh+$^nV^QRVD=^o1=pGY~Y8Ni}#FOtRfq-FV*q&TKK+sTOoZ&UpI zaJ~>u?%MV1TDR!yb>ypalBPI10wi<*NX_E8($w9+KL}ZxSi{iLNeM&u@*l4uYkE=y z$r+UBeB#?QDG*K5a$dYqKss(pJgB@w09jgn0?M2t|+gjW+`0Jydu zM6^ZHxN+T^fiSx!r@Mf82vtSCm(5W`3t4}_wH2NjK}*I$ zO7qbq9Fw+`WHAw?b~+csvtY5+Vtq?#v?)~GK)7)OWa+F&ITA?i9+R14HmhErl9Hlw zmk!_Uwq?QX*^Az&N5{S^{4w{&xM~Z}h)a?8+Sf#kk3Cg*_SCmBlg!A1_hn>|7q4F3 zK-eILc|=qFJ=I^!Lf0)K4IEkREZYU~NFh!x1ssCN^Fz-})mCpN>n|Xp%GNy}o8RJC zdXL;{*|@8opN34$^SA+b++&C>^c02$58p2A()2p|X#EEZY}HG~Hg-ClbFlj#>}RI0NT|S?E2&SV^}7G zcT#!GpN}-FDWLcZUXbu};08!jx5T{pJe$-?7e}O{0cZeED+!k+e!&m$uoH^P9jNV(iVqDoO zoF7VR;b~%%pMkhZL}3>1REyx)zh#iv&LPPW5U69iz1IAFO)`I|QHrj<_%&hxg;^@m z>GSvR54%x~trw<8G;;X3vyP!6;p>glwF`|ZA6w_Yu1+f@zYV8k`}wu3mCT)x!n|*b zDjE>hh0`VDPBb<09?2&C--ha*utc`o7vRnD@9=&xcV%@QYLqkI1aK_F{U7JfEw;v$!{?^VhFmr(9ll(eG}r_-@l& zsoVDtTBgi=9JnZ2^L!KEQ5~WVwD_;kWTu(f&UBpS_mZquPz7U_E?s)upjGWSgEh1+ zHRsy?G*!``VISaYZGh-!7O7tWOTS>feuKBeuTc`n!YaY1-&_Rc$)(3_vS?acNmE=T z(U)-b(%>H1@H(&8T3}yPFXNcqxcub6XwA$fL^<<+dSWs+J4p@G6GASeu@b;((>M^X zV%lKB)gN!(%mb+DhAsp)AgA2BL%Vi;=I6#VIc*^On-OL<1i%C$aO@b%8&e5-Wp-F| z))1V9q3g1vdUJ?`Tm-=AtF6q^fmc8v$g|uwd6|at;FRB9o9kyiTSqwoiwl;C`gzZS&8dxcS^&dA}3f3!# z6Gb&H%mfr;wkgd{#T9DP>fZSxf_}+HCh`{*g$h}Zy*39x-*R4`8PLU?qF*jH?Q%3I zmE$`sE@aAA$RSRzZ1bBs57n48Fq#<2(fIBzzLjJgzrnF5o5fcsrn>dI7~;Rx655A4 z5{`=5r@{xXL(y)$M4Tpty3EBh$bj2|E&6wRC)?yD`n#C74^^y zuv_@8C{g%Ag9QwYe6LVaC@e<}ci2zzasMz&XEyJC)#}yrx2EQ%l7Z>t8q1u>_3O$2 zpT@Th4dcdLwLptC%`NsZ7|4Cfx_U0mUeW+f}BEd^$r`5y%v|>sFuM4qd^xH>h77QIsRniKBVHJcLq5 zWCJqL0A`Hsb>D<{CJY{mAsY$`#;5}YJBJQDsjRNQI1XMW3vPDf6%~|k$oqsw($PK?g?LD}LTPZj%iIXx2PO3|7OdHGHE&c1dmUSUdiWCH~+H!n4 zDc3h+Gtc*OqYS9flf$u=Y{vnzcICy&h#BA<`^Rj68xr?S zU?wD@@0d0{j0X>1{^F`q(^#G1uf$ZYs;UYUI9bEEA>%fdMMt->H8&&UEYOkh=&xbU z-Y!Jbta|=lUQt#ugd#rje1lflZBqp2s!az>*$LesQ)DnY_G*?g&Ou|e>}R=)4589a zmCnZKe^*`Ckj9qo8#7`)u3;2%ly#$;9F4&%n%D&%%MCble>dO61JpobHbtixoXa3J z;JN-O*lW6q+`oJ*u2;mI+N9ql={$)#`)-T@FJXM92yPOX zDl_6>+FQ|iGFtNf>BR{!$`}NVkrS#35?-+II1y=6J{(B0!uw zXY)hT_$sp*;&slMq;Z=Z`#2fES;S|YMcG$4p$?p+A7iv?6w09IIb6QbOKHpo78A-g z6nI%{{*1kg9FayXofj6`bbyX-w6Y=Dl4h!F<0i5tk8gfuNo?O1ToE2sD!4~zuRshg z&HG;;-m{qgFmp#oq=*+TUW_L|7$+psOEQjPzoV+E6YNAi%`rLiyV5k$WC^i=AY;{+ zS4|nnvyT4@kcI&kUE+dzZNZ~QF^^V^RW@7{m6~`A4hdvI#_#yS63!_@lrb$@_52wKNyc!+4%V+I&@tT%Hyk-@ zV?IJEF_T2vELO$j>sD>r+@Oirx_vuZLCda1{S5IrP~^E5f8Cl&n?Xaot8R{1F#$|@ z6dFVe2}j0NdD}p?8@x-LF=E6;c%&q;+PgY?7kzfg6V)CZawv@V$lN2IabfOAfA1!|u$^ubC z)8Dsj(`L`%6+P&UaDT+=a8}%SL@cT3&`DMweDMXd3v^K)~)jq_h!rlMEr6E_;y3W zW4EJ`RFt*dswB4gU>IgY7gR^8sVrnb`GmmqN!Q`WvB5!K+0&hrU!NFqOPA9B`Q(M6m>~ zgfDjVh_qy-a31dqk^<=)jF?x#3Fi()XUK<<92IJJrOY!O9Ldyi)6Vw>!$7QSw_+Y^ z1RWCwxS=-MU{a)aMKOg5Mywlf>FwfaC+Sfy8O9q?v+TSg z7TDq4&B#5yuHK1CuwoC5Rg~=albm_xd;asSIqHQv&G5w)xtjXp-E9~M~) zJ~%Y{HB5`UOcA6c}%Zk=2x$QcGxM>IBWFEwtaf9dWa_uXd{J%SUFo; zZ~M#IqZb}7PioUPJlcuzY$g`0VEDysNXLZBXi+6FY{jL4@qrm+P;pNH_$r}$;EUq2 zDLY$bP*5r{a;7ZgL~XZSywWKE`+xz^aEKJ~4SVbnD@0K!AS7x_M#Eq$9Hy9Smf!p^ zgZiNek`;2cuwUUaJuvn&U@Ql)P$U>;Lz^G8f5He|$-5Q2RptkEBJQ@xvk_YgAh8I( zz?X0DCLj22Zt}MY0Geq9l8FfSUzB-HUCJ+Yw&G$b|)+; z9Hx-b(lBu@qpvnuG%d~{yKtH7653(yc}I&VWM?&^GhT_yToNv2kG9 z;~S1IXfzLoOS*~VLL;ed)^YX0HjwTfKDKQqpC%G!RX20e*cD>`=M+N)jgGTF*Cc!rdRoTt7ogj%7?5;#kOo4m}ve7g2iISBxWh#&fynNXgGDVh}dx6>fHU1RA0uB7XjlFU z?H*N9YxtzcNekKn@+S1jq4t8{u*c{gOnx+ozHhnaN!P5h-tlj2;~Xk*FH>@#8_#PNj+!dj^YTX$ zUphxjT$(A0hk$@m@!rRU1JZgN=Wf~3r8D6!LD6_6%z5z^5C$v-^s$ZCr>(%{U|A9KL`?J*)cNzp`L#v zANWAe+cq7j(KJ!o0TJcouU~^VM}KPduB1jJsfZLxY4rCuOd2zZvt*%wsynVnYLe?@x(Xy)vd}W-~TNL ze{sd?#?b+9H>mB~)s6z6DVGrdKnT%10?t*2OuKICi-JkwoE&!EF!pOA+5v8=D9K7i zn+H`SqBJN3^EM%68^K#{{F(LU&6_572Hdiy`KoKio3z(>R8RgQ55OaBGFe!pP&|f* zZ#RatlnRRlCO}@Ja9XO9m(wOW6TXLaI2pMSRqFdSDP>+4#% zxVWH{%4H!6{LsErmxCw~Nq(`k612ocrKRbV6jT~rP+c+&0N0Pl!QqGBSkRS;bk)=2 z!><I`!bVp&UVJms%d@uNErFfrLn zbY0H2O+>;W)~g=Ec~YTTgXW2~52~nywZSi5S2%yjuMP!io+)l7z@sF+>br|d>Bf9j z>KmaUlpWu+oU1BI8iL6mri}8~zsgbOQ0!82j;3?BO3(cTp&K{s(@{3mxltWM>3cBw zMgb&N2y|-}%Q_Or6(?Kd6R#Pmm9CTlqM(n}mf5uhUTSPFNwKoJ{yR;U>~JE5#6;*N z<>l7ko8W%*)X)<#1qYdn=?I<(ubD>vLU7YkRMlKBfmRcO4E3q##m)aE@?jz&jdeFx z1KMPKNB)3Xt;^sXLgh9xxXiAZeX!ZLi*O)%K_m#XWBa0N5g5{B<;R|Z#YTZ?cMiZU z+|&1SBH%+^ctAjNF>FEbl#&K{a%G8HYN+I-uKwcnzdby?y&p}x&I8j~ZW5%?P!JXV zFR}OJZfdpNe5b$~NeU20IgY4Y5uyM{Kk1QO$g;wOrH2EV1KoDw#5>d6`G1RedZy^k zTl;HoILZApvH^s(X`GHYisj^;LlMm}JtD3H2!IKPxKNR9uk$RJDt03eo0bkfiFTG2 zi1RLj0+7$12JdjnqA8~R7bax&oHYzSvh!^w_cUvI01XF*@Qn!sckDd<&4Scv4VZe| ziUWSm;=V0G8knp*fDo3bc#9UI=E096iNcBt4J`;TJU3rk+1>ZFc5}2|ez(63#ZGgB z{MDf9*4kqN9)%Wl9Ny9L3j?LHvV$Ah*J~}KMp#nVfw%C=ZxLu@wJs$(_D7tLX&FpD?ZlCZz}IuinGO8=1T^}T;dJrA z^;h+vc%n=s?Y;m6W1uQrVny)86oPsM>p@wd1*!BXa@@=UL~}hIs?)isF#ZdX(fMJJShiK0o4*)~6ok z|HY`PJrJ`Sf=7hlJCjXuBQ8`r6=(=tW%H`5AWHIWHUv;c_94jlG65}rXta&+nn@;SjM z1WD?rmXSSnTgl1@Eb$QQ%w*7>nxS{PiB1dFd2M}qMJcK4`H~Rb^dXJ=Ng$!{&Oo+D zR-4yzFjmF%LP~i>M`ChxVj4D>FDtfQ-HF_ZVJ>i;$VQ)D@rfj|u;s6X5ay)dka35k zPhkOCb1WwZldjUXhR-~th~pkZ|9&-|79i!80xKMYOM)&`91o0 zO`K3Ya2jGqCm)?rJL*Mq`WhQo;%h7oQI_lBn(Y!SDeLH7%t83iBB^& zX`vOzL#B&B<~HyqnZlD#%`i!br_YGAYMe zc9OXG35#a~-3>u^(vBDQ@JLMKiyx%yCZemyLQQrsL#kHSCYp^YM-f%pm?BU)@AD=9 zx^=h)(&)+BbxPy21eVAm#kZ9pU*g3srXQqG83h)77j%*WtS24t3j2GTvodhNng?6P z0r{ibYHjNeFVJqyXmWSBMn%%yPh&355ExQmA}$qrxNHI?#t^!kglji$)U#9VMhx4} zZtK?`U$JU}|#;=fBV4yB)m@)(JuD}(C%x9}mWjhO0iL$E7oJ$(j+QYvU=*~61h zREJpkzKHav#3Db+NF3*>oRt7ZVj&}NEhGBebMVkTu8)O?{-kfR;{CC+^SbH4E4O3u z056WbS68ef7C${U@zLTrbNv2ewon9&;qkQ+pqg>GC~9kyhEHn(EtHe_R`ZACgRlCL z(b+2@WwWe+O=0;q{A*HXQejy7pbrfhgY#Yhmj2nzS%vkA}g3%O_OuzI+^!%29# z@zJC44$^CUUEIm=WAY6Q9J^QREyqE{eeQy?XAz^sI+LnZ6ry?!|JV4l_Yf}bL%`k6 z6TjJt7otoI!k_fvyI@{C0^GgdjHQq9xo5wEr>AGavGbjMPZ`|x>^OZ_(RN6GrFnFU zpcz@&?9!9+mg-NDloQUaC$p)Sh9vjX)B8d^EwN;@ck5JrZS5GCSGP&0TWG{b8ENumY$>nS`ao^>P zB{;14T$%-OkLKT&_4LSmKm4VSN%Gd$-hW)R$v4sYks3LU$;XFx2+Wy;MOkW(E(Xoz zgc&knjby}{z9NEi$C46QZKXFFtye-cAIM(+tD>$Q=6BxJdkM6B(H$2rUc6yTuO(6` z%vi5IdNe5glhfoDZ=gU!*2~Zxyps?llx(7fg+a0ZN0tJWUotN=#zLi+f`qf{lQZQ< zDH$vwb(=eT@MT%M z-&22^98ft_`R(({;D|*QYu-WmTgMm9adElEGiIc-%YXp`Zi(?6+qFuLl5(>c7PyGf z4=PHz@x;6A%II5hF^sB;^ovNw5WsSgqDd40Ipi+cdZpN>fIqDI@v$Q@L>D?Tp>NnI z7GmTM@k4=X$eI?>BJpYBCZ3Q{%lnSs2@4gqAU+twHKvgeX%oN99uFgU?9vb3csh!V zDZ$N>)(TU6PC_Vn>4bP!RG{n1zkan|w$xdxckkY}irFX@^59#i>Pu03wk;mmw{oP< zVNBTI1?jZD-t-6{UA*aLS&XH{f1AH1X{Y+RKIa=Udyu5Yyek zclA27;>vI5$h=^6%epx&Ijt3i7tpj*9dx!U^wAr)cNC^sG<$OJ~S83ysIp3|L@ z6@WbWm7D!Ei7rp(f1HN6ll}$0M`H?c@p~k`$v_BM81=!3-h+`c2o}rDMs{QSDzSCP zj<@(9kFfqy(D%+^GZXghtu!=xhBa=|qy&3n(J`!>!Cbg_1EZ|?#|Y0XyZTDZo8SAT zdgOPHf&Ad+9Ofe`aHx{`Dhyf}kX2zI!zz9UZj7c3$JNMO=d%N_%6IBD}{x#3ZBP<~qPmjIOVqYplA%FU|} zot48k)t~X#APNd3M8RSO(9!Jt`SW5$EM@mIk8PEJI#&Zfv#~4G4D$GlO zcU5KUY{yjNhwl=#OB5bvjDKY-US}2sMmEZ=k!ZI`Qd4ZM~TA{ah86@M&f|67rF1Iy5NA z43Nle^68ssLG7r)xMs^%t`yHd*&oI{7^z)EQ)gH%z+MPcmM)7IA6)D^npd>JX3-R? z4{@g?WU?|~KdE9oKg9U?qwMTPbgc|VUxy-V2Nos!xB1Wd59hNOoTqt#a6+`P(S2mO zt)zEpadFolo9dYQVdYd#Tg3Fn{v|6B&}JqAC)^OG@< z{fqM_Gg`>1pAF;|PL2yfLkeMGDpyyyU0nYTYY8cLKobOTr9sH{LdbWIF%k^sx1+WP zjf2acF=HSAE5%?rD2xYcv8hkzySrzLN*tORALnXPA8Ct=iQ9kfl!_1cw30x_% zWw?#EL9x>m=Y&H^0{BUN4`zqHagU$bO(1N76cMu%a}I94;s2kMX~LMls2&0VR0v3} z$g4!>z&-;Me*XL+->WhrMdeuivoTW|neEuI3l^>-P^5qfl*3S`?XZ>fdYgBcQzvu_ zPu2(m+vnS#&SjO%E)Sr`xaY_f9t@t}U;|Qyql3RQ`mqZX>XA)xI;+RGp19NOrI5j%7CKf1xcEkbu-W5$ghnn z=q&|VgTEkd8m(7MDKDHwIf5d60)uP<#Qgfg`HV+B1ouyveW1SR%O$8xF@zIqA_|46 z=N7i-C$s9K35YSIvTSJ;VLCv1G})NR2JURH!VbVe(s?kJCd*&N{GMDwGgJ?JOgyXZ zC)x7np$G61UI;1Kkn)fCP~YtGL0kUr@lB)Q-ron^<6J?n`Qt_gAvufJ(EjJSI&s0Q z%O=3g4pX(X2w_-627M8tD!}(;r1X?V@&(`Zs&omWxNN1c#ky8NwmOX7IOFeigsDXFTR&(I=`t-4z!6_w zfM%tOCep#eE`(!|$K7jNND~>j=Y8Rt4IVXVP%2ST!H3Gt%h1ACGfGHIXB#=^qAdeJl zG1!AU%-9=yzG;qg8${;>;qZ8G8<=6Wu^D11`@LvYUUQ((Jxz~Yah(aL*TXKBT-I}H z3+N(N<-&0iuVU8vFvC|@^anu5ijvINK3Wh1bA2C?i2Yfp#H%)qiCl;|4-JRRpn%Vy zE*tNiq1ol)xUnOC@+cK=D{bZsR;e*y-&-mtohi%nX&hGO9TZnF?MZJ~2Z#s@l295h zh~xQ$e}Y&vg$Z}tWMriqb~Kb52Soi=vQh zkqR-E${I?NQfSd4OVNTcr0k3mCD}@LAri{IWQw-;bIm;O`yMmTar~eEGc%^{`}g~P zujM??>%30+6C#Oh+Cagc98>#53(FIZu_g>DcR1S7RVB)6w<6W1&z>c%&DL`$lxWQnz=&1ZrUa| zqKAVzUmM{Z!-T_-8p&yfB3qPc;!3g5e=bj2R=D$$7G;ixR39{J1+rIkOJkV|mw8GA z;0%`7pKYbqcMOVou1~|f=)<>bQ&v5AIcet7_GzmIE%B?@WcIMLUdy72K281OtX0=f zOR^G8zZTV7waUUr#kf&R{f&OBQ!f;Ds=U>|{WH_Jk2^dZwe0UcOL?}JYy4;KnvR}X z<%|~L+%c7rvORB3pPb&4gq%$uHDUe*hQ?$M8Uy%oTAdb=3?Q0sN1*x?Ng-pyKM1t!4GKKR+m*!>@eev~BDa zf)1~49wJglg9mgS$Hr(7fj}{UHpCR2FDE_c*mzk#0ab#!&3Vv8{4f;nEo8Zv z!&5HUTD)?ji)sw)L&dV0g(_XfM_&RG&j}vz)DR zlk+dmRu4#QU04|}3=R-Ec2m2b(ucH|4;!=K)O22V)E|#*j@+kPXosJgT8YkPEshdL zzZ8QlcqO3&xYqBEYSCwrLRqmCHBTq;Yhb2G&*)WB@Ce#aTfIumYRcXMyuZ zd;U`N_MA!@``K-_;N*L~Xd6%Y#$_>*4Ci{l170V;u0iF+Gnq%fTbP-0bWVmZtIk!} zOM&CpC7yq}Wu6Oet*AMeDl|ZK^cHno9JoQ-RhN7_N$gPX&_Q$uF7tc%fbm{8PbP?5 z_x|CwFbnGNDdWRR9b43t&+Hzt!M-W-hLj;BzbZZNr8+K2Qeye6Eg^Gi#z8$ z^5E1XGFqS_NdT-^WynEU7L&?$X<7EgleE>$P|Fw3oYYMbnS4DS*|rZ&(Ywe`j0>8C zhK*3T6QG!7#Mh}c80s9QUT(>Opp|MtF=FMP*S5GD5QKbFQTBf?EI>#sc5;cjJ?^GjF+&GBHE5uU&OfEq{xmWfL{E zp9g->s^6q+VOX)D=ferS^PJs-dNUiQjHI~~Mw!ZT8^y|Um^}iA;DCG(XLJmyA$rCn zMe%i$ZG{*lN}6dg1ND3*Yq<|kXWJuQOjFwbD-G;7TvwF32w65h7SB2?d?JSKG($4uLmeyHtPx3!1}=w;A|s3ehL<%`h+SRz^B7H= zC<{}WHDGMyI}^*ibXgf#wGXRN!9B6^f|*zQPdAyd>cxe}+QT_v0Okg*wkYLTP&-^t znNt|XK{n=7iu(k)kR_b1oHO`ni;gXKT4)d2Rk1?Z=WBY)hLTxK?OR$Oi)-Yq)the# zQ8wlL=}x#N13#hELF~U1B(Cm|gME~9?17gE%$QUsiBWL-_VL{6EpgoE<7iqTg&H+& zT9uSypZ}s55VQn-#C}{yP@b_JFQpNw>^sH2$bJRJ#G>nFK3tFYt?M=KIINK=pUrOZ zJdJsfl}#Flb-!Kt>sMc_oA_q|Q;#G9z)7FSlemMFmUOdA@3&Y=TJD%s(!vE(SAS;$ zSJVJBuqbPtZ^cCL@5yuz5yPCbBrCBg`kjDe-9453CK&+Rv#ZFt?> zVYL8jcQ6XXA#nl4phYmrO4~zCT3>WJpu(a`KobG>e-#lRk|XQo!zs;S=MD*4N=q{1{${1Ii7md)V21LYUDAQWc^1p9kBnJdr#Fv>Jr2PD$h99F}Dm z23=9_Xj3#$U(fB-^qQObD};dr%?3g%YS*&rxJB+F=$XS;yS5)qvQ0UD{1OvotV?V< zcu0pZji_SVo#JbLsa(U!VcN87zDa)N-zl2w40+hqbmhTc`+7~PHDFAo4s=OuRZMZO z0zfOxXBXAg+qXYb-k$FDMy{z^lx(SEhC@ZBc_3W{%RSJ{}!HEh_BLO^B#?bfHQWky2$ zp_o^s-~v^-qB-xt-5V5ffq{Yh5rfF)(v@lvV}eCzXsCW3O*lt}ikBxWBr8=BxioyS z{o%=B^c_CVpTAS^)~l(R^`DBRF!Hmjmy4TQjH_Y}|8DqMU+4ZHzfjvf1=NM!wxwF_ z->w<^Q6Og6m~jInJa0jNNd&n)ucFzIK(HWB_u|G2OIuI+Iy`iQjhkw4e(*yQ=)r;k z6V&4f&D|e=H%+uH{mtfAZDv*@x)dstY^k_Gj8i}LDisw!ACO1#Ka+2f%ivZE=3s(W zfK=p6>BlLq2r6e$K#PRg37U<zb4_xC=qs+>}PM*>hLKKH?Egcv-uA zJqWQ(q)KsjYWg-1CV;U6T-w6KqbG0^<#rN68y=k+H1)`Y&Pjj-k+s`ttZ&_F=0g@c zK$+FBo6(yw-G(J7ZcpwOfi-9#%_%t1Wxm-$tb)X2E<1Y*S_^Q8(4`x^e>uw71KKq| zYEc29L<~g2aQV6%V-%O=JGu5B>UFMuj%*ltKTHKuk)Y?NH~IP&r(?F7mYsd)3a+zU zX%FNAy=MO@%nlh`m{$5`;4!?RVm>78s;DqPX10Tqr9$WX(5p94rx! z8f!49*DeyP9lfhJZ`sn2m-F_aqxb!StNf@sdtaGft|%@J*}4X$f9bDY^-9gQ8hQiL zfeX0qRxZoQmwWqUnl|2hw{%95sz}Gs#jc_Q3=9ol>P&r?JYvE=Q zIvJ2O3I2k4yI_8yOtPYJxJ2nV7^sI76&BT<{kX#3a24s#?LQ9z9 zOjFq-_5ua43z5O7<10>|>@<7wWO^ITf`S4&fKq`yAj`vNTH%xFqu72D^2W|HrVT(P z8wuMVo!Wh*hD$x$PNUDSN)kg10n^A8w~ntrc$M^w>sANosUADVpU3rtPQ+RMFHIY)XKoN&KDJNz35Xcw3 zl;kaH6>&i2svm^uSkS+nn4!b;8?WLYcCrajA5Eoh7n4d03sv94tp{iXJx~~1 zjlJ2oS(7I70Hnpa^4&cnj$c%J8b$RpyrZaq zct={4>xWP$G2fWy<;{!X9f&5`&3L5Ax3{^;hRwf&9Z(U|iFZ0ReFCUgGSico5o-X()WJ$~R?X95JIsG^6&)g9eEPo|m zn4SX6`H}Np)BH`iUDDemLU^PO-A)l8I(ecSJ>(W1e;$;G<<72kO6s|wQ*!1;P#yU5 zhVu^F8H)IntWrE;`eiSBC-t!hrovWY0h#!2UH8_dbW4Y|_LpPIt$MB1ZLYJw=f%V` z-n-QK&TYFjSG`i-i7paP(if$ryPO>R=!ZVp2Af%OZl?0LQ;L@1V-5pb{_17@u6BT8 z`7^N9*AEq1ry(j6&r<4&Se_@>PN33R7se73#S4^fQ@)?bFNj=yq1WZQA|@s_M8&qJ z!~jMS0RQBYXYBxsNUUu~tQ$<;f-z81C-Xwa@Jetq&POp{Qtf+LI|a`D4d{s8Y>fV9HI4p z-PTX4AITncTIWn_k5XSIaa|cjny}#`Otx&*kVzt1UNMqKD@T%N{%tprg_#D~T}Jpz zBApeMY1gVLHcqn)jfrc2@(5;JDpZ(O5C2N6li3?jq_OVXEVPaOGJqTtrwN$`Ky5^- z7WZ`w2`$?6i=;gR)3k&0gs~J<$K^~)S!cC&ex~E+bmal@v}8emxXGViapd`nHR1$6 z)Tni8x*sMm^t)q7^8yu6>War9G{?cxzGXJ*XbNcYRHPLZo7fIF!VPykJ%+C5`X}$h z=70Y6?tT#Ie*c{XcrnINNTDyc-9Ak{;TCwnLK#N@C!=j>a8$piJ(DVrJ9OP|%Fd-W z#02nR(^TV?YC#tlo=TxtyhQB-z1uYCwNfVS*-F07=S3<_51-Rb+P5Bzrcz8k^T43ra?ktnHfxO(kc02N9bJ!C?Ma5lfD5GL`Mo%;et@^+t272(?2L#WRMfDAWGMO$+I5w?qm<6oE3VbwV#!f zrpv1yu(IFF`3NZb(3R5ywH8UW6h#n8LV601h+O3|>L2u2qqlDM-1uUK2fjjoah!fv z)$n22Xvp%qDN8rXriRt7ixzc1bBeI~GbvjWw%Mky)zguT6yFHd-|``N0CQ@s=I;yO zhSe!it5h^(HJ_6@k4`R{f_9z#9~$f{1TOta*kHMaNEneQ&sn&TG-v0W;y#-5Tgr$A zXiyTUq6$MJ8^J)pu9`c9xFEr!;8;Kv$GM}7kO8BggN9Qh@M5ko1_HHyoUg%npi|!P zDwsq#%|^rl%a%>S;OTcU?;OTOVvD8^ zk+mfYQ>i>b|295fiwv5xJHI5w!rFTJ(Ke7T=wgI>LjX;)p>jUsDb)$!<4x33GSf+J zJIKpxkX>696*9&U$1fF`W#I6iLy$+CGGN*xgVxD*-AfF{KKbj+vX)-(B|O`!4C~7y zp{$_*3IUTYzDh7$X1XUPZWHU}<{TF;+j74VA zHx%<_L@x}!q_{8~Sr-yYdeE_e&!9@0@hbI|B5))RxUaJlC$eZ-vZqIZip$jh*m7 zW+Db9N#+9}09%>y2+~&XR%2L9kOi7w!4%=*WZ?w&GlO!AQanP`uufTxMVbsSM!{Zx z4!WQQAI*l8vqd8MSc%!FU$WL!ygNCZ3wt5@oDEszLu>&BuX^|BOeUC4KR5qBJiI{(kuph_=uALF`WxG z`dJiMG|3rGrd+H1k@Y#QQ^TXet)pH8F z(X7PPYxdb?dO3Z8O&m8IpD5w4xQ)N_+(~=)m4R4$<{pi36 z_8MlhY?{vMH8<1Q?LaaeD+S1SW{<=Rmj1wEr3pf`i?vZ>+EZ^lXx}uV0qAq$>_2vt zwJS;c?)Ry??H`$RA>n6Jwfi22*3A*ZnY9`4h%o#&nzse)4VkwO^^BOP3i^d&S6FQ7 zD-m7rVDMB8pBID`eDs?Qi5`YPx3>zj@*cnPZ(P=Q?YU0P%+j^YlFtyI`bUM%v`~>w z6|$gs^xjP17nND~n<(cZ7kUirViNAB4K1O&WrWt9j-|~Mnp_?Gr@)@{6z_i!7618RO6=z5o z=Yw?-5V2Nk+yAuHz#DWK)ckUPDMoBQ>^-jAx_$fZ=?#VPU2~4yE4WsZT+Sdu)B8a= zb)KzSNEgJ8ck#`lw)y52xVMj8XLQ9N0kX{X1yvEqcnMiRJ#-q^?REO8={;#&>Wfp~ z+P667xd6u@P|w6ilJTXywDn%-!7T^@g13qG4XrUNf=A;pS9&n^5E@dzh=u#?K^L%_ z5TGwT-8<&D;K>O#LAAaW{o_MtuC^ULB%RI@AbLW6r!Ww{1_WhQ&lKn9&z~DD8Jbw* z;FR!Q-*|xAJzYL4VelJx!7HTHbY=OA8+K>sB`4``^GziAt`yUfwly^s<5tNEy4i*d zwb8EbpL&FzciZy;`S=0Qv>0AEZ4o|HecXaX1MP5&ii-@GSbwSob+F9UGgm)CyAv(4 zLwMnfC6Ez4=QFA}PSW5`|CT>UgfxuBr8o z`W2s!Zets35`XPlP2QPh%lhx^{V|z(t`YZx5oZ0N?KZuvRyB*dQL^DenF++oI0Qg| zCR3L!>q8=#OO-E!Sj0;i#QHgG=aVRcMCtnZ{u{Pu9(!ptcY$KIdu7DaYGfiV`<0fV zsiKwOHL;np1&3x+?0bWkUel$Jr60e&Z+MfE%wd80*Aowo+BQ~y+=7Dtr@0psF1~(e z*x0q}chvUv|7)Jz+Sy`!+I#k&FQ|gWnrmNPIT3Gi@@v18ohn3n)faTYviOd%K`~*0 zwcnVY-Q9L{yDL<7uFT=_Id-5A`u^>;mC%y{p5bN#oXXZ3Es$kuMw2z)68_vKN^6|I zp&@q{T^ykYS|nCIN2gj*1;P?pZ5>_Af7@bGDa*Kl&jsP&9{|U1gIBagt+mtACAGY5 zPR2>PVtUTDc@r}0%&L9LPMz6PkK7lZGu{$TL)~I79f%2#}$E>oK z_B&nIg$tE{lCL+8Giw}z4M*w%{kg^ldWV~RG3a>QW7#sG%2wFRaK{#+R*1>gmD~+P zD8cXc?d?<7z!4)C&49HD6yJx_y=GjP;t zDht2y5bxweb)66Kp|}B`97ZNXvABR64Y7OQ4cH(welpDj%E#aO{uxp=fLKBd*n*yn z9zywi6?+KyfG|+9K>5YeGi_y1?p;quRI{e`9~w1lR&BrjfpiaTg)u^X$m%_&1xR!TNX-}1AIG{Aw>jLIa_ZVjBDw` zO}H^>VPRwB9Pk*JQoW%;5+^0@hVZQKR)9f^hX*kchqIeaPY=CW1EkFV+BR_)9Wbk=W!JtvwMIgO{)JIgG;JtAkcPj^hWLJ>;=R{YW z1$7dA7(+w7&ZL)#-UD~W>)a`$C+C@md@wR(a z2jhi@RP9GIIUwJXk*TX085`9!x@h+}J0So@k$DwyV*|JKF)vf7yAOS_go@P>o$dtHW_~r+pNKAy_MDK|IwX zj?F`JE>3>8!>W8V5U)*zRh3TC#I7j!p;H_?z6$-@4$JwWv6`}LOqj|;JEL1oDueOV-fQ57NZ+p3}d-i;4yFTEB#Aty`EMC)wv zS&z5PbJqbcK6X;wypBm*M+oD_5A1q`;VDX+R9`E9%CW&K{11t?kl?afi_DV z6-6Y--Y+mr_cEZ5h-nmcv^+9ph3OONdsF}N-fEdmm+w%oIxB+^(8(}N)`Y6IEWA&> zfoXWdysQOn-|eY;ex;ZTrw;yu*If4Q{(DD*Xk2K+;}`cvDy7v)z4Q7SLedTMQHU2% zSHV{*GmlWlM?UMz_-)VJomvTEE+?Ei{0%T)58?|M{zs(9Gl@cG`zPix=Psi?6t@3U z2NYkA2tt&FYwvZiz8{U3hs7lW8eA&;7(ZDNHq%s5XPW!hkz|ESh1+-Loa+z$@{Iu( zQ?1x}SnpyG!2l>s%*5k!Mu|xkVkha#2{y9bib}mT^b8d`waOL#Gsm#08u+9HDZxay zZgqlWUF$nA5KR2WvDI-G(v{1{1m0QDUv>aeZN0)(2#`eO*$Tb&2g!R^SQmqahRBn` z>M6cv>5!F1u6=cNb!WXtEP;LGtd`-6c5?xvN30(LJO-o3&hfHm+mCQ2*{7ug)54Ww zr?RWM)>S_p9JjccvQ!MDd{VavCN=8&aIQ;C8Uwz-C10b&6vq@1c%X#=@Ap7c;hY?v zY#x8-3l<+aMaQ`(tT?)+8P%H-4~jyMf{A&cR*20=F^8ufjp@;xdaeWSU>?wE&ekho zS2$vY-nDzVe&VoABXW+`%HDfK#u;(S=3{II6%)gA9x<~olvuJ>OICz{K%>9hfkZ=58+}aKm?I`0TMRc!F9tQwEcJW}GQ>5bdgl)rIB@E|n?`}T zg%`cVqSG?bwvD&Jn0K8la~D88r*`e})nj*mrQX-UA%6X9$_Cu|^zWThb+7!K|1Gs9 z(L(h!?WhQ31ZEJywAH$=jt0Z)reK+^@}=VIJd+3d0}>vmH|RP!NHMT=cG;y)rpO5I)2a2Qgj~sV4*w)f`V3iIxxd z_S;>XkzWy}P>CJX>J+MD8yvU{D8-NF8B_zfOEwS4`0R(p@6b@*Snd({s>W8ILkLj6 z9N+|`NEM)@}0>s=b>pJ&1vFR&eD>DDRH%XajcI_zIq{AavoZzJtEPx+{D6sNm@M%t1C-taBM^luDU&CH6vWDYJZdZXViM?hKvq3$i1<-T0-|+!E;t zp|8b`mEKL|e~P7&->i<@cjtS-QVAW%Z%ZJOicA8V2Z=Zw7T_I$$7d(gsFiL%64rT> zqSd|^1$ri~25kZso|;b6=L?ic2#2jMyP!3RC>jVJMr%0?>;_W9pr980);s`K6D(TeFq108{=BY zW)l=pgwqJag^wRUUT?o~8Z{ArEe!3#vHPq(hG069u{@NDtzku%hfnC21@+;vd~;69 zw0PAFa6&oq01&LOn&55BjCKEYsMdyWBupMjQW_)d65w8@ADx=J*SK@%x+qJ9=!F@O zHWFA-8eTbqlz}V)Qo^h6VynPb-^4QfO~r9dz)H>?OdZamH-TuHZnqkLlgl)CZCTg2 zAdNwj*X_{;$QAPv&p| ze3g@WOPb+j6UQz?B(rRF-SaM$L?lkd-Yu>fj$60x&*$Uawv7#c7d!`8lM+F|1Zq=8Cz`*^83`b?x@iyc2R3L;iWOL*gMPNBf4X;RVQ`BVya3V3**_g2jGTfHx@c&(V0-50Q}WQDA~$-vcLaNs zBS`NoB5l68j9EDC9vlBs;Hu3Z34^cRz8y!w0{-xln~-WTAH{NXOpFt&5P=WSWq&zi zI(~4#dN0MsN1U{>Oa-q|rfg`;NDWD=Cakj&2O%mghzt#o7GC-Q<;dUoq4?8%AtQ*o z0D{)1YI&2%HUJi^Hnybu)1hhc8dc0~`X761>gh{-aO&sf;- zIxcRaw#hrS=6{pJk}>F1;K!?{Wbd4&VBOezg+`ltwLQ|SzBR$7b3bIH5amD@LTSSQ zimHh&?pwkx@`wCX*b-leVHz(%H2_#QXp9?XCR_;xMpfBe8+xv;=Ar&$Xg`1A0kay# z6R<-O&Cc58%L`-ZPfsj)Hbg|T3|iV=d}vuWrR=P!QX`T!EK-3i*gS;rnNY74LvnBDx#hxv(Ckr z^U89l@qB+wx-J8|5&6YU&h%k!)NPxb4#N`{-C(X0Yh;k~*gH_CMI+*ibt7#H^iORo zEsbsRAMRGixN*1&6IEhnMqJ|E#-oL-Ez?^+D51_rO3(Hu*O3QR_D(byfTR_buOWC4 zwR!iP%KR5j@CdlW?3{0x4y=HuEr83`tcgNcVN*`ZL}ve%30>AbMRIdxvKB_036Z%F z{RANyN`kuyyJ(S1(q#6qWM%1kV)B}?>%kJs9)uRzD)j8!ov-f{y0H&sF0{=#07>a0 zNO44~+!u#pI)tZ%)@vEE9%S-i|%{c@708mCL?Z0i?wsjLQA``1T;Nn{( zRTB~ts7~pK*`+nRC)MpR>bf*Tn@4XP8-Is|h(%YW@qT!Jq&aP=wA?_o;=KrEQ+lzt z+Tiu;)-j`grt19I_|W=gfbh!GyDO&Kc8mD7h*W!JX0JqxZ(JzhKsG(3x9J8ZE+TYM zstOs3?~m4x?a;pJX0I7gp%QG0gA$Q_EJ9C(u7x0}gmIl6Jd@sei`AD(@q z`00T(t+19JocvPvtf}21DBtzX?^K`Zd~RT814YWo{kh3;ixwr;jG5m2Ua9#z{e-{T zst=iF__o{LK}%<^Y1%nOuFe@53RQyro2BxP6K5`Hl1nsnyRdxI=;%x9@bS2sFV z{qNSGwt=5%k5=MqK$V%Zbytl&rcI}YEyEvS+9rPmXM`Tw@;d{ZwCd zX3YiZY{JaNWAj5K{lmAX=Mvi~#^yp}VIbOxs^@=3YT6Jgn6T+nx-sU|hezQ&KM9&E z7L^KRNSlir@A&<>zOoHxj!+LHe$R{DNI55>?520}+$0{a^*xQvN#)S)R)An&NC)_+ zhkNY?q13HDHXdfyw$^ERVGoaM^j53H1JUnFB- zUS{w7F!3S`lfCtz?FITqTP!0IB|5|5db%ce5}&r#Nw0$eWTf_K(Q%lhO4)S0z6denNu(Uo~ab@t9En7x~uJ{Lr6we-9Y=pD`{AY9n zZPF8ciPJRQP21&Znrz0#=jI?D2!#WStzwY6Hk`fLwfrM0%vkD!``bpCon z?TI_~pD*$Vg-3$15>5mG17}*i_hqIG#t9Iyu4iZ4!$(Ijk|!5~6HfsQ?Z-+0Q9b~w z_nhS$dM_liy{*pC{g5}y@j((F2pA&U;EF1ym<3?tc~cK1Eb5eh@u>KEcby0T(m1Ur zdGadeL=*_dOt@bxr$4_5WG{o5q)f56r#k>!l>vKk_2&Z>kue&K({=41SpQM&O7Y;Q z&=H3dKHuTzi+1Rk)HNm0-aXag@T^-4R^5aJRT;9;#&Y{|$gI?}{a+^N2O>jo_|nV# zQ{$RXUY6CPm7a>>2Q5aA#rF^4L=NkbL=S~RstZO04KBQRM#If7k$n^(Iq4a~MI1fsr)pkMSO*L*(Ho38a{n7cI1E~B zoO;Ck=v48vLsvsxAVTI(HwG=tua~!ei`w2k1~2U`t3Sh5sV3ZLJ>x$M5MFC1Uc9zr z+*p&=Rrb-0b#TH2eV|C#^neK4g5&d=c#ire58aG&RJiMpq&BZYpp#jkeO;ZAdi)+4dN=#o{RTG+lu|8BMOMfNBZ4?yB7AapTB=ep^c)KOYn`-d@-h^6CA zUN~O0ZZU6%SABGM#?=ecfkU${3}og+=B=O>ai^emCT}%3*4yRI6#X6Du zC)1iN+5*9lX&_m(M@7Z$q!$557_s*K2wZB(?ch&;*uSvYM)Askkd8E9g6%V@P8}<| zE2szDK|(#x*;Xa%w|=eo8VPMmczMMQ5`P4;wAj^v6RVgCA8MBr{Cr?S`MU>4gSdPh zrmFvqCyoDy4^FMOs@)=2f4aoSAvg2ee*a<{W;8y}VH@9%DT*~6^c^E9fY-I+pjS& z;r)=KULT^wg>_B-80sh*KIg5*9)ahu-ubd>PvVP0k)Sf4qyhdU3&faWYN?}hlfQD& z^BPT_6-5z^ZxLGX0TntGQ(3{b^Ql+OHf{H3jL1l|-f5XQYQvMUr!rlMhdYiJGTFwy#ml8E_?~_$WXd3J8BHdo~Q#e9&jWmTC+1UUIumP zoX`;N2B7D4;3BoySh`7BmB+sZV5~|-#UwdMF@|A{z%;PFd;t@)1vV~F3TM=fN9m~3ZPAwwu1o=0`m@CEg%Q7?Uy zfW$5>RiF7djn4KXMPEBH>Fy?E-i*r4h4Q0)^nubyrNy4S_hNo$MjU8RRK0VXBClR7 zi@iK9D7SL}#3DuI@iTMX!9eelZa}v{E0WIrS*-i!?c1nN*E;9iDTUcd)zurI-Z<0L z{*n8#WpPAb5pD7(#m^e2Vvum8Y1iT`_T1ZEfbO-F?xRHU#*1N>lN$d7t{I6b{O+V0PWe4hi5I1vUV?4V4 z3d(9qt%rQ-MNv_hp7AQ;DiFIYVHIYB7yP;$kWD~mk}`o1w?^?hk_XUt7v%6AG!^_b z0<{RwXxG`Fqr{-H@62mIdWxpcA4cw4LHW0DqQYvB)n-xub|ymxUL38JQ7*PBl&kH0 zkjkYp-^`9Tgtp8+M3LA5hSWuwjIMyoE+t{@*}+X~cGopFld}=$(cwCk-}wjCQ|lVj zAg=kACB<=(d)SNNCJj?Kr>e>h)4V-x-CKt5v!gYCU3mZ5v*FJQcF>6snW*l>bA*SE zSy`z04M$6nk}%3ia^{tYDiv9M06ixF&O?tL4;J)?^U(HTFv;Nkx_x&9ZiMQ>YwfM# z6_OTTy1;9_PJ#%8X3eFin6R>QZ2zfTx3-U;-MW3dIRjz9-_Sv8Z-|(Uw7@BSt!(k{ zLfUs}P|JYo7a+45sBG!S?*_YA$rLEYG{e^a+6h^pxG)|(HsrKvQ_oF5qz1inXHv%( z)zT1;CY8h@vJm<7`kLuO7xA-&{Kyu)r z=&ov+P(Mx8*BB_gYpUc8K%%hbGxW{3{1+SC>kF=eicbC*;h8f@FIGh}U%z`NMljN29i85~qQIb-Ano&MPuQ`Iew8_m5Ps_{T1c#=xXIZ& zbP)QjOZaBtA(_uqw46W2X@nc650J0p*x@UVkX>TOA<40r6vq(n75cVExSh%1JzW(F zi|>LUVF%_g(QrTW2VeIbJb19`7xQ;QVey_*yMDH6+1#nHg|g+YX}U>lmKYK0xi_)l zsp+?b7)E^{vsr}V%6Icl;b8*IEi-WtSx}lRuGl`?@i-VA!>4UQidfr_3Hu)2bLDOl zDc~Hw09#Q{$RGicsxb4`p#2s7;w5$Q5z#ctu2x!6S&awOX5ldlE)VF9WddRGarJ9Q zfYRz^S8i|H&z~Rh^5e%_5~tyZfi~lz3Q~377y90t|I1M$V6TZs10PwW>Gf=>K}%K9 z`o=BNu-)nrRnIBs$IHYt-+-ulhJy!dS6t}bZ9*3d?boFxC5#&%YCq_Js5_d;LcnKW^8VlySgEnu@(%SpbaqT+V zhX}*t2H7?vtH+R);A06JXxt(XbUnd`g^u40y@qI}dC9M^u;?Ch%M?`|dP`R7bUVxO zUsdRf*wJIg*EB&{+IFC6)F0cpyOb}OB6MYC^r%w+xR6X-&+Sf2}5c6xhp4VCxtf{I< zdETQ;peJv*2=kRg+z}I+he4T&UR3sRi?WtXsE4 zb2sy3Ka0?73+D#tx2)>fZbMl~E66i(ZNWOMA05^3J7uxnHM^Xjks4E&>>>5iKJ3tW z74>xZaN?<$!twUR6z<&c1w3;xFgsJYL7z9P{M6kjt34#9)`I>q{c|4{`|YmVfw-}7 zwpZB12Uo9l$ypi!{EOkiYFwnD_WLi0rF=Dmf#xs_54{XX53R8?Hv`py_Q{KxzPEUu z#>6zSH^+ESEas3cB|%k)w-dM}s2J&OG(8lnsxNAPWkR6j@3q-oBd#k7@6w(%8#2Ny zq+TwC_d@HL$UPQ@Jv=%6x3E{?na6l|4dW+p4%5*=+b$6wz#R-i!E)kzCsXe9Xrq8TomV|LX|b?qCU!C6y9(wYR(ibu?-b7P zjp8MqE4VV#8?WDLm>GUW(9uOL|E}zd$~k7ZH(fn2)(o$4Klm5o$WFt77P$do+S7~R zzI;CQvr2CNWJy(c?zn%a(OMTNR&N3hsh}d;o8fyn)8fP&gXLqL66$!F>15Vy6VFWk zLnN~et`5~1-n3PKlC=QM^IE$#Ykf+sW{?nTaX}H+V@IZEyjR|~Qp=j@FOogT-*L>H zQ9O@d;jqv(P49rA@}>rBn*SJl1Gxc|uIdnDQ%DUXP^4}Vjb%j$M+uFV_(YJ==h1q6 z`^MZ`Nc|;&Yv2OO)F8Mya}3i@Vjk_v@kDMRmADKjfK&H#E~uSC8Tq;>b@HL^wh4`> zP?(ebP7kU1@V?tAru^FEt*Q*U{femhSZSs{w%zncm1n^2BoOkp4E{mQ@To7F{CNw7 zzW6}#L1lrYV1jfWzh`;mjJlbfJudlB0Wb{S0&*+nh20HW*0OR#f~K+BwQ~P^$5JvC znp=!C8^dAv1ApIi2H(g`Yt42hxWW8F`WpbS!-b4b4=%fw%A>Obkd zEqWaa8Vc)q5SBU53&iXJ!L=;EYS+I}o?8Ed6LvSaL|0|k`lRKgHFVN850|Ue^Vd~d zXuG?And2)&mwfkdP`j;Kz4v|k!5CQ};wZk9(fM`jo1yt{@PcQZlNM{6nNZwuDhg`P zY}cCJ@%P{6 z*yyC<0f)bm1+R=QUyn72)IsD_yIF-yejvweI<%viOxCC`pXM$Ob->iXZNqYGVw}Ui zoEw0gg+r^-sC@jt#8$qa1E})ip2BR?b->Uvxb#hH={WVsMCSGyFI3x`U1KGrQeNOH z+irPQ&^?Xp7|l=uE+d1WMJYbA!|qF=V({Q;BeE$|qmQky(+J4Q&OSJ#i6gx-ej6%` z6YNVcjcAFLd!D(z0nH%`=o*I1x~wjGWJpCTKu}@Z#Tk(=hz;mYz#*X}sam0DuEO3B z%~Xf9S_H#l`}8xVqRO*BYJ)vn7!MfW0$z@`zr#OtUojCfeGbb9aGA3wv`a3I!g{hA=P>x_cJlc1q#Ovr?`Y@6HcQgbyNec=3kr6F_R1cQ zO&yHW{TSq9B@AYn6D5yuV^MurrUom@masLM^S;v&S#4j^sR;MTsEW$H>8MAjfe%dV z%B1a*wIjn!L%P%kwF(a_L0|gIQPE?{;mK(2TD;iZ=r>`ZQL>6UJjgpT>d&BK zbpuZ8ik>jCVCmu9=$V-b)Q3D83hYfzT?VvkMk+DHToYby(&@zx7t0ytk-2JYpOjCQ z@>W26U<$TnY|jdW*J1zg9wvfCBZ`;RB>>>C6_KYI+I^ya`d?r#PV( zS1qBeY^G_cnpA7vFa=>Lux(@ApoQu{b%rfGlOCS4aBsy|>BIr5+Nuwf_?ST{NHZ?g z*zJe=?xcCGtE}%NH2=O~yAbjB@%n2q5>%#FW~jdn0C)?fjg$1T;E!U99hDJ~9H1mgQiI=n0%4s7Y+B~jGhMc7ZbvT$PX9*A+BuY0DMGH^c> z9&CbzMI)lJu;i&THtAVBEZwm-&D*Bwik4RI&oI#}MssV6`jnm@U~C*(f4r1l<-1xQ zomy$0eg^YZUbIZ$Q_(TsZo+ILRwWVfe$bv8S18Gt)mdM)pc za~JV{xZz&q$JL`ZYFB;i+{JuF(p9=^@kd2i!HYI1_1F~?bAA0x#&&;ptb0DN1uow& zIKWsZsd;dm0qCc>S8b6vuN1w&vW@>0&+BHCk7|^; zp%3f@rC|{-nex3KGgrw^W|LqUEO3C$W+ARXl?WEIe4&`l23M_dL_JD zPEm-?1gvo*BUP%iLcPTRtDq2$$2s)l4u8#$;S+V)7GbP{3B{hp`>#UgXvNy#py;VbUV{b6A(dmu$RJEl1`W#VLx*8i{f^@VF7_K7JlVhk?IM8} zsz7F-Hx<-l&Lil!Mw3h`On5PU(#};7Dez3%R)cY8l78$`>_2#RRV5Cup?Q~`}R{h(&|sXZK0wxR^Vy;c`-+F6nce89zWgND)fX7;V` zn>qpM%8ERurq0i`)R~u=0z4{wWPimo8lhS>J=o_0^W9PnjHhuaNzE>(o;& zXd3UD*yue1b4qLE2fu#L{gEDU@9p^2Bg3DdfnRpfCGi?|PO(YnZ_&-Tvlm>284$Z* zYer#?f5`8q7Q=_-Hible{qaMzYv^7x<{ZpD2_7dBSSDdTFq!}(V(zUW8aP=&i`>4I z4{Dd3UXg8KlEl$ST3P-nlUbWb?-x*p?kErBp6Y|O0pYZuVy}QdrMKB2t969ps z4kju7=%EOJW)1!)08Q(CWq(mXh z3rN!y?Bq~!Hq^{GV>ZM)%p;V=pFm(==r(z7Alr+!7AUn(RDOCklKQ-85fND{SlfGn*fXZ8O&Ogl$sFk-g4itnxwzI=vJF_GAuOI6{y~d2XFJVbe z_QgYKJUta2U%F=Ha(?|>iWhXM>h|^z71<4PnyA+I4UWSuLFQv9EU0s-d1w1yT|U3R z+N@IvBNmK?2WndKL|f_k&A_5=XS2#Pr3RoEKGu7Fzwh?rNy8jvpZzBe6~CE5idedG zeC-r)()w|OGRFL>ltheK`lnip@HWWwxO0d1z2~k=m&YD)6b%67_h-xowSXG^Ia?rp z$UZQM*L-{xap)(|$OI@KFbjoY*NJhmxbXmZR?hkCc9*#bGS|eh7mySlMCQCZIl>f) zZ#5+KE81E*u#%v^!nFy%BVS2ngm)fAbN%6UX3@@?Ab&@|^Gfu@WB>}(d?sqdb#K6c z*96_Q>$mYbRJ?~CV=GGpI(OO{HvU`>nWyzH9cW8!f2u@N$h)n$j0o+=3vN4j*&P4H zs|Xp6|oB_g}<>7~bk5=mGzYoUJ?U%}Z}zr;1eYI|>W z^3_4oZ0ZI3(PDs0>tVO&Yp=jIO{P~lQq*hXt3amzWd`+#)kTp)UF?v}W)VOG* zgF49h?9XQnaP%$yE9AMw3@6fPZg66T6d6bXIT8ps4QvL=k{Et=c8ff zp$F|WCnNCP#r+kRX58{_gANEw2uLoomS@inrV*2Uc9dFpfQTjl4OA|o9kd%lu7L0g z%L!L6*Ojg&^7C#0=0x5x(v0r2!i%K|L1Lk@SNjO*E2oz|ABl-VQz{w%2{UPzqId0T z1>DPxcgcR|CtV67m4Xiw^rUk_1~2|`)ch0KOY^t`%GC4NIR>FDhX#}t-865sYHjn> zo`rdhX#MZYziz0X>4%0zTzM7rr7k5eJgBuq2?2fdhG#4*n0YfQ=Y1bB$P4|_Kz3B~ zXZVSab<6!fZ=Qbi4cj^1a@%}vXHm!vWWP`6Da)xjBkDUjs&9kR@6lp)P*LR$B_)b* z)s!`JXl9Luq$tL5QCP{LKtd0}xke=&gLQXJ2bwx22v6w5hwJ2f<)~%KhK7>Jdpe6u zwRUuw)q)m3lc7EtB}s82{3JlpH1G!F^;^btHIaLS*_plSwnM)jeRjR3r^vRnv>KK; zEjBgWZqehaho`5kv6IS33XqzrHRN-kbcx#7m~`=K{*j@fyg+A-#)f=#aVr(C5Ud<= zzuLuXb~$vZ&j8kHOn?%zOjKuxf@%r`CX10SIy!j2;)nblapNGCSluyVI!Hr6K_7a( zd>TsepQ`HF-?|C5mgI2+XxRBpSlRJ9A|0N4WQRw#W^VJaP+HH94^qXeckjgViB~-S zzPVk9u~fOg{bpIMU;0aFrHhPB+yoh(r~i=hfTB%SO$Kb=KKAHTQI~S<@hpv;*MbZ$ z+faZYvtXzpg{~Jh#aR%mQ%Y}yE$u*HWnl?LD_E4}?_UD|kcH4-d65ql-JKKjZ7$B+ zoX{$Xq+iVKCp)ZhY`hE@^+DR(ZGaKp$F{CbB3p*N^) zIlVTM9vSMH_;*>NkCMEF?l^-c_1bP@n1nwqb%x`HpPMOWh2LVOjx71AU7<0VW^6Oc zeq7H219~z*)y8GRw)Y{LCq9&v1nk}WEUMGuZt_KR9nO5ptee73RM*rT!zpvF^SkLd z+Af`sk0^hCt{~5$=F-MxLB?w!azsCbDuXc(S)d0Rdw4{;FkiGOBH@?vl_)4oH?m4S zwtj(~fO>K6v%w}=aPW4?{TQqFg zP|_B$39XqbpR9prZUW*X{ug8F0#oOpUEs(_$x8vYiHUWWYsUYPb2lAFo2*Ot1U%`x zt^kFY7%s{D2A{Ft+{^W-OVmmZ3b8n|q-S=Nv4Z1OZ}l`1)@F)7R>oK%vAlaR?M1o``X1CK@SD*bx`S)? zbs39~bVeY#fOj{ z(|0>jhVpOmy>ke7ww~MACN2Z?aE7$rk;T=7%DaSgDePd|#m5*5QO+wlbME~4OJ*=^0sd06scnd;b$D zDYC^3OjTy_#ODc$5XMn_tRG(+2vCkxO^onAoc6#XSHzss0wJ@r{WxgAf;@3;V$HdW ztPL)-N%%g{WTSS7tEQV9T^^$jVweDK2@o@N<@5fUfJ4%`Gw1ty9%W@%{7nH8=j09O zc`828+tFy{U5u4bQ<8)7R|MDHxZm^RlL(H`T~=%nJ9`eDfz$1rB};nM+W(rd_24b$ zK*=2u@vqVv8U?NuM^cIrqK~rtM-N8GZlEOc>AlKlU_IdTX~U@C!y9E%<~1~B;PmbQ zhvIRC~!( zcNfB#v|NN0fxVpPPnoYtWtTcHC_a8(YO6KK>6uGz?1PZvZ;ak?0`Oi5_lBjLjEj9^PYC0xF&7E0q#G$1~heB+t(jnt}xCb$lOps6AltZ0hyCP51xrgn}g_aLV=Ft{i)yFqc zKEgAr3(sC(>oU?yo-)=@b#Wk}P{YxB-TBN;{Q4pw8yRLWTGq?{*q$E#X-w@i!(J~W zw=mP!Tv_oI9vcuQto6~vEZ#zKz%l;&x^?f;tbg#EYn?%9+ryijG4pSfiE-1$gjV4a zgir|)1jU++w&O4JY@?RiLi0$BV#E~Xhmbm`tz$-q@a5-$z(f4WFd{+t*Epw_39EC6 z+KH7lhReY;fkjO@Q!d8u&X}riG}!QfnKYJ&HqJBgY0$9YjRSfwAMMi2`EBTauW6&H zbF+AfQXBIL5>WqrTU7(Fu&G>T3m~(|9D8;2N{fpb3Vul#5YCV>0gi%eo?;NmlGh+B z_r$M(83agLkoz^nKLlFhHULHcmG5|~T#FtJSm2^1&ewe_Bmxe*D|#w!7Y~OgLaVabn1*WBR0yoV3|M#55@x{2|6y59-91eEis~WJFZi^w9$O@+#Dt zG|`8(P3>C7C(=Pu$1Er9zO847r7Xv2I{minpM0adw6_TymfJUX>K`M3=V#NMj|H*3{C9^TWT+lnKs5it*_{Y zk)7~QiYad?4P~_s!&Tdv-Hp@mxEVXr(Z)}xv7ZoUL0h&oR6!z}f5DZFn)Rwa7Ker{ z7(91wA6Rr^F^=0dP;F(A@1ag`dh7f`AFN&M{CBe zEw8k3nmaet?aoy7^{>SkdaYG};wr8-0}g$RmNDPYvzQd6n4(vw>1_;;YPmdJJ?GqU ztaW6U&N2)`P;&}W$VazaSxs@+3h6UL7vg~enirAVlfGj3AXBhMhyJfiOO^bf{Hj0I z%=1BD!YbFGeV>PZ^P89dzTRq;LR)uHDC$vRbM%3FMFh;=l1VHmry2@gBN7-CsV-*1XQ0{}wG<@3{o^4%8$0Hv8N|83%)Rqm6n2EGd8;hjTuL4qn;IsV6Hu$i$h18CX_T*}JUv-+XP&wyMe_cYT|_gs)OrIOP1g zo|FgDfaB@(rL5FWCclAoi|Y(y;#(GEGHu2+3jLsnv>k8e#0Q#&M+ONhi}XdFH0a(f z)U{Z$s$Sp`rUTv%2CE^N)Ar)Jf3Mjk*4;Gi<=doYkZkXz z&w0r`5lWIkd+^<(h?a*{^wg*nV6$Yu)bLhx`359)-N$v-l@8m0kq+gPRO{TJ>+CMz zsHQ2`z2k761?}Wlu<4G?GlIyOOywk4dFcAsu5x zCE^&$JXD6zKpG51i3pj;Bce!2hA2bkbjvYI#?bKp>~o&A{_A;OYrX4z&x_pm@Av)g zeeG*s``UN;A>!B++xgD{LBBi}X2NE+Cxh)JJ;1R{3!6DHFeVt-@p*thlOSqm2CZ5@ zBv5Nur=`QgzGmz(^XwHWvMYoe2wBoF@21kM@gBKU$O3e%T!Kg^`Rxv!$qt7#&t^(F7hT`SKgK;r z@j7RbaH{5Q8rCb3DH1|*Z2uvt^ShwiZZmNCRWxzT8P!8U9anoZ-m)&3R#pnZhW@%~ z*OE4PbgoCN#+0S9V2KI4`t!Z#ckOyqvtMSYfF^zYHO{Hd#=l~7X2xFs!?y68O)^ac zEeI#D)2I!46sG1})EqoG`CnIcugmZm%sspt5az1eENO==ShWrPE!jgTVGh33Uno2- z>5kKs5oBfz-ngy;oWE(7ZP=oPjIha4(yd#!4n{j3)i}1%_acz$`RJzUYLW2 zE-+)iN16_JR9GK4%G7f=q4=nWY^l!6_TS(?u#QN6|Jl9WqzKWB?HW zxQaoutCnm1+vyJjQyE2`&8+9NICg|lGteiGlG&4)Gv6uRiP0K|gN63OO7IBG;cS>h z;$CcEy9o%cQt9Ko0S&dvIpwn-^Ct)o|LMh*VSaiDFH32uQ2JTYT<_Y&Z2R`@qX4f0 z%V2rp*;$|>he+e1I_weB_Uv8}(joAAiZ??JYZE=KJg|@m$g4&_|?b@`ew)8oXYPoq=Hr zM_Tm(rFQH*OK=MsLQL{LD5h}8V{_WW>Qx&Mg zPQ=ButB5V?8-3o!<<}uFTh9kELyanTP|0JXa1q;g(ULM7C^J2jk}zfA$;oZSIRU6p z^4>reTd;qJ(NeEYzFWMfaWN9x*>2ku?K$!b#7)jh3nL?y$_ZMXP6wcPuk_3N;`NeC zzv9U7P2;@^3kuwV|3=bMmm)T1;u+{<)vt*&XlL#4o^bHr?J7JsQ794Q;NuVZ{`PLc zrrxTQfycWS8V~q!{VtvSwSBXKsw!i+d?KZnWwQ8S;S2}D?(tc%o9QiNqz;gEtYfxW z+iKtcEZn`WRp)ry6%4@kd32CUzhr7Mz<(M6O@$9SwChLh`Gs@nXvn%N2F<(CyY7?O zxg9b>?o$!V;iLo05hV+3O`3>2G* z3L@j=Idi1zBpuAbT`XfG{$(|f?!3c_Jco$oEj56ct zwxrN(j*={<+R?CMABT-?T0Ioh2qV;xym*=7Zeb)wN)#tZnrhA)^n_zHB<8W=?wGEOLO+ud_C9!u2; zj4QP~zmXthEdxsi+~TG= z_lp${Z0z*Frm6He83;*iTAN!y3c&$8S>w^KUx;-;{P@FuC{9+motnJ=s@u8mIvMIK zyX=^#V~uit(CR1;R5>)DmG9rLhsESal)V4>H#q&`R*f9#RdLa|{L#8S=yp+5l z*<{IWLscLfQ_11g!^0BbqiKYhQXA%vbzX`ib1uFkj^m7)v5SnLApKnUdd3{L5H^TB z{l(;sOH)>}9^WVW)hgl=-Yn#Q=$V>6?B4iv6IHt4H@bb=M3!SyF2`!V-xeR=&F_YR zXf!=N6L&li>$I2G^li-Tq&W($_Mb6ehLu#3x|z0%wT=)Ql`|7oe_e`X1B4H15{eQY zP4(#wS>Ysu#p0<7w=J{5WJA8^XZGuf?J31zOH#0`v4$2_3u((p`W|?FLNA`%9I@S( z-Zcu=L=0a*TlX0LN_3=YmiD>10EvOxMfi@XKT^KRJUjF}?Zb{8JK8LowW>=`yAIE& zB0ux|ZC=e6De97zvG1H>jO|I8@9_wkL9-wf+$Dy2^cX9-xz@|urj?eLZ=`>Xhj+6s zRr>*PUy!8WkKu-sk}zs@)ift_B?DEv^zYx_DW(HWq=hPS?O)F8)W>$nkeA=Hgs$sj z8m=`;7D42m5B<^7sZ+}6gPhoy$3brvU^GqY=i!4JO!8lC{%ZiooR$cFInEsx+dDWY z@zQ2uS$3MFd9YyVLDBvDqnnK|)4SrhCjGAGy**(!=*7&#{{cR62(4ckT!(#0<+;&W zG|HEf8NY2QJ1piqwtAWLsVXWWLcUJn+~DBg6Ak;Gz12C~;`I2#t>SjH z;*Fy4q;R9jYz^M4^iy^t`{KJKDn#maXz`Zkk0_kenSqV{A)r0ual>ijE`{JlDeKKt z)7dP(&HH87!GWusKeS0OESfh3p<4gKR}wUlLJH)ZN!z+u`kM)(VP+ zCv>0Gap=gW;%mgHWa;*&!(y=y5Mw@Z9RFzWhA{qfn|05gHA)|Jd_s1d7_~I8x<2g- z5$kbvQ5AHj-$Up87@8chiqDzlN+d(8uis&L#qs}HzHSwDx8ulFwd#Au6@fSqi;U<} z5Y!ZP>{v!}z@ai7hRW`GKwTY5d})$H23}jU__qEH0s*)PiDBpPcmbIV8`XsrMDnA^ z+Q9_ARFgCgC?dB!ouVj>B*Ow9NZX8LJJ6Yub}NO{6#gFhbQIV0!05x*U@0(P@}@pO zKjaamX@O6GOkozjG_|AzOQrl^rf(Elocl+;lAj%Q|Fl{*l1jNJ_4!}3S`rOXNF^3s zN{G%f9}C`)x_!p!zOK{u=7<5(3fg_waycRM80?t|_a$kV#tg{cT;w$13hw6DM&|Ar zv4y#MoF9IM5HQG?xvA0+f7s{4vUYvOpgdA zAS@?o?r6Am(eLlGh(h@c@oj5Thk`|I{>PRmghg+}6_uS`sE8s)hQZxkVRszAe)hH* zGopqDFaU{;)0oU+Tp|rNJDcWpTlC@4AH!AR>4X55a)A3wV6MsId9(q@fM*|QUM+|w z+HKL8!c-JL3-$pIDii1&=q0{y+y=cHh|!|Y_1mQPdlXJKs02mF1_F5rBqT34K`tPB zA-U0S*a!RG5r^b_a&P6=H@ZBjJ+z#~0h-qBww|*(w*1Q8ut}Yczc~;0A&L zc4@96ny8*Ln3s0TH0hvDQIC%LnX_iidbtpsX7;dJSXY9vXtVhs+YZHD=VnnlHAXEW zwskj`jrdpj1O!G|7EU-NFF+p)nSxYWCasU1J}nh&;PDB&3%j8iLz1x3Jx%tHy$Q4> z6Ep_zh!Bl>-?r<4#iR)_Bt*2y`npPgxr6yKKCzjV$yeTEy% z!fjl3jTn)!a27e|GW4Phs>+ZBSsjamzD}yLahOVHjVu-uVu6%OL#J_+exoK$_7XzG zGN1488TXN(KckBE-CGXzDhY!U-Q@|o6 zKZkW#Pd`gEzrsKhCuCQ-JW5JkVYTUQn0D#n7F9m;-hs{MgP6nRBBF@{szY_qHQ%fe z!`Hvx(bl=ikXVfgfsQ|-LSvS}8;T>ejPjAOP*95Th8$KTbt#k!3^}X@W>U3XhuyBM z{!v2BAPS^EVc=X}b+}YqfihZA9D{iWe>{~JxU^tVnrIIB^I|^-p(7L&QDooJ0gOf> zguIS6J!0Lu2H*uBsyZx4|2{Y{u53Lpxm|`g(g&L^bGOIGGq+%@k*#RA_G>MH0d}fR z-zlMGOSgVHDV_whs#)bFjeFEq+n!mH1FjcWk&3=PN~CFxJ}4AM^I6v2BS*`Qojhqm zq$6cP6k$?hD#Z|z;Wv-mNJ6sz4Z~8B%yxb#)2ZQl<6uobmA@TpqUEbVn*d`fC=Z-z z?P=r(6*hP7r{)?tcs4d}*zh?IK-M^-h%o!@`KyPc@pjxx6&-^&Ke07!xAAMO^5KZK z)O`cYUlLuas;ebP@Kj{8NKN%J+60V1TVQm;_J$1r)CDe(DutlT&rqphHF%`yWET8@k zCSOwQnV{{XzYFS_PG^XY2J%bb`o zA2v(>Qdi8fYW79+CUxf0mZI2RzG$Vv>l{y1bkDgm>_P0C|JyyF?-WIN+5OG2278c9 zqcNxeUa^@euwW&alWp70(0Kr!p42!#qp2PIHup1b6OCC1FFM zfsMG6@i|d)A|@`i43MG~vm+Ww*H<;O_^Uy*&nn&KoQwa%vEVUbIi}lzt5SXF|?fY7dGZS|J(~J&7>;8?hZ%*6Tt6k8@ zA#L_ea@BG0HNApw5LsgsM}wIV+0A-(*^eWw^nLyOt{}vdJ~pj>(NS^CUO#PFzho`6 z`Rj>8Ig`6#y45sp>M|?Vj4neSguFEBBsomEyw?B7G`#Q7RLK4rNgae$)biF(e-FO_ z*cD6zzvvW^i4DOCBbRe?9Bt2rW`{tyumPdTMc(4e@?0PU6^*!h2vcciv=9!DtHW?y z+>Uf>wuA^8bPEa>IS}pr&w~(p^7{-Y<=wh9yoRklTX+C{blg~m3Nb_z_M3glADG56 zP+ux3hf#K#ngf0u1N$y7TnHY}ke35uprut6c{FZQ^v6HEOteO|*OSVc_Oob>#L9#a z!7E_Zz0=knjLedov#w|SU!@@p7_ZT{t5jH&qa;;FAou$=;K&Z_dUDxTL&c3uE)cw=A?qr4|$%A zC5~2`D0}ADcQ9PpuKU?Zu35UN zPdiH;q>HkFQl3WrR4ymt|C4x5a|pWRy$@;(DZ{RcNPdF%v}t*a8)0ov%+XD+v3j7L=3=E8M=u z6`h`f-Ud<%d#G2S@i$RtU3iaE!Ol|dJb!)>tWmcAe8?*BHJtw13m2-J-z=!@ihFOO zU+6^91ggYVO#%jOvRC-`xrl=InwxLYg}U=A{t)p6UP}3WrTL3|22Me`qsjpVKukHg z_K)}-)e*?)RzG@0e3c0s;jKCRgoN-;WiyD)6mKUowkos`5@H%5Lqr4~)ro_xb{%sY zU-0PBHW@A8!1`wGD{GabjV3eJbCEbS8d)8; zaoJ`d!!~wyp`CnP+E+H1&cruG#3i`&TT5W+*HS3dm2vw4yIue?L>ntJ72HRhC2eX% z7r6S@VmNF>MU1G1V6!O8RtYdmwNJfC<$;%;Gw+Pdm}uY-2b7A_XTsPN~?%m929w*g+XQb;fd9NErNKB8L0D`LL_;{H6=&clDo^a^LgoUPHy3i0e@ z!%2f~rb8S1f9ipZJJ?kx3@J@7`(7#6YV)3rTW7zyI;5({WzC5xlcz*JJv{Tw%tebb z7B*P8DC5Y5g^`Dzc8*Aje_-6uxj3i1>5gTYnws6(fBZP-!qHcy+6gUR6+Bt?G2!=; zD@$q%8r2Ug5td3XSzKDzyQ4pKOFB1*v~f5R>T5*_Ti z;+B`c=Y?zK5;iJGGZi_IP#dQH#+<77bD6yL3C87aFMO4OvQ&P z=5|oDlE;AY8f{(&%B;I>F6|W!0Y*aIUTc% z0v+4Cqr}*v6-FW({&kRhJ2TTg!T$US(7qa66n9(aW>~Mkclc`NpEWa8_WNyM5`5k_ zJM+VYzszGE1y>~-5zZZw4k>j9jrIuCy1u#N)|Jo4=oqt)GnzoN>&_eTB++{1-mEsU z3&lNAaL@4M9q0!mibbc&XlFhMU3~BW40V(Rg`jTLfG8jLfYB+;yM;e$rLBDlrS$E0 z-#m_ZCeS&k`1WqhsAoDdj$ZO_D<4H(%Gilyen7IXQ5}9>8!dA`fh$6#-c;i<3b6!7+X~N97>-6~PH|6U0)b%5aQ?=yO^7I`QiG_7-X!MB!L(P& z;9nqJub+LL)ZYZXDcs$F29P(5tz23n33L!}4LP#vwxEk%ac!<(z8O_rBH3dB4rG zthb#j-{h@h>JmO)?ZvAS`dA_?5>X_aqzot1Ch8{JJeMz*wf2nr7$HpqZ}1S{?)6us z|IQ|j{QC|*(C`Hx<<>G+G{^n7i~b~n;dU4iLExg(Q>>OTxeVJjno5Ln`4=pdOr7a1h#Dqh;1*PQ|rPL-CQkE&6Z zE$!UVa3CPLWEzCB#Sw&*ZY(Ef*HClg_)uaAX|}En$StnZcM4)6i!0^HsQDMg5s8+J z5%3m_6F+x;xR7E{lq#|T?p}b!pgM1Y4tQnN40;mVcIfztL-9{j(cHbVZk;-zmG*B> z;qU}to7KvzXU^g&W9=pEHRYRY->9rOs-4NO6X*hDqo9g` zwcq;wmPT!AzXd$4Z7$o~IH4BMe?C{%TRP?FmkjQ7I5#x5eW&1%iUDGAO#EI$33+p8^@F;K=whVlJ<-^rFmb${x6TM^&wPx$S-SZ6& z+&$-wY_?^C1`R5(1IcDc6Zx}aW2QG7kfUF7;rw~|3|vc@ z=EW074KAH*>41)xSF1@ez;e1{!PQG=+}$7ZWP-_;q^hUIg@Xn|*Tv(;rGkJ!cTL3} zj3T{xru30%>$FeYrkn@)4;TeqI9VSL!!rhCrGhBN>Rv8;RxeW`H`LJBO_k1=qLJ&P zZv)VUYmA(U2Y+qp{i@zY%DzSKzB!nfHErED)NP*$Dw~w9nR825EgrBdenf|O)Ad7d zBKxET46oIw-L^?bUr|NdB-#|}{Rl;GBIG(p1`uPJuiwQ2KpTn{n<%HBJ25}TI&-=y ztslC_F*Iy_lPp3)Z8$+etf|&s!96I#qbja=vr}uXp#WW|bKwbaRuW%lcReKdM;tE=yMtuO!KL89D-Gr(IfQd+MaH?@6G-4qmxB!ku7Oblu=0~uqU0sXnVBS z8hw(Y^$Ec(VyF9dp<4a=mhAcYx^w`dvz_EU&-068K>nW?w-Bz0XZW8L!?^N5C}Cx0 zh0tE_aZIZ`M$=~(Rd>O913U@lT@Ci2sX^({l&RSrAeR4ZrwXb5)?7%rUsYwx#DkZc zLWfUd>);mu^D7~%Ei_sks^-0fT+#jT!=w+PLhJ1oV+4W$IgIU)%CSbRLkxnx7l@yl z4T6U7|J%(^)5&NdXOA(gPnW;P>BZW-=beX78ur@AOur*fJK=rL!-o&&Cl4AF{lFd_ z-^FyBLwsp8y1F_##;yKjhyftqSSl!361D3M>eU;u*QEtr7kgwsf_7lO`!iX=;2~hL zy~lnn5bRV=XkzaG*^XoWI5?sFRe5GqjRi(rViZ{T?whRcqF_>FEXOF(piiJe>y?{v z?()Hj`yC<_>=arWp}i_iOy*^41mD=>hqbT-H09`>G3);8X#0=Da<>9QV*o=T={*KT zol1{FgcaJ)p(^t?4DQ9+kE%ePS42SD5v&m(GIIHyci)U3?S&svd)$jN*TuW&EEQu% z2wT~Z+00jD6$GD?b3aJGiL^27p5?YxWwUj>#QzonpbEWy$I&@v>N!e{9WP9Bui;*Y z>{fPaa|&<6!Kg?PL-pc1NGri3?(PEGz(%N#FR>sho^v9voe%Vi$S#JMU_|pr_o=8E ztalrE$mk!TRb%CHWb{B`dTTOIML~Hr)>K0U3Y6vRYBUn*9kw78jz8QCQvqJAc-7*y zxVmtxlR91&NAB&hyP}TRWo3M7YU=cZNl6nUns~36+lHk{$#dJjDapzj9Qu?T@skx1 zC>v8y2{`>#oh%1Tx#;15PL_kNr++NDR-RSM()Az;4>@*3V;l=)Uxp}sx$eV0|2}f| z>{+d_eWz9IbR73>UL9`-7e;((ggltym|XY?^80>}mL>t%cU_aYuKfr;eo% zpfi1y76tMg8rpq0Zu4kjW5AKa(>sfdZ0Us^GN6oPTo&hosXCkO-GE6IZ7E$Xt&1!v z(s>%m?%@ZhP(WMOTC-9^p?e_8VWh;o2*3lSdMPij0Um{%V9Fn{Z=e*J?C&oYhV5~d z6qhv^1EkkD7aQ+L^T2mLx0)u(321I6q{zuW>br8Rm1BVh#3+%NsoRD&)E;tUIn4hU3gHa|7!a2UkMJC> z52L!hD#N{AJI`d?Nx(fp9PZ%FoQ(8P&8-jA7| z7Fqb|tRL!fz@tOEu_!Cdw0iBH(BX-!Snr?SK$LU%J{V6tH=i)Qn&!p{$T=h{iy2=4 zLsE_gx7~AdN-0=A3E_x&?0lCf5PpIKTXkm=|HyC3}OZjs9j#-6qjM%(+PO24MwCaS~xz zi!BgZ#ZzsSUU3vS**$JNdE&YLC=%Dk^)@>7*$VsHgs{h-W9mw#+SLjs@Z+JW>ID4A z4EfSqvebjekEP&xIN(OZf3ELUfqvjY6={1D%Ou`#!7C7OBY3|_EPunFh_@uWVp+( zg82+ZQ3tDt4+n&YA0u0`K^04GKqJk>Gx>QHhv|sc7d6f&GJo65LI0CtTO{PL)W8?$ zBw`Ah2C^I#MWGvK6GZO^1bhNM;%$Nz#zL(*(UFsuuk*{SFSGt=ugCoO7XAqaDl}Hx zDmOYD{ihYLqCHbl0gXuOuYT37Z^JsoE?c(Dn(+aJiKRG5pkh?jxw(*$5)&9yGwNB) zqYNf`>2z{z>Y~b4BNu?QdgI0~smGM%MMG{tnFqeU-Hxt}9hq0CG#-6raqvg0+SXiD zB4smsO&`ns708e9jZigcXq!6jlXL3lb?Ni(9huk?eXLZCCnDo*cWJHKCQKPKM!T=S z`L>iu_Yj&247CebEPK8nz8dYCHvTvZ;#f+;))f&^`=9$zYiPK^A!nzvb_79Jcg;=; zpNQbGI;$C*pJ>-}SbZ<@6NJX?H}h#H6iseRZ?9QF6kqUfzAqjBHmAoYwU4rDhwMVn z@9`;r0p7u5lRN7#HSKl!F8`rMm0-7Nl5L}Aoj~bj5E5#uQbHY#d!Q+%r7pg!S=lke z{|@2YM1(i6FKy8o>aRn)JyuSE>*@nDf&(o*SlE1(V9z3 z*tr@yZ-c{b?c!Z!D)EUnUOI+Mt3`h4oHlh&(6BmrD2>}ir;~!1?f2F+Kbw6;Km_CU ze}q{W75H}wev;(2Pxo;N={_P(qeh+Bdz}w1$cL~fGu8ynsyjDhW3(M--=G2P3&{08 zkK>nvs2&k^gLr+&pbq6Dp#!};{Y?kLY}T*OFEzlb%}Vrj&qeOlugJ?UG2Xl7K4ul+ z^^`j9UKZigQZxIcR-AWv+kX71{poETf2^uhnqQc-5r4J-cP}XWv1X<5mNn%pAaIYc_>oIZSp#G-LsQ#eaY^v}7{#rRUV$ZOxQN#*sMjIf~^SNO^5f%6&j+g#6 z&KIYY>bH9d0&acPc#l(-Z!J$Pe^hec1yiO4Rq0_Q}>z`{q zgP;xQxB?$YJ3$%mQ2OL%-+LNL5jB17wt2 zg03p`Btr>Xb4pAb?4o&F(@|riRc#QjWeD6l07oosavR>E_amZwnHOtUvjdRAS;Vnd zufWxxU5qEwCviEZx|d$h&8^Q`GGwf)>FHBO$SOVbsWL6beR$2vRG}$hR53i1mM4d} zLC#q#Ihr&A_vBk*ROrBqhX38p-g1*R3_5te{jpiOSr*yyyy(7qu5 zuAOR-M(lQXcQ?m;mOZ?AcA=lvtIRF81FRWr%6=ILm~o1@-E+<}7aj@J4SKoa=g%y@ z-1>si1udIgC_Lx`K2{60*-;1xvj@v;1jD_N#68@TZc^?@>zf9Xn7xbYhxiS5W|5Pi z^{Q+dFqTPdWH1i@z4kI6 z&q5z10MuctQ~MLL{h5cKA{R3C7qWiQzTvlM zYm0#ocTKEu?cBE|h#a8_v~oIqd+O<5W7g;HPDv3cDT+Z589cxmxM8?X)r)`xwxYq& zIk!mYttecGkSXQe7)+-L0OYR*Ip3P9mv?4Exaf-zQ<=JY_T_asb1;OrrTc@Del55t z=*L3cKx8NM&D3l-{6I2?17~88X@dMn?OHx}LLF1uIPLFf@9JEA!sXGipx+BI`jxfF zuq<}u6_ja4A1@ToI4%4V?cE|i13@MF>gjJBby&qZWJbQx^xHdEUV6vr(a$PQ8zFf@b};)c$&Rl=H9^ z^D!+$!)uTl%(IKZ8+lnYG@PBwWTwoY&syl~%k-_FtXYpvS;nuB#UkS_EbT$_1>s+K zzUlu^zE)NtZBN+TsE0~lYGAn9$GU3g}W7va=g>5L`#^iMVmVD+W-4p^epocug+Bob8&!-k8IX_|-JIuS-e z9x<~K&)6eXtqJ((#`a!ab6f+ZGk-M9O9 zG>Oycn-oP-M4THKJOa_sMx|Ob8R$lhtvv^T^5lFm66kZNteI4geFcUSXcD z;T##HX_tY6y_bC6jAq+!F@f81k#P23##{dI2pc0(xHMw|E~BGZV-ehl1{axA2(qJfsVizkDdz)SBX z`SwM(Zgq{Z(cH;tpa1bmiwl!(A?{j?j7j{3DZpqnGxc$U`mABorsk9kluP2NDN0v9 z{{{zZThF#fJUT3)b|7v1(w21VWbol^2wP_7Kxt zb<3sI%vo!*)K#c;T2-Q2LYY#s(8j7n;)oP~vJ4o6Dk5qW0Ag1}iV?h;Qsen{H;N~T zvGTQKGLqH|Q#uXN9>C$+pI^ua($SCQp9*(RQ}pkjNS8u==te>a4t-sYWQi`%-`}F1 zH-X8tkBT95qQQFeQGwnu1*4q)%;3rq=IUOgNKn_*RDia znFiX(vX!;+*SC&bI-M717zjCuNE4(!F-4{7`{OxI=cXrILsB5@E3E<} zcpr&={Lql%o*T3&ixE$Y%`*CrkzO-8z$)Qc(){B7ZfZD$E6nYok`}(EO#roTgQX24 z2Z{X_MN$1?6jUJcY792GR~W6W=X@%G-4G-RNHQ^Qu|=25T!M&pAUa`^UFUCHxL`pl zR{_d#luIY;`ZP>Aa>tJyd(L+iO)0v$=M0RqDinH(R+pe!65EC&0-ng_okFkd#Y{p< zTgiD40W$hVo{$j};xhog*ME%Ai3X-Ex+du%#SrZ(Pm45Qt~|MV)heCN+hO(!$B3>$ zEg%6eGeVy@FDl=hK zm6p=*p;i{(1IeyTFo7>+hLEf^B+cV9rIZz{!10gd;oeMTs>lmFx&MV2zra&JL_iA; zFs9)F)iok>`4X{0q>)6BtNzPRUs>^Bi!O>yb+1xfecZYv>WwZBAEKP_Wn1b(FWj zPl;%MauW(`{Wa%-N9GofEdP2&-~^JYp?6?78xd<7DNu1$=Cp_|u&@{S2zzfGjs*`3i1&1}= zOS^Kf8#|o4skgY{-K6<(eK0 zn--N-%^9F^CHB2z*{@kRi=@2H_P^c&fOd^ONFN#;+{3;!h^q(tbBV4juiP}_kiuA4TDUdPjw}@|HEveh zf8dhO3Pcp8jDZ@Do=M>FPMrR)3kE?$LzXH+-YCcOHI8Vlbxp7`b<%#hQHefEQWf;N zAPPv9eAGeoB^h-~rj8VsRVqrZ*=BohtJ)6isE6~4tD1znz8I8~Md%H?tUm)kc$3B( zO_g0df|4g{ATvUsKU!i+A&&zd;XKcXazB5uN+Z-MIY%Vf?Qpisi{pm}Y6bDG3X4;J zbl+w39wnPa#Gg}L#HODCk9uTrna8FQSQ9ym`;?E4o3A>BCOADZSxw7>c7Xb)ACba4 zLUP|6;0SGkML}BF!Sx$$ckM(}M&viY_sRt**#Zhz%iO3b`g5_N7&lHNH0XYcI;Hez zS7hCSq=XfSY~B$KAKhV@y+uDsJxK?9vs0Hl`~{hi1JILsS|%nXB{2HW$(I4n^{xBe zpT6D~tl^IoUWKpwYfSIpYghmKS5+s-1`jE8t$eQ<)f~O^Ca3Ntu-g|@9aFy4&I!Ic z)yHQD?|x-@WP)a+Mxu6*2t@olPHw`Cem(6cvYRAFasdyx4P|`}z|8)*CCm~OOWlCu znxcnY=tT`S1^xo#)ytE6eqf?cGchK!zW-l~mamjP9jVFWUmCgM2jTxSY!vN~!IU$w zY?4|?_#@KiPWiha={krz^WXq0t1BF+x?0VHXhM9~+qzX5AinLi!*wpqo;uYu(NpKj z11Awgk|9(BvO7B&_|KR>2`nr^O0Lri`zE9m=9mHH1 zC}r2bgtJNF8Gqon66h3|XttrqZdij?iS}tbR<*~%Ai=Uq8DDV^1)B~6pWL!czUL6@q8ME zpu{Uw4KnG>d-lN@n@Y|)xN5P;j^GAgGm%M{O$+u?nD3nvo=~qFNw+x=#)Xc`tWzV*zxc42b~ntOQhb;Zw2W=2z5xxRsht;v z(eSV5CDF#?`ZO6cr;m!LL95_{Aa)EjOTDIRi*Por=!!P^9C!3-WsK&W~ieUDKm9LFYq;h>kpas>zs z*|x>~TH&_O2NF8_-ZkcBI0MWaY120w4p@WkQ)X15LW{jHJftaEvH6iH9C4_B(q7-& zumyH;X2G&Eg9)qv*d#`*H7?R!58a+X)h|*?o)SG8!>3pH!=jpl9oj~B$}G|x|@MD|Mkl$^lkF&i&^RyCe8V*IRd_>(F`)&KO?(B zcveR<08$slI>qy1!k z=Y!|vkXmItdmxBIdVwR5FAl-6?28u_GC6rM;|TozG~%q}7Q)b|^NRyke{%^+&N6+Z z=uDm{0K#Uyh9DR@+yORH|jbdhw z3$(w^W4Mj6hqQM|!b_Obr!(b{>ilbAbFcPiymK3={Aa+f@fPcEzj}OI`g_aRCs{2I zkKSJPvqN|v72Hj)i~4THX;IElDl_>z``G!iL#hGx)8AxD9;k7J?B;RIAWt5F(zSGXmkqb(z>#Orn@Hv!lBv@P^NYFd*`U zW5lVeMJ>_OtUA!5hQFOHqeWPf|(yAM<4RPE{t45V3_n zYF7>IsYxEBMQdez(mP(F&3TGji%Dc3zOyPM)0fZK*5)}%7c{7R{`nut#PoH+mW4di-QDA;h zgY~ro{~LW>y;{|&>T8|jq6}`)!U^=?vUIV6VgSj6AF3Goe-Q-1Kt}0H{XWmx2Qne2 z3wq$FjOsMm+?QNVtF$!}0E~k(W2}hXUjG8Sld(t<&jJEgN|+v_iJUN1(MZxD?bSDq zQB-%mZg=XWjWwuM?Ur*hY7pNTz~H*@De4_0m%emj1z5pgvCDils7^Esac#}6UsdE% z>k%3@b)GP~x2~x@n^VHxGEZ=JtDpH))l-IqIaJD=}Y+7T&+;dSu*X!q~erPLrh@ zKq+!1V&`YYhLhxFEu8E(VXYM?nK^JVhy;bB?2*;8gq3wLrsn6e{P2csm=oJ?==Dt& zm&sPDLV}1+NLkOoeQ*sF0aS!^O9>8$*QCip+p5Kdqx?t7>r93A9yv`!PbPpiau(E^ zn~aLCw^hb*`}X~{J?S+S?7E{zyFwnx=!&|onZYSp)Pm8{G3Z^ikc_)8NvYGy25ZN7 zxEtyjSusz~UM44lM{W1587K-RWilEIZ=)^48`G6a{J}kx84u79Q6tlT8I;fa$7h-{ z{E9rm4s0xok15G|glP?uhc6OznO5P_X7zt3qb&lcP2NT{nI>ph&!9gTW_(N%DEyr|~ zgJk~Xm-}j_3L&;iiS7=&XUg39Smx^CDwJexnSMSezxo(2Znu^+S)foLuzjiWiS}hl@h}TS^-KYj1wA|73JFA^-nGklw*>!}3w> z$hDCzid>da1Lo0|TE`G=V?rD`Z0qZl#aRi!%SK}kRW|Y5I~N3r$#;{BP$fFymt$p| z$J?wN+BN6KEeIIeV@6vvG&OTLd0t*6ZQE!l!hcCX#rC2yG7~wP><{J zks#gdhOfylc+K=*pI)<{XdRzwm4A{>n&>gcA2#EJRaRE+^ynZ}rmS4zD$C*w>PD$7 z_(FNy3;RP!2M->6FdE;f*r#LQM8wix=AHP{a#JbP!;4GKFR7c;XANTd#;^inxWs2$ z)Ot515}4mE%_9(B15r+XK8N<1ItwxGVx|Ckow)>+3B2S}a66R*rtmEym71F*nrP0M z!x?K^Ba(R>ffVC z=#5E?=H!SxQ_R{q0&)&*tsxSiEgF9}*AD5l&!dBV_m?!BL^lO4!5<=t5YTAkHC_+k zr7=p9!PHJbIhu!NM`}s-q^H)DkQS{*qCfGUo5V`8=#r4y)`S3D;GLl?biz+%)(2eh zp+!$bo863`gBer|pG*2OA_XVdAq;IJxF5S(@+i1k)H@U};^_djZiUj3$H`Lk!-Jpf z(R(QEjVuh^sR-f4pDUHCut-3EBCAT$o^2^x4sgiO+ltin%%vp=jBk5fTcxK>kbO=Towtx?#oH zjT<*!%ETgR&Ba#`8{vO#?&hsigB7|)PUc2&lbrQf&4xdGHsHnD_{Cd5-U5<20&sqN zvBDTTcC2VL|6^gH1EsrIuMnei+3|;$hN7S9KJq7Nxfz8p+=|ZA z5I((VPE`opc_k%-9{S*g{NKNR1_zH{`G9AZz#c+jj_Cc!CL1#TUT4KK78&>z zyR+eRqT)Q=$Q}sf;rTW&woKn3|4JX{D!iIhY^!1ue)~+y-tdvUA-(N-C2c_VC1G`Dfw2VCMV&!NSj{Oh68z41|yocld?iZmO~L67$Y`C z-b_-Kc8o(*t9%+0jqOJ5JzqjfcmXD|NgUxGF+dYFtE~P4|(BGo9bnDKU)*?R|V44^>5G40Mt%;~@Yjl+{BjtV@pH6EE921S^F_8uVo$ zz@pQxSGtRzGhN+5^fScHJo{mX%-#dqH8$+7@|=QA=~+9PxJtmd1UXJVO`Y+S$0d6u z5K05&(<*;UT}RZQu4>o6{}UTMZ7;no$4ZWJr&S2J&q?}ViV?dV8n@fhn{i}7lFayV z5oAJ>#{C+0FUsHtaNZ`d;|Ek((l`U4+wHq!iMLzn^m+k5&Z5^vcHWmSWDg+D9G2d~ z(9oF{oBNqECldZ=dQuXsxy zhsSRwOmL*HQzii7GDrTH?$gODGm#J)(K}$2EL28oEOtbAYp^FUevkEjt^#2Sqn8Lk zl(w$!YQ{`Z4DRHKAkgD_hqgQr+Fi}kOe^k+qJrhNhsuBuu6@;7yr_c5K|mTybeU0n zq;tIrZn%C$mXK)>D{{VNdK_MOXYso}>_H^S=z5oBG5I{0o0IDpTC+ z)-^EPpvD@ab?b&2H<&M5R^%{3A!_ElrlEk%(1eCIG9AuyX-{Z|TBqG1mb3&mFc{fs zB+I(7yOR+l%3ac?;6+&muB@Q~NOt^^`l#1Kj*IBqiSpW0q$8NRm&RK+%L1$0k@_x;*lEc>XF6V5J* z9tR7j%OHj{LJ3y)v{g8*N`4I0oD_i-HZ>eUDGL}QCb&qIVHd6=wn^|IqOeX6*d1TL z$|TyeyK>NtG41te_-i{}xIUh`Pu2ZpZxa(A&HR_bi6RO%&q%~9h_4MsQ(t#+Q*^|8 zu$eI)YdNE~tx|;c2a4n8<+$)6bu*S~(vSu?$OyKqz7r-8YLw^Oifek6N}REimJl^l z7+jW9MAX&M(ptfz1;p*+2Ecwq1I>ged3!hQGDHc3;sfA%Wx1u>bYdeaI&v?4wi%s()L+kTscBS&sMqw2sT6n&SY{da* z(>;>hHS$nhgu8wbxlg&HJMYcFa#s`jTQ6j5qm%S6vo9GDx&K{QHp}Q&^=Kh2C~i<5 zz!IB`Ge1}9Sm*erR&88%LF|dw0|BKpyG7fyivau}C_riO@va>-lAs~?@=&3{v593d z*6_@XAgg>?Nwjx(vUmwfe&S8$F|ULoPH0_77Xy+TN|&7C;^OD?G?+%Zf4>==&fURX zdWff2SjX7BjjOt^0Rc8#IR}`MvjlnZ6dFw81h*orqRxQ< zPC@(NW@(g06!SbILYA@HbR|*Q6z!Eq*hAkd_ ziYE)lQ;C=3LkbB3q_tsE&G!!zL|?}A2YXu1YwaC3Zk*+eyX~dD0AHR`t*i0H7 znd5lVSM&cUbnku{)hGwT2PnEXq_#$O>Jo#G;dn(fH|RTmdaSWTG#2|^M8U+U#pgk{ zKc6z?w%8X_QQZrHrJVv5MaMeqOgdFfvkFePnQ08YR)x>9g<*F6*$`ogx zh+g`5FewQTbEhoGabz2JPq!JK20sbh&u~JmS0i>rjlCK(nYd(WKJJRv)qNPK(~QybLNyH5KTE z*sGmw17`sVoe%szog4!Yt=o;pRlD~csF>3?Iv!8eD&9Jc!ivhlirWs-bRcuJ_sTrK z_$Z>1tGG1A6VTv3j1`KU$sx4&pJXg*YGe1%$+7+c(Pq#f1Cr%IT;&e%3^e@RhJKzz z3VJNWo4AQ*VgpToHe-pbk<%*^WI22^0STNe@G5UP-tMCZ015@OI~yxV2}G63l8^Bf zCrgz|TiTLqto`ZGU|rG^<|FUkJ$(`=gAwE;)TUid&A1l<;chkexefTp+^&iTQF5`)Me&IkP-{dH>E6oTs$)=e>q=$6=fF!?U$beC`aC@l7Y zxTwuWte+BxF3~=0!V9l;&?q{2)fe2ttZHZNnRlKftMoiY*^UgUWPTI&7paBKGmGqc z!e!W=59iLF4S4^+vfcik`cSpl;a6sTP3mc?X!j6=0K+ya_Bs$w{rF7!r~4;e=u5rO z>vbe_Z#d%y9XedI>HYEVzts$yKVY4M%G%i%VjmyXyVN;uDJ8wMsu->jwt!LihGFwg zZZ_+RN+9obdMC7-)G#hNZT98nteUe6Elu{gJMjknu_jD%TL)NzY2U-KWRo)n209zI ztFAtttXO;h-8bdHI3Xo$0}%7bIOhp@?@yn$=24QTAp*3uPw6o4D+C24nFL^RK@M^> zv>a#W8mvv9IUj);;Jb4>#_P0htz6%FBOLgD{c?F!ac8s`+uWiYUyM0`(-AhO5EXo5eJFw7mp9p%vF(^9Cd+d%LRVxVG*aG6Cjq_w~f(*BJ_dv6P*|UK*SXC z&!SQg@XbFojaVSNT*dSaC`Z>~2)B&}ie2ogVbt`nRg19%0&gNZ?=e!_F`}r)LRaUJ zGpR?brv%*yQBMm?ATpHxt&$v2|^FWd*IPkaG?{V2ah z;Vn}^fJ#C-r8>>??wot0y; z0h?a5#8`hFvGu3xEXPJZM;hw@&`7WhH%wcYLZ-J8)v=v$nrxVESlzIUIs z+vOS%T%0dNoQSfFi=M(r9xU_=*e7ZRX%1lKL6y$iZ&vE|>#&UNbKZm^`xQJ&WK}R4 zG8s*!N@7+s>f+ZEouAN(VY)+|r2N6%cXyL`Igi=3fFy(S_bJtoF0Df{APwh<=k8ui z(0tB)kc*9d8ZRL9lWoKSw(*qF;!2sQgPwU8NaEic8D^y(F=#^wxBw;$DZ^+F?wbVC_NwkE{*fC zIoAy~kS_2e&M64NG9{+hRxm$E5#ZUpozsxEod8se^h5)&efnIYYpL zOm?a41b=yA&&Cgkr37rm@-L`-UMG?>#o5NZ@f~aSDBf<^$W}2x2z?~&Hf_*yzh9*O zG1c)443>Fd`lf<7JU97?&Z+L5d~Vzv$a7aN@_+A%84zNJ#uqseBFY-PtZv=9xr;3! z21NwK(+gYJdEAM5M0ci-==|~!s2DV8ndgDXWMG}pNtOs<- zh!i{ZQO~;kADl49?%VKQ&y($zKIo{f&Wq2XEI`jSK&XH2y?4(y)Qw_=BvmmOIltNT z+N|d?YDLH@2(1YX%|Oxv;l zi11j4#K_@we>tADU^A(V*P>RU+B55ClAFia2E)ZPZ&*JxGGfhU@Ju(H;&vZRQ2I_^ z4_Fr+2_jRFpHrCvVZ>!B9Td(E^<@-6EC#su^S-|y2O0Ry^+V-2HeE0bwv%tnXZ!>b z6$J)&a|v6*z|_McUZt?g2^}0u3S*J(6AMIYOi8XJrbxr9L4yXRRvwf;yC2g5ynOAN zbNYjqc)k$f3_HKxW{WWw;YnY+7pfcMM^rt##otr9X7n?B&r~MWQ&&+{QE{>x>|vt6 z;*K-p$AzN|Lx5Yq6P=$0N6 z-dFpZSqx+WsO2&m5A=lk5Io&_|LDl!Bao*r{dT}9uStM+)BNy2icW`eT@c)5+VLF8 zQcTK0{G#T7>wtNbkq03H#vEw{&nWXSc1MRN_0@+QqX5DKSQj5+c|}0^eQ7Os-3Zx8 znm!IYBS@X8dWrmC1(qx(GaJosT=B@*9R*`S3Oj~C=t@-UnRit`Et9@G|4?>u@m5)< zaP8-4W2$1!)_w1mz5KTHLRdg|v;E;4=hqH@a7LlvlmV?0csXI2UEy;H;8e6&^Yt=_ za}Jugd~1hsM&E;Ej+Q{?gQN}#>6*tgK$W4_XOZ3FaAc?*@T%J%M&G$HaP5=7&a>ux zSF+O_&(u{Q3dPSLw;9uTh%#3v)AX~0wO$RJ_HR3g=~UxJ?#Q$+48-nJ$Q)9mnok%V zG#S-H5_f|@grSQ_>ES(?!)gCWJH*zD*moAgaZs)J$s$U7GTEI9{vmK$4J9GSb|rXL z$tE&l+Y-d4RO0ouk_RuJHar|Ot`cx$&WY_O{|lR}Uw>nxeekbX6(>bA#vx$*#Rm|) zxQDt>Y)nztu%7EzuTN;{R1kJfFZx`x`r;f5qXzZsV~o<+Z^GqTzd-XJJ0~&J+Td-` z>St|NLiJDsASsrbi%z@g_pJGE++|w|dMp|o-NV$J!tOeZN_yz)8*3ZbE5^^;To^oX zy$U_aO^4xbC_bH|2Fu;sUT!Kzt*?ZTD<3A?TwXs3sAAdVr zZg80C{OvK83XtT*ZzFYPu?a_;_d*UQf+IZb`p0AX>8U_iNpE0o*^$GvhWHEQo~c}O zA~gNnId_-^Y7`sHx--YMYDR+-1j55St2Ol>G7TnZ%9M7>umJ-G#62wlBFRASt`UlN z<6{h6^uwIk3mw6Ybw{iuwH&V0DJf4V8N|Pv(~bp(rRyV$FF`UVO7{uA%MpZh$5YP> z0X`RW0|I&alNNmknZaqV#rTM|AhOqUF=8mr#n3blD@yRVTo=B3U1h{2C%LauIkdV$ zGezb^d(VBYqJjv@(S85JQ~sln`wgKP05{Zg@nNo!ji=`kJ^#d3us|Z_3rP?o}T?l3N1*Oil zkuR+*yji0KyRRaeL-4H_S4#VfCleg^ZXUSN!SJ$ft=nS`_28U8i{NQg{S?`Fq1$WofNzp=+I+lOwKnSS#`EsfV_}ghq;U z!@F+mMGsxJMQ@#9a^3K=FtS~RBcuw{ab zlIzyRTznlGa3dpkrY*5?ep#^M!RFHXC#w`i(s*$h+b*1$49z>FxT*3><9-S!H5pDWRA z-e-a~@BMabOue5%M5cQ>Enocx)`~)klYk_y64zs&ti5}s(t6HRU-w9cKR=p*AFR&Z zpwY{%Xo8j{Euv{1S#AK-QB!(1XGA4)-19$RZmEsweY%eBpRk42^l{)SYp)vS=v$|z zge<3dn;YPKq|TMsNX=Df4%R-7Dy=nf_c+FI!n0?S>|EA{h9cIBgqX2O>7D-n$a)ia zuJ?A`pJt6hrCBnhku*q&B$->`X&$Mixo92?l?tg0DN2(mno&!$218VeYH37KvsF{k27bIUrVuZ-x)?pq_` z<~(A4vs+uwN0D-&Qg-6m!nV9cAy$2R;e^kKX4+&K5c4~4vzte_~VZzq95#~Z$M!rE9=QR&`<*c9w-kt5GzukhTPpTRaI3-DH5eZ0aJl^ znh>fJz#kO>uBhCA%LRp!E`yv-Yako>fP0ymR2#Te9GpPT`11nZ?CCwI*8E73fI8?=N#K@5Xh9gPnY3kLa{>Qax9Wj+fhpZ3&qLK6Q3cU6|5?D%nMmq-S zERD6)*cY<(DMg(7+O@jO&Y+GEnI%z;FFXf$0}48fBPu?Zm?zz&w_|rz3kr9dkmKr= z{db+@)E#g2r_KX3L)ZVcc*~@iPyJTuHxb*k+MZqWB%swghXYeKUSYF&=v{=MV7ZBqO*0PGk!b@n%g}97a4b&tTv3% zrUMIJ0G3Qp>pgc-`@fxkbaiUgH}3GDh7Q%y3E>;AE}VIBaM>46zKbL}LNo2SmP$0v z0b>25zz~NY(xHI-YU(V6J2S4Vu{Rnf4u`OgoD_B}-^|UO=(owSm|s%77GQ|6s1a*F zMXpc_4f5Tm7rfVLnz^jE#V$kY3V|Kbr~$|7PRoJqlOPJCE#)4mYnRs>=mQv%aN!H6 z8+g^`w2UePbiXy$3;sK|&%*Wfv1_JtxNem-s?Sf2#X*w44jO`brG(NSPp!>-E`2`y zVJ1&0+gk@b(o-_fDjxZU6dh1+`g*8nJwpE~FDnZx9xRL`18v0(%rl^!a;$40H<_!? zz(SJixa0V7w~(c>d{$XkIW!>8dhR<<+sg&ceZ$7|GEVT!JPz!^c@5{PUNX?z5dg@s$-I2{*&rxzCQ->lImw$GWK=?yz-_i44XYMZ6quUGY};R>)Q3-U(O z$M*Be$1Kk4_cWvDaIi`9vTo5dtjAbF;64s<*0KjLUX08w+FS{vFPhs&EqTG0Yzsb7^04pdS z$>94Im>_Y1Cm8s$5k2u8dIpqy!&fBRuS>NJ5CY6d6gtwPVdq3fX<8f=qTT#1#0;kJ zjZ50k%&^j%WN+><>DR~6Rw!{dm?=xd6pXK>R@6})9Y1QZleVc#&u3ZPUH?QSv zFq7H52;rRdC))&PlrHQa4_GlJ%tad&$iR9XK-61A?;2=5_nsRry zgXD@U!~+)r5Z~whJfKn?J++c-!c-C?r2PMxH@;2saM~Y~ws^y}S#}QYZSSDjv%XpzAIj0__yvsDEOKKwv-w#+hC3ZOsnzMdUiE}o=Q=({i(UrAbbd(^)iE7XH;G0Y zs7y!NAj}=0I|6oWr$SL1XuZgAmkI7CHYJl|aNbdDUp#~LILp5MKg zrSDc514Zbh%>|beks^I>Zck8<+Jn#gbx;#UCcP8328~6@16M=-p~reu`uYkAN6@q~ zezq9L0?Vm)*fC~CHB$DE3Y4RI&l-h+9MCl@kQ2ULdGT}hxW4ByD(`9sKcuS>xerKI zFD)%!l827o^P2Yb<)feE?o*!JdY_Ax{V@iM%pzClCe#Ky&vCe0>r;BMYJfpLuv4ck zp;U_69YfxblBVSe4Gjc;@l#I1%M_Y1GO_`{?#nRbFCwFFl9qGuHfNQje{r!L8ayc z2{8B66_N~FFQW}#_=2l9L!rfnnIorMPb84ASMVH(1Q}FM@l%s^gi+qQ zFQeDY=0Q{ZKKIs7cULMxkD_qv*&uDL>gAhmMz5+IJ}l8XK!5Mc^y18B*_vT{#l#(U z6)EF(uoZnImqp&~s|QRwe}i7X)1X=CbO>E`)z7w-_V{z*!idsq{edqipk!2q@k;{m zgn)9#O?@Yy>F-+v$EsF7)tnodcCFVj{pkn(D5|U+boeyRdJ!+1xr!nQAS;OuxH7<^ z9VRjS^+;3i!<-b6Tc8eTo96%U{IsPZZC&zyE{d%+yQOoHcFB~!vp+3dZvPDn|4Pzx?>Km{b*~PuDO~rzy@22&Y2hOs4Tq<5 z1LjbLg#3dSk41eV#sw?Z23a%)o%j{^-DcZ)7M_T)2*tRRKD2&_arbZ^g%!r$GmEhg zBk%fAWYC1?3{MZ29VJMDkrK-kM9a-v4J31rQ$$D_b+}YSEObW*OvhSOWvjcK8nkDS zveU^Y7!dmqxTp`)0r@GS%i5`aw;VryCG#4Lec5x6uud}tJ9qeduC2Mpqc2reR$-y` zbPhej7 zGU^OI&{pbfcYdAwOtyGRS2ykJF4)NPQ4q-6! z*9aPJ$d-?fd!z-UpMeJ2OQIzS#oWB2@g+GA2wv7h{rTc9R5~5VF>(xs0kR3<^@;r* ztvoXed!fh~l%=!?o_Souf)i78E;KU8n)!iiVm8C{lw$%rUhN%+XsKp`Bgm8~5RQ~( za1_`Xi$N~)5i&uXKh-+^2tre}L!R23r!e~)=%6E{55q8&8ePEC0H%N2KNu?$>CAB< zuZPq%$lAD!ifVIOqeKJ{J6Dbi@ccb%v%1f?>~tBGiBN!>E0Tiu0nd`{(A^Y}&Im{CokUw!ptdv^4+t1Du^cZJG!x0D#Z^^_Rn76`aQX zIipBpM|jm4alPx~zSr5|S#|`r+V2Q3O{?4(FedK5&ho{W{zU0VcB5{l0#)tOB>*Kf z?OTW7vvez3Kp=iQdk(+}ig3iLDZ}989lnek@rA^@o)}jA`n6)3{~M>34m?9uAG+>? zqsH8H7Q&WdM&yF7Pbf`;Ku&le+tGB>LQ&$<&-(LzfkK&%+CpPJ@!E#f;^UB({(k+s zb$(W-OQm2(L*+_G!Cw%Vu4PLpb_De%H07fCsQ>kT(AF0EOleDRjC?SQ&q(3X@aFw= z%Qxeh5xN8Ug%ykeF2fuOn~Os|2Zih`0yMYZy{B$cjDZzse#i_GX3k`oNAj zQ1UUf#Rcga$4EsQzHo4Sx1uXjLxzb^^;kd{0214hcL@-wPMew_ib9 zSBFK9D6qMDk9b(Zr|=~gy?XEcdA@W%ADM zY)XtKsqr@oyk!b>loMq3}zxA?L~ybofEeNCcW^Y$$C4C9X0(UJ1_eCRK$rX z2dQTyeo$-!m+D{KkJE5_*YbDo79}Z+6iV)5%BGWB)ze;G!J4ma+6c?o@1y%enJbZ- z+obs)xxRX&sid4+Bj)iz6I7lx#m5%|o~`cV$w%fG`RVlS+Y$ji6P-H&+<7Zv{e!-X zu`hvp;hZ@ZB`k{JUI?lHEUZ1e7S}{M~d3~ zIMX&xHZRGlI-(Tm4c8zo%0U2wxsVYMz|9arf_L5`oQaSM38jJrU$V((Fg13v-s9h( zbW5`Sahn6|#4Sft=*u)5OxGLYh-^NfI`pSG@fa7J`ohOK$zg7KI0 zO>toGQ{4=W%&{5Z`%NXptbhH}$pJm=?zVW)&;^*OoDxI~)Ny_@s93`97`3TJ+04$) zr@^yhhQ(~9;EXd$x);#OVSs)E35OUgzW@8<6&Lkh%Ub8N!?|1c?sPE&c-#}t+aW{9 z3hLFExZ|($IQ>YsXy?98Chd_uNAf^qO%~-%8viF|_{Kb#a@N(BP)1qik7T7c#l&kQ zsJu+vAF`Nbq)m#Ud6CIQKnYPuyN|B@^(zF24v;BYY_(69`9iE(ZBYs(jwZFF@K+@3 zf&bTk-nfnH==VEDCe3}gweD3#H`VqtcG=$DxlmKXKQM5}l>B@5`ac=>3Cw^2P5frt zw^R2X0$13n)-l1k*;(s$^He(;#;CWNIz#izX_)rTJ`f7p6*{!-Djg05GGv*)bscYm z`L6>DIxp-=L1TS6a}#SRG49*Nm1m?$cmOy@zu!8YQomsnK{>3HHqMNhXJc!(=o+hS zpcN_39ab`myOT{gz;m}MEZ=xr?OtBp%9+KsElyOvyySXr6QzxuYe+7fjb( zWi;)_BKFu;T$A3w?uRWfFqgjBb!<> z_b#gxNzHggh^>kOv&^;#KplU3F?KrhB_5)b*ez1<R71!W$U8pYE@$EjLqs53>_G48xQodwFyZi5dJXK|=^VSp$1Eymr0S+&5uNUoB`S zYye(4pFT)j6qg_$1otO;6nM81-X0St(e#5;3Cj1pXXnm(#g{MzTBxk6XY%m(6^kW< z8%&7GjWk}`7dxR^VX{fVhw*#LysTxmNKkk!l^4nz}xKzcd%@ z^wREE1a_>B?}LoNLJ1zc>X~pOeA<|kxU3ZW2sXdy)sfAVH;OG3_gMfzDFznU^5qd1 zuaxmv;FQ7vDtAM5+bQcN>5<-|io>yAe|s`C;MZvkIcYYbbY+X|>k|uGg8XK3O^9$@ zE_v=)u}hYcPGsrQU01Gb)I5HDx%7T8i`)6Fd>`2*bbav^;n;Ie;!&gWq|E^4n7S2rd*_> zHXRqIKnR>=??I_N<9>lz20yHy`ylo6hWc{lsdV^%|{{h!Bmt;?EVa?epB5$ zIdxo3w}z2PUrqj_5@2n0%UEwW(`+k0mX!1^_MTs#$^G!>8L=`8u$mI z)hY`)3Rt9SLbg#kY1O_w3I+4^!!uRCgQHR)ZpD7u%$+CoEb^EghHACSd%CT^Q_ zQpDmM5N3Pt(wSjS|Av3V=w>UwgIb!N$M54u$uCKV$e^4Q2D@eTbWwIAli^)joSsy| z--KZosGF%U#++3qOnBhj^W=Y*(}C{uHb0#Z0Kvyq5J4sLF{twUwQR`nt9+{J9rx+S zLmssAS*I&&mQpUXnX%2Q36O;gM^MJpMHht`YWw+$=Rku{or`8Rs>ayb2vTBa&%Z}U zGz<&b8g8&z{RV>C6{}a5@qO-L0+jxdEpUE3RYigRi1tgXE;}ZuZQH}JQT;=|o=Vn1 zQvGVp%maM1u$Tkx>{NzuY0Gu(-4j&K*54WZrlB|Bbc@-lm-{etA$`Mu6Vs+oe?%WK zq`kkj=Lc@xF5^}l;P$Ee+1C*L0^t~|V$#gC{_LA~tslUFre!&qs+^-K{XAV{_1!_K zF>itb5e{|*Y~Mv!fSL;Prr!T?_firoH2{6&1!6t;`uXPp*wVKs@28jKsIySSA}Ej3 zCNM`kg*gU+k$Kxu`b%3vYsXS&;y^#xHKVma&149R_8p5e);R_~hhtP-I#$Ii!MU#p zX~+)HcAv(_8MjC3G|OJ{x#*O1QuH4l}M!LwW=ci`0m&t zKIF2UrPv+#tc0lsplVsW3T89@Y<{c6bV~i!`}SwVDRtKYy=r@dazPwP8=a;P527-V z-Ics3r;pn^$^b60Y(R0pQ1jE13@PZTCVfDnk$RxKiJ7W)I?!%f@%#6-V83mA=R%Fj z6f}Z(jr-pz$o&(7f}Oq@t<*T=UHno zx0*hs&ujI`0S1AClvldD_oBMwHTF6a{Ie(PZN&Hb+kPqvV#p4PlF!WTkdD}5HiSbQ z4FUr>%17wRT*|%=S}ELXLuAnOHPU&K6EN&xSUB=cvA$B)2!~$@}9?Lf4~lHp}ckP){V_wVoG z$2dY$vtYprQ69i>r;^X@ds_w>x;cnt#b3XndPFGvKjH-tk2pdfG$FntLSe8XZAZ78 zeg8{g5o#%ESRIQ;+B~!aSl-F`iuYWuRt}0@0cAjqL%6xs{f4tvexz^y zaXldr&{=Z6R4+ZQyC;CD+jcZg%DU>wDJi;|n&xTgT4{NCgY7L`5adFmD$>eH0d=jO zea4JD0**DC7_Ma9N5wF#CcOYSqh<4Cc-&;daJ0dhgfdzZS;>YPf#%H)kSO);$9|*{ zJ~K|ovVpVW$;te+{*D-YpqtWx5lZm*9_lYHcXYu@l(fh|>Cy(Te?{*tgRz_}8Ks%> z19e%m62pZrUF!a0Ovg=G&5z(03ng zJy+CS{O+sSBcWmEAmyz_DcUz;41PcGg%CqR3qm3l+^PupD1FKhC=}UQEZ#k6-k2ky zu;t~^lLn!-(i*9R1jQH`TW1<@hN{AiSD&bv;ngH>-WI3<-OJ1C`@~dwD>ZddUfaMv z7k&CwOPt6tc8PvqNNXXd18*Plpi_)hdzz#1T+M};5`$QYs7O#{4EDL@z{E8EtioF^ z%f}E-(Yvq*%Hh}b`Ao5%O|8%7@m%t(9YzZ6plXOaPKe8l`2Ws~cG+^>x`9`KF3Xs$ z04H?%gMSs-AN#}4;eaZ>pCUO+2^kSzrrL@1U05X~493j*rE`(4DY{@jC~ii=PrU zFe@gpimmq8K!Fm_5wQs@WM1TC#*~!R$Z1+nT=vC8dD`84_H63K6j+?TPu@374*oS{ z^5lGrEgkV7FGwcL=v><6tC>jf|4bBgP8s*kttXpv-d>=^P;jp{aop?VnAXYPvlT^! zxQNNdRZ@%hqn{mnG8Mr;eRgf*a2~Iiu`-{D7g6fsgTh`xR>J5SFnt<*#*@T!^U))8 zZHKk`&d5YYBs5SXck}(FX`=`GLuAN87bJZP7nO{7aA;&EC!XT{7;}=0FSml)qMBz? zoUvf*Z{fJ9?F1Mx1n2B5Y8}D*LSrb@=EN>(%G_K^MoiF4g1aX@G0W+f|T`jIf}4X$Buq zw#ERA!PZpavE`S#_QTWm1XON^tYc5A?Kk`SD885+8EfMa$2;(nbkPgI1$a=b_q;qv zDQiss1ov1JSNxG^un+K{H7t^R=l+YC1K$^urEPA}!tC@UX_;3UA5w4Jrn~W8V72J{ z_vxiqYIyYcG)}p?&VZUvv@RTr0smP_qx8T3V{P|2%qhZqKwQ9ZyY~NqQC;^x^h28lZNuzm=_z_1p{I!+l(>Gbr^i>H6)7vUtmxY zrm!whHT;9Oa-_JrQM0H){*-pfYd%ghxc!8+$Z2ubOP-(i=e*5fueAt1M%vf8TrO=! z3EB#8jn<>LKECK1Za(dB^TF1x?`qxNl?TtbS-}f6i&W^(N6T6!!e{8$_Bb5KRxhSf zLT6s*1c@0k^n5kLlWYbURQ#8Q!>0A{PmIiw2lOu2J$v@7bKUH-`Kk*lLk&A{0or_by*IRPuMn#{xw!`ECHgws#Z;P` zSc(!|I2rw!-D+p6h)qPmm)RbV?u6>@88g?(c*5ex@i(Q*Us=98cV!BLpv6DXlj-AN zg&<4TABfEd#kGQ6?IzYpZo~S4n#^{UiFM*1B1F*Cyrq?Ymp>QaiF@3%^9#l>CrisF zzQZ6BxvY8QMoM?IYuW8bj~*2@WOW!^qFSi802@GHdGZQCekkmePVYaRI5U-s4Yf`1%$Jo zSb#HOapTpi?ic`}nAT2PPwzN~o|<}3RHR7sjF=aavj|%bh#}ZtJz~eezwTq-MLdBi z?k);OfU%%Ey_A*PPIcTHLw5_JJpR}pf8cL`4S;Oe5R*C~ZaFAI`V$YUC_>4Ad;&k^ zGwFjIu#k_SGTFItED=m*uP@M~I;Wk%3rGj5YTZzOS5_NVrqVX%rtwCB)1(4 z#m>x5&EYvL#E*}d{f17Or~W*C!GMHy&wZ`Z*Z3eB%Eg;g21g;uEt(9NQ)F?lTioH} zakF~HEgN*~+^3BxZrL7jpA3iAb#MQyFuCwND3)hC`! z#`6h<8;esOP|=a5&k+NpxkV)+%{N7i!OzQfeXa+f>a-VIBDy96^~RawyG$QHZk)71 zv^y@J-Fd{-bTB6@Zn;-9fW9;{dWCf~NL zxI~d!{0-2h%gouoYWYCVu6vHe>P|Xi>S*YY+YG@O0WHfx0rhgZMwnB7RR9VfxXe^r zOZV+wyy{|s286KW`E_lPr?uk>m(Tr(t&&qtrwWtvDJc^^j}nl8xWbBob~tts`tAb_ zg+|)Rk)vK^ie1)q%E4xU8ZUVUrIZwyoR8va3EfT~9>gCtP%NhElq3Vkj)9=B?ZQnm z)W_@LG(?B@pF%SqTF}WU$L;4-km>*bf9^f-{h*=KIL)MrnP-t%&$zf^vkYCRjJ0al z{n7I=b}DV?q|@ePtoGnZ_Ucd2yOtZS>;yjXhJwX~@Wiew^l7)qWh(wJIU)mHehWc0 zZ`D+?1cYlZ+#`@_d#=1P&&vVSRUt&pHbd%ksZ{^y!;=gOKYgehn9+PtbhF5#<2)+1 z7~S0e^_Tm2O9T82}o%J2fYP0MEF zUhJE-G&pJ#g>!@zlSp43RlkMnFJn2!ZwkggjPCpgC8TxAmGi~&_2E$Y!14Ti>yEYJ zBubVW;a+SJlKW6ucSy*m>GZ0diq8pHC3&3$!$)NfM48`)ViA3k*yYE<$;ep23TL--o%R~w2&aV%HXm7O2Z z(=Bul0RGVmVc>OEeJ$}*sZ%zp53Lv1(Xa%l7lKA>cRk&2X(~s!*++H>315hNBUtr&$O zO%`sg)1s8d7Mg=V`CP2;ozOx|=^LweIH5KE7BXVhxDXHJevGY$*L@vO{i0!yQA^Jcz!{HpDQf{5odYyY9T4L5zvv`O%QvZh{gp%8%g_^*3=a2Cv0Q`> zZFaVy&t_T0B#V&@C)lU619Q4=d;4eM(pSxB{*StFtLAv%j8W$ zg^)fKh&bDB8aqb1vH->UMF_g24mn%URwLjD65A!bHU_2EnbU>2F>2I%G7<5Ijy6w@>msPogH{| zqjiX(F^gJo9vg_z$SHt)ViyOrc2U&|VC`{6inf}6cQRs7kTRMEJC!9~?T8K?-K}_n zotiFj6@NbtQ$T}lqex#me9Rb?kxnKsCxZY8CsK-J5s1OBGt}CI8cT=@vX_`FawpU8 z-*1DEkVXy6%<}Z4M?ip;M#V>VQWs2w3#fMDv04;A;rbgeN7QTg>Dj5$X5L~*gg~$o z5itgevU?b8{GhVA?2F;g`ri?FY^VDUtwna$8qM6#cyV3KXr0Q+=C3+LDZW$vR7}7( zd=PjW$%p{TjO)?2>{s@pOn`KEOwMVMsu@uaVniRQqUZFX7Cs zfb<4O&E;82#agiB)@K+gdT<=~Ka%_-nt8leoL>GFcrR7l0F&_qmd#N$_g&-93x_4M zzX*b4#D{S`b(0O}vs42DS$8{&IuUbQYe$7iRFSl`M3qwFoX+x*bJJoBSm3b9zE88H z#KiW$XFKkmWZD({y?GHnF7{!qejvFKl^w@aV$@tGuwn6`_!fkNH#fX+~FO?dsDe-(e z#!l5BZrlXj$eR+OX@z8`;wMk&Q)U}l=&y1N!-z#)Yz?`vq5!2#qthdKx^}tV@4wZN zrkzkvW%%%O6G2LZrCapJI)^^Wj}~2kfe;S_VJs*<#Q9pvIuTyU;~B9L5qtYXRbXtP zuWrEL`Nh=M{Y+k=V-owm2s0sX^z6n2W#%Fp}U^y{z}^xQZPPwTz0ox`@~9r-|lJ2q>x% zsYL5P6&Gtp?sqKl((Eu4V=sGN^;`UcL?pn!7oP%Z9@&m8vXaxMmxfI2k>K8LC)ppU zg7$YS{azP8gVg*nb_^{pu+5@%w+K*b)2>~o^0AJM3~G*iIaK;INCjCXBc#q3jcJIK zu&NIAz2NxRzRt=9JM$*D6FT^9K4xVXMb!7VxUIZ)P#r#Cbe!TQ%xF~X25;p~%dax{ zsNrxl;^q3DnCQ|bJj6G3#0W)-hOHKN(S(Tc4)KVG!k2{85SX`GPSRi zDN9Hixo<(%pN*Q$cjatH=I$qAwne1K1l?siYJ$g9LI5n8Y)D)G``1uHUEQ1OV=UiP zvL?)s^FnP&zKUW3xM5YO&&8U{R3ZW4FE~&##7Q~u?f#3s7EehRhJEW+aaY>rS07sA zWbUCHdgkY2BR@s)HDsnv`A3f)n1K4NY#99g7xkDNJ%H@ND^eW$sIjX1AeWB)r|B%@ ztzmYTcm~$|2OpLg%<$6i5p6$@mrkd=eyyEVa1$4$l7pe7ge@p({$M%FPF(;}um?34q$9 zApH6~RdVU2nN=jS9A^0c39eaR0^XcCmJ)E$&IqH}-F8V)OjoGI}o4`(fSK|0=^NJo*r;Y_OW++JRJLqQ+ zk-@))!n!U5&9#1(Nq-D_8K;5D$fS`LzeOOF;%~$kALkKmwPM64!*sQxxytvltkCgl zxgn~92<0@Bp(V4CbMSRL<-qjJng?iF9BCuG1QLOc4s)wbfADJqeixv|R=ef6RZHb0 zhG?>nAa|n*S|AZkP=_Wh&L7A)-hCFa$dW$dm(7#wEG=at*Yy?WK9FdW^6QDjzUt4( z=6);JgyyXETiLcNyfIKQd`vRUrdG5D`**0M+nXFz!;4?4Fv6d|(Zq;KX7#qniUE)o zU{b=}pFq@V1E%dgdQ?LO;yCzL`v-O>eTZDpJaY zMwDIzIKq#=5v{iDE>h96x|K|mcRZ`Qu{Z?#z`=Ftu(oq=PQB1BEMw3d6zd*sklQrv zlHcaJ{pTa0_c03QjGv{dVNoK8!D92q|mzS4kLX@lH2*RsCN<0{Cy-W&1AefWP7h=?grAPbs zC;uU~>4Ym(SA7f|kQ8xgZ38{t1aAbUr-#&Qh^C6(Gb}X1%hjfhjn>jF#G+vCs zYc+W#_!Wm#6yU-8EjVmaOEL@~$nNP$w!~aGQ6W(I^lXOQ1HsBc21dcR^Tn|A)l>ed z*WVOckdHCJvI#kh-{@|fwU{jFKr6Nt^^YEVrY0yvX`#YCUEVk|^>)yf zmH~=M0ozy~`s&Qn?oZELc=P<#;>0!iE^DLT9*BSXYIC?>_wZHsu6kD;{^AR0B>&6XD|N9n~tg)^IIE zxkEgUrzl2p^(5<8uyTcFzcV|+wp5wuZJgz}j zLIlBC7D%oj>^^;}j+zwAyqDW}fHPIzT=kg;OrZuPchL?z{~)+WNV@i4ODAV%i-pO0 zwd)}s^An+~!AZKCz0~&?!HLJ4jHev+xga?7&r?xvfw)b4xduj-9dELV7pTLR$XZO- zjI2^nWsov4rbagNZr*|OiHX4tOF6qTi#K}H9C|JhSv7CoT)g7<#5vzWI`3uFpXbp^ z_7u|cdo1iTcI;R?dwY;<4bsKL+FEd(Bt{`gZ({pQPMV-^rnA9v)WYr$P_Qtah@_pm zNBU9Pj*J^OeCS8-DjrD;PCA4>F7ubhbgRz5B2>LXqLR(GZ{Pm4vT*jm0d?=5^b;{8 z^*lDW^Qq4aa*kYY7rpelg*P9fcFu9xbBUp z8|^oxXVj5Lda;fP%w%;YLd#eTnoiM4BI*qQiQ#KL`gxu0L7L4S5Q}!GnLTW@-}BwB z8}HNsA+khBz6Q1EG46oOwBT5U067~KO{rONWd|HNh#j&|A$W|PRi~Jeos&+^#{rBq zq&IPlR>D7eJ<<~DmQX(D-PnW)kw(GsUmkD;WN{07LT=I?u!&+t;KCTk8@!P}UY!xC|@;#-W%ON>QP-j#*0wws6_9-GHgE zQ^oZ8qN1Pe^|Ps}sw!msJw#C`gx^r-$p#2Hc z@Z9Y`C)t#{j#dE=Y;}DqRs*6Z<`I?Lt9rqZ4bnvg zjdo4y3yA-Kq;u&sKQC5#W!SOF7-jya{)&Z9xbjILBgMD#PD;3^3@C$x7EqL~rG)ib z{7z7hCqM5(5P{~)=rp&CIfw&uo{b_d(NT#nD$@@*#!U56?|jJLUnwpwPB1Kt*l;yK zw?V7YXrkgBquO!vANfvJIPqe8)fBaBkPI&TKE#hHhpMb43qv5_4Nrbn5L#yiV+ zjXu4a8d{fiQGJhstca$W(S~iX1+=!sP43>nNim3snk=i*;3H7380hO45u1-8kwyYT zT<&zpOcAOU5?GoD3IVVhL0hhkdKj|BQzbi%E!VQ0O1f*>0^zef9!j5w30X9C1a8%) z*1xCV+%974$B*~Y)M_W}YS==}`u0Pu;1A1#!j2v51&Zq;N1c@K=2mVoKkOv4g$P&J zofpVkt1i0P7^vTeDCh8}HOH0FKd}K`wYwirjCZtyHqIRbC@$YE&>Y#r#UY7C%L9(6 zHSy*jWL;*LT5;R&wri3+FH7u3`(P6S7gfq5p@1Ii{w$p?MyxkcUWx_-4{nc#-w4D~ zt@958s};`j%K1`a&{2hPiP&G_m%#Yy9Qy9hw=eN43|GH<+$6OYHEtphf@~FG=yJw8 zr-x@LkVbHOa_`js$FFbWsq7pPIEhosN~b9 z-E7k01u$N;0)&f#j-Gz<*A-KzbxQ5oA_&zKH#nV}7y8vC?G0Kfan8eA+*BlY)tCO} zuFK39J&Y`YJ&)c7;H8?TOe}gKNWk7pCnuIWTB6L~X`p`N z>C^<~KLYA6^#$t?SSI!2imgBXS2> z8{h%aY!uBcOIc(E9^5T8{t5_)DV1G?PNWqTlTm3n+j%e-#n*2t;SEe!5ELQ7NEu>4 zMe!<=pe&=9Vrci~PHsQ&*>^|@=djeXS(T2EsHUMNO7(bgj{lcPg{5+Ym zA^!nbngI67*dI1MhvI8h;O+rmWtktBPr#<)z(bP1dF%+t3~~;e^lOga?;;Q>3oZ|t zGbD0AB`@mpV6P)4c4B7&UxwXlA3$q7ObtLL<>36he36jd&D1NB(~6T`b_@* z1PCw0tdX1Uu}!=$v2tIsHJv*=ix~_Z(0>|!$9x*unv0bf$C~~u2>_cxBmE&mWQ{G- zxUsow52HpGMi=fTn}EI$gg?rw-} zIzT#Xak&OPIQw=bjZJ^nR)CjQQ^mltIiPBglALJ~d2p1smzu=#WaKZiZ^^WwE)=@BtBsy`<1} zjGrwi9pZs@oX%3RG0AHiR9||FKY!)6ZryqT3MvYe;|vu;rEViw-!1#FS8^BzekH&v zfInJ)E1JL}+By4#t3B>M+b4YQ@3*y;a8RKXagA^>GHUGarL*XQan6pSLeq5@&Yy4M zH`J_k)b8Wb4sxK3BZi~kf&r&bX~YQ;yDlPv%Yc3ds5}JdW@<(RUaSE|3CW_n){NL# zQ~0}@%&v#nN4VNN%IgdF?%ma%J$ei*?1$Vkr0z1^Ab$rOp%HzI%%W2Zaamf?NW_sU zNo#k#RxNx{wz#oh1|r(%MrIGgLp5J|UL(0A$QKUAhs? z?>wqenP8{!4TjI59kd}hq5p|=*gnux-DrgdK|O|}nzd+rcAhENBQHOl;3pQ~o8y!2 z6NdSpTMx!H@l!G&N_XQOm<`_=&g0|z;F*4yQ1OW2xF*Z*S!&2H23TrUW zg?dM%LHL0%)*Z>;a9Ok{fWnrK6CNIpPe0|o3=_h?&Rw!3@LF_5?#uiy4}k@@CcK>qtGZHLAEW%1vJ41LD{Rnx z1dkpyl3@{X;0EyVVA_28liM!QVCy&%L;M~Eqfhq5{GqvrRILe0}#WJPB{H5~z^- z5=Vc(J(FGq*ZY|T6U4YKtc}g)*{QC6g>>HpMtB#}Ct{%n2gA5>OSUjkZ? zQW_IkuEFFMPzKK|^X1D?miq3@=#(YB0b>YG$w@!=CkP>WVG|K&?J~{Fn^^Yd@xb zWPh{I%-9`3&R6v5Qx5qv`VL(}+$>yCN!Y5dN5vRBjC&{stdyDEUB&E4njD_VCE)W-13JTK(!G?ee8c;`b0h+91(56g4TD~} z1{BnD;ayGkh+`TM>}xW!L9^xB`z)iy3LQkY(ZurcJZoaRHiew@#(c(@!mBCfm)C-1 zOiNj|i$6;zV~{<6gt4*A*`VM+LbP$qM~Ij}s#DJIsXmLxZQ?ExB@li3g9n6C4$vV} zrMD%PAS__r^EkQ*F1r%ofvxk3r@RwsJAgSP8*n*IEY}axhSCh&v7@<9pMF|e0chIR zL|FS}f0mXQg`+i+4Pu8I@6uSYW%ai)eG4a}j7oBQuzS);AteRt;OCZn{CJ(}FQB-Bl`3=WZQY9!ZsQM@&sJG@ey87uSa0F#TA~$}4eKeBv=HMV_!2M`cIwC1 zD$`alPzX{Q-QW zMrAO9BMR`*q`xGYN_3}HV#rn>A0l$|{yFDPPdokh)XEqI11tZbZiT{mpjDT4ad{BA7L=7|~N&46?T zEJmB=Q-kSlS|@ifa{)97IO4FwV{xUe{A_T6Hr-gef5~T98Dvg^$%q9qXI)kfAmre3 zQML*DO$$(k%)9vQTlLDH*#sFSMlERS@mw*y`P269a}42S9G5;B($)}sO4xOphMh(F zW2}17^RT~R8&J|*7ndz~8c}r_e5xggA0kf&b8X3JeI}h!r<>v0rawQ!ZRFf=wVQz; zN*};>1~8rfJRY>LK~@fB{yG3wCYA2<=hLpg+26=1G&$1dS8(KY%94E?j1bNTKa_zr zRp_RDjWe|2X*BuB3OKyB#U!)Ay8KV~Z*7X}uU&tz>`-LokP9YWb>UDyvYd^1j8z4- z+ytgF+;~{%UUW{pLMkFiD_w?adE#v2*O^0J_JPPK@EDYM|=)2j*IS>MVU&c>}*p?99aTjby-t(gW)Hb>H5F z&1g1^3I^AD-Z1gZ1&J-w;|ddpC++1qpzdwWnEe2^hH zTvb%gwT}Ktl_CbRK%Ps`7G(6Py|`?H*C4&A%}b(JU2#r2q`T(DZ%+P}&40N!)m~kZ z*OVp$6f%QEA_fsrqjmOvY+FR-R8v#40zY`3gH+w6_hvy0sWIM2t0*hr31>Sn|GZl8 z<8AI%)T2o>79^B=;1QH}DLL+z*{yLT({rm1vC169AUCeGvdS1h)xdI<&CSDtecv5E z33O*0j&BM8yvMk;9684`Ge$7h-Ar^o@zsT^R zVu5~}@q*EH#HoJmZqhq|AJ|a$r5%w-G7JeR`+dOq?z$Eg-;Y%`-&pZ_-4M@Ts}6m9 z3q?~$5$Uo0B+4M+oW}J|VGym zR~DFHdf4A>)-DI*$p$#n{BWa956j<1B+l$?6+gtPx_0F)o{1q8BSVh_%GIU6lS00n zMdH!yd8Pdo)8z*ln;_qbTcbmRD=d|artog6P11RLf6#4W$h>uca0E_>dk z4}P5??kHgb#x@+#F5vXgU2meyCnG`lijOt?Ql z;e+M5Yz#sE)*2!yV4~*FBi{ALc#m{sq1+DE^4=FQ4T?;P?PFqd`tAo8NPt%uw`ih- zSc;K9axo<5ExI;|>pL^&@mpJJak6a`0J9)c1SP)UTe2cNw#T`NNPZ;0aTJe|V~sn{ z#w0J6jv8f+ybx(U-;^ zJ1IfB6CHkl4f{-K{8vmqG7S4EQ^Mw=rT8ltC~8=)cq*WE>+^l5o$o`QmER1+3w;q{^x_&1o)NSHizEd%W#$VAwGX64Ppj4*FX~)@{gyKjDf(H;2k8bniC7^kt3_f>vHJ zV86<%?Hb59)M^UH#+2&o>x)`IwmqRy0u-Ms{Vs#@F7q7x`BLhIzxpfyhB5Rvg2Cp^bfm zgWFSVVS$JW;KUc79C%{B6=jYU#1A(+`sJVEfce+bgAUX4ZX400HPP|^j_yW^k(`tdJ?&~tIsTMF_K z2plDNvw$z7dIee%$XQ>CtW2Zp2M-)bf)Qel=O+I^RMIS~^;;X!q)Z$nnt#}_V~2>B z0kY(v5pl6Y?VoXfy>?XdUY4^b8^Se#RLO!N&Q8$s(UajcNN^&E#m*~7F#7zco&C0k zt9?)=U0fGH1YAV!JJ+Z~SX4j`g&T#7EX5SuiPR#S1Ee9MQ8ja}0;PE5I;sVe8-wW0 zv?w{L6q51I8FzE8axzPg7fi!90=YqG{Cs8K^BE5x22qevi*!UhEo*&c@)E}iD;LYmv!uUfdm9aJ!7=46cMy|=kQ*(LyV;NsT2p1^+mS`Yh`u;3bb+xQ8wLdzJ%r! zrnXbr5pS2G{O#QE%V1lLCq6@{_2a-St-WJfM>X3o@e{O}p|M;4Dn6E-Di6)8^u(~2 zWU2Mittgm6o!(=2IBwlj0P* zT7;gmy$6_|-++=-k(ws$`E%ofZ|}I_66;2Pf_jnhFfc-FI29cn9ISKVJ}_oTY7y^J z5C3VjOfvQn^PuIHqxa|*Wrqrld@;}1VE1eQVuh%rIhQYAW>0FW*ej1vfAQo=7`HPS z;8?x<0fHT&xDfGYTr3FMeJqO=DCrE9P%cK*P2+ltP8c*LPw7v`%aIAWo&1bv`yIAd zi~6)t-h3*&4zW5ER2m)AW;Sz5q$6_JvHWGCH)z$B z9|_pg!7(Uw>(;TSDm#(ZD5TMpHXf(wLNkDmuJsu$m7W!&7cCi6l2>$=u98+Er1XZY zQx(&GR4QA;!ooJq&b)QYysa0zT@83D`wX9xM};)iSfuX*nMq}>LCD3}x2s>a)g)Rw zuG@C9v-EphS*3x=|A3)estUClr8NPGK4WBLH+aEA4N=(;pXKfYeeX25PZhHcz}74& z+jjeo9m(v%5~Fv@z0L7A&QV1?N^~8c9-ZSV*GrH~(g`nV9=c-dE+$_{xpF6Y(4uFB zO!x1*T(5-B*X9`)MF?bSC*$a^v0d%{N7leT zf$aB}5)$Kzpy$_;JfvAKknvN^u|mt5jn z+UhBOPOzcu_)wGkNp``^d3478*B6E6BBG;bd!yd&iALWqlZ48i7}W%5ISt2Iwh?e_ zMWVs$_psbX11?7CqO>CHOl4;VZ-dQ(22u}DrDZGrNte=AdFRRV^S=%rr)2WDRok|I zaw-5to1z01QihU5G!KwsvNbc4S+soN7)x1yhCHY)Bm0^h@m`tq_H627{Ib^Huk|46oAsfJsqY?;V6Z^$0|oGKy;-mM`+ttU zVct@?_G?9GPW%E-;hZ3A0=ta};W6hixu*VXt%?lh1C5Me3W|3eE7&U4H)tZm`aZZd z1nwx6rQEO)40i#z7S# z2FomNy-z`c9fQ)dE4kYUriXms>!=ojYKP=9kJ zQx{;AO`;lEMGtz9)rP(gCPR$GpmQjUmH6x<-Q%gg@r zhrdi&-cy0g^(gD{!UYQ`*H^Mi#w_sn$**2ZeprUJo?>UkO=Hj`GG{S^3)-}lcs^K{ zua3PN(Zy5IrOA#l&Jni=G48bOWMj*Jrgx5@XKh^FFpJtUu)W5c@Gan&x0#F0%H9DsM z0|0ocD7u(l;^IDHILe~wjxmq@B8hJDcYs#zg6_DET6G0%6POHXm3GdK57oGK3Ixz? z^rk7-FM$}%_XavEoNSsPU52(CV42l4_V)(>KF*FPWJO1j z)9OECtYm$JPyD?#FqT=@M+_3j6L7gG5(~U^ApM#Fr3J2r_kcl4Q!5Q`BW`s92&EHe zTCWM@Lnh*2yz*!kt;lilKsNj@fU8KTFzQbg5nzMrsN?^Dee&X{?2|T~>!S95+bN!=mrJpIyZ&D`%!bVZyv3JdMV!OB7p0?>ZdPaEz!Ew*Gn-(rOyF5HIbEL+b zHlHP=lET?^D~55ak~Aq&!w~paKQL_&JqM_W7(9!2Jy+H^!Xio>qXFBbMy{+p60_uF zbpoTz|HBcVnssg3@ee0efEf94dHfPIFKLut21AF=N41N`sjb^{NG`Fz1!!zyVX~JV z&mqKilJyuD;EbDTXC zp9bUma1Oy;ZMPDsNKi)Kq7RC|GJ&=xAo<}iU|aap7600NyX;j@NlbiA7?m|j6c{jO zkeeP=d%gxuKlvnE(IvO>Iz{Sy8Nno68s{H$9Og)_npmvszqg4W`<4FSwUhD!|6M~7 zyC63TrE5j&RkeIbTqn}#4v3-Rq%3SRd44?_X~OqhQn+k*;S9R|>p>C##)yWS3I1itUySAPUb-%Z97Z^f#K%6S)QEEOhhom||zY4j#7KCM-cBv5g8i(XBaq>r;+SVPfYF)#Gw#OUo&4~F7RXQ?;k z->CSyoI1|S1-!z>aYcA32Y4Ps#3X|4^uBqBE)V^nxo@L2U}HEQu+$nJK2}(X)UeH2 zHiMy8i$#XdqlcR2Sj~wtY(~>>-=o75_FqA59eV4y4zJfYE7)Ixj_w(rkxAnZE19Ms zMsPvn1q4Z)+!>suF#@_)lvn)TH)L{9@j)3w^0s~?T8S~3l$yP$v^0&tByL?iK%G8)tR^m6>p$eyMC)dWly+YTA&vAYy)DAtaCU`- zBdj_hMZlxOv47W$2lqIGbb5b4fyyWhrvL@grc;l=uEI#0cNP26RtU)f3!v5aj=nMM z3eN)=xH(zxIG;^YIrXOyl*n;uE?SZ`dy(TkzU%kvcXCR8^V5_1^(-Vfb*{V$t(490 zpa7Z&hx7S<6|b}iJscPr+q&D)x1*Wv=|kf_nB6#+b^*jv5($fmOzt%wNW|d$wmIx) zLLdU{(F`SAdUkf-zn7ZDI_6$py_n}Gn@L1Tj$)NT{R7k-SrIyvj|^G#AgxO#z+G?l z-fj=K5%_>j=-3>j!bw$KOp`e0|0xhb`EB0H|E+1jEgl`|% z!^HSm>?^`bg#KLfq~|oVr^JnH+O#~T)et9&MJYaRYm#0(S#-hqgtN773uB!Ah)B|1 zaA{BWNuJFUl@S}@i8)jP3{#us)?p$f{05D`D>6j}>&(kNA^(KsZ`*d?4^IJ7x2DH5 zKPf|^)Pab}gxEhcET;J*bOMi`-cTPGg@=G4T0$9HpeiKvO{nTd;gaJKH`1@=5K}T{ zWl*ko}JjrV+7JWfJS z4mD9MV(8+1k*^FAio3+mXY|C)VqYJD@NoB>AUGdLo~S^KMvfF-m>QXcE!+JpzgMJ% z?4IxbdlnoX0j?S+vO@}qY5g|wLq7Fdd`sjY~1@qASdM+_m}xbwECyXX?BWM$1O z^Fc|>$DqNIDxaB(BJs5%JefJqVQ$s6^T;))iXDSh63$zH{k+fp62_+rJno$r9xvU5 zt2X#aEmfxiAV+?FVEw4GKTkZ&$yW0+&m z3)Z0$M1;BAyy>JVQ}k(_xnl<&+ngW%KJZkKTSRLchZ!@BXnhH2dE{>@g%&?@4!+H< zp&h-|h4`(P=}3F2vm@ReZYd!Buw)HPN~n^S)Cp#csD<~ym2LaXHg&nwt)5{pcRm+{ z80cBg-b!Zz+lbq>>&p)n-56V#kVXA2M_kJ+GZpP<3^ysOThihX9^E6$g_kDMw3vSb zHd01OP^=UuTHRheZ_%P2MO~veXW*;R``F=Vm!KT`)8@ywD2+1HZLHQ%S$Ri4MI-Mr zS*kT4Q1URGS#u6cB$}+yUAwYKY-|X-1tQ6!uKj~UV@dY1C`1%RjJ8}HQn=P;;G#Np z>x$@$=kpUTK_z$q{e0So@Steh+r}ZPXogI=^;{4pL~X4V6+m{L2kXA%PRZ_DNouV% zIt4ekxTDY4j1A-_UcWOzvQP`_NCf@o%U$WteAcRFEIK@18h3sK?xSvMYtg0~)5&D6@}<8BcINW@n5jZ=YM95LNL>Z(NlFgCV{a zgNNJgi_cOG%yJ8sPf?z@?D+L7S2SO)z@0R&Z*H4qFIh_`K0Z<^!X+tCX41I#8FlUh z^gO?pw;*?i0{_9x>kXer*Hr_Jyib!Rfl)WEuLU#-e4gZ`)_JUv^Od^ww^^#ziY{%h zQZj^LSH#hI3RVK{$|R2%pJnrPq}E6e5sZbebG6EH+rP+}#G~uw?l$`K8aQpq z+Ujk7`@EUWtg6xT&LtNF&Dpu_(>foGdS5M4XT;DzS@f8+X!dMlxY5X=5!pww0=&M5 z_&h1o(J|8d>-*8_apk{0Q?uYdIO&M;xp7^ZGyib_(sAJ1)45YT*fy7&lYm?lW31k0 zRhCC6csjj*-VTSQaH2xjCdq1HVYM|s9cIo_C`ZV)uXQXblG*0HAtYqN){=Zk%CD%1 zZ~%T2-*X=J!%Q^@u!&xmT4O2bDGq{h8P|ORv)+zwOyaBIz5L06x$}ceb=0G~WtfWs zkmmpTPuBz#G~l~2*=`?+9cfptnlouOa*5|eSZbyIE_wAZ@5!H7LcxrNT|_act%k>c zLL7JxkaS`WYP&Ynm9gAih*?wXpQ+$9sz{Xh4{F^_)`|-_M{5$J3 zbz!_ab+V{bX02gT#;(h_JsU1Nhzpm-ubi&OUUhausx$KN!!Ey|KaS>}LyotD**X#$ zdKu&V1V9G5OjF46Wpxrn#vqg*LfRz8l5dckXI0H7t*bP@a1KcmYvJ-T%qAHfxV+>| z9dMbOXu)wo)0@6CTXvC3EeHRL2azS`n7Kpu_@t!p@b=OX5He*P2!J7z$2qZX@pKS% zk6%B;t5%W!jB^Ho8cCDSrXq7@8Q5~2Pc7tXMqfcvN4%w#&%lZj=j&^uE#q?^&rw zdD|b3Xi*CA@te?$@8a%vom8dv=WO-^!`fu3br>9`xz%pJA5VR^ZcRx^sU;$$w}T>g zoI@jA4=@hIA-wFLLC0d(HrqNq`kuQ6>&n}XI)AmC{BW z3B^-j>i`^CEl}U`_?cRpvI>KGI6K(*cm1k$d!K2}!r>p>9nNXF-M_h_zR#3cjbF?A zUn+R!Z=kGka?7Q&AwxK%;o)wo>#Cq-2!jXKg zq2Jg^`q9t)6Vu{4PdgrTr{BqEnPoXn3!(z*n#TlgufOEe<*2I5>(&ihUo!rZy6?hC z(QhIk@9;i#$)O^D`|ZiCTbed&CF;XRTg}#uFrrS4@08%Jg)L~ZCsA|f?oNr4N-4@(Ns$9EaC}ty14Sn zEF-M0Y-Ql2V#hTbMX*=^pjS8vN{Kj5j02@mq-v)RoIH0>Iepmx{AolIOFpN%5gq{^ zVi<*pT8?g32;GPcbReDl(bId7;l+}a;vVfF9xWx@0$JarXtn_TlwO67kZ$cpJ(Zex zch;0f9=HOyt~UCa@bGEzllIBC=JI2Qd9u>=?LV8(`XGuh4ry&^fr2-KI_S@&Q!XD! zL^uG_Ox}102aof;dzt1|MB;S&s(2~E#Rw3=B-XN1X(u*s+_(g@6w&6CTq1fC0Bj$O z;(6kzsKNh6-Fqh-JZKnQQ%pUTX20+EkJPv`<%tKnYd;~ug)z<&e;;gUF;R;_rd>07 zjhO7;p`uY_PMltEH>MqX(a`&rxamBzcE}HF5q198@bV8O84v8;7MeJXSyRzihp)7- zud*z9W<9yO%D6f}_h^|_K-M$sH9tFORF(dfP}-Q>E500OClu?@4UPt?= zTaJ(a-ijuM6RMj%-Ck>$_cID0neL>uk0Kisf1u}@1IB|?D&^-BG&Q*9k7Qa$CgAvE ztmntN=P)xvk+}5r)!Mk{2^4^kM8siwmq!1|98UpzvQ$WzIAjf;Jg_gxM~9<>5TwV8 zsmo$-4&6|&a-L1rT&G&6Hf|glAJ#u;fr)v3*v|j_p7-qCod5fKcD-2j>-Yi_r>e4M zRTg{Cyi9uVbbD5LnaZ>^h4a@G>Q{RD~1>jLeGP!@25CX z{8!i<@@rF%s?DuiwbEX7+Nev!3|j2Y8xfko#vy(tU(a!OHhpCP~!||Hms+wwXJ~TsdchcEUfeZ2Rw5)>K~EgN*8{ zD}wyal)CATU-NlT<>0Cbr`p%QnlT4+b}{jO=|nPZ41fI>`Hn{ZeS9@4IGKoaZVTv2 z;j8duL{)HNs@j`k(}=qlEPHZls@dM5kT{5EFqd?}`Re}LM6-X&BGZ9QH9GyNssBy- z{KFdd+OcNmY@`!GghHti_cNNLT>L@)fvfMc>-eYxxErc@Qde6e9}X|0Xp_4 z*H(PrMqiFu*mdGVLr+FI@#r2Xi-~oq3kgdG4|k6F^K%CodrlmPiuWpg?)`D^b4s)4 zs#IByF^tS8{N!rxT(zAJG5$=YohUJ3f2<9}n_B zU+357|9;Sa{`*yrpx1nZFNW0lC}&77=vfRe4O;IP1I`NQ!=+D7*7%$~{$%_5!=I;c zk5YBdX$>EJLR>eznD>$^OtD)0f!iUHHs-u* z!W1!r&}tVc?Uk;Me5`^~tp>bV*{mfS%S!H`^=m*z<(I3)wWWQ9s+#VIC zsq?c;FbYw{K#kAfMlG?7Ev*psA#$R9u+41B9X)iru)(;vjOF?=C9;(e8TmaZ{XBP4 zhKAuxgwpIEYkR7y4o+Keh6)<-gQv&1^`tnnu4XwWmZ7y6=Gn({1!n2{s1Rol)B`Cm zUv^NSL4SS_I0G2-SC`en}?oHOIc*{TNd?{S1Iy3ax6J?PW-|f z%PW8E%_<2gxSs$q&2*AN(4pe1A+119x6nyE{zLID}gk7R8+LFb|mkYQF2U31eA~;1q^= z@s>ZFZ7(IQT)g-dWTQw!b#-I=#{y0&1N88~>J1und1CBEdN3}vHm(1X4l9+!+5<37 zHmtLPcl?8lA5oh>ndUsUHrIP%C43h`g;T^g)~dEEIwLyPpS!Y3_Wby)4)#}r4=$n< zEG3$sEkjEn%U1#aFEE3$=f2*M9b;YM;ZFM@vlbr`+LlrI37lM6?9MF)e{H~|jL+$C z<}?<6c+{PN5ScDj`)1|C17=b`pn3@M3=!{g5&*&15B?;hWDBqHPPnp&@EVE)7m8Qy zTkrC}syI4GHbojWuxyvA7M-1zF$)ja*(`bmqB^teuqH~k2W7i~U zANy1Q0>N&H-OyM|`5+V$YG*_4eQ&F#rX69+p1B*Vw>?gDbA@6J1KQoXP^+(bMaaEx zZIeL{3OTGa`4o@?Upl?H+{BOZ79Lr#f!kp7`kdHkkV3;tZP#e-e0$S&O~ij1`hP-1 z))?ju0_~E~x$zklY|Pj)_03rwss9RzpU%2;pSZHYHrzlQOUa0bZ^qi2r#d?=O6x zmgR1Asl|4S1&?=^`|Uh`;7mJ>ouN4+3S9~+)@mhItt$PX6@AmKuw8Eyb}wh0%zAS% z;4Mw&lD&g0s{G%z&MEQ>Es2gYy|a2+>Cc-hI<(6v)2*6A1oz!>Kc_k|YxU-=oGMpy zkDR>c%3H=P$XQZaVj9;z`jg%eb>9#1J<7jJnRWhCzw*Dvul=yR)Xgs-$GGrkR6t2~ zc4FCusFZJ+9&h7&M1MCiTra6&$n)JT|I02o!h7ONa?{kD(EN@pW@PoSvKyEgC<4<2i-F3npNI= zf{Ir)grM&+d-O4Cb0ckrn}zkz{1#PKwZf&mT5IQ!bE`YWs5|}KqkocCe7W@+dk>xR zsZE&LqbKnEhtJj_#(bTVyZXzkbuZtwZ%IZI!N{9$q-(W=MdP{*37_Ba-_osI6AWjd zOWrM|6t@GyAt(S*ZSrFt5y%yt?GF0ot~Tq}F=*#kGbfu#>Tb_9%nDr9yt2iI#uI2$ z`*3?87X?U7*vFxI!O(OQtJ_C_)70<-+pQHV)3yp!rMlYM+VzL1xu2mKt06M5gtLVO z1?&o{F9sI~GQ{EpxTB#b`?JP5m#jOuxg}+|toac;2ljH#KCc(tL8)rzT&Oe+Dq4^) zK_0`)=qHyfU3vf#pU+cEHUaB8B^`J;55vAz`*L!=%|+>8g+OaHi{%-by_YsHxLsz= zmcT>@ek=ugSH!RVY2&Qyi~_Qr@uz-BnU99_wG~$|&s_IJOvwcWgMe;Y3KejR#Di@l z!=ig&A8!tLL8>Jch!YDjjDIJft*d4?X9>oovR~2&u}whs(qR!%`>H`+f4$nPeV4eb zK;v{eH^cPtGzMpN5mb*X|2CxX=@&ZN-Y;Ddh8V>^YiT^KABOjBf8;EmIaypq6@a3L z|1JkWtsyiv<0XLxRwzj|-tD_vdi&iUvb&^rXHqWqF)2qje zSLK;e?%Ll*OKoBX2RyVS1t~-TcB&#Oh`B%b0+`0G4ayl< zl~q4uTimjR7pJ|EM~P^A3JWx%jzLD8z^6e0DG+yl#ix%Sg;rL;aZ3F{kQYH3pyU4Y zKNak+38N=b>IKWkOmM)97&WzBaGnE9C#i#0i~vAT5_KgCEaE+PSN4JOtKZ1TP{#E{ z`X%tcH|?jXhroK4pns)s!MX9s>g9X7sdqf0Ba(KJy1*iToUCB<*Rt+QIR4ZkGnTw zm`Wtsbq2m~Mi2JIURXb$N2gaedGQ|KjA8brl#Jp!cTW*IBX4i-ec-<6EPD^w zqEWDJ{9&lXmOt$Mtk#sExK%Vz>%8v=$)ztShd6A`8>WdAppN%)cZ%#<`v8K!o)cUY zNHMAE`;_=~p>ap40wAYdC_P!I5%H=Np$-mR+_awdZFP34h4!pnFJ`69s|79_&P07U zgL>E&#Fs2592BL*T<~}V1rfLniaQa4tyUP2ljvw%{lN}&Ighq+b`YgC`Q2Qxnj{F~B&)y5_=f;u?4bvu{scLUeiMKlU*MbT4%i%n}Cjh?^uceeQ5~ zo=Ef2UMiANOXCdN08SCeY&|lHTM-k6->wlfzD5d!yrQa>cXP@WZgsPE?e6fI0$=R? zlGBoZCY-IytLy#nxV^ffXV2qyZCf|79_C=KUjr3R0Edtb=+H%W>+iR1(QWOih{HVGFi24!i(`*guS! zJNxe5A%p09jDdMCSK2Jjrr3S^nCYmXHq+E=uyQu4g86Z}i*reBqT;GnQV!<1dQ^c3N1!JVGHz_I^KM&U$P{59fUs<&a_1XM`0S!P5c%nq8m@#cdE|Cnwq54-(J)_YPj%Nc#L?}71 z#xC(n^9g!5I*>J=uZ!}muOkT`dN(1 zc~}i33#}!BG3-l$l+XK=-AT&I%3OT+Wi4^xI_Y(ARzT3h5%uE+&nbNso7crYF@EZ% zB@C^vncq*xbW%$0z?IuOw3{<)me$;t%U70jweDvU$AwAhCcP0B!=2K;MI7sv^1N#NyH>pPf6e_ps?LDn75vngE4kPyu{ zB?lPLOxGj_TU0k(wfYSjQ0hy`mFF0v<$C zz=-pjsE814psvCTK#wXXA8=blXl9dwf^7~zL@Mf!gwP)Y0md{k0F8bU{AjCFl+`tI z^YV;=2-Y1OHuk^AE@PO4Ky)$S33a^nPZ&qrNH|9~w2?^*QXFAOcVO2$9s@SCs&uY3 zdvfD@O|y0uywwSG4Ma{QwU5T~w9%b51x@O?-=$-wBlbSb4l1mbdJZrD?erACdIYZ* zXYbX%cSi6xaC4I++G2DlpI`7tHHzcr(asHDWgMY~VZ-(=WSA@&=!uEEYybuy zu{xzcs!P+>t$h$Z@q>4)yi}m%pwc`($`0~fsmf6$uo7tr1*Pax2At^My$hr0Dz2W{ zWmjejOo?zs2DsqF2Hdkz@e1mPLJ$}Et;=I|8;}1V{`C?Vt zM^uu!j8aj!iEJ>X{K<(Ey_io?GD7`mYY?g%_nzMa)DlIQok9Oz1D!9S?h+PAm-H&DL%BP-Ru+fz^EB0MHNyv%@k zlGD<<-fg7fUn3L_C+yX?h<~BNFFE^?$}QHv{QK-r>`wR?8yA<7`xm&)B^svBbRy5q;7 zorlM^dG3V4$(z6QjE-O{UR*)9UjN)T7OI_q?v}}-hzR>j-Rsx9U6x^9b+B%y`rCr+ zL0G;db+fG>+{3r~u+G&5Zj5J&OdBoTg`G~JX=w{@p;CMz4 zRyKRqw@0NFpwqJSEp%Y=7nZx*Mk`+r+S#$3;Zg;Ll~M!=B4Yh^^ns9?P{fxji>OwW z#zL!zClSd8Fp(Rpvudbz?fP))Ew!``)G9MEKQ9)VnP{}h!70qlX0tBl$ilAE>IQ$d z4|312am}om5}Bd3X7NO><^Z%!jKfNg%DM(N>qHwZ^ge_6rUpKZRnaycEL>ZDcd^Jwb@^i(c8KT%@p zypJE3`hB-*zPou+{+U(>16%PSg4(qe#`^5x(pATnjh#I8(`r}On&Q_+<3^h>_Q7;$ zQAQ19U=W!l=I?(qruE!L*HUjO``EAHQ5>;zvPC`(U+I;p&OVq@bu?Qwh$-{}Dl8;napYJF`Ab zEp!W}()pP~1uavkClh0XvOWweY@@HqgUN$iq!bfd27a+g2#5pnyZ#4&*N9T?xFJ+! zgIabT1O<;fch50V8d6ewR9A(N-nG{Z&AJT;>!HUcYb$Wwr(s7Q$`tMM%vr1Oa z!YlEKr&8HG(Hb2o6_nq*Q^(M6-kLN=HIPxr+Zr;aEw)rqC;V1SV@V0)U7%aqJA$l~ zWm3*lspdWO$5h?y5s13A;a*U>MjdNqhunsrpD>f7Zs@K}N|b#e#6|&h!9quY6;32U zZryyHengH2jqUAq-G@IHLpVCvw@G4Tc6-B4`iFarT@UErDDqUTly6@?@E?urdd)@H zvx;r093fkS<8Z~1;}8G*@%6yk!S_RCw0hpW+u(Zx)|4Kc{_IgpJ)@5df){@;c3YfH zK+v0bVqazJIPHf(+Hd>ZU#}6hl@@LFD_M^6CVqt*-xo!EO&Rh)Y@(OmH~;Q#6Mkdf z<5v9(vp6*L`vJ(6d(BApoZVyNJ}aBmH%fO6`^fiGnB87K8^>yJ6a}~N;ryiBj*e~c zW4u)PloRTCP;oLF>OnC;3an>5poSz-`>jQUD!-&}*RO2_5KX2;;Q!RU!r4j|= zB}~|Mv)B~m*iENqiYgRC8wFjG7dxXV0I54fwnR_JKgpuMfnhml*A;)~Bqro;Z9e=x zz9bEhbEZ6dwp#{eFxZzig>LyxqhrC_N8p$nq<{0%lhY=dm5kNA^^_fDojjBx1+Jhi zsTJ8_eV4mk{C6>kv=!qODyO%ErQWyA-PPOG8H+tGipU+rTEx_b2lLl|SvnD6Ieohp zYM{}fz34Fz3;`i^#QYd;6NjrU{hxuX_{6_3SALXK#?%BJ7xhKdWBh7(8?D;jPo6!i z&fcyPJaRSjgmNW#fL(7N+OtQ|TZmFn>snSl^zI+TfrH46XUnZC4rnd6e$z*fmCG4r zo?{&oGg0P-frsc9Tnhh=&bJiQ0ClamxWcSQ4rL}v>_vu^|Xh&hDO1rGRQ7Qc^3`#`hAzd8zOQ|hPRW{pSN|CMnfb{ zgh+sZJK=*SEQxy^vvwoyybeYi35c8BrU^bffA&Q)xMKyJY&o40HkGP~#I zefF6aqksL4l@X(LZ84SM%8wz-LRG}9ox1%lQVGw4Hh0Xd$zdAb`gn_-2z6d$=HD`$ zEECS4YTS+M7aQ3axzd_>WM4dGL#LPCF{U(hk+XA#b5d2EWPn)JAy(*n(5$Ru9~l)R zFv2hE(u)FkA?PQX4oA(3i-Gts&SZx!J+WqHmFW?{)+~&hP|V7V9+OR@i)XacbDhYZ zIQQ%sQE-bk2SznUU(#i+nbi(XWc_5M&dW|I{}&&w9)iFD(Mxc1kOT+lAjX%A!Mi|I zwljwJ@?FyjJ_o!+X-)@3V2VDUm|R`sA@cFkM=^HCN|}8)&M~0IT^sO}ijQ?K;8UBrXnvZ63j`dVD9lr@c*f~5W1N5uY2 zNRP;0idaIUTen8Yx)}q5Mr94oT7PY%mZZ~n^4A1Te5A(|7*a z$>RTOJZ*ko5*pp3tQJJ+Dby`J)mz!K2>RfnU_kZStcCOGp>XbL;7RO^o!H4n(+PH) z-OV-K*Lr4?WZx!tpdggUO!%EhtxUx6UAiw!O@nv&*H9Fu2Ui1Y*`&8yfig3^+etmM zfmYcp5CxG`io9)cyT@a+E3|hpQ<5Diwo?aAZnICOqb}ynaR3=it*O8+ zs?*$^ThdT+i&0x+$7QNn9kz|qs=kcMo1GVF)gkMXJX<-oiLM(4zk&2G z5U^nNA-P#P(uKLael8Ms>}Np6)m=nx(1deKA|EdryG>mQnZPh^^&_H+bcYZIa~t_(0+BHKw(58ozc zUI6oC9_l9X{?@Hq{f{q=a@FI(%Gy{QD}1OkQwBs(=2C!)@3xqABV4N!nLey>t=7E*a^fj-=_6wefwj8W~5 z>a&B+J_da4>#?2zWNUU=qIXc{aXBJSb^0k*7S{wWqN6fmV( zi*TCYC?=!*zya;XAV%xPfvf}hk4c24`#TSg|&^^eYBhvsCjqVe+~9l@j{O^ zgY9dK1u?dEZu-+k;9BHwqxW_9;XZbX|P6$?Y$Jla<2*(vUcF2AJ>_uQaN@Au@)CK0w^YBaQ2ml&&J*5c@#T1r~5@ z0hw2#Prq$EWUNd*!F}AM<3ns6^}yl!VV=CA{!Tyka7iU4kCT*~n&ad5T{MO1+OOZg zKV-Y8fFhmRp{`N-)y}I~E9yCQFKz=iL!EtkdiJ`+L3w{B`T;G(;w4LDE61KeD=lMt z%H)#lJU{{>x_1r+E8oL9o&IW?rfmI8#%)zA@9?__Hvz6`*-tsSw&oD-KX+^(6ZfI; zic}aKicXG?wvm`Npz}w_EI!9(o~W!_hZMZw-Be2=HQlJR9vJx9#csn;OEx3{>3TzB zQD!*;Q0pZe9Ik4d5!!sng*PT*Amcgp(1TmIyvbTK=gzIGGWSWm+!(|kvhas(6J!zU z*f!FHae{B7N-5ipaLgJ3AQo13XyE!?P{&I#fSi)n(WacKr+ndqsfh_Zzfz~4v?79v+M7vCL$W;V>mb99-tD+ z-iRZ8-Q3*P9o!wusdhOr(Hy#+ndrUL2&X^=5j(yu6GjfC7|hS#a@6j;znMcowJ-ZC zfRYQM#|-m`s@vA7x1UxIf`qIkA%}9`n`)(rCq8d4is?H>%2U%MYhvwul?@xREIBgs zi%sh=E`iR)Gv+Si7;GTI%9J&Sk`cdcODD6rk3bYSwas|bAzGuhYEW^43y2NgOx&iq z{gR{+qnHkc>kN@nWl79pI@n$|iVU@Xmt{3t0Y3Ec3IgldXX zO%GFeF(>A=6wa$|I>czS)1(*lWi_qy&wX|5t6FQ=MV^v$7P93aK&K&jcS)W7#&c)P z@I?H)wcAGX?;c2*XD}+uagYU3Ak@sYt_cVkW;)46Wt+c$rgFv)trr`93_bXRCMvml z`_UYIyj2JRqHm*kI05ql2eb|Rg>O&Zs!hxRz9(ENB9GOd#^{RYPWAor7;$3(^)H1{?_zvxyxa3Pa1&+)!VX|ZK_55L3XHAAP zig`)pnW977@$x1rGV3pcdptSb6w51a%&xoH44>Ou{5Avd(W1inskvd1FmAN63?@0J zMcZ*5(bV)@cZ|^fhDwx~EN}$HkkTIR6!fNFDo&j{H-7Ngk^hq8V=a-9sFADMb8K@- zj!-`%doBc!+Dn|P*$etFJTqPava}y^j5Qx(S_OWLdvNm0mwU;Ug1-ZxIe~ASx_yTg zhs8(>G~V5PlBe1_rB*a+39J}u!XPA;a8um)c~n4+6vn}9D>W-%4}N3UR5s{RI&qfJ z4)me@L9dZY9Y&bUoI-9Nf&ih{EBI=xW$c_kH8AX_XWETETBH}mFR^rR2GQ-W#UlolUD4dKQ4A`2L91Vea`##fAnpXlESOy?u z2f{18w*Hzno8vG}4J6gyXtQr6vs_#-*{QWYde{9nC}BVv>i8WTU55V!7os8TLI^KgRrQ)GB{~oz}>Xf{l=ggRq%r{md7rX)B zUgNY9DIidNa~c#O7?Po}WrYe6`ZcdkSV^?+V#oHgQzRGUzAZU>!2kTwu6*%Q`Y|sA3`o)R!m%#M$V`_o#hwc|k6Ax!ivV<(xl^j7#V!8jJFm>HwwhBY*~88 z;0rdgaxT3`SI40~83-c;cR>v<{Sl4=*GtT|M9x(SG~ak#YKoViMI5c2!?%;h5lgmx z90b{FMn@2pT?u71ni4~RbY3=f2_o$){Vi%VaN4Y9TykK?AehqOhaWn?7$b|XTtnpG z2G>+=qOv6&>Yhv~mcKOAqGObqFxsXwe`gN2-t_E@4BIPkGyGAvOxr2IW{!>cvlEu; z3K$?Vfk=(sh3Cg~9&2jmnJrJY(eu%G!=}mXW^u8M(g^|nKHesdu2|sSxlJpM%YtZx z7{Y+z7Pgl|Bn4x9d_3f5KBcVLS9}eqLOM8cVOW1aHAxx2)It6G_b+(f&353S{)t&yFbP>}_IYJBJybru ziB8G5r~TYA`sqB~(z7iiJ>nU=kC@L;9e~H_TI!3gFRwYFfr_YbiQHeeO^@!kh{1%_ z)xU;NRNO=>g^cG{RhhkrU-ZMXsC}d)%#;FT3v1O6BdQ(HG+_ z9ZP-d+s8ofh5H7cNBIM~N74D-SktY-`kB8?;mkpO3=BqpakbkOZBe+Ur{gOm zNpRV9__|u&w>!0%vZ6;^N|aijw&M=J8f$~o`^m@*g^`FkcAH&c9Ud}*-!*otn~i+} zvKD#fZhi@M?Hg0!UZLEmk!Uxv4M%2_ig^VC6q{+s&|we!FlRwh5qXfpqani>Q3PG# z+&-MndUGi2yZeKm5gMMw-+Cri-6I41%?1^C4Z!JL{K{Z6R|q6;Dz|18r=JXu5T|bv z3!Ha9+WH%uS=IoLf3VkYVD2Dya7Ph#mc^Q9Y!3RY3kvjBX=RfpO$Js4Z%@Su4xg#6 z^YqJ?+{?;RoiO3?#8@)NhR9!fLw;f0*L9a=;qvL6Wx#TPyw*BpgY8mRhGfs$Z657g zrjMq420P+p{UjKxKTuNt*lkDSE10f;Rh|nZ!ZnvC4$ZuIRfV>jmB?z{A7`!JGU|ZO zlDqWyo5I3c^Ryr|>M?G5J%7T|E-yCY|xr!JKhrZ{=0qNZJ1<{xW) z$!A&b)AUyj$V?3q1^ z`yBM1N*>uxM<6dy6zz)-W}DAdaplHP(8|i8C3QCI+$8h$cduL(pOyo$qo69#2ytlY zr7reRh(|ZlJ*2koJxgobA_4Ob1#9_OQ1>WMhiuw&boh^=v2k|X(LUI$F)(OFH#95! zbA`=#n*kvybJq9`RnPhLgWAC5XBt5Qx%vy^yIN<%hq&$}a0RlhNmi85Brq_rwRboz zl>qMq7T+4bqLvn*HH!XeGqmfrhBPq!#P`DG_|}Y3D{Gg zoWbQOlOG^J^_=bayofW%go|PZHIu08(w(9eC%yVixjw-`b=m10-Hps9@u6iKoZLxf^2gWNcS8N?XHJ6mC!CQzm0bO6psAu)#D>79d@W)*SyKZvPe$yu;^V~Y z{nkY_a=?ocP54%Rh24NZT|b8`3;aaZ@wM=(zaAc+Oju_Ik~CkN4<@k>=h=2M`uI=n z*zNlIYjoG^wrtrlwc{J;#%}hBdqmZOh5)CPbQt-!pDJ(t+1)fk2PnJF1VtM#sANoo(_eT8ou=;0nvv$Re;ZK>Hk#mHk zK_tMOV7T#3M6YI06a;HJ;KoUn#SNhrs8_Xo_1)(|WCiYnuk%W||F?lG@dBDv+)MKC zATqSndS${kJ)BNKL;|C_g@JG*{(6nOm>4P}B0)%d4YM^=V_QCY6xvNr)#y~)+b!aH z+sP-gK! zcOG03FpDW{i~+m!Vhh;#N z?s^6Q9!P=C%URphww~unwsIU1oK5|$z;VpHbhU?h8eW@7m$#Q?WM+OfS!w&9mPqqO zQzeJt0W4pemT3Lg%&T6;jvsujfPw>quOH?lf!3)hFto(>VzcD)cX#-j`f~M%i}!)a zWtSwU`WD|G(yUHy&)ydSO~t<)AP_}@25qfx2Ltw&ag7jEz=;e@P3u&-78uo(hf=>` z!&xXxeaabgaDJ=Wj8JR7!k)5PXeKUk6uFDp&nVNy01B;S*%iZ)R01Wxs$8ibOa&an zxsjYItLu?_gfY)pqq?DrY)N=ou32DZWMmXDH2=c~3uwwbn??MESt;I4Ma_nuF8WCy zRhkr9nOTb#rK93vTgSutr7|Hw!9&>K4pkM;JMYXAMC3u^@cV70@7Pkz9CksZRelcj}oO9cTx~WK1Z`g zd%Zt{b;+(fu2%`m%}5I(7vzB*0J>f?rGw_T=#7njuN70-j=2*!0Fn>R!A%~yXHc5P zIri7OSAClx2L7Pj;?PSoC!Uei;Vz^UA-GB22W?}U0Nw4Z2nt}T;i@2fCr+4_Jqv|Q zjc=5i+ObYAbb*`jF%erD*07=x0Xci~yl1Q|n`0L528gF%xMJ}}^@U!yjVJ65HN+6vUI0cR zYc^mD$JmSwmhCfwLLtRZ;WKvTFf;NwgFt_Br_#1DJT#W0T3A#$%*JOg&Bk6Zvy?Qk! z*8P4~gYV4YnlaDgNll)`51*J8CeEa}x$BuxKxD#`>EsmV6%j}c0F1A>dE=GXYAMGua`*xs0U_7%ur!=x`=S^48?# z$WRO*m3+AX?sRUDLR?-_hf?I$`IBwo?ZpC){2^vCyi3am`LZiz`DgrO{$R!~RAYS? zWG55b#q5#ngw0MDofev@Sc-+R8i+rUX$RSH0hsp{><;X`juGW);52uAO`E<-3R~Oz zt%@La0Zx)h7oI6~Yt}FSMzjy^pAnT!$~^;71}!wLOJuAc)=^!1b8irqj-N#Ol?El25#8s09?g}N`@2A zv=b^eCnY7NS72R4BejcPrRv1e-C|GKW|cY~!JutIfB}7qizwZRY{Zsp%c+V(6Mg9W zH{L0iW&?n(l68J1KTEb^`pmmYkYW6~7tkhVLnBCgdHXJSk(GUAJa^Wt5qxE{ghv0R zTFUX+@Y1GRJ;3)B1-r9zkwG&QeuW8 zoYEo48aTh)Lp5`frD)>KON?gWU?ap3;(3mJ{glN0g1FJH5ua0@KX12kWm$E~HcQRr zX2aV1bpLA(uMJd{{R)^ocR-pSO!@Qb{rg?7&b6T|;(<*9`&@fXHFEuQPEz%O){xC}I>44^Yyop0(7| z*^fLn+Ppc<)o`W}Wz9wWksRB@4+U<(vHJ~MQ24zFiI$NSo$48CA!XvyZ$Rg!65QL7 zc*C?tua_C$K0OQJmjwY9m*3`&Tc3hj@W3?Lj7g~=0o1>+$Z%8xcR5rTPmMd>&WtKs z+;Ih7C(7U+S;P9VJLqH#XB&r$3C<-YID?V2^8u@gQ!-Z$M6*0y7geC1EG{q)GCGjC zm=2Md1Fwr02hj08m}2iBwm%G5`k)Qu`I%;}j^0W7XC><`_)%#nPkj#IaS|b{2=Zco zOB^X47)aA8L>Q5=StFI*tQ3GAoe9oP8mNT2dsCfBbldD@!9k@yLdGrDlg8 z7GrJPi#1roM~r}i8HBHl$doVMSOyKj1syr}ty;IvWLlDn z_d3x`RxMDn%pua?lovq|&SZo_mMsvBWWO@Ot#^m?hU-O+QxJ3>WS6fyda}=S=T-fQt zCY3bOg0u;Q>+7tJ;HSpEveB^=Z7ZuJnJ3NVr5f4ky`k%fGEY7Mb_80s4WJ>zYqoC~}irHG+TOmmFEcO{L4_jccA3hCag0+SYo+LURR0VoS*GNJ^WKHb?( zMifa7LHb^@e8%)M#*^stCJHSH_5(*aJ0T-XDkpd{b z|A#&Y$dRf`Z0qadW#7I%{L+ca3HyxJsZUNHt&(+sysJ<1IpFXZ4ofJ-bUUwb6|%T+ zZ+E*g%Sg>7>qs>YdHtlLVQI}6m+FmY^r)*r3<*T5z~K?A8%C)E)$c>QD`Ho!71pa% z7aL#dzhTMG4l0@w7Ar%%PCUP;J;0#(m!)(@N|D>0agWHH9}!8v>asJF@tc&eheHxh zo;eeRR1UDG5m}JZCE#PJVPEn4lrD*&_{J=N2W<%`H_)&$G_XFr@#ePLx1VIEtk+%X213~Hp zb(_>@ndAxvs@ z;$JDwmq4?s{Q*}!eC?i&>9X;QeX*Erpi6^uk##UgUIreZ!f7lb2&p$BJbr0%#zh~? z=@I*Lnt^pB5?*NTrY&EtamWzsNvFe=)B&6#FNy*}0O39XTv5gs!^mnScojq9vY)UI zY1ILc0fMgMxb-H?lzEN1V{yR4ZSC4+i%jPm>IMz4uH7MLW6E%R)%ilT^7>uAqB!;m`3O znxs5r^e%u?+az~4$RaWN;@9SVjIFgf`V=z0EP!B+*-h@g^v5Uzk}0cnwJb?fVxdOu z%~z|yunACvq;(XxvUJ5SM`Zrog}iFr^FdIIFJ!w%W%Yx4O#=IzG|iu`fNTujkPzhS z?d_M=NvD(19STK$MseYNUBu9Lc)4=n|9HuC>_1vXRcbi8Bd*NbPnTc;xN@7(v%X($ zFTDb%M0E7*Y9P{ypc6&=q4PiMIc;W66sWRBaz;i`-Lg)H4D2~~5YU%jZn!4vqIeE+ z3ZZ_HB~7+ooJ|4Ue|53QV}J;Zj=Pn=7zM32dS5! zYW*!-b*U6;3t7Rt7Bv!Ob>We=2s-GONEY3W8BPH5>y!gdFa8DzL%qLHuN@XiT0iuDdv?R?Q;NhcS4`*^0B_{g``fwU@ z!Qs&S_DlaZG#Zi!glWf0S>z$}V{0b0!uRsKU>-bPMXyEA`}6vb{5XxY(Zwf__6Q+U zTuI{OqcIdPymFJh$0+aU6?;u`8;YeY3UW+`Ko4xWo2Ji9y(*{-^21` zjWnC*Tdw6U+G0C3u^aeCMegTrpFe+|yeIc_(8hCi#g$j?R~Fc4MC*0`vU=KtW1a4X zhv}z24@|uiwzT>>$KdK;35df^Ft5qTebcyL)9*z&vSLRz>Y$bqa$m-ECJ#2;0&XPh z;(+0=ks9eSln1!v$pUEtQzl-CP}vrwdc&~NFAM_qa!9Es00XDwVY8g>8$0D{==>&Q%w9|fbz~7x5eL1$iP1& zT)i##F{Bruvq(H=!?hMn_m`dF)W%CdP{_2+pS)gcBNgk~bWkr@NG}Z$a})g8RjT~N z{$A*`r~yRGO2aG5R{&J?kgegQb>dd(29Kpfn1_P72^KX0L28SA|JN)-Ai*ny17Qh) z`q@cjCLWgvOF7~5{x$>dY1-63!f#V(rRBzclk$@84C(bXXT_D~DFExnz#oN1m*9~P zKk~5p?%{iJg6^(opO>b6z+!|DKY*x!kjPOUez%{CX3g4wE?f>hW6?qjAZ4ZvY<@bo zzQtPmYgr%CGHyK2(CYYd?o!p1p+^>!4FE=zT@cV*@_~`pT%&0rNYiT4+Twcj|%o0>};-njYck__dx;=Og&& zUGd(TRtkljY?lVLH0WLi*&h1W{1!m)v1A+I#*8D5h;S1pmHgFj=+dN|p=XDL0^%UX z=m?_8>pDq*R_!$U??(n6wfrY(cBix6zo;&A3#mZ9NIthcF&hcAD_er(Nl@B~&7V}| zU_?kCnsRFtckGW3{pbva-44k3LH?Z=$194=Vg$W?nx2`Nxy%EwDCWI3vjDjq;=bX@ z-^X9H!!qz%ED zIq|R79nU{{_{HZ))ko#BW+MVSj%+}r$RN?*J=Ch=wflTpOP1EM1kI$joE>>J7 zoA3YS!K;=0Csu}Bm!im|NmHhHe03eNs>&j$b5maWT)3(Q&o6vEFmC~!(VV;YA3l6| zWW~GB{oFn;eTGJdR$XdPsBkt=R$ZUnX=&M@y9?)j{@QqHnbqCZ_NqGODTqj92tx=} zei$Bc!W^@m=(0-E^{c1ak*;HSnG9Ci!o?81zFYt0v^3<6ZB@Yt&ebf~RhgSbAtfOc zMMUlRqGwl{U!%geU^B$sUF;8(|5Z**Ox5`;8aYl-$_{mPa@&2SU}fibzch9p)Zq*n zZ)wq(?#t@tWL6-Tsd-ggCf9du8&BUgGqf6*ETK4Al3DPwHRgibR8GBpGZ;neTR| z396W;v(Y0|OcJh9P0QjAK5q4F`|9IhIr?x~jq23o_wA(dS`IJ6LpIH}Mhwpr(ZtbUsCNO;|6d;j978S8zX$L_P z>Dg=`XZz8i759jez1nO;`|$Lm?<@r>3XuZ~F$NFrw!|ZkVADu_Xgg2DMH@{!RQqFW z(=<6~S<7*?>(s%+cdTGNuwQ1o`*tUdMFGI%jLh8|4I3ZMj}h-eR`wL)8!k_uBRF%H z<>KZ^fCm_JT2UcRq8K$vow;)k+IEO+GZy5~9*Vp8flP0xS;B{?MrAX&_>K&JBU(X{ z#=)*vo23YVNX$tW<;${VV)qAl^5wu38uN|=)qE60*ckJnhF5z9LRbi93Hx#yeotzH zCz?LS^@vJ>fsygYOb8lv4#dBNR)&oPiT#7jzNiFJM>JUyov4};c}#{8KoedS6x<@o zj=p9edVn0k&)mb`(s!@?VRi2G*|Uz+Bka-K&Fd3HoAopm*9+Cjnn;9CFaIzeoroAE z@o1vx{=_1RYb$~?{sVdiH4=jcCxBV+0OYs3658dg=OLhforRHxoLHh=Z;a#>0#ZZf z8wB0zC$eW=kK?6j6xOHZ%+l*i%?!>YdnJH!L|A&imyh+6&8agH!o22Ki7br_4+1s{ zcyr7{tD$ds*DOIl-Z-}`|&()Ub&(~ugh;^%%5E~5IZUv zhf!d?HUH6kX<_;PW_t$@kT!*%ER*A)@FD?{lm<6oxaI`}hHUR4cEoazX8M-6s>Wxa zEq&d2(49QKMC=-f`dYwj()0p&2-ost|43gnjHebn=>u%xgU>WmFi{MNp|DP|bNap% zcCi3Rf)G&mWzm6j9*wJOg?E$ot31{{Bv>O9p9usqdUxM#)M4T$Mi{)t6Ee`g^gHO@ zvg&r#qbc=XOQJ2&R7NCZCLA8U!M9F1v9AuB13Idw`g>(Sf(m%5K-dvO73bg1-!!|c zx0tgqKO)RJJ@;LBg~Tq_$$uM&V4F@78Bu>A37;={hd~Ex7Iod@=9Il1y-3(5mEz*! zK2`Sh9jvF&IS7lQpb8ZtaZW-yy-Cp6zDO5IHKnQNz;NgCAE=DBL)!y$Z?qb(+^5Yp z`Nha6OE`%@G}may4F{S9_gy$ytp5QZr39?6s{M)zL;4XU*Wy@?jQH;478YwT{*kVU zKFk1%BeD*ZyD=&0>z6Mx=4i-F?bWicP0|*(2NtT5_PjuT& z|K=xr?DZPL(!L?VAg)=mk`#5BSw0bM5iOt;Y7&d`sCebqp~{fe3%q_Ae1NCZCfm#M z_dj!T+*YAG8w)YwUeL5QUvErYQUVZ492rx{Qdwyh%FDzaK21nEv^PNM90CI-&>B+D z$~HS#H>&JSBOR)yw{BF2iLARkW1&x}u=#4roE9iUzoouuLa8Jh0MRhj6#5qU>FeEQ zr$DtF+gJj<93xDsdQuzsYnuf)x7qUhCV3loZ|bgg1DOVey$xQ5-g|?M176iSiMZUa zkE1zNXz`0_+xjvCiC&;yyn3HVx@2zAE!QhPwK!mc)o`qIQ%dNC^5E^HvE*VerAi(f zn2M3bf`LYVZwKFzeBkxN{?zPL7J3Y&;-PjZ?z;2c{jH-8=uNuy+$ol43g#*7-2Ljr zZ^0F+DBdLjw$`#nC5yTWaXXc94J*=1JLO0?`Ob>w^VJ2T9yL9 ziGwaZ?$CKT$#oka*c)97MvnYzNPv0ojYnx zP?4Rjlq>1EtaTc-k<}g-Dhelr2kXqlgegNJ=7Gi)gRsePsUQ|IG8u>-B%`dG4F~ z{l4GN=bY^J3F_@o-d$OyY`6CGiF0C^C7$jGu2e_r6XIh384esR+3QVOQ8?hF$ z;a7$ix;ph&@si~*$mB0GP)+|n21bIbV)`zBBm~!MfLGPKLsO^AW*n)ZWty6~*z%0( zch4@I+I*x{qi7gnuBHm%QHDfeGUtJ`q|)R;`a!XZ-!p1emdiG!+!K(>bLV=SURztB z0Mo7s%qgccuK6XSe#iRx@jRTZ7n@HreD)8DrxHibR2HoIp`(tU0)>yqw}~J-bXBUptdn zq(#&@PK~Sx?pVLiK;@$(JG{Mn1SC$TFiEmD@}(>1$T9&*Q0$l{-Q*B= zd^=5T81vW=zNl=1!}PN_RmuuBFz877hNZ*-qmu&%()3icC)?TBCAC;U_MpM`ijdEH zik`k5e*3NN%<~O5i27n(no0HtM#)^J_*D~ayD^|vr+!nJapl<>(km6^CkYyU$HOm9 zb28oec)T7NWMTNJIxWuw%2W*5Fp#idsiJt(cpl9bwwnPRI&(Kjb7FwNY-Y*3>84CC z6R1V`E?PB^ysW8}9wCoKo*)-g^acd33Cz~xKVk{BCL$ORnrL+SGdn5ZMS4h*!tFsD z$}n@z6CCCiS*=CZ7pi%_x;?W_LM+9^{6XTCP6g<}nBOu+wed*rHrp8@(U)gN=YvlW z1ONwTH=JL{2cmViBqjzTS-yU(3u~t8a{Sj1}2_LMK79?J& zH_TLbQfC|DYb4YBypn=~0(BZSSi$V!W()BTnSChxY>Mpjpg>s2Cyn3Q+MAZygTuxR z98xg|g$jG3Rp3IW)Phv{1*q)*v+y=BY zEn2FnLir)9#to>JrIAYsC^NAn?@%tbR8WZQyzedp6o>j(i8uviAk6NdjBhHfwdFd8+33M6H{m+ zVqd--Zu!NY>e^1cJ7Jmn#WDtrqU1)Ykk4|zo!r8^~M24F0(|Dxy`wV z??3?VFq5#D-K7CY{i~3tK|7&5YbbGv0oJ-NgD*E3LxI+A$ZA~(Nx1|o>{6@y5XV_^ zMGQAUKFgJjrB)GV9H}EfWNK3?%iBv_ClA5lJSd7L8H%b(%E%*smTfTc>3o1kg!$}n zk}E-48~&m1hYJ+4MAEpGM_$@(u7A~Im7s~d7OxTOP-Obg^6!+OvipOkwGlWP@?Vv>`6@^eP%G$iWMskb9_*uY?7Ob%UkM|J=$a1 zGi7AS{KTq{J-3mI>>i(NEncUBs}Ov_3CSH)FeI?(QJ6A`I3X=*X};^4^yWGVQuy#}>XA-(o)kcfVubIxc>jm$SW zIWi&S)oDoo{Hm?R+`>K!?{#D$-955EAgxcR(}uns>ytJbT+7q5mJAAaoG8UWq=Od3 z8StS!Ue!SyxrZE6j>=7xiX!%a%@^YVh$4|LQ3W2*`~3^t$ykbmLF~C9!7h}EyQOIl zKh}8+%m*(xlz+3w^2nSA7)It$u(A!nWMx0%rDh>}w98}ClGBcolL z%N@4WDg2B#0T0sV=C(yA`xjf07o-{_18?pbl+HLWMayyogOlEbaycc}6OTH*$5e>B zn0C>=1E=&T6);y?8MsRfL+G{}z{>~ZN*vCi`-r>|QDMA5nt z{RzdBU>f1#aYuv|^ChQG65$W(Wqwu|N?IO`4FSk{>k`TeX;b5Zy!XK5{n#FCP|^|j zCD+#+hM3PD?BW7s7qVYL(tpdVI((63w*ian{seSw9%Mb)Bo>atK_qtEjsog>sT@$A zHbk36P007~h0jJIQU`6ruatoT?D;5SQG*~;e9{QG$BvF|6hYN^pTn(yhl{6JHy-Ln zn~5JsPk9Zt6f#)>d-BOY)p7aqxugwD%>3#$QjzsVP`MlFky6y>kS5}HblOLVmuZ!o zP{r>Pj1DsyJWnEJTKHye{fwc5bf*&~g#N|#uCJC?y=f|hicWNDUh_pk1@HY;=Q?{~ zdIT~(hE5@%>9E`0Lii&jAs;h2)C|u}$S%t`zrC{xF5tuxnxTguinzy!S`zFhCe<8- zB+|Q(BlIl}0V!r*UEK^zYf;(2DzUzJzh7Kz3St0r^@~;#ZH0-R&(G#=> zSu&TxKbn1&4|ahtHIlcDgxnmHTaw{nsvNa+*%b2NaN+`o@QJH|BtKlb)R60n@4zBMQQZ96UadI()j- z{$fJyKO0fW(wQ@x`<}fG-Av~VQ&B+_;y=%?Zf)ba<9=N=yXH$a2ExGeR2m4|u71$M zSEC+%NXg8nt)dU|MMg_a8fhR`<5S~fC*O|B7OvS z^n1vN7YVf1S>X9{J!7h8`K$EX)%lFf#P6bYF%e3G&erdL_oV?gFR!U*QuK_K!dRPPC5%mRi^9wtDZCqXv7FKqjJJd1$+vq$;!?;`mdG}J&n<~i*s8oG8ETH+<;l-nQB|={T+=8xqI!O9zhR8K3+C>6MwH*pMB~(24#<@VFC%_G@S@Row z^$o?N*6@-R^20^!$>ExW1wNK^o^5)yrxticY(_WN7GK_mRyTuXH8Uq=9x4@~LZkp% zZH_yi{A1NB^R*sd>DJ0>!WzvY!$;D5CHRh9Ik0eh+ots~6N8#~3&SLAA~;xD;AFb7 z9Jjckcfor4P?idj?LsPwR2*`K$g-J7s*g0kked6<)^V=0WeSx+ z92lOxWBuIUXg-3ZL9MHm@7k$+KmTnwy$e)sx1i6W2IsVB-F>1vgZq6JJ=7HK9*|F} zIg)Yxm&JEDt#p-CvzFz%xVgE#ayj+~*YClVRZD0DS^GZCe#^qtJ3IuLDn}o3oJT;C z5W6iQ%gFpz9)8lKk@L#%KJ=9#ghXR2RFpiqjHt?muMOI72N|?mn~|5? zy6ecMh@M3JOUgHFR!~pO10q7L@$5Xgd_7M?+cNMM(ASuhU=-eNGU^B3{&4hC;s9GH zTu^2bz?I)Au1V-M3aGjKkXaIkH;{OsIJ?kGYCm>{?oC>SX!FD^1G&2x$3UDNCxhd& zBzYRl+&RbnFW$vD6nfMQSQe`h@5=si&69uu;+hz0*^a;ei`0s(2X697FfX}$^=bsv zuvF>5C|M=N;SsYi2r1Xq8PVKZ0geEjpp&oaIj&sUcglJfm?R3Rc!Gb_p00DA zCZ`A?08exKwio@YC|tQB$UD<^ZLrNfrGK|_Xl$QWR+DYVIxC-R8gMW)GS#s7%Y}HS z)=El>Yeog%sBj|Yw480 zdlhF>#^hR#8#fM?`xZw@IxYwZa0>l{pdS>fJkxZ~edmE0%eseOygK1PSV$Drf{70Hui`B6H0 z%?hq?Ugy%cCGOKq(S~5H^HS%r+q-C=O+isS;I~00SWMgjCuCywb zeXDg<#5r_e$i^_r){wPQHt=kO=ZSc&>ZQxD$c-mzyOseQ6l4X%F?TwG*~C4O3SAlz zz`_&&upDc+om*JDn>Yk|O2=}N=|et(OTpY#padE3QV@Viyy*O>2BO{{ z9lQKHD_cuMY}CI6N&c&1;?4bAoPd^2d!AYZPU`PDAJxQ-RC#uWyZur<`D-^LkrwEBVP zX{~jkvfkEVK<{wlGWCRQ?fik+GL;89+5#EQAJ>GNret{+@{N{3zfT?P)31Ep7hi%3 z0#AouVB}zj-VO*lUemkroPMzPq&>^D5%LOBrCxG?N~2|CGc^`IcHFxck%kK1dqP6O z8-Egty%2JjmuGJ5Zg2y9Clz0IqB7-=_3CSsR-xc4SROHTBI#^EYzYLfKbJ>uJI+Eg zylxAF(hE8R1m2;Fkk${sBXfriL={QEA5gNKKh_t*Gl6yz{raGbPppJtQly?;rL2yQ zN{9lcNoB(-Hq&bwx6w7Ha7_YK%1Ar{Gfj(tk5}7oG(Qpr<=qEW2t~n7h)i*^k%HUN z@9*Q%)(6~RUL(5o6M)6ADdmLIy1#JRV~6@s^wpBD#Y3s3bY$=8NR%b?YBFUD51$K3 z_-5u#5)xYCM!?SzV5T32u+&>g}J$Xo+OL09LOHKUrwG@~rYSz5!@x zECkY+xF;^7fRRB=Opr;097WQsZ>jSF%22Qfh7?CBWTB0dCc9qeScUdiYyMbMrFA_W zPZ#@Gub8CmhpsrJ6{uX->~mADV$xbpCf!% zBHTVd(x_G-mvhcsmr|p zjCExrDB2*C=|f52G7d&!hqCKPi$T9_Q;2*S05W-3ufIml6}0WJKNTN8frWiLg?_(9 z=DYI8sKjGQF$(re1cTgM8b%$&=aF+Z5f=fh46Ay@HoQdCtCiPcNB2=5KP*7$0h_^K zTO8eJi9R~dVV(jAS2p@>*;KPQ(4+k*2J{3gr!J8w#bWJ0P^SR87D8aDZ_&R^J6X6O zP$GbA%c5r*88esfjyjWWi>>>!T@6pvHSl^*-2-*rgUZmpPtbFb~*?&3Pa>{m1i)M3sS7+Q!7A@G+B%qcN0YflX&3qd_yP)-N>; z*v*E*#P|h{+`6q-?|Pe=`75MREtx=Kq@@3_HB2KyZEU3|fCQQc`;(#j7i6~Rw15-0 zU@gUM0CL%2SoEUXH=hhp)KVx$nfy9ARaBalHGu}w0kYxk8AZEuzB^-82al+a)>Z>3m3i}AH0SJ z4%7qzXYdi@(#3H0JX~2W$L_o&nYTm&t%NBciRo2#_WUaez-6-f4E@5eGcZh#PEC`w zKr#%*1D06^l9^~!#2bbu?6&IIU$pxubPni8-@_=Bzf0yDEiS<1FR^*paH6p!o)r!b zWhXDkJ3R6ko>HS26_wC;{a&8SJ?f}#zg?01uEk#!!cs2vCOfV03iHZDz=%HpGQ8iJvoEGm(F+vNF1YAA={cR zhX$ec5Y)qGWCnEiW{urpBP-1{D<1in#On3<76KzE^Ry^p#0ek`t8!2E%Uws%_*YO) ze>7|4fLOBjt}fa&8MknlSAN}dw04-mF?x=zy20%^58Cgq*o{>SyjLu_nwHZMC>wRZ z_~bute>qIhE_naAXX+=8HqY{gU#8hQm=zlN`)D0WI}4~a0zQlayAd6T1*jIRetR5q z>y`&4h$M7kvTDwg;}p-d8%3WaNm~rT8L<#3jM9Lp!Hb2zd2tSCpLnMRGz@Y)=~iL~xVlOSS;4TG4H!AU;K7~?r?Z1#qi+|5Q$MGNxi? zP?*U)9FmDen7u?m(%?Xsvh$15TXdc{!gZ<9Vx!TdKA_%FE|n0|%-n-=%wPuB@`9O$ zCH#*u9_wjZ5mwo4C^g8u_O9mf9p9U2WFB|bRB?T8mN$c0b>fZ!yt1GyI+&!@)(-V% z*RFkQL?gcfQN zFpAccoK6>lF-IbNr{?*(0p9k9CFx%UDxtB|+R{$xNQAFhiiAPRKl`?DNf=2toVq(TSEQ9gZQx6!Hxs zJRds`6iG4gE2BV-@ZVB>`gZ!u)AS9JYaooQi<9;VG|15dD+Q$}E0=Z_V!z?KZN1j6 z9R;D?sk-JnE0^vtDN*t#2MY#*9+wP)Ui%ISPsR(;y~wmR>_=Qy>jt{rOGaPjq~wDSi}c#&$@wyDgnb3r}U&(!c0 zk1=8XWs8&CjOr>EsT!pV%8l9GQQPP|?@|mWI7(sJ`rkjluU;Yxr4{HEWS+lzPnXb4 zEz=(aqaFlf{k%I_Yj9vy!AG0*PWg>`zw^+wHwZc6<4!V- zuc;fApidA%CKCKL2Fhvx|0Nh%onLvKUxa3Rkkj9qI#tw(z>Bo<^?r z{89O-)T7L8N_q!r3GbWl*2o5Kz%xGa2NSA!I%?F$rye3L^V1v!dwuiL!Bt_~M!sIC z)}Tq_#u`+%RCIfJZPLf1Z-0ktE6phwxSQ-#r9N`;KKn*It1+U`A9m5Co`J8L$lOaZ zs;e5@kO2gGTyi34(fM`^`VJyJJxyA3+$Nh+13O8jPDXM zF?y^6NtI*1!(4N*m}?2Dpbp}ZeYo-0kI&WTa6T(B;x;*CgFfFLb6@}eyfnI)*J z*3N!Qrt(R>8OY)8Lm46Ai&|7sxCnMbj34kJYskDOt)Naa1ks(2y0j$PHrmNCHl?pbjfyqh)A9as3Xwh$9%;e0=k6&&-%L7U`p6F)DmuqBYfqCI&unW7D$m5X z^m?dc+wou5{9`_F22c~)EZC)EYgu4>2W7S|30(^vbE%ZDOLi7`p7M!gn zDNWTh>Gk8V{`(&){SWU+D*Hp+?g7ufU4E@CeMNqZ0;#%aill>sSX7}l?Flj%F-4-W z;QHnAKM(20OXELcZ)dfRuQ~-qBxCo`Xl~&$ogu|H%BOAbmMcW6#97cmkaMHD*|H!+ zA5vpbCJ%SoP7y8B#HeFl-~|A7aPyyTq!qqpi{YB6oQn}f_m2=G+Z=d#d`tW7b~#ac z?KQ^VsQ7y0VP)6wvVF&%lqbGlmSJ4ouWeJSZK)6Qf*F~-svS^Ob++iO_2;sVk1rKx z#5t99JzMlG1*J#r=F2O&f)>-_f>jd#(eO;Oo4+78O>HH&V*^Q4|WUhG6AbJsaRX<=;2+ z37eD*YA zVKa`5lt3enH1yMFs`=N9V+}_smEQ-jL2j)!Nzs0B`t(bmi$UC4+^ABRC=tN`@nLKB zKOz68HxRXBib;y}?1-WW7j;9S4k8nD$H2~JdVHFVa zwpsl;C@spSGO3J2Pij0kdEZVoe8In881Cn?vwz)lu%g1$laVZ|4cpT+-l9|-GGb%f zL)rRGK-p~$%*nrE(>>?r!rzBDzK$qz@N_qw|Fx^dr09y(_-{+KlTqU#h_dWn;B8?__|NeOk_iz)i+Zsg?FVp_iug7TD zD*r#8QsOu*CEU+2!98)i>SqU6umpFWJQ+6w@5^wXPg9gwd!Gi}optJr#ekQh%aZE(o6do>^wMZ5gOxU5r z_NCo_I!DTJpgCz@qcKXkc$H`fynh2;AFcEqt^2A?bziu63}*J!`OB#hWY>u2!2>_N zu7)Ble@ROg2hOvati(?KANG)Woz-#=Jow9oV4AJq5Q12WAWOuUpG;gk?Y zix=v*%`Yt6`fmAu|C(!ietw%)t%OD8%Y|0A+kVsM+2(IXrK^)&s+-l{Wcqdq0q1M! zR`<=39~Mo{aINZA*(>~_zflK^GsP1s*MBg}eOu^KHsRtlvN=leOAB)Id=j4-^;;8V zbi2xN^6@vjJr5PnsQJyh>bxmdlC*meQKL%z`GNV4pKiKZ@zV?Y@vkZd|HC@{^gkAE z3a5gC{i<^arUDKnwdiR_YQak@2P)r2@~q!hV@T7VfBg4<_Z<(0lZytpgGn&vyp8cO z(i!;R{d#k;SRE)R4%<@^Q-sI;=dUZ5YI^H}@#cJlk_;MDwUqK>I~y>l(rtzA3asg18h3sgygfL&l@IwMU{wwFgi9HRmoF|>KZ zhG)&MX&5yT;&jqK4C>GSQuFD2)qJPSZ;(HX!poL#M7w7Pfg|hl=jJ!awle1-n-&5A zzhthB{{H^CZH!!;a|26La{+F6Fk!?1=KwW!N z%dptnbM@Jju^`h9Cibto9Le?Kn5(iLM{fA zc?2?Eo?xUB;XJ0^`Me-DWt~b-`4;8ub7Z2?R^5O(rP<%bW|J#BW>-6V=Y@nt9UK{;cFd}3Y49w&^Hw^-MH%e~tGfEpEY!8= zOX-Afajv`G{E$_9S{H%Vis1Ew-Z}ul1wJP063n(76JrqCk5q3Rocs zAn?s*Ui;5a(1_X9#WobCxDW0CsAD3b?#Vi}ZrseN-g=fgrkR=9Ev4%J#zHS$qD+5? zHlTMTVu-MmjH_!}z)5oU+X$r-@}S`_JdNoQw#}$EXzK5P)GK)RpsQ5%TuXrVO@yKl zF(wZ2|NUGUU4EsqT_s$N=qbvasq|^_H3T8-csD+(+d$=y(WJiINEk@zu+SR5NhB42 zY$}Cg=z?ZnTk8wOsRA9ktlh=`++6c8iPccR;-wvI|K<6z=oQf9p3Su;qsOs@?I+5k zxEY@IIZpE&{i1v^|3j%JXFx(Xz(!WHLwjcQSoxp0;+qog^{yS|2E&6TY5hSSuL)(! zoo<_nN|>;p$aXF&R=!}{B}+bxyI8INhb~ibLR=s}s;xu--#x|s?eqUu41v934fl!w zSmvUii&~KErg_A;Q6{}@#(!IY^uf{em+hYd=V{m3j_azge}D3nkB*OT{pVPkXc)uR zp<~-hbP;ikdszhxv^3KxCwbVGaH*J*xedgWmez>aQ6a|J%jq4BIb8gxtEi(O9h?~W9r|R1epKnim);!n6Yvz%%LeOu}D*t6&PoB}M zIW2`QRCMRy*p-H?UMfuvzCvJs`}Q(y0%ahkQ}hy*&_!Nh;PAoWKr~-|aE>!CE**tb zMV>#(PT6q{SL@nmR5Fctj*e&=xs_k}eNY|hc-ErbwFy5HF;El}XjpogyPyO*OxJ*~!CxB;zsDHd5$(y zpkE?#m=Wlqm#wGxVXwn=u@3ZE6==}6e+N`Dntap)h zHC6R}YktA>IXt*^>=$E(f|I)WfnCM?t6_d!Z1vkJp5lo_4WNUuIHRk<$hdp?b^STS zSvag-EqjHzh_2`UMCU_ET3vra%~hN}m7mN_^`hDl@q;*$@|4e>^Lo~GR#{1jK9(t> zz$TOZuj03`j>C!-nUp$+(u*QK2~A6YgF#Inu(hUgiFeQ#I%x@5M64#zd@uvt`awj5 zuxpHQ|3cTu9N*aa9$n?D7y7=+90&V_i9^jdcFw?^5LJNJNBx>Ru3^asrN|ZUK$ihY z)-edWq3EW^zuoe+qCE-D_Ck}+C%;(0pxj-&@Uw%Q$;bC*=k)eC7IgKRZ|h*^4gsH| z0}d`@uj>qZM$ZK;js5wIev#_JU#YnwKfxt|q@I(M`Yi@Xm+Vujf6;9VCR( zy%%rvi(L#ZMF-t^F~J~t*c_YBc_S_ImQ8o4?t3!NyM|;+toF)}P@>V3$tO@A)`2_j zReR;~>6%$APT7grP8kCq9Gwv9;_=WUJg%lR_<_v2^#JXPREd*u8LwWGl+muW5jSPg zQ%h#u5UT9GzeSET(fFIgVTVFfMBi(f*Wji3%0#&c!I;VX+-KM9t~)X(&w5|I#0s@1bGz2f<&=HxvYkB-|Olqo3QiCr{BG zIOBiS~3#n$v`Z#soJDR@xVZ1 z`S9eFo`7-T`Bl?0O+6vx$0M40<&rg)jNf&TMyan1EH#Ote)i-`)QMT0*fC78#H~0$@VZco;S#f+9f8o0kla z85P&RzjDG+vqT&x7zP)4E{O&-C|R&q~OVWWTCo|5<^s? zyF$vUW7R7DI)u z1}dc!+HD`niH(uY_B+g>7!YZA;ZMFD*;Zz3#6AJq9wDc)iq6=IUdHm;e1jj6poNP8 zpLWF8V)m`NKfqCZ;c1N_3f~s890{DN!VCf(kn*&PbapHc4sEyA=#gKK$6h8)c?LiL z2&)jQXji`9L}s2CG`dGm$}-6sB6Th$Bo=expTGQW1R`pBPn*H8e5F(4{Y|tM^TL`_ zU-HL>kZqf9Aam13D>C1qo7L@COASA+3V$m!`z(WEE`0{+22#~S&dP9;ko$C&3rLB} zl)v|cD&ocJ94sLw-t?IkAR+j-YOU~4iPs!0wl50l*|BCN!k(ezcy2Oj<1lkvnR{7Y zea12_#4B+5<%Xki-)d7i$Ku!UMG>wp>&hq?*jbL5&<247Vv8=rmRVUARla27^d63|bcLH8)@XyrlL!FO#nqq7Y|Z&4Pwt(5 zGMIk5NFi!OQw+gQxa?8!9gUT?qL>zo7cR@Fon30>yP9~>)EntA{|6c%-p#q3^9E(_g}wxBie=vU>T>bWvZw!C~Ce70%%Ris5j;uBQ0O?4CGpdz0cOLCPXiiNi|j< zGoXm~j?mX^t{uw<<$ijX?W#cy=B=y?>YjUbQjgxZI!_DY_15Vp-GBX9uH+kc?Euv+ z%{C=2z*ahJS&`M9Ey30;!DP%mChgkQ(D~emn=o+GudeO|@)P2KvMKQ3dg1_NNC-+1 zVoftz4g`nft;52buMR3-_ipZf1%?N1MMkJ6MOj19_>><@1Hy3FLbQIQw{DX{n}SF+ zNCgUMRntFNws;85Y;oo_NA~8qF7WEX`gQAy7bcB%3uYu?{#sCJiSSI6a74feP^{im z)uwG8mmiBf;#5tI(l31+`4ACn?Jl%z)St~L12bs&MSf|}RS z4E_Ug#Q;>?2FV%>5xBYA6=$xlaE!XB=CU=iGV#UNl!xWUYM<3ycSNdo0{~TSi|Y*# z_P*}p`?V^l{Cexh(W)k~8sTNL?4s9?XjyUfCIj~*e|B-2bF|FJ?u%%~@ckPkS4Z_N zUjJ-%&hGkl`pQ@_03?WvXy#Q*!>BPwL0U{y((R6n6MBGpd_1F9B7Z|JGZIl)rH zV4`o>A^@{A);D13B(rC)DwoAl0m&r>&6~nK+#P%VALW7PJgzXE1h;SJ5|7u_@qCly z|2E_;o%E;laft4=e6Jhjr`FE8u(YfdE`wkZk+hcTB+ZBEpe^Rp9noSbC=87WDBamO0}iu3I48eWrqoSe zm3GR6C+Ge~xpm_vt-8&1>P^KOn!N=WjM3(a@t0a^SbZS(vES00lj~Ui%|5ky`^c_R zzypB=vC(akSxj%MPkKfdIf`3m8pJ^P6Qul6k%6TWc>y5Mi)%Hqc&dUv_^z@&OPmFe zz-8s+*ghyF)H#onflf$SQItqWUSJSCg>BrT2!bq8JCP4Y4DU&LIu0}zO^QQy;V|ar zWCbb6ZwmpXdi)15!?27yRGsHmEsogg2dkMHkRv9phWe7F*+8RxQRhX2)3%((Q8{ zkEvlOg5|V9ZpqXHSXV|;`NC5MjdX1Y;yO$LAp=SbS}+If=i~Qhawn&{B7ziG0z_6Y zA!UzO&?MQ89K?U4>~UB*sWeHh_!7-&|iEZcP$&^n8wy#moIexUY(*%zLiTTz}D z{RM|4vDg4}P=$5I0mT~HzZLVV;#U>rye`6{$Uit0Kq;PoWEF*2kiy}~3L0Wd>!b&p zQz(r}U;7y4fBh;`Y@t1>)f`r?oDX99Vf^%vF&`>w4fUBd@K?D}DDwubnC6nnD#rb# z!%Z24{IT)(DcM(~4zczIG;djJk8`}3OoZ5%)K+*Z_!I7Qdj~ssC-B*P@nv*7ma!|U|pvRd68G$mR-C0T9bHG zyP}cZqW-BZE7Ooh(dfsk7D`my1m?_{19sTP|GdTgic+Zx0hEP;w;9Ng0!pCDTUoKm zhrnoJre|YoeuY3Q!ldc$vb8SfLLkD>}0S0Y8*Lw zvZCkYGH4?dhx;)YeXRk6ha=Jo8%8lD$ucpnmK#TF!|IBs>N-~G&EyYO$uMXL5 zBm=xm^u)HhMGPHY7L2b1%07K9AF#(Z1g=a$EiEg@LtlvjB`v>Emo+T|cXA#K;oa$l zB=b4D<0phF&zc!V8QV%uZHiwB{a<17_$UsvlPH2zj@$)Rz?PiI9b#-^l08cXBgHoW zg~W~W*G=WKBDS>ETAlXvsn`lr-`yb`iNp%l;SH&PDY2b|_X&)f3js=``}*8OGAdvz z4%Qe7Lk8&k*d`~+U!>aShKk@(7B?d4cTs&TYPPnRi4mX-ev1!60YLgMNW1+Kl|0@m zwy*@dSoO*J+>u$xcj zSFo1xnMRVe0u_xoV+ff=&05G38SsMbw+{%WWwc-76JOJ6DMKZ$;$4gBDDYN?$|1A; zvx%4%z;<{q(#sHgy~fo)Pz=?(QcRlsBKl*8z$3-q6;A!4*1P7=fj5%p^Fe8 z?q^1fuhl|^b9n<2iHR#>>MRVefEyyNQtL%;-cqiS4jRJN#@hAuW8#9yb&JXA+Sjby z#8I95#YoyKMsVce$&ck4b=x<)BrBl_?lDVGMu}EOl#+1Dm{&Lbnw8QbGBR=nqeAH8 z<3MY~CQ0LS$wo}XjEi0n+ofB#hSbwIo|9h^sOS3j{YP3lh?8e6q!Gat6;BK%D?lP; z2zX|o)>W4EBWFk^MyI;3s+hNgc67n$`ghQ1ejmTPnR4$A!y6zEe>!~2&aYIjx2HTB z9WU<}JivEG2GEV{0mZ1U; zk*wRJGA3+#m|lECem%l*6hiOO+vuXoZ`S)ka{aWn{TA(d02lH=zayKfsYq2AKG$dj z_Sl59gN<<;mh1UcTqcK(1g)Z&wV?vV;97aDRV@epRhI6`EH1%;7||A})-q=u21gz6 z3ZO(VBi|(j^xC(`Jc*n%i6|pKiTDvjB;iIzyz4_ds4hA}YutgcMy!p$n|OjfI8sj{ z1>8iNAP)j4T*Yx}0x3sHVP81oqIBU(@C&WywY(UY zL?t3#;^II=-j%T#{sghI$#e|;;P{l!*PY-22gpwrCtR_Y!F*fxq)Qzlj9x+3=3F|+ zm8w=&8A<~H5l3g0#QN==W(cihB{f$nL%y>`S_1y@Kc<=PK&`jj0DUJc8FiwP^_RP}PNj6k?oy0}{sD-1YVwp2@9N{1)rEN)wJBp- zoPsZ7P+7OIvYnEWtWKcvZNs_1y|Rgp&T*g5X5MW+d4CbvzP`Q^T0kO#>y^A>zUxHp z1SdfDu)rW6A{Pg{Z0Eo;ysXJj7ncw|yT~L+k2LuD{LV{ zNMk}E)8(*$LWBN1H)Be)(Q(RpNZ(xq69dLPWcLpgAKTInB%IuDR*%FWvOm853Wm#s z(ggwDJvr09H&qm8Myf~SN@*o;Q`NsG-g{Em$^p(NPJJCgMaLx&D6;a1CY z+V{N;*eTHCsDA_d5{3i}aN*-aZNgN8Pkda)#X*xiD;Nz1GOU?F zk^9S-pUe`{_K{t9>WhK9SNz9NOc~&xxjpKXmBOnUh(q zn6MIc4QW7D(Kh zl<8v5LPh?7r9Y{z*3%_7uxU_C;vf>DpT0zD&*UaymR488uL$$zFAnBIVR|tF3q(uf z$m+z^c`xrW{T_qd+e^)8ICX1_R8!gEF0QTe1%?j2dCvyvKI|`IG=*`NyDKAzHtB1YFTVj8kcktHub9QaP?6&6(gpqD zRGr>81G5@^eBG4Ft7=HWLdUIWH3$Xt-iDX?0tCi_i8j$$C!u#`ECpdq3oTvaX+d%O zH-&P!4Vt}{#ncjsfhQyEmwX<-BB%4|egh6;HOls+!@EnXClA42ZPLc4#{?r`j#U-wev~N#@tPz)EZ{_|pFHR?ux~Qn43w;cL1S52gUYB?)_P4UP}#DBWFu&737zr4h>ntuc@;SC<4I^P z#T9>)dpCq*DW$Fq_fW>2!+wqwX9{V}q;G3xS4Ch72ULriuAY!U_#$&0>%T%Re2luC z!4k&iZYjm7>Wto%c>2xS(!ygiu@(|%n~AMk z6Ik$b>CHM%6a=7l;X*`2S7>q>52B%*N+40%t$a7_%z7{&=X=0`ZGS;fQQ*ejyjdSE z11xik|C3=IPV;eumsb082~3TgldN}xl$JRjjw<_Bt3DQqNDiSfK0mSoiMt{)7cS>0qHa>4K}fFc zy)|RX8_uFc;7rZ*tF za!o}djyd8jRjXo5(fcqch9^-9g;_1RmQW(nDn?ViRcu2(eX>6}l`a}ArFdLh?RPy3 z&-4%xlbFnMO3+vTmF8ud)A_hp@~4pypq9nguU`+skPcy(m=J-k(AKF$_*m7`$-fdA zDZWJ#sktmr^xEKOO*SQjMytJ~aj=kDb@t`ue+V(muQP;?j?imUI@@hMSRKs-Rk+#) z^i)z9O{zU_Ez!wi?Z@};Z}S(7iNR^?J%aj?&7BZ~`}z%YERNH?Zd6JclUhkMhh}x8 zo-3Gel`Q}Q0qK6rRtazoake_->ysBf-o<#G;0g~R-^f=CKGDuj7uzJr+2 zrz|f`@;6o~CH0S`p~{@#E75h6K7BhqRIOLHu54Wa2qYdHb~UX*hw7dHIYsaorIuc| zH+t;RB^(fp!=J9?GTx<@V@sKM^_9H4af-GyC6f3Wg8_EpJTDA7lqxol2f8aKgF2`< z9f(jq&4)WVISCCZ?bP?J-^CcOZQ7y#Htto#yj#sF0K`Us*SF(DR;z1jRa!)o(6%US znIw%&7`L*@97s|6!0R%RK>Wpkl+x`6=HSrT?}g4{5t0zfEE_x_)QEYXWurf08bmdq z-NMxt;tbl=hQo^U$N{3HBJWf`c%1-R>9vpukJ-+i@Zv2EgS#L+3cxRaBbXi5$)Z zjl`@EAH1nYs6*A#J&MaGz~yy;st9ch^FEqvG{~i_FCl3j3=18Q@{n~d)B9;%J|a`* z;tqBGQ$X--I5Na28**3kU;2#Qc8d-albD&%9GoEWhY_nLW{V&BzogAHpQkI-I(3+M z$%|(u&-BMK3~`@^+HgIU(5jI?%&}ONrY`rRZL&ZZE@r@hnJ$H^z_N3RH)5Rzkk2PJ z`#h7^f-)@v0`-+k?OQC894$czZ%t}_-tkxOMmJ(q@70wp-Snn;p0c2knToq4FBq!r zxmj1CApPK_AiB-jl|{D03u`l6g^W-NIU}?MYF&qWH_Mt@-ON|#drLqSCY@7o1AH5x6u^jtvaomzIP`a<<;U(eJ z&Cteq5}v0e2&fjP4)A(J#Ad>AZQJJlkBW#w8I`rF-CY+xAZRc2-kg+BZdu$>xDS*U4MaANd{aUb{fXwOXNLkMkARBc zAX~0A(!I;|z+>i}j+MXE3NK#RrC;esYta^Z;~&2vQSh$pS43VE%XHWqabV#eY0gXa z&i}RRYqvA4b^LIx%ds2ro#?)Z7JACcFEmzo#OfylW^5$I^IMMrsb*hy+AWTTAn?5| zT31Z2f%`lLCW*DWYvtD`O7;|~=r*!x_=xZf7Q+yOSfO&cBkkA@4zQ~J=qw#aAinst z(x8yC3%Qr;BVyJ{@r437hdX^;EH57}g!f;cO1e6%lF%o<{Jc1sqYggZxm?zN%TSVf zk2caPLw7SC*d{0yfUpA!mStW8v^Jp|dgq_|0^v5bis9A5KN5?3KpsQqo`Z__IcR~y z06kjarvag2gu11yL$84|zC;V%Nv(hjN^R5)au&pVfe+#+;0j|)=$l-GRlKsv6GKh9 zbF;;k4O}O|19&nH5^Z=|6&Fg3_JoHgG%BecgTvj4Pm?&=!B8NJs!uqReUtl#tq&m2 z6-wey=oP~tSShkN%jn;PPbZR{v7dwqgtJN^&bF^cr;j_Anl^ zP+yqkZX-RN#iL_VFio58oP*=g1w{KkY$Oof&&i5xqBEckNnnm@%jHlK%#|UH$@N!t z+;Y)Hcru{;0&--i$vKWwAN&s~DT~r|wqlmtCH}1+#UmOI zUWf%X6pGIofft|iy+TQaLQ{j6!x`gV7LdFXZ%vsg;xCJk8cZSHq;1NFeStj%8w%bM zb9*?;c;+I|W4bl7>|B02GRL=@H+*^zc8a>l%_SYlTG%#lpm96;H(#$Oe!I+v~~B*oBVq;WphP!pMFt z9zwmBI==QVugzeE+kH@HcRY=F2V&GDLD+IyVQG0eJ%D!7GUuzFy>KDn-o56$tklX^ zw+Mac-LPRY0aTj%X*R+Rpd_o7w^T8-*<}|U*WY;d7&Qcbyanl55EBd!%$8*dJSr)X zC?B1_)tv_@QcO|u&r5|;k>LYUh?UFNGY{;t$qTZ+10=b*ZJ9hSp3Nj0=}HZDA|L>$ ziB#mOK9oNq(uh3; z7Ige>a6x6xOS)mSa)c5e{Hpo@KEK-h4v|Cf=%pPd8fW5@5Kn>w$JpX2h6DGo99mdg!-c!Rh~U=j6&Y_@*o;&2C=Mf6P^q6NeiwFG*QD}gMQm$(mx zOc040n9xg~XF=E~`y7U=SeM;xedeY_LiCtuD^ZHj5#@^MkTVe`#oK(55eh7#m^-U4 zN=o2?_)w!B&b@!Q4n07Tavs39d`aOU1g8Zth(z>8`yG3+j-#*UZhXcEsiJ2N1g%S|ix?jS^p4+i+ zE{hm7k9o4tlW(lQvxzy$->uDP27pjs@c^Wm242ECC=oP^K{_XF)e!=D8?|o(`!X@7 zu96_tH^JA{)8rf&!SuxYsnXenP!kV*yf*~@NgtH}EKCE>;}$y|Ng~pQCeFr!Y{koo z`<}_)1SJG*(Iz7dWhP9W-lcU^w=P3DL$`n@Vi3#=IRvB|{LG-lPUR~CPk zwOS}%1_%x;l7YCVAfXNV1Z_B@$(OO`bdV!TxK4(8GtnJvy;lDhfEvgfs6FX4E? zwY4j7b{mCHAx%k9%J3&5U~_~x11pX(PN8|QUDTl*t?@11quv?@8NdUYQ8a&>xe3{= zHECV(GB~?rulh#OIr7$FpHJtyiHHeCkGdiNT(RZZG9WkqT^D*3YEw2Ae)}dn3K$1; zFLARoUMN`~X`Sp5hX64p1kztv9=N9~e_T8&xbd3sQ(3FiL$Wf|Iyy!v4_ydO@6h-%=7m1_ zf0$yY6OJ}rYypMT5bgtSr$;SlEyc)ZCWr}7M5)iNwxK2?p1QrALU@wp7rL|K?G~TG=X`vr)I-=k&=8SP6`CGm$Sh4O zQCQQ_AV@B3f3lJw7pu9CY(K3TzQ7245D)$}WG2O|u%BEZQ?TjqNTb}> zuj81}5Pwq0KWS@lVRnKn^?E;{;D!`*2z=?y3Z}Ern)=ssENI7y-3CMlwP0_l@d@$mBx)kriq!vn;d7dH1>VLJn1?&aWtr8d@AoJ)-gNE8>8QRbakk`&kblMU zD$0mmdwVCYNqVrJBe}mxtV!95?5yIn^3R1YxN7@5}4g>$B z^5o!)^iE1gn4sL3*N0H4+J5_3ee31ATa!wMEw6C-q^Z%mnSJ}w7Jbj3KfX0!b@xx# z&-Xj;adD!{bBiOR{4{!<_wIkpbE0n_jCJ%<^sF9Tn3?gm(kXq~<+lgl4!yK+|J(g3 zrOvBu+qde@GIf57P8ZzGmn@;G(HXhMtKu6fI~&}CL2wG*$mAU#8a8hpN{5Tt)Am!Q zOp*QxXMYjXIBXn_Y*U!Mvd=jHq2NNW@Hu9Zf!n+EHtlh6aF7{#($>7+fA3u}taopj zr{@B+Mh}1R*s1M_)e|O73L%@*u5V=Ca=~wuf0Q;VYy_j=cAvhl=v~6f>^EA0G($!SI+1RkUED_Hs-eX{v}$3OgKITU#0gy^Z%eCq| zS3^U!If9~%r!hQTX;b&^-50#qGe0&FYUnN}sf3E)kES;+EqSs7lk7gMK9db2gj*&K zt`98u)&L>#4M-P#L&JF-fA`IsA!(aRoC6n2yAFb)?!y<8sSW~O+e?0+y>*tPDuI9T z;vVhWw-4O6Pv#Ml@812Aoqsqq^rrB$e4EScP^GvF+`G4N-kqc*0|t?FjrQ$Uun}-D z<&-Sy5=|2&E~nI*ACd7}WUt+_`?oP%VHR+#oyJgXX2{5OD1q)yva05(m}~mwUAg4t zneD!8b9_!tPPd*t=h@hd;IGrXYox8MUC*J~{k)?iLqgN1PnQ8oI9w5y2|6al7|)s0 z8O;W05wO-wgxNf$P=rreni1xItYCCZG%|cMKyTBw`me6ONuBo@GVLCcr9%6mW^?8o zB$qe&{q|1n_UmbdC|O7|nh%DOyZ4C`C+fM*2@4Cm&vqtmv=AbksJ89ewSL!sf$5AH zK}fY^-=lmcQimAGQeoU{b+wn@dN~zrzirc|4db;DTp8NTJ+GZPc~YE1@3SwS57}-G z-*7~$k~1s`=}%hl;0A&(JIR(slGcOAkKb_ojam(ALtF*|&9k!$%*+r$O}8fmdO}DX zX_-gc07~ExN)=i0j9zyB^02R1M4-}$&3eLx7eCG*)%&4C0;hnUL3_%os<(IweXfBo zZo!|rryRXHNj1LhN>aiJm@DcjcXxL*|6{hd?-z)PE=j+2Ugn@K+z`5?c>xdp{<~_? zia#}?^>UjWiZYux(XU&Lv5Ew*((bJ;_qg-uQ7b?%)Q8vBt&O>z2`^p@-E<=dWa+l6 z=OhP*K8QAr7VOMhxO4B`(aV?X!`o(&&N)ksH=gFpir84!befSy<>8x&iQS&>UY60} zV@XA+p@D$`1@zdG;^%v|Y}ul3;5R&1@BdJB<^ehHTiZ`W2~8+-5>XKuGerZTGDRXo ziDZh*TS_SsQc=p32APL4WR65BMCR!v8e~>zRPSf)y`Sej&-vq=efHk!zJI^(cdct( z*R|HlZY=)2M>OUM1aUBAF}Lzg_{#X`2BwAKxxEj=cN*Eh?FvI3hVxnf7He)O-6sa>b ztX%o;;bX^|ysA!hFf0UN>>aowm^WMayyFK->$I!x6dBR!E+sblnnaWlJRLk1$^>;6 zR1bufdSk|nq0SE4vuBTPXrzt4pb}#FLvYW+PG+_3`d_4B075BX@7}hclRM9X?s!rh z_2B>uCM0}mwreN!lBu=9<{BrRHf>nlE#^y^%McqI3lBAHbt)>_Ma@kIC9H5DrBU!( zUaBxFty+2}Y#F?4OL%zr+t!gC{BQB<-oJk@!Yz_?5jwlTd1$NNgo7sQ&OjuH0h6)P zp)QJtD4N*e*A_AnQ9|rzBqKM9{hjRU9r>lQ;)n2Uw;n#!V5*x$FWN_IwN%GgHI5|j z3au}`3vN30ef9k>6gob=NT!X7j(!M+Wwq`a*;y*nh3y2nf7mlx;cg)NVL&MlU(N3@I^c3eROU_QxgDr)GJ11C;w zW4`M5!9Twh@62nes;YYT?p>L(P0a6%{0??e^jMd*JOh;>dv1`P3GFqEK#H!~?bLlh zS6V5A1!8R@j5LS)J}kD(MJ4ydQ$IrMG@=jzIMp8p=YehFgu1zr5vjY7w|Ds1u}7o| z+i#mTZWR2T4|R8Ww!6FIfoI1-Mq-_3lInKeAVaebf#4(uC0?saeU+GykWK3tvYw^OjWv- zO)FGG_nxfGb$m7n6zdpc0^tN&=JlB>^*dk~W+Rzr!jYYv0~g9KWFFJa!+-wzB^(8n zRR)`d8Gey}RnRSpknJf>Xx-9`jhSLn8v7ia&I`Er_W1z3Oytz?_nXr|Y%(JCn$MqL z7=G-2cP9;=;}duMxGk@fMyOl&?wuH>L;KbK8%a1hK51Qe!9fz!4LeoA?TjH09kCu{i|8+&`keMK``P`yP9rD3kF zda&H@$ZW9s*2}fD?r3RlZh%h;dM|YJK|W8{n{oTUe_zt^=^A(d&Q&fog2-Sz;j$;c zE@08Xw5GE3jf;zmI521{^53vQT75|~`UVE7OJ~-Nu(uC^6Lt0UtQ*-~U90j}z{ZWa z*Ve6db#?98uisoUTGwvf)|L0hK}->%wAre3Ox3WG_Z{=L^r$<f_ z28xwA`6A4ck*QGEcx@kTW0U!Ibwp_BFyOLJAG0wD6S|2)hXN7ZuutO_hE}}efG5Q; zW|>c0SWnJvmtGrCkLA;w-o1O5227Rq2V5&z`WCXUU3aRoib_5S?}~5T{s#}nSy);I zRs=7{TXGst4$sQ}qt%iCJkl(fRYF$m4 z8c>oL5y-xS}!IT-bO&bqD;y}bpwh_X5`I=U6`h*jl0 zcA1FlfxBCCvwnvCXc0(YPZXrbk5pXvq3q(V++0mNJ3H2(^tfQeN}@?Y!!_^t`aE5e zaNxi&sEwrLzkqj)$!;Q~4x5_l$@Rj+I%P>B z3sMG(DnMqhyp-eHyY7k4K4U-zF97?OIJkE1Yz{1twMy5nU6WZ!&6@uWyV#gsHqjhga>TeBoYQcOsu5BEE7cC zU{zpvhCJI6W|ds*IcR%aT->~8C)?%c=R2I3+JYb!y?<=3bucNxy!nn64Y;Wnr}x~Y z&8b5N5as0X;jT9V|A5ica=6p-cK{c02M(iYmDD6;KTr zx}Eo{nM?N@+sQ%aKw6DN*`rv=J($UG^P_jf1MXC}qXMf4c$&KWp3xQPd6^zT3ulPZB!K z;p4}TXmSmD_wEhWIL!6Grnb8_tKP%CdL${*Jzqqc$_X>9hN6jxYLB7lJHD@VaA;1; zMXMPdJ9dPm@DB}@dEqkKK}rG+*j@E!`r+m^pUXZthVR_T>uvqWCy4B)Q^s zp?cE(@Y|>Ji+v+W)IClw8p@tJbk4W$-i6=jW;<$>%Ccq4BtMe!d2`q4X-6J71J3K% z_}g0P79_}=J$v>8%y!7Dr;v8UlSZM3mg#a_dE2EWj|^mwIk|7ybjQU#`}Y?|gcMQ~ z`mQ8JlxU}`f2}wlwny)x!R4}fbV(>B4_N~N-malfqg;a>jk8*uhwkQ`pJ67o2!7xg z5+ibXFG#Rfq}*@zpPwSR@Nda7OaezVphqw-LAjOH9N9w+JVg0ysnET9ccH`(L<2rq zBex2T1UYj65<+l0VnpGZAGdt{>L#T2IK{n1NwLDXqtj=eI`9Xs%Vk0}cjI3fU0i@9 zTRi3Ne)h>mhK3WE$gnW}B5Q!t$lpKM%0^@mv79qa+gx?bvJTyBY7@)4_Uji37gCDi z_4DUX8ZdJ7f8GoEX>)02ixwuw;;T6FatO%F9PKWa%V$)5Va9?O}DM)rqX;^EbetBJWKSFP6q5q}N_dBt)b>?Heu$Vvs9UQZ4 zd#aoeLj{8hwBwBCi$`-Qfy)17j)hjn2G353Rt$q+wIde}cBeh$hQf;RIPW zZNs^m$96Gl*>I=FPONb=o_%1>X8k{tZU1{_s9x#XvPVx0lai9WE56rj+O+B8Cr^BT z7cJY$x#qFUPI>=9mYh!dZHDQEscUO*BI0m-6A}}P=H)Knwb6h$s$xjTj`ffX6DAz7 zRePHp+P=8H@?zm1m^CuwrmnNM_Z3^UpUKT$k2*M^1LSG* zrA6r*eCV%H~(v-x*lhyj3fe1nUNim?6NSR;i}C z2q39SParH^TKV3nC>tUVVER4i2A_Z|tL9i$y|KCaa{!99*XGTeO`jMX8`m8}*UfJPS~M$pL^3o3zcPPU7#E&F-RWzd z)f#!>p`p6oDdZUMs-L4O%>Lx@kK8Yr7j62Ko>F69p7QyAJZ_kp)8-vN{V+H-amw=L zXKf2}4Nm!5{nh9G_KN)qhJU7n@7lGivd-?Q7Z^_6XFn~2y!V8J1m$LpOP;OopmLVt z^e*v7xJkZyArDH*WZAB_T}4OKixGw+B|8dfsxtLGk3f5k`j@@j!(bblI`_`-A!1U z@q@TS?;6j%wwEd%30@g#D?EQn_%ENIi}so&{Bq1o_1zvGt~q-2Xg)>U;lojdKOEz% zRA7AlFsVY46cbEjj|Smgw)oO!8vW%*_fO>$9yB}StGLE&dujEhjtd&RPRSWk;{B&8 zg71qo83c5|Vq%(6*tts=F?T48-qm9te{ezde^Px-CSLT6*`BL?x0Q~L+s6!Y%hu=5 zr+|N?^*(TIfq=Q=j*xwH67%P_IT&^}Hn#mR%~y>bw#Y=moB?q3L2LqGDP?n}*EHv! zkdaBYcYcnxv+HbT7F|)Xns$yH5*=Zl`!kg0DSvpNJ3p`1Ts9YtSl$m}gNllZOucc6 z%x>oNZG+MeZ?f}`n?7rN@(#`U(xG6L`Lw6j3Gh&+5j!R+@AdgwqwJK?$!$n8w=d?| zRYQC)r^4$|ube>`j?J6NRfEKkS=n=rexqJM%VC2?(}ZnbVB`0It~CFdEJhlTXG_;e zenCM1tl90`w?%*AMU7|bSANCL!q$qJeH!X5=wM-0Ft($y@#js?0$`9 zz{K2IH*)XZyD%CrsDn1vE9Ca}T=={v#d7Jifkp=t5?JpYB>hdZlst@LVo*Y0 zS(Mv&4y>k6A2MbW4dP$fg@l`QFc7(rgI9T}L$Vi@r?hik2aa`hJo(zhqfvt0SFp(= z{M2k?2~fUD0`1A?>hN0ysLJPghqTBiFGC+PHt;$lJC%6|#Tkohe%EM!9l0jId@jf7ok!=dFvCXt+)dBb;iYX0ESqei*V z>_ypoATe>{v11ED-tTdToTb@6O##-C7c}_}GoJ2vC=5hd@pa{wnnO-q* z;-j|DI-8it%1#+tK$O1Q^FO~YhrU^J78ukdFaGFJL%_+04<8O3JGSNI$sSXuPBm+J zNa7ISk1`6`!}qn>o)8`im&VH4S{Avwbey*M(_hRD;pbURoVbItT$l9c5T%ZS$Fqhs zF44twA8(#p%Y2-%LOM~qQ-6TcMl8{M`sk76*sEg`t(QfRW-mzD&Fy8P| z?81C-c~p*$yZD6PiKtYkvL2d7sJrDsH){^?|B;|12#KYYe*$niguVb$)zZ{V1BUkM z+xHe93in!(;o{^2Qlb$rh0g&cG_Uxb>saHlU_qUR{n8Rhlq5j4Z1I64K7IcD{fXW? zE?pW3Op|47gobw?K4gNu#49FaLonGdr+p-4&9xj2kzLf7B|AC|l2C#}qn@ zDmyDC@yEHva8mfd8mOvniTTgQ4gz>d#O8& zKN&NA0RQZ8JG-WR`}WN*CfQ&yXTw1QUL=CU7lY5Sncr+Ljkz6@TYLn+ASYrjHW8Ue zMXm$?-y{z4OY6JsndeCWz!ELX$Z6SywUBmttfG}G&9&JyCCn#o37X+{Q4NCvI(+`} zG5Y90`Ps_$ATG)Pu4%gB5c5T%$+ zIgV3kM<9wwlN@^Md)lUPPnBJ%)AMalWB*()25XOQ5Y8PJnY-u z&@dSOktWY}WK4nTe3^1iD8?Y&7+lc)VN)Z6i{dMXirP+kQjOWjF zpn%8s>{1$IFmT{*fhk|#R9hahg+!$K@|ln=_y3>&lx~6>I5FdDYh(>BX*ze;^!PX* z>`nZRBHHTZTA-3NU%t#MfXgC-175vqgAG9JMr1>>+N~QmhEM63OD#+~+ekJ63yqjG z>6doO;7*`Vi{-CL7QR{i9x1u{hOeD6kaC^xB$|=9W|6z>89iR=aA@PkhPXP#t_~K= z=lTO=VYK47mCQ(rJ9sc+>{{K@6j}W8^8BJ~1bfB^H^vZSdaGw`nYHYkP&uv+kk)%jys)MXs%Y-`$!$jBwHF!83tQQySI1|%%-+Gaq^@+P?#2F z@wecpEV^iCo(6>Ac4)S5e;;j=$q6=oo@JhIiBd9b1U65#S8H+Da*!|XE*RsEOAmZ5 zd--Uk+0OHV+N_t`r8C5%;&qDf(ra25brA7 zB9XOA@+#qJiFL9}9EmCfaqg{mJr9f1z#LZ>m*Fs_;~X7FLJAG=t?RyXl%3riWO`}c z-~@3!EC4UQAFl=1%2pY1cw85~TfgxMA%qHJTll0!bTY#xrgqNt zx-${#4+AcLY#Fv1h_WCGag()Av%6FI0;Cko+BmLZnu#=d7#lZPv0??;Z4*J>HYUPG zA38FSPt=pCyFj7U72iAZPiWTk;_=%0%ar+Xmsc(#3X03GXP-VhySYxVD)3C;Lp?q* zt=zFcvuytTkA7aaC4SFQpnLV{Qve8!?&82t7Q3y`YMij@FAZE3;1m9C;NtUxNo17^ zJQ4MDm(HD)Ah%>_Dt|)E)j-44J|hf`WGSAis^yq5szye~6yH20eu6Ny+B(--`RQE8 z|1e-#Q4@X0QI%&0+scBkzx_-{9J58x+LB9%g zlfJZlI5h7)k%n&=k4gwk8Ry_&9NKTRz9xf;$MXM$r4tDs0-vYU)zXYpKu6_z6hZYQ z10&F*L@DAwF3{1qf|wmrmEYJoEM_c#gT%^Dmf;M2(1nyjT zBrrYQ3byq&%D4b=+6_xDe^2J(VsBf56>96YZ89YXUsCq(8C+dAo!Ksu46Ih@f;F-Qb9eCjc!$w%6fPrRO2h_>Dn}=FJb!xH^#3 zaR`P>r*&)e<-1f!QZ{3mmi~NRo{WpcI$1lhynl**moD^xHf6)HEkE(Wqes>G%i{Q@ zGW>#%N^tJQe2&4>`xDy*W|Fd8Dbb*cMK1*1Fl>PNK0&~%CSg0FFRADJk;uS#TiOnJ z^?x=E)a-A5Vc2JwJtVDuub%wofXYA}KCSlhiC){n!oqa->kRe2@HV6~H{R-Ci*6Gd zbnbj`@K}o$4G^vBU(d~*khp0L00{P71QgortQ;M+g~b7lKs`e~%81XV1!fMZiJ=k54++fijJa zwpv?pt(BWLJxH@KcEJhBZlj`ymQy34k?rmnr})g4{tfZ$MT4x%li$3Nz_s%@a)`l=fP69uQYrT- z#Lp6r4%0I3!8`f&>3AkEPze$yo?Ed-^T?g-Y?)jo!A5*Zu+FCvw-jjbxud^hPGgtFZ@;4+0< zGQ4h{;cv^CGh<-n)!VkcOInfMLA=j5kiCH>wI7Q%*K7*4(dS5tfmuKc4S^J0w{W^} zV&}fdsr)^oWs4Rv`-vfU$#=`0DZt6GHUL1TzqX_po6W=iXRAuk?%l0v+`U0%%ST^A zYJ=-y&XZ=_q!uk&kQM(9JSV2@A_cHS(e7b>ftmqYFSC#pdgA{83C0+?|CXYWd7Jpk zsEK3w%QaKqhEL0F=fgefI(Q6qY_mp<8j*l0(bLCzP8|M#nH0o>bk4e1)`7;X8gUA8 zt!-abRRvrGTQQE|?aWT$lP65DAmk=q%SgR7z690X_@;lt@v7N;UXg>v3kIlQvD1Bi z3#X|U)=1NHzxei|$6~M&xeZf_bg6KL%|Gv1S6Ld!m5_EYO-;?v9Xn=o=eM>so1kN4 z%!XX`RmvW!qyPN*6X-}()7%`ualmNlf8JJI6>us`{&0Rbq=u5m3rage z#N0Sr|5#n`zXj5N|0^tShoYnKDX9%aKCAAm0AAiPgwb{CE2Z4SV&}0d@+zen>*})lM<}uW<^*l0SkH71= z9@6VaVYW6t|6uaHuJSRkW5}D91S()u{amYU;vc~Ehwo5u#--J5?;e zQXG2p6)w|Pf0Ud14z5u6JMI_#WA&jyGqtky6VN7EhQA@`709g+4ZBRo^ zH|Tswy-(yJZ2G;`eZl-ADva?5w9fNt4{?@c9x5`Mg1B&aOR5X^n8)G%ltjc2D?<;I z&_!(pJ=jz_A^=V(5opsi`(JsR9BI#U+`MgDXUbwk(7*TLFu8)0Eh6N#%XVG6c9o?9 zV0tO%kYUJ@lUAD>HHz9x0kKqpwUXT~ZTiy?8_zoog>>Ze7F*WNp#9y})>e`w)el!} z6NItMTJwH&{_52sz{pu(0cryYcuaYbp-tk~msTiLP{hLcgoK@6hJ2#RkT6kL7)a6t zuD1FQCBgvUFlF){{L!=K&b`g;5LeB$FJpY9-e*J)AKm~z7ZPnG1hLD!c@@X@bSdp1 z93Kzv@5%P4w&V>l2{QvHM}OQ5s1yMLIQe8I2Vu_@g9(fwiKYt#py{GBoB%2QTEQOHPpP@gt9?HemQY30VW| zS3AdlY1m#!> z8QDjymI~qW<+Ud#_i>l`LRNlJHeHxX);KiYuT;F!c&n-GIbw%+g{}URvpCRevUhDw zG$&9|25ur--GU$mhfBjG9S`8%Xc zVZ29YtB#U?aH!=%Pf||o^|YS}j4nnMJv;wNFla(}OTbH&qy5i4ixQaH&B$mXHbj}r$U9K_aTzLv z>@QQ691f2g-bGv832Y=nAZ(o|7P3?Wa#mUzK~J&?GJ0qyYF*{q5Z#oVGH||Cz!ntT zsNK7BN-Y5nRL}|LfFQMu0)hh~dnCE`sr#yhh)Ftq`Z~Tj9!gE2V1~9^RjZeaWh{U z9G`S7QrETS6;`_AQ_lszeLD+vCWCGn+`r${QtKeP-s4#058!OMKX(3?ypg;Qq1!lG za{(bj^>gsgFx!_hVzflEDK(&LO>|TR8`~Jf4NEZ&;jt1w&^{$2~9YO zl>Bt~?jc^Itc`(OCY_7^fG9!hw(6yB(PC}qY!qVSzVz@Z`z8-@QRj1(fl^ z=WrHeK@KE_a2MqGs<8{NE|lS4j~_d*1N6IP|5LzmM(2(4_8u(8JDl*3=1+sDqF9n5 zT~E&+_$coBu&{?FF`*`1Mam}&$gUHytAh2#mx(p1ID6O5dNhllBjJP~JDc%LIAA_+%J>B3 z3x>1Q3**o3y3y?}y)V|*)_6c~QQUbI4nW@sVgnukPX-hSmf~Qm)_*D0H}y1sJQv<( z@F1Tj&>8~*m%m_=sa*Vd+3?qExy#TA!X5J+iq`(o zOJ0hx87@pI!K}V0oN%17!GhAKcSQfbeGh^{kp@tIT4Lj{qf#wBX9r>D4iz9LiC-Z* zJK>0ScH0jX83m$K@-@fu~i^?;i>PUe|v;nX5oRig@PemLVB6J)@UxVBLDJ(OxvKmwHGXNU@s&>m3 z>poqb(tfZGv;m<-mRE2S3xLqHA2Q}^ zUlY~!;5D&4QFo=`Y|jpww%N-6A8iisI%lePYbs+TC7)wS4K`a@0NEMq(hN#4>ug0& zR0t%iKXFh0&%V=w07c)I&@TqiEv_6M+KC1~s{W?|E|miUOpn=O{RO=`uE;nY5| z+<+%Bf>1u*(J`m=42?K4tOf~lJOmMEe%H5dz8a!ksfDEnRTbg-S^u!*F7}g`_7XuN(&5 z1saoBqYaWo=f*~fAfz4Oda;mLWwB7YtODz0b3c+P2pHWdct)}>vqLg}PenhU?=47zvfG`jHW&{VFHYitc-Rr%Hzt?tXj)P}4;dB_ zW<9?WD>IaKokRYpOKYbW`s7_(XLvWKHLrNA8Fx&R$UQ{ymIT25PT5T33ZJLrfB{i3odmda zP|1KbrTQpZ;;4DlPg+Omw-H+{7ij0jtIQ9Z^$&{^PRLeScY;E=lrAmioL(7!mhmT3TakLLMvja>al(#^<1=LjRSq;A6$uFD@Y`8~u=wBu2#}$4TbL5MBKj6!GFNva^?;Mb2rD^Bd7V(AtB9 zbn#_M-ti?s$d_%w4a(QcY|pV}w;FA59Qg|p0#ZTjNdX@LXU z=5gvpg1Y;(uRI8ZQXN@pdf?>A`(loZs9xz`ww9p-nGW_(TP#g}dFv4MRj75(M#>ty zOB{|(76-PNAjHCp{TYyBI@a5_&E}uoiHGlqV3^4s5te(^7MR;teVhc~L!RX^k+nN-c8y}$P!DDHvbW}+n{>5fP#p@jW{!O zxY(0XQ5b)#eX8z{5f1vb10&M$YX>_1)!fs~O`m>h>7Ed|k&{U;f@yk9VY%*602#cek}C8JPlX03_WH}5Q2&-48)E5_oXWz8?-j9C1;Ks4IDT%aZ#4nUPaT(E3+=K zU3{dESPR??wdsz^FNb#=hnbcekC{rVw#TOib9s+M*$gZUyrHFp5v2A9bmmMxuwdRi zx0lq$U$PDRnHaf6>$ZjP!q$VIzbRrRl}B>-J=1+l^J;>to7B84v(xXLTv&k4PW`_QdwM*664$HRX8rJKF%3mqE@>_uG^Ia@vS=_#s2TD|Z= z#5DP1CyF`)uu+@?x)w5A$bIBW)#lZ7JDS$F=*+qJ^nZN-wR@^}89L(j$R zi+Fw&F-7bPEMODEDroN7wBn1p44O{!o+RcLX)Jqo<(BhQ0KU;HTxHXXt*ZeD~_Pm(%-(bJ`V$KN)3@ zOH(d8S}LBkF~n4%w-Z?cy_B>jcjLw)(?8MnPEM`pqW~8Oj~961-M1en2Am|Vv~tev zFyt@&iLX+vpB87=`0S7T^}KFI)`)@0AuayhF!YZdxaKdP-80X@@h2uGnlD)PLNCl_ z+=L15Aj(Da<0o~D7%d%3sNyoog*e!g)*%WfTWq6&H=YL)1v zMlSb@A|=`u0x*~h5GSd=m8t&NSJX;<1xhd#;l;o~94A4uw2hjwuRezs6Uh`b3oE{b zGNFA;nkvWh#J@F81w98u-Qk9188?iez0(5SOxF971vS$(`3gdZhzduo?CR!bMa&Xz zH&R3WMc9n;)G$a9>I~31{+>G^04~H_3UTVP+U@)2Z4C;VMV%)N_cmJsNn0}_6}Qyr z@+WvFsQfiCUdL-RN;En+{qP*V3m=;7MxeIfw2L8%a-{K{~J@VB{+MmR?r`Nf}G5m#?J!JXBZ(pb-3AS zgo_2HN=+;F2ArrEvaybJgD}vAV8*mv^KxIPAEZ95E#iNt#(fbvjei4c_ppf*wHh_r zr0Ddk10fU6De(D$pN^wP2Y+8y)%XVw^br+;x5r2r7q~Su&2=@(o2}9y_SdkTn3ZwdzI4x=6>|nE1;t8cyUkPh*^ePX%ayn zPe;_>Q2GRZ1oTF+_F@$Nt3#6OKm0{Gq)7DjaPd(Qok z+1NZ**pCO?&xP-s78Vpx&)tA;Wlf2U;^!j%`2AZ3Fat}bEom>)0-_NRxUpo$8Qx@J<|&?zJ6{Y37tfkK+mfnR)F*Kt&=DdYL9RnG1Cxfg)h#VU zCDOxT0^^BvteCJZN4kh86x^2tIXF7XoKcBEkeiLSOtkXKK}fAv+9xus1s-b``l3aw z1hxpZ0MQpY$B7(IWo>~8lzQjxTmQ~_da8)#xZXtL;o97Wm^Hn)3$!}%#MHhtf z%nOcO_Ecjod(M0b?s`65qVQTGUg7e1!Ac(Pd1lg+x)d0*uz!uS0Q9cQcKRQOCKg$g zqDZA#v$@osYv!NgN2w241x;5oKQXg|ji?C^UowHRLu3r$9p(nxR-duY60s9u_4Yek zk^b}fnQx4;m_%tvtAHUUDH)3lfrY~DPfX(_Ms!;Y%5GAzbh%sADw#oqxFeA2dGZ`p zg3~Qh2@G@ppp0(Y3Q^z`&`$0m0t<)2PUy0a?jDGag{%HnbbHo{sDKU`c?Bm*wo zg6C`9u_ZAvW3=HcBCYAj=z@(jTB|PB#U^NU`b*6bp42QCmmBEp_?$XXiXnD$Hwv6@ z<$8}nM*{2VVa`R1f55RAtorpBQZv4}dh!i%7Oy$_ZIu`#P|T(VMNK1;2~eS8Haq!= zQj(eQ`?z?GPr8sqI8X%_PLf07pt328WZ=h-Vrwg(mb`6uSbAY@pp}e`jYVStDTq7; zpTMtn#4a4(alYNmnKC0>_sif>zBBHrfoesS#K_4N)1+P_(q|D^g_YuX2y04&Im1za zALDWf-(Z??Vsn9c$&c{7;DoDJt~|f6v=ewq_yqh-Ni0evy-vBXq$84X^II-!gb3zE zOG!XkkTcK9dUQRRD5kHtrD@MUeHa?V)UlakhpWgSIH*VAeeY3g2 z64Xv|rZc2B^e8b^wi>;lTMM9{$WMwJVEf+}uE2bZpK7}NYm~NF4ILuCky>`cU?h`X z{1=aI2>Esdw<*^-?fO|OJ&gb*iuhTy&|-g6r7YB4^q>^s%-8nrq`xk=={tYtBJpO1 zVcVdxQu3SN6GmSaWwaP+;1XoD7D0m!@$N!0a2UnbK$RJb{v#$0DCc@IJq4F|==)Kb z0Rau<#xp%nKmdh36^xK_#iO42#veOIl$gd#q|WNR;gS8~ipjc>JfseG%`5ARwvG@h z1BdbC7G9piaLO)0&IGd<1C8sv_03ziCeT)7790(2+7Qoa0wB#Ipi_d%Gprqk6Wh`I zhsQyBo{?;Yn5K6nY)Ng^KkI*{rYyb(lK@Fg2V)bSzASg<$THrgv2p2qP7VqlL4EWGsx!e5fAfL z9K73<3oOfMsC!umV#ix1e@nj(=cnh&&r=W#MK`9X;L_M zmriwZlJQKGQciSTFgZzSWlfDSm`?mH)E2Z|DIsDa^cr6{k{Jv$hcr8kzX@wgo<`C$ zhF-ouX-}^9AsL^S5kn`;3wl&@nUgOsn+q+|(Ruth`o&|%r@&ub5A%!m&`1A_aKmvg zO@?H(q6K@nbtTmMxCW#GL<9+?Ym($C_fup|R2(ur>SQLr82dm+svTI>DN9;ucUz63 zp)`EWUu{=hTB~!N=zozdl+Z1)*gE5mxenF9D@ZRdw?dCAii*X(eNI}y^>!!a3t}73 zn*l)$g}&yMzlgNko`%_B`L#te3sSAgOfRmj8^o_If}j%57hpot7}Q+!`wyP~7l@`t zC~l8p4#vrz&v&&W!}D$84(ti9M~x+mJm6KM*b)nh)ZTlZV@?Se2c3!&WdrTt`OAE> zMn9pPQdvA0WSx25xk_PHm79Z)jO!8_BQ8#lp{%j!zR?Yg8#j(tyFoxrLZlHREP+O@ zIlM_5cM2cvQK-$s&CB_R4Gr>yP3%~^rt@Va zsNmIK6FjJ;MrE`-f525Q|Oo`)?JtMMtdH??Xh_PEq2OX)vfVwF%aQJyvTn+`2Xn+3vsL^^jkS!=<{7cm(i?WUAE$u*3C)8?KjkYR9Q9lX0Eoq&!XT?0y-lOn4Wq3+$eA(p?eLP@Q7}+4QExVsH7$`y@Y(B)acghaqR5vsOg1# z-6}53E1M&LNP7rBMO;N$8)T|9Ids@`U{A$iL4AjD?}=^sR)Fjo{;+ESDn6K$Jgn{@7+EX!uWQU4m2W`& zHywtG5U?QDrdC};;|6Dd(r37(CjT0p6?Gi_nJ zERvHoY>nU90So?5mgN70`Nv*wmrmJ*kLbs$Y?Ql-T}^AFqoS64eQk!{bsHW-4)t}s zUl>>jc)`1Ozg(p>In}5{PYV$9t??O|cuq;P2G{+yMb#V?|1t|w2hQ73A)mTBCF|3j z5Xa4--%B<+QDjS3E|>r-b~vCEzSn3#hZanyfY}8K?sJJqf%_4{8Q=l97?9V=g0XM& zfFMjRr)KH^59Iu;FU?3YE{>^ekc$T?-YTC@2+P|?KZS0@=n{NT)IXw*y@(vL^0N-1 z7azz(*5wRd)F1o-yIIde>(Rv)TQL-Neb!?O3=bD$BK9`%`2q{M>6Sc0MC;7Y0nDzt z59uLqlGdk-^HpW&ifm;VJb3T)%PV^^h1~hsuV0@WfzKFE-_iL;@84g{5%7GM1wiV^CjDqWolH=}cxGc`quZ<3_|*p! z6J;XI6>uILZ7liI+&c8o;lr}{nVPn1x37bbJ(VBz)$IB6vncVq(m0Nl)@h)x*`PsZnBbki3Tv)y zNGvaj^RC4}VvZbb3wLAMHP@(Ag3%D|owi*L+8kz_$^IwO6P(}}Xat$!#>Q7X$ii5W z9c|%s%ij1+!0aUqKO-%n{ciA$pEE;&BH4e+8R94%Hm&Jfhm_lnFIr6ac-Yg@wsN0b zXsF_JTDvC==CGNeS%0rveFx4)xlUUI;yH_|+sF%*x`R&&kb4N)rkg6T9H(#+HdAIo z;&wQ*^u4V22sdfAn2}`v-5Kdk2xIQ0n&AA?IyQgjPW|+{r1T?P4zC%@*ZFKPH;jBo< z>&RJHiqjCzOn)cQI+naH1KFrE;Gk%*p;k0N=7+q><(myY_bHDP+K5j^>`up+>ecR3 z0;(|oOl}DrMp{LDsxKshw{d0;Vt){QH7? z@dCKD{AS1uEe7-p$4;z;DIP-1$m%(M&(59c;2ATfKv1c*N{@t{W`2g4Nni^)(c8q8 zHAO#{I|@F4h2*(a#Kt?4n>OaDG8K~4eq>y%|M%dnThlW#HgN$-<$=la=A)u!&7U9a zU?}V(;{+b!(j_X-shZ*C&%NZf{M|u*;lcx~DyWfBCMyp4j^&rRUU6$LV_pamSwGA3 zI)8MgQJf4j_kun2#&-hJ{-Nu#BtFw;3yrQjaNus_C}K!>-q0#{*e`H1j*9vimIJqJ z*>X2yDQS{6F}w?%$1IKF;PZzY&Eif;hnKLd@E?pe&uKEp2mrx%J8y4<2U0UQI9OUF z$*1qxp9m8LX?;j$%%l1P!LSC%f=M_zAw6*_3j>Ud-?(6gSc=6X4_-mNbVC>s41%3o zthOL>>9rJeOm3lZ?H#BH5Gqz*C@N|HEXdS&SZKIQ9A%s{=`+QY1KnSr3r`c162vcd zJ)ad?b*;thDLVSk5-q|*#>3>VA~HEr$pu^3Cre{HSKdo$v{Z4{BgMF7^yFS zUE!6BDhwPf^6aeXGM8R%k!X47UD+Ll8wxPCkvL3Mnuf^N_)@pLgUFH+(1@|X&*_{CA|L+C z@?GbR`UA$L^9hTm+AWQe`^uFa)vKWj@o|Z<04@$rMFd15 z!*ai<&9#@)T9Kf~!S9jJsYnH@~Wv-g5S zncucBW)4Y-;rIEQLT15tiBl57zHp4Q%cTtm&Yxd-^47LgRn96SR;5+9S%;yrQ-{%l za6Hn~NwdlI7cZQmp675MRpQo8X(DOowPGG>(|BSjDFirdS@cmlhDQ1EE) zGnvPBPB@DPgcc z{NR*$?x0X<=v5jmTFCCh-VrDkH{t7~%%HcAdav%|t%l;Ry1G`IuY^3l-N8^Px25;j z=SdrIN7VkvDazA}7>&A~bvpDdCqTw3A9w1_j)acEy~wGJcS6I{TE(R3Z)$H&oI7`p z0;hUZcdh@uw3QcW`@M^|jQUxn$2ij7-S5{VGBZdkJw4&To>EK1Iqqdm;rJfdLUr0^ zg%DL1<_%i?T~|Ro0mlvA3^x{|PS92XDV3`*hjLRcGSnMOkIYu%D%YOAMwfSoA79+! z;JAA#pW+4(>;lWL6(1i@M}$zH!qsCTM>nd;UpZ&R_8p@Sis1qjnnc~W@Ka>x#bc<# z;Eu%u)v||kP+*|-d5g<=>#lzvnK5&qZ`pOv$u)!CeT5pgNPk7t4Jc1tp;HwJb5-A? zSf25m^X55UE2{g=n85KVQYqo*-f_5}k-;EFQsAy5ueUe!Af(E62e9`X-1p{J&aeA6 zpq;tTmA88RAWEBzH-7Qs%Woj_NQ492My|NWTHSwPo?inI@>}gxZ*$ELi95c2kNO%_ zUna51*|=by^>LPWVpFWP(LDs9cP#Ai?Nkr562LO#LpnKR9^lk2Xx_u&weCygIL?Cs%4NIY~S1^%SUO;bP@gRWkrtd%99s6MwSguJ*A{)uI99T!|?|F z;LGN6*=_d@2*ZGLnj4E!MHkF%ZWzB;2r8VOTnrQ8$3QSLMwRc1Hd=O531#a<*7s`6 z71V#-;n`ZvAcUh)xqU3gf*{*Q08tspjDnk(Ss~q=U^dygEd({DD*k=(Y$9X93#dh3 z({}0>ROTGHbmQK~omF2ktkk@Y*<3X~qCB<}1SEa`NvzP&ac79idX9 zvuIj&t9k_&F#zK-A4|G3s7VE*U7Na+kmNFVuFQZYda@j&@2i!WtquOe_!F+~EnLeo zi-p;iPLX4_d#;20f~yx(hErs9M2}A;8DGm+)604O+dBHefGJ{2hosS~nNF+(0hiK> zLba61Uj|exangQV`|9C!90qLVk+xH`FJ{+?3?C^3}QbmstjS~N(SD3I8fa(JK(`o3?{d@u$O-HohILS0` zv!i3jkJlJ7pCooKHT*b?TY4DsK)K&3M$M{=aj;=vegkN<8HY#G-32Gaj?CDWh3tQz$SOWjAzO!mwhD%C#U`1{YFZrpfj zY5aE)EYg?kG`fiZBexME0hSAyTnF+6Peo0>-g=M_I6LfHBf_asS=sl`Ihj$8P^jRc*sCH{{US)% zbc@fNj+n@>7Q;F2>ok5ERvz*%P%@StCggjHi9fSl>%j2LNssBAWCTdtui3}Hz29Gh-A;L{l#Bf3ypB5W9T+GOKn$B#3oDfqi5 zgptf!lkmd@XQG^N;^Z;^Yu9{E-ma|`&=Y=e$BLDhUGZLSn0f7i)Bj#8{-Pi!|B!OJ z?xGd9(dc}i7S*1CFk=spEZP8o-d#Da%f97)&r=<13IhN`r26&>AiCi|$i~jPk<-LJ z&XHV012A@1@ybnmgYLJJ9SuzThIWO@n{)D0?&HTYgj=v3{3Oj9&t_g4Jvrg+>hEv; zmejIK@YAM37KvPkH)GE3-INRb(!{rCWMUdnEnoE#;nm< z&&XtPdXc0|-^8cN0wX%q)SEZAB032c<{RG|CyO3A)Zg*bh^rdTuU>;guaW(DpH_ZJ znKS=g8fgsl5kU|LEW#vaK=QsDG%h`JEfHYJijvcK9`L#tB(l+$#fliYV&sSsUedM? z`dE+pM95D2#JPYdzA@w@N6m`nV^{-en51m~kb6)SESJvU5Be|pRT*@o2n&J$D3Cy! z@`{Q!A~<8Ib)%srwQ$9kRl<$Z`OT0ZJI3K%07SyG4`(D7S+(fZN+x|Vs!x$Nz(&Jp znkQR9Kn6S`?{I%LXK-Q>`hU;?4SysHq&VYLR4sUpj9?i{ggb&}y+8iq!3o{Kq0*~Q zu_X<)1M-TBbXXI?+W7!(>WyA0#2%I(WEHfm>yGh`q%T4!zu$~ap~ACFG`|cqr8{ve z9F``&UVk$zCx;s5=q zc=D3f;M>;J)ee!qA)i0H0ef@38quOd@*|^ky8>Tn`o)vBEhbQ^2e`_e zH{jN;Gq2Um)cUi#9aJBV%z7Rn=SdSd6b>9d%n)Eja7@}ao)IrflfE*z44fLFFCLC9 zDDJdn+F2csuK%BQD?L*&XzqCH22{08GK<xMnElMu-qJqJ>mPBLdfjTVgBa=Yxu52+Y2#6-srbu0AL*myAh(4moVE{xeqV{1WC^$+9*-I^a1CQclu_ zLg25xX){D4%zPipx?oG$g~CM;E(nSO=X49^3HSEM)t8yp06EE^Fe~p!K*9rJ)1&>p zyveUy85y#y9Rzfk)oy^c=m!0vw3+IlymSL)sdK8&K4=cQMu#(mg+DEmf8n0Q006;% zL#8>fgg{!&aqNqen#UrI0t`g%KA=b8q`^P?%R^8Y-lV9YpusRm@6tIp_@ z?d&wONGtfn7svO`Mct4+4NM;3Tgr`uo+9%}yBNWADL)9`R5$v2u?@z8zs!&;#( zNDqW|={HqVGk`9)jOL^Z{e>%BrYJ$Vn0to^d#|gzp3B;?${yjEh(Ud`=hILxE(v4a z<`R$7^e$Z4`nNHK3H#h2gx)(VKOctV5Xqj{#ne;dM`xaWThEknZlc(oX?qc0>5X%k z@}osLVT3|{Rp|8Q2N@^K>&)kr$u~e~`Qv4fL_>+RLQI*W!_AFu8PC2Tsks>8Wlw5A zVrN&v^EfP+>i2`$=r9y z!*R_w0|Nf%Kg|ckb$&QAohORlwSI0}6}RF15t}x;W0{`*@V9t&FzJW~h_fiImhMe! zt#Kqa=cn>TjhomPYnNzMKo#>4jK{8^{_DkH3k6QdzT)$nYkb77@a4-k5(GG?#-)KaJ=e2m7oup;3y=o91xL^DTQm~&gM%z&cb~p} zalXoCdYaxWEG?Hg?9%Dgt+W%#AP1^$Jif4;oU!VUYD4%Tfw1q42$wa)vL_g}Cc4Yd zx_kUxE=f}bm^B$Z_9F5NB{`B+`t93|7iVj3wpyaG7BA?~FWZ7Lzineo25oUNRSd;a z8QUw_7XwuzF!S9Q^BBl0I`P^{L4;1Oz^ugM-<#|WVLBfbu+$7_kH&AJU;+NIjEF%| zaw&#-Ko5|SGo=b1&44grxB*dtK|u!THYb^KK=CRwx9CF{K#Jne?**B05ZJD$65Xk3 z-57KZq<57Bm2?d1V*U*++P~Y8!=6KN1})JLx^pMnQ0ecU+pSr(2BMT6!zk&nl8M2h z*kh1AZrA~n}nf&p%1YNN~q^8xD%r3X5%qfI6JpxB^XX6 zXo*d+u^qY1(sBnlkTFVxJK8b2Af|v_V;2EPi2Rez)43GR$N_rXE6Yz4-_FYSg?3*hTJ*%FIx(h zVoYO>o+#zhX7U~4-69rh-GtmAaQXW9$O0msgowgX4TCou!O&o4Ali}o`M)F-m!pI-ULayf1#M2J|iC|nzj1j>s-vW7VNNJvk3kce`>K%-n2}VX; z9N7gLIb5e7rvlYBB6ATOUVQYk^NHx_R3M6GEn3ukShi7*!66PUt~p(_2m zPpRn1Jb;+%z{*Jy6L|ysxhNpOeRr7Tv5-(i%YV?Tt_$EpLAbkJmILgFgyL1n+g{ApP=*oQ~#Q-XU-iZsCLGq?*&U(4)wPlQlYf z_v~qNbO~L*IA!u21gC?>b93oGid0L`Qo4aOqC>-gKzSoem)-|xk?TR&=79{idEVF0 z05os+`9=Th`u9vx_1JM@;`qMV8V(K%v#N;OJR7AO;3dwZeYgY-3_*eFuW6n}vneZ} zWEObrmXT04V!u&kSxFTuZgaF@H}Br{rhO{Be!e2`G6^^{hw!{$_|}6mj|bWO8{*pQyS@;qUC&Qq zNR3`u0z*tp=^skApa3Vh-e{ry=u#gu_cHqG+iBQ#wZY|_Z;8E(17R!0A>d`{WNIL> zq#!~&Jip{b6?Mt*YBWAB2rq;L1Q{212tm4O+#dAqJ+1whf}z*v6{L)#0YO|@K&oez z%aVTGrkI)aKVl_0f0eX8&t4eTUO0+XIkB$;1K@IkDX* z05C=*gdhO2Bv872@AtO|e7rWJb?fvo!h?pNG7?P#?fW*^7brNxMT^vVH^N=)>o!K5 zR39V7mfhi^xFG2RGy+xn)BdASN~duFcGID_5X&Pb>C_75d`}f)J@s=QY&Mm-;RdHb zsLq=hl9T0$U?Txw%XAf9i>C}zBT+c#k_5i4PLMbub74=~mEOC)W^0TXUpD|}Sr!2I;czaZ#>m4sDJn|2 ztv2=c!=TT|6kQ0klqMisX+a}%sz6;Bx@?!fkUxVTleIUu6xLAdFp&z;bQm;x2o<$!oP2&Qe<;HC)7QV>-}p23jh64;*krR3*5G5dToditUHh z?w7HBmChutP*PrPz~@7J6A&F4>T<#3}o_N z?2nG0QkvlLr!K~0YCZ-%mq;(a`1X$DCr;c#cR=Ue12T)mBeqi1&8Ha9MUcyOs1sxl z9=U*JBHZ{_zQz%z77#DXK`Uqc+uEWSJpf`4b|*O-DEb0ob58tTL=RH%fI5iVG@XjT zRM6;B&*RD{ohlL-fmN9qQU0;_S3^^eqvDk1)7M?mx&N_hB=uxPuU^igVAEe}v157J zBSZ9o9HS+3*Z2nq-zUXt@vIw1KL{0VF4hsELS)uSRBoJXVjcrzN8em$b(XJg^|vu- zf49*rGgc)*pJ*h*K9oB4YHP`-ISf&W;*u3YVAY6))TgNbNML2_j}~9%`1GCmfpJpL zU2P`eQOot+1X@O2a+0T6mArp7$UO7BVZ>ZTr}K+N_)Q;Bbv$?ixGcN73@Jn6g4gU=xX1&5Gc@&q6Jy}SR)$MQ2E>2O+-nf zrw7VLJI-pqKNOf{167XezY$i8t00z0K}KxD=uHY}ZU~aZvJUg3A1BA;qMnes!i5PF zmM!~Br3cH;bz9WU{%huLh!Ogp8$d&44ln9iAu{V}OwF1_QW_`-*i9{Bpksh+g*hX9 z!rll$Jz{tQ0d>f_{N}k#E&(Io4c6&f&H6;Rr5Pksdb>GVM*ZL6tY(p`>}cjmkCQ@79I zfpi$S$k21dKU$iaM6@RAD>II%hbvJI@*Vp%T056a%|4j4jcYqZY&i%*2X0;9dT}nX zL8a{H-lROMN;4jTThvS3~w5|&s16!yjy5^)miz4 zJ+kmL1bC2_9Xx@795$%2{rFGP6w>>?qnN0FI;!-m=3iG^<|Q0?JQCRZ%TPhd!*qsWiDr_Prn+hC2u*d9&FuqPDP~9P=wxi#!`sJtXhL51D#r}JDT9uz zFjg#1KA)tXu*C6Uz>K2M;?bp~?5d(8tAD;bXfSBKGJ-ix7<2SE+lnIU{%jp+ZbDRW zX#bs{6ibDWdp)yt;#gdOK*PlSj%~c%c=bN@;dPi1?xiMdTH9ZxX(L5XL-kFcTeq@} zQ88Dt-JEokWUT0v9{?-cMpZTP=Wxn<)Z!fmu6}w$e?5{PEn2PQROsncB~~ z>e8wL|7lYF@>U<|-|^KVw+tfwEQsGl-TQ}W+tT1=vvf5_o4%bUT0uTzL4&0BRkf9Q z4c-%b&X22j#hgt{@~=I7r{h^mJFrgY{0|KhV9UCn@BWA%D}fVmr5Bj%xOw|0j6)KJ z^ZOu@y6d3#Eu6MJC*ampm!`yTu*UCJP z@_U_E7reO0w2bsqTshgG5Z={VqUrYF0(k{qo! z{IEwqEmKw^4GgkzH#+mg83nQHpfdpK>png89hT=dzOGs{Z0s2mF)L7e)z{m92*b`q$|52Hl&b6jq)2tJbVYn6k5hm3 z>rkk(INa5V60)G}=i8m#krMu1mqa}xdjhEjovF%RyB2pPrV6t!pspYrq&3)!qR3r; zS`Vd^)__aodrfC3Oje7LDgg`b=H%o=Y@y0SP1{B^ZJ>MS zM&G9-66aFtp*>&hIOL*Yq%fesovCJECc~KsBabZ_xobbHb>2ZH9seZjiHa7brf>TJ zA=^+PM<7 z@|i&%q`0NZ-~&Hq#PnqGx!jKo{}!O{5z{wn6w+tm@+Hfcb8owXXd`o{eAcMSYhQgm z+qQ}48wVVzr@Fa~CSOZ?NMOe-YL)&utUzT>sH*3^sDM&e;l$V3qgrt|ES{{f7oPt=LTiZp5%9O>ThLiRM84N@|D-2$yf-s7pdL~RBaa+?Ory7u(C8);AXSRorx)3wgv zp#J=PHShiFq=fut-nLMtY*20!c4;WntJyvne{Ne4t0}pOa8vA0U^*`&) zJPe*J8$x7;AZja*n&Ha(DP|5mkvI^W6}~0HHgsYm&f3)G{R=E*imoFjvo zyh60lE82g-f>=A^W!bm&Bg2fw{=IAn6#}bndrb0;W(aw~Kk0$}qD^a})*Ty3VFnhc zAj=&njQ2&&=yXDdV+*6~LQY{Kjo+B)?=*5ZBM)Cc#-vbWI40NqT8>YBL0(zUvr+NH zuJKoLHIk-aNUSJhKlqhV646dh`HILS_-bql%c6n7_T0LuwK6YY&zgWkHcpl`L=l)L<&dZuBX9n-PQ?HpC6qqS zsN^UOCLp#S4s6|x(b}JozdR-kHtaHtXZCArFLV<4$GZM8QV7!^6_bt z%?4sZvh*LJ3BbAVxx`Y2+PfR5p{VK3s#ITC=fS>zydQ1+F~m`PV4sVqnCR49Vvl2 z6Gc_0{W~Zu#s75M*oZb)ZEoCnE8&iSf5P&#rgh@AnO zTWPnP|LN^p-<5~qJ|0tu(1zZo>iwC&R~JFnxh$7I_1vr(ToB1P$#&S>pE6^?;W0ug z1Hk}rXR`L1qn)Pf)G42<$*LMJ+^7Glj>`SW+T95;xopI8STolA)z8)ZPspgE#-x|L zlbxOYe!W@!`4CmIoPT{dI(g!Jq25hOR)KJk9;jBn3%veAOZA@i!lh;TNz5q(WGz4J zezsS?h_CCO`Oew`vSxyI|6j^iEJ+2Z29{$0@RZvsm^7rgG802iJpcZhdH5`17~_nO zL?lHoBPa-yT!v?!uvBlr&eRson-A`^x%}?08Olnbs?f<8I2%o`Yvh$E3lu0&NHXz? zS_9SotzuMIywdYdLBH*7n&@e@Xc%$ME#twSF;45}Gq~vT*}joeL&xl6_$(w1@y0s@0sGN#NS$gt?@eq-y_ITN45!UiUibgJo?0w5OEvnrPqjR(S zb-Hos>eY!SvySDI3>s7aQhkaAZcsToZ-+-bc}~2v3`nM|b#B`A^p`EOvX)(DlCde6 zJoQ4bzEc}$*9?C1X*na=9MEEpWl`p^ya?)Q`~iYwsHmWz_oxe2R#p$*jVUqEUH&c7Q2oEIK&9#A z=ta5)?Sr?s@qEfSIM++SaO~dxF0i9|e$ewbCfNH(M^gh&-DVGGYReskX}Z5{2?Y~> zL!^vxf6P7oWZBrCZyJgQ3lLj~8Orm_@9qY5KkZ}`899@f8-=@-?LP6jcjW~r&31nc zTCgm3=f1Sce9^Z~d7k%L)Vk77quMejVJT4@gtE24BIN4_WXi zEG$v5_~W;Q3l_{}fQJ`a$iWoV4AZjqrW*09Xg^dsb^7aRmhqxa^cVc-hD{op*{^nR zh#bD|Cl69)t-;fDSK2aF?MKI&RzJrpFim$_~@AD20Sju)4 zT69}j3^z(}mgIq1gx5SgVQBG2lWA<>&}s19zCW2mdE`kKBn1`oF82WFPJ<)jx$*YJ z>J+gXqq6@Wn_Hzf;EKv#G>`ha_zgwX^k!8RIn%1j(si9EinRmx@Lgx18kfHc!@$DF zcXTx}-bg0@%Ec|e{fB|3`$V0ypruEF=eP}3zdpuzUfS@fG^M`rF_QeAyP4E&>@I6& z>Jll8^7*o;oDyzQ4=5Vbx6~;%chM(izL#rst zI!x!@u_Mttj0`(KQ-0W?%qI3llR-~Ek9)3unAu%`-HGRB`-7fwK6oQT5r>w0^-ng5mL_pW10^BpH6X8~8!$o8fB|wgMp?zLW=rJqT+U z?(9@FS3)vD*309H@8mrmbC|`Q0VqTMjui9Y4d@_~a@KB%y~n@O!Ld&^N^z=!s?4Up=5f*`H zi}?T~ji5aD>iw|)vP?_(aMYa1@dM2U*fJ}YZrS4KoS8t6%ttn4fTM15j@jbAP|AmO ztF+&n)($GMJ1`QwVBXR90PM$wFlE0FRhwq3)yhtby|LjDJEp8P&#G7{6luFQcsn|q z?rvWjD9|Re4Bg-l=R*eeMe2R^+-o+;XdV3<9g+L#N^^D1rF7=vwI$Yi^H(RCfJHD@ ze1lGt6=D!2yE%B2f6?^ulr*w3{MV1qd*_{##^0^Jwz8w~?ZbN*-vhOEtNorUm3ZtM z40JZ&6WN(*GZAe;r&y7H8Ep}lQQDV*jN2=|eIMNZ{rAmQjXtrn0^Z(J-xK*j2SPi~ zY+_b2IwqkZh(5dyz@@M=iDQVgKu!ecRE9X7v zI^ax&-;7^O%(rm&Z)D50d9*FG=nsAnY?+$ zh(A<&ho{8E&P4DO-r=LQbwHXw8K0$Bz5~a`i)y|$csKf8ab>5=NLi`Lc!$ z&SP2ffHV;%LR=mr0|{vfloJlrA_IuiSqtKCvcvDIUf;g=-uS;5I#ceQm)Cu|s7;lx zHo_R{6SZhIv|n!kM*{6>1tU)mO{G$D37GKvuO}Mfb=f03vKXT*p6^WUSy64bX$LAZtu2aXgwP6bYCz~m7B0&$8hOwiEp{0ArhC&>(33yfhqNMM+iowf8pB%mFB_nv=JmOR-33B zXzmSQO+hKj5XMO2aCju z$MS4yVxcNukdS=lo=+p&o_Fj!!CQ&YtxN+Sf;q;b@uWXj!0rq^SdY{m!H{s%PtQbr z%CP%M<6*6-k(T3aL&>NNV7CQ4N6#{4OkAFdFMd38gmcP;bTKd5nX zN2!!J4tT0!>QappPcg&WD$lG&Jr z!o@r2Of4s0J_=D7k$B_o7YE)SEkKPRs8z0OGc4p1#lJgwaI9bZONrcJoqzZIm|DL) zzn!-;7)>>fNcTBTuJ85_W9(&{QP=am_y5k3uHGuUl8oiJo4Q=n>w5(Vteel!kV82) z{|3t1x*+DCK%c(1cvi8Q{xHh8v2EJ4Nx{j#qG(N(`9U(}>ZI>#Tc3q$QkvPP20|Sz zIbyB+s>!Zdp)t)sy(gS`a$@$G!HFw)v3sA+9>aZ4O`Si@7Z%>HTy5rnJ02T zYRx_yY;-^QCEflhOrjUL=k|EeKWo8(NtU}z`xIK;qT8exX((2LaRn!jtpTV}UukT5 z_`P0xfQJUhkiQ5V0oR26^br3pe$IO?UmhYNe(t@{!dw*LGCK`j5qz_7;^|TLt6)I< zV!lyKl@|uH8Qrpv!n2jPJD|9D7Udd7`~UFCW#8HFM~xh5yLxr_nM7X9--cW3rNsQK zo5Ed*jkC6}cs%Lhl!vDYlL{%dKaLnZeOy6P7fRRGP+UqW1P-mPYTHz~IkAw>_KoKK zDP|)&p-4q3<{OAhfkU2`H&o9eMPV#IR-}~O+!Yy+) z8^pjF9Jm!y@_jR8n=G)wr=kI)?;*%PI#%?j8=z|z%PLAaHijjC|E@d=ZDm>YiVcfP zB9@H%u+8wW|DR-7Iy%(oMGGgCH%+pB#2IUOXEC+?gq&siov5rP&}pLd^fR%4HfA|R z`wie?0`9YwnKL7j?Lm_;o_dfq)_TdHyIH|Nh>)BKL(WJ2SlO>Vq}<8s{)GVV(vn|M}2ko*RNmW z`4CJaCbMq-*}p*#pE*rEpZ2>0c%-oDqMOQRj6V)?5AY{3Px`vl3ciGja ze}YkJsCrAwN&S*DJp-ooX&VoUNQu!v_HM@&8d^^D71w z>gxAaXSB>-6l;HO#njNSFwaZZ(?S~_%zk6V#7t(aLCNMF-rgaX!g`+_VlsDj2cJfL zimoTOVQ9RpU?G&8YQm%frFrKLLB)TCe(8AjAKlq!J76Fdu<&x(HWF{!t&kB9-kvFI zTM`dXvoy*nB6h*km#4~!TYIwR2tWh%V}Aw8 zXrWoDXJ!Al!q}>Uw($7Q>ubDr6l~Jko!TeD#D?%OJ5l%atMmtd8WYvfA&M0>;5qZf z8VOmQnqQ0p6{N(VCx95|Doq|+^wAXTojQn5(xIuMO>ag7IFt>)K3fw0ATMU_Ma8Y8 z)WYQ*#=6MV26srozJIGp2hAf%Y4`0WJo~ZZ$QCJ|-mip9=EH`qYMcG$bo}gl zW(}K1O%P!A5Iyx5W2rh`I!8nL$q0RhtEW55C1irV|dFO?g4 z@8tUrMvp1Oy@Yn*Ck^Ure(%6T@F0ZWwY7GT6Pt>|Hp(Cmg1#s~r}z&a525lRoQKLt zCqNR3#T1&=Q(xm8|6cU0$yG(2F*&*c94b}`=q~ISIYSEk&*V983VS8ZNO>9))X0{c zxTmu4{d?1;OHVzpQ=kO_gM-1?IpL(ND&&s-y=?uV=3_aWGN;egC?XSzRmk@I^c^&r zicXco&Qw0??olK0Rcvd4`aJd5KH&zkOH{^StNd(S@|ito-i#eds=IHiuhh}$-Mdke zH569TiH&b)0p;>aLIB_fZyT%?11>`54qiXKARQpwg~SWILzGIN(HgUdTl60ka1!Sk z73j2o!iE7Jfm}YB4C<(My~AGH9s~DN#~j|0wQ#Wpa&i>TCOvL^{`9GZT3Ye|(XnxD zR8Lh_dIf73y&ep{$PUvz9D3r(TP}P2^Kc4bD$0znNf*8SnEjFoC*aF02D52lMfrjM z>09nWteuN{YZyI5Frz)xr?ceUwQjaqr{7ROS(~@&KlX?Q-}SN!I@$eETDuf4hWZ7y zvdvy*?xK9Bccn(;#WZBK&3)eaH1N75vvER5kn$Ka>2tf((sjrz_1Uln`kh*i410KX z@#_fHG}qSo(;_V&RA)XgUuq{++L+Ca7F`+G$FC_fg0~{BUw~WMsy z(6)hv&{6`kd$D+l(GKf*KJOEt+ORQW8lWxBQ~aw3idW!?!BJ~oC6-q)ClE; zV^j(4(hZJsgASuMtT^uC*0yPxtjd-OL~2iBdI|gDK=B%i?|SUESv2!vM*~)k-*Nxk z)RkafqQ{BO`9|DH^Dm*4L88(?G;OS+6N7PbHI+2OuQzycE?a86yb8*Go_HS#J8$^h zRxPr0gypC{yMF|#Ff2<)__$;p0AMyW37+h#J9mNF9;zM!<+Qovow`!j)AiQS1(S2A zki~UP0p?F5uenp!Om0x~u=mw`dguEYgvj6mw=OKGTd*M1?8{{BK=Tc%$y{G5<-Ub; zI+bq@eCf{U=_Aw=n0;(WzTOOEl{)=mTwMB9yW|yY+Fe6P{TaM`ZMdNiL3<6n98}eo zD_@qOEZW>(X#*v^ICD@$B!4=4zuleD&B~6_l&oPB44`Iao1R&StU(POf1=)fuKtRK zOpL*S0eGIBMf=CF?^-UFaQp563oS}#Q?J~+dDEy)=+oO9>EtrLkN6$NEqq!lUYxU? z?goo(bDG0Jsz#=5-Qzo6)6{Hb>u0E=tMe_=b3fp?$(euv^=5YhtgE4Oe^ae-U2H4{ z!hZ0;mA2|8|GahTy?L7k!6O&PWGyXNb2@5=amKzxHI1!RO3L)W*pHvcT1qepu==63 zd(^s-VP^l-@e4+et_gIWoz~oQ?ys+YR#OHRZs%wcc{jl{0XYfUhqfgc)J141-q9@} zGGx69+2b`pRw99h=&1@LcU%wf>AM}!@JQ+L#Su%aw+Cks`6sA7W#O^5S^@)eY=(7dC7=)b}Eg3`SPVWri#5sj1IwCbmUelVwwrn zD=RctLQ*Zmr4wJGtQV}5(Gd1;CHi%d`!2}rZgh}4zCR&Yv&RC)vKplJI0 z$&7Cb4HOj2#v9EndfxKwtUj1Ik~d}~2U&g3qal&D8QT2>sH@@g6J7{v6vTcZ@zI5j zvMiUgu9zK~L#xwfO6v{X zi8{v;Ac5k+F8i9y!Ir#E8bx`5^SB!9BKnOqCJSOSiqRyzhlHYpA9rrH%crLoC*L09 zsCwej++P`GmjK$ftwUTM_~IwOzT>AE&Nni(8%1V+407+zK#dz37pddz?8ec3KTFsc zlmBsLi^$?5M-qvY&=uols)bZRd}U@a6ejN7@2&4a`87vJZ9E_-6N|qWIXD2kHW4Tu z5$}kkMvh|S2E?e9cUz_O>BWXyp}n#f^*95z2Uf4J2+djJ7b05#5x*Y}ZZholMuE-N z9tGRTTB_f%=RwvurA_^2{$Do`v`fX`JD|PNJd$YRV|{ZH}Q z2(h@FoG$v!nBm(eIiC5+AOXSh)J*M)%}KVoZ6deD@z={DG6^DsRF|6faEiSRl1H1U zo*Ahg=NzFw`N-7hhS_PmDYp4pkj-Urdl`{ z2;WWIS)!0F{t|+NbD?BYx#&jmi=n7Rb2N0-%li;l;=hwp_v_4wcT}4%-d9@Irm%sM z#L#!I!9={z_-gqeY&){GYR@@OjN2$R4BdFh zk0uNa4yZR6GGx}O_ELTwof6qh!pln_r@%eqBD zNr~1d)s3H?>gCiOS(j-&9x%xAG@Pic<^&bA$yyuPY^IjJtz+t}Uke<*w+cRbHP3zi z^y`REwaqp9-x$eq%q~4&;p#~@A%=Yd*qcXMCN7%VM#nLgFW4?J{cQJ*jP-0wK@fIo zsfT%L|GHlvbxlgBX`(e3Qk29^IeF=kmP$K)kuAd8VdmCY;t6~!<4z$Atg_l?0{#bD z(P4;hA?I!)R|kf{Jd)z9i70ZhHOeQ9TC}s?1OOxGG!e{#I1SA$My)j3uPB{9JgrZ!ksc6Kxvyp1EOS+OsAP5d~t2XQ`u--fG6U z$>`i^hOunU?6r3lQlXzcd~7hee@*lb~&2v&?Wj^ExTb! zRK7yN;;-&6rGEtLCPs`$s>&r9T=g6h&RT7kR@bcq?whtXV${O1-dz4~yS>Y-hf?g)&RxWjOppzueSSfvZFO!L>1-q@Q$a!k zjE~MC&iOh#or+q;=27D2sX%D_2GkBT&&M^PY^=}kwEm%;j{5;AcjxRg4uF1%-~X9E z;<>EQ6N$1IXi*D_!{daUEoBchbQWH32bqH_XTOAl%^G>_`_EHC{3~c0L_jQrGyMKB zO0Sylxi&H=e2P5&5pCsMU&G1ICvNCjL-)a`t!O}@?Kjl_ zUIt9cXKeW)#={aEre6d}62B@;BwLiOMu>`t2urBdKlJtm8NuC&P(X<3>@FAZ-HK~> zd?8LqHB17IR{we#!93js`4r%g|pG|xg!Rkcq- zwsva`QOY|w2=Pbb(%3)3dw+0n2WS}On)ccZo!k|f4Ce(JWq(lUI8Q?hA6eQ7$1J9I zWg9uRvY1Dz2cp6oas$swBo4sd5bD(FCkSmCu1J<|F5KCGq zEW8V^jap?WSt;{}n`alB6hf-r`VTWl{AwK0KTRCSnOIA|XgIl~sz_g#QzL#n5^@9_ zY(9-l#)()-thLmix`kGSBeM_P1aWlO{T%Z50|W@N>c+nPxlbH`<7Tb<6LeOpD`r65 z8{2POvKs}Wc%_W^OR+{{bI`5O=!x^XoOrT~6yy0RahWo0er((&UN3YOwU#LATpzBX z{Gvdt&ry zte{OBWPhmO9v`!Oop1w!45b%xNUHL38GlcDGZ3FE&uI!43+yza17@O(l!ZOnj##!%6t zCgrY(WHZ6ZtfiENm`0n`xdYZJkj$)F9V`MY-+36fCi|$i9^T#+t!#CB?!FapeUnz} zT~3$hp>o~xPWFz>_u@R>i(0Krv*(99_{LUrImwUFB`g>f@&@Ab?euxxsjVY z>`{66{NAbkOK1qTZQC~Pz-w=w4ViGLtDe7gOi;LR^k6HrIy8`4&#faZlLpm(2l-*` z4>ghl1Fhn}EYC;&(i$|VEb`zxw|I|E?j02?IPTSRnGE*9(VIPn*PK1OJ+i8aynJwz6lEl-eV;WJ?!E9X^yR;Tr+v`8C*^? ztbRC4qSfZK%bp{48{VKLF1!R_tM~?vLj$Azy0Lxw+&z{dt}YW@WtY_{rfn#40Y5xR z0-}D~wz(HtEh6f+&*JbTzYV&H4=t;U_2foOQ+y* zcJ11E1{XLta2>o`YsA}N?YjFdNYY*qhiM#z`)6`!q+t$;^5TZM1KX24MY#}Y z9;rEbf>AVd9V6AOX-bDVDF1woi)YR2E3ojV01lbj*R0_}lB?^7v(uk8vh@qb&3;Jw z75x~bH*aZD*s6UQ*SsvG!2Yv~qn6OQiVFnwBj9`G|>IfeI!_O|geBJQtNIM)+ z;aXbWVA{4|cK~01E5nNooZdPTv%az;f~fDIA>9Ja`&5=Nm3NoIf9s`YrcQ&_ zKzuld$pR=6>I9eyK-h~`zwYQZ@1v*}xp+$-qv?(-XEMHp+PcTck)1Bj+n`{xLT}!_-H>_>m<)C~sOw!s=9{pY0$_Mc zkt>`b5dGaAX>o|jcQaNEz?eC}k^yazEW!+$mCr`lzl^;s=5CleC+jWHZrW$<$3)_ZTJlUS55R*rJEhm=qIw`t(WfF>B?q0bd0Lu zbA;~v&!Cs-K!4ayt%v>vKSEw?){f4Nft;P-T}QSbp;lw~@L_I1qUob4*u)HqK>2+! zzYa<65>I`-TJFwZHwJ*r0h{w5rvi7x-e2%w36S}oAIn#XxuxijC^&!_Z>Og21Y9Y< z+unvwV9}cel??x6W5hd%Ex^{c{Hw zH60u>2iS4=y}y_D8?h@%TTze7y>`#Fmpz{(6t&WIMcJa>KO=g??ZwGDL~m3cjqX25 zGn-*r`Xy?-x?218K^iM7&TMbDtW9>`h5OIdOg1<7`*=HX!*%~Inab%~{68F{_)huT zt%irdHnf`R1UiQ26M;#{9)?sK=o-!aorQL*prgyRv);f6qopL{8Z8>ZUadz^CR{qcRT{xLN6IMwr+{ytr zEi-XXhKCe;q0G#d*cip@_VZTMp%vq!39&Esa#>5+v(ty|!T?QWB$%SP3tBiGIEgAt z_E7*H#9g0+kfe-8KqfEw=#akl)aerz4&QeX_em;X8GNFyQ_B8Ti+D^a+gKxI6=U|j z(oK%)BJ488dVmZHU<)$sc}Z_9ABC!)1-e&HY%!zBPGp2uh77pS7$k`Tp0Ov%?m!EG zRm3<}himRUuRrlsh$^2G&Syr_=Jnh%4Ug4)QO>33rOJqxG%Q&kzb{C3dls?Q3*q+R z%GI>-Apnv|V_q(J|$^{=rBzlwnGMJvR` z5Iw8VzoNC`<`~lzHQ1*-A}3*0i1<&HCYHB??LsF%WP;Kpt3os9D-X|I(~r*8jk`FZ zdGozpbdTLHsXFY}zQk?+7@JP}8>0;~aSgnOX#2>Lzu$fYOx{{juH>z%Rgq7*UQo(0 zWi)UxV-O;;0q?uG>eWc7uR9d_1si>WJ`dLw?WmE_$u)Opw6T|hhb9h%SpQK|9d_t9 z^*Q2cX|~cv2kl&T{dy!lnZ;u zj}LC{v~|L~F+a4i6i|`{rmH`XXYcIGBF8>Mr3e-K|Vl2J7WEKkadTo#mwG z6CK-nS=z*q`CUKQ&bz+ODZaE2)qOg0B-;GCEtivRZM!G?TB_$5b;QNCu|Qj62xGo_05VdTrZ|9i4tC{gIv2pXQv`5j$LmAD$TA%z7lM!)I4Q zz}3>q-R%25WKULuy0$sy^*gq2@1QyX$zkV+^nC;eYwJ^A8N%vVGgK(+(!AB{5<+#3 ziop`~b^+jsw2-JLLPDd_Q)KEiSIt;on_=W08lxX2#e2ddb2OdI+4%ObkI>6bC_iGi zZyk@m@BWFBTc|V&eqAdvo_Fe(ZT&x}-kEOD<2!9D#s7}bJAkm;*x30udD_TW{YToO zjQrUa*B+#+SbBMRT55Forc=<(#pZVCmuW926&5_XygBfTk@a5h*q&!T{?uOhuEuyl z+{@_}=fnR#f5dLli-6q;L00XXHuBz|k#(D1?q=Ry{+=b2k4%1?WNH(+7?AUA2n&xarV{7o{~H znzQ51e(sv`6H5nE46oSRKhWn{xqt4%1aBp?#3hRt_jq~Rx^+O)XwZL~wEW?HZpFx; z=lYLPOK(Op(4F{rknS~V34&k8(5VNzj%uu~doPAEn)W510h*4DrgD>ep$()RcIY=D zP)*J3c#9nWBX>>ibzoG7DC_{W&J?`fMAwmsl6RCwj2s5cy4G4=i0A3zBW>~4xXn?) zqIYIv&!EYRCS1n>Dg^uT)9I;dcSSXl$ z*VkM?Wl^P&F{u|js;W84JVD5?)!itu1d>EtnavnXRi(J+gU}*~5$!y$mRp`$p!DxJ zYeA!LS9B!8{rhtN*Z;Fn5b+|$i|A2S;YJEyN9}YTa+2ByNj}HM0WA)c7QZtuR{M`h( z6^})Vfj(3Zd?v&;OlElNj1-Y4G{27Mtj3NVDylN0(L$!&U%xtq;{sB1b#ct zxR3l5@hG947cW|AQGu$Qw@js&cdIH40_7Bsu_4Q6@6az9*TgX}S~?_tXXh0kS+E3} zHk?rkJbsRINl=Tu-cq>`)v%NPw4&?oMK}H~&uq3jxvDQuB|kdc{RG4D?Dp;o1Ogf^ z?(+~gmIXbLKhF{uJwQjXWn`*1njeSU3*U?zJPes#U~KQ<`lBwgVN#~@zS`{i2Y4-Q z)qLU=fLz|9BlLCUc%sn|Xg04L%I10w;Y5IS@%uu1$N`9>l%gmS*&yd~=+kMmLQIxn z#M=bjA9J#I!R48aa1h52?OEEX#ZNBUO=Qzm(_>FZw8%CNaYrCtLn>>yhzF23f@rFB1Wd&W3?bk8Ko;to{xg(Vua`(oat3r?Iuh@BUU~4dT0Y8BhoI5ltnYUyIb5oDX z?=BCt`gQg|pRgrB^CznNy^22jrN8DMy%v30SJXG5x0Ki$7Y{V4YG+`JSXCv`TD?i z%5O~qj!rZ;-xFHQE}6+O{(gg6bqv=!ePGkK>a*5mkt1J#0cQ2iJ^TLXKA*i#7n+)N)f`iaBBSH2x2E5N0|Nu| zEk>AEFV+1Ko?Y25r#|XZ(#*|y0f$b0df01AM~gO5Mov{0oyv?ht3?c*zAP6HvX&oy zg$7KDENoeKChyX`b5OXq-3v?%G)9%3u+dQ28oMNIZuc*DotxJOuEf}NMfD%%c@rbI zAViDL__%Il)zj>amfZ$@Tpcyd?xAzUrLhAeZ8VZzByXyz$X#GIMr)x4ZPdxiUR%0b zmeg8251*V@XI|x#Jgxpn_Pb8YA4UEcyT!)PDM34OTHX?dT|53#jd!gNe7-=MuZ@wl z`!kLfw@-}=_~DW#7|2AmSnTlksEjE)3#yWkfGpp_8RMT@*Mqo5 zHo8wzvt8^SR`z$YiP|?fQF%)OFVxN8AZcX=#@aeL055R4C+_<`VDAb90yPkkCNxVn zg5fW(t=#n^l6o$^EUO>7(t6>jK9wfFl8}7K#EhU~GEUA51W(Sy+EzxeZ@QB$4xnkK zqkRWL(}UVgG?+NsQ z>k5ri2tx~P+qd5d#6xRy5~RH1^NV5Q{uE;v5j?RF79d^`gwzpNLZ-Mq`3}LQy;X5p z$zV!saMNcpT8*2bf5`@_7lfNkx|WaJ`#Yf@%T75PGS#RtZJ^bKMMj6505AVzmi9Dx zWIfbd?~!_0Sa`5sF+`=qg_ctf`Z?Qw&}**f-9N?RXIah2!rTRgr;bg3`sMic`y17hXJICR2$*p;aQ($Mpk%CR5$(~?`Hn~?)E!X zcB!g^E{h7-%F5yTAa-4encz2QLgVsSiw@;2dgfK__8a~2`(~`G*REM}-#PN6pL0Y- zu+r{Zhio+6T;02tE&sw7)?86(VO%iE
    go526=0i?(94>995D^Id+K56XRCy&l7 zJy_P>cUrlnMOoEf0XzWB+)*0siV|;s^sUd0>=GG$AuqQg@jx+(foTXt**ojN|EmhV zoiTmhfkxije0)?OgdErCV$5HXb+U;JQb6#yeZAR4S8r#GuFaP3fmx5wl*m>SU;+J~ z19X*aZQQ;oQ8MKNM_~eO{Vo1?S>ZE7xiZ#gPK(R)!anZ}wx6|X<;t*LFUo?97H16J z=yp9^&4|kr~;rzDoiytgs{6HhIo2%;Q>2*EIoUD~C$J%&12Xs{`*?h!&@xxJx zW2-#6rcK|hT)kz=qmP>gWUsco_R7o4&$+i|7hy$Yd=QRH_8&kr$tW6T$Ps}4j6l6} zSn}<%Z0;e>IEF+KQbxQ^LF$OO0&D4QrI?P3G02VzqL1uW7Soztx23A`S>4n#_7)fUg&ts^bOj`q%iPl&9~I<^o_O*g@M&nQModKZ-0E_be#;i zOh}ibOR7oFC+M}1y*)K-3Z}VOjADE?paT(fl_J6t9B*{G|L@`HaFIh>;x^)tdg(4$T6h@4dBFv+#YpUo zg^muo2!3(_l^Z*0`me48Pm~RuqB!PBh;(ddzl`}E-R<@acg_bs=i<}R;|3hHMS z&bmGze?cSfjy-;~E$AcrvXGDZ)f0)80c>R}tH5nr!5?W;T3q>v&I}Gfx(0+yRGXrz zAZR)tCITwjtF)QQ(gX;_ps)u2ccb$^W`qC7 zUoU8Btlsy3{KNnK*8Y2yF`8<+qZ)eE!?=ky3sstVi>?&9ASben#lk=M&!X}*R&e+C@t_4~tF zZ`^KpRzc_vHiBAj7~057F_Q#_GBvGYqx}6(|NlSB`l|CxkZ_Pan+-!VyoLvPjU&4L z9qihq$Iv9JNts-^(pag*`oKCv`>-nrS+uOq_n|FIEJP@LJQ#<5|@^f}Lklp!;4 zx*U`ORn?T^aadyUcc##RiCQ6c^lLOH3X)~1zHuks!A%$>7BU-4Tisv=vx>j;li77T zm+9+h>bo`Otyh;ybwCEyV>d=sHOOLmy$vg7WCJis5LhF9(VyTTCaF(8e&3Vrq~FP; zB;dq46W{Ebqpzt8NU$YXxy)z|IB+j7S`+1Bf9Kuqn) zbc1N=sfw2TtmRX3GTU-}iynnb{8lZT<`nPd3yv&(VPaAeD`vLVBxSdsiS!1LEi=${?4%F@b^u&qtk{>({Q?Cm8%J9U+gAqDGc!cKonmD>=ZV`V6*U z3Q)cS=oQY+zj}aScbOrRmJc>V0l&EGiBqWcUpmU8dNU8cNcV+Z9Z^hRDCA1nS60ri zs)j@i`A>y|i&+Chq&LEevIuSwh#DiMH{XwqOR#BU>H4UZON=Q?SM@)xZ9eYLnG6rD zc=4T?Wc#XWnl5qA6G;&(U>c~Ci6O`Vzr9crKxQ3>FG>7%uQTe74zCU}GX%h-(4ylY zG1z~MKp&0YQHOAyL4&g2SK?S{%fA-uehBu~Xm9jiai7HrjTIk@vBLxpo>R9HUoyb` zmRk|v%gk2QRR+Hsh^ddPxwvSnB%6o8Zh`Tj%(}Yc72wX%Jl=PpMRK%h zjTKD)bc{U-?jjp{>Chj1H+GZ}OA$5Gu6E-GOu$g0fdH!@!BTV5{A!Inh>iM+oE9Ah zZiU5@MG63c4>@}s2RrV&Ssgvz`qD)6G~evXc9D-xXMfuT7&MqY9@4Q=W^bbHFUTXM zb{M`!9f5Mk*Gu-Th4Os;XXzNeWg1f|IvxhJsp8|n)-Q3w7o{475%_jZZS7iT=MxmJ z2<~xZJV+p*ev-XTOj7V1#l8viuXkA42yw1M?4x_>K8_p1O$}x_SaUtpDrH@xy<3Wm5}N?DQPU&ujiWB_zllm z1UP(>iw@~(+g1jQ)wT2(Ju2f_a#F_H#*y|<4z_I30_(^o^i1+Y@TE0pu)5>uO>3fF zV6+l|H&q;vzEsB`*<$PrJnj2&g2n6AgNDUB894h-)#8QoeAnP=q5j$G$M^3_Z}_V+ zT7u;G@d=aWjB9%#J4T?Xu!!>8@cF1WaC5GuNtN=LI}BUZ1+LEyZGqv#)M3th{i=PD z&Oz|E3+jbE*OlrM71tv?@*eK_RQ5!S`kB;3^X0_ZKIBA4GGAa0WI!Q$b`uUc1zrg< z&#P3rH+|%QW0$&FUN`7d0+lnD`p9$D!7?udGHKr(7EcUKPoLrS;;n>@S*i=1JPNpC#p(CYhR>6VX)nPw@ZT+!A@z=A>>o zrHb}r%9Oy$ep3cbs&@M}*J|-%-)~t#Wr1~9+dt|Ll><=l_RmONdt>!O3AI{g%gskv zrlq$*LFcncFASn7ytpFw&E4YH^!@`z(cG7nMve7glF`{}$PPc}^G}vdKl5Zldem>5 zv)!;#kO}Ql3JNRgmg{%oJJJC+t{%8?ghxksa;M0=b7#+H^i-K-v$1nRYRPnMb-y1_ z^pgqDQI8IauKf3p9I`jMj1?MrEJ;rEQcGZ3|-~30f9#1wBW~~l=Kl|m#%n3Y> zxY5(JJUZ>a_hxQM^;+h9-VC!V99^3nkr%lVp<<%0UGd3^c*;S)%E;W0E&Il0+vvIk z4U0@vR@B=aloj)*RiX}a6V6(Je}LaqZJBy9i{0W2k2SI_c`<6nmxRC1C(nJ5w;bVp~;9I9|1}d)GVU^RQ77 zTT0fAJXvYAdBbI3j*Af+A7cyK!qwteV&d7l(|%i$Gk$2EaX-rgGcqv$kYNA4i|M13 zne|JCCRfHje|1Y+<`plM z{^mP^8eFfmhrzDWDf>p;I%R3*7a6wA(Komtx&7F0a~a_7*?Xk5O4a%`n0x+$3N=1| zfAG66{I*8Q%4Pcf9D^O7J!Yoe`_la{s|T5N zcz47grsg%NLD~7kg&B)aWP2&*2@Cby(~d*J3FnBI5?_8N<5&F}5Hr%i_D5~4=I}&O zN6r3c{s3fJn{UK?fiK>6=&G`|Tv)wJHvfD0rgjh26%G9-A@n2JmXL$W;F{JmksyZ| zSX^Zk>;40unZ>f?8mEfb*w|Z3r~Ey+!E1huhDdYFn0vN;=ET^Hfpa=*)+`0L$_#R) zf6}cYFpq|>OH18<2L2vJDWY%`@khi7RR0c5XI8gFt;UFCyv&cr7w%Vc#L6rbaLRO|TLnzfhGFEi? zOiAHDff|pYbF8XfbIxSU9U0K$_ z!F%CHw^v}W%n%892PK%S-4M18RdEA-dV0nZAAPU7bC--VowByQS@Ly%{DjLuQ5 zDBrO8bmsAAYwOyIuU{Sf%r_m#=HoQedr$Ewvq;Y_7cXAicuZl`uSDpqbQhTwy{}vQ zR&KF6Eh4A#rgzwJ|-l_YJP{9h#gqzU-u-te5yO^IZ!!M8ftAv3GBgw6#Gm!oc zB>rBpA}W-^j~dJSVDRtokhj(3e+7@YOEJaKEyi>kpWCz9q;BeMbfXjWn!b+Npql^n zBfQA4(#I*g6@qCfzf5G5_Kj*mS*MiVzgr<9xy zRz=1(o$)*(F ziG!?$vt1V0+o;JH^fLn6VpsB6d zvIyPK`*3@+?*IUzUqb2A`}`;tY~rY!zT)&5=q11eqo}`CIaD4wk-O1xLpZL-X^YZw z)ToJX!j4f^VNKEsD<4x+1+<>Bh>Ug7pfV4*1u!xOE6@>XUPPBM5_&nEMEbASz*u?F zBYmBnIo;3u_8znigALprWvKzwta7t#qcM!p(8N*ZF`=09SkGqt=|hK3{XgKNq^*l% zxOPJ+>|l{&ek?LiISD=qR~dVWI|gGy@l6(d^86<*rr3)$;K5l&~Of>?A^@Ub7!L$)A8zCc-($&qC>PKb@q zwxqznvbGEa?I^)`uhCp$!O-kj`^^1)#=*O8z^#L6$S3ZB)Rm zxnTYbT(#IiLj&HG=^9bOpmY6oc%gg^+%9C*X>Fa*cnAa3H1i>c-5O*+a>Ah@&Q(55 zojRkz6ZEcX9;09H8x}o7ruWy=v-+KlP!tj2Yj(l#f`z>gOTuM;U2vlb*Z^=!kvf*l z`%Sa>V30g9DM;aodXt|PEm~A!6w#LDp9;B|6+2ITM#Q5a7E8!x%NQL?M>p5`w!rN; z#SziLgW0Gf4E~bkEJsmCj%0vh7~$$e@a;u8-G}L}Hn$m~kRH`w`IEtlW`1462EKdV z_hgRjHGmeKg!G4br$ONa-+qXHV|q`XWRCa=DR~JdbP^OHq{C_Y@&42~H?sp*f-G%V z4w7)$YQ_)UjhuC740&$+`F$VP2rv`0NC$v4i{HP`1Qh$a>FEv8W=jY`)I5nE5 z#=F86!vfANbV2dKDT3LuuTE%rVyna_;7Dv{_;91XYNt(9W|oK=0o_H?VU`63ND- zN*2(62;uxg&vd$C7zox~{N|0$2Jxg4UKz;|H=2Z?r%dKas1&Ib)i^XQLwySxkwdzv zN*!TnEdLow>cja1V2w`?4wyAVw_E1E|3wCU>n4p2{Tnbb5qCkNsWWsgY9P+8p)3Mp z4&Zp!`^ka}_?HH}eLG+L;BbrDb>aiN6N4@$)PR*L05sp5yRw*}5DaCkTprC0N@R^% z)feJ*l_it^hTe-dZvd&Mc3|{uHLcQy+@(ZgC@OPg;o#m69#!H-i(zdQm_=(gA_KcR zva1P=-wT?mh4+X=L;dQ9@GFP@rJDrw%4ZhEF^NN1aPZ=wN@lN`X`{Wru;*P3$V^2@ zWe<&5yPq_E@MDT=FSr%OP}0k}&I0dRHD0Fl+!&=VWL}{qK@Qq?;|Ao5pnp0if6X2y z(-?VsFa?*?UiyZ`J; zFfh@v5?!k*-dJEam%*(2uTn#Xj0L};nLRlAYZzZEXpouqm7Uh$Ad1$lpWy;G{MEGa zs@_)IEw{!Ypxk1(>i6SKV~^}ZHh?j0K3TL1fGSJ7v0lf~07=EOez5HZ}xNCDhF25_KiDg0-% z*gUbIlPc$0zMih`QT$*?ko3;zmk05bp?=v$&#`By*mnj?PD%p8h;4NPKN3VkeAW; z;#0eT+1xx?_gWa%=n05)u~4h^hK^J9&R+ZF`aO|^E-zE#y17s{i^DEp*VFmF6azdD zPh{D|K||tIJ-HaWkvEiIgf!U%__emT$|#JJj{aA;3dqo^UAw!~j_>f(6-?b?%y-gI zUjIOJ55*W+t*iTViPenKfr`^QF~KR$Fug%TWw-as4roHHCOaW=6wY)yfYcS=Z@q-2 zN=5ycyU|B&%b2mF_*w^s1n97jkyj)4c! zWD=GM7;4`l!G|+brNNqTQE`tHh+Nw@2q}>oC1@S}h}+jk-BrL+4SU3MwT4zt=as8{ z7wI=uAbCqZy(|#q$dxZJs$i6>nEuaVj$_8DAdu<9#l=k<`P>3+7Rm|MIB;T|A~$C4 zq1b0WZN`bi=#d_P*A;eW#%Gx7&Ho%|%bE_pUs&Te+^p?de?{+No9z!9iI)du@F@uAj?u^+|k7uR*G7T?ZjP~EO>T` z9oF>=bf9sReu)!Yj=i@wE3g286BoZ2L_{aaapE80pGFKO_{&h5NP|#42gUFMz2=)w zcS?0?vlNd8|7!O>ziuGof&xAUbWWI;gmJwq77*Nyz`&DXXTq{}YO|wrmf0l@DM6ciXyp11f9>C+Sa?(Ox`HvyXBdw#_Tz@ZQtB??OO3yP{2w5#kA~i zCy(ahv&;j5(-}pWS0K7=!Q$BC%514yx%E;I)8QqYdP!b~)z;y^kw<=fOw17o6D7U$10drE z+x%5b*+*5^!aDKgR3pzST;LrE11=l$UvYq!#Xdb$ zcIynCeS5>%+0Xyor&4p{4<+qomgLZ~8k_VK(S-Uz%8fzONBjmrXJg17#A_#bx#n8uSMyr6AWa`&-?ME9Ruy!V9KwM}H?!+Y!tu9l$LOp!V zi1o|i7HW0s^qei~vj$~tZDQDS&6A+W(oG#)yb(R|O1E?7zA}gVD@kgr26(Wan>6kC z$CE$;{EOlf;;Zjs^! z5QUyWGcB-yfVK4IiMNVhI}RAwK5vV{K}!y&$jz@YuhL?>)Eozcp` zsTLPc23+NizZ?CL8lZM0JXJdsLY!94$J&c8Ook0hWP-%FlQK$$6e^CrLg`$2agL4b zs-jH93MTkNEygV2NjLA%6!Jpw+`Pvi6JNqN{4kBn;ByJH--^5gE&g1tS~3?OLM0^c z46-m-!H#70A(trf@0-8oB5Nc~94q+Q|5e_0>MeEj=6LDG24tZ^7ov~2UX%H`>FguY zX3ApKxIZG4kT=%=Kd|aIg)u8t4m`<-{FocEcAPDsN3@pY;x6bEhv(HY{Jol+u32+A zc|56m=&FwzL1nKW;?-JP(|($D;bpIeriFXAwE3G#^bU<6V50MiDc%IPTd&~cOC!$j zugse`Dr57l5aS7}^e+sXX7Xt6lDZWReF~L)c7`-POF|IeYK4*Y6&65LrA>p{`*=lJ zMuEMZW^*~UGQ}1Pvm>JF-SrN6VygqM$AwV;G5^9&lA=blM>X79-Ui4R?Bo$I%2UvB zit{zwKX#F|tlGtt?LVEWDP9b+unCmnHY<+39H(u{^|kLg_I)#lX_Ye}@Laq79Ovgd zhC~1}_9p-}9^+G>QoSF#h|0I&QKrdO(`wk)uISl(#`wPmFlST+Ftej3;h@2{+UVUr zhC_^uqO0mf8g>Q`T0@rIoZGekb^UzwS#-o=Icj%olfQN>DN?!I7jr;PPTZ_3Ms!Da zStlTiVURmvc^iD|5n$6o>7f_{O761jOUc>9lo1z<(7C6{A-b9QjMQ{)YLsD zXNqt9_^EDB_XgR$KUtlSA8~*S505mVziRF*8zH_?XIFmxS}er?H}iSfBTOh@e?S5~ z@$HJGN8b?W5vKXSda;DH_divYm5{#uNvZL`mLMm@t6|nQHfAyQ++J0AX@VsfE!cHh z>yhrIV6KklFT&8H+Eu|w9*Is^ZHBH?YP?7+LqXY zQS)v-a-<8BBz~NA8Cl?{aWgOVWe#UIxrI%07a#_cD-i*}M;{vWPmi$A5)`mXlzpG{%7z<$uS2`N3wv3r z`Y4dFh{$4!L{?2;kb9KjPZ-_!^am%sy$54Z* z{l39+03s&QrT-c$U`^vi3z4I4@8XxvA(Jo&xhMpxnKvV~=TshaMYq6aHSn7okJ*_*>K zvRj^YBOud(KiAXoF{9nyyYEkWlXt>gD1GN%(8A%%bN;4;m7as!eJ|9IE`(#l6hOR( zIgYpZWjs&gWy7+vCr#}7%e)kPjg^z8Fe}}9jA`2aONRZ+)0W!%Tl-(r{$iKEcgjz{ zIJXUDs~Y~VnJ}^#5^(*l2%Wja;+IJ1mwO}Nn5|#D@I?@+op8$tSy!3XUa!2{`pX%h zq@j>jz}7;|9(NCRN`FFzw(XIJ8HF$eM*XUGII*N-uG>0Dq;1PD0e2`cvUc#AuMZa+ zi0C-x2n>)Npbj7#h&=vFAU(9wPzGtHAFNEFngi-R3iFfT35zNNnBO=$v{5XO6}zd(%xw0Wi-m@!vPt)+R^4025d{Y<&fkZq zzjXfj-o1M5pgNa1ICV8X79sZo;WPUG7&3yhFWgcgv%mF%uPmmK!d)PTpOD;PvQlLu z%$ELuz}jcVul4$tP`#QqrkUPX;Xy(S#p6o&{1K7ja&i9rc^C_(TVi@e6Ry0lnueDj z$UK(zIQY(HU6r+9i+lF=Y5Ho;A?u45E`+_*<>APFT!M+vJ8&S`$B5ru-64EOi8nij zmOR|x`^e0ehRl#!)UWQ-V8A-*#c&;^r*AIdgVl6wnwvj)}IBiMQt;SvSI~%-*z(I*7i^uW9E%9ouv{5VHK}EA?pia%l=f~&Q z@uT#kLmB*)B*?c&+c~Z`7~=kluaA?1hc4%p-*+EqxUUJ=iYZWi%3&TL+h>cvkrd z0fi=m^MsIP=)Kk+??$^&p;O|x{q+w^6%b2c(~@||auiJtgaQgw;;T`8XHCtXEpo?( zMvax#sfC4wfR(ZUCGADiMrBMWjos4K*{me)WRRP(!n!>HB554`q*)n)J7XRaxbGIvM}4TR0TMSnm?p20m@Aq67h^li&x2}~$+ zH89h)pMUs}sBdVB(x?@&`YwWF98J9`YbVGN#A)_2oVKY$mt4d{isk;(9Xd64hyakq zKh~{sCOJTGcK-pl4y`yOrp%yw13HXXZkz&XMZr@?H&G?;V7;@h(5lFT9(Uro@>A#;EUj6J*{N2#i~3d)Q>ks$Rz19%sk4xZd1zu?wCT zl;h&8MyEA==CSE!_X-OmCyuObv=OKyVD-s$;_r?W3qH#aAeWNL3zUvF!Ef(UxD#=4 zAd&n`Su||*=k%g+{@w&>lD2Ygq0;td9hIX;+odlln2Cca-$!QtBwYZikyOOkNAjm- zp%Q}D%&*JS7Yb#ECb~3->QA6M>Z@W}DH$unPYqSt!A)Rf&`@KDO_^*uUJe?sfWhbC z-g)o8`BFUXcfJ)vJN7?XsY=&cJ@tbCm|z5@c<+e1sLHKn^mPT0^>LjG+Lm+zngcJW z5LleO@QClAXr0eEb*5XlZnfk=F(L|TH!@>-fRzKT`K=MzdJqB~C;WJxpPzsaD>(Jx zxPzndB)IiDIo{u2%u_HC<+YZ!^%zV3PuTekw?vok6(c}xWlYWgxgF5+%pDQS?5-sf z7Ey@V*#$XPon)Wv2;Tt|ZcJg~!G9XYdrWBLqCh2C&--niR` z?+rY&+Xxm*uJeH72lb)og`e+ z7wd%%G5){#SRMzv^!yXDFyiQ3S9kaB8-N68(Umu9^-Z7&!GMyf?E|5!zTB&;e*b~) zsp{m8s-vf%ItF&npfjt*h>y{T*BmrfjiE2!;-SaaO-@n34ZOVirhfPs~ZT&;F zBHL$~gUEPd^ZD~W{)YIa=yhx4vgvlfkADKh_Cw_8>xP+b-YT?rb{Rpyu zWE;j?R%G{)CDYX3tl-juXiCiLuDU~)DbNF%2NLg4XR@{$dvF2TJJ0t5!1Wn<^BW!4W5E{+M*wyjIw;d11HRRSKpe*sc= zusk1>|BI7oz9C2WT(Jm+Uri+<>i!gXYT<&ZCw z`<32pIlpK0X4aQlF=F6$#YhT&E_7f=U1ME zZ-Nm*QM6{Q?1{)^S|;WVAm>%7VaGVLW`!nMfdvqB_8KNDPWC<> zEaNkQpp~jC zTv)O)Nd5JU_0t@%rB4~IbEI&#jrXipeHFN|)xF2%RNh~qz7qo-+(o=ko+k2^&5wUX z>{N^nJT<=U0rxb}0HL)D7j2kLu6dWeT(!ra_NC@A?|1d|)HCATTqP@xkb8^4;v^Kn7!~y#a}os%8wbT# zZW;S(7JaawfX8i}P_z8d7|#X=CZBcEg?z`Uet35FF!~gw=)n5iem`vb-R_rlHt7E@ zwEycTUFS4%8C74=khzW$tj;?)MEtcFxa<}e zL}<#IZ55N%&=s)oHcZPZ3dqbfU;FHUp1pKNY`;5IZQgb?YRphg^T)a`S9_+?XsqBJ z0G}L)jLo0ROj~t<92ma%NRZxZ<{txp%w)qvYae1)f1e{PZfuIT{_r&wG6}81xv@OX zx{R!L8`uZZ7z4hR#{LEM%D_*6IP74I&o8I=HG~gf|(Ag55bPh18r+3;Z(k!o) z{`xEJOD@r1c>A#`S>wQ98Bj5qp|;5(qwQwz8ntAE$yV#nd)^%x;lQrfR^`K&M@@~r z?rm*Qo_AoJ>#OUwqTt3ZZ5)ewsi+64%Z91;vLH5rGkzd!w7>Qk>mmgkb-iDQr|pTn z8?blpyx;Y;lTS|%<^|$qV@({?An;_=`TnlIGTi3{xKm=OA;oyR541YLd<28PS3{D+p1p^ZEfA?b`h(XXZ^3hcMCar0whAcw{>p^M z6Ody~O>S5&lbwGT6aD&yRZL29`cmpgtNRCaqZYxEf^W{}OV{a%lb0m~p3;OQ(WC{F99);csEhi zi7B6~{kv5ZR{iZTQ@h>++ATTuzdL;NXiz`Jc$&6HRGqdxcIhnscH(@WoSFZwQ+K%1 z^p?(*a-vH|Ti+Xtq->`kt);@3)#}piF@MJHR8d_+V}4Ky$e)6Ik1+;U2#&%gWapMR zZ+3kgt^F!U&lsP*pyL&DB6P+Nm+kF~>TGRnoQ3PkHe*0Fb`c6&5g+^Jq2g;C+ITR= z%rZKrSzteL1!eDf@oz4z8x0aV>#pjKfH&^T*@IrV?(2AAmcVBaA{qpO2b0(IzS2%X zQ%lQ;Nkm5h=RhB2i-@c!q2!dwJQ!zJoc{b)0Y>+nc2#V*NY8mgZfw~9LPbUS-G?P~3x^KVyeefl zmW}2kH4N7~zpiCQgC2>l;hT9=MvN)091?r;Y|{{h1jxJIzCHUzY;ATx+E(5QRRAea zZy5!>TG5{Zs21sv1T^~3RUik-vMzEst6loUY7SbYL7b&(=SpN!xQ_la(QbZ_o?gs( z-w=&~=Q`DDpJ^c?DLVR8ns%(djv~kwnngW*Q-a35HqbmLvLXh|?Llx|xFJ;#->(}e z96j7ug73lPe;bvAih0`Hs=$eMr~t?=0t1hrXE}QfK>fS#WM*kkf$ukzmZC>!ySOuUPFC z{ETyomudrww0YFy-ao-kWlIDEFK+ZnvkjA5?YPdgL;)_x#LATtz6zYiQ)o~p$bmwJ z1sN}zFDl&!4b&kX=3ES#sJQTC+FRtRKia#NV(*|VB|_A!RoBCDP#$|>LOaKf?1^xak+a@79J)ceKd z_SZ4EOe4=UkG`(T0=EP*O?L6UaA3on zRSyRb81VSCvrZ$;rfiu8X-DL+#=B4>H+7|l^B`}G`FaugyE0v*O2XA8V+VzG|Nrca z@LIXjoleYKqZ_yjy`mKiU6w`K!4Wd zWoD5IlXd`nLFP##1dFvRqlRGS9>z0+%_anIu|9Xr)D~VMT$7>E6FFSc`NvUF?0-RE z6o-)WI|doh9N!_tO=Q0W_d5-LNcJ}H(q!{B-XS1!wV_U_$F?wh|L<}nlmw6eo^tz3hKu){yOZ=?GrGc^JO;}KZBElWY zNH}~UKHizqObC{ejluWCZyLl%Dg>O7qCc+C(GRz7ofV>biZ9II#_G}KVrzKsf;E>uj!ut8`*(2)gsJc9CG zy<<&-x+LqXhnmNXZJq0rMj!cunZppF=;{6SlV<~05pG7;d!L2Q+cL4gXNFSKyOoWZh`U4(?({EbQF z^Lf^QgSoGN$3{ovX09Q>5=uv6&dB3{?aFl_w~sHpFoT^vb60rn(p6V&-hBU&NpaV+ z-$(Sp08qA1Q5(v1pFwN-;-~y4ce66?=W`N~f;5wKY=Y49;u=0WQN2sQZOgm&?in*P zsQq;c$WB^tf~83ADCmuorUC1`hOX#>j*)hGEB~9m$<$TyH&A09pL$9n5^G}8Bhkm5 zcfHp<|I*T>+U6!m2qBT|DERYa!9a%SCE!|NX{l80Vq3VQu(;8mH^s$wxPfA60Ct+n zk~7R~DZI=WdZ^`&{j!96p*&%8D>#{MLYB6E#%aoh@RQiLL+_K+Gp(&Z+;OWZ?81s4 zoK-11`2;0D!WnzJ!Z($gmkCwos{?2+C`cbdPVy;`^nc*6*&MCCf3s~HTqQmC$}H_Q zvt8io299-*y)EGD2+|V}&J=izaELZ3TG_4v^#earrzAfgZ!4rvYJ|{z`z(12LSKfAb_~bHq zS$SnpbwujHw+66yOSj|rGD{fD-r@T)=Qs@uKxXGxUpa=!Xc~V#j<3bCf)ZRtUiQ$> z6LH-Zt29O_tc4CAdU?aY*HdvWZMWvnGh>5)w&{0uyNz|;c)w-^9p%)PZw zuCVrqUQ~yoPZrlQ6Bp!<0pobs!fIQD=D(}MK;caBM{m*W83)`^5I45xE0Y;sTBWr` z#Y9wsUEXWh%W*l2R;@R%(Va?bK!<=qnal~{`FF53SWCbV$veP^H~Y?jvOp}H!D^K^ zslOY#jydJUV$q2K+P8(6E7=2pR4PjHb`;r+j_c9Yd=-pxY|~V`O*ytT8IK$N3#-qv7{= zzm!G{&UrX0tkDZ-3I|>t$a~rte{N{E3f3R?M&ixyH2g;|C^_Psz0rTBn}>C=EZKSR zttf>|fa1cRkS+5spqUbAwN%S@=N9--VVM3!QXC|D!$x-r5UdM7NPlIAC35dkT&}LR z|EI3e%xLfbj0WH)+)<+ox<^K=9Yo7@oN-BQ@ZqQJprgNNyP<4_`Z!zL5_pWx5Jb6Y z>NpWo@N8pA@T*rxcsMc!WY>Ws9+>>mu*Q=ZTZ`?;zj6p_KzFjL=F|miS+lFDaJHA|U6oyKD7#>!Cc)FLHuJvh*w%w*SP6@CQJlgiM*_?J)ZxS>DzDxfO z+j2DCr8z_Suu5}oCG&gGcoHmWtW2wP_VGvTl3zpPCRJBg!wD0D^fo*7XdDk-aFDn! z+y0p2S~DqxVAhezH16{U@%YtW)j8O_+Io+gV@w@IHoA#eiFH~-jdIRj8Q|FcN(US3 z&uzGb4<{7ni_p|)Zov~L4zvYJUs)c2M6toyAphR+|)Sxw7EsP2L5d1 zc_Au34vXMRS|%$32+Tp82-*56noVU)pvS9`P27w>)jM^1AR`VjjJ30U{^;83rF_}|4jBFt$LQ>8{l+UPI;7{QhXVG< zW)6uVf`~A%^ro8v+79kEt#9CeoG9;-ty=Q5PWVfdQ$SKxb;kX8RwPp@0Fd4b$ghQ?Rb|)Bb+Fb4IwX~W41Vi-*beNT6%DJJ3cXR#owDEXoEd@?Bx|l8 zFB||k?uxmEZ5t{oUS3lfh4%HjiHXUKJ+~`e9EHzMz306aQ;w?I$G|y@%(6)yD)U z8RkwA7Z!j;B4U~BVyLY}4A5@_?-+*?0~**;ep)POSb9qMfuNS~$Yw=`XE8N__!zmm zc56{(o`17~9}`m`$U{a=wl7$?@YLI@;?FkVR&CMG*RY`BxNq+n~1woZdhNs4-kV`mze!++s8iyo36jedT0|q84CdbDc3aOBSQ+CCRrb79e zR)^f*Wa`b`^qQ?K&6wz4z48%UDp;@8zsndhGM+hUAEPpOY2kqB+Inp-0Bt0$$Zx8u z>IzN|j60V&vvsQy$Y{%K8xYZ=Lv+QFW_9DQW!n8KQ`-?2*Ax7Ut%{0qhNBqf6wL~1 zltwq1p*_QJvVw@6#7IY0!SZHA|H+Kag;?)f6OnbgdIO|b0qU9VNvx&3cSo+Pd?i9C z%%n=$y(JS{5b)Zfo&8w%9UQ6f*se!oil;8ltQZ%Z$>gsa@IjV(;uIv1`4<-J5GH${;ZTrLPct2iBHHK%jB8-@c2B)0#*)p~1Qq?ugl)X%!c8O=4k0jJdH39-g&hKJ9-cI?Rl#{{>gw4zjQEy*p?RWVha z)GI9FVt2>gJ@S@AA`=oVLr|eQB0MB>y1k9n$Ukf2>bjBs)|+OLSXN6J{w?2e`SRtP zFZP&y!+vJ@n>WGl1|D9u=a!wM#i|iXFw!zp__vvH!k1~WzrUF?LOf1J$m(00T~j?+O0EFOuCF` zY0^Oab^ByJc<@bYVWLUD+%B((&Im8XO@-GV&kLuc-e`X8Cc{ejExDAw7__dPjmd~9*+K_Q9-}Y=Dm}JQ9KyY zchW5{2XR8CA|}Vi>-TzQ^h#Mwx`*~Amh6y}R8x=5(3_Wg?>>s+BOp(zH8BFDEG-<) zd~)%_?DD_8zt_}^iLozTJG}nloGwcj%$oG@Yy38s`J?^`s#*B$1{FwVRG@qO1p>6U3_+j z@50uJ2MO@55H~sy9?s-*6^lJRZ6H*{(UUR|V=d#dy6=kxu|`C*W%pHtXmCK&jrKRO zZ`=U`BQ8+|LvFRvl?W#j0UmIn$Pt}lshjC4jL!vYuD}KEJm{3Xt<9bE-Q3i`r2?0~ zvh&)dgPEs!lgqrrZw23(ZCUz{z=ZtVkflqOh^-}4T((Dx6|&G_RK3DWwDun!qGta> z0g66ZFLj5mf5u{7uxH$L0YMW(g#UNI@bhBs0VlHxk?ZhNw5XttTQP@gk!XK zMohWD>%v{*r+9x}$mU{BXE{$!_A0s2rQV+6Q1&3?qN(1u4KaRUZA$xiK@wwr1Nx-7 zWsJ|QM~@~2l+9^Ropd{*-}>PRy6T~pt)U_^s+Emc+E2Wk!4HSF3+DE+HOk-V1sitZ z*Dx9Z#kQt)mjmCtg%C4LiZozT1pN;6~qIQR*G^UaMMD+E2}0;EQv0>cC#Y>k?1jBodYX^PK{m0+lTe#BFiCXXUbVI+N?|L93h%8;s zraG}ng(n{#5mCv^~~Wm#|_rgm?^kN_Kf z%xRZ?g--}c!}RatX9tvduUyF?JT|#kPa+K^Dy)WSZN}XXGq}ewNnJl+1f!f%S^@>8 zz9M?3E($iw3To@&;UVU+a*mZWLi}(^}GcaB%eYk74@n4v7=CdWdGF6SXEUWjh1x%o5%Qa}U6u#G4ll0tepT*Yo)5*_a-`(ffzVJ>O3@MX-wlp)HG=(kMkn z$D99k)YhQ+xV(Lf*uWtY~DYjlZul+Y^tcZA=yVR_#UHt2oT3SSVHI=Q3K-zTq6Tns}`@#<% zbe%VE3|}H~#K^-F#*pd}13y~smPmsnis}K;6V+zT#a0m2Kv;Yc$jq3 zPDke-U%m=(@B_o|QS^BbE=ITc4WD0}b;WH>J$d)F?@H$&Fz%;>tf(K55sN&8boe0!lzZM*vR6F;)qq79+J6+|vu54lkcc$B>eHsS?b}NgARZV&z1~L*TfM~pN=sag zDuJaMK1^^QCsoL~Pic4Wsysb)`ey>Fkf08?E`i|VdUjg~g69`LuS=)s6h4CjUCr`~ ziZQSrxu|AnwU-@!Fm}kvym!w?s9X`Av|E5Wu^bZr_{*#5L3WIu-)~Mz34LHxj_1h_ z6J{`X78(MM7Q#9MzGm?_{tkCCGsjksATW$!&kJ~y1d4#J{&iZtc;k|4T1a3k_ zyplM7m-%4-!GmRe^|?>Kk{7VDg*}l?Rkq%KeX#_-8Kz9I6cc7!&$5#_43h)Y__7?z zc>NypW9k!v1T9i>F)}hzvRbxmnZMOilc7W1KdAKkPoVkwyCT2OHBnDMWN@lk;{GGY ztoNv>?to`eX5NWcO&ELt-Ei|7Y7fkh=9gWWv0gRqY%hXxcK>4sTg$agSwFMi45L2C z9m*lQvhp%g9w+L_>rI+ki28nB_8~zcC3f(TOE>Q<4)$4T-BO?Ov;UCakJWYkr~5u~ zx40Ewm^cAjIro-ul{G|SD+wNDyDZ6IoE z)Me)}|D$;eja?Ya(H2cSD-oD-W&uB_>PGcAv-6+ry6Oh{OxeSetiQ;zxg5X{#`$Ti z3#NdieQNELyvCmZ%132$2*$xpy(ZwO#uz@6=0*+;W53*mi7QPyL6700D**Q-B14~4{A za(O*vR7&0FVbE047{gsgg2;@cgddA##4|LdkO(JM$o!es>^EQ%ZfQ8H?19d=4Y?{> zF@95K_=j5^Z&?|_b8GjhM|9*`*~Y{J1T*WP^`{D5bV#C&$5uZZ-D7$0-m=R;koJOt zLj*8v+`Y8F8AI=i(ic*C2R@4AOsEzV|LGmSEDeMHP9u>(QAyUc#KQpk>~R7v_-+{6 z{E{CPht{4CF1Q0(Zbw9fCPSK{-(g++r&HRHm)aQmFgU>Bs4WXi^XJw?oxY5PHvm~F ztaKirv3q74h^2GpKE3)MQ&8z?Kvaxb0J3hWgLex=*%|h!dQFq_R8pmMCASFP3{ehk z;?P(3s`^5juVpUZ0IYNI{-X2nF9oyb8j|A0y$4elk8A0aH8aKPfGZ%xjW@$pR8(>e z&NC^U#5z(ub70aJQ$725t8ae1T+Gr*j&_%qIZF>>Yn%G`aT~bryrS-rb&#sSzmVW{ zLrua{&n0+WgmR4`Zb(~>8lOA+&`3$Vj3P#?z4&$xbf9TDp1sZ- zf5yu*ZjwqE5bP3mXza3Pswyf{yL`m#;nmrlT{;M150ELU8QtrR)cO_{8;~kz;{EgWOLu4Ds2rmm(2o$Q+(+|#db4z91 z)1*n0^t`on9oX)-r>>%pzD+^(HR*8mx|7Xal+q{;F)qUP1Sg~8HL|-*T}x{hX@gM` zreaoY-pWoem8XR(?+cKaNW;=ZQ4#~~l`GrKd?o8SZNkVXIp@q;E zGQd%?-#LUBE+r`QrQDSDYx&<;E-k7CnZ6U%R9ogB?Kn3TBm2JIKhl4P>5ev*` z%|;U~vIgo~ntkHl^P>x20G|`%6ynx9duDquL#H7VoSYiWwPNirKDo&97?gGGN(WoS zo^nvNEe}zHIQae=9QSh5WU~n#MTO8xnr}6~1%*r#4F_0CDItc;WbDu&)b=0i2^(_D zlcDNgFG?BEImoiWacH}X|Jh<&C0>_!=2Df)UqrA<;niWR6L=G7HY90vTlpL_!;(>@$Jgc2$PJ~9XV zqQrs;naQnmx4)$>S;jzr-6$AJrZCm~O+qr#|NSTVdU7lOXADHbV#P*ePTHIF`cWa) zW4%=~RjCzt>;Mc#mfG%HkquK{au{(kD z-94RDqfK^b{v4W-?zSBVC82ldFKd4nQG%k|vT-55ol@4-YF%=6bF-vf{(4~(zja2y zIq#WkYS#`_tBjRnQ{rgg<= z`3BAgr_+6Hm*A9cV2xrtK#^&=ZwF(q%k80MaaQ^d8bsq531Ho%aye09Thm&B0Fm9u zrgzbQfL@FTJE6!0TMK~@Q~JXP6lL;|HE~ZCj9ASEV=)=BY-v0KU`GZnwre_e9UCgU zusEU(`oA6G?$pQLx$okF*pdXM0S4LYZA|wA2|K&b&krmsz*I3cRiVVOO)DJND6xbW zEi_Nw6Yf5KJm9{@ME3Ye+XU&NvZf}MpegE7Vj9d1r(Yd?wt^+;cpG3Q zVD{O$;+2*uC%sv_eLR%w@qxz}c=QK6;CSFRnmwqwrh4(}jJD&;ejx@E`$?~njx%Od zA>^mZn@H)HwrP%L#HPD>d06D1TQ{oY$4pK6C!)p9Xb{Y5sZ>wTm>DpHlzgtyG6BXJ^GNO-+T z9R*uuM#{zR-C(NU+t{EXrlc~I1x;%I_Ir0Tw;9vWkQy{n8`^fZS{e+WrQYXoiSg}v z1h$r#gE9O|QC(@@`#A;D*9)DRG8fh9A2Q8Ba}RAfYn%!7f{l@UgWe1ko-Wa@9Igx$ zorXXVBsr9eO!e&w&e7)!xOLfUGq;gKZznASJ##D<*Vp9$Af@Y~MZ2(Zef-9HM*LzA zk8Lz@<(Dy*TlxO+Es!7R*Hk0y^lT+!8!B|gw|VPT>2mzguJL_V(-i+YOY1D`&0e{*KC z=_puUQO4?&D<3vx@j z2Ec_3AH&1LmoYVwn(%s)@ezY9z|np%0^IocgAOYGT{6%%W0t57JDiDCE7!tm3B$zj zuT`bvWHy6vp|Nn%wkkN>?JuzJ`+v^hpEUZI-7*YK9*tU(cS2k#kAi~nSoAg*JI@f7 z^3of>l%rKHAllqverztS#L}&aOPrAEcV~w=9T(v7l8O(}qkG68c4RD*gnB0TYFa~r zAo*5-v}yJ>9e1BjgOyontP+!#sBbO$kRX%h_j5M6MO-?-lad_;P#1iv-?o%JzkpNd zRF-|pA1L}zM!g}W!}r=|%N+c}hl8IiyvOe+asK;HMkLWiRZT4`uiLp`7o%;ACK@7$ zwE!dp4PPC3saso}?W%h~Iet`qG&K)aR~B$>Y#-NCuPBwd z=zeuJf{zLMz6_FZVX}|X)x}^)lsy}rcDYfiAwhI?E$%(7qP$(t_vfsGy9#6Li|^xi zEB|W-b;~~l;w#w;aBTNbCVh*5@3}A%_ zPT~fopwg880v;iu<=);d?5pgW>L_yqu2a_QEpF&p^_+jz*Bf0nuqjWjHt{Ta$u$EcE+bJCDw7J4!*$oFrS}GgCE-1j~cQ5U_pHZP-9~Vs%2`Tu_Tr)A|4~PwknxISHeTQ`FNaKC6vWNWzo!>PW632_Y`OJ*b9PnzVOVH?n6e}w#UWZN`nNhul zDl?Ui`x%sV!kF-zpb10PyDMx2x`|hpr!PWlB9jH+FBoZ02mAIm>e82~s`xY()lexr z;BGm5u>pi}?rX!1G@@{&Rnig$e~48av)wc>`R0!@)`?09WeAJGJXGz;a5X_FTMbx=qvupR~(H}Qyxv9}?l zr`3Y$dvP0R>ASAVQ>P!N-k3+-M^{UMWDXH-Ixt@3EMGSjWx5JF(VlZsl7Dm-cTTEj z8QTB{a>#nWSeQe@M-D57Q9&G*woL%k0Rtva{Mawl_{515^?*8UdQG-o*9ESWc-R%y zX&Aa}DKrl`Ez~w~&qSk$ghA!rTi(iz%u5xSSlZr>;RIy*E}kUXAYPc>QfN&)ahOOJ^U0q`+E3P?fn-`9qhc! zf_lvtzf&2)8t6f;7k@egMp%ZY*Z4(0`7K-6&%10H;M{jufaYB^K|_|kR$#|GFK6(~ z!A*cQB^`j10fv?EGJx|sU>nNu!Y&p-8kmfkPAt?`aqyZsNetb~%E~~S+?R!n`=G7& zsmp6>FMbl%QnQ;7#}MG%!{wSOLI@Co5iUu>64||b_p?ZRRR7ey`olc57voVtpuYES zA8XUBq^wL&OB2%*P!nBHeW2SN8s~4`ys38X22@#-!_DW-E1xy`DmD^LCxs+kol*8c zT~jmA>iDgKf>}xLP~`E<0GdnHp32@`(0M&D^1S6;2jAX>f0hiO6BF|cOqhT)jcAB+!DxAf)5Xj5sjccR?0P~W@cyKnNm0y*IC1Lj+xKvp*B#dTkz;?_ zs;g`N^bjM2N~g&r}dR}^`-0AuTLLx9+KMX{3kH= zNxic2xMk>Y#J9tjF65euNi_XD(@B+&8hKq`?(Dw$_$I3b4l3TePT1*hF`eD&m&bRl zbOZ~9uB%$+e;JnBRn4NTnQrj(W9D0NV4nxx4ccRT|BFMbfDxz|HXpoNeeLn3<*Qa% z9kWx%Py$x1`l7>37WrI?aZw8_#Kk|zWP21lMqc&#}%8$m8vn*nuN%*|yCa3X4Q{3ee~0og0elfCBWnC&QX&Jiuen zL-~46HA@)+fs7s{c{=bien5oMMB@bl<&z(2`<~m@V!POG?JTsTR7M)VD^A~aeMhGoGxAVa`ERe6* znl%tG=!v7*7dImR@&RQ)LR*f(dFIoUJR>ShvV|zxRE{HM%y8bGMhDToim?AMP% zDvp@i95>+?G89*vnTo<5BGc?ZB=Y*>$3;Q&AMuQvYzEFYP}#;D zzP@+c2SOF)qml|_6inzgWUS8h$>Da(+)$V$t5%)|5Tr4SW>!Yh7OnG}9X-%EbyaBq zUeIpzT!cYTr75jlcxPR$^o`pa_6*Sm#m@b8l?d)zG$`Wf-ZuRl@qgqxb27jb8bq{r zc>tB@RbneGjU6S2>>){A_j^Re1CF)zataF!4tdsZeY_fz>Er-c5MbGzPOdR_zTJoz z0E2ukrMK+8?wXKGip4F0DE!f16juM4N@-UA=5K}{ER#fM{+z(cFc)c^ zWnVjOro68xs*Edrca$AqUXgdqLhLW;n(q&)8fR5D#d*PkOsIOOZT%v*S_|pq(JJ~) zrK>^OeWX`}>xBXBGRh9cSHDj!Q*UUgHFd(L3m>&l>;-HzOxvdNdiJbYmaoQfNq4T+SZQrlBs@B{EkUHvB7aE&JPY`EEE%gCzTjY9Mw|xQBn1-@M?Rw3A z@X2idq}vtMT`LjU1Sl!ViZ@b-%(N&SJgzLI>PN6)DIExx08aK&^bqX@1EmlXO)(YS z&dLx$M^en*n;&D=4-aH{+kO$9nL!EAgO{bi7GNbp{#upevsuCerC#7G3SE@;Q}zd6 zj;JI^sQOp(NCWZYL-fPAr(N}%lp0wdC#FC3oe^ZioiM&Ur+mxd!-tohBzGE+TN@XS zXm1!cHl}>Vp#yct+Jx3IepehZ$ojfd3Z|fFCWR|@HpeG!gSKjFVZYp~+nZ?(u?xt| zbKsZ|Z|-slWH5;PJkHXQvWm51g`ub=tPSKBMvm-u}8PNI(9RcnIJSZ`ThXPX_*(1%; z+In;*9oPG2Woj!sXJ)1pq^AdPW+gr_IO2yB1W1S^+hIEujOi|uUJ%6`Rm@n$>-lhGbimQmm_8Lz zj8O3~QVcmhZp*Q$9|d9N0x?@_GA2GNXlg~d<&4JhA5%VTQonq?4-IUI#_R)ykKQsJ zbou|NdK0J|_qP8#GYKIPqLM@z%9ybzDjCX5LIZ^iWhxa(DPx0DW+}7{nTd*0gh<6s zqLd+-l1fOtpToYN|61=}>%Q0X+?%?t^ZX6R@tuz7>+pm;Nm8htgKOuqa|ENtQ&>zP zCp(cJ=H%I;vQlmZr=r^hLWe?NctkwITH}+3RQFA2#{<5vHc4I#4g?&KXN{vkJ~6 z8Uc~>4DtSXG4K_*vk_I0Bb?{|Vz4@I4No{ zsEZDQpVTB~tfi#LV*l$hc<>dgM%$?KU$TWvj!b@Zdd^_Yju~4|ef~Ra8tJGcq;a}R zM}W#fRwPG|#F)VTej$J9eUNR;=#epnyK1XHYyn#b3)$U!PJ~2~GZ}+p(m&T~jrqjI zfFUJ>v`FxPu-Q=CWh1g4nJ*es+r3d;Vyn|dKQ7;xH)dD-VfDC(K65P{9kpo)VmPK} zDK=Mnv3z!uf+`9BV3){f9d4Ug zR~>b8Q(m82|JmHw+!9rN0+@_7z=j8y4Cx^60SK0Sq-yL~4!h#q!#Kj^$q|(wYz+z%~8?wS!e-m6F z^-jr$58DW842;c!Q$4I6fSX~Eu8(Nl9}#EfYDh@$d< zuM_eM4DJ|vNA2O@o~iwND2<3O+*2EJq{COA_0{jw;j;KRjZhgO93k(GD2vqrdE=KY zD3feTpapbM=K5aJA_LN`zP#<3QoGk3J0AFb*&(CG0Lnx>F&sWfSRmAQ0c;i9gh%jG z7hXTjMK39OJf>Y_TC<{xv98ZL*0V#eFN0otzy3D+{?g%|6oTAp;856^ZoTvfzqm6! z=XpGY7$>P`d3Lbb661(%aT2>lK?4det!d+$g4oJrnm6NrjP)-pDRw>7#Ak{@O2Fs5 zM?ad5X_muXNW0MW8k$u@e_yG6kBbz%20HvrJeDd{;do}sSYl3 zgdJ>e8cu@VM&;>U8`!ahNF&Mn7gCxtZjthq*isH}$#C``Z9}>* zyM?L4iebQMPalQ)u=d_oGn&Lz5w#Q5E7FRi5Gyi$ zDNPf#lx?7@`}_4y>EM^xCdM3@9C+e1#c*pUabkk%-6GrpgjM44IpYHJ##c-Y_O^-x zF=7Frg|)3}kBG0QsW$u?(ER}pXbOfy#no_ISKAK6_&BF@}dV@iO~G*fNqECNm?9>h+dPh?T(_^lL-tO(Xo3=z$re zRzGI!gty(tnvZ=ufybeJrhvOA$<1I2X>=Q>j(f1FkCmnW(@m;Ft}_t~ZJ1 zYb6IuGCJu5C^-EPJ|-HZq-+u7H{i;ZE8l-XR%ddxwD3tnW`YZqmU}(K6V52d-_$w9@hBQ>&rWSRIbjj_Jj?~4s^^E#<*FI-8yWa7M$Je{or;JU9Q1Z<@`sC{vs(;^2 zZ3AOhD~DzGAi0e*q93O=q@}>1?CY!xODv#P{I4$MSR@#4*o^ERE|NyB`?O()5K!wOn9m6w|~HaVe5rnCX1$3%(wIy z2d?p*>ylRO@6)GG~iD9D{bA(Xet*qPALFU7x#-`k~m5e zd=422op)``r+f6FW~O_z3#}%h+!A)_B`LSXXr)9THTpFei)h8*mWT&B0OOpCriEcg z`<-^Lr^tkH(`J`MH|^W4^32&~FNPwkvjj1Imw%yDuV@}ee!u?xyY=fgxv%P3jbKyr zM(PG}pf&|jkMB(QvtiwO^|V5jP-82%{Szimd{~`y2I4?M*hQ!TaL;{BM%56iFa?`e z2LLmIwry)$GoZ|UQu$0rAmHTYs5m>XqZRg_`H?zJ))>7&#jhXkP;qo5F@V3~g_M86 z%e2cpU~XP+z4h{dhA#VGzj_rG`%V*48%s=3C$4D()m%56O1IFir_lDetx7|~-$P5Q zzPo$n(gTOj6FOu#e(h>%VZ#~Ohc7`@ zvwuPB$js|^<=r#Xi+8WK|A>=g;WSJyP*1{E&6`(b)iof-?Eh3HFA`OKIRIV>c*3pv zhQBI93D}cjr0p`whvU#e{^bpl(_p2o_6`Vw;Jj9_>9Gnyun67|e$gxyuUXI(c?VfX-Dpt2XL?UJcw4oXn~>xE zw`CJ|eqb!^vH}U&sq@D#iE6crrvD2p8ibHUX;C41$JXA@G|?w>4Nam=+<+l=ed0l| z!U>!~hv0v7f{8WeBb&$jfCeTZ=I6YV7-8eZogd?Pzp#48fX!mvXnG3ObPA;49lQjj z5%`?>JclQ&@)bu{@2!iGgQH&;)IsJ28-xaX-auQa%&k-chl8X>?osjtrAr2?7is|p zPovsy7!VIEOM@82QJMqYp1}CY#!nUeMF<*L1&XYZD?euLL!YLp5MYD;1X=y zqqmK(W{={FRw+Vwb+Ne^rV_FmIX%}J@^@;=1~40xuHEfw(Zd9dUV@>dnWabaD~k0u_LBR1Bc%D=bGkU zxgktD|BG5KJxQ0N6@0UPTx;_mJ(bl)v-N>1-on^MsV>HbGn&T=5@1ES0c+Kt`%X-o zs71*yYmG$4QKb4^M;!#w5?F_$%NhF-ReQU0E^Tb~8)_(e8XA_@Cqx`VuaUWHH>Dlx z?zch^ z`9qtu?#=de!hh@fYsqXPM`Y{~_kaWh3HiuIU-WDqyIVjtadK_sZg2N=<^-L!qHh4} z+*U+_3@aq-{Aq969nR;4O!LvYmW=#S45@u}RuiY-8L|PE7eiFB;mOImz^7_-a+#|> z7jqhU*6iUB3^Evt=uRAA49VHv{`iD_-#QkWe1=>v!Ap|K&xPk{WANaIiQ7uWR;{vX zF2j5kdDo}|yGdsVZt~V+4jzN6ffk#FM6KON>C_WtsGrFPPrj`fZ+FE=D%B<$DOVaH?d3nHHTpU!HVtsY^QlgNnEP;Vo&Dv z``Ed-s+-ZxbEK7pmruGjHdj?4<_31DxB1VCCTnjA2L);WJ(&Z=cT@X7r)iUaD09&+ z1n$O>r$8CUv%UkUhQPFU@TV(iRshtBU%%dr6Br->2w_B?>j?~otjn>SQ|^6|1cYun z<4o1bQ(ZATqI5^{)BuM_T7G^MlqngJt@w3-5<6Bzdv7^V8WoBfaVf4%yDIQ#hCU znj?3kmUkXMw691Q*}*_S=x~|%@|2-V!Lz4^+nNh1eA0oHpIR|-X#DTrwGzWG(7STC zX#gIafC@J`{y^c{xWN$cyq&v%1_7+;C8JOcC{%~){ zeWf6kZ2lyfcW!BE~I?BhirIW?Rt-{!CILZKww2B+X;yB!-#Qi7XluC zvw45$bHSBKw(-2Z6kaC|L7;w0p4QvK3sohfk%FlD`DP4T^UwAhI84z-1101@Fh@U* zUfB1Hnh~?zI^fwU9|^}Va%|LXU&+`@D!Ucp(S*|k#a#dK*-!SV&QJJ%G?H*m+(>Ok z9sfT*$!PAFziiT5_0mgC(|A|mN9P|WM#EE2lSQBxukGcJWabf7@y6kkRLoC0oZt6! z_vQt3)2&as=W0#J7&-)_re`ypR25hDM=Qq!O5^<3N%F1n_eo!XVr4Q$b1x^)+2EGW zd1ur&Cf*yZGhVxbyM9X5b6dMc(0zo!Dtw=`qK&$GZ`*p^eV?2(?G@1O;`r@cQB-&e zqnGZT7n2(DQQ>=6sV1f$VD9L=WG~-k^Pse;`gz~gzYoqaSn%x0wKh>k`=3aVD@i+e zLh8{bGrM&WJuP7@f^S17KY(hh+B}_7l|H&+{ZISZP`y8j{`|fJ5Y$WOROP0Nind#O z4p50xc=!JO&wCCIPHu3-{^T$#{Hb1^)4XQ4W$cPB7J6DTyKAFvpH5pr+iF9ft#RpO zlWFfM;#7@GSGha4PdpA>;izm`w(45vxpfl20m~)G-|%(H=I!=coQvQ9ih&6ccz7ci z=bH~*zOzDcv!CmHXb-pV*287ZYXM|Z>vi)UK68zXmtX~~`1;xcCDqR9Cm%d2KMBE) z{D&`S>ea>2aQQG|(i#dE`D0D92Q6%BBd04*Q8wQh9Upz8x4JtQY{09CpUGv7EXXfY z_`Rfb)PR_Dds>7??#hD)4t&sW()_<}&C_#c?2o5f{y45pjMJ8j%L!$n97)^%FDsc~ zo~*M^HHP7wl)Ps(X8th<6+HO0=c(=oI-x%3yU?(EceAQHH1!iX!K&;QA$S096E+gb z@d!Fa;v3H50BhY|SJUhQ0A>((q+EP5+!CetI*gRCn@$KAWuP6MlZ;m2h_-;2miQLT z?W)l=A645hy0-XbpK+&?W)V+p&MipT0g^Xj$aXKV(+6K#Uf36<$bNTZB4VyX@KW5g zRz@YLUFsb+ko%HP;{ZnWb`m~-6QvngzF@${#!Y1$hUVwNv>MUztE?qBb0Lc_QNt&m`>X}wL;yolZD@7%T0Z$O_tZ=;&rAv7Aq*km}99Yub6Cn!z@XJ4|aBTBK|#;J5qa`r^s6oxUSuor=>jJ((;~xPCq{T za;FoQE>$<_6#wN~@YZ~fxe3~ zn5&9v3jk#SajCWo?YaYfDrl`ab$AHFgGhF`m6D=4&p^Q?L??0HBI_#GJJjYBHiBd! z2cjlqCbPj{X?&?v2~7?pf2Q~Bi}R5bc(|!jOGX|%+r9shsYtK|K&dBrw@AnQ0yA6~ zHZGzL5y(tF@z{9)jYbpm9osTbU7bT^17oi5Q_i$)X_64QqqKKeu;56k5-Bf6#eJqR z1wsy-C;zn5(a{n2VV0|Y4=(9e^Uq#CW|d-y4-<$=rwf7vmD1p;or6m{apG+Qmq)WL zFf3(!(1~f^5#mMVTC4$!X}Tbmi}k10tm+1^l!`H`v%qtK9g#baFfgNI7wM$i_PrM_ zUQ{WUnU@0On(xRB`Rzk0CV%!j0ww>$Bl`mOf8}~Q56#=T$F#iDbL3G`I8>Dv%`4vk zgqNx|xtGH004Tbk+OeK_qj0hp-@J&#V|aSf{fxwkz+Iqun^)ZSI$-Ws4lEUy(qd3agW1+ zVNj5HJ`#F@BI6Dyr6~Mi-?7;WGAkY)Of)9+o(SclIWLXhU)uMgZo+IU-4}XJg-YDD zS}koYz0CW5wmj6Z-p@QMaYPJ{CbtJ$4mVr7t7mJ*+@u916uw?uWh|DfRqE z`!e{Gl3FXj9xM<+edb6w!0WMCtmNEj-L`JfBk*a`A9TC0i~9C!GJ$Ik-8P8{QdiF_ zkVr@{0urY7Z``=i%k_gSf>9f~0R1yW^rV;T__q(b8HF(B^+s_wkEFy0t%J%H5ywR- z#yi%F_*>j>&;0P3o0#@lCa7HHSseRwgGazQ_{6;iu~)C)Cq{;|x|wO);{KTwzsLUH z^K*VU(h!0_qp2N~6JO}IVg(>UqulG8El%2h`t-?tzizN%V`QHRZZ!$O$ltKMPZCDrlt7am3+!W zJB(8eOi}~ds_NpB)yl%L@7(^J55hG3y1~ikS+^jrYj{D28UCX+?Yg~K6q9T|ePwt~ z%rY2pwtC6087H4epfx)wmoK6pA6tcq*G}au1-brD5Ati$c)Bd2a{zhRPs41Ju2h*< z%nbJKS_2ZqrQTp8^x>k8TMEg9)L5RqhSwrvS=zP@v+ChNoX;*qc0_3p%J^gjW1wX= z42XN`5ckAB+A}IPsRmkjXU)*0c;5W3tWg`b;KpR*rARGhxCH_SgRy@efuhPZW_BJi z)A!j$J*lS;rY`_*ut!A5pJXC-I+|7lKUptdMlfpTciY>r;Zl96_QXy#^?47FL!hRY ztY7fnIZdAn)DKdZ&x@lJ5xSP7(tar0{7SE}_3>^?bT}+MGA9!vbq%+V6K0GhE%V!m zO2}W9i{3>*Ne(mE(CVMNDLf&pcCao&K%m6?A#;>DHzJ4002G1hnQL>HmCx^+zP!Q| zB7k>*t!c#Z3so^YzIg3Buw!JyoUK1MDwPJu#km9|FZ#JeFbz@4Uz)vx*enWF$piP; z8d&!zBswE@iS{S^ISlza7fBhv)o9wIUAohCRajsRzGv-NM@hPT!XZBB%<_;u72f+fJ&&GA8NflCr|fKpo^jyoLQ6JCnoN&YQ>Qoa|+qkyvX!`tJVc zGr!)W5c*NPrK{wp<#_!$&TX_6wIGVso1v4eJ21_|g9{rT&7_<;0YC8tb# zLtEgL#t%CNF$xdr-gL3H_0&2)3aP$dx}gFE!Y$@!aL~|CTQRuUf8*F*4XK7lFW*OJ zW+am;u!ng4sjD&i+?SaXJW6sy+}efpsPqh`Tg}vr*_w)=vc*lHr??s~i-w2h_(oRk zg(5%yCHMUu(MCkt2H4l`AgNeXOJS|4R>)*O5X<3Dj6GxK%~x0}S;3VO>!M(8zs zQo;T07t{CgMjo-NtPWivzll8oUH`ylII+o7WbPCEx10B_&@>lLsf?m;{gw5T9t}O-)Pv29b&kO< zZ$VYWt{Hu+=SiBO6piNt^iFX>^4j@i4w?hYm!?%M1V*4^mEh8}o$cx5oRF_eqc zL3FPAcfwt+fHZ6GI#;LxS(Zxr?vO5pMAP@RX=9Tf>eAlpSo=Uq;&TvbU}CtN`O+(q zmP;%*`=@!P`uj15LA-8ma6eO)+X2{K?gOl`p4OMj;>9zVLkBCSLe^U|T<`Q-w(O#$ zU1=woI>hOfLVzmHG_MEoAcnhOpb%9wVlqM0aaWm~#0|q*0#7hKxXbMACz75mwKvDC z4%$#;(J3VI!2M~6PpFP0Tg+%#E^@0$?w#@%1b)f9d$;GEi||T+|B0%JqNJ}SL<54L z8{o)5w2*Dg`kEiOiLA|mwk10F5T*rI8tv_`4?_}LiQ!kmLJ`s+BuZGLYM^c3#-_(s zY&Q3O8#IAvPCRI=WYgRcXuVgsVz?!e4`qYh`9Llb#>O~sben3!v*UBiq+n;Q-GpZa zBBd11V5_S!;DgG!@7cEFPdI!7SOWcsytBCU3U@mE+a%!2i$*`*KHKYY~6tWEjd^lh2s^3IpS zbH7m-=&lIKc!51iwzrTl99o#8+0G~CULOS9{+boosEr2}Bx@8(+-gx{W6dEr{7m0d z4^p;sdQpyUSGFr{OdF3jgsVgH^#`o#`sk{9q_BOm@}J9vSOPq%Q1$u6 zu={n>P@+jT8d9#m&|oqlVE30z`vFdZBC7?LC5?j&)9sk(trnFh`Wx!LR2nNNhs1An zk9gd@@0@TaybAF;tpvL@uB^^Bj;R~hmBvD`8lU$@-|i4ln{#{o=Jt*uhA z76O5w%-z0i+qS}PR~%$=JRHanlr&so^hi=50|)Fm{cZ^n>YZ`>b1u6B!+?Ao_~e6; zbq8Z2SjrL)Rq$ch6$i@DZHm4#r>9q5+_Zx%j(ZNL1_x5$!9u^L{6J>&1#ncU^=r^; z&z&ox5Ecp3}Y%MBX_nxFp2toH^sFV4o?)tob@Kq)(7}nmjvu<8EDYPbKfJ0X}^KLSLjMYmYup z?3t!!%HdD3H-6`EzuXc};7OSe=|ZBks0G>Pl5dEtX8{`wZC_fi>iwX)2_{+h%PRFS zGxAF%P^EEt{K9F60iK6~95sGPVRRcE=3(>ZW4djN*>o@}YS!gL2}fG?Z{)46c0TF( z=iH18-8)YT%Vu=#JL~*rba8GV=W^wO(mLnF1UWaj(2cR%)T;y_ZwEdFr&<65Wc-iw z?fm>%iCeg_)T+9~gy*$!c~zSuf7|c!o_`-K<@3lb45T}DXXm)mM2%*NZZfG0Ti#|A z8g2w&3j0r;;QM$XJ)Mt~U@u4bm)^)vhzA=?@&NVbJ2=6T;M_&_R(W)a@~$BgX6QB9*NHiz%vnp;rAbKypf zoZLPZ}G8cU|l=JT^en zYD`-dF~$1cBcB7W2k2>DDwTWvPuR2eV7c@cn>A>X|DAQ&KR#^J$Vn}d%8wt|CcPuD zPzl-<89|KoXfKuMVQ2`_8;`|rz=|XF8^2XjyBJ9VVEalbdZESH<5PS2cUwrv6yBg# zt#(;8Y73u=k3gB|0%@hVQ<0==B5Ou;juRohZr1yGnC{wk>2mN*ZQZ-0yA12rV-n>o zZI@n34<^Z^Z{Hp6VH8eNcMvV=SArtRRf$qcJyTW3`gtZ65883Wc!A&TayLAjeR@#t znyXy;QC)Ve>NCpcS0sgrenA5;OX??VKwTX}>L!HbkM^UglCkELrwsCHgpe?A&MB8g zi+cPF$QcylaaK6X8+nv7)P$s9`mqN1GDx8rx-kp)tDe`4@;*_QhRM7*gnc5w!X*gj zhcBy%O}ZMyf=KghmjIfU{3+}9FHnHw5kT|Yq&0x>6cDZ0?uQP>sD~9k4)5v)pY&Nn zr!3VM*O*9}^6g?t)3q+I0m^Y$yjWDaLLR)*7;|(J1@Jv&bqY*>pBj<1e#)z&D@pT~ zzN=LFbg1Ldo%fclrzEduQ@TV+eYu+9jeWV#KVRRxZ{NO@)We(Fa784jXWp}1GOC7c4j2+D9>T!OoTf1~qu^ByDzuwD($@4n;_vdk?;33`q=HhU@+-*v zMjAhsF9du{L9qZW9fBGfu^8SeM|m5P6UY%rTluC*+11zUETTRJE7ml+w6~J8K}5q; zne&$}wW2RaMi~~V(z02z)_r=hygZ!xDCTRnQ0czF*=`+cmGy@LSfokhP*0w`<@l9W zy6e_nscFHIkQoSj2f13(HDNAwh)7SoCH%FJ^O`1O(o^y)$lJ6&GP)7WJ(1xa zJ4c67ZuVz1fJSvvg^j)LyN1~u03d-nso7XtP68-*@kcPdnOYMVo9n}}%6 zFi|?piX;L|a)p!DS*b-`@#UxzKK12;^sbivMVq!qCc2llys5W(bwpYpK85FoNu3M` zF)_=&il{IC;?N;J^w1F+fp$*YKdNTXD$dCLk24 z3B8hZjZ=XHwilPbxZWg2^$yugJHo=I_?mSSV=a%jdB_|Jd8Y1lbzA-UigR{-iV+C{ z;8G5IRY_l{uj<6Xu9sU%h{t&%I7EGoBB4dEf5%r2F>2=>n>8}&rVZhbL8{%YW3@L1 zo8QniJej-?WhC-1$#+7l8R+wwJ;ZDWCzGz<)^B?pV6m-rjQ@%+Mb*^|>>SbL+$ov5 zL>gyf*buL-odzuxQAPagr$wsBV4B4Y4e5A(ML#{{wP}$(Pj|?f%$e5;-v7kB8_6rW zg?U^Fx$TkTp;`Iu#^qffOALw{Yac0~jdb%$Y*rC|@l-7G$oy62`r=53G9YB%%J z?B2-x{^r_odjcQOVvMDwm&ExMGemsHJ%?tZ6&c+!&XiiMa;`F1W55vYyIzraLN$Hv z#y7Qq$3U=C@nCQf?$n7KJRKNPP*_-KpT2D~m?Pr1}L<5pX9|K>)&wHHw|qs?mkpDV8)u4bi@mlyeTh;vc_e6azFCy=Uy zMSGScX--`bB7Rg;f&%gyE;l*M!Ad`Xk)MX7*mCy_>Qfq(G(zQ&&Vvci)@0gDUdm`Q z4h97z0_y{&m9&BI=Kx!P4H9zp>+0>~ne-fM*u!_&uO6T8?K0N?R4qpEHjA!Zy&C9q zP+PhGWA8r0P~G4Yl>l$%JX6kuy>Vv(EVFcVsl=(int-#hq$7@eQRmrAovOlO2C5u6 z-uEeGwSVN(;Jd|KAb6-`6b#GE@I=2ZZQ6)_mVz+bedzt|)cV5wD)=C+>3pSkx5}aU zh#H`4r5Vb~E?<4bCeGAab8z!5ZY8Umrk&p!xjU^r^$2w^;|_MRdSQCjIo--wqmDDX<#uZ@w%zQF)h&SV#(XFur<08Odw7 zU^~oYFAWA6n+j__KflX&JHLA61>8h+Pcv0l5qf%oSV!ncIfOBQ%Vb5h&Yrnp({F~V z=S0qM%Z~7HNAH6-y=Q3soaSjceLp^!_!p2Zf?GIbSopWX_$gD_vX0YKIoH%w3K9;4ZP(*vk+go;_LP!2F=Rr+bnogR5b6@!a)r!^us&dQDMb_ zdx+2!2L<)f=J}M9QokOUxaIWe(}8dMHKY+_K_c8z(Ngp3DD0(o_(pEWV5W+-tj856a|8ifi|jLRJf#kAC{fVRyG)Z2O92H!@!hmpvZ> z1Qdvakw%t`RLkYn3DZwH;e8i~n~I4aRC7CejC$wJroT)93eL>iz+sNgt>sM_1iaPj zLGzrNZWzh~K!^YiVrUoGfHJm2zj=L$qk%bYS8|ro0#FxFEd&YJLp1{HKy2t38AY)f zc${{X!+!4Kgq6S>${x}&^@Eq(VV(mkYg^F;w^m^cU^l=GK$^6{hTp%eny(Jko5xRS zo*6#**h_=Q_1gVzyY@}`X>IQ`?O%iA)Kyfp6*mRCG=7N)ICtCo;ODo{!fm6gMIFVF0kvYf@)P(5F;iXr zM=uCo-yue|Se#)oLlwkRWVEx9cFF-}Cz|BhVSeT0K~!z7(%I3muyM@yI0nTehWJFk z9_dI-Q1FsVn$83Ef-HrlspK04T6}h<#v44u^23;JN#;wr(M!7QMl;WuK!T(o&xr9S zlS#PrN{^uHIfdg|v<@^HHVnpg&!=_Wamn))2&UP{>O&RbKo|6jIy!&rNy1Nttohg> z_}Pt4BRp3nEBYV?kP*}9Hh=&8(3h4g>XZH8o`sdHE6<5`0GEz1s=#dZdGoTgI1rc> zC;~y47N-Q~F}TT@zBR|>z(-7S`CG@QXBsmi_@ne(-J2S0ErK0}V0R7`PG+jQswRTD<1(?kS&;=n>(ei<*c_afYMb_0voSU4hy-v^P=@` zZj=1~pZIRcTj)8o@h<IE%BbtLk_O1Y!tMRzo(9gNP~9Py(&^Vv znf8-f)r{B3KCLHKI&``1@YiwgaCML5z}UQHi&$@Dyvfm{-GCfq2_+0YcG0};;2eW{ z88JOh8|%m0x8#cvs+Gk-%u%-h6v*n}a3jB`pxU>hn^Mb^4t~Ei7T^$9mjd*j;gD+$ zZMJz)J~loKdC&SO4Oc(@Q6pkKcaKafv6sN%veQ0|C9rP;1LqaEku=Z{14Htz0nwEo zHUlJ==m)+A_a78QR@mWwHzdjV>SV1a8OsGvLAyzw)R(2|ecfkIOD52&wR^KTt#Bj*4MfQ1w@aMY zZZxhFiO8_+Q`9hYCW__DH?@xWZQ4XVA8Y4ZH%-GRFt&Z5MN4}b_)=%#8gBJArd`md z=Lu5+<1l;xRY}vrPVF#YL+}A({nPJ;jDU!LJ~-z0wArAHqLZK!c}r6Yj3sh?4n&T2 zwC4Kv(-#7yHMVK~08IMcTE|#2mT)2{sTnq8jRAtD6K0YH=}Nf&?0|$*;m)vxm6WlZu!4d)6LIS-0Gk`%o#;6ml|yx?UJQ-KF?Jt^Q<>J;C-m z-3?|wJ2&0aH&XLq5wXvJj#7y56UTD8g51kE>g5YIS&dau^k31MC`}Rg2W~CC@gs*d zR?nz;ngS5N%un%soSwiPOZ4}r(}%WYwF0za<^EW_2yR>obzp#my?(*B-$A;X#_b+s zTezAoH;Mu-_*rthKDOUmbPd^_S+K3M*?*4e$~6u%2hATTXTRls;ob7Us+ zRwJG7?f7mL&EthK-N(P)hO9rEa)qosr5M#hy5}8aE}$z=j<>iTJuTLz`2BlJI5i3E z;uoRL-^9A~J49*+&?bH3jjU+|SrYluo5 zP=FsI#Q`{yW^O6LHT5P#qg+XSat0zAWx^pYMONn)mO5qZo%{Fo3Y^0k`-W0c5||0f z@{YZu+iq0h?%ZeB%>h4qcDqa@#K_x!%z`ODe__%goe-)1*x?+c z{eRiwpi9Q_Uw{25!vM`@j`UgM?Cqmnr|XTlH=qHvgqaRr$& z10j1JYzp@?|LaS#d5sq*YQ?BFrP68xw#S3-wcGq@LlxmI%5a$h-RP$dm98re%5F|j?!e(u5$FFu|VYeWIz+L)$e1SEtQmPLn{E;%nF(U8%1 z87w6-VVo|va!KT`U!Q?tP2K6ene-mc~)2PwP~bIv6WxNTKrXIXy^ za*~xr592l7jgf6VoROPc_^+5{%O#PX=o=m$p5m~?<@1;}Us5*If4{$B-<+D=`SvC* z29Y4r=!~$oG#gh%X(h)7&rTA90`e@Q{Vy)dk|^2jAtBZ~v%@&*_!LwMMogA6MxumT z@FjOUHTfg1F-eSi{`~pO>uXN0=rCTde(27V$hGZFHly%D-TW3IcXWAF{=fvHMh%v0 z83iv4s2Y)Bf8>fEx+jLJ)i>z7^2{#t!S*XVaSQKo(zZyC~MVdjC88coHGcfhb81)OJBWe`*6) z^_|`;{KxxIFoxF0C#rKKKcFplcr+8^Ub6)&;M^(`mq%Ukll@99(`;=4=ElV0ZlNCg z08LaMz5&OI8d*Bwm_}kxzHQ-5rDOl;bgaH5s)ql|W(7Yf!Ae~obGR1+U?C=`1lr;t zC4{y0Jn!mh$!7L+WS$Yf`0x0nrz6<1i0WuE?}nSlhz9{-7Dw-ebrtpyu*)8O&@MfY z8QnA|UNNiBRt-IwNJa@Odk;0&oxHp~5Y}5&dZyx&G3@Q^xo%xxGi#itFGpzx4#<|R z#Ce2@qF53ULiGpZCuvQ!Q2YgO_VU`Vp6d$Mbvko&lU97&>Fico;qyeEX$+k1i1;CY ztA6Z~iV5w0xDMX;xOsZQvizUEpRv6UA( zDgGR~6*)hUQuL69arKKqa<)xNY-97>@AXB|1JalM*U>pT_lQkBxMj<@GY8HgIHasM zB}agFKKxP&G>TG};gJxj6)?@>uoi)OSu!OvNkr{i0j(hCu{)f)3swk8{%DRXQS0md z(OexqoM}Za;M0%U7?una(N`ODv@j z`{7O5uz%)U=>xGyA>l@Jq2OxN?Dv5lK&Bl~YWycZJR{R4_=*l#A+f}DKn6~U$-I)R zX)1fQ6*==da!)*JYdCw(oL8SdO%R>Z>Z(w!1nrfPrnsicUR)n6dSr?|!2m%?&J!p{ zj+f|UAPvDbKx=}4IcKK)q5kM(YdU@s5TTqm2zup<^-cc1i4@#GU89Zsq)KAvWL6Nb zC%~1IAd_ES_{=mPbB1@OL6Xv$mYbgB>#FVLL(8Y!`0T39I^`F4{1wHtLqn1eW~`r_ zKN%;F48`GWliGq8@P^X%LEfP5rLFEJRZP=N(1~qtZC=6{tfTsKZ-2dC>#1&0H?pUq zO`DlS_662DOf+A1z@=QzW<$Rod%hn&d{`+yJa}guMV?yP;vBCr&d$!q>Kl~VZVwE6 zRC|5)X&ne(f`8a*e~frN)Dk1m7WaT+!@Z0Ec-t8Pq4jL)-P zBg3GmzPW}(4VRVG3ST-038NN{Z}{^Y?*$*DPHCsTixEdQ^)-}E2lJ#p;Ov29IDpJ! zBYnl>#O7&khw-S18Vdj2d^C9Ud_(AHq_#liBNCGT8^%JIm*3^Msl8+lJV<@}JP5d& zdHE%q2YahqY}yV}uyE$%kU7{wGr1V@6WouRc(?W+ZG!Js;yCy!89B3NzWv0U21AsZ z#b0ePwX)a3%jzb*y#*@W0iQO=oElltbU&G26Hrq|f78Z8E&o*Nc=WD+4kd?Y&F8v@ zTNeNokfEi65S>oHKf|=Rys?crC7ap<1=({Jcy0*OnQYet)?y(rq5_$ULubTK(^Zi` zK3Tc6Cur0S7Hl}Eo^RIFyuQI-ej?l?qPmQBRW4^T;pB2|?f*jpyL|8FwI5IY4&(R z00EK$b;{6Ek;-;&<+Oy?le@L+U)UH=k`?``P>yVJF+@@uuOwnz<~Not(QY4@s9Njp z_qxwy>qq{8#EvUaQyguykFyJ0;|-q%eWdMvYH(@^t%)QD4#PcD-YL&aQ391Lyo0j+QnpSLL=vfJ)=3aH9Y+`%)d9(u1Ot(`nvUYf-9Lb5Gj-^zq4 z|Is0F{TF-67+DN*3St64b4)ec{K)nJSv`W3oX+2dT4ox!FK4BU)57c|D~#?wod+W) z_(gSi9i&Cx%gU}*RaBS&uJD?jolm6PRZh@6`5_}E#bna%B`7PubhtH{!8yv`EL&Io zkiC2xtu;%L%FA`~yd+9QsUpy1G70IWS69Ex4`#782BfF_9}0+-#MbxF$>j|~8%$s0 z71(lSfO5NbG7|+x<0kWWfXx>Du%+V3w;ul=TKe|%OE4L3YdxKk!hzZqGw4_7$hw2> zAEzAwJZ)zjnD+Jjv&G!HOuMrD#VwJBi$<~P`-L%EuarGKcQobXgB%;0Eef)9DhHA3 z$B}xob5Lc@=D69XANI@dS+|TuM`}wzhPZM&r1ZD^SMCfMVd@ z0lB4-d-s<775g-2qk)nK@EzY}Ton%oCfNDRvE1vx+XH82JCE-q2A^Z%Vp?vrc>Q9x18{pE%T z5VOb6De1@NpN_+gSeiaGFCDi`)f6$;q{KpwC=N$a%uwDKw(}yaDQNWD`UdnzB2&W} zC@HZFqVEe2p~0vvY9fWWMkh$`s;H%$tNheOfn*f|Y#uRZoBj}=XbbxD!xW146=ui- z`X?@fmIC0!Z6a=08aqiN(^{BQP%x)y8WT#ld37zlo>BWaetjpY7J`TV-iIRsv(GI_ z*FiWYz&S@i$?Mm<Mp>ym_Dw6HSg~IZDkEQ66iMXuW zTsp#~W_P$Qjo**xetKh`a+7mVX1F)Zyc}oCV5(O4PB||n`khM?!%7dJL_hcI1GXT# z)9ietJ6)?x%v%%m~x*MgD`C4ilh1 z3Swm0D+5TPwrPkl7hpv~RY0#jYaYWIl~p(AKtfHZ0pt#RzSz2M(A{RXI_s%S9&m$t z{H*FJ+6{PBcu)Vo;+&tnF@xnvWZhBeqU6}_90p@}n;)M*!KT+b&}p@L9gvY{WiJ-$ zDiVQ={Rd;@GCrCOur*U-=ZKsRJ{0Y&Q9t5i4e#WcN8e*#l&=3{7{7-bMdmf*4cf)+ z1Jt;FxkLBdq4VEt{5-c8S(XGFHmiW2_{B zHC&cwQ8tJmmIpU#=AOV!FDKZIYc%0mvv2#gCJNp_=Pntvf)(*@65O&qJE^DT1T#EFSFLuia;SP*Oyt!XYmMp3dD&e1{zK#o6F)h4oI%j*63IPDs`b-W9&c{W z0#IYWf!31qOB?q(?nEbb^$Cnb`aXj9j_Rv4{{_yDuaLCpuk=6v$vH|w2`Y;D%Tc-2 zp<6o3s4xgjKNS4OD%?FxX1D%CmW+vQ%w%1qpM7M8A8Ij4yd;bC?%A9CBDDx_UkYZ60O${h=l%W*N$@#X%++f-Msg_U*rP%K?As7AI6shm{`WPLD$N$IF{?p z-K!1?E`5=p?=rn0d)&x%Uk<-}RfM!HEA_+voue*%$YAQ9?Ib`KF+yd&Sr<_} zIBdN*hc}z9-GD}p;zNmj&2O=SD9kA8Zn^C}y64>~ z7(Khmsr}EtIX2P%f5L9RJj3hR%)jK09!pOmlP3z%AzWJOS$KQmG3LrWdD4ysjU>GB zm=q*PTxxO-FbQ0uT(n`*mL#|6^qJQ^;C5ErllNAC`ka0*&T`0)b|=5PX1+|c~PGfbd3yl^8vg6NR7h`s4hfR+-IrX36}NA6*{O!{FfG zfX|1`#$>f0V|F&cXH>w&CIOe#+CVrMef|TXz+V;rK5K%KAR6gV>Re;33t4a24aV=2 z%-lNEBE#GGxTJ{ZiaREqmJU#dSZb-$I_VDzQS=PGK3zUaP!PsemY&}9Q9k5YG7s$Vb##s31o!7V*a=%A3t2*>M#&5W=^C zs0Jx#HL9GMoo_8N-JK8qb78>0b?IY!)fnWHh);HqgNw^uvXv-B4ZnMaX|-AV@oBx7 zYh8%a?SHFMX=M;P89-j=OV9KR-iz^q%NPnGlhHSV)dt>~OB5*R)h%WSP;EJ_=uRPC z4{}k^>i8Y^^mFw3gLF!M1;bOMY~pHDctpw=VxhFvs`z;*NO6zcCO8KeMd%o~Vgihn zThFyQQVswMw7)R_cSp}d234N@t47WYuDkoZa93fI!l@f7-6)Xj6y@HmUwq9x?7u@7?Q_TbH`w z)eXWciE!GVlJ0?CB5G^HEXJXfeG@!ZBFxFwl3+dfSa*H!8%`891iE9pRLm~nCy6)H%Ceeb+)Q154n!H zlOPZ!Zjgv4Y*_o^>yFLSKBV@;!lq|aiNmSU`@iG2)#g0I4>&O_=ph-y)ejo_Fe@Kj zbgx_8>Rx{?*fhXn4s8PKb{s-UP{f72d3tM(+y}rRfc7onVVBowNV7de0%GHYM>IYvFAYrYpB`__6vgoD@TAjO-0Zp%^;{Wgr4RRC6=Kmo~LIr+Xp%A~=tN z&C#jUgcAVVDTf=M-aT>G*rUZ@X~yqk&P+|qnAlmpYu7)Poxfqn=xw{5b=v2-?N`6o zUlbhIw|W=^g24E{Af{GvavkgOsA`pINfr&3T9tkYC#wI^s?)mgF0_sym%p_A;}$WyJ37+&69}EycY4%=xX6k znv+~LfBs?RVL-$k*#uL2O-oi^;^dekTX5sj_DhmJ(Z-%fw}_uoGzP0s4I(;*I;nE%5FdyW68jJEn_?r^ZG?gGG9{Wz`P znOYCJ$E@6X^5n_FqFA3L)6HHHDGETvB8pNx8~f_+=Ps@;F5EB8P;=kca<@yj!lxn; z)SPOM5S&wV>8f^W)VQ%5h?FY$mP~WLrBpkbeRKIv^khT-;v>{le!`%1v5_c|18nr%b z=Q4V;RNV4ND(nweZo7EmB%8TWvN2m|q5_-s?%^LBEZ#+gy`1s(!3Bb;Wr7qt87S!u zREoWw<=eVjAnv5*E$#Z%+wy+^j`te?90J~C0M)yKEAiirE^JYW|9V+-f(m#iq%nMgt#}Q7KXCqxbkiO6a08jmC~WmBU7sUNOWQot;(Xd{lu60!yV8!eo4AYyHn_R3p{MJ(pY{EDln}Zysev>jl))Vg9nBBU zP*a_n7GoK!MK6PpS6NkcEHhFse;x#e--FykEKf=;r$jAat3?L^&BY9^*TcwEbe8kV zQzoBZe0`)N+ZXxTD4AaJm1OHA%X8*>WlKAU@lurBtPAHI| zA;X3>AD6)rwA$v2)IqvUTlYp2=n?%pIP*;#*{Jks>KjR#6_AYrt+Lt3ym?GV-kpNX zjaE)^%UJY2XyHgD6JKKd(xOm}Q@~rY3W4xVY@3W-{MyCG-s zQGPX`zUDs@Wr{)NKvbqiW>%IJTfi@(3iDzTc6~J6!a08R;G1t<9CYcw=|UfoE`d#M zB9)N~DMi4wGIT&f$tlJRj)_~A&RPPjDWWgHYp^iIx52kFAp@3kn%`UxZ-g;iCdE=x z-DmPj=m>#gHV1YeM!5>m@CKL{8JIOn4UOubp&r9dz&euphrave=g&JiL4kZos4=+Z zexa??v2pM3Tjqsy?(Z^0g5)^NxycX2C;wF3D9S6N&9QG-PgGm>j*gr8s;+N=j!JlQH#B*HwBR=hJSenxy3nOm?1 z1*bXZhG=z&zoNoW+pnrsxlD4{kPpx#rxp|pE7*cKQNPXqiNN~`yM!E&NRy6!U6yyt z*}Q-V2L`6U0ZJ!vKoUh|Pa9!1`p9GfQJ8kj zyAqt#;+M`~w>(d}ht8)VrV$4&l#lz#LPrggOjBSF#A-}J)b0UeS5<1p<^Ybp`@ z+(#Wdr!15tZg!zH!=70x1RrE5&yAX+T@x*>t6snnut`y$V=p<&-R`9xX4MPUXc3)L z$;y{EeXR;cB5W=(>As)@-raYq^^uD?f2N5i1xlLOt+|)aO^K=?O;~U_RK=3=$RM$d zwT{8sg_fPVMX2KsBe<{G#v6dNRHTfjx{K(gfA4qW8y#Rn8Q|H#hb!ZN09Qe=DgTBL zwd(w(f($TG&+xU5wU1-)s|>sLef;k3#Xn)T({og7V%&{Y1MjM6qkb7OJ^4X>RNX@` z`t5l&;v7&=YRXUKXoGd{wg2s|42gKZ`W%)GQ2s4sX8}~7NXiWUsUFoV8_jl1;H+61 z5&c8`BFVkduwTwb4Zaqv*Z)cpZv-g;JA_O9echzRd-`7=yyVjC5qs`r2H&1yl#pYw zsg!4z=$7ZfsuxQ&PtJw3cR8x~x?a^!W^e-T$95u7?gA>j&x?;QJwr&Xniz&e4Z%08 zdTR>2cyva;U0b(GAcBG zW$n4mgg{E%-HX^MJL5BE=OvqZTX`T6GDBjXnifSR>Wb5&`<((FStWr2Aa3Kw#WZcEXup% zSN%39%;!222s9IaC5quoq0R^|(Z7&dyU$H|3Q1eT&HF0rVXz|%i_^O$FQeR>DVpS3 zLX7t2!Y!p8d{{GxT&*0{9l7(`hVGLymyqg=n&g=J0Z=oSA~_5L5RNxp5D8!d5vwv$ z7kE5ts%g8nZM#ixhUk;H%P{?JJss@rWy%MD9KWnsD`L%?h>Q0w9WFS3DtP4aO8U*Nl_{D0fZ9HTQ-K9wq5C@>8+2rq;G(Rd2)TMQg=?1Ap^}zLu}&6oK(x`&|hZW z$vgw-Ng9pDg49T6c3$m6N%cpfCBatUP&_h1-0luvo&`39JQj?-@>0hloJMK)%vWAE z{2{JY{=bTj`_#86)KgHfFg2Nq(so(IvgSFx)}rYV>l4&xXxyA}%PJ}>+n(9hXa`Z5 z)acs7hj+%-1d}vf!#dKYUiZNT|FG0JyxZJprDmvoKfDO~Sm7`vfMllfv%m>Wsj$=6 zqoS}*u3b+%|B~Ac8{1cAB$n01a|FO0sy?UpnZ26%aC#jy8K7TxCfnIC zv&o1Jzil(eI}Td(cLutu(TjNUyD9dV`V=(i&jg}!EfJFIPWwkiR2g(u-9aZ?KnNYC zW}-7dGjR(U;pW=I7@C-kcn(c0|64^>m5TMDS=>Bed7(4=#XMO|AWm>2`{g|mqM|HV z_3>$InnMY@@)|7S0kaMFlsV#Eo?VRlK9{Kr>XKkf)h~b$z>!$5h3i8V!DyY>w&w2f zG}(FX#S!+M0f1V@T=rdj>DtW_^-4?Fb0icrRj(6vSWTR$>iuqp(WoL)$Q9(&blucA z=9+B;^B7N0KY3-o&D&4&$*)b_v4;AYhugQP`GNnBsrL@#djH@5(NUs784VSZQp#vZ zn<9#q5~ppnODLtKL5Y%RXfJ7=3Z14XnGK>Tl}Jj9$TiMS%3Vm{sW(Tzt^2-W+rZ5U7=(iY;#m+JW^PTgC;QJT;1G6p=XZ% zjSF|5sY>doCxUn(PE?ul`HI#&n_9lx1;ZD>y~9<;TGZ>9Pl>a()^~D*_Y~H2*CqS2iE4K@yrNRSS`5WRCzEUIRQ$GK`^|*#@ZyhmDiUt^ z1pi@wQT?U<)hRnBBo5nQlWQ<(l=1X)6^dG_C+v4jcp2&}zt^gdx`AQpEC2Vko#!lG z9_18oe&oxb_n(bl-T!+3Yxc`k?_dA;krX%V&w)E%Z*e$Zy7ORiW$2(SUbANHIICh7 zuwIN{8Bj57*#7t5f3LXk`Q`5TvtxEC$8ZA?r4^YG7dEm=q~gli`=4A#w8TbQtay%4 zC@Uu=2P(zSjYCIk*Wp(D@UPpMa#$>j_gk`r!4kN)?JvhMlu7R9x%cj=9UL=foYldk zgL`tEm`%guF*%=>c-7Zy*+O_qCom_WNt4`5c`WmF$RJ`9M&=P#gt(>1)ETjGbl(Nq z*yeeGPZs~_eD+C-Mi&fFjV>|DcGh3T@Gtk9a?BP3?t%1!H zQPt!224ySXeDhu8Sgvd^TQng8t^V<9d9}-F)9tOt9vt8mKi8m7A5Hc$eQO~6_q#rA z{`~In1r+PG-?_QER@`pVN$40jd0T@m?JzXX+0m*dSfi}7AB?FThFM1rMk+tOwk^VC zoY|&CJ$wS{R<|ErYDpE}8S+5!%VaokqTS&U!ado{k7$=V;LMZxMPFy0xp(r$je!cX zl^TkXRj6H5Mt7pl9ne&V$gKk=DY}Jke&?~fyp~Gi6w8w$4UMzc=VKPvE$LBJ*cF2H zu?zO)8^C?C;8rP)n0jmo-wq(h(BZCnhxxN+>A!f7q~s!fvyW*j{uCe|>v^sJa%sQK z@!3fHGJdRdPFvzO%iCM_%a>svhy&{^0Bks6Q~RHv_xFot2E78N<^q;3tnImL)UKlC z370QxAJfd&A5-*2Mjj-67A*(yTv-^a^oC^OzW;!oqTR@mVby)~Mt8Lf-YR?hii#9t zwgrHLH1qt`@m>9>L+^Vrw_M-(!XO<$iypa$6;tL_Imr%CWQ*%ws@c(ZU^HYxd)=M14_C6P$EW&2wN zp8r2T4blzoIQTBYg;RuHisW~J*nLD?){DwF{64bNnpCQB)~|M<0EK1n=S#=;hJSu? z5~mB=Y__0U!Q0M!mT9sMy467v5&y64!tkwz4#f0U>lhlgG4ThxDpHC;4hd;V&HqCC zfTP1^+=|`4`eoU)IllKep@RCcWKC!|_f~cP)q96>4>H38v)jGYhE`r5*R!+fLOh%d zGR`}KBawO<$>M9Dm&n}hBZKUYo3|TVitVOBBmFOb-gT@x(kYj{5#hLOE~$z-`@Bi> zc7NQ${K@S|xElAF#CHHhRZ3{U%o#IlYr5)(ZT9V9(EYzfrh0w*_I2J-2*!d=L>ff< zd&VF(A`1NbPbY9gX=;J5V`5_`(4cMfYnAs$A$}<`a6s5WRxqA&`J8&tdrNfm6pDGU z1{i}Q!!iiO>*m#NhID|IJSrP^swjuo^Q#$Vbax2W$q@JobBg z!b~a=p#dKaEl1HMfA*5ykavX?#^R5MbKJ~17L2$@;<_^r2@~{TxVE?t_P*cAIMIzr-KO*4H8m5jnqx8Gd}%Ka z_kQjwkd2qO>T3CW${nxNcn{a^>_*NU(yeZF_@F%oYAIBv%~az{zOdw>*^5;0D$SSc z_lLX;FO5B7)u+!+GP6R(>s94ibK}zZ7V9q=lzk4}|Mu-$+x0%gG_}vsn!eebJQ4c= zI3aCFEW0y)-2eVsv4{l~(S{DfvgRWQ_Y>h*7NfB=557im;e{~D*1Y7vKvYJj00np* zAUuZ!b;w+?v8UJ!g6)ssIi@|hJat;-*i#n9%zSA^Ga%#;_yvG_U$AO3;@H9^j7i!i z-C^1QG;AY)y8JlI`CL7TXM; zE+1G)+4N6RsCLg$l*{bufkLW5jR5w+`01XR&%y-@M$7J|K8jo6-cV3_t-n5xj#XqH z?ER2P7;Zx_6GH=BSOm-FS+&JVQ#5nOwCYBA2gI3QvB@JkiWz`HQr*8(;y#>=uw=EE z4L>7o`1GJ`5ns!L{C>|g*)xC4zLftKLF`!3a_rcgg&yM^@j%~-fmdsU>7b8-u;# z{;JsQg2+r$8Aep01pXsWJI+%Rl+hY!h`#sxrtPWy#nY2$ORO4)juNa!wk(RCA7D_n zSMVQE5fQgYy+FC9%n%jCirQ%VoJrlaWMIYgS2PdYHwu(A-blhS*PR86^Q-SYecJDl ziovypKHAT^WQ-ZI>R-n}UBkRJ*SkIy6{)@7w5Zy?4w0^x=4v1=je+KhEGqp-AyaxY zLeXus@SJwfn*CWmsg-rn(pk%V3kp%wqponw0bKZ^_`MW5hTW^$;b9Knws|EoHr>e?r z`BCWRgQqY+5vYyPKU%66n($n(9#G&QOmOd_Ws!yrDS)+S&BNtplmF-L|2$do=O6B} zG>jTybye<2v~|rLB7c57d*Lel7C4z{V8=rDJUFbUifrwZeP+P+`6#zU-$|y6rIN*O zYntQz*+%u4x&0iO(F;HPD6*bX1>40#t_CM!uQrNqR;(z1QO$o&!Hfa!j-nvdcE?jd zlBN9u!SsH-t>?h?(Tv0+X5TyHJi4yRKGN_HcdWXyAFVR}^?gT=r|*(IX;2zVk;`!k~Ua;TJU#T6fli9Xazq5y3Y11 zvKAkUE8b0xfC7rm6Z9n@q^OfWFd^{bnJ{X8%RcF@hgZ8_D!jWDc!+eWSNsAjYKm<0 zq{JJMco7EdvJWi!i8Q3TOKj%xox1cP1O=!q%EREJxZPa&w|Y3(Pui*85uXz$vmo+# zMz_F`FeC5pQHl`nW$8X;duX#BmwX6=zjWtcH(SNN-@ zf(ytzO97l~*{0;lr^cTI`P0yyvY3A(N zMh9}Mf*y?d7iUxig)$C;!lPruc(aC9`1{xr|fRnH63Aa9Xd8q@4t7@TWYbl93< z3v>5bAiwyem_EwjmU6R`KgPh10`yXUj}83!YHjI-o)r$PezL+ z$XrOpVZpH~34NZbvm#Ol6g+<7mscA)~2l@2q&PhjT@ zHl|eb%f+Dg-o4TNpO^I}!f=TEcP2-)UT*!5lu_nO6rna~lXp{>t)e0!AswkoAKEJhJi zs@{TxdD`W#eb4(17_g5)q@F`7B7By=MqzroU8>9M$xFotg!6#F$|2m~K9ZT0C(jY* zSWS>FXhdK`y;NjqTu2f9sTkz*aR!ZYY=ICDNd5BZ=6HrBd|I}(3{$Zb)jJ?A@TS$F z_OdjG`T=yk^6%|ev>bA5`t9#6B^^C%*wAMc_2p+T+ zZOvid!V@z!0T^N11gouZX$f3o?tL0ro&2g8`{wVT?`3li4s09WSLwu3oDB!8cEvUa zys2l!+W<&iR9jk>b1Gx@l40?qIJUw0FbmxTg-Q(guA-tc6aRJPwFj!LUB=I)=jlzI zD$1L!?JdLkN4$(bue9#I{|>|Ju@ip8bsgOXYmUMtMdq zc*?9BbMiQ5>VgcKoIA0+*S)Rx-4mD+sl?dkL)jhhsOb}iao;~a=>d{Vhk7P!jCR28(h|5f+r)&b$zZ}p#Lskrq9J9@LFG zP^uDIc%ux3O!F>8Y=bUcl$jxsnJrTHe3()_Ix>|Wi9j;(uRot0aoUjs&1(+Dv5~ej zuXoYKidpktPIFOPWC0pq!UYpU#EHBA1-1hG_JbG-FK|w73v^pgV(RW{?5?qoOeH>n zwjX!gYw?S%gVh~-&RdwAZuZZw9wGkF4?M3|bQx;~1i1f_>#n`(#~J#9%5DeZtpvVN z>w1I~4sB3T==tByFxAMwgi<9jVYqgh=Vo(_EphLirbYMuXZ4P>@cdaOv+bv^#_gpK zguddbXvT0Cnmu>8-j|wxtEx%}Ob3Rnc`;M}1ynu?i#8@NqF2`MT{$G#_;ty$lA@v= ziyHJybv-wqj(P??bZZ*(#bEF^zN;sUWg`4H46Vsz3*vhm{yd#93j_ZIHXj1#q8yLNuHvj24MqF>1R}T9#dEeU4?R9lcicJdE-P;`nzA#{tt(>BJ2|IoAORWnXZxQttDyfQ;g(ZSND+PP zgA?{;P$+0FhXe3ns2w`@$D`xrb}lRh<6;hxXM=)z;LwdwI!8Vu%Dm)LwJd-^|~ctr6SP0{QXT;E`B7i18W6okG;`>lbO-M~KsB z>zsRSZfqWJzo4MRpOVj}Dxv$XyGOjwGv*-#$PH#1mU{&rLNq)U_y3?;Tc6JZ%M&}F zuckS$piPhgz;S z^P^d`?v%pu9ft1xs5krLpy6YJvSMe>UsV%(z~p=FkRdqd!aPBX&`2-T=F#yP=k{q; zTeWomcpyII6Xx?y8f%SFH1X?KkAOv;$JQxpf?xS`?k;4D<0D6kAH+$MHURwtW(6X#J%%B~fFWExt=@ zM=pX8*woVUileeptU=hF3C_+$GO^NPU>Buqk*}}jR&qD_3(*zRw177D4e~FGQAv%i6(kM)!essy^jcsCjZXyf5 zdH=qvuI8KaU>BZ^a9;|hP48gZ;3Q66Jbp$WjG0r`G^_ub!}pPmx~%^^MwIM>hz5`; zRFFA94J>9rvqX4LtvYH%R<~aX)!-rUaAK*%Y_@t+aR(*vjP8Dm7D^J+JY{>of9}pd z)~A2}Xo0LbdBV%3sx5o^S9{t7PbOr-K$N*R5oiKnCBiXQ6(}pkBSz+pRMzFRWZ>wuq1chV5SMguB-~X3O=;Ro3T#o5+q}kiA_e zMkpU2l^A2Q)&NFL`id+fE<+4^@HMAiy4_X!wA1Wr0n_E?{wODgB z$RwqOiVDq@?I6>JA>@XfqTGh%5y2CE3rt2vpywT{cVE9|Ly5sh$y_#UBc249=(qS~x0>!AIQ>ri zH4r=zSWH7xbEj!oEs&?(i3u#TG^v??ZSGsdB1Xwl$uZ+kV;6j?O^?JinLm^fjnj69 zEf|qJ?a|ymDT-ryy@9VA8QmjF*{R01g>N6@m~D?LYgg7tlztlHv{iE@SxblLgBkwt|Yjg`y^mvBmyZb2#uy5}$^BB$V7_m^H(uhgORGUh*@ z>o8!O-EhLyDZcJ{m!$*o73TtBO~3y8gH`1>N1_tO?$3h*7x1UTw}1cD&ei}A$$5C* zI7UCLop0+K2lnl|i?M*rc&L$V+W4{@Id0qM=q_qQOZcnfV+Q2(MiHeeU%*jtywV(2 z5sw@CuQRp=Nxv#b(^uI7=XPVwU+mjD+F1FG$jHd&;*3xrq%Ov^En>7f;YAq^&x3_5 z+Z}6l12jq@yEYE~qw@Xv@y9of&t3PqmB*PLi^lBj?_5Ln>z*$Oh zRT@z=%hW319?eSaOQO4XS=`gFWYsb^z0xmVn$J}4SxrX$R!d57RO#Y)l(a<0D@*Ch zm7CH>9qT_nIoi&Dch;GRY~~cy2f`~vv)|$taMret9#jBJmhn9rQ~q%ju`@{{BwO_V z=C#?-0b?p(#x7#wbnxK8{#V{9jdlAR)q4GNLYsobhV@rl8<(_%bm^Xb>)Eqsr+~%% z+eN)^;d#=wy2g*S220-V$Z5+Nv$t2_OZK=m_FK8l&|Vh3*GB`?&z(~h)~)1y-uA%# zedcX|fD>s*SiB81+6Mhmd%vv{m`&UEI(uxYLnLd$N8q$d{r$`+Xivr`A&UAJ&{BZ| zuLERVZ#JRm0WLH-48Ij)w668|FN;bd)e+_(e4}?5$x7L*<~I*H^(5jR$aXc? zL4AFH!}e;*(AGdW5tpCmQf`r$X&|%X%qIA&_oivP;e!*k@HzPn>cYlxOE0v>-&T8E ziL#dc{t{i5uahVtwS+OaDfw8papOi&2J`yyOK?$XrD+j4rHMIv6cDtDy(*wBE2O5R z4m;G}$EGTyQfcF$(~CSjI(N@;+SaqKD<*ez{?e*UO@yYI?2w6T$heL^xQ z2w9-jGh$8I(gENNEn~Lb%o{l$Q;M)pPT_tJ!lCD3= zUHrcshhzZ43R^TUuT@}RL$j~l=+}!9a)!KRA~73fP5$*)Y}lB9mXtc6dFtcJW8rbp zIu4N-;lwM$%CyDx5Ql{Zc! zq;1!}eJ8zKHE`fS*&mX=(N~c@8qN6khD91;(yuplo1-lhf@dXHZrn^-6>U=baQW`6 zUMrjB)nw7bIN$JJMCcX{2V8HRz&(ZzA(v!DC&-$(a;&bd1lnL?l@GxQCPTjJQ1~Pj z-@X;mP#&P{mj2<>)1IJ2wvJvDZEOu`b*zcA`&o_?2~bNZuH}%QdIitACAyWwm53y5 zsokaDdl#(l?^1L>5H-YL+K@RV#XEVuL!4)wPbp+*;8T8Z*Ty!#zW5rCTrb*qNL58k z3jSU&nYTkvDjPs%)Ca1x-tk@`eqZ4l^zyix5YiH##( z5?v?_5K`Fu^ex^0ziT{2t}%Cww2Vwfd1gvbQ(C7@;xx*P2%Wr|>}ltS&&VihW1BQS z;lHg_^z@*|(Pp!h(8OW*(%x+Fqx!??)GPpDO-`Ra4cYc?$*4F2$`KMi%7{&8o{3c# z2aq>1GjE&UY(yI>KG6(vS+~%tbvqzZOfS|`5qhv^g3n5cdw={tXB;t#qI~5QK>pnI z?s8?yw8hHqV69!ivYBP6d^@^fgUNM^6M1#}y_0U!QA4nl(RI`hNMIvuz5N%1LpIgv z`j<-&Bb_e4!9Hc9C7Izgk4k~2mG^+>M@@VJq?14McB&>GpEkr`L19Q4zt=>a-`wC2 zEH&n!3TN6V^^O7@JIEQ0HEDE!3pK0{PS5_ch|#11AC<&;m7_$6VJ|>DLzeC zsmY1}iSiL2cFW(^)C{_IEg)8{i?3lhr_<>=?bg*>#1lLs-iz^*#dRnGXE!ddl48oPx<^ut{SH#tuOxT;-x6V!K`Qth0 z6+~(cf`?;RbLc{y(>uHAhus1=>K*c6YtvNE_2Ur!v^m@3mlL-I`a51+Ok_p(A89BS zQ6suZ3uR!SgfGQ{$sW}MVDRYy4HbsZ=ye@9qje4<**&Cw45@7$J^V@Rtn6!z46!MG zXMFo&Z{~9-qK`~C9TWlu>n%z)+6n$D?3^tGoY}@Ba01nb&m>|2?3ErvEXi53dbK!h zc;s+wRpQ1jS#t5`fxYIeI>O&=3larI5+hJF_ zYH8i4F&#g}ny!n=*$WwF{0g&`Rdb_8Y-;dgiGY3UfUGqMaVl;BV;mZ$PfH5yXEZU= z!$|C$Vf6oT3#?bw(eO}*%9qYOoZRYpWELh-ifjW~)mX#bS_1{Z(|(RV`}UMu?_7~} zrAhuxe`fTdbI1NSHehft^xguu>aW~C?xcIu?ly!a%wVL7lD@FdlZulO#Tlm1RJ;T zuJn`XXRm(O@(YEseyOQhUH5?=nNbg6tysOzp%0XOMeOH{%bRRAYT&w12+=x`8iqh10?zwwp-tF7^cM4$>P}4J)xb3Mpg<~QE zAn{A6t#!uOkbA%Luc(5+gX}L+n3p@LWv3y1s+W}oPorC5)Vswt_*_jV=Ra1Ft;I!> z=uB~y&YZ#xgJ*gF97~z^3GZ^UP4P2p|7R{t$nXp_=0fY}6pm zVuA9&;0+!?W7uPdQrL?TB2ofKY8qPas<-IY?SnrLh-MM;kSh-<%zcXUd1I^jb)unH zO1&SE14H=v)Qlz1Q0rEWt*INcI3nX%&0%Nl!|iHKKvx>&9ZQ=q1r>DZlCZd-jbQ!NLTk#eivU6N0}Tzs=Rm8sgZPgOq-SlRaag-GM> z!z=d|?N4&GpZ?+L_QuXX+t>L|jWisuax8FY=Rj|mE}Q}+cHeZvzVP{FWk}wEMSji^ z5r--&Dw=(ZrWkamB2ZvkW}lI^##FHGUmY8MmV9@NG~D=}l!iT4W924p+brK1^hdUa z_vUCiui%ItwC}X;`Z3?Ds&K@!R$0q4-~BG`-r`~OU7oa1cZ+`wRjB?G#8I$j=|v3kEVdm zto-IZChEW5b;YYh^97plwJ||$FT1aCMc8I{`e${u4_MSQ-$9kzE&+$|z+~3UWmCmU zAnJ2{L-R~aJF?zg7rid*>RYDwz(;>@ylpk$n_rih$T;MS;H}bf&oKR zQ!qMJeS4lu8I}o~>`#0aMK;6;oX#Fm-Y&c{HhD>8YjT20j8o{oKaI~-6wa*wRZaGk zRe*^7;$KZa6g{@nv?wj2gu=wr6S@=xtH4=6PQm`T6I9l+s9ea5ltr=~C@;u$En5My zxwZ7lCrDaC4{v*!slJVR#6g8IXP;DJ;34=vY}haat1w?=16aP@9^;svv;X;=-x`hs zYs5mbv%;B&72cWz+hYE$!VOabj=E|qeEiX=^4izpV)r-h5E5m@Ed8T9j!S~li;S2K z5e4}q+F2uQOJS7cV}2g=FFa*PTy5IEfx6^bkY6_0uny^k*!7x0XU?|b}8-Ge)o7^igO>I z+HpI2V7G26-~@bPUb0f2_BAgNOm^O?ZT;(+c>7U#QE*DPap&A;|7P=)3b3_7*W29C zK|CfzX1=_9IyOsANy{mdthiwG3-E9E4r*!$e=S&VYWNxwsJt(F7?F)52lcHw1bX7$ zOh3bJ_zT_rgDwD=2D_Z?0&52Xm3XbvJ`mnw=@*T*Xo;p0V4BdzLiM*FK7<%LK;JC> z@ByWfEt(fGUSI;OFz@ZwbLsml+t~L~AHSkDce^o6AH4fo^C~$Yc6djn)=dOq`2cjt z;h;#MSWms_v~u&>ZFYCyYG1r`sf4n~W*vq~+0A+tsRn`q1rM))X((>yNJ}LXa-w^a zy~pcfdD2&-P*+E%>(?iAoAat_Oj$W0i%S(m+DBD!2O}|>KTZ4bVdiisss4Ft^g}Wz z%FZYQ9xIk*yago&?-LCQk4O8q+!YyV@Uq?a0kPvUhvHy3j0rEp%A?e@4J&s-hg_|ToW$HyJaPIj(GwC|<#7hS7{4;Q1J4IzKk zmG1Z6YPTm#L$%#DqkD4`WFmsTQ+SJXbkw`=VWtZy-irMVrl@8$A)JSx=%Pt9>UqQp zJ57_Ith$iITV2Brm)spwUCn(gQW}pz+{PcwPK|JdaxX3*qjFwA2gj1Rq^svDzu(+x zt;oEj2_cQGt^HzI=;dl5!ZG>s+2i`OP04No_GufA~!SzwM3- zSWE8?1I7*X7Tzq(J~0(05Ad^E`{=8BYX_`MfZn7xLgwRBNNSn++d`YM&|s}w9~HWFkoMXR=hHc7qL`y?ZN<2CEkaE&nHg_tC;cqXg;>NSZT)YG zsq587UUrz}J;&#Y&;6F;Wb<%GA#I&1$ol!|H}8_H)I2*`2{UupzEgcn4SG66ju_!p zqjHn_N@VDFn==50t0Bl&eS8u| zgLf3NFP4mS))M)JA_VmaUww;ZO%mAL19C~R&$5_93vP>akIV-(M2pip{-tckCbBG9 zy3`ZD1~)s)_HUv33IA^L`2*I8)0p+(LIz{#J?$(yNbo!n#!~CNr6Dr=*wJ%q)%n5k znIk(6yq7G6g)M}~xGPt>nwgo6_PILfUqM(WT&-v+wza^dN+BPA{3Qu zScH=^@FzYvLH6fh`!T^b zJWYI}6lZA-=0|7CV7{RW(_rmg_6UBT_|eXyJJPj8_> zgVSVldBG8#b{DW`3AJM9Gj|fC{bne^|BC85dG|AH%~beYHej6uR5^=|$CrDqj{xG# z-I4>53ddpxiic}ZwJ3OH4F~3hL8Juj5AT0kig_C}7W|}T=IuVK??47pVjoQ~)Kr_Y z*D54y%}C;|=w#buB487LbYYs^TVA*(1Fq{&RtF2G7E=BZSZp8;s@w@WS~e34CIfz+ zKt((#Diw>B|POm0rw5J zcLhe=ce1~SRbJYiJ9qR_#`tGFS?;p&lKH`@6%jz3ln(l#193Yg%*m*P^7b1%5FQF8 zAY!))%&5fa6e?3SOoBW|7<7GQl&na<1Xqz9&p;L8*chU%m!gdOJ!b|g? zgz_=SmK`VtZ4bPH;UdmEqN*leVQ%?9>%N1ITqA7Tyj%`(A?l#77hRmFT4)h5qMZP| zMb$;4*1EVJ0m`5$8E>m#oQvb4g8r%83 zO+i5sJ9lDe&_!0(g0N#zx{<+aqj}$Z?3Ln)<_d7NlmnCBF)U@jt$$0u<;yMmPlm>u zMJFtI5Lxp~c*tNijpi-BOex`ky28Lm@3%$2oIJv4_@UN~n?ax6l!o{=P zsgp}mYb=t1MfmOeE~_z3UlhM?sA9V%g-H+CaO_tm?tiDc9m#nPs`VU2c8>n;nO+ zM#{GTErjN2%OvMS!hP!IYpbg*Xo=B=!47|G(s_y3$@U(-W>N~BAmGX@0k0}jv(T)W zf%qhi>{_=6P3q2&T{20h;?7SJ@&KY@(VvoE81%c@IeuI^K*JOr^1O{rpK?$EZ`eH8 zn(+5v;-FL8N%vF`$7sG%)6(LWmQ9E|HC~-WrQx%)Q`9FY^1=`TGpAMy>VJ;7XcPU{ zSdMmEqJ;ts7s5r?oQxw8ND8%J@hGVp&7Keo*_UNF>v)gk09h+3j8W=*Bhv{`T@R>g z^M`qevJ#qZP%=2DC+N7f3lwIBdE+eutl_??jkJj0=vot!1p`rCM`ttA%MVlhY>-%S zfZmI*D}pJM^ASMC-6WNBnR(6lSdK40SE8yZ-K)G z|MKw(ry~rr^uh-XCOZiqSe$v-$c+8GBLxdGj^UAp1W~Se5fdqTgDBvPnjUS>di;Q=+Zj0N8v&-2#}Y(lCx8ZxBgpQrYq=Mo5+H%1?oz#0d7uXUGf`Z=fjph2K@6sI^cSa3J`&a8k?|GF!^7d|&r zpoKRm9m(Fg$ zEcs!j48Ewj!bbJ0n=4yeDqz_AutE+VM>FjU=qDEDealiSh+W;nx!aV>Cj4B3KjXQE zb~;abPs!YK_G=dh1J==+^qZX~CI^s7Ncs8KM?}}q8P9ESjBnLn5uejNa5*ITuB|ES z3L3G$%zGN4m{A4enk?TP`pk(!Yi_~yWqZ>Bhph`JEpO?rrGTJD5pz7Zb15{3^= z@}`kY`7)Z-co40=)gcIa74xG#eyR*)gz_Nfn0NSyiL;|W7N6+!tRFj< zm^7Lmn8x6>m~f|UMdH82gR)SREM9;pdTOWbWD9Wy4;vfG)qVKal8**C_* zm5s_d3r&#$7PZCE+_@j}PK2GR9E*b9BcI2+afNyHpLoFzfNmCJpiuX%5Fi(}Zc193 zGJhHF8ZktiJAO@G{GJY+E?^Ff*v8p6W7kf1wpEQ)RdRgwwJ_dw!TeP-=hyy-P15(&fc~%8F|oaWX+05X2i0koSIO~lBPX&)&0x7X`<}TG1vx7(yr=6`~q}Sz76_6Hm9fW^wkxQ zB7xp+ty3g{h=(B1q>+e~YppgVMQq+zi?a>Ds}(Rq)*S34d25&QMoW4|J&2dh49wpdLrg~$9SvVzA0rZM zd&T>S?~$JzI24Oi+p|ydPL1!(QrF%aLoVLWyl?%P14s+-hEb#FZcSP^XSPFzMc*$Q z*U32I8d9P>>F2=dd6>yMhN2dUi#9Z!IO&{g?wdi7xS9iOqbj^Nb4S5MN@f8(dEP9S zi-gap>N9GstO3zjKM&q2jJAf_!Ul`0I$jx|r9X*%ZdVp}h%|&J((=sY3)vh{vf@lS zW4B(rSyGQc+ld05Ex#pr+~oK;#@o~v;nu}qpP!m?@aPeg)F)tek~t=uJl+>-a_Qmr zo=#3qCjuMl6Mwv3^%1csvuvmT^6elK;j{y4nm-?j=+2p&u-%5hqNo#5;c9LF+_jqbOoorpJSZG?tt^3`@g(`t zTSDGfW2-?P5OD;OjlXH1c}@}E Qw1QATL! zTIlgfvvQBHXF1^MvIxxmxy}KXvhw}hU<%mO*2>pV0o179 zhk*;8oJm84zr@c^7?kWg9Fmbdl3acIzx`4~Zz^a3xxI8O(u=+o_~0^u<$T{kw%l*akqya6EfR_wu?(7Wjw}l*}O7whNcU2 zuIZ-yKfi(ldxj2kRLm?m7X51<4A=I54Z_(N>}`}|kgVK+HkX(iTt+7bP~Ti&gn|C& z98y}ZsXDvTv07IkVX*~1=jN;t+bufi#*G{5t+V&_?ctnZdToFu!p+Z~T8sTeY_Du|BozRPt?mkv_`Zpg=I$v@D zB}nd1hu}nKh*S{qf;kKjwq(x`Q=5H;D%qcxi!~H3EnC+9`VI#i%9&>Cl52mN^dyJE z_8pAA1<*5&U~7`r!>Wil1nEL|9i+P6IFSN=`j%%FJ;bdq0;>5G)8|cscQVL0|n7pytxGAQVeIN%=G9UkUMRL-oLEi zLb4=2m%NABxLD*581=#T{0GQf4$7i{xXUzIxCcUvKlF>SKQ}*Mma;A;L z_BngjM4JS3iZ6&Vnf*^i#Szpk0X5AUl&h58eRbQmQB4Jd7jtI0)NJzPQNwOeq1lZF z8sH$|o3{fD+l&dCOfiN20w@6UO<)#UD}{N%D(IbPRBcTv5D_uW}L5S|6mh-+(j~%e3v@tGrNeW#C$FFiFGB}MoFmATZKw-FHJ&s^_-#&ACgaQF-?#C3aiO1 zqO;+g;d+`V^Ww2AszrZwk>ZnXKA!GrjU!WA3^Ccw{~ zNx_*v=8TYPFW1Ji(*n8@yr?$z3zoy=Zu)DQ&mkRtMheZ7*m*&hm_>U6(1p18FiXW0a?wI zUhyZ)6>7l1Lqks5eBGh!E^<&km%GgzB86MYpikC%vKC?oVfP|njgple5n!mn7b42A z7k#NN#F9fGZ-M6~R6)J=+HK;tfl5L65YRqSdO8~YRswI{wQJI4HB*|Pmq}~()98N} zh7Geq!9f6-uOjD+D4o8$W5BE_4X*FXwS1?W>|l=Xom|)W7LTFEZp3p{RjZ@ys?{sz=kyx{~NJA^QLh1Ci{oUJiTSm3AD!&FSnVsI|}MQ>!vzt97K=-#Bg!` zlkVbtPKJb&&?f0N*V2653U(di2)o#p0ZF6&qj>2r*s0Y_Ss`x4;r8FKSkj17 zsx1Cqm3Y)p;;#D=?1 z>NV^4^RT}ob%0HPZ})!nfHK%};ZU!vtnyE_SBrTyC=CG?WUbC4i9wy%ZgYza$Oh_m zl*O%fMOfX=$kJ-$%Y0ShQyRl{oIshA z^_W@#m-H4Jd%);r0ncx#P)E@RQ%x$!M3Dry1nLMXpwV}M-x5Dx zdcvk?d5O$kBmuH9k3CYJ*Df;noF~$Y{|3~>yjU%@M`$*>bp}Z^QT~TI@hH>>A=5}4 zWOj&ckrVtB2m@Dd+~~dZ!?@$N3cjf^Nei+#8BNvH5UIB5ueC3QTQAO3wxOCD=Z#fW zy$AAlwZ}(Mt?1NM>_5goDimIh<`N~MHnn{{lH_uA%0>0 zp};*hm>k@UJTIeD0O4_X^7E$k3?6PfYO3DbC2fByvC!RZ=eG(6W51v#e0~&Hkb7&iPFHW1C zqU&a&$%Z@}-5@Zk#N&K|aEAU+hvo*B?)=BblajjbN;6UQ)`3{6*(r1JwphKeRGHS` zQ7@OOPo*iQf>PrL0Y?c;=hvJ(`eIN$A;JNd3E>l?;4-I9yEmi+?qlZ5cvM_sPJ?*Iw#?6W`QI>u zagfrw=**Xw=0!R9%tShESp3B?E_*Tlr!)hK-Nt$Z_1{~_xtjWSA%yJZ5;&zP9k?(D6;0IM6$f~2=wvz_`-fhKMDaX-lP(=Rot{N%Xo zS}x9CA!%Dlr*F&3-1MF-hqA5jT61-d;LfX8(*~K2{LVBl^ylM57E+5#KSaVmG85DyUcScvJ)S!dm-#Pa z^!lG~TYhVB`!X`=HFNKD3S&w~O#^!!9i1eM%cuV6Ar(1ov1r_0_;=GB6hE#G{P8|+ zE?h0OkuB`~4&b=tNr6E|QYDDdkjaqR4LLQykD&6yO2^-cH1uKiHTCbCzWYxPogSs^ zLI9Y>lLEhU{pFN=GiNJv1x6z?XbXjGoZL`9kP)pX|3pea*MT}07*pq-e?|c&HlMH` zWi^6qARywm8NBjR?z6k*R^V=8(*)A4Hfu6nd%W(mw!^xxow#^2F=$&tLc;ArPsI_u5!*u3A7$6JJg&Q7a6^9v zb~SptosZ`}JU?xq+(5i;*dJwmxo!tN96#+Hhp5kzNZLqG7!obI-w(NhL4I& z<^3`SP*g9pU9nRru;2gn*QJexLwGU9B{`Pqd$!QF$Ov_xrDYvf7fU z5N_!3`NiKo3f~`~fWJj7e>k^K{($Iv9_#qh??0J$OB{k7k8B@SA2!D&;w(-RD``6K zU?wGF@oIL5KG{EH;%|H*IPeeezlS*@!wK7^>6-zHv6(Y~h~>V|%g1Cr5gagD!-4BX z%roXmiftx7PwG{Y^&}!9Vruf{L9+^Q$=9D~zunM5wq6pQ*kN((>Zood?W{IA`nx!c z1;AUEtaJKN=PSpp{_1~W+KtQ6&-2QbGQ4}lyVkwgB&KJOQ?%K79!G218U|^y112_i z(6uyT8rE^lQG2JpyQ{XgvLqqplaQ~tD8oZG5U6?{(5yn%cR>9vZNAHhZ_M{jfkTmCmV>U38)v+ckdV@EPFw}p!ZJ5&6%BPi#Pvcv0ly@ebfe6T&Rs)g7%U7Yh71&>UcP;{ zvMa5{)S4E%IaG>ysg=;tVJhT-SIadayUovARe-TG#=l#9jsvT~M;`W<%Xb3toPmdc z3~sg=-f#Zn-|ZXd<#yU8ud@0^^VWPMtgX9bp9w;v!J-OIHvui!A>+dKmwSwh&4L5l zfq2X&(lEf3*h-WB`olMw{PEvf!F@D{hL4{~(1FErA$L_ThsdCHUAh{aMdpAl(wCn)>6K`Vfh)5;fN5*wrld7C&-?Q*++UnV{w?i3bw zK%!1V4v}3{Gh$E@KBVVey}j4&a$|7ORQz;?arz-W&LU1o{Ymr=GqA5qh6Q(acoq3yeBYx~ySc4I|x=?X)VR?6P0d!3m_iei`ApeNY!_!m@cxmr*>s(oa6b0f-&J$7?YbtK78R&f5Am%g7k#%&v}jX8%7!+xXJIv_rPR z5xTZxr4#k=@QLAL_gZurX_?2oSTV1-vy+c{qtx;iLwwCfAC8QS{l_CQx$!VWAW94pk2TWrx%T)|{DVWOujV za*E3!^%m2o^|0xo(`HT9xM(sH*_hp?w>!^1#ztqqnEKktsN2FkD>jFnnZ&Wq`ugh; zMR=}SEE88q(o =Y*VZ9lpJXOt$__`%i<+%gtbxw`WeqNLcN>VMJ;hLSDSQ;@ye( zjFHMWXk-r&U7FNxg5~}OBNQ8(4NKI;qT^J_4DS7*?#JI{t+CZ}xkv%gZcyIg0@}DY za36~Ko2fib*rBqQlT5iKH1rryG%;s?LtS|wo7|j7477exTm=H9u(j7j=Iw1#qxr^r znBVP9yx955K}zD*Ib0U%&6N*vBqaEy(`iRz0?eVmm%B{VZ$vWzrXgArGrz;H%Hv2c zRDM9c-mHDUX7+x&Gv^I@jfHV@?LpYC+@{0?-O9|Zy;b;ZNAbAR6=P$l#fh1X?EMC2 zJI&ZPgNrS&E*ve|3&;KawgG~8wSB9Js%XT!vR59xJ5zpxhPBev90B$qOTZn!Hu*>S za{7zY=Uy}l=u~q|#qIf7l~l)*G;94)TI%SCo=EgGB>M+GgUn*A9L7YODFy}x0{;n4 z!-(0P#Ac)0Z`HpGH)i_Tx4xM9)s0KoyUjhu(9|t3ccrw#zUofNTiJ+trEqO$_?%#L z=Kk4ZnSzd(xRhvRW@$MG%06=w3 zrmBuPIGcVi+1c3q@-Ag})NRVeCVxfvv4ae=siq8~Pr~0qmT@=cyCu3XV9*=uNfm@|uCN^O&0Eujd{<=>+nE+&b{)R1=@;yyF~ z=wyG(KEc$w(40-bI?#Cc-qw6gdvO}|Ns{dbSAMXqs)Q08oYB@_0ss#s60nHb>Gv=F zHK&_0nh@_Ziu|66N18;J@e#4H=6hVW)nt(OD z>IX5;APj{CXB==`d)Hu_Wf7*WcS>FKO3TU|Xo^YJ#{iY=uE>&EF4-TV0nLB0y+=1s zTi%tqr6oIxx3b8>^XHAJ9o`}WEdExN^%u4ww#x%ntoSVx!A0Qhvror=TU+!5Tf~h* z#&*XSPq=2Q9nyCH)g2vDEBzwJ>2s0w#ifep)Lx|@^A!3CSpx&YBgd0^X;#Ibz>+do zV(3dAr<9#@&6JrU&A6bH%qA(lAC>q|i~Q|;4`bmfBra|Si(L0|f=izV7z}L3WQ<&6 z%(4`w19UxG_=$5u4q0r1f2@>Ck-6nPo&9i1bz}fz4t;*)rPJE>`Gku?P7*k_7z)MX zX1To>TyY*O=H(d*HJV);JT7;%lfpq~2K4mQFw1`ZTrSm?<(#GvqB{w9Im;jEPVQ7d z=Oon{VbLjc&yIp@(50qboA6a=&&AqDfSoq6 z-)#3XqY{^B0ny1WvSAi@wg1V@ExJA1kuvl!hD6`82bQzNO+sbRn~zS!BW0;Y$Sg7; zP3U=XNARQ;Em|09bw?B~;fA&a#*znCMQ*AO^P&zm@^JaG-khnBp3%sEjXh=e(wIr) zs}%g2OAPw%rMMA34Utt`Bq8s=0me*$^+u1|Z?sRF7%RKW3=4=0Ui|}m%}Bb>4BW424}_Mr1lThjtIz z!}+yI75|7L-PrB$kC%)dxb%ZM7|Pu9fE{7>lkELkUXG79pb#*A6QK6|s!wNrbXUAl z$aq3YWlD7T-2#?+c%Y2ICCUpZOymiUJH++wV4XOFAUGwc@#g!?4nOuJb z^!Rn?%Ro&IA_gfb)IhLiTUIPzcrvcAb8kqrBlh>uG5Y#Eu5)0^eusB-i1_aJRj8-r z3jt4~I&TykPt3j|JFnKNw*sC8g9uaz9GLPyHbi2eFlEIer8s8m&X?wn$56|Gw+r^P zEq8Oue%SmQczqaO^ZVk*clY1E^zd9BMIp<0M|ya81Q*y^tELhmufjWQyu*5Wht!h! zbbK$ox?kNpC_wfUP-&UDr-n@ZGSb>Qon(!sOflWV2-68n<%HS zxFJ2;0(yuqFS z)3U}-Tj(G=0$3=@rspm7=ja~jQ##s*{S|R`uU}}f-oLX2sQ|sTYl!{rq25arp$y{a zDNRNf?a=)pJ3lrSjt8dxoIe~`hi?^2Yb}*Nqh&EMp>~C7adZ6mxFKZPG>jFw{zPNn zZ}XfG1es)S-WQ)qxjm2o1+E3P~J`}TG~k%S40F@hp8z6Wu)I;ka< z4PTb6+pD9#Ts;dp%qYy(YrX4ijT>bUN{#s#X$X!e#Y1g9oz45Y=ee%Z8+x+vYj?&= z`e9n!>dvKFj$OC}g0qt573bM5W>WKpiqrAXpuKb?w2Hm8HfPD429Ev4_mrE#N0Mn! zclMh0qU7xh45Q#h-{FQ+)b@FH{z1OJ&fJ*oX_!pQxC&>FHZUlgHIr>Mq{59qpYq(> z_1_zNl8}b{R<;p#9J1OTDhn&7*|c+eX5xIx8X!D{F0(70fOxX7nxG^hk+s1FZwQq?;qqr zm#6NNqH1*#SW+Qna0T#YamN(Rl9E5FEwMM4Jw2POaE4rEA%R<`f)*y8Vs zc<83G|C?ui&Ml$xovY^bPOMZm#9#~F-y@Q-<(mGg|M#nU)a%M9#}W|=k)UwKlNCE? zi^>L#v^0+1blUQKeC=@Bd6}9+2=hc_y!6x4<{*c0uQ)Kxr+1xPx$N`v+#?2$U2RWq zHJQnjzKB_|QHR5)Z7kr~Yh2Mp3vFaZJTUGa_U=9UMKOogB)7JUlDe4*j#tyk~+Pq!O``H9nnM=Xm z({8=mf|MvsaXwOO8W)fqkBa5hzqW4KlJ#^~Q;X{lJvYUjQt@cBKN_YHZ?=`X`Yr0u zxxki`UU3fPbMCuU@_vTxd%M@f#i{(E*tddM!R|YFdjSk0<7wCX$U?&h51Ml?nTzQi z$oF*AYWe?|dK0i7*Y*9o5Gu<`kup}ABvF|~Nui`vh7i$!%*s%tNYR7_Qz=6VEpw*K zp(x`LR^}m5#zaV^-p{qy-oO9rIQFrRy$yZ8&vW0`a9-zmUca|_`oi(a{%r-|KAkd? zOs;qnjpt#x zu*7b+Pt)HI&-I!6SglPP8A%iPVAZx?{Nx_I+c=W1`^C@|%oV)9R?Pluw5$d&oLg32 zzV%ZdKH@7bg*d;8Y|Y>H2I$W*q>fC#05`^<5OAL{(#Q8id|J}$)pZr8zHaLq972oM zreZm~3tXoP0Aytc!^|C{79SdUaiw98&*0Jbcg{~9wEUz;4s4TGxE*PRC7oIhs3Nu< z%nEN7t;m1&tRvJK2EGZ?%&i7JiXPxsn*aM_!J#_=OPB5i7eL3k5!P~)QsGVZL-h5( zxF_8$tA*Yhi-BQIbG@9-TNky>DsA>_#q(`nLzNaDJfF4j*^|My`((b5jTr!d;zcY% zbsmeXh0%KU&;4&MVHas3{U8A-Nadn4;LezzMn183sVZ$A_j9Qa^&4WljN(H!)GOi@ z)BzH%;pu(lVJ&|z83&P5?P0++!9HG{Id$@6)1s?;zt)xyciMB&+SvH+ve0SogAZGG zx})Nr_Cc~4y^OzrU##I`>)@61f6CCv(&{I89cnmP=L)D2r)3__-UTp}E}tt&O9DhU z)jicAdaE|Q8m!?&6c+FUs!L*f0C@3QVHsH5>{fp5;6=uFTro;-@qUX3qOaI9%y?VT z1prCaqf2=N>z;pG!EBT<+XA+-LBFL`8WmKQwiJ1QQo+kOvw{6SxFdfiX#aOIv4_CG zEHIJY;w$}6OyFJ+Sup@2`ZV8rwC?Sw@5AS-xYuf*PUEgjBau-@46c6Jm`SlME2?wm zeXnRt`|W=>J<|AiufBcv(ius^3Knx`YKwc2)Zq^1=X_^6#khZU|DnIbKr-6bg`xQu z>O74A;x4rg9c~hNw0g~qzf@M>c%Zt^)b|zBW>uZd`qtvzsShw3{-w+ad5WFZaV&H! zw7t(kZ|wg%05Q7ILqqFwf;u_P|KP{e;n?rcs@PlEtPZ;Wvf<6Ia@2_;pnFqy+`MSu z&`tvuMZr*_Wn(As#p>rytqMnxB|xgaaCE|u{-;h*T(dWVaUv1qF_jP~X zgAmCjZq$>tV=aEZJbh`Sx1oE73;)MwZ2fh0afuFPp@~o-sn&3^?k*%70_U*KD)YB9 zf*j4vhWAoP(a!b<3k$a#ZHbPhKeyOV$NSncXiB2KOhJ)taz^_f8bLS$a3aI4K79D_ zrFEOm1u6I^8NgxWJwS5_JpN~YlU=Yjl4JfU%{k0vo-r`tz|N^ABtfMDwHro)z2pWd z{diIypGo4MaLf>@_vKGL1{!TUAim8CoHdJ@rRXN9qw=9Yy+gAle)J&byf8Q$L70Y6 ziOH%!LBC}dwy*uRa9yIb7!S&36yg~rOn>&L(jUqi6OO5D4yI_ypH1y3oiXH61N3^d zH|@{uop^9M-l!~W!(ina0VR7-OFjXz%sUi~H>v6cJ$y&83El{NYIy(9tWjZ1wPzV*WRYH473a-h9I2=@_>wrU!Aw$QCb4ME<(*Hz0?wn~)vp(S&R>1{Y@$%r zI(8F8vZ?j&Dh&lrni%Wfcu@cK$`GfpR+9(M%O7G|RxmG*=hr8zT5QE-j|p9Y93}Gf zzJCoz)k}Z{`cf-w-)~<-RBrXJcNPo}yWMvMk>mUPLP?oz>KXsj>!^2r2m9`28{tmD zsK7OSWYb++09?JHGsE@kG@>??NmAU|v&;7!{!jhLs1pIYdWQ~9jN(}5`x02M>AZ{p z*VXFhf4tpFi4DD#jZu}dws@Hrk|H|@OgPmD1xt+Bt(O0YqqX%@TRUXp@?wNCItO1G z<5@)7Z9iwXwbz!x-s<(+eKx*ep788+eQLvEU~l_`U8dIuLxL(ie3+put)yQ6{u5fi zP7c^mzegrhKZo^C`_}s^?Tdm|-+pLGNP(!Eey6&a&4zXt){fTE1&(ki&Nk6!En z%jH|$bPoJSFD~}eFG^1wh`A6{84g0B)i1*M+V}d*Qi|Ph{@_tgUQMyLkO*!nXLCLo^nHj+-}I$dA0yR#Vf^@by@eO;O7! zu`Pe)bm`J1Uq34J!P&v+*h&VvryI2Ssxd)l35G1YOB{ZA{`~Rd_88CPP~C30IeB%v ze|+NH^`4`kOHvX@7X-%{7vtppN>^Zj5es-J6UOc63o}NH6Hrdchv38W*k6*iWI+(p z{A95s+nfNi zm>KYcEOHz)O~ynqcWXXk+qqCc$CKB{MB-KgEtM&T`udy6-UCq=o zL2L?EO)=Wjud=Igox89h7?c^nA^=nG>2>HAj|OKDXWo_gtZ?yv>BTGEYvL`lL4ZH- zDXHd#@%Q2PyA4OzDd%OY$4xg-yD{7Ai&qB019Ve^f;Tn7@-+`Z_Q@l+V+@hZj{>rQ z7h!XARKbd9R%%pMzhE}RHCGfT7f2O|1^q|`d_%SN?Qb9_S$8G_fJ0a98+%3Xh6cuX zjsV-ieq(4(g)Dpg_q-;7<>1fo^|J+0r-t?SGwmur#dcm6JUO3>a|b*8`?C zFXykiL#rB~Kg+GB7zgLRh;E71l=-7aQ}7m=?Xl$bM8pSC5nv@&R5@lVci$eAJ@DAa zpe>WAM#K=B-tneO=|%tn@E^vED+{}}Q<*R0MOYaM8K>8tzop!dY$Y7U()_J9g&@^8>9%63Wk^+G>6IL@7%GpqQ}C`xyP@z+=_h`Ij%z`fk+SoDP%*EC7+Pnm8Y*U%uiQ#Rf!qsRUSCBB1B`jw4A^ za3o*hWM|&BWT=^LC`yG#{^={r_{7GHWK&5WZMe9u*h)&(gfh^}V{Cx9{q3}0W5g&=TU7J>nAL!4sHJHclGqktF0 zlp)XR5-A)zVN752j*_1Kt`NTJ43y5fv=k(`KAK@0ckIJs3SS zUdewS^}DgzwBzvam;+(|@~W(i=col&sBzKNqhFVcV4qFuD{EDf(n{f5Y~YJ7NObP;x#(gKym7P-oucX|i*{sH5Jen^sOg`b zQnPlSvHeGIf06B?w}tzLTU7rupG!}Uy3pW)4o6?#&Z_8|)dhdftYYc2Ax_Rw=$noO z-AQ#L=`?MjrI0Jo#Ud*er8|s)(0^z{eYl)eGuID|1roqPH`X$1V^#h9n$iDPo(c!lE!ElpqL-IJGV4Q;q=#gtw9*`^e$eo`AB5I+{YUjhsWZN z?2C#s0|_&DPpf}tL66QaKlQMJn9>hk1lkMAu+g|%kr?x4`<8C2CU{0WtUv;LJ)(fy zCS4sgXL{i@PUMVag znAWra!V^C?f0<$+pAH6N7k&Lao$bwv(kF31*E0QJz^Hv;hR?MF6>5JG2CUi0#t{po zTC~c~DQuMl;a$vN@$6}(>wmpp28?1^4hX8wYJw~LBbHIe#Ne?}p~FpICOu-SMGZbo z)gExJ0D>JxH6lXp1!aJ3Ezq5oBj1^?<9%~I)Y`kydhoAOYjn;G2PK6!1Kg^AjuuWM zAe{oM5KTRm105GFI<6M6u0#JskXv(@d+g&LJkPXmK_l}=_U40_adPO}3Uv#zlc#;N z_eq^wCDlA3V1S{rO*i0z>VgJUh?=S+x_S0o((r{|0UtHkE_7CF$Zb-=@rIODRK!4# z34<%FORk}GYt$S7?zB}`D3>D22Ur3W$?n9vcU|qauzLfI_DO}F?ARe4h=LFn0ph2k zm*1f|HYs|#Q`PNAL;2kB zd@w_}U|4*xx=QEPtudXvww7ofh_^0SKb1f)y_rm!&U}KhsXcIssRA}xyQn@4D;OlY zC?Bnr&l3-5m)ryGb*b~zC3u$(&Ctch$E zY^!5s<>{37KGyhKNk+Vv)>-F3$57?9r3Dj51R4cBY2q-yUGT00wB*8CeG-(w9qY+_ zP5~@r&X>M~X)(yqTDs_XcD6S@hroF2WO)vz{5*hC?H z+7teUBl-|MD<#=g#{4*Px}mM7)D&YpfM{k&!|ZzBa88N;=WwBLg}w7thye5?oN(<9 zR~Y&u`8!zdWf?m}9Lr#iQ$|{#V$$h;VchP!rQc4a$9KfuRnO6+M{GK$tl3m>vXXXi zqsRwIj}P%+ImG!M1tQ$D`AEkpoBl?`hV|Zr{OK!nMO@f8hx684e37%TF~u^qw;(Gr z8f1Uctq`q!1C}-!Ab0TvABtl5ouSb6?A|KNCAD?H{{;^X?zrEK5g>I5BbV zm^p@LzqZq;DgXH)TkStu9_PKCFLv{F;R8vS6>9S>yP`(qM8QPlH(4uB5}JGLO=*8b_ zFj%SPa!UuFBwdU0f&QB=8C zB!{3sN&-q=G52+!magw0{!=LL#YKXsx#jhWKRON=bFOVJfg>+?Z^cz#mi_j$HogZr zfYy|ox09lt{viQkz?|4=t>eU0 z>((l9#pxMuDMxo-WbVnKzkKgr66p)mt_aHAXr&zBC8$0FVXo0vykSxX zyJ-5>@{P}WtF$#U)w~3}+O0zoAlarj<~LkhaH!0;KI|(gP7yjn$jKLx;kM3&*N3zy zvWuhQmra~DGu>!A=?B(8yHxd~OAwz&nRBQSCoEi17=DYnZHoS8(A8EYVynNc z!zA4w!fD|iAfs%(g3}=O zPB2GqFHx+e0RoRwf;I#Bv0JK{N;`){0k1#>dDkTQE5+GfN|D9+q}j-=)fh(2fNNlQ zb^)`QBp`{YSdmaN*>&!$t=;u%$k!F_^XLUNKFyfk=+;L)<3LC7$MXAG!kD#~xK&?u zLtKAnN`x4TM+DHdp;Yo}y30*0li+1Q@ZM|2#cH)QheLb!TE>Q; zjrq&3+I6cb$e&YVKLg(%@zSF(ffcff=ZTG zy0k!N)V|j=(Wvv4GM@cx+ihG6Zs_2Ky1?DGC%CAw3!hPh*k@ETP0JgU5&ll5a7>^Y z`>*%reUK2gd4`-o=qB|dZbcB8$n|C=ccC_hTUB-FGO*WPm#qcL0cSTA-&w+iUfAhQ zO7GYKi{F>N``ub)Jz=mh>p?HhsSW#|l$e-raoF#{q`mhLMy`RsLL;0;eSbobrOh-%(TQ!630ASt#M>SmL)rK`s^N@ zbdpt6Ak+E2^Ei=8f9ZTx-(tnTokk@3Nnb z8=||n;BwZ@4tY#OB2_OjRnPlovdPiuCXvjo-?L44&~hQ)#422exF8lPH2&h(h72$lKBirOVyLlK9{4iN75Yc?48(AmrO z1-Oi|93GmA{UtY^RT4#2d0tg^rn}D;u!0rX|%G&WT{iWXUf>CI1RoT>cFmIEsEesbk5jzbM>zg2vmFB9sz;&he3}< z<|2NI(;iC7$F!49JW6G$Lth|j*@0rqCU@G!h_eO`=X5Rj};v7 zD6aptCqB9ld9|3)#s^<|_H59-oKC@UX%k!%LQ?60OZrbR4Xmm1_ZzI=7AxwFG!oz` z`M{S1lq-`PvB?+MY}B5+nKiT_zh8`jxe~kz@Kj~^ zS9nIvkNSFhryFO_=Fc7KI4{7K0wEa@d6!{vxeRLTl1 zvlW^rUt;%zS;5s`c13w^DYNAmIWfG$a1#f3Sp-a-^GC+IRU}oFjMJ^U&O50jGXz4GhZiSs#!wAdZ$R2~UPDKl8+(jmMF`Vjyu_-uGl+`Ju_)eSu= z$gLOy@&_Rlo&PbSGE9}lxx1L{0JM_M#k%Ea(W7be=ilMpX;}sJ1OTuMDp#GO+KBBO z=tqSnSoqA;YenN?aQhbks*Ar6yEmw-C9cV+z!qOrqefa5S?7@!}L{R2A zRqY42`+tEY{0;2_k#G&eIEOzY``$sgDHd&DakgU;wVCx8>&Rq{Y^skfGFSLZb)^p-pgoV55<*+ECA4cH z;L-Nc=oFVPudS_3!ng~IGPNUq_RRGabArF^iE8OOx}XP$l(mpZpv>$fUodq=9$4CRSn8#jkIX;!7E{}wAtUf?h=O|B`) z7&ogezqqQcQ2z5!gi1TBe%_4iIXZ*8GJy1a|R=f5r6>b|&=8kV%r@ z$>;-l&&+A9sS*xeY81q?5nmXwMxd-E?cT-1l@|XBtVp~2@t3y^o#(^*`&Jsx!s4ss zdfCpQ09exY(-G6AN#a+0usUqSB#+Mh1ALp7T_xE{pg{b_sq&LnLiI2h`xPeQ%-=Df zh~bjyI;i8K+w=)bSXN)O_vYmcv-d_3Nyr6%ZIVlEN{#k)_N1xKfOH6gZBk;lsdqp0 zW&U(HyvSHo8M>_RG)P`|q~-W!VnqjCy-_ZnBr=1(^ulk8->>amdlWePLfpT;Xj`~i zlwah>%ZbT0PMXc$4UBql*sC+63{cpJZdu{)&}O<$)$8%`eHYa{xPCpZ&gBS}JPoNT zPbgpAyWJmB-SK^xu%;XjJKg*ft#VTz@6#*uDSMd~Du@oL@+aX-3>^szs-7OZAVKco z?}U)xWalqgfJ{tpw0>1Vcd3h|PlQyHPJo3Kri|zITN~j$khK{L@`*6e=s>pC$qjOfXuzO|ssA&j_VA953ubrT!a zuis*d&d0%QBu`$b8AQ3&CVuSwvYE2-2NsWGHp;&swvqHPv8~>Db#B)#rHkTgWU92t zha7BorhX~OS4F;{z{GgHL!(_c|6b}Kq;kzPpXiE2e z58k)Y+bjZ3{gc*C5}Z&J&DNb~G|SJt9{tJO0yR_QA9@ zBqr|;+wvr2g)WktY&# zod+p>)x)#YDgFBQ2aO(%xQF6gZe)ty(A_?lNK4bpu**75Bh-JjWMT=yw1kzu> zoI}Tk{=4*3w1YjTD$s%NhXW3gX(#oQ@uPDhLqNXkUrutu(4e%c7uC@3}aPzL8 zA8CeZs+6Zq@BBv1Wjp3~AK^_@R@}6EZ^1YfIg&upt5*+Q|8o=i0D5CZAwC7vElZ}_ zL0PKAy?TUI*rE0IufAjQ2ui7>z!|^(9R26wM~w}YfO4|LW)&#WC>(T zi9&E>W0db4Sl<=D4o1PjR)uS4`3(fEobdn{drTQn6@+UEZm8liA zn-p7ybNxySYXRZfFTRj)x9yhRb04?nTGFB#n5^})*l*~v;oQn?9UZ?vdJ>s+acx$m z`8#{Z>_MU29AOrOxKu3aElz&7({3=>`^7HSyoD>EzR`(L3=Lxp$GExovYV9KZ5oKi znz57l>@eIXo|CmQhAUe7+-`{(_(tF0w6$y2OnId$c#mq?keW&G@QVn@OqgX*&t>=3#2Qo--$ zVhby)R7&3H3!*SU)KIu{eh^MyT;VRTiMYYvPzs2{H6s^+0Ich)yKwWwNQ9KozIhhg zujp-Mw+552(O8BEUa zj?*aMX0wI95%F%d*2$X%pWD5o@0TT6*cX6YnlF)2E!zhD{Dj0Uzln+eY7ehOeJFp| z8C3vll`tce9U>Ek<-WHBDuQJq_WI~>yv?6O_&^*^6cnW~WQs@+wNos#xqla9i%_`9 zDbmW*oCuqq4H-r?8)!#BVmd<%@-M1Pe%#6TO>@oGt@YD`M7wS3li-^jWBt#yjr}Zp z{;nzb6x8ML){^o{!^zeGC5vkMRQz1Lp)c@n9xbVO1~LxIgbE`w(Yic+4htHp?yxAw=lfk19Mj4 z0z#px156Z*_VOduq7G9TEnlWkw_Bx^+tw@O(u^G|h5=`AlqbYY&o((kJk#gqL6bpd zl>bdeTi|vjFPMtL6bWR5C{r9B^$_Ol92ln;PwrQO(SEM_IV65uwM;W8rv&`v3HZr! zu=8HauKglim_xUq4v!>}6G=0Ou_aSJQR+)BKnni)<41I6J}s_h&#w*sv-$Xc4g|Ts zcJ{g5CsE%VR1|8tz`=A^1z(r5wb%@-SK2wI&D*LbI6ayU$G-z!z!dyD$L(hssv$k> zox!!HS597it5I9^?h4oWG>ixs{Wzg+g&5o$lDsvp!jSfSv0YyE&~IPr5nQ{OG{2B|jfO`*i5g$ zdi(f~t;T)ybe{FJylCC;r?q@LX3cYXV(z5q z;&P`+1#UL^&z}#zG@w#7x5WBvC|s#kUk29a{Hb#$nLQNxL59Wu=+;xrwCdOrD1OJs z%1uST=Rz02Bdg^~W>ZnlFV!2;KUE%>-Ri`i5pTF3@ZzIUiu3vzf}30lDIhZD?U+1y zGSGuCBK=L~6i*Kw7SnDE!1I586_{in7-P=No#-pW3YrXOY?cUO&5+&upi!UbqB7Fg zjKtPxC&gX6{HZDNH)Ll+v#7w5WzdacJ{Xnh>=}tL@EyGY6u-TsMCH6`m9ECE)1yAn zRyZ{69TA;(JZ~^)l}2@6TYFpE@mpnkK$EVgZ!yDEM~%W;?BblKOr%j5G-wdO%3jx* zZFcadnyfaWVWVNq4F1x?oCz7`7s>R(l0tOm8JsV@nVcj&^|<}?)YB5*gs&d!s9~+{ zzndec(1BJf-^X@=_wl1g9V~M8j@zHVs_$GSfaKO1a4ruYSjZ*-Dj zO-)T}P{R?%f2-H5%CaA?vM>q%B*Cm`RCbOw-a2sUAuY>|Qz8m$jvv9mggYy&8UoPm zzv~V3L;J`+1YTkmqcQpriTtD_S&u|xw1GAk8q(c<6PB=l7N?m)80l+jeW*ZiLsE%~ zbuG5Du~DEHD0mZ4IVI&$^WvoAc22gbHZ4jFGcya0SKVd*qyqVVOX}BXA z(g+GRm0VzoqTg{{I(S8QX%`tZ(q_@$ZYG<=DKBc^j))6K9W!g@XoKM6~gUE`)nHOM92V$-@3K|cOL;vh*mCvKdh)>o>(^8w$XEL7s) z$)8K#`kv3k?{-dhx=LZb%Y8`V(WB-R$u_%2M_v1exm^&e`o>k@6uUv|k6MPlw9!GB zbBC8{MIW!^Q@Bye@<8qu7nc!lfW9q9{`iFQoXn6{OdIdEsHtl6=4xRG)m_GoX^Tbk zk3UH|$%jrVUmF9;eO$BuwK9@X`j>@_96{<$CVz@A7=V^uA})f8 z#thjN0-B=M@t^^D_}^cleX{LzG$A-pvxzkyfdT=JCVXyc{NSITI||l=DS6S<@}*a= zTv?4$Q=unySFiJ@IDqNf7*yPI>)>=#WtCQ5^hJD;;@|JIA!e|&q6-U59LA*dOi0d2 z>~0WIu#wxLzi?_&e1Y+0-xZDvm(jwrjJHwLe$O};vafxs*T{hFo;!&I_1EqIBe2fCHj%88xHLDjM6SC5Ad0U7VUaG%|mNN`C~}r|O#T zvtz7N;*7Z@NCF*5Gl} z69)KsOi$&Aj5gWS+IaLz>Sd$;nvj~W6EK9j0Qp30A)bEb(~4ffF*A{Xnaw<)N#$6$ zSyID8fawScm3t_p%HsijVq#*{jZAc%pQcWnvoGSaS`%;ob&DL=@eF07A7N0GC}bJg zf(9fY7F}$AybY)!%jn58e#jLtqfP-DZ3NhEeel`hCY-|IfF=l5WKL|n$x#&kgyg)? zjj;2Lt(?1iTMVwW_vU)bCQ#9tJ^D@$mNLu9nYK`X1fIym*u2lr&);A!lu+o)=;*`R zr5W+@1ubA-t)@``!_uPwB(fX@N~)@=dU$I7j`E;wTdOM*wFfPg&%ll(N<+tghKyeD z=Z$2`z5g6~tZiRDaIc}-NK<_iR5|pscuX5b4cYkjc4{10SdpxoQr}_<6Wn#@CJ(0~ z)Om61*8SqWow-@&&fHIXHr3ZR zk1W)CSPE6`LT}q$qxpX#YH$FmZgj7?c>)jbg+D5~s8iJeP^wh;tj(tXX3Lgf523RLNY55-J%K$s=aJdc!$k zxT}YSA|5yG;yqy{57GJY>PoY;pRr%x-nv0@lC^l8a&d6MsV?AMkpaUoE=4yEB+r3! zVvL0z#W#MqDbyBw{k??j#Eh@N*TS_<1dtFphzEagvLI`!VjD2-`(ArupWadNe1T|; zYO10M$+A20^{ZE_xU+ZxOyj*P$FwG$i--nChz#mLtfhMnu}Zaj z%W8xx*!Msh%w(X;qtMU)&T^l8Mh!ysU@DgU&mozXfW3pbQ2LcXAQny*foiWwHhECZ z`WlERfX6Gz);UA(_=FFK{iyF0kdWENJ zc(ui~4d2=b%l_3wuI0V4Q9+-d_Qy$+;}Voue*Hd~N?ZpShPt9421>7S&wE>6{0JzQ zqLgblGgY-!#2Y@;aKof&vu0g~Xo1d4O@RhsFB!mXRSs0)|NP2xu_r=E6k&%CVgO^l zg3A#RGfTUK=3sV*g4%>%oBp~FAy(D_D9~e#DDDHY%%rB5+D~hjdFkJVYeWt4tXrLs z&T+~VozJLGDre|45zz)Fxc-KPeq&|=fN1@WJSu;jh z!+3CsPQL6GNd*#ays+{%kg7(7{P5Pz^u(wHn9<$em=lTRR|Q30X2&$w|J ziBBqMiv*syCyuJNro)k67H;a+mLgmEX@@~jV}0&jxzf1$ROhXlGj?$4GRx!2aDSl8 ziHeEQ13Stu96jVfBmL7ZvxM-BGh|ZtNqyjH9$vgnLts7m>Y#HnPB?R_i*Dt4ir5(h zOcL#Ox|tXaKQ*I`XO>oUq8^sLr9)n(gUH+D2BUjPzC@+2nr95wjyB%; zuA$uy1nQNQuZL4a*e~r)e0biL_#eadCVZo?dRbDE9=O(Zt}(<30%6|$`x}`$9`h|! zcfH?z0euM{>KYg1?!9{=V(oxD5v`K`g42T_Z9#^xhvT)3FT)J%8{ksD2g) z&o?MZx*)wndTP_L)!G)?=?^?Mn8B{me4XCA%_9}P2fS}~^C-{MjKcclB@0cdMwsTk zn-x2{7poC=_d1()$m3`@A;x>o946$;^SE0Ng< zU}p+ZAC~6zNcmq&A5UB_RA2RwkN|Sxklk24;B+bix~QZAYYQG#859yCM4j7F3%D#c zS!3WyZ0a@MAzwB-ZB0p`zzAm|dv$Sy;+3kLGrpP>oh3~&Uq)3yVbQt0$*_CF2aY_S zt!bX}LVf*3(LMVe9%bMSM6j}7f|EB*sqCY~s0Cs4I3Ls9j+uG`225;SOcg?lq|tnp zTf3R-V`54;?I2&{pN67Cfl(Boe)Py8wL?9k({42}-!S@Bk6rB}8hoUL+OtV(m%8UY z^KKoi&!~H%`k+GJ)Ff$nz-iA9vvV$9w8pl=_TaeKq#Rjk0&(BT;@ylnbK-1w=|KAj z-xFm85AN5OSB?2td=3V1D_^u~*DhV+ zfY?UMZbmMd(ATJY>?zn5sekJh)$U@*FTE+MCG*F#)H$`FRn4UE#x~wtZp86%Sd`o* zU%PAmqWre&GXC+Z{rW}~c&FJ^)CSsTwlrU1SAFVmSKZ2EdP#-sx>3bN%NI5c_EAaH zrnqD24)~{zaSn1c3D4AVni=0lWjm=p<7Z%T`+lvsb#=Wg$865nLIcI+KNci5| z$2H}uHEk1R2pw8wiVBX7^2H`x2svHdcht2*|ING+5qTRN$-Vqs^3JG*X^1~NU$CSO zoHvnG92|wv{xkm2y{>mK!8V#;+qT)Tq}m;-NekXZ&o40LUJYY*Ph;D<#}unoja^BK z?kmFR4sl&TezXHt8Axm+!ImwLJyLHN5;1#2AG0C0rRm$YO;T^BPZNbQB#wP#0B4jw z@nks-I0}^Z{en6pDYCO`LQxs9W{rBAHU?*68HRi9R*irX1812d%T^K@;8QNlU?m|t zN4s8e4KHlPpH*3xR2v#sH?pRXKSsS%u*2FKb+BeycYTXymoAk8$Q!p-fx5ma(|o4(8=jc5 zVTl+M^0!19%0&XwLKMZPD*y07mQ}d67)$tn`J(!Bb$C{sRE)S->c!lL;Fj|74E)@^ zQ!hcN<78j6P6NrZkfzinIkEodU4BBrb z8wZ7wg_#dQI=;|1r1nSmN4EE>#cYPTH)VNVeMX;oN7M_Gh+PXm-eZCu6{z6!vD3b8 zs7h|)>L)vZKv!6$8y>EtS_J&2|3TB#Lk%1Uyi;ny++3Z)6P1KCe8CeeXE?il+180a zVZ+lGOFpJWAD^Cf-~V$3p$L3eZ_5q$kox%QO0|+KC8ML&txY{fhy2*aumd5NTrtwC zEEN$pFAJ7I&B^mdBcF69+x+`0p;dN9$YJJI@~45D08Tdfo(2FD+tm2LvC{)Vud%Bi zNrwzhw5?y15vB4ytCwxswAqxq5FcBUdz~)0&tBV8r+eOQzX-O=Mzu6aQL}48%VZs~ z&1J!YBQw*c!18LXqJ-4w@|`<>`uM!oPx?_A3^Krrc@0KQWF1>~7E%EkE9b!bTkIbH za=pF#fL`}TM}fqW1}$I0xV?AG#8FO8LiFKAzU9z7J-@sy$`Rxs?3)w0thTnpoH+-o z)2{nkwD{u>BAAPw4uke{D{41$sbb1Rs4vqyC<(W#92DVfP$fakavC?YvYUP54~bRV zhle*FN+L}B-Jo)}b4t_N$cK@gBDJ^o9$@RR8$Y zo*dM;q$IJ#eO7I>4a$u zM~2)DH&nb5mew%w;^!GS<=w?MpENX_=ecj}eog+}wq87l8yhd|&{5NPdM0rw2@_hm zc%H-m8@S}-OWvxOvw~;6J2zQDCU%@zY^J_2Nxpm3(WYM+Rd15#&xo>xmriu;QdfL? zH`l;|j!prB;wY$}NqM)fTx@idM`bu+&uC*~ut9WXS4bfsp_w@@5S{=*gSotvk1(Kg z^Xrpd#*KmFp}#*KoQw;9Ql?yN8xNrQ*H?0|7hb=@yZJ}2P%p4o28VcVr2JYxeHe^Jrf;|p*p zVj5hSHn1-}!u2mdn)HnT(!IR&`=E|_Y~~U2fWTG^j7mx`=+tR=58(+X)y@CSwheT{ zNN`=In$Pw3cOdbzX3LVZxy8iySXAdXR@YSs$1&d#0ZM7VQm7LFb`=-PM$I{jQH zK-z6`1N#L#V02Cco^*pRWmDaJgGB}jXtAcE&&-(xK)H*v4>nj7rVX!rCaZ#|>#RcS zw33~M330yl>26U|7B5^E8y6?5{HQi814;{gjmn53hF2qmuTPhfNUR3?8OAAr6V-xl z|MJtPsj`yt$EQhy!|LgMM(A7zH`y6fe-Yi`gTkP>Y}AV~KH_w1Q}+|m!eKlX{B62I zecd+~A(cEGTsXwkuYTn3Gdo008(3c#92XadMXH0|n(HlGL!F%B9_~=9Y1>r-5vMdK z;Ef7Oyi%eulZIG+vZ$X9o=ngL=o&^3Ec_APr}Nje=C(~wOdMrnv(m$=jEMm8E9qanDy7*n_qGdvL%tRj4kmT2I(1sl^p?-Lmp01#BA&(n z{Z%rf%Sb&#l0pHD5rC;dLi=Y31#i?9LI54sHJ+Nx+NiQ`{x^a^AG?Nmr(w($tRqJ7 zw)c(R6<+<))M>N-4bm~_xVjgB;eaeH=WdtvCrUW6xMbX-KJ%^4=2oj7QW!i0+>(PI zR-c-in>)rx7gqpkpVv%&00p-2K?T%h$})F%_wL=1NTz@37J4oNuOnW=Mz$xz&?tHT zJ_h_mgx3@}Rw#BE*f~@xPq?`upp%~x0YbM=A5EMH4mR5!p{zKhYHJ5bV`6|zA<4xf z0x(n9HQHF#zfeXpEU0`qZvO*OyMfD6Q0LQdNWs8?5xE|0GT8Bl$9_M2D5{6r$o2OR zX;9udr487`0BUx}9^bmfKmRr&YMn#ZVde&_wCa}MfjaBrzKxeTfwu<@Y=~i!xG2>B zyx5RAp@U}o_T8@TAJDJg%(|!H`#s1#6ueSK@s2Xv?MPFHP=;a~*Nnm@8rqQU8RToP zWQ?SrvqO$99@jdDI)AlvKKc8){?bm98S_K2LB_4b8n(;aot6FeCdW1ZhWuC?o}pBI z-+m=&QROpekTfN+jLg%nI2JsHa@fW*>^6IFsG4?-F7KJN!`7G znQY!%WwRAKpKNtl35smgYI?>dep1Z56*Te1TER)3^jCRYuiH|y*xC{}MrJHFo~d5L z?)04tl?VYAqk+d+zmXjx9CU8fl$=X%FrlF*~1koY*{=v!C~E zaQ)&*3uZcmq&f9y$ow{wc_49F%HSiMZeOEWxVuKLUw@Uteu%D3NXc1GDpj@rAX$<{ zO87^)py5EbQD#00)T{+;{A2zI&v)e)cTNYlbzOV-`RyKLO_rp$0?qu+Glrqx)fJx#V*l3jt_>Gk6e%bwTP5*@T8Gd zx4O8N-qy9j`gIje_SVAt6hQ{8wivgp3}S$gykGi7g>R0$>c8^xh-@z1Yc8;vjz7+-QN6UbEo_y5VSE2qGSmRUE* zwg}W1EIWS7N&5c*8+M!fMJY;OCzEpjdxl`EcCa&lE$1ea`G5%Z|}cob*rY$c-F_a{~c$&>iwrr-CJf~6`#gNN-CH>j--BpS0%-r zR6n$qX#aH2JhSoqIDu!8MPnBp^MbS_ASVllsC?TTya7`1=%`OKwHcRQz)zt9kW%{i zdi$br{q`K(H|)vl((#%H43>6!r#|dL&IyGLM~0En__bjVngP;>B!x#7ij@=8IdyMUd0E*N<^cg4G%~S=f zL#@I8rVClHXi+I4U)VB*XR^jx?XU_?L=>3#@7Q2RNfS0&pEqW}3B3U?8U;4pfO{Wu zE$x!?+aGX0I;k_v_E9?3bwEHwZd{bQT+%s3`VrX+{}EJ#`g~nwo5#<`FP{ADz^Csn z#p$k-U%WVe`mo_CXEGw8Dlw|bnwA|?a_#M&D86Mj9%Iehn|y8_D{Io&(-{5R45>bM z%V|17d8bshra6e>!uu$Dza|`s?HX%4WHA%p{4Jau+2)Mo_pZ2U^qtQ-wP0}>0cvSn zQnLnUNA<%Sz_AEbH~HC^^Gs{Bx3}Wl4O{Nl9B%YDv%P}K7?Jzbu7udUwE zrM~yoq9O~2hy#6QE^qjIahThr8`{tbAs|HYwCp|n!(7>ok60L>)A1~4q& zqB3*2XJ)3CY1Q2(m9cwX8yl28ri6lz(^JSIRI@U%V>`!G;1EbyHm{fJ^wY__O4YC` zr*{9#+yEGs`s;^M`{^^2WMEc#?8UiYji}fy_ajHY9zI~h@j)#C+fPjF5!&#cLS@dg zGi@L@0oo8HHMbaDs89)m7H$nhSQ0qS`{-|m0#IT`e09qj145^UD_2lZKPEGPkg4d6 z4f@SfkDY`!Wq!ka6nJ@W7{@yPu%vM#0OXO+?Uw3CY2?XPTbfFP{>~*E0&SlD6st2) zMvz7sZL+D)>OomD3}nzm`&EsUM6DAv2#P?aN3Lu@ynAav<_Eae8Rd3!{roZ?96k7H zv2nLUNtDW$E92E45B_sj?8!3My4njm!$K%ki0Z=JOWcGESVuwlKvuN8Z6@@_DC4JHh` z7;HGC?yH3luyFa~!SnqC|GfVr<$oBGu5N;B_M(kPcpU?xoeE|_HHh^zD+AZTUMPk} zHX8+%h}`%;K`Pjsy#aJo)`18G&N1`4TUlz=*I(aeP#!}NYG?GeXy~k#-j_@LmZgoy zy`g!Y@;Mr>UwmaWgy6$p3Bs)>8w06}Z2+-y)aYQ%9e@#vV%qQ8p=nf0ErwUh5$5XZ z6Cy_+HSTw4=1_1<5TR?p+E5mcTJ-G?c=!$*Orev~1FVVyGRj(R7@K%NzsgwV9)#=2zeb;tsNXmZ?hs@Zhkxy&cTGBgTDhO6l$m!Yf2e zM(#&(2FLm%ioH|+Bps$0?ZRKBCgjxQUA!39Fy-7u@OSG=%K=h6E`;<~U=mzEPotx{ zzt~nQ8@6Luv2OQnpEW17;TiDiSLIxqj!Q4ePb9eaLgqX@7wm5Jr_40S?()tF>dr^D z1B`=mDKNg#0J#E(%*6okHW(5+In>gYfT^wLyO1#26c_iJVB*xr|AS^i8(_ID3=0s? zmQy*!EVOv1dqHbRfa1n#ZIKufu?@rO>x3>fveur_isJFuEl%r!pN{xkYe{kvM#7h` zUxmysi~vT!Zjks|oN(PW`fsdzKwNG?!sw-v2RAp#=5L_T#WsOBkxdT?3GrZShnmsM z5eHw6Hr99elx%Odg%T+xquk|*UTz*^gT`?Eup)C?cL7@KYe>wm$bsG$J-obj0K*kA znCWG2e#6&7cAoO@Pk__P8xU&T@!7?)igI0r49Sf(JM*TDTgI+i=SMRPJjBa@u$nz? zu3Da!N~GSS6^_20!riyyP(@ZBwrg}xFe)IBnxXyA^oA!&l8gO(BD`BpA6AY8el2w{ zANGxw2FmQ70dI8^tY86gXx0!4Y3F|+G2cm^-r%jj-m;a%Wqu|A{D z=iwe7HZ85S`L94GZ9^b$KE!07f#HLi!AAdgS1XTwePMFVx!J4}?cCkn8K2+g$1*HO z01?iEk7;y-;@OhpE&dEdfQ118HU3R=@vKu=iSyTO!>ovhEiP>`GC!j(az(EL=hji) z-nbEqOB5lmS!Ek7ttjeK=IJFYJ=snBteieb9f`BSu#mhR$Jd4_d7n`)au|`Zsj7J% z06UBAUy=sp{C~Jq1eIj;k*Pz(D~Y3Jj@y_UKZ5?jykLQJxl=qY!DkYwVna z1(WxA98JIarqZW*i2zF2^y>|96P^mRX-FtBa5#Y5--3?_(U?mrK478PJfTDU;U2Zp zvi%U9%|#3SAPe$|NjYFAMp=1IBlD!rkY)5pkEz!q`17#SoB?;h))=X}GF3VFc?JQP zNW}gvxm_u#PJD>}Y|4TaBPo)b3VbReOh(TM?~m$sI1Hac+2kGnh-zN{D4C;OS}q>T z2r2m;)M`^coOBHNPZtI%^#+(Py?w<>#l}LdmTrOTHyu;FiSx^VLSPUyUkSiJngb%7 zGXj)--?6JGegta)*YT%htM0_cTGXp~bidW8ox6_SsQ_TbFooFxLnF~azOIja?HN^9 z|Gd#t<|;z{1oULC(v``1QSi2mD2M1B$f7>rDxbNv=P(tdJo*FYo zG+s%IzejC&`DODa`|dm+4WG=U*fFu|*S{rRfkJHsvWqoy1nm&k6S+OAEcs;y8p}1v zHyEXjEIRCblg@t)6aKLJUqSc4x+Dv}jZR}qgbE7Lbr8LsK&)X>G zPSn?h+n6E)eDS;f&`1=!DY?p`$+XVU>B_$W%B7*9AvOpsnrj1f=lAnljTf)kSOdX|liN_WWOTgeNA{{%B21i@#gNYrGItp z-=+KQ-*C==0XEYh{QcM1ZfPgGn`2^-ifREvs?h=?-NsEpL1Za~9k>@ao`zz;zA#Iybq z)>*w8z`Vh3qn_Q+x`KuK)GzjV*9sz5>Utfuk+HEp%+f?@!{sp|oCp(z3wPAp;#dr>elck7bx|zFcL>U<30@%9R=BdqdqK<~A>Wz(Euh8d6eqjUQHWqZ&Fl+*v@P_%xK1g

    mdrU?=NL4KADyp7a(O!u^|CTt@6bg7K#7DKb!|WIky=tl z3bEE{)F@#>@f%%F?!t_NX_HLHrgY9uMCf&wt-TzKeN)a4psKmCaYP@6ttB&rnumv; z)=H_oprx43U(F0A{hmdP@Ih*-L~$Z`5ueOr>1R(mskB}Dt^t3$^5CtktN=UbH5xu_ zh>l^^@_EnCVO%S)_La-08dSooZ$Esvl?ySm&FpT?^Rm6qIb@BI&DhjYf7sxnW4Crx zRQqP4H{-^Lb`bO9g~{vI{Xsdqmj}QlgL_m+p3L)Zaw(w-All~X>CwbkIj+3H-UGTm zdGbWQKdVrpm|yWOGvH7uUv(A>9qL@v7s@3)&f|CwP9kg6MzXY zi8}@7cZ_mSb%XlJhHSN*k@R-SJ_b!XLAayu2(7eZw2aMhIkPh$5j~`e6 z{#$Lvj54RNKI4pAOCthy5{L;_YbEc*)erBC#C5VOJVJOoWt_)}`ClAI{O?!Y0pIKH zW4Kku*q#uM9KI}{b+~%FYhCc=)04iuW(CQy?Iv~C zO<1_mF*B&ep0PTH4|gwLwro%EMI_fk(#1(=G!?E>+o8e1!AD`XNWFbmvG5h{Qlubo zZRQJ8UJr=wn8(b;exV0VIiS2_b}<-;s%*L8!AbZ8nxO%bh8v5Flz%oI&Y#JnDG)mWwiozv3eIxIv&~54N3|YoWL*x$ds7b+UQL$F`*Vg z5ThI7$HcMwW;3UGRa%Y!g5KL1gJNU_c6R zwL#WrI4lFXy)>U?XPpX0)_Kq#j9B!ihTTQ_q#5+MT3T8{K$w0zwX5?Qqf_f>=-D~% zz_gOYeJp7EW{g!wXjRgXwP)NHOL>7VX2g;1Ksvx7Yhb4UkZbxFpSVPzN*&Sa^THRg zVE8Xzzh2E@Lqz$vR&(#)!NOLv$USx|Xn6 zNs=C|=gerbYhO7BRJnfp!y!%!4sR|oI3XLvDd-!C?aslc6M3;(4V&70Hsh{q*6xDG zu@iO;d7Q!7fBdyHyL#lr5C*RFmB$6#8#p*6`G7FWag^j%9H zK|yxOM~Z)nA||*~5@(oha*QO<=qTrfPA?7UPeb2qDhc?->E8bnI;g8BkKALNj3v-2 zCqoUR6_GE(#zhotKX#MjB2w{-qI7%8xUcF);l!)9Qk2qpVe;> z6Qp-rpL@91ql8XOqVk|-O6ny{a2EAFKI4GGmzKtxL4!k6rw6l6^m}-qTi^JN0HRMX zte6ycAM>YG;~u=rn&$ieF?AkrJ@4)RFETP)$PP)!C?UJj)IbzPMzUvw%#xx-lu?n9 zgb*3&SSjBkBO{57sECu5P$>PM*E#on|Nb72^SE#4G``=@_x--c>w3Ljm!}0|LVe_i z{7;=Cy)OMb+3&(3L*J~>MlBC#dmY>TwraDG5=J3mlF>nv3$8__mP?Y)!@wA`L&#S$ z)L|Meqc9GfH~=xBH`6l1P5U(J^RYhjkzi8@Yb>ySmu6{YHSc*^LfO8ADD8y|h60~l zuyd&DhCunYpr>*#{r_plSxf4=Kiq{fKGu*^I<-KEY*n`E{_XR>li-@ zVni>yy*&bdA7mKJ9EY1kB*ifz%I8B%_)Yq4{8&6LuIxKGBQYs7v zC-Gi|F-&`a_&Nwdk_BJHQ#&HRjKPUS@He{ZI|=F|9S9Oj=!M4N?^|?tQu%9g4qcAe zBBWt-8}qXE5H!5b`_fo&AUm0P*wW5R0ts zSNDv@!H6?x_U9%kwe0aEv_;Q^iWTo=IwKZXh8{<79K`d={Dg}`({yzm3WV&WE_-wR z8(?3Uy~!zH_YyD_7CObH_t$5yXdBM#^IF9qTMLlvspdB4#uDlTbj)JhGu?Y-^a z_t?IPsom|h=O_BpvYAj}bkf%k0;~+aad|*>zGYZiXBIXn7b%j1Ej2jjvhx;f7*_Bg zFk$$VCB#%9mW}FNtT2*u=A@t~oj)nPeE%$Km`RQVJsx z!r`IiC&WGi>L0us=Gak*Rkd_z2pwaIOQGv*-J!@CJ?muFd-pP&hA<%dGjjCfQ8tQF z>PU#Mbi8=?*Dm^~w%0>!V!J={g`Df>IQ$Uz^vmb|lV0(4*V1a1c3R=KWbqE0Xvpy% z%+G*zALm5++rhQTJEO48e)=?;LPY}(Edbd+F4!pxQJQX})z784)1ZV6V*~?SVtfV<0myrYcu6pD4+EiqzvE(yF8x3SANo}h05*>?a-Y&o&8f9 z-W$Y2w%y&+5pSlEhShPFD|x@=ch=f1N0RZmEqPkD1+l(Ran1*4<}#-0OInuZ={Z1&>RHW ziRBYfd(>3|etb)O8C;kaV8Ms@)kZu1%-OTKbN3jHLK(-snGWO&OLL2Sa40LJp-MQa zWM&ZgP!UM;<1ygu%-dOvvYx=`p-q8%)=DTDQXFpFM-~u<8?}Sf`aKWiXf*}RuJR0IqO+Rk6w9WJsub>@JMq<7y4n(s*C6LJ3 z?Ys>}nrP!`9W+huW?Ijg;IY}TQ^L5&#{1~(==3sja<)<(-v>wa>@o-lPikkd^1~p5 z!dD>vdlpQ)YVlZTmJfdRUf=GNA0shj`JQ~zykaTrId%Vq!&sh34a|e@xF!E|$uBqm zFZ1~uZr3rze*6gbF`sYHpRqtZCDxnR39yTY;Vd zXXQ?j+0q}wrp}+Jg0JcR6Z4k}9}lu7%16LlhVCJdx0of#`h#f{v?2zdpWo)7e)^Lq z+Eo*DS{4rLhj=8+?%quc$b=tdkS4A~8i!u5pCy1S%CgLS0K8*v=X(&;dHR3)X11m! z*Yth-cJj}C@HMT%J2b8SeZrfIs#W~37}o@SOj}qV8vm|M%D)X}za#0j%JB8k>JFh; zgB3g-rWQyhwB$>UDN~8|B)@J~<@;!_&i_55q2p${Y}XZ6yYCzY4&WDG$1td^b$we1 zM65Wz0eVz7{?q~Y|A0s4=0&qR+o^ao3iPK+jZe*?ojo%&v53!0Y6rwcVhK9FL3Htm zqm18oz-tt>zo@{u8NMdvfdg=#3vo{15#0XrgOQrjHBh)+iX3-zFV2UUs*hgrb!b=P zzuex{K{GY{Cdb{qcrc0KI{T+l{fJhHID4cbSXDK+M6I3T;owzT+S>Y8E3LiMV5dv4 zL(v$T+?A8hpl)*Ua^v%6BLHqW)Smga25OpffGQc*90c`+1uX6nB4+^bWMJ&cZ-R4x z$3SaTi?;x2XajRshPwPxXVfIde~g47^4*b%7;yk>lqnD?wZj&#g$NhlQ2FB|dE{M( zmivLS4Hp&|Q~Hc#2FV+F!-1BNC7%Mu(aoMD=FKB*-zI&eLaGJcp;zmR4+Pol=vmc8 z_>~Kp8%RxrF)-)5XGQCFQ`6Kd1Jmh$wGWa65WePk2UE&e!rE)tym={Cii!7rV&_AeYBf|{emfG zX$_g3P!SG(krJS|=eYX!!Hb|lj^0i4EWO694Y)Ul-KADd4(}!xvBKOxI@-04;TSe5 z==Lr^`9<{D=8NFM11lS(tNUSaO`TlpvK6;ThGkD}05 zclOzmcUUAquzAe~4ldg8uv0dfJtDRDeDip7T_lKfp~gnXY-4HET6`ShH1~Y* z1(Z7ClXj;e2nuH@^whbqQ^--_8;u4R-__zX?M+azQ;gF{l?_okHoK~Rnji_}^w9p# zTD>Fj|4QUaJ?<(BpyP@APMkQ$0}xs`77|K8B$9R5yR=s?J)8jfth(PNC1|;7e$=|N zZf@zs>Vb9v54BFd`TOr%xE;XcvG4!O6!q??;#Y@zfBEv|8me>{ExOJx>wvQ30J@db zsL`g)E!_D4Y|YLjduwQ1)Vb{lxCO9?Qv&YQtVC=zE- zAKG5+hZGGWi_pWA$#m8+?TP`S?}E={;|N3(MwK`$+XASzki`-j&@xe&h5=#pMUVwE z{A9ldqo~apVhBF1tgI~cGjs{vo?kT7^vX3xyELpU-0_l{Zapy@Pm_A|J-8rsfHn!) z;}#K0#-jh?XZLYIAZB;T8rHbC8W)d3udeY8`>Y!`Zw7IEM8^+)>cER1*Ww+c_2A!b zgcXY8x?V}ld@fHz{iq}Zys$ZsHqo=~xpvJI6THrY&*|MDuV$u(lP!$A-;L49dC-5* zg%%5`#6G;dGRa_(n#Vxpu}Tc{)Nh1qY&<$f-=b8_y>W*C)y3{>tvbb=0+dnJyMlht zAGdhsG-U5!FK2+Z5#N2xUB)GQSn4cV+o^xKwaX2=tQ#m)Y4h1D^~$r}BUm6p38#?M z$$3AQ;j{dW5UcB2h(jE;Q~boRZ5o_Ct)RmtI(lJcluEkS3-?U!J)8G0P8z%maL7&A zZfr{VIZP6wTw-D<%+Pk!JSg`qR_1f3LL5gHh6)!3mf`TtGhGZ$^)`ey+kPf=KtmX2 z;G=w;?#C-9xwgC0s6Q6R^yE96UpEv#{bKBgdWDI1MEBHAN-jXZ`fSuEP!-KN-~<1TJs6 zOu~QwDF}K&;L$v;)Bp9ykFg)l2(yGy744k(Rzk;1|4Qel?A1JnqsRnePOpYYM|2qL z!&0)>>jLGXO|5l-OY9p`U;1Z|A(bMJZsLiV)_9{*9XRyD8Ujc=L$VWQEjc%foGr|G zlbbQ^3YsO&yYw<#W9paVhdx%G__$n$TIKlnfF5^2E)2IO(Bax9&I!!!>L6=4f{vdX zZ4f8hNQpUwTD>`!#_ZhRCd0yNo+8}z5>#TsBc>z!sWJqWFJHaw$;=*qDm&EnyAj@J zT#ubaCAQ4eH4H+1Yc%y{a?mjtvGM=CWsGtG!$cfD=mBTYaC>la7(kjN(TMq+EW^% zZ5$*Ff1u89*fudJ5Wciwn`?|BxP!2%D#m|qUSJi)MExt+HoU6tgLTA|tG4E|FI|Cy zR=)1a+7?@nzy;(ODSjUSQQbG}Qr4Pcg8YWm>_|)oH!_&kR9D|XoiDt3s%1j56Mdq} zB71HX)7>TTajD~aeVRJXc$40_E2rkIsmaZ%r*v+_r{(K9K6<+>{`@^$l_LKxeLNt^ zMa_h`p$RvS{GLgEVw13!|M!SJ4ei(NZ5$b02ggT`wVt3p7U^+ct`h7-5)XILf_1~x z0sPEL zCpK36)whx1YT72v+Ge^-zVE#K#wx@B|Mtb6QoKmKH7!u^iO2!;XzW-yp1E>~csg-b;`?W4Ge z6O6=S?bypw*}sYb<@FJb}jUz*ByA$_o6<}Tb@COk|528lb%?2oxW zE<)jES4Yd@uC(xXk@)#kR~eF^iYgNprj5&w|FKGxbySE@T*Mn`!Lt-0d>H`&Z7EH> ze@gQ-dmBM^V2E{qvDY9$Jlle0pKW`^7OL zi@eII_=2r9*Q)W~5EQpM9ecF; zxy!RZp#w@ckKfkgzr>R5&Yo`fDgeKV8W-ukDXw+!5!N@wbuxP(DI~YM-nI4P9&tQn z1B|dVNb+a-j~L@1iSs<;=pi`>7d())cogm)-wj7NQ)_WRB4 zR{v7P;`Z3Osq4MK1oB|T--TjaH3lKq_8T@M0q)7GNE)L;zq~R znr9$;1&DBt7nbd%+9f|6CTf9u$?kQ!;~e_5jSR6k$wC6-n3ox77_+(q8vSX8X;aeH zZ!tf30C0{ei^Ysb0eS&5hOZwnl1rl=c}&$wr+1^plxdQ~dF#pWp`|?Bi!qP=iM*p@ z>oOtW7Rlbm^EF2`MkvFUQxYs@_A@QLYXIN<9cLlD3USUO7?Uh-*T$mYKCpdGuTB%in%W+xRNck)rBMO`& zb1!LVr1b#RE~+VBGbAWI?bBGe?RUPUFvt^{3t9^xBq@@Dwy5oC}1aHQBe-KWD4 z@~SjPG+BK{CPmjV_Z>HSbaNtt=sRtfO#kXu=vI?42TYYIE*e$$+_BWlOI~1+UC`EhJqVz7g2VOL>6Y1a zw$|(Qu@N_qF!Q>WHltW$M01`u6}LYF-5|&F|2LINGm6$z!`r1dPzI*aU>)px-BY!u zdIEBocP}mtge-yof^qH`#R6lFrh2^)KIJs9zWW8=DQ$-6b8 zA8U}(8DbOO$Bv0;DF=tHnD)77q}N(7FBOIJs<~xfx1mx>IPTMT10(lxGB>n&WS_ep ze?E)!7|IOLk20+-_EF*=jp3EUt4&>?js{E=>wpB?(^@#DU%kM%M0HnYt3cI7cG1D% z;o&~*1*@T9w*NjXW46QGz7v)>_n3HkT1cIGOg?B%lqNz}T^U|ewG)?Bv33vzDOi9P z7&Q2%(C#VbC9qIT6~B8IHU0H2^MlhE;tHj&D!<+w=yiOL>Fnf03VfPYLnPB2<4tHF zm@5G~?1dN-Z(E|*?nwD>?F`TMbEZx=Xfh??tOWj$`24yiCfeM3pu&BhQjX=#Lk!l$6gVoFB8{9y-F~ z+OWg&VMeMI(A$}zQ;hn96e+4a>4U}^H}qOmN+yyeVX`=sh6)iOK6Kq_>GIzNB=j=h zKW>S8%XX{f))4DSdvT5D0v%*l4ns&n#c*UzS{;6{2WCMGD`bFN?)rW95#A;+l19sx zR%B9fmZMNCwA^tHLa2_TcT~>_y+IV~>Uw|wvmLh%ExIXUeMoP6DQAj3#&x09f&X4V zsO`r4=yizWjFI}Ejj7&$fS(K$QXkeQu!XjvhYE8=^{H@kZhsx1YL0L3C(mDhwba&b zxUHzle~iU4a4g+VH5wxZWYnb6Q#fru`rkG|;p=Z78ij$wMQy|W4Aek;jz5UCJve3? zI(;Im(d3hRTmsC@4^~V)WYv^$&qyw)_MoM@1mG6Ej^Z`Gk@_Esmu#jiyc;p>EI@BO z2gTzR@0 zvH|0Bd}j1VFrUqxK;Mjs!9?#X59+~%^Rkc86ZSVpmj}=kNwsFZ85*r&J(lC0C_at^ zT#a@(o9hV`kDCEEtK7czg2-%7DNdG#Bw5Pfv~E!ilr#D}*%_32=~AfS#X9EW&%#4o{`Vu`VUrgNmr-va2<5)PMc{@h zF8eohSfaZ&%`nn*n@?-P2*> zto@z=y|OD~TQaP(5CJSMg!ICHI_*{MCo)A)YY(tUdZMQA3}n$0^hJvYHZFKVrX&8^ zkQy{8NYe%fOR>O$aQxd~zTIl^8KxX7!jTz;v}ITf)O8g`GLL(#ilZ_RgI?+zVa+0O zmih*Q+1wSLyMqw@CsV;t?g+u6Lx&Ewo%M1vc$=C-n^OZVl;S7nf$OR(yZ+}mRkhDE#&iJ< zQqes^-5~%_rnjBXt~k=KC{#h)c9#;A(=W?q;Xy8=b+?bcT%!(mvpFa44M>xe^<|^p z;sNo%{1V@9yNfFnNi9fWxZ{A)fRhb3?!KrM)XViu00MRZO_2@fUWo=W9 zd7|^*-oG^p!!K;Zda9s1?;_9I69X9t2AubQ+;^@OIriQThdO}-d#0-qS4Q$4B$ejg7>)4&&=aP!uEe$rJCs%~(GWfV&1a%}M51>nzz zteew=Y=6}kjnJt zl75NEy3ns+z1e+a;Yd=KTtrU%%!~ll!JmJxmjyCX8F4qWKrskyoKOcM{h1FHu_Ju< zfv-VhRA?_wH@lOU*Wa$-Kx3;g?R2QzCRiHKRhDpuVKzGvI|vu&E_U3)dM^IGkX8Uw zNh;Iva7Ir#tnu*lQ?{V8VoxHMm{MUcdjluqKoFHUWXW3KGE6YW`=`Bt^ec?PUMGx< zjTJ9ncAyI3s_s5k$8cf27xJwDFp_@giObF{ap=5EC`de`65>Lhf0zGEh#Bon4Ybz` z%CuhM^V3yCJ22c15SoC{0Mmo9N~46R?OH!6V&qXznh`zls7a~Ec2m)F;{`D?Na2_BAL*n4keclYpsAM@L` zrXIH=8Qzdqj#`K6Ht*svjTrU%cJzN%%szp!ACB(06UgN{r_*xa9YUCJz8MtZ4ZL_$ zN^G~$9k9Ql@D_Rm(Q!KtL5zIZH#vi!K6vGY+o0H7=VCkp8TL#KbKhR|Qwmp>0i#4` zyHI$O#@L(YQzyW2X!T;G_l6~fV5RnXt)~%ikcv1l;o?>9{;-;pE6M@hPK=<@yk1XB zkMiHL=B7p9ekgQBiVOC7f^x;(4%aPUd6HR^m*F@gMZIgXGv@vXp=57f{{CS-lut}o zL-y^PlJIR^;JdUEi!yUx=OuHso=?JB$pnC zxx-TI?MQ3fT5ynrV-veVn8E2S77!AKl;ktQiw->u0}0syCdLj+mK>mhfYVHe4?nY2 z=tq@+jxPjLCfS{(bbpAF3OpIOwXmNd5~vduPM$oey|Zn{>0`#LT>{;7 z8Q65|W`;-Yr`>|mMpu;kZ9O6(9mfy5p6l*YYrTxfe=?=}?%UDq6;wiCq!HUr_u*)Y z66^s~8Z?MRd$NB@yZTG;xDbSbrU#gR79KFivR=;>;c@1ZV|>jLBwHN+=wWLH@VT&r zFsPaC#dzvuTuE(%T18a#XP<|1(J#-|hXMRCAni!2!PSVg|Kk}zYctKm!yifuv@H7- z!Tn!WWtvZ&7v&558=jz0B8vqw$B|(#%(S4eFrih6=P8n~C0r=9uF(IFP)3Mq zm$5MPW);O>cFR8&v;fMG=gw;+BSDGc&ECV)SXMvb#O6s*Ml=)zgfO#e&C@jB5lUcE zzYmA>-4Yfi8v*^Q?BlmY*-ntUo^eTJ7wcvW7e)hmtio&oz+3v)+XV+3WAM3HHbhd| z@n(+D51Kwd8#d;^jy6&P-@m_2bZblsu-6hZ-dO{Fk1(Sf0tvJA&j*@+cy0@o-S#0X z7nM7+_g<{&-g-7j4<5IB#>E)5Pld-+FIK#{-EDmZGJRpm%$f)E`*M{J2H~ZpN)8o(^DgV3a>)Wh-vV&`c zBt@1~D*>UnlvP^(0|K_x={?vb*J5IIqat$Q?2z`gfIh^gVijkc;cS{knQZP&YqV>}8IKa=uFWQOH! zLK}Hix|%&_H0&+^NhUrh!~9a$lmV$Nzj%*WCq@GV(2$~b!TZB`%ys6hYih3w36K{2 z7@?X11x$O=?Clo3%2xIC1b{S(?!(5^USQ(Mr%$Whi`?-a3&O{vbRWmtYUm+yy1p zFcEswxacX`P^!=DVqOzLRtKhIJ(%5T>Kgl@v(7$zFq}KdCn2$0j2Kz$XaWETj= zFQY>v=5P-zdmkb{2=PbG)ahx;6sSi~-V0F4u@VZT%xr0pwBa#Q0@@V0xv+>EeV{+g zCPr?g2c({p$jNjxLWBY+6DKlAMMfWdN^x(<%B>AC%pn+!g ziOu70&r`FZjlZI@pymu(calR=JHDBR63K_zoDx+y8|{Uf-*wau(V9@O&pqE1nxj`c z^Cj6eI1u={Hn_Aumu)V1NZPQ|iYSowzx-qP+dv`fvd%7!rHC|(GU%%ev z#k1h>F9(w5Z-_!~_QEY7xCM~I89ZOGBVt%HI&w(`6hTOx&XisS>8HHb$-%b&t0^~!kiJ) zRz^7ZUEk5FJbt1xj00*MxW;(!J0%R>Jb)q}%LPDOOyLbEaAcn?*Fjv>iGC3Z<(UDB z%ERZfR@zjjHq2k)z=N)BjPJvC2Fq+zg!c@*h(T0qZtuDfMXNiwkopL z3d;605|R$XHi1q6y|N#r*v5BEf6Z(uDJ?+r>VGl1fPfS+or6v*v@xDeQE4d~F=kPP z!Kjyyg&8^mor>n;oN3?g@Ozl^-`?@$e8-*VOoD(#aq4WMa7biLcjwISfg948ZFnBt z;vX8-`yb+sch^zjK3)5huo_*XOGSGanRB5}`TPo$&ts?cXQSB3!fuM0%8a*qx|ScJ z>q1Eh(mA(RBZtNeoI0`Ti^6i|yTLI97Ht)SGL)q6D%()R!^kBfJ(_o3zh$J&2xtPd z_6_`~TfTCoK0lB9=0#D3Lu}uH1H~yT`eGjjNwhIH@xVhm)@sI745-ZTuJ}@3J{`1l z@W~~M+4G6h7L9167PG{wk1yVgt4%Mrb5$?z@(d=1rL<*#Lrh#}+P-jXAqb;`SH24iPpM<5Y^$@& z3nEm4O$of9y06~9KUPsg1HOZbTc)JMt{WU@vG`+L4xb80--fI#Mpj^ebbRe4d%<+eui2-Ubc>0b7xQLlP}@FI0u=cQC1rzW;X36nbd}zAU=m$>&^L zTpan~BArkDBE8+5Xq2+;)juwsf)sAgb%#&K4NJIZd8A z>4@3FdsE$22dSJJl=N`*RvzRK=NrnMtd23@1)d6~(>T4n%!Q-3d)gJmr@B?c3=En? z+wUs5I7fWfo;hhW&9Z*>`6q8TuCY5HD{6rJcdwk0dz?(!U1gFNz{c8CqlvQ_`t3T-h=2(rj*l-^PQW$<7-q-I33@F`$zL|4) z13Ccz6I$6hNV63NR^rA&4B9;{qZWL}qt+WN8owQgH!6zEjScOi`fi{YB||v!EKzTU z`M5i-jcujZ2!mxsP(GYlB)rjomnxmx>Nxu2dffvb*Pp-A@qIz}F}rINpP!aK`d%6L z@yGga>(>y)Z-jg{Z0#4WCHCM&f@H?$^=G6r-VG{G#;X&52yQPIiwRg_Gf|LpQ_s$bVRo9;y zccd99L)N13ltS~Zkv-7f-iE|)aqv(=t?kkqV?By4IbB`-Jp>L-NTINOkYcoaae@Ae z#+Kxw+q2O><{rQEQVMsZ?vHJE?62b6Z&ZjnW$zdGrr# zr5LuSgUJ$|^x0aUE^t5ekdaVadsPv=C7MNHnp*4o|qC)(}z%zIptM2J^QmsFW_D)-;T#(tp#nOuoq_n4h{h| zRx8{Pd#a8#)p;!!j@X=XV^>Oqj2c+&}upd9zrmMvgv*&eB z4cO`Ce_b|9jKf!q#kTN39aBQww6B$a`}Sy_h7qSptIwQ*{id^;KF2v;HAvI;Z?`6$ z%gZ?PtqkAJqrh^z(hYtEEO?RO3pz1q*{no=Sr6V#ee-*Na0RRf*@u(**kgZ*+VGJ} zOAQrGOCWl-7o)M07u>n)58f|E3o_z`VzNu$MbAwbTlKyQ>Jj;&Qa3VN``^i>XxH_d zRAb&Kq!i=@_g5G7@5j*dHIESnh{V*_#RGal>opBK_}7xxbr62V`E@9otiQa)=LWSG z`%k{?Gj_iEIg2boD2T?brd|wd7ML2`DBbeT?b|)wpHZ#R{r$6GqP)m5d4JLv{i9RU zhNIcyN}mQ(Y;q~?7W#uQZDmDfbynd%o!-RiRS~uu!PJz!95pi50A%m={7q9cn+BSD zQ-MfTY(4(|zK(nk)53{9d1}D}3v=^HFnI_0e9`$bLt$4R?RNP#S=bY~Ch8i<8Jmg3 z-Q`%wjNH)siya(>#+Z0GJ?q>pI9}>is%;#%cIx^+VIv?hkCtY6%j{P1&)g-)ZsMN zzs3QKSTOFS%UQ@ULJ5vds;IIy4Z75FioZ`!xe0(S%C#-eLlkH|aNq^+&-s~|mY19L zhJJ&=sF>2wb_@4@+}bIBo|3uL-4(;vRw*egBj$HW_49R~%QLel^eZW5j4dh^O%KK} z<_GV)#Yaw2Ngm@C8im!1KPqPEqp~_J`hjt<_bYrPC_9w0huQyEl2Mdq%aR7hFq#%s z(Udo3WqYQ@LKvGB@;xnJ1;arV)8j{pAUWlnUD=BYVsO6TDzzq$gxjJ!q|--aoHTSVobc^wAUpDQ?mu!`%CPgkB6@Lec)a0R_iD*7l8 ztv5v1$*D`sw1VHie_u|EF5L`0rr6yw#&KIX^{~_25B;2}Ul0K@@|M*|%09TiabIbl zNllRpWZ|Oe-ie2S=}fN+bbm5mL5Zq8&$OGr~KE1rE zAHSstSjo20D}E%Hp3qtV({xL*++HcAH+_Z2_MCe+3w>+vGJR3x)6{9E<~A15A~5~y z;^L8f15y1TCDi*K)vRu4QO}8X`7q-TmTjJf_=kOTI<;-<0ShK>Y8SEITksjW1S(@e zdvLPQ`n|FoimV}JIPuI%D`+9kZ0K%g&Wh}66wP+X=oh}D=PTk(iLp3k*Hr@5z5gp} z=gr9=wf1OqnC*|S7%}0$H_N>9@$87!l7YLaRXln6v}=&RvJX2LA1C+FxM-~%KgvO; zWrq$-z}G0fD>#3A;`hC6aD)yUJ!+AfRnYE$lJuU>LW7roq0=3 zoJl#;7S7ig_#Eqrhg`7fe^qC*KI~lB3__FH$ftH>+bac0ANc{2sbA z)1ltc&r?3ikXby|m@~29&4W(M&3fm1@g~JQ079~F`UAZ#4eKO7>Jf*N5u1nWvELSZ z9MiIor5GT2pW!aL)LCh#D!n$q=``&7IO~!aGEW z9pb^rJ;UKqh})>8(CS@d@0ypFmwI40hfGioJ}*sD0_I7wFINCq9tf!;5IjVz1py+` zX`{9A6k(R&$zf;{_=_CG=AGiAB7b&DQfJpWz$hWkF6tUpk*K5L>IpRv4X{e*WXdev zr&p05_?&>JGwzU@^Re+pD86-a+)ojk+}8PK12=Jo-~8LcMWT+Bh5+pImc$_T_;C{A z0o`OES-NN`N*NDYGNpeu%miJ#)0x5#08Gg%e@oaoUcqqsR{Y-PZz+2C2vNK+9$^-_ z9Xi_cd`A7(7x&}cp%FB*352?1I(9+(@&nZrnVEr9H6FmPTu!H6MzVVeZGS0GM>T8k zdOcL%bMg{J|CJ6gnh~3Lf-xBpqaj#L*K>2n4NbwaI@9p5qPK_tZ)d=Rb4w4_jurD6 z>fHpSVR0j0FcyqFF?-&;9)tT96cj*JY5_5NE>Y!7pm7XB6kuQt3>3k5T2t6EC`;hE zQXq(=hi*%hOVsGOPfM#G+=Nd5E~$6J*O2dz?Hrc|k~mSNgvbDDz?+VPUR0DCr?|IS zO?_{Rd`$=S$burQS^$0hQUjD6k~V6%T4M`BRo9@Xk(F<~{L%ld62TiIy4Fc1uglo5 zypn-$FOXpiUpJV) z-SKzP(*;7{vn3v?L#_;i8)V|Oe{4W-%A=h2P;$yUL_Aha1K^&HQ5|b{=k)8NZVmV# zG!+P8(-D3O-3I9nSVN^}X0HsxDn=B#`7W3|g8DX6e6L5)*L7KNF*Q}!YyHgy42k}s zz8o7rck8b4J$A~>x%veyA#eKdq;H4D=u065bcJPstPcYy=dH@hB(${~z}5b?7(f2X z>gA}G#JdjE=fwCD_35#F05KZ|h`9EMow;{RQr zCg$+azR2=oE^^}bZZ*sAY?%$NW9ab7)LiQ%m_Lz{zKQo`;gw0brMS9at)M(0>r|Pt z^cZAEZ{m8qI2b!KR~PB?vGw=5qZc;gom*C>3ABoq4B@tdvqgO=PWh#fbHpj0ESAet z+5gzpswiJ1qeLgzsj}?w>dIxzKkM@PB&jh6pEYL^-+hQMX<87v^3lBIb;ep3m8r&0 z)%nrW`ua>LcpsPv*zVn{^3STge&@nr(**e+fq65H!>l8?rXBQ`v3Fh&hYV_)T#TojAQ?`d zJ-b_@SCK=m29(_e2oPU(5YBEeY(Tf>t+NN4Z9$_02i_GoLfXbs-nE!1QJ~AO9&2%F z?a!r#Nxvt^oDudo##F)4(XFYKRI_5n#ldt`LdCX+Py=txmiED%zFs%|&*+dbLtCh; zA3STGSfbWQHMea103b91jL><>VHX#~ShN|mzS@MJR00CSC$aAH+y70^ZPvD8!T4hg zvgy7%Sv3YFj)3WQ8V@z>Wnlu@>65kK&9fuGMwdW5X73mbvAsje8kOb2aK4-O%slX< zp5bGHDP5TOF=)$K_+q)`0>1J}UBi42XZx zx?jw9+H~tjGIvE}phWX(e`Yc(3I17T$}%W5+}Fu!?@FSall5s?glY~QicCtLf;@}vdMpH83+?SUJ;s2ywIDhWk?j4>5 zySIiTte3T16|&|yCh$C6J0j_O!VZ~RR*S|(ja2jLDt4Ffq;dT1$2^Vd^MaMvxg~uB z*5YWK0N&WJF`OHbb+0!o2ie3XO)k3td#BbhzdVUWKEBoE))x+3%jtmWhR@GqeD`+}@5&3? z!p7igsFPG>`fT`qx0B)_U*uC?=c4nC8_%D&(|KxsL{Z1E`$Lz3bhs*XMBd)s8r%I| z8AOg%r{J$7hcX1}ofkLQRIAy05UI*;4vS(&XhPj%h$YlLkWF^4MnCBmR9o%LRe|W} zfIou1F=&W~Zz0^TvbLv2Lh&+L`KcXkkY??!$!*LTdI)=r885%3A0B>`<+8AksBNED zGm6HS_Dv~kYkNEHgr-T{a2iRJ95j!avs=4=>*+h5_To0J*9PBH`x)InN>n=wXA>C0 zkH%IsuQD;z`1nh=QnyAFD zsjak1ZB-AAqqpyl|03Ais`_mEIj>GpkA}sUHX=Wg8G)P{QoZm>nG@KII}sW1iH}RE zNUv9**~bz!y(XFy-pugSw0hk8@rf$*S&YeCj ziuVr%=R+aT5Xe*&!7H(cO3EwUmF-CoYUS(eE6wJFq`fYmBdZp7ZqjD1M*bWR2ohMX zd<854t;(cniD-ADwdIpPR8=x7$}*Fc3ENQ_Q+$IcE@hZ;rY`39_3lrk(E0v3=BORZaDmfPdcf`9g;zv9!4 z3mr-8|DI7!OoIkZDOC_F(LHoUq)T*SgqHjK59tSw@jE;o#Q-*8<8Yv#G-LjJ36TP6 zlOK+NXvReizS6KZ?S$H{QM&Qa9b`mLr7WliZN;jZU;U}r?JuOdytD3l_0RFVc~Yox zXeC3!aFGzkr6A{MT3l-^4y5=LfI8qOGs=G^!S^i*7;*vsO0{`&uHRlta25e=^Ni~U zn8$hOR<>Z)%$b?2jfjFZtHgNnfp*Quckg7;5_po#5x9xzngmDM`CVRLw{kzCk zQ5gtpQ#1po9PpnIzZL42vkVfU;ZVlx%=)N%1FTADTQbamvW|O|Zt%^p+AHi!aT%H) z=wqiE+!3ZX{^ci_v{Z?ko+RDkQ`K=G?IOWe!9??cnc)XZ7pdDb9iCH;Jjl|A<_zT7b_Ke*ALc0$I zY9`5vSF@3PlgpkGLk(Pq4#2V!?jVur3o20n>>T(QM7;A+(+)OfmZP9;L|!F()#tAF z#VnH9PR6rmd&sdcY}Uq}cj){PA8~&Q%%v1XfDIX3jI=d-FOs(Y{bS)$@nTjz7N_a< zEbOS#>1#44`B>5(w*^+C8usoQ+`x+XFIlbkZdtU+sXR6@xif`(7QI#RwdrmaE}Rir z=OXtH@{HH34q~l{lye`bFA6x%6>u^<&kW^(3FjOt+i8F*IyXi7poJW)=D>?~EBwcs zo0}e89qzj~Az;G>W$*wQV9Q*b+%5JdVE_AHr{Gh=575op&`?$)@q*x+c5%EvKX(d@ zDDI_2@=@}7VvZK3mzW2~0!@GzIko%Jvi^mCkWiE&*(t|UqlHT)?ru-X)R&Z8WE~Ybu`JMGohBVU2gP)*TRX{@RJCx)(bYG z98e{0u|1vFM3lW0;~v!V^mfMChTVhK)>K!cNzb@{zi;;Nw#IWzS&qhbJj?QRIBZI< z0eTU4Xq)LgOFbNaGZYFHOg6BaHRHNu&g8dsgJ#IirZ6t-SD7Az2?Qn0%!Z~W@?mPfyv^-DRId0-(<4u+OWx}o@~eN**>jy! zKR3)dd+as+5`kYy7}GHNgwjiw$Ia20Jb{M0zT(A<>lqmlK@mlTpI;Zo^>oO$3O>i) zD59IK)6LFdlXO?Cb7%~C3+_;a1g6YI*@QM4BA8Ty=^85SNH@l zo6_QaV*W1WlD6?2HLf!CYpl59PsO2L+m5ayz83qsG4E^R4rLM>DmZO|Nde;E9-60W?x}9_Du( z`*9F-`!0_|MkXAcw@gW7ucm1D032iCEp_{ywynxgES)G9Us3S@pR}Q^5oRK)P;(B9 z_ruNVoxO*6zuWfq_jyPlU{siQbwnpaUfecsdWywc)>H!q4}uW+hSMb^ORQVgex7Cf zzwW}byKB*WBKZoe^P-9di+~g`#q~kEV2g&$Jj|avjEct$O4em9(QO%|8X4oir=G&~ zM&pmN#fnu+a|@=f6&GXJKd5msGc&uU8?o;MwA7H@UU_-W?fh~rpl4|tc{Y@#MrXUG zzWNsN_}!4;+79O4Tl=QRHzh?&Qw>t3L5%}R>0z%Jb%+PkXhGskPVm4y0%VRQ%N00V z%uL+odp+UeYFbo?ZEaWsJ5@yKN?RNUmaEsVbqf94rkPpQD)f&{Zrra+#=Y_vIz5Vg;r+m9bWsBf2@gF6ah`k-G zmg^85C||SSccQAZ8&#VuGLXrHQ;W@+;iH8jmFcdw^6F=#1_*dC=|qOv zv|b}ES#U!S#4g?$^uG-Hr_GwB=jb1=8?pFde6Wj0cc%Xq?Gl;-k~})$s_93Z@9O%J z_sr)J2#SrjWO{bC%;`NZv|_6ak=@ek2@+ts=Y@$yrztH&lOu8-8KJa#X=5|T{9S$~ zm&9xKpD8AT2S3F4PbX*Ym^~(j4iNl9Jo+qD@xY*8tIJp6-ckdx?YQW>q1T=GS1yg_ zgk85cP>XD*@ZPL*b<_u)gvM}Xn?ULw;82qQVliXZtW5xLE;D+KXe*{8_39nOe~3AN zrUBX+_1Fy*`Arlx;S7$$Ams3fZL?;rpDFt#nI=%%ye5%irXdTZK?H@=kyXj4B4phz zu1=_LX!xs(ZM3v>w?@~5+;H=ta_D2_(57wMx?g}|<}P2%Yc;@)0>}=jat6o(bO>WE z2p&a!-7{xt6t)5{;Y5r7tnjeWcrvm;a#+@6@o)M9h%=frQ{3x^)qi+mrGMx zfeDOdLgM`P`ybVJ9;@8l>9GD4JJl}0z7Y3%3lVU-v6e9+rtp1Ue1dywsl z6)R`0e4BIS^Y1mji@TDDZS3rdVacTwJkIH$Whq|9O7t5&$K5|lxpgM<9?2f|k44hU zZas}RL~XiK^2p3yb_-BJsWxgPBuG@fLnoY^mR|DB2s%0alk7r}Es5mgp==2d2mc$Y z3lBZGDN`*B$wJ~}KMqZUEx#L5`_uZ__Xap*7WaSeur)0YJ+P4`RP;@onoOq} zS^MpQwMarq$+-v2gwbL~@Wjx)F~HjlI{V9&FW1Ny@m@DkQkL2TL{Df^zpmsG)CcWG ztlxX?oG~RU?QrXk>%OUfzWTejm^G)wq*AAo$+i42aVp8)X*95z2VKx%a97#}%ar5? zH-{j;F6QhVh9jsSk|DOjAJ6AMVM&F%Z6dcXF0luI67IB030{?_(*AVr)oTk8;F6oi z3(&P4JG79f7`aFCf{VGe@4l~oxwgWg`@(m!X{9*1pGW6w$5avr46ugo!cl&PcD7FD&|&CUv7N(@L^ST z#XaTRf}5Ay@7|P^%Q4)1xp2}Ymwj3Q6Ohk?K&7t!e7k4zl`M)@WbyEKx;)eU$f!!1 zvuPTU(L*LSJ~CzgvT=rKH>;jThnL1|ocO299Ov9CQL@c+}S)x2mvM&&D0?t!LB z(VpH&6&o;V6=dZ}RXfPD^XYp;Gn-#FF{7`<{HU$9Qf; zY0ZP1BZvpqsJCtH?LBX9>l9-$9tQM3e~L$B&IolvB`q{X7Cq!l^{tuNN#mi9_mkrI z881DIF4fzvc2ZYQ@7A;({f+caqReOz6%p_@NgEwYL>gSlyKl?fFZ*n#-=0qT<5KCGX*%4zfcPU^JwPNPmvYNd%>b2j5_^M$u1BGV#{ zZNFD~N&9I#)LXTh-Osgq(seFQhoL^xD%a`t@9+HQB_c)~hD9}C1v9u;%Gvz$SuZPk zobpe(vElgfOXJ=bmTM=*J`Z99S?9X1F-ao4POba&|3JL%i{OZk{Ou`yV zEP69V9E3mVL8u`QYmNgAMepmGSj;`>m6m_%>aS75*Mmh}t^R6v{PS-P1X4gP$&#pVk=y42N7v;ldaw$th5*BhMs>*M4WJ9hBMb2U7J<^VI zQ|czDFz{IS7k!WvCXjm{Zs^>TP2YDoC^wl%mVf^~lXvc-nffEA?CYAVy9z6^Yuk+e zT^fV)HwL!d&kMg=LT=e!FuKbIFwdOl;Z*+_=ccqz6aD|!Nc@B=p9zkk2om6v=h z%DGn_t!*E(o#OkgFeY=o1t+#}+Kfty1!(ve4pMk)D=$-J)wN=7p%J9(uoe7WZH z19~_6*yc+w`qt`ou)Lh};>ECMUL!8mF*D05Ggs^sxjmV0=FsjldQP4cbxEK3v!)*$ ziJ38%W-mYK&XgPO^EBf&^k2=%w|9T^JF{|l@5dwV4gT@6@?*{953!4*Zd~9M+agw> zHfR8GHYI98LEGBfKKqCO+emdqWo7frXN+F^HBGzc13u$_{`eRTO}Uv4Tqh;7WVygr zQ?|wFZs``9K5bjC4j+@_S~V`(>#{mT5xXjG*pXg#ka!lX^wa4Q5_dD;Ew8!X`Satn z0-65EI03vxdbYgZPt0{!^lxCe<%G?sQ4z`;_6`nG1uAQe5Jw45qh(tsua}uW^KNR8 z>-3eJ@7ZN@cC@I!aRV*OZ9hEc4>g(f-g|JUOJCP|>qPDL-dn=9Zg_u-Yj<4^yyrXi zQY79s9gpykszinw!ueX~S8H}+P*B%y-EOCN^DsO(AEKJzZa=L)A=*j|*Qc&64-7PE zVqZ!UX)9~BV95x}Ok_9@?fQ%*0}{JB+)a=6LKQWfSIj>Z2B$i0OuvRJBQ(^ByMmW^ z_3ls8O4m6K#m?kzExiY~Uwq|zO9Gkb)+uVjnV?N_SUoH7FH?gdcQU7EcxA@qZm@bu z<>o=%!?;$7Y>G+@W4!vrkJEz|-!cim?PxrA3*G|n_2>8AIueqtw5R3xb9B_o}bCc zRCw0>=qc-e&XgWmpkXM!5>xn(Gx+Ds!kH@?&UW-eAU9%{m;J~teS%iK%Vphp!m3Sk z&JRExb?KCEI+di-8E_G<=u1|}w+snYLhZLhwO4(R0G;}B6v3dE1d@>Ld;)oySPLFYRZ# zY0bbIMTC%)=(!y(`3^mdLTXgfVk{KTS*~3>l;Ssz@|vWxi;49^{NNEdIY8)<5kH46 zj?+dw} zPkmC-Aj8#CGia`7ppEYP@4qH@GgLz2V+Vj`V{gA>p}`sR)U~}Pr>bpC_alH3Ebopx zb8A}P(-=6o&RpEPnI?5^vrtZ`@vvtyU1!ey)IGST#?hzFI(tH|qt3a-v-h8JsuY~XDJp(@nzYj_*p(m; zp*;M-98YLOqO?uJ<1aT=SK}OhHJyhI6CkprY8a4wSYo1Nnv9dWP$HQY6(JP2?znEp zo1b5A^*m*BV-vhNd3~=sZkV{pEw0TLD*-G3&Qn)@?f|n(mThs&01VS9(Hsdk6qb&9 zl}humx^2Ye=dWkH4J%09%;N+Di;vfx;#;$P%DV5)JnmyIC)*(XI`jA2S)pQCDG8j* zaUb9HOZ$%;ERR=PWZ|k>v0sabR*xE01W)+36x3Y)_RV|Mh?K5sp~1o1>wJfDNK@8$ zkFC4m!0dOr3+;3g&PT01*D!sJRr=Mc^-IsDgoK7hHcNWz)r1g7E044I5Ev-6*(u|> zM~WLa#4_t}t`lbntHZYh%9g#?0;%#zv^<)4i>hpQvH9k1+01h%vU8<6`SI4lJtZJs zF0aq5U&f#1EazlmU=c2o2AJVdRPhBt69O`IPnHo3}%|CTz z)`ifSU^FGw{aI7MwG`&@@t^0ia!1mCDvui&@;qx>B@RnVmm2XZpxP>uO0~CGNry(M zJQ7(l096KH+l0@j%6$aIRKk4tX5Q)+_VeHGp0YZaRHSJ+79JQMdk?K%ZsT*x>2+n6 zK_Mc(*fQynvpIGBPs=|gE0Zrmr})pGUL7t~`p^>#t%IzEWARtmp(8%I-|ssz)snR? z8NK(+Q=hmcJp97%VdI_eK1#ej$9UFLC!;Q0pX*E{Xp96>I<6zN8LVT!?ynb~tg+#( z35{6^>fX)mznxqfHb}0Wr{sElxP?X%72K-CzK`B}udRB$S&2dAP`evhB_*CReLr=B zm*9la%b*TN6GtIqhw$Htx|iNz!M)d<2C<0*%3J;an0gbqp7XW;KiOp|DkNLcLQz>u zwno_^p{Ohs30cb?$`UErA|XX0lBg6>gi?ftMoF@znkJzd)JT5MYtDVn|L^gb`##4p z`hGvl^}epxcBQ?J;pBVYOXUxHyCIWp&n@nG;>Z~5K1H!rZ@}bwcUns>nKN>4>0Uq+ z5;$oBKfLCl-$?=G^aHj> z>Y@M5UiYv}lLfGh;&cI3o_}3E%7J4ny88Cr11MimlCo(a^=rNp@Ware^1!Hx_TL*h zRUQ5Bjr!+LZIF)Aw3%Ki-d80B_OFYVpIw|a5I6yN_ic2ksgH-`k@Ij!=->&LUKuWx=F$*A`xd_gpzR%BL zHQ`n7OG>K0e7W*+(OW?WSh&{|y?c~L?E>y3X zPFF{7Kheg<9gU6YrWMfL+=ECoSfVrU(j}X3USE22zG%CJZvgN?5*LBDC7yeL9j$lC zaJtiLbrnU|K5Lsz`tM=huHEDwH^;O+1=P$*v)k8K0sF_j&XnXn>weqe)6A6-ex03N z>!NRe{4w)pjJchi8#8gAK7Az6xN{bFTdQBcho62T5s9LZarla7j+5cz);M7$2oVd$ zA(;sOaIq-$pT90bYq0=)$L0~k{Kgpc?b~;TlR~?8+h{T`140K=2V4hhB*tzflCuYY zIntqRG^t`N4SZX`smva}HpJ-2L=COPuJ$JUyorvEj^_$y*c}1CUqzsb3wSn7ddpImrTMXYVqzLNSk#N= zVyvShXd-GfrhT0^x(m9caod_oDD38yhNtz~NCUIG=hjnvFVGILIY=<*{(I_$tusXH z45(v};o|zMD9NLxKzUayON_-y_j*&Mp)4y0u0oj@vb@M|!A`I}KE|;_m%mAX0(j*P zMi=v=clyLmnLPOn;*Qk1_kruuLL&8K`o|-1khcQ<^ov^I4Slu_2zGjb^5%)%QA#Ho|Z$Gs@P6 z>MTuD4UM)ffH}D%P%x%%<}@2JxW|yY@nuU?{kK(^Z3SjB(g>nC{e3{zEaO~nA~7z# z{sB`nj;ajOgLHquV>c2Mr_>9!BpQ4Yn2)9gS|I=LzE&1k+bYfE2j|V}9vu(JyTMQ8i4} z@*NN=dt78YtB`B70dyx4r$G~d{_ie@l9#BxucjBljBt4e%I9s3iwN&4nhxJ4O%fJ04dpkP{y^?cB|H6B`XlIR9Q*RxT6Z1FRP%g;=R6c&jtF-Fcn?;lzZuaDX zX&HRp4WClV4U%nDn-kQD+iN1zgOE-#!_rfrcz^i59Ox;xpyb(qhxOmDWOh>V-*%z+ z4K;fMcaA=_Etkn>`pue%b3oo8+!WWv$RX^$ck&wNYj?T8z0&u<;lmlQ1dsTk3T#Qa z(Dtg6M|Eg-Zno#PlTO=0=UqP9ISr*Raj)qV2@_3l0Pz7?{0?O#45EJ5HvtfYRqt0acz_qT`jiw(|r#XE0Isb z0w9Mf;r~TNMZLuZ%|$Uxre@}>S!p=!lyB-MlYRPd(Y{|5BchL>CcJ`qMm!!g%@TUL zWXX~ShEV%!oh|!1FD)JkdAudJUeiMGfwed2Ra<|<-JyuN>$i+;^NCdU4DFvFau?N^y!}F_fe#B>~5PH zov7T}-S7H<;#aR0y0-~(Z~9cR5IN^~MPuV#e5l$xEqi81Zu(z$5fpt=S4X*N@Wp4; zRtU@)w~}h|vb$0*TxrBan@^pWT}KSp%Le$5OCLngbWWc_Drr5irbK<1o!rNxDbQ9E zo$--$L}f9VQ|aT~j>NTkUw)WVN$42J4e7$4FW(tjN#|EzwofajVBFUmyR59ndA^h1 z(dsA)0R_TzuA~IDdK>8VI;^g41Gk!@$yh2o=taWI8a2-LzEEkPfnvQBJs-Ha=zV%2 zCC5xtm=tl>G8VmCm$ofr&tye_HZsoxRTH-olyLFEQToRBcsV9bnxvy^QeB8uVQ%AP zu$;}DaSZyRt723Wp)d$*8E>G%+hChf57rk%yLW%uZ#Ypq^zuz~R9xYsnnazSCPxTO zIhlkQe;@qifeEvtQQx4N#Gl7wVnlt-ePq6nlrX_>==p%9CF64FvKPJjA1p2|u1E8Y zXzs9L4D5!m^67`)%C%KM)Q^;R>aAHE0?es1qe8LdX^!wlua-w0Q%|M|2H5c{OFx zYtbP?a-i0n_GC(Pp1zjWwNJxhFVj(Ni-|!b@G$xAg$oxFl3a2AOjB)6h-A3;Cn>Mf z?2drLe>i`#ChrNI+A(BT{6S$4=>(pl=@*$EquSE=T@dtc9NL?|KA)v-0jWKK;qpta znS~MJzVw;adifn~MqEy0l!$LI*Z{xz|DLu=wv}Wjw=aJ(XR;qMbXQiHUiLX@fP35+#yzWX9{;esbIZ z7`}~A55q(C@ajoU1NIdNrQ|tMn_PSiBzr9y_0Jtca?0Mt4To9=Q7p6J~|8oVEGO6%e>w*&BZ~gr4-NBMBJM=ypa^jr3+u4ce;!qCs zL!qI1#f^i*w~B3`SQAxdxu%-c6!iLxA#|hn7-BB1bNF8e@%sjijRuPG&;d1{Zhm&$ z@(4p2#chX}4^YBp#gEYFN{^r~LAlH`efsvTZv63i5T2Kdcf5K(R6Y{kmARPjXpGoE zy50R4Q?}Oi=5`<5w(?z%Mf3J}3}o6jappfA>qKahgbyfy^(Ea1nh-#(|1kXNQl+QK z4~{=h+tniU$%uCixVIjg-wsz!Sh%J|Cz=C?iAxNfbsS4zi62zXvX#02fC{!Ir)%gOlFa6`ZMAUh%(frjsqpqilO7GF1kvu+(dIAIQ zrud1LGR%BqM@!o-<{MUpxc1>pC^XZ$EgLVfVp-4vYiRV#T`+(CdT|d4tv%GbMNl7N1CpmfVcfT{Y>`j1jA1?I- z+i`%^OckE7IjtdVe(X%~T8d2LtXCz+NL5VgXk>A!OTR*wnmHV1uML&$PMb~t5xplYU}UCoVaE$(-z)0On&GWdtI=Db30uMr7(B_op0E1;?5i6t z9<#?^Phu;D_6*l+|AFHU18vdvyL|URW3HJV`ITnjC@)1c?dGun4_VaAki->kC>Y+6 zm{J5InuUs6an=b32;NFqZo2h=tGq)coL(Hi4yu!BF86b~Yj*E$`06afM>^sOIxQ@k zmGRwY%|l5x%;(q7YYi$@LkVPA{M=#I-VPj$<&THG85fS@A-TftBB9?NUn)WIY%Un{w ze++4BbmAjLu*1htI`dSoyihExh{g6VzWnP1{_|~|a-I}$zD5?Grc|?-_g3g*Of#}> zN=!5PmrkfIyq}fTNnJgp>gBlD8Gd8Tb^YIWu?=0&!R+MJvQa!g=aO`{v_MK5GiSst z2r3w;UgLAWfB!DV2P_K-FaQVyeoatP95VR7ziI-rw3Pd^Tqi`y#L`xF;BoG3(Y$%a zr8!DE{LIEI3D&%S8n|`2l%2*L7>?T_1Vz)a^JYEoByeZR<}z5-$S`7r;to?DYH{ZaS*G-uG5<1L?i z{f3!=K(2ANhpjJ-ZdTT0h=-!FOYzD}#SxKuoHEZ)-!W#Mt=v8}x+JAMJm*L!H~>j< z<_TA)dT>V!cAg4|lhWf z(B;V$r@x|Wtw9)&2wY{}yu->@*IjBiuUoasnHB@Ic{|4zfY_0Ww4Gxf*>^L1dZ>hl zQGL(KvG!99H1?~%TX*2lA+5Rn0S9gEhGe~;vrXBb3UkMz%bU2lBT(?0`ZT6K{&s6S z58A_VZsOH?7S)=GM^>D{<(F^4ymD>RR5zB=PHgc}{8SgGHmRvt&oJ9NtS|JpR22lwNo7u|xa^p!hRo~O*{o9Sc8JGZ)+ zrF8g9fOOzKgRAr-?4IT2IY2sLPZviJb=I&b{~}y`&HCDs&eex$p8~zFrg*bYclUzZ$KJ0!{ZL>Ofw&5Hu6dg)G>bx3N^s^maW>f~>kgVO9!= zp~JWJn4)MNbSc#$m~crFE` zjiDQ%ga`yfKKCwbNYQVjV)2QtpzP0$pe`m5LLi%000uN~U%p>b!VDmlkw*n!>^`Kk z=T|dIsyk9GK9Q)pz}p%>o&PV#vFpC}n4HZX6NJaP{d1_i-y~$Cu|CcV>$Gga}-^sabn1} z&@r~ExXWa{GNkW0%*Go%-Bh&VIU~4=LN05rPe1%SpShq39I~a(QR8=S53in+{c!A~ zhBRF!P>w*-ea@}X_~S~(tFZ>4n}pZFJOXY!votUvkh3#b{&Etpjf$Qa%2P5J|ARKFKa0+VcMk#@q-Tk*n2FM5q4Gun z8YY%u))2gI`;Hyx_oVDvpER>;W9)U8Z&CN>nf+Z%J)_o?KAJ#=Unu$p3ZbwXc}cF} zu?bdqyw45_NcwTAtk<^>%5f{p40VF6w7HDx%Wb^G*+td_4OC%x7wb2P%IkhI{tfl3 z=VcJf1g9Q*vnRoA%OMBy{T}V%zZQ9v%RGeKo;py8s+X5}PKws34C>NWl$o-E3{jiB z&Nm&?&6l=}2PzUkT-DNr2gl*{Z$k!$+wW{ux1>i&=7xor$5?9y8$NYffd<*^q-L<+ zgw~BJ{@q7yesQo!U=g+$W46nC40{wwKNS%%g6;xlWmu2yV7R=o~9v z3i4&IRhWOMtn5Gc*|p_G-)7Dq?QlA#pU4FmYKH#{R^49q*^T2QIn?8L)cQ@U@Hq1|09 zi_GloXvcVK{7_$&{xBYO5q)U(kW_yMM~^|rx?`k3lF^C>n!#U4cnDI4O8qxxZ5s*% zUyw@PaA8i0u#%6Hr^N8EXu8{+G&|z<%3z?Pu}Y`xJv+LD-rvrv(#^>9#}bP#(ZR5R zvTlXWmW28t;Vc7ERww(w10@6NJ*$I|u%*dy)x)l~-` z^50DQ8V#x0HL_}Gy2+K-)Ft}FOu7b4!F!htY&K!N+KxND`p1a+2DWc3eGIl)q!+^Y zAZ1-%-2*iHSx6d|%8w*}pG&m0%J|WSdL%1iB>x%H(mVb<-OkgPZlOb%-pLCm+q2lebbV5rYVoIzcAgh#eI_=ms56x(iU%S09m9?Bim)v z)m~A;-e#;|4jV8Dd%%#&BXU_-yW#g+^&857S@W!^p}*I z>j)LmD-cn_1!_+9WBI5S;|?1P{6RGye7>s=VdI9|VO_*52UF#_@bT2jGkR@!?Sk7k zcMwK{^dy0?>uV66!~e##p=ptDAK_kqrKP2voglk% zg}YL}d@wXL8**$cfDjo;z$`+TDt#AArOZDFAjy?l1eYk=PKtg81_g=Q$BnEkF~C5_ zJT&%(?nDgPotq-@WuZHlI@rR`n9jmTakbEO$eA5+hL1+;8uiVl!)}*8;&ZCDQ}@D> z&=$X1%}luWTJG><8=Gq$H)G2Nmc8(rJL-Y=+q_XL%-hxOh~>dWPdBo?_x%YTp*Nc> zDi2WlHGC(VtCZ8`MO19+R06BoL2vezQ|51~f^48Ck8p54yba|ffeFV(`;6j}CcnVA zpO`Z0Q|c+bhci}hb-4FBGInnSn})teU9n}COPy<5xiDwfbUw zi{53mwYAu%o_|{X?3Hl>rEX|>Uh?}~*3TUFbH}K9n!Bni)b#XS`ZGcv8HT{b(Dl2{A6+d$$>}>aA}>#M8xxw+%RWWey%_G$>*! zpf9Oq&M#7JMx0`gGf(|FtuelAd^wtfgir4FZg?VOsAkfRSn4UojgRgyT($MMBMw_l z{qKynd+c~(Vml}Qg>ZYrYW_BhGz^%qb@n~;J7LDFdnKW03cJ6UT~e@z|t^>&wGmcLg*($pBM%&DY! z=xhr#+*xbOp7vtb<{knftk-gy+A&`mEegQB#e-SUcAqER6iYM;AzDxdpoOyzOtV!b zi9}(B;_w9*`%!bKfsCZxC>??i@Dq*Mr+4$^5Uf}1N_+B_XUQq@$+1DZp{8Cf9b>z< ziL14;KiUnumlm^^RE^RbrF1ECVIx#_;kA`Lxf&uou`lX({95QvpK*6qVjbYrUtZTm$?|PLGxk@!Y-G*dBMH zLDpA+HyOWZIxk+n_3&`3p}Gq^jB7(Dtb;Q94^=AQPVk_ryeFu-KA8Ulr+$VAmd(|f zoh4I@N5KR?wRNs>m$o$xl5xsuem~B2Pqc{EG%**rU z&dXu;yt*p1qt}p@Eq7aM?WUWm&39TU#yp%Q$BY;3dcHBjnUW1WZ@S*6{N(z^y!S>< zta$2CmzDX6dL6;X^sDPUnMA=W1fH)?Ow_2q%eNo2CF|}x&4hIs*Tk;E zM@*QY6D=e0wUzS~Bv-zq+{4^#rkxu}F|l)Rs>3mXCg>@})&xZAi2`8eMf)HmMm3a% z(wxeri~sw}yiJciW3Wki%t_m1*TOgB&9{cd=H35@u~RZ?e!Ur89~!>yY4i49yEcs8 zj6bLZJ_xtU&AW2U+-Ay@BM&SN4$tk+TgFK~?V`OQF&Ze9T{(2HzWy;F{`AqKN3Fx0 z5`EOi?hNyPk|27=mW@k(MV~&X^6tq(o1`d1q@)YKuC*Vq#iAEj?V!Hrc8QhoSQV?YyEEG3(}|eDr3QblW*#`mma80M*nL6 z*XUh~jl1^fu`ecO`j>8MYHm->xJYK|?GG2CuN79n>Wr~d=TosSXy3`eqG}b+HEe&b z@)TxwX$9@<w$5!RDdonE=_#IW>3tR(jh@dt141zb zsEjQ(^TW>w`bv6I)`JIofGQZ;a7$`hol!wlgq}+~vjMUU{Q1GS9ERtr7~l2l)`ci0 zQAi}g{noEWZ-o|ZyNqGb^jZ_9wc2B)-Sv@T4Tk@{vi`Fy;i*HPvh0TM;>G4l4JVwe zV`7#*-vXR$8vWq$KcA7Dsoy<-+XNi}f|F-+CtNPb3GuVo8Fg^j&ZeOwpL}5COw#{Mo!0KAL!1_hQqxNOPVuo#-_3+%xwLqM_AbmULC?k z8TP=fg3`}ah*N$l*lVey_4Tu@4(G$h(2gy)xJjq;a= z%9Yb;@086ys;(szN8bQ@>$E3c0GhGx0JVPmq2h2FvmcjW^!ljoCIBrUlyqb*N`nIZS3K<{70gbh@6V(k} zc-17wVar}98}+pqWOmuVm&MjL8RPorfN?M0w^74hW$G-{?ju+Q5)*?7`taHFnR9wA z+Q(VDztnxib$oRMD?Fg!oAO6MVshdqJbLvNKGt)(?^Tu!)`w1*6sk0*i99=!`vrVX z{QHKoe*Z+M@4PsZ|BBgv`bV;8{WzTAaM35sZd3D-*NLcTr5A6e>S_Z#9)9D&1@he7 z_}3aUuvf$G+UaXXt43ZAgXWWnHB@giPZfyU`Qb9WYqF706rmBwlKEeW+HBTsm~oY4obJbbuuOeg=5qeMtd zyNT~R{pnNNaIgFT-JM@1^cnf6x$Bqx-R7?d^AQFE>;R$nqr;E>#H3C7C6oo(Rr{_0 zO;@}f&ghHgP}a_Yyj^Em1HC2)4>zl39z$pL75*n*W=wM9p%bkA${>^;joBGAE+zOw#YPM z`aw*^-+cZhSi&DpN)>r z`4OjF#j(`W_d@q*LxS)giWWxBTWI`2rZuCcwAvP0w+{$mK+l}1y;|tJdxaea{u<** zf9r@*#THQ=t{@6!YWt9yMvG>up4Ko6X9bv-;;*ZUxj8rH+o~csqNQ&|PvO}(98~?H z-#Lx_Jt?y2?F?S^$lUT8t7evQ6AWZ4FhL$}9)yW(ZruFw+P?tZdPi*iN8kCz{yiHe zZJj}^6g8|_I+h`WfR_J7;kmZzJp#wX(y#ujJxD!bHHzh3V<;&Qa@<{oRpb=Djrev* z>-@>$NC!K+C74(DxuQz}8yE6J?#`%j??>~;c>iA%Qtt$es*R#+S={H<$Ni~FxkOg*}{pf2Z z&q|r;7(W4*v_Ml?I?W6U{)&;ad9q0Trbt$LYanUQTnyiL9M_ z>vZY&{A>-{fUe8Hz@r(leV4?}RQ2mHy5Rsrl3U@8-_E zm8>WFa^TIly>*Mm;Kc{d=pcwWLP45ng!`o```5c}zb zOURARvl*Rq{b>H>`4RX6yK?@>V2nT9&0>vSIwTw1poljyCKoKP(_a+{le;{ug2*UG)>vv=tx=yj zyNp<^Jrzv?&BM1DRoNHO(Ej_Y`pt*r4=4(FMbF8ZBylE;jWx!-6$f?J*z=*7_hR{{;8kgmwkyy5 zIHhdrRMa^r%w^Pm2vmK9@6*3HzjmDUmhKoyE-f*w{^T}wnd^I(d8Blm0aIq^*S1W{ z1&ECoTM{gqtwd00)RB0`@1;6mM_8Y5cyO8w6uXFY+8?jPHGG zvbA+XZf572PIh+n9}{yq$XVJxJ9*&MmaBVoX}inX%YOB*kp%67>N}W?cDv@2JIQ0B zon5KpB~%%F5LcrvAGV}XE`67b(hq3AbK1n{ya!1iKs!K1J?Fj0q*vg`AEm3&AMhD? z{PB?UNkhKm8Qe8qA3I&u4;XP`XA1_0^beCli|dtDRmV}>o0zBtv^sqFH`vhC%ki$7q{Z)WY0xYzQS#{SN=E1E@i`O6mJ(tS{Ina;tPB{bZzWz+u(;ew(A zAfhiKCjr#kqPgkF3}>u#UbVn^@sdoDtI(=JGa zJl2te+Zs^jQ6Vj0RmIRnZzR19Nal1t2C6jbBoNW#zr2xdD_tA(bXP5{M}p-CiNSdqho%-Vgm<6ygIg1awc?#Ckl&Z^mN{GAAH`E$bPOT1IuL{f}Cs znhwv}t_BQ1<}bD#!PKFrZ~7JwxEAgj-iaE-mR|_!N@P(r%{4+bNw;C^HPB2Z3Wa(I zda<|jbQ64dNXTB?o&f)DUh~4k!X8O4i6|%qXbAO@3$4lYUI(0DzBae>c6x;fraw@Q zq-pf(6lT-y{XdEM0R`%Jn?DVic8Vp`JO&-@?7W7IfAptWzp(R`Ig75W8KUAf&v12^ z7YfC@qB#Rtfo5ChvrAuJAM&H6FVnu;rgFjtMts{BSQ7@7A5<&&Uk zJ>wb%N9qNbTlH4GI6FRaO?syV>4@uu^7>-qJ%Jt%PiP=uG1w3J3YN*A49E)>71QN{ z+6TH3tH^!ju!Yv0-pVa#*y{~D(9_+$+kM*bYV%9}DYrs9_ zl<+%=3U_ibIJ5npy8?0elU5V z1pUd555h0RG=r>P>_+yQr~Y^F&ipdFnJd|iE+k$wK%49@sp`?1Y1N%B720l(D8C?s z)|eIF-sC!PyHn(rnY3p-;zta%5#=u)~?m`$4w$hgae9Yy7LCD#ecutf@Mcu#M zS>;8|moM2#N;6>maJVBA>z|Xpd)2Uqv%3!L*f%ETW#654c1O6Rom5oDZr+@_#ZmoQuofC zvv6Z7(EsdyyhVND@#wVToV@$+c`4K?M)7wz4vzln+&YR{SDv!moh>U8rz^1!L=B1A zl_5ij352;J-{*33&DQ)mh6&d9(ev*qLI+7xjEYFQI0hu73C)0Ugt5!3_(DquW+5r% z9Ic|trnknzw{l;c+v?oMdc(yU!kgR78NzVrNmbILY-lPb0?bi~fPyA|ANcg2mZv0_ zc#RosrQzHI^g*5y-D39##hrkF7A(n*HhWhedrGejc*lJ>HR|;$mJC$vT%2?I5;Phw z_`^Ofix;1__T4kiZfY}E8XagHHKs}&s3IB#@&Fl@Ra8?WMg=7%4gxrIMpmZ&<|pES zCV6vgS{3uO>3??TkOEOy~dP z?zX-pts-J+axQWwV*(~jG21$Odg$9m?p)J>vD6`CKpN64_NIpnoG&|VBqxgZ>cRH~ zPi%J1NDa+mW#7y=7rzZyR2S3?iuZ%Z(j53GYbcZTyaLBxYOgnxw1bFXXo_UM!bIkv zn);yyZE)8DRb$oO&GI*R`g>mzPTu43+yh>dIQiic-a&z+EFEMnnjnmRJ}a?H@0CF9U%RPIv zv~cdbeU5X?;$}0Z<=?yaTML_><{Q#sr=D<@E^Cn{4mdPRG~JJmMy0LlZbpa5RhLKb z##)g|t{9XZ7t>E*|H+yybe{f(!W=W6$e9zp-=LbRM-KewtCT?iT=bivd?F(@5m-;{ zrLnPO$?bif0jrb;T@HPs&%ta`fAUCRsIvM(Re${e*LPYWYB4dMPSkS5rzPP+c4aJWNj*iC_`CQL*0v7U%_7P`3Ug|17yY!jPF-#u+ZpLWF2y zED=utwd@dLpGZCQAL0apl97Z%dKw7`=AA8FvZB|gl>BiLLM!=P6zaqc)vgzF3s96+ z7Ig3h^}K>@^FPi5byq2WPr+stK%d>ce572U!$JNoxWkwh4{63$CXW2w=V0oq{ZL6~ z6CX2gnj{51^5-e{Yn8uwK=6!hvw_?xc{DI;e09?vJO~Gn=D0`Hx?&a^G2O@dYAX7mQJ$^BnhflK942CTM-hpX4J|cYSViVVN873G!wjiAnXjs zX@1GwV30BFOYW9N+cp`Ej|*)1?mYCPum?z36b;X}I^RR_nV_HNXru!lyMMLM>*^l`3HTb15rG ze>KqbB5;z^=Wb3;AZ&Zbo;@Y5y2;1JCv3d9OV=mYD#G92M7O|p%oyLjM@0L=hjF+H zd}A!~WIU+=k3;>UL0W~-nm%sn0Odguo$%}~shl-}-tO&*mA9H$m+YDvtxduNs^1r1 z8(?s$-uD5l3|+PE>Q4_-IzGn0qes@y?BiWVARt2Zz|pVh?DcqgASksU#!Zbd#Nz=8 zDfwQoVK8I6B+ZJzi8-F}W|P6^%8H5yw2vZS1l@IICKt)`1%q4vvj)_C{_;XIgF#?t zObjg8Vc;JL*+8NeTv5J6Hk=T(cbEgP7HPF7!at-JcPQ{ty%as&okrpZ;GUK4=W!E1 zL~e0zCoNM$&0a)fykHUmq~zia^87G?_i#6i0_~WXaGh~PJUcl72bjU!xt#k8s_RP+ zOK4oPrN9mw-uZP-SoW(|hD)T{^mXrzjP}g>7aIj$S(F!_smYnmzkh7-l8)fJtf z{n)KkrTXgp@uq!8H*@t}HY9|2gE?pN52;&ZhuVF4;%vm$R{c(Mc_#ooSPgV&!M451 zzNThoiXq-nqjr!RFIAp@ zbvzt7qsrrJ`P1n6Ff^oZ$}!{Yt8g{OAQ$)}(G=Jh;bTi?F{cTRUe+C3-=NiXt2ktW z#RC?AW=WEG5-j(vn*6)b!Ga&Q{EeK>&2a0t=}Z`8dY|7t@Q|*_@7QYW_De&H%0xBG zRcF3MO%Vk>N23;xTLFzp>(H{Tbl&k3ZunmKITKdGfB*iYUi~d?^_VmmQc)MIf^dM) zZXVh(ifh5#IRks|e{6UOYR{DhraFHf-rgPoK+u6}4 z<_>$a`8NQ2uZDjLPn-Iej{djgJaJl?);t}Q?rh?_JPQ#|F24IZB`i`;l9$HZaCI!Q zx~^T=zmG!j6@!w7S*=Jsr_ORy3mZXS8qRoPbuDr0m-JtRQT|l6gs}ybG&F3qK)RNmaas6Ts zNr(sbCKXJ(YqSWh>7pPCs4zAcrl0+A{M{1W=z@cxot3Ju&pxV{uzWoeF^|Eue;P2Q z5^Ff1Kry+Q>#3w;SC2|3O|nbd7CV-;mc;3t+~NH}vU5Q+O>uW*cIlNrQ)|VUq|isl zvsRDps2Gm~x_`m(c=JB$5+a|U3;g_c+{JCPl1nApC%E}8ms zQg1_@(h03Rzpl>m!96Xi=-Ul7N;C(h)E1=9^7%06FNyET`y(f~2u=R_Yw4_Mvziqu z6b|igYTA|RDQgNY*6loDX*JGi;=y#yq071eJLY`bqRo;f1f0^);~?ZvJ_h3F{xS~Z zLd|!M{dnSLA(lf&ZMl*8t$uFB1-pGOZ|w_Tl;By~vET$BtoYNw{J2_bMm%kR7~f#+ z57e7=-e!yPDlm)T@iC4xaY+LL)tC*_?c3Lnp`G?)m8WMUJ^s+(&)-@oj7B|P@6Ao; zb<9BH&P^6P2DPS;=HTYIK8;CUP_q3Kzc+HZkmbK8BrfmJy7fD}$s%EhTesj_^8KXs zuabt%UUIoEJG>w@k@J6g+mae&L)xx1HJmrrqYJU%z; zDUfmCZOb3i$W)v`1i2=&0?0&b zw1<~09ck4{)_Tk7D_R+%JG)`k!h0FIU{+i2&rbCx4SY0T+}H;nmN2qM?=~x7h9Gqh z70jheF>H3PtKWpkv%woX+Ftcy<8ET%if+~=cVL!D1 z-n_nQg-7n%6&@$tSI_$%mHoN;kC&#e$l7JTe#qMyN)QMaIq`@2hlWOM z+O+AR>A0;owMR}VIG9!vII`#xpNweTkyQ3*(3A=EU*nMew~_9e7AdVqbCCq{~9ja@{B?cuG{wt3uz=q7Xw?I!q@|fO^|TwnE=_grIN1A5`*!2k%MtT8 zsD9VBw2j?&k!HCRH|ZfuKcYlVabneILZ)f5MC~0NuH?2cx7Cu`_@G>Ayxkq1U|A~} z5UH%8v9x;j^y$wAy|ey){=uilb9!w=F2Ns=o*3_uClMer8#Bk7EFTRUlz#P7=aS~3 za|REbd!t!8ufBrNug<@czR%ChU#rU2+p4en0d@&@osA|KFLHK1Wv-j?>MrQbuR?9s z=7c8ks-XW5;f#Jtgs^qdv1f*6lA6&|iX?RNubzvP>Ue2!zu4K&%l+~wPo^;KKmaCl7zWup*ggQm?E!0pZzfDWd@gvzh~ zALm?icY%t(TlurUuR<#fhwuQ4duFO+z3l`%uPm2ga+C;f>TDD`3)7B!Oh=4x>a%Oc z^y%R--*#vzuLxINLuJFHBT+igWR9&Ac=9FEA06ihbPwgUE#kWcnc*w;6uLE3b26u6 zCeJ1CcaDjkM-cCPFq0U9E-BRcOD6i?dsH;(rS`v3b)rKMml>~D1w&=Xn>;$4CWui_ z*%=?RYOIl7&~mt9VM)UwwYy^_B%dV&vzI^b+p$aj_8hH0!=Y+pV${d(r*|(jdxR@N z(9Tn*PMKG&v;w1WbJ8|y{tei|5@9I1AM3E;#=#3twTi=o`ywv)GFY61R8tHkMzp-L zakK}eMPesZ-U{^kIR7M!h`iQ(nH zKo(Yl;!@e1+Llc}HrL=NJ+oWbohY=tZ=kQ^U!tgJ5^Hgl2Fiya+K zVe2nCol?O3>&|@{vnb2|BX4KOz7`Ix zH4ruO9wH5Did>w>FpG~||K?XCpGtaGDxBk0%S0=lN(oPC*@B@#jjCp3TD^WVA7;}n-JxL=8TuO6(QL(I7fjaU$A;rS-Lg3z zHgCMvr;Yd%INrsXEWRx%7|t5rqI>2Qkwe=7InU-578cu8HMcGKIHXHkdQ%9LJJ!aD z^yLO~7Y%7V4cQ|fnO@;xd;?I?8N`D_ix04E+=fA%7KY~Ell5;sOva(v9oT^(g z)G_cuZ2a2pY%!SK>VcA~-hqolztAJ#OhVjm&}MvqSeEbb20z7OwS>qD_L$A33C2EhNAD!{`0W|C(#BZXq;jt44Swvd|a(GRy{c5PykVny>F z$gAn~=kL8QHn~OLNB1ya!XxM{EJ)Ssq+{J{@ZiI^NR$BH<#KX?+|r9*j}dZ(d)wBz z&6QFs&5&+)TxqIBvUiGQ(s+&B$Oz58s^nS$wv7;nsp|EGmdF)&e99SJqhaD? zXMp^}mFPcf;(mC8t^ZW$3N#6Dbc{{{CQ*{JQv*18bh(R*?GIC2&-CygdaumHDMEiCyYQ1%Ek9HhghuSBZs!iGRAu=)1 zE2R7#{TlG{t~D;Q=z!U3Z}sF`bU>2&qrFXRZC0A`3%h~h?aRekX5z7tox zOrcUfE|ct;i!`UaBJnM&--bI2`t=Aw1RcU~($gffO5GhLTg5&6@p zz){Q81OBoL3KoRw%w9VjU(Qcd*qc-{0iG# zsOrtwLL9?3!1as!N0^#UJm|-SCD%%bkGi@K^=XgE%j+eo^0%4LaA@sbZVtcWEHI4p z@9HinTE;wS&1u&>v@A^P{@*Jz-n@z6MOaOmqx6%&Li7t=FQq1siBVF1Qfv=F_6EihhklJLgj~3d<|O7L>9q0X4+<&R6pqY^8L1p7ibEx}Pql zEH`c~^nv8u)A|kW4O+0S?Kd_63jVQ>@_xy8uYL0KEF@o6d$=NdM#h z)7LR^u-q<#HQaJ8$BLJYjI><9FH?io!>#}$y?8a>WyUY`D|Z~p zxc@vWv7rYr3%LrExUxol|l<0)>L@vV7@G z4uDzFffnymbZsk>lKR42T`uusKn2;!BM%XoI#BJ;VD2PA8u@j)^q8<2cVHpJy}B5$ znrf&l=3RNKeiy^DxC)HPWuTvPTkq7uXzmCZ^m#;k|DQ%nf5y301h~m$OjUjawD^Qa z`Zpj%79BK=l^r5$uD+#0=i#0mTO-SxzEh5{qruwc0j&-2*FvUBDBw8GuD( z0sKW#F%kU^tlO~{{n>BLL?}z;hGtsGm!b41z$+W%eBZ*UqDLP?mo}J@E_!#gi!NJI zI3Z44%1y9nm66b~D}Hl_}KZ42h3%too!?w>WzY45o^!Vu`cwEt| z0p^MSj`4NRe(8=~>4}K~qu4$`9C_fq zzi{BFS{_`mqsu_hxFs)aK-QO)Z$ym1uGWW)TC4_gJ-3|29q0zMXhG11+1$65Ahi-- zt$hmTDoZCO{ox%V{Rj(pkvWLBgCre9Omxcg?q_@)yR2K6E;M!x%Kr8L{3Cf03Kb7e z9wcG5BJLmHubg!S8aAVOfORMey-ZDy@x>^&e!6LsHU9T&e^6FgFLi>7e>Hv|4WyNg z>kmid*c(SZ-9vGG6m_Y%7AtzVds*WX8k>4>E ze<{5JL~b-w^3eHw{*xR4od{te7D!S_iOohb_^%j)+;O-mYgSbyt6dQ+onyvi_3`d- zs4mphF|RXRwgZS80sG2YM26uDb8jYRme5X{q>N&1ih0^m3fjTN3A3&oQe)zFrGgG^ z?ix9%*#5+DE*qGXXwLwsxHV05)}H~3QF1B7$!CiCRDsna&L}9XL6?MoJY$WwpFQWXVA@*uuBE7!scR(DZGCpLS+9a(ZasqsvgdR{ zR@jfdA*9NMi6xibgZ+P}*&=mp=N!5T->-MuJ1H95PI&iktMMyDF>$=GH+3jJ{-fw^ z?Uvvjr@WhduTTm>`KHlilF4#L7&Q8x?MztD7zF0r4tK`Mzz)3#5~grV4)EI*6LTme zgsirJ1_28hWbSNAB=mPqn%@t!U-WFv36s8kD7d1c|dCy z+0XK2{}}652Q$j!Tt*Abcg%7!NNVHHqq>xtYGI4D%$cxpSM%Zgy+egP&G3 zvqy}3^QC@hOT`-PJtC)bIJ=e3(0V`Z%?ZeRM7o}(|C~PEWE$&z6exS4syjD7cdyHfOf@mm&Q6_R>H7FW=5%X;siN2DNf$8J0m;mUsXTY5SE%1b5{yQSB z7W6Sf?@QnkEYLk{6J2nI@YY>j)vZ5gHUH`Go*0fo^QxQYdky4wB-F=1kzl!kMeILR zFkL!JpE)47R>`>ii$LXoAToHlP9R>h(>4tuf&Sm13{V1xQNIWjYHB^8Lt#o%^A;^e ztuxN`X@?1dQ<=pDZu`2o&Pe6?b$WL2Nf>usQRj|y8m3rnxT(Yk$x_V1k-Lz2l4bvd z_>KNq6|slDQImZu=BW9HymJVu+8@A^<#fH19JJu*)jSI2*+~iqDv#1N-8q+YVAuV` zC&0xDdFK6fwVsWC%%-Mx3qPkGQ!&nW8+IS~y(wG+?yu+G-u6#eXEeX%mBk}Z96sEq zyd!+!bn{;;1A2cLhAPWzGLX+#LRjX zRn?v7@H7*zCV4y&2?9fTGi$|SO0DFTL4q43cQRJ4-4J>G^Pa46ZO85{=6u?4b;PfM z<84)CPa+_X%smX#%+ahf?0UjA8)>h+&xkX)F30kF*O&gTA2yDX9vc_z_vcLjBE*~* zsyE=*k@XLh9^zn@uHW}R``d3N4&|u&1>8aa0?x3bt#TTEPFeo~qWn7(G^eNtic4Oc zG>BL0Gqg)v4pc7v9X`2e{(fb0hy(4lQ7Lp20;&y}N|=yJe}g2R5#J^0TQk^Bg;cwH~H+ z>G%xofg&`5f)t`ejET_&H2Gb8Ei)64Z|ZBrm)Rc<3DL<6qY0kAhFM_oSIy3b_tXDK z*1dek7=b^p0#yT$dr{Gosm)6KibB&`jW?;y@2RnW7vm<6YNntP!P0rli19KXqJ(W% zU(k}uHnuBh#{@}`!HhnA0=5P+kNVThp5fnx>_$X+opiOHK~?Ogc2(@1R* zwaZ2b7|lT=Z*in3FmoMT@cjz4{ek+!l;sY>M=(=maHgIoC=g&gc3#?Xq%({39Xq+& z(mohY{HeMMOlQEn)NFc-sPy!C%(9dC;aqKIEeHRTXsXY4TKKPq)iT*HmO?eRwTn^f zzN5*9+)CUPI2@?s`=o+-3ph=NMre2RD0?vUUEK_<+?w4NZ0(iN8{AJA1hLmr6zGat z(+2W0)&u&EE!(I^kp-3PP;>~N-$68#5^#!|6OKuC4g-Yd*37m%m?J5FJdq%g zT7U_%{en7Krq_eq9a`Ci=<$T5CwJ~<#+>(IMD;4rdL?dZe=(+C(Lg7s{f*u98CSuW z&&LE14k^SAbKtRNb7}MudbKkOaueV%DoR;Rt#5PPFS##J{V#gwJTPBV!}Pa|(R9Hd zLY;4YvsJacZ}q77kYBHvkB<12#45Ml^WHHimG)(#42Ogy5UUT1EW!y&t6~1XohCyM zRz+Y&f-5}E8P9t)-)s7q#qDgJ%RYTtvG%0FxZOZ;2R)Vsxd-wt%mAqAwcc;*|GIjO zT&?|d#SmAb@!c;zJq;%%+c~;m%A%yEoUUXNIX~?U5-8MyH%WGZ z5K4)i)|C@WFfYN&Mcsi|C4cGHFNqV#z=3f}r>zJ4T(oJ<>;d!BHa(9yVNbs9>!PA= z)a`m7B31qR+_icW#tmn9oQNWdhcc1|VBM@sBZdz*!oNxCj=)J&{w?9^-rqmJIhwWf zQqcKUy;yrSdhMm@L&CcwQK^5sqk}>n=ZBkf|6!OMhXnpj^aam3A3LTpu+<7&>F?gZ z-=3J5xaL!0*(4D*!p(eGdwiBGB;&TPVj}_MW1-Gkn4(s)xWUBE#oe#fr=^O|zkZnM@^QCUfYvMQpitfOSMR5)g3lvK(p zLPIG;MYfPVvdaFw?sJ~!`TcSJIOkB`@8|P=-{ZQk>$=k@MRCg!NW1iHeokqZCH9*~ z*&;CmTiL$q`J%b#TFBo|o;(@B3|;=?m>T#L*t*UwYa+|n70%|@$+j3-XPmydo>m^Y z+2*N9?>0Dor_^) z>zHUaJoT1Ph^bnC9a=JD#FI2XHZ8o|@{JNu=0w9Dn}6Q`8y}H^k}NWUP8#reYB#lv zq-VR;W_v8^)@DsVCMV%v3}W9M#flPteV5i6Vc#;<)`Zu(vbK+W4Tyc4NW<|WM8gdfJXZee*7A@aF(Rc96- z9dc~)^q~A>pUQp?^$F*PeZ-`lOa(Z2?dJ2(`rYytvXo}KwS8s7Y3^hF{`_76!&vcV zu*^F%Q%{dBx;K0Qt?P}|<1ekubIqc8^Y6T4!sl5>#~0$Mmlc9fW;;+LqrkaCm)yGC z5rhVBDfD_9K`JQCsT$!Njp40J1b72%0 z5K>a53631GYedl7>i<&=&~1riKs?1}>m9iD+Cb+%Lq9pT!#KWvZ?Oz5U>(@bHY$S$G_}n7_vBcizXc4vhbVa!d={iu4B-6wn#_*Ciy^6TKRmw0Q6= zb8}q5s-2({|8ADS?3b#-|4RpI0zCRUSWLPU*JEnTTr*{r3!y@!HTIcE4pi zB-V#jzkV{V8v46_mh9I->BPDZZ<6A2#vSsUpsCRi7LbuXWv5pEY;C`_LBBvocbj?Y zXk4$-iIoxp0}PPPuGy16%9|@?>xIBl0PLGE?gYB8-akH#B_ zKp)(fWf9^$2_FJ6B8ELgsgcLJLcg`-4$j-+@@?^|HqTcZtZt`yUm>95*_%s0hPF$rY-#ns zCHj-|LSTTGd;Mbfhj;Hz$vV|?X>jn+N{?dVgPbZL8fv5vz&@+_! zGHw3-K|<(&qwvS6`Z!2f8X-Nmyh*u+*&JiNb5mNX4k)as5Jxn$pMl@^+vw}*sqD5N zMTz47_9_-HrP!xz0P3Uxc_L){v-7r@S^ni+x>+R`CsiE_(~j#8PS=|EE4Z z*3|I9fVBKTZ!n#WG8k|2dmryZHUrU-LCwb!`$+3$p5kH5EHnvxj!8gAtO|OkX3HMh zLo?2wQ$5zS_6C1E`ON5=hTYVRhYje_qsM7zYXn_tCV@cULQr_f++S)P1T?0jR$G7@ ziczWp4Tc>PVyc}-V1KA}AS_2bS<&|0OoRm|&YJr|^{RMn*n>o~mn@IM?@sdkTb=U$ zX)gPuNK%|KMh4=0bHVKqej;Wo4S2>}@3PllFmjgjh+vlm+L}Fk{uP2lL2lIL6W&{* zImoQTh0b9z?@oEm^2;5%m+3|LA!^8TGkipyxp@BU&4d=QAYy-*4PbnGBagpe6%<$8 z?iQiYK7R=SwG5;{A8vp-(HxQ>_Yw4DyO7J(uA?@!8%{k&{nwlaUs^HM5s~j>dJ}1{ z_#DjKDeKRbYQ3S+?DBfhgV`oRt$p_($CkgAd}f+=K1i|Pyli6YGr0$J#5gw%YYUd- z#+jUB;H5yNmsx*a%_H2*cBR>_pLbj4_o11+?0=T2UoCRP1|OLG6b0TT5V!-&bYkTv z>|-rQ{a%BanaMsnH}zJ-n%t@fA`?c9(6ouT2GjC_zrGnmkt0$yVN<2vLRB_1GhrI| z65&!di)yUV=K~>A?+Hvoh7tw<#n1-2tLqq5DL3yFJFD@O+V6}o`#1fNcjpL2omuc( z@6O$34_Wnl2*Lw#F+}w3yjWY(wVUhC@M?wnB<+(yE!}Z@p$GVw27Wm-eOCQ|{iumU z+vqvuC9aJHhhi`W1dotq3`q3qlGj?@)L3+a+g9cL4I`TU_^fR5q>u0UMrnvW|A(?L z+eFCA?K=fUVOI+wn{KZa(cT*~KJn}20|_acp6DWtove<9?Hl=SQhV$@(@)f{icdS) zpnS+_%#!wqr3;rqR?_6ps&S716%c6zbN0W{!sJ&EIdI)li)p^O!M#ke_RZC@zf#TMOL7?ig6PI%6&vaQt&L9Tt8w*7rH=d_V>7KV&1o{h z#vc*vGX3NAPRq`>Sa!5UfP+D5?TfU!Wf@6hU$MXV0wp$<03Mumen??W*8V{_ZBLj= z(;Sy;l)A?BypHjt!uZM0@7m;9Sy?d_YIL^Y%&%u2O30vL5W5@&m=3;9nlXN>O82x# z{Ds8gguwR^Iy|?3>!Nb&8 z6|P~AY#~x{>SN8vaKI^=pJi6l4$_$+gM~n`o*{Qjbk?A+DOraMpVI%+m$e?q8J4z>1;@4gw4dt zhUS32KI-`i(@~>+L;aA{>;SH^sT~Q*Ec6ToP$*?sO3GC_XaDf7u`n9LXpkt6rPoG) z^uad+vub>+f8aSRHWdyBr^3b1zq=^zZ23yaDI(Q=@d60C3AXiOT|a<(#FKM4zj6g% zWY4qPeQVxT4{DndW4v;htX<_-{RH!uy@WtP0x!w}G=66H^IJE~TXOzeO-F*YNRMNU zJU7nm=yi13g!yTcH_dzhW&6WpZLeKi{LLitVYTJ58RN9BV6r0HD*1+apglyc$Hdr* zd9ut@)(?@W0?@UEFZ(-ZO#fx?o4WrV0JWQh%C^y_teu#pv)U$beVhH6^9eyr5v$&L z<5I$xsiu)ayM|Jy8HHjY+&4n~wmP>@r!LTzDnyRY`}xOmTNgF8ko`T4R==&T{^)$H zqwkaZ2MQk4YWHq?6YQ=Z(l7b}J?p^sOeJaZ#YTJu1t|mwdT-Mn$wbinf z{}h_7FmQ6p=rq|{^(Ei=fBfr(zz;;g6fyJmSIkdk!iKo@27Y^HC)M|)F7#;=@CEmi zh3G;QblnC%vdg^zdB4UEGy`0eA%s}5(fx;ohVBJ%`&bJo z`||Q@)^>B=q~e_zTh}x1;2#Y->5QCV9*cZicOLd8ZgTkha=39`GeZqZo@={r87~~M zPi_2$M&n_?<6Ow;5v}I=mA~|}u59gJSo3pdWzp9U1Gn6A4Ii`YdEc)q&wp)g`Ydsv z^Z!<5BYF%NqOo`kJTDNFvr|Lr@YImhIbwuB76R?idelIjTKV(`V?f{{u>Lk+pIj(# z>Vbm?JEl%I|6Tv2g{*Ru;7sGAudz-1X2=~|mv`MG{$G*v(pm0uQ@^b<3yX5G*Wych zPbuxKzB(%+giBZeuY}u|jzF*dqIo3FbJ`UYvVD|tr5@ORWjy4#MISErdU4dL^PHzL z3{Rg{A!CRqm525>+k5(Q*{+`pRfAhQrDKQRnBxmGY3iUgRkrBI$(LX0}vl!knQDsD`8<(46CS);F$axAj8$6ETNry5@8{y&l!eG`i0W06LbXCejjR zrED8*6TU8nfmQkg_^Fr(>GcsF0^dT%&`^y7OrgS1$sKqOEXMQhN>Hg!Xx$oofYv38 zSmCw6baokftpi1wX0KjNVEfO#Yr9e72uL->5qLq>>42p5qf0x0Si-yu`28~|F4?%y z)z)Qm;T6&+`qE33tXZCIqiZ4dOj0UlZ64qZXUQ{T#cb3s!v6R<B6<$q|`3MrJDyvoyn908^5aTpo~SqS2oJgqvudS9ejGZpY7GAa!N@LT+5wTkGt78FAEAX zPyc1QWJ}hwyxv1+_~tkLC?g@KD^Z#%FA-~|TeKKSAn2ED7 zbKM!gZ?}5i%i&O-~Z=a&UnlSL`2u1 zd3x5CGA@JL^o*);IW4wK^o1=&h5FR*O>^o-!g}#5M|Pk_oW%hHP5T}ceZo?aSxoN4 zqJg|X3e_Sm+6U*apT>M3{=p0p@2-CZcy}2umQk7KYQ4JLq^0V~XZq|e>lwLjfAtwU z2IdGxx@Y$o2pdqPo&xKx!Cw?81Ca;P*m#)A`WCM^&AF6A#H0jx z7as?)JVlh3`~h#9rJh>&KTtd?C4L%;7@DF0h(CJP^MQUjPY=FjHIpbj8ttV>6R$1C zJ7w@Js51&=bW2@w2+Mrz*@!!Y6l>-~&lc}#`N`K{@Bl@i9cg3u!#oGU_5_uIiRjvS zD?IzL2zue($P)O#vu*JEoP!v7ES=6k-aL>NwxCr$y{ouz@(=((6k_jK_uhDsgd1S; zr&y0b;mObu@_|TbUstDtjVZ+49p<&Z-C!ylqC~o0JGHxil?9G$8L#F6XtC=tI|+6N zC7bCkfME}LP8jw~HyNIRQrkS3@)|8Bb3e8P?jr<2iEY4=P|AJApQRtCX8|IRiBW1s zZy9d(7%4G;s>#DjyWpJZXI>G&&C5Kp1}-S=Jh>s%4TL#LW@jsZH{pAOG&3A`j&yJ>*(v-_qx|S{HCpzQE7i?7s#d3asD5u z-9Jfnwh)F^{IlCa(~3aSHnP8S_NUevk3)V)4#OErURC-jaqgai8mb2 z;=diB{{7w~NQSaf%QpGlOCD^$hIGRiA~*P9b=sTfPjr~La^R{TJ<4VGxW}yktLSoG z|J*^x@HcMc?s7UZw()gHyjq=2KJMhTsdwnG5gIE{QDEj0wXk(>Tu1U*Q#6)#=wNmM z&Xh2=4Nr$w`G*#brY7gEj$~Vxx_ZP*KPRVogF<(k1r)p!v-#5Un!bG{EHQr(-vrZc zb6Rwty%LV%+5GuDH><@RT*LuQM2fjjAI$tGN-f0}uG2i_3EbU0KzQ+ABmJ=)#_mk$ zTUeGlXRt;iM=2g)lzad3;ra7Uk7HtI5yod4wmYa+3_E}W3hChB&NXJhEbts~ zs2T^AoB6Yq6Hbk%FVfJ=n`GR3qYUVtJv<%Pky+Qv-(u{z^DdrXAwa1M8HbT^xMU^^ z9_*q=5(5fcbNb%TV=UEU_r8)j=m^|=Qlit$BTtQbo<9LUCK;FVbzyfHoqij(u3ngv z_mxUga~R<}pXg>fYG6|AS^K}ddoR0pNlXgC*k!KAn(s#tF~oBLj32U6Cn3Ud%rjIE zg+G-QAmT=JH;dgNN8`G*K$FLjlVs0{>9@RoA!97zUqj zY?5UVd@&sTW+#42{m&D+b+3t`)Uq}$H~(F5Q??hN6fYr%enBzSGy=m-*-9zBD$pLH1WLA~74ndOysYO2X=n zp+1O%UCM*5y4Bg4fq%~b$KHMZyl#)ah1!$n1U#eyG0l(BnUtQGN3F+1x;tGB#j{!J zaUC@Y1wbqS!jd7O3=k{EZK?e5$BCrf zEzY^pQIH~bGjbfWWGh6xJE}2%I@V*7qIlDL)`Ba0z-}fa+O_BNBzzht8-7RI+ES`| zx9_A6(~!d|KQeve#|f}11kvJ4!neRVx!`o9_t=jFI3a z&aQAbrd?~PSS`T0#RfBKDWh)BUcQ|3<@DEx8ONqDe-nm0&jKK1CzhwGss|ari933r z_l9;iS;w`#N8`*tMwF&Cls}E!D0z)0^lI?FX3fF?rf~V75452%20=^S+#b$bU?R#W zN;J(%A6N{=-kVX-2tm%C;vLAWPmpH;P4ynR!nmQ-h1Gy;?Kmol+}0(gOIlp%b-FFy zjGU*XzyOe9bj{3M&UAI|!Eh2DR3!i0tm)^S4`eTuJ02DUgDLkBL13a{3wjGqAmcjA zD{3uMDX*##pzus>kLI`Grjl`*2NCm`oLJh(eIv;9E*=i33 zq4(}3#?;hSM(_BPAuAfB!P01e@5t7!3Qj6&w(Os6k1EEykhqfIu)jHNx%Q_w|??D=p_;c zjkim^b+l>s$8s zX*ajQg9k6}Hj3_DvEAUn?p4(2gjx4g`*2>*LNL0DCQ{j3w%fGr=1s7dcNZS-O zV8L|A9jk$!5g0h?RxzrEnsxj3ZJ{i|+1BuF6Pc5hJIGa3xMFDxq&5ZO4a1zf${6cdM!nD}67vitB#5)qESh6Xg!d?(l^=^<8W4 z53)YB+5YQBp^eKtzeHWQf9m0e(YI-jEWpZ`$4_26J37F_((^uxMKD9x0@Q*?souMH zZ^#@>W|JTtqPhKajR~z%&d~4b&uHf54$z+-8GdTe?miFvrW76bFl3zYZxM6}BUwcK#Y!iPdRHmnt_phCD@BV$Um-zmx=fi1s1hQ8TwQEk- zqorY5J!{^)d4Wb#4jOpwcS^mr(_DhwFcp{-V)htc8o3P5mC zF9K$rt0A^3_{2Oqg$P*2QfBC~z8*YppCpzRG9HmSpFRwiq73JUXUk^im6ncOaqe{g zziJZ= z!GAj0w4d{AV9faIL6(Z{^7VfOQtgNQ(fKE`WAfKNKKSHAgM$JcTN>NGO`G*a9uBK4 zrWEV|>e+kiHQT~1lP$JmPgMQ*vEt9RKlfP!!C{B_2(U&Mc_HOQKfVOz3%l(-U}s1x z4US>x)T#{m9X5h60_ODp5vPsqPAt`cAAiQ##V7hl5(%ZSiTj2>YYL;1E8cg|UOL02 zA+Mz|!9`r7;ks>Zt2HUfzB9@J8Df#ya_`)6HJI6MGU8H&SW{mOxGVb@$_bYpe$T=C zjf)#btv4*LzsxAJu?3l;G-ed&WY+ih>(X})@U2u0y=rwE4-&8&8mAMp59gJ<0s$d_ zfCeZ29vji2Z{;g4K=$%)>T54-o86|Vf%@6=TWphVid=-EUOAVNt(Cz{{Rf?F#=ob^ zDYv^j$2#fe;il2+nZ_l<)~YgDI!9c$YvxwJ5}9uiR}^bhzQ5u!HSYl^2h{ zcjZdc`uEGSahtBnZK17Dz<3fEpBcmI7sKyy9v$iFvELLeAOvP9Ht;PJm!B=2%+_q) z+>GEldF#V|dhIV^RA3GcQU2tNebS};{6?Yw+_Rs(tpy29N`+2i0 zQ>8g$I&Jgy(Sp&VLxayb+051P0E9!#-fqg2DgO)!x+s#C-*xBzh~0@;Gc)K;s>UEe@uDwL}bCwGtL`)$3HaBBmMp z(z{+-Rn?J)vMD;c4WmP(NrDrrx^GRAryl~k#i|pu-tf*a7Ka9I1&5Mf9yV$2^b( zDV^qiZg_k~k^*zN28|mxmTsR0sM`D;)Kau(D)8>`8lI?hBEmRCV?cnNPtW&zh|aAS zNM%ph6#$C$Yw1QUdqvi%YsL&IF81+E@5v>T5h$=B;O-Oi8F51N}+EO6+iN7m>8tBbgx6K8s@vNDE0CBE8p zbnB6>b51CiV0n+>V+hnsfJgw|s?|w*fA91G`$>$zrPri3Mrt$9t}B#-JG!4H>M!k{`|7K>c1!`nX=Lz7%`g_rf{r%GD2; z0O>64+#WJNu#5<$Kp`&Nxf8Fbq?nyB|@X=DRiw)vJ5LqqBALiox)bcT7EC zXLQ$ns!O^@*y4F}`dH{AOgH$_alqQxi0zhoEgJLcr;0k2B0YO)t!3xIy)e|E(o=rK z@eyk`7#r(lFC1PXjFTfvjwk3}r5I!f_I-HaneGc-na~aToE%g+@yVmXm|H@t@wjR%sMkRaMmwCk9ZK z0!F0Y9&hMcu8`2l%>U4@ukeEA+&MR30&Pph+)Xw*XGBKPR6RUs2&i6bw7wqLUaNSu zSo>n{1E_0m^X7>e=H(rIyAq8kUnkhO>h8y{K-PXm7KwTJWvWT>y5L%fUIV*Z{EzdjKkq4(j0TMs)tt>rEUf|xh$h)_pSJ!1!=B>oZPAs)pBR|nk;MZ|>VqikbGv6|Ro;)Z4$ z-{ShlCE-SUk1h7M+}?X~pT+4OR28-9W-q@m8h&Sok;T&AKh3}uZ37=>OkCLR;7|*F zt|m=^QkkNqpK*bfwpU1Oa(3l0V0aJ@Vs`~4O6(>4MIJ0^za+%T>{zc>?o2G4Y>mWG zn}V58RX`ypjuNi(+w8yAMmC27YaE1+I3(!fIT!vQUiqr7LzfSnR1#pJ+H5Z4C;{nF=z{qPCG#K`1&k`4PUH`7* z{=EAAi)Jw|SfBg$>O2L%D2--iUhAY?-wimA(gzX-rV>Qy-esGt)y%)mI5JTWjC|IL z1se&w!oSGC0)>PJan)0p#aOvlnz}%U5M3X=g~{C8Mo3Z<&3%wLrCprLS&jx#Q#@Wj+nbe{lPXlKQG!``sf7Bu11 zCrCgxW02{9fP^AQCNJ=khzM4W+dND728mI7ogNJE`YLP)<5>l&h=WBK%g=c=tN7RJ zv+XpMYm6rrHdCJh-y&JFA7gAS+XI%LZ|_bw#G>Nm3Ex}IL(jc$tKxW0SG!|oPL4T> z=zX|Ha@fc+g;Z>jrd_sCBybVdFq7ia<78j4pP%1^RZ)%X4l^RS zh}JsfMeYpSdk=dx?HSohQ@Jd`R*@qgQ{x%3-cz;|^01guVgTVaa=@tnZof&ij-oqJ ze@yMTkz$|;eL0t^hH_Mvy>96`>{#=d^G%~v-HWE_VeYrrK3n(1D<_rjR5h7r49k{mX#e$f z^rLt<82$|5T4z3<7RBTGaxW!n;t@_lR#uh>I(Mr&^;Ak`%)@xMaE0SIhDYI`1-=(jWAMGN_vB5u9Qz zHJ4h;ma8wg?FG?AeH+bX_U&M|^6Q(p_&LvT+f#Cb8twCFvOB-H+|JY0wy+?|d|99X zMRYgJSr@KZT~xeeF$-fipxO7&WG1R_rZn_1T#qX*u*CS(3lWjl2xnHjsuNj1x(VSF zatpK(rEEfXN>t77-OSf0E|#tDlrc}Q?b*YmRr`#E=4mnGfS*e-soqKFfsIY#o;;&a zMW2U8OH!g@+UIdoK(ZUVdmGxQXkW7RSIP@<9zLUH%L4P zv*!K#YoNZccwb%2J+B z#Uh=zu@}k&e^9gZ=pSSICqji(2Lt0!%Ti1){7+h5if0N( ztuuoh^(I#mAeXkEPHjaQoKMhT!Y07~*tHq1jpMYMR_(NY-Q>n8MLm_W4hr88*??4+ ze^ge5IL<;3ik zfJXAe2+=K>+~6Sqny%HWm(6aEUdXAbNOdWj1f>n(TEvwOVtCgT;&?dU8)A_cBP9mr z8#pk4A!BF?D^#b~q!%bRY$&VNHG}=@cwDt#Z?5}#H&16=y7#p$G-U*A3_HnCOT5dA(W zKb%TdK42M_>17=+6|?I@4@dPF)qL!=o53qmznrmp{Z+sH#2p$&SH4Yt{lh0G^?G{O zoAr4O02ojNa_M7vPP+Bk2fyBA3@5HY zAQam@631&!3VH~#a`=`e;c@d9XWn_m-l>x(-*PT|{^7$y#@6M9nt?$@Cu<*!^WB-* zWkA&H=!2s2qHlmc6UJj8Wlp|gX@CrdEEGVA#b0U%ctWc(lgB{1SPcer=*JmLl@o%y zQ&d-zE?%9RN}2Jc_S6$af-!`KZM;z_Q+cd&ZXe%Q^JD9&gln9`ExaSyG(}s)D&v@z zJw|H9rn#J+QM_l^FkXA@)vm>Pf%o)ZjJfZv7xly5^L#0d43n2?m|T1{>v`w77iu}9 zPw?!q?GMHIV&<$_qIv;F%I5`=ptbHSXcN}xX}(9%k54CFtO5pkwd5&AJ<-ul$yuVi zVr+jS(^pKMN~jRUkzh^TPL*i(v79p(CNbi=1JJv1%-ISYb!2#Rm7tDW&1z=fZxL0S z$Hc;s$j-C9#3_;M{(YDD+)5qhp7$38%N7hm8NwKy9oMV?z~;m<+-SlhPm zbXmXokuFo~bXTvK^P~K=78^9zit}`?1)=lAzfi!o zc^_=1rKMH&RyrfD#Bma8c%M#1hkj`vcy~5)<@A&~^Dy&N8c*@>11l1y0h^*@pMC|N zPk6Z^b7Io=xdHzM>h3uQ$0(<*+r`FHDhV;I#qD-AUYe|M8Ly;dRp1{OIBD&!h$&tU zMVgVQ^D;LPkpxIP1ZAfQgqHcL{xdGFF%jw#}S<|lDa3YPma)2>fU=r0jr1oT4V=e>+j_KGJ#A*OqQ3Ancvu{=m`6{PFc9MsK5wKf<$@Y~L z`hxO;Sv&@W|M`B^AEo`e$7HAO&s#gl+%`mmuV25uS{zk2d!mwRtKKudfRj1=C-Pt% z&>WIBrG{DvS5#}z^_A#o>7ewcXP@e@&{w6{T`{3kijiM1AhwoL6j1nVd^{DXhlN`T zqgvKzipQzANV_e(%?|{*@2U{XX6JmPCb5XXGnZbNX_?&&s6Ih?rA~~Bqh7x`eb&aF z{&Y-RFFfvq$1A%{5yRh5Bo?ilS7LXvM#E4qT(?@e+kGlkmKHk4O^#ORm$G_a^)SBU zOn$3^cY-OFxMu;FbJ}IEA3!129h?sBg&0Lia9d$r z`~8cr2=1^nfQH`$$4y=S2+p(Hd2tEBH_nlVWTq!fI-mjQ`)gRIShcsI=hXarYt}JZ z5_du%?U+}amKA>qVwXaBzpZ4?xMu5KgKP4sbg=~MY&$_iS8w(6_YoXqxKw+DeQH8lG}5G9=z(uA9#IfBVm0418UI{}1Y zaYfQ)=VGh95xXsW2ekUxPcJf1Wo+z-Hwyi@*Hk6?XDqpHw*O5jJ0C5HF#{gH=wT#Y ze3`q#F2WS!Oe?}|8Fr{ujHUA(qz8%)6W8=fT!5h+rJi)U!W{T&22$;KC0#6hI`67{ zxrjEf87awR%Heihx*ThMRcC<38!8zw%vA_GmVV1pC2RbeCF}YZ=WS)V{F&-B8k^So z&W4l5Z|pulPTBLL!Mkr1xE*Ay1P~`83Gx=_X&l+=#@HX`oOmY^=w&!WX8 z$~(XfZQXcC7jgzgD60yR?VYE%xb$1}Vtf;b3)I>!S##s6R(mX1umEd?<0nr}o^iB- z8iJM0Z3JQ9^tQY^h3i<(UI9|I8Q&Wrr z5#1DCxe?t#AQ32U`$_D)XOWk2l4nKlnwb8ItulG7cnazHf!UPDJOQpR+R3(;ey#^D1|)urKkC)KgKv z$ugo>6;1I+pqollx}tEZ`-_?$nzG5B zzH+Sk6i)#6eTR=t{6GAO0*+8xM#jyk!-i&2vyv^a_xc4jz3_Bq% z>ScUP!u2;U!p*1V=p#i6hL_RI z3=jt>h$){)YKJ3#c7JlvDJ0e?ELD50Gb5_w>FErR+xo<9Dc`tpqr=Ty@T;C>{;pfQ zm^&y1aHrb5?#JsR_h3YNkZ*5P*7L?D60mV=Q!G54?iNRAhsT=}LJYhb4@^l(p>9;A z951|ft;K)=Cl}dvOI6LPx^u^;&!~g48JA%ogGBSPy^Rf`=Gr46j=vJJ@x@0qI(*IU z-5c<$G=tME+uyrGOOpJ&u$h{p(Hy3?1y@F1M|Vo3{aeOpEJ~81jADG!MLxxu|ym8OM4PGYjujFRTpzN#v1Kqv9>rbvl6-pa~(TW2)Hk; z9#l4!xiLHWauL;CVU8)D?bK}5oMz#K3O&AZ8DxtO0N9FU&red_CH-#NQcqkE#d%H6 zT+%pQ@LO%LDu`%xppl$6o{!vOZCp2WNEr%s&!Ot7wzy^|I)6Y z6PdCOio8rau%04qySA+cMq3qcZogN5Hb{4$Lch?iT9tru9-h5>AtMkE)ezVH4z?~Q zmN-E^5uz)Y##4HzO63_zAK$0$MmV z|G`lKR^9bmYI^lKWYym8JeDkL^zKAEr4gnaudKjNL2vq_E9^ zaM!~w8Hs1A+w9kU1r6p3pG_oWGzn2*Be#2zjgpl8^^~q0mK-k%2(+-&s$P2{>0Qqu zcLeO>pgd)+J!XeyKka4q{uvv%mG+%gW$t|B?gS8flsY!hWYiuu4VgK31!@qI+?_hh=3gd7ySWj@g^Efc{|jhVz_>;~g`e?NZT6Q>&;ldgc(Bwx%s_MaOHBaR}Fnc#16h7s=w`fgdLCwpJSeQRcI#Kq!< zwk31ZZPempE-d635D@CI!Yy}Lssf6pjUuKE08Syj2sBcmc6}^pTKo4WqyRX*K>(QW zwy-F{BWI(F(V#xs7H>FAwHhCLx-7WS%c2C8S+o644Kf|OkJ{47<4R1k$fBq>`tHC+ zGCiR3J*?>0I__g$*bw90>+zM{L{4qoWWUtcDJgfiZ8tZSN3?k}@*I8;o_ zNVXrJUmHiHtmmP$LvXS9Rk;jo!Ryk%CGaY<0y`_(J$L}p5qBzD2B;07#K@KmhP8!k z^_X3io?yLJO^7+92)?9KvT9J*WmE9+OK@VXfzZ zw&!rbor-bOc$p_Cdg-x12>ayI#6Ex7&Cb63-(Zku$C$Xhzlq7U(0+1nihtSMG*2%Rd}a^i3mLw-ofwT*%o4YQdTkC6*+ zGg4rFBU}=GbqHXQu4~KTs1Kxg;^DrsJVj0N`0d->m0e%&UPO!q;~vYBz}X-j`6p91 zO*^Qyw%uN@sKM+L?QpO6gXJ=Fq@y9}tr3$e_weRG4P!al;5nLLS+d_!Om}}t4Ql=1 zebeA#4Ao|uPKL&Tx`06I=&=kX6P6dkI0t2r>gtece+9CpQ@ z?#)Sj6@c(>zj6yY9m4*4@cTiJ4_T9C4!LHE3G?zFZ%@jvpxVNaYL$q5{%2p6AcnI| z9FX}=^!C{Fnz{X}wCPn#U`iw_>rXMV5SfDD3p4tx*n~!r!?(nn>v{DL;9ept=`Acq=u^oh zOR2JXkYdS>z{`R`fK0;}HuDRqG5W>Xq0ZgK)kE&lP#_L+`*xkCdGsNdNK&U){Ta$` z2REv$S6|!-1K$wOE?m6W$<`j)4&SpPIaqB)4oZ_weD_9Hja4zOu$tDX4n~E?$tzV= z?fF1oR#01}D5vTMc0SV3ulKA$He=>K8}xbFN3MZT&KRx9ehb(bUbGSSvV)8tmpqJJ<+1 z6QE*1|Gi2WHJx{si8T{|CLvT3Epd*bSh?{gBl{@rUwQ6Xl$d$d;Zq)+_j?tk97>}X z3KGEtXDOpK?VUO|ZWHP-U6Jt`S|*)}C;K@~;)ySOQ1Xl5vhw`k#T@$0wDqcGO56XS zH@}za;)s3t*qJe*xOG|^S%;NRXmBZFr)M{>)7=9i)iLZ71cN%BHZu?64#bC1$sLoa zQ)y$h(O%p0{AC{=zA&TH*EzTYIJlhl*QT>$`Z>MXwlSO1je5>kh=tn=JH+C16N`j< zYt-gkz;uM0r)0?SadruHu+aKvEG@9gc#T!iJ#p5k}$IE4;QW%I6GbYM59zmPG zoq>%va%&gi)WLv}!3iwQZNc7uOX-K!SA#j~G3b`HMS6@Q~oof5AKwO~GQL8@tFi zew6MZ&Z~zA>Xp0La&3vscponR)C~SHJik~fq7UTT>pY{0-WCqUs=eB~Srzyqb>b`G z%h(AAjzxo}B|l20so``=HK}BHcJb>(``>c}Yh|5@_?@EjUVFiU<1B=0ZgLWkjrbk0 z_CfZGp-kQ7(f`a-Ff2Dp6uS0ECcQF4b#v2{6i#cNMCkQt-@-Q5aApIJw>CrvvBHt9 zfEstR)D|3(nP&yG0~=cuq)<@9qEmYKm9XH>eV&_V4agRZRfZMq+Hm=($`W7g=;a6misn zHy6!xUbygh{QaYloBl&wig}5rG3!z@?<3_dlCUih2spOOad_dqv|zu1BR4m>d9f*J0DPkC%6P$P8FUudpz@CVvJ6fqcB%YrrDQ!JZcfosPWze`CLLh4548 zH7=ohrLb%UV{#S}y{A`pY$X!lKCyOs>dDc6v!hfh(XCyF3Bi8|M0duZBF-5*T+7C# zb-)iF{^Gx@MA*8+pfNiAQ1`KvbYb+8>F)VWPxk4aIRJ@QC_OW3u@DCUy1;#9*1XCh{(xZCu) zxi>V0tRB^8T0B`=ws$XnHj7FeW|yiI&*>&~ecB&w4xoXu6IDeslGt z1%k%#go&OYrZW3oHbV#ym=7#nx>TX;c^Xvnyy6cYS3&vUG4h)RP5RcAHx6-y{e~D{ z^_(8GAKr?%>dJ?QKLn7c(QMMh26+)a=SL4CB{GHY2N=Rg$d1S%OD?F_a$(L!-sfyp z6B1hY9G^S)!*KT~m5Y!u%ecw(_laE=+&t2H7tV|yN%Kc9L{qfqU%k2oBLk!c^SENA z6nrE3B>Ep$$~)(_a?L5BqdfE`cQXQP^4^|qf<20i^@y4x_0G6f#Lx?TZ$Af-q%+ds z!Yp53S!#R3B(|Wqcq36yXnvq-^<>}60?L?;ItqT%4?h-+!R*J6AAe-aa8}Xg*vo*b z%J(uiL)MEB0zl>X1Y)_v%{m?Uwga@G>nFxr=6M^%RX5VgmwiTps7ta(8oGbAgHWnjj(12~{%o<2xGi966sO8K9RtY=-f8iO2Zs z4f8~qXgr}+90`HA^iqDw6tBw8Za2Yl$>hlhHF&gT10 zG+vnKS*iVOrTBA)goJo>JrC!(A~*e1)u{ZPsesPZ75$j}d0pKV24x>o>6l9QBiFBw z7aWagCGpLjh@&yBo!-AH4J(_2w#sRg7YzZx08vwqxlM!`cAe#n$EN2LW787RrL<+( zc;%rHgS(|rh(~YicV$k#=d4*HgcZd4pyOFtHRM!AJ%-lL?h&Hc8+$Kj*$&dFY(VCN z6mbhnK`NZnJye)PHQozJGrsZfXuC1WYr8#|1&M-sR#66si+@-ijHW;7!ro5`pBE3$ zuiAJ;*&MD0e+3P?NvdXFmcM}iTtIs9&E0a(&TEAF}^`=FFKJz@1uak79#k25P4^ZIVu(XH#3Z zYuE05zh{Gcv0#hELRqsHI^&}Ti%}tMO{G=AQT(n+tXx#F&(I9+J~>Wv`NyYWt{r?4 zI~O%MCt`GBjK!j^rI0D(qh^HZAxa_Rh|iapi^+l-XjbSDWQ;3J4GbJ1_kF`^PG94A z{pXjC2u7RcvOq{{pj~L|@ka07K0H6H%-;sI$%C>~_=)Bx{~#HRzp_5*@BeHbo*`t^ z;b1X@g?A8_JdZFF0);uR$vd0FkQvk=Ku~(OQau90wHFzdUc39GxEvyg!f_toL|A7v z$b})c4%&V7jb%J3KqwyyprH{joEurqII9!5O2iICD-kUL_e@;7%%Pew0sN}Ypdw6TT{a%iCO(`0AnhKgOo-^9md_B&SnPM^`WArd7N8-)Fh{R(h zojWkLX$mA8;oCuxDg-nG|4*vAz2jd^m0Hnuj;%DminvDZA zk?974^<3gTHMcl?eXo1JC)p|3CdApY)gi)Y5(F6q=}+}+jQgyc z1kZuQzYdP5EDHy)+u8bUfK7-6m}f+rMKUF(YFr0Xf}080Fw|P<2W-i?ce9$o3)4K) z+JPV%IiwO#fixrnqHCfl8bc@)MGBvBY+%O-`k!)DcL#T)Z?N#iM6EJtFOfuuNkZCc zb&3-zj%l(+ajfI(v!_mtX8@qUfl8U9Hcs!^Nq%ydVJkO*ZBhdqAWg>GbUAzW>?g*! z6tHswhdDLKBdb)K2mY-0)BN6PPM^_crco;0M~y0+xZ_Ccn53w#XM;ZJ0B$<1T42(l z!oNSKQkI+)-Fe567D)_T`s&pkfg@^wG)bR-Gl9uq=tlWj$iF&MNr47VGTuQ`wgu(8 zU;T%f5W^Ubh~W!va>d=}(FOw!cOW73ns{jMw|~S+#plCrweEw~&gFT^+9C)VVM7OC zdWrHQ66Wi_bL8foFCJor?0Pl4v9l1uaDcyCLrq8BfB+=uUF zplWmxiekC{rLLb+p8m9}zozZ`d7sTBd;2(tALVAi;SyI~aNMXDr$)Hr|0MqX?w&30 zurOSZ_>J$bVIN^fv;#q$n5v{XXqy{4Cy{muPKw>LV0|=kP$>|lM4bQqJ)D6`#{*@f zdAWq@!0eFMbbAWe3^mFazJQ;p%Taj3(|cv^YH6nvnRsn!-MeYz83s3@(Q`(rYzvyK zlvkaOmCmALREnx zB97DzQy=nygiiqu!}k=(RhpI4&;CQzV|Vn?)|oeV?!th8S-e)j*-l-%c17@{VAZ1a z3fdGAoutRt{Lu!#x;{i9uDnTA?0c0oTf2DQ5$?ThqZQKVco%a@)l zE?pWiE$A0?Puhos-}G9!Z^rH*?~`5j!xI%ZOs*jAZ^J3wBVRlXcAxV-`hDev3*9cb zon-tYLViZn`Q*_GRkJZ;u0-^X*}M3B`l8Cj0lO2cBR zWYzf6_0uOCi+ z-DG)6p_1*pRYh;l5>Hj^v-F~h_Hbq=;JX6%|1#HaHd>JHyJ#3|NoJB>ng7PUSa~3G z)ogJrzTXwPN5aR|Tw3H`LVY zr}TCY_z=0G;!)oKX_2Sz%mH)|K_u1q;ph9IJsiHE9u_v`Z_{oVUp>d~RE{4ZsA0xa zy}GUn9FlzmLj}xY22EN9x!@4Yt1g9^UVqBt@0Z@VHPdx#q6b0{R4Nz&)m!4*Mhp#y$AVQD?tGgKC@Vg$DEmZ`3<2ig^kLIa`!~W@ zIYd#3Z4|vJW?{t_1n2lqa{(Ut((l=lCGYNqwRw?FJ>aZ;Xx6)Mw2^oZHw3c8v1c4N z?+}wyf#^=wTdJ5G9lvi){Nl!YiDwpz+ydRTXJ&5dvD!2BPsFm#vrRgX54dD-?n(K?V{S;wU!$(W)Rx!t_%=h}-`)ny(3yyB!8 zMS?dil#jv(=wAnv>vXJ7zt84s(Y&&5z>lD_wEg{u`T2tp@Q?YLv(9MkFR&Ji&$7zG z$+WIT%Cl}yzP=~1WB|IM)Kh<*cB*h{KvddVsw>@~oL`B1ZW^q3IPc}#IXTK>U*4mv zTl)faDLxyIpp?ne7A>pLbXmsu<@dB&jzb158|R!8e-N_Cn3WnXA$|VsI&RpW@lad) zqUi^SlYZnAMy7)0@+84mg~>W*;q8vyP21;14NrU;l^fvi@6NLkJ_K)u&iVz*n+5o# zG$XvNqwQg!ScE4JZhU*;OTBu+cz7oNS0wnK-`y7_+4fJy47i_VABXqzy!Ov-+yW1&b?StJ+wdbsi?u|0PBCB5Yr$a}kiFD73KV(0AD9)>Dv-|5s+BM! zMze7c7(?cVkRE1tS*VOMWDw;il3|e`?_9CP=(O6&_no2NkytsBCXJJ{Hbe|Bad`G~ z-7`Jy2|3x>vSk#3NuD4^?txW)3?Y7k78IHH^_90n7TF zSL?D#nVgpcTq?2KW=5L9L_``A_MXW`+e3sIipXble2w-uzBMJ&$8-Bv1qprNA_@?9 zJ;u7s66O!KYY;MoPO!ZGgVow$>zD56>8Z2i?VW_6vpD1%IDX&T9CeI;AP!2;ST?(-Q?uBoMDh#s<|BswO7Nrd#^Hs zp+=>hJgKXxnLF1k5REUO6QM~%YD6NvP`Y>}@koGVEFz13U>k#P9&=q|Fki(W;hu+0 z#>t%C%s1u5!nE0kKuZCxne(UD(JS{(eXkqc-d-z(&}OkHa7gZ(UtX3lf$Vss096jQ zg$wSVsi>Z}dn<@A>(Det-P_`~7oW=Wy2h{dzs0$9No%$Kz<)agaN{HB4yvik;k9 z20CuI_a8tJV-Ax92oFsSYTUs&+nl8iL@#jPpXQY zm&1CylqB*5cEBLfg^KX&uZjtBo=`E~AejkEfss&9)bd%Gk&SbDv~twvp)e7?4TT|i zpv&PK#a)eNVoUX6)9-50nBs~b0=QE_BvYUDz4S(-Rm$V6ldyoi$=yw5h~amz?WF0} z)*|8S580B25-B6ypvHoyrhlM+qUf;o-*$sp>o9a07ugjA_!b{UL`cXIVAzb&uI(S_ z?>>}%RLN$1R|ervQ$6CWRQ~{XQJKAVIU(gJlWpoJ8HalzUVvY zDh93nq^g`WqyVT5JQZT$0tg+WNSJN$wkNC}<@jVaH5s<_seA&tuZ%1_s zHOdDS@flX&h!3wguh2NiGmZvyN>ue?o~y$hrIrN*lnW5OWS1I%0OwkoZ4reRtxOUX;H6O07`{g?VwjZc%I%b2J zyB!h4@BH50dXPc7bUET!|5k77#Pf4ARa|y(IzKaQU-qF$4+M4_a}c;k5Zm?d#!mC0 zvQ?B7);hCO+#4@B`8S~OIdX>k1O9~U4gk$?V(cWIkB|`Z5wftf;~K4ML9C`)a~*Sw z?)!Dy9nKGUH8p0+9NIPEh0*4H0(6r3303D9N{4*y#}@({+bbWMGoPgW$HeG>$D*w! zdw81Xm{{`#F?Tw~V}@g-o_D{epBM?o7a;2j=FK|s}rX*#!(2$9jO4`>IX$NKD z&h7jrS&dF~yy1F+=OTxK@uiV@f%9iTGs3Mo5W8duX$^1BFw?vn1oDTK*>L4fiL2RY zEM63xly$sb;te&)AF2@378R#ZF(ZOmn6jy*;;Wu+{pK$Hiz88ZU!RibVfo2z%e^79 z4x9n(UbtvINGQ8N3V`l&BVX8Cde?xsSbWvHOE;V?#}9yp5&ay>-2-RyKn;9^%S?h3 z7By{>w(D>)R-l{NMZD(X7T#UMkM!YA(HhBwlhy){VYqMgv3K*kQPe%H*bFYjYYUBC z&q5@Q3Cmf!1;!{$-;YLH`0Ohz<8o#8Dx*=BG^l*2CU54tl#6WPsoE%T(I)IG^fT20 z8)hm|zbPE#;G9JWEhu@P18krfdm{g-NWfq&KBGT|8XLf5R_@}t*TFQI73lvQTwHF` zJ^er}&>}aV2sKxPm{)%%N8RWzK|m~ef#y_VH-rz)I=@s9YunLa(z2MFYLZbvI!FbT z=4|E=v>?Q9t0SymN~JPVkivpti6~m7WW?WKGtaI+F94Z_1Du)@-t{?od!S*YFYtj! z%h**jG+-;~=Z^=;a91OA=a7%?9z1XMwmyeIDlJ+7%CF$~h3|)^P-4+2C>&Qb&CFDf zeAK4o(u(@Bej_RAC+-%JT_RG5L;n&7g14zYE*Z$_1kpcq{CLlE!L3_{J1YAr#N}i{ z!6qjOMOlVkB%(oUK6XTY1|6XPl1CSJF9}l~&$U@^D6+Nb|o%hSjV1+ zmKa@t|Bay)xWr4a*-)qJ)_%QhYo$?Iz9~r>N(7eLm%>M0F|KJNamK=mH;j6D!K)^| z%lwtf{2i4iTWQv-TQ~AYO_O?~x7VN7Q_#P0i=j$%yJRrLs49b-ZCMY@XYNxsyD2{*1#C5#xUR@254wpEZxjzWW z1(YS0#r}3dVz<-GyQ;c6)-N$SH)SmZfw{L2kK>m?rm+Wd@v1jkwsT;uv5{?*LPZyw zc@hkdAYuNi``oFR*SpF8&JCMNk|ysfh%??%wqpu-!@p6ao9_k7XHJ}c!_ODqvA972 zx*s82ky@vEPv3#ofSOc`+o4ww@8nXOCJPApps45ziCK`LpGnx*!xQUH#G(-!1P09y}pJMSJtc6EK|Ntp0kJRWx@JqHv3W1U}9x#Ba29V#{-h5wPF; zzhyFRon!~gX=?V9buvB;p4VgCfvwQJp-Ur2)-PEv5rp}ja21F(H+g?leVv)IhYZn= zn5gS5c3II+ zIp5nP4N{WA0H^^?5x&$4YX$XIEea@l7(y8AICQ+JM zL4NJcx?iB>jwq|1s}H_cC(Pq;>D&nuzXe1riD)5qUmp`dljm^|G5fAVg{RnK43~```F%zPZ*~sx>kgR!%a(D` zv{UxPfurcXNha>KZkEhAQJq=C6lfW^&CGY*vwy!Rj5%dDtCoZea1N`N5EXIvpmMNq zA}Ng3aN9UB^U^-r3Sp5`!QbG#w9NJP_vL$Jkc&6}*!=EQTI1cO>?a`cISgOIm?38c^rXoi>$E=bIj{w7t$J@2?Bpq@r=7OFA`%Zp zy242wS!g0~6mjBqic%3h9VKC;^VI=u8zVr=dB1AMmqSB_4xRDh^3ZeDq%PR=B4Y-} z5T+UzZr^FKCMIOe6v0ghzkfpR6fMgr;+GI$P_U_1tquPg^!)w%6McR`Wj}1QZ8k{L zy9eQS#{Nu(6?5QY`N>DW)sGFEFPt5nDVOdk2hV_;8pQHY8bsU6KJyxO-FhYwy3IDZ zJ8)sSS?SaSVi^QU#_Veuk3AjS&x|A}k_3!Fgqe$q&d|9gZmV)FlVb{TJoEwzP$2hQ z13QLOlIxi<>}PbXghf|fCNy=!vfHTN=LhXPbf^;E1#c?2pI(ju&YItvs7 zKYNkQzzLqy$r`7c>UY1OJ6-7ke&<#_Dlry6WpJ484c3%zm||mIBG)RK#atX8>TG*h zpY$^n?90wxh;Do#ve41g(!YhCnUadHF6FYwco}Hj$le?3c-!*q`A8!!W})tGaPXby zwKZmp;=1O{J<^+@v9JwEZlv$xuvB-r%A0N4PgL4Bz}Q+T!eailO(VSm?KECM>S5j> zvfd%$865l?v>*dJN1-Xz>OB860@m@kwG@5rJVu1pWLH&Ow4LJw9-Q!2o{mV)K$(%2jOf>sTEh0s>`Z&RatODqZVIhqq3V?vBAftm#xwdkRUYVQ22q$}K4JC4&y38X zgG;e1ZeK=|>McAMaSq`d?ktMA0q(N-&5a>C{bQ|(vXI00iN@qDF`Qys12gAC;^+DP zy#@F60YE1yA(gs}v>Vy$dLN_uMkKEe?o=reuItOUAaEBd)6=VGeW4${re5$$J_T5J?Knhr?8Wp+IQ+3mkUvjDO_zKy^YiS z^kjh9*3}Rt_H9^#{*TvNcQ%2>*|N2TC!S9R!Xlep?+liEZ2j7aYAZWOjKUe$TV7aX zjrY^Z=sJ$0DCZ$JhtZ43-YhX6@mri3TF28V&Am&+qB|4PjupK9V|QY>bdpjQQO&e) zy)Yp`yu_?SY%afke|1(PZEEs{b;#j@V!jWQkvZj1Ey&Rq>P&0Xd*o~#j{^S?L-Tr` z>s(!>sTK7S^{EK!$@Hy@lh>ZpY}b79WXl0+YNND&ckW+c2gE^3B_3*GEeUiY*dW-9 zxLi@A%_exqW#y3ob4p*n^)I`}E46l9Z#MS`;3t1ED)EEAZjNvPIZ)3a%*q&=7O@4s zMDW|kQRfOo!W6=DTG8Ne`SRd0$48zpowOwp-!dy`;Ew0VDJkz7{ANAA2SVsG+MgV- zyJe<;p#0>2G=m zjwCj=g>k__5(A*|6Ov#@B&y=SwG&V6+<{$7sV6? z4lf=tK2Vz+^0&C0eP906SS0;s#G{~L>vXvSR=2~w;?b*l@2QxsM`8Xm=vQk0+Y zYYUeRHy0;0z~|4s7Qf8&Gq&|#n0cS^_->lm7^l*0%4#PgvpboQ`~58T{Ge+&UfWsC zp=Zm*nYK!u3g#Vg+fKg?Cin`@U*H0p%d*=9Jiz^eLL9I55C=e}k-%XG?&Y?;c7nP~ z9wtSR%UIqq4?h8{_oO2xqj%6i=+RfUjiv_^|)p#`b~X zkIr;rEJT-sTEYDRwkDIO?tFmvKP^sp!P^Hoq|kHs(ABln{^>u-NrfFeqhZW!KXT+q zQQ$@YfXU4A{VizPm4`BV6HkR6&xz?8!Pj;7hJm~yCC8y{iu-&IJ< zc3j}SZ#Xs94i1mT&kdV4ZNfVWy>4dm+$m>s({fb5G3RXoz6<61=v439gc1L6gg}Gbss<>4z)8A8a8-axNX2ES4(=w}a~`6q1Yvgh zwvKNk`#9hsW8!$H>??n&9&>w$aXV?YuqoBd>eb~wuTw^v(AfAoQR`=?7oVUUU+~R? z3q(W)=RG=PZa|R8daxyd@IM}qOaUFEYuNL;YR>HJl{FYZ3}N@nK37%>JL`bm4E6`; znYeHo#0U}d9{R`h_(jX3!W@;&%V)=ORH$K5E$(@>EtU3!yWIQ1!-shs%=pDef%be; zxw3;Pb;(roXyFABf^H>P-50bsvK!`wzPvrU=G;k) z<+46ZYyxZ(pG1C7{Kj|{$zUA>Qohw1U5zfA?!OX)DpshFB3(S67%Yp{$(T#AWnv|P zuhd`@Yo%H4`)TY8cm#A&+i1VONl%ZgkT(GTjDRL#gQDIeUtB$@>fPT2913i3rn#C1 z)fghCuF8kTXmtgT#z6adSy^o0zTBRa8M;?2tulxCPHJ7xKz#UcuR@#IaX z>v*=?b92hPmN7QxV-B=?M%`D+F9UvW2v)PpVt(YGcvp13c3ABvxo$V(Z>b6hfe>^g znwI?-M@FV@0Ef$oT_bmoCuBlDwm6a5-f50- z4V=*nxF6V)(}JLE1e1;lE+UvmYQ+$QVTbo}3fgM26}lqDl5Az+xt~Q#Lnh^~?hQ~4 z=`4m#^BXCzz%!+`GeDB>i;95vb6EXi@@@F+M~&?%$`*{cEi3WfFTbQ_Lj{Q>{5f|+ zv`#mQ`5Ono82Y_#Q`_xvsHe&2@Fyk2zD@xoi+X4u1nW>de(`>LOM$g-v2Y7yGYDr= zB)J+B_yk78v&oMS!{7TF#72l_nT{c~%v#4W3O>H8?Q0lXFIMfu5|UWf16+oR*@M!I z&9t~T+BX6$;nOA!W#?;QQj?~|D!@t+Cx)Cz^O z_X3u*(;NJ$`&F0a#Zh;=C)6=NJ@M{k!-Z~7=q`m~FNY!3*q-#T3_?lPIh`l-Ky$xMy1@^zui3;jgST-0QB)YaIu8^< z@TO~jleqgAe72$_hLk4x0C6%F9$7wUB7rJtu)Yr7>A=@c zQ4X8$-46R&KId)u&HU=AGpPs#5f=YHD5m=e8wGr(-1~kuhNthn){NBo>N=e6>owEC zx#r`TM~0;h3+ z)0vGOtS@gGG<0Z4jV<9`z%RM2bY`DFw%K=X_=uhwA~iCte$zNbSWwbmqI}Wq5GEzXek^s9!SgbpHxR) zUw^~C{KAE1(YY9b@xCQtV68E=pnBfjOGWvw&CNG@Q&LFvM4a`Y_9KaKEw?jNo$cVz zvg}fp{jMN6&m2`gZ7pDdQkq2}*CG|^W@~6X)_hT-ar5sLyYFoVQ^M|}U#ripd^&sitI6?qz6_2~iB>M} zLqQ|#VbBB7FM)NvTlI~3cJ##1TI4~Qtf8e508M}|o~-ym>t6h-H2KyJfyO`U%P3g^ zdBHqi2RB+I`^RNM8307Uu0C_^?UQ5&@#u8w?0Kq19h*9uV z(-{=@8u?DG*Hg&6yUH)kFAOt>)3zox2J~{R*oL2jCOxvq$e8 zzlXq_nWF8cvc^)i4=Ov@`3;PYsRx9(`Q{M;h2BnmBuu8%#?wjOH|TN@)%{scQJs|y zpP!jR==dCR@ldP1)@EiFAcOuOyX`#tGzrNs3>GV=L4(?|kcZ~mGQLUkkQS;Jh>fcP zu3!_4UY@F#0l}f!OWPUMpG@aDPJ7!IXvB=2Tb7COzPLN^jbcXjx2Zj0-*V!#>JVk^ z!JyD&PDjoGi|2MT#EO=!8UB0ti?NT7{6TryxddAGXk~A19!!pl_1m8LK(Uk6OZ3DOD!)Q4 ztSfto`1A{; zu{szf-C$O@d9_lFsxwmkSi~-NtyYzDrP3Rz z=9_7D*bBTrF1W>*dO0RW;Qk0QSj6>%I038#c5jY{9c0@pu-%^bUTTFg?FM z5@--;N$=>R79MBNoxOk*srZ!M!Q$ISk!0cnp2jmAOf%>@`eYqX>DR121#&fLzlCoE zvq8Q02rbAD^iHCD=Ec*J^_PtkXN!90&k}hD91SdaRC%o&DLhKWz>;pwGeA+i=)_o^ z10ggF%y0_5d;tMi<*=O|AC82`nRtMTzn#8L%nBV@Icg7`2n9?R^+A`;Qohm^%g=|0 z3X-so`r#mYqlq-nq7R(!9=BsheMonxfsR)%r#6*}mJ0BkiF;5B_b5HTNGqTT0<+d- z+pf9{I?}l(EUJCR!4=4WaIx^#JKxsWEbrzgQQ4X4j<8VDC;Dk2v`xcGtcGpzo?kb; z{ISSOK_{|f7#8|=;$uPG#^-io^g|FGl3S8pP%t^^UJ;XCFl3$TL>jN-!Ft$k2R;4z zn7NOnX9d!^6Jm4BDMzk2x(PZ6cfVfP_w^@RHk8dEyj<{vNmNU)MHa@T?1F3yI5 zZHwHVefL>GHIyL&n05;PN&+4&++5UIa9(ust|rj`LobjLy2(bNumcx8r$~OAJX`A= zDs%CKPlT!cuI)`YebQfXfXAIJN1}F^7{da?(Rfe@@tF+xoM&`4rH3NuL#}FINi}YT z6{bSgOV=UVepovqV2BugL#Qqyl}s6?X6n_f%68JkO24v994|OKzJOc_FnhvuS+(; zy$7G3+`1smb}2JF_48&sxx0}otyhOzY>9kf9MSBhx)qY6T_d3cIw?}J=V@QnkbuVLXFbBIo&&T_QdRs{43j9$I;U<$5t*9-J;Cy=W+B|m8AoY zCC9`pP^;a-FBizGA{}en_W0aUNQbZ=$^s$1J9WMkpkz>jl}wq=y<9K;=L^F~m53VU z)j){CP0yEspHSYwhr;H5-({ZZIT;!J*EN4IU7v?$!G(8CcEPObgqeN2sausZMTtv@ z6f~O}IgD$vpkChYqjkaw$^$aL7?M@RjRy{p@Y})oaskrBq+Vn-c-sp(P4@4>1#N3$ z>nh>aF}nwaxE(qyocZw|9n;U*tQG^YLlrI_^lbmR9S@~PM;Iu4AarPk=P4C?WH zYiRUj2du|W*bG##^h*q0#x3UIwIMuT&&$)vT=YhHxR=liAseMo>TA+b@gq(9m0f{Q z3ihYrU=1Hf7(R+33D5jNIgik^!%es%MY`^Dm3c5 z>|_&XRiR81_ctaCA`~JZFC*YMx-|OLyYJwoPg7Kq>~>K*cW=M=||tRS42KCl(jcm1Ks2kw@&s4CGIWYNIa&M{)F!tc6P>hV>28syzE#6%VZzJ?@x z_Q7iHT$tgaPW$|2AD^I8V|a~&sJqO4UMYoo!Ed4hHLCw1U|%<)kxN*+gIaGX)!|LH zqLw8?3*iyIn2aSadzmh-7Z()0!Oxt{5;poH=7&2r23*GWav*CeD3SyF&KeJY1^tr@ zVZ?n3Jq?bqLbnqnhwOE)jAG$`xhE9Hl#$jL!yC?K7d*I@(1EBjC{KH=pTXe_!^8mq zP-vN|pA~d8LQIy|N~2g4?E|uY`Us78%SI$VM*1>N=H+j~eR|^h99E6~_b6Nw1*}e+ z{cs7ma_-!@s&dB0vd@iCM}7B%goKB)v&k-Sj!X&0Vx>VPZDoGFFB3RPj2}LX155fd z4s+;dG~rznAEwQV*1LZ|I?pM`pk1yduy>l~~ z2|6uivShsUMcYgHFZ(TdG)VPwS_YGJxh>TFv`+pYJun*!j70o49VvnW{m(evmR~6K z&{rGw7=I98Ri@{E^1M<8<>?oaRh)A{chNKai}SsA_Ii}uNb>p}L6b}&!e(#TfVKHeen3`|mq{??yd`B2)-b}T;35<1`mw`V zebx-T4_D~~EFb2pl)6>ZL1}NYM)&Zru)EbwwH?M}eZGjUcwev`CGK2axL$QNL5`7P z79n*!Og&a|8;TMHn{~cqebth-KSNZ>l;TQFy#-EnO!K<+Osb<)5&`gX%&s%+6P-T( zvVV|@i$UZs=unrmFN}j^hDDlC2=@gf4@la!ZCiBgrIsP{H`G*}AJT7cbG_5sOe|Q* zlNr$aPe8-mo5A@3H#@v3&YU~fZNLB(MiY#l3lbNbeLKKhF}ilXFy+XrR2tE@CtJ-> zpO;b&Nb&A!z{Z0uvK9ttjlZVrubYSngNm*rUNd^54_*o=1SWw4+Zb{m8r#z!kMOWS zF#F^DLhH4!l~F*)tZLwu9q?MjY8oG$JqZbGc3zlvW%Xj<-q7(Mw{72ED@eO;_y((& zgKc`YtBvh;&zI39bI5y6fKm&hn!mibVBx~psMXWzg`f#D;hsTp(C=Vz!tti8o8A0- z%RJ;{VpHP&W@LMf4!8L#cu>VuRZ*4eEnM_yzz-_e>*?t|3jKV|+_#|^xj}-mz7_Pk zdsm|`vh@T|TObYw=pl1Hpl6l44q3%2sr5JuL|=Ey0)yHz_wYb!6`N13e~m z)>~^AVMo*?NRBw*B<%I>%5HY7QXOVzXGa4$bj&Ubr0=F+^k^UKe|&mI(~2lY#`gm3 zik<}!(#ty!+ICDea+33Y8*Wo5e1g3W8Yxx)ynzW{^mNV*=>75&UDxS8!x&HNMh9B} zP=>s?ux&N9krPF}VAAk#yO1K_;L5&fmaf@{1vdcTk}5$woa)z~{I~KD#@m{m=D$4O zY->+S3q^%2K7oK$;^QIo!f~g|`F!aA5?ozk9Wz>ezLAsTdsxvP^~t+SL*qu+Y&$#X z82_1bQ6G#|gUv@Z8olY~%Qn;rF!RSGE)+*ES(cXjr_5W`bowsFmEEtTInETj1Lol% zT{vy7AUF-UYL(fUp`D@TF2<$KBWk4?X+>uy+K*pub|XnCe&eqfj)PjLCCz`hY1I(h z?Eyb0n)U7ZVQtH#=nt+lf{X9RZ%D@i@$;58(1*lm+)81p88bD}^u3#UHS^NuakTJL;*T-TB@eEZxt-4$Gnk8JEv^h4kD zbY^XAgm)$F_wX2fVsQY{0dCMun+D-ymKTYuC)gssrO0E zP1m=YZA@=fkvvwngZ22F_4nvO)XUE28<+IS96T%A=mOI&^NFpsLVC~G0Q=r?@j|Y2 zrdeqoJ>``8o{I16@4YzKVf5(He4r*26lg;uFE@#f!y8^yCUho(bHJD>+X>a+r*yV3;>pyC?tr@e;0!u;f(yD5Rz36kD&t6zX zR!6P;>IjK`LD5Gh4>?w=V7*F0;DcFGudNLp#Fp%f(02d;Kigd);!=k?KRm0!;WoSX zIzEE`N*94na?Q6H9MuWjUHP|^A?9Dp2oAU9w;{}BJQ2p1qgvlemlE|gouXxguIJwt zz5o6}mD_z}QMs5ia~Zc0RfNzcn+LcwKN&+jWMDpapfBx2e{x9)PJ9qH#!?lsaY2br z_$4C&k=K*Q)7C$Ri;}?~z2sJXp?!m66SK@Wa?(wce;DO}=Xf z$FeC#vK|;eDNr&EFdV39b0Ly`i+OSL&YcfGU6C_pWe7AxC8J}q=w%F3o~yewd-dvN zU3~6th5bfv22xTxZc5*v_3Aqupi>5bB*UzBZaNt`;Cz&2?(r7xHcm#GDlc7p?ZMyjSQm|N z2ZPebq1T%-#lLDzy^89+{4S<{bBpJLm;O2w-?`jx#bN66ch$yBLIzIzE}pzSn-Kb7(xy%SNZ~r(ZFcQnl?% ziH^^CXcKW&YjU0+Jgbe*#m^s$PWA11e=(KDyVda@zh|v+Vt4jmw*vGRJdC>c{Nlaa zv0qor|KMUn-_4$x*M(ma;|F!Iu5!)IPmkSw`EZWs_o?13F@edvdgxxwgt8~y=5Cto z&C}MgGra5CDfH`x=$C^|9ZUej!R&0}>E=0Z#_bAyv~u&Gw>IrG!QpVr^MCE}8@f@s z;&9hjzaod7TD7Suv%zYdZ>A>Rc3M)bbl&jQVC(E;F5Fab`Re?i)lp*%&sMtGVFKj3$H~bl^1{eV7ubbBWvp@ebXwwRufa?9 zK6ssZyv0QupALZr-V0k*44qtc^!)i%iB~6Crh5e4&sBEwUfc3_XB+i|F3!!rU(&f6 z8{}i{nmFjyXS2c6djIzR8FcAt!1DLr4-?`$H!FF;#BKVQVyAso&^by#zK3L#KU@t+rKB*Ep8jZS3xphYts*rk}42f06t;A~$_Nk9}--P=n>MxGE z(kU(UA?h%@u`QF-CFt;4?CU*_)JSh>0;sZ{>*PLWUA zm4$#(e-X&N3|_;T5%;liwbP*Uic!;`)$iNZ^H9WEiQat=ARGsx^^XXsA{P_-*BR!Q zU-s41Zlz}QNjAYdMBd-%m$YYbJ?Ay9`9-fTI#0{Z%lFw=aoA;N!pWph-_HNEbv~_8 zofqgG#<#U1;&ex};;iPl=uUzfG0z+BHpBBn9oD zd(}bkL{@TaW0XT$i0izbj6_2)?$deN(F1nudQ*Z0Y6jkfid-0}^h=ib6RB+I#W^)l zQf~RT70174Z=)y-KDXcl67|~Zv|T6WA@H-pe%%7H2b$6N(Y`~65XYojf4f7InnnEr zhsNUOvF4*Z{rwbl{v)^k{qqwG#(H-&1x`OcHMTX%vgg#0uNh=CW;dqWEx1PF^efw8 z?+LS6tnHcYGx3!Tx3tpPFjWqmIx?xj=Z{>)1o5VU%H|rIeTd8{;rJv))@Z# zlg}=gx3czs^DiTs{Nrm9_Nzr!uka44zZmfT>lVIQd3pB|Z)F!1zf7aGYX>pEdh=B& z{eqcc3$;1uOW&t6chl zg^wKFzOsARTmhVdVZmmx$gJg z`0Oa7#D!EgLH*}>zuKt&x|^@{{K2=~FH~%)49~f}uXp=-O`I)Yu@B`d+n3p z!@roY@M?Vgn&=}v61rrrJ*50O^2X1e;oh49yQkL}lKdcY?OQ0(z}yy>kb3oQq3%Y19R+(WZuYT$cgR_x^4U$d&3 z^h%@UUvG{1^;)`L{`~r`{QCX;dh=Ro`98bv^F;aMEmXUgOm(SRbtrXq#(?{N!7tA_ z2HsW-tkzooz7=+|wa)(gVfe56<7(gmko}!w4!8XNGt+Gk$1=zCR(-X>j+>f~Qs3WkV4aMOs%ar6RWCz~Z5_l@?~grr-$;vpk30YMWpp+F`x9?gEA7Aj;J^QsnjfLTQ8&;&uV@B$BaRg~7o8%c(}b|+`lC&?Wl}4_ ztgOtOBoc%T7_+Bi0CaHKcF^NaWCo0-*xpIGf15%7$9*YTD+o_vpD54a|8Sty zx+^JHZxVh#Lm)tg?O2(RY>ko7OL6R4vuW03usqo$NmLtm_5a0ihe!_`K>1#0T* zLLg(>KNg@o@9j#sb~gC;ryeo-hX=$%`&DtPz9-yz@HulHEoijmrcF4%A;OnUY+QIo zS!cjV4h(RbF`W zP@%jjIM$4zv}}JA2E9WE?SKAhraaQ4kVHj>0|!qC7R*qMfd1GW-SuzdZ7Ld>IdfQ^ zb7;VSw8KOGn;+*zApSiqe*PpJY#XM4^n9wUjNr_HO8$BKO}Q<=j%>opd2kb@n{xRe zws*W;$Kt6yGdNpbe$NBQBJt|4+x=aql-g>f8_E&zT(ip{yu;q+yT>|gxjLB(j@Bep&eQmw3J2 z77QIS%$vg4HXu+;GhhXMlygmA8K`*VA76BXuKRiv%u}vr2>KQc2~H59h%lsZ9HHIG zc|2S^`15k`nYzLMY603J4IwNEliRNl$^OHIHQMR)uCYBtPbV-{2_mvRmcJENwT$bS zaL&8147 zk|23vX)HV}p5}t3ZQaqi;he*=*j}{_H~;e|jIfJptc<@^7!MH>As?Q6EmS(zarSi~ zhNKo3hk&Es`FjVBu=y1r=xMRr<#ck@PP*{9FO2{B=Xy@b_SfhRge#d}(oH!U@60x{ zHl*UxBrhC1dmfQOM6&W=ginGDj+hCYr_YVbO8M_%cJzMD+Gz@UgK`{onO>uL*MlWH z6l{{+mhIf}V1&J#0)tS#108iZ0TsFz*l)=0MalpCX(Mc+*tbQ)hp_Z*jeb)fm&pX(_wyOxZ1sruF9gZZI>Np9DM zng4!Va=vCnavN^d!rynys(cl)%cliKtuhR#*qveGQnm4G)b|zU>+j#%UtaXORq5ir z);F8!zWcFn(rfL(zXUfeZ|XL7pz3hV)Hz^(H6X56xWo~d0=0WM;2#h1)-xB^^kR9t z$MwppH$ET0R;UKVo&-us*3-a$z+&Z;IXP9e@LR@jk<(AUMWeNpX)Z;RU#j*$bgXiH zvRz--N&~cOtG}o%M_U8LRB#;_Mvg1b{nP#0s(h`q}!Rn7WN#$-ID=d4eG~6~SbmG7L-VmDh~!sdC}x)xYlFs;IV`u;EEUb#l7zklLnMz7Z=ce};dUn6+b+abWnV zQa6|Scb50*?0O_ZzU0g6JBIv^-_5w;|BLVT|K~sR=e{D1am?v0O$ ziMj0B=Kp?M8jevrKH@~DnO8S!|MbCxVA=~{L&Hg>eF?V*f$oqg_iEsL7?tM{Gdl^Z9{nk6>8o3B4h z$@##|2N};FvDRvh3BYDbtLAw@K&g!z zH!k_Qq+q#G=t_n7j+1)2G}AU#$toV5-TYtSDl$B@R-Bb`!)>$Uri(M!%2lg$TuO$i zHNApgpqZK3(b(8V#An?jKli9(Y10n=)47a9V%9t7qf8@U;d+J-@fS}cg@?hF}CyDx9`rKI|>R&`BF&w{TL+T zS)x)@RAif&?tkIv%j0FRVpHj8vDQ{nxRalMP1LP)7uV9#A{MUkR%>2-U=`?1sdiq$Du-GeXf@9#gvsaVBq-l!wS{@z=%Uy4dW{1167bs_dmpFUk)LF?A7 z`3TCz`Y%4Og}9W?5fN?R-Fx?Ht<_dwK}c5#r*m6M@*jVm#FV{`qZhW z?b-#OD*4c?y^VKI|AZZZ-5S395c<;dUxzk7Z{p^lIxqyTWn?s9Qlj9F$PC&nuZ| zYukjAZDMYoFeo!4<0&NC7Xx><-L3R=pil|!6ck(sE7h5s(^lR9cd-12r{u&r?zIjGYjuA!DvsbUicG5QNH$Tbyi~ilacT?EK3Ii;S0&&;7 zckf#7=QQoObZM}bwzk(=S+p!V+%FXsnai1T3Jo3LW;3;x-%p$t)yjJJ>ZN8MmzHKE zyO5Tvd&dSO({Rzs*;kE4QR@c%Fg-{6B?R#YSmx+<#4Q9Z_~?|V5C;Nv7-C+ zXCru23l=Px$p5W-@+08-Gxzl8n{U5P7?PQtJ$k}~W)!?0hYxq=Th0%0|B}ib_*dxi z_kQ*Kqo(WVwwv1&tc8U&7D53(NN8}X>gshEqi+KZwwydUZpq^zCuUvkJgjpwFqwdh zd700araW7k@%r-RAznkvw~m@q-Rwl|dp+JRueI4d5udPhv<7?k?j2FbmUozuo7>9z zR5)kLBQ&&Y<0eh!%%1Ij;)E6um#U%R5l^d#$VlGDc4$H(w_r%#s`{#FQS;lLbvNIS z{?>L5&IT)temR)lzn{0ck?HS*X3MU)O!mIK^Hi^^`y_}0Ct%##1uCSmk8L z9FQ2Z)?E?CAX&lP*S9%GuX+3StRhi(ghkz;-8XLBm;*O-1Mq%9~#-(+P-=7 zX74o9+G{$w9Wt}&Gqh@xpZ}WX8H=JjD%IJdUtaKGU7t@sH*gdXQF}8lFLZTNJp)?C^hT`*Czv9Uq>P*0pgSM3u1Nkouh0HnIXM=(dYyaT z`7x~aX=5uF#FLQL>MNL-nyz}Y%*4$2{<$aLU*9XfV>!4*!)@gsJ|~{8E-W;unz^^~ zSAPCFE(w#EflD!3;DColMQz_OZ_%RN6WHbw6%{4MH2)(AMe~2YPuaA-h&2U-$SGkw$Mc>}N8#QUt zIMe~ z&%utZpN>w~bj`QFza`&n*Rx9(?SO9Gz3;Z~^mxhpoazH3R8ycXbGuF44upRGoN%+c zZY1L3o{aF)%RTSdN4dKiko9D^!5zV#0$f(wHHC{feD3 zZQ64jDz>qpo}5`&I)56^ghe%M5p(yv%)?K8U6K0yb4EtLSHoLABA>d4g{cu&^00UZ zBki<(`}RBc@3$$Q&*vNspd^WxlihXj;C5UK>Z5CPymR=)a{kb2ZVe56)}hn6i9OT6 z4dajz$!1oCGZ1=2B+O?mpV~uz+qIO}6_hJG=wzRPv7=3H{&sq8yES8>*QY{>Z*Wa+ zu71vomoIftJV^PWz(y(eqeoSVbSY1sbh&it5@p4Xt5>f+sPPR5*yiosG#DoG(`U~V zkpd1Wt*IRu8mdUxx`>t%9CG`$YomP2A1=C((Q^(@-PiXz3HkVm6aE(`-+%C+EwRmU z`O>8$u+uIHQRw57ejVz?XWZ*Tj~{QKAnT%KONsocOHk7t@aJL)V>_`$2C7xn(5MSQ zor;vpU2Yisdd_V5`W1^*K0}}ITInvS49=}oWI~m90P<>a(t&zdeu~0uqSX)nvFWi> zfq|{z|8B)JKv996Q(iovRK6|KZO(DGQKJ!wH@%*IEKiFf)jzTpuxoh6$&> zWY+G{U%9#>9|KtIn6fZ5mw4(8pTrB>9aB@WWEh3sQdBEairu(Tm!S@2W%`UxhLJTS zgeg{5VNp@_^z`(OpFF8i-eHr3J3w0)-ChqKOpmKouYdpkpnvl1UJT6Xy+2!{JUBW& zUWL+0`<`uNM18 z+pBV%YaymtpDuScfVPdjPAwqa4`;o{W-Z+D0J!=naZ+6Wpw%`QK0K+<<71pCq;je# zM4NRnFtW64;ppU~gU_8@N-X;Z+$yZg)qVf?N*iQKBUspXWn*t`?ItADpaTbRg6}wY z?pz5tltw(>lHh`uyqhs~6nCFnJ7!GXtnBR1@K<$pjt~{|DCspdqg(gjQ=WOah)dng z(6CG0x^;W@?Q2ZINR>tm=(>6n0ZLfsx?kTG7~7Aee_J$Y%fdsq^hZ@r0@x= zNC(jJz5DlDa0*X`_6pWS6aK|w3mxt-*3;#w{dTUxLmKh`#=Q;Rw>H7!k2 z%!|UicU2f{Q3>P?U4Qd$T*kRWFXe^O2#VK;8v8KVlBM9zd_=Z0Pe+{9t9Nf5*vuIj z84}6N%o-w|x(;cuAO0cg8X7h8>e{bbwMsPgFcfz%OqgcmXuojbE+Ep>j112PsRX6a z@bH4rfTF;$iq%VVu$KO+sirJV-u>$ zbAvN;bKBRcSFe@=tyt>ZbH0ZUd!stAr-~KwB6`WY&3BlF#+y)R95!&r#4!(?~1}L=4*+VPoyqyXry*hceOtr@FR-ma4Ye zos>=-ln9xoc_KdEf5S1dJ(5>3xuqG{!MpSP5gM77-q_;U5~|H}-nQM7X7V(!*%`kv zJR+hFKTxfGK|w+7{n>T*XOCIDSer(eqWnNEjbe-Dr}V}z%bT7Nb@ZqL&6EC66DFR! zuq57lRQAcR(`Pr$Lc)(3G^y+uYRTf}Ydo$`Xg0g*=!iJ(CSfu(HdY3X;shq>B(-r- z)%u?0K{CX;+>}sCYqJbL%tuM<+raA|L#C3~zYNt_UKd!2fN<&$&*m#p;(GOg2i1UR%)2%;+*3NEr-)H=~h~arZ0@lsq1Y%M#aCxiAd`@(%%O25dZi$MTyDNLw zdOP|tI?~!0ldGu_1O`w+hSOK`_X>6ihnRbMNne2&ib{XscLQ@%(~;)pjVQy_q9@XV zQU|&dPf2+)RAWp{ULQ%#kdu@12~$~nD$m@GVLcBS(_KMT_o3?bTPJ>L(v{K|K8L34 zzH68HmHRWMOxY^6X;@e-4l96$J1=~Io?hswQ+@88l-NgRm-vJ;NJh%mn|JTue~!>! zyp$;d#JLB7OnpF)4<*KC)j&XETqx!%E&5Kfvf7@QIE+52YqxHrqoR&vXJzd@aA0Rq zkxfKYl$xGiE6!*uYr~M-?!j#jG=1Ups_gCCRi518v4)8c~jbL-I@Ud28jAX9z6=iEpV!Q%Pq!@(^>USR!YWC4=#`K*CK1k0xJ;= z_SxS^G&4o#8}SOor1BmIhWp3eCH8@j-9d&A^~DJ&BZB0y=kZn%l&2NC3{-QO~?XC0q(V98Db%Ag`OnhLdw0N>+U4F2D~c{$vX zwrnZDFMw|Gc0zl-?%XrFpSX|REYjOd?u>EB=NFkRxvX7L0WpVnNwhWHONhSotNjE8 zli9{Rf6Dmbu;SI@Ol(X|l?aU~Qd_}tc>m;T7dGng##1OxmoZ8Lle+%+aVNaxZo++? zXlqOHCmsuWggKW^GvriAJe)k46nd+dzzVIP6I)0pZNu4}e zgZ{O$xXjIcTRVL3+pU|2Brx(_TZ0WhW;D8!R@ejThc{qR2U*V|ABf*6adi+1Abj_I zu<1*TTexVRGypQOL^vJsm%?d}IA;IXkgQw0+pm&Uox^~ZeSjM~VwyLoj+wc+NVVx_ zQh-l9W6yy#i{U!($GNFH6bz2|Kq+{}C)Y@IAQfoMyr_)GcT9On(wKL>JKZ2r{5f~< z7!>SwxrmCAp=1h;5DpQ>qej(&b-kJV2D!N%PFAVhIisz^U%h&@_Q$8L_%`kq_ak_q zAXF(72cZ!Wcjr`B&k8$pMwu^rlQ*eddbzMQjk~Gg7bL7a%-*WBZ?YOTW^nz?JX- zOyxF85y`~4BPu)P8S`2+sADpI{8;`QSc=N++ZWB1cRN+BJz2z<8$laTvgi_F%(wCq z?iLwYS=Xowush|4+sy_u`+?c4aLL8-9E%b`ktj;dI$0`H)ah zi{5WYsO+fHpiTyW8=;a%wD<7g@oDzJ7spSX>aO`{#wdzM!QrTWOomS8&+sDNyLbDr zmWMdo-afxHg;fDI1c_$^5%swfr%W*_DW`g&zn3zDCz1d1)^L@E9_ZIhh770b{2Sof z+<5QZf&#yu-PKhZN~s0}sjjZBN}&Td(7k*2h`%V0N6eb_`=ssBwu%pEDlt-Q^mLW$ z_rA$LYLWDfR<0btLv=^#gXFO`<>*bu7KT}yTS+c#)~v^5{=l-nOLpV_1DLSS&yQHK zi#9&(#tknmDc6un@jE=z7PL=a*#36y5gpr;ojfNKunY|BPRxFkmDP~W!V!2$wYHk{ zha~AQgNG0AM8EW+-3k_fP$!7Jc@VB{oQQ8@b-H#R`#5M#aB@(-J}2W$T%2>@$*~>5 z*@6CD>#rXp=|Ew&(iOANio?qQouzx>Dy73RIZC$G&A*hG*ikHIgt*L+!*r}Eu}8I= z=f@{kn;`+kfxHzrc2l?;5W#gS7P`M8ePhd{?5ij7vwS#zTGaCWG$;9Ob^7%ac0`m# z@D?;o7p&Knzj?lQrJJ_i8xI_~ke_-*Ts?Q@jB2Sxe#5w9!NmHDe+6G6O5sPpWQG^`kvdr*uOkP1=aX{toy^dG*1^RsR?`HZorYv3) zm7s8Slsk9!hN{L<@w6RJ9U^@U-%3y@iVbob4^S@zK&>&BfxeTDZZMb?R^xooo+0$k z2Q>r7<}27NybtoBh_8$3!-eG;El4DM0I(xsJDTC%4D_)4M7+kd?0T+O%3X%FD}BQ~T^)vSiw@zQr@V(fXFP-p_8!n^e2o z2*Dbv2lG@SJyj*g#x}*e*GF6 z8TtS4bS7Xur|TOJNw%nHp-79p$WqBNq*AGdY>_2pB3rUAMb`GH7;DxrA&FA9v}a2c zqEHG+lI*+x=P`5s=bGy}=gf@y{l4G#eU|&apZmG{{KxiuuR|aNrlzJWEE6k3>W`}* zjO|+=9c(dRfTR`7SiDni#rHQ{aWXe2=LtY+HZJ20E^4riV3j~CqEx|Wurwyqs<3AB zgUduWL6KmImjC>I&hF^s)vJe0oJ!oGL(idRX3l;40WgQ{syI_HWH{q^3JL@kb_KM;#L8;JU}6f5RTt+(E(r2% z8=8n}h3OlrXs$lvmY#?yE~nhQbbE^T8tLyKujudC@jjGqN{gG}k$4LIlOQen29Gvc zbs`Y>mZvws@MmYx3fRuujA;6Otv)Ko=~v#0B{f3`q|ZPMJlGQ-6x)3MoL{>Nx78Zh zN520xYtA{K3{Zx0;;B^Aj*czj`p)+D9yV>-zCn%LR*su6VNIp8ll{g?QA_9BfBpW# zZmm{;#^%fC&$pA_1(bX(WogtGO3N4b;@j^O0Q2b4K)U5&6DDl8Xx6PqkIUP2Q({I( zP@EUNUYK3=j-J?AX^?5HTJC_Gw{Eq@Jud)1*Dp@QF2RC{b6dXdgK`im6kY~SMuUWO zrJ!KM>eXrYhj}n8BfpBXG8(+aGQe$}_J(Kw!g3JAmvg>OqKTH0lM2?LbLUMrCb+oJ zRW+9W9vbImESDRwgnJl#kte!7>q*d$U&oFg5AN&k894O$;NAI~)~;>9dAmA?ww>m) z)ZzpSs2k8*uaY|0as&2|=;y&&mSY zgz^Ozu&wk7<@L+;)z74H;-o=9`3f1#UT$l?WI%Dn3tD4oyNvI-l^L3J2ddt;Z!?dg z;>FE9>vQYeLfZOT=y21VD;6f<|FG=2-p9^0Bb}U#JgzeK}r z<5S7vy)?c_Nl8SErr>w8I8aVbP9AHgcz*U95)vAE_0pO8tuZm-;2#nxb)TlE7Ml$j z-+6U@)w_Dsg#b2B=v67OyRk83-P5NtB7dH{8K4@r3cG2VZNqmib9(zWKQK!04%(^; zJ{29po*=D>7#J9SNCflZGK}WU6MPqj_u02|g0J`ArQZg<4Ed<4fOW|seoCF+g_vP; z^3>GT8xf563|ev8DXWFCv9Xr6cJlcaXVU03x&2k)pC;=3+V;y@?fiMaQC+t#Yt%OA z$*-L0(s9cSt$1yLF#Ns?v}e)Jc(f8RzGK`@k&y+>FV`A7rWPb_TW~xk?sqlafcm3# zQ2iG_;383&N;II>;8?r;2tRw)Op0MJfz15;mUGSA4yx3>eY?=GOP5Ma`32M^xkv8V z6SiwtJGI`kAKst!eJU*<#Y}d_Cf_90Psd0$BztM?FeCxgpBHgwHPdQhZVs!Hyh4ZO zI8Wd)cD{_?E*mGMV@5F6KmoC?$i)gL7HZMP-CQ$8k7`;l(a^>B&bam@6KzRaaldq(}#hfpQ`UAcO77%d40%IN$r8V`3EdRH`-f#zK; zcb#k#G+45M$lPqj&DqMu1_xy6pYM-k$=G z{JLOREU+S>tufp$6rFj^*sW1f>p(g13>6vyVfk6N)g`fb$Ism@b(27j46aiYD89n$OF7T4@o z{;_52$Hf~4lfb3jMdVvU=BAly|#)g-IDn=U-lE>PdYj_pSEBWDSt9gQ@mW3KIHtBM5{if=%?v`5A%~$TEW_2<#akf6u z^y1Q%-zapRyng-oX&q_f1MC8uR9fGJ3fV|UDjUbUcJH1^U$lV3X}@`AV`NMPs;l_`P-@bh-%g|Yj zqktiZ@#}q{MJNZ@pQj)KCpo~o)i#q)lyi4{+ee$r;w|e9DN!Mf@e-3iDFEcQZQIm- z95KE>_pDvdQ)y`(_(>cdpQIs_iZ80GRc~u46pqHK{nt&UC&Z(a(t6$H{yq~=xi>%{ zUwq_3ZmG(idCkT|5RL(kBV%G7y;}3UemRrpU%M)LIBkcbkyVn!gg;+e;%Ovl$;L} zAgv;$JkfPIAynuClohJQ)e!;BvL6qQQqHz?xMkDWBWhSbh{@ z>HD>dHN^0<4uKiaS+IN9PFWgeyHCy=M|wE*z`;X@RB_3j3c+*U z)L40CrNPJL+}_>cZ_wstQOei<#}7!?C^jg>T@RdQ#rrb;z1b52X`S_{$&)7i7Lc$= zkD+SSZ+^gKKJQ9A0KI0Lsa>k?VK6&-+cu*Fr!qn)=Wnl~OLo)g91yfQ5MPbK{SSfE z&hRMQdsfg%sY9fdMoI+;CP$qL&J=n2W~b%Uv3ZUTv5gQh@)=Y0xf_@C$n zoLdf%#C8NHQrKeoBBJ&u&z?2b)ZA|V`pO0PsUhE9^=+e}VKsGX2igTBdKzm_R*^j&#{c8{LIeFld~(|Fe)l)6px?#7aJQ}&Jfp(6}A>f z5)xXO?QY3m8lg6Oy6s$X<2#>*jFbE#?MQ5_wa#^LeCy6$GZuKF!6DWuC=k5WVp=sj zd`1u-5WG;v2L%k)H`xVH%{Z$H{vR#-_B~@((}bKNcFt0;8g}lirkMqokD$)N(lQgm zf6#>Xp$jHarFWtuXkjRVfPv>P8j&T+`ShKJZ%PO7rL$05a=t)bFkkTPgnl*el3S?c104qjrAsXScg?`Kb+ z%4VX-j48}|c-Tx1ucmTZ&B{SRL)d);3Os!F?Czia(ypXyqsR(Yd?;PlZG>uXmo;*4KV_N-1#7*$bhmIW!*}96usno6C;W)SL2o%2Dph=95 z9{zBm*7p8ht4Er=0ue{-89+#+iMzUrAL3%Bm9qG`f8`4PH*AG9#V_X^h|_tdjpUq)gHHeDOKkB6_OJL%m{>EwG;b z#gkmopcY;piLSzWt1s%%t{@s^7RQx_Z$G6qj|+!3)Y42@o$2PAQc}{+->X4{odzCU=9}6u78N%^M&W~Uk~SA(IMt5MVRXU zliRIucV^YQ(Gw>I^N<#ndA(F{YNFa#z3&wwT|3?}O;;~GncSyOUyDcp)bZ2#@^FKca$EU1a(W@Mj}`47R>c{g>}v^_pmU_HBX7R+brOvxEm~{z+f3i<_33ju=Z-RcL#)-Tzy4~>Yx^fIF0#C}dOweL9mCt}5jnHZ zZSqav>$crFjgFI8tdD$&`)fmDwXa%)A^+)KIx1p~!G6(A?6IV=*njx&Mq#zNb3QB1 z{@O=@m;u_)){}0;J|b&4buNh3lKW;|J8Ggvv0(uyNp=Bqx?U>QwHuMiV(^Ah?@)MP z9H?_jCh^&#-4mz?sBCO{W)09$fi2m;fB#V1CX_q|*xtu>mOcm!ubq|gmu;1 zt>0K`p>SNI>Q<8*w>se0vU6&AES8c%P5c8DWb#rZ9;7j&zfe`FcKVL92fUQg?Zjdf4sd^2;OPnq>>U)*xrc{GZzMw9TW8e_ zH7?Q?K%OKDmH^SKo;V#wjf3^)+{I(7s;WZ$H#JBoIzda;9t7KGsZ(Fwr1YA1<6K?s zT>8slB#!5A@T`?DPx8U?Z#a$$GJMUDb#tG}iZ-JKh4XhnNihRMm2X|>bpG7A>3l#M zanrP4KUXnY*op~j@8YvV%%rHX3DpqUM*5t^s1vp!5DLI>~IpSZwr(xhFnvq>+Cp*c~Xm+!nYKFqzR zxw)o7kk*Y`w@x`tIB9;Mei2Qor1Qxp6RZ{PF85Gs-|ul9T`vLwci>pmcp5c}A865R zsAg!r0|B}JEMj$d+_7UT4G#RZN@#Y9_ggICEHs%P-{G+aHIqP#g?SPLGsihJA||80*Qymx@l>NcOwL<|MPAzH<54p+UyR7Jnt zd8x-laiN)jLPpHG?{{`bS<|4tDFz1`>U0aP_> z&YVh&cSgCm=sPUwti^0*Q(&X-JkABazN+5kAbZG_{kQxb%_*{Ae3v$F_(SD@;)o&Q zn~u~c1Ufaj#{%G+2$W70@VICLY>|AO37H3TP!3TGgr~1~=Je^8Z_?e}+WOILb5^5D zPWz^P{QljK{UqwL&zAAta^Mc(y$J?@8ZSnznvbLkm$B(M^@@g_3S<+B+*!AF#FyT| z;o&(*yc;O2Vf|P8P*JMdwL@WY+3RCIRTL6V^F4d^*uA-tnR)PTd3m_VFQC|9vw$0j@On-;KlWhc z*Pa@osS0M!4@8AcFXzc{Fb|9%E)7MtGaa(p(JfzkjiyBx9WW?;s#gEiptAGoQ;?`+ zV`*dmGV936KM0TdnS*`9R}b0OOTW99t+82Kt-fY2!i-iH6_fT6wxKrJwD@&zljcsk znqIaRJV+Wr^u^R9rlkQSgLG1kO2>RJ9_dhF)>ajW#!a#%qu=WjsA1jkB&E9Or@NU zbS}=#Or)3dYVh{RKJ@QMaR6GH(BdxKx3c|Fo6h~u1qP1TZDPPsS|*qS)G5>IELO@a zE1NbWdk~76fCi1O1hjK=D|NP?ey7BeA0j|Q>(&7@j2zGY?UzFWJ1eRGJKvBt@zN8g zPi}jb#~ZEw{GcH{2km7k-ziN%##F#18hsDM{U9dB*P*fzutqr?{RY z>wziUhz3Fz4biM|quK)i6S5_=P>9Cg>+818e)G-PG~sYK*kNH|xBe5ObEE_u=BV!; zkPYf0{%Q);cOfecUtN8P&F&pm&VmiFybaH`ZI)Ai2ik}Q*rg(QLpphVLoW`HNXXn)vd zZd2Q_wA709C$nbHjvD*IvE)u}+!!v=fn&#-O`Q0^{QNS+b*cxf=k%^`NmM~q)rn-| zH)LPxtBc{K7Kqb)tB&vlAdDj$G=2FA2C;hi?-=2rTi4>za{c3P{bGVXXR46{(AOhJK5Dc?vw$)^< zcZzx2X-kaw5VvmM9)`;4%yNMgAvDUlya?ukT6{F_+0(F5Po%o7b#=oDa~e4|QH5h$ z7hIfxVTpa@{eDvpl2HZCs;ozOxt1%a8kle-Vj4lMRxR`NgSnLd{TEr!m`Y;nL}lX9 z6NuhoqS?}ZGiP;wHOKPmwQC_Krad%ki%@CEGNX9)ezjwu{p6-!?VU}$v6(R{52`rl z9RpObtKFad|K-2>dM>#!_MoFn`D2x0Zi@f-4yIW zI1;}h9VK<+frI#W#0F#pC1Rx{>VNR@g?_@WBkrMWZ3*QA-T#Mv#PKkaxhBXP;3CkI448G4{6{*Fzy;V1c%&R`kI! za9uNWX;>&tbE!9E-DJhzN0D_5SoB=vB|b&n5)v1jI9z8mtZN&Q>Z0ecwkRPy$}0JJ zM;?0YQS9QlL+|$4u5Ij})GwE*TG~?nO~B)g=m@+%4CO|PAel}?i>;MMQ%@u*jrrAF zSy}JksUs=}cJ125P6Aow*Bd)-aQ7wm{l|4}Lx|UWI)KG;EvP4`6xv9?v?N`f>@gDs zK(&9z+Iv1W-+MGZz9s7*B4vx~~r%{4UkmEqp0Rwhg)PV9LneDA z^OE{L;o^h&yYv%`n378G=OX4kb1|Iq5`>fDD#?FPEG)zF9v>&Z(3 z={GuP$o-96p`$Xw+Rt!4h#&BzhP~$li+B#A5wt__@N+Z+B6SyYZUO zE#AF*XTQdYgnWPOlkh6jZoQ}juK53OQmpr3=1v1h%y!Si9d~YprBCIl@f)*s)wc{X z`E}sKXh^we%j*=gHqWToM7S>r%c z#PFLc za5y`+Vh?Su-a5az{fKj>d5_enI;CU5$&YTDrfzsz(e2xv8mJ%9H;H?8Lrb-5ZxL3E`pi9OSZ6zIgtEq90NMiW1uh%Qp}e_ zhWqRj2osUJE2Q(KS>d5N@NEgu4sE1NBs7g!HcsJqkL#m(q@{i*Lu;O@2F4cbJiB36 zizv%nhwcGwtJZZ=vA>=WTTp-Y+KTz}O@N<5NzTEsj~4QsnZlE^5IM)XaOH{AuS`;k z_(;3@hGWH^c~H&5?YUIVn=~KqgaZi)f9lL;#fZu6)utB_570K+0S0Ukkp=v z$o_QcOONWGm#)@pv}q$VDVy+4keYmI7G=$U;5(zDro{Tl^HH(78c`hrQ{V0ubl(`U zR@n{vQG_+df(_T5%C&N{w`t;3b2S_m#Gqjv;%1GyI2uy?&t&B{nfhMES5QNY1r(5A zz|3Oe;A9x{lw^Udc^&nIBxIDEQUC0{|9Ih@NM9v;@9J~NXhX%?D#+c*(eV_Eg<9xX zoO*DYJV@IqW&unkjr5RmR?tdDA6o!EEv~s2{_szu+V^uF-t=i{zi)fg*;R&*=2tGA zI>+v1fPwc*dwZQ;S|d8!N!tVrwCrkP0Y*}&48#bmUQ)6#Tg z;8_eH)@xiiJ=Gj7v_?W8GRyy_)Rl1qi96Z|p3Ey?6KJw;`mE~Zd|y5oy{n_n7)HiQ zyarC5I(4tb`8cMF3aX-m^C}}JlYs;Y+&ox65C;BU6WtVc>LZXC&MDa z%(*|bFes;ubxie-5NY0vOpSyIp!L5NDfPN= z%J+QLy)FIvZlu+O4cmmmq|adguVbHH{wvW{ZiCENaWnuD6CUVo{rhZ>*2)W>UQrc( ziSUc7$qsyt+3TS{y7cIwn%cTW0|WH;ejKuyioQg5CgeHf<*c)-?Q1^8f9TkMQ*c6m z=c0cO9xUvcKd17QS$wDJfUJ98S7~Wl9f`kXvCWr=uDY%NO^ftr3+<0Iu-(v^lFvHN zvDsggwvk(}0SgH<1=(d6Gpz7k7w=2as*jU5#(wyAZ^MM}>IY|zT3<&4dGGdZ??u|3 zuMe+WIg_IB%%W=^iHIIQP5sod0Zhr~COcysbmDbKqNSw2kTwJD<;4$YEm!)7e@Xb> zCF<7QXX~vFTx|c?r>%o#hN36JBU6@|2n0tXmc#tPh2;g0!>D~7cl2(+Kb6T63Z&9k zL5>a%nV=@BU@eqUUJb0s_%hSXFa}s_{+_mmO>XizRKgRDV~ylFQv=>6-(F z1?d?X2Rv0ayv<-4PY5FJh6;q=;)50^OwrG>R;01FJ0dZe-H_cCOvZxAz8&2-7*1Dv zML!mGu`{zBN*IxaF|QMXSZF$R045+qFWj3meR_lD%|kUVYbq-O*bRdMZ`i1jFIEId z_A04ZB?6)%YRU{Yd&(OVH|O47@qBnJ8gkjjgFF`jw)68ID8~5R05AFg_`S^Tm)=#e z?xHGpvad)C85qvZ z;QI!xYW`-@iw}?tPnfWqZ<^{u<@Ktv`h~Z{go6nQo8<{YH>@WRBKcj%I7S7%z07S8 z)&t8{e`|@Ww9mzG=61gGl;zIX+z@d~VFP+KPl!Co=?pTH@ADhli5aiB8rXbgUcDOk zsG@hX!>05_Sx8W%rj1-01NkU+7z}j06}Y$C2OF<`%ZuJG4KFG)O>i1B8@(vVz5)ZE z5=0p>3v3pFY8ZxzF^do?ELC&E@+DvTBi5lVL{87x?ue3&O#4_bFrRa;XJSJTxNdVF zrm9Xh(ZsYP5C7S9wBV7J#||GhlKFJXZ%+)A7A#tn#VF9gSDznuft!F+4q~oG@OYRu zVGx*FdFZ%jH#90{=S;dxur|Dp^N*Zxgt|V7m3s@nP-?fH5SA-{PsVN+F6<+dd%PXej}2nb>=v9#`SODGF*IYC@D43Z7uaH-9Rw@Z7+J)yG<$2dfF^ga-N@|g@)i7t3z zlVxts&TuHb`}DEGpHc?a;YCJ~fhi`fbrNNG1LJ+E?;{gQxdakM4ZwOyMTcHcr@| z8>GI$@5{68@HXqPSfI}dM1&$z9I-<6jh~-?_>KQ2T&XfLh8aGd1YUu7zQUoPg@&V@ zT{?s*6!dz+k2}CHv2^uu4;^a8f+62pup%;RS((2$_6iU$1Np31451KRGq&F1TA=;R zBV9C_RKC7HKBR2fTI9UvG#xC5j&<=}uu5lIHy@?IthIahPTgJX0{aJs^(*3Ih&Ul_ zZMFSZ?9eb@*rdPz&O#i(?V?L9_0*?F1|*^(q0#Mn)ks zm~0Wr1j`%)7rWFiA0)C;*c2dqg{e(U1jlu8v09k&2WW`JHQq@UwUI$z+CaTzO%x7H zK_b8U^!z&qPed-%26Sc(0<$vG*EX6yd-hl7PotrF@Ttk+%ss`d(V>IKnQfZ%74#ux%Cy)o<+UM>o=R$l$poyP*mA$B?>9I}?^T$c_A z6df$!Vjd!*k03B8^HH26=BiL z1o&M3p#jt0=^e2{mS3VRbgWp_@9-S#Vlt?-Fy{{CZFGW=N2C84%LHphH8g1f5F=@y zE1HX_i2@7!`TMGlO|;GD@3H&Vl^hi7=}L#gh@E5$)*oh@=tX6EopEzCLQuXAusq78 zXO}Kbs48G0#L8)cs;cGi`U`G(?slK@jv(7?@uMgIhlGfLM|ZlAYeMoejc~hAH`uiG4)bA zQolf+GG0JJY9t6KlnRz*=bg3B(Q$;qeRyCjfKgm71+Ta|Ajs;3(I1@}Q&8!g?IHiz zE`c2A+r%EBoMVioEH|~Kgf(>jt9UA<2hts(SOl@4(wbqNf)^AW4H}>(5Fo)PTOAms z%ut`%?nkdx>Y0Ehzhq&7!x_$ZX#~UA0=Y8<_qxM}|GIGRk*X+karrP*&bOG4tVYmg zwS+vBI)XXbKfA_6=sK2PPA8ut;8KhE1NK89VT$L^>%F~|wLowN_P^Vi;cclim{gm? z>Gd^6kZG788UX$~nzYa7BPf^rRXwz4 zFCk`Ly=u{+Jm}j2_*9BZ4Y{8V2IV|9!!5dpMlhV}F2h3fUsAKlDB9 zKpo?Uv12NtZbH~_B^_9Pb%=4n@{wDi z;i)Y86S7jS-tsqFg`vU*l9W&O*y61+3D0Y~ zq9&bA&=q|@Q%Y~mil3o}rH1^ETeD{M5pY5{WTYpalQCxL5k%1v>|Sg{yNWFkMvjy1 z+voKL*$vu#Iy~H|@2~+I|E&Z!pY-ux(nnfauC37CJWcj~iIWBt45L4Dllx>TUKWX! zlT-fqMm_7=Q__f!0g$BW!l!mP$2*^}I6)I3qx%45P0K%+{z>158&>t3$a5rnro3ej z2JcznVuyNm(lh}1h;&9cHvk2DRAezGNH{VPP;6JOw4@giWjUBBMw^V0u62FM*1M3o zc)4lN_G5c$P2zuitvXbukK~4n7a$HTVnQkd5q|#u^;iNVRev^Afg+cdoggP%ioDd9 zTJ;^Dk`p9ZD=0A8IP2bkds={rH+Gx1g7E=56!l~7=8k2Z!jq^J#Jq_dm!DR-z(UAI z!V#0gS9z8jxI~4gP8>UCMc|-hvhQO4=If-Y1s2E)=662r?SNH{j7tLMdQeN|TlX*) zpB&FZdK|o?n#-@9_wYz`#P?&YoAZw)W5cHW5`G&8dyK}4n>IZDzi zqajPbc~gZz^y2*@BNf8Cc)CfW0|+2iN>nfCe7F`{fV~bdGXmxY8=E1-CH`H=Dbq_! z+tXvu>$|--@~ry7n|y0WIylTebMu2~UmQasBdi&x=82=0P#?VkSc)GOv_N7PEyGhk z^RyAB?{>VKNIIhcFDC^F!43acW|^~=IE?^}rSt))Jcf@LQFX{k{Wu*zpk8yPR z0`V5focNAlrLm!LbB)%wmq2byd5k_+{YeCj5-r7g9KW#0+drjLW;Xf!QpNG`#r($3 z&MpARoyT=$$(dz!Q>jFvRz<|cRW~s*JCfvtEJeh3w8WERE0L6T06pEn{3SP)4=S?} zPsB9j^)}k-Jn)xWblIW=qL|5U8JhLPoj~>ZKMF=lDOSjMh%pY0nW8Af9#^9rKkBY8;OEj_BJ zyFdd3;+rUgi42)!!F8yDg-Ouv)~zWQmYbMGyZPkRt1w`D@xLR=*PZgL>rPR@9TBIM z;S(lk0!HXpZFmACB34@p9Fd5J-z+`8No>x~7mclCQe^Lig)S5xTlyV!viYM0dRv|# za7LE|UM;Q6CK?i)XKg0$9w>vOXvSztQg$BvQw@qxtlD_L6l6h;j+R3Zmbp~1;WHD@ z&41NFTV}cMp~L+7xw4ha;%uL`T6O^ux@2ZDGU+C)o+L}2Olw>Pq&8&SuP%PM2vziJ z4gCFoq=dwv35z;%3imT)=ls`q_v~?i(7mRmStCkgaw?Ljh<2a<3ihj8Q~ zMcR)W_u-FH+gag2telf4RD3fS)6v|m(TB2FsC>o};3)>)7!V^qjuMr41ERj?jXq(9 zNB??KO3W$xNUu+mpHIE+cw?xBmwPD18j~W^y}U%ByKJTQbSAgxA*9TN29>&lJ|lyE zV-rx#RawJDPEATMzs5SMg;On>vbq3WGM+0zqYmA`$qH7N#ojDrkoP zmD=&5Tem*D?~Sb6MX|`MLX#8;avlA-|0bTu&6D(&0+98bcv?|RT=< zs8ky@5N_D8vjYOt%`zYL!Q#)`!_*O|AK0c7*tTd^M4{^V^aUg*rXPmaSVia4w^2`+i!_ zv>tNzFX5KTG6vg9f~Mg%0f=Nq^bdT>hueCbTt@F&RMPa^j#YNS2t_L7)a- zm`rRz4srFvSO`-hw%x^+xh9uLpE-Z=-jE&-9U>FUSXZZ~E8v2fzphYNL_#GUq+}GB zHxDVK(6bw^%oV{?RaGUBCi)@OQs0uP{1m(MEqqJoT$AxU!IBtlF|>IGpo7<@5?1qP z9+lQXb>p`}aUt#>-20SOx*ZL`gko>cFF98@8lnp8w<$ARQ1?y)-q z9b>oogJD()uXs!FCd=#UdX0ZSj-w*tKn1aJleMNk4{a0T<87F*V#d$vVO~zlm8yYU zN`9$$ZDohv2x#Pc@?6+0Jm_RbOs4Bls<_Xe|8x9&i$NVWTMXb|756*_{9^aO)wg8K zKyu&84=;^ITe<)ih^GQBZVaELo*U`Fw-uu!4GpF8N#bIa!)142F$Q`R06xjD@4ZfTda}TR+|)!MG-e%KCr-rqGqxyO3A-2B6 zc18evzYNJ9DoBX{=)7lq?KNZ|-&2h6Vq*=NGQl+KP53?Wlp%1_?c>1Ik!c@^a!T!? zNPnA9KwjRtGqys@pFLUpJL$MG(5KIMV*}oap9Mw}*g`D;Lx47xe}cG;k%Gau2yI&J z+NEhmMEw@_`MvT>`m>bVDq~BT9NBu@=Q5b&MTCONbhv{<$d%K^hj-6Nbk%n@=-3R@ zgYdeZnn0EW^sjm7!|$fL3qhZ6rPn$gXJG+%-bA<&vC-a2hNC0m69({Sc+#n^Igcgy?=_hbbglMf+M= z&Cp@c@&K8smmN|xzfv<%TO?m7T0mL3pT|$VG|6N1!+-{j7Vz6E`do1Y;abjyWxUm` zUyl$R6&)Mft8Xpsc)tO;&v(pyTlqc*sDzZlxIcxWj3#516QQsa$V~wU}V+z zz1Fp95jM+>5e&x6{!R2b|aj~ki=wuum~U+ z-m@YiLK%B}5}6n!qreDYHE-yuq!koA43+Z+2%Dgu1z4frvQAJ{zwP0fQ9^mmeBr@m z-PKsF-CoYabmj@~r}+;&IrUNqeV`cK6E>SMzfoqQ=?wZJIJuM&ysS^`GcvuI+=5Y< z>`OwIg2-W6+QzqEHwqMdTR9Iq6@r+aARqlZ92pg~5&5stN|T@qmzyg#lo&_Fl9-rC zgMvM+HrUZ{mq;j^1Gq?wYD@XvXMfKINUih*P{9}V;-_J5cpu#1k64=keW zm4E0vtZ~sMH zh=_QnRjbV^>PcqV=9t5JwkM4tZOJ&)l_p_bAJGo+(3){K)Y;Nv=Tt zeeO@3a4T|e1nIf8-|?nX*0Y86D7es0iqoGA;(wXSy~SZDHX;F=ehH*i9@PG@vzgRI z;yBMQ<#2h@u8`2P$#O!@L5^HDuc}8_pa%~AuTgSrdSP3#LZ$@i(kEYDed+EG#^kyzse@FaDPoaC)CmJJrrH2$L|AZoR1E0EARqixLO{QMZLr^A6hvKn`c|4> za;+dABKdA`vDINU4wGyRP)O&P?VkAPGQMwIbpvlL#8{HMmb~bC9B4p{Yv?E1XxH9m zTn;_bf#HSLcFh>DxvA|q(*arpPf9WRqY^uab9Sl>xJxJDlVl7YKDR7Qmt^C5dPY++ zj}VEVz>TS3)DlL}vWRmdor72sPA7`i-MzBPAx~XPD~NVPtWemio5_iznKT-&_cxLW zh}J`(pt3##5csj>HZAe6s>HTd)DIB6QthJyy7Xb??t=^nu3GhN=?~%6L}3fVD%L&p zVi_2Wf~LsF0?+J{czqnaiCtCqH$lmWv>Ne&O7(({pg#Oh zP0?J??OPLOIoSJCrq<;RWq3h|YmNlBM{sQ@hv=^RA;WG{rfAefS19U`4)Z4uWBl*R zH_pG%L!(D0K3b;%fG5}}U5NmrKp>`wPCkGBEN0(AMKf{&aNMKfY{QFZ7nrjPV@V|A z)gA=?+LfCYD^k#j7pX{egb`yIwWuV-{7vk%VZ;KFbXeE0vsOC-M;1^zcZj+dv@Kog zetZk$j$KK<6G$07hCa{;&za8iv|y9BZ^nypsY86&D2-T>Nf5o zj!-x{Hdgq|WP;I89~6qx*+H|UK3E3c!(Jexlh%M39-bYH&$Z)R@TFw7l=Kz*vxs$Y3qjal~urz zW`e7OXj@o}la}*-R)x-i9nRM1J7E3{7(>xgZRk4 zMR=Ngcx_XsnpIBMWl@e)(&8fkQYznrsSn@$3oAc#4=6qvibE@A*WQ75I?<+$W{lsm zU}wZ4EHo?&2KG?eLqegA=6CB48Zh$F{9}gLsn|0RxfD%M3 zCg|bFaCi|$%@`v}0d`$=)vu_L{wzQLKz#gz4x)ujZnyHrD$$w+V>014d96&3&IpKt+@^&21)0t$6MVpmI_2p0mRLzC67 zvB=&iHgP|e&3`O%punC^*{$$6ZT(2h&0tD#loKn|tFtCht^pM)k!WW39gG~U zooIgeEezxwfIHPS31bwZx0%_dLx&8&X!Mve1dtVt5MBKJ3(ISaL`sqVVkP=`*;U_fvd2@^`S6{hD%i_R))%g>9rJ!)9+IE*MyS-SsdyN>HQB z9YMANo)^l0fyJH_wIB`|#f$>899`&5u-%sxkco%VF0|9t)fHI-c_>?YVL%Uwt>P># z6bvj-(yAB=DGD$Yj|MVQrGR~?bf6Jj0|Dkmtq6qo1{J4$&k*lmpR_MZTwm#7#XXq3 z&N%O?R0oZO0Ff;cqH*LBip)vA~(Sw0=;hYBYeP3QjnC88XHj2FGAG#u<3Tz0BzYlLh7kf6=1gc#C@$QSyXeh)H) zl&3>e*XiE{dv7OlnizZ%_ZXgvyka4emAv%BljPvg(9oc+C(g(^M>J4^ z#!Ba&Gmk-do`DrnnW&@?fPd+J1emyo=Rt$|(x0~ST5vtjmJ=!m1VC0}J3{kLaiiF4 z7DPvL`GdPIr;m@M=$Lj9`AAH;`51x2VJB&GKi! z5B5n-_3xBy2o_VeP|EfXp$a)vMsF{{?2V6~w2{N^iCu{Z?o?Z}0@|mYB-8#XZ|p%) zt1xxo`;bbQPtY*t%GB5xX>{9?VHwGdsNw?%-c;TH5O>utHKR(}w$2 zgl)#FFK@FTKsSCwV|UrLP1M>J)Sv`K{kZiXD77tJv?-RY_c~Dp^Fa0It07U#PITbJ zQzS~;f)dR>ueS6cBtLg$Izm2d;iu9`Us>}-gvq>0e30jTbi>*~)PY3J5%epv;u3aw zVaLfyvs8Uu8CmH}_Icd(?w1P!+zTNt89 z_70AHTveYBZXhPA%LW4@O8*sm-J-_Kzu?)z-7!75cP_Tv_p(V>Vo#h1Lp9<_NbAae z3c76>Y!itKe6#hH2k*@hB|RNti4kXTlZ3^M(Lr%Vd-v=es)Bu${1}0) z7qTCLbq_+4i|vjmL#F3FBdriwgk}(U03_pa&zQw?uxk2k0|w(F%Vh*1DJ{J){A0M) z3Ux*_c-f=KrFaK$Ze&xU@)Akm6qp))r`UyX9MQV;#vc=5f;ijOe!YamOE!++kc+}m zFw^v!uPcx|^XKgMis5>G_SYLf*01>3`{08J$I*;N$c|Q|3)DBbW;Fw+5o0iM3$UGW z1R*%zG1+3)0aGqCuOYktcsI8TBR#tl!QyjAPI9Qf$-L-}6GV5zMT&j3On6Oy?j4jN z0S2Ae#g!i%vf~iGsLFB2HH%kh7gO%PDDlYesWbm)huQ?m7E*kz)tjb^JXAJr{PqXl z)xS9zUFc82%s@@pBi;~dL8xLVX(JWKv_IiAWk6pc0P0Gtp_P*f>OAo1LGIUe^yWS0Qj;MM=R+H#(PDIB7eev+sY`AsPc zCoM0dM$MwyLU9_->K+-X(P(6thT)`cj1YpK6%a%3f!@EFEGq2Kh|Pzr;Y&-@ojB{(10D`d$)c z0|jQkT8rV=v17B3Uj`k}g{2k;1&B%cbNOk1AlpT$$FtMWkL_T&xGS$r1)Z+)#>`e< zcX2Hm+&aBpwljbYn){4g-i4W^H|4{mRzZ)%*so}5cHhl6@;&#eXS;#EtMRJ!*d7_H zKFS4$BI2*@Lou4cb(&hi-N&a_VcLzWSF2Cen%TlUa4w`_7rQtKq$ncrk^A21Y-J&B z0*4}yx(V&DKs_QBe)^4eXO%nN}a+0s`atr0&CK-vG<$?(b&kqKywX_K;vK95Gs?)N0njb}KZM%yM+in5{kiAFgHO}*FPjtKK zyX&`QI-8%IPmFr!H2utj7o0qHqbZ8*=A$+ zQ8OWs_JxZ14ka(i9ZBvj@30BUjKAO)E(||8 zbJ?&=@d$LItSzMoW7SShK1 z!0DfIwzuzelK{R=EapL%=(exa5oLrM2USzu?ghLSytk56hf%mu!-ve;o7r43?u4u& zs#(kmSx?RXM28vj@ba!BJHFrY9;6i!JfJ@kLYOj07g?|Zz(80QOQe#&!Xe>0&J9`k zdk6btqn=Mjm>l<77l-zNoqu`Q6FIDlAPI?ckyt$djXj|bReV#@C*{l-=OEeJC1V>Y zWvffQs^U@=a?$mk0_w&W$$;O;?=j4|MYv7Gv5%TbsN(#F-FSm)wFc2?f(gpl83kJK zD%GOt7NDeRi@j^Kc10YV-;(Zx)5YfPGd4Bz?HgG;6@BRakPW=-K~z+bGYIIreU`s* zV@<@#GRUUbxbfN?4>4Gl&Ukl?=gG@NY~ku*Sd40Bg~jSN)_ru~R$JXRj$at+ns@x! z@WN*$!$*{LuE7_Y%*}AO9$TGcjG84seKRJ&(FPO``^nWZK-$9B5h4!2Wui&Xo{k4= z`>f5Ql;-yeG>nXDEi4cu!7$6VyOvH)-jI67pkmgZsDu4kYS!)c!th6jK?-Cv&DsWs zBT0-LB~$;ei-YeKk6-@>5A4FyS}S%u1QdTdk2b8FXWgbpVYFegpb(UYqOgW3QKToB zXKGg%nIQ|1SdB7{Sxa!DsIS9x=ao$dV#|BWZSU92snuz^(JHDBHLR5MNIB-*UE*Bmo`Qje z^>tI-sW^{m%H#wwQx*eranfZcI0)kTJ(FBrMXic)Ma`%#TO(9V9NzlPse3mIcy-?6 zUPexrN=sJEzrh7FoaF41VrOsjb>84!EKHC(3GC2i6E{Mb05KMx_`|KR_UzV$N4rs2 zs2O*;dZ~GUwTe~1(+|q@F%rV*B4kLh9(7F6l#_>Reu8GWruhvZPDu2@l-KoOtwTs` zl}+KoBu0LNuSXiP^P=Xc?vw<~t%Va5`VXvwE2*!iw|{4QuL-ZGjkq;dN0dX1_MFXm zwsYkP!OSdBY(ojSyp>^pLW}?K_q%jR3M6 z?#^dH-#>PPJ19s7gE5j}ApXHRXbAhkd9mtks!ov+? z{(5BWhYFx2yQDp_^V6igH>*S7CoL-_D%3{=5zqrx*JDjb1^|U+N0%xk3LWItWdP2i zt&vwE5;lsmr(YxcZQE^_l%f-Sytw+s3w+?(QC&i(MWqgY6cuo)TgA&Y+*iZ_n%;8; zvVjGO@x0s|37=HBQ4P-}W;KacDPm`g^4?Ob!mp{ZwW9X*dPmhPQhi)o;H%J*dQbz2 zzv1!H0-AO9{Md!L&>;cz2`TYK*Pu-i2#%_#nfL=>{Q||nQ^}r7SQ+lul|e^zuf`t! zYfm1TZ$*J@_b|V(IyySq$g)>dZk*AQ#qU0Tw5627lNlCqB4KQQj z<03n=0ATj)W6Ze2*UUS6nK^hP%LsN>ojpGdk0&fo^c!j+4QB*Zbn@}nFPuN`kTj)p z&K%TBGTtwqQn~{*Am%kygOfFUjwK}I;Z;RKYJ>-HBUBCXLz=8T8QN%!<-U)(+B5++ zuh)Pd@)Qkg7c;O=_2IVIS1piR0nEZ*)D6~(4rt+Me73$QiMkUvq3~l`tglb!DHkmG zt|Xc(Ul5uZRkwFL-%;$H>$~+uY>2-rZHLzf4?efVDE7~@;XY@Ah<91A@UJ;E{;imp zMScEK+V9znk-e8xo!yeO+Q|ExlS{F!eT}M6MtB5JeFH0@#Va}C%x<~$w2sT{e~n(n zdtYNP{nt0U|KFp*JjKX|KfS{vrcU$x+2dER;o>D<_K)gXw`p_XuoI?^hwtX)H9<7v zdVmE)ve^O36xbu!?yrMAvZwouB<^a@yGR2bY5Q9C5}dz^I`;CokbcNpP}h>LgM$h{ z$t0$slzN*!Yoy+If+Ltsa0*uNl)3^C!YM`Dh9I;fD-S%e{ytU;9CV(&gmLVcfg?WW z){GfHK9~5N2zD*#FpzeC!l#tYW;U7%Z{TO1pdD0h*)j)06P);6?l-FyYnTKO;2$JH zB-eb%lov|lQ5E6c(1~0jVg{OrdvS(MVcRJ>hb>re%I*s%R!mhNUuS7VdhBZ&(`*W( zQG9gd5tm1wzxp>@*fIg~!r#ANEV?ArHQ6%h%Rs!}uOFX8qgHb1CQZ1E9cfXRiYF@{ zIeFrw@=q~z1@c*?udVjGhrJtE2~8KsorsuTKhjY-`k*V?vn4D))e)uU>CIt!C+gpK zk}4GUc>W2oQ=|=qkL>^K376R4n!p|8DCa?4zrlT4Moo@7$IKp=YRDTANCe|!N>wzQ88$t6(6qvncF{!3-(d4oqm&6)h z5Ux*0EF$F^oZU2w~Ref?UnhY6- zn5@(_&AWpj0ZsMH zEZgsC>A}%V>5blCX!o)OG`P{gR1;C@i!KV+&o?W3-{IK*>-nVEMpIKpI^@wY!zy)r zmo7(DM&{gmz%P&Nw-6<^SfLWwWcx3?h~#fZ;rx%UR-ob+_(jI{xaLr~GA0{*4_#!a zG;-zXYbEi@nhzdMtJBlgy?yxa)C)ft;pfNRUbWOe2wmU%g74ldViy zPm)&r`n9Ff0vVCc8JfYJ1$!>QKJ$5_u}@T^k|5g-hhjWl1E=5lff}zz32;BL>h16$ zYwDTY~7K;6mp_dCZN&pea3N~HlUG@Po4Rsj&Cf>X{I%BfvTi5 z&jOOb!h9_kB57wAc4D-aJ@O2&go)wT{}FX2U^(Y)`_B@|mQ;2vDy^36yRx-dl2VzZ zq9MzaY&9jMP+2P7#mJIG3#Eu6A)89M5|^&lq*zzu)(J zE$4Nf=LLy<4NL>PEla5j;f0ysEBP`SribYDvEyqEAm>&PkUehD9s6|uj4Lk{daBY);o&li@;saGH?)0iZE>9 ze!w^`#w+FC&HO4+l*v#2<3ZjUv^GNR17iq{jdjS#Y} zdZqXn{`&c?HyW!Ac_Ouk51`jAh&RT(FZ$Q0&lSVW(ldhYHztr@CY} zxbH2T+I~Ra?Bes=T=p97-26}$y(KL;O?vzG zTj!sA^Vha*7-Z|ox=}=Thw|fP~FQkyP*xoAJW#j(+hF;lU4mGPl2mJg_ zb`QXvI*1Km5vv)TVw@7n>+r7VmLT8-}y%bJ{eS#uatJ9yO@q}EMu zrQDNY-*ZzsXE6}A0cfXwR z?U+6E^e8S-x?CYia3Nbk_moA5l-~V3&M5It-+%ZZ<{F{xiUs^TW#^$X>eI9$i z{&IM28Dot|yB{+I8r}5_^2Kk8Hz;5}8utn8@SBl4-?-}$YGW$T$CKXJrVv7s0W_&x zh1vu>)}nRmI-)n7EX3k3dMT0RV;JCxrViYxFWua1kZ?HWHfj4hFqq|CkET`4Tl!ob z`Yl$Dt+mhp7+<`@V8DRf4MDNMiaHKCxw+#QfJ1R%4)TUu>#9LMN&Hs z-o=sLLG$GqJ1q6?-Miwu%XAtVbe+G?v?Qx=RZSDe)PN3OrnES}N5`dywyd@7&Mi8} z#%pkzQ+4JUgE5!%Ad4x1BwFjL6e4+)(@C7i)_6jAvnJ=xo%@Q5fS9b>U;Q^r&pfeo z>d=Qh6t*qew0Qy`VYTOPejej4lTRyQq7VqPj~>O2atp;2S{xP>r1u%DU=a?E_I2Z4P;Gp1WzZ(ej9cQP9R+MR-wZ3}97rZkUuUH{BnambPxpsuJ7Jg7s zBcdyzH}kpS#cafgom90ftz+_K?Eeb(tntKR)=nL|F%+(sEIkA4DQegrxhE^@3a43a15dV= zSRi>vCImG#dQI!;_c9D#R$sjujW${&mol^hwjBbf7qRwtIKd>wFrGvXu#;l+s{u?u z=q=SBxF(JEa36fc84&Yq>*Iq}Tk6A(BvkZzKLhfbCGNK1GC%>UTPP-OE+4sI-n^?R zb{&Q;eRhUc2mB1~>t-rOe?*d)%9nxBmKDs!0#>3X1?VNp7b>sO`s(jXd+tWTynFuL zgmf!TIWmoHjF%#q9mM}O_#oepV><6)N-|M)4_(P#H;a_eZ=dyKb_ zPbnswLUqay_f(Gv;}fZO>}b^LSSMUHMm$W$+6{D{M<>&;8;5?Qm+J5+k7WrdTxz(M zQG!A-FF?QlZYA(B%%n2lf(r~8WpDr<=uN|pN8c`5%!UiuDShhIHy0b^i&3a`io*$E zy_O6gG$;W*g!p}UcH*>a{?l!aMJHkMq@>6mQ2LPkre3%(0r&Cddr#ygyuGYcI95-W zg~Z11V<#H6k8RvC;V-%}@gID?a`}oC9ma0vhBpTpI*M_?oxlm3I*$YQB0@Z2|1A^S zjXdDSGQ%XoUnb#g#|DZ64-;Wb>4>b6lA%;o#AL*FRZo5WvDWI*CkGS8ou7sH(7BKg z(2U7CKKXK#t03fSLYK>>^m&jc>y_%%?QHbbfuP~eXv9{OS?Q}w%R_wN{5i!RRaRbk zk{~6kSlN0iPEgDl>Y(-VVq1P&=w5}HhrTG~C+{A4nF{tRLo_?{ zzQ&~Gp)l*Va*U1ye)ye>f03%u_0giK@2ekvnmv|{iU3#PFL!yd6la}P|mvs+a2z^Pv>nTz}^d5 z{C4_(VZX{b-AOo)XJ!A=r%wiGIjrNGX!LJYW-`U4Q|+2~K4>-{g&{~jZ2h3$LPRqI z5lARQd|Ztji&=%irOe^hG=AhVj&y?#fNBzQufCm82j|oB-ZpD0F z@SE4>mp~tB!bH7GE?v)6_Gj4X(4_z?C_yX$F5u^qbqw|OWv`<^@a)uqn?-V6OT&E_ zh>}M?i7#N1d&nex7gQe$_!mmxVB`7Fc8LvCJ5TsE8;ZS13=`38$IVzjR?m~nfqFyb zZSJD`@f_*dK0dB*JdR8>6gxo8UR!juq7=(#2`T`uPN7-`ICMQ}Ommnv`Aqb={<6lN zsp0`!yLcZ?T`w;_f~*l>V&`nV?FjBe-A9F7LPjEp1A9h7<_?ucv-fBBMRVs~WLD&J z?onm*6Z2#SG%kQ|4+0KdWR^&wcWy zE0Apqze>%1#$w-Z-|osx$XB7lk!t;zWw@bt_h0h+@Dj*{*EeSp{&=4nq z%cdIY9#|SyM5E+5*L*9un#p(iOK>)1@3I0y19R8)C2lzLG={s0LXxVF9g@A~tG<(vOu%p#A|Nd@RF=BLtquCnL2vyx`lOI!l zAtEmP=Y{E`WoY|h(wgSBy}fr_OR~uKiU-x{p+F!0D6*MxQ=dNz+Ue)04s+E}ZT%-o zz&{d0;3?UzJWU;r%qtW@ zHET^H2uNHO)kOhF=cot7Ym{bN4wq`Vxay+sLERuiw!UcQ`R+Ehwz6SYF~7&@)+C|u z*x{Qx4{-Qm*00}I1~^T|H6L6DAuE<2qNg4q{X>uO8$ICNXx@uwFD!x5^y5sUd28#Q zKHZv_c+fAgpzUOZ#dhJaYrSk!|IYx|k)wOQJtu#>d-oJz28slHCl#j4Z{>K~Asm(F zuzF=fH$WQm&mq0kBeo4()R`ZE+ku360(AJX8roiwpF?_iO4X;8_`FI}U%dsLREN#2 zy|a%TVRRkONIq$jFJ;}*>eGWM_r5Z|7r>i-M;e^No@01yVL$y?kLzvG3Dlz3w2p?_ zAI%fp;_$gZA?TI84d%3Sz;4^3%F0;e{kV5u1U3L7R90>F3Elfd#@yF#+^8iEI2FDb zB=Y*|Z6%{k6zW~N*fF`J&$F_z@lKnXEbd$0pu)wU&sHD*ywh5r1hp1I+g~gPL_~g` zCtg}!ZaTFjovvu92k{(obtos{9ip*pPGw5rw`&u$-(xmWkCH~{T9hlJ=^IT8%uPlH zGbI?A*EF#vI$`sw4gs@b2kdh&1}~`dAE2yJHpN-FEV3+$va3CYiw$pn?%n%mbk8Pn z>sW+^mCVh<11P}l--kFXYMhOx0m~Ed0*;PY0FmjBKQeWO!#@su-1-n;*~nktmxzfi zeIXTLNLKl3_-5NEp*^WVmcStUZ8}z}g6LRRL2RhUBOf)n-lAS^Kn|BUn z?M{GvDNA|O)QuaXoy-pubW_s2xpw$(&DrahaU-zh+4hd4fR`8VqqgsLeCZysD zn%(5dnelmX589PsVwU%rGPIJ9I7t0UkoL9f*Qe#Jciev{YybY~2OsB;)eB56t@0H^ zgBi#FFz-6d=~~P*m z^szTkmnJt-J$2xkJ5(wyR8%~;wgU44!!t%c3&Nkz+8)x)I7%!QTn#$Wjo8C(gi!bu ze6~Sk@akv(RQ0c~*v~0#x5E$QVvqm8<76D{bhjW-$nm*uSornH4%Pg3Y>QW=^AVU1 zawvm$lVgrv<0wJ(M~>VDQiQxAykXJ8g`!74#a+js2(vE2(o30LESVP14C)%N=sQ#x*KAIzw*hQiHUrdL$PaKF&8?k@T67o9jg-` z7pI?i1wbX^#`5wbwlof(CP#GQJL$S5Gjwx>4Wz59i&1kTJ&@u>sB^a))Fb41E}_lj z$xkSnY4PD^Ac4Ao>cFYdUBdL3MvK8qHsC(d6H~e zz;~sIMxLtTJbCZSlO4WF!c|agI6q1sbSlgt4Q|A^Fn6?e+!2IZQ)@gDfVOrHU8YCT z$nHHlC;9~Fmmauvps>32;h&)l5Bm-pbdg0(ZU!4NLW#yg!DI3B)y-Syern(#kVO&# zvcf`-{eZeCYDuJN?VD|ZNb(h=!iWDw<&u)maDa4VpsTxvcgKw2r+@P!P?Vmdb*Bmg zxn2*Q{2Cpl$=?;M#b7;m1!U_9c)tUkTLrG*^v%DKs$0+J~t6(zXPh%D;pNz+h>9{d~p&JU1S z&$?Q=*8Xau5tj>6)BD!t4>QMNpuPO{JrvN%_CM{b+89j^vv)~Np}ypep<+*|+7cyQ z7L2(j@5s%Z5QkU64ry@|flDauuw1zdn%eHbU2b@zg00@K2$Y@rdNt*0)=+)g4)ZJ7 z+H;Ri$kSJ^CQ@seoEib+*$cEQ)T1X#4cG)uMy^z5t=U3L*`Pc~-Me@#&4yLl^Fi0N z6KqqU(&f-4ajF^Yj5gegWAr74dSzg&)uwZQK#q=~qFK1`puLxanHK1k{A&z<9*qE2 zrv2tQbMEC}7A~d*_3K(Tdp%DCO}JywZ@*?f;f;ju@Q%_lPv2L~=C4a^#`;$br1)6rdNx(FQ?a=t( zby^yz{r_M}zT!xCqlL&W`Ub3x36q!&qB?bjTF=M*Gx@$l#&mNCk0UpXUpL&(IdPud zA3t{Q*kL;Om{S;a%E4TwzX}Eu5V{|i-|x+XAp$`0O`|T~ z3hsvS<#!{PLAn=$@E^$OqJXZ;3X$hwdy`=i)ojJ`0jn$i)eQ~~xo^qhgQJ}c9 z=2JT1X_Nf1AO#q16IYdb8eseyjA#?rH!qo734cQxemta13KA*gCnpYWWMA9;>#@H5 z`b{(1k?(P~v8>+~pq?`uh&&if#`p5?GpW1RIt`eA;|%C3t^~qkuc*)pwdPwO?Q$+% zISU;;`ACHEefp$+tdBFm83QIGG@5RE9u9wdFV|E$b4-oz9FL3}a>xjbIP}>9T2+Ji zb1|iJ@D&KBL#HY|2Lzh2s7rMR_bYiFqKe_e&Ym+TjCx+Jk7@b8gA4 z|IcpZCo~q>hYz2*diC9*qfsPhIIgq88Z7{`2QHex0s%y|=c=mQ)9PrqE%m=*r-oWP zbo1tlAWfBf91R#VI$&F@Gs=cg?cXy89gY}n5&tPKAAz9w9@L)+=wv(5`it5>E@d7I z7ha-0Cv>)kB`fwx!JqHW^$j|bu7QdR+DIwJCTDozcJz`NpGHoTp&0bxIEHnJFmmlV zv_vkKM|EISm9#~C1(?=ta1*Grb^q|`lV-DzJNo5rWM8;rjvma{9DYGEnSDm zE^)$KG69lMP9bF8s=i=LFtD<;&{%k^X3}BwF@wJPGK6qB%Jf!d^)t~M!2T=ZSOwCA z^LkQ%+{ndoLBlBegm%mW$Cg$xi*Mpa_kauseqzdpapO zEzO3${{6dmYGHf4@v;a9jA8uYq*cxs!<+CZejXs3d|d`0bJ<{toaBJjOM)9>1?6GG zhS9!8wX$fUnw8>UEKIyET~22Yv}7hlRnY!HbHIqp44tBFQ>3AVZ1*0byA;%9#jvEL zM_ul2PAB-cW^Thxj{H%uFXYV2Z-(0aSq$63_741m!Vae`m#Ndf!TFA{)~5gvHJr)f zbT`;(4rvWF|O<9D#qkT*3(D4tNOgjiQ=+m=H7cvy`-D)Hl|4l%TcqBHopyLl;rg3 zg0k1UN3GmSx}A?);3tle9v-8q{-_}|wKhC|`SJ+&7qwFnK+*g9`n|kZ%^C6FBf0Z$ z78MtV|7%D5L-Eh9f~8K#gcz-lv#^i_8b^UykRK{s(;hG6g*NZMzF~gmZ%rVk8 zGi$}LQXCkts@i*F1$G_dxPIa+0xtp5%GGoyQz_&aOjd1g)LFBPD|;T|%OtRnuXMBE z%Tpp68#3(>RT`3h0oOYrY?Gx}H*Y#U{{(?ajCPsSUBC-Qln3C3%z zN@-H!mE93pIC~ypgHJ2TTXnr;js?-m4&a*}7Z%*(O$U3jVUi!;g#B_wwmYNvPUB1z zmDEZm?MqJ?pZDr3CSf$~jKCrQ>cok^o=5+6~bVLsyj;5=pUK7R<@CB4=j=fc< zxZBP9#ic-V@YQcps1N)Vh$ImZemrPwub#kJt6tu%rp)b0iFBs2GUtZQXNnJ*doX}F z_}lTxn%|9^(G7S$#HL|Z$g7PzKt~&)W~*>I=cJ^i)&*y~nwU<(%@iE;C|#8XKgItGx0jJ`~YI(dsyudR_^+a^tq z+c$5%{r81DsZ;=Zk{2#vNE$#ltZfktw4E1cgj2+mQ75A#8Ed z2x0ID80DJhrz<9u;R)>Gc0FjFHuQ=_GP(!BT$z(G2k6J-9WM^!kEN@S)g!Tl2GAU4 z(KR`4S%iEwOJrOIR_{51anJdN#eK>g)yf54YU|Z1}6ckAp#jzYJKVJ5|-xHoSQ& ziouW5vxT4oI09w#*}^OXhV9K<8||hkEzT?Het5;FSIn5gWpA2vW7MzjJq{k0>mvnGWT{{A*QMAc&k3kAfhC z`dLQ@<0!r!n}~M!AK)g*Lw`st@PX7`hq>`An+rSZa!H82ZbL$8~foxeUf4R113HK9n92-2Q6QgMHAVX$z-v?>A z41*cVwc%GyPv4ipxXHG2!GbrQDc2bO$qZDk+oVZ#q$tkU>DQN;6$KCQt?HT$1*a*^ zpmO$NjsQQwHo>M*`lPOgG1dVTLd;?GF^Gv6YsG~8Di#Ok#9#;EyD{*t-*#h#R;DcnrhTMqEv z#Twz{$!|OE%+W0Fv*=!b%){*l#0- zsd1AV_9SM%jk-OeFlB8l4%c_%3OgxuE5kuQ+)8L_}m{4ftyfH3KB2>v@Z3 zbB@QM1J_VLCjA*1NCuA}EW&}J4yU)q5v~6I{YIL;J8742UgA*%AvQk1fd=p>EQ|H6 zvzbP;cOLqrTj$PY&a+l3YkW+Y)qBbzt8q3IM=d4~Wl+vdVo%UzIM~}qts03Std{>e zZtNTr4T@ccc`?z^_L;R)|KFXBY}h{k$Qf9G&T0b~-6rrj^%S<@drG*Z*~gA$W={HZ zuJIJV$=$8ZZJL6;o}~{B{Pe;pZSB2!|F{%UOaR$fAqS!VolERAs4%^j{i-t~LPca|gU$QzrN9%c+>Ja7@S?^|;DExU0Sd*H*n$30lf=i|kjpFw5 zX#?%3_Z-u{Mty!WdloJO3M*r&n8ljRmrW)eg1YXV`ZSUh5 z>hMxCVywQtzO2WF%W{GFQKNZP1w&X_AbT{@>f$@2hA?blkt{PcR|>(9KOb7mrgjPz8|UHVY3K1lq=L%5p!yGpHWO1o)V^ zq#wR{?OF_zojz>(f(OF}tGTrQ2FJ?dUau-}FS8Hn7P#_P+^Jb6zjb!GTNz(=LCJke zp;-&>tv78=7B>tn{@Ymw?So73S?Opo$2$8*0ZmcM&6nWe&ZUtzxNxv`@n5h<<4j%0 zY=3;T;Pwk5QG9-D1I5f$JDH`M6reG)G*M{zTuHdqy$k{)p{@ukQKJps<}j#|{+&Tm z92C*-Q>zOgEwpp*3kT>V*_nB3S&vol3J2jYAo&R-=6`|peP4RO~)=x0q&xfylb&;@;@lw7DuS(+8z zrhLaGM^V^Zxw3_L4H-@_Uh2AZzo+J)Fi7^6klK`_djLIi%pCm$p!XtomC@E*87H@# z>sT;~zW)et|Iih0E|K$vmPE)kT^vGVjMLJ1kPlH!@Ui*yrrq?-WUxWAX~i$vHX>*s zO^*jI2T+2?Cw(vfHk$f^=3fU772Mm@k6g(J<&EnqjlyHf*8C+0tdVN71yvU-+rqH< z4sMO1UOe?cJ@=amu!xLZ{(AKX!*&b>5ZKq&js=877yKXNP$$kNutT$1ECIWMLPELk zPRKJvM)A?JZFxPtNuXgmhWHe?G7Zj+e-fO4bHb{u%mco$Nc;dp zM5PBv&+-4wXYsF-6X9`Cb+n_Z9KB{q6$ALND|Oy4xihlom^%;bOoP4#vIz{OUV08j zA`HjwUKSe^OVas|SY(=Fa zUtg9}(zN#TE{j`{9+v^#hS0qi zLHStLiQY%9rcA7%v?yVlk!UfFp!e%61pr85^=9L*4AVmIhd{MGPAFDl3W1J6m|FpAzD%>Sv9 zfEvs`56*PT5T(^z6yZ3cvEQZ|zu8enBaFTs!|t&ehf3{1I)v3S=rfz)`8w>?=OhB4 z+A?R9!aN^pj1*k3ANnu2wUP1QRc6%wik|VMbNGo%p!dP!EoYv_KW>J%@uo_>Yw0?S zS~(7M2S1PeF!Kg4!jC_Z7tkZd8uGh0I~eNH=TFrlDRV@-8>#u2yc~T3G7WWCXkp<< zq_b%jJ^N|?nk2rLpFdyVrcgBlqh90$A@q34NfpmH)Xch_l7MqSzP}&Xd=k+qucKI) zc%;toZyT6+0;@8qcnqEf+Z*(NW~K(BH8I+5=@_T~XjQOjwbPDuujR>>;?Rh}8~vB$ zQb0smYyp1UST%HQ+3!tLjYhxGnLa(Dlsd@7brw9PPB0{p*3K@D)D$AHPlB@d)$7-T z9yc)-0@KMATX+FWX!PjM_2=AK2AK?EgyZ1rKd~&GxNG=!28>KUnXc`$|7)5nl=u>e z|Eoh6erelRC4KK|;BaY0gl&qB`wWe}?Pc}jxw&=BDi}V7<7P#t9Fdjfn^L|IJS`gq z3k$M;qnEitQ4mN^fR4yjv8&oQmN)PLHw%V~QY%%@TYU~W(DsSr2|Io;_uu(5$z$@O z{Vqh25ZsNj1h5Y?ATfynpitnmDEmm+c$>NOiN`WeES@hsSY~2NrHpW*U05+A7Gl;q zO!%fh-!s>V$%k271qKqX5o?VB)`Y97grVIOpXQCh?`Wrm%@2|WNeFYXE6t}`8Gz0L z1h*$70A2!~z)g>(4F3Qfn+GU?iylHethx?eYW*b>h7{~7y!{k;!D)_VG)Z@`eC2ga z^IOcCA}=ae+5Y9z&{WfRGYHa=g?B9kPNr^_^)UP^d+ELfQsCMe?Nt2eRhc%eCl9m~ z0Abr8UlDh$y1!%JYP37#FfS+yB86kp1u=b=LHWS`{i%g@xsEga_RqhYW5BWNeWcn5 zU`PE=gTHS>pA*9iy%=lVQp-0wp9>SVg*9G$n+o7AhN@I29qygs19$HA21e@Gq2ap3jB zQ<|Q=(gH6eOSR81tSH?Q9c~A~KHhUCw{~X7#F?xL3Z1>4EB~3yz%1uat>~2m(Td#t)`LHxZe)iFHO6aIV3XE)J?JRFux4 zjh8C)k2$1YS>){p8L3IbRswHVE^lH?22iB3L= zc6u@I?#|u4_-nm*)0P+PFU=2X$kZ|UM)Db@(l;-1MRS}6*0t51OnW8T09j}A^{a3; zcnb7eX7=_m7cO7kL`hlm;spf0-%{S@Qae+;CoK|3E8q}f5PQO$t*@GFEyX$X2}y}& zNNi?cdqcc6Es`wH#EB530L_88@(m`H{sX^Es%EB7(uv=-t3oXOk00;tlD)1O6Ph#C)w@A!YbYgXbu;!gy|Ai9 z{^*1I8U+LIEq5aP!&VIMGr9q-Br5r+OLIY-nyQ-U znU!9;GzSWRRYVvC(-8m`hw?cZ>lwTWVMR&sLPhKOsR1h_Damu@a0Y+8Hi3t)c%k|P z0pf=9y!O$Ihwh#VT9MMkjeb$Mg=i|*@*^V1&*CP|HmX^UKEK!PKx43)aM(m%_o??4 z=?@+y71Uh0eU}xMFbx>9te3e%@%~dz!uQ7rMl@E8lcaE`PmyVss1n}PF`M&;&Bz#Y zG>=xEAOaE)$X`3#?{tbIA<>_f(~`+D7UtsAsd8DSJa-uHD})4j0}&Cd`4ldoCtNu# zMhV3Al(Eh#s~Nb86Fa=0N=tttCN-PX6g+vxI9E8jqf}Ph;7U|7(}4YzI`iZ}Y7H}o z`0cHnA|Y@FG7MsICKgz{JdS)sIgdfIf=80j~%)0mhJUa%Wo2<$gfd zG+=irSNU_8u4DX0Q8$t_D=D~S{zqk?=ry4}Qw-+-ic9&c2ysju4Dybb4R@fR#UD2$P9uD{OnuW;9y-xfHZPxX)A}{H7zK^2lm|mI|c5?065~ z4Umkr+AU?e*vjbHwA;`j)?JLA}_A}|*KQHRV$Sv|C$ zO|i3+JycwDLPC|xtLY{RRRi7Ym6bfu*+2+%blH`q`#}C+R!jOK9`qQz8c&OsE4$@4 zIq3GK=hjlZH>bu};D7y>5IdLv2}gzLhDxhe;zbw?_e7Mf4)^~`NVv%FZqcfh#{L@H z!1S~>ao}O!P_GBh-Ndw%c% zQWjf7)q=@WkcG~X?E|4kICzf)t59CsmQM&l!UhTx7x(jTHZ%Sga~&Ob&2PV9*{eG- ze6l0VUBVkq6%-K2(=#uBc^ExxmLjgNX@|dLE{*oZq?6olg@F26*_Zwh&_AV(AMZmNC{XY+N}uOg6%k$* zr1Wz_14gGvw;x9E-b8Q{D4D+w8+?RJ#t{=;zmM^eQ%Pr}p@1Mg>Gk1iIC`&LUCc$k zLj+7*jRC9+6WH zeg4JB9o!2574CBTX6ri-`r{IL%{_hV)g3Hem7#?wo!}Z#Q5pB$(_DG&EZQcs_YG}S zRlQ)5DgAry&$<)D;lxT1hq6X<12lQIxo(?buwG?}A02}XugIPn!#cv`7S98w!Zbdn zxv%J`WTZ>HUDLQ>FVtZR6kbH+hTSGj>I;EGc#FCapcyf5l;Fc$o;ia!RE0I_7`Xcm zWeI%n>uIfbF&mGW7VjA?rp7L+E}3fp5dBsg#Dotie)ReCV}-8?$m04W z_YFBWbV2n=JvEwvzlvURtfeaf^We={b?R-T_7?9$k41|nQU4-ME^43$7XL2ci#Tj9 z!2J_dd&2)1*|Zpf7hnt4lQYBv-t(|~{ogC96~nU6my~Rv706*0v1@o>QK)juc@Ofk zt#%)Hr{CMh{D0$H;fzo`6Kz7*Mx~|sYsZm?dZIeb&8;i< zgNaRxX{fO{Aw{{#eFz4jF3Il*@t@ptht%0j1`m=c7|n>z)>4S{BH~%f#V)&rqvEenKXg$CV<2dr$aMD^X}yCd7S1j~@4&X93U=wN4{C@dXigv=pe zpd<$)X8DA1!tFysPJZRyL&Y+LoXy@Ry$_vHyLUrK{L==E23ZOa z&WJ=lKM8aj>T{>`d+YxLTHnt~f;R&zL3`ulizmaFZP`aJb4xTzBg`>#UH5Ol%0R$+qp!Qc%C9G=~~F_L5XML{5!Df64X*NSl=uiQr9k7)7CeGj|Ge z%5opUJkFLAEbih|^mD2B#a&M(?DK>0n3rvzvN9Nkq1yi2=q5-#>`d!1oNX!BKrtD; zI2+vlUexIo7x5_}!s<+3AFO)kaH16Mvuto>7UM0U_i5y<*IXW8)Ru9KCu}=eWr2u; zZ=dczt$HYZop=sIVGg zULXJ;5QFJnmHoS(?tok5cX2bpHUiRze}1V5E4{qJycr{Hg{>WTb!s!JN@4(LBVTy- zf(22VHf{3rtzdvbxMbmxWl0EhT5k$oK#pTGvc6I6k`Z!%$<(j-e%-T$fB)TsTek%N zh;ymv1+$eK)>wJ65#4^^){{wl|DuVOro3y{iSVO1%jg)zM>9TN3uvtto|hEKvEm1NqI7CyW|!hXgWH_utS4okZ*WjXl*m9se*DQl zp1{URp7QUUJ5l_~U*sywdYzP?Js>3uWk@Jpj8-TiMW|kMCh6qKpxoX4K*exjsOKD@ z48vI(6}>hm;p3-IkU{B|lt@;OM&SGlKtWlqco^IrUR)%Yf-{f+P`!T#v2zbUToh zBu$#N(m~=0=s{3-da=@+wwe2K1br0*0vT%5)W1I_I_m6;PV~Rv{Ft+E0Lw>CjkQo@ zpLwm7OFikB_rX~tGGvXt-D?Qv2GfX+)Z1*$wgc0{@&cMdbEG(6oc51K9tX1*NgGp# zej;%ooTgNuFj)sUOzQx_z_?XGhw&8NK0aiW2%XM12!NZVN8+?Y1R2l1NiG?QFioc! z>k8$%;(C`iqr#I%H{<9$UHumXCaGRvbkg7!tumN$$vpG&fkTHb%Mv}RWtj9&xs?wJ zS9W1EB$PbHA{v_msT4sxSv=rPz#CT)X!NZ5#L1H_r%|IsPC9BaU_dc}_0G?le=^CA zKvL=yuZ#wm_|%a@2`6Z~-OqPwuDW?t;N5}L9OR*4AFV$3&oE2!wvvigb1`gG=qe5&QbW&dYkSoqi5X)2kL8KVFi)SQo~?t zsZi-ihX0Y2`TPdJ6x2f`jj)%|!;d{-Wik#Myj)ID{-xL&q8sR>>4s#1?dew>YxdF= zaY~YbUq+w(1i8p^8}@m|!?$eNBGf_#kpk+2vJ(RL(R;xCN_bwho!z%&c5&*!)Y8$f z>!DVV-BgQua}NKrq0mUww~O>CdHg4k$Z!ltKsq58hyChnVq$C5f-8bDpAAvHpfVvo zPha}=HJ_VVn0zCM&VIcWqxK&-knqQGQZ{>ng+W=I7W|ig{g-S7G%W5yv(Vr{)CwvX^Lx{|HW~v=;1FW@KRLq zg?cSk%1@8}7pL^c>xWEc+SE$qb6C7kA?~AiaO&=eje|Ed2CMiIx*gyRpZu^P$fubV zxcaRJADr{PrdZCzr8Zk4+iu}pE`|jmqee)xI{>}XPb#VxexjCI#+={Q-%#-SL_ zdvD*lGrj`;F$=B9o)}Kz|Hb~F7Ehxf^w3(;5ErK2I$I7oEIX*-kL7P37`J-CNuu*= zOINZQFBZNlh5jBiZ@ro;K|bucJGh9?4sBko=?#;rSr}`;7Oj=GGq9c!$3u%FQ=%9K zgt8%UGyO!tnnMRiiU@&Jgp7VX$RL-Xo&lZXb~|?lkD$9nXWq&>qtt%;BVQEL1=5$v zx~x5W;_3+kLh8Uf{vinq&?T127+hlrlfxMAlH#vf)$41gfz)lsMyZSWTr3Vt`8zI{ z_MHB~>VWk9eMk~{Cr_Fly$QLln5X}RY2^C`(b4~W#D664ARAI_8Tn6Y*|Fcuv_3#2 z6*;e9M$|Ru$G{OrN(ZfF%b@A%23%%FQ~ne#1Yp;~;K1ycD3m(V1UD7Bk=4nkuuq;c zv*Lyy0hViPvUnb7KQUOQ)D}#*gwBLYnHxHXX^6~miJdYFNxAnKQrZ*PQsO2nlSI;p zYUVPEWkIXOeyD700i6#GGDi+MwtCl~+O@7JLwptM+#F|i6fNS>d6uArJ~vWhLUod7 zoemKdAkl}Qf%-o_+Mbb$(|O%gGs25<-))=|u4`t_=jS{%DImpX?eD*LLZH?#{T$0Y zbJ+5YV3}g|_2>tHI|=ca$=&!t5wt&)Rzf>tjMak>I(f2I*)i4tQUE@-ZEFvvw39z^ zE*(ho_>P4#^2jJhqaIAdwh=Yu7!-{V#L*8>sZrnyKpYvVhg^z$M%xkawcdcdVsPSI z9Ivn$^j&El;OjV_NSxy>*&IVVUW)%p$XR`PP?OmsuE6 zv18jdl?o4j5O;jcqkm@YGEvS#@}#W0A(0)R+>@|h^`DFf4RTi7Q@EKpTyzYPMLl4; z@u?A95ht}Jl%;4SOCkA>J%q9>pw}uqd$)uAs4F)H4Fzd=gt0f(3BzBfKX=%|@Q9{V zXXMvC++rE1lt*nz3@ws%+P2A{)e=qlsJz&142ITIJv;sR%`r9AmLf>g?SlJ+yLk&1 z@100p8s`x=EV4YKP@_gCvUjotH%w zs%o%t+0vzgUltdS`x3(9R-J;)QBl+KE6*h=K`fX6_*OvLuyTWE^+m z=;6bAAi^k4tS_OVIzY@WT3&uX33BZdQiE0SfPn+2`#q9+M14wKi=zMos?(^EV;m|{ zpp30gO48-MQ9~O)*isc9X7V3vv1a9-dvCT;CG2MqB~>kPVzR;NG(E~O8Rt_>K0#{% zusI{Czw&5qwzNrO*0qvuLTEYAS%s}i)HK!Xn6RQV#eldeQK6u;>x@CW*rI1=C!xfa zD(w8|ZLoRg-$H3()IYthbqz1lICuh}Hhd9|zQ)1e1vgSHX+xi1OTO5p-AqR4{kV%z zv6XmgQo2K5Iq6nDgy}eCksd3h{+kzzcGGd${a=qE|A|rytD7P)@8@hcq-`?3x$7>W zjP{Dn+}3>Xag@NEM{nU%Q7Vhr#o==Wwze=Vrac-F_&frAJvu$*N81X9mvU&3dpoN& z;_S9)#M&?6`O&hA_S9Y1dKpJvd0zs1?ArYhtDpQi33hv`nagEwEJa)qW z2GkU*E%#>g|1rC0rO~^hw}>NA%GZzvLX!3R(gV=Bih<`>4t&I>O(_%#p&bu`;umLD zgJF_Wm%P3=KnM|>#{Xt3GMcf#GLnBO#nH>sRgyLEgcv?>?O_Nc4iAh4q4|lo2wY{K z%6%;e>=&+IKVln_#Q`J5ix-2~{sJn&@m?d7xb1_OIO9s#SlQqu7*vxpTnxF4_Bt9F zq>z;3FK1erX-Ufm%pfFF9#KoQF0!+aqDX`!5d18Gx**!vryo3pJpoR;aIIxN0t!t; z)PrjW4|g%&h+%;7V5L&KckrEAecJoz#oB4KR0KAw=YP>sAkIQ&ZD?SSaw)7aBLtD@ zF)e1+!7%&mkI^Gjh-+^#MIdD3Cnr=d0vTxERYUXEfnW6kcQD>&XLd0|0?fGV{k}ve ztTTJ{MOP~de9KsUeQNsW=TKv%5{R))5g3byq-B$CH))c5qB&%a?kxVn1(;DaVy%XJT<1HsgVip}F~moPB@{wzL) z%&DoqZiXCOz&)?6b==psm%TxUv)^@T_B+f z=nqBP;57f=Or(Z<6B<_QfNE5@Qed(C*nf!kD43!`L($frLSPb^YRb-EPN`p_jh@f= zSh$z%dMcZk223DXNlu|X1-`@+cQpTiU&sAxsoAXd4#b5&Dv4#nV_oKdNOiHx;*$GG zplHgfO=s7?(58pfJb$%*?7JM1%(F}h3qdZsM%RG4y6D!eTG=i-P-YtQKOu^V?Ez3Z zfc;{OQ2s~P6^#eSAZqCY>IG1YgPE8A(djtokFGGNWJ{q~qENFOW+ZpkKv~t+YS++Z zk-*NfeVTDj1;9HM`aX16GE5hcwWymT%`WEeXQ9%HvkX5~hBH>?pE))n8q&?MbgyJS z1e;;n{t*j(__EWODf~6aw}QV?RpL>?_z-$v6G+-Ud-P}ma49-E$fj|sGsyzX=g|QUuD<; zr;;EktZ>^{y;$rrIa1#kzf%12xbA0!e0HuXCV@=-M|0$?6o^&_;c@G z4Xcc(*1nnh1yDpO8S0ghu`p^ z+aL$H@6Go0og%{2h55zGTanX}XuYazQcGky%YUJ#UQ_uoJ7fc@))3!0%U+c4> zGedgcxPCpE3ihU9?3J>z8Xoh)`^Pqcd#k(+3J7>d*=Zq`NTzN5p&63ATAqQbFK$W- zvxYGbX)YC=m8NP@EI#^9OD>xdaX#_N`UXNmx<)Q(G$-J~#fu%fc=YerFB$nVc8WVV ztElqqYd)Ed8g;s?;pHOuL&dnchzXVmLI}R0B}*9m%W8V3p#hz`cHPKOoA_>Zm-n;4 z`EI>a!6>SD=`WQR?_tP^)EBuiNjm&u(UpD{79#Os4kZpz&>WBO4F^@->La?%77W41 zik=&TKj;M_hGS|d!+9ZP;mz6!_eIFkNhCN$_tB8yLo=>GDI3hz8E)%C&MTixEWtd` z!cg3ZLqmA%M%pcx(;*}18``^R2_nt2j9TV_)MPFIBOO2WZZSg)6%CckOhhM<)KS*f zt@`zCR?6oeGcoe$OL5b+TepAbTcBBi%nkXRdr2sGneE_2Uq8KS)t?P-Ch-f?oHF7a zeu#LIdD=R>M?qEOeK&C;17_r`ls%7k?g+f)sZ-8;oE|B~c6({DCOr!5R^Aos$__bt zwbl4|y*A}mRPu>?d-x-oupHV=B^mOO&oDJaTvNs)zk6a?hW*G=*2(bw`;lVX{vMq- z6zvOh43(n$;8e0LhIWYVUv!4Pj=?E}sJIN1%`fJ)J#9znT1msr&%8j%EqDbDgjM|x zaAU3lUEt~0lgvXwgWk>c$EanMjVthPm+?K9 zu^HQVfUc;>!-K4xoHD@`i%M0iPde!tKq=XZ&LUXGt}XN&MMo zq!>IgJo1+sV4#&Ej1Py6O)(D|DRub6;n)|gep?1E*$mvwwq0EjlDw%h6wBLrH zkI!Os1_Br}{8|T6OYfv<2!=_42Kmx7_f*!2<>~*;)14;dwm7ev&?&aSjJfC zl$Cc!%1?kUJejw11B7=*#uRI64!7;=@|UhkWJ4FJRyz^(Q{XjNMQ4~4d-AsV)$eJE zAA{vBTnmWI@6U)fPcw7Qbo0c#5<{}aG1lh+Z-i<0t6v){leh!?{lvkGL&;3C+!Dci z2}MhphCeUZ0)u%mKH!nChqbA~=qVl!a`VgwZBVOA|htrj8|V|muIRG0irvme{tgI=gz*;;fyr06w`lRO`t zIk&?k0@KeaFx66>GH!Ko{rQqVX2K-i+=G|D+7=yQq!so4^Ji>-`cfaH+Wws=1vDxQ zSj9p%#k=HSS^N*(s|Zh&A1NG7#_7cv4Y#uVd>}aXGy}LWn>RxZSM^%;3)mWq{217h z{|Oq3PJp0>o$W?M0&ap6H?G+-)hoT*PAtPIY6PAb)en1rTpp8PdV0yz&3>=jsHhm} zEyqd63)sTxMdxOIZ>vVonhHx?2G@`*Y;DhGeDS07k_icz6eHQR4=3{T#W@nUX)B@* z;*BfA%&k`}T?*aEfI@K#{32PI7rJ(>Sho||#65)(!gcD`q#N$u_t=2AG z--(8^QI>&r(IUw@g&*!G3>E7s!MzqND96A?w)!!|p9^NQu&Q9YIKxmkPtS6{OgXXG z&uqoJ4LWY|?FZo_^zaal1mGu*DMoSC#))}`t9I_*y@i4#R{V4UcF&(Vvl`6H z1M3L@Ho9Mf&n@HHt=Al{ua4>9H1E(KE7mXz4f*wB#lLPK8_pq~*6DhMP$4{fB6W+t z@lZp=6dp;_hpLkkoCZ-A3%Ob-s|>T1*RLExU>B_&4_}-LW#&l&ayq?i0Mv-E`a{}{ z>vNLBDQt`5_nOH@qqx8=8EpHBd$nmXCz z3Hik~YYYvVhdsb$3_uEug@71+NT{cd=Rdgy(J-#^U)sI65 zKVj>W!x~K1+)cK&-texFfd?&taAky4mYZ1=m#a;D$D+yB&rL3~523uGj-+KQ2Do9SMz(Q%6o@{bG8M{$#*RD(%5uDFZJ0pKgM-fG*7)

    &*>K)*TMes10EHG6_N|3Wi!>xZjwFuy%3kTqb1R}+ui$qJYWz;lOtIX9mR{vUiNZ2 z^YdPDSBIiq!w!P#NfcY6AVUesd1in`P1#Ih#NZkx!S$f1l{^}URpnT)yk~21BUw?) z$7pf}B)b$4ow*o_q;H!BKPd|@c3PA3%hz>LC~0a5L~mG|HW)SVaceQC6DK3$9|iw8 zT48>92Xz@I34F!DO`#tt?p|H*Fv$D;KW}wotedu5?wNj~b=JZlXV>HPW${5l-MsIg z%u3iFOcB7hDTeonqmIN+@LxVgXvUomvx0Tw^f8U;$K16F>xsoO+vQIL<(Y& ztD$FWXQDrRt-@Y+Kxep|A)x{I? z-#PsJ{;5GQu4T(oz~(}i*f=$OsBL4ymtBUKU^m4pIa5p*Ht&cHnTr64R78{}ugdyO z7~NtC{4agR2($Nx0bfphND8Jgz^Xg5o8#B5oh_sk>U-(s&Vixk0aQx7K9Tz+@#-s`fx zmp&_2Xw!nadoRWjrlG7VgCBeMrH|9aZE-(lmYIAqzlc|L+E-q7+Ku2c!}E@x-X3^` zK~3ci&6&G$-n)2z9)!^XkiK#5@c%)wrzcW4#x1f>OYI8q^Mp~b49~l&E9i51!FnB?QyBZUit%fg3dzyHS#={PDRIJ|3X8S{};vv(VJQOjt zJz5p)lDiwJl)!3>$NtDE^~Mx11X|cfRC%K?={!c=_eF)aq{IzvwgEvGqmMf~eu{e4n=I%}RiD`6%GJ#S8K+!6JtVgvWK>3Sb zJWW;!(Dn>vwC6Oq%g>oLL^`Iy(O8kmJ~8r}HJi-xo0+oc5dlk*j>^_yGqgI~iMLIW zpTW@he?DO0%aapnIU6ZAz6yq1d1BcDF&;xCs(djV*j2p1X)#5?3S{S?m}o!MfN_Ni z>0X*OVfUAp&!+o)J8rtT$?(bkop`VL;`-*}GMaZ@_Yk5PB)Mj10Gi0(SLvj|7^f$_ zT0)|hp8|NoikN?A#$UU2>{vn+5(WkYdP_4cX|^d5g@ zdw@Au-|~L#awBEYNki(AI+Do29BMM*>Nx|L{Cgg_{ZZ?R%N10BP`d#dgAa=|j?YKY zwnML|h=YddkQLME^9;A74MXw~>aviq4z3oBmko3M$|e;gP*D^BT%YZmk0S+&&Qpue z?w)7}<$eqImT7SE)wzmUyUW*tG7?ENfZischq3HG!)3V1>8kST>jX4gz4+X>K-9u? zVi9FPK;FCI%%iD&`KlT}o3NBm{lI^UnYj54Je;GK0&BhdLfjqAd;iyVpgK@nNT!5i zz^e@$o0ch$llBZP#w(w@r%#cY6+3$n9+KQ_k>Pdi+Lb=^ph>U2n4KQq43I)04f(aP z2c(v?wC6!RDxLo)Pju{t^m*FUmDIL4jyecuo^h8`59Qw12k13T<}R?XgjD?z{Hf|7%;-%K^EJ>O_96o}$TH^pm} z_2J?F(ga!DM5Rs3@$tn?JHL2%-tU|19y7WGI;rW_i7W%V-T(oVzOHW4or?@s$VXx} zWEXEH9lLlXF)IDIsfD6yE>JY}v=+~zc}g8p-r3*s9f;qJ|I2_|4K|M=UH{^ zKhHj`Vh*J~+)FYlm!?B^v}7{5@|k4p%lU*^6(}p9g>sU&=e3mY*I@WT3sYV^`_Td?HXg9*#_iG9T7JN8my2uxQddQlc7%XuYX*Dd(o;-azfmRrjzWUhhaDchm3JFzQNdE@s zO&d49dH~

    i5l}$&m42#(1xb zR+!5iUba!Q0LDrTD98!c0001J0rP$oJwMTa>Cvu?ZUCfd#SZxm2aerIz5E!DapT7` zY@wonBSO!$<5WTh&1q{l0H|DOa@p<(9mBG@kisk!JXW;)=qp5xQO9`VW2EWCuT@iMAdQ!uL!I9rq6> z93`!DP*TtoT0N_OvUx7yH;-g}(Y$%|Ra5Ofd-j}Uju-N$>5W;kTP7_aAQgrf=tmjl z7bCx9nVNH1rnNm?L&|>qn!CBZU((1t%U+Xxte)YBR?%gD-LP20Jtb50?zFOZhR^C1 z+yHst5#q1YhD@t&`ueSzQ~BWX+cS(sU@|)^I}DHkz+%}H%{JLQ=u5?tx7odW|2ob6 zkhJ#t^|VMAbH|MvHjrx-VQRvB_pYMU;{ET0%_yb9TVrXNA&?Fa8yif0hJ8+f%P(>r ze27*}JK21}n*M=mbkch}4{PUsZ+qi9{Ur~nWkf>DmQaQ-=j7KLit>otF+nF8sMMx* zu3aae9Vx$oSVl(uopGqe*)IIYbG#vA4lH|rYkGLvuDy{HIFBEBSd{bisKUif znv@?nbZu0sH`^NNPy5YJ_<+qMmsEZ!#vqBmzY+V|Qb1X5*f3Z~JKiRFU%T_9fGqFM z!wy2omLZI>`SnscWR&imoJBKaDu;k7>qvo`Q-^mRHEAd3y_K@^U9>iCmCmbIcN;!@ zR_w&~sPp|iFP%~W2{g8|jxT>yliP4LzeDC)WOfj=+vw$TTffO48^pVQv)7-!*}$2T zRzN9tx4iZl0QET7O%bs8_rZzmk3hy1$L@Uw^eXI=&57UobG@Ewr%owo5#0S7#DhDS zkdZ5^O5$!{+8+C05m0JTrwqdCvV-_)& zw2vle61xMiQ3;JHY!+w<(glt{>pY4NGL6H$Vo_#Wej(a8861@6Yv|A*xO55qyOh+A zIZQe5(>KupgrNy36lSn>mL|zon_f@sh9et02y9PUld|FSx+@Tycgq_aC0$)=j;W6x zW!p)dFI1R2q%^cbZ%N%g25e5udF6@#U|E@EK-}y;veGn(vjFkV;}tA`?QTGPdOTmh zr@eQUFgKu^^|+J4@>#xF%WHbx=@?6UVH~*8Kc}R`l2juaIf`#F;iR|Jv`sTULl=h9 zrJktgsiB{gZfrJsycCR-Phs3fzK2O+@m#hDz-@V zae#k_{!cD{b0kXMAO^Uv8QDdzHevO6C*?L)CF|GAZXq)T?ay^n1)yo@k3Lo80#|Mn zd!&Dt70yCaSM1X;Oo@!|zHmfXL*roXD&?ba(e z<$&D!PaMU+0S->A18=$31QBxT4&Hr~oMF`ESw0>#G+$ zycFswD42{LWrORDe&Al9V^JST<(5|}nqBICiXm&r0NMyY`{Oksj44~jI?-MX1+?G) zaWKanwRs0)m-c?8H6s^p*3&|3;P-TG!>*UaLJFd!x{S%R!jwJ{tVWDzW%=7x?mtqE z)@y7B&rhIUYu;hx<_+8Yzc$-hpV2OoPar%CwH|{Q*0XGSbiLzpF0Xz(dPdoIL!~EL zK~yk|5bP!$6UfAnTyS4B-at5qAqlvu1zc=X$2le@)fUyLiXxT_{hs;2^iUrQF-hz+ z9=sHyNMamKHv_ue@ui>sslL^u+eq6mw;d}+JE`G&Oe0k5p3ll?|4WbyasbmVOh!F} zZMPlt6^WJho(}-8@plFsnT{I)!CWj=U?S}?BlvlJ(Z(h+3dvq=VIHuZM8VS`)llY#5X-4oyziawW0%4@ zz%ED>WlSf+=p+XOR%Hx?wp4r0H#z|V(gsoXXwX^1gLqKC*&}=dtCrPv;HN5?53F*? zOB}$-n=qR8q``vAnEN!$NSXa%{-Z~J~QC8 zPp1zTYg-7;4OPEocIiG&a78a8f#T543>kCjCapstwsDf9Xsr4#9l34)3;H|{1lVF6 z{q@ZivA{hyV<2tqkL2ZvK=aU5^9U)A9rT$W*zYiPcoH)hq9~_Ws-NAl^NrZp1&i1D zN+Z&??KsGCMidurE5S7PPilHPC$^nC;oPaB+W(10yA{-5R^?Va(ocxh^8t8NZHI@G zQWenD7mxKm%62^AK$|vcBJ1MtYYy+fAi=;nig4tWH6ZFph5I1K2Jw6>AR;7clj*^= zJ20IEh+wCTq zPZ@6&T!FIY5pV@)U(bxedgmy^dVH-jAXA3N>^wyEK(i%qYFWXvXMB5x!UBG`iHexi za~g_4xgESpD7+YvP~u)g5HmU&vePvFf2NuPGWjjMy|ld7`v z5B<3JjH^W|lmNdkay<<)_vEB|m8>hKJklv1T1+?=lYN5Ud{5Oa!C0$|KhSoc6G))K|Bu)-A2;$h4Wi@C=Kx5VceC=+mpw0@M(xm4uTBW zeZ>mb`I7^4&1TK&L~+vK&ic29eaV=)7^dODPci#Hvi2hrE#R^|V)DVHW@w~ghNER3 zgJg-bbK?HWCZor@5EL0I2R>_z1zx1f9n@N4qAW7^rKL$I9YlOj>f=uEEB1eKCLo&> zc`XAGL1e07?PH0pZL`m(1wW`cJo?1`Z~YwBwD%kRCVs^uDrXcT4hu`yZQd_@NkFp& z%;;kQXdFBK4Q(v3_3{43*OI*U?D31;ih05yY_4ThgYe3{8nr=7A9I4mcZA(RsBlap z3lrkwjWa9zpK(e2Dc2lyk(`2)LY9h0`LACA^*_o>r;!WK-bm0%928@o1Vk;8@zR=O z=_EP`G=Q`(>rkG2_kuL1ELmqcm*>fo^JDera>Pk}mMmSt3T?kW#oD?wxH2TI7@*u| z1Gk-Bf%X|r!QJwjb96TR@~-e!vYrnZZZfOJD1|3-q$&ZH7BUP?%1!Mx!tUIuL*`m1 zVV$8O_h4tKaVR-6dVXVa&eV#KF1`gX5PB@u}tT<+)xnis`xAXie9opK3si$&o`i|JPR z$98?jUp>mvlqECHEPz=yBjOYyTcz=B8oQk!#jLz9H9)_OWe&3Hk2B9svg0Z#tr$Dr zx^>jSC0P|Wsj(-8o3`agMzVy8u4(tS(|EcOYgz!Mk{yqM>zWxn^NXD~nQ46@?;~0- zaa-go%;VEDvi$Eu4=>2wc~W9xT#(dkD*XWj&Vh*hJ>K5o1cy|Uwbf7Z!>TL#_Pvwe zceACs-tv^{jpQhBzE4P+ktZIasbW7%@V7!(j$=FwS;zxuoWgTBO|cwwN}L!9>))Z7 zgei*rdGBgSNJ#qH_W-T^&&|~6I-mSIVBkQOC@g?3fB@{Ccs`esBZcgZtI?QkHlHcb z!+9VgDv3PaD$xAkT(>`5f7#@+bg6D#^$#Wf#=|F1EZ)?ex;FDw9(bEjE!c>39~aI>?!y!eCr9+0-^a8D{N@| z1tq7YRUNQUnV=@?;slN$K^#AKF8JdB^r;D;xC@s|Z~NyfJ>LIi4EWzYie{=ZYTnT#4BL@PqbhCi-D5St@$~O~yYYkjL3L?&Tn?AmX0xGN?Pl$?d7GXl?%bPpd{hHL-^l{1 zq%*uT=7G?mSvSg`u7e?w7~RPpDX|hI&WfOfsfji?o`VllLZbIm_7<67Ov=@2tBxIiX~)C zS?qL{!^2VVmno`cM+oX%h3s;_$RUPxt46|Bg(9%B>U#jG_l)cn&HnkvPwip+1)p@1 z|5+Tm7-B|UtGzZ25z<6D{Qo(tArr9ao{tSpJj3RwxMaSN&xp?}f1<+hL*bU{I#AVd z_%U&TMPz*B$dSvpw6i9>Oivf|NK# zvUBu-4;#Ias>#u!_*Na7O(||gpaQLLx>;$%zTLYg5B)F;l&X9A_B2ri%6JUrEfcVw zxT_G2{9%0U ze1fnDP#Kkfeq*&0uhtYb8+DdAvME?;WnD&e`8OLCKB9(xH{ZN5Jt;ZUf3Z0eh0MAc z?JCKY5xXzrW;HDvF@G!F5SX+B*)`Q7pWkre?m#vM0Kn1q5vNlqe85@LwdVp-*AOCPnIIW49@`jx=HCU*cF(hK zuJ@tTG&y(u2n`)c-DcCMT8y;h4x$wxnQFA7(!jDciL9mZR3Qa^aW0`4;{0@L?L2wB>Ye}P)1Dc1XU4h09 zi82(tdD-FidWeFhF2Pr^VI&WCk1V@qK`Yi!HZf;M7hNGG0mt@ZK!K?Hm^&=@ANI@t zsrJdxP?=aDKwFe@;i_o9qm7+l{6xsyQ;$Xq=n)WF3t zxJQVeHj`1z8_Z`~jUhCHhZ|)sgC8m0uuLC@8?~%(9dn1*{}Yj4rG^gl7`_D;%$jl{PM8kb91nAI!XBBOZo9J z9gDUb1e(i0+>Lc#)u@USm_7%ZJC2bQLb2#emrepWM#la|C_WUJZ^D={+qt7PWz|!H zd)vjTkS&(KyM9m9pK$Ck4`xiC9t{X18^8f+!_I3{*goNkTV;5@V4|O(@&V}&Uu`29 zP%Mn3-&kDrp_KPVXC^yR@#kY3-Go$?>q0Hd@-2Q60^R$JcJj(%-2QH^@u4>3kZ~O* zA&f<&W(JelZ_uEL-w#vzBv8DExnD%bb86_YK-31Lxzsw*dXxOc!(pL{zds%F5ppqB zy1f|XHRAS6M{N_eZ!Jckm{2J2TzIs$2XD_-PA3ZGBJdSwL4LF|B7AtR1im(je$uXG zRVZb|h!WOSrX0}>6I{?ulJ3Xb*!cf+b8v77-8G9ANaxwhfGG?tVxQqA+wR=o1{eU;RRZ?hzV()uo+GHqw+dp@|0^;x`zH@-38saUCJMJ*Z z5vdA~TZBX~NFd)nJQ8m8vE#-5akYQUl0K=rnM3*tc1X;zrm+d{a+Y81M;4g}pvU>d z4vcMl~C-csLw0V@SiR(r!!&8B$g(Ugz z{rmNCtWb38t*eVN)3#_D9=|vwjp}o#$D8xMn-spXh8}2DP;_(xY;k68D*5uc9d*y( zWaygu5{rmDB8u9`d>T0u&500plx7d88S9BsN5)BP4|OGIixC|xhUusUXxdIN29?@k zk?qKpZbS>RV?KDcYy(9IdZ4@Y<8~l8GL8p&lTR??I%9!7&ytK_-Hj z_cVXCnM6#qKgG!t?Ew!!yrs#1;m6zlw~H)XcPvGu?ae*`G&%w;^LL!+ssF>LQr8@1 zY_biRqQP9wK31Bh84qig37$rMK||1hM&=|6L>UFj%;5^XW?wILj3ya{E`)FFVH4s* z&K1lVjzSf0QnGPCY@@mW=1B_Ym6UucE;d88S3zaKGg&~#i8<`xCuwOIK2w;9eJ70s zRagS2h3qRpi+mZ8Dt913G@u^D7cA`0A%hjO`9B1Og&?%r6`;-tg@kLCD6y|t_$?+3 zK($5f8eUE_Kz44ershRA1BG{=Vu}YGO6>l*32td5@A4$?nzuU-Z!*HB|NDt}XObeT znO?nYmD^WPdx3z zO{g5i6I~Shkl632+X%jwNMr=q@eF^auF2B%O9b%?=g~@a%wdei^ILYNqr{MP3LX`0W`3Yfgfa^V!cJ%>x;1H%2Qu45JQ^9^!7(? zii~=*34n{BAb_BNF;T2d$uowgtAL$xoJcXEz&F5HPt$?C8pDdk_z@Zx8 z%)}FRh*$J#DF~U;2HH`s$jT7lB1Skxk|V?NR7j$ZqC4o(g0pE&X}U8Yz&7@iD*QhB z=If~Cncx@w34av6BcXYiC=__3x_m4!^>iHE{b$v`IMl5D5s zue8Zh*<6;_l%HsiJBU^ClpC`qg7FJWhahv}EB#tsI7pPU60fB$qsmCDUODNp8qetg zU8qE1IqJ);ii?X;y-#2;T5O_2JL=L`A7z;>m0TL66+PuP!gcD8-V7j$qD!2z>0$Q%0R{$BGuLsntTKY$pl$_rvtP0#nSuw4frg+jC#WJlL(5$&v*=B-Z{U{SPiDH63HN zMcpJ7v-HMomYrQ84I?k0!f|^l%`ek#4Qb?TUhbSqWb~_^cw*8@uo9`$xXcKPnuA($ zpT&NPM{w{&3llhnyI^+L@dgKW=|+f@dKrF#^k_Rs9c7NRzc5IL8Eq1%?9rpf0Nm(c zEcxPK*We;1_h!Xev6g_C7WQh6Tg|WJ+$o!ljKPV~JxGY7AuzDZJR&1$kUP?fP+x0m zX*I$S2t2GjIYyd3zGb&V$ubKcRG`;nI=&)~vMGdrHIKP<(Vp-~#0!TfH;z)b|4q-u z7FvEU&Q9+yJuyF7xC~CM_{kA9^|$=_>EJU^o3z#*%psncDtWmewu&tZNej@C=dD^8 z0MDVM$3$^AxW1IHSdPTg28b@4KfrFS4f+NmM0mMjZB51ZlPZesvKwC1Po4x&$i3>8 zbrPr5HD1%Zy3t3BCu*N&xiNRda2Q^#bR_OIk=iXp6Q*Mrw1cq`wd=74HA3s;*89ZDw9s(!O!aPI9SbG@UOS z8X0Lqg{?Yxr3N3tDt5_*Jh986)W*rdPMT|cX?X61K@l2c&ZEJ>mBkn08yVQ|<34Xz5{(3#u;At=XH!{~9c!bSYJYciJ zOT679MeP78QMhp0$v0STw|025CDC)sMFpfo9EPvW4csbg?E1% z+HSrY0vaq)5zN;eZ3;hj0j;rgXcCH$O!3dpuX|J&vIQm!TTHy886neRYKyAye{S>r zyL)D4XNz5t$yWdGEsSHf11<^!#edmJ)kvtXbZj@cbLZgZz09x;;5*&JCYTN%JS$+~ zVY2Z>>J4Z|p_OJeYd@#MmLR~l3a*G=4(Tv|;X-4s?#0WO{p;Vfx~ca{^i;l)qk&2*a1IB$ zoKJC2S+$FX7y#d!Zh`#W1B9q)!tM)GNuuE_|I^X-&~RuAP6isE#&couK(bWLWUCa; zh0JD=$QHX*KV1rfBd(BKu}dsZlKl~U-pHvJx^QqJ}Hh){dfsCVor z`=gjKwGTb-$NNvAk`-9|5g+M5ay~2hMr4`?5v1*DpRf|_JxXJm^Ym9~rhN|0X9WP# zVq}#&lVHJM;XCQ|M?db_p(e8-vKW_hLh#qsyu-1fs zZp5YGT}OjG!B?2W8Dhn-|Nj_!6R@81w*NbZQPvcawTKjoFt#Bjw2~HN%TgJ}STb1} zimchvE`w+YXV0CRuDifCu)})NihyAN^TG3}xu*@EOCuXI^UTpl&c3=fz9(uY z$Hr9`gw=SMR14(8+nFweGVC6LME{@*6vK$4*(BDg#(X1h1H2nY+0~ zFUwd_U3zJr^OU%k9b(0&Pj^0NUYV?4uLH zAZRSc;a-3Jwa&|O8MLWeckb)}%@-RM3i(Mn@re$z?@#kYixMh94yG8qs+(j*2;FbE z%M6&sA&VRfKq9tV%yr%d=bne%67jx#u*U{;10WUw23jfWR;qm;_Q1(XJUkT_W+5i3 zUgMrjBB%KMtZjEOGxPQS6I38~C{x(ZDisYSnw|T{5Ql2@!ZSaxDs|Pmb% zRs^68qnZGCr0TW+WRmJxm@}bSh^fC~oLdlZs@U+fTqIeTLdhxqhD0VmuRpof%>6~MMma?Hr#1_l`ccidu6{mJ0i`Q7x~ zqQTg%Ff5Y6n|MZ(KegXpD4L1tGLs=eSHK6Vq?>>LJtS$9z)WKHu2Eh-ok0~CCGiVz z?ni)UE7x8IA#GuPQ2t8Yh}G=6($hDKME3DbjDJO`aP!&#L$So$@Rf!#74OTFmaV?};Nc_=q^)aKV<) zyMiP*UsX7FS2=WWI`cS+Yi_FvWjdon({(%aYhLzQgs4MzKuHt!HeCcw;-U$CjCg5- zWJ1>3%KwI8+X&_^t%#ULmCm6T?SlI@luu5 zc=%oipBDcb(h{1g+o*bEB85Sl&=-%7T*f8k%Lp|~K_=pSNT;Xu+uldwNG2T^!E`9z z&)*@r3+7Qh)zngZKmlY;#O-ZB_4RG3B2lv4&X#teBzU&L?^Y_HGPMl&UYJ0f< z39v2kK&3q#3q)@tE0{sh)8yD2fnK*iJ^D0Aw$fuo(dOAEgb(bB$q|C&ECpRK&DiOAL)#O@yLv{-Hl z1(-3F(hl!Edqjj~^W-koa5>Hrl7vu%bF6>y+PREMLi{4RPGVlmvz|n;C!nIlMR){e z0lFIBK=Nx;l z#ltrmcKI_TL^C*(V&|7%e$lF_d-Vs1A$z@yshMD5FN;rF1%xJq5RBUSWofj&@y_|? zXaGmy4lN}wx0NBZDg*{Pf{5&|$&QACd+=aojTfT})wZ_qI+&_S%;a!bbV`QA;|8KB zVzh`g=Xm8eyhx!WE=@!n(n@1O|Q*4O#K;z z^!+&R+s3Tkg+3jW_srzk%IKZ?f$Zl(O@lstRMJF8q03fQny6XnK7mdy<1Ip@h^gR( z39lG<*@A8Cu1v|bu4i$N#QcZq^x_M3iV;|AErDnGYXZegFv5Qv`> z1{rGo`t@R7A+#-?qRNF^jkL5fZV@RIw@8XUQpIP?JHW3QTrH$vuK%262w#S~Ci4OU zvB;^-Ts~5G^&Y>9`HkEOax1++O9jqLQ1g@)Mh z5$82~9SK@mPqAyHG>p9bYzL=N_LVVhV4~TIL~4iUuhm19ZHD4b6tQ;%dU{DGRBdL> z77F177|8oh9kGFF;7P~Cqv#3xm%hA6;gh}9=aDXJUr0l;wHDE)e`l5%5gbIxBms}N zYW3-FOzEnmwIV@Mx}*|BBHQK|e~}gs;|7@!Jb3VcS>?axs96%jTa1mCjV3WfKk{_2 z*BvS!o&?KcW!EEJ_Gs1}lK#vNoYDbSmSbQOy#CwOxkW~oBDEsv7ur@38a7C|0=tzo z$X8*5EQP0ea1-}HDF|>y*Y4D8$Qp6NMfu!wh!t|*AVD^ptD zj}%`p3y7Hq^c}|woq!UQ1v;B6b)gbqjl_(r&&0B!icQ%1%`Ba-l23&0!9D@Lz#8;| zC~lC?^#UNweC^lTtM+fmJdj942Ej1GEay+YG|b6YWo5ESEb`YZ_#4((7E`nOZGyyL z;h-dDT7$P9_3Yv~x5>dKHH5b4)rSxF=m{=EhTtd(txmos7op$i1`fij_wU6RdvjT? zb}>A9D)dJ`SzKc@N#|tVt`5Ee|29ZNc~E23=L#S+RfZ^1c+sPIC6aam8#^c|Y0|_} zO(Qpii|ZW!IR~KVQf_j~c+okdsb7WqV54-nXrfga=fIkndW z9D{rF{Jok-n@yWGaW7Ix-3Ny)X-{|1;uwDmE$4*Rt%*#YUgbiJkzWq;knZITV62F& zB(<|=jy`J&EssIOU4rb~>+bfg6|eqYV&k>>Cn|k5K4SP2`Oo~n+mzl*N!iY+UV|5p z%wOrT@^&; z^}Tqk9$K8Lr^FVIb_#ge6NgJn%=?=Dit0l6BAA6oMsCM8lW6Bm96vmM)9imfvLPM3 zWh8I-IPy8AJM0nn_0kZE4frtUCi{oKxzC?KZuD#;-R( zmQVR1Cz(OLSF5)K&~VCEzRX$bwAg{8;hwoyf4s+F^Th9e9Uy|jwUMPkJnilD7GO)$ zYww(8BN!Dkm}Dw74AmU@y4>V8u)S@9ixt^0X5Q7GaQ}8+=6PASn`r!F&tF|? zbbaa4rQ%a2d^g&TOHfWgV)Gd;!Ga8;bu&Fvvty^BXBSR01(om^9@&Qm2L})DKzq)s zN|pH;K7C$a)dtRJ`yE=G%AxT4kX<=5XFkwP_T^%q;yCWRFvsa=!2>8DMXf+E?$mmF z<@maYMj3g{pBu*7fd-xr_SYB9}ByCR(*J;hUN)S`3}7pM>00MZa0c0 zip3q+B?*J6vH|sm6*R6`Xtja}aGXnSywoE~W3eVoMYJrwLe^ig;^?u0lb51q>Pw!c z)shI!4-?WdqU#F*7s>fXu^qw5jOib)yXN|_tgSrF?)`TlxEC-JpcfG~FJWr33 z($fAXl@8Yq7~*cgbZE0<9z7uy?dP~$E44|sGV*M*!H!|zh|hw zKn5N7amRelE}e?@rKV6jwG@sH4*^|n2G3a>AqEW+i)+%d+LcKl-bq6uQ>aMn08BULep@MM<~p(;m`SV5IG0EeEE{y#vu&& zP>-0Cu7>@x)TYcwQMv9@!4DJ_BG9DdWA1CTeEG@BWA^?1&5Rb2+QoWYR#d@NgGFS- zl$`{_dZ4zBj(N*p#19=t4^+-wlD?NwuF7?!fGYK#^riflPnDI)uoEVf>Xyk{n>ksX zBriS=RHB>9p6wzNu_WqdMuz!w1yzrM%zi|h#vE6~ZH()cW`EK8Yg}n*DcgOzl8;lU zXBi&{Ug>!d(_XTJ#hM?-BNT(~^XMNYQ!AOBB+v_jF9gPHx1n&6#(;WBmVodghu@p2 zoL_hG117`hC}ayc2TAxWv84tgZV=Ru@TMSzJZFvpccp$RzC}@Fzuz$@;W$&*ew&=P z+LYeVFm&@^4mth2&VcvDGZ7%6hA|tGm*+d!R@sh1-Jc6Zh!Qs(HWswJ(dw!A1OmVg zd~s+th%0TEEi)aF^(n|^JCJTpp1exVg2d7i%@cF=maxtTuS|9~Tx-!RgK3uxBChKM?iA`bS#b+6JvIK?^y$+Nk6pJP*p+^(-?)>(2+i=%RyB?WjBjn z-gd1_4yeeTCXa+oaT?Lotu%X7$c(v_#<~S(y+>5WIbHG|m}d{2{N` z>D>EFq!fTnl8?lfc~>VxE24ogX5LMtiCX{h$PqwGcC<~V21*8Y!Cu$@_eL~>A+JTV zDBX2w22;#VH7{!-=t{sFru;M0<@X(R6!*?2T7C9xPPV(?uBb`8;I42I)S1R=6kI3< zE#8GO13Qztc*L8+6*Udb5u~ijvo-DOgcPAk;N` z87$fe010O*89_zKW!Xsjxr%j}n~JE$#Q2oXB$_JKto>)iKKSlf(W*0B>%(ahwIpsOfO>VLJD`AWsSXuZA)&-BgWxYlEh3&Kq2Z`8OAUVs9*r= zT%qE;5~PYgci4mCbmuw$L2RU`5RH(WOK?OPu@im5Q4yOP&O&3mh>;-s9hz~Av9uy) zIdR&bPN}6^+d~yLZ5ta6tG0rHvp4bY3FCkUknX632dPzZb^% zuHO+_EO3Y(GXjSp%7i5^Sxm|7s`JeC8oY50H0$kfVa2X;v zebgKIh?}4l(*lSpDtkC3t~^BX6NML}>oCKp=3#wRjw3yQO0Qm1X(Ek_I6$r$BG4>vm6~aDnNSItYyW5R*nS`a0!mFYCl!1_NK|oT|gI;ka z&O@8$CH4uex^^=z({&PZJoo?Dn;2l@oe3vXen51>?U67$RJO5=P-EG!1YAKZ8B$Xzb~tAl`{Q z>^rz?+BAp7yKudjwBCeXBqKEis2&(CPK#Km7Qc^V9>1n|Ds#vzNZ z@E73r1d}AqQ1~f+_VD7b|2S^+(#&JqDRO(tux804Dvt>zu&B$}Hf~SDlCjn@DMNS~P zRX(mW?WjzyiE|Z+O$|Qae`X;);QGm=mc~PNKJxDjD9Yt2&=U*325z^AR-PVbc+&XS zpaep#qg7b|yN$>P^~*cs32Wg^^@4>eMN<%%JIU-X?&5T&33v4-M|crh3E{V`ZeVR3HiZE? zYruI#Gz=aB?j7*aW4f|IHn$ay+t;EY8BU)v6yf1RMw<{@F?!S}W$@qT$=jBHD6YG~ z-bWV6*qmKpB&Z}PH#@|F+=EqZz5u)O0QYioA;qNLx@Da4dZm+7K-|&|8#bIHq>?5E zTActk-rIF(pLDIZ6rwyrVUqJ{wzBYw0T+Rzm9}>DaKm-7?TmWE`{>yME$0=oJVEWl=CitJr2sNbA})=F#usb<2H$ICgQAwPLQAkx6Pp zkS84nJq#4U$-+e+F+z;nxRmBi#b^R}YanCob!gcmzX@P2KAqA!g!!fc%F(vWrI4{^ z@{0tW0t@U<5ezI;{yuk@&YW4-_>BqdIimFOAKg-|h2}(-XthVE#1VsAS8`}SK8ldo zm2OsunD8{<5 zD~?&7lQBBmw7gY^F1&OvajqCy^Y=)Gx|wr z%V4q`2mcplsx%C*UdhgxfED>AKr6hJG=J?5)UJ3Wefm@|QJfEW08c4d!j|viWiEeq zu^sgY7JPg89Q@Qs&!$GiIQ`Q84(tYXf>6K6#U&_z4b3+=dk{zPEumjL|GCJ+#3!5s zM!0L%6@|c+=rg*x1``NPqUg0HhMiOlEi3)=-!_Q`eFycHbPL`RoLe5(fHx zDFq+=@cX$$)%b2uw4m!OeRlCwWr*={@lSv#W7@ha5_cs6T!9Fru+)fMQAB_D4~js!Ggnr7IwmE#Cj?DpYYEldaAH$N|4QahaEh?RhvIfeE z@pNHQlwJP4#IMul?zlY2P!jV#>3|@hb`#YDV{40ALl9)dBLeYe;nZY83;yt;aX=@` z)Cl(h9&HqeD}zWELib#(C;3_NOd=VN z(DE+eDU<&HB=PindSKg0S$v3^VBqxOXN`}WerlvP1J>DN*Kn)%B%I*p<=VF}I>Jrk z_=(+dMuu*}p1z94UHbW_qhqsoQJbi|%?>@FGC6(`uV3a*T!yIiwoOZrkRdqV40rG> zB4B1`GCWn_E5pQ7r;Xh^;D&~xXIHqm{AGiyT}7^}p?GVW(8~dO`6r3n$E=IhiP;G} zeh#h=Va@_)GlU@6O2ApF{rlYFDN8Rggowuoi;hbKE27ll&yx*kcaE4m9JeZe^P>jx z;sC>Ef!~A9yxMEI#&!se8fHj4yv)MhrqK=*NxMpAi)2 z*}*v~xO3@>83V#~W9u3xIT-5g+`oUS-{2{>#H__I)%f^s(LbMuT^wVwQ!tZOnXI?3_NmxM9nf;K5LsgAY-vd~MfH^^tgA zh_H^)BZa=$uF-s9+ufS072vf7H|8u}Juu*Lj*)OPWL6G*`mX_%N{=77tP^R?P%DY3 zfixh&fg%bS`BwAh$@z62fdAeyogqpl5Gs}?o@2lZhJcI1ajFw3ODWdQQlwz_h`Wlh z*PtyKr}vyJg;jX7x^07D@0A=IfmNkpB1eAX$UM|_h}YstNL~`l<7SS)k#9Jw-d93A zpfD;>hguB3UXw+bp9q+Aez1D??%KPmh71`Zi)d8(nQI-I%&i^GNLLIG#*h6z+ifa} zA0Vv_g-ZXL_Y9^*wDIEMjDA(uE&QjJCoROsi11IE;B}$-?*-38K?DH|(Bw(7iA_!% z&dbw*+Lo{J(v^$+{XfRFe5IYN>~LB;^qZT?w^(cxnVlUO1#GuQ=*iqhsaxr6zyg zHOJp9DSnw-7zjFmbQfJfL#H2-xga5UF#+}+qxgdR-C)xrOICI5am@?Q=FMm`$hRA23B6MV?4f*|ef?Y$5%!@GHy0s}9F zjakVpss55%yr^1=xerH`W)`vEJTHZFHD!2rs#sO0F1L+4#^f91a6K$2m;^L5otTKh z3Sj-HnOFXh%|bzEM#OPvo9{3{7V|T0oS}k!qDqb2S@s}2b)}MWowR?_NmbOSCwd3Ib=cNDVV=cX(oZN!B18tB^kDqS5OEWZM zkt-!BFuV3x%Z+!=8|IRt6`)Oj)!(_t(T8$jT3A_Q?K^$pYjb?)gu^>IJwj4K-*3iO zV+W~7zvSdlg2t`axv{6redPG0zqu&QIN3#DZr(e9qVQJ@k{Jy^O=Qk?1M=jdk4v?p zsfA)&kADXTlIgy>O%N5IL^VX~w^-vSnkq^(p@7jox&mM_Xw+`UKr0K9!6-!7V5i4v zNdZP&nbuRhGwz{&z6ylAod54vbCSBm-0j742!Qxm`4+AU zDe-BoytOQ8VR`qvl(MVWtO@6R!r1KVZ_)pMevT!!G1i)NQvs!MWz$-3s~x&(1v`in z-#1!uyf<&&Z0PbrtKWFrM_K9@J-UQ$$Y30sUWVR~&ovq}>p=5lP|GclE{W|?7Et~ z{*Bf34)w+K15;OD;TqmTn#?_mr@|@u3^FToHF=G&BTP~H4S*G_KpJNXCpu7ud|;=> z?0{p^E39m&QVJ^0y;K9LQ(NvzX|)s3pAx?W zph|E*7A(?{NtzOKTH=shkkxzg;CEr#WTK}^fG8#D(xXPTbLLUaz`nEAS) z4BX1kcWzw+KeHJ!eGk7pL&X0zM?35FBh$4o{d>e>?*2HMNZBb%@H*5<<42_8+cB$v zu%LA3D|S$TZ#UU2NYvK-Yl3kf&$*jt!8RxV%5Hkb0c)r*wd!_#TblZ94*ee)g|%gM zyE+`cO7+G<iqT zbslUL;Z9HnYj?jH+>QSU3rKad(12!LtZ_2V_`OZc`zzLr@hsx5YD^g_S48hYXEr%erG-A3 z8b9XQz9jk6j0NRR>i9oCT#v2fZi|rL{K9$J-rngeb$yTRXBdJr#O&ca^m91X`B`$E6emnR81J-7-b8l=^r^J#SFkmx~ zVI>j!8OCUv5FkC(E&k@>u`yKW@XuiN#-4))PH;ZLH4o zyji_>AvZlAlBN*%828C#qy$!d^o~0$SD&{&iMDhpEA?bP$8!`!0A9dX|7mG71|>Lh=)|I%;&6yq7k z@7KLJY3=i9LxZz+p@-Q&kr#WNLYAHKu>gkRR=ml~-^~Svzc{*!OR)3T$F7VNID+l) zvTMp*Dnpa}{0Za>o5PMWR9dqp^FaP>wjZ%J@` zIf^5occ!?c=++c4Q@X7H3>6DLPK7|dR7*Or1x27r-15Gn!b@&XOuU0X5jmfX$F9C1 zJV$}97}`cI>nYl+*ykt-HXIwwSHiEE@w{&nEK(RPV6Ta+#1Z)I@MYV7`s} z02A=3!;gZ#i$&e#%TSb!s67CT7CpbTy3+t_jeD7$@wTUaV`gmUm30btm(QPVzpjTJ z)L&0;YDrbq)%tU|pA5j~U^AX#lwpjIV5S|1h4RZXmwlo71B28}E_+5l3K-3wnK$o+ zqeq9t(^F!PM2|^ZRn3P@dP z8K^qaK#2l+jlaAp4&;EJXz}D*hp@h{Q-$6}uh z1ZD1+WN#Uze7tFy;Ej|?VotVPtC3C=lLvO}Do&huJ8$l-^Xa>tf1Lg?kJ6P*Tx0-g z@BBKxEDwmCl5yYmh|`4%<0k_@hg#3K&*|&t|JdX9`mpDTGu<M(-%3ned5g>63j02Q{Rof5bv5>kfqZUYVG3@@Ypk_x3}~3 z-eJ9*=W%X!UdahRdBejqU|vwatAkp_tZ?A>dmUTBi^iPmw*F4$!OHO6q2N;H2j_z+ zy+mPh^!+88lGtu5fyIHs)>OPUyU?cn4;?lBmwKEs)VK>9lD+5>ZEg!c1u!i8_{3Hv z3G!d4?3_|*$Y;1;qv(oXoZ4OnDzwx#m$edirga$bKh&|`D_i4>!?8pZ&nL*dtPvGF zLF}jLc+76YDEN8$s$hdx?eIE*WXu+)pODsyIRwQ!Ax%H=x32-32D_Yx@-pbm`&mR@ zx%>V6=?u#_{P>COjDh-2*A4bmxNLH8JF?5QX}-gS=W$UV-x&qyZ+dinW1_{3*&Q4r zPdY>%*2p+y?-b~)R%CbTq|N>JeFmN>J2`i)a#7al_=oNtGy6CNy|4Vk+qq*v;E^XQ z*E$5fkMD8dde9`~H*^E*F=!yD<^u*vz9y6sEeEwj>0rEI!GgHdIhS4gDZtq=AM%m7 zC#%j4Rvfo6!^O5!$UMdWs@ndiYNN_T&HukQ0ssCdxdDpG;;!?*{3k7H_^_)Ax0*x{ z!Q*04QeXdcgR7&wj?F++0=EX7OSRp;*Nf*&8mF@DwKo@}M7z{iBO)|riq`>;uwF{W zwg3A+j9xx2!?Mo=%Y9&(+X<~p1nN1}4!D9?;FPFVWvXn`|Nr+-3qIs7Ziup_`s(hW z{-yILyXp6i@l=0&ZlliQ@Vi>glOOFO>wVg^mHH(8b#{F({7hpD0`^UqvDQ7Vf1ik9 z^Px^KRR}6IbLH~eRDYPiJS%tR+PEokU!osd*z1>tzMgh+O2~zAOM=}aP8!DEXz;Ew zHJ$C!)5qZ5r85~t%2lQfF?C-f;+%KBnlYJcny4T7vB>s<;fIY@J$zbdZYbS2uRA*w zXab@hg}-;bq4}jcap#yb!H;_RaN(ljDHYyLQnu+kxY&(C?M_ls!MaZR_h{lUu&;Yq8mXvk zk!Tp(EB5og%Do5A#B*#Nl0xl#wnca=oU2r=GTrq3Ge^G)(tYQ9B`?^fx+3w|J1K} z^8a$&ujr5{&{F=ocmbo zweBJIJ_dVYR_4xe`7JB%g?E z$F@B;-!JOpv)DNA4;p{iR(~*m5nJc#WOCi^%{9Fd4{iFn_@nBpf^aO2q@w=^$iGNZHWJf?rNC zG0|Etd>n8Lru$vKKaA`GC<2sh7^^hi)!Y5wAK+}|W{Zq3!i&;@>Hd z7Pc*)(qflV?tY4VibLixUepyL4@7dphNcN1IE=it)}O)o5z!f1JMr9w_5P%tY-lY)T{ZCg^(HLa`G7 zkpBV7b7nH+`vVSiun3|$Gn}#b;;M#qDtR)0m#H*4<* z6f1*BCIG|2o_cB2FNEQo%fynG_y#Z)WVM2f1PLSQuHK>lI5@d`NiOk$aQfx5E5Pl_ zq{a{QP%eA>)|B5OtD>~=XoZ9UV}C1c_7W%GmYTOWF${#vX8@rDV*>@*s7jxP_2&V) zQdi`CviTA=JH2_1ic6aOZyvp6EF=~x{6sM%rsw=KX(US(@;#D#BUIz3|bpt4yxaS%V(Trf?%Fn z#7V*C&Ug96b0XJ8uL7~~9()O*_$$_TX`yLFcAP(d{@&h(NBMcCp;<`6WDWtHK?-aL zW+qRJ|0C<#8OxEM426K-$dX^#6caN&+-BT9&_51Y%P}ZQL4pw&%3+5$HurgrTKr*d zHN})LUX@z6ZhoWH4t5TC)2QR?(oD`gZ%FD@$T}S-wD4B)+AWrL9B_-Kt8B$&z5p<` z6*hSPAWP-=C~EO+P*)PDF~vPLTa)L^8JoV4X~8{kOHu5~&qK!~S{`gJ5cCBUUgN9E z9G~lgseCAM%oz98EsWAM=mLu%Baos%N(8A0>^6<5G1DaLFcWXB#13uRgagovJrH9M zlsW2nGVH@4B@Bzr-aWg?9v9{fU#NJp_zfTAIqQjQbIMCGWZ z-sS%|eGVaV`nKa6)}v?74b*(RU0GDU^ymy=abZ6LT&6%rmD_pUf=GsL-0<-8baQhPcNLx(%7HGMIYkDrTR`gneq?#=U2p6-H!G^Bw&>g% z?jJ{92u<+%Xpf92@Zed5^QPp>3deQr(3YB+QuI;q6?2beE)8}{dkyo*bZrl4vqX%^ zkS%2KIwL08U7|U0hwNT%@!|k|{!wB4;#37A_b%F{h`WE%2g@x&IJl(7mAEyYC*s6J zMgiWPWl8=JXRs}Co|PTcL;z7`pf?7Z+q3wJhN2E1`i67HGoz)*Kj~SGkA*VAvh1kIG{Npi102_}uWZcHf5Bqgf z#%X-+X;qitgDv7aTYhx$kv zNOd23YOJ7|uE+KP@l8L7LFUfARad7l?!hcB1TUfN3KJ zeX{qc;;WKF4!Gk3?BS{#lr-P{!4d+{&?3*Ky56t>;ZKQPQpQX;y{WcyMB&AP#ar-J zW!a8!TL_uLX2%d=0qzW(-XX(>v$9qrX<*|pkU-(YLGd59(3=F^IEef00^H8K9Qk0K zS<6ZwX9{8{1es?mU{==&1}zIn;SK=d`rUKaSG*Paj;w~Q*CF_8`E_fa5?}j`7M>J% za351kYpIr;Hb68CYCeZpWdaOnZ!nLkFRv)1q>~fgv<<02f?>odg#e!YMO}|XH}*V!HjFsazEhD%ll-w08XmU zOUAptI#1p-#16v4Bx01H<3ur42Za^|YAnFO?RN>ic|MSYtTlZ65D8%_I3Tz30Wxsr zA6tCA4SUm`Hz)&!3#fke)Np?S;7`p&jjKgKvZo1u?tGaH*D9MJA+(6rUWj# z@->eWEAv_cl}(SC$e+a71fY5q_VdhIF+b8~QjL^UXM`s}sSJ%cj@cJ_P<$9Kg3EV@ z@e*oZ+?3IT0;OrL_vAdDa{lOCMyU_r4Q&OWzt#PH!r z!*w|lcOE?GNGmJoFs29sHx8M*_JG64LxCYHXIxS`o#Ec8d-qI|7hkhA2VSPXkgQ-F z+4dNT={;$8>5-J+fn&zZj@_W$S*uG^u1`RNmBFt~ew{Cdyr1wmapml2HN%%#iC4j8 zAAdP%JwUAa(2%f~O9pBnb+Fz1cMN4KLuTe45}yd>WiU!E1@yJJXde=nlW--+(K)uZ z`re>i=W$EyHtk`O4IMy1$jDF#qWKItw;}Me^~6o;I9f#cf$W)rqSSx{-97Vaw!1q633%Rr4~&M4h3Lg3YHZh|svPMf@y6g@b!TO%owSQAsI@ zhf5+^^Y(8|IJe^57}(x;m*akNFApM}NwCZ|IFc|@EN<&u9XHV8an;=1&;9N7cg6<{ zcC4?sK}MsT6CjDtl}eeY+M?gUt33+WYdN(Hal3cy?nXn8`ZOrevRn$_f#67yWCB#F z@*?N!26rD_$Jp7@MX3VegpA$BLVu6|#DK z$UhtY#WUJ~$+&1)V6QTFJJh^oYh@gv!9Ydcj^6O*-MfQQdd>G3^rwbmt|LhWY^yc3 zV*~{2ka;}{ItZb8ki~K9A#6SGW$0lEcZW!8wd2Srs5q|*3X(*^3WbD`C#GZjgcp-m zzs>L&H(4YDr1};&nWgc1bzcO|sBh`)Uf)9tr4iz{r5a)LMJNtQWluVZ8g%Mz-$3&t?II-!ivg>2%!9tjbDa}v~Wu0?B7Ftel?=U=L{ z81bzp89ckiO)6vAY`XH#zwqPaFhmoYVdie9M6kuQ0JWz$5_rrRy)6%!_^NkL1~aU9 z%66HNEZX?AiS`6iZzO}wQDeux!cdkAPM5OA@06EkKQL!ry{wukY4(x-23ap`_};mC z(agBH3})^O#T75l-A}+5nM*Ki!%KRZ8nR=p`%i5*!)+2z^T3nu;8ahCL~9bNU8V3D z{t$~14|2jpJPq@~ffedvVX=BVd|l{@4p*jn7r%LP2Yb%0I6nw)dd|VT0eX7vzZ7>;c48F-a{`{L*{3;WBp-NpaVJNT!#xHg(`#xd^8~Eij_;d- zxcfngd-!%}g=}iG?u`oA(jrqt_Ss^`c6Z$v7i@>Y7^uW-`C$5oDg- z(Au*XbO)|k3N(}lIXO$XCsN?q>cx4sRr!-ucyu9A)SF9>&S$(Y>@k@F-aqmu1*-Ri zu9`1o){Kt}*x8TyV$4L2t?@{R<6)tmws3D?(5OBm6ymjhOB+36EDxc&TILaeqBz?kq;ZH~ByqAlhx%5a_&M0iFb{#7z$!w#dv0r2arAmIGC9Qus7O z-{Ke*G*Q_g6N0CZ*%io{5sW@VZ1#gpxQZh3;7bxW%q;j9e?$dLvA)ikVeS<&a%C?u znjIgUStQ0YJp8{=VNll&*t8BBHEl=|e?vNM?xQpOQ}cWJqedO1ur=@SC-XK@$x6r1 zCCnbkEQhvr6)M(?1im=u3|ulmpTo#nc&yL*ePWC;!t?@e1(Qd5`%25vGetYY^<2au zlCq7z|2HI7+M|PfG#Il=J7x~(BvMRiBhVG-WQL&K=80=kUT_GU`VZvSG2S%~Q7U_Wd7?A` zIFfMi?;md7=2mYx7(_AiD@~Qmw1)vLV7+nrNu7~19rrFGjhRsra}Mz|cJB<0F)5EA zj(6!ioI*@OgA4Yp)e3gJm* zlXh@sI=UsQXl)8yCXNaWFsWYCI12E4@7%lBj+zJO6*yg^Wz2o&PXClYG=S}35#vm4 zPQR~ucxP+%GMY9KzjM07a1@{!uHl>braMuAtO1piT}(hgLQ+C_WaE(YS&*m$GvqS* ziT@3BEd_CGCF028mW!z$qL<5GvPKxZbvz zBUU^gC4Z8|44lwAB(=vKUI-XOSH&HEMWL@CIsqOJQ~1E8^F}M?-i9`T;8OIcgs}=- z;AArdj{{M?Z{=A~FE<6DdeT-7S^4tkz>j~OL4k>21VN5WaZ(333;h`6A)5m&Wk2YN z&A6tT1%%<<)3L!77tdzzp6VaDjWGiA<&d^;&QiktT4N-vW0as-MZ(nIBU5 zB$LI1ob|hKuH~~)(IWTqz%jombMg^@o7AkVX{D#@iD1%N(x)*Dzb8^%x@SBP{F^6v zZ0y~!qwfND4s>!x#%}t2_6SS@5f1$L#M)ZYA4kOn3>CZ#!2=42SIWD# z!CzVdVHC1p&QkXqDz-~usL{RerZLlizJ=x3*jR(CaxWEw0#M$zgoMv4^@}OJN>X4?ovd(1^Kp~-A&#wlwflb;b&8J$ z`UW%}=%#%ETLPW+L&rayXKj6;)1b9b(4;YpL{np&lWieeK~ zPnd;?w`;Sd=55+LFVfy;Fkb~OfoP2AjYJv4CCbM@Uuu3h#?a(%63EkEP22cn8<3HO zgDscg@f%Gypd9?$3PU{`3&7%8t*L3-{OCHC#%m0lP6+t6J^Ra2_|+JBbfoQuWwJda zM4el!{^bBbfv`VC*od+ieY(X@L+tc|MYqmOLRwHnEpIq)sJJyJX7qPDDR~(7d+c#p zRwMdhTBMn^1(EnFaGGrT$1+*qGq)!kcyXn`17zP0(N@q7VUi^#8WM?x=tXUx5v(8j z`nMVbB#CD9dtHBCKZMh8|Iv>D zpeM2ve2jKW@iWCD7#3_Vic+4Uh|>A1A~q86;>&w)fB%R2!(KQA0D;kjWO65ju1+N@ zuCdfCi+=mfilpkd{L|uP%XYKKfbLOXcTt2vLM8mP1Jj7br5qohWgYALD>^(NwK&i) zqW{rEx&_i9OPOf$#H!L@f$l(eut4Hl0nvi{meSDf)RL<+T6z?(L)V zGOd2?F=7QYdht-_4#?l&4_H(m9%vtDLXvnKUw|9e04eh+>jJe-MZPSYV5Z#c{xZ z>0Z>RHZ9ESXW_+($X*gA7gp>n=yMFHD9$y~*=)1a zWGs6@gpuOmOsyK&E1Jv4YZ(2o*V+y+I6xEF^M0S7*LBpYWB>eosZ;z>^574WMF1AJ zM9guC-OX|3?y<5eWeprJ{BXRl9Nb(sato#Pef}T(_iE+e`KfKJNcJFnrVP61vwW|F z?F!}$PWI__k1|QpwdldnV1hVs30sj6f)-}#I_XoFiBSc&-E`h>2)+B|9@!Ba8~f;S zhm9-E##2cNLd4m>Odr#CD{@8C}$!13IQ#+W101 zo&3erDau4eJtRFX?PYkFPv_ai)jJ)F2Yx>Nu}ksS^@n#puO6LKHNJR!&gIi<=9N{y zlO39(@#e!3^(OAU)2I%}lTRj*>YCUp%Qtv_tpo%Yyc_-6*OKI{GXrcQ6qz5GeO?W> z?F~^(Kz;J8xTuzw@n2+Ttt>hMhbI6uF3#>S0ezGC$M-%K-th<;SmwrxRNl=w1`Ed0C82`{;a?6Ws)KC ze%X3H08S&<$cdz9IIsRIe&TJZd~hqiMHlJ)eYF;U6OhM?eww89cqkaLZxHDS#}ctr%-bPfNEDK(WSlv->975ZX``V2kDixfF!Dgf(QF!) z9wR@lpyY&6xDW^fRmz_G3;E-B{`sdHLSH&KQyZJ?X_?kDXQpCREW~5#KMI%f>S_i! z`1*_^xUlwOPEhn^b4$zQyLb1JXT!t8h5m>VSeYt6_RGV+{}x>}qBD#K(y7EHb%~gP z#4hA!g0$t#A8Sd1l>G~Ug05}+s#4<@zXHKpUT%Ftqm`MNHx>0m00D}NWZ*tH065Oi zdLGSjusk?q3l+R+vxzDyi3~RJ(m|)O7*M$Ixhy&*Rvn{E&0T)H&}`~dSz4z+N_DE) zWP?o*M{$pN4A=tUMZ9JxE+B(-IT9$8QE+W*8yjgW+o~IEqumzkGFtf1xVxaQ(^>5U zwOL51Lf9NX{%B3j`tq`sh9U_P)-vgCBTJwj2UX+>s*s&6&K_ZlPi@(fq@Ox~L4Zhs zaLO>ZGdf`#-JZfN47i}=##+t*q*c^(2dPLns|$D#@bA5Irs7}4!2dW!!wURPFtL+8 z(hPmHfBcP3;1F#%6}JWaT;1XlstHbwG0q;c88|I%7csKYZE8bF5vYw!=Jt{ud=DP1 z$!uuCp^W_qLOSRDiCMfT3Yc23vtF+%OGWc0sc%eigJ1$KX#wJdF3ydEJA7o;GE#xXOflG zJ}Pq?QgPW0dH>W*gzud)?Vx**{umUB_Rc8inXd04;DuFGUk`?gg;eYfLo|)Rl2S-= zLLSJ)R2}i>;ltndoIns^J|V-r{XL*>Wp?On&m`zRy@6tf4ypfM(W2kjFhVCziB2z^ z;sw_&!zocFQefbTBc2>={*nYa$f|F!O5jG|96vul?sYWPmBWn0Z6<5aQL{@Sz)=-~ zJjGiCJ#gzY+Pof`D>jzsTs%j9jwj_YEw|#id4_Z|ac~F$_LOCjIG9rC`1MI%gZk&2 z+I;HaT^fp#ZOPl#OugF%kK0(Yiq0^VYFoBsLf?Kvy0{svafxFhYncFj(I3s)@y?p7}h^jn)TN~Q5{yP5Wsk8u;ggq)w5 z{rPTdtp0s4!e9_A`x36+``X^w*;z&~Z0Ql`nv?=#J;GsW9KvLdj}qBdh}=+cxOF#) z1HkiK!{p6J$S+H-Bpe2ZM7AWBw6w*%@GqG+kI+M}!f+BRlABCeS*&&JsUB6>eUiD1 zgb1v*F-OD*1d$%IN0pvE+f4w7KAVuR7%Z&BJsJb7*K{tl4z9J8OXZ+ZQWE-K(=gDX z2;xE)Ec;sUV{1K5Tgm(RR4l1T9w0dTNSC`qStKo=m7fy)s)tACN$Gz5)FCQSbfw_I z2tc6ozTqm$H5N#wT_{8LmOOl;W7b$l?7M)r_BiMoY~`qX>+~wf{CPdIYG%0+Lv($E7L9644~Ni53Zh|WZ!em8v2)-N5~jp_1r~-6M#e;-W90JK{ewYu zD8)=8Csblx&m`;)xzT$1^yB2lVpEx(6jHyNh_jF|o1D96uJr zx!0fAEJBiF>*1g^0~$Mu76}prv@{h3989_O zU+1)v#)e{P=#U|TdQ-wSXQI7*AT8EH_8F4ZJWDDcQ|tg$5s=%HyMd`@9lvNkKoT(TJe>&BOmdlwe|}M( z0RMyAe)X+gJrz+4qhribV!PyJhnt=l0P#%NSxjQWx~+6KH8(GW5fgj(4)w$~IBf9S zWFhSO_3IosAtgamtCu&1u?o{<00;y}LDYJ~2Na_R1zao|mXWIhM|( zVv)~swiYd0cIbFs1)q<^nt*nb(x;_pSY(1t`crT3Wj|v^6j-)+o}(kox5&5$0pKhR zIzI(Rq|TH+OOT!*%`@Mhsp2_~wBSs_7=%SWk+`{|SDSlx@5c^WCl*wj4h(W6J!_V%5d?qT^}vo52UXvOAL zKSVXFExtD8gfc}vDAB6@MQIu+LJk{x&bMo)H~ahm=$eH!CrKnST!KbaJgZoTSdHTv z_5)uFs>R}rNU~`6=a`rn$EfpmHa4{@{BcRgL{6mmc+(U$Yo9wQ<5J1GjD>l9{L^V> zqiBG6R>Rmt1$?)Uc_73QursF}m%l|5mDmF_l(phJ87V(y5Ds5)QNVIQPhoEeVCwQN zJC&h{Y|bpWy_0?vlc>D+KbiGW^RsWiC_Ozr%>5}#a>YcS#~eYPWtJsj2U4GCfnfiI z6DdI9dM#f3Dm3m1zgiaRahHSp&ygGs%}yq_1T4y%(a-Fg^@|UF{n`n%Zvwp`S5do9 zALG4Uw&|-*l6A+l{H^z_2jCt~u^_I`RHSF2DG9@b5W;PYK z*mgpyr3EL`CgC>B%g4cHKihBUfp{Og=Ft8p|8BoqOH;EMl^iNtYC{c14q_OBm&&oQ zFu(pg^>9}F(o<*8hRdD#L&6AFQj&##vf>ohmvCMP61b?KHCBU#w!N`G37SeKl>SIz z>7KcP2hx6{pxy$uEhs?#9=`-u&fiMi4-HN>k+bGx|&n)2$`)!@oUYkV4MUur00RaKCF!KtE*~Us}y@&78^a26{Q!1x8 zK7`pLl@XbYf>DMoJhX4vz&KXh2MWJ@)43u_5p)BqFB>BfCV`W`qSLfTpE1BzLTL~bEGGTGPJc;%}v z?T0XG*_j}t80K$g32{y?$7wWx*=2z%oUXbO4|Sy>B%dB!C)G?g;U14kUJuKXv#S-= z{38aCoac_!SyOj5?zLg9hGD< zPmGqQ=|PFEZI2fZOErSL;^K66kTrOH9Y@GYJybi#Q;0 zR}@6#4$^jceCkD#DxyH}lyiVk{KYlPAMG-^-|s|^WYW@Q%1%111&pr?i?ON(Vv)Ht z0rfUx>ZkF}djA@z zo%1|%bMvr=vv`Hb6U`$PLb`w)c;VOxAxlC0cM0;vvnK65sRiDK4>P+ z*3~s&k{^~Zi*{Sxz{zw+>P@0Y$DYab$HVM`wwX*VqTC=Ppff!HVDxW zNBXeXK6eu@Xm6*A!VvWUJ;ik!UXWS)rb0QgT2952U zbmnvdOdVo}$U|t<9`eESCYR{cR@c~mEPmf-a2Dy~G`#bj;K7Us>I%+rEMyS`14!?V zUW^+HH~G5mz1HA$O}{knOK+X2f98FeUhz`j(DnDajujXCZpfPX^6fRs1eipb3`ivI zVFJ2@PBQO9bY!HgiA+AlZO8Sn;;Pcn4fd@%y?EKFp-0x<;v^DAawq>!MI&pk9F2(3 zx_mnH%9Sy4m<}DPfHuRb&|5T1mq|y{%6up`3SYhQ_VFpE8k1{ICK|$sjc=?w^E&xi zYILR|phUZn-9o(R*t0Nk{v*HYF`0iDW{%bDzw-CUYgs>Rc-L^{8r`ZYHn<4chzJEt z{fUWF>gyOR1Do%VEu7Hm38~Uv3n&35H=*{Q=?w2v6054Jng;BA@_zDztRG6MO?!(p zgNBqF2bj2(9v;-Wp~S29udHB64ihKB_bj3+#g`D@$4oF!mT-*#CyDjwPL8MbK1g=L zhll82ursSYgk%ol4!~H(Ak_aw(NiB2EQ)Y_aVB+(ES^@t5XLo7yUgtgu9?3ko=J^NG=BM2al@NR ziVj&MOcw;&yM;d{yiqB@K)YFBDlfqZBJ#)b0~(5A-c6K|U?X%+1SN;;f3vN5@LX|Y z$VxNyt%jRwI|W|^CBw4=h;I-?C`h8v1ozV6v!)Adj~C30u0Y{O|WZ>!Wdl!Y*tx%{Q>!KlJ1 zi+A)sH8En;wmjMu4hvWUC6T@?_TYgX2LX|sK^x_vRx_}ytbdkUdd8B>zjXTZh_rH6 z8(wh)=aC^MAA2#4IE_m@1ibH)I{!$xhdo4Bw~X?9N*q|(ERr$7tq;h~4A6DR zvdwhU##F2ruig)Nkw8aAABLk+UDn|#+tNEx3RBDSsbpm-e>wN)q`kqxn_z-an0S>( z(Y>O%$%W!78M6-=ytjDj@%m&2g*abZc1hkPF@%Y~K=kZ(EkBy>*tDrJLAKet$jIki z4^9smPZ=u+|El0DsWvmx0t-I@Ztw!E@RLOA$wklASQ9jjb17Sg z$m&uUaf`(nmcVw{IU#TPWJX!?zFersx9f288mJIgvxy$Ts__>o8aW|1Wm7AYQUC#k zrrT_+Yg;tmaG5ACvj@BadJvKagER}4?F&69pNm2O-k=NA5t}|V^;+6y{mZlljGA)IDu~ElWXtZy@|&oNSODhsmSLn3w$9dQ2}7_5gf0loi{a;dAOi+A!14V3jSIGe!FI z50BSl)qp@wn0a;E5%EWk+@#o;gZVd^>qbtFZE|*&OuvvvWS6(S&P=W%sMf&e%dC3} zkSY&~C&%cfjau8otP0_4V^h+OzjW-aL2ZBE@T;;7S(KD;vrVQ`xxnU?9yZ!!uOsW` z31!+3ODS_+Q`v%V@qUIw%EJv6QF+r64z-HRE%dk78R6ohP9^}#@p-%xIj*{iM=Pk2 zS)?QUZCFn?ToBr!YgZ+1`vlr1%1Q1R(8I{dllMX7vBk5({>MfBke!!T>a&&`(z_$` z;hDbNzLC{Y=9_b!8~;zefbM6yo0~Yf;kh&r${!V03hGP(Uep_JHS)0*)*P4_m2HoK zezLT%*igNo$z66!ikT(Sr((0A_{IVM;AN@R#g#r1_D)PAsAsXOROS?5=mY{X?p)`U z{rmTa2z)6CpQcp6HejauMdGz2!dk}0ZgT-x#b_1&J}7H2bqWElff%NOwif&|Cb^rb zsX8?;o%={cxz#@a;&{J*;7NCg|MdRwIFMSuZqbJ*3%Dttg9KY6w$?vVucow=2{AApMtH1-4_{|-JenJ4l^6wKqd+q& z(nW)%7IlzRK>wvY1#U&@ma6nZ{m~vBi1a-M502o-Ak(m2wCK3%v&v?&xfOIowM$lY ze|>%Z|84DQ+pgU;zA|q4A%|ZVv221ukpu>TK0gNUHbHS;F`D0|X%?jiFLg(i=pq;rmGUF5Cz))@gJ= zEPT=ol&cvneg*d~sn@HYi{m$y$!R3BBtS8hmH7ei7p0W%^Uw~-GH}L9JzyY3*YWT> zqY=|dFYv0-adFqFf5jFQ)!0iSw`{lv-`0QDXWKJ#k3n0(%Xa~AVurqhiPdvCVhcrx zMjj_y4geQ#@%N<~!bjNK6# zsk`Vxh1Z7I%(x>i!XhZ*Vncr3e9-f4#3s$=LQEkEpcmamtHN}bh~tP&;7Bo@j|GP^ zG+*%`fJQBhlti$Tt-=I5K{K+inC=z&|xXyBFxXifXw7`Sz<9Vm3= z_wTdmnuL!Bx|WSz5s{JXURe{qzfG7?a!A0qej7BiLSA6M{ujE};oe7<0VuwBG3}$@ zH#Bc|DG_nT7qOTN5{@>7DXJv_FL*X=>yniWAOgUoKelXN%^8%k3lxz^(8F|Vi6)h+ zjHpy+NNNIHDSA6V1rN_(W!*SN$Q}EVc0ewg0MIp&W}51!gd`wxZl0+Z ztSpUd<25#m(G#wIf=d|{PWt=zROO2oEjj=~Ez{c^$9sG*iK3*?)Y`#n>miQV!3m+) zi1t~3R&71RJfKn44TDGfaN9-cNtX}4b$}zuh-pRq+?|XP$qlC!W30oHUkfBSrv2nd z)1FVvKfoTd{Gm~p{kO>_(^0PAtXC5mv^VxapTMWG;{|qFmVQv8H3RVE5gEG=(KR&< zBm6H01r&e^J>JMI$1#qr_o_{Ziwm9kU;|Vm>~=GX=u)_vqRs&$Kb8RO3M&T0DBCHS zI=~c3G?-ySuj^xzK-yw0dqa{s=%Y*qP)}dMRNLwOZF|S--otGB?+Xr=VW_hhHZ$=D z*)a}-cN8qU-&L?98pA|OB+{8H$7A3RGLk7M2DY*U>Adi0!2~v4%`WH_WV)3|*_V8a z^X3)Q9w-%MLyd3blE>h?bDo~=&HUFP$VV)HC1U#pNAi0K9mKbP67RShtCQ{zQuYFX zyUm$nO?mCRqTKi7a6~+|xZsk>cD#MI433|AE=l=(x(CZBfU;yYAt`KRi#G`N^Jt-7 z=Ejx+5m~CWMl~j?1VH9~k88HuxLB_uB>?;@Ej_)0c9RFuU^;Us>idI}(QknF_n^#X zKM}$eQA%MVIg8P+vOdd+TcWlVG@XpubyODafNi+blx~W+>R zfXQLY{)4m|Kw?U9!k<&+Br)6>_B_rec0CQav`HMHFteqJZ-=MPeW<@UiURL7wKkZm z^_Vde=KKPky8<=9JBa&P%fA$Vb`nMmD@pi|yeZ2_64lFT_+Tcpj$aJ(v)8#s?L%fT z#vX|?Bk+ZYSO^UT|Lo^&H>Z{0PZ0TX*X{sIp&Y%A_iVtO==TV%4ss(!_9}zFoZvby zqbUvp+0chyHRZVsn}ApUeC0#231TQ0mW1)suD|xSBiDeP5Q&HdF7WD!3qDd${qwZx zWr@y=weGaDsp}0XB*q(;JKX3CNrs)9{-ygcDC*|Q+)gRMMLrM6CriKttd&CT@$u-x zN9uYbYAZsdi;zh&I)61h(f7{i*nx@Z02~xPfVCm5LJ-vU7km@aNNlo0lRVtpQ_j66 z6N>LPE^uTfaDpdoLNY`%PBaw31qBd7#_pXACJf%7xv08w#;5vWN};1$@>yklc2PZw zj2)DbxS;$fGb%LOwiLVu!VtUJ=vD>Yy9G{VxxrQ%Iq?wY)n<~}P5Pgj%9$DHQ(HYY zT?$jmeibxXXj4RMz(tXwv#Ycdi zfcPo(9;E%XV{P>z?ThZqm<|Nj@OOs~nsxXly$Um-2syRqKv4|ZcsGVh{VCEg-jbsJ#W=`;`Cg#=;n3^q zz4?ZYvp!J{InT_#DTbD)r5m_5H1UxoWt<|qL2jhUtu6@~hyI{6R>1k6D2c4)9~Ko7 zCew<}{5KS1>JX^Vg7%Jdqjku`Zdn|5pv6TWcOk8eM- z2PK22sgA#9{B!W`pFOsHxK2c%%9UTp4>o>i+REqB8*I%k^C(5cNw&yIQ3GI`?Q zpO=6}d-;`J>f^OSvu7a{kPQz;oiOc||JOaRbB50~APCVTFe~Uz(t*QrxZz4d8wysj!NvVf4Tmhi-e$bZW9hF}jU!s@J$;kmg6jN3F6+|>o zxoB7A_KJxI>|9%raYiDQ;CjvWU-h?=-u48dQu|@=8NEZaM&YkUpVF3ANOnf@2w$eXf0`9P(r8gk%aQ5CcNa z1XcfQ?yyZVA7zx81LH-NG#vhxREDp+7;bjxGvzXEHE?wU*W0})!Vv9kZ|SY_`QYJY z#c(@t$5?@9-n!Mhox!akG&cg)P=!2Zx|TF3SXjo58w`5rq8Chkn$fClhYq^>rp1ru z46_W{S96etOm=aLFqC9Sbz>D5_fC&p$KoY`=nXFcBxW*IE$Sn3yfi}STa2f+j^hav z3T2f$6kJ4$N3&ZaM4%LGybjpiY+Ly^6fkSC9R34L4ljqB=HYHK}ZY`Q96aX1)o`{NJO4R07ArESz`++y^aNS$T(!Q zK{ToqaocvTOz&RuD~}1e6JyH;89&)cYoJc0i(8c@>>V$=QSaVITRj~wgUv@=MAcZ= z@$2zp;3};vEwzvEriML}LSEJklAH7BxQYKK@<#eqK?|X~mulQN%-(s#*j>9n=F4Cp zx;FnZ>xoDH{$%&{xGF?Z=E?IiHllPkK&~NFyoodyJ?tFs#Wi+i5uzH1e2OnVJl9yy zi>t680n)dTF(7t|zL}Jye3)a!J%jLD&r6F*O!Pl<=1lN{0Ug9&olX*W)lrO#P~OU? zl7z=`6@RJF0xtkqBL$1DH}K|X+guF0w-erWGqYj5Ug}=m@h{gvJGi9#4B>6u$j;sd z2qG@7fG^$Sehh{jM0`;yP|1U9j^idwXs=;)5kP<+yZJ*1Yb*i+s{hP3|Da0*1mc#_ z78qn!0NXeZpm@%_dsiS~mcd4TbY2AX`HBl!)Lu<3q*q?dzpzGxz;v=iwC$Y-2F;UAzz60onF~YnR!im z8n{Z=@E-T>+j3$U(4_J7r?1`?MMCOKy@Se^wV79ujZ)k~hOdi?D(u?s$KHputLt3jo+zCS0!)$XwK|xy;m6$nB zOa37GbFkZiF;TmVZvwrp4B-O@^ZLV|>v(MCe1GC?)6W|oQm^*)TJ0Ol0wJn(-sRfR zXHi1c(>6^(*T^kA0i7)4u0$Le3u73;W?~vYO~=eEoO;%g3(#b8WbcW8=_XC^YElf#VM#^+y0pEGVMx zQyP5jfjoH(t3jZA-p>kF8NENOwVUIh-BExw@(yVzJwfSCKORSJ;;rD)WEax9kM6-E zM|Qxw<3&XYbs#zUHrJA7tPx-@o8&gYj>rR$$x=k%kT3iUQI(2$mc#h(vyo>{@(!Tz zSvVnLg1&Ly&c^1`xp-nn2dpRP2qkt>->1~bu>s2~BCbQG%4Q&*&6R?JAMrJ^Jp=^m zU_ye-lYz{wNFG1ihe=8h3BjTP&&6iXA*Ga4%v*LpyoSYL+(&5cXiiP~^Lk1~;$M&E z4Jpyp7p!*l)e3wbJ8&zeB|YulIiXRbX91m*B}O!tXee5OLy87ZOn{*Ga0$1iu!I$E zdnW4CnIrS9C?=@YWZ?>-lDR@(RJNKl^EZQkYNhT(k|J}dJSYJYxrwZpGqBUB2h$rv z(=AmZC8isP%4NWoDD`q{rc1#;WGy#LV&=cmlWqoZiC4a&p!f_ z@16zU=Ka}k&TuXze5r7;>Zub2(XuF-=QE^7RR`QjW%-T3R@89`c`vZ~>59z`-zJAr zT%DG8gJ$f?;Roa5HZ$#AT@O=KzihXG?t#R_97->-=M(fMFYj}F;9AdDpwmT#90OUj zi04LM7GKDoPy{YwJS3z0CqCOkafpWw($Aa6BhwH5w<@XywV0RzoIhW)yapmmn%Z+n zaLc~cmdQ0YA)4HP^^>Jr0wd6v6drnR+lDhBuI>=n2c^bQA!3yt}SMLt8b~%f3+V`ABNqxV%lXX3l z7{b4^P;GggHy^J@;?^-?#wEv=d51o6_$X`akt#v%4p7MX01^RCwFrHJB8fC4)r}B; zU@Z4!5s!vhY{2(N3vC>BetdNF>B5>7IF77qW%#pRDM2H9tpB7fK-8yW_t!?WAcFr0ognvBbF-sl z!2$K>W`5=dO=Wd;J$;$_HF$Vw*t||I_lOUgefugSe5XTczQ(1HsD5YnDb;O94$_3A zdtbe6AW9&93#CIeSr)`(cGeOWM{nQmli#c} z+s~}6i(AF2U5pCYU4M>|pM>_OojSJz_m){-x?9CC?@84`Vh_Q~qSWhUu=Y;Ak{hy$ z_8Qk^sc3k=8C0$^S&Ob%?jWh=?xRO>xO54-OuZ7FoXouFO2^(4-oJkjQV@K|upgAy zwOlH*Bs|i#}aGj7ha0wo89HTb?vj)^}>p_Tn+)oV|M5SF6N{1FJqCygDHca zhrhq?Gbq)AkzAB{qugyOISbGDwg=DA%TV8vX=h=}!ep zrd2|yhM4V>!SdEM;WRojL{H~MB_Bf?V1jO9^inM?Eg80!H53h5+ZJ<8dB&@x- zZ9=4EQ*TpysHquhYkBq3dpCnN(9~k7gHDhG4zPToOz;(`Iq$^Y?mM-UOd;tw0kzmo zP2tgkRCU(SVE(!-esU_6N~m>{@tb6$tie$=CT5iyEiNilI&sdqXL zSw=#scpy6u-c8l!A!*f3i#;A0b>PU6+G~Op()|<17;MiJgMo{y`opce{8?^z%p^SK zA6vb#TwJglVSf3Hv?9KVO^#Cgr{&lc7#bNlbRJyBQX!_BXxF;MHMI@1&S%ICm}c1M zidkvK-m_W^a4@aaXykPl4K_$p_D}$lNOwZ=tc|khk{&7 z?^*lBC9I2M=Os>a@{^ypNv}C~8-A*RtD`O=+tD*l&)W21)zH~Z@yYqN#>cx08F3~J zq)2Sx_0j%l#qAG{AG0#xFE`s4-?~*?G+3Cfc}&UgLTRsKYd?QWSNA;s;>8QdY?%a> z2Ht!AvR$sJfYhNSUvpP?&Dwt9TkiW6wM&cNAlgcEeo-}g;ifsPETE1D&NB;fI(eb| z?ZoeEd6@#9UW$EltF`5&4|5i#MYp-xr}*LkQ|*CzfY@+UubHf4XLcj4lGW)Rd&nV~lxx{Ds#^ko|ribt~3tF0838@F9x0P12b> z`Ds7B>n<)Xiz{9X5qklyo`a)f3=v%v%*a+KqA&AF>A*)M-jRjX=wBT}v@5Eer};ec z$!BN|!L3NTkts3E{r|%k7rbe)azCZw0iL=>O#KDi_gW8!$Q8F%X(VT6}F^pk5P;G zatbPo+swk{MY?xC|Fg%++X8mWG%&>!E%2B*bKZ{CI9j6C&|3WrI2bq1k4lR|Cl0wv zf={oN6;7V5NBvhvX*Egl4#;b;94-~08MM3$`MxUuqH;bKO3pc{si{Sj!DW5kBQ7IR z;9oS2(!bedib9iFQNPJ+)5%ST(VBY4;?-II{^1g>yfJ{XGKvgzA`^lft)8({nDktn z$F_>HLYwgV9sIMc-{Fq?Gk-en@RGk*iOveRweh&UyQ4Q12|^EcCnJ-s7A~x?PZAV^ zav1G>vVGEsqLz%iQ7{d6Y!g&{qHW{#=rhF1Y>=9S)YCU8(u?Mz3wHm1%wiH=Tm{+t%%s}C_o*L6Rmud3AXpqWkp)tZFsrhS zE(C-pYtxAzrW-W-V#r1*Hj0bOL<)j|+HscJmZJ?$sAWK>*-?y-EIm$EGX}e~Kk8)B zJ;~ZZc^-pa119f2A?w=3YXEkRa~VxTMIS^TFcWJ^uR&q9*0noOLI@2)(STP)7hbFz z$O_Y&CeLrrFx5aV3Zc@IaS}P+GA%K+R~LW}^alK>8J?aAl?TT0wOVXTZ)0O_(1@7$ zFJqK$vuEEBQaRyyZSS*Z&kC%{Ue+r-11KoliKdj=jW1;l{p+76s_T%#bZc&r{B^-! zwE1}Y2+ztWf@im>-ER?%nnJ~0-=Qu>XF^8tr`7u`M?WCiAYSNwELHJ?2(#6wd_O=p zC|k6zRHjX)gK(lhFWI`x6bhROC19Z4A6B-`WCJF{1?ODuM!EW5zwc3PmBTx~T44k@U?G%s`WMHyS zjHLzH)Rn#~)?m+I)W82BWPa)8`#^jwn^p37^andmwdiglv>YE?Lu6Mx!6zik?9zn2 zcgo-ZjF)}*TS1=B9B#XrH4%BFgrE9OiD(*L}ld)%>7WmG; za9vNHgwuJ}sm(0s2fs#kPMukB(z6|Z%7c@wq<5a6FC0E?G6E=Tz6Gj8o1JUFhYFCC z^}{+zSv{8eKUjaf%;->a*f9ft;%Tgb<{Fpq}4I&?rwRcqR_Df6wr<#d#ISmL1 z!59)OGIA8go+IJ+Jp_BvP5237e<4n?R}6`X36+Asq{WS zpXxu08(m_y07#I6s@sUAz5Hdq2rNor!XJUTS>Y}U}7$h(ubr|mwdVx2|oT2#PMG)jw?EzDvp1Uj!UCl>oZyLbG*nayviZY^ zHV&d}P*-YN9kdlMS!4hT*t*uhkz75+52rsRjPh`dV7iPHvO)LAw2MEFy`L;2>Rcvq zqrZN=DfR=xk_V(RgL$0}kGUrqU*^pSPvRHc8zpnMPv1Y~587&JWmSbf2)b!kc=(@W zyU{^&$I~Ipa#^r3uhP|Z_hk;9OeX473N`WnORgp{` zqZIbS5zH?ss*%yB^S(~q?VURIoj!bkORrC7yqE?3Q~{K+E$FHL zbcDW`5RUoKsF(BM%h1Uw$B$cwe6yN7xzj)Ylt+!J6wrO5y%y$$&B zHX@0CMN4W(5RryL=s$S)P;J>z3W97HZJ}TlBIP1d$J}>XFdPYix5{~R7l55Imqtq- zmk>c=mBNhKOS@GzE1ixpSlz(UKRe>xA-Q>~^ zKXKc+^{99Zq($4t<{uZ=7qW&(lM3lIZ-+$i^1}zfxzXYI&FWQk#za`7VnnGddOn^( zFZ=Afc#u$D%fuQ&6H2$tygbKl?!RG(+NreDK`*v ziDwHYG%$c8?d*C5^}e0PzqP6zG^Cvyg4~9e2Cn5YPOvu(d#bBP$(S%D%^2Mfh_c!F zTY*`J4ksp(IY*GRpjBEE)k{zWsqY*vHqf*x<`X=6>%Hp(fN#!Y^q=ZJIV4zSEDj^wQbzM(9jUFz4vLom4_#vNnUYF zTPxKcoy}HXeB<(O+_185@{k!$(bZ8W-B+t-tBMcsfF;p}p+I*ksue3&%HTMmQ$~=) zZVZu!IDP;dA4DxY`mIf|{-p(-q?6(VLl)~zG_`~&1Dn4Ler(5e131*w(i*wI@4nc4 zQJ=!m?+g!rvM^-2Bf^pEObAsMZPgDnv0mIdG)rC3a85}h48fjuzIb*o_zD=If_O8CUdy5Qh{N#59KF||EmN^@qq=D{j#}BTKt?~9 zxnO1?hWN=yTepd(EA6ba?6@CrAHtaM#qj`nlRhTb*mTmG32rZ6Z2TSl>N zlJA(k(xpNJBvt_hd+y=hiQm#Bpz}Jfwt9w1{(IK}Fr`<2&kve}hK^!jH)lg;qvPXe zZ+`#NNrq^kSj3}Mk}zc!OB`v%I*7K5gCl;v6s%&_B;Yz@j_ouG$ZCrI z6z%I}Xm>tMuh^SpL5lt13N-eZ2F3HfmBLOE8I_3~+L*vn-oWkT(7VX92DY3@Q>I{O zwf{qt(H7m~PoVw))3pNVmJ7w0FXQmCU=hXJqGij(6P0G6EsocOhb6O~ghK|TAWs2c z&W3fvG~;EPa>h|!66&Tv$kCOa0JM~a!6Z%@svt)(Fz=LX)~I1anckrmhG?<47q1mK z$}#wwqvI>HfgVw#ynC8p|FNB1Ya6K#P9fkR18NF9r!ilSpn zSktziSu$bypiN~y9<=Nx4iq%OeHm&2A`I(4FSKLH*ZzQ$MR))X{~4TG*N-dE5#Kw~ znZ##7T$4Jp%7i8j%~C@$ig4NdtI<~;c?_WpDu&b~fZ8^iwXK!Yk-v?I2%`YHRzl!V zP+WcbbkO-*IqlI0HW1ns*z1m&kMq^svZ; z-sgjqbD0L5TUMS!`W6`+-Nb~y^-s{~Fv9Cc%;f;|AU!GRO(Y!TVKT$t9|=5dgZapn zD-Cc0gyP-%JkGrn-5M2d*JLM(P6zXx7(r#LcU@=Earq1ntA$9_Z0UTu6M zH|_;~qMecq`k&yH&)4_>e@`+3dL5dvn*cu<6)){3WTmxfF}9Jp6(v4tfbm=9n&CGk~)l1@y6t@5$sK= z2dQ_YV?*U<@vf#FJ1VYWoj_`?LXn23 z=qm6G+t>qs_S`mXKcNE3YXhW%gf9v>9i(F1%2d1v{CZ#dwS$X;Sj-kW>qqSm69s!D z$8EiTgt27W_Xbj5Fp5R;JHq~DYAZ>T3_$Rm4p0d8)YA*Z-HY!Q z*cu+T$?z2`&Z;izu1gRC1WJKHlC?;*bL?P|t=q_9Hqz&cgA;A-XbLxR&zn;6k$=uW zNV~3G$I^5N+mO6?8tFA2ctpYcz4pGN**}=y>+0+43+5uKYGA~hUY)S%Y`HEiZDnZe zEM$EnkaQ4&LG3f|3cq8=~zCN2iQJHIoP6O)_f%ra(T;TDbiIzzn#s(k6>81l7`7#K(vk#T!mPVlSO ztIT|V9_hD0REW^rOZAGI%fJo9t>W(sD^e_yv!mL3E~0f*AU6rW6>42;gM$&`WIs$Y zz^r7X06M2Hd&Se=`5mF4liH3aXQV%|d+F{X<>;QXx5BhlQ9O1XHG{}^0xh!~b?Hn9 z%N+l)PCxvY&B%t(?(B$x!?gU1Frzb#Wu_v8JBxZ6|FEW8>noC=dX>BCFzl zT{EMcy1{V;$YONrIDdXhhqFA{R$^BK$VWZI#xJe&qjLg92`vsy$3goz4M8OXP_bK0 z8L2UN#iGy$dO_K;M@fvh8K0)Pp;oj2?q9zB8ax+T(R(0G7(l2wrfW7AVULE!Xv#IV ziaA?jz>|g(fedQTL5nLMUlcfU9mLiGayxy@!&9(}(y0JqQtWAIKXjGZjREr?uOlk} zvRy-!PMjddUnLFEoWAB+x+HH3d)RsKiU2a37Z&<2?E*3hyRl!B`!Pm!1Jp{cpX|B8 z5uB%boOw|1*&6!)Riy3=DsZQxxQzOb=l+I@hlM@s|2{@RI|_eum0@kT6frhuKFc)w~nK913Dz7#c%rh%k?Xn$1lowEr^id}yDk0*(>OWtBw2rJ#JD zl^tG$1Q)kEA2jjTt9iQN{1n*_0M;{#i5p}uco-h~S@ZsmTqLk70&Tn8>VhBOKN{<} zt7|77>mKmktbX*E5xyvsc=zawO;)xH795?aH63ctvUGZlKf47jL2j-## ze^F1fK4(6~pR79OWMB6#qIk$I?ogw`_`UY#uC84Xe^Faxa8%Ohq6{OW9p900`as;x z0Hl*D2e&#FQuO(_Cm=v`4+P=l!qfR=U)h#M!{Rt~`cJ1cL_c|57bSr%Hc8*UUF-@)M%3QmJFE3L9$+#UA zaw%YaQK@KLxtT=pMt}VAuXjjfmrHe#r%s&$dcD8!d+xScyY z+~bP@>fav}KDs>_T|9zNboDDX`_!uzNA3z(K5V6Lo0YjWF6oBN8&aP1Ffg$9W_q?| z&5Xovyo)@n&&0I{0EG-YCh^+?B(uVSbMA$Irf_7iMX)_gT)W*qdS#FEM7_D`j0I1p zoAuf?y4ZaG`#q1A&tf9z`7hUKizdR_akP&!jmwgc=+bTr*Zq5;M(L42k?WLaaU|IAbMm>41+&4!mjzRm3# zr8nHVyA|B%#NsMjk|8-;Ox$IgD0~DJq1dakkfh(#ulkODeUC1Q`CNJMJqBKjmM@Pd zIH4_&#fwCMV*RoAs#<-9YW>yy*v7QM=@lsQ_G0s(AVVZH45G@j!CjtgEo0-f0TZKt zU5e2txqmRUPxIf7g5+8BG;P*VN3$@rA)RJW?}~<>AB06#^h{5h45`4mpu_1Tid%jZ zYCbn^+^|+%YRyhOywi~&j>5T=8zBTIOjNCRVS4$HvA-Lo{Mx0h-Sjs0ry#|PLA0gd zMKN;EBQgjt!@)E}-O9&{AqN&M4=d{&I$6xT0T>#p4$6)U5iJKIRC<&BaltCEYSZb% zB=u1z)*IW7bW{$?vPWeC6e8=&$Z`b3;h_Ii2)>!Iu88RsXGsfWf*byGXDY^MO z7XOUg*l`{HCEzs0lpnnMjkMBri_(Lg4-ve1B4gKh%ECk>-mgFy!uI>fggv*+gg8gw z6IUl>Z=%FzM@i3HC7Br+6z7>7XGUfemqz-ZJAdBp^M21AD^NL5oiCm-5kpm12FA5ux zW6(?+Q87Y{wzy0$n!e;fZq%@0$izi2(0SmZb-WbzjAnf3JATF*jJ}+A43Rnd+YxOy zmLp$9)NuuKQBp?fyHAtG93W&uNZmy%gD7S_zCeUD_Gt9IRdos#hh)6}@$Zza;C%cE2_`c)fFxvC@oT1WyU&lMeYnmaL(2QwwQ9F!8_5Nr zkq4>1()yfYy&0wlChSFJO-ut`Xj5Y6@TpTZNC13VviHxA6_jW)ngu&9%O3avtYH*; zw2G!THfXMi*VO42s1AGBm|zAK|B211R8N?mz>Y`KV(Qa)#DFj{zPF1Y{s})wk25wZ z=`ZIsF|rIO()W^Z$7m_9m4X)0f^M~{Q9iL0eR}trqJGP z=b~Zei9tjB&oSIk%A0LvLC{d!*@3$`PiH({j{-Q6bg8H@a#p5{1H<%x{Xj5ukdIc8 z6UPVwfLvC#7*h!#s+||tZ10-wn$0h)`YMFx)_uSy=z7HxTxtAYHZ2+1mgmngW-V+` z$(8w(eWFrDqfOYx)%g3^hc4nQLftGbEg;QJrb9k~e@$FdZ{G$WRoVh^r&`{#4N?$9 zTD&YU@HpZV;D{iC^aaF(ZaO+T4-56`k3neyjE*_;OGU-^PqIvRM(#P20_-9b5H_tB;qe0pYLNB3dwFR<&4i+SMVya zz5yj9K-~q|n-b#olkPaC$73iah_(7Y-ep#ncPQJfWk8bh zZPAh?l>5VEEjgj}9y^qsTOXianL)9^3{?no0-u(^xi)210%d01iY@md@QzsPXxj4+_%L?s z90w(nErdjlx5^Hu=etlWA3uKl0P_)RYO4d$U)D#q$ANuN> znubR2(0xI`b2?ZnB0YTd{(Y~w*9d53tecS#Pe*%;?t5^=5tS>Aaw+&NBN2@OHaNDK z54>x)wI^VItUBA)N*Fe3;|xWh|xIzGX`@U}|ye z5V6ZEk-9E|i6WNuF0CNwEze4R7nTiq$P|38V0v)ZCv5-~5a zQ~nI>z06HBkAk*q?XrD)$^gMqfGU7nr5qynj;oHC$V~xnk#RGE4dAUgDutd<0h|M= zp=2(LoF_ssf|wmA)>D+DQytg;*Q~CI;`$XnbSG%va8?~p7>r6kZTUf z3^d4PV+tIG*)V3eIQQU{+Gs#v8W)j{6;;3F|Cf1UBBrP;=r?~oU(D!BH0M`f26C|@ z2|B&|2%BX{?XRMb5xE)xM`mx>FPBOH89aEf_zX@dfnW)uKAY&>cQVbf&g4G7K_n~9 zT0c6J_@BODbmO?U(O}Cm>E?&VWCrAj1<}TjV!b&|hFL1-ArQ2f1aB$RnUtk!soC2v zln@D8aB)=>vW`o(GWo^E##WpbQRaeSi9L?)J9g|CX-!M90bz{1<8E|%&-1@gKW@N) zDgd~wf$}Wlu)biUuc#*4QJHR{U{qQ+F3rpRUQ^41Z)-GlgYt&{7L5N&ZD)C^qU%4r z7d5DYUW%ePel|hAXROBBi`qImIj6EGPg}F*!jz}^rEEW$)j*k=G`Q@Jjb;6%0*7gn zo(6WWIS4nTw5~6cZLUKrO48y{VCe}yuh=9)qSJ+|Fa{5Rm}{Vn6h-%2lXdJ@aF_u< zVBzfipv|taX*allx-6`)V2b#}lzoHOKIPz!6ieYg+`~1qYxp^v2mssH{-H_j)RLz6 zD%s1Amlij-g16X~6R10dWabpr-`8ze4@3V2R^V%@Tx4yWbNW79D6X&OPEIBdXCqya zdDr8d(pO{<3w!$=@_8C46H;|U^(v}<%o_v(-;+TTCE#Xk?jn^nG$P-8R(fkZ|g>4C0RQo#uZ1VyN+` zQ>Radp7+g39R1?Q?yedj54yyUqVwOc!e2F~;rLnh_^%UZcx|ts<}JWTPY;{Naq3OhcO zUe^pO(z&+&72^sQfbae)uPr3WXOw=YUoE_B1#FsJ{*=)=pn(|w>kCoAx8bDzOl(qZMxi!b&`HyhgP zNnpR^pnyF`-nB%p*zL5*@V$dqrc!N`8fe(Dc1&;0r7`V(O`)gM7kKNA@CRC6=E1kSqb&D&GImg zKWub}%)x@qG&aAC42s5A0Fx}2Xw*Ck$iY>PyZ;4q41WSz+GKkA52%Imfe{;gNo0fRvaZo<- zH)TZjbO@&bKccZ5N11D5GzK-^akl&l!vl3GyKczc3NlE~5!j!lY~;dWmx+%rnokAY&=fQtK>Pgj_`ZG?sC7SxPLYpdj8!tDr6WK*NDMR4 zwuw%UZo>UeacT}Kr?#KOs(3|Bs z0uzvk<_<8a7UO?HLx z<0ycxQ0$Q}U%z`tPMAD!*@upxX~a0OVUoR)sP?Itn-fJvKuLfzK@!1F6$mC)T4lR!ky!vNcV`%vQ8bJ0(A$OM* z2(q$U&@q5nVJs+hQqhirgA4ZoCx3%A7E~?Kc;AtGMQBU_bxU+zDfB+WT96K#fOs5c zT!4nfgg_Q8@Uw8|`;###A=^clN0wsziqbjFLS`TF2*7}8chYTX-^$BJ!%b7pG~^G9 zp}=_GptekaTLB_LruU#gfMRtZc}dyCF%&7W+H z%6{~pk7(Oj@yjPos5;f`3yoT19PtToR%k)s2sTKg_=+%z;O)SoukXfpqqVdv&+V<` zMtr&oxskb5kUO6aGXe^f+_+I4KsrixUd7rbNzokkQI#_7Xyw~YlG}g=3N7a_@MgK9 z>dn7%w#59{H@LoD>(I6%J0fszP(kO|Ltz2nl^2_}hrssHYtv_s`(6wPTvO?YO< zTKgBJRrw`&BReWKk+E8k3*08(#};O&AlPEZPNd=f|Y5(8B;6VV{$OkPRUKZ z(D6-whSD|>7t?CusREP;^6hCp{0OlvIUZM$ZEoVJv|yNP!d|6K7pk=B?^FNV{o*z;>ggkSH5TG9Sc$2cfc z7;Ps+aUrXxz2wqtK-xEOCn%qa6tU?1YtJlMg!I6I`!EPj_`Y!V=4%r>W+WZr~wJTB3j8V1MmrvB{2BJuz8qc)TT{?v4@0avjOvA zR$taTvk0c{+gaoZJhDuv)Qmf%SMeF;H!z^j;^4ZQ{eBIiBVsW|15UCy1rY|tpn`|A z|Ijk)*fy}Z+)U8O-^yp2Tv|qHoy32LaEwb-ENxv;Jv0xNpvKg*KvEo`X2ecWo8P|O zdi?l@{R)`cZX(pz_c~4!6K^foAy|$}NOl!ZP`GKZU=KKTPcwewC<-^MI-41XeTZKh zHS(h+TgXVOxfkmxrxnDJ3FqF|0?rP^Shv!$JQ@s&d$o%xkwugWGM>s)N;!S{I#^V$ z^8~T<mM_uZBZYsDZ-+3o14JIn~>R1dO|CKB-Y)piAB01V>dLyq~?1Jty0z?tFBGmNd$%dZV98i zzg)>5V3$rXfD|atAJO7=&|V5X01MPcvb@3P>CmT4Uh>@KN%A>~l_C(PaAWh+pO~2O ziKr`Oo0-gn!)@0mMARYM{2OZC(Gtt zRC_T<0m#X+8R#|o?~51LZZ35t0Lg>^Z<6A3&G)sok3x^=aPMgeHJH|9sEC~r8CY1- zq&Pr!GUD<1nlHaWT+|R;>ahG)fk!O8@D1pK-xEh4vKTNz#w(ry!0e(VrBuc@RKOS{ zGO>=EHyP%8Bi=)q(7@H$UL2Kg7_W(r4zyFi+ zFjU0YxS(Mp=n&6SHBuOs?#nADUYJ^!WqK2?hMCO+2M-3_ zdQ>t^Og4bJ2-li$1XP*icacV50+AhT)|LHN=;LtERKO51vOW?rQ|Ke)8MO;4LCUX5 z!H&xkb>3~&^Nv}247;Mg6A=<8#DSk|e-xa$i$Lfsp8z8hPDh+$nH8<`t|w6Qg@%f$XEhrW+m#dV3Wh8l#Z4-@W#$5B90;2$fC?+j=-x-vtoqoNKCfYBPcl zm7mnn_(juA{_^q#5JI1mIopS-&jq6*Fk6?Rj{Q+p(7b^W1DB&q=J%%&TB{?FM-zxG-!l`(8w%!gG4Fbv8J3uXTnqAAPVb(fu?JCo8J5KcMGW|1%|7_)#Hu zWQ7UGT-FPcjJ8d-HM_(p15tkkJ2Po1t{|)C9GQ?Pgu8yD*{nLRv@1j%c$omHl;2Kk zUD;to#4!iy^JGMyukV<6V1DWjC4CB?Ki5$7>Xl@_ud97a!iKC+5Gg7jL&39W zZ1O7RfgioZh!KunR6-mRoxy`UqbMhF+$LuF?hU_4J0Pc+?Bl?PMm`%gcQsa9i2jAO zrElJTgAlj_3d*+1p9!boWJ7%D-J!5W5r?9XZck#3bbAs{*sE7t^Hed(TzLhKHS`L_ zWGe_I_EOyD2>MQnCx|iqll{S|Mvu*X=rYkn-2&a0eQQMXJvA$c2zXQQBx0Y9`94g3 ztk^yPu=7vFVvlcf{r>%SXeDGX2==`ETaGcb*hYN2`Dpy#h~(+N#MFxB0W1Ldj6P4* za^l3lMl8#nWh_lHK)Bp|Nd5ybj0l$c-l}LMls|qx3VMRlEv*--`1ZkCO+o=+v-FW* z{*|9U|16oZg5)jRq38j0x$d<7?$MbfD^ykg4trt#VfO7AOyKQ~xMsYS2}QskJ{?cl z5)Cw1AfXFywCF|Sqi9m@vYu3E3x1+ZEpqYPKwg<}V6(W4w2DVA)(Wu@4K8tAzV)UV zxVd|kmR7O61^MoDYeqQxP&rgxYqAmc=6`*Z9^E_*qRdxR7Y)a&{|*OCJog~e0ZfG^ ze>5DzH-hBPXE(MyeV$B@1*5(3nKh)R7?u`uHEma zQ8ZmfQASbkM8U)yM&w){N`avaspic102nQCUN$vxv1iVhv7U{DaN3u1a^9WU`G|** znV;VQ!w8xP*q!$2>{&iQ(t@4}C--oBHT2eLHWMFaVbORf65ruy+ug7uIm3n#Pi2p+ zJYBFc1VUVbn6MI8JV+eB;ME+&+xv%X$Dj}zVZ3#i|7CVS zLeAamHXr(+A46+X-Kmnc%PyHU)r+vt&!VYVZ#ZT)|MZ~n2Xy+}fx2ZMO_49tYxuXG z$?rcvp@jm_)p2`hK-MUbRaEh@$PcAVqUvA7C+qq#iNfD(`$Y}`!=hdFH#HEX;Mg%i zqLvh2@Zj5tW5KKV zwww7*4Ma~*r5u(#`<%T_Zor>4yATV@BosQzof!Z8&q_$^jLNF{^9@+8gBih0&;x*8 zwYij|vUd%?Dq8Sn%PL;TE%H&H)0>@yC;}+4xY1)-f+W+w)!M!%H$IN~&{?OVC05^I z(-x8_T3{Ye5B~Vc6M&ElvQ=NmEX^R14V?kBL7UM^sUwki&vlp{1 zx(8VUv5wCo4s1fyKDuA8?eZxixO&>B9KsRy7+pX1YTl6gf~8?RlKbVIyH`|v`o|Xo zeC!W?V_LE(>ASj3c?!59aX$0G?)0E$_2Cgdt~TX8S692N%04+Ioo9i?u;T5rw5m4W z9nc@6HsCUgYZVGjF{Hy7MJi<8vRFZ;Rc!3qd)W}5+Pz%kSEZ%RhLhjrpz%MwqPCBG zv-|$jLoqiB{Mkfgv#s7C&R(4&iso-Hjf>3FP}!q>3MtX^q~Vm2?Eg&aO3oyj4;!(+ zM%(0L!mvYYYZC{XD<4lu;WqQCdL)jkRRX>TfIu=hkcNzosdS6&b+UpsYAvyjsy#y2 z!W)uZn&3C`uVX6Pvp^*6gPmi zR*?CL0>6pJ6_x9lBT@eU%}!ik)m>s8N81E*Z1oIXvG2Q)Ux`w*F(&1MeKOBfErk(E z|D$pH#_P#z0k~pS1a~(vHmvN;cjf`!MUE^skf_t(S(VdCb$_A&w%=ox6jOa~AFjNIg91ls+P<4(_)Z8c0}F+IL&THhY~O*dMAIPK$uAn4RWz zmjf$xcBS?ovE8vnKXtgf@rUfOjF96M)-=fy{3S(CsIgB$pH)MSF~$Ra`( z^9uubI}qw(gUDL($mCDca0fz{2%n+>zC_m`>i~gib}!kBq3$ydc?$PKoX#m1nzwEp zNBM&SM?ZHy3jPf8DgBoNhUu6s^c)>gI$U(^WH^W%?(gK}-z(5)LKKhL_C$5;ndoF^ zClflT^KOtNg}D))9|faqCgr(FOAhm<<&4VO4lgfX?Y542OZ_t0)!B~&vX`4Wl=J>F&xf%~>4e=E^(O0zxi)`}U5 z1`37ENONb3ZJ5%~R(qCX12|cRERPNN@Z~?<<7JrLxzZ60&N2NqNX62Z_uBl5B30;7sVV? zoixTLyzMmuO+?Oy&g$}>l<^do^l5+agliWRQX-54^kYoT(r<{!?OSZ?8d!#mw%y-c z-`ZE-Srn{1eva`**fY_hZ)(v_tMs_R0X`j2?`~)b9`PSEX804xoCCx|uc_`5vFIBh z%cz#GUcUTtOASjEXU?AwIN6s-!UECG;XHkeH$o0$mZ#NiP} zm%TXqsWD|y2Ab%eQ_fZ{%+*KHh~8L6SwLW@DrC||b^yaygW2V1EUg9G5~B~OW|`>( zX?O!NA~R-`fqhYC8|?EAs6G#+6hpNvgLOcA@Q#&FXOj8AkKXXygeU@urP1C=6*L59Si5(q2(Xq3S3M?ORp z2{rD;76*DFOQPP1-XWl$r#p^(gW```5@t%K(^r4f!afvY6M^rso+dJ<=VgF9>;e87 ziw@Okf{~OX8S)8NeXU&_PYj_63jp32M;QDtp!AG$qVqN$qf`L!7o%yXv4MWo=KBy_ zo;fTE8s}f$f7lmQ$i%XsrFi8ruy_3ANx|5_IAa|Tv5@;Z#KSt##qIfT>2%sU`{l}Z zi$DHwnY#rt4sGQmPIj=~S87(7PBsAq$u>1;i8-thL626j86K4wJ|(!E(BYDTb!APx z4IFX&#EC|iMvlX+Gk0kO*2a9m9@M-XDVujQR8;D5;|PDdC=E#=kH9czyde^5LUp|9 z=R^WpOrw#|8q5H}c-^N?rNFyRuS`L5YxxZ}bMj_V_$C;N0#hrisIZgjAf++XG%LSt zNIGiX-&9o6MV%|1KkqFjDJfTwu*0<&bZ=p5RJulhR|F_B%gbPwE&5MvA{tRJ%RwYs zP)v0eu`mNYd~WP+hIG0;ru%u;AGcvOa@#YMj?yQlY0UA(B5cE3&2RtsXP=~(Y$F9r z-kKv=qO&S;Iw7ZM#W7B{0Z}8q^gBE?Mq9%P+Cm=!>&PLezGBVur>Mk35jnf7-ow5a*-2wssRZehbU%7aIJ%CBuQ-z=MYm zH)rq0Wy+SjT#WYGodw0`)CYuz`;!?SdUgdp7Ry{)JG*;`8|X`pt)FKTJ+7x}PR%y# zj}YayWSt3j=K$Te($1x6X=x(L&u}v$a&H3E?dSB5UhlT;xBDTMP)|rm(0Vw_$7iU| zkCG1Dh?#JVkbq5OoL@~L+dmIg8L2AySwt&FYbqnW9R&rJIQr;iw-i~eKFaPFZbq%? zM?zv=jr&ukxz}JXAlR2Y1h%A6$2TI%XCmX9J9>m_mo7U&^c5@S9Z$4V?K@=1MUSeC z*m0BeVF`MabwO3tf)qt*@p9k7O9@NvSyc* zl!%y-=hIkxgmGdb;+xyrDixsqY{MI-Zxw4%X^rK%2;vPNB?T8PW4{~G4&i}J4PVL4 zZ3%kve|)_OJeGUA{(U!>CPgJpk|m^p&@34vN+Oy~4K!=D8a0s6NT@VZ6pB(Q%}9w_ zN=a!Yp-D-zq~ZOZJb^U(laGb|+oJ<@>OrQP-u<`h6+vnu* zZ91)~au*k0Cz_M28np^O=yYo6^aTSuul#sx0D7Z9!0oRqO~w0(jv(gLsmM7sp!zrW z3{oMkGgR#)Z~Kz}3QUq)V6ptQGAZTCi~-+nuQQKXsfB5xtVqj-com3Xb8WWgQ+juy z5_6^(0DCsB-TUE#-}i5?Q(Ct7*if)Ea{G1>@F+?)G@V|zKypep^=nA^72eU6rwi0C zOqCY~93fCTbBV|;t4+-w%t^}~J8^ddB(2}SmTy(8k<$YMqx#bin6R?iiGdr>NurRC zmYbXyM{f~j(0V?ts8~l%hHuz}r&wR$X%7mZkz>Z#Km){Jwd=0CeU$H(gL@0(Pqjnio!=95EsL#iM>L#Vd3 zfrUZl%R1}~-HoLUCI%~FU8NJ9gsvz^JU%HUPsm_9d^1K*`m-?ks<%{TQ;kxj}E8~f%N>gs9SG?OzPE- zGa5pLkR^?ef?ONKbjNy2XQ^;-T5JgIN`P0O4!Q_4&0#r8i*+IsMIHhseT7DC0baL? z`6(&itd;pPzjyS3mmVO+P~Td^4rb$l>+2ic zEo(w(tip-H^1C=*a<@0Hdk}ybW)^`NJSUj%zBcb!8Lw8W>OEg}A?*`7^TwR}Cfp37 zBM7n5k|j0IlTCvPju1G-7RFNjN&2Fm*iZ;&sPlNLetR!kXz>-6Xvu4rFi)1AmpO49 z?=tAaDr&Q}+~h1q0n3&xlkopwjtNYy%|x)`7l$tNhrjieQ!Qi%&S;o@gUdg@bRvn| zzj^u~=fQ4N+?``PSjc*nY49_rn>kLCCvW0I9^{Q|p3=^)`I@|Eo&csxk(0}VAjvZx z_F|sOe65~>F+qdXKR$O*xHAs}Jed2p-Z-6Z`VPhh`JN}B7CRCbpYp)f*i8X+y^)^y z&f30lRvAwh2mu$*OqDq%omQH516vd#yc}ML0IWvr$OUqKU_LgQ9H2t(>MlNdZkJd2 z2Zl7=sS_r#3T>X82O^s+a#Rkrw7a|rR2@c6+xlz8pzIny)9XF4se|dg<#&R&6#_*)Hs_OB9%T|;c)d4bB{{C@{;tHSPJ?%}@<=sTC{NcYkmz0#8WhT$JJ3jC6 zU?>?%)Hog61z&6ekIRE>BwVk8f`a4zb${`U%{0SH!5k%{$(WzQQ|!tlXv5Upb-q~D zF5*B!LMTb3oKlyTQFi5u^*IqCv6tta$d!16<+vOP2*OqV=IhdC%b~gQ0wO*+=c4U&~ulbt)A7mb{&u z=y%xBRD)`ez9SHViqR4mok;a)ia}vXI5`Ed9_02*_sHc2fQQzbBlCFWutBhkR_Ci{ zQ%s(~R7tF7akc0>c*$G)w2O509F}0x1(kVcvWV}ga};|BmKy7~Zw?`M^M_6xd#4Ue z61I*R{zDZez}M%y=L;yW9`vMbXhZTyHcf=1n_JV;xr5V~dS>S4CWiVt_{@S6XTbOs z2)i4AuV674Su{gp*XU8ZpbrKHh$bXC5J;{OY2N*)c_JQ+;<6(zgnkICB0MNm2=iHt z({`WF{ar*}aHV0-$j?tnPa08srF6kSw7|<3xdxKSb*k%aep@ye^iuLPu$%14#X?o` zF%X+PWf^~x7pz+)$HV7XJS%jE`NM^ zztN{<<3A62eEV~q_2i>HNIcs9(c%a$|n=l_#qnp*^ld+zkM$EPZ@*2}O<* zCvE_&T3P#Shl5KayXI2#Q$Sq@%MsT!Mz6+8qW=A2N=AW;h-wP3rz^?D8hRAXBBlfZ zB4)P!Ea5{zIX;Z|6}D}gd93TGI498i`n*5NktSMgn@JxAa`2Rt*?33Z+~<;>?CzFP zBr}YcX}{E?vf2T`=kw(D|8*WX7LkK*SKN1?bR(D=f6jL*QK>B7?C|gNQac@yV-8gHlU zd2-|b<9jbJ@UDLX?t_%E^yf?=vVc-=d}E#iskMm>Von)>dx>4g zzrNI|k#fNZf2CBiYDTk^6>F}SdjQ&sO(?^|UFqxp$MOHy7b)mx6l(T{nEI9eW$e_c zqZ_(xGSeS2q!S%u7U=l<><-h6tQJ>_Yn|lnLve8^Y8w#a8f?st)in6;>lEWy(w_(n zF1v+hZ3JhTA%VOSSVz0h`~H8w^8urCW^W{Y0C+<=e@E#pe!O3w{Q7BQx@5_cV9Z(= z9~Z?o@?_Zhm51J4RcWlVbHlD%gW*yj=uP~)tY3^{Q+|?~sakD~ouhOkx7J_YZLH4c z;1U~EyB{$%ZGxOuzx22CX$56TAU?A*|WBv+DF41*|C{gw>2_SV#K0L|f1 zM=}rTuA_6L-t5saE5F{Z3o%a^{)vlJ+4|yEoycz%)jjMMNzIKay82>=;Ck|F>i_GH zA+K!1(9M06o0AVIfMX6HcC9i&U`FKfeUo^vnd$zmKuiZnJZ`6EwZVqI74vDlhuSDK z!beu5rF^@j%@8*zQPLPnvKTBOK1v`Ry|Zf(+a zt4&GsAoB(3n}665h=+e@E-|p7~b*g)QTJAI9$hXx- zf1Z!2{5kR4=MOQRZ7e>wL><(doV|9W0)_xV3ny`ngq+H4)o?{xWf0ar^d* zxVSU3Be!P<`#nDMr~0?ry6&4Bxap4%EIb*Lzd!NRr;q;>>~G%9?(!5D=RsSZ5551~ zcbaKhU21w|&56hJ?s5 z$h$A=pCBQZ(~dwik&Y9v0S6KGDfXKta74`q-DN#$T^`Zg*Qb3{BQYk=l+A7B< zee%jL4`&JxVX3(m{1(#mMg*}#m%dX(MMXjKoBg)(|M#V+hq8GF!}>M(FN)3`!zc`& z$6W`;t8UQb()2enl6ASiorL;%PvzO|f`>J}Tb11vS`k?2Xw;sC`e z8+TX(WJ<-)F}lZ6Qd%RR68u0jJVfd0f69q&y`2(NnK1>PJ2#jT^zJ`}|DWTcsuVXi zCoiwT{`~9UK&546PUUHwNkBHA*j;0FJ`fCBC`fA38_-bqpbO;EK(esm>pp$;>Y=L3 zk^XZ`cI$7<_T)neLlMPKAl9^CYzhU#$Pwg9YSA&-9=~JlJ+slHTR~}53bhOXrp_oX zK9`ER{CA`-Sjw{$=lxq#S}Mg&+-0e8t@P>B zdaYY;jsJe5aZuLND+L8@1>pdmp;CDF>hONdVq4KzS&{ld=o_nT?<`l{j!CjL zcT#bH!76AYTn6j@MH|dxj2Z$EzNCXj7kT3xwB1-B4vb9aUE{g@_lNnftGDukQS>%e zqI9GhcR%4Kot}ZmN6-U4!U1#vgcCCO$T+9tZu&uUqdyP-`LzeHFpzS>lN`+>4aS^G zYE4`OOtQxQt_DB+q)f5f4#$3E@eG>TpII|bj&*cYDBO^QiscDdYkJ+j|N0RBDk1;% zmJ3cDpZ(;}TjKxINAoXJuQmd?!j-ZCB2P+U);YGN80eug@>T{_9>8-qKrVbj+Th)m z!KaGckfjLw3Z{a0c6mA-vj5?OdyzTLBnGJy3Ki~0D3Xx+%xO_>2< zRj#ghhkJ>IR$KPQ?^|E^8(I0Eg@rsqA8YmA0d6pJWIyJ;d(J!^ZX1IV3%#h=0|yj< zi-LpFN(aI#Vz3r-A~7}+Al99WAXJY_Kflce^hPYG5ELWj^3M$^m%b?>;x>L;elp*5 zc5LzQ;`+nWeoVT-54vz8rulz%S>zhbEAA(lE@{+u*9}|@oL{dQ@xNRUKITgGKLU1I zYG%yb@4ud_%+ORI5}n??t!B*7X4#4KazHgP93d=@Wjk7@gX(tSc5%uZvt?%=sHUtT zai@8NETFacBiC!~+^r zIV0=E6&zOglMlgzWgh}yNj>n_K07v2Ey-?Ym{1vXU+jht0^V7h=f7gth7B9~VGITH zvlEDr1*qW-)8Am6JiOdVxFByuo63u%cQ#tG_vHPTUsYZ! zE*Tg7x-G|0qQ9T#Eo|3{c-2kz|Ny1 z3QxqbS0I=Yml=q-;n7-NW3sX9(`vy26i`hp!L5NCKCIPO`hN=<{r<-JCL`?h&;)1yF&Oa`K|4AoR3faCH?USV)W$hx@(Eq+~{JPcjvzt%c zYfQ zzL>M*&t=!BOUI8ZLzH9gs*!TjP-Sf@EXsH9-*?AmEBtxf>eies%6fAQ6;n^16ilN1 z#zxd0PE?ei|BkJ~sixxjb4{kA#O|D%HyO-m*Y*&~wd1`vkCzJ$&PbxJE0 zm5BAGf9BdqQ=a~O=eF?h?n`>N@6h33Y^-^*b5FNDOob`WM-CsE28MLwb9iDem_QYm z^!oO-88M>XhmRklwg&{%53$rFkibxYVQUf7?=^#X%(|OnjEH!4z%w6y?rs{58a0wG z2x_%q|Nbm9XhuFRc+MZnZ6}#ot1aeJ40)X2MopMevSY^%3;p);msZ;w7jyeGm_Jvo zbF1LU#OgtvTTu}8<8ldF4Z+~n%IBu@4ms&|OreiUoecNw_-GwfU=s_?aOV{*L$W=A z|1r~Pk5~*YKwv_G&&jRBh#?$H!C0tA8+F<+$K$@wtXZ;=n;|7qrsg9^(T6Kscsg-QoXS@xs=B7^5cUF>=`8q8URCdWV$*lo8FI zJ#9wZzF{9`tOcAYbBF+gwPtI;yeV+w?^l~H`_PcOQdU{iQ($<_>0HY~-(|)|M(f=4 zt!-_a5qZ$6#QymZff6<5kC)jkC|bcrdR9lzG%GAhe2sAD%8QRTDrOgEt{m^^XlCiY zNy9$s&+N{vngFx=#Tsy=88~Rx&%1Ris#pEBX3xsUySmzdYqmi>z))}K%CE|3cOoBM zyLqz(EF)}>0^{PQZ+;QH%mo2+#C&;)aQ0T@t2#K`YX5j^?1fHPX)tAL`0mR@oj!f6 z88BV^k`N!?7##|X1DUUIKrJi6_U)Vc;s+ok2S_E*!2-H+ea^9_+DRDl**Q5)_1lk1 zKK^h{Pz}w8(%jHFE3)*_oo4Z~LZAgJ4<>MuBwk*$in%b8ib_15@NyQr4)#*E2(>Ku zr_d>s$*h3j=c_MSK*csdu)p%#tHTRe&@*>zj~>cRrGTK=3tKfABG18}FhR82Y<&3O zLD+P>*WcBCSKnx+v4(*FCh1Yz6Gt0qhBsw)$;N8UKL!t02jxtx=|35|9?!wj=-LHF z;bMa>=q{iN_3)%8#;xa8y%thwEWR%a&A{6ANqW_2$FvC!n0u*wW}` z(i_8%xdTgbHHZA=ef#d+Ku8nlsG?RK9u#I5$8ooYaYjUQ8QeE_&x@}sFYW1n)J#LM zF?*Yex8C_8EC4c|`Hcnpq~H#*jP>17kEzeL%#h(pmx>AtgYeMfLByn_Jb9C^0k}~X z`A3x9>+h3aEEs61ACE84_RpU`W8Y^xr!g<7qL7!x6NQ?vteSN$j>`JGqoZ$ZUY*oF zeoV&AP-j=YUcI=(88c4K?wwmae0oscv)n;nU8mf?eS6lIv{zHYM{0pxUv1xG(jlh= zrzV3RPUo@*d2P3gJ46^uo$S(Y(%!J`K4WH|d_?wAW#BhSt?sOw{&tn;iI9IT&fEZO z7jSY@YApCw7cfB^lhe{B83d{*JA++fa;qMO~e7!ik zH5u=j|G_)^PKhZWXiBHqHH&GSw*`|qC&|(mBqCTvD$rG5gBB3lMm9C$1F-hkvAO+= z&iUlrx@E-{brkV3LxKp{oLPh4Bj(VaXwuXSJlE}+d5CI`z6*WnTG|58z3#nxw}iDK zH32%N+|R z#Tp?2p4dtFVZ%kIm-gdyZi&6RGPnnWKxX&5BJm>{-0?+XtGC`wLS3GGTsqX**;&W( zkS@6fYKP>zy?c+n>jc-p`+z&UlN&2DyCwI^_?CSAj`1*Z=FY?#8TRArpzk`yCCD6U zkeactssO+kFyuCiGi?v}_^icf2bnHJE5kksbk;+7-8l5PR{M?N`}Y0(+gyz|YM9_u zIBR_2n)u}8Ey53gUm*CkJUmTJb~1(Ho#eNdCh}y)*8O?U^fn*wISFXNea4K&SdOyU z`>xT0-aBvJw2!L^r`kv_+1aec!`Ziyx~Y{WwkJk=_EXuIE&8@s%?;uz>vk!P8~)Jw z=^T?uqmSpQ&e#iVz>}V>&!~{5RxZiHg$5r!dJPWSs$n&2+oz*k z!c9)zwCwS8!N7{B3EM|%NqxafHZnGr3Zd)Po&!4eIIrI}RG#miVJq5`NIlq{Z9(>1ArWmGhib3(ZxVUFchDVD}fBH{z@DEHd0$@>=@<6DG#I|?A z%iGktY#sJ~dC{p4Hw&1;lll z0|vNyOyV-i(mK&}0dUXWlo*E!uJau~GCp2UKq%UeB zgb6w#p;;1DJ3~=Zuq{`?utrKp7%p|CE%!&ZZU7cWc|-dzz7I&HZ7>hawA`(ak-b%V^aQp3ndm7L8GJP@c6yiR-#a98RGF(qDQS>ct*O?Zz| z#q=V5D^=5;<$N4(Vi!P#a=IvTq?S2Qo}r;370c)r?xb)sDR2Qh(L)0cU!=-Ct!rQJ z*^l;oz->-WU(~3nr2E1;MoJP0DB+L#o}B#obZV;8-^Zt(?sx5v@R82P9GyB+g$cna zEp7bBYM|+sGiSc=J=nofeLpG1+`(bqZW<)8)W!xM6S-MiQ93a^t7?H;w85pD3D6uHgBqI0aK-Sc217Ftg^lF&_`VV9GWx%RN6KG@`j8BB*Dmu>^B?(l_5 zH2_S>9pQlBq+yF56`X(y$VD)K4AC^m95de55H&>WMY%7!M*ho~S4%h`cUW1IP6#}x z+$`M}ZgWf6JfS+wmwvcw!%=W5T@CKGx#-x9#ne zF9WiWFwCd{F$g(YYI*IMpJX$NpKbp4oat}cMCWc8om4!UD4d?+JuNRcx8iXU0u6;*D0m%msV>jZ``tF`=TpeC)|vc7A09b zMz&k6Gien+B$p?r;^Z7$!q$$|()8Slb~)&q!Dy{b3pOqLdz?;hsnRvwDC=MnbuSup zH#n?I>;8H6pGG|Inv@KVT=1oc4>$Vs7te=^dVmh|E?M7CaL^1t5E~mX^o6g=qfTmS zW~{+@Taxm(!2vz{6}@K<=RP;ypAWBbmt9Bw#LSS}-UlsGAI@p?V$h5UX}@lK@3wfv zgbgdSm4=M#`*3jXCBuNB+HG~bRCf5M96sEnRj|e13mvQ`81ZeT#wLE zy%aGeaxGI`iDLe7016`c?^gge9G{i)OCE0AKG!7SCaZlj!i^EPCy`dK@Z^%d=<>co*LN2{Ys zl!uJ3Clp%u7&c5eo{odRHY)i`xvHuPHF9H|C4^~!1Af@d_Qc#}f75Hw&+MFLc15>Aik5cMWB&Xnw}Z#T z+}kb59GfTje8Of+`aCQ3^ywgKoHl_50sTA{)#-I$yQQ#=Tt zS*BP+hYbsjxe$--OsoF=PcAXt@;v^)0kiJ0TMR8Myl5O2!ttV*y;5AP4v*Qpz6?Yl zi*26TWIEsc0FVwuicx`YcQhL_&CPA3E!U?QW!C%jz!`N9MVv;VNjANMqnNx@N+P3JMbL*|x;i>naEN^eV%Nji*rj{#DTSXV zP;f(2=n()#_LTZTrt%NmK`KNa&lIdd@7~864{vw4PfwjR|unz*PF6FkuBHi4g6g8^pbpncA3&qNqQcT4M@f2_4M+8!OrX4!$7;G~+392tMmr(nh?;UvY*!7#CP-Z9*|8AIM ztyg!0Lo%uICa&w}q8A00x7EJIwd>ch`U-#03Pps(|ISmQXox8@8^I0>8oAA-XL*#z ztXUxpQzCevJFJ(zH5yQV?Hc>DuI&yn{63hPy3O*wb9*$bJHaG^?`fH(BY?C*&%Gr=VNqsV^A|CCCw+V*$aeD>p`iBLMbj;aKOhl_9mSt zB&NN$|72>dxZl=&-N{W0^al*s@!*_y(7u~1)|#269c$Hoqm`i>^&_}m_`+Z}eeJTE zO2VIPs^gOfaiQ|sU8{9YbMQ7SJF@&js-`Xl?VMhl&L&kFew;eqtXHpwb&l?DUeo{Z z2rE`6PxKC2?{e_-xZt<(GjYna?3{*~CQA5jl*Sy2g!k&7KYVcVE$Yy;p(q>)RhaTO zBVVr{I9dGih1Ew+cS)NjGajo!-K-2}PA@yoyvCC*uABF|e$&Ozfy3(k;Q@75Q%pzx zAxt%v7uWiDQjY-xI^ZO6omrP{aZqAOJ(r?p4Fi^#ETBj)SUA(t>t-sddt2T3W56!a`hm?6=nzkbs5YoWt7Y&$c!gXx=}NtDj5Fmdbib#H;zQAV`Qts z2qYWk&y)0$Tg`UgnwOUG8SAbS*gW9EYWcK3kdS8klc}kid5Fox6esp+O-qyA4x8*< zTCON$Ok|teOZ)W6qYreL#qxhzk1Kfr!gNO@*p=Lp9C2XRhk3HWXlamLoRez1c7O{G zaep>M!Wgz9Kcp6~BpdCHr1+4L-Lbdx=tlL6K2hke^i*rVe_x5L2g^St_&?r5i1wPQ zUQRc1ls-=9ZPdr2=?WZm?l@R)k34(CsB;zsPWS(EI3dBda(p{VHhKX}*CN>KH6peb zEU|z&v#Dx`@mbilFq{ya@#&j4_H_Y~Th~+DdhqXz5kn?lZRc{g;0B;gE0Vg*a5*EV z{@_nQN}6=gez`;=xJt5GL|i4QBGdCffBF;>+&`jZ)3W&<9wTCP@p0~fz4)fZ(f0!A zc*RaYAJf4@`)2A;=>1w{kXmhDGJocN{I#TC$%s;z-cVFwKwq;<~5Aw|kd zKfn6#Oq}2?|B#oIItHwlBj6!KHAusf`2Y@ulOeL7;kmQYXx~I1)n4E2_JKOk*1)8s zUJ$R@E1;^FUuVcW_lrFNJ6!orF|*S<1kAyk53(X@qm`y<-Pf-=EBh!n9eHoUL)RID zK$SGZNBXRLkO-p6Bp{YhYT4pGGDmztF0%q>i4`l=hR*KPHpcPm==uf|NP2XyK6M?N zSUt=fI6Bl)GtTqR=fgf$^15QnT&(C$nhuK$y$HU5D1MY3yk)WAV0vB3q*tbL@9&No zj#A?E=x2oq9oCqc zD38Def?vq_$H`$rOZBp)(Hi0!1mm~57aR=BC2Vb9&#uiO)fPIcWoMqOn!T=gY~*hq zDfzbkyT3+Pg7+qU9&*}BW9GFkJbW>VBBD!35Mrs^M+ph&+Sz7PPrOf>xBAWNE37ST zfZAbGveP$m}rSEu}5v^)vj98!^!fmjYFxPnD6C!)|YoO4!YZ!6=5SmY+h#$vk zLS_^)E{CK7D|=t#mIF#A4|*}&C_f@TKKR!P>3*j;+}&YclJ@S9(y%giY7&}vjmGLoYX3oU9t&b!DUL3wBB zfX1zY8{l4~x!h}N((*0NJ3M3c66X}LL|J4_fX!DCYNAzK$oOuokDk)Cx_!EIe>8?! zn2<;j$55oftfeX1F3FddT(n-~=jYq<@zKPYrxUA2p#=H)?R8%sk7DVNeh~fpzt{9G z*e=a6FrV|e){Jy4Gdb-_x+5b)V_e!T>8G2*lP56(M7AXHqISypakypG!l`#_?-myR z5jb=D0cK~yM zct`eM%9xc?BLrN~knT*AhBjc^Z_X0xOQ7Wz@Gk|BzI|J{apT5}h612z>B9EFyKv?F zx=D%m&l~KsX=!Wm>#s#!M~#z8_{X=!0v|E>(|V1WM2K+QP@w^xXiYba&>odW%*=7H zNA6Xq_cD3Zw|3J z7ht7}_`|3F&PX-t`3EOwwnhi3R62M6E}9}?-w3!X(f|?nq2K{a*llf>|NmnD0uv{y$AdYNOazIm)`;=ClK)S9UdM+TcZnRv_thX&hDw>5jd+P(7( zrkx?~X7~TnnLP@Zk#Wm^fCvQrb#QRWA9nT@u{T#8Y~j*nwTr`@L%E-0D^F*-ySpFo z8{+25SrhJ#0A>vP^fo%<6m{o>mkn5J_|Z{RS>Vd=(061~Pe47(f+%nTL0IDskBp70 zU!OgcymI9ANo4TZ6RlkC_G#a)UBaooiFZzQ^82T0PED|M&*p~UPOtmh@}ZK4C%4c( zw^MJl|C-9Ggv6?|iFM<$JvtuwIgsQP(i@XEH9#W2ziR1Rp($ly>qHe&u>#7R-jC#? zJ+_!>n6c-c6tVEb^nI?Ue{^e+=xs6U&CKS{eu>5?MCB@-%2v$jb%*_8PM|0$*^dk7 zRdjwre-+Vo%%$_^SDX&8<$8-QQW#2Tbi~(Ei1Gl-yVYTho*LX}8*3zvT zO?oTWJ;?S@Eco`xN6s1dS0Er(26Zwz?FzP4w0&sC&5 z)X@To6Xtgu9bjmf2}ivj>=(G1aD6nWG`JHvH*SnzCy2~pA|fmbRKd^FM;g+SY0aH5 zx68bTTZTmVno#sHHPR|}U`m20B}C^bk-B0Ct-h9jH0`IH_8~*gH*V+oe(6xrLBULJ z2p$5Nz`%r&Ph6F?l~p6rAtL#37q%1@OYjwo-52}Xvv;=lOh-dw;~;Rl(B$~Uv1aW+ zvLs((kY4Y~q!W*kNW6QtV_L3NI<3D2Gzr^k+7GMZI$A?-Z||^?e}R6SUG!p0=6tek zn|d~j&0BHFA!X#oJ<0j|Z);(c@e}Uf-*@Is!+!nDIMa917tKVE5Ceg_w|2X|xHMvM zDr+8OA_QOZT}{n(1|lInKVc~@rrSuYWXadNFJIOpkL;o|K*1l4njH)bFFVALpc;-H zzxUk;lS+*Ubz$-Nq3G`dB7?t@CEw7)-Q96+!){bev8Jm&+~|2I-)a1K>*5ET`m)nE zm}onID29J(#O|RU!Gxphyns|`s z4iI&$n8)2XM|DcGv#s1y=#4q&ikZ&`6V3sA1$x7PvKb!;4o%9U#4dJuWj}w^0l6w| z=~>+aF`V3kX{&z(0n#hxKMH5$=!rQMF4Idk4zjjZbW%~VD83Ff%JfeI>*RTlnEpv; ztOlV1@l|4%nv2Z|Cw^^?&`JoSR%C-B`{G2li~VjWVhXaKs5ND(r6$Yuxn3 zeBs8@xWSCrLmKSB=q*(Fd0AO~I*@hW5=B%3x+3dIdTeR3c&+U?t=VV503i?e$Un)G zxTpR(R*!xXJ-7GK1{bPMa;`HTK1?t9yD=g3?2_uPBK?Jh(d&@r0232yFpsB93{Z|@j(Pi6^$F*8s|>$(|F5*7fbC=AuD-TG#1$UukE(_dmGX8F3k7O zJ)dQny_w8zGH}Mw#%6trp?wbGsmsX#O=?{A5#~eNHg+9*KO1C~&=GJ}A|1p7@+@Sc zHTdQ&OvReMAr!1OYrtvSX$lWoG`n<+A^0+*Z=cV*v>ccBn~? zn`K9c0~|5H8|}62c5d6TIul`dVM;D5!rnbAU9#|DjE0F&LMv72+E& zoSu+Ptl3kvjlA*xgGWhH=K1A^`+ueilhuZ|!SV$%yz(oqn+&r6mjug0e!yfi;^hEF z#Xaf}0$b45hR!<5goF{p*oU2xwAG$k&P5Db&a;$#(m!iobdyYmp*vQyt1iC2{lI(r z^cyWpVH^q|MkuMLV3cm_yM>IO@%P_v_ddErg(jv4GS&JQWuAeZ%pc;cS26LH>Jdx4 zS3d=qjgAf#c0qhRl%rrZGpBLm-ZN$>>*6};-i>0o^^63qNI0OJ3o%F*roxp;83zf! znA|L3ys~-kzXZ+RJXA#mR(Q5pg8rstFL=4xBzAruuLoYmV?1rz`qr&mZ75)&1-bLp zAF!Vd1!pS?)}Z$W!C4tn4B-VzyYJ3A3`Sr-n?!6&L0Kb_2iCqqfhCj3L(#T|s%^4u zH0{Zs7m@hLXcm#>LLkz%2{1>)4K}&yujel`9+sM8|n~y9Nkw|x7QOnEIul0kxQfR%3IfAVB)!qCU+zcz!FFnIYhAiq_u9cxb6HvWZ zujZhd+aFzqQh|a*VZ^h$8av4!yR>0s#aD{63IM!)#{(o^D5z32k zhDhkeo(5pw9XF`2XoY0=pICKJ?MQJ%%MH^3vUCgxQJ)=2@AidGeEi_WX#Ms*06I7# z|Kh^7r4namyXDj=qqZJ#gVmlMG!)hdd>=a9m85%UdOzHYFI+1Fcw;klxQcmy6Sw$V zFqLNA8_XRRH7$%jzt>FmxqdDsV-_qpK6yn7_&HZBefGq{Bi^}J;RQr5)>X)dYmg1@@rKuXG1ugg-PhOdc^bSekGXRl_9#U!rT}L6sb$zdVv3l3$XI`v%IBeY zG*GJPS@}qd{l$hM`iH4dZ>i%Dfd4W2EMZLM8U5JbEE_ia_%DWiLAD9G_(Dpn%U&=p z=!1weqG_P&7uy8aKs)QDw8iI1$)uu4*v9%P8dkp?HRiV~SE;H7Z$Zjdi>}FY@67jEwH5OxwC= zPiJX`op0OGFX+7+(fq2Sl-5Fq1xR$yZ?*2M9X|56sGA<8i2}FK)_j1ZGr`XRvQNx^ zVhU;-9$KFhDHa_ZUm1xL3Q-!5ONu!%2#Tp@S^McKr)iOdFOKQqMpO+F`KX7603EHt zi0BI1fTu&KB_6P>TXrmvvjj}-KjY>Vj0^(#&{)y-NSbFdp1Bp^_&O;Hm$DTU%X|Lu*<0d$LgpYDy@k$7* zj=*Y4$%$9ZL=U4_1JjaPXFZccU5Bdf=SMp@w8U5BI;Co8JzqS?b{fxa&x~i>i^0s6 zZZzwdq<#!waAd;^*j_3xbX~acM8M9#yk~72tcO5|XS6#L6hb|ye`g*LF-2Ti@G=IW zl8sKRAlBp})89yy1&gS1s+{YIUjg3^)C4T1*Fju6;3-KQJZYf-aA1j5FK?SJT+uaMvN3RSt<<{Q^V9bI5{zP z`%MwaQ7L-fKEo~wJfYrkmMzANNqIQVm+4eFVxt|SKIUGTaT4bDM$$;;jT_BqNk!Fy zi&EY4&mUonqe^Z$`Qq|+qCW>Xk=h=U!$LJ+{8A9Is1308#5`^zOa+=#m8s zt{}&e6~Uwsy_G5FZa~HjAicI7kpu^(`?>LuNwAvu4<&KgN9(^QC=}#>F^|%ScL(s& zEbFV#+T}KNYS&jY`~T=yT6Jgxg%T1ZXS+_>A@jv(g4~j{r+8#X=*Duz_=4{!356gc zrJQJwq-3Q_aZw);jCBdQMD}EJh=agSyK8FJrz$mP==lyefrGU0i<66jg@DWNXoOxe z>h3EBtYsj{SuA6y9M)_+>rpsZLVjUHctN{o&!XjfJ9g?6q53K{=++FCCVjLu7eN&Y z{jvw*;JOGpR#LmPnz)Mf^Ss(LF1*Js0QTR+Hpb6FohH&+{WUaRe{P(!-XF2!;`_bc z>3li$wRchf*O2#xi7uVpznR7+=Evu(vn0fp%@V?nv{`wVvO;X64`NcUp{;HG>aU?) zTl3G(%Pxb}v@A0l*M%Flla(wIf&`=lH=>WW;d4p^APL1%n0|e8GM`nHSC@1hvxKoT zm1`CZ9AV!G7<2jOw_dA$e0CLq25Vvtwa(NZBJ?|haXN|Q*Z|JNKNps%aM>eY52!c4 zDhqI%Ael+E!bapR06v0uG*kkSV?Mhc&MU#HU2}ypsdO-({Q(u4^Fj914dbq0bab2a7W9}T7wRd zvmjtb?N+H+Fi_~+INjZWxIy$?KeR8M{79{DY%ziY6TWTtZXpv3M-ypP2FOT13~hIv zKHNyi<+ME_fTsEt?nmSo3mi00;IYUeg9b|dl9Fv8cgtoSQB3h0#GMN+hvCs4OOSuEm1R3bx=!4gZm{nEp z*%oUs>qg1>#FB2+e%*|XHb36O1e2Q(_U@+m@&PyTm1O2DJGWldKr~yx-a%37L2tUk zhqj$Uc-|rbfV~Di8klSEa+_=PXF~&*mX8vbC)jGWE!*3!eS7PxP08VQXNro7dS=FQ zpJW&~t0=ik?S<~zFF}<$g`}kCZ&UbsOyO%Qm#m1y?hmjAc~@~mg%NK9oVf{B|OZ4i8y}d{p z3ggv$b&V=MR!!M&Bg)TXHD?oRwj{oaPuy*mxFNBziOc@^iD}bQs}djxB;fVB0s7cF zkS@)C^!xPLJ}LiD1O#v#saC(ZF%M97+XpDTEzo0Gj>Np9A*9Bry?W}oy~ff*>FPEU zE)yT-J*E^ic##9W74v^3A)xgn)d=b42}ikPDGT=HeJ!A!Oh9F^s*2QuOh@l0N?G$$a}$d7^F@lO!=ua56CP*|jtCkRv1# zk#N#pdU|=a>DFz`b_Ub+E_A6T1-1l`h&bPKSJq1OIAXXYOR(vfFogI6Zkx=6XzU_v zF%hm&Y)AglLr-aQO1kE47rQ(szQRjrdlGvAPNtQ3`@@sH5QLh`II<3rO#p?Q`DNLEG5O@pnFI!{ zxr0^SJEjeXIZke9&0dWWF;k5C^>eH>{r3H1{_Kn6U#pC;??@68G*T)KB2UEZeOXUM zwJ-Z~Bo+{}lPVlDzbC_Z6E`V{AexMqimT`-LYD}SAA%67=9Flx5X#1QdKOZbD zdT|GE>sgwRn0UEuDj*jjxM={Rpu!&@3`%^(J};hGNlA&8Q`*Ig7yntDG0@V`KbgM6 za9Y6w2++(A9sB&yA4ju{@DqK0PLi31M!>2W!xUwf+I6|A&;1cPKH(+lX} zhDr6dZ=IJuYGvR*qIK)mGO31~t@W*!O0(^4ocmW#-*1yT`cUe}QTFWq(#{`4#Ac>_ zWX=xP>^?3;c6wLonkh7*PO8+O-{^{5q&4*#+`i<$B5F>m%l;R@zhXuueH&cx)DpTH z7&M|QgWkXpj5M9B>|cP3mQ{W?;|`yz4l-BB;AMj#%VZZ+DQd6*oA|oW)mU+}fsRCq zU45x;bi&Ck`u6MI@^U%kB3+jeB||EV@I)6b03jh_+_(LEhc0W&y_CR!tI%4`Nuu}3 zgZU$@Dh*rLQ9&?RF!JdAWG}OH?SyiES?Vp~y!dqKtxh@jt#I+`%_GL`%{o`Gt+K{+ z=uFe4Yai5E`ZtnsVipwue^7y4tgQS6EKOcKT;zYuPXEJJ4-W2StOplEX!_C-LIj$v zI_g4xeS^j{@G{7Tyai)T*vn6Qh1FGeA zjP~ZV4WDJ9bX83)L#c<35>C3ymy4(-bm97C$Ic~Fa(o3J5l`QOpDT6hpGY*{$6Jcr zzCE{DhRi0bxE(=`I$HHRYR?%uPW&QBE_ut;d}qyar#Vu?0|C=>C$De4T&f2={Kidv z3aT}5<=1|)8sW?o0-6Dd5I8o?s_DKY^G2SzKF#W%*yZ?awU^Us=t`QMkqTMxbK7PB z2?l~3M*Y2!ndamQ1M7j$D=QNh{OmM%j(_1x74ReR%cGU5*z}I(0u#5v48{_Tj=6e` z;Oo93w0Ff0O-$z?iG_^{XGk6_+HJa?iY1_1TV?pp);qt(E)etzdgN^ff}$VQz}SgX zthaT4r4k+|9j2{Z`(#yJOGfWX`v=6;YS6z^wOj-0t7xc79?W9g+X`BHQ1y}JtGpYv zgrFeu5c%d5gE4^O9xLc;#pIRov>8=VtIh-0P}HW+rM0zH&jXU|b*c^c)NcnuT0#_s zniLNSCkv?^H=(#r{9#)Ly+`xg<>3OJ8iITh82{&dDQB!}B zeCj?IO?zTH5RqIXm^5vzP7P+o#X5m70M-iU^=Q8l-C{FXRZR9J!TF^PfCn!RhXyAb zq|J7C3L+MmI9lIE0pw-wyasO=62E99qutV=+TFS0TQ;SkXJPoRU3QwMb&~cQ?#%hy z{?v;~OD~s{4nT8M{KAPFJosGkH-_y3#!)B(4g2X{poMwzYOtdS#gQrVzc~IfL4nub zh;JfDjJucDxbm-CP>ca{S-!y~qd%wO-ubBrizHic!bMo1A%l3CFM-q*#&4N#C$@#Ss8G?1eYjs0gvk+{J&}hn9!0As&}sMX^|(l| zk^r7GeAW#4^W}kp{_VofB*9ayV4{OS14@@bX?2dLf4L$iFZe5QY7=|qRy<~k7-7$m z$YqMk4Al0AbJddZJ-?!w6O@N8r$Y17{e~)cQvdj!B)VJ=UcX-KdKr5`%5nVGIUl+n zYGAC$TOvauRK<<=Pzy{o9gU7d1mN%u!WK(w49y%SRZOXy9~2rXx#2a56%=D5*Vokr zFAiurYm6U$#O!G8EZfg3y6x-ltZ_f!$W}Aiq#`z7$krJuzNe*$p?6j4G6W+B5VS!f zcBcNl3ziWxJ*}CW=zGU6E339p4yiZ4i7B8F)xWIernLHFfJZ4NSnSD?&9zEpZ6N3R zW%O^X^>Dhj>vuS_LL~h#OsvGB4I9CrSQc?O*5^i-*sZo2+bO&FuHBKL(z8eO*P=F) ziTG8wLdN50;h0pH!;lh89jqzEafeJc2>WzpO~jzAdGn3Ft1rhi@&q^(8v~wB^qYnn z9ZHIs7){Tb>d$SM>&mnjfKvuYRQ>`EfDPJq%Ecv}j#!jb`&WJQ30!xY^(ThxW7tB) zCg!9(6UqIR7oY5!Wxv*K?vdh4E05Ga@h$evciT7_?QT2%hz%QlmxojPi&->L%(36u zdsZ6SqsTn}o*$*FCSRF!M~o~C==j>JOnBqfHl$gQIYgeYLkB%HGq&&8K^Th^qc?1p zc3Bp9YCniPeZBVtjW2!3Fm3Z+;X5Iee>f9_P*LHN&k~JO4I{h)8@J3P@rfZ+^`|PA zgttqxJq>_;gP==XdGchx+OLj%bBU8)0M0E--X58FIdP?ZeI^9`q)Pxzg+o>NC52vu zsNIU)7a?jdp$)K&)#95?YMa5nj8HOI#%>?7d-v{$x*b_h!;In)X{XLnb)j(qO|VXe z{1HQfG`2o9OaEiNi1o%$qtFp&fEb z*R@QBd}_``Va;bFrW;bK`WSsc5-UA1z|2(wZACGRx<%qY3P2RO;(4l@K6khbxQ{z( z=I9uf*?%H7Mg&EQOePxUr(eyUbk1xJC6llvCFnEFzI1Kw^pqsxx_Euim$;5xK}CN4 z{JC=yhyN{nm(ghY>+yxQ+1FG<3N4AL#&;&yH;CWg6K4|o8Gfe+=ZJm?giO|J;)^%I zYuzI#H?mJWtxjD{O$I=;Bq#sre`ZkKVwXp1TrPeO(uJ@h2DPG@_*6{+NIVlo zHnoVHfKwL}w@mQH!9%q6@Osw+c;<%o#Lz;HJg)~!Gcymr2jo7Pc8l(T^8UfYhq`-Q zU8)weB=A~USTy+8AW18t%3igzwr6~zrlSkovxremavqpLJdY}=yE!<k|I)i zQ1<`%q&YAD9qsQZ2TVu76sx~$_wFnX-==mu-`DqQ5cZ=wYLJbNMd6Vpx%+xzukdtm z%Dl8^_tpjrfdvD0cR#Pp$Ew_ZR#&k!b16>8?yMfOiW*1~;9YZ9Synrb0@RV`i>aCo#`J2*JZ(Th-bE&ni|9xTnt+rHSh2x$qlvvge zYh9T3rBp~tRa@zZ#J%z7_hlqX@ubIP-tpnzV+dU#>1I;EgF3 zROGZ-$;ccuQAUU$(X+o#`PSKaozm6HChbCuyY)_3Rh_jc{%38fFFu&UBvGp`SDLol z0=gH~{Cf8FdbvIed7C4E~5nLg^ zfQQ(1+tL!hpgz2HEKkae=`|l}=sxic%=U~S^u8=U-HTOF8z|4Ayw?Zxu!bBh8eD#S zF}Ig}6_n6pv|Xc*UpJPWf8@(@ruRae3){YZz4(;^1ISfEUL*d6w}-y&){mqCk6k8> za_bTGq>nxi>xnJOTZj~8Kn&flrGpj9UPR&AF&C`ZRLHDQYv?MDxVyKv^WnN3(Pm(m zn(OVKGaXXt+{z|dwS9YL;#SFI_@co%Aa>hSHi7fOGH6A4jI?OZrxjDsXbXjX@#03Z z3bYBcJ>gs-qPhh&%in12vQDcZU@{XBrVR0bzSk8j6xyup~d}z0qy8dh%I)48<7&o5Kv>m=i5qFw=zz@s++6h3$EVVnwUj(TlJ;OlU851t&0 z-*!OPDOQR@yQbVIP-iiaChMog$dJ__(QibX`djw4*ux{n{82XIdE$TDjB}>C>AW`S zLBsJob_#~$t|KR!z%>Th59yIPMPTXNp zQ9+1QjC?Sx5~b{ok;_^vQiB^${$^UB6K2>cqjD4%dJpI{zGv zcPH00nAt%6rH_hy2$ARo@oH?w;UUJ{OaJBI)0yO8TlX){?6Gs$=cj;pc!!ux7Z%ph3}1r}fa(tcdNb-l>yH&y%!cK%NO!d&W#>hNhCS z-EgN-6x|sz4jPNNJ9$xE4#Nxt1fd=YfXQd(;83;qU?FA`>YIP`&ggbtd$)Jt>zxVn z3qQIn{_w*h>)N&TL-W?2DXS)~gm2@D&_9+DN8K(iH)h82JAFVG0gXIt=WnQe@eoj% z@G>Z%J@EpS3e;rIgEhn|pwd&tv-g>gy~atbXy^*4)z8Fa`|EKghjyl!UYOmD1eb-( zoB~_n+~H=D-U26=(uBn0iF)16JGgd)gzG(d`kXnFlir=xLy9hEl%siL@bvr%j2sAQ z_ft|;gn7W(Rz3Kl!`3cG& zCNK9j$F)8Er{n<{vM9uWe$L}_E(!0mAXEz$#5{>1C$7AG|3fQfWZrhnA8-_)aB^$6 zsbYIM$nOtWx*Q2_&52)5Cmz<`dj0p1z56zU_C+(x4w!Hl`Yr!7OU&(sAHeuoSY>>! zwt*P~o~#TM0F+mQmB%K#&kO&x#^*RhLAf3HU1hSD;vI$>q8DZw(?z_uDGm!?`j1-n zOY|0i3{1E+&y=v|L3|px3%8+(`%Qxi@k^r%#*K57R;o2H>7h{$qP}H|^`C#9%3MN;#5yMW)CLHz@L|~AG>6hEGJd#k z(@mQPe+_$s)HdQS+hQC&)Glp_3cOG9Bn~(XcQiSE+zDSj%tkK5#@}rxM~EY7TvkC zmds;*WQED@-Y=%>C^v^_?CO3XblWx$-@VbThm9MzBh13Xt=ZiZvg9|Z zp!XqYT$nd&2-U^id-O2W40ODI0Gt89FLJ{Mj}vcYEa5Vxb0D;8TUY-3@ZrOYX50EP z^-Eu;(c~{?VrKU`OBTNvfVf@FER=fT)MpO-+ujtj`xFELqV=aQ@Iy-;)L$v$<1uPTyxRVP9Vc_ zl%>SCtxX&7P=76uA!}0Pz{{-@bqZs$;0&_Dfy?30m`@$Y@mqzml5r2!3)Vu}jw1|y|xA%g5PoD+FiQ~LGo8nFON>&{OPMfi>R|Xpp(czSjW{HXNFuSY zi`8kvP&lB^g`il75c&*D#6#U?a|MN`>s}5<+zVr?tmLf=3Y1VO_ zNULbo%<^M$Uewe)racxnImcxU9i@;Obt0-CuevU|G}`rSY3aTayOe|*>i~ejsUUPn z@*-Ka{KaDnf6laIRS|K>N**pbzvBp}xiblj1Yit`y-gu{vb!T7Il1@iWkUD&*QgyW z0K}h6le3ku!pyxqN-Ehe4Bl9uiZ714N9ub&`RI2ygOO!8r?;4%~`%d2?w}c>{qB ztr2THQL&dSQh7i)aJ(=He2}+}w`oJ^!OIOL*b6$b30S>wo_IJL$Lj!GK|;zORfOuY z){SD^xZ~rDeM^>kAi0$_c~XNpiRwQFa`gG2;D7aNV6q=RdSq4X1M~`2H#6L%Qg%g& zhCoEYSWo*c{$M~T={wjmnT|Q+tC6zHY9DlFH?-VBE%W2z_U>(lSd^8K@`GiTxx9Di z1{eUJXBT(m7S+}hwGEwtG{mq*0_1XY$j&!PpXaP<8ryE3xvFSZf;9aY8vJ@^1nj(i z!r9q56q)n`x_Sd!@eQH_Q&HIT!%aFowpHAJfsb1wkBh#7FA80Kz~D3>G3Y7jJ@HD& zVFq{sCmHVKBoO~w|5FKV8MK5_lPyy`fS-pnydeATa{T`h53Y#*x#lFER1igpLE%G1 zH#sw2ZwbC*5y*<=N6saELJ^zk;AZUTl(ipD+u4RMg@5}PN`=5Omsd~KPdWu(T(q9> zz_@e6nAW5aklLA-barbP%C<&MS5yPEdJbo(&}!S(a@er_h1K(bZ%ML1mp&PtgAuO4 zmlJlJTXAvlHeL@77i=sUyC;4D1`{BSh4PVHZe81d#Fui|yHH8!eY9z3(epiIH6&sp ziLnKBi8yrV?XZvS|0dvZx8(@NZJp)`#N})zP81ojX1VT>izx%Wx9*tvec6!36^Mg= zbu{d-j#6^%W1z3E{@2qPlZT;l{p&AHqyqnLeaO(e35%IK}Vk!az zO58Iw=exJwEs40HBJ;M}vO;k!gM<_quBiVBY_dKKA*ZMjFH}_Xb*1_gidWgkR!8W| zf-Tm63xS4IZ2G%T+gjQpqL1cv3&ef%=GXYs|J3DcrP*bStCLj|)}w1+P?umyE5D_2 zGfExk)G8i8o?$KW$Hinp-Qg%8RJGYkC205LyGUj_t@u`N;lFouYshLEexq5ZPJ8V% zdje*%H@pe59J!~NZ3tIf%Wbx2#&w`h=?ZD$is|iS+mIBoCm;Dkaz~;g6#Vn_k5`#? z>4Cn5HqAa_7&?A@aaOei;vhsAMni)>RyOY~+%uN0zDxQRptkq_1$9o|9ZH36L?8pXy=YF2vue1w1oo54EOH`HerB4eLuY<#5B>Sh zI?F|kCXIPT1t?5x5cDalxtdtndc(WydP`XC4nC0TG#D>0?oGUFz} zmWIKz>C=%kL1;tw7$2Qz)H3YS6l)yU{g%CY@q%}~tJ_~S>S}7F1Fn|V>+02a@l}Kz zH~@tUfeyz(y5bsVqX^oU@eU(sJ95?=!{YGeru3?v-2P+6fHNF6Hjj|csYC@`4T{fX za4r-5M|LyQRHAb)Pb|K*j{TH(j{95-=>0s-K6~8w@pn>Oo@otq!&bL=eP=_%${&Ma zfWW~e<}+o4e>7L|osg=r^LB6kqZw&DZ`x4GIwQ73p6Rs=oFO$IFN>r%P@SzAgN5G$ zev)__YFikROtbPVY;hH7gvq7C247CI!UIb$$Lqq23_Mr6+ssbDIp6@ z_`uO2RNgXm4t^vFvOp8YLwU&`HFIU@&=HD_F?9*Nna>>?uzX}0qvSdxp6z##W z6DK66laD{B8pODsLcj}9Z++Yf3cx0T&!kW05wS*utPyg<#;N~({Bj~8LLazR>y`Ae zK-z-ln0b@npk3fK+aH#Xn$<$XL|1p^3=gvbp25A!o8O*cu2FGr$zP`7!+1cEK9aj3 zG47xp#TL&&=LsU_S&>d1CvC>fPI3AFkXpo9FQMrqPe+?cyycmnADW0cOTH$XJ?ZN+ z!YifrB9s8L(4$9{%Tre7K?faos0~!Z5EIzyl<7_XnLo*1DAsiPvbe-2`-z${&#Yfi z%#QgRSM3-}eKEU0qE|Hb52lJ^<9jUjKX|#fN$krSM+a- zt_SZB_$yq?;i~)ohMN9^KTxl+>glxUp2C!6(qWj>71on>C8t|_n?ah3sV|Jlh&O@u z;s5+bb4)Kd&2Ox%B&|HWm#A-)#=m$NbX2XK zxnuew*NWN?%NKQY+TAbPJ$cuRNyi6(v$(L^4WRo+A2krmCwG*bkIl}oZsptloq7Cd z=mi!QonPLqtE=x;u2YOq_LDv`hsuIa5dl*ifI*r{x*Ei5(zUl4uQaj(v}d_>P;u4p zSWFxY94mnqHp`X`k~IQAXKc#crj^oGKW_#|Y&~!~f!iQFg=EG@*BQ>RA@bZUuRK2Q z$tD4trQ)JHkwp$KucgvJRyW*IWwqtK%NRad`z7{MlujqU+MnU#Rq@B8!rsg6)n2ws z@+pcagZvxzq#rx`6+nSUupzg3Jzx|&z!5%vXJ2h!6}w(|$@T9^q`JuQ;k^|nTRmQm zvqici$oTWWe(^YE=ANN*PtWaeeM6Q5AB&_8T=O_dl@-nBTz0sXa=G_HR1Z8F>8Ofw zOBSJy&Gu=)4f^F?W3isQ2Yxz=NKTT&u)?mQ{}c@haAvB@x*fliW+%_xmy)8xpiPT! z7Ok#T8-8H>+DlX7-fuPOb-8g)Qc7GbQ-A-Hqn3S@K6| z!CArA3_hfg-t1|WivQU}&|fM2qBUep5>Xy&n$zYtZI?U=aB0x-bHgOjGOoJ9c5;@3 z+e+PCmsB>CiLdSZ8J_34l^N=8`s%%n$M5j<{t{z z+d5%Vg~q6Etvzao!r~ks-ho`Sg&0?}``PS>*s8;u%uMSJMfoUuPq77GL~XIC0NbMw}8 z_tNn3oH>*Zbc{avijT1fH|AkhA&WDW#B->?0&p8s7}-^ChNk=;oVtRU;&A2j@rZ1* zxE^n1L`U;1J?;HBtp>#{_PEx#hL1A7^zx5=;iW|VAU%Fih?ZA-K0#L#Hxu_j$L+P_ z^kOZHTDB-Vxo>re58F^TeJuJk0zuwlLty(4o*vJ`U%mEm{Im7=R=@ab^)~*Vh4DM< zOu5#TmQVBz!bg4~Ld8o(zR z<+gYNL{LFN@x>4g@?=Coab5rO;JU3tLS(1-TZiN(#X>|}Gm{-h0130a@y~x5+3{G|4sH5i0Q1cv8GnDyEM*ve)8>v9<}q}Y%+OB^el;UX$y-R zpih;VRvKMMqjzi`mAFk=o1m&{0n8P7VuIVD&y;grbjOVEWgCY6j-);%0*uOYaQfGm zYD0Sshw1&Ab{evdI%@^5OEsf+cL})IYHi6kED!HxZH-=R0U}(Qj2TyrxC zf-8x0VEKPXZhv>BSaj+{K1~WWPt& zXm|cSp5U;wbW!8V){7Md0~-~)TDpZFV2rc+)TQ*CJ0ayr$b5c!?{Ucyk13=EvAs^o z`%_teY@-;rB}20h*xWZlJ`M{v&vtR~W#f2ZCYv;~j{;?tOgnVd^|30COe%{3>yxxF zY@Xs?+1dK-U{?4QLOSxZ9ftNbHPyYxf|yk1LGicJY{^Dze&Ay!Q7NO0oG||%Ek8}( z-p8-P_LZ~wj<0NG@}ZxT<)L5R-rvBtC0rjwNBWo!Vya^N5TmiY%&XYsFpeG`S(|iY zBsdz2?jj?fTMOVUX=qFWtny0flQCMXJ}?q`*H~f_&P}eYWEod!hPq4^@QPdrZj#u{ zPjE8CMNX?UYR}ukuq%sPc^<^$)<63u$I9{GewiUa)>0<*Ay-N`ecTWjlp>%==XEF@ z=z{ewVh%ua+T3bs$hjC`?a3o6K;#|h_|;f4rY8Geo&sTj8Q9w2Ka~|iZ8PdFjeQ%p z=CI(R@VJF}XZW zm^-dt=(AkTjcX9~E+4yHS-76#~*s4va z$F8_!6N(!$rMR?7Z+q9f-0#u30Z)6yj`wRY8%S_`*~P8|=C7SGIbc)b4Y9(t~CYjBC|!^NYteL^ZzCk{&R~G3W7l zlz}McyBc0fXCfZouEfUtz<`&N*aR7|vZAnGi}TmCetoZco$7dM@Ss7F^BRxQN}NJJ z!e9*Jq@3t{E5W=K(M=%zWUBbGomm`nLNlN)8=P*9+j6v6( zmo8s+f4LA!AoJ3q!pKS+z4C#no(F%Nq~%Nb6+6?KjMyW$ZPr~;{fOb=$z@Bo{bTlB zhB|sO?tIHAn>o#~lLu%t1t>@0;rq**y{?~lI;hI-7Ryt1d{Bq~q zp1|9=FVuq1zqWLV;i6VJ&%e4%wg{eHMuy}X!rN|4zyG<_X6?3ZB|2_<-`O3-+kUC< zebGRPSeOwu08VmCePui($C3a5}MBL+G$IEYU3;WxMvh$#RrkQG%I^svn$#d$#X6zmxa?g~S(d4hB}jA`mqS zTu0i4w=DfO^;H4vb9&{a$^7;0-@o7XVvU9DkV6nJRJIslUjMgf{`0}VQs=UeYwVd7 zgJJd*80W5_o!ER&A0x9g-tVJjODb-zn@buQiAl3@8R78!U-QSh@7#CyVcRtS<78hI zBLXpC)_qYGi{XND#H=bHCfJt`-K@#5t?6BHGg>m5%G@f16~(4NrWmNy0jWn>R8kBA zCg$y*$mM0T=o+|qq3})eT6M1nj6g^EkfwRKy}fP?e;kiTml7~GOl*wA2l99cWYI5>Pq0>&gipJU7iR6Cw2 z8>Ue<3Hhi^ytA$kG5xDVvV@SNvdDEHRhMaQ1Rxda0iUWFi>|KbA6E^+sP zKK7KHG^Wbt+3A!}2CX#`*(bCiuXE?eUap4Vf;7XV6=s;`yktO+ ztCD*JnwZrL_LUfBMB&Q}X;bC%+ChErT({4+-8)pIjy&Wr)2Fea$rey!jTBQHl5s7w zE(Wq|B5ttt;z1qlYaSze<%IaM@e@85RBo(-@tXn}iv^JbcmRmiT;I145yNcJ3+Lp`7V+8>a6g)ak?EN=d4$Mo)- zy1ol}yvWdBPjjwo{x251ApaWE+}L>Ol}{cSjT1AH0;aTeYv5y+y3k**d9;ll(sC2k z<>R_-y>nzd0tqzHjSHJ*R39~Q?b2(i-Xhbq0jtw@>ZT32=P}jMPRFR-^epos$_-D6)@8 zK_bjW@-tdSI7+$=d0ZkH#_cAEGF_AUhgx_z*bDjKc@@GBsPAj`&2fG%UTJZxtrdfY21orn;oNs2wNnb zbW>d1d%)aBVA^%j_ZZu^j8-DZMUO|4LRkVkBDVt2$s?M@Qv3(X6ib!NzNrrZ;R<-c zb0Mr11tM+&Q8PZZnoc~L_r`9AHsC=}{cm8{1p@*ZwRk z;ZKW8nHXt>^Wdjr-Ly80Dg789wL8V+^ZOTm?A~BS15#%VY9QyXE|!Ui3K3fIQgU4y z*b`AEk*s1B7HkU5gNRAyz_SfV4f8*8TWX9fAZC|+eKDp3Xu`hVOPD#vnq(rf9-a0y z<4e%h^y8irP#l@=&Iz|%HIZaj?Av%CX5nr3)`q#uD;E5X$RKFq6>uh-b(j8IU%*6? zz`wm@a<~(ze`1Se1_5$U!ixkQHYYD`sT$MWHvp0Mm}MV_{Hg8qKNAvgsuseT)!Y*Q z69O4Kc00Yn)v4^PPj<`E1I)}XTuU%twZB(5xBSVICq;t~Uxo@*!cL8d=4Qm2!`0ZL z+-hD zBZaA+LJ;J>v+fjtGiD<~{Ax9PDZP-Q04MzBN)MAQKD3bE7;O|Moouh+>N!3nhCLbF z${hzSl33gGgNHfM4k09=3))hgOYuS?WUwi7#)-aeXkAyXUq3P~KE$vpBIB~|rQiv@ zT+RzW^y*b(x&p2wk_6QG{T9&5$G1urtl+}F+|e@D%N)hs?t@wP+mpCaqMljxRx!Iw z@`SB<=y=Q}Oa>G!&F*qjKH3=gM#58s9EX{WMyw$J$;^|kM zja(xe8v+iwyBl^1@{JgolkNzUCwn~!5+`sj`Auei+TwL%;a(zDFKvIZ0>E^9504%L z8Y@<*PIXl7e%m||P`p(2iUJ$Zy2vp5H);Rvl;H&uS(x7byr)|`RbQ{kNe_EH>TP$9 zG!4}UGYtam3`%Wk!6=*~{l*4&(Coa%KKAl>&77B)$x>z0*I9$*BX(|_nmyL0*b~NG zd>SPsi3bj-0SOiXGl=GlR#7Z&>I3$$2CfXLfH=06(JV4ySwW0T7~g-u%eAXlC0Xm? zix-g;QXf|1(qQ{0_ggXM-fRYrE?e|GgZj1ay~mvJQu-6CB_*$v3*79ToZQl9y}eeJ z5wkGcE3)KKK)m{n$cgKcvrYQA7GG`F!0zzftN8tl2PJQwupvXWc!+_Q%LwZo76<5{ zIaUqcnRj0{6WOhR3l!y;pzhE%GQbo-y}kq3lmib?A`uXH1Y_iaA z6JlXPet#cU=+&FE!rs~l_QS1N(@^3kyD721ykz11V!b?kjn)*cM0Cjb^pGJALO|drZYvwb*YjQW_L*kV3@&=v$4h;slT;$f9C!{o zbu#*XgG+l&re8+XC~7#)c-_ilk|=-w{5XoN?Z*reZ%c{`%St(?B41+6lxP4+r`$ZN z4NH~y1N9dL-m3n4VNaf>k0a0IF%uHSJNo5Rsv{JL?6ekp5MZ^++GVoHhAKQDkza#yM}zkkvZr6Bik7m&iONEX|n3vKeQR2SGcp5k*=x;nXa(MqE+Jl zv4xsj@#ojVpVawdD7<*{Hyhtid5Qzy)z{m_!69R0yR2tQ#B*}-{oQC5>U-ax-`;Qk zYid)NzED|^0X=Vt#H>-jiUw3vIHFIxUe&NJ~aG%U@5tgnX{=5=SUWQHEQl$oQB2_>>+K=>FHVKH3aHk{BadhyMdwbdPjL zO~NH#3ci)S=)KR$rC*y`q;}}OrNJ+b;DSTim!5x|%Vr!!T1C(=-a|zDe27b$(FRzE z+f+TLeQgsxn2IM@F7I>t#>Pti3-8@9f5(+1=syp`uKBxXcK~rF{9mXRo|N|>{hoVc zA-QfHo?Kt_AzZ1j`b}6i+?Eg@=|!~PETuV}3%4h+s9c^BVT}*JJY(kWloY?1D^SK) zRl0aC|Ng=DMIM4#m!TT2y%P`2fpr#nKGZj-DBD(E4dc z5|PH52zS7S+!;j z!9=4{l$V)mujex{EEySSJ?2@9jHFZf!|p2uMBn6%BvQF)hd>O|zDm?(N+*3xWPl5l z2?P}NiA>l#{If)O|M>F8tELtRTnGr+tRtu{+RFm0RZ`mf4IC(OnGE^NpIKv8dZ?Ke zH-GFkY^`ST01~eW25FIg9vfXFrx_tXJ~g5?7voj+JYnJ_J3+;tMLURCAX!Ft9vPCM zRR_dRoVwa-L_+>Nm%_eC0YLLKX1~}M_F+_;L4WXZBPcyOdrH_y1S0%A>F5!H1bi}` z-J_S5n#dZFZ$!MaJm0#nKz))`C+r#ujIg4I%l`UPc2vb=>3lBOyiG-+f6iOuy%2%9 zddC|dzAsDE>822fHqOu~3_bvGqn-PELn}t%Cl|AysSOvfiF;Lc0JvP9u8t-Fk!bVJ z2c8}41nykr`*GCQ_4nS5L0gGp%Z)rKTdwx@f48u(u^&;2X#1R9xo$ zprKRY3;(ZEl_;cTKfh%cDw`&JIGU=I{3h@2x1E;hlS@lVbjg`CTChx={jUdG-|%p8 zsaqpMx;3V`^a<&qH&hCL_h1h?4e*(0HE->h)exy$HmYT2jDQ;y0lSr-3Vj%?h%QkH z<>r&K)E#tF^BWmt>^*>VlXw-kr3XFUJQm^=*^hFJT@e<6U0HjG0k0@^3QjyVa!-EW zt1suzXB>p9rGTU_Y`>3zP)1+jRU}5AkJHoM$l~eX$+Kpyr_AJKej7F~_vq&ivh564 zc7^ItbRi5Mkl7;jry1ogjO@-*JVUV4M%>c;3V^SZ=YzA;9~?OHQPjsh27t`cX4$s=7?($_lSB*CB0@$@W05&m}svjb0j+o1)?0&On%r9P<&EMlaE?+C~CBRVTa zH;tWru2oc&;P8lCQpUwN7aPzN-3UIV|5;hWHf(4{8iOQOF^Hhd$p2QX+PVHDw}AZq z4IA@mQw}=uh$$38IZSzY`;J67kiAss*IVRSDx=Qvt$EWm;8u;GIMeG_^lmk?Y}$+E zH{1yhJ4@eQlU2=)B5Qgmbg~%eywRqB(=3mv%pu&F%QV3pG?>yAmXId6H5NyyG$EATC>n;&aQ;Ja|4wR zlG!HK6;@bN8il%@*|8S}>|4j@b#eL4QWRBw%l#LRO?No@IGJG$!p3=O+^gH#X>W!= zm0SJMj33#>ylGZF?HCob^ghF^Tbb{=t9N^^x~-3oo-hE~6&*-v{l7{At}2kq`t19y z{&`}yy<}dYGNPZeIi8d#6e4q+0)x|d2F%RS&1$uO1j|!Hr3lCSDnbqv2eWHTqSQzL z5p-ADn(Rng@j!MtSuyS#U9h=v9YsP{sH@6~w zLALj6CaKKNA!pq9A$(}-ZI7?w)aT9ns8S)_z22L?eD{hi(Wg)y%EtlS zA(#{ysif#_A!QokV2PC!s!nqMto%!)n(E2LDAkYC*0DA4X=y~c~yC#r$9Q(&S?21q> zDH`Sc+v_K3qAQTqP6`FU#H9Uw0-PWWB2bBPS$I#M9>v^h>EUs-b4Jy{@~gFJSr)fD z-PpfQ57N-k{x=hW-R(rkS~2&d02hpmB=?mhCJXmNzNqusae7?Xkj~%?sKi{x* z+oXj4=O;@T+Vtth;t!!cX`Z)Xt71c05t4La2azAJVpuX}k-1yObqmmJ(L!>?C|Xts zfyD<&ncS8A?2q-Bw``R&^Qk`%;}G>Vclcnh7Ov0Y)~!=x)MT{FXke7l+VX7B2(i-8 z#WJTz1TRF7AstiRx5YI|n7IgFUrG55w@ZFVTQU8^gC$}e|Mp(r)y)m&==h=NTcfL& z8}#hijH6J8w(IJaXB3X-0Wyqh#52T`XvqMD5GZTG?C5%c+t|;8m zrxUAK4D>5WnW*wCCr$eEx3gi&rh(cSeH5-$o$v?mRfKYrtsP`~f8sl{Pc`V!CEZU^ zKrv@Dt1&+q`y!!yniN>1jc>!3xw7zx2|R76mZAB_$p0!!)nV>4$n8Wc!$ z3RXT@QidR};`PDmc1T%pDmPNVxkO!Eid1pYpWlP|+=%~m%^7TF)(T?TyO)+;b$Z_C z<$Z|ZR}^p`1g@*)3lc(65h`IGM{Xy78OJGSgSIj~5rd<+6k|rWPQCt8cHu#!KuSt} zef|330bkOCDLA#e_D0O?!LQPxGf;>PF{k1R0&a0)`1)EO@W|vgRibYHrI3sj_4SE; zlw+c9Qre^Zj*KjX0RgY3fLb4k9SXKR_w_APz-nu>S~BqA6;uQ8fDz`<$mS8I7)14! zQ>N@2KG%^ijdBMsAhv3OJp1-t1A4%vBIHY?A1Ic*Y#w-cs6-M!rcZy_IzIRL`$hjn zO;ma8A7ANABQ5BXr{{?Zx1l>64Y6CSDm91T$n)$&$exWZGEJzRQV_oazuqEDMzNG{ z^fLO@sCt4=BIml9aE1A}Eh0d15g_sMN{aSb)_GdbuP$DRx>x#}_CMfnnOq*w+1soE z7rs;>43|t#JZYA%Q`&h3tkqrd_N&W0^2Hl*TZPBQYM}1ug(8A3t1f9C_yhb-0@kS> zDV5Ur(Yx>3coyIXKi)N+&J$gpFXG%J!2BxwW+=I?ck66m@b2Hc1C>=&qL)pif3sdU zTRp3H(bkko$07mB|t>NAyEJw znQ|$>eU&O>g50P>e|^$KO5h9}h&$jBPsOCv*JkJ%sc`Rq|5~aD&CCp3J9RJ7-afy7 ze&_cJBmozFH{lB51>)-FnqSEUM+wxnDgM6o>h{{S?iL!WY+}FeA#{|ELu?=*Z00|9 zb9IRwkVj`&PFLA5C+sLdP4|GicFE;BJ$v3a_ zjQpz8@?S(l;_v|_x7dGu?ti;wj@XwiBV|K3e>Y!xrvBiN z?r&U{)}zA0va(iPjaE$*M7T}LFw@8=-nQ+I{DVHb60?=F0gb%z*eXJK@!p{NmnI(F zT6#06)8{wU+dkc=zxe5vKM&PRU16(z(^s|z<4)o6k{oCW^%PAl3K+hOGA5!s5g4Wh3_7A~ zW2LShGo|xsvJ@Y4=Zk=pt^>FeQ-UfWZ&AK#@jNkQYCw8O zkpKbX`L$W`@AYYY$oa9rEry0jo}Z*7AG20>pQ*rO@zQC)Wh0j0s&dUS{>x?qdF8;Y z6rYyq@eusN(J|OlAmuTYB{LkNVxELL11@SlW-0-e?wwxVgsXrO2o>k-mvi@~%7wFi zxW*)A5L%FS$~oQ1Xt?hMnS2X3GhNFef5@89A1DSr;C7s$>Ra13Yt(2Z z%PD{&1=B#YoOUTnX=YUEyypltdTNg4qV zl*bG?wVW^Ey7*Y9f6A`bL_Z`=A`L7WPCoC1aorXKTG3+3c0>Mr5><#1?pBU}6KJsp zoja!*cBln-leWznoiYgWoiib4N}_sS3YPt5x~!YnJ2b?O7<=MC>xKFS5!r9ev1M+V zw57MJR}usaACE03n+J%mqSX9o?V~yUB#5))kY%bW;R0D$4}X(lifyHhp4Sck?l&gB z&&wV5rUCeQ8O@lkv2hfAQvR_C-)|~sya|Xb@dLkD@$F$lutjgk)?28gn^YY1e{Xho zn2@r(4-pU06dc*>F>$DUr+pH1g6wCFlC4}63H3*Q_iR$u-l z&yqN~Wh~#?V?tF@@sH9__Sj(cvPdmSdJsGTB?kquzC~WKZost(u1oC`-n`YP#p7TI z21$==*5cgsKH3tS58w2N@4NHOiB5-ea?VNvi77}285D;?P+r7Er<=H)|I)PSnsD)m zlHxRAfHoRTXBQV)lx#ah?Ea`@8YqDDmc`UcY6#aSOP0zwG@d`lxmGX#M53eamf5r` z!q;uzo@*Q>Tn{a_G9qQ9?6AIuEq%AJI1y0{*k(dTMl^31VTurfR3o`x7dT#MJ>jtP zKB+_Y8e&7)n?P&q*J3cjrhE5O^NZi+&)7hWA|~Qt2LfgJ0awG( zvFjq?3d&HAa=9v@9!C6i$a3!=h}_5vFR;K_b;~LKw!(#X{;;TF;?glLF4>o=^-PPW z9hz42zWk^%F;F5G1RWGyT!j40b&r4qg>aH!NK9J~xJ&X{r0_+-2FaTP%x|o5Y^Np$ zcJQF6jbU1Ks}le>!x@wpyuJ5pLYK5?T`E{l&y%fq3M6%5wOlEfxU4MCplp%{5@Vl0 z1YU#cA2uaOWhJSff(8N4`VjP(^6?#U(_DNHQKuF&y3GCf%!z!X6K=2k0qHpH5*jQa z&tx#m5+U6nPky|BYl6nILZVohFIsBtE)Q%v_fehl*O(VK4|QU2JUp}7oR&|ltfY-v zb1%FayeBQcDSdAE@Y=b@$H#s_30Neg{Nu;guLOV=vFWs-E`tx-s;|!;+fC9u`DycsooQ!ihzKKI8BB^?5^rK;B-bXpNF)qHgUm!?6|jt18NC39GPw4BcYM5U)of%t zdT+MH$9KQBLXffi?i^q#qJU}Q=Yr-SX3Qq#ag@XEEt@wBP)(1rnnyfNxtw+7U+A#;t_M=3Owid-h!Hb=7kOkOTQK763=Mt;CQCYH=#>d%R<-t#87(B8d!Xw9Ymfc{<5G0t5%zw-+an6#Xf zTyM4=FeHVHc~cHp?Dp1RUs|E6ORH3yyQ6MAPf1(w6uemPO`8rK)IkRn1$3FH2|ORK zNUG|#cFCjw`kvgqX>pg6?mbt}Tf1*_&p`c1w>Jx0w`t=GrcG%bMyZ)YldIBTpzW!l zHos2K3_fEvEF??U+0Cs){4w0YvMp^pn}OpFq#F16n(}wrnGqE=d(QhkV%%NHwjwXR zqsyY&FWm(XqoeCK_OL}-$Wz>(h(BaGSxJfQ3+-uYmIutQPRpJ0_t2WZHwX`m_v0aD z`5P(kF~8c-)QM3Rt$!hyI9kYG0}lNWg(LK=*s1{D3cY(Fn)spJ{U|5i_D9F0A>2{F zyIot6WVkEJ;R{G{o9SnM930vh{zmVOhu5;mf1ZL@)D!6m`eK-Kc;W6X*TinzSb*Fl zp!3g)2ribef1V}*q%nh~fB7vv7hN=s1pXwKCtt>Ie!kqgTA9HlDE`BuLD-#TUkU${ z4^$#$H&7JT&hO@OW*D-JthevKFO%K?(6gaJu54=C!6Th?w(FEW4H5+sZlGnqf9}|f z2?6mZF@Nqg9uC8YC-5Ld%-P4R>Ev{2$=;r>K2w%Acl}Dn30+??;0@aU4TOv();PgE zNnjD5PdS<-&^(T~E;kP#sH}6(*CU0JC4vY z)}Kdp@dPF?T5Im>1YSCt1peXL5&K-c=d6%V*}Uf|Drar-dicWQ0P9JkztAD z)5L(e0#>_DG$!ETY{P>lb{`+p=2iSK1;1hp6#>KKsZ+c39_Mv<(s){JqTeNhn1SEw zYru*Xr-zqmR-E1vG%vkblHt&(M>;P2nUg06QEOGVP{-h{e zbn?Ihe#Z1iob<$*U_PJ_;a86g?Amoi%3Ix6Kewi3oJd;lVjI&XI{L(qo?&<`G^Y=B zDNShKbpM2|4k7BzQ}0R^ulSodgu<&`y?IlX(n#wsF=fnH2KFwLvb2w5X*bwn+HTbX^+X5Ue5_)-dmu$G zq4%++Xw8Hw7oiIwdAQ0=7k_xNV9Rl<+xE~oZRn2x2t?@3_|YY3YOb;1QHbG{6-1Ow ziYdHO#Vy#7=So(NQdP^ij*0`?P0~Pw1>G#)AePr9-JJkq>SCBcSLi-uqt(vNBX&Y; zQN>7=>LQah%OV7lLFB5G^kL79L2mrS8+{(&R#-yW1Gm3f@yxS{oW8-BK7?7-#c0~$hXz~L1_{N9biKk$`TIV>%Ow9Jv_LOv{oLj6Si18`pNE^R(`A(*eKI!7+`r zkr|BEu|;)7eku53lZ0yyr%k>;qa{`<3(Z z7`X`}U;&VPFAjp|hVy>C1sUCx(uPk7m}N3l6ray?kNtJ2X2fXy7tau3G;~~@csr-? z*AQk`P%$(Ba(^L!;gJOH3|+n2F2?3W)p`;}HqiNy0=vnduMB)FVE1M@DL|t%wY^vi z2Bc-4_AltH!a3P2XvU0gLa{4^P9j|9PJ(aWzC5fW51KL2@oYXo@WZE2!1y%xO_iB2 z(f~ZI7Y`ak-^>&Z|3ZZoUW%gk`AZa6)0mR)=$+K2d32(k#8LU&IZQCfq zMwWg5&|uQ(WtKnyTyrd!9aS2%IpzSpJWsuX%T8qMv~1uR)86$vPOX({A02H)o(p9@ zL!%u#1i=)Wi8O?wUUB*mZM(qhbO1_e2J2e4Y{`!I)|?_1#*zBa0BK^v!Gj~CDf#d( zHCgj!zIW}hZ7m(s$>p`JdF~0^Qe&)bP6hNAQW#JF6vJyX?5RczR=ctx~jciaL~v;&3K?NUGMQ4LorgVa7}mJW8^>Av9E> ztgI{>v&6+kFLaeJjPiV1M~EexSa?ai|&I#w?EjTdZFj*BD!we%`5fL*PTqg`F!F z|2!U$crR>O!6&v_n#*^SK+`aAIo+* z@I~nrLGvY12yvXS*C61s$p(|oGU6?7Q?wouqHF}qyLYAN-EKxh69pKvg5Mi znThM>5auY}KSfJw_!oYhk|SXIab2 zv9$rHWZpwzLEs%wiRHm@E1ZNE z64ySNRUCf4T=lFWrn@OgB6;IQGM%>0Df7=B3lSY~kT0eq3Wsa}D0> zLRsMfIQsXKjbFl6k9Ax^4lP=FKBEFO`Cc)@cM^60cSa#l;_cZ0mwG-u`9_@{PMBuN zP2?ti$VZk}mAw3t){tfW03KFRF4&E<`j(Qr+oV~6@B%U=P+$rq96%r=hI;ZGM(i4y z!}I=tU&Z)TY#wcer3i6heU)&R#`d?i(z}U3>`G7qWSi+JEXxEE*w*^wo9z z8nolh%zvM~c%dqD|E=u$lp>Z#uc2~FxG~K?l9#;H)1%|?F*vo1kuvStd2LiwA+=TE z`sFC`4du|`qbXb#$>q}D@?ejq)oqRdowx3JWmIyxR3O@1k!UflaS;Ih%Zx1TOK0=3E#3{s#fTJFGY4nS(BOjH-#8kwX=OjXJ_T@t;5Mae;S1z|C4cURYryh zjSbr8Zm#&hMR`w`AQxZ%{>OLv7>5^zqAZcI8V%IOvC+6i&)t{J=|a99J$fj3?`o6N z?hV2VEr4*#c{F8(B>jk}?;ytk`iLwTeqi;kCUnqT@OqVdf%`UY-fXZSf&a&E7oT|c z!_6FcE+*Z5E^o)Q-W@h=cEr03{F94l_5>L{8S-b(_@N}C7i{W0!n6DL$fAed92a_- zP$GI(e1F;|b|@{`IPn20#BVK6wNKD?tiqDv*?dgMsMxtB9=ep+F4f_Hm|aMj8NthMfD&&pypb$Rpq(Z}z?@YBcPX%XM^ ze?l1|O4VW7okmXK56DdlYS4epgX00UBg2IJW3!ihXOLn%_wex={=+js(?Scq`Df!y z<{Y>vAqxIsWUzXWnjgXyj^{7s&=;WDpcNc{nS4Q@`R)KM#9_*q6k7C9btb|xOXh48 z0o5;_iz^D)U*h%O~_~v>=u-rm^e9ky%!gPz=!_+!Y14>fsF|bwmGiDNQnn;@;(`Io6Xu~;(xE;N(hz2oikDj)d&iWBu zqioQj)sVv=(*aLK5xwOur>N-jew!Z0lSa*$Un;gE-d9w&z;2Bhhk!D-J5D)e5DmsK z{&-LW%5ekd9k-8RKN5MCcD?W`&wUwKAUeWH>*A3x37R^DIhn@Zmivb1Th{YM&xq<` zR~rUpsT7mN@@$MML-BRt)=Sko#t8%F!btT-Znd%=+oXF3-m zE2nRfo>*bhs#UZ0{jSa^Jj040LEUIlhS6*(3hv$O2I{AaLZgo3&5F8 z#@&ZX7JD;ILMh$kuBQWYr%GHLT40GW485Qu7mb_{rWal8@{GyC1HdQiaHWyNJpbQ2 zZQ}<72h2}M)a9Cy`gML&vQka=7^d^r_fEB=+LB#d!jZ{zx|plXhbKcEJ=P<-SRUQ@ zjRywDosURP)|WU%P#3d*Z0Zpawrf{AKrWVPIo;4Lo;HCKEXcYfIy7!<#c#>BOK7~d zBxA}B@&!8QXg6~H9`Y6?3KdmoJ>a6#T^~)gw#f7UY#hmS_mC?Uc@I+=BgM@3C+cl! zd!45+;?8UQ7;c&FlhFv?PgT;fZU2V|H_2BjynXxouGg6hN!vl6tTk{!Hy}~5d!eV; z=r}FE4M2^kY~^;0vh@g4)t+iza79ZEQFtZ3C~Y(o!?vQ$gU zjR4{?rQznI^{Dwa^>&WGjHMjKIc&?8E#;p(LX5QI_AEhuE?d9E5J$Ts@Uwj%2@B@r z2j-2KF&(Htq~Z*D7C3Zc@~!A<4Rpf_y_?sRBD{jG1U7Oww_*ZXt+6jEl;JL@`IHFa z*Biy2=0nEv9}6&#B6>;9p^S{iR1jq3*7$uSmpC_91W#?_q5~>C+pyFZT_4i*qX7|n zxtJf*-#{#^T(f4_-*5Vg!k1+3kt0Wn{!x~+%Qg*wMS0aE38>OmB9;-$C`o!1hJJau zx#D;lvUgNBjr72JX|8WYW5O#BNN*nfh|WnCgIln>Kkn|5Vk^;5aQJ*BeiP&L_uQWTM!)uJXmY zcOJ)M+6>Nxl3hkyXR~5rJYs$-NPR{G0NJ@Hh)nxCuCqhbT3Aq^V208%3uuXA>%7)b z27c6(ezPLLmO@3O;^?S)ybQ4t9Xa({(uJ{)OLqyn4PeIz`3MzG@n8|R0Wp$AXUkm_ zl~^fpXWnQ=d0fYayXErU{zt_gnAo5vuinZTUy|rxUS5~)lst%(dC{I_ReSz+-MiMr zHKZ4kE$Ko&e{S{iTT6Ha2^JO&FfzpYJYNg<$POkRJN^Ww469Sm6NVRUPU_5Q=A}Ti ztomKeY71ST;Gm$K=8XV4EViHwD z^XZT#()-6^6`;TI;m)RgfpI7z`B_I_KYYU93q(<33#sUG`#>WOr5N!zim~h}hdB!; zg`TF1UmBCqZi`kNO(_Ro_jylAzi4MJY+(tXUN--27cWxY=8pqy*ADYw(TpU-i)6@b z!D&9aISkblwBxCn*9@E&#Brfls07f<2nfuf)b8U_IbIY{gw`UC%4U`leg+sF9mKAy zD;R%me7=!8C|A0)-jo{IMX?=Shlo+TDYxXIMq=0sSalfjU6c(KR2D6M_F-A$oHbdf zT`8~znyW9UCSHGuia`G9rR5i|x51^*8G#cJ^kiCC^-bSr2@fq%Y}~y(>q5}#HB1L( zlqhX**+S$T{K_`fD$7flB1^%S9suOB8X@x24p>7CI7%xxRw56szK%NtG&%Oj6m?WH zLTu@gokJ4+3!2W%jgz%Gl#TDOt`-NT?l=R<^WoaL(=Bd=kVi~ZAOe=>OkuCjf~uG@ zq1ZKeb?y3f@z(aXv=>W&VFT+ z@On(W1_jCg+4sWX@$JIU2d%@(2nXcZL z+PPaF3Fhbt^#>xGa+0tK>xx(^qtPS>v0FYd4X598-6lLywq z!XmkqCMpx!?NLhQ{1u7yA!gPxbJRz9?Lotxcvm-Z~^c=3k6$y61FX_3ibrhy}j?=DA z^t))O+RN4rIcm~>U=z>yd>e^NHl!l^T%<`9!!jss&EV6t-C{lBek0IhlaU@s_(q#> z@LdavH;Nyw7>&_46Adf)_+73fsZ9!sPyom%JoMSw9@=eG$~cl~-eR#mJpG8!@k-R!qocn`80f$LC9 zh{5`XmICK%+*lWS%dpa4+0;{Pg>Gj)m$StW5WwAQ+@x$5wR<%ifgZ8W$nOk`6p|BV`n}V@$nB@~)xw2l{qc>aD zOa_(6$qA#gN1Z8abJ^dl&+lM35^=zmIX(Cps62VdL&fJ{1XB!6t%&FmyHT*wvkhAM= zSi4sLY}>Gp<6Q5!ybGo>bBk>xLFc~&AS!0qt*9|00Ab{wMc`;=mghMsm+&pgcBQwB znm#)9bU)2)|8({DF(3fODL=f>FJFY zFaGXXbDnJNLAL8Ei|B!*#ga7|Jknx-XGWE^EfjSS4>6_#4I*Kgm-X=q`94w`xrnSE zJtiU`W>!1Ke85u4mIUV26(IpZ*K)7X9dZx-JG=Cftz2x@Epz)>&S0D+-lVs1Bru(m z3qtFOb_orQ=nLS$>&pp7+cd%>)eeD3688?`4Aui}1c~(PW$AnqKEJIvaXuM-2w3~wgpj$}wj5ue&mnRY!rWe`#BfJ3W(v_fPz|TSq5gzDGQFF>) zMHWsedJ83vwO%ua^V1umet^ik~QIb$K;~FToDOpThsOsiBlD zj!Ob*WgIQ};zq|=ZHK2KJXx9k6#O{$mgDkl7?-BZYm$lwLSW%v`|10~sgTmd6E`kd zO#l9BP{$a%aDImF`x|R2b=KIDHA?Ci=$H^2ZLh=n2>?JVp{Uz|1(P}B{f|Cf#4QOw zgmU$N`gqa9Q#Jn=hrgtQDoAUuIS(vAIX8~?-JCQYkpMavzQf&iT}^WPY-DZzbEnyCjwGz5xMLNf_7Op7na=qpIb{+^=>CkRE zx>g~%c>rY@!CS0Wv{V+vX^-`+_tZ*ZLN2lqgO`DU%S#H5ps_h}mz@H-4C zuPykhh#p{&kYQ{ZVl`9bL3w$r070nPF8i+qFTbLr5^nj(@v|8L@6kDmBNpF29kb0DQF%B&P2?+uXec z)TO|f$vCTg7$!udnmvBD8ZT4K7f8NiZQ?VI&3oQ7^g^+r<=+eIbog)Ubsw^1C&Cjn zFihe*Owu7n^-7Ot3cwr6O_y=QR~ zAPKP0uA~7ISu%}MtbW|zur4+vQ}LfzwgT*5_n|Fp=0%Zcy<+@=xLvHwiJ3x zM=DVf|9P#3e@RX_kjw`QMP&RtWmr}(UOKgzQ?w-L(Yec zH!QHVEiwFlqzMjZ`ag-8_Y3lvGmb`I8fS730Ma8Y1a1&MfzkBfR|#PkYX!GRJp~4$ zi2eJYdn9+$v3nysU8G*g%!h~sg>@;7NX@T1LMq80U48nkTfe@P5+fZ|?{uVQQ6d_IDbtJVzw*M=B4)=2 zZ{EZJAv8a$zKZ6+hde|p^TbKk@2-@V+C?r4^znfSZS768Le{=s!}edF?Bk<^Bvdj( z_#{MpPy8u3vTU#TZ_$IU*ZPb%!zKt4Lt;nlpqn>ut~q$HN24B%OhQ=u>@h}>T-Bj) z^mTLX829ehqmV8Qk8wnO%h%QYOD!hvLCDC($fo-_jRVRH+CFgMthEhq(6-51Z&qDM zhs{C@Db;KD9Ldt)r#_d+IW>ka`^3meij4H=r{pox){O6Qv|RI^#Z%l!d1fkVTA}b0 zh_h2JPi?W~xJR%T6Qxjn%pbCK0~TMnaBzlfCxuDvVw`$&=FXk&@vAMvl<228#PTq5 zxsYo&zUGhH*s+lab|6A-F1`b`C0Rrug$IT|DdXkHq)#?OL+A92V+4qjQ-8YZbkB(= z3wU1OhwH;a;}HD2V$1d}j@v^8<$!9?GWAiW^fyY_r1pKVFIP0?$U2Lk-dP&o(qjG0-$Ag;!=a}mK8CY~o=7fKjT#F8V&>RhXq0~@qzMzr** z8S{XmaEV6%ReZR3$XGYg94bzh+_to~E}Bwv-NnRd(m=px6ylM>C6g>Eun6rl0=3or zt)JxT_FMX;Tj&LYutT^1U3K11azyh9L(k}JTWW7o2 z{>Y&rB|_F+nLV9uvUrp$-}6Kx7Zw4;Y4tGM%2(A~+XDnZ$`bQC88JQ5H^2Gp zd#sLMAYC}QcoJ=!hy)x7mJq};VIqk0==yyG%_8Wn%GG1v<4DDq^Z|YsV}|gxnV0^V z$ncCsSrI?7j5>At$BI0>t-5yB5$m!V^LKt7OmMf0=V z?WTAneiAJ$1Bd|VShoa(mRAK?Qq#HP{Fm1n(3k1g`|~9XnYQH8h(QQqL$aD!SCHwh zVU{j0A7z@gtO`aopFbB(nGfV>8w`;c+(w+RD7SM2hoBlE}$*7u& zEKyb_$u%=#MCQcw>a7;&W4I(_+fLtUy(`PhUHp7-S-A8^^Tcj|<`$hzr}49A9=vB@ z_c7EiS=Le_iLQ$w;1(9KInVy*6$=Ne0VvS<|STmUOD5W5aXk8@yU*BrIao1Km^TYnNQC%bdSqCtU*dn0ST2QKV^ zp{O3}irDpAm{q|v9`FtpeS4RCRj^MlEhcxc@HTq3Fcj1+l+f!oGu_d`!y~Udx99&*_9kFGuWQ?XETIgmlFUP? zR5C@GLz)xK6lzHsO3OS;q)26GBx6!k3Z=}kP*P?pLxiwUl6lDV|6JDE&;Q+f?c;fm z*YO{VX`7iKY_D$&a&V%o%a+;q4E~lAo z)YLetyR~gkKt1+xIOro_BE0i;a2w1@W^+nJfdUN+5Vdbn_DD<1RXOd{>2Jc;wv5v> z=-G3HabtkDX1XU(FPo%39oRTr2zjjXGjA!?b_rpLnYU9R6ii)9&_bMlWKsQJzv2OC_CS`_s5%iEXn z;hc=U1@$EhslayO5eR*bcqTSpF#kl8CasIP;sMP-d#31I5J^o)U(MXV!dW~GIxQqg z21Nv1nPDm{m%k8B&2HL|gN3@p3Bx`25ecg1^2vic*|8z6oT4+AC1A8Tq0kt3(!p?$ zCjoyd7$`X~!W{&mLQK{ufMhQvKv3(U%T8FQ8@louocH*-*@yhbuLS<2woAq)&^vM) z^cFJGv}qVvuZ|^c8y{JB{};fppQDq7ua@8HQ9tg=2I7_(Ehp@KdGb`P&7n9!`}BTY zSr9P(?Vls&L=W9Oa9QGuPWpQb;U!4h#nhdMTa$jJOr^x+wV~xBm}-%5VOo(Qrn+6& z2Y_GtWvG%tufxVT95BL=m>%t;1N)dEk~d4c-lrSQ>fau-q57D)>ZDr(85NMQZtshp zf}e?T=Vxt)UE0$U;$xh?LWp{IhVu&fNsf4@Fz`#cd`KfxrDf%Z&*hD%DnT|Fj)sb1 zwGao<2l6vSqKg83BIwdW+&qL^#j6k_Y~ynZZOa#|Tf0_|L7&20;t6 zajU=}ihsR4bq?95wfE9XQ*+&I!tYg-QF9|3l^w8k@1C#3D|f@1IH_+LOpB=$cswh1 z?)aInU}A>`eB(&l!#8by0C~v15C(|iRZ4Jq@bF>1DN~+$45b(!QU592Y;lkisSDdy z<#xz&Cpwtk?0M03AKtrlQ=^^MyRL<;^`YN~!i`=z5wbY}%xG3EzGu(g8f$>2A7P6U zajX&I{_Jhp8xz${Q|Fz=Cp`=>D+voStp$_epS(W%>rh$SNFmElHQyEdz&mFKw^B^- zIRR8YXTVTcGxf+LAi04iAQ+Tz+4Lnc6gX|o0JH?-=M&Mb3kJ^~((Bs3-2rs6E0B(c z;NGH^pA@{0o{2OHrH@tNV;;TtjQQ4W^;yki#qbhmMr77Q=r_S#;ERmlc8h7fTK}of zmV-vO5OF0{f&c?lm$TbXnKDJH7W$xlACK^Dd1B`CD#5(Wl3m~;K}PU~bs<@zoP5bVCN?%>@@?mqOx-WdKe;M6 zJ4;a0%*=o-rMnD3rDoFNGX7R&u;w$!_cdSD%1pVBv2kXWJzbN=W`*=Mjq6Yvb08?QKE9kFJT{qiVnrnT(Z*?r^1_-jVK_p^ijyU zyR~j=Y5b%t-GT;^8aA`z(lM^C7QBnlXm9E=Y7$(X_08KiY1T|!>onJW{`9GF2Rr_4 zMrh_kVr9+TCGREE5$Q8F_vS{)y^}hW{-0slio5H%A;Q*Ta!tq=@(%%Y?ppK;vWgYW z2PzV?Z#Zj&61K%&{BggcOP31Ja{=)v5R~LC5|qpeF*^t)%a96$u_n6v2`w75Q|2q_ z;-tQ5AVKNC$zPPE=QPG;o;uyJDym%Zav^Dm6 z_if5qrPdTVGRO`eUSxrwQchJ)9+Qm{dSQS`>&WDoEq6le#XLMWBHzMf%UViEOuuAX z4dpHPMzTuF;WGbV^#;RpkQ>}dr^TC?tMC)J`g(?j4IFOonf`K4jw0cf4(JtZQP>je z^5;NlmDqG%=i(A9BmcMKp zB|~VuvUg@?He)9Oa!;@lS))gTC^jGbst0^8Xs9(Px^ktE?&4xQ2(z?109lRCnH6Gt5=w?I8b6`Cp~$q^%J1Xjv6UYatUPq!4Q_+xqlsom=rV zX)*%0RuMT~t}R=!C@aIzLJ_J1#}wR)dk*CP!_VzEhSZx(0@@0xc*u~vfpG>DltM%n z(lRKQxwm)H)i2Bmq?wnU5kM=;=FH9qJ~lhlpZoH2nD*#gGbzv-|N3hoZ47hOwNQS& z>Hmxk)Lt(f%){RhIV_z+B8IsX76@ll-1BOdG)nOyJBKoihT9WEY_(KRfs{${w;B?? zLfn<@vSAgp?EM|Dl7_rj(;YqEe8bdoy+BORzw$57hf00_Zl0Fqmj<}dP+-nP?9Z4- zMMp3y5{I~xt!&sV-Q?lz*k#+mZ`?A*uAsYZWhi#1b*jMmn$6IbDx*|?%X<)~>{0FqRi+(^HUXow;14ieB ziN20ql(cPPeGH%`v-UM>RPXF?nuXJ|Rs&lQN*W!t>Y=B#^!1fcX?P!?yy2NXHnuqM zcxHd|_wlwJBO)W!+uoPw3YiGxfv-p(lLqUtEi_xRa(w1Q6|!fKm5YD+_shuD?qpHO zi=EM5`v7<1XhbZUPpGH#FmTPbvONOmA`sG_g)Ko%-l-Y=nVm_fu}MolVw5)TrUn1jHXNW zE^LePA9}?DZjXXN(L2iW0y>ye9&x5vKc;TdS2S2OMkV-)-f5Fni+ea)euKeNN+Uac!{nGcf6SI7jBt$WvpcN0QkB4QShWm)k+*CameLKowf4x2Ec z1Lt;HjxCAmqbx6?1!orbkbmUG?>ls#f|zX^O~4QVQ+7|#9wuTv?;J&sug@0%7Ds39 z-?huR<{R!FGSZZ5u33@LbLVQoJb#e(PI)(nh7G%zwrn!O&qERhbXGrx{N!DOI ztpfH(yVVi8P9wp$#N>&A6f{MqEv*Q_=(F}%zi&~xePl^(H-q%~M`Zd+RF%>NpJAs)2(~aTjhmYenpdTL{bQ>Ba*q}s1`+XR zAL0R^1Rf)_hAiLWqN21z`yu2neqW_XVN9O01dfV2I2z zTHp=Cj=`38d`;_sA=A8Cwe{%d>-(hbk5gc0j~E{0^K=gIa;4WM=M^%-6%-1sh$cc> z(Hv1>(eJL+FmXNRss{b_J#H9#w&Glh zKAIk)Mg1Lmr+gvWf`f@YpwQyk3Chc2z$_&i{!1be;{YZNoH~8_$U1c~D&Xk}3IX6q zi61rct4XUnZ@*cdE+|O3m#)MFyO#(SC!oiymfjJ@%Nw(H15GAM{51Y z!+`o+i?3 zb5_OmjBuxvcBssS3Lincu5-_Od`XTt9dTbMRtFNyj^~v=hYR6Pp-(vdPXYUEms7-px7G?3) zfzn%h|CAM3Npm#6Ow<*oq*D6}u?nX2_(}Kg!6)ODTk*X`q01D<#o0w1XyOcvQupWjA>1c5EymhD8BAd+yFmD#C95JD zlcXGdgb;B=>M z038YDM%w#9j0u>`s9u=lmq4NfYbR{GcH_I5?Z0s0!W!4v^XDJB>U7n^y(GEQa6xN# zS*|_9gq*Ie(C_{uLBo#B)D$#~u`$I;xVpdp<);frtdC1b(C@p?2AK_UB=;Mg6TY;~ zseu8KMxYxU+21+ls;d`EWN~6ZJuwVuFu$t7#u8exf&6^Gj4tqRsbcY|l!ghvO|{Tv z>CBznj_*S}2^mR+GYYp2@|{}!8Qg|8_j8RKWKK#myZ3_?1lGBnTG7AL2k8{YU78lx z_-pNF1d0eOA&*Vi6?#;!P*hZ;QW}@g#_e5;=9l zZ(5<4>!EZZHQ~PO)@6juX|b6R3l0QURe7V$+f+B9u*$4>?KtP|Qzn{ZI?x@Fj#1f! zRoL*9gMZ?Bqwk`rAe|iYaQq|E)ZoI?ug)37{o7=R6JEe(fY7rVre zy1B9M`@7PlJ>S7kP%Xb$H%vckFW#RZa8N7w4+}-QlF~z^CVR%TjIGqM2x~FE)m=-~ zr>XOLY-}Q`99!F9gpuDH=U(>ClB6=P%(Kie8?o(IzU#=~LJAx*eW z-oazL4Ue6_n<$hkyHCtk|i0~_Yv9}C$N&f>x^I}G@n8eiqW+{L;CdZ@avd> zMy$y_H2rok_<=%d&P-E~qqX5**mkk%`0+ssqJGqhF#efZmqtu|efu(yqLysnn$0M{ zS60{ZUmQ{%9vPiZhh+5Ul^yGaT7?t=)^CV5`(ZN&fAuiztq6{;nK7y_Yotqw~v|$y*!cENZWzDmYM=>!JbWGEy zaWG^YSdJ>^{*oSzP=0?bE8KFeP_C7hN%+Y)Pw7iXA9WcO*B_$gAxAcCW0dUfI*PYO z*%}sFBsLcy5(;C1R0USPrri|{<;r%D8sPs{%7;XYp&=;;e-i{dk% zsf%750nHx(Ho8hfdimR^Xg(_ zPcp≠6ge)#D3hz)^I&(E8!)g^~@qBFvHHQbVl){dQ?`fWg>V28-a3q7(--zL9kQ zvwuDCQ{reZwz^7Tz-;3zJ+~BKSd|^Q^PL)(Ux7TR<)(uq3oei}B&2da?QXR^T|%}D*S^GqhDLX>uRW#yLNN?^d`@rKNo9I&0s}Rv99j!vVjgCC)xjq*|}2_ ztVHSI9cFpld|eZJ@0?#HC^PRL`GLLa6y|GE_bA^fa}9(E3W0AzK>hISlP(AYz#~Zc zpO%^u4bSKGIFf7_G)|5C2Eo@rTnk@RWw@@y-|NX^Q}qVEMK>6j3A>LHC^S2Jc5a9` zpNS0|#nmdwpW~=+c`A zUBG@(rC6aVgs%0+D$&mtZCUT zr5wzNdpouW`T4MLWDdpQ;|yZ>Vegf+A-4*>jdDq0IoLoYMOwh*ZFf~T%A4}1^Y+PN z)@oLaEV;+jI-}0rcU8(vT7Kb)nnh+ED;k@M(hJ>s>gp|9w)8U?u%fEaIY2RG>luXZ zTen66JJ3G59WP6>nQ+?w6qZNKlm~a*U!k;!z)a!NVs8^M;cOh19#O|GJyFaONSM!Jg!{6ta~cv zK`m3tCcqm#HkMDF?LL8qT>HwtV|0rxEu9v%Ld!>12NZ8r7pRj5;LscCjA~Y?> znavzo9!=oVyz*c(<5lJ?iU6^&;w<+rC}jwzND*OXZ@*S0E46h)i~23bhui*9-=Lh$ z^-k&<8tfO_xS>YT=A*04C%{1l^$;k!&W~@lf@VZzT4a%r+L@N`Ba>ZjoHQYc(T8r( zy7j@SJpDrl$`Y6e<*DbqOBw*S$_?Uk3Oy2BL0BN7c%g$JW8~*dyYY&hCY78^L3Raz z;9!d=_VF^c7n7bmeyq}j@?b23i~0&EL6(z!N^dPF;;^FzL|^y-?^tF=hdzv!_Au<) zzN?iZVr@72&Jsl!(`lzodK{f!x4YesJZfRRpu5)v#W+$X7b z>&X4#?L`eW5f=`j#4`PQ{rZuWkZPb_XX%L|K_`a&N9DjrK}5X2To~@$8o)cq@fCfbqQV zmcTRSJ+^A~HPKPwe{S671=m)BD$FhO`iknrf>~81`bjQb&wW(Hs}xCbN3p}?$KsF{ zAP<}5eh!{PufV#w;x^^81h*nQ3n{ zl?FB(I9ui#;N2D!z`(9bZ7#xwfKD(keVvFY1`gcpljf{LO-m8ROpgPe_CCme+RUSO z-G-i`SNmI8NAm(r^z@lC_4z3FS_biuis4kiJm{KYVyuAf3ba91IY8G~jgQkQ-71ckQVP_gu+F;#^5)L?sunmtv=9z>7Y(?U(l__eM5N#)7@P6?Y` z`}9eRboayG?^in7PhUUx!C_|H)~z5+@`mIxQBjqB1Je>+J&mV#cbD>P5go+?MEPt}PIodt0CM56vWzn%hRi70c~drYZuQu> zLA6rPCH)=9Kzduvkt%`^Fm*2Pp%h)Per7@fB?ZM%|^g7w-vRynB83URue+Klp{)FlaZ4tXm? z*+NafTemI^v(zI$-_N@X4eK4Ou|3$Q4Sy$gHBPH>oa6>lzkM{x zGdFs<;zq_mx!)VbHW?8R7*+Ru(CP2{e~Vo2G{$e5cjvX1U9PNS2A_LtQRVqJTsV?v55@!Y1eM8IC>ZGGB4fUnm! z!{NTPa%!JanTOc&ELHAa)rta@T?6R;95RQbZ+>7gWQXEm$1A^vZ?e^&+2r?02XFg$ zTFe}ZG~nH4Ou={`5$-N)y&TfVxflkAUsI0fbC8ImYt4`brKDIiXr!lFpUWta8LXyf z1Ki7xK|C`q=dnk?3`MFlPC?IF^Kg{(Idr)reyVkiho1C}oYg9u*GMD!mbQFGfj zehm{RF=7{urF9_~M*qd{^zg=Ye)NStT4+lI1bC^z+<|^iZ?^b3M@T z=z82M_>I|XL`>)eZYSG8kRyjNkEI)^FTU+X8galxWIe(DBqbqP^mo%qJ7h*$6`Gww z+$m9op#&Zlyj#T9C>AtlxrD-n(~Mx6iR;p4pHX-TfiuXetGON=6r1H*-MU4LJn8#W zp{z2kwoH(Ktkda!Yh7c$2@?s3!Fosx|IfsyG@~+I$JF#>+cr&8n^xQ@FKC@Q1;f*=7{4s)}1>X8amj|zDf;37ird_0dgWS z^wb@>NdZ;>uE@gr>D$-keDDfo&qmBZdi6TsF@Z$8M*Rp%ZzXkSqlf5c%;wK!La*J~ zy=78E1@4xTCPutX2wFZ#!CJACr%VyPw!o|(M!ffJY6JPDxLBF;$t=aB`ckbbMAS6r zY)+kL*fYD#EeQ##P1+Sl<6dWR{-+d)5(So(2wd?@uAZO*EBkyzlTp*3{P|~eKhm^eN$47byBy`=))MLQ9(V%+x;TmkFY~rVrlJYg*e|(BjRqq5@VN`lrG3s zsM(7igY{b>cg^xlQZHU!{9qq}8zmxn$sR+lcsLzh8aPy$s=D0Ud(8f7+npxzZ;$k{?(ahj5JiC8tDE~GaDt^qj zY1-mZ^$LET*jylUkPi<3N?3jzF~1Xg_Il(3Q%C#A7803*i9ifvo-y{@at#pG7Ud07 z^<;`!bj0zhL1vW+!5nQa!<@8?d%s5s5CpUrUpiyry-?nmsXQOyl)}fO*kVxc z6Fr*DW}O)?>z`x+2^B!nOzqV9+GDG&DTK6u;gX$-zcRD!*bdRcxm zsc8daj1j~XoD)mCrrQvjOciCQpX@b1L zU;!uPz3hM8X5=@kq|^{;7_>Htzx$~muSq9t_%|5-z>W*=zgycfLTvF{w(aKpxcg}* z=cSPPolMa$v}b8Yr%<3<$b;uACzSN>lVYepHOoHAKh1RGgT=1xD(Z$6^xvDFQI>on zU2?H!Dbrs{P9Tx$=|OZ$j(gM%2`+t{XM4v->yy4;HXhOli+5|o!+u^{ zZR=D|oNTav!085!{Kl0oRE{ai2fVOI-_bg=3Fl5cYlN)M)uBlZrOkQBdsi^{ahy&w zN#?XOXNr}eERRG};-@s8*43+hPY&eYYz!QO;HO2`%f>SAcB7PL&_eKqy~mc?=?eQli&A;a-R{PnRyc-1d(1 zrxu8sp2gv;pZekfkWVGa5Mn`B_aih3Sodgmm{e?%uIYeXbBIz4;Q%rSU1_~+54B|O zDsVP}kjj-eo7Hk6<==Aj5B+dk+)3Xi2QKlbY>s5x8RFp!4-q4oggq~wGbSEw$*OW2=A`} zErE9~Wjxwo7;IWQ))#p1WD+B-1N96XkJW|EXCA#XUI-gCEw)@bN8=Up;zxsUi9MY{$>nPQZ+($)tSt*H-XO)9jAXJ zIn2Gp-{4iZO)2g-oX};Q_qYT^1Kvg;TS$zQ_1ZKEef5`|qC0{w(M`&AK0C85aq)wY zWwv5mv9hec!Z?go3#Y7oBH~3=m1Wk-u*3v={kAKQ1&5B>!``7CBa zcK)%r3s26#z^bUIK&$k-gI87$VH0wN;isPi8BTZaTc+shTH3aIw-v=UaZnXCx7_3Z zd-v-;_1E|7p%Z z2Xp&@*;F!p+hiIz5YMX6*xPz3BMB?Ro(}Fifo;ir6lGl$I~FL_-N&t61yMr15qr_ z*jBb%nudP;_Q(HL?qK9%Z5_7ZOy88PBj2Z`-P)H5OVmtxI`9!X#IyK&gxpa*Z9aLp zR!ZWV(WOpst(Pxf?l(yr=o6mPPL3xbtr3Lsddz`mXdN9NxnE{|QxC71x^@Np8x6y; z%i$?ilEbx|i_8JDTaWH)Ect7!e;mj_BH3$GLl%Vbo@Jpsc#O%dVy0VaRG4Dg7F;iQ z&6MCPjSYH?+0o5XBw%OI9^!_Z-PX0aVoj^^2 zFO)H@)UsIAqw#`_*h&n*gkMIQ5Npt%K3fC6dtia z+SPm7b#2^Gvn!g0#z(i^A5I4?wu9I+YHf3+WzjrS|H%wb^{DzS#g41{)BrNef+Qc-VFGcOx!4LQV3UL{x^gj}YI={<&cBt;8*ZU= z`etx7g;`9aCM9B`Y8o6HnlrFFLAc_x@`U34OcX) zxAJu75I`_Dd(}{x)@hoSg`|i4YT!J&*Q*g}E9!NwdBEhP-T0@_mUa&BDdyE|E5O54 zND6eEmy8@K39ahigyd_*9HF2%F0-+tVe|6(WFG1DSe>Ue*UKgMP=WBf%>hLZp}qvm*qo9F zRxD^q^X|$a7Pppi9e_3SY5pbhHVHCYnNTREUhcZb79}5)9KlkTiStRNY>4dfJSL>*4=E#;Yr6x@x z{0cDOxE-cAxa0a;ztV6it9Dd8%v=~L+G>^1)=gJk{p57DewgJhrL@5=u7QcV!!e^| z@+QuWW{s=CXTc6nt(nCXJ9R@+qiv)1m%7=}Nr&|xVXx67y-aD}6d#`l7@}E`vzGt3 z9itqrqW@+m_@QIeuCO$E$dCi+Nl8w%@6Fx6VA3OSE<;Y{=*a|RDP7TI%OW5gJsZ9W z1lEFt!W1f$Am&Sg{)Eh!9a&l;k#Rle zPdncq)tzt=&6d0ImZ1nZZrD`@H`hPlU|YZEDJZqye9}Xb#wbUeu|WL9MGgDp?bCQM z_M`glJXbd_O?k`a&1zcMIp6zj2HEsOs&|-UE2c%-CHt;V?YnbI^OdJnvNd*BX3*f~ ze-5o`0VzyY0jn2V##8f>;6QMF^ixmvL=zERto_q(PjF6pB&D^XQ2=zvEJVujxRkZ9HmW zX(PUxXCSIRZu^l%Z*_^ALamajol0R--)U;PqqpCk+$I@PgHWTz+|JB%=L;+9wC*nl zhh5t(f+fmd1WU(sJFI5G4^&6t3PK^6i7Sa@AV6pcIQDsMtmt-<-!@O&Y^3IU%E5R^ zERK;x#40mfMWe+j7}ZuXrnYs)n@wLyoQgv&zn$b zc?`g!Mg1hSyd0hCt)Eo3zQcC5YlA7cH_$o|X=%^vHElTJ@zcS`Ow2Fv1Rny`GnCsq z`Jj!gSyT(#_2M-cT2X_M5Nc(C2?JGXPKFdlcmMF* zsMhvYm})t!IzjQtWUt)t_+S7bQEpRiSm+L?kP!|g3p|pq-QELTMC>%gbA$YJ{mQP4 z$$QWIwM&uaX*u@^cs1QUr!1#p!Pam=ul~FVCabX_JFuviZ&!2yC7AR1W zV~zSi?Quqi^q4X-L=eAI`+;4d(FkcOj4Am8Cb0rDF8ls@O7txk*A7vB-L}MRz2IY1 z^w@C(by$;w1;vw+ttfo3GJQ2L)k>>{ggr8E!Qz}Kv% zKY0iV)cj~XV?+4)bYrp?!_mXTz~@WXBcsx4bpMiWP;|g{(oBEh8W5&m@E)rCPToMX zm&fS{@kiWQr`7{STl#4KP&pvsH;V6G4W2n2`PPOJ2ve%OBmz-YVV zTV}T(JC@}ovGt}tNnMme?cN3TeYZekWLeT(#Rf6?qGm(q^GH-J+$iyW0~LAz0a8TV z<|c_ow^=)`$OSx&W4Hgv62HCGiBY6&%nF4UBfcd%zDsPPR@3w&ml76T^l}^hm?n41lw=Yv z?TN6;=;Rj{1vHx$q^~t|j&=~XEHG%jA>9a?io!IES@~(OOfW%hlNk`oL_xk})EFE{ zF&h)Ty{y?|a!EqFr=yE+fJ zNE^?x<6tOCuyPn_1S3BXN|4KSlV1s`M$0UE~XeTYK~a-8NFnkYux1>8Md=b^w7%feGW337 z=Es?*Wj=m%Cv0`n_LZC8B?h<2O>+!WMhwCN#es3%tG=9&Z3~&1p@*D5?4ymNYw`&m z2e`QO)Y2y~iYDc*F)w}-x;X=uW6R?4pogK*Hb9TWff+9_-3g62!s6OMV=6;nH3bu6 z;{xdVa8WaK$3^<#m&@K^MEfl}4qmvNN77CtAQTxyaPsf3AoEE;Mua()qL-GqbWPon zczzr6*fVsAM2v83%Xh+pxwQHAC>%DXOlgG0kJ0eiNHQoNWlxd0e!7V)9On>=rp)Fb z0U|TKin7kCX@`O86r(Va&vIZ2darU5i)>KU#@Mro`>~`woEg(sm zr0s+ClQoznhVsbe5ZOEJ>!zpI8Iv<#7nj%#mB*--g(wC`s~)BKe&;N)>ydbs>whz( z@t|QTYAP1HcUiXxuBN^av?ebvaM!68@E|Jm>OZ;Jkm;in0#7b($r;K^H?WyuWG zDv{jLG)}~IKnS};Bw6CNGZ0w&H)szT7h$f4T|bui%Nl|(iQQ&LbX_LC$<~c0lFp5z zZ*zYAGxpH4tPxb71u~mXSnaxoD?*s;tr88 z_F8DI$62lC3CWdN z?^)!u0{BXm3L8MsFJV8B9HRnBL1EraUHNwDz6biM1FVaIH=A?T(?cpqu^>ev7G~jV zCVDS@D^=r919<2eQeg7zXa22BV)NS{Xw@G+cyKjY6pE7|((>D^Ah!d-3bk)?T|@7s zJwJJ7FU3|WtF_vraK1f(VD+eT>;ih-Ns`&TN zJMkbw@Q7xgU{ogy!&;jzU3jpORU5=>Kz9IZSmd{%la<&%aQ%@TIxIzM$kL!JEEC)Y z?SR>qb>iaX%a(u=)L=HBAvn1Age(h*kycwKRnazvH0^@nP-`al-D42ZSsp!6Lp(9K znuS0%TQbshSIK(rvB&1>OUl8TU1ldCGc^t98hayyfDZ4W-6W+B?To`NgFy%c#R{AU zC9GB&ae2ng+xI7pO&YnizbeI!Ip?#U0w+#pQ>aZ0-USlVde46`R{jO>%kKNWz8<1w zWRyKBTHm7eF2gx#X16Vf1`u%rU4In7GrNWhoKWkTC&^uqfiLe3EHnMy=$s(oxoW!^ zOx^p5)k1>t@Txyi&C5#Dda2qfz~VB7U|K9*s$xUk2m3KW8R#*%>OyD`N}VC@WY1S} zt$`s^O7|DySz3~wPg^u`#*FolGFm7aXI1SMYY}C@_pBq^`_1qiH)c-}vqxDtbO48s z%E55vhsOh#iyhg_nFmw`>@{)ta0Ns})Jl%eClCzq=+P15WjLqWy;pl&!4sc<5@{f` z*Tgp;7qB3Zlp;QK{GKKsM{Av1(3m+e5LjsA;|u>xWC^ zjD`dsF-GBfRo@%Kpp(z%9lfcw*3Q!F^|%URzF9VKn~I;->HuuaxnjHeFFCvxVaKpr z4**r9YZ1i^tRrcnL8@+rIWUnBw>nvLf#w8`%5uD)&+HbsOKJb;XvA<6Cei*kq76cyU!L2cuaPOn-q#~+w%Aw zdSy3m_+NV@!;$+v8E;+NGrAKGWzU@@Bw{{jh@l3KBf?+i?DKB4+~350JIWA21`;r_ z#QjCgx+X{6RDq1jy4%KZw#|@9;Jn{##*A9sZpPu)`jeKmC}AMn>L#D2hoP0$GB$3> zILaqs`}XGKO2X&}>+TsTEi?!Xlu8dOE6vpV({@D?v}EOz+%fuCo2&RXJABqBOO&lZVG|^zJLt@%+$wsqKd{55+iOV=N$K z3&k^c#9AM8Q%pwZ4dS(-3~9((i>Yz7PA$;DgvP!{Ef*Fy8A3Q%NtctC@%?Pt`+Fw_ zZsJ@ARY)sm8}cKBd|`d#DH$Q(_=Q3k!3CG8K&l}Gzg;}a}p;KjH$dQFUu8u#rh7fyPsVJT-M@D-UO zVl;;}2e79KzYY2O)`9bs;aF1!O4BY6|G@)??#B-!2!c|W&%Aui_@lrw;zR`y`hClk z*r0AVBU;;!m~c}pS!7*V`HWK&DSu=l0OqiY8cYfTVT=fk4Jr={Hm*K@*LKidN@+1z zV%f*mb+g7hBEZ2E$z2#T{Em)K2njl;kzDKjD`@oj9lgtrWD zrB^U6F=g0_L&LHvU%vd^!c1R1AWCbvTIH46UzZKKKOuUUS@gZzm);K8^R(~rEw3)V zxEnR7_U;l7P~|R^(a{LUic!`1k`29Q%;?GJGM;CiHsSc8*d7z=wsp8fy}N+~ka}ZM zhoQ^HcSsqir)OQZ-)l$Zv_GQX=A{%a@K3MN$vph!@c!DG7caE={L$Qt1>Zf#kKYRB zkT5ss>4OKIfo$U87}zabc!UUOfreO95WGEcRErxS;7`L^hr#fs=>yH|YOQP{qUEdkUif&;;WbX;|DTc%rIQ` zc>jJqc@5(4!KWLq+AYY9#=&KsGiO}!8C-PK)b`~E@AOA)4?oS<`MA(OAYfZYLSu#V z*it{?1ixX|@uKfvkJ3xoL7*A>>G;Q2uQsUZ*jleq(=mPaQU8(k?}J7cy-)o99wtFD zYhX6lEHZGM4H%L2EaLiBi&kOoezV3}l4^E5Fn7M0#`3!Xh|rdU#oC#T7*RUvJHpcl z{*+|6JJZT_p?;3%;hrZ`l#YsGg-8*TnG^^eD1WZmFV`zjR@dvy0;CAZ*eAcd9Q2tI zxAa5GBwiKE>Qrefi?;OJ|M=3DG2o&)80@?$XytXDTEoKJhoEId`q`e?LNz5Axna)+ zYr?KCPtuFVMXzYvpu;)SZ#$V+s5h99x7d}zm_7o_SmNk~p+CNMp}vSCauy>sEjW92 zgkMRjX0Bgpv`4Prn!LO`Kr|P@;wcJZ7&qB2U3%j5sV9|{THQTzHb5zKziVw4KhpnZ zckt_~chLm1jS#HD!)U7;cltxK_`%#8*19?ft3}s+bV2QQpG%wH=P72JoBJfFTc|Pw zm^FKLWo(|$toH5OkL<7_?7X4-+@`S5_+xH(V%SLY$4oVi0`Bx}gJWLASwnfkk7GugMvXT8Y)(J(g#I`i1-E=g$Y*1T zgpbkP#!@S9nqH--HRnlnbvIfG*C330;;4SlQ#y6WZD<2<>3QZ?Pq=_@fa2|TnuYpc z{aD+fVzc~oYkzeM$31?}?;Sr(?^8?@SQ?x~3)w_L-!(nfqp4e(lTJ~5_bq9=TF8_5 z`6n-?pjB8)eJAtg9S7?jo&7`&vuEeRopUGq`s$?E#zphFiy)}UJ4O=r>79vVTlBAv zd*U*PENPD@uvAr3kP@`hAG2?3Y_~C^D3NI(@%jL23WtGzxi&>_KVIQL8qqW{jjV4*5l)&HIKe`p0)dqXU@rOet~(~ zV`jgd7C6LzTiM4w)AF|Ge$Put|MsT8ee#gOKl(J!ti74DY{;hZ@!9P?3Tp<;+wtgo z$s7IfGqnS@W$n1y|A;3qC31b2FnI?5{>NQUUTeq?xfG&F;iJ7^wm7#KOO`eVL%?BeNG=4plh>tA>?VP`#c&QhbN zke{#P5aE7&e&*JpL7wSunFb&%QAWPS=Sfhe&uT|)?e7Q-S^5J8z!T2dw5{9EXJ>U# zP3c&!>R9!^|3y``jrWSNMiRp?^nQHsifIgBy=|6vfe%Hq5wc?3LpE|(E^v{}%^M8M z{_zrM`7sHiDhO;M8VGpzO>Y%Ktu46~?+xY95FwbXdccJJYlsxW104(fTd%f6KNr?!+GX&>J`^JYZZ&4XocqYiEI8+0JH=8Dsuqh&v~>+^3;9&lsv zH~AOG$4B=(`eWaK>3iarSKrP~@cXfG{N3-7BdVX;7sXvW^fa=}H0R_Aj~Q0G2cEM1 z-86GYq3#%uWu5dVH0)U0koTwV$f$ zv=vX>j8b;uH?;U)TL_Q$az06sZQ%hH?=u)=f+Y+jHf?-0tl`<%I+V>|6Rj9o0hJ}Ul)pa zG`{stFDvr8$$n#=Dyx+DJT8v2`?mG);lp4H7f7S_o8l$7{@b5tL1R0A=y=TW3{9L5 z#AVw2F4*Zs0j%5MCYwxYq@^{R9m$Kne#&x5b-+&f?+o#Oe4C2jIK5-tzs=CSjN`Wr zA4!{EV*{_yntZHdV&XPxhiP|6P4QScZR(}}&qwjYhp?)Uq-Ps$ItZ^UiSn!z9?mb+ z1x%>2wzemRJsavPDksnJ{#VTS*MBBO$`+?LFuUGAMeH>IOegrtq9scrcsIRwC4@Ge z(D~4!x0Cs`H*kd_^76hd8My|asXu)NyyK;qh+JF_NB(4uJ-Cyt(eN`WSo zmwYshusod+8e#17usHd>&i^&!{4rz3Kxd6$1n-60)`tqMEXk_;a+2b`*_4!=|BoZn zX3Vf*%VP~(ZOzRISUbND>B-O(kOe`i;Oe7ce4tyt>n}4cHG^6IPNbxwN!u4b_?u zeW=K#m;e~+?!*F{NtTDL!;>}*G~3*(bawLlQ~q2Lj|)?)p*Dq?n=5En&uNqSO?PA2 z&4`qYDbCmG>L!_LUB6cMdgYg2)Uvi#2igv?aQu|Fw6mT2jPcFi#$JoxXz1n9aeZ|8 zsmb~|%kuvC)-vkDfeC6+DOrnuSQ$oev0{6AdGh|cBCw3Ygop0HIhiG;*J)BDDpM!u z)*Yw1Q&U&h2I`8TbZOVG|38kJqq6Uj`n^@7c-+Msy_T`!5@t=bt9BWU{D8o^oPm4( zc|4=se2?hsT2w@8P4<1i+obt&y>Kl`sTg8q5whGAo>To{P=C5>X|cmm6)z_#55mG& zC6iz{=s$mjC>>mH)=?9F+O~s1fU#5C3vrrF^4~qb(jlXpTDxAqErRJYyDf16%c zFcEMk6JuM~z$?X7m#XMGRosC>iXW{ssA|s=4tU0e}3`F&wsXSGhlWyUg4H(f;A)yHr%nT z@3d$}cXlvrCjYlDo_6M3NxjI2HI)bUpRBVzSy$CfqAZN8)=N!TZps{|0FJ%7Baue0 z&D_FyfBou7B@f=~sQS9x{UOiU*HFW&bG*x}vGC7ZsIOiVhL>r(L0bR$3Z|WB@ycUb zv)ZpC;ju(rS4mk%FPD#YW&@LetY>mn80ZvnNqn%y`R5Bs@=1pFiy`DgvQ;qzi~FCC;v4e(>duvfp-sCM z>72RM&_b#tBC-h^($^9@4h?0K{`mvpg=yofW zl(_~4%$CW0>%&gyxAmveP71nl_okg?K3xSgnmsb|{|34dq+w-M=B@&8(B|E$J~7gls^W0-brKhTDmYo>?s2=xIv3k)oxLPEW|j@l}hCuPcD zBQ0`#A)tiMnGmJuZew~VDJw@qr?;IuH%E-;(|V|F=|3w8WAm~Hxq#k#s%EJ9_Wi$i zF+WT2=TB+XZPr~*i7D$c^7;Cv@v(=k8ru22+xzbPo_TEo3<}=8I;lT!lDBtc?Blap z!(I%5A)v!>Jc>QtbE*lih1;c@xKkqUOMe)o{Dd+ir$JMKIyhFPSPe4z`<@gPyd9Xd!0uAut>o`+I2t757GpH8sziHe zH7*9kEmbgc^? zr4XJfA3oG%h7kDlDNx?yh@K~}zB}5wNq$8aFvD!NluWywoe~xX``aC~!x!{QDLh{x z`B_M5MTMs{LY@b+vZ6r$yM7)7mN1)5O=%vv>CpcRZ%$d4V6mxulT$QCavoqtB|BpL ztf-2u4+c3mx!8Sw*k|9%){A1w*Hz3Ld|hkKm*(>}l^?loaQNuqE}1NV**MVS%AS~t zn)Bn!R)&4{G2XaUt4-H#>#fe6-a5_Bf54@@4teX#$`;senlnEtGHz&QUG>EkM}zj) z9WlJ){k*VCVEn7V=QG`NEH2cXS1a4UF2yh+#c;LtogNp;YxJ@{TA1^gy26Z!rmQ>l z_Woc5JP!`B?R-N0-`n0R=2z`v#Ar zpt#t!BO!}9-bN@S){79`EfYqB*rys@ED2?oeWy50z(UxRTTA5;47mxF#{%R zO#FS4?WoBLY8^UsXOTti^f5)#tct$%%gw+0CVZ3EpOG8-WtK;kZ7N@odiL(QxI1^_ zudmDb{;juH)zl$7vhGBc-CSupr^v6z01LyR@>B|VD%}QjPvNP!m3>^@>*))vHeG@& zr&-lq{I;aD(Dz!yLtWn1ovb*=-{wrU`t!uH`;+xAc9ciF|R{6{rv6YeR&#iw11X8 zjJH!h*&WW#UR=9s@7}qiGpZ)+z1aI;<2mh|XZPv4=eo&&8ai59R|ntcHg&%;7`48dLMYAN~Vjr@)_GMu5&JQ^In#aaPw?U5WF5h}EqSvXE?Bw@nd^Mf3l4~O_ zm=*cjRNr)pFK~(<5%SSJCh2|TUk99PW}(c^$PoQ{Di)5WpFUD;_oQ=;Wg1h&G+3MI zQL`KWs4G+h`*(A0)GGxL^8)CLwt+#_${(v%u3QOk&Iy+PrT_8}uE#bVRPY#tIGG2A z(o+i)R%sZsvTSZwv1e?fa!Z5rz`by^b+$$Sqbyeq+|+bH!DHa2WEg&G8XCF`MFlBN z7#ei4uYcK#JPU8ZRv|9g0(d$N8KU!#U)ynU@l5BD=L!oipy+kkfuc7XFi*p6= zWDwf}fk_}*UU-(WmU}#vc0Fs1(nf)MZl#yIW<^-bxqF&8Ihr&sSS_X`0T6j`z@!lvMVb z5I9~Sw>vr0s;s?1_Z)|wnsTM1v=nuHBF>H!lmU2TQdR3VXLt#Zp-=5zH__$KvEBUF zCCt01zk~ry*rqX)=M?)jWm3O;-3ISJzjgrbrArP^cw0Bh-=pH`wB9We8fDHHq)-yc zoL+ZSOb+?!JlMQ1R+c|;7N$LEU38{Fr8M$6uaY+I@IKku^8M4JU!ARNuQOhHBEtgu z!7T&BEv_zJa`T$sxKrT%ly>1^n_LUijxMd;jjMzvjugH3)|%*h1dLeo86p8E*@61( z$jj~4ep{V*^03;?lZo zP@0-I#Ba!sun$K2#vLBTP6REjKT8XvDvW$qY%=|}cK8@=w_|gsJ-@bVH6FCK^IzW8 zX9yyLJG!B(cUNt5VjdDBqdjCakKt4q&D-j;Wzd3s%=A29Z}?T|6;0k(y_hP@32^^2 z^w3LwOGek!HSElDs#>#SfZ-CDUkV#o39@CNZ8lUauYF#pdO-Q=JulV=3I3L)A8t>X z4Q=RP|3w&dZaA^e07=(6@9+yk5jkFyTR*`Je zqRrri2jt1ee0l%auVOgnvTuAFLbfQpZVZ@J6>#f7 zJxX{>=1&g$J%?nB%{ud7(2bfKIo_}^^~UTY^|epbQ3SqpyfMtX@=%`M%X!`T!7?$#j z@6=Pa-Pj}GK;SqsncHwjbqd}#7+dt6w{^k5rJs6{n%828@~-xNCyxH`>C-dPCbbcp z3g&&X3bFlw*$sqqM+EzYN{<1|FBImz=b4xCkXWl7P8zMG|4>*?-aC!?I47G zM0%nO6BNRZ0zIVNyFMO8=@vyt8JzKPvqv0f zh%f2;3kwSBT|IZK!E%7f(@;PGa(JZ-MheLsI1W#$n!0&+b#)tzwL!=ItNQD|fq4Og zq$LIf5L3z6zr1;RWjzw+)bc%U&-bmzdt=kH#W!t_+ZMA_0xhB9f&~lo!s`RKHo%yT z1GRhIWG6o03f>yOVic_!tb(0TIwzMtO`O4tflWIMG>r8rQ4MPS0|%9UrkQmB4bck^ zrrCQ4?&x^`M%_uRF5ZK#>mKUz=wPy`P3Ff4osBQ=9)wG``9&vtCFl2w-)Z}%-rU!L zZ|$2C)F(ywBtAvaiU1~jND`Adk4?2rn`u>3|3AXs1gz)$ZU4@eLB`lx5F<&bQHiLL zwUCO0p|PYwA)&IC>_W0rky2@~OO{F{dt_-LBxQ?gDocq{{a;t+ci+F~IR4M`+{b;) z@0b}=-|y%1eqYOZo#%NetYx&S4smh|D;8)?Y!a8blHNz>^>RL%zZe+h&f&rRVHI-t zID&d$?6$Wj7M;Gy7#+!FUtIm4W$!i=h`FcdgvUeEs3}Z4n6V}IV4+(d&D&5^gulrL zkzG)t{WbmmwmpX_ltyuo*SE7VJ+$WIqC9`zU4W}SKMo(T-k0tD@l(}6_$ zai!HW;r{{c$r`sllaK!nSbFy2#am>9mc#zeJ@5dkL>28Z0eWnDU1e)6ilH>)HO}|RxU<>+2B0itBj?PB79Grn2O7)~p%uTXW@dcy{Xet($!f$wa1b#!6*Q}Zz(GmXuPamzEa^&VbKLv(SpY^;Bv+qhBdNm zvrX4OhoKr)c!z>07Q?IPh7egoR#R#HA>GQ|0ce-}#6Yo95?nRD}qPSD4 zgBJfzp|pihF1DFm%S~{kWEm_ft3$?f!{k$#*}!gHELf{|Em2(jw88xb)INFvPXr~$ zY2__L@r@q2S!puh7rV8I!vBD!vfQUPoLk7Yacl-p1YOGMmv&**^xh2o+Z>*4$&=pW6+0O02_sG$?3^7&b32}HZDtdytB90&a{-jb<-9%bEs~f#kll%aCx*Rx8 z1@9O#cEDc=@6-n_RHcpB2*LSyIcX<8oGhENDb3*#W2IcyJ;65EglXkH zqi^oaiAF=q@>6#5ja`;ijF*I&*5#{Qd;V~HFvC&!)G94X!0Snksw}?6UNwpsRpDo2 zCD`GQ>8jX&U%b<~D52DyBM?13-hi&@q4vTn8Lg&EaZ0W8tgt7OH7-*`nhrL@eu)81FE>cyU0}N zqwW>_aAq6BouC0HBD>x-X6*^zMyaK#IRY;xf1xm{0stD*MxWqZ4%9A8SgR7%GkN38C7gex+wx?bQ_#kaf#??K+{D{30snssp(&rE~3NWgFZ=eOll z=G4J0*-`|;u(@#2pLxmNf9L&CS(9n(S=d83(-JfBI^y@OcthKjgBo1e0#^{~~Yz4hVZZbD6z zTwk}k@&f-de8SNYcLMwO;=yjAT0e0wVse6KEnWvlDSjH>4~x!1FZb->dcyA3vIr~M zpgCi`_Y`LK`}#*hr3tk+Z~dwL#&SN!EBVqi^h(=q#r+8Wpqkk4BlT4D%p_xZ$wa8D z81TbDd-Ni{S|kO~+-TE|pFyG4GNtxwXM`FX=#678G{04MB!q?G401IzHE)EbAVTO& zOlVG4f(?=FeX+OyUO3nC=5Zox-7=j2{O?I%$*Cg)Z$0=1>*^{xL5R2$YV<{WLTBcn7!+mB{hx0rY-aNmgkRtE;~F}TX>5c!F} z2GPL%G?Kz#L8SYK1cc=zDk_K%{=C` zZLOydttov08bB-EyV;q`_Ra(%w?Rk^u<^X0QX)JOT$r)LIXN`{tt-98l$5*!E`{;2 zy;{K=7P%Y@w4JJy1;YS9kRfj5xGY!|q#aBJc?|&*-b13lrq$mB_n}jl1@+KWp`cvs zak0m$>wBs-@5jv2>77urd8cXhFMLSbS#D3y9yOj58K$jm7pKh`I8;y-mmc7~jxWCf zDT9c-5M5aY8MYuI2lZ`Nf`2)U(3!fEtA8=T=LvSI*}BHfCY-e&excv3fZInYgWGWG zZ!ryeoa6ES6&&DJq;~Qy<_g*6=}&V-(zCnQ_^uR~_X{dX9!D3yx)yDBp%1{wP8pfs zpX=wTO1;XTqrt5x$$40CF2}6xo)cSGaRzh>7g(qWC2-$JiDtxS4x>`uK4er*TQ$ zkCpACYG<*mF#Dj86O9cvcO#&r+PaNh-uuAo=j@E8*FeB|yv5q1 z+1SBL-!eXIM!aM`eeC5G!-07N^T;P)U&>bxy%~;3N}A*8EbHQtvd?Gh*w4oXfG?zjKiN!otP(y{UW`=3cyCFd*;K(G{Owf5^Sm zkCEei$LSZkc+Ct2Azd*Jv8gq9;bu9Y(O6pPF*c9mmEnvN~AK zl-xb1d-%xmTmokDkI&=CseHdq0}dS+LNf%DejU#q*lnvseEU7n4$Ea~;7->#h@D zUS84z^1*t#aIuhz+;%vcs{bl@jmWvGgX$87(iY+Gvm1fheOIF*h<#$yEBzfM#}+AZ z!B#fG>!*y_%z8p9T@Eg24rGoooE+xD^00(XYGNhZ!8<-Hg#f1 z#NdkUJ>QjjkAqet=mIO(yguDjk?I?w*W$u-HKgY4M8op+>n$>n<-S4REi+VD!8JoP z=(XROgGzrBX66w2;!KHoKxWF!oYI2~#06duLAkc}oe$4Q#p~#xLYb1_3w$AURSB(>Aor^#l z8+NOCN?g*bJayoqu3z_&9ejIE>B4$t=M6Cx9&w-Jrp-?p(!7)iCs@;@6H~0zt;~I< z@!_&N_lne6LDA})YMTtqV$L}u$i>1|4(l44mDzuF6&SGOgSmf{_TU{zipctyIA>zOun>ehTu}n{H1=zG%d_DMw$~@rz`Of zAhMBjcyN>YL(4|_82(YakS{JljnKQVV2ERuTYu4Yfub!fOpob0(nH0YK2~(md~U=(HDu&KUZ=5gmed`1kdwg z-&Ip1`#EK;B2iu%GHD^Pu-VA1;Z`_Rc*T7BT6MbK9kw32&Csr$BdvpvTMwVYuj$Zx zVpH;dh|QN`!1kVkkt=LA`UPBbJ7+4U*{{{EZO7ERdJ{qjba~}PFyR#F%$KRve zE)-t8FtBZwCigADazSt9XD1QM4w#=kVRWbLAyXSEu$u%4b9ly)H6ccBT?qs8^yXjX zoe;bsIPL&aJYlQ)VQA8G#@{rm&Wk!&+h%zC6lSIzEho`R0%SH6rW{LcJCu|iJJw{m zr&Qr!&!Rm*|@P&$>q>ht(V0$472b*&jdfWVyD z)6ebby=l{?*-uVRL-jo&s+rentxayp-_5pYz*uUHXEuMrfusk?qc`2t)h_;kZa zr@QID8&I^68gj@4D39dSM>;J>2yV?wh^dMH*Gz*Et|X;*y?ZP?V|VjsIJ5W}&de_P zWKh}yQNsbXu5U^mx;R!fD}HjU%&Rt~ zzHVIX7|Rg-R`bRO8*c(V38fmlysXr2y%K$tgik>e2vcgh2dx1(GZrg`+x(PMvOvIe;q*HnB`sBk+i^oZRaSHo>Tyh1ykxgF#SVd zBYWOH{u-0rHh)M?G?ax@$?2!9F1s~_#pdDko>HDdjc`XLbhQTu;92v2(G%B;?m zM8E@fU?Z#ZqtJE6$NkrI=2~|^@1wN&l3`@r%Zu$-x0|*6s_u*gLuj81Kfa{V+hMvf zwoWe~VQ&V1bDidwx@l5=meskVK3Vr3KYsjSd`9v6$8(+|=e9VYBP)!Y?(S>Bowb}` zkKL}4ND7@co)AT1{h7agWVjJK_x?5xy!4AiWQCUtyCsyCBN3o^Gxyo{p13G`yu)(K ziGZ{%CuWASuJer~Pi7l@T0x=1=MHFA>wTciWg9)q;8d$!C& zl-nDMhI_jHeQ6;T(H1&yt<8l`@^e5#)EN_e`*gco`xlc~_Bm?1vTniKw-H+p0lTim zcIv!cev#R|nk$#KJ66oNC3gp4NUmwW4Y(B@}+9 z=Z!sC{HFsX)`GqiY*^C1)uUzihr=08U0VFLijXSJgVM$3d07s848^Rcov%~;h~3qO zwwK%GCeOk;k%sEHZJS~!h)?ZTMOBEi6VHRr0QI6;2Ws=mmi#xHDPj3+!Ok{5?EFS} z6LeZ_@7~F?gg~qQ_$23p262GJ?$AZmi70h?UGAO@9^skLvexBqkM}Eakwjn^6lnT# z(#tZPC-duXB^qB4sD(L&R)8HlA;qjzT$va}2EJ_DNHZNG!cO{tYZTX*r7ft*H({7IGo^9Lf`#GY-Kb93g`{c=WbrmYIyl3IH1bVN@+sPUgSlCvE}Mbw0jfR{g7695l^< zm=Vi=UHs$Q63#sHl6t!VUXWsY~u#A6dkeYQ9H2TCJPvneAInx4lc zJ!&g6PtP!csH}Ne>roiNQfzz}ej@1o%dM~^B+8$?o#-rr{z!hT-cZYp*wj+3iyJma zEm1NsaxzozxYcrd>6k-L{WBkeJ)cETv7uzRdr6Zg88k!}wjZduxkn~z-Qi6rzQ09l z{>v}FG&Hks-h{s#2N%I6WiLU6w`;ZI+L@B(S(QCLl#jdcNF{of<`ZJKXTI!$Wr655 z&G#P@nqOcGBEz8Q$#t@+4M2>P)YK}18a;}%>dX<0X_g>oh5Oa- za(4Y`onobR9{HpWHbw(qjSX=WOIL>ggo(vm2osOWxH*|YBeQ(Jsuy>hW2vlI` z`n&@~P`x`H@o6l$b$m_p*ezSOjEyWFQ+lzKC`=(I4n~;Eowr*UkexP~pQlvVIfb=3 zodz!S1={UYT37_2X~T`ZaQ^T2jGu~4Pf?o}mvC;2m-FZQfZn|Uw=hquK7Hltp|91n zm5wwtibgITy*4Bqnnmz4vYT1pllwT=C=a{mPuLC~x3ynZ^291>LRN74bT-~IT{ZW9 z?n5M(JHzmC35{*j^3mqzos^n%#d?fd3?pUWmbQW*R_tBg?X|SuBriHpGS(leFVY*Y zZPY|DTD`wpXQuA(E-ej?+f5vQkHGy}X5mzI#ZOKx_&9jR>cR9I4SzsqxkG$qKV!s1 z3qHPW{V}EXA8CrsY}hTZyP|cVFKX#G$Yy0e#k|N-Z@}&YIm_+F9Z!PgV~?tt;ph3W zRV#Z37vFz+ySVDz>gtN#vd@+I&hguhdDW|#TQC-VL^d*fcx-R>veQ0YRXTKt=(>Pi zB9G3_9y!Rw1pmqA96zI?xb3|!K%)R(Z!544fahbI@g8hRZONec2LwTdmtH>7DWlf_ zzJ+DkZpeYiEd+&dxC^u-BPdEA9iQB{GU-yQ0eHpv1M9z{T>5KPB5#zu4gOchnvsHh+OkNCMMS0%uI}wdHWKll)5?$ zT3*&g<}nOSn%<;5ULIV77+ceBlI+p-VDk(S1dIl!lAWnFydBoMo`^gWJ1`PNBu4$K zn@TH}7@r>J4p+BZ(5CwRc#g8Z#q0-(OQmV052JmgYJZySy?Xt_0SsA~*p97=N^E~G z#-!hBT0PnP30$SY$U?jWkS=FCda&h=P>E#ov&VgBe}+OSP0h|#_n6U4Q8A3?6Y2YP zCYD3oWM(N#!`k%qsxFEG!-`IeeB+&;RqR%Ovc8%Q4n%IzgdcMkmK)D~7&Ks<`nt(R znaAqNxk}j);VJ>oqe9kfBabQQ9qx3J(vHG3QOo@B_+Mw}hc2qva*68vSB`Q2?IVJr zE~o%O7f(vt(P@C}(G`rDLS1+x^t1y!EiBM_fZR}ts8)g~Xo+;$A4B*xR!%I-UaAa6j9ey_8d~;)m8@`mLPrg?4#A7Wx z4VWKM<{0-x(^Jn+MRkg;}8tAun-ieqoMc~-~-21jtL*t z$gR)TFU&6h89MbfSX=KIR8^AGhAH&`o&!$9`Oow!^J^F-V+y-4`)F4l^K$$N!pQ5+ zS&^+tTRoqL|b{->tMU;l2u?r zEF0~=%F}iJrBcx5>);clGRpJyz?x73Y94eR0YTj6eE@U;10JtzBI7xkx~lxq7A_fI z=MS(P3V9~QrxyKX%%FV2cze*d-35s}`9R|t0Wn{Shw5H{CG-}ffYf|}53#}XtA4v_ zQGS2g*bSu?m+sedbY&j|)3aTaVKU!T9RK}8WGovdFW7vhrfmnbO7mM|5)~p`9U70e zSXTpPU8TyT9+QU7yolU{m?v4Wa z06`vld-EY#UPAOWVy&1UEqSv!)m9m#XB5v&2osKGiXY2fxRq~c_jf+C`%P!g^s9sY zP|>>j03(=CAS*}>ltu!=h>t_1`BIj3X-V8XnIvuyx}k=Zrcl2%^l$w+C8cjjovA0^ zfTnu%dh2{L;Z>~q$hp>E77okI{hs^Y7}phj0Xf+HBm^ViCK)cnLOW$W_VCN_Nyj!3 zKd;}rC!_!pv+e|R$Fy3KHjC#`!vBUBIsHwewX0?()2mdDO=Vmcwfa>(V?CMskzBlL zD|3VqLsodikayJ3ZY&koFxwV=(;E6ybC75;3)+s=JHeu57%QP{-T~1Dfp# zLw&yfqb93+0CJd{HxVoYZlbh-;BuY(cFoVu@>oqZ zC`NZ_dgLT@{u?~w8VC6Ch!^x-STFRQd1VlT>dnYS{&^AKUVc96JmU*q=CN2FQo*qR zv8T3jX7F-~{&27kt)#*=e?5r!kdi`Lae8oAjZ1VP)yl@PFgwH|oQGAs#0fd}{M7pb z(4L&C&)4ZbV`uyFyD^EUv3HaWl}k#W52#G-7fg9WqGI=Avh)7BWh^(HNs1il+Ap$L zd;CHVlVD!`RZa{I?fmYIKF*E1N|_Uej?uhZi^(Y~jAAs-T`KDLhUtUNh53_ZgrQmU zo!Mcp??>tqLi8OKNoW%$?0W9=%av(WT+plVbQkJ-E4ht3u1f-kncJAb7H8K$_1QZB zU@442|3k_)eTX@h;>GdYNb8;2VK_#VvT~B+vQY2T`QGkYU(Z`Z*&{e?k}(opvpLnL zL>u8XDJ=e2FuX(GDd``)+o`GNw#~IMzX&eJTDwr7uC1neIBePCsmS~LHGj@<{9 zaew3uIZApDW3~`I^;}qL4TP-gG&j4M!Q!Ukm>Dq^!o}9=I(h~aRXdOp-h7(T9`v~d zw6+OhqgwFoJHf`$o_h9ocph^<4n9@UY%?Il>l<5v{UzEE6D$pkkJNpAj*}uUmKn~| z&<|j++qEZzDnKN2uJEb`)!CR*SKT*txiRb^UhlKF;S)jeU&|_g*wFhOoZjsAHo-ui zg315fY;hCBLvSc2Q$iTn`f}>{ehEbbg?ETVS~%+JMPrTb-cPiiiK(5?5o9cGzB=eF zuU>od@eYjq=jdfVK_59GbXX&4cq!~TFf)8$*#=F`p&eh(u(P_V{LD+GC!XeHl71O2 zUtWqjs6?dWYwfVh2!8IGh){?sfyY?9S6Ai{_-xr_ob(wHT2l||91d=E*`uf(ZHi=x zeXsSrDW^u#8xa<1wgU@B6abg08aVwToHmk$WCAfD1+TRsvjU0Th^|Rm{_gRq96(tq zJcXPF+m7< z`;|tqKb1st()4g39vQ{HU``GIFdEc&7gV-z>y?LIyr_OTGoNB$1XCKn=V}*0q_`0U zpe3oY5*m0wp08xfr{-}Nle3=JJiiC(-snp2cAX8Jd^0GB^OF6%Pg;*n<`#8lqCl3q zMJW4(-=koMgsx2?A+r+x)IEH-bo%n-I25*T?;UJI7-!dNXTJ8jpOdcHFi+-Yq^&Nv z+DPITjmP9KrYk-wkcWo@nk}iWaH%{mwqrQplXY8@;RSZrT^0aPmZ0(r2y|;INfvMJ zG)`KC=~hdXKNjj$tJ`0Gr~_#>$jHr~E7VH*YAV!>ZR&zCQ3N_r?XQ6IMM2iu$jE3z z+l}?`P0sgtG^z2X(i zFiW5tYU~qdsy<{ip}PjM5w-|0(njW`0@Iv0lf0fk2s@GjpAfKXz=A1OR=pf_@Olu~ z1V)$=bQZ`iebO|}(eMf9Mlp=+F)BUbb(uyB$p;`_X1@VTdAQ zHJORY>N4)J}30RSHYY59q7~3Y({m+5U51l8`81AN8YdP zzi?(=B(2T~4c4kGXz{Ud-6lv;;vG#RW(J)|@>_U^+GN)Tboi$Iwl{B;x3Yo7ffOn5 zC}13(n>Rfy0^_)+q4Y1`UTx{Nr}8`bM>umdAVNNo?nHo!q(Ofs$^4MJXsEiQY=-Ls zU{;1g6$5@sYVl0+zKhWS1U3NKi3ATapzp;X1Ta!=qW z1eNaf%86}&k`mGgaS~^vYaP;#CHBf@L7tG-gComJzZ~Ty*|XGyn6L%VpV8$`a5G1y zeNv(EFRV;ENyrgiCbdY+`SA23LpswQ%iD7BCy_NM>YM6>Q=(gRYm_{Kqh(OhAFcamw%KmEC;_8$vUWuy;%-Vbs$ zBc?+O)wSO7bELKAVYc@>8@zj}*Se{YII+)g&6xEWw3N8Wl-_Oe_4%l=1==u}_rMRZ zr~q_va;t@`NEQo3AzE>^<>10A_gl3m0ctr3ssTMN_yOzy=p6j3mOk@VuAr8TQ3w@k zp2mlP?qm%sS$BxS_4Y;5wSvMg_Vk<_^=!MJpW*ieIDcv~g=EOrsf!20f=~3W;5tl0 zbu;q4;fx8n0IEW2Zfbg)8(Ew>rivCUdU#$K$P_vrfJJIDrNe>R1SYYDkY9O0DhjYMDQg%UAHP^ig9uMS3JO>kiZ>k(7&;Zx z@`C5>wZD4|DzMC47B5+n%G5wXb9x!gS~$QIC&*y_ysw04n<#5_q5BZnWxik&Hfn3r zn_3B1!0E-(nl|aU>AC=VAOS@^XB~d=Vk{rBm1NAKBA4t3NeH=x5e%F;@UpYdr`OG7 zzzP9Y)oW(o5|bgCQAr=n6<4Faw5eJd^DNnKiNt%J65e97Vdrq;){@AD$wa-@164p# zY~93!U~X8x^tu=Tts{g4-gKT_E!CM!FsGh#Xpvk_19JVrgI}3$PMnpS;xm+~qaYcx zIlJaFUQ(SOf1jV=Xhl`0(W8ezquCh2^A=LUtCWuXh9#Jp$RvRC;+!+CMYrkw;lZY% z5DhN;{tp*${;SClt{+9)^uUC#fbxR#AXe`r;2oy($1Od7(K(|r1yd@|@DmmK8$P{x z?=C>A*902^m=+a-fbrCyZQzRFV4%ngA5Mm+c9*eTrPe*5S3TTkg<}oOsJ~*ER=DI7 z@#2Jvq%$L-11f{fhYsbMY!3nxla(LgGu@*!-{(pOf?X2IX<)1F*qkqZZDxc(*D&W( z%cxXH6yOOKIbSYkpB%67jn+$eW^&Je6rRxM9JB$tt_|%Kd;Quqy@gNp_UZnuOv2Z% z{$7?CIcrVMhA^MH@4bX>O@A29b@~SFZO6<|q^kc5lOMZ0Mi6iUFLi)H+KzM5VNnE8 zXb3NHPwi?kq{MAbdM&9~nC)at`eZ);6x}JAehXb8PFrH&+p}cz4-k2FfdH%tcZS)I zi$hX`Q+;UuEuk6kqr(Xx(Y8^)0vOE6?7X@>Xo_jf8xh0F7m$oT!`Z(EAtF zwveWdcl3!4U9)-G%7at_M|mJctMU%*@k_|TXy2Dg{e-Jn5#}jd1@`ML^aZ>GQhn#C z?ZCZaPE&-W4TuS>A}9=96doa<67>SlT3Xm`%DMm`AEC>tO+ zlb5Ih)MBCcIfrws<^w)cknubI(`S?ca5@7|HcWya=<(3F>qo`WRF>Ae}9+x6qCc&vG4S+gjK;2h~yS5u{!(m5*m zH%joHOA8_6Qx2=diwrYnJ!t z^o+!Tm<}FU)MIIclSxi+jhIh>t|m_F+CxGwSe89^xp+Z>V+GPQsXHqV+swniR%~-J z4jMt*BN|)id4J_dWJje@mA9hQMvU;9KT$l7KoYemH!?u0l$XDb!)oUd-FMk}-0|^O zGB$QuI@)6F__X`;TQa_p>dziVWw&nKz(O7QQN@OXWf7Uog5BJ90>{@{*Eb7rp)DX*8Bii9Q2Xcr(ZlH3`kX zgl9(VC(L{yhQ<`PdJ*Shd%W9~KdC3l!lxhnv+s$i=9}D__heyFOV2p%gYeZKpiH*7 z0nACG2dJ^9wnl;^CMuL6!5)3XtX*$WTgk{|Dh)?M-2UvsEbn{}(mT7_I2Ajt3sB3- zPZNq0H&b?WNOb^?W)Cn}JdXsKaem%->df-wnqRNh8{7EJ_;e=Z?|PS&wOx>`(@GV$ zxSeZKbmz_(Svp7WVEI07b3BAuL~L7y#{&t}-rAwoPi7HNa7^oexL;XQYnQZEuP|Xv zO+EvKUB()F*1RvLXy4s)oN}dkZmx<63_Ykrmop-rSFAQXlf5vP%sFrU>j(kEpaL`f zx)w2ZuhbIKoiY_L)IR>4B8rmSDRZ{98w6{P+6DE6SeH|0QVBnW3n$YHI{L`f^SqZP z^>c$b+g@Gm(NmQ)q*B+Iq*01fCy3TXgW5r?+J*2Xv=0gIT=onCzLkfCY>j}0BN|ht zvKv4IRR2ts5r8N>K_%zUKCZmzd1Tc8lWb?u&Di4?V7fa9>Q{{vqo!}D0fGJ|?47>P zl<~8DNCm1U9oW4Y4tsBy4>a80Cav|A-fso0rT}-_Gy4*ZFn`cy&FYtJhW=2%#HOjy zoVc^eStBi;Z$D>ZZ0b?d*w3AE(A3tWI+BRfabi(j{`6YDLGj?++>$F4+|ExdK_^r`q{)Pi){4=ci}Po5}Kt=uNH>LW`MIujpi z{_dw+Os9&T;HP|*DyMowo&Tc0GeZ0gb&r|ds_^>gL?orx8OiC^hxsJre#W#+-`FZP zF}6mLUSDwblbVS{(58m{uiL9fGnzA`C5?LM0!o+)wk(fTbnCq#vpzn z5H5&a>5~5-INR1mXo*60VCdO&n$`Ckn;!5BV4`fKe(G#F>$uM~*>or_YR7za2k$BN z-L`eBlx?55P@S&S;h}9c>3S2F^RBnSOo0JDR#e=8!>nZe6;^vI{CepgM%3ow9G-dO z$9?8{=3tZAbMi=u#y1vEG}{;X=ba>Yxf|Qp?$xz@o3y;VD+(%^ti_RKIA<$J$RiUwhrJn`lm{xu&~rE`cnK=| zO3&KbkMtojtN?+V;?1hw6-buFekb4%dJ&|dh{lcs8R$;geD5g zN-16mMVBrX!g>=A^>!JufR*5kYJ%-=OwTiQ;5dK~l*Zln6=evWgjV&Dx^osOQ^(X# z{{}12Jbszg_QSVSoBiJ7)Wf%yf6sB@N9(g=hTu>2S-?XO7{lJaV71#!3P^RK5T{-b zx>dooY*K&k^!_gu6yrUB;fj~cFuHke&Ft8+qnXr8p-`N_!^Tf&>avhAj7S9^hYST7 zgKr_LT(*V%P7fJ?<;n0@1zdqm;8TGbr)^lvDl32>4>qx1VO-yAp8JeIzO$4dcN zpK4}C;xz{b6Vz7jC7FQs*(|oY1`|eqz&nL8`L4t6siC8i;UM^|P>lU;%lc10Hs@A_w%@=lp3eSLf1fLze#C<}#t@AZ>JwAl>+8IQ_}s$0_)+OE zDOeOzNxy~~ajW%?8}2FNAf3oLr{HX)ci-wHDJK+;wg}c5SdESPW1b3owgC5o{(~+T zFS0A6Pvz?tKZ>{&5t0bRKELe7li8M*N}%&wnQ|RJGXvT_z0XZ#5OcEquP;AU9{2nr z!3Ma6p)A+$u;SbYCMg)3uG$^Nx(c5J>_0&#zoeKYFDX z1zIN;9ctOC)kshwS<+%_W_T9mvvdUr>Zni*Rn5jnao$01wxpf-zGDtyjF3nZWu!WM zOufcwFso>+Q+mHnI=HA?#H@uA(KS?VegVHse%}4OZx)NnA|kgwSx@B(fU_Yc7ZZ|g z*d)FJWQ%Ndz(4(1lIHRngU5O`h1rYxXD&OP>(6e{4FsSQXJ*AOZHBaLjGo5U-bKqN z6xG!uTjtj@!x=bPXMfpYEK(LVFc3YTP~QDoaNnv7D}+wo#<4d79G~1Uz6bEq1c)Ns zD*p6<>e{CKajS~{&@JK<62f>nvTz9Bk8QLxeqDzc&)A-sWiW5mOviaQDqZewX=^zL z(Ufqas=t7xF!ir`(AHWmAcG7pAm?7K|LNhI+XSu|tf6u3?X$|FC*{3Dyw;O!6jo%- zJBHeawYvndjVKA;a8+)Sj546P(4HRh>a!Ow;dThsM*!VO1txKl zoZ1b^Z33rsee`mvljg$F&vWaZ`p3N@I}yswr z?#+pbHLF{lm1c^Vmi%Q^KizrWu=;PM0VP};2>9%bFT;0=7{2oA0Fi4bo7RB z?kV|&Tu&nX|1ohmHHeT80z|Ri zM>~hp@tz_9M3w5F*7vW|LPti4F)+;|;FiN5rDi3$2cLW?a=KA^g&P<>;b>zHvn+q| zs>m2kGB65Ip4qqJ;Ybx+P(bqb{yBBeJ`gTE2t+F_z-##4`K2y)?sr?NuBl>{Wd?|2 zgE;nl;L={YMb#~2KE!{>e6)Ys!{X$7ym4_0Vl*~V2%U`7VQEH{>^qqrsP8^MxGtg5 zS|an6s=`XfE5b0O%)A*rtPvpYF$MFhhcm6mp*yFxNz)5UO;5aewfS> z(9}4y$k5L3Y60fW2)m!&9h}u^VP9-Y%_#u!ku^0l_QPswbYi+91roMCFSD1Jj-mS&xoPFRyyOMEeqhT~X3*b<`&4m%zZD*G z%MKxKZ>Jsac|u_Uq4y3TM?(3y^zM#q$p9uiT6Z!fAK{Z4b7TcrH9E7BUldd&BUR!= zsT~WMR-$&{$io+Bho$>{FwS5$yp#J+H`Q!g*cJCR^K09u5FthFbN)CIF}66|6!Y zy+zX?o9x%FRR~|Ekkt3mn%hOWj>Suk+oKo<>hJ`?S47nMIm8kI~U+J*n~NSP-}BDS?ddc)L!ZRp_l2p{dWE6 zZdEz-l4r@325Rj6>j5LX_Zm?9hk|%*3q1DG^(ipV|v` z7vd$ zO6N{#w*>OqB#JKfeIg7{?4cXj|I{UwQ*N^k>}qrNN9mt}Esq9)6TxA_1zNW3$y>eG z)vmBn+(knzZ*ptr+s0T{PEGb2OdY-j7zgK}Uha2~yU9EdAPQX1z-jmPHA7PYWY`1t zu*&pt%+SwMh&+5Y9tNM(sI1CfG06sQ@|slk$zCDjlqAwnSW6V7Ib1<9o%th~MAL<~rQ{@Um=NR(awx z8hD8G*kB#iEzk>F9Z6|4w%_(eH*V1l_JHJz zD~(OQiBH|1Opbz&H_wCy)Xs0MH?P7;#a_+v??+3`^QtaF4wES!@{Hou)m33&2hI`*IX7tv-U4yKDK;ygQbFR+^T4(fz3|3#4RZc4n% z`(Sh@|DZ|CA!`Z7)SekIs`tJaQ}gHrGS^H-P~09{8vDFKkA3PHS<0{zxNXOj;Xkm| z`_t>&qk<77GXiD&;yGy}^B%S7gE~7}Si0Y974hen-12TF%aml852?-X^Nn#9Unpmk zY4|C6fT)F!JOTHUre-G#b&i!dQ7jN##2-mCt<+rMG9{^~7==-TzDAR8XI;n3x>fPv zkibzncZvDEQzj|g>ur(S$Ltwb(vT636`qeP{V}5=suM}x5^Udc6_k@ZHl5QtG;Irb+RnEH#_aA|5Ja1J-pdhL^gywLDkQ5 zb0Vrsmi#+JH$Jt@-u`zgB^J-m`MhAnxM>I8>Wp^qcs~qanC@NCY)a&w%sJ0k299h^ zI8RuowS)#JnQ=FBE5pQD5h<-dsMr@N`YOE4*7z{jK(E=Uxk8-~%M>iTTX7osnDy3_ z8A`-v&xCP~RZCokgg^Nb*jd>(aGmH;c(01V&c-rrMHf;X_dGY)g-(0~Ol^LP7G#l! z(c`B^Y8ib@QU6Y9=+i1)v>-1^=pbUl10G)ekh8 z+f;VhuU20;kVuIn|8{uu>HKtJ5E!mgMQ4|8K{X7rqNkkd#C@02iO+Ntw6Vp-3lZ!Z zltL$ZQcOFB#Pj1Ek}00Tm|h_nF(dTu*}syX1?Q?Wgus}WU91-dSryiPOg9#w8&<>w zNN`fx%}MvWqSY-;Z!j58 zfK*E2=JYyo0k0QXB-21Qfk9(XJs@0J$V!N+$Haw%asv4-lcNZl&tLJw1+U|cXI%(J zs-N!Cn<1HCoA6k>0U}dKHV1=rES_3^tg`4}Wsyphk@F)32p@*3!)uAJQqPMU(7Z)Gc$ilmPMtHTL19GbwfKOl+4q2vTLsZ?k zz6WA;dQbC@fJ~xDpn>0NzUrpsfi^P^P{&IhTAcKfWuYGGdWC>?QlJYF3tFel+$o); zwqv!M)`RXBcG*<9O~10DtuP`&h7Ofiji94z8N)-p8Ik9l8;*1jj`UVOM<>6F$NKIW zO!%j=gJZFw=}7IyZky-Kf*449x^Rz6o~hv_Q%CC;80lJ-xqD(cbz=;&m*a)qP%@)G}7aJ~4 zdSH8WgsaQXhO$p#EmPVcLl=j*UlTd%*8%&OEMKGdwcRJeW%F)f&tH6Q9CJ_U%F1dp zn+}k7v$Stc>9)rxX73c+fBI&Pv^%*MLAB>`2gk9c7n@&EyPHHoYhF6(u5UcJ!zWHP zJ+L%?p0hVU$=&|sQonHz=8er=X(3B_WwKoCo7SzE&in>mZQB^o1RA#t^o@y3d9ohK z#A`~Z;)e-Q#zwBso1%Zg7;HeAd#;RSf=zO?jk*Q&cPuoVSN(qF&>tT+7^_Fcj6OEz zSNDK5gY{`TCXb#l3&Qm9p}tQ-rVd500KkTk$s>?q*}_PZZ^pM3N23ifw+!|W62zTb zhJHr*UPAE`4DXi~E!KsEggiTv82F{`m205^=MM88?}ekg$uN>XJ)9C&i2Y*UCA4gQ z&bImkF=txn&Y3nbu}igUZZ<;JJFy?$r+C*4{I|x6$j#oG;L{IHg`hYTZ48~(0n9*R zzGq+UX`5kZp64l6NHlYbq>c2$gR=_yL%tsk3EOhu-N+~X@l1`My)MAKXxYs25=&~` z5ul1<8V2ytXpGmI4^rPBGVG$?k^o_4=N&#+86UJ$eGQCiHI^$MW zC-+EQ_cWKSK=PXim*}AH{}cHpySn0@8_SVyqKr6)s~lvyL&nzI^61T38`Qgty87c0 zmmutgKNxn@40Lcbu~{~bbEGH}aCgktFxNADU%MgRx^IZhIdzxzxi-bHj{ZX(T=lY3 z*RBmi2^xIG5Ub)Yg9Z+K5`F%ZCa%vtbq_T8HY-;zr(84XOwr3opQM3cW{CDW_TJs@A-LsXM?mU1 zlv<-8D~10XuH1a!2c0*5ps^LxuV}*3TeEm>?e~3wMlLaF3%8YCd-1u`hWOL%3kUw; zen0q0Tyocdhp}H+U$?Jr!-B0H>p#pCJ`U+n4-WyyTekZ1o*NuIe1J6;jfJI0%gwsbc?~P&T?77_b%C=(4wOQzcl;l zMn3G&y|8?h!AvRmc0E(){jY=FbZc>@yqa~$5bzSbwDO}OwELCFE05d0r!a$RA=}7Cwe2fQC z8Ku5wLVZClj4~m`t3OMfYOtz|no*>El!(ng&dtrtPlit|q8;-7@!SHLQ86t7RpXXN zy_u+0M!h?C|FajLgB<6&-&>$mm|z`{dpUi;a=WbRRraR$9*iEfZ#sAGI`LmxQmWrK z45bzJthrl7?<$fXZp7Sdmqij4<0i=i;s9By_qaKz*1)E^Kk%5Td(f04I-KrX9n{r# z;4IKHAYmbSn`)bfVJ?nAoHuStd9aIg~m> zI3B2(uq0RuQ{JrhMeg{S0MhDs2|}l}tneo{k>KWD;t>H|2pa{4NKQOAOEgd zdO!X;K+%0huXNYxg+V5#*afKN-1A6#g?s1SI4rne804B@)+SfOo7r|>vlm?C#`GhYH=L_v*1*Hd5|KD)R|pJluU9L*s#H`|JeS3fPkj;wAdrX4@P_a zV8DiR``#(|CF>*cUEa2bwz%rH7iA95g~paU}x!2p5Y& z=fFUd*=1b5d*HWX1|weo&lrpP`pWO{y~E4C2`nIe)Qw}_Mh%a{?En3sSfmpSsdf9p zJ?q$eQuHo9GOK-j$8eRKtvx(wiAF|#v)?r|D983vSESLjyBqXkNYHk-*0ZNi+3M=$ zb)~wV)76ppDJzcV#C15|@OXaHe;KR5`h`)W-X3yG-u*x8wek`#F&K_`ma?}~!#R<) z9F<cyk{OkHF=3k(D;+}>>(oQC!;y(`Id1BOl7YIx;g$5o79B7!{|I*;OiUBp=!qG(D za1Bl_I&I;or=$%1WBTP?x5n*T_WuzDWT#5Ew!4#I0yi}q2I?X74k7d6R;P9aY}l4U zt#f}3g+j{(g?kz{7;2wl|B@=G&w3h489o#bieZ@w|KBL0KKAgC)ykMYFmR5ZyM1W` z4yI5lse9Z}h5DiXCTfpi@5srcQEfdFIbI+@@^sN4#;^Pm>FW!qe+R%-X>)5;Et&l5m(e@AeDj zeSz^iLJs|01v0UWK9-&J8XWQcOuxsWmBfvF8seg({=d(_BBSyP$pDU64_?P_2rB|{ z74pLTXesm?K|vBaw$KObXm}k?mgNcca~A#k%C#L+hB^K`XaDn1y#p9r`4X8?LCq8~2QxKU z{0)2PG~+`{8JjRhRoQJ6{EUe?E8~ib`#{e#aDMbVMA-=mD*yd44@|n^_;Ue~!?b%N zMnzK8_SojQtWoydcPv&{9* zZ&3k;VUU31S9}TscxMf}N0|E1HPZphdXoG_up49h`Q~lb0Pmk&`x4mU7%0mg+VaAS zSJTs7f4q?EFltoA?5|~ceNm3>XWAhnC$mxPkg!(QF9vqb1l5u~tW*IO%$J(2#Ugy% z8pwLn&|Qk(5}YAt|TP| z?)~3~FlNxrh}}hnzdYFy>g4LGE4y#FZYOL%qp)LTD&|PtA)s~U?J(3;O|2xea2V|}m0EBkHV-oEW_viCSFabzugaXFl!v}m&t|5G+_ zXLom3_H|i%dRHN1l@2I9WjUW0A`AmsIAGkUhGy9zEM1>y&-Sc_@=GK02#{$S{gpOd z%zj)W%qir1X}Fzk=Go-Qd)EIl%XPUMkREs|y=OxvmgrF1rqvC@YtNtCL8tG{U`Lrr z9{Um2%5FfYS(?B9el`2k9w#~e=YMhkxnO;F(TA2kFYDroMFzM}OgrbWiPmfkAU?m% zJn&b-FuXeppiZ`RANjAhXnBL3nK~U6Jnts$1N_(n41pi!E?Smx)OlImXJr288#%uJ z`3}S0l4Xwx>yrVP1U;6xT<)5k^2a~uh-u0i_LzcA$I_o56WYzF^e*-H9JnM7bM~7) z)He7}Fp&4sJG`KDbfcmTrTHndkZkBO#Aw_3(7v3P!GErt$XHkxr1u~k^f55bvbxV1 zQ5cZhuz>2^zhB(uF4G1J0D+!dk8$vXkYfOe~%U; zyPt<^-%`q&-4F#WKv9oDAd*$-vRe}$5LqYFQ&V#kk4tII`_kQ!Kzg8nSnjliaE$cW7nl(S);c9 zmVt8k0ypoEX%di#MSz?kP+1$OFg+M090Xyzi!+Q9v+{N-DhJ<|gZYk>2hU43?-#27 z?{m&g3E6pL$Im zkAuTozTc9C`zE~&LPa4YdnC~#~N4>S`Wk-GD#OeObf5~T{$(6S)XP3s! z(2R!BRdx;XqS`Gld7^96!3>e+;eYa{u||SL#Zaj&FHIj+xUtk%XBt&bPefD24__?n zofJ=10O~ad#3mNeAe-FU5egbn!lQTm3=4QWYHfyQqichC4`acBBS@O*X@^I-ydLSl zQeL!~&0AtX(ShySy54s#SlX?q>0&!7_kG~jcRjNV3NN0xP#$tSsEyQ&xd-#UC2V(twif*39z4;ixL^+r+X$mS>c)O580n&*927Ft_L0kJ(y+kwTxtVkO3 zIX`;Z`AGJyMylK_ynNa$-N>k|jk!A(4{E?4vUDACKGnwfU%UFi923`*?xvg;-@IJR8(oQ?V?OwdfAya8)e`5RKtj9(l z>JVD6t)rVPH^(B`irakPgVOAMOU@3{Q7=--6s?owPUx$9z--6yznWTF=FDI$)%!Pc zPY>RcjwwsyVQap>J2ba6RzyuiC@d?YTG+Pl>85U`NTu44Rq%%19n6GqBT0gL zH;$=L1OvULj1+7~YqlR--!eeyG=-~NYuU~zG#(4^2hEzF^&p0cG!PhrmeJr zg4K^;y%%B6SP;9%3zDQ!;YR(R?ymf==KWi5L&gk6l2D{n8c2gkNgIu59#m*RC6PG| zj?hSRDkaU63QeM2I7xPqW(~qoQk`^^O7nfzaqfMc`}*F$;QHa{ys-CYc)!E~Z@l=;%lr&&D7k5+aw16SmRz)fvmA@3xi_Z!DipNC*b?-k~D$+?UyNol?L)C#{| zc{)XQ02P2ZnC*dpJNb)NFIggl@DPf2tOicwknv%tScy|G2ex<$cE=bVqVo=_69$;O ztd%SAPs94CyEy8PIN%owXvO`KY{wh+?V4*F+FE$x%V?(-$M(FQu z(PoyyEdwJ`Qfr@!iHKSh^|W?5YOI<;=6H6PAOo$w<sts$` z0>P18enL6vM~a1Mv1=iuJz`N zedllf#Dp90-vF#>3#6TixXymaZ>Kg2IBC6_{7o+lUA~7v9xtMnr6V4=t29(oZlNvn z3JpxaB)iZTw(pLJVk;ulAoUFJ_QvQ&`Q4an@PkmiEo-+HgiCHzHxIB75N%&!HwsHg zREjEo*rnAIBH=gr1Bs4h7+TBTFlE}ZZJQPy3kcsDlix6voQUIy3JwG~Dy9$v<|;P= zw?|YX3jfFLt2fK!hq_KtAptsw4_p;+QfVUVu?2Y6J#wSCJ=&91(NfFZ^VO*WO}8*q zhQ5$v#>9*%l+f>dy%_IZjz^RTzHl&LcT^@zak&`VjP~bO_{K5~-da@`lY$O&1*W!P zguV}o9m43;$v`BHSyhW!Kv68ZTVP}RVrCo)ei~S-R1^A0O@Ug-EIp~2h@iQy{I7f{ z=g{GZQAPv2C3+zw``Dfrf5Kw@@Z;)ew{m3#;hxQ6$fZL7erbYcfeywV`a*E3$hj1L zaF$o9`jGU&rydg><#^!jMS zB81ZeQBeCKPM~Q8I-^cIYRf+JJw8o#P1h2PIE?7)UfIR(BwxXBJL0rx;` z6HR>Ui-raxs3r=cXA=m$jb+*UuWv%1uB$001+8{1aBtROzL&mH5e**@gASnXPI4c@ zfvJ~euJ{VmQW)3Jr8|c+MIeyR3RGT6*ij<B|DzL(wX~3OVRvg|kVgpE5MtvB zdduUO_oS-dvns`AYKK4;ih+j^yXD(6Q&!uC6z94mfeApnHL+sJ5gtJM8f^2ww>3CA%3Jmz*n?$mJPA*Xw1XwZ00_!) z?Y6*BsKNo*Apg@9%vQqX^uP*!1?MAZA~WgokwS-JG3!g33?SnG_rC%OC-LiMd33sU zrsL)4p%;j`pUX=@_{Y4TBof(&P};G3|2U6S<*IMr;me#7E*xvc{J+xy)OaKTsRvch zG=sEYP##}leRY6J8cv4+4v{6>1EBj6K{X^HWuTx1k-*l)Rf#PpxCiKk0pux#m9m>7 z&L09j>4Pu_tB!~SmK2i;CM15hfL{8cU*N5to)xM0aO$mwU5jQG!l5sQc`-0`VQZ>-8cB3gNPjtM*^1sOR?eG&WMV+Z1X-s^O@<&;t!C z%{coGFc17W-AS+iW! zVi%K1gej652hag)&n5&lXBodacvK4+)Sm|;jo4=~IDwjh)Z5WaUcSj{CUYiyU^f2b zN6LKF`_S{NbLUP@dQ44tp%0L9@q{FNsY8oVaZKm>tz>E{?|inZZp_h@Pp3Z46AkzC z^}cfXyd+;W(_n{g&!M8YHS)om*(rsHQR({G{@WuXBd70RP%)Gnu6VIOkr4^q;K=<*}=&9Dk~ zPI`K}>=aU%#f;#HhNy` zls4#wY?Q;CV|rU@d0qT@y9;;voS z;MixuPzOAd+!~>1;%LvmIrMZB(h)?+Y@Q0>#2lBYGZEF_-|viyWibN_>CV;y{r39S zj4yqs^DKnK26FM3%aQuab`%yC5rqu3knWY%)acP1 znBkp>h*Y0}gHxlo+8nFTj}H>$#0Ug-pE)#)$l`03*f%B#Xcb3GXkrW$L=XUUnk>TSS7iO zS=-jFXJjOTH^fliz59By63o6NBqRv=F{|KRwv3JOPBcSzuU2utCl&x0iy;G;Urb!Q zW>gzEfQQ;feeJ~#vyd%>AoYq%`hBCBga4D_h>Q;s`}Xc##vm|*>!RG;2f4YS2xflX zUv(Cvn2usBhAfxoHY@#7$1F34*19v4lQJlW-g=hJp|Yejb^FhWJ`ZVz4F=-RN~M?! zB1`UJE&hhgp9=O2|G8FmmJw>(xVaP&!A(p|yrD*hlcY4&Q7}E=!ReI8+@*LO-4?NF zOG*I=Cg>O7Ql<&w<7(4dAl1ttxWF!KgTkK4u@6(-uDhGQAkOMUV%6(vrEXBw9^pcl z>G#(H%XR1LH$(ZHrQ|khkYZV=de5Ira|-M@^BCLBGBPtS7ma;;;!=_@y1?!xU+?Wr zFAqlEdQn>XwayKB0PQ_AC!%DmzZ}X-lRiw-oLgfFL}mmc{MoS=u2vFdtizb$-9h9$Iq`;+)4e42~wXI&!6wCsi`>=ZX7&M`RB(OI;Lt_ z{0BB$kF{R^w$E)Krn%^Q8NZpMdi$VUKtaLz%-GHNOhEn&DE0&xJv6kKH*emVe67U8 zbFIpT1lZ>y(;fM`aXy<3mo6odCrwRX zMj!(3^1>vWy`k29fBtz-;kR6Q`#dusBQ}V|I?GoDovL&@A!cQACBoYqiMP@8`!$_l zo*d}w2Yrr*(GSt%V{GKlGcN<+NYLG+Kk&yky(m?jie_7{&a%64ihs!fQi;FH_?-@6 zIhf=eSpckmTAo?v;IY!E;E<4Me^Zr{l57(`8|Id|Ey!(ql-7mMn!zBG4*kx)zU+(U z3c3eU{CS}IpKYs%XlXJ%eg%uPUi!8uOD+BENwkYMdu?3F5?9I@>+ zM9h$CM1UHCyz>UChG$h(*`{B*M^`T)`Bz4^rLJ*E|JKOp=#?>B!!$X-B#-IG(ESVs zg;cT4`5uBYyoew7@85q4754Wvaf|aJFhBRzP3JAmjoQG$+H;9Wf7x>4x7T5bHH!wN@TOfMGefnO)pwsL81>r z0p$3L-R+Isa0Y{%$k3M^?=~)xaeP~EP*NDTKh0pUK}_X`aU+UkmO>Ap?ztZai>F_( z*qv#ttIGur=GhbG+-84--~ zh>D9}VX@qDDxYy~pSnG&l-*;NaYO==4H*AWJ$!f#z!yJ2Nu+v&uu{%u(?}`1qJp0% zV9m>U7gz_=9h^!^me+L3AMt4Tfh5BkkBF$f0A9#d@^#Ts)c9b@xW{~7zCluQ0T3iO zgS-F+e*<*OWA(-c28V!OJaX=shwZ%)20rdsOwDsK9!|qxsqyl)Fl2= zURik+a%Uu~g(bK%NiQxnbq=;(-ie$zx4jmP56*@)8tmWC_-Gja4qXHXi*M+Y%yJWm zENE^X0Y?mq^IUL*;{|3oL{tN^? z&)uji{7%*eWICG}=6Z=S**pEDzC`iMq;U z?9Zrw2c-P;b#;U6e3=3We5@7UumMW6obdF{+=4NT2Oz1sfb8)l)@EfbODG_f$NA*R zr(l2wg@nvFaG=E{$0_W#CtiVLzqGIS3I{Zt7-Xc`Q)f|h;eo2hAhT|T*ymlK&CTDl zv(6>YVB6M9LlSN7V~)u80N+NvI$W2iz5V>d{QT=6`72NS(1(&1&(ni%7$T{vBWx!c2HDqOYNQfs4WoDhYmW8~N{E19W zO-ZKeOWqGTu}*6ka_)^ud$Z^6sR^$%xx@u4w3^zG|901Hhc2U-nAj{g4m{ZHK02!^Yu*~AtsY}*gUAOJVn$z+kM*E*-TJ8z9C7txf)I<8`172 zb9J0{q!p=CyfD678&DYD$yh>Kzi|ti9}S^^C>AgRV`ARF4ZLlDv6F@#A+%PmKR=V8%%nPrv%J4+Q)ID!{T2C!JSLvP+HH!dC1R~(X? zdfS13M`P;yO-=25b*|tmvOOjo0QYcUw?TKqowcc6NdODtKDrRBSi7$ybC$Px$R;)C zkDxaH*>iBpKKLxynoCgUz*u4t;O8VmUOqm%w>|39?jj$H0EZK?7j6*pUv=12S_VZ% z+WFpp#aVx@_)AB}r}HIa76Y@H1_6kul@%4gqca$QIx-GekO?M5lD{D_e2(Ia^mhnL z>gxn1eFIVC^;FpU8xunXCG|ym`f)}Xa{Dxd2A&&h>pf{q*(ZQF{PJT5X12Bb1yoi8 zO)mpjveA9~g7=)7+S*glg(KyzD2)ZWmp*rPdI7t%0%!#u-E-{sYWSV4#1JG3>bzoy z+o_p+;I~hXRC!ndLMLgbBX6R8;A?`BBnEJL_&CLUk>04&0s7|e7~@DbG%_(s9n|zH zndo(btj80LMy)!7s3)r^UypzSE1S(GLvehCcHhAx^{%^S=vqfueFKmvLUcNuXxA_( zNA_VLp9m@|uf~P^eC_0(V0sQI@crCeCRCI4^z~u;IER7!RVTzTAr83PeoM=Rfax)F z9EWrUv#_Or!-#7J(0*^uaV`HG=JoEHr1@wc01}Zp1U#55y-52CIuFFHN4W?soKgq^ zZUi(5+wH^0blxexC%^if@M*_DtkpIN)UQgaX6E zujAm2kWfrf#-Ob#6afln49{j!y?ib_bZtu+j5H z|Lz&&0a1=+oZXK7vj@o-n042Y@!46*UBFkicXS*^TLS99PHY`V9?ss=IuBHkulis} zMgX9jS3!XS958jw&HZt-llKI2%2_I==~DD)K_`>*F(1vUJ__LpQ~(qkLDYVUeQ|1R z$dObfkTlWYT(ZvXWQDo+)--CPB+{`17^N;!j;)*14ZR9FEzGhR=)QR+*-li6j=WTv{O!k&GbEIb(&{=m zphzppsWCAr=@VKJ?+Tm*kX-YxQizNt8Y--y0I<;MgK^TJ+qeAy)X~@lR0{077W?Gp zCKo2jD5uxkFXiF+1f2U3PD%95=c1F1eX*z0vKrjuO^~H@g=8RXxi?@baUg4LZ*TwT zF*PPYZv{CkfaMvC`zJr`BsmIPSQ)q;q|AYQ?Gv8c795L^j(GZ^=5^5GJRwFRQs7yV zW1T8frVRDt$M5a6aCa}p%F(0l1L$=Gq)=;vm>x*>eG)|ME$Zy^Fb$-GDLn~@cYpy# z)7U}r@oP_=KhJyq{BZcfpwy;y!onIjq42dD7%zhP;4aYcC4Ld9D=W3938Ho8q9U+( zv7lTEvCv&!rj(X4r|s`7sjRFNd8ZklTyNf69w=X0vEF`OS!K+Rm?t>?xtA>y!F&Y~ zZn|0vbEuUcE@dH(lV@vB3+qw=ob9wRkF&znau1U^hoPaZU6+>O7|nh0{TonFa@k?M z65`@;Dh*f1^lT}ctw}e5v^3r*W3Wv3;M9&fJuC=nG1Ooh<-z^?Z|#Epp?uUl~m zC?5b_;+P;8iW#yvz?qbk#LdGatTHuH^#b@$P8PD4Zi$w_p0b9@cgn`4vST@kiA)f% zCDDyp0%|bzr|6+!ZM34H!!_%ph>q74rlewmRU47Si-@<@po4Z z%Mlmf)l!g+2Y}@}w{Lw=lT0H}@UQ+pSh_V3MC03JbrtiE`_WiT0)E<1r2-@fPnS4N zRt^puKn3~Z)2F|QWFS*roE$HFbCi@g_!Le=qwC;<*9SVXy1KeIx>@f(K1v%g@%ENg zVxcWwev(}~`0(LFGueg2f07Z17UJg0bHif&HKQ*Y8@h*CSNg6~Pb)1weJ6>R0Iw`I zqkqa-Kjpgbzgbhiezza?_4TDe#7r43Rb*p`Ro~xi>&xA512&TZuc6lL9r)p2zrIK- zD)NDyp^6dqf8=A=tzR#Mlnu2HyuvKBywvg6T~Igw;s~d%+6F0XJw3f{i#Kp=fgq30 z>P5&#@QdZ5*9d(SPBVFUyFsH~XGMS&c)X;?N78yAo!rIy0AbxZ(U0;CSbb~A9}@kTf1 zTK(&${`t`{)b>B_m;Ok{^Z)$YOP7@X^Zoz&tyxsW``6w5`@ea*+KYw$_mju}*(V?g z{*TM3+l@l=U!VBv$J85ESC;?#Bme$cm;c4gP5<$^@$$a^pD#FdYDO^Yy0Y^`xB>&e OG Date: Tue, 25 Oct 2022 18:30:09 -0400 Subject: [PATCH 151/300] Working GW150914 --- example/ParameterEstimation/GW150914.py | 18 ++++++++++-------- 1 file changed, 10 insertions(+), 8 deletions(-) diff --git a/example/ParameterEstimation/GW150914.py b/example/ParameterEstimation/GW150914.py index 46c2d2a0..d5dfba08 100644 --- a/example/ParameterEstimation/GW150914.py +++ b/example/ParameterEstimation/GW150914.py @@ -81,7 +81,7 @@ def L1_LogLikelihood(theta): psd_list = [H1_psd, L1_psd] response_list = [H1_response, L1_response] -logL = make_heterodyne_likelihood_mutliple_detector(data_list, psd_list, response_list, gen_IMRPhenomD_polar, ref_param, H1_frequency, gmst, epoch, f_ref, 101) +logL = make_heterodyne_likelihood_mutliple_detector(data_list, psd_list, response_list, gen_IMRPhenomD_polar, ref_param, H1_frequency, gmst, epoch, f_ref, 301) n_dim = 11 @@ -111,8 +111,10 @@ def L1_LogLikelihood(theta): print("Initializing MCMC model and normalizing flow model.") -# prior_range = jnp.array([[10,80],[0.125,1.0],[0,1],[0,1],[0,2000],[-0.1,0.1],[0,2*np.pi],[-1,1],[0,np.pi],[0,2*np.pi],[-1,1]]) -prior_range = jnp.array([[10,80],[0.125,1.0],[0,1],[0,1],[0,2000],[-0.1,0.1],[0,2*np.pi],[0,np.pi],[0,np.pi],[0,2*np.pi],[-np.pi/2,np.pi/2]]) +prior_range = jnp.array([[10,80],[0.125,1.0],[-1,1],[-1,1],[0,2000],[-0.1,0.1],[0,2*np.pi],[-1,1],[0,np.pi],[0,2*np.pi],[-1,1]]) +# prior_range = jnp.array([[10,80],[0.125,1.0],[-1,1],[-1,1],[0,2000],[-0.1,0.1],[0,2*np.pi],[0,np.pi],[0,np.pi],[0,2*np.pi],[-np.pi/2,np.pi/2]]) +#prior_range = jnp.array([[10,80],[0.0,0.25],[-1,1],[-1,1],[0,2000],[-0.1,0.1],[0,2*np.pi],[0,np.pi],[0,np.pi],[0,2*np.pi],[-np.pi/2,np.pi/2]]) + initial_position = jax.random.uniform(rng_key_set[0], shape=(int(n_chains), n_dim)) * 1 for i in range(n_dim): @@ -124,11 +126,11 @@ def L1_LogLikelihood(theta): initial_position = initial_position.at[:,0].set(guess_param[:,0]) # initial_position = initial_position.at[:,1].set(guess_param[:,1]) -initial_position = initial_position.at[:,1].set(q) +# initial_position = initial_position.at[:,1].set(q) from astropy.cosmology import Planck18 as cosmo -z = np.linspace(0.0002,0.02,1000) +z = np.linspace(0.002,3,10000) dL = cosmo.luminosity_distance(z).value dVdz = cosmo.differential_comoving_volume(z).value @@ -142,13 +144,13 @@ def top_hat(x): def posterior(theta): q = theta[1] iota = jnp.arccos(theta[7]) - dec = jnp.arccos(theta[10]) + dec = jnp.arcsin(theta[10]) prior = top_hat(theta) theta = theta.at[1].set(q/(1+q)**2) # convert q to eta theta = theta.at[7].set(iota) # convert cos iota to iota theta = theta.at[10].set(dec) # convert cos dec to dec - jacobian = jnp.log((1/(1+q)**2)-2*q/(1+q)**3) - jnp.log(jnp.sin(iota)) - jnp.log(jnp.sin(dec)) - return logL(theta) + prior + jacobian + # jacobian = jnp.log((1/(1+q)**2)-2*q/(1+q)**3) - jnp.log(jnp.sin(iota)) - jnp.log(jnp.sin(dec)) + return logL(theta) + prior #+ jacobian model = RQSpline(n_dim, 10, [128,128], 8) From dc1cfb62d366d9addeff5a1f4b9bd533df85bef4 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Tue, 8 Nov 2022 01:23:23 -0500 Subject: [PATCH 152/300] Commit latest version --- example/ParameterEstimation/GW150914.py | 8 +- example/ParameterEstimation/GW170817.py | 122 ++++++++++-------- .../Injection_withParser.py | 21 +-- .../configs/injection_example.yaml | 4 +- .../gen_injection_config.py | 7 +- 5 files changed, 90 insertions(+), 72 deletions(-) diff --git a/example/ParameterEstimation/GW150914.py b/example/ParameterEstimation/GW150914.py index d5dfba08..03250b13 100644 --- a/example/ParameterEstimation/GW150914.py +++ b/example/ParameterEstimation/GW150914.py @@ -86,12 +86,12 @@ def L1_LogLikelihood(theta): n_dim = 11 n_chains = 1000 -n_loop_training = 20 -n_loop_production = 10 +n_loop_training = 40 +n_loop_production = 20 n_local_steps = 200 n_global_steps = 200 learning_rate = 0.001 -max_samples = 50000 +max_samples = 100000 momentum = 0.9 num_epochs = 60 batch_size = 50000 @@ -184,6 +184,8 @@ def posterior(theta): batch_size=batch_size, use_global=True, keep_quantile=0., + train_thinning = 40 + ) nf_sampler.sample(initial_position) diff --git a/example/ParameterEstimation/GW170817.py b/example/ParameterEstimation/GW170817.py index 9bb9c591..52078067 100644 --- a/example/ParameterEstimation/GW170817.py +++ b/example/ParameterEstimation/GW170817.py @@ -21,44 +21,65 @@ from flowMC.nfmodel.utils import * minimum_frequency = 23 -maximum_frequency = 2048 +maximum_frequency = 700 trigger_time = event_gps("GW170817") duration = 128 -post_trigger_duration = 2 +post_trigger_duration = 32 epoch = duration - post_trigger_duration gmst = GreenwichMeanSiderealTime(trigger_time) f_ref = minimum_frequency -H1_data = TimeSeries.read('/mnt/home/misi/projects/cbc_birefringence/GW170817/raw_data/H-H1_LOSC_CLN_4_V1-1187007040-2048.gwf','H1:LOSC-STRAIN') -H1_data = H1_data[(H1_data.times.value >= (trigger_time-epoch)) & (H1_data.times.value <= (trigger_time+post_trigger_duration))] -n = len(H1_data) -H1_data = np.fft.rfft(H1_data.value*tukey(n, 0.00625))/4096. -H1_frequency = np.fft.rfftfreq(n, 1/4096.) -H1_psd = np.genfromtxt('/mnt/home/misi/projects/cbc_birefringence/GW170817/psd_data/h1_psd.txt') -H1_psd = interp1d(H1_psd[:,0], H1_psd[:,1], fill_value=np.inf,bounds_error=False)(H1_frequency[H1_frequency>minimum_frequency]) -H1_data = H1_data[H1_frequency>minimum_frequency] -H1_frequency = H1_frequency[H1_frequency>minimum_frequency] - -L1_data = TimeSeries.read('/mnt/home/misi/projects/cbc_birefringence/GW170817/raw_data/L-L1_LOSC_CLN_4_V1-1187007040-2048.gwf','L1:LOSC-STRAIN') -L1_data = L1_data[(L1_data.times.value >= (trigger_time-epoch)) & (L1_data.times.value <= (trigger_time+post_trigger_duration))] -n = len(L1_data) -L1_data = np.fft.rfft(L1_data.value*tukey(n, 0.00625))/4096. -L1_frequency = np.fft.rfftfreq(n, 1/4096.) -L1_psd = np.genfromtxt('/mnt/home/misi/projects/cbc_birefringence/GW170817/psd_data/l1_psd.txt') -L1_psd = interp1d(L1_psd[:,0], L1_psd[:,1], fill_value=np.inf,bounds_error=False)(L1_frequency[L1_frequency>minimum_frequency]) -L1_data = L1_data[L1_frequency>minimum_frequency] -L1_frequency = L1_frequency[L1_frequency>minimum_frequency] - -V1_data = TimeSeries.read('/mnt/home/misi/projects/cbc_birefringence/GW170817/raw_data/V-V1_LOSC_CLN_4_V1-1187007040-2048.gwf','V1:LOSC-STRAIN') -V1_data = V1_data[(V1_data.times.value >= (trigger_time-epoch)) & (V1_data.times.value <= (trigger_time+post_trigger_duration))] -n = len(V1_data) -V1_data = np.fft.rfft(V1_data.value*tukey(n, 0.00625))/4096. -V1_frequency = np.fft.rfftfreq(n, 1/4096.) -V1_psd = np.genfromtxt('/mnt/home/misi/projects/cbc_birefringence/GW170817/psd_data/v1_psd.txt') -V1_psd = interp1d(V1_psd[:,0], V1_psd[:,1], fill_value=np.inf,bounds_error=False)(V1_frequency[V1_frequency>minimum_frequency]) -V1_data = V1_data[V1_frequency>minimum_frequency] -V1_frequency = V1_frequency[V1_frequency>minimum_frequency] +# H1_data = TimeSeries.read('/mnt/home/misi/projects/cbc_birefringence/GW170817/raw_data/H-H1_LOSC_CLN_4_V1-1187007040-2048.gwf','H1:LOSC-STRAIN') +# H1_data = H1_data[(H1_data.times.value >= (trigger_time-epoch)) & (H1_data.times.value <= (trigger_time+post_trigger_duration))] +# n = len(H1_data) +# dt = H1_data.dt.value +# H1_data = np.fft.rfft(H1_data.value*tukey(n, 0.2))/4096 +# H1_frequency = np.fft.rfftfreq(n, dt) +# H1_psd = np.genfromtxt('/mnt/home/misi/projects/cbc_birefringence/GW170817/psd_data/h1_psd.txt') +# H1_psd = interp1d(H1_psd[:,0], H1_psd[:,1], fill_value=np.inf,bounds_error=False)(H1_frequency[H1_frequency>minimum_frequency]) +# H1_data = H1_data[H1_frequency>minimum_frequency] +# H1_frequency = H1_frequency[H1_frequency>minimum_frequency] + +# L1_data = TimeSeries.read('/mnt/home/misi/projects/cbc_birefringence/GW170817/raw_data/L-L1_LOSC_CLN_4_V1-1187007040-2048.gwf','L1:LOSC-STRAIN') +# L1_data = L1_data[(L1_data.times.value >= (trigger_time-epoch)) & (L1_data.times.value <= (trigger_time+post_trigger_duration))] +# n = len(L1_data) +# dt = L1_data.dt.value +# L1_data = np.fft.rfft(L1_data.value*tukey(n, 0.2))/4096 +# L1_frequency = np.fft.rfftfreq(n, dt) +# L1_psd = np.genfromtxt('/mnt/home/misi/projects/cbc_birefringence/GW170817/psd_data/l1_psd.txt') +# L1_psd = interp1d(L1_psd[:,0], L1_psd[:,1], fill_value=np.inf,bounds_error=False)(L1_frequency[L1_frequency>minimum_frequency]) +# L1_data = L1_data[L1_frequency>minimum_frequency] +# L1_frequency = L1_frequency[L1_frequency>minimum_frequency] + +# V1_data = TimeSeries.read('/mnt/home/misi/projects/cbc_birefringence/GW170817/raw_data/V-V1_LOSC_CLN_4_V1-1187007040-2048.gwf','V1:LOSC-STRAIN') +# V1_data = V1_data[(V1_data.times.value >= (trigger_time-epoch)) & (V1_data.times.value <= (trigger_time+post_trigger_duration))] +# n = len(V1_data) +# dt = V1_data.dt.value +# V1_data = np.fft.rfft(V1_data.value*tukey(n, 0.2))/4096 +# V1_frequency = np.fft.rfftfreq(n, dt) +# V1_psd = np.genfromtxt('/mnt/home/misi/projects/cbc_birefringence/GW170817/psd_data/v1_psd.txt') +# V1_psd = interp1d(V1_psd[:,0], V1_psd[:,1], fill_value=np.inf,bounds_error=False)(V1_frequency[V1_frequency>minimum_frequency]) +# V1_data = V1_data[V1_frequency>minimum_frequency] +# V1_frequency = V1_frequency[V1_frequency>minimum_frequency] + +data = np.load('./data/GW170817_data.npz',allow_pickle=True) + + +H1_frequency = data['frequency'] +H1_data = data['data_dict'].tolist()['H1'][(H1_frequency>minimum_frequency)*(H1_frequencyminimum_frequency)*(H1_frequencyminimum_frequency)*(H1_frequencyminimum_frequency)*(L1_frequencyminimum_frequency)*(L1_frequencyminimum_frequency)*(L1_frequencyminimum_frequency)*(V1_frequencyminimum_frequency)*(V1_frequencyminimum_frequency)*(V1_frequency0.25,1] = 0.249 -# guess_param[:,6] = (guess_param[:,6]+np.pi/2)%(np.pi)-np.pi/2 -# guess_param[:,7] = (guess_param[:,7]+np.pi/2)%(np.pi)-np.pi/2 print("Preparing RNG keys") @@ -160,8 +175,8 @@ def LogLikelihood(theta): print("Initializing MCMC model and normalizing flow model.") -prior_range = jnp.array([[1.18,1.21],[0.125,1],[0.0,0.3],[0.0,0.3],[1,75],[-0.1,0.1],[0,2*np.pi],[0,np.pi],[0,np.pi],[0,2*np.pi],[-np.pi/2,np.pi/2]]) -# prior_range = jnp.array([[1.18,1.21],[0.2,0.25],[0.0,0.3],[0.0,0.3],[1,75],[-0.1,0.1],[0,2*np.pi],[0,np.pi],[0,np.pi],[0,2*np.pi],[0,np.pi]]) +prior_range = jnp.array([[1.18,1.21],[0.125,1],[-0.3,0.3],[-0.3,0.3],[1,75],[-0.1,0.1],[0,2*np.pi],[-1,1],[0,np.pi],[0,2*np.pi],[-1,1]]) +# prior_range = jnp.array([[1.18,1.21],[0.2,0.25],[0.0,0.3],[0.0,0.3],[1,75],[-0.1,0.1],[0,2*np.pi],[0,np.pi],[0,np.pi],[0,2*np.pi],[-np.pi/2,np.pi/2]]) initial_position = jax.random.uniform(rng_key_set[0], shape=(int(n_chains), n_dim)) * 1 for i in range(n_dim): @@ -185,7 +200,7 @@ def LogLikelihood(theta): from astropy.cosmology import Planck18 as cosmo -z = np.linspace(0.0002,0.02,1000) +z = np.linspace(0.0002,0.03,10000) dL = cosmo.luminosity_distance(z).value dVdz = cosmo.differential_comoving_volume(z).value @@ -198,19 +213,18 @@ def top_hat(x): def log_likelihood(theta): theta = theta.at[1].set(theta[1]/(1+theta[1])**2) # convert q to eta - # theta = theta.at[7].set(jnp.arccos(theta[7])) # convert cos iota to iota - # theta = theta.at[10].set(jnp.arccos(theta[10])) # convert cos dec to dec + theta = theta.at[7].set(jnp.arccos(theta[7])) # convert cos iota to iota + theta = theta.at[10].set(jnp.arcsin(theta[10])) # convert cos dec to dec return logL(theta) def posterior(theta): q = theta[1] prior = top_hat(theta) theta = theta.at[1].set(q/(1+q)**2) # convert q to eta - # theta = theta.at[7].set(jnp.arccos(theta[7])) # convert cos iota to iota - # theta = theta.at[10].set(jnp.arccos(theta[10])) # convert cos dec to dec - jacobian = jnp.log((1/(1+q)**2)-2*q/(1+q)**3)# - jnp.log(jnp.abs(-jnp.sin(iota))) - jnp.log(jnp.abs(-jnp.sin(dec))) + theta = theta.at[7].set(jnp.arccos(theta[7])) # convert cos iota to iota + theta = theta.at[10].set(jnp.arcsin(theta[10])) # convert cos dec to dec - return logL(theta) + prior + jacobian + return logL(theta) + prior model = RQSpline(n_dim, 10, [128,128], 8) diff --git a/example/ParameterEstimation/Injection_withParser.py b/example/ParameterEstimation/Injection_withParser.py index 65d2f952..96ad3150 100644 --- a/example/ParameterEstimation/Injection_withParser.py +++ b/example/ParameterEstimation/Injection_withParser.py @@ -15,7 +15,7 @@ from jaxgw.PE.generate_noise import generate_noise from flowMC.nfmodel.rqSpline import RQSpline -from flowMC.sampler.MALA import make_mala_sampler +from flowMC.sampler.MALA import make_mala_sampler, mala_sampler_autotune from flowMC.sampler.Sampler import Sampler from flowMC.utils.PRNG_keys import initialize_rng_keys from flowMC.nfmodel.utils import * @@ -125,9 +125,8 @@ L1 = get_L1() L1_response = make_detector_response(L1[0], L1[1]) -f_ref = 20.0 +f_ref = 30.0 trigger_time = 1126259462.4 -duration = 16 post_trigger_duration = 2 epoch = duration - post_trigger_duration gmst = GreenwichMeanSiderealTime(trigger_time) @@ -167,7 +166,7 @@ def gen_waveform_L1(f, theta): psd_list = [psd_dict['H1'], psd_dict['L1']] response_list = [H1_response, L1_response] -logL = make_heterodyne_likelihood_mutliple_detector(data_list, psd_list, response_list, gen_IMRPhenomD_polar, ref_param, f_list, gmst, epoch, f_ref, 101) +logL = make_heterodyne_likelihood_mutliple_detector(data_list, psd_list, response_list, gen_IMRPhenomD_polar, ref_param, f_list, gmst, epoch, f_ref, heterodyne_bins) # Fetch sampler parameters, construct sampler and initial guess @@ -199,7 +198,7 @@ def gen_waveform_L1(f, theta): print("Initializing MCMC model and normalizing flow model.") -prior_range = jnp.array([[10,80],[0.125,1.0],[0,1],[0,1],[0,2000],[-0.1,0.1],[0,2*np.pi],[0,np.pi],[0,np.pi],[0,2*np.pi],[-np.pi/2,np.pi/2]]) +prior_range = jnp.array([[10,80],[0.125,1.0],[-0.5,0.5],[-0.5,0.5],[400,1000],[-0.5,0.5],[-np.pi/2,np.pi/2],[-np.pi/2,np.pi/2],[0,2*np.pi],[0,2*np.pi],[0,np.pi]]) initial_position = jax.random.uniform(rng_key_set[0], shape=(int(n_chains), n_dim)) * 1 for i in range(n_dim): @@ -208,7 +207,7 @@ def gen_waveform_L1(f, theta): from ripple import Mc_eta_to_ms m1,m2 = jax.vmap(Mc_eta_to_ms)(guess_param[:,:2]) q = m2/m1 -# initial_position = initial_position.at[:,0].set(guess_param[:,0]) +initial_position = initial_position.at[:,0].set(guess_param[:,0]) # initial_position = initial_position.at[:,1].set(guess_param[:,1]) # initial_position = initial_position.at[:,5].set(guess_param[:,5]) @@ -245,12 +244,12 @@ def posterior(theta): posterior = posterior dposterior = jax.grad(posterior) -mass_matrix = jnp.eye(n_dim) -mass_matrix = mass_matrix.at[1,1].set(1e-3) -mass_matrix = mass_matrix.at[5,5].set(1e-3) +mass_matrix = np.eye(n_dim) +mass_matrix = np.abs(1./dposterior(true_param))*mass_matrix +mass_matrix = jnp.array(mass_matrix) local_sampler_caller = lambda x: make_mala_sampler(x, jit=True) -sampler_params = {'dt':mass_matrix*3e-3} +sampler_params = {'dt':mass_matrix*1e-1} print("Running sampler") nf_sampler = Sampler( @@ -271,6 +270,8 @@ def posterior(theta): batch_size=batch_size, use_global=True, keep_quantile=0., + local_autotune=mala_sampler_autotune, + train_thinning = 40 ) nf_sampler.sample(initial_position) diff --git a/example/ParameterEstimation/configs/injection_example.yaml b/example/ParameterEstimation/configs/injection_example.yaml index c1f51b28..53883bbc 100644 --- a/example/ParameterEstimation/configs/injection_example.yaml +++ b/example/ParameterEstimation/configs/injection_example.yaml @@ -7,7 +7,7 @@ downsample_factor: 10 seed: 1234 f_sampling: 2048 -duration: 4 +duration: 16 fmin: 30 ifos: - H1 @@ -26,7 +26,7 @@ inclination: 0.5 polarization_angle: 0.2 ra: 1.2 dec: 0.3 -heterodyne_bins: 201 +heterodyne_bins: 301 # Sampler parameters diff --git a/example/ParameterEstimation/gen_injection_config.py b/example/ParameterEstimation/gen_injection_config.py index 807bf3db..99313d55 100644 --- a/example/ParameterEstimation/gen_injection_config.py +++ b/example/ParameterEstimation/gen_injection_config.py @@ -1,6 +1,6 @@ import numpy as np -prior_range = np.array([[20,50],[20,50],[-0.5,0.5],[-0.5,0.5],[400,1000],[-2,2],[-np.pi/2,np.pi/2],[-np.pi/2,np.pi/2],[0,2*np.pi],[0,2*np.pi],[0,np.pi]]) +prior_range = np.array([[20,50],[20,50],[-0.5,0.5],[-0.5,0.5],[400,1000],[-0.5,0.5],[-np.pi/2,np.pi/2],[-np.pi/2,np.pi/2],[0,2*np.pi],[0,2*np.pi],[0,np.pi]]) N_config = 960 @@ -26,7 +26,7 @@ f.write('downsample_factor: 10\n') f.write('seed: '+str(np.random.randint(low=0,high=10000))+'\n') f.write('f_sampling: 2048\n') - f.write('duration: 4\n') + f.write('duration: 16\n') f.write('fmin: 30\n') f.write('ifos:\n') f.write(' - H1\n') @@ -43,10 +43,11 @@ f.write("polarization_angle: "+str(polarization_angle[i])+"\n") f.write("ra: "+str(ra[i])+"\n") f.write("dec: "+str(dec[i])+"\n") + f.write("heterodyne_bins: 301\n") f.write("n_dim: 11\n") f.write("n_chains: 1000\n") - f.write("n_loop_training: 20\n") + f.write("n_loop_training: 40\n") f.write("n_loop_production: 10\n") f.write("n_local_steps: 200\n") f.write("n_global_steps: 200\n") From 0aad34b77090f8c288142912ccb4fff731e57fc8 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Tue, 29 Nov 2022 01:46:23 -0500 Subject: [PATCH 153/300] Update Injection_withParser.py --- .../Injection_withParser.py | 29 +++++------ .../gen_injection_config.py | 14 +++--- example/ParameterEstimation/make_ppPlot.py | 46 ++++++++++++++++++ test.png | Bin 1168472 -> 0 bytes 4 files changed, 66 insertions(+), 23 deletions(-) create mode 100644 example/ParameterEstimation/make_ppPlot.py delete mode 100644 test.png diff --git a/example/ParameterEstimation/Injection_withParser.py b/example/ParameterEstimation/Injection_withParser.py index 96ad3150..3d99e034 100644 --- a/example/ParameterEstimation/Injection_withParser.py +++ b/example/ParameterEstimation/Injection_withParser.py @@ -188,17 +188,14 @@ def gen_waveform_L1(f, theta): guess_param = np.array(jnp.repeat(true_param[None,:],int(n_chains),axis=0)*(1+0.1*jax.random.normal(jax.random.PRNGKey(seed+98127),shape=(int(n_chains),n_dim)))) guess_param[guess_param[:,1]>0.25,1] = 0.249 -guess_param[:,6] = (guess_param[:,6]%(2*jnp.pi)) -guess_param[:,7] = (guess_param[:,7]%(jnp.pi)) -guess_param[:,8] = (guess_param[:,8]%(jnp.pi)) -guess_param[:,9] = (guess_param[:,9]%(2*jnp.pi)) print("Preparing RNG keys") rng_key_set = initialize_rng_keys(n_chains, seed=seed) print("Initializing MCMC model and normalizing flow model.") -prior_range = jnp.array([[10,80],[0.125,1.0],[-0.5,0.5],[-0.5,0.5],[400,1000],[-0.5,0.5],[-np.pi/2,np.pi/2],[-np.pi/2,np.pi/2],[0,2*np.pi],[0,2*np.pi],[0,np.pi]]) +prior_range = jnp.array([[10,80],[0.125,1.0],[-0.5,0.5],[-0.5,0.5],[400,1000],[-0.5,0.5],[0,2*np.pi],[-1,1],[0,np.pi],[0,2*np.pi],[-1,1]]) + initial_position = jax.random.uniform(rng_key_set[0], shape=(int(n_chains), n_dim)) * 1 for i in range(n_dim): @@ -208,12 +205,10 @@ def gen_waveform_L1(f, theta): m1,m2 = jax.vmap(Mc_eta_to_ms)(guess_param[:,:2]) q = m2/m1 initial_position = initial_position.at[:,0].set(guess_param[:,0]) -# initial_position = initial_position.at[:,1].set(guess_param[:,1]) -# initial_position = initial_position.at[:,5].set(guess_param[:,5]) from astropy.cosmology import Planck18 as cosmo -z = np.linspace(0.0002,0.02,1000) +z = np.linspace(0.002,3,10000) dL = cosmo.luminosity_distance(z).value dVdz = cosmo.differential_comoving_volume(z).value @@ -226,14 +221,13 @@ def top_hat(x): def posterior(theta): q = theta[1] - # iota = jnp.arccos(theta[7]) - # dec = jnp.arccos(theta[10]) + iota = jnp.arccos(theta[7]) + dec = jnp.arcsin(theta[10]) prior = top_hat(theta) theta = theta.at[1].set(q/(1+q)**2) # convert q to eta - # theta = theta.at[7].set(iota) # convert cos iota to iota - # theta = theta.at[10].set(dec) # convert cos dec to dec - jacobian = jnp.log((1/(1+q)**2)-2*q/(1+q)**3)# - jnp.log(jnp.sin(iota)) - jnp.log(jnp.sin(dec)) - return logL(theta) + prior + jacobian + theta = theta.at[7].set(iota) # convert cos iota to iota + theta = theta.at[10].set(dec) # convert cos dec to dec + return logL(theta) + prior model = RQSpline(n_dim, 10, [128,128], 8) @@ -244,12 +238,13 @@ def posterior(theta): posterior = posterior dposterior = jax.grad(posterior) + mass_matrix = np.eye(n_dim) -mass_matrix = np.abs(1./dposterior(true_param))*mass_matrix +mass_matrix = np.abs(1./(jax.grad(logL)(true_param)+jax.grad(top_hat)(true_param)))*mass_matrix mass_matrix = jnp.array(mass_matrix) local_sampler_caller = lambda x: make_mala_sampler(x, jit=True) -sampler_params = {'dt':mass_matrix*1e-1} +sampler_params = {'dt':mass_matrix*3e-2} print("Running sampler") nf_sampler = Sampler( @@ -276,7 +271,7 @@ def posterior(theta): nf_sampler.sample(initial_position) -labels = ['Mc', 'eta', 'chi1', 'chi2', 'dist_mpc', 'tc', 'phic', 'inclination', 'polarization_angle', 'ra', 'dec'] +labels = ['Mc', 'eta', 'chi1', 'chi2', 'dist_mpc', 'tc', 'phic', 'cos_inclination', 'polarization_angle', 'ra', 'sin_dec'] print("Saving to output") diff --git a/example/ParameterEstimation/gen_injection_config.py b/example/ParameterEstimation/gen_injection_config.py index 99313d55..671fe23d 100644 --- a/example/ParameterEstimation/gen_injection_config.py +++ b/example/ParameterEstimation/gen_injection_config.py @@ -1,6 +1,6 @@ import numpy as np -prior_range = np.array([[20,50],[20,50],[-0.5,0.5],[-0.5,0.5],[400,1000],[-0.5,0.5],[-np.pi/2,np.pi/2],[-np.pi/2,np.pi/2],[0,2*np.pi],[0,2*np.pi],[0,np.pi]]) +prior_range = np.array([[20,50],[20,50],[-0.5,0.5],[-0.5,0.5],[400,1000],[-0.5,0.5],[0,2*np.pi],[-1,1],[0,np.pi],[0,2*np.pi],[-1,1]]) N_config = 960 @@ -12,10 +12,12 @@ dist_mpc = np.random.uniform(prior_range[4,0],prior_range[4,1],N_config) tc = np.random.uniform(prior_range[5,0],prior_range[5,1],N_config) phic = np.random.uniform(prior_range[6,0],prior_range[6,1],N_config) -inclination = np.random.uniform(prior_range[7,0],prior_range[7,1],N_config) +cos_inclination = np.random.uniform(prior_range[7,0],prior_range[7,1],N_config) +inclination = np.arccos(cos_inclination) polarization_angle = np.random.uniform(prior_range[8,0],prior_range[8,1],N_config) ra = np.random.uniform(prior_range[9,0],prior_range[9,1],N_config) -dec = np.random.uniform(prior_range[10,0],prior_range[10,1],N_config) +sin_dec = np.random.uniform(prior_range[10,0],prior_range[10,1],N_config) +dec = np.arcsin(sin_dec) directory = '/mnt/home/wwong/ceph/GWProject/JaxGW/RealtimePE/ppPlots/configs/' @@ -43,14 +45,14 @@ f.write("polarization_angle: "+str(polarization_angle[i])+"\n") f.write("ra: "+str(ra[i])+"\n") f.write("dec: "+str(dec[i])+"\n") - f.write("heterodyne_bins: 301\n") + f.write("heterodyne_bins: 1001\n") f.write("n_dim: 11\n") f.write("n_chains: 1000\n") f.write("n_loop_training: 40\n") f.write("n_loop_production: 10\n") f.write("n_local_steps: 200\n") - f.write("n_global_steps: 200\n") + f.write("n_global_steps: 100\n") f.write("learning_rate: 0.001\n") f.write("max_samples: 50000\n") f.write("momentum: 0.9\n") @@ -58,4 +60,4 @@ f.write("batch_size: 50000\n") f.write("stepsize: 0.01\n") - f.close() \ No newline at end of file + f.close() diff --git a/example/ParameterEstimation/make_ppPlot.py b/example/ParameterEstimation/make_ppPlot.py new file mode 100644 index 00000000..17eb42b6 --- /dev/null +++ b/example/ParameterEstimation/make_ppPlot.py @@ -0,0 +1,46 @@ +import numpy as np +from scipy.optimize import minimize + +def get_all_quantile(filename): + data = np.load(filename) + + chains = data['chains'] + true_param = data['true_param'] + chains[:,:,1] = chains[:,:,1]/(1+chains[:,:,1])**2 + chains[:,:,7] = np.arccos(chains[:,:,7]) + chains[:,:,10] = np.arcsin(chains[:,:,10]) + + median = np.log10(0.5) + def compute_percentile(value,data): + f = lambda x : np.abs(np.quantile(data, 10**x) - value) + result = minimize(f,median,method="Nelder-Mead",bounds=[[-4,0]]) + return np.abs(10**result.x[0]-0.5)*2 + + result = [] + for i in range(11): + result.append(compute_percentile(true_param[i],chains[:,:,i])) + + mean_local_accs = data['local_accs'].mean() + mean_global_accs = data['global_accs'].mean() + + return np.array(result), true_param, mean_global_accs, mean_local_accs + +directory = '/mnt/home/wwong/ceph/GWProject/JaxGW/RealtimePE/ppPlots/balance_1001/' +result = [] +true_param = [] +mean_global_accs = [] +mean_local_accs = [] +for i in range(960): + name = directory+'injection_'+str(i)+'.npz' + local_result = get_all_quantile(name) + result.append(local_result[0]) + true_param.append(local_result[1]) + mean_global_accs.append(local_result[2]) + mean_local_accs.append(local_result[3]) + +result = np.stack(result) +true_param = np.stack(true_param) +mean_global_accs = np.stack(mean_global_accs) +mean_local_accs = np.stack(mean_local_accs) + +np.savez('/mnt/home/wwong/ceph/GWProject/JaxGW/RealtimePE/ppPlots/combined_quantile_balance_1001',result=result, true_param=true_param, mean_global_accs=mean_global_accs, mean_local_accs= mean_local_accs) diff --git a/test.png b/test.png deleted file mode 100644 index 24fa4fb0662dd7a938f014cbecbbb4a064b6951a..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 1168472 zcmdRX2UwJ6w=HUHvBoZnSVmE4ih|O_62y@vNS7v3q$@)asm2&J7IY9$s!T}nEB>?_q+GnYpwlydXJjYv>*9@ z_+dd_Vt&T7!6P;oS-R^M611TLk}b`2tPOQ~vBteVK({3Mz_ZqjHx3IoIn?+Cr><@pJMxpa>Q=|Z;#d1} z-|;Q@af3{U!R%$p(*--mmA$y@(yvAu| zdCG4adHeR}gngN6x!!{v^+6^>F)mRF0b)WPH^6op*nmO6cbG zx<)SL%M+3xOju<8`Q;zIpRfBlVcqKQafVv&t6N_zcc4S=>-}-k!vh>n*+<>{XX{3I8^3;`D-!d~uV(8rL`W zwbqNIo$8ji;b3xq`C6RxUBLNVm1?!6x6WT~;Oiq!T5+iKhE4B|9FTEsy?k!dyYC*2 zx!*sYX{t!rS7FF1oHKvERad#LIyQ(^Y2+sQrDVPh;-jFiB`E?yqiB2}I?VM2cJ0c; zhwiyD2?pseG?WAa818*d(Tz>Cz~M(eo=$UkE0f#*w!NWAw!bwswZA>B7di^sU#}jtvUl=WH7C*t@$NwE~zUFI)|q#40e{L*_7Ml^vC4=^P+P$ zzxN%Nf}hcjm?u?m8$OUWdwhz)#HY-iEwXR_iMc6-Qg6(g%Wk?4^)|-6*_U?k zVpqk!q&|IoDW|j|UO$z=^k1b~lH=7sRd9=Ec>2j1g5l={WQ#H@f(!I7x$qWTFJ+FX z=cIi0Tphh-{q4Cv4)?x2^~8zLw@pZAdoCKXd3VphOp-<;!#U=`R9?Gb;iL*;Bl*+=(alSnh z)Qzx_I`Z}X)FpD89dmju&o!1gkLFBeu`%m&u46rWKM4+BRv2tjm;Q8iL&t-Og67}9 zerPI>)7`y$cY|zWY+T|Ctcnx1-A8C~V5H2!jCIE+OnZU%+w;j_hIHM9mCKj9mN13t z(<_bhzuavPHyQXipVR3z*j347&($?NoWp9&7`|Xyozr zKFIx)yvb$-m3Z0E+3IrZl>}V0h6dYx8qaPh(tH0s>%$G7;r`mHgV)c^>&?c}_HEM@ zMI>arOT4@ou|c`Z?oQJUcO<9O=GeF*x0>G;Ib3GA_qSy>z8Nl(ZD%m0UYj0&vUfFa z;qpbstZ%T53eb6h7zx5Mtjrh9G@OnTHU50<(Ut^GiL!V-KH=c2G08Kwe82ki6Yb;C z$)}7CJs2t+!mCy(x@0evXG{E8hZ$k)GF>7{_)2j zQyPM8D^@>uskV-;uVEE)<70{ZQLRi9x3@0R%`w+CwX`l}cUKFyB;|Tjp!*Db667BF zvkQ_JxZOH5L+ZdBx;^oMS&ep08Ar5z}hrXUvsB4OjC>l3I?m|gPiCs=_ zgY?H!E1K`#Lt2;F^wzml&b~dn$M_v;86&USHfb=-_qC75@L-QW14X*%>bBzzvX%(b zVYRLCsv%PDO;JHkPZwI%B|lq|zrs(BU9M{)@73R3c*QFG3i7u5E2#kUr)L)~l(f-O z+x+2I_i9Q&QY#ii;BMzt2BVCz@b6Nbn!LK9!!-W5NjqOY&d zj!aA2c#dE4MRbI>jQTMC9judJQ0V5M+VC&Wa+Ovm?bXgHo;VL|hHCIeW`B3v2G8$b z-76Ms@jQY}+FaXZEAbDH^zn!F2l>v+e!0Ea^Xr2uo45q3Om)q1Nks^Z(Cg=A)-omj zqvU;%?~Y4L;N;n>%n2sZsiO_)c)mg(vEa`L2ncX*Nyzkm85(?^SF}v?v`6BZx0ZZ+ z`u_un9UGG~<^3?=o{+zgx*EzN5apiQ`unq@Vq@2Cuq=2EJQDG6dzH?}%lQV+oVo88 zm2f#}Afl%2&l1ur3CbLA(NeVS5DoZ9*b;a@CL{gVxfcNXxkx8d`FT%HR&sa0w7x<=K z@?F6D!`5*tmr{Gh{q=I|_J2R8%~1Vf0fB@QpPq3wiP4g-V!=Wahi0w)oauH$&vxJ8tda zFJA2at4I1i=7+~IZuRx`)|H7yUO~Kf;hUzm-;0Nd`S|XCT=w6*EdM|4*W7ZqF?;sT zojW%@LkI8{>uh5$&kGv8S(8sCKNc1hRb?`nSZ-rm+rUegBClQBzKe%Fd?%=GJ-gBv zIcM$KwVL=%xJ6q_E7GIq%gy-sIirp3)ort99=N$jM`u;RXc#N(`5&?^+FG^aU#yDy=*^{fGU;oFyjf~opw){zW94s^2(Jf z*NBQHs>15mvQcJ%dHBk(Mvv?Oaate&UwO z%$Wz0*NBU&-*s(cGsN$G-QHEpdvo7A8vk_}LQ4bZrz`5(HM2hs+)xWMm>9r&COS+{ zC8AMn@By3NW%qA?Mm;bX1yzJn^HmAnH^vx^H4|as|7mJyT*PnV&&S2YJYVE^4}bXE z5f0pqZ}X=poYQv{0JZrlKkpUvI6z-?cX!7w-Fe9iy5q6D@9}M4v2^D-Vh#|VSwI?ljg52eCj-H) z5f+ZS{$lj|dViYvX`=9-q1Jqtci8z%m;I05gSD~kV@;X58XCzj*N=Se<2Jm{eYN3- z$L~EZ1qR+gE&el{|0UtA{M~nfr(a4+O6%=U=c|Ao=WkBDc5V6FyKd?h7EyndU*$Pt z*6aDQ+3Ymi%EX*}-dp%8a^W2BkN{hK%om^_uN4-azxl?sYuC=l@=^?+&2ira z|5B_`Gz7FpbNYbZShL8K6p6jjYJ~}z?}9rzI>w(DMFBx&5YQe)BLfS@nrZ!OcwG&g zQ{guHc6=gB{tqi9H*ZY?RCu!e8EtBOeEjskKF*l+-(L|V(bQxUaCWvskwX#tb(DaO znLXC*>k8z2WWMG2ZOUSV%|(1~bV`cmD1qxM13Zy_H9lVD^OTWY^D0Hu+qbTjR$p`e%6dk2_vAcTDnx`2YQumyI8jY_S!osXED#tWZQg}9MvGFC?a}jZ;6w!+%d(3&mdJ{T zK&NA$u$T0`;AVatg}z^}Pdc?t0Kq-GkJmq4E&9J3bpPE5oDkjp0e~&@<)|ut)H82Q9nsRP`=k><{<=wkcp_efxDBMR=d*zJSoz^(itTh}+Dj$kW$ zy!~t>8sn03UP!&M^9TJQ_2{>0`D^3i**)((#N!h0G=HpKI}$x*r+i;)9z<(?1<&FD zO#tvr{7}5`q#!j53yYz*rt5Flv6V}%EBO(5z;t>tSC`x5YwH>XK=E+A*l2jVDrKFZpgcy(zTd-v8iM}#q+%~%gz#kuwMdSdUp!G913;QsB&d{&i3P(6_xwL{&R zX?9f|6O;NsoD^(8D_le^bDqCYv0K4uq43L7#P6*W-Iq}S3MC9^^&a#M_pc8ovOude zfaX$zAT!_E1c~#CyoW2v5bo8w`1e4)xKFPJi-;FP$Vk%NuMbc(S#*AWCEalO0GpwN z5MJ&-yOq z+Hw6m&n~za=32eRD)jJExm*s!i_k_lufEPEY}sY2K|J3^Vh6W%4eeny6y!lP(D$mZ zQ}^jB1*I(u=I+!e4Cs0>*ZxehWe>G-rO5~0~sqv*|-5joyFJp z55PUDmZVvi#%DDIOZR=g&Ov}gyR=kTzrEw)6$Jk9X&qLc4hiRO?Vv+=4pakK;zu2z z@TgposGpeNL9Gcs_zPlyNzlkSu;u&v<3xmjjlK*ToB`hEfR5h&6P}O}IB9||Wf}|R zk2-giMkxoir`h9`m^st5rEJPpM*z#q*?NLLw=Rz@_q2^|3MJYLu>G6d0Xo)bcE)@1 z4%)5n(%Ys6n&D7!gS3O|)z665Rvil3*C_&L7GXmfk0&DpT2E)Dux zK_*1F%3@IJ7c*6|GV?(ohvHMnQuHDS zB%D9LY=w@5AhDg-S2SMw%k582IC)la2^rjkh?tiV^cgsO9x7JzdL)IS_YaP9a3(8` z6ya!xKoe>J!K#|+@Rm5_I+Er-hmRmH)5Ei8j*A($spilQb{(X=u*S!6H&jC!NCgDt z_x*UqB77=}DSkhNx(hpO=FIGe@aE4DPQ3lmv%fK;a$~4#k#u7n5_TTqGsLnmSk&1Q z@@ff4zs0Vv_h+GC#J;IS9>kgdB&(}Fv|Lf%05~lZk&k8DRAqsbid)-C%hwjK9 zoX`n6{?ZElRmB}p&FYW=n8QPZ;F1D|2Kz}EQHV7j$#>tAA)q(k&lk@PWmG{x$Suu; z#zJsj;b1oO>WZEEXUdVdE*u*-vElnCzv5z_Ar60G7BpGbi#5@a>JeM$+?B>^$C278 z4MCFia@#~oPV_ywZbMKbZ@+Zw{PNc0*n-qmZ%(@lqK|QVM0%Cp}mQ9RKUF3 zMG&q!6oxqpM2DB;`V8}D_Y;NFS3XecEKuPTz1^13RnfTwLjQ$Dh z<$-q%)Yj3j_n@wKgkFkI_kDf)@TU%>ByE#Nn-w-#6~za*l?S=DRoCNG7Hue&-XN8vyz~-z{_lY%n<9OyumC)YN4PXj zsR1e7g4)6(t3Yj&^3}G?+8mkH>lKJK!mq%~!cBT9f3_cHr?X|85J<%S;2=pHdVjxN zK-bVP)L(r|)^y=GBS0sv?SWM+zlr1&q`HfYE67XB+iamGr5k?891)iq5kSu`EZgB! zu>P4=PZ$J3P|u>mhr0I4Go z`u-;mpB`Hot4Gr|6_Bbi$b#)+UY6R(vc!nevOs1M3$aVd>*@AEy}~;MwG752qi(M3 z?g6n(y{0M(jf72EOuIl2GEpN^W!$3|HkE`851f}?1szwB1cp|mT5GK05{MS@2t&ej zEq5Q=K)tabEozHbUmXyE3b33pAm7;q>z6OGyS1$daLfHE{2fKTNYEWa{q0ne&kVd9 z?0zGDw>3q1s3Ryh67-$XU02mOodlw|P6~Q&MolL>L}`jezM}WDl|hDV2yT&3t(k13 z%H@9Y-Fg73v7v77+!bup(0Uo9+oV5FA4$XaKQ3LG@x#s7*xhV$e@vNr3WCmB#7^~C z^@aON3c}?*ZpONax%gBfe4#spR+{)^>m+)4qFB)%sq`Pq?Wy(Ts1np_ZqF<*B~GvAO%4n&vB3gd~uW$C}fMoHVh zx=*GQl4Hg`BTvB!rSmEXI!&!K=eqvDr@^ybDx(B7IaJXa3PUP2EL^O<h4JA+>FNO-?3yQF4?QEe+6<;%fv#i zjp_cFiNs6A#i#7f!2gvao6^~lck%7p^RLRH;d-6}g72TSGg7sN$;5F*&fO^(5E71~ zKCy{`tFif4)*sjzXyV;FXP&oDMtRSq923=CrIBp5aWS&l1+-6%XiXB@B}`AdwkAKj z0ry(%o8cWfN-*E(YytMDUTdGcuew<^Fef-D;OP8r`4Oh#>IV`iH(Hl4m?dZ?;eo1T zNpc#0Ue%o6*;LAcKCR=Uo>N>igj(tPN9}cL8KJ|Z>wNl{kyaie;w8DM^Qdj#KfYYS zG?TgW=TABHphxd@`}-NaOC)W|4xS!NEG#b3aZKqfzZYNDGVJu{C-AjU`oFE%i2ect zU7r{)9{EiF_~Eg)@5L2LmLDIVMz?kF(6VGKFaM|{b-!fC*7)Bjf!o162y>P)4rSTW z9Xve2xAoETr8+ynS*WwmB3~kN+9v$^_=b8!`03d~MI5A&xQyMSL}cLMv1XD0Jb@oS ze(Y?i6emXj9BP@(7hZ=-v*uYB0_KP#S(|l{d&RQu`NecY>5={9yA?lcX51gB+FM|F zDMSk$wxMYA3h~?lCwd7=U1(&y&q~T`x7PWq7a>0z4N{xhRCcrU<7ysAVpK!c8GT(fqqft?5Margp7 zOLj@FLu775>jvOB?2krmJ(EZx3J-_O0W+#MU1k z=X?U3D#Te&0sM`Iki*pUI$<6nW#54mVg(p%4X4-9?$#^RuTU?Jbs`6(#^$N-bI<;% zkxdcMMJhstmWi1(^3s6^l3+ba z86N5<=NAJwPTwLxTf*-3EqsO*sF5aQ>NG^Z-;1V^7y}FFZiTn@KZ;cs7^hcV)^OT{ zTmmENTv5+(dDg$dC~g&dWlVY) zI{Ss7hAvQdnuVScHoJjURHT9UQUE9_nVaKdV{MXi4|v11(_Eju^)2}L8Z+r{aX=Vt z)#-8PX7Tct;MT1oc|j)LS9DFhB!M`NxNMGxHHcUoiWzL;DsmYSc?a0AmW-ysn;gRx zhQ9grmM3`!FsuRZu$ODLju$BhM-ld&N90dC<*Sz?za}15sott$??6TqT(Cn!J-JpV z1zc%OIB@o@RG074;%yHpq8v)*^K-u|cIBC!E=~9S?fMNNZ(<^E`E=CfrEAX`35Hvf zet66Vo|GY=>40-gJ@-xMfktj`1MrU|xRQmio)GX_eDw1lAPe)+E(O42A_5Q;+g>uJ zS@Ew9Vf=YnM4~2md=xU7Se{z-T|M7(E7o!?P^*(GHWwYQeDBvsGxqA~T}DT9Bj)oP zKP%WrRZ(H5%Y42XidMiKDY-#fda9{DwU;PhNDg9$Y8oSa!i}?!X8p$d2+esCf-{BT zI;Njh82DNPh(+iy<=fLm(TZp0G1&kOH_+2m0qG(%?$&|yQihdl`O*m@90;8A_y>-D zI5Gd%MrgI4LQP5- zeVQ`8;|kka;iN1g_bqLh!f3~#wJ)E+ zdsC4+6Wo8%L*hYUxkN-tZeMwlQ@OTol+W-`7EUNJSC;6pRg;XfJ7BL|175XUjy1gD zb!WtfSR8hwlrx3k$Wx!p+h=}!n$&an-*b;t$-Vx!@c zpMn}lWDZD;N;iGpo831nTRP5+DS?$nZ!ZF9V!?J)pBn;1%QcB*);KAH4Y*XQCM!xs zfo+bHN?i+=^>}%8TbFj9*+269XiY3STs2ArmnM=UC6nwvSwJi?!Ep8teJGxvdE zrv0X;b$@?sq9#t;!-o$MwJVuAhKHXRq6X=M_hrE(sauT9>BXHR0G-vP@11azY<9?B zLQG<}rY@2anYiSLsbz6^7>-|kXQ_H|YgKAX)^6VCyP6^S+E%AcjZIi@e>` za=_dyi@;~Xc8(O-Q>HYUJ+_M4ejSs{pm78`lfW(#R2nJq5=3_Y{KrZ7P%ntU-CqSV zw)t+slN-ox`e3No6uU!ieOUF7`6iz0W!vwu$g1k__D(%m!$R7p+nT%&b5%-oHr|Nl zOqPHsb8f4aB*!Djl)F@q$>F^OnVxv>E~AfT1cg9eFqmi`H`J0)4>C}Rs`N@`5oj#> zd7^t#Wq8ohw>rH5RV5PrD|gZXY(Nb>+F7udOE}d%C;w-cQ8m5rR~S8OGKM_~6M`4A zf&(!NS-TW`8^s&E_;&b`@n9K3R{Pvv$>`e}SO=O7z)++#-3YNPRo#@(uGw7nP;F+v zdTwu_V+mfsxurrBLloMSf#8HaayW}@leq!!4$`^Ho@pan zB{B$C;!o5l5r-t6^PLM8F_2<%-JOu|7w{mML=kg`4T_t%vH6FIK0-!&Vkmhle)Xtd zEQf1BL1CXci`Uxnqlejv@)8NIq&MGWhz zTeogW<&U?i!eO*=-p;cazC1MPl;{prxgdr+rUN7(9HCbeFOeZqI~*KVC{z_LSxZF) z%a0W3Y9BbI>>BB^0C^y^j6T1HBuh>CHr_Bva(`Dt?X0bD#w{5+ zOIMF!lYYY{nV*xa;F`H9rEq?DJgP?gDD%(qPCYQn|>XQWu zqM$T4M#w@H2YI%TT4>`Hlm`1t1KD?6+ky)>)yiQo%Y_<01Cw0Dz(S}jZ1P8E;iK9P z3hcVD*McOCEmb-e`efjrS&v`0Qfo z6xU7POn#g)h4m$?aTSvYJbct!6}5*UAT>Uot**i*8JyaazrE-Ehm%i!A>G5MFAw3XbLISd`1r#9YL~b8dQw{U%$m<0i8y7(ye+Z@qA|Ac!x!<3N zr$ENHK+=;&)cTYeAdwfKLCr1o#v@tMOd$RQc-UVT=Ju{vqn(%H`MfueNj|X}vN^wP zJU27tWeS@-c=8r)WUq7GxQwBa0(gSgH%ig2TFo6pI|FDyYv(uB_Yt&@XXx6JoNSMC>%! zT^KGei;iSBn`X&qUX6I;djN@CNeL;fQ&>)6bcc0Bs&8iyt zflNqomY@;y0P=!}K7gjV?_33qsIlvSK52d;_V|8u7j6!>Kl}!mE5}R$fwhv!?VBJD zx`S^e<(8)J^bwV@mBQ$hUwQaE&$Hu&%>B&JrQIgRp5LBe>>>_($YxNP5BY8NUaw>ggeip50{_B;1d$wIv5TLYjH05i@^yPP~`&nyrCVZ z!)>q8wG80Q4i~xjlgjZ0dq9s&`6IQ~yWir)r8Vi8N&#^YUs}Kc&Ff}9yFIA{kN+ z8Z_tE{J?oa;YZALjNR*@4c1}ksPEljG$3m1R_OB}yo_>6Ycb&4EG+DZq)j#%l;k{Q zDLwfHD}{dj-=+%$L${O0bg%-=PErGres>u!N|)wIESVXCYDvR7j6AS&moQATlG%;r zBX&ZX=K7%2M=A%2(I8a^V%G|#y%20j1R-Nsk{lB;T2o)W0UUND&ZKlBV&(>7pEX|F39Z&E*CFNsJ&OUZQvpuO~G(2O8=f+!_# zRWEv@RCI9`(Doo2#lAhqTlM=A4p*k13V?B+#BEH%vB2Qhk)4O?9GHzQ6nWgl1-An^ zS~>V)gIjLD5hXtYYgex>1zs!sNony#O7t|j&Laz|DFVlf);dvBHiVdufqmA1E z-Ui&7y6*;h*c7x!jr7=)g+ecoCTAO}2;)$3dPnQUoR zLm8_+Jr1W*8tMm|0kX{H9nvigJ^u0KuQ%OJCi?y{53-Z zjS5^YX8->E4&W;30||P5-6q=hBkwT-?ZG#RFzE!*v|H1gOh;ms$YVuARczWrrW0bE z=GqA2vPHfJKZz>GM+hcpgsDIZrJPce(%F!Q2)2KCbh1%5w@;f|75GjY!7;0{u_1Nj zT)^214;0Vy>X+P!=3Qi_3pB;LWXEJmX%0*}=D z;6qHG4yK~69|nt~%r-=QpmqFh+M+-lUNP$Ex-LU?U&)j>{P8gca9obHKt@%qwQd0& zNOQk*RH)Q-065gaO2MK@Lnx08WI!S;06I#P>_&9PK-w~f=c~mf42~!oeD_nXeh@gW zJr0?WV{ZaTP7a~s4Gs?P529NqcF?;!HA3{zxhV42c|%rS#q>rkDNZd`l;38T2w-S| ziM`?^@2)ro*CPN4*BlGUWP^RCu9;*_vR<2+)44sBQ9F*gVge8prXow{33@GvGqc|xxTN_FH4}; z$8JYHwJn-fS2(T09xf`H1|q|+m9j% z|4w_D#osyEj8Yl}cl_;-zY{r#4x^tnXZ)gJKDMhKC|D7grz`}GHNsMfJS=@w$Gf}`_;M_Z5 zE4jyuRQS&JS_R_PxZVIH6>>D-$^(UZl+eC+F0P;+D1fq%ct|rYe>)+Nj~0?BBRVX! z&1?cl&|D6)>uAIle1J__1!_K5@4z{58KgnHu}f7&Z4SrP7t^KDp|AiRsV6IEfna!BgPK~d;0Plq%_{lXm&Flt zMC_J715$I`bjvXGmJnj{s1-H@?I~%{qHx?C!0H9a*+7svqR8^Q*d!}0TyOTXD+)F~ z$0jO^PN+HAm{OB8VZM~M6D>nFn`ZQ1)Gs+ z*$j$%{EuQ0=di3X$skM!G0+hPH4~&Pmc;<5QDu{02A|N0~j<8O=epQ7@Uh>M=+8{ zBI+iRE)_vmVb!brE1Yxn)Hbl`7KEgG9lP1)q`wfbU@Tl`x|Il@@rfHYEv*0^9e@j^ z0Iw59?gK&Z@B2|nPf~thvZ;YaN-L%x1m)F;-OtCd3t*bM6~ua(w!! zbercuunLM+JHow$jmt`}0v%3B!}iN3KH z(&k09($Z8&=vEfMqfJCd;;?dgMSz_0Vx08Yh(AlnueY-73HP6ely3(MDU*FhJZCcr z#MIItPYUC_>K`~KDM8j$@Sk*(jsz`;GRWo^ zJxEqgI@Y~K!|k&lHz3o$KmiLHk>0q53Fu<=STZIyhxC>BloxABga8IhqN_q6&T-yYW=6V<#HkY^*)fP)sVEw^4ti$JkbI4l?RzM;A>tf6y{vw z*;oV!mFG6jjAf4mQ{|XDm{PJ^*aC_L2@M_XKEtv^zY`yUODxuq3!nSEXd=n}OSp!f z7w+SH@}H3%w2$ZX;WM?)R3QQx>>-zshc7CWRU}RN9nA)i8RpZRYcMbKZ+in3 zr68GG5dQ~2(LvM-pbp(L(*Y**1_4-vEiZkOkB)m83W;6RACbyJt{;2HQHn3nAYW^% zNN@L?H~@@|#A6f15DyU9WmKYBPl6?}IuPinP2t+!C`rI~u1#Rw5SM#!3h1&U(vyf8 zK(I|Q%#Ddz4ei*2Bs7vS$sxwJ15uKN+DD8&4IAX)!l@tzy;2v`jFt39D2|)i>roqr z2&5C=1qwx=2rh>>=19}M3DoY=oh{{UL55CDHs(Xb^60=cOXKX5h`}!C`SHcD4t#cm zKVZvP2*w6tg+HUWN!=$HzGNY$8m` z{u{FftC-vl2}PdhW4ai_Urpv_w-gUM@F}kuvwj|vC~t(I6+R8} z>JCDaj6A$%D3b7cI_uPOHh-f_mFQ|41`%$#Zf{A0BqYP8%ce-+L*1{f!$DSK;}S2f z>2tSm!X?V3blp$aU2E$~4@Vs;315*cD zD{&A^GIBtg*D0>T=K3?lvpY4rPMZ!>EheQ1kkqe^9B@cq)IT%wB8CSVn_JdY8rUWP z$_ZEl^pN$pv$;I3Z#NM^xqZ&5wS!;WU_AJ4)<+l8aetZS>2il6!5hZBx{ZQd$myXI z!?mx_NEFytI`4G96kS$RtT60JBhb|Bm$#7P+8uzagRbSlDN@DcMq@uYPa0+I zO%c1j6j+|gMmZBAe7HD0k)G1*pFugv8*c2OM9m9$-Q>Y24h!W?=GXCs7Lw zvdFT~Vn!q7Q5P~@zVjt=cfV5GD2o}0Hgez~X3lK7)){g`Yf!|q`msz=|2+lP!Yl9=4=MusTv|C!Z`%Z@% z7n=XWYXyO(AldE;Y!z%`r<6gaZ}8}D#gK$7ktFEOO`?$16~4>2y~$K33oigLOvvUS zj>^4|J+qKuJ225C#&jYB4z=EFGJO$0%4U!P+BNyW%bDaa12Nb!maL_HVzWmcDQ2Rg z%QrR>f!oD~xsa`3VNW+yZNnXeRGqzol7EKmXHO}k6y@E~_64cLA7ea}Gg@PPU|oMz&prRn}H%!!zzbyV+YYcHTpCcI5P6f$cNR>9qK z21I31zd@JwIbor>grKm43$Cisz%A}6Dc8L#whOKtOd+>is!hs&&)9$2_k zC?J+N&UE{j1VWr7Jp^q&sL%kq>O4faKQsYh6km6`ZmqfNXwMh<+8&xu?L~u0n45eM)IF0B)I0IcoxWnMo_)#I1Y596x!64Tu%}>V#dcr~S852@*3(L7HSRi7g{~lbADV?u*OQ)!S+?eDp@MW(U@!ErFzSf8cUyTb+HHXDk5DX4VW3|x#C>WV z$17XzcYT3SMx-W4}#$c-8-Hl}bF5}qgD zCINJucW>f!aX65cH*w8m&|B(buAkrC;Zy0MhHmIs`)YtPx=4}mIiiEwBI;x4_OHCm zWyur^IBZ6cO1Nzdv;uvo!Q5I81`%s&4ra8OF?wk)|5=&Irv%V3^`h5K#kH#3{Xl*u zoNEpVncjhHTu8V=y>HvSb?4WYQE;&tbdactBZoQS(3xs1K~1VM9AeI~9fc%~?0@uI zJrv3U(x!?7#IH~}`csV~aUR##D$#HOxi7c?8#_t^nq;*qr91Y#I$x2~pcEPsdB>?m zo5x?X7FnFGZzO!dbb|k@fMstD9*UsM00AzvNlq{)5Pw@#48omcHGR1FIFOWxel$L~ zlxGg&3!H#Zm&KFJ9{|bqTLIYo^C_QCkPwaD$Yt-}7 zfuq5CAew>RVxJ;}S_oAuG^~cyk`m8A_w+fz2iQP|1Lj@LVp%<>!4EThcER6B#%696 z$FkGFJk5El(mXNNk(Ax?Xm7O1t~{RB0%8C)O15)m0Ynax1)EDh2crkMb+iyqwfOMk zpUNxuxuT}=%#gs)3>adeGHeL}6pxo-jJmj#;}8Wa^B^h)k`0LN5bhxNDvpnXHw3x{ zBqQ$6!SWlQxVzUF1fg3u)`RdHmx#k1eoHYAv==69A8x-6=tI{~S|L-Z{3#Npz_dn( zk*;{kF3*h=2HAp_2qWbTK7{Q=g@60>%nHI3H4t=9GMPj<17h)XX&s*D5~wJZvQDP)kR z6o~W|eJ2S`JhKBx)at}so#zN@!^SRgRq~pW0c??dng)%{x6UP391l9gmX5wU^+8)6h5?``Lxr4z~ccctb|!0Ql6a zlh2FVT5?5@-KD%yC-6t&4S@+c{t*LQ<1YzXmf%&mq|j`&&C8hNGp2h0%5`_ln>$xj zb5|=!B(lnYkqNFtbttAeHB>4h`|@ZwW!!|RyV!I=1tFJG$lToa*oh1m~jD?Byrl=;grUn|DCL6K9 z;v~#ByKhoH88J{NL?0;?X{Qm7M}vPv7?7uv_!57v+6t3V%KZx@wa)8Hr0HBrT0{oW zlpeX?XtDs4w2P16v9%)RxB_W6{xU`EIZgJ_JTB;<7nH)NQk`k?=XD~!PohcVoP}wt z5dRkFSKa7yXq2mUL4&lMTs<%kL1IlMV?Oyhkhq#zicD-1LvxNe1t^CF{tw(=?xxT1-j+c7#td@G9<&1SW3W!ar$+%3%+&=bVR26?QuUOR zqSuh`BnV>@6Hbpo1S~e63?#%^Go4V%8^DUiY+>5b$)xiGc7R5+C|?XAe~Wb*VeM%A zmWB^;xa)DfFSr*u&Fn8kQke`k>oU35g~QHeM*zSM7qAdUCSx@t|_)rfnT^VJK22yC>%ayle z&3;A2VK>=m-`9SbWN^bmsQr0*Aw!WWgqDok{dU;zY6b2}ntqz9QWg+)3LpoOup~_8 z;K5Ji#385&S`g|dijD9AB{G%Zjcd?TQzVgW$zal;0#0}XF%dvif6BcWo`kI+=kk`( zj29!18Liryt*>c}3AFDj3V!lg5&=m9v=fA8OUS8YZ9sC>C@z;$p|PW`)B8bPIZ$gw zIwRRkP&e2NDiKswnlI>~%O$HJ?H%Dr3G_aoE9Ofct;M#55E!CaBtYwIa-ET0L{0+O z=IH7lRYWDB6KM!hcQX=nA@C9n+n{Tz-_8^=Wsf-w$dk73>j=V)YfHF}?5}-e*Vv#YEL{_@qAPcE;1G?xZUq_dGYn%Q* zn-HW1I9|(=ssEzUa-wc2B;i?xAH|5xy=8)AKQi^Y==7(=V71GIPSds2lu6ljSC3B* zZe%2bveO-worjC^;fW*_m@+?7edT6G1unPvQ<_2+OTUv1Vu(H>T`EH~C0V8E?9z-n z(}^zfppGp}l~+jao4tHL&Wwv|kZ$RPdXWjjG22ev4tT@w@1$w+fWRx2K<=m+5vQ!E zWoJylF0;7v9h6l8?j*SLiPT~;*_5of1)b*CF*{^;al07+zdPG;oZ0ZgC5Ci_ahg~4 zcGsnlHHNYQ{Us0#f?*U=f|>ff+MuU`>4{bZR3X_=h`J_bln8M83kGQ5f#`G$X&%nu z5S}7?BUYGrVYb%97xU(bTzBmPUY~E|tii4$7K~skLmJpPZig8bu1juNGWk!?)+=K& z%loB^KPVj7L4WfA~Oq z&Li8L;bBbibT6O$Y?aPAtx2!uNtVt^?^!+Bx&R*2!islW(%pn@{Hhby8rZaFTslxb zN%Fw>(*>OVN5#ETD}PwX=y~IkEuPmu$X{HUC;d%nNoC7uJMJ5KY1KG0%T#^Li=<^pv_eoV%SKqMvaAZ56oTO{3 z9+N26jjRS0rr&lm6pTOyUgS~AqsyJE89LW~o{S8|OioVjLydI)@bJVBescpD6DLlb zyGHY8&4B}}1Q%`by#3>x)mP8V7jH(9xp3~>xp`uSG0PQy|BQ5H^YZGk`bm3vdq}^( zce#k?o^X7dtE(&R6HZ|~nyMFeQu*)R8Q13rRU#uKt<{b_2=H%^Y^He6v=|1~v?Jfl znCt-UU{7~r;R(k-WPzW|-gvb0AhQI{#q^_HHtt7{+5n~metiufknU8!n@n0X08d{B zJn}-z_4}DKrFb#t#v*~P=5l5FYJjkA_-#2kIR+E6@fkI;hu-^6U%dG$298@e#R@?W zOkVpxpS+8Qd~GwpJ%T2G`Xzqt|3d_V={g;;}n=dMd%j?~QYoQ-n?mL&$1o z2}+E0Zyz2b^Ves%>m(BOQNM5V=FJCzHTZEWRcL6a^Y^d&CGB4C$qy3uv6&Qi=)*&H zB%rDiqQc{2R%T`bRH4zdPzavc@XgaWN0hG)Y3C7t5kS@+IU%q^!^XEOH`YAu7z<+yMd|}yf zr-p(xOc@wC_u5XHG-(f38$%Y+$a*cXMy@rwlSc2(m%TajcEkv4^A4B$Swv1ahJ zQ%p^8``Rhy%&zuY6I|AQsdM$Z&drQK1s`v!p1`IrVa{eS@v+Kqy@YnWUb0r)qRB0# z(SDef5^K^}(DLmke>}k}@R$tw>bEU8)u$`PbrTJPW!(}1+E;jZc!bG&=zwvlKtM&Q?2|Wm6k<({(P{is_G`@vwlB&R%_R;Q&ADu zuOF}pIQ#SG%A^RW-pVQ< zRi0#GvdQtCN@5V-*5y}JRtZ2-yHW{{E3bs$!(6C%9qBqIukg*AeJDJ8K~R+??AtVL zk(3JXO;AEY0;36^-pS2P7ZI^HOY`0-XsW`PJ;k*`C1`>=@tD*2H||2hx=>|TfkP_^ zgQMckow2wkPh!E;z?|_@=4l}^(QiayGT}2$IpvSmwzjVK55_V5&MzHwix_rtaw@^O zD$nuqFuHd{15DiMoZ%r4QKLIr7;nqLdR#{!=-~?2m4_xpj=lbBwUCfdZ-2i+W`e)J zzsf3qqyo*qt~tgB)6@x`f!M;OI%)14OG(FfhG6@3Q1-gOuQWr!!a!i4p`oGHy(8bs z#Ab^h1S&~MPuB(+jf1&P-t+q&oDogffGlinsqUu7`i)9WR0zDi&c^1Mt(`N$CE&pFtNa;Xz0tL)ddsulDAXR z1YZ}65!iz;emb#O$_={X?&rE6Ir7|GV$H^lomblM$p!`o=koLa?Pg_c+N!8CzLyyR zVnznI7PJi3Z;FIz3nhLAr+4A*-MctzNrqWYQLd+tOvcH$c>MTrAQ-xEvZ$gLu2m|T9JhjSB zdV7iKy62^(oT$d6eVK{dckGaK`(l`GSEV^|`r2{Ha@#r0$omxkZ+^#pKpuo6|P_>M-bl$;@H_Y8f@&Ntze%i&vv1V*@dwWrF zadA9zl!eH&3oE(_I zGDWr}>#le2Fdqa&p?N28da-aV<>7qFy0-4Y`E+h5Sg0MV#Z(yjex0NchTi&;J1<}E zIdSr&ElPbL-zNk*()M+sYW{OOU0?jYR;UMj8EbYVn;{h%aGC6~DVdqNh{|-J4(ZLC zKSN~j$C2NCY4sn#OSZV2nty}k*+i4vMC8Yt2q=EJBe&i`my15XaMmn#OrMzXy%<P4&%a~NtqwI8k(Bc z2nq@^$?7j+^0$=4$M{X#WsP4HfRltyrU1(OZiN+kH@X4DNB@h-$CBy^!< z#A!zDUTczb56xU64&}YF8am(T1y*ruJ{=OK-$QGN5?a77l+IDBt+B++RMWo&6W>z& z?FkoFQBY!I{I6fXp5a)Vh`;4&7vi%N)D3|krX~_1xn7BKpJ_$`*--_SEcRc6 zRrE+l&iXDWiAR^x3~Z+hCGRIdmVF|45$BfJi=6L>-;cU8V` z8f&(z^vAhtBQX?2+!BeWzt0uEfew+XCHfQ%JG=NOdwBH95{+bWR#bt_bSYhIaTnj)Q5jk~VXKc2JERI4g1Vu6ynx z{E9^wKY#vwm)6%Bz?wQh;47KHnYw5>MU>QrLZYLi0YnmUNhcHBRuF^m5zc7|INeX< z^Jk@a^f(x0IVq~Csm1Qg{4%>3UAwrCkI$2*Ph)Yc?m&fz6Xo4!@#klrfzPaKhx+bN zvl$As()8)m`;ZUnTUz1(7Ua^iQS-n(DAU)#0OZcgKb6K z4XCp7w&_P8n8fKO>EP=DxMS&Uj@LeXO&gpf0>YiZHB}Qt{z@j=^jFB%7V8$&wYNu@ z3KSI;8QgKc85$Ob9g#w@V(3@!E|;Y@_wCGEl|F?}Q#p^lGTp1+6{gYMR8n$tb3wOU z2R@B;s(%4U@vRy+@oG@WV&kG~eTI$D?3aT_nLBINHH5ScB9$Ng&F}L>*CNoFCCETa zXUy=ks?bO~sDaA?_M)^=V#9j%l&x4Zh9T&m$6(%JHJ+a?xMiSBYUlNDSrJy;1^M$| z#+z^^ZwzcI3z%tTWtE=`a_0;Id+540KoqfiqtB!Bi3AB58~=2KChc#K$j3(f2qm(aq84ERLKB9w#Q=q zS+n}@@HL%#b?_24+afv_u*()eUERK=u8!fK_w!$aXMB8LcYZjE@dl4M7-ZKu7&-;1*ug~Xso_S2&%k{gi^E}SuIL`C>kbQ1d z%*s`(p5^9>P_C1e>DV)v{Fa{GC^`!N33c=bxYvi~(vASqRb2H~ibG?vqcCfAr{4?YecxzE=I9fCwG!tHnEb z-kCS<2cC6FN1N&k?i2Y{W zXdN9Lokyc+{{5Jeux>StItV&SNEkJA=uiMt3~JC|h32+x z+o{ILi*@pcWNGsYQ;Irkf@du zYpWucISf=ZZP6lxhIcpXF=NIo?xq>8z544HfwJ7=sShAdcI-JDrTpE854W+@ZC`l~ zQAe#lshVapaNa|Bu?rlwAi zq!tsiLbdsal*tG2EdJ9o%f;B(SaP~MZiA5g?N-l zaP@u3`46>;a5C4|RKCJGm`RKfICUp0Yc@QAWMkiMw(mxd+Op^R%=dY>MoAb0Oh{#s z^JCDAtc06RwRrzaV8zRqM^HCzyE9KLrQB06z5iZ0Jg}AH`1@4Qd$)utoqW%pJ==Tc zwdQY6L_{bL?bOx*hZE-KPapqqN&s=n0L&{aOA0LGb2MM9u0l?(D8N7 zSv!UbZp>t`w}i{NnLVTkBTq<^fW#smk#7I~{ZpUdk+217`Y5)v8|EO~%CXAlRKsU$ zg9e|y*~6pVPrBGZdnoeYY%OQc4(64*D*1vD>sIr|`I+J2%xIpAQ_NuX*VvzH{F>jm zev6;iE6#G3{8z{VKyV7_VHIMoBUqGrO3&iZW~3a+`hs{tgTg3o9vOIIdZJVG#3MEb z0FFxc#`V}0d!43DYvc;R(#iiXUdHFtLjOl`6#m0Wf0jgCtHa~kz=(}j#hyKz5jo2Y z(Z*V+n2UeZnZdEcZN!FE(%q5)3nVUnGLn81>ow`-!QRFIH{Kv(Wg%bv+qSw!peQ<>QxgdVQ=5Q?c3{MV4&b9)8L=;wlntuaKE1)$;WhQ zu>75Roc~~~!Q=AC7iSheTKgvFY#64TxfCA09+=Wo860g9zVM%|BLAzVi??`o&Ep)t zmyBUbhuL_~qkBR6mMug2R4Cz_Z&9QRVkTFG60`{leo21%;atX+FIf20ygQcrjn-H? z)OD!&=FOW@xO^#)uf()#+x8e>)n@oK@d;j7TfLv-d5x`|eh`^_0wYyN?M^a>3R+OA z{QuA)pfjOEc#7|einc3er1;xYgH9-Xe3=@`gc8#R4iQE{NIEH;yV;&lznfEF;Id?i zB|l_UUXWLQAB{W>7Tuufcom~2!?5JcCJ>ck%H;uf7uHDiG&i?%!ii6)BynjNxt2~z=~mx7uNdr z%zBb53BdfE<3GPt{=aVZL@Ro7-kMPJ9&k37!$Hm)kBfB4yQfMCdepHj+ul>Z-QSax z@vlnfe9;}apO6dl`gcD4(wb2D^zq|^$abbE^{E{8e(=j==rOE=wULo0%z@vD>K!xG zx8E%6-#?@%bS6Xjg=1>T3Zfc``mDX(Vls)w}9Gx6(jvQ+#bQhEpwfY!T zlHWbb-dhVJ!r3uLKn{43%f4E$U;!8;5tcjcTbq!CRCf)Zue|Uen1BZlzD3X9_t#(3 zd++|qs}lNgsN>vQduIQ=u8?qoH;K=}jO}Eg18^#zvn+(DL;#S7UeBK6fBWq>v&};D z6aRu%7HW8AZv76opezFJ+?d{*x0-U`08=?DeUtD%`nG7$r}@t(A|pi{eM)uk-;6xk zB$|{F>s$M$pL}>6iac^`cHHVX#tOB^Yi{XK%kyLxg+ejmmyWRx&}cucs`O2A2J%fh zDZf+V>6DT>L$a-|LHa{5j|`kpr)jtGWc$7g9~yo7_%WDBduH{wbC?p3@`3V6&-tLc z{2o<|_B7Jc>R@JOMq>C**=-&Rl<> zB^vq-ekKP zfHQ|x6 zdz;OvxYSTS`M|LB9@>K>&Ou+DSzdHV>Jn;X6BVDFus>tgtizN8T_7(wjJ_T}?5f`% zrn*o*iCDd}6!%dz+lt-DnoTDB+dgwXCy3O(XA$^wrvQp2! zTD&5J=a+qYQ6LK-8&lGS)Ffn%ll=NK%B3jNf1OcJ#fMRG-~r$p6-}XLqT%hmr(!f-#R8Mq2L*3bczWWfZshHbF}( z?Q+(@^C|b6R9;Svv@SoU|{&j4jJGYd7EIrsz1A58(g!}mTKtY@I#!6`oKlAM2!~HNFbHPi>v<29Z zr06|{yRdUY1yA5PVCz^|G7Tg5X?)rVI_w^>N4Ji^Nd-2jGi0M7qI)_ge<)%=0bm`f%r|oC$XDi`!*f^Hpqo-8D z&<7 z=1K^>d-FJ|b7-C{{usq0v32vBX*y|;HF4`Y$v zk4i6cNMi4aR3B(}iHEmZ9h*EzX-?zlll$!;A#k)ge;P`}0~X1 z%Zab8CU>PRRs?9NtO%83Zb$z(kDi%rMvWPo@c8nkjMIyz30i0W8@N2*40b(C@uLi1 zT09-G4%6sk`(C|zDXk3f-N608@(nBMy?acEeW!v+S)@4X;zN|*lDh{je(D2KpkV)M ze3$JS26{l(UA%DNO+00R^wT{Gc}=*kQnhM;+jCOs@IPvwy&bdqD|Mw1YJi`^pu~g~ z{ik1T0F;{xB6cr$_{+wvu4omn@5flP9o#!;KY&3j1Dg7V5yZ{zt)Q5Nhm@j(;`8#$ z)iex{D27~!o^mQ+)22?_83h{sOOYYfK4G3SpxfwIl~+*)g}$g=s6Xt z6cOqeglO>i1H<))e1&`O%kT=f5>%%zp);zQx$Udk(0K%u;NT%c*tTEqoq~DftLlz# zQs6Fij|iK#c6RasNqs&-?-L;AAe7F@J{c19`j0xziZN`27_F%SX1Ix$k?W^=OX_6@ zE}y@w%8C{cZaWnmro<#YkM+Z-%%#?r8EELo`4l5#A|jX-+Fd#4)}bx%<4=Yp@6g>2 zZ{B#pE4n!HaJ;%Z{Usp|eq&CW+Ur>k`#FQJQZ4sXWaNqvkNWL~O5RCJ|6BpM-E6DH zYc-(YVh_khm8IF%VT3}7kYf(*D4hZOAaQn9abm-7TJ~xJn}quUz9dCeHCYaNs$IXn zREW>=@;+TV1$v<>(6ww);6BLX%W0|#HGyRgF_#!)6Pt_6XJbhj0kN-Jfg{EB)y3f` zeIf+Nuv*Jn8e{(&I00cxx#TmxjZmOU%KT+OZlyUDe?Vz0Pe=9*QxV?Z8*gpCrAfnv z2Uyvp?CeR9U#s4<^p}=JSzydohuL0pMPFR`=>#`m%y!0JZ8>(IffeKF+zxmUI?SmH zU_}nR%nYB4N1m;>2DDH%x?jcmIBRM~R5KnmN;pS(9{f=#sYj$5U zkUwECAlSM;7&UTa(v|h?Y87<)fo<0G?Q|ee`SxCPAP36S-DT~c@CW0e^u_Mh`QlU+ zk>ZWpgWT(Tt$Dr8%fex>$*?zJDCurlhnHtUvmTR8mVAbi?X0K06jfR&QPLt{*tvjR z3n!UxiCXz-@%?C*QG&CXQRHv1>kvi2ZhDZp9L)t<~GK9_HtVvUet3oQll`7X*c<)CtI<&%8rtM(*zT zMMFb*;|ItvTa^>Hdi9ppgUr(c15L+vx;3|**4>z8;ffGS4X-&NrdXST= zoMh*;Z`kUgR?o-Q_4f9Ag@fr}cFNuXZ+E8{vWIxyIv-b(v(YjN>Xg`>S>k*!zv2U? zb%f(N9ucw4+uIxAN|C6gv1!Yeo!DXyk3f&$E++Hflsa*Yxr~V85^4xY6TsriOOjAM4g3yIQ~u ze}`~^sk^FJwI5PgP+-!tI0`qe+o(N5^@E$YX|qoqb#`;YnsW0C=WFbJCx~d3^h-BZx?I$R6y;iTIuU4W~E>B*R*K3j*Hqh)pkvEGVX9 z(mZ&+=1@Qx(Jr%y2K^G}R217emU)!N9Hgk4*Xxqymn^0t1M zcO3H?!e%5yP&H90+k-ie!<$(wAEBYub@1THd>)UPdlL+MW5v31H>8%^zQJ$1BoPP= z4}pjd@qJ6k-E#;}E&I>g6>1%R3`?zHP7&IvF54%>(*E&Z90=PhNQ(QveEA}#cBwT4 zF%0I8G9SdK{pbdR8#;W$kk|z2lY$}X_U#Wluei@c)|98j2Rq6MQzVv()Jtk|CLe{4 zKd@HxR2&j7*l8(0XPz^O1TC}hTANZHYR@W55GFcEBK<8VHiUpE4v_3K zu5(o>F*C2XJZc*`Qq^sgG015YpGwvC^~L`9R22awBjl-zxrIK%K4qAU*>jK)pT(0P zrXDIcw}UD8dV}jU>+u4X58CcQ*Ddd}o;=Zk77Ca?SFU?&(!9AubfpUY^B|vEjPER0 zde@Se6(z^8#)nXe&tVvya$?0fDL7>Mw`K$jAp#Kp{x+?g^J@L6OrtZsF?kBArQDIi zmIVO{Ff=uRT2TUHGa=r$cWVu~p`%1%2_M|Fpwsd%IjemW0ygDFt@;v+Gv9ZbW1}>9 z_%g`El)9^dxVG$k)AuPeL7<_T1qX9Z?z!cD7U-tZD%ov1j!A~w{rvnyg&;LLqqhTB zc)sg|%ifP$RWf6x>M*jo|Tjt!gT?qalDmG zZK=L`akL*^w6yFG?Z90UvTS*yvmD8!n%Y*}xVi^xcrBcmvweoMxUUTQp z-PzqvAozC7EziCVc(5`MAC0J2I-_^oWI2duLxpl6E1rCH1LD<6x~n-l6C<21tJH@Q zrut$ZQe<`N)M=!6IN0$srWMWn@-ZiVk52wLrM|ffZIEOG|4)-uGKeJiMl=d3`tZ4i z;TDs=MW3seW^p#5v;W>$mN~mm@zYC;ke< z|K>rQKFVuygzvW|;58cCq)tgF!xD62>u-r08k-W_pjGadN*dZdDt-m-;L~VwD56CV7L8ZJF z04AA=M`2kG4N>^Ev9+Wlx>{I8ip!Z7{;jF5ZaiS@kJ?Sr=w&|$-cLmR0Wwgr@BlN( zX~nqDbqx$A317W*>9}_-$tC`uM|t2UOpf!8^SFQ3tDkB5K9!c8i__A_O_RIrxpp{IYT*|iFr4VMn+EtrMn+NyD3~qQ*tSAhDKIcC`rpKH0=|^k zg;~HXVN+e-<_yWG>V&inNp-W8t7Y0=n@HpPy1M=5c-(S0Jm9E9-u1BE?rWZkm@KLx z5H^#v`V>7)0>pxxSl|BOM0jv3!QJ(`TFaBcw})4W8=8_-#jv=g(DR_a;>e{AUarE zTSuQcbLif`_x>yXfvLeLN5QC-Ee9^#zjEbDO8~RH1IpWNavna0 ze)7yOzdQ%AEZzB!i>fS;3;ZO_)*71YQf=^*Nncw(yRpxXif#1k-VV)O4h3hF&Za6{r$ep;VD^gCpO(m$mLYdh_tFA~ z4a~lK_weYt=U?hUYjEep@s;DWF6RDZV&nd?HTG8%2+I)rD|G_j?0B!|+L_Sx3%)7?y7it@>j)O>|blwP+QN89HdM@b5R;N zIOVTM?YHatbH$XBBE|ccm6xF)R2b9Be}qi-+L311@L%tAKCFYbuL=2qUi%$LzI(HS zD*uv((ZL2Vc&MBq*k>5qYEDS$H}zt*HbdOg-~Dy}^1psKcI!_Yh9r=+#CtKwz#tUq zQkYE;g^M_h%8h@&UnnqdAAu~*wTPEvoSd9g?zN2eT*>{>Sj|gnPsI$;o=X>~C@eSt zedlNQl^`|gtb3Fgi{+uB@9yb2b*u@a#y^+(|b4kF+CO^ z_H&Bt(8GeHHhc}%EM^5$GNyGY?U=pOBh=a8h;!P#{>F|!z9Y;3c(GcZ!627e_2IVcWB zU)QO4G5*PDFhWxIDd#4$EzCK)>5$E0vtlpZ!q6a67zTP8jD8VsnpAy4!h^B;_xJAa z7(*q@y09e(-3pH4bp|kxT{AMMo2Z_PaU%dhLhbqzNJk{Zcz%>>Og!dMnAp&gA~TU2Hf%^S z6iCU3r9-QU7K8Ny70#HT%AYWU-=I!#{2S^d@o2+!(am{I>ZO&8(LP-!hS(b|rqS*X z_D>)uHjE7-YAriLD{8I}yOcJwVrBu%mDi1S>HO%&Oxf~Xv0w)|zWh9$nx_j~q>6R; zRFs>^EhPH^=(7=an2>9FJRC2avGF>@K7nph*$OQe{kUTBcscD_>gbN0uFs65@(ZU^ zFBD2bus;Yogy1b@Is+2MBV(^g<^#cAde6YK;IaROScs%8@xkr1PO}`R$ zaX+s5%@8RW=WgR4_gC^>IrE%*XwU4~TnbNmjHD13Ysj5>;>r_i6P${J$oK?DY&(=E z+IT~hY0^OW@Vw?Kn%z}uq5Gek^GK}!$GJ)K*_AlmJt(()oyz~Kz+kisux8`dt-d(& zv2u3LOFbrCoI)>u?+z5L3IAadnLrQ>v92Gsx9j^1t)bj3B*!lU6RZ3DONZF)Xo&R~ zGdNMAlhWwg4^sLc+Vo?ROGRok8m-)}*$cES4pe3XPKunl1z(}}rv>Go-~(!T*@_1b@K zbz&jY6ylVKp8o&KDdBdu?6b*L#G-^4S|NV>l>OjAK*#nK+fTj1d1=jF3@-U_o6KTK zjK+W#;5GFRmvM2fv+5YiuGK#b=^IE(oa$g?+9{jqD*DoI8-uBDzxe((sw6-b?vN`JbGeZ0g|P z5Mp0jV>TIAdVfGAVt&sr_>@Kkeny94n zh5k#Ck4owMe)jtH(G44Z5<(gwVFIq%CFEzzV@-|B`=I~gm!QEwdTiKXo!y|o_3%PP znN~0^1vSL25fabAhBQcpY!mCa2&nMB2=be79|aLL$AgR}#kxgE5ld4yky1Y@xrUE1 zt`{^KaUTc{A_ThUM&lj?9OM2W_8J8Sh}EW+G4zWMU4(=2C5;NALDCW|tyFN8 zdMF9?+YUbdOoyW#c0?t(LSo0d4))joyA)gQgfeC5(CXSv%qhsAkf+9{2!Y zHG`!2nY9tyYPS?}cZ2W;+}Uyfo;Dxqr(AZ0ca$<;kXBmhk8cO$%*MK7;v~*D2)22&v6IfG1pwP%XJzqCSG_lRWNEV}QShq#x{7K)b7uPioZ63ZCRbSj=Dy^ImuW+tS?|9*6J4Cdfz*9v&XdObeI3Vrl)C69pXE@uHWQJe5#1=8JcF4e+E^ zQ?GkaQyl%pU3@+=`;E!KCZuBl@X#euoKX>x;ZUX5KjO@qc(@MfLJe4sep>l_Xr#5K z+;><@`!aNxIAkP3!@Y>IS*1!9|LAF1wZd4&+q0zm zjlvr3D-8RW*fuC(qVRbiglU_Nnz(1kic=on%9e?9n%NtBnW)u9q#6jR;iL7dyA4Ng z2kuFbA{-8lJo=yF#i}AEYDm9Ivj&)=I(I&t-lJ+rJPi(@WGUCN@2YLZJ;)qTbIZ? zN6;N41&E833sw%0WJG)AQ;yp`qEdMB%&cd)LFXck2$I$WB0Ba|FCFAoqa211GEFRF zq=}7RyiHiaC(>T{Ip`z+_y)`=@x zH%P`Tfy%pU*Dm9Xl7WAH7(8*JSMu6U?>`$JUFo3p8H7_dYg*CM@SKF0Mk7azu=Qtq zjz%53#rt46)JD`7mEG%{L^W{UM5s32CWQPZd>(i~^bnz<#Il2-bw_Zi*5`ATf|y45 zuqsX|4H=0*nhA!k;LPrmi)*J(!)J2kZg(uOy;Cn}E8_4xg|R!NUqnX$ z+>&ogpI&XKG^eESZ`p0d5xQb!sYE9f{g`)?m?*tEvh)m~uz>c5c}jR{S5aBwL4Qpv z*?J0|VDJM_J3wF!GPQ~7n8Vpn1XNm$q*d$x^kI$e_auY%%Gr?p5|7#rUD;*Ss8P~E z%6ZKE@~J3l>ZKYuU=q@XGCNp^c2M!F-yk+E)dDw zgG$WZeJ2i;j!>2VV5fHvG%v?*o)G7;3P-02$@#X}HRy5KscSJ36l+12mq`#H;jVeq z^A&#_OuY;TbZ;YPLE=0gDGYOi_-zy+xVxaMEa>qQADFYW{4hqz^m=OelwtQ7CAru> z&cj{|3Q02_Ru}I)?zscPLkYnryw~%T0DYi8Wy&0=FKMi#tHv_iVTc$BL15;TLskck zz1&7MT$-0QLSzl*r_yhobofRI-`zxo8|Le^`v-iEpL{pSy>)}nCH0*6oyad9%O@JW zA_j_4YHPb;M2V$&Sh|EuM_XP}TI|flSxuCz6ZmojCLV!(?gaO9<=mzt9;(}^S(S<)obY|kb5mc>W@DxRIIJ$!h`EcOe? zMC{$qoLe z_Mdcp2}K9$#-)lzbbPvf1@fcZTlGr&L*sJJ4#Q&z?TJDeNQ1w07eOaaI=3=fEEX6a zCZMRK0v(VBIw+m2{RhZE51FDv=mZ|VWRheW`A!OI?DB)g^`=ck0F@<&%(%J_n>@HY zO#!_EN^`Q1GZl$&MtM!w4N#G;`D@sS)p=b062L_Umq(-pPMgm|{haQ>N|g889X-l% z?jbvC!P)n|<=0_LsDa3k;SOS}+%s^|K{Ra_CPx097+0Cm@Ck+f{sV1QN70||645vQ zqkI%IP3tPHhpML-!`LyQaC`D3Zuk%B`uhc+JwZ>ZBXolft;97)>sBZ#m$+&qEbtt= zy{5rH&(6;7{ux(Om}G(RbPjE!}rt8MSE2;v^#i9TUm;& z<3C6w1T@Sb!nm@DgxnPeA20@$NuabmaYP#1q z8*so22I)yJrkGa&b>)2i;N>Td9C^IDo19g!q%w_aFksM9(15W1i2u@u%O_4J5C@$3 zHH!llgf->rojbNXws9xkKf0d5R$0YVP9T>B9YYu;4RcZM(v;wwNGPbCCM%%{)8Sk;$i4+elpHy4{%yc;|SRhnt_iU_8myUuf$8crgN zxPdS2{ac^%kA#qKyR}x|z1My3UpzKB`XJL?`%@fY1oAhq!H$*l?@gccgx&Vk7TrArDCRJzDHOoj1oC{KF^ zxg1hB6Y|Yo^;ln=NFy#28l{_6hQ5y7-FpOv7a1%BKiu$b&NRBU^~Ue_R7g*<@8^+$ zsao?U!ya#Jtb33EXR4BcEl@4uFp{w|()z~1@BFN3C6K6LKITmQ4Pud&Ok1z*;9$O< z@b`uOtZ>5T98CHB``^~8XNdiq9VU`q6{m0-h4rH-#2N8=cK)Il#^~x;l%&+{~#_svwl)xnB<$HiatGY_qfi3pK7v z$5%adjBgM7P@cvM0(R|s^@K?i7{&qGH^^lq(0X&`VIZhV&7-*-GlelJ z;v6DE+!AvzkWQTbI3K8c(q!+6{he?n$}KJ&Kc|*ci4zja%6uIz)IWD15} zsni7Y@{K^6pv-s@wR#WTXg25SVdUjvb6Q8!-R4uSS#g2{sZd*65y>01ev2F{l^t!a zG^M(*KnYs?SGMqay*DfKt<{`g#Kz(+GuR*!#f~OBO*+K*HTk$A{D`(8L_=EO%>xRQ zgK?5U=x3gBMCPs$%%%TWny)xv0`%DyaWcP4h5^v;d7E%`0L4Ibq&dX0gZWQ?+yKe3 zI{#AN{;hUXTBu-!UP$92-OhcPH7CO>_ynt*I!COo!=-6_IQa%5tCW`y#Nx(BT6^`v z-Yv~@tdi{OD_h%y0W8C)wnbPwX-L;F_4QGx^Ki?8b~eCPR0AE2mvG2S|9(l&939PH$e{4ZqDp=<>gJy%nGv$=h0b^xkJ}Np_KGM zS(DE9Ouiu+WV*Dv(m~J_V+H=qn^+*z500oK(j9`Tbo0md!e<>U8adRJI02YH8_M%a zsIyl`LB_#8D6QAhiNM=+4sDnAH?J24BT3Sb;cpV{r0l}mA+rNm5;37N;f$|9m$lm$ zeaxv)W6}Xw-sSQ1sTxlxTzvUCfgcj1N{gRs%)fueO0L|y^G79c?>T+@v8qn^cx2|2 zg68sxLNBMO6D@vq@?Pf7tg{_RN}@*SpZhqRcsVwzw2;tuG;-Znu{d&iZMRk*g4e!A z{kBJlY%+IFif4QbRtygjmmStCCNtgi)T}BZws3a1oR4qb$G8?Okstq9`V}*`Ff~$? zAzH{7EqMKL*&{lRrNopBljHGuK7o2Hg_AFB*3WP3>nbT4QcAFc!nTSN6``9`YZ%$H zOd(gPsiV6hcGC|)eA|JBAjVgYKrm&Hs_h?oe%1aHm2|vWv!2dA@z5H8;y%jRF2EulmLfT%jJ6f@Y z(pr)jE~o)Z}^DEER(%#=|gko)4kgewJZjgU$L@pIymCB=0z8Zp_2ed!D$ zl?j1Bd8nFgiYWuqdQj4dGZA@SIzDk#9A?6!&C*X|$iS|u3rJ@ITko@o35}lY(6bti zMNMcuX|fgJ6KPON2$`Zp5ej7)hSy)EhCF(S2iP?F-}__R8|H(e%sl=hUZM~MZ744! zLtkptYaPs<#cez|%Jj0uItp>#QnsyVv~-V3vBcn6(Gv(K1Mss+>!q+}vH(<|Ox>Dv z~cNIAma69VX;da?t}SIlr1zKSHyTlSdD9Urr1k1=^bZQQ&+MM z?brV?XUK1G)?JBB-Iq73rTq&>jb1fU{LMb$$m_zNqYZ}qxFQz=NT!MOsuSZt72=qZ z9y^Nae3C-Sy$r$&!d}&v;fv+G z#Mu(T(0ivBi8w@bg-%Ih2XssLT6UM%q$4bnpa<2w-_&L^BkyQ62;*7B&iZ!$moe^3 zuBjE!#<_2NyC~&h0JfywURAn82Du1<1Fa-p7fyhwjK`7EaM-Z8YRfuRqPaD~JBw8< z1dD`(dzl3X8!jFd1}0iy#}grqD_Ml7mw7Vi`8)RRy=#%+MWmE-$wImre$aN->2$t> z?uC7u1TO*@Wc-L2Y`|8oNZj-9oEQP?PoT@NSZ2;9^V*Y{on0DIf)htg%E@Em?|9~g zrU#CypB$Mms!+5G3{YNsa^vRBQ+){h6dH55y)!|$Q|Nuz*CmhRYYE)sN8GuG42D7F*)+ zkQK4N+t8S%n(>=jpRgXO2tn+|0=~7R8=pX>20c!CL)|R1MO6N73p&N4zXPKM?gEu1hM^4}S;P&l_;HjO2 zE`iz1GE9!dounLnYGKPXzXZ()mS9*R287Wn2>fnwPt>ClMz;L9283+ZJCBuZksW20 z3S%Fd={uwo6^j{-o`?%6HzgpG&nP1xB>IYL2dKl*JFBq4%kkrn$++;bMz25)UiEqp z_C`&l(^lwQX^;Vza~e;yp7jET27Xyy7mO59?2PCpDuF1J%6y}DyYZAj4RekZDHeq)37L%2O2$HaW!H&EFuDCMqjylXWiqEYoY~~AoF=%Ye^ z?k&mz)#gl^W@M%eJcM8BY)i@wfY^7t_KsoY(~%iLLI8kU6}0~R>2 z)lK1QEm<9!za+~WZ+Xw#iO~m_!pMWU%b39n@`gbVmwA)Gg-rH-AkA^k2s20|DuwiW zaV5pWDcI2T7#eZrq|VPD7QkdM&L@+|%e?Y77^)}vv;ECzt&u5V>3j>ePQuGp4I56a z)sH=u{B{WJgir(=e;M2-460-%X=Fj%j6&qA+jg*x{9nNi(A)E~j<0#*b}QmPhPuct ziPJ|Pm{Vy1zrD|XrQIp<#HO+4SN(^?00~^FK!}ZIl!@DlyZqGfU%uzUr9C>f{@@-X z@*0y9VN(0e&$eW-Z$b>gpdIGDli&3v`qa5mR_6$4YkW|!= z04QTDmZFvvI1kBO==dI)X1biB_~shO17y@i9Oy0drHDV&utJ>z%r zD3xi1?kD&4nLi~)ja|E%!G>y^6)seil1XGeZ3wX9NgH=G94J6apMQ z?CLf(;U($*{jw0lqKEGrF$an1_{M)Pc;HJ?7ZsV46havwCl5R?u#0T?a`zK9$*)Am zR4H^>P;znzOoI%gwnPjk3|a6vq)jn{?l}0>Y^~NO(crlut%EewxJf6D^x|U}Ba0?$ z9pM5WUus&_O8uYGVtGvBI)Pna-oZzUyJ)fFR3bH6R%kS7isp5ih6JH zdjwjtjNVg7D+cT7BSuXrZ}CrnXo;XQh>NYlMExv3`%XEI73M6a*B+SVz)u0wDq}JY zwP9k@21Nx0ZU^bsTjI)8mjIu*scMk6M%Y z?agD%B)Z?5|KOb8?q*Sd=}hQZYxoAK;;xpK9ioAZBtuYErs0HbLe)i_R;mcDhY8MS zzc>|+{N6yG(ntT|)QY|4W1#A;jmQ(!>0?i*ZXE8Dsjo+qg%NQUnK6n{vhk!bsR4tQD&T_Fwt@YEG^-&BO&WGiI zd9tQQu6x3#GMRzPq%+O?j4TzQ3}HifA|Rf?6Xm;>+N-dT$x17n8C{0~dSl5KZu^qn zY)a)%@mpQt&3BNvq@A_AL_9@gc(dpT2x%gHXbL5GE}c7RbZC(R;R)X;Sw*yErS$oe zcc-{lj2FQ*nWtlC)Qzwp<3E_Qt>r2SF?uxNs*8xIM8`bP`z_IA@UlqBW1D+9%n<*U z>|~|6%zXdw%2v3^w7~P%Ad;W7`Sfv$L~hwYP$1mUJz8XBsuITX>~ajC^O>c6$-_VO zsgNeJJDJxZ<{#`D6k=w&%k{}qadDZ!<;@fzuzN~7MPM0 zhYiZkBS9up=nq+q03lEXGmzZqAOt3vMy9-HgjkH?OiVCwKT`V~iI?%el0SHdm}nKM`jVuUr`^^I#Ry zUPF8M@&5C&9GzFc%IcMziF`ol@DsuaKniEXj<%!c)nn%VZjfs5_K2HDI9()wleukw zg?6_UVn{L?Wtm(G&tQsQfg2f=hg@r`W?e#%9K4p>nqOb--DQl)=#>t)PQ;(P0q&3b zV#r)b5$_SlCa|tgUhlHzbcsqT^*3bC<1K}vs=}fQ`Nfym5xg9`_s8@-^XJbO0Ys)I z6I8Z~g9Cjfm{==JkXTDZHm4euz`}>=oR>Nw&g;VGN`io=+HaoWhUF_(bTu$AaB$4E z)oQiNsB~)oy~6AX%?@x$qucig5u_^=9^b`^Q`P9z3bLh8MUpMC($X_2!@TiZectVY z)g_-h6;jDj_yl}{7iLW(%t~LiLiVSKmFz^}3gmFftUut(<0%>sr^t*ky!vzw$Vo2r z=YyH5L|sM8bW1^g*O9v;PU0eSaF?qXCCN8Ztgs|Y+W9LJxDO~sb0+m`w3Uh6k9UlpSA@VP>z&s z%|d_ud^r-(cj-cy=$vP=oLQU>#=;Oy^2A zunxUIQw2{*RorDvG$Xf!P6u2G@Q|SfsKE^?b)RTF?5CQi89~2^HQi|Ay|i?+GR`r@ zGQ6yCg~=eCTj}Mpp?E4$6kXBAnOP^|zFFNBGJQi3DqmM(IRr5m2ATLXG;QD8MFU?E z;zgRI;CS}vQQ<|`nCR#bR8y5&#_!0;4o1#2NS?&~>W7=PZRVpU4TvjAW}m_RI7TmE zun-lKQuP(*V&_u1s6f?NOY7Z57bdYnBZ_^ndv8uTK_u zlEDYL#%~psJ3&+qC#uJL`bpFEKk6VG5%<05tmQhu4*0;%rR&zOpFx(CYDfO63mgkk0m6hoz+5R%L8e=N6Lpdki%IgXuenHxkj%BEn z6WDzUCFjt)?Qrbw2Lpg;>QT~ApJkjvW1}EXSzy_yjG$4-+*@{rO3kSJF2n`7 zQfp-#C79SDh%vYMrZU?bZ1}pmb>*4Zsw$ejVkd%7#P27Z7^Yh(Qc;=|Lkv!xgaI`x z_M+mX#*COK$BNBiJG}&)Q07S{8mB=LV)V^;U1+wmm z6SN#$U~Q5OumiZ2? z`p>(`;g{ng!$koeGCy8O8gi)}#!zMPMvaf*2?ug#^=i3A-*ra?E#Nu8>_oMAs$`bj zlD0Lf>kIG7B6rCMGRBnXu>jh6yq+-*i&w6L@Ag-p_!R4ED@pbm_zWbeSlS$Gz zxTn5gQs}C=2&GcFuq}2i%lIk29JIm;yGgUdS(6x-tWwfF4D;$_6sx)Uyi?@+WH{OS6 zN-w0~Ct!k9a56fXfEl;2Zg|TEbit)@(q!K$#UjLN>vawLW_W?Cl!AokfftqjJ-Q3n`EWj46k;1mWFi6{Uk)rBTp*mE+^P6hW#h&Ni)JyGkCu#>nE<6HGFpU-}o zLQ=&(d_?AZiN65AT7Jg~F;}db)FP-GG9XFV8@bR%oMJLOiocd-4E+)(4Y#%bUvon} zmuTNv+Tiowl-->Z`np{bUO-+Ka$G8Zp6Ls~qN;ed-%(6WlP-2Qiv*5~2!M)2^t~5p5d%~T|8IW81qO}bCARLLj;0kx3) z8Fke#U;-*_$W*s)_Hs|TySBl_&U&fNA4Ot&(5OBgp1_Ap}$43gWu#3~4+;4z;P3B!}ZHm$?u^^uQzM zQ))>U3D*!B62Z%Pd#?c4H#gJX$Jf%#z3HkAg)zhVk?7-n6rW6DxR(6V^hWE z56{!+=St0M8lHaJJ9r&I7nvY55Ds<7BN=OW(J}9)4B--%3CT}JfXmbe0Z>ekmI*J? z=?4%L*~_drijtl^C9>l=Reu$jM?Hp@#F${4k=blh9J!oug2~ z2(=_bG)kEA_3yI^kzE01UVEy345vDmVKh)7%ASosO`+Dw6q6B0&l%5?@BK51JnjPK zTkT!)_L3%l;-&Uq^@CfGo=k?2aekrkFqn;XhP3% ze4Wk?rx*TW{?mY*p_?FRr87W8ABy_V!xJhy&^-^EZliIIyD-RUueQ`> zrWIkfG3{*}wL?pF7yQMnB5|g3eB^otxr+odoD?WzlZhZAp;qmdHhOt4I#L=h07$~7 zq5di@2fiZ8$YI7XAiv0y#LIt`!C2%FRdIZwE00XIBRJrt*kpN1fJ!l2r5thQ1Iw9L zgutfjFIlHku)zCwJ1~cDo3~oS3`GDIZU-kCIK`RwAHSnFTnHA;(Zp7=N{hZ@LjU5zk zndn47mZXfW-x90c*j+t;;iEL zO@;tWfAEAQzZNh|E>*;*broanAe(8my){-ohpbeHN1}9jaxm8 zQ`TwVz+D{QoeR!=>U1Z3RD67L@+KZi)t=n@>o)3mrz^#g?b@_y^YSCEJ53Bz9Z%8gx#;^NHJoxr6(b*A(bP>l#M%gHX%MeMo${I z$etRh@*n*(OKNT4X>H)B98NfDejl;mMS4gh!aroCAAqeF&2})G*Fk}vyEM`w*Ts+7 zx77dZhRvJDy!P`s06@G7p48aYdq!=9-|9|ld(eqc&1tP3x7uF0e3=E*Q+#dk z)}dRspI5B-Z1bpRBR-sVC+!c#B{;R_7>)6Z*}s2(wYO6lhJJR%01Z-!>B9R&M{-^r z=&y9{<)jFG472kpQ{lU!p?@H`!2H1Q>Xf}k1OS_}?)`}S5icg)GD$jI)oEnlHm>2& z7%xRtNFmo?E?LqSZ`z{UWJlK5h*W3f=%~%Y93)!oAijZMrOz8&kjN*kO`Ts=b-&+O zE_|u{$H~;sGmECb3w)Yg0~bAZ$(a9=I_%nCgSO)Z>P4p$aU_M7x{DZ@YW?(+BfRbN zH{(;ZwDt~O-kYf2b2XJ$LhIp4wSg6G2*Z?AfbBovQK?1iavxL@DXm(3HRYH`7a-mo zr%l^_WJT*?9)AG`wOX`D zv||hcGY1|&d)DOb9(1zpWM)X)F$6AK(88cyot!&|4I8CFXt=h|uo>G4iS{zz90uTL zII^ik&qW4LhFov#yOP0$Oj%~RJ&zvk4ha)t9+(&1+qK(@j|J`Vb}YILag&OfmKi2l ze=leUEi;WZJfudy7leb7;=ETF$y!RCg<@Wv?i|IbqHHMv3R=t+0Ei` zTT^aYT+86dJ;RU%OsOF(%3P=K!1%Wo)k4LZlYXcWo*UJ|!Xa=U57#SI@4G)4krU73GyA5Qk9=}-)=hu_UwiBGttGj=D zQ(7mq*v7>c|N7&rbcpFxhdH2TwftiM*K0do`ng6`YHzbmDt|A&FA(wxc>p?!6Xy74 z_hpG#an5&6=&-Y0 zLjU+p;hgfsWyg4*!Z^m6Jz43%L!%&zXGv|N7wf>IJf_%XJg~m}=X7**DxxojqhCof z*8|&b8QBr2qvNyl2}a$NuenWEQTp=)I3ge_k6loKHyTd?Sj) z$KiVSBDJIV{T$r$baLFD)TN1WIOHw&Wa%>LzzKTq3#>v`c6M*Vj>e`->cqOQ|BszN zeT1{>;AsTqN%Qu;L5#yrUmGy-m_q3Q(J^AAa$?xTEu@L{U}~v!VN%wn&R+TG*IJxB znPe|}6lM!MUOZcC(ZYofQHA=28?rlyiZHf&@UwS5Gm9vQ@G5yJ-v=1FHZs!XMBMX3 z=i3?a5E8d_tO>Y%3bNmUjB`7i1K>h+tKq=S>^8YT#4+@w@%80EcC$az*v{_m^o$Ow z$u)jo!&&6Y${DiWYz3RBIZas1M}W+3lTZ7w)6pWDxHRvsr&rq(yBTC*ujW5L<(I7T zd%2c?tPlsW86p98($mu$H>yt~N**fj_2J?AT!aU;=?O8qh{=hxYg{1lej-6PAL8DZ z05uIT{`C4@Ki=%{=<80qc|@H{{XZ;mv#uPtjg5OB?ptdE1&5;hsj<=pZDVWO3%(D1 z@-k+SJM2L4bj_ZB##6SRr&kGJ|1%ZI0kn)3wo4BcKm;v*bg?>RWF8rk5L$IVY3=Ea zeRpGjgRaqFwpY&_jp3G-jlqOZ_%t6se@-wUrjBAuT$|Q$`%7TLjvn?cH`I!1Ok*PX zX(0!1pk@TZi(}uGx^}0!QHu{~_u%xRj&Ea&>fyD5T-ZjY(QDl)?}lSfMk&|D#dtyT z4<|h@=d7W;6*}3KOP8vCoU-91f2+aH4$4k>$%wN?6my)&4O_Q9Cr3Rmxp5dNWeY9a z3n^!*PlnsrGzWsUfUcN^cNA;bCN>yle;Z8j*MTM@*cID{Y@O(D&yUn%(#n!8+qXC3 z3Z^{EZIwg!h7Ah887?=>rGW0s#l#m1SL2D^kL&x0eN6u$NnYIkXmTWFto^cgxhW|W zlY^L|%eTgR8h=kGA${9nN*EhEI~U$>Nw{Go9m7A%2h)&G2A*WY;-YIIoTvk{h;+He zbIS$Co;7W>JI#Ll_H7D#USPvvvw;48UZRI95e*Q2;4%h*-Iu0n;0D_KK3X^34T*i= zTwDV6jMz8V#eOo1sdHc%HDzfxTr7`zCN88NVDzdt2T!w?eJQ(g4S~4N5Hqts`&lG4 z-nGho$sxq&<>|SUv)AT+?TRsnFd(U!ibP-<{ZsulpnadSn3A>VwRY;;vIV=9D6Ndj z)>*`2KOr4@d3@xq2lsZ(IK>rzwq4V)uj0fQ#;O38#$Cp1=MSYWP%<620;*(37Vdw9<-5T3+l&Lvq1v8$aulit3LOqF^aEfj(A2}!dM>C zqNPjw*K{y{95-$J*hlMed28XD8QQjW>uc*UcDEkJZX#pp|y|`bOsOp3k&w;R?l~&i9Y0UqS zuhC=5xoaFbq)wNGE-r!CYVsUb&+N@09r$K5?5@stPjv@j)Hr|l?q0&~-b`k$?B=vP z&YIP1ZtFalh2>v9_QHnOSkH0;fZLo~y=c(dmVZC`tKpj)DJk_eYS#SIW@7+W z6CzN-yLT67_wCJ9t2{S(eX^g~&hvXr=5ViPJq>iU_>7F5ycw-?*9jumhlPiO>Ep^$qfVJM$ zs#Uv*^U}q-Z^gze|0kslD=46wM66}MokFhYMP%_}pdr6DoUPHqU`ZETeL;kpMZ>}2 z4N*r0XpvQ@W9rM}5;TY65#F=bhk_k!1|>(<-Mfugj=g(ZHRzLF z0$vMQJdFB}-3Hjk-sLj>=kr&ttS6p7dHh)QqV1uZ&#Cw|x=cLMoG}pX_4GEfBQ<+z z-T*Z{diClW+yoeNQfRvVzqg}mXOQU`qSQXHSm!g(v4EatAnK?=Gr{eDVeKR;_JV6* zGH?==|6^*eHSH;!q(DKe+^mD6tEzU^i6MdR1Nv5 zTIboWqrq&D1(M-6)bgSqjT(87Ykp&rgYLP2sHlDvZ|v764^pb1VXP!-6uy67-N~R7=OZz^joidqluct*8aKDH%!MC5^skxpZ@^@%I0ic) z-f`?K3|Vh8^V+TpaG5{VuKk#5VNAf#eHf9onl!mg#k!;Ivt7LQyX?8)n!PUQ^Gz@z zG+6NeF!$zhJ?HP+_oqk?-gi6us*fGIFHjZqp7!ky;d>m zznr-WaCM2l(Zkbo20_;k=P8Bvn}?fFSVZMOxrEZ(DBYriL{rkbcx%?I)EhU}5@fsh zWJ)?9N z8nx(A9QisUx=#pHzBZuXaGhfT{Xg0<5G!DyrDG$KyNOtvnVNX;NjDasLDql%fPm=RJY~&8AJwX<|LS zyrjf4vx$^iwZKg5@+uzzu-w>H!1tkJpLV94wNHaJvLCoZ6qwnZNTL+Td-*b$iVrvl zmtoMt7Qg-W?CY%dCf>oktj0~7E<1a65X^T6vZrxPgGkV)<)}zGizy)4SGm%>FXDkx z5RNj}2dZc4=;>wbOpIl!)}mO){pbfDqqT0MMsn=7%Bb-fPUS(glteH)N?rJ)n-uf2 z?;UK(cih7d=RJiWpK@5nZBdJR?u|5fQPA@ zhYZ{WaWmm#Be1X3F{tI#Ql!F#kB=O%pjYCV_djzZ*By`D{Vu!4zmdj|X)k@`?Kg?f zSc?1tO;`gheFZicr0m6;H@84k(2iT2NA?<>Xsd~Q(P??A;2kjSK4mxhRNQ%$^_RQ*+h(wtoXeHAq1>kkRMl>C<-9FlEIHC($mWXlkz@whBgCWRI^| z?O#{RI!ygLe&M1;?|EMH@EfD7yFxHUL!knwR5W3j?S++lhB<){xiQ=WE)Y(p!FJv{znfAn%nMpL%%x-0uXGYgCr(Jwos?VylfH)9upLD?&BaQDnm#tq z5kFm_F*9;r^9QsSL%Cg0huphrZFlQ;Dwcr()&1`Ep`E-$x*=Kcx;ESI>V_Px_Ai}n zb(~s0$@3*DUX@6)W{_xXjXXoIHE44SfO9vL$^8`Wy+sWbEsd2Iy>v2Q-01e zD<|G8IO|D*PgD-pI!O#Tkrs5v?W_x8YaAQ0Zk;P3&nzRSf6}c@S+nv{H+0t4R&eP_ zMXSgyOWb=qp1I0)H!VZwpSXfuwY7urnbBuquFPRg*LuTKuLfmOG{A!ljH;}Zszv!0 zMvvFejqMl}M8anjvF@{X=lbB222_6kh)aE7aVPjua9O%xmp|V=n%%A@+7Xz&8+lv9 zx7ryXP?y3A$cd=e^Pc28K~$iHx5A?FB`<=g-gs*F?i1lbpZr_65G^8rY{eIWvrW#`Wi-r!w+n7qI!-nwnu+M?{zAxx_!Q23Z4dYcaC zG0?AK1M#aA349rjE3AcCW&W-V6`q_z0y4u3NtG33Q?Sg7NXQ2&kIDC;uB)&8hgpXA zAJk>syruA$N72dfg_cpNlaO3FKSid0ye~2c?*a%ziBab;l01T2%i>lPia^q7+(M;D zvS@q%593y>y@s~6_OEoCBfpvb#t~r%n(agLZs9Wl?s8cQ4{z;hE3KwgPNSo2W8(dj47wVCi{VH3^3f?HUM-fT~ z0s{kK%dT_3oTj?DxTKLfM2-yTg>1GDRFu-&d5*l-y1Vl`Ji9 zm3MM*+W8pnec8IKI7OWo-lm@0&#SN^9h5a6q_lI#j>_LlL|Loh zyudJ=hQ*U|4$%GCn#S`zJ(Zj;Z(2QYE+v#TeGE-~n|>bJx!<}ZY7`aF$U2flH#;s; zQ(&O%eW01sl1Q3Bg2PgjcTwvH^y?=GI{ZiqK~h>x=xEP`xj6}Kfg}4Xn7fdtRW#vr z?XVskZ0Wl3g|d^%QYHE%u7x@9Qn#_&74&}q#u$IxpzQMbcz0q;)*=JNb~HA=NPcRn?>5K>a2J(4;FBcf z3Gm5Hb#3bg7gf8giaXaLKFNz%c5LCAy{mNV+8wkvVA#rO+jVfwAd9Jm=7ziYsme9Q zVJ%`|RumqrMpTU4+zTivkHH8)M(sBt)q!jxWKp(S@Z`B9RcT1cOOGHd_$^*Y#{2m$ z^!3fAUiy|t14eb!+yc?Ui?jalobT9jYWxomCT?s*{oo-P6+^j>W&MutIXxaZ>CuIy zPK>i$XWXHm|7T+bF=Ym+i#yn`ef!uo-3QsdFD-q_mCL`rc|GZ4Gu?72)8k6|OyZg^ zoy?bvWAh=EFY1sO`$9_SdS>3<-Ur9ct8p>eyfw&vUQ~ekv(}yGyqXdp6ge=_Mkxt~ zuj|Qz^WR2dQ8g%@y}7d~g6mf=wf!t?WM|UX5#vg+G-MdyB9Z+0!PX>00Hv-ZOng~9 zcL{(pSBDS*xT9P|7-3PL&Jym;)VkrR7kY z5qkmao);8crYD+vE*>OVU`K9;n{#Ep-I4FR_TUv^1J1to5{A!|dgr#*Z{R2zudpGfLSBDBI@KxsFjo(5MAP zj`oSqdmV^GwHe4VCB<->iIVe$PdR=CGmD;Xn&^1p+O^e4**%D<{R*c}o$5gBqjK&9 zOA8itm~z|R9W>AZV4pNknK9rPL-rlUgw_;3eY%)4ARNbsnN$jORaKv}h&`+G51p#z z&-b0Zm1gheT>_qOWCE!vDjs4bMpY@ZjGgqg1U~@Za%Cd?b?(_M99F@cfTQ80W)9yR zJ4a8=QK_zq%4J_a#`YxkGOLXd{7uw7*Aac_Yc)VrvqZX|+~TjT+nffv0~*SwBIan_ z@be?1$Y-Nf+_GiznU7mcHgkl!XxXYYXaE#jm#gdO|M9NQY3VI)RKG*8;#Z-pg!MSp$?!#`Q&D-&?fHc5RWaoyB`Mk2O`v!rwIaoD>9&@H z&!Krve&zz-Z`1wsqL1A{)Vk>FZ=lR!-8MFk-I!+MHi z<^RECH0k#10)&|GBbsmu202bni_|I6@JXsm!Gnr8SE4G0au?3LB35QDX59kd4#2rNBC9pR|PNe3V*7=nKB4&`q-3A?@j%WoXNrS4>-Q zz5>z-pQ~8xI(~-+h=hQ~o@4#-c+d`cDnumG`Ave5?UXIG?{9GM)iRyyDya(Qkgw8+ zNQ^=rmk&3m>NK>Tf{j3p@K_Q4rqlI|jdUn2fB8*IF=)|;?Lf1V6~VJ+LVQ4Nh993s zlAC=yPEEn^vVp2Gl^xHFB-j|WNTEr#iM*V)tG-@0PCi0;oxUFfj;$Ess0*hU9r#O( zYbyf*@#G$+wTmJL>lP?>E3-bf+d8Q|I|H*y4Hl0^_<5s<&n$ z>OisK@aV414`;$_*)7bFv+hyK}Voe=}(}35m5QWFCyGgf*Me%QpbGFqt76=bFx{aBr9a4(t~w1i7mhW-xop?Q2=sO;(uub$8NPvc^^Mep&D43V}_7vI7LNo;lm zf}yApnukHvIL>3ssZR0p@b33I=|rWQpAi*Kf42%T*t3Fy;b@>@XUihE=qq>m{ulio zrKhU}2WG_SYB!(e);pX>>UCv%zDgLFsw|Eh;5 zVV~{Z?5LbaXw3AE&2jWy3qnyp2vFIJVB+>rbvq&`RtjsvJaE14pY!)!;_j7y^#YBm z;2Ox*X7Hsw?@V9b#GzZsI6F-tc)VFuKxPM-yR;*C9-B7OQad2ZZ^xX?t#nzgM{}VntxBoNQ|RT3;Oa{b9Owa3HUr>w--H8}6Hu96 zK>_{j%TEm&r7g+FIW$A^1L*EzuVM^Oa%BnM#TDoqze@ULwhK0d; zrZhX8Km{1_EAyP_%Nt@S8XU?s(rs)quTtnVnc@W@q3t@}c^(K#^WKgbL@~w7y9Zi` zDlK3~%{$_$-n4UDQEU<8EYSgmoe^4z@vXW+GcFaZ*tA5U#0FmTA>&sxZrQS>+bI5E zWFPnD3I^Y&(~)LQ*=0GQpmrgYR+WSYFiN$O4-CD_K#U;r3^8p_GN>!WXgsinv_ASMkUHW%ic`Sj2o?Pc8{lxbf4VYh@| zbq=seIVCf5J?FJc#W*Ze(T5K8@Hl*G*PhzvKG?a}&%vu4=&+%b>rJQkuy;tSUf5>f zLJeu^qStr6!n>-(y!0nEwF}wV;lkfJP@O2Mduy{RUFc~V3M6*sp)3LJjr_7+0i7>V&;@Wj%CKS zOOiDtHGRwtXa5qT4C*ODse`gVn_7;F$BtosBnnuo)}i@bR1j@(q&qHDLMi60JaE8$ zb#llL3J%MMc4jsiGC3eZBj`HDQw~*D5H}Pg*WpJ#F{I%HPILyucPlVI&u5Qgu_(((1Kty`}CuYyf_DtkP352qL>H8O-dmcKFOQ?i2@@wb;+pw}|O# zwKnn$dL#{_2{*&caChiOZk<8k#GG*(U3>{)xfCRRkk?wy)C{0c9$E>i7g5Q&{TvUB z7)eMd%TI4YE^6@(h})e2G>VVjZ^7F(G~lC322u9Z{*~{u$>aTS!#>u-8qm-;Kjh0@ z6kf(@_$6o%4NXnu6pn~eH-yhiE`-%ERoR$=xX=1kP%>Q^2}2s+KIz_|$Mbcl{*udu|2ED)K1byi4uM@pZ@r2Z0-sxD zSQ!ATHEZfhxdyq_SVJQP>>*>ZK@;oq?TH;b=XXq>F+1p0k-AgqmyBp-C%EoBdYV;l znPIdDsCcUCm2(K^NKsV4ETmAoIUVYW>x#w47s&G-{Vr+`R~Vy$w@bzxHIC~~OUlq~ zIwi#{@+4oUG@*(wB-k`X=?w6%GYoc@CLBF>AbNM^soX}jADy|m&Y-t+NtLay&SsJ+uT^3Sp!Sj z3R9372R<2z#aPs%w358u?f?k3!xm>#VuqYTb&D zlvoQ-*YDCygTAJXXe<~8OPytGi}V?6CuHTy5vyNeSMLV+fg|-=$a#Z+>V+Yv=XZXA zA}S4%To69y2fCh2nET`${$LSYKp_%nylh{5{SRN$a+H$63_`_LQ-8tJ;ISJQKSFt` zpeLQPB4Q#!Ev9c8y}Cv%vv=}|UX%g4`*mTT zh+h~C{0atp8EDe3O@-eisH_U?D-@B?d`os2HB_m?tt{mD7Es{{8w9bY;Y^OIipu-67Sr;Ii$kCnK>|%Uf}L12mYyKgC%k|i zI#WsO`JZ`sXiDqFomm>WZ}I~n2OBnAyI_>aGNtCFP|;0}wHyvNY&iHip<$Jm%!Qb} zJ{%9fcjd}TV6r*}+dc@;&P=iG6vjvTiBgZcOq9Aj@TLnog^U4r)17v1AWGnBCgs!; zy#D;tm8eFE&Fb<$0QuwGrv7;2VtjuNKAQ)wC}LFjE6le0-aEyhlqdQjTC=@ev?ac@ zzsd$vFEgvYn!U3|cclqOhQ+ZvF-wQhjRB0^s`00r*Sn?(lZKeLQ&8k~2fuCC8O@#h z^)m{Nt2;jfEK$+c(MS384>wqE0M)I#DK>+k{l5eT@K@r375Qv?K2?@LS~S6z5#@;! z-pH4FrUZZj^&n1&mG>KHl?;3zcw$b}8Ee`NYK134m-(o7YX6E;t(WRJooRhHsxt*v zM=N0KQcccdO{&QCE4xv5M8D+ZAz7$-{-wE zVip_vvq~`U=dWe_THpjQAKhQ~Hx(~`WvBO#CzGn1K;uAF=iFS z19hjun4y96k8{tIs)+xLg8t~4Xq}Hf{PD$_-;N$NyoGkl@3;&^-4LiXVYm*KN#{kL zReZooKI<$IYc=RR{;#cRB)U+t&z{gX)muTZZlaOhu!sg=QNdZkt-(&@c>}?LQ}nx6 ziIP2wik~7`P8|XM!sEw-EIx)f#|htoe~EN^z%zS^iBhfS(F*Nh1qr)BAh|eabK;bAbjc$Z)Y zBoXIl#>Vm@t`r3`mu`rBb{~2*Q9I8&58bm3IFQ=o23-m&?HQ9c-9)-ZZPtOi&nDx% zsB#AWO;EKeJDGIlPwD>iq;SgNNim??9R&t%9Y>aN8;oFu6!4sxm;x=19JfYjQjw!* zgFW74IJ96lk~@0IP#&Xbu_;4Of$+%7B0?81<0f%eYDJXZ(i{_?xd0Gzd9PlrhIf@B zrOZPsh3BI*>;B;m#K`8;a*z*D>Z_!#$)AEIAo@<%AD>28#GN6p>p|If;ic41;huRc zSWw81C@AAu8me|66a1bPLwmtHE5j882ZR(}sr&_mfqj#zczUH}WtZRw4>}m}j$>!X z!a|7vn5(~c{60-&^Q-0u)O`Iy)x3<(RB&DrAd0!sYd33$>3%5ASx>S3KdN}3o_3Lg9M|6OC^Qo(7epk>nYM|4hC8t0ySkFX87>o?X1Y%Xy zUw$bnKhPEF0yRY)gCY0w@dDODBZ97fGqU*&6uP9WO%Gjus9b}C<672B4bvSsunmiL z*7T~gD{EF(a!N{yee4qs8wiZDfl{a(3oWgyAWk>jF;@!WQlT*usxj$iGgPlxRlu{W zNsIH4X|eI(CWaeZW?hM4K=}I3oKgz>@BB#}Gf}3jNvo^(cFJGy;G)C>!M;gKDJ>}p zp8p51Ci0GKP)~Y+P0qL8oNs&I{C4>6!GWBZwI3^SsT)RQaX23^h!eesZ=|3BH-b)~ zzVD4(earOi;@+5K+Nfbekpyz@u9&pb(3n2XpFDST)>KBMEsFUGt#%QVq(&< z;PT_NnO&1JDcI3tD3OD9GOJ3(WO5;|&2&%wd$+f5&7JmRDAMU{>}1#yUy&OCuv?rX zwU08Si<~kN^zeC6(d>2x&9ogl`hc;`s4V_Xa`sXc;)wg=mm^9juj0x2^lf zK&C-N>CAJ5Wd4#-MF3rb3jJpVq=5a?!`!4Yj8_{|I1}+4K=t-77}6wNPudxpA24o* z_&j9NqY!(D{#YvR=I;tw$JDh=L+&P!F?SlgnyVS2v|O|p+46Ouxx zGDsrmKWbq++vM8$27)JfoLR4pjsp0fr5l0XfzHeo6Nadrlebz<`i%7Z@QD*{2(=2_?!y1k=+-$*V>rIPPyEJzuoBV7 zz%v-ZWMQ+DLR})?onC0waZ??gWk691gIz{;7ZLGslI>_=;d+J&SXfqOofY$A(CAn+ zM9HAOpu)o9Cz~qEjvI#Lk#sKYdLU*cfje1+4UvJ$u{^mcesMkdv+- z0W;-H=*}wGgN%X7^Z5M8_TEL6<4R2_q#8sMVoQ(j_V5aXBa#(Qe0BshLROqnD3G0L zb;&RaY@VhQWMt-}-z{%XTx2@JA#~+RRZ-z6It^c=ANU5k-in_i^J~z9qvo|o!pE$W zloxkD3+w{yhUuU)m4iUn47Y6qQT)z7Q1B=PH@QzS-I{6(^HV`yn&vPUl0wyq4z(k! zzpz2@pH1KO`0+`FMQjYRy2$T)iTs9Bwkxnks5`A7bPbwq+pfj&%xL|1nS?^uvwr?T z+IZcB{-#uAFNkqyH8meUB zMunpqHwsM(^-~$?G_6%JeYN0@y3~}=&X*rNhyq71p{(&h(E_nE-SITJNW07Hh)J2p zLPF|Or^IuEL0_`iFwJy%(56lA@BQZ?_t({q2qygzBm|avNg;sihthObBfMtE0FcL1 z_%(dhE&}wdVzJnfPwfde%rY1RwnNxJwP^dXPvmcplbZ#B=Q+8%w=S&aAu7Fme7Y~K zj7`>AP8sFB8)NEIm=M&?^_}#J7bLOv`OB9THon03NuZtzu#?f8kBmaXHRDLz?fSj0 z43EhiH(?WMJ5q&!u@dTN9OL*ccs9pC@y(+Lmp}QBo^RD#Han(CSHTV)f`aIP6&`>7 zDOk_O-qT`)DWcDG{~ggyP-NpHV~hq}-}iW2FX}lwf!6`(dtrw~OX>Rci}rsW)q}k+ ztU40yEjWz*5!z6oATW*OrWrEcq#Zx(9_5xiKw3#r?yJ5#$7|!=k8l3oOb%&{2od*m;WuFmgo=Q| zVZdPw7#dL;Hn+l6<6tOfN~#yfWq?VUM^K`>?b zE6;m$uys-^&z%s(BEsvqMV4ToegY~4krwv6%Kh=_b=+sAySgqZZ@Rntb%Lx&rL_9~ z(FVP6Ay_S=9TCLm@Sc-@;qY9ckP)&RZFgT)Jwv-&FR4ltgsv4-tZBZc;g!;9k47&pH02quw&#=(o2UruC$KW&NUDSEbZ0MKjjrqIkn{M z*|V25uhqXXUB5&GWdD#jH=+El@%PK{g}e$rE5kS&I6qOtCdC^Z8skp+#^R!3~<4#d9#9iW{w{-R0g}?-7bMW+4|3|jd(O} z9C~M`x|7=B>8+ z!BEkCa?41bb++?+(i6T=p08;;AVOdmI&YLLKs8;6=-Uw}>;AlQNXAJsyS!fX(3!`u zgRVNYu(Tr;geaQt^|vcOOdWl>bxaeVP>#$Ia9vb^ODNPtCPyG|iZBiE>eM!QzO!)kuVEiV84Q zyZ^%z2)b`4Jq;f~5OPvoKKxOE7u8=6WPm$qa`{M?lW$(S#Q)n0)zDJs^-(sBtm>uG z@6BX55IV6Onqjo}d1^%^56sO)WQFQm#w1X1^&2oirtq}mk4AS!4WT)x1~yb71I&~};g2$N zb4~9RlIY>LjZm1EKp{k*-tgeTgHrkzn*X27Ku>zU8n5$y;L&5l z%Q?H5%$2z8>rc3lQEqv6GRDN@)UlW6WEPS5Oimq(-~d{u>APf#)HL5m_wN02&&!Vk z#Kff!HDrH;&!7v2s+SqS)9PnX(=ZG6rA@F}aMaK_L1~LktcN99eKFNf|Lt*)ABjR8 zud%8IPD9P5jv@X!^O+xkt|`?~@gQ5sQ|9wDcRzb!x_0;O*Q&2naGQ=Sc<0D}X})LJ zufwYd#XmJ_)RnA&B1q?ECI6kFq#@kgbQY{w_&T1Nm)1tzm#$yNlW?`#H`PQ&jw0^4 zBHX%4NWKZuxj3@d(3-a6PO&#Y#F6A}@m94vY}s7278t`QQV28^1zYCosdp#Nssqxp zx_WV9VxmQk^zl5o`a#^ZFdD89_dfMxT~*B{oIJg>NXCM`@%FSuo z?%bbmqASme2)foUoulJa4N`J)TA<0`VhtzHf19yKKk1BGQsx3V9vFeUd*$e7cRNZQ zv`wI0s*+OB&FA^~8$)|{R<~QA-@A9>!qeAf95)C#yu%&;0=jc@|0eK1s!IZIL(K** zaVpkzoKRx4fd+!`JsemQz>wYV>PIQaw3-m@)x*vpV%|3kHDq?uUo0C4E-uR zC^j$BQ%ZQm}0o5_Njn znH{8LD2*Y;T#M+rCEpQjrtiv2$RdYRMr0eeQBhM(rH*D4(kpKNRCMm|M>XA~ru&TrV*05%#T*q6TwqJA`Xlw{K>J9BXAT272lLJJ}q#-&S# zEpew6oJJT>PXVNNRnMDrt*M6fOA|(^&C$ocY_u7v7x;?I1-jQEsE5?L%R~&cnWn4+NNqUJC(v+g2&m(wzzM& zo<}5EpK3wGA!N8xWceU>p$Bs`rkUxPm_!R#3|J`>CeUVbk!e>rQ!A*3lYn>yrK2^M zueH^G!JCH`G&H1!oge_|`>paV=w7rC>hh=J(EI>722_`h5HhyBd`w9)_t6#?AAunm zxM*_r3MUgSfS_SK$1VrXaTf%})@h?rhZQd^6;dqFVj)otrp4plr12gui(r(*VSmI~ zjV^l^fF~34hA1e^-%UeosNE&M%a0XQSj&A&wZ;NO(Z8f3=fjQSX9}-WdSSFxf)Fyj z*WRy0iO=jppCBdyR7s^Q15_|7wfRD#c7Q~73L4myas2Uqh3y%H`A3i>xS$vJuYbl1-Zf55RUU)h+V+JgVPHx5H>z}id<^NR(ObTNGv$OSU=f5Y01=dqQ_vRlKy~mp(J3xxh=buB zCJ)};uH(Thm;!cl?fUg&!m6rs0M4&bKZ6Lm(tW0%bIiAxK-<6}l}duBGD=@t#cF+k zaZBS_8gde8wPpAKR)XW-Qp7VvTyAn6oq#-ent2ZZNBf+=A3S)9x#9E*_MI;!wKu2z zQt|j&?IT6BP!H5+fb-v#4$DwaG}hF-$hA~LBsKc*Th68jJuN8pFalT;|Fs<*qE4S4 zK$*G9w09T&L1U_`^TQzzTh~;hDiA;=pVRRi+WiXDEjC%pcycp;|DB-`)=t3dHRcV3 zXYHOOhn5Vg5+n!VbQcJF?R1YwD@qxx6q`w)LRnHx8~Es){}v7p2Ya{u^G*~MBk|9Z z3LhMn_LCahJqw zNPKzS)>~2o?^4Lz0%8Qhxz8U5lDwFcvzZLEnWrpknaZk~rJiwPnO7t~CW>o5j+;^U zT`Ax_J*~owPg!*Lz~;&d!4T;Y`~Ue`iyTqNft8^+`VO>hVqdop_Ww*(tF^<~UaD@! zE9&{mv0K=$J_{G-6&7xOdEX(}CzB_C`N@-MN(Qyd%~0`B!r zhmt3}hl_`rjr%K_NZf|Cd5S%H9BANI&uTK8dr+}Sxz9Pda{BU}7d`qXrXg*mP!g3q zF7g*BjfCb`03)+cYc*Duw&X1P9EZu1MPz>%Ckix&R{T57H*=aJMo?e!Wr6qK*=wu&JuD#H}EF8uOgHtEjncH2}N9_&J9ruWfz$^a_S3we2+Ah1|F zB8iME0hOSl+G64RxuOEWZCiZiuJX?3n!bJg+6L^E!PI5~K+qm>ZErl<*I+w?*lOqg zqW2_GwPbb#u;PTp@nB~c^6t;#O7ZV6XWQN^9i<7uH$Z^ze8aec&$LA8bMv_#AY7T1tcTwF6Qq) zcm@&8I@?zf#iWvLkrt1K4RtE^;7|;0SdT95OA%I$-R3wZlG_*E$9|=iVJfF z4Z8fEu%nRm&o>uZTR&-p5~Js&@+ak|mc8^xXO34GYNUJ(xOqT-s6j$DwnN7KVJzaBKSre;ZkiPqcKD=wQ z*XmAiJ_3u;-$jq;AHN)$+Y?AtFJZ|bL|eWCHWA1jrPQ5F#mf)UF}jYJlj z#osS9E)p2g$wB?tQO4x@6t z&J+Z?d#9MjCGKU*n@CkjP&WBj3Fj-zlc!c>P4nd4u5)yNLjhrXjqL|t)V{FDE3 zO?_O_gS1lXHUSjJS4`n_aculL2Rb5@+>zWQL)dd}M8gz{E9u}VrY_-9v}@#;P$(MN_bt88t;@I)qaLgT0NwZ3VjfpJ{BrkD{lkLl z{967C*W@P+qtz8YYK?vviU__M(gUSAGNvDAMP|6TxV()P6q(eyj@d>&=DEsf_-I?$ zZ`!1-p)Up-+1XzROPPpbiK(-vRJL>%riZLUcB3hRL>2!j0t6RZRoqmlot`2A10vxa zwk(cjoEEm10woU3IErJV^r{N75%L-te!{bO^Mu|b@|Qn<`}U7^iT9CsdwG=2rUp^Y z0|zSvt?fkLLQ+VbBqk7Z`haJY&><+qUXGV!_xrm&QMqoTI!vw|gz0%Q^h8=OO+DAW z0*ZkGvXMsFN`E^`bp1}^7(?wUx)uexlxuuts?6V3+})0LWJf zf(!AWg)>fkHybfc;nGH?G8zob31!1fkTJtuOfED7sw#WP96iO}6(}#NutuE5%h$S> zwtqtQ5HwX;pYD|YCL%s@M81U)icu^jQh_}wQSd567s29Iw$Ha$97gGiuYOml+P_z2 zA|yivG3NLp2w*|>84%#PcE0=ROK&^k z_>eQq=85VONGSD8FRmA_E|#Y&cUl4&dj+@Vf0ffg%!TS%OEtIWg>_?}4)U=e>JS@T zBR(u-%p_xGXC+!Tv4!GAT%gPaG?%o_blWI;E{0U?_ph5}T+hE4ai4RLac5zzVC&Im zPdVw);l|reKgQg!R!6+e8pz0mSZ0tSj5s*~HD4AeurN}ppr&A?yAyzKO9$8ee60_x zXb$30*&jntv=2j`XVk!4+vv;9x@1t{i2|rq+m;A8cuI1p2#*3nJ;`rOKo z=Xn*4}nC`l6(}!v& z?dHvOyb>?E4xOA9;Pzk)bq-^GPDj4!Ugt^3``*JE&qJKVbCEv}7Ypdf{m zlXT+7E6Abc6uo{z=bHyqJua znm1t1hVL@S>O)~^{b35oDOb84f7@uyDjNxzI%|6!f)| zk0sDdtHqQt{znx=l`p>j--fcvy*xZ9^rPXjmF0DH{d|1nYLoTpAy;pguHlIm1Y*z< zD(C@V+fnd4ygz03UnHvhb@BXMew-{!S)tzc8p;>>(1`Eh9GWxaK&{y`IT8Ke)|v6! zCoogvF@Ju&!zYcLakRB4j%gR`0RCC#A8V?PBjtm{3D!9d-S<6d9g)!3%AE;Os|3#l z3$9$c)T!teBqDY^WNjmhsZ;sP3N0)lnN~hp@eq;NTItz%oF%f3_SaP%-?!)eJJFp< zS zFJ$Uf7pRW5?6*fJvv#^mRD}{!R6yX#29-$>k=t<|UJlfyBZGYsawf_9>+>-I8_0oeC_&o?t_S>M(F%b<|r~mI^nz zg8BWOQ`PLk`8u%MD${SrSryG26I!@!E{Q1D)oAqkn>#)ve zJd~1JV6mXTpz-8$ey7sDp_WuGnl#&d67suc@;aR=4)u*~%f+QBpkc&kS~_XaNkO8H zo;_}7u>D?Sa(?BH7f@raV8lk4YEFc-V+hK2z!3}SM}QIhnEa;qYGK8+__s4?TdZ@& zzC?d1LOp6I`M{6g_IN*#HD^MgtqLIJxq3%+jC#n`mnxdsV8CX;!?)v4^+G#KeXu1* z>U5g&-FvUs9#wEw|2f!YhKxQ?$8N>giuNcZA&I{Utersn9{ozYN~PlqBy!CsZ#1U z|JBB{9H5EA95aq!(9)%KfKyX3F@q!6U35R4zf&=m>WiJITMl~Mr_hv%8gbh~dk1uB zeCS%!P&YceU&O?Su73oDGIl0~R7G*r+d?{k$a!L%&KkT%A)ihojl>}9^}GZ}miYx}x8rWd4~LaKp1K(EZkB{51#HIs_K>=Hnk-SL~a zr+5Xkh0hbT$lmV_AynEu1U^@o*O7jRmo)y+7<1e8Tq%Gc0|loN9Z(o(an_DustQgp z<`98Pat1>SL7k}3>vDKjh-D7HozlI?_Ix^x0FkwihS49k<~lu2<2)tR2$=FySjdZ6 zS)s%?o=jV8g~u@AJ$$p)b^h`qQEL3$GzIk&jHDq!1A)qIzur{5L`i^h6*7V|W{=!Z zL!rd{w^1SWXWj}XW!3yM_>#d0^-jxS>)A?bB{5i0z+1}<8|achBQO7EK&Ux0y^tP| zh8wDbBCP=OS`H2v2-CEi;tNtk!g)4X9={$YI?VUq@qxQxC={A;?2+Naa+BwW@EfJ8 z5E2F&Z#a?IZg-c)Rs+BfI@4x|%?sUgvmh`lJ9MSIH%Xf$(tCaA40%QJMCIqxU-I>3 zJ&7LBbMk(N_4Gkf2(zW}X~#n?WKs3mDa z3v3kz?m6i08I1B}i^t8XKZl>jqiJ}lsCah2xbfB{~V0E*2S0Lu0h!n4Bq3%e3r5t}eDw-+8QxzOfWG4!O zaRJkwg6Yq;ord5=0B^oO28hu$<>$BE5K&g2Ipd@6Xm<>2OhLx@iTBp$1S%)UF*eaP zVEfFMKpWAXh*Fbr7C9-@c|{I?oM8iU3ZFI#w;yPa#g!H0K_OGMaD@PbvkE^OQ2C+- z7kD_MY5mB9r%t<2t?{D7Q z1P~MesdO^}(Th$VWvv8%v5+o0Tl;kXzHh00^Uaf#48kWm`Yh)GmNBp!#;`4ZE;ohp zA#vCpV`O*&6;Q~%Dx4sp#du~z;5Kmtqg8c1PVn z5qy|wS{Zzy^FjhDqbLIE&(>6@8QxAkAzT|1%cd21bz~5fgbi_Zg_@q)J&U1P<|EPp zKyK{vvoOE?H`zu^+U02Tia`v7;ACRCrP~|Y7Nqb-1Q!~%vG{y?sa~l`Fw$z^X)=|q za~tHLh?b=4Pl|k{3U035o8yCIh2LQ!AYM6h-hP;67*M^CCwG|%ES#E0{_|24l#Q+2 zKP1u6fPyRXfcry^Pvobdt2$Y^i?-2oo!Bd&si8}vN*#T$_}!yZ!vrh!Ya8JxgLN`4 zEW^^!hhu;QTN*lBZvA6ktt;@pdYo#KO*hKimoHzM=TNSNd@@`fkCUD}T9R{Vg4eux zM`jI1%A`QmWQDLp~indbATFA9A@uTo$%%o4R%WnDwa` z;iZw(XdS3OQ^@1^zBSUw-gH$nBH)YO)UIKhbUv7tPOE9CrZbU4C$t17_yvQR)1hk= z2;p%~nI>w5}kvpVYviNz&Rq!O|p~(PC-A3JbYAbi{p_2bK^c$Jv<78(N!Vgzs&a< zDU2Rb`~zsq%ou;Q_3xd1RbKd&2btc#@otb|Z>ibRm)wyQaN-m$vPjKQLhZ)>grW)x zm=a;SDYx-2^8qIin>ct+1&0IGOG-G-d#Wgp{uhrAc6Mx8GqH`89jD?lCl*Rodb|CB z<~JCn#`>(D1R2h;Xr~E4mn$#X+ATqxA6X8T=e;A25b~AOX8ls zhcMA6T{o?lO3HI!>QSRf{_%|jYk=71iHS@EP;ZZ(_n2B{ab##@sk}wdQDO9~R8nJH zdfVk3CXb{!1cF~{7r6h)_gFZ>rwgmpI*6UTyj|EqBKRe;%8jXyP%S=KfTb8UUD*7o z5Mu)Okq~#0SE{MM#jf_u$9t306YT!Y;pT=d>ZKm(2UzE_;FE~DTTFZQI3WPJ#jkyj0~&roPX_Sliz>h`2c?izANz8_C$CT$;wX2 zY5~~Y;YuQ-Ga;wznD(F=5sNLxc#*BFX`W7uDA4A=F zlDZ3xYCe-7azlC0c2JgOt+0&pI@z;-jz7tTQLJd8Q(<$dKF5F_P;q1!-R)j0enqWR zcIvILGkeWjLnS)kZh3EBH$T}K{RilxL}dDcS*0H_XXuf5CM+)+OireWcnRW{gh2K1 z!i=rl7-mrT+w@`oh}q`Q?in-B=&32kMUbJ6g12b^K*?0)r=8JSLqe*Ne}MPkjL?^^s2lzwoTztK^$intL8?8NxfFt|4l z50B{tkAgqWk(fq#+mz9JZ+tn(=6v5KhJQTjN1?I#bul8mlG4%(Ak0}+wE*3#WnG`1 zx3P>6<(-1t5#d^n6s9KvJ5U@$swtk8Uh>T+Ph!b9wCBho7cD84?8Zk2ad4rA@HCl; z|HuK3nh5*&oO`Y)#>_qWim}g9pZ#BKR>+t$JLevNYib&H-f?d@mYnBUb*U&Pgxi2W087v ze&}Ff9y5mTlg3;;N2DxC-U4NNnnQ7Z{<7HEY4PtIT|dw5l70Wphl1zNFG2Y0PE2SM zhz!AK`8Gffy;fzjYyn_HOsvnb@IbK@Tr3yFGFEjvxa{n|HpB4ov}b~nCaqh!V#PdU z0t~PUiWx@b{O>QTo=j3IZo7|<#)W9-^Ld&xXRnRgJz#loTtdb0*xh|mz_Gj3eO$iJ zb4cy{NY(Q0BG>9(EsNxXLn1BI@@Oe@sQf)!1zmr7?t#+y2~g2YDl;?oxEA?-f zk4}q?v;X|jI55m7=R^nXWHb0}eoP4GA+%rQ+rc!7EPKYCJfvlqoY*H&j3@mJLdCk}2 zh)CR`f`_T=AT7%s2h%q=ml>0TQj`MRoPPCi>w zeYd*f-7S+c$GMiB@ozM0{DRLT4<&~$T4!$iH(6WP{;;;#dD3|rwXb1NOnk8@E=Og} z2M#pL$ovy@SO!5vbSH!$_0pJ26KgA)W{*-zveBUf0sM+Q?U7c<{gd$`6mO<|12_7$ zb62^$Ek;g-TTzO!~FXHOhi z{MU#6no0BryPmcEwbD4-^7^p`!Bos_VYq8QHBBHqX?l_K*uK}S-Iy*NH26(BM+Ibt z)z=-jiJ{f!;{)CP%$#{+dvbR8?mk;gChJ@dE9}<4JVP($#G;IzpZ_t7c)uivn?pY!@QQZ_F=}8u zDe1%RS`C3^m))oO{Zlt(E@l^L3Fd}^Vk3)^(H2RBF#Tu&>3*m?y^kceM*oECTORR3 zhQ3@x#}a9$Z_G< zITOxio-h39&g;o3n{?tQnM(cNE5IwS-oJSFuKk`Sk)3l0eQUW}r62=_GcMKzMwF;u zN2P(~iJaQ3=Il$z3)Z%ricsjkzv4{SM%Ic$-*Zm-&i@V?$bU%R`I}*~qEZ&hp;cN! z%dN7s!>;R%)5jCI2vJCuX1Z+h`0LlqP|J%S+N_G(8q+Cs<5AApzo{(r&W<4aE;4Wq zg|uZ&uVFfN@(c;eV@l5O+vAG*Yg(N;SkeXoSuipOH}95ew*I=|4~mz@#diAl=QOrz z3pg&j=gczNk7Q-6(aGq81c668=hN-&OYU{tV#J8a$9nbfc9kV`pwI?`yQ=xLi`kX& z)xW-)TGFg-yQe)lSW>q6SDo_yt@?RP4|`IG`ZeV3?+{6W^PE48wvPHzx#`&EO#$uO zqMMmJ_uZOaordi1?PBQVA7MJ8R(Ty}BEj1xu<3~tCo+!(T4r?DYpC5#JEBtO<5}N- z{>G!nOzWk_Oxsu426M2%Umm&nJ3K9yjo9&r8jhbf(&f`kxiBM)m*s{SVLjo~V3+ ztovz5?{l&n2#CRL`meQKul!FZ-Fm4Iu0xoA^IV6cKA|f6yjHE_DF4&y7Y;NkXv4(5 zxeJ#9gn+p>qvaaGFasm1-@e56uk`I7`Tcju+tj&=@EX%OXYZCBJBBro>iWr(!RhlM z!HdSOt$ksgjpbAD>qsy?#xrKQ$5prk+9Fv}Zq#VK_ue1;y$n|?JAL~a4&nyQPh%Bb zU3PBQ&8eiMVwejb@bACl+i&aG(P1loV7u*2y0ZcJ;ZDg-%?|<6@qb#a-IqLV&e0FW zPA)E+E^itbYxt_zttj(XXXiKz>ALhLwZIrKD>>tPP>TISCmx9%MZDQhFlV5+3Ahfg!wD^A?lK#(u>i-Y_R)S^d9f+sD z#y{@;Eiw_U_APp{#>r)O#;lRfpAx6BEyAK0w7Tb{%}f_<+}MOE#ACCzU6^ipw`$}y z`_EpGGqL;2kAEmkyB+ImJ#EDBFV!xee&~Y;%V$314FAtjM9`x*@<;LRx8L!9`?uG+ z?5h85xj_1Ugfi=dmUSnSVt-({XZ5!u_x=CmTkQHL0$H}Aa1$TARSD9Mu%WkuLG2G= z)#Q6xjtd1Z97j!8H?xX!hhzW$cJ9KuG}SnIi?Tf(NK)p|Uw%_MtgJH6mS)iPz8qs^c+)qj*pJ2 zEba3n@7`8z5t-C+N?u@jFRsv1=LNaTa?VKt34oAQg4cotIsHb}`fj1#aY2#w#Jmxg zBjE^e-`fr!D*o@f>Z?Y@42*(5yJ9kdEnZFERGwj`qxqrwjT)Vp^_ag}3G0#>03WQN z)4&`HsJe(whNemZB$%=D3rmqLiuD@W!tZ^$`Ynmww4E@);eA)XVb?%`$$NWDC@_9MN$O4+ITLiS~f z-_dFkRsAfe&=?|toP!jKtNdI2bJ z!Aw!$QkO}DX4rRNode-_;=ezA=YaHv(@&i~%?Mo$p2cIpa77iAoo#esKC%FjJ-mCD z_(LkRG#O{6rxu?wk?{SnsWV zXU}>nDzYj|=EcpR24tQXVFXf|7#aMcf;5CILkPB`m$}0-scNnV7tR9w{%#90=xE30 zhmC{kwH$CU3uFY~!1-!zHpg5i;B&~q<${HSh@8RzS=1l2KMh(L2LmZ@z$FXq@-SLY zQPmCK{CVg@5Ez*{L`to^9V8Ye`_91q890_AcauRlaj~Jz7GV(ua^}?|7BRjAT7c%2 zBKalki133BXf`J4T&nRJcF%rNS1$-3V>$ztMN8iHrqoW6q4`Iy zy;L?ly61`o^MxDGipxAZ@Sf1xw7t&(H)XCL$(|j1v>PN;q#0A2gTJQ$By_Wl`$?8S z(YqCRXr+2L*@C^44E=KKs*wr7Z?0suZi9nU@++IOHV0)7#d;ljc3`dnLcBuJWnr%; ztbSJKkzHhK5=`qlHp?R6n{v)CNuHV?qYrbMyX1bH}mYbV})0LpOcT!r%T8Ht*+PB_?Q`QN(N=#uSyx`mki_#kiwk)H`Fg9}~H z3&(jZJLtY4QSq~tlX1~CT@FF(5E$z2CPmn=aZua+$ zTB3kM-XojDhHvf?wj=E4IS!inBh~+5+}PkWdfp*?+4+_~uc|!{F>sG#MbLfGt26v4 zpPvK3Ucn${kc?pZn4^>@g`yc7ffWF|1CmY^?J2jJ$M@mBhHxcjht<3*aU@0f%#Siz)<;2Af{gKQgD4dK>yI8L-DP1 z0Wo6GM|tUmAUI|Id&bqx?}t7FF|7Go)tiD;SQWJdHtZB&IxhM=>~qCfRg6K`@qNYC ztpE`$-}wmi3M%F54BrP2A0oYRmesI|S2>BXlUznN&*x!+=!2jm(%Q;_!D~EtO)qPm zOAx{Iz$}gl40E^hy|XJkJpAjH+5-sr^-6}SrV_#~L8euJaAZ6^4EjlBD1stDiUPnj zxUuhcE;4_^&*xH4e0kCiHu*2`{XY*Ly5DyCp~v?ZcT?G^ zy~MWc>xBX3ova)F^eVZquwQ|FWL0uvarBJ0En{Z}WcsKRgK1+FBD7(%B(B{xEp8kb zqyL&u!D55ONGfchx_C-sP!2UYr05nfkiWwPFi{Lgz~d_iHmmVLZDhO6`lk-dG=~2B zf8wHQyv_8mE53s)m}t|Z9NS){v6MBkGYK67mu$p=yF2gB(S>Fqj&Hs<+v?Vw`*fD3;&@%XxxMNWdoE`LvS|hCzMV`Pk$LNg79CO8XxxPMB0u2-$`&qb>+9x) z*Ee)n>R9|WCt=sIlCNKU5%)A{J9EyQ{W()&*I(b#w>J-B$bq}i%tiLky?-4LzVlu2 zSN_s#nUfAhz~$|naDc_hE(blKo$k4t`d?b}`CNOy1Ld3N&i98bPN@B4!E(ELF2R^ziST zKipfiYbOPP!2cRertlf6CO%&J-)}k8uYOJCGG>d4f=+HS2H;kQAq<-L1c6kfslN#; zX(BxCPmH@eM@(CVKW0H4to2cB2xJ*Zz0|tpdgzIAlClGeRt=`4k{Vwx^-&<)kNOYrx%#eI|(F4 z!LozlcR?c=L=&SmuC6Zn7Q}DbdU|?y%XL0bB3MCBCO%@?10dFI|HPvs-KihBUqjSZ zDxvnBP^s6r*2VdDg@W5h#ckEx?!KIAx1K1CfVRYjbkT{fO?6QN3?NBJmJ)Iv?SO=8 zk`Z?XZ-w?E5Vs8NNRyz}wQE=6wP=!DU0spuZKj$Lk+c2pU7s@`f0Oc53x@aLe$S!R zv)(>ApUDeWOJQ!(z`#IBHt|6g)R&Ql!#uX}Qx~A|M**z7X=Iwn+H7~Po|kd!mIi-| z)MUVbH-NK5j0Qb{aZI-v2BrQYoMEFTFXCxoVfmpGyEemlW#h=mG`PNz(M*mSVz0)B zCRlTgF;RJTB6sKib`awB)l3OV5Bw|4#*)s_@GGfOOt3NEArx5QOOU;2ON|wsicD*8 zTk-N}QyjT(AFBFqB@bxtu-OB0{`!3~`ikvq#IBTj06w$Rh53%>18%^ZpY*FVq?#9} z(`$=lU>fsw5;x( zA+(XT9Z+)^IJZH)za2;i_ns~j23(ulli7aWi7by}@{2XcO~ewDz4WNxV+IG_ZCjEK+lF`d%1>Qg|c3Ba6YaJHFG0`gQfbkW-P+IfyDk#z;8XvN##p zmfTk6_t+O_P6)8v+^a1BC)!Zmz8FB+j2bm=-}pxiC3Dt}pxWj&9?BTxd{rYE|Bi!S zl24wzh54lL=#;z`C=A&ZVp-Wl+t$3?XZD;q*T^3-r_d6mv#4E&h>Y#67S*G*wKdhg zY`Hb zqOUKVU>j4vNaU+89%>9tb#ZqOiHd3oqcdjSLH-Br@3a{c|8xI4_78X&be~Gp;qY`N znkAH@4N+LZrP;`o&A=SCU>X@2aU9bq=~&(!x9rmGTLgnhWX1}5qANFUG~^+@Sm^BR ze5Mfj(P}CYsaM4~i971^wYt9)N7Nt4kq6!E-+j|+BBul^wA&V9k%nSh+ytScg-VAo z)cy6^vWjv44`F8>)^pyz{cmK+n(Rgt6%|8vSxWX~Ym-8ht&E*}qE%xlOGuG5N>WKO zVk|9av8Pg5DGe z(56Tz9n@z;Wy@R(jT!NxpfpM4>pjBf7umw3HOuPPuMbSdMAh>BZGY{i{JU^E!ZLV1 z*0l?dpO{oayp{F=ZN{+0U3RY*fRD|mOVx*C<})h*Cv2p7^Vzn#2T6_+dOX!Lt);nJ zD~lPqAr#S8ipXqo3BwbHa>#WDk|UPM$~}z}ACD`y+%zGGjvvnv<)7A24pBR5;FvS| z|3)m0cXu{o_ZoHMxh1)NR5_S4U8RR5ig}{^0*Z$RG4}QmYG0<3v?)uNNtH7}4Mj!y zN#Dr3&X1i6Dd^e1Gy3d0MBz-$>Um=~pR0x29T~Day&%$L{6maNL3dGWmU!O8?Y@q2_^qC^E zm*PJyZ41v>+)}YXWjSm&!UdWsV&W-Fd&~_nd{^BuZZvd&%!p)Xx8()#JI${wYTdfE zN6ovzJmEA_oQ#Pw+VeFt+{8_jA3-@3xc4a4hB$eH$0U@^NpSPsy}Q-s&6{s|8r3}_ zgvHt31xw2EG|u57AiQjVp#b*ft@MoU-TGsB(Rjgcz~Z4a@9`hD^ph4SK})hi9>eTq z)tuIb4v(XnXsXee?Q^xyzu%OPXKdGBHBMwZGnG6Kc^0}lxqsq(23nB`IpL;gX#RLa zSOr@L>Z$VuK-0iJEs8(27Ly!#RL}Vk+^ybuTkV6hoa2VOe+{4z7Yk#qu;5a%rt#bR zvVpwyq46_jzg+DccYvRSq<4XQ2b6=aa%1xMjToYt&>yXz?EjMr9e|Qae{oD23zUPH zm{Ek7RLT)?|!RaGohdMR&#H`vDXS7HD;3m=6mlu_l5| zDj*?GJg?HuexGgh2{a3^-ri`Dl823p%1*u52V4g4U%@dsiw0XvR&a&nFs*t2bUy=@ zdxrhKtk?}RMQzoW#l>T7Y=ZjjGYz|j_aTiw=IIW+S|X0NsG(5pctu5(wja6v-_ILJ zQkC$1g_1#_=_Xz_Sx9D-w*72Jp`jNVlP zcf`Bf&}ZmH>pXk<)ck3usXk@X5#q_Hs&H!PBdE0W$~LHQFUN$LAIn%=*x4e&(#F=- z!O3a=i^tqs*vi;>*Bi2uaU$A9)IAVcJua4TWW@}hVnZM)6q4Dh5#>u4$JuhAHpa%T z^dB{H$GATL+<<;`8M3GY$}EK=zMr{g@<=EEhc@dl*HC*pD&kcmS&|4Qi&IL6$Jy>U zd{~_e%j5PZUVT8oBSYpeY5^-r1K_G@oIs}N9OBw6rBUr&<4hosAPqF49zH|pf8e|} zWT$q7^CSEuB{T93oMM-)dS#cb7hxQLYudRvMW5W`UwQXM+f)S6 z0$;up^E)c`O~aR~?hPX_cyx>Xqy+H~h!86tBOWPSvKrE%|}jfFCTgt>x?RGtAB zl4ryb^V_tk!ONE~kwX*|6^({(mbFej?GuJGEg;+?@!MhEyxp%ZnJ;*In=Zaq_3%7Q zFMQVJSAP8Hz>h)zRX^FecXj%~W5+bbx(Fyn)NaH1Bdv=ErR>@vNFp0bS#O^k|6Z0q za5QfQ>bECo3tAwPIyyL*09+j#q!hXyelMr= zt=T*8_D>jWqpEL>W_<@1Z=p)-4|btNJ_^LRA*jz1E4{Y7Z%2M0hD|;kL$N5Os94Mz zBJAl-a4zz{D193*h?jqMV7(W~!lmM6bDD1E-tL$xOa+^v(oK{ekS%tGuB<2*^i zCP}B-jlDw?gklCgwRqpmUA%Y#GvJ7`>q{^Df;ruu0l+^|P4U<1Yj)_~y*s6$th@Rf z(J@`ViMK#t_8lnx!l-D9x&5L-!PRIL14{C9P!*+u8nOx=wbCvB^x8rsxG(??&$<$@ zfn`^gx+}?y06a)f!UtoCaw$!pjK`)6Zu}aOnChzX?gSmNGQG9rGw27PzE$dl4jnoy zxko(I>M++0vJiYRIy7S%$I5bj!+-G_Ql1sod>+lnxTHyrSrxk-#Bq|YL7-{a#$*}b zqL7fuNRBN`js;3*=6*IuM7+L`SKG3TTi`&%IGOFt_=Oh%l}e+CjEyU!2&}+9V|aWw zfb~>*lUDrNv}se``wa{MPYwDVm=9}-9ZCzrjA(rUuQJUs@qHh(;Rp-+h%Qz}B$y22 zqa4=TIoqdf6oaoq?bFsd1Pv$SX@<1`1c_nbK&XLGAc^?d!`)VpndCXbPZ$gsaGbXa zuQmcQCJeW%CnJ36mC`gSdiRbOzhsD8)fw{z>G-hn@)x zn-RUBMHzS598)ny<>!2tJowY~hRLw|*OFH4Yh=&~kf^u-&xN$OQ2x=)R zEsZ+VL|6a}bmY-c9Z!WKY(3lvdXWVqs*K5mg=xdSP|l=8 zILu$L;0J)Cc9NjOYnKJ5reRdwGo1C}_L_mEb zwvlMalAHKEql3VUYfWo`(4q8@v2)3zOkKKttJ=-mU5$&i+$Kt1Mhx3vkb@#u|Cl z0zMVg*=A0pl0kH5!2d5L%cEP{>*rC$-H)+1`d?YzU8)0@FK;^{BVI%=h7wWK>9PwN z@>6EHp#xAY*bL48d*`|+W)*nC-sJ+Z^lg}=A?}?tK&e;sOK7vyH;(vK?>~3c=u+1~ zxP!1(hqg)tJ6ptc41P-h!aEhHBflLxc37%bZ#`$*8?EIP*GfMb6lBFS-z$x`u6RXh zAM*SR44M>?P|}5+qo@h#@%XJy92a_w*dkM5;!48R6w#-gGwDDT*a8zlq2|X-8vKJO zfB~jgL)#VBSTeMfb9(k|U`#-b`~kiV8#hjHoT*NF1}4q9;v$xNV)%l>X#rnQmQ^EJ zFDNb!Q~sTCFKItx71iOFUslsYwJCLuvz09XXpj$$*uQ`OZjcASRBj)*Q9Ll|RhF}X zLni)-jYF{hK$9<6RjdHt9noqP!<{q0Y(hh4*LMV6`hkXf^l3~;9mLe+2j`KrJa82{D-A6SIz^3;vlx zQ4oZuMP-Y+@o~k^mv5tL{-0eFI-7GohOT*~N-_xJ^L_jBWV!T%h;^|P+C;>jbFNff z$b0FTDqjku%$3B%SPh(pH8~Bz`S%X(2MhHWN=mW3QrV-enN0iPTg4fuSMWnw3uGd3 zyc(FOS~uuYr0b>JAxV_@ybm4Rx%v82R2&~ad7|$)GEqmm-h_1#F!EKzn}TrzR(ZZt z*8c>ndd_y~p&HpjN+rYCaJr=NVNae#C6Nz6OIk81lsfI*BO+5nWOB z>Ghs|7$;BbNU>itwx;dR(9*|qTh~k8K~q2ykBHndV@uU2ZjyqD9UZUp=m`EbIdCibS;C?H~5*AFK)D=Qshru$0UOJmU)hjdtP+0)N2dD zrF0ay;Op1CNs|!fL3pe(K?l`!Ed6?$Li-}9R&klKz8FLJSAg4~<)ttN7+BhtGZ+yh zMgIYM0{xKXGgq&aym7f~kSMssZ6XX6p{nOn2~USHssu$$r6!-aa1-KD2-`G zrJi_cDCE8qb8-6E0A9f@_Os=B>y0BN^$|;`PbXf9?r!mh0RD|R6d>|1Vx!N7qs5p- zQ4}-+t%L#%Ksnv5pG;sSGX%hBqGG2VczYYsS64{_*$1SrCw@l7D^! zjmx`#eXj5GT87SHkHr*Ak}hLtVl;R4>T|p0?~DZH3D*wZ7ys5`kJ-j|u3a;USqCdH z9;hHAgbXuCkXXOzI;2Jhe7k~8RLi`Jh+hD{LN2BlD#2JyXkbu&Jp-@QK>hHi`;0t2 z0CS`zVF8#;)=TTR@XiD}fQ8CKXYfJz{(&_YplpaaYO|jtC3(STWIpR8V?N>#dXXlQ>PyEIC0IO*vsA5OuCthu(xL~9@Bu)tYr3pJKx^f@v$DqH#j zLPFE~hq9JUV61lU2z|wL<1NnLNg4*cP~k_R1ydq>IxSx8Ez6aVU(0~dQf zcMD&-aVpyu+&eVWsgW#{vw40F@Dh@-nE6^&5c-rytu@V)A3h8sYbpr(pU19lpsXx| zN0NuYMJrdXoJZi7d8VAdxSxL#ArM%!kl9Jt`~hY8i8}`_BazCXCav?!o5zJt_aisN zGFn8M6Sr3E0!~m}3!le0u4hKkzv*ETs!v(Ar=EsR!eKGP-~h;!DDlogJT{s@w3IQ%oWU;PB9>;M-Gqsy|m~poHAIL=8w{P#j zlprxy6P_YAm6$#`A}^cQCCjy$-{&9{6Nt+<|L)I&xj|Dy9v-9CFmPQN0)uTnG;I-+ z?}0fn{JyCKdBuY%w4W=!EiF9VC*%gCugr3LhOaP#vmErkeo4eFqJC z354MJZK0M?!n3Kl?5rVFJGoV`ZFeitUdntDu1eFUjvQN-i-}}q!oz7ldNlvf|0%z2 zP?htGYTd72H!G+#LcU#rh+z*+^CT>eUA}&bSak=bBRwK<5^8bPG-rQpC z$k{jg>5|G%h?Jqa`j~kHF0Qh6!nCc?VXpCNz7sv6*1uh^`BuG}Lu&$X1ssac!i+ij z(7OU+C@1>in#4NAh}o2Gw#B0AhECoJ+c8uI_f0Iefz<04nVKe$ai zt*h3)W5@jk`S)0q3Cg7)k>ynM27K=^WfAu68^-8#A8#@XqUdRRPS)(oZsC=4c9uKc zKdOWZ{TP6rPo4o&FR-5Y=K~fZ+w)o)c`eLUtU^*8uBrzHQ4`;3w8e@28=WxJXJO~0s`^0T1Fl%HYSq@&v!b#ydH#4Xh%}F+zNb%WN=waNy|$x5l3i1L zTiQpB1hZmFvC?*2J@hB_#d@>MjTJ?J$&mZ}Sto(uISO7D*itN1Hl=HDnWGhVq-mKpHeIjTg#kdd*^xD#bn*gQu^( z#QwioD}J|E(1iY&6CULfnJ&3Wp_DvqO0M^m{K#IqvY1Z6IM*-B7wnVfAdHYiOHQ?S z_MMGYgN00>^CpW<6;dUkd;`dbDW{W`xF2fax7hVAOGsY8pg>Z@F1*|6&Ye4jcb*p% z96cAzI+qbZEz>V~SSV51E05jQE{xo#;#Whh%_P>w%#lz|10F5$4WNu^vCG96LRALl zDA7i9M$2J&nHUlCMqfMvausEmnomLL&>6rj>@h+CTN949>$ zrY`5vtb%4B?XLE&5DRYf)yO8qc#Ao<2TRHT$O{;-`Qb0jRnsxdBj$03=CLLOCK@jM zc;35;5)5!z zr6v2IbQXV#LcN}9)Pf?yl~fFTNiHcS$Gf#kzB2WMu*FDIQxg*rjhp49`V#~IQV}{l z4Y5|HHuE3e2bra*>RL)~KFzS3tCRO3>8Ou5so`8)sop-0+r}SUML?B>jN*O;N-*fx zF9dAKGSUM)18v(@zlMp0mfnW`hj~h}4T0irJmeSNQ3uW2PW^u}#^1oDb}}Vb=8yHO zueb@`_77dxt)nseI}c}8HwA!#rEUMu_495l|JnC>FylD&C!zDfu?G_11tK(bJTvj=0Jdm(SxS(?Rtu`vlV zpJQY5aB$x4|6m8LVBUKq(%%wttb*ohk9*}vjby1Ybtf(sA;uf(^&%(SwKbb_X^k%e zM2ER^jfql4oo7T?l3zN55JghKny1Y9T}|93laHkb2V=65H8U&#x_bZE=yf$;qUV&b zA+_Ev;%jjNSAOoic~|-Cu;D$G=gKu`uAmt^dDU-xH=6{vp^Wx$i@>Di z@#_DRAqeH_ z5Aj!{$tI7$3l$6H{CSr&N{8*7F(r|=k%$9yYFZU8K^IjZR-yDx04zJk609nVFeMGn>*T z^RoLaQ&BU5bQ~5g97o}+rv3zlEX!+@jYV>bLDqqHlYC;dK?f4bk!i||Bs8~l_i6o` zo@pNXt8!GvV$906u06%|@6fU1O5hhLCoH!MghY#1T~+JzZ$pSX3eh;IoYEfK-fd44 zhkUT}F-l$8Vsh1hqC}KJY~k{S!pb5Zm7QJdNQ$D|>`o(&8w1@NzdT<1zel1~b;A)V z%(Tl!1Znv)`63byoTTs3{!VmxPu~6^TtOFHU3{=Ml}p#e81+&>jUZE4Fa`*W__hu> zIWo7bHMvrRC}HbuDCa&!3iq1`A+Zz{*aR-pI@D?h-cNVC*!8oa=~5=9@oVrQ^m69K z;!URe5fiLTee-zIR;zvnLvrjXs+p=&m(_l{|xOTA!v!ZzJBDOUs=?q!PO!t zY2U5X+32IsS}ZQK_PUDCpTFnW{X|?;o$lyX+T1N&`clLWCoBtyvnhFbUAzEEVWtgY zsp8PeR@qcyh2mCzB|nqHA;#MZ+^3(z_(?An&On6E5L4(G$A_7(ggn;v@ekl^mkvBl z{1Fi&-s%#RsOt$83ut!{+IkkLEpZw|lB0W?26mA~xTLT9_xon7ZQktD1%~8 z-)7`~y`OKK`nOrmGZtkodbTlDYmGWAbdRAi2|K&T1@8m%$EUuosm!kYyohwnoVZhd z!U$oL(HV#n90jmm*>cs^rkG^a@Vqo}LN3Jaj6Og}{P+=YR=wbl+q zTYLysRD1YPPA^-Pj&;tebf1;)bvU7{kHGK9^?4V*e!HJrnPAjvA5(N~`K?y0BjmD+<7yFl86f?{0l5Yx9sd-+BiRLGK|v z=S!k5bu8-rQS{XVoJaAa#040hLoUPIWy_8%EKLMiAjzf>naEof<4JnevDfBJ6hA=- zUosrjj_&4`U5_lRX>0#}{1v}UTbLuU$|6`bayXrl-k?#V`aR3m*3D-P7nPt=fS$Vw zuFr`4nnB!Quizh_EAN3O9MV=&hS{nsma3EX$E;L;MpFgeqyBxWTiJk2(~JAxz~S*w zF=MyNRTgp-23q!2VBW^Y$(dm3@{b=2*>~Yn`NJ3t4o-jypWDKHs)^5W_yASZ`=AR1 za}M4Y;D*#Xbvm%P%KqOFqcCwwJqKUL^^3Rx%>3vEE~W&z$H937B9l)DghWPY+~t zK$NCQ2uEz-xSf~JIPTxI-0-U{Y+J+R=03>1T4-dPKXqx)9<=p77j6aloqjii*M`fs z>6b3q&I94J8qTGt;e%=$&Y7V-p`OFdJvq74>C;n^iZYu=ES8VOHpFys6H+jLB!42} z>_S2?9JM&W5xyG1Nz1`O0e6onc?BP6p#0F)r{5ny*A;nX@5ZWByoSvvO4P2fSfa{q z`ShP2-Q^xiv16bcfZ$cfMGb6?aZKC0rZlz~TI!)hdCiQ9YL;~s`7QRI5rw#FYt`02Cwd>x%LBeQ9KmuK)R50# zy_&#PK|C9vHoPjF4Cgt+%9EOdCo&fRk9&kvY!ox&&_=xy1I4E8l#`I~LjK@M8CU0K z+zVkHX|_J!|Fzy$dIrCa4ZQNnfFX(V%bqn6q6nxaZR3c3m4HF^>~`mrz-g3I30Ur* zLF1EjiORGH)`LD|&&pC;GtP(~Y!-Xa?!b|(!^rAX#pSi74U_mNF1)-$)sRGi&eH&j zx_ROB*EstMX+_g0)fr;lUX@hP1p$EW9E;;Ez+7-6(n#Y%2$5`KW0Tx5+&tnHtueaa z1snlsqd>8RFTP9rO)NQRuKHnb!=awXwH9=lGlT^mqA$SvLS}er*(t8L*I27;r;OGe z>#Ay^br)HeWK5TuZ>uN%`>dL{?H|z8XA{m4RNxp>rt@ttLX(rE#jz4Foa*ZLe%tru z&x4oVUX#5@tVam67nynEZxknFl(6-Uorb-B`*wS5YcbqE~}%L;X3<+tm(tj zQthjnWzsA9)wN#yfJJC(X+ecQeoDDTog}-jft8QYa7EtjoqXX7K@0Lp$)Uj-o~P28 z^XL6O=T*U@+LTZgA0QbaK%T^A_6r*QMzDS?+b`%*u+iy2&~fX z-^acxcha9;GROgnZ$T0k2TQztz;5e5AX1Pzh0-p|`SpA|q7ZE%__oxcCzf1Z=;$cz z1hAY%WNwE}o$5(%VEc|8>?!!R{$TArn&8FTK7kMFiME@z3lM7r3aCkc#vL5Eq@mY^ z3xlD}BSt3DNtQLx3f|@S6QjmynrFc$%gQd8IOd8JJ{R}qUs+e*7y^*1V3Eo>9+^{( z)CU#jZn{&VjiBfg#|5C8wca=xd*d_fTQ#sS4bAAg=G8Q4%XMsorcy5CMXgLlkn97F z;2EXz$VKu_AO2fN+}K;PP)vAsAE%;|QxDFwu&@w1fg#wxxHa=>0#Wk3#Cent|RY|x=9EQG(>JCAZXAnt1&s=4KEmRAtKlLUx3h`w|seB^o3sk zW@gG`_AOM3<|NMF*v~)6MNEi!6x^3@TlfQ!`BU{y>i3F7@_?zn|WoHkZ{A7#a zuwiVf{hutj#0%}fDQRr;+hjI@FEVk@z>QUpIl&u(rUjn9mPrt)LX1Kf?LT;M+M`Du zSqKmonOVY1sPVL7!rR}61Ju7M~?_3TC%fv7jkeOixA!5Y+ed=A$i{7 zYvX8{{BKql^t+qP`kl2<}wCy@J#M#&HVbU65lIr;aJla-CvsrL}` zR~hD2Y-fQy(JG66xRhB*GALlU=A>GO_9kvac^&XHfiK=w>^l1slACamNHhXnXWrat z6bz#9q?YAMNbCyBZs-33M-g=FH|VN?BxKPO?TJ(s-%(~(>QwxouzOr2)(z7e+aQVu zDjWj3XaWkI}t=X`4LrPaVTNIv%uT6$pBQ-nOEbtLB@mbPZS zDJbTVu5cZeoo+O;ikjDJ%d{SC2SUs*(D$-X8Pajvg*QPHW?xF2#HHp3i+j(_BcYJ9 zg6!Z{r0XxG)yTmYl?=R~yQgs;Y3E4)jDDIV4{3~faj!DzzoGaFxYDTGzYjS@W&Q@9 zaK{cEpgCHV#qu6v=~WS)sppDmCp*RM7y~?9fo96^_xE>*OMIzL=zhvKCqo6(BNw~} zb2bfSg@8)pkI=!ep!qKh+*-=9@j zUg;I^1^+<(5q`6iiCc5j)c^|iY;C6AqGmPsdtJ{#3`KJe0oE5W+=L0kM)I0$bGCd| zQ3CurWf^3M0vhmJ-f(p9UcGGCHUy#*^&i}*Sm7cI+_ey`_AnAGgj5fzM()1>vD+qa z$R3e*!K}9OV*cemXFR_4f4V9@5te#%lTTWP|AO8})#CeauxNx=u6a9j3Z~fUHR-bB zr(>&X;x^1Fr;^>RN635HE?4LtQYLPJ`;h%6;PwO7b`z%wg+vesZZ|xNR2|_=e}ZGD zLBEN}~PjIi~Dxw~7Dg8nx&2@upXURb)d4|1;2$0gm-| zE8{0Q?dq=i_57x+MbBQmpB!x+{5b-;K03Ln>y&p5>Hrf`mLF5!qDQ+1kE| zGkR9D)EqikB?&cE5Kh39KgVe~NF0gP+Z(8B(NH9*d%y?WscU~T(prQitJLrNqKtG> z9z3v&(AuZ^Y*9Sk7|={w@F?BJZtY8Ex0z?_KKc(xSqqQT*3q3r(oIz~7qUf2S6=%r zf<1m4t)KS)R`z{Z#P?EBInn(S?LO#E7uPmXYp`J$_Dtd+l;RY|)9^;J^MAg`-DuIL z=(FKvHM~prLwMC%p^?9*bd`&>}Kt9v7$# zd13*ISBNXwnKMWEY30^xS?;kNx^!t_OeKA^u6?-CSsu#|KWuIyoz!lWk5nq6ECI9w z&=-OquzHeOPNxPU@7mL+hqz&^s`4E$vk2wJ*wBnKe`~xm=Qr0ZTn!N_Qy8GA*A#VI zBj=7FYLIX%XtIi&g7aIr@t5!4zA3;ilLOtu!#LOm_&(A|2L>#pbER^AS1;VT&3C-v z$?nqgis*@FI<>C>Dh$5NNd_HZCDWch9d7lKGI|j@7KDl)Q4RBxw}yu=@(xbajnFtz zgnyz?&C+gA%j9A8gO%zA=QAsQeIUH#M+8G{Mt1uTEmn0YvzJB=i?`@|1bmog!YbB# zz^%`xKkR)mtm$TqN zF<|C+pL7I0+E10wV45rqX_g7+t%j1>E>D(DD%dp*o!6!vJ0>4md1>0)L`{u`GTF*) z+;H(C1>Dnlh+kDUClag1@=6*07XLxD50e{6uLhFI^u`lV1|ft2sY8*aq9Y?pTA&vr zA<%niLbd#oTn;;@=M$!-(NOkK$WlI#Eqhh}Qft=eFqGX!5F76Dc^|- zfd7{~JJ_m0lXFxHS5pn8F?2MOQ_~q@Fw3SF4=@f`ssPDjm3osH` z1Hp6GI}7_!mZ|OGs(|Jh1vw7D5JP~RA+sCAf{I^1ds)6ZH8qtYSRFEie>;|82b$VS z4v%+tFHQ=)S6byD*8)4k95(igu}*`P~MHG~#V_nrix||5L54pETu+?Z3lG z;duXrN(R(Kyw3>$<4>LJ*@eTvc&78Pt;h9J-MXliQf>1rL`o~|Q#kk4pg@k@zLz!M z2D^O!D%PmVG83(kDNirtu-FrAf@?z_3;`%!rnFgTACHT+7Qtu3mR~hW;B_vl+g@rr>VkSnV6uQW{Y;Jr!b&UdgFj@~6`CN~`$B#gfI?u3@ypdH6T~qS z^eSR2*npc{?JJJguV1fQ+ctR(NmeAOT>Nw4U16SO{tEPr2G5@_?yotzp)4jMBn3gKGKD{vqic!U+Ip<9 zX7WBv;8X70*-qS%Y2z`=IXQcG1^pW&)b1freTjFes~rKuZ=Rr)(;!JLv}V=J(2ThZ z^=;Uo#PQ;1HEh&q%Ygif#1zr16TM}ofht}p>FLwXUWn}}ijh?-JLp|RblQUW24JGd z{LyvVvPE7rm%@x6Ash9Gci%$Xv13qD&=jG+zo&CoWCJ{7zO`Ro?-FT!9zA!jO}E}) z1EpO(rgl-Vj8nV^6mS&23CrlQrV|<=8GiJ>sq3b$cuP}?NfOyggGh2{+pbr0=Pg@y z4<>`1;q{EYv$Jv3Bbdrml4;XlPTlj_iLvorL8z)`J`>E_mp0Q|GyFG-pPJ*lFBJcWky5(%` z$OmU|zo43x32G4Nb>bEbdk>otCBb~{(Ht72J=bxT0d5?BoLBpfYC9FaQX!Atd97K_ zeWJ8c-M7}jUjD!zh}*}&*Sg)I&?c@+f* zC4*(u6>>LGAtlq}Ym!7`4!U4_~A%>) zf>WWfkjmgXfRsht(?zfWZG@Ohm$f4^u62h$0TAwnlLSfo(U{|P6%({1c42d}8)K;X zWmChvz0x@ ztRnmVSYrwT69qP(dNfG~k|Ug#rE+92NRF5gLA#zRR%S?4{L~rq_(yUVd)mnz7D;~|??PLdlJY*aun*UneLJ6VKJgvJ zK`X;KPDF8Bj?|82rKOXYkm2&8!&1Tp$fab*)~)6yL41f%f^WGWDOr{sHi0==mzL0#ggWjUkm87Z3eKt-R zAh)p=Ydeq$4$}{Jcs@4EYR{29KJGt&lH;CM;!G2_-HjPbrz1{@6dAc;f*v%SkQb8w(Royj}$e zR|#qQGn~&f`?zQDdW^%vdFp7)>)l8C5x?BtG|YV7{Q1e;AW1~T;?c;pfBVghGLnC2 zDhf3?=-=|L>_=v!SAs1;FoP9Mjh$k2o}YP0NBahhol+HMcSTD}I2|&RLlC1|FI`=f zsXNitku$H+U%Xmv@*U(Z+Wj3#@;y;6j-G*i!_kMVx9dlO^5fPbF zCjao_nI4r*aYq-alN1!l+eJzbxv#_UAY`hnQfA`2`mWxwEGA%x7K39T^(IewCTsAR8JmY5j%mATA!B^Uix;fot`UDsutT7o08B?1jwN!D6`yh=l ztA(qyvYoBQ=O=DS$j&w&c09DK=>G3dJ$EE{+9TpGF5!Ok$1|8u^yvK3Ur5TgV(;I- zZ&M=fg@orlT>r0?Wig$Zt@SuGP?`+jYKC!T3=;rT8kdOE5T+no`DMsQ=+Rxo8!`x; z>Ax-Jt;wC^cm$u<_eO_cc+oBwD=>USaaaiGW zWPK)gyDuvt!u0Y>dY_(p*sy^y94nRoj=1+AIVDW+7DBo6A(cd}P23!XZ8IfcdBc-+ zP87(vZFxg|%iso|Z#)OHNIh!#+!1j`i(a_T`ga*);^Ub(AVT9y^h_HY=`S+aB{CuB zxK44l(HrN3Fw=k-XJTWgM+VaryAKQi7>=QZkWl<3(T>?N82~djvkKYh-QG|DFaYSv z`p;X5xU#Mfd4iY-fLo^IJcekQ@Wn%4fTd->%uUykmjJCspBl7}Nv?$yw&H2WAwpU> zju(sfnqS*fUs3qOvec`ZS8ros{&ny@ov>99qZCr@%R11yqXr?`w^Pt4Gd=fpcW`j9 z`9P@yC(g+-&qTj~XVG^`Eh*Y4Bu4huOB48uwemZB4mw?on`Ot=`2U^IxIMZ7Hv#X%Xl(LbRj zRg>e%Gn)Dw9aRG^Y<+4lX{UVNWZp2JL1pdICdIt9MOY0etAEl2Dx*1zt z_!o^2ghLZl41FH*wXqNAKoX$BH=O7<*uX%D4r4Rzz5wJfbKE^9(Ra#qeMe_W+ci8YjGG0YeoJf#s)iUs58;_>twIH}il#(hLJwRWNfcl`#lL zVl@nC>VpSu2wkFG|9dp%fBvp_{#KJqQO3pDj6hDdg6r-4Nz+01u|U6pIL?XE`O)Ry z`@m3~4EBT@rPCq^%Jqw_%l-LMiWpG?WM@vFIp#mX~j8@Qv)GK*yH>xC2rXX0o z-pLERQTpuheZ+ZSMAeQRZyukxM@nht+>-P2?VZX_?u5HPgRHgqciuOmFCS(Oz2ChQ zEP+f@mWEPbhpL*w7mj}}X zI!mnvElI)IXP?Cj|EOJ?Z!ZD z%GJZG%Qc>H8Dg+8GxwUCM-l zgzS?-Rwm&H6@CLzeC^`pD;e~jl(=t27LQHj^}JZQG!P%DhI63P=+246hKP6TI}B(& zAB)MKpiHFg504;vWjMf@6il3xYeXSZ{aNNIsCKuH3;khRcvA~*y%hw=T$7a2mL%{5}4;EBf|xr z*$YH;Kub(oGTCcA$6V?YMdF=GE$_efJrtedFEsnGrD2pYf&+Watf2KHV?Tf|A9S#A z@6z+Y&aeMfJ!2xDjAqkFR+N722GYE7IW~T`k_-auIEr#Bl{tx z8BT^LDa>P{f^E42Gm>&qhRB*O1wTM);EAUhsFuZ7D$Sc~!}iD%xazzxSRWb%n2oL2 z3d&YY1W(reQNv1)HvepQfpPYS5G!Y@JxpyL*oQdMsJ6Xo)8@@5uNXOd!AZ2G9)mZW zD9a!=lRWBUp(B=Rif>%@mEIoGUgGL8eB1%6aLZ>DT5GmOrt`oyq7z5vHVTYYG^;g3 z+onx#0`^;}tBY)qDFKB~+n%mfA2g}fWJfYZy8-$-hBmu~Z(2f~*8!h@8MYym61N5| z&od+K20DkSK+rAZJ{X$?-lZU6?*8(8qhXo(!&Y@=ecKv7Wk(bleHiqB|-q6pyXXwq+1?(t!uWKt*=$(P2*z0ns0}%4>ey z-tULNJfvN^8{!8{2fz<$5AqmI8hr{dK@;Ogc9_$6w`~Y9B2Kn6JSMGN{4P;;Ivd|{ zeQz{+EoLCMO0HAniIO?;R5jp<(2CtakDfiz#=kLHz?)O?$V2Xz)(KOlz#8sh!{m+E znJ)5X={W0n=kYFNBsxR&1b$H>o59i;vh)K|7RIA)Ldds%wi>V?JG2yhloZZ9JWK1& zoeymv|LcFM*;w~LuONI%_$_svCZ$Ay0~pi^=23rOTMkYeZT}AbgUCn$hB>T zC0JNvYvt<1$;&WwVZUT$p&drI|&QJ7ACm$*n=bYMaer%FGv_KIV z4}edX#L{@oNIZ+1}CL(Wi^?q-yKMnek~p z8`X_GI{T>1sryr}yD?!iJF%>Z`dMuAFKzMX387E2zS+^yDxNBj$+GRO7T*~9vS zg^CkUIbdhNWUOyq#VdxurMUz`m*GU}BAm;`iiU|R;-*z(tL$jI_CD*H4;?Z@l%h!6 zM35#Eci3??>pWx^q4d_i%5Ib6J?oCZ%j}1|Z9&RDI^06c4#2Zqz zU48H%fap(@YAM?1QYJ0MVuDk312}W`3saJJ$>EBVR#EN13uZahVrL}w7d{c=zD$J~ zPpYfufK!3WjuDs&UR@`+p0~lRn|RhPuXe`9&6_`>xS`>HbYlb&pi7wzWC&?M94#sA zLj;y@KBgwCt{|zl+D7A-=#yAidyuZvYj+PIW=AA43hH=q3d$)*E&bu;-Ct-LMoc^A zy(*E20`XJ~|1D;aDqEK3Cma?zdd*6bSOpHmyD0oba1Mx7Q;)%r_32**(jx_5vpaSt zxlA<0(r}hJ+=%(QKi~HWdx_Jm_tK*V7S_geN#gQRxX7HcF_5`5qW6sh14{=#xt-|y zs|HrnF_lC0txwGb$tkFb>l02HO%)#uv=}ZfE?TZ#DW>}O|8suuK2^F=^!c~I@n?QH zjY%eg2ECmZ1!bdYdS*JuiG9N0!Glo`iI{;nek~An}%?>TK!49*Lz`K02rfB$}6r4ab`3J^-0cV**|9*<=CwHdFS zzoDfz58SM7_L`l#_?{}JdFOp~Ml}z7!e-nb=%Lqp zd}wHBVA8>D+a{gbx>!d?ha;`(6EUY+`+48Pux&yMDo}xR=y9rDSJD?~LxAASD=r3{ zxaY{9_)QxbUuT}?Tc0Kc|1p569o8(?&VGxSa(R!;z$j!kME)xzFb5C;`aM9jzm=~UbK>0|Jr6j}gJVEs1VS+BeL!7! z{g5MB-5MF5u~@u5DN!6Ls>G0l0m&7Zl1aHjbtYz<@Dq!W-C&6y0I=7(tR1XccMf8h zs3);NB)b$J6W6#=rco6;B9xE?xnB71sjOaIF+Uoit2K41Ou7)~ZyF9BKD;xHB?SR1 z;BDMm7~#atCGIDta#RL#UcQ8K(31 zlcR)oWV+?L^x$}QvNDf+y@2OIqx0pq${xv{n|AJ;>hFl&ZqH}W?LU>xRe3ZSe&bcB zbaCYkFqk*V`0bPGMFoYcFD1_4!c329-N*0(iyyQ*ThqNgGSc0Y+$n7z@v>gTSi!9- z-kZW;-d357zBhK`07-%+F@ z(|`yR%6Jb(TU#*ejO;|rh@nVdqBs`;awY2Lji^2oB697_1qJjeZZ z6VLoVA9&RIU-DZs((~Qx$C<)Mt7)4i8-8I5Jk78SydglJHzX$1Tr)<|HSzyvNr6^w z_72{33+O0?!)ak=QzLBx;Ah5^NH>qFnE`sD z75(uiLGL=g24qg4P@DZbOs|O0B!#0W3H$oc==Br-tR#)eBW)9XdBrJB{yMOKKhWr1u0XsKWJe@`Y#Qs8<8uzNwVZHt{o%t71TA)hy?uL!OG?eY2uJ;I7{9OI zv;*Ajizb7%(^ZyClcK~0RUCz)EgxKlhdAVJY_{N1dQ{eH+XFhXe(0V%nw6_GO5n>a z5Md5a;okn#?t%tKHoiIS3+-{I)qa;&(Bbni?!6o z>zsbD-0UkQ2M!!a{c1i;CRO7!XV;_!Sa;3b5*QfxQTsa&UF4)xclU?gR~XobPyqhI z!$rrD4li~S(jBFU*bVct$3EWR+lDx(f>$2$ekk&NuO7nt(dd}lWu%)A%`YmcFi4!6 zE!JTXH!5PJHEO}IOGgqoje+evu(p^mJ=@p|=&y!|n<}dHDTk9$6FS0%&vLxf_r&|} z`UY^%CorpIlCtM9jaJZa&4hm4dnk80`tMi38{6o1j ziK7OLJ zsR>?l%0o6R)T}L~tys>2VxbB0q?btrgb8H+`rH3jb*o0`Os?mE7Q(E2vYRK@yNOm8 z)fz1M1NxLQA=P^MkW-jv_@4e@u33V~Qcojx@F<*qQLT9jURTgZzt-Mris)E4CMW1_ zA{P8}aYi>`X~=#SOQH&m91vM5kS%DfS3d&d!$F{ufZ$jc#N;ERH#g_}W1oWfTkPO? zJX5}iua-L{G_ zGVDO7iJu4k)Qd2meLy+6)`=8tNn*VE#Y>ELpc;RBa{k?t-^OcMn72)4!s;4tdPC3( z8*V&D*H^6tv+aZF#%pM4jZtw;=>ggoXGmw~PD`R?2p=OMfH;MXN4XmXnCR9-h%1e5 zaL{yk8??q0z^~e;_wjH8tV%DF0VNwQ!hG-=7C>UiD-Cs88|LR;60YuoJ?<|afOos5 z^|n#&08NvNevk7EDRljouAz`#{T-1n>o$p8h~DS~Th|7zt%0;G{5^4Ip~^S607 zBbh&PgMKOida$tJgf|tB7NSoA5zNIAjwjOE?U~3$d2I?I_O+7_wE+pTJ|jxG34!wP z*+!o?k--EhDFj9FpOn=IU`6^%nJ*}gcNa#ffbZ%w+lK$?*R`?N*7$t$krrpLcXpsR!zd&=%$2Ee^3r+bLLX@9EK!r+*b41(Q)a zHiu#CREfwxEJQ=IHmR&>>z$d&zjFEfMg+)m7P4PP)4`Lg0jhu$5s}8hgLaX3`*_TP zQ7HhS$(tfH^%$~{J^{Z)6kQB+;VU~aV?Amj^J|x6M8~=LMF0>q2Oq8ne1Y!c@eI+3 zj1;U$t3>t=x?Z zOs~|=X(%qf#;lDv(q!0AW3H+u-x?>F8o%Xyl%L=FbrzKdbB>R5ANoz$42rIAuryxg z@zk*<{z1@!@??98BCOwDW|EN-pR|m$vCr#aosC^k2S>*uo66VT^$H57#pv2A?Y?=B^}>w~E?q@XvL6Hx*f!U- z567wpEa1(@p3eP=X|@aKHx9LWd5&34fUjwgZE&g8PmQR?<@q11>N!9uOgwKWt_=iq zd&gnAAMKZBuYusuIAM6xh)%~2gvoQMk(uHPtT}x6Fgp%3ks3>nfKD{kh3t4HQS_kR zz4>@FL@-KT;bU%jWSX#ui$AV>lm`-FSjlJY$m}d|vO9CCoOSTL#oFfOj;mI#yo!jN zVo}tPE@@(Z4X@5Bkga@0s+j`Px~^?HG%z_KBi3;|5sC@(=b*;ls7M)|*#V}*s&{`u zHK9(XS@D0+u^HG;_aF5XbcW1w{Q2i<{}P$$tl1J|Dqo(G(45ha_}ni27dE4rLD!~n zpFS~d-q|OoWA;p6UrAU864Hn8RBmZ!oE)Jjoh^l#c;%+ zGRPlU(8BD$Nwl>nXi&(s5ASwRwVW~vBDt*>Mxx!XR(3&BAq!*cS zHtzr&C<^ppOVe1Z1S19ydj}nz%v)Zu`mjdx9*^b3;&)&gaaYA&d@khpC&Q^TdYe^t zVU>C#V~pksEw#sG1!S?%`C_4Y- zB}VSlk)&kJPj^belvE@8S~(%=1vw2CEEbyHGRFHqghEy2S5g?v5Dl|?%GaAPV}T@r zL6(u#0IIcj-)N`qLa&@;@5DL@P+yr)E$DL*rG$vcapl#`*M z&5&1Mi*V=guzj#m!ajjTWrIA^M><5%%fD?o2bU}yH)oZSUn*aKiMWCl+}87@PVg-# zD6qMZY z=0Gsn*cK0DD5;`tCENV`79!q5tn7`&gnjKhmtm1_4|pR@-z!1P38IyXTtx^M>A)Y1e<6+#RM+P}XXR~aEC zA=g;!yztZ)e-P`G9LHwJ1AyA1tb!T2&%2p_I3`NtOS+ehTXxGm{xpxJVW~hMb5uFV zW4_Al@zMsOJh^q3dWlZ8TZS!xM|Q>#6QGt(Z0te>-*VUdlnhT{a1a_wR|+YcM@C+P z4x($cbQPepHtAfj@N@6iARIzG7jf3;zi;h&!IEAw9S^ND0cN@*26Eg0pS81?i2zs^ z-+o?}!h4cmhfI>iJmO->&zEq4qGKmITcYgL@^1jFg)}H)->+V|vO05V_yo5N2RH$Y zU2J7#43ANVX9&DvJessWLctOQ(G}ghD0_{lpUi^#ZUF0vO*%`7x-2@BFzOF}9LB=B zf*at!Wg@cE4xM{LW^X%teE!`7R@E~xLZsYOV2IhOG>G6jr{cBVRRxv9#($c03L#Ip zR~XafxQ=xHW6M{+tP2j05@XLks$8S~T0bIkwjuP3g$D!YMtm(%(u$1;ys)qxz@yM} zbH5*=b3w`yMGfN)+fZ-8&!`nw(FzT8cH`R2h#Wl1EITck@gi|}0pz5Spvo&M#NtlI zPz^?P1q#J?UC#C3N$F^4C@9=y2On(*wa)fLL7T+!#1N%}G&JVTotwn_q!HmQyHiU` zZ5w%>=Gh6>mit0Mo<)``F4xvhs60{SHZ|AIBsYT~>9}bh!;24TXb>udlYNmMY+q)) z5JkMh_ov53HxiZ=>Ea}6YX+dYfECaTOC?0NGNj_QiI-~D8y8Zfl;YsLl&`-)LZQy_ zY}jXQj2O=XX`K8LwU+Us$1!$%N*5*c4*(n{NKWJ2yra3Uv21G0m{ra!e_vu2-a z!Vf3J7`fLae4W$vr;^abC#EdQQnkJ~4@jq!|H|{H%g$aer}dTZ@ABZ0Za&F-kSa%Y(eyYRL@=wK3|HMT!M!eS2Z6#U4^$9W0Lvga&tA->|h7ibMt7!NlS&^)JNB%;CQe1Qy?xMQHjlSW2C~O6sOA+D> zzqL<=iyf+MwGU^!#Zcm)lN5G_5Zl5st%*FJk5Fly#e$Nz*%z~eM~{X+%H4K4UdxNS z1QL^G7E{1XBca{-T#BzgWo(Xqkn_BG$2RU~GB(1pXU%l&HR>X86MX?UN^J09pA`?0 z22ygK;33hq8ZdSV3|TuL6-V@aZRm-RiyCMpZnc5s7O^_QK4uJGY_pr}fw8#EU$e#D zefrv%WJnHeO#>KAo&*W0mzO z%ykyC$zV`AZySOte|@$@xD*_dc9Z3p0lNd=jRt7ynmp*wT|f}<`%P&uy>G=;_jrJ~i@98&Y(d-v|eMw-JvHJIRG+zWQ- zQ(1mj7LYOH9#Cz5iw-R_6%6IvC2l7y{?#X9)nF#qup;TGe`Vp)rOl68R@4`24EpOi zC5Qw?GL_87(!9FH>G}&_S00ZeS5+oUym&IqD&Fgp7yc6|tXi1%$*R6XoeqIgtc6W) z_h$bI-aNJ@j~+hkxgoqywB^TmS;MXXwBB;3K{ze4c}i~1`(Rin8X+yAL=k;?mLH*@ zS56`n0UPhOzl%IXAKYU?&yxRX5&1|fjrcB3BiQ$dn~^vSiYo@oMTo;6$JVs&rb)fx z0K9#Q4>HBF%pTI@6w#`S%QP5icnjquu*?m9EMh1M+)?WiGp$s!;x^SR+7$cZ?D_L2 zI3;ZHnpC!0vKH+nC7kQ6PPN;3^H@{q-YE4{GMGJ3&x>GPU-$?tR=8=hIEIL(JtPLj zE$_UMtVy;1cBCQ{_IWPf2Ng;)7LkeKlXeSLFgi^sh~A8t0d=KhLq-KFah8<~lwx0a zoENJHutL2vPFXq>tbwO0)>3LdN^rmY(beJIl+VR~H@AjBPM+#}g1tXc8tu^3NWDgu zg`*F}UDh(vQkLC8kkcwY2Axhq`Gdr&BypG`oX$L3JPjgvv|% zcu3U8-x(yLg#DPE@HT;lC^=dODF@F8TE={ME}uprD|(XB(_8weFB^e>x@y+1t4OX2 zfoL#B+P2Nlg2m`j1(39|5>fc=IN=4_i=}r%!(l6Pdl*oF_O_9_-pn>&3)@#BtF(~` zXk*C@$k#Q73>jh_ILuuXR;OnVQ;0a3M&RiyBR{Py&FSs&{qqdO-Z5^qEbz^zqcJU_ z+kws$kbK_MNaM+soakwyua)a=*KeCX-R<4{*j zwX_Ual@G93hfbf!D}(|SSvg~KvLFx*Fr9^Ca1FZ4E*=Qt<%{~1hO1If)>pwV*Mv+9 z`*pr35als{OTb5qCC)@GA=(w6N^|>IH@+v@_Bb4vFslit?<8Vc3S@c*J2!0_yS1{W zd*h}}i*UP@X$m;z)W}XJG8TWTb?wx#Bh*Q=WaTrH&CKKn!aWs|Get!6$aM+YL1N%s z_!i1m>AMi*LM$KG^qB3#zZ*-xSKX)jGKVXe%8-NAmdZT^^bA|SFE6^YySqCe##ilp zjtKa$97*CbBBx));5+4=H=`t!PO|(1!c=RB;8osDcUW7DuMa}6OsDhC(HzImD*9$O zuJP{SFwtnFlbyeJLVej)x~7;*@r(9OzyE{;bh|4V%q1G%!7zl(`09HDnJ@rl8Ji9H z4e-X9+=BA`Thw(~*AdDoM1f&JmxNQa1#1@gCT);2bBZFfW~8@pggvjBv?eoOJj8&S zrfqr}^MF@*&C?cOyx#`K{8;1=9gVZSVxt6pHe%`Ix+mv*?9n(jO!sg(oqBk<@9Su-9v1Nfl8w{u%bRkBJ*I_4aOek`-sE@%sZ!NsvT|650brm@-FGgS4WedncG zP+>ym3A!d{iL*9CdE42xhQH`nm)w@r$RJ22nXMxXAJY;+#VNCz2tXZa^)cfyohKwK zcHz5&0Md!yzx)EjSPYZR6i+ix&$=OXpDQbSP+{_rKX5OR*0nB;E%koJ$4p~F5jeGn zfr5zNbjS3Qiso~o3J3Lb+y8a{x8S6_xVAzmmWi57{8>pri{ZMFi0OdWRQz`O`7!#t zhmswndYuYOoG_`R3aNBVhoyLSXJC-m(YSHrqx9%xoeG3iUn~zvRHST~m(IhZd5p3s zV5qiDU1=vx#*88Y8wSF0?vj?5_U@kOzI!%kRxleGe8}(bJHy))@>S)TaoX6^b>xLD zy~&q&3lzXN0POB_1~B&nHN? zT)Ms(oB^E1kXQn9T)@Mkum${)n??*r( zICl(bSzry^rmT(#Ee&PTI>n)=&nQ->R=57xl24q9GY$XV0!ET_Ob8n$>w{p?#J7z8 z59e$wbbv$rcse9nLp=er57~H$B)f==K_^Qc51DwoceQpP-g0iE%g$m9fme-JnVatW^)A6wG}Kb=^Z4Y90tX!zEfT$$ zI5MfOR?o&sD9hlh#Nk3tH>3nfrnd+MAObQYt@MjmQn|+hF2wYbEdUC> zayY+@npo+6g1akym?Ykx*k9B3FgrMC2Y+=e^a?6L;XhBpU>!;et(mpm&+jF;QwCZ| zM}kcRMDi=^urnc~mym<#;7uNyOW+}-ObG1$?^N7avo^FgOUZzPNPo=)<^yWIIkLA< zA~%Vq|J7YVw$TlfU?4ez(x${?p0L|@Ns%3guA0B~Wt+la za@{o^N9>t1l*IdaU6LGFFv0Ysc+0a6iDPN@k7x(>NgM@@&2D@Q&J8jR6Sbjy6YjHY zu18VL=)M;BH$=J$E-pPsb#a&GEwh>2Z`{odATm&3YZgx(H=G}GlMl5HZ7|6}@HzoV zY@}E>mPC%xopJhU`{?NeI+^x_7sU0h6$oPyIV7UfnwmT)Y2EC8N^o0K7_h)LIFuM6 zy{4s;_t22AWnOfSk~xwTzL5-<;(}}5S@n+~=V0+6;}eR2pW0(HSX(a*V;||FNq>we zb#-08`8Sf7v51%vbHse*Z|LM?>W>1NS!{LLr#6l8}jvW!d|7~g)D8~dW@tQ`Y% z$jLB4pp4WEn|9bw%dDeP@8|P=?|toSU;Em1%Ljk_@@4D^7u__g zqoLY=lzMqr));9wQ&YRd-xaop2z80b3{Tdng}RFP3G%pbp!q(nZ9|!#gyVm~6Xy~o zLbi!>0w=R4%dL4ixMp{}JAh#}**48X85z}$7Ot$4r?IT_ghyMNZ7O>IzFWGX$-0hnwAHlhcs%$tDm&Z4&po#3S>t=5Z-VyE{%ggo z2YX&z`1HnW0k=!WdNJIjmiuCV{^D=pR{D(_^~4z_*slnh0q*EUmDfR`G}Jy-ld4!MAnFq?d}i(W zr{+Iy>DRCwRkp7{Vg8uiV&G#46Dr~a0z_n?(#S~5T%4WA*hg1))VGa~eT&#)CL52g zQHP5Nko=!@wKB(Hm#)r$xQCYOQx6z;ty|UJo62hC)=!^5Kc{N{%%(mcu5<7|>{Scr zM}z^VDsNhN$PDY7pw?%Z^-Kba`IqD56#OL{FL-g350kdCE9eUXP8k`#%%_pnfd1y| z4p^i0p7rd{#;!I-NV;(*);F4Oh8!yMl9df6o;Pj)a2B(Af?mZiq^rDqvFxIGC~I0$URsR% z@xu37ML=b)$YP~5#i?le*@twUp!7bWU8&>uZwYVv{GYd!F!#P zG}LboK88q%y+C$R=7-1EetJ)O;js7{fDrshTkQ+Y}rX9fcwYUlpR+SDC zyvTtq!MTDOsR93Dxmdq`B7Uvks&4=w`UJU;Vv2!)`Xt1oU{NG_JB-)-vKsKDLi9O& z6N7iUOWM8bZV1W6pAl0#c3B1P*)uszR+~xb1ol!$xs3pDBEY@BL2{%`Xg~OW832qe zeDY*!!cUkpBxi_CYRG6m@(d}Vq^wL?b;h#BVkZBX%Fi!{(Z?AcmY^Eln~7_1BdfHw^Cf zrvXbHQtJ{AkH06ZH#hAz1uGY36w`5pC0WxaLUleE*sDJQ1!t7aCszjA$4MJn_Ine? zYBp?rY*~KOcAkRth_du}WNfbd^mk-Qlq#X1qLZw6gR>=bXj9SP#KM*fS?#8fMIv;B zuiI~fv?GHO_#`1xBZl3p;+U9^>Bfu`Nc&`Pej9ZEtubkk$ zi79^0Dh)u%{@C7zT?qceRv1UhN#2z{_e-qwtpkGYzWe;^Ot_JY?=01a1E15Z{)`tWiYMQ8X)qFYILLndZIe+zF8jNmM4DbIT<2 z0~Y0WbJ*(=_c(oNc9|D1=jJvhlz~`eq(aScKont6Ry1uVK&7v^dBm6IhvjyRMfRIJ z6ab?0XkY^!TOKwn+xDD_GNAQ<9jGIEvHn140DiL+2}mO4K@RQ470bl7A-_clJf_W&{*(OQATATm3ohKrs5&u&BP12)E4@X_v9HRB^T$*d{NU+uXv7hl7(XW8 z*{dFff+=jX(2l$&LA{X63qd3ZSH=@=n>@NmPX&1f`Kk%TL2G9R&;b~(rZ$>a=F=MS z5#W7gJ3OTMP+IcLTSLL^oK>=WiUfOfD++Usp8PSAlN1^#OpgLEvzFcBhD?Sn0t6l= zhQZowC7<-<#pwQyV!wXI73) zS12Vy+Mp2#>FblMup8#tt~1tmw2%OX;;qaKPh`vdGy0$w#x7MpFpvjVbA@lHU~0~Q z++iMhP)I;VNKqo>&Fm`Ft>+8bK4DkDI>gY^j;N@|fAt{VVCPrE@8r#bUC$EegZ5yA z!F0V3uj>%!M^?|WX0{WSW+p|?Oxq1PT5k+$!Cu*4kEv+|<<@Aj3cem1%GuhPi8$oj z$~rAG{Kv1pJb_1tu`&LEP62N)u-b!{nQRz|EsnGTR+|K5xllz&LWK>R$b<^8>pt=p zMBlB4tnNQHK8M3bH((ChD<#u2DOCby8~Xh^FZqd@f`YZ#$}_E#jvt@!zA(8=UyJ7Q z$iqtiXCT;bzRL=3f~H16(Rt%F?j*D;piKy(qL(j^T!^=f0%9IPc*E>&Al2e0++uSt ztXM2ID&<8rs0?6V)*(LS`nJZp;Tq;X?1MPpiw09uQX*4?LAaMQdD+mk+F?3OFNY29 zg3#AP&Hg0r($?Hq^T~g%zPOK&h9Y=*AzdAsQHMZ8e2HJTgqMj#)q()V`;(+I_d?OB zGid2#hz6=gCN?!_3E5GBaK3l8;)*D9dD;q~a3_{jUN@>$Ui^(| zYuP`EE!uM)ivY^R4B>~#*2-cLh^okGD`I_ils;Y()Ra;3nK^&$C%|SD=;yKNm`{j5 zVj%h(`Zc1cxt?j>fUY|1Y%L!$Vdi~m0tv>T0-2$Zu_$y$xu8BVC!^ran>iLgQw5Yt z@3AkdyEEh4nBRhYS3EwR>xV!!WL2E*78D}^4!vT$d(k%5RVNm@lA@fX1lw~dKS^{<4!y~V8Rz>u+i*zgx#A$suZXS8{7de zC*hrQHNJ=y0bv9JMO1uItDJ^anaD^wZ|Z{a`}gmst@8h)EmpX|WFB37f?7jTDA-2q zzWE1-2``918q?n}ExE?4fT%5jk2Sw>V!>oaS^+I8(?Q|pm09?UAi+^tNb`n_wVB*b z16=e8QA0u=evT>)j|sJxV3x;>(AOW7vI zc_OmTJ_jmlU}}5ZeB7-(-g53GTlm>MgI4qs9UOdz*^rU0vIbc63t)-5O}*mjG{oY> zM*OB?yM{uJt<2pB^L{5C=b1y4saEz>aRU4yv{Ki8k~4c4&%C|~=o2$U|D?wcAMPbd z?GW3dD_25XgFyyIIc=ipr4Lj++rUqWm|D{Zhqlhvx73T!gu-Mf0O1EYWOV|UEnKKg zE~Rf0M&ZEoGzYHzw$qT_@A!+x!>Vj)jMLv>5^XSp2>}&HnS`f~(92i*w*sPF>c+D7 z{%qx@Zz&>}+sJ~M*-!JW)8szs7hb9(-9*`%0QaX;sonP<%>!;64^)TOEZhO9^o%W$ zP+3^fZ%vE?$18*b6Y&6jZ)i!WqWQ39UoNRuB`mTVwwAGgxKnDkJKd?!rP3gk$gVYr zt;Exte0>)p570I;m4zvEqQ}!85!Qv~8#J8uNSBT{%zy!z_LF%5S$^ix<_8rlQotI> zgLt2G_{Vg9%ZB*9qlP4VM2hCGg>$wI5I(>bRH_hek0 z$_>5qG0SHLJ;Bnm2*#w9{@G6-?V@MbAB_NJQPMhP432-m_e^I_+oh9sTB9s#8ldSS za=Gam@xNHOu=1hBziW%MMsLs86PtY44or}B$PJvO8l)Tg2(SUO!F?h#Kvu>OgN^9; zz1l)s9KciC1e;|(mdS&LB5Y358{;2AwQ z4Bhpu??o&~S9jD%jHsC=u#YdH_ITEftStSzyUALO{~061%v4nyr8$(VFHKJ=OV|7w z#J~-eMJDry=xR=i@HNUNw^bQHc~h+yam>gt3s0Q_QVTS z!tX`w@VLOB4hSzrU?l3=>6ev3W|6+-I546CiF{iBq9ZJ$bO3#Ld9PCnNf0=IAC!5) z)5{?aL^MjceWkJcdv{dj<{F7qD{p=}Id)Tc*~PotbSJ`V#7Zcb26o*rq%kEYZ5h~Y!t!cT8som0}cd| z)Q4)NC$z2Tz`+nuqt*8Y`&&tK0sf5=LD(6T#iD4!4AUvV4M$-#OGn8ZQj` zHJQ=&rb@VkBP-&f6#Ww@yL634Hcw^n;2GhpJn**l%Wo`|vmD!HNV4k!>M(Le4fs$N z9KXsvTnih+tp0p4?PO&&6G#x}4KJm9fYJD$P1H=5$BA^7QdU`f(8VTLEygtu#wiw?DJ1L7f1R28nQOacwybBcw`$@!AG2OHm zFzvMU4UafHv&QFm`<)Xs&gQ&&XxDM%y@!T9#WI6iEpmTi9#WyUvkR^f0bdzTgJ5>5 zN+rh=LVO_s8M_w;AV8xEqLKuwA5+cW{g~^TQ_*v9eM)ORLSD#t&q*lD7l#&EK87r%NXcA2ws2Ekvd4EDwI7^oipg{&JbkrlrmJgoRl)G)0TxkI*&pz+=q>gZGy-CMpX2(;R9{u2 z@+Wowgz2fbeKe(6A@uvj>R$t?OI5;SMwJe8B0h<9j*BlA&Y8bZ2r8}Kt_Zh&~LGxC~6p=%r7lWH2^ z>W1hXDLfdi1h9;ptMx4s|A0kgEbU)Dn&Phvyqyia!W%LO_ycTEpM8m5v1n}+xn(Zk zm?n-V)xJm5_F_m$HQeBlBMzb&UwMGJSVt?!nUSaZtFjd|U3ZqHN)CQ}@t^i#*TD^GsqOudQ1K*IWF2ltlS+EJcQZ^Q}EBXhZCFsXEqc2WQ zzOGa$_KUFx;NH%m~Tj?8=7`@ zY4EUrmWqoEgBU4pa*P5xK4iLr(8Av_miMlL|edvv?HPL zXq65kAx7Z89fn273ixn6#K+;E9%HfuJFA>)?M;j|p6BL75$W?R&zy#SGS0?=JIY20=kPbeqM`ZI20J`Cm`SB2Nrt~bn-mSGnwcS;ZE5e5 zUB9sv9s#^%z&oy;OsByq?dCoeL&p#!b?z_y%%v1dH%>@KZMA7MHt3{GKs_%$h3HH2VpGaEeZt)U z2&G7Ma@z6s9P^rbRJ*idoyZ|bi%U}$a45OM&QMH)R##3~k;0cjUt%4leHL<<)4LwC zFi^<&ul9cw1uUG}S=Lv(X6}rma8*)Zk(@KRUE;tDwXc8%oZ;*o9KLjBwF~jGgh%9m zdS{g->GAB+cP)9Ohv_qzFMlnSH-(=xuxP)CKl<{kjcGQ@CLate9KKMXra$7k|K&9CDdynrZGu%wzp1h z7R=ig4K5d|7hbmP- zQtl99*2&P!u`#EA4}c}PhORqV)mJz^jD%A%9qjC;&zl#3n=mBfgkNoBcxNt*s@UjJ zCyL=>cG+4UnG&L)8ujZtRrh{D$H6?buNJo&_W@seD_p7)N^arkIW4kXbmX=+`$KNu zEdT+By1F1&7b`Tqy$PtRq#Hz9%VirHk>^0^5Ml1-J+Q2^5oDZ8tp=WUPBP4|aeAO< zDPGtUH{aF&H{5LJTe)=gJ=U0kwinW}ljW#W;86%UCnWSBNI(o(# zVy<>iXTwuUY;o`d22vfH{$AGwx1Po4kVUx6{vQNlCSG9&#sygu%AR#~A9MsF{u37n zzFmk1eKix$v-rMdw0d|esYZT{A4RPW$GCoGgi|~{aNXp&=aZ2Yyw_aCdLEXUy7BkcZe*(ElYeR*GIagP#TKLN@AD&+JIK2WXwjoLY z80)Q~HCd5x$9nBRwE7%EqE;&kT^`(hmidY_o>6qV&0q;(fjNCPsVY>VXL0c1c+4iW za905&hixOTMPM?9yWym7KZ&eO^{r|T9C&_a4rxXF`Ldu&T9770SanMOFOC4&)rjECMAn)pkA3^5(^1-6B4sV$@7{b7gF~V{RE^?i5&AXvz?s3;QC3a6P1y~z zfe^N_f=V;=b=lVU1N%a8Ne#@Or^E;j&+(;dv0VERLSDu>{k{7^8qoZxZMw<8I3f0H zB;9GStdQJz7L7k~@4pl5r>oS65@MB$K@8Q>IA^V8S2ZqsJXHqS;0y|Ev-(3frl zegAB*qWn1Y1(-1QznhwTi)P&9Sa)9k@4b{^c;pf_CGn$uJ4mk9(P7GId%R@z)T1#p4xK*fk!YNiR>GWJ`B@rHhcowM}F>v z*hPx7uR+%%>*4A;L3_lf+S`MucBP35Un2T0{#@YVu=w;>CGPE&B3-7T$6S|*_$8cc zE3-}{xk(J)fzQLH@5{gQk@`#f)w8?4tp~4c>H~N5?CDdr4xLY>2y42j#Ym<2a z*Dax9EvxC?u}M8YuP5f+57l)#a04?O63Ss3uhbkL=%b;5)|#CjL8|I}`8YIOBKbKb zuyJ$8{;W-JA172FT{y+@h0lgbyS(f^E_ijU)qaE#v&CJH+gbjkk>w<;2`IOfExT(s zYuYs8)T4K>8k9#b5WP=W=fsx}M-*f};VqIP)G2j*erv9B*zJd`M>);m^1D$Xr+%?+%bvYr}$T8$LgW9R7fz6n*FQv*(+?U)R zy%V}Snsk8-g)5kZ>`Bz5@m5)5;yLd0g7wAIM+U!|V zR2Z;q-xvshb2|Lh)seyk zf~AT3m)(36?H5XkYYG6RC}4Od1{LvX!q6(+JX@--#mnChMa?(@o~1k0o8BPog8 z%d__Lk$SE9wOTAFVBte^5>lZ#Fly5P-1baeWeF76f#5+oSE*;^v?a+9_Y^U^<{UKW zDFR!5Hmn^Dg)}&9_0|hqU9*^<^@o+fhi^LP&Li*!JEu5IwVq<(x9!Z$=>H6={@8x1 zQ(V!$LvG#aQr3*j4~b9T$?_`VU)Fu_tr(+pmkXp%W3SNtn+_-+>I6L|r9wonthSVW?T;8=;MPA-XHaJT`*Wz@m~p zgZLn8l=Np->qQP=3(zW#!O{f^*pl#Z+@ka|e^uA@TGv#sfkT9EYT1F*cMm@|m@SG} zx&!>qJxt*6_dp;YI5_yjM+R(p^N@`gHDob+)>1MC&s9xp;l!BY?AoB5DG6V+0I?Zm zWIlzsn=k+Q>Bq=nP+B2wI^44InrTCfD}+2n{Gpw^;eAQjhWCSol2=ggW@Kk~%a}r( zj3llJ7Uu>Ry?W&j1El8Y9!to~x}TFU4em-p0No3ZB}>xL%0il}lsfLWEX>)Gdf?}& zv)S+_{Cc*K1F?U zam%U{ju%!`<_f*PzdE`+bMoZLuMB0OlX!*$T~OdKmeNUgrZvcyI_ENn;Xaue_-O8# z>YF|MaPBP%1PP}A3-&oi@SVy7`RaqIQ>GJrL&%viZDM2!irMT7m>%;%4Xp2cJ`^+6N_O?M=~k;|%{-RlfkMUs zm6=D|_asx@IFMm~bR{>q7SQPeLAj%P?Gk)Bu1b{^`hGQP*8HDwu@nj?RJ@?MYEF2s z;h+2Xo&xomlRGGEFv+#nK}O^}vI#gc!A2Hxf&ayYL$-bbDtA6j1hS1o778E{D4{ps zpfXOdzM7+%MPX1F#W7#&IV~=Ew}>gI@WxUbSjH9Ef?1W$djCv*@&mzwj3?3I*DP7; zXx9UsUC;O5C}w_p%|h&BW`6W}HwQ5*VW(Lg(=$5(X zkSYn*lOgiu43BL7|3G(TJACd-JiKjvYi*^9lZRPM-Ija-Wd@_yS6gd+JF(fE+=#9o zcn?6g^qfMAKzHU+8r=QY*8<&0M#Ef$-nO_@erb4)T!svDwC7s_E}hi)yv+@!1DefW zXtmQe>H#cXCi4mAkwrv4al&1B=KgK#!tcGbkJQ_xtEh;zVsICc>hgUs-m$M!HO63UKyHXi&et9k>}jUG{E%ex3iuxFGpc zq3qjLK)3`w_hMl45(R;%$-tu`*8CrZ6WPmgQ7E&Nlw*VyCUw2mIWvj%fCU!7v>7aw z1ATR|DX{QCW=Pmi?5;t{#X(nAQJEC?bB>g3H0Kr21VWLN2jRANS2C# zM#xPlCS^s%=e^T-_4}u-CPs`vpC@BP%+d-0543L5tl3CmhfifUF^=c>PlC6C+*=O} zXHw%SY|vQCg@}wln|K~C{&djU)mE(@@~?PnfG6P=oC{wni=`GWJpN=c<^!GY`J`&3 zsq@)&PDM20;W0)Y$fcl^a3NUCT@+SrL{~`!a>8iuiXFf<5z!|yF#t=V21&{mJo%vg zJ&Jsv#5KG)6TJkop)A(Ee4KZ0pmdy4kg<}+&apNz)~Uv+^Y+!r)zG`a0x^yq zErqO#;pUuY76O!xoRD&&81dqdNJ9kTB07 zGa@D*fqWB3ahREt^jBfuuF~3KGPWTlAEV#B(o6oc$al1Zy4sI%DUczRg;(DI9`Ie2 z#d9*d?CuDMSwk!nLFURh+^r+0*~j%AIk21K{o%GVx*=ih1bzRU%xKs3Pm~% z@aHg%>^RcPu2le=b}2o*h7ZX5RsKtm?<$ke!@r$HDI_gZUv)0xmSaDXP0x4(TKyOPvP5dy^U4f1F_2q> zfhLqxgjM)7k54bhrUX_n4=a>32F7oOXyDz-(F`rTSl>@-1$uq5`HfzRdd|2tkT^(w zn)py8MA+vaTW}rF>>m&i0g%ya*lsoo9I~QxXQ`ECk(78sfpf1n>>xMQ2YjA>&Wkx{ z^B!U02sfpr=-WSAuVSl+h*NU5W(qLeR5{akiFgp(=yH!#^AATXA1 zZDxP4ILeQfiD}8vCgXBz($a?YigKd$KQknPReSu5Gqe|MyjAU|1 zIr6G94$J4#8`AGTHPu6jw^?*reb5o9!!Xngzp9gLvrIqKJgWT> zw@J;ZaiJ*JRwrdyK(0)ct?vkH51x9^n{3WYq_&h5yT5Zqs>l#vDnP!A1ORWtaHzIg z$?)a0Nm(X$2?|0XD+K6}TCJ-==thyCDLZ|tZ*_gGDmx{>eK40Y#|`QkcXtr~u2#5# z|DL^jX#?ya#3x+%OP%y$u6={;N67ahuk0Cl_%d#I;tL$cYH6+68xnF}MeiFia^!0b z)x0@S(7=N(+iU~yGhgFR@8&j}<>UjpQJ*-Aw1CMIc%MXOGr208l9CK@&6c4r7&Z^q zEs@!WQ^AM>6irZNLGAui3;qnuehC)JPG%AH>?7|E|8NR5cL408PoB-2PkjTY-GIC9 zPyZKYAE9B|vHD+KOx|*4OguGoSPG!i7Wxxx8CCUb#XvI*mLeZNqKYnVP_J~|faKTH z>xh(8#*FzZ$m449b~q$RD-O~4;Zw@EFsbws47hWRhC>!i!k};OsbGyl4M&TSveMHa zPgiE3K|4yhYfT+Lye9NPHtY}%wct=kQ~FV$0)8d}PV!W`>zb%~-Vg8Jhs*vOz}A29 z%laUpz=~h2wXi`>!JdjOCm-hS;(bAI*?Qlz?tDjfhr`+{g_jx6b;pK~qOlfOUzn4Z znLoNmp~>Q>qc<3zaB-j)Vz}{j?l$D^Q8DNrcoC6B%M_VA4XBB8FISxd*oeGR#50f# zGU3QsQ1InDW!RBa7==`ro>{+j8zdu1pgqWlHuPH2=014XKMMLpgmbb>m4GFDS9$zZ zMt-4PCt&Zzu%|CGo5JIHG z!U`-bV=QluDEXwIZJ-GmQ{$JdQr9TC)>hii@h!_uV)!(0+)eM7m1kJo>8qmjw_&IHw?^85amSBKy$( zK9CZn4T+=Esv%@k*!o6kKOgiWmNDVV?wbH$+a&f+qo(I590e~WW(@4#U-5y`RCSvh z$zCMcR_Z@4bah?SqE##DqBCSl*LXVAN4ZV8IkgQh_y}iS#v`Gwe^nN5)6CBa3k#F; zO#L^5o6WZ$5%sYJCR7^5-_`%T)K$yOOva2Sy+l%u4O;Un3<1>YcQ15GNgoO!Z0gKk z{lwnVHu6p3A||~kuScT5rPFzk&P-wk3UkCIJ~lBgC{R#90@6aC+F1h59uyR)dWqiZC# z-T!{^-mt6{9CL`i$#c68z$;R>_GyL-BUjo{Xa%PXS;Ky}X{q&1YYl8tZ@Y=-?np-I z;EXjXnDMcNHu68YH3BcDGA|pv!4NJ{!89xRN}t7P)~pP&c9VJOwQ@sqh4LlD?BTPW zP<8=jNHSDZeJxl}{r%@xH*A_32YvLp4@dI~XE-VanSY>2J(BS;Bs!?hhDI!&6;gyA zzNApDLFS6Cua%viJb8k6R(JUnYH3Q*;YiU?0+wLKWSM$&(iI3MkPMCGj);V>Q$`j$ z%9>2@wF|Er^hW42Au@-2y>UICp(urT3(zE))MwY9TuS?QqfN#%lD-t597BNZWVbxt znr1<;Luki7eVQV9)imZ=>P=1PrYe>*FAN>e_p7W>c7j8YEd+v<;CMtUMM5fy?c4O; zclMx!#hSZA2+0=6T=#VGFI9_4vPCM1Ytp5gH@jx*&*0Kt%E;IVAD`lI#05yh?Fgp* zA?K0QKvuaOV;>xE6sVfEqhnnB;RIF`Te#K`PX*B(qg7DQCh)P0>drPCjNj5DnsHH879hi!M_rbneKYM5Bv=gUJ$?jrmWKlRk=U{~~dUMRfNX)eH z9Kc;9n;*c$(X0!LqpkGCl^mFYEt3es#hQU|@oGsdkB_tbHx;~ikE#9Ca-iU6fIDLI z?mu64^^BU+Rw-4xu$lni&N*alWOMqFx=6`?%MUpNGZrjp+rc-YU{qTe=NYK0drplZ zQgL?uv$x+G?&9bo)g^lI_zUyh-M4}>Xs`C1T8ja@V^iJko_SGB?If*nTA&Jv$6&dW z%w1>-p%_;+0Pm-$Sxzo4BScbAA#1Og=|z?)olrEID47NSJnTnCvWi;j3BiL(b{WP( z@cnCLA-L-OI67MnAfr=`pt6_pk-4 zor}%~TE01Za7}Hi!f)y=kH*-{o;B;p6k~re5kep))di8457So2nHP&wDT~oKU=Sti z&BmYV`UC+((dwT>ccBNVF?g2@zU`m-W=r=uOJs7vIz?nO(4e44M&HCUByvX~+RV3~ zjpF02-2qI<);n*+qK3G+Oawkua_9N`->2&Enu6j`q~n>1uhiT3an`WLjV zmj`&1Cz;%-)2LA+guehOYL+%aXedOCENTtCm1J&`RGQo_5!`EMy?V6y7p$3%x0)JeL zM@5rdb|0SJTbKu0b)R6(2 zYuSY>&B);>5GI{^$rXz9y1Q#_N5>7_yoPtWzJ%i@tA`n`0)R$y>&gz^*fZK&1iivr)hUG9Y`_SyPyi>t!z`bo)t0(GWC`Vo8uJjNyD4-_VwK%9v5Y#t7hZSOF>;(yHBxy+%J{q*?sBN|XJMOoK$({1hKn-SeY z{@a6Hu-(V5J+K=B=NVmt-xfJs+1?AD43e2%o|4Q#@|kC$=QP_}@Q;6~rtx>H#%9<@ zjz^Lv8>($ai0IPfVzXf4eK9A-tZp#0%yDLa<=&A`1a;BhTxt6w@$~7AL`PZSz*j8w zdXyu3<~T*v`ytbJ^Yl12bCMp~mCT<#`>1L)wUmb25J!cHt-oGZ5KZ+L?dxmC+D{oZ zEjZrZ%BgF|q~8VZEIMx3tclGir9$Y3joCaj9)x(l_0<2WI|3vnYd^yiZdX2hANiYC zXLe}2{->yXS1NmzOC*L@RIwB&IDBtb*ER2fjEwPh*^~w&l7=pWJF1*ZE1_#;Y9An= z=*hWxbP`;n!z}F0;@%vi)+ZJ&M$3o_^hkxbD0RriFk={$v^>1%5mdwg4B8nJF`JsR z%k%Q9JGK2>Ztm6K`7!1}%f(~I*PB=f5b>}KP@^^j93EBeQG7VxRkqVm!iXsjLl|OH z8yRiO@I%Py6C6_6^js%N>Z=}O9fx?zG6WOE+c#t#-R}Rbx;tBtC zC&Q3-PgkkuV8kfvxi?~+g)ZR|W7^+8yMAoYB`k3~Y1E zRKgwBgtEgoH|RzQc%26!qled{7e%aE-Z1xacgGstMK_sVWVFmNKa_N!*R|Uc6!sEF zqlfH#7_n}*;^%LKCEuRULS&Owg(%!D<309c3c}H@XKK6DeEydnbe-r(i5VsJ@7yQR zW1Fk3qUwtFU8mqstY;jREoc-z2+RNGvF;hTtQ%FS|B{Pcu$4iFj`s2d_;;pbH9R^D zfQk=g*cJ-m(#JbLT`YpfLvZNuO}6mzI?4mjFyl-$t3Z^Y%KW(LwnTKNzYST1VsK18 zb;6x`QLM-0=g_|H7?XFmWJn`F-N-AGr&zK0TZ;`xDLQ2bU=;a;!$G)^%7gzWuj?sR zgEIewJ>L~x!2M;7ek8!-fwWX(H z5buR_pY|6W%>DEgjzd5ZAV*Q8FA7}52mWqAh0N1TTadh4cZkyq8JHx ze{9Y~@jMfH!@S=4yZa{o;lkemx1X-JlfUa0W9?5yKH2(wSX44fFL-;JJ18nRCG zJ`0hW&eUSg@w;q0XMH81gfzA>Z{%kOskrr-{;k4uaUDTYcliOD3Th%kL~tVQfy>{R zq_E&WYfCH6)Bnlp%S;s9@sTQTcB@g%hK7FdSf ztmoUqu!@tOFmiqk-DK$#z>KD=2E7I`^PupM8jDngZ1pntDYWixI3^yp%z8j1Ttd06 z?#QhO2L{Em!$HKoOmxZ^Fr0zk`t`%ZesCRX@CB)bK$*4=$KdqxJ*L)+|Ae2@!G43q zvF^z>o2fUpR4B#2KePr|CI>=ZASE+HG7KQxgmIu&H|BzEvV7%AUqn?^HHa5R1>XqW zz|NYpclM_x^FlnYt=rsnOR|r2%_(sn(tMh3i+?)Sz+?Evh>x4AXY&#e_25+DlvGp0 zxS{N_c>!DrT=s~>p^ynbrfDoLXrw?I`IM?1jo)EsF7G7Wde>+Adm`lzx{x)dT*xFL zO_UG&V}Y=7(fBcDJ>u#7nFn?5(&g>G`D>$tZMWa7-vmrjPhypbb`$JS)zppG}Ml zkZ5JwNwpjAqirp^^$(rmst^CMf_(KFdP>#&Nm*;kD99nkU1bS9QEnmaFNENt<>_jF z>Jm6rfGhD9^c`|37eFD2_WJc#G5cjS0x&@ioG`cg^Euc9OWKm8$lY*;VT~%6#OYxI zD~^IpP*T5bq=6F(TP_|P#24{BCE$z7dCG<5NCie9I)Y`p!Xso`RJ0#oBmy(HkSO zxqMIn7rp1@nOv!cK4A0=*Dc@A0fla3t*Geh{xFR)DY*5A6KEH%UpG4!PhY(}!8eV9 zto9{NsHJ;0&&h+5jiyiV^nIqXLGJ{nYYMRu<>>EN@;CivD8y~3OG!3j-PEDO;h8tT z7>Z1qc%^47=nF2^|8%2n^lN{s@F|kbtz)}Cz<-V~PC><`Jc_bD%MN%5%H4$9Fn~+s zQLsu!D`laW>LrRM%R#$F9&Sh2l`Y%)4?dMf?-=IUSxXx{RfYQ&O0ym4gZ^0-^n#9u z*uvr4s!pGcupF{DZw06R!^Jn94Z+6ZZ-uc5@&xfC@R2!@hkwg$l}?fTqQ zuT`AlJx{ltJbCgzK4m(E-YtTyh@vZGdYpGT=gEZu^m)XFl2pNmY9W)LpmSUnb(k9y z((lmlO^e8dVzt5(woCSmQa|$D9#^}DGM+PWSbm>O_40YrOgoVUwwGK^#Z9FeG zW7{K@Rg>6bN45cfM9hlWT5VI?!6y4mWO0!2sa2iPp5EUtXIruJZ#1r&?X^_MZ$~>V#f@xKxSfKbcV^6 z0A^D@TVpP#{`+Xmti8<+oFVW_SDe@z^7Wva*05?uKhH7}0H5as zT8i>!{pM^`9%#Kf*>Al?*$rs^1WH?8aOn?C+iEj?zs8TRjIrGx0Rb+PY0N~*(*bac zs|guOj3Q7(`0-(aVKM34t%C}e7X+%JgdaHa;OoL8_1p|8r=IlW*P7p-!LtK_q_;dA z6TXDA`C@Gn?iDh5DJCyqW z_#x0O{8U)Lc*=)Y^d!{09AUEtT(`bNS4K=B)g1i|;tW(>{t?76iJQ&HIsBsCX8m1{ zxf?`D=EvE4Xl`IkH&D1V-nbk~0i@!755%ODx`le?&g*PfYB z?J$!Dn~3H3+RP%z&Vcej=A8zL-63Ft_xurTwh)tWZ6nQ{6T~!&Vbse)`Z3`f{^rE- z;|C$$O;^ZN^Z1xv@l}n{@@hc#mZ-}2B=9cu z^bD}>`HI(!NsTP8qtV44w5-0U-Y6Q^mcE{Gy@8+3bBcoi+^u}1@2zsN?IQ$*3U)k% zPSqGn1~8>x$zI(%22EvyYSzxK^)3NLQRF%8P_boh&s>wLv>&xbr@7S;()Ip-HRfF3 z$#h!6MC^%g-1VT2WI2fAnvt%2jQpW#qgjgeEQJ8-F&hrbO?yL6cbLS zf23&+eW2s^*8)c(?C##P8>%_BE91G82PRQRacHGVAuEzzE_0EKtHQp2`<4j(DEm7_ zIm566bjEDB0m#@6xl62(f}7Mi6zThZFCdCKUw`uAQu}nKQ{Q~?a-KD7qzvAZ{9@gy zn2=0xxMcwR#N8&l!1S~|~Fi$IL>AXKHX49+W$nZ zBi=7Eg~ba&X@iazoZk~yE0K!uh6Yf9L%A#9-TI8Zi@?|K(4iUYoIM)_F9ABJnvh!o z=vMaQIX~UAe*)Ca&<=|xj}D`s#%QrEB|5$&?!X1_0Mjd#etD{OkTJ`ZTUY(6I>)H8 zSLrVojUIjtS9!H)czaGC(OmjK{o>Rcsaf#&@nhLHMghT%(;ayu;cS|P zOjspoacvs>?s_R1dJXS!y+MLs!VS^YmC1@y;*GFJ)(-^ktCqY%ZjONG8E(EJ1*a>m zn%PLRhY!LKae9(j3WzufAsVA|H@Fmw!y}W(GPjuo443n!5Nv?8==M36Wzc+)7>+`oLPen<9{sX>(SwE#*iOh zpfUP3HYo3Yx}tRTxuwK)@?Raa>4OGM`8H?;yZa2M!7;92&`AM4E%z>D^k#IzHz?gk zrfYtodM%3Up?Pn1uWocYrql3}_L0eF!*jeZySP{yDL$n?b}&mr;LXg>H@w>)8AVE9 zOj(74xqiJ2UA=e9{;a?%ZARDVsm)oU^%!um`qw_@Nfo(D;c#)59o^f0g+34iG^9n+r$rmO zHAy;+N?2k{4u5t$!iWYm0IWR{Vv<)P%#r4^9_7W5kaf8B>}%083zgC9E!3`JD8 z)uP}0)?h{aO=O;5oCHC?Aq(xWl@teG+6vEr5hVRqfGL{V%_S9UVB&D0#cvJc>JCjHN>tNA&@Wu1#!;bg(0WvwufCn`qZWJU9oT-ap>}YW1QjX|)hVF*=&&3K5rVrn z8Mm5+BA;6xz?gwqL>Y<0ad-M=rZaSj%{g_Poe^maU-_2PlUu^=>U z-ucHKt09pS9EWzn9C>d3Ui5U#S_#r8y=8*IsL?t_#VzGqAHyRVM2>UBS~``l9ve-r zy}d`RTD8b(ENQl1XH4v(^=O&!^vu7nX6hQ_nI>;<+_gzAT!U_sIg^{O>N*$RW&ic4rTL&|zV0YwYjol^Cq0J1hksDY9eQfPxw=D9!CETC z6J~jIowGfJLZ6U5(2yGE_Tf>D&`i$FkJb>k;A_`9=f7-Tctn^5ri%FHaIXQ@H^;Rl zVv9|ZLS{r}Y+-TMAjG6TP`kJIn0+?u{ zose-W%=1rmXHP;10lB!WEC;ZQP15Inlj}SuOd{-O2GjyXSfq8-?ZAtYl3lKucX6$( zJV^A&eMvyL9^DHX=NhFG@Q<>IS(tQ) z4uK*fN}*$gV$2V?^OS$V=L{`o`njWCQ`n|~T+s~H0b#3|`Z1Z&)v#j7NO9TYEq0&{ z8fD-3g9WM)n`q~)Z3gd!r8x2+G1UbCO#5IuBbCr)n#SI%qua`sEZ}cb>J(s|di_I5 zb8?k^WZ;|eP!4hMlD|NE;`i!|-zJ%GMQ1ftH^JHzc zEY21h2zZ(Dl0qfx;$m*8m<)2&%%*NZ4o!Q{iN>&rXRAl7?A6KnW0y`l-_hr)$K~m5 zmDN}D#ASeGKnRW$R0|JDZ4*9wa^g0_*#lI;vyokb!$2=|R<6dUzFeDY!oYAVYl5k!M zuG9FWo#DWR=|BVpUhJFbfkkFrC@R3lLxkt3ySL|n3r6`*iP_M-WB}24b=yB;s(kFsk!B}N5 z2n?qf=p@l`5uNt1K^hf#F^n!;xvc9JejW0%bxA7l>m{8<1Z=WqypP+f?`%Kg_lUzF zAY?XU9BA~*Mv%2+K3TyOfY?k%d5htvBwmUyJ8UyA6oUdYKx0(2Q}CCjM`mDi-o*<> z1CNxk#B{N@A}pDCwsA~Kg@=-Y>!SBhgg@gyCK~TT^(CD#*ko~s=8zwyR8q)xQ~->w zk-VS#Np8*Y>!}fbRiqtP^YcTQ`qx=8mK37H{44OXbAHu-^qST2m6LiEuH`8*x;PLo z2wA=*Gsqx~V$ziGl)~<~xM(=HMfbtHz|jNV@*goRgm>eFR|j`hV$&LJt8`XZ%FL~M z{EjPGS(^ogu{n>1tBH)+Z3V4lpvVW{%eV!78u7rD;aFG@>4BG(ZG+gRZ^efdKV-;0 zKY$tyNYuEp1c9vwyIUUpONW7aguKmFRMwk1yyq`(;Iw>V1+=u5RL@&RgQ~?UgYqvo zd5K%MDQ8Vk?pV6aFLxJLBX9tiOIEDqam93U#Dkcxp@*jsFj@5y<{QNGr;LQM+CXOa z!2snp>Ai7Q5vMT>YH0sLzrwP@@ha^1l`1H#<2}9eGmd~C&3w9jHaB&(C{nsSxj@2>L z>N$iddFiC_Ocn>Bk&Jv;1r;@$`%B8$z#Xxq!dzaU1IyYLSSD>K{;;@KBP#yw@o01e zOG>V(GpHX{ye|9^{Jmdfq)Tif$OL_lexZd3h=D@ajqY`)lfE4*v=@ac^t@$*s@T+l zDtgiLuG_S8(9_nmY-Rz_|7V>j3vQ`c7%Cem&H#cqD9={LjY3Gtg%W2WF2r1hhRnxL z{L`g*yJ_KzN%Qc@0VdEiJp!|Xmz5vMQFIuLs*V-jcGF+eZ2ta!;RJVdxB8;W?q^j0 z-r2R4nqzv$b1X5e7c^%{5ULNcwMKF_7ABZ;C>};Ku?w8@V1qCvX%0_IXl^N`&R#`; zgppUNcvdF^{IXdkLeD`dBkBEr<9XB@@eI_o*IfNe>-vdUR zoT_oP2)zOJJ$T@%5w#u}2kZCg6T%BvNUxOE5F?(EAVeAK;1B>6qEySS)0{T%A4^x0 zH9`gtN!bJQE zndqDWI>CWvOoA7f;|3v1&%^e;!q8ohTpBvrKLeY8;&l*tARo&C~=g&QR z|GuGY3;X==C_*OLF2?~o39$%v->dH?^_)z_^Kx+n*+r5uADG;B3ZH$?5HBOq=Cp4= ztjyH`-6VdG4Sjf#&z?UQ=N4vTv~_d{f3;wA*RkrHzuQp|Dti&YkvoqDI}seDY0bQg zj0z*op&Ogf=_3V^}2x{^H17zTjK>!Xy! z^fx&G)XS5G1xG=}))7e=AMgZS5>x(Sf23ONOH>+GjGW31pW*OFH9bZJ9nQbkh0 z3k}1AtV2^bss}l_msLq+H`_}pzJpiXY%NdnpPyh$Z$1z&zuQ=rr6ELoBbz} zs1~~5s&KUlrq%SWmHguGqoJV*e*fV^nPZSuvpo0_2tqpJ?k3WC>TmH;%c7*P{M*kzz^6P z*Gzo4-MiZ%_XBvpcA$eQJCVgZ0v_`juAjKbIR{v{y7k_E+VXr}L5X#;6qyVcAOlxH z4kgmXtka@+9+(L~BAq8*2KYD;0#iC21X=u5WikrnWDqwM4)sgCFTO2iDH*hUrX+ZH zB;DU)K}S%N-~@Mdg@!jVs(1Fz3M^E2XQEycAnrvChBD0Q|;FI*So-^mABB{{1n`a^xRqMPgte7h!J^YH6u@p|a?eqx9Rfv>b2{gib#K&Yo94xe zH}@KGRHds*Js>1EZU2MZ?ec6K`qR-rrcr;$J}o|`|6iL@!x>(g#^*^U_ zirCfpd8@sHsU;iG42BS{t8_h44iZJHZNe-e(g+$+sRF=IWUXSrf7HcDX;aD;CH%&2FrcGTc z#+6Tg@~=ifH&EhR#~$432gPW|;&gml2!_5yc(MDn+INxp8#Y8LlW{PmRBtfOVe({I z-3Wum%D>k9<-`0S@e|^&6uo|J&g%*dDkHuSwkWnc@I6M&b(uSJrYTh)YXoE!lsHQh z*cG?ftDvf49W^2*|0CNDA?E9w+A7>pATkOKoxxV8TdEEVGOz`ac}5WxEBelVbd3aw z8ga}*V@E&B!XIJ(rb>L(+J9Mn27@5U(M(Q==pxdIIa|yBACF4Q$dR0q`R!^vefe@{ zkzsntjiCF)*StPDU$M4e<&eAtG*M3r>~8KAXSk z3h#Y7+kyG;q$B@!p9aBHtxA34vzzQq#!X|!5T%F+%(RWdnDY=1FeayTfpdIIy&uev zddKN@+7h4soLFpmXULf5YUlC8kB&7Ma$r*$okZUHQuin+@ir(bbNRyHI(j=go7DuqgCCUfX&SLsW3XmQxaCzU| z8)L0u55*I;BXcxN#>-f-lvhuOfC%Xz%8ft{n#SJ?wejJ_w4=kWXFmywjN&K)3?w_; z>D`gS<}mUEdsj?XsgFnoYq6^nWFdDR9$|&I^??eG-4(*wHy#3p=1h#H~P_ zCM(Hx0PqstL{zUc^uIVyHKx;RHg~yx#}dpaqw#DjK>Q+366TS;n8)DlR$`PX5gx0O zarw@fY*pL>+(#+dJ95!W-eIV@6M1(!zz{eo8$+3MYBsyxNT$K09-z9%u*3yMB6e!Z zUPQOznJarw$-}VE{Bsq)(v-RHenz$*v01v|K&6nfS@cYcMxErD31|Z&ixnxYe5 zt1oQ659x!D+#)Gy=;y-6+)8Q-(Uoy^^{q>q7jEPlON3I;9ohc$KCm+!!7++xQrm3a z6Z!=P-$wFocH9~H#qCphxvba3>=o?ahT8A2ZP2GPh2Dn|{}6Q6@?U!Q#zMS>EZwg+ z5q%3SVwwCDbco1HJTiGlsQn+978dhjde6<%Ar;A9K!}Q&pjX~NOHv!5Ztjx{=hQzp zsTMgWHNC9D1X+liHSabOw*p2u@W*VRkV>;ppFZh}z2l()_-0}z1t3c&bcMQ8xrvAz z2-WA}$dn5gL|i8(#JKX%A<7?vB`!Za7e>zqj--(VRBUCKia{Qn=gRViMb5_131JTAh==W?#JP&iL08 zrpWLi-yBfQiEU&uH=oXw5!frw`%u(qtPf|jKuDMJ@HxXP5SD98ZC3F3!+AMm;!c;B zZZj+YmV)WRFrLfP=g%W5evy#Hm58VwZ50_y`wa}>_o;u;8eU;0e6i?*dHFJ5DH$v% zD5z=9U{)u~5ChQUw$4FYu?7v$R0@iA_YW@mRJL9CR=l#<`{nGeB0EgD94g_%Z;8Jg zuRtObxKG?!*{05+oJD5}9mz&02Z1))WXFHDnY}H%IBl-O3J!b!dq-k+QaeulmfEDq z_&F1S9O%Kh2XYL$RqrEl4kfjO6mrI(N%?B4L%-DCuFj7>BGSR^{rg~aQ z&uiE2i)}2!&X6HwrjTUH5He?uiV#W&i3|-YGi^gcsff^kk`OX9n4>`|gj8q}(nuxh zeAnL3c^~^d=X^fm0DX+H>|@wC^Xt&3{5UXsY!5{=nj^uPcgJ#nNE zBO;8rE^+7o*wK~3llrnw@)AD|jSid&N!e?qr;pfRl9~EzYs+Maqy+Nvrw#+TSp6}Q zQ72VOAChzl`!|M4O(b~DGa83em7l+oS#*eB$c0=i8^JJmgSD1A{DT87Q)v3?2@7lB ztu?6N&Conx{)_yVdx*xu?wy&R0w3uPvs;H5_=34cKV<7R%)NrgQ4eHLfpCXHzBAtO zj4^w{`E4yn&68aHI zXqk!9Ux7Uuc{?uz?ar?0s+bY8Pw(uDmEv&BM8;hv{uo9T97U!*qINL}hkx4(T3a@} zFG#BT@#6~Vh^D;(MFI{Y4=Kdp2c^e!*73$DrUWI|ak2Q0UT2)e)B?7N(puq6t=%@7 zxD3!l&B1c~zd__fy62C-d}}XJHb{elr?!`tF)%{OF#?KE3tGo{NOS`6CH?`)&ncz|YE6z5JdXyoF1W>piK!5F8c6R%r#ZZuRlEtoRM1a4}l?9H{@nahvB!%{?{%>ZW4*8eVR;lXWr zKoi<7y*&TiNYKaduQf{dRb^y=nB9as1*%GPKypZ>7P=pQq`o* zd;$pE zv0gTdpc((|jkFrka{(m8HbK<96fWexZ4A*!isu6Kt~sc}7wN^0Sh_mg+)D(Kahk_^ zUKg6XVZ(-}*sMjQg6?dC9vaGW{=bcyD_wrhRMb|$6BsTIT<|Q-%ph0;%RU7xj@9|- z<3sasZcb#DnjMN_Qo?}1@bRno&K*IN59u1Xu6dnD2|;O_5}CV;?L24~gCR$OaQNKv5OG;xdN&fLL^PY!`c!Bhnk({z{Y2W(&kfxx9KU`bw z)tGroK^!9iw6YLw{`~Z-X12itKti-6MCb;@lC`izqC5DGd#qgImuj>OO~ z2!wm0DzYU&(f*R;L266>{PXwxoE6b2=wVJpKfW>yGI@$~(#&w67mGto*Fs^VUFD2+-+U5|v@RxX(Muyj zo2m9bd<~5TdM{WK}&Xb=Zm@lROkX-4(@2m(g~Kxs^1D+px?LMsQiM0 zC-QNk7EZUNfR$!1lHOgUP2!VGiK+2{Ud~-u+#|HE`7rpgJOZ=~0{#P_$D`SSWDid< z)9S~CUdDqCnBmuve|ddPhQ|9#O9|L0|y!L8M7Z5EIuI1$zd% zM1v-zJah=7HRw2>*yooP7oWnCL!JvMT#_&O;={sN2ufMsAd)%e_C&xbYC(c~w-Yl= zIbR(GxX`0TgA%K|jIXO8_Jofr&OsB<4NzE&R?6!7BYu4{S9tEWl@vw92F2!|vzj2A z6^cA=2CRvBlrhw3n`#Q%LumUz98X~BWHkyYZu(m5zY29oh>YHgmRf9}$Q;spr|#=s zugLXxP_Vi5K1wDt_9H%W4CQ|Nj@ojP{_Jde*IS2n3Vx6q|H(+~pkQLeBao+}QTyUr zbhBVu3Gw`$$ICg$fQ1i6qhpJF+R~R5(3DX)p7T0{fogYv*2JDRM$hPQr)Ij*0jqY2n*e&5zbLsx z1oWcI#H_6cqq7Zf>&K@JTeEUXVc9dgXqbjJFS-ZQe|X8v%ws1n@N(?ff*=bQCGP#$bl4_lNZ z+_Ur{v7P6iy$I=x3Ham-Y1BFe&ra8cPLkDX&(S@yR4MA^*=pZM<13!sz(I~x4%5P_ zJ~VFfX}b{;wmGI_3pRInabe!Lp&ZD^P&?Y`(7(=a`97j)s)2Q8_{V2&9hqkBPVO|tJ%cE>3dmDQ3h*mn)`W8-Tcl}RquRZ}Hd`=Lj_?X#V&un%WH^v5EeQI~ zeDQnx$pcj~r@TH8o99}LD-ah*BmjtjWgrzgL?|J9W{V{!QGQ|Rr2cYq4xL2mncfR3 zYhT?iD()3mRFqwm7-XkENnP=9^OY-3S2Rs&c|K+Mq*>#d|IxC_u;onme$&P{{@HTe z-bqC}Gl%bNnX;{A%8`uxgCmB=^qKrP=vmU|TX|hxpNPuO%%!N z~vYpC}H~4sX~vi84ekS>}$nA`}CNs&MB$CDwxZzsnusC-m_JSt1T>cFv;qs z@z=_M$JA;SQ_2qhwQpa1vG4Ph-`bb#QU7ldpzk_l2P=&_b5`@gIB|xEH{5I@gg%D4lC=^(8uSdOwTrD{pItW6!w$l*J0<^>MTY z){oe29}y6yezYxPjkGn{ytCD;dGC=NDj8&losqq#ymZGU>Roho=T*(~Pj0TN$}Hf# z-TTbKc0e1l;YS-&>35`1X~a^AqSk5>Fw!KW#EBNbh->W=dNV-?pc*SGBDBV69!`To z>09-QH`togVD$K}g2E}$4LMIWzbY0HaAQptIeUIC5g!v0nJ$JSa1!%6ZqYp*;%ao> zu=|deGiqOZodCEYS~8&su^klgB_JT+l;L^hnzvLJXh#Q7bM1mW?G-WK)HDUTjWiSZ zyoanbUsB>1voKah)_E4?kM{hv%l^1!no5fM(L=*{tV4#DduH4yh}k2dl63cu^iE#$ zxRlsz)K77GVEM&MrM=57Ipf~Pj@cp^@b&dA|C(AaohP@Sp;Wk%*!1-DcQ1En!B_kl zU0bs`8hD+19Y#c(cOcoWr=Qb3A$&)#f?1b=WS&dn%t4>v3B;3t+PimzGw7cbzK41} zg*IGZmmFvg*^)wHMkdndey0X7hwXl<@zf?RT^exrGwJK|uFq!Ox_93ta~tH8DC7M; z$wp1$zZ^Ua1U|LDf*le&()aJinv?t;l@1A3$-uW2#p4(Yo+48Hb_#)^wrdWsi-1y$ z{iNFF=H}d~x^P*`NJ4SL_{1WIWAZWnU93}i@R3L!E^92dku)EVp$AK zG_dsYt^9hsNE|((J{I$>bX_9tD?VyudBUxwR#&haFJR$Jr&(J=fU6>dhhl@9516Ck zbaI0KEDcXgZjbo-PF=ZD11^H+m2*zxzVwz?AjHSSiCb1o#VF#KT&J0gFw((iZ}@l3 zCawi+aQE3#Q?&=|TE;M2a?F-3;)aBVq&hCYR8Pje$uog~(S#GjRe1umdya-&W&kQd zP75$kbjr|`k5k_o8X9^eEgD# zx#afd!?K8l3pL|ZrMySSpdh*d1*hu52*<3X#Kc4l+UkQ@wC$^jJw7L6N{)_J@@LWBX^cg;Ucy6=F8?YFi&d|d=R>Z?J%#L0K zhTQ4l(K5RF>+PwUvF%KssHD^}Pck{@e24~NJGJJ=>0Hx-6-R4FBV(OZG;4Cnp}^SO zcKphyZasUJ#nk?qF|Pxhn{;cVwcQ*8X zO}F2)HuhU`w}kaBdBe1cqWU5Y*8(xF-bz!B6)*0&j=VxCin%#QyuC3M4roY*4Y^d?y z`%PuDB!i&q%PRq^8mf1hU8tQfXO&8r*Wbc=AEW+_m6hk1wp?a6CXN<3!K5z1@p_;6 zaUV^!=z!wO%B-_VohAHauI$oueGQ#<2OgaoJeWli4<0>QcKak7eT27o@L=5esptHa zQVW8^qMFk1lTHuE2v>tPI?I$nksjaIYt4f!5tS5}RV%u(&Xz5;3l0tf9ztyoWm?lM z_~X(%$~bn|EoL>Cb>Q)woTYM6fR5(HmBc=poMmWYp^bb-BpUVX-TN+?Q>=oL-_Q&TiCx?Fe4V`%f zXDKsy?0V_BJO|yC4SX^-F%aWZI2gNElU!}4PZb_tJvN)Fz6q)c7#e=3KqC@5TxQ0* z!6^>!3ky#yak~{4k2d4!%yn}^jnRMmTI~v>F|^F|GzJ-uMw`xGdxFkfU7rYzr{@W* z^n;q15x*>--5}eE+B7Wsheqn&!%4uK_8gTQGBV?u!n;jyQ~L`7gf`&p@3Y6&ZvD8W zwz?DSBwOX%QcFp71mKf=i{7HXkvoQUilQmuKwA<7Utg3~zpC49%z-EvcwmT*_R~&V zUg#}gX;co-X7M!vxI<)l@J^>4$A52`2EF)wPKvFi=k7kcNgmc>i- zIn?h*4j(>a*E!#T7?T@QTT?fsrW8BtPX_HT(8Hz;I0HQ|onLN!WMrfaU$RvK575uo zyx?qR_X{S>H)pB2R?>Kr@yd(oq1CU=ttz8UC<@WZlPA|-*lm&xA&4Sf1|xY7>#sSN ztdT{|g9o>!j-VBy^EQSKw0fxHIGC#|9m1G zeQF%GgVRsJx`dJ*%l@(?;LVq7PYaxd0itTur_O=Rd-`oCtpO*jVHLO)!)B~cx#lJ9 zuYCjBMtk#+|en2CrWSerPR-*!+#oXhBMo|efxMY0gJn; zm12A06HQB$+p_drm%;xuKG-t8-At*^380RGFxdG84h=hBS!QJ=3cv7$rJ8C%V1ts? z5kAo?gTLO64i8_y`!d(O$;FyMc;Fg`nH_+sCexgPL=y@g`Njx8okiWkjZ zko&m2c45G(Mg^Zf9rLe>)*zliT<_rw>M*Oiqa&LWh(}wbv4?WP z93nu%0@JNBquG5#g_BaUZ_2N`Nw92-Ze+su3Li=B!4j}uCauo>&I5gz);V`yW@lo6 zJcbr6?sJFA3b!n_qbo|Owx09SCoS2?OlC{}Q$)u(-)ioP#WJ-d=WO}yX^eQWO+X+3 z|Ifjxel4?*Mh*vvkl)@cYKM9R+flsFZ+Z9|_bJ{|IEs?hC%bU3T`Ip|+f}87KG!xX zH*H$~@s))+We}3*VKiVF)Y;v5|JSYJqXvO-GYb)@(oo$-6CIwT$FN~<-WhKqyQNmY ze))26>bI0mNpdw0EMy1;ZTtkr5)B)Tvr`-tQzyUyVRIWK@x)e{2!^HVt=jQnJOaf> z)OJ>{uLP?Z3Gw` zrKDdwx1lFgFB}9{tB?|$A+l^}*k1X57a%oe(bp4??frq-D5n^uPXN^GgqEA&kGECT zxMwT<$W9wN2a62Fqt|J>MFs|qA>ryrOu_xX8NE(?=Vf2W6)?3d3I%oD-1zzEm?hO| zy+b}dxV6>k#~mLO8)B(_2KK?>%~u(XyyMduKc;YN?J6_`xj+4kT7`g(bnJ^46XKrm zUntPD5|2!DoYQSGcJBN`6p#M?{-r*i^KbonH@=lMyl*TfN#ZR}De`!IuWA2+okJSA zMp>koe2CN1J-@c}Ow3|CTiXG>)PgKle0KMZJGidwO}hc%>hAV2mUdtAmc}H;*U;O$ zbp5*V_Ek2GTeLWmHgebMwvHvHIR|~6PDZ+(4vkm#Jd*Stx-xIYYNCcX1Elu0)O2qC z9NLRM=~_&AcXX^ilOGb&?U;|(x3*e4?W^Z7W<<@r7L}B;;$@D}^7OBRb*RQgcFFKI zRzaf3LU0+#>yC36H6%NFHJL`87RcD+Pe1^5e{AV5Miyx8VxihfyQ?2PGRg@rI-Bc? zU@{$q=-vg+{E>Im6j*Qh@lo15cd@0FNiC3!VrNIbPpgHSpXOH#IU#ir$Sk|CFp&tN ze9?Tl>+|PxL^%e;q__zJ#FVqa4UV^cd6u{McOJKiejlEs7vFrqqDXsfU|bjQH&9Ak z0A;&{&3e_}O5VtfoKOE(tL2(@2F6%{BD9%f;n@}{Dh%0fznjh7Zip%S`%MdXjU{LO zt-fJJF02T7?P#Y)D`IWz_r8Xpz8*7> zsOu`D@0AD(}AprE_~3bGlU zNA&pnW#lT$5Dv+3@_lC^w|U=AWr;&XGr*Ijg!??icGSNfR7b-xqFoSv@WHBCSn!EG zBQ3h~oM*VcM=-4<(c^f#v{|5gnP5x^Luh49lEeL9Mg@R$hIoqeC1_!zMswN5~p^2A2l{&)khc0$5rnv)h6za`&5?gu>0hY4YBM% zIe$0*-Gw~QjOg;8n{;sb4v4HJg=_tEn!H6V@?4Ybva+c$Cobq6X=3dIWb&{cReS97 zSB3^&N88-WN0dGw(xo6aIpb{lS)Qb4g;w~lglE5MHePj&FIeXRt_q7abTODV&DkQq z{(zWB$0PY?hkJc;JHkk%w2)z{sB}0QxRUo!#0XBh&0u1-Yfz3 z4`7TpYSRkp~Gk2Z+?3>_|rSxWC!Ldn_dXdbwAzYiw0t9k2!zHB9P!;kkJ-O zS#NH(acO3lu}fmL^(eQ@@`@RGUPEkt{M>M5xLc+wvVjLYL#eW#N6(6k!hZ^&Ak)yW zs_+h#$b+h=|HGX`rx^##6R|3(N>qwSCN7C18Ny1$A-OR3FwQd_-a=!|L&d|xb16ug z@>`BB$vd+3Rr_)N$;Ds*jNig9oKoq~K?HrUDK?1@JtEdURWEb#RQ3rs4(Z;zcOwk1 zRE-S=$Q?Rvb2!2}y7+rym9w{vOQ(r8$07|T?E0Lr^z@<2-5p{Kc0Yan_U(xD%gVZL zwpS8|JWLEr!Z%b%GER! zm-#tGmvNKFfq$shj2XW_{HBMt{$Ce!*9ub#6`GGaD>8_s4B^xm=6B6(%6j3U!-lns zbXl-K4W(Jp;QxfjUx+$n8BJ6=<5e?Ym@4y*+jwYrQejv?_tGgHKMyL)nXY5+21yfs z|E>Lw{IB0C2D~dWxET4XMklbK24rB9{nwDRiJhDu0D4i`oWfy2@~CKqa2>HcLGMu1 z=`}NUmM-4m}1k5 z8FFan)dwLm8w67|TugYq}JWM}w7WbRb1F*1I>ap>s-R zQBmxI@ZoO1zlAzn%?XIE>ZQ~)2&2Ogy`OG#nWb-q=V{NzW@(>)>KPfrQ;3OD+`7Y_ zUhC9yBYtKsD&0KFy=+2WX|+@FFt;q zI_5U6Lq?t})>}B~`IE1@G5fQ7#YAbUUdb9#WZ-_!-8$xJ_Qz?Xit-|cIL91LintK< zB=*qh%<^AJGmGN#Kc!#F%8%e#oPY6Y$oDo*ndt-4A}mi9yCi&b9%1*}Tk354^y9`E zqksP=|9wmk;yWv9|Hi@m_uuTapUBpb|No!LoTbvd#sBwAxY~J?{O2f*TT_K876cjz zcOSKK*9rSiAvFlwZ2JHF$&yFza(P`<_wL`{jd5HP6BDMr%_=qwKmc8r%@c)#lEy_g z{)Z}*Z4W389wIL&|C+_XpxBcz% zUew8@Qjs;~Uj5g1&eW464lZ2=9oDT!5A&rv?l$)8Dq?rtEP_j}6z0j%R2=@fO5(DSUwtff;kdje241UY715EBZIzTX& z`iPvQtVaU}IS}(`x%#*oAiV4@!@PtgT4Do_QdumI@pd-9 z0qu}6Eg&$=?)|Pc{QEtH9sc8it#D={93a{l2247XHcXXh8kQXx5TGD!FCpG3ub_o0 z@*xk}z7+d7n$F1Lq+KDpRRSm^0c)W1=ezcUi2{;|0V{(U``O;|9zTA3s*2s2 zGWaV4I#FeJZDM{sf`63ijn1bsX{-K$id&IQV#AC>5;vW^mDKD6<%&wHR${$ZuJN>O zyJsl}WXT!&B^lkpvd}wo6L`)FQnHg%u_CYVg;Jv}=u4y(Av!T$6gAugy8cz4Lc%_x z3jnYCtVw(JON*6rr@oa|JL_z4`H(1Ugre4aa*-gEO^%kIo{zCtlygtIF&yQ!Oq)dI z!uR>CkyS{~FU&iFXhpW*kk-9Vms-G(!a0dA8p0uou}j-f2M=ewx+TsyoFk!45l;D& z{NOd0RxOxqXh_}uC@HBKQ=tqE_M(R3prs6-zQ|;8>a*+tzpBN{qom~S+qbesH|ksI z?cilDTVjTkS4Cee{9jf=FC4;CwQSruyV^`sK=_Oyg@Ua6#Bhj;GwiZN&rw(C2s*9$ zbm!=06Y-nB15KJnw)Qztv-a(d3c4$jb!ywY0EWKsnF_sd5J2mTkrnI0D)e==Dx2L zwAVjmXX~hi`{*)b&PfSEo5M-o!Mx8*d`VUH^58?HY@j9FD_vdzsHHQFRu0}#xUT`L zUiOirD00B%e$KmZ-gL+`Q(NMW#-cy0x(o;foLEFpdMZa)Y&uXE#BJISS`(*6Vb1v4 z7gbGrVaqG+CypPWxo7;Z1XhYjxhr$Vk~stoN%t zHEraef2RjmHGrccKm0zX7;)SWF42tLpA_A1zdS-6?5L_txX_6@_x%d7Q&ccq?fQ3A z7dhR9j|N^OUb=`uM2{X58ts#jNnSFekAsjnO?Gk`0V2ppE9rPplgmTV2?sx!CCEp< zxumTa=<_rQjU*pff-hbEsi-c>%|642GSPFo4>Fs3$X)3bqw5SAaukL{k^qe!sfo~u z_Ij~TP%;!S)_e`&F>;AGd3!p!*lYGPk50<;$o^>U>FxcPlPMmS7=zSF+1`NFH95TB zIl%>rV0UkLq1o`U>K41)8GAyrF?ss*rx6Rm930ZwCK>G$%xlR`FF+Snv<-bt$shHv_!aEgp9`JCC&?XWn1OF)c*f66c^`QAK9r)-MfB z(TNIodLs)w)@WbgqQ56R92r1&eDQ5V46g9A*>9p(`L6EkY*RzQN)43?x30F-cL3f_3v8{X*B1} z&#%2pn*txuZ$te~;+?}U&(Sy~qhEY@inr+u7SP=q@1LCW(L5P8f#P*Y~@i zot}h66mo?m#Bq$7X_v7Nl17~EFV$V|1V3TeWSICFRe=0dr23X^YvT5!YUO8~;e#4GOyA^2;%3`UwMU zP!vR%1&5UHe^9TE`mkZgkWqFIU}fQ{im*L<8VDUl?5q0jv|e*m#PQVZ6(UmTI1zoZ zOp@_pHS(YHiX|=1g7^iSc8J|thY z|1xuP!KS_7#@+63*YIpi^HXG|mk_pisk29n(A$QVDT(nfeUt%^44;xsQar}Yso^v#7u<54b_>m_9^ zwTWMlb=2YB?U89ehwA-S3NuTrJGS!K;L`Omjhi)F4v`}o2qE=v!~iqV#O>moe(VtQ zk5rh>rOuo?6#h$)jfmcWyH>}_^8^{MNz+g|-|nb%p3yH)Yu(bhmEz?KZvK$-yS6o9#D@Fe3Q25XK2fQ-!k=DxY;^llTdR&ukX3$WeMuuT3*Z4HD zc4YGN=N@qVs774(?d!_VLcf!1mtjpHfWMkWD~CC*FW;4&O9J5B3iTC0K_=>T^gHq7+q z=&<=4BD#0$reI~%MI2cf8vlr-8JWO_&o2+q$J4=1cp0YGd>O0s>wU#*Xztv|wdi&2 zEsOH2zijL{F>K0@)|LgT3oGN3l4dlDkDNrwAu<^P1TM553lT$xW7Upbh?r!{G56WZGzF)>TnLnLQnTn->jB;xls@_GFZ4sIRN;)Hq`p1Py{yR!I z=18b#$AC2o@NxtMh?0d?aSQN*s#;<*wA*pI3u4*cqSfCR79j8GiFk6fA{$kfBM(AI zh^zNLWsC3pAF8UV+}=lA1<3Qp#A^#Y=xu#}?^xn|aNzrzv%PG4Yis|>Atd&q%ajop z-ir*=@~eGDZXV2=68;26UT5?7)pB?kHjU${W>vnsgI?exvx0<8;jh5upB647gevgVvKOAoEa8nW(?Tmfh0YviN`t- z%^(g7vQLL=m<`V%Vym2-9FyYx<+yhclTZI)DnP<(TW zs-QrS{!ZXGiOmvyFoTDez!acrk4x)_iRk@jx|#OKKZ*#SCe>2T4Kp{GFon!fltogU zO9RV0d@PQ6EN5dthxnH-TNA6gE6ju44D7pD!U^L7yT9Y5c$Cc%vRsMHZ8PUllz14Q z>9Sdvbfq0feUeZ3DK~i>1B4PRNhkfi>C14ft4$5Tnk zgdL{!jdm0qO}NhIA0tE|4tPLAY3@Si2qhvte`Eq=c!NPv<+XY-)cy>o?Lu)Jw6EWT zPAywL;Bt5}zNN{w=F12TxPhKa+;HoV?Kj9@ql+V7VkR!C7)Eq;xzV(DMWc@IklMtF zv1-x7{}+KBw)>9a=;wmY7m@H!BN&{02t$+r7KbzDzL!Dq8s3R#zAg}^tqK~y?V8* zX%++1Ew#UfAPa?0>t)o9W4?opOe`{e$AxEbD`qY71zGTe#sk@6eOaHyd6V&sPR`|r z%&w_*9)JV$P2`QzG-t4k$?v6eah02;RT&u4CE#`sM0KxuXUSRyP2{K)M3zmGFX*~kaN&; zyKe>r1(lNfkOg3tyiKJet0NV;pS*$Q9XcfPqpl{T9``k7UjJ-7FnQ$BN-a%4d!oG|p^6vyYar=`)+R5aIOtpi4MNALF zbqX$rh)nclX3%lp#cK;3=hfp5^hG0mbIq^ilODDP=|Ph(dG^`wwLLE_5Oze|_6YH0 zifzO=5sArP1b$0t4Eqk9b$+UcCpejU79HEXy! za-f!mu8t0X5W(B}&fU9;%sWM8AvyQ>?sjYRE6AEfU-y-(AujLUCumiN^VJ!tsk6^F zM4H}pbnT7(`}WNaK3G*<%@dk|0P4r zl=dG-HwOje=H5&0+1-U+x#Db%{Vds&`mOs+rCz5oUkX<{ai4!kQjkQsO`Br@iv1Q{ zZQ1INf!f-qaq^mTVeTQ`vxT|&_6h#)31T8@0AL+*ci)QUg&>7FNJFp`swBpyQ$n6` z^lmzo^^kg%94BFc>KFgvByx8iW{! z%4p_j(^laW;vaYQB~dXz7hl$+*nLLwn6l(Lz zYNO*PFBVqqW4p~^sHjUE?7&@Dntymf7x65kcJ+AbZ+Tj3}VWN!>mPAE}|eT`NY(gl^K+1^&0JEU$o&65>5BynF+Y{y{D z8jz6VhGAnQ+Ce$$ta9dQ_GY!p?#owh4?T}OEw}2Zk&Tkn$*@O=mhQtBN_R+dCBZ;8kZ_kI7*+F9m;#FZNuT2&I;Ngle~O7Z^y!pF@eZ;AmI8L9Kw?rJ49 zM9a2}bM>U)lx-LWYadZV3i-$l5-;p1i@_rq#%CICE}B_B{lwkI%lK77ICF1EhC3r7 zA`JWX(kal)h+IuH!AB(<<`&$i>^RjpG={PaZS`r6hjbuOi~U9Z7InHnf0E!i%D9tx zt8iT8vsp2KMsc~Erzc%1Y91u6NwC<0g;CT8Ty3}cDVMkns(KQTf^=Z8?m#1Sj&B)c znY&MFf4F}^MG|U6+(qi0G;Lb<&Yib0=fO#^9T)`WWMl(f9Oz{`ZoMg8bo%w1ZguE< zwMhij4XgGT7K(++DO#RUM14DM;>0ox?vU5BUNr7X9dA&$(3zwt*0%JzpU;F@cL`G{ ze0=l{QiKDG8Rv+lo4J)6BOE#%sw*S%g{l_3zbv5*<{|gku_OCm(&2f0|yQ_JXNQcOZVts?N*OdiDTrinMW1U$e*Wc^J%(<+W3%&A^0MQm> zDzSNFm&;awJ+nE&q6og>A=O(95ND^`F$yB_;y{;h4~n9<>D7;#N>y!4-{+IEW%n8h zl9@))M8%?ihPpLq*bRD*PqH4XMbrn<&R_(M`mx)6`&+HYzZUhwn9pqu7b+@;sMlZW zpk3{-+t?w5;Rq=o8NZi_Lqhp`VlP}ib2jt~H11oF2yg`q1li*YC+0koqvYB%CM9Ny zvNjY&vk(hBN9li|C^)mzq;&X#3Jp#D+})kabjo!D7LB?cn^RFXXoFipMLxW8FC(`N zpw5YyGF2iy_gvYTK;L$|4m3IQ2fMK0{J3y#7)x3(YBw8QN47t`v3HY$r(u|9={>2hcWNm(2X zLzI&`3Mlv?noHuBRh@5Zg>gKkL7BSR+V-Ok` z^)41)^pc&fm!;NwYi(^UN;mpvkNbyF!1PIK)!o2jG1x*(t{8OnqG?wAq^IA^h&2l- zkH=Fc>p1(K_Oq$FnU((_zEW+$wUtKK^Hs-cj?IYLoObG)a+6+A1kEN@FWBqqihEg0 z;GN8{2Cp2n;@O>3B~08lF%3fvg!Ktupt=-?B}jk+|ExC{;qr5CRRvWZDcT)Ek`@rQ zy>V@j_mK4E@I|e(gGlBTi^-ofn>mTnWI`wE5A}kqn?KFmbqe#lB0r=S1NO_V3Fw?s z0X<;KD0TL$rV?&u*aQ1Zs|+tK9y3S7=xCOOU$b+Tt=+9TFvPN0;FA0)j801E_p~n` zVdmvk5;fg54e(~m+{m|$GsgZd>?+NPYUYYr*#JmF-xDWjC;6*1ZBIxCAKd4MfnZXo ze8lNTMAek5l}L5PD@t4?VAf#Fn6uAPR(-nYEE5a^aG__kbgiweISjne4>r<3lq4g= zdNdIs;im+7-Zx|7YVMk>=tNjF?z%ST7*WAto*;DqYJSE{3Nn_#nO^ObdaXzJwQCrt znO0>mB)~_4#5r_!U$m59$Z4bmquSIcyt%>pHi92dKM>r1+8eUc9y`wd#Q59~Hnf0c zP!ww^`EJx{(ytdX6JQSkXW|ld;X@NUrpTbW7=Z7Ff=hBcj2FLAacPIgSVHx~*{VJL z#-2286yVVGethf-cg;AVU3AhQc(pBCwgfiGA+X~)cyi2=nH;CUCO&J{VYEvZ==Jqg ziAumsq%QZ!^0MkdywiEPR58O+bz5s1D`WNr7wX1UwJN5@Sgr>;0s-`n~Mydq8rfjhT}W&|M{ z?T&nO4_$s2c-)8Ap}QcX@_o?2qWQIDqFdH5LHls>#vJY21B13FFeLPr&T?FZu4sF2 z2wY#)&u8_XC^zX>U|O3yXN}$tGPh^r%}Fs&VG6-k;ZTgoXc%b!gAcG2gds(QaAQ;` zPUlxM7%Yl*k!~;w2%D$uzgGoJb{Dx51Dd4U>5Fp<0r_APwvoCQIn6Re}m3;_H#v zW_8SG-nYo_E){JaCxkN8S2#DE!GuczXX^Yh>-QN(3fL>9nGGJ$IPf9dXE~KMks}H+ zltsbPK_d}J;Up|82V1C7^k7TKq2j&`CS9NXs0n{J7HAKTx+P;qgGslGD@3}13|T?O z4X6!t=H$~(@rTi1RG|(f5nd_2mhFRo1|+X2{`M`G>_|8ki)zR}TcgS98X9q@zU^(@ z*8uo{o58x1{f>sr(a8O<>W9+STS^}PwB3Y9huYLd>_8zW`vRHGb7PEKKP=9NYD0#P znD_e0tCpJxkdo2Zi0k9e_D|s1fdv+VwP=kG<7RYCZ#+QyPJknt;4^O~GIjuExxC%> zCx5|S0vQP`vrN+qS}%MUc|qe8K6kxZwS?91*jLG`0etMF`TdV%@cCw6WC-N-Id2xd zNl&-1vhw+y@9xP9mksGsJi`5ZtUbF1p0*ndN+b0Gp^*rZDd=N}>z-E6x{PFCiktQ^ zB~XdaHCw+CA9=W7d_+w%?``?Jte`=uC_D@16|xwwi-Y{ciRb^oYa(1N!hYw~v~pQSU)klX5kX;l=kh9k2rC=g^CK9cml)b@7@*Po9V;TkcnZc zbOC()MClpu+{I@L=v*HdEvvu8sS&c-kPEcBBD);2MV4_udpw?bGnhXms0=m5V!Zef z$d4jMJzyAk?(I!|VF3hm;JBfa6uK~LlT0HtzdDeH-^-WA^B|BeMMR{{hg_jZG|^aI z@YKl1;Ya!T%qpdtqT#Z)6P+o}GHr>!Aby>ZexKhAoiKhpyj3Dx)D6_TjQNVDkm9hN zTIH0fQ|AO{ynge>5b&?o;KK%SelZF(VP2P=yE637w*oN+Fe44kvjgdZrGt|-v-`v= zf%u3J0fuvGwk~BzZ*}_h14Yr*ZUj1+?hrf$)mFS zmXF9|D+^3O1agQ1kzu9E)mn>}w|XuGH5q}MseqU9Ttq6tX+0XI^wUSNe3BH7Bf@>E zfF>)CA3EOp2-hQS{3E`Vc;HQ)|Ed)n1~gBvR-Hwku!WlfD>2(;$lp338L)wa`Rl-e zTp~4UKKTc^-$9dHYX3jf6aY~&frYy1$#?VYvd}G84!@|rwx!mEjg60??iG7s>Mw#A zVv3_`k3sF4Kn*Q{e^nsn9)op{R6hg+TtFkjJTO2`WpXx^(D@bBs=fzTr-Y2CVz&@?mZfOd)V&QkAZ8X*`h0*_MBtg|PGF>1pUObue6h z$8-_A40Co5#A5(SIAQz)1SwchbyCRHRB`%wVZmA@8Y`fx5}p78Hm*)@N>H(H&db|< z_Uzd!^f{Nc-L%)u?GZDb{3fxH&)vj9!`^rtfc%@@46ewfOP6N4q-C^LRXrO~#DYb> z-Z&&tWZ1UB-cm1jIfj#!q2jcPFhoH#5g?OutMnX7$2rs#j2#Ez0x>ps-iQ$+#NZNO zO!K*0eE&c=I9h*sv_*tbag4o2hlmUq1nw3~G(N-3b2B?f#g20M=?y9hw~TL{dwb^^ zJ<)_gk)qNbh2?Xnt&rRO5phQqH5W4%7`AM}B5GC{9Uzl6%h{ro`*YdjbygoM24y%x z7a4C~i0fm~@X6B)Gn`kLbr!ppB7nDu30A zT^ABGTmx0H9^h+x{<_X-hMXB!wD~A}9uZC-1V)_)>~WZf`UhFzjWn_Ib)C_liZ4|r$1gF6f?A*EqI;u>-(h^ zMqe!2xchNI{-Msb&K|9>?xJh$nSMUn@E<*terBE{1v*9}j~K69RGAgG`=Zwg-=~8{ zxi$MuMfjkaL7`fi;+O6cXGi#RvGbuyJap{XUaTVt5mKU(wPdZlSITbfFvZJ=j|?dy zW!jAAD#;NJIHH-QG$yT2;Z0I8qcx9H0TLqT< zvFpXbD~%tF)N*zogLlVKB8#l}rtAot6Do_K+lmMh%c9hh(ss>vTy#gaL4gVD?R zxcD6Ox_?VZK?>$B$f(e@ORu+Jz2m%Y>PLs{9&r$TRWiGJq3R*Za=Btjr6Z?Lnk1VB zD6q#idLrMDJW*iOXJ9~L*MNEtgjZr{b3l~$nq|v^b~Z(FbYW+G5gsugg7EwK=0wnv z@|&+^iY-q-uOOek(C1e6*jRM+;PCFsN24=V|Hy80hc+@D1WhBeJH=b)8gKIiLJOfx z?r0X(*n}@4uXLVUh0}@xl=Czx#DDuT@Qn$wrjaKUFPHPF;pOuo@Pzw-e*SpkGgWgu zeDpb36%gTRihKgZ5*{pV93caP!nTQ($IWvuJ}Hk`K6b|;)B*xHIHZ5JYq#>5g)KO} zF(3ya-$jijdP%QdwK*^WLEuSzgNg9@)S)lnUW+Q=-lcRQSwZ;hnz|^QaMs`X#|GM^ z9u<=-^n&MTz;4KNIdaaxlsrRhZ2{gQxTGG0(a(iRa;!FhKjHSvqzQ*(9r{*eJ*8&# zA3!0M2TutM3R3CNA@q;BAaWXqtoJjtfl50Kw2m{RQ%%Zj5`jeIt9168S)WXG zZr!|zRX$%nYOODHA&7Az%e{DhM*1<_b>NU{{qPNa7s#b5ItYjmd9w8W>hF42rm0@v zs=5&7#fN3zF!dh2W^Er;Q@Mj8Vja3PAwQ>|9MLG?+A&rD|5U0lfx^WGO@lNQArBEw2Y=*dkSFOwxgEf z<$*<0ybFvR8HM{_vsou9q8|ph(E_3Sg(+_%j&$ypOH^8n-6Ch8_o62>8sl_cD$V1S zP-;T8;J|r`v<9MSVrN$|RjYp|`arx)$uN8f*{Pu1)O%@9T#q;hj50KqEB5v)JJBE< z&%=Qs|2%_=o7}SeoL<=g-c&LRctnDL`U7th)-*OC)HGEa%FSs&JFFhjDS!#js%U3O zbBc|Lq(dQNxx>qmJHRb#jjeZJhF;YljP2k+`1gJjthw5-$2B@>a-<$L^yrWmDR7YZ zVwc_9mxNeb>SfNeEVQ6jcDM9SZZ4gvdrROApo$yvGyrA27Tma$lF~1{@fIyHre>1j zriwRvg{7Fm1s%1LSRL*@YL&zmNqtNUS?459o@hQx3%B0@1w!Ol(B_j?3XtFunTc=a z?!S%}-d!3CV(l+Lk&(aQ%-ayM&vV~Is|gJb+q{;xF=Ni-MAyYCbVikglcf+b@ai`W z0vW|wfBWGMQSS=!@*YtT%GyCuh4HUMouE?V(9az;oX~6>D%vD+i8*7m(XMRk{NZJ1GSStG!u{a1xZ>x>9z3@&N6`_Z-AfpjC+Z# zlx#tC-xTe`SOI;+(_^xmwCVph&>*4mHO5Pe7}*~JbOZG-RR%kDOEv?PJbd_Yyj5F2 zy*7oPKE)B1g-d6j>-&9L(Wgiggo-Y)>HnG#?fjq`p=AMS6IP@FEn?)+2FE)rd~ukY zKn-fO*65gt6*-;F6GFX-bkbyDuaVqbP=R6_mJEYI;=BsMutEjX*AsT!eUsu+ z3yUruZ${RC$Fy1ClWQcKH*Vb$uUJ}&;!wqpq#7S*r4=$CLjRlT(;q*6q`;_%JdysJ ztRsznI?b+M2+@XuxU;zD^|Lz1Nfo9~MDtK4r7d6C&qn;2dTGg6I$Y=8Zeqn1TF)_n zbok@Nn3K%AzRT$~kGc5!cb3NwPpwXk@q_K=PVsAXB9rFT+iF;j!duD=)3Hp^;EnUbb$$s)p3?9yc(StS})xGlj z8HJk{-lf4TZdd7y}`~dZPnd29Xp*B`k|np%HSkU4TFZVeSsh?gQ|qc`f!x8dk+|b zaAk#C%~~+v4k&{?ocv2{iXg|Hwu>Q2&|^9Sl%j1Xi|l002k%IRxaeujytkBlD|Hr3 zYbiXOh6Q}gA}j6lw4U#QAt1GdRXV+;wg&E{_&``n&wm%RX+XrJ);#q|8B)bthPFWidVi5ytqKJ}M z9WZbfiYx4b#<(@i@s?5s=-}reGx4O|Cxj^R^DgZ>)XRtFqgp58js-z8kg`0{eB_`Z2RVOt`M0#m*Gi2oG{&6cXWjCrrV|KxElLc zQ9V+kJ8NmwHKSL^%T;OLehQ=+v{C#jr9HVG27!+L^9Q|t{W=+|0*KFtL^81*oeRrk zHZ!(4$S|Lm?S#Fn*q4kpefRd+x((J#lv-(cbpC?X^=jlobj6IK{ax)QLW0wvTjADG zL}`2jDM+YUWy}#9kjprjpw^xaM=rA^fD@O3!fX7b2T)V8CYIqK@i}$emf(xSo`nzv z#|AcHPLx3D!4_$q`^)V)Nn$`neYBkROI#fo^q-hqY{{Qhfi` z*B{3iOopmt)(M`GudT)p z740_>O`8AGlNQO|3q9*z$nk`FggHBUcCEMcBLFVK;X74i*a?>b+_h}CNfYltye0p5 zM{ShW-k)GX+CL-%t#oRIu8+B-;fL;=F8j)Bv9=c1Ew0xItz+6ZPU%0Z! ztA$(#H-eq)mY^%|Q!tEImroY^V=>-bKG!?9uu)w&`U0ueCo=}#4kkyk8F@gq@Hnh& z<>~Hzhe?AQzF%=&^d{B;kI)|W2F&R;376=F<$&DWRT zfF#(eobO5YYSF3FL8uy){Vlubz_ctQ3V&hq1(Ix8Z%p@#aC?@BAlew|MV!mcPB%vy zJo?RYW3!lSu>L-ho96q{bLYf98`DA9& z(4_Ay_MCps-`hgoNkRA8%>;r;xWW5YkkiN%M85&8%=5xJz_1a!F zBZN0GO?sBpcWlyaX20u)nhjxIu+D=>ue`UBAgkO?^Y*$8%FZWZ_%=IU z>XbKAQf|_4`L_nD^kor$`=TzSfg>$KskdN>rHBA*8B&2Ndk#XwsQhLP0#!sKKdN3L z1>Ny7e)X1%d_uxCT6A{+dGgKm%XfqnEoTCyax~Q>RkzQ)M$cs#JyBq)6n>i3JgzU5wL<_7C`wS>Dhv++j#vuj3W_m4S{F7xL zkhIrF9Psg^fDnJPd-v{nEPf_Vp`7-n3Uo8fup^IeQ?g(Iy%nhvy3UukZ6M;~ z^on1YEE1oyyxw0>ZTk>%$wHzn!~19^33Wu`C}JGP?*q7p0H+*n-_)vAokgCYm&Ger z#!>oq;-H`0F(vBGFHxJap2;MtcCApFEA5>U3N1>tZlwv=HQ7fr`MC zc4}SXG&9x}JJq|)v~r!Cs>rUHqaam~G|T2ONIuaLaPrVK+tLgX_ckdUrA0uivx?6> z6s{8qVRvfMtR&g*2nZ`7q!P$~syW*wx-nNuTvw1)$w}FuBw3Dz)Q#UaxYw7yzat5K z*NQ!Jn(K^qF|mr%LKbHjyk`)WvZ))z(psn*1=!XjmaB%3<>P1=x$=;!a3^bWvGT}C z;yA>F?#f+)Hh>enWCqEOQ6K*^IWGfQ2A@xpuUTds+y_0k?n!EiAl@_XCIDGM;DRBc zICVRImJ|boX~ffA4u+C62D)E5H;f8ue;aL~ds+IpL98n{6y+8@7gCQ_ zV2bB-=niV2x;&_5=yPcXD>F&u)S**Nn62S-XU1%$DCcRfgLCZ@(l&Y;(lzPkF*=|i zdjv#yx^m?Y!|Ps5>JiF$Bs@39M)ywe?>uNwD^G)6AO*;7piRE+it|dx1Dc%}(P?yY zs28rz3He4191bwqjdX?F^nb{D^SGS%E$si!EK^%D4;eCKNVbr%U52C#4Tew}3@K9? zE0wuyGm%K9*hvzVDP?F7QX(OwqL3ts=6>(B_kNz|*uOu{>vhgS-S_wV`K)!VYhCMF zxgZ|7HH^{3elPq~#}<~x^-2M(iRJ7sLl2KXwazYfSb4*>pD?$|(lVu3o*0u&O>lckhXpeqAwzV9lr0xb^DQ>wP5!oe&;V?dGf-G(xys z5f;#fhI707R@rt|Fi}JL=n`odV#IXe`SI-=wPel7*2QNTat(7B^1r%q%69y#m-R6? zw=DoIIt{`)qDkev96GxMo3|~XrFqmz6iLo%Q@8v0*vQ_4_}rsRBl3R!aOi$IeX)dK zxQT2Y0B$!eAuLGW*ASd2P-2ifJ8Ef#b=)G=Du(nOkfun_5&IDtu*TTzK<>I&L+0&e zpMyxl5xVK}b?;)ly{r=ufO$LlS{$$Ul;xsSfUHV|L~#$;y>{J77~-z7>j_lp74c`> z)~Jr|dm-|%LDf_b9p^xu;;e(O%1DS@*TSpFA;i`8pb(TJtstoQ7MQAvCX$YSMcN$| zx_;TzB)FvgeOHmMxObR6qfa09YoqF7@aaBMD(9yQzL}`esUwUuZqx{ipc0f@sUQyb zB}MJA)`9NjP(k9|9O#bYjPF-JOkJ7Dhbjl_mffkAG0}fCLvXM6W5Z5%d%?~5ZrLk8 zO7kp>hCpyKAs}@!fGPTWiZ=jAoqf+;)#5~K#rZ@&cCWT^%s7a6oE8X-tw6inPDV|k z`+848w0X(;)6z*@?A>dwy49@VPl*fGiw%Fy7o{QhLjW-G6A{U|D-;tdh)K8&q)KsP z!mf@8Zq0fj}`;dW6b-D$CifKEM5^n-lFtYrVl6df_md?7P(L!9QJo@lo* z57F%XHR`{VRQl2Grsv-Fk3^Bio#qBjK9hbIvhE|zcOJMvKK{=<2RCsGBAP3twv-i! ziFP!4FXDVDi=2=I2;21JD8w1lTw+jx^M#0saUwB<1x5y@5BG-a1px9neqHddDUZu= zH zvwUAKdV_tgbQ-qYi-T&rbm=3UwZ26+;}E07P{=ed9pe=j5*XXg1MAS0W2MV-6qhK2 z`Kt|RuwZLLncrY{1{)!1N%G(E;F5_%(lV!bC}|yHhwN+41 zG@K{%iP}LK#3ZRRCG|p9Sg$V?tAYm^Uneq=yJ5oynb1RatI=_RlM{Xg+Pn{=<&Pgd zYR=$39aYFLaH0TO&zh0YMp)|EUvI{xCZRNL)=Zq8WLp)!EFB92uMhMG+rpRXD)$(Z zHN2d9t-+Wd>qwx8eHwYO(n_UEm%xJXOQd>VubCCC_j50d3`g83QWiLm?g?e;kSU2z zZ*(I;^Qq9yBLrg;^8xM@cJU7(8fDI%871VI8vTMpj-PF^C&(I<`dwxdZl)Gx;+EwN zrp!T{MO=_*1b_by3dM$iNgvI7Sxos>lqFdBP2yehcT7iby8JtBS%IpwIpBaI7N>7V z1R*PTMBRY*3ISkhjB5Ti{-M%Wiu{j}_A#J69_1-gCdK@dx5cD&8XvNDlZbkej)D6T z$I0SjhTb=v8Ih3|UDbXG%ztCIWqsyu*(=znbLR_dRIu-WzS!^>MV zC}hV%h&HkHzzXIACh(Dg9frs9e2>XWz)9FKh*Ama(YMk?bv&UVM=Y#SZS~aEEf1RW zwu1kPWmbcGQ5v(EZPkT>1A^s|0obORSLq)2U>fWuU!8$TZEBVY>|0KpPn00Fo}vS? z?&Wy8rvz68D$}7UE?Xnicip_X{@A(Q^sc9hpGRb)%wz_p*`N+BI=D4ak z1`V2uIip51oeZL=`|jMhq9bv%-W#jy;20#Zv~tL?G0O^mOc3SB$&-VkuV8{e zvjrS?JKu4xPR~v(!V?FaA->Y+7}UF78#T50sXu5DhHQSKzNAZ1D7`Vvnwp=!IbG{= zotIR;NI&A!(!{dwrtXK0?k^(Otmh-{X`NPrLdWvz_M>j2uZCovT~(<^4-W#OXyxoN z{7Oi9@XQHruP{Fp0d*V#FK;#UbaITe#QsPcepIz@Nm%#SX^h##F7IdY$;r*2n6ML^ zL}y5WxYO*}<1O0FEO@A}r4M+{zDA3FdGnjs#!jpse&wn7Z6g?FDwHKw1MiNT$M|}|A>>lddHzZ?vx<)MLy%>KPrHdqv zkrpxcw>df+}Y&L1qgE=XE zN6@2FN;_9CXxX0*OP5~Q9B{cwQqZz%M~+0L7qutLavEh!Ui9(0y4iToq7eZKNmXxS zb0Ru{hjiZqSM5!>05E#A4CH5~S@_47JKvU_;(rI-KlEjH%ck12EyTDPf(OF|+`6{H z?S7-Ka7JUJUU+$S#+^}PK{fNeZnSHf*RsnrFli8&tpOIYnKSVOOBpIcA5~Oxj2sdVQL#xc?(~ z4p?T_g>Bpv(Fk)+%!#~r6SFoU>JYxF?#Z*^QCbGp3lVH~S zpAm+HM~@~qR%aN3{3iw`ydIJaS_LoyoE8sl_l+BFXMAs^U5<#r>aQJ-l|O}Epq|$J zNyO0bYK_7V{uuF^%oQ@IM3{A%Yx9kEAgV07W)q>roJ(~{i2{tkAQ-hD;AzD?RfJCu znNJ8-fOy6ijrKK~zkF&W1(ui;QDVIP{JCn-nTlt!6NRTHqjWS@C8cnjqN2^<6= z-9F~5**u3-QV$x9sgALs)4?_@1(|p-)O^+Lgr6Gfv=@d5M%0O-j2h@A;oPl93 zItRcO;4xdkB(ZawBx{X+{XR!N{9k|UuAA3dPt^O|e>yM5gBdb~jJ0VjKWVpcVWRhM z-4(#f@yE1YU0L+F4rW}^S^hNgnr!zJGs*Yk9v-EHjYl*M<&s@P;1P%BA)E(ZuLEQmV*+ zCP7YSEI=j5LO!r^#mls?hZd#DtF6PHU`Q`T5dD9Vf`*4rvoy&IGC6!-mf$hx1vazqFzmk5QksI~>%)M-C$XNj?ng2Tcn2RcuV~R< z!${~d^!lMoo#wukMb0uR2Hhg9@~EhUJ9qShf6T|%m70Qgjc-T(hw7n&2Aw228KsAE z3&f_9LI}Jkv)F7D21rRs$CtWa6Y_YG$n8ONx^K?1nNCD0u;aRK!qoeJm37i^d>dHf z$oklFtHN*VC=a_29H@f1$4$+7!?!kwqa7(4X-b&~8Hyj7y|3hJ^5Rjw&_n4)(x?1b z(7uja%&MFl{kTJ1^SWLzx$_s@UD}&iBa`y*&fLra04H~c+g1EOobL1H{U$L&HaxL1 zk$9k>4f-f5{eNBJNvUsI6#Y!Ux<*VW$5W@{4Zzym(e3o-QA!3OW#kY}xi<6QY_a=D z5HF_A=!*(g$Or{|gY&3ENU!NX>8{ABCoRN(oJT%A4K`p9N)Ck8gOFEg7A*?MxsWF7 zI#21fzTgxkblzXdk0=`y4qD|yOoJJ84c`bO1R>pti``I}C4J5A0x$d$e+z>7gZe zt}0_p(I0l)?6l!;NJ|ipZ`aP527k9yi1vgJZU$7*IA4iI7Im_(vfkFwyJ?!dclE@TQ3{}XX&Ya` z;oM6f#$rmvOQ-^4gXbJIy*HMxmG9zT%}4e^Qpx!Re>d%V@n;|P)v&)rSMd)yqm=L} zHS)XrEHEq*Dh<60v#*^!+r<0*nU_KTq2^6=wJf??4X0CC7+?6^cZf%A^%_~V2+>MI zy9uAi{EL`YN+Ba-$_$B|1SuT0dOK)v)l+Ix?o*tMldzTA*THd;7YzWX#P%p4fX$+u z-nMMjMf`!Kt3+I*n%^5w_jT^K~NXq;%DxaZ)0s^pGiJjCsS#?_g6 z4F=gQa@3ydEiyl<=V>gJH=eC_^Fwt;{WwyYQ|b`wUItqFv6E~Lf0j{-e>jXu@)Za^ zqQr1u^d7nSJd&xbJ(PT~DZ#kQz^9Q83_9LZ8A+*APMfi9gjL1Qp%g)a>+noKJFppb z3Z<}=^Khf~YtM7pA$#P2>w56x=clfAWUpm*l#uj1dZF-`1m=(q8MfTc@cl{DBdV|# z9DDY>W;ZbiNGVE@3Yv;Q?`65=j6VwJ>i*sF#@JV}4gxbwxwN6aqv$&@L!IY?o7EF)Qp(v|FR5{$)1J4{}x@^RVg%vg8;TF1) zmg_j$x|ae12dwmc!(Guu<1GEl<6PSYo}Jsh>{e8sGMX|O{%%9W*N87Q-z!}zyq-U3 z-u-4X8xfPwcRQvXalhcta|RXocuVYNW|ak3P7s{oDbmwTs z?IAY8CT&JoEp~zYZ~?g27AKc>yRO_EDk%bcLL$wHwi~_)mHuyagGb9=v2^ZR%?$~^Eh_s}eAY=2? z?RDqTm|_gA*M!`6&!|vkMwulk^o2)0S+(*+M}s>#-D;tDWI6)Ahpw4x*ZV(%HqN9| zyYSIbrQ~ez}a68PZ6>f4V=pIDWENu!|nhFl4*UqSDO|;d^_h zHv+B3`2-$V zFwGM_ddkmhHz$HT$`N@e%8+#Y?SjSG6=^kPB}1oWIw@b@{|@w`cgj4Rz(C|I-1ece ziEd9+Y=PzS;Uh-mzI*rmPBDm?0cp|w61ilvJRF@N3KN;-#PwC68`Wsl{4qLPE~GD} zF>>kr=yOfHC;pi+5ip}LHr2$Bbx z2I1U=tv%DOU55^`L{)v+lAfyyHFy(8Pi*x6Pb#veOWX;X=m97D@W^$8M00>~=wrva zvb7DjGm+UtGB3d7WZla*WFoOwm*%Dn3t)2fuyiIwgbcRx3ofE3Vj=~j+0nqT`@<;~ z#bgXgdmrCzR%b|Ahp_dc_^_I}q1#X~*XF@6K#=q;Xa00f+Bl9f8MoXj-8kBk>QY!^ z>9OJkgh)y(0{!U!@&Hm+=^W4Kr1C^O#Eu<1#=_h{dTP!;zswbBH`-@G2w1xzupz+k z!rW|{WSj>VekD4<>`#EFFGELUedqTLbeF|A8>!WJ`asQGcMRy*x^w5rh`Zcyq=Fg&EuchkN($-D@P$THKCNzSP`*xd-Y~^R_RF z`(Y1C)2{fn3bP_5H>ID%)PaKYP`ur%4f=D0thC3$sTeOD%!A#i}wx3_fLjvo@Z5OR`GGiMq ztDL%^b2J>$rOB^__6I{k#JBi1*0K_lDl-B0?GajH-%4sT@oN(~rVOM}9V>SQV%mr_)}9f%Ei~uR+(~I#qFAOjmt$IUqa8)VoHk^yNy$_>AlV;x4BxB z!oG6aF^k&((@S8VjFB!~xNf-4x^gE#vZk&+l7{Oqb?=G0w5q*AzYCpnPlfm-Gds zV)#cl^(U_T?_B#a!oX;b2!&t~L;xA%zAnh}d5H2hC0R`^q}YcKeqn{52pE98Uc50x zf2sV$j-xn3rN({(mDCe=t1Y{LBt`^?yi9m~u@Z&7)L#BpPaJS_n>=LZLxk>9pY!jE zkq%8|M&!`o%Fp6WNVcY;U}07d=2rlQY&xafs(0(*!+sO&-T+!4dSu{=#_dc#;RVe- z?Q%HwNSUSWyD8-QPDbI89WcH4_05*MX()*#E#J>i<+3Akt|M57J_Z9ch#YGlHHcI1 z|KT&fD|o2zTg^2+=a(LiVs8uWQsi)_DYm3hLST^lnwnm42|E&GO0vNZ8H{VT_Sww* zRiQfZA%q=znSbEP4Drbhx;N{W`BP?bFt8b&d7-l>M5NFSz#_DUCjis!p7G`}BjT@Y zsBKbPN2}_T4^Kzoptg+TDQ;j?&i_o+{Z)1Bz=1fVZiNf(YTpUis?4j+G?etV`T+w*&v1MueAd_yDAr_D|0 zyI7{uMvxaKp;ji}xTG$ljzQW$$9RUe84xz-+DY>k9ds6($3$(;MEW5kL+GujMBep2 z`V%QkDz4g`6R{PfnO8nUtJsyr$M8CJy+D@q__BFtucFuOr^xs!g2zQ8d3&wtfl*>7 z@HIu@&!Z25h|;pFXyCGZ`K`3Hebgzxb?GvNghFb%dgF#2^9x^x$xIKFBEG~e!6ZEE zy#;!ItcD9|O9=j2EL9aM8qWKhiQ8UN0+i`OF0nSBqFbNBYaAmk;gSTdnBL zDk^Xs*gD@EessUTzc|4v!9>M5j#=>JPamIYnp)tC0h!l6TE1o_NA&k=KIa+v<7`Yc zF|p8}Kw~Rx@XEkW@RK6Dqu&Q@E;?`QkIdkkHpR49Jh(k$=wTg4P&DP;D2jP7c#Z{2 zzYc0yKjKG={UgvxU0q#)5}{w+Tks&cG%x)8{4R1TJ9g^yyOVVi@6z9d0mI}76`OV zC|;zLLl*uPX-RVF4^gK;F_Zyqfa&e|xR1;V?q{@X{^#!93n6y@o>Ap|yvuw}scMJe zw?cLez-pcjlPt@2b?*vTKmvvEYUW@;Twy6m|(a_I~O=%W&evX&ViS%)GkZDEUF8m03_; z{Nd?iTmx9<9v0!Eb2|Ol7dNkZaaLmxaZtO^04pEWm)Bv3*bo+1d<5y^SbcgW##^L& z)D_e7*a%{Q_uuwy+Zuvp?xJisTv-Al6B*Be?Xy63vc_!Ig%&b?z~AsKvH>Pgq{(>w z#|n^o-L#lCqq{VBvtk}z!8yOS_y&kn=A89*Sz4ZHBZfNlon0&g%>E-9jT(A(v)4UY z>*xfaK_w{0K(rj_lURIN1}8vaUq9`fJzDnOQ!;6ew}k+fM28l+KH}N4#a4&lS5;cJ z)F--rYvu1|MX#MN+nr7*TG2r`J9ZpZLzA;BqsYAG%K+n{=e}*bcLM-gcOXjf_jlHl zL}ZbQLc}AC+v;}CV8DwMBx8egQD_H^MAMlP>yy8a?g>W$N^|6d?2ADE%WI2m`6~|p zNOIQ&XJ3SklnFisHE2-8jrDt_t4d4-Eyb_NTK_XG??;pyLULTUNnbdtur%vU+w01P zNPiwY4llk`zh1qI{7?m0#A#~g2ujsF8n3gq-i77A&&|? zn?KkQ8oK0QHtR%s1T~wFZPKMq4;V;R3>|u}wQptOj&=q#Lq$*}T`aiKjLbngI#OU0EtW&6 z)_?=6)+&cR1(3dAxrjpeEIQ}qLw-=^iVFsQUbqZtPEZOXozNpvK7DiHwQFyCznKH( zdCwr0%sr;-OWs1Eh)>acWK!4KvoBJQhT_O7@s2%oeOlmD_p+Ec}-8EFl3o6M!g)I+WcnZ#A z;Qzd~ZI-phFPk)3Dd$M?a~xN_&q>ANv#-!naPeNEM@kV4xKrBR+W zN9p!2wroVdbuXW3QPh*DQB(A7eDTJRg(K=`^k?x>BCe+{c5-U!g|QEhNVszxNUBzb zYIsJKdT52`uW>3J2z`8s1C$>C;Z+v1Z>CAe4xA&NC<3x|(UGkAbh$m#Q z!6edm^SM^k0E11k>`@i_U!0*ImXE;=i_sd#nQsi4dn0OkNljDQ%`Mlbq(!az$V>97 zy%{6ANbb;#%vCH2dK~_bhJ@AWL!&*a{dv3JDoP!hv-txWGN)b&y^Ry;6%oNI^%iwH z=(FU=AwRlpBB4iavI7AYWYg}SziUKUth5#9i1gJjJ0^0H#HK`f%SJ%b9zK1&E7jks z`y$7tsFXE2m|e+$V4n8eZFM}irX1!K^>TehD>Hh1GfsLfHv{p#(6L zaHH(@dL8Wx?a(NM6|&dK zyL{=Q1q)git+w`P9Y0;=!I7awGqQ43D?4{+-TDmuC_e1iT5bA4+ke(*)xA1VcW6X) zVw0D>cCjA&j9HTom(hC&-7dm?tJ; zF*TlGZyjC89iy4lorWo8>Y!zBqPtugzJob^a>98$k6|So&K;44C#W(Yxdh(OBt#^8 zi!HW%m_+Ph?6Zz`sGetBzG)BZnKS+J?zrpsH$j|p4;KnQs2oO)j|1}?Zyg*v?!;6% zd}y8I4+#8ufLyU_v)ra0dj%$EkV9GzSqG?pVjdq8D(}Pm0YFtimy~4O+gwCB3R!;| za=d`~9Z`XUdPD{8IxE+6qLWwGsQXW^K-BL>DkSc%oaVc9ap1C>G1S`l%l@IutYqCE zkl*up#?M$_KqD`DU>_ofTbWXYn3U=xW4Vvmr8EXt3o&mM;VZ=aFeUBcNG43OKYo<0 zj5^t8X(1h-+FR^8`MUFX=&aERgrG5{!m>{(t*Le+s>n7tA}+Pw<5*8m^2VvgjoX2n zcn*xPCc%X0()VEuPsGpSfibJ30UKqJ{KCp_!vl`^9QXKf@sUbObK%i2pO$DTvpwvV zAA4fz+q}H-9MaNft1F3Kod1S;1u4h0>e^0xkAMTc97M+%-oV5+V-806wfco+9jlL} z!C0f9Z)cl)60zU^u}On;17VH>h;8sY*IG^v0tJ5bHaZQwq|^4vcJt=l0yoPx5g`Tu zah5ae%R6&T;;4p&OXmJRdu5IGRwh(nc&Q5?C(Yr=p#0cLFc%R!QGVY$ZQKM|jWK{o zcAAGheAc0|6|%XCN>uP6rUt67mLgD=-%mLtYa#$)dp|eH;kS0@0!l_A0P$Xv5bvsn zb}So3Ae5YhH`R`Rl%j->9=h`mwO)5{s}sVqi4DF zl->!`N1f^SVhXnBUG<-8eG~X-GABeI>&abZ&!rqD8JUDm7#3DSsCpc3H^JmV905KY zqXZdw<>0(vzL}x26NCVIY&y4%_SCXs{rQz;U6?z5Y#!hQx)lC`I9mKg=3xY=PRhi^L5nel<%27ki$bhCekfe3O~b zGj*F~*@5zx4|MnL-MWl$GbU!hSl$Lduc!PvI%!Sl+&r6e)OZ);eIk+n$9!)nrJLcj zpg+q-l8ouJu%AJHYtwc)uP7he1o!pTc@chR4g3QmY zBTnuhoZzHu2>B4X*c00{8400OV(0umg3BlSfZA2X_2VGqxNWj8gL2lq#A?aBd3q>7 zu$h_`IIE&hJsm@yPm@2S1TSV-JdNkC~NoncwxxE*F4i`m9dIx2oJ17F#v{pHO~ zR>7nw;nsDQ(jw`?=d)9LW41KuS{Xb#rnGUe`>^M>Sk_Pfb;UctbhDwJaTA%lWSS0maB^yIc9p3R%k4Pm6VDm#^XI7$ z@!k_)WF!wnDZY9p@#l%gV&o>aC?N7aRR5PY&E@RKVohN*IKsIsog&?sxi=-`iXR1m zjp%$jeKf)}mJv=Xaf5MOlyRYxILO_ow|yDi#{rZB4{BWxp*WV-Wnd0+Ft1lGo4;lpV>#fKLoI6nW` z577ef8Q`MOc9>iEjtfA%I0yo8vHz_rPZm95dL*7B{#6Z4*UVZvQKhxh$;S7oB5qekh!{{8+}l5XKcQ!erC%G!r-bOhtEt8 z5u;DwVOMghET8mGgwCD9pJK$UOaesBwv`7&t+XjJa@En=H{3Pv#<5kOAX#WTR(

  • *`uCxUva5~Haf$>WXN1DD_)~gc8ldzX9xv_$7stm-BY^kV(zbuUezMgI zK$Mu6xJ~#@C~>v{q=2Humz+X+d&&BONo05%x+D|NwLj=D(SV!BmIGNnZCQww=DFa9 zQB2B_Z+)1loL-sGAORkM z(G-M7GVd*9<@f(3aB^Zv1wO-;zx~?*O15S*y8}od6UCw4J&d^^A!|bDbjMCx%S zjyBo)PfM%7xqJnwdC<~V!?+o0%QDY{Q(9t?MQoM*5Uk=ed^VCIg7@Yu28d`T*y2>B@DbL0d`Ts#ysWIaJ3oeEnTG0EzT3co z0}U^3H#XX`b?f4d-53rwHhu3i6Cd=IMQ=KJ2X{WG<2XIwLUC-KTi}H2^A;@FihvVB zNOA*xxt=fmF90b;h3Q(l7&WpFhDFnD_H7p2Nf%@wyMGCf2_oJgGKkQMO)(Q)r`({m z$uFTGRBhMJ2XN2v>4x>dJ8mw>eLoW#)Lk3Ut?+5VXX5D$(m2t0D^gr-(A_!n;C-KS zjl4~RpKiX(3D}K@f?E6b)2D-9^dDhY_YM!16*VmP*=>KNt?`nql987pBi|DuML0#~ zm1d~=8A~fjCYgR=pdhK{hMw`)Gki#{{B*O!U%2(sXX9Xs)>A~VL`a>=R{bpx0OMsj zDmeSdJjI+~bB|S7aN#d5+SG-=r#~udd{DW_T)L>fU%eXn=!ce7vow zgAZ$KZqUx`RWQO(pWJqrUX&73#`Z^*4^nfyl}f=sp&w@z>#>t?Lz@W@+8mrPXu(5o{tuWU{*PT`$-N{7`zx)c_ijbG zJ{3`dG=sT0Iu?1h?Er?nY99KDYx7f%1@Jtor-trOy-YG!Ka+Df>h>f+9UoElCVl;9 zzY~0Q_NPxW`=~l{0#mtn$jeHDv#M<&>=t=6XW1fYK=z%0^7xC}vEVwY_kzza>yYUz zX9cDCtBkoU=4oikaPv@oJ%nh9WufObi(iy79uxKYDTTLjVpVM)B&p`kY8oulF zH=JS#v!dPrYV<5WSu?DsrYREj1 zUXjA?z(B-a9~Nj1iMtT~T}<+PRC~!HMcMT><3dL?IudUie+ii$f=w&KzB5$6`pnJe zLSLnVBW{I#IOO<;oEkhRgy;qjF+AqVC@o4717>z!v#0FGJOf0p8)f@aMUM1`d=>z;qx> zzP+D>tWmAV6GF`=AmFu@#ayiuC5t#8EA+oXzV}hqOV}maUj)WoigGI!o6J2qwlTDw z4_qFqhEohfNyOqiP+z|Z)NItc3xfbuzIyv!Hb5NhQt{n^nT-cD$He7?S+%#Ys^SNB z#-szY(YfwFgl`4`P#rw++;*x(r{VSD>gg1uha}Pmkq(leB5J@L9d%nGy%x;|!Q%lo zWeNrz%e2%Uw(6$Kml#LAi%w*6gzq&ren3vtOz!)?n?o8^v)QMPt${Lq3PcO>4yv*> z5v;j|Bo9L%;Z0#`Gt9iu6E4x$7l$9bixdpn-z-)V1da zI?5rXHj*U5A;o%n9Dg&(UngXG*F7RDgk1w*G>qH)lI_$XtH$*Q$&8&nL>SeCCr?zp zgX`>ldi()dhD+A)IPH7l=9u_4dpzq0uju?ypYxx)IgaO4Tv}SaAXCf0zyJ)f1L+Oo zkD-uj-QaEdSWV(%$fIJ&9t#|2rq}qS@S5UzB`m%7x>)7`dNN%r zUxpJT+cc8J!A?dFcs$>-?>1z*udyBgNBT_wa7PousUt|P?A^2Ixz&#MmWccSDTJ@s)G z=AMJ03`uxHA`hKt!haOa4+bn{6ATP_2T)Q{N%^X z!T_(wa{JqjT)X^exVffpPj#8;{CDr_VKmp|KiQ*lbd=jcDfAcwFtJDz*C1TE14ebx zugXyCZ)vXi<$r^>N4u6Plk`5eVGf%P|0xsijS1K9(B<^FQ@c;uU(Yz%4!Fw!<~(bSF?)>ZsL{bym2z zJQ>OAqmPJ8_=LBy?bNnQIK3#Q_EKPZiD8^~Xy<3}qpbpF^MviWJmOTtG|1cc@8h9w zbeRApv}ZUS>OP@4=kB|O*H`e2@X!psPo=|~Tn#Rf#Wlng#~STKPUYI_3LPj*uKuUN z-+yg>d)GFDQjPkR{l9q|-w+#yq130G z5N}E#Bnad_|K~k)mnvrUWox+1xTiK^! z7Gw9F0vLkP#db?ZC;`~wn9gp<%O;>VEUh)f$AQ|FpRA-^IP&$YS2=S!R!-ySH1#47 z?1gs={ef0QToJhJb;XB{aa#tStc4#THp=>0zP?nu%cd_N8u0eF@Z99!(uY5E@_14# zaQ+ZS))~H#PTzg1lC~q)4x}gGe{c`Xf?RY+360#mrG&_HG@MoX? zZQHh;ie`t8C3F3J$l-3)X9u@k4OtBvdIzrzFTaDAA%j=)5g)CFeZh^d1?o6Tx*qP` zYA(k(X?w>+eFWc9d%iZyx!M)YR0qcWqQkwDPlVx+Mb6Oh>6l zG4_a{emkkDrl zOWuEfzglb~AlyENB*L-UI5_walX(vd;1jUFkjXAZXo!y*T==6Riii1lKVgI*D>|q} zj|ZPzPL*jDz4kUCOl-SVRNwgxc4$crT{rb%pr6*Jrdchs;;)wV_B9HPf(vSfLaEMi zPF3TY>KON9G{XREcH|rd`>aNc>pv=XT&DVj#gUDkEU;RC-Xv3dRdrSMja7?i3C*N- z1g|duRU#7F3BJ%+8^bqh4j;+ukl`$}Y?h(ZGNZO}AI0;PuF~%aGzI%|g9Tqx$cj>! z=^I3ek?p5sDi06$?V!5UN@{d|Wgoy!L~-w)J$!s@6-grbNE}GTXCK%b~K<%*?{HGbSN)?qX_U zRvpWVUT3EIidm1;Pa}#BspZB{$Q=bm4?YB8qvLp*p}-YUf3APc7@>;H^2fUk za4)Y}Xm5YuZLNWWTW6$2p;1o`4k@WQ^k~JeJ+Fnwe|kOV5h7@<%^M2fDa0B zDn=w!qaPQtd4V}+nTZEK(f-wk8H>GZ6ui6;S#&vRZ&!l`=oqvhuXdOQ%a0yYT7T~P=_-Hv!tP3G&BsdMvz8Uth9Xj@_F!vyy&kX zg{MxQWNST^I~(b4KIUi@-L&r-6Y{*a4IU!X_nouTe^_ZZb*c(a{Vp@T5;@y0HS66f z%RQy4s{Ha94R^XX`y-nF^41_e7tI*Zy*|A;10kianGpECyWY0y0MepC`WqU0Ja1X7 zWPSA3WKb(3*yvr5XC*^C47O-w8)D=DdW{$0W*`WRb-ehS zkKqc5YmlSwpo$tVJ@Wg)$-LqgvJPT!VVBYq5Q~T+^UgLMwmvB#p@A1It%6mQ(Pn(m z(8jHIv*4G25oEa~=D|H>dDe z$pbH@c-A!8>qpgC2YqnK*elX%;2fZDe2n4)n!^yjRqSG9EE2RLaGf%)$GT3a=V6Lv zZ)<2@E#+TdVvq4fYHGAwiztN1^V)wA0=IAzhn1hhhuJ2!b zRyH~iCJhF6sl^OOC3K_9=P z3}rrNJEP$&cF6&aOHTqSeDUjDU)6e(=4YhxUVkuQA`fvOKZ1j7J?etFrl`4jA#$LYgbS-Fmj0zIuaH^? zO`r`;9eLKA*vEvT&=i4dN>~|m3Z0$x|9|r{Isu9kph3VukVn_#wz zeCB2#l0#_f1~M2u=ccQL^`xrcb!*ouQe)Q3__gY*$-(An?6yK|Xv9e$=KB4w&3EHw z(AS;GCK;}NcVzQYxT&eB6-CiBw3FE(1W7vSeYI;o07UD$2YSs!>EJ3>)e5n*28zUF zzN$C~hf2L72!oo=fRluf5ST1Cs)d=hNs#FZ0NVAHAN6I@oA}Nw7Kr#FdK2+-BZ01^ zE5sn^7X3fCh_dB^#*0iDP`*8;bVYdENLkrv`30PaP`et_u;UFi1n|*y*-j3?%4Qcy zDl_v4z4#TtpPQXZh#QNtVnzXWa!KA)s4~UFptk`Lm?LAnJd0otZM2=PH=|$DBU}=o zU)3Agxk8VkE6GngcgF6F^RxCNdKk!!;uRQRPs6!NynTBE)d=%>^#xt=Po?b)%X|O| zA2B(6&qTn0&h4;eG6P66t-h=cx<1PA*T7Jf_TB0aC-j{NBe#$XnZIZ&-w)d&>6ws3 z1W$p^utGXt6y(02??|VUBch5AIK#kxRLTlm4`nkXZA~QfcWC`Pupd=Sj`)1c)Wx9=yiI_XiEYTFgS+7oulcvP!YgsHA;U2?ov}XFBDdw)|#QkS;fCdiQUhdk8d2*PE7my+7gg?re8*Nf6I>WlGud;6pif+x9+F!xl8SgEVUQ`X z+s~hug%paF8%LBNFa0Q-h{Qgh`TtvRx0viNk1mk)^MvRe$}9VOdp7W-K745OpH*ft z272cq(YaBnNIe@g?^tHx)*s?atkO6sF?=zJuX$Oid0th0P4#2V?Y*8D&bU|=+g<&Ja*;0Mf*tW@_e>&2+ec)azM z#DIVyh^o9F4H`6%F$iwUyXaf#ee~gWy|yblRz3<&6^X10)ljF0syjV{`+? z9)vBgQYs^&7)+=1AY~kNSQOz=L-SjD)A&ZeK#dElG&HG^YAj>={i%*(s_MwR7 zH+JK*xauzWPuTJQ{>HZDEtWo$?&<3<13yhb4c+^n20%8f4?VeRw|)kFQPiMfHa2_F zo!Jbwb18uc>7l`2PI|A_b1p_ave(wDXOF$D__C-NMu|-xbrrgNLl%7Rno(YImv}@ zDFBm43~H0J#q`Gp`g!OZCKpEoo;kf!Q49-d&PEE%15VP$;ASnS6$*&)Klw?F^8$9IuPj-_Eo(LbLUDyyfk4v_25zgh`=7UM^ zaWYiTFI|KMFHl1+5g$by3$ds<2>k0gd^hJC{k}SHK*p#rBlt?+DY?B2_rR@9)$|=U zVgyu8QjdTe|F6mX_kVkt>Q+B|CM~P^-GT9j$9w(@M&{O$Rjo>&rkw4rQ?a4)c&ME#ET?(*;ahVG7JL>$8L_VA@Z&Klh?r@*w z8baLx8fK2?CJNC+QX;vX412#l(D<|4eCo4^3uPG`T~WKxB>#w`8e{BL^R3NTODI|$ zezHQ?Bglt)hqMA81y}J6F7Yd?JPBmQuKvIC{ zT~CZmg3ViY?hG_tBWfB-zJ*{1-0cDZ6lMtY?UMmZ@d*BR@*?CJ`Ok~awK5u-wQx1zBl$F4w!Nr*Z#B!HYZfp)$0|SE8d*ANnWLVQT)qx z$`X*I{v)k{8QfAOCid%6#7~g}vINi|lzpWiUk04fm4=~owTVm90L$X`7%-#UrGiF0 z_w_CvEaD0oO-X#bT82Ikn%esV4k8w;DCE-6Sr_v}mOb1CLd2F^iGmyerw^o);3?u% zdi;b<*&&3Xfrt#qZN5Vu|9WIn|14^)QrV6?3557-&b2|p!Ep5PtT;+5Qq%zOMTTg# zX*NzQ4C*y2`<0s6+nhyh1ses zw0wjP1ihaf>D*thS6pWuzua% z>e&&aQesDDi13M&Cu?V9#*_;#ziPyY5!A~y7H=oPaWFE$M~~OyBk6|L+kmLp< zOtZk+qQ)oGxK-aM-vt0>{D=G#BdbR~^4ICBfEnji)ngQk^x%iu3kYHSZH4nl8jWoUo;(;Lkv5Z=z_lXGz@XHXIKYfGYp1 zVVHw!0MlpMSZ1TVFG=?c4~(VdY8*Ja6H@|UKUq;h;b{NpXgkB3CEde+mY?4BpEWRD zQr0x~=I6CQVH7q%Q?w7X9_7awL^_d&yYla4{SldbSetNVmWL_!QuyE2QwTG0uZUtrPG zRbZZsZptu%e-qc*A35<&2=sUx*W|r|tCFscvXygVg$4yzJ)h=l2yelAhOJgYI8|#Jqa0Jl2s&BcfAXQ3bvG??BbRn)>#%MG@<6Zn|oSZsxa>_XAO9ey8D z>k08RHrO2GrON9%nSK>o{S0(5S#}z=Xh8jNUYe}dgMmfCCRCch(nE)49DSR2NG#Y< zK4axA{pm5u`Ir7jB1ls9Sn3+vEgUf`SZXXUSu7sN0zIjN#HNx5{yS47@`q?@JpG>S zq$kSaspgTE)lVAS$&t}HQGlb6HEX4^$Nd0aFB-*826IETJ8KP6S#SI%J^Bj%t6uu`+K+R^_4y_cj6vQ*}hT#1a{VD zc*QdY(W+>zpe61Okv5m<3{rZGZY_ygR;STQk_}lrVTZYYOsKjdwI-=<4-7xrkNu-| z9XZkgI$8v+(z%*E)rrP2Q7d~}jQVosiX!e^kJoX9KI39Yu3 zxf7lZ13_Oc7NLF-rMZyskN`=r7nhzzF&mE_i63A~f2?KmCrXIcgir;&NVijdJGHev zTJ|@Ec&Bfp6DUJ|JU&z(zrRR-5wi`pwjuj^dGi}luwKfXG+^dM1z|zGr`~W#lYlLA zY%|r^Zg1Qx*XQ&NXoY(?U#woNr^l#}oME5222n%)gtg1>*A1;=n^TX;Hs3zHk#$?IryDDbY z&io|axCk%cpo0N)yx+6rY3n6nQ+s>()#m^93jmA@Gbn1j0dTdSkjbQ+Vc_VSc{Q+k zsf-mM82y8n!V1!|69W!bwb81WL6N|r6f!Ab$3jMBC|p*oStCe$t(%)z4au@8irTi! zI3R%*Wf;wb@x5F)t!b#X0S_(WIYH1JK664da=`NsYST`A+7UPky(v1ts8-LOH4>;$eGXQl=m8#QL7*Mtuf zoHWL&)u#h_F*Gf6-oEuh+eEeUOmk5QNm1gULbQCC+zeW5h7IR3Px6mRH_Yr9lbE8VrRrpJ8u@ zFB#Wn_8;e&vLD{TY>^he^~9KXc}J>I_PQ70%8%d{&G;_wzm36ybl) z@fV$1*UgGlH|0;He=5r1lZSntM2?bU46{BXYf|}pi%?57FZz4vj2Y;XO@@6_^jlHx1*5j-5IQaR=8g`-h|s=h(B% zDJksS>W}^`*CcNC5-L>h;UUD7@K0nDnUa%InFvmQW7p7*7&ng=2SWrN(jKB8tauA^ ziD+=2l=aXCVU~W?N9dsb+j+&kd=P}#>y8&w?=dpKY3czbBnzNG)*|st&Sj=Mi2tB2 zno1Doy8SfRCY$%h197=^@u{hMZ0>H=xK^Jh8we|OGH*^>UFXw}?%K#m36a+;!q2!r ztYbbEsNj7n9{cL<^=HpUlKNsJBnCfdyXw`7y`eM1^)Ej|CnN#~q+Snr!YswGh|a#) zWfo%w=w(hrg78UXjscW?^yPzJH`HuS_0*G9MNGcCyL6!AMt`pK<^k(tR5>bU??3S= zTn~2_%Bu4ye_0*O^fhc(om+g{RfB5C;!5H~L6^0Gt>!WjSGzrYKE%(u!EZS@XN;v$ zrjuglj<07UJ;I03L}Ub^>8T|j6SWAq{%>P|k;bLUZM&87F<2a<9JjfC%O?Fad;z*5 z%%;GDysr;w567a@CNHw_PBzg7G%(94sVa9-h>DfoROHM2*^bKWf(SC5cFlP{hU1el zBqp?rT2SN>P`9k!N&ite%Tci6f`-pwa;w8CLy7o?Mv@G^41GGiwkqw zfpZlY6Cp>tIN;!4LwOJOvQEo*el%rjZR&|BOYm9Rz+sg&g>{$_`c>#yFN{9Vir#2ote;K`bcxs#x(Wf~PerjCpZ1#Awq)rwl@=H_U5Gm=a4 z=KdcqD=wX!Y9yb>3pxwxV-~7LnosnUF7r2lxbSwBGb%?>aKqWE{`T7+M7fVyv7XyA zx_Jyhnr=s+6Ga0uDte`*gQhr5eaNhyo@xYo^3f6m$a*r!3I5CZ@?|pEG}t&e<(m zgPwbP81jGedz)74RhijQEvHG`74|N{SkUZI5IZcssW{g0 z>!h%vGU?MkgztT;_FQYuzwmtMJ814?Q>=*!pAu-%xf2-tE3v=j*ci{rJPSEG0GB?N z+sbq!Awc=PN{qSNZt76MW|>L&LOvuo3W8*8LC^x`YHyi2B&o{X!AwjtS1dzgx1XK6 zZVrD%nS)X%o4C}ca}gI*&v@LSGHkR=%x>ph$@1vV_P>&u?%5Y%r%tam8MB{&oE9=e zOFk~6-AYV3dA~7$2oe>FDR>;Jv83XTX(WvmQ20C$Q(BlK0e16hH@ zs}yf!Lc7dcGlLvOg2}+(CiU{b?Ix({xKdaU|HraZtX+?{A0f?(2GeTdX*2cpYM{M!euJY;3h{p%$BBIpXqeP~`g`|MHU={oc43 zQ_YD6h~yv*Ced!NgO&8$i}c0k#us$O>eUMfaj?y^mpO6()D#@~UMOW2aiKK?M?Gh}huZ$wl8jvB74B|r)3 z1Uz@l>J}qZRtd4hJB4|x$bfch_oMx~{$f0#?|vhu7;~f`Md?6GyVQ3?F^Ih?P&WWu03~u8C zRbJ>CF71>RIb&hO4&_Ac14`UYO7bDM)8aeKa&0)P497+#v-t;97iT#}fVgd7c+prx z^9zN8#q=Fn_E5r5w29us^mL15CEi!*RU*a6hJyiHcEPy*W>oan9uZy?`!1ja;4mHkv;BgWJxNr3B^#mm&s5_=rv5gvz-FThu-B0yMuKoVejE8$L z?QYpp3O*PpB~JbD;M4!Tj&mm7(A?h?gPM$=tzvsD>1(nmbGl*Xr)I@VC}LwRrmBP9E`*y{wAVp)r;9R?6--Hu~XSzzPK9KeC>QT)|VZwrKvOv|wBYvqJOc&HI{O zSwjP8Dthc=BM)~q*s56ca;3+l&sp8HDMO4t4T}B9?Lp#88DxZ`DbV5+iX!3TxowQD zI+C{@GAckFH5<#CN?kklex$Fq-r9yf{5WZwzMhGK28OB`AjRXB@c?B#e9Mv^%WY1?~E^ICp~J z{C}ny#-Es)%9sn)^+eia@GNN+RWM!87LQmGcqlF}GCW+?;dC6adWW<)E3Dd84u6bg zPEcX_4{N9Tzl!>+G;3~*n!DKwqWB^w1cG`3^cxg73ep6Wp<3!cx&zn7D1_jmK6{Z% z|DhA=FRyx9S4rqT6pS*0!oeR$39@fgVA--kLxwEijzX2}B1QcT>Bo3pc%o}N+xSex zrH2sFI=+j!mQn;mq9!$u5EoFcGy1j%t**pAJ%d6X4-RqN0cv@EjN0de#9hkICtrTL znYWzO7^FfRlG9565O0S~=ut1)B{ru%-wt>u+@9d@(u{WKlv?&8bk4PwqW*X2?Ni1GH8gp!4eE+mzCIkahjfrG*(;&C%<1Y!8pFmS?zuI^)WHEm_ z$>GSvC7Ryrg9Ry2_;S>ZhJEhfab}MnWjFSLGFT0A{+5^AB+h(nd*;V!4af#vQBIHu zB*&pPG+p!9mHu1;1gflR7zM#C23E<1uq^(opH?ievpW_1 zhlAvp9fO3GRxaAI9gsCrFT(4~wQbn2;p#81f)DM<7oRWsXZs?Tp}(hooVav%ITBxc z&(Te~#&qGAgT)Mp{U}FX4PAyxz<1C8j;VLEaKGa9oKB&PfdT!+gx&fz;>@y}2fOV9 zxzwSjVKVTuBq0WuctTtl?qFh8)|fZz?J{$2dQ@AQC9_)zmk!Q>f}WDiC9Q!8Rvo?k zS{v)19t)BCoOTSPruc`DXfN_01Lh2Tw5Mxba6m)D)j=2F)5D?vV9LeG0Gh9?tPG^X z6qh|Mu_R?5h=V4Ev-JAm?FNso^wL7*PuY@17We_1ye0rdLS9iBKpA8^pYrA?(o7 z8ZGM>d$=QoOc2p*?Qu1f+X+AyKA1hLe7<=yN}~J zp8el9bzQ&T_q*1)&ULPHp%s#1gg<4T*;L2g32)x57P)TVok?rFcPHm z2&mDKFZyB$LQ#ods9xS}Ue&l>R2(phUq8KW0N}90D@NML zq&`>YINUcKGgE=|dx6$(T;`EcQBDMd&lfKaW*$gozLTS)86SiKLIv988X6ETX?103=!Qwd6Aihx zPdYgUcI@%Rr5&k?SI0aNo{D2g5Z>3j#QobRFM?PAr&v6zbPA2vtFGDwd0E&(q4CQZ z^E@m%C)^=VvCV*<+mBgl=C46j-`^|)L%Mc-xocNj%O=c0s>m7*LM^{&WSDE*2^&!` zQShwCXGK9ZrLgbPx80JU3s~J44_|$#Wd=Wl3Lz@5H&J2CYV$#k0((dv-RN_|I44T0 zRpL!@kDfiNn~j^{4@W3+72Q)0q|XSsUE_E6}SVXAAlnWQ6spd zPonQ3KCm;-UBldN~e>hFp6nZ>U&S6#32FlwfgZ9{NV61n z3ng+~*u8a>lVanuz`?xFO}smjHtjw zp}KP1@BEU71%f`n5s(pO5l&{8#?Am<5JIa%r*J+)N>c5MrUSz_=eZm}UEb>)LO2P) zAQ$=}t@-S+je(q;>ovO{o)_c2C(;^ai2`P{qMoG1l6G;&3=HL1$|?+eieVC+Ajjhq zkl}f915VNLo*lMp5JD|k9*_N(Ci5DgG7(&~-M&NqFS5j^i%E~76V7f-Pl8AjqdWz4 z63x5ri3LK^sy3KVx_sMdFtPZG5mfZqaZ4l$3;l4|iEBgM+t@SILw%BtWqA|g5p57- zjP+nZDYhE?H7Ya$I>HEGgZ<5J$EP!*scmJ*E+okvD05HO#wf)mSW zrW|@_oaaA#5xqsE32$fa7C1iCy3>SH9%MaL?169r0%aW|b?%LXe~cGXq&TPA!d&`<%=C|A{`PP>xYpp)Fgj7i1(lS}+A_ z`<)c`(r%Ddrj(SjLvZa_QvvC8&KrZ|KuCA3$I~CvX5L=q4u)zncSZ!9MSn}~3GCym z+zAqJ7|5ep4FDeD9LL?|t{x$+aQHTgH^ooOk|P&fSK12yj29c6vt`;UK~6Z((XAP7 z)v#M@HGBF6KR0YlhYU`sJ5t&iU`V{`1FX$o;c%TTY|zbJz9?GUoF@@N0go$bjnZHi zPc2X$8{5)M^1Z^#MVcJ`P$LlbS}b?@mTOUO(z+Mx=?+bb$2V-!=`3;2q;njF6bRVT*Nmwo4 zhMCC>s#h4idt4iAP;LUZMEJ`&v1j;9)*U~q{e}usB9PRZy6$*ba& zIBVIfjxoJ;@7~9KPtZIxLBg*9#|OOnl4;SYRdxNVXM~_88A8#<(`;m3>(Ge2ILzUI z7(!LkfbNd*!GSFu-C6*-Y2-HXppeu4`V1oUhb?h`xpt?kUN^Txcf#Rj`Q)Cqt-e5| zV~xR5D6hPHf><%S2GG_b7V$CZhq9|C?P6=)v8VIKg=L6AB~9%jxGRXG-Ei`N=q)Av zCX<{*nk>97+1GrI-JT9(+Hw!B@v$&quq-N>_PZcfAgJ*PSdW(e zxZb*ZSMS+{pT22rcyOHV)~MeZOA>CQyd3*!lv7xzZOb#{q@t!7!udvqV7{dPyg=J| z=e%7HLW#&Ym8x_7qM<6DvoF=1a{2xG`7Q;8<3dKY=8jyYj;Fu1W89A6-(8g|_@dz7 zh^{SfQX|Ba4K+2@r&-5WAgR)EwcQsl-D5t)PGYOfPol3}L#Kf#;gf!?D_cwMxJ8al z5(ygf83uvPnR(tj52Ry`;uwcN2UrhOXLDMVeH@@7^CENqI`2p7V<$`!MJ~8iCD!)! zq|sTnqvHuR{EaQnfy76CzUAUq{em85#8s3n-L@^^pmcc9FjC;QSKr)wO{9An)s|$Q z@Nse>@P|_St7oXY?<0>1gEH2_QE>RlDWh>0flnHWu9Z>nKVkC)*uzH8uIT#*yGv2s zi0GZr2=>54Tw4mCn>U9Q?>QNfI&g~5F{jX#v5lKFY5T3jTbRugU9&1_Ud0i1#FGqus?x&B@2<4TPKN&31B+csz=rvnXNtn-oVNW|lEv8NHt08|*oMG! z^(X;j_qk(;*(UrH7SrtteEyxw`6p3G zy26y?PHS&@c@3CKSHseHt}r(nqQ(>yn?1}g^!->d{`XFMa z1>@j}o{xt&C}@=SrRZ%Zo3WM+;3MBxcI+N^s$c0Uy>c#VGBcpmg>6gM&YfE=vP!jD zj?onWDuEu5L^95rQg9po3cwbd2``@Qs+x3U#niUI{bko!BZ>%AzT7Vo z^gf~ldax5qU+zkvCL|{gB(eSNAJUeY5Sw?a1sK7V0;N;nazI$aPiYOC<2A4AV^X1Y zy)duJSV_%%RC)>VJPH5FX|K789?O&>Mb(AeS|Q6UR#csmDdZQRV+JiEh*q&^<_Spi zluu|o#jz_}wISZ5M6#`xfFk?DhYyOo$|I*to-7uY!VoY2P7g|qiN&sH`SN4Ldtr_y zCV%V*v6(Tlx$eETtu*k^!Zk|Q>ICZLMMc@BC@6)H=xC9?!RcC<2HM`IDJA6t4FMTDU(`Ze zTcAT^Q;}zAol9Kh@WJs$huBDf&@YLRDW02B`iNwqI{EN(4KKZ1P>yHB12(t)TGd|ytiZt#?e>s86W`DUO$2r zVLi)!D0R_pgnyy-r1`nhji1O&`=L^h|gw z{>N8ni15@TMYL?!?wEG!=Oe&f>Dw?YZh@qU<&Qlmv5{Q10vrqhKpXi5Lm)J^ zm-cm^yE+AI>4;A_y?mjpY!96Ut1DJDRQMbjnc$&6A?jr^Ax~u^@tcJ7 z5*P*9ELaO5td0HBgtsLpIDW~q3tXA_lblJ}qK}9eT-OXgSEJ^2xeT&9>B0JV5p(kN zb{xj0;!N6>ii4C_7quhWoW1c9G<9Zv=>PUpN8?C@z3M&6Kx*+%19sePP0q&hP3a1c zzh<2}_a8q(?!**!GtbZmR0cN}%y;KhqBFcgfgKqYl>>K5l{0i!LD~*d=|?C%L6)$; zqgVIIZp7^L_*QK@eFp7>my8Luh61bS0nsKpNp4~XG_|OTglIv0kPg`g#7yD76BGZC zf@b2Q!zj9m#~X<>)8@~=!~aIbS&N7IVEtLfdFzySbu2ytn_1;9!+n$<;z^61Lundy zG-G}G&pzye_<){}1y?W-u!~C;_s>qAK7W2)k=kdUxv_rt7hmu+*3A82y!EIr52Uty zGPN7@KCz*l(-!)p_h>B+@ojONOSlrhJRYnZe?eX_g<>Ee<}+Lpx9h1x{rZZ(HJ=pM z0M^-BI>*uNA$W*wIu*Jz<0IU)FM%gN6y)R#M!gF2M|ZF%M`RtB9firx6Gir6k!WR5)ABd4G}W(gVz8iXi_ee;Uqi+qU;rXkKE*@JD!;f9eHTxa z%{%avqB_&=*It7z{x&8G>0dhX96-hCiK7$UN6ig$+F@~Ic2pB$H3wbk71sE?nBg zB<(r#xIM~-6&MDAm>f<~7IyN5C_$Y1$Vt_%45Q7a%)4=V_lZXoj4^7quk&5|> z!}iY;ojDU3*uO|yhujqayiiXKuZI)3thQ8Os@HqR_%?1j6rO}C8Sj8V3&CUZLD~B! z=X{CiQU+0Jm@o^lwz4zcxDuqEED}P1ABcy#2&1F`L74c0|H8UV$3eD{)YLLoN7ad% zZJ2Z1prC0ySTQr8)~ibYkB-nfE);)2@ir?ikYKI!{Qdi_moHy_wsqLylX5TMb4VkC zc%aji(0~c=OSn`J-MHq%czU0@#nJgfP$4Ob?7+GK&{9Xy(-S^|sDoDR8DGTMAbFQ}Cj2W1)kpAzNEcSm(Ko;D@*}jXy1CWtKvSWneIse0O)>w_XoEO)g zQc9G8qI3LSzEWf$u!SiUPMeeY$OiIxNx$bWT^fUi{Pu=!hzs!C7>H6B5ITz%+F^R1 ziOz3iJvLK%Pz0m64ELvQ{hBE32v|@jbS-vCp%7iMt459p2@Z@!^#~6pyC(jY5-Aw) z{}Og%2FIm_OqfcP~*qNU$Yg$#x4cmUvW2 z>m4pczA?4s0Y)~?fr{X=Ax_YsmOql=4LJ#fPnu0Hcw9WsO^{=+-}TsKO}ng${qa7A zRP!Vt5jx6IpqvjV&%{n}2L9UOM9DX0DdkM`)#iM+rD}D(XUv_ug||(g852r1`oYln zgB=|m_qUor|F1pV%-X>5gK9oq#kd_c-o5e`ViUiyX~(nGeuLEI790)sN%gF?p$abx zSy1ds{Wmwg?;35UTA;{iLe(O!d8!S@NgU!^{!rgDF2#&QZX|cZi;}l^HAd5S9-Qnb zuNj6&sviD+Itfr5-*^CH`Y(gUeZ(FVW^NAA|Ku9BB%_OlkO6&s+pi=i=WjY`*a%No zdSgnOeU~`uM&vy#j@m!;n>C795A7NWZq}kMLL(qvt5|y>+$qiI8L9MR<3sNeuo4Uc z{jfSgPDjKlB9F+#-cO&PJtN|)7)7nWbBL6fe8HX1*$mbhP1Wn%?$1|qjJrbXCtxo? zOS(3n4K4X6n}&s12=X>VERvUI!VO9OhId;CDI~KEG-V)d?ml*6Ns5@`N~yt+6IMd^ zhBdV;GBV1}_k&@<%>4!N1ny0yx4B+w<;hL5o^T#|WL3~l?t_R1Oqc`ouG75lcKXJ5 zA>2fm1a{47J{Wr=hR4LT85OvIta2k$%7O{_$jNYG!6HzI{0%J8u!J_U&>ZPmd6Ayr z-&W*3N_^-vkw0Cc=fMxjAmzcEzrt`&>mykAK~=fE@z&y!!Vt(BnW{K7ZNMYHv&|Xl z>Cwd#f0BF0M*8B6=bvHhaxykC7fGSZGqD4V@W#H@0j4qeHbN6XDgJ#g@% zn2?qq1gJ4ZwSLa5zP_JydbT3i4Iol~4m|&8 z#=LpA`8W++%E2QF+p!vD=R~JO9~I@j4Erev#M60zMwSgv6t>1Ko3WL>SMFh_Ae--G zx&TZlu!jHdNYdf}=0sG(A-(cycVQ&%T#;_frP|YA8}RVCv}H(A-oRd(wgCxClg&@9m!doxr9&+P%ff^rX+)BE1p%^UdP&J6GVY1(5iHlaPhCq0%YYq#ld z`3?#)Gq65Zps_G>{Z}D0IKNmCYRFh6O%u{*E7S0nV89ewYbZa(LyfuQ1K<@{o7a#6 z?rd@Nud*@IuxsWxNAtNc{LP%#uT>6@_^dZ&Q&;ySy;+0ut>)bQQkwp`bRV{h-dT)7^6*_@U3G8b_&Yxes;%r9xa!nCO9FP?@4M%RH-q$ ztmN+JQa_vGq;^5YA$*)gYnKp4#kUExBS!8rs|qc7W^c~T8x~o?^L7t@GHxDUrXsH} z@{H@jrJpaRA}#?0+mq{<@KXg}Fj3_NeS})wfnY~o(|%pp5_2emy=)kv;%>`X7sm-s ziokw;fx(Mb^8)i*Rye;HYGcd6u?ZjX$>rgf!k~9$8(9;Xmrsp{x4$ykgn3Wz;{CJB z%1up8#Y{A3&z?PzX!?Lp%-IRn+3TE%Z0Bd$yqSp%&SoC)TO>Oq8$YO*h|EMfghE82 zST!N@sMV=g@XdAn!_IBS6ganywi#ACs4Kmr#5;HD${^eA)L*0vHH$iy*{yae{rZ)u ztEYvA3*J_1*XGdfx08vi*8|$sqgMJW*0rgpN+U$j3bDe6hL;w{(a}TSPWM85p~L>h zMe`$zzlR{2kRS%M?G7QBj0%wNpziwwxxILCKq68EaK*Y)6SsVGZGj6K5n)NtNCpvr zLND*Py_dS8#p`(Y@Y@ddESvEXEfxJ4@!F+&?vkOE9rSLcgOk%u7(h|iGJ?;_q%h zFF@ZS9UH14D=ssRmtmzRr=pJ$G6mIy3<3hFj)KE&+`IfvfetFSaC_wOhy>OL6P4u$ z(2IwIoD4_wP-lP(^}np(Jov~s9jbeWF6 z#zzsU26%XTPyb(!J{=UVuXa`KvnIi=Q{2~(!eG%Ldy4SsBxeD{K)@gCpXX0%j2Ovs z+S~CpYk^cY6LK(APNBSzV<>7kz`tloCQcVo0}^`a9TFb5jz6(_>eQ*SrxQ9E1YYn1 z6uV9w_%`mD?i&sqnp1`T*guwbU=)M+tq-8Uq;j_!REvNi^xnduFdaq3^u0^Nw?qwI zt7Ky_fuVQIe?Te7*=Rz!_rpa~qq7L>u{jN%`XJKjVHeg(q|(uo9UQ`=q@0IOwgYWV zj#=Ty*;K=Zh$ky79k?_-rc1`09vr_ztiTafz6YsV*hdm_j!Pq{@2Q~_(}7!t+vm1jV|4bzvD`yG6-_#E?>Bq*^3_=S1lUHhk7dOZ|05=p@@NsX zc#ifU*}l!o;;s|H2)BX~_$}g8K-(kZi3_A4>N6SJsZyniUeBIKIqPVeqL5C>v^mLM z6m#fCU@_=mRzoAP^x{_jP#S78CMSBVk#SWP3WY}Xk?M~99dgF0c=nzRkUPq&k)2ht zEDyQQH1_!tJ%r9J9L5|D5wUWff1? zAfESjgnE2HcG;(bMnt;7w_)l-I#n^3FQcfbMX@IP(Pgm`L(0Nl0fk{1zjdfG;-WvZ z=fb%_ctXiYWz0OmJtlC~&+RX_dJ3lma+LJ|d_u}Pb4Kb0f=fj+s=&Gg-3Ls;VIz^G zW1+!%D0&Q!dc^+T;u8pmd;a`+@eKeN9xJ{e9xHJ4`uh5IbhGKD zH=>s!nytr2VO8X=B01-2*1UpZ?@;kC6}}N)Nr5qu7T+7p|7${nAq(K#IpZ$`FT**& zhJ3^k5<&in$3{P)H8Eua-B`K|88>C6|q%-gP>J)g$u6F@DzR{`}?o-2g)FY9wv^qWvXs9r9y=V7cujQt_>YV z-<1B>j(BLCBwBj%=I3-BR?~-SUqT|IsMq)dU*YseuQp$m4n|Q3E115B93spf6m8$; zCF2>Zxx$Q=sAPE0Qao`r?0A}RJ)_H4%Iq0xca?-Q!-n7DPOSL!sy^*q3Zf3C&dJG` zjM9l*ly|GKjCK{P+PH`dG!XI81?OXVtA~n<41a_7bcp$&^_w=yo=fse$hhz(qz2iT zxqib2F`kDa=#Wula$L9u&X#85#<-P*l1qMPgc7u615s=DHEzlWBer;gAYYfe^4HNSZ+L@Rwe4-gyig`HKD` zET6hl)mG|ie)LuDF(H}NlsE2A^`FXAZ!O3J-dF-%U?NdAUc+_eo{)ech4e)!$h3i! zbucodkhV^L@jcn}D6oQmvs_XJ-=u_V(54L=O#SnpAkO8~j#$!zkaX-5}V=w!12lh z+)%6^{&IS~*(mO}CjOlYf=lVh>LK#lflx|M&+Sp~Fbi2V*iv*z2xj;G`G~V5O;3td z^UHD3z^D#vRVEoP@aK*f~Ch zBp2|U?96}^m&L&Z*7!idO>53-P*E+8hyLE{Q9K`?=kos2g-AAMtBR$&a;8EuobEB_V8R%Z0&Y6<9U zk^aE_<lc7Ll*>u1UN!c64on=x&7R=*@xch{DLJ5i} zV@`N%tPx<$*d0)7jwaAD^*)?7pnrwow*iRsEWOR=v8S}m zhgrDddUg2%BJSYYq?3q#f3V-ZAxXO%7>FfV)?YJ`4cd*^}q%_s^dvj4pFkSxj(#e8ZQlElTYh6JkWS|rixQ1>7uoop_It!!4DPzx` ziDVv6ZT^xhp-r=%w)v0CuVhOY_0JLFtWem(VO%5JM}f@wg?__c;) z^atZ8wlf1lh}53#gAWBogB3;qVn@Z3AV{Gv?U|AsHj<75x2FNRU&=Lslq2aOB*5v4 z44rqbLs7;=h4ktzSEtE581dYC>g;TAQ8MKeP%VfO!sZMb4w0MwJx~%+aKt&H*?b?sq2R>Z-&a@jtGy^U< z&)jwEa*2{CU-)?A-c{13bSZhd94YXXm=g%Zs7VVhpCz1MO-|mVtfID%abC(ZQ=X4( zVW51HcP7rX;G>B#?oO87JNVs9O4`VsE9Ndc!;iu?4F-Nfbku7b-~~r0P?#R~7T72S z*+q*ss>bJ*-7Y(DDvUawgv6Hig#s{a3P3`Pucx5a&&fi=e&V~9KaiT+SMAid^E*Qt zsH-Q?qT(;sMn@$bayjNQuf>PA)6>NAwk3yN)?yN;>v>R?54G7sBg>wHB_RR^V`9t{ zv(Kjhwwu1qtrZv@4Vv^#Yt@$lb&tUB8a*Ox&+$FT=e zKuB~@ik~jedXbx}p7A2VamXP*kYd$B|HR-+d}5ZNEO~E0vcFjSuHYC}oR|O;cnuZ7b+lsC^Geh3oe?_l8otF`R*^zbGYOTy$_ zI|Nbo??0uh9&LDtrxSLoV*E*Nc6;?iPBaD{ae`ntk$l= zjqsoM9lKbzAUaWE4dIB&Is|D4;Y}|BJ>xM@w|r0eUojRGT8HP-In&+j+CW~4CyImf zzC(kjQfe|@QqsCVikwUWcnm>?(YZ_^Q0*Q7N=Ph~EO(BWV?08)8-<>d* z+sZP9z3%K6!SMA&Yv#~eK9w4kSSfb)vP_1Id*a*50`8S4fO(nwI(jI9imG~0^oTh) zsP#IJse{d#S2fB+NJu)U{wF|B<6S?8IUl)WvAe8yl2o5hN$lY|4%482|52H=UfUCV zaK|MoX__8x+Ah%fl~>QU4Nze@AjL## z5~^Ynu|1FS;*0i?78JBH{7PXWc7^N}R>w?q;pf?UnqsFOSN^d!u};5T82`)t{+3xB zHX%#MSx012!#1}3kji;D!5qFQ&n!9nkz47@M$eyqS|@t$hec+eqOd-9zIF?EiWj(_ z>66>6GbWEMrZW~>_P(DuBa)os|LUT>Xa?Oq#wl;@-yMK}lTMTwut1sy^69AmFh{#O zr?4UDBbX?)A3b%{*#h=0u)64!MIE*1K_^2=dSW2T|FjSp6{BJ0Y4iNsTHX*tM4M<` z>0|Qt%h^_~4H+>xQp7eO61nwgXsXKF=grB=3g{1|Q-~a80G1=n@v37~{-G+zesx5* zh1^Y1n^CUiUn-1^YQGV{BFiyk`jZd+m~hcy^x^09cgjDdk5r(v8JTkR-!RF)Xn1Z@ zjwC!!)6>-3Gh9Z>D6+8jd|Z-=uG7EM#BQ|Pqzm0fOWjKus>lJ_lFkiLI~ecTMf zElMA0Bu=E#I$x`-BBlV+^CTV%jDX^D`7|V!NCYdRz<=0J6e|}Bxt&p9O9o+YkBPT< zD--I)sbG;qoC*!gk-H3LkA^TYA@W^-Z6JV1k5Aid+w*`YGgACF2nL06?1?E84|8z% z6Q~gSEOw-I6eJ4~OrR^(JURRHf}b|qV%GoRWM&@8(>KK-o9L}vy*gB}EBaMNc3SaV=#4;u+Kc9Z*MCCF(iSD zkOi}hPX7@Tc0C`XEl5`A=DHc*dA)~lG@y4_8zxSacK|;zO20Msi(F;!z^SOH>Byip z0wZWjb+S1l-MK}`w*bcp96Su|w6LI(K}O({`xpJqp8x&h;-jTKuv2bZqnNO4E6nkp zUh&$nWk?$y$kp`p^q5Xe@=?BSAX~UvecsHMFGi3LH`8d&=Dtqd_Y4;uIp|P?6C7xz zn10I#AMLE>d8M)Glh{c6H zNAT%=Z2SPChtR&zTX{2>Xp)_qtkZZNkkb~DOYRfhh23FZGUVLS?duv^p~dm_j1IBgSy8xE_-_HaZ3XqgJp zh~ti2Nfd+fJV|vn^W3%4{H{(DzYA@It1@;(_Hq5PeVLtD2y+nfkRxI@pnWS>FCfKX zgqzG4n~q(;*|-WAi{zi)LBs8LLIfAR$>>O(nM`X*yt}x-@Cr|}4gZ!AK~J}kM@sbq z0q(wdAJsvXQ=Ef(XM1SY5o(44mxwT0#J9vL!CR>Grg7KNKb8lSJ%Zcei95)gBTH8a zyNi%tU=$OatEbe8)QQzZ3l9;3K53|rd7bj8{U_$pIN6?O(|^^=MNh{tE@931btv`+PPruCeo;pAp8QOqDnA?RD5;AedKlOtGHj-2yjwul1hpVxPN!s) zWjP!y*~uW=mjPhj2%qFDl8Xd>gRs^3ii`^1@(r=?w&%pRQ(xR(#s`-bH(ETk-SFMp zAm_HRijCHUcy*Hx@~Kw(%8VC#IHwVPwR7)Ds`#HipkZih@%z9l= zfALhxG2YW`Zi5$%S$FlRH9?0~;{BMpoOy=1_W+x^FMB&d zZabg2O<(oMU^15QE%3P4##Fwm#xP@3Ey-pEkKEJXi!Ml+R6boXO0eBvB{M%HA zZnEP}5STaw^vZG>3=tz+7pMc;5t;!|YxsdVg@vgifuz03@2||BuIlAQ7QaC!N;=C_ zY5Y2bH5wv>=7GkBq#}|Sg}N1%Kqkjg(T&-iVpkd7B9BxlAO5dY@P)XpD@3UqJV9p_ z)bLF5`;|+_O+LJjuDjB9H)$U0p0-54&kDYSEU{;F#`%mdAldsKFD6xg1CbK$B zmDv}r@h!fN05Y)vq2lv?H7dd4eO6p5>2l5TjQDJ9-ApSu9NP)Br#Wd{rg-XDspL5l zGbthPR>X&>p8OBUMC1t&D{|mTg1!l?m%c7wUmhS|PM=a3>6v$$qc*2g6hR_@<6w!p zk@*4!>ZYF$WT~Z?-2&wI6X-=^EPLxvFR`sY@#f8rx8`_o;I5#rqao#o+_si_`zxF~ zz->)Ni-S&jWSX>%keDV&IP740E zUOVl2F!`+IN`1p{Mv{zoi&h-yzKnOlPm;}DqEcr$W%}@qJ6dUIF&dAAKrBeR*v8)C zAdBRSmW3cIG-@(w%W0LpQ)KxWd>##-`lT1y`Q=BaxyoGv6q@6?Y0I+Q$~ueSpEM=2 z#K@ZO((skM2RdTN{U}F3vb}UQ{xnt!OFl@N6Trey{ru?T3jt?AkYwXt5Bn$g^Z(~M zsl8CK2j^)+B;{p6h29{1GczRXw2()SLS0K2R<5`|7wsb^b+k0iNSARTzA|mbaiQHL zI9w=Q@gB#%UD+8VCS0($*+4d=Q+pUzBR}RK z<`j(2ph}2^_w^dG#*WvIgNjHKWeX}ApkVS6@wCQ@@vjTWHqw^n@yd>Hx+KQnfjvE( zG=MEvE?HRaY`dwk&k$&c>K@Pvkf3(+Ws99S_l~XSeVb-oZCQ;7km%L55Ohj*%Yo_v z0=X@VLq&9z6GX_dXT6n-u|x4u)A8xlsiW(Ft9b|OnZTq`Z_g76yxbE0M7}GZ>jD#c zBI6|bODljA5kN3ceLp5vbcR~+_PoTb{+G|`z(R}6oLBRb#KLfLAg2N#kwBYS)@<=U zwqChz`pZkxzUcX3?jB024SVfDKdzc}s*@dNPTk2#|4v`cv##lhD{vzmv?_(izq-u7 zHkPItBLK%}{!610s3kl*wOVD`JV8^OAi*L$i+G1_EPoKEE-`rq-65aYO1S&6r_j*F z@nm~ke=ja<9&!EQHP|%rv*@#hOXTDVEkvx8eyB@jMG1bIO)OkP)Q)@(R6KkGBidT*T&9JTFmQKj2Bh1vG@ z^%<~LevU=*_u$xl>=@-k$d(kT8W1bMY7{f)1KuA(Z0UdjD23CHHQ;vR(#zC==7?q` zi%0=(qoY&03JPEo`lKq-o8XSlEjnxNuYHxd15oxT`oomRSLxvoCi}I~o&0ai1{PHO{LVjDJ|;7tit_$2Dxnr67ibtfTFeZi_c`{9zytL`n7$Ei zT}e6-6lca@%ua8>V9g|~5v(M`%J3IQP8J>TY_CkydzEy_tal>rnT%ja1KQWXytk!a1nq4?;o9u%38)J)u*;*>8GhW7l%5=aNSvq0= zmKe|z&H+0;A_qS5BnBRW@z=D|?_naQ%2yu$LyPl^O62ViLxmXYJyKQDcS>$$ba_LzlGw zvGMH)1eXHI#{tL!mox8pnz9N`%n=Ew;{3|9^Xt2G4v*~`L8bGEx@8ffH$!v)A90Nb zW5yRY+S>}&V*tGb!__UdY@?t}Wz6#V)BC;%ji>QO<-!5`6ovk-kYS1Fl2`Ct8ofJ4 zHp(;U%K2q>^n2P_T6g@`KLS$L!*_EozXiKm+|pwppN^r-{|OOEqgT`i9C$nxjJXMq ziO}c$d1~t>LRTRP`jgm4bg02U$Wjtef`!;bA1{r%!S_3{)KSZpqFo23085y<5KIj+ zUi<>cJk9DfAXJdkLBuTUDLpB_pXS$NVP$&95 zC7QTT5ub`~_sY79tN{@p&3?Z=`frH=)a%u1)Yw$hr3W**2;v)pqH%YFL}|!STriCv->BVYw#RLkf=6B51nhuKK*h96 z&| z(ye}l2;PTHZ5-}H5D+48AT;_xESY%?)ov^Pto&#qK@LOxJm9QhCN<5~o$#F`c-hg4 z5&;}{t_VQ>w<>>j(S%-_b!0sek)iMW<1c09@Ai+ zo>8BQ=_aBXCHTJt_~Xpz!EM|fPzU^w!heumxfC1Hp%Y@AtnE)kGxHgv-S;u~i%*`< z+sAWTeEe!R-RIkAPx*r2NBsKRUiQ#GGiVM8H3B%8bY=l<7dQ%2`tC9-PbkYJU0@?v z--9tBKXRv_MoPSCJ92isN`;nY##-uY=|Ev?1^3qJc-uTyn?MeV^pf8QeOjNc2z2)iDl6==;ZRJf-_ zPFi;)A|g7%$tuO6+lH0>9~jn!DJV{*o)rB+%fB`qDdVMdR#3#vlGWQX$i{=HqN=X0 z?DBP0WtOtOlW(%>kVE?S3(*8!aWse>-=Q!rb(qQ9lIg*3T<>NEFOIp)W4o1KH2q-; zApkK!O_yh%{2#cv9xD5MBQw=Ix-9C;SD*KVLwCOI6?`)Em+St~Zrn)KNt8wkFio3* zYnAh|Iv2(52+>|O%wJoDFf`8k}}t7(2fKe3#@-Oua@q!0)KSu;2&`DGmE|@hUyB9}fx?E7eJFSEr3yJE4wxw`!BESMvfnETSXQHuqo8 z6%3#z7&$+HyvuQsG694P-61AcRA)kWY3a=O3t55@871!>x>F+aMms}P)t@|e0P&pi zy6J8v6^N13X`RTxC2{0{Oz@VvjUwtwMCam(UgkrWNM|Ixw#@=WllsG~ju)gcHhSH4 zOI2AzNF9~Sd5>B3c{7b_m_K#)3xO5_w!*+vplRe>&?3 zCw96)6Dg_?NlUjvT<&=SrmtdkzFj1y@SlH7LR&TS@ie<%R8rE8)xnf4RH{agA3T0M zv0?B3!j@sHLAst5yXHHZo9r`~t%iav;m)0{ls#i;DyP1OY?BZvu^Ol>3-0JE4OTYk zs5U?z4Ykd-Rs*YmW2c?m?qG@R%bwqkLTCfrbk4hXmc$z&773=Mxo2raCo%g$z~HRf ze}0r(Aewe`>UFQsaTB`N@e*y)V(W4BCk~uT*Gl4$3^NITL0%GK9k%5t&K0%>@4^yX z5RqSj1)~^6*QP1?%A^l3+_F!Xe_!&)4JQt^YOD5`HiZKHA7#yZ?=mrMp%a(8L zXArDH9yjAi!SK^K{ylw@9`+3(D^_vLzbHD5xWh-2G(FD8GtQ^@jd?8J@^Y{zyIOG>Gkc%^sUJ#F676ZL~dTAFIctVUT z957V02$B`d*iwC!)Z9EydsWD!b6u##XOf3WBG1w0jWnt-fo_v7KOg-VJhyHC=o|t3GvYNn4RP6dH)n*9Okr04;pJBig|OIF9iRVZ+qbn1Vk==vLLPq*E)qf zILZqanhtYl5$uVzRL0kryKG!l@H%AH3ABw~|6x<`Jc3%G+(wa8p|~ z3oZdQ#S9KWIlb|`mqH(KT*NMpwzMBG?(JJG_v-VxrEp*)=)lX4cFIS;7VAuW2r!)1 zBL)9%&1Urwvsus@ZdIn5s+B27g&6gspN&P zV{)MRaZAkS;Av)&^C+(Cc#vQ|()ACZ-x*SKl6Iz;CUBXgN}@Zyj=cJuem3Hb^W+aa zp!jpj0VxR_X2eEmHOfS_P9rgxCv*Hv`?QT$r^#3l^(9*nJM$a|^X4E}YI2UedgdSZ z_8w-vsnxh;lvT2rc)4vS!L8!%1E?0>nelI-ubwTVBM?NLVH=Z$oSX?EYXv0CP@Zsa zm#=zUFIk>q`&Y!T#cM+U`UhK`g(8M2{*u-~yLt2GWRkj6Pg~Eb*iW50RhS`QhC+N$ z;2cZE-Nx!rQls(pVNA4{+rD2&fj!Qxh;l1nEAR??OqqEE)GmJ=;`8%Xk58G(#x67T?wQEGz%^gjy ze|lsW`KkAb1CH}w7pqNC%eD{j^*O|a+-UQ?HQJn9y#7FCeo!SGPE#4lUA(wf#+`c) zOQ_~3r(3KjdHjl@g;|myuzsU;Be1;Hm;E&!7#4PJp1*Uet&{sn@ArWXIzNsujQ=G^ zn4GLRQdRcd!kJV!?2~T-2KTJ{(pxkmoP>3}o%GN#?a&w{5UL%SgaV`-14G)ntdkYk z2$1^UKTV>uY4bK>C&z@6TD&+ojP%GGz?ecZsB%Y@Tk+}z%MUPDq0?1}>s>dF2(JGmfy1c&HUZA3|M^NR=+%<|eD z934&YkZ%p!*^SPFYxUpcUz<%J zt_fo!wy0=gFpT+=N>5@oQPp)(C0(B9-1j`$tDLn@&B`#4&ZSIW^HJYYHOX(F%MV{i zCd!5h26tHX8ZuqQr;Xo-^xoeV)02>^VAXu1N!6B3hf+Z3jl5aLsevF%0O|`cq1M#K zeRf>M@NSquGQu&Eyh2TB$R(`yC-_Ws9%vwYO%AmEH5_Z_d3{ihB$7LqeVHfSDY(4z zZWVpu}H3FI5WH>ppzaIw7lD*N-&N#q{{vH zJ6*1MUvzCKtGIX_3b-3`)pqGif#*1%8qrt1#?Ef$wH^oMT{tGH@>pd{iu5&sf18f0 z>)7skrMDF+wb-jGY2x9Erm#nZGuu`9P73JqA#*1G3$*wbwP>yKlVf2+WhDTSk^t4S zFud2E;YekuZ(7KQ=Tw}6b`%5hm|+H|BUkPkvrBiZuD`bVHIRw|SA$wh8{Bp1=2~<= zz`nVF7es=B^GUXQZ2K`q`=J(V5gF4(uGd7ohh(`bEar9M?V;X%OKm=L?m^^?rdy&E zeKskJ{vV7^3K@Mjere=e-?i<3djqFUEher5e#jnH{1@W+BVwlnf(yp^Mzrm;#!QB2 zYDhPX!ZsiFQhe{YM{r!qs=8O@(6gUX)*vaI7e9i=^@5k;UHG-Q6cfRpvueK9(=%ZZ zuZi@mBpG8wpy3}^M4h~6xK-o5E{TJV2FTo|h>M_635d!oN?vXNH&xlDXLqf=x79EC z$VAKb@D6KT-n&RO=}^leGr}ND_ASR9&CXI0$~XvRnYbR2`IMgNxHtAm8Z{4rs&LZE z%DZ;~R=0AeJ`j5f@=md^s*?uo`(kUi)+u)KDd4KB6EFe0*%Blxf>tQBBHV7L zQ8sY^?59s(N46)g1Cn~ z>|X6W53op5eCvVSG_+6di|05+PUa}dw4q#j_-sZ^J02hGAF&^c9C-Qe(9wq>{N%gC zd!)l`q9&_)h2_c^iOf|Ws;l*JE3O*k$H2Kc1qDsb)BgK}2~#apt4g+cv8koF>xf!T zTDM>~y2lH-3!~;i-$q79Gp)Xrv@Nd|qG#W8Aod{^Z~ta(U^{ihPKPkXw)lN+Gx}LwR{+zcMj-#&V+gjEo-EW8eF-@8H_w z$9>d-{|ka%2V%b|M>50$&)Q6mG=K_=*f!{#DWkB-NCo3D0C^Q=7;lCj>D$X*K`NMu z4&?2#%lp8Xvb;K^f3y026T~58yYARpbev%jFy~uty-EAyz}A7aO@f=YYu8Tth9Y!? z3fjoK;W({~&^_sCX8nIwk&PQQ+GV!+Ka`~XrSwgMgzRMBk$?;7)&TNqT%lLQ5k8HR zfb^SW@Abmx3QCt5yejyII(!LHJW88|5CGv7;UE7!K68il@U{VCOPmav46dNXu;uEs z6@^DbkMy!S^kzkPMT}YQsV@aHXS?s*e7soKVRH2!yBumeYt84(@@sup_P6PGq4n{k z1#^qy9;W;97NbKBL`S$k&=^}-d*|l&Wf^4;Hcc!mjfyIZ^ZiPvMS}kB;YT$~Vr7D%#Uc7Ua%NkaB+WR^w|0P#`*>Lr_8kn3|BfLvzC(0z)O} z0$g zS@`BVf|$|#^`8VY3)M2;Z8vJc7#-z0l6_QV!6^Na5acZidWm^ogvZ)DEp9P6Y9w%cJW-Gdv)RA1--&wb)!)~ZnL%a%;J)ge8kc@sK>T^=ujaH7xmhYVF&m! z5ek#_J<0QFJn5QG<9_{21hct^l_P!WQB;Lt-0S{~CN=j`E}HJ(vNuANqL~rV1I$Kg zVg+W@yX2xEPZGf-)E4}IEo4>l8399+x;px4z5-22*cWO8^H=h;))IFpJy}}maC`3# z+}9P;n8>76Ae1RF1kBKG&~(*bt5}+c^R~D8qQV7kx^MZ&G|~>Pm0H|15yEY5Q+9FB z@U}9uyktq8@ur8iSuCVX5mAKnKBzY6>?YCN4om2$Hl}f%>drF~udDiRDL^CxL3R5k z*=a!wUuqLS4d4HLBGVdWD4EP6x&yj}G3S9}v)_V7YyCt&*QazG6Tvb{?a@g$D(TJ+ zQmQfoULbK6Pl-!A}|iGCBD@sU{y z*+B!_J!`~Yh~SI>W}bI7e?7nayM0p~PuFWC!q;hsYVh&}CPDJ}6YxUn4)dVe*l@|0 z-(5sqAgi0deR(IaN9;dI$1i9HpB=PaOW&68!61y&;kOQN72`&LzDmBlGWNr?O?k_@x!3YaN>hd6hz8#U*tou`bg+i z{`S?YYk=!CWK}bQyj*s~mgY6(=nMtP3!sweW)k4+Rp#>RG-=KMf3cAtmyTIGK@(*Y z&}gEbW*w{Mn@tpiWN}WX<5DkXUv?LcfmO2|*B^GK%#!jLo}Ve12^W`y&Tl4lL4qpD zjwtZ_0HeI#?ht5Z+H(srpTPh!q3w z_Z`LwblP~#d4xwqVa&I}N0gh*P*&_7-d0$)pt6e0gR`8XKCOs5oB#OU*DXU1jV&qD zDYSbq?R7!Zr?%%_-Jv59og5KehN|l98@KSMckbnQ9$z%OgO9COd)6q>0%~%- zm$aUIW zmBKO6+$FBE9OTvi^~st|;EVkI*EIzH(fIXzqxau=&+Y!mWU1o6U+~Yrsp9ej_1%{v7R{r@h}OwC%b3-kG$eYHrnb34YVN)6l7FJz%U~C zcwETG9$R~@yQ-SEXlO~X&PCgi^X3M(9dC5}S=U2l1@*@Um-XI!{5!Fx^PE`*O~NnQ z4olowY;d;OCbQw}$|}415{gvx(Fz`noT5E;|DGbVPREq>|GqGOHFMv--2z&`z_;aO zBk}}K^bgrC#0#)c`%kbMX0>+r&$n@XJTpA?+}$fjo-Lj5vSZ{HHa75wTe==>iSUZS zst=z(C*HpOhr7G`zpsG5nbH2A2a?AspjQC$x=*62cxlV@`X{)(4 z8LIW-*dlGM2hVw$adB~DzkU4tIoiTV>*Hs}i<(H21tDc?NG+=4HGt7cGcI*~H|d|_ zabmEFy&W1DT0vrG3^*3WqKG@7_C-TH+1y?j_OBma1noqB-!rDQx64?eJVxf2X_xaa z+A;K*cq!P!Uzy|Dw@)8TnCC`+H_@KyM12AMFbj6RFnkiN2T^H*tpMVifx=dZ2tW{< zyuEJ@Ej#1z+ou{TpGvci0=8-cb;$=tVEpiD#6ti`EXrEj{n|b8%cD4+@qGXWAM{}- z(us@MkmP6f^=EbO=ehnNMMBM*HR;;bracn&={3gXxwY#@UPm1u)emE8ifQOhR;*qv z!$rW>1}EReo>}nEYl&>3<2ez7+})yrz(_{iWsrMdZlw>mqL_y6LjVv!3ANjN@3N%} z7hd7Hm9IvBwrtrda}&c&zkZ$x-rU^3gX4+!kR0NpO3$06f-xa=^Svu5NyRS&_W|)~ z2MCAK$Ux0aD?+<|=6Gtb-bl`50~ObS@8I8MN?-I5!>AX{c6NH)sChbHm^j^@DoZ6g z24q9~W4+UFk9OGd@b5#fZ}+dPc>3Uhh6ms5BWX4r-6bDr6O9G%pblS|?1!r73U8># zhE0Db6SGb-Kqs35;U9#}r0B?}g@v_qeLgfj%~^~5AME?6pq?=dg?OPM467#N zXCOySz8+k2B5{Ev;B)%xPo_ruBSu<_utyzfDDpw#F>1T#?OIasu}a$9wy*yLtQ71x zNJ<`*GyCGU&C=5xWZ7#ab{sSt2Y~`H(QJjRoF>NxCJl#J`Nu0Tp{;X;zcHwDAq%pZMA9=t1yu3uG}32{zqvI+^^qTX>&ZJrI&;h7DO|9zW z+s4V)*)PO_IR5%;^p_Rh#|wrni94C70U{W5)XMkdlBwoP^8Y8{(1t;#>we}oY4a5$ zis04NkK>cCrfi}0ng zlHMy5X%17*%D)&men_iGqEx!|uG8!`|3IYrBUS9LP+bsP>w!HW8MhG|MO-6;1Q(Zd zp!Fh#a4<14cE{nAA0eFzSTkw$%1k*3o1GMY=IZ^pYJQrx{5!OEb#-AsEP!GxDY6f#%F@p({ZD5s!s9R;PPt#~9a&?`4G?zqOc&!j9KR5I+o2|EsJx z_ua`w`ZQqRk~An2*HhYbqtF*`Iy5b}=ZP2+-WDU5@>%m{nK%6>YAVTRbLTpNxB$44ST381z_>jo^TzKbvHtMj? z-x0;r7|8@B$=VkV&ZnNGB5rD@(eF$e0|8(e*<E60EoDXmKFMZ`hN6K9gC(5AG|`TNKr&qNA%pIzWw9jS17*3A=$rrJ6CR5I&eG zs{S{!8~!|Op80Ryd`mGQ5g*)UF_Ap7HPhIz_2J{=%|1s|Hay)QvdHhjczOatq>wMg zKuK&wDUH`(aR28emwt2ktYWVq)az9?^KUk@+a3}HUI3B?6wc9lM19v`lm7-f3AF~P z1@U=IDWT&t9x`^)X>Xps330g7Ui)s)rV5(TMe7O}8do-kqichrnBp7YRTqT41~m@- zc8z)Mex3*|E!%oj@9WbX6Yx(EmXtBQ;>ENJ#XO#{A+tB?L)!oR(;was>ra*{?CcJW zPT(#(5liJA-f-vjMzst2*D2MSG+b@;g#o>Lf4#gE`J0faGOnr1pu+sWPMvpUUZ#_U zYDe4H{tEI*-LbAMo)hY6;K(H=t!RY0Zji?12lv$CUH^YUU(5O*Yee(OMRtc#rkHiT zdKl4?Y6P{0&gM2hr)9h<a5g{9dKr#5?*+oV*x={|MK06i=5TN_PY&&DP zNhv8Ry?UJ+dDa;N7f4t?^&Lxv&R=)cBQP*)b%D%igEpj}f+kW27CGoaC5T6^P;dqN z%%3)S5!;SA7s*tRI^AirqPn_Ou(59Q8{FmBL}<9I+R;zp!B!yELUq&4_Gawi2*?|0lwbH5LWW*bG^4NAPh@W@c&`$&EtCBxAy;pO{PuAEYcuFgE2$V zKoXMJWlDotC_+Sr423A9B2g-{$~+aCq>@Y-GL<4Tp+V{QT=urlx$p1&=kGke_jjMO z_g0_J`~7;Yb**b%>so?2F^eG{ia!hToP4b#uO}uv+4a5q@7H=is!qJVuU%6#5A8_` z(|KFcD5H@y6t(iN|HMy0rJ&TPQI)F@$581gQ@surq3Ht|{t zvb8Ng|9neuMBC9jC^8`c_X9B#mIfAbO7@}ZKw1%YcGJri#SswKnqGr|E6Fb}k~avg z#Cc8NavwCVBcGlI&1}}_z9f>!fF>E<$K4Uhfh#XW za5wN0mk2|yd^&0X(Ta-hkGxP>;KIMw9+osl#s_862SkTP=_b(U+UfuOkha#>{;b+} z-Xw69co;&|i!oNSV7Z10*Dh6bR&DjA{e(?dr^e0p`IV>B{^(q&cJ!6L=H{q_{r@~V z$XGYJ)Tnaf$il9N?Uz@j1-n=DzMN!H)X)Dz#KNQR=k!03*e7B@0Ao-&`muZHTesou zt(adS>^;fyT)}2Bmog~0+&3mg$)N9jvIFW2|IufBuuh}Zv?U=&+3^O35zGtM;)~G2 z@y&3RmJk`A>45&*bpGnGvZi{8D7;XrxU-*vluuvU_^?^Mr{jM12WZ1_%2))EJ%~O# z=lv>J*W}2uyjS1HF=r?GS$f$IWhP6C0e+LrCByDlTBLtNup^_lXdz9zpMY9L zWcvRi++hrZDs00Np2U`ohL8=+o%xB)&g76P6cF8)Z#|>bpn)?4Ix|9T2V4yK{3SId zWQhB)m>jGaj$)H+hMsQ$%GEn6vOgJ(R(;f z=Fa93di2I13-SuSM_gFBVw5A@4pADYd@Nh|w+^XlinB8@n~CB%=g$1No%p-em~x>} zOvrc7JC=*TzjF{<@W0uIW*79BE#>NNY_4^4m`-N|Ae-kWR;)+Ey`9Ei{VnMeoSn1w zo$In)!(g|yCbAwg^xD}`Xwxi$Q^FIsg?7TEJw3T_dD{Lt(P}8>-yn(%ctjD zGC4&jA_^Ktyzc1ex+UuM&w*8cS^M?oq{N$yACVsD{RGqTs;jf~YIIz!GSSzc#E_8h z;xeT#ON4*^{4u}Bq;sFHx*;# z)SEC(VCk39_MZ!ys&ZBXVa`r6?6RZ!V0@y@SAnQseWpQ^GBO4 zncI3;ZuPgZ-Y=RzDO`KK%`k%(UiF)eb>5=wel4%r9P7jFHOE9j^)vCcB4G<;5rfGn zl{~m=HgDw)(lPj!o}7O(sL$^xru_M9@39Td%S<%?#o|@F@K~o>t9eP;;K#ta z4Vx`c>nP9ufu__3tg`j{{EWqpkJz&C_kZknb^FZ%LN=xX8v$Y6MLi>4Je11$i7R$f z9qxWCvPWiu77(^_9{u;nh~LpkC7FzjbJDHWEdrZw^(U}#~HW$@n1|JNgN_?+f# z@XcsUexOcFnwwj0wU^DWXR%4wpu}sVc6{27enaB@y^k{l^NEcEG?Y`}tC(19MX*vw zu3X%Q%nG!8aq{ecVW|MOS#M*q#Y{r>wYwu||< zt5p9zsZIV^+xc*EGr#fHn$5vrow=J29(WHtJFd|3W#NC*B7gHEx*dl7{?+lTl~W=E zxlnSS4>mDVpYVeotR>Ul!}8`JM;Q4JpYrz~bWZMP#nz=7&Z6ChW}2z9Cg*D3j7$5? z>RQg-S0{cZErWhnE}B>y_3LNu-YXD|w}of@hy(NOhpEziVsiiKz){AUnwz_f$XPxo z!sq_e6A)qt!=s<21RQ=xhcCy`bzh#GlQ;XXF=15ciuxh$# zDAp10Xb-s6ESTeF)pUDK%lp56opOq?ZlC;vo3tZK`&B-gr=T-Lx7hz?p{0^@V3#xP z=i7B0wy5JUW#ioH>VaoAJ8tYyc>VtUK9ADAJNn{>x;P3zDto?tVkAKP4-#?YV z`bEEs?62VGV68d!)YB7Jr*6FeW;J49IY8a2m77a->wd(cf;T z`vy}dd&+P~aB5+p+wAJ!T)O^aqrZ82rDng+;70TI8XJe7V)i11bT1=(L}{U(h$H5n z_J2Pc<%W1>#bbN~smvn4mI#jwPv~GLfF4443apEQxvp`@Y?y*jJuK}R8(bi0Ol@K; z+f{XqeIPtHL^_=(--q{szcO^N(dzfNRWE?IkIYAa_8KQ*pkz<6Yi)zJQw*jdH!PeM zsi}QA4gD(0UvQjMzhWPT*x{Qo3JYa3T2kmj0??X}DT_fH#h#E5^a!?4X7A+_h3u+V zx30K@BQHw@HTFYG!wBI^UWNcb`dc!7CbL0!Mno3Gr8EGt{in*`dZzSO%ON|`%H`i9 zlN+j>Qs?<)?cUek(lK^A*1Ig|Qv~KpN zo`G5H@Ae=xshmEEeth}D*z z6n$1(3~VoRf=)iqs4IQ%Gew40pXeNdAT zg(ts$WB#*OO%*#2uw(V#x^=5pC^=T9NKuKgsOwhMK*~!)JMr4?LaJgT;1TQb`>|o{h5EkgG0nv|6bvEy57-ukc_ZqPQZFEsrLGyg3 zF-}W7X!M8Vyu4lfeAyr)vx=9FT(SkU%Zd&p*bM3U&bP=gdxNr>G8U*2JownPjy;bdgG1;0Nn z&o_5P9SU7G;aCj>V;lfg);w|#WP|}B0|Je_*+2f%hl}sS2|0k;5e@rpbXlc*UXF1I zb>fE)9|$%p7VM^*Vt^gpKT{>*A1gHSojawMeGxZ2jYB>s1s^XmJG1As8|E+~vWN5O z)2B2@np5qGL`qm?hWI?UJ0@B&A>cw`8F4zBQUa^B)2Z)CSE9nETGOClVCkAAc4r;JC)IkY5qY>#M0WY|tRmF=-_nR-IKuDcR14LS@FN{&=;0U;fyLe$tB< zLpZ(S$|0qe%#ty1{|nu`Kfnz!|C4<;#k$s`XZZa3fW0g4ul8zW%(Ex~Nluj%!`CJT&Ytzoz&x8}U z3Y*bpIJvkqf047n(r&kjPoj|#lTyp1?*&t^TU7{;Z_6os&~k~9J+CRGh9~28a2gN~ zf0>*;^;Pty417LW@m0+P-YBY37yeIXw;{l|#ib5+uKU}oF$zV@VY1PG%a+D6^Z>gT zac>w5D)s#z!Vu6Z5SMU@VX<1_Pp&1))v?f1K6x3%DPAG;Rl0aPQg*`xuY-$QfIA>f z*qQJlz90WGkKBr=iLcX=&dOFAT^tE5%z2&WEkTWfeLAUVG!3&~azj1smM`kO}`b9YZ<+`Tc#0Z7_~t2Ty!bU&tX9zY`u=GDOWU z`xrW2BuBf*82fD6BeH#gq@03oMo1`Phu)jasroi2r;o%lJ%=SC5*k8q@{l$enT&R< zG=orhW!?L3y0VD_mIVFf<`FlKdLK~B&Ey}+i{mN@`!2fo1Eazp&8{|Kt*N}5%CECf z+Gc|YXeSlXWBQnyiz5EW`@hT+^HD%4Sn}h1zxwbFS;BoC@>do~(SS>S_^=}CeRXxU z!+8%XJzbGbs?Nnyd^BiIx?D!H#EeD~T>jbc@D5-qQE^Ey<6jp?H=fQ=5R6Ia)ZeNQ zkW45DvRMIVT6B3ZOtpl7kMJy2Xw)80eGCa00!J$M2PQ=M!&39e=9G@4Heo#}=JXWcaH0qZY>ntV z>E3rSm|y@6Song3t~$4nX-%#Ugry7YAkVZZ(UZy2pQ%4u27bE8Vpe$CV%}!4UL%Ln zhPA-VeYG|e(WC-*cC_+-^9QB_w3^VS2fN zoJ9U%AvjHv7x4q}*UWn>#0QK^!mm{Yeo_E*0p<0s`H^Ijj-3jmga zWXPlW@v^;0#BnBDRQeHlrtD(kYt{oL3c4Wb@Q_kn4RZ-hSX}T$YIBXd(8?KaG^$y} zK#HyX1e4+h&SH+mcN233zFUv{YDWg}Xdnom^rz+qSb*J0Elx4W36<$IWfucw^nk_x z^H+ynlrraOGoT>jo1|7T57r2#HJV|EcWi&*@FZ7-o<=Dq zUoTaUwjRScz2rfV4U41xL^kQnuy^sHa_Os}iE#K^6^(@H*`;F~9G3OS8)mvl{QP`F zw{;sM)=sk6fSVVH$c8pQ$hIBmRg3cG#jnf_(G zM81;BW&DWoWQ=Iq?{oq?u42yi7CVcjEkXX5Pt~|>FByZUM~ETUC_PDSXey$)0$Rnv)*?cg9J1S#DbX?S#!l}Xv z1Ve}qn@$Xqa)ztVli9%kog`@5DCMF3h(X*eO*RUPTFR7R?0L^+)82 zP3EmAODL)q(zYhBT_d9y_ba*y2#{C)w^zmjG+H(-Rdl~$gh5uDdAw6;5N*B=$9Uf}uu{sodf-q@O}@)aBO5s3(7~=iCSS(rhRD z*yA!mn67zOuHigrWmQPCE<-<2b1^;J=nCyp)6*nIX{rbG1U>YVO0{nj$6Or9^;<+$<%XLIJNvOneBWV`!Sm(pGNP zI5%@+3D+cr7nfglUB%T5LI6>kjHQI|&;=<<%%ohExu=0%9_L~P5#>Ab^=DAC;zi@a zf;?JhB^=X(Z|8GW+TbVyB8jF90eY`--AKhf+0nWpgcLeJ_ zjBg?~B^2fy++49$``A$-pV% ziz@^d87t?~r{Z0Sphy9B%Zyh}c2?tw#UKq3sLE4d_f6^5Ik6h=-n}a?iH#8ZJX&?K z(n(z7xVE`W7?&f z#5c6!wdw#7WRw1+uBIvBio`?UR#tW5vTrvc%kf^EGiJaD$G&3hs&e{=P2H~XRTU@g zSSM}q-njS54J>)&Uc_OOOD~6Uj9Zh6^bUNHMcRzUeqCNoE?z}VCxg;>=dB74pL}pb zhU>EV4@t@b`(d#P`NZPa%apALMV4s9h?+8%$fJ-BCv{==q|2Sr$HBeDkt&7zAv%B( zd!{qD>>;#7rL{T09D9wZpP1OVThL@GxXyRmh|Y$2nd~h z@n_&$7bEo)OIz2c{^}M4CwyHtyTsjmTEY`z(Y1jc zVTU$VUZAb~=|kyQMzN(&&w&shdY0#Gql)2rwd%IN4OZhae*LEdf1_Q=p&j7tKsm;^=yH>OfZl@1ZMueeR9+g#YrjpYEX1 zNqvH+I3BGw%6$~kMR(dPAF_v}GzBDrYspBZ=uG%B>F{QuIEYAqfnE^0nybr5<(;h^ zMSt24P)DelK@1Qn0q1Bt6q`8UG465J^lLf)!W)S zHGMz7C1ztip=-CB!77`il`D>X`0F>~az^sKj*eO7o5Rax z`B@{w`|qU1AClJr*H84Lz8AEsaCWo7*h&Tj=|194U8=RcmRYx6RVQSAtFoHFQtRpD zR&tNQO|lk+=O0VOFAfUemh^mMYA+V<1oHzwG1yGf7z$k}ZB8+@xEwf{8c)C-);Kf+ z4p5nhcRYpa`p?$G&8gm1VaD~fyir*C0aj(3bJ?S|TZSTv&Kg@p^7kXUECj-$3A;f! zVC+?P1u@IK;!`n~a7776=|GZ65>ze!cpz?EhgSUdo`e6v> zN>Z`D6#gM%&xdkupw#!IGD2`N7$$M%q*#h=0K2eoAh%SZd8m34FXVhu3oPL*5fJOH zLcWVdU!l!gH#aw7fMfw5>g?w<8b$fVw}+5y!SYM`LIwO58FD9mo@AFESf};j2(ENI zW(y2;IZY}4_;EC`M@%BX1FoFYX2CCkP0BLnDK#3hn19$$G4UqJG-WUp7}0O}cU^hQ zgcW|l$g%s=JrG1W9sl#cRFNlLZ`}A(V^3rF4pLuIp7ed@&;K}j{Dk{@K4o;=jm2dRtl{_(<$qoqbNzqftYE?$%=Rqn>$nNi$x0ofR}Aj zrTzJ6L}k%`z+d7g~W~zBZ-nDVnJK- zf|sHfv0=^-1l$ppBJ@hmoowjV&x26G(V^&R_g$%LwB^6z7Gl%34j~G*d1ILezELG(-uH$y_rfRh8KfW zi(kfZqb+U)CW2W-v{vrDCp=t+ z!LzfwXUSw5*!EF3C3_K1YV28Om;1wJvsLBqA@Pc3+C#Dwsfr6Ips+mK%J40&4`S^P z0p2h#`|y=3S8Q4?#doLs-U1Cn$!s9cFSKc7JhSu*Df%{g-LSs%=jDDKtlAU%3T~8a zmw}%4(iacwLZ>kD~|2xW7aKm!-*qkz*Q zs1;#Sv77F`*Z92)NZ*nXZMePqj$U8Y2GN%FkN=%iF z(cqRgj5$c@BfK<&&Qj-*qM$x>@3sgM^BP_qt_u-FU;~C;<;N1NhtooHRZXz(YWQ@k z2=@3}wOSi{_XE;3KK+w$Q*7l(t=&g&E9Dn`d|8twvPlBa9c~O+dNecxpz+tRHcSFO z_Oc~Do?Bvb&r);+*mE7iNlHs+>y?YU?~$`-fVb5d+$FGzb*PX&FyT^I!FG3sU4y3R zarX>lP7FV#_>BpqIR1CqmqvGx{VJTYXu^nae008R>C%U}Ms0kG zInG;7Jy7=msXu2B@@cP3Rpn-W6aF4uRagaE5a#pmXpYi;q_Xs?!k{I@gMw(DM#2#& zjAFk4*dm%+z>UwCXa7!+tr)Ixe51?-Nbi;if~Wp9Ch>=JunzGxLVRBTnaME{LP&x7 z_2+BVkI=*Ns!_hl=L3wS^Mxd0kX2B!DT+Orw-rGQ zaocF|yVk&&85~FzE#*P9Yk-Z2=978tqoCMUFUe?1r6C8$ab?9i&emsIxrkHS2X1KL zvSMy~d|nDqR*L+Bdf^4%<<~k%u*h~gDr`cVb$V%(AkC6zNl9LBI`K#_BRphxNLlR4 zJ)iz$a$$c=L2VhzqArOAcklrdILwe0Y0%EN_BPpBwG+6+zLV7`Pvq^9tc7R*sF*u= z?D2k-o;HTFY`S8e^y_H={y6HtfGh~Klu0s$M0y=Irh>U5@#~V5X#Z#>0&7nkzA0SOwx@Xly$l zGCnn75rY+46m9YbxB)#~uQ!!YeDdT;udo-idd1Ur9 zap$?oEo2C+{H5Kl`(H?eM5n2VDu4X(M*)keI5ukD&kX#G^HL`(z1yb@x36zHP3n_q-gi$?Ue!Am%ag^9A2xD$wYVS(zMSF|dc5s#xu zpllu6$~Q6~a`J6WiGR|*WK2^0cK~Xys|TtyIOMDI8(J!N&>r%xIZLpzVH0F?A*+^l zcrEisC2)&`FFrm*FAif)tC6#M|L+;e?ms_;3|ihts!cHcB9^ZZD1vw}{J;*!SFT;N zMmkmGuDbj9Y>-bcw?F217&6dD&=-$oE|yqX6n_Jax=_Wxys>i=D8{$VTAG0(AE&V` z)fEl^9HxLWjm9xp_D{E1BVHhn!!0a#DJ+tmiU>J;-y~;R2ROvtBtmSeTrwj4&P%cK~@6D zkD`W@-V#NkEIz|-b2rCC9Ll)t%VnJnf%C{Tz|VKaM`;S*0?gsFZp|S;A*e|B78@(sHA&-6RB-HJoIk3vWyOwd zdYaxP%YG|11Di59?#pyJ$z>=PTl6dNZ?Xn7(p*VMXH+4w;10G-dcg3A&ri<#g0R{5 zustifTOZffIaWlsi4vyn(z5P9rHqMwCc%eBFabjW>=;X|QW@k(8k1pJ9Qf$ZX3>NY z!k9u=j7A(6xpar2KjrpV1=m^v$G6;z9_Rjzs`8tfc4W`4j$n2-dwE+Q;8dg$i z!&d|PoXHaLhnJaJdORThh=~vXtkQK_5Xb@FSIk6ugX?MdkMNr4RSZfHjSd8I$AIwcpINJXJY1NcG6K~ihZJdAyOIJ>>t&0vF0;N7aYhV zn^r{5N-T++dR3A6zijR^g`LV|Rouh?QSxu>k5Q97qd@ZFL(K^hqd}kLq$Ig1L_hI3 zA|Pxa+4ZD+vQe5Z-wxUaY#gTZaOH98(@&HXWyZU&UVQlikJC*wMhuABh&N>Q0FFu) zzT=fgFeA#*0rrO;`9ou5nB7N<8ncME&YBz)INkzTN5D_sXb*P8gk18I4%QG{4wwGbGhD{L@)FM8y%f4pV|t@E+RC+e1BT|UOWu=X(BQM@J5Qpiv9^?vD1thWmBeYL!l%_#j9-k4zf7< z9?T@$-Czo8OOKNWS(!U)s@c39?JycjljV@?WY~mWid$G2d03CC!i6TA$V>>GKBp=r z)6QaCj&^}2Xh4`j&cGdhmT+;yp=l;F7kd4tRG1J2 z_i>EHev-FHBjtwv_GC^u%#Ft1_IeH)0WfME2QH;HjjC|ka$rr3<_0kp;>Y+Jx!vPg z*SgMwI#>i}DYNDP9QWd^Hi(5xf%pp865iJOAxTd#2Ut#yB%+twAkPKuLqRd~KiVki z@j<3yH6_ABlC>;vK~C`b?@|TIMbt{<*kl01swMZ5P!W#g)eFH-!%LPO_WJ8UH;4h& z(QdLB7YFMlKE8ajBC)d~ztaZAT|Y-(rvP;2QF2>@!;wk46Up+Y5BXrNKB8io@BI&B zDOw8zC!ark>V&ls5%0Qpc9TN|k7yvQWEg(INS{lzlm+dvJ`30`iv+|OX#RZFH%2q_ z&l%8jJ;2|9WT;2Lm07a-KXm8AN^rsiKSOHOzLo9m*OFxIw9@1a^V9^`fVZ&==ZSE8m917*Y|B1mUAENg6kxBPR>=$^nv1Rz17Oo^iVSKRKh!8l5B z0)x({dH%abk97J5=_MDJV~&hFZf4t5Xfi|U(fSqB&ocE=v;PSi8xA@VmyuldgKYu? zB%PII*_JE=Knzn%<5lJ_Ro|P?cP_82WKn`~n0Iq$I`vmB`tBP2Owpaxy^BI!zdz5^cB7d1;!Xeh0=o{ zZYGzxdPjp*w;&-Qg{aA`b4;vSn^%SuY9aD1k--!7jnJ(U0j@FZ1ivJmD7}EIu7>gK z3YJ0@*__!iYJ_B#p8y!wTeTmtcc2mHrI(5E*Pl(5%Y3#cgQS}m-wn`nOngcBX1|tY z4Y^>F1RxPQ5>ywe9njbr1Qbkz8IUOhN>)u^AjnZO>%ICiVcCUPB()H^WwEA|SO~q& z(;x#=h{Lu5vwPwI3(W9C3&2%z;TH?Rbmd|LJ9rt$u=kg(9GryPL9r;hO%auTO#Bf* z4@maf5Gw$O#T;`PSrCmpP>S26@}_hzWER@D{U{WW2Zk|Q=d-{O#5;{+EqW9FerRk9 zj#6JJzg}8zRkUqij@WB^k^quR9fy94h%$*L2JfUNRt#ABxs4jtFp$k#tmGKXV-;LP zLUkd~z3_iA7XJNejmyWQ$&oiY*3^WxX}>MVP~XPR&hFiZfYZy}{>qy$V7$qN<4-Q1 zd3$_v>j&ZcLZ-(5(WmRwRg2QAem-CFaaZf9_2-mb+Pk4%$npzB53f*0doh=tP?VOI=DTiP zJc34tQ#+Y)_J6J3wDuy8D6w#(@6hyv)0&)gxi1_(;H(t#INKd_-m$~7`HZ$(G&9|X znVZYDW~2{YF6MRJX(eQ~om~s*-H>ghG=(}bpu7vW*w4e+Ue_7s(5g>vtBxIkAS=${ z1~c+Q^?d`P(?3uC$E8ll^_?A+Osq$eKUvecUj(1SToLi-4+L|IP9OSt*v z#}~&gefk9RQN=-K^XAPD=$sKBv^sZQ&AUUuwI1FU7&UNZmHm;f25M?+pa`W0PlViI zeQtk4))vWjZH~Cg;DGttH*VV0MOSxoTwI*pTdU*Cw{=zNfyf0qEYL@`AJSLa?w5?( z=FXNib!0f6ts&y&0c?n#0HAiqO{spvhJ~PZal?=`5uAnX=iAS6LF*{O9|UT*7-fa1 z8ZLDht5sQ`CRTcF{6p$KYqD(FGXCIJ#*0_;1@WIYW6t*mpE)ie!5Nx^I&3pplycP+ z4qhSd)G2vnP-0!oS{ZK{n#>=vw6xqu0?GLPCv#mQ)+LG*J>z7(LXPQ60zma!2kjA?XqWO1*6*2mZMSBYaPRhF}}vuwT**#-*Edpu7$7+!h^6rm4SM!0qy;8h2v_kO z_SD(YX^rQJc`*aw|URcAg zD4{J+oYK@WSy^?RCskB)C(PU0@5qrORZHycTXXvI*)+*<)M#G&_1m|idFFC61JbEm zH-C}}W^mV#IJpK|-7dd~H0?99Kd4y7x}Bg{9#yS_A$V08&Hr07dl4ggCLfH^TX+0N zW>ROhZ*zj~d~~$7wmzJP9w-#ywk$qTfcGha_9IlOnf;|7Z5wLcDfqtn-WF|wm3@4C z*p<4Ki6ZW`!ohp9DL_WTA{1pD;|z;y1HajbekC9#iqz?GzyZ-y?iy~hle4qjfqW*ZV1 zo!P^>H?At80ckJNyBZ!4G16*6vr`ThdBg1{m+4FnD9vh6yRfkE^ps(Iw>HS$q&^^1 zio^whsF*&LtM~@!u%C04uV}AIM!G~lxB8=e+h}~!c4M13U3>~|(_$cBJ zGFkIBZTzgFBf%k5w+ib_hj@DrWTwpHt2;xJTn*{ zW@o0P_)-`|jX%O^u~={Vf}5q_=NxiCZ8i_LMT5G*!NDP6VahFArqVrY!?sJ}GLi_X z$9k4`0UbwTiy>=#jKzDC&n2W_TD^^YbVD^yc4^ zaePzyp@ze}K7A{|{!kIi;Q*zgq9P6w{32OyB6y77k92(2`uCb0*2Zi&^(?ljs-u7zDx;K%I=wn0^fWCx`=^rs0`BO&_j1m<9kmU+(S=nw2!w&HJg0Eck zgkihtZT~qk&+31m07BHKie`!i%Hd08YaH;QoD@DSFyy7Vv#V3eAB$;F+@cw3Yc5y8+VA7EOXteH^|hmxwf zib@*W>Q+-+3s} zhzSwWv&Cgga|??M_ao!SkN@(PmpgFT*ZQk?wK9Ed>|MSJAE35d*7Go*(;NWxczUR@ zldvy0q)XSX8woookvin(=L?nLWT#Y zTO@g^mR#zPMlPc?WIzL$#KRd9ZM)z0kQCTNt^*wC4Cm|}RvH|wC`f`!%gX`)gfapOD$BoU0$Wldg+jI?68qw1zx(Z!q2A@6R@1_yiER$IQXzg(mxuA{-U9srM>4cJ+zRl9OxGcc$qQc)aJ$v!hUqzO(E__pDch{o;iST?pn1%-Yy2 z9`j=oRkG>gP7NA2-sz}^2n@`JqoYi7W=86{H4ovJy#hl?V}Z8~8r< zv%20~DcLK@M^y-0VvH}5j0_ivJM(A6P~_!{vfW z)1CMPatboip4F=@Q-}Vxhjyek0q)VG8miK4wg?5ftKi)g3lgHR1@0PTn`cK92r@PW`Eu7nUY zO0qDDsXXShO%3~6@nb+>_KDu-b(aQCN(K+ZESuc+{AlS%<3f~e90^{&EjSmVQvGbp z)~z@43&#HybxN9Qxw*MS+0g-G@6YU&G0BR2zZIm~ZKr=oNNb{Jr-mtp{wJ*cLPKTM zqjcj3M}}0A`XLQ7O<))ch(pp%gHRDsL3rJvYxK+(Ejp$9^&&SpM zNAC4EdatgozJQ@cO0iW#qpng&N&uQUQ|4E_AT7BR1#rQrCDcN@B7F?nq3iJFwu-%RNnurjwh)iL}E*gBTL_jpDd_t z29_Ci$WAVyaMQ3qNc|+>PfGL-rP?ii(5E5LeEG1bTzqosjM_WUZ}sY0T&!EeAMDvK z!xmtw$lRH)sp@$fnYM#SkPHC3(*^6*wlSfN>bY*0E?vH2_o!-Smh4({#I74mzCQ*t z)NI?sTM(N9ST-R~lW+c|-(W#tBqK>zP;WD&;mP$CT{HwzGqrhF`>y4*f}fz8w2~9} zFH*;OJ1YMV$X`CHe00MUQ&ZCiXWZH$>XC(HqA3&ko3dYY(M@{4?f+IdXz`3Ik863! z23|(C=2rTC0mB4CdXx5q;+6TLMd|LSk_4IOH2g43L6?^_9aG z&;A0DibM{y=rnceU@T8X)gTf_hR;adYY_`N^A8Z3ja51;{S0CP3oZiBj#b_O%#?-0 z3Vz#na5!G^kOxOD-`+xLIslqzRKiL%Y2|Yg#ay#w-hAQ)Qk<>;ap!;9ZwN~S3{olV zeDUL(qew@YL9De3?I@?Rp0q0=rpN==9y|!)BRd2vy?f8|dREqEx~9@VrHP(HdApPY zDl9VYYKJBz^KC464+eevz9N_9v$xh^MbN|$t7y|hcA7tq*>o*uehmav$ZeGJn^ZcM zjt0f|%Ws}rrdr>C9#c5T?4+)%*ci#Uehr5i&PJ_UH>)Wzv9ZHI z6jH-e%HPxZN%y9A?%a{03F(}4RVdgktgRc-5pLt3LNl}+*bM(qG{FhC zX6G5TAJP%x7%GVU-LZhiW5$e;edlp;`h2<&ntQ?X(q^Pko1*sG@@_mSvj>oL9r200 zeJ5k2X**t28Ur-o6nD5iSLZ*6clr(?gQRh<(_~_q=D>k_Xrf0?;FKE-P8rd_V=shy z!&Mw}qltlk1w9Vo0`rYo+_}ZiPl*ObL$YN?aC%yr2SMXna&j%#zDfM}&p>uL09YBS z?b5T4>qM7yKR1iC*#O%=;ffFipjNi)CKVHz^bh3->u0>#!-b{rAPM~%O(nuo`ern5 z0blM6Ehi%dB0=J6%vicvr$vL6N`+ z-9A8V@uEd9dDSv>AoV&tA&mxd^-CZ>1jQIs0KywksBuN}mR0kQKq!8|FIkvEA{HGt zbR}#~2iD|8#>S@0rc5rGEJlH%4tkx=ha@0Z{9N;~O!B0iyvKorXrX4zrLy;JVaUOlh% zpDd$a;`=K~VHyO##(-J4&gF`J+q&CzBkr@>$5LutL@w$Mb-JqeUxxwTI+*tYL`A@& zo=*UEO~ve-wDP*7Bp2omW;N*D(y?7zP~+Ob_`$Gkp$>+;d`X=tB159rv^f(`nsOIG zXoKh%$UYoNvlIyu@A!fuSE3Lq-k`d2+q$-#foYgezz-@=yu!7tJAK+S=FzyWEf`yF z0Ih&?(M`$}hLFU6Qt-4W`rwI%2DHzB{r8wVaeGk9&W8toI{hGF+9vPI6Hs)v@*EMc zWy>^5>MsvI!Kpcs105=j-@JO2j)a~b>=h_6@c%T2*VAXuwnE=9^WNc>P;&B7DL)H~ zic*O%qI_xBXYRJ7`4jBz222sEs^=%YlT_*_tU*fMbcNDy z()$;%u4-5@I;TO~wrvwg_pUb^k+`%PtXm^pjplyHyft>4cjrwJ@Xexfr_P>T+5197 zmz3005pdP>nQNg?OF?0{`4Br(Gc##MgO&3p%;QFamKAk$bPzQDa40w=J{Ta}RVf>z zL^_C!)q3Q}lkX1)!G=@%W^$Wx*0DZw6n(Vh3>sFEcHKC?>9=nyj~Fo`D?2+rF7C>; zYl{32qkyi*`dJO%(Mc)4&YC^4?T(tOZnSL%4=!O+4mVH|mI7L(5ohNvU3yM8@eP@> zn|p|_dcU!U?9hR>B${p~AP18++l&A7)Q|Ha^!Nro_snrW$b)`%J=GKye34%4) zaaX36#y1DSiGT~200vUe>VE6o9m0&?6bt<$ zR!``*gxba+0cU}UzJ*0IrqkUxzMo#q~OIs#2`3aI0$hFaA3((E0O#>!sw`jl@m%J@oV}-(pnUdOzntY?U zIE&VhA3%q==_i>Gt7-MeOAcHR1kS0;K#LMPn+h=yKEgNG|ZvTX3HeF zBGwj{Pm$pPggLJ-`8XN*CxmI`%AuhQ&N&Ul)G{`nvhhsa|5>*xUY${7vZNkMNsqBK zq6O_$1s+}(y-i!U4vV%$lsr_yLH!I9l@E75=f}2g+t%65E!@Sur{i{TRNVbUj3!orR-qe5%Tu$;_k1@+ViWtOAiRrfW?$ z*nWP8+-IG);0H1b8@f*2@579D78MQ~n55BCpuJ#7KpYrAfzhDQLrd@N5WKi$s2NSm zo_iV%U}%C&jnt#x$T8yx&{DY*VI=bt-D~+p8Z2D6kWd&L({L0KL$;HmeoanJ?lSJ{ z98RkEc5Hi{c3=xpN~V{L5_9nSySJG4&M(a1gB1B$OdfOOp2&#dh$xN&4|&#Xp)pcR zsH}IM!_{XxJF9IvLN`fVkN=*67WZe)F#UH8+Cv~YY0UuYEcoTJl{cY(c6PRmCrkeo z-iD5$l;Xn-Or3XhRBv!Iq5t_gz;Igm=NTX3f;_x%e1u}B(-Xqq^>>~&Z5@>Zm8tpA zp^AQyLw8!OyL@>N+3h8HW6_8FHoQvlu3Nj-ENspDcT=65uJQzC=0?^<94`KNVv0q- zmFJQ#E>x zwQJWV-0CFtIIj$`y;n4-s0ilH-_2c;!hwb!cT~we=J`d`fpt+^HC}~yFbqk|^xpyhPWXlq@H}i_HeGwdsjkTMU;rS<0$mghl-8N9W>Ekg=rFXr`mQi{);9+IU61S#-gBB7Bof{)l8wo{9zQ!PDN!XRHq zgt8c`MIazpQP$U8z94f9wMcoB!}_LE0LXAIY?KUB7(LyJj0PpcC>o$L1||>E;>>@IwWtx}2t zFnq^6lW*U?iL`XU;&%$lbr(Il*&*nPm@=3T8y0oqM8v$y!vhNj%F1e2SJzoy(1#qN zXo~uSJWJ=68OL-kI==_)teWem*Eu(^j^$||++a0_fKWjgE&48QR6NOHr9m%bX|G;iKb&~_GZ)R!h0!$2e_6k1^} zD=v}?w!NMgdb{%y2rrrGrGhh{cz|g;`D6gD+07PGQCRsY=E zAtT9xmRtPUvw4+IV^eP3YO14iMDLc)|315Kx0fz4O&UYjAuJCC&?@T&uhOmc(b3M z6WS{wbAk1I*Hf-Hb&M?R?L)lR_M>y)aD15Z#+`V{y!9A6cC3T>S%9kU*)B~jh>V-as9Paz$2{tP<#qc4IL0;K6><4K9tO#iSY{vVv(|$bX_Sy zN%(g*yJc_WbrpiS>5CqmaQg6lLu6=IZr=`i{ra`_fYlBYC%R0VX5sB!<@40ST6xcb zMS(lor?hCWuIfCs)QuBb7RSbm{Q^H(oB{xre+d0SVj1%Gz(VR>p6=*x7hZM>9;+D7 z7oB#bW5y8O=)qanuh)mLs;JqaNEyCNybkF1h>8Z-tH~P$gJlH9J-qttnd9PH)?#r6 zk5f!3A^DX3+JZ&sKc0Eq!rSN{>AcclI3vc|CIr7_YP7!84#={g{{{3gQyMaMa<&Q_ zBix)A;|1$E?v1+ETU+t3$IJ5)rj4Lu&G2?gZmvQ4qZaiOJtHF{TN^n0>Nc6kP&-^r zmD)`w8Vo`ZB=nmLD~D*Ob9#!C^n6oq-P#7K{lbfGf26N=&|ux@wXN%~e(*}RTy5M~ zUqogXFD}1e!oQ^Ap7cM#!oTKYMxs1Ah%;4HV{f6hg<5myb7=VkPOHZ$RW1x{(`S;Vq*cDij7{%% zyZna zxW4PC(xF550lDC$p);<=(2F4=B7!u1m0?Cz;BZ&0P{QvI_uIR-BcIlVXEp2lxcxTQ zM_D7$&9bX*u+DVxl?(L2f-B4C5AkR^LT|dg@V$xvQedMA-t#!F)pKeLp)l z%YaD_!|k%QSKAZmw@v8uN5;Ag?Yhp?GH?iVl19TAcJAC+rof;Xq)ZahJ70?)p=45& zeN4wpJ6r@kD`TnfU1N{}k|jrh8eYzcdH$GW))(EfFAOyEdFB9%laphiVc{8Nvg`Hh znZZ3K-{62JJbO5Q$EQyV9O}eo$B&M-HDCr(fm0VfZmxFIs>kQ>ouJ1sl`sCK>;-}@ z@9&ET4H_gG4W2QOe}Mgj2?Z}-ra=69RCU%MX`|q&e}%zYk5luMGTdz*pBt*Yb3~F! z-je4v{W-x$X;kj>H*aoGC<(C#3V#;f&-!tlYAAY{sV1I@h3V-D*5I6@w)Ag!25M-=v>rRzu+Q-zDgZe*}(BKDZ1Z2 z;&6<={r0^-gM*Pkg+MPbU*U-++!RK1`}w!7=F`oMj52mDs9WAKx7hcq2E`4AFO+)2 zr8oPvWV^&mzf&><&RvuO9H4?TNvy+npbDtbiJ}eYOXeOKHf`eR+0l0vTYjGWNVe0W z{Ge~bz)#?GZzo&6aO~KNwskrWrfd}4DxLwtt=*W`OZ(OH=U1Tc$ln2@B4ZD{TKo0- z>xJ9PC;U(yOvCX~cF^Fgloa)cKRSKms^~dXO`x)Z?)p+g3vl?!aMV09{`TSN<;-oJ z&01r~fW)ih`^aSxVB}5f)>(v!i{!3(gKmG@$D1tif2MxBzvh8uaP<@!2WU8bF+?pS z3P>&zc|7B3EVY$_&3TV$Qxd&12-ES%8i~jxb(yt=Qla!2wSGnIm*-reMmg;2>?LIo zg!W~o>74_QB{#rK3`Om=lvx($vt7Z(_V)HRocZua_r6trK7C`wNDO@golF?d_dGnp zW9f2$5)Ck7Z(Hs#`k!s@^7oFPyVMHi>U1J8+7p@|_D8I!7@)J&T4Ff4!|O#I^1}PM zo5p%jN$#o{Woaqv;FvEB17V4`8&94MWNwE`OF7>tOk;u0837Lut&YreNp$a z(`oN7eAhEHWV||9pItPh!0P~F83E)L^j{I9o$EOeRYyxvIKv_o<&iRS;N%3X=+@u0 za)$5m!Xv=K1vL10dn3^Dggsm6>3M$S`NXf6^?LQPQwcOHa96+F_x4-3)Zw1qC11|3 z$To=lZ)u@K^75bMe+}5^wQQM;)oV51FeFmvrg)>G#U_r`wu-iWn>`NnF4IxO57BNf z(Q0x>kOD`f_EbdZ$S}HPCva<}2TX_a`f+=sRG|hSu@q}Jk7P+pI$ev3HPVfcgR#l@Nzd%v~<6|Aa>S^I!$NHk86+uQ0k z*S7E;-bM2p%_gPX1KamMs;{IJ=%UtLSC@sm1BtNGOO<}{(wo*gUkrQB?A6xmV#3=_ zo!|fUXwoRqrx|7PECxah-R*5DHD;k}`{klY(^NW;1aPRAy)lFB+64v%N=D-0VUwhf zXp9OX;q}CJd@N^vh>%IZ0%@#O3{r93O3e2l%vZdE<_k6cb2z`59K!wz9U6`@PsseG zMs{@r<(&6~q~{#Y{%50X4~ejjqD_>Gum=lZ3v|C#j9AFRHJKhqm&{=;M4wOuWw;L} zMpi{hsvtGUT-(Eu=eV}B?$4M$Jx%yU!VjD!?dNSo|JSz}N;JW{qMVJcZ${xw<{>2F8tINRd+ zp=dBGbV1Yql@sdMaa`kbdhk(>m{`+Lb!HamXM8&Vg#s;PxUkroYjVn8Q&(3PlUEJ+ z(V)7D&w5h!%g6QgtpgYwMm2f6J0W<20u*Oxk^N9NxzO+w+qZ2SOilW7>DHX#i|abG zTn+eIOGtrUvx1x|fOm{-Dv{zwz#fQ=8BeonZZpj_KB_!_I2(n*#^a$-ayZ#Ni&Q@G zNSlFGK|3s8TYO=f9^F|K3ct{G;VaEqRfWvjFIv3&x-JzcC!-#8tk59Zuavc&Zzm_4 zY1ND0L0wex?r`a`cs8>$+>uGo%MaG&*!b9zFPf8wY2?M7s^bg>0(s!WQCqsp5fLLt zj$}GnkrInbWk~e!3hkrr?yy)*)6C4w&(DuMcR<-`cCRq&_gX<0ttJ@Qqi-uoYi?HH z>PbI9VJYqi#GMpaT<#T0nON83OK0uRo!OdUVK{zbtu!aGT2Q(|^?DxCCG0J-fF0B| zPP1lZQ0%uJxHPWHxM4y7VSvN9%|}|WGCP5$f})qhI3#u%Z%h6+mKTGga&3sg1p6Jw zC*I`H+k-wimoAVu2~mf8h!D8%yv=mqIVi&GAUqDw%bCSbK)Ny|Y~IOjuiHa_c>KyC z_&v?=i`GR!WOnWxK2oO ztM=WRx(0J(fIb#nW-*c^Oga(LD4d7Qc=bAq4na0YU7EMzl?BsB#fQ9gEk!IJotmMo z(bIVosy-$EselT&&j^=GUoCZ<4Pn?iW{${AXt%5TfA^w(#oY^^PBh;*yzj*5c9*CW zMyWLKYEi~96HhB~FE$F&DylP?{sF4R1s8fueAXPX7}Bf_9L|iu@v}ak&rjwyF;d=u zYTAyHmaDrWaOuH!4N~Y2{5cqE;`W}Z(SEjvT1AWWM896XoU)FWbnd zD9=Y{tD&_@OSjj^z!|84&b6#uu|9jGApqm(#IsF6WZXNM=J!;Y2Etb4cp}&Es{DHB zrW+LVXKXf}Q@oKv&_9hEf3fBs=Sm;R?lzyFtX+86%uFqM>KNsX(R-S%OR0WGXNmig zapE!pV=z!u?o1(=5Sk=EdK3ng!aQXTDB|?@ksfDnP}ByW9qpmrvv1$gDz#9uOYZ`k zr&aB9Lz(tjhlGp(9!7(fw=*-)ngNq zETNse-vOgN8Ya@0la@NTxOvBpdylzQo)`=Ap^z6tb}G_th&oef+hMo_WJsegi#}%l zg}_9_T*%X#Vz&X2&Ap;a-=n1eAhaaH9laiY~l{hIM=NSi)o%oLyM~@@e&l927cqFG~pIbg%WB0Tw)E?S zOQch&g&>*VC24{9Yn)kjl6qt;uH@2YqB(2OyZ1EeDCB<(=zeKraW`Tuk&ai?k?QSs zOPd+%PPI=7GH%p)i-GU|>fv&4yp@c7Rz7Gobid8M+?lA<2S?imG7n+Zg8zRNFOIw< zZffG$2DY5ta(y4_k` zK`=063N3y3*Bw^TRZDK<@swgt>USET7!U7cv{*fA>2Nn(ciZ4|3-f->(We?XuEWq^ z=(`D>o6a=rKLr39uy^ki!zJAbUu4=;G_tq1BtF4?1#@)7gSR0HVCr{Fe+YJ=OSUdB zuo>4sYmpCLwz52eZUtplOq(U&KNk+8JG|QV&;gJ>NTJnED+~HBY;kiU_%M9d^~t$Y zM{VI1`C{o{_kL`;rvWX_MmHA>;@>7QxzljrX!f@#U`+yp<8BXEXroJ~+M!1Llr39W*MK+L+}G$3F?`*02T>=(X%M}xg1OO1D3RG- zlDDOa1B9M6`6D0%Q^t0p*@K6!?e&m*O6X6h+RJoHzwGZ^lx)gf>?BMmb6r4KqT6}+ z^mt7-ld4Z<37-!Pje5Zk$BCKx`A^C~gfabSIL2~*#oYjes^IryCz%TZv2)k1^$~!I zDuA$h+pUx)yf7FFGBAP7vGgtB@0sh5;a>JHbKGz9%ZIba(ksRrGri#alQq zdYS6<%)jI{cJJMlO#g(vx&t+U28|*?#N;qSfpm9aN>XTlii@$l7uXtyfckDhL1a75 z&$L%@S=MXr9~xVa?}-!+tDUo>-X7T8?MBJ#$oZ@J0?m;Ha$|GcZ+ng3ea>Fli%y)Q z%DX@6)ob3Og}PpC{XTufut;VH=<zHcsH{dBYz7@wW_0M{ zp5w3PXfG?Jnty1Vj_{&Es~(#X2(;bu2Ow3rGh`6BQ8{BYFd-^Qy|X8!yr>k&;lXvc z0C|H_y)Lw$NDzqPmh~GjV2Qb64$4uW*Di>W0CuBhB0T5Is+&$6VN&&hw9 zOoiP>(gA~5rxS{0Na2YpTT5u3no_(Z9(SEqv!hOeZB5e4+AVH>o{931-)p-n>}$Tp`N41A%+~pT(AUEkjy*Hu7#4ttxWUEq`dMW})ygQxDj^?^ zJ8Ns(>{AfAjd+SPXj;DH*`Cv^kyTzdp@b=)HqaygA6su8*5lf?e}@cZN@hZ6AQ>uC znUbN>L@X4E6f#AFu_T!yX;zdBg;2;G%21LBk%WXwB2!pI^!psv{ci8J?)!QEc(!}3 zrF_5Fb)LttAN#%^3Uq0aotC8J5Zl*|-aCdmz_#}Y)SbMh1q;Ns%$j3<2uA^`ez-x+ z(9rTW$=&|&ASC!slcfKOo+YZz1RqMED|pcflm24aHPVmE+8-GGoq zi1f2Phpnq^6M8ZqZw^_FMRHD>c=hhjj==RDfo$}u9OmzBL) zT>}LqR!J@}ETKe()HcUFoLNUkR+g-Wr(N4f;w1JuQ*9cq8V-|{@$!VAr_c$9A~k!k z@)sHmg@An*0(18&K(^vINj|R@JLNga_lxUYn7D@z^*dU1=MkN=gqPX4194*#(G>uJ@JE(ELl+SgadoHnbU~cqWou zMidvU#T8nKpb4q#WT%F0TerqR#~`?!p(}7cEsT`^ut@0=ZQicp!jv-+nmTRTXVSQ= zrJ+`hW-l_wsjdvfEHQexHe3)jRs?W3DM;=-{JT-!SWYaBg8t&CgJ}=e)28w}nSe{5 z(}KIr7>jf`7m&_q>$RIZ`rbjophv|?w=FR2^XD_G%J1~u?&`^UATi54iIku6oSZO+ zBC2ABue&NoLdGiOdHzp`Zd4+)#G&J!{so#v+(S2Lu`H=NM%oAhR+tc_i9eL2?1^r~ zG-D4~RR4vKf*Ii5qjVHxQ*JG@QE%y881N^eBjucQMbAhdaU5!f_={45vLt4|?jzhf zkE?7_a&JWxfnB|g-T(;mR@{i%lV`NI&4MIdV-;{sfoIk*c;T(cmS)!Rd_kMPQ$45m ziWs-DviqCH*4C3u&U6y#0vOaGc%-h`Ts zKaoXtkh};+Z*-x#((fmH+y-cuM7c)w1iJN#b40m5zxC7urPLwoH*J!vy!Ul4z!m5o z^#azl?-}~n6fPVt0Kv637V3XP59OWTyMJF6W1``C3Hgc3D}^x!b1tQ(xZLpdXES}L zlGe}PaS9+3CAbom{e?7Vk#C4>S}F?eWz>KLlQ+HK-_ymX?8)9keosTZCnqa|mC7P$2s6GGxdQf~$6% zFB{wSi^|WP8%$ffk;)R-f^t_pDtHCZEMzE6C(eNzLlR*a`hooR$LUIC^a}ogtwMtH1lg4BOxkKcLt>Q( zLKNsza;nZW(l-UVgE^oI>=6dT1&-du$yb4=*U4eUfKT5wt^tfF=Z9 z+HsjjPtp-}T z;PQhvM_Gt42AudjUm$4fu)M3F)#>Es9ybr)DM@DOKu{qkj1pn*>fis#f)f6@0fBSx=)`c!R@*?Yu{x@ zd!>v?IuUA1dk4JzZ$6ISbC0T`#u+$8R#WYXHyp&y5N*AL8@u75e1_{k2>{JD8x9Q< zdl!Vypx2Z2en2slijg-RbWRGvPBc+- z?bWpOR(zZ(_lTZ|KfKd=T~i;QUurGS`z$bfKT8g4>=lg`TRbnB2b4yBdS5gI4VAj- zSQ(yK-Lth02;*>3sfEXCBqb#!Vs%9`hM#PGu|L9@oR4tw=~ysC*;6g&P8lgeXfefbpmR!;jA^et6bCk1nkq#1CIa zG^u@7Z)EP?@jVXVz{||@c1Vvbnq?sp(1{&CapIo&)7Bk2+~g{jd7Mv3&?KBD5N4w1 z|Af+)(()OV$*D*JQP_srq63BXWPi30F&C~ZODKXL%pU;A@X+3o9*lNI*s{@Y7Fgds zVAWAbZ#-&{aPz-gs;R}iISuS*zycA^tV_17E~so)8s3&yUk>sq7DRhTZIWpnchKWv zC-eThTQB`qE=f_sB|0ID>^GLV6{TkKTqpEs#p>>8dLY-{JA#ANE`&-=;ckhm;RhCI+nRzMQC@mAa24@0mNt&c|kGG%V-eQ-* z+-AZc0ep8%{pr}MqN41SR)uXI=FZ||!0=%z3WG|mP&;0&5M=i7rxUeqao%!>_v3rC zlEiCNFbym~B!l>%uLa*DfLnW1aFn%1{M};ZN;4%j`bS80(hYL93z-RU?e-F9QNg!mZJA|b zH-H)i{jZ%5NLZJ?9GDY8J3zPb?cYtsK!7_5Zf1AMMEvaL@)D8RRa|VkE~dM7iDLfz)7DoT z!ZZO%I*-8vRTz1R_eaCtT7TH$Ri9sNQ#)9+-?TYne`K!vYf9j5Li*+p%E*at3ZO6& zSC5^|#)_5@(5`5iW<&L!`tL2og)+9Y=iS!Xi9Jq!NH-2{53Oe{v^i0(h$T6goMGqg zy_7Q21~fbo|0%TnHop%TK#_{k%@ZTV7>z>Vl(JiKx~~lb{Q0Mi{wh7A5mdlctMAm! z$@b$gVd4=)vG~e9ew6 z>Z*X=H)|dW7!M(o?e<2dv`rsP8e7jaFflN7V7^I=y!DG2=V=r)(<7duX$99v^ zR6jyJq9+l(kSw?=LNVmnUgbl7XOzkGs5vbdw6XxIA9>12zrqw-z(lF~c@JkkMt5Nm z_y5{Vz0WCf>4j>NhtTqs)Bob z51kPfJo+j3fb_7Efb62*;%sv62-oxB3C_G@d&ljtq_`VZwSWW^NIbowNlV9@hy4BX zlq_eGFv3-V>d^h#?*TNSj1GmRL4{XGrVdo*mVZA(1lG#Tvpzx3fH`SKe*S=nbA1wq zY(153@qV$Y>zMiIKGqT}#l;xYGT_l0OFAmVW+phkHTpc@m>YMp{li`Xs|-vER%FE| z2UXDLH&_2;l)?(g>gjj@tVGqPzBSgfwen(K8wD8E;aJ|-m@TdF9E_q|a$HERX}LG_rQ=-_(PZNY-ACVQXr)da17*;q*g)^F~A^;uK< z`;v8;=e9;xE7yc{)7sj)S7fDku>KbP-kwFx9JV)iIJP78)R6_d;{1;l{#~HH|MT{7 z2{I@Js}K5-h8+kTbOAj2cI!9P^gwK8BdwA|NE7-^oj7L9`8jE(4 zjnjut$CTP@Sf$Ku)AP++9x4&9&u#ZmkEyhN?TtJSsZ3N#+0ehI7hMyxo4xmqxXEN< zuEFm9^~20V-dMHw+uxX;={Yfk@}AN|+jQi(WNpXbn=EEXPW@rXDE3`l^LBTh1eraDXTsEf@? z?1@e?By+!QOy#%0R(d_vG8E%Fa_u2oP}+2Q_Usu+dF1bAu`Ru~wg}_TR{5J7zKe={ z_Q-x(Mm$4p?_*-sNtNa4>sdWq=ij~acYW{Wb@doJ$qB{=m5R;dofr3C-Z zV1gB0M`-~s?Z(Mk9HQ!IK)j;oqIDn;qck_^HigSxu#B4an|x| zSCx2i_b2xOmO{nyw|;Au^%o78fv04{l(!QnuQ#GqGu}5OJTWm*w}+XI%6 ziOcHuMcJCNU#gswe-(V&a;gZt%=*SBjNO@pu}6y$YP zOUw-1P0R$&As3JF^z=+!S4u3CW??|7yEp!&!(Tz&O8A=InwG0xTQYk^Q2=l zIvEEw4RJc5uJ;2^>ALXeg#G()Hlha#9NL)@zzK%@)(Z?FB7%zv!(%Edq4@6mNJ&&^ zsD2K4b?SNM7@sft+3-aS160f|YH4oU`>XGB;23^EozU}}uGA`JcrNXvucx<{NfT}e zCPCe93&lW`Z@FdfDl3HnM4JvtSvTjMPQUd-@qVe zlJ6-oGytLY8-4IR#ihoy;*JyE$N)$KZ=bV(G{KBjya73h@Rv8x%e$OUU#r&Lfl%%?zpi8O zXFT|1uRO$Qf5NLILOLoEhx3tV9D%p3dPjF=YD+j!VrqN!>Q((tZk;=J>@`FIyrZ8* z(23%@h&5%MrrE_;o6osl*%8mzWjDC9FhSPdQFOR}hi%^1^6|&waT`!^K)_me2H}6peE~XaHHy|+3a@lN$X`^?Py368NKp?fnQRlZEQRR`%- zeKW1#@bz=K1CU(iCB5Z|LpJEdl>hues9Lt00_N8py6jxaY}i(F2)@ZSUtz4LdsMslQ$a2Lo=q?Z5yY?1Vw?lq;yY9 zgK&rl&5?o@@L$93s1lEP;J@L zAIA!uu4jS3i6a74!;SYL6l1yJW(f#8Q%W{ru|j5hxY7X!DaLc4R$&fhGA*Fuvb8$O z=zuDz{$txd@;L1 z?#JIZk)5wxkc6+^y;QudvDIov{!}38#WU6}OrE%R{1RBVZyqLDn3**qvd!g~I+mrn zi{aq08BL7=9cdTL|GNI*LI3-?sd?WVXb>rd6*QJVhzLgQ*|@RTb}@g!V9$n1U}y8L z|C5|YT7M*_BA$|_3wgbQs@uhJE$zv2v*C(!P(%kzH@y9iTdY`h3Nip_%n`|(@^sy? zO&ZOFEf2&g)2@{J7IT6Z?|!SJV!-?`Zs)^S#!ibDOJVtkCJHot!!wSjAf-Bqp9eRQ z^vYDGch-Z1(bxi^ACdVsXdLe`caoV!?T7@L2z(0IBHk=juKe>Yjju!|%=!%TkqsL*1ccyL zU!(R$H2@l)%^85NFnaI!@u%ni#rS*-FWF^KcZcBVeE@uR%#YSk$w^jtNeCCi9q1ZD zH)Bylx2zVwr(mz&A-LVJ1S{`(vu2Hi_34*!$Z%2EK0{E%Rv=MMJWJU*Pc(wWAci{% z2uQl>>NcUdq5q9cOx5(8I%m2%nBX0JtYmd4$W^PJw(F@a?@?a#A2WxCE>2Qd@d$Z# z;kt_)6B!dhFMsnOO~W`Rx#e6^-MpgI0l-5UupvYa1pbS?9S8jYr%pJYAnPUNlIk*gw&SnPwl+MB} zGR#}f$>XLr&@j5^GRHe2oWBm^$qks?PyrO!kKKQ73C?jiKwLvTzJdV<%47Vc))8aw z0wgayALZjpEhnA=NQq_iECN$uuV2(s@$M;;D_s2HBJU;Mq>R_qhWt>0%Q_75*LzFk(gu%GB(jm`%*KD=7#e+Hg1pR+{Hz+?;Yq$e0}46 z{(Js~%?Q-$-G}Hu6=Yq4p%7!J?;5IV|4uNkDFpdA14$zFnBpgv1iAqn{B1;;|Psnc;Zs9HyohG z!2A0W2(^L^iHln9*SS;F4~*~84r>u07Xn*dXmI|MOz`cIu`C1x4cL~T*N}|X+a{86 zHi}4+`I?C0iW-`$M>6u8l%o+cf0?2aV(@rQB!)h+c?(!F?7+Mi#n-tZ3?8}?yGn`3 z3a-jax=!!}s~W}8;XmNfa`u; zv-i#i8#8sSbJsBF3;h=BqLcwS=o4RK`$g6Ju)~yyRy23e0Z{9;rF45i10MZmuC0dO zRO|D`BVV5Ak4iR~zLWFbo~D))C*20E#YC`_W1XA-?IvUXl);BN;#k&9fM*7J-IMGJ z<7OX0NH8lg5&}>a%*sCLI%|jF(5+jyPJdw*B1fK6-Q#%lLqhh5uNA2%R)K^}U8PPq z6pN>W0*Uyc&R8GT7*ZEHA#>-+?IY1ccoGLIs4+-w`#pK`NmKU9Abfp+-x0+PH(aKU zwG&%hU*}ejSqAS+f*xpwY}Cn`ZJ$4X0MB6#0T2*Qns-8KUZDCNS0o2pX#?Op#z(qx&B`7&;a~1Xash@nEZ$~}#i5gscwszlhYXS3 zVvYG2C$R$J=w@rtqV+D*_N-k{8b7#+{s;92wQUWeBaNG=YAe85vGMfmPh`8-p+37l zF&zpI@dN#)F6)%RfiwXV#&LWv=|YNMD;^WUZeZYTo#0I9ro~!nImm<%DeS! z)LktViyW)JR5zQK&(H2Xe(>i1E??H&33yiU-gu4_q|^hmaD$Wb7g%rtym@B4j8j0P zsUchjMSp0$!6f8G#`wIBcORSSH0uR@T18q>C$=z!@dBy8Xl%VeAWz7c3EsKNYqt65pdzK6p@qmM0!38scb zxg(3hWOzm9g&_KV($!5Y8i}_rAjb04#B&@6^e_c|nlFYzlqn3R-uLf* zJ#TC0twBMNI}XN#+iZ>Rvx+2B4#AH}80N5Hy{Yr2rhL^nxHJ6I>eMvfo|!YyXeq!b z*YcQdPN8__7@=-K(enF7Zd^M(6lKISeC+yAW#xumv$KEQAw6=k<*ad@MaT3<@kzq+ zrya0t4~VLN&kc&s#3X6M00Aoky131qXpOy590u%h1r)`k@baJj~rmj|#EK2;q zCAg1XyYCkK)j!(PGXIq8L{Uj%5xOxfEKK)7G;epZ-+_Ui!*ad6{C=nvY>XMpkZ_rS zEt#gbH9TNXDelgXjrPMSlMDDf_s? z_YnnYGI55BMW-p2qKDld=lY@%15ugrehQ4n(65m8e-WEnkh9^&$Vxo5%)>|O?L5=N zX_3aTNJ0yFZF{9<%$Ji7@~!kM*05kzyb00IhZNNt!afzEkyo4Z6*_Bf&r1)x`}Oh1 z)7KBCRt>m*FxvIowCgOzrN*p#oIU42f>;ZTb@+B9Fm1Q@k$CQ2v)omw-OW-Y4tonSYF^sugxQ1ux+|i?b(14Dgo8-tiy5f0|93 zJ%))s=k;#&tFP(X!e1U|;{Ue)gxD5XM2Be|zVD&pKe{|lX!j7`2+;R+=VE)p_<|rO zgKGkzbI!^`G-_zSHxl#33IP)4c2v5lZ=GAUmz#+JqYP~d#X%VN^|m-Q$e``?EehAt z>}1?XWmTT#+3x2%x%4-WY(X*;D&fJDROQ{a%sm=mVnq-Aj}s>r%juOr;(N~+SW$|0 z`r_ohhX<19vtar#5D|I?xusUSB#ncA;PdLpaOg;~zC!#s(Y{HG&t2v!XkJ_3v0}ww zj6*Pu8@)RwnIe@QQCxRitutiGOG!?_Ep0a%Jh2F;Tq3suG%IssujS@8W6(?tXOd7A z?cmwT$*-)NsS zhay=-9$fYm#C`A|1sob`t>V0!l8*)~G}B}5ChBwmX}QUW$Xq)Qf|7fM;SdvsrL?)C z<$HIg&YGG}rl4$De3g|HO%$j(OJao}*rCG~@By-**%@8zEh(-QWQX${opFXkfYDDf zeui`Bm$ATsI_Okn{=EL8cmQ-Ja`9b4eJR|uS6i7RO*TN`2o|so^R82yr>NghVkm0H z6G13>kf?09tn=LsqW9w&|3{uDy*JEAF~rSUza0KC6~{WJ>tcw=4|>_#s1Bd2aVYXf z^cN#trOR~7HnIz0smaaF+59GPFrtGg+ideM28X~+H(b-&`%R_urtXc}xf6%fwI+Mb zv{Wrt`LDn=QFu4B1^pO(3Dp*G7IMQCRvyuAcrRNzbzGe%XH9NS%_g{%Z64g@_XqRW zF+ow`xStH<7m}6RV3z`auY2toRwW51OfL$@--3d+1VtY6CTL$oB&&%}A;of!eRAO5 z+A$0v`dSwFjY*vqH`zN3BC~oot@?J_=*w}NX}XrS+SO|);HYdd2a^=pka*W5oN@2w zq_2%CLK~~oHfuCnuhslDr1v1a6k=!BJwZ1r<|FEQsVm44S2MddX7Glw+gd(_;3!l; zn!H++;`3Wi%0%UcUY%Kt8IMPSl6@aR(7s4blX=%Qdew7YZ)J_CD9!kZKWnc+GqEV% zhE+NkT?uzicuG{C>gRI`aC^8nuRjG*{K`uL`Ov2D$(`udiP2umk}VrE(FeH6f{peF z&uOeCqE)-k88mi4FFxhQ=3T5QA~5LhChBtXIW^6cBPVyM`w&AkK72lK;DDk2Mz1AH zF4J~XdEu)oyG~Et?=UKh z0gbShW%zaY7%HHbK*VyxAYi&&@J+=Z_zo=>q%O)!sR-a$IBQ=Gm(58;+PBlwnJiOU zNf%@IXn~))hfJEVF~p!$3?V3$s$Ui_O*(p1gvkWWxs1GpgATTr!edu6?9v&F7`9^U zp;sXscm=KNgO(xqd*_O(q7fC>n9jG6;*wiQjfD0n`{ua1dOg?KIy%Pj!91ASNO)It z`fp72`N@w~jYM2}hiARA=DVJ>DKx*Z7>wUKr;p}3^@KeHyYU5jC#mJ2Gk?VC23R2a zPTn0pplX*c+b>=EwY;lz*@0@8qFOqes<&y?Dt=-$k93~Tu>EFxAh1Xj;WV>awVeB# z9qEXAmBL^mXw0OyhbQSsN}-2flslW#D60i-XV=RfY3aVl{Crai0%)sWe0zTaPLT4*>`D*^M6a~Ce)6MGvR)HXBaz$T6O>d?A+ z0kQ`Om`TK8m`Kfge5xl8Ct`n!VtZ7xkJ5cv8_5#6JXyHHJbXb=%r_}4{sG=fIX2mmcGz-2V64tUL=2itnM5pGmXZR4#)Vp4 zLE2KgWtBNi_->+uLcF)vHc{|BUaLS+vZILvDGLqGUmUxC7S~Pzt&l%8CLn0vT3YLB zH`o`@Hn_4ce{}o)2Bt?Go$GAog*+p0-QPOD7^*?O?10!tpe6VV{RRw(*nZ@wC=;t| zgcdA?{AVy%n{8vm-nX;cPDVvM3baNyr3tvVPQ z8OaJ8X4qMjFL~SIe%RwI+&g;kQ8*2*sqdWYyIwVRKYzErLwBu{CPJD+8_6?5HmkX{ zOe6*r7a)QysPJe%z9W0AF0TD;3Rr8K@4~c_7)i~m^UpF7HuiQ&dN(f4hn@#U8_EI*WxDuhF}fIHK+A_kBp;&T*%=?*G`n>(Y16|h7W(m2*&xF7z9m2+cxR}U?c6pM zF^OOVTejTPTe@x_K58}qF%hra6Kuum9w*(Dk2!eL^VPIk`nzaV^-J)dI#v#V@jj17 zv_OYFhOGM{VikZSk!)UE^>qtyi!A2A2ae}iLbGA~a=?C25Y}XBINU2CQ9MH49K0g! z0}D)nCg-vZOB~i1<`@9zt*QOBTv)=QuEK-$`t`AGHX}IHElks}%A3DyXb>rn@s}{H zkRL&H-laBdvVc;EM*)3B94p8e{>hg4h>1V`^|AtH@p`&5h8fb?{E_8Ai!`nOrgjbz zSDhh2uZSR~^+%E5C~L*xN>D{mJyxvx|1qr$gp0!~Ko{o9UO|S`VgpD1w zmU#8izOkq|fNO#b&)@m&JdfrS>g~BDg6z_-&b{E6D$@iAPzryxsWPwzV=Mz(6I2W^ zy&vfL`5NihmM<4@i~n+P)p2-Z-XUumD@MP-fum~8MSSJB4nP0;xgSUo(iV9@_8^H; zL7*}ouxpU?mYq7qT3&9=|F6t0xuuE` zq%rxD-A&GsZ_T$7-0C=w3+%SY#yaa{C&rlsAx#ghUU0@3jL2Y)YcpZ@#j~tEBX=uQ zSnqDDGiftNyriGgEKzDv~#3kyXkDGOiOY)L5X4xl5(#dHZ& z8CG;K`n>~fbs~fZ{sPw#S$BTjOoh;*Au4#S&%_>$_N6Y zMO5POHzuMAXr1RI#pPMNJA+VGIvj_;>vE!~uCA*F?cQXO`O0+w9pvIn-2S1Q<8W32V zH_}B{!I=245qC*hmJ6IL>f&JjW_ZP$H{$^RgjNCOXnA^QMJ;zBv2$*trcE)xXo!|k zjCG4eY=WXMZly)qh~JA!Qr)5f!bxe^dpDyn*3{8?vtYTBnWyq{FE1BrBucp6)BX(R z)$iTz->blk48h)Jwl5X@VXjpz{}|pPC$HAFOK7+A3m5e7@Q*qLR!v>!1=IWfo4dz9 zReZ`<#J8<3EDgY1b{t7PBwHC7*ik-8%LgX*$CCng6>itf6Mw!xSPm0LgTif(@962h zfs=$Aj<_%LEHWQHoIIlqH4-z2C%2D|Ur4aXe%eq0_%o_34R8NAoFk-MW-ZNeHDBeY zD96^4;oe-#<-7X@cJF53&k+HVtdEN1&s0blc5VB_=s3Mc$3favHS^3eJNAK+l~htE ze#@Y7=KLitepBhjZF>)b1xcwc?sHs}r4;duBRq72EI;~|ep&kEv{0-W_)6H}FI-U6 z#=KEj%OLikg@RL_L%tQc}79{Ms4TFoJ^7!1-E+RT?))_B6?=+qfDeE zbbw@V0|5s=kLO_vvp^jhCKu!m#xF~gJ&%I+iIKPZgmaw#^^GmBlg1akqJk2O}Bf;XN?kEi`wLM;Yn@M zN5qDi73W{?+_md7rY&pU->y#nesIHp{ZAXla(ZPU4>CGHOby8W~DuFhSU z`>_RKi#^NZjV<4pl7t_zu2A0y*v|qGSy|JA1-s*-)l+z0w`dEdQhg;ZyT=igqGzr{& zVR88$svW9Pn(khv$|n16lmP>YZWRDu09Pjb^7gnPHd>xHlx^ehg7Dq%t!-sO!BDOhPWc3|jL}))3)4*n^ez7y$$_Jka<%5PWVquYh zM3=Pq+*{#fE6AXOXbAr&GOGi;jqsAV;SFtX&7|R-7*LS+l6n+`n|a?A=MNP-#KeR9tKV~&~dw27vaF?`)n9XTRIsrhm0oD~uEpnqp*{TS!wP(wc1yCj+myDI9 zUJ(0dS$Gh=V8JR*=TfLe|8adHzgTSibg*LEo%l7r*^U0w2UiIq{5r+UYc<^pUBSRT)MrO{!dFcwfXgC`JTEX*gbeZq45>TM+ljIM6z9r}0 zPaC7E<+HWfqR?HFG4IOzZtQ+FR~HULq(dU4(=ea(NVR)iCp2Ln!On#a*^4Ia>OR$W z(DVwA;F#+grW5}!9A~G0I}M|iP1^Nai%2+vOOg|&8M@UNy54tg6+lTnhTURKBEf`- z5dXo1Z!lst397~n_-$yJwCUJp2idQ9cq< zaWS2OZJ>4S&MRsc<9$=R?vEN`uKtQi)nY@QIx|ZV6hhtF!$_qfvaR#(J4@Ak7O}r4 zn+_3fe9*%(!(n?5QVinO3a&1{M`{<0AWO}P%=O@|5opoMyK~48v0kMmXXm=*zzGK? z>4=ph8+ahYNlk30F)#vyq9 z$JEpT@DyZwlmeZ8mfCjr+$NMH+D1k@VKe}375#sw{2wfB6q`5zw}l=#E&eg22;Fxr z#bg$4CH865y-*E%LG5F3vXt)(Tg#H~BV%PU92h7!O%$F09nznCudYrwe3lZ)uJc)o zx+h}1KaQNj1rlpUF~s0U2!#jQ>rG>vg>P;)RiKD&IU)O~f#nO$o?YVX!T><_HA0NT zp+)w0|1Mvj?fS`0t-$AqS^4H$qnG>kbRVa#<+I5bt`6;+T?Y%qV(>r4AXU*feNBog zoG|^#%8^qV>T$}uE!^P$Z?y_X{`++AuNU5tCFhXH;GDLGe9q_899x5lrhkumSr;PedZA)AK*aq}`+dW&1kgaig5*Q3Zi~ zC9LF|1}|2OU7y^{DY)GQ=f5~Z1l66e;>Xi6$=X+ zuW~bSxjpyLJx`;we9NO&HCVx5LP!LUU8!%dyx7-CHwp7AKx;`^@sYbC1 zaY&2Yu4bqoak;xTu(Z%8*1oTMg2P4$?T3JX%^lSXeR4JfuNSTy-&XI8@ertUS^%_I zY*G5KPVnZ#)zJ=D#M)k{GqSHoY+%owlWmraLcqnesWkrMxI_aKJ^vR@Mywv#gdC6f zeHE8G-sIez_T?5vn>90O#Q}0ECY_;!VNTIC|839AMHom^uup)k&aV_9J`ljIfcMIZ zMxPy*sPv*8prO2uuu-_2RJfJ}z%?jEG%(r&qqDml^WIr5YcFS1tTym=PkdAo_`h8y+l-m3@dSs>3AGcUtD!cu zA$ugEg8qk-$i`d%zs3yfWJNHYgaPc4zWRnccjsIN5TuZ62%pdb_>$oJ#1q$OfrgO?aNmj4t#3P zSI+|7AmeG`ol%vxt4->f#b~jrih;F-*-y7UfzcLJKCwE-DmJb%h6N^lZ9=^e+%jAd zfgI!qODZDD&3)GIcw9Gq2Q7T0LZ+v38N*i!jR@2F82_0?ju8*+lEIFh&t?ahYeTLoZbq+5k;O!q=FvFFVUCD|OvuQA5ue7tdYPO?o?d za`wCtMF2d|(b-vOt6gxo_OVw3)$R=q=BG2-p^6b^D7+*v=J-l)xx%nFYy&zW))4y; z=)J+m8mkiX^Ag=c!yH!$=FTN3dHXg8R8d?V$=2a_3c1S?t6-G?TbdFz%0ACWvm?s; zCr@_Koudn)_^;25>t3SFa`arW)@X=1rZjclL}guAj+~4o;ruE zae+e5A`8W%-+#qhj5w>Pfk+Cx^~yYG6kAe!^g=0jC3yw zGf89>%BCl`5b^}S166ba5oA81>#*rc{0TubbKK+NR}=_c1MNVPi^hbX3RHLjbn3+;V8d+8e6gB;~Wqp)JJtKFV}$L zNY&DEEuq~vKPtz!9{jYFtrgr_s7NJH6X=7!s$y!?*ZpwFH+$^RD$hrFyjSey|&c5zVF~OW@A3Jy9G#^m=75cD_ z%W)m#NhK};GeDi7`3M%kortH=sun z-)0d!b4;r%4#vGEm4Y5uA2sQY_Fqb9$MoTxhurb)qL{0I^D15jv_5@3DPhy8?Y>Ey z%^5<`XcwmYrsoC)JQj=%vlT431#%RJDSCzLuywh6U1_{Ql4}>NU$V}vi|f$n*k3<9 zLSRx?KMl=OGCD~xlC@dtpJw?V?K~T4?qwPTc1DQj{qf^_C;(g|r|3kayj?lYFY3!~ z?P0#-%HLIv{`5Z_l98*t(TkbIw4+Y!FVBe(%jH9U)~nMRv`cbC3~V%+o1 zRv=XG&(8`923@NnG1ij`+3BUto|aI;hVSr?nzWqv={Ne*rnorAptB)%0*-<5jPKEk z+cT<-=bT7hM!kUd^B%8{wG9l3iu(DRO{+3bHQuK_$R%*e~=agXWJJCQ^7rc<~Sa^xG8~u)D@}}OOkqLm6YiFGg%`JD2VsBqwbHNs|jZV z3vE`FD8R5=7&99;7h<}cC46SY*s}(oSwWk*qu{{TLE>bFIRA79C@9$5WN`L{2MQ)_ z-sbGj(}y^4d<1O-7W9;J<~y(^MZ3Ne_3A7@AU^R%{_DY&l@uj^mU$DUA=D;9P!p~A z`A(ciAu{0YC8KwMR6v7z0Y5YOdlkO3BU~uM&^qTbMlCEh_A_ocyu4n+!ZGB$ZPB-M zB)U)%0#NGP#e2>{g`ASp-B=Lvz_SDT_g6I24793H2V(8pIAi_))h5lmGwS;mDYGaz z`!RW9WGWXE)Foy1L~B`fO;NfXUk`cc;4`w8n#wSz*(^Y6*^I?un+Q*nB|GB;+yWLR zJ23Gv+dVI@prBUw*DdA$AlFPOW#3Pp^bkX~nT4(3Fn8rSrM$Li>uxI_@(%T>0{bFt z&gg1sHK2NM7Mm|?aGd8Saa~{S>U&c7G2H! z$N7(_^TAEYSQJ-OWaZ>N&Q3!XlR=n@O#Bgl;}U-jE^EoBZ-K=LIKtos1K3@{WLiBW zWhfTRX581vQ-3znDfngg**6g;)~o^c5;^xse)ewb_~C1%^H%d!}|sCc>t%238g zR~|fQWfnNTar5R`Y-Oe4VLgB_MQIvQ>?3^;NBZ;ewO1!Gg0-KWeZlMPMqmS-2xV>v)C)7gyBOfn5oRAxB2s6YU}>1k z1j2%N+#W&kvuDqs(QcQ)CDZ8tP{=Syy+8Nodln;^UA^H}R_9KbMI*YM+gP8efCLcxV)!R&vAz#F{<{dg zQak&@(QCVmY6p31C$9Fh-DAAB)$(?kE=_iHEq)PLr{h5He(jpHOKsX^_NYx4EV4en z=#!aLH~h?l4a%oA(ntIlGB+aLYFy*MOW(Z8*1cNcp1b;4%$?7}w?8k=T&=C*f2v^u zRr6ke%Iefu(DV~f_W%wc4>hIAb0&V;jdtQeownbGyoAC1LC_aLIHB*&L~#JhSD5N` z{2~@@~oJzb@+9;+G^jCc&AY_0$$N(jHY8^^^H)DcuBJ0KnhxpacOnh z_m_?RgA6YDoAR98)#~hSU*YHbW8bmi?^~5Eh`=%LoGTP#fA-YUaHL|P)k8T00MuBa7p#FN_PzpdH(KY zS=pHR^L6OHgjZGF=fN8KH~XYW!oL9`K*)20k(KZPUvaX>HqMCu+=sNWwcR8WqXMug zS!oOSyXqjjn2$&;rNC%p%CX~Cdv*bZz(eUdVc+^^D}e?LYI=Vi067}q?Ev_NU8L9! zQ-Dc748U}fCr%k{W@Y8m_o;=$^yzF6>p{IYR%tke^i3>oqlPS(wLW)OR$kxJtJy#e zjnU;7^U9GI_y}K@j;zkL`U5J?el46Erw3)EdeveIOJ>+rzYft2h!6@^7C;yamV=MY zJ8i#mk0sbj@uz36$Ewz;i;-T+|&MZwAKov5L>?fnl35$ujMG4@!MJ%@{ z8O0)11_UZ9>TSm`R4o2<@AY*cWF9>f)|)hmfo(RQ<_~e--Cta-&m^Yjh(m)ED-5#L z5<@!jEO{++z*^Ay2Qo|-9_h+e^o6oLj&@K~_YIO5Q0nX%j7>&oF^A#1vlFx4T zWdteU6axt797w9J6Z$sx*Fi?{5u*O$qRJQ4?U7`0waAV$H=$BZhYU&MYjf8vE@46&y;_%b@$E z-=squVtbXBXTjS$%cm8QYZhZx;yw$B% zbW@t8@#2tiup>jmO*G<={;%^*Ym%3nYUe5Jx%g)5IO`L6##?K&*g)}Y&YW3CJZ*y4 zOk+0woF(UI`(Rx7xQ$u;VJEdUT5YUVJc9f`OLQTs7U%8$Z^1%sGxsU8YKs+1$@JMu zZn$9f3Z$5OcHi4J`qDLaT4=ioS2=578ugZ-GTmR$5#27ef;fUWQ^p0V8Oo6MK1*3Z zVaAX?p7mPVuBN2YrP8ZU3pO>PJ6dZ!%Zf0x)_1h?x|%ZjSvR!>2?Qj+sTiVHIh}PM zZB&cY&slF1BoMWOx&iJ@uP22PYSNV9~W`u0*J7wQn?4BuYuAZRYFK9N3Dx%z=SBX!Z}!@p_`)R8+RMw)z<%C# z&y-P8EKxYzI@vC{@)1jUX|AxloS9?)|N3IVcB1FOeHo%{=zGf%z>q&dl2Xa}muU7M4vIaQ1ObyG0O0RJ?lsuZo9B z9T7~-bEniNO+M`@_sZ@u;ZV%Iu+{4joSSqn7g&LDfj zjRv5b8=Y*Mn6f!T#B76#y+>iU2EOk^HWVH^vF7ZhpXbM}>JdB5Zz>V8pPruVQXD(% zcWGFp+s}=5xXu<7&eKL8hL0AKKI3P{ z>w`PHWck%=*a+!5u1U-^L{L!udqI=XX;VP$i+9eO2DL#i4EKBuzbKYo-g`T#M=M(J zhGtvcEc*`40X1qWLnwOq1mYg&fTVg22LZu%eQQ2(PInf-RXD+VZ}rP46Y%X ze|&;IZoBi9i~8#I?)dRNWXS_jzG`iY*^DU|%$Y5Vz&O`XLC72B=H})hPp({iQ=+UG zEu~vJ#+C{~i#=m~Gn>3bJ4-sH4Px5>pmGkOiYd zvb>p!s8L69)5FE z)h}DWH>+Fgca)B?Wfwveofu3+?0@GjS{vB|R$A&TIe=;bnaFK@NBneZe3K@#o~{X0 z0K1IfipEb0XB`?mK)axi`SkG!OCi>|Pj`HE$8Jjyfa$UZSLUpmWy>`bZXBp8gUJz3 zzE)ftzdZV>QI|K}M7-7m{9t5I2r=8KI8r@H2%n(68>*q_FtUADGV(6%N z`S$ImOoNDC^vI&A7wd_|~bX5kl0q z8%$;`aW0_FSlj=_N%}q6@CD2A~Og{92(i z>>btc($5JP=llO{liHlZRaPuvU&CgW*+i5N8;+|V`(aUS&itvt@sEf|+Bbw(>d1I)M#m%dzoak{&^zHyrqtAM|oK2F}- zZ8`15rThGO^rir|rzb1*}HH?!5K^yZ^BoZUFX^n>k^s2%Cst9E|7RNUwE z2*3~!3I!l`Kp$7$Omd8N(|76ZR{gH{Q4_zyob}{LJ(M@R+~$yIFl2N4H!tTMGVbkB zn(`oyVC=oo)jnyUzp>?y&YBJNMvO@R_V@JvaTKau{JSMeGqr&Ll`<$K8_0zrZyf*( zv3s>+-+mlTaJ4j)Wi}va3^K3)8$)6I?8=1F&gLuURKv*8CAr%HaV>AF)Q~vnm=FEjx=FwG_|#{ zFc*NA8KL*w4dWA6^f~RiaZmnyC^1UJu2)1W1-7(Xe5m2vm5{Hlj;yT30N%apRGoQ0 ze*VM>_YNtqteUSa%4C=m%tSYG_HJ&-y(LuX@bFf2qJj|%g3XmE<;zi8brI29n`tha zCLf6?QES?w$t%s!dbXxmi{NNehOcTJ!=bxq9;+@zePw|{W3 z_JYEDes~s3CFhQkm;!b1e>Uw}VYb0YWhUd(H)B@qWU4H2ruau{ z4(#prpbDKbceoc4L=TX0F{CCo0qpM+k=QCljjCL`FR7EEOEMHfk^xJMR}K=J$UeC+t1T={rd3E)|29y zwu-IUhgHi{G(p?UTFx!|D}AE7%Iaw?!a^tRDQyzjxrJWO|0>@aJ*(g4qOm2bYvB)y zL$-K;3{!^JHd0dmnG!AGPmN7)NULop55T zI`sWk@;HjB?p?dqLAimD@blQ88sFWzD~u)2pBW~JmuM9S!JViLiR&t@V>R%01=}(1 z{Zy2FFm{%Bgu43|?Nt0Bn2WB8!GlHC%+J;dw7mEemP&$8foE!(UP}AG)YW;Vjl1cp zC*P)A@%0z{1`e$P{J~$j>-TN!_O-g|a1xV;;A1Dww$N+erj6FtOfSRH;tScYSd^CotgW^ z1U;A&QM8+)ULt}x*V)!5ukkYd#u<6r+tda-wW#mbC3~;-$(a@+ij$p1ScsPOP+3hu zMoH8#Z(rBb_PuEoN(o4Iit_;61uBKk|NryaA?C!QN)moDGlk+uAvfAlxYEqX{S0_O z$SmWBg|i1Lyo9o+Nr*Y^3w-?TlDwgBFO1zDf9^7Wv&b}j7hQ;RY{l@Fv~No*Q+)|0 zEXYy-LM6HssekyB(zZj}-?jJM2+%)tR)YqeZL+TqhbT4y&~p zN@eiTKu~*u*GX#=F)m`if{_7*SsAp_1~Y-KYd-ka=HxF`(w7A|0Re(#G2o4hA&j6#!+0|Vg& zT`U=PvwhFWg?FbuiV1#t4KtnMn*+4Q)J#sa9aFCN+FeDA$ti}(vD8f4+;6r1Z->FU zqgM2)djdSE>h-qfwQ*jNMbt=^zpiGXNw+#-m3oG9$1TZF>}F{ zPc~A86x=n-bexFm<;a7|;a_%SigS20u|T}>*t2~_T725C-IcYQ0&yH-BL=l6YjaKL zJ^_#UKtsjV9)n*c0|NtQkV5xY-~`=C>5Si_1Z9R$Xv~MuQkM~igl@?1Z`r;*mGAnR zW1c=2->oqJuI;2xY2dG5@7@<&x(x%DJ6^LcK!%VBVmd2XV5JG|IPK^AA>x{Y1Lc*s zaNB2MRZO{-!U7jO=Ei~KHC?8_5;^bR)#V`WAG5XN`Y{Na-f6pfFXDu3M`==nl=sI~9b6e|F<&M;a#%^{Y4Si4Kn#D7^XD;Db+LwDe zG;^Y@kl`f@itq{3{$CBR`kj?r60xuyY-E|A=n|wx10G0Gz~GC+D!cIHQ~=Ke>L`fv1o?`~=BD>E z(OM{YDmT<@@r0UQmOTla4swn^bjKRQM!%mBxP(pf|JZu-xSaF1{Xbi_kS!t#$sQ_8 z){=J1SO$@hijXBCkr0WL5-AF)EMsgTLS-r1v|&h;rBt?3(SrP*r}_T#nfv$0{kR{G z&&+gP*Zci?oy&0?=W%jhTKeBP*Kh3fiBq_7ejg4@^xc24x4ypB(SJb2C?)$`+c$lh zwl1^I7-Sw687r><(W!Sr!gv76)Hm-BJ~dl8?S^$oLE1bf>SAVxLZeLS}w)<0E2oAn81>a@L zAo4HblwlSW6~{_vFK|zSLt|7-5#5l^9F2Fy#k(kEsY}F1*mTkL+e4s8$dHHTPk;FE z;o7U+xvN_>ZZzjzEj%Yxzy{84@|XN}UAp}BXxr>gIb)NR zJB?X<>DskoR9*|2wU#8#3y~Wp2c<0G*Ghj+Cma9zwaE{E_8B&;qrr={jW_yLeHQ~m z>>t>{SP4>^D;AB+z_yBv?ftF^bq0g-JGgqV1GiCjRM^GU`8PgB5kBga<)`U&1@ZpF zSAV94cOGx_U+kn=#r_}NMt%QfgWXTr6 zf~^s@h`1F-`7Br8K2m8RVOv4mT#g?BMgV2XvNSrDdBKCnu*?eifp;;yK)> z9)J9JCa3T|WxM@sZFg!<(RYK{+Yfz1MZlTd{bOssa^$YB(*veZf86qKV$1tY*NGE< z8;nKi$YhNj@RbB?NGa-BbEZ=Q&v{KJ$3v~yFZTj&$}}B+{gVV z2UN(`7lY?a5Yu+8RPR#MY`aqO2-lVpc45fZ#I~6Lrm_e=s-4bvpDd5245{B`UUwPQz~ix zqDQ9RzwaeWuUQBqu@>L+F*PS>5f!TgqiPaw?KzY#h@-H7Jn+rzKIfIK`SQk?9ru-X zKXZ!1x{+k;;Z7>j=0q;9G^+j7!_@=UOob=Yhs6Ns4V;mSZHTkXt)RN+ZSMmZA}*u` zt{XLabXvkLL?UTOJboYUIxTvxC;IbmTv+0_@sMJd-Swyb>sy%ICO_4m{k)svTL00G z znxoo9l5EQB6|54W1R=l^YGu~?d8w`4BVr#_vbx*<@L>xAsY{CuEmQ_3(^u-R|Ja#c zk3r8@Z{HdrccUm~#KO6%WZK4h(*0Wg-sTMp+ui#0gK-YAf~0;1!pgn4s1YynpV0}L z+6Q*nY{0IdH8oq_$hjG6zJ1~19Zpm$2KCJoH9o0bfGZZ3n|mP!3_N_0 z%3ULzS5KZYMUEBh;}>@5%%FY_s|*Pa9)dNe^(xb5iH&P_lEvU`iZ5r5drN$f8ziGT zZrzp@^p*;lunRhmP%Y%cX@~j3(~Cz5K-N<;zew)i_t3o^ijkE;W_!WpG5Ip%(!+6?q@5%dwx z0Xuk-$>(iDWP>JL#xQ{bsUNnk+4;z$J;NN`x5W{dVoR0}oF4EjEp32lr*7>Sz zPsne?Nt068gewgcf?nY=+$2n=85nI$8hea_$$m!}y(p)#=S_hM82IHK+;ghVx~Eup zQ$&H!2@2)*REAK6@g<*#<1F29E@(O?pQ_6K{ z#wg-0xwrQ`VQn=Wybb=w(~-D;eAZc6)|(Uh0ClG{fTgTjW?m#ZFCF3MGd7NM4xi48 zbf!3_0Tgn;;BYfrE?{npfonx zBm=WalQd~2$;nvy2h|_Kj!W&{5mmfJ|Ni@LGav}?u9hu*2DW{T#2@Lx{?O1#;DLZU zy#&_i_bz9t9>1!w!G%98I;}F&2s%nXCi5Ir|G6mTx}N(0s7jP-r>ty119qLtwEL&1 zl}xqxtK(L8nfU`TTLyL)FL5G*e*HuzmZHoC$F1QL(Iylt?l~7n6m@0SH)rnCh+8EnBvf0DyRpi!4WxcPJxM;@E&n z9ALWEwZks~=|Mf9ghupVHgxRtTXq!p(H(D6FpjG&Y){q|TNHwu9BBRfk4q=CTNky# z+YR)G&P}IgWMsW>cqJz3R-sndUibPo%Rhuvk{$29srE^vdHb+W>%kjgK$~)saQLiD z>a?JoNUO`a6DF77D}!P(87b-tnWkYaWflEs8JrfL>PgKzZ@y z)C63qM6gZ9G&}!LHFJtCJRDd++!JB00&)9H^-7HuQiTMm?nSjaX*P_kOL46c+KQ5m~UZes20UFsF~UC|`a1+!bi z0&bAPOy_<4W-@ec_7{*Cw_;F&MmasT)_pw|74t_#HvMqVgtAw!6ukd`u8DfSMWP-3 z73Q7&QD~hYUy*Utx^)9x;v^B9NGvTl=|kOKGD9U|T{_E}sMIVC3 z=;$JS^C1q47H!+Lt0~p|kJyOpD^M_gWSa22LH9a&eNIX0|m7GuB0`nw(#Ntq@mcv7@7ihIA+HxAXJMF?Fbp zAnFgnH(DI^xlVG75p*lI?AR)-%zZ<|kjTEvR=l3sKi}@b%{pt*zWw^OOMQ!I*&%i% zX8{V^jo1|$zo7I6DFV&@zz3gb%@6UD@CZA6uF?`V2LClV{XAa*U6ALvqHm;03Mw9r z!JStT6uf}Gls32qPg2Q@+>rce*;Sc?0TAJ?irHgfSq~i@owRtgX1frKKt$A{K_}m2 ze^{SQ2m`IYM?YIzbZ7y~kxl%LH7{8|(_*l4;K>ipgC7~&EI8yfYFyES+A*xrsC|yH z{z5QjWb>Y|KtrKk;anUp-h51Y7vQ>=$zBZ{erYWpVN)$05yYh$EQo{}qkunjD0|se z5CqN$tKJQ~GI4Xdqv&*vpI~tKdJR?NA>*msc6+#4=g0Zv6Q#KY-1i2uNmLVKOX>%>fvY{nN2qA-KjqaI&ZlrqvVI29k^1OJ-Z8kLTd0ZI|`X_BXXr!OlIG z@n!QjhK2yhP@~SEic@<#sMWe4-Kx4Fk&|^5jlR2j*%aSrDfC;l3scX{N+TMP>P}Dy z;BR2IL@%Z=zxOo6o3f0J-zuAAZ-u3QIR(qvFga%x_UX-qgGKnbWBjzX&u zWw9!_@6^hws>V4xg3)6qB0Hns66vWR`#4YGSZoU)rFb>YEAgI1@oDTD*Mv=1AKgdi zSAB|Op8leeugjC&2aLBq(kiY^m*=N8&;B{lXna?reZ79xf>J)9w3Y^fbnpgHUd-*N zmn&I(n#Bm7TAra%W+S7_v#2FS^-DoTaW>HAR%cwyl3u1|ebYbnJ%D&CmR5}9qH1sk zRgM4oZC+f#wa3SRR1?$EEVulkk03yUG&c~tqcIOL4*}nXVZ#;}nNjMaD|4L`FRj4o z^Tjd$x?^nj{4Wqu*mTuH?d1?`A|6MhISrQv;AXMX;e^R}V|u8*?(7dNi!I_e3FoWr zX5J=ZQ&q_}*g#V5s6z+4|CM=om=DC1m~EbQuNg57c@nHa%i9bF6gCVVy16Rb^!E6q z3GQZ>y549JdgD4<|Agh{6uG^$!^kq_TEUhyY*H1_-LCSRHXhzWOEO6jc z{R=Ya6qxGg47^De83edL%T4-a8&7JSkI*H5IR=qEihzxt@=67y7 zw8peE6Q)xcQi`ek7sE?Dv|vjLa~lmtLUF`=xgvN@etvi8AJVZ)1`gYU_z7wJXjPo1 zL zDzd$SF**kbby7SK!leNe2{gJ(%|3l}k$nwJ`bbqOx} z0Duo|J|S!C7&i(@jCITr4t8mBt z6!~|dgM$fVpUQ^hs+km=J|hSA>$h825d;#H)Jsu(`{f0`$%(AdGuoKfHjwF>ofCSf zKZ00|_O1nh>~FeZ3vI-xq~YSWDKkk)05ufKGVG-BX;i3=y@g`+u@Qy^8oNVheR7`w9yT5Ii%b|5h??T^VVJQ0ZVDxUa8xtnf2#F zj>!xyEe->GDL_-XtP}AQl@Ku#K=2fh_SP+J&%(-l-+#k;_T$0=xmw(PEJog>-N+8TG!scN6#& z<_m7Ge%i$dvDhN+i^1W*NmK5QzOzf@alJ>kleN|$Z;wmXnK>Qdt{Ya49yLn%wOMB$ zzL^l%O=cpH>?n{9|6FzJI2G;uKuD~j;TmIs78>Rx9%W`lXiXeieR%?vkaZO>*s4Ll~jz*MNqL`G}s=nA^Vq)Ya7*>|Gmz?8x|&h5|QF<@32yi8g1=jWC)Ohif+7JGi^ zx9UEoGuSh^uoR~&T;Q??DCiEio!-tH_9N|LqYj;Y*}X$uD!iC{{?DozppN?(Prvg4 zz~&Umbn9zv*LHOEz{<@?5=^JwEiid}XloM1+ zQclSJYRtJbwYB|t1Syp!#>Od)v}jIbH3)T1`o(yV#Mr5XtkGPi_w19+(8?HTG0}zNm2F-nbza&jOrH0?D(M3nUGx?5BdsY zOsRQh*10bJ?|z7ry3zyfj#;`lADbCh^UO+opKNX~CU%r^)R=_oF_|y=HZJQU>&EGS zF{%+-ROW>1eign7N1h%Xs%A zQYkN6mJ0Ro3{L@S!Q)VeWQw&k8e3{!Hx+kn#PKwITI0rbM{MVL0RyHx8w%WaSwy|z z%_{0UmP{Bqvg83a*WfWh@L$KXmx{ES5_LrXlew@LL}%OO%hQl;ID!%?2yOhLp0gFe z^JRS%UkC~mw^D9J@}<>v*q+O}JBWThkl|=s1x8r6B@f z(0*pjgC5NjVE~lI&1z0v#^Y{z8|ERp*&>l$8h#8NH$gkj^O=}_@7%rt3Nn?XcfwOZBr_tgw*M3pV`bKm%Pbiega% z1r4;iOVE%m1g=aZTo}ga=X^zI-LuW=%+APxK~eY2ZY)6_us>F9-$rTq3hw9dsPl8CZ( z%e6hw(i=xJ5IU+@QlC&em?xqK+frY<#7Zq1oxV(&1I=?t{?Tt|!vZg!82Q8m9^Npb zsAG#JWY?(Q{m`cSVERR+H`JK9^8M(>F!ph^YXS2FoFr3AL5lJz^V+9o`Gd$6n~Avs z8w=M?w2OEfsWCaRZ^uxjb;_O|wp!S5ta$v5GQ8%E&tN9QU(pmrzxCCohh(>SAF6Mo z>=lF(gTw5^2yBm_nY+7_XCnM~Ht0L=v-!WQ-VY9u0t$kH)802pNgUiZTdi+;hlGSQ zn4od84bs>6y7vR>s}p`Tkg*caZkC#&FJrc_!F6=o%&2SNML>%xYbVv$I6G5pHpF-y z5&&rsGk+gC-jnD~OO)DHI9{D=r39~Edygm}TZ$;Y7(v1-?U(h-mKuX@u&$C) z{laW-e^GnpcDzbrr-QwB{FCVJ{-7bXTD`PcX;PaTiR!>XhkmX4yX9QeePVD1`ibHh zBMb{tkU)g~Dub*T`jSV+`z`vO%IQ$(%uoiXy7)k1+h)CG0Zm~=p}EGV8&8ZSbx~N- zX|sf67y10f@yrG4Iafm-Z4}@LL0>BWOx`&tholg3PJv+py<9po!KS=SO!!y?9ESRW zwU)K$F-3xVvn;-m;H}*L2dCFP&=Qxh=_5u!ol{6N5GTfj+rz~6v8F)*8+Uy2wc zyMAzT)XT%3HyDB>VEte<(Av>!{$}(*8UrfdFIXtRNCXy*WJCo7ra`iX${DGRilsUz7X4gNxfo6vwso9=ow(;ui0} z_K~1+P$5W4W|Cp)CMe57QEp8LF=D7%e$w0Qz}t`VyVS(mAb>Z*Bztwc=o`+J-_@_b{)*EwN$6O zxU@MwYQqONT&@&QGF5Dn*lpsxcCGHxpRwVLOgX0?cO6pq8b1WP*8*{_{bXCidttNL zP68PSf{v^HnXLzpd{V(67m~1nc;3flpQi;>aVhPu;-GS#l=^#5Ek{%QdASr$Gzj^Egwzo=b;uV{3Cw!y9bA18LH70vEQJsHTa4>o!H}g3dcQt{XzR{{K10+>-2?|R zHjc|azLVDtZP#Mw-B&U5mvzFIadFlUd1y-yg!XM^{JtrC>K~_`jbK4SeZnQdx@Ud< z9&-4w4g-#)9_{my@-B|Ng>)7NvPZOMm6)&8Pd@2KD|29J8c-J^-GNv~s~CKd@1`*H-J zwOShbGWP5~o-12UQn*$XE*|UJ{=3)+Wz*iSBeV;yo!UoJoV$DO)5>iSto%4OhB%K` z$T_NOR&7yw&DKpb;86V1`HpUNcV_OtccE|!EBl&tz?zN}ETRB1jqWW3Hb-8UargHu z>G8iuz1A$gXn;l1#@%G44|`W&y$(qxHC9}&ss?1)+oz{R)NqM^dy`W@gfMb|;z;~6 zSLDS^_FK9A%C0<-VbQJd{a_Jl-yK;s5;sU7QsThaOuw%MduN6o3Z0;2JX_Pm4bYHc zr8#0DTRMek_QPScbNw4%Z^=Z#RgM>$Q`lPKwxGImcrqB1ir9Q5AKRBm$XrK0p6v>eG2>;h;o<7Mrm=&hg1#2lH#p zs8Qo<-*u&((|0axhJiV3q0}JL8RB7tI3Oac+i}DW+(}%#(`fgFe&q&7G6hPyvvQq} z1(m+`-=u6j_YZMh1f!KN!INiNBK}=vKD|CXYryM#cRG=vU^R5cNdlt=&zkZs2Hpq3TT&cNyS_vgn8aWu$BQaYOAj z94`*TA|y6dKi{GvxIKPaaESR}y~%@Bba$Xn&=pk+)eT&qc$V{HqDwB5@#GI7 zyfEKUe&BOy4Tn0u@*F2r3Jr^#61oF9Nn-ELA17uSGny+RmT7sNetehOmC{UB&soQB z4w30$AQT;9DglS`Y}?0^%bGC_z{bJf@>_;3H9pzo@t7Il(J&ogF_==Y_80Xbw>^1j z_;r`TEME^ac3O3Wh!Ov*e$$ViKgF&R2~V=KT29&g0Rzqse55&)LFy33M3OGu*fN?* zF}dL-o_boDNSXgnyifGcZbHq7`RB=yyJ z!}#WjIi`gSatUpY$igLKKvMl6n_F|r>bj3{m9rWw}IN}IgTNWH4L0T|i zossn&Ic9(sIsA{rY{kfq&Cya6`YrBDp#9hKV$}hI6$oAs{jg`aCFykK%qvA6J8^Ab zgN#M(QE@o1dSS0bdqb^92fc5bfKsm=@PCLtG~125)W=$mY$H4T)vPVyk0aTHS4Z`rnnsemkyRY<`M zR7cV$Lu>ZNXHNI8zI6y0x$DjR4a<*)q&tarr3GWSAh&kvUk8E{xR?&&4om$q%i6jT~LM)AC$OqgV@LXKGq}nPB3IiqJYu12y*)4g7 z%4sOFMNZa9-TN<|^)+`lYj|nHlcyH{0+O^Bi&w461P9fuUh=4yv4KTv_fmk^kbOOH zX(3E8NOt1UwzkY7;J!@o(9gN6tU2vq)otre)KNj*IxtCp>{FeHJ!!)3 z;#46+Z}7|~Fh)~sIYInH_iA^zL668-9ju4ACg_aWdy7UGoRFhw-!^2JxPeL~F>l3G z(g@}pTfMJ-&tjj(LawF)^BH>q9X$_0#teWJxeY$c>+ah7@C~=G{r%r%^-g+NB&(ED zJ}*R^5_BPsy}^J!)D$5ZJ#Pc-G0Da}05*z~sokyEek#$Bb}yQtp2mY<*`<5wm9ch$ zb56hxWBs)ewd8~4!az=GvG#=DDEuS8_Tn?<#uRlX-S@i?G`5)U+0x* zJXyC9K#dd4)Bvg#tc2fyNcsER)XxU*XDX@Zv9}16L{G^AEj#61=AuxdJj=gJ$)IH7m@eK%9H-9c5wq`mQqCsHfL~xQHNk zRn46SAdv=VpG;?*Bd$7ZL`QGKxgH)^&dIJMZW>eB{@Ed>?<+sCC>8pPA^l!cyN+>P z9TbzEkuha`X`XO$--l2?B8@a#xbXP2>`wybd{4Qdw&5#7O6g=P%Dyu%M*Kt{p`&*B z)xA?>LF!NyHoe6T9?txyTExN`)2GkDM~Rzh2YrQh3@fNazVcoa-|zTS z*ks7scax-OlK(?t&+TkVaFE$d@XVBjmx8nz?BZIa{!}<~e9-ROk6K!gx9u*rw{}Ac z1?;*6=tOEs@?th{0nm6NeO}d`WlBj1sE=)_V^2vN$`Uw5W5pg*)`RJI?i*Lv47H5t zr19L0DgJI7RzWn>^M9jF?+k-Y+tCM8KfKL z2?U3=Vl~R|Ent&z386F=7D!M~!Q8M%k+4s{-88aQaMn6)9+pTTsdRZS(SLNV-`(X~ zRaN?{lHiTCRBjEk*ik`!tTav=&Cml&hfbCu@ntG(>seedU0uxCKIYC)2?g17xIPuQ zct3XYvJ;O&K~-<5GVjx#>KE6ydBvk>6;aeQhdD`Jqaj2SRa?`AL_5W@YF;2x@uYkA z?k#O_6dAWnjiBx4aqk8Y>vHwhs{#?D7mf?nX6sQJ0wwWt68RV?;*-%A=%#p7(vL$@ zS2DpNC>O_5w4?l;T5Y93*V(OYkrGPDP8Pu*Hv`RM)wd?d552ro(o5DV2fldbqQBI0 z<+f-48f92^`95#X|J1f3@m6|$PU)#!?tIs72s7o?QPux(cGe(53;~VBmTbZmXh^(z zP_#(Vj*^=V&oZkm3nL?Xe*5lSWiWQIP+7uSp}Wu*PO)a;o~B-EyDmLo2VbjtwY7J%%ARJ` zMpmwayO6Uk)_8pHlsBz*XJut6d9d*^gv<|3-3t6`aHp!?rw)9SikM`G`iL8l$gTdG z@c|dstTnQGfa5Qd0&q?ulI2XoCDWeuxh7OiM)cqG=$ud0e9N-#TH-OqTpp{J<3FA} zJ~i22)u>w{*s%1Zl(MW>lxjBOMWjyP)8e$7nzGk&x#8Tr@x&(@M%$_zIS(eW+BDg3 z3_myElzFuZpT;uG7PmqBacQ=Cp_k+H0Ly7BvOgzRmWbZFny~1emr|N}bs!QkS#wNhqCM30hdh$tFCy>UXYVAA+FJ{GhkkL%0@*A4oGWY(Q^9f?{Oz z24bs)S&|f~7!xy31ps}KDO>tw=G!rLKV%%~xN23K$k=O9``-Rz3OX)wPSUgpujvtH zmfiAFscF@*duKQI&d)OvTJGwyn|J-L@5PpF+MFWW@wn-J=UiRZk$KbCTz`G-c~~e> z;v6%ycBLED_%x2zL?gk2VFOTbmgX5pWWNG6U|tY0cXp{F8=0H{U3ZXyaV$f{)hExL zJLT?WPu(jHuQa@>iT0%l`_kXk^J`;|MOpfX`&6wo$9YMp9b^^*-|WRRv>e3ZGU*o{ zyO+-dsBqouhoUy9)fZrW41zO~7l_Eh%4_Y|%2&InK%@D0rFZwJzx8qAnan#Q8MNb8 zD3s#|+GX>&C7-9VI&q)kpi}#9sgx9&CVV#181d7Su3DzS37Wa&V6Wu^e=eCrwzqN( zQ!U-Fn4*Q|J1|opE*acaM2(EzAQSEU_<>HgYk?bYJo#x#g#(8Uf`atc$ zvsvfTW{O(SQv~2m4C*JfGVK4%yw_isWm}#Cvx6qx2c6qjSXiiJ+t^5u7fL}IFGloS zGFaMWP6I`AdPZ5sAcPEn3g((ag5Hm2az^uH7zUJ;Z zH-EL}^b~vi`mRoUC{p#j?X!58oRJ0#(Xp6x@{6>8`SPW&^VSp@9+7!>iUsYmUww{k zw!QdFuTSKg_?c!%+qxy9c%f%h zYqU{@BYW)j!5pODv8mkGl;O+z_3yt&f-{%h`R)ByamF@_dA^dkXqCIZI6t663qrpS zYEHTh@drZ-tX8(kU#rV}_T&n#wDv74&T|EQ?0e!3A_ zt-t6Wsm(b|;1FymL$1cHeuV&*j(;gx2@X+TI#*Z-7|LmH;uOziT3^eIQdo!WKoe0oDZtz62R4Dh0N*FRHGiib1~Ss~{XW|SA$uO!Qe%IbmxxjQ~V_Q(|ty`L2suEQ#9d&0jpX_W?0;&jJ z(1nN&6-7W-HC;On|IIe|s_CRvF`&N&r~yC%v!RC-$_t!FR+N`#4Y(B$I!qX1@JHH< z;|Q^Zk>Ur5aXutvKeRSdhJ!8(u5{&B`99BJW$&NYM6pDg5EB7T&ijrLW+yrkUE+G0 zxFeN_{bK?9yGJ}R$A)7;FLlc=_=kIx)=#^D7CiC57mH-b4?OH~a`NY~JyK?`Jdiw4E*rA*&t`1dd>0mvX6FQ)hrwnr#U*1bXJcyfCoLeOKr zcd0a=Jo#W)&u(gJ49e_(SxK5ItUp+IWgS%vXr=DoZxN{hD0dRy1Mi-?(+6s4n2vk@ zhImB?Y4EGcv{G1Mgh-%)O+Bg?)JI!C~{{kLFRdtdc$|Px}rJoh``&)`kIf1}`G7lK1yC^s@PsRJ( zf`p^QghoR^CgqyF)tRnta431$&)a@gpH>rR5qNWJ;u1dKc%eD_69>2^%wzEXGA#|a zy}DNv*U{Tv#oWxpgC18J_im%t%OB0nH2@O_aAlL?9t`{LwC%86?~XUiW`Aq3ls$vM zVZ2&gmwnw6!e8;=iE(xz^_&TG(S2{)OUOf4366JL#*^5`?q{oaraUAz_*iJ@z)p>j z1nuzgvGgh>;&Y`M{JOrQR4lP6JUXyWZAD8}P!IgRgeZy_Fw-qEGYWyS;={o;LssK% z1+D}YAoB#Xk838)F>h^oe>EB^2r$5qtkTo_JHbgXSJoF??C7bT<2aY7a(USgd)g#7 zH|nXyC_xG`#MZZgnhW~Qo$f!D%2K?^g$$udX09rvhgQ3s#swV5CeblPA)*gJ0LE@I zaZCmg+;*$2H)w)Q{Vz_#*NjfxyUXzH$Te>!dVN0yc!*bDH<~<3ahdU=Mmf;?aDQDg zCk31i%_EwAjMx|9E}$AZ?a``x$ai=u((6ccN1-Vr2%tS8q!pc<_7uxwJ{`{Vd0Fi& zTeM`Zod*P*W1pq7eJ{Hgr!iP7*$LE?sEQEX84|tw7}w*vUJpUC5q?qjEpf4qM!gA$ zK}Z$>D_k}!CxPy*Mh=Kw2-RSw^3`I3ZS#JLF=%2`pPL|EB)P}BemRjpP9_RSi1Zsq zOEzEJQP@4GzCO~j!gXhyc>;{@qrm}c(YcK(TTRZbh+ww)^M{_g$z zzh=x)>DaONHTUT^`{=vJEPtAD{AauW>S(sVD9>+}NSV$=&cP!;CE5M_Cn zqNXD5iLE1e{z683kS&BVOc!^s=|lAv;Gx{vE@nR0hPbE>NjFr|x#8_ZPQG`ArbK)w z@l%uTiisgP?g)|rQIQhKKo=*-VV>TB#Xfr`I=_3HTL&a2tdfp$oWVqUr1 z(+fz4SP4ec1_IZI5i^Vf+KV<20m*fiB+J9$Z2!F=p*xZ>()!^d*L6L*^=a1h(yyK< z>%Jr=(c=a<>;&J$&pndBd;Bl{;7TdSnv0~sz z)N%igVgpMb4qv#6iF%3Em{y>TAo4MF5vH!n3}U`q^#5nL!QlZ%H8sk4rP;JRWDx6$wJ0dNl{IhYll= zy&WjnyMtgJhc%)E%rB|A`l2HI`hh0cnYW!4^3uAh$L>Fwmui(i1=q|5Ivw7i5ulXs zfsX@$l@M!0c_0fPBs*hm&P*+ET2t;ls;~3;Uy;+RGMRVn+6J%f^*pd`SG;?X5(cJm z2%Bw{&iJ%h8@eNUCR`t(pCAdQQfl;Lc8W-R>NlIUO{4B27zyZOv~i-Kok@xd)SaPg{1jV+&)I*E^@5 zNw0{TO@81E;<%d(5XRoPB|s_);xXe{DH>|8aY4+pL+YzG4nuYlzmg1CvT9`C()T=J^5@6W7r>dc%h zS83>&n9lP$?d#>8$Y0m}E6$L%#Y93&B|>UofmNIZ>lLc3}ToWW=l3E1W1 zv!OmWr-|)5CECYvTD$FBWJegCNo!HuBCpl#*Uwyg?)w2(ELUppKR#fvOr45H4ucqc z%c7A9g{e8z;0>f@pA~WzqhnChaU$?-4am|9Msg-&oZ7mg~KT?qGM!Mb>_@ zxU4)Ue$fXXE$9Tj~8_%>JcSola}g^955GVrM&&b5$T zit0v8a&jH)$TPp;4Jb^xDC9>49kto(84frIIMkpV$y(Q2ZMJ_DDxJhoylkayN7f*d zrCQUhq`aC`{QC7asxc515076;pVl)#j+&nkYqjGHGPUeP$}upJ9vCBb(6$biv5$Mh z%L)=i$4Uh+AmqOkcP5`*IC3=r1U=(qdSpRfSYE(vY{?kK()do8>0Nt5$7JH6a$jft z$2nW-O8XE9nR7l#Ha5|ZB`x`Y#}08R`9q37W~9&vh+7$dxn0+;mgwWE@@FGs6^n?i zA7iwXBOlzqugFvjA|+vr`Nc)RMYQ_j#UXZwpDLivqf1JhtY^fAQz0Y0@CKRT^c-B3 z$0|$}f31muiMC!nn{NcyzFf7f_az&LFD_sQP;bf%Z;M%m)jz@k@Rh`-0&*Hrc=rDo8X$q&XtP7;Qi%y~Nph5-;x*6M!;vDxm37&f$Wf!aPT10X zxmcy)AS*=^t&0GN&V>(->`k0vVX;3 z0+XKYNE7;>`=hzko=LwqJ} z^X&b)XqVDX0Tkh^TDRFiPq{35QUIyS40Nv<{AHZA>BgNMhDKTL=&j$r$V7uZzo~$c zYg}AlAH9eoovTzC{aNCD^9L|v(emNfsGVhYUyASY>Y34Y=6GV*@4dI9N5e~y53-X> zLEb(xI>++5@tPIiN9_;&;Oui;(HqJ# zm=ggYXwhD4(kp%sf&d-(Rqph`#U8y_IY$j80_ZP{L0*KFIyz>f2qC%BJ1F z8;k9<0J+9Pd1dtp9g6ykaSoa^UbKv1B8i~ctWlb>1(8Jb$O?*iAs7gp)7Iav_?I=I zeQ(5N1w-%eQ~31UJX1S6yP&uJq1|CG3+Ke#>hSS{5iV;8qIns{^jog%<$^jHh6HTH zoMui?EdS5Kqcrc@VCERr}+~xT;{as8(7Dsl=8ipg z5X)FDWe5_4C_D9%!n!w!&5=vVxFCRQ#irOn4)`3fsNoJOASvU>-6NLjqs1g@G6Ce4 zG~$NYVCA&Wb6|e&5Tj7%ihzY?%=6lpaQ}j*0W(8`5{KMy#oK{?Y9c<4%;b;BTW=$q zMc_=YBcGr>VTKjXK18V!KfH!!?Y-?c#LENZAnS79_$L#vUXZW~;U~f)ti%}rZv^r3 zuijeY2B zUv5xu;@DYHuwe5S(NzGQtBG_XwF(QcsSlraGr8sy%}x>~RVehquY zMx|X{ zd&!5OW{%nK{I!&a-p{QxDmMD~G_E<~wRb#>F`H}rImz=?+*P+1ss$Z!{c>$*Ro8Ob z*V-H1%>7wHJ1imyOFRF((^UaQBA?#<&#=y?Io4vf$a@U?>EL+#fXfSEyKv~@u@s%1 zGv&Y8>&LZ?r`iVg>(>wUZR*{o=VtksL^Q}TsbjQhH++pQdxSq1B(#b2ef*Q8$aikT z`VPzZ&jo-bA?&>qy%ye^;qXqPp#xM4Mk?*O=WOm!!YWhRg|n%t_)QrRaha;TL>7p) zh&0YpeScfa*QK7Ipfs1I%+vi;mNDp~fQW0wi|cL8*8a2@T=i&YR8*Apkn|K|*=$T> z+QeY}v9u{O10jqFj&fe9e8uJ&wUo0<8&ADmqqd5gcG{4J_clI#I?bl%Zfo66Sy5~A zb941QkN8{?0W|*tBSx}uOy;#mulnN;e{26wc*hZ|Pf<)Lh_)4Wrt?)No(9uvxc=?W ztfY%g^BbBA5MUQ@YUt2F5{qH@9}+dirKl1l+n>IreY&0YhE}%f{^!fAz=WmZKP5zz}W-~=C}__bI-F+zYD zK6G90)GjE*xUZ-q#UT5S9+nj4Z)iHyV7>B9Or+ySC7HJlrt~>MeZ!f#_L$hH&l+d+AKhqeh}OgLf)vFqS1yvvIho}WxdV8VZw=z6I+9WzvUHk1#?gi=b8`bBSLpO^}!+W$~}xz zXwL=y5X;J|%MN}FTFtm@Y}T25GT{Kc2?EAA$KwwpgZA%l80i~R{q^fX)7`V_syycm z_*vYi+qH;@#X*@*vTV;;MA@RLVhbl;W|;&T21e%w)#`X8pdgaP7-l|YI#u6Ctp62R za`)EUg71k(tWCZ4KmEL;?w>yHQ@i+@s0?oqJue(_Q-*g^sI`TrW~YlH<3>3x$O830 zC(qccmaGu;x(wi9i4~CA&z+2gB!2}V-1H*B;}~~V!SQ@^WG#ppFH@W{Zs`*hWgqi- zI=H9Q+8_fCs}H2NrmY|~l7V|`Xc$}=32lgVglLMe*H%TR-fW(Kq-3psnwpw= zytbmQ&b@urd{a6A{T8mRcHg%w(>VFzdZR{-#8j3e8yTrdQPg{?kGHo5e~$qSbgDyi zbt#wx3C_A?uH8-l621rn%UI4R4hsNdLv?i{NC#Zrl)K;3rS)N=0QG`h_N*x=$Y#|i zaCT7-F%v!9$6tJn44%8YbA)B1CmMT%RI;Ly|0nd$sLV%v3z>6J5Uya?E>pjTWqe}W z_U&a=vg#<8rGB6O3O|v$VO>FLn?t$jB{%&bUcR3k5k{k?kkQcwvL3!Y3@fef4T9zl$bNc4(4W0J{B@@+Os~ekd%|JT}$qhRHuN^Hv+( z-u57NxOikmGkXfUF7mYyhEOOyb8gBK`c}_ZzFRtKw}=cjRyshhPhl`rY2{O1m+P^* z-F4@Cu@?31x*zB3E_tnd`p2T_HZQB$Ns%&k$c32YOE;}bHaGzfunon#O`3c`NzApxlR}L!(KN?4D{6Sp z%2&IoVn|STd5`c7o?{lC0so@PZx4=!(6vE>1|vt0euNnVJ*9Z%OKBWbV)5UyRKs;f z{u$5CVr9k{jw1C+(90Fb%6}+}kFv zy(mU3g9ggD7z#>p$Or$ilsDL^png!#BLtj=S?E(&%%3lKtNpg%9>Ott)6<8AkM zET(^8t?CI==&v$#Su;?<i}Sof+6# zcAqjGew;T!@9hs>!IyMiF(PW@WiX1)SNG*yUFGe42L1|>U$d=JKgo$D5MKgCtsZr> zv|y`dP)?sM`m0|l_`mBOWV5hSw{8z%u4Rx`FnsC{fi#F0Vvj`&i@C>0knfFK%6$F& z-XU0u9{Gv>DD~2u0WVDj?_1snfn)eJvkOm_i6Oyt`aA#}F;$Q($~e?CjPm0?{P=Jo zf&EGM8004G7GH0}oS_}5RspteWTx@8?H+V0MB|2?3+G(LNU{6)Euwtq^s@kWp}l<) zL9JC}(uPwT=DMt2ovcyk^z34Iwk>jm&u!Vn%2zb3?m=WB0QB;CNaapyXVRE)RM`r% zI_~6#Q>f_!$M4%j^*gIFHw0uG;V)?OTS<*F4XK80ea&7Wf`P5xH`!hvEVlrM*<8 z?c25uI9rhVU$)c0zkQjt#w>bfP)<|s4%c$Ox)PEOh6IU<5!^K(1Uy#<53U}*$CSSo z2})a00NA}ybQHluwt-w+21$?^B+>R#i_M8y5pt`nY&1x{v*8w|sT2oSQ}n|f(enhT zFI)0}l*Trz`(Sz;u<$3#WSq5&uhzV#I`hr4`}5x^`rf{F<%%7~xa?2TtA5Rw~4%xbRvzxhwN{0@1 z2g*&_c{fp1?4swvycjyyaq^~MYdEI5%yIJ{n_+YzBvxz~@E<41BorP>D zNWnW+UTkzkquT65a%f{)5k!Z57W&t4j?ANp!w)bmT=T=#>ar7uWD!nlX;NTH(z73G zOgdOQ#zT`v3J5RSm8a8~>X0gr{zuii;Uo23+StmTe)4{^Z@A}`^~+%AsX!GGXk!V1 zP9tba)65Z3oN7>wa>SnzJr6W#Xykvsk5RWl1s!wTwJt|2!dTs^%K%I#>X#!Wfhu$M z&_88n-LLysVy@K-)3Ia5Z1retE8y{me_CmHrk(h&pRaOZM-H*#A8haxe@4!u>R7ic zA4m31nl3U0wkE82IkaNW#~r3bC%CG}pK-QO|S(2IhAC;ND&x?gV2224~Y2}=|H8ja>zX5Xe zjFORuNbs`eh^h#8m5wy`BStQ3mZLTdL_sk!HmrH$RWmeB&g@^Bd%2IlR)s7ddAVE3 z*m)cpy1^iP4|T>486tB`h`QITTlcW%(JSmMyg*L$ zq<9;KIhMAZ|6W8?!Z`ZPIfC(>z+|XHg)v-edij@F-Ela$Pg9znM`9N~>nuyqPJGPb z_e1Mp>8?tFtnYV*qaZ3;z%N;|KyPfnW-{0t2q`32cXf4j&t4aESScVCs&oX<9fX=0 zC~iHChn7YRj=ObhZ~m6`vVj5au%YAKmG0BhLWce`R>q?6zGw-mM@jI_D2r z-`DpCtOM`HQOgm+n|qKsWIn13l+y6>8K9HDy`mh^qYag5^SOK30BkSw8hF^)jQHoH zn1X>&^Yf6c*xgBGu%zb$)03UN?fb;gONjO>{qV4~SEpZxCjYQ9r!^4o1o0_>kmUIx z4)32=(41caaB>;Jy=RgQx3|LmkL&CS=u-OJt)Pi5J9-pyo_94`JaVn4OTjI;KaQ=* zpyi7f8zB?grq^R0w~EB0f4^ZTcvgnX@i!23%k0?rKk@iZ z!mjEGoaAdO=Sz*mB&l%vVD_&;lo>s$0BU8JQmVvCr|&`>b0edspQ*faW!6~{2(iRx zx^|&?(h_l3aB{-vA@g9%CwW>ZIl+Y*2&ftn6h|ez)$sEI(Y4Zs$q*JZA{yt)m9nU< z)l99IAnHxs*_s)sh@dCCW_HlivY8H!$F!9IsDJ=6UBfFh8GQnD!5hVe=+G{#IkVlP z&Ggsf)^^owp2%oI$f>;LsI9Rk1sa!8Oj>V;nYX5b@7XWe!Mj|t`XBzTa35S35%qF6 z*R^QRz=`y~5O6AirC^H5J7eK-I^WtieUp<}qMd^b3{go*=p;Rfp9q$3&FBnJat5QV zBCH--ylnhLCI%-X87X{<;A*emTqL?pU0SZele?&MRMnd(d?WPhcwo_pb`|tbo@ak1 zu2ezvE;7@H#gI8)M=XV)NjU5BP5BVlm7Z*10 zpQgdCxbqw4lbA)1gfM*I8mHLTz~C|hB}me#WGnDl+>*BZ{EC@b9ljPZ`CnO{xk2a_ zN@Wp{$6g3)KfUp3WjXBT{vk)kW#vWkE2ZUOw&22V+~dy6m)%H3WJ0!iw7X>KDTB?n z{w1^C4?ug@+q2OLP34=cO8`|enA-uRl*}b^db`B!`{f{G6tn;_7edu`{g2I_2oW$2<#gKUHL(njZR!qc%=Y{ zIPj&G^SsjsKtDuzLd&n<=(p61vb>4FE3ylMb^*<}16NRSbZn$)|9@B(msVomL|tot zOT%{`Ec=%i85@paC9K8z{!*P}pKm3V&4xyf?%{I2Yt=RoX^ClUc5({>cGIZ^ycKz!9&kD&;G6N>CF`j*i#m@uV)Nq2{CPcICggZ@5BXA6Wyix-s3<##%rW=) z9E+6F3L}-SE%RH!m$4XGMuou^zO#{fFiASvY%>B1)Dy*dF}!ufAG$SnJEK0-{Sg+p zDR{@@ElWPPwEXE)<=zB~uhE~`=20Fm8zkVAF{oT0a{(GUwNaD*hURTwZq05ubgM8~ zjKhnQ5QVNRDWcU4RDbtw7?0@fkyY_`Qd9R}^`nD-`-(7}-xr-P-0qBtR|l}xKXc(^ynE7y7*2m2IkS%-T-<*4=HmN7b&I@dx)D! zY`a}|#%t^9cO25>S2whA9I`f1OSnr4829$i%sfOZFax;-36wFy)OPcNx&d3?O-pMD zl7UZ%?#NKo)?|X#k=^!beomjn-olTHSuCxL`?Lf-6W0q?E!D4O?iY$|x-?Vt0py7x zC0#~{unSGqwkyx3E`F)}YceykJ9vvSB!~#6(QVpx-%)LRI`r%LAOPXk$a{K_JxnQ! zGZwMF2)|fUToy`7`i{Pj!B5IcTYV5&_K2Py*fZ!wn$pWmAjxFo2jJf_;|qN*Y)u%aP|S?>BL%U2Sy{17_eu0) zRVzObBOm|+fX33My}2QxC;Z-d$Q8^1mWHwo<{$&L4W+j(?J!rdapPpaTQmyyaZE;) zgS~Kn({oF9xp<^s*fwPQFF~At4;E~l*7i-=c4EqOFbQVNQ$Vf*mJE#!>4&M@);mA< zRPhlGtM&CXw~V4`q5W-uxm})8^{kXZ=K#^71MTYEEz8c=gtzbB-EZzW2ViEX$zFL+ zQ7Bcu_|xh?WJAq`@_d#doO7KC@kUTGrsXaQ_`2~SIxP^GMi8N59ZhGn8c#YkW4ONU z@brh5TSv_9#m27(i_AKJ{`I3bX37|ldq zHbUI0y4^<((7bhP97AW?m}14*YQ0LfFu&k<&-K}^Kt9Co zCX~9!9KK)hPOHDxUPZ+V91VUJv`MDRs$07BxVnixI%o7g3hc!#22eFFn!9Y-U)dQ8 z{NFwQld_k;D>6!%5n|yuIxnvAvhsCjEH$*|7wiv|1vz3@wN39*GNsk!zfWxg>uj<|phMK2;TW|vL4n@L%yhF;n{yTgTTO8NHJ5CnR zu@FiM8qxlPl|5V-@Ci|B?0P0X?Vh+jmGrNKIr|_~B3$lbr6j?(X zLYC~5NLhwd))-NevbCwSKCg4k{k=WU^Zn!g%{@bXKJWK+oy&0?=W(v3S#PUbd>S>{ zFCEJg2&|qNGRYt&%(yyeV^PZDFTCevKmD)G1AZO0A-%g^x}oMJdZ}a1MYbja9KpNf>hv(3w>OsN z?U+9MDJ7D^zdqsR!wn77kA%)yw-BO<2^`OMF2A;L?haSqj^+i_wq;gl6Qo4eZ zF}Ph%@PtlE7E`dPc0E_`jz$5(clj!n}0|GA~!_-N8g z^#U8WIAqoIzQt?6%tRJ3#nNrxwAScY9+2{0vXZ^$`qg3Lu>iRvi@~tuy)j`~@E`W5 z_V2eJ@i02Wa9hX0`S|tsD(7IM$e(=7t0wKa`=ptXU{@tP-gd-J318x zO5_EAuwv#O`*6cKtAse2rb0<#FVI~5J=VHcpcp-e!R%&jAe;p0? z0uS!|vO1~-loadOej$aHy`Rc5iBfwOXU&g|DJ|Od>h43*M-lb}s(<_;(pD)Dfe(~A z1s)YM;B&rq%d&kpQoqT%5o!R|d+bkqh#?Q#j2Szf^U1TJm=v_1`ID&;ni&*)EQ)Ln z90MN9ylarz4sGBKU<+(g1JusDOQ*hLhDeiL?`)EJ2Z&&T-mJt>geOn{O!Ryl@+Ymd ztaO4cutunhMMvC%*OObNeQ1?mvIc1ox8T{}Kpj=O^$C|g7Z(@r`e%6hHphmq*lsAR z?u?C%$)uqaRSIH6Aqio~>e3Y}Mlz}P!UJ6?4U7!rQ*_s#QyxK7`SbcW87WQh^@Rjo zPu+*uJWqBM17PrqN!Q06+VAT?9H7`|)$72k-wzxH7rU3xnMxtdEZBjpO!uyQivs=p zKRXtys(%=WC3U^@$N4up&f57x_N0&lr2-_Z;aYRy%aeVrt(v=C)nG%qR@CGeQNod+ z2pyb=6Dk>~1asqkD69L8Wf%JYmjK%7@?pAeX&;%V7wIF#c72GdKDNNHWMbxVly7lL z+pz2q>BP47OY=W}-p1Dg7C~A1;i0kqA6wgR+qkLKHEaSX#+p++g0(`j0d+#lS(KRWt= z9j6?H-fdDn4F}A`HvVT;wKp`>> z0rM}<#^ICC!4M1gGWUwFo#Ij(3=<9SWFbS6%6f^?{m1#;hx4>^sq7`^QHCjq?-oLf zet#Tmrg1IbbE)TFf8`Oqr~;j8jU$c6Iy#2-d^4Ypc+cnY`hQndRb}8fH09|q1hhCo z^$ANM$oBK7dz&|Z}58F)aw&sPrqd7_?VW_ z)^*2b9DVxjdyB`9ZFQ#pF{yObp*t=A>K1?DdcW&mygsgqa{~H~9s3uUAp>Y3d7_n2 zT3~@@FZ@j_yt%03R`6le!*xv|A<1NJ+_Rn+ zolYA@ks{kYJ>NXLZmFABzYpJo7b$=Bew4a;s>-z3qYKtI^PaCVZ4vbTy~D$Y^1^}K zdM1}&uc)nd`?SLAXT12sgB(~QZxK$Cmp2ovN$GN{fUn+NG0QMbI`{O+**AHquk(6^ zG6(-FpYZL4M}O;$eps`MgBkOBvK3CAK9A}sr%$rt37^84s*0j6 zL?sfH9aM^+-(;5zNFs&Y4^zhO>SntG)hqG_y91+dLhht<=y2wh{d2^h2C53kK?j;YHE4>5v5wR*k548i`jul)r-LCBbcqkydhfNAhLE zCk^4(qmB>di;cKm4Rwubw>KpkWv1PF6@uv20h&iM@`Js*M~z>|nmtA*kH}0!GE#@YXxWP^VzHLV~Qc_B15{n*vG+r*OPK3Vr7~~*8 z=#X1muYpn_rybX^d*qU1(`@+5qxmnO>f$z+W+NWB*jUJv`&~Wrq=DSb4Y%oEG$IYl zVvmCe2-C#71TeQCk2G62xZMFJ0UNZ`NQ&2u@UUtq0v3=prCTie(cXBclzOp&<@~(7 z(r0(*z_d4}{hxazH%i;M+CU?Th%=IeCAKmAA2&u?u%znsU|HqP(2?PZj~+E+zYqvF zF#ZSX%{Mnz?FMve=e4me)d-hJtX9$F%Cu_HFaD`9>}`-@>u9AD1|;%x`Zf~+F^ zwCMy`c(hzH^MoVM_=StW>mqubn^}^dA$qXe{CFsjIJ-mjwd3z~8ZdWKaG!Sxfw0 zMqIb`rWkjUZbC*l>3!}K!w?TZy{mK4mAH1WLalTy)o?Z=@`zkRwdG5F#=nBkK{X!Y zi0XACQrX(V#LVm<`_9RYcV4~>U9Mk!>BCAEQUS|{ct zGnx$1Bm!C3LkoUp)e;rlKUwkk#$g%d4>XwNl zT(K5GBJ0MXW00<5c8mO&^M5{iP?VD*>l!4*0FGibb%3AmU1GRk{`|H6sY{6x)}e34 zhc!w%cKz0St_(sc)wPJQO9bM*-rn03Qp7E2;S1lcR?J`=JHE5~Z+FvbYOQ}0x5~wi zBFbPQoGKuR9qP$3I|sCFhF*=jn_j>cag6{o)Da)SQa(Hg`P^tx;$?nW3qE&!u~B)T zkx`UL8A+p}fXh&_WArnV>7a}THev&VZNstZ1YyBBfzNY|8EA$T?uQSxtmH=RCG8!= z2Aa-^SiUpGlEhWXZr{9w&0Jgt1=*t$M}Lj!58 zah+)}{RNE^lAk}HtlT1bg;#$T^8m3&jNZKo3q+Lrw-Lg?!$#QIr)OoEJTE(f{_}+9 z&x`tQygb;k<|J`hD^9O|Za}cb3@)aIg=5@0&QsKu_&3299cLD~p%jVFFWz6-*;$zN5ViZgsqJ{N8it4EPcg z%3k3O8M%5MC9@c$q6pE!HR?fL3JZRh(rbw2H^4|6j<}HR)TJo*ThNY>*0KHbi74;t zy?lCw@58}F7a)p|@~xqnw@yBr3#4JqJ!7hZ@-`iNBt+1{8j^6>8P+@bRul}J=P4)X zQf$}!b0_TZ7#Gikl-98kvv~1c{_!P9fd8x9Vv;mIb74H!QnO8)B|IdL9{H7(mGw2> zs7>)-Mi0wR+whknF@j<3!Q789`2or2$U|<$ACCXd8$7tle)b|(900uz$ z2KT9~Jx^8GG3E$-t9S(MZqT<3mk)o0`Ce}Nzmk$v_;!(t+1O6aKcoKVg?2+<$*gAw zT5l1J@p=sm4Yzu_-GBK~^N>vnv9XZZa7GSb_H_0ycJ%N#ZkHBG=Cuy(VLZU&$yz#O zS)<{EJJ8HA18ZikKsJp5{W#LSf#KH5^ca*#fF`C-V@{a-g^-o>beB-u<)3fJ+5jJ< zETozlcqQb+UJ5(haJA4kpp?o0WcACd!J;GL)*C(>b9K&f83nd|DQPK2^h3HR7S^qO zNnu4z7A=hT4qF+xhJ`~Su!%4i#6;mrXJ3l*S6pql^mmk;AAl7^eYo!%f0L?fBT-aV zc(ZK+np73+L6#jeptqjJVdACvudGT24;dnK^CW%yxL$u5N3*<_X<$|FHKV;>8;C1a z($x-MFTVt4P@V9z&~{Ocd~)DeStVQnMi)AGX_Q$Bw?B~Qy%gmHuuI#Vs);lLGDSjr z`JRL;E5fNBrAX)FB~$Q8-c3yGoSAdeCfXuAO`&BXE+2UwFJvqMwQKMZ2UxZ_Z*L4n zl0Kn+0QGVX-;WW=jWR0#{!SwynfW(^yhIHmTdrzdus1uj|^5D;)L)SwHUk-(fMy5{EO z_>s*iAn`FBv2PJQ2@|_bxmcK^9Et1Q-^gh4Ndx>L4#79=0=r$*F|1-pi1i3QLU7-g zdG_MTHiqJzr_hc5>A}~^LV;?6y*qO_MUzia@{Vn$mS1{cES{K^)rm3&JcmlrhH%P< z6n6>`$oEW9!2gS<5HVJVbXEE(%@30gSu+o!Z`jIv=+K&mnyP8FSdQt{JNNQ!3K^Ar z4G$?xws`@gsLsUh)oekVF~NFAZ%m%e7V@?A8XSuo{XB3fCq;f2P<%R(NH0U8){!Gx za5HA4&G>8Iz%nOLQi{r$svec`VKtj|buPnN{CyySU%MyC$s-3Z=^^cytZEx_Xa~s2L`0sT03GJ+N1G@e zsn)S)_FfNmcJ@Tv=w|`sdwK67;;V%Q)58eefCfA#u_c7T}h&mK$IvlebJ~jKP zelpO|MBd#uox8+1Y;}R7=z2&kbftlpZhk<8bcKSMSCr+ot8Z_h3Dz+i-D;s6UQ@u) z;i-*+s$wdg3fBFJ7j@mlld(1Q1@jqqOP&L%Y(sy}$iW?oV%~X6-X@`(9B3Bvj8$?M z^z2RP69}^Q3&q+q;Ir&Dp-Jk>TM~B)GP@lvW>zV+a*NQX2Ya;a4oA7!=!JP`Q=v>NJI*&~h7WMVQhN43ddn0~|spXisPv*^tNWj{5 z2&0)9djuYEKg=Y-bE-;{CbyAIolq=8A<6Mm2ri6-1x|N!%qp8yms#s%g}PSCW8w)u zOHFxWMtbd|2C~&xCeMHTaK~zl^f@qn4Q|5UH3u#{XxD5h*uw<#Ta?`=WC8muKa=Y} z@-rF{sib)##09T7LKtx;3o7_78)03P^YYcJ?nmo@0C&Pvi?caWHB^}eeWuxu<@bY; z5I`H<;$bD6sIOU_F!u#0bk5X4PXiWi3Ud<&lmnZ9QH2x7?gmUU9mC8zt>*U>Mv~tOjq1Xz@iLZZyV%Z9x^pb=D&K5gykw&BmNb z&C!{4(~%XScGvXxSasZ_sCCw7N6cwKpOdq)!sM^LzBDmBtuJ4ghh@n+F4N32|5)qd)Z-0+KcZEG<{5*JA@pzY(2t?S5>yh)vhppS=>pOysWqa9QWx*Eh z>crZ*pWj_FZ>|}rKu59t+<;vbC7eeheKM9GR$ObEeJnNF$|hfGQ2mT&u`ZqbJsP%g zK5%nU8pMgNhfGLJKIOQMBvMyX-ihp!gDD=san7etR-6>(;ezab_T)mK_S{ApAg)gQ zVR5SC4_iBG83FKfz#x`J3nFOBH4Is8?AMkx353q4 zR0Wi~qR^r0*o?ZEG)mP?H{<7YI*MdYLA{yA+<>=7L9&^DNj{T)pMJm%P?`!Q*k~<9 z%z#fzwm;?xhXPIUIH2c(`nVV?5SQ-q7pdyp5LcuZ^l~q_yE-s!`W9EF;&Fn{?zOTs-2TkeePlvHc6(9qQle+y+-}9o zos!@)9LN`PVLz8dMz*=@YI6kbB(smAy@Zu**Uo4@TkSTJ8+NfVoZi=z!`5Z^bLu(q zP(~S1zhy{YxNK|g)@b>^3R?hxot7Tyh z)msa``AncD$vrsDoL!VPx__qnZsa#O8NCp+o zracbQH|Hi|lhyv~pdawF4& z)~F>ieak-;Q#uX`92cE&Ze@|1;KY9w6h z|60hY-i&1u@Ixc*lefHAO*J$!qH%foz6`$`b833P>tt@$`k4+j_3qys?z> z;tfEWZI2vD8>>N6%asKb);E0XI%4la+V5*CTxe|mH#6+Pe7!cWX_H2ty$umoGz z2Q)phFoMpUz-tWJRtQr1%h+gf!23SlR~}_%4oQB##DI;1ZsZiZKCcBWHU?alGNQ@3p~){2TPK3)zDr5iAfOY4`c z{Tr;Mx}@lJ!=%z~oAG(&C|Q4Ms;&Q2q7#!wY=ghYO-CL+DbC))XJen+2`QKjg{YU4 zl3hVmil(2|C*R3M;z^66K3Cm_04S=~;5o7bh9aK&Zw8lCuB|wDkRphuBgwW18RcIh zZ&GO4tv&(MoS&)QQ#0x^l`2XExSFo?omp`Kpt>0t&AhAY6&zp6i3Gjwa!5xbdkiMX zGnhMJ-APAyUBm`bLh4P7Qeh+p{nIv%!eZpjE1UV}%FweUq=C9{JVUs>|)&0v65vW0{BATbOF zH04{O{oLuemsamK5mSyKgP&db&!`_FBag>9*IeV)jXL=U(&ZFTF zsxx|*j`;F{ba-c6f)_9pRA+PoQJG^DcJ z(V~GH6>ZSMgq20w0Ln7q%JE&)ar1ADvU$>n%T4?{U>8B&^(@LBVHaU#Ml2IKvYq`{ z+dB6ZJg)9eaeBbb$A~W0uOqXzfRWI7fUg?LDWT*}=Wi=cNhP|Ty>MYBHHtvDprzcu z10J>t;tBxwN9MZ!pYEIE@F!3I0YPySFMZ$U#LV>`?R$e)IGd7FR!q=i z-E|z?MGho7YQQm>`jqU3?J~Ls>+7RAL4LNwKHurgX-Fb;y!%@72B3m5HhcZ9-W7%G z7$jz&*753&2(qyHP}_5VU%zgksQ{E+Ghb8^%m8TtXFkSgJaO}^D0HPdu5TH)^{GiG z;2FEOewc3-J)66dsz$7)zP!F12tFXSH5%Jnyh&y=MUIx$Ki*_CZ&mwdSEgajv97?k zK&;&;YB$qPW^-A2(Xz9kQS5Rcb|$c6%|9tmIHICzAm(0W3z9T!PoD%FZLcT}qQ;myy2%XLkmR&mlxWMi&Y0gCJ%ZCD;Sx-NH zftekxZ0)t&G})?2fwYlrAR@^p*>Kxzf7TG+gz<86U~m#V?fF2L3fg2bM50t`$Q7vk zXq1AuQ&IuJ@BQb`5BV!6rd}-`xcNo4li8PtT2*;pb=9jz%>BLOb;($yuDTSwmWmoi zn*CO{d8$)xt{%WsjiP6nVD6@xHa7|uu|Id5T5%_2tVfR>H6B9_^|p)L@=3EYatYOj zP=DO1aC$%?s9}UA;_z>k(NIy*t7PR50x|imGsvj0HarCO2!sw=;uhfAg6>(S)1bFG zv|G~mpOfZv-V2+-@8@_d-^hEcR|uCu-ibP>hzPWv1TG52m#<%sa=z$6tw_J*ihm1f zRu(?QJZl>_9Z8>GR7RK=&#sRG@u%)gk7vGR^X$FaSvXJhzM2JZAj_k1hy!u5)uS>6 z`_JK~ATS6b^CevyW?_4)U1nJ+(OGHS3pj?{a^|AX6DFZSDS4!%vNqMdR%1AX$V#xW zY%_0cx0eYk+CQsuI=hf!8Bw3(s?>_}*?0E(JRd#W;?j#JDVBZU5iw+Uj81b{DTqH- z;bqMj2H~zmih{JG z?T^##!({=CmUiETpK8UISpgXraA!w!Dm4W&BWB}CLKDv%@}}Y;O#KA(f5d|v%%BT- zn%sk_e&gzd)oCx_zV_0XU!ZtU;LE4bAFuUqa*`t_UcB5O2&5mey;IrP4QDGY1$X+k zf6Fe~H#g^7JkBbaUUnV6H`5`o9sW@0=ay2PQt>78(go*`bH&Mi@Oebm3gipc#JkAbZm!&lvR$4WK+C6yYiKE~1&NZws zp8d%n^GW%>3Lg{0Wve!|Z}B0tWj_m(tag>F4NA||daa_k!l$AiC>DV3RjMGYzVg7F zq^Bc`VE^1aGShuN`UdGtxZ1wy^QP7LR1T<2`Od3At<^m_zcgjIy83Rb)QiSi zld=E%+0OR=3X>WKY58nh)Dd3Imar=G)S?C@fy~d0Q%0l5mQMfJ*hRtWZ7chTI6{0M zHEE(4N2ptn5Fl331Z`a!GEM?a#b(7RvZIWcF!u1UHYoWil@>2q^0Of1DTN>Xw&mb7 zdW5-!_v88;VqFh$>+I~*jy#3MI2dYattDR|uIb37-Gr-d@XyJy)0>$RtJEXZc8ho* zWbwAn-SRd*c^J}TZ~C?6^vb+V!540E$$UOOQTcQJeCEiP)6O>_W*AiHyv-x!O1Y-M zIP{M1okx;J|0c6205cqktG{g{6EgJ8AQ$UpPawYC=Yg+r1mvLvE}u>pnNU|dhB(PE zp7<&dLGE5V11y#f{z7c06LW=Q1msGY^EYT}J^zV4qLIq>W9b>a_XAE};}$Kt@yAFA zyTmpf)tR*WCUw>Xv6V)ZRE1jJ>FuN(rWG2p$Aa^Gx zP5S0cl$A|wBw)Vlvv=;YqMDG~vTfT3{Fo5awJd|o-hAj#2XH^ibYr3xlD$EEhQB&x zEg#Z>fA=SZiWiBP`i7;U72nTn_;HoeUq+F&vX_R&J2`@}+z;-)J6-LHG^3yEN>Vo! zt(yNVe;ES~K~S58gcMl;6dC!kyy9)E-R3%zfq>;pO1xurf?RC$D7dt=wYvV^y(xE2b5L#CxUnd3KRnzU z$iv(PGNEu{(QNErJu#*KwUCI_9zu4(E0lZ!|Q!rJ3m}}8*#w`Ut z;ocda;jY?9WF@!Bci;`5rz(%VRA?QfeRp7NKWI+~0s{b7xiex3NjloG_?_~YY`Do%UBj|Vx|rJTWOR_D_-pGJU7AE_H;>KG(5o8LxR zxR0@HIU?kJF~G?@X$rdci)BkCP|B1?! zdaYViWwCk8r`u0>Jb7G0(UeJ6;$4Y9GorFF1g7l%JLtX6qEe98$sffFyl~*6i99V- zP7R1I?jRaBysP$bguE|S=H!){y@=g7;fF?&aa~UU*8#j3;N@onFYjsfdqEaEx-|E^ z{5RMHzU`#jMwLe#z!cD3ipDW>)erk5oHMtvtQExJr-5JXXsigPz_js-sR!_*EmNH4 z0(Y|bufMwSc&(oWQ>pRTk607k_+&C*e^<0CEKgOR~`o9t|a4 zI6qB<@uY+BjC?&j1KJqQvf61-z;|pajm8D1v@J<7jP}T2|9>iNU_1J(9I_VIt#)>c z@0)+|@UHitl+Dc2#t@IUU9aU=^~lwsuA)zYU@9ssoWajT>rH!omtV(*ZVmLbV!i^o zJM6&?jx!?+b|>s~Q6%s%COY-L8$qfJXg^quQjc#Y@p?X+vp6LFcMOSfmb-pJ$5%45=c~b(!Gev662pe5Z4SeFjt=obNgx85QIwY zPuROdsjDVDyQ2_UFq42O4~Or;=B~C}5=x0zV6fEX5rnZxtSN;#7|*B_wr>&|1_N%z zoqhr8`u%FCnSWN0)9D=(H5UGAc^+{w2!JgJoJU|*aBJ8Fv(e_pL9&T{>oU?!^40BS zdnK2VJUJ(>aG8wI4Ydgwoe;hLC4aY`-sDrEaV{Fa9buhN%TB5nE?#s8k5TC`2n;Lg&IO;Z5$9zvk!&y$w3%fUx0uW2sWc!zZ(G^n^2G@Q_7q7*GAdMdAc}63VR-7!`$1hAfeE=1 zQL^k$=yatnYp88L2ef~@)cW2G6$6qi0FJ>BkV9gQ)hR6Q;fkuagVJ z2zCw>`+8q@E?Q2=#J9ovrwC_&7v6K;&OMIikFXYl)*oHd2E@mU(J|;wEz~_^vl(`e z+R2UiSAK9K#yQ2QV12`;9`t-S8nyr;Jhq_e>{+ucNdwGGO5fUZ>Xq@z#$NHMoMc_q zHevP7At#fVLxdJZ2!4ic@K(cC7Oo{N3#7^&9w`Wq^QBX2NwSBSH|hqaDLpM67DoBH zh%s(~R4|`tXC7&1HrBsFw$Ji!H1XRfOweT5kvj)nJlLKzL}$iP-h>3jr#w{1K2oQA zI%;$hs%Cq&jKHdS8cC)0R$LGuMPj*<)tb@2F~F9$txJ1#?bWLnW{&&qnwhx-b!V|I za&6$q;N7PxtN7-0ovg^vHj7EEK8d{S4#;^SwhGX2+@P+XDiLQS(u>QUYf#}?ggOFd z+4a5c2oA7`(!VP}9J_N?l2Z1#o%dGUwCCP)*_T4t0l$rbSaM`eh|4N7G@xtMUpjXUg(zBr>JFN6&ylKm#6Q-b zDQKVEoF8NEzi4;&iCyWt?;=(gZI&SJgs+~{t_5?!GN9zFGUoMfiw4t@zev+M1D1By z-=QsW&dg1D^X4E>A;j!KX;BJHpF3HTA09iQqp$BtT*nR@T)HM`SRItf5epd2&*g(|P_(P?G^O znLo~(D-3`r^(#7pN#t~)(NuYMk<&9!Vj{e=xD)DnFu%_<8CSDiM zA2(JxQ+MDZ89*fO6h&O|AJb;G{w=ioztp+5>?wukN?s@piG5~0C!F`5&FymWb3dpl zG>XJ{&VSS!A8<6$Y0_za;#D2Ybgk|!{UJG;Qd)Ea^cA`lL%@kY5mLDrA9(B@7c;-F z{=&x>xww3J1-Jx(3zjTwF^47L@JxiL&jSPRVnompMYJZ326^}~@kTxF?(T}4m}gMT ziR~oby=o9Lw|JoxK%Af^R59d$9i;yBnK38ed-V)d{SGH*tiMqkQSG z3G2z*h?^*uJ<=*tb7MH7Ck+fRf0uDFtQF)g^8plSU_hd*F<>to5zW|kX{vC*K=qJA#=11%J=?FTJZ;5?Fs zZd3n;g5OZ(2>~H355WR^O4)`Z7V0E9)G%qVIVi&0&e?}HfXmeG-2taDK98}9K5?U~ z%3ic$k#>Jm(EsG*<3+Eu`HN@X<3~hASx`!q(-kK)gkGLyU#5tt2oSe;{{Y0wd`t)) z99&}*UmfOT*`6?6gM34_CNpM65fW1T^9**YqG+bNr!PG|q%iR9R$#Ei+1e}d~a(@e<&dpzWW1$I%24V zFTID&nl_!uq2l8;@UQq=^>tAmZ6jGl(f@eTD&ivANMwGp4ML>s*OPnw-h7@LJ*nO~ z5PVtg0$sPNs;n3OaIC&)JaFJ;y>6C2P^$(THHQkP;tA$g_XXHCF>EhuIED)lLH?u# zU53~+@x?w9U!R`6dMP(<{EkKx7RNioQ*2#HovUgp2LR#s*ssfXy|1`#747z@W|)=Oid|ZZ@RO7D^owv4d+Expio^7wcdDc1Dh5sDJTRX0bYGiN&{zWi;4w8`8J4}tncLUpp5gAWf7>b zH>tGYh&jitJ3{7XhGx@+3;kfre!9Nau1!rz8FfPI65JZX?}byHdJwB7P)`cJEAavH z383da(eqZFpR3f=Wbwq?uJF==o2?~h3w8;>pTY&Y%5W*O-7+u^J4sHKx1u0|dm?~! z?m=jRLaZ*5KhN{TI9UO-eAwgGDqh{e28dIkXc)ME$yAUi4t8ABJ*}Eug-t;KBFsY2 z$uyjBNtW}+J5QBC%-++iDfNYWZQ#CU6oC^E_Fx!4hmsq-9YpP!b^-AW)ZZy(w^H))C>J4%<>DWWK3BQTp{d z!9ImRCA%VsH_F#+q`lVYk<4J>AP}m74U-O=`0hX}m*JC?RG@(XiUn~cxO7jPBBu40*TvCW zxBs5uYgVA`-nx5thV7h3*U11fF@{qD8j^E|y|z6DK%;Vh;4s-Nnca)aeMJ~+BWV5o|tzw zybGt)*K5$&Sh7bzhoO52T4!;}0xJ)F_qiup6qv+!jOig>>19)*#E>YUGH2I@nB&NF z1zn(W|McTT?priM$PYyhfWScCghJ5Y(`^MWNQ5uY5+W}FJMH$qZ_7o+d8zpGe57}l zDjuv>HpyV7_y|J=?~6G^dk}k6*_fGJxwqy_7I#@Z5`Sv03Kq9QPWDweknf68z6rE*PBeJ&Mb#Y|k?c zqpqR2)Ml)%|NNJQ(VK6(xw*TKU?x|%dDwlSX!)2k=gb*RV{&Yg<5dc?y6Tb^^x2A9 zJTu`=XRAz8Et*nV(s$Cj`VE73elM2|0JJz+H``nM!`LUt6v7Iw(PmG%pM>uyLKX7p z?M#hu+}a7R0!9QaJR&+n=s^DEeZts{%deh0yqEbSn11wq`)BLtqS}d)~ijyA5rhM_2^k7-HA7@* z>O_!9@mYIivE%&2W?(k*M`i8^AdvG>Q=mN5B4#2A4uj0sSQFiXgcHFz(UJSvE^Zi_ z0)c^uN}~~ch!Vd{amLD(R}vrg+b}ZxJ$DXzXCGQWL^?AM8d061jj0E(jdo7zEnq_K z>;)#LsNAU+`gz>kq_l9^kV9q}cQ0n{su++|wwfg=T9jbeM-;=-ihBkCu9(bF9t(6T z%dwHGxgVC3&iW$m^rqdrCkDiB<2lOQX<%TW-Nt+X2pMCevl3;KAn3q|;gG)+JDW(@ zJ3(8i;hHot^jH(#bo?g9thhEjO|&-pzsBm@GQGd;BBkd!M3y{5d5xKw`-$6<(-9;n563`{IX# zEu4O!WZnrx2N?jT^e+iGY?T_>8s(GC#wHrUsx!uZcXqkvQ**8Ox=r*asWFX{lb`I2IQl)I zXxh5I1~*z@hiLF%MS^dgaeAGJ@6DAfSN2^~HXq<2_)-uj1`WFARPP@pri@G0izAA!8eK}= zvuTrQvjykofBO93N$T<~@4esKs%52aKAO_QqNX5hjLJW6`;-TY+!bR8X;aAApc85{ zURI5Q;FCQNM5{=ON1;l<5Jn9>-T7;edJxAE)hPcLiG~J*j2tu@{mz4`m@QjIpL#f$ zO^s++D0M<8`@3sA28LBzFmI*B;TD7c{Eg`BoUNUvm?Y{`4hHJzaOiEag&G!loM<=z zHwZC~%|^hCCGbDgX&~pEJw>WW!Ww4EFH+wIxY*Fki#U={O258~Dop27C3$lz!39=S zfPYGl+?W!E+av@aM_G&BlGr!4ut1fI0m3iL5W3(aZ>Hn8hb%D=@sv30b9x2T0+=gi zq#i-deekK^e1@grh?lAu*XwLK6;e5e;V8;M(XavC6T5_oY~IOO#d3q^e`?*26)!q*b?+xrshb%wCEznc!#wni4TR{h4aSSMA1mZ7vI1d@j1GYfq3OVmR_6!WMyWXPgkzo4@;E;hh6 zqP->g@{K#V48Pyjflo_izWWzo({9njf37a=1~^z>`{ZHV8vDQ9)1kGDEK>!`94p`< zay7EO%uk%`uB$Eo4djE~!{X{!?mdDm8TzE(*-Vkgqw4BgTWu1T{SVJk@JZkzrn&|` zpEHg%g;bS-SHi$cjldMMVw=4-(K%Gm4UeF?7yEp(=Up~bEMe$G$YaWuZ2qHteYZ`X z%a`Yp!sxJN5?V%2JQ_GIaR*A_tvDhqQ6P>e`JPb{F}v!;o3jv<#IKqE8~oAO)t`Ff zAsTQVlE}S8Y7Sw`>Ve>$jwJ5&0FRVb=>xI;!2(WOG&O)=*ARIVC@LZi zUzC4)O;Urw-o*xv)uo@-QO80R%AO{2-cl$FznIZKD=SB~y&M$OQW}Z+Gb?9gasukf z5RaKgxUnd1#aIWxuKToW{Q)NG3Wm`iD>ZZ&QQekdG};zfog&69k9!5uGdv_3NfS-j zl$|Zg9S>7NG(qn*x%T_Vk0WXKRL%_h zYqoa3Nlr1OYT>xnKFU!wnT$M`A0_4u++Ku6;yXlpmrEsb5EO!pAbP^y({J3Pa6<;I z4ZcE%*ueVTAXqCajTSi*Nt0@~LBKS4RYo4h5S)B<^;u(ASC)&LVP*OEup^(<%&P|p z3(f-{)K+rIDDI(j49#AYuQEMFwPauuoslp5FsKB?K!T3B?VIyG&cHB;iwYS-uzdwQ zVFie#vt`<0;;9lc%5I*DrgLcHAk~A14%MS~Z#(2rb1ab2T+j@e%&OszN;At{Fu+=s z`8nB%NF72olbc0Mw5;^yR1<7TUhin$fHno{kkNtFtIt!2T7CWrZO7NNIhgW-f|aoq z5VVHkl2HD9g?25ZPZ2@|q*9Hz3>H92DoqsMqC*Atm8$(;UTGol0iTERyD=Sri0_p) zJE@53oaM0rc(_wgN`FS-DGM`EZ!CObNIK_Xi6@niE_?uSW%)cz#`tIaO{8SbnitSW zEBoehsE8#)w)FNnLv|BO4^%CuhXo2M1(<66>^1k1K_scrVQ6yclHou*;y@xOI9V9n zC*IEW6dw!BLzBe`v6nUvcnanvNHeg8NEU5MFcjUuxffp&^i@gxBO>YW;_J0#)ETNWW?Ith(#V=skW(i|TuL3%+8ZklXE zCa?f`cV9QMYB}M&i=Li8cu+PJ3K$vA5m)TaP5v$07KQ7aypT)P?uO2Ta;XDxS_Vj{ z(ed!M9;D6T2a&J&aB@=IhRzKFpq=R`fO!))$nJ>+gPxI2I#gRCRjJ@}B{2(gcc z_g31I@c^?cJ3ZuUO3_HT6w`duB;^W@c?SEs{A}%-6!imMYK=ggTa7jI_=Dvw@$#bX z79)l1$dlq@2i7oQVT&eBwt6~)Pljw&CP&LfhiTXC|B;Qnz*1&M>;~ z@_1>=$vfgd!MV+evtiQ;vVK(>qRixCL8-|c{=B3B-WQq!RJ8rq)bq%Ib$7`HJ2}>fYw0ckWII*x*LCs zf+lzSv{TJ!6dzyhOFgaJteI4nqMDV_W7Lw=`KSz~+#-V^uF5HGSR5v6H_*EKlM#^^ zw1HihJgh!z8V3Mxg+r7)v=b^^-|gT~bPRWihVU!0e2zdO{+^A2V#a@@W}MAwl5trQ zruW2lr-@}UGtEfM8O0_9i*eLP`P3PJa@)hUYiQTohg%6ABQo7a?V*Xthu^l{pz#q1 zC44f(<%loxFAD83d>`;U$$d1~Hzpt3Jk8%GCFvyp_`Gb00Jt>Gs|cfllgR+U;ukoZ z%24`FeS3Wh#FKAoDKFuKa+YZ?0v?6}a#Z%(flvXwMRQm}#VT|he8foNihw+#L~h`QD6BiRY8`%8!yB(MVBNijE(2?+(OVD7_{ns_fMS(yTs^S)88- zT;75IBK*g0Q*P11;kFynb~~TH zh*Ttt^amBK2hvAS*QypxMaHq8qPf0&KW6IyLoOzUIsa&FDKIjvFORUWs7MB6`Iys{ z1HM8XfWC=U3$E@twYQ+ zuo^cUKB}1k8d3S;&71ag*t&&fm}H?-N}^#5&OLx=e89ZP!r%gMciGsZsx-gLI-kxy z7LfW;Fsf9bauj8N15cbz^BPx;0fvj9+7L+-GDy*UazERDF6tRS4Go}ho#P<|=zZ=; zW4@;+-jz=)TyuUq^ydC9UA6F49Ln^iKMHnG^5c_ET6I2`YU@)*j3q6n$2jx>Y2A!D z7KN5uEbGUVF?V_Bjg5}-9ezwK(4M- zhSrOlXxqFwaNvOG35k%$CiOFY zmZEx*u`0s-meRv@t5!xq1InH|<^_eLf-Vr%H1~cc4ThlCy#EYH=t0|;QXohXf!?6F zqG+bpvD}z#+r~u9U0ylkCDi~rr|}E_TCpMu0vJid!y^SNSbjo}Mz50H-==7uP|!>V z1}z+Oz^E6|GlI+llH>a#Z!)a}up<*44an+@j4Y|HSR#-Y-8b8!qyUvR%D-`IYMb^{ zY$Uc#^sB#YMrPpQ1DXv`A7QwFcLf0I8=Pvz9Iwcjd8g-m>uRl@X*X@v(~71CAZy{l z9l2qb+m)4;PP#n<#R~DtLfMG7B7{7-(Siy+{^vP$^Xg2DRmKt{+fe3HQRGMq4the& zx%)2XBOxC(Mtzv}k9<-w=U~p~31^r1wf{)xc~k>{RtkcB7-_7q^!e}ZO; zCWezxkETqGvcsIZC3qFkjF_|FR%9$x7ISyfGf{I8XEq@|7HuW?qG;;5wEBV~XMg;d zOhQ>n`K!>T-=DHKLb#&m`lHRO5w89%(ilSWOavCNP+O43WPm#ZP$W?)t}Op{$j}?O zSDvc0bKthfMc+ZH@#PAOi)Cs^tX%OUrp5`Rzo)-Aw@QxS<$|;wfs3hDJC4?km&IrWWRkLRy#13Ne z8;Ed>CS)UF5m5=;wn)S&EE!k2`{KpGWOsfCDQYCp59soiNprrFJ*0`^O6j?-saY`q zo(>dV6f%q2Ug!Go50ji0u$3tM*<3U%=hS9h0+yy-$!c%h%3P--jWemDkY-P!tSps+ zVU|VP+@!vrY9^hYJTbw?TZBh+)UwbS;4qr1O(ypci@{Cp*UHJlg`R(Rmw^hR)jRed zaaTjWBJ=o>J7P?l3^(AX*C=4*{|fW$^5TpJZiu6fAsb?@Xgk1xCn;OTS-58Mi#Qu8 z4<6{J538IpS(pK4K;dJ}BJB{D@1J27#<7x>>5S!&qjY$*3HF1=+M;2@evN08%qR?T zR@Rw=oe%|8P(P-8ajT7(ps)3 z&9Ev&bwE&>ZC1k#6$>ecxEDK7fn)F;$fnnb-8Ot32H5pQGA6UPuex>U&;cJA38e~x z2GE}|m<|dmH${AK3Cwgg;TsYu6}Ox|y&xiY@>IkVwty;EUDrNA&=fI`I5hrTSnDmNJVp^>+X9YK-YX~^cEeY1LNd}REe1ld8i)N zoM;*Uq!s`NbQJPmqv{mVc^{+ za{B>5YCe=mTqyVzW-v|$VrK7Vmv1ii{&Z-284O8k+jb!85z0;|(g3sz{a{LI=7m?0A$SrE9&-KOCRdYA zq3Rl&Qn_MkpbHbDr0q0LX3gI|*G>8^Bf&C$s9)ve=*Xqe?QHNr(%OGe<(zzRLjzus zDSP^Ee&%M*rEKw`rV$A`&rT8%g^m&>g8SQ4zbWF&dPU3Fsnf{WU_-JzmT0J}Bf}yP zaHr!Q{oFHG?DvA4nm26JNaQlHGwUXUFhqb+1ue|TUncUibp|yF^c)}=*wD+;s6l^y zJ7^dpJYx?Vr}Lqr_TL84OP%*8Q9Y;Q_&#|K`%J#FId% z&VRM14dciLNGeZNdrhyg{r;aoj~1poE)6+II{Q-G2%4)rIVkV1@*ZDD5d*n|@*!EN zVna|816@uX0B9hRN~z2#5fkPOhw+M=`)n%-_bk{6FM0=fxnNZyjA8!{ZxIHh=2`h) zzs4kyvdRdPX;PIFKlNV`nbVgoNuM1u1CK#Ui`4h_?e!EI^(^N$ZF2xE+MW9c2KJ%R>in;m zVGy4)nCmY*G_<+Qy$gOqdG!I6M-+MU554j4uJ=}I+N_!TzX$41)8=p`>zQX(+eNJvb~ zWEgaPNqQiXN?ewxC?0D*sgy;VeUXYpkQ>DvhGtfp_k_CI zhrm;s?0RCEuxX^--N8CWcp)?H+9CfEs56|Z%8)h-8e2s7i->Gx`m4mW2!4$x|5ZRK z4vM{xkURjm!3AcN-Lv@^{n1#WiWpB7 zu;39@K(qaQy1E>;+J-E?rl3D}66Nl@?CkZx)S&q?wE#l582K7|Dm&;p?x!1+H43zx zftLr=%!0@UXgy(ocO$&dr#gSJsP+T z6)V;EEb3gw4-Nt*q`?45)krCXOi)(yGB*z2XfZvNeoCy(Mdm>25f2ghgIFyAkp1iM zCqu&;!zsxjRbL8ElZBi#4a-x%*9Vi!i*7x6apCCa0i{1?Tg9YqDC}Xsbo(3=KIRLD z^4+No49@*LpF4M~N;6%bugibb)~vzB+C|J);ru8#3Ah`0%GGwRRWA9n~2K~O<$|n!0oCDO}*Xc{V#ww4(jVO*Y_q2Fz^MCmx6Zue9%mC9IcQet|J7Q zWC9h*RIo|HyiA`Vflo|L?UQ&W{@Lc6B19A`7@61!kT^#(9!c7f4L<^YlJD>S98*X| zCHjZlI>I`=S`XuaIh)fkX83#GZh8}8_H_G}vq()2IX6Wv8=$nHS0H=h$HNrB2-MKw zUdqgyj6n9gfv82xEvOgguidc2y!z~df~_$z^SlHoLU8fDxPKjQhFB+)q{&Z?RotwZTa{K$ zVU0{mgE}0brHLu)Ab4G?gP-p%uU`ih;xYa<-_;lWF$I6G$`KcL7PO_LzF9AF?y z&mJ}pjRp+ZK4}88NiW|u>AWQ|uUCOo?9{V^5mSJPib_flM)*jy*qSop!JA|6k`ZR{2kPc-B643=)8Fh~G}UR7L(r+078~dOLd>y#S7u43)HssXgky#ZL74=L&1wV3sAAAQ- z(`Mq9xXSjXKA2e?I5JZ`;ZkT>7jLb}h-_`<sb~a2VPP;F7X}-3DSq(b8+=>B*R*UvsFIg4v4Yx$K_eTULEJKhpEQojQ4Fp@uWim2 zRW}Dr7KJv|tO$#E0(ecRi5^A36Go4LtSR?fD9HCFSR`j|vTIzl20`I5m_y`K?`O9d zQO(EF4F^*epAkSuaq9-+@cE=ksKeYP@Q>`DA(;hRl91O8Pj0mfNvW{Nv^aKn&Y_as zsijfu25aB$|HubB87H9!BO+q5E87Jm3M_hA?h}f zWun0mwFsC;Zg#A|ROl~OmwmZUT_d9wx)JKC-lcLR5-SO@+9R<5Vtmxx!U9vxqeA>Mo4_sU zUZOi|kV>t~-BV~?1g!#0;Ipm^39*mZ=}2?HY=Aommmu^UF5OR8d|H$4@!DL^=~|3< z3bZwm8!OW!)VK^N^`#+vI`>Cv$CIk&{$UYNGu8CskdCCtz>OR4rgq$=y2OYYBv)!m zu8xR!iSLruxjYx}IdtDo)t2++%UEh38Sut64`&sgT12OsuPm@SaV?^b@3E!yI`Twj za_pTdDk~~vlS1U9s=auc{S77=TvlM-xM=9Vwm=tVN7Lkpwhxioc@DeihdE(fjXRGX zZRfii_*Ab%!NUG1UGc?)BO`?#M1$F21|trLdeOc%Y}n)ve*@Ea*5e42#I`hoP*u=@ z9=~6Tpz6}Knx;&dykumD79gB@O_YrMa6N|?t5>d+mX4uzb+4Xm1!14VB-Mu?h+Mfp z0coZGq0*#NRMXY@6Y5kl0tF?#VDQ?P1I6l&`yumJazGffMBEh{HoPaH*<=d0H2B?E zctD?Gj|~+@gw(B14Q;=3I|SGzXf7dJN>Zhy6;FS{bCqk(7#cl5-)@`~DG*@*aH@FK z>5@<94Ha!@Vb8<`H&<4c^&4WQL_I6pXTCxE$*$VTH97m*D=YT7~H3S|fzsLe5N^{b<^sV4zO^09_tEED<;FgY#bRrp|@2<^JW(MnE`=x zpot?}i5?IXZYFwKK|+}bVcepff>7JgY@GZx>jQo{QdHM6kSvlgUe4AsAMcH?V<~`? z#?78pI;N&OAIe1hN0CDHgA~o7!wc@?a+r4^!?S)L6IPXUg@<+HzsdA8Z;oxRXC{sM ze~lr99J zMo5|`EJ0(Prno2xFM-}6n?NSNl*&r?p635($e=yo)iE3VpZ964>gPY%Pnt{w*2>ooLs2p2;z`H& z-(J2ydAfrh0u`DY5V4XKS+d56$vheT4mvw z;TNIiL<((177)=D&kRTi_XC6vUPftJM&TwJ7{)di+#rixi zDI40M+eZII2@h*qW_|BeRC7hMxOSeaYa$?~9?UsU!wobYAD}?uu$bC_(w-T6fXY`Y z6JQ#(Qsr?` zBVg56QY!m!jv$?!p8mP+R;$0i0@>5(v1T)sDa=igXKGHTyCsWwGAqR>(PADKpV|!i zK8dW!I*CX|C75k(`6Ax@pC92jfr~_CS++2%jzQS3^75vzV|=OVqSGUdjMzn@#8 zrrC`di-X~x5vjK*G(Z|%{y&fPyLrTcZ*NW$ds(yTMPL%XkQ;=*N$=a=uy~^e#Yhlo z&JgM7p^QQSu>?1Fg6{XG7ykZD|Bj^mS94=}N8N21GFvh4; zqS2}qQ>6WYVd$Qq0v2J0Orr7p*6E*T_S=t7?Dq?^Y1%cC;`q4|pKuhZ{r_=@pTpjq zz+c2GnI67`r~q|m!0msY%5R_5*DNo#k+0Yc61?;w={mM<-nMNcW3F{Qyi`E4bUoRL z8h&RXs(2zclgrx3;f&lr{`TSi`Ju&Ew|f3i$bmaHrJ3-j>^4kx$;cWs1@Rw1(Yb_N zD<=+u2)CW1534ioAd6uAkm~&9|0TYD`}v+83HHK*p`YTf%r@Ak6V_AV#P}_ME|QfP zkx8eW{@bv<)(KY<@7yakf9zOd-mPbRQfW!E>>X*R;-|G!g%s*D+pt52o3C~{B~&bC zK}2}=!Jlmt!cMuY(W;#LEof9ir+5c1qC`qnbNvwO(ube)+y^SJvDYXw+*Xu(Rwbvj z^z8P_MukNq{&h^(|Fo|d-@OwmvD=KV9A(TAWFnSwnDy&8bSm_j&c%xYw29_~lDBee zyWbXk{1aWw*8Q5V=tw9Q290ULMT{Mi|I9&vloy9=$9)_@IUsJ#K#|3?Lau$WQu^&N z{r5E-EAOu82r&``vKCsEIKPBJ73xgsgYf|qX?a<7RbDgwICQ7%Q3Nl4L3cCz`N-cw zzDn^7l)5s?h|E|#7P;)0Q4hazN1wK&pb_2yGIKy+`>?*If1O>k_B->lMq?u&rJk@t zaOfb-n#PF5gzIOiDJq@zw^x^4LtddlK7I%MW)JA!+tFkGu#ejfT z(>tgqlF%52LxtAM{i?jzh3<$5%&1+so6Yckx8`@@_%~+t!h?T3VqddP_vdrbcQY0O#=V&^8kb_i!x_qG981wS{Z ztqcv>nEtfTLipjj(S0u3=R&nE%>}9$Bs}6QfGV#U{m|+r`#b*io^sT8V?9h0UQjt7 zT(L=5&uznNuEhodDintU>q|vTC}rpiC{VeV*EIia>a=|)(wEeh5hH>dF<4Fb(#2*^ zy6K?4#nTQ#MG>w$czk$(gp}@K9oiV9*iCURYaydmTtRUYgm*;aMj4f}s$bX1BnQ)D z4}p9ocT3~;q3l2TIY zf&J(&YZk<#!r=co0~w#9bKj`ezfWtEVE}G=((M5tMyqrd_r)caKy5*(M`78Kxy9E! zHL&b!asH56^bf}EFxx9Ok)h=oOQUXkDO2{BOWD;V;&|#F=-%-WqdR+PY6RL)q1#ibOLseRu6eZ$#1)qvm zV#D}j;}oYW;82Q`Ko;2|jR|~CP)vn+5MWC?WY`JoZ^It^a~+8m?UiUFA`~hy&JmGPtYcg0GVqX4v@ zB%{#4!7UzbxH>Lys<*3s*v}U|mO_8nrTOHmK$KFMt5MPYDK;In!nR;*ai8qoJN|xy zMpMGA%uf(FdAYD|tx&v*Oez{A$K6_3;If>U`kVf}1i^DN03Q|g zUz3vIyn-jso=Mp#uBpgjW`@Mlb!2D&ka|njL37b&+9wCIqtpg*1pl8bl@METKE_!P z!p|3X7B;u#=wd_w*zES4U)jtRPP0q~32ADLB=0Cswi#K%M?%BEWZo8otH?izt#!Ij`73}W z7InQp_WyS5_3+e5IJ-2XM+aH^u3V!mDKTU6D^ieYYHoYg8=*hqHW&5!9QxyDrK{)n zC*fwT$wND$30@MHz?FTT@yWZ%Ov@ zPe9r(p(Sx@e4L_8EYg6-gjpP(gq4DmkP&E(rQOhp3E`v+1%LD}K1Ymj2Kduu?`xZ3 z02^JlCXi^}b617q+MhgY?%XZXD4+G5V}L|rc2p7VNfCO%nbw)GV$1xtDCwkT!pRm^ zZ|nji{tF+edSiFhzph{LKuZIc4H&HoM?{Mo4oqb&Ln*e15~*IzsSu?PKx9LysM~oe zwdrOouQ(#_g|VW~aDUUy|6V`Ojif=A3TYM^Y^=p%pRTB?!0P1WqzO?XRbbuW{q>1| zDKxOaigB9)C80~-K>F8IGYHIeOWQE?z?l>>G4BF!cMQAAk&ah9T)PK5F0O78dlYCl zA-er+QCePB)^gAfG(3z$&i?X^ra9BGhIMQj_hK+e6vilrS3lyL0^&C@+C$}+_IgFxk540L zNZ>cti=N&!C?)b`Cbg($jQ!uS&C~w>VchUvWktQ>+NSFqz}^u6Xf$wZJXTOrbFGpd ze_4O+{{1FQ_W3AoW+?g^OY9g|;5giZ?QH+@2m7V(!`IRiPUqW-meb9F^g{8l&0q~U z6P@x~BDI5kHve89F9LX`F*jx?5X}25{|L{{PK4yw1w4()Q8mtFJnuwN9z@rbzMhiL zJFk*=4ONQ!o11&Bn!0X5_FSl8g)xe*%sDv$X=Q77*EfAl-@7t72VL!me2qE8t?&b~ z+JuHfsPUA)t>N3Bu`u{^CxMJ}5rW0Bf^Y>?IedTi&%#p)B7o%PgmEx9cP$yJWMn`( z={IlIr0*q#!r6X*<03elV73Kx01~(^`ZAYn>(J0y`$BWC_KW$(72O>sAScj9?LB5e zPo`l|0K6wC1}HjmdUjl}UqeFb&+DG>cp3Rnf%54`o5S0`fQE{u-Flqgb=EI>FpRZ{ z;v4eq$)zPxnXWk#81sl}QKTR7vRHPeybca4Q4kbKer#!Q;-6$-9$w!UwUr+a{45#{ zNyUiRM96O;ofKKkMQ}dDGzHb{Cj38n7hoi_lav2z@8!D@%?^SO+%h~-D`CB_ z7rm7GuUH_bP?4ax`{4QW9_O-JX=q>!lMpv#P2_QYN{b;W4r}46Va6j-Q%FyQHwrM@ zb}BvUl**61+@nzmgO4PS`@1;$*+zTc+KI5T(!C}Q%m8%>VMV?$SMu|qtma7ytH+0} zYYZmv`kL|&Wz)`c4c(xM$YaIzny3>^&%DxX*|N-M_*ak6SeuFi@owWso@kN~G^E~l z*ilK^_G$ZQ_8pk44|8mk-n~;oAPfPoHVL3rTv}TFVZ(KzZr#@a!uzkhQ1mFEF8X1g z`*%ER=Q)cjS4U#2%%>1r;47WcRG<=2U@-5`aw&iu)goc+zv}*>74dXDkun@Y7;wg# zrRHqTGWEQ7)NvW>HvqEgk#}XDum<+Z##c_BBA`vhp%@iT;t1nA|J;z5aGV@!MYHE~ zN=5(~YEG}Ns)PoAFz38mHY~sBYq_{g=Y6}JJnJY7tVn*QNPxH;t36&3 zip6~!#%wr!d*Pr!Yqp;=_*CaIQepS+a;S1V2L2s-wc94lGajk=v+Qd%;ZYo9`Ho+( z1rewLVh622pi2IdmPX!=HUHFAzDDBGbm7zT{_O4+bVZ8nNXN`X#f!qds9Vmk2gj| zMP*i%!X^<}x|YaTG&UmilLKEr|EFAdEF>*&JsH&^ZKWG`?kERYHPhApv0~zJej?VF zJB7)B1%?(mgUlP)i#|_q&%ZI70=K*!mJp9z$$|0Dj*Rk6ym9~jnEGjn>|FU4T;Dec zSAQ7X9W+eFplIW5-vV_}cncqn<IE3XF((1tt5FrzgCPq4|i{?baIoE`J-7-AW_OK}fAtO%HxF&n_xCc6#Pai~UUl`ghz|X)!G-XF_m`A03KUmA9#ywyn_f z93k*3bHQ2ci-Egmi;)kiDxsJ1iYI&e{@*$<(uJ+4N5T`Tf|olDXygcZe1i6T#+%23!i?KKGUqLHggN9MyxEQHxq! zr33dbSZ?vbo)>0{ zc-#{o$(?JMbVg`w#K^5Q)0kXKmiePk%bCnY%p~?%#nYw@3Ddr%=d-okytmUG zhU$=(VO}-!gu$#7Pp5oCua186ulWDGRnd8CzxdL_4x=1bHTv%K!st=y_VQ&<|MeIv zk5=YS@M=9Ic;c;n6K~Z|sGgWtnV-CK@vT!1vkve?>lYBLr7K8G!*U`fw0E5*UQ_Mu z56>U>>k?;gG@U|=*zc6fumZAJ4*8l|?iD&-9g*;hxB&W~YwQk|50F=KKYeO5>mXw8 z9JI?|tK18E|A6CxMxS~QW_p!%NqF-dLLU%e9fFzaCYtL&YT4i;gfx?kLbRimjh=dU z8TW?BC!+=(#wnLKOZd38!THIR4}0E%{Rr~2WkHFuB6t;!A>hR&uZ^ZPg%ZB8@v zYWCN|iQ3w4$iE)W_|;p>mp%E{!@0iWDJB%ThyHpvUbp^yI05o-Tn{7+&FfWME|zlu zZC{U;6onSmMJx}1YZUXNGaGLzfFb~LD0GI!z0AmHT(|Bfoh-JitRr&iVMAXt*7%>g z_hZzf#f@Qc5D!D%Xyx1<{O5>1<>q#xf-Lvqsms(Mq@SE7sxnzyhQ5oBP@3_g);}q{ zV#n2z?VH=XX8-7;|F-ou9a{45F1|akq|w}|OVY#VrVprlXV2MVCtW8@(*MwAG9Q#Y8)ii*ruWvP1bVM6XU=6QC@46|ULwSFg`ktyfF&&!1FF@p^TS z>{~{a)iaraVK(J9@;6zqVilolfQdj7@t+m# z9@V&l(8!=7c6Gj4_~`S(4V+&TpXL2xU)wY`a@=ont~}xGlykq#2U;6VUiBt{ZTog!Xo*NxohE$Oly_-m1BO6`C)%ff99_- z&vaE*j}qYpkSH4Jo8C!~5uCxUG;j>7a1WiG20_Y3$R=D3WTP@4D*_|VkjL#1%vbq) z2$N1#J9+)u zRs8;9>Uk>1U-D^T3~?yaS+H9(2$5oBsQ}7TfCAq@_{0JzJ7(|SN8_&(^1uJ_#kAPI zzpv#~#x@Za6*FUrsjDdnLxwT=Chs_SV)4%#@G5jx`q#Jm|M`g58(&}6QKjZ`rY?C? z&SW)UIUWW?bQh-G&OvbFc?%bYS*b#>W5X*KKiyG`Mm z^=s*NC<`$DlIQEGt+IN6{DH&vd~?O?!T$80M^y_miM+HnkNFnm|;f?@7 z?PQ83ZjwVtN{R&wVt=04-%e%qW3g$*JW(xp3qLwy+rNJ3zkXMG#=RZ%n3pq<5xd}) zOL2$THl7E6L_Pfdx9XKWYIskIM^sdJD!iT2FV83`F|pQKl$g`!%-Pq@0~Z9J+iNO* zj;XrLVd612ghgQY!!f%a?b=;GRK`3Y8e%a#4g=aTt!}yS5(1B25Way{9XSxY5*ni- z1Xv0*6V3>L;#4?%*f3GkDX@%q8}s1EZe1_ThaDFf4!G^jsFqV2{q}UgxL7seiZSwR zJV9QhB#=BKc^!7$yH?)P*br8hzOc{DJ{@9Vxzj-GS%Wraeuchwo@jJ?cAFCnGao$= zrr(o;_1#nAj+_tf`XrivD1;tPkxk}qnwJ;n)Bw)bhCNgts9y{N}2av8$ z10;>3&aT0XY&zXT=_c6NWT>SjB_%~p&Jd9nP>ynLCV(MzH~i2(8U->+Lg|lwV#&Vw zg$NfY3}k|a8e98ejK<)mKc?I{&JCKy1FBQnff44hU4qhM0P-<#A3Wy~01R<-6Iw@j z1A6@s7FRvkdoNqniS;)Cb#^Z1`kZZW~V&h<<1X7#5GDR;}{XAcQA6^ zyl&voT4H7?yUZC#SD<=W7cl07X*ja9Ce4~9QM5x%+Wj%>=8YSoy%b>v(EMS{M!dp0 zR###m!jQ zV`Swz^{eY%dVx;)7e7zgwncyC<&D#-;~)Nf<^SVu$iQGqAKrmF=A`~}CYkPV$bA3s zfknaUY3vi0(Qe9hoWGg>8#i|BSOCYe&vy`93D*lG*4_Oazf6#-uyk{Fbf8{+S8%t@eYZ#eS>wW1sG*C~(6I|ID|rhu}y!7xoEVVrYO znDT%1o9%^vR>LkXGiC%h?*-1trdS@{b(rMo$&+yA4KnyIKp+q||3KW-C}Kt%rOi8rv#w)m2kkF(vd-n^vtxtX@6HD$=&7 zjFym_!fjlRx%xg1!y_K{aJ|&-JIe{_r;X83bFEr1RO`q0!itua`K#1@BiBDjV89MC zQTL$N;>C;I98hxw#2#aE;rp%&!{UaM3Va-XjL5Sqtm)du#nlVC=2d(vO--A5^Nz2+ z+bbd=a_QCb!0OuGd)9|lT147*EZ-J6_u%lu*J;4vLGx>6w~)DI^!C1b3_hPd{Mo^8 zt2%3I=MT=vRMN71?_(8tgSdNQ{IMkm_wU)0|H3rWFgVgPd~BDj7Z+@!wfpvM-l4-` z<)$082RmoRTV{8*ch2|7xTkO+>~UAq*zyD|75#JR5tTY8aziqImAV@Ba&H~*ZROEZ z3kq8I>C@-oD&@$+1lAg#inc7bvud$2K5D7a&XdJtFby}&e!Y9|JT*A;jQ`1*nQKOV z$jIEvAW^Lb2f2HUC%#LE`O=qtZ$V2kXD90ssdp%#y;1! zpKE8@rStqLakmt|_NiJDdil-sL7KA+HXf{7=Gm{{;BJ3^t)A82;<|1#-L>6(*R=91 zRbR_$tHk8w!mAfXTP5!IrHP&v`IqHFf^5D>T z7w9G1|i%J!9i{1@}Bu3mk4_S*mc@Bcv@e&NaJ)iEta$@uzb@@O)c<)o2g6G2&Zqws`Ywre$cw{&Yk1uBuBizIQ+0) z|Be|^FRrYxv)26CQMeHX1Pc4N;sCZhpk$*NuGz|ZPV%H<505PGmbFA&yWCJRxyN>o z>|0Qt?!Rip8> zq2c=F?3FO4072XtU$82d>uDjr6uDoakFJ?l%bZ z+N&;aTrsoUw%EKK!diua126adBbm(=7WQgi%D&GHiBVA2xZS!pVbhE`Y7Ge%CIuH{uxTvu%T zgA!t6W2vKQuGhMHV>=RXs?a*dNH%ScBl+Y($|NXxrLh8d?AP%NlP6DBtX^iLucvqY z(WB-TYN0F)sk0Vznfv;+{~UWMaJzJo<}$96nJ+G2P=^#DF)2w8-G*}G6k@1J*xCt(%26W%86y`61D$+;cW2I`$AYoYE~a(7ZWx&#fI0?NbG?7Y-nb=M+hMhnt{K*t1>79^2z&iUCG zAc&?-U;k`Ie53lcjG7{-5*a_c-SK!BwLU}t(_yfp4{oWh1? zSE%K80S5r%wTW|CP!Uw!bMV3_mqFbuteIJ6t%7WLL^?6P=wceprbt`Mv18SEYaCjC zHU(*YukUF4A(h(jcIj|L8@xd%rdx;~CA~AI)OW!P3;qUHPGXuh$sY3~=C)KdG*anN z9d$?;BHg7AyXxbU0@|Dbj8j1Po!N-7QwF3;8_+yiPW$m#F-f-m1(+ zh>VBP>-HTwfFi1w^jtF2(NWgiDR5WI!{aD}yc2_?b=!5{b=!(x-dntp4jjNt^DuK| zsm|eD)s)05hGsq+;d2mlQQdA}xS-SiH?7*V8ASt$LQ!!#$y5N|q*V+#TnAoaA}#hK zbv{LX8_J%$8;8CsUiT7DWCm=sIhmO4=_I)U0%ST%>^csO)_E%%CJ-=u{qn|C_6tBx zvqHGe@ZrP1eSDhrT44%v8vU6gT00p0q0bq?69og7;IoOqSfoV1=%6twBtBu3x*DqwfEbmp(MXv8#n7m0KtO<) z4RBS&!vnOQHw$5mn+kgxvJ4A{nVH=Kkuph;3LyqT`Dlt4TMxk?F1YtW;wwU-%j5~V z45MRLnRvQZ?N`kSd2vfx9PH+!VCXvjID?OolOF@66m+ns;}^Itc#eh@0EF_5opOh| zjZD{xFrEgw&761nbuy!8|!~MrxYhb^w4qE!(N^ z4K5b9ML-=8r`B{Zx2s`nT14Wrz;!_5-zUXY**9<0w^YyB`k8TaJa<;5eA09s*O5()GSwvwAHn# zwW})VU@*1GO$e6dbZ6NjBQkOc+^o2swCF$}m{4MP+|o~*DaDz`lQ%CibBPp(Jmd8nauO>-M$^IN^c(2M3=oM zPMm0MF$v!y5PKpR8^iPu9eVZZ zd2D*@*R|_}?ann)aK)FG7*b@pE}J`Y+PL z4Mti4MBKFPO5z_;*S)qQc~4}-@Psnl$12C(%WhLc_hyC|X89&xP^P`<_0J-walAV} zJ^yUI$(Db)m3u>>?3oQlu=RjwB8bIRl_3-tlV4M;8fHnObcWt%WTi-9d%}=*> zv9FU~$7Xvn#=e(xhLNF^Bj z@s*BBJ$F%haVc*;eyq;NmdSB0WTr<(#Xj*GVv0)<|Ad^LZm$6Qlx};V0K-P>aHNh5 zdTOjml|XS>+rr&qtd82xvNFZd=Vp(28eml~G5!Eklqrut(`aKdGvxTzZ&NHYKJDxp zGyd49aa-p(Irgvte`{K(>09nH zKo^6PRaI5_LLqYiWMZI1h)4V30}Fs9cpAZ2ht?HYG4;at_n}_m3yr3dof#q5%XM2P z#&^%2X;ijbNNn>a~ zo|kMZ15EH1zZR*(9Ra}ItGrNh5V(^$zL-`2v3;Nq^b}7#3Mm9E3A@+andnqn% zC*%nW%kFToFc^R3^6B{wYpcq9dTEB)`}gjBQ&157IPwJViKwvwrZvFdf9=q)L&$|? zIX!e0J^j3~L;Q7gp0b^se;}w$(+`hN$yQkR+iQ9NwiRBJU%oE03~53l?iO`*B#gB5z-$kKAtwZ#iW$++=(_Djn3!fS&07xWa`Y*TXYo>NkmCsbc zcs8N{H4C*{k$o0i0aXz>86-<{{GAT3URBR~ zVoOQ7X!sZa(#be<07oi3>ckThthbSSb&u$IZu0y%vvGn=(B2qkuPNO7$oAPETNibN z2w^rvhK0q@?|3>~5v#juXxr%RRilal7cyGYN@l$1P@_6oPq|lkaW!-cGQ43jgvjqh z2M`dl3A*_S_PLwOn`vtHyu8Wd)3au>*}K$^c=+dCJ;i!?iK~1j@oL8}uiiDzn4SB8 z2eS?2r5J~I-MVShrdMuogzf1vP&eIYOq%JL-Vt}Z1Sz4a4$=4#YM2^`B!cQe`r}x% z=+^zT%hAcnj9U?Zv^&Z0Jp*4AxcVBMFzy!VsXbu2CBh!9q$P)7$WU5*LELh3+SS{r zA>@Jcw1sb@`no-uT0Y@noy7>%DgwgxuT zrOJlR)b1SfI#hqCs=n{GG&Fg`50#e-@P_g17WF`_BKFS@><0X4+chH zRB@YXi6=P&C2OetgxRxoBqHzwK*3^E%cOJ22g5n%CoesBa&*|b)pvKdPKrNb{L$sJ zIpX`Azl5}u6VvC;?K*JNrOTJ;725DUWP%h{KgNHITa)s!BfH%{WKt8g#5Oef904ti zuFx)=L79WQOy$#0eR}tn-8=H+fmpe8rRfJ!GmLy3aX#X>L&8B)RSmxzrzpk?JOUdj zD+eYylpXjIQ9NS$^h1MoTQ|htQQ`$v0i6pe@j@SZ{!i9@Y+5fd^EIgp05Ff@5;J5faau%yf}c4<2&0vio|rg(I2rM|BMZ~MwYklnON4p8(MB2iap732Je&LlUwXlyok_R-jERPaW>GobsoCp7nKhVur>O?!`}C}LqW@WekMna zs($*AsY@uYInsdNNJhMAbF{T^f4dKx0m1cLOY|YwL(S(O!D!fjgS~5U^r0_yOyA4m zTyOyc)b`C+Fjm_7{A4;=8@9Kam|1CYn}N=?i4R7D{$oy0`eTGFwjpBcPbTOytJOY=~eZsGaQ^F&;DzS=#Dric%NA_aOwUdfuJee z3Ghh%{MSA%p}lCvC|Gq_8bbsAFd!N7DS#Q@6>D6xwPoYV#53)oCIapDZQ8Iw6ovde zadjYj=Rtg6{L+fAO$JP+CYQz5pwB4u4;759iy0Z0HEn)oaQa++yaN7k4bX@~+vvgv zzRGRJMjJ|Fx*3r)eTjg^RUx)F@0D~ZxZ1yua+~T|?$x~>6r|oOQ+~Q5aQjOyy_0GA z?~iVM^|{m_{gHWQ`8sVCs*qVRC-_j2p%6@ zdhGQfB2X2Zuj!;f;O4ZYr~AN%g{X9dPzfukX2Rzy=o2ZJ_}+;9>%%#;fE2Wq61DYg z$%-*0H~mYrB70X4GwQ8ono-jD*XnEk(=>ggGPI7GjkyP3 zeE>W2z|~C3j3`?C9T5WZzNuGN20>IZb&Oa@+`Z8IOsZrdV06+XhS?|)g{mwx;9Oy ztr2p;%FWrL%d`L~o)!4Uefq29;1YiLu=q^2L&pjPmlZQ1hhojZmXl~L@AHUXRl1$> zP?0r&q3#2{Elm=>UEE&3_x7SQnrlXJJ7k9sYKWUGo@>-;(xgeEfR{02vZC!LJGzH4 z#=>j6t$mx&9`j_5tIvfg*nR+flF*Y*ytpXrK%;IQ0$Mi%dykqp@rliV=YtQIjUie8 z%-vxmDgp(GLP*iSe%-UDg_z0lrXd5JNpEglQhE;i(7-aIk}9j@$EPWn!{uE&6gJc- z+FJK}dQ`d+pf6cI2L!$yu&<~^C2n}a+%SGQ@?hBo9f z@l1pj9`&xO28%b-Y2AVHRt}p+1Bmo0YH}81_3+9@&?#m=w5sD*6<^CbhvhR zuYPApJ3dkgnE&Y+YqvUMY?XnZfm34JcSpZ&G^Bz7)??b;*Sc*mM(4mU**?3)bE)@(n zH)rr^r2>aRYs+(~@Q8hV7dVyDaMos#h31nVuLHN%G!dY(1w5<-w+lgYkL`t)$Nfn-29 z@=cA}fCXN|`G_{=Kii?RuJmMS9BL39n|b+9&vxdrS}k71NSMf^IZFEALbbx~bWHfB zl+zvd8h@nPlM+-I9zVy+ymQ$V&8;rLbwR3R%i`7vCFoT z0mIhQjc~D$$_K7XmN#No`{k2!q?axS)q2WzhkA)rF*Oh6Dmd2zQ%y&R?e^?4gxeW; z)-G*26I%3_e-YABtXEi&XR)l!PUDfGt2P+>k0E9X6M#3dY+hy$E~w>eTF(S=&W-#GUu_ABo8-ll+uH zVYy@5Mc%`h)3Z7HX822?Z^=OI%+@&tEL-0o0I^^#H5}J_?G`F?z0eh_1cveEo zni1q7hV?Es1PX+6+7%2d6WKnyv|)ha6;;A9xXEPjcPmje1nHm8?_5*?X zYWlUmBP21mL(J7MlVDV82IC82^+f?CdPHU+*MTX-q!N52OBnb^BQD;hX(*dYMvqnz zRV@f`=$D=lA#l_ z9;Q>^k>amO!+>l`sUtQcY;`>}wu`ua;Hvf-!J+P>CQX~(w#_Vnr%T+-Ah2xVYwT$dT=6w(a&#oz7D!UFqO)}gEqy$NBB+OTx zE=u`5(^frJA=T1sRYyiIbsIn5p;1MrW`5r>+T-0WL5cgi+@`@!Rj_+|tlEc7*qL!% z#d{+7Gt2%I?~_j*oYbA{B8nu#=mg~%TqqdH=A30t?yN;0RO@3iTX#WL;h;_`EjF_) zifVybdW%NGW#EUWv-t^qj#sle?P**k4(6B@Vg^_vngoWi?(x-$! zGg?PYoQMd)GBzAC)+^KzR}kV}jVD)E_2c~A{<0Jh+(w%2F3VpUHKltbx+W%)L|^`^ zoJNhXw>*_z{rb5O_N0e+^Fk|J+(@~sW(~7e7^Ujlaa7p8w%VnqQ6pJKWWwW&e z7O2i11Mh!&ammj7-mYn~;sJeFjfsGWm<1KEqu~2?+9%h{<$^hVe<}~;&Mod72 z5oRRXa3>t+&7%k9GMf~G17G#WzDk+NOoC~!b*Vywc^5*wGt+KcTj;Ih7!=d6xc?u2 zFH7jY7WlCPJ96ltu*G*$qvI{9cj+L~5#zU5>idTTVxxrN>Rqq^PcNZj4HsD9B0Y?l zl2L9w8NhiL+;-zwAt(@rN0AQ+__YzbLd9Zp65Z|S+C=vNx@)pyRJ~=O)f3O!2 z_S+!E`y2F)?5<%h1sT{Re2nM;eRXqH?&`N$+ncN3w|}$4*A^ja7vmMac1LaP^?YO~ z^qw}#0WY>{vYQ$C8{0Riam|vwzuUp)z17Pm0?YgqCr023I#IT1T;~t7=Iv*}CsW@v zqk>TEr!<;D*bzHyA(^NUXL1tx=g^I9npQlC01DrkOIwB>Qm_AXR(9nq?P(vU9W}iz zJL(YEjXWQMO1Z9T?L{Kz;(ACc5h9V!Tr^t7SFc_%Mz4f$4bwQnjtd;0!JG&65U?TB z!td&|%Fy41s;lcl^MSe^K8dhwN;tg5(}q#n*Lo~csIP`}%A?aiR5amd)m8P$Drja^ zkP8*!w%Nzmw_cm%o#eHrCq6F8x}+OqMJ(2i+OQhNl3qrTokv6x5Lt{+6yP0?pw=r` zAj*<%YFmAMElSH2z1sL28Gg+V7H`mn6paZz}Z9&Hx zV!Ns^AZanTc=6JuV!S9lzk=u9Hp1=EAr$!z&7cq6cC}9I?UF8#M~&^}`=BfTs)9sPO{^~uU#g@$6AUjUeU)6;wgV&9BZWcTfxG=;hRv&f zz1LhZiBe=%a+WyFg71Bvy?OoG7~N`(i7`%o6>FG{J|_F)eIPQ(&&{D2YmQk~BBN-G zG4hE(S<_Kj3*hF>-^2HVoG!;qWc37|8*r2a&D*1Ti=H$LIl^R_3YJ`V}A4Q(Y ziV!hq*HKfF1#HX};$-Fmvt>4X_=n>-LrHrGSoN0QEvvyHkyiiEf5&<%Dmg{S7lECq z4-SnzQu)m6*u&fDGsKx=O04x#hn@bNEY@^UsW~LA;S~SA6dF884GVWAAeMjUjo4<2 zY6O(Wh^N@K7`vI@wGm~342RhT(9(y7oa0XiRa)?(lEgy0ci1F%cX1XHLVc^I(LvJL zE~JhKRAiMJ%hIp05gR7#EIwZ1_ex#C=tBEmz5IECK|o7Izt5MzCWpDM6^Iu_1R^Ij zYXFOvc+eP!hXKxIqx9X}2}}x6DS@+vDC9t~(nyAwX~(Do$YVxib3m}@aRLM(l@r5j z8c=I(qeUQq4a|p4UM`(}1KwW_cc|s4t+dxTp(I#Mna`;yJ6iZYq>Z6Vr}<|#9pqWD zhwfR`J$*0IOk-^&y&E(})gGcUD0wLH zi$bX1?4^G|##O|40TV;>w$I=OWEd~Fcj)?r#eG#PUf@0!!(_37IIckgDJ$6)m>L?b z`j#K?`N7GJd`;N^N{2?a3vf0>bwWtjz;aJc-|gEosa53Spuh$UqZkHYoLb`@^l{vPZ0&L;nIGxVEQFpZP8>=9nUT zo2j3P=rLwzmN@7gWtAsAgyDzpi#A3EC8MHTHutcgWqRxR^Hv*%njq|~msx4fBcHhR z;ws8{RAZYyj@;FfvdHbrQfg1b+(_~=6TVBSsMFzy8#ecA#`@B=*p%GP*3i(v*S4e9 z{YbQUQVnCHo=z6^Hj90{)jfp>`JQho&W=Jxf@SH5=-Av?v~;O6{d(y#@MB`QkYfv% zVB1f;?Oo|-1QgP#J_4e3`y&^+Ii-=VqW8645iVD#IA&R_v~&%( zB{DrZKUbYtBIeM-AQ3EO4S_fxP&JCejw-2LmoA&3mgpVb1XGIvuB?c}Kfyq!S;P>8 zMLx7r>7I&xbJu22@+I1Rmos*5-+`A+@=b z{>!VExVzU<%`%{NZLyX;b7Kn$u(JAriWv+Dhge(aD_}7OiW(ehx;O<$=4mWAk_Nac((*?noQ+81VLzoW%Ngn(Uo-!yM^CMJY8s zObUx{c7ycL!fznQ@#uGFC;SiPRkdnsyhqLWJ{0f*a;2Y1(cEduL4y{3DS?5Xi5wQ0 z4iW;27))g&n-4E6%&vNRW7|T3dB5P0)+QdBxzca*W=Bda$ukEv8v5y5^VMwtAs)^a zU4yJd6oN3UKSUvs@%po8WoMrkk!ZM&GEXa}Pxf(v@Xb+i)>KW>X(lh`GBhAs5e`J! zx}K)sZ*sY;uA!cm`CP%6t!Fo@7avFwd3gM>XxJ4JZFP8@ww}fCEjLk0bKclwzlZFY z(#U`ZDf9Eu)6{|Y;-W3nyUZ*&abjhy$imy>>(;F+29Vf=iX|*R%!Tj1vgV?KVQ$*x zQ)BC!+@@{YYAy8hk;1ZlZOO}b5Ipe73>Edb$&?{CDF}oR1xml8|p`AH!M8VE2_L%+U3+bgC zr*{-;;(5#bu(Zv!?!P=j#lUj{e#DS_!fI&Ild@7Jr|6KrEr#$}h3yw|FKghhiMWh;0&8u+vdTtSE$ zllu|dmr3!f2S6^rrT!AAJmc0b(6G@W@x$^k{OmMJUD-Vb|KEWYbE;=4k2?$3c@F31(9qBW zomMm5BfU4q`t|9zDv(2Dvl8{m$alx18q;Y<1A3c4A~HKF2m>khCOf9e0rO3j- zrU6Hm@Eeq~W+9pynyOQ*2kVMMTGh|o#=;=;6r(8y2q~Q@@mw!V`b~^&?0=@>fubG; z(Lm`@pzD;7fw3lqa%IW>Olz3nEV~7blW-}#(U@b&J5u-}-ihw}3KlQpPwPv=0*LI&8O99}jKqBesR0UkKq`xfwMHR3SQSAvS@3GSmF|R)0|%b4`-yz%1nCIB?y$5*rAQuRzX&bV6ttM4Nu=Sj zGOaoLhGEnfbBK77I$Mr&dAEmz*vCEZ*`!UIZMdwWiU$cap{GYZh0?kv*;=eq=+xFi zT9%WOL$x?GE`5rn)&tX}VwQ<7+tT}G;C2ZA>Tj&PsXNHW@ z`uU83T6sorZsYzKo9b`a(R-qq+4@&`@L~MK%+oVWWfK;^-zi`m*cGo3zdcH>rOr8qU-mMS!&i|<$KlShES!xkCQxTKAe=7D z4On;1xJV1&3x4GVJ2=JmLWH~`d~G{Lz52Wsf|3d0nC?~{xJ-037OlIs#0u0l8Wy!1 zssR~GXuc!TO!zA%$7nYR-HC`qj6}f(!rq856xETmF2C-1s^Z;3+i)F4(N^?3vQm{L zHzH<}-3%1IBKb>wBvb^%fa@cMZk-7yO8)a=Y*0)M@T}-h`pxBx$5?cWT>K;B27f2h z{kTu!dyUlM$-f1O*CT$q)^?=z;K-hriXYDxva)Z&shS+ehbzW(txTZ!f_s!rC+Nw2 zckHMRbE%Lm%!Fx$Uc`T&$Cn|^d8IoYBI4rWMEC{r+m-V%E5bs51m#5rrKZlLn78T~ z?N8vdBKkx?uMj1Kn!+`yd)3&KBEf`A&SDTzw!swW+i!P93o3>X0JSf4SY%RByrmfF z?lSF1gCPx|I>_w`vLtH7Fga@e3;EJs*w;`54CDCEc@CIHBAAm+Zd!Fj4WUEwHuadVp!Te}2*xbj?()Ijg z+Hdv6LfHLe5`ECXEK6K()4WEI-2>3^;YuU=HdrvLlF0{KayWSfb{FPN;?3QnOb}8P z=1Pl{@%3tZBKFD+eq|KH=199Z^n@uErZOxuIr);#RYZuJpahxT+KVd-^o$IKO956? zWWxiLmrN|337#%EX~eh)%`^qN>IhHNR%?Lw^C<5Hqm$_ntR5emF_5GqK>$S;9-2+0 zUyPyvpo67cI=#pi5R7y*WS3y>t%h8yPEkfd;$}ub;;iP6>O75m{y96rh6xMK7vA>W zO@H(Qz7gaXgL1X>?m?Q)!~(K?lx9y@8bCgRYbH^;Zs|6PEpaGB00~Ycl+cbUK4!s( zNgOgKPw|cI$~Ke2Hb$O!hm_G@=DaEM{Fr_vL`Z1Ebh@LCPRgOcWTJ13?;WlsO0+MO z*KF;&#m!$o-R_I23KC4f-&(U}pSG?{w;Z9>X$G1upqT5+xIUC1iB#^WF`Oq>L+7?r zAe~sAxx?j9__n!^@csN#lJbx_q}FoG{5dhyc%;840&qaVpW2Otv#fp~Mk8YlMNXyN zwEu|DGm!g=JEv%Hot)NRy?V7{h-Ei2qS#Whg%t1=hw&1=;SwYh?7PcA)tHddaPax1FkU-| zW?2-!pw`(qK+|as4N?7J{+_pf@$%)S&6)){PXe+5={8u5{P&H&u3nO*l!(Q?Ur31T z^xeHdou+vXPzvp#IGj5T^^WLyjXK|H%$|0RM~n87tc8?dvX$aMZ+3`)PMS}2zooE4 zTop$8`I@#OHCm@TX=x)-S|nf4p^&hZTO7WWq9lfkh4kHqsUCmbTL1OJ`SZ77??;yD z%q|m-J{8(N_xVh=tfOv`wH3Ul;Q~TwKQhLkqT9K4flSlG@wcJ4f`TeZ`c6@n(ld0| z5k&JG6)L-q4IB_ly!|#A{K=sOkgi81O<$g+nI8)j0jDN;h#fp3!qeB!G`yGmLmg9aVIoRoQ3MQtT3X%cdO6J~o!%}MQWc*2uP zV5|Ak?4Wo2;c3d$EEj;%YJOWJoe4Jr zOd|`mwiIJqVPHd$4v8R8XUx!Pod)S4Z-!{QK*fM323l(Ys)=N_il{MCmPoOcM#H;#0&!d&kott#-6OS&~rkwE>5- z8;_iuhdLqnPK|FP*wIr*Ivn^sAwke*nw^H8UsTk4b))A6H<{3ZSRBJzUhe)L)Ivbq zv@f43>Vb;6_Zyhm_@hS^|B?cc1J_Qa>A&Y#za&+hQ`F@#!x)0U;Cu%jdI;Bg@W8Ka+5SI>58;Er%B5=p!y(ZmwyR20fmD^rlE|e zKx8Oe`vl*wG1N3e-88Lo-pSb;+Xlmo`P`25s34fjRuBLDEBNDesvGhhyR26RvA>v2 z{Z(|%`9LUx6T+BrPVu4MhB_#rs;n!$e8W8G?0QJqiSrN3U@H$emunU0#S&h)F|4W{ ze0<^=#q*OxMyBli)_+Hvfz9dR!|ed-I>IT$Z*1W3WSOIQAAS1pG_?~V7Gl@ilMQ;{Ehpsy??VKc;i%Iti&uTkbG$?4jiZ&&cNYI8b z0KcqUmRTkw%O9HM#u0iU!a((JmoiZ(Jphp|qYD_g^y59t30>Po9zlI6YaQiqp-_`1 z+|$V8Tvu6n${o%5^l22|-u6XfaFqzU5J`z5o&&aWSOlk!iF{vB$B}%u7RaMG(!16; zz|%5egifxLus)DpNFJ|gO=w|S9vQaXmuXn#PYD|X%UiC=yl3cjVey%;Ty@ifM(W)s z*OHbFIzj92Ei~fnnXYSaqJ8`Jwyig5bZM*a9-bMMaaDv|Doc3P!47fmmOVg$J&Ryr zFS{o?pd${dMvHDJgA{P_F&%no6+Nejh6IVxnd-3F(lp)3G~@m^|N9;-pBX7%oo0D* zDn3}#X)P(pkTc>1#p?HpQAP2qFqDUd@@8Izr#cfzZk+Sef!T^eobR0;pLt+Aw9~LT zSy4=5+@{7KSy@!%SJ3pvgKxUQ-^=TKb{V zq36Lw6?_ijt@vD6g@`Dm8i24vAOQ9v?Aq;aGq&P^N0AHQ{pIUYv)`u`9B$j}PfKJr znBElYdf-LGnTIGQG>@zZq!B97f{)%|$dH4KBK)<3I!`^fx&ahXUG{u~UNHimxG#@= z(9&Rs9SuhYLw7Lu>`Pm1?auuy+!v1+F;yCZ5XfNd<(t>_rRw`t`y)VZ;pD!!jGct3{Gq2*{;SpTdKowt7nV_nfg|%NcfPZH0;G?HC6vs?RA0ZU~7{Qlb$}E z)M$M&Bi&>N{_gH0S|c=JNNTEve9lyQaVs%VfT08+c2ZMZS#@euzE3o4fX@G@FLOvy zy1V=6>CLG4S&T)A3S2iIh@iYG&S#3Hy+f_)WWwb{rVHhs!IFvP532wP^Dut(0630t zKB?=@NTk58&J^Dd<`#3DC&1(D&^& zpv)?KM149nP)9Ww4W1tJ9b%foRa%t7l|j5>_pB53C9up7Ek1;3E!E@%Sjh*^x5K<; zVM-PpoUl_fmvv+3up($EF590Z&3`!O! z1R$dG?3b2p65}`Kr>aQ318azlDu}MbQ@8Vz@6wjat43fwDCvC~{BX@h9Q8Ic_G@ns z${edy_e_S&*suCA4>2q~m7t^ExL_XNHbI-_@)h_FO_JnDBb-i3*cF8Fc@&W5q)ZQs`JbuyG+_=viZ~Cf zrIKT)F&{0V_~PIMC8NNAxnksZz2-kNfDK|5UKCmj>0|J6yb9mB)MF)5z{{IB{_b@C z1-mYm__VLYqnPnX=Z#nIEO>CV38gQc;usFi*t9a1%%E5W6v9|o=^0NW1wu5g9WXF9ViGQm@j-A?QaI7>2vbq}(L7mR>HRBuGAmV+8h`?92%)5yWA zVNBG2JLCe92=UJ*Mmsnre-6<8F~MyJvRmu(Q%MDU*!Mg_xijSE469kC9f7_gO+%L{ zj3E)`z~EJVlfI(AA|i;cSnGZe2H<@PlzLey>IFFVo<-Z2&^=2yQ%Ch>-e;GH< z+GY~HX*O+c!&inGaOTUW1ES%4X_%O??v%-O+36e*&;%?ngJe>t?gQ~#$2$iBO9U)q z!JK2S)ADh>f&ZZb9(UZj5pv&nLR`ZcpxN1X1;lZeNs$1ImE9v4qud$RbQcDO9N@p zQ|+gC7o)L*-!UcHwZPPy#NKJhkhcB$^<&P>mk&-wFcbLfHkhND#QIr^$X|v*fwV6f zz8141U3O?wN9Zj-UXEjxY=(qhK%&rB_9w9AkRHK(7C^T&nEmI{9x8E?oGgQcIg*XE zXZTVKoL+*rlL7Li4vN=H*Be;=!Lx9UQ;Jl#)M~D-9vq%k@_4x0Aos_iCqU8)!ujpgD+`V1DhbT>YSjP{q}ts zpPgy_{ay{c(faJ#`7*(7X!O`jMs+r;=?=v-g>o8agI&B7H)@%T5Bl_jQ2D?KL}zX1 z`-#scpMBW!<^2&MUVHw0;LQ#DxvOF5@MJod7)n_X9QM7z@6y3|X%@81s~huYD*LJ5#YYezO#fu(+iuJGn1O(krBcdfEAtGIE}7X`|Wrc`*E zqY<$jB6qyJq;A)M3Jh{0a3La z(z!zdh(T9}dF95Ab0+9s3;B;Uy9Ee`AR%4kIuE-J@QvIZ*9GK3JPP3#GTMo;10WY*F(XFI-`nKbMt!g8pf+^EOC`8rmEEQJZ@2=@Dw!*06tn z)|ALNEF-V4xdY;U%4v>ay&24}6-!X18!bi^pDZmLkJ3#lte0$S>Q>^PXeZ-zjOB{J z7x-@Q*bB1;$j1Qt_6$WL0&W4pC$(eSw$`W>DoUe<<`65z)eSeH6(~@w z%SJ#4d;ZE($2{;ZPiD-qhs_Gzqktbgl z6j+idCQ>iMRt0a+<$r|p0cg3lCc3Z}*#F2;Lx>CtgC!rI=t1A_cb>DXK6-ox38Boy z+djtbDFaBC;CY}DoaqQ)=#la|w=?#eRQCM=X%MI^`*vDhN&U%hRZ&+rH#1B0`Y^R? zJHHLzBIi7sbN=gB-oqQVnIh^!M1)AorszE1(udv=I>m-y`#Rc&8`u~q6RR*lZ&%$6 zHgu~Vi4yzl(od5WxD7UdHuC8W%!n8$CtRI5e?AHO@b{JJ(1NULz{77H0fE``YjnWZ zzE#vqb*)-*U`EziO*(Yp2oK!$eSaXAm3{<^0<4;Q$dY&}=LVn-$SgT!1nNj~f?;%G zQ&rXFG*yKd;+V*u21x(ZM$7rSTt1FPQ#7lb0C7;EpT8gUcp9yhaF%$gUytr)mji_& zEi4`{Hb}$S!XXlB5S*J)lZxf?6VZkkqz!q0!Tr*Li+63k)eKR0qXP3A}@gR)ko1QvkbI8XwRSOwoj$v1(!E%dnah@ z-9BmrzD}GwLWGP&K^5Kse(-RHf)N2h28Fv7wW4cZ?ls2Y=~Y1ixZ%LYk(z+H>!g0d)mbtPCj+yKq5GcitmT+(rBZseH!M$^8C%c@ax8A8 zrq*XOC36fSoxwa`XdsxwHQH8iW;s^NcUwc0%=_USfk(U#U~The8Cw zY8Ny*J|g!rv<|WFR0oxsZ}q|hyEIcx9uE%_MM;^OI2&~FhQeB!s(|d&WbujuwhIgr zCUr&Y7v%r8|A(tLf$KSM+x{zrY?UI!v?sEZELmHWHc8pZ(jpmKWSNjiw4sgcl3yi^ z-Pre5A%%&tlq?kn*(#*}`<&~#@8_QXyq@QEUo&Is_xpZ7pL03R<2cU2%fj#o0VU~I z8H%Ls7fw?5hEfV2Qa*C$7>3)7Ey(c@mh~G-Ar(=9Vvc`>5=}NTkSR&v>v`Kkyrc3E zP7iHNT$4E}S5M0jT!~)PW6AddCq6v`Jif40)?o6MD>?=0Z`k~$xvV=F0fmtBhRXX! z)lo_}(X^4-oc`Fz@Ic0}LF$d@ewqTbJR=YoWWNXyJeuBBm})}!wn zCfCn*CMVF0<4WNLd38*IiII^s-AZs!8}82x{`J(PI^*AF&s7$28GuB^Leqt(Pln65;VnA>$FZnw_ZIszsdraM<@e73^)E_OcyaP#D5T*2in)^43Q^-0WAuuuzR<}d zT@-0aM%TkaG2py1vB_Tyw~y0jQqm&I*TZ@ESumx}{r^ZJ1`fPDnv^r)a_A5+drIpr z;`70G7OrV@yB;n~sUWId!A_JR;%~b9iMnsbFb>+rYKOS42l~F~(o22CT=2&na42O; z;H>eFHX1FYd#@e;$Z@t)RgTQ_=4|`i#fE8!?}GfjW@$?s7r#gwm3yyv0NrCj&A0i_ zwth9l@j93LtSp{0qI4f%PP+w~?%l2z!gXmi5I8(I?jQ4`rT*DM?r0|`t*y6Bq7wP%dalCefU&Y=}Re8;069Dd>T--D}fL|%>Zr1 zf$XE}H(<*h`!9cb!$644udmlkL-bh_TL%(U7Cq>%0CEjtxgF;&|t2)bgUKrYU-g+q> zzQC;kN;Ll^_1oqe(;iF~?fKz2rU1JRx%DxjEnw8*OL;@ydf^cLb^;0LZp(w?Kllo% zn-+p|AsnWkQ(f+!##===Cp<8ZgZF=+qFfj^$QXjMJ1sMCX+opsW)dj4luS*g)4O~;w`RD8BbDZhLP~!)^Tm_@M07R6 z?K@JMT`yVGa@?lKqa;gVXHqraZ1K)Zb63dHb^oAeG?aI>LcmLp;bb6hh}M+o&BP;F zBnd!pp$+v*Isc}~C9_Y-Bm9S5e8#lNdgG!k!;81oIB)U?nt>K=!WfqMS$^rDfdg-p ztfyQ{JF|+UH|NPYZHR!P`v6h$iF@D~XCa#C|G-UT8D@mXDVTYv`@C+mL_qA#XU>%E zSmaap6Z4?4fUO8|0mCDVz9v-Y#uMWLH~Kd4Gx=ANNd7Ttj@h&+fUZ>L(44y&fq~8- z`40h>1Ja(uyJ7S6xGsxM1>SWuT@Z@?o;om}V(I0v2^Sz5H$CU}+VLY1tgOPBZ{+qjs6w>EXmj4oSw#3&Dz&kApr8J|ujoFXIRT;rv=AwWw0kwX%+)TadA@=<7OZ$7$yGg~( zm|Z$B{+ZAh#952_lj|)~BvGUBZpi|zXvikMm_Yc4<{V8e#%1**)bnx+zF_(tN8klg zvxX2K5HsM{d;67*)Up*p^B}Uma9$Ibwz%Dj(lh7J)6M2<4cpVlQS1LsQ{$?Cd{(h= za%(6o00f^ER{8e;Qmm7XAKk#yFH*MyDVz@z%LIsiw z+}m=Vh$=7NxcOcvJFNCOb}T5mM0Mp{We|Wq`&ZCK=kFTSpU9ZdsGmwLJh=Vie&7Bh zUbpX;Y9x4_pOsMWCTxyhvmi7yG;m-a{B^7+v^eKuVHT#6o{ClH^~h>Sc8F%5uLP^^8^9T1l+VdGQ!o&VU={5*k&GfttN0f|$} zF02YFwZ|$Vj9Lo=js}LO=EYS0w>p~I4x}@NBdpJ~ZMWRneb_q2+f}7~17}Q!b(<^&+ef`2cSl%Q=tUw%<^hQ>t zQAt1>vt(Wo{)mMR|Ez(gq2mYh=qe7*I|$vI!mq8a?x94Nub1sQE~;#s$=Cj^rO}3^ zcuErS@^#x7D}tb)?z&y$?47jM#(bsIyBSDVwnP+O#ztKU`E}fpPn;kbt)hgui!&Fk z%k5N$7!-!RY1cV8q#HBPYRlDoC_M=-`{-WjbT>13Yppxr3X!`{ux1nzUy7k%RK515 z2KXOaGE!s6oR6>v)j2I{>wMkdVX~Qy_@KhDM0#AP`isBHZ0BQ^f@1V_=ZXvm&|I^L z$BjO$I*ds~z!vYnhW1~a_V2;Ol?7{U-Zro=D~AMrHne^qk*8wQu%Bg62VMGhezYmp zGc)Vq!w$X8uk>tUd3l}9TY$%ff29y`ypXGE6&%3*)WPkaoyoo)9a9ueD#v)|X&nb& zWKk_Q05sg>dO*P*CZ6EXb=jwE^ooxAyDCws_%+p}BqjE~v}D@k2TSSWq4_I?Sw&!j zi8z9xQuWub{VLPWvoV97^JBqxzI+gpYBB?)H9>HgCO@y#dbY z4h)TqhL5-a(qd9Geq~jY#DpPQ)Bpv>^N^vrZ z>)1rtbfh|qk;xOHAVX9&>uTKp$S88;4APkrbnzc2B4)%@x}C%jfDsvkFKV`FUD`so zWVNsP`MQ3y??n>J#gkKSCD2n)?@SxF*(ZX%;~RG}Yd)knm1iW%69}TH8&{J$poC+k zd7N^WDC5XG-49D%!ZQ3skOMdcUC)13`MB&1U7Bi()Fx|=WRt^%Or#_c^4y-Y#hCGK z+j$^_;lQ-TlfOCLvTgaZFn^%Ac`O-Q841~8t?VJiUyK7+66h{n9yDM;UQO932S1g1 zBkf3M5YKg3lxW~#mti+@gW}x`=O*X&Brx%&#CG6aGwU}oFjzy#WOeAimd*{6g&PB| z|1Rm~I%LR&BvDe^v8#u-##Jf-mUqRe2V#;hWy8S4zJjJurQeWtg0)}UGmn1JPpeHT zo-p%9l5>|~F=Gyv{=z|e4hB8)i*KHv>c4EmO551!ZE4Igg^xVzKjRk@E@A08=V{tz zJ@)CV90Th^XN>R!&f4JXf2nI~UgIm#S!H0V5SU)j2tCy1h9ygvj;e`$1YCk~UKTx& z*RHy5XRqR7&}*#z?TVuTgog@HJ0)Thg-nZ>MyPKuDd|MY}7K zZ?8*Fa{yJ{pT!e3kvGg`iq$dNG|n&s}1grmY0|X;)J8o`f;# zs+EoR8?BzOY}pyxtC`b6>%=k)Z|&eI)dD{;M-d4s1roupPf+>rT~~Zv=&OR5@9Py+ zmjPPPl@7Y~MX7H0$G`^HkikR$wueD9Cw8XZ>VoiAXH* z%E7B?Q2n5<4Lr;_500+p6tS$k5=IUG?xQ8G2RXf|4aZ&_0|5y>K6tENGr76*y@b!( zlM=8q)H=6rIz9kMY=tWadg{ZO6z2ta{+GM5}nlZ2BMpE%(kX9rS;o}Y( zV@c`j`+UrT*WFDUFGE>Bf6pM->a-eNxk=*|!m zbgsy{o#*r(J>=RFp18}2ceRa$>j>nda)&sj)u@T)9<>gh*REb&4FgSFg;efP!C37Y z{?pZR(xI8NLJr2d9jxwQ@!5FQq}(({?!xcp?OVdbult_#SZipjwQ?XRhW9}mAN-po z87cabPXw^<-L1an2-Z%4YCBW*^7PCjOGJ$c~xOhp%5> zfrMJk{VNG<<2b!66MuN)ZY*C~GS{V5)llZIo#FExMGXU1BoB>-A%57ZQ1+xBxI2ER z!$On0v#bVIOe(ij>d!*`DlQw#V)bv?ETX~WH>Z=& z<+}j9i2M1)!)D??-&u3l%5Oy$FD`A~eQ$#joRWJQji?0@X}zjqdnBZfO_Gh9s1lxm zDTxCRk%(F+@zqykP%9Q(%kROc9G;woKgK%(`kw53cy{<6I#L z5VX+Wm%VzGzUY=%aB%L37o+K_#+>fdwym$n_m+#FSe<-aGjyu!{L&v&Ek5hstSq(P zS0oUnOo};+{v{X5(0e^IUo5HGF(qY_Vx?8zgpJxV#>NLudM&`VGFqvREA#GrA3eJy zTw(O2P5$qHvT%gwx640a87O%y0Zt_)xT^T$$_dlH8x@^SOiaAswtW^`?Yp1%v`r0v zQ1kTirG9}UC$(tPrUIysGMV=B@S-*hY`P2F$(T;2P1Lf|KKm^O)y{u6Lp%$B+&>O6 z=u65%j?h|UG@#eAJD3W=13M)KJ>drrc876wg`G(c&$QEwSPdVm3>K>&i@Djk%Ij_# zzB9n9t-gtI)$r(hy%p15LI3AvSitGw)>UyXIK^9XHF%XXxvyf~##rz5=lCS2fjIzw zL*ezPXujWqKJ4^4pc)*K*Q`g%_FWxGp{VJC{+^6a*G%MjwZ@o%8cAr_>Ul zkc=w5bT(uxdQAalrn=oYxmDFv?M|J#XB6N@Q00=?(gX7<=Gs`p355Wlzdg)B2145y zj9Ugh0Fm)(W$9{`%(PHcKcM;XcgxcjKUF08SY}o}$2B5Lxnf#K#bJJzvIlLSxzUX(0Pu&8G?gPmO zOB-m_&52j{cO-nFAEx$g)EjeYFg@MJ?%mE|hQCnl2Iam6zACtgy}jVym0t|q1KDgB z>IptVQuYk!hSICS+mux^Y(YO_*t*e+&^>=1i;0TTXIYe!R*g&GL4{VHXA11oPvrHM zLF%qdbXPM&l94reR_F^;+v)0>4@%$AWyrUKr)>3{q6kA!@FJOff?>;Wj?V(8OYLFZ zr^`M}KqO1@XCM##+%v~Zvn^T}gR#$?#pxs<{p9ZFMkQ&AD#NtzhmvlO>o}}M(_<&& zO$@;L#=n2@JIRRB)Ozvjq$yv$XfVU*EQx+qE*G0cm3$C#5gaE9{7CeasHRDUeel^3 z$_JMi*UEBaDZC%#IXEtm6 z7eE>_%ue_Jx^-OL6s{gwpCuL*PM2-mO6NTO482p*G^)*PG)~+U|4P?liY;k~LDJ4F zs%R|(w}30IPMM#;nA7_Y*=E%T1M(cz^WAi!ttS6LVKA)TEogNp4K}U0dB+Wo?vIk& zaRhj2V#%RrF?TGDP2y%(Kn@r{gtHl#Td;0dN*N0Il1`Q58AZcNBY%r8scdRGUFc+c zl&p%iXI~`<3Vt<53LO?EcJow3UadFIv@yR3lbiI}5o$F&N?H_*YCR7Y-~P6+cBA<5Pd-AkU15&(S*>?TA7Dluf8mkp%mo&&c0- zf?m*#cq}v%sw4MtB{V<&0fY1V3PVw+Q>RZbzB`{Dy7Hgv=gXj^Xh+ARJU%{@rk0lC z#4fV8GPCIXF{gJWjdJh-x|qp{gKF*?Y1k)@(y|Zz2PrB$Ou5m5;9w*AGtzWkrs|g& zG`<;&VKMKzdU${)mv07~6bawZ#l$*1CS9*uw-XDU`Kkh?;s78>Ii)3(h;lOa$KQV9 z2g~#l$)*EEAyhJS2=Xe?!nv9(xyK@He-ekdUSs-B{~(&KxJsP|$SGlVNAn~YK*UG* zI>$er@Aakz6>lsKz$lV$)O4>3(Qlqbo$KFzv%nEQD=I6uz6xE@>cWK!P!XlHT6kl^ z>at%AqoQ~8{ne2#JrNEMV4>JDvOz&|%)=8EtDXw33S*$<6>bII1wY1@rdug@>rs?U zU5yD)3b8aW`rPNVtx0Qp7oT!eY_-y@>fdd2DneWyO8lOc!ibooiy+S=rt-@W!w%8R zlR>QHI!W6@Em2p~qE)Mx+!k>Irh<~5&v8?iTeMQ}Lj%A=OWzLtWX%9*7x9LDST=Yh zO1$zuCOl7MPZWCav<LD&p!+QFbEF{p(Z0*=S~_z){vjTY^>nw zAqA({5<*Wg)V|Xe%uPBxfmrTtyaB?#>KHF3mU(F+x9-84e z+U9Rmac<&Mb#c9I`N~6d4r1yJawIkcyIzw2rS7C=k{K{VeeSD^$vsNnc5oR8`}uzZ zA0>K^!z(A<@as1%d)+LXRo{G&O;cgDt~?j%ta6Okn=46 z8zYTEF(!dCcD3ql<8LDAh5L_Fi}+ZEoU%p?i}AaRtHr94YUJ)x?E4*zj+UnYyk2c^9D zLmPo9r60?H$3|bXtNQtg?KjH3;Yi{&uVxe-Lf0#>wzysp^R`6%*y9r!{(%32p3>0D zYRtTYR(~dK+>Fsp#9kg{!ok8-&a};FQIw{0lpE^4HW?;+V94X*zzO5_78hH_;{ab! zEm#8R4_|fG(es~5QoyODTcyT^Fn0HEkEAZJhtN$c-Er=)O_RB=>IREeLF#Q)EUw2- zx$5Z}FksdlJz6GqQe_twjtY2~d}e)>A-oDP$VN3IE+@ixqzsjLBa?)L-1Q*WMAW;G z8p7{7gpQJYEw>T7_F1#`5B@C&Iv9;9pu>O_wMPd}%<_OZ-0O0ix@p>x{zl|aH?KKc z#&vYMTDUr?+^WavvqxSIF#UfXRcqe=!jizK_%;4!O+F0EM(-S26->=j_*Xj4Kr%~D znRYppd|z|obzKthCWKBd^e~|^Jb2(ibY-WlkdCMo2WFa##ih^w2gT9A#ZNB$mPkn> zWgv8%K8lrbTXSYHu%k4TUZ2R<=g_>X4PA)wr`4hMUFJ0|xM;?BjF49>*r z8XlIIcaZFS=gu7|$1FJb1znZ6&>VS_O!-5l?KN=Oo)%mcWBbFq>yE@va+P8eU_f2!c8G%D|>*2p+V-o zFJ!qpc1njQqiBPG!ok}-RbOGG3jBKwS2mjw`;Dy^Dn{s~kLgwg)e3L1qUH}=kZ-!b za}~9kxIl6x#kezLr&jO}E2oK!^+~ZJyECu7v))MkyH#0z=6*MqAz#eECTw9?;Sy7q z7D`yaVN1`zz|MVDU{_(NFisHdBf#YhgsDX1r%#`P4lsWv zlnAEs0-1mowz@*h5P2h3^yH<9H_JGF&{!b$(Ms(^c1pR0h1lAs{dNvrIce&jygV8%vSP?2fv0y*add?aTS;#u#%1rMS+P`%!=rwhXu8<9ZFoUQpj#zd% zk2^kVg3#GaT)SO^!GWT@m$Tk0;422Z-3{7S!qk6oB4CrP$}x$Kg|-qg-fwemQruyP zvYXKC3Exa1yk+Q1cRBIQ0)5V!ETX&qboq)FG$dkl4? zdAW9)vjzLxU0aR}>ZV)q|2PL_6%LZ<6jt_C+dB6=yN_6tQLuOTHB4p*B|zhS9^1Ok z7BL7lk5JCJ{{_8X4qH4s{;>r*b;XIB5R#mWs2E@w}5r4c4u-s8Ph-mQM(Is8#EoPbl2!y8yTR+n*>zXL}s;Qt%O= zPM0m|nV5B-n@bM!z0}L^m_zf4Pl6irbAc8T6ZS)Jp1buOp})= zCp7My&|9^4*S@Zvu!lS^tODs#a?*<4@ShC}jVjONp)yz3)omF!C;bxq9^yb#&0XC- zr$w&K3fq2izO%z$&UZbO^F61zz|m#>hT4zPi&Nkc)*Mj;Hg1&-7l?Uxo>{!&n|@Kf zbEHw9b|g!wW@wOTtXi*lvkwlhEPjcLOEK)|)~&Z&fp-Q8Nm>vt1Cz=a$9?f$J6&qR z6|23eIOrzDpB=1IIk-tIPie)E+IXnZo!48h^zDQXGZW)<1F+qV>A8&I5F>QQ)iGSQ9?YT!6tuYUNR_y88gEZtyLFYkb~px=$c8n(9%9A+uI8^}jO$YX%bY&Smr&H?f_vs)G$#Cu#_5_qbEt2oZmLd~sHh7)6va9(3Z(dD9GWdm;bYr+Gd@ z=Rf0b?Pj4%ie3Kymm<4za(|l-f5+*bGD>b{kKJ=Cp3pOJZz0U{!#Z4eN1yQF(?VX7S2sdMu z1WL20DWS&K$(o=1dT*3C0H%7QyT%nS7N=hTqj=ddh;8@>i%+a3)$F__;azD(8KiR} zHo#76R2U6C!!t_NoOPEF=7AU}WQsIid|^ui7lCr{#d1d;30|f5GHHwlil7TZF}TJ$ zdZtFp$hjrk?(YP~=jk{iQ974~tFPY8KVc2E-5tEYn0Rig? z!en#jKmMjS{>16vD<)X`IbZvo*=ffYkN0_W6-oioUab4@h8pR9#JmXd6p$}ZRzws` z6X=!!1e(ydH7=u3qfn7IeQ8CVU;8~to^IdRQK;DZ?c*XoOe-odEm#-)v4B}mRIX+K z_Q>*KCBd3s)ppa@SH?7bWJC;NaQRuH+#-Am_|C-ZIz^5sxVSRK8de#}u;HbN=RR28 zo9NQf5L=1~6_Yw^Yd0~99~KS%JRaHbKzZ-n@} zgg*>u^7FFQ**tbXf{u}6n4{s9WBi?+QFhYIrdfWe+sCa7JDl>Ew!6y?C*Ru7VpOCO zwX*(hlqQOUWM)ihRkfRgeG>mK(E1I7^*uUN1+QMs{4kLFDaLQ4-J@V9&^*)H4j9$S zEUbjq{D@`DpO6muhe7bbP?=sq1t56x>{A_GT?_nHuOO44IxJv5DMoS}o1|&)T7%p0 z9;M)7_-F&;`RPEU0fEKk8I$VRgK)Sm0`Qc zV}yKpdj6ibA)n0v{qe+D1F*e-AmXRejYBV03avX#FJrzdJ_m6ySVXHK<}lcSlF)xW zevbF^B5B4RRp$iNP6VuO85t{>FPYD)(=yYU1`q6^w|)=t52Iyb`XBPB)>_Or+C?^> z5n%8wYX%~&J7(m3@$IAY^yj@waO8{&Vq;xzWVE|G;3o1a@Uhk~&YYwr2_DScfZnH5rjYe0R-|IeusIKRIT%1<-GJCS~g-1egr-bL@ zna_MtZA>96HXVpu{DDlZ=0BY>|HC;F6QD*o@nT_Bu`xS0)*#g8AT!U+n>XEA8goXf zLNU9&%JYe5w;@!7)>poQ_4CNK-L&n~gA{lTe^f_qzNgmBW|pc|HLmy-rA4@f3>n$& zjGL>Ts)PVwubbpy5ko-(5PJrQqDjaqJQ&xBIFl(Fz0*F5T=7Y!O@ps2^f{1Ek#18J z(Ak@}bnUT!Ihqo^(T!)$n|Jtxo6Gu1NZzd;^^(|5c1bbR>@;Y}p}~JxSy=3)hoTUG z&(uF|z|W+dHJ&@5(5|I{^$K|1cI8u?sVM)HjzaUWcBo61y)9S)v@}Xz-|pRvHAiH* zia2D#X*xc^(pYpFJef!wNJ$q}n!W*6__NL6EwW*B6XD?b%YVGtgh(yfmrhi5*d`O> zp+l#>3rB#G@1cLvNWJ^COBZg}6rY;9u-WFuKHrMZH{2Y&qBf3v2OKPXUjk`j?ks2T z?q0I>Pq>v(F)JgpIM3NI{Lp5108*)Ydv7;#H#z7a0mMgPIsXSyc>DG(e-n541hvw# zL)+WID`R8IVDC3{kNF$-ngvlxfzo>|PF>f~HhtCWWu_aF?`m#c8BJ~0#AnY?S9pQm zbTjMDpFeMI<&I%y%>0CY-g>pG-tJmDL967^y7y1g4D}2iWFLMo`QC%&Z!iA(*5c2s zdG{vx^oq(ke5~f_^7pNfHXrKREYc~t_4)7h$4YmSYYb+6j9E0TWa^D~Z*RVRx6?Y{ z)ymtSh)e(T*N^#nEwV2849&J}nL@iWr}R>1a4(Xq@y^r5<=~oN&nOP;AcPDHT(#jU zV;mAP%Fo5*%m{U3iYL_;^Eoa7KuH+w1;H5AZ`>V?0T1^j)N=7^qbQlVe?fb8W?|-U zt`;Xb1S8Obll$UtmW<$Dh?z5(wLhYD(4)NR@iIV^F(?2AEVVK%_mP@_O@fB-u&39}S#o)h0e*5(YoBC!B5vcbrtB zJc~)#ISc08Lf3nL(7ePJ zSw^GXJx4Uo_wL@q)}7lY@?OTU2p{?&E*h9^Nl-V)`AEVd*s!Q^&e6Z235m9f(h`ES zR`+qSU=FXN77|~TkmT=NcAK1q)`sLK+aD-zD7!S^9{c$zYA1g?jRaE$6n8h8Tjm(9 zEUeOA-m*Ck?Kfwk6<3Qei1=_rJt5`+7jFQYLu9~6Q}~jxIjrW*&D)wZYibb%U%3pY zH-H=V8j$9I@Vl~lP0TFSy3zW4ZhYC9&sP3;dy!Mw3xi|GVS7_eXth=6WeLrh_jGsA zcyIi-!eGW=2^JAP;o~B0!vZY}nYRLV z>Ctyj>=qUi9j*EN!HbF^Ra1I3&r(DQ0k`1z-wo}>CNZV$gIhqNxBT~jbTSQCG^z>y z6EoLjd>4lnO;oohh)Yb8DutSMwB-3K&qpLd*}UjmHii6>`i@Xnt@ zTZB=cQ?`%>Uo;RfJBcovUw;Dv8ctmZ9A3t?&g?nq0&T6=hQoXJr(|Hn05n(`8S~gY8?E|&ok>$Nwj{@*1ZGH zz7X9bCu1}}mtIRw4~fU6dL`dw+>a%=W{M6pq=1zUvK`RIRM+ay4Hj_pC`~nVbRwA+ z5Vvyf-P3|SP8!NQJ!dwEOp{)|jeX$j@+Fjoq&9kht6buUlsE82DR&hU(BJQ-NR4_g zfH$~W&j{bd7kXR9=zXOfx2fqQ!a9nT6A%oA<4S-MAqGYjkZqdryF}H&S@aKj&^~KI zQ;Wbun=jN(QaHu8Y+g?p9|_|UsfI|-8Cy3xiq}MVg)eg-3bdnt{PAZ@*ve0@HjIv# z&b)+9M1XxJC4VlzRZ=oOvR^@xneXR-IG}R!t1u0?r{YgCb(EW%AHBK~(<8@bK4hx1 zEbvTeJ931hV|3xX`SXvQZsmNEMHKzWyx|yA(OBL1aLEMwJ)77;{x!X~^}0&3`0d+g zGjw}GL%aVH){GD=c)5i2@I9g5niC6U5Xa-1cz((6>TW;1#1H`QsAy-GghrzsBcq~Z z&9@R$4MW!>cM*^~xw#pUV$5=m<#*0Pr{7vHYnkOhBaQjR&MNC<>oJ)pgYifCialD+ zMl)I34Td9o@fJ*5i3#w!!_~&v6c^9{itjadL(oX3okf>dtrCxUvXGc=3 zB_7FW_`1`ZccRQw5upL{!>&}La7naF#Mk&LQdKZT5Yc0&Pvc^#{dWm_ukkhQ$unMR* zawQIlyVcW`F_|pg|95)(7`^zD6DIOC;P!0D%nVdZ)(ISU`s8Eo<=Lh0yHGP}tWmF8 z1a)$c)HM{9{qCopeX&MMN5={5dKn22kEZFIjgS-HV>fvB{#sKwE7pG8-0?mAI+`53 z+}`Z39t6}M)w#d?zy?f~0fT#vh6i|Tr&ahxvbu<{0UU}iuWHcxF2(>hruU{GvH;di zr;jk(GHBJ$3$?4L@|fzfy@X{=nKhI6-B}HNJGP=w&jmCAB(3^=)b?Es%##VXyv^bJ zijcJDQnjzGJj6X$g4xnKj6XM;(p0O|b>WOy=Az)NVTbcfqts)A?m2G~p(=`Rgum?c z=&%&3s{S@H?j8}?!MlV3>hL0LjMpOHR77sJdbRv^a<5;|z%s(H7Of^3N7fl) zDDhd%jxBdr>7ePFM9;fPqj zz9~OjE7zMIEBlxZG>GVsltt=^-`Zo>p@hLCi#uSSbcb_HXvtXW&UOrCegwf^{2IqR z87cE-YI-pbhCBBI%|q%)**VFvz~W014css1u}P+Xk7qQqHZr%Qr2U=d_KD}m>AVT^ zn{%ue!=#B9FCBVYYI$ycx6R$|*Es!M0rVA^Gt*hc1WA&!OZM3jiW$q(wcuQ<5$WcSfwZlgu+RWLqZV86jX_9lGY-V({}DAlOVwnH8MQdqzRex$+hn+8N&<=7pr8VbaFN=7 zryA+dZ;lR`Ofb&!8NR-W<)s7?&0l^Qg`ER6)$ap_#x9=bTu^Uj)?hqi(YUFpIR7tS)-Ytq{Yb|RA<*JXFB7(^VJ8J!T~-BAF^L@nfSIq@u+#l8b{391>;ead z{IP$9hUk31X!Ogp#mq%yaQyn783>$FXs>*Y-aXJ2*>YTk07ds2*z-H*|Keri_U~T! z+6CGr7|ond7$bZZpB>CcH!?AauKs}($15_p&zpWXF!&zsltohv(J#{QVh5Roo33VT&y6Tu=Rg>B@^#N#J;@y&sI zUC~ejw^o3^C{f(sPkA%8`1wSZz0%6c?1Z~w4``OP_y!+3_S@8isVx}O!o^Bou(NOQ zEx*VY;RxWB zhOBQz&6A~p?UoU|1B(^SmAHWSy5AAZLyR!qyxDPWhPgF27SqB6+GMb+o7}9sr$UqC zEw*4TEczO5CeY}~tEtnexG>LQ4C1QwvejrD0jQj~3bOn?aNc;JgVf>8q3mvdZOIW7 z`x4GxZ^k2II^Q&^NaHNT=p0qd>y@%-+!;hUDQ&fMuJhZ3p9S^y$A{}}953chpiUp| zw*k`Ymc!pyttPsQK^(h~W_jpt(coWh9lG*dq}dSASf)(Qqx0rc)H6s|DZ@PuZrm2J zk^$xulN(bvw;0eSjqck-Oz^o^A zQ(-*b!un}#Y0NQ=#7r!19B?s_tr)BrrPqWjZ%b_C(a@5_a*6S;HGBLHBxc*|b25+T z^)phHjo(BR&|NfcEKO8Fm`9~#p1QFH4H_5Q!Qzp}+SFk8-#yc!i}vKJBka?3_kl<% zKmizYi`-jePTNr)#;1oB{X|a{%0$0D1QTe58)#T8aJX#d-;C zs8$(yiJVx>v1rV!airnp%PwJA!3sj4Rw8>!3R0JEE4J_bV_m5;K3jV8|YVKRh+fXl}gc)RScZjzL zBzFaNL(Aw8YAL><+Vo`xNk-@5*Ipt?vxtMjHILeI!_!R-mN?rGhM7U{GU#V%24SF# zoKL}Z7eya{7UM@Zjm<63y`T8{I#Fize|B|&X8%)Cre?l?z2(@f2Of+5V~kSK8&S6i zbV-rK)xWD1p5cHeCS-@Mn{Sx64RB#F^N+HmeJ^*M0|32ZY{yRyTxdIHOw!Xcf5ttZ z7lN{ABR8B8?GA(Jv0%pk`s>vveUJ!G&mDC;H55MQ8Rk80re|Zi)^)C92x6W7D~K!*9sIU)JC4s|NFr)!~2UTPMQ>*y6)JC5--{OAOdh=MC7)|OiW+! zn?nkK&6_nudBUcFq$h~rI-`)r? zsxvlk16>}HY|f%E3#j=%r#=^H4Bu?{+fBniMmkqLNSbm|@%%3qU?CHsNzYpT3oiI} z=1=feCpsG&o_%G z_}%DQTxtO`d8PVg`OIw)=dJV1RDc2&?<3_|2v!uXRe8K7@>I@OmlGd7%5bZGbRSg{GhHWT+ZVW^Yv6wBvl^Igc?3G6FD0Wi0> z`#83inwY3%oK+O~4c9YqShJ+^j_s00|AH658S2*LX(UcF!ylVkc-U{9LHtER0m!gW zgv@=0rS14aylh*&Bp+KInZ*e6V*c0DXV0EhPY};>?kUTBf)*~k2BzAy>3Z|?+h)NU z;hYQpDkEMBUahVkN_U=|1V1;VN%o~2HRw6Yz``P)8zk12%#ua5Tcpv((z3%|VtFjI z5kj_*pCN%Kq}trUc|erq#8EtpolZUJ%OMov!0XpHaC!KgdN~#*!f}-Bz!5Q%e$N55 zAV<>icI4b-3$NmG=*0zHlOA8EjDUEU%M1@wdAecY(m3;Lybd&anwPs2_nu^kM7!C*=N!EN9XxT zCp~zqw;1Ytp3)h*p*ZM?Fc^5#ZqBSo>QEsMiewGvh^(a_;5lY=`c%vB2-9+3mGEFh zyU6=~Hw6e<|#v#aUD&-&%?Odw0Mcd9)} znhRFIgcfhQV#4J`g@1>rKUc>G$JJ?a`}(iW05Aqa6B9K<3l6t2}OE_ z{mSUbL5ffIv>bfly-zRYXV2fZDjMb%m$nB~NVXL!uKIJtM~W#0>O^`-yJf!32pn`Y z*MKc$0M5)jowtq^d^D(9^soJ^LT=IWQ@dqEUN7hdF;nPvf}1k5c3xV?rWbsKn^mS3 z_c55ITz@A2&Ye42&jZXXkML7ezt3~l+j8&fa%y&Qq#>3G3dF*!9eY=PJEDJ952s4Q z`~Ff!6D?!`3@>w$gmSfC--`QzeB8qr}KgiMhZ(0OsYYPBJpy(g(|#KI@q>?eW(rG8iP6VPMv)=!`5&F>VXs0G1GDJ3vj>@Btk_)h z_Cdw2bI83eOaw<*iU@7@ zK;QaK=Ka~&Id~0!<+Mip`WWvGSdykv4cP4Y*_A+)3#K8VcEO*n(TGa1i^zhzMYV}2 zLja-JZg!L#ns?eq@{`!k(t@bpFw>|yLCbq~q$0cI-daZl>>SX})Q_s5;Ea1S5m3uE zBrF1{N-SPcQ)+kH>{969w(#LVwis<6JsD1#@PbKSv{$mMd|3VGwy6*UEeP4XcDLtu1TmSBMRV??tlr(Hh*AdnkinKk zP&iZLY8mG%(F5)F-!sC|t2YFpU)?6iDV6XFa&d20jawvd~9($k^J* z%oKcCoQRPD+40rn3xTMRr{YDwS{Kkr>H->nkc{EY<`fH6fYzHHLpBY zeGsi9JO>8|S8Vu&>BSZYd6AqEm0+J^ireR=VU9iNjBC1yIw8#hAhc0%?qMr;Ps9`* ztt2i{wL?U9(r7c^$LAXUyJl17U|?tdEsD3NHX>gkntuphjHtM0S}Vl~+Us$9rx*>9h^kGN3++KDsN*zasMVb~55|qN7FmQt5Fa3^q zqVWHH7TPWZ5zV@|iIj={+^~8(cdw^U@dW+MH{O7p0Q}_HYs@k!2c-cTxqeEAZM}%F zZCJNJ>@a?^9u6!6l!nhe3;t&g`Op7dp?N&~!FZPtcdZ;x2Gqjl0YRdV3F$Lw<%$)& z3h_3%|8Oz3>$Q+vvofHkG)Y(F<{nlL$sT!-1* z&-RbTZ#&YKyCKLKt8~_G?Pd+`cn$Q3iyzfh+g?^~6PEwcE4^QTuOF`RIguKMYZJEt zU-4X?Ghj|_)~<8s9??P05lWsHHlCb98$?M6sFl{K*S4)&yQ*o@{S*W&c1rkbIpQ@w z?2KXZ8Aq5HEPl;`r8wP33J*rm%rh=%SAa%1&*+qQ2nudEa% zx>Va6O+7os?=*(iynzP?G(cO|VuZ$jX!pCn?_%U7pNs;OTu>D?0^l8bBs~(#&|K)n zpg_phDAkyPb(99zo^;Jz{*ZitNc+lPe+|6edbQ=W)|oatw|MPgn6h9>%N8x}w*AY+ zsK*_=6Y|Qw8Lc-D+(CP8#BvA$1n{bXNsBd5D@ux)EdI)L{5lhl4}e190ZRqFCOn*5 zY4NR3Bb*debhM3kjs|ZNyBXW|HvsOY<66W75A2lItB3itOS-j#+$U(N?AW%ARVzLp zE*NR&rTJqVDodi{{&=$?_GYr9<^{!)DYLCQK6`KyII5-fN(|l;2@#fX)EV#z!H?#v zycBU5>OTe=ExB4$hNGK}`e|mA;X&idp@cu%2J=8b1#In_a5Iu#oiQZY@wk+AV(Sub z%V7fk@qFKRvr7cp71ht=kshndZ5gvbT!84WChg&-U` znQX0PjK6zpKJZN0#f=0)g4#8LD-UHaM~@kIB%&<*&bFq$PdxkuxXgujuk=K*1zo|^ zGkVOzoChnK_g<$1EXgyM4dS9T8W3eXcWLpL__ZM0WHJ1@z%BNfsx!-!WdS_d;kwD} zHkO(fB%GnC3~5Qn*Pggf3YblU0Q=^*>ZKnq-J=kI-C@zs{dG5_xm#XY%N}r{7OL+ zvy#QoqJOKjHGZ?-Gsm$ewax&)<%7Sr?i=>bB#=m+w`bVpr`L;$ES@ww7yALJ6E73M z$E>`oP5XyRW--bMjT{_z_VM$x3?E~a@OX8C;n@ejEn5}`)AK;1c%yD+{NQypBIK(5 z{{1;vG4O=A;_#M18>j?Y{pj4mK-(NL{1>&p`Hzkm^=4Cu%jvm1uyj%UZI8mw7e2bq*`H!AoHj9=CL;@gd5%dXo;5qy_iD~>t{9Q*a^>O<> zR#R3BRglQFR>yqs;K$SB-S>Kw!9txGKN_S_w5U>4MXyT;4wrv2KGx1%4XjUXXB#Texyh*AH~Ga|k@}NXWSfc% z{AHR2<%z6m`12O*F0}|1pa`<5Y`Ug6AQNKtd9eLu)m7(HjW!M1lH-=&;@L!p*%b+} z&lh1^DLViN4f}Csg)Rn(T2{T*NDw49DWTD5+q{= z-Qk{-o$ZATnUyu1tsryn`K^Y4FD?aAEiy~Sw^L5M5byyKf}p$o$y&DA4U?fB!?lRE z+bg1Pv#Gxzk{1O%=@<*?$@7PBtQf~Ef>I~^I9MrJOp#{NmWJ_jfRMl6SIZ&;^VTa6 zje>dIo^G|28RHqyI=B@wNt<#pWbA;`_)$T@8p&hNWMpT%mvy#<^6vNrh=n%JrQqu-$&b8WHFIsWUgzWqCIi7GMq48V6_ME+c#qUN8(PY#}*DNDMUGPHrc&ST0 zO+jIra(hOC+Bpoa=C`+swucIl4+(~Y=}*bQd5JEG?Q9s@8rd{Xs^cr!;(?+K5Q=6@+`m+R}jvrPOw2u zb@dEFLs(Du6Fj9kj3WT-oAti~`Q;7(KCj5TIvSs&pl;N=a;iFr81F2rQ~(|c6<^AD+je@Yl{9HZ zp$JJuEeM4c94LqO3OU$PwWW#|qN;(YPnofSU7hOT51DO~4Qn2X!k(5aAE=An(7xM? z!atn0F+V%YDP$`6+4*?gcEbESw$I2exiRtR%VWBqxX+^RlZsp)L15PRZ_ z@z0IE-;3o=aHu}f!Lyb?|5AU^w**lLP`}DzR`5)f;ED(s7|Q2-E;0;Zv}l4h3uL*Y zuKkU?ABj#YNtEYp$4_)N_wo1t;#{6XGzRw&BPpf_G7wTt-qCx83NVoIz=7LoxMbS- zY_74$;rNHj9oj}33qyuSiz(Gq$BnvRQ3339aXqkccHox+LK=~`7%3xHU<8@*%UXwo zWv@5|5E!dAOip`g`Q}6YWp~OZ!C7;2M?}_m?ISO%9$DmRNIwGAK=}?_9wmMC?w$&# z=$F8P!#l<2tOZ2*#{m!VR2;@U8qYe?vOTWC0=-vbUurA`&W zo;cF<$p%IU%b9BVP%Rbt>(M<1&J!ec(1+@3xus(@{5|0=47WP+zr3G+{>RN#jV9`S z-E8nAD{;zb%WxV#lAhqIOdp>)ejatsUa@XWk}#y%+DdnY+-;PPk3mXIXNxh1l2YI6 zeTmFhX!u)N?vXy1+KRI6712ty$yhQ)IpkOIL?Vf&FB{UkxB4CJY|BMA|Cm*z!le|{ z4BZi}x+#Tvs!c7_J9vrU8dYk_iX~g(_vhiHR{r9}a9Unr(C9ccIyc}Jd9=|{1UVev zxu5$PDX%wIFX+f@&XGSQ&jIu{vuAs4?QDs#FnyUNOQNZ2mt?$p>t4G@+(U7HMany1 z9mulRnFTX&A2@^eBG6Xb)(K)C0|v!-0H8)ZNd~CWVr7BVF9S$ow3|V(P}Sx1*aKts zg5m`BY;^9AU+2%4#S{X)+>O%bSTo#E`et{OvWEm__DAOZ9SWpLxztPL^~ zc&n6@Zjm{jzh0;6VC*}haN9pbLzQEzuIm(mD>C2fu;Lb#G-cNb^g<+8d%mbdYMNms z*$zzp)@a-II>U2ip&IB`bo+Ef=F(!N$pD|Xx5c2RyJoW$2lkb4S+WOY5-T&KvykG@ zN0w82!jKv2+Smsq&y<@fgwTXe^A<`jC;bVxkCxZ@%a6D&9OZqCh-3`SQxrJ0dOKSw zVVf~##IR^A>cX(%AZdr`297L8X~tQF~>#P7wZa1 zX%;PIzh-MZZ}>M}k}SUyeRR{{zkg+Y$(eKK7ERtKJO!u>Vj)ciTANKz zzifpTOqojnFNc8vX`JU60DRC(36M4e)~V`|XLN~th}Im=e?X?73ETnz6WO`|Kbhw1 z3iS%=#FtV6mKrwCN^ti~c-+W;^@9dgq66xR6LTN+1Ij9%(vKAY4N1{jRsBZitF{Ms%f_>vU?UeNjwGj6co;1ITGu0+{?W zI`;5j#s$<#v zhhONrC>*N?>pf0ZytnvWXP|GyfSjHIZ*SdS<#O`tsi>E+^+OW64BO*^4Wl$>#KshR z55Qk>%OaGiH>p+hxoW(04VZvptA5X0Ym(#s8u{_z_2O2yK_Fz}14z=Qa!M0WXC<0jE2oOt{;R6#gDnL24-3q*3GGqqX?;h6!Y z(`fI$efvh2_w_Xl+OYU_t?8rDd?sk|1qq8gvqA%k2UT)w8|K5XB0{5IqP!NuD^-;G z;e|@1Hd$Sd?_-+?yBQuwO*@zHTH6&3LU1FP3a?b6f9U+{J8Dm*NwEgQ$4WmwQ1k5ZFDwhPS=zIKq9$VboR8JJHf`DzdFbTHld{=ZcknWGvI<&0 zvn`2@uVTfO`e05o-w4h8%bQ61dM|_K&6(l}hAG`Pbwv&%M{y4$hLY!T9*}k#Dx8=% z0ptcPS&~likvXsh=>5Jwel^eOQdnFQ*Cl6nMZ@3E2m0pD$oYD?;og8p51walEyO!b zICA3;Pq_+0O7;8t;!iVbyXA~eWWsT0fT8Do%80zat;m`NpO0N?QqDAnPq;H&)2XAh=5pP6oE6 zSl`D*FRxRm96NMoT<^6nZftLt^EAMz@Y$Ga51w0BojDADfVE4zX`4SWWO?1gw0T*d zb58(d7?HHB%!-9a51^%vORZ<75df&!(DT8q;&2zY$Vv1`rWu)Y$99~i&GpiF1*NV0 zDB!)w<*lUMXMoP}VP8k)()v@VGEjFweV+*mz(0=c`dl2=V9jVfx=6VaIJ{V#~z3H&T_9St51nvRW#^bnec=(VegKUCJBfIR4CwNi(Kb02+|#UY>A7nD|V`AZPu=rnX{ou zpBoSw7||QhObHwT-&DO*Cs$5)M5KvX@o(?En)o=22QCL#C4~vgwyAqqaci8lb|0e5 zH;E-uqX8&_L%*b83^~%59kL=DeLVym=hD|m=P>GEpV_mc8AlU7@)H)rFNkM2PC>dK z&%rSDdz2=nDGR2)K5bN#!^y1v{5cDrH=ji36#B3?ybSy4(^IB)LXxXqokJtvwym4c z-w2@&TK=>k1PZ&~$I0Gx@SMcge*l{0@`C_)`sg$q8h4O|YyQwzamt8bBmnV&zRZwE za&dJ1R;%hfk1WNWJ=3TR(_u}XOOIcj)wjHQ%tNMbU2Frdetty}YGFK8rSD&6av zyOS@ba&Ftf;#(s#%RXer8}GS65h40Dih17qrSISeO>0=jRCN7t;IA8Tuu>HGC|)HAs?pm4v9*a+CXRyijB^f-hdC|kPI$sNdx zAMRgtwZvTgpvT@d12WkALrXyLsyb&&kPJuJT9qBmD%B_oabM;-fInFShYT5lhj+*!!{GA0l{S> ziiL#+6(w_sGt{lzzqUPnX{X93)YR0BsP^78&cFA6w9CCb~lyGLlWS0x64RtU^56$+bzrJN*_cP|7*Ih5!6mEK^Xm zia$o8f5$7>+UM{AQx`{dk$oDG*>*ynm!84${@W^qPTRTbvR>Ur@c{}o+=BD4G1UXQZ1U3F0m z{LR}K&zkk^0wNKzw>Kz~DcOmcPHS%Kf1o8NeRz1#QD2o|RD^g2wb9JGwVjm9cJ#(nkkAvebEjA} z@>DVx%~#eQsH%g4=a84^e{7)r&82H1F1Ube=^&Dn_u`4Y zJZneEDYuIkE)b70+zbYv%ouia3j;n+)5WEO`$o;4O`)~)+dDOEaQe;o>#~mO!g=#@ z@>kWi2@VdnF^Lx+A0!;erdNx%O9f&kOCL@eF_g0`oyv9grYMhYrP48&Yw@7InyemA1*XLdzpwGt#PH7 zfdQKSZuqp3KIq}$vGczQc9Y8s6(xnFk4wlU=ga6IkL3 z?fOZPvVre5PlOd|rRkq8*;l5~2BOv^H4 zZM^|N5nWGtwRi7|YXu2hLuK87fb|VJWRwNBMb4`|*%ZwNg2&IiXGnq(Xf~)ky&!tgahh5^mu;OeHP#qz!AB<1 z%N=uk@@|e*SuYb%zS|e4IM9ZfP3vSZ@7DbrO3Ne zz;lKN9By+PBn>bGAlSZb+n938hJd-j1^#GH5+2wi3*%VUOAEdcnoklC4KM^i?11_4 z=kLQICXuhu8|;G4#et5CK%06i!JMX0Qwmdk+U2nB>|*Q-AFF`L?6K121clj#YQu{! zAJJHEYmjaurF6{-VZ{JhPGkcm}rxrk`7Tj z=2@v53u3iDtJgKEQ+DIbL#{_}aEF>%tLW}l>x@|!<2gbpD!`_o@ektSsLXdj*O`nv z5ijN~CqnF3K&S2f{)pVt;i5~=AvTrXE)nK%J`2{_Zi3mTS_}>e(I!{i+1TiJ_)FQ$ z$@`bp#$u5UyQHbBih*Z?zQy`4E@$#qzN;MPFB~^+T=0h2ST~gs8)P;M1_}jb_mE{6 zF31*hLIs~* zcUye|>1HI`lz-I$ddlZx8_D^6+dO-dcL-{F{Zbp=+`~2*D<8gQHr*MX-7BalsnGGE zB-T2;yuZB@UUpY0ALwL2$M4d%uwSt4klU#P&*sVv#Ijmk0Gd0-z9C3(UfHxJgKY3J zK8U*+O%H;4&%GTR7hKZGXT}UDg2oi`7G87N07fg77z7@p%T2q@z}NyL>j+lFn+fJ|5ahqS6RK}R6fi6i->Y|a0hkv848B-wchJ**pWKIe zRM^~?JO|=dv|ZxQ_u1#|?POP5O&z(D;6f|l75SWwhnP2^Je~G5t-lOGa?dR?mCUYR zoqBD(vL5B$G}y!Fo?~SmY@;^}e=kc#m**?q-qb|RDMPfN#(uwyb8>20qH2nP01*Rf z=3#_TX4YGI9o*|01OnD6&n9pB!;B zMw3?csGBxT;y{A;0=OSsV0Ksxm|=jv;VspG*trl+HX6>MX(9`e(Z8xI8R8OJIq;%? zwd+cE_nv5Cq-7$bsGK`$h7B4XSIE3GLV&J;@|#$0K3mn!5^f zb+P1S`nIEwk)AwXRozVMPy400S1Gf7-Of(Zj6IDrXoJG=(nxIw?%v)d^*Z}`TxU*+ z@`va@VJRgf;s_RvY~izOhK|08pahP=2aZ4(>Pf!($q&`l)K*g_0z})CZcn)F7#TdK zM-V=GT2Jr9X(aC@OgC+!VrHAu4Ls#=2{tt3J`(JLz%ys0;h39ozt2Az` zdT000fKGwLE)cc|DL`K278=Z-n`YfaQrAwSlf8c|U13gHJUzeV>oS?NAb;xpb}aU_ z4c|`Hho~rc;V+u-R%$)_FWFc!28|HGQMN#kN}SBi6(HGUJ+5_tb$(V;yWaM;9ktEd zH1LNh*YdvLg{Fd7(3NTe#G_^p8lBX zh}$uSYte$7Q9$T;bmbsBal!nKux8vzd@3EVL~jVEbe;-6J6=b#q^)KP1SHGL%t?@WrNGY zI*;x;{m2)-3Z_kQr#pGV?zU{HX|ynWYj#VjOMqAK*Ztr8ZA(Ywn{HioD@whoAxdF} z71l8shCU&I%eMUZP^8X*MBJ}MPD3LYEoT~PcK-JVcpV%MFhV-B<^nB;BECzP#drVS zPZ>vj%hU#K$fWZma~mxZP}W0(Ufj}N-AZ-B+#5ahkERl7(x?>pKE>NxH#?3p+~V%G zyPCbqEO$~JzNF*E6xR3bowl2wf(6738J!>*<9A8$Jcd_kpU|>;OXBiaK~L%>KTbF| zqF?(Ck4tkR>Y>v@Vb3gEUAfg3qq?1KD}ov>&`J1jBk<%7E)ymQ3W=^2IL47i zOtWMAsY%7R1ogodW$Iu{u=juJX&?h)!vcV~x7N-vTrZ9zi_5vbtLNJJ71o60O z$TgjWhlaQs`EA-r{*slc8NCEnSWHu=UPP%(^#ZBhINwwadD>@SH4$U$v$0yz_Sw6z zdA|~C*@A}31RLS*|0p{nI0GpZ4t@_hvMftmb?CjpuL4#dnKdHvvt5rs>~p^mu%XA$ zXj>n?r!qDjirU+z11+9xU?|g74MDZA&dP1hiyjnlm|DQDm>3(%r*+r%(`cO61o48Z z{}NP+L5)xFV3E71MC@b$rnqiQT z;UpYQLy!8Bn!0xnW>9V(cVD7=+z|htzpFp-UN=o1b5LV~O4_Oy7%s)z)R-d9k)4FH z)|U|Efus|TSCb|;oY^bRA%Ow_ndu%->)kiQMSb+2EViN(&TUql^v&yw#j+VTABN4i z17%{X)}^(&x>8NA9EEpS#}cl@-PvcmU=9|lf;k0kXsCw^C)omSY8@n>=jt#dRh>58)tS9!} zn;{Dwcn(a44q=f_6s^_;IJp~Mnt(tF=)ihj@e7{^ZH;t`wgJPPEx6A>Edm)E8x?6K zAz>Dq-h#Mc`wRMFXpP+eAf!~td``|Ay1uXGmaP&;E$}OK)&^n`+6tG}c$jzimr?dM5LI}GsX$=!T{nS#<(P^Y7Ia(<#-jXd8 zA|vG)j$D%NvVzKKr*pv+azGN(=3HJJ9qxeX;c8lE)n}r@x%SkLSm7AL3w(l}GJoo?65sHglI`df3Bcu5~6QelGG)^$>dLQMv8>KrG zLL(ksBDD_pHgd}rbDkg6Ml*tr(Ypj$j}vkwGG-Lxq?>~sn&BzQ#Jpl#jQha6Vifx3 zP2#y-I=>vsJ22kd>sQ0%x=0yu91sxQYXV^M>}QgE;$su4n3vu(Yfmbb{qGW{xzgRZOm#Jfe z)B*jZUm&|B2xY5&l_fbG?OC?eM8D8fhkcRi9c?S0#J;?IH=J5U`3j$VbmREMcYoyO zj$q{!SlRW+xM9#4GTBVw{!)C3HB{*TSD_Vyd(cLPEh8!9yU}>XQmBg%9{Hq42STPX zAG+zgU+ChimCUBjx3E~_T#Z@ID#S{%#RNjsq2NB*FtOCKgrOwJ9%8=88ZoSuom>0} zS)azU_a?-0%0=!CL)qe;hjWyZkHn>9mvww-QJXNa7{+gggp?@+Tnc~noU==7 zuNA@f;`igIV65nRF>&4tJ+BPPG4sg=z(4vjt%!I-92*JHck+KRjLJ&x^GoS<9S9!z zvje9Ga+5#XwD)K_Ax%@YZ9Kw3egw&N=IMoD3PCM66tvHXc_JGJ097yLPnUu|cMGZv^(~ zd*~=yzMXLM=nA6tJv0NwFg2(*=j^ZQ>iq@NnBlfp>!p3mX6WMgG0u*SNLRYBaR7<~ zZU>K#OVI}IyaGM|sOw&$(vlu~FRwmmbF zP7KLFX5&fZ8P0vC?}Q-%csA9_S~{{_1u18Y(?KRWhVij)@^jqSrL$*L*Ir~ZeDAWK zJehdss6)4zKigejRpy<)Fi^%rwTB;3?W&F|*!v|#l^!Z(|%(fAcY6*$ap`Fp~LgM2uI6zXl; z9xOfGARz7&HU;A0!ET$sSA0E5ZAsscICyYU`X4YAZ;6M(Imji&6q6Hq9egLq3@YIC z%m=I+&m|R6gY2cXs&DyA2GQ8wh>8+LSXB8C(&4=K4-FBQXVR}wfXI)TS7aZev3opS zG+-ouQnhwMgtnFF{uS6D7e21FUqm_VC!nX0Z{Vlb4Bx>GKt^#FAqfvdlg*!c0RqIf zr0<`@#?G*@_)cC4sqPG3kdlfnM3)Zs;RdQvRfP!t)sV?TRP!wCzrKWsPG6P#KNMyu z=B3GKx&iOlRqqytfT<}8Aew>V2*v2kcEXww>?)=gb~N_!(YQIvU9x zcBhGS_4)J8jT$vlJ07Kplr^(iA?J}HtM*94iC+!puJLIF0h9IkaeGvaqPO?%E%p7- zx|%5B9y^v~U31pWm#4R6s+=@`pZCS-%>Dp%{mCb3%aw&}L-OuG&8FO<#e;!Enb8we z2J1TlU|;z3z~#%AV@B5Le;AqQA``Zd3TrI(;ijUgG3g!+6^>j>4UJ3^V9~@R>-}nu zcJ$`$exx7d_^*Hc85NiEmSgF~b!Kjl!XpO;4_^5bS>=eRZ2NXSn?GV949Us*sOxAZ zAK9U^^T4#jlp#dq24p}G_&V3attz+KzW(&3^o|Uz0mjH3T@3PO6mHUL-mIB7c|l4x z5Gh4BLyHXweMJKgz+T=WKtVRKPS4`}?nG-ugk|K*V4WLBUf+-z0vbrRI?u`r})P8anR1_BKCvHZxd4 z>=zFHWLH{AT|)C1epMsfEZZ#DHhhsl111=>e+`m6xe7Xo^7{KTci<%~GjDv&xb2o4 zZK09Yzbc2jQ+XF}P*?FfEMU#?`4fNWA^@L4K7-0EoT4+!nw7-re85EbTTv!35Y*PM zWzgl0nM3~6`U{d^CGEka#7JMUV@>n^)= z-%S*)aMO43*}76FvGQg!8b=y7a>+C`ZK3MeE+hivskjts{u;tM1sStI{@G6~*I14s zi2lRkCTa6e`%OAS#40eR*1Kk2M_qS*ToGE^A@{X|OV?#;T~3KDPurD~)xG{;OiwfnnYLlPQ<1%KKW#5A z|6cg?BYc$0pmXdJq~}FPr30*fm+swj-UaVPgNUB6$A514$B88n(Iw_M#%)6WrVIG%LdVB`&KC}J9gat(MV>a0v+2*+Y_nH z>g43);Nwl$NJF36AehD zJm^9J+@|-Gz4Y@-uo3gc&Kj>OniCA z<4Bw01ABBay&veeZUm*ei^ErCini)2L^sfBKd2E%}-&$Dx^`SXNT`yj2XM}%tj z49+dDw052QBhGT)sD$ct@9#hFKKx!gx^#QVb$9gZc8Nw-gSMr-4tA>Q06mO9_S1vk zv$C=j^&f9fsn(lqg0aOd!{~h#?!#@4tySlla;H>e=`~rA6(YvPWd}vq7{Ew`%JxDK z-|Lc+`ZXUMcgnh8-#Z+*R!S-S4u97!+YLW-zgQ6x>hZqHYQdiVmH=CXBAp?Jya;u# zjAf#OlR%whMFJ8Tm7G%UNOE@On>%rF@b5MJr z7|7U@bjsj>VIWF?fK@z4Ms$9dZh*%`0tice0e?2KIaTL1+G?S{_as(hZ`a! z@PK@`q9~r~MnrzJCxm5Zs1g!=KmBGAWkN*=exc+3b1-H8dPY@feZh40$clOJu%fHy zrwz5+JMQ)V-{B>%Aq^f4GiVmMDHD&%t6<%%BhxVBS6M|EwLI$DewS|5TaoH?7JhqQ{n~mK!9w0jj-6w`P%xRRuCDGVutoW!@3r>$p zv_04ZA{qGnmd3L<@o=#sVIQPqM@dCT(9XI0rp=puDT0_XaoKCGND1z9`r$($D(_G+ zUwKPttNDr6FeaIGQ6aay3g7a#$n3))DIbCsuSYv7(m|swxqFF{;=}buvjFXyi47;`dDrH`SS{9 z`_BEh?I8btbGudRVBM?vVSR0N4!9(v1za1PqxAI1wCU@QqiH73WEj;wzX6YFWjH>~ zaJwcNCPGy`)q)l-9bBv%c)%i&-18y+xH{LNq+|QfY`yc3*{=Z7jNz)hTn%3an!aNc z(f-|>e5Up~zg+Xd-|y`cxpYyV}^zonTLLVYS!z^Hif~qw!xA3 zn8FKLrR1R-;N>@2J!Xc@WxSK`8DHouv!OhAx=$xy6EtqCkTw+k!aHZ5O>{~`KNIM9 zmw^&?~6HJGo&sm?V$lSkfo^(2>WRU8|NZ8nOd8kfXs~wPVEF5pg$p z(jrGcY6#W4H)#;z-v;u9!e==bW#fE_v-D2En)IgDb4d@(%VHzQeuhUFK$kG3D2rlL z)V2lbs2)b20UUs=*x#}B(5=$BWv+zan$5M&`sy;cRJ*G zHXc1hE~PZ(!MUGK5iAv7xDQSJ63~j^0{-y*%%Z1l9=p2#pOXIXfBCuyGs>S6@)Ov- z)oP<|-2__jrB9zcd3a`K8{l}}$(??2YyX`zNa%Brf~en(Nv}X<37piSyE=h!N^m|x?JGkgrLvAD4 zTYOp(3*F||rNco-idU?xxEY%RL>>wgFD}3LB28>St@rN(?8V)B+LQ50oT^E?1@l)L zHGW_MANBI!IO;kCB8^#&mORE|>eSV+smOS-eSA74;4AGmkvq5~mr%&$eqKc(E4#dj zqLPK^BfdHVsFuyxv^BnI_Zr$Ja_JKmZT~yOgH$@^kJ;mQ#BtzelU7-hkVWhss-JJ^AYb@DBYgCj{W9lt5m9aWtM_275V9Si zJ#o$#GZzpsszn7eHf$nWqp+tekh3G8hcZiMKq_agTzO$ylnf$W&j-Q}-MMKTT}Ns? z1=%jTJU%<2xknjh4?RP!%Yva5Bh_7PJa2#Nkg0%lU*QV;d*&BS%Abjr*}2UWo4~V(4?*Aad=DFPA2m&9$w31JqUY zbG~4d+;!3hki4ONbsx+}A)K#5+5?|)mFFy}8r_F-G=gRo(h2%z z9KtCmX%jQ+_F7t6f!qs36`7VwKUcV{F1fKsqijeP{pEGT{d{<@uNk-V0tQGaMhsLG zUDf{MnymdRdy;CrH+@J{b}UBdnZ?EJ5Wi^{-itJMSge{iUKS_AR#R!kL;W`F41U}P zM=Z8TJq~UEzZTQq|57zKswz39?zM~!q1PxROw!TRvGgcY-i$>Bpp%pqgeFJ~rl>D5 zpDSup{5A9xwVPp76;Dyblqi{-Ht-OvzMse=UB|f7gQ>o7!V_0!0$J2< zU9Xe77{7mKmF5$~NK-iV5#pzgP#T_hrRjMMwhfW04@&#k@cFQH2PPXhEC8%#lZ5fI zxqIl(O&A);&OJuPY`|_=3t+FPh7_f(Mukb=T)$K< zV)i<9Rc)GTiG6dA>89^dR%|4qd=;|?M4pWK8~t_l=i|W!OnT9n^!{0EWaM@WBqE{) zawJ0A(fHcP!ZWI)X>_|7r*|-Z_0c|J%a$v|ScM^OQw+ZEbo+4k0vbe+M0X5)$f&pc zqSGv08l@!Il;SOIC)2HgQLItjs+wgy`|!At$qcF#q_A$xh&|1dS3zS}7>3MXZ?2|ZKS*&=#* zIV57xD&50qK0N>#fxXv^${rADeMz^~8W4oEIO za@k%-Uaoc9-TBWRtzh$g7=>X7!(`VR=|m19o3H4jB2<6A^R1;eLJxH zbU5nNZ6PASv6gWJCXf!IS3nT!SrIU&y)9XOLWmM-e$4Srrj{2Ftdnj&X{A2Lw92fDW$2Z#VK4n3HblL+oQX-I@EBL+EvE9Y9=(Nnp!c15p zbzM7Voi@0QBvy({M`8fIt5@TiHEx`9a%MF{yJF5pI}M`1=B3JPUthiWdqh-wIBlfC zHrtwX9SIQFRgh5E0(~chooqPfRHn_}i3nCqjpIk!T~C1|c~GBXXSx3|jl(<8dAW7i zmL-W4J|*dw?6@IUY|EPZKa*KT?uPDX%x^MrPZ2Sd$s}0a0V{v@1z6S(ZMl)IWHf(@ zMTalzl+PWcHET>$fQn@VA5O#=SmgPF?q!~U%Qhd^{rT5~Mw;Hbg$eQ-M7)?AsAe3* zO+vwIoP&paIc1s(*TG+R;OS!qulNHNSNzUol^NF>{b;p8D|a@R0Y{o?`J4^tL2;E@)=v!cb zvinEd1M7T|8cQj`caq7)i`v5gQL?Uy#qE8%*(Q%Mnt9(qfw7u8H%kw+x39UvRIVGi zWxiFv>JB47j6r4L$y^MXC+Nk)>}+cdZDiNEJ57!+bwWt+Jv@cqAc7vGFTfsErQ=Tc zH8jdM?P?P~l>Fuc+n-Em+$XCc z8k^Q2JsI-{0UBMLoNr}JyHb2(P^rY~Rzz3%=|oIk;A?Qy^>FEl($}wtBTi#vHe>oM zdg!uwl%I(mwj(u}$Vup3K&vZXH_PAHg~Fc>%NufQ(E9Zr*hvG!MhCY(DHQ7HM7vh7 z{?c+_pp4p%U2{{+A0%BQhTZzk!A+>_%FWP1?o<8HduyE2x9Z+`e5b?Y&M*rpL0(+i!yO!Kw1 zVc1ST$T7BR_oZ$+5qC>NQ(aNeu^SFiU+{p}Fzw#O{f&_CfnvX~&`nKU-2%-{EN*Wc z5yzDG{8d$;9VkFp`)DwgBzr!_c5k)As3OH%odU^tQTZ4{O?nx9(ZPt>Ji{3rEaRo0 zSo=H=BRhcWS~RuPyx8Op3;k0}M1ayXsfBO#IQfcm{gCa?3IxrZ$U8Lj{`VixLGJ#U zUSJd1giHZCmB4Dz;mqO><8e{rDBW3>2SY9sTXW`wCnhF#_p9x*m*ov>Z5Bj^81ZH6 zC;0C->(_6Lr8`@lNBjTyLqv| zY7T?ra6Z9HXNXlu&phNrS05y<*;L}QZCApnhl*mj4c0&TgF`q87H>Y5K% zFMksdCuozkk14LVup~-;S@%zC=Xw2pOaCk5(KIS{FtqaO7)*Sj3uWe#a$jv|lI!7Q zMiAbuMP2rj;kb<Dq}K05#w4NjP#E3>0$iUYhgxY%{84<|hedi?^j|;8@4!HAk5t*4y-R?9?js zzrNX$Js`J%2N(p34*O0ElA73m1N@NC3Ns^R2xnaG837SOeicn9X=w1@gX|Jzv#0j@ zHhzXak*~K=q_H;VyhWw)I534vFPyLfHCS|cf3M|q z)<+HSpw=ETI%Oq)5F~~N|9EPXYma@?R%)&KWmfI z);i5K=Vr?8h}pKz*Wn$jOTL3+WMCEx0m_#V=n?S#LbAByQ2xl|F)mb#GSo)sl@~%| zN!D*MO$R4q&L8AswIsxX8bqgt=KwN0u z@{Ix|yo`4$in`RQ!!&x*Jh6Iy;d>%?9)cbmx*;jaH!UycN5B74?!PCj{H~Nj<_UiF zn8Jr9%@iWw-%Gg=I*<;2zTG*uv9j_$Y61o)f_S*zJwiS|4>dH71?@^$ffvj0ui(w` zr5RDJv|Tp>`{Ca|U$@{!%h(^uQre{&8kY0sop`noWjf3IUY3?#2X|7Gh><4W4$Y92 zxK{@0WQ#19PN=xs@qL9`y;j|)k}ep_nk4OH69XUNO50(#R+PT1TrdZz4taF6FXEFc z2hZx?awPViFHnkKShTNDO){I;-K3N8f6Eem63_em9U&aR&dc+jC(kqD6F4uPXr>W zM(H+JTaQVf7$1tQ0{$>1U?XWa@bFT2mt!8paxwq)N0i14&1_M1F;LB=MJXOA>n2myM^zMBZejeN>u}KucSc91|+q8FKrCs>}Q;x9!DynPtNOz|C zF+P8r0^}sKfO4O}Lsrq=%ORgSVz$oT^JZGN*no~S57jDP$F{1&P8;)=KEGjC`%x~_ zrZO2`@#w_VdVtu5eikV=Lk=o-Q60#3WEcl+>mQ@(vqV;SEa&`@#lqmmAV8*1er@|8 z-nM}U-!NnT-HZ&2iA(abmb~7g9W*p)ZA>PY2G#`s+6Yo^VsL+)fv(b&q`JE(e$Vh7 z6fm?s^s_H22w{wF4+3z|J>^EWB>a8Ujxy5$!c3unr?9*xJrN?ArkUTt;E{J`NP$Kr z^@tQI4Gy$xV~$PKq(4FJVuPy(sam$w@SHZCRmC(42Vt=nVMHa7pY5@m#Fk0T$ejyb zc&$zw1n0ESz&rGXwe{OL$!sK-0uc*sKXdY=`Wmw#ESkFVs4&112{Ov{wyiV^ zF&kvk(iFsYpg!z^k4TO=v-4S>#zETStv1!jy|Xw13|-ZM2n4}3blKo+YX^+a=i{HL zuz$BEFqQ$EHsOtVzIp0n0kS!=O`SKo0Od6kby}127-|!qp0`#*I0W6AjZSfQY3c$3 zm5GnI-V;~af@!Q0{)97Ix7T@jv)`?|weB${Smwo`3c#1&gDy}JXXoWL0p)TBp!Rbj zYok2gqx;X+P#q2!IB+z=H`HFE8{1J6AlvYylT`wp zdad^ks9k>F7L>LaKe6RvYW5H>GY+rwd2!_v2^O`77=g-GNBeWSQA6lY$LH^Q(%(8{ zJEnpym=n^V5EpEK;)2=vP1L0{ANGTyDOR@=wnN(9Al0__t0KT=Os}w~%(>19y#Z2} zF%;1Oa@3@d5T6O~Il2;058ml&`u;}7)YyQYZf{g@(w|Q%4CNCN2s{yoi7UR4 z(ml3ylI}iY8U?U|WAGt7|0vVGc|B`S*8b}`uNOx_q>~# z2J0@$+uAYHDEwQkzw*AX95zMf9~Y@yH$u-x^SH~CsG@7wwE>2Uc;!yTOUR^YOFlWm zR6R^xsE1L;3mQM5k+1M+Op)Abdc3GDX=mEqgKCiUrLj?tNx=6g@6pPbHGblS~qUjvu9XZa80+&i~-lGIsgE z_i>+f77^azqq8$}uRgb0X+F2xDc8gICVYhR!}KN?G0ptZHT(}bSOa=;0}rV!%r#uR zII)XKUnbUMfP!)oX$j-1E!*7^e>x}N1OaJ)sc9u*$1&Tt#;1yF9dSSNE0^7q z`NLN~2a#UVsv9M@xsFz37@?Q_b+X2sOFsUBE7^^;G)*Rn`8l;=`@Mm|=#~V^Ud|1e&XrB(A zFXnFV%Uy%m1UzPxIhvLB&`_aGCAUn*mAOj{7w{a_%uBncQsEPWo4@;&3?(T}*5;)r zJ)34*ziZd^bfc$LLrv)qYc!w2kKzq&jJeJk>5E4=66hJ9&qZr*)El`Yv(M$43~F@6 zNjvR}vvu^g@nI_&HIyxU^r?n%|!dyDWXLK)@^>{2ETzb@;BvPvVuojf-W z9qxawOX%Q_VXfM>O%KREn|GwslHZSQi{})*iYreF8{9l}a2-x)yfwKn3NnlUAI}EL z6aXUGL<)O`a%>h32BRiI;V^u_FK2;T3TM^J?iAoMR0cc5YlEIo(>)cTgO5+TylOXi zk@pDZ&<-&%<*KUJZ9leTSm~bKcRv_cJ+xYo8p8H*&$KsTZ7*qR{w%(!oc zyoCK740TOTeG|B@FlnV1xd<)eRrcL8uYd>SK1pCTG#|rh1NcUTx5Kvx98vP3PNMQfJ*6qNR=VNZt*{5Q7Q#150K7J*U{BG zHP%^l>7Y%fNE$nlgehGrB(q$hJIrF@urPo9IqDU?C%y3Aui3oo`tLSwmsXmBs+oTY z!o=j=ceeI!jMv)LZ^4)zt!+Jya*$-^m9{wlPy6hEo?F7#32*LtPTc+>T*c}OsVd#6 zw!IACGV2kk>iHQ?o^Gn|EsKn$$MY2&MU>DEvf5h_hxQe7lQ-OZx08c4U)#>!)v9Gn z>BciAtg&HXNzQ7%5xk32%~(wL2md$+Pv>L1x|9~R^cavveWs0tU_Z3A-T^Ah{@wA+ z;6r055JyKG(gwVgh0a8pWYoz09uxr8s74R`IM)TmTkp)>T(P$hm%LlUtOYc|&~Da8 zKeoSHPw1KT;v)r%KoTpOG}-!Xz*b;vB~G*mDP*>blA8NnggOD@gJjxMs>koO;=IX^ z!bYl)g<=%l+ukUdP2Yt%%|-{)9X1a(AoMXAps8<4YboQ6nxFRj)0sx zCr-dfB(yUz@N5ft{zNURFs6*|O+owZ_`*nM0}Z$3`>(VcBt(dx4zWNQZ&dy0jq6c` z%NF<|zpPqb0WDiD%<$lyRFBh6e2VN&*G2Xj(cWN$>IKVEp@$21HLgrX(*g%#Ni$4- z4@b@irJu*d-Jk=CXpEAZfb?XiDrqRBa)|t*bj=`WJ*djuE^T(-0SV7`gg5;mR=6=GMry8l)7DX@rC#!;;t*oM`01Rd@j&OVZgF42?eJf6%}vh;K~AYE~M0a zznvSkjIHZMX?cG2GwMda*Oi6C8Tx8fu5%q5Nx-Jiuq5};@t+W;V=S~6Lwf-B@bh*y z7N?G6fLZub=qF`M{Ii)Naw0qtyzr>QoUkSX&=@xP91b>^d?I6TW~2&SxjnJ^xBD9i zfQ*-O5SmaWxqbSc&|JZG^T6DQE2veV&B#iQKm$4f@zl_6<84;qb@bUgekU}Y*u&8$ z6mcSZ)Am0zfiH*r?T+m~P~~={zZ9kn2#EnxyliB4tlTLT584{VzW%@bOpQvKh|O#h zCFlx8?&ZCO2P1FS(X3i-u@)3Enog{Y8UYZEE;qdlfPZI(40Q)P}F5M*oMOW z3wNHIYt2`ckPZKszpx_WNmjzcZ>P6RTnoPXwK;8DPR>e4al2-?M`6-s4#{GLms&EC zM(C3ksq|_pe{&;?xKtU}uAy@iC6`h0;2vGE6q41d{A#nb*6lVSA96_ucFwb(AI-BN zg{r|``(#cT&t;lfxXh__u;zvmt!>T)m!acP$cZF|Th-35<#w4Th<}4nq5R@vzydAu zU|2zJLVVat6&znEc6okMO%uTPgM-Os#Ip?k1U9O6hoWxs?Wf~>#B z_p-9nrRA$*Nr<&=29Me=Qf=P;X`9a7PdsZ=GsBX9}mY6JD5v?J*&vxeK zfd!8C;4XB|Lffl-)t0FlUOXGqJ5U#j<_em%-I-_nrg_jYG$Wa`dg`n1^ZS8J@e%dR z_ZQ?}@?Symv&SPAw98T#yYg>B962krAGp|nvyO>OU+8v&B~_6fG9y(o;a)P~(3etQ zZrijNyR6|PD8;{fd`fikKFWv_sFIX$`&&xVy%~?r&Th{U*X7Mn=%1MX+FEpdj0Xmn z){}V$1P>Qh)ym8CnUQX!*%b!r%hJ=3+*4^>VF4}J_s-7PSVMR>+@-(dUJr+_Mc~o2 zqgga>9_7nH++-BKY`<`uwX>@U8?yL+;5ZA#6b#NE2cEClEOjDKn3SroL2X?G~-o| z=JYTCa)*^I(qVuK6UP8L+MCIoijq$DU!DN@(qHD?7?I>hP78VWCv$iJD3N|k$P_L9 z#}yzCh6r{cEyFf44~0}v;~s|*1zG6q&}?%vv~eav49kA*^k;((k8v6MxWvY@EFohu zQ-~M4nFjkScd4#3gC)zH?3+2v0J$L*oH2bKVd4M?uwTt&yp1d_lOnvMSL{9DaY8nU zRUcP)^tm~cQucr|f-i~D6AljVqK4W-OL3Img1We=z&T55uIYFR@x!#3T;vz2Vq#cY z2z~HilitXXl5Y1IJOnb6%xewic-ZRAV-iiIf|N&6MRHdp8VW>8O{D24pAy|EKTtKQ z^8ZnOdwKmfm3!)vT&*JeO{h&$(RcBjTUs3fJe^>ZD}lJG#m6_>t$IxCw%I~9AS(bf z5@FX~Ekh85r=G&3%8$=22?UC-J7yAJsy?QFQpjxf;wCggD|QVjUE!rMGkh!Z`;D3< zboVo2lO?C9o9f*f;&f}+P5Q2l2kUM~`Zg|gfvy4!P0P|8+E^h6U57_Zg_r`L%VtHgg%8G`hqjc-LFMed(75_C9Hv-1S=0Np2&mpZex>);Lls zWF(RP2a+CDUhcsIg;C}DviQZqvd7n|h-|mqb)zB~u0Dv~fSplIw!FFi9?=Y=y@P27 zpFe;81Y=C_idnKagEIx+t3Yp2mio$C3;JRr=Toarr!8~wXW{AY&hhnOuN%E!UCZ#J zuAV5oPERjV0L~`zE4W-1H8f^El{+XSaXc<%6T{?BH|$`FQWDZS8R_c6m&DK}T zr#v`T=lSW#wL>|bOn#?sZ$7O4gv^g~3eF5{oi%QtGu!Rco3u(dOvy68|IyK5mX&3F zjRuM!?AO12wl!&UUMnsA`QW(Hm(Cw|nHn#Qja}$-d&RRQ+dB+Q#h%IGd7IVdTB?of zEB*WbhW)C#1HZ*erZrp|ZV;ib1I^@Ru_b#xyGuVau-MSR(52tjKT<;ao%n?2>dvv> zdQ5Z7d2xK$*hyRBwP9n8XAgzLwgk;?WO$gD;OpD{l9;eIr}*~un|lEw(m289q3O%A?#kB$ zQju;p;d03Lg}u@$g<_pL9Nx(((KCZ=*-Z~=uQg^DIB8j4yn7vERO2v1$vvM*cCI8L`59R zTmUq20ufebR4@^iK0OH5XwmcfYXVf@#qiDYK#A)VUAP`jfFev6xt}c6@`-=Q(r zma%4_VQa@D<2xoP4^}W3xeU(mLX|tuP zJ{it&)~q9gB3`bDSE=jSmU^S5DJ|bCPz8(B&)LTx;!cZ0p=2h)lSKBe)}c~XiS7mE zck1(LUS2`0b=|w_tl0L@w?LC&;P&3jdP~q4ZZVyJZWVFXw1gNW@6xzPcg<5sziZC@ zPVHVmG?ti7CmtbIUs~Hx7rW!@?I}6&fNBllm(e2k|GN$UTnf$MCDUq$E?>T!VpMIb z-S@bbLAo+vE{Y=D}-t`X2%q;X$n+vSz~bDaLB* zYA(8u#gF@qB7ynqj71-X93`Y>J$x8Li$Zt89wd?=D+ey>pqw48*^>Sibr7O~@m>D_ zy~Y4L#nSdgA7>DR4sb>AChrKDhbYY&Hf#vVfQTf^;o==VYbU48m}vBcLWS0i!RmuE zw9Rv~E)(ZBK~wUmklNOK{Ls3mYg4V*Ye7gdFu_4;@dX+Dqn8fu?r{`=BE^KH6YW`@ z+Uy^7W=O1c2)4XK77<_NS|WdKLNY6*!Z?}libior92iv}jVd07xp)1i-#gTblQW22 z+#vI#;Qpqif{K>I0&(+bQR~PShnO^FhG|jx2$lME)(N)#Io1^3G9RBUJIP4h|OVyTeelq293?|ex0pbJd zesgK2Fg!zzs6XNCk(wjWZcgkEX4V1GOg+A!08oqAs! zt0^27&>zcyAS64~GKtU|$!9l3&U;VE8GE+#n!4s2Jwu22ce4GYbSbIRf0NwripGsn zEEx~(qH5l&KFRnE)bAkwUt@khDJWnymA_~+_$mGG%LE-04&og_l93&80YUcLzGb*4 zx|Eg76SyK6RzB*1kDD045WK>jSBH* z=H?D)%CKL*5>6aTLHpl(*a>U`=ompO2MdWQG&^O@IQk5raQr>D9_%s!P_$<1lV&A1 z+2&Ah_l7MF z48Nom2DYnN^vu3_ORgBG@#RF&N?^Wd3BW3uI{$tpdKj0w|%Na9e(X?4JQ9Z+wY+om1qV?~V zoM70zi+N`uaucD z$XW{1n8SqtA8HlkO~Pvu21tl{`#lM#%*?54>ZVhXc(>`9Th*h#v?(GY)OeHLfakzp zX}@D1*h1X!q^>esgWNS?<|NxAJ9en{HjE6kS6_tjP4vW$Oo0n`AGrSOUZ@*nE)%y~ z?8`6qZLM>i1M#n+F11@GHJU)8w#GgVA$NW;*%am3W;3?^9#2U-GLuZ1#|}Ju#%1ZR zZR@H|i@|&KeYhRDn=(oI{ZS8H0ajpZHqz*z0V9nspzs%Fm1FwP_OBg~$?Q}aN3Fa) ze!zPG8E)7OF_$3`B=hiKM{kdrKaLws%GF|r2R9m?*P0VjG|@>t*t=w)dd(8~*6fi0 zBg-=Rs#2bCyUP{7iqLNJ$kuJz{a;8~w|SmwOC4$M!cU895)Ed95u&#fh6in7CWTeh zMU9)pUM{jXbcwXZiKvBy`OE#o^(sR@lQa4?az*SUPXKIP)#c-4EF*ad-9sB zH+LFI<5>P1ofdLtB(YiJ+*EcneXL~(!gk*}GxYDhPEjU7;jO37$3=QzW)lmIEPZ+? zDiKILK09(ix5SpyN8Nq$WKe8hOO_6DdI^g1bOm{)?DMIyeA5iJ0R( z-=fJv_S5X(gmcFX;v@2lim*AI>=`&JHt8QX4f<%jRs{Zh`&mwIc z7O3x}Q~@_ct(y<+n~|TdO8SyfiJMamF4}0H028UVYj?=-bFb8{w)O6-bG1l`^H4*` z9f#^Mnxxe$-;s}qZ>Ju}{WT1*Y%HT?<;?aZ5{os@9z}V+o8?Y5=p=*<;+X;;Sis5+ zuQ_H=GBTzq3yx`5?mK?`j+k|zz+JT9nXQA%>T&#eD#7}9pI^U|z>J!hHp5PxSXH|m z`^~!1=lZDD6y+e901v!`Qv34N-A+~xkhWnVv$+C_i`v&3OEiJEyorGiRE6Zv-3<;G z>g>MT;Y#G3CchDVDSe5q??Is`qh_4VnVFwNd$psv2NTQ|fjb;QyqVajjzhM9u$-XS zRkRgw%(^`fA3yHI#GB}_xLo(k29e<9bCM4{y}UB{X;_FT-1WbKdLwtv0&>|@8bX@t z^C;JMV$BmKj3i@Av}kCCBq2U4$=lA>tq6~c08^mTe0WeQB+=RsO>hqAGx|;v$xY%u zF-X))TsE^NJvsc%8?TPh@+Y{_T<+|a(^F0g?I@!$Nnv3lPueW}pR9JcaU;XkuDEtL zyF#hiZ^&Gf5qlSZv3*~tJ$2Tsn*?UzZ=pxo>3cR?a{*hmRSyW;T0y*D_-H+`w_H7QdumGA-2-ThLo=~Uj z(q7}@$|@u9awZs&u6#oGS<8NX(`RSxRM4#OV+cq^Sp*Jj+_Wj+O_ACZ@hrWbh7@$Z zoLg+ZU%hytmA*^II_bOOrRv%f94Wd{#AMT4eChB8A-Z;6TO~#rb>zH`c?Pqb=t}v zKm$?01XHAMKvKW9^U&X!4<0vT>ePLww6qdqPAAxW2dxK7?aR21h}Il;4HyP$DOsSY z>36GX=KJrThL3f&qA`B*4peYS%mtR*O!4tKaO$Ad&T-xJBVS{3GG)h_divq}n0FB4 zQRW(i3*%rIGJ2M$+e+O=XOVtSqREcxM*7!lWb%se-Lz>_=_^4y-5b}5{x;M%+qA@W zR&><_6$hQORQ2?d>K*KwMFw1b`O}3ulZkTyV!0nj6b2sPF!1l~QZ|z8>fLpoj}3mi zyni|v9uzD8YL9r-b_5*}!NLKHS`B=S2#t4a>QWo7vu|V2rAtHjvpA{dRXlq1ND&#~ zp7$4QWr$t~MKC%5UsOsgutFf;Kq_!;?KOlzy`8uAsM|KUPl1-ni~&d_60Rt<;~~V` ztRH+q)-S)XxE>g$aoYsk6QXnCe3^ejq$A622w0J(7e&Lzv6;t48rT6_5~YIQ z;{HXt*_v8LMlMMU$}|IOlJ+xCKD#qAnX7OVG3eo?Fhv|k#Ao@%53dJTh;M$y2k;(t ze4{hfVpHp0Qut8*NfVe*T(unk%IjPXVlqH>5u=@~Yl*o3OZ}XhEiYi}@y;yhk-vbD zrue;n{n-x=>I|~olv$ewsy)dpuY%*ALAjv&GIms`tkaHtn2OzA|QU z+p%xj+3K`spB60|dgJ|vZvM@Gy6c_7DQ(luA>qIrh3O+4>>eL=J=#u+QsQmk_;1lm z<6P1^>sUXin7H%QK`sw1BOw~3$%$UK+^=>$gV*H?-PJcGQa-L*c|4T8uY^+?b_!vC zO)OuCch6_IYE#Dnj1LDEKQ-3YR$>+xU0_M|G&?23Nx$dReOst;y6)-eeTTKolJm~} zW47(Xfnoe)H6Tp{?_s~!Q}W0MBh>L>Ej^9$NE-dhzy9^twzxK&Mh2ta@}Z?ZX0Aq6 zRW%R@O>UA6$ozs}4_cs-@KMs3bsS;$$vHCer)eMTe*s_8mZI-Tft0|*lLay>d%yd& z>KZE|cvYupq?`9jID77#lWXs?gteJAN;lvSh{cQ4hnFvZ{vS$HvZPnjms;KOMeWv? zf(zuG?faC+*v11O#0y z-57jlsWqd?^W}qbqTIv<14wD!%dGHiQ&9&HP{mV@2XNQSd;yMFAod1VIQXN(hG-;& z>r8Yg-yy^OVqCgsPbV0kk!L59!8qB{Y2=0$K|lTfo3eQ|nkI8P(9aTEFe-B6r)&2V z2F|lni<+KlGl#Wp@Chsxk-ZR95%HihXsFe+P{bh^a|2qhcI%5zf_tKd2CSk=V1fFY znq@RB6u$?0jbD2=ZBC>7@-1Cb({;jP>qByHf6}ejI&2INz3AAJx6=SHatV2XoT5P9Isy?Q_I#2+?yfaLfL3zm1; zz941jPQ`i+r_7lnWzhf(Mjb0KK{*z+l5p$Q#hcxtE8Xw^QT7h~SN*$piXsOz{jL5} zR?flNz0}&a5#ZU9`%hFrJF>~(g?(s=OYDvbB}5f5TL$LAZpi?{vPZlWETYVDahN-I zGq;7xC1Tw-8orTr2V!j@H)sl7rw2tue@XwC<~wZ}5g$|L|Fz+Kn6;&4Uq(2|DwvqN zt&gvT0?~GwXJeK!{69Gn?*8)+t@<8HM`34jZg=G=7moXjWTnIvOC8fO9AL%^i|Xi1 z(9|DsrPkqeR`~ScAsGgv4}>~~JLvy^WSw_B*Ztf6KWHxvEvY0UEzzVwLm?q)DT%bS zMLSYg3Js$|OQEE$2GQVZBC9E-G?Ys&DAVl9Yzf#BellJ9& z+EdDM=9@yEjR=8u9)wSef!=8M~m!3ou9Vl+T#oPk7y~qWHSD|fh&vXbD#4jj)g>}GO zW9rkY=x{uXFgD>&2B$Y1G(Y&!J>#K2TK{)fQ%CvQ&f?gHY4T&p_V)XO-?JG4e2l8& z>YW0e;p$cFQdI!Fk1zfrDlHNdImPYumOMUVD;Yz+`s|t0j_O}i-@Mh-P4|5uk>wm6 z#*kmN)Z*e1r9ax8k|qldVe+Y}Qy&`DT*86w0DmP23FGPw@78jtw7ClRep)lB*XD!8 zbu}m8=q7v$l{}ldJgBOV>+?2;hqs&RnJ00mw<={(R}+)HG(HQq$FuoHrjn@nb zYFaNFQtL+iP>joO!qNEEX>Xp(3yA)0AU@m*2!k0^UL$yJpTB)8CH7&&X0_nG`Y z^(J*wtI#|+z{QQ|!PI3!bUqBUSPjyB%Ps=ytmYj%ZpS4A<9zwO%mmTh)6lvk*H5W< zb*smi!2lSOYW|}1S7e=fvQ5)?s|@2tG#~A;5<9hshhD6MbMw804$gaPD?iOe!tZnB zWETnTzq~{8gCDKJy(>OGG9WEvtlH2yyFiMvZ8|fxEK!&k-uM2;h}>^l4OiRlAEb1w zSl7$zv!y@4Iefc}E&!aWL$#X`MTj!82VF*|h}>fCNouvy4f&Inm7Y!|uf^^-!-eZ{R!S74VJ?Zn*)}wNm(TMrN(%LmP6fxb)%%GHWW~m={^hVMgl(>D<#+ zjT@mPW-)npMp<_4{q5^l*|?7T05Z#9vdNsE?SOh{T${)|3$9-8pPb(b!6%S8;Xx!2 z6c%_p4}Wb@v~4Nl?hIDq4>XhFps;miuc1CiUtiYxckS|XSyeyZM?C+Z6{UeyMaKHH z13FYtD2S^5bZQwh91*84`}vTTu4wV)>({)vQxIr(c{K{Y;Bmt~HCyMu#6^4qaiu(f>#44_ic zof;!^V@gSf40QC416s<8YswR(-Je!|2-GZ=h8{dq#*LpoefoyFA4*lgcw{?gSU(3! zxl2E41VD6SGOg%zfX@nh6<7saxnf!#*1+6-j8WGa9&};Qw^;Mv;xw0b8)TRAwj&$P zdA48@7um^&7Fd9MbuUEy9Gb>JN$eYMGd^@`Z*TrQzuQ02c}6pSX>Vddj}xDG?*d8q$Oym1$ehb-1I2VH48Jy2|h4ic7ty zfl&jzYfG$9HkiUv)3BM}qvZRX!SbWJtl8ZCD3 z^YuA2>YJ)tit{l(+Guj2kxnNaB!sDyBbv{Ozqs+!$ls?_fph^nz+iSi*#ful1)Lj{ zjPD=plumV;h6;x>j{;@dj^&AG&d3HL*b*j+G*z``*P*JC?U|&Fez)uMKRV$~M069~ zPRVzx0A=NGLkGvJJfkftt<~G^-jg-TFbM*_315ZE9HnsV7sV3ruWqasApq_&PX#a& zd<^rpFC39i>f0DjL_Zt>vU-+@UcT+t5r;KROdNM4+#*2*RIVjFPPNu+8q}y}7!@p- zVhq!XG9k#k*>1sE(URc9dyT82KQ$qy=ljfVg6s2eD`x&?{-E2WKifeg)bxJ&*C@n| zJ+~&FJ*yH_w_AkaqxHC;n)Pa9^Y)|GqG)hf@wY%`PUbvtvx}{V6;uLLcUxG$#^d8( zn3^L|Gc+0MT{qU3_f5 zOtd$xe>6d0S5GCbi?ssy zc{KmRqq6I`HTdvs@H=C`W@UO2dR>{jr!o;(iQaC)jDZ6Nv;itUXzJ7KSmgPbxoe1M z9Pm{{0+2<^SMQH+r&G5>EhFYT=&}3i6d4a?b~Y=t0pIKUvu_NtZAW2t#`){BDv zT}iQ_zxX1vXWUQy-nKpBhyPdeQLEv-SDopIQRl*J8$wf~bp9Fo-6E(B@qO%o363Mw zw^n`suuo&)(Z=@cW?6lnlw8hX6d@=55@cyzLcmKpY{H%pj!bCYK!;B)u7L(uF_VEq z$NImxnK&U_$G?8sv?p$-T9v)zP5h_!=%PH@7^uv<-&zqkk3UB ziY%KBOm=`iE*o85NW59fwjrk1#o>g(qagGC?GLk)O(<`Q8l6^8EN2^H12h`xQvH030gD}B8vh( z`}Dl+@K*xZiNKa9E_E-|1sJ=#SGNaJ^Wu^!2m=@I2K^3l@Ms*iFy%$TE_wkPlwcmg z+($1zQsa>og18PBPj?-smf56jzxjt|1Z)`FQ2*Sc=g()H7^L&rY~ASeJmqUwTEm1J z>#ku?Fv9T%s)yP4{2^tDhS9S!c2t~ooFfpRf{JCAyFhEKP+*eWBJAf;5FJ^aA*}$z zg;1+8@#{E=9P#m}N!(f;zU*Dy-PAU$$K+;amm?e1kH2Yt*e>&qiRFQEqP3Kfu#pwN zlrvprJLZDKSV%pwe+7&_92=X#eB7rKyWz~)Ra*8ny^#0Mm4l0RgNf@ zD?YK2HF)#Gke|QT&bxnfd(VEA@Wdrq%anaz6#60uWpaZCVT*7qKas&byRvq2-Ty1* z^EErv)fAO1E<*<=Mzn+$s4eTOU00u1#OQ>0e@UU+>2AGmfeRUH8|G7U6Paw{=!Ns5 zd5*yTM6A&C!mRh$ycM7icX$-TyWVi%JEL;v59$*WG=+l)PErWh?H4kp~Cm- zg0Vh?-ncf~!L!0aXtM5I#*FIc$5juwZL@%n$hz{LMJ<~bY{x&j2FgrSb47+*rIUuw zgS}C_L(zg!`^>D5pC`+R{S$En_4y`;JL%$S<*rvZZaPdb1cu0Ec#F%S><`oxgI;sg z#lfTfWzrCgaVB@+&nfX6`T10db-BplL)V_0G#&ufK)eA#=f#Gc?^j*kuIum{OvViw zJXn?l@sLt+aUex%gR|Y&gmXQ#bK;gf)LW zwMVqY#?F>Y53~%i%fS@l_1bXRK9<;QBNCXL&mn**mJhwHJ`G10x0#L?49qlSSHT}> z8f2v%Xcr2eX(d?;go@=2irf+&$8dhG_Qj#%eokH`UwlD608^etRJ;JXIsG(9od~pu zuF`tIPGxHqgJ9+|&Wlv(Ok(1$I~Nmmk6_h<=Ym)YahyxYMCu`T7D%u_he`G@&;@N+j*_kJl$Gc93aCUbT!^Z2OIv6PEj+OCUFWCu;`6M#A*^=R$QD)1ZT|t8$gL~ zGkEBoe8cQ`Gk-?X!+QR$N3r=95fS0CU_lPSj^2N2^5*W0sU#Fzg97QZTk%Fk%-KIC z6!YDf3}|k5CDQ$8P@*WTlb=m|30Cl%Pp?wY&j zJZU>(@JQF(uFbLC{dKCk4t)H)ds@WO8&F9sK9c-Ozs?S!OgL|f z^3Ww`qg_kea`JMR<6jl|>z zWm^2#i<2p|#n=~ihY+6Y_>nZu$!KJkvV~sd|58-kL`Knf)ldA7Ol95idnft-upUsN zVA!%{%fs&{Kn3LALG4ih?V#^xI?ZusK9jZBPqv`HfJc~$*dn?9t-7X>AVSap8lumD zE-1OSPq}WRnM;<~lFyP%(#^+=kyvqR#-#u<5PBav!uiwtGA|%TOPu?b&}LM@46^T; zW!HJgstwQzbYL+M0dWUGGuH*5KA%>UL=hXzaFMtP5Xt~lCPy!FUKRWLO&y`fxm zbER|a>fuu#oTu5mD*w#+^RYhfkv#D ztL0je5$w26c;(dz4++(!cRFflNC|`Z`!$J{HfmI~!JLH)t$B3%6#N|iV5ga^3>i)@ zy_&*TBx6rxbr`lzu?~^5LvVacr<)Pyh9^%3N2GWIl7neBLTN?)-Bx5XG!-bJWNw?B zMr&`?W?O50d+i4`B|u#zwUNMI2>zAqm^^|E78=m&`Hwv5sDP-^_b_O72alK?RBczP zzfU`wXVAQXcTZtM5z$WEa+|->rFpCWr@G(h)6le+s|Sx7cQ09?$J>+O%M8*BYBUMe z%w!_9j4aybeXVd)Nn*&LJ!55ywt`+U=7ddI`l``IL2^Vv&uI2vuWXo%22N(Vpxs#w zU7j-cOmcE^OJsrEHh{OiHt)pS5qIVW%fiiWfHEV3yw78X;t^v;&QsQ|E^Jo$$n0gq0WUaW!yQ-10vU zu-p64ioMnvDWZX6qF%&ovu8kAroG7vL|Q96xZ?6FhrMV z6(u#*EbMSsty;C$@Fp)-7i^Z&6%(oMsz!P@u3p{1Z_U1<+F`&pKogxFZQHf8;Oer} z@eoxcs(i-_5mmQn-Q~SfY1~)4{5pGgV+stu#%TCsaDpPqsVOPNMMc-8Z~o{*2A@qL zl~RAVl{w%wjFrFNpq~0j5{?2D3D`?~7X2%#XPn1m!87wZO94#Oo~3A?&*Qq!n?4O( z@3ObH((KW}X$t)hBbhPmIgA|OdHmz5kTX*fJ8Z1gf<~ZGY6(d#AL8d>wYVf53(C2IAgrQ&&J(<8_>NHB-jzyrim ziMG=G(9Ov7C|IKmOmg>Iv~9bEDR>B{Ih0><0jc2jtJq~uj+#Sh%o2vp>{K~JY$tQ4 z^8>+V50U7pYR*CpgR-^5Z9v#>@I9WqlsZ(Xa!@(*at?SSU*HB|&@$EZve;ypbNl_F zkAug4IQaEoDeB>hFU3DdCZHlB#p3l+*U1pK(UHaf<{*4OR`pxEmE)R#+Ct2kfDcO} z(pnNRWMt!kQupD%Eql4^85U^hnoZC=>d?J^RC6$(`p3HV4;ig3TXfPFJwRbY{KuML z3pv-OQmn$vI;J;}ZGa~Yrcc<{r3WNbIKztMRvm17srIzYmCPLPsVZpwf}DHr#CWIE zlsvyv!K(VzL%sVYH&dPsecAilk(K)zZ7zz??IToS&Q3n~*Z3`|#V zs=dok@7RMry{2;SzJ0O3&)aBxZpC&P45KONrTl;sV#;UKL&hKgj8$D*steG>-C^M712cj=pk%UHK+~;twOSW&{ z9*a3~>_y>=yD<*kCtT`0`_^9B2_V@Qq;d*NNGJ~A2t`#R9Xn&=l+iBqK3T5obY42R z);H7-XqNXQAIBOb%^CPb_M2{;=>Nw87(K-La2~Sxo1SW98`u6!Dv{@*zI&)<^9-iX zIQa9^o}WX`CnV&uNOs}W?Gjh_5LaatB!l#`$3FxOEHy&WuUb9v%i1Y=o$Hzb#43# z+0sCJrT4g`tF^4vrZ-pCD8SZnN%{bFlzLkL4lu@!fOXxOW2U&Z|dJCbRqot-_+Qii*m~J5GRh@>_(n0$s{p-x8Kc3#Ht|)CxR11qfq!asu@_W{j zC6Ptny%xpl1eeetig1vGAc`lRO&_!jzdjjU^08_R#&#h4ze<16TKD@E*L@cyq%3!0 z8H5E20)4bpy!SaYo5Vtc^m)413b%aSXtU;XM7#Orzj-KS$0w{Q`$@Oqc`5Jvk00V+ z(@ibF!8tEBoxC&%S(n7Osb_V^tqe~T_c>`XnKDRiKSndNn|+fEV!cw*$Y+q%S8{Fv z?bNXgyzkjsH~q$q3c~K0HQ(Gha<&Bi{%M0-LeX<<;~ABtg_7sXeqik6roLY9Eu@rI zJGKc26!mGS|1kaG!vPP9gGcpf@72Fm%sBE4Z%UQDJh-g2BIn|$VTKZucNfD+z^+TT z)s{Gi;|^VQ(Sk-0KbhA>79w5VYP%jfK*vllE5s>NJ%`Z+!FVJ!{i@gu5EIibGzI=o&wUvY z4?i*SgdZX+5z!%}O|1-d+&{(4Od*!Hd3Utt`{VJv4LN?*WE$fRRDAVoTCSS&2ZeFP zuP<{&9}h2M-)kDEA?tT!$#Wb@2-n%F?@yMP4BpCQOverAJY%L#^0-p-e6HCB(l>nu zJDpmSiSIh{?1!J2oEFo3pkY2N~d}KNWLT z(t(T>oS4*0@CL{pe)m6A^?1IR=YbAXN;IIkXio6K>Ynm=(bEg#-z^U+nYn)fL|qsOA4SXI@jpGO#cU76C8YF=N#Mf27<2e7HS{wYwZ@4 zr=^PtgUYdvGwfM-|G6}$t=KhUGE8A|n9iwPyLQd7B&9R)+L1EYqol#1PS)1l``z6Q z`JdI>4Ty8Mv)`Ouc&Sk3D3aDb^10lE78IeC5i1%2h+nf-YD zD`e1n;v7F5-3PVL66QeMNR2pGnm#t+%gy3I$$&pKo@nmr9IXH)?L99ApG(3`3`h^N z<2lFgg|rN*9O?c!q&y5PsP}@6+r(7`oL7w2_yusqY8#+sU$Qofc_E{C^O-X>IU{e% z%H|LHW5b$0UX;D;GjnA=4U$aUHBhi4Cp?b862;*ALYl;3;}d(}Yy;+Bc0XE;R{B8kobs$c}P;tHce zF=pk?;;_PW;+a_)&Cow-1)laQC8hr9l+wn|c~i2DjYub8mSf11GVV*~En}>2O(;wx zO5Po(sXK1*?-{fvzMPCQ5W1?IrGO5c7-+svU7Yb23E6%rk-kza-@7=#Yi(Dj#CsQotcz`ngJ%X45dSb{S zzV&rcqyIt2Bt_;`4K-e|(T8P))Md^I?JqDCK!FBgUPKYalifp^dzU3S*d>hyMdrXX zVh2bME|T7mA%Bm+fA_=^dGrA&Wlp#5lbuAh#~sbve%?ADDk_T6ttLQc;=M?9GxylR zG*YJ@<8Cs(O+RxbWtgv~KE=jds#}JhX`#*eW0sHR89yI0!lQq#c~FFd#yO}^(U+4o zQEkj3`FVm9KK|C@fhqf)WquoQ5^B+n|3Yi2lRmYIUW(J(8O8)Y>EwMge`7Dc(?4w2?TzdzS~0>x<4d=uvPg5<;(*id zO(AF2ao>FUehQq~VM7|?$%;Bq;Z-%6fI!VKmIq9MbM5hC<6D_|JS3U_mO_l#U!R&^ zbN$SXH{L{)bAU&T$HFx-un68LhGf8~{qZ|@?*hUsQG`L0 z@Be$@fuO?DVG7~C_-Q*3b>3&GMNVE+Pn|7;4Gc~<$vASPq|F!PAdiPGqz_g9kR$U7 ztg{gUhTfv$^$1Pf=4KpKkjAEz*y3KxnR~Nt{c(rxqjzYCjFWI2OC~ETSRpfHxa*K- z7!V@9>~P32WeA(Ehybv^YwiTCvvpj#k)Sgh84jBN)gVOc+e%{1(Js4MHFGs>W?8!F$}|kCbS>`tYGTRkwgExtaGSPI1&(%nKZ*?Z6GVYRf{~ z|00P+p{i2XRZJ}T1PNP*|8@%Ne9g3e)^OavHWE(e^$gWWqbMrFYfj!58AOJD$s6Tp zowpCKAQ!GaWET)&tzr8M3j*>5np%5zDs|3|bN zkh8|zUEGnZzQw%K9#2#2m~NvFEPDCU7gZ;b>j+(yTrwvw3&iglY1mpF>e6h%qcdHo zIa!%4i$^ib6PFXlXmc;zkdl&XHH$|iDF1&*Py~r)2?<=3(2fTPUYl9EYLkIg8P4HB z?#7n;wKdYbp`#X6s!rDQ;SD78buheATMaMbZ&s@qum3QjJk zjVf~f$!YF-0KqX`!V}J(jpk1nU7WDEFfsU1T{k*!V2HgWYn=u4TgjqnWPUkR7qeX) z9UOMOe;!5%1_V($_GBDTGyut=6^^hr*MT8dD23Mhq%Dz|i^1u3L7-dl8kB}9v4*%^ zQM7Dg-C00ogFeI`n&$X~gzGsuV`Hy*Ui%>io^)k8wG>&S7hL4W8LKDpsPW#OzGA_C zq=DCOf9J~lG8&*eRW1jkOf zqTLAzNL>lSM8je-M_J22%O87Vipo$av}oPh9OY@hIvOCsVI5~GiMF}cIUrn3+3=cA zd#XhqYiW2gSG5lX_ko8)xzl7gVC+bupWD1jcE7k_0H=^`4pY}Hca2$<1;lgo*SfYC z!DbBe**SJR0s+y;5_iU2UNW!ik(wRsXEZX(F86K_0*qF2Wtn@_YQ)60Z3ZFJ-W$3E z^i&pc%IQG;CuU`^YOUD0$e;!lSTS~T;?IZD^bA^ma@NL6m(~Y-^w4L~Zw~0jF(g`K zg<=je&4tW}GvE93TfVcB5IgjpwuT4enYfhAKpfrqUx&u{H}sE=)cG+K_cPW&;5;b{ zXGuY21+`yia%`#C9@CtN5n8=^xlCxdg%0_5YOE8Hsu~#Hlj)yEo0XA|5RG(e54K@& z#e8z}Yj?&3+{n$(xBXsW!u_Vt@`tty;jxgec==o9TmxNXF zfhFD1rP#r0a0DT3nA95bIj0(UR0MqrSogjfsa+2EaQI+=V$@fMwV~6AB8si6{=FOB z9~webz)=JiN}Es))fvtSzQ0ua_ohDWVt(yy(H_mF$Hk)y%qlywhgu^fPNN(g)PUP` z2(%9>*9aWu-{lSmu^4KX4gC7OIpAfjMx5ahe#Cpor)zwO#Z4NfP z+}Wwt)c^HlEh0#y=g zpXscO&$H-RC&j0B7&8AbpYuscM-b{%*!n8hWw3W7S6#qH7#^y8DK}_y$8b7XX0yr_AI~04*gBntkw? zOE(0jAq6%h=u3)1$R1H4{u*){IvU>f8WqR-_0?+iGoquS7@}xL$iQFq1&bocpO1im zaRd}1iU&I$Cfwmk2g~x+D0a8sIqj5E?^twN%hR1(wv6ghFX)f`8-`e3d-`ra-N zKb=SMT&R`BKuo52R-E4bbQ-9-kTHD6?7QEAVQ{Wm(4bi}*@modqG`40MDP4RDPkaN zunl2J(Lt9Vr(i;4+wg${P6^)D_A!^;b3fU-=UlpUsc)jH@&^Ihk&qC{$!`xGbur^0 z%;$XEPq8#^;w}xQ?GXQ9l+NHt5R)&mH4ng1+F^ufyPmzgJkY2gxrWw$`x^>&i7nkuUy4dc4(IEj>wZk@YD%?-T zl6_@T7;17E7BdKP0+9|83Q7rzMC9qc&x8G_MX$6k2AA33c%eU;`yYiP%vJiGJE~Ao zAVKwx)h}c}!ByIW`2~sk!sk#-@twEOu!{DQ(plGe6YZwMNyptX$AO-L=Sw-#jN2e= z3z0H9HdZt)iQWbwsZ;D3&`sr^T|jS7)sg<-!Ndg@!53b_)gv>P1{=ttY{j|eF+0ij znb-f~ihjmrlr$WAvtjPdn@#YrBBV8F)aW`lrj6r<-%o!QO-`I=0myHz1w`>rUEZ7%6i$wLOF2c?Fikw+bJ&lGTLGG ztkAN1b16JvpCt)n%*73rN`mQ8>+dxqP-b_c?FO;m46+A3V%gvR>)M~lDQWgg38_p{ zomuzGgEzhV&!^;Y=_?O;-!^FTz@&4w6{| zpu|+%N#F)SOge49mY?{D4JX7JY$@)Mwq)r0WyEXc;iF~ri%JK@!I%|RR*ks;3ge>Q z{rP^QSsaCezdzunfPfVVl>1+Pe_J4CzE3Znym@%NKc#wT*iMXCQjw=c z3|OR&wUCEeXERPG&eZGto;0!4e8`jy!0TA>EEJNtbVAJMwqOP4^^A-yylaXB!+`_m z0^YN}LY8nYl|bPvHf>&*m;d#?xjo`Qn)Z@RCJ9E5M}97Y+t)6GXv}Nkcmj>~1_PZ~ z20r~yoajMdm#u*N2d}i56=ZuI(3bOd@6uXCe-}scu^>~=H#ey@V>F_~x0cwSfW{rE z>=;_&(1yxF({RAKu~Cr8SDjBSFlG3#%jse(Bk%SlBT4a|<8!bzm+iymDK zhO74<+4XXMq{K7MD~q0#gRYEii1CS4SnpovTi8!u=6ZgdlU4+mb|O8VG?p@42bJTX}nnIZS*qA2cHcFSzXOxR5PHxz6=9TIW%4JA9Euv43seD#Br zBQa}E*S0S@1Hp7XHFY(l!AQR**O(&b*Gunla{XntXJc4)i|-T%6ASQT75|a^h0Vhl z+M@nZ4+x;QXdOf;Em#l#4MG(DawH}LB_7n|xa~Cq@}pqQ9eQ}oWMPKjU4H%k)`QnP zo60uIx%i)$*zjhf|cdPzng zPc?v)_#11!w_X)(4q6VJxo^WU$!R{mtfjv!!~mtG+(&zWuy|kdEe&hh2nt0A=0A#h z!Q^CUC*N>%!R9Vt2O*|Aw{3HSrc(d~Ydf9IBv8)Q%MwLA2~UyiSr!#~Vyb=2@Ev(| zTsa&<5{%INewOOP1=+J?E8_h9)6S+OPw76C_4ph=EBHZ;z(xjhr9|E_wfO0|xL3M9`_Y6Z}0bHTTD96afuh9(9V3q(x@A}cy(Svt$ikZjRL zj-bFrsxE4Dl5D?41%K~0h2-NS%6b;@@}hCe(meX!>?%wU)?)9*OnGiqA{(iMgDK-bUoj2*75NRNx2pL z_>6taUaiHEk1PU;9oL-C`5%(VrS+4vQq*ancE{B!=KK} ziGnhEN1(vQTxvCB6S3{4HQHO$=ZJkomu2BrN9gSX(en23u3A-3wUae?21Q8>y%&qe z4kBxmEx|Bfn}AL!+Q1=(I;OjbfIwqV5Z*pPY_0_g4|=QJL4S_%{Hhd=-}~>Y(Wx;T z*7R8A1OLtMr1sqW0ixI`<5UkS1Ma>CQ;>lE8BmZ{1|Oa9ucT-eWCQ|d5zL{efY!zU zR1DYr-bI*rb*BYHT&>e87KxhQ+ zjb@Y~$r-WV_(tZ;o6}7qxiU}N(4b}j%8}V)qDdx4r_NxsqlatDJi}0`JF@wh6|1F( zX0Q-tVJ?@~^V3So`FRD2aaEN#;En)#yYHl0RjRqiF|7n{l`24w!3-3MdLy5-cr%*e zX$;JxK+{FT%j_c}W_{<&Lqxv9XQI0rwL-`Jal(;@TMJ2zDn99kWgF4u10c%$Cl$2g z$$rx4*e@eBJAF~MO}jgb_g(n~*ipd;v>njgz-beRlg0f-OF&|n!QDrfN&wxfmh+$^nV^QRVD=^o1=pGY~Y8Ni}#FOtRfq-FV*q&TKK+sTOoZ&UpI zaJ~>u?%MV1TDR!yb>ypalBPI10wi<*NX_E8($w9+KL}ZxSi{iLNeM&u@*l4uYkE=y z$r+UBeB#?QDG*K5a$dYqKss(pJgB@w09jgn0?M2t|+gjW+`0Jydu zM6^ZHxN+T^fiSx!r@Mf82vtSCm(5W`3t4}_wH2NjK}*I$ zO7qbq9Fw+`WHAw?b~+csvtY5+Vtq?#v?)~GK)7)OWa+F&ITA?i9+R14HmhErl9Hlw zmk!_Uwq?QX*^Az&N5{S^{4w{&xM~Z}h)a?8+Sf#kk3Cg*_SCmBlg!A1_hn>|7q4F3 zK-eILc|=qFJ=I^!Lf0)K4IEkREZYU~NFh!x1ssCN^Fz-})mCpN>n|Xp%GNy}o8RJC zdXL;{*|@8opN34$^SA+b++&C>^c02$58p2A()2p|X#EEZY}HG~Hg-ClbFlj#>}RI0NT|S?E2&SV^}7G zcT#!GpN}-FDWLcZUXbu};08!jx5T{pJe$-?7e}O{0cZeED+!k+e!&m$uoH^P9jNV(iVqDoO zoF7VR;b~%%pMkhZL}3>1REyx)zh#iv&LPPW5U69iz1IAFO)`I|QHrj<_%&hxg;^@m z>GSvR54%x~trw<8G;;X3vyP!6;p>glwF`|ZA6w_Yu1+f@zYV8k`}wu3mCT)x!n|*b zDjE>hh0`VDPBb<09?2&C--ha*utc`o7vRnD@9=&xcV%@QYLqkI1aK_F{U7JfEw;v$!{?^VhFmr(9ll(eG}r_-@l& zsoVDtTBgi=9JnZ2^L!KEQ5~WVwD_;kWTu(f&UBpS_mZquPz7U_E?s)upjGWSgEh1+ zHRsy?G*!``VISaYZGh-!7O7tWOTS>feuKBeuTc`n!YaY1-&_Rc$)(3_vS?acNmE=T z(U)-b(%>H1@H(&8T3}yPFXNcqxcub6XwA$fL^<<+dSWs+J4p@G6GASeu@b;((>M^X zV%lKB)gN!(%mb+DhAsp)AgA2BL%Vi;=I6#VIc*^On-OL<1i%C$aO@b%8&e5-Wp-F| z))1V9q3g1vdUJ?`Tm-=AtF6q^fmc8v$g|uwd6|at;FRB9o9kyiTSqwoiwl;C`gzZS&8dxcS^&dA}3f3!# z6Gb&H%mfr;wkgd{#T9DP>fZSxf_}+HCh`{*g$h}Zy*39x-*R4`8PLU?qF*jH?Q%3I zmE$`sE@aAA$RSRzZ1bBs57n48Fq#<2(fIBzzLjJgzrnF5o5fcsrn>dI7~;Rx655A4 z5{`=5r@{xXL(y)$M4Tpty3EBh$bj2|E&6wRC)?yD`n#C74^^y zuv_@8C{g%Ag9QwYe6LVaC@e<}ci2zzasMz&XEyJC)#}yrx2EQ%l7Z>t8q1u>_3O$2 zpT@Th4dcdLwLptC%`NsZ7|4Cfx_U0mUeW+f}BEd^$r`5y%v|>sFuM4qd^xH>h77QIsRniKBVHJcLq5 zWCJqL0A`Hsb>D<{CJY{mAsY$`#;5}YJBJQDsjRNQI1XMW3vPDf6%~|k$oqsw($PK?g?LD}LTPZj%iIXx2PO3|7OdHGHE&c1dmUSUdiWCH~+H!n4 zDc3h+Gtc*OqYS9flf$u=Y{vnzcICy&h#BA<`^Rj68xr?S zU?wD@@0d0{j0X>1{^F`q(^#G1uf$ZYs;UYUI9bEEA>%fdMMt->H8&&UEYOkh=&xbU z-Y!Jbta|=lUQt#ugd#rje1lflZBqp2s!az>*$LesQ)DnY_G*?g&Ou|e>}R=)4589a zmCnZKe^*`Ckj9qo8#7`)u3;2%ly#$;9F4&%n%D&%%MCble>dO61JpobHbtixoXa3J z;JN-O*lW6q+`oJ*u2;mI+N9ql={$)#`)-T@FJXM92yPOX zDl_6>+FQ|iGFtNf>BR{!$`}NVkrS#35?-+II1y=6J{(B0!uw zXY)hT_$sp*;&slMq;Z=Z`#2fES;S|YMcG$4p$?p+A7iv?6w09IIb6QbOKHpo78A-g z6nI%{{*1kg9FayXofj6`bbyX-w6Y=Dl4h!F<0i5tk8gfuNo?O1ToE2sD!4~zuRshg z&HG;;-m{qgFmp#oq=*+TUW_L|7$+psOEQjPzoV+E6YNAi%`rLiyV5k$WC^i=AY;{+ zS4|nnvyT4@kcI&kUE+dzZNZ~QF^^V^RW@7{m6~`A4hdvI#_#yS63!_@lrb$@_52wKNyc!+4%V+I&@tT%Hyk-@ zV?IJEF_T2vELO$j>sD>r+@Oirx_vuZLCda1{S5IrP~^E5f8Cl&n?Xaot8R{1F#$|@ z6dFVe2}j0NdD}p?8@x-LF=E6;c%&q;+PgY?7kzfg6V)CZawv@V$lN2IabfOAfA1!|u$^ubC z)8Dsj(`L`%6+P&UaDT+=a8}%SL@cT3&`DMweDMXd3v^K)~)jq_h!rlMEr6E_;y3W zW4EJ`RFt*dswB4gU>IgY7gR^8sVrnb`GmmqN!Q`WvB5!K+0&hrU!NFqOPA9B`Q(M6m>~ zgfDjVh_qy-a31dqk^<=)jF?x#3Fi()XUK<<92IJJrOY!O9Ldyi)6Vw>!$7QSw_+Y^ z1RWCwxS=-MU{a)aMKOg5Mywlf>FwfaC+Sfy8O9q?v+TSg z7TDq4&B#5yuHK1CuwoC5Rg~=albm_xd;asSIqHQv&G5w)xtjXp-E9~M~) zJ~%Y{HB5`UOcA6c}%Zk=2x$QcGxM>IBWFEwtaf9dWa_uXd{J%SUFo; zZ~M#IqZb}7PioUPJlcuzY$g`0VEDysNXLZBXi+6FY{jL4@qrm+P;pNH_$r}$;EUq2 zDLY$bP*5r{a;7ZgL~XZSywWKE`+xz^aEKJ~4SVbnD@0K!AS7x_M#Eq$9Hy9Smf!p^ zgZiNek`;2cuwUUaJuvn&U@Ql)P$U>;Lz^G8f5He|$-5Q2RptkEBJQ@xvk_YgAh8I( zz?X0DCLj22Zt}MY0Geq9l8FfSUzB-HUCJ+Yw&G$b|)+; z9Hx-b(lBu@qpvnuG%d~{yKtH7653(yc}I&VWM?&^GhT_yToNv2kG9 z;~S1IXfzLoOS*~VLL;ed)^YX0HjwTfKDKQqpC%G!RX20e*cD>`=M+N)jgGTF*Cc!rdRoTt7ogj%7?5;#kOo4m}ve7g2iISBxWh#&fynNXgGDVh}dx6>fHU1RA0uB7XjlFU z?H*N9YxtzcNekKn@+S1jq4t8{u*c{gOnx+ozHhnaN!P5h-tlj2;~Xk*FH>@#8_#PNj+!dj^YTX$ zUphxjT$(A0hk$@m@!rRU1JZgN=Wf~3r8D6!LD6_6%z5z^5C$v-^s$ZCr>(%{U|A9KL`?J*)cNzp`L#v zANWAe+cq7j(KJ!o0TJcouU~^VM}KPduB1jJsfZLxY4rCuOd2zZvt*%wsynVnYLe?@x(Xy)vd}W-~TNL ze{sd?#?b+9H>mB~)s6z6DVGrdKnT%10?t*2OuKICi-JkwoE&!EF!pOA+5v8=D9K7i zn+H`SqBJN3^EM%68^K#{{F(LU&6_572Hdiy`KoKio3z(>R8RgQ55OaBGFe!pP&|f* zZ#RatlnRRlCO}@Ja9XO9m(wOW6TXLaI2pMSRqFdSDP>+4#% zxVWH{%4H!6{LsErmxCw~Nq(`k612ocrKRbV6jT~rP+c+&0N0Pl!QqGBSkRS;bk)=2 z!><I`!bVp&UVJms%d@uNErFfrLn zbY0H2O+>;W)~g=Ec~YTTgXW2~52~nywZSi5S2%yjuMP!io+)l7z@sF+>br|d>Bf9j z>KmaUlpWu+oU1BI8iL6mri}8~zsgbOQ0!82j;3?BO3(cTp&K{s(@{3mxltWM>3cBw zMgb&N2y|-}%Q_Or6(?Kd6R#Pmm9CTlqM(n}mf5uhUTSPFNwKoJ{yR;U>~JE5#6;*N z<>l7ko8W%*)X)<#1qYdn=?I<(ubD>vLU7YkRMlKBfmRcO4E3q##m)aE@?jz&jdeFx z1KMPKNB)3Xt;^sXLgh9xxXiAZeX!ZLi*O)%K_m#XWBa0N5g5{B<;R|Z#YTZ?cMiZU z+|&1SBH%+^ctAjNF>FEbl#&K{a%G8HYN+I-uKwcnzdby?y&p}x&I8j~ZW5%?P!JXV zFR}OJZfdpNe5b$~NeU20IgY4Y5uyM{Kk1QO$g;wOrH2EV1KoDw#5>d6`G1RedZy^k zTl;HoILZApvH^s(X`GHYisj^;LlMm}JtD3H2!IKPxKNR9uk$RJDt03eo0bkfiFTG2 zi1RLj0+7$12JdjnqA8~R7bax&oHYzSvh!^w_cUvI01XF*@Qn!sckDd<&4Scv4VZe| ziUWSm;=V0G8knp*fDo3bc#9UI=E096iNcBt4J`;TJU3rk+1>ZFc5}2|ez(63#ZGgB z{MDf9*4kqN9)%Wl9Ny9L3j?LHvV$Ah*J~}KMp#nVfw%C=ZxLu@wJs$(_D7tLX&FpD?ZlCZz}IuinGO8=1T^}T;dJrA z^;h+vc%n=s?Y;m6W1uQrVny)86oPsM>p@wd1*!BXa@@=UL~}hIs?)isF#ZdX(fMJJShiK0o4*)~6ok z|HY`PJrJ`Sf=7hlJCjXuBQ8`r6=(=tW%H`5AWHIWHUv;c_94jlG65}rXta&+nn@;SjM z1WD?rmXSSnTgl1@Eb$QQ%w*7>nxS{PiB1dFd2M}qMJcK4`H~Rb^dXJ=Ng$!{&Oo+D zR-4yzFjmF%LP~i>M`ChxVj4D>FDtfQ-HF_ZVJ>i;$VQ)D@rfj|u;s6X5ay)dka35k zPhkOCb1WwZldjUXhR-~th~pkZ|9&-|79i!80xKMYOM)&`91o0 zO`K3Ya2jGqCm)?rJL*Mq`WhQo;%h7oQI_lBn(Y!SDeLH7%t83iBB^& zX`vOzL#B&B<~HyqnZlD#%`i!br_YGAYMe zc9OXG35#a~-3>u^(vBDQ@JLMKiyx%yCZemyLQQrsL#kHSCYp^YM-f%pm?BU)@AD=9 zx^=h)(&)+BbxPy21eVAm#kZ9pU*g3srXQqG83h)77j%*WtS24t3j2GTvodhNng?6P z0r{ibYHjNeFVJqyXmWSBMn%%yPh&355ExQmA}$qrxNHI?#t^!kglji$)U#9VMhx4} zZtK?`U$JU}|#;=fBV4yB)m@)(JuD}(C%x9}mWjhO0iL$E7oJ$(j+QYvU=*~61h zREJpkzKHav#3Db+NF3*>oRt7ZVj&}NEhGBebMVkTu8)O?{-kfR;{CC+^SbH4E4O3u z056WbS68ef7C${U@zLTrbNv2ewon9&;qkQ+pqg>GC~9kyhEHn(EtHe_R`ZACgRlCL z(b+2@WwWe+O=0;q{A*HXQejy7pbrfhgY#Yhmj2nzS%vkA}g3%O_OuzI+^!%29# z@zJC44$^CUUEIm=WAY6Q9J^QREyqE{eeQy?XAz^sI+LnZ6ry?!|JV4l_Yf}bL%`k6 z6TjJt7otoI!k_fvyI@{C0^GgdjHQq9xo5wEr>AGavGbjMPZ`|x>^OZ_(RN6GrFnFU zpcz@&?9!9+mg-NDloQUaC$p)Sh9vjX)B8d^EwN;@ck5JrZS5GCSGP&0TWG{b8ENumY$>nS`ao^>P zB{;14T$%-OkLKT&_4LSmKm4VSN%Gd$-hW)R$v4sYks3LU$;XFx2+Wy;MOkW(E(Xoz zgc&knjby}{z9NEi$C46QZKXFFtye-cAIM(+tD>$Q=6BxJdkM6B(H$2rUc6yTuO(6` z%vi5IdNe5glhfoDZ=gU!*2~Zxyps?llx(7fg+a0ZN0tJWUotN=#zLi+f`qf{lQZQ< zDH$vwb(=eT@MT%M z-&22^98ft_`R(({;D|*QYu-WmTgMm9adElEGiIc-%YXp`Zi(?6+qFuLl5(>c7PyGf z4=PHz@x;6A%II5hF^sB;^ovNw5WsSgqDd40Ipi+cdZpN>fIqDI@v$Q@L>D?Tp>NnI z7GmTM@k4=X$eI?>BJpYBCZ3Q{%lnSs2@4gqAU+twHKvgeX%oN99uFgU?9vb3csh!V zDZ$N>)(TU6PC_Vn>4bP!RG{n1zkan|w$xdxckkY}irFX@^59#i>Pu03wk;mmw{oP< zVNBTI1?jZD-t-6{UA*aLS&XH{f1AH1X{Y+RKIa=Udyu5Yyek zclA27;>vI5$h=^6%epx&Ijt3i7tpj*9dx!U^wAr)cNC^sG<$OJ~S83ysIp3|L@ z6@WbWm7D!Ei7rp(f1HN6ll}$0M`H?c@p~k`$v_BM81=!3-h+`c2o}rDMs{QSDzSCP zj<@(9kFfqy(D%+^GZXghtu!=xhBa=|qy&3n(J`!>!Cbg_1EZ|?#|Y0XyZTDZo8SAT zdgOPHf&Ad+9Ofe`aHx{`Dhyf}kX2zI!zz9UZj7c3$JNMO=d%N_%6IBD}{x#3ZBP<~qPmjIOVqYplA%FU|} zot48k)t~X#APNd3M8RSO(9!Jt`SW5$EM@mIk8PEJI#&Zfv#~4G4D$GlO zcU5KUY{yjNhwl=#OB5bvjDKY-US}2sMmEZ=k!ZI`Qd4ZM~TA{ah86@M&f|67rF1Iy5NA z43Nle^68ssLG7r)xMs^%t`yHd*&oI{7^z)EQ)gH%z+MPcmM)7IA6)D^npd>JX3-R? z4{@g?WU?|~KdE9oKg9U?qwMTPbgc|VUxy-V2Nos!xB1Wd59hNOoTqt#a6+`P(S2mO zt)zEpadFolo9dYQVdYd#Tg3Fn{v|6B&}JqAC)^OG@< z{fqM_Gg`>1pAF;|PL2yfLkeMGDpyyyU0nYTYY8cLKobOTr9sH{LdbWIF%k^sx1+WP zjf2acF=HSAE5%?rD2xYcv8hkzySrzLN*tORALnXPA8Ct=iQ9kfl!_1cw30x_% zWw?#EL9x>m=Y&H^0{BUN4`zqHagU$bO(1N76cMu%a}I94;s2kMX~LMls2&0VR0v3} z$g4!>z&-;Me*XL+->WhrMdeuivoTW|neEuI3l^>-P^5qfl*3S`?XZ>fdYgBcQzvu_ zPu2(m+vnS#&SjO%E)Sr`xaY_f9t@t}U;|Qyql3RQ`mqZX>XA)xI;+RGp19NOrI5j%7CKf1xcEkbu-W5$ghnn z=q&|VgTEkd8m(7MDKDHwIf5d60)uP<#Qgfg`HV+B1ouyveW1SR%O$8xF@zIqA_|46 z=N7i-C$s9K35YSIvTSJ;VLCv1G})NR2JURH!VbVe(s?kJCd*&N{GMDwGgJ?JOgyXZ zC)x7np$G61UI;1Kkn)fCP~YtGL0kUr@lB)Q-ron^<6J?n`Qt_gAvufJ(EjJSI&s0Q z%O=3g4pX(X2w_-627M8tD!}(;r1X?V@&(`Zs&omWxNN1c#ky8NwmOX7IOFeigsDXFTR&(I=`t-4z!6_w zfM%tOCep#eE`(!|$K7jNND~>j=Y8Rt4IVXVP%2ST!H3Gt%h1ACGfGHIXB#=^qAdeJl zG1!AU%-9=yzG;qg8${;>;qZ8G8<=6Wu^D11`@LvYUUQ((Jxz~Yah(aL*TXKBT-I}H z3+N(N<-&0iuVU8vFvC|@^anu5ijvINK3Wh1bA2C?i2Yfp#H%)qiCl;|4-JRRpn%Vy zE*tNiq1ol)xUnOC@+cK=D{bZsR;e*y-&-mtohi%nX&hGO9TZnF?MZJ~2Z#s@l295h zh~xQ$e}Y&vg$Z}tWMriqb~Kb52Soi=vQh zkqR-E${I?NQfSd4OVNTcr0k3mCD}@LAri{IWQw-;bIm;O`yMmTar~eEGc%^{`}g~P zujM??>%30+6C#Oh+Cagc98>#53(FIZu_g>DcR1S7RVB)6w<6W1&z>c%&DL`$lxWQnz=&1ZrUa| zqKAVzUmM{Z!-T_-8p&yfB3qPc;!3g5e=bj2R=D$$7G;ixR39{J1+rIkOJkV|mw8GA z;0%`7pKYbqcMOVou1~|f=)<>bQ&v5AIcet7_GzmIE%B?@WcIMLUdy72K281OtX0=f zOR^G8zZTV7waUUr#kf&R{f&OBQ!f;Ds=U>|{WH_Jk2^dZwe0UcOL?}JYy4;KnvR}X z<%|~L+%c7rvORB3pPb&4gq%$uHDUe*hQ?$M8Uy%oTAdb=3?Q0sN1*x?Ng-pyKM1t!4GKKR+m*!>@eev~BDa zf)1~49wJglg9mgS$Hr(7fj}{UHpCR2FDE_c*mzk#0ab#!&3Vv8{4f;nEo8Zv z!&5HUTD)?ji)sw)L&dV0g(_XfM_&RG&j}vz)DR zlk+dmRu4#QU04|}3=R-Ec2m2b(ucH|4;!=K)O22V)E|#*j@+kPXosJgT8YkPEshdL zzZ8QlcqO3&xYqBEYSCwrLRqmCHBTq;Yhb2G&*)WB@Ce#aTfIumYRcXMyuZ zd;U`N_MA!@``K-_;N*L~Xd6%Y#$_>*4Ci{l170V;u0iF+Gnq%fTbP-0bWVmZtIk!} zOM&CpC7yq}Wu6Oet*AMeDl|ZK^cHno9JoQ-RhN7_N$gPX&_Q$uF7tc%fbm{8PbP?5 z_x|CwFbnGNDdWRR9b43t&+Hzt!M-W-hLj;BzbZZNr8+K2Qeye6Eg^Gi#z8$ z^5E1XGFqS_NdT-^WynEU7L&?$X<7EgleE>$P|Fw3oYYMbnS4DS*|rZ&(Ywe`j0>8C zhK*3T6QG!7#Mh}c80s9QUT(>Opp|MtF=FMP*S5GD5QKbFQTBf?EI>#sc5;cjJ?^GjF+&GBHE5uU&OfEq{xmWfL{E zp9g->s^6q+VOX)D=ferS^PJs-dNUiQjHI~~Mw!ZT8^y|Um^}iA;DCG(XLJmyA$rCn zMe%i$ZG{*lN}6dg1ND3*Yq<|kXWJuQOjFwbD-G;7TvwF32w65h7SB2?d?JSKG($4uLmeyHtPx3!1}=w;A|s3ehL<%`h+SRz^B7H= zC<{}WHDGMyI}^*ibXgf#wGXRN!9B6^f|*zQPdAyd>cxe}+QT_v0Okg*wkYLTP&-^t znNt|XK{n=7iu(k)kR_b1oHO`ni;gXKT4)d2Rk1?Z=WBY)hLTxK?OR$Oi)-Yq)the# zQ8wlL=}x#N13#hELF~U1B(Cm|gME~9?17gE%$QUsiBWL-_VL{6EpgoE<7iqTg&H+& zT9uSypZ}s55VQn-#C}{yP@b_JFQpNw>^sH2$bJRJ#G>nFK3tFYt?M=KIINK=pUrOZ zJdJsfl}#Flb-!Kt>sMc_oA_q|Q;#G9z)7FSlemMFmUOdA@3&Y=TJD%s(!vE(SAS;$ zSJVJBuqbPtZ^cCL@5yuz5yPCbBrCBg`kjDe-9453CK&+Rv#ZFt?> zVYL8jcQ6XXA#nl4phYmrO4~zCT3>WJpu(a`KobG>e-#lRk|XQo!zs;S=MD*4N=q{1{${1Ii7md)V21LYUDAQWc^1p9kBnJdr#Fv>Jr2PD$h99F}Dm z23=9_Xj3#$U(fB-^qQObD};dr%?3g%YS*&rxJB+F=$XS;yS5)qvQ0UD{1OvotV?V< zcu0pZji_SVo#JbLsa(U!VcN87zDa)N-zl2w40+hqbmhTc`+7~PHDFAo4s=OuRZMZO z0zfOxXBXAg+qXYb-k$FDMy{z^lx(SEhC@ZBc_3W{%RSJ{}!HEh_BLO^B#?bfHQWky2$ zp_o^s-~v^-qB-xt-5V5ffq{Yh5rfF)(v@lvV}eCzXsCW3O*lt}ikBxWBr8=BxioyS z{o%=B^c_CVpTAS^)~l(R^`DBRF!Hmjmy4TQjH_Y}|8DqMU+4ZHzfjvf1=NM!wxwF_ z->w<^Q6Og6m~jInJa0jNNd&n)ucFzIK(HWB_u|G2OIuI+Iy`iQjhkw4e(*yQ=)r;k z6V&4f&D|e=H%+uH{mtfAZDv*@x)dstY^k_Gj8i}LDisw!ACO1#Ka+2f%ivZE=3s(W zfK=p6>BlLq2r6e$K#PRg37U<zb4_xC=qs+>}PM*>hLKKH?Egcv-uA zJqWQ(q)KsjYWg-1CV;U6T-w6KqbG0^<#rN68y=k+H1)`Y&Pjj-k+s`ttZ&_F=0g@c zK$+FBo6(yw-G(J7ZcpwOfi-9#%_%t1Wxm-$tb)X2E<1Y*S_^Q8(4`x^e>uw71KKq| zYEc29L<~g2aQV6%V-%O=JGu5B>UFMuj%*ltKTHKuk)Y?NH~IP&r(?F7mYsd)3a+zU zX%FNAy=MO@%nlh`m{$5`;4!?RVm>78s;DqPX10Tqr9$WX(5p94rx! z8f!49*DeyP9lfhJZ`sn2m-F_aqxb!StNf@sdtaGft|%@J*}4X$f9bDY^-9gQ8hQiL zfeX0qRxZoQmwWqUnl|2hw{%95sz}Gs#jc_Q3=9ol>P&r?JYvE=Q zIvJ2O3I2k4yI_8yOtPYJxJ2nV7^sI76&BT<{kX#3a24s#?LQ9z9 zOjFq-_5ua43z5O7<10>|>@<7wWO^ITf`S4&fKq`yAj`vNTH%xFqu72D^2W|HrVT(P z8wuMVo!Wh*hD$x$PNUDSN)kg10n^A8w~ntrc$M^w>sANosUADVpU3rtPQ+RMFHIY)XKoN&KDJNz35Xcw3 zl;kaH6>&i2svm^uSkS+nn4!b;8?WLYcCrajA5Eoh7n4d03sv94tp{iXJx~~1 zjlJ2oS(7I70Hnpa^4&cnj$c%J8b$RpyrZaq zct={4>xWP$G2fWy<;{!X9f&5`&3L5Ax3{^;hRwf&9Z(U|iFZ0ReFCUgGSico5o-X()WJ$~R?X95JIsG^6&)g9eEPo|m zn4SX6`H}Np)BH`iUDDemLU^PO-A)l8I(ecSJ>(W1e;$;G<<72kO6s|wQ*!1;P#yU5 zhVu^F8H)IntWrE;`eiSBC-t!hrovWY0h#!2UH8_dbW4Y|_LpPIt$MB1ZLYJw=f%V` z-n-QK&TYFjSG`i-i7paP(if$ryPO>R=!ZVp2Af%OZl?0LQ;L@1V-5pb{_17@u6BT8 z`7^N9*AEq1ry(j6&r<4&Se_@>PN33R7se73#S4^fQ@)?bFNj=yq1WZQA|@s_M8&qJ z!~jMS0RQBYXYBxsNUUu~tQ$<;f-z81C-Xwa@Jetq&POp{Qtf+LI|a`D4d{s8Y>fV9HI4p z-PTX4AITncTIWn_k5XSIaa|cjny}#`Otx&*kVzt1UNMqKD@T%N{%tprg_#D~T}Jpz zBApeMY1gVLHcqn)jfrc2@(5;JDpZ(O5C2N6li3?jq_OVXEVPaOGJqTtrwN$`Ky5^- z7WZ`w2`$?6i=;gR)3k&0gs~J<$K^~)S!cC&ex~E+bmal@v}8emxXGViapd`nHR1$6 z)Tni8x*sMm^t)q7^8yu6>War9G{?cxzGXJ*XbNcYRHPLZo7fIF!VPykJ%+C5`X}$h z=70Y6?tT#Ie*c{XcrnINNTDyc-9Ak{;TCwnLK#N@C!=j>a8$piJ(DVrJ9OP|%Fd-W z#02nR(^TV?YC#tlo=TxtyhQB-z1uYCwNfVS*-F07=S3<_51-Rb+P5Bzrcz8k^T43ra?ktnHfxO(kc02N9bJ!C?Ma5lfD5GL`Mo%;et@^+t272(?2L#WRMfDAWGMO$+I5w?qm<6oE3VbwV#!f zrpv1yu(IFF`3NZb(3R5ywH8UW6h#n8LV601h+O3|>L2u2qqlDM-1uUK2fjjoah!fv z)$n22Xvp%qDN8rXriRt7ixzc1bBeI~GbvjWw%Mky)zguT6yFHd-|``N0CQ@s=I;yO zhSe!it5h^(HJ_6@k4`R{f_9z#9~$f{1TOta*kHMaNEneQ&sn&TG-v0W;y#-5Tgr$A zXiyTUq6$MJ8^J)pu9`c9xFEr!;8;Kv$GM}7kO8BggN9Qh@M5ko1_HHyoUg%npi|!P zDwsq#%|^rl%a%>S;OTcU?;OTOVvD8^ zk+mfYQ>i>b|295fiwv5xJHI5w!rFTJ(Ke7T=wgI>LjX;)p>jUsDb)$!<4x33GSf+J zJIKpxkX>696*9&U$1fF`W#I6iLy$+CGGN*xgVxD*-AfF{KKbj+vX)-(B|O`!4C~7y zp{$_*3IUTYzDh7$X1XUPZWHU}<{TF;+j74VA zHx%<_L@x}!q_{8~Sr-yYdeE_e&!9@0@hbI|B5))RxUaJlC$eZ-vZqIZip$jh*m7 zW+Db9N#+9}09%>y2+~&XR%2L9kOi7w!4%=*WZ?w&GlO!AQanP`uufTxMVbsSM!{Zx z4!WQQAI*l8vqd8MSc%!FU$WL!ygNCZ3wt5@oDEszLu>&BuX^|BOeUC4KR5qBJiI{(kuph_=uALF`WxG z`dJiMG|3rGrd+H1k@Y#QQ^TXet)pH8F z(X7PPYxdb?dO3Z8O&m8IpD5w4xQ)N_+(~=)m4R4$<{pi36 z_8MlhY?{vMH8<1Q?LaaeD+S1SW{<=Rmj1wEr3pf`i?vZ>+EZ^lXx}uV0qAq$>_2vt zwJS;c?)Ry??H`$RA>n6Jwfi22*3A*ZnY9`4h%o#&nzse)4VkwO^^BOP3i^d&S6FQ7 zD-m7rVDMB8pBID`eDs?Qi5`YPx3>zj@*cnPZ(P=Q?YU0P%+j^YlFtyI`bUM%v`~>w z6|$gs^xjP17nND~n<(cZ7kUirViNAB4K1O&WrWt9j-|~Mnp_?Gr@)@{6z_i!7618RO6=z5o z=Yw?-5V2Nk+yAuHz#DWK)ckUPDMoBQ>^-jAx_$fZ=?#VPU2~4yE4WsZT+Sdu)B8a= zb)KzSNEgJ8ck#`lw)y52xVMj8XLQ9N0kX{X1yvEqcnMiRJ#-q^?REO8={;#&>Wfp~ z+P667xd6u@P|w6ilJTXywDn%-!7T^@g13qG4XrUNf=A;pS9&n^5E@dzh=u#?K^L%_ z5TGwT-8<&D;K>O#LAAaW{o_MtuC^ULB%RI@AbLW6r!Ww{1_WhQ&lKn9&z~DD8Jbw* z;FR!Q-*|xAJzYL4VelJx!7HTHbY=OA8+K>sB`4``^GziAt`yUfwly^s<5tNEy4i*d zwb8EbpL&FzciZy;`S=0Qv>0AEZ4o|HecXaX1MP5&ii-@GSbwSob+F9UGgm)CyAv(4 zLwMnfC6Ez4=QFA}PSW5`|CT>UgfxuBr8o z`W2s!Zets35`XPlP2QPh%lhx^{V|z(t`YZx5oZ0N?KZuvRyB*dQL^DenF++oI0Qg| zCR3L!>q8=#OO-E!Sj0;i#QHgG=aVRcMCtnZ{u{Pu9(!ptcY$KIdu7DaYGfiV`<0fV zsiKwOHL;np1&3x+?0bWkUel$Jr60e&Z+MfE%wd80*Aowo+BQ~y+=7Dtr@0psF1~(e z*x0q}chvUv|7)Jz+Sy`!+I#k&FQ|gWnrmNPIT3Gi@@v18ohn3n)faTYviOd%K`~*0 zwcnVY-Q9L{yDL<7uFT=_Id-5A`u^>;mC%y{p5bN#oXXZ3Es$kuMw2z)68_vKN^6|I zp&@q{T^ykYS|nCIN2gj*1;P?pZ5>_Af7@bGDa*Kl&jsP&9{|U1gIBagt+mtACAGY5 zPR2>PVtUTDc@r}0%&L9LPMz6PkK7lZGu{$TL)~I79f%2#}$E>oK z_B&nIg$tE{lCL+8Giw}z4M*w%{kg^ldWV~RG3a>QW7#sG%2wFRaK{#+R*1>gmD~+P zD8cXc?d?<7z!4)C&49HD6yJx_y=GjP;t zDht2y5bxweb)66Kp|}B`97ZNXvABR64Y7OQ4cH(welpDj%E#aO{uxp=fLKBd*n*yn z9zywi6?+KyfG|+9K>5YeGi_y1?p;quRI{e`9~w1lR&BrjfpiaTg)u^X$m%_&1xR!TNX-}1AIG{Aw>jLIa_ZVjBDw` zO}H^>VPRwB9Pk*JQoW%;5+^0@hVZQKR)9f^hX*kchqIeaPY=CW1EkFV+BR_)9Wbk=W!JtvwMIgO{)JIgG;JtAkcPj^hWLJ>;=R{YW z1$7dA7(+w7&ZL)#-UD~W>)a`$C+C@md@wR(a z2jhi@RP9GIIUwJXk*TX085`9!x@h+}J0So@k$DwyV*|JKF)vf7yAOS_go@P>o$dtHW_~r+pNKAy_MDK|IwX zj?F`JE>3>8!>W8V5U)*zRh3TC#I7j!p;H_?z6$-@4$JwWv6`}LOqj|;JEL1oDueOV-fQ57NZ+p3}d-i;4yFTEB#Aty`EMC)wv zS&z5PbJqbcK6X;wypBm*M+oD_5A1q`;VDX+R9`E9%CW&K{11t?kl?afi_DV z6-6Y--Y+mr_cEZ5h-nmcv^+9ph3OONdsF}N-fEdmm+w%oIxB+^(8(}N)`Y6IEWA&> zfoXWdysQOn-|eY;ex;ZTrw;yu*If4Q{(DD*Xk2K+;}`cvDy7v)z4Q7SLedTMQHU2% zSHV{*GmlWlM?UMz_-)VJomvTEE+?Ei{0%T)58?|M{zs(9Gl@cG`zPix=Psi?6t@3U z2NYkA2tt&FYwvZiz8{U3hs7lW8eA&;7(ZDNHq%s5XPW!hkz|ESh1+-Loa+z$@{Iu( zQ?1x}SnpyG!2l>s%*5k!Mu|xkVkha#2{y9bib}mT^b8d`waOL#Gsm#08u+9HDZxay zZgqlWUF$nA5KR2WvDI-G(v{1{1m0QDUv>aeZN0)(2#`eO*$Tb&2g!R^SQmqahRBn` z>M6cv>5!F1u6=cNb!WXtEP;LGtd`-6c5?xvN30(LJO-o3&hfHm+mCQ2*{7ug)54Ww zr?RWM)>S_p9JjccvQ!MDd{VavCN=8&aIQ;C8Uwz-C10b&6vq@1c%X#=@Ap7c;hY?v zY#x8-3l<+aMaQ`(tT?)+8P%H-4~jyMf{A&cR*20=F^8ufjp@;xdaeWSU>?wE&ekho zS2$vY-nDzVe&VoABXW+`%HDfK#u;(S=3{II6%)gA9x<~olvuJ>OICz{K%>9hfkZ=58+}aKm?I`0TMRc!F9tQwEcJW}GQ>5bdgl)rIB@E|n?`}T zg%`cVqSG?bwvD&Jn0K8la~D88r*`e})nj*mrQX-UA%6X9$_Cu|^zWThb+7!K|1Gs9 z(L(h!?WhQ31ZEJywAH$=jt0Z)reK+^@}=VIJd+3d0}>vmH|RP!NHMT=cG;y)rpO5I)2a2Qgj~sV4*w)f`V3iIxxd z_S;>XkzWy}P>CJX>J+MD8yvU{D8-NF8B_zfOEwS4`0R(p@6b@*Snd({s>W8ILkLj6 z9N+|`NEM)@}0>s=b>pJ&1vFR&eD>DDRH%XajcI_zIq{AavoZzJtEPx+{D6sNm@M%t1C-taBM^luDU&CH6vWDYJZdZXViM?hKvq3$i1<-T0-|+!E;t zp|8b`mEKL|e~P7&->i<@cjtS-QVAW%Z%ZJOicA8V2Z=Zw7T_I$$7d(gsFiL%64rT> zqSd|^1$ri~25kZso|;b6=L?ic2#2jMyP!3RC>jVJMr%0?>;_W9pr980);s`K6D(TeFq108{=BY zW)l=pgwqJag^wRUUT?o~8Z{ArEe!3#vHPq(hG069u{@NDtzku%hfnC21@+;vd~;69 zw0PAFa6&oq01&LOn&55BjCKEYsMdyWBupMjQW_)d65w8@ADx=J*SK@%x+qJ9=!F@O zHWFA-8eTbqlz}V)Qo^h6VynPb-^4QfO~r9dz)H>?OdZamH-TuHZnqkLlgl)CZCTg2 zAdNwj*X_{;$QAPv&p| ze3g@WOPb+j6UQz?B(rRF-SaM$L?lkd-Yu>fj$60x&*$Uawv7#c7d!`8lM+F|1Zq=8Cz`*^83`b?x@iyc2R3L;iWOL*gMPNBf4X;RVQ`BVya3V3**_g2jGTfHx@c&(V0-50Q}WQDA~$-vcLaNs zBS`NoB5l68j9EDC9vlBs;Hu3Z34^cRz8y!w0{-xln~-WTAH{NXOpFt&5P=WSWq&zi zI(~4#dN0MsN1U{>Oa-q|rfg`;NDWD=Cakj&2O%mghzt#o7GC-Q<;dUoq4?8%AtQ*o z0D{)1YI&2%HUJi^Hnybu)1hhc8dc0~`X761>gh{-aO&sf;- zIxcRaw#hrS=6{pJk}>F1;K!?{Wbd4&VBOezg+`ltwLQ|SzBR$7b3bIH5amD@LTSSQ zimHh&?pwkx@`wCX*b-leVHz(%H2_#QXp9?XCR_;xMpfBe8+xv;=Ar&$Xg`1A0kay# z6R<-O&Cc58%L`-ZPfsj)Hbg|T3|iV=d}vuWrR=P!QX`T!EK-3i*gS;rnNY74LvnBDx#hxv(Ckr z^U89l@qB+wx-J8|5&6YU&h%k!)NPxb4#N`{-C(X0Yh;k~*gH_CMI+*ibt7#H^iORo zEsbsRAMRGixN*1&6IEhnMqJ|E#-oL-Ez?^+D51_rO3(Hu*O3QR_D(byfTR_buOWC4 zwR!iP%KR5j@CdlW?3{0x4y=HuEr83`tcgNcVN*`ZL}ve%30>AbMRIdxvKB_036Z%F z{RANyN`kuyyJ(S1(q#6qWM%1kV)B}?>%kJs9)uRzD)j8!ov-f{y0H&sF0{=#07>a0 zNO44~+!u#pI)tZ%)@vEE9%S-i|%{c@708mCL?Z0i?wsjLQA``1T;Nn{( zRTB~ts7~pK*`+nRC)MpR>bf*Tn@4XP8-Is|h(%YW@qT!Jq&aP=wA?_o;=KrEQ+lzt z+Tiu;)-j`grt19I_|W=gfbh!GyDO&Kc8mD7h*W!JX0JqxZ(JzhKsG(3x9J8ZE+TYM zstOs3?~m4x?a;pJX0I7gp%QG0gA$Q_EJ9C(u7x0}gmIl6Jd@sei`AD(@q z`00T(t+19JocvPvtf}21DBtzX?^K`Zd~RT814YWo{kh3;ixwr;jG5m2Ua9#z{e-{T zst=iF__o{LK}%<^Y1%nOuFe@53RQyro2BxP6K5`Hl1nsnyRdxI=;%x9@bS2sFV z{qNSGwt=5%k5=MqK$V%Zbytl&rcI}YEyEvS+9rPmXM`Tw@;d{ZwCd zX3YiZY{JaNWAj5K{lmAX=Mvi~#^yp}VIbOxs^@=3YT6Jgn6T+nx-sU|hezQ&KM9&E z7L^KRNSlir@A&<>zOoHxj!+LHe$R{DNI55>?520}+$0{a^*xQvN#)S)R)An&NC)_+ zhkNY?q13HDHXdfyw$^ERVGoaM^j53H1JUnFB- zUS{w7F!3S`lfCtz?FITqTP!0IB|5|5db%ce5}&r#Nw0$eWTf_K(Q%lhO4)S0z6denNu(Uo~ab@t9En7x~uJ{Lr6we-9Y=pD`{AY9n zZPF8ciPJRQP21&Znrz0#=jI?D2!#WStzwY6Hk`fLwfrM0%vkD!``bpCon z?TI_~pD*$Vg-3$15>5mG17}*i_hqIG#t9Iyu4iZ4!$(Ijk|!5~6HfsQ?Z-+0Q9b~w z_nhS$dM_liy{*pC{g5}y@j((F2pA&U;EF1ym<3?tc~cK1Eb5eh@u>KEcby0T(m1Ur zdGadeL=*_dOt@bxr$4_5WG{o5q)f56r#k>!l>vKk_2&Z>kue&K({=41SpQM&O7Y;Q z&=H3dKHuTzi+1Rk)HNm0-aXag@T^-4R^5aJRT;9;#&Y{|$gI?}{a+^N2O>jo_|nV# zQ{$RXUY6CPm7a>>2Q5aA#rF^4L=NkbL=S~RstZO04KBQRM#If7k$n^(Iq4a~MI1fsr)pkMSO*L*(Ho38a{n7cI1E~B zoO;Ck=v48vLsvsxAVTI(HwG=tua~!ei`w2k1~2U`t3Sh5sV3ZLJ>x$M5MFC1Uc9zr z+*p&=Rrb-0b#TH2eV|C#^neK4g5&d=c#ire58aG&RJiMpq&BZYpp#jkeO;ZAdi)+4dN=#o{RTG+lu|8BMOMfNBZ4?yB7AapTB=ep^c)KOYn`-d@-h^6CA zUN~O0ZZU6%SABGM#?=ecfkU${3}og+=B=O>ai^emCT}%3*4yRI6#X6Du zC)1iN+5*9lX&_m(M@7Z$q!$557_s*K2wZB(?ch&;*uSvYM)Askkd8E9g6%V@P8}<| zE2szDK|(#x*;Xa%w|=eo8VPMmczMMQ5`P4;wAj^v6RVgCA8MBr{Cr?S`MU>4gSdPh zrmFvqCyoDy4^FMOs@)=2f4aoSAvg2ee*a<{W;8y}VH@9%DT*~6^c^E9fY-I+pjS& z;r)=KULT^wg>_B-80sh*KIg5*9)ahu-ubd>PvVP0k)Sf4qyhdU3&faWYN?}hlfQD& z^BPT_6-5z^ZxLGX0TntGQ(3{b^Ql+OHf{H3jL1l|-f5XQYQvMUr!rlMhdYiJGTFwy#ml8E_?~_$WXd3J8BHdo~Q#e9&jWmTC+1UUIumP zoX`;N2B7D4;3BoySh`7BmB+sZV5~|-#UwdMF@|A{z%;PFd;t@)1vV~F3TM=fN9m~3ZPAwwu1o=0`m@CEg%Q7?Uy zfW$5>RiF7djn4KXMPEBH>Fy?E-i*r4h4Q0)^nubyrNy4S_hNo$MjU8RRK0VXBClR7 zi@iK9D7SL}#3DuI@iTMX!9eelZa}v{E0WIrS*-i!?c1nN*E;9iDTUcd)zurI-Z<0L z{*n8#WpPAb5pD7(#m^e2Vvum8Y1iT`_T1ZEfbO-F?xRHU#*1N>lN$d7t{I6b{O+V0PWe4hi5I1vUV?4V4 z3d(9qt%rQ-MNv_hp7AQ;DiFIYVHIYB7yP;$kWD~mk}`o1w?^?hk_XUt7v%6AG!^_b z0<{RwXxG`Fqr{-H@62mIdWxpcA4cw4LHW0DqQYvB)n-xub|ymxUL38JQ7*PBl&kH0 zkjkYp-^`9Tgtp8+M3LA5hSWuwjIMyoE+t{@*}+X~cGopFld}=$(cwCk-}wjCQ|lVj zAg=kACB<=(d)SNNCJj?Kr>e>h)4V-x-CKt5v!gYCU3mZ5v*FJQcF>6snW*l>bA*SE zSy`z04M$6nk}%3ia^{tYDiv9M06ixF&O?tL4;J)?^U(HTFv;Nkx_x&9ZiMQ>YwfM# z6_OTTy1;9_PJ#%8X3eFin6R>QZ2zfTx3-U;-MW3dIRjz9-_Sv8Z-|(Uw7@BSt!(k{ zLfUs}P|JYo7a+45sBG!S?*_YA$rLEYG{e^a+6h^pxG)|(HsrKvQ_oF5qz1inXHv%( z)zT1;CY8h@vJm<7`kLuO7xA-&{Kyu)r z=&ov+P(Mx8*BB_gYpUc8K%%hbGxW{3{1+SC>kF=eicbC*;h8f@FIGh}U%z`NMljN29i85~qQIb-Ano&MPuQ`Iew8_m5Ps_{T1c#=xXIZ& zbP)QjOZaBtA(_uqw46W2X@nc650J0p*x@UVkX>TOA<40r6vq(n75cVExSh%1JzW(F zi|>LUVF%_g(QrTW2VeIbJb19`7xQ;QVey_*yMDH6+1#nHg|g+YX}U>lmKYK0xi_)l zsp+?b7)E^{vsr}V%6Icl;b8*IEi-WtSx}lRuGl`?@i-VA!>4UQidfr_3Hu)2bLDOl zDc~Hw09#Q{$RGicsxb4`p#2s7;w5$Q5z#ctu2x!6S&awOX5ldlE)VF9WddRGarJ9Q zfYRz^S8i|H&z~Rh^5e%_5~tyZfi~lz3Q~377y90t|I1M$V6TZs10PwW>Gf=>K}%K9 z`o=BNu-)nrRnIBs$IHYt-+-ulhJy!dS6t}bZ9*3d?boFxC5#&%YCq_Js5_d;LcnKW^8VlySgEnu@(%SpbaqT+V zhX}*t2H7?vtH+R);A06JXxt(XbUnd`g^u40y@qI}dC9M^u;?Ch%M?`|dP`R7bUVxO zUsdRf*wJIg*EB&{+IFC6)F0cpyOb}OB6MYC^r%w+xR6X-&+Sf2}5c6xhp4VCxtf{I< zdETQ;peJv*2=kRg+z}I+he4T&UR3sRi?WtXsE4 zb2sy3Ka0?73+D#tx2)>fZbMl~E66i(ZNWOMA05^3J7uxnHM^Xjks4E&>>>5iKJ3tW z74>xZaN?<$!twUR6z<&c1w3;xFgsJYL7z9P{M6kjt34#9)`I>q{c|4{`|YmVfw-}7 zwpZB12Uo9l$ypi!{EOkiYFwnD_WLi0rF=Dmf#xs_54{XX53R8?Hv`py_Q{KxzPEUu z#>6zSH^+ESEas3cB|%k)w-dM}s2J&OG(8lnsxNAPWkR6j@3q-oBd#k7@6w(%8#2Ny zq+TwC_d@HL$UPQ@Jv=%6x3E{?na6l|4dW+p4%5*=+b$6wz#R-i!E)kzCsXe9Xrq8TomV|LX|b?qCU!C6y9(wYR(ibu?-b7P zjp8MqE4VV#8?WDLm>GUW(9uOL|E}zd$~k7ZH(fn2)(o$4Klm5o$WFt77P$do+S7~R zzI;CQvr2CNWJy(c?zn%a(OMTNR&N3hsh}d;o8fyn)8fP&gXLqL66$!F>15Vy6VFWk zLnN~et`5~1-n3PKlC=QM^IE$#Ykf+sW{?nTaX}H+V@IZEyjR|~Qp=j@FOogT-*L>H zQ9O@d;jqv(P49rA@}>rBn*SJl1Gxc|uIdnDQ%DUXP^4}Vjb%j$M+uFV_(YJ==h1q6 z`^MZ`Nc|;&Yv2OO)F8Mya}3i@Vjk_v@kDMRmADKjfK&H#E~uSC8Tq;>b@HL^wh4`> zP?(ebP7kU1@V?tAru^FEt*Q*U{femhSZSs{w%zncm1n^2BoOkp4E{mQ@To7F{CNw7 zzW6}#L1lrYV1jfWzh`;mjJlbfJudlB0Wb{S0&*+nh20HW*0OR#f~K+BwQ~P^$5JvC znp=!C8^dAv1ApIi2H(g`Yt42hxWW8F`WpbS!-b4b4=%fw%A>Obkd zEqWaa8Vc)q5SBU53&iXJ!L=;EYS+I}o?8Ed6LvSaL|0|k`lRKgHFVN850|Ue^Vd~d zXuG?And2)&mwfkdP`j;Kz4v|k!5CQ};wZk9(fM`jo1yt{@PcQZlNM{6nNZwuDhg`P zY}cCJ@%P{6 z*yyC<0f)bm1+R=QUyn72)IsD_yIF-yejvweI<%viOxCC`pXM$Ob->iXZNqYGVw}Ui zoEw0gg+r^-sC@jt#8$qa1E})ip2BR?b->Uvxb#hH={WVsMCSGyFI3x`U1KGrQeNOH z+irPQ&^?Xp7|l=uE+d1WMJYbA!|qF=V({Q;BeE$|qmQky(+J4Q&OSJ#i6gx-ej6%` z6YNVcjcAFLd!D(z0nH%`=o*I1x~wjGWJpCTKu}@Z#Tk(=hz;mYz#*X}sam0DuEO3B z%~Xf9S_H#l`}8xVqRO*BYJ)vn7!MfW0$z@`zr#OtUojCfeGbb9aGA3wv`a3I!g{hA=P>x_cJlc1q#Ovr?`Y@6HcQgbyNec=3kr6F_R1cQ zO&yHW{TSq9B@AYn6D5yuV^MurrUom@masLM^S;v&S#4j^sR;MTsEW$H>8MAjfe%dV z%B1a*wIjn!L%P%kwF(a_L0|gIQPE?{;mK(2TD;iZ=r>`ZQL>6UJjgpT>d&BK zbpuZ8ik>jCVCmu9=$V-b)Q3D83hYfzT?VvkMk+DHToYby(&@zx7t0ytk-2JYpOjCQ z@>W26U<$TnY|jdW*J1zg9wvfCBZ`;RB>>>C6_KYI+I^ya`d?r#PV( zS1qBeY^G_cnpA7vFa=>Lux(@ApoQu{b%rfGlOCS4aBsy|>BIr5+Nuwf_?ST{NHZ?g z*zJe=?xcCGtE}%NH2=O~yAbjB@%n2q5>%#FW~jdn0C)?fjg$1T;E!U99hDJ~9H1mgQiI=n0%4s7Y+B~jGhMc7ZbvT$PX9*A+BuY0DMGH^c> z9&CbzMI)lJu;i&THtAVBEZwm-&D*Bwik4RI&oI#}MssV6`jnm@U~C*(f4r1l<-1xQ zomy$0eg^YZUbIZ$Q_(TsZo+ILRwWVfe$bv8S18Gt)mdM)pc za~JV{xZz&q$JL`ZYFB;i+{JuF(p9=^@kd2i!HYI1_1F~?bAA0x#&&;ptb0DN1uow& zIKWsZsd;dm0qCc>S8b6vuN1w&vW@>0&+BHCk7|^; zp%3f@rC|{-nex3KGgrw^W|LqUEO3C$W+ARXl?WEIe4&`l23M_dL_JD zPEm-?1gvo*BUP%iLcPTRtDq2$$2s)l4u8#$;S+V)7GbP{3B{hp`>#UgXvNy#py;VbUV{b6A(dmu$RJEl1`W#VLx*8i{f^@VF7_K7JlVhk?IM8} zsz7F-Hx<-l&Lil!Mw3h`On5PU(#};7Dez3%R)cY8l78$`>_2#RRV5Cup?Q~`}R{h(&|sXZK0wxR^Vy;c`-+F6nce89zWgND)fX7;V` zn>qpM%8ERurq0i`)R~u=0z4{wWPimo8lhS>J=o_0^W9PnjHhuaNzE>(o;& zXd3UD*yue1b4qLE2fu#L{gEDU@9p^2Bg3DdfnRpfCGi?|PO(YnZ_&-Tvlm>284$Z* zYer#?f5`8q7Q=_-Hible{qaMzYv^7x<{ZpD2_7dBSSDdTFq!}(V(zUW8aP=&i`>4I z4{Dd3UXg8KlEl$ST3P-nlUbWb?-x*p?kErBp6Y|O0pYZuVy}QdrMKB2t969ps z4kju7=%EOJW)1!)08Q(CWq(mXh z3rN!y?Bq~!Hq^{GV>ZM)%p;V=pFm(==r(z7Alr+!7AUn(RDOCklKQ-85fND{SlfGn*fXZ8O&Ogl$sFk-g4itnxwzI=vJF_GAuOI6{y~d2XFJVbe z_QgYKJUta2U%F=Ha(?|>iWhXM>h|^z71<4PnyA+I4UWSuLFQv9EU0s-d1w1yT|U3R z+N@IvBNmK?2WndKL|f_k&A_5=XS2#Pr3RoEKGu7Fzwh?rNy8jvpZzBe6~CE5idedG zeC-r)()w|OGRFL>ltheK`lnip@HWWwxO0d1z2~k=m&YD)6b%67_h-xowSXG^Ia?rp z$UZQM*L-{xap)(|$OI@KFbjoY*NJhmxbXmZR?hkCc9*#bGS|eh7mySlMCQCZIl>f) zZ#5+KE81E*u#%v^!nFy%BVS2ngm)fAbN%6UX3@@?Ab&@|^Gfu@WB>}(d?sqdb#K6c z*96_Q>$mYbRJ?~CV=GGpI(OO{HvU`>nWyzH9cW8!f2u@N$h)n$j0o+=3vN4j*&P4H zs|Xp6|oB_g}<>7~bk5=mGzYoUJ?U%}Z}zr;1eYI|>W z^3_4oZ0ZI3(PDs0>tVO&Yp=jIO{P~lQq*hXt3amzWd`+#)kTp)UF?v}W)VOG* zgF49h?9XQnaP%$yE9AMw3@6fPZg66T6d6bXIT8ps4QvL=k{Et=c8ff zp$F|WCnNCP#r+kRX58{_gANEw2uLoomS@inrV*2Uc9dFpfQTjl4OA|o9kd%lu7L0g z%L!L6*Ojg&^7C#0=0x5x(v0r2!i%K|L1Lk@SNjO*E2oz|ABl-VQz{w%2{UPzqId0T z1>DPxcgcR|CtV67m4Xiw^rUk_1~2|`)ch0KOY^t`%GC4NIR>FDhX#}t-865sYHjn> zo`rdhX#MZYziz0X>4%0zTzM7rr7k5eJgBuq2?2fdhG#4*n0YfQ=Y1bB$P4|_Kz3B~ zXZVSab<6!fZ=Qbi4cj^1a@%}vXHm!vWWP`6Da)xjBkDUjs&9kR@6lp)P*LR$B_)b* z)s!`JXl9Luq$tL5QCP{LKtd0}xke=&gLQXJ2bwx22v6w5hwJ2f<)~%KhK7>Jdpe6u zwRUuw)q)m3lc7EtB}s82{3JlpH1G!F^;^btHIaLS*_plSwnM)jeRjR3r^vRnv>KK; zEjBgWZqehaho`5kv6IS33XqzrHRN-kbcx#7m~`=K{*j@fyg+A-#)f=#aVr(C5Ud<= zzuLuXb~$vZ&j8kHOn?%zOjKuxf@%r`CX10SIy!j2;)nblapNGCSluyVI!Hr6K_7a( zd>TsepQ`HF-?|C5mgI2+XxRBpSlRJ9A|0N4WQRw#W^VJaP+HH94^qXeckjgViB~-S zzPVk9u~fOg{bpIMU;0aFrHhPB+yoh(r~i=hfTB%SO$Kb=KKAHTQI~S<@hpv;*MbZ$ z+faZYvtXzpg{~Jh#aR%mQ%Y}yE$u*HWnl?LD_E4}?_UD|kcH4-d65ql-JKKjZ7$B+ zoX{$Xq+iVKCp)ZhY`hE@^+DR(ZGaKp$F{CbB3p*N^) zIlVTM9vSMH_;*>NkCMEF?l^-c_1bP@n1nwqb%x`HpPMOWh2LVOjx71AU7<0VW^6Oc zeq7H219~z*)y8GRw)Y{LCq9&v1nk}WEUMGuZt_KR9nO5ptee73RM*rT!zpvF^SkLd z+Af`sk0^hCt{~5$=F-MxLB?w!azsCbDuXc(S)d0Rdw4{;FkiGOBH@?vl_)4oH?m4S zwtj(~fO>K6v%w}=aPW4?{TQqFg zP|_B$39XqbpR9prZUW*X{ug8F0#oOpUEs(_$x8vYiHUWWYsUYPb2lAFo2*Ot1U%`x zt^kFY7%s{D2A{Ft+{^W-OVmmZ3b8n|q-S=Nv4Z1OZ}l`1)@F)7R>oK%vAlaR?M1o``X1CK@SD*bx`S)? zbs39~bVeY#fOj{ z(|0>jhVpOmy>ke7ww~MACN2Z?aE7$rk;T=7%DaSgDePd|#m5*5QO+wlbME~4OJ*=^0sd06scnd;b$D zDYC^3OjTy_#ODc$5XMn_tRG(+2vCkxO^onAoc6#XSHzss0wJ@r{WxgAf;@3;V$HdW ztPL)-N%%g{WTSS7tEQV9T^^$jVweDK2@o@N<@5fUfJ4%`Gw1ty9%W@%{7nH8=j09O zc`828+tFy{U5u4bQ<8)7R|MDHxZm^RlL(H`T~=%nJ9`eDfz$1rB};nM+W(rd_24b$ zK*=2u@vqVv8U?NuM^cIrqK~rtM-N8GZlEOc>AlKlU_IdTX~U@C!y9E%<~1~B;PmbQ zhvIRC~!( zcNfB#v|NN0fxVpPPnoYtWtTcHC_a8(YO6KK>6uGz?1PZvZ;ak?0`Oi5_lBjLjEj9^PYC0xF&7E0q#G$1~heB+t(jnt}xCb$lOps6AltZ0hyCP51xrgn}g_aLV=Ft{i)yFqc zKEgAr3(sC(>oU?yo-)=@b#Wk}P{YxB-TBN;{Q4pw8yRLWTGq?{*q$E#X-w@i!(J~W zw=mP!Tv_oI9vcuQto6~vEZ#zKz%l;&x^?f;tbg#EYn?%9+ryijG4pSfiE-1$gjV4a zgir|)1jU++w&O4JY@?RiLi0$BV#E~Xhmbm`tz$-q@a5-$z(f4WFd{+t*Epw_39EC6 z+KH7lhReY;fkjO@Q!d8u&X}riG}!QfnKYJ&HqJBgY0$9YjRSfwAMMi2`EBTauW6&H zbF+AfQXBIL5>WqrTU7(Fu&G>T3m~(|9D8;2N{fpb3Vul#5YCV>0gi%eo?;NmlGh+B z_r$M(83agLkoz^nKLlFhHULHcmG5|~T#FtJSm2^1&ewe_Bmxe*D|#w!7Y~OgLaVabn1*WBR0yoV3|M#55@x{2|6y59-91eEis~WJFZi^w9$O@+#Dt zG|`8(P3>C7C(=Pu$1Er9zO847r7Xv2I{minpM0adw6_TymfJUX>K`M3=V#NMj|H*3{C9^TWT+lnKs5it*_{Y zk)7~QiYad?4P~_s!&Tdv-Hp@mxEVXr(Z)}xv7ZoUL0h&oR6!z}f5DZFn)Rwa7Ker{ z7(91wA6Rr^F^=0dP;F(A@1ag`dh7f`AFN&M{CBe zEw8k3nmaet?aoy7^{>SkdaYG};wr8-0}g$RmNDPYvzQd6n4(vw>1_;;YPmdJJ?GqU ztaW6U&N2)`P;&}W$VazaSxs@+3h6UL7vg~enirAVlfGj3AXBhMhyJfiOO^bf{Hj0I z%=1BD!YbFGeV>PZ^P89dzTRq;LR)uHDC$vRbM%3FMFh;=l1VHmry2@gBN7-CsV-*1XQ0{}wG<@3{o^4%8$0Hv8N|83%)Rqm6n2EGd8;hjTuL4qn;IsV6Hu$i$h18CX_T*}JUv-+XP&wyMe_cYT|_gs)OrIOP1g zo|FgDfaB@(rL5FWCclAoi|Y(y;#(GEGHu2+3jLsnv>k8e#0Q#&M+ONhi}XdFH0a(f z)U{Z$s$Sp`rUTv%2CE^N)Ar)Jf3Mjk*4;Gi<=doYkZkXz z&w0r`5lWIkd+^<(h?a*{^wg*nV6$Yu)bLhx`359)-N$v-l@8m0kq+gPRO{TJ>+CMz zsHQ2`z2k761?}Wlu<4G?GlIyOOywk4dFcAsu5x zCE^&$JXD6zKpG51i3pj;Bce!2hA2bkbjvYI#?bKp>~o&A{_A;OYrX4z&x_pm@Av)g zeeG*s``UN;A>!B++xgD{LBBi}X2NE+Cxh)JJ;1R{3!6DHFeVt-@p*thlOSqm2CZ5@ zBv5Nur=`QgzGmz(^XwHWvMYoe2wBoF@21kM@gBKU$O3e%T!Kg^`Rxv!$qt7#&t^(F7hT`SKgK;r z@j7RbaH{5Q8rCb3DH1|*Z2uvt^ShwiZZmNCRWxzT8P!8U9anoZ-m)&3R#pnZhW@%~ z*OE4PbgoCN#+0S9V2KI4`t!Z#ckOyqvtMSYfF^zYHO{Hd#=l~7X2xFs!?y68O)^ac zEeI#D)2I!46sG1})EqoG`CnIcugmZm%sspt5az1eENO==ShWrPE!jgTVGh33Uno2- z>5kKs5oBfz-ngy;oWE(7ZP=oPjIha4(yd#!4n{j3)i}1%_acz$`RJzUYLW2 zE-+)iN16_JR9GK4%G7f=q4=nWY^l!6_TS(?u#QN6|Jl9WqzKWB?HW zxQaoutCnm1+vyJjQyE2`&8+9NICg|lGteiGlG&4)Gv6uRiP0K|gN63OO7IBG;cS>h z;$CcEy9o%cQt9Ko0S&dvIpwn-^Ct)o|LMh*VSaiDFH32uQ2JTYT<_Y&Z2R`@qX4f0 z%V2rp*;$|>he+e1I_weB_Uv8}(joAAiZ??JYZE=KJg|@m$g4&_|?b@`ew)8oXYPoq=Hr zM_Tm(rFQH*OK=MsLQL{LD5h}8V{_WW>Qx&Mg zPQ=ButB5V?8-3o!<<}uFTh9kELyanTP|0JXa1q;g(ULM7C^J2jk}zfA$;oZSIRU6p z^4>reTd;qJ(NeEYzFWMfaWN9x*>2ku?K$!b#7)jh3nL?y$_ZMXP6wcPuk_3N;`NeC zzv9U7P2;@^3kuwV|3=bMmm)T1;u+{<)vt*&XlL#4o^bHr?J7JsQ794Q;NuVZ{`PLc zrrxTQfycWS8V~q!{VtvSwSBXKsw!i+d?KZnWwQ8S;S2}D?(tc%o9QiNqz;gEtYfxW z+iKtcEZn`WRp)ry6%4@kd32CUzhr7Mz<(M6O@$9SwChLh`Gs@nXvn%N2F<(CyY7?O zxg9b>?o$!V;iLo05hV+3O`3>2G* z3L@j=Idi1zBpuAbT`XfG{$(|f?!3c_Jco$oEj56ct zwxrN(j*={<+R?CMABT-?T0Ioh2qV;xym*=7Zeb)wN)#tZnrhA)^n_zHB<8W=?wGEOLO+ud_C9!u2; zj4QP~zmXthEdxsi+~TG= z_lp${Z0z*Frm6He83;*iTAN!y3c&$8S>w^KUx;-;{P@FuC{9+motnJ=s@u8mIvMIK zyX=^#V~uit(CR1;R5>)DmG9rLhsESal)V4>H#q&`R*f9#RdLa|{L#8S=yp+5l z*<{IWLscLfQ_11g!^0BbqiKYhQXA%vbzX`ib1uFkj^m7)v5SnLApKnUdd3{L5H^TB z{l(;sOH)>}9^WVW)hgl=-Yn#Q=$V>6?B4iv6IHt4H@bb=M3!SyF2`!V-xeR=&F_YR zXf!=N6L&li>$I2G^li-Tq&W($_Mb6ehLu#3x|z0%wT=)Ql`|7oe_e`X1B4H15{eQY zP4(#wS>Ysu#p0<7w=J{5WJA8^XZGuf?J31zOH#0`v4$2_3u((p`W|?FLNA`%9I@S( z-Zcu=L=0a*TlX0LN_3=YmiD>10EvOxMfi@XKT^KRJUjF}?Zb{8JK8LowW>=`yAIE& zB0ux|ZC=e6De97zvG1H>jO|I8@9_wkL9-wf+$Dy2^cX9-xz@|urj?eLZ=`>Xhj+6s zRr>*PUy!8WkKu-sk}zs@)ift_B?DEv^zYx_DW(HWq=hPS?O)F8)W>$nkeA=Hgs$sj z8m=`;7D42m5B<^7sZ+}6gPhoy$3brvU^GqY=i!4JO!8lC{%ZiooR$cFInEsx+dDWY z@zQ2uS$3MFd9YyVLDBvDqnnK|)4SrhCjGAGy**(!=*7&#{{cR62(4ckT!(#0<+;&W zG|HEf8NY2QJ1piqwtAWLsVXWWLcUJn+~DBg6Ak;Gz12C~;`I2#t>SjH z;*Fy4q;R9jYz^M4^iy^t`{KJKDn#maXz`Zkk0_kenSqV{A)r0ual>ijE`{JlDeKKt z)7dP(&HH87!GWusKeS0OESfh3p<4gKR}wUlLJH)ZN!z+u`kM)(VP+ zCv>0Gap=gW;%mgHWa;*&!(y=y5Mw@Z9RFzWhA{qfn|05gHA)|Jd_s1d7_~I8x<2g- z5$kbvQ5AHj-$Up87@8chiqDzlN+d(8uis&L#qs}HzHSwDx8ulFwd#Au6@fSqi;U<} z5Y!ZP>{v!}z@ai7hRW`GKwTY5d})$H23}jU__qEH0s*)PiDBpPcmbIV8`XsrMDnA^ z+Q9_ARFgCgC?dB!ouVj>B*Ow9NZX8LJJ6Yub}NO{6#gFhbQIV0!05x*U@0(P@}@pO zKjaamX@O6GOkozjG_|AzOQrl^rf(Elocl+;lAj%Q|Fl{*l1jNJ_4!}3S`rOXNF^3s zN{G%f9}C`)x_!p!zOK{u=7<5(3fg_waycRM80?t|_a$kV#tg{cT;w$13hw6DM&|Ar zv4y#MoF9IM5HQG?xvA0+f7s{4vUYvOpgdA zAS@?o?r6Am(eLlGh(h@c@oj5Thk`|I{>PRmghg+}6_uS`sE8s)hQZxkVRszAe)hH* zGopqDFaU{;)0oU+Tp|rNJDcWpTlC@4AH!AR>4X55a)A3wV6MsId9(q@fM*|QUM+|w z+HKL8!c-JL3-$pIDii1&=q0{y+y=cHh|!|Y_1mQPdlXJKs02mF1_F5rBqT34K`tPB zA-U0S*a!RG5r^b_a&P6=H@ZBjJ+z#~0h-qBww|*(w*1Q8ut}Yczc~;0A&L zc4@96ny8*Ln3s0TH0hvDQIC%LnX_iidbtpsX7;dJSXY9vXtVhs+YZHD=VnnlHAXEW zwskj`jrdpj1O!G|7EU-NFF+p)nSxYWCasU1J}nh&;PDB&3%j8iLz1x3Jx%tHy$Q4> z6Ep_zh!Bl>-?r<4#iR)_Bt*2y`npPgxr6yKKCzjV$yeTEy% z!fjl3jTn)!a27e|GW4Phs>+ZBSsjamzD}yLahOVHjVu-uVu6%OL#J_+exoK$_7XzG zGN1488TXN(KckBE-CGXzDhY!U-Q@|o6 zKZkW#Pd`gEzrsKhCuCQ-JW5JkVYTUQn0D#n7F9m;-hs{MgP6nRBBF@{szY_qHQ%fe z!`Hvx(bl=ikXVfgfsQ|-LSvS}8;T>ejPjAOP*95Th8$KTbt#k!3^}X@W>U3XhuyBM z{!v2BAPS^EVc=X}b+}YqfihZA9D{iWe>{~JxU^tVnrIIB^I|^-p(7L&QDooJ0gOf> zguIS6J!0Lu2H*uBsyZx4|2{Y{u53Lpxm|`g(g&L^bGOIGGq+%@k*#RA_G>MH0d}fR z-zlMGOSgVHDV_whs#)bFjeFEq+n!mH1FjcWk&3=PN~CFxJ}4AM^I6v2BS*`Qojhqm zq$6cP6k$?hD#Z|z;Wv-mNJ6sz4Z~8B%yxb#)2ZQl<6uobmA@TpqUEbVn*d`fC=Z-z z?P=r(6*hP7r{)?tcs4d}*zh?IK-M^-h%o!@`KyPc@pjxx6&-^&Ke07!xAAMO^5KZK z)O`cYUlLuas;ebP@Kj{8NKN%J+60V1TVQm;_J$1r)CDe(DutlT&rqphHF%`yWET8@k zCSOwQnV{{XzYFS_PG^XY2J%bb`o zA2v(>Qdi8fYW79+CUxf0mZI2RzG$Vv>l{y1bkDgm>_P0C|JyyF?-WIN+5OG2278c9 zqcNxeUa^@euwW&alWp70(0Kr!p42!#qp2PIHup1b6OCC1FFM zfsMG6@i|d)A|@`i43MG~vm+Ww*H<;O_^Uy*&nn&KoQwa%vEVUbIi}lzt5SXF|?fY7dGZS|J(~J&7>;8?hZ%*6Tt6k8@ zA#L_ea@BG0HNApw5LsgsM}wIV+0A-(*^eWw^nLyOt{}vdJ~pj>(NS^CUO#PFzho`6 z`Rj>8Ig`6#y45sp>M|?Vj4neSguFEBBsomEyw?B7G`#Q7RLK4rNgae$)biF(e-FO_ z*cD6zzvvW^i4DOCBbRe?9Bt2rW`{tyumPdTMc(4e@?0PU6^*!h2vcciv=9!DtHW?y z+>Uf>wuA^8bPEa>IS}pr&w~(p^7{-Y<=wh9yoRklTX+C{blg~m3Nb_z_M3glADG56 zP+ux3hf#K#ngf0u1N$y7TnHY}ke35uprut6c{FZQ^v6HEOteO|*OSVc_Oob>#L9#a z!7E_Zz0=knjLedov#w|SU!@@p7_ZT{t5jH&qa;;FAou$=;K&Z_dUDxTL&c3uE)cw=A?qr4|$%A zC5~2`D0}ADcQ9PpuKU?Zu35UN zPdiH;q>HkFQl3WrR4ymt|C4x5a|pWRy$@;(DZ{RcNPdF%v}t*a8)0ov%+XD+v3j7L=3=E8M=u z6`h`f-Ud<%d#G2S@i$RtU3iaE!Ol|dJb!)>tWmcAe8?*BHJtw13m2-J-z=!@ihFOO zU+6^91ggYVO#%jOvRC-`xrl=InwxLYg}U=A{t)p6UP}3WrTL3|22Me`qsjpVKukHg z_K)}-)e*?)RzG@0e3c0s;jKCRgoN-;WiyD)6mKUowkos`5@H%5Lqr4~)ro_xb{%sY zU-0PBHW@A8!1`wGD{GabjV3eJbCEbS8d)8; zaoJ`d!!~wyp`CnP+E+H1&cruG#3i`&TT5W+*HS3dm2vw4yIue?L>ntJ72HRhC2eX% z7r6S@VmNF>MU1G1V6!O8RtYdmwNJfC<$;%;Gw+Pdm}uY-2b7A_XTsPN~?%m929w*g+XQb;fd9NErNKB8L0D`LL_;{H6=&clDo^a^LgoUPHy3i0e@ z!%2f~rb8S1f9ipZJJ?kx3@J@7`(7#6YV)3rTW7zyI;5({WzC5xlcz*JJv{Tw%tebb z7B*P8DC5Y5g^`Dzc8*Aje_-6uxj3i1>5gTYnws6(fBZP-!qHcy+6gUR6+Bt?G2!=; zD@$q%8r2Ug5td3XSzKDzyQ4pKOFB1*v~f5R>T5*_Ti z;+B`c=Y?zK5;iJGGZi_IP#dQH#+<77bD6yL3C87aFMO4OvQ&P z=5|oDlE;AY8f{(&%B;I>F6|W!0Y*aIUTc% z0v+4Cqr}*v6-FW({&kRhJ2TTg!T$US(7qa66n9(aW>~Mkclc`NpEWa8_WNyM5`5k_ zJM+VYzszGE1y>~-5zZZw4k>j9jrIuCy1u#N)|Jo4=oqt)GnzoN>&_eTB++{1-mEsU z3&lNAaL@4M9q0!mibbc&XlFhMU3~BW40V(Rg`jTLfG8jLfYB+;yM;e$rLBDlrS$E0 z-#m_ZCeS&k`1WqhsAoDdj$ZO_D<4H(%Gilyen7IXQ5}9>8!dA`fh$6#-c;i<3b6!7+X~N97>-6~PH|6U0)b%5aQ?=yO^7I`QiG_7-X!MB!L(P& z;9nqJub+LL)ZYZXDcs$F29P(5tz23n33L!}4LP#vwxEk%ac!<(z8O_rBH3dB4rG zthb#j-{h@h>JmO)?ZvAS`dA_?5>X_aqzot1Ch8{JJeMz*wf2nr7$HpqZ}1S{?)6us z|IQ|j{QC|*(C`Hx<<>G+G{^n7i~b~n;dU4iLExg(Q>>OTxeVJjno5Ln`4=pdOr7a1h#Dqh;1*PQ|rPL-CQkE&6Z zE$!UVa3CPLWEzCB#Sw&*ZY(Ef*HClg_)uaAX|}En$StnZcM4)6i!0^HsQDMg5s8+J z5%3m_6F+x;xR7E{lq#|T?p}b!pgM1Y4tQnN40;mVcIfztL-9{j(cHbVZk;-zmG*B> z;qU}to7KvzXU^g&W9=pEHRYRY->9rOs-4NO6X*hDqo9g` zwcq;wmPT!AzXd$4Z7$o~IH4BMe?C{%TRP?FmkjQ7I5#x5eW&1%iUDGAO#EI$33+p8^@F;K=whVlJ<-^rFmb${x6TM^&wPx$S-SZ6& z+&$-wY_?^C1`R5(1IcDc6Zx}aW2QG7kfUF7;rw~|3|vc@ z=EW074KAH*>41)xSF1@ez;e1{!PQG=+}$7ZWP-_;q^hUIg@Xn|*Tv(;rGkJ!cTL3} zj3T{xru30%>$FeYrkn@)4;TeqI9VSL!!rhCrGhBN>Rv8;RxeW`H`LJBO_k1=qLJ&P zZv)VUYmA(U2Y+qp{i@zY%DzSKzB!nfHErED)NP*$Dw~w9nR825EgrBdenf|O)Ad7d zBKxET46oIw-L^?bUr|NdB-#|}{Rl;GBIG(p1`uPJuiwQ2KpTn{n<%HBJ25}TI&-=y ztslC_F*Iy_lPp3)Z8$+etf|&s!96I#qbja=vr}uXp#WW|bKwbaRuW%lcReKdM;tE=yMtuO!KL89D-Gr(IfQd+MaH?@6G-4qmxB!ku7Oblu=0~uqU0sXnVBS z8hw(Y^$Ec(VyF9dp<4a=mhAcYx^w`dvz_EU&-068K>nW?w-Bz0XZW8L!?^N5C}Cx0 zh0tE_aZIZ`M$=~(Rd>O913U@lT@Ci2sX^({l&RSrAeR4ZrwXb5)?7%rUsYwx#DkZc zLWfUd>);mu^D7~%Ei_sks^-0fT+#jT!=w+PLhJ1oV+4W$IgIU)%CSbRLkxnx7l@yl z4T6U7|J%(^)5&NdXOA(gPnW;P>BZW-=beX78ur@AOur*fJK=rL!-o&&Cl4AF{lFd_ z-^FyBLwsp8y1F_##;yKjhyftqSSl!361D3M>eU;u*QEtr7kgwsf_7lO`!iX=;2~hL zy~lnn5bRV=XkzaG*^XoWI5?sFRe5GqjRi(rViZ{T?whRcqF_>FEXOF(piiJe>y?{v z?()Hj`yC<_>=arWp}i_iOy*^41mD=>hqbT-H09`>G3);8X#0=Da<>9QV*o=T={*KT zol1{FgcaJ)p(^t?4DQ9+kE%ePS42SD5v&m(GIIHyci)U3?S&svd)$jN*TuW&EEQu% z2wT~Z+00jD6$GD?b3aJGiL^27p5?YxWwUj>#QzonpbEWy$I&@v>N!e{9WP9Bui;*Y z>{fPaa|&<6!Kg?PL-pc1NGri3?(PEGz(%N#FR>sho^v9voe%Vi$S#JMU_|pr_o=8E ztalrE$mk!TRb%CHWb{B`dTTOIML~Hr)>K0U3Y6vRYBUn*9kw78jz8QCQvqJAc-7*y zxVmtxlR91&NAB&hyP}TRWo3M7YU=cZNl6nUns~36+lHk{$#dJjDapzj9Qu?T@skx1 zC>v8y2{`>#oh%1Tx#;15PL_kNr++NDR-RSM()Az;4>@*3V;l=)Uxp}sx$eV0|2}f| z>{+d_eWz9IbR73>UL9`-7e;((ggltym|XY?^80>}mL>t%cU_aYuKfr;eo% zpfi1y76tMg8rpq0Zu4kjW5AKa(>sfdZ0Us^GN6oPTo&hosXCkO-GE6IZ7E$Xt&1!v z(s>%m?%@ZhP(WMOTC-9^p?e_8VWh;o2*3lSdMPij0Um{%V9Fn{Z=e*J?C&oYhV5~d z6qhv^1EkkD7aQ+L^T2mLx0)u(321I6q{zuW>br8Rm1BVh#3+%NsoRD&)E;tUIn4hU3gHa|7!a2UkMJC> z52L!hD#N{AJI`d?Nx(fp9PZ%FoQ(8P&8-jA7| z7Fqb|tRL!fz@tOEu_!Cdw0iBH(BX-!Snr?SK$LU%J{V6tH=i)Qn&!p{$T=h{iy2=4 zLsE_gx7~AdN-0=A3E_x&?0lCf5PpIKTXkm=|HyC3}OZjs9j#-6qjM%(+PO24MwCaS~xz zi!BgZ#ZzsSUU3vS**$JNdE&YLC=%Dk^)@>7*$VsHgs{h-W9mw#+SLjs@Z+JW>ID4A z4EfSqvebjekEP&xIN(OZf3ELUfqvjY6={1D%Ou`#!7C7OBY3|_EPunFh_@uWVp+( zg82+ZQ3tDt4+n&YA0u0`K^04GKqJk>Gx>QHhv|sc7d6f&GJo65LI0CtTO{PL)W8?$ zBw`Ah2C^I#MWGvK6GZO^1bhNM;%$Nz#zL(*(UFsuuk*{SFSGt=ugCoO7XAqaDl}Hx zDmOYD{ihYLqCHbl0gXuOuYT37Z^JsoE?c(Dn(+aJiKRG5pkh?jxw(*$5)&9yGwNB) zqYNf`>2z{z>Y~b4BNu?QdgI0~smGM%MMG{tnFqeU-Hxt}9hq0CG#-6raqvg0+SXiD zB4smsO&`ns708e9jZigcXq!6jlXL3lb?Ni(9huk?eXLZCCnDo*cWJHKCQKPKM!T=S z`L>iu_Yj&247CebEPK8nz8dYCHvTvZ;#f+;))f&^`=9$zYiPK^A!nzvb_79Jcg;=; zpNQbGI;$C*pJ>-}SbZ<@6NJX?H}h#H6iseRZ?9QF6kqUfzAqjBHmAoYwU4rDhwMVn z@9`;r0p7u5lRN7#HSKl!F8`rMm0-7Nl5L}Aoj~bj5E5#uQbHY#d!Q+%r7pg!S=lke z{|@2YM1(i6FKy8o>aRn)JyuSE>*@nDf&(o*SlE1(V9z3 z*tr@yZ-c{b?c!Z!D)EUnUOI+Mt3`h4oHlh&(6BmrD2>}ir;~!1?f2F+Kbw6;Km_CU ze}q{W75H}wev;(2Pxo;N={_P(qeh+Bdz}w1$cL~fGu8ynsyjDhW3(M--=G2P3&{08 zkK>nvs2&k^gLr+&pbq6Dp#!};{Y?kLY}T*OFEzlb%}Vrj&qeOlugJ?UG2Xl7K4ul+ z^^`j9UKZigQZxIcR-AWv+kX71{poETf2^uhnqQc-5r4J-cP}XWv1X<5mNn%pAaIYc_>oIZSp#G-LsQ#eaY^v}7{#rRUV$ZOxQN#*sMjIf~^SNO^5f%6&j+g#6 z&KIYY>bH9d0&acPc#l(-Z!J$Pe^hec1yiO4Rq0_Q}>z`{q zgP;xQxB?$YJ3$%mQ2OL%-+LNL5jB17wt2 zg03p`Btr>Xb4pAb?4o&F(@|riRc#QjWeD6l07oosavR>E_amZwnHOtUvjdRAS;Vnd zufWxxU5qEwCviEZx|d$h&8^Q`GGwf)>FHBO$SOVbsWL6beR$2vRG}$hR53i1mM4d} zLC#q#Ihr&A_vBk*ROrBqhX38p-g1*R3_5te{jpiOSr*yyyy(7qu5 zuAOR-M(lQXcQ?m;mOZ?AcA=lvtIRF81FRWr%6=ILm~o1@-E+<}7aj@J4SKoa=g%y@ z-1>si1udIgC_Lx`K2{60*-;1xvj@v;1jD_N#68@TZc^?@>zf9Xn7xbYhxiS5W|5Pi z^{Q+dFqTPdWH1i@z4kI6 z&q5z10MuctQ~MLL{h5cKA{R3C7qWiQzTvlM zYm0#ocTKEu?cBE|h#a8_v~oIqd+O<5W7g;HPDv3cDT+Z589cxmxM8?X)r)`xwxYq& zIk!mYttecGkSXQe7)+-L0OYR*Ip3P9mv?4Exaf-zQ<=JY_T_asb1;OrrTc@Del55t z=*L3cKx8NM&D3l-{6I2?17~88X@dMn?OHx}LLF1uIPLFf@9JEA!sXGipx+BI`jxfF zuq<}u6_ja4A1@ToI4%4V?cE|i13@MF>gjJBby&qZWJbQx^xHdEUV6vr(a$PQ8zFf@b};)c$&Rl=H9^ z^D!+$!)uTl%(IKZ8+lnYG@PBwWTwoY&syl~%k-_FtXYpvS;nuB#UkS_EbT$_1>s+K zzUlu^zE)NtZBN+TsE0~lYGAn9$GU3g}W7va=g>5L`#^iMVmVD+W-4p^epocug+Bob8&!-k8IX_|-JIuS-e z9x<~K&)6eXtqJ((#`a!ab6f+ZGk-M9O9 zG>Oycn-oP-M4THKJOa_sMx|Ob8R$lhtvv^T^5lFm66kZNteI4geFcUSXcD z;T##HX_tY6y_bC6jAq+!F@f81k#P23##{dI2pc0(xHMw|E~BGZV-ehl1{axA2(qJfsVizkDdz)SBX z`SwM(Zgq{Z(cH;tpa1bmiwl!(A?{j?j7j{3DZpqnGxc$U`mABorsk9kluP2NDN0v9 z{{{zZThF#fJUT3)b|7v1(w21VWbol^2wP_7Kxt zb<3sI%vo!*)K#c;T2-Q2LYY#s(8j7n;)oP~vJ4o6Dk5qW0Ag1}iV?h;Qsen{H;N~T zvGTQKGLqH|Q#uXN9>C$+pI^ua($SCQp9*(RQ}pkjNS8u==te>a4t-sYWQi`%-`}F1 zH-X8tkBT95qQQFeQGwnu1*4q)%;3rq=IUOgNKn_*RDia znFiX(vX!;+*SC&bI-M717zjCuNE4(!F-4{7`{OxI=cXrILsB5@E3E<} zcpr&={Lql%o*T3&ixE$Y%`*CrkzO-8z$)Qc(){B7ZfZD$E6nYok`}(EO#roTgQX24 z2Z{X_MN$1?6jUJcY792GR~W6W=X@%G-4G-RNHQ^Qu|=25T!M&pAUa`^UFUCHxL`pl zR{_d#luIY;`ZP>Aa>tJyd(L+iO)0v$=M0RqDinH(R+pe!65EC&0-ng_okFkd#Y{p< zTgiD40W$hVo{$j};xhog*ME%Ai3X-Ex+du%#SrZ(Pm45Qt~|MV)heCN+hO(!$B3>$ zEg%6eGeVy@FDl=hK zm6p=*p;i{(1IeyTFo7>+hLEf^B+cV9rIZz{!10gd;oeMTs>lmFx&MV2zra&JL_iA; zFs9)F)iok>`4X{0q>)6BtNzPRUs>^Bi!O>yb+1xfecZYv>WwZBAEKP_Wn1b(FWj zPl;%MauW(`{Wa%-N9GofEdP2&-~^JYp?6?78xd<7DNu1$=Cp_|u&@{S2zzfGjs*`3i1&1}= zOS^Kf8#|o4skgY{-K6<(eK0 zn--N-%^9F^CHB2z*{@kRi=@2H_P^c&fOd^ONFN#;+{3;!h^q(tbBV4juiP}_kiuA4TDUdPjw}@|HEveh zf8dhO3Pcp8jDZ@Do=M>FPMrR)3kE?$LzXH+-YCcOHI8Vlbxp7`b<%#hQHefEQWf;N zAPPv9eAGeoB^h-~rj8VsRVqrZ*=BohtJ)6isE6~4tD1znz8I8~Md%H?tUm)kc$3B( zO_g0df|4g{ATvUsKU!i+A&&zd;XKcXazB5uN+Z-MIY%Vf?Qpisi{pm}Y6bDG3X4;J zbl+w39wnPa#Gg}L#HODCk9uTrna8FQSQ9ym`;?E4o3A>BCOADZSxw7>c7Xb)ACba4 zLUP|6;0SGkML}BF!Sx$$ckM(}M&viY_sRt**#Zhz%iO3b`g5_N7&lHNH0XYcI;Hez zS7hCSq=XfSY~B$KAKhV@y+uDsJxK?9vs0Hl`~{hi1JILsS|%nXB{2HW$(I4n^{xBe zpT6D~tl^IoUWKpwYfSIpYghmKS5+s-1`jE8t$eQ<)f~O^Ca3Ntu-g|@9aFy4&I!Ic z)yHQD?|x-@WP)a+Mxu6*2t@olPHw`Cem(6cvYRAFasdyx4P|`}z|8)*CCm~OOWlCu znxcnY=tT`S1^xo#)ytE6eqf?cGchK!zW-l~mamjP9jVFWUmCgM2jTxSY!vN~!IU$w zY?4|?_#@KiPWiha={krz^WXq0t1BF+x?0VHXhM9~+qzX5AinLi!*wpqo;uYu(NpKj z11Awgk|9(BvO7B&_|KR>2`nr^O0Lri`zE9m=9mHH1 zC}r2bgtJNF8Gqon66h3|XttrqZdij?iS}tbR<*~%Ai=Uq8DDV^1)B~6pWL!czUL6@q8ME zpu{Uw4KnG>d-lN@n@Y|)xN5P;j^GAgGm%M{O$+u?nD3nvo=~qFNw+x=#)Xc`tWzV*zxc42b~ntOQhb;Zw2W=2z5xxRsht;v z(eSV5CDF#?`ZO6cr;m!LL95_{Aa)EjOTDIRi*Por=!!P^9C!3-WsK&W~ieUDKm9LFYq;h>kpas>zs z*|x>~TH&_O2NF8_-ZkcBI0MWaY120w4p@WkQ)X15LW{jHJftaEvH6iH9C4_B(q7-& zumyH;X2G&Eg9)qv*d#`*H7?R!58a+X)h|*?o)SG8!>3pH!=jpl9oj~B$}G|x|@MD|Mkl$^lkF&i&^RyCe8V*IRd_>(F`)&KO?(B zcveR<08$slI>qy1!k z=Y!|vkXmItdmxBIdVwR5FAl-6?28u_GC6rM;|TozG~%q}7Q)b|^NRyke{%^+&N6+Z z=uDm{0K#Uyh9DR@+yORH|jbdhw z3$(w^W4Mj6hqQM|!b_Obr!(b{>ilbAbFcPiymK3={Aa+f@fPcEzj}OI`g_aRCs{2I zkKSJPvqN|v72Hj)i~4THX;IElDl_>z``G!iL#hGx)8AxD9;k7J?B;RIAWt5F(zSGXmkqb(z>#Orn@Hv!lBv@P^NYFd*`U zW5lVeMJ>_OtUA!5hQFOHqeWPf|(yAM<4RPE{t45V3_n zYF7>IsYxEBMQdez(mP(F&3TGji%Dc3zOyPM)0fZK*5)}%7c{7R{`nut#PoH+mW4di-QDA;h zgY~ro{~LW>y;{|&>T8|jq6}`)!U^=?vUIV6VgSj6AF3Goe-Q-1Kt}0H{XWmx2Qne2 z3wq$FjOsMm+?QNVtF$!}0E~k(W2}hXUjG8Sld(t<&jJEgN|+v_iJUN1(MZxD?bSDq zQB-%mZg=XWjWwuM?Ur*hY7pNTz~H*@De4_0m%emj1z5pgvCDils7^Esac#}6UsdE% z>k%3@b)GP~x2~x@n^VHxGEZ=JtDpH))l-IqIaJD=}Y+7T&+;dSu*X!q~erPLrh@ zKq+!1V&`YYhLhxFEu8E(VXYM?nK^JVhy;bB?2*;8gq3wLrsn6e{P2csm=oJ?==Dt& zm&sPDLV}1+NLkOoeQ*sF0aS!^O9>8$*QCip+p5Kdqx?t7>r93A9yv`!PbPpiau(E^ zn~aLCw^hb*`}X~{J?S+S?7E{zyFwnx=!&|onZYSp)Pm8{G3Z^ikc_)8NvYGy25ZN7 zxEtyjSusz~UM44lM{W1587K-RWilEIZ=)^48`G6a{J}kx84u79Q6tlT8I;fa$7h-{ z{E9rm4s0xok15G|glP?uhc6OznO5P_X7zt3qb&lcP2NT{nI>ph&!9gTW_(N%DEyr|~ zgJk~Xm-}j_3L&;iiS7=&XUg39Smx^CDwJexnSMSezxo(2Znu^+S)foLuzjiWiS}hl@h}TS^-KYj1wA|73JFA^-nGklw*>!}3w> z$hDCzid>da1Lo0|TE`G=V?rD`Z0qZl#aRi!%SK}kRW|Y5I~N3r$#;{BP$fFymt$p| z$J?wN+BN6KEeIIeV@6vvG&OTLd0t*6ZQE!l!hcCX#rC2yG7~wP><{J zks#gdhOfylc+K=*pI)<{XdRzwm4A{>n&>gcA2#EJRaRE+^ynZ}rmS4zD$C*w>PD$7 z_(FNy3;RP!2M->6FdE;f*r#LQM8wix=AHP{a#JbP!;4GKFR7c;XANTd#;^inxWs2$ z)Ot515}4mE%_9(B15r+XK8N<1ItwxGVx|Ckow)>+3B2S}a66R*rtmEym71F*nrP0M z!x?K^Ba(R>ffVC z=#5E?=H!SxQ_R{q0&)&*tsxSiEgF9}*AD5l&!dBV_m?!BL^lO4!5<=t5YTAkHC_+k zr7=p9!PHJbIhu!NM`}s-q^H)DkQS{*qCfGUo5V`8=#r4y)`S3D;GLl?biz+%)(2eh zp+!$bo863`gBer|pG*2OA_XVdAq;IJxF5S(@+i1k)H@U};^_djZiUj3$H`Lk!-Jpf z(R(QEjVuh^sR-f4pDUHCut-3EBCAT$o^2^x4sgiO+ltin%%vp=jBk5fTcxK>kbO=Towtx?#oH zjT<*!%ETgR&Ba#`8{vO#?&hsigB7|)PUc2&lbrQf&4xdGHsHnD_{Cd5-U5<20&sqN zvBDTTcC2VL|6^gH1EsrIuMnei+3|;$hN7S9KJq7Nxfz8p+=|ZA z5I((VPE`opc_k%-9{S*g{NKNR1_zH{`G9AZz#c+jj_Cc!CL1#TUT4KK78&>z zyR+eRqT)Q=$Q}sf;rTW&woKn3|4JX{D!iIhY^!1ue)~+y-tdvUA-(N-C2c_VC1G`Dfw2VCMV&!NSj{Oh68z41|yocld?iZmO~L67$Y`C z-b_-Kc8o(*t9%+0jqOJ5JzqjfcmXD|NgUxGF+dYFtE~P4|(BGo9bnDKU)*?R|V44^>5G40Mt%;~@Yjl+{BjtV@pH6EE921S^F_8uVo$ zz@pQxSGtRzGhN+5^fScHJo{mX%-#dqH8$+7@|=QA=~+9PxJtmd1UXJVO`Y+S$0d6u z5K05&(<*;UT}RZQu4>o6{}UTMZ7;no$4ZWJr&S2J&q?}ViV?dV8n@fhn{i}7lFayV z5oAJ>#{C+0FUsHtaNZ`d;|Ek((l`U4+wHq!iMLzn^m+k5&Z5^vcHWmSWDg+D9G2d~ z(9oF{oBNqECldZ=dQuXsxy zhsSRwOmL*HQzii7GDrTH?$gODGm#J)(K}$2EL28oEOtbAYp^FUevkEjt^#2Sqn8Lk zl(w$!YQ{`Z4DRHKAkgD_hqgQr+Fi}kOe^k+qJrhNhsuBuu6@;7yr_c5K|mTybeU0n zq;tIrZn%C$mXK)>D{{VNdK_MOXYso}>_H^S=z5oBG5I{0o0IDpTC+ z)-^EPpvD@ab?b&2H<&M5R^%{3A!_ElrlEk%(1eCIG9AuyX-{Z|TBqG1mb3&mFc{fs zB+I(7yOR+l%3ac?;6+&muB@Q~NOt^^`l#1Kj*IBqiSpW0q$8NRm&RK+%L1$0k@_x;*lEc>XF6V5J* z9tR7j%OHj{LJ3y)v{g8*N`4I0oD_i-HZ>eUDGL}QCb&qIVHd6=wn^|IqOeX6*d1TL z$|TyeyK>NtG41te_-i{}xIUh`Pu2ZpZxa(A&HR_bi6RO%&q%~9h_4MsQ(t#+Q*^|8 zu$eI)YdNE~tx|;c2a4n8<+$)6bu*S~(vSu?$OyKqz7r-8YLw^Oifek6N}REimJl^l z7+jW9MAX&M(ptfz1;p*+2Ecwq1I>ged3!hQGDHc3;sfA%Wx1u>bYdeaI&v?4wi%s()L+kTscBS&sMqw2sT6n&SY{da* z(>;>hHS$nhgu8wbxlg&HJMYcFa#s`jTQ6j5qm%S6vo9GDx&K{QHp}Q&^=Kh2C~i<5 zz!IB`Ge1}9Sm*erR&88%LF|dw0|BKpyG7fyivau}C_riO@va>-lAs~?@=&3{v593d z*6_@XAgg>?Nwjx(vUmwfe&S8$F|ULoPH0_77Xy+TN|&7C;^OD?G?+%Zf4>==&fURX zdWff2SjX7BjjOt^0Rc8#IR}`MvjlnZ6dFw81h*orqRxQ< zPC@(NW@(g06!SbILYA@HbR|*Q6z!Eq*hAkd_ ziYE)lQ;C=3LkbB3q_tsE&G!!zL|?}A2YXu1YwaC3Zk*+eyX~dD0AHR`t*i0H7 znd5lVSM&cUbnku{)hGwT2PnEXq_#$O>Jo#G;dn(fH|RTmdaSWTG#2|^M8U+U#pgk{ zKc6z?w%8X_QQZrHrJVv5MaMeqOgdFfvkFePnQ08YR)x>9g<*F6*$`ogx zh+g`5FewQTbEhoGabz2JPq!JK20sbh&u~JmS0i>rjlCK(nYd(WKJJRv)qNPK(~QybLNyH5KTE z*sGmw17`sVoe%szog4!Yt=o;pRlD~csF>3?Iv!8eD&9Jc!ivhlirWs-bRcuJ_sTrK z_$Z>1tGG1A6VTv3j1`KU$sx4&pJXg*YGe1%$+7+c(Pq#f1Cr%IT;&e%3^e@RhJKzz z3VJNWo4AQ*VgpToHe-pbk<%*^WI22^0STNe@G5UP-tMCZ015@OI~yxV2}G63l8^Bf zCrgz|TiTLqto`ZGU|rG^<|FUkJ$(`=gAwE;)TUid&A1l<;chkexefTp+^&iTQF5`)Me&IkP-{dH>E6oTs$)=e>q=$6=fF!?U$beC`aC@l7Y zxTwuWte+BxF3~=0!V9l;&?q{2)fe2ttZHZNnRlKftMoiY*^UgUWPTI&7paBKGmGqc z!e!W=59iLF4S4^+vfcik`cSpl;a6sTP3mc?X!j6=0K+ya_Bs$w{rF7!r~4;e=u5rO z>vbe_Z#d%y9XedI>HYEVzts$yKVY4M%G%i%VjmyXyVN;uDJ8wMsu->jwt!LihGFwg zZZ_+RN+9obdMC7-)G#hNZT98nteUe6Elu{gJMjknu_jD%TL)NzY2U-KWRo)n209zI ztFAtttXO;h-8bdHI3Xo$0}%7bIOhp@?@yn$=24QTAp*3uPw6o4D+C24nFL^RK@M^> zv>a#W8mvv9IUj);;Jb4>#_P0htz6%FBOLgD{c?F!ac8s`+uWiYUyM0`(-AhO5EXo5eJFw7mp9p%vF(^9Cd+d%LRVxVG*aG6Cjq_w~f(*BJ_dv6P*|UK*SXC z&!SQg@XbFojaVSNT*dSaC`Z>~2)B&}ie2ogVbt`nRg19%0&gNZ?=e!_F`}r)LRaUJ zGpR?brv%*yQBMm?ATpHxt&$v2|^FWd*IPkaG?{V2ah z;Vn}^fJ#C-r8>>??wot0y; z0h?a5#8`hFvGu3xEXPJZM;hw@&`7WhH%wcYLZ-J8)v=v$nrxVESlzIUIs z+vOS%T%0dNoQSfFi=M(r9xU_=*e7ZRX%1lKL6y$iZ&vE|>#&UNbKZm^`xQJ&WK}R4 zG8s*!N@7+s>f+ZEouAN(VY)+|r2N6%cXyL`Igi=3fFy(S_bJtoF0Df{APwh<=k8ui z(0tB)kc*9d8ZRL9lWoKSw(*qF;!2sQgPwU8NaEic8D^y(F=#^wxBw;$DZ^+F?wbVC_NwkE{*fC zIoAy~kS_2e&M64NG9{+hRxm$E5#ZUpozsxEod8se^h5)&efnIYYpL zOm?a41b=yA&&Cgkr37rm@-L`-UMG?>#o5NZ@f~aSDBf<^$W}2x2z?~&Hf_*yzh9*O zG1c)443>Fd`lf<7JU97?&Z+L5d~Vzv$a7aN@_+A%84zNJ#uqseBFY-PtZv=9xr;3! z21NwK(+gYJdEAM5M0ci-==|~!s2DV8ndgDXWMG}pNtOs<- zh!i{ZQO~;kADl49?%VKQ&y($zKIo{f&Wq2XEI`jSK&XH2y?4(y)Qw_=BvmmOIltNT z+N|d?YDLH@2(1YX%|Oxv;l zi11j4#K_@we>tADU^A(V*P>RU+B55ClAFia2E)ZPZ&*JxGGfhU@Ju(H;&vZRQ2I_^ z4_Fr+2_jRFpHrCvVZ>!B9Td(E^<@-6EC#su^S-|y2O0Ry^+V-2HeE0bwv%tnXZ!>b z6$J)&a|v6*z|_McUZt?g2^}0u3S*J(6AMIYOi8XJrbxr9L4yXRRvwf;yC2g5ynOAN zbNYjqc)k$f3_HKxW{WWw;YnY+7pfcMM^rt##otr9X7n?B&r~MWQ&&+{QE{>x>|vt6 z;*K-p$AzN|Lx5Yq6P=$0N6 z-dFpZSqx+WsO2&m5A=lk5Io&_|LDl!Bao*r{dT}9uStM+)BNy2icW`eT@c)5+VLF8 zQcTK0{G#T7>wtNbkq03H#vEw{&nWXSc1MRN_0@+QqX5DKSQj5+c|}0^eQ7Os-3Zx8 znm!IYBS@X8dWrmC1(qx(GaJosT=B@*9R*`S3Oj~C=t@-UnRit`Et9@G|4?>u@m5)< zaP8-4W2$1!)_w1mz5KTHLRdg|v;E;4=hqH@a7LlvlmV?0csXI2UEy;H;8e6&^Yt=_ za}Jugd~1hsM&E;Ej+Q{?gQN}#>6*tgK$W4_XOZ3FaAc?*@T%J%M&G$HaP5=7&a>ux zSF+O_&(u{Q3dPSLw;9uTh%#3v)AX~0wO$RJ_HR3g=~UxJ?#Q$+48-nJ$Q)9mnok%V zG#S-H5_f|@grSQ_>ES(?!)gCWJH*zD*moAgaZs)J$s$U7GTEI9{vmK$4J9GSb|rXL z$tE&l+Y-d4RO0ouk_RuJHar|Ot`cx$&WY_O{|lR}Uw>nxeekbX6(>bA#vx$*#Rm|) zxQDt>Y)nztu%7EzuTN;{R1kJfFZx`x`r;f5qXzZsV~o<+Z^GqTzd-XJJ0~&J+Td-` z>St|NLiJDsASsrbi%z@g_pJGE++|w|dMp|o-NV$J!tOeZN_yz)8*3ZbE5^^;To^oX zy$U_aO^4xbC_bH|2Fu;sUT!Kzt*?ZTD<3A?TwXs3sAAdVr zZg80C{OvK83XtT*ZzFYPu?a_;_d*UQf+IZb`p0AX>8U_iNpE0o*^$GvhWHEQo~c}O zA~gNnId_-^Y7`sHx--YMYDR+-1j55St2Ol>G7TnZ%9M7>umJ-G#62wlBFRASt`UlN z<6{h6^uwIk3mw6Ybw{iuwH&V0DJf4V8N|Pv(~bp(rRyV$FF`UVO7{uA%MpZh$5YP> z0X`RW0|I&alNNmknZaqV#rTM|AhOqUF=8mr#n3blD@yRVTo=B3U1h{2C%LauIkdV$ zGezb^d(VBYqJjv@(S85JQ~sln`wgKP05{Zg@nNo!ji=`kJ^#d3us|Z_3rP?o}T?l3N1*Oil zkuR+*yji0KyRRaeL-4H_S4#VfCleg^ZXUSN!SJ$ft=nS`_28U8i{NQg{S?`Fq1$WofNzp=+I+lOwKnSS#`EsfV_}ghq;U z!@F+mMGsxJMQ@#9a^3K=FtS~RBcuw{ab zlIzyRTznlGa3dpkrY*5?ep#^M!RFHXC#w`i(s*$h+b*1$49z>FxT*3><9-S!H5pDWRA z-e-a~@BMabOue5%M5cQ>Enocx)`~)klYk_y64zs&ti5}s(t6HRU-w9cKR=p*AFR&Z zpwY{%Xo8j{Euv{1S#AK-QB!(1XGA4)-19$RZmEsweY%eBpRk42^l{)SYp)vS=v$|z zge<3dn;YPKq|TMsNX=Df4%R-7Dy=nf_c+FI!n0?S>|EA{h9cIBgqX2O>7D-n$a)ia zuJ?A`pJt6hrCBnhku*q&B$->`X&$Mixo92?l?tg0DN2(mno&!$218VeYH37KvsF{k27bIUrVuZ-x)?pq_` z<~(A4vs+uwN0D-&Qg-6m!nV9cAy$2R;e^kKX4+&K5c4~4vzte_~VZzq95#~Z$M!rE9=QR&`<*c9w-kt5GzukhTPpTRaI3-DH5eZ0aJl^ znh>fJz#kO>uBhCA%LRp!E`yv-Yako>fP0ymR2#Te9GpPT`11nZ?CCwI*8E73fI8?=N#K@5Xh9gPnY3kLa{>Qax9Wj+fhpZ3&qLK6Q3cU6|5?D%nMmq-S zERD6)*cY<(DMg(7+O@jO&Y+GEnI%z;FFXf$0}48fBPu?Zm?zz&w_|rz3kr9dkmKr= z{db+@)E#g2r_KX3L)ZVcc*~@iPyJTuHxb*k+MZqWB%swghXYeKUSYF&=v{=MV7ZBqO*0PGk!b@n%g}97a4b&tTv3% zrUMIJ0G3Qp>pgc-`@fxkbaiUgH}3GDh7Q%y3E>;AE}VIBaM>46zKbL}LNo2SmP$0v z0b>25zz~NY(xHI-YU(V6J2S4Vu{Rnf4u`OgoD_B}-^|UO=(owSm|s%77GQ|6s1a*F zMXpc_4f5Tm7rfVLnz^jE#V$kY3V|Kbr~$|7PRoJqlOPJCE#)4mYnRs>=mQv%aN!H6 z8+g^`w2UePbiXy$3;sK|&%*Wfv1_JtxNem-s?Sf2#X*w44jO`brG(NSPp!>-E`2`y zVJ1&0+gk@b(o-_fDjxZU6dh1+`g*8nJwpE~FDnZx9xRL`18v0(%rl^!a;$40H<_!? zz(SJixa0V7w~(c>d{$XkIW!>8dhR<<+sg&ceZ$7|GEVT!JPz!^c@5{PUNX?z5dg@s$-I2{*&rxzCQ->lImw$GWK=?yz-_i44XYMZ6quUGY};R>)Q3-U(O z$M*Be$1Kk4_cWvDaIi`9vTo5dtjAbF;64s<*0KjLUX08w+FS{vFPhs&EqTG0Yzsb7^04pdS z$>94Im>_Y1Cm8s$5k2u8dIpqy!&fBRuS>NJ5CY6d6gtwPVdq3fX<8f=qTT#1#0;kJ zjZ50k%&^j%WN+><>DR~6Rw!{dm?=xd6pXK>R@6})9Y1QZleVc#&u3ZPUH?QSv zFq7H52;rRdC))&PlrHQa4_GlJ%tad&$iR9XK-61A?;2=5_nsRry zgXD@U!~+)r5Z~whJfKn?J++c-!c-C?r2PMxH@;2saM~Y~ws^y}S#}QYZSSDjv%XpzAIj0__yvsDEOKKwv-w#+hC3ZOsnzMdUiE}o=Q=({i(UrAbbd(^)iE7XH;G0Y zs7y!NAj}=0I|6oWr$SL1XuZgAmkI7CHYJl|aNbdDUp#~LILp5MKg zrSDc514Zbh%>|beks^I>Zck8<+Jn#gbx;#UCcP8328~6@16M=-p~reu`uYkAN6@q~ zezq9L0?Vm)*fC~CHB$DE3Y4RI&l-h+9MCl@kQ2ULdGT}hxW4ByD(`9sKcuS>xerKI zFD)%!l827o^P2Yb<)feE?o*!JdY_Ax{V@iM%pzClCe#Ky&vCe0>r;BMYJfpLuv4ck zp;U_69YfxblBVSe4Gjc;@l#I1%M_Y1GO_`{?#nRbFCwFFl9qGuHfNQje{r!L8ayc z2{8B66_N~FFQW}#_=2l9L!rfnnIorMPb84ASMVH(1Q}FM@l%s^gi+qQ zFQeDY=0Q{ZKKIs7cULMxkD_qv*&uDL>gAhmMz5+IJ}l8XK!5Mc^y18B*_vT{#l#(U z6)EF(uoZnImqp&~s|QRwe}i7X)1X=CbO>E`)z7w-_V{z*!idsq{edqipk!2q@k;{m zgn)9#O?@Yy>F-+v$EsF7)tnodcCFVj{pkn(D5|U+boeyRdJ!+1xr!nQAS;OuxH7<^ z9VRjS^+;3i!<-b6Tc8eTo96%U{IsPZZC&zyE{d%+yQOoHcFB~!vp+3dZvPDn|4Pzx?>Km{b*~PuDO~rzy@22&Y2hOs4Tq<5 z1LjbLg#3dSk41eV#sw?Z23a%)o%j{^-DcZ)7M_T)2*tRRKD2&_arbZ^g%!r$GmEhg zBk%fAWYC1?3{MZ29VJMDkrK-kM9a-v4J31rQ$$D_b+}YSEObW*OvhSOWvjcK8nkDS zveU^Y7!dmqxTp`)0r@GS%i5`aw;VryCG#4Lec5x6uud}tJ9qeduC2Mpqc2reR$-y` zbPhej7 zGU^OI&{pbfcYdAwOtyGRS2ykJF4)NPQ4q-6! z*9aPJ$d-?fd!z-UpMeJ2OQIzS#oWB2@g+GA2wv7h{rTc9R5~5VF>(xs0kR3<^@;r* ztvoXed!fh~l%=!?o_Souf)i78E;KU8n)!iiVm8C{lw$%rUhN%+XsKp`Bgm8~5RQ~( za1_`Xi$N~)5i&uXKh-+^2tre}L!R23r!e~)=%6E{55q8&8ePEC0H%N2KNu?$>CAB< zuZPq%$lAD!ifVIOqeKJ{J6Dbi@ccb%v%1f?>~tBGiBN!>E0Tiu0nd`{(A^Y}&Im{CokUw!ptdv^4+t1Du^cZJG!x0D#Z^^_Rn76`aQX zIipBpM|jm4alPx~zSr5|S#|`r+V2Q3O{?4(FedK5&ho{W{zU0VcB5{l0#)tOB>*Kf z?OTW7vvez3Kp=iQdk(+}ig3iLDZ}989lnek@rA^@o)}jA`n6)3{~M>34m?9uAG+>? zqsH8H7Q&WdM&yF7Pbf`;Ku&le+tGB>LQ&$<&-(LzfkK&%+CpPJ@!E#f;^UB({(k+s zb$(W-OQm2(L*+_G!Cw%Vu4PLpb_De%H07fCsQ>kT(AF0EOleDRjC?SQ&q(3X@aFw= z%Qxeh5xN8Ug%ykeF2fuOn~Os|2Zih`0yMYZy{B$cjDZzse#i_GX3k`oNAj zQ1UUf#Rcga$4EsQzHo4Sx1uXjLxzb^^;kd{0214hcL@-wPMew_ib9 zSBFK9D6qMDk9b(Zr|=~gy?XEcdA@W%ADM zY)XtKsqr@oyk!b>loMq3}zxA?L~ybofEeNCcW^Y$$C4C9X0(UJ1_eCRK$rX z2dQTyeo$-!m+D{KkJE5_*YbDo79}Z+6iV)5%BGWB)ze;G!J4ma+6c?o@1y%enJbZ- z+obs)xxRX&sid4+Bj)iz6I7lx#m5%|o~`cV$w%fG`RVlS+Y$ji6P-H&+<7Zv{e!-X zu`hvp;hZ@ZB`k{JUI?lHEUZ1e7S}{M~d3~ zIMX&xHZRGlI-(Tm4c8zo%0U2wxsVYMz|9arf_L5`oQaSM38jJrU$V((Fg13v-s9h( zbW5`Sahn6|#4Sft=*u)5OxGLYh-^NfI`pSG@fa7J`ohOK$zg7KI0 zO>toGQ{4=W%&{5Z`%NXptbhH}$pJm=?zVW)&;^*OoDxI~)Ny_@s93`97`3TJ+04$) zr@^yhhQ(~9;EXd$x);#OVSs)E35OUgzW@8<6&Lkh%Ub8N!?|1c?sPE&c-#}t+aW{9 z3hLFExZ|($IQ>YsXy?98Chd_uNAf^qO%~-%8viF|_{Kb#a@N(BP)1qik7T7c#l&kQ zsJu+vAF`Nbq)m#Ud6CIQKnYPuyN|B@^(zF24v;BYY_(69`9iE(ZBYs(jwZFF@K+@3 zf&bTk-nfnH==VEDCe3}gweD3#H`VqtcG=$DxlmKXKQM5}l>B@5`ac=>3Cw^2P5frt zw^R2X0$13n)-l1k*;(s$^He(;#;CWNIz#izX_)rTJ`f7p6*{!-Djg05GGv*)bscYm z`L6>DIxp-=L1TS6a}#SRG49*Nm1m?$cmOy@zu!8YQomsnK{>3HHqMNhXJc!(=o+hS zpcN_39ab`myOT{gz;m}MEZ=xr?OtBp%9+KsElyOvyySXr6QzxuYe+7fjb( zWi;)_BKFu;T$A3w?uRWfFqgjBb!<> z_b#gxNzHggh^>kOv&^;#KplU3F?KrhB_5)b*ez1<R71!W$U8pYE@$EjLqs53>_G48xQodwFyZi5dJXK|=^VSp$1Eymr0S+&5uNUoB`S zYye(4pFT)j6qg_$1otO;6nM81-X0St(e#5;3Cj1pXXnm(#g{MzTBxk6XY%m(6^kW< z8%&7GjWk}`7dxR^VX{fVhw*#LysTxmNKkk!l^4nz}xKzcd%@ z^wREE1a_>B?}LoNLJ1zc>X~pOeA<|kxU3ZW2sXdy)sfAVH;OG3_gMfzDFznU^5qd1 zuaxmv;FQ7vDtAM5+bQcN>5<-|io>yAe|s`C;MZvkIcYYbbY+X|>k|uGg8XK3O^9$@ zE_v=)u}hYcPGsrQU01Gb)I5HDx%7T8i`)6Fd>`2*bbav^;n;Ie;!&gWq|E^4n7S2rd*_> zHXRqIKnR>=??I_N<9>lz20yHy`ylo6hWc{lsdV^%|{{h!Bmt;?EVa?epB5$ zIdxo3w}z2PUrqj_5@2n0%UEwW(`+k0mX!1^_MTs#$^G!>8L=`8u$mI z)hY`)3Rt9SLbg#kY1O_w3I+4^!!uRCgQHR)ZpD7u%$+CoEb^EghHACSd%CT^Q_ zQpDmM5N3Pt(wSjS|Av3V=w>UwgIb!N$M54u$uCKV$e^4Q2D@eTbWwIAli^)joSsy| z--KZosGF%U#++3qOnBhj^W=Y*(}C{uHb0#Z0Kvyq5J4sLF{twUwQR`nt9+{J9rx+S zLmssAS*I&&mQpUXnX%2Q36O;gM^MJpMHht`YWw+$=Rku{or`8Rs>ayb2vTBa&%Z}U zGz<&b8g8&z{RV>C6{}a5@qO-L0+jxdEpUE3RYigRi1tgXE;}ZuZQH}JQT;=|o=Vn1 zQvGVp%maM1u$Tkx>{NzuY0Gu(-4j&K*54WZrlB|Bbc@-lm-{etA$`Mu6Vs+oe?%WK zq`kkj=Lc@xF5^}l;P$Ee+1C*L0^t~|V$#gC{_LA~tslUFre!&qs+^-K{XAV{_1!_K zF>itb5e{|*Y~Mv!fSL;Prr!T?_firoH2{6&1!6t;`uXPp*wVKs@28jKsIySSA}Ej3 zCNM`kg*gU+k$Kxu`b%3vYsXS&;y^#xHKVma&149R_8p5e);R_~hhtP-I#$Ii!MU#p zX~+)HcAv(_8MjC3G|OJ{x#*O1QuH4l}M!LwW=ci`0m&t zKIF2UrPv+#tc0lsplVsW3T89@Y<{c6bV~i!`}SwVDRtKYy=r@dazPwP8=a;P527-V z-Ics3r;pn^$^b60Y(R0pQ1jE13@PZTCVfDnk$RxKiJ7W)I?!%f@%#6-V83mA=R%Fj z6f}Z(jr-pz$o&(7f}Oq@t<*T=UHno zx0*hs&ujI`0S1AClvldD_oBMwHTF6a{Ie(PZN&Hb+kPqvV#p4PlF!WTkdD}5HiSbQ z4FUr>%17wRT*|%=S}ELXLuAnOHPU&K6EN&xSUB=cvA$B)2!~$@}9?Lf4~lHp}ckP){V_wVoG z$2dY$vtYprQ69i>r;^X@ds_w>x;cnt#b3XndPFGvKjH-tk2pdfG$FntLSe8XZAZ78 zeg8{g5o#%ESRIQ;+B~!aSl-F`iuYWuRt}0@0cAjqL%6xs{f4tvexz^y zaXldr&{=Z6R4+ZQyC;CD+jcZg%DU>wDJi;|n&xTgT4{NCgY7L`5adFmD$>eH0d=jO zea4JD0**DC7_Ma9N5wF#CcOYSqh<4Cc-&;daJ0dhgfdzZS;>YPf#%H)kSO);$9|*{ zJ~K|ovVpVW$;te+{*D-YpqtWx5lZm*9_lYHcXYu@l(fh|>Cy(Te?{*tgRz_}8Ks%> z19e%m62pZrUF!a0Ovg=G&5z(03ng zJy+CS{O+sSBcWmEAmyz_DcUz;41PcGg%CqR3qm3l+^PupD1FKhC=}UQEZ#k6-k2ky zu;t~^lLn!-(i*9R1jQH`TW1<@hN{AiSD&bv;ngH>-WI3<-OJ1C`@~dwD>ZddUfaMv z7k&CwOPt6tc8PvqNNXXd18*Plpi_)hdzz#1T+M};5`$QYs7O#{4EDL@z{E8EtioF^ z%f}E-(Yvq*%Hh}b`Ao5%O|8%7@m%t(9YzZ6plXOaPKe8l`2Ws~cG+^>x`9`KF3Xs$ z04H?%gMSs-AN#}4;eaZ>pCUO+2^kSzrrL@1U05X~493j*rE`(4DY{@jC~ii=PrU zFe@gpimmq8K!Fm_5wQs@WM1TC#*~!R$Z1+nT=vC8dD`84_H63K6j+?TPu@374*oS{ z^5lGrEgkV7FGwcL=v><6tC>jf|4bBgP8s*kttXpv-d>=^P;jp{aop?VnAXYPvlT^! zxQNNdRZ@%hqn{mnG8Mr;eRgf*a2~Iiu`-{D7g6fsgTh`xR>J5SFnt<*#*@T!^U))8 zZHKk`&d5YYBs5SXck}(FX`=`GLuAN87bJZP7nO{7aA;&EC!XT{7;}=0FSml)qMBz? zoUvf*Z{fJ9?F1Mx1n2B5Y8}D*LSrb@=EN>(%G_K^MoiF4g1aX@G0W+f|T`jIf}4X$Buq zw#ERA!PZpavE`S#_QTWm1XON^tYc5A?Kk`SD885+8EfMa$2;(nbkPgI1$a=b_q;qv zDQiss1ov1JSNxG^un+K{H7t^R=l+YC1K$^urEPA}!tC@UX_;3UA5w4Jrn~W8V72J{ z_vxiqYIyYcG)}p?&VZUvv@RTr0smP_qx8T3V{P|2%qhZqKwQ9ZyY~NqQC;^x^h28lZNuzm=_z_1p{I!+l(>Gbr^i>H6)7vUtmxY zrm!whHT;9Oa-_JrQM0H){*-pfYd%ghxc!8+$Z2ubOP-(i=e*5fueAt1M%vf8TrO=! z3EB#8jn<>LKECK1Za(dB^TF1x?`qxNl?TtbS-}f6i&W^(N6T6!!e{8$_Bb5KRxhSf zLT6s*1c@0k^n5kLlWYbURQ#8Q!>0A{PmIiw2lOu2J$v@7bKUH-`Kk*lLk&A{0or_by*IRPuMn#{xw!`ECHgws#Z;P` zSc(!|I2rw!-D+p6h)qPmm)RbV?u6>@88g?(c*5ex@i(Q*Us=98cV!BLpv6DXlj-AN zg&<4TABfEd#kGQ6?IzYpZo~S4n#^{UiFM*1B1F*Cyrq?Ymp>QaiF@3%^9#l>CrisF zzQZ6BxvY8QMoM?IYuW8bj~*2@WOW!^qFSi802@GHdGZQCekkmePVYaRI5U-s4Yf`1%$Jo zSb#HOapTpi?ic`}nAT2PPwzN~o|<}3RHR7sjF=aavj|%bh#}ZtJz~eezwTq-MLdBi z?k);OfU%%Ey_A*PPIcTHLw5_JJpR}pf8cL`4S;Oe5R*C~ZaFAI`V$YUC_>4Ad;&k^ zGwFjIu#k_SGTFItED=m*uP@M~I;Wk%3rGj5YTZzOS5_NVrqVX%rtwCB)1(4 z#m>x5&EYvL#E*}d{f17Or~W*C!GMHy&wZ`Z*Z3eB%Eg;g21g;uEt(9NQ)F?lTioH} zakF~HEgN*~+^3BxZrL7jpA3iAb#MQyFuCwND3)hC`! z#`6h<8;esOP|=a5&k+NpxkV)+%{N7i!OzQfeXa+f>a-VIBDy96^~RawyG$QHZk)71 zv^y@J-Fd{-bTB6@Zn;-9fW9;{dWCf~NL zxI~d!{0-2h%gouoYWYCVu6vHe>P|Xi>S*YY+YG@O0WHfx0rhgZMwnB7RR9VfxXe^r zOZV+wyy{|s286KW`E_lPr?uk>m(Tr(t&&qtrwWtvDJc^^j}nl8xWbBob~tts`tAb_ zg+|)Rk)vK^ie1)q%E4xU8ZUVUrIZwyoR8va3EfT~9>gCtP%NhElq3Vkj)9=B?ZQnm z)W_@LG(?B@pF%SqTF}WU$L;4-km>*bf9^f-{h*=KIL)MrnP-t%&$zf^vkYCRjJ0al z{n7I=b}DV?q|@ePtoGnZ_Ucd2yOtZS>;yjXhJwX~@Wiew^l7)qWh(wJIU)mHehWc0 zZ`D+?1cYlZ+#`@_d#=1P&&vVSRUt&pHbd%ksZ{^y!;=gOKYgehn9+PtbhF5#<2)+1 z7~S0e^_Tm2O9T82}o%J2fYP0MEF zUhJE-G&pJ#g>!@zlSp43RlkMnFJn2!ZwkggjPCpgC8TxAmGi~&_2E$Y!14Ti>yEYJ zBubVW;a+SJlKW6ucSy*m>GZ0diq8pHC3&3$!$)NfM48`)ViA3k*yYE<$;ep23TL--o%R~w2&aV%HXm7O2Z z(=Bul0RGVmVc>OEeJ$}*sZ%zp53Lv1(Xa%l7lKA>cRk&2X(~s!*++H>315hNBUtr&$O zO%`sg)1s8d7Mg=V`CP2;ozOx|=^LweIH5KE7BXVhxDXHJevGY$*L@vO{i0!yQA^Jcz!{HpDQf{5odYyY9T4L5zvv`O%QvZh{gp%8%g_^*3=a2Cv0Q`> zZFaVy&t_T0B#V&@C)lU619Q4=d;4eM(pSxB{*StFtLAv%j8W$ zg^)fKh&bDB8aqb1vH->UMF_g24mn%URwLjD65A!bHU_2EnbU>2F>2I%G7<5Ijy6w@>msPogH{| zqjiX(F^gJo9vg_z$SHt)ViyOrc2U&|VC`{6inf}6cQRs7kTRMEJC!9~?T8K?-K}_n zotiFj6@NbtQ$T}lqex#me9Rb?kxnKsCxZY8CsK-J5s1OBGt}CI8cT=@vX_`FawpU8 z-*1DEkVXy6%<}Z4M?ip;M#V>VQWs2w3#fMDv04;A;rbgeN7QTg>Dj5$X5L~*gg~$o z5itgevU?b8{GhVA?2F;g`ri?FY^VDUtwna$8qM6#cyV3KXr0Q+=C3+LDZW$vR7}7( zd=PjW$%p{TjO)?2>{s@pOn`KEOwMVMsu@uaVniRQqUZFX7Cs zfb<4O&E;82#agiB)@K+gdT<=~Ka%_-nt8leoL>GFcrR7l0F&_qmd#N$_g&-93x_4M zzX*b4#D{S`b(0O}vs42DS$8{&IuUbQYe$7iRFSl`M3qwFoX+x*bJJoBSm3b9zE88H z#KiW$XFKkmWZD({y?GHnF7{!qejvFKl^w@aV$@tGuwn6`_!fkNH#fX+~FO?dsDe-(e z#!l5BZrlXj$eR+OX@z8`;wMk&Q)U}l=&y1N!-z#)Yz?`vq5!2#qthdKx^}tV@4wZN zrkzkvW%%%O6G2LZrCapJI)^^Wj}~2kfe;S_VJs*<#Q9pvIuTyU;~B9L5qtYXRbXtP zuWrEL`Nh=M{Y+k=V-owm2s0sX^z6n2W#%Fp}U^y{z}^xQZPPwTz0ox`@~9r-|lJ2q>x% zsYL5P6&Gtp?sqKl((Eu4V=sGN^;`UcL?pn!7oP%Z9@&m8vXaxMmxfI2k>K8LC)ppU zg7$YS{azP8gVg*nb_^{pu+5@%w+K*b)2>~o^0AJM3~G*iIaK;INCjCXBc#q3jcJIK zu&NIAz2NxRzRt=9JM$*D6FT^9K4xVXMb!7VxUIZ)P#r#Cbe!TQ%xF~X25;p~%dax{ zsNrxl;^q3DnCQ|bJj6G3#0W)-hOHKN(S(Tc4)KVG!k2{85SX`GPSRi zDN9Hixo<(%pN*Q$cjatH=I$qAwne1K1l?siYJ$g9LI5n8Y)D)G``1uHUEQ1OV=UiP zvL?)s^FnP&zKUW3xM5YO&&8U{R3ZW4FE~&##7Q~u?f#3s7EehRhJEW+aaY>rS07sA zWbUCHdgkY2BR@s)HDsnv`A3f)n1K4NY#99g7xkDNJ%H@ND^eW$sIjX1AeWB)r|B%@ ztzmYTcm~$|2OpLg%<$6i5p6$@mrkd=eyyEVa1$4$l7pe7ge@p({$M%FPF(;}um?34q$9 zApH6~RdVU2nN=jS9A^0c39eaR0^XcCmJ)E$&IqH}-F8V)OjoGI}o4`(fSK|0=^NJo*r;Y_OW++JRJLqQ+ zk-@))!n!U5&9#1(Nq-D_8K;5D$fS`LzeOOF;%~$kALkKmwPM64!*sQxxytvltkCgl zxgn~92<0@Bp(V4CbMSRL<-qjJng?iF9BCuG1QLOc4s)wbfADJqeixv|R=ef6RZHb0 zhG?>nAa|n*S|AZkP=_Wh&L7A)-hCFa$dW$dm(7#wEG=at*Yy?WK9FdW^6QDjzUt4( z=6);JgyyXETiLcNyfIKQd`vRUrdG5D`**0M+nXFz!;4?4Fv6d|(Zq;KX7#qniUE)o zU{b=}pFq@V1E%dgdQ?LO;yCzL`v-O>eTZDpJaY zMwDIzIKq#=5v{iDE>h96x|K|mcRZ`Qu{Z?#z`=Ftu(oq=PQB1BEMw3d6zd*sklQrv zlHcaJ{pTa0_c03QjGv{dVNoK8!D92q|mzS4kLX@lH2*RsCN<0{Cy-W&1AefWP7h=?grAPbs zC;uU~>4Ym(SA7f|kQ8xgZ38{t1aAbUr-#&Qh^C6(Gb}X1%hjfhjn>jF#G+vCs zYc+W#_!Wm#6yU-8EjVmaOEL@~$nNP$w!~aGQ6W(I^lXOQ1HsBc21dcR^Tn|A)l>ed z*WVOckdHCJvI#kh-{@|fwU{jFKr6Nt^^YEVrY0yvX`#YCUEVk|^>)yf zmH~=M0ozy~`s&Qn?oZELc=P<#;>0!iE^DLT9*BSXYIC?>_wZHsu6kD;{^AR0B>&6XD|N9n~tg)^IIE zxkEgUrzl2p^(5<8uyTcFzcV|+wp5wuZJgz}j zLIlBC7D%oj>^^;}j+zwAyqDW}fHPIzT=kg;OrZuPchL?z{~)+WNV@i4ODAV%i-pO0 zwd)}s^An+~!AZKCz0~&?!HLJ4jHev+xga?7&r?xvfw)b4xduj-9dELV7pTLR$XZO- zjI2^nWsov4rbagNZr*|OiHX4tOF6qTi#K}H9C|JhSv7CoT)g7<#5vzWI`3uFpXbp^ z_7u|cdo1iTcI;R?dwY;<4bsKL+FEd(Bt{`gZ({pQPMV-^rnA9v)WYr$P_Qtah@_pm zNBU9Pj*J^OeCS8-DjrD;PCA4>F7ubhbgRz5B2>LXqLR(GZ{Pm4vT*jm0d?=5^b;{8 z^*lDW^Qq4aa*kYY7rpelg*P9fcFu9xbBUp z8|^oxXVj5Lda;fP%w%;YLd#eTnoiM4BI*qQiQ#KL`gxu0L7L4S5Q}!GnLTW@-}BwB z8}HNsA+khBz6Q1EG46oOwBT5U067~KO{rONWd|HNh#j&|A$W|PRi~Jeos&+^#{rBq zq&IPlR>D7eJ<<~DmQX(D-PnW)kw(GsUmkD;WN{07LT=I?u!&+t;KCTk8@!P}UY!xC|@;#-W%ON>QP-j#*0wws6_9-GHgE zQ^oZ8qN1Pe^|Ps}sw!msJw#C`gx^r-$p#2Hc z@Z9Y`C)t#{j#dE=Y;}DqRs*6Z<`I?Lt9rqZ4bnvg zjdo4y3yA-Kq;u&sKQC5#W!SOF7-jya{)&Z9xbjILBgMD#PD;3^3@C$x7EqL~rG)ib z{7z7hCqM5(5P{~)=rp&CIfw&uo{b_d(NT#nD$@@*#!U56?|jJLUnwpwPB1Kt*l;yK zw?V7YXrkgBquO!vANfvJIPqe8)fBaBkPI&TKE#hHhpMb43qv5_4Nrbn5L#yiV+ zjXu4a8d{fiQGJhstca$W(S~iX1+=!sP43>nNim3snk=i*;3H7380hO45u1-8kwyYT zT<&zpOcAOU5?GoD3IVVhL0hhkdKj|BQzbi%E!VQ0O1f*>0^zef9!j5w30X9C1a8%) z*1xCV+%974$B*~Y)M_W}YS==}`u0Pu;1A1#!j2v51&Zq;N1c@K=2mVoKkOv4g$P&J zofpVkt1i0P7^vTeDCh8}HOH0FKd}K`wYwirjCZtyHqIRbC@$YE&>Y#r#UY7C%L9(6 zHSy*jWL;*LT5;R&wri3+FH7u3`(P6S7gfq5p@1Ii{w$p?MyxkcUWx_-4{nc#-w4D~ zt@958s};`j%K1`a&{2hPiP&G_m%#Yy9Qy9hw=eN43|GH<+$6OYHEtphf@~FG=yJw8 zr-x@LkVbHOa_`js$FFbWsq7pPIEhosN~b9 z-E7k01u$N;0)&f#j-Gz<*A-KzbxQ5oA_&zKH#nV}7y8vC?G0Kfan8eA+*BlY)tCO} zuFK39J&Y`YJ&)c7;H8?TOe}gKNWk7pCnuIWTB6L~X`p`N z>C^<~KLYA6^#$t?SSI!2imgBXS2> z8{h%aY!uBcOIc(E9^5T8{t5_)DV1G?PNWqTlTm3n+j%e-#n*2t;SEe!5ELQ7NEu>4 zMe!<=pe&=9Vrci~PHsQ&*>^|@=djeXS(T2EsHUMNO7(bgj{lcPg{5+Ym zA^!nbngI67*dI1MhvI8h;O+rmWtktBPr#<)z(bP1dF%+t3~~;e^lOga?;;Q>3oZ|t zGbD0AB`@mpV6P)4c4B7&UxwXlA3$q7ObtLL<>36he36jd&D1NB(~6T`b_@* z1PCw0tdX1Uu}!=$v2tIsHJv*=ix~_Z(0>|!$9x*unv0bf$C~~u2>_cxBmE&mWQ{G- zxUsow52HpGMi=fTn}EI$gg?rw-} zIzT#Xak&OPIQw=bjZJ^nR)CjQQ^mltIiPBglALJ~d2p1smzu=#WaKZiZ^^WwE)=@BtBsy`<1} zjGrwi9pZs@oX%3RG0AHiR9||FKY!)6ZryqT3MvYe;|vu;rEViw-!1#FS8^BzekH&v zfInJ)E1JL}+By4#t3B>M+b4YQ@3*y;a8RKXagA^>GHUGarL*XQan6pSLeq5@&Yy4M zH`J_k)b8Wb4sxK3BZi~kf&r&bX~YQ;yDlPv%Yc3ds5}JdW@<(RUaSE|3CW_n){NL# zQ~0}@%&v#nN4VNN%IgdF?%ma%J$ei*?1$Vkr0z1^Ab$rOp%HzI%%W2Zaamf?NW_sU zNo#k#RxNx{wz#oh1|r(%MrIGgLp5J|UL(0A$QKUAhs? z?>wqenP8{!4TjI59kd}hq5p|=*gnux-DrgdK|O|}nzd+rcAhENBQHOl;3pQ~o8y!2 z6NdSpTMx!H@l!G&N_XQOm<`_=&g0|z;F*4yQ1OW2xF*Z*S!&2H23TrUW zg?dM%LHL0%)*Z>;a9Ok{fWnrK6CNIpPe0|o3=_h?&Rw!3@LF_5?#uiy4}k@@CcK>qtGZHLAEW%1vJ41LD{Rnx z1dkpyl3@{X;0EyVVA_28liM!QVCy&%L;M~Eqfhq5{GqvrRILe0}#WJPB{H5~z^- z5=Vc(J(FGq*ZY|T6U4YKtc}g)*{QC6g>>HpMtB#}Ct{%n2gA5>OSUjkZ? zQW_IkuEFFMPzKK|^X1D?miq3@=#(YB0b>YG$w@!=CkP>WVG|K&?J~{Fn^^Yd@xb zWPh{I%-9`3&R6v5Qx5qv`VL(}+$>yCN!Y5dN5vRBjC&{stdyDEUB&E4njD_VCE)W-13JTK(!G?ee8c;`b0h+91(56g4TD~} z1{BnD;ayGkh+`TM>}xW!L9^xB`z)iy3LQkY(ZurcJZoaRHiew@#(c(@!mBCfm)C-1 zOiNj|i$6;zV~{<6gt4*A*`VM+LbP$qM~Ij}s#DJIsXmLxZQ?ExB@li3g9n6C4$vV} zrMD%PAS__r^EkQ*F1r%ofvxk3r@RwsJAgSP8*n*IEY}axhSCh&v7@<9pMF|e0chIR zL|FS}f0mXQg`+i+4Pu8I@6uSYW%ai)eG4a}j7oBQuzS);AteRt;OCZn{CJ(}FQB-Bl`3=WZQY9!ZsQM@&sJG@ey87uSa0F#TA~$}4eKeBv=HMV_!2M`cIwC1 zD$`alPzX{Q-QW zMrAO9BMR`*q`xGYN_3}HV#rn>A0l$|{yFDPPdokh)XEqI11tZbZiT{mpjDT4ad{BA7L=7|~N&46?T zEJmB=Q-kSlS|@ifa{)97IO4FwV{xUe{A_T6Hr-gef5~T98Dvg^$%q9qXI)kfAmre3 zQML*DO$$(k%)9vQTlLDH*#sFSMlERS@mw*y`P269a}42S9G5;B($)}sO4xOphMh(F zW2}17^RT~R8&J|*7ndz~8c}r_e5xggA0kf&b8X3JeI}h!r<>v0rawQ!ZRFf=wVQz; zN*};>1~8rfJRY>LK~@fB{yG3wCYA2<=hLpg+26=1G&$1dS8(KY%94E?j1bNTKa_zr zRp_RDjWe|2X*BuB3OKyB#U!)Ay8KV~Z*7X}uU&tz>`-LokP9YWb>UDyvYd^1j8z4- z+ytgF+;~{%UUW{pLMkFiD_w?adE#v2*O^0J_JPPK@EDYM|=)2j*IS>MVU&c>}*p?99aTjby-t(gW)Hb>H5F z&1g1^3I^AD-Z1gZ1&J-w;|ddpC++1qpzdwWnEe2^hH zTvb%gwT}Ktl_CbRK%Ps`7G(6Py|`?H*C4&A%}b(JU2#r2q`T(DZ%+P}&40N!)m~kZ z*OVp$6f%QEA_fsrqjmOvY+FR-R8v#40zY`3gH+w6_hvy0sWIM2t0*hr31>Sn|GZl8 z<8AI%)T2o>79^B=;1QH}DLL+z*{yLT({rm1vC169AUCeGvdS1h)xdI<&CSDtecv5E z33O*0j&BM8yvMk;9684`Ge$7h-Ar^o@zsT^R zVu5~}@q*EH#HoJmZqhq|AJ|a$r5%w-G7JeR`+dOq?z$Eg-;Y%`-&pZ_-4M@Ts}6m9 z3q?~$5$Uo0B+4M+oW}J|VGym zR~DFHdf4A>)-DI*$p$#n{BWa956j<1B+l$?6+gtPx_0F)o{1q8BSVh_%GIU6lS00n zMdH!yd8Pdo)8z*ln;_qbTcbmRD=d|artog6P11RLf6#4W$h>uca0E_>dk z4}P5??kHgb#x@+#F5vXgU2meyCnG`lijOt?Ql z;e+M5Yz#sE)*2!yV4~*FBi{ALc#m{sq1+DE^4=FQ4T?;P?PFqd`tAo8NPt%uw`ih- zSc;K9axo<5ExI;|>pL^&@mpJJak6a`0J9)c1SP)UTe2cNw#T`NNPZ;0aTJe|V~sn{ z#w0J6jv8f+ybx(U-;^ zJ1IfB6CHkl4f{-K{8vmqG7S4EQ^Mw=rT8ltC~8=)cq*WE>+^l5o$o`QmER1+3w;q{^x_&1o)NSHizEd%W#$VAwGX64Ppj4*FX~)@{gyKjDf(H;2k8bniC7^kt3_f>vHJ zV86<%?Hb59)M^UH#+2&o>x)`IwmqRy0u-Ms{Vs#@F7q7x`BLhIzxpfyhB5Rvg2Cp^bfm zgWFSVVS$JW;KUc79C%{B6=jYU#1A(+`sJVEfce+bgAUX4ZX400HPP|^j_yW^k(`tdJ?&~tIsTMF_K z2plDNvw$z7dIee%$XQ>CtW2Zp2M-)bf)Qel=O+I^RMIS~^;;X!q)Z$nnt#}_V~2>B z0kY(v5pl6Y?VoXfy>?XdUY4^b8^Se#RLO!N&Q8$s(UajcNN^&E#m*~7F#7zco&C0k zt9?)=U0fGH1YAV!JJ+Z~SX4j`g&T#7EX5SuiPR#S1Ee9MQ8ja}0;PE5I;sVe8-wW0 zv?w{L6q51I8FzE8axzPg7fi!90=YqG{Cs8K^BE5x22qevi*!UhEo*&c@)E}iD;LYmv!uUfdm9aJ!7=46cMy|=kQ*(LyV;NsT2p1^+mS`Yh`u;3bb+xQ8wLdzJ%r! zrnXbr5pS2G{O#QE%V1lLCq6@{_2a-St-WJfM>X3o@e{O}p|M;4Dn6E-Di6)8^u(~2 zWU2Mittgm6o!(=2IBwlj0P* zT7;gmy$6_|-++=-k(ws$`E%ofZ|}I_66;2Pf_jnhFfc-FI29cn9ISKVJ}_oTY7y^J z5C3VjOfvQn^PuIHqxa|*Wrqrld@;}1VE1eQVuh%rIhQYAW>0FW*ej1vfAQo=7`HPS z;8?x<0fHT&xDfGYTr3FMeJqO=DCrE9P%cK*P2+ltP8c*LPw7v`%aIAWo&1bv`yIAd zi~6)t-h3*&4zW5ER2m)AW;Sz5q$6_JvHWGCH)z$B z9|_pg!7(Uw>(;TSDm#(ZD5TMpHXf(wLNkDmuJsu$m7W!&7cCi6l2>$=u98+Er1XZY zQx(&GR4QA;!ooJq&b)QYysa0zT@83D`wX9xM};)iSfuX*nMq}>LCD3}x2s>a)g)Rw zuG@C9v-EphS*3x=|A3)estUClr8NPGK4WBLH+aEA4N=(;pXKfYeeX25PZhHcz}74& z+jjeo9m(v%5~Fv@z0L7A&QV1?N^~8c9-ZSV*GrH~(g`nV9=c-dE+$_{xpF6Y(4uFB zO!x1*T(5-B*X9`)MF?bSC*$a^v0d%{N7leT zf$aB}5)$Kzpy$_;JfvAKknvN^u|mt5jn z+UhBOPOzcu_)wGkNp``^d3478*B6E6BBG;bd!yd&iALWqlZ48i7}W%5ISt2Iwh?e_ zMWVs$_psbX11?7CqO>CHOl4;VZ-dQ(22u}DrDZGrNte=AdFRRV^S=%rr)2WDRok|I zaw-5to1z01QihU5G!KwsvNbc4S+soN7)x1yhCHY)Bm0^h@m`tq_H627{Ib^Huk|46oAsfJsqY?;V6Z^$0|oGKy;-mM`+ttU zVct@?_G?9GPW%E-;hZ3A0=ta};W6hixu*VXt%?lh1C5Me3W|3eE7&U4H)tZm`aZZd z1nwx6rQEO)40i#z7S# z2FomNy-z`c9fQ)dE4kYUriXms>!=ojYKP=9kJ zQx{;AO`;lEMGtz9)rP(gCPR$GpmQjUmH6x<-Q%gg@r zhrdi&-cy0g^(gD{!UYQ`*H^Mi#w_sn$**2ZeprUJo?>UkO=Hj`GG{S^3)-}lcs^K{ zua3PN(Zy5IrOA#l&Jni=G48bOWMj*Jrgx5@XKh^FFpJtUu)W5c@Gan&x0#F0%H9DsM z0|0ocD7u(l;^IDHILe~wjxmq@B8hJDcYs#zg6_DET6G0%6POHXm3GdK57oGK3Ixz? z^rk7-FM$}%_XavEoNSsPU52(CV42l4_V)(>KF*FPWJO1j z)9OECtYm$JPyD?#FqT=@M+_3j6L7gG5(~U^ApM#Fr3J2r_kcl4Q!5Q`BW`s92&EHe zTCWM@Lnh*2yz*!kt;lilKsNj@fU8KTFzQbg5nzMrsN?^Dee&X{?2|T~>!S95+bN!=mrJpIyZ&D`%!bVZyv3JdMV!OB7p0?>ZdPaEz!Ew*Gn-(rOyF5HIbEL+b zHlHP=lET?^D~55ak~Aq&!w~paKQL_&JqM_W7(9!2Jy+H^!Xio>qXFBbMy{+p60_uF zbpoTz|HBcVnssg3@ee0efEf94dHfPIFKLut21AF=N41N`sjb^{NG`Fz1!!zyVX~JV z&mqKilJyuD;EbDTXC zp9bUma1Oy;ZMPDsNKi)Kq7RC|GJ&=xAo<}iU|aap7600NyX;j@NlbiA7?m|j6c{jO zkeeP=d%gxuKlvnE(IvO>Iz{Sy8Nno68s{H$9Og)_npmvszqg4W`<4FSwUhD!|6M~7 zyC63TrE5j&RkeIbTqn}#4v3-Rq%3SRd44?_X~OqhQn+k*;S9R|>p>C##)yWS3I1itUySAPUb-%Z97Z^f#K%6S)QEEOhhom||zY4j#7KCM-cBv5g8i(XBaq>r;+SVPfYF)#Gw#OUo&4~F7RXQ?;k z->CSyoI1|S1-!z>aYcA32Y4Ps#3X|4^uBqBE)V^nxo@L2U}HEQu+$nJK2}(X)UeH2 zHiMy8i$#XdqlcR2Sj~wtY(~>>-=o75_FqA59eV4y4zJfYE7)Ixj_w(rkxAnZE19Ms zMsPvn1q4Z)+!>suF#@_)lvn)TH)L{9@j)3w^0s~?T8S~3l$yP$v^0&tByL?iK%G8)tR^m6>p$eyMC)dWly+YTA&vAYy)DAtaCU`- zBdj_hMZlxOv47W$2lqIGbb5b4fyyWhrvL@grc;l=uEI#0cNP26RtU)f3!v5aj=nMM z3eN)=xH(zxIG;^YIrXOyl*n;uE?SZ`dy(TkzU%kvcXCR8^V5_1^(-Vfb*{V$t(490 zpa7Z&hx7S<6|b}iJscPr+q&D)x1*Wv=|kf_nB6#+b^*jv5($fmOzt%wNW|d$wmIx) zLLdU{(F`SAdUkf-zn7ZDI_6$py_n}Gn@L1Tj$)NT{R7k-SrIyvj|^G#AgxO#z+G?l z-fj=K5%_>j=-3>j!bw$KOp`e0|0xhb`EB0H|E+1jEgl`|% z!^HSm>?^`bg#KLfq~|oVr^JnH+O#~T)et9&MJYaRYm#0(S#-hqgtN773uB!Ah)B|1 zaA{BWNuJFUl@S}@i8)jP3{#us)?p$f{05D`D>6j}>&(kNA^(KsZ`*d?4^IJ7x2DH5 zKPf|^)Pab}gxEhcET;J*bOMi`-cTPGg@=G4T0$9HpeiKvO{nTd;gaJKH`1@=5K}T{ zWl*ko}JjrV+7JWfJS z4mD9MV(8+1k*^FAio3+mXY|C)VqYJD@NoB>AUGdLo~S^KMvfF-m>QXcE!+JpzgMJ% z?4IxbdlnoX0j?S+vO@}qY5g|wLq7Fdd`sjY~1@qASdM+_m}xbwECyXX?BWM$1O z^Fc|>$DqNIDxaB(BJs5%JefJqVQ$s6^T;))iXDSh63$zH{k+fp62_+rJno$r9xvU5 zt2X#aEmfxiAV+?FVEw4GKTkZ&$yW0+&m z3)Z0$M1;BAyy>JVQ}k(_xnl<&+ngW%KJZkKTSRLchZ!@BXnhH2dE{>@g%&?@4!+H< zp&h-|h4`(P=}3F2vm@ReZYd!Buw)HPN~n^S)Cp#csD<~ym2LaXHg&nwt)5{pcRm+{ z80cBg-b!Zz+lbq>>&p)n-56V#kVXA2M_kJ+GZpP<3^ysOThihX9^E6$g_kDMw3vSb zHd01OP^=UuTHRheZ_%P2MO~veXW*;R``F=Vm!KT`)8@ywD2+1HZLHQ%S$Ri4MI-Mr zS*kT4Q1URGS#u6cB$}+yUAwYKY-|X-1tQ6!uKj~UV@dY1C`1%RjJ8}HQn=P;;G#Np z>x$@$=kpUTK_z$q{e0So@Steh+r}ZPXogI=^;{4pL~X4V6+m{L2kXA%PRZ_DNouV% zIt4ekxTDY4j1A-_UcWOzvQP`_NCf@o%U$WteAcRFEIK@18h3sK?xSvMYtg0~)5&D6@}<8BcINW@n5jZ=YM95LNL>Z(NlFgCV{a zgNNJgi_cOG%yJ8sPf?z@?D+L7S2SO)z@0R&Z*H4qFIh_`K0Z<^!X+tCX41I#8FlUh z^gO?pw;*?i0{_9x>kXer*Hr_Jyib!Rfl)WEuLU#-e4gZ`)_JUv^Od^ww^^#ziY{%h zQZj^LSH#hI3RVK{$|R2%pJnrPq}E6e5sZbebG6EH+rP+}#G~uw?l$`K8aQpq z+Ujk7`@EUWtg6xT&LtNF&Dpu_(>foGdS5M4XT;DzS@f8+X!dMlxY5X=5!pww0=&M5 z_&h1o(J|8d>-*8_apk{0Q?uYdIO&M;xp7^ZGyib_(sAJ1)45YT*fy7&lYm?lW31k0 zRhCC6csjj*-VTSQaH2xjCdq1HVYM|s9cIo_C`ZV)uXQXblG*0HAtYqN){=Zk%CD%1 zZ~%T2-*X=J!%Q^@u!&xmT4O2bDGq{h8P|ORv)+zwOyaBIz5L06x$}ceb=0G~WtfWs zkmmpTPuBz#G~l~2*=`?+9cfptnlouOa*5|eSZbyIE_wAZ@5!H7LcxrNT|_act%k>c zLL7JxkaS`WYP&Ynm9gAih*?wXpQ+$9sz{Xh4{F^_)`|-_M{5$J3 zbz!_ab+V{bX02gT#;(h_JsU1Nhzpm-ubi&OUUhausx$KN!!Ey|KaS>}LyotD**X#$ zdKu&V1V9G5OjF46Wpxrn#vqg*LfRz8l5dckXI0H7t*bP@a1KcmYvJ-T%qAHfxV+>| z9dMbOXu)wo)0@6CTXvC3EeHRL2azS`n7Kpu_@t!p@b=OX5He*P2!J7z$2qZX@pKS% zk6%B;t5%W!jB^Ho8cCDSrXq7@8Q5~2Pc7tXMqfcvN4%w#&%lZj=j&^uE#q?^&rw zdD|b3Xi*CA@te?$@8a%vom8dv=WO-^!`fu3br>9`xz%pJA5VR^ZcRx^sU;$$w}T>g zoI@jA4=@hIA-wFLLC0d(HrqNq`kuQ6>&n}XI)AmC{BW z3B^-j>i`^CEl}U`_?cRpvI>KGI6K(*cm1k$d!K2}!r>p>9nNXF-M_h_zR#3cjbF?A zUn+R!Z=kGka?7Q&AwxK%;o)wo>#Cq-2!jXKg zq2Jg^`q9t)6Vu{4PdgrTr{BqEnPoXn3!(z*n#TlgufOEe<*2I5>(&ihUo!rZy6?hC z(QhIk@9;i#$)O^D`|ZiCTbed&CF;XRTg}#uFrrS4@08%Jg)L~ZCsA|f?oNr4N-4@(Ns$9EaC}ty14Sn zEF-M0Y-Ql2V#hTbMX*=^pjS8vN{Kj5j02@mq-v)RoIH0>Iepmx{AolIOFpN%5gq{^ zVi<*pT8?g32;GPcbReDl(bId7;l+}a;vVfF9xWx@0$JarXtn_TlwO67kZ$cpJ(Zex zch;0f9=HOyt~UCa@bGEzllIBC=JI2Qd9u>=?LV8(`XGuh4ry&^fr2-KI_S@&Q!XD! zL^uG_Ox}102aof;dzt1|MB;S&s(2~E#Rw3=B-XN1X(u*s+_(g@6w&6CTq1fC0Bj$O z;(6kzsKNh6-Fqh-JZKnQQ%pUTX20+EkJPv`<%tKnYd;~ug)z<&e;;gUF;R;_rd>07 zjhO7;p`uY_PMltEH>MqX(a`&rxamBzcE}HF5q198@bV8O84v8;7MeJXSyRzihp)7- zud*z9W<9yO%D6f}_h^|_K-M$sH9tFORF(dfP}-Q>E500OClu?@4UPt?= zTaJ(a-ijuM6RMj%-Ck>$_cID0neL>uk0Kisf1u}@1IB|?D&^-BG&Q*9k7Qa$CgAvE ztmntN=P)xvk+}5r)!Mk{2^4^kM8siwmq!1|98UpzvQ$WzIAjf;Jg_gxM~9<>5TwV8 zsmo$-4&6|&a-L1rT&G&6Hf|glAJ#u;fr)v3*v|j_p7-qCod5fKcD-2j>-Yi_r>e4M zRTg{Cyi9uVbbD5LnaZ>^h4a@G>Q{RD~1>jLeGP!@25CX z{8!i<@@rF%s?DuiwbEX7+Nev!3|j2Y8xfko#vy(tU(a!OHhpCP~!||Hms+wwXJ~TsdchcEUfeZ2Rw5)>K~EgN*8{ zD}wyal)CATU-NlT<>0Cbr`p%QnlT4+b}{jO=|nPZ41fI>`Hn{ZeS9@4IGKoaZVTv2 z;j8duL{)HNs@j`k(}=qlEPHZls@dM5kT{5EFqd?}`Re}LM6-X&BGZ9QH9GyNssBy- z{KFdd+OcNmY@`!GghHti_cNNLT>L@)fvfMc>-eYxxErc@Qde6e9}X|0Xp_4 z*H(PrMqiFu*mdGVLr+FI@#r2Xi-~oq3kgdG4|k6F^K%CodrlmPiuWpg?)`D^b4s)4 zs#IByF^tS8{N!rxT(zAJG5$=YohUJ3f2<9}n_B zU+357|9;Sa{`*yrpx1nZFNW0lC}&77=vfRe4O;IP1I`NQ!=+D7*7%$~{$%_5!=I;c zk5YBdX$>EJLR>eznD>$^OtD)0f!iUHHs-u* z!W1!r&}tVc?Uk;Me5`^~tp>bV*{mfS%S!H`^=m*z<(I3)wWWQ9s+#VIC zsq?c;FbYw{K#kAfMlG?7Ev*psA#$R9u+41B9X)iru)(;vjOF?=C9;(e8TmaZ{XBP4 zhKAuxgwpIEYkR7y4o+Keh6)<-gQv&1^`tnnu4XwWmZ7y6=Gn({1!n2{s1Rol)B`Cm zUv^NSL4SS_I0G2-SC`en}?oHOIc*{TNd?{S1Iy3ax6J?PW-|f z%PW8E%_<2gxSs$q&2*AN(4pe1A+119x6nyE{zLID}gk7R8+LFb|mkYQF2U31eA~;1q^= z@s>ZFZ7(IQT)g-dWTQw!b#-I=#{y0&1N88~>J1und1CBEdN3}vHm(1X4l9+!+5<37 zHmtLPcl?8lA5oh>ndUsUHrIP%C43h`g;T^g)~dEEIwLyPpS!Y3_Wby)4)#}r4=$n< zEG3$sEkjEn%U1#aFEE3$=f2*M9b;YM;ZFM@vlbr`+LlrI37lM6?9MF)e{H~|jL+$C z<}?<6c+{PN5ScDj`)1|C17=b`pn3@M3=!{g5&*&15B?;hWDBqHPPnp&@EVE)7m8Qy zTkrC}syI4GHbojWuxyvA7M-1zF$)ja*(`bmqB^teuqH~k2W7i~U zANy1Q0>N&H-OyM|`5+V$YG*_4eQ&F#rX69+p1B*Vw>?gDbA@6J1KQoXP^+(bMaaEx zZIeL{3OTGa`4o@?Upl?H+{BOZ79Lr#f!kp7`kdHkkV3;tZP#e-e0$S&O~ij1`hP-1 z))?ju0_~E~x$zklY|Pj)_03rwss9RzpU%2;pSZHYHrzlQOUa0bZ^qi2r#d?=O6x zmgR1Asl|4S1&?=^`|Uh`;7mJ>ouN4+3S9~+)@mhItt$PX6@AmKuw8Eyb}wh0%zAS% z;4Mw&lD&g0s{G%z&MEQ>Es2gYy|a2+>Cc-hI<(6v)2*6A1oz!>Kc_k|YxU-=oGMpy zkDR>c%3H=P$XQZaVj9;z`jg%eb>9#1J<7jJnRWhCzw*Dvul=yR)Xgs-$GGrkR6t2~ zc4FCusFZJ+9&h7&M1MCiTra6&$n)JT|I02o!h7ONa?{kD(EN@pW@PoSvKyEgC<4<2i-F3npNI= zf{Ir)grM&+d-O4Cb0ckrn}zkz{1#PKwZf&mT5IQ!bE`YWs5|}KqkocCe7W@+dk>xR zsZE&LqbKnEhtJj_#(bTVyZXzkbuZtwZ%IZI!N{9$q-(W=MdP{*37_Ba-_osI6AWjd zOWrM|6t@GyAt(S*ZSrFt5y%yt?GF0ot~Tq}F=*#kGbfu#>Tb_9%nDr9yt2iI#uI2$ z`*3?87X?U7*vFxI!O(OQtJ_C_)70<-+pQHV)3yp!rMlYM+VzL1xu2mKt06M5gtLVO z1?&o{F9sI~GQ{EpxTB#b`?JP5m#jOuxg}+|toac;2ljH#KCc(tL8)rzT&Oe+Dq4^) zK_0`)=qHyfU3vf#pU+cEHUaB8B^`J;55vAz`*L!=%|+>8g+OaHi{%-by_YsHxLsz= zmcT>@ek=ugSH!RVY2&Qyi~_Qr@uz-BnU99_wG~$|&s_IJOvwcWgMe;Y3KejR#Di@l z!=ig&A8!tLL8>Jch!YDjjDIJft*d4?X9>oovR~2&u}whs(qR!%`>H`+f4$nPeV4eb zK;v{eH^cPtGzMpN5mb*X|2CxX=@&ZN-Y;Ddh8V>^YiT^KABOjBf8;EmIaypq6@a3L z|1JkWtsyiv<0XLxRwzj|-tD_vdi&iUvb&^rXHqWqF)2qje zSLK;e?%Ll*OKoBX2RyVS1t~-TcB&#Oh`B%b0+`0G4ayl< zl~q4uTimjR7pJ|EM~P^A3JWx%jzLD8z^6e0DG+yl#ix%Sg;rL;aZ3F{kQYH3pyU4Y zKNak+38N=b>IKWkOmM)97&WzBaGnE9C#i#0i~vAT5_KgCEaE+PSN4JOtKZ1TP{#E{ z`X%tcH|?jXhroK4pns)s!MX9s>g9X7sdqf0Ba(KJy1*iToUCB<*Rt+QIR4ZkGnTw zm`Wtsbq2m~Mi2JIURXb$N2gaedGQ|KjA8brl#Jp!cTW*IBX4i-ec-<6EPD^w zqEWDJ{9&lXmOt$Mtk#sExK%Vz>%8v=$)ztShd6A`8>WdAppN%)cZ%#<`v8K!o)cUY zNHMAE`;_=~p>ap40wAYdC_P!I5%H=Np$-mR+_awdZFP34h4!pnFJ`69s|79_&P07U zgL>E&#Fs2592BL*T<~}V1rfLniaQa4tyUP2ljvw%{lN}&Ighq+b`YgC`Q2Qxnj{F~B&)y5_=f;u?4bvu{scLUeiMKlU*MbT4%i%n}Cjh?^uceeQ5~ zo=Ef2UMiANOXCdN08SCeY&|lHTM-k6->wlfzD5d!yrQa>cXP@WZgsPE?e6fI0$=R? zlGBoZCY-IytLy#nxV^ffXV2qyZCf|79_C=KUjr3R0Edtb=+H%W>+iR1(QWOih{HVGFi24!i(`*guS! zJNxe5A%p09jDdMCSK2Jjrr3S^nCYmXHq+E=uyQu4g86Z}i*reBqT;GnQV!<1dQ^c3N1!JVGHz_I^KM&U$P{59fUs<&a_1XM`0S!P5c%nq8m@#cdE|Cnwq54-(J)_YPj%Nc#L?}71 z#xC(n^9g!5I*>J=uZ!}muOkT`dN(1 zc~}i33#}!BG3-l$l+XK=-AT&I%3OT+Wi4^xI_Y(ARzT3h5%uE+&nbNso7crYF@EZ% zB@C^vncq*xbW%$0z?IuOw3{<)me$;t%U70jweDvU$AwAhCcP0B!=2K;MI7sv^1N#NyH>pPf6e_ps?LDn75vngE4kPyu{ zB?lPLOxGj_TU0k(wfYSjQ0hy`mFF0v<$C zz=-pjsE814psvCTK#wXXA8=blXl9dwf^7~zL@Mf!gwP)Y0md{k0F8bU{AjCFl+`tI z^YV;=2-Y1OHuk^AE@PO4Ky)$S33a^nPZ&qrNH|9~w2?^*QXFAOcVO2$9s@SCs&uY3 zdvfD@O|y0uywwSG4Ma{QwU5T~w9%b51x@O?-=$-wBlbSb4l1mbdJZrD?erACdIYZ* zXYbX%cSi6xaC4I++G2DlpI`7tHHzcr(asHDWgMY~VZ-(=WSA@&=!uEEYybuy zu{xzcs!P+>t$h$Z@q>4)yi}m%pwc`($`0~fsmf6$uo7tr1*Pax2At^My$hr0Dz2W{ zWmjejOo?zs2DsqF2Hdkz@e1mPLJ$}Et;=I|8;}1V{`C?Vt zM^uu!j8aj!iEJ>X{K<(Ey_io?GD7`mYY?g%_nzMa)DlIQok9Oz1D!9S?h+PAm-H&DL%BP-Ru+fz^EB0MHNyv%@k zlGD<<-fg7fUn3L_C+yX?h<~BNFFE^?$}QHv{QK-r>`wR?8yA<7`xm&)B^svBbRy5q;7 zorlM^dG3V4$(z6QjE-O{UR*)9UjN)T7OI_q?v}}-hzR>j-Rsx9U6x^9b+B%y`rCr+ zL0G;db+fG>+{3r~u+G&5Zj5J&OdBoTg`G~JX=w{@p;CMz4 zRyKRqw@0NFpwqJSEp%Y=7nZx*Mk`+r+S#$3;Zg;Ll~M!=B4Yh^^ns9?P{fxji>OwW z#zL!zClSd8Fp(Rpvudbz?fP))Ew!``)G9MEKQ9)VnP{}h!70qlX0tBl$ilAE>IQ$d z4|312am}om5}Bd3X7NO><^Z%!jKfNg%DM(N>qHwZ^ge_6rUpKZRnaycEL>ZDcd^Jwb@^i(c8KT%@p zypJE3`hB-*zPou+{+U(>16%PSg4(qe#`^5x(pATnjh#I8(`r}On&Q_+<3^h>_Q7;$ zQAQ19U=W!l=I?(qruE!L*HUjO``EAHQ5>;zvPC`(U+I;p&OVq@bu?Qwh$-{}Dl8;napYJF`Ab zEp!W}()pP~1uavkClh0XvOWweY@@HqgUN$iq!bfd27a+g2#5pnyZ#4&*N9T?xFJ+! zgIabT1O<;fch50V8d6ewR9A(N-nG{Z&AJT;>!HUcYb$Wwr(s7Q$`tMM%vr1Oa z!YlEKr&8HG(Hb2o6_nq*Q^(M6-kLN=HIPxr+Zr;aEw)rqC;V1SV@V0)U7%aqJA$l~ zWm3*lspdWO$5h?y5s13A;a*U>MjdNqhunsrpD>f7Zs@K}N|b#e#6|&h!9quY6;32U zZryyHengH2jqUAq-G@IHLpVCvw@G4Tc6-B4`iFarT@UErDDqUTly6@?@E?urdd)@H zvx;r093fkS<8Z~1;}8G*@%6yk!S_RCw0hpW+u(Zx)|4Kc{_IgpJ)@5df){@;c3YfH zK+v0bVqazJIPHf(+Hd>ZU#}6hl@@LFD_M^6CVqt*-xo!EO&Rh)Y@(OmH~;Q#6Mkdf z<5v9(vp6*L`vJ(6d(BApoZVyNJ}aBmH%fO6`^fiGnB87K8^>yJ6a}~N;ryiBj*e~c zW4u)PloRTCP;oLF>OnC;3an>5poSz-`>jQUD!-&}*RO2_5KX2;;Q!RU!r4j|= zB}~|Mv)B~m*iENqiYgRC8wFjG7dxXV0I54fwnR_JKgpuMfnhml*A;)~Bqro;Z9e=x zz9bEhbEZ6dwp#{eFxZzig>LyxqhrC_N8p$nq<{0%lhY=dm5kNA^^_fDojjBx1+Jhi zsTJ8_eV4mk{C6>kv=!qODyO%ErQWyA-PPOG8H+tGipU+rTEx_b2lLl|SvnD6Ieohp zYM{}fz34Fz3;`i^#QYd;6NjrU{hxuX_{6_3SALXK#?%BJ7xhKdWBh7(8?D;jPo6!i z&fcyPJaRSjgmNW#fL(7N+OtQ|TZmFn>snSl^zI+TfrH46XUnZC4rnd6e$z*fmCG4r zo?{&oGg0P-frsc9Tnhh=&bJiQ0ClamxWcSQ4rL}v>_vu^|Xh&hDO1rGRQ7Qc^3`#`hAzd8zOQ|hPRW{pSN|CMnfb{ zgh+sZJK=*SEQxy^vvwoyybeYi35c8BrU^bffA&Q)xMKyJY&o40HkGP~#I zefF6aqksL4l@X(LZ84SM%8wz-LRG}9ox1%lQVGw4Hh0Xd$zdAb`gn_-2z6d$=HD`$ zEECS4YTS+M7aQ3axzd_>WM4dGL#LPCF{U(hk+XA#b5d2EWPn)JAy(*n(5$Ru9~l)R zFv2hE(u)FkA?PQX4oA(3i-Gts&SZx!J+WqHmFW?{)+~&hP|V7V9+OR@i)XacbDhYZ zIQQ%sQE-bk2SznUU(#i+nbi(XWc_5M&dW|I{}&&w9)iFD(Mxc1kOT+lAjX%A!Mi|I zwljwJ@?FyjJ_o!+X-)@3V2VDUm|R`sA@cFkM=^HCN|}8)&M~0IT^sO}ijQ?K;8UBrXnvZ63j`dVD9lr@c*f~5W1N5uY2 zNRP;0idaIUTen8Yx)}q5Mr94oT7PY%mZZ~n^4A1Te5A(|7*a z$>RTOJZ*ko5*pp3tQJJ+Dby`J)mz!K2>RfnU_kZStcCOGp>XbL;7RO^o!H4n(+PH) z-OV-K*Lr4?WZx!tpdggUO!%EhtxUx6UAiw!O@nv&*H9Fu2Ui1Y*`&8yfig3^+etmM zfmYcp5CxG`io9)cyT@a+E3|hpQ<5Diwo?aAZnICOqb}ynaR3=it*O8+ zs?*$^ThdT+i&0x+$7QNn9kz|qs=kcMo1GVF)gkMXJX<-oiLM(4zk&2G z5U^nNA-P#P(uKLael8Ms>}Np6)m=nx(1deKA|EdryG>mQnZPh^^&_H+bcYZIa~t_(0+BHKw(58ozc zUI6oC9_l9X{?@Hq{f{q=a@FI(%Gy{QD}1OkQwBs(=2C!)@3xqABV4N!nLey>t=7E*a^fj-=_6wefwj8W~5 z>a&B+J_da4>#?2zWNUU=qIXc{aXBJSb^0k*7S{wWqN6fmV( zi*TCYC?=!*zya;XAV%xPfvf}hk4c24`#TSg|&^^eYBhvsCjqVe+~9l@j{O^ zgY9dK1u?dEZu-+k;9BHwqxW_9;XZbX|P6$?Y$Jla<2*(vUcF2AJ>_uQaN@Au@)CK0w^YBaQ2ml&&J*5c@#T1r~5@ z0hw2#Prq$EWUNd*!F}AM<3ns6^}yl!VV=CA{!Tyka7iU4kCT*~n&ad5T{MO1+OOZg zKV-Y8fFhmRp{`N-)y}I~E9yCQFKz=iL!EtkdiJ`+L3w{B`T;G(;w4LDE61KeD=lMt z%H)#lJU{{>x_1r+E8oL9o&IW?rfmI8#%)zA@9?__Hvz6`*-tsSw&oD-KX+^(6ZfI; zic}aKicXG?wvm`Npz}w_EI!9(o~W!_hZMZw-Be2=HQlJR9vJx9#csn;OEx3{>3TzB zQD!*;Q0pZe9Ik4d5!!sng*PT*Amcgp(1TmIyvbTK=gzIGGWSWm+!(|kvhas(6J!zU z*f!FHae{B7N-5ipaLgJ3AQo13XyE!?P{&I#fSi)n(WacKr+ndqsfh_Zzfz~4v?79v+M7vCL$W;V>mb99-tD+ z-iRZ8-Q3*P9o!wusdhOr(Hy#+ndrUL2&X^=5j(yu6GjfC7|hS#a@6j;znMcowJ-ZC zfRYQM#|-m`s@vA7x1UxIf`qIkA%}9`n`)(rCq8d4is?H>%2U%MYhvwul?@xREIBgs zi%sh=E`iR)Gv+Si7;GTI%9J&Sk`cdcODD6rk3bYSwas|bAzGuhYEW^43y2NgOx&iq z{gR{+qnHkc>kN@nWl79pI@n$|iVU@Xmt{3t0Y3Ec3IgldXX zO%GFeF(>A=6wa$|I>czS)1(*lWi_qy&wX|5t6FQ=MV^v$7P93aK&K&jcS)W7#&c)P z@I?H)wcAGX?;c2*XD}+uagYU3Ak@sYt_cVkW;)46Wt+c$rgFv)trr`93_bXRCMvml z`_UYIyj2JRqHm*kI05ql2eb|Rg>O&Zs!hxRz9(ENB9GOd#^{RYPWAor7;$3(^)H1{?_zvxyxa3Pa1&+)!VX|ZK_55L3XHAAP zig`)pnW977@$x1rGV3pcdptSb6w51a%&xoH44>Ou{5Avd(W1inskvd1FmAN63?@0J zMcZ*5(bV)@cZ|^fhDwx~EN}$HkkTIR6!fNFDo&j{H-7Ngk^hq8V=a-9sFADMb8K@- zj!-`%doBc!+Dn|P*$etFJTqPava}y^j5Qx(S_OWLdvNm0mwU;Ug1-ZxIe~ASx_yTg zhs8(>G~V5PlBe1_rB*a+39J}u!XPA;a8um)c~n4+6vn}9D>W-%4}N3UR5s{RI&qfJ z4)me@L9dZY9Y&bUoI-9Nf&ih{EBI=xW$c_kH8AX_XWETETBH}mFR^rR2GQ-W#UlolUD4dKQ4A`2L91Vea`##fAnpXlESOy?u z2f{18w*Hzno8vG}4J6gyXtQr6vs_#-*{QWYde{9nC}BVv>i8WTU55V!7os8TLI^KgRrQ)GB{~oz}>Xf{l=ggRq%r{md7rX)B zUgNY9DIidNa~c#O7?Po}WrYe6`ZcdkSV^?+V#oHgQzRGUzAZU>!2kTwu6*%Q`Y|sA3`o)R!m%#M$V`_o#hwc|k6Ax!ivV<(xl^j7#V!8jJFm>HwwhBY*~88 z;0rdgaxT3`SI40~83-c;cR>v<{Sl4=*GtT|M9x(SG~ak#YKoViMI5c2!?%;h5lgmx z90b{FMn@2pT?u71ni4~RbY3=f2_o$){Vi%VaN4Y9TykK?AehqOhaWn?7$b|XTtnpG z2G>+=qOv6&>Yhv~mcKOAqGObqFxsXwe`gN2-t_E@4BIPkGyGAvOxr2IW{!>cvlEu; z3K$?Vfk=(sh3Cg~9&2jmnJrJY(eu%G!=}mXW^u8M(g^|nKHesdu2|sSxlJpM%YtZx z7{Y+z7Pgl|Bn4x9d_3f5KBcVLS9}eqLOM8cVOW1aHAxx2)It6G_b+(f&353S{)t&yFbP>}_IYJBJybru ziB8G5r~TYA`sqB~(z7iiJ>nU=kC@L;9e~H_TI!3gFRwYFfr_YbiQHeeO^@!kh{1%_ z)xU;NRNO=>g^cG{RhhkrU-ZMXsC}d)%#;FT3v1O6BdQ(HG+_ z9ZP-d+s8ofh5H7cNBIM~N74D-SktY-`kB8?;mkpO3=BqpakbkOZBe+Ur{gOm zNpRV9__|u&w>!0%vZ6;^N|aijw&M=J8f$~o`^m@*g^`FkcAH&c9Ud}*-!*otn~i+} zvKD#fZhi@M?Hg0!UZLEmk!Uxv4M%2_ig^VC6q{+s&|we!FlRwh5qXfpqani>Q3PG# z+&-MndUGi2yZeKm5gMMw-+Cri-6I41%?1^C4Z!JL{K{Z6R|q6;Dz|18r=JXu5T|bv z3!Ha9+WH%uS=IoLf3VkYVD2Dya7Ph#mc^Q9Y!3RY3kvjBX=RfpO$Js4Z%@Su4xg#6 z^YqJ?+{?;RoiO3?#8@)NhR9!fLw;f0*L9a=;qvL6Wx#TPyw*BpgY8mRhGfs$Z657g zrjMq420P+p{UjKxKTuNt*lkDSE10f;Rh|nZ!ZnvC4$ZuIRfV>jmB?z{A7`!JGU|ZO zlDqWyo5I3c^Ryr|>M?G5J%7T|E-yCY|xr!JKhrZ{=0qNZJ1<{xW) z$!A&b)AUyj$V?3q1^ z`yBM1N*>uxM<6dy6zz)-W}DAdaplHP(8|i8C3QCI+$8h$cduL(pOyo$qo69#2ytlY zr7reRh(|ZlJ*2koJxgobA_4Ob1#9_OQ1>WMhiuw&boh^=v2k|X(LUI$F)(OFH#95! zbA`=#n*kvybJq9`RnPhLgWAC5XBt5Qx%vy^yIN<%hq&$}a0RlhNmi85Brq_rwRboz zl>qMq7T+4bqLvn*HH!XeGqmfrhBPq!#P`DG_|}Y3D{Gg zoWbQOlOG^J^_=bayofW%go|PZHIu08(w(9eC%yVixjw-`b=m10-Hps9@u6iKoZLxf^2gWNcS8N?XHJ6mC!CQzm0bO6psAu)#D>79d@W)*SyKZvPe$yu;^V~Y z{nkY_a=?ocP54%Rh24NZT|b8`3;aaZ@wM=(zaAc+Oju_Ik~CkN4<@k>=h=2M`uI=n z*zNlIYjoG^wrtrlwc{J;#%}hBdqmZOh5)CPbQt-!pDJ(t+1)fk2PnJF1VtM#sANoo(_eT8ou=;0nvv$Re;ZK>Hk#mHk zK_tMOV7T#3M6YI06a;HJ;KoUn#SNhrs8_Xo_1)(|WCiYnuk%W||F?lG@dBDv+)MKC zATqSndS${kJ)BNKL;|C_g@JG*{(6nOm>4P}B0)%d4YM^=V_QCY6xvNr)#y~)+b!aH z+sP-gK! zcOG03FpDW{i~+m!Vhh;#N z?s^6Q9!P=C%URphww~unwsIU1oK5|$z;VpHbhU?h8eW@7m$#Q?WM+OfS!w&9mPqqO zQzeJt0W4pemT3Lg%&T6;jvsujfPw>quOH?lf!3)hFto(>VzcD)cX#-j`f~M%i}!)a zWtSwU`WD|G(yUHy&)ydSO~t<)AP_}@25qfx2Ltw&ag7jEz=;e@P3u&-78uo(hf=>` z!&xXxeaabgaDJ=Wj8JR7!k)5PXeKUk6uFDp&nVNy01B;S*%iZ)R01Wxs$8ibOa&an zxsjYItLu?_gfY)pqq?DrY)N=ou32DZWMmXDH2=c~3uwwbn??MESt;I4Ma_nuF8WCy zRhkr9nOTb#rK93vTgSutr7|Hw!9&>K4pkM;JMYXAMC3u^@cV70@7Pkz9CksZRelcj}oO9cTx~WK1Z`g zd%Zt{b;+(fu2%`m%}5I(7vzB*0J>f?rGw_T=#7njuN70-j=2*!0Fn>R!A%~yXHc5P zIri7OSAClx2L7Pj;?PSoC!Uei;Vz^UA-GB22W?}U0Nw4Z2nt}T;i@2fCr+4_Jqv|Q zjc=5i+ObYAbb*`jF%erD*07=x0Xci~yl1Q|n`0L528gF%xMJ}}^@U!yjVJ65HN+6vUI0cR zYc^mD$JmSwmhCfwLLtRZ;WKvTFf;NwgFt_Br_#1DJT#W0T3A#$%*JOg&Bk6Zvy?Qk! z*8P4~gYV4YnlaDgNll)`51*J8CeEa}x$BuxKxD#`>EsmV6%j}c0F1A>dE=GXYAMGua`*xs0U_7%ur!=x`=S^48?# z$WRO*m3+AX?sRUDLR?-_hf?I$`IBwo?ZpC){2^vCyi3am`LZiz`DgrO{$R!~RAYS? zWG55b#q5#ngw0MDofev@Sc-+R8i+rUX$RSH0hsp{><;X`juGW);52uAO`E<-3R~Oz zt%@La0Zx)h7oI6~Yt}FSMzjy^pAnT!$~^;71}!wLOJuAc)=^!1b8irqj-N#Ol?El25#8s09?g}N`@2A zv=b^eCnY7NS72R4BejcPrRv1e-C|GKW|cY~!JutIfB}7qizwZRY{Zsp%c+V(6Mg9W zH{L0iW&?n(l68J1KTEb^`pmmYkYW6~7tkhVLnBCgdHXJSk(GUAJa^Wt5qxE{ghv0R zTFUX+@Y1GRJ;3)B1-r9zkwG&QeuW8 zoYEo48aTh)Lp5`frD)>KON?gWU?ap3;(3mJ{glN0g1FJH5ua0@KX12kWm$E~HcQRr zX2aV1bpLA(uMJd{{R)^ocR-pSO!@Qb{rg?7&b6T|;(<*9`&@fXHFEuQPEz%O){xC}I>44^Yyop0(7| z*^fLn+Ppc<)o`W}Wz9wWksRB@4+U<(vHJ~MQ24zFiI$NSo$48CA!XvyZ$Rg!65QL7 zc*C?tua_C$K0OQJmjwY9m*3`&Tc3hj@W3?Lj7g~=0o1>+$Z%8xcR5rTPmMd>&WtKs z+;Ih7C(7U+S;P9VJLqH#XB&r$3C<-YID?V2^8u@gQ!-Z$M6*0y7geC1EG{q)GCGjC zm=2Md1Fwr02hj08m}2iBwm%G5`k)Qu`I%;}j^0W7XC><`_)%#nPkj#IaS|b{2=Zco zOB^X47)aA8L>Q5=StFI*tQ3GAoe9oP8mNT2dsCfBbldD@!9k@yLdGrDlg8 z7GrJPi#1roM~r}i8HBHl$doVMSOyKj1syr}ty;IvWLlDn z_d3x`RxMDn%pua?lovq|&SZo_mMsvBWWO@Ot#^m?hU-O+QxJ3>WS6fyda}=S=T-fQt zCY3bOg0u;Q>+7tJ;HSpEveB^=Z7ZuJnJ3NVr5f4ky`k%fGEY7Mb_80s4WJ>zYqoC~}irHG+TOmmFEcO{L4_jccA3hCag0+SYo+LURR0VoS*GNJ^WKHb?( zMifa7LHb^@e8%)M#*^stCJHSH_5(*aJ0T-XDkpd{b z|A#&Y$dRf`Z0qadW#7I%{L+ca3HyxJsZUNHt&(+sysJ<1IpFXZ4ofJ-bUUwb6|%T+ zZ+E*g%Sg>7>qs>YdHtlLVQI}6m+FmY^r)*r3<*T5z~K?A8%C)E)$c>QD`Ho!71pa% z7aL#dzhTMG4l0@w7Ar%%PCUP;J;0#(m!)(@N|D>0agWHH9}!8v>asJF@tc&eheHxh zo;eeRR1UDG5m}JZCE#PJVPEn4lrD*&_{J=N2W<%`H_)&$G_XFr@#ePLx1VIEtk+%X213~Hp zb(_>@ndAxvs@ z;$JDwmq4?s{Q*}!eC?i&>9X;QeX*Erpi6^uk##UgUIreZ!f7lb2&p$BJbr0%#zh~? z=@I*Lnt^pB5?*NTrY&EtamWzsNvFe=)B&6#FNy*}0O39XTv5gs!^mnScojq9vY)UI zY1ILc0fMgMxb-H?lzEN1V{yR4ZSC4+i%jPm>IMz4uH7MLW6E%R)%ilT^7>uAqB!;m`3O znxs5r^e%u?+az~4$RaWN;@9SVjIFgf`V=z0EP!B+*-h@g^v5Uzk}0cnwJb?fVxdOu z%~z|yunACvq;(XxvUJ5SM`Zrog}iFr^FdIIFJ!w%W%Yx4O#=IzG|iu`fNTujkPzhS z?d_M=NvD(19STK$MseYNUBu9Lc)4=n|9HuC>_1vXRcbi8Bd*NbPnTc;xN@7(v%X($ zFTDb%M0E7*Y9P{ypc6&=q4PiMIc;W66sWRBaz;i`-Lg)H4D2~~5YU%jZn!4vqIeE+ z3ZZ_HB~7+ooJ|4Ue|53QV}J;Zj=Pn=7zM32dS5! zYW*!-b*U6;3t7Rt7Bv!Ob>We=2s-GONEY3W8BPH5>y!gdFa8DzL%qLHuN@XiT0iuDdv?R?Q;NhcS4`*^0B_{g``fwU@ z!Qs&S_DlaZG#Zi!glWf0S>z$}V{0b0!uRsKU>-bPMXyEA`}6vb{5XxY(Zwf__6Q+U zTuI{OqcIdPymFJh$0+aU6?;u`8;YeY3UW+`Ko4xWo2Ji9y(*{-^21` zjWnC*Tdw6U+G0C3u^aeCMegTrpFe+|yeIc_(8hCi#g$j?R~Fc4MC*0`vU=KtW1a4X zhv}z24@|uiwzT>>$KdK;35df^Ft5qTebcyL)9*z&vSLRz>Y$bqa$m-ECJ#2;0&XPh z;(+0=ks9eSln1!v$pUEtQzl-CP}vrwdc&~NFAM_qa!9Es00XDwVY8g>8$0D{==>&Q%w9|fbz~7x5eL1$iP1& zT)i##F{Bruvq(H=!?hMn_m`dF)W%CdP{_2+pS)gcBNgk~bWkr@NG}Z$a})g8RjT~N z{$A*`r~yRGO2aG5R{&J?kgegQb>dd(29Kpfn1_P72^KX0L28SA|JN)-Ai*ny17Qh) z`q@cjCLWgvOF7~5{x$>dY1-63!f#V(rRBzclk$@84C(bXXT_D~DFExnz#oN1m*9~P zKk~5p?%{iJg6^(opO>b6z+!|DKY*x!kjPOUez%{CX3g4wE?f>hW6?qjAZ4ZvY<@bo zzQtPmYgr%CGHyK2(CYYd?o!p1p+^>!4FE=zT@cV*@_~`pT%&0rNYiT4+Twcj|%o0>};-njYck__dx;=Og&& zUGd(TRtkljY?lVLH0WLi*&h1W{1!m)v1A+I#*8D5h;S1pmHgFj=+dN|p=XDL0^%UX z=m?_8>pDq*R_!$U??(n6wfrY(cBix6zo;&A3#mZ9NIthcF&hcAD_er(Nl@B~&7V}| zU_?kCnsRFtckGW3{pbva-44k3LH?Z=$194=Vg$W?nx2`Nxy%EwDCWI3vjDjq;=bX@ z-^X9H!!qz%ED zIq|R79nU{{_{HZ))ko#BW+MVSj%+}r$RN?*J=Ch=wflTpOP1EM1kI$joE>>J7 zoA3YS!K;=0Csu}Bm!im|NmHhHe03eNs>&j$b5maWT)3(Q&o6vEFmC~!(VV;YA3l6| zWW~GB{oFn;eTGJdR$XdPsBkt=R$ZUnX=&M@y9?)j{@QqHnbqCZ_NqGODTqj92tx=} zei$Bc!W^@m=(0-E^{c1ak*;HSnG9Ci!o?81zFYt0v^3<6ZB@Yt&ebf~RhgSbAtfOc zMMUlRqGwl{U!%geU^B$sUF;8(|5Z**Ox5`;8aYl-$_{mPa@&2SU}fibzch9p)Zq*n zZ)wq(?#t@tWL6-Tsd-ggCf9du8&BUgGqf6*ETK4Al3DPwHRgibR8GBpGZ;neTR| z396W;v(Y0|OcJh9P0QjAK5q4F`|9IhIr?x~jq23o_wA(dS`IJ6LpIH}Mhwpr(ZtbUsCNO;|6d;j978S8zX$L_P z>Dg=`XZz8i759jez1nO;`|$Lm?<@r>3XuZ~F$NFrw!|ZkVADu_Xgg2DMH@{!RQqFW z(=<6~S<7*?>(s%+cdTGNuwQ1o`*tUdMFGI%jLh8|4I3ZMj}h-eR`wL)8!k_uBRF%H z<>KZ^fCm_JT2UcRq8K$vow;)k+IEO+GZy5~9*Vp8flP0xS;B{?MrAX&_>K&JBU(X{ z#=)*vo23YVNX$tW<;${VV)qAl^5wu38uN|=)qE60*ckJnhF5z9LRbi93Hx#yeotzH zCz?LS^@vJ>fsygYOb8lv4#dBNR)&oPiT#7jzNiFJM>JUyov4};c}#{8KoedS6x<@o zj=p9edVn0k&)mb`(s!@?VRi2G*|Uz+Bka-K&Fd3HoAopm*9+Cjnn;9CFaIzeoroAE z@o1vx{=_1RYb$~?{sVdiH4=jcCxBV+0OYs3658dg=OLhforRHxoLHh=Z;a#>0#ZZf z8wB0zC$eW=kK?6j6xOHZ%+l*i%?!>YdnJH!L|A&imyh+6&8agH!o22Ki7br_4+1s{ zcyr7{tD$ds*DOIl-Z-}`|&()Ub&(~ugh;^%%5E~5IZUv zhf!d?HUH6kX<_;PW_t$@kT!*%ER*A)@FD?{lm<6oxaI`}hHUR4cEoazX8M-6s>Wxa zEq&d2(49QKMC=-f`dYwj()0p&2-ost|43gnjHebn=>u%xgU>WmFi{MNp|DP|bNap% zcCi3Rf)G&mWzm6j9*wJOg?E$ot31{{Bv>O9p9usqdUxM#)M4T$Mi{)t6Ee`g^gHO@ zvg&r#qbc=XOQJ2&R7NCZCLA8U!M9F1v9AuB13Idw`g>(Sf(m%5K-dvO73bg1-!!|c zx0tgqKO)RJJ@;LBg~Tq_$$uM&V4F@78Bu>A37;={hd~Ex7Iod@=9Il1y-3(5mEz*! zK2`Sh9jvF&IS7lQpb8ZtaZW-yy-Cp6zDO5IHKnQNz;NgCAE=DBL)!y$Z?qb(+^5Yp z`Nha6OE`%@G}may4F{S9_gy$ytp5QZr39?6s{M)zL;4XU*Wy@?jQH;478YwT{*kVU zKFk1%BeD*ZyD=&0>z6Mx=4i-F?bWicP0|*(2NtT5_PjuT& z|K=xr?DZPL(!L?VAg)=mk`#5BSw0bM5iOt;Y7&d`sCebqp~{fe3%q_Ae1NCZCfm#M z_dj!T+*YAG8w)YwUeL5QUvErYQUVZ492rx{Qdwyh%FDzaK21nEv^PNM90CI-&>B+D z$~HS#H>&JSBOR)yw{BF2iLARkW1&x}u=#4roE9iUzoouuLa8Jh0MRhj6#5qU>FeEQ zr$DtF+gJj<93xDsdQuzsYnuf)x7qUhCV3loZ|bgg1DOVey$xQ5-g|?M176iSiMZUa zkE1zNXz`0_+xjvCiC&;yyn3HVx@2zAE!QhPwK!mc)o`qIQ%dNC^5E^HvE*VerAi(f zn2M3bf`LYVZwKFzeBkxN{?zPL7J3Y&;-PjZ?z;2c{jH-8=uNuy+$ol43g#*7-2Ljr zZ^0F+DBdLjw$`#nC5yTWaXXc94J*=1JLO0?`Ob>w^VJ2T9yL9 ziGwaZ?$CKT$#oka*c)97MvnYzNPv0ojYnx zP?4Rjlq>1EtaTc-k<}g-Dhelr2kXqlgegNJ=7Gi)gRsePsUQ|IG8u>-B%`dG4F~ z{l4GN=bY^J3F_@o-d$OyY`6CGiF0C^C7$jGu2e_r6XIh384esR+3QVOQ8?hF$ z;a7$ix;ph&@si~*$mB0GP)+|n21bIbV)`zBBm~!MfLGPKLsO^AW*n)ZWty6~*z%0( zch4@I+I*x{qi7gnuBHm%QHDfeGUtJ`q|)R;`a!XZ-!p1emdiG!+!K(>bLV=SURztB z0Mo7s%qgccuK6XSe#iRx@jRTZ7n@HreD)8DrxHibR2HoIp`(tU0)>yqw}~J-bXBUptdn zq(#&@PK~Sx?pVLiK;@$(JG{Mn1SC$TFiEmD@}(>1$T9&*Q0$l{-Q*B= zd^=5T81vW=zNl=1!}PN_RmuuBFz877hNZ*-qmu&%()3icC)?TBCAC;U_MpM`ijdEH zik`k5e*3NN%<~O5i27n(no0HtM#)^J_*D~ayD^|vr+!nJapl<>(km6^CkYyU$HOm9 zb28oec)T7NWMTNJIxWuw%2W*5Fp#idsiJt(cpl9bwwnPRI&(Kjb7FwNY-Y*3>84CC z6R1V`E?PB^ysW8}9wCoKo*)-g^acd33Cz~xKVk{BCL$ORnrL+SGdn5ZMS4h*!tFsD z$}n@z6CCCiS*=CZ7pi%_x;?W_LM+9^{6XTCP6g<}nBOu+wed*rHrp8@(U)gN=YvlW z1ONwTH=JL{2cmViBqjzTS-yU(3u~t8a{Sj1}2_LMK79?J& zH_TLbQfC|DYb4YBypn=~0(BZSSi$V!W()BTnSChxY>Mpjpg>s2Cyn3Q+MAZygTuxR z98xg|g$jG3Rp3IW)Phv{1*q)*v+y=BY zEn2FnLir)9#to>JrIAYsC^NAn?@%tbR8WZQyzedp6o>j(i8uviAk6NdjBhHfwdFd8+33M6H{m+ zVqd--Zu!NY>e^1cJ7Jmn#WDtrqU1)Ykk4|zo!r8^~M24F0(|Dxy`wV z??3?VFq5#D-K7CY{i~3tK|7&5YbbGv0oJ-NgD*E3LxI+A$ZA~(Nx1|o>{6@y5XV_^ zMGQAUKFgJjrB)GV9H}EfWNK3?%iBv_ClA5lJSd7L8H%b(%E%*smTfTc>3o1kg!$}n zk}E-48~&m1hYJ+4MAEpGM_$@(u7A~Im7s~d7OxTOP-Obg^6!+OvipOkwGlWP@?Vv>`6@^eP%G$iWMskb9_*uY?7Ob%UkM|J=$a1 zGi7AS{KTq{J-3mI>>i(NEncUBs}Ov_3CSH)FeI?(QJ6A`I3X=*X};^4^yWGVQuy#}>XA-(o)kcfVubIxc>jm$SW zIWi&S)oDoo{Hm?R+`>K!?{#D$-955EAgxcR(}uns>ytJbT+7q5mJAAaoG8UWq=Od3 z8StS!Ue!SyxrZE6j>=7xiX!%a%@^YVh$4|LQ3W2*`~3^t$ykbmLF~C9!7h}EyQOIl zKh}8+%m*(xlz+3w^2nSA7)It$u(A!nWMx0%rDh>}w98}ClGBcolL z%N@4WDg2B#0T0sV=C(yA`xjf07o-{_18?pbl+HLWMayyogOlEbaycc}6OTH*$5e>B zn0C>=1E=&T6);y?8MsRfL+G{}z{>~ZN*vCi`-r>|QDMA5nt z{RzdBU>f1#aYuv|^ChQG65$W(Wqwu|N?IO`4FSk{>k`TeX;b5Zy!XK5{n#FCP|^|j zCD+#+hM3PD?BW7s7qVYL(tpdVI((63w*ian{seSw9%Mb)Bo>atK_qtEjsog>sT@$A zHbk36P007~h0jJIQU`6ruatoT?D;5SQG*~;e9{QG$BvF|6hYN^pTn(yhl{6JHy-Ln zn~5JsPk9Zt6f#)>d-BOY)p7aqxugwD%>3#$QjzsVP`MlFky6y>kS5}HblOLVmuZ!o zP{r>Pj1DsyJWnEJTKHye{fwc5bf*&~g#N|#uCJC?y=f|hicWNDUh_pk1@HY;=Q?{~ zdIT~(hE5@%>9E`0Lii&jAs;h2)C|u}$S%t`zrC{xF5tuxnxTguinzy!S`zFhCe<8- zB+|Q(BlIl}0V!r*UEK^zYf;(2DzUzJzh7Kz3St0r^@~;#ZH0-R&(G#=> zSu&TxKbn1&4|ahtHIlcDgxnmHTaw{nsvNa+*%b2NaN+`o@QJH|BtKlb)R60n@4zBMQQZ96UadI()j- z{$fJyKO0fW(wQ@x`<}fG-Av~VQ&B+_;y=%?Zf)ba<9=N=yXH$a2ExGeR2m4|u71$M zSEC+%NXg8nt)dU|MMg_a8fhR`<5S~fC*O|B7OvS z^n1vN7YVf1S>X9{J!7h8`K$EX)%lFf#P6bYF%e3G&erdL_oV?gFR!U*QuK_K!dRPPC5%mRi^9wtDZCqXv7FKqjJJd1$+vq$;!?;`mdG}J&n<~i*s8oG8ETH+<;l-nQB|={T+=8xqI!O9zhR8K3+C>6MwH*pMB~(24#<@VFC%_G@S@Row z^$o?N*6@-R^20^!$>ExW1wNK^o^5)yrxticY(_WN7GK_mRyTuXH8Uq=9x4@~LZkp% zZH_yi{A1NB^R*sd>DJ0>!WzvY!$;D5CHRh9Ik0eh+ots~6N8#~3&SLAA~;xD;AFb7 z9Jjckcfor4P?idj?LsPwR2*`K$g-J7s*g0kked6<)^V=0WeSx+ z92lOxWBuIUXg-3ZL9MHm@7k$+KmTnwy$e)sx1i6W2IsVB-F>1vgZq6JJ=7HK9*|F} zIg)Yxm&JEDt#p-CvzFz%xVgE#ayj+~*YClVRZD0DS^GZCe#^qtJ3IuLDn}o3oJT;C z5W6iQ%gFpz9)8lKk@L#%KJ=9#ghXR2RFpiqjHt?muMOI72N|?mn~|5? zy6ecMh@M3JOUgHFR!~pO10q7L@$5Xgd_7M?+cNMM(ASuhU=-eNGU^B3{&4hC;s9GH zTu^2bz?I)Au1V-M3aGjKkXaIkH;{OsIJ?kGYCm>{?oC>SX!FD^1G&2x$3UDNCxhd& zBzYRl+&RbnFW$vD6nfMQSQe`h@5=si&69uu;+hz0*^a;ei`0s(2X697FfX}$^=bsv zuvF>5C|M=N;SsYi2r1Xq8PVKZ0geEjpp&oaIj&sUcglJfm?R3Rc!Gb_p00DA zCZ`A?08exKwio@YC|tQB$UD<^ZLrNfrGK|_Xl$QWR+DYVIxC-R8gMW)GS#s7%Y}HS z)=El>Yeog%sBj|Yw480 zdlhF>#^hR#8#fM?`xZw@IxYwZa0>l{pdS>fJkxZ~edmE0%eseOygK1PSV$Drf{70Hui`B6H0 z%?hq?Ugy%cCGOKq(S~5H^HS%r+q-C=O+isS;I~00SWMgjCuCywb zeXDg<#5r_e$i^_r){wPQHt=kO=ZSc&>ZQxD$c-mzyOseQ6l4X%F?TwG*~C4O3SAlz zz`_&&upDc+om*JDn>Yk|O2=}N=|et(OTpY#padE3QV@Viyy*O>2BO{{ z9lQKHD_cuMY}CI6N&c&1;?4bAoPd^2d!AYZPU`PDAJxQ-RC#uWyZur<`D-^LkrwEBVP zX{~jkvfkEVK<{wlGWCRQ?fik+GL;89+5#EQAJ>GNret{+@{N{3zfT?P)31Ep7hi%3 z0#AouVB}zj-VO*lUemkroPMzPq&>^D5%LOBrCxG?N~2|CGc^`IcHFxck%kK1dqP6O z8-Egty%2JjmuGJ5Zg2y9Clz0IqB7-=_3CSsR-xc4SROHTBI#^EYzYLfKbJ>uJI+Eg zylxAF(hE8R1m2;Fkk${sBXfriL={QEA5gNKKh_t*Gl6yz{raGbPppJtQly?;rL2yQ zN{9lcNoB(-Hq&bwx6w7Ha7_YK%1Ar{Gfj(tk5}7oG(Qpr<=qEW2t~n7h)i*^k%HUN z@9*Q%)(6~RUL(5o6M)6ADdmLIy1#JRV~6@s^wpBD#Y3s3bY$=8NR%b?YBFUD51$K3 z_-5u#5)xYCM!?SzV5T32u+&>g}J$Xo+OL09LOHKUrwG@~rYSz5!@x zECkY+xF;^7fRRB=Opr;097WQsZ>jSF%22Qfh7?CBWTB0dCc9qeScUdiYyMbMrFA_W zPZ#@Gub8CmhpsrJ6{uX->~mADV$xbpCf!% zBHTVd(x_G-mvhcsmr|p zjCExrDB2*C=|f52G7d&!hqCKPi$T9_Q;2*S05W-3ufIml6}0WJKNTN8frWiLg?_(9 z=DYI8sKjGQF$(re1cTgM8b%$&=aF+Z5f=fh46Ay@HoQdCtCiPcNB2=5KP*7$0h_^K zTO8eJi9R~dVV(jAS2p@>*;KPQ(4+k*2J{3gr!J8w#bWJ0P^SR87D8aDZ_&R^J6X6O zP$GbA%c5r*88esfjyjWWi>>>!T@6pvHSl^*-2-*rgUZmpPtbFb~*?&3Pa>{m1i)M3sS7+Q!7A@G+B%qcN0YflX&3qd_yP)-N>; z*v*E*#P|h{+`6q-?|Pe=`75MREtx=Kq@@3_HB2KyZEU3|fCQQc`;(#j7i6~Rw15-0 zU@gUM0CL%2SoEUXH=hhp)KVx$nfy9ARaBalHGu}w0kYxk8AZEuzB^-82al+a)>Z>3m3i}AH0SJ z4%7qzXYdi@(#3H0JX~2W$L_o&nYTm&t%NBciRo2#_WUaez-6-f4E@5eGcZh#PEC`w zKr#%*1D06^l9^~!#2bbu?6&IIU$pxubPni8-@_=Bzf0yDEiS<1FR^*paH6p!o)r!b zWhXDkJ3R6ko>HS26_wC;{a&8SJ?f}#zg?01uEk#!!cs2vCOfV03iHZDz=%HpGQ8iJvoEGm(F+vNF1YAA={cR zhX$ec5Y)qGWCnEiW{urpBP-1{D<1in#On3<76KzE^Ry^p#0ek`t8!2E%Uws%_*YO) ze>7|4fLOBjt}fa&8MknlSAN}dw04-mF?x=zy20%^58Cgq*o{>SyjLu_nwHZMC>wRZ z_~bute>qIhE_naAXX+=8HqY{gU#8hQm=zlN`)D0WI}4~a0zQlayAd6T1*jIRetR5q z>y`&4h$M7kvTDwg;}p-d8%3WaNm~rT8L<#3jM9Lp!Hb2zd2tSCpLnMRGz@Y)=~iL~xVlOSS;4TG4H!AU;K7~?r?Z1#qi+|5Q$MGNxi? zP?*U)9FmDen7u?m(%?Xsvh$15TXdc{!gZ<9Vx!TdKA_%FE|n0|%-n-=%wPuB@`9O$ zCH#*u9_wjZ5mwo4C^g8u_O9mf9p9U2WFB|bRB?T8mN$c0b>fZ!yt1GyI+&!@)(-V% z*RFkQL?gcfQN zFpAccoK6>lF-IbNr{?*(0p9k9CFx%UDxtB|+R{$xNQAFhiiAPRKl`?DNf=2toVq(TSEQ9gZQx6!Hxs zJRds`6iG4gE2BV-@ZVB>`gZ!u)AS9JYaooQi<9;VG|15dD+Q$}E0=Z_V!z?KZN1j6 z9R;D?sk-JnE0^vtDN*t#2MY#*9+wP)Ui%ISPsR(;y~wmR>_=Qy>jt{rOGaPjq~wDSi}c#&$@wyDgnb3r}U&(!c0 zk1=8XWs8&CjOr>EsT!pV%8l9GQQPP|?@|mWI7(sJ`rkjluU;Yxr4{HEWS+lzPnXb4 zEz=(aqaFlf{k%I_Yj9vy!AG0*PWg>`zw^+wHwZc6<4!V- zuc;fApidA%CKCKL2Fhvx|0Nh%onLvKUxa3Rkkj9qI#tw(z>Bo<^?r z{89O-)T7L8N_q!r3GbWl*2o5Kz%xGa2NSA!I%?F$rye3L^V1v!dwuiL!Bt_~M!sIC z)}Tq_#u`+%RCIfJZPLf1Z-0ktE6phwxSQ-#r9N`;KKn*It1+U`A9m5Co`J8L$lOaZ zs;e5@kO2gGTyi34(fM`^`VJyJJxyA3+$Nh+13O8jPDXM zF?y^6NtI*1!(4N*m}?2Dpbp}ZeYo-0kI&WTa6T(B;x;*CgFfFLb6@}eyfnI)*J z*3N!Qrt(R>8OY)8Lm46Ai&|7sxCnMbj34kJYskDOt)Naa1ks(2y0j$PHrmNCHl?pbjfyqh)A9as3Xwh$9%;e0=k6&&-%L7U`p6F)DmuqBYfqCI&unW7D$m5X z^m?dc+wou5{9`_F22c~)EZC)EYgu4>2W7S|30(^vbE%ZDOLi7`p7M!gn zDNWTh>Gk8V{`(&){SWU+D*Hp+?g7ufU4E@CeMNqZ0;#%aill>sSX7}l?Flj%F-4-W z;QHnAKM(20OXELcZ)dfRuQ~-qBxCo`Xl~&$ogu|H%BOAbmMcW6#97cmkaMHD*|H!+ zA5vpbCJ%SoP7y8B#HeFl-~|A7aPyyTq!qqpi{YB6oQn}f_m2=G+Z=d#d`tW7b~#ac z?KQ^VsQ7y0VP)6wvVF&%lqbGlmSJ4ouWeJSZK)6Qf*F~-svS^Ob++iO_2;sVk1rKx z#5t99JzMlG1*J#r=F2O&f)>-_f>jd#(eO;Oo4+78O>HH&V*^Q4|WUhG6AbJsaRX<=;2+ z37eD*YA zVKa`5lt3enH1yMFs`=N9V+}_smEQ-jL2j)!Nzs0B`t(bmi$UC4+^ABRC=tN`@nLKB zKOz68HxRXBib;y}?1-WW7j;9S4k8nD$H2~JdVHFVa zwpsl;C@spSGO3J2Pij0kdEZVoe8In881Cn?vwz)lu%g1$laVZ|4cpT+-l9|-GGb%f zL)rRGK-p~$%*nrE(>>?r!rzBDzK$qz@N_qw|Fx^dr09y(_-{+KlTqU#h_dWn;B8?__|NeOk_iz)i+Zsg?FVp_iug7TD zD*r#8QsOu*CEU+2!98)i>SqU6umpFWJQ+6w@5^wXPg9gwd!Gi}optJr#ekQh%aZE(o6do>^wMZ5gOxU5r z_NCo_I!DTJpgCz@qcKXkc$H`fynh2;AFcEqt^2A?bziu63}*J!`OB#hWY>u2!2>_N zu7)Ble@ROg2hOvati(?KANG)Woz-#=Jow9oV4AJq5Q12WAWOuUpG;gk?Y zix=v*%`Yt6`fmAu|C(!ietw%)t%OD8%Y|0A+kVsM+2(IXrK^)&s+-l{Wcqdq0q1M! zR`<=39~Mo{aINZA*(>~_zflK^GsP1s*MBg}eOu^KHsRtlvN=leOAB)Id=j4-^;;8V zbi2xN^6@vjJr5PnsQJyh>bxmdlC*meQKL%z`GNV4pKiKZ@zV?Y@vkZd|HC@{^gkAE z3a5gC{i<^arUDKnwdiR_YQak@2P)r2@~q!hV@T7VfBg4<_Z<(0lZytpgGn&vyp8cO z(i!;R{d#k;SRE)R4%<@^Q-sI;=dUZ5YI^H}@#cJlk_;MDwUqK>I~y>l(rtzA3asg18h3sgygfL&l@IwMU{wwFgi9HRmoF|>KZ zhG)&MX&5yT;&jqK4C>GSQuFD2)qJPSZ;(HX!poL#M7w7Pfg|hl=jJ!awle1-n-&5A zzhthB{{H^CZH!!;a|26La{+F6Fk!?1=KwW!N z%dptnbM@Jju^`h9Cibto9Le?Kn5(iLM{fA zc?2?Eo?xUB;XJ0^`Me-DWt~b-`4;8ub7Z2?R^5O(rP<%bW|J#BW>-6V=Y@nt9UK{;cFd}3Y49w&^Hw^-MH%e~tGfEpEY!8= zOX-Afajv`G{E$_9S{H%Vis1Ew-Z}ul1wJP063n(76JrqCk5q3Rocs zAn?s*Ui;5a(1_X9#WobCxDW0CsAD3b?#Vi}ZrseN-g=fgrkR=9Ev4%J#zHS$qD+5? zHlTMTVu-MmjH_!}z)5oU+X$r-@}S`_JdNoQw#}$EXzK5P)GK)RpsQ5%TuXrVO@yKl zF(wZ2|NUGUU4EsqT_s$N=qbvasq|^_H3T8-csD+(+d$=y(WJiINEk@zu+SR5NhB42 zY$}Cg=z?ZnTk8wOsRA9ktlh=`++6c8iPccR;-wvI|K<6z=oQf9p3Su;qsOs@?I+5k zxEY@IIZpE&{i1v^|3j%JXFx(Xz(!WHLwjcQSoxp0;+qog^{yS|2E&6TY5hSSuL)(! zoo<_nN|>;p$aXF&R=!}{B}+bxyI8INhb~ibLR=s}s;xu--#x|s?eqUu41v934fl!w zSmvUii&~KErg_A;Q6{}@#(!IY^uf{em+hYd=V{m3j_azge}D3nkB*OT{pVPkXc)uR zp<~-hbP;ikdszhxv^3KxCwbVGaH*J*xedgWmez>aQ6a|J%jq4BIb8gxtEi(O9h?~W9r|R1epKnim);!n6Yvz%%LeOu}D*t6&PoB}M zIW2`QRCMRy*p-H?UMfuvzCvJs`}Q(y0%ahkQ}hy*&_!Nh;PAoWKr~-|aE>!CE**tb zMV>#(PT6q{SL@nmR5Fctj*e&=xs_k}eNY|hc-ErbwFy5HF;El}XjpogyPyO*OxJ*~!CxB;zsDHd5$(y zpkE?#m=Wlqm#wGxVXwn=u@3ZE6==}6e+N`Dntap)h zHC6R}YktA>IXt*^>=$E(f|I)WfnCM?t6_d!Z1vkJp5lo_4WNUuIHRk<$hdp?b^STS zSvag-EqjHzh_2`UMCU_ET3vra%~hN}m7mN_^`hDl@q;*$@|4e>^Lo~GR#{1jK9(t> zz$TOZuj03`j>C!-nUp$+(u*QK2~A6YgF#Inu(hUgiFeQ#I%x@5M64#zd@uvt`awj5 zuxpHQ|3cTu9N*aa9$n?D7y7=+90&V_i9^jdcFw?^5LJNJNBx>Ru3^asrN|ZUK$ihY z)-edWq3EW^zuoe+qCE-D_Ck}+C%;(0pxj-&@Uw%Q$;bC*=k)eC7IgKRZ|h*^4gsH| z0}d`@uj>qZM$ZK;js5wIev#_JU#YnwKfxt|q@I(M`Yi@Xm+Vujf6;9VCR( zy%%rvi(L#ZMF-t^F~J~t*c_YBc_S_ImQ8o4?t3!NyM|;+toF)}P@>V3$tO@A)`2_j zReR;~>6%$APT7grP8kCq9Gwv9;_=WUJg%lR_<_v2^#JXPREd*u8LwWGl+muW5jSPg zQ%h#u5UT9GzeSET(fFIgVTVFfMBi(f*Wji3%0#&c!I;VX+-KM9t~)X(&w5|I#0s@1bGz2f<&=HxvYkB-|Olqo3QiCr{BG zIOBiS~3#n$v`Z#soJDR@xVZ1 z`S9eFo`7-T`Bl?0O+6vx$0M40<&rg)jNf&TMyan1EH#Ote)i-`)QMT0*fC78#H~0$@VZco;S#f+9f8o0kla z85P&RzjDG+vqT&x7zP)4E{O&-C|R&q~OVWWTCo|5<^s? zyF$vUW7R7DI)u z1}dc!+HD`niH(uY_B+g>7!YZA;ZMFD*;Zz3#6AJq9wDc)iq6=IUdHm;e1jj6poNP8 zpLWF8V)m`NKfqCZ;c1N_3f~s890{DN!VCf(kn*&PbapHc4sEyA=#gKK$6h8)c?LiL z2&)jQXji`9L}s2CG`dGm$}-6sB6Th$Bo=expTGQW1R`pBPn*H8e5F(4{Y|tM^TL`_ zU-HL>kZqf9Aam13D>C1qo7L@COASA+3V$m!`z(WEE`0{+22#~S&dP9;ko$C&3rLB} zl)v|cD&ocJ94sLw-t?IkAR+j-YOU~4iPs!0wl50l*|BCN!k(ezcy2Oj<1lkvnR{7Y zea12_#4B+5<%Xki-)d7i$Ku!UMG>wp>&hq?*jbL5&<247Vv8=rmRVUARla27^d63|bcLH8)@XyrlL!FO#nqq7Y|Z&4Pwt(5 zGMIk5NFi!OQw+gQxa?8!9gUT?qL>zo7cR@Fon30>yP9~>)EntA{|6c%-p#q3^9E(_g}wxBie=vU>T>bWvZw!C~Ce70%%Ris5j;uBQ0O?4CGpdz0cOLCPXiiNi|j< zGoXm~j?mX^t{uw<<$ijX?W#cy=B=y?>YjUbQjgxZI!_DY_15Vp-GBX9uH+kc?Euv+ z%{C=2z*ahJS&`M9Ey30;!DP%mChgkQ(D~emn=o+GudeO|@)P2KvMKQ3dg1_NNC-+1 zVoftz4g`nft;52buMR3-_ipZf1%?N1MMkJ6MOj19_>><@1Hy3FLbQIQw{DX{n}SF+ zNCgUMRntFNws;85Y;oo_NA~8qF7WEX`gQAy7bcB%3uYu?{#sCJiSSI6a74feP^{im z)uwG8mmiBf;#5tI(l31+`4ACn?Jl%z)St~L12bs&MSf|}RS z4E_Ug#Q;>?2FV%>5xBYA6=$xlaE!XB=CU=iGV#UNl!xWUYM<3ycSNdo0{~TSi|Y*# z_P*}p`?V^l{Cexh(W)k~8sTNL?4s9?XjyUfCIj~*e|B-2bF|FJ?u%%~@ckPkS4Z_N zUjJ-%&hGkl`pQ@_03?WvXy#Q*!>BPwL0U{y((R6n6MBGpd_1F9B7Z|JGZIl)rH zV4`o>A^@{A);D13B(rC)DwoAl0m&r>&6~nK+#P%VALW7PJgzXE1h;SJ5|7u_@qCly z|2E_;o%E;laft4=e6Jhjr`FE8u(YfdE`wkZk+hcTB+ZBEpe^Rp9noSbC=87WDBamO0}iu3I48eWrqoSe zm3GR6C+Ge~xpm_vt-8&1>P^KOn!N=WjM3(a@t0a^SbZS(vES00lj~Ui%|5ky`^c_R zzypB=vC(akSxj%MPkKfdIf`3m8pJ^P6Qul6k%6TWc>y5Mi)%Hqc&dUv_^z@&OPmFe zz-8s+*ghyF)H#onflf$SQItqWUSJSCg>BrT2!bq8JCP4Y4DU&LIu0}zO^QQy;V|ar zWCbb6ZwmpXdi)15!?27yRGsHmEsogg2dkMHkRv9phWe7F*+8RxQRhX2)3%((Q8{ zkEvlOg5|V9ZpqXHSXV|;`NC5MjdX1Y;yO$LAp=SbS}+If=i~Qhawn&{B7ziG0z_6Y zA!UzO&?MQ89K?U4>~UB*sWeHh_!7-&|iEZcP$&^n8wy#moIexUY(*%zLiTTz}D z{RM|4vDg4}P=$5I0mT~HzZLVV;#U>rye`6{$Uit0Kq;PoWEF*2kiy}~3L0Wd>!b&p zQz(r}U;7y4fBh;`Y@t1>)f`r?oDX99Vf^%vF&`>w4fUBd@K?D}DDwubnC6nnD#rb# z!%Z24{IT)(DcM(~4zczIG;djJk8`}3OoZ5%)K+*Z_!I7Qdj~ssC-B*P@nv*7ma!|U|pvRd68G$mR-C0T9bHG zyP}cZqW-BZE7Ooh(dfsk7D`my1m?_{19sTP|GdTgic+Zx0hEP;w;9Ng0!pCDTUoKm zhrnoJre|YoeuY3Q!ldc$vb8SfLLkD>}0S0Y8*Lw zvZCkYGH4?dhx;)YeXRk6ha=Jo8%8lD$ucpnmK#TF!|IBs>N-~G&EyYO$uMXL5 zBm=xm^u)HhMGPHY7L2b1%07K9AF#(Z1g=a$EiEg@LtlvjB`v>Emo+T|cXA#K;oa$l zB=b4D<0phF&zc!V8QV%uZHiwB{a<17_$UsvlPH2zj@$)Rz?PiI9b#-^l08cXBgHoW zg~W~W*G=WKBDS>ETAlXvsn`lr-`yb`iNp%l;SH&PDY2b|_X&)f3js=``}*8OGAdvz z4%Qe7Lk8&k*d`~+U!>aShKk@(7B?d4cTs&TYPPnRi4mX-ev1!60YLgMNW1+Kl|0@m zwy*@dSoO*J+>u$xcj zSFo1xnMRVe0u_xoV+ff=&05G38SsMbw+{%WWwc-76JOJ6DMKZ$;$4gBDDYN?$|1A; zvx%4%z;<{q(#sHgy~fo)Pz=?(QcRlsBKl*8z$3-q6;A!4*1P7=fj5%p^Fe8 z?q^1fuhl|^b9n<2iHR#>>MRVefEyyNQtL%;-cqiS4jRJN#@hAuW8#9yb&JXA+Sjby z#8I95#YoyKMsVce$&ck4b=x<)BrBl_?lDVGMu}EOl#+1Dm{&Lbnw8QbGBR=nqeAH8 z<3MY~CQ0LS$wo}XjEi0n+ofB#hSbwIo|9h^sOS3j{YP3lh?8e6q!Gat6;BK%D?lP; z2zX|o)>W4EBWFk^MyI;3s+hNgc67n$`ghQ1ejmTPnR4$A!y6zEe>!~2&aYIjx2HTB z9WU<}JivEG2GEV{0mZ1U; zk*wRJGA3+#m|lECem%l*6hiOO+vuXoZ`S)ka{aWn{TA(d02lH=zayKfsYq2AKG$dj z_Sl59gN<<;mh1UcTqcK(1g)Z&wV?vV;97aDRV@epRhI6`EH1%;7||A})-q=u21gz6 z3ZO(VBi|(j^xC(`Jc*n%i6|pKiTDvjB;iIzyz4_ds4hA}YutgcMy!p$n|OjfI8sj{ z1>8iNAP)j4T*Yx}0x3sHVP81oqIBU(@C&WywY(UY zL?t3#;^II=-j%T#{sghI$#e|;;P{l!*PY-22gpwrCtR_Y!F*fxq)Qzlj9x+3=3F|+ zm8w=&8A<~H5l3g0#QN==W(cihB{f$nL%y>`S_1y@Kc<=PK&`jj0DUJc8FiwP^_RP}PNj6k?oy0}{sD-1YVwp2@9N{1)rEN)wJBp- zoPsZ7P+7OIvYnEWtWKcvZNs_1y|Rgp&T*g5X5MW+d4CbvzP`Q^T0kO#>y^A>zUxHp z1SdfDu)rW6A{Pg{Z0Eo;ysXJj7ncw|yT~L+k2LuD{LV{ zNMk}E)8(*$LWBN1H)Be)(Q(RpNZ(xq69dLPWcLpgAKTInB%IuDR*%FWvOm853Wm#s z(ggwDJvr09H&qm8Myf~SN@*o;Q`NsG-g{Em$^p(NPJJCgMaLx&D6;a1CY z+V{N;*eTHCsDA_d5{3i}aN*-aZNgN8Pkda)#X*xiD;Nz1GOU?F zk^9S-pUe`{_K{t9>WhK9SNz9NOc~&xxjpKXmBOnUh(q zn6MIc4QW7D(Kh zl<8v5LPh?7r9Y{z*3%_7uxU_C;vf>DpT0zD&*UaymR488uL$$zFAnBIVR|tF3q(uf z$m+z^c`xrW{T_qd+e^)8ICX1_R8!gEF0QTe1%?j2dCvyvKI|`IG=*`NyDKAzHtB1YFTVj8kcktHub9QaP?6&6(gpqD zRGr>81G5@^eBG4Ft7=HWLdUIWH3$Xt-iDX?0tCi_i8j$$C!u#`ECpdq3oTvaX+d%O zH-&P!4Vt}{#ncjsfhQyEmwX<-BB%4|egh6;HOls+!@EnXClA42ZPLc4#{?r`j#U-wev~N#@tPz)EZ{_|pFHR?ux~Qn43w;cL1S52gUYB?)_P4UP}#DBWFu&737zr4h>ntuc@;SC<4I^P z#T9>)dpCq*DW$Fq_fW>2!+wqwX9{V}q;G3xS4Ch72ULriuAY!U_#$&0>%T%Re2luC z!4k&iZYjm7>Wto%c>2xS(!ygiu@(|%n~AMk z6Ik$b>CHM%6a=7l;X*`2S7>q>52B%*N+40%t$a7_%z7{&=X=0`ZGS;fQQ*ejyjdSE z11xik|C3=IPV;eumsb082~3TgldN}xl$JRjjw<_Bt3DQqNDiSfK0mSoiMt{)7cS>0qHa>4K}fFc zy)|RX8_uFc;7rZ*tF za!o}djyd8jRjXo5(fcqch9^-9g;_1RmQW(nDn?ViRcu2(eX>6}l`a}ArFdLh?RPy3 z&-4%xlbFnMO3+vTmF8ud)A_hp@~4pypq9nguU`+skPcy(m=J-k(AKF$_*m7`$-fdA zDZWJ#sktmr^xEKOO*SQjMytJ~aj=kDb@t`ue+V(muQP;?j?imUI@@hMSRKs-Rk+#) z^i)z9O{zU_Ez!wi?Z@};Z}S(7iNR^?J%aj?&7BZ~`}z%YERNH?Zd6JclUhkMhh}x8 zo-3Gel`Q}Q0qK6rRtazoake_->ysBf-o<#G;0g~R-^f=CKGDuj7uzJr+2 zrz|f`@;6o~CH0S`p~{@#E75h6K7BhqRIOLHu54Wa2qYdHb~UX*hw7dHIYsaorIuc| zH+t;RB^(fp!=J9?GTx<@V@sKM^_9H4af-GyC6f3Wg8_EpJTDA7lqxol2f8aKgF2`< z9f(jq&4)WVISCCZ?bP?J-^CcOZQ7y#Htto#yj#sF0K`Us*SF(DR;z1jRa!)o(6%US znIw%&7`L*@97s|6!0R%RK>Wpkl+x`6=HSrT?}g4{5t0zfEE_x_)QEYXWurf08bmdq z-NMxt;tbl=hQo^U$N{3HBJWf`c%1-R>9vpukJ-+i@Zv2EgS#L+3cxRaBbXi5$)Z zjl`@EAH1nYs6*A#J&MaGz~yy;st9ch^FEqvG{~i_FCl3j3=18Q@{n~d)B9;%J|a`* z;tqBGQ$X--I5Na28**3kU;2#Qc8d-albD&%9GoEWhY_nLW{V&BzogAHpQkI-I(3+M z$%|(u&-BMK3~`@^+HgIU(5jI?%&}ONrY`rRZL&ZZE@r@hnJ$H^z_N3RH)5Rzkk2PJ z`#h7^f-)@v0`-+k?OQC894$czZ%t}_-tkxOMmJ(q@70wp-Snn;p0c2knToq4FBq!r zxmj1CApPK_AiB-jl|{D03u`l6g^W-NIU}?MYF&qWH_Mt@-ON|#drLqSCY@7o1AH5x6u^jtvaomzIP`a<<;U(eJ z&Cteq5}v0e2&fjP4)A(J#Ad>AZQJJlkBW#w8I`rF-CY+xAZRc2-kg+BZdu$>xDS*U4MaANd{aUb{fXwOXNLkMkARBc zAX~0A(!I;|z+>i}j+MXE3NK#RrC;esYta^Z;~&2vQSh$pS43VE%XHWqabV#eY0gXa z&i}RRYqvA4b^LIx%ds2ro#?)Z7JACcFEmzo#OfylW^5$I^IMMrsb*hy+AWTTAn?5| zT31Z2f%`lLCW*DWYvtD`O7;|~=r*!x_=xZf7Q+yOSfO&cBkkA@4zQ~J=qw#aAinst z(x8yC3%Qr;BVyJ{@r437hdX^;EH57}g!f;cO1e6%lF%o<{Jc1sqYggZxm?zN%TSVf zk2caPLw7SC*d{0yfUpA!mStW8v^Jp|dgq_|0^v5bis9A5KN5?3KpsQqo`Z__IcR~y z06kjarvag2gu11yL$84|zC;V%Nv(hjN^R5)au&pVfe+#+;0j|)=$l-GRlKsv6GKh9 zbF;;k4O}O|19&nH5^Z=|6&Fg3_JoHgG%BecgTvj4Pm?&=!B8NJs!uqReUtl#tq&m2 z6-wey=oP~tSShkN%jn;PPbZR{v7dwqgtJN^&bF^cr;j_Anl^ zP+yqkZX-RN#iL_VFio58oP*=g1w{KkY$Oof&&i5xqBEckNnnm@%jHlK%#|UH$@N!t z+;Y)Hcru{;0&--i$vKWwAN&s~DT~r|wqlmtCH}1+#UmOI zUWf%X6pGIofft|iy+TQaLQ{j6!x`gV7LdFXZ%vsg;xCJk8cZSHq;1NFeStj%8w%bM zb9*?;c;+I|W4bl7>|B02GRL=@H+*^zc8a>l%_SYlTG%#lpm96;H(#$Oe!I+v~~B*oBVq;WphP!pMFt z9zwmBI==QVugzeE+kH@HcRY=F2V&GDLD+IyVQG0eJ%D!7GUuzFy>KDn-o56$tklX^ zw+Mac-LPRY0aTj%X*R+Rpd_o7w^T8-*<}|U*WY;d7&Qcbyanl55EBd!%$8*dJSr)X zC?B1_)tv_@QcO|u&r5|;k>LYUh?UFNGY{;t$qTZ+10=b*ZJ9hSp3Nj0=}HZDA|L>$ ziB#mOK9oNq(uh3; z7Ige>a6x6xOS)mSa)c5e{Hpo@KEK-h4v|Cf=%pPd8fW5@5Kn>w$JpX2h6DGo99mdg!-c!Rh~U=j6&Y_@*o;&2C=Mf6P^q6NeiwFG*QD}gMQm$(mx zOc040n9xg~XF=E~`y7U=SeM;xedeY_LiCtuD^ZHj5#@^MkTVe`#oK(55eh7#m^-U4 zN=o2?_)w!B&b@!Q4n07Tavs39d`aOU1g8Zth(z>8`yG3+j-#*UZhXcEsiJ2N1g%S|ix?jS^p4+i+ zE{hm7k9o4tlW(lQvxzy$->uDP27pjs@c^Wm242ECC=oP^K{_XF)e!=D8?|o(`!X@7 zu96_tH^JA{)8rf&!SuxYsnXenP!kV*yf*~@NgtH}EKCE>;}$y|Ng~pQCeFr!Y{koo z`<}_)1SJG*(Iz7dWhP9W-lcU^w=P3DL$`n@Vi3#=IRvB|{LG-lPUR~CPk zwOS}%1_%x;l7YCVAfXNV1Z_B@$(OO`bdV!TxK4(8GtnJvy;lDhfEvgfs6FX4E? zwY4j7b{mCHAx%k9%J3&5U~_~x11pX(PN8|QUDTl*t?@11quv?@8NdUYQ8a&>xe3{= zHECV(GB~?rulh#OIr7$FpHJtyiHHeCkGdiNT(RZZG9WkqT^D*3YEw2Ae)}dn3K$1; zFLARoUMN`~X`Sp5hX64p1kztv9=N9~e_T8&xbd3sQ(3FiL$Wf|Iyy!v4_ydO@6h-%=7m1_ zf0$yY6OJ}rYypMT5bgtSr$;SlEyc)ZCWr}7M5)iNwxK2?p1QrALU@wp7rL|K?G~TG=X`vr)I-=k&=8SP6`CGm$Sh4O zQCQQ_AV@B3f3lJw7pu9CY(K3TzQ7245D)$}WG2O|u%BEZQ?TjqNTb}> zuj81}5Pwq0KWS@lVRnKn^?E;{;D!`*2z=?y3Z}Ern)=ssENI7y-3CMlwP0_l@d@$mBx)kriq!vn;d7dH1>VLJn1?&aWtr8d@AoJ)-gNE8>8QRbakk`&kblMU zD$0mmdwVCYNqVrJBe}mxtV!95?5yIn^3R1YxN7@5}4g>$B z^5o!)^iE1gn4sL3*N0H4+J5_3ee31ATa!wMEw6C-q^Z%mnSJ}w7Jbj3KfX0!b@xx# z&-Xj;adD!{bBiOR{4{!<_wIkpbE0n_jCJ%<^sF9Tn3?gm(kXq~<+lgl4!yK+|J(g3 zrOvBu+qde@GIf57P8ZzGmn@;G(HXhMtKu6fI~&}CL2wG*$mAU#8a8hpN{5Tt)Am!Q zOp*QxXMYjXIBXn_Y*U!Mvd=jHq2NNW@Hu9Zf!n+EHtlh6aF7{#($>7+fA3u}taopj zr{@B+Mh}1R*s1M_)e|O73L%@*u5V=Ca=~wuf0Q;VYy_j=cAvhl=v~6f>^EA0G($!SI+1RkUED_Hs-eX{v}$3OgKITU#0gy^Z%eCq| zS3^U!If9~%r!hQTX;b&^-50#qGe0&FYUnN}sf3E)kES;+EqSs7lk7gMK9db2gj*&K zt`98u)&L>#4M-P#L&JF-fA`IsA!(aRoC6n2yAFb)?!y<8sSW~O+e?0+y>*tPDuI9T z;vVhWw-4O6Pv#Ml@812Aoqsqq^rrB$e4EScP^GvF+`G4N-kqc*0|t?FjrQ$Uun}-D z<&-Sy5=|2&E~nI*ACd7}WUt+_`?oP%VHR+#oyJgXX2{5OD1q)yva05(m}~mwUAg4t zneD!8b9_!tPPd*t=h@hd;IGrXYox8MUC*J~{k)?iLqgN1PnQ8oI9w5y2|6al7|)s0 z8O;W05wO-wgxNf$P=rreni1xItYCCZG%|cMKyTBw`me6ONuBo@GVLCcr9%6mW^?8o zB$qe&{q|1n_UmbdC|O7|nh%DOyZ4C`C+fM*2@4Cm&vqtmv=AbksJ89ewSL!sf$5AH zK}fY^-=lmcQimAGQeoU{b+wn@dN~zrzirc|4db;DTp8NTJ+GZPc~YE1@3SwS57}-G z-*7~$k~1s`=}%hl;0A&(JIR(slGcOAkKb_ojam(ALtF*|&9k!$%*+r$O}8fmdO}DX zX_-gc07~ExN)=i0j9zyB^02R1M4-}$&3eLx7eCG*)%&4C0;hnUL3_%os<(IweXfBo zZo!|rryRXHNj1LhN>aiJm@DcjcXxL*|6{hd?-z)PE=j+2Ugn@K+z`5?c>xdp{<~_? zia#}?^>UjWiZYux(XU&Lv5Ew*((bJ;_qg-uQ7b?%)Q8vBt&O>z2`^p@-E<=dWa+l6 z=OhP*K8QAr7VOMhxO4B`(aV?X!`o(&&N)ksH=gFpir84!befSy<>8x&iQS&>UY60} zV@XA+p@D$`1@zdG;^%v|Y}ul3;5R&1@BdJB<^ehHTiZ`W2~8+-5>XKuGerZTGDRXo ziDZh*TS_SsQc=p32APL4WR65BMCR!v8e~>zRPSf)y`Sej&-vq=efHk!zJI^(cdct( z*R|HlZY=)2M>OUM1aUBAF}Lzg_{#X`2BwAKxxEj=cN*Eh?FvI3hVxnf7He)O-6sa>b ztX%o;;bX^|ysA!hFf0UN>>aowm^WMayyFK->$I!x6dBR!E+sblnnaWlJRLk1$^>;6 zR1bufdSk|nq0SE4vuBTPXrzt4pb}#FLvYW+PG+_3`d_4B075BX@7}hclRM9X?s!rh z_2B>uCM0}mwreN!lBu=9<{BrRHf>nlE#^y^%McqI3lBAHbt)>_Ma@kIC9H5DrBU!( zUaBxFty+2}Y#F?4OL%zr+t!gC{BQB<-oJk@!Yz_?5jwlTd1$NNgo7sQ&OjuH0h6)P zp)QJtD4N*e*A_AnQ9|rzBqKM9{hjRU9r>lQ;)n2Uw;n#!V5*x$FWN_IwN%GgHI5|j z3au}`3vN30ef9k>6gob=NT!X7j(!M+Wwq`a*;y*nh3y2nf7mlx;cg)NVL&MlU(N3@I^c3eROU_QxgDr)GJ11C;w zW4`M5!9Twh@62nes;YYT?p>L(P0a6%{0??e^jMd*JOh;>dv1`P3GFqEK#H!~?bLlh zS6V5A1!8R@j5LS)J}kD(MJ4ydQ$IrMG@=jzIMp8p=YehFgu1zr5vjY7w|Ds1u}7o| z+i#mTZWR2T4|R8Ww!6FIfoI1-Mq-_3lInKeAVaebf#4(uC0?saeU+GykWK3tvYw^OjWv- zO)FGG_nxfGb$m7n6zdpc0^tN&=JlB>^*dk~W+Rzr!jYYv0~g9KWFFJa!+-wzB^(8n zRR)`d8Gey}RnRSpknJf>Xx-9`jhSLn8v7ia&I`Er_W1z3Oytz?_nXr|Y%(JCn$MqL z7=G-2cP9;=;}duMxGk@fMyOl&?wuH>L;KbK8%a1hK51Qe!9fz!4LeoA?TjH09kCu{i|8+&`keMK``P`yP9rD3kF zda&H@$ZW9s*2}fD?r3RlZh%h;dM|YJK|W8{n{oTUe_zt^=^A(d&Q&fog2-Sz;j$;c zE@08Xw5GE3jf;zmI521{^53vQT75|~`UVE7OJ~-Nu(uC^6Lt0UtQ*-~U90j}z{ZWa z*Ve6db#?98uisoUTGwvf)|L0hK}->%wAre3Ox3WG_Z{=L^r$<f_ z28xwA`6A4ck*QGEcx@kTW0U!Ibwp_BFyOLJAG0wD6S|2)hXN7ZuutO_hE}}efG5Q; zW|>c0SWnJvmtGrCkLA;w-o1O5227Rq2V5&z`WCXUU3aRoib_5S?}~5T{s#}nSy);I zRs=7{TXGst4$sQ}qt%iCJkl(fRYF$m4 z8c>oL5y-xS}!IT-bO&bqD;y}bpwh_X5`I=U6`h*jl0 zcA1FlfxBCCvwnvCXc0(YPZXrbk5pXvq3q(V++0mNJ3H2(^tfQeN}@?Y!!_^t`aE5e zaNxi&sEwrLzkqj)$!;Q~4x5_l$@Rj+I%P>B z3sMG(DnMqhyp-eHyY7k4K4U-zF97?OIJkE1Yz{1twMy5nU6WZ!&6@uWyV#gsHqjhga>TeBoYQcOsu5BEE7cC zU{zpvhCJI6W|ds*IcR%aT->~8C)?%c=R2I3+JYb!y?<=3bucNxy!nn64Y;Wnr}x~Y z&8b5N5as0X;jT9V|A5ica=6p-cK{c02M(iYmDD6;KTr zx}Eo{nM?N@+sQ%aKw6DN*`rv=J($UG^P_jf1MXC}qXMf4c$&KWp3xQPd6^zT3ulPZB!K z;p4}TXmSmD_wEhWIL!6Grnb8_tKP%CdL${*Jzqqc$_X>9hN6jxYLB7lJHD@VaA;1; zMXMPdJ9dPm@DB}@dEqkKK}rG+*j@E!`r+m^pUXZthVR_T>uvqWCy4B)Q^s zp?cE(@Y|>Ji+v+W)IClw8p@tJbk4W$-i6=jW;<$>%Ccq4BtMe!d2`q4X-6J71J3K% z_}g0P79_}=J$v>8%y!7Dr;v8UlSZM3mg#a_dE2EWj|^mwIk|7ybjQU#`}Y?|gcMQ~ z`mQ8JlxU}`f2}wlwny)x!R4}fbV(>B4_N~N-malfqg;a>jk8*uhwkQ`pJ67o2!7xg z5+ibXFG#Rfq}*@zpPwSR@Nda7OaezVphqw-LAjOH9N9w+JVg0ysnET9ccH`(L<2rq zBex2T1UYj65<+l0VnpGZAGdt{>L#T2IK{n1NwLDXqtj=eI`9Xs%Vk0}cjI3fU0i@9 zTRi3Ne)h>mhK3WE$gnW}B5Q!t$lpKM%0^@mv79qa+gx?bvJTyBY7@)4_Uji37gCDi z_4DUX8ZdJ7f8GoEX>)02ixwuw;;T6FatO%F9PKWa%V$)5Va9?O}DM)rqX;^EbetBJWKSFP6q5q}N_dBt)b>?Heu$Vvs9UQZ4 zd#aoeLj{8hwBwBCi$`-Qfy)17j)hjn2G353Rt$q+wIde}cBeh$hQf;RIPW zZNs^m$96Gl*>I=FPONb=o_%1>X8k{tZU1{_s9x#XvPVx0lai9WE56rj+O+B8Cr^BT z7cJY$x#qFUPI>=9mYh!dZHDQEscUO*BI0m-6A}}P=H)Knwb6h$s$xjTj`ffX6DAz7 zRePHp+P=8H@?zm1m^CuwrmnNM_Z3^UpUKT$k2*M^1LSG* zrA6r*eCV%H~(v-x*lhyj3fe1nUNim?6NSR;i}C z2q39SParH^TKV3nC>tUVVER4i2A_Z|tL9i$y|KCaa{!99*XGTeO`jMX8`m8}*UfJPS~M$pL^3o3zcPPU7#E&F-RWzd z)f#!>p`p6oDdZUMs-L4O%>Lx@kK8Yr7j62Ko>F69p7QyAJZ_kp)8-vN{V+H-amw=L zXKf2}4Nm!5{nh9G_KN)qhJU7n@7lGivd-?Q7Z^_6XFn~2y!V8J1m$LpOP;OopmLVt z^e*v7xJkZyArDH*WZAB_T}4OKixGw+B|8dfsxtLGk3f5k`j@@j!(bblI`_`-A!1U z@q@TS?;6j%wwEd%30@g#D?EQn_%ENIi}so&{Bq1o_1zvGt~q-2Xg)>U;lojdKOEz% zRA7AlFsVY46cbEjj|Smgw)oO!8vW%*_fO>$9yB}StGLE&dujEhjtd&RPRSWk;{B&8 zg71qo83c5|Vq%(6*tts=F?T48-qm9te{ezde^Px-CSLT6*`BL?x0Q~L+s6!Y%hu=5 zr+|N?^*(TIfq=Q=j*xwH67%P_IT&^}Hn#mR%~y>bw#Y=moB?q3L2LqGDP?n}*EHv! zkdaBYcYcnxv+HbT7F|)Xns$yH5*=Zl`!kg0DSvpNJ3p`1Ts9YtSl$m}gNllZOucc6 z%x>oNZG+MeZ?f}`n?7rN@(#`U(xG6L`Lw6j3Gh&+5j!R+@AdgwqwJK?$!$n8w=d?| zRYQC)r^4$|ube>`j?J6NRfEKkS=n=rexqJM%VC2?(}ZnbVB`0It~CFdEJhlTXG_;e zenCM1tl90`w?%*AMU7|bSANCL!q$qJeH!X5=wM-0Ft($y@#js?0$`9 zz{K2IH*)XZyD%CrsDn1vE9Ca}T=={v#d7Jifkp=t5?JpYB>hdZlst@LVo*Y0 zS(Mv&4y>k6A2MbW4dP$fg@l`QFc7(rgI9T}L$Vi@r?hik2aa`hJo(zhqfvt0SFp(= z{M2k?2~fUD0`1A?>hN0ysLJPghqTBiFGC+PHt;$lJC%6|#Tkohe%EM!9l0jId@jf7ok!=dFvCXt+)dBb;iYX0ESqei*V z>_ypoATe>{v11ED-tTdToTb@6O##-C7c}_}GoJ2vC=5hd@pa{wnnO-q* z;-j|DI-8it%1#+tK$O1Q^FO~YhrU^J78ukdFaGFJL%_+04<8O3JGSNI$sSXuPBm+J zNa7ISk1`6`!}qn>o)8`im&VH4S{Avwbey*M(_hRD;pbURoVbItT$l9c5T%ZS$Fqhs zF44twA8(#p%Y2-%LOM~qQ-6TcMl8{M`sk76*sEg`t(QfRW-mzD&Fy8P| z?81C-c~p*$yZD6PiKtYkvL2d7sJrDsH){^?|B;|12#KYYe*$niguVb$)zZ{V1BUkM z+xHe93in!(;o{^2Qlb$rh0g&cG_Uxb>saHlU_qUR{n8Rhlq5j4Z1I64K7IcD{fXW? zE?pW3Op|47gobw?K4gNu#49FaLonGdr+p-4&9xj2kzLf7B|AC|l2C#}qn@ zDmyDC@yEHva8mfd8mOvniTTgQ4gz>d#O8& zKN&NA0RQZ8JG-WR`}WN*CfQ&yXTw1QUL=CU7lY5Sncr+Ljkz6@TYLn+ASYrjHW8Ue zMXm$?-y{z4OY6JsndeCWz!ELX$Z6SywUBmttfG}G&9&JyCCn#o37X+{Q4NCvI(+`} zG5Y90`Ps_$ATG)Pu4%gB5c5T%$+ zIgV3kM<9wwlN@^Md)lUPPnBJ%)AMalWB*()25XOQ5Y8PJnY-u z&@dSOktWY}WK4nTe3^1iD8?Y&7+lc)VN)Z6i{dMXirP+kQjOWjF zpn%8s>{1$IFmT{*fhk|#R9hahg+!$K@|ln=_y3>&lx~6>I5FdDYh(>BX*ze;^!PX* z>`nZRBHHTZTA-3NU%t#MfXgC-175vqgAG9JMr1>>+N~QmhEM63OD#+~+ekJ63yqjG z>6doO;7*`Vi{-CL7QR{i9x1u{hOeD6kaC^xB$|=9W|6z>89iR=aA@PkhPXP#t_~K= z=lTO=VYK47mCQ(rJ9sc+>{{K@6j}W8^8BJ~1bfB^H^vZSdaGw`nYHYkP&uv+kk)%jys)MXs%Y-`$!$jBwHF!83tQQySI1|%%-+Gaq^@+P?#2F z@wecpEV^iCo(6>Ac4)S5e;;j=$q6=oo@JhIiBd9b1U65#S8H+Da*!|XE*RsEOAmZ5 zd--Uk+0OHV+N_t`r8C5%;&qDf(ra25brA7 zB9XOA@+#qJiFL9}9EmCfaqg{mJr9f1z#LZ>m*Fs_;~X7FLJAG=t?RyXl%3riWO`}c z-~@3!EC4UQAFl=1%2pY1cw85~TfgxMA%qHJTll0!bTY#xrgqNt zx-${#4+AcLY#Fv1h_WCGag()Av%6FI0;Cko+BmLZnu#=d7#lZPv0??;Z4*J>HYUPG zA38FSPt=pCyFj7U72iAZPiWTk;_=%0%ar+Xmsc(#3X03GXP-VhySYxVD)3C;Lp?q* zt=zFcvuytTkA7aaC4SFQpnLV{Qve8!?&82t7Q3y`YMij@FAZE3;1m9C;NtUxNo17^ zJQ4MDm(HD)Ah%>_Dt|)E)j-44J|hf`WGSAis^yq5szye~6yH20eu6Ny+B(--`RQE8 z|1e-#Q4@X0QI%&0+scBkzx_-{9J58x+LB9%g zlfJZlI5h7)k%n&=k4gwk8Ry_&9NKTRz9xf;$MXM$r4tDs0-vYU)zXYpKu6_z6hZYQ z10&F*L@DAwF3{1qf|wmrmEYJoEM_c#gT%^Dmf;M2(1nyjT zBrrYQ3byq&%D4b=+6_xDe^2J(VsBf56>96YZ89YXUsCq(8C+dAo!Ksu46Ih@f;F-Qb9eCjc!$w%6fPrRO2h_>Dn}=FJb!xH^#3 zaR`P>r*&)e<-1f!QZ{3mmi~NRo{WpcI$1lhynl**moD^xHf6)HEkE(Wqes>G%i{Q@ zGW>#%N^tJQe2&4>`xDy*W|Fd8Dbb*cMK1*1Fl>PNK0&~%CSg0FFRADJk;uS#TiOnJ z^?x=E)a-A5Vc2JwJtVDuub%wofXYA}KCSlhiC){n!oqa->kRe2@HV6~H{R-Ci*6Gd zbnbj`@K}o$4G^vBU(d~*khp0L00{P71QgortQ;M+g~b7lKs`e~%81XV1!fMZiJ=k54++fijJa zwpv?pt(BWLJxH@KcEJhBZlj`ymQy34k?rmnr})g4{tfZ$MT4x%li$3Nz_s%@a)`l=fP69uQYrT- z#Lp6r4%0I3!8`f&>3AkEPze$yo?Ed-^T?g-Y?)jo!A5*Zu+FCvw-jjbxud^hPGgtFZ@;4+0< zGQ4h{;cv^CGh<-n)!VkcOInfMLA=j5kiCH>wI7Q%*K7*4(dS5tfmuKc4S^J0w{W^} zV&}fdsr)^oWs4Rv`-vfU$#=`0DZt6GHUL1TzqX_po6W=iXRAuk?%l0v+`U0%%ST^A zYJ=-y&XZ=_q!uk&kQM(9JSV2@A_cHS(e7b>ftmqYFSC#pdgA{83C0+?|CXYWd7Jpk zsEK3w%QaKqhEL0F=fgefI(Q6qY_mp<8j*l0(bLCzP8|M#nH0o>bk4e1)`7;X8gUA8 zt!-abRRvrGTQQE|?aWT$lP65DAmk=q%SgR7z690X_@;lt@v7N;UXg>v3kIlQvD1Bi z3#X|U)=1NHzxei|$6~M&xeZf_bg6KL%|Gv1S6Ld!m5_EYO-;?v9Xn=o=eM>so1kN4 z%!XX`RmvW!qyPN*6X-}()7%`ualmNlf8JJI6>us`{&0Rbq=u5m3rage z#N0Sr|5#n`zXj5N|0^tShoYnKDX9%aKCAAm0AAiPgwb{CE2Z4SV&}0d@+zen>*})lM<}uW<^*l0SkH71= z9@6VaVYW6t|6uaHuJSRkW5}D91S()u{amYU;vc~Ehwo5u#--J5?;e zQXG2p6)w|Pf0Ud14z5u6JMI_#WA&jyGqtky6VN7EhQA@`709g+4ZBRo^ zH|Tswy-(yJZ2G;`eZl-ADva?5w9fNt4{?@c9x5`Mg1B&aOR5X^n8)G%ltjc2D?<;I z&_!(pJ=jz_A^=V(5opsi`(JsR9BI#U+`MgDXUbwk(7*TLFu8)0Eh6N#%XVG6c9o?9 zV0tO%kYUJ@lUAD>HHz9x0kKqpwUXT~ZTiy?8_zoog>>Ze7F*WNp#9y})>e`w)el!} z6NItMTJwH&{_52sz{pu(0cryYcuaYbp-tk~msTiLP{hLcgoK@6hJ2#RkT6kL7)a6t zuD1FQCBgvUFlF){{L!=K&b`g;5LeB$FJpY9-e*J)AKm~z7ZPnG1hLD!c@@X@bSdp1 z93Kzv@5%P4w&V>l2{QvHM}OQ5s1yMLIQe8I2Vu_@g9(fwiKYt#py{GBoB%2QTEQOHPpP@gt9?HemQY30VW| zS3AdlY1m#!> z8QDjymI~qW<+Ud#_i>l`LRNlJHeHxX);KiYuT;F!c&n-GIbw%+g{}URvpCRevUhDw zG$&9|25ur--GU$mhfBjG9S`8%Xc zVZ29YtB#U?aH!=%Pf||o^|YS}j4nnMJv;wNFla(}OTbH&qy5i4ixQaH&B$mXHbj}r$U9K_aTzLv z>@QQ691f2g-bGv832Y=nAZ(o|7P3?Wa#mUzK~J&?GJ0qyYF*{q5Z#oVGH||Cz!ntT zsNK7BN-Y5nRL}|LfFQMu0)hh~dnCE`sr#yhh)Ftq`Z~Tj9!gE2V1~9^RjZeaWh{U z9G`S7QrETS6;`_AQ_lszeLD+vCWCGn+`r${QtKeP-s4#058!OMKX(3?ypg;Qq1!lG za{(bj^>gsgFx!_hVzflEDK(&LO>|TR8`~Jf4NEZ&;jt1w&^{$2~9YO zl>Bt~?jc^Itc`(OCY_7^fG9!hw(6yB(PC}qY!qVSzVz@Z`z8-@QRj1(fl^ z=WrHeK@KE_a2MqGs<8{NE|lS4j~_d*1N6IP|5LzmM(2(4_8u(8JDl*3=1+sDqF9n5 zT~E&+_$coBu&{?FF`*`1Mam}&$gUHytAh2#mx(p1ID6O5dNhllBjJP~JDc%LIAA_+%J>B3 z3x>1Q3**o3y3y?}y)V|*)_6c~QQUbI4nW@sVgnukPX-hSmf~Qm)_*D0H}y1sJQv<( z@F1Tj&>8~*m%m_=sa*Vd+3?qExy#TA!X5J+iq`(o zOJ0hx87@pI!K}V0oN%17!GhAKcSQfbeGh^{kp@tIT4Lj{qf#wBX9r>D4iz9LiC-Z* zJK>0ScH0jX83m$K@-@fu~i^?;i>PUe|v;nX5oRig@PemLVB6J)@UxVBLDJ(OxvKmwHGXNU@s&>m3 z>poqb(tfZGv;m<-mRE2S3xLqHA2Q}^ zUlY~!;5D&4QFo=`Y|jpww%N-6A8iisI%lePYbs+TC7)wS4K`a@0NEMq(hN#4>ug0& zR0t%iKXFh0&%V=w07c)I&@TqiEv_6M+KC1~s{W?|E|miUOpn=O{RO=`uE;nY5| z+<+%Bf>1u*(J`m=42?K4tOf~lJOmMEe%H5dz8a!ksfDEnRTbg-S^u!*F7}g`_7XuN(&5 z1saoBqYaWo=f*~fAfz4Oda;mLWwB7YtODz0b3c+P2pHWdct)}>vqLg}PenhU?=47zvfG`jHW&{VFHYitc-Rr%Hzt?tXj)P}4;dB_ zW<9?WD>IaKokRYpOKYbW`s7_(XLvWKHLrNA8Fx&R$UQ{ymIT25PT5T33ZJLrfB{i3odmda zP|1KbrTQpZ;;4DlPg+Omw-H+{7ij0jtIQ9Z^$&{^PRLeScY;E=lrAmioL(7!mhmT3TakLLMvja>al(#^<1=LjRSq;A6$uFD@Y`8~u=wBu2#}$4TbL5MBKj6!GFNva^?;Mb2rD^Bd7V(AtB9 zbn#_M-ti?s$d_%w4a(QcY|pV}w;FA59Qg|p0#ZTjNdX@LXU z=5gvpg1Y;(uRI8ZQXN@pdf?>A`(loZs9xz`ww9p-nGW_(TP#g}dFv4MRj75(M#>ty zOB{|(76-PNAjHCp{TYyBI@a5_&E}uoiHGlqV3^4s5te(^7MR;teVhc~L!RX^k+nN-c8y}$P!DDHvbW}+n{>5fP#p@jW{!O zxY(0XQ5b)#eX8z{5f1vb10&M$YX>_1)!fs~O`m>h>7Ed|k&{U;f@yk9VY%*602#cek}C8JPlX03_WH}5Q2&-48)E5_oXWz8?-j9C1;Ks4IDT%aZ#4nUPaT(E3+=K zU3{dESPR??wdsz^FNb#=hnbcekC{rVw#TOib9s+M*$gZUyrHFp5v2A9bmmMxuwdRi zx0lq$U$PDRnHaf6>$ZjP!q$VIzbRrRl}B>-J=1+l^J;>to7B84v(xXLTv&k4PW`_QdwM*664$HRX8rJKF%3mqE@>_uG^Ia@vS=_#s2TD|Z= z#5DP1CyF`)uu+@?x)w5A$bIBW)#lZ7JDS$F=*+qJ^nZN-wR@^}89L(j$R zi+Fw&F-7bPEMODEDroN7wBn1p44O{!o+RcLX)Jqo<(BhQ0KU;HTxHXXt*ZeD~_Pm(%-(bJ`V$KN)3@ zOH(d8S}LBkF~n4%w-Z?cy_B>jcjLw)(?8MnPEM`pqW~8Oj~961-M1en2Am|Vv~tev zFyt@&iLX+vpB87=`0S7T^}KFI)`)@0AuayhF!YZdxaKdP-80X@@h2uGnlD)PLNCl_ z+=L15Aj(Da<0o~D7%d%3sNyoog*e!g)*%WfTWq6&H=YL)1v zMlSb@A|=`u0x*~h5GSd=m8t&NSJX;<1xhd#;l;o~94A4uw2hjwuRezs6Uh`b3oE{b zGNFA;nkvWh#J@F81w98u-Qk9188?iez0(5SOxF971vS$(`3gdZhzduo?CR!bMa&Xz zH&R3WMc9n;)G$a9>I~31{+>G^04~H_3UTVP+U@)2Z4C;VMV%)N_cmJsNn0}_6}Qyr z@+WvFsQfiCUdL-RN;En+{qP*V3m=;7MxeIfw2L8%a-{K{~J@VB{+MmR?r`Nf}G5m#?J!JXBZ(pb-3AS zgo_2HN=+;F2ArrEvaybJgD}vAV8*mv^KxIPAEZ95E#iNt#(fbvjei4c_ppf*wHh_r zr0Ddk10fU6De(D$pN^wP2Y+8y)%XVw^br+;x5r2r7q~Su&2=@(o2}9y_SdkTn3ZwdzI4x=6>|nE1;t8cyUkPh*^ePX%ayn zPe;_>Q2GRZ1oTF+_F@$Nt3#6OKm0{Gq)7DjaPd(Qok z+1NZ**pCO?&xP-s78Vpx&)tA;Wlf2U;^!j%`2AZ3Fat}bEom>)0-_NRxUpo$8Qx@J<|&?zJ6{Y37tfkK+mfnR)F*Kt&=DdYL9RnG1Cxfg)h#VU zCDOxT0^^BvteCJZN4kh86x^2tIXF7XoKcBEkeiLSOtkXKK}fAv+9xus1s-b``l3aw z1hxpZ0MQpY$B7(IWo>~8lzQjxTmQ~_da8)#xZXtL;o97Wm^Hn)3$!}%#MHhtf z%nOcO_Ecjod(M0b?s`65qVQTGUg7e1!Ac(Pd1lg+x)d0*uz!uS0Q9cQcKRQOCKg$g zqDZA#v$@osYv!NgN2w241x;5oKQXg|ji?C^UowHRLu3r$9p(nxR-duY60s9u_4Yek zk^b}fnQx4;m_%tvtAHUUDH)3lfrY~DPfX(_Ms!;Y%5GAzbh%sADw#oqxFeA2dGZ`p zg3~Qh2@G@ppp0(Y3Q^z`&`$0m0t<)2PUy0a?jDGag{%HnbbHo{sDKU`c?Bm*wo zg6C`9u_ZAvW3=HcBCYAj=z@(jTB|PB#U^NU`b*6bp42QCmmBEp_?$XXiXnD$Hwv6@ z<$8}nM*{2VVa`R1f55RAtorpBQZv4}dh!i%7Oy$_ZIu`#P|T(VMNK1;2~eS8Haq!= zQj(eQ`?z?GPr8sqI8X%_PLf07pt328WZ=h-Vrwg(mb`6uSbAY@pp}e`jYVStDTq7; zpTMtn#4a4(alYNmnKC0>_sif>zBBHrfoesS#K_4N)1+P_(q|D^g_YuX2y04&Im1za zALDWf-(Z??Vsn9c$&c{7;DoDJt~|f6v=ewq_yqh-Ni0evy-vBXq$84X^II-!gb3zE zOG!XkkTcK9dUQRRD5kHtrD@MUeHa?V)UlakhpWgSIH*VAeeY3g2 z64Xv|rZc2B^e8b^wi>;lTMM9{$WMwJVEf+}uE2bZpK7}NYm~NF4ILuCky>`cU?h`X z{1=aI2>Esdw<*^-?fO|OJ&gb*iuhTy&|-g6r7YB4^q>^s%-8nrq`xk=={tYtBJpO1 zVcVdxQu3SN6GmSaWwaP+;1XoD7D0m!@$N!0a2UnbK$RJb{v#$0DCc@IJq4F|==)Kb z0Rau<#xp%nKmdh36^xK_#iO42#veOIl$gd#q|WNR;gS8~ipjc>JfseG%`5ARwvG@h z1BdbC7G9piaLO)0&IGd<1C8sv_03ziCeT)7790(2+7Qoa0wB#Ipi_d%Gprqk6Wh`I zhsQyBo{?;Yn5K6nY)Ng^KkI*{rYyb(lK@Fg2V)bSzASg<$THrgv2p2qP7VqlL4EWGsx!e5fAfL z9K73<3oOfMsC!umV#ix1e@nj(=cnh&&r=W#MK`9X;L_M zmriwZlJQKGQciSTFgZzSWlfDSm`?mH)E2Z|DIsDa^cr6{k{Jv$hcr8kzX@wgo<`C$ zhF-ouX-}^9AsL^S5kn`;3wl&@nUgOsn+q+|(Ruth`o&|%r@&ub5A%!m&`1A_aKmvg zO@?H(q6K@nbtTmMxCW#GL<9+?Ym($C_fup|R2(ur>SQLr82dm+svTI>DN9;ucUz63 zp)`EWUu{=hTB~!N=zozdl+Z1)*gE5mxenF9D@ZRdw?dCAii*X(eNI}y^>!!a3t}73 zn*l)$g}&yMzlgNko`%_B`L#te3sSAgOfRmj8^o_If}j%57hpot7}Q+!`wyP~7l@`t zC~l8p4#vrz&v&&W!}D$84(ti9M~x+mJm6KM*b)nh)ZTlZV@?Se2c3!&WdrTt`OAE> zMn9pPQdvA0WSx25xk_PHm79Z)jO!8_BQ8#lp{%j!zR?Yg8#j(tyFoxrLZlHREP+O@ zIlM_5cM2cvQK-$s&CB_R4Gr>yP3%~^rt@Va zsNmIK6FjJ;MrE`-f525Q|Oo`)?JtMMtdH??Xh_PEq2OX)vfVwF%aQJyvTn+`2Xn+3vsL^^jkS!=<{7cm(i?WUAE$u*3C)8?KjkYR9Q9lX0Eoq&!XT?0y-lOn4Wq3+$eA(p?eLP@Q7}+4QExVsH7$`y@Y(B)acghaqR5vsOg1# z-6}53E1M&LNP7rBMO;N$8)T|9Ids@`U{A$iL4AjD?}=^sR)Fjo{;+ESDn6K$Jgn{@7+EX!uWQU4m2W`& zHywtG5U?QDrdC};;|6Dd(r37(CjT0p6?Gi_nJ zERvHoY>nU90So?5mgN70`Nv*wmrmJ*kLbs$Y?Ql-T}^AFqoS64eQk!{bsHW-4)t}s zUl>>jc)`1Ozg(p>In}5{PYV$9t??O|cuq;P2G{+yMb#V?|1t|w2hQ73A)mTBCF|3j z5Xa4--%B<+QDjS3E|>r-b~vCEzSn3#hZanyfY}8K?sJJqf%_4{8Q=l97?9V=g0XM& zfFMjRr)KH^59Iu;FU?3YE{>^ekc$T?-YTC@2+P|?KZS0@=n{NT)IXw*y@(vL^0N-1 z7azz(*5wRd)F1o-yIIde>(Rv)TQL-Neb!?O3=bD$BK9`%`2q{M>6Sc0MC;7Y0nDzt z59uLqlGdk-^HpW&ifm;VJb3T)%PV^^h1~hsuV0@WfzKFE-_iL;@84g{5%7GM1wiV^CjDqWolH=}cxGc`quZ<3_|*p! z6J;XI6>uILZ7liI+&c8o;lr}{nVPn1x37bbJ(VBz)$IB6vncVq(m0Nl)@h)x*`PsZnBbki3Tv)y zNGvaj^RC4}VvZbb3wLAMHP@(Ag3%D|owi*L+8kz_$^IwO6P(}}Xat$!#>Q7X$ii5W z9c|%s%ij1+!0aUqKO-%n{ciA$pEE;&BH4e+8R94%Hm&Jfhm_lnFIr6ac-Yg@wsN0b zXsF_JTDvC==CGNeS%0rveFx4)xlUUI;yH_|+sF%*x`R&&kb4N)rkg6T9H(#+HdAIo z;&wQ*^u4V22sdfAn2}`v-5Kdk2xIQ0n&AA?IyQgjPW|+{r1T?P4zC%@*ZFKPH;jBo< z>&RJHiqjCzOn)cQI+naH1KFrE;Gk%*p;k0N=7+q><(myY_bHDP+K5j^>`up+>ecR3 z0;(|oOl}DrMp{LDsxKshw{d0;Vt){QH7? z@dCKD{AS1uEe7-p$4;z;DIP-1$m%(M&(59c;2ATfKv1c*N{@t{W`2g4Nni^)(c8q8 zHAO#{I|@F4h2*(a#Kt?4n>OaDG8K~4eq>y%|M%dnThlW#HgN$-<$=la=A)u!&7U9a zU?}V(;{+b!(j_X-shZ*C&%NZf{M|u*;lcx~DyWfBCMyp4j^&rRUU6$LV_pamSwGA3 zI)8MgQJf4j_kun2#&-hJ{-Nu#BtFw;3yrQjaNus_C}K!>-q0#{*e`H1j*9vimIJqJ z*>X2yDQS{6F}w?%$1IKF;PZzY&Eif;hnKLd@E?pe&uKEp2mrx%J8y4<2U0UQI9OUF z$*1qxp9m8LX?;j$%%l1P!LSC%f=M_zAw6*_3j>Ud-?(6gSc=6X4_-mNbVC>s41%3o zthOL>>9rJeOm3lZ?H#BH5Gqz*C@N|HEXdS&SZKIQ9A%s{=`+QY1KnSr3r`c162vcd zJ)ad?b*;thDLVSk5-q|*#>3>VA~HEr$pu^3Cre{HSKdo$v{Z4{BgMF7^yFS zUE!6BDhwPf^6aeXGM8R%k!X47UD+Ll8wxPCkvL3Mnuf^N_)@pLgUFH+(1@|X&*_{CA|L+C z@?GbR`UA$L^9hTm+AWQe`^uFa)vKWj@o|Z<04@$rMFd15 z!*ai<&9#@)T9Kf~!S9jJsYnH@~Wv-g5S zncucBW)4Y-;rIEQLT15tiBl57zHp4Q%cTtm&Yxd-^47LgRn96SR;5+9S%;yrQ-{%l za6Hn~NwdlI7cZQmp675MRpQo8X(DOowPGG>(|BSjDFirdS@cmlhDQ1EE) zGnvPBPB@DPgcc z{NR*$?x0X<=v5jmTFCCh-VrDkH{t7~%%HcAdav%|t%l;Ry1G`IuY^3l-N8^Px25;j z=SdrIN7VkvDazA}7>&A~bvpDdCqTw3A9w1_j)acEy~wGJcS6I{TE(R3Z)$H&oI7`p z0;hUZcdh@uw3QcW`@M^|jQUxn$2ij7-S5{VGBZdkJw4&To>EK1Iqqdm;rJfdLUr0^ zg%DL1<_%i?T~|Ro0mlvA3^x{|PS92XDV3`*hjLRcGSnMOkIYu%D%YOAMwfSoA79+! z;JAA#pW+4(>;lWL6(1i@M}$zH!qsCTM>nd;UpZ&R_8p@Sis1qjnnc~W@Ka>x#bc<# z;Eu%u)v||kP+*|-d5g<=>#lzvnK5&qZ`pOv$u)!CeT5pgNPk7t4Jc1tp;HwJb5-A? zSf25m^X55UE2{g=n85KVQYqo*-f_5}k-;EFQsAy5ueUe!Af(E62e9`X-1p{J&aeA6 zpq;tTmA88RAWEBzH-7Qs%Woj_NQ492My|NWTHSwPo?inI@>}gxZ*$ELi95c2kNO%_ zUna51*|=by^>LPWVpFWP(LDs9cP#Ai?Nkr562LO#LpnKR9^lk2Xx_u&weCygIL?Cs%4NIY~S1^%SUO;bP@gRWkrtd%99s6MwSguJ*A{)uI99T!|?|F z;LGN6*=_d@2*ZGLnj4E!MHkF%ZWzB;2r8VOTnrQ8$3QSLMwRc1Hd=O531#a<*7s`6 z71V#-;n`ZvAcUh)xqU3gf*{*Q08tspjDnk(Ss~q=U^dygEd({DD*k=(Y$9X93#dh3 z({}0>ROTGHbmQK~omF2ktkk@Y*<3X~qCB<}1SEa`NvzP&ac79idX9 zvuIj&t9k_&F#zK-A4|G3s7VE*U7Na+kmNFVuFQZYda@j&@2i!WtquOe_!F+~EnLeo zi-p;iPLX4_d#;20f~yx(hErs9M2}A;8DGm+)604O+dBHefGJ{2hosS~nNF+(0hiK> zLba61Uj|exangQV`|9C!90qLVk+xH`FJ{+?3?C^3}QbmstjS~N(SD3I8fa(JK(`o3?{d@u$O-HohILS0` zv!i3jkJlJ7pCooKHT*b?TY4DsK)K&3M$M{=aj;=vegkN<8HY#G-32Gaj?CDWh3tQz$SOWjAzO!mwhD%C#U`1{YFZrpfj zY5aE)EYg?kG`fiZBexME0hSAyTnF+6Peo0>-g=M_I6LfHBf_asS=sl`Ihj$8P^jRc*sCH{{US)% zbc@fNj+n@>7Q;F2>ok5ERvz*%P%@StCggjHi9fSl>%j2LNssBAWCTdtui3}Hz29Gh-A;L{l#Bf3ypB5W9T+GOKn$B#3oDfqi5 zgptf!lkmd@XQG^N;^Z;^Yu9{E-ma|`&=Y=e$BLDhUGZLSn0f7i)Bj#8{-Pi!|B!OJ z?xGd9(dc}i7S*1CFk=spEZP8o-d#Da%f97)&r=<13IhN`r26&>AiCi|$i~jPk<-LJ z&XHV012A@1@ybnmgYLJJ9SuzThIWO@n{)D0?&HTYgj=v3{3Oj9&t_g4Jvrg+>hEv; zmejIK@YAM37KvPkH)GE3-INRb(!{rCWMUdnEnoE#;nm< z&&XtPdXc0|-^8cN0wX%q)SEZAB032c<{RG|CyO3A)Zg*bh^rdTuU>;guaW(DpH_ZJ znKS=g8fgsl5kU|LEW#vaK=QsDG%h`JEfHYJijvcK9`L#tB(l+$#fliYV&sSsUedM? z`dE+pM95D2#JPYdzA@w@N6m`nV^{-en51m~kb6)SESJvU5Be|pRT*@o2n&J$D3Cy! z@`{Q!A~<8Ib)%srwQ$9kRl<$Z`OT0ZJI3K%07SyG4`(D7S+(fZN+x|Vs!x$Nz(&Jp znkQR9Kn6S`?{I%LXK-Q>`hU;?4SysHq&VYLR4sUpj9?i{ggb&}y+8iq!3o{Kq0*~Q zu_X<)1M-TBbXXI?+W7!(>WyA0#2%I(WEHfm>yGh`q%T4!zu$~ap~ACFG`|cqr8{ve z9F``&UVk$zCx;s5=q zc=D3f;M>;J)ee!qA)i0H0ef@38quOd@*|^ky8>Tn`o)vBEhbQ^2e`_e zH{jN;Gq2Um)cUi#9aJBV%z7Rn=SdSd6b>9d%n)Eja7@}ao)IrflfE*z44fLFFCLC9 zDDJdn+F2csuK%BQD?L*&XzqCH22{08GK<xMnElMu-qJqJ>mPBLdfjTVgBa=Yxu52+Y2#6-srbu0AL*myAh(4moVE{xeqV{1WC^$+9*-I^a1CQclu_ zLg25xX){D4%zPipx?oG$g~CM;E(nSO=X49^3HSEM)t8yp06EE^Fe~p!K*9rJ)1&>p zyveUy85y#y9Rzfk)oy^c=m!0vw3+IlymSL)sdK8&K4=cQMu#(mg+DEmf8n0Q006;% zL#8>fgg{!&aqNqen#UrI0t`g%KA=b8q`^P?%R^8Y-lV9YpusRm@6tIp_@ z?d&wONGtfn7svO`Mct4+4NM;3Tgr`uo+9%}yBNWADL)9`R5$v2u?@z8zs!&;#( zNDqW|={HqVGk`9)jOL^Z{e>%BrYJ$Vn0to^d#|gzp3B;?${yjEh(Ud`=hILxE(v4a z<`R$7^e$Z4`nNHK3H#h2gx)(VKOctV5Xqj{#ne;dM`xaWThEknZlc(oX?qc0>5X%k z@}osLVT3|{Rp|8Q2N@^K>&)kr$u~e~`Qv4fL_>+RLQI*W!_AFu8PC2Tsks>8Wlw5A zVrN&v^EfP+>i2`$=r9y z!*R_w0|Nf%Kg|ckb$&QAohORlwSI0}6}RF15t}x;W0{`*@V9t&FzJW~h_fiImhMe! zt#Kqa=cn>TjhomPYnNzMKo#>4jK{8^{_DkH3k6QdzT)$nYkb77@a4-k5(GG?#-)KaJ=e2m7oup;3y=o91xL^DTQm~&gM%z&cb~p} zalXoCdYaxWEG?Hg?9%Dgt+W%#AP1^$Jif4;oU!VUYD4%Tfw1q42$wa)vL_g}Cc4Yd zx_kUxE=f}bm^B$Z_9F5NB{`B+`t93|7iVj3wpyaG7BA?~FWZ7Lzineo25oUNRSd;a z8QUw_7XwuzF!S9Q^BBl0I`P^{L4;1Oz^ugM-<#|WVLBfbu+$7_kH&AJU;+NIjEF%| zaw&#-Ko5|SGo=b1&44grxB*dtK|u!THYb^KK=CRwx9CF{K#Jne?**B05ZJD$65Xk3 z-57KZq<57Bm2?d1V*U*++P~Y8!=6KN1})JLx^pMnQ0ecU+pSr(2BMT6!zk&nl8M2h z*kh1AZrA~n}nf&p%1YNN~q^8xD%r3X5%qfI6JpxB^XX6 zXo*d+u^qY1(sBnlkTFVxJK8b2Af|v_V;2EPi2Rez)43GR$N_rXE6Yz4-_FYSg?3*hTJ*%FIx(h zVoYO>o+#zhX7U~4-69rh-GtmAaQXW9$O0msgowgX4TCou!O&o4Ali}o`M)F-m!pI-ULayf1#M2J|iC|nzj1j>s-vW7VNNJvk3kce`>K%-n2}VX; z9N7gLIb5e7rvlYBB6ATOUVQYk^NHx_R3M6GEn3ukShi7*!66PUt~p(_2m zPpRn1Jb;+%z{*Jy6L|ysxhNpOeRr7Tv5-(i%YV?Tt_$EpLAbkJmILgFgyL1n+g{ApP=*oQ~#Q-XU-iZsCLGq?*&U(4)wPlQlYf z_v~qNbO~L*IA!u21gC?>b93oGid0L`Qo4aOqC>-gKzSoem)-|xk?TR&=79{idEVF0 z05os+`9=Th`u9vx_1JM@;`qMV8V(K%v#N;OJR7AO;3dwZeYgY-3_*eFuW6n}vneZ} zWEObrmXT04V!u&kSxFTuZgaF@H}Br{rhO{Be!e2`G6^^{hw!{$_|}6mj|bWO8{*pQyS@;qUC&Qq zNR3`u0z*tp=^skApa3Vh-e{ry=u#gu_cHqG+iBQ#wZY|_Z;8E(17R!0A>d`{WNIL> zq#!~&Jip{b6?Mt*YBWAB2rq;L1Q{212tm4O+#dAqJ+1whf}z*v6{L)#0YO|@K&oez z%aVTGrkI)aKVl_0f0eX8&t4eTUO0+XIkB$;1K@IkDX* z05C=*gdhO2Bv872@AtO|e7rWJb?fvo!h?pNG7?P#?fW*^7brNxMT^vVH^N=)>o!K5 zR39V7mfhi^xFG2RGy+xn)BdASN~duFcGID_5X&Pb>C_75d`}f)J@s=QY&Mm-;RdHb zsLq=hl9T0$U?Txw%XAf9i>C}zBT+c#k_5i4PLMbub74=~mEOC)W^0TXUpD|}Sr!2I;czaZ#>m4sDJn|2 ztv2=c!=TT|6kQ0klqMisX+a}%sz6;Bx@?!fkUxVTleIUu6xLAdFp&z;bQm;x2o<$!oP2&Qe<;HC)7QV>-}p23jh64;*krR3*5G5dToditUHh z?w7HBmChutP*PrPz~@7J6A&F4>T<#3}o_N z?2nG0QkvlLr!K~0YCZ-%mq;(a`1X$DCr;c#cR=Ue12T)mBeqi1&8Ha9MUcyOs1sxl z9=U*JBHZ{_zQz%z77#DXK`Uqc+uEWSJpf`4b|*O-DEb0ob58tTL=RH%fI5iVG@XjT zRM6;B&*RD{ohlL-fmN9qQU0;_S3^^eqvDk1)7M?mx&N_hB=uxPuU^igVAEe}v157J zBSZ9o9HS+3*Z2nq-zUXt@vIw1KL{0VF4hsELS)uSRBoJXVjcrzN8em$b(XJg^|vu- zf49*rGgc)*pJ*h*K9oB4YHP`-ISf&W;*u3YVAY6))TgNbNML2_j}~9%`1GCmfpJpL zU2P`eQOot+1X@O2a+0T6mArp7$UO7BVZ>ZTr}K+N_)Q;Bbv$?ixGcN73@Jn6g4gU=xX1&5Gc@&q6Jy}SR)$MQ2E>2O+-nf zrw7VLJI-pqKNOf{167XezY$i8t00z0K}KxD=uHY}ZU~aZvJUg3A1BA;qMnes!i5PF zmM!~Br3cH;bz9WU{%huLh!Ogp8$d&44ln9iAu{V}OwF1_QW_`-*i9{Bpksh+g*hX9 z!rll$Jz{tQ0d>f_{N}k#E&(Io4c6&f&H6;Rr5Pksdb>GVM*ZL6tY(p`>}cjmkCQ@79I zfpi$S$k21dKU$iaM6@RAD>II%hbvJI@*Vp%T056a%|4j4jcYqZY&i%*2X0;9dT}nX zL8a{H-lROMN;4jTThvS3~w5|&s16!yjy5^)miz4 zJ+kmL1bC2_9Xx@795$%2{rFGP6w>>?qnN0FI;!-m=3iG^<|Q0?JQCRZ%TPhd!*qsWiDr_Prn+hC2u*d9&FuqPDP~9P=wxi#!`sJtXhL51D#r}JDT9uz zFjg#1KA)tXu*C6Uz>K2M;?bp~?5d(8tAD;bXfSBKGJ-ix7<2SE+lnIU{%jp+ZbDRW zX#bs{6ibDWdp)yt;#gdOK*PlSj%~c%c=bN@;dPi1?xiMdTH9ZxX(L5XL-kFcTeq@} zQ88Dt-JEokWUT0v9{?-cMpZTP=Wxn<)Z!fmu6}w$e?5{PEn2PQROsncB~~ z>e8wL|7lYF@>U<|-|^KVw+tfwEQsGl-TQ}W+tT1=vvf5_o4%bUT0uTzL4&0BRkf9Q z4c-%b&X22j#hgt{@~=I7r{h^mJFrgY{0|KhV9UCn@BWA%D}fVmr5Bj%xOw|0j6)KJ z^ZOu@y6d3#Eu6MJC*ampm!`yTu*UCJP z@_U_E7reO0w2bsqTshgG5Z={VqUrYF0(k{qo! z{IEwqEmKw^4GgkzH#+mg83nQHpfdpK>png89hT=dzOGs{Z0s2mF)L7e)z{m92*b`q$|52Hl&b6jq)2tJbVYn6k5hm3 z>rkk(INa5V60)G}=i8m#krMu1mqa}xdjhEjovF%RyB2pPrV6t!pspYrq&3)!qR3r; zS`Vd^)__aodrfC3Oje7LDgg`b=H%o=Y@y0SP1{B^ZJ>MS zM&G9-66aFtp*>&hIOL*Yq%fesovCJECc~KsBabZ_xobbHb>2ZH9seZjiHa7brf>TJ zA=^+PM<7 z@|i&%q`0NZ-~&Hq#PnqGx!jKo{}!O{5z{wn6w+tm@+Hfcb8owXXd`o{eAcMSYhQgm z+qQ}48wVVzr@Fa~CSOZ?NMOe-YL)&utUzT>sH*3^sDM&e;l$V3qgrt|ES{{f7oPt=LTiZp5%9O>ThLiRM84N@|D-2$yf-s7pdL~RBaa+?Ory7u(C8);AXSRorx)3wgv zp#J=PHShiFq=fut-nLMtY*20!c4;WntJyvne{Ne4t0}pOa8vA0U^*`&) zJPe*J8$x7;AZja*n&Ha(DP|5mkvI^W6}~0HHgsYm&f3)G{R=E*imoFjvo zyh60lE82g-f>=A^W!bm&Bg2fw{=IAn6#}bndrb0;W(aw~Kk0$}qD^a})*Ty3VFnhc zAj=&njQ2&&=yXDdV+*6~LQY{Kjo+B)?=*5ZBM)Cc#-vbWI40NqT8>YBL0(zUvr+NH zuJKoLHIk-aNUSJhKlqhV646dh`HILS_-bql%c6n7_T0LuwK6YY&zgWkHcpl`L=l)L<&dZuBX9n-PQ?HpC6qqS zsN^UOCLp#S4s6|x(b}JozdR-kHtaHtXZCArFLV<4$GZM8QV7!^6_bt z%?4sZvh*LJ3BbAVxx`Y2+PfR5p{VK3s#ITC=fS>zydQ1+F~m`PV4sVqnCR49Vvl2 z6Gc_0{W~Zu#s75M*oZb)ZEoCnE8&iSf5P&#rgh@AnO zTWPnP|LN^p-<5~qJ|0tu(1zZo>iwC&R~JFnxh$7I_1vr(ToB1P$#&S>pE6^?;W0ug z1Hk}rXR`L1qn)Pf)G42<$*LMJ+^7Glj>`SW+T95;xopI8STolA)z8)ZPspgE#-x|L zlbxOYe!W@!`4CmIoPT{dI(g!Jq25hOR)KJk9;jBn3%veAOZA@i!lh;TNz5q(WGz4J zezsS?h_CCO`Oew`vSxyI|6j^iEJ+2Z29{$0@RZvsm^7rgG802iJpcZhdH5`17~_nO zL?lHoBPa-yT!v?!uvBlr&eRson-A`^x%}?08Olnbs?f<8I2%o`Yvh$E3lu0&NHXz? zS_9SotzuMIywdYdLBH*7n&@e@Xc%$ME#twSF;45}Gq~vT*}joeL&xl6_$(w1@y0s@0sGN#NS$gt?@eq-y_ITN45!UiUibgJo?0w5OEvnrPqjR(S zb-Hos>eY!SvySDI3>s7aQhkaAZcsToZ-+-bc}~2v3`nM|b#B`A^p`EOvX)(DlCde6 zJoQ4bzEc}$*9?C1X*na=9MEEpWl`p^ya?)Q`~iYwsHmWz_oxe2R#p$*jVUqEUH&c7Q2oEIK&9#A z=ta5)?Sr?s@qEfSIM++SaO~dxF0i9|e$ewbCfNH(M^gh&-DVGGYReskX}Z5{2?Y~> zL!^vxf6P7oWZBrCZyJgQ3lLj~8Orm_@9qY5KkZ}`899@f8-=@-?LP6jcjW~r&31nc zTCgm3=f1Sce9^Z~d7k%L)Vk77quMejVJT4@gtE24BIN4_WXi zEG$v5_~W;Q3l_{}fQJ`a$iWoV4AZjqrW*09Xg^dsb^7aRmhqxa^cVc-hD{op*{^nR zh#bD|Cl69)t-;fDSK2aF?MKI&RzJrpFim$_~@AD20Sju)4 zT69}j3^z(}mgIq1gx5SgVQBG2lWA<>&}s19zCW2mdE`kKBn1`oF82WFPJ<)jx$*YJ z>J+gXqq6@Wn_Hzf;EKv#G>`ha_zgwX^k!8RIn%1j(si9EinRmx@Lgx18kfHc!@$DF zcXTx}-bg0@%Ec|e{fB|3`$V0ypruEF=eP}3zdpuzUfS@fG^M`rF_QeAyP4E&>@I6& z>Jll8^7*o;oDyzQ4=5Vbx6~;%chM(izL#rst zI!x!@u_Mttj0`(KQ-0W?%qI3llR-~Ek9)3unAu%`-HGRB`-7fwK6oQT5r>w0^-ng5mL_pW10^BpH6X8~8!$o8fB|wgMp?zLW=rJqT+U z?(9@FS3)vD*309H@8mrmbC|`Q0VqTMjui9Y4d@_~a@KB%y~n@O!Ld&^N^z=!s?4Up=5f*`H zi}?T~ji5aD>iw|)vP?_(aMYa1@dM2U*fJ}YZrS4KoS8t6%ttn4fTM15j@jbAP|AmO ztF+&n)($GMJ1`QwVBXR90PM$wFlE0FRhwq3)yhtby|LjDJEp8P&#G7{6luFQcsn|q z?rvWjD9|Re4Bg-l=R*eeMe2R^+-o+;XdV3<9g+L#N^^D1rF7=vwI$Yi^H(RCfJHD@ ze1lGt6=D!2yE%B2f6?^ulr*w3{MV1qd*_{##^0^Jwz8w~?ZbN*-vhOEtNorUm3ZtM z40JZ&6WN(*GZAe;r&y7H8Ep}lQQDV*jN2=|eIMNZ{rAmQjXtrn0^Z(J-xK*j2SPi~ zY+_b2IwqkZh(5dyz@@M=iDQVgKu!ecRE9X7v zI^ax&-;7^O%(rm&Z)D50d9*FG=nsAnY?+$ zh(A<&ho{8E&P4DO-r=LQbwHXw8K0$Bz5~a`i)y|$csKf8ab>5=NLi`Lc!$ z&SP2ffHV;%LR=mr0|{vfloJlrA_IuiSqtKCvcvDIUf;g=-uS;5I#ceQm)Cu|s7;lx zHo_R{6SZhIv|n!kM*{6>1tU)mO{G$D37GKvuO}Mfb=f03vKXT*p6^WUSy64bX$LAZtu2aXgwP6bYCz~m7B0&$8hOwiEp{0ArhC&>(33yfhqNMM+iowf8pB%mFB_nv=JmOR-33B zXzmSQO+hKj5XMO2aCju z$MS4yVxcNukdS=lo=+p&o_Fj!!CQ&YtxN+Sf;q;b@uWXj!0rq^SdY{m!H{s%PtQbr z%CP%M<6*6-k(T3aL&>NNV7CQ4N6#{4OkAFdFMd38gmcP;bTKd5nX zN2!!J4tT0!>QappPcg&WD$lG&Jr z!o@r2Of4s0J_=D7k$B_o7YE)SEkKPRs8z0OGc4p1#lJgwaI9bZONrcJoqzZIm|DL) zzn!-;7)>>fNcTBTuJ85_W9(&{QP=am_y5k3uHGuUl8oiJo4Q=n>w5(Vteel!kV82) z{|3t1x*+DCK%c(1cvi8Q{xHh8v2EJ4Nx{j#qG(N(`9U(}>ZI>#Tc3q$QkvPP20|Sz zIbyB+s>!Zdp)t)sy(gS`a$@$G!HFw)v3sA+9>aZ4O`Si@7Z%>HTy5rnJ02T zYRx_yY;-^QCEflhOrjUL=k|EeKWo8(NtU}z`xIK;qT8exX((2LaRn!jtpTV}UukT5 z_`P0xfQJUhkiQ5V0oR26^br3pe$IO?UmhYNe(t@{!dw*LGCK`j5qz_7;^|TLt6)I< zV!lyKl@|uH8Qrpv!n2jPJD|9D7Udd7`~UFCW#8HFM~xh5yLxr_nM7X9--cW3rNsQK zo5Ed*jkC6}cs%Lhl!vDYlL{%dKaLnZeOy6P7fRRGP+UqW1P-mPYTHz~IkAw>_KoKK zDP|)&p-4q3<{OAhfkU2`H&o9eMPV#IR-}~O+!Yy+) z8^pjF9Jm!y@_jR8n=G)wr=kI)?;*%PI#%?j8=z|z%PLAaHijjC|E@d=ZDm>YiVcfP zB9@H%u+8wW|DR-7Iy%(oMGGgCH%+pB#2IUOXEC+?gq&siov5rP&}pLd^fR%4HfA|R z`wie?0`9YwnKL7j?Lm_;o_dfq)_TdHyIH|Nh>)BKL(WJ2SlO>Vq}<8s{)GVV(vn|M}2ko*RNmW z`4CJaCbMq-*}p*#pE*rEpZ2>0c%-oDqMOQRj6V)?5AY{3Px`vl3ciGja ze}YkJsCrAwN&S*DJp-ooX&VoUNQu!v_HM@&8d^^D71w z>gxAaXSB>-6l;HO#njNSFwaZZ(?S~_%zk6V#7t(aLCNMF-rgaX!g`+_VlsDj2cJfL zimoTOVQ9RpU?G&8YQm%frFrKLLB)TCe(8AjAKlq!J76Fdu<&x(HWF{!t&kB9-kvFI zTM`dXvoy*nB6h*km#4~!TYIwR2tWh%V}Aw8 zXrWoDXJ!Al!q}>Uw($7Q>ubDr6l~Jko!TeD#D?%OJ5l%atMmtd8WYvfA&M0>;5qZf z8VOmQnqQ0p6{N(VCx95|Doq|+^wAXTojQn5(xIuMO>ag7IFt>)K3fw0ATMU_Ma8Y8 z)WYQ*#=6MV26srozJIGp2hAf%Y4`0WJo~ZZ$QCJ|-mip9=EH`qYMcG$bo}gl zW(}K1O%P!A5Iyx5W2rh`I!8nL$q0RhtEW55C1irV|dFO?g4 z@8tUrMvp1Oy@Yn*Ck^Ure(%6T@F0ZWwY7GT6Pt>|Hp(Cmg1#s~r}z&a525lRoQKLt zCqNR3#T1&=Q(xm8|6cU0$yG(2F*&*c94b}`=q~ISIYSEk&*V983VS8ZNO>9))X0{c zxTmu4{d?1;OHVzpQ=kO_gM-1?IpL(ND&&s-y=?uV=3_aWGN;egC?XSzRmk@I^c^&r zicXco&Qw0??olK0Rcvd4`aJd5KH&zkOH{^StNd(S@|ito-i#eds=IHiuhh}$-Mdke zH569TiH&b)0p;>aLIB_fZyT%?11>`54qiXKARQpwg~SWILzGIN(HgUdTl60ka1!Sk z73j2o!iE7Jfm}YB4C<(My~AGH9s~DN#~j|0wQ#Wpa&i>TCOvL^{`9GZT3Ye|(XnxD zR8Lh_dIf73y&ep{$PUvz9D3r(TP}P2^Kc4bD$0znNf*8SnEjFoC*aF02D52lMfrjM z>09nWteuN{YZyI5Frz)xr?ceUwQjaqr{7ROS(~@&KlX?Q-}SN!I@$eETDuf4hWZ7y zvdvy*?xK9Bccn(;#WZBK&3)eaH1N75vvER5kn$Ka>2tf((sjrz_1Uln`kh*i410KX z@#_fHG}qSo(;_V&RA)XgUuq{++L+Ca7F`+G$FC_fg0~{BUw~WMsy z(6)hv&{6`kd$D+l(GKf*KJOEt+ORQW8lWxBQ~aw3idW!?!BJ~oC6-q)ClE; zV^j(4(hZJsgASuMtT^uC*0yPxtjd-OL~2iBdI|gDK=B%i?|SUESv2!vM*~)k-*Nxk z)RkafqQ{BO`9|DH^Dm*4L88(?G;OS+6N7PbHI+2OuQzycE?a86yb8*Go_HS#J8$^h zRxPr0gypC{yMF|#Ff2<)__$;p0AMyW37+h#J9mNF9;zM!<+Qovow`!j)AiQS1(S2A zki~UP0p?F5uenp!Om0x~u=mw`dguEYgvj6mw=OKGTd*M1?8{{BK=Tc%$y{G5<-Ub; zI+bq@eCf{U=_Aw=n0;(WzTOOEl{)=mTwMB9yW|yY+Fe6P{TaM`ZMdNiL3<6n98}eo zD_@qOEZW>(X#*v^ICD@$B!4=4zuleD&B~6_l&oPB44`Iao1R&StU(POf1=)fuKtRK zOpL*S0eGIBMf=CF?^-UFaQp563oS}#Q?J~+dDEy)=+oO9>EtrLkN6$NEqq!lUYxU? z?goo(bDG0Jsz#=5-Qzo6)6{Hb>u0E=tMe_=b3fp?$(euv^=5YhtgE4Oe^ae-U2H4{ z!hZ0;mA2|8|GahTy?L7k!6O&PWGyXNb2@5=amKzxHI1!RO3L)W*pHvcT1qepu==63 zd(^s-VP^l-@e4+et_gIWoz~oQ?ys+YR#OHRZs%wcc{jl{0XYfUhqfgc)J141-q9@} zGGx69+2b`pRw99h=&1@LcU%wf>AM}!@JQ+L#Su%aw+Cks`6sA7W#O^5S^@)eY=(7dC7=)b}Eg3`SPVWri#5sj1IwCbmUelVwwrn zD=RctLQ*Zmr4wJGtQV}5(Gd1;CHi%d`!2}rZgh}4zCR&Yv&RC)vKplJI0 z$&7Cb4HOj2#v9EndfxKwtUj1Ik~d}~2U&g3qal&D8QT2>sH@@g6J7{v6vTcZ@zI5j zvMiUgu9zK~L#xwfO6v{X zi8{v;Ac5k+F8i9y!Ir#E8bx`5^SB!9BKnOqCJSOSiqRyzhlHYpA9rrH%crLoC*L09 zsCwej++P`GmjK$ftwUTM_~IwOzT>AE&Nni(8%1V+407+zK#dz37pddz?8ec3KTFsc zlmBsLi^$?5M-qvY&=uols)bZRd}U@a6ejN7@2&4a`87vJZ9E_-6N|qWIXD2kHW4Tu z5$}kkMvh|S2E?e9cUz_O>BWXyp}n#f^*95z2Uf4J2+djJ7b05#5x*Y}ZZholMuE-N z9tGRTTB_f%=RwvurA_^2{$Do`v`fX`JD|PNJd$YRV|{ZH}Q z2(h@FoG$v!nBm(eIiC5+AOXSh)J*M)%}KVoZ6deD@z={DG6^DsRF|6faEiSRl1H1U zo*Ahg=NzFw`N-7hhS_PmDYp4pkj-Urdl`{ z2;WWIS)!0F{t|+NbD?BYx#&jmi=n7Rb2N0-%li;l;=hwp_v_4wcT}4%-d9@Irm%sM z#L#!I!9={z_-gqeY&){GYR@@OjN2$R4BdFh zk0uNa4yZR6GGx}O_ELTwof6qh!pln_r@%eqBD zNr~1d)s3H?>gCiOS(j-&9x%xAG@Pic<^&bA$yyuPY^IjJtz+t}Uke<*w+cRbHP3zi z^y`REwaqp9-x$eq%q~4&;p#~@A%=Yd*qcXMCN7%VM#nLgFW4?J{cQJ*jP-0wK@fIo zsfT%L|GHlvbxlgBX`(e3Qk29^IeF=kmP$K)kuAd8VdmCY;t6~!<4z$Atg_l?0{#bD z(P4;hA?I!)R|kf{Jd)z9i70ZhHOeQ9TC}s?1OOxGG!e{#I1SA$My)j3uPB{9JgrZ!ksc6Kxvyp1EOS+OsAP5d~t2XQ`u--fG6U z$>`i^hOunU?6r3lQlXzcd~7hee@*lb~&2v&?Wj^ExTb! zRK7yN;;-&6rGEtLCPs`$s>&r9T=g6h&RT7kR@bcq?whtXV${O1-dz4~yS>Y-hf?g)&RxWjOppzueSSfvZFO!L>1-q@Q$a!k zjE~MC&iOh#or+q;=27D2sX%D_2GkBT&&M^PY^=}kwEm%;j{5;AcjxRg4uF1%-~X9E z;<>EQ6N$1IXi*D_!{daUEoBchbQWH32bqH_XTOAl%^G>_`_EHC{3~c0L_jQrGyMKB zO0Sylxi&H=e2P5&5pCsMU&G1ICvNCjL-)a`t!O}@?Kjl_ zUIt9cXKeW)#={aEre6d}62B@;BwLiOMu>`t2urBdKlJtm8NuC&P(X<3>@FAZ-HK~> zd?8LqHB17IR{we#!93js`4r%g|pG|xg!Rkcq- zwsva`QOY|w2=Pbb(%3)3dw+0n2WS}On)ccZo!k|f4Ce(JWq(lUI8Q?hA6eQ7$1J9I zWg9uRvY1Dz2cp6oas$swBo4sd5bD(FCkSmCu1J<|F5KCGq zEW8V^jap?WSt;{}n`alB6hf-r`VTWl{AwK0KTRCSnOIA|XgIl~sz_g#QzL#n5^@9_ zY(9-l#)()-thLmix`kGSBeM_P1aWlO{T%Z50|W@N>c+nPxlbH`<7Tb<6LeOpD`r65 z8{2POvKs}Wc%_W^OR+{{bI`5O=!x^XoOrT~6yy0RahWo0er((&UN3YOwU#LATpzBX z{Gvdt&ry zte{OBWPhmO9v`!Oop1w!45b%xNUHL38GlcDGZ3FE&uI!43+yza17@O(l!ZOnj##!%6t zCgrY(WHZ6ZtfiENm`0n`xdYZJkj$)F9V`MY-+36fCi|$i9^T#+t!#CB?!FapeUnz} zT~3$hp>o~xPWFz>_u@R>i(0Krv*(99_{LUrImwUFB`g>f@&@Ab?euxxsjVY z>`{66{NAbkOK1qTZQC~Pz-w=w4ViGLtDe7gOi;LR^k6HrIy8`4&#faZlLpm(2l-*` z4>ghl1Fhn}EYC;&(i$|VEb`zxw|I|E?j02?IPTSRnGE*9(VIPn*PK1OJ+i8aynJwz6lEl-eV;WJ?!E9X^yR;Tr+v`8C*^? ztbRC4qSfZK%bp{48{VKLF1!R_tM~?vLj$Azy0Lxw+&z{dt}YW@WtY_{rfn#40Y5xR z0-}D~wz(HtEh6f+&*JbTzYV&H4=t;U_2foOQ+y* zcJ11E1{XLta2>o`YsA}N?YjFdNYY*qhiM#z`)6`!q+t$;^5TZM1KX24MY#}Y z9;rEbf>AVd9V6AOX-bDVDF1woi)YR2E3ojV01lbj*R0_}lB?^7v(uk8vh@qb&3;Jw z75x~bH*aZD*s6UQ*SsvG!2Yv~qn6OQiVFnwBj9`G|>IfeI!_O|geBJQtNIM)+ z;aXbWVA{4|cK~01E5nNooZdPTv%az;f~fDIA>9Ja`&5=Nm3NoIf9s`YrcQ&_ zKzuld$pR=6>I9eyK-h~`zwYQZ@1v*}xp+$-qv?(-XEMHp+PcTck)1Bj+n`{xLT}!_-H>_>m<)C~sOw!s=9{pY0$_Mc zkt>`b5dGaAX>o|jcQaNEz?eC}k^yazEW!+$mCr`lzl^;s=5CleC+jWHZrW$<$3)_ZTJlUS55R*rJEhm=qIw`t(WfF>B?q0bd0Lu zbA;~v&!Cs-K!4ayt%v>vKSEw?){f4Nft;P-T}QSbp;lw~@L_I1qUob4*u)HqK>2+! zzYa<65>I`-TJFwZHwJ*r0h{w5rvi7x-e2%w36S}oAIn#XxuxijC^&!_Z>Og21Y9Y< z+unvwV9}cel??x6W5hd%Ex^{c{Hw zH60u>2iS4=y}y_D8?h@%TTze7y>`#Fmpz{(6t&WIMcJa>KO=g??ZwGDL~m3cjqX25 zGn-*r`Xy?-x?218K^iM7&TMbDtW9>`h5OIdOg1<7`*=HX!*%~Inab%~{68F{_)huT zt%irdHnf`R1UiQ26M;#{9)?sK=o-!aorQL*prgyRv);f6qopL{8Z8>ZUadz^CR{qcRT{xLN6IMwr+{ytr zEi-XXhKCe;q0G#d*cip@_VZTMp%vq!39&Esa#>5+v(ty|!T?QWB$%SP3tBiGIEgAt z_E7*H#9g0+kfe-8KqfEw=#akl)aerz4&QeX_em;X8GNFyQ_B8Ti+D^a+gKxI6=U|j z(oK%)BJ488dVmZHU<)$sc}Z_9ABC!)1-e&HY%!zBPGp2uh77pS7$k`Tp0Ov%?m!EG zRm3<}himRUuRrlsh$^2G&Syr_=Jnh%4Ug4)QO>33rOJqxG%Q&kzb{C3dls?Q3*q+R z%GI>-Apnv|V_q(J|$^{=rBzlwnGMJvR` z5Iw8VzoNC`<`~lzHQ1*-A}3*0i1<&HCYHB??LsF%WP;Kpt3os9D-X|I(~r*8jk`FZ zdGozpbdTLHsXFY}zQk?+7@JP}8>0;~aSgnOX#2>Lzu$fYOx{{juH>z%Rgq7*UQo(0 zWi)UxV-O;;0q?uG>eWc7uR9d_1si>WJ`dLw?WmE_$u)Opw6T|hhb9h%SpQK|9d_t9 z^*Q2cX|~cv2kl&T{dy!lnZ;u zj}LC{v~|L~F+a4i6i|`{rmH`XXYcIGBF8>Mr3e-K|Vl2J7WEKkadTo#mwG z6CK-nS=z*q`CUKQ&bz+ODZaE2)qOg0B-;GCEtivRZM!G?TB_$5b;QNCu|Qj62xGo_05VdTrZ|9i4tC{gIv2pXQv`5j$LmAD$TA%z7lM!)I4Q zz}3>q-R%25WKULuy0$sy^*gq2@1QyX$zkV+^nC;eYwJ^A8N%vVGgK(+(!AB{5<+#3 ziop`~b^+jsw2-JLLPDd_Q)KEiSIt;on_=W08lxX2#e2ddb2OdI+4%ObkI>6bC_iGi zZyk@m@BWFBTc|V&eqAdvo_Fe(ZT&x}-kEOD<2!9D#s7}bJAkm;*x30udD_TW{YToO zjQrUa*B+#+SbBMRT55Forc=<(#pZVCmuW926&5_XygBfTk@a5h*q&!T{?uOhuEuyl z+{@_}=fnR#f5dLli-6q;L00XXHuBz|k#(D1?q=Ry{+=b2k4%1?WNH(+7?AUA2n&xarV{7o{~H znzQ51e(sv`6H5nE46oSRKhWn{xqt4%1aBp?#3hRt_jq~Rx^+O)XwZL~wEW?HZpFx; z=lYLPOK(Op(4F{rknS~V34&k8(5VNzj%uu~doPAEn)W510h*4DrgD>ep$()RcIY=D zP)*J3c#9nWBX>>ibzoG7DC_{W&J?`fMAwmsl6RCwj2s5cy4G4=i0A3zBW>~4xXn?) zqIYIv&!EYRCS1n>Dg^uT)9I;dcSSXl$ z*VkM?Wl^P&F{u|js;W84JVD5?)!itu1d>EtnavnXRi(J+gU}*~5$!y$mRp`$p!DxJ zYeA!LS9B!8{rhtN*Z;Fn5b+|$i|A2S;YJEyN9}YTa+2ByNj}HM0WA)c7QZtuR{M`h( z6^})Vfj(3Zd?v&;OlElNj1-Y4G{27Mtj3NVDylN0(L$!&U%xtq;{sB1b#ct zxR3l5@hG947cW|AQGu$Qw@js&cdIH40_7Bsu_4Q6@6az9*TgX}S~?_tXXh0kS+E3} zHk?rkJbsRINl=Tu-cq>`)v%NPw4&?oMK}H~&uq3jxvDQuB|kdc{RG4D?Dp;o1Ogf^ z?(+~gmIXbLKhF{uJwQjXWn`*1njeSU3*U?zJPes#U~KQ<`lBwgVN#~@zS`{i2Y4-Q z)qLU=fLz|9BlLCUc%sn|Xg04L%I10w;Y5IS@%uu1$N`9>l%gmS*&yd~=+kMmLQIxn z#M=bjA9J#I!R48aa1h52?OEEX#ZNBUO=Qzm(_>FZw8%CNaYrCtLn>>yhzF23f@rFB1Wd&W3?bk8Ko;to{xg(Vua`(oat3r?Iuh@BUU~4dT0Y8BhoI5ltnYUyIb5oDX z?=BCt`gQg|pRgrB^CznNy^22jrN8DMy%v30SJXG5x0Ki$7Y{V4YG+`JSXCv`TD?i z%5O~qj!rZ;-xFHQE}6+O{(gg6bqv=!ePGkK>a*5mkt1J#0cQ2iJ^TLXKA*i#7n+)N)f`iaBBSH2x2E5N0|Nu| zEk>AEFV+1Ko?Y25r#|XZ(#*|y0f$b0df01AM~gO5Mov{0oyv?ht3?c*zAP6HvX&oy zg$7KDENoeKChyX`b5OXq-3v?%G)9%3u+dQ28oMNIZuc*DotxJOuEf}NMfD%%c@rbI zAViDL__%Il)zj>amfZ$@Tpcyd?xAzUrLhAeZ8VZzByXyz$X#GIMr)x4ZPdxiUR%0b zmeg8251*V@XI|x#Jgxpn_Pb8YA4UEcyT!)PDM34OTHX?dT|53#jd!gNe7-=MuZ@wl z`!kLfw@-}=_~DW#7|2AmSnTlksEjE)3#yWkfGpp_8RMT@*Mqo5 zHo8wzvt8^SR`z$YiP|?fQF%)OFVxN8AZcX=#@aeL055R4C+_<`VDAb90yPkkCNxVn zg5fW(t=#n^l6o$^EUO>7(t6>jK9wfFl8}7K#EhU~GEUA51W(Sy+EzxeZ@QB$4xnkK zqkRWL(}UVgG?+NsQ z>k5ri2tx~P+qd5d#6xRy5~RH1^NV5Q{uE;v5j?RF79d^`gwzpNLZ-Mq`3}LQy;X5p z$zV!saMNcpT8*2bf5`@_7lfNkx|WaJ`#Yf@%T75PGS#RtZJ^bKMMj6505AVzmi9Dx zWIfbd?~!_0Sa`5sF+`=qg_ctf`Z?Qw&}**f-9N?RXIah2!rTRgr;bg3`sMic`y17hXJICR2$*p;aQ($Mpk%CR5$(~?`Hn~?)E!X zcB!g^E{h7-%F5yTAa-4encz2QLgVsSiw@;2dgfK__8a~2`(~`G*REM}-#PN6pL0Y- zu+r{Zhio+6T;02tE&sw7)?86(VO%iE
    go526=0i?(94>995D^Id+K56XRCy&l7 zJy_P>cUrlnMOoEf0XzWB+)*0siV|;s^sUd0>=GG$AuqQg@jx+(foTXt**ojN|EmhV zoiTmhfkxije0)?OgdErCV$5HXb+U;JQb6#yeZAR4S8r#GuFaP3fmx5wl*m>SU;+J~ z19X*aZQQ;oQ8MKNM_~eO{Vo1?S>ZE7xiZ#gPK(R)!anZ}wx6|X<;t*LFUo?97H16J z=yp9^&4|kr~;rzDoiytgs{6HhIo2%;Q>2*EIoUD~C$J%&12Xs{`*?h!&@xxJx zW2-#6rcK|hT)kz=qmP>gWUsco_R7o4&$+i|7hy$Yd=QRH_8&kr$tW6T$Ps}4j6l6} zSn}<%Z0;e>IEF+KQbxQ^LF$OO0&D4QrI?P3G02VzqL1uW7Soztx23A`S>4n#_7)fUg&ts^bOj`q%iPl&9~I<^o_O*g@M&nQModKZ-0E_be#;i zOh}ibOR7oFC+M}1y*)K-3Z}VOjADE?paT(fl_J6t9B*{G|L@`HaFIh>;x^)tdg(4$T6h@4dBFv+#YpUo zg^muo2!3(_l^Z*0`me48Pm~RuqB!PBh;(ddzl`}E-R<@acg_bs=i<}R;|3hHMS z&bmGze?cSfjy-;~E$AcrvXGDZ)f0)80c>R}tH5nr!5?W;T3q>v&I}Gfx(0+yRGXrz zAZR)tCITwjtF)QQ(gX;_ps)u2ccb$^W`qC7 zUoU8Btlsy3{KNnK*8Y2yF`8<+qZ)eE!?=ky3sstVi>?&9ASben#lk=M&!X}*R&e+C@t_4~tF zZ`^KpRzc_vHiBAj7~057F_Q#_GBvGYqx}6(|NlSB`l|CxkZ_Pan+-!VyoLvPjU&4L z9qihq$Iv9JNts-^(pag*`oKCv`>-nrS+uOq_n|FIEJP@LJQ#<5|@^f}Lklp!;4 zx*U`ORn?T^aadyUcc##RiCQ6c^lLOH3X)~1zHuks!A%$>7BU-4Tisv=vx>j;li77T zm+9+h>bo`Otyh;ybwCEyV>d=sHOOLmy$vg7WCJis5LhF9(VyTTCaF(8e&3Vrq~FP; zB;dq46W{Ebqpzt8NU$YXxy)z|IB+j7S`+1Bf9Kuqn) zbc1N=sfw2TtmRX3GTU-}iynnb{8lZT<`nPd3yv&(VPaAeD`vLVBxSdsiS!1LEi=${?4%F@b^u&qtk{>({Q?Cm8%J9U+gAqDGc!cKonmD>=ZV`V6*U z3Q)cS=oQY+zj}aScbOrRmJc>V0l&EGiBqWcUpmU8dNU8cNcV+Z9Z^hRDCA1nS60ri zs)j@i`A>y|i&+Chq&LEevIuSwh#DiMH{XwqOR#BU>H4UZON=Q?SM@)xZ9eYLnG6rD zc=4T?Wc#XWnl5qA6G;&(U>c~Ci6O`Vzr9crKxQ3>FG>7%uQTe74zCU}GX%h-(4ylY zG1z~MKp&0YQHOAyL4&g2SK?S{%fA-uehBu~Xm9jiai7HrjTIk@vBLxpo>R9HUoyb` zmRk|v%gk2QRR+Hsh^ddPxwvSnB%6o8Zh`Tj%(}Yc72wX%Jl=PpMRK%h zjTKD)bc{U-?jjp{>Chj1H+GZ}OA$5Gu6E-GOu$g0fdH!@!BTV5{A!Inh>iM+oE9Ah zZiU5@MG63c4>@}s2RrV&Ssgvz`qD)6G~evXc9D-xXMfuT7&MqY9@4Q=W^bbHFUTXM zb{M`!9f5Mk*Gu-Th4Os;XXzNeWg1f|IvxhJsp8|n)-Q3w7o{475%_jZZS7iT=MxmJ z2<~xZJV+p*ev-XTOj7V1#l8viuXkA42yw1M?4x_>K8_p1O$}x_SaUtpDrH@xy<3Wm5}N?DQPU&ujiWB_zllm z1UP(>iw@~(+g1jQ)wT2(Ju2f_a#F_H#*y|<4z_I30_(^o^i1+Y@TE0pu)5>uO>3fF zV6+l|H&q;vzEsB`*<$PrJnj2&g2n6AgNDUB894h-)#8QoeAnP=q5j$G$M^3_Z}_V+ zT7u;G@d=aWjB9%#J4T?Xu!!>8@cF1WaC5GuNtN=LI}BUZ1+LEyZGqv#)M3th{i=PD z&Oz|E3+jbE*OlrM71tv?@*eK_RQ5!S`kB;3^X0_ZKIBA4GGAa0WI!Q$b`uUc1zrg< z&#P3rH+|%QW0$&FUN`7d0+lnD`p9$D!7?udGHKr(7EcUKPoLrS;;n>@S*i=1JPNpC#p(CYhR>6VX)nPw@ZT+!A@z=A>>o zrHb}r%9Oy$ep3cbs&@M}*J|-%-)~t#Wr1~9+dt|Ll><=l_RmONdt>!O3AI{g%gskv zrlq$*LFcncFASn7ytpFw&E4YH^!@`z(cG7nMve7glF`{}$PPc}^G}vdKl5Zldem>5 zv)!;#kO}Ql3JNRgmg{%oJJJC+t{%8?ghxksa;M0=b7#+H^i-K-v$1nRYRPnMb-y1_ z^pgqDQI8IauKf3p9I`jMj1?MrEJ;rEQcGZ3|-~30f9#1wBW~~l=Kl|m#%n3Y> zxY5(JJUZ>a_hxQM^;+h9-VC!V99^3nkr%lVp<<%0UGd3^c*;S)%E;W0E&Il0+vvIk z4U0@vR@B=aloj)*RiX}a6V6(Je}LaqZJBy9i{0W2k2SI_c`<6nmxRC1C(nJ5w;bVp~;9I9|1}d)GVU^RQ77 zTT0fAJXvYAdBbI3j*Af+A7cyK!qwteV&d7l(|%i$Gk$2EaX-rgGcqv$kYNA4i|M13 zne|JCCRfHje|1Y+<`plM z{^mP^8eFfmhrzDWDf>p;I%R3*7a6wA(Komtx&7F0a~a_7*?Xk5O4a%`n0x+$3N=1| zfAG66{I*8Q%4Pcf9D^O7J!Yoe`_la{s|T5N zcz47grsg%NLD~7kg&B)aWP2&*2@Cby(~d*J3FnBI5?_8N<5&F}5Hr%i_D5~4=I}&O zN6r3c{s3fJn{UK?fiK>6=&G`|Tv)wJHvfD0rgjh26%G9-A@n2JmXL$W;F{JmksyZ| zSX^Zk>;40unZ>f?8mEfb*w|Z3r~Ey+!E1huhDdYFn0vN;=ET^Hfpa=*)+`0L$_#R) zf6}cYFpq|>OH18<2L2vJDWY%`@khi7RR0c5XI8gFt;UFCyv&cr7w%Vc#L6rbaLRO|TLnzfhGFEi? zOiAHDff|pYbF8XfbIxSU9U0K$_ z!F%CHw^v}W%n%892PK%S-4M18RdEA-dV0nZAAPU7bC--VowByQS@Ly%{DjLuQ5 zDBrO8bmsAAYwOyIuU{Sf%r_m#=HoQedr$Ewvq;Y_7cXAicuZl`uSDpqbQhTwy{}vQ zR&KF6Eh4A#rgzwJ|-l_YJP{9h#gqzU-u-te5yO^IZ!!M8ftAv3GBgw6#Gm!oc zB>rBpA}W-^j~dJSVDRtokhj(3e+7@YOEJaKEyi>kpWCz9q;BeMbfXjWn!b+Npql^n zBfQA4(#I*g6@qCfzf5G5_Kj*mS*MiVzgr<9xy zRz=1(o$)*(F ziG!?$vt1V0+o;JH^fLn6VpsB6d zvIyPK`*3@+?*IUzUqb2A`}`;tY~rY!zT)&5=q11eqo}`CIaD4wk-O1xLpZL-X^YZw z)ToJX!j4f^VNKEsD<4x+1+<>Bh>Ug7pfV4*1u!xOE6@>XUPPBM5_&nEMEbASz*u?F zBYmBnIo;3u_8znigALprWvKzwta7t#qcM!p(8N*ZF`=09SkGqt=|hK3{XgKNq^*l% zxOPJ+>|l{&ek?LiISD=qR~dVWI|gGy@l6(d^86<*rr3)$;K5l&~Of>?A^@Ub7!L$)A8zCc-($&qC>PKb@q zwxqznvbGEa?I^)`uhCp$!O-kj`^^1)#=*O8z^#L6$S3ZB)Rm zxnTYbT(#IiLj&HG=^9bOpmY6oc%gg^+%9C*X>Fa*cnAa3H1i>c-5O*+a>Ah@&Q(55 zojRkz6ZEcX9;09H8x}o7ruWy=v-+KlP!tj2Yj(l#f`z>gOTuM;U2vlb*Z^=!kvf*l z`%Sa>V30g9DM;aodXt|PEm~A!6w#LDp9;B|6+2ITM#Q5a7E8!x%NQL?M>p5`w!rN; z#SziLgW0Gf4E~bkEJsmCj%0vh7~$$e@a;u8-G}L}Hn$m~kRH`w`IEtlW`1462EKdV z_hgRjHGmeKg!G4br$ONa-+qXHV|q`XWRCa=DR~JdbP^OHq{C_Y@&42~H?sp*f-G%V z4w7)$YQ_)UjhuC740&$+`F$VP2rv`0NC$v4i{HP`1Qh$a>FEv8W=jY`)I5nE5 z#=F86!vfANbV2dKDT3LuuTE%rVyna_;7Dv{_;91XYNt(9W|oK=0o_H?VU`63ND- zN*2(62;uxg&vd$C7zox~{N|0$2Jxg4UKz;|H=2Z?r%dKas1&Ib)i^XQLwySxkwdzv zN*!TnEdLow>cja1V2w`?4wyAVw_E1E|3wCU>n4p2{Tnbb5qCkNsWWsgY9P+8p)3Mp z4&Zp!`^ka}_?HH}eLG+L;BbrDb>aiN6N4@$)PR*L05sp5yRw*}5DaCkTprC0N@R^% z)feJ*l_it^hTe-dZvd&Mc3|{uHLcQy+@(ZgC@OPg;o#m69#!H-i(zdQm_=(gA_KcR zva1P=-wT?mh4+X=L;dQ9@GFP@rJDrw%4ZhEF^NN1aPZ=wN@lN`X`{Wru;*P3$V^2@ zWe<&5yPq_E@MDT=FSr%OP}0k}&I0dRHD0Fl+!&=VWL}{qK@Qq?;|Ao5pnp0if6X2y z(-?VsFa?*?UiyZ`J; zFfh@v5?!k*-dJEam%*(2uTn#Xj0L};nLRlAYZzZEXpouqm7Uh$Ad1$lpWy;G{MEGa zs@_)IEw{!Ypxk1(>i6SKV~^}ZHh?j0K3TL1fGSJ7v0lf~07=EOez5HZ}xNCDhF25_KiDg0-% z*gUbIlPc$0zMih`QT$*?ko3;zmk05bp?=v$&#`By*mnj?PD%p8h;4NPKN3VkeAW; z;#0eT+1xx?_gWa%=n05)u~4h^hK^J9&R+ZF`aO|^E-zE#y17s{i^DEp*VFmF6azdD zPh{D|K||tIJ-HaWkvEiIgf!U%__emT$|#JJj{aA;3dqo^UAw!~j_>f(6-?b?%y-gI zUjIOJ55*W+t*iTViPenKfr`^QF~KR$Fug%TWw-as4roHHCOaW=6wY)yfYcS=Z@q-2 zN=5ycyU|B&%b2mF_*w^s1n97jkyj)4c! zWD=GM7;4`l!G|+brNNqTQE`tHh+Nw@2q}>oC1@S}h}+jk-BrL+4SU3MwT4zt=as8{ z7wI=uAbCqZy(|#q$dxZJs$i6>nEuaVj$_8DAdu<9#l=k<`P>3+7Rm|MIB;T|A~$C4 zq1b0WZN`bi=#d_P*A;eW#%Gx7&Ho%|%bE_pUs&Te+^p?de?{+No9z!9iI)du@F@uAj?u^+|k7uR*G7T?ZjP~EO>T` z9oF>=bf9sReu)!Yj=i@wE3g286BoZ2L_{aaapE80pGFKO_{&h5NP|#42gUFMz2=)w zcS?0?vlNd8|7!O>ziuGof&xAUbWWI;gmJwq77*Nyz`&DXXTq{}YO|wrmf0l@DM6ciXyp11f9>C+Sa?(Ox`HvyXBdw#_Tz@ZQtB??OO3yP{2w5#kA~i zCy(ahv&;j5(-}pWS0K7=!Q$BC%514yx%E;I)8QqYdP!b~)z;y^kw<=fOw17o6D7U$10drE z+x%5b*+*5^!aDKgR3pzST;LrE11=l$UvYq!#Xdb$ zcIynCeS5>%+0Xyor&4p{4<+qomgLZ~8k_VK(S-Uz%8fzONBjmrXJg17#A_#bx#n8uSMyr6AWa`&-?ME9Ruy!V9KwM}H?!+Y!tu9l$LOp!V zi1o|i7HW0s^qei~vj$~tZDQDS&6A+W(oG#)yb(R|O1E?7zA}gVD@kgr26(Wan>6kC z$CE$;{EOlf;;Zjs^! z5QUyWGcB-yfVK4IiMNVhI}RAwK5vV{K}!y&$jz@YuhL?>)Eozcp` zsTLPc23+NizZ?CL8lZM0JXJdsLY!94$J&c8Ook0hWP-%FlQK$$6e^CrLg`$2agL4b zs-jH93MTkNEygV2NjLA%6!Jpw+`Pvi6JNqN{4kBn;ByJH--^5gE&g1tS~3?OLM0^c z46-m-!H#70A(trf@0-8oB5Nc~94q+Q|5e_0>MeEj=6LDG24tZ^7ov~2UX%H`>FguY zX3ApKxIZG4kT=%=Kd|aIg)u8t4m`<-{FocEcAPDsN3@pY;x6bEhv(HY{Jol+u32+A zc|56m=&FwzL1nKW;?-JP(|($D;bpIeriFXAwE3G#^bU<6V50MiDc%IPTd&~cOC!$j zugse`Dr57l5aS7}^e+sXX7Xt6lDZWReF~L)c7`-POF|IeYK4*Y6&65LrA>p{`*=lJ zMuEMZW^*~UGQ}1Pvm>JF-SrN6VygqM$AwV;G5^9&lA=blM>X79-Ui4R?Bo$I%2UvB zit{zwKX#F|tlGtt?LVEWDP9b+unCmnHY<+39H(u{^|kLg_I)#lX_Ye}@Laq79Ovgd zhC~1}_9p-}9^+G>QoSF#h|0I&QKrdO(`wk)uISl(#`wPmFlST+Ftej3;h@2{+UVUr zhC_^uqO0mf8g>Q`T0@rIoZGekb^UzwS#-o=Icj%olfQN>DN?!I7jr;PPTZ_3Ms!Da zStlTiVURmvc^iD|5n$6o>7f_{O761jOUc>9lo1z<(7C6{A-b9QjMQ{)YLsD zXNqt9_^EDB_XgR$KUtlSA8~*S505mVziRF*8zH_?XIFmxS}er?H}iSfBTOh@e?S5~ z@$HJGN8b?W5vKXSda;DH_divYm5{#uNvZL`mLMm@t6|nQHfAyQ++J0AX@VsfE!cHh z>yhrIV6KklFT&8H+Eu|w9*Is^ZHBH?YP?7+LqXY zQS)v-a-<8BBz~NA8Cl?{aWgOVWe#UIxrI%07a#_cD-i*}M;{vWPmi$A5)`mXlzpG{%7z<$uS2`N3wv3r z`Y4dFh{$4!L{?2;kb9KjPZ-_!^am%sy$54Z* z{l39+03s&QrT-c$U`^vi3z4I4@8XxvA(Jo&xhMpxnKvV~=TshaMYq6aHSn7okJ*_*>K zvRj^YBOud(KiAXoF{9nyyYEkWlXt>gD1GN%(8A%%bN;4;m7as!eJ|9IE`(#l6hOR( zIgYpZWjs&gWy7+vCr#}7%e)kPjg^z8Fe}}9jA`2aONRZ+)0W!%Tl-(r{$iKEcgjz{ zIJXUDs~Y~VnJ}^#5^(*l2%Wja;+IJ1mwO}Nn5|#D@I?@+op8$tSy!3XUa!2{`pX%h zq@j>jz}7;|9(NCRN`FFzw(XIJ8HF$eM*XUGII*N-uG>0Dq;1PD0e2`cvUc#AuMZa+ zi0C-x2n>)Npbj7#h&=vFAU(9wPzGtHAFNEFngi-R3iFfT35zNNnBO=$v{5XO6}zd(%xw0Wi-m@!vPt)+R^4025d{Y<&fkZq zzjXfj-o1M5pgNa1ICV8X79sZo;WPUG7&3yhFWgcgv%mF%uPmmK!d)PTpOD;PvQlLu z%$ELuz}jcVul4$tP`#QqrkUPX;Xy(S#p6o&{1K7ja&i9rc^C_(TVi@e6Ry0lnueDj z$UK(zIQY(HU6r+9i+lF=Y5Ho;A?u45E`+_*<>APFT!M+vJ8&S`$B5ru-64EOi8nij zmOR|x`^e0ehRl#!)UWQ-V8A-*#c&;^r*AIdgVl6wnwvj)}IBiMQt;SvSI~%-*z(I*7i^uW9E%9ouv{5VHK}EA?pia%l=f~&Q z@uT#kLmB*)B*?c&+c~Z`7~=kluaA?1hc4%p-*+EqxUUJ=iYZWi%3&TL+h>cvkrd z0fi=m^MsIP=)Kk+??$^&p;O|x{q+w^6%b2c(~@||auiJtgaQgw;;T`8XHCtXEpo?( zMvax#sfC4wfR(ZUCGADiMrBMWjos4K*{me)WRRP(!n!>HB554`q*)n)J7XRaxbGIvM}4TR0TMSnm?p20m@Aq67h^li&x2}~$+ zH89h)pMUs}sBdVB(x?@&`YwWF98J9`YbVGN#A)_2oVKY$mt4d{isk;(9Xd64hyakq zKh~{sCOJTGcK-pl4y`yOrp%yw13HXXZkz&XMZr@?H&G?;V7;@h(5lFT9(Uro@>A#;EUj6J*{N2#i~3d)Q>ks$Rz19%sk4xZd1zu?wCT zl;h&8MyEA==CSE!_X-OmCyuObv=OKyVD-s$;_r?W3qH#aAeWNL3zUvF!Ef(UxD#=4 zAd&n`Su||*=k%g+{@w&>lD2Ygq0;td9hIX;+odlln2Cca-$!QtBwYZikyOOkNAjm- zp%Q}D%&*JS7Yb#ECb~3->QA6M>Z@W}DH$unPYqSt!A)Rf&`@KDO_^*uUJe?sfWhbC z-g)o8`BFUXcfJ)vJN7?XsY=&cJ@tbCm|z5@c<+e1sLHKn^mPT0^>LjG+Lm+zngcJW z5LleO@QClAXr0eEb*5XlZnfk=F(L|TH!@>-fRzKT`K=MzdJqB~C;WJxpPzsaD>(Jx zxPzndB)IiDIo{u2%u_HC<+YZ!^%zV3PuTekw?vok6(c}xWlYWgxgF5+%pDQS?5-sf z7Ey@V*#$XPon)Wv2;Tt|ZcJg~!G9XYdrWBLqCh2C&--niR` z?+rY&+Xxm*uJeH72lb)og`e+ z7wd%%G5){#SRMzv^!yXDFyiQ3S9kaB8-N68(Umu9^-Z7&!GMyf?E|5!zTB&;e*b~) zsp{m8s-vf%ItF&npfjt*h>y{T*BmrfjiE2!;-SaaO-@n34ZOVirhfPs~ZT&;F zBHL$~gUEPd^ZD~W{)YIa=yhx4vgvlfkADKh_Cw_8>xP+b-YT?rb{Rpyu zWE;j?R%G{)CDYX3tl-juXiCiLuDU~)DbNF%2NLg4XR@{$dvF2TJJ0t5!1Wn<^BW!4W5E{+M*wyjIw;d11HRRSKpe*sc= zusk1>|BI7oz9C2WT(Jm+Uri+<>i!gXYT<&ZCw z`<32pIlpK0X4aQlF=F6$#YhT&E_7f=U1ME zZ-Nm*QM6{Q?1{)^S|;WVAm>%7VaGVLW`!nMfdvqB_8KNDPWC<> zEaNkQpp~jC zTv)O)Nd5JU_0t@%rB4~IbEI&#jrXipeHFN|)xF2%RNh~qz7qo-+(o=ko+k2^&5wUX z>{N^nJT<=U0rxb}0HL)D7j2kLu6dWeT(!ra_NC@A?|1d|)HCATTqP@xkb8^4;v^Kn7!~y#a}os%8wbT# zZW;S(7JaawfX8i}P_z8d7|#X=CZBcEg?z`Uet35FF!~gw=)n5iem`vb-R_rlHt7E@ zwEycTUFS4%8C74=khzW$tj;?)MEtcFxa<}e zL}<#IZ55N%&=s)oHcZPZ3dqbfU;FHUp1pKNY`;5IZQgb?YRphg^T)a`S9_+?XsqBJ z0G}L)jLo0ROj~t<92ma%NRZxZ<{txp%w)qvYae1)f1e{PZfuIT{_r&wG6}81xv@OX zx{R!L8`uZZ7z4hR#{LEM%D_*6IP74I&o8I=HG~gf|(Ag55bPh18r+3;Z(k!o) z{`xEJOD@r1c>A#`S>wQ98Bj5qp|;5(qwQwz8ntAE$yV#nd)^%x;lQrfR^`K&M@@~r z?rm*Qo_AoJ>#OUwqTt3ZZ5)ewsi+64%Z91;vLH5rGkzd!w7>Qk>mmgkb-iDQr|pTn z8?blpyx;Y;lTS|%<^|$qV@({?An;_=`TnlIGTi3{xKm=OA;oyR541YLd<28PS3{D+p1p^ZEfA?b`h(XXZ^3hcMCar0whAcw{>p^M z6Ody~O>S5&lbwGT6aD&yRZL29`cmpgtNRCaqZYxEf^W{}OV{a%lb0m~p3;OQ(WC{F99);csEhi zi7B6~{kv5ZR{iZTQ@h>++ATTuzdL;NXiz`Jc$&6HRGqdxcIhnscH(@WoSFZwQ+K%1 z^p?(*a-vH|Ti+Xtq->`kt);@3)#}piF@MJHR8d_+V}4Ky$e)6Ik1+;U2#&%gWapMR zZ+3kgt^F!U&lsP*pyL&DB6P+Nm+kF~>TGRnoQ3PkHe*0Fb`c6&5g+^Jq2g;C+ITR= z%rZKrSzteL1!eDf@oz4z8x0aV>#pjKfH&^T*@IrV?(2AAmcVBaA{qpO2b0(IzS2%X zQ%lQ;Nkm5h=RhB2i-@c!q2!dwJQ!zJoc{b)0Y>+nc2#V*NY8mgZfw~9LPbUS-G?P~3x^KVyeefl zmW}2kH4N7~zpiCQgC2>l;hT9=MvN)091?r;Y|{{h1jxJIzCHUzY;ATx+E(5QRRAea zZy5!>TG5{Zs21sv1T^~3RUik-vMzEst6loUY7SbYL7b&(=SpN!xQ_la(QbZ_o?gs( z-w=&~=Q`DDpJ^c?DLVR8ns%(djv~kwnngW*Q-a35HqbmLvLXh|?Llx|xFJ;#->(}e z96j7ug73lPe;bvAih0`Hs=$eMr~t?=0t1hrXE}QfK>fS#WM*kkf$ukzmZC>!ySOuUPFC z{ETyomudrww0YFy-ao-kWlIDEFK+ZnvkjA5?YPdgL;)_x#LATtz6zYiQ)o~p$bmwJ z1sN}zFDl&!4b&kX=3ES#sJQTC+FRtRKia#NV(*|VB|_A!RoBCDP#$|>LOaKf?1^xak+a@79J)ceKd z_SZ4EOe4=UkG`(T0=EP*O?L6UaA3on zRSyRb81VSCvrZ$;rfiu8X-DL+#=B4>H+7|l^B`}G`FaugyE0v*O2XA8V+VzG|Nrca z@LIXjoleYKqZ_yjy`mKiU6w`K!4Wd zWoD5IlXd`nLFP##1dFvRqlRGS9>z0+%_anIu|9Xr)D~VMT$7>E6FFSc`NvUF?0-RE z6o-)WI|doh9N!_tO=Q0W_d5-LNcJ}H(q!{B-XS1!wV_U_$F?wh|L<}nlmw6eo^tz3hKu){yOZ=?GrGc^JO;}KZBElWY zNH}~UKHizqObC{ejluWCZyLl%Dg>O7qCc+C(GRz7ofV>biZ9II#_G}KVrzKsf;E>uj!ut8`*(2)gsJc9CG zy<<&-x+LqXhnmNXZJq0rMj!cunZppF=;{6SlV<~05pG7;d!L2Q+cL4gXNFSKyOoWZh`U4(?({EbQF z^Lf^QgSoGN$3{ovX09Q>5=uv6&dB3{?aFl_w~sHpFoT^vb60rn(p6V&-hBU&NpaV+ z-$(Sp08qA1Q5(v1pFwN-;-~y4ce66?=W`N~f;5wKY=Y49;u=0WQN2sQZOgm&?in*P zsQq;c$WB^tf~83ADCmuorUC1`hOX#>j*)hGEB~9m$<$TyH&A09pL$9n5^G}8Bhkm5 zcfHp<|I*T>+U6!m2qBT|DERYa!9a%SCE!|NX{l80Vq3VQu(;8mH^s$wxPfA60Ct+n zk~7R~DZI=WdZ^`&{j!96p*&%8D>#{MLYB6E#%aoh@RQiLL+_K+Gp(&Z+;OWZ?81s4 zoK-11`2;0D!WnzJ!Z($gmkCwos{?2+C`cbdPVy;`^nc*6*&MCCf3s~HTqQmC$}H_Q zvt8io299-*y)EGD2+|V}&J=izaELZ3TG_4v^#earrzAfgZ!4rvYJ|{z`z(12LSKfAb_~bHq zS$SnpbwujHw+66yOSj|rGD{fD-r@T)=Qs@uKxXGxUpa=!Xc~V#j<3bCf)ZRtUiQ$> z6LH-Zt29O_tc4CAdU?aY*HdvWZMWvnGh>5)w&{0uyNz|;c)w-^9p%)PZw zuCVrqUQ~yoPZrlQ6Bp!<0pobs!fIQD=D(}MK;caBM{m*W83)`^5I45xE0Y;sTBWr` z#Y9wsUEXWh%W*l2R;@R%(Va?bK!<=qnal~{`FF53SWCbV$veP^H~Y?jvOp}H!D^K^ zslOY#jydJUV$q2K+P8(6E7=2pR4PjHb`;r+j_c9Yd=-pxY|~V`O*ytT8IK$N3#-qv7{= zzm!G{&UrX0tkDZ-3I|>t$a~rte{N{E3f3R?M&ixyH2g;|C^_Psz0rTBn}>C=EZKSR zttf>|fa1cRkS+5spqUbAwN%S@=N9--VVM3!QXC|D!$x-r5UdM7NPlIAC35dkT&}LR z|EI3e%xLfbj0WH)+)<+ox<^K=9Yo7@oN-BQ@ZqQJprgNNyP<4_`Z!zL5_pWx5Jb6Y z>NpWo@N8pA@T*rxcsMc!WY>Ws9+>>mu*Q=ZTZ`?;zj6p_KzFjL=F|miS+lFDaJHA|U6oyKD7#>!Cc)FLHuJvh*w%w*SP6@CQJlgiM*_?J)ZxS>DzDxfO z+j2DCr8z_Suu5}oCG&gGcoHmWtW2wP_VGvTl3zpPCRJBg!wD0D^fo*7XdDk-aFDn! z+y0p2S~DqxVAhezH16{U@%YtW)j8O_+Io+gV@w@IHoA#eiFH~-jdIRj8Q|FcN(US3 z&uzGb4<{7ni_p|)Zov~L4zvYJUs)c2M6toyAphR+|)Sxw7EsP2L5d1 zc_Au34vXMRS|%$32+Tp82-*56noVU)pvS9`P27w>)jM^1AR`VjjJ30U{^;83rF_}|4jBFt$LQ>8{l+UPI;7{QhXVG< zW)6uVf`~A%^ro8v+79kEt#9CeoG9;-ty=Q5PWVfdQ$SKxb;kX8RwPp@0Fd4b$ghQ?Rb|)Bb+Fb4IwX~W41Vi-*beNT6%DJJ3cXR#owDEXoEd@?Bx|l8 zFB||k?uxmEZ5t{oUS3lfh4%HjiHXUKJ+~`e9EHzMz306aQ;w?I$G|y@%(6)yD)U z8RkwA7Z!j;B4U~BVyLY}4A5@_?-+*?0~**;ep)POSb9qMfuNS~$Yw=`XE8N__!zmm zc56{(o`17~9}`m`$U{a=wl7$?@YLI@;?FkVR&CMG*RY`BxNq+n~1woZdhNs4-kV`mze!++s8iyo36jedT0|q84CdbDc3aOBSQ+CCRrb79e zR)^f*Wa`b`^qQ?K&6wz4z48%UDp;@8zsndhGM+hUAEPpOY2kqB+Inp-0Bt0$$Zx8u z>IzN|j60V&vvsQy$Y{%K8xYZ=Lv+QFW_9DQW!n8KQ`-?2*Ax7Ut%{0qhNBqf6wL~1 zltwq1p*_QJvVw@6#7IY0!SZHA|H+Kag;?)f6OnbgdIO|b0qU9VNvx&3cSo+Pd?i9C z%%n=$y(JS{5b)Zfo&8w%9UQ6f*se!oil;8ltQZ%Z$>gsa@IjV(;uIv1`4<-J5GH${;ZTrLPct2iBHHK%jB8-@c2B)0#*)p~1Qq?ugl)X%!c8O=4k0jJdH39-g&hKJ9-cI?Rl#{{>gw4zjQEy*p?RWVha z)GI9FVt2>gJ@S@AA`=oVLr|eQB0MB>y1k9n$Ukf2>bjBs)|+OLSXN6J{w?2e`SRtP zFZP&y!+vJ@n>WGl1|D9u=a!wM#i|iXFw!zp__vvH!k1~WzrUF?LOf1J$m(00T~j?+O0EFOuCF` zY0^Oab^ByJc<@bYVWLUD+%B((&Im8XO@-GV&kLuc-e`X8Cc{ejExDAw7__dPjmd~9*+K_Q9-}Y=Dm}JQ9KyY zchW5{2XR8CA|}Vi>-TzQ^h#Mwx`*~Amh6y}R8x=5(3_Wg?>>s+BOp(zH8BFDEG-<) zd~)%_?DD_8zt_}^iLozTJG}nloGwcj%$oG@Yy38s`J?^`s#*B$1{FwVRG@qO1p>6U3_+j z@50uJ2MO@55H~sy9?s-*6^lJRZ6H*{(UUR|V=d#dy6=kxu|`C*W%pHtXmCK&jrKRO zZ`=U`BQ8+|LvFRvl?W#j0UmIn$Pt}lshjC4jL!vYuD}KEJm{3Xt<9bE-Q3i`r2?0~ zvh&)dgPEs!lgqrrZw23(ZCUz{z=ZtVkflqOh^-}4T((Dx6|&G_RK3DWwDun!qGta> z0g66ZFLj5mf5u{7uxH$L0YMW(g#UNI@bhBs0VlHxk?ZhNw5XttTQP@gk!XK zMohWD>%v{*r+9x}$mU{BXE{$!_A0s2rQV+6Q1&3?qN(1u4KaRUZA$xiK@wwr1Nx-7 zWsJ|QM~@~2l+9^Ropd{*-}>PRy6T~pt)U_^s+Emc+E2Wk!4HSF3+DE+HOk-V1sitZ z*Dx9Z#kQt)mjmCtg%C4LiZozT1pN;6~qIQR*G^UaMMD+E2}0;EQv0>cC#Y>k?1jBodYX^PK{m0+lTe#BFiCXXUbVI+N?|L93h%8;s zraG}ng(n{#5mCv^~~Wm#|_rgm?^kN_Kf z%xRZ?g--}c!}RatX9tvduUyF?JT|#kPa+K^Dy)WSZN}XXGq}ewNnJl+1f!f%S^@>8 zz9M?3E($iw3To@&;UVU+a*mZWLi}(^}GcaB%eYk74@n4v7=CdWdGF6SXEUWjh1x%o5%Qa}U6u#G4ll0tepT*Yo)5*_a-`(ffzVJ>O3@MX-wlp)HG=(kMkn z$D99k)YhQ+xV(Lf*uWtY~DYjlZul+Y^tcZA=yVR_#UHt2oT3SSVHI=Q3K-zTq6Tns}`@#<% zbe%VE3|}H~#K^-F#*pd}13y~smPmsnis}K;6V+zT#a0m2Kv;Yc$jq3 zPDke-U%m=(@B_o|QS^BbE=ITc4WD0}b;WH>J$d)F?@H$&Fz%;>tf(K55sN&8boe0!lzZM*vR6F;)qq79+J6+|vu54lkcc$B>eHsS?b}NgARZV&z1~L*TfM~pN=sag zDuJaMK1^^QCsoL~Pic4Wsysb)`ey>Fkf08?E`i|VdUjg~g69`LuS=)s6h4CjUCr`~ ziZQSrxu|AnwU-@!Fm}kvym!w?s9X`Av|E5Wu^bZr_{*#5L3WIu-)~Mz34LHxj_1h_ z6J{`X78(MM7Q#9MzGm?_{tkCCGsjksATW$!&kJ~y1d4#J{&iZtc;k|4T1a3k_ zyplM7m-%4-!GmRe^|?>Kk{7VDg*}l?Rkq%KeX#_-8Kz9I6cc7!&$5#_43h)Y__7?z zc>NypW9k!v1T9i>F)}hzvRbxmnZMOilc7W1KdAKkPoVkwyCT2OHBnDMWN@lk;{GGY ztoNv>?to`eX5NWcO&ELt-Ei|7Y7fkh=9gWWv0gRqY%hXxcK>4sTg$agSwFMi45L2C z9m*lQvhp%g9w+L_>rI+ki28nB_8~zcC3f(TOE>Q<4)$4T-BO?Ov;UCakJWYkr~5u~ zx40Ewm^cAjIro-ul{G|SD+wNDyDZ6IoE z)Me)}|D$;eja?Ya(H2cSD-oD-W&uB_>PGcAv-6+ry6Oh{OxeSetiQ;zxg5X{#`$Ti z3#NdieQNELyvCmZ%132$2*$xpy(ZwO#uz@6=0*+;W53*mi7QPyL6700D**Q-B14~4{A za(O*vR7&0FVbE047{gsgg2;@cgddA##4|LdkO(JM$o!es>^EQ%ZfQ8H?19d=4Y?{> zF@95K_=j5^Z&?|_b8GjhM|9*`*~Y{J1T*WP^`{D5bV#C&$5uZZ-D7$0-m=R;koJOt zLj*8v+`Y8F8AI=i(ic*C2R@4AOsEzV|LGmSEDeMHP9u>(QAyUc#KQpk>~R7v_-+{6 z{E{CPht{4CF1Q0(Zbw9fCPSK{-(g++r&HRHm)aQmFgU>Bs4WXi^XJw?oxY5PHvm~F ztaKirv3q74h^2GpKE3)MQ&8z?Kvaxb0J3hWgLex=*%|h!dQFq_R8pmMCASFP3{ehk z;?P(3s`^5juVpUZ0IYNI{-X2nF9oyb8j|A0y$4elk8A0aH8aKPfGZ%xjW@$pR8(>e z&NC^U#5z(ub70aJQ$725t8ae1T+Gr*j&_%qIZF>>Yn%G`aT~bryrS-rb&#sSzmVW{ zLrua{&n0+WgmR4`Zb(~>8lOA+&`3$Vj3P#?z4&$xbf9TDp1sZ- zf5yu*ZjwqE5bP3mXza3Pswyf{yL`m#;nmrlT{;M150ELU8QtrR)cO_{8;~kz;{EgWOLu4Ds2rmm(2o$Q+(+|#db4z91 z)1*n0^t`on9oX)-r>>%pzD+^(HR*8mx|7Xal+q{;F)qUP1Sg~8HL|-*T}x{hX@gM` zreaoY-pWoem8XR(?+cKaNW;=ZQ4#~~l`GrKd?o8SZNkVXIp@q;E zGQd%?-#LUBE+r`QrQDSDYx&<;E-k7CnZ6U%R9ogB?Kn3TBm2JIKhl4P>5ev*` z%|;U~vIgo~ntkHl^P>x20G|`%6ynx9duDquL#H7VoSYiWwPNirKDo&97?gGGN(WoS zo^nvNEe}zHIQae=9QSh5WU~n#MTO8xnr}6~1%*r#4F_0CDItc;WbDu&)b=0i2^(_D zlcDNgFG?BEImoiWacH}X|Jh<&C0>_!=2Df)UqrA<;niWR6L=G7HY90vTlpL_!;(>@$Jgc2$PJ~9XV zqQrs;naQnmx4)$>S;jzr-6$AJrZCm~O+qr#|NSTVdU7lOXADHbV#P*ePTHIF`cWa) zW4%=~RjCzt>;Mc#mfG%HkquK{au{(kD z-94RDqfK^b{v4W-?zSBVC82ldFKd4nQG%k|vT-55ol@4-YF%=6bF-vf{(4~(zja2y zIq#WkYS#`_tBjRnQ{rgg<= z`3BAgr_+6Hm*A9cV2xrtK#^&=ZwF(q%k80MaaQ^d8bsq531Ho%aye09Thm&B0Fm9u zrgzbQfL@FTJE6!0TMK~@Q~JXP6lL;|HE~ZCj9ASEV=)=BY-v0KU`GZnwre_e9UCgU zusEU(`oA6G?$pQLx$okF*pdXM0S4LYZA|wA2|K&b&krmsz*I3cRiVVOO)DJND6xbW zEi_Nw6Yf5KJm9{@ME3Ye+XU&NvZf}MpegE7Vj9d1r(Yd?wt^+;cpG3Q zVD{O$;+2*uC%sv_eLR%w@qxz}c=QK6;CSFRnmwqwrh4(}jJD&;ejx@E`$?~njx%Od zA>^mZn@H)HwrP%L#HPD>d06D1TQ{oY$4pK6C!)p9Xb{Y5sZ>wTm>DpHlzgtyG6BXJ^GNO-+T z9R*uuM#{zR-C(NU+t{EXrlc~I1x;%I_Ir0Tw;9vWkQy{n8`^fZS{e+WrQYXoiSg}v z1h$r#gE9O|QC(@@`#A;D*9)DRG8fh9A2Q8Ba}RAfYn%!7f{l@UgWe1ko-Wa@9Igx$ zorXXVBsr9eO!e&w&e7)!xOLfUGq;gKZznASJ##D<*Vp9$Af@Y~MZ2(Zef-9HM*LzA zk8Lz@<(Dy*TlxO+Es!7R*Hk0y^lT+!8!B|gw|VPT>2mzguJL_V(-i+YOY1D`&0e{*KC z=_puUQO4?&D<3vx@j z2Ec_3AH&1LmoYVwn(%s)@ezY9z|np%0^IocgAOYGT{6%%W0t57JDiDCE7!tm3B$zj zuT`bvWHy6vp|Nn%wkkN>?JuzJ`+v^hpEUZI-7*YK9*tU(cS2k#kAi~nSoAg*JI@f7 z^3of>l%rKHAllqverztS#L}&aOPrAEcV~w=9T(v7l8O(}qkG68c4RD*gnB0TYFa~r zAo*5-v}yJ>9e1BjgOyontP+!#sBbO$kRX%h_j5M6MO-?-lad_;P#1iv-?o%JzkpNd zRF-|pA1L}zM!g}W!}r=|%N+c}hl8IiyvOe+asK;HMkLWiRZT4`uiLp`7o%;ACK@7$ zwE!dp4PPC3saso}?W%h~Iet`qG&K)aR~B$>Y#-NCuPBwd z=zeuJf{zLMz6_FZVX}|X)x}^)lsy}rcDYfiAwhI?E$%(7qP$(t_vfsGy9#6Li|^xi zEB|W-b;~~l;w#w;aBTNbCVh*5@3}A%_ zPT~fopwg880v;iu<=);d?5pgW>L_yqu2a_QEpF&p^_+jz*Bf0nuqjWjHt{Ta$u$EcE+bJCDw7J4!*$oFrS}GgCE-1j~cQ5U_pHZP-9~Vs%2`Tu_Tr)A|4~PwknxISHeTQ`FNaKC6vWNWzo!>PW632_Y`OJ*b9PnzVOVH?n6e}w#UWZN`nNhul zDl?Ui`x%sV!kF-zpb10PyDMx2x`|hpr!PWlB9jH+FBoZ02mAIm>e82~s`xY()lexr z;BGm5u>pi}?rX!1G@@{&Rnig$e~48av)wc>`R0!@)`?09WeAJGJXGz;a5X_FTMbx=qvupR~(H}Qyxv9}?l zr`3Y$dvP0R>ASAVQ>P!N-k3+-M^{UMWDXH-Ixt@3EMGSjWx5JF(VlZsl7Dm-cTTEj z8QTB{a>#nWSeQe@M-D57Q9&G*woL%k0Rtva{Mawl_{515^?*8UdQG-o*9ESWc-R%y zX&Aa}DKrl`Ez~w~&qSk$ghA!rTi(iz%u5xSSlZr>;RIy*E}kUXAYPc>QfN&)ahOOJ^U0q`+E3P?fn-`9qhc! zf_lvtzf&2)8t6f;7k@egMp%ZY*Z4(0`7K-6&%10H;M{jufaYB^K|_|kR$#|GFK6(~ z!A*cQB^`j10fv?EGJx|sU>nNu!Y&p-8kmfkPAt?`aqyZsNetb~%E~~S+?R!n`=G7& zsmp6>FMbl%QnQ;7#}MG%!{wSOLI@Co5iUu>64||b_p?ZRRR7ey`olc57voVtpuYES zA8XUBq^wL&OB2%*P!nBHeW2SN8s~4`ys38X22@#-!_DW-E1xy`DmD^LCxs+kol*8c zT~jmA>iDgKf>}xLP~`E<0GdnHp32@`(0M&D^1S6;2jAX>f0hiO6BF|cOqhT)jcAB+!DxAf)5Xj5sjccR?0P~W@cyKnNm0y*IC1Lj+xKvp*B#dTkz;?_ zs;g`N^bjM2N~g&r}dR}^`-0AuTLLx9+KMX{3kH= zNxic2xMk>Y#J9tjF65euNi_XD(@B+&8hKq`?(Dw$_$I3b4l3TePT1*hF`eD&m&bRl zbOZ~9uB%$+e;JnBRn4NTnQrj(W9D0NV4nxx4ccRT|BFMbfDxz|HXpoNeeLn3<*Qa% z9kWx%Py$x1`l7>37WrI?aZw8_#Kk|zWP21lMqc&#}%8$m8vn*nuN%*|yCa3X4Q{3ee~0og0elfCBWnC&QX&Jiuen zL-~46HA@)+fs7s{c{=bien5oMMB@bl<&z(2`<~m@V!POG?JTsTR7M)VD^A~aeMhGoGxAVa`ERe6* znl%tG=!v7*7dImR@&RQ)LR*f(dFIoUJR>ShvV|zxRE{HM%y8bGMhDToim?AMP% zDvp@i95>+?G89*vnTo<5BGc?ZB=Y*>$3;Q&AMuQvYzEFYP}#;D zzP@+c2SOF)qml|_6inzgWUS8h$>Da(+)$V$t5%)|5Tr4SW>!Yh7OnG}9X-%EbyaBq zUeIpzT!cYTr75jlcxPR$^o`pa_6*Sm#m@b8l?d)zG$`Wf-ZuRl@qgqxb27jb8bq{r zc>tB@RbneGjU6S2>>){A_j^Re1CF)zataF!4tdsZeY_fz>Er-c5MbGzPOdR_zTJoz z0E2ukrMK+8?wXKGip4F0DE!f16juM4N@-UA=5K}{ER#fM{+z(cFc)c^ zWnVjOro68xs*Edrca$AqUXgdqLhLW;n(q&)8fR5D#d*PkOsIOOZT%v*S_|pq(JJ~) zrK>^OeWX`}>xBXBGRh9cSHDj!Q*UUgHFd(L3m>&l>;-HzOxvdNdiJbYmaoQfNq4T+SZQrlBs@B{EkUHvB7aE&JPY`EEE%gCzTjY9Mw|xQBn1-@M?Rw3A z@X2idq}vtMT`LjU1Sl!ViZ@b-%(N&SJgzLI>PN6)DIExx08aK&^bqX@1EmlXO)(YS z&dLx$M^en*n;&D=4-aH{+kO$9nL!EAgO{bi7GNbp{#upevsuCerC#7G3SE@;Q}zd6 zj;JI^sQOp(NCWZYL-fPAr(N}%lp0wdC#FC3oe^ZioiM&Ur+mxd!-tohBzGE+TN@XS zXm1!cHl}>Vp#yct+Jx3IepehZ$ojfd3Z|fFCWR|@HpeG!gSKjFVZYp~+nZ?(u?xt| zbKsZ|Z|-slWH5;PJkHXQvWm51g`ub=tPSKBMvm-u}8PNI(9RcnIJSZ`ThXPX_*(1%; z+In;*9oPG2Woj!sXJ)1pq^AdPW+gr_IO2yB1W1S^+hIEujOi|uUJ%6`Rm@n$>-lhGbimQmm_8Lz zj8O3~QVcmhZp*Q$9|d9N0x?@_GA2GNXlg~d<&4JhA5%VTQonq?4-IUI#_R)ykKQsJ zbou|NdK0J|_qP8#GYKIPqLM@z%9ybzDjCX5LIZ^iWhxa(DPx0DW+}7{nTd*0gh<6s zqLd+-l1fOtpToYN|61=}>%Q0X+?%?t^ZX6R@tuz7>+pm;Nm8htgKOuqa|ENtQ&>zP zCp(cJ=H%I;vQlmZr=r^hLWe?NctkwITH}+3RQFA2#{<5vHc4I#4g?&KXN{vkJ~6 z8Uc~>4DtSXG4K_*vk_I0Bb?{|Vz4@I4No{ zsEZDQpVTB~tfi#LV*l$hc<>dgM%$?KU$TWvj!b@Zdd^_Yju~4|ef~Ra8tJGcq;a}R zM}W#fRwPG|#F)VTej$J9eUNR;=#epnyK1XHYyn#b3)$U!PJ~2~GZ}+p(m&T~jrqjI zfFUJ>v`FxPu-Q=CWh1g4nJ*es+r3d;Vyn|dKQ7;xH)dD-VfDC(K65P{9kpo)VmPK} zDK=Mnv3z!uf+`9BV3){f9d4Ug zR~>b8Q(m82|JmHw+!9rN0+@_7z=j8y4Cx^60SK0Sq-yL~4!h#q!#Kj^$q|(wYz+z%~8?wS!e-m6F z^-jr$58DW842;c!Q$4I6fSX~Eu8(Nl9}#EfYDh@$d< zuM_eM4DJ|vNA2O@o~iwND2<3O+*2EJq{COA_0{jw;j;KRjZhgO93k(GD2vqrdE=KY zD3feTpapbM=K5aJA_LN`zP#<3QoGk3J0AFb*&(CG0Lnx>F&sWfSRmAQ0c;i9gh%jG z7hXTjMK39OJf>Y_TC<{xv98ZL*0V#eFN0otzy3D+{?g%|6oTAp;856^ZoTvfzqm6! z=XpGY7$>P`d3Lbb661(%aT2>lK?4det!d+$g4oJrnm6NrjP)-pDRw>7#Ak{@O2Fs5 zM?ad5X_muXNW0MW8k$u@e_yG6kBbz%20HvrJeDd{;do}sSYl3 zgdJ>e8cu@VM&;>U8`!ahNF&Mn7gCxtZjthq*isH}$#C``Z9}>* zyM?L4iebQMPalQ)u=d_oGn&Lz5w#Q5E7FRi5Gyi$ zDNPf#lx?7@`}_4y>EM^xCdM3@9C+e1#c*pUabkk%-6GrpgjM44IpYHJ##c-Y_O^-x zF=7Frg|)3}kBG0QsW$u?(ER}pXbOfy#no_ISKAK6_&BF@}dV@iO~G*fNqECNm?9>h+dPh?T(_^lL-tO(Xo3=z$re zRzGI!gty(tnvZ=ufybeJrhvOA$<1I2X>=Q>j(f1FkCmnW(@m;Ft}_t~ZJ1 zYb6IuGCJu5C^-EPJ|-HZq-+u7H{i;ZE8l-XR%ddxwD3tnW`YZqmU}(K6V52d-_$w9@hBQ>&rWSRIbjj_Jj?~4s^^E#<*FI-8yWa7M$Je{or;JU9Q1Z<@`sC{vs(;^2 zZ3AOhD~DzGAi0e*q93O=q@}>1?CY!xODv#P{I4$MSR@#4*o^ERE|NyB`?O()5K!wOn9m6w|~HaVe5rnCX1$3%(wIy z2d?p*>ylRO@6)GG~iD9D{bA(Xet*qPALFU7x#-`k~m5e zd=422op)``r+f6FW~O_z3#}%h+!A)_B`LSXXr)9THTpFei)h8*mWT&B0OOpCriEcg z`<-^Lr^tkH(`J`MH|^W4^32&~FNPwkvjj1Imw%yDuV@}ee!u?xyY=fgxv%P3jbKyr zM(PG}pf&|jkMB(QvtiwO^|V5jP-82%{Szimd{~`y2I4?M*hQ!TaL;{BM%56iFa?`e z2LLmIwry)$GoZ|UQu$0rAmHTYs5m>XqZRg_`H?zJ))>7&#jhXkP;qo5F@V3~g_M86 z%e2cpU~XP+z4h{dhA#VGzj_rG`%V*48%s=3C$4D()m%56O1IFir_lDetx7|~-$P5Q zzPo$n(gTOj6FOu#e(h>%VZ#~Ohc7`@ zvwuPB$js|^<=r#Xi+8WK|A>=g;WSJyP*1{E&6`(b)iof-?Eh3HFA`OKIRIV>c*3pv zhQBI93D}cjr0p`whvU#e{^bpl(_p2o_6`Vw;Jj9_>9Gnyun67|e$gxyuUXI(c?VfX-Dpt2XL?UJcw4oXn~>xE zw`CJ|eqb!^vH}U&sq@D#iE6crrvD2p8ibHUX;C41$JXA@G|?w>4Nam=+<+l=ed0l| z!U>!~hv0v7f{8WeBb&$jfCeTZ=I6YV7-8eZogd?Pzp#48fX!mvXnG3ObPA;49lQjj z5%`?>JclQ&@)bu{@2!iGgQH&;)IsJ28-xaX-auQa%&k-chl8X>?osjtrAr2?7is|p zPovsy7!VIEOM@82QJMqYp1}CY#!nUeMF<*L1&XYZD?euLL!YLp5MYD;1X=y zqqmK(W{={FRw+Vwb+Ne^rV_FmIX%}J@^@;=1~40xuHEfw(Zd9dUV@>dnWabaD~k0u_LBR1Bc%D=bGkU zxgktD|BG5KJxQ0N6@0UPTx;_mJ(bl)v-N>1-on^MsV>HbGn&T=5@1ES0c+Kt`%X-o zs71*yYmG$4QKb4^M;!#w5?F_$%NhF-ReQU0E^Tb~8)_(e8XA_@Cqx`VuaUWHH>Dlx z?zch^ z`9qtu?#=de!hh@fYsqXPM`Y{~_kaWh3HiuIU-WDqyIVjtadK_sZg2N=<^-L!qHh4} z+*U+_3@aq-{Aq969nR;4O!LvYmW=#S45@u}RuiY-8L|PE7eiFB;mOImz^7_-a+#|> z7jqhU*6iUB3^Evt=uRAA49VHv{`iD_-#QkWe1=>v!Ap|K&xPk{WANaIiQ7uWR;{vX zF2j5kdDo}|yGdsVZt~V+4jzN6ffk#FM6KON>C_WtsGrFPPrj`fZ+FE=D%B<$DOVaH?d3nHHTpU!HVtsY^QlgNnEP;Vo&Dv z``Ed-s+-ZxbEK7pmruGjHdj?4<_31DxB1VCCTnjA2L);WJ(&Z=cT@X7r)iUaD09&+ z1n$O>r$8CUv%UkUhQPFU@TV(iRshtBU%%dr6Br->2w_B?>j?~otjn>SQ|^6|1cYun z<4o1bQ(ZATqI5^{)BuM_T7G^MlqngJt@w3-5<6Bzdv7^V8WoBfaVf4%yDIQ#hCU znj?3kmUkXMw691Q*}*_S=x~|%@|2-V!Lz4^+nNh1eA0oHpIR|-X#DTrwGzWG(7STC zX#gIafC@J`{y^c{xWN$cyq&v%1_7+;C8JOcC{%~){ zeWf6kZ2lyfcW!BE~I?BhirIW?Rt-{!CILZKww2B+X;yB!-#Qi7XluC zvw45$bHSBKw(-2Z6kaC|L7;w0p4QvK3sohfk%FlD`DP4T^UwAhI84z-1101@Fh@U* zUfB1Hnh~?zI^fwU9|^}Va%|LXU&+`@D!Ucp(S*|k#a#dK*-!SV&QJJ%G?H*m+(>Ok z9sfT*$!PAFziiT5_0mgC(|A|mN9P|WM#EE2lSQBxukGcJWabf7@y6kkRLoC0oZt6! z_vQt3)2&as=W0#J7&-)_re`ypR25hDM=Qq!O5^<3N%F1n_eo!XVr4Q$b1x^)+2EGW zd1ur&Cf*yZGhVxbyM9X5b6dMc(0zo!Dtw=`qK&$GZ`*p^eV?2(?G@1O;`r@cQB-&e zqnGZT7n2(DQQ>=6sV1f$VD9L=WG~-k^Pse;`gz~gzYoqaSn%x0wKh>k`=3aVD@i+e zLh8{bGrM&WJuP7@f^S17KY(hh+B}_7l|H&+{ZISZP`y8j{`|fJ5Y$WOROP0Nind#O z4p50xc=!JO&wCCIPHu3-{^T$#{Hb1^)4XQ4W$cPB7J6DTyKAFvpH5pr+iF9ft#RpO zlWFfM;#7@GSGha4PdpA>;izm`w(45vxpfl20m~)G-|%(H=I!=coQvQ9ih&6ccz7ci z=bH~*zOzDcv!CmHXb-pV*287ZYXM|Z>vi)UK68zXmtX~~`1;xcCDqR9Cm%d2KMBE) z{D&`S>ea>2aQQG|(i#dE`D0D92Q6%BBd04*Q8wQh9Upz8x4JtQY{09CpUGv7EXXfY z_`Rfb)PR_Dds>7??#hD)4t&sW()_<}&C_#c?2o5f{y45pjMJ8j%L!$n97)^%FDsc~ zo~*M^HHP7wl)Ps(X8th<6+HO0=c(=oI-x%3yU?(EceAQHH1!iX!K&;QA$S096E+gb z@d!Fa;v3H50BhY|SJUhQ0A>((q+EP5+!CetI*gRCn@$KAWuP6MlZ;m2h_-;2miQLT z?W)l=A645hy0-XbpK+&?W)V+p&MipT0g^Xj$aXKV(+6K#Uf36<$bNTZB4VyX@KW5g zRz@YLUFsb+ko%HP;{ZnWb`m~-6QvngzF@${#!Y1$hUVwNv>MUztE?qBb0Lc_QNt&m`>X}wL;yolZD@7%T0Z$O_tZ=;&rAv7Aq*km}99Yub6Cn!z@XJ4|aBTBK|#;J5qa`r^s6oxUSuor=>jJ((;~xPCq{T za;FoQE>$<_6#wN~@YZ~fxe3~ zn5&9v3jk#SajCWo?YaYfDrl`ab$AHFgGhF`m6D=4&p^Q?L??0HBI_#GJJjYBHiBd! z2cjlqCbPj{X?&?v2~7?pf2Q~Bi}R5bc(|!jOGX|%+r9shsYtK|K&dBrw@AnQ0yA6~ zHZGzL5y(tF@z{9)jYbpm9osTbU7bT^17oi5Q_i$)X_64QqqKKeu;56k5-Bf6#eJqR z1wsy-C;zn5(a{n2VV0|Y4=(9e^Uq#CW|d-y4-<$=rwf7vmD1p;or6m{apG+Qmq)WL zFf3(!(1~f^5#mMVTC4$!X}Tbmi}k10tm+1^l!`H`v%qtK9g#baFfgNI7wM$i_PrM_ zUQ{WUnU@0On(xRB`Rzk0CV%!j0ww>$Bl`mOf8}~Q56#=T$F#iDbL3G`I8>Dv%`4vk zgqNx|xtGH004Tbk+OeK_qj0hp-@J&#V|aSf{fxwkz+Iqun^)ZSI$-Ws4lEUy(qd3agW1+ zVNj5HJ`#F@BI6Dyr6~Mi-?7;WGAkY)Of)9+o(SclIWLXhU)uMgZo+IU-4}XJg-YDD zS}koYz0CW5wmj6Z-p@QMaYPJ{CbtJ$4mVr7t7mJ*+@u916uw?uWh|DfRqE z`!e{Gl3FXj9xM<+edb6w!0WMCtmNEj-L`JfBk*a`A9TC0i~9C!GJ$Ik-8P8{QdiF_ zkVr@{0urY7Z``=i%k_gSf>9f~0R1yW^rV;T__q(b8HF(B^+s_wkEFy0t%J%H5ywR- z#yi%F_*>j>&;0P3o0#@lCa7HHSseRwgGazQ_{6;iu~)C)Cq{;|x|wO);{KTwzsLUH z^K*VU(h!0_qp2N~6JO}IVg(>UqulG8El%2h`t-?tzizN%V`QHRZZ!$O$ltKMPZCDrlt7am3+!W zJB(8eOi}~ds_NpB)yl%L@7(^J55hG3y1~ikS+^jrYj{D28UCX+?Yg~K6q9T|ePwt~ z%rY2pwtC6087H4epfx)wmoK6pA6tcq*G}au1-brD5Ati$c)Bd2a{zhRPs41Ju2h*< z%nbJKS_2ZqrQTp8^x>k8TMEg9)L5RqhSwrvS=zP@v+ChNoX;*qc0_3p%J^gjW1wX= z42XN`5ckAB+A}IPsRmkjXU)*0c;5W3tWg`b;KpR*rARGhxCH_SgRy@efuhPZW_BJi z)A!j$J*lS;rY`_*ut!A5pJXC-I+|7lKUptdMlfpTciY>r;Zl96_QXy#^?47FL!hRY ztY7fnIZdAn)DKdZ&x@lJ5xSP7(tar0{7SE}_3>^?bT}+MGA9!vbq%+V6K0GhE%V!m zO2}W9i{3>*Ne(mE(CVMNDLf&pcCao&K%m6?A#;>DHzJ4002G1hnQL>HmCx^+zP!Q| zB7k>*t!c#Z3so^YzIg3Buw!JyoUK1MDwPJu#km9|FZ#JeFbz@4Uz)vx*enWF$piP; z8d&!zBswE@iS{S^ISlza7fBhv)o9wIUAohCRajsRzGv-NM@hPT!XZBB%<_;u72f+fJ&&GA8NflCr|fKpo^jyoLQ6JCnoN&YQ>Qoa|+qkyvX!`tJVc zGr!)W5c*NPrK{wp<#_!$&TX_6wIGVso1v4eJ21_|g9{rT&7_<;0YC8tb# zLtEgL#t%CNF$xdr-gL3H_0&2)3aP$dx}gFE!Y$@!aL~|CTQRuUf8*F*4XK7lFW*OJ zW+am;u!ng4sjD&i+?SaXJW6sy+}efpsPqh`Tg}vr*_w)=vc*lHr??s~i-w2h_(oRk zg(5%yCHMUu(MCkt2H4l`AgNeXOJS|4R>)*O5X<3Dj6GxK%~x0}S;3VO>!M(8zs zQo;T07t{CgMjo-NtPWivzll8oUH`ylII+o7WbPCEx10B_&@>lLsf?m;{gw5T9t}O-)Pv29b&kO< zZ$VYWt{Hu+=SiBO6piNt^iFX>^4j@i4w?hYm!?%M1V*4^mEh8}o$cx5oRF_eqc zL3FPAcfwt+fHZ6GI#;LxS(Zxr?vO5pMAP@RX=9Tf>eAlpSo=Uq;&TvbU}CtN`O+(q zmP;%*`=@!P`uj15LA-8ma6eO)+X2{K?gOl`p4OMj;>9zVLkBCSLe^U|T<`Q-w(O#$ zU1=woI>hOfLVzmHG_MEoAcnhOpb%9wVlqM0aaWm~#0|q*0#7hKxXbMACz75mwKvDC z4%$#;(J3VI!2M~6PpFP0Tg+%#E^@0$?w#@%1b)f9d$;GEi||T+|B0%JqNJ}SL<54L z8{o)5w2*Dg`kEiOiLA|mwk10F5T*rI8tv_`4?_}LiQ!kmLJ`s+BuZGLYM^c3#-_(s zY&Q3O8#IAvPCRI=WYgRcXuVgsVz?!e4`qYh`9Llb#>O~sben3!v*UBiq+n;Q-GpZa zBBd11V5_S!;DgG!@7cEFPdI!7SOWcsytBCU3U@mE+a%!2i$*`*KHKYY~6tWEjd^lh2s^3IpS zbH7m-=&lIKc!51iwzrTl99o#8+0G~CULOS9{+boosEr2}Bx@8(+-gx{W6dEr{7m0d z4^p;sdQpyUSGFr{OdF3jgsVgH^#`o#`sk{9q_BOm@}J9vSOPq%Q1$u6 zu={n>P@+jT8d9#m&|oqlVE30z`vFdZBC7?LC5?j&)9sk(trnFh`Wx!LR2nNNhs1An zk9gd@@0@TaybAF;tpvL@uB^^Bj;R~hmBvD`8lU$@-|i4ln{#{o=Jt*uhA z76O5w%-z0i+qS}PR~%$=JRHanlr&so^hi=50|)Fm{cZ^n>YZ`>b1u6B!+?Ao_~e6; zbq8Z2SjrL)Rq$ch6$i@DZHm4#r>9q5+_Zx%j(ZNL1_x5$!9u^L{6J>&1#ncU^=r^; z&z&ox5Ecp3}Y%MBX_nxFp2toH^sFV4o?)tob@Kq)(7}nmjvu<8EDYPbKfJ0X}^KLSLjMYmYup z?3t!!%HdD3H-6`EzuXc};7OSe=|ZBks0G>Pl5dEtX8{`wZC_fi>iwX)2_{+h%PRFS zGxAF%P^EEt{K9F60iK6~95sGPVRRcE=3(>ZW4djN*>o@}YS!gL2}fG?Z{)46c0TF( z=iH18-8)YT%Vu=#JL~*rba8GV=W^wO(mLnF1UWaj(2cR%)T;y_ZwEdFr&<65Wc-iw z?fm>%iCeg_)T+9~gy*$!c~zSuf7|c!o_`-K<@3lb45T}DXXm)mM2%*NZZfG0Ti#|A z8g2w&3j0r;;QM$XJ)Mt~U@u4bm)^)vhzA=?@&NVbJ2=6T;M_&_R(W)a@~$BgX6QB9*NHiz%vnp;rAbKypf zoZLPZ}G8cU|l=JT^en zYD`-dF~$1cBcB7W2k2>DDwTWvPuR2eV7c@cn>A>X|DAQ&KR#^J$Vn}d%8wt|CcPuD zPzl-<89|KoXfKuMVQ2`_8;`|rz=|XF8^2XjyBJ9VVEalbdZESH<5PS2cUwrv6yBg# zt#(;8Y73u=k3gB|0%@hVQ<0==B5Ou;juRohZr1yGnC{wk>2mN*ZQZ-0yA12rV-n>o zZI@n34<^Z^Z{Hp6VH8eNcMvV=SArtRRf$qcJyTW3`gtZ65883Wc!A&TayLAjeR@#t znyXy;QC)Ve>NCpcS0sgrenA5;OX??VKwTX}>L!HbkM^UglCkELrwsCHgpe?A&MB8g zi+cPF$QcylaaK6X8+nv7)P$s9`mqN1GDx8rx-kp)tDe`4@;*_QhRM7*gnc5w!X*gj zhcBy%O}ZMyf=KghmjIfU{3+}9FHnHw5kT|Yq&0x>6cDZ0?uQP>sD~9k4)5v)pY&Nn zr!3VM*O*9}^6g?t)3q+I0m^Y$yjWDaLLR)*7;|(J1@Jv&bqY*>pBj<1e#)z&D@pT~ zzN=LFbg1Ldo%fclrzEduQ@TV+eYu+9jeWV#KVRRxZ{NO@)We(Fa784jXWp}1GOC7c4j2+D9>T!OoTf1~qu^ByDzuwD($@4n;_vdk?;33`q=HhU@+-*v zMjAhsF9du{L9qZW9fBGfu^8SeM|m5P6UY%rTluC*+11zUETTRJE7ml+w6~J8K}5q; zne&$}wW2RaMi~~V(z02z)_r=hygZ!xDCTRnQ0czF*=`+cmGy@LSfokhP*0w`<@l9W zy6e_nscFHIkQoSj2f13(HDNAwh)7SoCH%FJ^O`1O(o^y)$lJ6&GP)7WJ(1xa zJ4c67ZuVz1fJSvvg^j)LyN1~u03d-nso7XtP68-*@kcPdnOYMVo9n}}%6 zFi|?piX;L|a)p!DS*b-`@#UxzKK12;^sbivMVq!qCc2llys5W(bwpYpK85FoNu3M` zF)_=&il{IC;?N;J^w1F+fp$*YKdNTXD$dCLk24 z3B8hZjZ=XHwilPbxZWg2^$yugJHo=I_?mSSV=a%jdB_|Jd8Y1lbzA-UigR{-iV+C{ z;8G5IRY_l{uj<6Xu9sU%h{t&%I7EGoBB4dEf5%r2F>2=>n>8}&rVZhbL8{%YW3@L1 zo8QniJej-?WhC-1$#+7l8R+wwJ;ZDWCzGz<)^B?pV6m-rjQ@%+Mb*^|>>SbL+$ov5 zL>gyf*buL-odzuxQAPagr$wsBV4B4Y4e5A(ML#{{wP}$(Pj|?f%$e5;-v7kB8_6rW zg?U^Fx$TkTp;`Iu#^qffOALw{Yac0~jdb%$Y*rC|@l-7G$oy62`r=53G9YB%%J z?B2-x{^r_odjcQOVvMDwm&ExMGemsHJ%?tZ6&c+!&XiiMa;`F1W55vYyIzraLN$Hv z#y7Qq$3U=C@nCQf?$n7KJRKNPP*_-KpT2D~m?Pr1}L<5pX9|K>)&wHHw|qs?mkpDV8)u4bi@mlyeTh;vc_e6azFCy=Uy zMSGScX--`bB7Rg;f&%gyE;l*M!Ad`Xk)MX7*mCy_>Qfq(G(zQ&&Vvci)@0gDUdm`Q z4h97z0_y{&m9&BI=Kx!P4H9zp>+0>~ne-fM*u!_&uO6T8?K0N?R4qpEHjA!Zy&C9q zP+PhGWA8r0P~G4Yl>l$%JX6kuy>Vv(EVFcVsl=(int-#hq$7@eQRmrAovOlO2C5u6 z-uEeGwSVN(;Jd|KAb6-`6b#GE@I=2ZZQ6)_mVz+bedzt|)cV5wD)=C+>3pSkx5}aU zh#H`4r5Vb~E?<4bCeGAab8z!5ZY8Umrk&p!xjU^r^$2w^;|_MRdSQCjIo--wqmDDX<#uZ@w%zQF)h&SV#(XFur<08Odw7 zU^~oYFAWA6n+j__KflX&JHLA61>8h+Pcv0l5qf%oSV!ncIfOBQ%Vb5h&Yrnp({F~V z=S0qM%Z~7HNAH6-y=Q3soaSjceLp^!_!p2Zf?GIbSopWX_$gD_vX0YKIoH%w3K9;4ZP(*vk+go;_LP!2F=Rr+bnogR5b6@!a)r!^us&dQDMb_ zdx+2!2L<)f=J}M9QokOUxaIWe(}8dMHKY+_K_c8z(Ngp3DD0(o_(pEWV5W+-tj856a|8ifi|jLRJf#kAC{fVRyG)Z2O92H!@!hmpvZ> z1Qdvakw%t`RLkYn3DZwH;e8i~n~I4aRC7CejC$wJroT)93eL>iz+sNgt>sM_1iaPj zLGzrNZWzh~K!^YiVrUoGfHJm2zj=L$qk%bYS8|ro0#FxFEd&YJLp1{HKy2t38AY)f zc${{X!+!4Kgq6S>${x}&^@Eq(VV(mkYg^F;w^m^cU^l=GK$^6{hTp%eny(Jko5xRS zo*6#**h_=Q_1gVzyY@}`X>IQ`?O%iA)Kyfp6*mRCG=7N)ICtCo;ODo{!fm6gMIFVF0kvYf@)P(5F;iXr zM=uCo-yue|Se#)oLlwkRWVEx9cFF-}Cz|BhVSeT0K~!z7(%I3muyM@yI0nTehWJFk z9_dI-Q1FsVn$83Ef-HrlspK04T6}h<#v44u^23;JN#;wr(M!7QMl;WuK!T(o&xr9S zlS#PrN{^uHIfdg|v<@^HHVnpg&!=_Wamn))2&UP{>O&RbKo|6jIy!&rNy1Nttohg> z_}Pt4BRp3nEBYV?kP*}9Hh=&8(3h4g>XZH8o`sdHE6<5`0GEz1s=#dZdGoTgI1rc> zC;~y47N-Q~F}TT@zBR|>z(-7S`CG@QXBsmi_@ne(-J2S0ErK0}V0R7`PG+jQswRTD<1(?kS&;=n>(ei<*c_afYMb_0voSU4hy-v^P=@` zZj=1~pZIRcTj)8o@h<IE%BbtLk_O1Y!tMRzo(9gNP~9Py(&^Vv znf8-f)r{B3KCLHKI&``1@YiwgaCML5z}UQHi&$@Dyvfm{-GCfq2_+0YcG0};;2eW{ z88JOh8|%m0x8#cvs+Gk-%u%-h6v*n}a3jB`pxU>hn^Mb^4t~Ei7T^$9mjd*j;gD+$ zZMJz)J~loKdC&SO4Oc(@Q6pkKcaKafv6sN%veQ0|C9rP;1LqaEku=Z{14Htz0nwEo zHUlJ==m)+A_a78QR@mWwHzdjV>SV1a8OsGvLAyzw)R(2|ecfkIOD52&wR^KTt#Bj*4MfQ1w@aMY zZZxhFiO8_+Q`9hYCW__DH?@xWZQ4XVA8Y4ZH%-GRFt&Z5MN4}b_)=%#8gBJArd`md z=Lu5+<1l;xRY}vrPVF#YL+}A({nPJ;jDU!LJ~-z0wArAHqLZK!c}r6Yj3sh?4n&T2 zwC4Kv(-#7yHMVK~08IMcTE|#2mT)2{sTnq8jRAtD6K0YH=}Nf&?0|$*;m)vxm6WlZu!4d)6LIS-0Gk`%o#;6ml|yx?UJQ-KF?Jt^Q<>J;C-m z-3?|wJ2&0aH&XLq5wXvJj#7y56UTD8g51kE>g5YIS&dau^k31MC`}Rg2W~CC@gs*d zR?nz;ngS5N%un%soSwiPOZ4}r(}%WYwF0za<^EW_2yR>obzp#my?(*B-$A;X#_b+s zTezAoH;Mu-_*rthKDOUmbPd^_S+K3M*?*4e$~6u%2hATTXTRls;ob7Us+ zRwJG7?f7mL&EthK-N(P)hO9rEa)qosr5M#hy5}8aE}$z=j<>iTJuTLz`2BlJI5i3E z;uoRL-^9A~J49*+&?bH3jjU+|SrYluo5 zP=FsI#Q`{yW^O6LHT5P#qg+XSat0zAWx^pYMONn)mO5qZo%{Fo3Y^0k`-W0c5||0f z@{YZu+iq0h?%ZeB%>h4qcDqa@#K_x!%z`ODe__%goe-)1*x?+c z{eRiwpi9Q_Uw{25!vM`@j`UgM?Cqmnr|XTlH=qHvgqaRr$& z10j1JYzp@?|LaS#d5sq*YQ?BFrP68xw#S3-wcGq@LlxmI%5a$h-RP$dm98re%5F|j?!e(u5$FFu|VYeWIz+L)$e1SEtQmPLn{E;%nF(U8%1 z87w6-VVo|va!KT`U!Q?tP2K6ene-mc~)2PwP~bIv6WxNTKrXIXy^ za*~xr592l7jgf6VoROPc_^+5{%O#PX=o=m$p5m~?<@1;}Us5*If4{$B-<+D=`SvC* z29Y4r=!~$oG#gh%X(h)7&rTA90`e@Q{Vy)dk|^2jAtBZ~v%@&*_!LwMMogA6MxumT z@FjOUHTfg1F-eSi{`~pO>uXN0=rCTde(27V$hGZFHly%D-TW3IcXWAF{=fvHMh%v0 z83iv4s2Y)Bf8>fEx+jLJ)i>z7^2{#t!S*XVaSQKo(zZyC~MVdjC88coHGcfhb81)OJBWe`*6) z^_|`;{KxxIFoxF0C#rKKKcFplcr+8^Ub6)&;M^(`mq%Ukll@99(`;=4=ElV0ZlNCg z08LaMz5&OI8d*Bwm_}kxzHQ-5rDOl;bgaH5s)ql|W(7Yf!Ae~obGR1+U?C=`1lr;t zC4{y0Jn!mh$!7L+WS$Yf`0x0nrz6<1i0WuE?}nSlhz9{-7Dw-ebrtpyu*)8O&@MfY z8QnA|UNNiBRt-IwNJa@Odk;0&oxHp~5Y}5&dZyx&G3@Q^xo%xxGi#itFGpzx4#<|R z#Ce2@qF53ULiGpZCuvQ!Q2YgO_VU`Vp6d$Mbvko&lU97&>Fico;qyeEX$+k1i1;CY ztA6Z~iV5w0xDMX;xOsZQvizUEpRv6UA( zDgGR~6*)hUQuL69arKKqa<)xNY-97>@AXB|1JalM*U>pT_lQkBxMj<@GY8HgIHasM zB}agFKKxP&G>TG};gJxj6)?@>uoi)OSu!OvNkr{i0j(hCu{)f)3swk8{%DRXQS0md z(OexqoM}Za;M0%U7?una(N`ODv@j z`{7O5uz%)U=>xGyA>l@Jq2OxN?Dv5lK&Bl~YWycZJR{R4_=*l#A+f}DKn6~U$-I)R zX)1fQ6*==da!)*JYdCw(oL8SdO%R>Z>Z(w!1nrfPrnsicUR)n6dSr?|!2m%?&J!p{ zj+f|UAPvDbKx=}4IcKK)q5kM(YdU@s5TTqm2zup<^-cc1i4@#GU89Zsq)KAvWL6Nb zC%~1IAd_ES_{=mPbB1@OL6Xv$mYbgB>#FVLL(8Y!`0T39I^`F4{1wHtLqn1eW~`r_ zKN%;F48`GWliGq8@P^X%LEfP5rLFEJRZP=N(1~qtZC=6{tfTsKZ-2dC>#1&0H?pUq zO`DlS_662DOf+A1z@=QzW<$Rod%hn&d{`+yJa}guMV?yP;vBCr&d$!q>Kl~VZVwE6 zRC|5)X&ne(f`8a*e~frN)Dk1m7WaT+!@Z0Ec-t8Pq4jL)-P zBg3GmzPW}(4VRVG3ST-038NN{Z}{^Y?*$*DPHCsTixEdQ^)-}E2lJ#p;Ov29IDpJ! zBYnl>#O7&khw-S18Vdj2d^C9Ud_(AHq_#liBNCGT8^%JIm*3^Msl8+lJV<@}JP5d& zdHE%q2YahqY}yV}uyE$%kU7{wGr1V@6WouRc(?W+ZG!Js;yCy!89B3NzWv0U21AsZ z#b0ePwX)a3%jzb*y#*@W0iQO=oElltbU&G26Hrq|f78Z8E&o*Nc=WD+4kd?Y&F8v@ zTNeNokfEi65S>oHKf|=Rys?crC7ap<1=({Jcy0*OnQYet)?y(rq5_$ULubTK(^Zi` zK3Tc6Cur0S7Hl}Eo^RIFyuQI-ej?l?qPmQBRW4^T;pB2|?f*jpyL|8FwI5IY4&(R z00EK$b;{6Ek;-;&<+Oy?le@L+U)UH=k`?``P>yVJF+@@uuOwnz<~Not(QY4@s9Njp z_qxwy>qq{8#EvUaQyguykFyJ0;|-q%eWdMvYH(@^t%)QD4#PcD-YL&aQ391Lyo0j+QnpSLL=vfJ)=3aH9Y+`%)d9(u1Ot(`nvUYf-9Lb5Gj-^zq4 z|Is0F{TF-67+DN*3St64b4)ec{K)nJSv`W3oX+2dT4ox!FK4BU)57c|D~#?wod+W) z_(gSi9i&Cx%gU}*RaBS&uJD?jolm6PRZh@6`5_}E#bna%B`7PubhtH{!8yv`EL&Io zkiC2xtu;%L%FA`~yd+9QsUpy1G70IWS69Ex4`#782BfF_9}0+-#MbxF$>j|~8%$s0 z71(lSfO5NbG7|+x<0kWWfXx>Du%+V3w;ul=TKe|%OE4L3YdxKk!hzZqGw4_7$hw2> zAEzAwJZ)zjnD+Jjv&G!HOuMrD#VwJBi$<~P`-L%EuarGKcQobXgB%;0Eef)9DhHA3 z$B}xob5Lc@=D69XANI@dS+|TuM`}wzhPZM&r1ZD^SMCfMVd@ z0lB4-d-s<775g-2qk)nK@EzY}Ton%oCfNDRvE1vx+XH82JCE-q2A^Z%Vp?vrc>Q9x18{pE%T z5VOb6De1@NpN_+gSeiaGFCDi`)f6$;q{KpwC=N$a%uwDKw(}yaDQNWD`UdnzB2&W} zC@HZFqVEe2p~0vvY9fWWMkh$`s;H%$tNheOfn*f|Y#uRZoBj}=XbbxD!xW146=ui- z`X?@fmIC0!Z6a=08aqiN(^{BQP%x)y8WT#ld37zlo>BWaetjpY7J`TV-iIRsv(GI_ z*FiWYz&S@i$?Mm<Mp>ym_Dw6HSg~IZDkEQ66iMXuW zTsp#~W_P$Qjo**xetKh`a+7mVX1F)Zyc}oCV5(O4PB||n`khM?!%7dJL_hcI1GXT# z)9ietJ6)?x%v%%m~x*MgD`C4ilh1 z3Swm0D+5TPwrPkl7hpv~RY0#jYaYWIl~p(AKtfHZ0pt#RzSz2M(A{RXI_s%S9&m$t z{H*FJ+6{PBcu)Vo;+&tnF@xnvWZhBeqU6}_90p@}n;)M*!KT+b&}p@L9gvY{WiJ-$ zDiVQ={Rd;@GCrCOur*U-=ZKsRJ{0Y&Q9t5i4e#WcN8e*#l&=3{7{7-bMdmf*4cf)+ z1Jt;FxkLBdq4VEt{5-c8S(XGFHmiW2_{B zHC&cwQ8tJmmIpU#=AOV!FDKZIYc%0mvv2#gCJNp_=Pntvf)(*@65O&qJE^DT1T#EFSFLuia;SP*Oyt!XYmMp3dD&e1{zK#o6F)h4oI%j*63IPDs`b-W9&c{W z0#IYWf!31qOB?q(?nEbb^$Cnb`aXj9j_Rv4{{_yDuaLCpuk=6v$vH|w2`Y;D%Tc-2 zp<6o3s4xgjKNS4OD%?FxX1D%CmW+vQ%w%1qpM7M8A8Ij4yd;bC?%A9CBDDx_UkYZ60O${h=l%W*N$@#X%++f-Msg_U*rP%K?As7AI6shm{`WPLD$N$IF{?p z-K!1?E`5=p?=rn0d)&x%Uk<-}RfM!HEA_+voue*%$YAQ9?Ib`KF+yd&Sr<_} zIBdN*hc}z9-GD}p;zNmj&2O=SD9kA8Zn^C}y64>~ z7(Khmsr}EtIX2P%f5L9RJj3hR%)jK09!pOmlP3z%AzWJOS$KQmG3LrWdD4ysjU>GB zm=q*PTxxO-FbQ0uT(n`*mL#|6^qJQ^;C5ErllNAC`ka0*&T`0)b|=5PX1+|c~PGfbd3yl^8vg6NR7h`s4hfR+-IrX36}NA6*{O!{FfG zfX|1`#$>f0V|F&cXH>w&CIOe#+CVrMef|TXz+V;rK5K%KAR6gV>Re;33t4a24aV=2 z%-lNEBE#GGxTJ{ZiaREqmJU#dSZb-$I_VDzQS=PGK3zUaP!PsemY&}9Q9k5YG7s$Vb##s31o!7V*a=%A3t2*>M#&5W=^C zs0Jx#HL9GMoo_8N-JK8qb78>0b?IY!)fnWHh);HqgNw^uvXv-B4ZnMaX|-AV@oBx7 zYh8%a?SHFMX=M;P89-j=OV9KR-iz^q%NPnGlhHSV)dt>~OB5*R)h%WSP;EJ_=uRPC z4{}k^>i8Y^^mFw3gLF!M1;bOMY~pHDctpw=VxhFvs`z;*NO6zcCO8KeMd%o~Vgihn zThFyQQVswMw7)R_cSp}d234N@t47WYuDkoZa93fI!l@f7-6)Xj6y@HmUwq9x?7u@7?Q_TbH`w z)eXWciE!GVlJ0?CB5G^HEXJXfeG@!ZBFxFwl3+dfSa*H!8%`891iE9pRLm~nCy6)H%Ceeb+)Q154n!H zlOPZ!Zjgv4Y*_o^>yFLSKBV@;!lq|aiNmSU`@iG2)#g0I4>&O_=ph-y)ejo_Fe@Kj zbgx_8>Rx{?*fhXn4s8PKb{s-UP{f72d3tM(+y}rRfc7onVVBowNV7de0%GHYM>IYvFAYrYpB`__6vgoD@TAjO-0Zp%^;{Wgr4RRC6=Kmo~LIr+Xp%A~=tN z&C#jUgcAVVDTf=M-aT>G*rUZ@X~yqk&P+|qnAlmpYu7)Poxfqn=xw{5b=v2-?N`6o zUlbhIw|W=^g24E{Af{GvavkgOsA`pINfr&3T9tkYC#wI^s?)mgF0_sym%p_A;}$WyJ37+&69}EycY4%=xX6k znv+~LfBs?RVL-$k*#uL2O-oi^;^dekTX5sj_DhmJ(Z-%fw}_uoGzP0s4I(;*I;nE%5FdyW68jJEn_?r^ZG?gGG9{Wz`P znOYCJ$E@6X^5n_FqFA3L)6HHHDGETvB8pNx8~f_+=Ps@;F5EB8P;=kca<@yj!lxn; z)SPOM5S&wV>8f^W)VQ%5h?FY$mP~WLrBpkbeRKIv^khT-;v>{le!`%1v5_c|18nr%b z=Q4V;RNV4ND(nweZo7EmB%8TWvN2m|q5_-s?%^LBEZ#+gy`1s(!3Bb;Wr7qt87S!u zREoWw<=eVjAnv5*E$#Z%+wy+^j`te?90J~C0M)yKEAiirE^JYW|9V+-f(m#iq%nMgt#}Q7KXCqxbkiO6a08jmC~WmBU7sUNOWQot;(Xd{lu60!yV8!eo4AYyHn_R3p{MJ(pY{EDln}Zysev>jl))Vg9nBBU zP*a_n7GoK!MK6PpS6NkcEHhFse;x#e--FykEKf=;r$jAat3?L^&BY9^*TcwEbe8kV zQzoBZe0`)N+ZXxTD4AaJm1OHA%X8*>WlKAU@lurBtPAHI| zA;X3>AD6)rwA$v2)IqvUTlYp2=n?%pIP*;#*{Jks>KjR#6_AYrt+Lt3ym?GV-kpNX zjaE)^%UJY2XyHgD6JKKd(xOm}Q@~rY3W4xVY@3W-{MyCG-s zQGPX`zUDs@Wr{)NKvbqiW>%IJTfi@(3iDzTc6~J6!a08R;G1t<9CYcw=|UfoE`d#M zB9)N~DMi4wGIT&f$tlJRj)_~A&RPPjDWWgHYp^iIx52kFAp@3kn%`UxZ-g;iCdE=x z-DmPj=m>#gHV1YeM!5>m@CKL{8JIOn4UOubp&r9dz&euphrave=g&JiL4kZos4=+Z zexa??v2pM3Tjqsy?(Z^0g5)^NxycX2C;wF3D9S6N&9QG-PgGm>j*gr8s;+N=j!JlQH#B*HwBR=hJSenxy3nOm?1 z1*bXZhG=z&zoNoW+pnrsxlD4{kPpx#rxp|pE7*cKQNPXqiNN~`yM!E&NRy6!U6yyt z*}Q-V2L`6U0ZJ!vKoUh|Pa9!1`p9GfQJ8kj zyAqt#;+M`~w>(d}ht8)VrV$4&l#lz#LPrggOjBSF#A-}J)b0UeS5<1p<^Ybp`@ z+(#Wdr!15tZg!zH!=70x1RrE5&yAX+T@x*>t6snnut`y$V=p<&-R`9xX4MPUXc3)L z$;y{EeXR;cB5W=(>As)@-raYq^^uD?f2N5i1xlLOt+|)aO^K=?O;~U_RK=3=$RM$d zwT{8sg_fPVMX2KsBe<{G#v6dNRHTfjx{K(gfA4qW8y#Rn8Q|H#hb!ZN09Qe=DgTBL zwd(w(f($TG&+xU5wU1-)s|>sLef;k3#Xn)T({og7V%&{Y1MjM6qkb7OJ^4X>RNX@` z`t5l&;v7&=YRXUKXoGd{wg2s|42gKZ`W%)GQ2s4sX8}~7NXiWUsUFoV8_jl1;H+61 z5&c8`BFVkduwTwb4Zaqv*Z)cpZv-g;JA_O9echzRd-`7=yyVjC5qs`r2H&1yl#pYw zsg!4z=$7ZfsuxQ&PtJw3cR8x~x?a^!W^e-T$95u7?gA>j&x?;QJwr&Xniz&e4Z%08 zdTR>2cyva;U0b(GAcBG zW$n4mgg{E%-HX^MJL5BE=OvqZTX`T6GDBjXnifSR>Wb5&`<((FStWr2Aa3Kw#WZcEXup% zSN%39%;!222s9IaC5quoq0R^|(Z7&dyU$H|3Q1eT&HF0rVXz|%i_^O$FQeR>DVpS3 zLX7t2!Y!p8d{{GxT&*0{9l7(`hVGLymyqg=n&g=J0Z=oSA~_5L5RNxp5D8!d5vwv$ z7kE5ts%g8nZM#ixhUk;H%P{?JJss@rWy%MD9KWnsD`L%?h>Q0w9WFS3DtP4aO8U*Nl_{D0fZ9HTQ-K9wq5C@>8+2rq;G(Rd2)TMQg=?1Ap^}zLu}&6oK(x`&|hZW z$vgw-Ng9pDg49T6c3$m6N%cpfCBatUP&_h1-0luvo&`39JQj?-@>0hloJMK)%vWAE z{2{JY{=bTj`_#86)KgHfFg2Nq(so(IvgSFx)}rYV>l4&xXxyA}%PJ}>+n(9hXa`Z5 z)acs7hj+%-1d}vf!#dKYUiZNT|FG0JyxZJprDmvoKfDO~Sm7`vfMllfv%m>Wsj$=6 zqoS}*u3b+%|B~Ac8{1cAB$n01a|FO0sy?UpnZ26%aC#jy8K7TxCfnIC zv&o1Jzil(eI}Td(cLutu(TjNUyD9dV`V=(i&jg}!EfJFIPWwkiR2g(u-9aZ?KnNYC zW}-7dGjR(U;pW=I7@C-kcn(c0|64^>m5TMDS=>Bed7(4=#XMO|AWm>2`{g|mqM|HV z_3>$InnMY@@)|7S0kaMFlsV#Eo?VRlK9{Kr>XKkf)h~b$z>!$5h3i8V!DyY>w&w2f zG}(FX#S!+M0f1V@T=rdj>DtW_^-4?Fb0icrRj(6vSWTR$>iuqp(WoL)$Q9(&blucA z=9+B;^B7N0KY3-o&D&4&$*)b_v4;AYhugQP`GNnBsrL@#djH@5(NUs784VSZQp#vZ zn<9#q5~ppnODLtKL5Y%RXfJ7=3Z14XnGK>Tl}Jj9$TiMS%3Vm{sW(Tzt^2-W+rZ5U7=(iY;#m+JW^PTgC;QJT;1G6p=XZ% zjSF|5sY>doCxUn(PE?ul`HI#&n_9lx1;ZD>y~9<;TGZ>9Pl>a()^~D*_Y~H2*CqS2iE4K@yrNRSS`5WRCzEUIRQ$GK`^|*#@ZyhmDiUt^ z1pi@wQT?U<)hRnBBo5nQlWQ<(l=1X)6^dG_C+v4jcp2&}zt^gdx`AQpEC2Vko#!lG z9_18oe&oxb_n(bl-T!+3Yxc`k?_dA;krX%V&w)E%Z*e$Zy7ORiW$2(SUbANHIICh7 zuwIN{8Bj57*#7t5f3LXk`Q`5TvtxEC$8ZA?r4^YG7dEm=q~gli`=4A#w8TbQtay%4 zC@Uu=2P(zSjYCIk*Wp(D@UPpMa#$>j_gk`r!4kN)?JvhMlu7R9x%cj=9UL=foYldk zgL`tEm`%guF*%=>c-7Zy*+O_qCom_WNt4`5c`WmF$RJ`9M&=P#gt(>1)ETjGbl(Nq z*yeeGPZs~_eD+C-Mi&fFjV>|DcGh3T@Gtk9a?BP3?t%1!H zQPt!224ySXeDhu8Sgvd^TQng8t^V<9d9}-F)9tOt9vt8mKi8m7A5Hc$eQO~6_q#rA z{`~In1r+PG-?_QER@`pVN$40jd0T@m?JzXX+0m*dSfi}7AB?FThFM1rMk+tOwk^VC zoY|&CJ$wS{R<|ErYDpE}8S+5!%VaokqTS&U!ado{k7$=V;LMZxMPFy0xp(r$je!cX zl^TkXRj6H5Mt7pl9ne&V$gKk=DY}Jke&?~fyp~Gi6w8w$4UMzc=VKPvE$LBJ*cF2H zu?zO)8^C?C;8rP)n0jmo-wq(h(BZCnhxxN+>A!f7q~s!fvyW*j{uCe|>v^sJa%sQK z@!3fHGJdRdPFvzO%iCM_%a>svhy&{^0Bks6Q~RHv_xFot2E78N<^q;3tnImL)UKlC z370QxAJfd&A5-*2Mjj-67A*(yTv-^a^oC^OzW;!oqTR@mVby)~Mt8Lf-YR?hii#9t zwgrHLH1qt`@m>9>L+^Vrw_M-(!XO<$iypa$6;tL_Imr%CWQ*%ws@c(ZU^HYxd)=M14_C6P$EW&2wN zp8r2T4blzoIQTBYg;RuHisW~J*nLD?){DwF{64bNnpCQB)~|M<0EK1n=S#=;hJSu? z5~mB=Y__0U!Q0M!mT9sMy467v5&y64!tkwz4#f0U>lhlgG4ThxDpHC;4hd;V&HqCC zfTP1^+=|`4`eoU)IllKep@RCcWKC!|_f~cP)q96>4>H38v)jGYhE`r5*R!+fLOh%d zGR`}KBawO<$>M9Dm&n}hBZKUYo3|TVitVOBBmFOb-gT@x(kYj{5#hLOE~$z-`@Bi> zc7NQ${K@S|xElAF#CHHhRZ3{U%o#IlYr5)(ZT9V9(EYzfrh0w*_I2J-2*!d=L>ff< zd&VF(A`1NbPbY9gX=;J5V`5_`(4cMfYnAs$A$}<`a6s5WRxqA&`J8&tdrNfm6pDGU z1{i}Q!!iiO>*m#NhID|IJSrP^swjuo^Q#$Vbax2W$q@JobBg z!b~a=p#dKaEl1HMfA*5ykavX?#^R5MbKJ~17L2$@;<_^r2@~{TxVE?t_P*cAIMIzr-KO*4H8m5jnqx8Gd}%Ka z_kQjwkd2qO>T3CW${nxNcn{a^>_*NU(yeZF_@F%oYAIBv%~az{zOdw>*^5;0D$SSc z_lLX;FO5B7)u+!+GP6R(>s94ibK}zZ7V9q=lzk4}|Mu-$+x0%gG_}vsn!eebJQ4c= zI3aCFEW0y)-2eVsv4{l~(S{DfvgRWQ_Y>h*7NfB=557im;e{~D*1Y7vKvYJj00np* zAUuZ!b;w+?v8UJ!g6)ssIi@|hJat;-*i#n9%zSA^Ga%#;_yvG_U$AO3;@H9^j7i!i z-C^1QG;AY)y8JlI`CL7TXM; zE+1G)+4N6RsCLg$l*{bufkLW5jR5w+`01XR&%y-@M$7J|K8jo6-cV3_t-n5xj#XqH z?ER2P7;Zx_6GH=BSOm-FS+&JVQ#5nOwCYBA2gI3QvB@JkiWz`HQr*8(;y#>=uw=EE z4L>7o`1GJ`5ns!L{C>|g*)xC4zLftKLF`!3a_rcgg&yM^@j%~-fmdsU>7b8-u;# z{;JsQg2+r$8Aep01pXsWJI+%Rl+hY!h`#sxrtPWy#nY2$ORO4)juNa!wk(RCA7D_n zSMVQE5fQgYy+FC9%n%jCirQ%VoJrlaWMIYgS2PdYHwu(A-blhS*PR86^Q-SYecJDl ziovypKHAT^WQ-ZI>R-n}UBkRJ*SkIy6{)@7w5Zy?4w0^x=4v1=je+KhEGqp-AyaxY zLeXus@SJwfn*CWmsg-rn(pk%V3kp%wqponw0bKZ^_`MW5hTW^$;b9Knws|EoHr>e?r z`BCWRgQqY+5vYyPKU%66n($n(9#G&QOmOd_Ws!yrDS)+S&BNtplmF-L|2$do=O6B} zG>jTybye<2v~|rLB7c57d*Lel7C4z{V8=rDJUFbUifrwZeP+P+`6#zU-$|y6rIN*O zYntQz*+%u4x&0iO(F;HPD6*bX1>40#t_CM!uQrNqR;(z1QO$o&!Hfa!j-nvdcE?jd zlBN9u!SsH-t>?h?(Tv0+X5TyHJi4yRKGN_HcdWXyAFVR}^?gT=r|*(IX;2zVk;`!k~Ua;TJU#T6fli9Xazq5y3Y11 zvKAkUE8b0xfC7rm6Z9n@q^OfWFd^{bnJ{X8%RcF@hgZ8_D!jWDc!+eWSNsAjYKm<0 zq{JJMco7EdvJWi!i8Q3TOKj%xox1cP1O=!q%EREJxZPa&w|Y3(Pui*85uXz$vmo+# zMz_F`FeC5pQHl`nW$8X;duX#BmwX6=zjWtcH(SNN-@ zf(ytzO97l~*{0;lr^cTI`P0yyvY3A(N zMh9}Mf*y?d7iUxig)$C;!lPruc(aC9`1{xr|fRnH63Aa9Xd8q@4t7@TWYbl93< z3v>5bAiwyem_EwjmU6R`KgPh10`yXUj}83!YHjI-o)r$PezL+ z$XrOpVZpH~34NZbvm#Ol6g+<7mscA)~2l@2q&PhjT@ zHl|eb%f+Dg-o4TNpO^I}!f=TEcP2-)UT*!5lu_nO6rna~lXp{>t)e0!AswkoAKEJhJi zs@{TxdD`W#eb4(17_g5)q@F`7B7By=MqzroU8>9M$xFotg!6#F$|2m~K9ZT0C(jY* zSWS>FXhdK`y;NjqTu2f9sTkz*aR!ZYY=ICDNd5BZ=6HrBd|I}(3{$Zb)jJ?A@TS$F z_OdjG`T=yk^6%|ev>bA5`t9#6B^^C%*wAMc_2p+T+ zZOvid!V@z!0T^N11gouZX$f3o?tL0ro&2g8`{wVT?`3li4s09WSLwu3oDB!8cEvUa zys2l!+W<&iR9jk>b1Gx@l40?qIJUw0FbmxTg-Q(guA-tc6aRJPwFj!LUB=I)=jlzI zD$1L!?JdLkN4$(bue9#I{|>|Ju@ip8bsgOXYmUMtMdq zc*?9BbMiQ5>VgcKoIA0+*S)Rx-4mD+sl?dkL)jhhsOb}iao;~a=>d{Vhk7P!jCR28(h|5f+r)&b$zZ}p#Lskrq9J9@LFG zP^uDIc%ux3O!F>8Y=bUcl$jxsnJrTHe3()_Ix>|Wi9j;(uRot0aoUjs&1(+Dv5~ej zuXoYKidpktPIFOPWC0pq!UYpU#EHBA1-1hG_JbG-FK|w73v^pgV(RW{?5?qoOeH>n zwjX!gYw?S%gVh~-&RdwAZuZZw9wGkF4?M3|bQx;~1i1f_>#n`(#~J#9%5DeZtpvVN z>w1I~4sB3T==tByFxAMwgi<9jVYqgh=Vo(_EphLirbYMuXZ4P>@cdaOv+bv^#_gpK zguddbXvT0Cnmu>8-j|wxtEx%}Ob3Rnc`;M}1ynu?i#8@NqF2`MT{$G#_;ty$lA@v= ziyHJybv-wqj(P??bZZ*(#bEF^zN;sUWg`4H46Vsz3*vhm{yd#93j_ZIHXj1#q8yLNuHvj24MqF>1R}T9#dEeU4?R9lcicJdE-P;`nzA#{tt(>BJ2|IoAORWnXZxQttDyfQ;g(ZSND+PP zgA?{;P$+0FhXe3ns2w`@$D`xrb}lRh<6;hxXM=)z;LwdwI!8Vu%Dm)LwJd-^|~ctr6SP0{QXT;E`B7i18W6okG;`>lbO-M~KsB z>zsRSZfqWJzo4MRpOVj}Dxv$XyGOjwGv*-#$PH#1mU{&rLNq)U_y3?;Tc6JZ%M&}F zuckS$piPhgz;S z^P^d`?v%pu9ft1xs5krLpy6YJvSMe>UsV%(z~p=FkRdqd!aPBX&`2-T=F#yP=k{q; zTeWomcpyII6Xx?y8f%SFH1X?KkAOv;$JQxpf?xS`?k;4D<0D6kAH+$MHURwtW(6X#J%%B~fFWExt=@ zM=pX8*woVUileeptU=hF3C_+$GO^NPU>Buqk*}}jR&qD_3(*zRw177D4e~FGQAv%i6(kM)!essy^jcsCjZXyf5 zdH=qvuI8KaU>BZ^a9;|hP48gZ;3Q66Jbp$WjG0r`G^_ub!}pPmx~%^^MwIM>hz5`; zRFFA94J>9rvqX4LtvYH%R<~aX)!-rUaAK*%Y_@t+aR(*vjP8Dm7D^J+JY{>of9}pd z)~A2}Xo0LbdBV%3sx5o^S9{t7PbOr-K$N*R5oiKnCBiXQ6(}pkBSz+pRMzFRWZ>wuq1chV5SMguB-~X3O=;Ro3T#o5+q}kiA_e zMkpU2l^A2Q)&NFL`id+fE<+4^@HMAiy4_X!wA1Wr0n_E?{wODgB z$RwqOiVDq@?I6>JA>@XfqTGh%5y2CE3rt2vpywT{cVE9|Ly5sh$y_#UBc249=(qS~x0>!AIQ>ri zH4r=zSWH7xbEj!oEs&?(i3u#TG^v??ZSGsdB1Xwl$uZ+kV;6j?O^?JinLm^fjnj69 zEf|qJ?a|ymDT-ryy@9VA8QmjF*{R01g>N6@m~D?LYgg7tlztlHv{iE@SxblLgBkwt|Yjg`y^mvBmyZb2#uy5}$^BB$V7_m^H(uhgORGUh*@ z>o8!O-EhLyDZcJ{m!$*o73TtBO~3y8gH`1>N1_tO?$3h*7x1UTw}1cD&ei}A$$5C* zI7UCLop0+K2lnl|i?M*rc&L$V+W4{@Id0qM=q_qQOZcnfV+Q2(MiHeeU%*jtywV(2 z5sw@CuQRp=Nxv#b(^uI7=XPVwU+mjD+F1FG$jHd&;*3xrq%Ov^En>7f;YAq^&x3_5 z+Z}6l12jq@yEYE~qw@Xv@y9of&t3PqmB*PLi^lBj?_5Ln>z*$Oh zRT@z=%hW319?eSaOQO4XS=`gFWYsb^z0xmVn$J}4SxrX$R!d57RO#Y)l(a<0D@*Ch zm7CH>9qT_nIoi&Dch;GRY~~cy2f`~vv)|$taMret9#jBJmhn9rQ~q%ju`@{{BwO_V z=C#?-0b?p(#x7#wbnxK8{#V{9jdlAR)q4GNLYsobhV@rl8<(_%bm^Xb>)Eqsr+~%% z+eN)^;d#=wy2g*S220-V$Z5+Nv$t2_OZK=m_FK8l&|Vh3*GB`?&z(~h)~)1y-uA%# zedcX|fD>s*SiB81+6Mhmd%vv{m`&UEI(uxYLnLd$N8q$d{r$`+Xivr`A&UAJ&{BZ| zuLERVZ#JRm0WLH-48Ij)w668|FN;bd)e+_(e4}?5$x7L*<~I*H^(5jR$aXc? zL4AFH!}e;*(AGdW5tpCmQf`r$X&|%X%qIA&_oivP;e!*k@HzPn>cYlxOE0v>-&T8E ziL#dc{t{i5uahVtwS+OaDfw8papOi&2J`yyOK?$XrD+j4rHMIv6cDtDy(*wBE2O5R z4m;G}$EGTyQfcF$(~CSjI(N@;+SaqKD<*ez{?e*UO@yYI?2w6T$heL^xQ z2w9-jGh$8I(gENNEn~Lb%o{l$Q;M)pPT_tJ!lCD3= zUHrcshhzZ43R^TUuT@}RL$j~l=+}!9a)!KRA~73fP5$*)Y}lB9mXtc6dFtcJW8rbp zIu4N-;lwM$%CyDx5Ql{Zc! zq;1!}eJ8zKHE`fS*&mX=(N~c@8qN6khD91;(yuplo1-lhf@dXHZrn^-6>U=baQW`6 zUMrjB)nw7bIN$JJMCcX{2V8HRz&(ZzA(v!DC&-$(a;&bd1lnL?l@GxQCPTjJQ1~Pj z-@X;mP#&P{mj2<>)1IJ2wvJvDZEOu`b*zcA`&o_?2~bNZuH}%QdIitACAyWwm53y5 zsokaDdl#(l?^1L>5H-YL+K@RV#XEVuL!4)wPbp+*;8T8Z*Ty!#zW5rCTrb*qNL58k z3jSU&nYTkvDjPs%)Ca1x-tk@`eqZ4l^zyix5YiH##( z5?v?_5K`Fu^ex^0ziT{2t}%Cww2Vwfd1gvbQ(C7@;xx*P2%Wr|>}ltS&&VihW1BQS z;lHg_^z@*|(Pp!h(8OW*(%x+Fqx!??)GPpDO-`Ra4cYc?$*4F2$`KMi%7{&8o{3c# z2aq>1GjE&UY(yI>KG6(vS+~%tbvqzZOfS|`5qhv^g3n5cdw={tXB;t#qI~5QK>pnI z?s8?yw8hHqV69!ivYBP6d^@^fgUNM^6M1#}y_0U!QA4nl(RI`hNMIvuz5N%1LpIgv z`j<-&Bb_e4!9Hc9C7Izgk4k~2mG^+>M@@VJq?14McB&>GpEkr`L19Q4zt=>a-`wC2 zEH&n!3TN6V^^O7@JIEQ0HEDE!3pK0{PS5_ch|#11AC<&;m7_$6VJ|>DLzeC zsmY1}iSiL2cFW(^)C{_IEg)8{i?3lhr_<>=?bg*>#1lLs-iz^*#dRnGXE!ddl48oPx<^ut{SH#tuOxT;-x6V!K`Qth0 z6+~(cf`?;RbLc{y(>uHAhus1=>K*c6YtvNE_2Ur!v^m@3mlL-I`a51+Ok_p(A89BS zQ6suZ3uR!SgfGQ{$sW}MVDRYy4HbsZ=ye@9qje4<**&Cw45@7$J^V@Rtn6!z46!MG zXMFo&Z{~9-qK`~C9TWlu>n%z)+6n$D?3^tGoY}@Ba01nb&m>|2?3ErvEXi53dbK!h zc;s+wRpQ1jS#t5`fxYIeI>O&=3larI5+hJF_ zYH8i4F&#g}ny!n=*$WwF{0g&`Rdb_8Y-;dgiGY3UfUGqMaVl;BV;mZ$PfH5yXEZU= z!$|C$Vf6oT3#?bw(eO}*%9qYOoZRYpWELh-ifjW~)mX#bS_1{Z(|(RV`}UMu?_7~} zrAhuxe`fTdbI1NSHehft^xguu>aW~C?xcIu?ly!a%wVL7lD@FdlZulO#Tlm1RJ;T zuJn`XXRm(O@(YEseyOQhUH5?=nNbg6tysOzp%0XOMeOH{%bRRAYT&w12+=x`8iqh10?zwwp-tF7^cM4$>P}4J)xb3Mpg<~QE zAn{A6t#!uOkbA%Luc(5+gX}L+n3p@LWv3y1s+W}oPorC5)Vswt_*_jV=Ra1Ft;I!> z=uB~y&YZ#xgJ*gF97~z^3GZ^UP4P2p|7R{t$nXp_=0fY}6pm zVuA9&;0+!?W7uPdQrL?TB2ofKY8qPas<-IY?SnrLh-MM;kSh-<%zcXUd1I^jb)unH zO1&SE14H=v)Qlz1Q0rEWt*INcI3nX%&0%Nl!|iHKKvx>&9ZQ=q1r>DZlCZd-jbQ!NLTk#eivU6N0}Tzs=Rm8sgZPgOq-SlRaag-GM> z!z=d|?N4&GpZ?+L_QuXX+t>L|jWisuax8FY=Rj|mE}Q}+cHeZvzVP{FWk}wEMSji^ z5r--&Dw=(ZrWkamB2ZvkW}lI^##FHGUmY8MmV9@NG~D=}l!iT4W924p+brK1^hdUa z_vUCiui%ItwC}X;`Z3?Ds&K@!R$0q4-~BG`-r`~OU7oa1cZ+`wRjB?G#8I$j=|v3kEVdm zto-IZChEW5b;YYh^97plwJ||$FT1aCMc8I{`e${u4_MSQ-$9kzE&+$|z+~3UWmCmU zAnJ2{L-R~aJF?zg7rid*>RYDwz(;>@ylpk$n_rih$T;MS;H}bf&oKR zQ!qMJeS4lu8I}o~>`#0aMK;6;oX#Fm-Y&c{HhD>8YjT20j8o{oKaI~-6wa*wRZaGk zRe*^7;$KZa6g{@nv?wj2gu=wr6S@=xtH4=6PQm`T6I9l+s9ea5ltr=~C@;u$En5My zxwZ7lCrDaC4{v*!slJVR#6g8IXP;DJ;34=vY}haat1w?=16aP@9^;svv;X;=-x`hs zYs5mbv%;B&72cWz+hYE$!VOabj=E|qeEiX=^4izpV)r-h5E5m@Ed8T9j!S~li;S2K z5e4}q+F2uQOJS7cV}2g=FFa*PTy5IEfx6^bkY6_0uny^k*!7x0XU?|b}8-Ge)o7^igO>I z+HpI2V7G26-~@bPUb0f2_BAgNOm^O?ZT;(+c>7U#QE*DPap&A;|7P=)3b3_7*W29C zK|CfzX1=_9IyOsANy{mdthiwG3-E9E4r*!$e=S&VYWNxwsJt(F7?F)52lcHw1bX7$ zOh3bJ_zT_rgDwD=2D_Z?0&52Xm3XbvJ`mnw=@*T*Xo;p0V4BdzLiM*FK7<%LK;JC> z@ByWfEt(fGUSI;OFz@ZwbLsml+t~L~AHSkDce^o6AH4fo^C~$Yc6djn)=dOq`2cjt z;h;#MSWms_v~u&>ZFYCyYG1r`sf4n~W*vq~+0A+tsRn`q1rM))X((>yNJ}LXa-w^a zy~pcfdD2&-P*+E%>(?iAoAat_Oj$W0i%S(m+DBD!2O}|>KTZ4bVdiisss4Ft^g}Wz z%FZYQ9xIk*yago&?-LCQk4O8q+!YyV@Uq?a0kPvUhvHy3j0rEp%A?e@4J&s-hg_|ToW$HyJaPIj(GwC|<#7hS7{4;Q1J4IzKk zmG1Z6YPTm#L$%#DqkD4`WFmsTQ+SJXbkw`=VWtZy-irMVrl@8$A)JSx=%Pt9>UqQp zJ57_Ith$iITV2Brm)spwUCn(gQW}pz+{PcwPK|JdaxX3*qjFwA2gj1Rq^svDzu(+x zt;oEj2_cQGt^HzI=;dl5!ZG>s+2i`OP04No_GufA~!SzwM3- zSWE8?1I7*X7Tzq(J~0(05Ad^E`{=8BYX_`MfZn7xLgwRBNNSn++d`YM&|s}w9~HWFkoMXR=hHc7qL`y?ZN<2CEkaE&nHg_tC;cqXg;>NSZT)YG zsq587UUrz}J;&#Y&;6F;Wb<%GA#I&1$ol!|H}8_H)I2*`2{UupzEgcn4SG66ju_!p zqjHn_N@VDFn==50t0Bl&eS8u| zgLf3NFP4mS))M)JA_VmaUww;ZO%mAL19C~R&$5_93vP>akIV-(M2pip{-tckCbBG9 zy3`ZD1~)s)_HUv33IA^L`2*I8)0p+(LIz{#J?$(yNbo!n#!~CNr6Dr=*wJ%q)%n5k znIk(6yq7G6g)M}~xGPt>nwgo6_PILfUqM(WT&-v+wza^dN+BPA{3Qu zScH=^@FzYvLH6fh`!T^b zJWYI}6lZA-=0|7CV7{RW(_rmg_6UBT_|eXyJJPj8_> zgVSVldBG8#b{DW`3AJM9Gj|fC{bne^|BC85dG|AH%~beYHej6uR5^=|$CrDqj{xG# z-I4>53ddpxiic}ZwJ3OH4F~3hL8Juj5AT0kig_C}7W|}T=IuVK??47pVjoQ~)Kr_Y z*D54y%}C;|=w#buB487LbYYs^TVA*(1Fq{&RtF2G7E=BZSZp8;s@w@WS~e34CIfz+ zKt((#Diw>B|POm0rw5J zcLhe=ce1~SRbJYiJ9qR_#`tGFS?;p&lKH`@6%jz3ln(l#193Yg%*m*P^7b1%5FQF8 zAY!))%&5fa6e?3SOoBW|7<7GQl&na<1Xqz9&p;L8*chU%m!gdOJ!b|g? zgz_=SmK`VtZ4bPH;UdmEqN*leVQ%?9>%N1ITqA7Tyj%`(A?l#77hRmFT4)h5qMZP| zMb$;4*1EVJ0m`5$8E>m#oQvb4g8r%83 zO+i5sJ9lDe&_!0(g0N#zx{<+aqj}$Z?3Ln)<_d7NlmnCBF)U@jt$$0u<;yMmPlm>u zMJFtI5Lxp~c*tNijpi-BOex`ky28Lm@3%$2oIJv4_@UN~n?ax6l!o{=P zsgp}mYb=t1MfmOeE~_z3UlhM?sA9V%g-H+CaO_tm?tiDc9m#nPs`VU2c8>n;nO+ zM#{GTErjN2%OvMS!hP!IYpbg*Xo=B=!47|G(s_y3$@U(-W>N~BAmGX@0k0}jv(T)W zf%qhi>{_=6P3q2&T{20h;?7SJ@&KY@(VvoE81%c@IeuI^K*JOr^1O{rpK?$EZ`eH8 zn(+5v;-FL8N%vF`$7sG%)6(LWmQ9E|HC~-WrQx%)Q`9FY^1=`TGpAMy>VJ;7XcPU{ zSdMmEqJ;ts7s5r?oQxw8ND8%J@hGVp&7Keo*_UNF>v)gk09h+3j8W=*Bhv{`T@R>g z^M`qevJ#qZP%=2DC+N7f3lwIBdE+eutl_??jkJj0=vot!1p`rCM`ttA%MVlhY>-%S zfZmI*D}pJM^ASMC-6WNBnR(6lSdK40SE8yZ-K)G z|MKw(ry~rr^uh-XCOZiqSe$v-$c+8GBLxdGj^UAp1W~Se5fdqTgDBvPnjUS>di;Q=+Zj0N8v&-2#}Y(lCx8ZxBgpQrYq=Mo5+H%1?oz#0d7uXUGf`Z=fjph2K@6sI^cSa3J`&a8k?|GF!^7d|&r zpoKRm9m(Fg$ zEcs!j48Ewj!bbJ0n=4yeDqz_AutE+VM>FjU=qDEDealiSh+W;nx!aV>Cj4B3KjXQE zb~;abPs!YK_G=dh1J==+^qZX~CI^s7Ncs8KM?}}q8P9ESjBnLn5uejNa5*ITuB|ES z3L3G$%zGN4m{A4enk?TP`pk(!Yi_~yWqZ>Bhph`JEpO?rrGTJD5pz7Zb15{3^= z@}`kY`7)Z-co40=)gcIa74xG#eyR*)gz_Nfn0NSyiL;|W7N6+!tRFj< zm^7Lmn8x6>m~f|UMdH82gR)SREM9;pdTOWbWD9Wy4;vfG)qVKal8**C_* zm5s_d3r&#$7PZCE+_@j}PK2GR9E*b9BcI2+afNyHpLoFzfNmCJpiuX%5Fi(}Zc193 zGJhHF8ZktiJAO@G{GJY+E?^Ff*v8p6W7kf1wpEQ)RdRgwwJ_dw!TeP-=hyy-P15(&fc~%8F|oaWX+05X2i0koSIO~lBPX&)&0x7X`<}TG1vx7(yr=6`~q}Sz76_6Hm9fW^wkxQ zB7xp+ty3g{h=(B1q>+e~YppgVMQq+zi?a>Ds}(Rq)*S34d25&QMoW4|J&2dh49wpdLrg~$9SvVzA0rZM zd&T>S?~$JzI24Oi+p|ydPL1!(QrF%aLoVLWyl?%P14s+-hEb#FZcSP^XSPFzMc*$Q z*U32I8d9P>>F2=dd6>yMhN2dUi#9Z!IO&{g?wdi7xS9iOqbj^Nb4S5MN@f8(dEP9S zi-gap>N9GstO3zjKM&q2jJAf_!Ul`0I$jx|r9X*%ZdVp}h%|&J((=sY3)vh{vf@lS zW4B(rSyGQc+ld05Ex#pr+~oK;#@o~v;nu}qpP!m?@aPeg)F)tek~t=uJl+>-a_Qmr zo=#3qCjuMl6Mwv3^%1csvuvmT^6elK;j{y4nm-?j=+2p&u-%5hqNo#5;c9LF+_jqbOoorpJSZG?tt^3`@g(`t zTSDGfW2-?P5OD;OjlXH1c}@}E Qw1QATL! zTIlgfvvQBHXF1^MvIxxmxy}KXvhw}hU<%mO*2>pV0o179 zhk*;8oJm84zr@c^7?kWg9Fmbdl3acIzx`4~Zz^a3xxI8O(u=+o_~0^u<$T{kw%l*akqya6EfR_wu?(7Wjw}l*}O7whNcU2 zuIZ-yKfi(ldxj2kRLm?m7X51<4A=I54Z_(N>}`}|kgVK+HkX(iTt+7bP~Ti&gn|C& z98y}ZsXDvTv07IkVX*~1=jN;t+bufi#*G{5t+V&_?ctnZdToFu!p+Z~T8sTeY_Du|BozRPt?mkv_`Zpg=I$v@D zB}nd1hu}nKh*S{qf;kKjwq(x`Q=5H;D%qcxi!~H3EnC+9`VI#i%9&>Cl52mN^dyJE z_8pAA1<*5&U~7`r!>Wil1nEL|9i+P6IFSN=`j%%FJ;bdq0;>5G)8|cscQVL0|n7pytxGAQVeIN%=G9UkUMRL-oLEi zLb4=2m%NABxLD*581=#T{0GQf4$7i{xXUzIxCcUvKlF>SKQ}*Mma;A;L z_BngjM4JS3iZ6&Vnf*^i#Szpk0X5AUl&h58eRbQmQB4Jd7jtI0)NJzPQNwOeq1lZF z8sH$|o3{fD+l&dCOfiN20w@6UO<)#UD}{N%D(IbPRBcTv5D_uW}L5S|6mh-+(j~%e3v@tGrNeW#C$FFiFGB}MoFmATZKw-FHJ&s^_-#&ACgaQF-?#C3aiO1 zqO;+g;d+`V^Ww2AszrZwk>ZnXKA!GrjU!WA3^Ccw{~ zNx_*v=8TYPFW1Ji(*n8@yr?$z3zoy=Zu)DQ&mkRtMheZ7*m*&hm_>U6(1p18FiXW0a?wI zUhyZ)6>7l1Lqks5eBGh!E^<&km%GgzB86MYpikC%vKC?oVfP|njgple5n!mn7b42A z7k#NN#F9fGZ-M6~R6)J=+HK;tfl5L65YRqSdO8~YRswI{wQJI4HB*|Pmq}~()98N} zh7Geq!9f6-uOjD+D4o8$W5BE_4X*FXwS1?W>|l=Xom|)W7LTFEZp3p{RjZ@ys?{sz=kyx{~NJA^QLh1Ci{oUJiTSm3AD!&FSnVsI|}MQ>!vzt97K=-#Bg!` zlkVbtPKJb&&?f0N*V2653U(di2)o#p0ZF6&qj>2r*s0Y_Ss`x4;r8FKSkj17 zsx1Cqm3Y)p;;#D=?1 z>NV^4^RT}ob%0HPZ})!nfHK%};ZU!vtnyE_SBrTyC=CG?WUbC4i9wy%ZgYza$Oh_m zl*O%fMOfX=$kJ-$%Y0ShQyRl{oIshA z^_W@#m-H4Jd%);r0ncx#P)E@RQ%x$!M3Dry1nLMXpwV}M-x5Dx zdcvk?d5O$kBmuH9k3CYJ*Df;noF~$Y{|3~>yjU%@M`$*>bp}Z^QT~TI@hH>>A=5}4 zWOj&ckrVtB2m@Dd+~~dZ!?@$N3cjf^Nei+#8BNvH5UIB5ueC3QTQAO3wxOCD=Z#fW zy$AAlwZ}(Mt?1NM>_5goDimIh<`N~MHnn{{lH_uA%0>0 zp};*hm>k@UJTIeD0O4_X^7E$k3?6PfYO3DbC2fByvC!RZ=eG(6W51v#e0~&Hkb7&iPFHW1C zqU&a&$%Z@}-5@Zk#N&K|aEAU+hvo*B?)=BblajjbN;6UQ)`3{6*(r1JwphKeRGHS` zQ7@OOPo*iQf>PrL0Y?c;=hvJ(`eIN$A;JNd3E>l?;4-I9yEmi+?qlZ5cvM_sPJ?*Iw#?6W`QI>u zagfrw=**Xw=0!R9%tShESp3B?E_*Tlr!)hK-Nt$Z_1{~_xtjWSA%yJZ5;&zP9k?(D6;0IM6$f~2=wvz_`-fhKMDaX-lP(=Rot{N%Xo zS}x9CA!%Dlr*F&3-1MF-hqA5jT61-d;LfX8(*~K2{LVBl^ylM57E+5#KSaVmG85DyUcScvJ)S!dm-#Pa z^!lG~TYhVB`!X`=HFNKD3S&w~O#^!!9i1eM%cuV6Ar(1ov1r_0_;=GB6hE#G{P8|+ zE?h0OkuB`~4&b=tNr6E|QYDDdkjaqR4LLQykD&6yO2^-cH1uKiHTCbCzWYxPogSs^ zLI9Y>lLEhU{pFN=GiNJv1x6z?XbXjGoZL`9kP)pX|3pea*MT}07*pq-e?|c&HlMH` zWi^6qARywm8NBjR?z6k*R^V=8(*)A4Hfu6nd%W(mw!^xxow#^2F=$&tLc;ArPsI_u5!*u3A7$6JJg&Q7a6^9v zb~SptosZ`}JU?xq+(5i;*dJwmxo!tN96#+Hhp5kzNZLqG7!obI-w(NhL4I& z<^3`SP*g9pU9nRru;2gn*QJexLwGU9B{`Pqd$!QF$Ov_xrDYvf7fU z5N_!3`NiKo3f~`~fWJj7e>k^K{($Iv9_#qh??0J$OB{k7k8B@SA2!D&;w(-RD``6K zU?wGF@oIL5KG{EH;%|H*IPeeezlS*@!wK7^>6-zHv6(Y~h~>V|%g1Cr5gagD!-4BX z%roXmiftx7PwG{Y^&}!9Vruf{L9+^Q$=9D~zunM5wq6pQ*kN((>Zood?W{IA`nx!c z1;AUEtaJKN=PSpp{_1~W+KtQ6&-2QbGQ4}lyVkwgB&KJOQ?%K79!G218U|^y112_i z(6uyT8rE^lQG2JpyQ{XgvLqqplaQ~tD8oZG5U6?{(5yn%cR>9vZNAHhZ_M{jfkTmCmV>U38)v+ckdV@EPFw}p!ZJ5&6%BPi#Pvcv0ly@ebfe6T&Rs)g7%U7Yh71&>UcP;{ zvMa5{)S4E%IaG>ysg=;tVJhT-SIadayUovARe-TG#=l#9jsvT~M;`W<%Xb3toPmdc z3~sg=-f#Zn-|ZXd<#yU8ud@0^^VWPMtgX9bp9w;v!J-OIHvui!A>+dKmwSwh&4L5l zfq2X&(lEf3*h-WB`olMw{PEvf!F@D{hL4{~(1FErA$L_ThsdCHUAh{aMdpAl(wCn)>6K`Vfh)5;fN5*wrld7C&-?Q*++UnV{w?i3bw zK%!1V4v}3{Gh$E@KBVVey}j4&a$|7ORQz;?arz-W&LU1o{Ymr=GqA5qh6Q(acoq3yeBYx~ySc4I|x=?X)VR?6P0d!3m_iei`ApeNY!_!m@cxmr*>s(oa6b0f-&J$7?YbtK78R&f5Am%g7k#%&v}jX8%7!+xXJIv_rPR z5xTZxr4#k=@QLAL_gZurX_?2oSTV1-vy+c{qtx;iLwwCfAC8QS{l_CQx$!VWAW94pk2TWrx%T)|{DVWOujV za*E3!^%m2o^|0xo(`HT9xM(sH*_hp?w>!^1#ztqqnEKktsN2FkD>jFnnZ&Wq`ugh; zMR=}SEE88q(o =Y*VZ9lpJXOt$__`%i<+%gtbxw`WeqNLcN>VMJ;hLSDSQ;@ye( zjFHMWXk-r&U7FNxg5~}OBNQ8(4NKI;qT^J_4DS7*?#JI{t+CZ}xkv%gZcyIg0@}DY za36~Ko2fib*rBqQlT5iKH1rryG%;s?LtS|wo7|j7477exTm=H9u(j7j=Iw1#qxr^r znBVP9yx955K}zD*Ib0U%&6N*vBqaEy(`iRz0?eVmm%B{VZ$vWzrXgArGrz;H%Hv2c zRDM9c-mHDUX7+x&Gv^I@jfHV@?LpYC+@{0?-O9|Zy;b;ZNAbAR6=P$l#fh1X?EMC2 zJI&ZPgNrS&E*ve|3&;KawgG~8wSB9Js%XT!vR59xJ5zpxhPBev90B$qOTZn!Hu*>S za{7zY=Uy}l=u~q|#qIf7l~l)*G;94)TI%SCo=EgGB>M+GgUn*A9L7YODFy}x0{;n4 z!-(0P#Ac)0Z`HpGH)i_Tx4xM9)s0KoyUjhu(9|t3ccrw#zUofNTiJ+trEqO$_?%#L z=Kk4ZnSzd(xRhvRW@$MG%06=w3 zrmBuPIGcVi+1c3q@-Ag})NRVeCVxfvv4ae=siq8~Pr~0qmT@=cyCu3XV9*=uNfm@|uCN^O&0Eujd{<=>+nE+&b{)R1=@;yyF~ z=wyG(KEc$w(40-bI?#Cc-qw6gdvO}|Ns{dbSAMXqs)Q08oYB@_0ss#s60nHb>Gv=F zHK&_0nh@_Ziu|66N18;J@e#4H=6hVW)nt(OD z>IX5;APj{CXB==`d)Hu_Wf7*WcS>FKO3TU|Xo^YJ#{iY=uE>&EF4-TV0nLB0y+=1s zTi%tqr6oIxx3b8>^XHAJ9o`}WEdExN^%u4ww#x%ntoSVx!A0Qhvror=TU+!5Tf~h* z#&*XSPq=2Q9nyCH)g2vDEBzwJ>2s0w#ifep)Lx|@^A!3CSpx&YBgd0^X;#Ibz>+do zV(3dAr<9#@&6JrU&A6bH%qA(lAC>q|i~Q|;4`bmfBra|Si(L0|f=izV7z}L3WQ<&6 z%(4`w19UxG_=$5u4q0r1f2@>Ck-6nPo&9i1bz}fz4t;*)rPJE>`Gku?P7*k_7z)MX zX1To>TyY*O=H(d*HJV);JT7;%lfpq~2K4mQFw1`ZTrSm?<(#GvqB{w9Im;jEPVQ7d z=Oon{VbLjc&yIp@(50qboA6a=&&AqDfSoq6 z-)#3XqY{^B0ny1WvSAi@wg1V@ExJA1kuvl!hD6`82bQzNO+sbRn~zS!BW0;Y$Sg7; zP3U=XNARQ;Em|09bw?B~;fA&a#*znCMQ*AO^P&zm@^JaG-khnBp3%sEjXh=e(wIr) zs}%g2OAPw%rMMA34Utt`Bq8s=0me*$^+u1|Z?sRF7%RKW3=4=0Ui|}m%}Bb>4BW424}_Mr1lThjtIz z!}+yI75|7L-PrB$kC%)dxb%ZM7|Pu9fE{7>lkELkUXG79pb#*A6QK6|s!wNrbXUAl z$aq3YWlD7T-2#?+c%Y2ICCUpZOymiUJH++wV4XOFAUGwc@#g!?4nOuJb z^!Rn?%Ro&IA_gfb)IhLiTUIPzcrvcAb8kqrBlh>uG5Y#Eu5)0^eusB-i1_aJRj8-r z3jt4~I&TykPt3j|JFnKNw*sC8g9uaz9GLPyHbi2eFlEIer8s8m&X?wn$56|Gw+r^P zEq8Oue%SmQczqaO^ZVk*clY1E^zd9BMIp<0M|ya81Q*y^tELhmufjWQyu*5Wht!h! zbbK$ox?kNpC_wfUP-&UDr-n@ZGSb>Qon(!sOflWV2-68n<%HS zxFJ2;0(yuqFS z)3U}-Tj(G=0$3=@rspm7=ja~jQ##s*{S|R`uU}}f-oLX2sQ|sTYl!{rq25arp$y{a zDNRNf?a=)pJ3lrSjt8dxoIe~`hi?^2Yb}*Nqh&EMp>~C7adZ6mxFKZPG>jFw{zPNn zZ}XfG1es)S-WQ)qxjm2o1+E3P~J`}TG~k%S40F@hp8z6Wu)I;ka< z4PTb6+pD9#Ts;dp%qYy(YrX4ijT>bUN{#s#X$X!e#Y1g9oz45Y=ee%Z8+x+vYj?&= z`e9n!>dvKFj$OC}g0qt573bM5W>WKpiqrAXpuKb?w2Hm8HfPD429Ev4_mrE#N0Mn! zclMh0qU7xh45Q#h-{FQ+)b@FH{z1OJ&fJ*oX_!pQxC&>FHZUlgHIr>Mq{59qpYq(> z_1_zNl8}b{R<;p#9J1OTDhn&7*|c+eX5xIx8X!D{F0(70fOxX7nxG^hk+s1FZwQq?;qqr zm#6NNqH1*#SW+Qna0T#YamN(Rl9E5FEwMM4Jw2POaE4rEA%R<`f)*y8Vs zc<83G|C?ui&Ml$xovY^bPOMZm#9#~F-y@Q-<(mGg|M#nU)a%M9#}W|=k)UwKlNCE? zi^>L#v^0+1blUQKeC=@Bd6}9+2=hc_y!6x4<{*c0uQ)Kxr+1xPx$N`v+#?2$U2RWq zHJQnjzKB_|QHR5)Z7kr~Yh2Mp3vFaZJTUGa_U=9UMKOogB)7JUlDe4*j#tyk~+Pq!O``H9nnM=Xm z({8=mf|MvsaXwOO8W)fqkBa5hzqW4KlJ#^~Q;X{lJvYUjQt@cBKN_YHZ?=`X`Yr0u zxxki`UU3fPbMCuU@_vTxd%M@f#i{(E*tddM!R|YFdjSk0<7wCX$U?&h51Ml?nTzQi z$oF*AYWe?|dK0i7*Y*9o5Gu<`kup}ABvF|~Nui`vh7i$!%*s%tNYR7_Qz=6VEpw*K zp(x`LR^}m5#zaV^-p{qy-oO9rIQFrRy$yZ8&vW0`a9-zmUca|_`oi(a{%r-|KAkd? zOs;qnjpt#x zu*7b+Pt)HI&-I!6SglPP8A%iPVAZx?{Nx_I+c=W1`^C@|%oV)9R?Pluw5$d&oLg32 zzV%ZdKH@7bg*d;8Y|Y>H2I$W*q>fC#05`^<5OAL{(#Q8id|J}$)pZr8zHaLq972oM zreZm~3tXoP0Aytc!^|C{79SdUaiw98&*0Jbcg{~9wEUz;4s4TGxE*PRC7oIhs3Nu< z%nEN7t;m1&tRvJK2EGZ?%&i7JiXPxsn*aM_!J#_=OPB5i7eL3k5!P~)QsGVZL-h5( zxF_8$tA*Yhi-BQIbG@9-TNky>DsA>_#q(`nLzNaDJfF4j*^|My`((b5jTr!d;zcY% zbsmeXh0%KU&;4&MVHas3{U8A-Nadn4;LezzMn183sVZ$A_j9Qa^&4WljN(H!)GOi@ z)BzH%;pu(lVJ&|z83&P5?P0++!9HG{Id$@6)1s?;zt)xyciMB&+SvH+ve0SogAZGG zx})Nr_Cc~4y^OzrU##I`>)@61f6CCv(&{I89cnmP=L)D2r)3__-UTp}E}tt&O9DhU z)jicAdaE|Q8m!?&6c+FUs!L*f0C@3QVHsH5>{fp5;6=uFTro;-@qUX3qOaI9%y?VT z1prCaqf2=N>z;pG!EBT<+XA+-LBFL`8WmKQwiJ1QQo+kOvw{6SxFdfiX#aOIv4_CG zEHIJY;w$}6OyFJ+Sup@2`ZV8rwC?Sw@5AS-xYuf*PUEgjBau-@46c6Jm`SlME2?wm zeXnRt`|W=>J<|AiufBcv(ius^3Knx`YKwc2)Zq^1=X_^6#khZU|DnIbKr-6bg`xQu z>O74A;x4rg9c~hNw0g~qzf@M>c%Zt^)b|zBW>uZd`qtvzsShw3{-w+ad5WFZaV&H! zw7t(kZ|wg%05Q7ILqqFwf;u_P|KP{e;n?rcs@PlEtPZ;Wvf<6Ia@2_;pnFqy+`MSu z&`tvuMZr*_Wn(As#p>rytqMnxB|xgaaCE|u{-;h*T(dWVaUv1qF_jP~X zgAmCjZq$>tV=aEZJbh`Sx1oE73;)MwZ2fh0afuFPp@~o-sn&3^?k*%70_U*KD)YB9 zf*j4vhWAoP(a!b<3k$a#ZHbPhKeyOV$NSncXiB2KOhJ)taz^_f8bLS$a3aI4K79D_ zrFEOm1u6I^8NgxWJwS5_JpN~YlU=Yjl4JfU%{k0vo-r`tz|N^ABtfMDwHro)z2pWd z{diIypGo4MaLf>@_vKGL1{!TUAim8CoHdJ@rRXN9qw=9Yy+gAle)J&byf8Q$L70Y6 ziOH%!LBC}dwy*uRa9yIb7!S&36yg~rOn>&L(jUqi6OO5D4yI_ypH1y3oiXH61N3^d zH|@{uop^9M-l!~W!(ina0VR7-OFjXz%sUi~H>v6cJ$y&83El{NYIy(9tWjZ1wPzV*WRYH473a-h9I2=@_>wrU!Aw$QCb4ME<(*Hz0?wn~)vp(S&R>1{Y@$%r zI(8F8vZ?j&Dh&lrni%Wfcu@cK$`GfpR+9(M%O7G|RxmG*=hr8zT5QE-j|p9Y93}Gf zzJCoz)k}Z{`cf-w-)~<-RBrXJcNPo}yWMvMk>mUPLP?oz>KXsj>!^2r2m9`28{tmD zsK7OSWYb++09?JHGsE@kG@>??NmAU|v&;7!{!jhLs1pIYdWQ~9jN(}5`x02M>AZ{p z*VXFhf4tpFi4DD#jZu}dws@Hrk|H|@OgPmD1xt+Bt(O0YqqX%@TRUXp@?wNCItO1G z<5@)7Z9iwXwbz!x-s<(+eKx*ep788+eQLvEU~l_`U8dIuLxL(ie3+put)yQ6{u5fi zP7c^mzegrhKZo^C`_}s^?Tdm|-+pLGNP(!Eey6&a&4zXt){fTE1&(ki&Nk6!En z%jH|$bPoJSFD~}eFG^1wh`A6{84g0B)i1*M+V}d*Qi|Ph{@_tgUQMyLkO*!nXLCLo^nHj+-}I$dA0yR#Vf^@by@eO;O7! zu`Pe)bm`J1Uq34J!P&v+*h&VvryI2Ssxd)l35G1YOB{ZA{`~Rd_88CPP~C30IeB%v ze|+NH^`4`kOHvX@7X-%{7vtppN>^Zj5es-J6UOc63o}NH6Hrdchv38W*k6*iWI+(p z{A95s+nfNi zm>KYcEOHz)O~ynqcWXXk+qqCc$CKB{MB-KgEtM&T`udy6-UCq=o zL2L?EO)=Wjud=Igox89h7?c^nA^=nG>2>HAj|OKDXWo_gtZ?yv>BTGEYvL`lL4ZH- zDXHd#@%Q2PyA4OzDd%OY$4xg-yD{7Ai&qB019Ve^f;Tn7@-+`Z_Q@l+V+@hZj{>rQ z7h!XARKbd9R%%pMzhE}RHCGfT7f2O|1^q|`d_%SN?Qb9_S$8G_fJ0a98+%3Xh6cuX zjsV-ieq(4(g)Dpg_q-;7<>1fo^|J+0r-t?SGwmur#dcm6JUO3>a|b*8`?C zFXykiL#rB~Kg+GB7zgLRh;E71l=-7aQ}7m=?Xl$bM8pSC5nv@&R5@lVci$eAJ@DAa zpe>WAM#K=B-tneO=|%tn@E^vED+{}}Q<*R0MOYaM8K>8tzop!dY$Y7U()_J9g&@^8>9%63Wk^+G>6IL@7%GpqQ}C`xyP@z+=_h`Ij%z`fk+SoDP%*EC7+Pnm8Y*U%uiQ#Rf!qsRUSCBB1B`jw4A^ za3o*hWM|&BWT=^LC`yG#{^={r_{7GHWK&5WZMe9u*h)&(gfh^}V{Cx9{q3}0W5g&=TU7J>nAL!4sHJHclGqktF0 zlp)XR5-A)zVN752j*_1Kt`NTJ43y5fv=k(`KAK@0ckIJs3SS zUdewS^}DgzwBzvam;+(|@~W(i=col&sBzKNqhFVcV4qFuD{EDf(n{f5Y~YJ7NObP;x#(gKym7P-oucX|i*{sH5Jen^sOg`b zQnPlSvHeGIf06B?w}tzLTU7rupG!}Uy3pW)4o6?#&Z_8|)dhdftYYc2Ax_Rw=$noO z-AQ#L=`?MjrI0Jo#Ud*er8|s)(0^z{eYl)eGuID|1roqPH`X$1V^#h9n$iDPo(c!lE!ElpqL-IJGV4Q;q=#gtw9*`^e$eo`AB5I+{YUjhsWZN z?2C#s0|_&DPpf}tL66QaKlQMJn9>hk1lkMAu+g|%kr?x4`<8C2CU{0WtUv;LJ)(fy zCS4sgXL{i@PUMVag znAWra!V^C?f0<$+pAH6N7k&Lao$bwv(kF31*E0QJz^Hv;hR?MF6>5JG2CUi0#t{po zTC~c~DQuMl;a$vN@$6}(>wmpp28?1^4hX8wYJw~LBbHIe#Ne?}p~FpICOu-SMGZbo z)gExJ0D>JxH6lXp1!aJ3Ezq5oBj1^?<9%~I)Y`kydhoAOYjn;G2PK6!1Kg^AjuuWM zAe{oM5KTRm105GFI<6M6u0#JskXv(@d+g&LJkPXmK_l}=_U40_adPO}3Uv#zlc#;N z_eq^wCDlA3V1S{rO*i0z>VgJUh?=S+x_S0o((r{|0UtHkE_7CF$Zb-=@rIODRK!4# z34<%FORk}GYt$S7?zB}`D3>D22Ur3W$?n9vcU|qauzLfI_DO}F?ARe4h=LFn0ph2k zm*1f|HYs|#Q`PNAL;2kB zd@w_}U|4*xx=QEPtudXvww7ofh_^0SKb1f)y_rm!&U}KhsXcIssRA}xyQn@4D;OlY zC?Bnr&l3-5m)ryGb*b~zC3u$(&Ctch$E zY^!5s<>{37KGyhKNk+Vv)>-F3$57?9r3Dj51R4cBY2q-yUGT00wB*8CeG-(w9qY+_ zP5~@r&X>M~X)(yqTDs_XcD6S@hroF2WO)vz{5*hC?H z+7teUBl-|MD<#=g#{4*Px}mM7)D&YpfM{k&!|ZzBa88N;=WwBLg}w7thye5?oN(<9 zR~Y&u`8!zdWf?m}9Lr#iQ$|{#V$$h;VchP!rQc4a$9KfuRnO6+M{GK$tl3m>vXXXi zqsRwIj}P%+ImG!M1tQ$D`AEkpoBl?`hV|Zr{OK!nMO@f8hx684e37%TF~u^qw;(Gr z8f1Uctq`q!1C}-!Ab0TvABtl5ouSb6?A|KNCAD?H{{;^X?zrEK5g>I5BbV zm^p@LzqZq;DgXH)TkStu9_PKCFLv{F;R8vS6>9S>yP`(qM8QPlH(4uB5}JGLO=*8b_ zFj%SPa!UuFBwdU0f&QB=8C zB!{3sN&-q=G52+!magw0{!=LL#YKXsx#jhWKRON=bFOVJfg>+?Z^cz#mi_j$HogZr zfYy|ox09lt{viQkz?|4=t>eU0 z>((l9#pxMuDMxo-WbVnKzkKgr66p)mt_aHAXr&zBC8$0FVXo0vykSxX zyJ-5>@{P}WtF$#U)w~3}+O0zoAlarj<~LkhaH!0;KI|(gP7yjn$jKLx;kM3&*N3zy zvWuhQmra~DGu>!A=?B(8yHxd~OAwz&nRBQSCoEi17=DYnZHoS8(A8EYVynNc z!zA4w!fD|iAfs%(g3}=O zPB2GqFHx+e0RoRwf;I#Bv0JK{N;`){0k1#>dDkTQE5+GfN|D9+q}j-=)fh(2fNNlQ zb^)`QBp`{YSdmaN*>&!$t=;u%$k!F_^XLUNKFyfk=+;L)<3LC7$MXAG!kD#~xK&?u zLtKAnN`x4TM+DHdp;Yo}y30*0li+1Q@ZM|2#cH)QheLb!TE>Q; zjrq&3+I6cb$e&YVKLg(%@zSF(ffcff=ZTG zy0k!N)V|j=(Wvv4GM@cx+ihG6Zs_2Ky1?DGC%CAw3!hPh*k@ETP0JgU5&ll5a7>^Y z`>*%reUK2gd4`-o=qB|dZbcB8$n|C=ccC_hTUB-FGO*WPm#qcL0cSTA-&w+iUfAhQ zO7GYKi{F>N``ub)Jz=mh>p?HhsSW#|l$e-raoF#{q`mhLMy`RsLL;0;eSbobrOh-%(TQ!630ASt#M>SmL)rK`s^N@ zbdpt6Ak+E2^Ei=8f9ZTx-(tnTokk@3Nnb z8=||n;BwZ@4tY#OB2_OjRnPlovdPiuCXvjo-?L44&~hQ)#422exF8lPH2&h(h72$lKBirOVyLlK9{4iN75Yc?48(AmrO z1-Oi|93GmA{UtY^RT4#2d0tg^rn}D;u!0rX|%G&WT{iWXUf>CI1RoT>cFmIEsEesbk5jzbM>zg2vmFB9sz;&he3}< z<|2NI(;iC7$F!49JW6G$Lth|j*@0rqCU@G!h_eO`=X5Rj};v7 zD6aptCqB9ld9|3)#s^<|_H59-oKC@UX%k!%LQ?60OZrbR4Xmm1_ZzI=7AxwFG!oz` z`M{S1lq-`PvB?+MY}B5+nKiT_zh8`jxe~kz@Kj~^ zS9nIvkNSFhryFO_=Fc7KI4{7K0wEa@d6!{vxeRLTl1 zvlW^rUt;%zS;5s`c13w^DYNAmIWfG$a1#f3Sp-a-^GC+IRU}oFjMJ^U&O50jGXz4GhZiSs#!wAdZ$R2~UPDKl8+(jmMF`Vjyu_-uGl+`Ju_)eSu= z$gLOy@&_Rlo&PbSGE9}lxx1L{0JM_M#k%Ea(W7be=ilMpX;}sJ1OTuMDp#GO+KBBO z=tqSnSoqA;YenN?aQhbks*Ar6yEmw-C9cV+z!qOrqefa5S?7@!}L{R2A zRqY42`+tEY{0;2_k#G&eIEOzY``$sgDHd&DakgU;wVCx8>&Rq{Y^skfGFSLZb)^p-pgoV55<*+ECA4cH z;L-Nc=oFVPudS_3!ng~IGPNUq_RRGabArF^iE8OOx}XP$l(mpZpv>$fUodq=9$4CRSn8#jkIX;!7E{}wAtUf?h=O|B`) z7&ogezqqQcQ2z5!gi1TBe%_4iIXZ*8GJy1a|R=f5r6>b|&=8kV%r@ z$>;-l&&+A9sS*xeY81q?5nmXwMxd-E?cT-1l@|XBtVp~2@t3y^o#(^*`&Jsx!s4ss zdfCpQ09exY(-G6AN#a+0usUqSB#+Mh1ALp7T_xE{pg{b_sq&LnLiI2h`xPeQ%-=Df zh~bjyI;i8K+w=)bSXN)O_vYmcv-d_3Nyr6%ZIVlEN{#k)_N1xKfOH6gZBk;lsdqp0 zW&U(HyvSHo8M>_RG)P`|q~-W!VnqjCy-_ZnBr=1(^ulk8->>amdlWePLfpT;Xj`~i zlwah>%ZbT0PMXc$4UBql*sC+63{cpJZdu{)&}O<$)$8%`eHYa{xPCpZ&gBS}JPoNT zPbgpAyWJmB-SK^xu%;XjJKg*ft#VTz@6#*uDSMd~Du@oL@+aX-3>^szs-7OZAVKco z?}U)xWalqgfJ{tpw0>1Vcd3h|PlQyHPJo3Kri|zITN~j$khK{L@`*6e=s>pC$qjOfXuzO|ssA&j_VA953ubrT!a zuis*d&d0%QBu`$b8AQ3&CVuSwvYE2-2NsWGHp;&swvqHPv8~>Db#B)#rHkTgWU92t zha7BorhX~OS4F;{z{GgHL!(_c|6b}Kq;kzPpXiE2e z58k)Y+bjZ3{gc*C5}Z&J&DNb~G|SJt9{tJO0yR_QA9@ zBqr|;+wvr2g)WktY&# zod+p>)x)#YDgFBQ2aO(%xQF6gZe)ty(A_?lNK4bpu**75Bh-JjWMT=yw1kzu> zoI}Tk{=4*3w1YjTD$s%NhXW3gX(#oQ@uPDhLqNXkUrutu(4e%c7uC@3}aPzL8 zA8CeZs+6Zq@BBv1Wjp3~AK^_@R@}6EZ^1YfIg&upt5*+Q|8o=i0D5CZAwC7vElZ}_ zL0PKAy?TUI*rE0IufAjQ2ui7>z!|^(9R26wM~w}YfO4|LW)&#WC>(T zi9&E>W0db4Sl<=D4o1PjR)uS4`3(fEobdn{drTQn6@+UEZm8liA zn-p7ybNxySYXRZfFTRj)x9yhRb04?nTGFB#n5^})*l*~v;oQn?9UZ?vdJ>s+acx$m z`8#{Z>_MU29AOrOxKu3aElz&7({3=>`^7HSyoD>EzR`(L3=Lxp$GExovYV9KZ5oKi znz57l>@eIXo|CmQhAUe7+-`{(_(tF0w6$y2OnId$c#mq?keW&G@QVn@OqgX*&t>=3#2Qo--$ zVhby)R7&3H3!*SU)KIu{eh^MyT;VRTiMYYvPzs2{H6s^+0Ich)yKwWwNQ9KozIhhg zujp-Mw+552(O8BEUa zj?*aMX0wI95%F%d*2$X%pWD5o@0TT6*cX6YnlF)2E!zhD{Dj0Uzln+eY7ehOeJFp| z8C3vll`tce9U>Ek<-WHBDuQJq_WI~>yv?6O_&^*^6cnW~WQs@+wNos#xqla9i%_`9 zDbmW*oCuqq4H-r?8)!#BVmd<%@-M1Pe%#6TO>@oGt@YD`M7wS3li-^jWBt#yjr}Zp z{;nzb6x8ML){^o{!^zeGC5vkMRQz1Lp)c@n9xbVO1~LxIgbE`w(Yic+4htHp?yxAw=lfk19Mj4 z0z#px156Z*_VOduq7G9TEnlWkw_Bx^+tw@O(u^G|h5=`AlqbYY&o((kJk#gqL6bpd zl>bdeTi|vjFPMtL6bWR5C{r9B^$_Ol92ln;PwrQO(SEM_IV65uwM;W8rv&`v3HZr! zu=8HauKglim_xUq4v!>}6G=0Ou_aSJQR+)BKnni)<41I6J}s_h&#w*sv-$Xc4g|Ts zcJ{g5CsE%VR1|8tz`=A^1z(r5wb%@-SK2wI&D*LbI6ayU$G-z!z!dyD$L(hssv$k> zox!!HS597it5I9^?h4oWG>ixs{Wzg+g&5o$lDsvp!jSfSv0YyE&~IPr5nQ{OG{2B|jfO`*i5g$ zdi(f~t;T)ybe{FJylCC;r?q@LX3cYXV(z5q z;&P`+1#UL^&z}#zG@w#7x5WBvC|s#kUk29a{Hb#$nLQNxL59Wu=+;xrwCdOrD1OJs z%1uST=Rz02Bdg^~W>ZnlFV!2;KUE%>-Ri`i5pTF3@ZzIUiu3vzf}30lDIhZD?U+1y zGSGuCBK=L~6i*Kw7SnDE!1I586_{in7-P=No#-pW3YrXOY?cUO&5+&upi!UbqB7Fg zjKtPxC&gX6{HZDNH)Ll+v#7w5WzdacJ{Xnh>=}tL@EyGY6u-TsMCH6`m9ECE)1yAn zRyZ{69TA;(JZ~^)l}2@6TYFpE@mpnkK$EVgZ!yDEM~%W;?BblKOr%j5G-wdO%3jx* zZFcadnyfaWVWVNq4F1x?oCz7`7s>R(l0tOm8JsV@nVcj&^|<}?)YB5*gs&d!s9~+{ zzndec(1BJf-^X@=_wl1g9V~M8j@zHVs_$GSfaKO1a4ruYSjZ*-Dj zO-)T}P{R?%f2-H5%CaA?vM>q%B*Cm`RCbOw-a2sUAuY>|Qz8m$jvv9mggYy&8UoPm zzv~V3L;J`+1YTkmqcQpriTtD_S&u|xw1GAk8q(c<6PB=l7N?m)80l+jeW*ZiLsE%~ zbuG5Du~DEHD0mZ4IVI&$^WvoAc22gbHZ4jFGcya0SKVd*qyqVVOX}BXA z(g+GRm0VzoqTg{{I(S8QX%`tZ(q_@$ZYG<=DKBc^j))6K9W!g@XoKM6~gUE`)nHOM92V$-@3K|cOL;vh*mCvKdh)>o>(^8w$XEL7s) z$)8K#`kv3k?{-dhx=LZb%Y8`V(WB-R$u_%2M_v1exm^&e`o>k@6uUv|k6MPlw9!GB zbBC8{MIW!^Q@Bye@<8qu7nc!lfW9q9{`iFQoXn6{OdIdEsHtl6=4xRG)m_GoX^Tbk zk3UH|$%jrVUmF9;eO$BuwK9@X`j>@_96{<$CVz@A7=V^uA})f8 z#thjN0-B=M@t^^D_}^cleX{LzG$A-pvxzkyfdT=JCVXyc{NSITI||l=DS6S<@}*a= zTv?4$Q=unySFiJ@IDqNf7*yPI>)>=#WtCQ5^hJD;;@|JIA!e|&q6-U59LA*dOi0d2 z>~0WIu#wxLzi?_&e1Y+0-xZDvm(jwrjJHwLe$O};vafxs*T{hFo;!&I_1EqIBe2fCHj%88xHLDjM6SC5Ad0U7VUaG%|mNN`C~}r|O#T zvtz7N;*7Z@NCF*5Gl} z69)KsOi$&Aj5gWS+IaLz>Sd$;nvj~W6EK9j0Qp30A)bEb(~4ffF*A{Xnaw<)N#$6$ zSyID8fawScm3t_p%HsijVq#*{jZAc%pQcWnvoGSaS`%;ob&DL=@eF07A7N0GC}bJg zf(9fY7F}$AybY)!%jn58e#jLtqfP-DZ3NhEeel`hCY-|IfF=l5WKL|n$x#&kgyg)? zjj;2Lt(?1iTMVwW_vU)bCQ#9tJ^D@$mNLu9nYK`X1fIym*u2lr&);A!lu+o)=;*`R zr5W+@1ubA-t)@``!_uPwB(fX@N~)@=dU$I7j`E;wTdOM*wFfPg&%ll(N<+tghKyeD z=Z$2`z5g6~tZiRDaIc}-NK<_iR5|pscuX5b4cYkjc4{10SdpxoQr}_<6Wn#@CJ(0~ z)Om61*8SqWow-@&&fHIXHr3ZR zk1W)CSPE6`LT}q$qxpX#YH$FmZgj7?c>)jbg+D5~s8iJeP^wh;tj(tXX3Lgf523RLNY55-J%K$s=aJdc!$k zxT}YSA|5yG;yqy{57GJY>PoY;pRr%x-nv0@lC^l8a&d6MsV?AMkpaUoE=4yEB+r3! zVvL0z#W#MqDbyBw{k??j#Eh@N*TS_<1dtFphzEagvLI`!VjD2-`(ArupWadNe1T|; zYO10M$+A20^{ZE_xU+ZxOyj*P$FwG$i--nChz#mLtfhMnu}Zaj z%W8xx*!Msh%w(X;qtMU)&T^l8Mh!ysU@DgU&mozXfW3pbQ2LcXAQny*foiWwHhECZ z`WlERfX6Gz);UA(_=FFK{iyF0kdWENJ zc(ui~4d2=b%l_3wuI0V4Q9+-d_Qy$+;}Voue*Hd~N?ZpShPt9421>7S&wE>6{0JzQ zqLgblGgY-!#2Y@;aKof&vu0g~Xo1d4O@RhsFB!mXRSs0)|NP2xu_r=E6k&%CVgO^l zg3A#RGfTUK=3sV*g4%>%oBp~FAy(D_D9~e#DDDHY%%rB5+D~hjdFkJVYeWt4tXrLs z&T+~VozJLGDre|45zz)Fxc-KPeq&|=fN1@WJSu;jh z!+3CsPQL6GNd*#ays+{%kg7(7{P5Pz^u(wHn9<$em=lTRR|Q30X2&$w|J ziBBqMiv*syCyuJNro)k67H;a+mLgmEX@@~jV}0&jxzf1$ROhXlGj?$4GRx!2aDSl8 ziHeEQ13Stu96jVfBmL7ZvxM-BGh|ZtNqyjH9$vgnLts7m>Y#HnPB?R_i*Dt4ir5(h zOcL#Ox|tXaKQ*I`XO>oUq8^sLr9)n(gUH+D2BUjPzC@+2nr95wjyB%; zuA$uy1nQNQuZL4a*e~r)e0biL_#eadCVZo?dRbDE9=O(Zt}(<30%6|$`x}`$9`h|! zcfH?z0euM{>KYg1?!9{=V(oxD5v`K`g42T_Z9#^xhvT)3FT)J%8{ksD2g) z&o?MZx*)wndTP_L)!G)?=?^?Mn8B{me4XCA%_9}P2fS}~^C-{MjKcclB@0cdMwsTk zn-x2{7poC=_d1()$m3`@A;x>o946$;^SE0Ng< zU}p+ZAC~6zNcmq&A5UB_RA2RwkN|Sxklk24;B+bix~QZAYYQG#859yCM4j7F3%D#c zS!3WyZ0a@MAzwB-ZB0p`zzAm|dv$Sy;+3kLGrpP>oh3~&Uq)3yVbQt0$*_CF2aY_S zt!bX}LVf*3(LMVe9%bMSM6j}7f|EB*sqCY~s0Cs4I3Ls9j+uG`225;SOcg?lq|tnp zTf3R-V`54;?I2&{pN67Cfl(Boe)Py8wL?9k({42}-!S@Bk6rB}8hoUL+OtV(m%8UY z^KKoi&!~H%`k+GJ)Ff$nz-iA9vvV$9w8pl=_TaeKq#Rjk0&(BT;@ylnbK-1w=|KAj z-xFm85AN5OSB?2td=3V1D_^u~*DhV+ zfY?UMZbmMd(ATJY>?zn5sekJh)$U@*FTE+MCG*F#)H$`FRn4UE#x~wtZp86%Sd`o* zU%PAmqWre&GXC+Z{rW}~c&FJ^)CSsTwlrU1SAFVmSKZ2EdP#-sx>3bN%NI5c_EAaH zrnqD24)~{zaSn1c3D4AVni=0lWjm=p<7Z%T`+lvsb#=Wg$865nLIcI+KNci5| z$2H}uHEk1R2pw8wiVBX7^2H`x2svHdcht2*|ING+5qTRN$-Vqs^3JG*X^1~NU$CSO zoHvnG92|wv{xkm2y{>mK!8V#;+qT)Tq}m;-NekXZ&o40LUJYY*Ph;D<#}unoja^BK z?kmFR4sl&TezXHt8Axm+!ImwLJyLHN5;1#2AG0C0rRm$YO;T^BPZNbQB#wP#0B4jw z@nks-I0}^Z{en6pDYCO`LQxs9W{rBAHU?*68HRi9R*irX1812d%T^K@;8QNlU?m|t zN4s8e4KHlPpH*3xR2v#sH?pRXKSsS%u*2FKb+BeycYTXymoAk8$Q!p-fx5ma(|o4(8=jc5 zVTl+M^0!19%0&XwLKMZPD*y07mQ}d67)$tn`J(!Bb$C{sRE)S->c!lL;Fj|74E)@^ zQ!hcN<78j6P6NrZkfzinIkEodU4BBrb z8wZ7wg_#dQI=;|1r1nSmN4EE>#cYPTH)VNVeMX;oN7M_Gh+PXm-eZCu6{z6!vD3b8 zs7h|)>L)vZKv!6$8y>EtS_J&2|3TB#Lk%1Uyi;ny++3Z)6P1KCe8CeeXE?il+180a zVZ+lGOFpJWAD^Cf-~V$3p$L3eZ_5q$kox%QO0|+KC8ML&txY{fhy2*aumd5NTrtwC zEEN$pFAJ7I&B^mdBcF69+x+`0p;dN9$YJJI@~45D08Tdfo(2FD+tm2LvC{)Vud%Bi zNrwzhw5?y15vB4ytCwxswAqxq5FcBUdz~)0&tBV8r+eOQzX-O=Mzu6aQL}48%VZs~ z&1J!YBQw*c!18LXqJ-4w@|`<>`uM!oPx?_A3^Krrc@0KQWF1>~7E%EkE9b!bTkIbH za=pF#fL`}TM}fqW1}$I0xV?AG#8FO8LiFKAzU9z7J-@sy$`Rxs?3)w0thTnpoH+-o z)2{nkwD{u>BAAPw4uke{D{41$sbb1Rs4vqyC<(W#92DVfP$fakavC?YvYUP54~bRV zhle*FN+L}B-Jo)}b4t_N$cK@gBDJ^o9$@RR8$Y zo*dM;q$IJ#eO7I>4a$u zM~2)DH&nb5mew%w;^!GS<=w?MpENX_=ecj}eog+}wq87l8yhd|&{5NPdM0rw2@_hm zc%H-m8@S}-OWvxOvw~;6J2zQDCU%@zY^J_2Nxpm3(WYM+Rd15#&xo>xmriu;QdfL? zH`l;|j!prB;wY$}NqM)fTx@idM`bu+&uC*~ut9WXS4bfsp_w@@5S{=*gSotvk1(Kg z^Xrpd#*KmFp}#*KoQw;9Ql?yN8xNrQ*H?0|7hb=@yZJ}2P%p4o28VcVr2JYxeHe^Jrf;|p*p zVj5hSHn1-}!u2mdn)HnT(!IR&`=E|_Y~~U2fWTG^j7mx`=+tR=58(+X)y@CSwheT{ zNN`=In$Pw3cOdbzX3LVZxy8iySXAdXR@YSs$1&d#0ZM7VQm7LFb`=-PM$I{jQH zK-z6`1N#L#V02Cco^*pRWmDaJgGB}jXtAcE&&-(xK)H*v4>nj7rVX!rCaZ#|>#RcS zw33~M330yl>26U|7B5^E8y6?5{HQi814;{gjmn53hF2qmuTPhfNUR3?8OAAr6V-xl z|MJtPsj`yt$EQhy!|LgMM(A7zH`y6fe-Yi`gTkP>Y}AV~KH_w1Q}+|m!eKlX{B62I zecd+~A(cEGTsXwkuYTn3Gdo008(3c#92XadMXH0|n(HlGL!F%B9_~=9Y1>r-5vMdK z;Ef7Oyi%eulZIG+vZ$X9o=ngL=o&^3Ec_APr}Nje=C(~wOdMrnv(m$=jEMm8E9qanDy7*n_qGdvL%tRj4kmT2I(1sl^p?-Lmp01#BA&(n z{Z%rf%Sb&#l0pHD5rC;dLi=Y31#i?9LI54sHJ+Nx+NiQ`{x^a^AG?Nmr(w($tRqJ7 zw)c(R6<+<))M>N-4bm~_xVjgB;eaeH=WdtvCrUW6xMbX-KJ%^4=2oj7QW!i0+>(PI zR-c-in>)rx7gqpkpVv%&00p-2K?T%h$})F%_wL=1NTz@37J4oNuOnW=Mz$xz&?tHT zJ_h_mgx3@}Rw#BE*f~@xPq?`upp%~x0YbM=A5EMH4mR5!p{zKhYHJ5bV`6|zA<4xf z0x(n9HQHF#zfeXpEU0`qZvO*OyMfD6Q0LQdNWs8?5xE|0GT8Bl$9_M2D5{6r$o2OR zX;9udr487`0BUx}9^bmfKmRr&YMn#ZVde&_wCa}MfjaBrzKxeTfwu<@Y=~i!xG2>B zyx5RAp@U}o_T8@TAJDJg%(|!H`#s1#6ueSK@s2Xv?MPFHP=;a~*Nnm@8rqQU8RToP zWQ?SrvqO$99@jdDI)AlvKKc8){?bm98S_K2LB_4b8n(;aot6FeCdW1ZhWuC?o}pBI z-+m=&QROpekTfN+jLg%nI2JsHa@fW*>^6IFsG4?-F7KJN!`7G znQY!%WwRAKpKNtl35smgYI?>dep1Z56*Te1TER)3^jCRYuiH|y*xC{}MrJHFo~d5L z?)04tl?VYAqk+d+zmXjx9CU8fl$=X%FrlF*~1koY*{=v!C~E zaQ)&*3uZcmq&f9y$ow{wc_49F%HSiMZeOEWxVuKLUw@Uteu%D3NXc1GDpj@rAX$<{ zO87^)py5EbQD#00)T{+;{A2zI&v)e)cTNYlbzOV-`RyKLO_rp$0?qu+Glrqx)fJx#V*l3jt_>Gk6e%bwTP5*@T8Gd zx4O8N-qy9j`gIje_SVAt6hQ{8wivgp3}S$gykGi7g>R0$>c8^xh-@z1Yc8;vjz7+-QN6UbEo_y5VSE2qGSmRUE* zwg}W1EIWS7N&5c*8+M!fMJY;OCzEpjdxl`EcCa&lE$1ea`G5%Z|}cob*rY$c-F_a{~c$&>iwrr-CJf~6`#gNN-CH>j--BpS0%-r zR6n$qX#aH2JhSoqIDu!8MPnBp^MbS_ASVllsC?TTya7`1=%`OKwHcRQz)zt9kW%{i zdi$br{q`K(H|)vl((#%H43>6!r#|dL&IyGLM~0En__bjVngP;>B!x#7ij@=8IdyMUd0E*N<^cg4G%~S=f zL#@I8rVClHXi+I4U)VB*XR^jx?XU_?L=>3#@7Q2RNfS0&pEqW}3B3U?8U;4pfO{Wu zE$x!?+aGX0I;k_v_E9?3bwEHwZd{bQT+%s3`VrX+{}EJ#`g~nwo5#<`FP{ADz^Csn z#p$k-U%WVe`mo_CXEGw8Dlw|bnwA|?a_#M&D86Mj9%Iehn|y8_D{Io&(-{5R45>bM z%V|17d8bshra6e>!uu$Dza|`s?HX%4WHA%p{4Jau+2)Mo_pZ2U^qtQ-wP0}>0cvSn zQnLnUNA<%Sz_AEbH~HC^^Gs{Bx3}Wl4O{Nl9B%YDv%P}K7?Jzbu7udUwE zrM~yoq9O~2hy#6QE^qjIahThr8`{tbAs|HYwCp|n!(7>ok60L>)A1~4q& zqB3*2XJ)3CY1Q2(m9cwX8yl28ri6lz(^JSIRI@U%V>`!G;1EbyHm{fJ^wY__O4YC` zr*{9#+yEGs`s;^M`{^^2WMEc#?8UiYji}fy_ajHY9zI~h@j)#C+fPjF5!&#cLS@dg zGi@L@0oo8HHMbaDs89)m7H$nhSQ0qS`{-|m0#IT`e09qj145^UD_2lZKPEGPkg4d6 z4f@SfkDY`!Wq!ka6nJ@W7{@yPu%vM#0OXO+?Uw3CY2?XPTbfFP{>~*E0&SlD6st2) zMvz7sZL+D)>OomD3}nzm`&EsUM6DAv2#P?aN3Lu@ynAav<_Eae8Rd3!{roZ?96k7H zv2nLUNtDW$E92E45B_sj?8!3My4njm!$K%ki0Z=JOWcGESVuwlKvuN8Z6@@_DC4JHh` z7;HGC?yH3luyFa~!SnqC|GfVr<$oBGu5N;B_M(kPcpU?xoeE|_HHh^zD+AZTUMPk} zHX8+%h}`%;K`Pjsy#aJo)`18G&N1`4TUlz=*I(aeP#!}NYG?GeXy~k#-j_@LmZgoy zy`g!Y@;Mr>UwmaWgy6$p3Bs)>8w06}Z2+-y)aYQ%9e@#vV%qQ8p=nf0ErwUh5$5XZ z6Cy_+HSTw4=1_1<5TR?p+E5mcTJ-G?c=!$*Orev~1FVVyGRj(R7@K%NzsgwV9)#=2zeb;tsNXmZ?hs@Zhkxy&cTGBgTDhO6l$m!Yf2e zM(#&(2FLm%ioH|+Bps$0?ZRKBCgjxQUA!39Fy-7u@OSG=%K=h6E`;<~U=mzEPotx{ zzt~nQ8@6Luv2OQnpEW17;TiDiSLIxqj!Q4ePb9eaLgqX@7wm5Jr_40S?()tF>dr^D z1B`=mDKNg#0J#E(%*6okHW(5+In>gYfT^wLyO1#26c_iJVB*xr|AS^i8(_ID3=0s? zmQy*!EVOv1dqHbRfa1n#ZIKufu?@rO>x3>fveur_isJFuEl%r!pN{xkYe{kvM#7h` zUxmysi~vT!Zjks|oN(PW`fsdzKwNG?!sw-v2RAp#=5L_T#WsOBkxdT?3GrZShnmsM z5eHw6Hr99elx%Odg%T+xquk|*UTz*^gT`?Eup)C?cL7@KYe>wm$bsG$J-obj0K*kA znCWG2e#6&7cAoO@Pk__P8xU&T@!7?)igI0r49Sf(JM*TDTgI+i=SMRPJjBa@u$nz? zu3Da!N~GSS6^_20!riyyP(@ZBwrg}xFe)IBnxXyA^oA!&l8gO(BD`BpA6AY8el2w{ zANGxw2FmQ70dI8^tY86gXx0!4Y3F|+G2cm^-r%jj-m;a%Wqu|A{D z=iwe7HZ85S`L94GZ9^b$KE!07f#HLi!AAdgS1XTwePMFVx!J4}?cCkn8K2+g$1*HO z01?iEk7;y-;@OhpE&dEdfQ118HU3R=@vKu=iSyTO!>ovhEiP>`GC!j(az(EL=hji) z-nbEqOB5lmS!Ek7ttjeK=IJFYJ=snBteieb9f`BSu#mhR$Jd4_d7n`)au|`Zsj7J% z06UBAUy=sp{C~Jq1eIj;k*Pz(D~Y3Jj@y_UKZ5?jykLQJxl=qY!DkYwVna z1(WxA98JIarqZW*i2zF2^y>|96P^mRX-FtBa5#Y5--3?_(U?mrK478PJfTDU;U2Zp zvi%U9%|#3SAPe$|NjYFAMp=1IBlD!rkY)5pkEz!q`17#SoB?;h))=X}GF3VFc?JQP zNW}gvxm_u#PJD>}Y|4TaBPo)b3VbReOh(TM?~m$sI1Hac+2kGnh-zN{D4C;OS}q>T z2r2m;)M`^coOBHNPZtI%^#+(Py?w<>#l}LdmTrOTHyu;FiSx^VLSPUyUkSiJngb%7 zGXj)--?6JGegta)*YT%htM0_cTGXp~bidW8ox6_SsQ_TbFooFxLnF~azOIja?HN^9 z|Gd#t<|;z{1oULC(v``1QSi2mD2M1B$f7>rDxbNv=P(tdJo*FYo zG+s%IzejC&`DODa`|dm+4WG=U*fFu|*S{rRfkJHsvWqoy1nm&k6S+OAEcs;y8p}1v zHyEXjEIRCblg@t)6aKLJUqSc4x+Dv}jZR}qgbE7Lbr8LsK&)X>G zPSn?h+n6E)eDS;f&`1=!DY?p`$+XVU>B_$W%B7*9AvOpsnrj1f=lAnljTf)kSOdX|liN_WWOTgeNA{{%B21i@#gNYrGItp z-=+KQ-*C==0XEYh{QcM1ZfPgGn`2^-ifREvs?h=?-NsEpL1Za~9k>@ao`zz;zA#Iybq z)>*w8z`Vh3qn_Q+x`KuK)GzjV*9sz5>Utfuk+HEp%+f?@!{sp|oCp(z3wPAp;#dr>elck7bx|zFcL>U<30@%9R=BdqdqK<~A>Wz(Euh8d6eqjUQHWqZ&Fl+*v@P_%xK1g

    mdrU?=NL4KADyp7a(O!u^|CTt@6bg7K#7DKb!|WIky=tl z3bEE{)F@#>@f%%F?!t_NX_HLHrgY9uMCf&wt-TzKeN)a4psKmCaYP@6ttB&rnumv; z)=H_oprx43U(F0A{hmdP@Ih*-L~$Z`5ueOr>1R(mskB}Dt^t3$^5CtktN=UbH5xu_ zh>l^^@_EnCVO%S)_La-08dSooZ$Esvl?ySm&FpT?^Rm6qIb@BI&DhjYf7sxnW4Crx zRQqP4H{-^Lb`bO9g~{vI{Xsdqmj}QlgL_m+p3L)Zaw(w-All~X>CwbkIj+3H-UGTm zdGbWQKdVrpm|yWOGvH7uUv(A>9qL@v7s@3)&f|CwP9kg6MzXY zi8}@7cZ_mSb%XlJhHSN*k@R-SJ_b!XLAayu2(7eZw2aMhIkPh$5j~`e6 z{#$Lvj54RNKI4pAOCthy5{L;_YbEc*)erBC#C5VOJVJOoWt_)}`ClAI{O?!Y0pIKH zW4Kku*q#uM9KI}{b+~%FYhCc=)04iuW(CQy?Iv~C zO<1_mF*B&ep0PTH4|gwLwro%EMI_fk(#1(=G!?E>+o8e1!AD`XNWFbmvG5h{Qlubo zZRQJ8UJr=wn8(b;exV0VIiS2_b}<-;s%*L8!AbZ8nxO%bh8v5Flz%oI&Y#JnDG)mWwiozv3eIxIv&~54N3|YoWL*x$ds7b+UQL$F`*Vg z5ThI7$HcMwW;3UGRa%Y!g5KL1gJNU_c6R zwL#WrI4lFXy)>U?XPpX0)_Kq#j9B!ihTTQ_q#5+MT3T8{K$w0zwX5?Qqf_f>=-D~% zz_gOYeJp7EW{g!wXjRgXwP)NHOL>7VX2g;1Ksvx7Yhb4UkZbxFpSVPzN*&Sa^THRg zVE8Xzzh2E@Lqz$vR&(#)!NOLv$USx|Xn6 zNs=C|=gerbYhO7BRJnfp!y!%!4sR|oI3XLvDd-!C?aslc6M3;(4V&70Hsh{q*6xDG zu@iO;d7Q!7fBdyHyL#lr5C*RFmB$6#8#p*6`G7FWag^j%9H zK|yxOM~Z)nA||*~5@(oha*QO<=qTrfPA?7UPeb2qDhc?->E8bnI;g8BkKALNj3v-2 zCqoUR6_GE(#zhotKX#MjB2w{-qI7%8xUcF);l!)9Qk2qpVe;> z6Qp-rpL@91ql8XOqVk|-O6ny{a2EAFKI4GGmzKtxL4!k6rw6l6^m}-qTi^JN0HRMX zte6ycAM>YG;~u=rn&$ieF?AkrJ@4)RFETP)$PP)!C?UJj)IbzPMzUvw%#xx-lu?n9 zgb*3&SSjBkBO{57sECu5P$>PM*E#on|Nb72^SE#4G``=@_x--c>w3Ljm!}0|LVe_i z{7;=Cy)OMb+3&(3L*J~>MlBC#dmY>TwraDG5=J3mlF>nv3$8__mP?Y)!@wA`L&#S$ z)L|Meqc9GfH~=xBH`6l1P5U(J^RYhjkzi8@Yb>ySmu6{YHSc*^LfO8ADD8y|h60~l zuyd&DhCunYpr>*#{r_plSxf4=Kiq{fKGu*^I<-KEY*n`E{_XR>li-@ zVni>yy*&bdA7mKJ9EY1kB*ifz%I8B%_)Yq4{8&6LuIxKGBQYs7v zC-Gi|F-&`a_&Nwdk_BJHQ#&HRjKPUS@He{ZI|=F|9S9Oj=!M4N?^|?tQu%9g4qcAe zBBWt-8}qXE5H!5b`_fo&AUm0P*wW5R0ts zSNDv@!H6?x_U9%kwe0aEv_;Q^iWTo=IwKZXh8{<79K`d={Dg}`({yzm3WV&WE_-wR z8(?3Uy~!zH_YyD_7CObH_t$5yXdBM#^IF9qTMLlvspdB4#uDlTbj)JhGu?Y-^a z_t?IPsom|h=O_BpvYAj}bkf%k0;~+aad|*>zGYZiXBIXn7b%j1Ej2jjvhx;f7*_Bg zFk$$VCB#%9mW}FNtT2*u=A@t~oj)nPeE%$Km`RQVJsx z!r`IiC&WGi>L0us=Gak*Rkd_z2pwaIOQGv*-J!@CJ?muFd-pP&hA<%dGjjCfQ8tQF z>PU#Mbi8=?*Dm^~w%0>!V!J={g`Df>IQ$Uz^vmb|lV0(4*V1a1c3R=KWbqE0Xvpy% z%+G*zALm5++rhQTJEO48e)=?;LPY}(Edbd+F4!pxQJQX})z784)1ZV6V*~?SVtfV<0myrYcu6pD4+EiqzvE(yF8x3SANo}h05*>?a-Y&o&8f9 z-W$Y2w%y&+5pSlEhShPFD|x@=ch=f1N0RZmEqPkD1+l(Ran1*4<}#-0OInuZ={Z1&>RHW ziRBYfd(>3|etb)O8C;kaV8Ms@)kZu1%-OTKbN3jHLK(-snGWO&OLL2Sa40LJp-MQa zWM&ZgP!UM;<1ygu%-dOvvYx=`p-q8%)=DTDQXFpFM-~u<8?}Sf`aKWiXf*}RuJR0IqO+Rk6w9WJsub>@JMq<7y4n(s*C6LJ3 z?Ys>}nrP!`9W+huW?Ijg;IY}TQ^L5&#{1~(==3sja<)<(-v>wa>@o-lPikkd^1~p5 z!dD>vdlpQ)YVlZTmJfdRUf=GNA0shj`JQ~zykaTrId%Vq!&sh34a|e@xF!E|$uBqm zFZ1~uZr3rze*6gbF`sYHpRqtZCDxnR39yTY;Vd zXXQ?j+0q}wrp}+Jg0JcR6Z4k}9}lu7%16LlhVCJdx0of#`h#f{v?2zdpWo)7e)^Lq z+Eo*DS{4rLhj=8+?%quc$b=tdkS4A~8i!u5pCy1S%CgLS0K8*v=X(&;dHR3)X11m! z*Yth-cJj}C@HMT%J2b8SeZrfIs#W~37}o@SOj}qV8vm|M%D)X}za#0j%JB8k>JFh; zgB3g-rWQyhwB$>UDN~8|B)@J~<@;!_&i_55q2p${Y}XZ6yYCzY4&WDG$1td^b$we1 zM65Wz0eVz7{?q~Y|A0s4=0&qR+o^ao3iPK+jZe*?ojo%&v53!0Y6rwcVhK9FL3Htm zqm18oz-tt>zo@{u8NMdvfdg=#3vo{15#0XrgOQrjHBh)+iX3-zFV2UUs*hgrb!b=P zzuex{K{GY{Cdb{qcrc0KI{T+l{fJhHID4cbSXDK+M6I3T;owzT+S>Y8E3LiMV5dv4 zL(v$T+?A8hpl)*Ua^v%6BLHqW)Smga25OpffGQc*90c`+1uX6nB4+^bWMJ&cZ-R4x z$3SaTi?;x2XajRshPwPxXVfIde~g47^4*b%7;yk>lqnD?wZj&#g$NhlQ2FB|dE{M( zmivLS4Hp&|Q~Hc#2FV+F!-1BNC7%Mu(aoMD=FKB*-zI&eLaGJcp;zmR4+Pol=vmc8 z_>~Kp8%RxrF)-)5XGQCFQ`6Kd1Jmh$wGWa65WePk2UE&e!rE)tym={Cii!7rV&_AeYBf|{emfG zX$_g3P!SG(krJS|=eYX!!Hb|lj^0i4EWO694Y)Ul-KADd4(}!xvBKOxI@-04;TSe5 z==Lr^`9<{D=8NFM11lS(tNUSaO`TlpvK6;ThGkD}05 zclOzmcUUAquzAe~4ldg8uv0dfJtDRDeDip7T_lKfp~gnXY-4HET6`ShH1~Y* z1(Z7ClXj;e2nuH@^whbqQ^--_8;u4R-__zX?M+azQ;gF{l?_okHoK~Rnji_}^w9p# zTD>Fj|4QUaJ?<(BpyP@APMkQ$0}xs`77|K8B$9R5yR=s?J)8jfth(PNC1|;7e$=|N zZf@zs>Vb9v54BFd`TOr%xE;XcvG4!O6!q??;#Y@zfBEv|8me>{ExOJx>wvQ30J@db zsL`g)E!_D4Y|YLjduwQ1)Vb{lxCO9?Qv&YQtVC=zE- zAKG5+hZGGWi_pWA$#m8+?TP`S?}E={;|N3(MwK`$+XASzki`-j&@xe&h5=#pMUVwE z{A9ldqo~apVhBF1tgI~cGjs{vo?kT7^vX3xyELpU-0_l{Zapy@Pm_A|J-8rsfHn!) z;}#K0#-jh?XZLYIAZB;T8rHbC8W)d3udeY8`>Y!`Zw7IEM8^+)>cER1*Ww+c_2A!b zgcXY8x?V}ld@fHz{iq}Zys$ZsHqo=~xpvJI6THrY&*|MDuV$u(lP!$A-;L49dC-5* zg%%5`#6G;dGRa_(n#Vxpu}Tc{)Nh1qY&<$f-=b8_y>W*C)y3{>tvbb=0+dnJyMlht zAGdhsG-U5!FK2+Z5#N2xUB)GQSn4cV+o^xKwaX2=tQ#m)Y4h1D^~$r}BUm6p38#?M z$$3AQ;j{dW5UcB2h(jE;Q~boRZ5o_Ct)RmtI(lJcluEkS3-?U!J)8G0P8z%maL7&A zZfr{VIZP6wTw-D<%+Pk!JSg`qR_1f3LL5gHh6)!3mf`TtGhGZ$^)`ey+kPf=KtmX2 z;G=w;?#C-9xwgC0s6Q6R^yE96UpEv#{bKBgdWDI1MEBHAN-jXZ`fSuEP!-KN-~<1TJs6 zOu~QwDF}K&;L$v;)Bp9ykFg)l2(yGy744k(Rzk;1|4Qel?A1JnqsRnePOpYYM|2qL z!&0)>>jLGXO|5l-OY9p`U;1Z|A(bMJZsLiV)_9{*9XRyD8Ujc=L$VWQEjc%foGr|G zlbbQ^3YsO&yYw<#W9paVhdx%G__$n$TIKlnfF5^2E)2IO(Bax9&I!!!>L6=4f{vdX zZ4f8hNQpUwTD>`!#_ZhRCd0yNo+8}z5>#TsBc>z!sWJqWFJHaw$;=*qDm&EnyAj@J zT#ubaCAQ4eH4H+1Yc%y{a?mjtvGM=CWsGtG!$cfD=mBTYaC>la7(kjN(TMq+EW^% zZ5$*Ff1u89*fudJ5Wciwn`?|BxP!2%D#m|qUSJi)MExt+HoU6tgLTA|tG4E|FI|Cy zR=)1a+7?@nzy;(ODSjUSQQbG}Qr4Pcg8YWm>_|)oH!_&kR9D|XoiDt3s%1j56Mdq} zB71HX)7>TTajD~aeVRJXc$40_E2rkIsmaZ%r*v+_r{(K9K6<+>{`@^$l_LKxeLNt^ zMa_h`p$RvS{GLgEVw13!|M!SJ4ei(NZ5$b02ggT`wVt3p7U^+ct`h7-5)XILf_1~x z0sPEL zCpK36)whx1YT72v+Ge^-zVE#K#wx@B|Mtb6QoKmKH7!u^iO2!;XzW-yp1E>~csg-b;`?W4Ge z6O6=S?bypw*}sYb<@FJb}jUz*ByA$_o6<}Tb@COk|528lb%?2oxW zE<)jES4Yd@uC(xXk@)#kR~eF^iYgNprj5&w|FKGxbySE@T*Mn`!Lt-0d>H`&Z7EH> ze@gQ-dmBM^V2E{qvDY9$Jlle0pKW`^7OL zi@eII_=2r9*Q)W~5EQpM9ecF; zxy!RZp#w@ckKfkgzr>R5&Yo`fDgeKV8W-ukDXw+!5!N@wbuxP(DI~YM-nI4P9&tQn z1B|dVNb+a-j~L@1iSs<;=pi`>7d())cogm)-wj7NQ)_WRB4 zR{v7P;`Z3Osq4MK1oB|T--TjaH3lKq_8T@M0q)7GNE)L;zq~R znr9$;1&DBt7nbd%+9f|6CTf9u$?kQ!;~e_5jSR6k$wC6-n3ox77_+(q8vSX8X;aeH zZ!tf30C0{ei^Ysb0eS&5hOZwnl1rl=c}&$wr+1^plxdQ~dF#pWp`|?Bi!qP=iM*p@ z>oOtW7Rlbm^EF2`MkvFUQxYs@_A@QLYXIN<9cLlD3USUO7?Uh-*T$mYKCpdGuTB%in%W+xRNck)rBMO`& zb1!LVr1b#RE~+VBGbAWI?bBGe?RUPUFvt^{3t9^xBq@@Dwy5oC}1aHQBe-KWD4 z@~SjPG+BK{CPmjV_Z>HSbaNtt=sRtfO#kXu=vI?42TYYIE*e$$+_BWlOI~1+UC`EhJqVz7g2VOL>6Y1a zw$|(Qu@N_qF!Q>WHltW$M01`u6}LYF-5|&F|2LINGm6$z!`r1dPzI*aU>)px-BY!u zdIEBocP}mtge-yof^qH`#R6lFrh2^)KIJs9zWW8=DQ$-6b8 zA8U}(8DbOO$Bv0;DF=tHnD)77q}N(7FBOIJs<~xfx1mx>IPTMT10(lxGB>n&WS_ep ze?E)!7|IOLk20+-_EF*=jp3EUt4&>?js{E=>wpB?(^@#DU%kM%M0HnYt3cI7cG1D% z;o&~*1*@T9w*NjXW46QGz7v)>_n3HkT1cIGOg?B%lqNz}T^U|ewG)?Bv33vzDOi9P z7&Q2%(C#VbC9qIT6~B8IHU0H2^MlhE;tHj&D!<+w=yiOL>Fnf03VfPYLnPB2<4tHF zm@5G~?1dN-Z(E|*?nwD>?F`TMbEZx=Xfh??tOWj$`24yiCfeM3pu&BhQjX=#Lk!l$6gVoFB8{9y-F~ z+OWg&VMeMI(A$}zQ;hn96e+4a>4U}^H}qOmN+yyeVX`=sh6)iOK6Kq_>GIzNB=j=h zKW>S8%XX{f))4DSdvT5D0v%*l4ns&n#c*UzS{;6{2WCMGD`bFN?)rW95#A;+l19sx zR%B9fmZMNCwA^tHLa2_TcT~>_y+IV~>Uw|wvmLh%ExIXUeMoP6DQAj3#&x09f&X4V zsO`r4=yizWjFI}Ejj7&$fS(K$QXkeQu!XjvhYE8=^{H@kZhsx1YL0L3C(mDhwba&b zxUHzle~iU4a4g+VH5wxZWYnb6Q#fru`rkG|;p=Z78ij$wMQy|W4Aek;jz5UCJve3? zI(;Im(d3hRTmsC@4^~V)WYv^$&qyw)_MoM@1mG6Ej^Z`Gk@_Esmu#jiyc;p>EI@BO z2gTzR@0 zvH|0Bd}j1VFrUqxK;Mjs!9?#X59+~%^Rkc86ZSVpmj}=kNwsFZ85*r&J(lC0C_at^ zT#a@(o9hV`kDCEEtK7czg2-%7DNdG#Bw5Pfv~E!ilr#D}*%_32=~AfS#X9EW&%#4o{`Vu`VUrgNmr-va2<5)PMc{@h zF8eohSfaZ&%`nn*n@?-P2*> zto@z=y|OD~TQaP(5CJSMg!ICHI_*{MCo)A)YY(tUdZMQA3}n$0^hJvYHZFKVrX&8^ zkQy{8NYe%fOR>O$aQxd~zTIl^8KxX7!jTz;v}ITf)O8g`GLL(#ilZ_RgI?+zVa+0O zmih*Q+1wSLyMqw@CsV;t?g+u6Lx&Ewo%M1vc$=C-n^OZVl;S7nf$OR(yZ+}mRkhDE#&iJ< zQqes^-5~%_rnjBXt~k=KC{#h)c9#;A(=W?q;Xy8=b+?bcT%!(mvpFa44M>xe^<|^p z;sNo%{1V@9yNfFnNi9fWxZ{A)fRhb3?!KrM)XViu00MRZO_2@fUWo=W9 zd7|^*-oG^p!!K;Zda9s1?;_9I69X9t2AubQ+;^@OIriQThdO}-d#0-qS4Q$4B$ejg7>)4&&=aP!uEe$rJCs%~(GWfV&1a%}M51>nzz zteew=Y=6}kjnJt zl75NEy3ns+z1e+a;Yd=KTtrU%%!~ll!JmJxmjyCX8F4qWKrskyoKOcM{h1FHu_Ju< zfv-VhRA?_wH@lOU*Wa$-Kx3;g?R2QzCRiHKRhDpuVKzGvI|vu&E_U3)dM^IGkX8Uw zNh;Iva7Ir#tnu*lQ?{V8VoxHMm{MUcdjluqKoFHUWXW3KGE6YW`=`Bt^ec?PUMGx< zjTJ9ncAyI3s_s5k$8cf27xJwDFp_@giObF{ap=5EC`de`65>Lhf0zGEh#Bon4Ybz` z%CuhM^V3yCJ22c15SoC{0Mmo9N~46R?OH!6V&qXznh`zls7a~Ec2m)F;{`D?Na2_BAL*n4keclYpsAM@L` zrXIH=8Qzdqj#`K6Ht*svjTrU%cJzN%%szp!ACB(06UgN{r_*xa9YUCJz8MtZ4ZL_$ zN^G~$9k9Ql@D_Rm(Q!KtL5zIZH#vi!K6vGY+o0H7=VCkp8TL#KbKhR|Qwmp>0i#4` zyHI$O#@L(YQzyW2X!T;G_l6~fV5RnXt)~%ikcv1l;o?>9{;-;pE6M@hPK=<@yk1XB zkMiHL=B7p9ekgQBiVOC7f^x;(4%aPUd6HR^m*F@gMZIgXGv@vXp=57f{{CS-lut}o zL-y^PlJIR^;JdUEi!yUx=OuHso=?JB$pnC zxx-TI?MQ3fT5ynrV-veVn8E2S77!AKl;ktQiw->u0}0syCdLj+mK>mhfYVHe4?nY2 z=tq@+jxPjLCfS{(bbpAF3OpIOwXmNd5~vduPM$oey|Zn{>0`#LT>{;7 z8Q65|W`;-Yr`>|mMpu;kZ9O6(9mfy5p6l*YYrTxfe=?=}?%UDq6;wiCq!HUr_u*)Y z66^s~8Z?MRd$NB@yZTG;xDbSbrU#gR79KFivR=;>;c@1ZV|>jLBwHN+=wWLH@VT&r zFsPaC#dzvuTuE(%T18a#XP<|1(J#-|hXMRCAni!2!PSVg|Kk}zYctKm!yifuv@H7- z!Tn!WWtvZ&7v&558=jz0B8vqw$B|(#%(S4eFrih6=P8n~C0r=9uF(IFP)3Mq zm$5MPW);O>cFR8&v;fMG=gw;+BSDGc&ECV)SXMvb#O6s*Ml=)zgfO#e&C@jB5lUcE zzYmA>-4Yfi8v*^Q?BlmY*-ntUo^eTJ7wcvW7e)hmtio&oz+3v)+XV+3WAM3HHbhd| z@n(+D51Kwd8#d;^jy6&P-@m_2bZblsu-6hZ-dO{Fk1(Sf0tvJA&j*@+cy0@o-S#0X z7nM7+_g<{&-g-7j4<5IB#>E)5Pld-+FIK#{-EDmZGJRpm%$f)E`*M{J2H~ZpN)8o(^DgV3a>)Wh-vV&`c zBt@1~D*>UnlvP^(0|K_x={?vb*J5IIqat$Q?2z`gfIh^gVijkc;cS{knQZP&YqV>}8IKa=uFWQOH! zLK}Hix|%&_H0&+^NhUrh!~9a$lmV$Nzj%*WCq@GV(2$~b!TZB`%ys6hYih3w36K{2 z7@?X11x$O=?Clo3%2xIC1b{S(?!(5^USQ(Mr%$Whi`?-a3&O{vbRWmtYUm+yy1p zFcEswxacX`P^!=DVqOzLRtKhIJ(%5T>Kgl@v(7$zFq}KdCn2$0j2Kz$XaWETj= zFQY>v=5P-zdmkb{2=PbG)ahx;6sSi~-V0F4u@VZT%xr0pwBa#Q0@@V0xv+>EeV{+g zCPr?g2c({p$jNjxLWBY+6DKlAMMfWdN^x(<%B>AC%pn+!g ziOu70&r`FZjlZI@pymu(calR=JHDBR63K_zoDx+y8|{Uf-*wau(V9@O&pqE1nxj`c z^Cj6eI1u={Hn_Aumu)V1NZPQ|iYSowzx-qP+dv`fvd%7!rHC|(GU%%ev z#k1h>F9(w5Z-_!~_QEY7xCM~I89ZOGBVt%HI&w(`6hTOx&XisS>8HHb$-%b&t0^~!kiJ) zRz^7ZUEk5FJbt1xj00*MxW;(!J0%R>Jb)q}%LPDOOyLbEaAcn?*Fjv>iGC3Z<(UDB z%ERZfR@zjjHq2k)z=N)BjPJvC2Fq+zg!c@*h(T0qZtuDfMXNiwkopL z3d;605|R$XHi1q6y|N#r*v5BEf6Z(uDJ?+r>VGl1fPfS+or6v*v@xDeQE4d~F=kPP z!Kjyyg&8^mor>n;oN3?g@Ozl^-`?@$e8-*VOoD(#aq4WMa7biLcjwISfg948ZFnBt z;vX8-`yb+sch^zjK3)5huo_*XOGSGanRB5}`TPo$&ts?cXQSB3!fuM0%8a*qx|ScJ z>q1Eh(mA(RBZtNeoI0`Ti^6i|yTLI97Ht)SGL)q6D%()R!^kBfJ(_o3zh$J&2xtPd z_6_`~TfTCoK0lB9=0#D3Lu}uH1H~yT`eGjjNwhIH@xVhm)@sI745-ZTuJ}@3J{`1l z@W~~M+4G6h7L9167PG{wk1yVgt4%Mrb5$?z@(d=1rL<*#Lrh#}+P-jXAqb;`SH24iPpM<5Y^$@& z3nEm4O$of9y06~9KUPsg1HOZbTc)JMt{WU@vG`+L4xb80--fI#Mpj^ebbRe4d%<+eui2-Ubc>0b7xQLlP}@FI0u=cQC1rzW;X36nbd}zAU=m$>&^L zTpan~BArkDBE8+5Xq2+;)juwsf)sAgb%#&K4NJIZd8A z>4@3FdsE$22dSJJl=N`*RvzRK=NrnMtd23@1)d6~(>T4n%!Q-3d)gJmr@B?c3=En? z+wUs5I7fWfo;hhW&9Z*>`6q8TuCY5HD{6rJcdwk0dz?(!U1gFNz{c8CqlvQ_`t3T-h=2(rj*l-^PQW$<7-q-I33@F`$zL|4) z13Ccz6I$6hNV63NR^rA&4B9;{qZWL}qt+WN8owQgH!6zEjScOi`fi{YB||v!EKzTU z`M5i-jcujZ2!mxsP(GYlB)rjomnxmx>Nxu2dffvb*Pp-A@qIz}F}rINpP!aK`d%6L z@yGga>(>y)Z-jg{Z0#4WCHCM&f@H?$^=G6r-VG{G#;X&52yQPIiwRg_Gf|LpQ_s$bVRo9;y zccd99L)N13ltS~Zkv-7f-iE|)aqv(=t?kkqV?By4IbB`-Jp>L-NTINOkYcoaae@Ae z#+Kxw+q2O><{rQEQVMsZ?vHJE?62b6Z&ZjnW$zdGrr# zr5LuSgUJ$|^x0aUE^t5ekdaVadsPv=C7MNHnp*4o|qC)(}z%zIptM2J^QmsFW_D)-;T#(tp#nOuoq_n4h{h| zRx8{Pd#a8#)p;!!j@X=XV^>Oqj2c+&}upd9zrmMvgv*&eB z4cO`Ce_b|9jKf!q#kTN39aBQww6B$a`}Sy_h7qSptIwQ*{id^;KF2v;HAvI;Z?`6$ z%gZ?PtqkAJqrh^z(hYtEEO?RO3pz1q*{no=Sr6V#ee-*Na0RRf*@u(**kgZ*+VGJ} zOAQrGOCWl-7o)M07u>n)58f|E3o_z`VzNu$MbAwbTlKyQ>Jj;&Qa3VN``^i>XxH_d zRAb&Kq!i=@_g5G7@5j*dHIESnh{V*_#RGal>opBK_}7xxbr62V`E@9otiQa)=LWSG z`%k{?Gj_iEIg2boD2T?brd|wd7ML2`DBbeT?b|)wpHZ#R{r$6GqP)m5d4JLv{i9RU zhNIcyN}mQ(Y;q~?7W#uQZDmDfbynd%o!-RiRS~uu!PJz!95pi50A%m={7q9cn+BSD zQ-MfTY(4(|zK(nk)53{9d1}D}3v=^HFnI_0e9`$bLt$4R?RNP#S=bY~Ch8i<8Jmg3 z-Q`%wjNH)siya(>#+Z0GJ?q>pI9}>is%;#%cIx^+VIv?hkCtY6%j{P1&)g-)ZsMN zzs3QKSTOFS%UQ@ULJ5vds;IIy4Z75FioZ`!xe0(S%C#-eLlkH|aNq^+&-s~|mY19L zhJJ&=sF>2wb_@4@+}bIBo|3uL-4(;vRw*egBj$HW_49R~%QLel^eZW5j4dh^O%KK} z<_GV)#Yaw2Ngm@C8im!1KPqPEqp~_J`hjt<_bYrPC_9w0huQyEl2Mdq%aR7hFq#%s z(Udo3WqYQ@LKvGB@;xnJ1;arV)8j{pAUWlnUD=BYVsO6TDzzq$gxjJ!q|--aoHTSVobc^wAUpDQ?mu!`%CPgkB6@Lec)a0R_iD*7l8 ztv5v1$*D`sw1VHie_u|EF5L`0rr6yw#&KIX^{~_25B;2}Ul0K@@|M*|%09TiabIbl zNllRpWZ|Oe-ie2S=}fN+bbm5mL5Zq8&$OGr~KE1rE zAHSstSjo20D}E%Hp3qtV({xL*++HcAH+_Z2_MCe+3w>+vGJR3x)6{9E<~A15A~5~y z;^L8f15y1TCDi*K)vRu4QO}8X`7q-TmTjJf_=kOTI<;-<0ShK>Y8SEITksjW1S(@e zdvLPQ`n|FoimV}JIPuI%D`+9kZ0K%g&Wh}66wP+X=oh}D=PTk(iLp3k*Hr@5z5gp} z=gr9=wf1OqnC*|S7%}0$H_N>9@$87!l7YLaRXln6v}=&RvJX2LA1C+FxM-~%KgvO; zWrq$-z}G0fD>#3A;`hC6aD)yUJ!+AfRnYE$lJuU>LW7roq0=3 zoJl#;7S7ig_#Eqrhg`7fe^qC*KI~lB3__FH$ftH>+bac0ANc{2sbA z)1ltc&r?3ikXby|m@~29&4W(M&3fm1@g~JQ079~F`UAZ#4eKO7>Jf*N5u1nWvELSZ z9MiIor5GT2pW!aL)LCh#D!n$q=``&7IO~!aGEW z9pb^rJ;UKqh})>8(CS@d@0ypFmwI40hfGioJ}*sD0_I7wFINCq9tf!;5IjVz1py+` zX`{9A6k(R&$zf;{_=_CG=AGiAB7b&DQfJpWz$hWkF6tUpk*K5L>IpRv4X{e*WXdev zr&p05_?&>JGwzU@^Re+pD86-a+)ojk+}8PK12=Jo-~8LcMWT+Bh5+pImc$_T_;C{A z0o`OES-NN`N*NDYGNpeu%miJ#)0x5#08Gg%e@oaoUcqqsR{Y-PZz+2C2vNK+9$^-_ z9Xi_cd`A7(7x&}cp%FB*352?1I(9+(@&nZrnVEr9H6FmPTu!H6MzVVeZGS0GM>T8k zdOcL%bMg{J|CJ6gnh~3Lf-xBpqaj#L*K>2n4NbwaI@9p5qPK_tZ)d=Rb4w4_jurD6 z>fHpSVR0j0FcyqFF?-&;9)tT96cj*JY5_5NE>Y!7pm7XB6kuQt3>3k5T2t6EC`;hE zQXq(=hi*%hOVsGOPfM#G+=Nd5E~$6J*O2dz?Hrc|k~mSNgvbDDz?+VPUR0DCr?|IS zO?_{Rd`$=S$burQS^$0hQUjD6k~V6%T4M`BRo9@Xk(F<~{L%ld62TiIy4Fc1uglo5 zypn-$FOXpiUpJV) z-SKzP(*;7{vn3v?L#_;i8)V|Oe{4W-%A=h2P;$yUL_Aha1K^&HQ5|b{=k)8NZVmV# zG!+P8(-D3O-3I9nSVN^}X0HsxDn=B#`7W3|g8DX6e6L5)*L7KNF*Q}!YyHgy42k}s zz8o7rck8b4J$A~>x%veyA#eKdq;H4D=u065bcJPstPcYy=dH@hB(${~z}5b?7(f2X z>gA}G#JdjE=fwCD_35#F05KZ|h`9EMow;{RQr zCg$+azR2=oE^^}bZZ*sAY?%$NW9ab7)LiQ%m_Lz{zKQo`;gw0brMS9at)M(0>r|Pt z^cZAEZ{m8qI2b!KR~PB?vGw=5qZc;gom*C>3ABoq4B@tdvqgO=PWh#fbHpj0ESAet z+5gzpswiJ1qeLgzsj}?w>dIxzKkM@PB&jh6pEYL^-+hQMX<87v^3lBIb;ep3m8r&0 z)%nrW`ua>LcpsPv*zVn{^3STge&@nr(**e+fq65H!>l8?rXBQ`v3Fh&hYV_)T#TojAQ?`d zJ-b_@SCK=m29(_e2oPU(5YBEeY(Tf>t+NN4Z9$_02i_GoLfXbs-nE!1QJ~AO9&2%F z?a!r#Nxvt^oDudo##F)4(XFYKRI_5n#ldt`LdCX+Py=txmiED%zFs%|&*+dbLtCh; zA3STGSfbWQHMea103b91jL><>VHX#~ShN|mzS@MJR00CSC$aAH+y70^ZPvD8!T4hg zvgy7%Sv3YFj)3WQ8V@z>Wnlu@>65kK&9fuGMwdW5X73mbvAsje8kOb2aK4-O%slX< zp5bGHDP5TOF=)$K_+q)`0>1J}UBi42XZx zx?jw9+H~tjGIvE}phWX(e`Yc(3I17T$}%W5+}Fu!?@FSall5s?glY~QicCtLf;@}vdMpH83+?SUJ;s2ywIDhWk?j4>5 zySIiTte3T16|&|yCh$C6J0j_O!VZ~RR*S|(ja2jLDt4Ffq;dT1$2^Vd^MaMvxg~uB z*5YWK0N&WJF`OHbb+0!o2ie3XO)k3td#BbhzdVUWKEBoE))x+3%jtmWhR@GqeD`+}@5&3? z!p7igsFPG>`fT`qx0B)_U*uC?=c4nC8_%D&(|KxsL{Z1E`$Lz3bhs*XMBd)s8r%I| z8AOg%r{J$7hcX1}ofkLQRIAy05UI*;4vS(&XhPj%h$YlLkWF^4MnCBmR9o%LRe|W} zfIou1F=&W~Zz0^TvbLv2Lh&+L`KcXkkY??!$!*LTdI)=r885%3A0B>`<+8AksBNED zGm6HS_Dv~kYkNEHgr-T{a2iRJ95j!avs=4=>*+h5_To0J*9PBH`x)InN>n=wXA>C0 zkH%IsuQD;z`1nh=QnyAFD zsjak1ZB-AAqqpyl|03Ais`_mEIj>GpkA}sUHX=Wg8G)P{QoZm>nG@KII}sW1iH}RE zNUv9**~bz!y(XFy-pugSw0hk8@rf$*S&YeCj ziuVr%=R+aT5Xe*&!7H(cO3EwUmF-CoYUS(eE6wJFq`fYmBdZp7ZqjD1M*bWR2ohMX zd<854t;(cniD-ADwdIpPR8=x7$}*Fc3ENQ_Q+$IcE@hZ;rY`39_3lrk(E0v3=BORZaDmfPdcf`9g;zv9!4 z3mr-8|DI7!OoIkZDOC_F(LHoUq)T*SgqHjK59tSw@jE;o#Q-*8<8Yv#G-LjJ36TP6 zlOK+NXvReizS6KZ?S$H{QM&Qa9b`mLr7WliZN;jZU;U}r?JuOdytD3l_0RFVc~Yox zXeC3!aFGzkr6A{MT3l-^4y5=LfI8qOGs=G^!S^i*7;*vsO0{`&uHRlta25e=^Ni~U zn8$hOR<>Z)%$b?2jfjFZtHgNnfp*Quckg7;5_po#5x9xzngmDM`CVRLw{kzCk zQ5gtpQ#1po9PpnIzZL42vkVfU;ZVlx%=)N%1FTADTQbamvW|O|Zt%^p+AHi!aT%H) z=wqiE+!3ZX{^ci_v{Z?ko+RDkQ`K=G?IOWe!9??cnc)XZ7pdDb9iCH;Jjl|A<_zT7b_Ke*ALc0$I zY9`5vSF@3PlgpkGLk(Pq4#2V!?jVur3o20n>>T(QM7;A+(+)OfmZP9;L|!F()#tAF z#VnH9PR6rmd&sdcY}Uq}cj){PA8~&Q%%v1XfDIX3jI=d-FOs(Y{bS)$@nTjz7N_a< zEbOS#>1#44`B>5(w*^+C8usoQ+`x+XFIlbkZdtU+sXR6@xif`(7QI#RwdrmaE}Rir z=OXtH@{HH34q~l{lye`bFA6x%6>u^<&kW^(3FjOt+i8F*IyXi7poJW)=D>?~EBwcs zo0}e89qzj~Az;G>W$*wQV9Q*b+%5JdVE_AHr{Gh=575op&`?$)@q*x+c5%EvKX(d@ zDDI_2@=@}7VvZK3mzW2~0!@GzIko%Jvi^mCkWiE&*(t|UqlHT)?ru-X)R&Z8WE~Ybu`JMGohBVU2gP)*TRX{@RJCx)(bYG z98e{0u|1vFM3lW0;~v!V^mfMChTVhK)>K!cNzb@{zi;;Nw#IWzS&qhbJj?QRIBZI< z0eTU4Xq)LgOFbNaGZYFHOg6BaHRHNu&g8dsgJ#IirZ6t-SD7Az2?Qn0%!Z~W@?mPfyv^-DRId0-(<4u+OWx}o@~eN**>jy! zKR3)dd+as+5`kYy7}GHNgwjiw$Ia20Jb{M0zT(A<>lqmlK@mlTpI;Zo^>oO$3O>i) zD59IK)6LFdlXO?Cb7%~C3+_;a1g6YI*@QM4BA8Ty=^85SNH@l zo6_QaV*W1WlD6?2HLf!CYpl59PsO2L+m5ayz83qsG4E^R4rLM>DmZO|Nde;E9-60W?x}9_Du( z`*9F-`!0_|MkXAcw@gW7ucm1D032iCEp_{ywynxgES)G9Us3S@pR}Q^5oRK)P;(B9 z_ruNVoxO*6zuWfq_jyPlU{siQbwnpaUfecsdWywc)>H!q4}uW+hSMb^ORQVgex7Cf zzwW}byKB*WBKZoe^P-9di+~g`#q~kEV2g&$Jj|avjEct$O4em9(QO%|8X4oir=G&~ zM&pmN#fnu+a|@=f6&GXJKd5msGc&uU8?o;MwA7H@UU_-W?fh~rpl4|tc{Y@#MrXUG zzWNsN_}!4;+79O4Tl=QRHzh?&Qw>t3L5%}R>0z%Jb%+PkXhGskPVm4y0%VRQ%N00V z%uL+odp+UeYFbo?ZEaWsJ5@yKN?RNUmaEsVbqf94rkPpQD)f&{Zrra+#=Y_vIz5Vg;r+m9bWsBf2@gF6ah`k-G zmg^85C||SSccQAZ8&#VuGLXrHQ;W@+;iH8jmFcdw^6F=#1_*dC=|qOv zv|b}ES#U!S#4g?$^uG-Hr_GwB=jb1=8?pFde6Wj0cc%Xq?Gl;-k~})$s_93Z@9O%J z_sr)J2#SrjWO{bC%;`NZv|_6ak=@ek2@+ts=Y@$yrztH&lOu8-8KJa#X=5|T{9S$~ zm&9xKpD8AT2S3F4PbX*Ym^~(j4iNl9Jo+qD@xY*8tIJp6-ckdx?YQW>q1T=GS1yg_ zgk85cP>XD*@ZPL*b<_u)gvM}Xn?ULw;82qQVliXZtW5xLE;D+KXe*{8_39nOe~3AN zrUBX+_1Fy*`Arlx;S7$$Ams3fZL?;rpDFt#nI=%%ye5%irXdTZK?H@=kyXj4B4phz zu1=_LX!xs(ZM3v>w?@~5+;H=ta_D2_(57wMx?g}|<}P2%Yc;@)0>}=jat6o(bO>WE z2p&a!-7{xt6t)5{;Y5r7tnjeWcrvm;a#+@6@o)M9h%=frQ{3x^)qi+mrGMx zfeDOdLgM`P`ybVJ9;@8l>9GD4JJl}0z7Y3%3lVU-v6e9+rtp1Ue1dywsl z6)R`0e4BIS^Y1mji@TDDZS3rdVacTwJkIH$Whq|9O7t5&$K5|lxpgM<9?2f|k44hU zZas}RL~XiK^2p3yb_-BJsWxgPBuG@fLnoY^mR|DB2s%0alk7r}Es5mgp==2d2mc$Y z3lBZGDN`*B$wJ~}KMqZUEx#L5`_uZ__Xap*7WaSeur)0YJ+P4`RP;@onoOq} zS^MpQwMarq$+-v2gwbL~@Wjx)F~HjlI{V9&FW1Ny@m@DkQkL2TL{Df^zpmsG)CcWG ztlxX?oG~RU?QrXk>%OUfzWTejm^G)wq*AAo$+i42aVp8)X*95z2VKx%a97#}%ar5? zH-{j;F6QhVh9jsSk|DOjAJ6AMVM&F%Z6dcXF0luI67IB030{?_(*AVr)oTk8;F6oi z3(&P4JG79f7`aFCf{VGe@4l~oxwgWg`@(m!X{9*1pGW6w$5avr46ugo!cl&PcD7FD&|&CUv7N(@L^ST z#XaTRf}5Ay@7|P^%Q4)1xp2}Ymwj3Q6Ohk?K&7t!e7k4zl`M)@WbyEKx;)eU$f!!1 zvuPTU(L*LSJ~CzgvT=rKH>;jThnL1|ocO299Ov9CQL@c+}S)x2mvM&&D0?t!LB z(VpH&6&o;V6=dZ}RXfPD^XYp;Gn-#FF{7`<{HU$9Qf; zY0ZP1BZvpqsJCtH?LBX9>l9-$9tQM3e~L$B&IolvB`q{X7Cq!l^{tuNN#mi9_mkrI z881DIF4fzvc2ZYQ@7A;({f+caqReOz6%p_@NgEwYL>gSlyKl?fFZ*n#-=0qT<5KCGX*%4zfcPU^JwPNPmvYNd%>b2j5_^M$u1BGV#{ zZNFD~N&9I#)LXTh-Osgq(seFQhoL^xD%a`t@9+HQB_c)~hD9}C1v9u;%Gvz$SuZPk zobpe(vElgfOXJ=bmTM=*J`Z99S?9X1F-ao4POba&|3JL%i{OZk{Ou`yV zEP69V9E3mVL8u`QYmNgAMepmGSj;`>m6m_%>aS75*Mmh}t^R6v{PS-P1X4gP$&#pVk=y42N7v;ldaw$th5*BhMs>*M4WJ9hBMb2U7J<^VI zQ|czDFz{IS7k!WvCXjm{Zs^>TP2YDoC^wl%mVf^~lXvc-nffEA?CYAVy9z6^Yuk+e zT^fV)HwL!d&kMg=LT=e!FuKbIFwdOl;Z*+_=ccqz6aD|!Nc@B=p9zkk2om6v=h z%DGn_t!*E(o#OkgFeY=o1t+#}+Kfty1!(ve4pMk)D=$-J)wN=7p%J9(uoe7WZH z19~_6*yc+w`qt`ou)Lh};>ECMUL!8mF*D05Ggs^sxjmV0=FsjldQP4cbxEK3v!)*$ ziJ38%W-mYK&XgPO^EBf&^k2=%w|9T^JF{|l@5dwV4gT@6@?*{953!4*Zd~9M+agw> zHfR8GHYI98LEGBfKKqCO+emdqWo7frXN+F^HBGzc13u$_{`eRTO}Uv4Tqh;7WVygr zQ?|wFZs``9K5bjC4j+@_S~V`(>#{mT5xXjG*pXg#ka!lX^wa4Q5_dD;Ew8!X`Satn z0-65EI03vxdbYgZPt0{!^lxCe<%G?sQ4z`;_6`nG1uAQe5Jw45qh(tsua}uW^KNR8 z>-3eJ@7ZN@cC@I!aRV*OZ9hEc4>g(f-g|JUOJCP|>qPDL-dn=9Zg_u-Yj<4^yyrXi zQY79s9gpykszinw!ueX~S8H}+P*B%y-EOCN^DsO(AEKJzZa=L)A=*j|*Qc&64-7PE zVqZ!UX)9~BV95x}Ok_9@?fQ%*0}{JB+)a=6LKQWfSIj>Z2B$i0OuvRJBQ(^ByMmW^ z_3ls8O4m6K#m?kzExiY~Uwq|zO9Gkb)+uVjnV?N_SUoH7FH?gdcQU7EcxA@qZm@bu z<>o=%!?;$7Y>G+@W4!vrkJEz|-!cim?PxrA3*G|n_2>8AIueqtw5R3xb9B_o}bCc zRCw0>=qc-e&XgWmpkXM!5>xn(Gx+Ds!kH@?&UW-eAU9%{m;J~teS%iK%Vphp!m3Sk z&JRExb?KCEI+di-8E_G<=u1|}w+snYLhZLhwO4(R0G;}B6v3dE1d@>Ld;)oySPLFYRZ# zY0bbIMTC%)=(!y(`3^mdLTXgfVk{KTS*~3>l;Ssz@|vWxi;49^{NNEdIY8)<5kH46 zj?+dw} zPkmC-Aj8#CGia`7ppEYP@4qH@GgLz2V+Vj`V{gA>p}`sR)U~}Pr>bpC_alH3Ebopx zb8A}P(-=6o&RpEPnI?5^vrtZ`@vvtyU1!ey)IGST#?hzFI(tH|qt3a-v-h8JsuY~XDJp(@nzYj_*p(m; zp*;M-98YLOqO?uJ<1aT=SK}OhHJyhI6CkprY8a4wSYo1Nnv9dWP$HQY6(JP2?znEp zo1b5A^*m*BV-vhNd3~=sZkV{pEw0TLD*-G3&Qn)@?f|n(mThs&01VS9(Hsdk6qb&9 zl}humx^2Ye=dWkH4J%09%;N+Di;vfx;#;$P%DV5)JnmyIC)*(XI`jA2S)pQCDG8j* zaUb9HOZ$%;ERR=PWZ|k>v0sabR*xE01W)+36x3Y)_RV|Mh?K5sp~1o1>wJfDNK@8$ zkFC4m!0dOr3+;3g&PT01*D!sJRr=Mc^-IsDgoK7hHcNWz)r1g7E044I5Ev-6*(u|> zM~WLa#4_t}t`lbntHZYh%9g#?0;%#zv^<)4i>hpQvH9k1+01h%vU8<6`SI4lJtZJs zF0aq5U&f#1EazlmU=c2o2AJVdRPhBt69O`IPnHo3}%|CTz z)`ifSU^FGw{aI7MwG`&@@t^0ia!1mCDvui&@;qx>B@RnVmm2XZpxP>uO0~CGNry(M zJQ7(l096KH+l0@j%6$aIRKk4tX5Q)+_VeHGp0YZaRHSJ+79JQMdk?K%ZsT*x>2+n6 zK_Mc(*fQynvpIGBPs=|gE0Zrmr})pGUL7t~`p^>#t%IzEWARtmp(8%I-|ssz)snR? z8NK(+Q=hmcJp97%VdI_eK1#ej$9UFLC!;Q0pX*E{Xp96>I<6zN8LVT!?ynb~tg+#( z35{6^>fX)mznxqfHb}0Wr{sElxP?X%72K-CzK`B}udRB$S&2dAP`evhB_*CReLr=B zm*9la%b*TN6GtIqhw$Htx|iNz!M)d<2C<0*%3J;an0gbqp7XW;KiOp|DkNLcLQz>u zwno_^p{Ohs30cb?$`UErA|XX0lBg6>gi?ftMoF@znkJzd)JT5MYtDVn|L^gb`##4p z`hGvl^}epxcBQ?J;pBVYOXUxHyCIWp&n@nG;>Z~5K1H!rZ@}bwcUns>nKN>4>0Uq+ z5;$oBKfLCl-$?=G^aHj> z>Y@M5UiYv}lLfGh;&cI3o_}3E%7J4ny88Cr11MimlCo(a^=rNp@Ware^1!Hx_TL*h zRUQ5Bjr!+LZIF)Aw3%Ki-d80B_OFYVpIw|a5I6yN_ic2ksgH-`k@Ij!=->&LUKuWx=F$*A`xd_gpzR%BL zHQ`n7OG>K0e7W*+(OW?WSh&{|y?c~L?E>y3X zPFF{7Kheg<9gU6YrWMfL+=ECoSfVrU(j}X3USE22zG%CJZvgN?5*LBDC7yeL9j$lC zaJtiLbrnU|K5Lsz`tM=huHEDwH^;O+1=P$*v)k8K0sF_j&XnXn>weqe)6A6-ex03N z>!NRe{4w)pjJchi8#8gAK7Az6xN{bFTdQBcho62T5s9LZarla7j+5cz);M7$2oVd$ zA(;sOaIq-$pT90bYq0=)$L0~k{Kgpc?b~;TlR~?8+h{T`140K=2V4hhB*tzflCuYY zIntqRG^t`N4SZX`smva}HpJ-2L=COPuJ$JUyorvEj^_$y*c}1CUqzsb3wSn7ddpImrTMXYVqzLNSk#N= zVyvShXd-GfrhT0^x(m9caod_oDD38yhNtz~NCUIG=hjnvFVGILIY=<*{(I_$tusXH z45(v};o|zMD9NLxKzUayON_-y_j*&Mp)4y0u0oj@vb@M|!A`I}KE|;_m%mAX0(j*P zMi=v=clyLmnLPOn;*Qk1_kruuLL&8K`o|-1khcQ<^ov^I4Slu_2zGjb^5%)%QA#Ho|Z$Gs@P6 z>MTuD4UM)ffH}D%P%x%%<}@2JxW|yY@nuU?{kK(^Z3SjB(g>nC{e3{zEaO~nA~7z# z{sB`nj;ajOgLHquV>c2Mr_>9!BpQ4Yn2)9gS|I=LzE&1k+bYfE2j|V}9vu(JyTMQ8i4} z@*NN=dt78YtB`B70dyx4r$G~d{_ie@l9#BxucjBljBt4e%I9s3iwN&4nhxJ4O%fJ04dpkP{y^?cB|H6B`XlIR9Q*RxT6Z1FRP%g;=R6c&jtF-Fcn?;lzZuaDX zX&HRp4WClV4U%nDn-kQD+iN1zgOE-#!_rfrcz^i59Ox;xpyb(qhxOmDWOh>V-*%z+ z4K;fMcaA=_Etkn>`pue%b3oo8+!WWv$RX^$ck&wNYj?T8z0&u<;lmlQ1dsTk3T#Qa z(Dtg6M|Eg-Zno#PlTO=0=UqP9ISr*Raj)qV2@_3l0Pz7?{0?O#45EJ5HvtfYRqt0acz_qT`jiw(|r#XE0Isb z0w9Mf;r~TNMZLuZ%|$Uxre@}>S!p=!lyB-MlYRPd(Y{|5BchL>CcJ`qMm!!g%@TUL zWXX~ShEV%!oh|!1FD)JkdAudJUeiMGfwed2Ra<|<-JyuN>$i+;^NCdU4DFvFau?N^y!}F_fe#B>~5PH zov7T}-S7H<;#aR0y0-~(Z~9cR5IN^~MPuV#e5l$xEqi81Zu(z$5fpt=S4X*N@Wp4; zRtU@)w~}h|vb$0*TxrBan@^pWT}KSp%Le$5OCLngbWWc_Drr5irbK<1o!rNxDbQ9E zo$--$L}f9VQ|aT~j>NTkUw)WVN$42J4e7$4FW(tjN#|EzwofajVBFUmyR59ndA^h1 z(dsA)0R_TzuA~IDdK>8VI;^g41Gk!@$yh2o=taWI8a2-LzEEkPfnvQBJs-Ha=zV%2 zCC5xtm=tl>G8VmCm$ofr&tye_HZsoxRTH-olyLFEQToRBcsV9bnxvy^QeB8uVQ%AP zu$;}DaSZyRt723Wp)d$*8E>G%+hChf57rk%yLW%uZ#Ypq^zuz~R9xYsnnazSCPxTO zIhlkQe;@qifeEvtQQx4N#Gl7wVnlt-ePq6nlrX_>==p%9CF64FvKPJjA1p2|u1E8Y zXzs9L4D5!m^67`)%C%KM)Q^;R>aAHE0?es1qe8LdX^!wlua-w0Q%|M|2H5c{OFx zYtbP?a-i0n_GC(Pp1zjWwNJxhFVj(Ni-|!b@G$xAg$oxFl3a2AOjB)6h-A3;Cn>Mf z?2drLe>i`#ChrNI+A(BT{6S$4=>(pl=@*$EquSE=T@dtc9NL?|KA)v-0jWKK;qpta znS~MJzVw;adifn~MqEy0l!$LI*Z{xz|DLu=wv}Wjw=aJ(XR;qMbXQiHUiLX@fP35+#yzWX9{;esbIZ z7`}~A55q(C@ajoU1NIdNrQ|tMn_PSiBzr9y_0Jtca?0Mt4To9=Q7p6J~|8oVEGO6%e>w*&BZ~gr4-NBMBJM=ypa^jr3+u4ce;!qCs zL!qI1#f^i*w~B3`SQAxdxu%-c6!iLxA#|hn7-BB1bNF8e@%sjijRuPG&;d1{Zhm&$ z@(4p2#chX}4^YBp#gEYFN{^r~LAlH`efsvTZv63i5T2Kdcf5K(R6Y{kmARPjXpGoE zy50R4Q?}Oi=5`<5w(?z%Mf3J}3}o6jappfA>qKahgbyfy^(Ea1nh-#(|1kXNQl+QK z4~{=h+tniU$%uCixVIjg-wsz!Sh%J|Cz=C?iAxNfbsS4zi62zXvX#02fC{!Ir)%gOlFa6`ZMAUh%(frjsqpqilO7GF1kvu+(dIAIQ zrud1LGR%BqM@!o-<{MUpxc1>pC^XZ$EgLVfVp-4vYiRV#T`+(CdT|d4tv%GbMNl7N1CpmfVcfT{Y>`j1jA1?I- z+i`%^OckE7IjtdVe(X%~T8d2LtXCz+NL5VgXk>A!OTR*wnmHV1uML&$PMb~t5xplYU}UCoVaE$(-z)0On&GWdtI=Db30uMr7(B_op0E1;?5i6t z9<#?^Phu;D_6*l+|AFHU18vdvyL|URW3HJV`ITnjC@)1c?dGun4_VaAki->kC>Y+6 zm{J5InuUs6an=b32;NFqZo2h=tGq)coL(Hi4yu!BF86b~Yj*E$`06afM>^sOIxQ@k zmGRwY%|l5x%;(q7YYi$@LkVPA{M=#I-VPj$<&THG85fS@A-TftBB9?NUn)WIY%Un{w ze++4BbmAjLu*1htI`dSoyihExh{g6VzWnP1{_|~|a-I}$zD5?Grc|?-_g3g*Of#}> zN=!5PmrkfIyq}fTNnJgp>gBlD8Gd8Tb^YIWu?=0&!R+MJvQa!g=aO`{v_MK5GiSst z2r3w;UgLAWfB!DV2P_K-FaQVyeoatP95VR7ziI-rw3Pd^Tqi`y#L`xF;BoG3(Y$%a zr8!DE{LIEI3D&%S8n|`2l%2*L7>?T_1Vz)a^JYEoByeZR<}z5-$S`7r;to?DYH{ZaS*G-uG5<1L?i z{f3!=K(2ANhpjJ-ZdTT0h=-!FOYzD}#SxKuoHEZ)-!W#Mt=v8}x+JAMJm*L!H~>j< z<_TA)dT>V!cAg4|lhWf z(B;V$r@x|Wtw9)&2wY{}yu->@*IjBiuUoasnHB@Ic{|4zfY_0Ww4Gxf*>^L1dZ>hl zQGL(KvG!99H1?~%TX*2lA+5Rn0S9gEhGe~;vrXBb3UkMz%bU2lBT(?0`ZT6K{&s6S z58A_VZsOH?7S)=GM^>D{<(F^4ymD>RR5zB=PHgc}{8SgGHmRvt&oJ9NtS|JpR22lwNo7u|xa^p!hRo~O*{o9Sc8JGZ)+ zrF8g9fOOzKgRAr-?4IT2IY2sLPZviJb=I&b{~}y`&HCDs&eex$p8~zFrg*bYclUzZ$KJ0!{ZL>Ofw&5Hu6dg)G>bx3N^s^maW>f~>kgVO9!= zp~JWJn4)MNbSc#$m~crFE` zjiDQ%ga`yfKKCwbNYQVjV)2QtpzP0$pe`m5LLi%000uN~U%p>b!VDmlkw*n!>^`Kk z=T|dIsyk9GK9Q)pz}p%>o&PV#vFpC}n4HZX6NJaP{d1_i-y~$Cu|CcV>$Gga}-^sabn1} z&@r~ExXWa{GNkW0%*Go%-Bh&VIU~4=LN05rPe1%SpShq39I~a(QR8=S53in+{c!A~ zhBRF!P>w*-ea@}X_~S~(tFZ>4n}pZFJOXY!votUvkh3#b{&Etpjf$Qa%2P5J|ARKFKa0+VcMk#@q-Tk*n2FM5q4Gun z8YY%u))2gI`;Hyx_oVDvpER>;W9)U8Z&CN>nf+Z%J)_o?KAJ#=Unu$p3ZbwXc}cF} zu?bdqyw45_NcwTAtk<^>%5f{p40VF6w7HDx%Wb^G*+td_4OC%x7wb2P%IkhI{tfl3 z=VcJf1g9Q*vnRoA%OMBy{T}V%zZQ9v%RGeKo;py8s+X5}PKws34C>NWl$o-E3{jiB z&Nm&?&6l=}2PzUkT-DNr2gl*{Z$k!$+wW{ux1>i&=7xor$5?9y8$NYffd<*^q-L<+ zgw~BJ{@q7yesQo!U=g+$W46nC40{wwKNS%%g6;xlWmu2yV7R=o~9v z3i4&IRhWOMtn5Gc*|p_G-)7Dq?QlA#pU4FmYKH#{R^49q*^T2QIn?8L)cQ@U@Hq1|09 zi_GloXvcVK{7_$&{xBYO5q)U(kW_yMM~^|rx?`k3lF^C>n!#U4cnDI4O8qxxZ5s*% zUyw@PaA8i0u#%6Hr^N8EXu8{+G&|z<%3z?Pu}Y`xJv+LD-rvrv(#^>9#}bP#(ZR5R zvTlXWmW28t;Vc7ERww(w10@6NJ*$I|u%*dy)x)l~-` z^50DQ8V#x0HL_}Gy2+K-)Ft}FOu7b4!F!htY&K!N+KxND`p1a+2DWc3eGIl)q!+^Y zAZ1-%-2*iHSx6d|%8w*}pG&m0%J|WSdL%1iB>x%H(mVb<-OkgPZlOb%-pLCm+q2lebbV5rYVoIzcAgh#eI_=ms56x(iU%S09m9?Bim)v z)m~A;-e#;|4jV8Dd%%#&BXU_-yW#g+^&857S@W!^p}*I z>j)LmD-cn_1!_+9WBI5S;|?1P{6RGye7>s=VdI9|VO_*52UF#_@bT2jGkR@!?Sk7k zcMwK{^dy0?>uV66!~e##p=ptDAK_kqrKP2voglk% zg}YL}d@wXL8**$cfDjo;z$`+TDt#AArOZDFAjy?l1eYk=PKtg81_g=Q$BnEkF~C5_ zJT&%(?nDgPotq-@WuZHlI@rR`n9jmTakbEO$eA5+hL1+;8uiVl!)}*8;&ZCDQ}@D> z&=$X1%}luWTJG><8=Gq$H)G2Nmc8(rJL-Y=+q_XL%-hxOh~>dWPdBo?_x%YTp*Nc> zDi2WlHGC(VtCZ8`MO19+R06BoL2vezQ|51~f^48Ck8p54yba|ffeFV(`;6j}CcnVA zpO`Z0Q|c+bhci}hb-4FBGInnSn})teU9n}COPy<5xiDwfbUw zi{53mwYAu%o_|{X?3Hl>rEX|>Uh?}~*3TUFbH}K9n!Bni)b#XS`ZGcv8HT{b(Dl2{A6+d$$>}>aA}>#M8xxw+%RWWey%_G$>*! zpf9Oq&M#7JMx0`gGf(|FtuelAd^wtfgir4FZg?VOsAkfRSn4UojgRgyT($MMBMw_l z{qKynd+c~(Vml}Qg>ZYrYW_BhGz^%qb@n~;J7LDFdnKW03cJ6UT~e@z|t^>&wGmcLg*($pBM%&DY! z=xhr#+*xbOp7vtb<{knftk-gy+A&`mEegQB#e-SUcAqER6iYM;AzDxdpoOyzOtV!b zi9}(B;_w9*`%!bKfsCZxC>??i@Dq*Mr+4$^5Uf}1N_+B_XUQq@$+1DZp{8Cf9b>z< ziL14;KiUnumlm^^RE^RbrF1ECVIx#_;kA`Lxf&uou`lX({95QvpK*6qVjbYrUtZTm$?|PLGxk@!Y-G*dBMH zLDpA+HyOWZIxk+n_3&`3p}Gq^jB7(Dtb;Q94^=AQPVk_ryeFu-KA8Ulr+$VAmd(|f zoh4I@N5KR?wRNs>m$o$xl5xsuem~B2Pqc{EG%**rU z&dXu;yt*p1qt}p@Eq7aM?WUWm&39TU#yp%Q$BY;3dcHBjnUW1WZ@S*6{N(z^y!S>< zta$2CmzDX6dL6;X^sDPUnMA=W1fH)?Ow_2q%eNo2CF|}x&4hIs*Tk;E zM@*QY6D=e0wUzS~Bv-zq+{4^#rkxu}F|l)Rs>3mXCg>@})&xZAi2`8eMf)HmMm3a% z(wxeri~sw}yiJciW3Wki%t_m1*TOgB&9{cd=H35@u~RZ?e!Ur89~!>yY4i49yEcs8 zj6bLZJ_xtU&AW2U+-Ay@BM&SN4$tk+TgFK~?V`OQF&Ze9T{(2HzWy;F{`AqKN3Fx0 z5`EOi?hNyPk|27=mW@k(MV~&X^6tq(o1`d1q@)YKuC*Vq#iAEj?V!Hrc8QhoSQV?YyEEG3(}|eDr3QblW*#`mma80M*nL6 z*XUh~jl1^fu`ecO`j>8MYHm->xJYK|?GG2CuN79n>Wr~d=TosSXy3`eqG}b+HEe&b z@)TxwX$9@<w$5!RDdonE=_#IW>3tR(jh@dt141zb zsEjQ(^TW>w`bv6I)`JIofGQZ;a7$`hol!wlgq}+~vjMUU{Q1GS9ERtr7~l2l)`ci0 zQAi}g{noEWZ-o|ZyNqGb^jZ_9wc2B)-Sv@T4Tk@{vi`Fy;i*HPvh0TM;>G4l4JVwe zV`7#*-vXR$8vWq$KcA7Dsoy<-+XNi}f|F-+CtNPb3GuVo8Fg^j&ZeOwpL}5COw#{Mo!0KAL!1_hQqxNOPVuo#-_3+%xwLqM_AbmULC?k z8TP=fg3`}ah*N$l*lVey_4Tu@4(G$h(2gy)xJjq;a= z%9Yb;@086ys;(szN8bQ@>$E3c0GhGx0JVPmq2h2FvmcjW^!ljoCIBrUlyqb*N`nIZS3K<{70gbh@6V(k} zc-17wVar}98}+pqWOmuVm&MjL8RPorfN?M0w^74hW$G-{?ju+Q5)*?7`taHFnR9wA z+Q(VDztnxib$oRMD?Fg!oAO6MVshdqJbLvNKGt)(?^Tu!)`w1*6sk0*i99=!`vrVX z{QHKoe*Z+M@4PsZ|BBgv`bV;8{WzTAaM35sZd3D-*NLcTr5A6e>S_Z#9)9D&1@he7 z_}3aUuvf$G+UaXXt43ZAgXWWnHB@giPZfyU`Qb9WYqF706rmBwlKEeW+HBTsm~oY4obJbbuuOeg=5qeMtd zyNT~R{pnNNaIgFT-JM@1^cnf6x$Bqx-R7?d^AQFE>;R$nqr;E>#H3C7C6oo(Rr{_0 zO;@}f&ghHgP}a_Yyj^Em1HC2)4>zl39z$pL75*n*W=wM9p%bkA${>^;joBGAE+zOw#YPM z`aw*^-+cZhSi&DpN)>r z`4OjF#j(`W_d@q*LxS)giWWxBTWI`2rZuCcwAvP0w+{$mK+l}1y;|tJdxaea{u<** zf9r@*#THQ=t{@6!YWt9yMvG>up4Ko6X9bv-;;*ZUxj8rH+o~csqNQ&|PvO}(98~?H z-#Lx_Jt?y2?F?S^$lUT8t7evQ6AWZ4FhL$}9)yW(ZruFw+P?tZdPi*iN8kCz{yiHe zZJj}^6g8|_I+h`WfR_J7;kmZzJp#wX(y#ujJxD!bHHzh3V<;&Qa@<{oRpb=Djrev* z>-@>$NC!K+C74(DxuQz}8yE6J?#`%j??>~;c>iA%Qtt$es*R#+S={H<$Ni~FxkOg*}{pf2Z z&q|r;7(W4*v_Ml?I?W6U{)&;ad9q0Trbt$LYanUQTnyiL9M_ z>vZY&{A>-{fUe8Hz@r(leV4?}RQ2mHy5Rsrl3U@8-_E zm8>WFa^TIly>*Mm;Kc{d=pcwWLP45ng!`o```5c}zb zOURARvl*Rq{b>H>`4RX6yK?@>V2nT9&0>vSIwTw1poljyCKoKP(_a+{le;{ug2*UG)>vv=tx=yj zyNp<^Jrzv?&BM1DRoNHO(Ej_Y`pt*r4=4(FMbF8ZBylE;jWx!-6$f?J*z=*7_hR{{;8kgmwkyy5 zIHhdrRMa^r%w^Pm2vmK9@6*3HzjmDUmhKoyE-f*w{^T}wnd^I(d8Blm0aIq^*S1W{ z1&ECoTM{gqtwd00)RB0`@1;6mM_8Y5cyO8w6uXFY+8?jPHGG zvbA+XZf572PIh+n9}{yq$XVJxJ9*&MmaBVoX}inX%YOB*kp%67>N}W?cDv@2JIQ0B zon5KpB~%%F5LcrvAGV}XE`67b(hq3AbK1n{ya!1iKs!K1J?Fj0q*vg`AEm3&AMhD? z{PB?UNkhKm8Qe8qA3I&u4;XP`XA1_0^beCli|dtDRmV}>o0zBtv^sqFH`vhC%ki$7q{Z)WY0xYzQS#{SN=E1E@i`O6mJ(tS{Ina;tPB{bZzWz+u(;ew(A zAfhiKCjr#kqPgkF3}>u#UbVn^@sdoDtI(=JGa zJl2te+Zs^jQ6Vj0RmIRnZzR19Nal1t2C6jbBoNW#zr2xdD_tA(bXP5{M}p-CiNSdqho%-Vgm<6ygIg1awc?#Ckl&Z^mN{GAAH`E$bPOT1IuL{f}Cs znhwv}t_BQ1<}bD#!PKFrZ~7JwxEAgj-iaE-mR|_!N@P(r%{4+bNw;C^HPB2Z3Wa(I zda<|jbQ64dNXTB?o&f)DUh~4k!X8O4i6|%qXbAO@3$4lYUI(0DzBae>c6x;fraw@Q zq-pf(6lT-y{XdEM0R`%Jn?DVic8Vp`JO&-@?7W7IfAptWzp(R`Ig75W8KUAf&v12^ z7YfC@qB#Rtfo5ChvrAuJAM&H6FVnu;rgFjtMts{BSQ7@7A5<&&Uk zJ>wb%N9qNbTlH4GI6FRaO?syV>4@uu^7>-qJ%Jt%PiP=uG1w3J3YN*A49E)>71QN{ z+6TH3tH^!ju!Yv0-pVa#*y{~D(9_+$+kM*bYV%9}DYrs9_ zl<+%=3U_ibIJ5npy8?0elU5V z1pUd555h0RG=r>P>_+yQr~Y^F&ipdFnJd|iE+k$wK%49@sp`?1Y1N%B720l(D8C?s z)|eIF-sC!PyHn(rnY3p-;zta%5#=u)~?m`$4w$hgae9Yy7LCD#ecutf@Mcu#M zS>;8|moM2#N;6>maJVBA>z|Xpd)2Uqv%3!L*f%ETW#654c1O6Rom5oDZr+@_#ZmoQuofC zvv6Z7(EsdyyhVND@#wVToV@$+c`4K?M)7wz4vzln+&YR{SDv!moh>U8rz^1!L=B1A zl_5ij352;J-{*33&DQ)mh6&d9(ev*qLI+7xjEYFQI0hu73C)0Ugt5!3_(DquW+5r% z9Ic|trnknzw{l;c+v?oMdc(yU!kgR78NzVrNmbILY-lPb0?bi~fPyA|ANcg2mZv0_ zc#RosrQzHI^g*5y-D39##hrkF7A(n*HhWhedrGejc*lJ>HR|;$mJC$vT%2?I5;Phw z_`^Ofix;1__T4kiZfY}E8XagHHKs}&s3IB#@&Fl@Ra8?WMg=7%4gxrIMpmZ&<|pES zCV6vgS{3uO>3??TkOEOy~dP z?zX-pts-J+axQWwV*(~jG21$Odg$9m?p)J>vD6`CKpN64_NIpnoG&|VBqxgZ>cRH~ zPi%J1NDa+mW#7y=7rzZyR2S3?iuZ%Z(j53GYbcZTyaLBxYOgnxw1bFXXo_UM!bIkv zn);yyZE)8DRb$oO&GI*R`g>mzPTu43+yh>dIQiic-a&z+EFEMnnjnmRJ}a?H@0CF9U%RPIv zv~cdbeU5X?;$}0Z<=?yaTML_><{Q#sr=D<@E^Cn{4mdPRG~JJmMy0LlZbpa5RhLKb z##)g|t{9XZ7t>E*|H+yybe{f(!W=W6$e9zp-=LbRM-KewtCT?iT=bivd?F(@5m-;{ zrLnPO$?bif0jrb;T@HPs&%ta`fAUCRsIvM(Re${e*LPYWYB4dMPSkS5rzPP+c4aJWNj*iC_`CQL*0v7U%_7P`3Ug|17yY!jPF-#u+ZpLWF2y zED=utwd@dLpGZCQAL0apl97Z%dKw7`=AA8FvZB|gl>BiLLM!=P6zaqc)vgzF3s96+ z7Ig3h^}K>@^FPi5byq2WPr+stK%d>ce572U!$JNoxWkwh4{63$CXW2w=V0oq{ZL6~ z6CX2gnj{51^5-e{Yn8uwK=6!hvw_?xc{DI;e09?vJO~Gn=D0`Hx?&a^G2O@dYAX7mQJ$^BnhflK942CTM-hpX4J|cYSViVVN873G!wjiAnXjs zX@1GwV30BFOYW9N+cp`Ej|*)1?mYCPum?z36b;X}I^RR_nV_HNXru!lyMMLM>*^l`3HTb15rG ze>KqbB5;z^=Wb3;AZ&Zbo;@Y5y2;1JCv3d9OV=mYD#G92M7O|p%oyLjM@0L=hjF+H zd}A!~WIU+=k3;>UL0W~-nm%sn0Odguo$%}~shl-}-tO&*mA9H$m+YDvtxduNs^1r1 z8(?s$-uD5l3|+PE>Q4_-IzGn0qes@y?BiWVARt2Zz|pVh?DcqgASksU#!Zbd#Nz=8 zDfwQoVK8I6B+ZJzi8-F}W|P6^%8H5yw2vZS1l@IICKt)`1%q4vvj)_C{_;XIgF#?t zObjg8Vc;JL*+8NeTv5J6Hk=T(cbEgP7HPF7!at-JcPQ{ty%as&okrpZ;GUK4=W!E1 zL~e0zCoNM$&0a)fykHUmq~zia^87G?_i#6i0_~WXaGh~PJUcl72bjU!xt#k8s_RP+ zOK4oPrN9mw-uZP-SoW(|hD)T{^mXrzjP}g>7aIj$S(F!_smYnmzkh7-l8)fJtf z{n)KkrTXgp@uq!8H*@t}HY9|2gE?pN52;&ZhuVF4;%vm$R{c(Mc_#ooSPgV&!M451 zzNThoiXq-nqjr!RFIAp@ zbvzt7qsrrJ`P1n6Ff^oZ$}!{Yt8g{OAQ$)}(G=Jh;bTi?F{cTRUe+C3-=NiXt2ktW z#RC?AW=WEG5-j(vn*6)b!Ga&Q{EeK>&2a0t=}Z`8dY|7t@Q|*_@7QYW_De&H%0xBG zRcF3MO%Vk>N23;xTLFzp>(H{Tbl&k3ZunmKITKdGfB*iYUi~d?^_VmmQc)MIf^dM) zZXVh(ifh5#IRks|e{6UOYR{DhraFHf-rgPoK+u6}4 z<_>$a`8NQ2uZDjLPn-Iej{djgJaJl?);t}Q?rh?_JPQ#|F24IZB`i`;l9$HZaCI!Q zx~^T=zmG!j6@!w7S*=Jsr_ORy3mZXS8qRoPbuDr0m-JtRQT|l6gs}ybG&F3qK)RNmaas6Ts zNr(sbCKXJ(YqSWh>7pPCs4zAcrl0+A{M{1W=z@cxot3Ju&pxV{uzWoeF^|Eue;P2Q z5^Ff1Kry+Q>#3w;SC2|3O|nbd7CV-;mc;3t+~NH}vU5Q+O>uW*cIlNrQ)|VUq|isl zvsRDps2Gm~x_`m(c=JB$5+a|U3;g_c+{JCPl1nApC%E}8ms zQg1_@(h03Rzpl>m!96Xi=-Ul7N;C(h)E1=9^7%06FNyET`y(f~2u=R_Yw4_Mvziqu z6b|igYTA|RDQgNY*6loDX*JGi;=y#yq071eJLY`bqRo;f1f0^);~?ZvJ_h3F{xS~Z zLd|!M{dnSLA(lf&ZMl*8t$uFB1-pGOZ|w_Tl;By~vET$BtoYNw{J2_bMm%kR7~f#+ z57e7=-e!yPDlm)T@iC4xaY+LL)tC*_?c3Lnp`G?)m8WMUJ^s+(&)-@oj7B|P@6Ao; zb<9BH&P^6P2DPS;=HTYIK8;CUP_q3Kzc+HZkmbK8BrfmJy7fD}$s%EhTesj_^8KXs zuabt%UUIoEJG>w@k@J6g+mae&L)xx1HJmrrqYJU%z; zDUfmCZOb3i$W)v`1i2=&0?0&b zw1<~09ck4{)_Tk7D_R+%JG)`k!h0FIU{+i2&rbCx4SY0T+}H;nmN2qM?=~x7h9Gqh z70jheF>H3PtKWpkv%woX+Ftcy<8ET%if+~=cVL!D1 z-n_nQg-7n%6&@$tSI_$%mHoN;kC&#e$l7JTe#qMyN)QMaIq`@2hlWOM z+O+AR>A0;owMR}VIG9!vII`#xpNweTkyQ3*(3A=EU*nMew~_9e7AdVqbCCq{~9ja@{B?cuG{wt3uz=q7Xw?I!q@|fO^|TwnE=_grIN1A5`*!2k%MtT8 zsD9VBw2j?&k!HCRH|ZfuKcYlVabneILZ)f5MC~0NuH?2cx7Cu`_@G>Ayxkq1U|A~} z5UH%8v9x;j^y$wAy|ey){=uilb9!w=F2Ns=o*3_uClMer8#Bk7EFTRUlz#P7=aS~3 za|REbd!t!8ufBrNug<@czR%ChU#rU2+p4en0d@&@osA|KFLHK1Wv-j?>MrQbuR?9s z=7c8ks-XW5;f#Jtgs^qdv1f*6lA6&|iX?RNubzvP>Ue2!zu4K&%l+~wPo^;KKmaCl7zWup*ggQm?E!0pZzfDWd@gvzh~ zALm?icY%t(TlurUuR<#fhwuQ4duFO+z3l`%uPm2ga+C;f>TDD`3)7B!Oh=4x>a%Oc z^y%R--*#vzuLxINLuJFHBT+igWR9&Ac=9FEA06ihbPwgUE#kWcnc*w;6uLE3b26u6 zCeJ1CcaDjkM-cCPFq0U9E-BRcOD6i?dsH;(rS`v3b)rKMml>~D1w&=Xn>;$4CWui_ z*%=?RYOIl7&~mt9VM)UwwYy^_B%dV&vzI^b+p$aj_8hH0!=Y+pV${d(r*|(jdxR@N z(9Tn*PMKG&v;w1WbJ8|y{tei|5@9I1AM3E;#=#3twTi=o`ywv)GFY61R8tHkMzp-L zakK}eMPesZ-U{^kIR7M!h`iQ(nH zKo(Yl;!@e1+Llc}HrL=NJ+oWbohY=tZ=kQ^U!tgJ5^Hgl2Fiya+K zVe2nCol?O3>&|@{vnb2|BX4KOz7`Ix zH4ruO9wH5Did>w>FpG~||K?XCpGtaGDxBk0%S0=lN(oPC*@B@#jjCp3TD^WVA7;}n-JxL=8TuO6(QL(I7fjaU$A;rS-Lg3z zHgCMvr;Yd%INrsXEWRx%7|t5rqI>2Qkwe=7InU-578cu8HMcGKIHXHkdQ%9LJJ!aD z^yLO~7Y%7V4cQ|fnO@;xd;?I?8N`D_ix04E+=fA%7KY~Ell5;sOva(v9oT^(g z)G_cuZ2a2pY%!SK>VcA~-hqolztAJ#OhVjm&}MvqSeEbb20z7OwS>qD_L$A33C2EhNAD!{`0W|C(#BZXq;jt44Swvd|a(GRy{c5PykVny>F z$gAn~=kL8QHn~OLNB1ya!XxM{EJ)Ssq+{J{@ZiI^NR$BH<#KX?+|r9*j}dZ(d)wBz z&6QFs&5&+)TxqIBvUiGQ(s+&B$Oz58s^nS$wv7;nsp|EGmdF)&e99SJqhaD? zXMp^}mFPcf;(mC8t^ZW$3N#6Dbc{{{CQ*{JQv*18bh(R*?GIC2&-CygdaumHDMEiCyYQ1%Ek9HhghuSBZs!iGRAu=)1 zE2R7#{TlG{t~D;Q=z!U3Z}sF`bU>2&qrFXRZC0A`3%h~h?aRekX5z7tox zOrcUfE|ct;i!`UaBJnM&--bI2`t=Aw1RcU~($gffO5GhLTg5&6@p zz){Q81OBoL3KoRw%w9VjU(Qcd*qc-{0iG# zsOrtwLL9?3!1as!N0^#UJm|-SCD%%bkGi@K^=XgE%j+eo^0%4LaA@sbZVtcWEHI4p z@9HinTE;wS&1u&>v@A^P{@*Jz-n@z6MOaOmqx6%&Li7t=FQq1siBVF1Qfv=F_6EihhklJLgj~3d<|O7L>9q0X4+<&R6pqY^8L1p7ibEx}Pql zEH`c~^nv8u)A|kW4O+0S?Kd_63jVQ>@_xy8uYL0KEF@o6d$=NdM#h z)7LR^u-q<#HQaJ8$BLJYjI><9FH?io!>#}$y?8a>WyUY`D|Z~p zxc@vWv7rYr3%LrExUxol|l<0)>L@vV7@G z4uDzFffnymbZsk>lKR42T`uusKn2;!BM%XoI#BJ;VD2PA8u@j)^q8<2cVHpJy}B5$ znrf&l=3RNKeiy^DxC)HPWuTvPTkq7uXzmCZ^m#;k|DQ%nf5y301h~m$OjUjawD^Qa z`Zpj%79BK=l^r5$uD+#0=i#0mTO-SxzEh5{qruwc0j&-2*FvUBDBw8GuD( z0sKW#F%kU^tlO~{{n>BLL?}z;hGtsGm!b41z$+W%eBZ*UqDLP?mo}J@E_!#gi!NJI zI3Z44%1y9nm66b~D}Hl_}KZ42h3%too!?w>WzY45o^!Vu`cwEt| z0p^MSj`4NRe(8=~>4}K~qu4$`9C_fq zzi{BFS{_`mqsu_hxFs)aK-QO)Z$ym1uGWW)TC4_gJ-3|29q0zMXhG11+1$65Ahi-- zt$hmTDoZCO{ox%V{Rj(pkvWLBgCre9Omxcg?q_@)yR2K6E;M!x%Kr8L{3Cf03Kb7e z9wcG5BJLmHubg!S8aAVOfORMey-ZDy@x>^&e!6LsHU9T&e^6FgFLi>7e>Hv|4WyNg z>kmid*c(SZ-9vGG6m_Y%7AtzVds*WX8k>4>E ze<{5JL~b-w^3eHw{*xR4od{te7D!S_iOohb_^%j)+;O-mYgSbyt6dQ+onyvi_3`d- zs4mphF|RXRwgZS80sG2YM26uDb8jYRme5X{q>N&1ih0^m3fjTN3A3&oQe)zFrGgG^ z?ix9%*#5+DE*qGXXwLwsxHV05)}H~3QF1B7$!CiCRDsna&L}9XL6?MoJY$WwpFQWXVA@*uuBE7!scR(DZGCpLS+9a(ZasqsvgdR{ zR@jfdA*9NMi6xibgZ+P}*&=mp=N!5T->-MuJ1H95PI&iktMMyDF>$=GH+3jJ{-fw^ z?Uvvjr@WhduTTm>`KHlilF4#L7&Q8x?MztD7zF0r4tK`Mzz)3#5~grV4)EI*6LTme zgsirJ1_28hWbSNAB=mPqn%@t!U-WFv36s8kD7d1c|dCy z+0XK2{}}652Q$j!Tt*Abcg%7!NNVHHqq>xtYGI4D%$cxpSM%Zgy+egP&G3 zvqy}3^QC@hOT`-PJtC)bIJ=e3(0V`Z%?ZeRM7o}(|C~PEWE$&z6exS4syjD7cdyHfOf@mm&Q6_R>H7FW=5%X;siN2DNf$8J0m;mUsXTY5SE%1b5{yQSB z7W6Sf?@QnkEYLk{6J2nI@YY>j)vZ5gHUH`Go*0fo^QxQYdky4wB-F=1kzl!kMeILR zFkL!JpE)47R>`>ii$LXoAToHlP9R>h(>4tuf&Sm13{V1xQNIWjYHB^8Lt#o%^A;^e ztuxN`X@?1dQ<=pDZu`2o&Pe6?b$WL2Nf>usQRj|y8m3rnxT(Yk$x_V1k-Lz2l4bvd z_>KNq6|slDQImZu=BW9HymJVu+8@A^<#fH19JJu*)jSI2*+~iqDv#1N-8q+YVAuV` zC&0xDdFK6fwVsWC%%-Mx3qPkGQ!&nW8+IS~y(wG+?yu+G-u6#eXEeX%mBk}Z96sEq zyd!+!bn{;;1A2cLhAPWzGLX+#LRjX zRn?v7@H7*zCV4y&2?9fTGi$|SO0DFTL4q43cQRJ4-4J>G^Pa46ZO85{=6u?4b;PfM z<84)CPa+_X%smX#%+ahf?0UjA8)>h+&xkX)F30kF*O&gTA2yDX9vc_z_vcLjBE*~* zsyE=*k@XLh9^zn@uHW}R``d3N4&|u&1>8aa0?x3bt#TTEPFeo~qWn7(G^eNtic4Oc zG>BL0Gqg)v4pc7v9X`2e{(fb0hy(4lQ7Lp20;&y}N|=yJe}g2R5#J^0TQk^Bg;cwH~H+ z>G%xofg&`5f)t`ejET_&H2Gb8Ei)64Z|ZBrm)Rc<3DL<6qY0kAhFM_oSIy3b_tXDK z*1dek7=b^p0#yT$dr{Gosm)6KibB&`jW?;y@2RnW7vm<6YNntP!P0rli19KXqJ(W% zU(k}uHnuBh#{@}`!HhnA0=5P+kNVThp5fnx>_$X+opiOHK~?Ogc2(@1R* zwaZ2b7|lT=Z*in3FmoMT@cjz4{ek+!l;sY>M=(=maHgIoC=g&gc3#?Xq%({39Xq+& z(mohY{HeMMOlQEn)NFc-sPy!C%(9dC;aqKIEeHRTXsXY4TKKPq)iT*HmO?eRwTn^f zzN5*9+)CUPI2@?s`=o+-3ph=NMre2RD0?vUUEK_<+?w4NZ0(iN8{AJA1hLmr6zGat z(+2W0)&u&EE!(I^kp-3PP;>~N-$68#5^#!|6OKuC4g-Yd*37m%m?J5FJdq%g zT7U_%{en7Krq_eq9a`Ci=<$T5CwJ~<#+>(IMD;4rdL?dZe=(+C(Lg7s{f*u98CSuW z&&LE14k^SAbKtRNb7}MudbKkOaueV%DoR;Rt#5PPFS##J{V#gwJTPBV!}Pa|(R9Hd zLY;4YvsJacZ}q77kYBHvkB<12#45Ml^WHHimG)(#42Ogy5UUT1EW!y&t6~1XohCyM zRz+Y&f-5}E8P9t)-)s7q#qDgJ%RYTtvG%0FxZOZ;2R)Vsxd-wt%mAqAwcc;*|GIjO zT&?|d#SmAb@!c;zJq;%%+c~;m%A%yEoUUXNIX~?U5-8MyH%WGZ z5K4)i)|C@WFfYN&Mcsi|C4cGHFNqV#z=3f}r>zJ4T(oJ<>;d!BHa(9yVNbs9>!PA= z)a`m7B31qR+_icW#tmn9oQNWdhcc1|VBM@sBZdz*!oNxCj=)J&{w?9^-rqmJIhwWf zQqcKUy;yrSdhMm@L&CcwQK^5sqk}>n=ZBkf|6!OMhXnpj^aam3A3LTpu+<7&>F?gZ z-=3J5xaL!0*(4D*!p(eGdwiBGB;&TPVj}_MW1-Gkn4(s)xWUBE#oe#fr=^O|zkZnM@^QCUfYvMQpitfOSMR5)g3lvK(p zLPIG;MYfPVvdaFw?sJ~!`TcSJIOkB`@8|P=-{ZQk>$=k@MRCg!NW1iHeokqZCH9*~ z*&;CmTiL$q`J%b#TFBo|o;(@B3|;=?m>T#L*t*UwYa+|n70%|@$+j3-XPmydo>m^Y z+2*N9?>0Dor_^) z>zHUaJoT1Ph^bnC9a=JD#FI2XHZ8o|@{JNu=0w9Dn}6Q`8y}H^k}NWUP8#reYB#lv zq-VR;W_v8^)@DsVCMV%v3}W9M#flPteV5i6Vc#;<)`Zu(vbK+W4Tyc4NW<|WM8gdfJXZee*7A@aF(Rc96- z9dc~)^q~A>pUQp?^$F*PeZ-`lOa(Z2?dJ2(`rYytvXo}KwS8s7Y3^hF{`_76!&vcV zu*^F%Q%{dBx;K0Qt?P}|<1ekubIqc8^Y6T4!sl5>#~0$Mmlc9fW;;+LqrkaCm)yGC z5rhVBDfD_9K`JQCsT$!Njp40J1b72%0 z5K>a53631GYedl7>i<&=&~1riKs?1}>m9iD+Cb+%Lq9pT!#KWvZ?Oz5U>(@bHY$S$G_}n7_vBcizXc4vhbVa!d={iu4B-6wn#_*Ciy^6TKRmw0Q6= zb8}q5s-2({|8ADS?3b#-|4RpI0zCRUSWLPU*JEnTTr*{r3!y@!HTIcE4pi zB-V#jzkV{V8v46_mh9I->BPDZZ<6A2#vSsUpsCRi7LbuXWv5pEY;C`_LBBvocbj?Y zXk4$-iIoxp0}PPPuGy16%9|@?>xIBl0PLGE?gYB8-akH#B_ zKp)(fWf9^$2_FJ6B8ELgsgcLJLcg`-4$j-+@@?^|HqTcZtZt`yUm>95*_%s0hPF$rY-#ns zCHj-|LSTTGd;Mbfhj;Hz$vV|?X>jn+N{?dVgPbZL8fv5vz&@+_! zGHw3-K|<(&qwvS6`Z!2f8X-Nmyh*u+*&JiNb5mNX4k)as5Jxn$pMl@^+vw}*sqD5N zMTz47_9_-HrP!xz0P3Uxc_L){v-7r@S^ni+x>+R`CsiE_(~j#8PS=|EE4Z z*3|I9fVBKTZ!n#WG8k|2dmryZHUrU-LCwb!`$+3$p5kH5EHnvxj!8gAtO|OkX3HMh zLo?2wQ$5zS_6C1E`ON5=hTYVRhYje_qsM7zYXn_tCV@cULQr_f++S)P1T?0jR$G7@ ziczWp4Tc>PVyc}-V1KA}AS_2bS<&|0OoRm|&YJr|^{RMn*n>o~mn@IM?@sdkTb=U$ zX)gPuNK%|KMh4=0bHVKqej;Wo4S2>}@3PllFmjgjh+vlm+L}Fk{uP2lL2lIL6W&{* zImoQTh0b9z?@oEm^2;5%m+3|LA!^8TGkipyxp@BU&4d=QAYy-*4PbnGBagpe6%<$8 z?iQiYK7R=SwG5;{A8vp-(HxQ>_Yw4DyO7J(uA?@!8%{k&{nwlaUs^HM5s~j>dJ}1{ z_#DjKDeKRbYQ3S+?DBfhgV`oRt$p_($CkgAd}f+=K1i|Pyli6YGr0$J#5gw%YYUd- z#+jUB;H5yNmsx*a%_H2*cBR>_pLbj4_o11+?0=T2UoCRP1|OLG6b0TT5V!-&bYkTv z>|-rQ{a%BanaMsnH}zJ-n%t@fA`?c9(6ouT2GjC_zrGnmkt0$yVN<2vLRB_1GhrI| z65&!di)yUV=K~>A?+Hvoh7tw<#n1-2tLqq5DL3yFJFD@O+V6}o`#1fNcjpL2omuc( z@6O$34_Wnl2*Lw#F+}w3yjWY(wVUhC@M?wnB<+(yE!}Z@p$GVw27Wm-eOCQ|{iumU z+vqvuC9aJHhhi`W1dotq3`q3qlGj?@)L3+a+g9cL4I`TU_^fR5q>u0UMrnvW|A(?L z+eFCA?K=fUVOI+wn{KZa(cT*~KJn}20|_acp6DWtove<9?Hl=SQhV$@(@)f{icdS) zpnS+_%#!wqr3;rqR?_6ps&S716%c6zbN0W{!sJ&EIdI)li)p^O!M#ke_RZC@zf#TMOL7?ig6PI%6&vaQt&L9Tt8w*7rH=d_V>7KV&1o{h z#vc*vGX3NAPRq`>Sa!5UfP+D5?TfU!Wf@6hU$MXV0wp$<03Mumen??W*8V{_ZBLj= z(;Sy;l)A?BypHjt!uZM0@7m;9Sy?d_YIL^Y%&%u2O30vL5W5@&m=3;9nlXN>O82x# z{Ds8gguwR^Iy|?3>!Nb&8 z6|P~AY#~x{>SN8vaKI^=pJi6l4$_$+gM~n`o*{Qjbk?A+DOraMpVI%+m$e?q8J4z>1;@4gw4dt zhUS32KI-`i(@~>+L;aA{>;SH^sT~Q*Ec6ToP$*?sO3GC_XaDf7u`n9LXpkt6rPoG) z^uad+vub>+f8aSRHWdyBr^3b1zq=^zZ23yaDI(Q=@d60C3AXiOT|a<(#FKM4zj6g% zWY4qPeQVxT4{DndW4v;htX<_-{RH!uy@WtP0x!w}G=66H^IJE~TXOzeO-F*YNRMNU zJU7nm=yi13g!yTcH_dzhW&6WpZLeKi{LLitVYTJ58RN9BV6r0HD*1+apglyc$Hdr* zd9ut@)(?@W0?@UEFZ(-ZO#fx?o4WrV0JWQh%C^y_teu#pv)U$beVhH6^9eyr5v$&L z<5I$xsiu)ayM|Jy8HHjY+&4n~wmP>@r!LTzDnyRY`}xOmTNgF8ko`T4R==&T{^)$H zqwkaZ2MQk4YWHq?6YQ=Z(l7b}J?p^sOeJaZ#YTJu1t|mwdT-Mn$wbinf z{}h_7FmQ6p=rq|{^(Ei=fBfr(zz;;g6fyJmSIkdk!iKo@27Y^HC)M|)F7#;=@CEmi zh3G;QblnC%vdg^zdB4UEGy`0eA%s}5(fx;ohVBJ%`&bJo z`||Q@)^>B=q~e_zTh}x1;2#Y->5QCV9*cZicOLd8ZgTkha=39`GeZqZo@={r87~~M zPi_2$M&n_?<6Ow;5v}I=mA~|}u59gJSo3pdWzp9U1Gn6A4Ii`YdEc)q&wp)g`Ydsv z^Z!<5BYF%NqOo`kJTDNFvr|Lr@YImhIbwuB76R?idelIjTKV(`V?f{{u>Lk+pIj(# z>Vbm?JEl%I|6Tv2g{*Ru;7sGAudz-1X2=~|mv`MG{$G*v(pm0uQ@^b<3yX5G*Wych zPbuxKzB(%+giBZeuY}u|jzF*dqIo3FbJ`UYvVD|tr5@ORWjy4#MISErdU4dL^PHzL z3{Rg{A!CRqm525>+k5(Q*{+`pRfAhQrDKQRnBxmGY3iUgRkrBI$(LX0}vl!knQDsD`8<(46CS);F$axAj8$6ETNry5@8{y&l!eG`i0W06LbXCejjR zrED8*6TU8nfmQkg_^Fr(>GcsF0^dT%&`^y7OrgS1$sKqOEXMQhN>Hg!Xx$oofYv38 zSmCw6baokftpi1wX0KjNVEfO#Yr9e72uL->5qLq>>42p5qf0x0Si-yu`28~|F4?%y z)z)Qm;T6&+`qE33tXZCIqiZ4dOj0UlZ64qZXUQ{T#cb3s!v6R<B6<$q|`3MrJDyvoyn908^5aTpo~SqS2oJgqvudS9ejGZpY7GAa!N@LT+5wTkGt78FAEAX zPyc1QWJ}hwyxv1+_~tkLC?g@KD^Z#%FA-~|TeKKSAn2ED7 zbKM!gZ?}5i%i&O-~Z=a&UnlSL`2u1 zd3x5CGA@JL^o*);IW4wK^o1=&h5FR*O>^o-!g}#5M|Pk_oW%hHP5T}ceZo?aSxoN4 zqJg|X3e_Sm+6U*apT>M3{=p0p@2-CZcy}2umQk7KYQ4JLq^0V~XZq|e>lwLjfAtwU z2IdGxx@Y$o2pdqPo&xKx!Cw?81Ca;P*m#)A`WCM^&AF6A#H0jx z7as?)JVlh3`~h#9rJh>&KTtd?C4L%;7@DF0h(CJP^MQUjPY=FjHIpbj8ttV>6R$1C zJ7w@Js51&=bW2@w2+Mrz*@!!Y6l>-~&lc}#`N`K{@Bl@i9cg3u!#oGU_5_uIiRjvS zD?IzL2zue($P)O#vu*JEoP!v7ES=6k-aL>NwxCr$y{ouz@(=((6k_jK_uhDsgd1S; zr&y0b;mObu@_|TbUstDtjVZ+49p<&Z-C!ylqC~o0JGHxil?9G$8L#F6XtC=tI|+6N zC7bCkfME}LP8jw~HyNIRQrkS3@)|8Bb3e8P?jr<2iEY4=P|AJApQRtCX8|IRiBW1s zZy9d(7%4G;s>#DjyWpJZXI>G&&C5Kp1}-S=Jh>s%4TL#LW@jsZH{pAOG&3A`j&yJ>*(v-_qx|S{HCpzQE7i?7s#d3asD5u z-9Jfnwh)F^{IlCa(~3aSHnP8S_NUevk3)V)4#OErURC-jaqgai8mb2 z;=diB{{7w~NQSaf%QpGlOCD^$hIGRiA~*P9b=sTfPjr~La^R{TJ<4VGxW}yktLSoG z|J*^x@HcMc?s7UZw()gHyjq=2KJMhTsdwnG5gIE{QDEj0wXk(>Tu1U*Q#6)#=wNmM z&Xh2=4Nr$w`G*#brY7gEj$~Vxx_ZP*KPRVogF<(k1r)p!v-#5Un!bG{EHQr(-vrZc zb6Rwty%LV%+5GuDH><@RT*LuQM2fjjAI$tGN-f0}uG2i_3EbU0KzQ+ABmJ=)#_mk$ zTUeGlXRt;iM=2g)lzad3;ra7Uk7HtI5yod4wmYa+3_E}W3hChB&NXJhEbts~ zs2T^AoB6Yq6Hbk%FVfJ=n`GR3qYUVtJv<%Pky+Qv-(u{z^DdrXAwa1M8HbT^xMU^^ z9_*q=5(5fcbNb%TV=UEU_r8)j=m^|=Qlit$BTtQbo<9LUCK;FVbzyfHoqij(u3ngv z_mxUga~R<}pXg>fYG6|AS^K}ddoR0pNlXgC*k!KAn(s#tF~oBLj32U6Cn3Ud%rjIE zg+G-QAmT=JH;dgNN8`G*K$FLjlVs0{>9@RoA!97zUqj zY?5UVd@&sTW+#42{m&D+b+3t`)Uq}$H~(F5Q??hN6fYr%enBzSGy=m-*-9zBD$pLH1WLA~74ndOysYO2X=n zp+1O%UCM*5y4Bg4fq%~b$KHMZyl#)ah1!$n1U#eyG0l(BnUtQGN3F+1x;tGB#j{!J zaUC@Y1wbqS!jd7O3=k{EZK?e5$BCrf zEzY^pQIH~bGjbfWWGh6xJE}2%I@V*7qIlDL)`Ba0z-}fa+O_BNBzzht8-7RI+ES`| zx9_A6(~!d|KQeve#|f}11kvJ4!neRVx!`o9_t=jFI3a z&aQAbrd?~PSS`T0#RfBKDWh)BUcQ|3<@DEx8ONqDe-nm0&jKK1CzhwGss|ari933r z_l9;iS;w`#N8`*tMwF&Cls}E!D0z)0^lI?FX3fF?rf~V75452%20=^S+#b$bU?R#W zN;J(%A6N{=-kVX-2tm%C;vLAWPmpH;P4ynR!nmQ-h1Gy;?Kmol+}0(gOIlp%b-FFy zjGU*XzyOe9bj{3M&UAI|!Eh2DR3!i0tm)^S4`eTuJ02DUgDLkBL13a{3wjGqAmcjA zD{3uMDX*##pzus>kLI`Grjl`*2NCm`oLJh(eIv;9E*=i33 zq4(}3#?;hSM(_BPAuAfB!P01e@5t7!3Qj6&w(Os6k1EEykhqfIu)jHNx%Q_w|??D=p_;c zjkim^b+l>s$8s zX*ajQg9k6}Hj3_DvEAUn?p4(2gjx4g`*2>*LNL0DCQ{j3w%fGr=1s7dcNZS-O zV8L|A9jk$!5g0h?RxzrEnsxj3ZJ{i|+1BuF6Pc5hJIGa3xMFDxq&5ZO4a1zf${6cdM!nD}67vitB#5)qESh6Xg!d?(l^=^<8W4 z53)YB+5YQBp^eKtzeHWQf9m0e(YI-jEWpZ`$4_26J37F_((^uxMKD9x0@Q*?souMH zZ^#@>W|JTtqPhKajR~z%&d~4b&uHf54$z+-8GdTe?miFvrW76bFl3zYZxM6}BUwcK#Y!iPdRHmnt_phCD@BV$Um-zmx=fi1s1hQ8TwQEk- zqorY5J!{^)d4Wb#4jOpwcS^mr(_DhwFcp{-V)htc8o3P5mC zF9K$rt0A^3_{2Oqg$P*2QfBC~z8*YppCpzRG9HmSpFRwiq73JUXUk^im6ncOaqe{g zziJZ= z!GAj0w4d{AV9faIL6(Z{^7VfOQtgNQ(fKE`WAfKNKKSHAgM$JcTN>NGO`G*a9uBK4 zrWEV|>e+kiHQT~1lP$JmPgMQ*vEt9RKlfP!!C{B_2(U&Mc_HOQKfVOz3%l(-U}s1x z4US>x)T#{m9X5h60_ODp5vPsqPAt`cAAiQ##V7hl5(%ZSiTj2>YYL;1E8cg|UOL02 zA+Mz|!9`r7;ks>Zt2HUfzB9@J8Df#ya_`)6HJI6MGU8H&SW{mOxGVb@$_bYpe$T=C zjf)#btv4*LzsxAJu?3l;G-ed&WY+ih>(X})@U2u0y=rwE4-&8&8mAMp59gJ<0s$d_ zfCeZ29vji2Z{;g4K=$%)>T54-o86|Vf%@6=TWphVid=-EUOAVNt(Cz{{Rf?F#=ob^ zDYv^j$2#fe;il2+nZ_l<)~YgDI!9c$YvxwJ5}9uiR}^bhzQ5u!HSYl^2h{ zcjZdc`uEGSahtBnZK17Dz<3fEpBcmI7sKyy9v$iFvELLeAOvP9Ht;PJm!B=2%+_q) z+>GEldF#V|dhIV^RA3GcQU2tNebS};{6?Yw+_Rs(tpy29N`+2i0 zQ>8g$I&Jgy(Sp&VLxayb+051P0E9!#-fqg2DgO)!x+s#C-*xBzh~0@;Gc)K;s>UEe@uDwL}bCwGtL`)$3HaBBmMp z(z{+-Rn?J)vMD;c4WmP(NrDrrx^GRAryl~k#i|pu-tf*a7Ka9I1&5Mf9yV$2^b( zDV^qiZg_k~k^*zN28|mxmTsR0sM`D;)Kau(D)8>`8lI?hBEmRCV?cnNPtW&zh|aAS zNM%ph6#$C$Yw1QUdqvi%YsL&IF81+E@5v>T5h$=B;O-Oi8F51N}+EO6+iN7m>8tBbgx6K8s@vNDE0CBE8p zbnB6>b51CiV0n+>V+hnsfJgw|s?|w*fA91G`$>$zrPri3Mrt$9t}B#-JG!4H>M!k{`|7K>c1!`nX=Lz7%`g_rf{r%GD2; z0O>64+#WJNu#5<$Kp`&Nxf8Fbq?nyB|@X=DRiw)vJ5LqqBALiox)bcT7EC zXLQ$ns!O^@*y4F}`dH{AOgH$_alqQxi0zhoEgJLcr;0k2B0YO)t!3xIy)e|E(o=rK z@eyk`7#r(lFC1PXjFTfvjwk3}r5I!f_I-HaneGc-na~aToE%g+@yVmXm|H@t@wjR%sMkRaMmwCk9ZK z0!F0Y9&hMcu8`2l%>U4@ukeEA+&MR30&Pph+)Xw*XGBKPR6RUs2&i6bw7wqLUaNSu zSo>n{1E_0m^X7>e=H(rIyAq8kUnkhO>h8y{K-PXm7KwTJWvWT>y5L%fUIV*Z{EzdjKkq4(j0TMs)tt>rEUf|xh$h)_pSJ!1!=B>oZPAs)pBR|nk;MZ|>VqikbGv6|Ro;)Z4$ z-{ShlCE-SUk1h7M+}?X~pT+4OR28-9W-q@m8h&Sok;T&AKh3}uZ37=>OkCLR;7|*F zt|m=^QkkNqpK*bfwpU1Oa(3l0V0aJ@Vs`~4O6(>4MIJ0^za+%T>{zc>?o2G4Y>mWG zn}V58RX`ypjuNi(+w8yAMmC27YaE1+I3(!fIT!vQUiqr7LzfSnR1#pJ+H5Z4C;{nF=z{qPCG#K`1&k`4PUH`7* z{=EAAi)Jw|SfBg$>O2L%D2--iUhAY?-wimA(gzX-rV>Qy-esGt)y%)mI5JTWjC|IL z1se&w!oSGC0)>PJan)0p#aOvlnz}%U5M3X=g~{C8Mo3Z<&3%wLrCprLS&jx#Q#@Wj+nbe{lPXlKQG!``sf7Bu11 zCrCgxW02{9fP^AQCNJ=khzM4W+dND728mI7ogNJE`YLP)<5>l&h=WBK%g=c=tN7RJ zv+XpMYm6rrHdCJh-y&JFA7gAS+XI%LZ|_bw#G>Nm3Ex}IL(jc$tKxW0SG!|oPL4T> z=zX|Ha@fc+g;Z>jrd_sCBybVdFq7ia<78j4pP%1^RZ)%X4l^RS zh}JsfMeYpSdk=dx?HSohQ@Jd`R*@qgQ{x%3-cz;|^01guVgTVaa=@tnZof&ij-oqJ ze@yMTkz$|;eL0t^hH_Mvy>96`>{#=d^G%~v-HWE_VeYrrK3n(1D<_rjR5h7r49k{mX#e$f z^rLt<82$|5T4z3<7RBTGaxW!n;t@_lR#uh>I(Mr&^;Ak`%)@xMaE0SIhDYI`1-=(jWAMGN_vB5u9Qz zHJ4h;ma8wg?FG?AeH+bX_U&M|^6Q(p_&LvT+f#Cb8twCFvOB-H+|JY0wy+?|d|99X zMRYgJSr@KZT~xeeF$-fipxO7&WG1R_rZn_1T#qX*u*CS(3lWjl2xnHjsuNj1x(VSF zatpK(rEEfXN>t77-OSf0E|#tDlrc}Q?b*YmRr`#E=4mnGfS*e-soqKFfsIY#o;;&a zMW2U8OH!g@+UIdoK(ZUVdmGxQXkW7RSIP@<9zLUH%L4P zv*!K#YoNZccwb%2J+B z#Uh=zu@}k&e^9gZ=pSSICqji(2Lt0!%Ti1){7+h5if0N( ztuuoh^(I#mAeXkEPHjaQoKMhT!Y07~*tHq1jpMYMR_(NY-Q>n8MLm_W4hr88*??4+ ze^ge5IL<;3ik zfJXAe2+=K>+~6Sqny%HWm(6aEUdXAbNOdWj1f>n(TEvwOVtCgT;&?dU8)A_cBP9mr z8#pk4A!BF?D^#b~q!%bRY$&VNHG}=@cwDt#Z?5}#H&16=y7#p$G-U*A3_HnCOT5dA(W zKb%TdK42M_>17=+6|?I@4@dPF)qL!=o53qmznrmp{Z+sH#2p$&SH4Yt{lh0G^?G{O zoAr4O02ojNa_M7vPP+Bk2fyBA3@5HY zAQam@631&!3VH~#a`=`e;c@d9XWn_m-l>x(-*PT|{^7$y#@6M9nt?$@Cu<*!^WB-* zWkA&H=!2s2qHlmc6UJj8Wlp|gX@CrdEEGVA#b0U%ctWc(lgB{1SPcer=*JmLl@o%y zQ&d-zE?%9RN}2Jc_S6$af-!`KZM;z_Q+cd&ZXe%Q^JD9&gln9`ExaSyG(}s)D&v@z zJw|H9rn#J+QM_l^FkXA@)vm>Pf%o)ZjJfZv7xly5^L#0d43n2?m|T1{>v`w77iu}9 zPw?!q?GMHIV&<$_qIv;F%I5`=ptbHSXcN}xX}(9%k54CFtO5pkwd5&AJ<-ul$yuVi zVr+jS(^pKMN~jRUkzh^TPL*i(v79p(CNbi=1JJv1%-ISYb!2#Rm7tDW&1z=fZxL0S z$Hc;s$j-C9#3_;M{(YDD+)5qhp7$38%N7hm8NwKy9oMV?z~;m<+-SlhPm zbXmXokuFo~bXTvK^P~K=78^9zit}`?1)=lAzfi!o zc^_=1rKMH&RyrfD#Bma8c%M#1hkj`vcy~5)<@A&~^Dy&N8c*@>11l1y0h^*@pMC|N zPk6Z^b7Io=xdHzM>h3uQ$0(<*+r`FHDhV;I#qD-AUYe|M8Ly;dRp1{OIBD&!h$&tU zMVgVQ^D;LPkpxIP1ZAfQgqHcL{xdGFF%jw#}S<|lDa3YPma)2>fU=r0jr1oT4V=e>+j_KGJ#A*OqQ3Ancvu{=m`6{PFc9MsK5wKf<$@Y~L z`hxO;Sv&@W|M`B^AEo`e$7HAO&s#gl+%`mmuV25uS{zk2d!mwRtKKudfRj1=C-Pt% z&>WIBrG{DvS5#}z^_A#o>7ewcXP@e@&{w6{T`{3kijiM1AhwoL6j1nVd^{DXhlN`T zqgvKzipQzANV_e(%?|{*@2U{XX6JmPCb5XXGnZbNX_?&&s6Ih?rA~~Bqh7x`eb&aF z{&Y-RFFfvq$1A%{5yRh5Bo?ilS7LXvM#E4qT(?@e+kGlkmKHk4O^#ORm$G_a^)SBU zOn$3^cY-OFxMu;FbJ}IEA3!129h?sBg&0Lia9d$r z`~8cr2=1^nfQH`$$4y=S2+p(Hd2tEBH_nlVWTq!fI-mjQ`)gRIShcsI=hXarYt}JZ z5_du%?U+}amKA>qVwXaBzpZ4?xMu5KgKP4sbg=~MY&$_iS8w(6_YoXqxKw+DeQH8lG}5G9=z(uA9#IfBVm0418UI{}1Y zaYfQ)=VGh95xXsW2ekUxPcJf1Wo+z-Hwyi@*Hk6?XDqpHw*O5jJ0C5HF#{gH=wT#Y ze3`q#F2WS!Oe?}|8Fr{ujHUA(qz8%)6W8=fT!5h+rJi)U!W{T&22$;KC0#6hI`67{ zxrjEf87awR%Heihx*ThMRcC<38!8zw%vA_GmVV1pC2RbeCF}YZ=WS)V{F&-B8k^So z&W4l5Z|pulPTBLL!Mkr1xE*Ay1P~`83Gx=_X&l+=#@HX`oOmY^=w&!WX8 z$~(XfZQXcC7jgzgD60yR?VYE%xb$1}Vtf;b3)I>!S##s6R(mX1umEd?<0nr}o^iB- z8iJM0Z3JQ9^tQY^h3i<(UI9|I8Q&Wrr z5#1DCxe?t#AQ32U`$_D)XOWk2l4nKlnwb8ItulG7cnazHf!UPDJOQpR+R3(;ey#^D1|)urKkC)KgKv z$ugo>6;1I+pqollx}tEZ`-_?$nzG5B zzH+Sk6i)#6eTR=t{6GAO0*+8xM#jyk!-i&2vyv^a_xc4jz3_Bq% z>ScUP!u2;U!p*1V=p#i6hL_RI z3=jt>h$){)YKJ3#c7JlvDJ0e?ELD50Gb5_w>FErR+xo<9Dc`tpqr=Ty@T;C>{;pfQ zm^&y1aHrb5?#JsR_h3YNkZ*5P*7L?D60mV=Q!G54?iNRAhsT=}LJYhb4@^l(p>9;A z951|ft;K)=Cl}dvOI6LPx^u^;&!~g48JA%ogGBSPy^Rf`=Gr46j=vJJ@x@0qI(*IU z-5c<$G=tME+uyrGOOpJ&u$h{p(Hy3?1y@F1M|Vo3{aeOpEJ~81jADG!MLxxu|ym8OM4PGYjujFRTpzN#v1Kqv9>rbvl6-pa~(TW2)Hk; z9#l4!xiLHWauL;CVU8)D?bK}5oMz#K3O&AZ8DxtO0N9FU&red_CH-#NQcqkE#d%H6 zT+%pQ@LO%LDu`%xppl$6o{!vOZCp2WNEr%s&!Ot7wzy^|I)6Y z6PdCOio8rau%04qySA+cMq3qcZogN5Hb{4$Lch?iT9tru9-h5>AtMkE)ezVH4z?~Q zmN-E^5uz)Y##4HzO63_zAK$0$MmV z|G`lKR^9bmYI^lKWYym8JeDkL^zKAEr4gnaudKjNL2vq_E9^ zaM!~w8Hs1A+w9kU1r6p3pG_oWGzn2*Be#2zjgpl8^^~q0mK-k%2(+-&s$P2{>0Qqu zcLeO>pgd)+J!XeyKka4q{uvv%mG+%gW$t|B?gS8flsY!hWYiuu4VgK31!@qI+?_hh=3gd7ySWj@g^Efc{|jhVz_>;~g`e?NZT6Q>&;ldgc(Bwx%s_MaOHBaR}Fnc#16h7s=w`fgdLCwpJSeQRcI#Kq!< zwk31ZZPempE-d635D@CI!Yy}Lssf6pjUuKE08Syj2sBcmc6}^pTKo4WqyRX*K>(QW zwy-F{BWI(F(V#xs7H>FAwHhCLx-7WS%c2C8S+o644Kf|OkJ{47<4R1k$fBq>`tHC+ zGCiR3J*?>0I__g$*bw90>+zM{L{4qoWWUtcDJgfiZ8tZSN3?k}@*I8;o_ zNVXrJUmHiHtmmP$LvXS9Rk;jo!Ryk%CGaY<0y`_(J$L}p5qBzD2B;07#K@KmhP8!k z^_X3io?yLJO^7+92)?9KvT9J*WmE9+OK@VXfzZ zw&!rbor-bOc$p_Cdg-x12>ayI#6Ex7&Cb63-(Zku$C$Xhzlq7U(0+1nihtSMG*2%Rd}a^i3mLw-ofwT*%o4YQdTkC6*+ zGg4rFBU}=GbqHXQu4~KTs1Kxg;^DrsJVj0N`0d->m0e%&UPO!q;~vYBz}X-j`6p91 zO*^Qyw%uN@sKM+L?QpO6gXJ=Fq@y9}tr3$e_weRG4P!al;5nLLS+d_!Om}}t4Ql=1 zebeA#4Ao|uPKL&Tx`06I=&=kX6P6dkI0t2r>gtece+9CpQ@ z?#)Sj6@c(>zj6yY9m4*4@cTiJ4_T9C4!LHE3G?zFZ%@jvpxVNaYL$q5{%2p6AcnI| z9FX}=^!C{Fnz{X}wCPn#U`iw_>rXMV5SfDD3p4tx*n~!r!?(nn>v{DL;9ept=`Acq=u^oh zOR2JXkYdS>z{`R`fK0;}HuDRqG5W>Xq0ZgK)kE&lP#_L+`*xkCdGsNdNK&U){Ta$` z2REv$S6|!-1K$wOE?m6W$<`j)4&SpPIaqB)4oZ_weD_9Hja4zOu$tDX4n~E?$tzV= z?fF1oR#01}D5vTMc0SV3ulKA$He=>K8}xbFN3MZT&KRx9ehb(bUbGSSvV)8tmpqJJ<+1 z6QE*1|Gi2WHJx{si8T{|CLvT3Epd*bSh?{gBl{@rUwQ6Xl$d$d;Zq)+_j?tk97>}X z3KGEtXDOpK?VUO|ZWHP-U6Jt`S|*)}C;K@~;)ySOQ1Xl5vhw`k#T@$0wDqcGO56XS zH@}za;)s3t*qJe*xOG|^S%;NRXmBZFr)M{>)7=9i)iLZ71cN%BHZu?64#bC1$sLoa zQ)y$h(O%p0{AC{=zA&TH*EzTYIJlhl*QT>$`Z>MXwlSO1je5>kh=tn=JH+C16N`j< zYt-gkz;uM0r)0?SadruHu+aKvEG@9gc#T!iJ#p5k}$IE4;QW%I6GbYM59zmPG zoq>%va%&gi)WLv}!3iwQZNc7uOX-K!SA#j~G3b`HMS6@Q~oof5AKwO~GQL8@tFi zew6MZ&Z~zA>Xp0La&3vscponR)C~SHJik~fq7UTT>pY{0-WCqUs=eB~Srzyqb>b`G z%h(AAjzxo}B|l20so``=HK}BHcJb>(``>c}Yh|5@_?@EjUVFiU<1B=0ZgLWkjrbk0 z_CfZGp-kQ7(f`a-Ff2Dp6uS0ECcQF4b#v2{6i#cNMCkQt-@-Q5aApIJw>CrvvBHt9 zfEstR)D|3(nP&yG0~=cuq)<@9qEmYKm9XH>eV&_V4agRZRfZMq+Hm=($`W7g=;a6misn zHy6!xUbygh{QaYloBl&wig}5rG3!z@?<3_dlCUih2spOOad_dqv|zu1BR4m>d9f*J0DPkC%6P$P8FUudpz@CVvJ6fqcB%YrrDQ!JZcfosPWze`CLLh4548 zH7=ohrLb%UV{#S}y{A`pY$X!lKCyOs>dDc6v!hfh(XCyF3Bi8|M0duZBF-5*T+7C# zb-)iF{^Gx@MA*8+pfNiAQ1`KvbYb+8>F)VWPxk4aIRJ@QC_OW3u@DCUy1;#9*1XCh{(xZCu) zxi>V0tRB^8T0B`=ws$XnHj7FeW|yiI&*>&~ecB&w4xoXu6IDeslGt z1%k%#go&OYrZW3oHbV#ym=7#nx>TX;c^Xvnyy6cYS3&vUG4h)RP5RcAHx6-y{e~D{ z^_(8GAKr?%>dJ?QKLn7c(QMMh26+)a=SL4CB{GHY2N=Rg$d1S%OD?F_a$(L!-sfyp z6B1hY9G^S)!*KT~m5Y!u%ecw(_laE=+&t2H7tV|yN%Kc9L{qfqU%k2oBLk!c^SENA z6nrE3B>Ep$$~)(_a?L5BqdfE`cQXQP^4^|qf<20i^@y4x_0G6f#Lx?TZ$Af-q%+ds z!Yp53S!#R3B(|Wqcq36yXnvq-^<>}60?L?;ItqT%4?h-+!R*J6AAe-aa8}Xg*vo*b z%J(uiL)MEB0zl>X1Y)_v%{m?Uwga@G>nFxr=6M^%RX5VgmwiTps7ta(8oGbAgHWnjj(12~{%o<2xGi966sO8K9RtY=-f8iO2Zs z4f8~qXgr}+90`HA^iqDw6tBw8Za2Yl$>hlhHF&gT10 zG+vnKS*iVOrTBA)goJo>JrC!(A~*e1)u{ZPsesPZ75$j}d0pKV24x>o>6l9QBiFBw z7aWagCGpLjh@&yBo!-AH4J(_2w#sRg7YzZx08vwqxlM!`cAe#n$EN2LW787RrL<+( zc;%rHgS(|rh(~YicV$k#=d4*HgcZd4pyOFtHRM!AJ%-lL?h&Hc8+$Kj*$&dFY(VCN z6mbhnK`NZnJye)PHQozJGrsZfXuC1WYr8#|1&M-sR#66si+@-ijHW;7!ro5`pBE3$ zuiAJ;*&MD0e+3P?NvdXFmcM}iTtIs9&E0a(&TEAF}^`=FFKJz@1uak79#k25P4^ZIVu(XH#3Z zYuE05zh{Gcv0#hELRqsHI^&}Ti%}tMO{G=AQT(n+tXx#F&(I9+J~>Wv`NyYWt{r?4 zI~O%MCt`GBjK!j^rI0D(qh^HZAxa_Rh|iapi^+l-XjbSDWQ;3J4GbJ1_kF`^PG94A z{pXjC2u7RcvOq{{pj~L|@ka07K0H6H%-;sI$%C>~_=)Bx{~#HRzp_5*@BeHbo*`t^ z;b1X@g?A8_JdZFF0);uR$vd0FkQvk=Ku~(OQau90wHFzdUc39GxEvyg!f_toL|A7v z$b})c4%&V7jb%J3KqwyyprH{joEurqII9!5O2iICD-kUL_e@;7%%Pew0sN}Ypdw6TT{a%iCO(`0AnhKgOo-^9md_B&SnPM^`WArd7N8-)Fh{R(h zojWkLX$mA8;oCuxDg-nG|4*vAz2jd^m0Hnuj;%DminvDZA zk?974^<3gTHMcl?eXo1JC)p|3CdApY)gi)Y5(F6q=}+}+jQgyc z1kZuQzYdP5EDHy)+u8bUfK7-6m}f+rMKUF(YFr0Xf}080Fw|P<2W-i?ce9$o3)4K) z+JPV%IiwO#fixrnqHCfl8bc@)MGBvBY+%O-`k!)DcL#T)Z?N#iM6EJtFOfuuNkZCc zb&3-zj%l(+ajfI(v!_mtX8@qUfl8U9Hcs!^Nq%ydVJkO*ZBhdqAWg>GbUAzW>?g*! z6tHswhdDLKBdb)K2mY-0)BN6PPM^_crco;0M~y0+xZ_Ccn53w#XM;ZJ0B$<1T42(l z!oNSKQkI+)-Fe567D)_T`s&pkfg@^wG)bR-Gl9uq=tlWj$iF&MNr47VGTuQ`wgu(8 zU;T%f5W^Ubh~W!va>d=}(FOw!cOW73ns{jMw|~S+#plCrweEw~&gFT^+9C)VVM7OC zdWrHQ66Wi_bL8foFCJor?0Pl4v9l1uaDcyCLrq8BfB+=uUF zplWmxiekC{rLLb+p8m9}zozZ`d7sTBd;2(tALVAi;SyI~aNMXDr$)Hr|0MqX?w&30 zurOSZ_>J$bVIN^fv;#q$n5v{XXqy{4Cy{muPKw>LV0|=kP$>|lM4bQqJ)D6`#{*@f zdAWq@!0eFMbbAWe3^mFazJQ;p%Taj3(|cv^YH6nvnRsn!-MeYz83s3@(Q`(rYzvyK zlvkaOmCmALREnx zB97DzQy=nygiiqu!}k=(RhpI4&;CQzV|Vn?)|oeV?!th8S-e)j*-l-%c17@{VAZ1a z3fdGAoutRt{Lu!#x;{i9uDnTA?0c0oTf2DQ5$?ThqZQKVco%a@)l zE?pWiE$A0?Puhos-}G9!Z^rH*?~`5j!xI%ZOs*jAZ^J3wBVRlXcAxV-`hDev3*9cb zon-tYLViZn`Q*_GRkJZ;u0-^X*}M3B`l8Cj0lO2cBR zWYzf6_0uOCi+ z-DG)6p_1*pRYh;l5>Hj^v-F~h_Hbq=;JX6%|1#HaHd>JHyJ#3|NoJB>ng7PUSa~3G z)ogJrzTXwPN5aR|Tw3H`LVY zr}TCY_z=0G;!)oKX_2Sz%mH)|K_u1q;ph9IJsiHE9u_v`Z_{oVUp>d~RE{4ZsA0xa zy}GUn9FlzmLj}xY22EN9x!@4Yt1g9^UVqBt@0Z@VHPdx#q6b0{R4Nz&)m!4*Mhp#y$AVQD?tGgKC@Vg$DEmZ`3<2ig^kLIa`!~W@ zIYd#3Z4|vJW?{t_1n2lqa{(Ut((l=lCGYNqwRw?FJ>aZ;Xx6)Mw2^oZHw3c8v1c4N z?+}wyf#^=wTdJ5G9lvi){Nl!YiDwpz+ydRTXJ&5dvD!2BPsFm#vrRgX54dD-?n(K?V{S;wU!$(W)Rx!t_%=h}-`)ny(3yyB!8 zMS?dil#jv(=wAnv>vXJ7zt84s(Y&&5z>lD_wEg{u`T2tp@Q?YLv(9MkFR&Ji&$7zG z$+WIT%Cl}yzP=~1WB|IM)Kh<*cB*h{KvddVsw>@~oL`B1ZW^q3IPc}#IXTK>U*4mv zTl)faDLxyIpp?ne7A>pLbXmsu<@dB&jzb158|R!8e-N_Cn3WnXA$|VsI&RpW@lad) zqUi^SlYZnAMy7)0@+84mg~>W*;q8vyP21;14NrU;l^fvi@6NLkJ_K)u&iVz*n+5o# zG$XvNqwQg!ScE4JZhU*;OTBu+cz7oNS0wnK-`y7_+4fJy47i_VABXqzy!Ov-+yW1&b?StJ+wdbsi?u|0PBCB5Yr$a}kiFD73KV(0AD9)>Dv-|5s+BM! zMze7c7(?cVkRE1tS*VOMWDw;il3|e`?_9CP=(O6&_no2NkytsBCXJJ{Hbe|Bad`G~ z-7`Jy2|3x>vSk#3NuD4^?txW)3?Y7k78IHH^_90n7TF zSL?D#nVgpcTq?2KW=5L9L_``A_MXW`+e3sIipXble2w-uzBMJ&$8-Bv1qprNA_@?9 zJ;u7s66O!KYY;MoPO!ZGgVow$>zD56>8Z2i?VW_6vpD1%IDX&T9CeI;AP!2;ST?(-Q?uBoMDh#s<|BswO7Nrd#^Hs zp+=>hJgKXxnLF1k5REUO6QM~%YD6NvP`Y>}@koGVEFz13U>k#P9&=q|Fki(W;hu+0 z#>t%C%s1u5!nE0kKuZCxne(UD(JS{(eXkqc-d-z(&}OkHa7gZ(UtX3lf$Vss096jQ zg$wSVsi>Z}dn<@A>(Det-P_`~7oW=Wy2h{dzs0$9No%$Kz<)agaN{HB4yvik;k9 z20CuI_a8tJV-Ax92oFsSYTUs&+nl8iL@#jPpXQY zm&1CylqB*5cEBLfg^KX&uZjtBo=`E~AejkEfss&9)bd%Gk&SbDv~twvp)e7?4TT|i zpv&PK#a)eNVoUX6)9-50nBs~b0=QE_BvYUDz4S(-Rm$V6ldyoi$=yw5h~amz?WF0} z)*|8S580B25-B6ypvHoyrhlM+qUf;o-*$sp>o9a07ugjA_!b{UL`cXIVAzb&uI(S_ z?>>}%RLN$1R|ervQ$6CWRQ~{XQJKAVIU(gJlWpoJ8HalzUVvY zDh93nq^g`WqyVT5JQZT$0tg+WNSJN$wkNC}<@jVaH5s<_seA&tuZ%1_s zHOdDS@flX&h!3wguh2NiGmZvyN>ue?o~y$hrIrN*lnW5OWS1I%0OwkoZ4reRtxOUX;H6O07`{g?VwjZc%I%b2J zyB!h4@BH50dXPc7bUET!|5k77#Pf4ARa|y(IzKaQU-qF$4+M4_a}c;k5Zm?d#!mC0 zvQ?B7);hCO+#4@B`8S~OIdX>k1O9~U4gk$?V(cWIkB|`Z5wftf;~K4ML9C`)a~*Sw z?)!Dy9nKGUH8p0+9NIPEh0*4H0(6r3303D9N{4*y#}@({+bbWMGoPgW$HeG>$D*w! zdw81Xm{{`#F?Tw~V}@g-o_D{epBM?o7a;2j=FK|s}rX*#!(2$9jO4`>IX$NKD z&h7jrS&dF~yy1F+=OTxK@uiV@f%9iTGs3Mo5W8duX$^1BFw?vn1oDTK*>L4fiL2RY zEM63xly$sb;te&)AF2@378R#ZF(ZOmn6jy*;;Wu+{pK$Hiz88ZU!RibVfo2z%e^79 z4x9n(UbtvINGQ8N3V`l&BVX8Cde?xsSbWvHOE;V?#}9yp5&ay>-2-RyKn;9^%S?h3 z7By{>w(D>)R-l{NMZD(X7T#UMkM!YA(HhBwlhy){VYqMgv3K*kQPe%H*bFYjYYUBC z&q5@Q3Cmf!1;!{$-;YLH`0Ohz<8o#8Dx*=BG^l*2CU54tl#6WPsoE%T(I)IG^fT20 z8)hm|zbPE#;G9JWEhu@P18krfdm{g-NWfq&KBGT|8XLf5R_@}t*TFQI73lvQTwHF` zJ^er}&>}aV2sKxPm{)%%N8RWzK|m~ef#y_VH-rz)I=@s9YunLa(z2MFYLZbvI!FbT z=4|E=v>?Q9t0SymN~JPVkivpti6~m7WW?WKGtaI+F94Z_1Du)@-t{?od!S*YFYtj! z%h**jG+-;~=Z^=;a91OA=a7%?9z1XMwmyeIDlJ+7%CF$~h3|)^P-4+2C>&Qb&CFDf zeAK4o(u(@Bej_RAC+-%JT_RG5L;n&7g14zYE*Z$_1kpcq{CLlE!L3_{J1YAr#N}i{ z!6qjOMOlVkB%(oUK6XTY1|6XPl1CSJF9}l~&$U@^D6+Nb|o%hSjV1+ zmKa@t|Bay)xWr4a*-)qJ)_%QhYo$?Iz9~r>N(7eLm%>M0F|KJNamK=mH;j6D!K)^| z%lwtf{2i4iTWQv-TQ~AYO_O?~x7VN7Q_#P0i=j$%yJRrLs49b-ZCMY@XYNxsyD2{*1#C5#xUR@254wpEZxjzWW z1(YS0#r}3dVz<-GyQ;c6)-N$SH)SmZfw{L2kK>m?rm+Wd@v1jkwsT;uv5{?*LPZyw zc@hkdAYuNi``oFR*SpF8&JCMNk|ysfh%??%wqpu-!@p6ao9_k7XHJ}c!_ODqvA972 zx*s82ky@vEPv3#ofSOc`+o4ww@8nXOCJPApps45ziCK`LpGnx*!xQUH#G(-!1P09y}pJMSJtc6EK|Ntp0kJRWx@JqHv3W1U}9x#Ba29V#{-h5wPF; zzhyFRon!~gX=?V9buvB;p4VgCfvwQJp-Ur2)-PEv5rp}ja21F(H+g?leVv)IhYZn= zn5gS5c3II+ zIp5nP4N{WA0H^^?5x&$4YX$XIEea@l7(y8AICQ+JM zL4NJcx?iB>jwq|1s}H_cC(Pq;>D&nuzXe1riD)5qUmp`dljm^|G5fAVg{RnK43~```F%zPZ*~sx>kgR!%a(D` zv{UxPfurcXNha>KZkEhAQJq=C6lfW^&CGY*vwy!Rj5%dDtCoZea1N`N5EXIvpmMNq zA}Ng3aN9UB^U^-r3Sp5`!QbG#w9NJP_vL$Jkc&6}*!=EQTI1cO>?a`cISgOIm?38c^rXoi>$E=bIj{w7t$J@2?Bpq@r=7OFA`%Zp zy242wS!g0~6mjBqic%3h9VKC;^VI=u8zVr=dB1AMmqSB_4xRDh^3ZeDq%PR=B4Y-} z5T+UzZr^FKCMIOe6v0ghzkfpR6fMgr;+GI$P_U_1tquPg^!)w%6McR`Wj}1QZ8k{L zy9eQS#{Nu(6?5QY`N>DW)sGFEFPt5nDVOdk2hV_;8pQHY8bsU6KJyxO-FhYwy3IDZ zJ8)sSS?SaSVi^QU#_Veuk3AjS&x|A}k_3!Fgqe$q&d|9gZmV)FlVb{TJoEwzP$2hQ z13QLOlIxi<>}PbXghf|fCNy=!vfHTN=LhXPbf^;E1#c?2pI(ju&YItvs7 zKYNkQzzLqy$r`7c>UY1OJ6-7ke&<#_Dlry6WpJ484c3%zm||mIBG)RK#atX8>TG*h zpY$^n?90wxh;Do#ve41g(!YhCnUadHF6FYwco}Hj$le?3c-!*q`A8!!W})tGaPXby zwKZmp;=1O{J<^+@v9JwEZlv$xuvB-r%A0N4PgL4Bz}Q+T!eailO(VSm?KECM>S5j> zvfd%$865l?v>*dJN1-Xz>OB860@m@kwG@5rJVu1pWLH&Ow4LJw9-Q!2o{mV)K$(%2jOf>sTEh0s>`Z&RatODqZVIhqq3V?vBAftm#xwdkRUYVQ22q$}K4JC4&y38X zgG;e1ZeK=|>McAMaSq`d?ktMA0q(N-&5a>C{bQ|(vXI00iN@qDF`Qys12gAC;^+DP zy#@F60YE1yA(gs}v>Vy$dLN_uMkKEe?o=reuItOUAaEBd)6=VGeW4${re5$$J_T5J?Knhr?8Wp+IQ+3mkUvjDO_zKy^YiS z^kjh9*3}Rt_H9^#{*TvNcQ%2>*|N2TC!S9R!Xlep?+liEZ2j7aYAZWOjKUe$TV7aX zjrY^Z=sJ$0DCZ$JhtZ43-YhX6@mri3TF28V&Am&+qB|4PjupK9V|QY>bdpjQQO&e) zy)Yp`yu_?SY%afke|1(PZEEs{b;#j@V!jWQkvZj1Ey&Rq>P&0Xd*o~#j{^S?L-Tr` z>s(!>sTK7S^{EK!$@Hy@lh>ZpY}b79WXl0+YNND&ckW+c2gE^3B_3*GEeUiY*dW-9 zxLi@A%_exqW#y3ob4p*n^)I`}E46l9Z#MS`;3t1ED)EEAZjNvPIZ)3a%*q&=7O@4s zMDW|kQRfOo!W6=DTG8Ne`SRd0$48zpowOwp-!dy`;Ew0VDJkz7{ANAA2SVsG+MgV- zyJe<;p#0>2G=m zjwCj=g>k__5(A*|6Ov#@B&y=SwG&V6+<{$7sV6? z4lf=tK2Vz+^0&C0eP906SS0;s#G{~L>vXvSR=2~w;?b*l@2QxsM`8Xm=vQk0+Y zYYUeRHy0;0z~|4s7Qf8&Gq&|#n0cS^_->lm7^l*0%4#PgvpboQ`~58T{Ge+&UfWsC zp=Zm*nYK!u3g#Vg+fKg?Cin`@U*H0p%d*=9Jiz^eLL9I55C=e}k-%XG?&Y?;c7nP~ z9wtSR%UIqq4?h8{_oO2xqj%6i=+RfUjiv_^|)p#`b~X zkIr;rEJT-sTEYDRwkDIO?tFmvKP^sp!P^Hoq|kHs(ABln{^>u-NrfFeqhZW!KXT+q zQQ$@YfXU4A{VizPm4`BV6HkR6&xz?8!Pj;7hJm~yCC8y{iu-&IJ< zc3j}SZ#Xs94i1mT&kdV4ZNfVWy>4dm+$m>s({fb5G3RXoz6<61=v439gc1L6gg}Gbss<>4z)8A8a8-axNX2ES4(=w}a~`6q1Yvgh zwvKNk`#9hsW8!$H>??n&9&>w$aXV?YuqoBd>eb~wuTw^v(AfAoQR`=?7oVUUU+~R? z3q(W)=RG=PZa|R8daxyd@IM}qOaUFEYuNL;YR>HJl{FYZ3}N@nK37%>JL`bm4E6`; znYeHo#0U}d9{R`h_(jX3!W@;&%V)=ORH$K5E$(@>EtU3!yWIQ1!-shs%=pDef%be; zxw3;Pb;(roXyFABf^H>P-50bsvK!`wzPvrU=G;k) z<+46ZYyxZ(pG1C7{Kj|{$zUA>Qohw1U5zfA?!OX)DpshFB3(S67%Yp{$(T#AWnv|P zuhd`@Yo%H4`)TY8cm#A&+i1VONl%ZgkT(GTjDRL#gQDIeUtB$@>fPT2913i3rn#C1 z)fghCuF8kTXmtgT#z6adSy^o0zTBRa8M;?2tulxCPHJ7xKz#UcuR@#IaX z>v*=?b92hPmN7QxV-B=?M%`D+F9UvW2v)PpVt(YGcvp13c3ABvxo$V(Z>b6hfe>^g znwI?-M@FV@0Ef$oT_bmoCuBlDwm6a5-f50- z4V=*nxF6V)(}JLE1e1;lE+UvmYQ+$QVTbo}3fgM26}lqDl5Az+xt~Q#Lnh^~?hQ~4 z=`4m#^BXCzz%!+`GeDB>i;95vb6EXi@@@F+M~&?%$`*{cEi3WfFTbQ_Lj{Q>{5f|+ zv`#mQ`5Ono82Y_#Q`_xvsHe&2@Fyk2zD@xoi+X4u1nW>de(`>LOM$g-v2Y7yGYDr= zB)J+B_yk78v&oMS!{7TF#72l_nT{c~%v#4W3O>H8?Q0lXFIMfu5|UWf16+oR*@M!I z&9t~T+BX6$;nOA!W#?;QQj?~|D!@t+Cx)Cz^O z_X3u*(;NJ$`&F0a#Zh;=C)6=NJ@M{k!-Z~7=q`m~FNY!3*q-#T3_?lPIh`l-Ky$xMy1@^zui3;jgST-0QB)YaIu8^< z@TO~jleqgAe72$_hLk4x0C6%F9$7wUB7rJtu)Yr7>A=@c zQ4X8$-46R&KId)u&HU=AGpPs#5f=YHD5m=e8wGr(-1~kuhNthn){NBo>N=e6>owEC zx#r`TM~0;h3+ z)0vGOtS@gGG<0Z4jV<9`z%RM2bY`DFw%K=X_=uhwA~iCte$zNbSWwbmqI}Wq5GEzXek^s9!SgbpHxR) zUw^~C{KAE1(YY9b@xCQtV68E=pnBfjOGWvw&CNG@Q&LFvM4a`Y_9KaKEw?jNo$cVz zvg}fp{jMN6&m2`gZ7pDdQkq2}*CG|^W@~6X)_hT-ar5sLyYFoVQ^M|}U#ripd^&sitI6?qz6_2~iB>M} zLqQ|#VbBB7FM)NvTlI~3cJ##1TI4~Qtf8e508M}|o~-ym>t6h-H2KyJfyO`U%P3g^ zdBHqi2RB+I`^RNM8307Uu0C_^?UQ5&@#u8w?0Kq19h*9uV z(-{=@8u?DG*Hg&6yUH)kFAOt>)3zox2J~{R*oL2jCOxvq$e8 zzlXq_nWF8cvc^)i4=Ov@`3;PYsRx9(`Q{M;h2BnmBuu8%#?wjOH|TN@)%{scQJs|y zpP!jR==dCR@ldP1)@EiFAcOuOyX`#tGzrNs3>GV=L4(?|kcZ~mGQLUkkQS;Jh>fcP zu3!_4UY@F#0l}f!OWPUMpG@aDPJ7!IXvB=2Tb7COzPLN^jbcXjx2Zj0-*V!#>JVk^ z!JyD&PDjoGi|2MT#EO=!8UB0ti?NT7{6TryxddAGXk~A19!!pl_1m8LK(Uk6OZ3DOD!)Q4 ztSfto`1A{; zu{szf-C$O@d9_lFsxwmkSi~-NtyYzDrP3Rz z=9_7D*bBTrF1W>*dO0RW;Qk0QSj6>%I038#c5jY{9c0@pu-%^bUTTFg?FM z5@--;N$=>R79MBNoxOk*srZ!M!Q$ISk!0cnp2jmAOf%>@`eYqX>DR121#&fLzlCoE zvq8Q02rbAD^iHCD=Ec*J^_PtkXN!90&k}hD91SdaRC%o&DLhKWz>;pwGeA+i=)_o^ z10ggF%y0_5d;tMi<*=O|AC82`nRtMTzn#8L%nBV@Icg7`2n9?R^+A`;Qohm^%g=|0 z3X-so`r#mYqlq-nq7R(!9=BsheMonxfsR)%r#6*}mJ0BkiF;5B_b5HTNGqTT0<+d- z+pf9{I?}l(EUJCR!4=4WaIx^#JKxsWEbrzgQQ4X4j<8VDC;Dk2v`xcGtcGpzo?kb; z{ISSOK_{|f7#8|=;$uPG#^-io^g|FGl3S8pP%t^^UJ;XCFl3$TL>jN-!Ft$k2R;4z zn7NOnX9d!^6Jm4BDMzk2x(PZ6cfVfP_w^@RHk8dEyj<{vNmNU)MHa@T?1F3yI5 zZHwHVefL>GHIyL&n05;PN&+4&++5UIa9(ust|rj`LobjLy2(bNumcx8r$~OAJX`A= zDs%CKPlT!cuI)`YebQfXfXAIJN1}F^7{da?(Rfe@@tF+xoM&`4rH3NuL#}FINi}YT z6{bSgOV=UVepovqV2BugL#Qqyl}s6?X6n_f%68JkO24v994|OKzJOc_FnhvuS+(; zy$7G3+`1smb}2JF_48&sxx0}otyhOzY>9kf9MSBhx)qY6T_d3cIw?}J=V@QnkbuVLXFbBIo&&T_QdRs{43j9$I;U<$5t*9-J;Cy=W+B|m8AoY zCC9`pP^;a-FBizGA{}en_W0aUNQbZ=$^s$1J9WMkpkz>jl}wq=y<9K;=L^F~m53VU z)j){CP0yEspHSYwhr;H5-({ZZIT;!J*EN4IU7v?$!G(8CcEPObgqeN2sausZMTtv@ z6f~O}IgD$vpkChYqjkaw$^$aL7?M@RjRy{p@Y})oaskrBq+Vn-c-sp(P4@4>1#N3$ z>nh>aF}nwaxE(qyocZw|9n;U*tQG^YLlrI_^lbmR9S@~PM;Iu4AarPk=P4C?WH zYiRUj2du|W*bG##^h*q0#x3UIwIMuT&&$)vT=YhHxR=liAseMo>TA+b@gq(9m0f{Q z3ihYrU=1Hf7(R+33D5jNIgik^!%es%MY`^Dm3c5 z>|_&XRiR81_ctaCA`~JZFC*YMx-|OLyYJwoPg7Kq>~>K*cW=M=||tRS42KCl(jcm1Ks2kw@&s4CGIWYNIa&M{)F!tc6P>hV>28syzE#6%VZzJ?@x z_Q7iHT$tgaPW$|2AD^I8V|a~&sJqO4UMYoo!Ed4hHLCw1U|%<)kxN*+gIaGX)!|LH zqLw8?3*iyIn2aSadzmh-7Z()0!Oxt{5;poH=7&2r23*GWav*CeD3SyF&KeJY1^tr@ zVZ?n3Jq?bqLbnqnhwOE)jAG$`xhE9Hl#$jL!yC?K7d*I@(1EBjC{KH=pTXe_!^8mq zP-vN|pA~d8LQIy|N~2g4?E|uY`Us78%SI$VM*1>N=H+j~eR|^h99E6~_b6Nw1*}e+ z{cs7ma_-!@s&dB0vd@iCM}7B%goKB)v&k-Sj!X&0Vx>VPZDoGFFB3RPj2}LX155fd z4s+;dG~rznAEwQV*1LZ|I?pM`pk1yduy>l~~ z2|6uivShsUMcYgHFZ(TdG)VPwS_YGJxh>TFv`+pYJun*!j70o49VvnW{m(evmR~6K z&{rGw7=I98Ri@{E^1M<8<>?oaRh)A{chNKai}SsA_Ii}uNb>p}L6b}&!e(#TfVKHeen3`|mq{??yd`B2)-b}T;35<1`mw`V zebx-T4_D~~EFb2pl)6>ZL1}NYM)&Zru)EbwwH?M}eZGjUcwev`CGK2axL$QNL5`7P z79n*!Og&a|8;TMHn{~cqebth-KSNZ>l;TQFy#-EnO!K<+Osb<)5&`gX%&s%+6P-T( zvVV|@i$UZs=unrmFN}j^hDDlC2=@gf4@la!ZCiBgrIsP{H`G*}AJT7cbG_5sOe|Q* zlNr$aPe8-mo5A@3H#@v3&YU~fZNLB(MiY#l3lbNbeLKKhF}ilXFy+XrR2tE@CtJ-> zpO;b&Nb&A!z{Z0uvK9ttjlZVrubYSngNm*rUNd^54_*o=1SWw4+Zb{m8r#z!kMOWS zF#F^DLhH4!l~F*)tZLwu9q?MjY8oG$JqZbGc3zlvW%Xj<-q7(Mw{72ED@eO;_y((& zgKc`YtBvh;&zI39bI5y6fKm&hn!mibVBx~psMXWzg`f#D;hsTp(C=Vz!tti8o8A0- z%RJ;{VpHP&W@LMf4!8L#cu>VuRZ*4eEnM_yzz-_e>*?t|3jKV|+_#|^xj}-mz7_Pk zdsm|`vh@T|TObYw=pl1Hpl6l44q3%2sr5JuL|=Ey0)yHz_wYb!6`N13e~m z)>~^AVMo*?NRBw*B<%I>%5HY7QXOVzXGa4$bj&Ubr0=F+^k^UKe|&mI(~2lY#`gm3 zik<}!(#ty!+ICDea+33Y8*Wo5e1g3W8Yxx)ynzW{^mNV*=>75&UDxS8!x&HNMh9B} zP=>s?ux&N9krPF}VAAk#yO1K_;L5&fmaf@{1vdcTk}5$woa)z~{I~KD#@m{m=D$4O zY->+S3q^%2K7oK$;^QIo!f~g|`F!aA5?ozk9Wz>ezLAsTdsxvP^~t+SL*qu+Y&$#X z82_1bQ6G#|gUv@Z8olY~%Qn;rF!RSGE)+*ES(cXjr_5W`bowsFmEEtTInETj1Lol% zT{vy7AUF-UYL(fUp`D@TF2<$KBWk4?X+>uy+K*pub|XnCe&eqfj)PjLCCz`hY1I(h z?Eyb0n)U7ZVQtH#=nt+lf{X9RZ%D@i@$;58(1*lm+)81p88bD}^u3#UHS^NuakTJL;*T-TB@eEZxt-4$Gnk8JEv^h4kD zbY^XAgm)$F_wX2fVsQY{0dCMun+D-ymKTYuC)gssrO0E zP1m=YZA@=fkvvwngZ22F_4nvO)XUE28<+IS96T%A=mOI&^NFpsLVC~G0Q=r?@j|Y2 zrdeqoJ>``8o{I16@4YzKVf5(He4r*26lg;uFE@#f!y8^yCUho(bHJD>+X>a+r*yV3;>pyC?tr@e;0!u;f(yD5Rz36kD&t6zX zR!6P;>IjK`LD5Gh4>?w=V7*F0;DcFGudNLp#Fp%f(02d;Kigd);!=k?KRm0!;WoSX zIzEE`N*94na?Q6H9MuWjUHP|^A?9Dp2oAU9w;{}BJQ2p1qgvlemlE|gouXxguIJwt zz5o6}mD_z}QMs5ia~Zc0RfNzcn+LcwKN&+jWMDpapfBx2e{x9)PJ9qH#!?lsaY2br z_$4C&k=K*Q)7C$Ri;}?~z2sJXp?!m66SK@Wa?(wce;DO}=Xf z$FeC#vK|;eDNr&EFdV39b0Ly`i+OSL&YcfGU6C_pWe7AxC8J}q=w%F3o~yewd-dvN zU3~6th5bfv22xTxZc5*v_3Aqupi>5bB*UzBZaNt`;Cz&2?(r7xHcm#GDlc7p?ZMyjSQm|N z2ZPebq1T%-#lLDzy^89+{4S<{bBpJLm;O2w-?`jx#bN66ch$yBLIzIzE}pzSn-Kb7(xy%SNZ~r(ZFcQnl?% ziH^^CXcKW&YjU0+Jgbe*#m^s$PWA11e=(KDyVda@zh|v+Vt4jmw*vGRJdC>c{Nlaa zv0qor|KMUn-_4$x*M(ma;|F!Iu5!)IPmkSw`EZWs_o?13F@edvdgxxwgt8~y=5Cto z&C}MgGra5CDfH`x=$C^|9ZUej!R&0}>E=0Z#_bAyv~u&Gw>IrG!QpVr^MCE}8@f@s z;&9hjzaod7TD7Suv%zYdZ>A>Rc3M)bbl&jQVC(E;F5Fab`Re?i)lp*%&sMtGVFKj3$H~bl^1{eV7ubbBWvp@ebXwwRufa?9 zK6ssZyv0QupALZr-V0k*44qtc^!)i%iB~6Crh5e4&sBEwUfc3_XB+i|F3!!rU(&f6 z8{}i{nmFjyXS2c6djIzR8FcAt!1DLr4-?`$H!FF;#BKVQVyAso&^by#zK3L#KU@t+rKB*Ep8jZS3xphYts*rk}42f06t;A~$_Nk9}--P=n>MxGE z(kU(UA?h%@u`QF-CFt;4?CU*_)JSh>0;sZ{>*PLWUA zm4$#(e-X&N3|_;T5%;liwbP*Uic!;`)$iNZ^H9WEiQat=ARGsx^^XXsA{P_-*BR!Q zU-s41Zlz}QNjAYdMBd-%m$YYbJ?Ay9`9-fTI#0{Z%lFw=aoA;N!pWph-_HNEbv~_8 zofqgG#<#U1;&ex};;iPl=uUzfG0z+BHpBBn9oD zd(}bkL{@TaW0XT$i0izbj6_2)?$deN(F1nudQ*Z0Y6jkfid-0}^h=ib6RB+I#W^)l zQf~RT70174Z=)y-KDXcl67|~Zv|T6WA@H-pe%%7H2b$6N(Y`~65XYojf4f7InnnEr zhsNUOvF4*Z{rwbl{v)^k{qqwG#(H-&1x`OcHMTX%vgg#0uNh=CW;dqWEx1PF^efw8 z?+LS6tnHcYGx3!Tx3tpPFjWqmIx?xj=Z{>)1o5VU%H|rIeTd8{;rJv))@Z# zlg}=gx3czs^DiTs{Nrm9_Nzr!uka44zZmfT>lVIQd3pB|Z)F!1zf7aGYX>pEdh=B& z{eqcc3$;1uOW&t6chl zg^wKFzOsARTmhVdVZmmx$gJg z`0Oa7#D!EgLH*}>zuKt&x|^@{{K2=~FH~%)49~f}uXp=-O`I)Yu@B`d+n3p z!@roY@M?Vgn&=}v61rrrJ*50O^2X1e;oh49yQkL}lKdcY?OQ0(z}yy>kb3oQq3%Y19R+(WZuYT$cgR_x^4U$d&3 z^h%@UUvG{1^;)`L{`~r`{QCX;dh=Ro`98bv^F;aMEmXUgOm(SRbtrXq#(?{N!7tA_ z2HsW-tkzooz7=+|wa)(gVfe56<7(gmko}!w4!8XNGt+Gk$1=zCR(-X>j+>f~Qs3WkV4aMOs%ar6RWCz~Z5_l@?~grr-$;vpk30YMWpp+F`x9?gEA7Aj;J^QsnjfLTQ8&;&uV@B$BaRg~7o8%c(}b|+`lC&?Wl}4_ ztgOtOBoc%T7_+Bi0CaHKcF^NaWCo0-*xpIGf15%7$9*YTD+o_vpD54a|8Sty zx+^JHZxVh#Lm)tg?O2(RY>ko7OL6R4vuW03usqo$NmLtm_5a0ihe!_`K>1#0T* zLLg(>KNg@o@9j#sb~gC;ryeo-hX=$%`&DtPz9-yz@HulHEoijmrcF4%A;OnUY+QIo zS!cjV4h(RbF`W zP@%jjIM$4zv}}JA2E9WE?SKAhraaQ4kVHj>0|!qC7R*qMfd1GW-SuzdZ7Ld>IdfQ^ zb7;VSw8KOGn;+*zApSiqe*PpJY#XM4^n9wUjNr_HO8$BKO}Q<=j%>opd2kb@n{xRe zws*W;$Kt6yGdNpbe$NBQBJt|4+x=aql-g>f8_E&zT(ip{yu;q+yT>|gxjLB(j@Bep&eQmw3J2 z77QIS%$vg4HXu+;GhhXMlygmA8K`*VA76BXuKRiv%u}vr2>KQc2~H59h%lsZ9HHIG zc|2S^`15k`nYzLMY603J4IwNEliRNl$^OHIHQMR)uCYBtPbV-{2_mvRmcJENwT$bS zaL&8147 zk|23vX)HV}p5}t3ZQaqi;he*=*j}{_H~;e|jIfJptc<@^7!MH>As?Q6EmS(zarSi~ zhNKo3hk&Es`FjVBu=y1r=xMRr<#ck@PP*{9FO2{B=Xy@b_SfhRge#d}(oH!U@60x{ zHl*UxBrhC1dmfQOM6&W=ginGDj+hCYr_YVbO8M_%cJzMD+Gz@UgK`{onO>uL*MlWH z6l{{+mhIf}V1&J#0)tS#108iZ0TsFz*l)=0MalpCX(Mc+*tbQ)hp_Z*jeb)fm&pX(_wyOxZ1sruF9gZZI>Np9DM zng4!Va=vCnavN^d!rynys(cl)%cliKtuhR#*qveGQnm4G)b|zU>+j#%UtaXORq5ir z);F8!zWcFn(rfL(zXUfeZ|XL7pz3hV)Hz^(H6X56xWo~d0=0WM;2#h1)-xB^^kR9t z$MwppH$ET0R;UKVo&-us*3-a$z+&Z;IXP9e@LR@jk<(AUMWeNpX)Z;RU#j*$bgXiH zvRz--N&~cOtG}o%M_U8LRB#;_Mvg1b{nP#0s(h`q}!Rn7WN#$-ID=d4eG~6~SbmG7L-VmDh~!sdC}x)xYlFs;IV`u;EEUb#l7zklLnMz7Z=ce};dUn6+b+abWnV zQa6|Scb50*?0O_ZzU0g6JBIv^-_5w;|BLVT|K~sR=e{D1am?v0O$ ziMj0B=Kp?M8jevrKH@~DnO8S!|MbCxVA=~{L&Hg>eF?V*f$oqg_iEsL7?tM{Gdl^Z9{nk6>8o3B4h z$@##|2N};FvDRvh3BYDbtLAw@K&g!z zH!k_Qq+q#G=t_n7j+1)2G}AU#$toV5-TYtSDl$B@R-Bb`!)>$Uri(M!%2lg$TuO$i zHNApgpqZK3(b(8V#An?jKli9(Y10n=)47a9V%9t7qf8@U;d+J-@fS}cg@?hF}CyDx9`rKI|>R&`BF&w{TL+T zS)x)@RAif&?tkIv%j0FRVpHj8vDQ{nxRalMP1LP)7uV9#A{MUkR%>2-U=`?1sdiq$Du-GeXf@9#gvsaVBq-l!wS{@z=%Uy4dW{1167bs_dmpFUk)LF?A7 z`3TCz`Y%4Og}9W?5fN?R-Fx?Ht<_dwK}c5#r*m6M@*jVm#FV{`qZhW z?b-#OD*4c?y^VKI|AZZZ-5S395c<;dUxzk7Z{p^lIxqyTWn?s9Qlj9F$PC&nuZ| zYukjAZDMYoFeo!4<0&NC7Xx><-L3R=pil|!6ck(sE7h5s(^lR9cd-12r{u&r?zIjGYjuA!DvsbUicG5QNH$Tbyi~ilacT?EK3Ii;S0&&;7 zckf#7=QQoObZM}bwzk(=S+p!V+%FXsnai1T3Jo3LW;3;x-%p$t)yjJJ>ZN8MmzHKE zyO5Tvd&dSO({Rzs*;kE4QR@c%Fg-{6B?R#YSmx+<#4Q9Z_~?|V5C;Nv7-C+ zXCru23l=Px$p5W-@+08-Gxzl8n{U5P7?PQtJ$k}~W)!?0hYxq=Th0%0|B}ib_*dxi z_kQ*Kqo(WVwwv1&tc8U&7D53(NN8}X>gshEqi+KZwwydUZpq^zCuUvkJgjpwFqwdh zd700araW7k@%r-RAznkvw~m@q-Rwl|dp+JRueI4d5udPhv<7?k?j2FbmUozuo7>9z zR5)kLBQ&&Y<0eh!%%1Ij;)E6um#U%R5l^d#$VlGDc4$H(w_r%#s`{#FQS;lLbvNIS z{?>L5&IT)temR)lzn{0ck?HS*X3MU)O!mIK^Hi^^`y_}0Ct%##1uCSmk8L z9FQ2Z)?E?CAX&lP*S9%GuX+3StRhi(ghkz;-8XLBm;*O-1Mq%9~#-(+P-=7 zX74o9+G{$w9Wt}&Gqh@xpZ}WX8H=JjD%IJdUtaKGU7t@sH*gdXQF}8lFLZTNJp)?C^hT`*Czv9Uq>P*0pgSM3u1Nkouh0HnIXM=(dYyaT z`7x~aX=5uF#FLQL>MNL-nyz}Y%*4$2{<$aLU*9XfV>!4*!)@gsJ|~{8E-W;unz^^~ zSAPCFE(w#EflD!3;DColMQz_OZ_%RN6WHbw6%{4MH2)(AMe~2YPuaA-h&2U-$SGkw$Mc>}N8#QUt zIMe~ z&%utZpN>w~bj`QFza`&n*Rx9(?SO9Gz3;Z~^mxhpoazH3R8ycXbGuF44upRGoN%+c zZY1L3o{aF)%RTSdN4dKiko9D^!5zV#0$f(wHHC{feD3 zZQ64jDz>qpo}5`&I)56^ghe%M5p(yv%)?K8U6K0yb4EtLSHoLABA>d4g{cu&^00UZ zBki<(`}RBc@3$$Q&*vNspd^WxlihXj;C5UK>Z5CPymR=)a{kb2ZVe56)}hn6i9OT6 z4dajz$!1oCGZ1=2B+O?mpV~uz+qIO}6_hJG=wzRPv7=3H{&sq8yES8>*QY{>Z*Wa+ zu71vomoIftJV^PWz(y(eqeoSVbSY1sbh&it5@p4Xt5>f+sPPR5*yiosG#DoG(`U~V zkpd1Wt*IRu8mdUxx`>t%9CG`$YomP2A1=C((Q^(@-PiXz3HkVm6aE(`-+%C+EwRmU z`O>8$u+uIHQRw57ejVz?XWZ*Tj~{QKAnT%KONsocOHk7t@aJL)V>_`$2C7xn(5MSQ zor;vpU2Yisdd_V5`W1^*K0}}ITInvS49=}oWI~m90P<>a(t&zdeu~0uqSX)nvFWi> zfq|{z|8B)JKv996Q(iovRK6|KZO(DGQKJ!wH@%*IEKiFf)jzTpuxoh6$&> zWY+G{U%9#>9|KtIn6fZ5mw4(8pTrB>9aB@WWEh3sQdBEairu(Tm!S@2W%`UxhLJTS zgeg{5VNp@_^z`(OpFF8i-eHr3J3w0)-ChqKOpmKouYdpkpnvl1UJT6Xy+2!{JUBW& zUWL+0`<`uNM18 z+pBV%YaymtpDuScfVPdjPAwqa4`;o{W-Z+D0J!=naZ+6Wpw%`QK0K+<<71pCq;je# zM4NRnFtW64;ppU~gU_8@N-X;Z+$yZg)qVf?N*iQKBUspXWn*t`?ItADpaTbRg6}wY z?pz5tltw(>lHh`uyqhs~6nCFnJ7!GXtnBR1@K<$pjt~{|DCspdqg(gjQ=WOah)dng z(6CG0x^;W@?Q2ZINR>tm=(>6n0ZLfsx?kTG7~7Aee_J$Y%fdsq^hZ@r0@x= zNC(jJz5DlDa0*X`_6pWS6aK|w3mxt-*3;#w{dTUxLmKh`#=Q;Rw>H7!k2 z%!|UicU2f{Q3>P?U4Qd$T*kRWFXe^O2#VK;8v8KVlBM9zd_=Z0Pe+{9t9Nf5*vuIj z84}6N%o-w|x(;cuAO0cg8X7h8>e{bbwMsPgFcfz%OqgcmXuojbE+Ep>j112PsRX6a z@bH4rfTF;$iq%VVu$KO+sirJV-u>$ zbAvN;bKBRcSFe@=tyt>ZbH0ZUd!stAr-~KwB6`WY&3BlF#+y)R95!&r#4!(?~1}L=4*+VPoyqyXry*hceOtr@FR-ma4Ye zos>=-ln9xoc_KdEf5S1dJ(5>3xuqG{!MpSP5gM77-q_;U5~|H}-nQM7X7V(!*%`kv zJR+hFKTxfGK|w+7{n>T*XOCIDSer(eqWnNEjbe-Dr}V}z%bT7Nb@ZqL&6EC66DFR! zuq57lRQAcR(`Pr$Lc)(3G^y+uYRTf}Ydo$`Xg0g*=!iJ(CSfu(HdY3X;shq>B(-r- z)%u?0K{CX;+>}sCYqJbL%tuM<+raA|L#C3~zYNt_UKd!2fN<&$&*m#p;(GOg2i1UR%)2%;+*3NEr-)H=~h~arZ0@lsq1Y%M#aCxiAd`@(%%O25dZi$MTyDNLw zdOP|tI?~!0ldGu_1O`w+hSOK`_X>6ihnRbMNne2&ib{XscLQ@%(~;)pjVQy_q9@XV zQU|&dPf2+)RAWp{ULQ%#kdu@12~$~nD$m@GVLcBS(_KMT_o3?bTPJ>L(v{K|K8L34 zzH68HmHRWMOxY^6X;@e-4l96$J1=~Io?hswQ+@88l-NgRm-vJ;NJh%mn|JTue~!>! zyp$;d#JLB7OnpF)4<*KC)j&XETqx!%E&5Kfvf7@QIE+52YqxHrqoR&vXJzd@aA0Rq zkxfKYl$xGiE6!*uYr~M-?!j#jG=1Ups_gCCRi518v4)8c~jbL-I@Ud28jAX9z6=iEpV!Q%Pq!@(^>USR!YWC4=#`K*CK1k0xJ;= z_SxS^G&4o#8}SOor1BmIhWp3eCH8@j-9d&A^~DJ&BZB0y=kZn%l&2NC3{-QO~?XC0q(V98Db%Ag`OnhLdw0N>+U4F2D~c{$vX zwrnZDFMw|Gc0zl-?%XrFpSX|REYjOd?u>EB=NFkRxvX7L0WpVnNwhWHONhSotNjE8 zli9{Rf6Dmbu;SI@Ol(X|l?aU~Qd_}tc>m;T7dGng##1OxmoZ8Lle+%+aVNaxZo++? zXlqOHCmsuWggKW^GvriAJe)k46nd+dzzVIP6I)0pZNu4}e zgZ{O$xXjIcTRVL3+pU|2Brx(_TZ0WhW;D8!R@ejThc{qR2U*V|ABf*6adi+1Abj_I zu<1*TTexVRGypQOL^vJsm%?d}IA;IXkgQw0+pm&Uox^~ZeSjM~VwyLoj+wc+NVVx_ zQh-l9W6yy#i{U!($GNFH6bz2|Kq+{}C)Y@IAQfoMyr_)GcT9On(wKL>JKZ2r{5f~< z7!>SwxrmCAp=1h;5DpQ>qej(&b-kJV2D!N%PFAVhIisz^U%h&@_Q$8L_%`kq_ak_q zAXF(72cZ!Wcjr`B&k8$pMwu^rlQ*eddbzMQjk~Gg7bL7a%-*WBZ?YOTW^nz?JX- zOyxF85y`~4BPu)P8S`2+sADpI{8;`QSc=N++ZWB1cRN+BJz2z<8$laTvgi_F%(wCq z?iLwYS=Xowush|4+sy_u`+?c4aLL8-9E%b`ktj;dI$0`H)ah zi{5WYsO+fHpiTyW8=;a%wD<7g@oDzJ7spSX>aO`{#wdzM!QrTWOomS8&+sDNyLbDr zmWMdo-afxHg;fDI1c_$^5%swfr%W*_DW`g&zn3zDCz1d1)^L@E9_ZIhh770b{2Sof z+<5QZf&#yu-PKhZN~s0}sjjZBN}&Td(7k*2h`%V0N6eb_`=ssBwu%pEDlt-Q^mLW$ z_rA$LYLWDfR<0btLv=^#gXFO`<>*bu7KT}yTS+c#)~v^5{=l-nOLpV_1DLSS&yQHK zi#9&(#tknmDc6un@jE=z7PL=a*#36y5gpr;ojfNKunY|BPRxFkmDP~W!V!2$wYHk{ zha~AQgNG0AM8EW+-3k_fP$!7Jc@VB{oQQ8@b-H#R`#5M#aB@(-J}2W$T%2>@$*~>5 z*@6CD>#rXp=|Ew&(iOANio?qQouzx>Dy73RIZC$G&A*hG*ikHIgt*L+!*r}Eu}8I= z=f@{kn;`+kfxHzrc2l?;5W#gS7P`M8ePhd{?5ij7vwS#zTGaCWG$;9Ob^7%ac0`m# z@D?;o7p&Knzj?lQrJJ_i8xI_~ke_-*Ts?Q@jB2Sxe#5w9!NmHDe+6G6O5sPpWQG^`kvdr*uOkP1=aX{toy^dG*1^RsR?`HZorYv3) zm7s8Slsk9!hN{L<@w6RJ9U^@U-%3y@iVbob4^S@zK&>&BfxeTDZZMb?R^xooo+0$k z2Q>r7<}27NybtoBh_8$3!-eG;El4DM0I(xsJDTC%4D_)4M7+kd?0T+O%3X%FD}BQ~T^)vSiw@zQr@V(fXFP-p_8!n^e2o z2*Dbv2lG@SJyj*g#x}*e*GF6 z8TtS4bS7Xur|TOJNw%nHp-79p$WqBNq*AGdY>_2pB3rUAMb`GH7;DxrA&FA9v}a2c zqEHG+lI*+x=P`5s=bGy}=gf@y{l4G#eU|&apZmG{{KxiuuR|aNrlzJWEE6k3>W`}* zjO|+=9c(dRfTR`7SiDni#rHQ{aWXe2=LtY+HZJ20E^4riV3j~CqEx|Wurwyqs<3AB zgUduWL6KmImjC>I&hF^s)vJe0oJ!oGL(idRX3l;40WgQ{syI_HWH{q^3JL@kb_KM;#L8;JU}6f5RTt+(E(r2% z8=8n}h3OlrXs$lvmY#?yE~nhQbbE^T8tLyKujudC@jjGqN{gG}k$4LIlOQen29Gvc zbs`Y>mZvws@MmYx3fRuujA;6Otv)Ko=~v#0B{f3`q|ZPMJlGQ-6x)3MoL{>Nx78Zh zN520xYtA{K3{Zx0;;B^Aj*czj`p)+D9yV>-zCn%LR*su6VNIp8ll{g?QA_9BfBpW# zZmm{;#^%fC&$pA_1(bX(WogtGO3N4b;@j^O0Q2b4K)U5&6DDl8Xx6PqkIUP2Q({I( zP@EUNUYK3=j-J?AX^?5HTJC_Gw{Eq@Jud)1*Dp@QF2RC{b6dXdgK`im6kY~SMuUWO zrJ!KM>eXrYhj}n8BfpBXG8(+aGQe$}_J(Kw!g3JAmvg>OqKTH0lM2?LbLUMrCb+oJ zRW+9W9vbImESDRwgnJl#kte!7>q*d$U&oFg5AN&k894O$;NAI~)~;>9dAmA?ww>m) z)ZzpSs2k8*uaY|0as&2|=;y&&mSY zgz^Ozu&wk7<@L+;)z74H;-o=9`3f1#UT$l?WI%Dn3tD4oyNvI-l^L3J2ddt;Z!?dg z;>FE9>vQYeLfZOT=y21VD;6f<|FG=2-p9^0Bb}U#JgzeK}r z<5S7vy)?c_Nl8SErr>w8I8aVbP9AHgcz*U95)vAE_0pO8tuZm-;2#nxb)TlE7Ml$j z-+6U@)w_Dsg#b2B=v67OyRk83-P5NtB7dH{8K4@r3cG2VZNqmib9(zWKQK!04%(^; zJ{29po*=D>7#J9SNCflZGK}WU6MPqj_u02|g0J`ArQZg<4Ed<4fOW|seoCF+g_vP; z^3>GT8xf563|ev8DXWFCv9Xr6cJlcaXVU03x&2k)pC;=3+V;y@?fiMaQC+t#Yt%OA z$*-L0(s9cSt$1yLF#Ns?v}e)Jc(f8RzGK`@k&y+>FV`A7rWPb_TW~xk?sqlafcm3# zQ2iG_;383&N;II>;8?r;2tRw)Op0MJfz15;mUGSA4yx3>eY?=GOP5Ma`32M^xkv8V z6SiwtJGI`kAKst!eJU*<#Y}d_Cf_90Psd0$BztM?FeCxgpBHgwHPdQhZVs!Hyh4ZO zI8Wd)cD{_?E*mGMV@5F6KmoC?$i)gL7HZMP-CQ$8k7`;l(a^>B&bam@6KzRaaldq(}#hfpQ`UAcO77%d40%IN$r8V`3EdRH`-f#zK; zcb#k#G+45M$lPqj&DqMu1_xy6pYM-k$=G z{JLOREU+S>tufp$6rFj^*sW1f>p(g13>6vyVfk6N)g`fb$Ism@b(27j46aiYD89n$OF7T4@o z{;_52$Hf~4lfb3jMdVvU=BAly|#)g-IDn=U-lE>PdYj_pSEBWDSt9gQ@mW3KIHtBM5{if=%?v`5A%~$TEW_2<#akf6u z^y1Q%-zapRyng-oX&q_f1MC8uR9fGJ3fV|UDjUbUcJH1^U$lV3X}@`AV`NMPs;l_`P-@bh-%g|Yj zqktiZ@#}q{MJNZ@pQj)KCpo~o)i#q)lyi4{+ee$r;w|e9DN!Mf@e-3iDFEcQZQIm- z95KE>_pDvdQ)y`(_(>cdpQIs_iZ80GRc~u46pqHK{nt&UC&Z(a(t6$H{yq~=xi>%{ zUwq_3ZmG(idCkT|5RL(kBV%G7y;}3UemRrpU%M)LIBkcbkyVn!gg;+e;%Ovl$;L} zAgv;$JkfPIAynuClohJQ)e!;BvL6qQQqHz?xMkDWBWhSbh{@ z>HD>dHN^0<4uKiaS+IN9PFWgeyHCy=M|wE*z`;X@RB_3j3c+*U z)L40CrNPJL+}_>cZ_wstQOei<#}7!?C^jg>T@RdQ#rrb;z1b52X`S_{$&)7i7Lc$= zkD+SSZ+^gKKJQ9A0KI0Lsa>k?VK6&-+cu*Fr!qn)=Wnl~OLo)g91yfQ5MPbK{SSfE z&hRMQdsfg%sY9fdMoI+;CP$qL&J=n2W~b%Uv3ZUTv5gQh@)=Y0xf_@C$n zoLdf%#C8NHQrKeoBBJ&u&z?2b)ZA|V`pO0PsUhE9^=+e}VKsGX2igTBdKzm_R*^j&#{c8{LIeFld~(|Fe)l)6px?#7aJQ}&Jfp(6}A>f z5)xXO?QY3m8lg6Oy6s$X<2#>*jFbE#?MQ5_wa#^LeCy6$GZuKF!6DWuC=k5WVp=sj zd`1u-5WG;v2L%k)H`xVH%{Z$H{vR#-_B~@((}bKNcFt0;8g}lirkMqokD$)N(lQgm zf6#>Xp$jHarFWtuXkjRVfPv>P8j&T+`ShKJZ%PO7rL$05a=t)bFkkTPgnl*el3S?c104qjrAsXScg?`Kb+ z%4VX-j48}|c-Tx1ucmTZ&B{SRL)d);3Os!F?Czia(ypXyqsR(Yd?;PlZG>uXmo;*4KV_N-1#7*$bhmIW!*}96usno6C;W)SL2o%2Dph=95 z9{zBm*7p8ht4Er=0ue{-89+#+iMzUrAL3%Bm9qG`f8`4PH*AG9#V_X^h|_tdjpUq)gHHeDOKkB6_OJL%m{>EwG;b z#gkmopcY;piLSzWt1s%%t{@s^7RQx_Z$G6qj|+!3)Y42@o$2PAQc}{+->X4{odzCU=9}6u78N%^M&W~Uk~SA(IMt5MVRXU zliRIucV^YQ(Gw>I^N<#ndA(F{YNFa#z3&wwT|3?}O;;~GncSyOUyDcp)bZ2#@^FKca$EU1a(W@Mj}`47R>c{g>}v^_pmU_HBX7R+brOvxEm~{z+f3i<_33ju=Z-RcL#)-Tzy4~>Yx^fIF0#C}dOweL9mCt}5jnHZ zZSqav>$crFjgFI8tdD$&`)fmDwXa%)A^+)KIx1p~!G6(A?6IV=*njx&Mq#zNb3QB1 z{@O=@m;u_)){}0;J|b&4buNh3lKW;|J8Ggvv0(uyNp=Bqx?U>QwHuMiV(^Ah?@)MP z9H?_jCh^&#-4mz?sBCO{W)09$fi2m;fB#V1CX_q|*xtu>mOcm!ubq|gmu;1 zt>0K`p>SNI>Q<8*w>se0vU6&AES8c%P5c8DWb#rZ9;7j&zfe`FcKVL92fUQg?Zjdf4sd^2;OPnq>>U)*xrc{GZzMw9TW8e_ zH7?Q?K%OKDmH^SKo;V#wjf3^)+{I(7s;WZ$H#JBoIzda;9t7KGsZ(Fwr1YA1<6K?s zT>8slB#!5A@T`?DPx8U?Z#a$$GJMUDb#tG}iZ-JKh4XhnNihRMm2X|>bpG7A>3l#M zanrP4KUXnY*op~j@8YvV%%rHX3DpqUM*5t^s1vp!5DLI>~IpSZwr(xhFnvq>+Cp*c~Xm+!nYKFqzR zxw)o7kk*Y`w@x`tIB9;Mei2Qor1Qxp6RZ{PF85Gs-|ul9T`vLwci>pmcp5c}A865R zsAg!r0|B}JEMj$d+_7UT4G#RZN@#Y9_ggICEHs%P-{G+aHIqP#g?SPLGsihJA||80*Qymx@l>NcOwL<|MPAzH<54p+UyR7Jnt zd8x-laiN)jLPpHG?{{`bS<|4tDFz1`>U0aP_> z&YVh&cSgCm=sPUwti^0*Q(&X-JkABazN+5kAbZG_{kQxb%_*{Ae3v$F_(SD@;)o&Q zn~u~c1Ufaj#{%G+2$W70@VICLY>|AO37H3TP!3TGgr~1~=Je^8Z_?e}+WOILb5^5D zPWz^P{QljK{UqwL&zAAta^Mc(y$J?@8ZSnznvbLkm$B(M^@@g_3S<+B+*!AF#FyT| z;o&(*yc;O2Vf|P8P*JMdwL@WY+3RCIRTL6V^F4d^*uA-tnR)PTd3m_VFQC|9vw$0j@On-;KlWhc z*Pa@osS0M!4@8AcFXzc{Fb|9%E)7MtGaa(p(JfzkjiyBx9WW?;s#gEiptAGoQ;?`+ zV`*dmGV936KM0TdnS*`9R}b0OOTW99t+82Kt-fY2!i-iH6_fT6wxKrJwD@&zljcsk znqIaRJV+Wr^u^R9rlkQSgLG1kO2>RJ9_dhF)>ajW#!a#%qu=WjsA1jkB&E9Or@NU zbS}=#Or)3dYVh{RKJ@QMaR6GH(BdxKx3c|Fo6h~u1qP1TZDPPsS|*qS)G5>IELO@a zE1NbWdk~76fCi1O1hjK=D|NP?ey7BeA0j|Q>(&7@j2zGY?UzFWJ1eRGJKvBt@zN8g zPi}jb#~ZEw{GcH{2km7k-ziN%##F#18hsDM{U9dB*P*fzutqr?{RY z>wziUhz3Fz4biM|quK)i6S5_=P>9Cg>+818e)G-PG~sYK*kNH|xBe5ObEE_u=BV!; zkPYf0{%Q);cOfecUtN8P&F&pm&VmiFybaH`ZI)Ai2ik}Q*rg(QLpphVLoW`HNXXn)vd zZd2Q_wA709C$nbHjvD*IvE)u}+!!v=fn&#-O`Q0^{QNS+b*cxf=k%^`NmM~q)rn-| zH)LPxtBc{K7Kqb)tB&vlAdDj$G=2FA2C;hi?-=2rTi4>za{c3P{bGVXXR46{(AOhJK5Dc?vw$)^< zcZzx2X-kaw5VvmM9)`;4%yNMgAvDUlya?ukT6{F_+0(F5Po%o7b#=oDa~e4|QH5h$ z7hIfxVTpa@{eDvpl2HZCs;ozOxt1%a8kle-Vj4lMRxR`NgSnLd{TEr!m`Y;nL}lX9 z6NuhoqS?}ZGiP;wHOKPmwQC_Krad%ki%@CEGNX9)ezjwu{p6-!?VU}$v6(R{52`rl z9RpObtKFad|K-2>dM>#!_MoFn`D2x0Zi@f-4yIW zI1;}h9VK<+frI#W#0F#pC1Rx{>VNR@g?_@WBkrMWZ3*QA-T#Mv#PKkaxhBXP;3CkI448G4{6{*Fzy;V1c%&R`kI! za9uNWX;>&tbE!9E-DJhzN0D_5SoB=vB|b&n5)v1jI9z8mtZN&Q>Z0ecwkRPy$}0JJ zM;?0YQS9QlL+|$4u5Ij})GwE*TG~?nO~B)g=m@+%4CO|PAel}?i>;MMQ%@u*jrrAF zSy}JksUs=}cJ125P6Aow*Bd)-aQ7wm{l|4}Lx|UWI)KG;EvP4`6xv9?v?N`f>@gDs zK(&9z+Iv1W-+MGZz9s7*B4vx~~r%{4UkmEqp0Rwhg)PV9LneDA z^OE{L;o^h&yYv%`n378G=OX4kb1|Iq5`>fDD#?FPEG)zF9v>&Z(3 z={GuP$o-96p`$Xw+Rt!4h#&BzhP~$li+B#A5wt__@N+Z+B6SyYZUO zE#AF*XTQdYgnWPOlkh6jZoQ}juK53OQmpr3=1v1h%y!Si9d~YprBCIl@f)*s)wc{X z`E}sKXh^we%j*=gHqWToM7S>r%c z#PFLc za5y`+Vh?Su-a5az{fKj>d5_enI;CU5$&YTDrfzsz(e2xv8mJ%9H;H?8Lrb-5ZxL3E`pi9OSZ6zIgtEq90NMiW1uh%Qp}e_ zhWqRj2osUJE2Q(KS>d5N@NEgu4sE1NBs7g!HcsJqkL#m(q@{i*Lu;O@2F4cbJiB36 zizv%nhwcGwtJZZ=vA>=WTTp-Y+KTz}O@N<5NzTEsj~4QsnZlE^5IM)XaOH{AuS`;k z_(;3@hGWH^c~H&5?YUIVn=~KqgaZi)f9lL;#fZu6)utB_570K+0S0Ukkp=v z$o_QcOONWGm#)@pv}q$VDVy+4keYmI7G=$U;5(zDro{Tl^HH(78c`hrQ{V0ubl(`U zR@n{vQG_+df(_T5%C&N{w`t;3b2S_m#Gqjv;%1GyI2uy?&t&B{nfhMES5QNY1r(5A zz|3Oe;A9x{lw^Udc^&nIBxIDEQUC0{|9Ih@NM9v;@9J~NXhX%?D#+c*(eV_Eg<9xX zoO*DYJV@IqW&unkjr5RmR?tdDA6o!EEv~s2{_szu+V^uF-t=i{zi)fg*;R&*=2tGA zI>+v1fPwc*dwZQ;S|d8!N!tVrwCrkP0Y*}&48#bmUQ)6#Tg z;8_eH)@xiiJ=Gj7v_?W8GRyy_)Rl1qi96Z|p3Ey?6KJw;`mE~Zd|y5oy{n_n7)HiQ zyarC5I(4tb`8cMF3aX-m^C}}JlYs;Y+&ox65C;BU6WtVc>LZXC&MDa z%(*|bFes;ubxie-5NY0vOpSyIp!L5NDfPN= z%J+QLy)FIvZlu+O4cmmmq|adguVbHH{wvW{ZiCENaWnuD6CUVo{rhZ>*2)W>UQrc( ziSUc7$qsyt+3TS{y7cIwn%cTW0|WH;ejKuyioQg5CgeHf<*c)-?Q1^8f9TkMQ*c6m z=c0cO9xUvcKd17QS$wDJfUJ98S7~Wl9f`kXvCWr=uDY%NO^ftr3+<0Iu-(v^lFvHN zvDsggwvk(}0SgH<1=(d6Gpz7k7w=2as*jU5#(wyAZ^MM}>IY|zT3<&4dGGdZ??u|3 zuMe+WIg_IB%%W=^iHIIQP5sod0Zhr~COcysbmDbKqNSw2kTwJD<;4$YEm!)7e@Xb> zCF<7QXX~vFTx|c?r>%o#hN36JBU6@|2n0tXmc#tPh2;g0!>D~7cl2(+Kb6T63Z&9k zL5>a%nV=@BU@eqUUJb0s_%hSXFa}s_{+_mmO>XizRKgRDV~ylFQv=>6-(F z1?d?X2Rv0ayv<-4PY5FJh6;q=;)50^OwrG>R;01FJ0dZe-H_cCOvZxAz8&2-7*1Dv zML!mGu`{zBN*IxaF|QMXSZF$R045+qFWj3meR_lD%|kUVYbq-O*bRdMZ`i1jFIEId z_A04ZB?6)%YRU{Yd&(OVH|O47@qBnJ8gkjjgFF`jw)68ID8~5R05AFg_`S^Tm)=#e z?xHGpvad)C85qvZ z;QI!xYW`-@iw}?tPnfWqZ<^{u<@Ktv`h~Z{go6nQo8<{YH>@WRBKcj%I7S7%z07S8 z)&t8{e`|@Ww9mzG=61gGl;zIX+z@d~VFP+KPl!Co=?pTH@ADhli5aiB8rXbgUcDOk zsG@hX!>05_Sx8W%rj1-01NkU+7z}j06}Y$C2OF<`%ZuJG4KFG)O>i1B8@(vVz5)ZE z5=0p>3v3pFY8ZxzF^do?ELC&E@+DvTBi5lVL{87x?ue3&O#4_bFrRa;XJSJTxNdVF zrm9Xh(ZsYP5C7S9wBV7J#||GhlKFJXZ%+)A7A#tn#VF9gSDznuft!F+4q~oG@OYRu zVGx*FdFZ%jH#90{=S;dxur|Dp^N*Zxgt|V7m3s@nP-?fH5SA-{PsVN+F6<+dd%PXej}2nb>=v9#`SODGF*IYC@D43Z7uaH-9Rw@Z7+J)yG<$2dfF^ga-N@|g@)i7t3z zlVxts&TuHb`}DEGpHc?a;YCJ~fhi`fbrNNG1LJ+E?;{gQxdakM4ZwOyMTcHcr@| z8>GI$@5{68@HXqPSfI}dM1&$z9I-<6jh~-?_>KQ2T&XfLh8aGd1YUu7zQUoPg@&V@ zT{?s*6!dz+k2}CHv2^uu4;^a8f+62pup%;RS((2$_6iU$1Np31451KRGq&F1TA=;R zBV9C_RKC7HKBR2fTI9UvG#xC5j&<=}uu5lIHy@?IthIahPTgJX0{aJs^(*3Ih&Ul_ zZMFSZ?9eb@*rdPz&O#i(?V?L9_0*?F1|*^(q0#Mn)ks zm~0Wr1j`%)7rWFiA0)C;*c2dqg{e(U1jlu8v09k&2WW`JHQq@UwUI$z+CaTzO%x7H zK_b8U^!z&qPed-%26Sc(0<$vG*EX6yd-hl7PotrF@Ttk+%ss`d(V>IKnQfZ%74#ux%Cy)o<+UM>o=R$l$poyP*mA$B?>9I}?^T$c_A z6df$!Vjd!*k03B8^HH26=BiL z1o&M3p#jt0=^e2{mS3VRbgWp_@9-S#Vlt?-Fy{{CZFGW=N2C84%LHphH8g1f5F=@y zE1HX_i2@7!`TMGlO|;GD@3H&Vl^hi7=}L#gh@E5$)*oh@=tX6EopEzCLQuXAusq78 zXO}Kbs48G0#L8)cs;cGi`U`G(?slK@jv(7?@uMgIhlGfLM|ZlAYeMoejc~hAH`uiG4)bA zQolf+GG0JJY9t6KlnRz*=bg3B(Q$;qeRyCjfKgm71+Ta|Ajs;3(I1@}Q&8!g?IHiz zE`c2A+r%EBoMVioEH|~Kgf(>jt9UA<2hts(SOl@4(wbqNf)^AW4H}>(5Fo)PTOAms z%ut`%?nkdx>Y0Ehzhq&7!x_$ZX#~UA0=Y8<_qxM}|GIGRk*X+karrP*&bOG4tVYmg zwS+vBI)XXbKfA_6=sK2PPA8ut;8KhE1NK89VT$L^>%F~|wLowN_P^Vi;cclim{gm? z>Gd^6kZG788UX$~nzYa7BPf^rRXwz4 zFCk`Ly=u{+Jm}j2_*9BZ4Y{8V2IV|9!!5dpMlhV}F2h3fUsAKlDB9 zKpo?Uv12NtZbH~_B^_9Pb%=4n@{wDi z;i)Y86S7jS-tsqFg`vU*l9W&O*y61+3D0Y~ zq9&bA&=q|@Q%Y~mil3o}rH1^ETeD{M5pY5{WTYpalQCxL5k%1v>|Sg{yNWFkMvjy1 z+voKL*$vu#Iy~H|@2~+I|E&Z!pY-ux(nnfauC37CJWcj~iIWBt45L4Dllx>TUKWX! zlT-fqMm_7=Q__f!0g$BW!l!mP$2*^}I6)I3qx%45P0K%+{z>158&>t3$a5rnro3ej z2JcznVuyNm(lh}1h;&9cHvk2DRAezGNH{VPP;6JOw4@giWjUBBMw^V0u62FM*1M3o zc)4lN_G5c$P2zuitvXbukK~4n7a$HTVnQkd5q|#u^;iNVRev^Afg+cdoggP%ioDd9 zTJ;^Dk`p9ZD=0A8IP2bkds={rH+Gx1g7E=56!l~7=8k2Z!jq^J#Jq_dm!DR-z(UAI z!V#0gS9z8jxI~4gP8>UCMc|-hvhQO4=If-Y1s2E)=662r?SNH{j7tLMdQeN|TlX*) zpB&FZdK|o?n#-@9_wYz`#P?&YoAZw)W5cHW5`G&8dyK}4n>IZDzi zqajPbc~gZz^y2*@BNf8Cc)CfW0|+2iN>nfCe7F`{fV~bdGXmxY8=E1-CH`H=Dbq_! z+tXvu>$|--@~ry7n|y0WIylTebMu2~UmQasBdi&x=82=0P#?VkSc)GOv_N7PEyGhk z^RyAB?{>VKNIIhcFDC^F!43acW|^~=IE?^}rSt))Jcf@LQFX{k{Wu*zpk8yPR z0`V5focNAlrLm!LbB)%wmq2byd5k_+{YeCj5-r7g9KW#0+drjLW;Xf!QpNG`#r($3 z&MpARoyT=$$(dz!Q>jFvRz<|cRW~s*JCfvtEJeh3w8WERE0L6T06pEn{3SP)4=S?} zPsB9j^)}k-Jn)xWblIW=qL|5U8JhLPoj~>ZKMF=lDOSjMh%pY0nW8Af9#^9rKkBY8;OEj_BJ zyFdd3;+rUgi42)!!F8yDg-Ouv)~zWQmYbMGyZPkRt1w`D@xLR=*PZgL>rPR@9TBIM z;S(lk0!HXpZFmACB34@p9Fd5J-z+`8No>x~7mclCQe^Lig)S5xTlyV!viYM0dRv|# za7LE|UM;Q6CK?i)XKg0$9w>vOXvSztQg$BvQw@qxtlD_L6l6h;j+R3Zmbp~1;WHD@ z&41NFTV}cMp~L+7xw4ha;%uL`T6O^ux@2ZDGU+C)o+L}2Olw>Pq&8&SuP%PM2vziJ z4gCFoq=dwv35z;%3imT)=ls`q_v~?i(7mRmStCkgaw?Ljh<2a<3ihj8Q~ zMcR)W_u-FH+gag2telf4RD3fS)6v|m(TB2FsC>o};3)>)7!V^qjuMr41ERj?jXq(9 zNB??KO3W$xNUu+mpHIE+cw?xBmwPD18j~W^y}U%ByKJTQbSAgxA*9TN29>&lJ|lyE zV-rx#RawJDPEATMzs5SMg;On>vbq3WGM+0zqYmA`$qH7N#ojDrkoP zmD=&5Tem*D?~Sb6MX|`MLX#8;avlA-|0bTu&6D(&0+98bcv?|RT=< zs8ky@5N_D8vjYOt%`zYL!Q#)`!_*O|AK0c7*tTd^M4{^V^aUg*rXPmaSVia4w^2`+i!_ zv>tNzFX5KTG6vg9f~Mg%0f=Nq^bdT>hueCbTt@F&RMPa^j#YNS2t_L7)a- zm`rRz4srFvSO`-hw%x^+xh9uLpE-Z=-jE&-9U>FUSXZZ~E8v2fzphYNL_#GUq+}GB zHxDVK(6bw^%oV{?RaGUBCi)@OQs0uP{1m(MEqqJoT$AxU!IBtlF|>IGpo7<@5?1qP z9+lQXb>p`}aUt#>-20SOx*ZL`gko>cFF98@8lnp8w<$ARQ1?y)-q z9b>oogJD()uXs!FCd=#UdX0ZSj-w*tKn1aJleMNk4{a0T<87F*V#d$vVO~zlm8yYU zN`9$$ZDohv2x#Pc@?6+0Jm_RbOs4Bls<_Xe|8x9&i$NVWTMXb|756*_{9^aO)wg8K zKyu&84=;^ITe<)ih^GQBZVaELo*U`Fw-uu!4GpF8N#bIa!)142F$Q`R06xjD@4ZfTda}TR+|)!MG-e%KCr-rqGqxyO3A-2B6 zc18evzYNJ9DoBX{=)7lq?KNZ|-&2h6Vq*=NGQl+KP53?Wlp%1_?c>1Ik!c@^a!T!? zNPnA9KwjRtGqys@pFLUpJL$MG(5KIMV*}oap9Mw}*g`D;Lx47xe}cG;k%Gau2yI&J z+NEhmMEw@_`MvT>`m>bVDq~BT9NBu@=Q5b&MTCONbhv{<$d%K^hj-6Nbk%n@=-3R@ zgYdeZnn0EW^sjm7!|$fL3qhZ6rPn$gXJG+%-bA<&vC-a2hNC0m69({Sc+#n^Igcgy?=_hbbglMf+M= z&Cp@c@&K8smmN|xzfv<%TO?m7T0mL3pT|$VG|6N1!+-{j7Vz6E`do1Y;abjyWxUm` zUyl$R6&)Mft8Xpsc)tO;&v(pyTlqc*sDzZlxIcxWj3#516QQsa$V~wU}V+z zz1Fp95jM+>5e&x6{!R2b|aj~ki=wuum~U+ z-m@YiLK%B}5}6n!qreDYHE-yuq!koA43+Z+2%Dgu1z4frvQAJ{zwP0fQ9^mmeBr@m z-PKsF-CoYabmj@~r}+;&IrUNqeV`cK6E>SMzfoqQ=?wZJIJuM&ysS^`GcvuI+=5Y< z>`OwIg2-W6+QzqEHwqMdTR9Iq6@r+aARqlZ92pg~5&5stN|T@qmzyg#lo&_Fl9-rC zgMvM+HrUZ{mq;j^1Gq?wYD@XvXMfKINUih*P{9}V;-_J5cpu#1k64=keW zm4E0vtZ~sMH zh=_QnRjbV^>PcqV=9t5JwkM4tZOJ&)l_p_bAJGo+(3){K)Y;Nv=Tt zeeO@3a4T|e1nIf8-|?nX*0Y86D7es0iqoGA;(wXSy~SZDHX;F=ehH*i9@PG@vzgRI z;yBMQ<#2h@u8`2P$#O!@L5^HDuc}8_pa%~AuTgSrdSP3#LZ$@i(kEYDed+EG#^kyzse@FaDPoaC)CmJJrrH2$L|AZoR1E0EARqixLO{QMZLr^A6hvKn`c|4> za;+dABKdA`vDINU4wGyRP)O&P?VkAPGQMwIbpvlL#8{HMmb~bC9B4p{Yv?E1XxH9m zTn;_bf#HSLcFh>DxvA|q(*arpPf9WRqY^uab9Sl>xJxJDlVl7YKDR7Qmt^C5dPY++ zj}VEVz>TS3)DlL}vWRmdor72sPA7`i-MzBPAx~XPD~NVPtWemio5_iznKT-&_cxLW zh}J`(pt3##5csj>HZAe6s>HTd)DIB6QthJyy7Xb??t=^nu3GhN=?~%6L}3fVD%L&p zVi_2Wf~LsF0?+J{czqnaiCtCqH$lmWv>Ne&O7(({pg#Oh zP0?J??OPLOIoSJCrq<;RWq3h|YmNlBM{sQ@hv=^RA;WG{rfAefS19U`4)Z4uWBl*R zH_pG%L!(D0K3b;%fG5}}U5NmrKp>`wPCkGBEN0(AMKf{&aNMKfY{QFZ7nrjPV@V|A z)gA=?+LfCYD^k#j7pX{egb`yIwWuV-{7vk%VZ;KFbXeE0vsOC-M;1^zcZj+dv@Kog zetZk$j$KK<6G$07hCa{;&za8iv|y9BZ^nypsY86&D2-T>Nf5o zj!-x{Hdgq|WP;I89~6qx*+H|UK3E3c!(Jexlh%M39-bYH&$Z)R@TFw7l=Kz*vxs$Y3qjal~urz zW`e7OXj@o}la}*-R)x-i9nRM1J7E3{7(>xgZRk4 zMR=Ngcx_XsnpIBMWl@e)(&8fkQYznrsSn@$3oAc#4=6qvibE@A*WQ75I?<+$W{lsm zU}wZ4EHo?&2KG?eLqegA=6CB48Zh$F{9}gLsn|0RxfD%M3 zCg|bFaCi|$%@`v}0d`$=)vu_L{wzQLKz#gz4x)ujZnyHrD$$w+V>014d96&3&IpKt+@^&21)0t$6MVpmI_2p0mRLzC67 zvB=&iHgP|e&3`O%punC^*{$$6ZT(2h&0tD#loKn|tFtCht^pM)k!WW39gG~U zooIgeEezxwfIHPS31bwZx0%_dLx&8&X!Mve1dtVt5MBKJ3(ISaL`sqVVkP=`*;U_fvd2@^`S6{hD%i_R))%g>9rJ!)9+IE*MyS-SsdyN>HQB z9YMANo)^l0fyJH_wIB`|#f$>899`&5u-%sxkco%VF0|9t)fHI-c_>?YVL%Uwt>P># z6bvj-(yAB=DGD$Yj|MVQrGR~?bf6Jj0|Dkmtq6qo1{J4$&k*lmpR_MZTwm#7#XXq3 z&N%O?R0oZO0Ff;cqH*LBip)vA~(Sw0=;hYBYeP3QjnC88XHj2FGAG#u<3Tz0BzYlLh7kf6=1gc#C@$QSyXeh)H) zl&3>e*XiE{dv7OlnizZ%_ZXgvyka4emAv%BljPvg(9oc+C(g(^M>J4^ z#!Ba&Gmk-do`DrnnW&@?fPd+J1emyo=Rt$|(x0~ST5vtjmJ=!m1VC0}J3{kLaiiF4 z7DPvL`GdPIr;m@M=$Lj9`AAH;`51x2VJB&GKi! z5B5n-_3xBy2o_VeP|EfXp$a)vMsF{{?2V6~w2{N^iCu{Z?o?Z}0@|mYB-8#XZ|p%) zt1xxo`;bbQPtY*t%GB5xX>{9?VHwGdsNw?%-c;TH5O>utHKR(}w$2 zgl)#FFK@FTKsSCwV|UrLP1M>J)Sv`K{kZiXD77tJv?-RY_c~Dp^Fa0It07U#PITbJ zQzS~;f)dR>ueS6cBtLg$Izm2d;iu9`Us>}-gvq>0e30jTbi>*~)PY3J5%epv;u3aw zVaLfyvs8Uu8CmH}_Icd(?w1P!+zTNt89 z_70AHTveYBZXhPA%LW4@O8*sm-J-_Kzu?)z-7!75cP_Tv_p(V>Vo#h1Lp9<_NbAae z3c76>Y!itKe6#hH2k*@hB|RNti4kXTlZ3^M(Lr%Vd-v=es)Bu${1}0) z7qTCLbq_+4i|vjmL#F3FBdriwgk}(U03_pa&zQw?uxk2k0|w(F%Vh*1DJ{J){A0M) z3Ux*_c-f=KrFaK$Ze&xU@)Akm6qp))r`UyX9MQV;#vc=5f;ijOe!YamOE!++kc+}m zFw^v!uPcx|^XKgMis5>G_SYLf*01>3`{08J$I*;N$c|Q|3)DBbW;Fw+5o0iM3$UGW z1R*%zG1+3)0aGqCuOYktcsI8TBR#tl!QyjAPI9Qf$-L-}6GV5zMT&j3On6Oy?j4jN z0S2Ae#g!i%vf~iGsLFB2HH%kh7gO%PDDlYesWbm)huQ?m7E*kz)tjb^JXAJr{PqXl z)xS9zUFc82%s@@pBi;~dL8xLVX(JWKv_IiAWk6pc0P0Gtp_P*f>OAo1LGIUe^yWS0Qj;MM=R+H#(PDIB7eev+sY`AsPc zCoM0dM$MwyLU9_->K+-X(P(6thT)`cj1YpK6%a%3f!@EFEGq2Kh|Pzr;Y&-@ojB{(10D`d$)c z0|jQkT8rV=v17B3Uj`k}g{2k;1&B%cbNOk1AlpT$$FtMWkL_T&xGS$r1)Z+)#>`e< zcX2Hm+&aBpwljbYn){4g-i4W^H|4{mRzZ)%*so}5cHhl6@;&#eXS;#EtMRJ!*d7_H zKFS4$BI2*@Lou4cb(&hi-N&a_VcLzWSF2Cen%TlUa4w`_7rQtKq$ncrk^A21Y-J&B z0*4}yx(V&DKs_QBe)^4eXO%nN}a+0s`atr0&CK-vG<$?(b&kqKywX_K;vK95Gs?)N0njb}KZM%yM+in5{kiAFgHO}*FPjtKK zyX&`QI-8%IPmFr!H2utj7o0qHqbZ8*=A$+ zQ8OWs_JxZ14ka(i9ZBvj@30BUjKAO)E(||8 zbJ?&=@d$LItSzMoW7SShK1 z!0DfIwzuzelK{R=EapL%=(exa5oLrM2USzu?ghLSytk56hf%mu!-ve;o7r43?u4u& zs#(kmSx?RXM28vj@ba!BJHFrY9;6i!JfJ@kLYOj07g?|Zz(80QOQe#&!Xe>0&J9`k zdk6btqn=Mjm>l<77l-zNoqu`Q6FIDlAPI?ckyt$djXj|bReV#@C*{l-=OEeJC1V>Y zWvffQs^U@=a?$mk0_w&W$$;O;?=j4|MYv7Gv5%TbsN(#F-FSm)wFc2?f(gpl83kJK zD%GOt7NDeRi@j^Kc10YV-;(Zx)5YfPGd4Bz?HgG;6@BRakPW=-K~z+bGYIIreU`s* zV@<@#GRUUbxbfN?4>4Gl&Ukl?=gG@NY~ku*Sd40Bg~jSN)_ru~R$JXRj$at+ns@x! z@WN*$!$*{LuE7_Y%*}AO9$TGcjG84seKRJ&(FPO``^nWZK-$9B5h4!2Wui&Xo{k4= z`>f5Ql;-yeG>nXDEi4cu!7$6VyOvH)-jI67pkmgZsDu4kYS!)c!th6jK?-Cv&DsWs zBT0-LB~$;ei-YeKk6-@>5A4FyS}S%u1QdTdk2b8FXWgbpVYFegpb(UYqOgW3QKToB zXKGg%nIQ|1SdB7{Sxa!DsIS9x=ao$dV#|BWZSU92snuz^(JHDBHLR5MNIB-*UE*Bmo`Qje z^>tI-sW^{m%H#wwQx*eranfZcI0)kTJ(FBrMXic)Ma`%#TO(9V9NzlPse3mIcy-?6 zUPexrN=sJEzrh7FoaF41VrOsjb>84!EKHC(3GC2i6E{Mb05KMx_`|KR_UzV$N4rs2 zs2O*;dZ~GUwTe~1(+|q@F%rV*B4kLh9(7F6l#_>Reu8GWruhvZPDu2@l-KoOtwTs` zl}+KoBu0LNuSXiP^P=Xc?vw<~t%Va5`VXvwE2*!iw|{4QuL-ZGjkq;dN0dX1_MFXm zwsYkP!OSdBY(ojSyp>^pLW}?K_q%jR3M6 z?#^dH-#>PPJ19s7gE5j}ApXHRXbAhkd9mtks!ov+? z{(5BWhYFx2yQDp_^V6igH>*S7CoL-_D%3{=5zqrx*JDjb1^|U+N0%xk3LWItWdP2i zt&vwE5;lsmr(YxcZQE^_l%f-Sytw+s3w+?(QC&i(MWqgY6cuo)TgA&Y+*iZ_n%;8; zvVjGO@x0s|37=HBQ4P-}W;KacDPm`g^4?Ob!mp{ZwW9X*dPmhPQhi)o;H%J*dQbz2 zzv1!H0-AO9{Md!L&>;cz2`TYK*Pu-i2#%_#nfL=>{Q||nQ^}r7SQ+lul|e^zuf`t! zYfm1TZ$*J@_b|V(IyySq$g)>dZk*AQ#qU0Tw5627lNlCqB4KQQj z<03n=0ATj)W6Ze2*UUS6nK^hP%LsN>ojpGdk0&fo^c!j+4QB*Zbn@}nFPuN`kTj)p z&K%TBGTtwqQn~{*Am%kygOfFUjwK}I;Z;RKYJ>-HBUBCXLz=8T8QN%!<-U)(+B5++ zuh)Pd@)Qkg7c;O=_2IVIS1piR0nEZ*)D6~(4rt+Me73$QiMkUvq3~l`tglb!DHkmG zt|Xc(Ul5uZRkwFL-%;$H>$~+uY>2-rZHLzf4?efVDE7~@;XY@Ah<91A@UJ;E{;imp zMScEK+V9znk-e8xo!yeO+Q|ExlS{F!eT}M6MtB5JeFH0@#Va}C%x<~$w2sT{e~n(n zdtYNP{nt0U|KFp*JjKX|KfS{vrcU$x+2dER;o>D<_K)gXw`p_XuoI?^hwtX)H9<7v zdVmE)ve^O36xbu!?yrMAvZwouB<^a@yGR2bY5Q9C5}dz^I`;CokbcNpP}h>LgM$h{ z$t0$slzN*!Yoy+If+Ltsa0*uNl)3^C!YM`Dh9I;fD-S%e{ytU;9CV(&gmLVcfg?WW z){GfHK9~5N2zD*#FpzeC!l#tYW;U7%Z{TO1pdD0h*)j)06P);6?l-FyYnTKO;2$JH zB-eb%lov|lQ5E6c(1~0jVg{OrdvS(MVcRJ>hb>re%I*s%R!mhNUuS7VdhBZ&(`*W( zQG9gd5tm1wzxp>@*fIg~!r#ANEV?ArHQ6%h%Rs!}uOFX8qgHb1CQZ1E9cfXRiYF@{ zIeFrw@=q~z1@c*?udVjGhrJtE2~8KsorsuTKhjY-`k*V?vn4D))e)uU>CIt!C+gpK zk}4GUc>W2oQ=|=qkL>^K376R4n!p|8DCa?4zrlT4Moo@7$IKp=YRDTANCe|!N>wzQ88$t6(6qvncF{!3-(d4oqm&6)h z5Ux*0EF$F^oZU2w~Ref?UnhY6- zn5@(_&AWpj0ZsMH zEZgsC>A}%V>5blCX!o)OG`P{gR1;C@i!KV+&o?W3-{IK*>-nVEMpIKpI^@wY!zy)r zmo7(DM&{gmz%P&Nw-6<^SfLWwWcx3?h~#fZ;rx%UR-ob+_(jI{xaLr~GA0{*4_#!a zG;-zXYbEi@nhzdMtJBlgy?yxa)C)ft;pfNRUbWOe2wmU%g74ldViy zPm)&r`n9Ff0vVCc8JfYJ1$!>QKJ$5_u}@T^k|5g-hhjWl1E=5lff}zz32;BL>h16$ zYwDTY~7K;6mp_dCZN&pea3N~HlUG@Po4Rsj&Cf>X{I%BfvTi5 z&jOOb!h9_kB57wAc4D-aJ@O2&go)wT{}FX2U^(Y)`_B@|mQ;2vDy^36yRx-dl2VzZ zq9MzaY&9jMP+2P7#mJIG3#Eu6A)89M5|^&lq*zzu)(J zE$4Nf=LLy<4NL>PEla5j;f0ysEBP`SribYDvEyqEAm>&PkUehD9s6|uj4Lk{daBY);o&li@;saGH?)0iZE>9 ze!w^`#w+FC&HO4+l*v#2<3ZjUv^GNR17iq{jdjS#Y} zdZqXn{`&c?HyW!Ac_Ouk51`jAh&RT(FZ$Q0&lSVW(ldhYHztr@CY} zxbH2T+I~Ra?Bes=T=p97-26}$y(KL;O?vzG zTj!sA^Vha*7-Z|ox=}=Thw|fP~FQkyP*xoAJW#j(+hF;lU4mGPl2mJg_ zb`QXvI*1Km5vv)TVw@7n>+r7VmLT8-}y%bJ{eS#uatJ9yO@q}EMu zrQDNY-*ZzsXE6}A0cfXwR z?U+6E^e8S-x?CYia3Nbk_moA5l-~V3&M5It-+%ZZ<{F{xiUs^TW#^$X>eI9$i z{&IM28Dot|yB{+I8r}5_^2Kk8Hz;5}8utn8@SBl4-?-}$YGW$T$CKXJrVv7s0W_&x zh1vu>)}nRmI-)n7EX3k3dMT0RV;JCxrViYxFWua1kZ?HWHfj4hFqq|CkET`4Tl!ob z`Yl$Dt+mhp7+<`@V8DRf4MDNMiaHKCxw+#QfJ1R%4)TUu>#9LMN&Hs z-o=sLLG$GqJ1q6?-Miwu%XAtVbe+G?v?Qx=RZSDe)PN3OrnES}N5`dywyd@7&Mi8} z#%pkzQ+4JUgE5!%Ad4x1BwFjL6e4+)(@C7i)_6jAvnJ=xo%@Q5fS9b>U;Q^r&pfeo z>d=Qh6t*qew0Qy`VYTOPejej4lTRyQq7VqPj~>O2atp;2S{xP>r1u%DU=a?E_I2Z4P;Gp1WzZ(ej9cQP9R+MR-wZ3}97rZkUuUH{BnambPxpsuJ7Jg7s zBcdyzH}kpS#cafgom90ftz+_K?Eeb(tntKR)=nL|F%+(sEIkA4DQegrxhE^@3a43a15dV= zSRi>vCImG#dQI!;_c9D#R$sjujW${&mol^hwjBbf7qRwtIKd>wFrGvXu#;l+s{u?u z=q=SBxF(JEa36fc84&Yq>*Iq}Tk6A(BvkZzKLhfbCGNK1GC%>UTPP-OE+4sI-n^?R zb{&Q;eRhUc2mB1~>t-rOe?*d)%9nxBmKDs!0#>3X1?VNp7b>sO`s(jXd+tWTynFuL zgmf!TIWmoHjF%#q9mM}O_#oepV><6)N-|M)4_(P#H;a_eZ=dyKb_ zPbnswLUqay_f(Gv;}fZO>}b^LSSMUHMm$W$+6{D{M<>&;8;5?Qm+J5+k7WrdTxz(M zQG!A-FF?QlZYA(B%%n2lf(r~8WpDr<=uN|pN8c`5%!UiuDShhIHy0b^i&3a`io*$E zy_O6gG$;W*g!p}UcH*>a{?l!aMJHkMq@>6mQ2LPkre3%(0r&Cddr#ygyuGYcI95-W zg~Z11V<#H6k8RvC;V-%}@gID?a`}oC9ma0vhBpTpI*M_?oxlm3I*$YQB0@Z2|1A^S zjXdDSGQ%XoUnb#g#|DZ64-;Wb>4>b6lA%;o#AL*FRZo5WvDWI*CkGS8ou7sH(7BKg z(2U7CKKXK#t03fSLYK>>^m&jc>y_%%?QHbbfuP~eXv9{OS?Q}w%R_wN{5i!RRaRbk zk{~6kSlN0iPEgDl>Y(-VVq1P&=w5}HhrTG~C+{A4nF{tRLo_?{ zzQ&~Gp)l*Va*U1ye)ye>f03%u_0giK@2ekvnmv|{iU3#PFL!yd6la}P|mvs+a2z^Pv>nTz}^d5 z{C4_(VZX{b-AOo)XJ!A=r%wiGIjrNGX!LJYW-`U4Q|+2~K4>-{g&{~jZ2h3$LPRqI z5lARQd|Ztji&=%irOe^hG=AhVj&y?#fNBzQufCm82j|oB-ZpD0F z@SE4>mp~tB!bH7GE?v)6_Gj4X(4_z?C_yX$F5u^qbqw|OWv`<^@a)uqn?-V6OT&E_ zh>}M?i7#N1d&nex7gQe$_!mmxVB`7Fc8LvCJ5TsE8;ZS13=`38$IVzjR?m~nfqFyb zZSJD`@f_*dK0dB*JdR8>6gxo8UR!juq7=(#2`T`uPN7-`ICMQ}Ommnv`Aqb={<6lN zsp0`!yLcZ?T`w;_f~*l>V&`nV?FjBe-A9F7LPjEp1A9h7<_?ucv-fBBMRVs~WLD&J z?onm*6Z2#SG%kQ|4+0KdWR^&wcWy zE0Apqze>%1#$w-Z-|osx$XB7lk!t;zWw@bt_h0h+@Dj*{*EeSp{&=4nq z%cdIY9#|SyM5E+5*L*9un#p(iOK>)1@3I0y19R8)C2lzLG={s0LXxVF9g@A~tG<(vOu%p#A|Nd@RF=BLtquCnL2vyx`lOI!l zAtEmP=Y{E`WoY|h(wgSBy}fr_OR~uKiU-x{p+F!0D6*MxQ=dNz+Ue)04s+E}ZT%-o zz&{d0;3?UzJWU;r%qtW@ zHET^H2uNHO)kOhF=cot7Ym{bN4wq`Vxay+sLERuiw!UcQ`R+Ehwz6SYF~7&@)+C|u z*x{Qx4{-Qm*00}I1~^T|H6L6DAuE<2qNg4q{X>uO8$ICNXx@uwFD!x5^y5sUd28#Q zKHZv_c+fAgpzUOZ#dhJaYrSk!|IYx|k)wOQJtu#>d-oJz28slHCl#j4Z{>K~Asm(F zuzF=fH$WQm&mq0kBeo4()R`ZE+ku360(AJX8roiwpF?_iO4X;8_`FI}U%dsLREN#2 zy|a%TVRRkONIq$jFJ;}*>eGWM_r5Z|7r>i-M;e^No@01yVL$y?kLzvG3Dlz3w2p?_ zAI%fp;_$gZA?TI84d%3Sz;4^3%F0;e{kV5u1U3L7R90>F3Elfd#@yF#+^8iEI2FDb zB=Y*|Z6%{k6zW~N*fF`J&$F_z@lKnXEbd$0pu)wU&sHD*ywh5r1hp1I+g~gPL_~g` zCtg}!ZaTFjovvu92k{(obtos{9ip*pPGw5rw`&u$-(xmWkCH~{T9hlJ=^IT8%uPlH zGbI?A*EF#vI$`sw4gs@b2kdh&1}~`dAE2yJHpN-FEV3+$va3CYiw$pn?%n%mbk8Pn z>sW+^mCVh<11P}l--kFXYMhOx0m~Ed0*;PY0FmjBKQeWO!#@su-1-n;*~nktmxzfi zeIXTLNLKl3_-5NEp*^WVmcStUZ8}z}g6LRRL2RhUBOf)n-lAS^Kn|BUn z?M{GvDNA|O)QuaXoy-pubW_s2xpw$(&DrahaU-zh+4hd4fR`8VqqgsLeCZysD zn%(5dnelmX589PsVwU%rGPIJ9I7t0UkoL9f*Qe#Jciev{YybY~2OsB;)eB56t@0H^ zgBi#FFz-6d=~~P*m z^szTkmnJt-J$2xkJ5(wyR8%~;wgU44!!t%c3&Nkz+8)x)I7%!QTn#$Wjo8C(gi!bu ze6~Sk@akv(RQ0c~*v~0#x5E$QVvqm8<76D{bhjW-$nm*uSornH4%Pg3Y>QW=^AVU1 zawvm$lVgrv<0wJ(M~>VDQiQxAykXJ8g`!74#a+js2(vE2(o30LESVP14C)%N=sQ#x*KAIzw*hQiHUrdL$PaKF&8?k@T67o9jg-` z7pI?i1wbX^#`5wbwlof(CP#GQJL$S5Gjwx>4Wz59i&1kTJ&@u>sB^a))Fb41E}_lj z$xkSnY4PD^Ac4Ao>cFYdUBdL3MvK8qHsC(d6H~e zz;~sIMxLtTJbCZSlO4WF!c|agI6q1sbSlgt4Q|A^Fn6?e+!2IZQ)@gDfVOrHU8YCT z$nHHlC;9~Fmmauvps>32;h&)l5Bm-pbdg0(ZU!4NLW#yg!DI3B)y-Syern(#kVO&# zvcf`-{eZeCYDuJN?VD|ZNb(h=!iWDw<&u)maDa4VpsTxvcgKw2r+@P!P?Vmdb*Bmg zxn2*Q{2Cpl$=?;M#b7;m1!U_9c)tUkTLrG*^v%DKs$0+J~t6(zXPh%D;pNz+h>9{d~p&JU1S z&$?Q=*8Xau5tj>6)BD!t4>QMNpuPO{JrvN%_CM{b+89j^vv)~Np}ypep<+*|+7cyQ z7L2(j@5s%Z5QkU64ry@|flDauuw1zdn%eHbU2b@zg00@K2$Y@rdNt*0)=+)g4)ZJ7 z+H;Ri$kSJ^CQ@seoEib+*$cEQ)T1X#4cG)uMy^z5t=U3L*`Pc~-Me@#&4yLl^Fi0N z6KqqU(&f-4ajF^Yj5gegWAr74dSzg&)uwZQK#q=~qFK1`puLxanHK1k{A&z<9*qE2 zrv2tQbMEC}7A~d*_3K(Tdp%DCO}JywZ@*?f;f;ju@Q%_lPv2L~=C4a^#`;$br1)6rdNx(FQ?a=t( zby^yz{r_M}zT!xCqlL&W`Ub3x36q!&qB?bjTF=M*Gx@$l#&mNCk0UpXUpL&(IdPud zA3t{Q*kL;Om{S;a%E4TwzX}Eu5V{|i-|x+XAp$`0O`|T~ z3hsvS<#!{PLAn=$@E^$OqJXZ;3X$hwdy`=i)ojJ`0jn$i)eQ~~xo^qhgQJ}c9 z=2JT1X_Nf1AO#q16IYdb8eseyjA#?rH!qo734cQxemta13KA*gCnpYWWMA9;>#@H5 z`b{(1k?(P~v8>+~pq?`uh&&if#`p5?GpW1RIt`eA;|%C3t^~qkuc*)pwdPwO?Q$+% zISU;;`ACHEefp$+tdBFm83QIGG@5RE9u9wdFV|E$b4-oz9FL3}a>xjbIP}>9T2+Ji zb1|iJ@D&KBL#HY|2Lzh2s7rMR_bYiFqKe_e&Ym+TjCx+Jk7@b8gA4 z|IcpZCo~q>hYz2*diC9*qfsPhIIgq88Z7{`2QHex0s%y|=c=mQ)9PrqE%m=*r-oWP zbo1tlAWfBf91R#VI$&F@Gs=cg?cXy89gY}n5&tPKAAz9w9@L)+=wv(5`it5>E@d7I z7ha-0Cv>)kB`fwx!JqHW^$j|bu7QdR+DIwJCTDozcJz`NpGHoTp&0bxIEHnJFmmlV zv_vkKM|EISm9#~C1(?=ta1*Grb^q|`lV-DzJNo5rWM8;rjvma{9DYGEnSDm zE^)$KG69lMP9bF8s=i=LFtD<;&{%k^X3}BwF@wJPGK6qB%Jf!d^)t~M!2T=ZSOwCA z^LkQ%+{ndoLBlBegm%mW$Cg$xi*Mpa_kauseqzdpapO zEzO3${{6dmYGHf4@v;a9jA8uYq*cxs!<+CZejXs3d|d`0bJ<{toaBJjOM)9>1?6GG zhS9!8wX$fUnw8>UEKIyET~22Yv}7hlRnY!HbHIqp44tBFQ>3AVZ1*0byA;%9#jvEL zM_ul2PAB-cW^Thxj{H%uFXYV2Z-(0aSq$63_741m!Vae`m#Ndf!TFA{)~5gvHJr)f zbT`;(4rvWF|O<9D#qkT*3(D4tNOgjiQ=+m=H7cvy`-D)Hl|4l%TcqBHopyLl;rg3 zg0k1UN3GmSx}A?);3tle9v-8q{-_}|wKhC|`SJ+&7qwFnK+*g9`n|kZ%^C6FBf0Z$ z78MtV|7%D5L-Eh9f~8K#gcz-lv#^i_8b^UykRK{s(;hG6g*NZMzF~gmZ%rVk8 zGi$}LQXCkts@i*F1$G_dxPIa+0xtp5%GGoyQz_&aOjd1g)LFBPD|;T|%OtRnuXMBE z%Tpp68#3(>RT`3h0oOYrY?Gx}H*Y#U{{(?ajCPsSUBC-Qln3C3%z zN@-H!mE93pIC~ypgHJ2TTXnr;js?-m4&a*}7Z%*(O$U3jVUi!;g#B_wwmYNvPUB1z zmDEZm?MqJ?pZDr3CSf$~jKCrQ>cok^o=5+6~bVLsyj;5=pUK7R<@CB4=j=fc< zxZBP9#ic-V@YQcps1N)Vh$ImZemrPwub#kJt6tu%rp)b0iFBs2GUtZQXNnJ*doX}F z_}lTxn%|9^(G7S$#HL|Z$g7PzKt~&)W~*>I=cJ^i)&*y~nwU<(%@iE;C|#8XKgItGx0jJ`~YI(dsyudR_^+a^tq z+c$5%{r81DsZ;=Zk{2#vNE$#ltZfktw4E1cgj2+mQ75A#8Ed z2x0ID80DJhrz<9u;R)>Gc0FjFHuQ=_GP(!BT$z(G2k6J-9WM^!kEN@S)g!Tl2GAU4 z(KR`4S%iEwOJrOIR_{51anJdN#eK>g)yf54YU|Z1}6ckAp#jzYJKVJ5|-xHoSQ& ziouW5vxT4oI09w#*}^OXhV9K<8||hkEzT?Het5;FSIn5gWpA2vW7MzjJq{k0>mvnGWT{{A*QMAc&k3kAfhC z`dLQ@<0!r!n}~M!AK)g*Lw`st@PX7`hq>`An+rSZa!H82ZbL$8~foxeUf4R113HK9n92-2Q6QgMHAVX$z-v?>A z41*cVwc%GyPv4ipxXHG2!GbrQDc2bO$qZDk+oVZ#q$tkU>DQN;6$KCQt?HT$1*a*^ zpmO$NjsQQwHo>M*`lPOgG1dVTLd;?GF^Gv6YsG~8Di#Ok#9#;EyD{*t-*#h#R;DcnrhTMqEv z#Twz{$!|OE%+W0Fv*=!b%){*l#0- zsd1AV_9SM%jk-OeFlB8l4%c_%3OgxuE5kuQ+)8L_}m{4ftyfH3KB2>v@Z3 zbB@QM1J_VLCjA*1NCuA}EW&}J4yU)q5v~6I{YIL;J8742UgA*%AvQk1fd=p>EQ|H6 zvzbP;cOLqrTj$PY&a+l3YkW+Y)qBbzt8q3IM=d4~Wl+vdVo%UzIM~}qts03Std{>e zZtNTr4T@ccc`?z^_L;R)|KFXBY}h{k$Qf9G&T0b~-6rrj^%S<@drG*Z*~gA$W={HZ zuJIJV$=$8ZZJL6;o}~{B{Pe;pZSB2!|F{%UOaR$fAqS!VolERAs4%^j{i-t~LPca|gU$QzrN9%c+>Ja7@S?^|;DExU0Sd*H*n$30lf=i|kjpFw5 zX#?%3_Z-u{Mty!WdloJO3M*r&n8ljRmrW)eg1YXV`ZSUh5 z>hMxCVywQtzO2WF%W{GFQKNZP1w&X_AbT{@>f$@2hA?blkt{PcR|>(9KOb7mrgjPz8|UHVY3K1lq=L%5p!yGpHWO1o)V^ zq#wR{?OF_zojz>(f(OF}tGTrQ2FJ?dUau-}FS8Hn7P#_P+^Jb6zjb!GTNz(=LCJke zp;-&>tv78=7B>tn{@Ymw?So73S?Opo$2$8*0ZmcM&6nWe&ZUtzxNxv`@n5h<<4j%0 zY=3;T;Pwk5QG9-D1I5f$JDH`M6reG)G*M{zTuHdqy$k{)p{@ukQKJps<}j#|{+&Tm z92C*-Q>zOgEwpp*3kT>V*_nB3S&vol3J2jYAo&R-=6`|peP4RO~)=x0q&xfylb&;@;@lw7DuS(+8z zrhLaGM^V^Zxw3_L4H-@_Uh2AZzo+J)Fi7^6klK`_djLIi%pCm$p!XtomC@E*87H@# z>sT;~zW)et|Iih0E|K$vmPE)kT^vGVjMLJ1kPlH!@Ui*yrrq?-WUxWAX~i$vHX>*s zO^*jI2T+2?Cw(vfHk$f^=3fU772Mm@k6g(J<&EnqjlyHf*8C+0tdVN71yvU-+rqH< z4sMO1UOe?cJ@=amu!xLZ{(AKX!*&b>5ZKq&js=877yKXNP$$kNutT$1ECIWMLPELk zPRKJvM)A?JZFxPtNuXgmhWHe?G7Zj+e-fO4bHb{u%mco$Nc;dp zM5PBv&+-4wXYsF-6X9`Cb+n_Z9KB{q6$ALND|Oy4xihlom^%;bOoP4#vIz{OUV08j zA`HjwUKSe^OVas|SY(=Fa zUtg9}(zN#TE{j`{9+v^#hS0qi zLHStLiQY%9rcA7%v?yVlk!UfFp!e%61pr85^=9L*4AVmIhd{MGPAFDl3W1J6m|FpAzD%>Sv9 zfEvs`56*PT5T(^z6yZ3cvEQZ|zu8enBaFTs!|t&ehf3{1I)v3S=rfz)`8w>?=OhB4 z+A?R9!aN^pj1*k3ANnu2wUP1QRc6%wik|VMbNGo%p!dP!EoYv_KW>J%@uo_>Yw0?S zS~(7M2S1PeF!Kg4!jC_Z7tkZd8uGh0I~eNH=TFrlDRV@-8>#u2yc~T3G7WWCXkp<< zq_b%jJ^N|?nk2rLpFdyVrcgBlqh90$A@q34NfpmH)Xch_l7MqSzP}&Xd=k+qucKI) zc%;toZyT6+0;@8qcnqEf+Z*(NW~K(BH8I+5=@_T~XjQOjwbPDuujR>>;?Rh}8~vB$ zQb0smYyp1UST%HQ+3!tLjYhxGnLa(Dlsd@7brw9PPB0{p*3K@D)D$AHPlB@d)$7-T z9yc)-0@KMATX+FWX!PjM_2=AK2AK?EgyZ1rKd~&GxNG=!28>KUnXc`$|7)5nl=u>e z|Eoh6erelRC4KK|;BaY0gl&qB`wWe}?Pc}jxw&=BDi}V7<7P#t9Fdjfn^L|IJS`gq z3k$M;qnEitQ4mN^fR4yjv8&oQmN)PLHw%V~QY%%@TYU~W(DsSr2|Io;_uu(5$z$@O z{Vqh25ZsNj1h5Y?ATfynpitnmDEmm+c$>NOiN`WeES@hsSY~2NrHpW*U05+A7Gl;q zO!%fh-!s>V$%k271qKqX5o?VB)`Y97grVIOpXQCh?`Wrm%@2|WNeFYXE6t}`8Gz0L z1h*$70A2!~z)g>(4F3Qfn+GU?iylHethx?eYW*b>h7{~7y!{k;!D)_VG)Z@`eC2ga z^IOcCA}=ae+5Y9z&{WfRGYHa=g?B9kPNr^_^)UP^d+ELfQsCMe?Nt2eRhc%eCl9m~ z0Abr8UlDh$y1!%JYP37#FfS+yB86kp1u=b=LHWS`{i%g@xsEga_RqhYW5BWNeWcn5 zU`PE=gTHS>pA*9iy%=lVQp-0wp9>SVg*9G$n+o7AhN@I29qygs19$HA21e@Gq2ap3jB zQ<|Q=(gH6eOSR81tSH?Q9c~A~KHhUCw{~X7#F?xL3Z1>4EB~3yz%1uat>~2m(Td#t)`LHxZe)iFHO6aIV3XE)J?JRFux4 zjh8C)k2$1YS>){p8L3IbRswHVE^lH?22iB3L= zc6u@I?#|u4_-nm*)0P+PFU=2X$kZ|UM)Db@(l;-1MRS}6*0t51OnW8T09j}A^{a3; zcnb7eX7=_m7cO7kL`hlm;spf0-%{S@Qae+;CoK|3E8q}f5PQO$t*@GFEyX$X2}y}& zNNi?cdqcc6Es`wH#EB530L_88@(m`H{sX^Es%EB7(uv=-t3oXOk00;tlD)1O6Ph#C)w@A!YbYgXbu;!gy|Ai9 z{^*1I8U+LIEq5aP!&VIMGr9q-Br5r+OLIY-nyQ-U znU!9;GzSWRRYVvC(-8m`hw?cZ>lwTWVMR&sLPhKOsR1h_Damu@a0Y+8Hi3t)c%k|P z0pf=9y!O$Ihwh#VT9MMkjeb$Mg=i|*@*^V1&*CP|HmX^UKEK!PKx43)aM(m%_o??4 z=?@+y71Uh0eU}xMFbx>9te3e%@%~dz!uQ7rMl@E8lcaE`PmyVss1n}PF`M&;&Bz#Y zG>=xEAOaE)$X`3#?{tbIA<>_f(~`+D7UtsAsd8DSJa-uHD})4j0}&Cd`4ldoCtNu# zMhV3Al(Eh#s~Nb86Fa=0N=tttCN-PX6g+vxI9E8jqf}Ph;7U|7(}4YzI`iZ}Y7H}o z`0cHnA|Y@FG7MsICKgz{JdS)sIgdfIf=80j~%)0mhJUa%Wo2<$gfd zG+=irSNU_8u4DX0Q8$t_D=D~S{zqk?=ry4}Qw-+-ic9&c2ysju4Dybb4R@fR#UD2$P9uD{OnuW;9y-xfHZPxX)A}{H7zK^2lm|mI|c5?065~ z4Umkr+AU?e*vjbHwA;`j)?JLA}_A}|*KQHRV$Sv|C$ zO|i3+JycwDLPC|xtLY{RRRi7Ym6bfu*+2+%blH`q`#}C+R!jOK9`qQz8c&OsE4$@4 zIq3GK=hjlZH>bu};D7y>5IdLv2}gzLhDxhe;zbw?_e7Mf4)^~`NVv%FZqcfh#{L@H z!1S~>ao}O!P_GBh-Ndw%c% zQWjf7)q=@WkcG~X?E|4kICzf)t59CsmQM&l!UhTx7x(jTHZ%Sga~&Ob&2PV9*{eG- ze6l0VUBVkq6%-K2(=#uBc^ExxmLjgNX@|dLE{*oZq?6olg@F26*_Zwh&_AV(AMZmNC{XY+N}uOg6%k$* zr1Wz_14gGvw;x9E-b8Q{D4D+w8+?RJ#t{=;zmM^eQ%Pr}p@1Mg>Gk1iIC`&LUCc$k zLj+7*jRC9+6WH zeg4JB9o!2574CBTX6ri-`r{IL%{_hV)g3Hem7#?wo!}Z#Q5pB$(_DG&EZQcs_YG}S zRlQ)5DgAry&$<)D;lxT1hq6X<12lQIxo(?buwG?}A02}XugIPn!#cv`7S98w!Zbdn zxv%J`WTZ>HUDLQ>FVtZR6kbH+hTSGj>I;EGc#FCapcyf5l;Fc$o;ia!RE0I_7`Xcm zWeI%n>uIfbF&mGW7VjA?rp7L+E}3fp5dBsg#Dotie)ReCV}-8?$m04W z_YFBWbV2n=JvEwvzlvURtfeaf^We={b?R-T_7?9$k41|nQU4-ME^43$7XL2ci#Tj9 z!2J_dd&2)1*|Zpf7hnt4lQYBv-t(|~{ogC96~nU6my~Rv706*0v1@o>QK)juc@Ofk zt#%)Hr{CMh{D0$H;fzo`6Kz7*Mx~|sYsZm?dZIeb&8;i< zgNaRxX{fO{Aw{{#eFz4jF3Il*@t@ptht%0j1`m=c7|n>z)>4S{BH~%f#V)&rqvEenKXg$CV<2dr$aMD^X}yCd7S1j~@4&X93U=wN4{C@dXigv=pe zpd<$)X8DA1!tFysPJZRyL&Y+LoXy@Ry$_vHyLUrK{L==E23ZOa z&WJ=lKM8aj>T{>`d+YxLTHnt~f;R&zL3`ulizmaFZP`aJb4xTzBg`>#UH5Ol%0R$+qp!Qc%C9G=~~F_L5XML{5!Df64X*NSl=uiQr9k7)7CeGj|Ge z%5opUJkFLAEbih|^mD2B#a&M(?DK>0n3rvzvN9Nkq1yi2=q5-#>`d!1oNX!BKrtD; zI2+vlUexIo7x5_}!s<+3AFO)kaH16Mvuto>7UM0U_i5y<*IXW8)Ru9KCu}=eWr2u; zZ=dczt$HYZop=sIVGg zULXJ;5QFJnmHoS(?tok5cX2bpHUiRze}1V5E4{qJycr{Hg{>WTb!s!JN@4(LBVTy- zf(22VHf{3rtzdvbxMbmxWl0EhT5k$oK#pTGvc6I6k`Z!%$<(j-e%-T$fB)TsTek%N zh;ymv1+$eK)>wJ65#4^^){{wl|DuVOro3y{iSVO1%jg)zM>9TN3uvtto|hEKvEm1NqI7CyW|!hXgWH_utS4okZ*WjXl*m9se*DQl zp1{URp7QUUJ5l_~U*sywdYzP?Js>3uWk@Jpj8-TiMW|kMCh6qKpxoX4K*exjsOKD@ z48vI(6}>hm;p3-IkU{B|lt@;OM&SGlKtWlqco^IrUR)%Yf-{f+P`!T#v2zbUToh zBu$#N(m~=0=s{3-da=@+wwe2K1br0*0vT%5)W1I_I_m6;PV~Rv{Ft+E0Lw>CjkQo@ zpLwm7OFikB_rX~tGGvXt-D?Qv2GfX+)Z1*$wgc0{@&cMdbEG(6oc51K9tX1*NgGp# zej;%ooTgNuFj)sUOzQx_z_?XGhw&8NK0aiW2%XM12!NZVN8+?Y1R2l1NiG?QFioc! z>k8$%;(C`iqr#I%H{<9$UHumXCaGRvbkg7!tumN$$vpG&fkTHb%Mv}RWtj9&xs?wJ zS9W1EB$PbHA{v_msT4sxSv=rPz#CT)X!NZ5#L1H_r%|IsPC9BaU_dc}_0G?le=^CA zKvL=yuZ#wm_|%a@2`6Z~-OqPwuDW?t;N5}L9OR*4AFV$3&oE2!wvvigb1`gG=qe5&QbW&dYkSoqi5X)2kL8KVFi)SQo~?t zsZi-ihX0Y2`TPdJ6x2f`jj)%|!;d{-Wik#Myj)ID{-xL&q8sR>>4s#1?dew>YxdF= zaY~YbUq+w(1i8p^8}@m|!?$eNBGf_#kpk+2vJ(RL(R;xCN_bwho!z%&c5&*!)Y8$f z>!DVV-BgQua}NKrq0mUww~O>CdHg4k$Z!ltKsq58hyChnVq$C5f-8bDpAAvHpfVvo zPha}=HJ_VVn0zCM&VIcWqxK&-knqQGQZ{>ng+W=I7W|ig{g-S7G%W5yv(Vr{)CwvX^Lx{|HW~v=;1FW@KRLq zg?cSk%1@8}7pL^c>xWEc+SE$qb6C7kA?~AiaO&=eje|Ed2CMiIx*gyRpZu^P$fubV zxcaRJADr{PrdZCzr8Zk4+iu}pE`|jmqee)xI{>}XPb#VxexjCI#+={Q-%#-SL_ zdvD*lGrj`;F$=B9o)}Kz|Hb~F7Ehxf^w3(;5ErK2I$I7oEIX*-kL7P37`J-CNuu*= zOINZQFBZNlh5jBiZ@ro;K|bucJGh9?4sBko=?#;rSr}`;7Oj=GGq9c!$3u%FQ=%9K zgt8%UGyO!tnnMRiiU@&Jgp7VX$RL-Xo&lZXb~|?lkD$9nXWq&>qtt%;BVQEL1=5$v zx~x5W;_3+kLh8Uf{vinq&?T127+hlrlfxMAlH#vf)$41gfz)lsMyZSWTr3Vt`8zI{ z_MHB~>VWk9eMk~{Cr_Fly$QLln5X}RY2^C`(b4~W#D664ARAI_8Tn6Y*|Fcuv_3#2 z6*;e9M$|Ru$G{OrN(ZfF%b@A%23%%FQ~ne#1Yp;~;K1ycD3m(V1UD7Bk=4nkuuq;c zv*Lyy0hViPvUnb7KQUOQ)D}#*gwBLYnHxHXX^6~miJdYFNxAnKQrZ*PQsO2nlSI;p zYUVPEWkIXOeyD700i6#GGDi+MwtCl~+O@7JLwptM+#F|i6fNS>d6uArJ~vWhLUod7 zoemKdAkl}Qf%-o_+Mbb$(|O%gGs25<-))=|u4`t_=jS{%DImpX?eD*LLZH?#{T$0Y zbJ+5YV3}g|_2>tHI|=ca$=&!t5wt&)Rzf>tjMak>I(f2I*)i4tQUE@-ZEFvvw39z^ zE*(ho_>P4#^2jJhqaIAdwh=Yu7!-{V#L*8>sZrnyKpYvVhg^z$M%xkawcdcdVsPSI z9Ivn$^j&El;OjV_NSxy>*&IVVUW)%p$XR`PP?OmsuE6 zv18jdl?o4j5O;jcqkm@YGEvS#@}#W0A(0)R+>@|h^`DFf4RTi7Q@EKpTyzYPMLl4; z@u?A95ht}Jl%;4SOCkA>J%q9>pw}uqd$)uAs4F)H4Fzd=gt0f(3BzBfKX=%|@Q9{V zXXMvC++rE1lt*nz3@ws%+P2A{)e=qlsJz&142ITIJv;sR%`r9AmLf>g?SlJ+yLk&1 z@100p8s`x=EV4YKP@_gCvUjotH%w zs%o%t+0vzgUltdS`x3(9R-J;)QBl+KE6*h=K`fX6_*OvLuyTWE^+m z=;6bAAi^k4tS_OVIzY@WT3&uX33BZdQiE0SfPn+2`#q9+M14wKi=zMos?(^EV;m|{ zpp30gO48-MQ9~O)*isc9X7V3vv1a9-dvCT;CG2MqB~>kPVzR;NG(E~O8Rt_>K0#{% zusI{Czw&5qwzNrO*0qvuLTEYAS%s}i)HK!Xn6RQV#eldeQK6u;>x@CW*rI1=C!xfa zD(w8|ZLoRg-$H3()IYthbqz1lICuh}Hhd9|zQ)1e1vgSHX+xi1OTO5p-AqR4{kV%z zv6XmgQo2K5Iq6nDgy}eCksd3h{+kzzcGGd${a=qE|A|rytD7P)@8@hcq-`?3x$7>W zjP{Dn+}3>Xag@NEM{nU%Q7Vhr#o==Wwze=Vrac-F_&frAJvu$*N81X9mvU&3dpoN& z;_S9)#M&?6`O&hA_S9Y1dKpJvd0zs1?ArYhtDpQi33hv`nagEwEJa)qW z2GkU*E%#>g|1rC0rO~^hw}>NA%GZzvLX!3R(gV=Bih<`>4t&I>O(_%#p&bu`;umLD zgJF_Wm%P3=KnM|>#{Xt3GMcf#GLnBO#nH>sRgyLEgcv?>?O_Nc4iAh4q4|lo2wY{K z%6%;e>=&+IKVln_#Q`J5ix-2~{sJn&@m?d7xb1_OIO9s#SlQqu7*vxpTnxF4_Bt9F zq>z;3FK1erX-Ufm%pfFF9#KoQF0!+aqDX`!5d18Gx**!vryo3pJpoR;aIIxN0t!t; z)PrjW4|g%&h+%;7V5L&KckrEAecJoz#oB4KR0KAw=YP>sAkIQ&ZD?SSaw)7aBLtD@ zF)e1+!7%&mkI^Gjh-+^#MIdD3Cnr=d0vTxERYUXEfnW6kcQD>&XLd0|0?fGV{k}ve ztTTJ{MOP~de9KsUeQNsW=TKv%5{R))5g3byq-B$CH))c5qB&%a?kxVn1(;DaVy%XJT<1HsgVip}F~moPB@{wzL) z%&DoqZiXCOz&)?6b==psm%TxUv)^@T_B+f z=nqBP;57f=Or(Z<6B<_QfNE5@Qed(C*nf!kD43!`L($frLSPb^YRb-EPN`p_jh@f= zSh$z%dMcZk223DXNlu|X1-`@+cQpTiU&sAxsoAXd4#b5&Dv4#nV_oKdNOiHx;*$GG zplHgfO=s7?(58pfJb$%*?7JM1%(F}h3qdZsM%RG4y6D!eTG=i-P-YtQKOu^V?Ez3Z zfc;{OQ2s~P6^#eSAZqCY>IG1YgPE8A(djtokFGGNWJ{q~qENFOW+ZpkKv~t+YS++Z zk-*NfeVTDj1;9HM`aX16GE5hcwWymT%`WEeXQ9%HvkX5~hBH>?pE))n8q&?MbgyJS z1e;;n{t*j(__EWODf~6aw}QV?RpL>?_z-$v6G+-Ud-P}ma49-E$fj|sGsyzX=g|QUuD<; zr;;EktZ>^{y;$rrIa1#kzf%12xbA0!e0HuXCV@=-M|0$?6o^&_;c@G z4Xcc(*1nnh1yDpO8S0ghu`p^ z+aL$H@6Go0og%{2h55zGTanX}XuYazQcGky%YUJ#UQ_uoJ7fc@))3!0%U+c4> zGedgcxPCpE3ihU9?3J>z8Xoh)`^Pqcd#k(+3J7>d*=Zq`NTzN5p&63ATAqQbFK$W- zvxYGbX)YC=m8NP@EI#^9OD>xdaX#_N`UXNmx<)Q(G$-J~#fu%fc=YerFB$nVc8WVV ztElqqYd)Ed8g;s?;pHOuL&dnchzXVmLI}R0B}*9m%W8V3p#hz`cHPKOoA_>Zm-n;4 z`EI>a!6>SD=`WQR?_tP^)EBuiNjm&u(UpD{79#Os4kZpz&>WBO4F^@->La?%77W41 zik=&TKj;M_hGS|d!+9ZP;mz6!_eIFkNhCN$_tB8yLo=>GDI3hz8E)%C&MTixEWtd` z!cg3ZLqmA%M%pcx(;*}18``^R2_nt2j9TV_)MPFIBOO2WZZSg)6%CckOhhM<)KS*f zt@`zCR?6oeGcoe$OL5b+TepAbTcBBi%nkXRdr2sGneE_2Uq8KS)t?P-Ch-f?oHF7a zeu#LIdD=R>M?qEOeK&C;17_r`ls%7k?g+f)sZ-8;oE|B~c6({DCOr!5R^Aos$__bt zwbl4|y*A}mRPu>?d-x-oupHV=B^mOO&oDJaTvNs)zk6a?hW*G=*2(bw`;lVX{vMq- z6zvOh43(n$;8e0LhIWYVUv!4Pj=?E}sJIN1%`fJ)J#9znT1msr&%8j%EqDbDgjM|x zaAU3lUEt~0lgvXwgWk>c$EanMjVthPm+?K9 zu^HQVfUc;>!-K4xoHD@`i%M0iPde!tKq=XZ&LUXGt}XN&MMo zq!>IgJo1+sV4#&Ej1Py6O)(D|DRub6;n)|gep?1E*$mvwwq0EjlDw%h6wBLrH zkI!Os1_Br}{8|T6OYfv<2!=_42Kmx7_f*!2<>~*;)14;dwm7ev&?&aSjJfC zl$Cc!%1?kUJejw11B7=*#uRI64!7;=@|UhkWJ4FJRyz^(Q{XjNMQ4~4d-AsV)$eJE zAA{vBTnmWI@6U)fPcw7Qbo0c#5<{}aG1lh+Z-i<0t6v){leh!?{lvkGL&;3C+!Dci z2}MhphCeUZ0)u%mKH!nChqbA~=qVl!a`VgwZBVOA|htrj8|V|muIRG0irvme{tgI=gz*;;fyr06w`lRO`t zIk&?k0@KeaFx66>GH!Ko{rQqVX2K-i+=G|D+7=yQq!so4^Ji>-`cfaH+Wws=1vDxQ zSj9p%#k=HSS^N*(s|Zh&A1NG7#_7cv4Y#uVd>}aXGy}LWn>RxZSM^%;3)mWq{217h z{|Oq3PJp0>o$W?M0&ap6H?G+-)hoT*PAtPIY6PAb)en1rTpp8PdV0yz&3>=jsHhm} zEyqd63)sTxMdxOIZ>vVonhHx?2G@`*Y;DhGeDS07k_icz6eHQR4=3{T#W@nUX)B@* z;*BfA%&k`}T?*aEfI@K#{32PI7rJ(>Sho||#65)(!gcD`q#N$u_t=2AG z--(8^QI>&r(IUw@g&*!G3>E7s!MzqND96A?w)!!|p9^NQu&Q9YIKxmkPtS6{OgXXG z&uqoJ4LWY|?FZo_^zaal1mGu*DMoSC#))}`t9I_*y@i4#R{V4UcF&(Vvl`6H z1M3L@Ho9Mf&n@HHt=Al{ua4>9H1E(KE7mXz4f*wB#lLPK8_pq~*6DhMP$4{fB6W+t z@lZp=6dp;_hpLkkoCZ-A3%Ob-s|>T1*RLExU>B_&4_}-LW#&l&ayq?i0Mv-E`a{}{ z>vNLBDQt`5_nOH@qqx8=8EpHBd$nmXCz z3Hik~YYYvVhdsb$3_uEug@71+NT{cd=Rdgy(J-#^U)sI65 zKVj>W!x~K1+)cK&-texFfd?&taAky4mYZ1=m#a;D$D+yB&rL3~523uGj-+KQ2Do9SMz(Q%6o@{bG8M{$#*RD(%5uDFZJ0pKgM-fG*7)

    &*>K)*TMes10EHG6_N|3Wi!>xZjwFuy%3kTqb1R}+ui$qJYWz;lOtIX9mR{vUiNZ2 z^YdPDSBIiq!w!P#NfcY6AVUesd1in`P1#Ih#NZkx!S$f1l{^}URpnT)yk~21BUw?) z$7pf}B)b$4ow*o_q;H!BKPd|@c3PA3%hz>LC~0a5L~mG|HW)SVaceQC6DK3$9|iw8 zT48>92Xz@I34F!DO`#tt?p|H*Fv$D;KW}wotedu5?wNj~b=JZlXV>HPW${5l-MsIg z%u3iFOcB7hDTeonqmIN+@LxVgXvUomvx0Tw^f8U;$K16F>xsoO+vQIL<(Y& ztD$FWXQDrRt-@Y+Kxep|A)x{I? z-#PsJ{;5GQu4T(oz~(}i*f=$OsBL4ymtBUKU^m4pIa5p*Ht&cHnTr64R78{}ugdyO z7~NtC{4agR2($Nx0bfphND8Jgz^Xg5o8#B5oh_sk>U-(s&Vixk0aQx7K9Tz+@#-s`fx zmp&_2Xw!nadoRWjrlG7VgCBeMrH|9aZE-(lmYIAqzlc|L+E-q7+Ku2c!}E@x-X3^` zK~3ci&6&G$-n)2z9)!^XkiK#5@c%)wrzcW4#x1f>OYI8q^Mp~b49~l&E9i51!FnB?QyBZUit%fg3dzyHS#={PDRIJ|3X8S{};vv(VJQOjt zJz5p)lDiwJl)!3>$NtDE^~Mx11X|cfRC%K?={!c=_eF)aq{IzvwgEvGqmMf~eu{e4n=I%}RiD`6%GJ#S8K+!6JtVgvWK>3Sb zJWW;!(Dn>vwC6Oq%g>oLL^`Iy(O8kmJ~8r}HJi-xo0+oc5dlk*j>^_yGqgI~iMLIW zpTW@he?DO0%aapnIU6ZAz6yq1d1BcDF&;xCs(djV*j2p1X)#5?3S{S?m}o!MfN_Ni z>0X*OVfUAp&!+o)J8rtT$?(bkop`VL;`-*}GMaZ@_Yk5PB)Mj10Gi0(SLvj|7^f$_ zT0)|hp8|NoikN?A#$UU2>{vn+5(WkYdP_4cX|^d5g@ zdw@Au-|~L#awBEYNki(AI+Do29BMM*>Nx|L{Cgg_{ZZ?R%N10BP`d#dgAa=|j?YKY zwnML|h=YddkQLME^9;A74MXw~>aviq4z3oBmko3M$|e;gP*D^BT%YZmk0S+&&Qpue z?w)7}<$eqImT7SE)wzmUyUW*tG7?ENfZischq3HG!)3V1>8kST>jX4gz4+X>K-9u? zVi9FPK;FCI%%iD&`KlT}o3NBm{lI^UnYj54Je;GK0&BhdLfjqAd;iyVpgK@nNT!5i zz^e@$o0ch$llBZP#w(w@r%#cY6+3$n9+KQ_k>Pdi+Lb=^ph>U2n4KQq43I)04f(aP z2c(v?wC6!RDxLo)Pju{t^m*FUmDIL4jyecuo^h8`59Qw12k13T<}R?XgjD?z{Hf|7%;-%K^EJ>O_96o}$TH^pm} z_2J?F(ga!DM5Rs3@$tn?JHL2%-tU|19y7WGI;rW_i7W%V-T(oVzOHW4or?@s$VXx} zWEXEH9lLlXF)IDIsfD6yE>JY}v=+~zc}g8p-r3*s9f;qJ|I2_|4K|M=UH{^ zKhHj`Vh*J~+)FYlm!?B^v}7{5@|k4p%lU*^6(}p9g>sU&=e3mY*I@WT3sYV^`_Td?HXg9*#_iG9T7JN8my2uxQddQlc7%XuYX*Dd(o;-azfmRrjzWUhhaDchm3JFzQNdE@s zO&d49dH~

    i5l}$&m42#(1xb zR+!5iUba!Q0LDrTD98!c0001J0rP$oJwMTa>Cvu?ZUCfd#SZxm2aerIz5E!DapT7` zY@wonBSO!$<5WTh&1q{l0H|DOa@p<(9mBG@kisk!JXW;)=qp5xQO9`VW2EWCuT@iMAdQ!uL!I9rq6> z93`!DP*TtoT0N_OvUx7yH;-g}(Y$%|Ra5Ofd-j}Uju-N$>5W;kTP7_aAQgrf=tmjl z7bCx9nVNH1rnNm?L&|>qn!CBZU((1t%U+Xxte)YBR?%gD-LP20Jtb50?zFOZhR^C1 z+yHst5#q1YhD@t&`ueSzQ~BWX+cS(sU@|)^I}DHkz+%}H%{JLQ=u5?tx7odW|2ob6 zkhJ#t^|VMAbH|MvHjrx-VQRvB_pYMU;{ET0%_yb9TVrXNA&?Fa8yif0hJ8+f%P(>r ze27*}JK21}n*M=mbkch}4{PUsZ+qi9{Ur~nWkf>DmQaQ-=j7KLit>otF+nF8sMMx* zu3aae9Vx$oSVl(uopGqe*)IIYbG#vA4lH|rYkGLvuDy{HIFBEBSd{bisKUif znv@?nbZu0sH`^NNPy5YJ_<+qMmsEZ!#vqBmzY+V|Qb1X5*f3Z~JKiRFU%T_9fGqFM z!wy2omLZI>`SnscWR&imoJBKaDu;k7>qvo`Q-^mRHEAd3y_K@^U9>iCmCmbIcN;!@ zR_w&~sPp|iFP%~W2{g8|jxT>yliP4LzeDC)WOfj=+vw$TTffO48^pVQv)7-!*}$2T zRzN9tx4iZl0QET7O%bs8_rZzmk3hy1$L@Uw^eXI=&57UobG@Ewr%owo5#0S7#DhDS zkdZ5^O5$!{+8+C05m0JTrwqdCvV-_)& zw2vle61xMiQ3;JHY!+w<(glt{>pY4NGL6H$Vo_#Wej(a8861@6Yv|A*xO55qyOh+A zIZQe5(>KupgrNy36lSn>mL|zon_f@sh9et02y9PUld|FSx+@Tycgq_aC0$)=j;W6x zW!p)dFI1R2q%^cbZ%N%g25e5udF6@#U|E@EK-}y;veGn(vjFkV;}tA`?QTGPdOTmh zr@eQUFgKu^^|+J4@>#xF%WHbx=@?6UVH~*8Kc}R`l2juaIf`#F;iR|Jv`sTULl=h9 zrJktgsiB{gZfrJsycCR-Phs3fzK2O+@m#hDz-@V zae#k_{!cD{b0kXMAO^Uv8QDdzHevO6C*?L)CF|GAZXq)T?ay^n1)yo@k3Lo80#|Mn zd!&Dt70yCaSM1X;Oo@!|zHmfXL*roXD&?ba(e z<$&D!PaMU+0S->A18=$31QBxT4&Hr~oMF`ESw0>#G+$ zycFswD42{LWrORDe&Al9V^JST<(5|}nqBICiXm&r0NMyY`{Oksj44~jI?-MX1+?G) zaWKanwRs0)m-c?8H6s^p*3&|3;P-TG!>*UaLJFd!x{S%R!jwJ{tVWDzW%=7x?mtqE z)@y7B&rhIUYu;hx<_+8Yzc$-hpV2OoPar%CwH|{Q*0XGSbiLzpF0Xz(dPdoIL!~EL zK~yk|5bP!$6UfAnTyS4B-at5qAqlvu1zc=X$2le@)fUyLiXxT_{hs;2^iUrQF-hz+ z9=sHyNMamKHv_ue@ui>sslL^u+eq6mw;d}+JE`G&Oe0k5p3ll?|4WbyasbmVOh!F} zZMPlt6^WJho(}-8@plFsnT{I)!CWj=U?S}?BlvlJ(Z(h+3dvq=VIHuZM8VS`)llY#5X-4oyziawW0%4@ zz%ED>WlSf+=p+XOR%Hx?wp4r0H#z|V(gsoXXwX^1gLqKC*&}=dtCrPv;HN5?53F*? zOB}$-n=qR8q``vAnEN!$NSXa%{-Z~J~QC8 zPp1zTYg-7;4OPEocIiG&a78a8f#T543>kCjCapstwsDf9Xsr4#9l34)3;H|{1lVF6 z{q@ZivA{hyV<2tqkL2ZvK=aU5^9U)A9rT$W*zYiPcoH)hq9~_Ws-NAl^NrZp1&i1D zN+Z&??KsGCMidurE5S7PPilHPC$^nC;oPaB+W(10yA{-5R^?Va(ocxh^8t8NZHI@G zQWenD7mxKm%62^AK$|vcBJ1MtYYy+fAi=;nig4tWH6ZFph5I1K2Jw6>AR;7clj*^= zJ20IEh+wCTq zPZ@6&T!FIY5pV@)U(bxedgmy^dVH-jAXA3N>^wyEK(i%qYFWXvXMB5x!UBG`iHexi za~g_4xgESpD7+YvP~u)g5HmU&vePvFf2NuPGWjjMy|ld7`v z5B<3JjH^W|lmNdkay<<)_vEB|m8>hKJklv1T1+?=lYN5Ud{5Oa!C0$|KhSoc6G))K|Bu)-A2;$h4Wi@C=Kx5VceC=+mpw0@M(xm4uTBW zeZ>mb`I7^4&1TK&L~+vK&ic29eaV=)7^dODPci#Hvi2hrE#R^|V)DVHW@w~ghNER3 zgJg-bbK?HWCZor@5EL0I2R>_z1zx1f9n@N4qAW7^rKL$I9YlOj>f=uEEB1eKCLo&> zc`XAGL1e07?PH0pZL`m(1wW`cJo?1`Z~YwBwD%kRCVs^uDrXcT4hu`yZQd_@NkFp& z%;;kQXdFBK4Q(v3_3{43*OI*U?D31;ih05yY_4ThgYe3{8nr=7A9I4mcZA(RsBlap z3lrkwjWa9zpK(e2Dc2lyk(`2)LY9h0`LACA^*_o>r;!WK-bm0%928@o1Vk;8@zR=O z=_EP`G=Q`(>rkG2_kuL1ELmqcm*>fo^JDera>Pk}mMmSt3T?kW#oD?wxH2TI7@*u| z1Gk-Bf%X|r!QJwjb96TR@~-e!vYrnZZZfOJD1|3-q$&ZH7BUP?%1!Mx!tUIuL*`m1 zVV$8O_h4tKaVR-6dVXVa&eV#KF1`gX5PB@u}tT<+)xnis`xAXie9opK3si$&o`i|JPR z$98?jUp>mvlqECHEPz=yBjOYyTcz=B8oQk!#jLz9H9)_OWe&3Hk2B9svg0Z#tr$Dr zx^>jSC0P|Wsj(-8o3`agMzVy8u4(tS(|EcOYgz!Mk{yqM>zWxn^NXD~nQ46@?;~0- zaa-go%;VEDvi$Eu4=>2wc~W9xT#(dkD*XWj&Vh*hJ>K5o1cy|Uwbf7Z!>TL#_Pvwe zceACs-tv^{jpQhBzE4P+ktZIasbW7%@V7!(j$=FwS;zxuoWgTBO|cwwN}L!9>))Z7 zgei*rdGBgSNJ#qH_W-T^&&|~6I-mSIVBkQOC@g?3fB@{Ccs`esBZcgZtI?QkHlHcb z!+9VgDv3PaD$xAkT(>`5f7#@+bg6D#^$#Wf#=|F1EZ)?ex;FDw9(bEjE!c>39~aI>?!y!eCr9+0-^a8D{N@| z1tq7YRUNQUnV=@?;slN$K^#AKF8JdB^r;D;xC@s|Z~NyfJ>LIi4EWzYie{=ZYTnT#4BL@PqbhCi-D5St@$~O~yYYkjL3L?&Tn?AmX0xGN?Pl$?d7GXl?%bPpd{hHL-^l{1 zq%*uT=7G?mSvSg`u7e?w7~RPpDX|hI&WfOfsfji?o`VllLZbIm_7<67Ov=@2tBxIiX~)C zS?qL{!^2VVmno`cM+oX%h3s;_$RUPxt46|Bg(9%B>U#jG_l)cn&HnkvPwip+1)p@1 z|5+Tm7-B|UtGzZ25z<6D{Qo(tArr9ao{tSpJj3RwxMaSN&xp?}f1<+hL*bU{I#AVd z_%U&TMPz*B$dSvpw6i9>Oivf|NK# zvUBu-4;#Ias>#u!_*Na7O(||gpaQLLx>;$%zTLYg5B)F;l&X9A_B2ri%6JUrEfcVw zxT_G2{9%0U ze1fnDP#Kkfeq*&0uhtYb8+DdAvME?;WnD&e`8OLCKB9(xH{ZN5Jt;ZUf3Z0eh0MAc z?JCKY5xXzrW;HDvF@G!F5SX+B*)`Q7pWkre?m#vM0Kn1q5vNlqe85@LwdVp-*AOCPnIIW49@`jx=HCU*cF(hK zuJ@tTG&y(u2n`)c-DcCMT8y;h4x$wxnQFA7(!jDciL9mZR3Qa^aW0`4;{0@L?L2wB>Ye}P)1Dc1XU4h09 zi82(tdD-FidWeFhF2Pr^VI&WCk1V@qK`Yi!HZf;M7hNGG0mt@ZK!K?Hm^&=@ANI@t zsrJdxP?=aDKwFe@;i_o9qm7+l{6xsyQ;$Xq=n)WF3t zxJQVeHj`1z8_Z`~jUhCHhZ|)sgC8m0uuLC@8?~%(9dn1*{}Yj4rG^gl7`_D;%$jl{PM8kb91nAI!XBBOZo9J z9gDUb1e(i0+>Lc#)u@USm_7%ZJC2bQLb2#emrepWM#la|C_WUJZ^D={+qt7PWz|!H zd)vjTkS&(KyM9m9pK$Ck4`xiC9t{X18^8f+!_I3{*goNkTV;5@V4|O(@&V}&Uu`29 zP%Mn3-&kDrp_KPVXC^yR@#kY3-Go$?>q0Hd@-2Q60^R$JcJj(%-2QH^@u4>3kZ~O* zA&f<&W(JelZ_uEL-w#vzBv8DExnD%bb86_YK-31Lxzsw*dXxOc!(pL{zds%F5ppqB zy1f|XHRAS6M{N_eZ!Jckm{2J2TzIs$2XD_-PA3ZGBJdSwL4LF|B7AtR1im(je$uXG zRVZb|h!WOSrX0}>6I{?ulJ3Xb*!cf+b8v77-8G9ANaxwhfGG?tVxQqA+wR=o1{eU;RRZ?hzV()uo+GHqw+dp@|0^;x`zH@-38saUCJMJ*Z z5vdA~TZBX~NFd)nJQ8m8vE#-5akYQUl0K=rnM3*tc1X;zrm+d{a+Y81M;4g}pvU>d z4vcMl~C-csLw0V@SiR(r!!&8B$g(Ugz z{rmNCtWb38t*eVN)3#_D9=|vwjp}o#$D8xMn-spXh8}2DP;_(xY;k68D*5uc9d*y( zWaygu5{rmDB8u9`d>T0u&500plx7d88S9BsN5)BP4|OGIixC|xhUusUXxdIN29?@k zk?qKpZbS>RV?KDcYy(9IdZ4@Y<8~l8GL8p&lTR??I%9!7&ytK_-Hj z_cVXCnM6#qKgG!t?Ew!!yrs#1;m6zlw~H)XcPvGu?ae*`G&%w;^LL!+ssF>LQr8@1 zY_biRqQP9wK31Bh84qig37$rMK||1hM&=|6L>UFj%;5^XW?wILj3ya{E`)FFVH4s* z&K1lVjzSf0QnGPCY@@mW=1B_Ym6UucE;d88S3zaKGg&~#i8<`xCuwOIK2w;9eJ70s zRagS2h3qRpi+mZ8Dt913G@u^D7cA`0A%hjO`9B1Og&?%r6`;-tg@kLCD6y|t_$?+3 zK($5f8eUE_Kz44ershRA1BG{=Vu}YGO6>l*32td5@A4$?nzuU-Z!*HB|NDt}XObeT znO?nYmD^WPdx3z zO{g5i6I~Shkl632+X%jwNMr=q@eF^auF2B%O9b%?=g~@a%wdei^ILYNqr{MP3LX`0W`3Yfgfa^V!cJ%>x;1H%2Qu45JQ^9^!7(? zii~=*34n{BAb_BNF;T2d$uowgtAL$xoJcXEz&F5HPt$?C8pDdk_z@Zx8 z%)}FRh*$J#DF~U;2HH`s$jT7lB1Skxk|V?NR7j$ZqC4o(g0pE&X}U8Yz&7@iD*QhB z=If~Cncx@w34av6BcXYiC=__3x_m4!^>iHE{b$v`IMl5D5s zue8Zh*<6;_l%HsiJBU^ClpC`qg7FJWhahv}EB#tsI7pPU60fB$qsmCDUODNp8qetg zU8qE1IqJ);ii?X;y-#2;T5O_2JL=L`A7z;>m0TL66+PuP!gcD8-V7j$qD!2z>0$Q%0R{$BGuLsntTKY$pl$_rvtP0#nSuw4frg+jC#WJlL(5$&v*=B-Z{U{SPiDH63HN zMcpJ7v-HMomYrQ84I?k0!f|^l%`ek#4Qb?TUhbSqWb~_^cw*8@uo9`$xXcKPnuA($ zpT&NPM{w{&3llhnyI^+L@dgKW=|+f@dKrF#^k_Rs9c7NRzc5IL8Eq1%?9rpf0Nm(c zEcxPK*We;1_h!Xev6g_C7WQh6Tg|WJ+$o!ljKPV~JxGY7AuzDZJR&1$kUP?fP+x0m zX*I$S2t2GjIYyd3zGb&V$ubKcRG`;nI=&)~vMGdrHIKP<(Vp-~#0!TfH;z)b|4q-u z7FvEU&Q9+yJuyF7xC~CM_{kA9^|$=_>EJU^o3z#*%psncDtWmewu&tZNej@C=dD^8 z0MDVM$3$^AxW1IHSdPTg28b@4KfrFS4f+NmM0mMjZB51ZlPZesvKwC1Po4x&$i3>8 zbrPr5HD1%Zy3t3BCu*N&xiNRda2Q^#bR_OIk=iXp6Q*Mrw1cq`wd=74HA3s;*89ZDw9s(!O!aPI9SbG@UOS z8X0Lqg{?Yxr3N3tDt5_*Jh986)W*rdPMT|cX?X61K@l2c&ZEJ>mBkn08yVQ|<34Xz5{(3#u;At=XH!{~9c!bSYJYciJ zOT679MeP78QMhp0$v0STw|025CDC)sMFpfo9EPvW4csbg?E1% z+HSrY0vaq)5zN;eZ3;hj0j;rgXcCH$O!3dpuX|J&vIQm!TTHy886neRYKyAye{S>r zyL)D4XNz5t$yWdGEsSHf11<^!#edmJ)kvtXbZj@cbLZgZz09x;;5*&JCYTN%JS$+~ zVY2Z>>J4Z|p_OJeYd@#MmLR~l3a*G=4(Tv|;X-4s?#0WO{p;Vfx~ca{^i;l)qk&2*a1IB$ zoKJC2S+$FX7y#d!Zh`#W1B9q)!tM)GNuuE_|I^X-&~RuAP6isE#&couK(bWLWUCa; zh0JD=$QHX*KV1rfBd(BKu}dsZlKl~U-pHvJx^QqJ}Hh){dfsCVor z`=gjKwGTb-$NNvAk`-9|5g+M5ay~2hMr4`?5v1*DpRf|_JxXJm^Ym9~rhN|0X9WP# zVq}#&lVHJM;XCQ|M?db_p(e8-vKW_hLh#qsyu-1fs zZp5YGT}OjG!B?2W8Dhn-|Nj_!6R@81w*NbZQPvcawTKjoFt#Bjw2~HN%TgJ}STb1} zimchvE`w+YXV0CRuDifCu)})NihyAN^TG3}xu*@EOCuXI^UTpl&c3=fz9(uY z$Hr9`gw=SMR14(8+nFweGVC6LME{@*6vK$4*(BDg#(X1h1H2nY+0~ zFUwd_U3zJr^OU%k9b(0&Pj^0NUYV?4uLH zAZRSc;a-3Jwa&|O8MLWeckb)}%@-RM3i(Mn@re$z?@#kYixMh94yG8qs+(j*2;FbE z%M6&sA&VRfKq9tV%yr%d=bne%67jx#u*U{;10WUw23jfWR;qm;_Q1(XJUkT_W+5i3 zUgMrjBB%KMtZjEOGxPQS6I38~C{x(ZDisYSnw|T{5Ql2@!ZSaxDs|Pmb% zRs^68qnZGCr0TW+WRmJxm@}bSh^fC~oLdlZs@U+fTqIeTLdhxqhD0VmuRpof%>6~MMma?Hr#1_l`ccidu6{mJ0i`Q7x~ zqQTg%Ff5Y6n|MZ(KegXpD4L1tGLs=eSHK6Vq?>>LJtS$9z)WKHu2Eh-ok0~CCGiVz z?ni)UE7x8IA#GuPQ2t8Yh}G=6($hDKME3DbjDJO`aP!&#L$So$@Rf!#74OTFmaV?};Nc_=q^)aKV<) zyMiP*UsX7FS2=WWI`cS+Yi_FvWjdon({(%aYhLzQgs4MzKuHt!HeCcw;-U$CjCg5- zWJ1>3%KwI8+X&_^t%#ULmCm6T?SlI@luu5 zc=%oipBDcb(h{1g+o*bEB85Sl&=-%7T*f8k%Lp|~K_=pSNT;Xu+uldwNG2T^!E`9z z&)*@r3+7Qh)zngZKmlY;#O-ZB_4RG3B2lv4&X#teBzU&L?^Y_HGPMl&UYJ0f< z39v2kK&3q#3q)@tE0{sh)8yD2fnK*iJ^D0Aw$fuo(dOAEgb(bB$q|C&ECpRK&DiOAL)#O@yLv{-Hl z1(-3F(hl!Edqjj~^W-koa5>Hrl7vu%bF6>y+PREMLi{4RPGVlmvz|n;C!nIlMR){e z0lFIBK=Nx;l z#ltrmcKI_TL^C*(V&|7%e$lF_d-Vs1A$z@yshMD5FN;rF1%xJq5RBUSWofj&@y_|? zXaGmy4lN}wx0NBZDg*{Pf{5&|$&QACd+=aojTfT})wZ_qI+&_S%;a!bbV`QA;|8KB zVzh`g=Xm8eyhx!WE=@!n(n@1O|Q*4O#K;z z^!+&R+s3Tkg+3jW_srzk%IKZ?f$Zl(O@lstRMJF8q03fQny6XnK7mdy<1Ip@h^gR( z39lG<*@A8Cu1v|bu4i$N#QcZq^x_M3iV;|AErDnGYXZegFv5Qv`> z1{rGo`t@R7A+#-?qRNF^jkL5fZV@RIw@8XUQpIP?JHW3QTrH$vuK%262w#S~Ci4OU zvB;^-Ts~5G^&Y>9`HkEOax1++O9jqLQ1g@)Mh z5$82~9SK@mPqAyHG>p9bYzL=N_LVVhV4~TIL~4iUuhm19ZHD4b6tQ;%dU{DGRBdL> z77F177|8oh9kGFF;7P~Cqv#3xm%hA6;gh}9=aDXJUr0l;wHDE)e`l5%5gbIxBms}N zYW3-FOzEnmwIV@Mx}*|BBHQK|e~}gs;|7@!Jb3VcS>?axs96%jTa1mCjV3WfKk{_2 z*BvS!o&?KcW!EEJ_Gs1}lK#vNoYDbSmSbQOy#CwOxkW~oBDEsv7ur@38a7C|0=tzo z$X8*5EQP0ea1-}HDF|>y*Y4D8$Qp6NMfu!wh!t|*AVD^ptD zj}%`p3y7Hq^c}|woq!UQ1v;B6b)gbqjl_(r&&0B!icQ%1%`Ba-l23&0!9D@Lz#8;| zC~lC?^#UNweC^lTtM+fmJdj942Ej1GEay+YG|b6YWo5ESEb`YZ_#4((7E`nOZGyyL z;h-dDT7$P9_3Yv~x5>dKHH5b4)rSxF=m{=EhTtd(txmos7op$i1`fij_wU6RdvjT? zb}>A9D)dJ`SzKc@N#|tVt`5Ee|29ZNc~E23=L#S+RfZ^1c+sPIC6aam8#^c|Y0|_} zO(Qpii|ZW!IR~KVQf_j~c+okdsb7WqV54-nXrfga=fIkndW z9D{rF{Jok-n@yWGaW7Ix-3Ny)X-{|1;uwDmE$4*Rt%*#YUgbiJkzWq;knZITV62F& zB(<|=jy`J&EssIOU4rb~>+bfg6|eqYV&k>>Cn|k5K4SP2`Oo~n+mzl*N!iY+UV|5p z%wOrT@^&; z^}Tqk9$K8Lr^FVIb_#ge6NgJn%=?=Dit0l6BAA6oMsCM8lW6Bm96vmM)9imfvLPM3 zWh8I-IPy8AJM0nn_0kZE4frtUCi{oKxzC?KZuD#;-R( zmQVR1Cz(OLSF5)K&~VCEzRX$bwAg{8;hwoyf4s+F^Th9e9Uy|jwUMPkJnilD7GO)$ zYww(8BN!Dkm}Dw74AmU@y4>V8u)S@9ixt^0X5Q7GaQ}8+=6PASn`r!F&tF|? zbbaa4rQ%a2d^g&TOHfWgV)Gd;!Ga8;bu&Fvvty^BXBSR01(om^9@&Qm2L})DKzq)s zN|pH;K7C$a)dtRJ`yE=G%AxT4kX<=5XFkwP_T^%q;yCWRFvsa=!2>8DMXf+E?$mmF z<@maYMj3g{pBu*7fd-xr_SYB9}ByCR(*J;hUN)S`3}7pM>00MZa0c0 zip3q+B?*J6vH|sm6*R6`Xtja}aGXnSywoE~W3eVoMYJrwLe^ig;^?u0lb51q>Pw!c z)shI!4-?WdqU#F*7s>fXu^qw5jOib)yXN|_tgSrF?)`TlxEC-JpcfG~FJWr33 z($fAXl@8Yq7~*cgbZE0<9z7uy?dP~$E44|sGV*M*!H!|zh|hw zKn5N7amRelE}e?@rKV6jwG@sH4*^|n2G3a>AqEW+i)+%d+LcKl-bq6uQ>aMn08BULep@MM<~p(;m`SV5IG0EeEE{y#vu&& zP>-0Cu7>@x)TYcwQMv9@!4DJ_BG9DdWA1CTeEG@BWA^?1&5Rb2+QoWYR#d@NgGFS- zl$`{_dZ4zBj(N*p#19=t4^+-wlD?NwuF7?!fGYK#^riflPnDI)uoEVf>Xyk{n>ksX zBriS=RHB>9p6wzNu_WqdMuz!w1yzrM%zi|h#vE6~ZH()cW`EK8Yg}n*DcgOzl8;lU zXBi&{Ug>!d(_XTJ#hM?-BNT(~^XMNYQ!AOBB+v_jF9gPHx1n&6#(;WBmVodghu@p2 zoL_hG117`hC}ayc2TAxWv84tgZV=Ru@TMSzJZFvpccp$RzC}@Fzuz$@;W$&*ew&=P z+LYeVFm&@^4mth2&VcvDGZ7%6hA|tGm*+d!R@sh1-Jc6Zh!Qs(HWswJ(dw!A1OmVg zd~s+th%0TEEi)aF^(n|^JCJTpp1exVg2d7i%@cF=maxtTuS|9~Tx-!RgK3uxBChKM?iA`bS#b+6JvIK?^y$+Nk6pJP*p+^(-?)>(2+i=%RyB?WjBjn z-gd1_4yeeTCXa+oaT?Lotu%X7$c(v_#<~S(y+>5WIbHG|m}d{2{N` z>D>EFq!fTnl8?lfc~>VxE24ogX5LMtiCX{h$PqwGcC<~V21*8Y!Cu$@_eL~>A+JTV zDBX2w22;#VH7{!-=t{sFru;M0<@X(R6!*?2T7C9xPPV(?uBb`8;I42I)S1R=6kI3< zE#8GO13Qztc*L8+6*Udb5u~ijvo-DOgcPAk;N` z87$fe010O*89_zKW!Xsjxr%j}n~JE$#Q2oXB$_JKto>)iKKSlf(W*0B>%(ahwIpsOfO>VLJD`AWsSXuZA)&-BgWxYlEh3&Kq2Z`8OAUVs9*r= zT%qE;5~PYgci4mCbmuw$L2RU`5RH(WOK?OPu@im5Q4yOP&O&3mh>;-s9hz~Av9uy) zIdR&bPN}6^+d~yLZ5ta6tG0rHvp4bY3FCkUknX632dPzZb^% zuHO+_EO3Y(GXjSp%7i5^Sxm|7s`JeC8oY50H0$kfVa2X;v zebgKIh?}4l(*lSpDtkC3t~^BX6NML}>oCKp=3#wRjw3yQO0Qm1X(Ek_I6$r$BG4>vm6~aDnNSItYyW5R*nS`a0!mFYCl!1_NK|oT|gI;ka z&O@8$CH4uex^^=z({&PZJoo?Dn;2l@oe3vXen51>?U67$RJO5=P-EG!1YAKZ8B$Xzb~tAl`{Q z>^rz?+BAp7yKudjwBCeXBqKEis2&(CPK#Km7Qc^V9>1n|Ds#vzNZ z@E73r1d}AqQ1~f+_VD7b|2S^+(#&JqDRO(tux804Dvt>zu&B$}Hf~SDlCjn@DMNS~P zRX(mW?WjzyiE|Z+O$|Qae`X;);QGm=mc~PNKJxDjD9Yt2&=U*325z^AR-PVbc+&XS zpaep#qg7b|yN$>P^~*cs32Wg^^@4>eMN<%%JIU-X?&5T&33v4-M|crh3E{V`ZeVR3HiZE? zYruI#Gz=aB?j7*aW4f|IHn$ay+t;EY8BU)v6yf1RMw<{@F?!S}W$@qT$=jBHD6YG~ z-bWV6*qmKpB&Z}PH#@|F+=EqZz5u)O0QYioA;qNLx@Da4dZm+7K-|&|8#bIHq>?5E zTActk-rIF(pLDIZ6rwyrVUqJ{wzBYw0T+Rzm9}>DaKm-7?TmWE`{>yME$0=oJVEWl=CitJr2sNbA})=F#usb<2H$ICgQAwPLQAkx6Pp zkS84nJq#4U$-+e+F+z;nxRmBi#b^R}YanCob!gcmzX@P2KAqA!g!!fc%F(vWrI4{^ z@{0tW0t@U<5ezI;{yuk@&YW4-_>BqdIimFOAKg-|h2}(-XthVE#1VsAS8`}SK8ldo zm2OsunD8{<5 zD~?&7lQBBmw7gY^F1&OvajqCy^Y=)Gx|wr z%V4q`2mcplsx%C*UdhgxfED>AKr6hJG=J?5)UJ3Wefm@|QJfEW08c4d!j|viWiEeq zu^sgY7JPg89Q@Qs&!$GiIQ`Q84(tYXf>6K6#U&_z4b3+=dk{zPEumjL|GCJ+#3!5s zM!0L%6@|c+=rg*x1``NPqUg0HhMiOlEi3)=-!_Q`eFycHbPL`RoLe5(fHx zDFq+=@cX$$)%b2uw4m!OeRlCwWr*={@lSv#W7@ha5_cs6T!9Fru+)fMQAB_D4~js!Ggnr7IwmE#Cj?DpYYEldaAH$N|4QahaEh?RhvIfeE z@pNHQlwJP4#IMul?zlY2P!jV#>3|@hb`#YDV{40ALl9)dBLeYe;nZY83;yt;aX=@` z)Cl(h9&HqeD}zWELib#(C;3_NOd=VN z(DE+eDU<&HB=PindSKg0S$v3^VBqxOXN`}WerlvP1J>DN*Kn)%B%I*p<=VF}I>Jrk z_=(+dMuu*}p1z94UHbW_qhqsoQJbi|%?>@FGC6(`uV3a*T!yIiwoOZrkRdqV40rG> zB4B1`GCWn_E5pQ7r;Xh^;D&~xXIHqm{AGiyT}7^}p?GVW(8~dO`6r3n$E=IhiP;G} zeh#h=Va@_)GlU@6O2ApF{rlYFDN8Rggowuoi;hbKE27ll&yx*kcaE4m9JeZe^P>jx z;sC>Ef!~A9yxMEI#&!se8fHj4yv)MhrqK=*NxMpAi)2 z*}*v~xO3@>83V#~W9u3xIT-5g+`oUS-{2{>#H__I)%f^s(LbMuT^wVwQ!tZOnXI?3_NmxM9nf;K5LsgAY-vd~MfH^^tgA zh_H^)BZa=$uF-s9+ufS072vf7H|8u}Juu*Lj*)OPWL6G*`mX_%N{=77tP^R?P%DY3 zfixh&fg%bS`BwAh$@z62fdAeyogqpl5Gs}?o@2lZhJcI1ajFw3ODWdQQlwz_h`Wlh z*PtyKr}vyJg;jX7x^07D@0A=IfmNkpB1eAX$UM|_h}YstNL~`l<7SS)k#9Jw-d93A zpfD;>hguB3UXw+bp9q+Aez1D??%KPmh71`Zi)d8(nQI-I%&i^GNLLIG#*h6z+ifa} zA0Vv_g-ZXL_Y9^*wDIEMjDA(uE&QjJCoROsi11IE;B}$-?*-38K?DH|(Bw(7iA_!% z&dbw*+Lo{J(v^$+{XfRFe5IYN>~LB;^qZT?w^(cxnVlUO1#GuQ=*iqhsaxr6zyg zHOJp9DSnw-7zjFmbQfJfL#H2-xga5UF#+}+qxgdR-C)xrOICI5am@?Q=FMm`$hRA23B6MV?4f*|ef?Y$5%!@GHy0s}9F zjakVpss55%yr^1=xerH`W)`vEJTHZFHD!2rs#sO0F1L+4#^f91a6K$2m;^L5otTKh z3Sj-HnOFXh%|bzEM#OPvo9{3{7V|T0oS}k!qDqb2S@s}2b)}MWowR?_NmbOSCwd3Ib=cNDVV=cX(oZN!B18tB^kDqS5OEWZM zkt-!BFuV3x%Z+!=8|IRt6`)Oj)!(_t(T8$jT3A_Q?K^$pYjb?)gu^>IJwj4K-*3iO zV+W~7zvSdlg2t`axv{6redPG0zqu&QIN3#DZr(e9qVQJ@k{Jy^O=Qk?1M=jdk4v?p zsfA)&kADXTlIgy>O%N5IL^VX~w^-vSnkq^(p@7jox&mM_Xw+`UKr0K9!6-!7V5i4v zNdZP&nbuRhGwz{&z6ylAod54vbCSBm-0j742!Qxm`4+AU zDe-BoytOQ8VR`qvl(MVWtO@6R!r1KVZ_)pMevT!!G1i)NQvs!MWz$-3s~x&(1v`in z-#1!uyf<&&Z0PbrtKWFrM_K9@J-UQ$$Y30sUWVR~&ovq}>p=5lP|GclE{W|?7Et~ z{*Bf34)w+K15;OD;TqmTn#?_mr@|@u3^FToHF=G&BTP~H4S*G_KpJNXCpu7ud|;=> z?0{p^E39m&QVJ^0y;K9LQ(NvzX|)s3pAx?W zph|E*7A(?{NtzOKTH=shkkxzg;CEr#WTK}^fG8#D(xXPTbLLUaz`nEAS) z4BX1kcWzw+KeHJ!eGk7pL&X0zM?35FBh$4o{d>e>?*2HMNZBb%@H*5<<42_8+cB$v zu%LA3D|S$TZ#UU2NYvK-Yl3kf&$*jt!8RxV%5Hkb0c)r*wd!_#TblZ94*ee)g|%gM zyE+`cO7+G<iqT zbslUL;Z9HnYj?jH+>QSU3rKad(12!LtZ_2V_`OZc`zzLr@hsx5YD^g_S48hYXEr%erG-A3 z8b9XQz9jk6j0NRR>i9oCT#v2fZi|rL{K9$J-rngeb$yTRXBdJr#O&ca^m91X`B`$E6emnR81J-7-b8l=^r^J#SFkmx~ zVI>j!8OCUv5FkC(E&k@>u`yKW@XuiN#-4))PH;ZLH4o zyji_>AvZlAlBN*%828C#qy$!d^o~0$SD&{&iMDhpEA?bP$8!`!0A9dX|7mG71|>Lh=)|I%;&6yq7k z@7KLJY3=i9LxZz+p@-Q&kr#WNLYAHKu>gkRR=ml~-^~Svzc{*!OR)3T$F7VNID+l) zvTMp*Dnpa}{0Za>o5PMWR9dqp^FaP>wjZ%J@` zIf^5occ!?c=++c4Q@X7H3>6DLPK7|dR7*Or1x27r-15Gn!b@&XOuU0X5jmfX$F9C1 zJV$}97}`cI>nYl+*ykt-HXIwwSHiEE@w{&nEK(RPV6Ta+#1Z)I@MYV7`s} z02A=3!;gZ#i$&e#%TSb!s67CT7CpbTy3+t_jeD7$@wTUaV`gmUm30btm(QPVzpjTJ z)L&0;YDrbq)%tU|pA5j~U^AX#lwpjIV5S|1h4RZXmwlo71B28}E_+5l3K-3wnK$o+ zqeq9t(^F!PM2|^ZRn3P@dP z8K^qaK#2l+jlaAp4&;EJXz}D*hp@h{Q-$6}uh z1ZD1+WN#Uze7tFy;Ej|?VotVPtC3C=lLvO}Do&huJ8$l-^Xa>tf1Lg?kJ6P*Tx0-g z@BBKxEDwmCl5yYmh|`4%<0k_@hg#3K&*|&t|JdX9`mpDTGu<M(-%3ned5g>63j02Q{Rof5bv5>kfqZUYVG3@@Ypk_x3}~3 z-eJ9*=W%X!UdahRdBejqU|vwatAkp_tZ?A>dmUTBi^iPmw*F4$!OHO6q2N;H2j_z+ zy+mPh^!+88lGtu5fyIHs)>OPUyU?cn4;?lBmwKEs)VK>9lD+5>ZEg!c1u!i8_{3Hv z3G!d4?3_|*$Y;1;qv(oXoZ4OnDzwx#m$edirga$bKh&|`D_i4>!?8pZ&nL*dtPvGF zLF}jLc+76YDEN8$s$hdx?eIE*WXu+)pODsyIRwQ!Ax%H=x32-32D_Yx@-pbm`&mR@ zx%>V6=?u#_{P>COjDh-2*A4bmxNLH8JF?5QX}-gS=W$UV-x&qyZ+dinW1_{3*&Q4r zPdY>%*2p+y?-b~)R%CbTq|N>JeFmN>J2`i)a#7al_=oNtGy6CNy|4Vk+qq*v;E^XQ z*E$5fkMD8dde9`~H*^E*F=!yD<^u*vz9y6sEeEwj>0rEI!GgHdIhS4gDZtq=AM%m7 zC#%j4Rvfo6!^O5!$UMdWs@ndiYNN_T&HukQ0ssCdxdDpG;;!?*{3k7H_^_)Ax0*x{ z!Q*04QeXdcgR7&wj?F++0=EX7OSRp;*Nf*&8mF@DwKo@}M7z{iBO)|riq`>;uwF{W zwg3A+j9xx2!?Mo=%Y9&(+X<~p1nN1}4!D9?;FPFVWvXn`|Nr+-3qIs7Ziup_`s(hW z{-yILyXp6i@l=0&ZlliQ@Vi>glOOFO>wVg^mHH(8b#{F({7hpD0`^UqvDQ7Vf1ik9 z^Px^KRR}6IbLH~eRDYPiJS%tR+PEokU!osd*z1>tzMgh+O2~zAOM=}aP8!DEXz;Ew zHJ$C!)5qZ5r85~t%2lQfF?C-f;+%KBnlYJcny4T7vB>s<;fIY@J$zbdZYbS2uRA*w zXab@hg}-;bq4}jcap#yb!H;_RaN(ljDHYyLQnu+kxY&(C?M_ls!MaZR_h{lUu&;Yq8mXvk zk!Tp(EB5og%Do5A#B*#Nl0xl#wnca=oU2r=GTrq3Ge^G)(tYQ9B`?^fx+3w|J1K} z^8a$&ujr5{&{F=ocmbo zweBJIJ_dVYR_4xe`7JB%g?E z$F@B;-!JOpv)DNA4;p{iR(~*m5nJc#WOCi^%{9Fd4{iFn_@nBpf^aO2q@w=^$iGNZHWJf?rNC zG0|Etd>n8Lru$vKKaA`GC<2sh7^^hi)!Y5wAK+}|W{Zq3!i&;@>Hd z7Pc*)(qflV?tY4VibLixUepyL4@7dphNcN1IE=it)}O)o5z!f1JMr9w_5P%tY-lY)T{ZCg^(HLa`G7 zkpBV7b7nH+`vVSiun3|$Gn}#b;;M#qDtR)0m#H*4<* z6f1*BCIG|2o_cB2FNEQo%fynG_y#Z)WVM2f1PLSQuHK>lI5@d`NiOk$aQfx5E5Pl_ zq{a{QP%eA>)|B5OtD>~=XoZ9UV}C1c_7W%GmYTOWF${#vX8@rDV*>@*s7jxP_2&V) zQdi`CviTA=JH2_1ic6aOZyvp6EF=~x{6sM%rsw=KX(US(@;#D#BUIz3|bpt4yxaS%V(Trf?%Fn z#7V*C&Ug96b0XJ8uL7~~9()O*_$$_TX`yLFcAP(d{@&h(NBMcCp;<`6WDWtHK?-aL zW+qRJ|0C<#8OxEM426K-$dX^#6caN&+-BT9&_51Y%P}ZQL4pw&%3+5$HurgrTKr*d zHN})LUX@z6ZhoWH4t5TC)2QR?(oD`gZ%FD@$T}S-wD4B)+AWrL9B_-Kt8B$&z5p<` z6*hSPAWP-=C~EO+P*)PDF~vPLTa)L^8JoV4X~8{kOHu5~&qK!~S{`gJ5cCBUUgN9E z9G~lgseCAM%oz98EsWAM=mLu%Baos%N(8A0>^6<5G1DaLFcWXB#13uRgagovJrH9M zlsW2nGVH@4B@Bzr-aWg?9v9{fU#NJp_zfTAIqQjQbIMCGWZ z-sS%|eGVaV`nKa6)}v?74b*(RU0GDU^ymy=abZ6LT&6%rmD_pUf=GsL-0<-8baQhPcNLx(%7HGMIYkDrTR`gneq?#=U2p6-H!G^Bw&>g% z?jJ{92u<+%Xpf92@Zed5^QPp>3deQr(3YB+QuI;q6?2beE)8}{dkyo*bZrl4vqX%^ zkS%2KIwL08U7|U0hwNT%@!|k|{!wB4;#37A_b%F{h`WE%2g@x&IJl(7mAEyYC*s6J zMgiWPWl8=JXRs}Co|PTcL;z7`pf?7Z+q3wJhN2E1`i67HGoz)*Kj~SGkA*VAvh1kIG{Npi102_}uWZcHf5Bqgf z#%X-+X;qitgDv7aTYhx$kv zNOd23YOJ7|uE+KP@l8L7LFUfARad7l?!hcB1TUfN3KJ zeX{qc;;WKF4!Gk3?BS{#lr-P{!4d+{&?3*Ky56t>;ZKQPQpQX;y{WcyMB&AP#ar-J zW!a8!TL_uLX2%d=0qzW(-XX(>v$9qrX<*|pkU-(YLGd59(3=F^IEef00^H8K9Qk0K zS<6ZwX9{8{1es?mU{==&1}zIn;SK=d`rUKaSG*Paj;w~Q*CF_8`E_fa5?}j`7M>J% za351kYpIr;Hb68CYCeZpWdaOnZ!nLkFRv)1q>~fgv<<02f?>odg#e!YMO}|XH}*V!HjFsazEhD%ll-w08XmU zOUAptI#1p-#16v4Bx01H<3ur42Za^|YAnFO?RN>ic|MSYtTlZ65D8%_I3Tz30Wxsr zA6tCA4SUm`Hz)&!3#fke)Np?S;7`p&jjKgKvZo1u?tGaH*D9MJA+(6rUWj# z@->eWEAv_cl}(SC$e+a71fY5q_VdhIF+b8~QjL^UXM`s}sSJ%cj@cJ_P<$9Kg3EV@ z@e*oZ+?3IT0;OrL_vAdDa{lOCMyU_r4Q&OWzt#PH!r z!*w|lcOE?GNGmJoFs29sHx8M*_JG64LxCYHXIxS`o#Ec8d-qI|7hkhA2VSPXkgQ-F z+4dNT={;$8>5-J+fn&zZj@_W$S*uG^u1`RNmBFt~ew{Cdyr1wmapml2HN%%#iC4j8 zAAdP%JwUAa(2%f~O9pBnb+Fz1cMN4KLuTe45}yd>WiU!E1@yJJXde=nlW--+(K)uZ z`re>i=W$EyHtk`O4IMy1$jDF#qWKItw;}Me^~6o;I9f#cf$W)rqSSx{-97Vaw!1q633%Rr4~&M4h3Lg3YHZh|svPMf@y6g@b!TO%owSQAsI@ zhf5+^^Y(8|IJe^57}(x;m*akNFApM}NwCZ|IFc|@EN<&u9XHV8an;=1&;9N7cg6<{ zcC4?sK}MsT6CjDtl}eeY+M?gUt33+WYdN(Hal3cy?nXn8`ZOrevRn$_f#67yWCB#F z@*?N!26rD_$Jp7@MX3VegpA$BLVu6|#DK z$UhtY#WUJ~$+&1)V6QTFJJh^oYh@gv!9Ydcj^6O*-MfQQdd>G3^rwbmt|LhWY^yc3 zV*~{2ka;}{ItZb8ki~K9A#6SGW$0lEcZW!8wd2Srs5q|*3X(*^3WbD`C#GZjgcp-m zzs>L&H(4YDr1};&nWgc1bzcO|sBh`)Uf)9tr4iz{r5a)LMJNtQWluVZ8g%Mz-$3&t?II-!ivg>2%!9tjbDa}v~Wu0?B7Ftel?=U=L{ z81bzp89ckiO)6vAY`XH#zwqPaFhmoYVdie9M6kuQ0JWz$5_rrRy)6%!_^NkL1~aU9 z%66HNEZX?AiS`6iZzO}wQDeux!cdkAPM5OA@06EkKQL!ry{wukY4(x-23ap`_};mC z(agBH3})^O#T75l-A}+5nM*Ki!%KRZ8nR=p`%i5*!)+2z^T3nu;8ahCL~9bNU8V3D z{t$~14|2jpJPq@~ffedvVX=BVd|l{@4p*jn7r%LP2Yb%0I6nw)dd|VT0eX7vzZ7>;c48F-a{`{L*{3;WBp-NpaVJNT!#xHg(`#xd^8~Eij_;d- zxcfngd-!%}g=}iG?u`oA(jrqt_Ss^`c6Z$v7i@>Y7^uW-`C$5oDg- z(Au*XbO)|k3N(}lIXO$XCsN?q>cx4sRr!-ucyu9A)SF9>&S$(Y>@k@F-aqmu1*-Ri zu9`1o){Kt}*x8TyV$4L2t?@{R<6)tmws3D?(5OBm6ymjhOB+36EDxc&TILaeqBz?kq;ZH~ByqAlhx%5a_&M0iFb{#7z$!w#dv0r2arAmIGC9Qus7O z-{Ke*G*Q_g6N0CZ*%io{5sW@VZ1#gpxQZh3;7bxW%q;j9e?$dLvA)ikVeS<&a%C?u znjIgUStQ0YJp8{=VNll&*t8BBHEl=|e?vNM?xQpOQ}cWJqedO1ur=@SC-XK@$x6r1 zCCnbkEQhvr6)M(?1im=u3|ulmpTo#nc&yL*ePWC;!t?@e1(Qd5`%25vGetYY^<2au zlCq7z|2HI7+M|PfG#Il=J7x~(BvMRiBhVG-WQL&K=80=kUT_GU`VZvSG2S%~Q7U_Wd7?A` zIFfMi?;md7=2mYx7(_AiD@~Qmw1)vLV7+nrNu7~19rrFGjhRsra}Mz|cJB<0F)5EA zj(6!ioI*@OgA4Yp)e3gJm* zlXh@sI=UsQXl)8yCXNaWFsWYCI12E4@7%lBj+zJO6*yg^Wz2o&PXClYG=S}35#vm4 zPQR~ucxP+%GMY9KzjM07a1@{!uHl>braMuAtO1piT}(hgLQ+C_WaE(YS&*m$GvqS* ziT@3BEd_CGCF028mW!z$qL<5GvPKxZbvz zBUU^gC4Z8|44lwAB(=vKUI-XOSH&HEMWL@CIsqOJQ~1E8^F}M?-i9`T;8OIcgs}=- z;AArdj{{M?Z{=A~FE<6DdeT-7S^4tkz>j~OL4k>21VN5WaZ(333;h`6A)5m&Wk2YN z&A6tT1%%<<)3L!77tdzzp6VaDjWGiA<&d^;&QiktT4N-vW0as-MZ(nIBU5 zB$LI1ob|hKuH~~)(IWTqz%jombMg^@o7AkVX{D#@iD1%N(x)*Dzb8^%x@SBP{F^6v zZ0y~!qwfND4s>!x#%}t2_6SS@5f1$L#M)ZYA4kOn3>CZ#!2=42SIWD# z!CzVdVHC1p&QkXqDz-~usL{RerZLlizJ=x3*jR(CaxWEw0#M$zgoMv4^@}OJN>X4?ovd(1^Kp~-A&#wlwflb;b&8J$ z`UW%}=%#%ETLPW+L&rayXKj6;)1b9b(4;YpL{np&lWieeK~ zPnd;?w`;Sd=55+LFVfy;Fkb~OfoP2AjYJv4CCbM@Uuu3h#?a(%63EkEP22cn8<3HO zgDscg@f%Gypd9?$3PU{`3&7%8t*L3-{OCHC#%m0lP6+t6J^Ra2_|+JBbfoQuWwJda zM4el!{^bBbfv`VC*od+ieY(X@L+tc|MYqmOLRwHnEpIq)sJJyJX7qPDDR~(7d+c#p zRwMdhTBMn^1(EnFaGGrT$1+*qGq)!kcyXn`17zP0(N@q7VUi^#8WM?x=tXUx5v(8j z`nMVbB#CD9dtHBCKZMh8|Iv>D zpeM2ve2jKW@iWCD7#3_Vic+4Uh|>A1A~q86;>&w)fB%R2!(KQA0D;kjWO65ju1+N@ zuCdfCi+=mfilpkd{L|uP%XYKKfbLOXcTt2vLM8mP1Jj7br5qohWgYALD>^(NwK&i) zqW{rEx&_i9OPOf$#H!L@f$l(eut4Hl0nvi{meSDf)RL<+T6z?(L)V zGOd2?F=7QYdht-_4#?l&4_H(m9%vtDLXvnKUw|9e04eh+>jJe-MZPSYV5Z#c{xZ z>0Z>RHZ9ESXW_+($X*gA7gp>n=yMFHD9$y~*=)1a zWGs6@gpuOmOsyK&E1Jv4YZ(2o*V+y+I6xEF^M0S7*LBpYWB>eosZ;z>^574WMF1AJ zM9guC-OX|3?y<5eWeprJ{BXRl9Nb(sato#Pef}T(_iE+e`KfKJNcJFnrVP61vwW|F z?F!}$PWI__k1|QpwdldnV1hVs30sj6f)-}#I_XoFiBSc&-E`h>2)+B|9@!Ba8~f;S zhm9-E##2cNLd4m>Odr#CD{@8C}$!13IQ#+W101 zo&3erDau4eJtRFX?PYkFPv_ai)jJ)F2Yx>Nu}ksS^@n#puO6LKHNJR!&gIi<=9N{y zlO39(@#e!3^(OAU)2I%}lTRj*>YCUp%Qtv_tpo%Yyc_-6*OKI{GXrcQ6qz5GeO?W> z?F~^(Kz;J8xTuzw@n2+Ttt>hMhbI6uF3#>S0ezGC$M-%K-th<;SmwrxRNl=w1`Ed0C82`{;a?6Ws)KC ze%X3H08S&<$cdz9IIsRIe&TJZd~hqiMHlJ)eYF;U6OhM?eww89cqkaLZxHDS#}ctr%-bPfNEDK(WSlv->975ZX``V2kDixfF!Dgf(QF!) z9wR@lpyY&6xDW^fRmz_G3;E-B{`sdHLSH&KQyZJ?X_?kDXQpCREW~5#KMI%f>S_i! z`1*_^xUlwOPEhn^b4$zQyLb1JXT!t8h5m>VSeYt6_RGV+{}x>}qBD#K(y7EHb%~gP z#4hA!g0$t#A8Sd1l>G~Ug05}+s#4<@zXHKpUT%Ftqm`MNHx>0m00D}NWZ*tH065Oi zdLGSjusk?q3l+R+vxzDyi3~RJ(m|)O7*M$Ixhy&*Rvn{E&0T)H&}`~dSz4z+N_DE) zWP?o*M{$pN4A=tUMZ9JxE+B(-IT9$8QE+W*8yjgW+o~IEqumzkGFtf1xVxaQ(^>5U zwOL51Lf9NX{%B3j`tq`sh9U_P)-vgCBTJwj2UX+>s*s&6&K_ZlPi@(fq@Ox~L4Zhs zaLO>ZGdf`#-JZfN47i}=##+t*q*c^(2dPLns|$D#@bA5Irs7}4!2dW!!wURPFtL+8 z(hPmHfBcP3;1F#%6}JWaT;1XlstHbwG0q;c88|I%7csKYZE8bF5vYw!=Jt{ud=DP1 z$!uuCp^W_qLOSRDiCMfT3Yc23vtF+%OGWc0sc%eigJ1$KX#wJdF3ydEJA7o;GE#xXOflG zJ}Pq?QgPW0dH>W*gzud)?Vx**{umUB_Rc8inXd04;DuFGUk`?gg;eYfLo|)Rl2S-= zLLSJ)R2}i>;ltndoIns^J|V-r{XL*>Wp?On&m`zRy@6tf4ypfM(W2kjFhVCziB2z^ z;sw_&!zocFQefbTBc2>={*nYa$f|F!O5jG|96vul?sYWPmBWn0Z6<5aQL{@Sz)=-~ zJjGiCJ#gzY+Pof`D>jzsTs%j9jwj_YEw|#id4_Z|ac~F$_LOCjIG9rC`1MI%gZk&2 z+I;HaT^fp#ZOPl#OugF%kK0(Yiq0^VYFoBsLf?Kvy0{svafxFhYncFj(I3s)@y?p7}h^jn)TN~Q5{yP5Wsk8u;ggq)w5 z{rPTdtp0s4!e9_A`x36+``X^w*;z&~Z0Ql`nv?=#J;GsW9KvLdj}qBdh}=+cxOF#) z1HkiK!{p6J$S+H-Bpe2ZM7AWBw6w*%@GqG+kI+M}!f+BRlABCeS*&&JsUB6>eUiD1 zgb1v*F-OD*1d$%IN0pvE+f4w7KAVuR7%Z&BJsJb7*K{tl4z9J8OXZ+ZQWE-K(=gDX z2;xE)Ec;sUV{1K5Tgm(RR4l1T9w0dTNSC`qStKo=m7fy)s)tACN$Gz5)FCQSbfw_I z2tc6ozTqm$H5N#wT_{8LmOOl;W7b$l?7M)r_BiMoY~`qX>+~wf{CPdIYG%0+Lv($E7L9644~Ni53Zh|WZ!em8v2)-N5~jp_1r~-6M#e;-W90JK{ewYu zD8)=8Csblx&m`;)xzT$1^yB2lVpEx(6jHyNh_jF|o1D96uJr zx!0fAEJBiF>*1g^0~$Mu76}prv@{h3989_O zU+1)v#)e{P=#U|TdQ-wSXQI7*AT8EH_8F4ZJWDDcQ|tg$5s=%HyMd`@9lvNkKoT(TJe>&BOmdlwe|}M( z0RMyAe)X+gJrz+4qhribV!PyJhnt=l0P#%NSxjQWx~+6KH8(GW5fgj(4)w$~IBf9S zWFhSO_3IosAtgamtCu&1u?o{<00;y}LDYJ~2Na_R1zao|mXWIhM|( zVv)~swiYd0cIbFs1)q<^nt*nb(x;_pSY(1t`crT3Wj|v^6j-)+o}(kox5&5$0pKhR zIzI(Rq|TH+OOT!*%`@Mhsp2_~wBSs_7=%SWk+`{|SDSlx@5c^WCl*wj4h(W6J!_V%5d?qT^}vo52UXvOAL zKSVXFExtD8gfc}vDAB6@MQIu+LJk{x&bMo)H~ahm=$eH!CrKnST!KbaJgZoTSdHTv z_5)uFs>R}rNU~`6=a`rn$EfpmHa4{@{BcRgL{6mmc+(U$Yo9wQ<5J1GjD>l9{L^V> zqiBG6R>Rmt1$?)Uc_73QursF}m%l|5mDmF_l(phJ87V(y5Ds5)QNVIQPhoEeVCwQN zJC&h{Y|bpWy_0?vlc>D+KbiGW^RsWiC_Ozr%>5}#a>YcS#~eYPWtJsj2U4GCfnfiI z6DdI9dM#f3Dm3m1zgiaRahHSp&ygGs%}yq_1T4y%(a-Fg^@|UF{n`n%Zvwp`S5do9 zALG4Uw&|-*l6A+l{H^z_2jCt~u^_I`RHSF2DG9@b5W;PYK z*mgpyr3EL`CgC>B%g4cHKihBUfp{Og=Ft8p|8BoqOH;EMl^iNtYC{c14q_OBm&&oQ zFu(pg^>9}F(o<*8hRdD#L&6AFQj&##vf>ohmvCMP61b?KHCBU#w!N`G37SeKl>SIz z>7KcP2hx6{pxy$uEhs?#9=`-u&fiMi4-HN>k+bGx|&n)2$`)!@oUYkV4MUur00RaKCF!KtE*~Us}y@&78^a26{Q!1x8 zK7`pLl@XbYf>DMoJhX4vz&KXh2MWJ@)43u_5p)BqFB>BfCV`W`qSLfTpE1BzLTL~bEGGTGPJc;%}v z?T0XG*_j}t80K$g32{y?$7wWx*=2z%oUXbO4|Sy>B%dB!C)G?g;U14kUJuKXv#S-= z{38aCoac_!SyOj5?zLg9hGD< zPmGqQ=|PFEZI2fZOErSL;^K66kTrOH9Y@GYJybi#Q;0 zR}@6#4$^jceCkD#DxyH}lyiVk{KYlPAMG-^-|s|^WYW@Q%1%111&pr?i?ON(Vv)Ht z0rfUx>ZkF}djA@z zo%1|%bMvr=vv`Hb6U`$PLb`w)c;VOxAxlC0cM0;vvnK65sRiDK4>P+ z*3~s&k{^~Zi*{Sxz{zw+>P@0Y$DYab$HVM`wwX*VqTC=Ppff!HVDxW zNBXeXK6eu@Xm6*A!VvWUJ;ik!UXWS)rb0QgT2952U zbmnvdOdVo}$U|t<9`eESCYR{cR@c~mEPmf-a2Dy~G`#bj;K7Us>I%+rEMyS`14!?V zUW^+HH~G5mz1HA$O}{knOK+X2f98FeUhz`j(DnDajujXCZpfPX^6fRs1eipb3`ivI zVFJ2@PBQO9bY!HgiA+AlZO8Sn;;Pcn4fd@%y?EKFp-0x<;v^DAawq>!MI&pk9F2(3 zx_mnH%9Sy4m<}DPfHuRb&|5T1mq|y{%6up`3SYhQ_VFpE8k1{ICK|$sjc=?w^E&xi zYILR|phUZn-9o(R*t0Nk{v*HYF`0iDW{%bDzw-CUYgs>Rc-L^{8r`ZYHn<4chzJEt z{fUWF>gyOR1Do%VEu7Hm38~Uv3n&35H=*{Q=?w2v6054Jng;BA@_zDztRG6MO?!(p zgNBqF2bj2(9v;-Wp~S29udHB64ihKB_bj3+#g`D@$4oF!mT-*#CyDjwPL8MbK1g=L zhll82ursSYgk%ol4!~H(Ak_aw(NiB2EQ)Y_aVB+(ES^@t5XLo7yUgtgu9?3ko=J^NG=BM2al@NR ziVj&MOcw;&yM;d{yiqB@K)YFBDlfqZBJ#)b0~(5A-c6K|U?X%+1SN;;f3vN5@LX|Y z$VxNyt%jRwI|W|^CBw4=h;I-?C`h8v1ozV6v!)Adj~C30u0Y{O|WZ>!Wdl!Y*tx%{Q>!KlJ1 zi+A)sH8En;wmjMu4hvWUC6T@?_TYgX2LX|sK^x_vRx_}ytbdkUdd8B>zjXTZh_rH6 z8(wh)=aC^MAA2#4IE_m@1ibH)I{!$xhdo4Bw~X?9N*q|(ERr$7tq;h~4A6DR zvdwhU##F2ruig)Nkw8aAABLk+UDn|#+tNEx3RBDSsbpm-e>wN)q`kqxn_z-an0S>( z(Y>O%$%W!78M6-=ytjDj@%m&2g*abZc1hkPF@%Y~K=kZ(EkBy>*tDrJLAKet$jIki z4^9smPZ=u+|El0DsWvmx0t-I@Ztw!E@RLOA$wklASQ9jjb17Sg z$m&uUaf`(nmcVw{IU#TPWJX!?zFersx9f288mJIgvxy$Ts__>o8aW|1Wm7AYQUC#k zrrT_+Yg;tmaG5ACvj@BadJvKagER}4?F&69pNm2O-k=NA5t}|V^;+6y{mZlljGA)IDu~ElWXtZy@|&oNSODhsmSLn3w$9dQ2}7_5gf0loi{a;dAOi+A!14V3jSIGe!FI z50BSl)qp@wn0a;E5%EWk+@#o;gZVd^>qbtFZE|*&OuvvvWS6(S&P=W%sMf&e%dC3} zkSY&~C&%cfjau8otP0_4V^h+OzjW-aL2ZBE@T;;7S(KD;vrVQ`xxnU?9yZ!!uOsW` z31!+3ODS_+Q`v%V@qUIw%EJv6QF+r64z-HRE%dk78R6ohP9^}#@p-%xIj*{iM=Pk2 zS)?QUZCFn?ToBr!YgZ+1`vlr1%1Q1R(8I{dllMX7vBk5({>MfBke!!T>a&&`(z_$` z;hDbNzLC{Y=9_b!8~;zefbM6yo0~Yf;kh&r${!V03hGP(Uep_JHS)0*)*P4_m2HoK zezLT%*igNo$z66!ikT(Sr((0A_{IVM;AN@R#g#r1_D)PAsAsXOROS?5=mY{X?p)`U z{rmTa2z)6CpQcp6HejauMdGz2!dk}0ZgT-x#b_1&J}7H2bqWElff%NOwif&|Cb^rb zsX8?;o%={cxz#@a;&{J*;7NCg|MdRwIFMSuZqbJ*3%Dttg9KY6w$?vVucow=2{AApMtH1-4_{|-JenJ4l^6wKqd+q& z(nW)%7IlzRK>wvY1#U&@ma6nZ{m~vBi1a-M502o-Ak(m2wCK3%v&v?&xfOIowM$lY ze|>%Z|84DQ+pgU;zA|q4A%|ZVv221ukpu>TK0gNUHbHS;F`D0|X%?jiFLg(i=pq;rmGUF5Cz))@gJ= zEPT=ol&cvneg*d~sn@HYi{m$y$!R3BBtS8hmH7ei7p0W%^Uw~-GH}L9JzyY3*YWT> zqY=|dFYv0-adFqFf5jFQ)!0iSw`{lv-`0QDXWKJ#k3n0(%Xa~AVurqhiPdvCVhcrx zMjj_y4geQ#@%N<~!bjNK6# zsk`Vxh1Z7I%(x>i!XhZ*Vncr3e9-f4#3s$=LQEkEpcmamtHN}bh~tP&;7Bo@j|GP^ zG+*%`fJQBhlti$Tt-=I5K{K+inC=z&|xXyBFxXifXw7`Sz<9Vm3= z_wTdmnuL!Bx|WSz5s{JXURe{qzfG7?a!A0qej7BiLSA6M{ujE};oe7<0VuwBG3}$@ zH#Bc|DG_nT7qOTN5{@>7DXJv_FL*X=>yniWAOgUoKelXN%^8%k3lxz^(8F|Vi6)h+ zjHpy+NNNIHDSA6V1rN_(W!*SN$Q}EVc0ewg0MIp&W}51!gd`wxZl0+Z ztSpUd<25#m(G#wIf=d|{PWt=zROO2oEjj=~Ez{c^$9sG*iK3*?)Y`#n>miQV!3m+) zi1t~3R&71RJfKn44TDGfaN9-cNtX}4b$}zuh-pRq+?|XP$qlC!W30oHUkfBSrv2nd z)1FVvKfoTd{Gm~p{kO>_(^0PAtXC5mv^VxapTMWG;{|qFmVQv8H3RVE5gEG=(KR&< zBm6H01r&e^J>JMI$1#qr_o_{Ziwm9kU;|Vm>~=GX=u)_vqRs&$Kb8RO3M&T0DBCHS zI=~c3G?-ySuj^xzK-yw0dqa{s=%Y*qP)}dMRNLwOZF|S--otGB?+Xr=VW_hhHZ$=D z*)a}-cN8qU-&L?98pA|OB+{8H$7A3RGLk7M2DY*U>Adi0!2~v4%`WH_WV)3|*_V8a z^X3)Q9w-%MLyd3blE>h?bDo~=&HUFP$VV)HC1U#pNAi0K9mKbP67RShtCQ{zQuYFX zyUm$nO?mCRqTKi7a6~+|xZsk>cD#MI433|AE=l=(x(CZBfU;yYAt`KRi#G`N^Jt-7 z=Ejx+5m~CWMl~j?1VH9~k88HuxLB_uB>?;@Ej_)0c9RFuU^;Us>idI}(QknF_n^#X zKM}$eQA%MVIg8P+vOdd+TcWlVG@XpubyODafNi+blx~W+>R zfXQLY{)4m|Kw?U9!k<&+Br)6>_B_rec0CQav`HMHFteqJZ-=MPeW<@UiURL7wKkZm z^_Vde=KKPky8<=9JBa&P%fA$Vb`nMmD@pi|yeZ2_64lFT_+Tcpj$aJ(v)8#s?L%fT z#vX|?Bk+ZYSO^UT|Lo^&H>Z{0PZ0TX*X{sIp&Y%A_iVtO==TV%4ss(!_9}zFoZvby zqbUvp+0chyHRZVsn}ApUeC0#231TQ0mW1)suD|xSBiDeP5Q&HdF7WD!3qDd${qwZx zWr@y=weGaDsp}0XB*q(;JKX3CNrs)9{-ygcDC*|Q+)gRMMLrM6CriKttd&CT@$u-x zN9uYbYAZsdi;zh&I)61h(f7{i*nx@Z02~xPfVCm5LJ-vU7km@aNNlo0lRVtpQ_j66 z6N>LPE^uTfaDpdoLNY`%PBaw31qBd7#_pXACJf%7xv08w#;5vWN};1$@>yklc2PZw zj2)DbxS;$fGb%LOwiLVu!VtUJ=vD>Yy9G{VxxrQ%Iq?wY)n<~}P5Pgj%9$DHQ(HYY zT?$jmeibxXXj4RMz(tXwv#Ycdi zfcPo(9;E%XV{P>z?ThZqm<|Nj@OOs~nsxXly$Um-2syRqKv4|ZcsGVh{VCEg-jbsJ#W=`;`Cg#=;n3^q zz4?ZYvp!J{InT_#DTbD)r5m_5H1UxoWt<|qL2jhUtu6@~hyI{6R>1k6D2c4)9~Ko7 zCew<}{5KS1>JX^Vg7%Jdqjku`Zdn|5pv6TWcOk8eM- z2PK22sgA#9{B!W`pFOsHxK2c%%9UTp4>o>i+REqB8*I%k^C(5cNw&yIQ3GI`?Q zpO=6}d-;`J>f^OSvu7a{kPQz;oiOc||JOaRbB50~APCVTFe~Uz(t*QrxZz4d8wysj!NvVf4Tmhi-e$bZW9hF}jU!s@J$;kmg6jN3F6+|>o zxoB7A_KJxI>|9%raYiDQ;CjvWU-h?=-u48dQu|@=8NEZaM&YkUpVF3ANOnf@2w$eXf0`9P(r8gk%aQ5CcNa z1XcfQ?yyZVA7zx81LH-NG#vhxREDp+7;bjxGvzXEHE?wU*W0})!Vv9kZ|SY_`QYJY z#c(@t$5?@9-n!Mhox!akG&cg)P=!2Zx|TF3SXjo58w`5rq8Chkn$fClhYq^>rp1ru z46_W{S96etOm=aLFqC9Sbz>D5_fC&p$KoY`=nXFcBxW*IE$Sn3yfi}STa2f+j^hav z3T2f$6kJ4$N3&ZaM4%LGybjpiY+Ly^6fkSC9R34L4ljqB=HYHK}ZY`Q96aX1)o`{NJO4R07ArESz`++y^aNS$T(!Q zK{ToqaocvTOz&RuD~}1e6JyH;89&)cYoJc0i(8c@>>V$=QSaVITRj~wgUv@=MAcZ= z@$2zp;3};vEwzvEriML}LSEJklAH7BxQYKK@<#eqK?|X~mulQN%-(s#*j>9n=F4Cp zx;FnZ>xoDH{$%&{xGF?Z=E?IiHllPkK&~NFyoodyJ?tFs#Wi+i5uzH1e2OnVJl9yy zi>t680n)dTF(7t|zL}Jye3)a!J%jLD&r6F*O!Pl<=1lN{0Ug9&olX*W)lrO#P~OU? zl7z=`6@RJF0xtkqBL$1DH}K|X+guF0w-erWGqYj5Ug}=m@h{gvJGi9#4B>6u$j;sd z2qG@7fG^$Sehh{jM0`;yP|1U9j^idwXs=;)5kP<+yZJ*1Yb*i+s{hP3|Da0*1mc#_ z78qn!0NXeZpm@%_dsiS~mcd4TbY2AX`HBl!)Lu<3q*q?dzpzGxz;v=iwC$Y-2F;UAzz60onF~YnR!im z8n{Z=@E-T>+j3$U(4_J7r?1`?MMCOKy@Se^wV79ujZ)k~hOdi?D(u?s$KHputLt3jo+zCS0!)$XwK|xy;m6$nB zOa37GbFkZiF;TmVZvwrp4B-O@^ZLV|>v(MCe1GC?)6W|oQm^*)TJ0Ol0wJn(-sRfR zXHi1c(>6^(*T^kA0i7)4u0$Le3u73;W?~vYO~=eEoO;%g3(#b8WbcW8=_XC^YElf#VM#^+y0pEGVMx zQyP5jfjoH(t3jZA-p>kF8NENOwVUIh-BExw@(yVzJwfSCKORSJ;;rD)WEax9kM6-E zM|Qxw<3&XYbs#zUHrJA7tPx-@o8&gYj>rR$$x=k%kT3iUQI(2$mc#h(vyo>{@(!Tz zSvVnLg1&Ly&c^1`xp-nn2dpRP2qkt>->1~bu>s2~BCbQG%4Q&*&6R?JAMrJ^Jp=^m zU_ye-lYz{wNFG1ihe=8h3BjTP&&6iXA*Ga4%v*LpyoSYL+(&5cXiiP~^Lk1~;$M&E z4Jpyp7p!*l)e3wbJ8&zeB|YulIiXRbX91m*B}O!tXee5OLy87ZOn{*Ga0$1iu!I$E zdnW4CnIrS9C?=@YWZ?>-lDR@(RJNKl^EZQkYNhT(k|J}dJSYJYxrwZpGqBUB2h$rv z(=AmZC8isP%4NWoDD`q{rc1#;WGy#LV&=cmlWqoZiC4a&p!f_ z@16zU=Ka}k&TuXze5r7;>Zub2(XuF-=QE^7RR`QjW%-T3R@89`c`vZ~>59z`-zJAr zT%DG8gJ$f?;Roa5HZ$#AT@O=KzihXG?t#R_97->-=M(fMFYj}F;9AdDpwmT#90OUj zi04LM7GKDoPy{YwJS3z0CqCOkafpWw($Aa6BhwH5w<@XywV0RzoIhW)yapmmn%Z+n zaLc~cmdQ0YA)4HP^^>Jr0wd6v6drnR+lDhBuI>=n2c^bQA!3yt}SMLt8b~%f3+V`ABNqxV%lXX3l z7{b4^P;GggHy^J@;?^-?#wEv=d51o6_$X`akt#v%4p7MX01^RCwFrHJB8fC4)r}B; zU@Z4!5s!vhY{2(N3vC>BetdNF>B5>7IF77qW%#pRDM2H9tpB7fK-8yW_t!?WAcFr0ognvBbF-sl z!2$K>W`5=dO=Wd;J$;$_HF$Vw*t||I_lOUgefugSe5XTczQ(1HsD5YnDb;O94$_3A zdtbe6AW9&93#CIeSr)`(cGeOWM{nQmli#c} z+s~}6i(AF2U5pCYU4M>|pM>_OojSJz_m){-x?9CC?@84`Vh_Q~qSWhUu=Y;Ak{hy$ z_8Qk^sc3k=8C0$^S&Ob%?jWh=?xRO>xO54-OuZ7FoXouFO2^(4-oJkjQV@K|upgAy zwOlH*Bs|i#}aGj7ha0wo89HTb?vj)^}>p_Tn+)oV|M5SF6N{1FJqCygDHca zhrhq?Gbq)AkzAB{qugyOISbGDwg=DA%TV8vX=h=}!ep zrd2|yhM4V>!SdEM;WRojL{H~MB_Bf?V1jO9^inM?Eg80!H53h5+ZJ<8dB&@x- zZ9=4EQ*TpysHquhYkBq3dpCnN(9~k7gHDhG4zPToOz;(`Iq$^Y?mM-UOd;tw0kzmo zP2tgkRCU(SVE(!-esU_6N~m>{@tb6$tie$=CT5iyEiNilI&sdqXL zSw=#scpy6u-c8l!A!*f3i#;A0b>PU6+G~Op()|<17;MiJgMo{y`opce{8?^z%p^SK zA6vb#TwJglVSf3Hv?9KVO^#Cgr{&lc7#bNlbRJyBQX!_BXxF;MHMI@1&S%ICm}c1M zidkvK-m_W^a4@aaXykPl4K_$p_D}$lNOwZ=tc|khk{&7 z?^*lBC9I2M=Os>a@{^ypNv}C~8-A*RtD`O=+tD*l&)W21)zH~Z@yYqN#>cx08F3~J zq)2Sx_0j%l#qAG{AG0#xFE`s4-?~*?G+3Cfc}&UgLTRsKYd?QWSNA;s;>8QdY?%a> z2Ht!AvR$sJfYhNSUvpP?&Dwt9TkiW6wM&cNAlgcEeo-}g;ifsPETE1D&NB;fI(eb| z?ZoeEd6@#9UW$EltF`5&4|5i#MYp-xr}*LkQ|*CzfY@+UubHf4XLcj4lGW)Rd&nV~lxx{Ds#^ko|ribt~3tF0838@F9x0P12b> z`Ds7B>n<)Xiz{9X5qklyo`a)f3=v%v%*a+KqA&AF>A*)M-jRjX=wBT}v@5Eer};ec z$!BN|!L3NTkts3E{r|%k7rbe)azCZw0iL=>O#KDi_gW8!$Q8F%X(VT6}F^pk5P;G zatbPo+swk{MY?xC|Fg%++X8mWG%&>!E%2B*bKZ{CI9j6C&|3WrI2bq1k4lR|Cl0wv zf={oN6;7V5NBvhvX*Egl4#;b;94-~08MM3$`MxUuqH;bKO3pc{si{Sj!DW5kBQ7IR z;9oS2(!bedib9iFQNPJ+)5%ST(VBY4;?-II{^1g>yfJ{XGKvgzA`^lft)8({nDktn z$F_>HLYwgV9sIMc-{Fq?Gk-en@RGk*iOveRweh&UyQ4Q12|^EcCnJ-s7A~x?PZAV^ zav1G>vVGEsqLz%iQ7{d6Y!g&{qHW{#=rhF1Y>=9S)YCU8(u?Mz3wHm1%wiH=Tm{+t%%s}C_o*L6Rmud3AXpqWkp)tZFsrhS zE(C-pYtxAzrW-W-V#r1*Hj0bOL<)j|+HscJmZJ?$sAWK>*-?y-EIm$EGX}e~Kk8)B zJ;~ZZc^-pa119f2A?w=3YXEkRa~VxTMIS^TFcWJ^uR&q9*0noOLI@2)(STP)7hbFz z$O_Y&CeLrrFx5aV3Zc@IaS}P+GA%K+R~LW}^alK>8J?aAl?TT0wOVXTZ)0O_(1@7$ zFJqK$vuEEBQaRyyZSS*Z&kC%{Ue+r-11KoliKdj=jW1;l{p+76s_T%#bZc&r{B^-! zwE1}Y2+ztWf@im>-ER?%nnJ~0-=Qu>XF^8tr`7u`M?WCiAYSNwELHJ?2(#6wd_O=p zC|k6zRHjX)gK(lhFWI`x6bhROC19Z4A6B-`WCJF{1?ODuM!EW5zwc3PmBTx~T44k@U?G%s`WMHyS zjHLzH)Rn#~)?m+I)W82BWPa)8`#^jwn^p37^andmwdiglv>YE?Lu6Mx!6zik?9zn2 zcgo-ZjF)}*TS1=B9B#XrH4%BFgrE9OiD(*L}ld)%>7WmG; za9vNHgwuJ}sm(0s2fs#kPMukB(z6|Z%7c@wq<5a6FC0E?G6E=Tz6Gj8o1JUFhYFCC z^}{+zSv{8eKUjaf%;->a*f9ft;%Tgb<{Fpq}4I&?rwRcqR_Df6wr<#d#ISmL1 z!59)OGIA8go+IJ+Jp_BvP5237e<4n?R}6`X36+Asq{WS zpXxu08(m_y07#I6s@sUAz5Hdq2rNor!XJUTS>Y}U}7$h(ubr|mwdVx2|oT2#PMG)jw?EzDvp1Uj!UCl>oZyLbG*nayviZY^ zHV&d}P*-YN9kdlMS!4hT*t*uhkz75+52rsRjPh`dV7iPHvO)LAw2MEFy`L;2>Rcvq zqrZN=DfR=xk_V(RgL$0}kGUrqU*^pSPvRHc8zpnMPv1Y~587&JWmSbf2)b!kc=(@W zyU{^&$I~Ipa#^r3uhP|Z_hk;9OeX473N`WnORgp{` zqZIbS5zH?ss*%yB^S(~q?VURIoj!bkORrC7yqE?3Q~{K+E$FHL zbcDW`5RUoKsF(BM%h1Uw$B$cwe6yN7xzj)Ylt+!J6wrO5y%y$$&B zHX@0CMN4W(5RryL=s$S)P;J>z3W97HZJ}TlBIP1d$J}>XFdPYix5{~R7l55Imqtq- zmk>c=mBNhKOS@GzE1ixpSlz(UKRe>xA-Q>~^ zKXKc+^{99Zq($4t<{uZ=7qW&(lM3lIZ-+$i^1}zfxzXYI&FWQk#za`7VnnGddOn^( zFZ=Afc#u$D%fuQ&6H2$tygbKl?!RG(+NreDK`*v ziDwHYG%$c8?d*C5^}e0PzqP6zG^Cvyg4~9e2Cn5YPOvu(d#bBP$(S%D%^2Mfh_c!F zTY*`J4ksp(IY*GRpjBEE)k{zWsqY*vHqf*x<`X=6>%Hp(fN#!Y^q=ZJIV4zSEDj^wQbzM(9jUFz4vLom4_#vNnUYF zTPxKcoy}HXeB<(O+_185@{k!$(bZ8W-B+t-tBMcsfF;p}p+I*ksue3&%HTMmQ$~=) zZVZu!IDP;dA4DxY`mIf|{-p(-q?6(VLl)~zG_`~&1Dn4Ler(5e131*w(i*wI@4nc4 zQJ=!m?+g!rvM^-2Bf^pEObAsMZPgDnv0mIdG)rC3a85}h48fjuzIb*o_zD=If_O8CUdy5Qh{N#59KF||EmN^@qq=D{j#}BTKt?~9 zxnO1?hWN=yTepd(EA6ba?6@CrAHtaM#qj`nlRhTb*mTmG32rZ6Z2TSl>N zlJA(k(xpNJBvt_hd+y=hiQm#Bpz}Jfwt9w1{(IK}Fr`<2&kve}hK^!jH)lg;qvPXe zZ+`#NNrq^kSj3}Mk}zc!OB`v%I*7K5gCl;v6s%&_B;Yz@j_ouG$ZCrI z6z%I}Xm>tMuh^SpL5lt13N-eZ2F3HfmBLOE8I_3~+L*vn-oWkT(7VX92DY3@Q>I{O zwf{qt(H7m~PoVw))3pNVmJ7w0FXQmCU=hXJqGij(6P0G6EsocOhb6O~ghK|TAWs2c z&W3fvG~;EPa>h|!66&Tv$kCOa0JM~a!6Z%@svt)(Fz=LX)~I1anckrmhG?<47q1mK z$}#wwqvI>HfgVw#ynC8p|FNB1Ya6K#P9fkR18NF9r!ilSpn zSktziSu$bypiN~y9<=Nx4iq%OeHm&2A`I(4FSKLH*ZzQ$MR))X{~4TG*N-dE5#Kw~ znZ##7T$4Jp%7i8j%~C@$ig4NdtI<~;c?_WpDu&b~fZ8^iwXK!Yk-v?I2%`YHRzl!V zP+WcbbkO-*IqlI0HW1ns*z1m&kMq^svZ; z-sgjqbD0L5TUMS!`W6`+-Nb~y^-s{~Fv9Cc%;f;|AU!GRO(Y!TVKT$t9|=5dgZapn zD-Cc0gyP-%JkGrn-5M2d*JLM(P6zXx7(r#LcU@=Earq1ntA$9_Z0UTu6M zH|_;~qMecq`k&yH&)4_>e@`+3dL5dvn*cu<6)){3WTmxfF}9Jp6(v4tfbm=9n&CGk~)l1@y6t@5$sK= z2dQ_YV?*U<@vf#FJ1VYWoj_`?LXn23 z=qm6G+t>qs_S`mXKcNE3YXhW%gf9v>9i(F1%2d1v{CZ#dwS$X;Sj-kW>qqSm69s!D z$8EiTgt27W_Xbj5Fp5R;JHq~DYAZ>T3_$Rm4p0d8)YA*Z-HY!Q z*cu+T$?z2`&Z;izu1gRC1WJKHlC?;*bL?P|t=q_9Hqz&cgA;A-XbLxR&zn;6k$=uW zNV~3G$I^5N+mO6?8tFA2ctpYcz4pGN**}=y>+0+43+5uKYGA~hUY)S%Y`HEiZDnZe zEM$EnkaQ4&LG3f|3cq8=~zCN2iQJHIoP6O)_f%ra(T;TDbiIzzn#s(k6>81l7`7#K(vk#T!mPVlSO ztIT|V9_hD0REW^rOZAGI%fJo9t>W(sD^e_yv!mL3E~0f*AU6rW6>42;gM$&`WIs$Y zz^r7X06M2Hd&Se=`5mF4liH3aXQV%|d+F{X<>;QXx5BhlQ9O1XHG{}^0xh!~b?Hn9 z%N+l)PCxvY&B%t(?(B$x!?gU1Frzb#Wu_v8JBxZ6|FEW8>noC=dX>BCFzl zT{EMcy1{V;$YONrIDdXhhqFA{R$^BK$VWZI#xJe&qjLg92`vsy$3goz4M8OXP_bK0 z8L2UN#iGy$dO_K;M@fvh8K0)Pp;oj2?q9zB8ax+T(R(0G7(l2wrfW7AVULE!Xv#IV ziaA?jz>|g(fedQTL5nLMUlcfU9mLiGayxy@!&9(}(y0JqQtWAIKXjGZjREr?uOlk} zvRy-!PMjddUnLFEoWAB+x+HH3d)RsKiU2a37Z&<2?E*3hyRl!B`!Pm!1Jp{cpX|B8 z5uB%boOw|1*&6!)Riy3=DsZQxxQzOb=l+I@hlM@s|2{@RI|_eum0@kT6frhuKFc)w~nK913Dz7#c%rh%k?Xn$1lowEr^id}yDk0*(>OWtBw2rJ#JD zl^tG$1Q)kEA2jjTt9iQN{1n*_0M;{#i5p}uco-h~S@ZsmTqLk70&Tn8>VhBOKN{<} zt7|77>mKmktbX*E5xyvsc=zawO;)xH795?aH63ctvUGZlKf47jL2j-## ze^F1fK4(6~pR79OWMB6#qIk$I?ogw`_`UY#uC84Xe^Faxa8%Ohq6{OW9p900`as;x z0Hl*D2e&#FQuO(_Cm=v`4+P=l!qfR=U)h#M!{Rt~`cJ1cL_c|57bSr%Hc8*UUF-@)M%3QmJFE3L9$+#UA zaw%YaQK@KLxtT=pMt}VAuXjjfmrHe#r%s&$dcD8!d+xScyY z+~bP@>fav}KDs>_T|9zNboDDX`_!uzNA3z(K5V6Lo0YjWF6oBN8&aP1Ffg$9W_q?| z&5Xovyo)@n&&0I{0EG-YCh^+?B(uVSbMA$Irf_7iMX)_gT)W*qdS#FEM7_D`j0I1p zoAuf?y4ZaG`#q1A&tf9z`7hUKizdR_akP&!jmwgc=+bTr*Zq5;M(L42k?WLaaU|IAbMm>41+&4!mjzRm3# zr8nHVyA|B%#NsMjk|8-;Ox$IgD0~DJq1dakkfh(#ulkODeUC1Q`CNJMJqBKjmM@Pd zIH4_&#fwCMV*RoAs#<-9YW>yy*v7QM=@lsQ_G0s(AVVZH45G@j!CjtgEo0-f0TZKt zU5e2txqmRUPxIf7g5+8BG;P*VN3$@rA)RJW?}~<>AB06#^h{5h45`4mpu_1Tid%jZ zYCbn^+^|+%YRyhOywi~&j>5T=8zBTIOjNCRVS4$HvA-Lo{Mx0h-Sjs0ry#|PLA0gd zMKN;EBQgjt!@)E}-O9&{AqN&M4=d{&I$6xT0T>#p4$6)U5iJKIRC<&BaltCEYSZb% zB=u1z)*IW7bW{$?vPWeC6e8=&$Z`b3;h_Ii2)>!Iu88RsXGsfWf*byGXDY^MO z7XOUg*l`{HCEzs0lpnnMjkMBri_(Lg4-ve1B4gKh%ECk>-mgFy!uI>fggv*+gg8gw z6IUl>Z=%FzM@i3HC7Br+6z7>7XGUfemqz-ZJAdBp^M21AD^NL5oiCm-5kpm12FA5ux zW6(?+Q87Y{wzy0$n!e;fZq%@0$izi2(0SmZb-WbzjAnf3JATF*jJ}+A43Rnd+YxOy zmLp$9)NuuKQBp?fyHAtG93W&uNZmy%gD7S_zCeUD_Gt9IRdos#hh)6}@$Zza;C%cE2_`c)fFxvC@oT1WyU&lMeYnmaL(2QwwQ9F!8_5Nr zkq4>1()yfYy&0wlChSFJO-ut`Xj5Y6@TpTZNC13VviHxA6_jW)ngu&9%O3avtYH*; zw2G!THfXMi*VO42s1AGBm|zAK|B211R8N?mz>Y`KV(Qa)#DFj{zPF1Y{s})wk25wZ z=`ZIsF|rIO()W^Z$7m_9m4X)0f^M~{Q9iL0eR}trqJGP z=b~Zei9tjB&oSIk%A0LvLC{d!*@3$`PiH({j{-Q6bg8H@a#p5{1H<%x{Xj5ukdIc8 z6UPVwfLvC#7*h!#s+||tZ10-wn$0h)`YMFx)_uSy=z7HxTxtAYHZ2+1mgmngW-V+` z$(8w(eWFrDqfOYx)%g3^hc4nQLftGbEg;QJrb9k~e@$FdZ{G$WRoVh^r&`{#4N?$9 zTD&YU@HpZV;D{iC^aaF(ZaO+T4-56`k3neyjE*_;OGU-^PqIvRM(#P20_-9b5H_tB;qe0pYLNB3dwFR<&4i+SMVya zz5yj9K-~q|n-b#olkPaC$73iah_(7Y-ep#ncPQJfWk8bh zZPAh?l>5VEEjgj}9y^qsTOXianL)9^3{?no0-u(^xi)210%d01iY@md@QzsPXxj4+_%L?s z90w(nErdjlx5^Hu=etlWA3uKl0P_)RYO4d$U)D#q$ANuN> znubR2(0xI`b2?ZnB0YTd{(Y~w*9d53tecS#Pe*%;?t5^=5tS>Aaw+&NBN2@OHaNDK z54>x)wI^VItUBA)N*Fe3;|xWh|xIzGX`@U}|ye z5V6ZEk-9E|i6WNuF0CNwEze4R7nTiq$P|38V0v)ZCv5-~5a zQ~nI>z06HBkAk*q?XrD)$^gMqfGU7nr5qynj;oHC$V~xnk#RGE4dAUgDutd<0h|M= zp=2(LoF_ssf|wmA)>D+DQytg;*Q~CI;`$XnbSG%va8?~p7>r6kZTUf z3^d4PV+tIG*)V3eIQQU{+Gs#v8W)j{6;;3F|Cf1UBBrP;=r?~oU(D!BH0M`f26C|@ z2|B&|2%BX{?XRMb5xE)xM`mx>FPBOH89aEf_zX@dfnW)uKAY&>cQVbf&g4G7K_n~9 zT0c6J_@BODbmO?U(O}Cm>E?&VWCrAj1<}TjV!b&|hFL1-ArQ2f1aB$RnUtk!soC2v zln@D8aB)=>vW`o(GWo^E##WpbQRaeSi9L?)J9g|CX-!M90bz{1<8E|%&-1@gKW@N) zDgd~wf$}Wlu)biUuc#*4QJHR{U{qQ+F3rpRUQ^41Z)-GlgYt&{7L5N&ZD)C^qU%4r z7d5DYUW%ePel|hAXROBBi`qImIj6EGPg}F*!jz}^rEEW$)j*k=G`Q@Jjb;6%0*7gn zo(6WWIS4nTw5~6cZLUKrO48y{VCe}yuh=9)qSJ+|Fa{5Rm}{Vn6h-%2lXdJ@aF_u< zVBzfipv|taX*allx-6`)V2b#}lzoHOKIPz!6ieYg+`~1qYxp^v2mssH{-H_j)RLz6 zD%s1Amlij-g16X~6R10dWabpr-`8ze4@3V2R^V%@Tx4yWbNW79D6X&OPEIBdXCqya zdDr8d(pO{<3w!$=@_8C46H;|U^(v}<%o_v(-;+TTCE#Xk?jn^nG$P-8R(fkZ|g>4C0RQo#uZ1VyN+` zQ>Radp7+g39R1?Q?yedj54yyUqVwOc!e2F~;rLnh_^%UZcx|ts<}JWTPY;{Naq3OhcO zUe^pO(z&+&72^sQfbae)uPr3WXOw=YUoE_B1#FsJ{*=)=pn(|w>kCoAx8bDzOl(qZMxi!b&`HyhgP zNnpR^pnyF`-nB%p*zL5*@V$dqrc!N`8fe(Dc1&;0r7`V(O`)gM7kKNA@CRC6=E1kSqb&D&GImg zKWub}%)x@qG&aAC42s5A0Fx}2Xw*Ck$iY>PyZ;4q41WSz+GKkA52%Imfe{;gNo0fRvaZo<- zH)TZjbO@&bKccZ5N11D5GzK-^akl&l!vl3GyKczc3NlE~5!j!lY~;dWmx+%rnokAY&=fQtK>Pgj_`ZG?sC7SxPLYpdj8!tDr6WK*NDMR4 zwuw%UZo>UeacT}Kr?#KOs(3|Bs z0uzvk<_<8a7UO?HLx z<0ycxQ0$Q}U%z`tPMAD!*@upxX~a0OVUoR)sP?Itn-fJvKuLfzK@!1F6$mC)T4lR!ky!vNcV`%vQ8bJ0(A$OM* z2(q$U&@q5nVJs+hQqhirgA4ZoCx3%A7E~?Kc;AtGMQBU_bxU+zDfB+WT96K#fOs5c zT!4nfgg_Q8@Uw8|`;###A=^clN0wsziqbjFLS`TF2*7}8chYTX-^$BJ!%b7pG~^G9 zp}=_GptekaTLB_LruU#gfMRtZc}dyCF%&7W+H z%6{~pk7(Oj@yjPos5;f`3yoT19PtToR%k)s2sTKg_=+%z;O)SoukXfpqqVdv&+V<` zMtr&oxskb5kUO6aGXe^f+_+I4KsrixUd7rbNzokkQI#_7Xyw~YlG}g=3N7a_@MgK9 z>dn7%w#59{H@LoD>(I6%J0fszP(kO|Ltz2nl^2_}hrssHYtv_s`(6wPTvO?YO< zTKgBJRrw`&BReWKk+E8k3*08(#};O&AlPEZPNd=f|Y5(8B;6VV{$OkPRUKZ z(D6-whSD|>7t?CusREP;^6hCp{0OlvIUZM$ZEoVJv|yNP!d|6K7pk=B?^FNV{o*z;>ggkSH5TG9Sc$2cfc z7;Ps+aUrXxz2wqtK-xEOCn%qa6tU?1YtJlMg!I6I`!EPj_`Y!V=4%r>W+WZr~wJTB3j8V1MmrvB{2BJuz8qc)TT{?v4@0avjOvA zR$taTvk0c{+gaoZJhDuv)Qmf%SMeF;H!z^j;^4ZQ{eBIiBVsW|15UCy1rY|tpn`|A z|Ijk)*fy}Z+)U8O-^yp2Tv|qHoy32LaEwb-ENxv;Jv0xNpvKg*KvEo`X2ecWo8P|O zdi?l@{R)`cZX(pz_c~4!6K^foAy|$}NOl!ZP`GKZU=KKTPcwewC<-^MI-41XeTZKh zHS(h+TgXVOxfkmxrxnDJ3FqF|0?rP^Shv!$JQ@s&d$o%xkwugWGM>s)N;!S{I#^V$ z^8~T<mM_uZBZYsDZ-+3o14JIn~>R1dO|CKB-Y)piAB01V>dLyq~?1Jty0z?tFBGmNd$%dZV98i zzg)>5V3$rXfD|atAJO7=&|V5X01MPcvb@3P>CmT4Uh>@KN%A>~l_C(PaAWh+pO~2O ziKr`Oo0-gn!)@0mMARYM{2OZC(Gtt zRC_T<0m#X+8R#|o?~51LZZ35t0Lg>^Z<6A3&G)sok3x^=aPMgeHJH|9sEC~r8CY1- zq&Pr!GUD<1nlHaWT+|R;>ahG)fk!O8@D1pK-xEh4vKTNz#w(ry!0e(VrBuc@RKOS{ zGO>=EHyP%8Bi=)q(7@H$UL2Kg7_W(r4zyFi+ zFjU0YxS(Mp=n&6SHBuOs?#nADUYJ^!WqK2?hMCO+2M-3_ zdQ>t^Og4bJ2-li$1XP*icacV50+AhT)|LHN=;LtERKO51vOW?rQ|Ke)8MO;4LCUX5 z!H&xkb>3~&^Nv}247;Mg6A=<8#DSk|e-xa$i$Lfsp8z8hPDh+$nH8<`t|w6Qg@%f$XEhrW+m#dV3Wh8l#Z4-@W#$5B90;2$fC?+j=-x-vtoqoNKCfYBPcl zm7mnn_(juA{_^q#5JI1mIopS-&jq6*Fk6?Rj{Q+p(7b^W1DB&q=J%%&TB{?FM-zxG-!l`(8w%!gG4Fbv8J3uXTnqAAPVb(fu?JCo8J5KcMGW|1%|7_)#Hu zWQ7UGT-FPcjJ8d-HM_(p15tkkJ2Po1t{|)C9GQ?Pgu8yD*{nLRv@1j%c$omHl;2Kk zUD;to#4!iy^JGMyukV<6V1DWjC4CB?Ki5$7>Xl@_ud97a!iKC+5Gg7jL&39W zZ1O7RfgioZh!KunR6-mRoxy`UqbMhF+$LuF?hU_4J0Pc+?Bl?PMm`%gcQsa9i2jAO zrElJTgAlj_3d*+1p9!boWJ7%D-J!5W5r?9XZck#3bbAs{*sE7t^Hed(TzLhKHS`L_ zWGe_I_EOyD2>MQnCx|iqll{S|Mvu*X=rYkn-2&a0eQQMXJvA$c2zXQQBx0Y9`94g3 ztk^yPu=7vFVvlcf{r>%SXeDGX2==`ETaGcb*hYN2`Dpy#h~(+N#MFxB0W1Ldj6P4* za^l3lMl8#nWh_lHK)Bp|Nd5ybj0l$c-l}LMls|qx3VMRlEv*--`1ZkCO+o=+v-FW* z{*|9U|16oZg5)jRq38j0x$d<7?$MbfD^ykg4trt#VfO7AOyKQ~xMsYS2}QskJ{?cl z5)Cw1AfXFywCF|Sqi9m@vYu3E3x1+ZEpqYPKwg<}V6(W4w2DVA)(Wu@4K8tAzV)UV zxVd|kmR7O61^MoDYeqQxP&rgxYqAmc=6`*Z9^E_*qRdxR7Y)a&{|*OCJog~e0ZfG^ ze>5DzH-hBPXE(MyeV$B@1*5(3nKh)R7?u`uHEma zQ8ZmfQASbkM8U)yM&w){N`avaspic102nQCUN$vxv1iVhv7U{DaN3u1a^9WU`G|** znV;VQ!w8xP*q!$2>{&iQ(t@4}C--oBHT2eLHWMFaVbORf65ruy+ug7uIm3n#Pi2p+ zJYBFc1VUVbn6MI8JV+eB;ME+&+xv%X$Dj}zVZ3#i|7CVS zLeAamHXr(+A46+X-Kmnc%PyHU)r+vt&!VYVZ#ZT)|MZ~n2Xy+}fx2ZMO_49tYxuXG z$?rcvp@jm_)p2`hK-MUbRaEh@$PcAVqUvA7C+qq#iNfD(`$Y}`!=hdFH#HEX;Mg%i zqLvh2@Zj5tW5KKV zwww7*4Ma~*r5u(#`<%T_Zor>4yATV@BosQzof!Z8&q_$^jLNF{^9@+8gBih0&;x*8 zwYij|vUd%?Dq8Sn%PL;TE%H&H)0>@yC;}+4xY1)-f+W+w)!M!%H$IN~&{?OVC05^I z(-x8_T3{Ye5B~Vc6M&ElvQ=NmEX^R14V?kBL7UM^sUwki&vlp{1 zx(8VUv5wCo4s1fyKDuA8?eZxixO&>B9KsRy7+pX1YTl6gf~8?RlKbVIyH`|v`o|Xo zeC!W?V_LE(>ASj3c?!59aX$0G?)0E$_2Cgdt~TX8S692N%04+Ioo9i?u;T5rw5m4W z9nc@6HsCUgYZVGjF{Hy7MJi<8vRFZ;Rc!3qd)W}5+Pz%kSEZ%RhLhjrpz%MwqPCBG zv-|$jLoqiB{Mkfgv#s7C&R(4&iso-Hjf>3FP}!q>3MtX^q~Vm2?Eg&aO3oyj4;!(+ zM%(0L!mvYYYZC{XD<4lu;WqQCdL)jkRRX>TfIu=hkcNzosdS6&b+UpsYAvyjsy#y2 z!W)uZn&3C`uVX6Pvp^*6gPmi zR*?CL0>6pJ6_x9lBT@eU%}!ik)m>s8N81E*Z1oIXvG2Q)Ux`w*F(&1MeKOBfErk(E z|D$pH#_P#z0k~pS1a~(vHmvN;cjf`!MUE^skf_t(S(VdCb$_A&w%=ox6jOa~AFjNIg91ls+P<4(_)Z8c0}F+IL&THhY~O*dMAIPK$uAn4RWz zmjf$xcBS?ovE8vnKXtgf@rUfOjF96M)-=fy{3S(CsIgB$pH)MSF~$Ra`( z^9uubI}qw(gUDL($mCDca0fz{2%n+>zC_m`>i~gib}!kBq3$ydc?$PKoX#m1nzwEp zNBM&SM?ZHy3jPf8DgBoNhUu6s^c)>gI$U(^WH^W%?(gK}-z(5)LKKhL_C$5;ndoF^ zClflT^KOtNg}D))9|faqCgr(FOAhm<<&4VO4lgfX?Y542OZ_t0)!B~&vX`4Wl=J>F&xf%~>4e=E^(O0zxi)`}U5 z1`37ENONb3ZJ5%~R(qCX12|cRERPNN@Z~?<<7JrLxzZ60&N2NqNX62Z_uBl5B30;7sVV? zoixTLyzMmuO+?Oy&g$}>l<^do^l5+agliWRQX-54^kYoT(r<{!?OSZ?8d!#mw%y-c z-`ZE-Srn{1eva`**fY_hZ)(v_tMs_R0X`j2?`~)b9`PSEX804xoCCx|uc_`5vFIBh z%cz#GUcUTtOASjEXU?AwIN6s-!UECG;XHkeH$o0$mZ#NiP} zm%TXqsWD|y2Ab%eQ_fZ{%+*KHh~8L6SwLW@DrC||b^yaygW2V1EUg9G5~B~OW|`>( zX?O!NA~R-`fqhYC8|?EAs6G#+6hpNvgLOcA@Q#&FXOj8AkKXXygeU@urP1C=6*L59Si5(q2(Xq3S3M?ORp z2{rD;76*DFOQPP1-XWl$r#p^(gW```5@t%K(^r4f!afvY6M^rso+dJ<=VgF9>;e87 ziw@Okf{~OX8S)8NeXU&_PYj_63jp32M;QDtp!AG$qVqN$qf`L!7o%yXv4MWo=KBy_ zo;fTE8s}f$f7lmQ$i%XsrFi8ruy_3ANx|5_IAa|Tv5@;Z#KSt##qIfT>2%sU`{l}Z zi$DHwnY#rt4sGQmPIj=~S87(7PBsAq$u>1;i8-thL626j86K4wJ|(!E(BYDTb!APx z4IFX&#EC|iMvlX+Gk0kO*2a9m9@M-XDVujQR8;D5;|PDdC=E#=kH9czyde^5LUp|9 z=R^WpOrw#|8q5H}c-^N?rNFyRuS`L5YxxZ}bMj_V_$C;N0#hrisIZgjAf++XG%LSt zNIGiX-&9o6MV%|1KkqFjDJfTwu*0<&bZ=p5RJulhR|F_B%gbPwE&5MvA{tRJ%RwYs zP)v0eu`mNYd~WP+hIG0;ru%u;AGcvOa@#YMj?yQlY0UA(B5cE3&2RtsXP=~(Y$F9r z-kKv=qO&S;Iw7ZM#W7B{0Z}8q^gBE?Mq9%P+Cm=!>&PLezGBVur>Mk35jnf7-ow5a*-2wssRZehbU%7aIJ%CBuQ-z=MYm zH)rq0Wy+SjT#WYGodw0`)CYuz`;!?SdUgdp7Ry{)JG*;`8|X`pt)FKTJ+7x}PR%y# zj}YayWSt3j=K$Te($1x6X=x(L&u}v$a&H3E?dSB5UhlT;xBDTMP)|rm(0Vw_$7iU| zkCG1Dh?#JVkbq5OoL@~L+dmIg8L2AySwt&FYbqnW9R&rJIQr;iw-i~eKFaPFZbq%? zM?zv=jr&ukxz}JXAlR2Y1h%A6$2TI%XCmX9J9>m_mo7U&^c5@S9Z$4V?K@=1MUSeC z*m0BeVF`MabwO3tf)qt*@p9k7O9@NvSyc* zl!%y-=hIkxgmGdb;+xyrDixsqY{MI-Zxw4%X^rK%2;vPNB?T8PW4{~G4&i}J4PVL4 zZ3%kve|)_OJeGUA{(U!>CPgJpk|m^p&@34vN+Oy~4K!=D8a0s6NT@VZ6pB(Q%}9w_ zN=a!Yp-D-zq~ZOZJb^U(laGb|+oJ<@>OrQP-u<`h6+vnu* zZ91)~au*k0Cz_M28np^O=yYo6^aTSuul#sx0D7Z9!0oRqO~w0(jv(gLsmM7sp!zrW z3{oMkGgR#)Z~Kz}3QUq)V6ptQGAZTCi~-+nuQQKXsfB5xtVqj-com3Xb8WWgQ+juy z5_6^(0DCsB-TUE#-}i5?Q(Ct7*if)Ea{G1>@F+?)G@V|zKypep^=nA^72eU6rwi0C zOqCY~93fCTbBV|;t4+-w%t^}~J8^ddB(2}SmTy(8k<$YMqx#bin6R?iiGdr>NurRC zmYbXyM{f~j(0V?ts8~l%hHuz}r&wR$X%7mZkz>Z#Km){Jwd=0CeU$H(gL@0(Pqjnio!=95EsL#iM>L#Vd3 zfrUZl%R1}~-HoLUCI%~FU8NJ9gsvz^JU%HUPsm_9d^1K*`m-?ks<%{TQ;kxj}E8~f%N>gs9SG?OzPE- zGa5pLkR^?ef?ONKbjNy2XQ^;-T5JgIN`P0O4!Q_4&0#r8i*+IsMIHhseT7DC0baL? z`6(&itd;pPzjyS3mmVO+P~Td^4rb$l>+2ic zEo(w(tip-H^1C=*a<@0Hdk}ybW)^`NJSUj%zBcb!8Lw8W>OEg}A?*`7^TwR}Cfp37 zBM7n5k|j0IlTCvPju1G-7RFNjN&2Fm*iZ;&sPlNLetR!kXz>-6Xvu4rFi)1AmpO49 z?=tAaDr&Q}+~h1q0n3&xlkopwjtNYy%|x)`7l$tNhrjieQ!Qi%&S;o@gUdg@bRvn| zzj^u~=fQ4N+?``PSjc*nY49_rn>kLCCvW0I9^{Q|p3=^)`I@|Eo&csxk(0}VAjvZx z_F|sOe65~>F+qdXKR$O*xHAs}Jed2p-Z-6Z`VPhh`JN}B7CRCbpYp)f*i8X+y^)^y z&f30lRvAwh2mu$*OqDq%omQH516vd#yc}ML0IWvr$OUqKU_LgQ9H2t(>MlNdZkJd2 z2Zl7=sS_r#3T>X82O^s+a#Rkrw7a|rR2@c6+xlz8pzIny)9XF4se|dg<#&R&6#_*)Hs_OB9%T|;c)d4bB{{C@{;tHSPJ?%}@<=sTC{NcYkmz0#8WhT$JJ3jC6 zU?>?%)Hog61z&6ekIRE>BwVk8f`a4zb${`U%{0SH!5k%{$(WzQQ|!tlXv5Upb-q~D zF5*B!LMTb3oKlyTQFi5u^*IqCv6tta$d!16<+vOP2*OqV=IhdC%b~gQ0wO*+=c4U&~ulbt)A7mb{&u z=y%xBRD)`ez9SHViqR4mok;a)ia}vXI5`Ed9_02*_sHc2fQQzbBlCFWutBhkR_Ci{ zQ%s(~R7tF7akc0>c*$G)w2O509F}0x1(kVcvWV}ga};|BmKy7~Zw?`M^M_6xd#4Ue z61I*R{zDZez}M%y=L;yW9`vMbXhZTyHcf=1n_JV;xr5V~dS>S4CWiVt_{@S6XTbOs z2)i4AuV674Su{gp*XU8ZpbrKHh$bXC5J;{OY2N*)c_JQ+;<6(zgnkICB0MNm2=iHt z({`WF{ar*}aHV0-$j?tnPa08srF6kSw7|<3xdxKSb*k%aep@ye^iuLPu$%14#X?o` zF%X+PWf^~x7pz+)$HV7XJS%jE`NM^ zztN{<<3A62eEV~q_2i>HNIcs9(c%a$|n=l_#qnp*^ld+zkM$EPZ@*2}O<* zCvE_&T3P#Shl5KayXI2#Q$Sq@%MsT!Mz6+8qW=A2N=AW;h-wP3rz^?D8hRAXBBlfZ zB4)P!Ea5{zIX;Z|6}D}gd93TGI498i`n*5NktSMgn@JxAa`2Rt*?33Z+~<;>?CzFP zBr}YcX}{E?vf2T`=kw(D|8*WX7LkK*SKN1?bR(D=f6jL*QK>B7?C|gNQac@yV-8gHlU zd2-|b<9jbJ@UDLX?t_%E^yf?=vVc-=d}E#iskMm>Von)>dx>4g zzrNI|k#fNZf2CBiYDTk^6>F}SdjQ&sO(?^|UFqxp$MOHy7b)mx6l(T{nEI9eW$e_c zqZ_(xGSeS2q!S%u7U=l<><-h6tQJ>_Yn|lnLve8^Y8w#a8f?st)in6;>lEWy(w_(n zF1v+hZ3JhTA%VOSSVz0h`~H8w^8urCW^W{Y0C+<=e@E#pe!O3w{Q7BQx@5_cV9Z(= z9~Z?o@?_Zhm51J4RcWlVbHlD%gW*yj=uP~)tY3^{Q+|?~sakD~ouhOkx7J_YZLH4c z;1U~EyB{$%ZGxOuzx22CX$56TAU?A*|WBv+DF41*|C{gw>2_SV#K0L|f1 zM=}rTuA_6L-t5saE5F{Z3o%a^{)vlJ+4|yEoycz%)jjMMNzIKay82>=;Ck|F>i_GH zA+K!1(9M06o0AVIfMX6HcC9i&U`FKfeUo^vnd$zmKuiZnJZ`6EwZVqI74vDlhuSDK z!beu5rF^@j%@8*zQPLPnvKTBOK1v`Ry|Zf(+a zt4&GsAoB(3n}665h=+e@E-|p7~b*g)QTJAI9$hXx- zf1Z!2{5kR4=MOQRZ7e>wL><(doV|9W0)_xV3ny`ngq+H4)o?{xWf0ar^d* zxVSU3Be!P<`#nDMr~0?ry6&4Bxap4%EIb*Lzd!NRr;q;>>~G%9?(!5D=RsSZ5551~ zcbaKhU21w|&56hJ?s5 z$h$A=pCBQZ(~dwik&Y9v0S6KGDfXKta74`q-DN#$T^`Zg*Qb3{BQYk=l+A7B< zee%jL4`&JxVX3(m{1(#mMg*}#m%dX(MMXjKoBg)(|M#V+hq8GF!}>M(FN)3`!zc`& z$6W`;t8UQb()2enl6ASiorL;%PvzO|f`>J}Tb11vS`k?2Xw;sC`e z8+TX(WJ<-)F}lZ6Qd%RR68u0jJVfd0f69q&y`2(NnK1>PJ2#jT^zJ`}|DWTcsuVXi zCoiwT{`~9UK&546PUUHwNkBHA*j;0FJ`fCBC`fA38_-bqpbO;EK(esm>pp$;>Y=L3 zk^XZ`cI$7<_T)neLlMPKAl9^CYzhU#$Pwg9YSA&-9=~JlJ+slHTR~}53bhOXrp_oX zK9`ER{CA`-Sjw{$=lxq#S}Mg&+-0e8t@P>B zdaYY;jsJe5aZuLND+L8@1>pdmp;CDF>hONdVq4KzS&{ld=o_nT?<`l{j!CjL zcT#bH!76AYTn6j@MH|dxj2Z$EzNCXj7kT3xwB1-B4vb9aUE{g@_lNnftGDukQS>%e zqI9GhcR%4Kot}ZmN6-U4!U1#vgcCCO$T+9tZu&uUqdyP-`LzeHFpzS>lN`+>4aS^G zYE4`OOtQxQt_DB+q)f5f4#$3E@eG>TpII|bj&*cYDBO^QiscDdYkJ+j|N0RBDk1;% zmJ3cDpZ(;}TjKxINAoXJuQmd?!j-ZCB2P+U);YGN80eug@>T{_9>8-qKrVbj+Th)m z!KaGckfjLw3Z{a0c6mA-vj5?OdyzTLBnGJy3Ki~0D3Xx+%xO_>2< zRj#ghhkJ>IR$KPQ?^|E^8(I0Eg@rsqA8YmA0d6pJWIyJ;d(J!^ZX1IV3%#h=0|yj< zi-LpFN(aI#Vz3r-A~7}+Al99WAXJY_Kflce^hPYG5ELWj^3M$^m%b?>;x>L;elp*5 zc5LzQ;`+nWeoVT-54vz8rulz%S>zhbEAA(lE@{+u*9}|@oL{dQ@xNRUKITgGKLU1I zYG%yb@4ud_%+ORI5}n??t!B*7X4#4KazHgP93d=@Wjk7@gX(tSc5%uZvt?%=sHUtT zai@8NETFacBiC!~+^r zIV0=E6&zOglMlgzWgh}yNj>n_K07v2Ey-?Ym{1vXU+jht0^V7h=f7gth7B9~VGITH zvlEDr1*qW-)8Am6JiOdVxFByuo63u%cQ#tG_vHPTUsYZ! zE*Tg7x-G|0qQ9T#Eo|3{c-2kz|Ny1 z3QxqbS0I=Yml=q-;n7-NW3sX9(`vy26i`hp!L5NCKCIPO`hN=<{r<-JCL`?h&;)1yF&Oa`K|4AoR3faCH?USV)W$hx@(Eq+~{JPcjvzt%c zYfQ zzL>M*&t=!BOUI8ZLzH9gs*!TjP-Sf@EXsH9-*?AmEBtxf>eies%6fAQ6;n^16ilN1 z#zxd0PE?ei|BkJ~sixxjb4{kA#O|D%HyO-m*Y*&~wd1`vkCzJ$&PbxJE0 zm5BAGf9BdqQ=a~O=eF?h?n`>N@6h33Y^-^*b5FNDOob`WM-CsE28MLwb9iDem_QYm z^!oO-88M>XhmRklwg&{%53$rFkibxYVQUf7?=^#X%(|OnjEH!4z%w6y?rs{58a0wG z2x_%q|Nbm9XhuFRc+MZnZ6}#ot1aeJ40)X2MopMevSY^%3;p);msZ;w7jyeGm_Jvo zbF1LU#OgtvTTu}8<8ldF4Z+~n%IBu@4ms&|OreiUoecNw_-GwfU=s_?aOV{*L$W=A z|1r~Pk5~*YKwv_G&&jRBh#?$H!C0tA8+F<+$K$@wtXZ;=n;|7qrsg9^(T6Kscsg-QoXS@xs=B7^5cUF>=`8q8URCdWV$*lo8FI zJ#9wZzF{9`tOcAYbBF+gwPtI;yeV+w?^l~H`_PcOQdU{iQ($<_>0HY~-(|)|M(f=4 zt!-_a5qZ$6#QymZff6<5kC)jkC|bcrdR9lzG%GAhe2sAD%8QRTDrOgEt{m^^XlCiY zNy9$s&+N{vngFx=#Tsy=88~Rx&%1Ris#pEBX3xsUySmzdYqmi>z))}K%CE|3cOoBM zyLqz(EF)}>0^{PQZ+;QH%mo2+#C&;)aQ0T@t2#K`YX5j^?1fHPX)tAL`0mR@oj!f6 z88BV^k`N!?7##|X1DUUIKrJi6_U)Vc;s+ok2S_E*!2-H+ea^9_+DRDl**Q5)_1lk1 zKK^h{Pz}w8(%jHFE3)*_oo4Z~LZAgJ4<>MuBwk*$in%b8ib_15@NyQr4)#*E2(>Ku zr_d>s$*h3j=c_MSK*csdu)p%#tHTRe&@*>zj~>cRrGTK=3tKfABG18}FhR82Y<&3O zLD+P>*WcBCSKnx+v4(*FCh1Yz6Gt0qhBsw)$;N8UKL!t02jxtx=|35|9?!wj=-LHF z;bMa>=q{iN_3)%8#;xa8y%thwEWR%a&A{6ANqW_2$FvC!n0u*wW}` z(i_8%xdTgbHHZA=ef#d+Ku8nlsG?RK9u#I5$8ooYaYjUQ8QeE_&x@}sFYW1n)J#LM zF?*Yex8C_8EC4c|`Hcnpq~H#*jP>17kEzeL%#h(pmx>AtgYeMfLByn_Jb9C^0k}~X z`A3x9>+h3aEEs61ACE84_RpU`W8Y^xr!g<7qL7!x6NQ?vteSN$j>`JGqoZ$ZUY*oF zeoV&AP-j=YUcI=(88c4K?wwmae0oscv)n;nU8mf?eS6lIv{zHYM{0pxUv1xG(jlh= zrzV3RPUo@*d2P3gJ46^uo$S(Y(%!J`K4WH|d_?wAW#BhSt?sOw{&tn;iI9IT&fEZO z7jSY@YApCw7cfB^lhe{B83d{*JA++fa;qMO~e7!ik zH5u=j|G_)^PKhZWXiBHqHH&GSw*`|qC&|(mBqCTvD$rG5gBB3lMm9C$1F-hkvAO+= z&iUlrx@E-{brkV3LxKp{oLPh4Bj(VaXwuXSJlE}+d5CI`z6*WnTG|58z3#nxw}iDK zH32%N+|R z#Tp?2p4dtFVZ%kIm-gdyZi&6RGPnnWKxX&5BJm>{-0?+XtGC`wLS3GGTsqX**;&W( zkS@6fYKP>zy?c+n>jc-p`+z&UlN&2DyCwI^_?CSAj`1*Z=FY?#8TRArpzk`yCCD6U zkeactssO+kFyuCiGi?v}_^icf2bnHJE5kksbk;+7-8l5PR{M?N`}Y0(+gyz|YM9_u zIBR_2n)u}8Ey53gUm*CkJUmTJb~1(Ho#eNdCh}y)*8O?U^fn*wISFXNea4K&SdOyU z`>xT0-aBvJw2!L^r`kv_+1aec!`Ziyx~Y{WwkJk=_EXuIE&8@s%?;uz>vk!P8~)Jw z=^T?uqmSpQ&e#iVz>}V>&!~{5RxZiHg$5r!dJPWSs$n&2+oz*k z!c9)zwCwS8!N7{B3EM|%NqxafHZnGr3Zd)Po&!4eIIrI}RG#miVJq5`NIlq{Z9(>1ArWmGhib3(ZxVUFchDVD}fBH{z@DEHd0$@>=@<6DG#I|?A z%iGktY#sJ~dC{p4Hw&1;lll z0|vNyOyV-i(mK&}0dUXWlo*E!uJau~GCp2UKq%UeB zgb6w#p;;1DJ3~=Zuq{`?utrKp7%p|CE%!&ZZU7cWc|-dzz7I&HZ7>hawA`(ak-b%V^aQp3ndm7L8GJP@c6yiR-#a98RGF(qDQS>ct*O?Zz| z#q=V5D^=5;<$N4(Vi!P#a=IvTq?S2Qo}r;370c)r?xb)sDR2Qh(L)0cU!=-Ct!rQJ z*^l;oz->-WU(~3nr2E1;MoJP0DB+L#o}B#obZV;8-^Zt(?sx5v@R82P9GyB+g$cna zEp7bBYM|+sGiSc=J=nofeLpG1+`(bqZW<)8)W!xM6S-MiQ93a^t7?H;w85pD3D6uHgBqI0aK-Sc217Ftg^lF&_`VV9GWx%RN6KG@`j8BB*Dmu>^B?(l_5 zH2_S>9pQlBq+yF56`X(y$VD)K4AC^m95de55H&>WMY%7!M*ho~S4%h`cUW1IP6#}x z+$`M}ZgWf6JfS+wmwvcw!%=W5T@CKGx#-x9#ne zF9WiWFwCd{F$g(YYI*IMpJX$NpKbp4oat}cMCWc8om4!UD4d?+JuNRcx8iXU0u6;*D0m%msV>jZ``tF`=TpeC)|vc7A09b zMz&k6Gien+B$p?r;^Z7$!q$$|()8Slb~)&q!Dy{b3pOqLdz?;hsnRvwDC=MnbuSup zH#n?I>;8H6pGG|Inv@KVT=1oc4>$Vs7te=^dVmh|E?M7CaL^1t5E~mX^o6g=qfTmS zW~{+@Taxm(!2vz{6}@K<=RP;ypAWBbmt9Bw#LSS}-UlsGAI@p?V$h5UX}@lK@3wfv zgbgdSm4=M#`*3jXCBuNB+HG~bRCf5M96sEnRj|e13mvQ`81ZeT#wLE zy%aGeaxGI`iDLe7016`c?^gge9G{i)OCE0AKG!7SCaZlj!i^EPCy`dK@Z^%d=<>co*LN2{Ys zl!uJ3Clp%u7&c5eo{odRHY)i`xvHuPHF9H|C4^~!1Af@d_Qc#}f75Hw&+MFLc15>Aik5cMWB&Xnw}Z#T z+}kb59GfTje8Of+`aCQ3^ywgKoHl_50sTA{)#-I$yQQ#=Tt zS*BP+hYbsjxe$--OsoF=PcAXt@;v^)0kiJ0TMR8Myl5O2!ttV*y;5AP4v*Qpz6?Yl zi*26TWIEsc0FVwuicx`YcQhL_&CPA3E!U?QW!C%jz!`N9MVv;VNjANMqnNx@N+P3JMbL*|x;i>naEN^eV%Nji*rj{#DTSXV zP;f(2=n()#_LTZTrt%NmK`KNa&lIdd@7~864{vw4PfwjR|unz*PF6FkuBHi4g6g8^pbpncA3&qNqQcT4M@f2_4M+8!OrX4!$7;G~+392tMmr(nh?;UvY*!7#CP-Z9*|8AIM ztyg!0Lo%uICa&w}q8A00x7EJIwd>ch`U-#03Pps(|ISmQXox8@8^I0>8oAA-XL*#z ztXUxpQzCevJFJ(zH5yQV?Hc>DuI&yn{63hPy3O*wb9*$bJHaG^?`fH(BY?C*&%Gr=VNqsV^A|CCCw+V*$aeD>p`iBLMbj;aKOhl_9mSt zB&NN$|72>dxZl=&-N{W0^al*s@!*_y(7u~1)|#269c$Hoqm`i>^&_}m_`+Z}eeJTE zO2VIPs^gOfaiQ|sU8{9YbMQ7SJF@&js-`Xl?VMhl&L&kFew;eqtXHpwb&l?DUeo{Z z2rE`6PxKC2?{e_-xZt<(GjYna?3{*~CQA5jl*Sy2g!k&7KYVcVE$Yy;p(q>)RhaTO zBVVr{I9dGih1Ew+cS)NjGajo!-K-2}PA@yoyvCC*uABF|e$&Ozfy3(k;Q@75Q%pzx zAxt%v7uWiDQjY-xI^ZO6omrP{aZqAOJ(r?p4Fi^#ETBj)SUA(t>t-sddt2T3W56!a`hm?6=nzkbs5YoWt7Y&$c!gXx=}NtDj5Fmdbib#H;zQAV`Qts z2qYWk&y)0$Tg`UgnwOUG8SAbS*gW9EYWcK3kdS8klc}kid5Fox6esp+O-qyA4x8*< zTCON$Ok|teOZ)W6qYreL#qxhzk1Kfr!gNO@*p=Lp9C2XRhk3HWXlamLoRez1c7O{G zaep>M!Wgz9Kcp6~BpdCHr1+4L-Lbdx=tlL6K2hke^i*rVe_x5L2g^St_&?r5i1wPQ zUQRc1ls-=9ZPdr2=?WZm?l@R)k34(CsB;zsPWS(EI3dBda(p{VHhKX}*CN>KH6peb zEU|z&v#Dx`@mbilFq{ya@#&j4_H_Y~Th~+DdhqXz5kn?lZRc{g;0B;gE0Vg*a5*EV z{@_nQN}6=gez`;=xJt5GL|i4QBGdCffBF;>+&`jZ)3W&<9wTCP@p0~fz4)fZ(f0!A zc*RaYAJf4@`)2A;=>1w{kXmhDGJocN{I#TC$%s;z-cVFwKwq;<~5Aw|kd zKfn6#Oq}2?|B#oIItHwlBj6!KHAusf`2Y@ulOeL7;kmQYXx~I1)n4E2_JKOk*1)8s zUJ$R@E1;^FUuVcW_lrFNJ6!orF|*S<1kAyk53(X@qm`y<-Pf-=EBh!n9eHoUL)RID zK$SGZNBXRLkO-p6Bp{YhYT4pGGDmztF0%q>i4`l=hR*KPHpcPm==uf|NP2XyK6M?N zSUt=fI6Bl)GtTqR=fgf$^15QnT&(C$nhuK$y$HU5D1MY3yk)WAV0vB3q*tbL@9&No zj#A?E=x2oq9oCqc zD38Def?vq_$H`$rOZBp)(Hi0!1mm~57aR=BC2Vb9&#uiO)fPIcWoMqOn!T=gY~*hq zDfzbkyT3+Pg7+qU9&*}BW9GFkJbW>VBBD!35Mrs^M+ph&+Sz7PPrOf>xBAWNE37ST zfZAbGveP$m}rSEu}5v^)vj98!^!fmjYFxPnD6C!)|YoO4!YZ!6=5SmY+h#$vk zLS_^)E{CK7D|=t#mIF#A4|*}&C_f@TKKR!P>3*j;+}&YclJ@S9(y%giY7&}vjmGLoYX3oU9t&b!DUL3wBB zfX1zY8{l4~x!h}N((*0NJ3M3c66X}LL|J4_fX!DCYNAzK$oOuokDk)Cx_!EIe>8?! zn2<;j$55oftfeX1F3FddT(n-~=jYq<@zKPYrxUA2p#=H)?R8%sk7DVNeh~fpzt{9G z*e=a6FrV|e){Jy4Gdb-_x+5b)V_e!T>8G2*lP56(M7AXHqISypakypG!l`#_?-myR z5jb=D0cK~yM zct`eM%9xc?BLrN~knT*AhBjc^Z_X0xOQ7Wz@Gk|BzI|J{apT5}h612z>B9EFyKv?F zx=D%m&l~KsX=!Wm>#s#!M~#z8_{X=!0v|E>(|V1WM2K+QP@w^xXiYba&>odW%*=7H zNA6Xq_cD3Zw|3J z7ht7}_`|3F&PX-t`3EOwwnhi3R62M6E}9}?-w3!X(f|?nq2K{a*llf>|NmnD0uv{y$AdYNOazIm)`;=ClK)S9UdM+TcZnRv_thX&hDw>5jd+P(7( zrkx?~X7~TnnLP@Zk#Wm^fCvQrb#QRWA9nT@u{T#8Y~j*nwTr`@L%E-0D^F*-ySpFo z8{+25SrhJ#0A>vP^fo%<6m{o>mkn5J_|Z{RS>Vd=(061~Pe47(f+%nTL0IDskBp70 zU!OgcymI9ANo4TZ6RlkC_G#a)UBaooiFZzQ^82T0PED|M&*p~UPOtmh@}ZK4C%4c( zw^MJl|C-9Ggv6?|iFM<$JvtuwIgsQP(i@XEH9#W2ziR1Rp($ly>qHe&u>#7R-jC#? zJ+_!>n6c-c6tVEb^nI?Ue{^e+=xs6U&CKS{eu>5?MCB@-%2v$jb%*_8PM|0$*^dk7 zRdjwre-+Vo%%$_^SDX&8<$8-QQW#2Tbi~(Ei1Gl-yVYTho*LX}8*3zvT zO?oTWJ;?S@Eco`xN6s1dS0Er(26Zwz?FzP4w0&sC&5 z)X@To6Xtgu9bjmf2}ivj>=(G1aD6nWG`JHvH*SnzCy2~pA|fmbRKd^FM;g+SY0aH5 zx68bTTZTmVno#sHHPR|}U`m20B}C^bk-B0Ct-h9jH0`IH_8~*gH*V+oe(6xrLBULJ z2p$5Nz`%r&Ph6F?l~p6rAtL#37q%1@OYjwo-52}Xvv;=lOh-dw;~;Rl(B$~Uv1aW+ zvLs((kY4Y~q!W*kNW6QtV_L3NI<3D2Gzr^k+7GMZI$A?-Z||^?e}R6SUG!p0=6tek zn|d~j&0BHFA!X#oJ<0j|Z);(c@e}Uf-*@Is!+!nDIMa917tKVE5Ceg_w|2X|xHMvM zDr+8OA_QOZT}{n(1|lInKVc~@rrSuYWXadNFJIOpkL;o|K*1l4njH)bFFVALpc;-H zzxUk;lS+*Ubz$-Nq3G`dB7?t@CEw7)-Q96+!){bev8Jm&+~|2I-)a1K>*5ET`m)nE zm}onID29J(#O|RU!Gxphyns|`s z4iI&$n8)2XM|DcGv#s1y=#4q&ikZ&`6V3sA1$x7PvKb!;4o%9U#4dJuWj}w^0l6w| z=~>+aF`V3kX{&z(0n#hxKMH5$=!rQMF4Idk4zjjZbW%~VD83Ff%JfeI>*RTlnEpv; ztOlV1@l|4%nv2Z|Cw^^?&`JoSR%C-B`{G2li~VjWVhXaKs5ND(r6$Yuxn3 zeBs8@xWSCrLmKSB=q*(Fd0AO~I*@hW5=B%3x+3dIdTeR3c&+U?t=VV503i?e$Un)G zxTpR(R*!xXJ-7GK1{bPMa;`HTK1?t9yD=g3?2_uPBK?Jh(d&@r0232yFpsB93{Z|@j(Pi6^$F*8s|>$(|F5*7fbC=AuD-TG#1$UukE(_dmGX8F3k7O zJ)dQny_w8zGH}Mw#%6trp?wbGsmsX#O=?{A5#~eNHg+9*KO1C~&=GJ}A|1p7@+@Sc zHTdQ&OvReMAr!1OYrtvSX$lWoG`n<+A^0+*Z=cV*v>ccBn~? zn`K9c0~|5H8|}62c5d6TIul`dVM;D5!rnbAU9#|DjE0F&LMv72+E& zoSu+Ptl3kvjlA*xgGWhH=K1A^`+ueilhuZ|!SV$%yz(oqn+&r6mjug0e!yfi;^hEF z#Xaf}0$b45hR!<5goF{p*oU2xwAG$k&P5Db&a;$#(m!iobdyYmp*vQyt1iC2{lI(r z^cyWpVH^q|MkuMLV3cm_yM>IO@%P_v_ddErg(jv4GS&JQWuAeZ%pc;cS26LH>Jdx4 zS3d=qjgAf#c0qhRl%rrZGpBLm-ZN$>>*6};-i>0o^^63qNI0OJ3o%F*roxp;83zf! znA|L3ys~-kzXZ+RJXA#mR(Q5pg8rstFL=4xBzAruuLoYmV?1rz`qr&mZ75)&1-bLp zAF!Vd1!pS?)}Z$W!C4tn4B-VzyYJ3A3`Sr-n?!6&L0Kb_2iCqqfhCj3L(#T|s%^4u zH0{Zs7m@hLXcm#>LLkz%2{1>)4K}&yujel`9+sM8|n~y9Nkw|x7QOnEIul0kxQfR%3IfAVB)!qCU+zcz!FFnIYhAiq_u9cxb6HvWZ zujZhd+aFzqQh|a*VZ^h$8av4!yR>0s#aD{63IM!)#{(o^D5z32k zhDhkeo(5pw9XF`2XoY0=pICKJ?MQJ%%MH^3vUCgxQJ)=2@AidGeEi_WX#Ms*06I7# z|Kh^7r4namyXDj=qqZJ#gVmlMG!)hdd>=a9m85%UdOzHYFI+1Fcw;klxQcmy6Sw$V zFqLNA8_XRRH7$%jzt>FmxqdDsV-_qpK6yn7_&HZBefGq{Bi^}J;RQr5)>X)dYmg1@@rKuXG1ugg-PhOdc^bSekGXRl_9#U!rT}L6sb$zdVv3l3$XI`v%IBeY zG*GJPS@}qd{l$hM`iH4dZ>i%Dfd4W2EMZLM8U5JbEE_ia_%DWiLAD9G_(Dpn%U&=p z=!1weqG_P&7uy8aKs)QDw8iI1$)uu4*v9%P8dkp?HRiV~SE;H7Z$Zjdi>}FY@67jEwH5OxwC= zPiJX`op0OGFX+7+(fq2Sl-5Fq1xR$yZ?*2M9X|56sGA<8i2}FK)_j1ZGr`XRvQNx^ zVhU;-9$KFhDHa_ZUm1xL3Q-!5ONu!%2#Tp@S^McKr)iOdFOKQqMpO+F`KX7603EHt zi0BI1fTu&KB_6P>TXrmvvjj}-KjY>Vj0^(#&{)y-NSbFdp1Bp^_&O;Hm$DTU%X|Lu*<0d$LgpYDy@k$7* zj=*Y4$%$9ZL=U4_1JjaPXFZccU5Bdf=SMp@w8U5BI;Co8JzqS?b{fxa&x~i>i^0s6 zZZzwdq<#!waAd;^*j_3xbX~acM8M9#yk~72tcO5|XS6#L6hb|ye`g*LF-2Ti@G=IW zl8sKRAlBp})89yy1&gS1s+{YIUjg3^)C4T1*Fju6;3-KQJZYf-aA1j5FK?SJT+uaMvN3RSt<<{Q^V9bI5{zP z`%MwaQ7L-fKEo~wJfYrkmMzANNqIQVm+4eFVxt|SKIUGTaT4bDM$$;;jT_BqNk!Fy zi&EY4&mUonqe^Z$`Qq|+qCW>Xk=h=U!$LJ+{8A9Is1308#5`^zOa+=#m8s zt{}&e6~Uwsy_G5FZa~HjAicI7kpu^(`?>LuNwAvu4<&KgN9(^QC=}#>F^|%ScL(s& zEbFV#+T}KNYS&jY`~T=yT6Jgxg%T1ZXS+_>A@jv(g4~j{r+8#X=*Duz_=4{!356gc zrJQJwq-3Q_aZw);jCBdQMD}EJh=agSyK8FJrz$mP==lyefrGU0i<66jg@DWNXoOxe z>h3EBtYsj{SuA6y9M)_+>rpsZLVjUHctN{o&!XjfJ9g?6q53K{=++FCCVjLu7eN&Y z{jvw*;JOGpR#LmPnz)Mf^Ss(LF1*Js0QTR+Hpb6FohH&+{WUaRe{P(!-XF2!;`_bc z>3li$wRchf*O2#xi7uVpznR7+=Evu(vn0fp%@V?nv{`wVvO;X64`NcUp{;HG>aU?) zTl3G(%Pxb}v@A0l*M%Flla(wIf&`=lH=>WW;d4p^APL1%n0|e8GM`nHSC@1hvxKoT zm1`CZ9AV!G7<2jOw_dA$e0CLq25Vvtwa(NZBJ?|haXN|Q*Z|JNKNps%aM>eY52!c4 zDhqI%Ael+E!bapR06v0uG*kkSV?Mhc&MU#HU2}ypsdO-({Q(u4^Fj914dbq0bab2a7W9}T7wRd zvmjtb?N+H+Fi_~+INjZWxIy$?KeR8M{79{DY%ziY6TWTtZXpv3M-ypP2FOT13~hIv zKHNyi<+ME_fTsEt?nmSo3mi00;IYUeg9b|dl9Fv8cgtoSQB3h0#GMN+hvCs4OOSuEm1R3bx=!4gZm{nEp z*%oUs>qg1>#FB2+e%*|XHb36O1e2Q(_U@+m@&PyTm1O2DJGWldKr~yx-a%37L2tUk zhqj$Uc-|rbfV~Di8klSEa+_=PXF~&*mX8vbC)jGWE!*3!eS7PxP08VQXNro7dS=FQ zpJW&~t0=ik?S<~zFF}<$g`}kCZ&UbsOyO%Qm#m1y?hmjAc~@~mg%NK9oVf{B|OZ4i8y}d{p z3ggv$b&V=MR!!M&Bg)TXHD?oRwj{oaPuy*mxFNBziOc@^iD}bQs}djxB;fVB0s7cF zkS@)C^!xPLJ}LiD1O#v#saC(ZF%M97+XpDTEzo0Gj>Np9A*9Bry?W}oy~ff*>FPEU zE)yT-J*E^ic##9W74v^3A)xgn)d=b42}ikPDGT=HeJ!A!Oh9F^s*2QuOh@l0N?G$$a}$d7^F@lO!=ua56CP*|jtCkRv1# zk#N#pdU|=a>DFz`b_Ub+E_A6T1-1l`h&bPKSJq1OIAXXYOR(vfFogI6Zkx=6XzU_v zF%hm&Y)AglLr-aQO1kE47rQ(szQRjrdlGvAPNtQ3`@@sH5QLh`II<3rO#p?Q`DNLEG5O@pnFI!{ zxr0^SJEjeXIZke9&0dWWF;k5C^>eH>{r3H1{_Kn6U#pC;??@68G*T)KB2UEZeOXUM zwJ-Z~Bo+{}lPVlDzbC_Z6E`V{AexMqimT`-LYD}SAA%67=9Flx5X#1QdKOZbD zdT|GE>sgwRn0UEuDj*jjxM={Rpu!&@3`%^(J};hGNlA&8Q`*Ig7yntDG0@V`KbgM6 za9Y6w2++(A9sB&yA4ju{@DqK0PLi31M!>2W!xUwf+I6|A&;1cPKH(+lX} zhDr6dZ=IJuYGvR*qIK)mGO31~t@W*!O0(^4ocmW#-*1yT`cUe}QTFWq(#{`4#Ac>_ zWX=xP>^?3;c6wLonkh7*PO8+O-{^{5q&4*#+`i<$B5F>m%l;R@zhXuueH&cx)DpTH z7&M|QgWkXpj5M9B>|cP3mQ{W?;|`yz4l-BB;AMj#%VZZ+DQd6*oA|oW)mU+}fsRCq zU45x;bi&Ck`u6MI@^U%kB3+jeB||EV@I)6b03jh_+_(LEhc0W&y_CR!tI%4`Nuu}3 zgZU$@Dh*rLQ9&?RF!JdAWG}OH?SyiES?Vp~y!dqKtxh@jt#I+`%_GL`%{o`Gt+K{+ z=uFe4Yai5E`ZtnsVipwue^7y4tgQS6EKOcKT;zYuPXEJJ4-W2StOplEX!_C-LIj$v zI_g4xeS^j{@G{7Tyai)T*vn6Qh1FGeA zjP~ZV4WDJ9bX83)L#c<35>C3ymy4(-bm97C$Ic~Fa(o3J5l`QOpDT6hpGY*{$6Jcr zzCE{DhRi0bxE(=`I$HHRYR?%uPW&QBE_ut;d}qyar#Vu?0|C=>C$De4T&f2={Kidv z3aT}5<=1|)8sW?o0-6Dd5I8o?s_DKY^G2SzKF#W%*yZ?awU^Us=t`QMkqTMxbK7PB z2?l~3M*Y2!ndamQ1M7j$D=QNh{OmM%j(_1x74ReR%cGU5*z}I(0u#5v48{_Tj=6e` z;Oo93w0Ff0O-$z?iG_^{XGk6_+HJa?iY1_1TV?pp);qt(E)etzdgN^ff}$VQz}SgX zthaT4r4k+|9j2{Z`(#yJOGfWX`v=6;YS6z^wOj-0t7xc79?W9g+X`BHQ1y}JtGpYv zgrFeu5c%d5gE4^O9xLc;#pIRov>8=VtIh-0P}HW+rM0zH&jXU|b*c^c)NcnuT0#_s zniLNSCkv?^H=(#r{9#)Ly+`xg<>3OJ8iITh82{&dDQB!}B zeCj?IO?zTH5RqIXm^5vzP7P+o#X5m70M-iU^=Q8l-C{FXRZR9J!TF^PfCn!RhXyAb zq|J7C3L+MmI9lIE0pw-wyasO=62E99qutV=+TFS0TQ;SkXJPoRU3QwMb&~cQ?#%hy z{?v;~OD~s{4nT8M{KAPFJosGkH-_y3#!)B(4g2X{poMwzYOtdS#gQrVzc~IfL4nub zh;JfDjJucDxbm-CP>ca{S-!y~qd%wO-ubBrizHic!bMo1A%l3CFM-q*#&4N#C$@#Ss8G?1eYjs0gvk+{J&}hn9!0As&}sMX^|(l| zk^r7GeAW#4^W}kp{_VofB*9ayV4{OS14@@bX?2dLf4L$iFZe5QY7=|qRy<~k7-7$m z$YqMk4Al0AbJddZJ-?!w6O@N8r$Y17{e~)cQvdj!B)VJ=UcX-KdKr5`%5nVGIUl+n zYGAC$TOvauRK<<=Pzy{o9gU7d1mN%u!WK(w49y%SRZOXy9~2rXx#2a56%=D5*Vokr zFAiurYm6U$#O!G8EZfg3y6x-ltZ_f!$W}Aiq#`z7$krJuzNe*$p?6j4G6W+B5VS!f zcBcNl3ziWxJ*}CW=zGU6E339p4yiZ4i7B8F)xWIernLHFfJZ4NSnSD?&9zEpZ6N3R zW%O^X^>Dhj>vuS_LL~h#OsvGB4I9CrSQc?O*5^i-*sZo2+bO&FuHBKL(z8eO*P=F) ziTG8wLdN50;h0pH!;lh89jqzEafeJc2>WzpO~jzAdGn3Ft1rhi@&q^(8v~wB^qYnn z9ZHIs7){Tb>d$SM>&mnjfKvuYRQ>`EfDPJq%Ecv}j#!jb`&WJQ30!xY^(ThxW7tB) zCg!9(6UqIR7oY5!Wxv*K?vdh4E05Ga@h$evciT7_?QT2%hz%QlmxojPi&->L%(36u zdsZ6SqsTn}o*$*FCSRF!M~o~C==j>JOnBqfHl$gQIYgeYLkB%HGq&&8K^Th^qc?1p zc3Bp9YCniPeZBVtjW2!3Fm3Z+;X5Iee>f9_P*LHN&k~JO4I{h)8@J3P@rfZ+^`|PA zgttqxJq>_;gP==XdGchx+OLj%bBU8)0M0E--X58FIdP?ZeI^9`q)Pxzg+o>NC52vu zsNIU)7a?jdp$)K&)#95?YMa5nj8HOI#%>?7d-v{$x*b_h!;In)X{XLnb)j(qO|VXe z{1HQfG`2o9OaEiNi1o%$qtFp&fEb z*R@QBd}_``Va;bFrW;bK`WSsc5-UA1z|2(wZACGRx<%qY3P2RO;(4l@K6khbxQ{z( z=I9uf*?%H7Mg&EQOePxUr(eyUbk1xJC6llvCFnEFzI1Kw^pqsxx_Euim$;5xK}CN4 z{JC=yhyN{nm(ghY>+yxQ+1FG<3N4AL#&;&yH;CWg6K4|o8Gfe+=ZJm?giO|J;)^%I zYuzI#H?mJWtxjD{O$I=;Bq#sre`ZkKVwXp1TrPeO(uJ@h2DPG@_*6{+NIVlo zHnoVHfKwL}w@mQH!9%q6@Osw+c;<%o#Lz;HJg)~!Gcymr2jo7Pc8l(T^8UfYhq`-Q zU8)weB=A~USTy+8AW18t%3igzwr6~zrlSkovxremavqpLJdY}=yE!<k|I)i zQ1<`%q&YAD9qsQZ2TVu76sx~$_wFnX-==mu-`DqQ5cZ=wYLJbNMd6Vpx%+xzukdtm z%Dl8^_tpjrfdvD0cR#Pp$Ew_ZR#&k!b16>8?yMfOiW*1~;9YZ9Synrb0@RV`i>aCo#`J2*JZ(Th-bE&ni|9xTnt+rHSh2x$qlvvge zYh9T3rBp~tRa@zZ#J%z7_hlqX@ubIP-tpnzV+dU#>1I;EgF3 zROGZ-$;ccuQAUU$(X+o#`PSKaozm6HChbCuyY)_3Rh_jc{%38fFFu&UBvGp`SDLol z0=gH~{Cf8FdbvIed7C4E~5nLg^ zfQQ(1+tL!hpgz2HEKkae=`|l}=sxic%=U~S^u8=U-HTOF8z|4Ayw?Zxu!bBh8eD#S zF}Ig}6_n6pv|Xc*UpJPWf8@(@ruRae3){YZz4(;^1ISfEUL*d6w}-y&){mqCk6k8> za_bTGq>nxi>xnJOTZj~8Kn&flrGpj9UPR&AF&C`ZRLHDQYv?MDxVyKv^WnN3(Pm(m zn(OVKGaXXt+{z|dwS9YL;#SFI_@co%Aa>hSHi7fOGH6A4jI?OZrxjDsXbXjX@#03Z z3bYBcJ>gs-qPhh&%in12vQDcZU@{XBrVR0bzSk8j6xyup~d}z0qy8dh%I)48<7&o5Kv>m=i5qFw=zz@s++6h3$EVVnwUj(TlJ;OlU851t&0 z-*!OPDOQR@yQbVIP-iiaChMog$dJ__(QibX`djw4*ux{n{82XIdE$TDjB}>C>AW`S zLBsJob_#~$t|KR!z%>Th59yIPMPTXNp zQ9+1QjC?Sx5~b{ok;_^vQiB^${$^UB6K2>cqjD4%dJpI{zGv zcPH00nAt%6rH_hy2$ARo@oH?w;UUJ{OaJBI)0yO8TlX){?6Gs$=cj;pc!!ux7Z%ph3}1r}fa(tcdNb-l>yH&y%!cK%NO!d&W#>hNhCS z-EgN-6x|sz4jPNNJ9$xE4#Nxt1fd=YfXQd(;83;qU?FA`>YIP`&ggbtd$)Jt>zxVn z3qQIn{_w*h>)N&TL-W?2DXS)~gm2@D&_9+DN8K(iH)h82JAFVG0gXIt=WnQe@eoj% z@G>Z%J@EpS3e;rIgEhn|pwd&tv-g>gy~atbXy^*4)z8Fa`|EKghjyl!UYOmD1eb-( zoB~_n+~H=D-U26=(uBn0iF)16JGgd)gzG(d`kXnFlir=xLy9hEl%siL@bvr%j2sAQ z_ft|;gn7W(Rz3Kl!`3cG& zCNK9j$F)8Er{n<{vM9uWe$L}_E(!0mAXEz$#5{>1C$7AG|3fQfWZrhnA8-_)aB^$6 zsbYIM$nOtWx*Q2_&52)5Cmz<`dj0p1z56zU_C+(x4w!Hl`Yr!7OU&(sAHeuoSY>>! zwt*P~o~#TM0F+mQmB%K#&kO&x#^*RhLAf3HU1hSD;vI$>q8DZw(?z_uDGm!?`j1-n zOY|0i3{1E+&y=v|L3|px3%8+(`%Qxi@k^r%#*K57R;o2H>7h{$qP}H|^`C#9%3MN;#5yMW)CLHz@L|~AG>6hEGJd#k z(@mQPe+_$s)HdQS+hQC&)Glp_3cOG9Bn~(XcQiSE+zDSj%tkK5#@}rxM~EY7TvkC zmds;*WQED@-Y=%>C^v^_?CO3XblWx$-@VbThm9MzBh13Xt=ZiZvg9|Z zp!XqYT$nd&2-U^id-O2W40ODI0Gt89FLJ{Mj}vcYEa5Vxb0D;8TUY-3@ZrOYX50EP z^-Eu;(c~{?VrKU`OBTNvfVf@FER=fT)MpO-+ujtj`xFELqV=aQ@Iy-;)L$v$<1uPTyxRVP9Vc_ zl%>SCtxX&7P=76uA!}0Pz{{-@bqZs$;0&_Dfy?30m`@$Y@mqzml5r2!3)Vu}jw1|y|xA%g5PoD+FiQ~LGo8nFON>&{OPMfi>R|Xpp(czSjW{HXNFuSY zi`8kvP&lB^g`il75c&*D#6#U?a|MN`>s}5<+zVr?tmLf=3Y1VO_ zNULbo%<^M$Uewe)racxnImcxU9i@;Obt0-CuevU|G}`rSY3aTayOe|*>i~ejsUUPn z@*-Ka{KaDnf6laIRS|K>N**pbzvBp}xiblj1Yit`y-gu{vb!T7Il1@iWkUD&*QgyW z0K}h6le3ku!pyxqN-Ehe4Bl9uiZ714N9ub&`RI2ygOO!8r?;4%~`%d2?w}c>{qB ztr2THQL&dSQh7i)aJ(=He2}+}w`oJ^!OIOL*b6$b30S>wo_IJL$Lj!GK|;zORfOuY z){SD^xZ~rDeM^>kAi0$_c~XNpiRwQFa`gG2;D7aNV6q=RdSq4X1M~`2H#6L%Qg%g& zhCoEYSWo*c{$M~T={wjmnT|Q+tC6zHY9DlFH?-VBE%W2z_U>(lSd^8K@`GiTxx9Di z1{eUJXBT(m7S+}hwGEwtG{mq*0_1XY$j&!PpXaP<8ryE3xvFSZf;9aY8vJ@^1nj(i z!r9q56q)n`x_Sd!@eQH_Q&HIT!%aFowpHAJfsb1wkBh#7FA80Kz~D3>G3Y7jJ@HD& zVFq{sCmHVKBoO~w|5FKV8MK5_lPyy`fS-pnydeATa{T`h53Y#*x#lFER1igpLE%G1 zH#sw2ZwbC*5y*<=N6saELJ^zk;AZUTl(ipD+u4RMg@5}PN`=5Omsd~KPdWu(T(q9> zz_@e6nAW5aklLA-barbP%C<&MS5yPEdJbo(&}!S(a@er_h1K(bZ%ML1mp&PtgAuO4 zmlJlJTXAvlHeL@77i=sUyC;4D1`{BSh4PVHZe81d#Fui|yHH8!eY9z3(epiIH6&sp ziLnKBi8yrV?XZvS|0dvZx8(@NZJp)`#N})zP81ojX1VT>izx%Wx9*tvec6!36^Mg= zbu{d-j#6^%W1z3E{@2qPlZT;l{p&AHqyqnLeaO(e35%IK}Vk!az zO58Iw=exJwEs40HBJ;M}vO;k!gM<_quBiVBY_dKKA*ZMjFH}_Xb*1_gidWgkR!8W| zf-Tm63xS4IZ2G%T+gjQpqL1cv3&ef%=GXYs|J3DcrP*bStCLj|)}w1+P?umyE5D_2 zGfExk)G8i8o?$KW$Hinp-Qg%8RJGYkC205LyGUj_t@u`N;lFouYshLEexq5ZPJ8V% zdje*%H@pe59J!~NZ3tIf%Wbx2#&w`h=?ZD$is|iS+mIBoCm;Dkaz~;g6#Vn_k5`#? z>4Cn5HqAa_7&?A@aaOei;vhsAMni)>RyOY~+%uN0zDxQRptkq_1$9o|9ZH36L?8pXy=YF2vue1w1oo54EOH`HerB4eLuY<#5B>Sh zI?F|kCXIPT1t?5x5cDalxtdtndc(WydP`XC4nC0TG#D>0?oGUFz} zmWIKz>C=%kL1;tw7$2Qz)H3YS6l)yU{g%CY@q%}~tJ_~S>S}7F1Fn|V>+02a@l}Kz zH~@tUfeyz(y5bsVqX^oU@eU(sJ95?=!{YGeru3?v-2P+6fHNF6Hjj|csYC@`4T{fX za4r-5M|LyQRHAb)Pb|K*j{TH(j{95-=>0s-K6~8w@pn>Oo@otq!&bL=eP=_%${&Ma zfWW~e<}+o4e>7L|osg=r^LB6kqZw&DZ`x4GIwQ73p6Rs=oFO$IFN>r%P@SzAgN5G$ zev)__YFikROtbPVY;hH7gvq7C247CI!UIb$$Lqq23_Mr6+ssbDIp6@ z_`uO2RNgXm4t^vFvOp8YLwU&`HFIU@&=HD_F?9*Nna>>?uzX}0qvSdxp6z##W z6DK66laD{B8pODsLcj}9Z++Yf3cx0T&!kW05wS*utPyg<#;N~({Bj~8LLazR>y`Ae zK-z-ln0b@npk3fK+aH#Xn$<$XL|1p^3=gvbp25A!o8O*cu2FGr$zP`7!+1cEK9aj3 zG47xp#TL&&=LsU_S&>d1CvC>fPI3AFkXpo9FQMrqPe+?cyycmnADW0cOTH$XJ?ZN+ z!YifrB9s8L(4$9{%Tre7K?faos0~!Z5EIzyl<7_XnLo*1DAsiPvbe-2`-z${&#Yfi z%#QgRSM3-}eKEU0qE|Hb52lJ^<9jUjKX|#fN$krSM+a- zt_SZB_$yq?;i~)ohMN9^KTxl+>glxUp2C!6(qWj>71on>C8t|_n?ah3sV|Jlh&O@u z;s5+bb4)Kd&2Ox%B&|HWm#A-)#=m$NbX2XK zxnuew*NWN?%NKQY+TAbPJ$cuRNyi6(v$(L^4WRo+A2krmCwG*bkIl}oZsptloq7Cd z=mi!QonPLqtE=x;u2YOq_LDv`hsuIa5dl*ifI*r{x*Ei5(zUl4uQaj(v}d_>P;u4p zSWFxY94mnqHp`X`k~IQAXKc#crj^oGKW_#|Y&~!~f!iQFg=EG@*BQ>RA@bZUuRK2Q z$tD4trQ)JHkwp$KucgvJRyW*IWwqtK%NRad`z7{MlujqU+MnU#Rq@B8!rsg6)n2ws z@+pcagZvxzq#rx`6+nSUupzg3Jzx|&z!5%vXJ2h!6}w(|$@T9^q`JuQ;k^|nTRmQm zvqici$oTWWe(^YE=ANN*PtWaeeM6Q5AB&_8T=O_dl@-nBTz0sXa=G_HR1Z8F>8Ofw zOBSJy&Gu=)4f^F?W3isQ2Yxz=NKTT&u)?mQ{}c@haAvB@x*fliW+%_xmy)8xpiPT! z7Ok#T8-8H>+DlX7-fuPOb-8g)Qc7GbQ-A-Hqn3S@K6| z!CArA3_hfg-t1|WivQU}&|fM2qBUep5>Xy&n$zYtZI?U=aB0x-bHgOjGOoJ9c5;@3 z+e+PCmsB>CiLdSZ8J_34l^N=8`s%%n$M5j<{t{z z+d5%Vg~q6Etvzao!r~ks-ho`Sg&0?}``PS>*s8;u%uMSJMfoUuPq77GL~XIC0NbMw}8 z_tNn3oH>*Zbc{avijT1fH|AkhA&WDW#B->?0&p8s7}-^ChNk=;oVtRU;&A2j@rZ1* zxE^n1L`U;1J?;HBtp>#{_PEx#hL1A7^zx5=;iW|VAU%Fih?ZA-K0#L#Hxu_j$L+P_ z^kOZHTDB-Vxo>re58F^TeJuJk0zuwlLty(4o*vJ`U%mEm{Im7=R=@ab^)~*Vh4DM< zOu5#TmQVBz!bg4~Ld8o(zR z<+gYNL{LFN@x>4g@?=Coab5rO;JU3tLS(1-TZiN(#X>|}Gm{-h0130a@y~x5+3{G|4sH5i0Q1cv8GnDyEM*ve)8>v9<}q}Y%+OB^el;UX$y-R zpih;VRvKMMqjzi`mAFk=o1m&{0n8P7VuIVD&y;grbjOVEWgCY6j-);%0*uOYaQfGm zYD0Sshw1&Ab{evdI%@^5OEsf+cL})IYHi6kED!HxZH-=R0U}(Qj2TyrxC zf-8x0VEKPXZhv>BSaj+{K1~WWPt& zXm|cSp5U;wbW!8V){7Md0~-~)TDpZFV2rc+)TQ*CJ0ayr$b5c!?{Ucyk13=EvAs^o z`%_teY@-;rB}20h*xWZlJ`M{v&vtR~W#f2ZCYv;~j{;?tOgnVd^|30COe%{3>yxxF zY@Xs?+1dK-U{?4QLOSxZ9ftNbHPyYxf|yk1LGicJY{^Dze&Ay!Q7NO0oG||%Ek8}( z-p8-P_LZ~wj<0NG@}ZxT<)L5R-rvBtC0rjwNBWo!Vya^N5TmiY%&XYsFpeG`S(|iY zBsdz2?jj?fTMOVUX=qFWtny0flQCMXJ}?q`*H~f_&P}eYWEod!hPq4^@QPdrZj#u{ zPjE8CMNX?UYR}ukuq%sPc^<^$)<63u$I9{GewiUa)>0<*Ay-N`ecTWjlp>%==XEF@ z=z{ewVh%ua+T3bs$hjC`?a3o6K;#|h_|;f4rY8Geo&sTj8Q9w2Ka~|iZ8PdFjeQ%p z=CI(R@VJF}XZW zm^-dt=(AkTjcX9~E+4yHS-76#~*s4va z$F8_!6N(!$rMR?7Z+q9f-0#u30Z)6yj`wRY8%S_`*~P8|=C7SGIbc)b4Y9(t~CYjBC|!^NYteL^ZzCk{&R~G3W7l zlz}McyBc0fXCfZouEfUtz<`&N*aR7|vZAnGi}TmCetoZco$7dM@Ss7F^BRxQN}NJJ z!e9*Jq@3t{E5W=K(M=%zWUBbGomm`nLNlN)8=P*9+j6v6( zmo8s+f4LA!AoJ3q!pKS+z4C#no(F%Nq~%Nb6+6?KjMyW$ZPr~;{fOb=$z@Bo{bTlB zhB|sO?tIHAn>o#~lLu%t1t>@0;rq**y{?~lI;hI-7Ryt1d{Bq~q zp1|9=FVuq1zqWLV;i6VJ&%e4%wg{eHMuy}X!rN|4zyG<_X6?3ZB|2_<-`O3-+kUC< zebGRPSeOwu08VmCePui($C3a5}MBL+G$IEYU3;WxMvh$#RrkQG%I^svn$#d$#X6zmxa?g~S(d4hB}jA`mqS zTu0i4w=DfO^;H4vb9&{a$^7;0-@o7XVvU9DkV6nJRJIslUjMgf{`0}VQs=UeYwVd7 zgJJd*80W5_o!ER&A0x9g-tVJjODb-zn@buQiAl3@8R78!U-QSh@7#CyVcRtS<78hI zBLXpC)_qYGi{XND#H=bHCfJt`-K@#5t?6BHGg>m5%G@f16~(4NrWmNy0jWn>R8kBA zCg$y*$mM0T=o+|qq3})eT6M1nj6g^EkfwRKy}fP?e;kiTml7~GOl*wA2l99cWYI5>Pq0>&gipJU7iR6Cw2 z8>Ue<3Hhi^ytA$kG5xDVvV@SNvdDEHRhMaQ1Rxda0iUWFi>|KbA6E^+sP zKK7KHG^Wbt+3A!}2CX#`*(bCiuXE?eUap4Vf;7XV6=s;`yktO+ ztCD*JnwZrL_LUfBMB&Q}X;bC%+ChErT({4+-8)pIjy&Wr)2Fea$rey!jTBQHl5s7w zE(Wq|B5ttt;z1qlYaSze<%IaM@e@85RBo(-@tXn}iv^JbcmRmiT;I145yNcJ3+Lp`7V+8>a6g)ak?EN=d4$Mo)- zy1ol}yvWdBPjjwo{x251ApaWE+}L>Ol}{cSjT1AH0;aTeYv5y+y3k**d9;ll(sC2k z<>R_-y>nzd0tqzHjSHJ*R39~Q?b2(i-Xhbq0jtw@>ZT32=P}jMPRFR-^epos$_-D6)@8 zK_bjW@-tdSI7+$=d0ZkH#_cAEGF_AUhgx_z*bDjKc@@GBsPAj`&2fG%UTJZxtrdfY21orn;oNs2wNnb zbW>d1d%)aBVA^%j_ZZu^j8-DZMUO|4LRkVkBDVt2$s?M@Qv3(X6ib!NzNrrZ;R<-c zb0Mr11tM+&Q8PZZnoc~L_r`9AHsC=}{cm8{1p@*ZwRk z;ZKW8nHXt>^Wdjr-Ly80Dg789wL8V+^ZOTm?A~BS15#%VY9QyXE|!Ui3K3fIQgU4y z*b`AEk*s1B7HkU5gNRAyz_SfV4f8*8TWX9fAZC|+eKDp3Xu`hVOPD#vnq(rf9-a0y z<4e%h^y8irP#l@=&Iz|%HIZaj?Av%CX5nr3)`q#uD;E5X$RKFq6>uh-b(j8IU%*6? zz`wm@a<~(ze`1Se1_5$U!ixkQHYYD`sT$MWHvp0Mm}MV_{Hg8qKNAvgsuseT)!Y*Q z69O4Kc00Yn)v4^PPj<`E1I)}XTuU%twZB(5xBSVICq;t~Uxo@*!cL8d=4Qm2!`0ZL z+-hD zBZaA+LJ;J>v+fjtGiD<~{Ax9PDZP-Q04MzBN)MAQKD3bE7;O|Moouh+>N!3nhCLbF z${hzSl33gGgNHfM4k09=3))hgOYuS?WUwi7#)-aeXkAyXUq3P~KE$vpBIB~|rQiv@ zT+RzW^y*b(x&p2wk_6QG{T9&5$G1urtl+}F+|e@D%N)hs?t@wP+mpCaqMljxRx!Iw z@`SB<=y=Q}Oa>G!&F*qjKH3=gM#58s9EX{WMyw$J$;^|kM zja(xe8v+iwyBl^1@{JgolkNzUCwn~!5+`sj`Auei+TwL%;a(zDFKvIZ0>E^9504%L z8Y@<*PIXl7e%m||P`p(2iUJ$Zy2vp5H);Rvl;H&uS(x7byr)|`RbQ{kNe_EH>TP$9 zG!4}UGYtam3`%Wk!6=*~{l*4&(Coa%KKAl>&77B)$x>z0*I9$*BX(|_nmyL0*b~NG zd>SPsi3bj-0SOiXGl=GlR#7Z&>I3$$2CfXLfH=06(JV4ySwW0T7~g-u%eAXlC0Xm? zix-g;QXf|1(qQ{0_ggXM-fRYrE?e|GgZj1ay~mvJQu-6CB_*$v3*79ToZQl9y}eeJ z5wkGcE3)KKK)m{n$cgKcvrYQA7GG`F!0zzftN8tl2PJQwupvXWc!+_Q%LwZo76<5{ zIaUqcnRj0{6WOhR3l!y;pzhE%GQbo-y}kq3lmib?A`uXH1Y_iaA z6JlXPet#cU=+&FE!rs~l_QS1N(@^3kyD721ykz11V!b?kjn)*cM0Cjb^pGJALO|drZYvwb*YjQW_L*kV3@&=v$4h;slT;$f9C!{o zbu#*XgG+l&re8+XC~7#)c-_ilk|=-w{5XoN?Z*reZ%c{`%St(?B41+6lxP4+r`$ZN z4NH~y1N9dL-m3n4VNaf>k0a0IF%uHSJNo5Rsv{JL?6ekp5MZ^++GVoHhAKQDkza#yM}zkkvZr6Bik7m&iONEX|n3vKeQR2SGcp5k*=x;nXa(MqE+Jl zv4xsj@#ojVpVawdD7<*{Hyhtid5Qzy)z{m_!69R0yR2tQ#B*}-{oQC5>U-ax-`;Qk zYid)NzED|^0X=Vt#H>-jiUw3vIHFIxUe&NJ~aG%U@5tgnX{=5=SUWQHEQl$oQB2_>>+K=>FHVKH3aHk{BadhyMdwbdPjL zO~NH#3ci)S=)KR$rC*y`q;}}OrNJ+b;DSTim!5x|%Vr!!T1C(=-a|zDe27b$(FRzE z+f+TLeQgsxn2IM@F7I>t#>Pti3-8@9f5(+1=syp`uKBxXcK~rF{9mXRo|N|>{hoVc zA-QfHo?Kt_AzZ1j`b}6i+?Eg@=|!~PETuV}3%4h+s9c^BVT}*JJY(kWloY?1D^SK) zRl0aC|Ng=DMIM4#m!TT2y%P`2fpr#nKGZj-DBD(E4dc z5|PH52zS7S+!;j z!9=4{l$V)mujex{EEySSJ?2@9jHFZf!|p2uMBn6%BvQF)hd>O|zDm?(N+*3xWPl5l z2?P}NiA>l#{If)O|M>F8tELtRTnGr+tRtu{+RFm0RZ`mf4IC(OnGE^NpIKv8dZ?Ke zH-GFkY^`ST01~eW25FIg9vfXFrx_tXJ~g5?7voj+JYnJ_J3+;tMLURCAX!Ft9vPCM zRR_dRoVwa-L_+>Nm%_eC0YLLKX1~}M_F+_;L4WXZBPcyOdrH_y1S0%A>F5!H1bi}` z-J_S5n#dZFZ$!MaJm0#nKz))`C+r#ujIg4I%l`UPc2vb=>3lBOyiG-+f6iOuy%2%9 zddC|dzAsDE>822fHqOu~3_bvGqn-PELn}t%Cl|AysSOvfiF;Lc0JvP9u8t-Fk!bVJ z2c8}41nykr`*GCQ_4nS5L0gGp%Z)rKTdwx@f48u(u^&;2X#1R9xo$ zprKRY3;(ZEl_;cTKfh%cDw`&JIGU=I{3h@2x1E;hlS@lVbjg`CTChx={jUdG-|%p8 zsaqpMx;3V`^a<&qH&hCL_h1h?4e*(0HE->h)exy$HmYT2jDQ;y0lSr-3Vj%?h%QkH z<>r&K)E#tF^BWmt>^*>VlXw-kr3XFUJQm^=*^hFJT@e<6U0HjG0k0@^3QjyVa!-EW zt1suzXB>p9rGTU_Y`>3zP)1+jRU}5AkJHoM$l~eX$+Kpyr_AJKej7F~_vq&ivh564 zc7^ItbRi5Mkl7;jry1ogjO@-*JVUV4M%>c;3V^SZ=YzA;9~?OHQPjsh27t`cX4$s=7?($_lSB*CB0@$@W05&m}svjb0j+o1)?0&On%r9P<&EMlaE?+C~CBRVTa zH;tWru2oc&;P8lCQpUwN7aPzN-3UIV|5;hWHf(4{8iOQOF^Hhd$p2QX+PVHDw}AZq z4IA@mQw}=uh$$38IZSzY`;J67kiAss*IVRSDx=Qvt$EWm;8u;GIMeG_^lmk?Y}$+E zH{1yhJ4@eQlU2=)B5Qgmbg~%eywRqB(=3mv%pu&F%QV3pG?>yAmXId6H5NyyG$EATC>n;&aQ;Ja|4wR zlG!HK6;@bN8il%@*|8S}>|4j@b#eL4QWRBw%l#LRO?No@IGJG$!p3=O+^gH#X>W!= zm0SJMj33#>ylGZF?HCob^ghF^Tbb{=t9N^^x~-3oo-hE~6&*-v{l7{At}2kq`t19y z{&`}yy<}dYGNPZeIi8d#6e4q+0)x|d2F%RS&1$uO1j|!Hr3lCSDnbqv2eWHTqSQzL z5p-ADn(Rng@j!MtSuyS#U9h=v9YsP{sH@6~w zLALj6CaKKNA!pq9A$(}-ZI7?w)aT9ns8S)_z22L?eD{hi(Wg)y%EtlS zA(#{ysif#_A!QokV2PC!s!nqMto%!)n(E2LDAkYC*0DA4X=y~c~yC#r$9Q(&S?21q> zDH`Sc+v_K3qAQTqP6`FU#H9Uw0-PWWB2bBPS$I#M9>v^h>EUs-b4Jy{@~gFJSr)fD z-PpfQ57N-k{x=hW-R(rkS~2&d02hpmB=?mhCJXmNzNqusae7?Xkj~%?sKi{x* z+oXj4=O;@T+Vtth;t!!cX`Z)Xt71c05t4La2azAJVpuX}k-1yObqmmJ(L!>?C|Xts zfyD<&ncS8A?2q-Bw``R&^Qk`%;}G>Vclcnh7Ov0Y)~!=x)MT{FXke7l+VX7B2(i-8 z#WJTz1TRF7AstiRx5YI|n7IgFUrG55w@ZFVTQU8^gC$}e|Mp(r)y)m&==h=NTcfL& z8}#hijH6J8w(IJaXB3X-0Wyqh#52T`XvqMD5GZTG?C5%c+t|;8m zrxUAK4D>5WnW*wCCr$eEx3gi&rh(cSeH5-$o$v?mRfKYrtsP`~f8sl{Pc`V!CEZU^ zKrv@Dt1&+q`y!!yniN>1jc>!3xw7zx2|R76mZAB_$p0!!)nV>4$n8Wc!$ z3RXT@QidR};`PDmc1T%pDmPNVxkO!Eid1pYpWlP|+=%~m%^7TF)(T?TyO)+;b$Z_C z<$Z|ZR}^p`1g@*)3lc(65h`IGM{Xy78OJGSgSIj~5rd<+6k|rWPQCt8cHu#!KuSt} zef|330bkOCDLA#e_D0O?!LQPxGf;>PF{k1R0&a0)`1)EO@W|vgRibYHrI3sj_4SE; zlw+c9Qre^Zj*KjX0RgY3fLb4k9SXKR_w_APz-nu>S~BqA6;uQ8fDz`<$mS8I7)14! zQ>N@2KG%^ijdBMsAhv3OJp1-t1A4%vBIHY?A1Ic*Y#w-cs6-M!rcZy_IzIRL`$hjn zO;ma8A7ANABQ5BXr{{?Zx1l>64Y6CSDm91T$n)$&$exWZGEJzRQV_oazuqEDMzNG{ z^fLO@sCt4=BIml9aE1A}Eh0d15g_sMN{aSb)_GdbuP$DRx>x#}_CMfnnOq*w+1soE z7rs;>43|t#JZYA%Q`&h3tkqrd_N&W0^2Hl*TZPBQYM}1ug(8A3t1f9C_yhb-0@kS> zDV5Ur(Yx>3coyIXKi)N+&J$gpFXG%J!2BxwW+=I?ck66m@b2Hc1C>=&qL)pif3sdU zTRp3H(bkko$07mB|t>NAyEJw znQ|$>eU&O>g50P>e|^$KO5h9}h&$jBPsOCv*JkJ%sc`Rq|5~aD&CCp3J9RJ7-afy7 ze&_cJBmozFH{lB51>)-FnqSEUM+wxnDgM6o>h{{S?iL!WY+}FeA#{|ELu?=*Z00|9 zb9IRwkVj`&PFLA5C+sLdP4|GicFE;BJ$v3a_ zjQpz8@?S(l;_v|_x7dGu?ti;wj@XwiBV|K3e>Y!xrvBiN z?r&U{)}zA0va(iPjaE$*M7T}LFw@8=-nQ+I{DVHb60?=F0gb%z*eXJK@!p{NmnI(F zT6#06)8{wU+dkc=zxe5vKM&PRU16(z(^s|z<4)o6k{oCW^%PAl3K+hOGA5!s5g4Wh3_7A~ zW2LShGo|xsvJ@Y4=Zk=pt^>FeQ-UfWZ&AK#@jNkQYCw8O zkpKbX`L$W`@AYYY$oa9rEry0jo}Z*7AG20>pQ*rO@zQC)Wh0j0s&dUS{>x?qdF8;Y z6rYyq@eusN(J|OlAmuTYB{LkNVxELL11@SlW-0-e?wwxVgsXrO2o>k-mvi@~%7wFi zxW*)A5L%FS$~oQ1Xt?hMnS2X3GhNFef5@89A1DSr;C7s$>Ra13Yt(2Z z%PD{&1=B#YoOUTnX=YUEyypltdTNg4qV zl*bG?wVW^Ey7*Y9f6A`bL_Z`=A`L7WPCoC1aorXKTG3+3c0>Mr5><#1?pBU}6KJsp zoja!*cBln-leWznoiYgWoiib4N}_sS3YPt5x~!YnJ2b?O7<=MC>xKFS5!r9ev1M+V zw57MJR}usaACE03n+J%mqSX9o?V~yUB#5))kY%bW;R0D$4}X(lifyHhp4Sck?l&gB z&&wV5rUCeQ8O@lkv2hfAQvR_C-)|~sya|Xb@dLkD@$F$lutjgk)?28gn^YY1e{Xho zn2@r(4-pU06dc*>F>$DUr+pH1g6wCFlC4}63H3*Q_iR$u-l z&yqN~Wh~#?V?tF@@sH9__Sj(cvPdmSdJsGTB?kquzC~WKZost(u1oC`-n`YP#p7TI z21$==*5cgsKH3tS58w2N@4NHOiB5-ea?VNvi77}285D;?P+r7Er<=H)|I)PSnsD)m zlHxRAfHoRTXBQV)lx#ah?Ea`@8YqDDmc`UcY6#aSOP0zwG@d`lxmGX#M53eamf5r` z!q;uzo@*Q>Tn{a_G9qQ9?6AIuEq%AJI1y0{*k(dTMl^31VTurfR3o`x7dT#MJ>jtP zKB+_Y8e&7)n?P&q*J3cjrhE5O^NZi+&)7hWA|~Qt2LfgJ0awG( zvFjq?3d&HAa=9v@9!C6i$a3!=h}_5vFR;K_b;~LKw!(#X{;;TF;?glLF4>o=^-PPW z9hz42zWk^%F;F5G1RWGyT!j40b&r4qg>aH!NK9J~xJ&X{r0_+-2FaTP%x|o5Y^Np$ zcJQF6jbU1Ks}le>!x@wpyuJ5pLYK5?T`E{l&y%fq3M6%5wOlEfxU4MCplp%{5@Vl0 z1YU#cA2uaOWhJSff(8N4`VjP(^6?#U(_DNHQKuF&y3GCf%!z!X6K=2k0qHpH5*jQa z&tx#m5+U6nPky|BYl6nILZVohFIsBtE)Q%v_fehl*O(VK4|QU2JUp}7oR&|ltfY-v zb1%FayeBQcDSdAE@Y=b@$H#s_30Neg{Nu;guLOV=vFWs-E`tx-s;|!;+fC9u`DycsooQ!ihzKKI8BB^?5^rK;B-bXpNF)qHgUm!?6|jt18NC39GPw4BcYM5U)of%t zdT+MH$9KQBLXffi?i^q#qJU}Q=Yr-SX3Qq#ag@XEEt@wBP)(1rnnyfNxtw+7U+A#;t_M=3Owid-h!Hb=7kOkOTQK763=Mt;CQCYH=#>d%R<-t#87(B8d!Xw9Ymfc{<5G0t5%zw-+an6#Xf zTyM4=FeHVHc~cHp?Dp1RUs|E6ORH3yyQ6MAPf1(w6uemPO`8rK)IkRn1$3FH2|ORK zNUG|#cFCjw`kvgqX>pg6?mbt}Tf1*_&p`c1w>Jx0w`t=GrcG%bMyZ)YldIBTpzW!l zHos2K3_fEvEF??U+0Cs){4w0YvMp^pn}OpFq#F16n(}wrnGqE=d(QhkV%%NHwjwXR zqsyY&FWm(XqoeCK_OL}-$Wz>(h(BaGSxJfQ3+-uYmIutQPRpJ0_t2WZHwX`m_v0aD z`5P(kF~8c-)QM3Rt$!hyI9kYG0}lNWg(LK=*s1{D3cY(Fn)spJ{U|5i_D9F0A>2{F zyIot6WVkEJ;R{G{o9SnM930vh{zmVOhu5;mf1ZL@)D!6m`eK-Kc;W6X*TinzSb*Fl zp!3g)2ribef1V}*q%nh~fB7vv7hN=s1pXwKCtt>Ie!kqgTA9HlDE`BuLD-#TUkU${ z4^$#$H&7JT&hO@OW*D-JthevKFO%K?(6gaJu54=C!6Th?w(FEW4H5+sZlGnqf9}|f z2?6mZF@Nqg9uC8YC-5Ld%-P4R>Ev{2$=;r>K2w%Acl}Dn30+??;0@aU4TOv();PgE zNnjD5PdS<-&^(T~E;kP#sH}6(*CU0JC4vY z)}Kdp@dPF?T5Im>1YSCt1peXL5&K-c=d6%V*}Uf|Drar-dicWQ0P9JkztAD z)5L(e0#>_DG$!ETY{P>lb{`+p=2iSK1;1hp6#>KKsZ+c39_Mv<(s){JqTeNhn1SEw zYru*Xr-zqmR-E1vG%vkblHt&(M>;P2nUg06QEOGVP{-h{e zbn?Ihe#Z1iob<$*U_PJ_;a86g?Amoi%3Ix6Kewi3oJd;lVjI&XI{L(qo?&<`G^Y=B zDNShKbpM2|4k7BzQ}0R^ulSodgu<&`y?IlX(n#wsF=fnH2KFwLvb2w5X*bwn+HTbX^+X5Ue5_)-dmu$G zq4%++Xw8Hw7oiIwdAQ0=7k_xNV9Rl<+xE~oZRn2x2t?@3_|YY3YOb;1QHbG{6-1Ow ziYdHO#Vy#7=So(NQdP^ij*0`?P0~Pw1>G#)AePr9-JJkq>SCBcSLi-uqt(vNBX&Y; zQN>7=>LQah%OV7lLFB5G^kL79L2mrS8+{(&R#-yW1Gm3f@yxS{oW8-BK7?7-#c0~$hXz~L1_{N9biKk$`TIV>%Ow9Jv_LOv{oLj6Si18`pNE^R(`A(*eKI!7+`r zkr|BEu|;)7eku53lZ0yyr%k>;qa{`<3(Z z7`X`}U;&VPFAjp|hVy>C1sUCx(uPk7m}N3l6ray?kNtJ2X2fXy7tau3G;~~@csr-? z*AQk`P%$(Ba(^L!;gJOH3|+n2F2?3W)p`;}HqiNy0=vnduMB)FVE1M@DL|t%wY^vi z2Bc-4_AltH!a3P2XvU0gLa{4^P9j|9PJ(aWzC5fW51KL2@oYXo@WZE2!1y%xO_iB2 z(f~ZI7Y`ak-^>&Z|3ZZoUW%gk`AZa6)0mR)=$+K2d32(k#8LU&IZQCfq zMwWg5&|uQ(WtKnyTyrd!9aS2%IpzSpJWsuX%T8qMv~1uR)86$vPOX({A02H)o(p9@ zL!%u#1i=)Wi8O?wUUB*mZM(qhbO1_e2J2e4Y{`!I)|?_1#*zBa0BK^v!Gj~CDf#d( zHCgj!zIW}hZ7m(s$>p`JdF~0^Qe&)bP6hNAQW#JF6vJyX?5RczR=ctx~jciaL~v;&3K?NUGMQ4LorgVa7}mJW8^>Av9E> ztgI{>v&6+kFLaeJjPiV1M~EexSa?ai|&I#w?EjTdZFj*BD!we%`5fL*PTqg`F!F z|2!U$crR>O!6&v_n#*^SK+`aAIo+* z@I~nrLGvY12yvXS*C61s$p(|oGU6?7Q?wouqHF}qyLYAN-EKxh69pKvg5Mi znThM>5auY}KSfJw_!oYhk|SXIab2 zv9$rHWZpwzLEs%wiRHm@E1ZNE z64ySNRUCf4T=lFWrn@OgB6;IQGM%>0Df7=B3lSY~kT0eq3Wsa}D0> zLRsMfIQsXKjbFl6k9Ax^4lP=FKBEFO`Cc)@cM^60cSa#l;_cZ0mwG-u`9_@{PMBuN zP2?ti$VZk}mAw3t){tfW03KFRF4&E<`j(Qr+oV~6@B%U=P+$rq96%r=hI;ZGM(i4y z!}I=tU&Z)TY#wcer3i6heU)&R#`d?i(z}U3>`G7qWSi+JEXxEE*w*^wo9z z8nolh%zvM~c%dqD|E=u$lp>Z#uc2~FxG~K?l9#;H)1%|?F*vo1kuvStd2LiwA+=TE z`sFC`4du|`qbXb#$>q}D@?ejq)oqRdowx3JWmIyxR3O@1k!UflaS;Ih%Zx1TOK0=3E#3{s#fTJFGY4nS(BOjH-#8kwX=OjXJ_T@t;5Mae;S1z|C4cURYryh zjSbr8Zm#&hMR`w`AQxZ%{>OLv7>5^zqAZcI8V%IOvC+6i&)t{J=|a99J$fj3?`o6N z?hV2VEr4*#c{F8(B>jk}?;ytk`iLwTeqi;kCUnqT@OqVdf%`UY-fXZSf&a&E7oT|c z!_6FcE+*Z5E^o)Q-W@h=cEr03{F94l_5>L{8S-b(_@N}C7i{W0!n6DL$fAed92a_- zP$GI(e1F;|b|@{`IPn20#BVK6wNKD?tiqDv*?dgMsMxtB9=ep+F4f_Hm|aMj8NthMfD&&pypb$Rpq(Z}z?@YBcPX%XM^ ze?l1|O4VW7okmXK56DdlYS4epgX00UBg2IJW3!ihXOLn%_wex={=+js(?Scq`Df!y z<{Y>vAqxIsWUzXWnjgXyj^{7s&=;WDpcNc{nS4Q@`R)KM#9_*q6k7C9btb|xOXh48 z0o5;_iz^D)U*h%O~_~v>=u-rm^e9ky%!gPz=!_+!Y14>fsF|bwmGiDNQnn;@;(`Io6Xu~;(xE;N(hz2oikDj)d&iWBu zqioQj)sVv=(*aLK5xwOur>N-jew!Z0lSa*$Un;gE-d9w&z;2Bhhk!D-J5D)e5DmsK z{&-LW%5ekd9k-8RKN5MCcD?W`&wUwKAUeWH>*A3x37R^DIhn@Zmivb1Th{YM&xq<` zR~rUpsT7mN@@$MML-BRt)=Sko#t8%F!btT-Znd%=+oXF3-m zE2nRfo>*bhs#UZ0{jSa^Jj040LEUIlhS6*(3hv$O2I{AaLZgo3&5F8 z#@&ZX7JD;ILMh$kuBQWYr%GHLT40GW485Qu7mb_{rWal8@{GyC1HdQiaHWyNJpbQ2 zZQ}<72h2}M)a9Cy`gML&vQka=7^d^r_fEB=+LB#d!jZ{zx|plXhbKcEJ=P<-SRUQ@ zjRywDosURP)|WU%P#3d*Z0Zpawrf{AKrWVPIo;4Lo;HCKEXcYfIy7!<#c#>BOK7~d zBxA}B@&!8QXg6~H9`Y6?3KdmoJ>a6#T^~)gw#f7UY#hmS_mC?Uc@I+=BgM@3C+cl! zd!45+;?8UQ7;c&FlhFv?PgT;fZU2V|H_2BjynXxouGg6hN!vl6tTk{!Hy}~5d!eV; z=r}FE4M2^kY~^;0vh@g4)t+iza79ZEQFtZ3C~Y(o!?vQ$gU zjR4{?rQznI^{Dwa^>&WGjHMjKIc&?8E#;p(LX5QI_AEhuE?d9E5J$Ts@Uwj%2@B@r z2j-2KF&(Htq~Z*D7C3Zc@~!A<4Rpf_y_?sRBD{jG1U7Oww_*ZXt+6jEl;JL@`IHFa z*Biy2=0nEv9}6&#B6>;9p^S{iR1jq3*7$uSmpC_91W#?_q5~>C+pyFZT_4i*qX7|n zxtJf*-#{#^T(f4_-*5Vg!k1+3kt0Wn{!x~+%Qg*wMS0aE38>OmB9;-$C`o!1hJJau zx#D;lvUgNBjr72JX|8WYW5O#BNN*nfh|WnCgIln>Kkn|5Vk^;5aQJ*BeiP&L_uQWTM!)uJXmY zcOJ)M+6>Nxl3hkyXR~5rJYs$-NPR{G0NJ@Hh)nxCuCqhbT3Aq^V208%3uuXA>%7)b z27c6(ezPLLmO@3O;^?S)ybQ4t9Xa({(uJ{)OLqyn4PeIz`3MzG@n8|R0Wp$AXUkm_ zl~^fpXWnQ=d0fYayXErU{zt_gnAo5vuinZTUy|rxUS5~)lst%(dC{I_ReSz+-MiMr zHKZ4kE$Ko&e{S{iTT6Ha2^JO&FfzpYJYNg<$POkRJN^Ww469Sm6NVRUPU_5Q=A}Ti ztomKeY71ST;Gm$K=8XV4EViHwD z^XZT#()-6^6`;TI;m)RgfpI7z`B_I_KYYU93q(<33#sUG`#>WOr5N!zim~h}hdB!; zg`TF1UmBCqZi`kNO(_Ro_jylAzi4MJY+(tXUN--27cWxY=8pqy*ADYw(TpU-i)6@b z!D&9aISkblwBxCn*9@E&#Brfls07f<2nfuf)b8U_IbIY{gw`UC%4U`leg+sF9mKAy zD;R%me7=!8C|A0)-jo{IMX?=Shlo+TDYxXIMq=0sSalfjU6c(KR2D6M_F-A$oHbdf zT`8~znyW9UCSHGuia`G9rR5i|x51^*8G#cJ^kiCC^-bSr2@fq%Y}~y(>q5}#HB1L( zlqhX**+S$T{K_`fD$7flB1^%S9suOB8X@x24p>7CI7%xxRw56szK%NtG&%Oj6m?WH zLTu@gokJ4+3!2W%jgz%Gl#TDOt`-NT?l=R<^WoaL(=Bd=kVi~ZAOe=>OkuCjf~uG@ zq1ZKeb?y3f@z(aXv=>W&VFT+ z@On(W1_jCg+4sWX@$JIU2d%@(2nXcZL z+PPaF3Fhbt^#>xGa+0tK>xx(^qtPS>v0FYd4X598-6lLywq z!XmkqCMpx!?NLhQ{1u7yA!gPxbJRz9?Lotxcvm-Z~^c=3k6$y61FX_3ibrhy}j?=DA z^t))O+RN4rIcm~>U=z>yd>e^NHl!l^T%<`9!!jss&EV6t-C{lBek0IhlaU@s_(q#> z@LdavH;Nyw7>&_46Adf)_+73fsZ9!sPyom%JoMSw9@=eG$~cl~-eR#mJpG8!@k-R!qocn`80f$LC9 zh{5`XmICK%+*lWS%dpa4+0;{Pg>Gj)m$StW5WwAQ+@x$5wR<%ifgZ8W$nOk`6p|BV`n}V@$nB@~)xw2l{qc>aD zOa_(6$qA#gN1Z8abJ^dl&+lM35^=zmIX(Cps62VdL&fJ{1XB!6t%&FmyHT*wvkhAM= zSi4sLY}>Gp<6Q5!ybGo>bBk>xLFc~&AS!0qt*9|00Ab{wMc`;=mghMsm+&pgcBQwB znm#)9bU)2)|8({DF(3fODL=f>FJFY zFaGXXbDnJNLAL8Ei|B!*#ga7|Jknx-XGWE^EfjSS4>6_#4I*Kgm-X=q`94w`xrnSE zJtiU`W>!1Ke85u4mIUV26(IpZ*K)7X9dZx-JG=Cftz2x@Epz)>&S0D+-lVs1Bru(m z3qtFOb_orQ=nLS$>&pp7+cd%>)eeD3688?`4Aui}1c~(PW$AnqKEJIvaXuM-2w3~wgpj$}wj5ue&mnRY!rWe`#BfJ3W(v_fPz|TSq5gzDGQFF>) zMHWsedJ83vwO%ua^V1umet^ik~QIb$K;~FToDOpThsOsiBlD zj!Ob*WgIQ};zq|=ZHK2KJXx9k6#O{$mgDkl7?-BZYm$lwLSW%v`|10~sgTmd6E`kd zO#l9BP{$a%aDImF`x|R2b=KIDHA?Ci=$H^2ZLh=n2>?JVp{Uz|1(P}B{f|Cf#4QOw zgmU$N`gqa9Q#Jn=hrgtQDoAUuIS(vAIX8~?-JCQYkpMavzQf&iT}^WPY-DZzbEnyCjwGz5xMLNf_7Op7na=qpIb{+^=>CkRE zx>g~%c>rY@!CS0Wv{V+vX^-`+_tZ*ZLN2lqgO`DU%S#H5ps_h}mz@H-4C zuPykhh#p{&kYQ{ZVl`9bL3w$r070nPF8i+qFTbLr5^nj(@v|8L@6kDmBNpF29kb0DQF%B&P2?+uXec z)TO|f$vCTg7$!udnmvBD8ZT4K7f8NiZQ?VI&3oQ7^g^+r<=+eIbog)Ubsw^1C&Cjn zFihe*Owu7n^-7Ot3cwr6O_y=QR~ zAPKP0uA~7ISu%}MtbW|zur4+vQ}LfzwgT*5_n|Fp=0%Zcy<+@=xLvHwiJ3x zM=DVf|9P#3e@RX_kjw`QMP&RtWmr}(UOKgzQ?w-L(Yec zH!QHVEiwFlqzMjZ`ag-8_Y3lvGmb`I8fS730Ma8Y1a1&MfzkBfR|#PkYX!GRJp~4$ zi2eJYdn9+$v3nysU8G*g%!h~sg>@;7NX@T1LMq80U48nkTfe@P5+fZ|?{uVQQ6d_IDbtJVzw*M=B4)=2 zZ{EZJAv8a$zKZ6+hde|p^TbKk@2-@V+C?r4^znfSZS768Le{=s!}edF?Bk<^Bvdj( z_#{MpPy8u3vTU#TZ_$IU*ZPb%!zKt4Lt;nlpqn>ut~q$HN24B%OhQ=u>@h}>T-Bj) z^mTLX829ehqmV8Qk8wnO%h%QYOD!hvLCDC($fo-_jRVRH+CFgMthEhq(6-51Z&qDM zhs{C@Db;KD9Ldt)r#_d+IW>ka`^3meij4H=r{pox){O6Qv|RI^#Z%l!d1fkVTA}b0 zh_h2JPi?W~xJR%T6Qxjn%pbCK0~TMnaBzlfCxuDvVw`$&=FXk&@vAMvl<228#PTq5 zxsYo&zUGhH*s+lab|6A-F1`b`C0Rrug$IT|DdXkHq)#?OL+A92V+4qjQ-8YZbkB(= z3wU1OhwH;a;}HD2V$1d}j@v^8<$!9?GWAiW^fyY_r1pKVFIP0?$U2Lk-dP&o(qjG0-$Ag;!=a}mK8CY~o=7fKjT#F8V&>RhXq0~@qzMzr** z8S{XmaEV6%ReZR3$XGYg94bzh+_to~E}Bwv-NnRd(m=px6ylM>C6g>Eun6rl0=3or zt)JxT_FMX;Tj&LYutT^1U3K11azyh9L(k}JTWW7o2 z{>Y&rB|_F+nLV9uvUrp$-}6Kx7Zw4;Y4tGM%2(A~+XDnZ$`bQC88JQ5H^2Gp zd#sLMAYC}QcoJ=!hy)x7mJq};VIqk0==yyG%_8Wn%GG1v<4DDq^Z|YsV}|gxnV0^V z$ncCsSrI?7j5>At$BI0>t-5yB5$m!V^LKt7OmMf0=V z?WTAneiAJ$1Bd|VShoa(mRAK?Qq#HP{Fm1n(3k1g`|~9XnYQH8h(QQqL$aD!SCHwh zVU{j0A7z@gtO`aopFbB(nGfV>8w`;c+(w+RD7SM2hoBlE}$*7u& zEKyb_$u%=#MCQcw>a7;&W4I(_+fLtUy(`PhUHp7-S-A8^^Tcj|<`$hzr}49A9=vB@ z_c7EiS=Le_iLQ$w;1(9KInVy*6$=Ne0VvS<|STmUOD5W5aXk8@yU*BrIao1Km^TYnNQC%bdSqCtU*dn0ST2QKV^ zp{O3}irDpAm{q|v9`FtpeS4RCRj^MlEhcxc@HTq3Fcj1+l+f!oGu_d`!y~Udx99&*_9kFGuWQ?XETIgmlFUP? zR5C@GLz)xK6lzHsO3OS;q)26GBx6!k3Z=}kP*P?pLxiwUl6lDV|6JDE&;Q+f?c;fm z*YO{VX`7iKY_D$&a&V%o%a+;q4E~lAo z)YLetyR~gkKt1+xIOro_BE0i;a2w1@W^+nJfdUN+5Vdbn_DD<1RXOd{>2Jc;wv5v> z=-G3HabtkDX1XU(FPo%39oRTr2zjjXGjA!?b_rpLnYU9R6ii)9&_bMlWKsQJzv2OC_CS`_s5%iEXn z;hc=U1@$EhslayO5eR*bcqTSpF#kl8CasIP;sMP-d#31I5J^o)U(MXV!dW~GIxQqg z21Nv1nPDm{m%k8B&2HL|gN3@p3Bx`25ecg1^2vic*|8z6oT4+AC1A8Tq0kt3(!p?$ zCjoyd7$`X~!W{&mLQK{ufMhQvKv3(U%T8FQ8@louocH*-*@yhbuLS<2woAq)&^vM) z^cFJGv}qVvuZ|^c8y{JB{};fppQDq7ua@8HQ9tg=2I7_(Ehp@KdGb`P&7n9!`}BTY zSr9P(?Vls&L=W9Oa9QGuPWpQb;U!4h#nhdMTa$jJOr^x+wV~xBm}-%5VOo(Qrn+6& z2Y_GtWvG%tufxVT95BL=m>%t;1N)dEk~d4c-lrSQ>fau-q57D)>ZDr(85NMQZtshp zf}e?T=Vxt)UE0$U;$xh?LWp{IhVu&fNsf4@Fz`#cd`KfxrDf%Z&*hD%DnT|Fj)sb1 zwGao<2l6vSqKg83BIwdW+&qL^#j6k_Y~ynZZOa#|Tf0_|L7&20;t6 zajU=}ihsR4bq?95wfE9XQ*+&I!tYg-QF9|3l^w8k@1C#3D|f@1IH_+LOpB=$cswh1 z?)aInU}A>`eB(&l!#8by0C~v15C(|iRZ4Jq@bF>1DN~+$45b(!QU592Y;lkisSDdy z<#xz&Cpwtk?0M03AKtrlQ=^^MyRL<;^`YN~!i`=z5wbY}%xG3EzGu(g8f$>2A7P6U zajX&I{_Jhp8xz${Q|Fz=Cp`=>D+voStp$_epS(W%>rh$SNFmElHQyEdz&mFKw^B^- zIRR8YXTVTcGxf+LAi04iAQ+Tz+4Lnc6gX|o0JH?-=M&Mb3kJ^~((Bs3-2rs6E0B(c z;NGH^pA@{0o{2OHrH@tNV;;TtjQQ4W^;yki#qbhmMr77Q=r_S#;ERmlc8h7fTK}of zmV-vO5OF0{f&c?lm$TbXnKDJH7W$xlACK^Dd1B`CD#5(Wl3m~;K}PU~bs<@zoP5bVCN?%>@@?mqOx-WdKe;M6 zJ4;a0%*=o-rMnD3rDoFNGX7R&u;w$!_cdSD%1pVBv2kXWJzbN=W`*=Mjq6Yvb08?QKE9kFJT{qiVnrnT(Z*?r^1_-jVK_p^ijyU zyR~j=Y5b%t-GT;^8aA`z(lM^C7QBnlXm9E=Y7$(X_08KiY1T|!>onJW{`9GF2Rr_4 zMrh_kVr9+TCGREE5$Q8F_vS{)y^}hW{-0slio5H%A;Q*Ta!tq=@(%%Y?ppK;vWgYW z2PzV?Z#Zj&61K%&{BggcOP31Ja{=)v5R~LC5|qpeF*^t)%a96$u_n6v2`w75Q|2q_ z;-tQ5AVKNC$zPPE=QPG;o;uyJDym%Zav^Dm6 z_if5qrPdTVGRO`eUSxrwQchJ)9+Qm{dSQS`>&WDoEq6le#XLMWBHzMf%UViEOuuAX z4dpHPMzTuF;WGbV^#;RpkQ>}dr^TC?tMC)J`g(?j4IFOonf`K4jw0cf4(JtZQP>je z^5;NlmDqG%=i(A9BmcMKp zB|~VuvUg@?He)9Oa!;@lS))gTC^jGbst0^8Xs9(Px^ktE?&4xQ2(z?109lRCnH6Gt5=w?I8b6`Cp~$q^%J1Xjv6UYatUPq!4Q_+xqlsom=rV zX)*%0RuMT~t}R=!C@aIzLJ_J1#}wR)dk*CP!_VzEhSZx(0@@0xc*u~vfpG>DltM%n z(lRKQxwm)H)i2Bmq?wnU5kM=;=FH9qJ~lhlpZoH2nD*#gGbzv-|N3hoZ47hOwNQS& z>Hmxk)Lt(f%){RhIV_z+B8IsX76@ll-1BOdG)nOyJBKoihT9WEY_(KRfs{${w;B?? zLfn<@vSAgp?EM|Dl7_rj(;YqEe8bdoy+BORzw$57hf00_Zl0Fqmj<}dP+-nP?9Z4- zMMp3y5{I~xt!&sV-Q?lz*k#+mZ`?A*uAsYZWhi#1b*jMmn$6IbDx*|?%X<)~>{0FqRi+(^HUXow;14ieB ziN20ql(cPPeGH%`v-UM>RPXF?nuXJ|Rs&lQN*W!t>Y=B#^!1fcX?P!?yy2NXHnuqM zcxHd|_wlwJBO)W!+uoPw3YiGxfv-p(lLqUtEi_xRa(w1Q6|!fKm5YD+_shuD?qpHO zi=EM5`v7<1XhbZUPpGH#FmTPbvONOmA`sG_g)Ko%-l-Y=nVm_fu}MolVw5)TrUn1jHXNW zE^LePA9}?DZjXXN(L2iW0y>ye9&x5vKc;TdS2S2OMkV-)-f5Fni+ea)euKeNN+Uac!{nGcf6SI7jBt$WvpcN0QkB4QShWm)k+*CameLKowf4x2Ec z1Lt;HjxCAmqbx6?1!orbkbmUG?>ls#f|zX^O~4QVQ+7|#9wuTv?;J&sug@0%7Ds39 z-?huR<{R!FGSZZ5u33@LbLVQoJb#e(PI)(nh7G%zwrn!O&qERhbXGrx{N!DOI ztpfH(yVVi8P9wp$#N>&A6f{MqEv*Q_=(F}%zi&~xePl^(H-q%~M`Zd+RF%>NpJAs)2(~aTjhmYenpdTL{bQ>Ba*q}s1`+XR zAL0R^1Rf)_hAiLWqN21z`yu2neqW_XVN9O01dfV2I2z zTHp=Cj=`38d`;_sA=A8Cwe{%d>-(hbk5gc0j~E{0^K=gIa;4WM=M^%-6%-1sh$cc> z(Hv1>(eJL+FmXNRss{b_J#H9#w&Glh zKAIk)Mg1Lmr+gvWf`f@YpwQyk3Chc2z$_&i{!1be;{YZNoH~8_$U1c~D&Xk}3IX6q zi61rct4XUnZ@*cdE+|O3m#)MFyO#(SC!oiymfjJ@%Nw(H15GAM{51Y z!+`o+i?3 zb5_OmjBuxvcBssS3Lincu5-_Od`XTt9dTbMRtFNyj^~v=hYR6Pp-(vdPXYUEms7-px7G?3) zfzn%h|CAM3Npm#6Ow<*oq*D6}u?nX2_(}Kg!6)ODTk*X`q01D<#o0w1XyOcvQupWjA>1c5EymhD8BAd+yFmD#C95JD zlcXGdgb;B=>M z038YDM%w#9j0u>`s9u=lmq4NfYbR{GcH_I5?Z0s0!W!4v^XDJB>U7n^y(GEQa6xN# zS*|_9gq*Ie(C_{uLBo#B)D$#~u`$I;xVpdp<);frtdC1b(C@p?2AK_UB=;Mg6TY;~ zseu8KMxYxU+21+ls;d`EWN~6ZJuwVuFu$t7#u8exf&6^Gj4tqRsbcY|l!ghvO|{Tv z>CBznj_*S}2^mR+GYYp2@|{}!8Qg|8_j8RKWKK#myZ3_?1lGBnTG7AL2k8{YU78lx z_-pNF1d0eOA&*Vi6?#;!P*hZ;QW}@g#_e5;=9l zZ(5<4>!EZZHQ~PO)@6juX|b6R3l0QURe7V$+f+B9u*$4>?KtP|Qzn{ZI?x@Fj#1f! zRoL*9gMZ?Bqwk`rAe|iYaQq|E)ZoI?ug)37{o7=R6JEe(fY7rVre zy1B9M`@7PlJ>S7kP%Xb$H%vckFW#RZa8N7w4+}-QlF~z^CVR%TjIGqM2x~FE)m=-~ zr>XOLY-}Q`99!F9gpuDH=U(>ClB6=P%(Kie8?o(IzU#=~LJAx*eW z-oazL4Ue6_n<$hkyHCtk|i0~_Yv9}C$N&f>x^I}G@n8eiqW+{L;CdZ@avd> zMy$y_H2rok_<=%d&P-E~qqX5**mkk%`0+ssqJGqhF#efZmqtu|efu(yqLysnn$0M{ zS60{ZUmQ{%9vPiZhh+5Ul^yGaT7?t=)^CV5`(ZN&fAuiztq6{;nK7y_Yotqw~v|$y*!cENZWzDmYM=>!JbWGEy zaWG^YSdJ>^{*oSzP=0?bE8KFeP_C7hN%+Y)Pw7iXA9WcO*B_$gAxAcCW0dUfI*PYO z*%}sFBsLcy5(;C1R0USPrri|{<;r%D8sPs{%7;XYp&=;;e-i{dk% zsf%750nHx(Ho8hfdimR^Xg(_ zPcp≠6ge)#D3hz)^I&(E8!)g^~@qBFvHHQbVl){dQ?`fWg>V28-a3q7(--zL9kQ zvwuDCQ{reZwz^7Tz-;3zJ+~BKSd|^Q^PL)(Ux7TR<)(uq3oei}B&2da?QXR^T|%}D*S^GqhDLX>uRW#yLNN?^d`@rKNo9I&0s}Rv99j!vVjgCC)xjq*|}2_ ztVHSI9cFpld|eZJ@0?#HC^PRL`GLLa6y|GE_bA^fa}9(E3W0AzK>hISlP(AYz#~Zc zpO%^u4bSKGIFf7_G)|5C2Eo@rTnk@RWw@@y-|NX^Q}qVEMK>6j3A>LHC^S2Jc5a9` zpNS0|#nmdwpW~=+c`A zUBG@(rC6aVgs%0+D$&mtZCUT zr5wzNdpouW`T4MLWDdpQ;|yZ>Vegf+A-4*>jdDq0IoLoYMOwh*ZFf~T%A4}1^Y+PN z)@oLaEV;+jI-}0rcU8(vT7Kb)nnh+ED;k@M(hJ>s>gp|9w)8U?u%fEaIY2RG>luXZ zTen66JJ3G59WP6>nQ+?w6qZNKlm~a*U!k;!z)a!NVs8^M;cOh19#O|GJyFaONSM!Jg!{6ta~cv zK`m3tCcqm#HkMDF?LL8qT>HwtV|0rxEu9v%Ld!>12NZ8r7pRj5;LscCjA~Y?> znavzo9!=oVyz*c(<5lJ?iU6^&;w<+rC}jwzND*OXZ@*S0E46h)i~23bhui*9-=Lh$ z^-k&<8tfO_xS>YT=A*04C%{1l^$;k!&W~@lf@VZzT4a%r+L@N`Ba>ZjoHQYc(T8r( zy7j@SJpDrl$`Y6e<*DbqOBw*S$_?Uk3Oy2BL0BN7c%g$JW8~*dyYY&hCY78^L3Raz z;9!d=_VF^c7n7bmeyq}j@?b23i~0&EL6(z!N^dPF;;^FzL|^y-?^tF=hdzv!_Au<) zzN?iZVr@72&Jsl!(`lzodK{f!x4YesJZfRRpu5)v#W+$X7b z>&X4#?L`eW5f=`j#4`PQ{rZuWkZPb_XX%L|K_`a&N9DjrK}5X2To~@$8o)cq@fCfbqQV zmcTRSJ+^A~HPKPwe{S671=m)BD$FhO`iknrf>~81`bjQb&wW(Hs}xCbN3p}?$KsF{ zAP<}5eh!{PufV#w;x^^81h*nQ3n{ zl?FB(I9ui#;N2D!z`(9bZ7#xwfKD(keVvFY1`gcpljf{LO-m8ROpgPe_CCme+RUSO z-G-i`SNmI8NAm(r^z@lC_4z3FS_biuis4kiJm{KYVyuAf3ba91IY8G~jgQkQ-71ckQVP_gu+F;#^5)L?sunmtv=9z>7Y(?U(l__eM5N#)7@P6?Y` z`}9eRboayG?^in7PhUUx!C_|H)~z5+@`mIxQBjqB1Je>+J&mV#cbD>P5go+?MEPt}PIodt0CM56vWzn%hRi70c~drYZuQu> zLA6rPCH)=9Kzduvkt%`^Fm*2Pp%h)Per7@fB?ZM%|^g7w-vRynB83URue+Klp{)FlaZ4tXm? z*+NafTemI^v(zI$-_N@X4eK4Ou|3$Q4Sy$gHBPH>oa6>lzkM{x zGdFs<;zq_mx!)VbHW?8R7*+Ru(CP2{e~Vo2G{$e5cjvX1U9PNS2A_LtQRVqJTsV?v55@!Y1eM8IC>ZGGB4fUnm! z!{NTPa%!JanTOc&ELHAa)rta@T?6R;95RQbZ+>7gWQXEm$1A^vZ?e^&+2r?02XFg$ zTFe}ZG~nH4Ou={`5$-N)y&TfVxflkAUsI0fbC8ImYt4`brKDIiXr!lFpUWta8LXyf z1Ki7xK|C`q=dnk?3`MFlPC?IF^Kg{(Idr)reyVkiho1C}oYg9u*GMD!mbQFGfj zehm{RF=7{urF9_~M*qd{^zg=Ye)NStT4+lI1bC^z+<|^iZ?^b3M@T z=z82M_>I|XL`>)eZYSG8kRyjNkEI)^FTU+X8galxWIe(DBqbqP^mo%qJ7h*$6`Gww z+$m9op#&Zlyj#T9C>AtlxrD-n(~Mx6iR;p4pHX-TfiuXetGON=6r1H*-MU4LJn8#W zp{z2kwoH(Ktkda!Yh7c$2@?s3!Fosx|IfsyG@~+I$JF#>+cr&8n^xQ@FKC@Q1;f*=7{4s)}1>X8amj|zDf;37ird_0dgWS z^wb@>NdZ;>uE@gr>D$-keDDfo&qmBZdi6TsF@Z$8M*Rp%ZzXkSqlf5c%;wK!La*J~ zy=78E1@4xTCPutX2wFZ#!CJACr%VyPw!o|(M!ffJY6JPDxLBF;$t=aB`ckbbMAS6r zY)+kL*fYD#EeQ##P1+Sl<6dWR{-+d)5(So(2wd?@uAZO*EBkyzlTp*3{P|~eKhm^eN$47byBy`=))MLQ9(V%+x;TmkFY~rVrlJYg*e|(BjRqq5@VN`lrG3s zsM(7igY{b>cg^xlQZHU!{9qq}8zmxn$sR+lcsLzh8aPy$s=D0Ud(8f7+npxzZ;$k{?(ahj5JiC8tDE~GaDt^qj zY1-mZ^$LET*jylUkPi<3N?3jzF~1Xg_Il(3Q%C#A7803*i9ifvo-y{@at#pG7Ud07 z^<;`!bj0zhL1vW+!5nQa!<@8?d%s5s5CpUrUpiyry-?nmsXQOyl)}fO*kVxc z6Fr*DW}O)?>z`x+2^B!nOzqV9+GDG&DTK6u;gX$-zcRD!*bdRcxm zsc8daj1j~XoD)mCrrQvjOciCQpX@b1L zU;!uPz3hM8X5=@kq|^{;7_>Htzx$~muSq9t_%|5-z>W*=zgycfLTvF{w(aKpxcg}* z=cSPPolMa$v}b8Yr%<3<$b;uACzSN>lVYepHOoHAKh1RGgT=1xD(Z$6^xvDFQI>on zU2?H!Dbrs{P9Tx$=|OZ$j(gM%2`+t{XM4v->yy4;HXhOli+5|o!+u^{ zZR=D|oNTav!085!{Kl0oRE{ai2fVOI-_bg=3Fl5cYlN)M)uBlZrOkQBdsi^{ahy&w zN#?XOXNr}eERRG};-@s8*43+hPY&eYYz!QO;HO2`%f>SAcB7PL&_eKqy~mc?=?eQli&A;a-R{PnRyc-1d(1 zrxu8sp2gv;pZekfkWVGa5Mn`B_aih3Sodgmm{e?%uIYeXbBIz4;Q%rSU1_~+54B|O zDsVP}kjj-eo7Hk6<==Aj5B+dk+)3Xi2QKlbY>s5x8RFp!4-q4oggq~wGbSEw$*OW2=A`} zErE9~Wjxwo7;IWQ))#p1WD+B-1N96XkJW|EXCA#XUI-gCEw)@bN8=Up;zxsUi9MY{$>nPQZ+($)tSt*H-XO)9jAXJ zIn2Gp-{4iZO)2g-oX};Q_qYT^1Kvg;TS$zQ_1ZKEef5`|qC0{w(M`&AK0C85aq)wY zWwv5mv9hec!Z?go3#Y7oBH~3=m1Wk-u*3v={kAKQ1&5B>!``7CBa zcK)%r3s26#z^bUIK&$k-gI87$VH0wN;isPi8BTZaTc+shTH3aIw-v=UaZnXCx7_3Z zd-v-;_1E|7p%Z z2Xp&@*;F!p+hiIz5YMX6*xPz3BMB?Ro(}Fifo;ir6lGl$I~FL_-N&t61yMr15qr_ z*jBb%nudP;_Q(HL?qK9%Z5_7ZOy88PBj2Z`-P)H5OVmtxI`9!X#IyK&gxpa*Z9aLp zR!ZWV(WOpst(Pxf?l(yr=o6mPPL3xbtr3Lsddz`mXdN9NxnE{|QxC71x^@Np8x6y; z%i$?ilEbx|i_8JDTaWH)Ect7!e;mj_BH3$GLl%Vbo@Jpsc#O%dVy0VaRG4Dg7F;iQ z&6MCPjSYH?+0o5XBw%OI9^!_Z-PX0aVoj^^2 zFO)H@)UsIAqw#`_*h&n*gkMIQ5Npt%K3fC6dtia z+SPm7b#2^Gvn!g0#z(i^A5I4?wu9I+YHf3+WzjrS|H%wb^{DzS#g41{)BrNef+Qc-VFGcOx!4LQV3UL{x^gj}YI={<&cBt;8*ZU= z`etx7g;`9aCM9B`Y8o6HnlrFFLAc_x@`U34OcX) zxAJu75I`_Dd(}{x)@hoSg`|i4YT!J&*Q*g}E9!NwdBEhP-T0@_mUa&BDdyE|E5O54 zND6eEmy8@K39ahigyd_*9HF2%F0-+tVe|6(WFG1DSe>Ue*UKgMP=WBf%>hLZp}qvm*qo9F zRxD^q^X|$a7Pppi9e_3SY5pbhHVHCYnNTREUhcZb79}5)9KlkTiStRNY>4dfJSL>*4=E#;Yr6x@x z{0cDOxE-cAxa0a;ztV6it9Dd8%v=~L+G>^1)=gJk{p57DewgJhrL@5=u7QcV!!e^| z@+QuWW{s=CXTc6nt(nCXJ9R@+qiv)1m%7=}Nr&|xVXx67y-aD}6d#`l7@}E`vzGt3 z9itqrqW@+m_@QIeuCO$E$dCi+Nl8w%@6Fx6VA3OSE<;Y{=*a|RDP7TI%OW5gJsZ9W z1lEFt!W1f$Am&Sg{)Eh!9a&l;k#Rle zPdncq)tzt=&6d0ImZ1nZZrD`@H`hPlU|YZEDJZqye9}Xb#wbUeu|WL9MGgDp?bCQM z_M`glJXbd_O?k`a&1zcMIp6zj2HEsOs&|-UE2c%-CHt;V?YnbI^OdJnvNd*BX3*f~ ze-5o`0VzyY0jn2V##8f>;6QMF^ixmvL=zERto_q(PjF6pB&D^XQ2=zvEJVujxRkZ9HmW zX(PUxXCSIRZu^l%Z*_^ALamajol0R--)U;PqqpCk+$I@PgHWTz+|JB%=L;+9wC*nl zhh5t(f+fmd1WU(sJFI5G4^&6t3PK^6i7Sa@AV6pcIQDsMtmt-<-!@O&Y^3IU%E5R^ zERK;x#40mfMWe+j7}ZuXrnYs)n@wLyoQgv&zn$b zc?`g!Mg1hSyd0hCt)Eo3zQcC5YlA7cH_$o|X=%^vHElTJ@zcS`Ow2Fv1Rny`GnCsq z`Jj!gSyT(#_2M-cT2X_M5Nc(C2?JGXPKFdlcmMF* zsMhvYm})t!IzjQtWUt)t_+S7bQEpRiSm+L?kP!|g3p|pq-QELTMC>%gbA$YJ{mQP4 z$$QWIwM&uaX*u@^cs1QUr!1#p!Pam=ul~FVCabX_JFuviZ&!2yC7AR1W zV~zSi?Quqi^q4X-L=eAI`+;4d(FkcOj4Am8Cb0rDF8ls@O7txk*A7vB-L}MRz2IY1 z^w@C(by$;w1;vw+ttfo3GJQ2L)k>>{ggr8E!Qz}Kv% zKY0iV)cj~XV?+4)bYrp?!_mXTz~@WXBcsx4bpMiWP;|g{(oBEh8W5&m@E)rCPToMX zm&fS{@kiWQr`7{STl#4KP&pvsH;V6G4W2n2`PPOJ2ve%OBmz-YVV zTV}T(JC@}ovGt}tNnMme?cN3TeYZekWLeT(#Rf6?qGm(q^GH-J+$iyW0~LAz0a8TV z<|c_ow^=)`$OSx&W4Hgv62HCGiBY6&%nF4UBfcd%zDsPPR@3w&ml76T^l}^hm?n41lw=Yv z?TN6;=;Rj{1vHx$q^~t|j&=~XEHG%jA>9a?io!IES@~(OOfW%hlNk`oL_xk})EFE{ zF&h)Ty{y?|a!EqFr=yE+fJ zNE^?x<6tOCuyPn_1S3BXN|4KSlV1s`M$0UE~XeTYK~a-8NFnkYux1>8Md=b^w7%feGW337 z=Es?*Wj=m%Cv0`n_LZC8B?h<2O>+!WMhwCN#es3%tG=9&Z3~&1p@*D5?4ymNYw`&m z2e`QO)Y2y~iYDc*F)w}-x;X=uW6R?4pogK*Hb9TWff+9_-3g62!s6OMV=6;nH3bu6 z;{xdVa8WaK$3^<#m&@K^MEfl}4qmvNN77CtAQTxyaPsf3AoEE;Mua()qL-GqbWPon zczzr6*fVsAM2v83%Xh+pxwQHAC>%DXOlgG0kJ0eiNHQoNWlxd0e!7V)9On>=rp)Fb z0U|TKin7kCX@`O86r(Va&vIZ2darU5i)>KU#@Mro`>~`woEg(sm zr0s+ClQoznhVsbe5ZOEJ>!zpI8Iv<#7nj%#mB*--g(wC`s~)BKe&;N)>ydbs>whz( z@t|QTYAP1HcUiXxuBN^av?ebvaM!68@E|Jm>OZ;Jkm;in0#7b($r;K^H?WyuWG zDv{jLG)}~IKnS};Bw6CNGZ0w&H)szT7h$f4T|bui%Nl|(iQQ&LbX_LC$<~c0lFp5z zZ*zYAGxpH4tPxb71u~mXSnaxoD?*s;tr88 z_F8DI$62lC3CWdN z?^)!u0{BXm3L8MsFJV8B9HRnBL1EraUHNwDz6biM1FVaIH=A?T(?cpqu^>ev7G~jV zCVDS@D^=r919<2eQeg7zXa22BV)NS{Xw@G+cyKjY6pE7|((>D^Ah!d-3bk)?T|@7s zJwJJ7FU3|WtF_vraK1f(VD+eT>;ih-Ns`&TN zJMkbw@Q7xgU{ogy!&;jzU3jpORU5=>Kz9IZSmd{%la<&%aQ%@TIxIzM$kL!JEEC)Y z?SR>qb>iaX%a(u=)L=HBAvn1Age(h*kycwKRnazvH0^@nP-`al-D42ZSsp!6Lp(9K znuS0%TQbshSIK(rvB&1>OUl8TU1ldCGc^t98hayyfDZ4W-6W+B?To`NgFy%c#R{AU zC9GB&ae2ng+xI7pO&YnizbeI!Ip?#U0w+#pQ>aZ0-USlVde46`R{jO>%kKNWz8<1w zWRyKBTHm7eF2gx#X16Vf1`u%rU4In7GrNWhoKWkTC&^uqfiLe3EHnMy=$s(oxoW!^ zOx^p5)k1>t@Txyi&C5#Dda2qfz~VB7U|K9*s$xUk2m3KW8R#*%>OyD`N}VC@WY1S} zt$`s^O7|DySz3~wPg^u`#*FolGFm7aXI1SMYY}C@_pBq^`_1qiH)c-}vqxDtbO48s z%E55vhsOh#iyhg_nFmw`>@{)ta0Ns})Jl%eClCzq=+P15WjLqWy;pl&!4sc<5@{f` z*Tgp;7qB3Zlp;QK{GKKsM{Av1(3m+e5LjsA;|u>xWC^ zjD`dsF-GBfRo@%Kpp(z%9lfcw*3Q!F^|%URzF9VKn~I;->HuuaxnjHeFFCvxVaKpr z4**r9YZ1i^tRrcnL8@+rIWUnBw>nvLf#w8`%5uD)&+HbsOKJb;XvA<6Cei*kq76cyU!L2cuaPOn-q#~+w%Aw zdSy3m_+NV@!;$+v8E;+NGrAKGWzU@@Bw{{jh@l3KBf?+i?DKB4+~350JIWA21`;r_ z#QjCgx+X{6RDq1jy4%KZw#|@9;Jn{##*A9sZpPu)`jeKmC}AMn>L#D2hoP0$GB$3> zILaqs`}XGKO2X&}>+TsTEi?!Xlu8dOE6vpV({@D?v}EOz+%fuCo2&RXJABqBOO&lZVG|^zJLt@%+$wsqKd{55+iOV=N$K z3&k^c#9AM8Q%pwZ4dS(-3~9((i>Yz7PA$;DgvP!{Ef*Fy8A3Q%NtctC@%?Pt`+Fw_ zZsJ@ARY)sm8}cKBd|`d#DH$Q(_=Q3k!3CG8K&l}Gzg;}a}p;KjH$dQFUu8u#rh7fyPsVJT-M@D-UO zVl;;}2e79KzYY2O)`9bs;aF1!O4BY6|G@)??#B-!2!c|W&%Aui_@lrw;zR`y`hClk z*r0AVBU;;!m~c}pS!7*V`HWK&DSu=l0OqiY8cYfTVT=fk4Jr={Hm*K@*LKidN@+1z zV%f*mb+g7hBEZ2E$z2#T{Em)K2njl;kzDKjD`@oj9lgtrWD zrB^U6F=g0_L&LHvU%vd^!c1R1AWCbvTIH46UzZKKKOuUUS@gZzm);K8^R(~rEw3)V zxEnR7_U;l7P~|R^(a{LUic!`1k`29Q%;?GJGM;CiHsSc8*d7z=wsp8fy}N+~ka}ZM zhoQ^HcSsqir)OQZ-)l$Zv_GQX=A{%a@K3MN$vph!@c!DG7caE={L$Qt1>Zf#kKYRB zkT5ss>4OKIfo$U87}zabc!UUOfreO95WGEcRErxS;7`L^hr#fs=>yH|YOQP{qUEdkUif&;;WbX;|DTc%rIQ` zc>jJqc@5(4!KWLq+AYY9#=&KsGiO}!8C-PK)b`~E@AOA)4?oS<`MA(OAYfZYLSu#V z*it{?1ixX|@uKfvkJ3xoL7*A>>G;Q2uQsUZ*jleq(=mPaQU8(k?}J7cy-)o99wtFD zYhX6lEHZGM4H%L2EaLiBi&kOoezV3}l4^E5Fn7M0#`3!Xh|rdU#oC#T7*RUvJHpcl z{*+|6JJZT_p?;3%;hrZ`l#YsGg-8*TnG^^eD1WZmFV`zjR@dvy0;CAZ*eAcd9Q2tI zxAa5GBwiKE>Qrefi?;OJ|M=3DG2o&)80@?$XytXDTEoKJhoEId`q`e?LNz5Axna)+ zYr?KCPtuFVMXzYvpu;)SZ#$V+s5h99x7d}zm_7o_SmNk~p+CNMp}vSCauy>sEjW92 zgkMRjX0Bgpv`4Prn!LO`Kr|P@;wcJZ7&qB2U3%j5sV9|{THQTzHb5zKziVw4KhpnZ zckt_~chLm1jS#HD!)U7;cltxK_`%#8*19?ft3}s+bV2QQpG%wH=P72JoBJfFTc|Pw zm^FKLWo(|$toH5OkL<7_?7X4-+@`S5_+xH(V%SLY$4oVi0`Bx}gJWLASwnfkk7GugMvXT8Y)(J(g#I`i1-E=g$Y*1T zgpbkP#!@S9nqH--HRnlnbvIfG*C330;;4SlQ#y6WZD<2<>3QZ?Pq=_@fa2|TnuYpc z{aD+fVzc~oYkzeM$31?}?;Sr(?^8?@SQ?x~3)w_L-!(nfqp4e(lTJ~5_bq9=TF8_5 z`6n-?pjB8)eJAtg9S7?jo&7`&vuEeRopUGq`s$?E#zphFiy)}UJ4O=r>79vVTlBAv zd*U*PENPD@uvAr3kP@`hAG2?3Y_~C^D3NI(@%jL23WtGzxi&>_KVIQL8qqW{jjV4*5l)&HIKe`p0)dqXU@rOet~(~ zV`jgd7C6LzTiM4w)AF|Ge$Put|MsT8ee#gOKl(J!ti74DY{;hZ@!9P?3Tp<;+wtgo z$s7IfGqnS@W$n1y|A;3qC31b2FnI?5{>NQUUTeq?xfG&F;iJ7^wm7#KOO`eVL%?BeNG=4plh>tA>?VP`#c&QhbN zke{#P5aE7&e&*JpL7wSunFb&%QAWPS=Sfhe&uT|)?e7Q-S^5J8z!T2dw5{9EXJ>U# zP3c&!>R9!^|3y``jrWSNMiRp?^nQHsifIgBy=|6vfe%Hq5wc?3LpE|(E^v{}%^M8M z{_zrM`7sHiDhO;M8VGpzO>Y%Ktu46~?+xY95FwbXdccJJYlsxW104(fTd%f6KNr?!+GX&>J`^JYZZ&4XocqYiEI8+0JH=8Dsuqh&v~>+^3;9&lsv zH~AOG$4B=(`eWaK>3iarSKrP~@cXfG{N3-7BdVX;7sXvW^fa=}H0R_Aj~Q0G2cEM1 z-86GYq3#%uWu5dVH0)U0koTwV$f$ zv=vX>j8b;uH?;U)TL_Q$az06sZQ%hH?=u)=f+Y+jHf?-0tl`<%I+V>|6Rj9o0hJ}Ul)pa zG`{stFDvr8$$n#=Dyx+DJT8v2`?mG);lp4H7f7S_o8l$7{@b5tL1R0A=y=TW3{9L5 z#AVw2F4*Zs0j%5MCYwxYq@^{R9m$Kne#&x5b-+&f?+o#Oe4C2jIK5-tzs=CSjN`Wr zA4!{EV*{_yntZHdV&XPxhiP|6P4QScZR(}}&qwjYhp?)Uq-Ps$ItZ^UiSn!z9?mb+ z1x%>2wzemRJsavPDksnJ{#VTS*MBBO$`+?LFuUGAMeH>IOegrtq9scrcsIRwC4@Ge z(D~4!x0Cs`H*kd_^76hd8My|asXu)NyyK;qh+JF_NB(4uJ-Cyt(eN`WSo zmwYshusod+8e#17usHd>&i^&!{4rz3Kxd6$1n-60)`tqMEXk_;a+2b`*_4!=|BoZn zX3Vf*%VP~(ZOzRISUbND>B-O(kOe`i;Oe7ce4tyt>n}4cHG^6IPNbxwN!u4b_?u zeW=K#m;e~+?!*F{NtTDL!;>}*G~3*(bawLlQ~q2Lj|)?)p*Dq?n=5En&uNqSO?PA2 z&4`qYDbCmG>L!_LUB6cMdgYg2)Uvi#2igv?aQu|Fw6mT2jPcFi#$JoxXz1n9aeZ|8 zsmb~|%kuvC)-vkDfeC6+DOrnuSQ$oev0{6AdGh|cBCw3Ygop0HIhiG;*J)BDDpM!u z)*Yw1Q&U&h2I`8TbZOVG|38kJqq6Uj`n^@7c-+Msy_T`!5@t=bt9BWU{D8o^oPm4( zc|4=se2?hsT2w@8P4<1i+obt&y>Kl`sTg8q5whGAo>To{P=C5>X|cmm6)z_#55mG& zC6iz{=s$mjC>>mH)=?9F+O~s1fU#5C3vrrF^4~qb(jlXpTDxAqErRJYyDf16%c zFcEMk6JuM~z$?X7m#XMGRosC>iXW{ssA|s=4tU0e}3`F&wsXSGhlWyUg4H(f;A)yHr%nT z@3d$}cXlvrCjYlDo_6M3NxjI2HI)bUpRBVzSy$CfqAZN8)=N!TZps{|0FJ%7Baue0 z&D_FyfBou7B@f=~sQS9x{UOiU*HFW&bG*x}vGC7ZsIOiVhL>r(L0bR$3Z|WB@ycUb zv)ZpC;ju(rS4mk%FPD#YW&@LetY>mn80ZvnNqn%y`R5Bs@=1pFiy`DgvQ;qzi~FCC;v4e(>duvfp-sCM z>72RM&_b#tBC-h^($^9@4h?0K{`mvpg=yofW zl(_~4%$CW0>%&gyxAmveP71nl_okg?K3xSgnmsb|{|34dq+w-M=B@&8(B|E$J~7gls^W0-brKhTDmYo>?s2=xIv3k)oxLPEW|j@l}hCuPcD zBQ0`#A)tiMnGmJuZew~VDJw@qr?;IuH%E-;(|V|F=|3w8WAm~Hxq#k#s%EJ9_Wi$i zF+WT2=TB+XZPr~*i7D$c^7;Cv@v(=k8ru22+xzbPo_TEo3<}=8I;lT!lDBtc?Blap z!(I%5A)v!>Jc>QtbE*lih1;c@xKkqUOMe)o{Dd+ir$JMKIyhFPSPe4z`<@gPyd9Xd!0uAut>o`+I2t757GpH8sziHe zH7*9kEmbgc^? zr4XJfA3oG%h7kDlDNx?yh@K~}zB}5wNq$8aFvD!NluWywoe~xX``aC~!x!{QDLh{x z`B_M5MTMs{LY@b+vZ6r$yM7)7mN1)5O=%vv>CpcRZ%$d4V6mxulT$QCavoqtB|BpL ztf-2u4+c3mx!8Sw*k|9%){A1w*Hz3Ld|hkKm*(>}l^?loaQNuqE}1NV**MVS%AS~t zn)Bn!R)&4{G2XaUt4-H#>#fe6-a5_Bf54@@4teX#$`;senlnEtGHz&QUG>EkM}zj) z9WlJ){k*VCVEn7V=QG`NEH2cXS1a4UF2yh+#c;LtogNp;YxJ@{TA1^gy26Z!rmQ>l z_Woc5JP!`B?R-N0-`n0R=2z`v#Ar zpt#t!BO!}9-bN@S){79`EfYqB*rys@ED2?oeWy50z(UxRTTA5;47mxF#{%R zO#FS4?WoBLY8^UsXOTti^f5)#tct$%%gw+0CVZ3EpOG8-WtK;kZ7N@odiL(QxI1^_ zudmDb{;juH)zl$7vhGBc-CSupr^v6z01LyR@>B|VD%}QjPvNP!m3>^@>*))vHeG@& zr&-lq{I;aD(Dz!yLtWn1ovb*=-{wrU`t!uH`;+xAc9ciF|R{6{rv6YeR&#iw11X8 zjJH!h*&WW#UR=9s@7}qiGpZ)+z1aI;<2mh|XZPv4=eo&&8ai59R|ntcHg&%;7`48dLMYAN~Vjr@)_GMu5&JQ^In#aaPw?U5WF5h}EqSvXE?Bw@nd^Mf3l4~O_ zm=*cjRNr)pFK~(<5%SSJCh2|TUk99PW}(c^$PoQ{Di)5WpFUD;_oQ=;Wg1h&G+3MI zQL`KWs4G+h`*(A0)GGxL^8)CLwt+#_${(v%u3QOk&Iy+PrT_8}uE#bVRPY#tIGG2A z(o+i)R%sZsvTSZwv1e?fa!Z5rz`by^b+$$Sqbyeq+|+bH!DHa2WEg&G8XCF`MFlBN z7#ei4uYcK#JPU8ZRv|9g0(d$N8KU!#U)ynU@l5BD=L!oipy+kkfuc7XFi*p6= zWDwf}fk_}*UU-(WmU}#vc0Fs1(nf)MZl#yIW<^-bxqF&8Ihr&sSS_X`0T6j`z@!lvMVb z5I9~Sw>vr0s;s?1_Z)|wnsTM1v=nuHBF>H!lmU2TQdR3VXLt#Zp-=5zH__$KvEBUF zCCt01zk~ry*rqX)=M?)jWm3O;-3ISJzjgrbrArP^cw0Bh-=pH`wB9We8fDHHq)-yc zoL+ZSOb+?!JlMQ1R+c|;7N$LEU38{Fr8M$6uaY+I@IKku^8M4JU!ARNuQOhHBEtgu z!7T&BEv_zJa`T$sxKrT%ly>1^n_LUijxMd;jjMzvjugH3)|%*h1dLeo86p8E*@61( z$jj~4ep{V*^03;?lZo zP@0-I#Ba!sun$K2#vLBTP6REjKT8XvDvW$qY%=|}cK8@=w_|gsJ-@bVH6FCK^IzW8 zX9yyLJG!B(cUNt5VjdDBqdjCakKt4q&D-j;Wzd3s%=A29Z}?T|6;0k(y_hP@32^^2 z^w3LwOGek!HSElDs#>#SfZ-CDUkV#o39@CNZ8lUauYF#pdO-Q=JulV=3I3L)A8t>X z4Q=RP|3w&dZaA^e07=(6@9+yk5jkFyTR*`Je zqRrri2jt1ee0l%auVOgnvTuAFLbfQpZVZ@J6>#f7 zJxX{>=1&g$J%?nB%{ud7(2bfKIo_}^^~UTY^|epbQ3SqpyfMtX@=%`M%X!`T!7?$#j z@6=Pa-Pj}GK;SqsncHwjbqd}#7+dt6w{^k5rJs6{n%828@~-xNCyxH`>C-dPCbbcp z3g&&X3bFlw*$sqqM+EzYN{<1|FBImz=b4xCkXWl7P8zMG|4>*?-aC!?I47G zM0%nO6BNRZ0zIVNyFMO8=@vyt8JzKPvqv0f zh%f2;3kwSBT|IZK!E%7f(@;PGa(JZ-MheLsI1W#$n!0&+b#)tzwL!=ItNQD|fq4Og zq$LIf5L3z6zr1;RWjzw+)bc%U&-bmzdt=kH#W!t_+ZMA_0xhB9f&~lo!s`RKHo%yT z1GRhIWG6o03f>yOVic_!tb(0TIwzMtO`O4tflWIMG>r8rQ4MPS0|%9UrkQmB4bck^ zrrCQ4?&x^`M%_uRF5ZK#>mKUz=wPy`P3Ff4osBQ=9)wG``9&vtCFl2w-)Z}%-rU!L zZ|$2C)F(ywBtAvaiU1~jND`Adk4?2rn`u>3|3AXs1gz)$ZU4@eLB`lx5F<&bQHiLL zwUCO0p|PYwA)&IC>_W0rky2@~OO{F{dt_-LBxQ?gDocq{{a;t+ci+F~IR4M`+{b;) z@0b}=-|y%1eqYOZo#%NetYx&S4smh|D;8)?Y!a8blHNz>^>RL%zZe+h&f&rRVHI-t zID&d$?6$Wj7M;Gy7#+!FUtIm4W$!i=h`FcdgvUeEs3}Z4n6V}IV4+(d&D&5^gulrL zkzG)t{WbmmwmpX_ltyuo*SE7VJ+$WIqC9`zU4W}SKMo(T-k0tD@l(}6_$ zai!HW;r{{c$r`sllaK!nSbFy2#am>9mc#zeJ@5dkL>28Z0eWnDU1e)6ilH>)HO}|RxU<>+2B0itBj?PB79Grn2O7)~p%uTXW@dcy{Xet($!f$wa1b#!6*Q}Zz(GmXuPamzEa^&VbKLv(SpY^;Bv+qhBdNm zvrX4OhoKr)c!z>07Q?IPh7egoR#R#HA>GQ|0ce-}#6Yo95?nRD}qPSD4 zgBJfzp|pihF1DFm%S~{kWEm_ft3$?f!{k$#*}!gHELf{|Em2(jw88xb)INFvPXr~$ zY2__L@r@q2S!puh7rV8I!vBD!vfQUPoLk7Yacl-p1YOGMmv&**^xh2o+Z>*4$&=pW6+0O02_sG$?3^7&b32}HZDtdytB90&a{-jb<-9%bEs~f#kll%aCx*Rx8 z1@9O#cEDc=@6-n_RHcpB2*LSyIcX<8oGhENDb3*#W2IcyJ;65EglXkH zqi^oaiAF=q@>6#5ja`;ijF*I&*5#{Qd;V~HFvC&!)G94X!0Snksw}?6UNwpsRpDo2 zCD`GQ>8jX&U%b<~D52DyBM?13-hi&@q4vTn8Lg&EaZ0W8tgt7OH7-*`nhrL@eu)81FE>cyU0}N zqwW>_aAq6BouC0HBD>x-X6*^zMyaK#IRY;xf1xm{0stD*MxWqZ4%9A8SgR7%GkN38C7gex+wx?bQ_#kaf#??K+{D{30snssp(&rE~3NWgFZ=eOll z=G4J0*-`|;u(@#2pLxmNf9L&CS(9n(S=d83(-JfBI^y@OcthKjgBo1e0#^{~~Yz4hVZZbD6z zTwk}k@&f-de8SNYcLMwO;=yjAT0e0wVse6KEnWvlDSjH>4~x!1FZb->dcyA3vIr~M zpgCi`_Y`LK`}#*hr3tk+Z~dwL#&SN!EBVqi^h(=q#r+8Wpqkk4BlT4D%p_xZ$wa8D z81TbDd-Ni{S|kO~+-TE|pFyG4GNtxwXM`FX=#678G{04MB!q?G401IzHE)EbAVTO& zOlVG4f(?=FeX+OyUO3nC=5Zox-7=j2{O?I%$*Cg)Z$0=1>*^{xL5R2$YV<{WLTBcn7!+mB{hx0rY-aNmgkRtE;~F}TX>5c!F} z2GPL%G?Kz#L8SYK1cc=zDk_K%{=C` zZLOydttov08bB-EyV;q`_Ra(%w?Rk^u<^X0QX)JOT$r)LIXN`{tt-98l$5*!E`{;2 zy;{K=7P%Y@w4JJy1;YS9kRfj5xGY!|q#aBJc?|&*-b13lrq$mB_n}jl1@+KWp`cvs zak0m$>wBs-@5jv2>77urd8cXhFMLSbS#D3y9yOj58K$jm7pKh`I8;y-mmc7~jxWCf zDT9c-5M5aY8MYuI2lZ`Nf`2)U(3!fEtA8=T=LvSI*}BHfCY-e&excv3fZInYgWGWG zZ!ryeoa6ES6&&DJq;~Qy<_g*6=}&V-(zCnQ_^uR~_X{dX9!D3yx)yDBp%1{wP8pfs zpX=wTO1;XTqrt5x$$40CF2}6xo)cSGaRzh>7g(qWC2-$JiDtxS4x>`uK4er*TQ$ zkCpACYG<*mF#Dj86O9cvcO#&r+PaNh-uuAo=j@E8*FeB|yv5q1 z+1SBL-!eXIM!aM`eeC5G!-07N^T;P)U&>bxy%~;3N}A*8EbHQtvd?Gh*w4oXfG?zjKiN!otP(y{UW`=3cyCFd*;K(G{Owf5^Sm zkCEei$LSZkc+Ct2Azd*Jv8gq9;bu9Y(O6pPF*c9mmEnvN~AK zl-xb1d-%xmTmokDkI&=CseHdq0}dS+LNf%DejU#q*lnvseEU7n4$Ea~;7->#h@D zUS84z^1*t#aIuhz+;%vcs{bl@jmWvGgX$87(iY+Gvm1fheOIF*h<#$yEBzfM#}+AZ z!B#fG>!*y_%z8p9T@Eg24rGoooE+xD^00(XYGNhZ!8<-Hg#f1 z#NdkUJ>QjjkAqet=mIO(yguDjk?I?w*W$u-HKgY4M8op+>n$>n<-S4REi+VD!8JoP z=(XROgGzrBX66w2;!KHoKxWF!oYI2~#06duLAkc}oe$4Q#p~#xLYb1_3w$AURSB(>Aor^#l z8+NOCN?g*bJayoqu3z_&9ejIE>B4$t=M6Cx9&w-Jrp-?p(!7)iCs@;@6H~0zt;~I< z@!_&N_lne6LDA})YMTtqV$L}u$i>1|4(l44mDzuF6&SGOgSmf{_TU{zipctyIA>zOun>ehTu}n{H1=zG%d_DMw$~@rz`Of zAhMBjcyN>YL(4|_82(YakS{JljnKQVV2ERuTYu4Yfub!fOpob0(nH0YK2~(md~U=(HDu&KUZ=5gmed`1kdwg z-&Ip1`#EK;B2iu%GHD^Pu-VA1;Z`_Rc*T7BT6MbK9kw32&Csr$BdvpvTMwVYuj$Zx zVpH;dh|QN`!1kVkkt=LA`UPBbJ7+4U*{{{EZO7ERdJ{qjba~}PFyR#F%$KRve zE)-t8FtBZwCigADazSt9XD1QM4w#=kVRWbLAyXSEu$u%4b9ly)H6ccBT?qs8^yXjX zoe;bsIPL&aJYlQ)VQA8G#@{rm&Wk!&+h%zC6lSIzEho`R0%SH6rW{LcJCu|iJJw{m zr&Qr!&!Rm*|@P&$>q>ht(V0$472b*&jdfWVyD z)6ebby=l{?*-uVRL-jo&s+rentxayp-_5pYz*uUHXEuMrfusk?qc`2t)h_;kZa zr@QID8&I^68gj@4D39dSM>;J>2yV?wh^dMH*Gz*Et|X;*y?ZP?V|VjsIJ5W}&de_P zWKh}yQNsbXu5U^mx;R!fD}HjU%&Rt~ zzHVIX7|Rg-R`bRO8*c(V38fmlysXr2y%K$tgik>e2vcgh2dx1(GZrg`+x(PMvOvIe;q*HnB`sBk+i^oZRaSHo>Tyh1ykxgF#SVd zBYWOH{u-0rHh)M?G?ax@$?2!9F1s~_#pdDko>HDdjc`XLbhQTu;92v2(G%B;?m zM8E@fU?Z#ZqtJE6$NkrI=2~|^@1wN&l3`@r%Zu$-x0|*6s_u*gLuj81Kfa{V+hMvf zwoWe~VQ&V1bDidwx@l5=meskVK3Vr3KYsjSd`9v6$8(+|=e9VYBP)!Y?(S>Bowb}` zkKL}4ND7@co)AT1{h7agWVjJK_x?5xy!4AiWQCUtyCsyCBN3o^Gxyo{p13G`yu)(K ziGZ{%CuWASuJer~Pi7l@T0x=1=MHFA>wTciWg9)q;8d$!C& zl-nDMhI_jHeQ6;T(H1&yt<8l`@^e5#)EN_e`*gco`xlc~_Bm?1vTniKw-H+p0lTim zcIv!cev#R|nk$#KJ66oNC3gp4NUmwW4Y(B@}+9 z=Z!sC{HFsX)`GqiY*^C1)uUzihr=08U0VFLijXSJgVM$3d07s848^Rcov%~;h~3qO zwwK%GCeOk;k%sEHZJS~!h)?ZTMOBEi6VHRr0QI6;2Ws=mmi#xHDPj3+!Ok{5?EFS} z6LeZ_@7~F?gg~qQ_$23p262GJ?$AZmi70h?UGAO@9^skLvexBqkM}Eakwjn^6lnT# z(#tZPC-duXB^qB4sD(L&R)8HlA;qjzT$va}2EJ_DNHZNG!cO{tYZTX*r7ft*H({7IGo^9Lf`#GY-Kb93g`{c=WbrmYIyl3IH1bVN@+sPUgSlCvE}Mbw0jfR{g7695l^< zm=Vi=UHs$Q63#sHl6t!VUXWsY~u#A6dkeYQ9H2TCJPvneAInx4lc zJ!&g6PtP!csH}Ne>roiNQfzz}ej@1o%dM~^B+8$?o#-rr{z!hT-cZYp*wj+3iyJma zEm1NsaxzozxYcrd>6k-L{WBkeJ)cETv7uzRdr6Zg88k!}wjZduxkn~z-Qi6rzQ09l z{>v}FG&Hks-h{s#2N%I6WiLU6w`;ZI+L@B(S(QCLl#jdcNF{of<`ZJKXTI!$Wr655 z&G#P@nqOcGBEz8Q$#t@+4M2>P)YK}18a;}%>dX<0X_g>oh5Oa- za(4Y`onobR9{HpWHbw(qjSX=WOIL>ggo(vm2osOWxH*|YBeQ(Jsuy>hW2vlI` z`n&@~P`x`H@o6l$b$m_p*ezSOjEyWFQ+lzKC`=(I4n~;Eowr*UkexP~pQlvVIfb=3 zodz!S1={UYT37_2X~T`ZaQ^T2jGu~4Pf?o}mvC;2m-FZQfZn|Uw=hquK7Hltp|91n zm5wwtibgITy*4Bqnnmz4vYT1pllwT=C=a{mPuLC~x3ynZ^291>LRN74bT-~IT{ZW9 z?n5M(JHzmC35{*j^3mqzos^n%#d?fd3?pUWmbQW*R_tBg?X|SuBriHpGS(leFVY*Y zZPY|DTD`wpXQuA(E-ej?+f5vQkHGy}X5mzI#ZOKx_&9jR>cR9I4SzsqxkG$qKV!s1 z3qHPW{V}EXA8CrsY}hTZyP|cVFKX#G$Yy0e#k|N-Z@}&YIm_+F9Z!PgV~?tt;ph3W zRV#Z37vFz+ySVDz>gtN#vd@+I&hguhdDW|#TQC-VL^d*fcx-R>veQ0YRXTKt=(>Pi zB9G3_9y!Rw1pmqA96zI?xb3|!K%)R(Z!544fahbI@g8hRZONec2LwTdmtH>7DWlf_ zzJ+DkZpeYiEd+&dxC^u-BPdEA9iQB{GU-yQ0eHpv1M9z{T>5KPB5#zu4gOchnvsHh+OkNCMMS0%uI}wdHWKll)5?$ zT3*&g<}nOSn%<;5ULIV77+ceBlI+p-VDk(S1dIl!lAWnFydBoMo`^gWJ1`PNBu4$K zn@TH}7@r>J4p+BZ(5CwRc#g8Z#q0-(OQmV052JmgYJZySy?Xt_0SsA~*p97=N^E~G z#-!hBT0PnP30$SY$U?jWkS=FCda&h=P>E#ov&VgBe}+OSP0h|#_n6U4Q8A3?6Y2YP zCYD3oWM(N#!`k%qsxFEG!-`IeeB+&;RqR%Ovc8%Q4n%IzgdcMkmK)D~7&Ks<`nt(R znaAqNxk}j);VJ>oqe9kfBabQQ9qx3J(vHG3QOo@B_+Mw}hc2qva*68vSB`Q2?IVJr zE~o%O7f(vt(P@C}(G`rDLS1+x^t1y!EiBM_fZR}ts8)g~Xo+;$A4B*xR!%I-UaAa6j9ey_8d~;)m8@`mLPrg?4#A7Wx z4VWKM<{0-x(^Jn+MRkg;}8tAun-ieqoMc~-~-21jtL*t z$gR)TFU&6h89MbfSX=KIR8^AGhAH&`o&!$9`Oow!^J^F-V+y-4`)F4l^K$$N!pQ5+ zS&^+tTRoqL|b{->tMU;l2u?r zEF0~=%F}iJrBcx5>);clGRpJyz?x73Y94eR0YTj6eE@U;10JtzBI7xkx~lxq7A_fI z=MS(P3V9~QrxyKX%%FV2cze*d-35s}`9R|t0Wn{Shw5H{CG-}ffYf|}53#}XtA4v_ zQGS2g*bSu?m+sedbY&j|)3aTaVKU!T9RK}8WGovdFW7vhrfmnbO7mM|5)~p`9U70e zSXTpPU8TyT9+QU7yolU{m?v4Wa z06`vld-EY#UPAOWVy&1UEqSv!)m9m#XB5v&2osKGiXY2fxRq~c_jf+C`%P!g^s9sY zP|>>j03(=CAS*}>ltu!=h>t_1`BIj3X-V8XnIvuyx}k=Zrcl2%^l$w+C8cjjovA0^ zfTnu%dh2{L;Z>~q$hp>E77okI{hs^Y7}phj0Xf+HBm^ViCK)cnLOW$W_VCN_Nyj!3 zKd;}rC!_!pv+e|R$Fy3KHjC#`!vBUBIsHwewX0?()2mdDO=Vmcwfa>(V?CMskzBlL zD|3VqLsodikayJ3ZY&koFxwV=(;E6ybC75;3)+s=JHeu57%QP{-T~1Dfp# zLw&yfqb93+0CJd{HxVoYZlbh-;BuY(cFoVu@>oqZ zC`NZ_dgLT@{u?~w8VC6Ch!^x-STFRQd1VlT>dnYS{&^AKUVc96JmU*q=CN2FQo*qR zv8T3jX7F-~{&27kt)#*=e?5r!kdi`Lae8oAjZ1VP)yl@PFgwH|oQGAs#0fd}{M7pb z(4L&C&)4ZbV`uyFyD^EUv3HaWl}k#W52#G-7fg9WqGI=Avh)7BWh^(HNs1il+Ap$L zd;CHVlVD!`RZa{I?fmYIKF*E1N|_Uej?uhZi^(Y~jAAs-T`KDLhUtUNh53_ZgrQmU zo!Mcp??>tqLi8OKNoW%$?0W9=%av(WT+plVbQkJ-E4ht3u1f-kncJAb7H8K$_1QZB zU@442|3k_)eTX@h;>GdYNb8;2VK_#VvT~B+vQY2T`QGkYU(Z`Z*&{e?k}(opvpLnL zL>u8XDJ=e2FuX(GDd``)+o`GNw#~IMzX&eJTDwr7uC1neIBePCsmS~LHGj@<{9 zaew3uIZApDW3~`I^;}qL4TP-gG&j4M!Q!Ukm>Dq^!o}9=I(h~aRXdOp-h7(T9`v~d zw6+OhqgwFoJHf`$o_h9ocph^<4n9@UY%?Il>l<5v{UzEE6D$pkkJNpAj*}uUmKn~| z&<|j++qEZzDnKN2uJEb`)!CR*SKT*txiRb^UhlKF;S)jeU&|_g*wFhOoZjsAHo-ui zg315fY;hCBLvSc2Q$iTn`f}>{ehEbbg?ETVS~%+JMPrTb-cPiiiK(5?5o9cGzB=eF zuU>od@eYjq=jdfVK_59GbXX&4cq!~TFf)8$*#=F`p&eh(u(P_V{LD+GC!XeHl71O2 zUtWqjs6?dWYwfVh2!8IGh){?sfyY?9S6Ai{_-xr_ob(wHT2l||91d=E*`uf(ZHi=x zeXsSrDW^u#8xa<1wgU@B6abg08aVwToHmk$WCAfD1+TRsvjU0Th^|Rm{_gRq96(tq zJcXPF+m7< z`;|tqKb1st()4g39vQ{HU``GIFdEc&7gV-z>y?LIyr_OTGoNB$1XCKn=V}*0q_`0U zpe3oY5*m0wp08xfr{-}Nle3=JJiiC(-snp2cAX8Jd^0GB^OF6%Pg;*n<`#8lqCl3q zMJW4(-=koMgsx2?A+r+x)IEH-bo%n-I25*T?;UJI7-!dNXTJ8jpOdcHFi+-Yq^&Nv z+DPITjmP9KrYk-wkcWo@nk}iWaH%{mwqrQplXY8@;RSZrT^0aPmZ0(r2y|;INfvMJ zG)`KC=~hdXKNjj$tJ`0Gr~_#>$jHr~E7VH*YAV!>ZR&zCQ3N_r?XQ6IMM2iu$jE3z z+l}?`P0sgtG^z2X(i zFiW5tYU~qdsy<{ip}PjM5w-|0(njW`0@Iv0lf0fk2s@GjpAfKXz=A1OR=pf_@Olu~ z1V)$=bQZ`iebO|}(eMf9Mlp=+F)BUbb(uyB$p;`_X1@VTdAQ zHJORY>N4)J}30RSHYY59q7~3Y({m+5U51l8`81AN8YdP zzi?(=B(2T~4c4kGXz{Ud-6lv;;vG#RW(J)|@>_U^+GN)Tboi$Iwl{B;x3Yo7ffOn5 zC}13(n>Rfy0^_)+q4Y1`UTx{Nr}8`bM>umdAVNNo?nHo!q(Ofs$^4MJXsEiQY=-Ls zU{;1g6$5@sYVl0+zKhWS1U3NKi3ATapzp;X1Ta!=qW z1eNaf%86}&k`mGgaS~^vYaP;#CHBf@L7tG-gComJzZ~Ty*|XGyn6L%VpV8$`a5G1y zeNv(EFRV;ENyrgiCbdY+`SA23LpswQ%iD7BCy_NM>YM6>Q=(gRYm_{Kqh(OhAFcamw%KmEC;_8$vUWuy;%-Vbs$ zBc?+O)wSO7bELKAVYc@>8@zj}*Se{YII+)g&6xEWw3N8Wl-_Oe_4%l=1==u}_rMRZ zr~q_va;t@`NEQo3AzE>^<>10A_gl3m0ctr3ssTMN_yOzy=p6j3mOk@VuAr8TQ3w@k zp2mlP?qm%sS$BxS_4Y;5wSvMg_Vk<_^=!MJpW*ieIDcv~g=EOrsf!20f=~3W;5tl0 zbu;q4;fx8n0IEW2Zfbg)8(Ew>rivCUdU#$K$P_vrfJJIDrNe>R1SYYDkY9O0DhjYMDQg%UAHP^ig9uMS3JO>kiZ>k(7&;Zx z@`C5>wZD4|DzMC47B5+n%G5wXb9x!gS~$QIC&*y_ysw04n<#5_q5BZnWxik&Hfn3r zn_3B1!0E-(nl|aU>AC=VAOS@^XB~d=Vk{rBm1NAKBA4t3NeH=x5e%F;@UpYdr`OG7 zzzP9Y)oW(o5|bgCQAr=n6<4Faw5eJd^DNnKiNt%J65e97Vdrq;){@AD$wa-@164p# zY~93!U~X8x^tu=Tts{g4-gKT_E!CM!FsGh#Xpvk_19JVrgI}3$PMnpS;xm+~qaYcx zIlJaFUQ(SOf1jV=Xhl`0(W8ezquCh2^A=LUtCWuXh9#Jp$RvRC;+!+CMYrkw;lZY% z5DhN;{tp*${;SClt{+9)^uUC#fbxR#AXe`r;2oy($1Od7(K(|r1yd@|@DmmK8$P{x z?=C>A*902^m=+a-fbrCyZQzRFV4%ngA5Mm+c9*eTrPe*5S3TTkg<}oOsJ~*ER=DI7 z@#2Jvq%$L-11f{fhYsbMY!3nxla(LgGu@*!-{(pOf?X2IX<)1F*qkqZZDxc(*D&W( z%cxXH6yOOKIbSYkpB%67jn+$eW^&Je6rRxM9JB$tt_|%Kd;Quqy@gNp_UZnuOv2Z% z{$7?CIcrVMhA^MH@4bX>O@A29b@~SFZO6<|q^kc5lOMZ0Mi6iUFLi)H+KzM5VNnE8 zXb3NHPwi?kq{MAbdM&9~nC)at`eZ);6x}JAehXb8PFrH&+p}cz4-k2FfdH%tcZS)I zi$hX`Q+;UuEuk6kqr(Xx(Y8^)0vOE6?7X@>Xo_jf8xh0F7m$oT!`Z(EAtF zwveWdcl3!4U9)-G%7at_M|mJctMU%*@k_|TXy2Dg{e-Jn5#}jd1@`ML^aZ>GQhn#C z?ZCZaPE&-W4TuS>A}9=96doa<67>SlT3Xm`%DMm`AEC>tO+ zlb5Ih)MBCcIfrws<^w)cknubI(`S?ca5@7|HcWya=<(3F>qo`WRF>Ae}9+x6qCc&vG4S+gjK;2h~yS5u{!(m5*m zH%joHOA8_6Qx2=diwrYnJ!t z^o+!Tm<}FU)MIIclSxi+jhIh>t|m_F+CxGwSe89^xp+Z>V+GPQsXHqV+swniR%~-J z4jMt*BN|)id4J_dWJje@mA9hQMvU;9KT$l7KoYemH!?u0l$XDb!)oUd-FMk}-0|^O zGB$QuI@)6F__X`;TQa_p>dziVWw&nKz(O7QQN@OXWf7Uog5BJ90>{@{*Eb7rp)DX*8Bii9Q2Xcr(ZlH3`kX zgl9(VC(L{yhQ<`PdJ*Shd%W9~KdC3l!lxhnv+s$i=9}D__heyFOV2p%gYeZKpiH*7 z0nACG2dJ^9wnl;^CMuL6!5)3XtX*$WTgk{|Dh)?M-2UvsEbn{}(mT7_I2Ajt3sB3- zPZNq0H&b?WNOb^?W)Cn}JdXsKaem%->df-wnqRNh8{7EJ_;e=Z?|PS&wOx>`(@GV$ zxSeZKbmz_(Svp7WVEI07b3BAuL~L7y#{&t}-rAwoPi7HNa7^oexL;XQYnQZEuP|Xv zO+EvKUB()F*1RvLXy4s)oN}dkZmx<63_Ykrmop-rSFAQXlf5vP%sFrU>j(kEpaL`f zx)w2ZuhbIKoiY_L)IR>4B8rmSDRZ{98w6{P+6DE6SeH|0QVBnW3n$YHI{L`f^SqZP z^>c$b+g@Gm(NmQ)q*B+Iq*01fCy3TXgW5r?+J*2Xv=0gIT=onCzLkfCY>j}0BN|ht zvKv4IRR2ts5r8N>K_%zUKCZmzd1Tc8lWb?u&Di4?V7fa9>Q{{vqo!}D0fGJ|?47>P zl<~8DNCm1U9oW4Y4tsBy4>a80Cav|A-fso0rT}-_Gy4*ZFn`cy&FYtJhW=2%#HOjy zoVc^eStBi;Z$D>ZZ0b?d*w3AE(A3tWI+BRfabi(j{`6YDLGj?++>$F4+|ExdK_^r`q{)Pi){4=ci}Po5}Kt=uNH>LW`MIujpi z{_dw+Os9&T;HP|*DyMowo&Tc0GeZ0gb&r|ds_^>gL?orx8OiC^hxsJre#W#+-`FZP zF}6mLUSDwblbVS{(58m{uiL9fGnzA`C5?LM0!o+)wk(fTbnCq#vpzn z5H5&a>5~5-INR1mXo*60VCdO&n$`Ckn;!5BV4`fKe(G#F>$uM~*>or_YR7za2k$BN z-L`eBlx?55P@S&S;h}9c>3S2F^RBnSOo0JDR#e=8!>nZe6;^vI{CepgM%3ow9G-dO z$9?8{=3tZAbMi=u#y1vEG}{;X=ba>Yxf|Qp?$xz@o3y;VD+(%^ti_RKIA<$J$RiUwhrJn`lm{xu&~rE`cnK=| zO3&KbkMtojtN?+V;?1hw6-buFekb4%dJ&|dh{lcs8R$;geD5g zN-16mMVBrX!g>=A^>!JufR*5kYJ%-=OwTiQ;5dK~l*Zln6=evWgjV&Dx^osOQ^(X# z{{}12Jbszg_QSVSoBiJ7)Wf%yf6sB@N9(g=hTu>2S-?XO7{lJaV71#!3P^RK5T{-b zx>dooY*K&k^!_gu6yrUB;fj~cFuHke&Ft8+qnXr8p-`N_!^Tf&>avhAj7S9^hYST7 zgKr_LT(*V%P7fJ?<;n0@1zdqm;8TGbr)^lvDl32>4>qx1VO-yAp8JeIzO$4dcN zpK4}C;xz{b6Vz7jC7FQs*(|oY1`|eqz&nL8`L4t6siC8i;UM^|P>lU;%lc10Hs@A_w%@=lp3eSLf1fLze#C<}#t@AZ>JwAl>+8IQ_}s$0_)+OE zDOeOzNxy~~ajW%?8}2FNAf3oLr{HX)ci-wHDJK+;wg}c5SdESPW1b3owgC5o{(~+T zFS0A6Pvz?tKZ>{&5t0bRKELe7li8M*N}%&wnQ|RJGXvT_z0XZ#5OcEquP;AU9{2nr z!3Ma6p)A+$u;SbYCMg)3uG$^Nx(c5J>_0&#zoeKYFDX z1zIN;9ctOC)kshwS<+%_W_T9mvvdUr>Zni*Rn5jnao$01wxpf-zGDtyjF3nZWu!WM zOufcwFso>+Q+mHnI=HA?#H@uA(KS?VegVHse%}4OZx)NnA|kgwSx@B(fU_Yc7ZZ|g z*d)FJWQ%Ndz(4(1lIHRngU5O`h1rYxXD&OP>(6e{4FsSQXJ*AOZHBaLjGo5U-bKqN z6xG!uTjtj@!x=bPXMfpYEK(LVFc3YTP~QDoaNnv7D}+wo#<4d79G~1Uz6bEq1c)Ns zD*p6<>e{CKajS~{&@JK<62f>nvTz9Bk8QLxeqDzc&)A-sWiW5mOviaQDqZewX=^zL z(Ufqas=t7xF!ir`(AHWmAcG7pAm?7K|LNhI+XSu|tf6u3?X$|FC*{3Dyw;O!6jo%- zJBHeawYvndjVKA;a8+)Sj546P(4HRh>a!Ow;dThsM*!VO1txKl zoZ1b^Z33rsee`mvljg$F&vWaZ`p3N@I}yswr z?#+pbHLF{lm1c^Vmi%Q^KizrWu=;PM0VP};2>9%bFT;0=7{2oA0Fi4bo7RB z?kV|&Tu&nX|1ohmHHeT80z|Ri zM>~hp@tz_9M3w5F*7vW|LPti4F)+;|;FiN5rDi3$2cLW?a=KA^g&P<>;b>zHvn+q| zs>m2kGB65Ip4qqJ;Ybx+P(bqb{yBBeJ`gTE2t+F_z-##4`K2y)?sr?NuBl>{Wd?|2 zgE;nl;L={YMb#~2KE!{>e6)Ys!{X$7ym4_0Vl*~V2%U`7VQEH{>^qqrsP8^MxGtg5 zS|an6s=`XfE5b0O%)A*rtPvpYF$MFhhcm6mp*yFxNz)5UO;5aewfS> z(9}4y$k5L3Y60fW2)m!&9h}u^VP9-Y%_#u!ku^0l_QPswbYi+91roMCFSD1Jj-mS&xoPFRyyOMEeqhT~X3*b<`&4m%zZD*G z%MKxKZ>Jsac|u_Uq4y3TM?(3y^zM#q$p9uiT6Z!fAK{Z4b7TcrH9E7BUldd&BUR!= zsT~WMR-$&{$io+Bho$>{FwS5$yp#J+H`Q!g*cJCR^K09u5FthFbN)CIF}66|6!Y zy+zX?o9x%FRR~|Ekkt3mn%hOWj>Suk+oKo<>hJ`?S47nMIm8kI~U+J*n~NSP-}BDS?ddc)L!ZRp_l2p{dWE6 zZdEz-l4r@325Rj6>j5LX_Zm?9hk|%*3q1DG^(ipV|v` z7vd$ zO6N{#w*>OqB#JKfeIg7{?4cXj|I{UwQ*N^k>}qrNN9mt}Esq9)6TxA_1zNW3$y>eG z)vmBn+(knzZ*ptr+s0T{PEGb2OdY-j7zgK}Uha2~yU9EdAPQX1z-jmPHA7PYWY`1t zu*&pt%+SwMh&+5Y9tNM(sI1CfG06sQ@|slk$zCDjlqAwnSW6V7Ib1<9o%th~MAL<~rQ{@Um=NR(awx z8hD8G*kB#iEzk>F9Z6|4w%_(eH*V1l_JHJz zD~(OQiBH|1Opbz&H_wCy)Xs0MH?P7;#a_+v??+3`^QtaF4wES!@{Hou)m33&2hI`*IX7tv-U4yKDK;ygQbFR+^T4(fz3|3#4RZc4n% z`(Sh@|DZ|CA!`Z7)SekIs`tJaQ}gHrGS^H-P~09{8vDFKkA3PHS<0{zxNXOj;Xkm| z`_t>&qk<77GXiD&;yGy}^B%S7gE~7}Si0Y974hen-12TF%aml852?-X^Nn#9Unpmk zY4|C6fT)F!JOTHUre-G#b&i!dQ7jN##2-mCt<+rMG9{^~7==-TzDAR8XI;n3x>fPv zkibzncZvDEQzj|g>ur(S$Ltwb(vT636`qeP{V}5=suM}x5^Udc6_k@ZHl5QtG;Irb+RnEH#_aA|5Ja1J-pdhL^gywLDkQ5 zb0Vrsmi#+JH$Jt@-u`zgB^J-m`MhAnxM>I8>Wp^qcs~qanC@NCY)a&w%sJ0k299h^ zI8RuowS)#JnQ=FBE5pQD5h<-dsMr@N`YOE4*7z{jK(E=Uxk8-~%M>iTTX7osnDy3_ z8A`-v&xCP~RZCokgg^Nb*jd>(aGmH;c(01V&c-rrMHf;X_dGY)g-(0~Ol^LP7G#l! z(c`B^Y8ib@QU6Y9=+i1)v>-1^=pbUl10G)ekh8 z+f;VhuU20;kVuIn|8{uu>HKtJ5E!mgMQ4|8K{X7rqNkkd#C@02iO+Ntw6Vp-3lZ!Z zltL$ZQcOFB#Pj1Ek}00Tm|h_nF(dTu*}syX1?Q?Wgus}WU91-dSryiPOg9#w8&<>w zNN`fx%}MvWqSY-;Z!j58 zfK*E2=JYyo0k0QXB-21Qfk9(XJs@0J$V!N+$Haw%asv4-lcNZl&tLJw1+U|cXI%(J zs-N!Cn<1HCoA6k>0U}dKHV1=rES_3^tg`4}Wsyphk@F)32p@*3!)uAJQqPMU(7Z)Gc$ilmPMtHTL19GbwfKOl+4q2vTLsZ?k zz6WA;dQbC@fJ~xDpn>0NzUrpsfi^P^P{&IhTAcKfWuYGGdWC>?QlJYF3tFel+$o); zwqv!M)`RXBcG*<9O~10DtuP`&h7Ofiji94z8N)-p8Ik9l8;*1jj`UVOM<>6F$NKIW zO!%j=gJZFw=}7IyZky-Kf*449x^Rz6o~hv_Q%CC;80lJ-xqD(cbz=;&m*a)qP%@)G}7aJ~4 zdSH8WgsaQXhO$p#EmPVcLl=j*UlTd%*8%&OEMKGdwcRJeW%F)f&tH6Q9CJ_U%F1dp zn+}k7v$Stc>9)rxX73c+fBI&Pv^%*MLAB>`2gk9c7n@&EyPHHoYhF6(u5UcJ!zWHP zJ+L%?p0hVU$=&|sQonHz=8er=X(3B_WwKoCo7SzE&in>mZQB^o1RA#t^o@y3d9ohK z#A`~Z;)e-Q#zwBso1%Zg7;HeAd#;RSf=zO?jk*Q&cPuoVSN(qF&>tT+7^_Fcj6OEz zSNDK5gY{`TCXb#l3&Qm9p}tQ-rVd500KkTk$s>?q*}_PZZ^pM3N23ifw+!|W62zTb zhJHr*UPAE`4DXi~E!KsEggiTv82F{`m205^=MM88?}ekg$uN>XJ)9C&i2Y*UCA4gQ z&bImkF=txn&Y3nbu}igUZZ<;JJFy?$r+C*4{I|x6$j#oG;L{IHg`hYTZ48~(0n9*R zzGq+UX`5kZp64l6NHlYbq>c2$gR=_yL%tsk3EOhu-N+~X@l1`My)MAKXxYs25=&~` z5ul1<8V2ytXpGmI4^rPBGVG$?k^o_4=N&#+86UJ$eGQCiHI^$MW zC-+EQ_cWKSK=PXim*}AH{}cHpySn0@8_SVyqKr6)s~lvyL&nzI^61T38`Qgty87c0 zmmutgKNxn@40Lcbu~{~bbEGH}aCgktFxNADU%MgRx^IZhIdzxzxi-bHj{ZX(T=lY3 z*RBmi2^xIG5Ub)Yg9Z+K5`F%ZCa%vtbq_T8HY-;zr(84XOwr3opQM3cW{CDW_TJs@A-LsXM?mU1 zlv<-8D~10XuH1a!2c0*5ps^LxuV}*3TeEm>?e~3wMlLaF3%8YCd-1u`hWOL%3kUw; zen0q0Tyocdhp}H+U$?Jr!-B0H>p#pCJ`U+n4-WyyTekZ1o*NuIe1J6;jfJI0%gwsbc?~P&T?77_b%C=(4wOQzcl;l zMn3G&y|8?h!AvRmc0E(){jY=FbZc>@yqa~$5bzSbwDO}OwELCFE05d0r!a$RA=}7Cwe2fQC z8Ku5wLVZClj4~m`t3OMfYOtz|no*>El!(ng&dtrtPlit|q8;-7@!SHLQ86t7RpXXN zy_u+0M!h?C|FajLgB<6&-&>$mm|z`{dpUi;a=WbRRraR$9*iEfZ#sAGI`LmxQmWrK z45bzJthrl7?<$fXZp7Sdmqij4<0i=i;s9By_qaKz*1)E^Kk%5Td(f04I-KrX9n{r# z;4IKHAYmbSn`)bfVJ?nAoHuStd9aIg~m> zI3B2(uq0RuQ{JrhMeg{S0MhDs2|}l}tneo{k>KWD;t>H|2pa{4NKQOAOEgd zdO!X;K+%0huXNYxg+V5#*afKN-1A6#g?s1SI4rne804B@)+SfOo7r|>vlm?C#`GhYH=L_v*1*Hd5|KD)R|pJluU9L*s#H`|JeS3fPkj;wAdrX4@P_a zV8DiR``#(|CF>*cUEa2bwz%rH7iA95g~paU}x!2p5Y& z=fFUd*=1b5d*HWX1|weo&lrpP`pWO{y~E4C2`nIe)Qw}_Mh%a{?En3sSfmpSsdf9p zJ?q$eQuHo9GOK-j$8eRKtvx(wiAF|#v)?r|D983vSESLjyBqXkNYHk-*0ZNi+3M=$ zb)~wV)76ppDJzcV#C15|@OXaHe;KR5`h`)W-X3yG-u*x8wek`#F&K_`ma?}~!#R<) z9F<cyk{OkHF=3k(D;+}>>(oQC!;y(`Id1BOl7YIx;g$5o79B7!{|I*;OiUBp=!qG(D za1Bl_I&I;or=$%1WBTP?x5n*T_WuzDWT#5Ew!4#I0yi}q2I?X74k7d6R;P9aY}l4U zt#f}3g+j{(g?kz{7;2wl|B@=G&w3h489o#bieZ@w|KBL0KKAgC)ykMYFmR5ZyM1W` z4yI5lse9Z}h5DiXCTfpi@5srcQEfdFIbI+@@^sN4#;^Pm>FW!qe+R%-X>)5;Et&l5m(e@AeDj zeSz^iLJs|01v0UWK9-&J8XWQcOuxsWmBfvF8seg({=d(_BBSyP$pDU64_?P_2rB|{ z74pLTXesm?K|vBaw$KObXm}k?mgNcca~A#k%C#L+hB^K`XaDn1y#p9r`4X8?LCq8~2QxKU z{0)2PG~+`{8JjRhRoQJ6{EUe?E8~ib`#{e#aDMbVMA-=mD*yd44@|n^_;Ue~!?b%N zMnzK8_SojQtWoydcPv&{9* zZ&3k;VUU31S9}TscxMf}N0|E1HPZphdXoG_up49h`Q~lb0Pmk&`x4mU7%0mg+VaAS zSJTs7f4q?EFltoA?5|~ceNm3>XWAhnC$mxPkg!(QF9vqb1l5u~tW*IO%$J(2#Ugy% z8pwLn&|Qk(5}YAt|TP| z?)~3~FlNxrh}}hnzdYFy>g4LGE4y#FZYOL%qp)LTD&|PtA)s~U?J(3;O|2xea2V|}m0EBkHV-oEW_viCSFabzugaXFl!v}m&t|5G+_ zXLom3_H|i%dRHN1l@2I9WjUW0A`AmsIAGkUhGy9zEM1>y&-Sc_@=GK02#{$S{gpOd z%zj)W%qir1X}Fzk=Go-Qd)EIl%XPUMkREs|y=OxvmgrF1rqvC@YtNtCL8tG{U`Lrr z9{Um2%5FfYS(?B9el`2k9w#~e=YMhkxnO;F(TA2kFYDroMFzM}OgrbWiPmfkAU?m% zJn&b-FuXeppiZ`RANjAhXnBL3nK~U6Jnts$1N_(n41pi!E?Smx)OlImXJr288#%uJ z`3}S0l4Xwx>yrVP1U;6xT<)5k^2a~uh-u0i_LzcA$I_o56WYzF^e*-H9JnM7bM~7) z)He7}Fp&4sJG`KDbfcmTrTHndkZkBO#Aw_3(7v3P!GErt$XHkxr1u~k^f55bvbxV1 zQ5cZhuz>2^zhB(uF4G1J0D+!dk8$vXkYfOe~%U; zyPt<^-%`q&-4F#WKv9oDAd*$-vRe}$5LqYFQ&V#kk4tII`_kQ!Kzg8nSnjliaE$cW7nl(S);c9 zmVt8k0ypoEX%di#MSz?kP+1$OFg+M090Xyzi!+Q9v+{N-DhJ<|gZYk>2hU43?-#27 z?{m&g3E6pL$Im zkAuTozTc9C`zE~&LPa4YdnC~#~N4>S`Wk-GD#OeObf5~T{$(6S)XP3s! z(2R!BRdx;XqS`Gld7^96!3>e+;eYa{u||SL#Zaj&FHIj+xUtk%XBt&bPefD24__?n zofJ=10O~ad#3mNeAe-FU5egbn!lQTm3=4QWYHfyQqichC4`acBBS@O*X@^I-ydLSl zQeL!~&0AtX(ShySy54s#SlX?q>0&!7_kG~jcRjNV3NN0xP#$tSsEyQ&xd-#UC2V(twif*39z4;ixL^+r+X$mS>c)O580n&*927Ft_L0kJ(y+kwTxtVkO3 zIX`;Z`AGJyMylK_ynNa$-N>k|jk!A(4{E?4vUDACKGnwfU%UFi923`*?xvg;-@IJR8(oQ?V?OwdfAya8)e`5RKtj9(l z>JVD6t)rVPH^(B`irakPgVOAMOU@3{Q7=--6s?owPUx$9z--6yznWTF=FDI$)%!Pc zPY>RcjwwsyVQap>J2ba6RzyuiC@d?YTG+Pl>85U`NTu44Rq%%19n6GqBT0gL zH;$=L1OvULj1+7~YqlR--!eeyG=-~NYuU~zG#(4^2hEzF^&p0cG!PhrmeJr zg4K^;y%%B6SP;9%3zDQ!;YR(R?ymf==KWi5L&gk6l2D{n8c2gkNgIu59#m*RC6PG| zj?hSRDkaU63QeM2I7xPqW(~qoQk`^^O7nfzaqfMc`}*F$;QHa{ys-CYc)!E~Z@l=;%lr&&D7k5+aw16SmRz)fvmA@3xi_Z!DipNC*b?-k~D$+?UyNol?L)C#{| zc{)XQ02P2ZnC*dpJNb)NFIggl@DPf2tOicwknv%tScy|G2ex<$cE=bVqVo=_69$;O ztd%SAPs94CyEy8PIN%owXvO`KY{wh+?V4*F+FE$x%V?(-$M(FQu z(PoyyEdwJ`Qfr@!iHKSh^|W?5YOI<;=6H6PAOo$w<sts$` z0>P18enL6vM~a1Mv1=iuJz`N zedllf#Dp90-vF#>3#6TixXymaZ>Kg2IBC6_{7o+lUA~7v9xtMnr6V4=t29(oZlNvn z3JpxaB)iZTw(pLJVk;ulAoUFJ_QvQ&`Q4an@PkmiEo-+HgiCHzHxIB75N%&!HwsHg zREjEo*rnAIBH=gr1Bs4h7+TBTFlE}ZZJQPy3kcsDlix6voQUIy3JwG~Dy9$v<|;P= zw?|YX3jfFLt2fK!hq_KtAptsw4_p;+QfVUVu?2Y6J#wSCJ=&91(NfFZ^VO*WO}8*q zhQ5$v#>9*%l+f>dy%_IZjz^RTzHl&LcT^@zak&`VjP~bO_{K5~-da@`lY$O&1*W!P zguV}o9m43;$v`BHSyhW!Kv68ZTVP}RVrCo)ei~S-R1^A0O@Ug-EIp~2h@iQy{I7f{ z=g{GZQAPv2C3+zw``Dfrf5Kw@@Z;)ew{m3#;hxQ6$fZL7erbYcfeywV`a*E3$hj1L zaF$o9`jGU&rydg><#^!jMS zB81ZeQBeCKPM~Q8I-^cIYRf+JJw8o#P1h2PIE?7)UfIR(BwxXBJL0rx;` z6HR>Ui-raxs3r=cXA=m$jb+*UuWv%1uB$001+8{1aBtROzL&mH5e**@gASnXPI4c@ zfvJ~euJ{VmQW)3Jr8|c+MIeyR3RGT6*ij<B|DzL(wX~3OVRvg|kVgpE5MtvB zdduUO_oS-dvns`AYKK4;ih+j^yXD(6Q&!uC6z94mfeApnHL+sJ5gtJM8f^2ww>3CA%3Jmz*n?$mJPA*Xw1XwZ00_!) z?Y6*BsKNo*Apg@9%vQqX^uP*!1?MAZA~WgokwS-JG3!g33?SnG_rC%OC-LiMd33sU zrsL)4p%;j`pUX=@_{Y4TBof(&P};G3|2U6S<*IMr;me#7E*xvc{J+xy)OaKTsRvch zG=sEYP##}leRY6J8cv4+4v{6>1EBj6K{X^HWuTx1k-*l)Rf#PpxCiKk0pux#m9m>7 z&L09j>4Pu_tB!~SmK2i;CM15hfL{8cU*N5to)xM0aO$mwU5jQG!l5sQc`-0`VQZ>-8cB3gNPjtM*^1sOR?eG&WMV+Z1X-s^O@<&;t!C z%{coGFc17W-AS+iW! zVi%K1gej652hag)&n5&lXBodacvK4+)Sm|;jo4=~IDwjh)Z5WaUcSj{CUYiyU^f2b zN6LKF`_S{NbLUP@dQ44tp%0L9@q{FNsY8oVaZKm>tz>E{?|inZZp_h@Pp3Z46AkzC z^}cfXyd+;W(_n{g&!M8YHS)om*(rsHQR({G{@WuXBd70RP%)Gnu6VIOkr4^q;K=<*}=&9Dk~ zPI`K}>=aU%#f;#HhNy` zls4#wY?Q;CV|rU@d0qT@y9;;voS z;MixuPzOAd+!~>1;%LvmIrMZB(h)?+Y@Q0>#2lBYGZEF_-|viyWibN_>CV;y{r39S zj4yqs^DKnK26FM3%aQuab`%yC5rqu3knWY%)acP1 znBkp>h*Y0}gHxlo+8nFTj}H>$#0Ug-pE)#)$l`03*f%B#Xcb3GXkrW$L=XUUnk>TSS7iO zS=-jFXJjOTH^fliz59By63o6NBqRv=F{|KRwv3JOPBcSzuU2utCl&x0iy;G;Urb!Q zW>gzEfQQ;feeJ~#vyd%>AoYq%`hBCBga4D_h>Q;s`}Xc##vm|*>!RG;2f4YS2xflX zUv(Cvn2usBhAfxoHY@#7$1F34*19v4lQJlW-g=hJp|Yejb^FhWJ`ZVz4F=-RN~M?! zB1`UJE&hhgp9=O2|G8FmmJw>(xVaP&!A(p|yrD*hlcY4&Q7}E=!ReI8+@*LO-4?NF zOG*I=Cg>O7Ql<&w<7(4dAl1ttxWF!KgTkK4u@6(-uDhGQAkOMUV%6(vrEXBw9^pcl z>G#(H%XR1LH$(ZHrQ|khkYZV=de5Ira|-M@^BCLBGBPtS7ma;;;!=_@y1?!xU+?Wr zFAqlEdQn>XwayKB0PQ_AC!%DmzZ}X-lRiw-oLgfFL}mmc{MoS=u2vFdtizb$-9h9$Iq`;+)4e42~wXI&!6wCsi`>=ZX7&M`RB(OI;Lt_ z{0BB$kF{R^w$E)Krn%^Q8NZpMdi$VUKtaLz%-GHNOhEn&DE0&xJv6kKH*emVe67U8 zbFIpT1lZ>y(;fM`aXy<3mo6odCrwRX zMj!(3^1>vWy`k29fBtz-;kR6Q`#dusBQ}V|I?GoDovL&@A!cQACBoYqiMP@8`!$_l zo*d}w2Yrr*(GSt%V{GKlGcN<+NYLG+Kk&yky(m?jie_7{&a%64ihs!fQi;FH_?-@6 zIhf=eSpckmTAo?v;IY!E;E<4Me^Zr{l57(`8|Id|Ey!(ql-7mMn!zBG4*kx)zU+(U z3c3eU{CS}IpKYs%XlXJ%eg%uPUi!8uOD+BENwkYMdu?3F5?9I@>+ zM9h$CM1UHCyz>UChG$h(*`{B*M^`T)`Bz4^rLJ*E|JKOp=#?>B!!$X-B#-IG(ESVs zg;cT4`5uBYyoew7@85q4754Wvaf|aJFhBRzP3JAmjoQG$+H;9Wf7x>4x7T5bHH!wN@TOfMGefnO)pwsL81>r z0p$3L-R+Isa0Y{%$k3M^?=~)xaeP~EP*NDTKh0pUK}_X`aU+UkmO>Ap?ztZai>F_( z*qv#ttIGur=GhbG+-84--~ zh>D9}VX@qDDxYy~pSnG&l-*;NaYO==4H*AWJ$!f#z!yJ2Nu+v&uu{%u(?}`1qJp0% zV9m>U7gz_=9h^!^me+L3AMt4Tfh5BkkBF$f0A9#d@^#Ts)c9b@xW{~7zCluQ0T3iO zgS-F+e*<*OWA(-c28V!OJaX=shwZ%)20rdsOwDsK9!|qxsqyl)Fl2= zURik+a%Uu~g(bK%NiQxnbq=;(-ie$zx4jmP56*@)8tmWC_-Gja4qXHXi*M+Y%yJWm zENE^X0Y?mq^IUL*;{|3oL{tN^? z&)uji{7%*eWICG}=6Z=S**pEDzC`iMq;U z?9Zrw2c-P;b#;U6e3=3We5@7UumMW6obdF{+=4NT2Oz1sfb8)l)@EfbODG_f$NA*R zr(l2wg@nvFaG=E{$0_W#CtiVLzqGIS3I{Zt7-Xc`Q)f|h;eo2hAhT|T*ymlK&CTDl zv(6>YVB6M9LlSN7V~)u80N+NvI$W2iz5V>d{QT=6`72NS(1(&1&(ni%7$T{vBWx!c2HDqOYNQfs4WoDhYmW8~N{E19W zO-ZKeOWqGTu}*6ka_)^ud$Z^6sR^$%xx@u4w3^zG|901Hhc2U-nAj{g4m{ZHK02!^Yu*~AtsY}*gUAOJVn$z+kM*E*-TJ8z9C7txf)I<8`172 zb9J0{q!p=CyfD678&DYD$yh>Kzi|ti9}S^^C>AgRV`ARF4ZLlDv6F@#A+%PmKR=V8%%nPrv%J4+Q)ID!{T2C!JSLvP+HH!dC1R~(X? zdfS13M`P;yO-=25b*|tmvOOjo0QYcUw?TKqowcc6NdODtKDrRBSi7$ybC$Px$R;)C zkDxaH*>iBpKKLxynoCgUz*u4t;O8VmUOqm%w>|39?jj$H0EZK?7j6*pUv=12S_VZ% z+WFpp#aVx@_)AB}r}HIa76Y@H1_6kul@%4gqca$QIx-GekO?M5lD{D_e2(Ia^mhnL z>gxn1eFIVC^;FpU8xunXCG|ym`f)}Xa{Dxd2A&&h>pf{q*(ZQF{PJT5X12Bb1yoi8 zO)mpjveA9~g7=)7+S*glg(KyzD2)ZWmp*rPdI7t%0%!#u-E-{sYWSV4#1JG3>bzoy z+o_p+;I~hXRC!ndLMLgbBX6R8;A?`BBnEJL_&CLUk>04&0s7|e7~@DbG%_(s9n|zH zndo(btj80LMy)!7s3)r^UypzSE1S(GLvehCcHhAx^{%^S=vqfueFKmvLUcNuXxA_( zNA_VLp9m@|uf~P^eC_0(V0sQI@crCeCRCI4^z~u;IER7!RVTzTAr83PeoM=Rfax)F z9EWrUv#_Or!-#7J(0*^uaV`HG=JoEHr1@wc01}Zp1U#55y-52CIuFFHN4W?soKgq^ zZUi(5+wH^0blxexC%^if@M*_DtkpIN)UQgaX6E zujAm2kWfrf#-Ob#6afln49{j!y?ib_bZtu+j5H z|Lz&&0a1=+oZXK7vj@o-n042Y@!46*UBFkicXS*^TLS99PHY`V9?ss=IuBHkulis} zMgX9jS3!XS958jw&HZt-llKI2%2_I==~DD)K_`>*F(1vUJ__LpQ~(qkLDYVUeQ|1R z$dObfkTlWYT(ZvXWQDo+)--CPB+{`17^N;!j;)*14ZR9FEzGhR=)QR+*-li6j=WTv{O!k&GbEIb(&{=m zphzppsWCAr=@VKJ?+Tm*kX-YxQizNt8Y--y0I<;MgK^TJ+qeAy)X~@lR0{077W?Gp zCKo2jD5uxkFXiF+1f2U3PD%95=c1F1eX*z0vKrjuO^~H@g=8RXxi?@baUg4LZ*TwT zF*PPYZv{CkfaMvC`zJr`BsmIPSQ)q;q|AYQ?Gv8c795L^j(GZ^=5^5GJRwFRQs7yV zW1T8frVRDt$M5a6aCa}p%F(0l1L$=Gq)=;vm>x*>eG)|ME$Zy^Fb$-GDLn~@cYpy# z)7U}r@oP_=KhJyq{BZcfpwy;y!onIjq42dD7%zhP;4aYcC4Ld9D=W3938Ho8q9U+( zv7lTEvCv&!rj(X4r|s`7sjRFNd8ZklTyNf69w=X0vEF`OS!K+Rm?t>?xtA>y!F&Y~ zZn|0vbEuUcE@dH(lV@vB3+qw=ob9wRkF&znau1U^hoPaZU6+>O7|nh0{TonFa@k?M z65`@;Dh*f1^lT}ctw}e5v^3r*W3Wv3;M9&fJuC=nG1Ooh<-z^?Z|#Epp?uUl~m zC?5b_;+P;8iW#yvz?qbk#LdGatTHuH^#b@$P8PD4Zi$w_p0b9@cgn`4vST@kiA)f% zCDDyp0%|bzr|6+!ZM34H!!_%ph>q74rlewmRU47Si-@<@po4Z z%Mlmf)l!g+2Y}@}w{Lw=lT0H}@UQ+pSh_V3MC03JbrtiE`_WiT0)E<1r2-@fPnS4N zRt^puKn3~Z)2F|QWFS*roE$HFbCi@g_!Le=qwC;<*9SVXy1KeIx>@f(K1v%g@%ENg zVxcWwev(}~`0(LFGueg2f07Z17UJg0bHif&HKQ*Y8@h*CSNg6~Pb)1weJ6>R0Iw`I zqkqa-Kjpgbzgbhiezza?_4TDe#7r43Rb*p`Ro~xi>&xA512&TZuc6lL9r)p2zrIK- zD)NDyp^6dqf8=A=tzR#Mlnu2HyuvKBywvg6T~Igw;s~d%+6F0XJw3f{i#Kp=fgq30 z>P5&#@QdZ5*9d(SPBVFUyFsH~XGMS&c)X;?N78yAo!rIy0AbxZ(U0;CSbb~A9}@kTf1 zTK(&${`t`{)b>B_m;Ok{^Z)$YOP7@X^Zoz&tyxsW``6w5`@ea*+KYw$_mju}*(V?g z{*TM3+l@l=U!VBv$J85ESC;?#Bme$cm;c4gP5<$^@$$a^pD#FdYDO^Yy0Y^`xB>&e OG Date: Tue, 29 Nov 2022 02:05:22 -0500 Subject: [PATCH 154/300] Bump injection parser to v0.0.9 --- example/ParameterEstimation/Injection_withParser.py | 12 +++--------- 1 file changed, 3 insertions(+), 9 deletions(-) diff --git a/example/ParameterEstimation/Injection_withParser.py b/example/ParameterEstimation/Injection_withParser.py index 3d99e034..f612756c 100644 --- a/example/ParameterEstimation/Injection_withParser.py +++ b/example/ParameterEstimation/Injection_withParser.py @@ -15,7 +15,7 @@ from jaxgw.PE.generate_noise import generate_noise from flowMC.nfmodel.rqSpline import RQSpline -from flowMC.sampler.MALA import make_mala_sampler, mala_sampler_autotune +from flowMC.sampler.MALA import MALA from flowMC.sampler.Sampler import Sampler from flowMC.utils.PRNG_keys import initialize_rng_keys from flowMC.nfmodel.utils import * @@ -26,9 +26,6 @@ from tqdm import tqdm from functools import partialmethod -tqdm.__init__ = partialmethod(tqdm.__init__, disable=True) - - import sys sys.path.append('/mnt/home/wwong/GWProject/JaxGW') @@ -243,15 +240,13 @@ def posterior(theta): mass_matrix = np.abs(1./(jax.grad(logL)(true_param)+jax.grad(top_hat)(true_param)))*mass_matrix mass_matrix = jnp.array(mass_matrix) -local_sampler_caller = lambda x: make_mala_sampler(x, jit=True) -sampler_params = {'dt':mass_matrix*3e-2} +local_sampler = MALA(posterior, True, {"step_size": mass_matrix*3e-2}) print("Running sampler") nf_sampler = Sampler( n_dim, rng_key_set, - local_sampler_caller, - sampler_params, + local_sampler, posterior, model, n_loop_training=n_loop_training, @@ -265,7 +260,6 @@ def posterior(theta): batch_size=batch_size, use_global=True, keep_quantile=0., - local_autotune=mala_sampler_autotune, train_thinning = 40 ) From 4649b234c77feec25e1770dcb3f5d77b4561e1c9 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 2 Jan 2023 18:35:34 -0500 Subject: [PATCH 155/300] Use HDI for pp plot --- example/ParameterEstimation/make_ppPlot.py | 50 +++++++++++++++------- 1 file changed, 34 insertions(+), 16 deletions(-) diff --git a/example/ParameterEstimation/make_ppPlot.py b/example/ParameterEstimation/make_ppPlot.py index 17eb42b6..18810fa4 100644 --- a/example/ParameterEstimation/make_ppPlot.py +++ b/example/ParameterEstimation/make_ppPlot.py @@ -1,32 +1,48 @@ import numpy as np -from scipy.optimize import minimize +from scipy.optimize import minimize_scalar +import arviz as az +from scipy.interpolate import interp1d + +q_axis = np.linspace(0.1,1,100) +eta = q_axis/(1+q_axis)**2 +q_interp = interp1d(eta,q_axis) def get_all_quantile(filename): data = np.load(filename) chains = data['chains'] true_param = data['true_param'] - chains[:,:,1] = chains[:,:,1]/(1+chains[:,:,1])**2 - chains[:,:,7] = np.arccos(chains[:,:,7]) - chains[:,:,10] = np.arcsin(chains[:,:,10]) - - median = np.log10(0.5) + true_param[1] = q_interp(true_param[1]) + true_param[7] = np.cos(true_param[7]) + true_param[10] = np.sin(true_param[10]) + def compute_percentile(value,data): - f = lambda x : np.abs(np.quantile(data, 10**x) - value) - result = minimize(f,median,method="Nelder-Mead",bounds=[[-4,0]]) - return np.abs(10**result.x[0]-0.5)*2 + f = lambda x : np.min(np.abs(az.hdi(data, hdi_prob=x)-value)) + result = minimize_scalar(f,bounds=[0.001,0.99],method='bounded') + return result.x + + + # Multi-modal HDI works only for data that are actuall multi-modal. + # If it is not, it may actually screw up the result + def compute_percentile_multimodal(value,data): + f = lambda x : np.min(np.abs(az.hdi(data, hdi_prob=x,multimodal=True,max_modes=4)-value)) + result = minimize_scalar(f,bounds=[0.001,0.99],method='bounded') + return result.x - result = [] + result = [] + result_multimodal = [] for i in range(11): - result.append(compute_percentile(true_param[i],chains[:,:,i])) + result.append(compute_percentile(true_param[i],chains[:,:,i].reshape(-1)[::10])) + result_multimodal.append(compute_percentile_multimodal(true_param[i],chains[:,:,i].reshape(-1)[::10])) mean_local_accs = data['local_accs'].mean() mean_global_accs = data['global_accs'].mean() - return np.array(result), true_param, mean_global_accs, mean_local_accs + return np.array(result), np.array(result_multimodal), true_param, mean_global_accs, mean_local_accs directory = '/mnt/home/wwong/ceph/GWProject/JaxGW/RealtimePE/ppPlots/balance_1001/' result = [] +result_multimodal = [] true_param = [] mean_global_accs = [] mean_local_accs = [] @@ -34,13 +50,15 @@ def compute_percentile(value,data): name = directory+'injection_'+str(i)+'.npz' local_result = get_all_quantile(name) result.append(local_result[0]) - true_param.append(local_result[1]) - mean_global_accs.append(local_result[2]) - mean_local_accs.append(local_result[3]) + result_multimodal.append(local_result[1]) + true_param.append(local_result[2]) + mean_global_accs.append(local_result[3]) + mean_local_accs.append(local_result[4]) result = np.stack(result) +result_multimodal = np.stack(result_multimodal) true_param = np.stack(true_param) mean_global_accs = np.stack(mean_global_accs) mean_local_accs = np.stack(mean_local_accs) -np.savez('/mnt/home/wwong/ceph/GWProject/JaxGW/RealtimePE/ppPlots/combined_quantile_balance_1001',result=result, true_param=true_param, mean_global_accs=mean_global_accs, mean_local_accs= mean_local_accs) +np.savez('/mnt/home/wwong/ceph/GWProject/JaxGW/RealtimePE/ppPlots/combined_quantile_balance_1001',result=result, result_multimodal=result_multimodal, true_param=true_param, mean_global_accs=mean_global_accs, mean_local_accs= mean_local_accs) From d9a297f275868ba7f3446ed9e0f5703cb8fc6433 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 2 Jan 2023 20:09:55 -0500 Subject: [PATCH 156/300] Extend Injection Prior --- .../Injection_withParser.py | 2 +- .../configs/injection_debug.yaml | 33 +++++++++++++++++++ 2 files changed, 34 insertions(+), 1 deletion(-) create mode 100644 example/ParameterEstimation/configs/injection_debug.yaml diff --git a/example/ParameterEstimation/Injection_withParser.py b/example/ParameterEstimation/Injection_withParser.py index f612756c..d9132aed 100644 --- a/example/ParameterEstimation/Injection_withParser.py +++ b/example/ParameterEstimation/Injection_withParser.py @@ -191,7 +191,7 @@ def gen_waveform_L1(f, theta): print("Initializing MCMC model and normalizing flow model.") -prior_range = jnp.array([[10,80],[0.125,1.0],[-0.5,0.5],[-0.5,0.5],[400,1000],[-0.5,0.5],[0,2*np.pi],[-1,1],[0,np.pi],[0,2*np.pi],[-1,1]]) +prior_range = jnp.array([[10,80],[0.125,1.0],[-0.5,0.5],[-0.5,0.5],[0,2000],[-0.5,0.5],[0,2*np.pi],[-1,1],[0,np.pi],[0,2*np.pi],[-1,1]]) initial_position = jax.random.uniform(rng_key_set[0], shape=(int(n_chains), n_dim)) * 1 diff --git a/example/ParameterEstimation/configs/injection_debug.yaml b/example/ParameterEstimation/configs/injection_debug.yaml new file mode 100644 index 00000000..79312f0f --- /dev/null +++ b/example/ParameterEstimation/configs/injection_debug.yaml @@ -0,0 +1,33 @@ +output_path: /mnt/home/wwong/ceph/GWProject/JaxGW/RealtimePE/ppPlots/injection_26 +downsample_factor: 10 +seed: 9866 +f_sampling: 2048 +duration: 16 +fmin: 30 +ifos: + - H1 + - L1 +m1: 45.83666518760738 +m2: 43.85387081147894 +chi1: -0.3884050202764605 +chi2: -0.01385254347363285 +dist_mpc: 411.78955142985495 +tc: 0.14209878911731888 +phic: 4.037087228130469 +inclination: 1.4931823395002113 +polarization_angle: 2.6636577704157407 +ra: 6.204781034047059 +dec: 0.24329325205940763 +heterodyne_bins: 1001 +n_dim: 11 +n_chains: 1000 +n_loop_training: 40 +n_loop_production: 10 +n_local_steps: 200 +n_global_steps: 200 +learning_rate: 0.001 +max_samples: 50000 +momentum: 0.9 +num_epochs: 60 +batch_size: 50000 +stepsize: 0.01 From d8a48826a0cca6b4f63343e1c28033a90eae4c2e Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 2 Jan 2023 20:10:16 -0500 Subject: [PATCH 157/300] Update make pp plot --- example/ParameterEstimation/make_ppPlot.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/example/ParameterEstimation/make_ppPlot.py b/example/ParameterEstimation/make_ppPlot.py index 18810fa4..30dd4b19 100644 --- a/example/ParameterEstimation/make_ppPlot.py +++ b/example/ParameterEstimation/make_ppPlot.py @@ -40,7 +40,7 @@ def compute_percentile_multimodal(value,data): return np.array(result), np.array(result_multimodal), true_param, mean_global_accs, mean_local_accs -directory = '/mnt/home/wwong/ceph/GWProject/JaxGW/RealtimePE/ppPlots/balance_1001/' +directory = '/mnt/home/wwong/ceph/GWProject/JaxGW/RealtimePE/ppPlots/2to1/' result = [] result_multimodal = [] true_param = [] @@ -61,4 +61,4 @@ def compute_percentile_multimodal(value,data): mean_global_accs = np.stack(mean_global_accs) mean_local_accs = np.stack(mean_local_accs) -np.savez('/mnt/home/wwong/ceph/GWProject/JaxGW/RealtimePE/ppPlots/combined_quantile_balance_1001',result=result, result_multimodal=result_multimodal, true_param=true_param, mean_global_accs=mean_global_accs, mean_local_accs= mean_local_accs) +np.savez('/mnt/home/wwong/ceph/GWProject/JaxGW/RealtimePE/ppPlots/combined_quantile_2to1',result=result, result_multimodal=result_multimodal, true_param=true_param, mean_global_accs=mean_global_accs, mean_local_accs= mean_local_accs) From cb198d103171e31db869fe47d17eada3afccf78f Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 2 Jan 2023 20:46:57 -0500 Subject: [PATCH 158/300] Boost stepsize --- example/ParameterEstimation/Injection_withParser.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/example/ParameterEstimation/Injection_withParser.py b/example/ParameterEstimation/Injection_withParser.py index d9132aed..b98e6117 100644 --- a/example/ParameterEstimation/Injection_withParser.py +++ b/example/ParameterEstimation/Injection_withParser.py @@ -240,7 +240,7 @@ def posterior(theta): mass_matrix = np.abs(1./(jax.grad(logL)(true_param)+jax.grad(top_hat)(true_param)))*mass_matrix mass_matrix = jnp.array(mass_matrix) -local_sampler = MALA(posterior, True, {"step_size": mass_matrix*3e-2}) +local_sampler = MALA(posterior, True, {"step_size": mass_matrix*1e-1}) print("Running sampler") nf_sampler = Sampler( From 64974ed7a37e1992770db31a4364934f32008876 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 2 Jan 2023 21:31:27 -0500 Subject: [PATCH 159/300] use narrow time prior --- example/ParameterEstimation/Injection_withParser.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/example/ParameterEstimation/Injection_withParser.py b/example/ParameterEstimation/Injection_withParser.py index b98e6117..0eb5ea2b 100644 --- a/example/ParameterEstimation/Injection_withParser.py +++ b/example/ParameterEstimation/Injection_withParser.py @@ -202,6 +202,7 @@ def gen_waveform_L1(f, theta): m1,m2 = jax.vmap(Mc_eta_to_ms)(guess_param[:,:2]) q = m2/m1 initial_position = initial_position.at[:,0].set(guess_param[:,0]) +initial_position = initial_position.at[:,5].set(guess_param[:,5]) from astropy.cosmology import Planck18 as cosmo @@ -240,7 +241,7 @@ def posterior(theta): mass_matrix = np.abs(1./(jax.grad(logL)(true_param)+jax.grad(top_hat)(true_param)))*mass_matrix mass_matrix = jnp.array(mass_matrix) -local_sampler = MALA(posterior, True, {"step_size": mass_matrix*1e-1}) +local_sampler = MALA(posterior, True, {"step_size": mass_matrix*5e-1}) print("Running sampler") nf_sampler = Sampler( From fb2823caa9c527c1a45f85ad7f39077f9f19f7e9 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Tue, 3 Jan 2023 11:43:25 -0500 Subject: [PATCH 160/300] Fix heterodyne likelihood bugs with wrong frequency array --- .../Injection_withParser.py | 39 +++++++++++++++++-- 1 file changed, 35 insertions(+), 4 deletions(-) diff --git a/example/ParameterEstimation/Injection_withParser.py b/example/ParameterEstimation/Injection_withParser.py index 0eb5ea2b..b3ae1f2d 100644 --- a/example/ParameterEstimation/Injection_withParser.py +++ b/example/ParameterEstimation/Injection_withParser.py @@ -147,22 +147,53 @@ def gen_waveform_L1(f, theta): true_param = jnp.array([Mc, eta, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle, ra, dec]) +from scipy.interpolate import interp1d +q_axis = np.linspace(0.1, 1.0, 10000) +eta_axis = q_axis/(1+q_axis)**2 +true_q = interp1d(eta_axis, q_axis)(eta) +cos_inclination = np.cos(inclination) +sin_dec = np.sin(dec) +true_param_trans = jnp.array([Mc, true_q, chi1, chi2, dist_mpc, tc, phic, cos_inclination, polarization_angle, ra, sin_dec]) f_list = freqs[freqs>fmin] H1_signal = gen_waveform_H1(f_list, true_param) H1_noise_psd = noise_dict['H1'][freqs>fmin] +H1_psd = psd_dict['H1'][freqs>fmin] H1_data = H1_noise_psd + H1_signal L1_signal = gen_waveform_L1(f_list, true_param) L1_noise_psd = noise_dict['L1'][freqs>fmin] +L1_psd = psd_dict['L1'][freqs>fmin] L1_data = L1_noise_psd + L1_signal ref_param = jnp.array([Mc, eta, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle, ra, dec]) data_list = [H1_data, L1_data] -psd_list = [psd_dict['H1'], psd_dict['L1']] +psd_list = [H1_psd, L1_psd] response_list = [H1_response, L1_response] +def LogLikelihood(theta): + theta = jnp.array(theta) + # theta = theta.at[1].set(theta[1]/(1+theta[1])**2) # convert q to eta + # theta = theta.at[7].set(jnp.arccos(theta[7])) # convert cos iota to iota + # theta = theta.at[10].set(jnp.arcsin(theta[10])) # convert cos dec to dec + theta_waveform = theta[:8] + theta_waveform = theta_waveform.at[5].set(0) + ra = theta[9] + dec = theta[10] + hp_test, hc_test = gen_IMRPhenomD_polar(f_list, theta_waveform, f_ref) + align_time = jnp.exp(-1j*2*jnp.pi*f_list*(epoch+theta[5])) + h_test_H1 = H1_response(f_list, hp_test, hc_test, ra, dec, gmst, theta[8]) * align_time + h_test_L1 = L1_response(f_list, hp_test, hc_test, ra, dec, gmst, theta[8]) * align_time + df = f_list[1] - f_list[0] + match_filter_SNR_H1 = 4*jnp.sum((jnp.conj(h_test_H1)*H1_data)/H1_psd*df).real + match_filter_SNR_L1 = 4*jnp.sum((jnp.conj(h_test_L1)*L1_data)/L1_psd*df).real + optimal_SNR_H1 = 4*jnp.sum((jnp.conj(h_test_H1)*h_test_H1)/H1_psd*df).real + optimal_SNR_L1 = 4*jnp.sum((jnp.conj(h_test_L1)*h_test_L1)/L1_psd*df).real + + return (match_filter_SNR_H1-optimal_SNR_H1/2) + (match_filter_SNR_L1-optimal_SNR_L1/2) + + logL = make_heterodyne_likelihood_mutliple_detector(data_list, psd_list, response_list, gen_IMRPhenomD_polar, ref_param, f_list, gmst, epoch, f_ref, heterodyne_bins) # Fetch sampler parameters, construct sampler and initial guess @@ -183,15 +214,15 @@ def gen_waveform_L1(f, theta): stepsize = args['stepsize'] -guess_param = np.array(jnp.repeat(true_param[None,:],int(n_chains),axis=0)*(1+0.1*jax.random.normal(jax.random.PRNGKey(seed+98127),shape=(int(n_chains),n_dim)))) -guess_param[guess_param[:,1]>0.25,1] = 0.249 +guess_param = np.array(jnp.repeat(true_param_trans[None,:],int(n_chains),axis=0)*(1+0.1*jax.random.normal(jax.random.PRNGKey(seed+98127),shape=(int(n_chains),n_dim)))) +guess_param[guess_param[:,1]>1,1] = 1 print("Preparing RNG keys") rng_key_set = initialize_rng_keys(n_chains, seed=seed) print("Initializing MCMC model and normalizing flow model.") -prior_range = jnp.array([[10,80],[0.125,1.0],[-0.5,0.5],[-0.5,0.5],[0,2000],[-0.5,0.5],[0,2*np.pi],[-1,1],[0,np.pi],[0,2*np.pi],[-1,1]]) +prior_range = jnp.array([[10,80],[0.125,1.0],[-0.5,0.5],[-0.5,0.5],[100,2000],[-0.5,0.5],[0,2*np.pi],[-1,1],[0,np.pi],[0,2*np.pi],[-1,1]]) initial_position = jax.random.uniform(rng_key_set[0], shape=(int(n_chains), n_dim)) * 1 From a302ce3fa463f702ff1407e2c18290e45b406a83 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Wed, 4 Jan 2023 18:38:43 -0500 Subject: [PATCH 161/300] Add debug script --- .../AnalyzeInjection.ipynb | 134 +++++++ .../Injection_withParser.py | 43 ++- .../Injection_withParser_debug.py | 331 ++++++++++++++++++ .../gen_injection_config.py | 11 +- example/ParameterEstimation/make_ppPlot.py | 6 +- 5 files changed, 505 insertions(+), 20 deletions(-) create mode 100644 example/ParameterEstimation/AnalyzeInjection.ipynb create mode 100644 example/ParameterEstimation/Injection_withParser_debug.py diff --git a/example/ParameterEstimation/AnalyzeInjection.ipynb b/example/ParameterEstimation/AnalyzeInjection.ipynb new file mode 100644 index 00000000..38f2497c --- /dev/null +++ b/example/ParameterEstimation/AnalyzeInjection.ipynb @@ -0,0 +1,134 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import arviz as az\n", + "import matplotlib.pyplot as plt\n", + "from scipy.interpolate import interp1d\n", + "import corner\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 335, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 3.40979325e+01 2.45388759e-01 -1.00265932e-01 2.25096589e-01\n", + " 7.56303706e+02 8.18942360e-02 1.19036560e+00 1.31411074e+00\n", + " 2.02418843e+00 3.65204542e+00 -6.61721882e-01]\n", + "0.00428 0.00176 804.5702769247524\n", + "806.7223827358287\n" + ] + } + ], + "source": [ + "data_path = '/mnt/ceph/users/wwong/GWProject/JaxGW/RealtimePE/ppPlots/injection_203.npz'\n", + "data = np.load(data_path)\n", + "chains = data['chains']\n", + "true_param = data['true_param']\n", + "print(true_param)\n", + "print(data['local_accs'].mean(),data['global_accs'].mean(),data['log_prob'].mean())\n", + "print(data['true_log_prob'])" + ] + }, + { + "cell_type": "code", + "execution_count": 336, + "metadata": {}, + "outputs": [], + "source": [ + "q_axis = np.linspace(0.1,1,10000)\n", + "eta = q_axis/(1+q_axis)**2\n", + "q_interp = interp1d(eta,q_axis)\n", + "true_param[1] = q_interp(true_param[1])\n", + "true_param[7] = np.cos(true_param[7])\n", + "true_param[10] = np.sin(true_param[10])" + ] + }, + { + "cell_type": "code", + "execution_count": 337, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.2538761253371305" + ] + }, + "execution_count": 337, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "true_param[7]" + ] + }, + { + "cell_type": "code", + "execution_count": 338, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "

    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig = corner.corner(chains.reshape(-1,11)[::10],truths=true_param)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10.4 ('GW')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.4" + }, + "vscode": { + "interpreter": { + "hash": "c1b26637a459b71d5a98be81c2c552e2aef4ac924b44e1d1dcc4c383679c0a72" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/example/ParameterEstimation/Injection_withParser.py b/example/ParameterEstimation/Injection_withParser.py index b3ae1f2d..17f2fdc9 100644 --- a/example/ParameterEstimation/Injection_withParser.py +++ b/example/ParameterEstimation/Injection_withParser.py @@ -15,7 +15,7 @@ from jaxgw.PE.generate_noise import generate_noise from flowMC.nfmodel.rqSpline import RQSpline -from flowMC.sampler.MALA import MALA +from flowMC.sampler.MALA import MALA, mala_sampler_autotune from flowMC.sampler.Sampler import Sampler from flowMC.utils.PRNG_keys import initialize_rng_keys from flowMC.nfmodel.utils import * @@ -121,6 +121,8 @@ H1_response = make_detector_response(H1[0], H1[1]) L1 = get_L1() L1_response = make_detector_response(L1[0], L1[1]) +V1 = get_V1() +V1_response = make_detector_response(V1[0], V1[1]) f_ref = 30.0 trigger_time = 1126259462.4 @@ -145,6 +147,14 @@ def gen_waveform_L1(f, theta): hp, hc = gen_IMRPhenomD_polar(f, theta_waveform, f_ref) return L1_response(f, hp, hc, ra, dec, gmst, theta[8]) * jnp.exp(-1j*2*jnp.pi*f*(epoch+theta[5])) +def gen_waveform_V1(f, theta): + theta_waveform = theta[:8] + theta_waveform = theta_waveform.at[5].set(0) + ra = theta[9] + dec = theta[10] + hp, hc = gen_IMRPhenomD_polar(f, theta_waveform, f_ref) + return V1_response(f, hp, hc, ra, dec, gmst, theta[8]) * jnp.exp(-1j*2*jnp.pi*f*(epoch+theta[5])) + true_param = jnp.array([Mc, eta, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle, ra, dec]) from scipy.interpolate import interp1d @@ -166,11 +176,16 @@ def gen_waveform_L1(f, theta): L1_psd = psd_dict['L1'][freqs>fmin] L1_data = L1_noise_psd + L1_signal +V1_signal = gen_waveform_V1(f_list, true_param) +V1_noise_psd = noise_dict['V1'][freqs>fmin] +V1_psd = psd_dict['V1'][freqs>fmin] +V1_data = V1_noise_psd + V1_signal + ref_param = jnp.array([Mc, eta, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle, ra, dec]) -data_list = [H1_data, L1_data] -psd_list = [H1_psd, L1_psd] -response_list = [H1_response, L1_response] +data_list = [H1_data, L1_data, V1_data] +psd_list = [H1_psd, L1_psd, V1_psd] +response_list = [H1_response, L1_response, V1_response] def LogLikelihood(theta): theta = jnp.array(theta) @@ -185,13 +200,16 @@ def LogLikelihood(theta): align_time = jnp.exp(-1j*2*jnp.pi*f_list*(epoch+theta[5])) h_test_H1 = H1_response(f_list, hp_test, hc_test, ra, dec, gmst, theta[8]) * align_time h_test_L1 = L1_response(f_list, hp_test, hc_test, ra, dec, gmst, theta[8]) * align_time + h_test_V1 = V1_response(f_list, hp_test, hc_test, ra, dec, gmst, theta[8]) * align_time df = f_list[1] - f_list[0] match_filter_SNR_H1 = 4*jnp.sum((jnp.conj(h_test_H1)*H1_data)/H1_psd*df).real match_filter_SNR_L1 = 4*jnp.sum((jnp.conj(h_test_L1)*L1_data)/L1_psd*df).real + match_filter_SNR_V1 = 4*jnp.sum((jnp.conj(h_test_V1)*V1_data)/V1_psd*df).real optimal_SNR_H1 = 4*jnp.sum((jnp.conj(h_test_H1)*h_test_H1)/H1_psd*df).real optimal_SNR_L1 = 4*jnp.sum((jnp.conj(h_test_L1)*h_test_L1)/L1_psd*df).real + optimal_SNR_V1 = 4*jnp.sum((jnp.conj(h_test_V1)*h_test_V1)/V1_psd*df).real - return (match_filter_SNR_H1-optimal_SNR_H1/2) + (match_filter_SNR_L1-optimal_SNR_L1/2) + return (match_filter_SNR_H1-optimal_SNR_H1/2) + (match_filter_SNR_L1-optimal_SNR_L1/2) + (match_filter_SNR_V1-optimal_SNR_V1/2) logL = make_heterodyne_likelihood_mutliple_detector(data_list, psd_list, response_list, gen_IMRPhenomD_polar, ref_param, f_list, gmst, epoch, f_ref, heterodyne_bins) @@ -222,7 +240,7 @@ def LogLikelihood(theta): print("Initializing MCMC model and normalizing flow model.") -prior_range = jnp.array([[10,80],[0.125,1.0],[-0.5,0.5],[-0.5,0.5],[100,2000],[-0.5,0.5],[0,2*np.pi],[-1,1],[0,np.pi],[0,2*np.pi],[-1,1]]) +prior_range = jnp.array([[10,80],[0.125,1.0],[-0.5,0.5],[-0.5,0.5],[300,2000],[-0.5,0.5],[0,2*np.pi],[-1,1],[0,np.pi],[0,2*np.pi],[-1,1]]) initial_position = jax.random.uniform(rng_key_set[0], shape=(int(n_chains), n_dim)) * 1 @@ -237,7 +255,7 @@ def LogLikelihood(theta): from astropy.cosmology import Planck18 as cosmo -z = np.linspace(0.002,3,10000) +z = np.linspace(0.01,0.4,10000) dL = cosmo.luminosity_distance(z).value dVdz = cosmo.differential_comoving_volume(z).value @@ -246,7 +264,7 @@ def top_hat(x): for i in range(n_dim): output = jax.lax.cond(x[i]>=prior_range[i,0], lambda: output, lambda: -jnp.inf) output = jax.lax.cond(x[i]<=prior_range[i,1], lambda: output, lambda: -jnp.inf) - return output+jnp.log(jnp.interp(x[4],dL,dVdz)) + return output#+jnp.log(jnp.interp(x[4],dL,dVdz)) def posterior(theta): q = theta[1] @@ -259,7 +277,7 @@ def posterior(theta): return logL(theta) + prior -model = RQSpline(n_dim, 10, [128,128], 8) +model = RQSpline(n_dim, 5, [128,128], 8) print("Initializing sampler class") @@ -272,7 +290,7 @@ def posterior(theta): mass_matrix = np.abs(1./(jax.grad(logL)(true_param)+jax.grad(top_hat)(true_param)))*mass_matrix mass_matrix = jnp.array(mass_matrix) -local_sampler = MALA(posterior, True, {"step_size": mass_matrix*5e-1}) +local_sampler = MALA(posterior, True, {"step_size": mass_matrix*3e-3}) print("Running sampler") nf_sampler = Sampler( @@ -292,7 +310,8 @@ def posterior(theta): batch_size=batch_size, use_global=True, keep_quantile=0., - train_thinning = 40 + train_thinning = 40, + local_autotune=mala_sampler_autotune ) nf_sampler.sample(initial_position) @@ -309,4 +328,4 @@ def posterior(theta): output_path = args['output_path'] downsample_factor = args['downsample_factor'] -np.savez(args['output_path'], chains=chains[:,::downsample_factor], log_prob=log_prob[:,::downsample_factor], local_accs=local_accs[:,::downsample_factor], global_accs=global_accs[:,::downsample_factor], loss_vals=loss_vals, labels=labels, true_param=true_param) +np.savez(args['output_path'], chains=chains[:,::downsample_factor], log_prob=log_prob[:,::downsample_factor], local_accs=local_accs[:,::downsample_factor], global_accs=global_accs[:,::downsample_factor], loss_vals=loss_vals, labels=labels, true_param=true_param, true_log_prob=LogLikelihood(true_param)) diff --git a/example/ParameterEstimation/Injection_withParser_debug.py b/example/ParameterEstimation/Injection_withParser_debug.py new file mode 100644 index 00000000..0159a94b --- /dev/null +++ b/example/ParameterEstimation/Injection_withParser_debug.py @@ -0,0 +1,331 @@ +# Import packages +import lalsimulation as lalsim +import numpy as np +import jax.numpy as jnp +import jax +from lal import GreenwichMeanSiderealTime + + +# from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar +from ripple import ms_to_Mc_eta +from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar +from jaxgw.PE.detector_preset import * +from jaxgw.PE.heterodyneLikelihood import make_heterodyne_likelihood_mutliple_detector +from jaxgw.PE.detector_projection import make_detector_response +from jaxgw.PE.generate_noise import generate_noise + +from flowMC.nfmodel.rqSpline import RQSpline +from flowMC.sampler.MALA import MALA, mala_sampler_autotune +from flowMC.sampler.Sampler import Sampler +from flowMC.utils.PRNG_keys import initialize_rng_keys +from flowMC.nfmodel.utils import * + +import argparse +import yaml + +from tqdm import tqdm +from functools import partialmethod + +import sys +sys.path.append('/mnt/home/wwong/GWProject/JaxGW') + +parser = argparse.ArgumentParser(description='Injection test') + +parser.add_argument('--config', type=str, default='config.yaml', help='config file') + +# Add noise parameters to parser +parser.add_argument('--seed', type=int, default=None, help='seed for random number generator') +parser.add_argument('--f_sampling', type=int, default=None, help='sampling frequency') +parser.add_argument('--duration', type=int, default=None, help='duration of the data') +parser.add_argument('--fmin', type=float, default=None, help='minimum frequency') +parser.add_argument('--ifos', nargs='+', default=None, help='list of detectors') + +# Add injection parameters to parser +parser.add_argument('--m1', type=float, default=None, help='mass of the first component') +parser.add_argument('--m2', type=float, default=None, help='mass of the second component') +parser.add_argument('--chi1', type=float, default=None, help='dimensionless spin of the first component') +parser.add_argument('--chi2', type=float, default=None, help='dimensionless spin of the second component') +parser.add_argument('--dist_mpc', type=float, default=None, help='distance in megaparsecs') +parser.add_argument('--tc', type=float, default=None, help='coalescence time') +parser.add_argument('--phic', type=float, default=None, help='phase of coalescence') +parser.add_argument('--inclination', type=float, default=None, help='inclination angle') +parser.add_argument('--polarization_angle', type=float, default=None, help='polarization angle') +parser.add_argument('--ra', type=float, default=None, help='right ascension') +parser.add_argument('--dec', type=float, default=None, help='declination') +parser.add_argument('--heterodyne_bins', type=int, default=101, help='number of bins for heterodyne likelihood') + +# Add sampler parameters to parser + +parser.add_argument('--n_dim', type=int, default=None, help='number of parameters') +parser.add_argument('--n_chains', type=int, default=None, help='number of chains') +parser.add_argument('--n_loop_training', type=int, default=None, help='number of training loops') +parser.add_argument('--n_loop_production', type=int, default=None, help='number of production loops') +parser.add_argument('--n_local_steps', type=int, default=None, help='number of local steps') +parser.add_argument('--n_global_steps', type=int, default=None, help='number of global steps') +parser.add_argument('--learning_rate', type=float, default=None, help='learning rate') +parser.add_argument('--max_samples', type=int, default=None, help='maximum number of samples') +parser.add_argument('--momentum', type=float, default=None, help='momentum during training') +parser.add_argument('--num_epochs', type=int, default=None, help='number of epochs') +parser.add_argument('--batch_size', type=int, default=None, help='batch size') +parser.add_argument('--stepsize', type=float, default=None, help='stepsize for Local sampler') + +# Add output parameters to parser + +parser.add_argument('--output_path', type=str, default=None, help='output file path') +parser.add_argument('--downsample_factor', type=int, default=1, help='downsample factor') + +# parser + +args = parser.parse_args() +opt = vars(args) +args = yaml.load(open(opt['config'], 'r'), Loader=yaml.FullLoader) +opt.update(args) +args = opt + +# Fetch noise parameters + +print("Constructing detectors") +print("Making noises") + +seed = args['seed'] +f_sampling = args['f_sampling'] +duration = args['duration'] +fmin = args['fmin'] +ifos = args['ifos'] + + +freqs, psd_dict, noise_dict = generate_noise(seed+1234, f_sampling, duration, fmin, ifos) + + +# Fetch injection parameters and inject signal + +print("Injection signals") + +m1 = args['m1'] +m2 = args['m2'] +chi1 = args['chi1'] +chi2 = args['chi2'] +dist_mpc = args['dist_mpc'] +tc = args['tc'] +phic = args['phic'] +inclination = args['inclination'] +polarization_angle = args['polarization_angle'] +ra = args['ra'] +dec = args['dec'] + +Mc, eta = ms_to_Mc_eta(jnp.array([m1, m2])) + +heterodyne_bins = args['heterodyne_bins'] + +H1 = get_H1() +H1_response = make_detector_response(H1[0], H1[1]) +L1 = get_L1() +L1_response = make_detector_response(L1[0], L1[1]) +V1 = get_V1() +V1_response = make_detector_response(V1[0], V1[1]) + +f_ref = 30.0 +trigger_time = 1126259462.4 +post_trigger_duration = 2 +epoch = duration - post_trigger_duration +gmst = GreenwichMeanSiderealTime(trigger_time) + + +def gen_waveform_H1(f, theta): + theta_waveform = theta[:8] + theta_waveform = theta_waveform.at[5].set(0) + ra = theta[9] + dec = theta[10] + hp, hc = gen_IMRPhenomD_polar(f, theta_waveform, f_ref) + return H1_response(f, hp, hc, ra, dec, gmst , theta[8]) * jnp.exp(-1j*2*jnp.pi*f*(epoch+theta[5])) + +def gen_waveform_L1(f, theta): + theta_waveform = theta[:8] + theta_waveform = theta_waveform.at[5].set(0) + ra = theta[9] + dec = theta[10] + hp, hc = gen_IMRPhenomD_polar(f, theta_waveform, f_ref) + return L1_response(f, hp, hc, ra, dec, gmst, theta[8]) * jnp.exp(-1j*2*jnp.pi*f*(epoch+theta[5])) + +def gen_waveform_V1(f, theta): + theta_waveform = theta[:8] + theta_waveform = theta_waveform.at[5].set(0) + ra = theta[9] + dec = theta[10] + hp, hc = gen_IMRPhenomD_polar(f, theta_waveform, f_ref) + return V1_response(f, hp, hc, ra, dec, gmst, theta[8]) * jnp.exp(-1j*2*jnp.pi*f*(epoch+theta[5])) + +true_param = jnp.array([Mc, eta, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle, ra, dec]) + +from scipy.interpolate import interp1d +q_axis = np.linspace(0.1, 1.0, 10000) +eta_axis = q_axis/(1+q_axis)**2 +true_q = interp1d(eta_axis, q_axis)(eta) +cos_inclination = np.cos(inclination) +sin_dec = np.sin(dec) +true_param_trans = jnp.array([Mc, true_q, chi1, chi2, dist_mpc, tc, phic, cos_inclination, polarization_angle, ra, sin_dec]) + +f_list = freqs[freqs>fmin] +H1_signal = gen_waveform_H1(f_list, true_param) +H1_noise_psd = noise_dict['H1'][freqs>fmin] +H1_psd = psd_dict['H1'][freqs>fmin] +H1_data = H1_noise_psd + H1_signal + +L1_signal = gen_waveform_L1(f_list, true_param) +L1_noise_psd = noise_dict['L1'][freqs>fmin] +L1_psd = psd_dict['L1'][freqs>fmin] +L1_data = L1_noise_psd + L1_signal + +V1_signal = gen_waveform_V1(f_list, true_param) +V1_noise_psd = noise_dict['V1'][freqs>fmin] +V1_psd = psd_dict['V1'][freqs>fmin] +V1_data = V1_noise_psd + V1_signal + +ref_param = jnp.array([Mc, eta, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle, ra, dec]) + +data_list = [H1_data, L1_data, V1_data] +psd_list = [H1_psd, L1_psd, V1_psd] +response_list = [H1_response, L1_response, V1_response] + +def LogLikelihood(theta): + theta = jnp.array(theta) + # theta = theta.at[1].set(theta[1]/(1+theta[1])**2) # convert q to eta + # theta = theta.at[7].set(jnp.arccos(theta[7])) # convert cos iota to iota + # theta = theta.at[10].set(jnp.arcsin(theta[10])) # convert cos dec to dec + theta_waveform = theta[:8] + theta_waveform = theta_waveform.at[5].set(0) + ra = theta[9] + dec = theta[10] + hp_test, hc_test = gen_IMRPhenomD_polar(f_list, theta_waveform, f_ref) + align_time = jnp.exp(-1j*2*jnp.pi*f_list*(epoch+theta[5])) + h_test_H1 = H1_response(f_list, hp_test, hc_test, ra, dec, gmst, theta[8]) * align_time + h_test_L1 = L1_response(f_list, hp_test, hc_test, ra, dec, gmst, theta[8]) * align_time + h_test_V1 = V1_response(f_list, hp_test, hc_test, ra, dec, gmst, theta[8]) * align_time + df = f_list[1] - f_list[0] + match_filter_SNR_H1 = 4*jnp.sum((jnp.conj(h_test_H1)*H1_data)/H1_psd*df).real + match_filter_SNR_L1 = 4*jnp.sum((jnp.conj(h_test_L1)*L1_data)/L1_psd*df).real + match_filter_SNR_V1 = 4*jnp.sum((jnp.conj(h_test_V1)*V1_data)/V1_psd*df).real + optimal_SNR_H1 = 4*jnp.sum((jnp.conj(h_test_H1)*h_test_H1)/H1_psd*df).real + optimal_SNR_L1 = 4*jnp.sum((jnp.conj(h_test_L1)*h_test_L1)/L1_psd*df).real + optimal_SNR_V1 = 4*jnp.sum((jnp.conj(h_test_V1)*h_test_V1)/V1_psd*df).real + + return (match_filter_SNR_H1-optimal_SNR_H1/2) + (match_filter_SNR_L1-optimal_SNR_L1/2) + (match_filter_SNR_V1-optimal_SNR_V1/2) + + +logL = make_heterodyne_likelihood_mutliple_detector(data_list, psd_list, response_list, gen_IMRPhenomD_polar, ref_param, f_list, gmst, epoch, f_ref, heterodyne_bins) + +# Fetch sampler parameters, construct sampler and initial guess + +print("Making sampler") + +n_dim = args['n_dim'] +n_chains = args['n_chains'] +n_loop_training = args['n_loop_training'] +n_loop_production = args['n_loop_production'] +n_local_steps = args['n_local_steps'] +n_global_steps = args['n_global_steps'] +learning_rate = args['learning_rate'] +max_samples = args['max_samples'] +momentum = args['momentum'] +num_epochs = args['num_epochs'] +batch_size = args['batch_size'] +stepsize = args['stepsize'] + + +guess_param = np.array(jnp.repeat(true_param_trans[None,:],int(n_chains),axis=0)*(1+0.1*jax.random.normal(jax.random.PRNGKey(seed+98127),shape=(int(n_chains),n_dim)))) +guess_param[guess_param[:,1]>1,1] = 1 + +print("Preparing RNG keys") +rng_key_set = initialize_rng_keys(n_chains, seed=seed) + +print("Initializing MCMC model and normalizing flow model.") + +prior_range = jnp.array([[10,80],[0.125,1.0],[-0.5,0.5],[-0.5,0.5],[300,2000],[-0.5,0.5],[0,2*np.pi],[-1,1],[0,np.pi],[0,2*np.pi],[-1,1]]) + + +initial_position = jax.random.uniform(rng_key_set[0], shape=(int(n_chains), n_dim)) * 1 +for i in range(n_dim): + initial_position = initial_position.at[:,i].set(initial_position[:,i]*(prior_range[i,1]-prior_range[i,0])+prior_range[i,0]) + +from ripple import Mc_eta_to_ms +m1,m2 = jax.vmap(Mc_eta_to_ms)(guess_param[:,:2]) +q = m2/m1 +initial_position = initial_position.at[:,0].set(guess_param[:,0]) +initial_position = initial_position.at[:,5].set(guess_param[:,5]) + +from astropy.cosmology import Planck18 as cosmo + +z = np.linspace(0.01,0.4,10000) +dL = cosmo.luminosity_distance(z).value +dVdz = cosmo.differential_comoving_volume(z).value + +def top_hat(x): + output = 0. + for i in range(n_dim): + output = jax.lax.cond(x[i]>=prior_range[i,0], lambda: output, lambda: -jnp.inf) + output = jax.lax.cond(x[i]<=prior_range[i,1], lambda: output, lambda: -jnp.inf) + return output+jnp.log(jnp.interp(x[4],dL,dVdz)) + +def posterior(theta): + q = theta[1] + iota = jnp.arccos(theta[7]) + dec = jnp.arcsin(theta[10]) + prior = top_hat(theta) + theta = theta.at[1].set(q/(1+q)**2) # convert q to eta + theta = theta.at[7].set(iota) # convert cos iota to iota + theta = theta.at[10].set(dec) # convert cos dec to dec + return logL(theta) + prior + + +model = RQSpline(n_dim, 5, [128,128], 8) + + +print("Initializing sampler class") + +posterior = posterior +dposterior = jax.grad(posterior) + + +mass_matrix = np.eye(n_dim) +mass_matrix = np.abs(1./(jax.grad(logL)(true_param)+jax.grad(top_hat)(true_param)))*mass_matrix +mass_matrix = jnp.array(mass_matrix) + +local_sampler = MALA(posterior, True, {"step_size": mass_matrix*3e-1}) +print("Running sampler") + +nf_sampler = Sampler( + n_dim, + rng_key_set, + local_sampler, + posterior, + model, + n_loop_training=n_loop_training, + n_loop_production = n_loop_production, + n_local_steps=n_local_steps, + n_global_steps=n_global_steps, + n_chains=n_chains, + n_epochs=num_epochs, + learning_rate=learning_rate, + momentum=momentum, + batch_size=batch_size, + use_global=True, + keep_quantile=0., + train_thinning = 40, + local_autotune=mala_sampler_autotune +) + +nf_sampler.sample(initial_position) + +labels = ['Mc', 'eta', 'chi1', 'chi2', 'dist_mpc', 'tc', 'phic', 'cos_inclination', 'polarization_angle', 'ra', 'sin_dec'] + +print("Saving to output") + +chains, log_prob, local_accs, global_accs, loss_vals = nf_sampler.get_sampler_state(training=True).values() +chains, log_prob, local_accs, global_accs = nf_sampler.get_sampler_state().values() + +# Fetch output parameters + +output_path = args['output_path'] +downsample_factor = args['downsample_factor'] + +np.savez(args['output_path'], chains=chains[:,::downsample_factor], log_prob=log_prob[:,::downsample_factor], local_accs=local_accs[:,::downsample_factor], global_accs=global_accs[:,::downsample_factor], loss_vals=loss_vals, labels=labels, true_param=true_param, true_log_prob=LogLikelihood(true_param)) diff --git a/example/ParameterEstimation/gen_injection_config.py b/example/ParameterEstimation/gen_injection_config.py index 671fe23d..4af01123 100644 --- a/example/ParameterEstimation/gen_injection_config.py +++ b/example/ParameterEstimation/gen_injection_config.py @@ -1,6 +1,6 @@ import numpy as np -prior_range = np.array([[20,50],[20,50],[-0.5,0.5],[-0.5,0.5],[400,1000],[-0.5,0.5],[0,2*np.pi],[-1,1],[0,np.pi],[0,2*np.pi],[-1,1]]) +prior_range = np.array([[20,50],[20,50],[-0.5,0.5],[-0.5,0.5],[400,1000],[-0.1,0.1],[0.1,2*np.pi-0.1],[-0.8,0.8],[0.1,np.pi-0.1],[0.1,2*np.pi-0.1],[-0.8,0.8]]) N_config = 960 @@ -33,6 +33,7 @@ f.write('ifos:\n') f.write(' - H1\n') f.write(' - L1\n') + f.write(' - V1\n') f.write("m1: "+str(m1[i])+"\n") f.write("m2: "+str(m2[i])+"\n") @@ -49,14 +50,14 @@ f.write("n_dim: 11\n") f.write("n_chains: 1000\n") - f.write("n_loop_training: 40\n") - f.write("n_loop_production: 10\n") + f.write("n_loop_training: 5\n") + f.write("n_loop_production: 20\n") f.write("n_local_steps: 200\n") - f.write("n_global_steps: 100\n") + f.write("n_global_steps: 200\n") f.write("learning_rate: 0.001\n") f.write("max_samples: 50000\n") f.write("momentum: 0.9\n") - f.write("num_epochs: 60\n") + f.write("num_epochs: 240\n") f.write("batch_size: 50000\n") f.write("stepsize: 0.01\n") diff --git a/example/ParameterEstimation/make_ppPlot.py b/example/ParameterEstimation/make_ppPlot.py index 30dd4b19..b8fb7387 100644 --- a/example/ParameterEstimation/make_ppPlot.py +++ b/example/ParameterEstimation/make_ppPlot.py @@ -40,13 +40,13 @@ def compute_percentile_multimodal(value,data): return np.array(result), np.array(result_multimodal), true_param, mean_global_accs, mean_local_accs -directory = '/mnt/home/wwong/ceph/GWProject/JaxGW/RealtimePE/ppPlots/2to1/' +directory = '/mnt/home/wwong/ceph/GWProject/JaxGW/RealtimePE/ppPlots/'#balance_1001/' result = [] result_multimodal = [] true_param = [] mean_global_accs = [] mean_local_accs = [] -for i in range(960): +for i in range(256):#960): name = directory+'injection_'+str(i)+'.npz' local_result = get_all_quantile(name) result.append(local_result[0]) @@ -61,4 +61,4 @@ def compute_percentile_multimodal(value,data): mean_global_accs = np.stack(mean_global_accs) mean_local_accs = np.stack(mean_local_accs) -np.savez('/mnt/home/wwong/ceph/GWProject/JaxGW/RealtimePE/ppPlots/combined_quantile_2to1',result=result, result_multimodal=result_multimodal, true_param=true_param, mean_global_accs=mean_global_accs, mean_local_accs= mean_local_accs) +#np.savez('/mnt/home/wwong/ceph/GWProject/JaxGW/RealtimePE/ppPlots/combined_quantile_balance_1001',result=result, result_multimodal=result_multimodal, true_param=true_param, mean_global_accs=mean_global_accs, mean_local_accs= mean_local_accs) From 5d52e279291d04d23e0dd21ad349b861f5cd2c34 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Thu, 5 Jan 2023 14:16:20 -0500 Subject: [PATCH 162/300] Matching prior in inference to population improves the ppPlot --- .../Injection_withParser.py | 2 +- .../configs/injection_debug.yaml | 33 ++++++++++--------- .../gen_injection_config.py | 8 ++--- example/ParameterEstimation/make_ppPlot.py | 4 +-- 4 files changed, 24 insertions(+), 23 deletions(-) diff --git a/example/ParameterEstimation/Injection_withParser.py b/example/ParameterEstimation/Injection_withParser.py index 17f2fdc9..5bb37f65 100644 --- a/example/ParameterEstimation/Injection_withParser.py +++ b/example/ParameterEstimation/Injection_withParser.py @@ -277,7 +277,7 @@ def posterior(theta): return logL(theta) + prior -model = RQSpline(n_dim, 5, [128,128], 8) +model = RQSpline(n_dim, 10, [128,128], 8) print("Initializing sampler class") diff --git a/example/ParameterEstimation/configs/injection_debug.yaml b/example/ParameterEstimation/configs/injection_debug.yaml index 79312f0f..569de250 100644 --- a/example/ParameterEstimation/configs/injection_debug.yaml +++ b/example/ParameterEstimation/configs/injection_debug.yaml @@ -1,33 +1,34 @@ -output_path: /mnt/home/wwong/ceph/GWProject/JaxGW/RealtimePE/ppPlots/injection_26 +output_path: /mnt/home/wwong/ceph/GWProject/JaxGW/RealtimePE/ppPlots/injection_203 downsample_factor: 10 -seed: 9866 +seed: 6199 f_sampling: 2048 duration: 16 fmin: 30 ifos: - H1 - L1 -m1: 45.83666518760738 -m2: 43.85387081147894 -chi1: -0.3884050202764605 -chi2: -0.01385254347363285 -dist_mpc: 411.78955142985495 -tc: 0.14209878911731888 -phic: 4.037087228130469 -inclination: 1.4931823395002113 -polarization_angle: 2.6636577704157407 -ra: 6.204781034047059 -dec: 0.24329325205940763 + - V1 +m1: 44.9874933444562 +m2: 34.22893332856121 +chi1: -0.10026593180890286 +chi2: 0.2250965892530974 +dist_mpc: 756.3037060150606 +tc: 0.08189423602712018 +phic: 1.1903655975899228 +inclination: 1.3141107448156706 +polarization_angle: 2.0241884314944927 +ra: 3.6520454167972867 +dec: -0.6617218817600116 heterodyne_bins: 1001 n_dim: 11 n_chains: 1000 -n_loop_training: 40 -n_loop_production: 10 +n_loop_training: 5 +n_loop_production: 5 n_local_steps: 200 n_global_steps: 200 learning_rate: 0.001 max_samples: 50000 momentum: 0.9 -num_epochs: 60 +num_epochs: 240 batch_size: 50000 stepsize: 0.01 diff --git a/example/ParameterEstimation/gen_injection_config.py b/example/ParameterEstimation/gen_injection_config.py index 4af01123..50234a2a 100644 --- a/example/ParameterEstimation/gen_injection_config.py +++ b/example/ParameterEstimation/gen_injection_config.py @@ -1,12 +1,12 @@ import numpy as np -prior_range = np.array([[20,50],[20,50],[-0.5,0.5],[-0.5,0.5],[400,1000],[-0.1,0.1],[0.1,2*np.pi-0.1],[-0.8,0.8],[0.1,np.pi-0.1],[0.1,2*np.pi-0.1],[-0.8,0.8]]) +prior_range = np.array([[10,80],[0.125,1],[-0.5,0.5],[-0.5,0.5],[300,2000],[-0.5,0.5],[0,2*np.pi],[-1,1],[0,np.pi],[0,2*np.pi],[-1,1]]) N_config = 960 m1 = np.random.uniform(prior_range[0,0],prior_range[0,1],N_config) -m2 = np.random.uniform(prior_range[1,0],prior_range[1,1],N_config) -m2,m1 = np.sort([m1,m2],axis=0) +q = np.random.uniform(prior_range[1,0],prior_range[1,1],N_config) +m2 = m1*q chi1 = np.random.uniform(prior_range[2,0],prior_range[2,1],N_config) chi2 = np.random.uniform(prior_range[3,0],prior_range[3,1],N_config) dist_mpc = np.random.uniform(prior_range[4,0],prior_range[4,1],N_config) @@ -50,7 +50,7 @@ f.write("n_dim: 11\n") f.write("n_chains: 1000\n") - f.write("n_loop_training: 5\n") + f.write("n_loop_training: 20\n") f.write("n_loop_production: 20\n") f.write("n_local_steps: 200\n") f.write("n_global_steps: 200\n") diff --git a/example/ParameterEstimation/make_ppPlot.py b/example/ParameterEstimation/make_ppPlot.py index b8fb7387..c1c950e5 100644 --- a/example/ParameterEstimation/make_ppPlot.py +++ b/example/ParameterEstimation/make_ppPlot.py @@ -46,7 +46,7 @@ def compute_percentile_multimodal(value,data): true_param = [] mean_global_accs = [] mean_local_accs = [] -for i in range(256):#960): +for i in range(192):#960): name = directory+'injection_'+str(i)+'.npz' local_result = get_all_quantile(name) result.append(local_result[0]) @@ -61,4 +61,4 @@ def compute_percentile_multimodal(value,data): mean_global_accs = np.stack(mean_global_accs) mean_local_accs = np.stack(mean_local_accs) -#np.savez('/mnt/home/wwong/ceph/GWProject/JaxGW/RealtimePE/ppPlots/combined_quantile_balance_1001',result=result, result_multimodal=result_multimodal, true_param=true_param, mean_global_accs=mean_global_accs, mean_local_accs= mean_local_accs) +np.savez('/mnt/home/wwong/ceph/GWProject/JaxGW/RealtimePE/ppPlots/combined_quantile_balance_LVK',result=result, result_multimodal=result_multimodal, true_param=true_param, mean_global_accs=mean_global_accs, mean_local_accs= mean_local_accs) From c57b5ff338b64460feb604fbedc3c3809a74a983 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Tue, 10 Jan 2023 14:04:04 -0500 Subject: [PATCH 163/300] pp plot working --- .../AnalyzeInjection.ipynb | 216 ++++++++++++++++-- example/ParameterEstimation/GW150914.py | 1 - .../Injection_withParser.py | 2 +- .../gen_injection_config.py | 16 +- example/ParameterEstimation/make_ppPlot.py | 16 +- 5 files changed, 219 insertions(+), 32 deletions(-) diff --git a/example/ParameterEstimation/AnalyzeInjection.ipynb b/example/ParameterEstimation/AnalyzeInjection.ipynb index 38f2497c..d76e567b 100644 --- a/example/ParameterEstimation/AnalyzeInjection.ipynb +++ b/example/ParameterEstimation/AnalyzeInjection.ipynb @@ -2,37 +2,70 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import arviz as az\n", "import matplotlib.pyplot as plt\n", + "import matplotlib as mpl\n", "from scipy.interpolate import interp1d\n", + "from scipy.stats import norm, uniform\n", "import corner\n", - "%matplotlib inline" + "%matplotlib inline\n", + "params = {\n", + " \"font.size\": 18,\n", + " \"legend.fontsize\": 18,\n", + " \"legend.frameon\": False,\n", + " \"axes.labelsize\": 18,\n", + " \"axes.titlesize\": 18,\n", + " \"xtick.labelsize\": 18,\n", + " \"ytick.labelsize\": 18,\n", + " \"figure.figsize\": (7, 5),\n", + " \"xtick.top\": True,\n", + " \"axes.unicode_minus\": False,\n", + " \"ytick.right\": True,\n", + " \"xtick.bottom\": True,\n", + " \"ytick.left\": True,\n", + " \"xtick.major.pad\": 8,\n", + " \"xtick.major.size\": 8,\n", + " \"xtick.minor.size\": 4,\n", + " \"ytick.major.size\": 8,\n", + " \"ytick.minor.size\": 4,\n", + " \"xtick.direction\": \"in\",\n", + " \"ytick.direction\": \"in\",\n", + " \"axes.linewidth\": 1.5,\n", + " \"text.usetex\": False,\n", + " \"font.family\": \"serif\",\n", + " \"font.serif\": \"cmr10\",\n", + " \"mathtext.fontset\": \"cm\",\n", + " \"axes.formatter.use_mathtext\": True, # needed when using cm=cmr10 for normal text\n", + "}\n", + "\n", + "\n", + "mpl.rcParams.update(params)\n" ] }, { "cell_type": "code", - "execution_count": 335, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[ 3.40979325e+01 2.45388759e-01 -1.00265932e-01 2.25096589e-01\n", - " 7.56303706e+02 8.18942360e-02 1.19036560e+00 1.31411074e+00\n", - " 2.02418843e+00 3.65204542e+00 -6.61721882e-01]\n", - "0.00428 0.00176 804.5702769247524\n", - "806.7223827358287\n" + "[ 1.32596611e+01 2.23837734e-01 -2.57752574e-01 1.90366052e-01\n", + " 1.05185065e+03 2.45513561e-01 5.73357951e+00 4.61125436e-01\n", + " 2.84415670e+00 3.94116461e+00 1.39966765e-02]\n", + "0.3293969849246231 0.3485075376884422 213.68014348997855\n", + "216.8159238593497\n" ] } ], "source": [ - "data_path = '/mnt/ceph/users/wwong/GWProject/JaxGW/RealtimePE/ppPlots/injection_203.npz'\n", + "data_path = '/mnt/ceph/users/wwong/GWProject/JaxGW/RealtimePE/ppPlots/injection_0.npz'\n", "data = np.load(data_path)\n", "chains = data['chains']\n", "true_param = data['true_param']\n", @@ -43,7 +76,7 @@ }, { "cell_type": "code", - "execution_count": 336, + "execution_count": 206, "metadata": {}, "outputs": [], "source": [ @@ -57,34 +90,111 @@ }, { "cell_type": "code", - "execution_count": 337, + "execution_count": 207, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0\n", + "0.2804925\n", + "0.99045625\n", + "0.3117975\n", + "0.5384325\n", + "0.38452\n", + "0.42758\n", + "0.0627275\n", + "0.70695\n", + "0.990815\n", + "0.0104925\n" + ] + } + ], + "source": [ + "for i in range(11):print(np.where(chains[:,:,i]" ] }, - "execution_count": 337, - "metadata": {}, - "output_type": "execute_result" + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "true_param[7]" + "fig = corner.corner(chains.reshape(-1,11)[::10],truths=true_param)" ] }, { "cell_type": "code", - "execution_count": 338, + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "ppPlot_data = np.load('/mnt/home/wwong/ceph/GWProject/JaxGW/RealtimePE/ppPlots/combined_quantile_balance_LVK.npz')\n", + "result = ppPlot_data['result']\n", + "result_multimodal = ppPlot_data['result_multimodal']\n", + "true_param = ppPlot_data['true_param']\n", + "mean_global_accs = ppPlot_data['mean_global_accs']\n", + "mean_local_accs = ppPlot_data['mean_local_accs']" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "def makeCumulativeHist(data):\n", + " h = np.histogram(data,bins=100,range=(0,1),density=True)\n", + " return np.cumsum(h[0])/100.\n", + "\n", + "N = 10000\n", + "uniform_data = np.random.uniform(size=(N,1000))\n", + "cum_hist = []\n", + "for i in range(N):\n", + " cum_hist.append(makeCumulativeHist(uniform_data[i]))\n", + "cum_hist = np.array(cum_hist)\n", + "upper_quantile_array = []\n", + "lower_quantile_array = []\n", + "percentile = 0.05\n", + "for i in range(100):\n", + " upper_quantile_array.append(np.quantile(cum_hist[:,i],(1-percentile/2)))\n", + " lower_quantile_array.append(np.quantile(cum_hist[:,i],(percentile/2)))" + ] + }, + { + "cell_type": "code", + "execution_count": 112, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", "text/plain": [ - "
    " + "Text(0.5, 1.0, 'Combined p-value: 0.698')" + ] + }, + "execution_count": 112, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
    " ] }, "metadata": { @@ -94,9 +204,75 @@ } ], "source": [ - "fig = corner.corner(chains.reshape(-1,11)[::10],truths=true_param)" + "from scipy.stats import kstest\n", + "axis_labels= [r'$M_c$', r'$q$', r'$\\chi_1$', r'$\\chi_2$', r'$d_{\\rm{L}}$', r'$t_c$', r'$\\phi_c$', r'$\\cos{i}$', r'$\\psi$', r'$\\alpha$', r'$\\sin{\\delta}$']\n", + "\n", + "start = 0\n", + "count = 1200\n", + "plt.figure(figsize=(10,9))\n", + "bins = np.linspace(0,1,101)\n", + "bins = (bins[1:]+bins[:-1])/2\n", + "plt.fill_between(bins,lower_quantile_array,upper_quantile_array,alpha=0.5)\n", + "pvalues = []\n", + "for i in range(11):\n", + " pvalues.append(kstest(result[start:start+count,i],cdf=uniform(0,1).cdf).pvalue)\n", + " plt.plot(np.append(0,bins),np.append(0,makeCumulativeHist(result[start:start+count,i])), label=axis_labels[i]+\" \"+str(round(pvalues[-1],2)))\n", + "plt.legend(loc='upper left',fontsize=14)\n", + "plt.xlabel(r'Confidence Level')\n", + "plt.ylabel(r'Fraction of Samples with Confidence Level $\\leq$ x')\n", + "plt.title('Combined p-value: '+str(round(kstest(pvalues,cdf=uniform(0,1).cdf).pvalue,3)))" ] }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "KstestResult(statistic=0.36469647931640514, pvalue=0.08102220470941102)" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "kstest(pvalues,cdf=uniform.cdf)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "pvalues = []\n", + "for i in range(11):\n", + " pvalues.append(kstest(result[:,i],cdf=uniform(0,1).cdf).pvalue)\n", + "print(kstest(pvalues,uniform(0,1).cdf))" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "KstestResult(statistic=0.24252371720146648, pvalue=0.4652064216351961)" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, { "cell_type": "code", "execution_count": null, diff --git a/example/ParameterEstimation/GW150914.py b/example/ParameterEstimation/GW150914.py index 03250b13..280806c3 100644 --- a/example/ParameterEstimation/GW150914.py +++ b/example/ParameterEstimation/GW150914.py @@ -185,7 +185,6 @@ def posterior(theta): use_global=True, keep_quantile=0., train_thinning = 40 - ) nf_sampler.sample(initial_position) diff --git a/example/ParameterEstimation/Injection_withParser.py b/example/ParameterEstimation/Injection_withParser.py index 5bb37f65..5bcb39d3 100644 --- a/example/ParameterEstimation/Injection_withParser.py +++ b/example/ParameterEstimation/Injection_withParser.py @@ -240,7 +240,7 @@ def LogLikelihood(theta): print("Initializing MCMC model and normalizing flow model.") -prior_range = jnp.array([[10,80],[0.125,1.0],[-0.5,0.5],[-0.5,0.5],[300,2000],[-0.5,0.5],[0,2*np.pi],[-1,1],[0,np.pi],[0,2*np.pi],[-1,1]]) +prior_range = jnp.array([[10,50],[0.5,1.0],[-0.5,0.5],[-0.5,0.5],[300,2000],[-0.5,0.5],[0,2*np.pi],[-1,1],[0,np.pi],[0,2*np.pi],[-1,1]]) initial_position = jax.random.uniform(rng_key_set[0], shape=(int(n_chains), n_dim)) * 1 diff --git a/example/ParameterEstimation/gen_injection_config.py b/example/ParameterEstimation/gen_injection_config.py index 50234a2a..507151f8 100644 --- a/example/ParameterEstimation/gen_injection_config.py +++ b/example/ParameterEstimation/gen_injection_config.py @@ -1,12 +1,20 @@ import numpy as np -prior_range = np.array([[10,80],[0.125,1],[-0.5,0.5],[-0.5,0.5],[300,2000],[-0.5,0.5],[0,2*np.pi],[-1,1],[0,np.pi],[0,2*np.pi],[-1,1]]) +def Mc_eta_to_ms(m): + Mchirp, eta = m + M = Mchirp / (eta ** (3 / 5)) + m2 = (M - np.sqrt(M ** 2 - 4 * M ** 2 * eta)) / 2 + m1 = M - m2 + return m1, m2 -N_config = 960 +prior_range = np.array([[10,50],[0.5,1],[-0.5,0.5],[-0.5,0.5],[300,2000],[-0.5,0.5],[0,2*np.pi],[-1,1],[0,np.pi],[0,2*np.pi],[-1,1]]) -m1 = np.random.uniform(prior_range[0,0],prior_range[0,1],N_config) +N_config = 3000 + +mc = np.random.uniform(prior_range[0,0],prior_range[0,1],N_config) q = np.random.uniform(prior_range[1,0],prior_range[1,1],N_config) -m2 = m1*q +eta = q/(1+q)**2 +m1,m2 = Mc_eta_to_ms(np.stack([mc,eta])) chi1 = np.random.uniform(prior_range[2,0],prior_range[2,1],N_config) chi2 = np.random.uniform(prior_range[3,0],prior_range[3,1],N_config) dist_mpc = np.random.uniform(prior_range[4,0],prior_range[4,1],N_config) diff --git a/example/ParameterEstimation/make_ppPlot.py b/example/ParameterEstimation/make_ppPlot.py index c1c950e5..346cd074 100644 --- a/example/ParameterEstimation/make_ppPlot.py +++ b/example/ParameterEstimation/make_ppPlot.py @@ -17,9 +17,13 @@ def get_all_quantile(filename): true_param[10] = np.sin(true_param[10]) def compute_percentile(value,data): - f = lambda x : np.min(np.abs(az.hdi(data, hdi_prob=x)-value)) - result = minimize_scalar(f,bounds=[0.001,0.99],method='bounded') - return result.x + return np.where(data Date: Thu, 12 Jan 2023 15:25:52 -0500 Subject: [PATCH 164/300] Add Initial readme --- README.md | 19 +++++++++++++++++++ .../ParameterEstimation.md | 3 +++ 2 files changed, 22 insertions(+) create mode 100644 README.md create mode 100644 example/ParameterEstimation/ParameterEstimation.md diff --git a/README.md b/README.md new file mode 100644 index 00000000..8810de97 --- /dev/null +++ b/README.md @@ -0,0 +1,19 @@ +# JaxGW - A JAX-based gravitational-wave data analysis library + +In contrast to other library such as `lal`, `pycbc` and `Bilby`, `jaxGW` does +not just aim to give you a nuke button to do your analysis. Instead, we take a +more modular approach, providing thin wrappers around other workhorses such as +`ripple` and `flowMC`, as well as example oriented documentations. We still give +you the nuke button, but more importantly you should be able to build your own +nuke button. + +## Installation + +`pip install jaxGW` + +## Directory + +Parameter estimation examples are in `example/ParameterEstimation`. + +## Attribution + diff --git a/example/ParameterEstimation/ParameterEstimation.md b/example/ParameterEstimation/ParameterEstimation.md new file mode 100644 index 00000000..f595ee39 --- /dev/null +++ b/example/ParameterEstimation/ParameterEstimation.md @@ -0,0 +1,3 @@ +# Parameter Estimation with JaxGW + +In this subfolder we host the examples and tools for parameter estimation in gravitational-wave. \ No newline at end of file From eac4494dddffae0db43b42cea8bb3bac33fea096 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Thu, 12 Jan 2023 15:26:06 -0500 Subject: [PATCH 165/300] Update GW150914 and GW170817 --- example/ParameterEstimation/GW150914.py | 21 ++++++++------------- example/ParameterEstimation/GW170817.py | 10 +++++----- 2 files changed, 13 insertions(+), 18 deletions(-) diff --git a/example/ParameterEstimation/GW150914.py b/example/ParameterEstimation/GW150914.py index 280806c3..348f640f 100644 --- a/example/ParameterEstimation/GW150914.py +++ b/example/ParameterEstimation/GW150914.py @@ -9,8 +9,8 @@ from jaxgw.PE.detector_projection import make_detector_response from flowMC.nfmodel.rqSpline import RQSpline -from flowMC.sampler.MALA import make_mala_sampler, mala_sampler_autotune from flowMC.sampler.Sampler import Sampler +from flowMC.sampler.MALA import MALA from flowMC.utils.PRNG_keys import initialize_rng_keys from flowMC.nfmodel.utils import * @@ -86,8 +86,8 @@ def L1_LogLikelihood(theta): n_dim = 11 n_chains = 1000 -n_loop_training = 40 -n_loop_production = 20 +n_loop_training = 20 +n_loop_production = 10 n_local_steps = 200 n_global_steps = 200 learning_rate = 0.001 @@ -112,8 +112,6 @@ def L1_LogLikelihood(theta): print("Initializing MCMC model and normalizing flow model.") prior_range = jnp.array([[10,80],[0.125,1.0],[-1,1],[-1,1],[0,2000],[-0.1,0.1],[0,2*np.pi],[-1,1],[0,np.pi],[0,2*np.pi],[-1,1]]) -# prior_range = jnp.array([[10,80],[0.125,1.0],[-1,1],[-1,1],[0,2000],[-0.1,0.1],[0,2*np.pi],[0,np.pi],[0,np.pi],[0,2*np.pi],[-np.pi/2,np.pi/2]]) -#prior_range = jnp.array([[10,80],[0.0,0.25],[-1,1],[-1,1],[0,2000],[-0.1,0.1],[0,2*np.pi],[0,np.pi],[0,np.pi],[0,2*np.pi],[-np.pi/2,np.pi/2]]) initial_position = jax.random.uniform(rng_key_set[0], shape=(int(n_chains), n_dim)) * 1 @@ -125,8 +123,6 @@ def L1_LogLikelihood(theta): q = m2/m1 initial_position = initial_position.at[:,0].set(guess_param[:,0]) -# initial_position = initial_position.at[:,1].set(guess_param[:,1]) -# initial_position = initial_position.at[:,1].set(q) from astropy.cosmology import Planck18 as cosmo @@ -149,8 +145,7 @@ def posterior(theta): theta = theta.at[1].set(q/(1+q)**2) # convert q to eta theta = theta.at[7].set(iota) # convert cos iota to iota theta = theta.at[10].set(dec) # convert cos dec to dec - # jacobian = jnp.log((1/(1+q)**2)-2*q/(1+q)**3) - jnp.log(jnp.sin(iota)) - jnp.log(jnp.sin(dec)) - return logL(theta) + prior #+ jacobian + return logL(theta) + prior model = RQSpline(n_dim, 10, [128,128], 8) @@ -162,15 +157,13 @@ def posterior(theta): mass_matrix = mass_matrix.at[1,1].set(1e-3) mass_matrix = mass_matrix.at[5,5].set(1e-3) -local_sampler_caller = lambda x: make_mala_sampler(x, jit=True) -sampler_params = {'dt':mass_matrix*3e-3} +local_sampler = MALA(posterior, True, {"step_size": mass_matrix*3e-3}) print("Running sampler") nf_sampler = Sampler( n_dim, rng_key_set, - local_sampler_caller, - sampler_params, + local_sampler, posterior, model, n_loop_training=n_loop_training, @@ -188,3 +181,5 @@ def posterior(theta): ) nf_sampler.sample(initial_position) +chains, log_prob, local_accs, global_accs = nf_sampler.get_sampler_state().values() +np.savez('/mnt/home/wwong/ceph/GWProject/JaxGW/RealtimePE/GW150914.npz', chains=chains, log_prob=log_prob, local_accs=local_accs, global_accs=global_accs) \ No newline at end of file diff --git a/example/ParameterEstimation/GW170817.py b/example/ParameterEstimation/GW170817.py index 52078067..a836f6bc 100644 --- a/example/ParameterEstimation/GW170817.py +++ b/example/ParameterEstimation/GW170817.py @@ -15,7 +15,7 @@ from jaxgw.PE.detector_projection import make_detector_response from flowMC.nfmodel.rqSpline import RQSpline -from flowMC.sampler.MALA import make_mala_sampler +from flowMC.sampler.MALA import MALA from flowMC.sampler.Sampler import Sampler from flowMC.utils.PRNG_keys import initialize_rng_keys from flowMC.nfmodel.utils import * @@ -241,15 +241,13 @@ def posterior(theta): mass_matrix = mass_matrix.at[9,9].set(1e-2) mass_matrix = mass_matrix.at[10,10].set(1e-2) -local_sampler_caller = lambda x: make_mala_sampler(x, jit=True) -sampler_params = {'dt':mass_matrix*3e-2} +local_sampler = MALA(posterior, True, {"step_size": mass_matrix*3e-3}) print("Running sampler") nf_sampler = Sampler( n_dim, rng_key_set, - local_sampler_caller, - sampler_params, + local_sampler, posterior, model, n_loop_training=n_loop_training, @@ -267,3 +265,5 @@ def posterior(theta): ) nf_sampler.sample(initial_position) +chains, log_prob, local_accs, global_accs = nf_sampler.get_sampler_state().values() +b \ No newline at end of file From 2e01763d75af5228431152d366ede46a10e6227a Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Thu, 12 Jan 2023 15:27:18 -0500 Subject: [PATCH 166/300] minor update --- example/ParameterEstimation/GW170817.py | 1 - 1 file changed, 1 deletion(-) diff --git a/example/ParameterEstimation/GW170817.py b/example/ParameterEstimation/GW170817.py index a836f6bc..3bd233be 100644 --- a/example/ParameterEstimation/GW170817.py +++ b/example/ParameterEstimation/GW170817.py @@ -266,4 +266,3 @@ def posterior(theta): nf_sampler.sample(initial_position) chains, log_prob, local_accs, global_accs = nf_sampler.get_sampler_state().values() -b \ No newline at end of file From 8b51e6edd049b4bd832cdc7db5a12734ca8e0528 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Thu, 12 Jan 2023 15:45:13 -0500 Subject: [PATCH 167/300] Delete outdated files --- example/ParameterEstimation/GPUprofiling.py | 154 ------------------ example/ParameterEstimation/optimize_param.py | 112 ------------- 2 files changed, 266 deletions(-) delete mode 100644 example/ParameterEstimation/GPUprofiling.py delete mode 100644 example/ParameterEstimation/optimize_param.py diff --git a/example/ParameterEstimation/GPUprofiling.py b/example/ParameterEstimation/GPUprofiling.py deleted file mode 100644 index dda32b26..00000000 --- a/example/ParameterEstimation/GPUprofiling.py +++ /dev/null @@ -1,154 +0,0 @@ -# Import packages - -from xml.sax.handler import property_declaration_handler -import scipy.signal as ssig -import lalsimulation as lalsim -import numpy as np -import jax.numpy as jnp -import jax - -# from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar -from ripple import ms_to_Mc_eta -from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar -from jaxgw.PE.detector_preset import * -from jaxgw.PE.heterodyneLikelihood import make_heterodyne_likelihood - - -from flowMC.nfmodel.realNVP import RealNVP -from flowMC.sampler.MALA import make_mala_sampler -from flowMC.sampler.Sampler import Sampler -from flowMC.utils.PRNG_keys import initialize_rng_keys -from flowMC.nfmodel.utils import * - -import matplotlib.pyplot as plt - -psd_func_dict = { - 'H1': lalsim.SimNoisePSDaLIGOZeroDetHighPower, - 'L1': lalsim.SimNoisePSDaLIGOZeroDetHighPower, - 'V1': lalsim.SimNoisePSDAdvVirgo, -} -ifos = list(psd_func_dict.keys()) - -# define center of time array -tgps_geo = 1126259462.423 - -# define sampling rate and duration -fsamp = 8192 -duration = 4 - -delta_t = 1/fsamp -tlen = int(round(duration / delta_t)) - -freqs = np.fft.rfftfreq(tlen, delta_t) -delta_f = freqs[1] - freqs[0] - - - -# we will want to pad low frequencies; the function below applies a -# prescription to do so smoothly, but this is not really needed: you -# could just set all values below `fmin` to a constant. -fmin = 30 -def pad_low_freqs(f, psd_ref): - return psd_ref + psd_ref*(fmin-f)*np.exp(-(fmin-f))/3 - -psd_dict = {} -for ifo in ifos: - psd = np.zeros(len(freqs)) - for i,f in enumerate(freqs): - if f >= fmin: - psd[i] = psd_func_dict[ifo](f) - else: - psd[i] = pad_low_freqs(f, psd_func_dict[ifo](fmin)) - psd_dict[ifo] = psd - - - -rng = np.random.default_rng(12345) - -noise_fd_dict = {} -for ifo, psd in psd_dict.items(): - var = psd / (4.*delta_f) # this is the variance of LIGO noise given the definition of the likelihood function - noise_real = rng.normal(size=len(psd), loc=0, scale=np.sqrt(var)) - noise_imag = rng.normal(size=len(psd), loc=0, scale=np.sqrt(var)) - noise_fd_dict[ifo] = noise_real + 1j*noise_imag - - - -# These are the parameters of the injected signal -m1 = 50.0 -m2 = 10.0 -Mc, eta = ms_to_Mc_eta(jnp.array([m1, m2])) -chi1 = 0.4 -chi2 = -0.3 -dist_mpc = 1000.0 -tc = 2.0 -phic = 0.0 -inclination = np.pi -polarization_angle = np.pi/2 -ra = 0.3 -dec = 0.5 - -n_chains = 100 - -detector_presets = {'H1': get_H1()} - -theta_ripple = jnp.array([Mc, eta, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle]) -theta_ripple_vec = np.array(jnp.repeat(theta_ripple[None,:],n_chains,axis=0)*np.random.normal(loc=1,scale=0.01,size=(n_chains,9))) -theta_ripple_vec[theta_ripple_vec[:,1]>0.25,1] = 0.25 - -f_list = freqs[freqs>fmin] -hp = gen_IMRPhenomD_polar(f_list, theta_ripple) -noise_psd = psd[freqs>fmin] -data = noise_psd + hp[0] - - -@jax.jit -def LogLikelihood(theta): - h_test = gen_IMRPhenomD_polar(f_list, theta) - df = f_list[1] - f_list[0] - match_filter_SNR = 4*jnp.sum((jnp.conj(h_test[0])*data)/noise_psd*df).real - optimal_SNR = 4*jnp.sum((jnp.conj(h_test[0])*h_test[0])/noise_psd*df).real - return (-match_filter_SNR+optimal_SNR/2) - -theta_ref = jnp.array([Mc, 0.138, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle]) - -h_function = lambda f,theta:gen_IMRPhenomD_polar(f,theta)[0] - -logpdf = jax.jit(make_heterodyne_likelihood(data, h_function, theta_ref, noise_psd, f_list, 101)) -d_logpdf = jax.jit(jax.grad(logpdf)) - -L1 = jax.vmap(LogLikelihood)(theta_ripple_vec) -L2 = jax.vmap(jax.jit(logpdf))(theta_ripple_vec) - - -#def mala_kernel(rng_key, position, log_prob, dt=0.1): - -dt = 1e-7 -def mala_kernel(carry, data): - rng_key, position, log_prob, do_accept = carry - rng_key, key1, key2 = jax.random.split(rng_key,3) - proposal = position + dt * d_logpdf(position) - proposal += dt * jnp.sqrt(2/dt) * jax.random.normal(key1, shape=position.shape) - ratio = logpdf(proposal) - logpdf(position) - ratio -= ((position - proposal - dt * d_logpdf(proposal)) ** 2 / (4 * dt)).sum() - ratio += ((proposal - position - dt * d_logpdf(position)) ** 2 / (4 * dt)).sum() - proposal_log_prob = logpdf(proposal) - - log_uniform = jnp.log(jax.random.uniform(key2)) - do_accept = log_uniform < ratio - - position = jax.lax.cond(do_accept, lambda: proposal, lambda: position) - log_prob = jax.lax.cond(do_accept, lambda: proposal_log_prob, lambda: log_prob) - return (rng_key, position, log_prob, do_accept), (position, log_prob, do_accept) - -mala_kernel = jax.jit(mala_kernel) -state = (jax.random.PRNGKey(1),theta_ripple, logpdf(theta_ripple), False) -# jax.lax.scan(mala_kernel, state, jax.random.split(jax.random.PRNGKey(1),10)) -def mala_update(rng_key, position, logpdf, n_steps=100): - carry = (rng_key, position, logpdf, False) - y = jax.lax.scan(mala_kernel, carry, jax.random.split(rng_key,n_steps)) - return y - -with jax.profiler.trace("./", create_perfetto_link=True): - mala_update = jax.jit(jax.vmap(mala_update)) - result = mala_update(jax.random.split(jax.random.PRNGKey(1),100), theta_ripple_vec, jax.vmap(logpdf)(theta_ripple_vec)) \ No newline at end of file diff --git a/example/ParameterEstimation/optimize_param.py b/example/ParameterEstimation/optimize_param.py deleted file mode 100644 index 243cbf4c..00000000 --- a/example/ParameterEstimation/optimize_param.py +++ /dev/null @@ -1,112 +0,0 @@ -# Import packages -from curses import KEY_REPLACE -import lalsimulation as lalsim -import numpy as np -import jax.numpy as jnp -import jax - -# from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar -from ripple import ms_to_Mc_eta -from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar -from jaxgw.PE.detector_preset import * -from jaxgw.PE.heterodyneLikelihood import make_heterodyne_likelihood -from jaxgw.PE.detector_projection import make_detector_response - -from flowMC.nfmodel.rqSpline import RQSpline -from flowMC.sampler.MALA import make_mala_sampler, mala_sampler_autotune -from flowMC.sampler.Sampler import Sampler -from flowMC.utils.PRNG_keys import initialize_rng_keys -from flowMC.nfmodel.utils import * - -psd_func_dict = { - 'H1': lalsim.SimNoisePSDaLIGOZeroDetHighPower, - 'L1': lalsim.SimNoisePSDaLIGOZeroDetHighPower, - 'V1': lalsim.SimNoisePSDAdvVirgo, -} -ifos = list(psd_func_dict.keys()) - -# define sampling rate and duration -fsamp = 2048 -duration = 32 - -delta_t = 1/fsamp -tlen = int(round(duration / delta_t)) - -freqs = np.fft.rfftfreq(tlen, delta_t) -delta_f = freqs[1] - freqs[0] - -# we will want to pad low frequencies; the function below applies a -# prescription to do so smoothly, but this is not really needed: you -# could just set all values below `fmin` to a constant. -fmin = 30 -def pad_low_freqs(f, psd_ref): - return psd_ref + psd_ref*(fmin-f)*np.exp(-(fmin-f))/3 - -psd_dict = {} -for ifo in ifos: - psd = np.zeros(len(freqs)) - for i,f in enumerate(freqs): - if f >= fmin: - psd[i] = psd_func_dict[ifo](f) - else: - psd[i] = pad_low_freqs(f, psd_func_dict[ifo](fmin)) - psd_dict[ifo] = psd - -rng = np.random.default_rng(12345) - -noise_fd_dict = {} -for ifo, psd in psd_dict.items(): - var = psd / (4.*delta_f) # this is the variance of LIGO noise given the definition of the likelihood function - noise_real = rng.normal(size=len(psd), loc=0, scale=np.sqrt(var)) - noise_imag = rng.normal(size=len(psd), loc=0, scale=np.sqrt(var)) - noise_fd_dict[ifo] = noise_real + 1j*noise_imag - -# These are the parameters of the injected signal -m1 = 10.0 -m2 = 10.0 -Mc, eta = ms_to_Mc_eta(jnp.array([m1, m2])) -chi1 = 0.4 -chi2 = -0.3 -dist_mpc = 30.0 -tc = 2.0 -phic = np.pi/4 -inclination = 1.57*np.pi/8 -polarization_angle = 1.2*np.pi/8 -ra = 0.3 -dec = 0.5 - - - -H1 = get_H1() -H1_response = make_detector_response(H1[0], H1[1]) -L1 = get_L1() -L1_response = make_detector_response(L1[0], L1[1]) - - -def gen_waveform_H1(f, theta): - theta_waveform = theta[:9] - ra = theta[9] - dec = theta[10] - hp, hc = gen_IMRPhenomD_polar(f, theta_waveform) - return H1_response(f, hp, hc, ra, dec, theta[5], theta[8]) - -def gen_waveform_L1(f, theta): - theta_waveform = theta[:9] - ra = theta[9] - dec = theta[10] - hp, hc = gen_IMRPhenomD_polar(f, theta_waveform) - return L1_response(f, hp, hc, ra, dec, theta[5], theta[8]) - -true_param = jnp.array([Mc, eta, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle, ra, dec]) - - -f_list = freqs[freqs>fmin] -H1_signal = gen_waveform_H1(f_list, true_param) -H1_noise_psd = noise_fd_dict['H1'][freqs>fmin] -H1_data = H1_noise_psd + H1_signal - -L1_signal = gen_waveform_L1(f_list, true_param) -L1_noise_psd = noise_fd_dict['L1'][freqs>fmin] -L1_data = L1_noise_psd + L1_signal - -ref_param = jnp.array([Mc, eta, chi1, chi2, dist_mpc, tc+1, phic, inclination, polarization_angle, ra, dec]) From 1a6819c62e039afb22be99d7a2db002393618d56 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Thu, 12 Jan 2023 15:53:02 -0500 Subject: [PATCH 168/300] Add preliminary structure to parameter estimation --- example/ParameterEstimation/ParameterEstimation.md | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/example/ParameterEstimation/ParameterEstimation.md b/example/ParameterEstimation/ParameterEstimation.md index f595ee39..2b314472 100644 --- a/example/ParameterEstimation/ParameterEstimation.md +++ b/example/ParameterEstimation/ParameterEstimation.md @@ -1,3 +1,11 @@ # Parameter Estimation with JaxGW -In this subfolder we host the examples and tools for parameter estimation in gravitational-wave. \ No newline at end of file +In this subfolder we host the examples and tools for parameter estimation in gravitational-wave. + +gen_injection_config.py + +injection_withParser.py + +make_ppPlot.py + +RealDataAnalysis.py \ No newline at end of file From 5132b1c34d998ba44f869d98edc992f4385a3eb8 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Tue, 24 Jan 2023 16:45:34 -0500 Subject: [PATCH 169/300] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 8810de97..51ca960b 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ -# JaxGW - A JAX-based gravitational-wave data analysis library +# Jim - A JAX-based gravitational-wave inference pipeline -In contrast to other library such as `lal`, `pycbc` and `Bilby`, `jaxGW` does +In contrast to other library such as `lal`, `pycbc` and `Bilby`, `jim` does not just aim to give you a nuke button to do your analysis. Instead, we take a more modular approach, providing thin wrappers around other workhorses such as `ripple` and `flowMC`, as well as example oriented documentations. We still give From adbc3bc90f701635fefa2afeada874e3427098b7 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Wed, 25 Jan 2023 14:31:56 -0500 Subject: [PATCH 170/300] Update heterodyneLikelihood.py Remove unnecessary cropping of the data array --- src/jaxgw/PE/heterodyneLikelihood.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/jaxgw/PE/heterodyneLikelihood.py b/src/jaxgw/PE/heterodyneLikelihood.py index ea0c1aea..ff58ebbf 100644 --- a/src/jaxgw/PE/heterodyneLikelihood.py +++ b/src/jaxgw/PE/heterodyneLikelihood.py @@ -107,7 +107,7 @@ def make_heterodyne_likelihood_mutliple_detector(data_list, psd_list, respose_li def hetrodyne_likelihood(params): - theta_waveform = params[:8] + theta_waveform = params theta_waveform = theta_waveform.at[5].set(0) ra, dec = params[9], params[10] @@ -129,4 +129,4 @@ def hetrodyne_likelihood(params): return output_SNR - return hetrodyne_likelihood \ No newline at end of file + return hetrodyne_likelihood From 5f5c51622121cc3b175a0cf0ee00be0f8040b23f Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Wed, 25 Jan 2023 14:34:44 -0500 Subject: [PATCH 171/300] Update heterodyneLikelihood.py remove unnecessary cropping of the input array --- src/jaxgw/PE/heterodyneLikelihood.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/jaxgw/PE/heterodyneLikelihood.py b/src/jaxgw/PE/heterodyneLikelihood.py index ff58ebbf..1583e2a5 100644 --- a/src/jaxgw/PE/heterodyneLikelihood.py +++ b/src/jaxgw/PE/heterodyneLikelihood.py @@ -70,7 +70,7 @@ def heterodyne_likelihood(params): def make_heterodyne_likelihood_mutliple_detector(data_list, psd_list, respose_list, h_function, ref_theta, freqs, gmst, epoch, f_ref, n_bins=101): num_detector = len(data_list) - theta_waveform = ref_theta[:8] + theta_waveform = ref_theta theta_waveform = theta_waveform.at[5].set(0) raw_hp, raw_hc = h_function(freqs, theta_waveform, f_ref) index = jnp.where((jnp.abs(raw_hc)+jnp.abs(raw_hp)) > 0) From 18bc14eea182eb0e6aba9c6839bb527306b3e155 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Thu, 9 Feb 2023 15:36:42 -0500 Subject: [PATCH 172/300] Update heterodyneLikelihood.py --- src/jaxgw/PE/heterodyneLikelihood.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/src/jaxgw/PE/heterodyneLikelihood.py b/src/jaxgw/PE/heterodyneLikelihood.py index 1583e2a5..d7a6ee50 100644 --- a/src/jaxgw/PE/heterodyneLikelihood.py +++ b/src/jaxgw/PE/heterodyneLikelihood.py @@ -3,8 +3,6 @@ from scipy.interpolate import interp1d import jax.numpy as jnp -from xarray import align - def max_phase_diff(f, f_low, f_high, chi=1): gamma = np.arange(-5,6,1)/3. From f7f5920083fd0165e475d8469b76e718bfd6164e Mon Sep 17 00:00:00 2001 From: Max Isi Date: Thu, 9 Feb 2023 17:29:53 -0500 Subject: [PATCH 173/300] updated setup.cfg --- setup.cfg | 14 +++++++++----- src/jaxgw/PE/__init__.py | 0 src/jaxgw/__init__.py | 0 3 files changed, 9 insertions(+), 5 deletions(-) create mode 100644 src/jaxgw/PE/__init__.py create mode 100644 src/jaxgw/__init__.py diff --git a/setup.cfg b/setup.cfg index fd14e14d..02e994ef 100644 --- a/setup.cfg +++ b/setup.cfg @@ -1,5 +1,5 @@ [metadata] -name = jaxGW +name = jaxgw version = 0.0.2 author = Kaze Wong author_email = kazewong.physics@gmail.com @@ -13,10 +13,14 @@ packages_dir= =src packages = find: install_requires = - jax - jaxlib - flax -python_requires = >=3.7 + jax==0.4.1 + jaxlib==0.4.1 + flax==0.6.3 + flowMC + ripple @ git+https://github.com/tedwards2412/ripple.git + gwpy + corner +python_requires = >=3.10,<3.11 [options.packages.find] where=src diff --git a/src/jaxgw/PE/__init__.py b/src/jaxgw/PE/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/src/jaxgw/__init__.py b/src/jaxgw/__init__.py new file mode 100644 index 00000000..e69de29b From b088864ed76c54b251d6fca3e0971e0edd6743b0 Mon Sep 17 00:00:00 2001 From: Maximiliano Isi Date: Fri, 10 Feb 2023 11:26:49 -0500 Subject: [PATCH 174/300] Update README.md --- README.md | 43 +++++++++++++++++++++++++++++++++++-------- 1 file changed, 35 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index 51ca960b..bcaa7ad2 100644 --- a/README.md +++ b/README.md @@ -1,15 +1,41 @@ -# Jim - A JAX-based gravitational-wave inference pipeline +# Jim :smoking: - A JAX-based gravitational-wave inference toolkit -In contrast to other library such as `lal`, `pycbc` and `Bilby`, `jim` does -not just aim to give you a nuke button to do your analysis. Instead, we take a -more modular approach, providing thin wrappers around other workhorses such as -`ripple` and `flowMC`, as well as example oriented documentations. We still give -you the nuke button, but more importantly you should be able to build your own -nuke button. +Jim comprises a set of tools for estimating parameters of gravitational-wave sources thorugh Bayesian inference. +At its core, Jim relies on the JAX-based sampler [flowMC](https://github.com/kazewong/flowMC), +which leverages normalizing flows to enhance the convergence of a gradient-based MCMC sampler. + +Since its based on JAX, Jim can also leverage hardware acceleration to achieve significant speedups on GPUs. Jim also takes advantage of likelihood-heterodyining, ([Cornish 2010](https://arxiv.org/abs/1007.4820), [Cornish 2021](https://arxiv.org/abs/2109.02728)) to compute the gravitational-wave likelihood more efficiently. + +See the accompanying paper, [Wong, Isi, Edwards (2023)](https://github.com/kazewong/TurboPE/) for details. + +_[Documentatation and examples are a work in progress]_ ## Installation -`pip install jaxGW` +You may install the latest released version of Jim through pip by doing +``` +pip install jaxGW +``` + +You may install the bleeding edge version by cloning this repo, or doing +``` +pip install git+https://github.com/kazewong/jim +``` + +If you would like to take advantage of CUDA, you will additionally need to install a specific version of JAX by doing +``` +pip install --upgrade "jax[cuda]"==0.4.1 -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html +``` + +_NOTE:_ Jim is only currently compatible with Python 3.10. + +## Performance + +The performance of Jim will vary depending on the hardware available. Under optimal conditions, the CUDA installation can achieve parameter estimation in ~1 min on an Nvidia A100 GPU for a binary neutron star (see [paper](https://github.com/kazewong/TurboPE/) for details). If a GPU is not available, JAX will fall back on CPUs, and you will see a message like this on execution: + +``` +No GPU/TPU found, falling back to CPU. +``` ## Directory @@ -17,3 +43,4 @@ Parameter estimation examples are in `example/ParameterEstimation`. ## Attribution +Please cite the accompanying paper, [Wong, Isi, Edwards (2023)](https://github.com/kazewong/TurboPE/). From 56d7a4230d08f74ef2daf167b235f36cd2fa58a3 Mon Sep 17 00:00:00 2001 From: Maximiliano Isi Date: Fri, 10 Feb 2023 13:07:20 -0500 Subject: [PATCH 175/300] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index bcaa7ad2..3d1b39b7 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -# Jim :smoking: - A JAX-based gravitational-wave inference toolkit +# Jim jim - A JAX-based gravitational-wave inference toolkit Jim comprises a set of tools for estimating parameters of gravitational-wave sources thorugh Bayesian inference. At its core, Jim relies on the JAX-based sampler [flowMC](https://github.com/kazewong/flowMC), From 7f0d6fbc43c389ffa452b9ce6571d45b3e46a1cb Mon Sep 17 00:00:00 2001 From: Max Isi Date: Mon, 13 Feb 2023 12:16:15 -0500 Subject: [PATCH 176/300] updated ripple dependency --- setup.cfg | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.cfg b/setup.cfg index 02e994ef..1febcc27 100644 --- a/setup.cfg +++ b/setup.cfg @@ -17,7 +17,7 @@ install_requires = jaxlib==0.4.1 flax==0.6.3 flowMC - ripple @ git+https://github.com/tedwards2412/ripple.git + ripplegw gwpy corner python_requires = >=3.10,<3.11 From 264f80f6c458b7de99c8250549071cfd3e286824 Mon Sep 17 00:00:00 2001 From: Max Isi Date: Tue, 14 Feb 2023 17:30:36 -0500 Subject: [PATCH 177/300] started refactoring --- .gitignore | 3 +- example/gw170817.ipynb | 313 ++++++++++++++++++ example/run_GW170817_nolal.py | 179 ++++++++++ setup.cfg | 2 +- src/jaxgw/PE/__init__.py | 0 src/jaxgw/PE/detector_preset.py | 85 ----- src/jaxgw/PE/detector_projection.py | 171 ---------- src/jaxgw/{PE => }/constants.py | 4 +- src/jaxgw/detector.py | 216 ++++++++++++ src/jaxgw/{PE => }/generate_noise.py | 0 src/jaxgw/{PE => }/heterodyneLikelihood.py | 0 src/jaxgw/likelihood.py | 57 ++++ src/jaxgw/{PE => }/single_event_likelihood.py | 0 src/jaxgw/{PE => }/time_and_date.py | 0 src/jaxgw/{PE => }/utils.py | 0 src/jaxgw/wave.py | 111 +++++++ 16 files changed, 882 insertions(+), 259 deletions(-) create mode 100644 example/gw170817.ipynb create mode 100644 example/run_GW170817_nolal.py delete mode 100644 src/jaxgw/PE/__init__.py delete mode 100644 src/jaxgw/PE/detector_preset.py delete mode 100644 src/jaxgw/PE/detector_projection.py rename src/jaxgw/{PE => }/constants.py (68%) create mode 100644 src/jaxgw/detector.py rename src/jaxgw/{PE => }/generate_noise.py (100%) rename src/jaxgw/{PE => }/heterodyneLikelihood.py (100%) create mode 100644 src/jaxgw/likelihood.py rename src/jaxgw/{PE => }/single_event_likelihood.py (100%) rename src/jaxgw/{PE => }/time_and_date.py (100%) rename src/jaxgw/{PE => }/utils.py (100%) create mode 100644 src/jaxgw/wave.py diff --git a/.gitignore b/.gitignore index 04b822a1..f62c1c25 100644 --- a/.gitignore +++ b/.gitignore @@ -134,4 +134,5 @@ data slurm_script* build* -log* \ No newline at end of file +log* +*.swp diff --git a/example/gw170817.ipynb b/example/gw170817.ipynb new file mode 100644 index 00000000..85dc558a --- /dev/null +++ b/example/gw170817.ipynb @@ -0,0 +1,313 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "eb8130ea-eeb1-48f3-a870-db230d0f93ab", + "metadata": {}, + "source": [ + "# Analyzing GW170817\n", + "\n", + "We will demonstrate how to use _jim_ to analyze the binary neutron star GW170817 using the IMRPhenomD waveform." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "29f96c4b-7aee-4bc0-a9b7-0684291d9091", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "%config InlineBackend.figure_format = 'retina'" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "e2290d54-57fa-46d2-a3b4-f2e91b40cc68", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import jax.numpy as jnp\n", + "import jax\n", + "\n", + "from gwpy.timeseries import TimeSeries\n", + "from gwpy.frequencyseries import FrequencySeries\n", + "import requests\n", + "\n", + "from astropy.time import Time\n", + "\n", + "from scipy.signal.windows import tukey\n", + "from scipy.interpolate import interp1d\n", + "\n", + "\n", + "from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar\n", + "\n", + "from jaxgw.PE.detector_preset import *\n", + "from jaxgw.PE.heterodyneLikelihood import make_heterodyne_likelihood_mutliple_detector\n", + "from jaxgw.PE.detector_projection import make_detector_response\n", + "\n", + "from flowMC.nfmodel.rqSpline import RQSpline\n", + "from flowMC.sampler.MALA import MALA\n", + "from flowMC.sampler.Sampler import Sampler\n", + "from flowMC.utils.PRNG_keys import initialize_rng_keys\n", + "from flowMC.nfmodel.utils import *" + ] + }, + { + "cell_type": "markdown", + "id": "0ecaca16-1029-47f3-a1f2-46cf8c686209", + "metadata": { + "tags": [] + }, + "source": [ + "## Data and conditioning\n", + "\n", + "We will fetch the GW170817 strain data recorded by LIGO and Virgo from [GWOSC](https://gw-openscience.org) using the [GWpy](https://gwpy.github.io) package; we will also download power-spectral densities (PSDs), made publicly available by LIGO-Virgo." + ] + }, + { + "cell_type": "markdown", + "id": "f02e0e83-05f3-466f-99f6-5ed55a059078", + "metadata": {}, + "source": [ + "### Strain\n", + "\n", + "To do so, we need to know the GPS time associated with the event (in this case, $t = 1187008882.43 s$).\n", + "We also need to prescribe how much data we wish to analyze around the event (in this case, $T = 128 s$, aka, the _segment length_ or _seglen_). We will place the trigger $2 s$ before the end of the analysis segment, following the LVK convention.\n", + "\n", + "> 👉 _**NOTE:** if you don't know the tigger GPS time, you may obtain it from the event name using the [`datasets.event_gps`](https://gwosc.readthedocs.io/en/stable/reference/gwosc.datasets.event_gps.html#event-gps) utility from the [gwosc](https://gwosc.readthedocs.io) package, e.g., `event_gps(\"GW170817\")`_.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "4eb06ba0-e822-4d35-b942-d7317fed1950", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "trigger_time = 1187008882.43\n", + "seglen = 128\n", + "\n", + "# determine segment bounds, placing trigger 2s before the end\n", + "post_trigger_duration = 2\n", + "start = trigger_time - seglen + post_trigger_duration\n", + "end = trigger_time + post_trigger_duration" + ] + }, + { + "cell_type": "markdown", + "id": "681fde34-453e-4918-954a-fe6e89a2eff0", + "metadata": {}, + "source": [ + "With those parameters, we can now fetch the data from GWOSC using `fetch_open_data()`. For GW170817, We make sure to specify `version=2` to get the version of data without the glitch in Livingston (see [GWOSC docs](https://doi.org/10.7935/K5B8566F) for this release)." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "3b929ad4-6c4b-4fbc-9762-95f4a6f8fadb", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "ifos = ['H1', 'L1', 'V1']\n", + "data_td_dict = {i: TimeSeries.fetch_open_data(i, start, end, version=2)\n", + " for i in ifos}" + ] + }, + { + "cell_type": "markdown", + "id": "38c01f46-88e9-4b26-8435-397e14a8503e", + "metadata": {}, + "source": [ + "For the likelihood computation, we will want frequency domain data. We can IFFT the above data after applying a window function; following common LVK practice for this event, we apply a Tukey window with a slope parameter `alpha=0.00625`.\n", + "\n", + "> 👉 _**NOTE:** different `alpha` values may be appropriate for different events, e.g., `alpha = 0.4` is standard for shorter binary black holes._" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "ee162bc3-c25c-4a5a-8762-b0335be47a43", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "tukey_alpha = 0.00625\n", + "data_fd_dict = {}\n", + "for ifo, d in data_td_dict.items():\n", + " w = tukey(len(d), tukey_alpha)\n", + " f = np.fft.rfftfreq(len(d), d=d.dt)\n", + " data_fd_dict[ifo] = FrequencySeries(np.fft.rfft(d*w)/d.dt, frequencies=f)" + ] + }, + { + "cell_type": "markdown", + "id": "2b970fac-7a39-4e26-8de2-961273620880", + "metadata": {}, + "source": [ + "### Power spectral densities (PSDs)" + ] + }, + { + "cell_type": "markdown", + "id": "6a1a4cc9-e4d5-4daf-bf1e-df79dd186738", + "metadata": {}, + "source": [ + "Besides the strain, to compute the likelihood we will need a PSDs characterizing the noise at each detector. Although we could estimate this oursevles directly from the data (e.g., [arXiv:1907.06540](https://arxiv.org/abs/1907.06540)), we will forgo that step and download precomputed PSDs made available by the LVK collaboration in [LIGO-P1800061](https://dcc.ligo.org/LIGO-P1800061/public).\n", + "\n", + "> 👉 _**NOTE:** you may load any PSD you wish for this step, whether from disk or computed on the fly._" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "6e87c095-801e-49c7-829c-53673b42110f", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "psd_url = \"https://dcc.ligo.org/public/0150/P1800061/011/GW170817_PSDs.dat\"\n", + "with requests.get(psd_url) as r:\n", + " psd_data = np.genfromtxt(r.iter_lines())" + ] + }, + { + "cell_type": "markdown", + "id": "869c43f8-7608-4f05-a3ea-0c0fc39ebd71", + "metadata": {}, + "source": [ + "The `psd_data` object is a 2D array where the first column is frequency and the rest are the corresponding PSD values for H1, L1 and V1, in that order. For convenience, and because these PSD data are not uniformly sampled, we will turn this into interpolants that we can evaluate over any frequency bins for each detector." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "eb7e241e-26fa-403f-bf08-f9b696499a26", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "psd_dict = {}\n", + "for i, (ifo, d) in enumerate(data_fd_dict.items()):\n", + " p = interp1d(psd_data[:,0], psd_data[:,i+1], bounds_error=False,\n", + " fill_value=np.inf)\n", + " psd_dict[ifo] = FrequencySeries(p(d.frequencies), frequencies=d.frequencies)" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "f307d2b8-d2d7-45ad-9871-1ee420014fd9", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "a = 1\n", + "b = 2\n", + "\n", + "def test_func(x):\n", + " \"\"\"Test string {a}.\n", + "\n", + " This is a stest {b}.\n", + " \"\"\"\n", + " return 2*x\n", + "\n", + "test_func.__doc__ = test_func.__doc__.format(a=a, b=b)" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "782054dc-2ef2-4de6-9321-c911224b5fd6", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\u001b[0;31mSignature:\u001b[0m \u001b[0mtest_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mDocstring:\u001b[0m\n", + "Test string 1.\n", + "\n", + "This is a stest 2.\n", + "\u001b[0;31mFile:\u001b[0m /tmp/ipykernel_2794160/1016926636.py\n", + "\u001b[0;31mType:\u001b[0m function" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "test_func?" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "a369a6e6-9d77-4c82-a49a-3d421d8c4952", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.deg" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "10cf6b4f-e583-413f-a6c5-a88e0f40a7b2", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/example/run_GW170817_nolal.py b/example/run_GW170817_nolal.py new file mode 100644 index 00000000..60e7232e --- /dev/null +++ b/example/run_GW170817_nolal.py @@ -0,0 +1,179 @@ +import numpy as np +import jax.numpy as jnp +import jax + +#from lal import GreenwichMeanSiderealTime +from astropy.time import Time +from gwosc.datasets import event_gps +from gwpy.timeseries import TimeSeries +from scipy.signal.windows import tukey +from scipy.interpolate import interp1d + + +from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar +from jaxgw.PE.detector_preset import * +from jaxgw.PE.heterodyneLikelihood import make_heterodyne_likelihood_mutliple_detector +from jaxgw.PE.detector_projection import make_detector_response + +from flowMC.nfmodel.rqSpline import RQSpline +from flowMC.sampler.MALA import MALA +from flowMC.sampler.Sampler import Sampler +from flowMC.utils.PRNG_keys import initialize_rng_keys +from flowMC.nfmodel.utils import * + +minimum_frequency = 23 +maximum_frequency = 1792 + +trigger_time = 1187008882.43 +duration = 128 +post_trigger_duration = 2 +epoch = duration - post_trigger_duration +#gmst = GreenwichMeanSiderealTime(trigger_time) +gmst = Time(trigger_time, format='gps').sidereal_time('apparent', 'greenwich').rad +f_ref = 20#minimum_frequency + +H1_frequency, H1_data_re, H1_data_im = np.genfromtxt('../data/GW170817-IMRD_data0_1187008882-43_generation_data_dump.pickle_H1_fd_strain.txt').T +H1_data = H1_data_re + 1j*H1_data_im +H1_psd_frequency, H1_psd = np.genfromtxt('../data/GW170817-IMRD_data0_1187008882-43_generation_data_dump.pickle_H1_psd.txt').T + +H1_data = H1_data[(H1_frequency>minimum_frequency)*(H1_frequencyminimum_frequency)*(H1_frequencyminimum_frequency)*(H1_frequencyminimum_frequency)*(L1_frequencyminimum_frequency)*(L1_frequencyminimum_frequency)*(L1_frequencyminimum_frequency)*(V1_frequencyminimum_frequency)*(V1_frequencyminimum_frequency)*(V1_frequency0.25,1] = 0.249 + + +print("Preparing RNG keys") +rng_key_set = initialize_rng_keys(n_chains, seed=42) + +print("Initializing MCMC model and normalizing flow model.") + +prior_range = jnp.array([[1.18,1.21],[0.125,1],[-0.05,0.05],[-0.05,0.05],[1,75],[-0.01,0.02],[0,2*np.pi],[-1,1],[0,np.pi],[0,2*np.pi],[-1,1]]) +optimize_prior_range = jnp.array([[1.18,1.21],[0.2,0.25],[0.0,0.3],[0.0,0.3],[1,75],[-0.01,0.02],[0,2*np.pi],[0,np.pi],[0,np.pi],[0,2*np.pi],[-np.pi/2,np.pi/2]]) + +initial_position = jax.random.uniform(rng_key_set[0], shape=(int(n_chains), n_dim)) * 1 +for i in range(n_dim): + initial_position = initial_position.at[:,i].set(initial_position[:,i]*(prior_range[i,1]-prior_range[i,0])+prior_range[i,0]) + +from ripple import Mc_eta_to_ms +m1,m2 = jax.vmap(Mc_eta_to_ms)(guess_param[:,:2]) +q = m2/m1 + +from astropy.cosmology import Planck18 as cosmo + +z = np.linspace(0.0002,0.03,10000) +dL = cosmo.luminosity_distance(z).value +dVdz = cosmo.differential_comoving_volume(z).value + +def top_hat(x): + output = 0. + for i in range(n_dim): + output = jax.lax.cond(x[i]>=prior_range[i,0], lambda: output, lambda: -jnp.inf) + output = jax.lax.cond(x[i]<=prior_range[i,1], lambda: output, lambda: -jnp.inf) + return output+jnp.log(jnp.interp(x[4],dL,dVdz)) + +def log_likelihood(theta): + q = theta[1] + theta = theta.at[1].set(q/(1+q)**2) # convert q to eta + theta = theta.at[7].set(jnp.arccos(theta[7])) # convert cos iota to iota + theta = theta.at[10].set(jnp.arcsin(theta[10])) # convert cos dec to dec + return logL(theta) + +def posterior(theta): + log_prior = top_hat(theta) + log_like = log_likelihood(theta) + + return log_like + log_prior + +model = RQSpline(n_dim, 10, [128,128], 8) + +print("Initializing sampler class") + +posterior = posterior + +mass_matrix = jnp.eye(n_dim) +mass_matrix = mass_matrix.at[0,0].set(1e-5) +mass_matrix = mass_matrix.at[1,1].set(1e-4) +mass_matrix = mass_matrix.at[2,2].set(1e-3) +mass_matrix = mass_matrix.at[3,3].set(1e-3) +mass_matrix = mass_matrix.at[5,5].set(1e-5) +mass_matrix = mass_matrix.at[9,9].set(1e-2) +mass_matrix = mass_matrix.at[10,10].set(1e-2) + +local_sampler = MALA(posterior, True, {"step_size": mass_matrix*3e-2}) +print("Running sampler") + +nf_sampler = Sampler( + n_dim, + rng_key_set, + local_sampler, + posterior, + model, + n_loop_training=n_loop_training, + n_loop_production = n_loop_production, + n_local_steps=n_local_steps, + n_global_steps=n_global_steps, + n_chains=n_chains, + n_epochs=num_epochs, + learning_rate=learning_rate, + momentum=momentum, + batch_size=batch_size, + use_global=True, + keep_quantile=0., + train_thinning = 40, +) + +nf_sampler.sample(initial_position) +chains, log_prob, local_accs, global_accs = nf_sampler.get_sampler_state().values() +np.savez('../data/GW170817.npz', chains=chains, log_prob=log_prob, local_accs=local_accs, global_accs=global_accs) diff --git a/setup.cfg b/setup.cfg index 1febcc27..43e717ad 100644 --- a/setup.cfg +++ b/setup.cfg @@ -3,7 +3,7 @@ name = jaxgw version = 0.0.2 author = Kaze Wong author_email = kazewong.physics@gmail.com -url = https://github.com/kazewong/JaxGW +url = https://github.com/kazewong/jim description = Gravitatioanl wave data analysis tool in Jax keywords = sampling, inference, machine learning, normalizing, autodiff, jax license = MIT diff --git a/src/jaxgw/PE/__init__.py b/src/jaxgw/PE/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/src/jaxgw/PE/detector_preset.py b/src/jaxgw/PE/detector_preset.py deleted file mode 100644 index fdd2ed5e..00000000 --- a/src/jaxgw/PE/detector_preset.py +++ /dev/null @@ -1,85 +0,0 @@ -from jaxgw.PE.detector_projection import construct_arm, detector_tensor, get_vertex_position_geocentric -import jax.numpy as jnp - -# See https://git.ligo.org/lscsoft/bilby/-/tree/master/bilby/gw/detector/detectors for detector parameters. - -degree_to_radian = jnp.pi/180 - -def get_H1(): - """ - Get the detector response matrix and the vertex position for H1. - - Returns - ------- - H1_detector_response : ndarray - The detector response matrix for H1. - H1_vertex : ndarray - The vertex position for H1. - """ - H1_lat = (46 + 27. / 60 + 18.528 / 3600) * degree_to_radian - H1_long = -(119 + 24. / 60 + 27.5657 / 3600) * degree_to_radian - H1_xarm_azimuth = 125.9994 * degree_to_radian - H1_yarm_azimuth = 215.9994 * degree_to_radian - H1_xarm_tilt = -6.195e-4 - H1_yarm_tilt = 1.25e-5 - H1_elevation = 142.554 - - H1_arm1 = construct_arm(H1_lat, H1_long, H1_xarm_tilt, H1_xarm_azimuth) - H1_arm2 = construct_arm(H1_lat, H1_long, H1_yarm_tilt, H1_yarm_azimuth) - - H1_vertex = get_vertex_position_geocentric(H1_lat, H1_long, H1_elevation) - - return detector_tensor(H1_arm1, H1_arm2), H1_vertex - -def get_L1(): - """ - Get the detector response matrix and the vertex position for L1. - - Returns - ------- - L1_detector_response : ndarray - The detector response matrix for L1. - L1_vertex : ndarray - The vertex position for L1. - - """ - L1_lat = (30 + 33. / 60 + 46.4196 / 3600) * degree_to_radian - L1_long = -(90 + 46. / 60 + 27.2654 / 3600) * degree_to_radian - L1_xarm_azimuth = 197.7165 * degree_to_radian - L1_yarm_azimuth = 287.7165 * degree_to_radian - L1_xarm_tilt = 0 - L1_yarm_tilt = 0 - L1_elevation = -6.574 - - L1_arm1 = construct_arm(L1_lat, L1_long, L1_xarm_tilt, L1_xarm_azimuth) - L1_arm2 = construct_arm(L1_lat, L1_long, L1_yarm_tilt, L1_yarm_azimuth) - - L1_vertex = get_vertex_position_geocentric(L1_lat, L1_long, L1_elevation) - - return detector_tensor(L1_arm1, L1_arm2), L1_vertex - -def get_V1(): - """ - Get the detector response matrix and the vertex position for V1. - - Returns - ------- - V1_detector_response : ndarray - The detector response matrix for V1. - V1_vertex : ndarray - The vertex position for V1. - """ - V1_lat = (43 + 37. / 60 + 53.0921 / 3600) * degree_to_radian - V1_long = (10 + 30. / 60 + 16.1878 / 3600) * degree_to_radian - V1_xarm_azimuth = 70.5674 * degree_to_radian - V1_yarm_azimuth = 160.5674 * degree_to_radian - V1_xarm_tilt = 0 - V1_yarm_tilt = 0 - V1_elevation = 51.884 - - V1_arm1 = construct_arm(V1_lat, V1_long, V1_xarm_tilt, V1_xarm_azimuth) - V1_arm2 = construct_arm(V1_lat, V1_long, V1_yarm_tilt, V1_yarm_azimuth) - - V1_vertex = get_vertex_position_geocentric(V1_lat, V1_long, V1_elevation) - - return detector_tensor(V1_arm1, V1_arm2), V1_vertex diff --git a/src/jaxgw/PE/detector_projection.py b/src/jaxgw/PE/detector_projection.py deleted file mode 100644 index 67f859ca..00000000 --- a/src/jaxgw/PE/detector_projection.py +++ /dev/null @@ -1,171 +0,0 @@ -# Credit some part of the source code from bilby - -import jax.numpy as jnp -from jaxgw.PE.constants import * - - -def make_detector_response(detector_tensor, detector_vertex): - antenna_response_plus = make_antenna_response(detector_tensor,'plus') - antenna_response_cross = make_antenna_response(detector_tensor, 'cross') - def detector_response(f, hp, hc, ra, dec, gmst, psi): - output = antenna_response_plus(ra, dec, gmst, psi)*hp + antenna_response_cross(ra, dec, gmst, psi)*hc - timeshift = time_delay_geocentric(detector_vertex, jnp.array([0.,0.,0.]), ra, dec, gmst) - output = output * jnp.exp(-1j * 2 * jnp.pi * f * timeshift) - return output - return detector_response - -########################################################## -# Construction of arms -########################################################## - -def construct_arm(latitude, longitude, arm_tilt, arm_azimuth): - """ - - Args: - - latitude: Latitude in radian - longitude: Longitude in radian - arm_tilt: Arm tilt in radian - arm_azimuth: Arm azimuth in radian - - """ - - e_long = jnp.array([-jnp.sin(longitude), jnp.cos(longitude), 0]) - e_lat = jnp.array([-jnp.sin(latitude) * jnp.cos(longitude), - -jnp.sin(latitude) * jnp.sin(longitude), jnp.cos(latitude)]) - e_h = jnp.array([jnp.cos(latitude) * jnp.cos(longitude), - jnp.cos(latitude) * jnp.sin(longitude), jnp.sin(latitude)]) - - return (jnp.cos(arm_tilt) * jnp.cos(arm_azimuth) * e_long + - jnp.cos(arm_tilt) * jnp.sin(arm_azimuth) * e_lat + - jnp.sin(arm_tilt) * e_h) - - -def detector_tensor(arm1, arm2): - return 0.5 * (jnp.einsum('i,j->ij', arm1, arm1) - jnp.einsum('i,j->ij', arm2, arm2)) - -########################################################## -# Construction of detector tensor -########################################################## - -def make_get_polarization_tensor(mode): - - """ - - Since most of the application will only use specific modes, - this function hoist the if-else loop out from the actual kernel to save time from compiling the kernel. - - Args: - mode: string - - """ - - if mode.lower() == 'plus': - kernel = lambda m,n: jnp.einsum('i,j->ij', m, m) - jnp.einsum('i,j->ij', n, n) - elif mode.lower() == 'cross': - kernel = lambda m,n: jnp.einsum('i,j->ij', m, n) + jnp.einsum('i,j->ij', n, m) - elif mode.lower() == 'breathing': - kernel = lambda m,n: jnp.einsum('i,j->ij', m, m) + jnp.einsum('i,j->ij', n, n) - - # Calculating omega here to avoid calculation when model in [plus, cross, breathing] - if mode.lower() == 'longitudinal': - def kernel(m,n): - omega = jnp.cross(m, n) - return jnp.einsum('i,j->ij', omega, omega) - elif mode.lower() == 'x': - def kernel(m,n): - omega = jnp.cross(m, n) - return jnp.einsum('i,j->ij', m, omega) + jnp.einsum('i,j->ij', omega, m) - elif mode.lower() == 'y': - def kernel(m,n): - omega = jnp.cross(m, n) - return jnp.einsum('i,j->ij', n, omega) + jnp.einsum('i,j->ij', omega, n) - else: - raise ValueError("{} not a polarization mode!".format(mode)) - - def get_polarization_tensor(ra, dec, gmst, psi): - gmst = jnp.mod(gmst, 2 * jnp.pi) - phi = ra - gmst - theta = jnp.pi / 2 - dec - - u = jnp.array([jnp.cos(phi) * jnp.cos(theta), jnp.cos(theta) * jnp.sin(phi), -jnp.sin(theta)]) - v = jnp.array([-jnp.sin(phi), jnp.cos(phi), 0]) - m = -u * jnp.sin(psi) - v * jnp.cos(psi) - n = -u * jnp.cos(psi) + v * jnp.sin(psi) - - return kernel(m, n) - - return get_polarization_tensor - - -def make_antenna_response(detector_tensor, mode): - kernel = make_get_polarization_tensor(mode) - def antenna_response(ra, dec, gmst, psi): - polarization_tensor = kernel(ra, dec, gmst, psi) - return jnp.einsum('ij,ij->', detector_tensor, polarization_tensor) - return antenna_response - -def time_delay_geocentric(detector1, detector2, ra, dec, gmst): - """ - Calculate time delay between two detectors in geocentric coordinates based on XLALArrivaTimeDiff in TimeDelay.c - - Parameters - ========== - detector1: array_like - Cartesian coordinate vector for the first detector in the geocentric frame - generated by the Interferometer class as self.vertex. - detector2: array_like - Cartesian coordinate vector for the second detector in the geocentric frame. - To get time delay from Earth center, use detector2 = np.array([0,0,0]) - ra: float - Right ascension of the source in radians - dec: float - Declination of the source in radians - gmst: float - Greenwich mean sidereal time in radians - - Returns - ======= - float: Time delay between the two detectors in the geocentric frame - - """ - gmst = jnp.mod(gmst, 2 * jnp.pi) - phi = ra - gmst - theta = jnp.pi / 2 - dec - omega = jnp.array([jnp.sin(theta) * jnp.cos(phi), jnp.sin(theta) * jnp.sin(phi), jnp.cos(theta)]) - delta_d = detector2 - detector1 - return jnp.dot(omega, delta_d) / speed_of_light - -def get_vertex_position_geocentric(latitude, longitude, elevation): - """ - Calculate the position of the IFO vertex in geocentric coordinates in meters. - - Based on arXiv:gr-qc/0008066 Eqs. B11-B13 except for the typo in the definition of the local radius. - See Section 2.1 of LIGO-T980044-10 for the correct expression - - Parameters - ========== - latitude: float - Latitude in radians - longitude: - Longitude in radians - elevation: - Elevation in meters - - Returns - ======= - array_like: A 3D representation of the geocentric vertex position - - """ - semi_major_axis = 6378137 # for ellipsoid model of Earth, in m - semi_minor_axis = 6356752.314 # in m - radius = semi_major_axis**2 * (semi_major_axis**2 * jnp.cos(latitude)**2 + - semi_minor_axis**2 * jnp.sin(latitude)**2)**(-0.5) - x_comp = (radius + elevation) * jnp.cos(latitude) * jnp.cos(longitude) - y_comp = (radius + elevation) * jnp.cos(latitude) * jnp.sin(longitude) - z_comp = ((semi_minor_axis / semi_major_axis)**2 * radius + elevation) * jnp.sin(latitude) - return jnp.array([x_comp, y_comp, z_comp]) - - - - diff --git a/src/jaxgw/PE/constants.py b/src/jaxgw/constants.py similarity index 68% rename from src/jaxgw/PE/constants.py rename to src/jaxgw/constants.py index 01f87e88..74237b37 100644 --- a/src/jaxgw/PE/constants.py +++ b/src/jaxgw/constants.py @@ -7,5 +7,7 @@ Mpc = 1e6*pc.value/c.value euler_gamma = 0.577215664901532860606512090082 MR_sun = 1.476625061404649406193430731479084713e3 -speed_of_light = 299792458.0 +C_SI = 299792458.0 +EARTH_SEMI_MAJOR_AXIS = 6378137 # for ellipsoid model of Earth, in m +EARTH_SEMI_MINOR_AXIS = 6356752.314 # in m diff --git a/src/jaxgw/detector.py b/src/jaxgw/detector.py new file mode 100644 index 00000000..a955de4f --- /dev/null +++ b/src/jaxgw/detector.py @@ -0,0 +1,216 @@ +import jax.numpy as jnp +from .constants import * +from .wave import Polarization + + +DEG_TO_RAD = jnp.pi/180 + +class Detector(object): + """Defines a ground-based gravitational-wave detector. + + Argument + -------- + name : str + interferometer name, e.g., 'H1' for LIGO Hanford. + coordinates : dict + optionally, provide custom detector arm and vertex coordinates. + """ + def __init__(self, name, coordinates=None): + self.name = name.upper() + self._coordinates = coordinates or {} + + @property + def coordinates(self) + """Coordinates defining a triangular detector (angles in radians). + """ + if not self._coordinates + if self.name == 'H1': + # LIGO Hanford + self._coordinates = dict( + lat = (46 + 27. / 60 + 18.528 / 3600) * DEG_TO_RAD, + lon = -(119 + 24. / 60 + 27.5657 / 3600) * DEG_TO_RAD, + xarm_azimuth = 125.9994 * DEG_TO_RAD, + yarm_azimuth = 215.9994 * DEG_TO_RAD, + xarm_tilt = -6.195e-4, + yarm_tilt = 1.25e-5, + elevation = 142.554, + ) + elif self.name == 'L1': + # LIGO Livingston + self._coordinates = dict( + lat = (30 + 33. / 60 + 46.4196 / 3600) * DEG_TO_RAD, + lon= -(90 + 46. / 60 + 27.2654 / 3600) * DEG_TO_RAD, + xarm_azimuth = 197.7165 * DEG_TO_RAD, + yarm_azimuth = 287.7165 * DEG_TO_RAD, + xarm_tilt = 0 , + yarm_tilt = 0, + elevation = -6.574, + ) + elif self.name == 'V1': + # Virgo + self._coordinates = dict( + lat = (43 + 37. / 60 + 53.0921 / 3600) * DEG_TO_RAD, + lon = (10 + 30. / 60 + 16.1878 / 3600) * DEG_TO_RAD, + xarm_azimuth = 70.5674 * DEG_TO_RAD, + yarm_azimuth = 160.5674 * DEG_TO_RAD, + xarm_tilt = 0, + yarm_tilt = 0, + elevation = 51.884, + ) + elif not self._coordinates: + raise ValueError(f"unknown detector {self.name}") + return self._coordinates + + @static + def _get_arm(lat, lon, tilt, azimuth): + """Construct detector-arm vectors in Earth-centric Cartesian coordinates. + + Arguments + --------- + lat : float + vertex latitude in rad. + lon : float + vertex longitude in rad. + tilt : float + arm tilt in rad. + azimuth : float + arm azimuth in rad. + """ + e_lon = jnp.array([-jnp.sin(lon), jnp.cos(lon), 0]) + e_lat = jnp.array([-jnp.sin(lat) * jnp.cos(lon), + -jnp.sin(lat) * jnp.sin(lon), jnp.cos(lat)]) + e_h = jnp.array([jnp.cos(lat) * jnp.cos(lon), + jnp.cos(lat) * jnp.sin(lon), jnp.sin(lat)]) + + return (jnp.cos(tilt) * jnp.cos(azimuth) * e_lon + + jnp.cos(tilt) * jnp.sin(azimuth) * e_lat + + jnp.sin(tilt) * e_h) + + @property + def arms(self): + """Detector arm vectors (x, y). + """ + c = self.coordinates + x = self._get_arm(c['lat'], c['lon'], c['xarm_tilt'], c['xarm_azimuth']) + y = self._get_arm(c['lat'], c['lon'], c['yarm_tilt'], c['yarm_azimuth']) + return x, y + + @property + def tensor(self): + """Detector tensor defining the strain measurement. + """ + #TODO: this could easily be generalized for other detector geometries + arm1, arm2 = self.arms + return 0.5 * (jnp.einsum('i,j->ij', arm1, arm1) - + jnp.einsum('i,j->ij', arm2, arm2)) + + @property + def vertex(self): + """Detector vertex coordinates in the reference celestial frame. Based + on arXiv:gr-qc/0008066 Eqs. (B11-B13) except for a typo in the + definition of the local radius; see Section 2.1 of LIGO-T980044-10. + """ + # get detector and Earth parameters + lat = self.coordinates['lat'] + lon = self.coordinates['lon'] + h = self.coordinates['elevation'] + major, minor = EARTH_SEMI_MAJOR_AXIS, EARTH_SEMI_MINOR_AXIS + # compute vertex location + r = major**2*(major**2*jnp.cos(lat)**2 + minor**2*jnp.sin(lat)**2)**(-0.5) + x = (radius + h) * jnp.cos(lat) * jnp.cos(lon) + y = (radius + h) * jnp.cos(lat) * jnp.sin(lon) + z = ((minor / major)**2 * r + h)*jnp.sin(lat) + return jnp.array([x, y, z]) + + @property + def delay_from_geocenter_constructor(self): + """Gives function to compute the delay from geocenter for any sky + location and GMST. + """ + delta_d = -self.vertex + def delay(ra, dec, gmst): + """ Calculate time delay between two detectors in geocentric + coordinates based on XLALArrivaTimeDiff in TimeDelay.c + + https://lscsoft.docs.ligo.org/lalsuite/lal/group___time_delay__h.html + + Arguments + --------- + ra : float + right ascension of the source in rad. + dec : float + declination of the source in rad. + gmst : float + Greenwich mean sidereal time in rad. + + Returns + ------- + float: time delay from Earth center. + """ + gmst = jnp.mod(gmst, 2 * jnp.pi) + phi = ra - gmst + theta = jnp.pi / 2 - dec + omega = jnp.array([jnp.sin(theta)*jnp.cos(phi), + jnp.sin(theta)*jnp.sin(phi), + jnp.cos(theta)]) + return jnp.dot(omega, delta_d) / C_SI + return delay + + def antenna_pattern_constructor(modes='pc'): + """Gives function to compute antenna patterns for any sky location, + polarization angle and GMST. + + Arguments + --------- + modes : list,str + list of polarizations to include, defaults to tensor modes: 'pc'. + """ + detector_tensor = self.tensor + wave_tensor_functions = [Polarization(m).tensor_from_sky_constructor + for m in modes] + def aps(ra, dec, psi, gmst): + """Computes {name} antenna patterns for {modes} polarizations + at the specified sky location, orientation and GMST. + + In the long-wavelength approximation, the antenna pattern for a + given polarization is the dyadic product between the detector + tensor and the corresponding polarization tensor. + + Arguments + --------- + ra : float + source right ascension in radians. + dec : float + source declination in radians. + psi : float + source polarization angle in radians. + gmst : float + Greenwich mean sidereal time (GMST) in radians. + + Returns + ------- + Fps : list + antenna pattern values for {modes}. + """ + antenna_patterns = [] + for pol_func in wave_tensor_functions: + wave_tensor = pol_func(ra, dec, psi, gmst) + ap = jnp.einsum('ij,ij->', detector_tensor, wave_tensor) + antenna_patterns.append(ap) + return antenna_patterns + aps.__doc__ = aps.__doc__.format(name=self.name, modes=str(modes)) + return antenna_patterns + + + +def make_detector_response(detector_tensor, detector_vertex): + antenna_response_plus = make_antenna_response(detector_tensor,'plus') + antenna_response_cross = make_antenna_response(detector_tensor, 'cross') + def detector_response(f, hp, hc, ra, dec, gmst, psi): + output = antenna_response_plus(ra, dec, gmst, psi)*hp + antenna_response_cross(ra, dec, gmst, psi)*hc + timeshift = time_delay_geocentric(detector_vertex, jnp.array([0.,0.,0.]), ra, dec, gmst) + output = output * jnp.exp(-1j * 2 * jnp.pi * f * timeshift) + return output + return detector_response + + diff --git a/src/jaxgw/PE/generate_noise.py b/src/jaxgw/generate_noise.py similarity index 100% rename from src/jaxgw/PE/generate_noise.py rename to src/jaxgw/generate_noise.py diff --git a/src/jaxgw/PE/heterodyneLikelihood.py b/src/jaxgw/heterodyneLikelihood.py similarity index 100% rename from src/jaxgw/PE/heterodyneLikelihood.py rename to src/jaxgw/heterodyneLikelihood.py diff --git a/src/jaxgw/likelihood.py b/src/jaxgw/likelihood.py new file mode 100644 index 00000000..a2bd1e77 --- /dev/null +++ b/src/jaxgw/likelihood.py @@ -0,0 +1,57 @@ +import numpy as np +from gwpy.timeseries import TimeSeries +from gwpy.frequencyseries import FrequencySeries + +class LogLikelihoodTransientFD(object): + """Object to construct a frequency-domain JAX-based log-likelihood function + for transient gravitational-wave signals detected by ground-based + detectors with stationary Gaussian noise. + + Arguments + --------- + waveform : + frequency-domain waveform function, must accept an array of frequencies + and a set of parameter values, like + `waveform(frequencies, [param1, param2, ...])` + """ + def __init__(self, waveform, heterodyne=True): + self.waveform = waveform + self.heterodyne = heterodyne + self.data = {} + self.psds = {} + + @property + def ifos(self): + """Names of interferometers to analyze. + """ + return list(self.data.keys()) + + def add_data(self, ifo, data, **kws): + """Add frequency-domain strain data for a given detector. + + Arguments + --------- + ifo : str + interferometer name, e.g., 'H1' for LIGO Hanford. + data : array,FrequencySeries + frequency-domain strain data. + """ + if isinstance(data, FrequencySeries): + self.data[ifo] = data + else: + self.data[ifo] - FrequencySeries(data, **kws) + + def add_psd(self, ifo, data, **kws): + """Add power spectral density (PSD) for the noise of a given detector. + + Arguments + --------- + ifo : str + interferometer name, e.g., 'H1' for LIGO Hanford. + psd : array,FrequencySeries + power spectrum data. + """ + if isinstance(psd, FrequencySeries): + self.psd[ifo] = psd + else: + self.psd[ifo] - FrequencySeries(psd, **kws) diff --git a/src/jaxgw/PE/single_event_likelihood.py b/src/jaxgw/single_event_likelihood.py similarity index 100% rename from src/jaxgw/PE/single_event_likelihood.py rename to src/jaxgw/single_event_likelihood.py diff --git a/src/jaxgw/PE/time_and_date.py b/src/jaxgw/time_and_date.py similarity index 100% rename from src/jaxgw/PE/time_and_date.py rename to src/jaxgw/time_and_date.py diff --git a/src/jaxgw/PE/utils.py b/src/jaxgw/utils.py similarity index 100% rename from src/jaxgw/PE/utils.py rename to src/jaxgw/utils.py diff --git a/src/jaxgw/wave.py b/src/jaxgw/wave.py new file mode 100644 index 00000000..9afd4667 --- /dev/null +++ b/src/jaxgw/wave.py @@ -0,0 +1,111 @@ +# Credit some part of the source code from bilby + +import jax.numpy as jnp +from jaxgw.PE.constants import * + +KNOWN_POLS = 'pcxybl' + +class Polarization(object): + """Object defining a given polarization mode, with utilities to produce + corresponding tensor in an Earth centric frame. + + Arguments + --------- + name : str + one of 'p' (plus), 'c' (cross), 'x' (vector x), 'y' (vector y), 'b' + (breathing), or 'l' (longitudinal). + """ + def __init__(self, name): + self.name = name.lower() + if self.name not in KNOWN_POLS: + e = f"unknown mode '{self.name}'; must be one of: {KNOWN_POLS}" + raise ValueError(e) + + @property + def tensor_from_basis_constructor(self): + """Constructor to obtain polarization tensor from waveframe basis + defined by orthonormal vectors (x, y) in arbitrary Cartesian + coordinates. + """ + if self.name == 'p': + def kernel(x, y): + """Plus polarization from (x, y) waveframe basis elements. + """ + return jnp.einsum('i,j->ij', x, x) - jnp.einsum('i,j->ij', y, y) + elif self.name == 'c': + def kernel(x, y): + """Cross polarization from (x, y) waveframe basis elements. + """ + return jnp.einsum('i,j->ij', x, y) + jnp.einsum('i,j->ij', y, x) + elif self.name == 'x': + def kernel(x, y): + """Vector-x polarization from (x, y) waveframe basis elements. + """ + z = jnp.cross(x, y) + return jnp.einsum('i,j->ij', x, z) + jnp.einsum('i,j->ij', z, x) + elif self.name == 'y': + def kernel(x, y): + """Vector-y polarization from (x, y) waveframe basis elements. + """ + z = jnp.cross(x, y) + return jnp.einsum('i,j->ij', y, z) + jnp.einsum('i,j->ij', z, y) + elif self.name == 'b': + def kernel(x, y): + """Breathing polarization from (x, y) waveframe basis elements. + """ + return jnp.einsum('i,j->ij', x, x) + jnp.einsum('i,j->ij', y, y) + elif self.name == 'l': + def kernel(x, y): + """Longitudinal polarization from (x, y) waveframe basis elements. + """ + z = jnp.cross(x, y) + return jnp.einsum('i,j->ij', z, z) + else: + raise ValueError(f"unrecognized polarization {self.name}" + return kernel + + @property + def tensor_from_sky_constructor(self): + """Constructor to obtain polarization tensor from sky location and + orientation parameters. + """ + kernel = self.tensor_from_sky_constructor + def get_pol_tensor(ra, dec, psi, gmst): + """Computes {name} polarization tensor in celestial + coordinates from sky location and orientation parameters. + + Arguments + --------- + ra : float + right ascension in radians. + dec : float + declination in radians. + psi : float + polarization angle in radians. + gmst : float + Greenwhich mean standard time (GMST) in radians. + + Returns + ------- + tensor : array + 3x3 polarization tensor. + """ + gmst = jnp.mod(gmst, 2*jnp.pi) + phi = ra - gmst + theta = jnp.pi / 2 - dec + + u = jnp.array([jnp.cos(phi) * jnp.cos(theta), + jnp.cos(theta) * jnp.sin(phi), + -jnp.sin(theta)]) + v = jnp.array([-jnp.sin(phi), jnp.cos(phi), 0]) + m = -u * jnp.sin(psi) - v * jnp.cos(psi) + n = -u * jnp.cos(psi) + v * jnp.sin(psi) + + return kernel(m, n) + get_pol_tensor.__doc__ = get_pol_tensor.__doc__.format(name=self.name) + return get_pol_tensor + + + + + From 4c1e7e0b8addb6a2942636f7073d0ac6d457fef5 Mon Sep 17 00:00:00 2001 From: Max Isi Date: Tue, 14 Feb 2023 18:09:34 -0500 Subject: [PATCH 178/300] compute response --- src/jaxgw/detector.py | 24 +++++++++++------------- src/jaxgw/likelihood.py | 12 ++++++++++++ 2 files changed, 23 insertions(+), 13 deletions(-) diff --git a/src/jaxgw/detector.py b/src/jaxgw/detector.py index a955de4f..ebfd4289 100644 --- a/src/jaxgw/detector.py +++ b/src/jaxgw/detector.py @@ -156,7 +156,7 @@ def delay(ra, dec, gmst): return jnp.dot(omega, delta_d) / C_SI return delay - def antenna_pattern_constructor(modes='pc'): + def antenna_pattern_constructor(self, modes='pc'): """Gives function to compute antenna patterns for any sky location, polarization angle and GMST. @@ -200,17 +200,15 @@ def aps(ra, dec, psi, gmst): return antenna_patterns aps.__doc__ = aps.__doc__.format(name=self.name, modes=str(modes)) return antenna_patterns - - -def make_detector_response(detector_tensor, detector_vertex): - antenna_response_plus = make_antenna_response(detector_tensor,'plus') - antenna_response_cross = make_antenna_response(detector_tensor, 'cross') - def detector_response(f, hp, hc, ra, dec, gmst, psi): - output = antenna_response_plus(ra, dec, gmst, psi)*hp + antenna_response_cross(ra, dec, gmst, psi)*hc - timeshift = time_delay_geocentric(detector_vertex, jnp.array([0.,0.,0.]), ra, dec, gmst) - output = output * jnp.exp(-1j * 2 * jnp.pi * f * timeshift) - return output - return detector_response + def construct_fd_response(self, modes='pc', epoch=0): + get_delay = self.delay_from_geocenter_constructor + get_aps = self.antenna_pattern_constructor(modes) + def get_det_h(f, polwaveforms, ra, dec, psi, gmst): + dt_geo = get_delay(ra, dec, gmst) + aps = get_aps(ra, dec, psi, gmst) + h = jnp.sum([aps[i]*polwaveforms[i] for i in len(aps)], axis=0) + h *= jnp.exp(-2j*jnp.pi*f*(dt_geo + tc - epoch)) + return h + return get_det_h - diff --git a/src/jaxgw/likelihood.py b/src/jaxgw/likelihood.py index a2bd1e77..39fcf355 100644 --- a/src/jaxgw/likelihood.py +++ b/src/jaxgw/likelihood.py @@ -55,3 +55,15 @@ def add_psd(self, ifo, data, **kws): self.psd[ifo] = psd else: self.psd[ifo] - FrequencySeries(psd, **kws) + +def make_detector_response(detector_tensor, detector_vertex): + antenna_response_plus = make_antenna_response(detector_tensor,'plus') + antenna_response_cross = make_antenna_response(detector_tensor, 'cross') + def detector_response(f, hp, hc, ra, dec, gmst, psi): + output = antenna_response_plus(ra, dec, gmst, psi)*hp + antenna_response_cross(ra, dec, gmst, psi)*hc + timeshift = time_delay_geocentric(detector_vertex, jnp.array([0.,0.,0.]), ra, dec, gmst) + output = output * jnp.exp(-1j * 2 * jnp.pi * f * timeshift) + return output + return detector_response + + From ad839034b6e1819e5381990d29a725846edd64ae Mon Sep 17 00:00:00 2001 From: Max Isi Date: Fri, 17 Feb 2023 12:33:32 -0500 Subject: [PATCH 179/300] detector projection --- src/jaxgw/detector.py | 61 ++++++++++++++++++++++++++++++- src/jaxgw/heterodyneLikelihood.py | 39 +++++--------------- src/jaxgw/likelihood.py | 11 ------ 3 files changed, 68 insertions(+), 43 deletions(-) diff --git a/src/jaxgw/detector.py b/src/jaxgw/detector.py index ebfd4289..0baaa9f7 100644 --- a/src/jaxgw/detector.py +++ b/src/jaxgw/detector.py @@ -201,14 +201,71 @@ def aps(ra, dec, psi, gmst): aps.__doc__ = aps.__doc__.format(name=self.name, modes=str(modes)) return antenna_patterns - def construct_fd_response(self, modes='pc', epoch=0): + def construct_fd_response(self, modes='pc', epoch=0.): + """Generates a function to return the Fourier-domain projection of an + arbitrary gravitational wave onto this detector, starting from FD + polarizations defined at geocenter. + + Arguments + --------- + modes : str + polarizations to include in response, defaults to tensor modes 'pc' + epoch : float + time corresponding to beginning of segment, def. 0. + + Returns + ------- + get_det_h : func + function to produce the detector response for arbitrary input + polarizations in the Fourier domain. + """ get_delay = self.delay_from_geocenter_constructor get_aps = self.antenna_pattern_constructor(modes) - def get_det_h(f, polwaveforms, ra, dec, psi, gmst): + def get_det_h(f, polwaveforms, ra, dec, psi, gmst, tc): + """Project Fourier-domain '{p}' polarizations onto {i} detector, + taking into account antenna patterns and time of flight from + geocenter. + + The response is defined by + + .. math:: h(f) = \\sum_p h_p(f) F_p(\\alpha, \\delta, \\psi) \\exp(2\\pi i \delta t) + + for polarization functions :math:`h_p(f)` delayed apropriately + relative to geocenter by a time :math:`\\delta t(\\alpha,\\delta)`, + and antenna patterns :math:`F_p(\\alpha, \\delta, \\psi)` for each + included polarization :math:`p`. + + Arguments + --------- + f : array + frequency array over which polarizations are evaluated. + polwaveforms : list + lenght-{n} list of arrays containing '{p}' polarizations, each + assumed to be defined at geocenter and evaluated over the + frequency grid `f`. + ra : float + source right ascension in radians. + dec : float + source declination in radians. + psi : float + source polarization angle in radians. + gmst : float + Greenwich mean sidereal time (GMST) in radians. + tc : float + time of arrival (coalescence) at geocenter in second measured + from epoch {t}. + + Returns + ------- + h : array + Fourier domain detector response. + """ dt_geo = get_delay(ra, dec, gmst) aps = get_aps(ra, dec, psi, gmst) h = jnp.sum([aps[i]*polwaveforms[i] for i in len(aps)], axis=0) h *= jnp.exp(-2j*jnp.pi*f*(dt_geo + tc - epoch)) return h + get_det_h.__doc__ = get_det_h.__doc__.format(p=str(modes), i=self.name, + n=len(modes), t=epoch) return get_det_h diff --git a/src/jaxgw/heterodyneLikelihood.py b/src/jaxgw/heterodyneLikelihood.py index d7a6ee50..51f03af5 100644 --- a/src/jaxgw/heterodyneLikelihood.py +++ b/src/jaxgw/heterodyneLikelihood.py @@ -43,29 +43,8 @@ def compute_coefficients(data, h_ref, psd, freqs, f_bins, f_bins_center): B1_array = jnp.array(B1_array) return A0_array, A1_array, B0_array, B1_array -def make_heterodyne_likelihood(data, h_function, ref_theta, psd, freqs, n_bins=101): - f_bins, f_bins_center = make_binning_scheme(freqs, n_bins) - h_ref = h_function(freqs, ref_theta) - h_ref_low = h_function(f_bins[:-1], ref_theta) - h_ref_bincenter = h_function(f_bins_center, ref_theta) - - A0, A1, B0, B1 = compute_coefficients(data, h_ref, psd, freqs, f_bins, f_bins_center) - - def heterodyne_likelihood(params): - waveform_low = h_function(f_bins[:-1], params) - waveform_center = h_function(f_bins_center, params) - - r0 = waveform_center/h_ref_bincenter - r1 = (waveform_low/h_ref_low - r0)/(f_bins[:-1]-f_bins_center) - - match_filter_SNR = jnp.nansum(A0*r0.conj() + A1*r1.conj()) - optimal_SNR = jnp.nansum(B0*jnp.abs(r0)**2 + 2*B1*(r0*r1.conj()).real) - - return (match_filter_SNR - optimal_SNR/2).real - - return heterodyne_likelihood - -def make_heterodyne_likelihood_mutliple_detector(data_list, psd_list, respose_list, h_function, ref_theta, freqs, gmst, epoch, f_ref, n_bins=101): +def make_heterodyned_likelihood_multiple_detectors(data_list, psd_list, + response_list, h_function, ref_theta, freqs, gmst, epoch, f_ref, n_bins=101): num_detector = len(data_list) theta_waveform = ref_theta @@ -87,9 +66,9 @@ def make_heterodyne_likelihood_mutliple_detector(data_list, psd_list, respose_li raw_hp_bin, raw_hc_bin = h_function(f_bins[:-1], theta_waveform, f_ref) raw_hp_bincenter, raw_hc_bincenter = h_function(f_bins_center, theta_waveform, f_ref) for i in range(num_detector): - h_ref.append(respose_list[i](freqs, raw_hp, raw_hc, ra, dec, gmst, ref_theta[8])*jnp.exp(-1j*2*jnp.pi*freqs*(epoch+ref_theta[5]))) - h_ref_low.append(respose_list[i](f_bins[:-1], raw_hp_bin, raw_hc_bin, ra, dec, gmst, ref_theta[8])*jnp.exp(-1j*2*jnp.pi*f_bins[:-1]*(epoch+ref_theta[5]))) - h_ref_bincenter.append(respose_list[i](f_bins_center, raw_hp_bincenter, raw_hc_bincenter, ra, dec, gmst, ref_theta[8])*jnp.exp(-1j*2*jnp.pi*f_bins_center*(epoch+ref_theta[5]))) + h_ref.append(response_list[i](freqs, raw_hp, raw_hc, ra, dec, gmst, ref_theta[8])*jnp.exp(-1j*2*jnp.pi*freqs*(epoch+ref_theta[5]))) + h_ref_low.append(response_list[i](f_bins[:-1], raw_hp_bin, raw_hc_bin, ra, dec, gmst, ref_theta[8])*jnp.exp(-1j*2*jnp.pi*f_bins[:-1]*(epoch+ref_theta[5]))) + h_ref_bincenter.append(response_list[i](f_bins_center, raw_hp_bincenter, raw_hc_bincenter, ra, dec, gmst, ref_theta[8])*jnp.exp(-1j*2*jnp.pi*f_bins_center*(epoch+ref_theta[5]))) A0_array = [] A1_array = [] @@ -104,7 +83,7 @@ def make_heterodyne_likelihood_mutliple_detector(data_list, psd_list, respose_li B1_array.append(B1) - def hetrodyne_likelihood(params): + def heterodyned_likelihood(params): theta_waveform = params theta_waveform = theta_waveform.at[5].set(0) ra, dec = params[9], params[10] @@ -115,8 +94,8 @@ def hetrodyne_likelihood(params): raw_hp_center, raw_hc_center = h_function(f_bins_center, theta_waveform, f_ref) for i in range(num_detector): - waveform_low = respose_list[i](f_bins[:-1], raw_hp_edge, raw_hc_edge, ra, dec, gmst, params[8])*jnp.exp(-1j*2*jnp.pi*f_bins[:-1]*(epoch+params[5])) - waveform_center = respose_list[i](f_bins_center, raw_hp_center, raw_hc_center, ra, dec, gmst, params[8])*jnp.exp(-1j*2*jnp.pi*f_bins_center*(epoch+params[5])) + waveform_low = response_list[i](f_bins[:-1], raw_hp_edge, raw_hc_edge, ra, dec, gmst, params[8])*jnp.exp(-1j*2*jnp.pi*f_bins[:-1]*(epoch+params[5])) + waveform_center = response_list[i](f_bins_center, raw_hp_center, raw_hc_center, ra, dec, gmst, params[8])*jnp.exp(-1j*2*jnp.pi*f_bins_center*(epoch+params[5])) r0 = waveform_center/h_ref_bincenter[i] r1 = (waveform_low/h_ref_low[i] - r0)/(f_bins[:-1]-f_bins_center) @@ -127,4 +106,4 @@ def hetrodyne_likelihood(params): return output_SNR - return hetrodyne_likelihood + return heterodyned_likelihood diff --git a/src/jaxgw/likelihood.py b/src/jaxgw/likelihood.py index 39fcf355..9c27faaf 100644 --- a/src/jaxgw/likelihood.py +++ b/src/jaxgw/likelihood.py @@ -56,14 +56,3 @@ def add_psd(self, ifo, data, **kws): else: self.psd[ifo] - FrequencySeries(psd, **kws) -def make_detector_response(detector_tensor, detector_vertex): - antenna_response_plus = make_antenna_response(detector_tensor,'plus') - antenna_response_cross = make_antenna_response(detector_tensor, 'cross') - def detector_response(f, hp, hc, ra, dec, gmst, psi): - output = antenna_response_plus(ra, dec, gmst, psi)*hp + antenna_response_cross(ra, dec, gmst, psi)*hc - timeshift = time_delay_geocentric(detector_vertex, jnp.array([0.,0.,0.]), ra, dec, gmst) - output = output * jnp.exp(-1j * 2 * jnp.pi * f * timeshift) - return output - return detector_response - - From b326e3f1f4bb61410d6561ed49fb9e936477e368 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 27 Feb 2023 12:21:01 -0500 Subject: [PATCH 180/300] Rename package as jim --- setup.cfg | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/setup.cfg b/setup.cfg index 1febcc27..9a25c6bb 100644 --- a/setup.cfg +++ b/setup.cfg @@ -1,9 +1,9 @@ [metadata] -name = jaxgw -version = 0.0.2 +name = jimGW +version = 0.0.4.3 author = Kaze Wong author_email = kazewong.physics@gmail.com -url = https://github.com/kazewong/JaxGW +url = https://github.com/kazewong/jim description = Gravitatioanl wave data analysis tool in Jax keywords = sampling, inference, machine learning, normalizing, autodiff, jax license = MIT From 708658c159c6a239d1d01c7d78d5fa4bd0ed85a3 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 27 Feb 2023 12:29:07 -0500 Subject: [PATCH 181/300] Rename jaxGW import as jimgw --- example/ParameterEstimation/GW150914.py | 8 ++++---- example/ParameterEstimation/GW170817.py | 6 +++--- example/ParameterEstimation/GW170817_optimize.py | 6 +++--- example/ParameterEstimation/Injection_test.py | 6 +++--- example/ParameterEstimation/Injection_withParser.py | 8 ++++---- example/ParameterEstimation/Injection_withParserBNS.py | 8 ++++---- example/ParameterEstimation/Injection_withParser_debug.py | 8 ++++---- example/ProjectionCrossCheck.py | 6 +++--- src/jaxgw/PE/__init__.py | 0 src/jaxgw/__init__.py | 0 src/{jaxgw => jimgw}/PE/constants.py | 0 src/{jaxgw => jimgw}/PE/detector_preset.py | 2 +- src/{jaxgw => jimgw}/PE/detector_projection.py | 2 +- src/{jaxgw => jimgw}/PE/generate_noise.py | 0 src/{jaxgw => jimgw}/PE/heterodyneLikelihood.py | 0 src/{jaxgw => jimgw}/PE/single_event_likelihood.py | 4 ++-- src/{jaxgw => jimgw}/PE/time_and_date.py | 0 src/{jaxgw => jimgw}/PE/utils.py | 0 18 files changed, 32 insertions(+), 32 deletions(-) delete mode 100644 src/jaxgw/PE/__init__.py delete mode 100644 src/jaxgw/__init__.py rename src/{jaxgw => jimgw}/PE/constants.py (100%) rename src/{jaxgw => jimgw}/PE/detector_preset.py (97%) rename src/{jaxgw => jimgw}/PE/detector_projection.py (99%) rename src/{jaxgw => jimgw}/PE/generate_noise.py (100%) rename src/{jaxgw => jimgw}/PE/heterodyneLikelihood.py (100%) rename src/{jaxgw => jimgw}/PE/single_event_likelihood.py (79%) rename src/{jaxgw => jimgw}/PE/time_and_date.py (100%) rename src/{jaxgw => jimgw}/PE/utils.py (100%) diff --git a/example/ParameterEstimation/GW150914.py b/example/ParameterEstimation/GW150914.py index 348f640f..dbfd7c0f 100644 --- a/example/ParameterEstimation/GW150914.py +++ b/example/ParameterEstimation/GW150914.py @@ -4,9 +4,9 @@ from lal import GreenwichMeanSiderealTime from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar -from jaxgw.PE.detector_preset import * -from jaxgw.PE.heterodyneLikelihood import make_heterodyne_likelihood -from jaxgw.PE.detector_projection import make_detector_response +from jimgw.PE.detector_preset import * +from jimgw.PE.heterodyneLikelihood import make_heterodyne_likelihood +from jimgw.PE.detector_projection import make_detector_response from flowMC.nfmodel.rqSpline import RQSpline from flowMC.sampler.Sampler import Sampler @@ -75,7 +75,7 @@ def L1_LogLikelihood(theta): 1.33131339e+00, 2.33978644e+00, -1.20993116e+00]) -from jaxgw.PE.heterodyneLikelihood import make_heterodyne_likelihood_mutliple_detector +from jimgw.PE.heterodyneLikelihood import make_heterodyne_likelihood_mutliple_detector data_list = [H1_data, L1_data] psd_list = [H1_psd, L1_psd] diff --git a/example/ParameterEstimation/GW170817.py b/example/ParameterEstimation/GW170817.py index 3bd233be..209c5f8c 100644 --- a/example/ParameterEstimation/GW170817.py +++ b/example/ParameterEstimation/GW170817.py @@ -10,9 +10,9 @@ from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar -from jaxgw.PE.detector_preset import * -from jaxgw.PE.heterodyneLikelihood import make_heterodyne_likelihood_mutliple_detector -from jaxgw.PE.detector_projection import make_detector_response +from jimgw.PE.detector_preset import * +from jimgw.PE.heterodyneLikelihood import make_heterodyne_likelihood_mutliple_detector +from jimgw.PE.detector_projection import make_detector_response from flowMC.nfmodel.rqSpline import RQSpline from flowMC.sampler.MALA import MALA diff --git a/example/ParameterEstimation/GW170817_optimize.py b/example/ParameterEstimation/GW170817_optimize.py index 006c99c7..0a5a6c61 100644 --- a/example/ParameterEstimation/GW170817_optimize.py +++ b/example/ParameterEstimation/GW170817_optimize.py @@ -10,9 +10,9 @@ from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar -from jaxgw.PE.detector_preset import * -from jaxgw.PE.heterodyneLikelihood import make_heterodyne_likelihood_mutliple_detector -from jaxgw.PE.detector_projection import make_detector_response +from jimgw.PE.detector_preset import * +from jimgw.PE.heterodyneLikelihood import make_heterodyne_likelihood_mutliple_detector +from jimgw.PE.detector_projection import make_detector_response from flowMC.nfmodel.rqSpline import RQSpline from flowMC.sampler.MALA import make_mala_sampler diff --git a/example/ParameterEstimation/Injection_test.py b/example/ParameterEstimation/Injection_test.py index e220f7a1..e6364998 100644 --- a/example/ParameterEstimation/Injection_test.py +++ b/example/ParameterEstimation/Injection_test.py @@ -8,10 +8,10 @@ # from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar from ripple import ms_to_Mc_eta from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar -from jaxgw.PE.detector_preset import * -from jaxgw.PE.heterodyneLikelihood import make_heterodyne_likelihood_mutliple_detector +from jimgw.PE.detector_preset import * +from jimgw.PE.heterodyneLikelihood import make_heterodyne_likelihood_mutliple_detector -from jaxgw.PE.detector_projection import make_detector_response +from jimgw.PE.detector_projection import make_detector_response from flowMC.nfmodel.rqSpline import RQSpline from flowMC.sampler.MALA import make_mala_sampler, mala_sampler_autotune diff --git a/example/ParameterEstimation/Injection_withParser.py b/example/ParameterEstimation/Injection_withParser.py index 5bcb39d3..26295a61 100644 --- a/example/ParameterEstimation/Injection_withParser.py +++ b/example/ParameterEstimation/Injection_withParser.py @@ -9,10 +9,10 @@ # from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar from ripple import ms_to_Mc_eta from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar -from jaxgw.PE.detector_preset import * -from jaxgw.PE.heterodyneLikelihood import make_heterodyne_likelihood_mutliple_detector -from jaxgw.PE.detector_projection import make_detector_response -from jaxgw.PE.generate_noise import generate_noise +from jimgw.PE.detector_preset import * +from jimgw.PE.heterodyneLikelihood import make_heterodyne_likelihood_mutliple_detector +from jimgw.PE.detector_projection import make_detector_response +from jimgw.PE.generate_noise import generate_noise from flowMC.nfmodel.rqSpline import RQSpline from flowMC.sampler.MALA import MALA, mala_sampler_autotune diff --git a/example/ParameterEstimation/Injection_withParserBNS.py b/example/ParameterEstimation/Injection_withParserBNS.py index b3a92f4c..c8f05f07 100644 --- a/example/ParameterEstimation/Injection_withParserBNS.py +++ b/example/ParameterEstimation/Injection_withParserBNS.py @@ -7,10 +7,10 @@ # from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar from ripple import ms_to_Mc_eta from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar -from jaxgw.PE.detector_preset import * -from jaxgw.PE.heterodyneLikelihood import make_heterodyne_likelihood_mutliple_detector -from jaxgw.PE.detector_projection import make_detector_response -from jaxgw.PE.generate_noise import generate_noise +from jimgw.PE.detector_preset import * +from jimgw.PE.heterodyneLikelihood import make_heterodyne_likelihood_mutliple_detector +from jimgw.PE.detector_projection import make_detector_response +from jimgw.PE.generate_noise import generate_noise from flowMC.nfmodel.rqSpline import RQSpline from flowMC.sampler.MALA import make_mala_sampler diff --git a/example/ParameterEstimation/Injection_withParser_debug.py b/example/ParameterEstimation/Injection_withParser_debug.py index 0159a94b..f0b32695 100644 --- a/example/ParameterEstimation/Injection_withParser_debug.py +++ b/example/ParameterEstimation/Injection_withParser_debug.py @@ -9,10 +9,10 @@ # from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar from ripple import ms_to_Mc_eta from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar -from jaxgw.PE.detector_preset import * -from jaxgw.PE.heterodyneLikelihood import make_heterodyne_likelihood_mutliple_detector -from jaxgw.PE.detector_projection import make_detector_response -from jaxgw.PE.generate_noise import generate_noise +from jimgw.PE.detector_preset import * +from jimgw.PE.heterodyneLikelihood import make_heterodyne_likelihood_mutliple_detector +from jimgw.PE.detector_projection import make_detector_response +from jimgw.PE.generate_noise import generate_noise from flowMC.nfmodel.rqSpline import RQSpline from flowMC.sampler.MALA import MALA, mala_sampler_autotune diff --git a/example/ProjectionCrossCheck.py b/example/ProjectionCrossCheck.py index b18f1af3..6c81fafb 100644 --- a/example/ProjectionCrossCheck.py +++ b/example/ProjectionCrossCheck.py @@ -78,9 +78,9 @@ import numpy as np from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar from ripple import Mc_eta_to_ms -from jaxgw.PE.detector_preset import * -from jaxgw.PE.heterodyneLikelihood import make_heterodyne_likelihood_mutliple_detector -from jaxgw.PE.detector_projection import make_detector_response +from jimgw.PE.detector_preset import * +from jimgw.PE.heterodyneLikelihood import make_heterodyne_likelihood_mutliple_detector +from jimgw.PE.detector_projection import make_detector_response def get_lal_waveform(f,theta): Mc, eta, a_1, a_2, distance, t_c, phi_0, theta_jn, psi = theta m1,m2 = Mc_eta_to_ms([Mc,eta]) diff --git a/src/jaxgw/PE/__init__.py b/src/jaxgw/PE/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/src/jaxgw/__init__.py b/src/jaxgw/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/src/jaxgw/PE/constants.py b/src/jimgw/PE/constants.py similarity index 100% rename from src/jaxgw/PE/constants.py rename to src/jimgw/PE/constants.py diff --git a/src/jaxgw/PE/detector_preset.py b/src/jimgw/PE/detector_preset.py similarity index 97% rename from src/jaxgw/PE/detector_preset.py rename to src/jimgw/PE/detector_preset.py index fdd2ed5e..19c3fbef 100644 --- a/src/jaxgw/PE/detector_preset.py +++ b/src/jimgw/PE/detector_preset.py @@ -1,4 +1,4 @@ -from jaxgw.PE.detector_projection import construct_arm, detector_tensor, get_vertex_position_geocentric +from jimgw.PE.detector_projection import construct_arm, detector_tensor, get_vertex_position_geocentric import jax.numpy as jnp # See https://git.ligo.org/lscsoft/bilby/-/tree/master/bilby/gw/detector/detectors for detector parameters. diff --git a/src/jaxgw/PE/detector_projection.py b/src/jimgw/PE/detector_projection.py similarity index 99% rename from src/jaxgw/PE/detector_projection.py rename to src/jimgw/PE/detector_projection.py index 67f859ca..6f7ab619 100644 --- a/src/jaxgw/PE/detector_projection.py +++ b/src/jimgw/PE/detector_projection.py @@ -1,7 +1,7 @@ # Credit some part of the source code from bilby import jax.numpy as jnp -from jaxgw.PE.constants import * +from jimgw.PE.constants import * def make_detector_response(detector_tensor, detector_vertex): diff --git a/src/jaxgw/PE/generate_noise.py b/src/jimgw/PE/generate_noise.py similarity index 100% rename from src/jaxgw/PE/generate_noise.py rename to src/jimgw/PE/generate_noise.py diff --git a/src/jaxgw/PE/heterodyneLikelihood.py b/src/jimgw/PE/heterodyneLikelihood.py similarity index 100% rename from src/jaxgw/PE/heterodyneLikelihood.py rename to src/jimgw/PE/heterodyneLikelihood.py diff --git a/src/jaxgw/PE/single_event_likelihood.py b/src/jimgw/PE/single_event_likelihood.py similarity index 79% rename from src/jaxgw/PE/single_event_likelihood.py rename to src/jimgw/PE/single_event_likelihood.py index e0fc4c2a..327f4e56 100644 --- a/src/jaxgw/PE/single_event_likelihood.py +++ b/src/jimgw/PE/single_event_likelihood.py @@ -1,6 +1,6 @@ from jax import jit -from jaxgw.PE.detector_projection import get_detector_response -from jaxgw.PE.utils import inner_product +from jimgw.PE.detector_projection import get_detector_response +from jimgw.PE.utils import inner_product def single_detector_likelihood(waveform_model, params, data, data_f, PSD, detector): waveform = waveform_model(data_f, params) diff --git a/src/jaxgw/PE/time_and_date.py b/src/jimgw/PE/time_and_date.py similarity index 100% rename from src/jaxgw/PE/time_and_date.py rename to src/jimgw/PE/time_and_date.py diff --git a/src/jaxgw/PE/utils.py b/src/jimgw/PE/utils.py similarity index 100% rename from src/jaxgw/PE/utils.py rename to src/jimgw/PE/utils.py From 7a3e0375d0db6004539806a747206116daa39729 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 27 Feb 2023 12:32:34 -0500 Subject: [PATCH 182/300] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 3d1b39b7..1b9d6080 100644 --- a/README.md +++ b/README.md @@ -14,7 +14,7 @@ _[Documentatation and examples are a work in progress]_ You may install the latest released version of Jim through pip by doing ``` -pip install jaxGW +pip install jimGW ``` You may install the bleeding edge version by cloning this repo, or doing From 57ffe9f464053796278892bbed1ce4af8a210453 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 10 Apr 2023 16:28:50 -0400 Subject: [PATCH 183/300] WTF the __init__.py keep disappearing? --- setup.cfg | 4 ++-- src/jimgw/PE/__init__.py | 1 + src/jimgw/__init__.py | 1 + 3 files changed, 4 insertions(+), 2 deletions(-) create mode 100644 src/jimgw/PE/__init__.py create mode 100644 src/jimgw/__init__.py diff --git a/setup.cfg b/setup.cfg index 9a25c6bb..238f8f6c 100644 --- a/setup.cfg +++ b/setup.cfg @@ -1,6 +1,6 @@ [metadata] name = jimGW -version = 0.0.4.3 +version = 0.0.4.4 author = Kaze Wong author_email = kazewong.physics@gmail.com url = https://github.com/kazewong/jim @@ -20,7 +20,7 @@ install_requires = ripplegw gwpy corner -python_requires = >=3.10,<3.11 +python_requires = >=3.8,<3.11 [options.packages.find] where=src diff --git a/src/jimgw/PE/__init__.py b/src/jimgw/PE/__init__.py new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/src/jimgw/PE/__init__.py @@ -0,0 +1 @@ + diff --git a/src/jimgw/__init__.py b/src/jimgw/__init__.py new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/src/jimgw/__init__.py @@ -0,0 +1 @@ + From 68c9a269bb4ffba6e51af6f09cd6f383d3693345 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Thu, 11 May 2023 11:13:20 -0400 Subject: [PATCH 184/300] Add example and bump version --- example/ParameterEstimation/gw170817.ipynb | 333 +++++++++++++++++++++ setup.cfg | 2 +- 2 files changed, 334 insertions(+), 1 deletion(-) create mode 100644 example/ParameterEstimation/gw170817.ipynb diff --git a/example/ParameterEstimation/gw170817.ipynb b/example/ParameterEstimation/gw170817.ipynb new file mode 100644 index 00000000..a1f64c95 --- /dev/null +++ b/example/ParameterEstimation/gw170817.ipynb @@ -0,0 +1,333 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "eb8130ea-eeb1-48f3-a870-db230d0f93ab", + "metadata": {}, + "source": [ + "# Analyzing GW170817\n", + "\n", + "We will demonstrate how to use _jim_ to analyze the binary neutron star GW170817 using the IMRPhenomD waveform." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "29f96c4b-7aee-4bc0-a9b7-0684291d9091", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "%config InlineBackend.figure_format = 'retina'" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e2290d54-57fa-46d2-a3b4-f2e91b40cc68", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-03-30 14:13:39.741712: W external/org_tensorflow/tensorflow/tsl/platform/default/dso_loader.cc:66] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /mnt/sw/nix/store/wxp5xscxcqq0l1nlrv8k136qs5wqaln6-vscode-1.73.1/lib:/mnt/sw/nix/store/hayjz1l94cb2ky37bhcv71aygjzq7fci-openblas-0.3.21/lib:/cm/shared/apps/slurm/current/lib64:/run/opengl-driver/lib\n", + "2023-03-30 14:13:40.065011: W external/org_tensorflow/tensorflow/tsl/platform/default/dso_loader.cc:66] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /mnt/sw/nix/store/wxp5xscxcqq0l1nlrv8k136qs5wqaln6-vscode-1.73.1/lib:/mnt/sw/nix/store/hayjz1l94cb2ky37bhcv71aygjzq7fci-openblas-0.3.21/lib:/cm/shared/apps/slurm/current/lib64:/run/opengl-driver/lib\n", + "2023-03-30 14:13:40.076833: W external/org_tensorflow/tensorflow/tsl/platform/default/dso_loader.cc:66] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /mnt/sw/nix/store/wxp5xscxcqq0l1nlrv8k136qs5wqaln6-vscode-1.73.1/lib:/mnt/sw/nix/store/hayjz1l94cb2ky37bhcv71aygjzq7fci-openblas-0.3.21/lib:/cm/shared/apps/slurm/current/lib64:/run/opengl-driver/lib\n" + ] + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click here for more info. View Jupyter log for further details." + ] + }, + { + "ename": "", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31mCanceled future for execute_request message before replies were done" + ] + } + ], + "source": [ + "import numpy as np\n", + "import jax.numpy as jnp\n", + "import jax\n", + "\n", + "from gwpy.timeseries import TimeSeries\n", + "from gwpy.frequencyseries import FrequencySeries\n", + "import requests\n", + "\n", + "from astropy.time import Time\n", + "\n", + "from scipy.signal.windows import tukey\n", + "from scipy.interpolate import interp1d\n", + "\n", + "\n", + "from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar\n", + "\n", + "from jaxgw.PE.detector_preset import *\n", + "from jaxgw.PE.heterodyneLikelihood import make_heterodyne_likelihood_mutliple_detector\n", + "from jaxgw.PE.detector_projection import make_detector_response\n", + "\n", + "from flowMC.nfmodel.rqSpline import RQSpline\n", + "from flowMC.sampler.MALA import MALA\n", + "from flowMC.sampler.Sampler import Sampler\n", + "from flowMC.utils.PRNG_keys import initialize_rng_keys\n", + "from flowMC.nfmodel.utils import *" + ] + }, + { + "cell_type": "markdown", + "id": "0ecaca16-1029-47f3-a1f2-46cf8c686209", + "metadata": { + "tags": [] + }, + "source": [ + "## Data and conditioning\n", + "\n", + "We will fetch the GW170817 strain data recorded by LIGO and Virgo from [GWOSC](https://gw-openscience.org) using the [GWpy](https://gwpy.github.io) package; we will also download power-spectral densities (PSDs), made publicly available by LIGO-Virgo." + ] + }, + { + "cell_type": "markdown", + "id": "f02e0e83-05f3-466f-99f6-5ed55a059078", + "metadata": {}, + "source": [ + "### Strain\n", + "\n", + "To do so, we need to know the GPS time associated with the event (in this case, $t = 1187008882.43 s$).\n", + "We also need to prescribe how much data we wish to analyze around the event (in this case, $T = 128 s$, aka, the _segment length_ or _seglen_). We will place the trigger $2 s$ before the end of the analysis segment, following the LVK convention.\n", + "\n", + "> 👉 _**NOTE:** if you don't know the tigger GPS time, you may obtain it from the event name using the [`datasets.event_gps`](https://gwosc.readthedocs.io/en/stable/reference/gwosc.datasets.event_gps.html#event-gps) utility from the [gwosc](https://gwosc.readthedocs.io) package, e.g., `event_gps(\"GW170817\")`_.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "4eb06ba0-e822-4d35-b942-d7317fed1950", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "trigger_time = 1187008882.43\n", + "seglen = 128\n", + "\n", + "# determine segment bounds, placing trigger 2s before the end\n", + "post_trigger_duration = 2\n", + "start = trigger_time - seglen + post_trigger_duration\n", + "end = trigger_time + post_trigger_duration" + ] + }, + { + "cell_type": "markdown", + "id": "681fde34-453e-4918-954a-fe6e89a2eff0", + "metadata": {}, + "source": [ + "With those parameters, we can now fetch the data from GWOSC using `fetch_open_data()`. For GW170817, We make sure to specify `version=2` to get the version of data without the glitch in Livingston (see [GWOSC docs](https://doi.org/10.7935/K5B8566F) for this release)." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "3b929ad4-6c4b-4fbc-9762-95f4a6f8fadb", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "ifos = ['H1', 'L1', 'V1']\n", + "data_td_dict = {i: TimeSeries.fetch_open_data(i, start, end, version=2)\n", + " for i in ifos}" + ] + }, + { + "cell_type": "markdown", + "id": "38c01f46-88e9-4b26-8435-397e14a8503e", + "metadata": {}, + "source": [ + "For the likelihood computation, we will want frequency domain data. We can IFFT the above data after applying a window function; following common LVK practice for this event, we apply a Tukey window with a slope parameter `alpha=0.00625`.\n", + "\n", + "> 👉 _**NOTE:** different `alpha` values may be appropriate for different events, e.g., `alpha = 0.4` is standard for shorter binary black holes._" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "ee162bc3-c25c-4a5a-8762-b0335be47a43", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "tukey_alpha = 0.00625\n", + "data_fd_dict = {}\n", + "for ifo, d in data_td_dict.items():\n", + " w = tukey(len(d), tukey_alpha)\n", + " f = np.fft.rfftfreq(len(d), d=d.dt)\n", + " data_fd_dict[ifo] = FrequencySeries(np.fft.rfft(d*w)/d.dt, frequencies=f)" + ] + }, + { + "cell_type": "markdown", + "id": "2b970fac-7a39-4e26-8de2-961273620880", + "metadata": {}, + "source": [ + "### Power spectral densities (PSDs)" + ] + }, + { + "cell_type": "markdown", + "id": "6a1a4cc9-e4d5-4daf-bf1e-df79dd186738", + "metadata": {}, + "source": [ + "Besides the strain, to compute the likelihood we will need a PSDs characterizing the noise at each detector. Although we could estimate this oursevles directly from the data (e.g., [arXiv:1907.06540](https://arxiv.org/abs/1907.06540)), we will forgo that step and download precomputed PSDs made available by the LVK collaboration in [LIGO-P1800061](https://dcc.ligo.org/LIGO-P1800061/public).\n", + "\n", + "> 👉 _**NOTE:** you may load any PSD you wish for this step, whether from disk or computed on the fly._" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "6e87c095-801e-49c7-829c-53673b42110f", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "psd_url = \"https://dcc.ligo.org/public/0150/P1800061/011/GW170817_PSDs.dat\"\n", + "with requests.get(psd_url) as r:\n", + " psd_data = np.genfromtxt(r.iter_lines())" + ] + }, + { + "cell_type": "markdown", + "id": "869c43f8-7608-4f05-a3ea-0c0fc39ebd71", + "metadata": {}, + "source": [ + "The `psd_data` object is a 2D array where the first column is frequency and the rest are the corresponding PSD values for H1, L1 and V1, in that order. For convenience, and because these PSD data are not uniformly sampled, we will turn this into interpolants that we can evaluate over any frequency bins for each detector." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "eb7e241e-26fa-403f-bf08-f9b696499a26", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "psd_dict = {}\n", + "for i, (ifo, d) in enumerate(data_fd_dict.items()):\n", + " p = interp1d(psd_data[:,0], psd_data[:,i+1], bounds_error=False,\n", + " fill_value=np.inf)\n", + " psd_dict[ifo] = FrequencySeries(p(d.frequencies), frequencies=d.frequencies)" + ] + }, + { + "cell_type": "markdown", + "id": "e9842c0f", + "metadata": {}, + "source": [ + "### Forming the likelihood " + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "a369a6e6-9d77-4c82-a49a-3d421d8c4952", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from jimgw.PE.detector_preset import * \n", + "from jimgw.PE.heterodyneLikelihood import make_heterodyne_likelihood_mutliple_detector\n", + "from jimgw.PE.detector_projection import make_detector_response\n", + "\n", + "H1 = get_H1()\n", + "H1_response = make_detector_response(H1[0], H1[1])\n", + "L1 = get_L1()\n", + "L1_response = make_detector_response(L1[0], L1[1])\n", + "V1 = get_V1()\n", + "V1_response = make_detector_response(V1[0], V1[1])\n", + "\n", + "def LogLikelihood(theta):\n", + " theta = theta.at[1].set(theta[1]/(1+theta[1])**2) # convert q to eta\n", + " theta = theta.at[7].set(jnp.arccos(theta[7])) # convert cos iota to iota\n", + " theta = theta.at[10].set(jnp.arcsin(theta[10])) # convert cos dec to dec\n", + " theta_waveform = theta[:8]\n", + " theta_waveform = theta_waveform.at[5].set(0)\n", + " ra = theta[9]\n", + " dec = theta[10]\n", + " hp_test, hc_test = gen_IMRPhenomD_polar(H1_frequency, theta_waveform, f_ref)\n", + " align_time = jnp.exp(-1j*2*jnp.pi*H1_frequency*(epoch+theta[5]))\n", + " h_test_H1 = H1_response(H1_frequency, hp_test, hc_test, ra, dec, gmst, theta[8]) * align_time\n", + " h_test_L1 = L1_response(L1_frequency, hp_test, hc_test, ra, dec, gmst, theta[8]) * align_time\n", + " h_test_V1 = V1_response(V1_frequency, hp_test, hc_test, ra, dec, gmst, theta[8]) * align_time\n", + " df = H1_frequency[1] - H1_frequency[0]\n", + " match_filter_SNR_H1 = 4*jnp.sum((jnp.conj(h_test_H1)*H1_data)/H1_psd*df).real\n", + " match_filter_SNR_L1 = 4*jnp.sum((jnp.conj(h_test_L1)*L1_data)/L1_psd*df).real\n", + " match_filter_SNR_V1 = 4*jnp.sum((jnp.conj(h_test_V1)*V1_data)/V1_psd*df).real\n", + " optimal_SNR_H1 = 4*jnp.sum((jnp.conj(h_test_H1)*h_test_H1)/H1_psd*df).real\n", + " optimal_SNR_L1 = 4*jnp.sum((jnp.conj(h_test_L1)*h_test_L1)/L1_psd*df).real\n", + " optimal_SNR_V1 = 4*jnp.sum((jnp.conj(h_test_V1)*h_test_V1)/V1_psd*df)." + ] + }, + { + "cell_type": "markdown", + "id": "10cf6b4f-e583-413f-a6c5-a88e0f40a7b2", + "metadata": {}, + "source": [ + "### Constructing the sampler" + ] + }, + { + "cell_type": "markdown", + "id": "a134ec89", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "GW", + "language": "python", + "name": "gw" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/setup.cfg b/setup.cfg index 238f8f6c..d627ccc4 100644 --- a/setup.cfg +++ b/setup.cfg @@ -1,6 +1,6 @@ [metadata] name = jimGW -version = 0.0.4.4 +version = 0.0.4.5 author = Kaze Wong author_email = kazewong.physics@gmail.com url = https://github.com/kazewong/jim From 00f84aeb7ffd8e71c5b13f3c52942e0909e2e93d Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Thu, 11 May 2023 13:24:34 -0400 Subject: [PATCH 185/300] use evosax for optimization --- .../ParameterEstimation/GW170817_optimize.py | 101 +++++++++++------- 1 file changed, 64 insertions(+), 37 deletions(-) diff --git a/example/ParameterEstimation/GW170817_optimize.py b/example/ParameterEstimation/GW170817_optimize.py index 0a5a6c61..b4d90f92 100644 --- a/example/ParameterEstimation/GW170817_optimize.py +++ b/example/ParameterEstimation/GW170817_optimize.py @@ -15,50 +15,38 @@ from jimgw.PE.detector_projection import make_detector_response from flowMC.nfmodel.rqSpline import RQSpline -from flowMC.sampler.MALA import make_mala_sampler +from flowMC.sampler.MALA import MALA from flowMC.sampler.Sampler import Sampler from flowMC.utils.PRNG_keys import initialize_rng_keys from flowMC.nfmodel.utils import * minimum_frequency = 23 -maximum_frequency = 2048 +maximum_frequency = 700 trigger_time = event_gps("GW170817") duration = 128 -post_trigger_duration = 2 +post_trigger_duration = 32 epoch = duration - post_trigger_duration gmst = GreenwichMeanSiderealTime(trigger_time) f_ref = minimum_frequency -H1_data = TimeSeries.read('/mnt/home/misi/projects/cbc_birefringence/GW170817/raw_data/H-H1_LOSC_CLN_4_V1-1187007040-2048.gwf','H1:LOSC-STRAIN') -H1_data = H1_data[(H1_data.times.value >= (trigger_time-epoch)) & (H1_data.times.value <= (trigger_time+post_trigger_duration))] -n = len(H1_data) -H1_data = np.fft.rfft(H1_data.value*tukey(n, 0.00625))/4096. -H1_frequency = np.fft.rfftfreq(n, 1/4096.) -H1_psd = np.genfromtxt('/mnt/home/misi/projects/cbc_birefringence/GW170817/psd_data/h1_psd.txt') -H1_psd = interp1d(H1_psd[:,0], H1_psd[:,1], fill_value=np.inf,bounds_error=False)(H1_frequency[H1_frequency>minimum_frequency]) -H1_data = H1_data[H1_frequency>minimum_frequency] -H1_frequency = H1_frequency[H1_frequency>minimum_frequency] - -L1_data = TimeSeries.read('/mnt/home/misi/projects/cbc_birefringence/GW170817/raw_data/L-L1_LOSC_CLN_4_V1-1187007040-2048.gwf','L1:LOSC-STRAIN') -L1_data = L1_data[(L1_data.times.value >= (trigger_time-epoch)) & (L1_data.times.value <= (trigger_time+post_trigger_duration))] -n = len(L1_data) -L1_data = np.fft.rfft(L1_data.value*tukey(n, 0.00625))/4096. -L1_frequency = np.fft.rfftfreq(n, 1/4096.) -L1_psd = np.genfromtxt('/mnt/home/misi/projects/cbc_birefringence/GW170817/psd_data/l1_psd.txt') -L1_psd = interp1d(L1_psd[:,0], L1_psd[:,1], fill_value=np.inf,bounds_error=False)(L1_frequency[L1_frequency>minimum_frequency]) -L1_data = L1_data[L1_frequency>minimum_frequency] -L1_frequency = L1_frequency[L1_frequency>minimum_frequency] - -V1_data = TimeSeries.read('/mnt/home/misi/projects/cbc_birefringence/GW170817/raw_data/V-V1_LOSC_CLN_4_V1-1187007040-2048.gwf','V1:LOSC-STRAIN') -V1_data = V1_data[(V1_data.times.value >= (trigger_time-epoch)) & (V1_data.times.value <= (trigger_time+post_trigger_duration))] -n = len(V1_data) -V1_data = np.fft.rfft(V1_data.value*tukey(n, 0.00625))/4096. -V1_frequency = np.fft.rfftfreq(n, 1/4096.) -V1_psd = np.genfromtxt('/mnt/home/misi/projects/cbc_birefringence/GW170817/psd_data/v1_psd.txt') -V1_psd = interp1d(V1_psd[:,0], V1_psd[:,1], fill_value=np.inf,bounds_error=False)(V1_frequency[V1_frequency>minimum_frequency]) -V1_data = V1_data[V1_frequency>minimum_frequency] -V1_frequency = V1_frequency[V1_frequency>minimum_frequency] +data = np.load('/mnt/home/wwong/ceph/GWProject/JaxGW/RealtimePE/GW170817_data.npz',allow_pickle=True) + + +H1_frequency = data['frequency'] +H1_data = data['data_dict'].tolist()['H1'][(H1_frequency>minimum_frequency)*(H1_frequencyminimum_frequency)*(H1_frequencyminimum_frequency)*(H1_frequencyminimum_frequency)*(L1_frequencyminimum_frequency)*(L1_frequencyminimum_frequency)*(L1_frequencyminimum_frequency)*(V1_frequencyminimum_frequency)*(V1_frequencyminimum_frequency)*(V1_frequency Date: Fri, 12 May 2023 10:36:12 -0400 Subject: [PATCH 186/300] Add optimize using flowMC evolutionary Optimizer --- .../ParameterEstimation/GW170817_optimize.py | 50 +++++++++++-------- 1 file changed, 28 insertions(+), 22 deletions(-) diff --git a/example/ParameterEstimation/GW170817_optimize.py b/example/ParameterEstimation/GW170817_optimize.py index b4d90f92..5e65f675 100644 --- a/example/ParameterEstimation/GW170817_optimize.py +++ b/example/ParameterEstimation/GW170817_optimize.py @@ -130,25 +130,31 @@ def ridge_reg_objective(params): y(initial_guess) print("Done compiling the function") -import jax -from evosax import CMA_ES - -# Instantiate the search strategy -rng = jax.random.PRNGKey(0) -strategy = CMA_ES(popsize=100, num_dims=11, elite_ratio=0.5) -es_params = strategy.default_params -es_params = es_params.replace(clip_min=0, clip_max=1) -state = strategy.initialize(rng, es_params) - -# Run ask-eval-tell loop - NOTE: By default minimization! -for t in range(1000): - rng, rng_gen, rng_eval = jax.random.split(rng, 3) - x, state = strategy.ask(rng_gen, state, es_params) - theta = x*(prior_range[:,1]-prior_range[:,0]) + prior_range[:,0] - fitness = y(theta) - state = strategy.tell(x, fitness.astype(jnp.float32), state, es_params) - if t % 10 == 0: - print(f"Generation {t}, best fitness: {state.best_fitness}") - -# Get best overall population member & its fitness -state.best_member, state.best_fitness \ No newline at end of file +from flowMC.utils.EvolutionaryOptimizer import EvolutionaryOptimizer + +optimizer = EvolutionaryOptimizer(11, verbose = True) +state = optimizer.optimize(y, prior_range, n_loops=1000) +best_fit = optimizer.get_result()[0] + +# import jax +# from evosax import CMA_ES + +# # Instantiate the search strategy +# rng = jax.random.PRNGKey(0) +# strategy = CMA_ES(popsize=200, num_dims=11, elite_ratio=0.5) +# es_params = strategy.default_params +# es_params = es_params.replace(clip_min=0, clip_max=1) +# state = strategy.initialize(rng, es_params) + +# # Run ask-eval-tell loop - NOTE: By default minimization! +# for t in range(1000): +# rng, rng_gen, rng_eval = jax.random.split(rng, 3) +# x, state = strategy.ask(rng_gen, state, es_params) +# theta = x*(prior_range[:,1]-prior_range[:,0]) + prior_range[:,0] +# fitness = y(theta) +# state = strategy.tell(x, fitness.astype(jnp.float32), state, es_params) +# if t % 10 == 0: +# print(f"Generation {t}, best fitness: {state.best_fitness}") + +# # Get best overall population member & its fitness +# state.best_member, state.best_fitness \ No newline at end of file From 089932335708c85e7a9076614e82618df32f4281 Mon Sep 17 00:00:00 2001 From: Max Isi Date: Tue, 30 May 2023 09:25:54 -0400 Subject: [PATCH 187/300] bug fixes (td ap prep) --- src/jaxgw/detector.py | 20 ++++++++++++-------- src/jaxgw/likelihood.py | 17 ++++++++++++----- 2 files changed, 24 insertions(+), 13 deletions(-) diff --git a/src/jaxgw/detector.py b/src/jaxgw/detector.py index 0baaa9f7..81f2443d 100644 --- a/src/jaxgw/detector.py +++ b/src/jaxgw/detector.py @@ -20,10 +20,10 @@ def __init__(self, name, coordinates=None): self._coordinates = coordinates or {} @property - def coordinates(self) + def coordinates(self): """Coordinates defining a triangular detector (angles in radians). """ - if not self._coordinates + if not self._coordinates: if self.name == 'H1': # LIGO Hanford self._coordinates = dict( @@ -61,7 +61,7 @@ def coordinates(self) raise ValueError(f"unknown detector {self.name}") return self._coordinates - @static + @staticmethod def _get_arm(lat, lon, tilt, azimuth): """Construct detector-arm vectors in Earth-centric Cartesian coordinates. @@ -91,8 +91,8 @@ def arms(self): """Detector arm vectors (x, y). """ c = self.coordinates - x = self._get_arm(c['lat'], c['lon'], c['xarm_tilt'], c['xarm_azimuth']) - y = self._get_arm(c['lat'], c['lon'], c['yarm_tilt'], c['yarm_azimuth']) + x = self._get_arm(c['lat'], c['lon'], c['xarm_tilt'], c['xarm_azimuth']) + y = self._get_arm(c['lat'], c['lon'], c['yarm_tilt'], c['yarm_azimuth']) return x, y @property @@ -117,8 +117,8 @@ def vertex(self): major, minor = EARTH_SEMI_MAJOR_AXIS, EARTH_SEMI_MINOR_AXIS # compute vertex location r = major**2*(major**2*jnp.cos(lat)**2 + minor**2*jnp.sin(lat)**2)**(-0.5) - x = (radius + h) * jnp.cos(lat) * jnp.cos(lon) - y = (radius + h) * jnp.cos(lat) * jnp.sin(lon) + x = (r + h) * jnp.cos(lat) * jnp.cos(lon) + y = (r + h) * jnp.cos(lat) * jnp.sin(lon) z = ((minor / major)**2 * r + h)*jnp.sin(lat) return jnp.array([x, y, z]) @@ -199,7 +199,7 @@ def aps(ra, dec, psi, gmst): antenna_patterns.append(ap) return antenna_patterns aps.__doc__ = aps.__doc__.format(name=self.name, modes=str(modes)) - return antenna_patterns + return aps def construct_fd_response(self, modes='pc', epoch=0.): """Generates a function to return the Fourier-domain projection of an @@ -263,6 +263,10 @@ def get_det_h(f, polwaveforms, ra, dec, psi, gmst, tc): dt_geo = get_delay(ra, dec, gmst) aps = get_aps(ra, dec, psi, gmst) h = jnp.sum([aps[i]*polwaveforms[i] for i in len(aps)], axis=0) + # note, under our sign convention the phase shift below corresponds + # to a time shift t -> t - dt_geo - tc + epoch + # this makes sense: a waveform tha that peaks at t=0 at geocenter + # will peak at t=dt_geo at the detector, so dt is indeed a delay h *= jnp.exp(-2j*jnp.pi*f*(dt_geo + tc - epoch)) return h get_det_h.__doc__ = get_det_h.__doc__.format(p=str(modes), i=self.name, diff --git a/src/jaxgw/likelihood.py b/src/jaxgw/likelihood.py index 9c27faaf..cf6a5c00 100644 --- a/src/jaxgw/likelihood.py +++ b/src/jaxgw/likelihood.py @@ -12,11 +12,18 @@ class LogLikelihoodTransientFD(object): waveform : frequency-domain waveform function, must accept an array of frequencies and a set of parameter values, like - `waveform(frequencies, [param1, param2, ...])` + `waveform(frequencies, [param1, param2, ...])`. + heterodyne : bool + whether to approximate likelihood through a heteredoyne. + earth_rotation : bool + whether to include Earth's rotation in the antenna pattern. """ - def __init__(self, waveform, heterodyne=True): + def __init__(self, waveform, heterodyne=False, earth_rotation=False): self.waveform = waveform self.heterodyne = heterodyne + # whether to include Earth's rotation in the antenna pattern + # TODO: implement automatic defaults based on IFO names + self.earth_rotation = earth_rotation self.data = {} self.psds = {} @@ -39,9 +46,9 @@ def add_data(self, ifo, data, **kws): if isinstance(data, FrequencySeries): self.data[ifo] = data else: - self.data[ifo] - FrequencySeries(data, **kws) + self.data[ifo] = FrequencySeries(data, **kws) - def add_psd(self, ifo, data, **kws): + def add_psd(self, ifo, psd, **kws): """Add power spectral density (PSD) for the noise of a given detector. Arguments @@ -54,5 +61,5 @@ def add_psd(self, ifo, data, **kws): if isinstance(psd, FrequencySeries): self.psd[ifo] = psd else: - self.psd[ifo] - FrequencySeries(psd, **kws) + self.psd[ifo] = FrequencySeries(psd, **kws) From e117092c57040297d115831e2add15063597485d Mon Sep 17 00:00:00 2001 From: Max Isi Date: Tue, 30 May 2023 12:43:28 -0400 Subject: [PATCH 188/300] earth rotation APs draft --- src/jaxgw/constants.py | 3 ++ src/jaxgw/detector.py | 69 +++++++++++++++++++++++++++++++++++++----- src/jaxgw/wave.py | 4 +-- 3 files changed, 66 insertions(+), 10 deletions(-) diff --git a/src/jaxgw/constants.py b/src/jaxgw/constants.py index 74237b37..ea3ae17c 100644 --- a/src/jaxgw/constants.py +++ b/src/jaxgw/constants.py @@ -11,3 +11,6 @@ EARTH_SEMI_MAJOR_AXIS = 6378137 # for ellipsoid model of Earth, in m EARTH_SEMI_MINOR_AXIS = 6356752.314 # in m + +DAYSID_SI = 86164.09053133354 +DAYJUL_SI = 86400.0 \ No newline at end of file diff --git a/src/jaxgw/detector.py b/src/jaxgw/detector.py index 81f2443d..b8920933 100644 --- a/src/jaxgw/detector.py +++ b/src/jaxgw/detector.py @@ -1,10 +1,18 @@ import jax.numpy as jnp from .constants import * from .wave import Polarization +from scipy.signal.windows import tukey DEG_TO_RAD = jnp.pi/180 +def np2(x): + """Returns the next power of two as big as or larger than x.""" + p = 1 + while p < x: + p = p << 1 + return p + class Detector(object): """Defines a ground-based gravitational-wave detector. @@ -158,12 +166,16 @@ def delay(ra, dec, gmst): def antenna_pattern_constructor(self, modes='pc'): """Gives function to compute antenna patterns for any sky location, - polarization angle and GMST. + polarization angle and GMST. The antenna pattern is defined + instantaneously under the long-wavelength approximation. Arguments --------- modes : list,str list of polarizations to include, defaults to tensor modes: 'pc'. + aps : func + function to compute antenna patterns for any sky location, + polarization angle and GMST. """ detector_tensor = self.tensor wave_tensor_functions = [Polarization(m).tensor_from_sky_constructor @@ -201,7 +213,8 @@ def aps(ra, dec, psi, gmst): aps.__doc__ = aps.__doc__.format(name=self.name, modes=str(modes)) return aps - def construct_fd_response(self, modes='pc', epoch=0.): + def construct_fd_response(self, modes='pc', epoch=0., earth_rotation=False, + earth_rotation_times=None, data_frequencies=None): """Generates a function to return the Fourier-domain projection of an arbitrary gravitational wave onto this detector, starting from FD polarizations defined at geocenter. @@ -212,6 +225,9 @@ def construct_fd_response(self, modes='pc', epoch=0.): polarizations to include in response, defaults to tensor modes 'pc' epoch : float time corresponding to beginning of segment, def. 0. + earth_rotation : bool + whether to account for Earth rotation in antenna patterns, + def. False. Returns ------- @@ -221,6 +237,28 @@ def construct_fd_response(self, modes='pc', epoch=0.): """ get_delay = self.delay_from_geocenter_constructor get_aps = self.antenna_pattern_constructor(modes) + if earth_rotation: + if earth_rotation_times is None: + if data_frequencies is None: + raise ValueError("Must provide data frequencies and epch," + "or explicit time grid to evaluate antenna " + "patterns under Earth rotation.") + else: + # TODO: move this to likelihood! construct_fd_response + # should only accept a time grid. + + # construct time grid on which to evaluate antenna patterns + # the time grid should as long as the data segment implied + # by the provided frequency array, i.e, T = 1/df. + seglen = 1/(data_frequencies[1] - data_frequencies[0]) + # the grid spacing should be as coarse as possible while + # still resolving the evolution of the antenna patterns, + # which has characterisitc frequency of up to 2/(sid_day). + dt_sid = np2(DAYSID_SI)/4 + N = len(data_frequencies) + earth_rotation_times = jnp.arange(N)*dt_sid + epoch + w = tukey(N, 0.1) # TODO: do not hard code alpha! + def get_det_h(f, polwaveforms, ra, dec, psi, gmst, tc): """Project Fourier-domain '{p}' polarizations onto {i} detector, taking into account antenna patterns and time of flight from @@ -260,13 +298,28 @@ def get_det_h(f, polwaveforms, ra, dec, psi, gmst, tc): h : array Fourier domain detector response. """ - dt_geo = get_delay(ra, dec, gmst) - aps = get_aps(ra, dec, psi, gmst) - h = jnp.sum([aps[i]*polwaveforms[i] for i in len(aps)], axis=0) - # note, under our sign convention the phase shift below corresponds - # to a time shift t -> t - dt_geo - tc + epoch + dt_geo = get_delay(ra, dec, gmst) + if earth_rotation: + # antenna patterns are a function of time to be evaluated at + # sparsely over a grid of times spanning the data segment + # the result will be FFTed and convolved with the waveform + aps = jnp.vectorize(get_aps)(ra, dec, psi, earth_rotation_times) + delta_t = 0.5 / f[-1] + aps_fd = jnp.fft.rfft(aps*w[:,jnp.newaxis], axis=0) * delta_t + # TODO: ^do we want to zero pad? + # now, we convolve the antenna patterns with the polarizations + h = jnp.zeros_like(polwaveforms[0]) + for p in range(len(modes)): + # TODO: do we want jnp.fftconvolve? + h += jnp.convolve(polwaveforms[p], aps_fd[:,p], mode='same') + else: + aps = get_aps(ra, dec, psi, gmst) + h = jnp.sum([aps[i]*polwaveforms[i] for i in len(aps)], axis=0) + # note, under our sign convention for the Fourier transform the + # phase shift below corresponds to a time shift + # ``t -> t - dt_geo - tc + epoch`` # this makes sense: a waveform tha that peaks at t=0 at geocenter - # will peak at t=dt_geo at the detector, so dt is indeed a delay + # will peak at t=dt_geo at the detector, so dt is indeed a delay. h *= jnp.exp(-2j*jnp.pi*f*(dt_geo + tc - epoch)) return h get_det_h.__doc__ = get_det_h.__doc__.format(p=str(modes), i=self.name, diff --git a/src/jaxgw/wave.py b/src/jaxgw/wave.py index 9afd4667..280f3e0d 100644 --- a/src/jaxgw/wave.py +++ b/src/jaxgw/wave.py @@ -1,7 +1,7 @@ # Credit some part of the source code from bilby import jax.numpy as jnp -from jaxgw.PE.constants import * +from .constants import * KNOWN_POLS = 'pcxybl' @@ -61,7 +61,7 @@ def kernel(x, y): z = jnp.cross(x, y) return jnp.einsum('i,j->ij', z, z) else: - raise ValueError(f"unrecognized polarization {self.name}" + raise ValueError(f"unrecognized polarization {self.name}") return kernel @property From 741f24f4c342eda323f1f710d6b0cef644cab66a Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Thu, 1 Jun 2023 10:22:04 -0400 Subject: [PATCH 189/300] Create python-publish.yml --- .github/workflows/python-publish.yml | 39 ++++++++++++++++++++++++++++ 1 file changed, 39 insertions(+) create mode 100644 .github/workflows/python-publish.yml diff --git a/.github/workflows/python-publish.yml b/.github/workflows/python-publish.yml new file mode 100644 index 00000000..30f2bc2f --- /dev/null +++ b/.github/workflows/python-publish.yml @@ -0,0 +1,39 @@ +# This workflow will upload a Python Package using Twine when a release is created +# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python#publishing-to-package-registries + +# This workflow uses actions that are not certified by GitHub. +# They are provided by a third-party and are governed by +# separate terms of service, privacy policy, and support +# documentation. + +name: Upload Python Package + +on: + release: + types: [published] + +permissions: + contents: read + +jobs: + deploy: + + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v3 + - name: Set up Python + uses: actions/setup-python@v3 + with: + python-version: '3.x' + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install build + - name: Build package + run: python -m build + - name: Publish package + uses: pypa/gh-action-pypi-publish@v1.8.4 + with: + user: __token__ + password: ${{ secrets.PYPI_API_TOKEN }} From c002a8edcdae009461960f68753363f99beab0e1 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Wed, 5 Jul 2023 15:10:11 -0400 Subject: [PATCH 190/300] Blow examples up --- example/DataProcessing.py | 17 - example/{ParameterEstimation => }/GW150914.py | 0 example/{ParameterEstimation => }/GW170817.py | 0 .../Injection_test.py | 0 .../Injection_withParser.py | 0 .../Injection_withParserBNS.py | 0 .../Injection_withParser_debug.py | 0 example/JaxPjitExperiment.py | 24 -- .../ParameterEstimation/GW170817_optimize.py | 160 --------- .../configs/BNS_injection_example.yaml | 0 .../configs/injection_debug.yaml | 0 .../configs/injection_example.yaml | 0 .../ParameterEstimation.md => examples.md} | 0 .../gen_injection_config.py | 0 example/gw170817.ipynb | 313 ------------------ .../{ParameterEstimation => }/make_ppPlot.py | 0 .../AnalyzeInjection.ipynb | 4 +- .../GW150914.ipynb | 0 .../gw170817.ipynb | 0 example/run_GW170817_nolal.py | 179 ---------- {example => test}/ProjectionCrossCheck.py | 0 21 files changed, 2 insertions(+), 695 deletions(-) delete mode 100644 example/DataProcessing.py rename example/{ParameterEstimation => }/GW150914.py (100%) rename example/{ParameterEstimation => }/GW170817.py (100%) rename example/{ParameterEstimation => }/Injection_test.py (100%) rename example/{ParameterEstimation => }/Injection_withParser.py (100%) rename example/{ParameterEstimation => }/Injection_withParserBNS.py (100%) rename example/{ParameterEstimation => }/Injection_withParser_debug.py (100%) delete mode 100644 example/JaxPjitExperiment.py delete mode 100644 example/ParameterEstimation/GW170817_optimize.py rename example/{ParameterEstimation => }/configs/BNS_injection_example.yaml (100%) rename example/{ParameterEstimation => }/configs/injection_debug.yaml (100%) rename example/{ParameterEstimation => }/configs/injection_example.yaml (100%) rename example/{ParameterEstimation/ParameterEstimation.md => examples.md} (100%) rename example/{ParameterEstimation => }/gen_injection_config.py (100%) delete mode 100644 example/gw170817.ipynb rename example/{ParameterEstimation => }/make_ppPlot.py (100%) rename example/{ParameterEstimation => notebooks}/AnalyzeInjection.ipynb (99%) rename example/{ParameterEstimation => notebooks}/GW150914.ipynb (100%) rename example/{ParameterEstimation => notebooks}/gw170817.ipynb (100%) delete mode 100644 example/run_GW170817_nolal.py rename {example => test}/ProjectionCrossCheck.py (100%) diff --git a/example/DataProcessing.py b/example/DataProcessing.py deleted file mode 100644 index ddfa4463..00000000 --- a/example/DataProcessing.py +++ /dev/null @@ -1,17 +0,0 @@ -from gwosc.datasets import event_gps -gps = event_gps("GW150914") -start = int(gps) - 16 -end = int(gps) + 16 - -from gwpy.timeseries import TimeSeries -data = TimeSeries.fetch_open_data('L1', start, end) - -from scipy.signal.windows import tukey - -data_window = data * tukey(len(data), alpha=0.2) -data_fft = data_window.fft() -f = data_fft.frequencies.value - -from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar - -params = jnp.array([30,0.249, 0.1,0.1, 400, 0.0, 0.0, 0.1, 0.0]) diff --git a/example/ParameterEstimation/GW150914.py b/example/GW150914.py similarity index 100% rename from example/ParameterEstimation/GW150914.py rename to example/GW150914.py diff --git a/example/ParameterEstimation/GW170817.py b/example/GW170817.py similarity index 100% rename from example/ParameterEstimation/GW170817.py rename to example/GW170817.py diff --git a/example/ParameterEstimation/Injection_test.py b/example/Injection_test.py similarity index 100% rename from example/ParameterEstimation/Injection_test.py rename to example/Injection_test.py diff --git a/example/ParameterEstimation/Injection_withParser.py b/example/Injection_withParser.py similarity index 100% rename from example/ParameterEstimation/Injection_withParser.py rename to example/Injection_withParser.py diff --git a/example/ParameterEstimation/Injection_withParserBNS.py b/example/Injection_withParserBNS.py similarity index 100% rename from example/ParameterEstimation/Injection_withParserBNS.py rename to example/Injection_withParserBNS.py diff --git a/example/ParameterEstimation/Injection_withParser_debug.py b/example/Injection_withParser_debug.py similarity index 100% rename from example/ParameterEstimation/Injection_withParser_debug.py rename to example/Injection_withParser_debug.py diff --git a/example/JaxPjitExperiment.py b/example/JaxPjitExperiment.py deleted file mode 100644 index bb0ff210..00000000 --- a/example/JaxPjitExperiment.py +++ /dev/null @@ -1,24 +0,0 @@ -import jax -import jax.numpy as jnp -from jax.experimental import maps -from jax.experimental import PartitionSpec -from jax.experimental.pjit import pjit -from jax.scipy.special import logsumexp - -import numpy as np - -def dual_moon_pe(x): - """ - Term 2 and 3 separate the distribution and smear it along the first and second dimension - """ - term1 = 0.5 * ((jnp.linalg.norm(x) - 2) / 0.1) ** 2 - term2 = -0.5 * ((x[:1] + jnp.array([-3.0, 3.0])) / 0.8) ** 2 - term3 = -0.5 * ((x[1:2] + jnp.array([-3.0, 3.0])) / 0.6) ** 2 - return -(term1 - logsumexp(term2) - logsumexp(term3)) - -mesh_shape = (4, 1) -devices = np.asarray(jax.devices()).reshape(*mesh_shape) -# 'x', 'y' axis names are used here for simplicity -mesh = maps.Mesh(devices, ('x', 'y')) - -input_data = jnp.array(np.random.uniform(size=(100000,5))) diff --git a/example/ParameterEstimation/GW170817_optimize.py b/example/ParameterEstimation/GW170817_optimize.py deleted file mode 100644 index 5e65f675..00000000 --- a/example/ParameterEstimation/GW170817_optimize.py +++ /dev/null @@ -1,160 +0,0 @@ -import numpy as np -import jax.numpy as jnp -import jax - -from lal import GreenwichMeanSiderealTime -from gwosc.datasets import event_gps -from gwpy.timeseries import TimeSeries -from scipy.signal.windows import tukey -from scipy.interpolate import interp1d - - -from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar -from jimgw.PE.detector_preset import * -from jimgw.PE.heterodyneLikelihood import make_heterodyne_likelihood_mutliple_detector -from jimgw.PE.detector_projection import make_detector_response - -from flowMC.nfmodel.rqSpline import RQSpline -from flowMC.sampler.MALA import MALA -from flowMC.sampler.Sampler import Sampler -from flowMC.utils.PRNG_keys import initialize_rng_keys -from flowMC.nfmodel.utils import * - -minimum_frequency = 23 -maximum_frequency = 700 - -trigger_time = event_gps("GW170817") -duration = 128 -post_trigger_duration = 32 -epoch = duration - post_trigger_duration -gmst = GreenwichMeanSiderealTime(trigger_time) -f_ref = minimum_frequency - -data = np.load('/mnt/home/wwong/ceph/GWProject/JaxGW/RealtimePE/GW170817_data.npz',allow_pickle=True) - - -H1_frequency = data['frequency'] -H1_data = data['data_dict'].tolist()['H1'][(H1_frequency>minimum_frequency)*(H1_frequencyminimum_frequency)*(H1_frequencyminimum_frequency)*(H1_frequencyminimum_frequency)*(L1_frequencyminimum_frequency)*(L1_frequencyminimum_frequency)*(L1_frequencyminimum_frequency)*(V1_frequencyminimum_frequency)*(V1_frequencyminimum_frequency)*(V1_frequency 👉 _**NOTE:** if you don't know the tigger GPS time, you may obtain it from the event name using the [`datasets.event_gps`](https://gwosc.readthedocs.io/en/stable/reference/gwosc.datasets.event_gps.html#event-gps) utility from the [gwosc](https://gwosc.readthedocs.io) package, e.g., `event_gps(\"GW170817\")`_.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "4eb06ba0-e822-4d35-b942-d7317fed1950", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "trigger_time = 1187008882.43\n", - "seglen = 128\n", - "\n", - "# determine segment bounds, placing trigger 2s before the end\n", - "post_trigger_duration = 2\n", - "start = trigger_time - seglen + post_trigger_duration\n", - "end = trigger_time + post_trigger_duration" - ] - }, - { - "cell_type": "markdown", - "id": "681fde34-453e-4918-954a-fe6e89a2eff0", - "metadata": {}, - "source": [ - "With those parameters, we can now fetch the data from GWOSC using `fetch_open_data()`. For GW170817, We make sure to specify `version=2` to get the version of data without the glitch in Livingston (see [GWOSC docs](https://doi.org/10.7935/K5B8566F) for this release)." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "3b929ad4-6c4b-4fbc-9762-95f4a6f8fadb", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "ifos = ['H1', 'L1', 'V1']\n", - "data_td_dict = {i: TimeSeries.fetch_open_data(i, start, end, version=2)\n", - " for i in ifos}" - ] - }, - { - "cell_type": "markdown", - "id": "38c01f46-88e9-4b26-8435-397e14a8503e", - "metadata": {}, - "source": [ - "For the likelihood computation, we will want frequency domain data. We can IFFT the above data after applying a window function; following common LVK practice for this event, we apply a Tukey window with a slope parameter `alpha=0.00625`.\n", - "\n", - "> 👉 _**NOTE:** different `alpha` values may be appropriate for different events, e.g., `alpha = 0.4` is standard for shorter binary black holes._" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "ee162bc3-c25c-4a5a-8762-b0335be47a43", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "tukey_alpha = 0.00625\n", - "data_fd_dict = {}\n", - "for ifo, d in data_td_dict.items():\n", - " w = tukey(len(d), tukey_alpha)\n", - " f = np.fft.rfftfreq(len(d), d=d.dt)\n", - " data_fd_dict[ifo] = FrequencySeries(np.fft.rfft(d*w)/d.dt, frequencies=f)" - ] - }, - { - "cell_type": "markdown", - "id": "2b970fac-7a39-4e26-8de2-961273620880", - "metadata": {}, - "source": [ - "### Power spectral densities (PSDs)" - ] - }, - { - "cell_type": "markdown", - "id": "6a1a4cc9-e4d5-4daf-bf1e-df79dd186738", - "metadata": {}, - "source": [ - "Besides the strain, to compute the likelihood we will need a PSDs characterizing the noise at each detector. Although we could estimate this oursevles directly from the data (e.g., [arXiv:1907.06540](https://arxiv.org/abs/1907.06540)), we will forgo that step and download precomputed PSDs made available by the LVK collaboration in [LIGO-P1800061](https://dcc.ligo.org/LIGO-P1800061/public).\n", - "\n", - "> 👉 _**NOTE:** you may load any PSD you wish for this step, whether from disk or computed on the fly._" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "6e87c095-801e-49c7-829c-53673b42110f", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "psd_url = \"https://dcc.ligo.org/public/0150/P1800061/011/GW170817_PSDs.dat\"\n", - "with requests.get(psd_url) as r:\n", - " psd_data = np.genfromtxt(r.iter_lines())" - ] - }, - { - "cell_type": "markdown", - "id": "869c43f8-7608-4f05-a3ea-0c0fc39ebd71", - "metadata": {}, - "source": [ - "The `psd_data` object is a 2D array where the first column is frequency and the rest are the corresponding PSD values for H1, L1 and V1, in that order. For convenience, and because these PSD data are not uniformly sampled, we will turn this into interpolants that we can evaluate over any frequency bins for each detector." - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "eb7e241e-26fa-403f-bf08-f9b696499a26", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "psd_dict = {}\n", - "for i, (ifo, d) in enumerate(data_fd_dict.items()):\n", - " p = interp1d(psd_data[:,0], psd_data[:,i+1], bounds_error=False,\n", - " fill_value=np.inf)\n", - " psd_dict[ifo] = FrequencySeries(p(d.frequencies), frequencies=d.frequencies)" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "id": "f307d2b8-d2d7-45ad-9871-1ee420014fd9", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "a = 1\n", - "b = 2\n", - "\n", - "def test_func(x):\n", - " \"\"\"Test string {a}.\n", - "\n", - " This is a stest {b}.\n", - " \"\"\"\n", - " return 2*x\n", - "\n", - "test_func.__doc__ = test_func.__doc__.format(a=a, b=b)" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "id": "782054dc-2ef2-4de6-9321-c911224b5fd6", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "\u001b[0;31mSignature:\u001b[0m \u001b[0mtest_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mDocstring:\u001b[0m\n", - "Test string 1.\n", - "\n", - "This is a stest 2.\n", - "\u001b[0;31mFile:\u001b[0m /tmp/ipykernel_2794160/1016926636.py\n", - "\u001b[0;31mType:\u001b[0m function" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "test_func?" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "id": "a369a6e6-9d77-4c82-a49a-3d421d8c4952", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.deg" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "10cf6b4f-e583-413f-a6c5-a88e0f40a7b2", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.4" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/example/ParameterEstimation/make_ppPlot.py b/example/make_ppPlot.py similarity index 100% rename from example/ParameterEstimation/make_ppPlot.py rename to example/make_ppPlot.py diff --git a/example/ParameterEstimation/AnalyzeInjection.ipynb b/example/notebooks/AnalyzeInjection.ipynb similarity index 99% rename from example/ParameterEstimation/AnalyzeInjection.ipynb rename to example/notebooks/AnalyzeInjection.ipynb index d76e567b..338b5b6d 100644 --- a/example/ParameterEstimation/AnalyzeInjection.ipynb +++ b/example/notebooks/AnalyzeInjection.ipynb @@ -283,7 +283,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.10.4 ('GW')", + "display_name": "Python 3.10.4 ('jim')", "language": "python", "name": "python3" }, @@ -301,7 +301,7 @@ }, "vscode": { "interpreter": { - "hash": "c1b26637a459b71d5a98be81c2c552e2aef4ac924b44e1d1dcc4c383679c0a72" + "hash": "6f4a06cc15340400a6203557802841098014e10be4eca28bd03d990b467d3dc1" } } }, diff --git a/example/ParameterEstimation/GW150914.ipynb b/example/notebooks/GW150914.ipynb similarity index 100% rename from example/ParameterEstimation/GW150914.ipynb rename to example/notebooks/GW150914.ipynb diff --git a/example/ParameterEstimation/gw170817.ipynb b/example/notebooks/gw170817.ipynb similarity index 100% rename from example/ParameterEstimation/gw170817.ipynb rename to example/notebooks/gw170817.ipynb diff --git a/example/run_GW170817_nolal.py b/example/run_GW170817_nolal.py deleted file mode 100644 index 60e7232e..00000000 --- a/example/run_GW170817_nolal.py +++ /dev/null @@ -1,179 +0,0 @@ -import numpy as np -import jax.numpy as jnp -import jax - -#from lal import GreenwichMeanSiderealTime -from astropy.time import Time -from gwosc.datasets import event_gps -from gwpy.timeseries import TimeSeries -from scipy.signal.windows import tukey -from scipy.interpolate import interp1d - - -from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar -from jaxgw.PE.detector_preset import * -from jaxgw.PE.heterodyneLikelihood import make_heterodyne_likelihood_mutliple_detector -from jaxgw.PE.detector_projection import make_detector_response - -from flowMC.nfmodel.rqSpline import RQSpline -from flowMC.sampler.MALA import MALA -from flowMC.sampler.Sampler import Sampler -from flowMC.utils.PRNG_keys import initialize_rng_keys -from flowMC.nfmodel.utils import * - -minimum_frequency = 23 -maximum_frequency = 1792 - -trigger_time = 1187008882.43 -duration = 128 -post_trigger_duration = 2 -epoch = duration - post_trigger_duration -#gmst = GreenwichMeanSiderealTime(trigger_time) -gmst = Time(trigger_time, format='gps').sidereal_time('apparent', 'greenwich').rad -f_ref = 20#minimum_frequency - -H1_frequency, H1_data_re, H1_data_im = np.genfromtxt('../data/GW170817-IMRD_data0_1187008882-43_generation_data_dump.pickle_H1_fd_strain.txt').T -H1_data = H1_data_re + 1j*H1_data_im -H1_psd_frequency, H1_psd = np.genfromtxt('../data/GW170817-IMRD_data0_1187008882-43_generation_data_dump.pickle_H1_psd.txt').T - -H1_data = H1_data[(H1_frequency>minimum_frequency)*(H1_frequencyminimum_frequency)*(H1_frequencyminimum_frequency)*(H1_frequencyminimum_frequency)*(L1_frequencyminimum_frequency)*(L1_frequencyminimum_frequency)*(L1_frequencyminimum_frequency)*(V1_frequencyminimum_frequency)*(V1_frequencyminimum_frequency)*(V1_frequency0.25,1] = 0.249 - - -print("Preparing RNG keys") -rng_key_set = initialize_rng_keys(n_chains, seed=42) - -print("Initializing MCMC model and normalizing flow model.") - -prior_range = jnp.array([[1.18,1.21],[0.125,1],[-0.05,0.05],[-0.05,0.05],[1,75],[-0.01,0.02],[0,2*np.pi],[-1,1],[0,np.pi],[0,2*np.pi],[-1,1]]) -optimize_prior_range = jnp.array([[1.18,1.21],[0.2,0.25],[0.0,0.3],[0.0,0.3],[1,75],[-0.01,0.02],[0,2*np.pi],[0,np.pi],[0,np.pi],[0,2*np.pi],[-np.pi/2,np.pi/2]]) - -initial_position = jax.random.uniform(rng_key_set[0], shape=(int(n_chains), n_dim)) * 1 -for i in range(n_dim): - initial_position = initial_position.at[:,i].set(initial_position[:,i]*(prior_range[i,1]-prior_range[i,0])+prior_range[i,0]) - -from ripple import Mc_eta_to_ms -m1,m2 = jax.vmap(Mc_eta_to_ms)(guess_param[:,:2]) -q = m2/m1 - -from astropy.cosmology import Planck18 as cosmo - -z = np.linspace(0.0002,0.03,10000) -dL = cosmo.luminosity_distance(z).value -dVdz = cosmo.differential_comoving_volume(z).value - -def top_hat(x): - output = 0. - for i in range(n_dim): - output = jax.lax.cond(x[i]>=prior_range[i,0], lambda: output, lambda: -jnp.inf) - output = jax.lax.cond(x[i]<=prior_range[i,1], lambda: output, lambda: -jnp.inf) - return output+jnp.log(jnp.interp(x[4],dL,dVdz)) - -def log_likelihood(theta): - q = theta[1] - theta = theta.at[1].set(q/(1+q)**2) # convert q to eta - theta = theta.at[7].set(jnp.arccos(theta[7])) # convert cos iota to iota - theta = theta.at[10].set(jnp.arcsin(theta[10])) # convert cos dec to dec - return logL(theta) - -def posterior(theta): - log_prior = top_hat(theta) - log_like = log_likelihood(theta) - - return log_like + log_prior - -model = RQSpline(n_dim, 10, [128,128], 8) - -print("Initializing sampler class") - -posterior = posterior - -mass_matrix = jnp.eye(n_dim) -mass_matrix = mass_matrix.at[0,0].set(1e-5) -mass_matrix = mass_matrix.at[1,1].set(1e-4) -mass_matrix = mass_matrix.at[2,2].set(1e-3) -mass_matrix = mass_matrix.at[3,3].set(1e-3) -mass_matrix = mass_matrix.at[5,5].set(1e-5) -mass_matrix = mass_matrix.at[9,9].set(1e-2) -mass_matrix = mass_matrix.at[10,10].set(1e-2) - -local_sampler = MALA(posterior, True, {"step_size": mass_matrix*3e-2}) -print("Running sampler") - -nf_sampler = Sampler( - n_dim, - rng_key_set, - local_sampler, - posterior, - model, - n_loop_training=n_loop_training, - n_loop_production = n_loop_production, - n_local_steps=n_local_steps, - n_global_steps=n_global_steps, - n_chains=n_chains, - n_epochs=num_epochs, - learning_rate=learning_rate, - momentum=momentum, - batch_size=batch_size, - use_global=True, - keep_quantile=0., - train_thinning = 40, -) - -nf_sampler.sample(initial_position) -chains, log_prob, local_accs, global_accs = nf_sampler.get_sampler_state().values() -np.savez('../data/GW170817.npz', chains=chains, log_prob=log_prob, local_accs=local_accs, global_accs=global_accs) diff --git a/example/ProjectionCrossCheck.py b/test/ProjectionCrossCheck.py similarity index 100% rename from example/ProjectionCrossCheck.py rename to test/ProjectionCrossCheck.py From 8e85e0f0e416d4b9deceb07997239ef3dd1f80fc Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Wed, 5 Jul 2023 15:11:38 -0400 Subject: [PATCH 191/300] Nuke jaxgw folder --- example/examples.md | 8 ++++++++ src/{jaxgw => jimgw}/constants.py | 0 src/{jaxgw => jimgw}/detector.py | 0 src/{jaxgw => jimgw}/generate_noise.py | 0 src/{jaxgw => jimgw}/heterodyneLikelihood.py | 0 src/{jaxgw => jimgw}/likelihood.py | 0 src/{jaxgw => jimgw}/single_event_likelihood.py | 0 src/{jaxgw => jimgw}/time_and_date.py | 0 src/{jaxgw => jimgw}/utils.py | 0 src/{jaxgw => jimgw}/wave.py | 0 10 files changed, 8 insertions(+) rename src/{jaxgw => jimgw}/constants.py (100%) rename src/{jaxgw => jimgw}/detector.py (100%) rename src/{jaxgw => jimgw}/generate_noise.py (100%) rename src/{jaxgw => jimgw}/heterodyneLikelihood.py (100%) rename src/{jaxgw => jimgw}/likelihood.py (100%) rename src/{jaxgw => jimgw}/single_event_likelihood.py (100%) rename src/{jaxgw => jimgw}/time_and_date.py (100%) rename src/{jaxgw => jimgw}/utils.py (100%) rename src/{jaxgw => jimgw}/wave.py (100%) diff --git a/example/examples.md b/example/examples.md index 2b314472..28578b5b 100644 --- a/example/examples.md +++ b/example/examples.md @@ -2,6 +2,14 @@ In this subfolder we host the examples and tools for parameter estimation in gravitational-wave. +### Single event analysis + +### Injection recovery + +### Population analysis + +### Batch runs + gen_injection_config.py injection_withParser.py diff --git a/src/jaxgw/constants.py b/src/jimgw/constants.py similarity index 100% rename from src/jaxgw/constants.py rename to src/jimgw/constants.py diff --git a/src/jaxgw/detector.py b/src/jimgw/detector.py similarity index 100% rename from src/jaxgw/detector.py rename to src/jimgw/detector.py diff --git a/src/jaxgw/generate_noise.py b/src/jimgw/generate_noise.py similarity index 100% rename from src/jaxgw/generate_noise.py rename to src/jimgw/generate_noise.py diff --git a/src/jaxgw/heterodyneLikelihood.py b/src/jimgw/heterodyneLikelihood.py similarity index 100% rename from src/jaxgw/heterodyneLikelihood.py rename to src/jimgw/heterodyneLikelihood.py diff --git a/src/jaxgw/likelihood.py b/src/jimgw/likelihood.py similarity index 100% rename from src/jaxgw/likelihood.py rename to src/jimgw/likelihood.py diff --git a/src/jaxgw/single_event_likelihood.py b/src/jimgw/single_event_likelihood.py similarity index 100% rename from src/jaxgw/single_event_likelihood.py rename to src/jimgw/single_event_likelihood.py diff --git a/src/jaxgw/time_and_date.py b/src/jimgw/time_and_date.py similarity index 100% rename from src/jaxgw/time_and_date.py rename to src/jimgw/time_and_date.py diff --git a/src/jaxgw/utils.py b/src/jimgw/utils.py similarity index 100% rename from src/jaxgw/utils.py rename to src/jimgw/utils.py diff --git a/src/jaxgw/wave.py b/src/jimgw/wave.py similarity index 100% rename from src/jaxgw/wave.py rename to src/jimgw/wave.py From 5c8b0e74a7242e800d6a321bf473817076cd349c Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Wed, 5 Jul 2023 15:12:08 -0400 Subject: [PATCH 192/300] Nuke more stuff --- src/jimgw/PE/__init__.py | 1 - src/jimgw/PE/constants.py | 11 -- src/jimgw/PE/detector_preset.py | 85 ------------ src/jimgw/PE/detector_projection.py | 171 ------------------------ src/jimgw/PE/generate_noise.py | 53 -------- src/jimgw/PE/heterodyneLikelihood.py | 130 ------------------ src/jimgw/PE/single_event_likelihood.py | 13 -- src/jimgw/PE/time_and_date.py | 38 ------ src/jimgw/PE/utils.py | 51 ------- 9 files changed, 553 deletions(-) delete mode 100644 src/jimgw/PE/__init__.py delete mode 100644 src/jimgw/PE/constants.py delete mode 100644 src/jimgw/PE/detector_preset.py delete mode 100644 src/jimgw/PE/detector_projection.py delete mode 100644 src/jimgw/PE/generate_noise.py delete mode 100644 src/jimgw/PE/heterodyneLikelihood.py delete mode 100644 src/jimgw/PE/single_event_likelihood.py delete mode 100644 src/jimgw/PE/time_and_date.py delete mode 100644 src/jimgw/PE/utils.py diff --git a/src/jimgw/PE/__init__.py b/src/jimgw/PE/__init__.py deleted file mode 100644 index 8b137891..00000000 --- a/src/jimgw/PE/__init__.py +++ /dev/null @@ -1 +0,0 @@ - diff --git a/src/jimgw/PE/constants.py b/src/jimgw/PE/constants.py deleted file mode 100644 index 01f87e88..00000000 --- a/src/jimgw/PE/constants.py +++ /dev/null @@ -1,11 +0,0 @@ -from astropy.constants import c,au,G,pc -from astropy.units import year as yr -from astropy.cosmology import WMAP9 as cosmo - -Msun = 4.9255e-6 -year = (1*yr).cgs.value -Mpc = 1e6*pc.value/c.value -euler_gamma = 0.577215664901532860606512090082 -MR_sun = 1.476625061404649406193430731479084713e3 -speed_of_light = 299792458.0 - diff --git a/src/jimgw/PE/detector_preset.py b/src/jimgw/PE/detector_preset.py deleted file mode 100644 index 19c3fbef..00000000 --- a/src/jimgw/PE/detector_preset.py +++ /dev/null @@ -1,85 +0,0 @@ -from jimgw.PE.detector_projection import construct_arm, detector_tensor, get_vertex_position_geocentric -import jax.numpy as jnp - -# See https://git.ligo.org/lscsoft/bilby/-/tree/master/bilby/gw/detector/detectors for detector parameters. - -degree_to_radian = jnp.pi/180 - -def get_H1(): - """ - Get the detector response matrix and the vertex position for H1. - - Returns - ------- - H1_detector_response : ndarray - The detector response matrix for H1. - H1_vertex : ndarray - The vertex position for H1. - """ - H1_lat = (46 + 27. / 60 + 18.528 / 3600) * degree_to_radian - H1_long = -(119 + 24. / 60 + 27.5657 / 3600) * degree_to_radian - H1_xarm_azimuth = 125.9994 * degree_to_radian - H1_yarm_azimuth = 215.9994 * degree_to_radian - H1_xarm_tilt = -6.195e-4 - H1_yarm_tilt = 1.25e-5 - H1_elevation = 142.554 - - H1_arm1 = construct_arm(H1_lat, H1_long, H1_xarm_tilt, H1_xarm_azimuth) - H1_arm2 = construct_arm(H1_lat, H1_long, H1_yarm_tilt, H1_yarm_azimuth) - - H1_vertex = get_vertex_position_geocentric(H1_lat, H1_long, H1_elevation) - - return detector_tensor(H1_arm1, H1_arm2), H1_vertex - -def get_L1(): - """ - Get the detector response matrix and the vertex position for L1. - - Returns - ------- - L1_detector_response : ndarray - The detector response matrix for L1. - L1_vertex : ndarray - The vertex position for L1. - - """ - L1_lat = (30 + 33. / 60 + 46.4196 / 3600) * degree_to_radian - L1_long = -(90 + 46. / 60 + 27.2654 / 3600) * degree_to_radian - L1_xarm_azimuth = 197.7165 * degree_to_radian - L1_yarm_azimuth = 287.7165 * degree_to_radian - L1_xarm_tilt = 0 - L1_yarm_tilt = 0 - L1_elevation = -6.574 - - L1_arm1 = construct_arm(L1_lat, L1_long, L1_xarm_tilt, L1_xarm_azimuth) - L1_arm2 = construct_arm(L1_lat, L1_long, L1_yarm_tilt, L1_yarm_azimuth) - - L1_vertex = get_vertex_position_geocentric(L1_lat, L1_long, L1_elevation) - - return detector_tensor(L1_arm1, L1_arm2), L1_vertex - -def get_V1(): - """ - Get the detector response matrix and the vertex position for V1. - - Returns - ------- - V1_detector_response : ndarray - The detector response matrix for V1. - V1_vertex : ndarray - The vertex position for V1. - """ - V1_lat = (43 + 37. / 60 + 53.0921 / 3600) * degree_to_radian - V1_long = (10 + 30. / 60 + 16.1878 / 3600) * degree_to_radian - V1_xarm_azimuth = 70.5674 * degree_to_radian - V1_yarm_azimuth = 160.5674 * degree_to_radian - V1_xarm_tilt = 0 - V1_yarm_tilt = 0 - V1_elevation = 51.884 - - V1_arm1 = construct_arm(V1_lat, V1_long, V1_xarm_tilt, V1_xarm_azimuth) - V1_arm2 = construct_arm(V1_lat, V1_long, V1_yarm_tilt, V1_yarm_azimuth) - - V1_vertex = get_vertex_position_geocentric(V1_lat, V1_long, V1_elevation) - - return detector_tensor(V1_arm1, V1_arm2), V1_vertex diff --git a/src/jimgw/PE/detector_projection.py b/src/jimgw/PE/detector_projection.py deleted file mode 100644 index 6f7ab619..00000000 --- a/src/jimgw/PE/detector_projection.py +++ /dev/null @@ -1,171 +0,0 @@ -# Credit some part of the source code from bilby - -import jax.numpy as jnp -from jimgw.PE.constants import * - - -def make_detector_response(detector_tensor, detector_vertex): - antenna_response_plus = make_antenna_response(detector_tensor,'plus') - antenna_response_cross = make_antenna_response(detector_tensor, 'cross') - def detector_response(f, hp, hc, ra, dec, gmst, psi): - output = antenna_response_plus(ra, dec, gmst, psi)*hp + antenna_response_cross(ra, dec, gmst, psi)*hc - timeshift = time_delay_geocentric(detector_vertex, jnp.array([0.,0.,0.]), ra, dec, gmst) - output = output * jnp.exp(-1j * 2 * jnp.pi * f * timeshift) - return output - return detector_response - -########################################################## -# Construction of arms -########################################################## - -def construct_arm(latitude, longitude, arm_tilt, arm_azimuth): - """ - - Args: - - latitude: Latitude in radian - longitude: Longitude in radian - arm_tilt: Arm tilt in radian - arm_azimuth: Arm azimuth in radian - - """ - - e_long = jnp.array([-jnp.sin(longitude), jnp.cos(longitude), 0]) - e_lat = jnp.array([-jnp.sin(latitude) * jnp.cos(longitude), - -jnp.sin(latitude) * jnp.sin(longitude), jnp.cos(latitude)]) - e_h = jnp.array([jnp.cos(latitude) * jnp.cos(longitude), - jnp.cos(latitude) * jnp.sin(longitude), jnp.sin(latitude)]) - - return (jnp.cos(arm_tilt) * jnp.cos(arm_azimuth) * e_long + - jnp.cos(arm_tilt) * jnp.sin(arm_azimuth) * e_lat + - jnp.sin(arm_tilt) * e_h) - - -def detector_tensor(arm1, arm2): - return 0.5 * (jnp.einsum('i,j->ij', arm1, arm1) - jnp.einsum('i,j->ij', arm2, arm2)) - -########################################################## -# Construction of detector tensor -########################################################## - -def make_get_polarization_tensor(mode): - - """ - - Since most of the application will only use specific modes, - this function hoist the if-else loop out from the actual kernel to save time from compiling the kernel. - - Args: - mode: string - - """ - - if mode.lower() == 'plus': - kernel = lambda m,n: jnp.einsum('i,j->ij', m, m) - jnp.einsum('i,j->ij', n, n) - elif mode.lower() == 'cross': - kernel = lambda m,n: jnp.einsum('i,j->ij', m, n) + jnp.einsum('i,j->ij', n, m) - elif mode.lower() == 'breathing': - kernel = lambda m,n: jnp.einsum('i,j->ij', m, m) + jnp.einsum('i,j->ij', n, n) - - # Calculating omega here to avoid calculation when model in [plus, cross, breathing] - if mode.lower() == 'longitudinal': - def kernel(m,n): - omega = jnp.cross(m, n) - return jnp.einsum('i,j->ij', omega, omega) - elif mode.lower() == 'x': - def kernel(m,n): - omega = jnp.cross(m, n) - return jnp.einsum('i,j->ij', m, omega) + jnp.einsum('i,j->ij', omega, m) - elif mode.lower() == 'y': - def kernel(m,n): - omega = jnp.cross(m, n) - return jnp.einsum('i,j->ij', n, omega) + jnp.einsum('i,j->ij', omega, n) - else: - raise ValueError("{} not a polarization mode!".format(mode)) - - def get_polarization_tensor(ra, dec, gmst, psi): - gmst = jnp.mod(gmst, 2 * jnp.pi) - phi = ra - gmst - theta = jnp.pi / 2 - dec - - u = jnp.array([jnp.cos(phi) * jnp.cos(theta), jnp.cos(theta) * jnp.sin(phi), -jnp.sin(theta)]) - v = jnp.array([-jnp.sin(phi), jnp.cos(phi), 0]) - m = -u * jnp.sin(psi) - v * jnp.cos(psi) - n = -u * jnp.cos(psi) + v * jnp.sin(psi) - - return kernel(m, n) - - return get_polarization_tensor - - -def make_antenna_response(detector_tensor, mode): - kernel = make_get_polarization_tensor(mode) - def antenna_response(ra, dec, gmst, psi): - polarization_tensor = kernel(ra, dec, gmst, psi) - return jnp.einsum('ij,ij->', detector_tensor, polarization_tensor) - return antenna_response - -def time_delay_geocentric(detector1, detector2, ra, dec, gmst): - """ - Calculate time delay between two detectors in geocentric coordinates based on XLALArrivaTimeDiff in TimeDelay.c - - Parameters - ========== - detector1: array_like - Cartesian coordinate vector for the first detector in the geocentric frame - generated by the Interferometer class as self.vertex. - detector2: array_like - Cartesian coordinate vector for the second detector in the geocentric frame. - To get time delay from Earth center, use detector2 = np.array([0,0,0]) - ra: float - Right ascension of the source in radians - dec: float - Declination of the source in radians - gmst: float - Greenwich mean sidereal time in radians - - Returns - ======= - float: Time delay between the two detectors in the geocentric frame - - """ - gmst = jnp.mod(gmst, 2 * jnp.pi) - phi = ra - gmst - theta = jnp.pi / 2 - dec - omega = jnp.array([jnp.sin(theta) * jnp.cos(phi), jnp.sin(theta) * jnp.sin(phi), jnp.cos(theta)]) - delta_d = detector2 - detector1 - return jnp.dot(omega, delta_d) / speed_of_light - -def get_vertex_position_geocentric(latitude, longitude, elevation): - """ - Calculate the position of the IFO vertex in geocentric coordinates in meters. - - Based on arXiv:gr-qc/0008066 Eqs. B11-B13 except for the typo in the definition of the local radius. - See Section 2.1 of LIGO-T980044-10 for the correct expression - - Parameters - ========== - latitude: float - Latitude in radians - longitude: - Longitude in radians - elevation: - Elevation in meters - - Returns - ======= - array_like: A 3D representation of the geocentric vertex position - - """ - semi_major_axis = 6378137 # for ellipsoid model of Earth, in m - semi_minor_axis = 6356752.314 # in m - radius = semi_major_axis**2 * (semi_major_axis**2 * jnp.cos(latitude)**2 + - semi_minor_axis**2 * jnp.sin(latitude)**2)**(-0.5) - x_comp = (radius + elevation) * jnp.cos(latitude) * jnp.cos(longitude) - y_comp = (radius + elevation) * jnp.cos(latitude) * jnp.sin(longitude) - z_comp = ((semi_minor_axis / semi_major_axis)**2 * radius + elevation) * jnp.sin(latitude) - return jnp.array([x_comp, y_comp, z_comp]) - - - - diff --git a/src/jimgw/PE/generate_noise.py b/src/jimgw/PE/generate_noise.py deleted file mode 100644 index 02084c35..00000000 --- a/src/jimgw/PE/generate_noise.py +++ /dev/null @@ -1,53 +0,0 @@ -# Import packages -from typing import List, Tuple -import lalsimulation as lalsim -import jax.numpy as jnp -import jax -import numpy as np -jax.config.update('jax_enable_x64', True) - -psd_func_dict = { - 'H1': lalsim.SimNoisePSDaLIGOZeroDetHighPower, - 'L1': lalsim.SimNoisePSDaLIGOZeroDetHighPower, - 'V1': lalsim.SimNoisePSDAdvVirgo, -} - -def generate_noise(seed: int, f_sampling: int = 2048, duration: int = 4, f_min: float = 30., ifos: List = ['H1', 'L1']): - - - # define sampling rate and duration - - delta_t = 1/f_sampling - tlen = int(round(duration / delta_t)) - - freqs = np.fft.rfftfreq(tlen, delta_t) - delta_f = freqs[1] - freqs[0] - - # we will want to pad low frequencies; the function below applies a - # prescription to do so smoothly, but this is not really needed: you - # could just set all values below `fmin` to a constant. - def pad_low_freqs(f, psd_ref): - return psd_ref + psd_ref*(f_min-f)*jnp.exp(-(f_min-f))/3 - - psd_dict = {} - for ifo in ifos: - psd = np.zeros(len(freqs)) - for i,f in enumerate(freqs): - if f >= f_min: - psd[i] = psd_func_dict[ifo](f) - else: - psd[i] = pad_low_freqs(f, psd_func_dict[ifo](f_min)) - psd_dict[ifo] = jnp.array(psd,dtype=jnp.float64) - - rng_key = jax.random.PRNGKey(seed) - rng_keys = jax.random.split(rng_key) - - noise_fd_dict = {} - for ifo, psd in psd_dict.items(): - rng_keys = jax.random.split(rng_keys[0], 3) - var = psd / (4.*delta_f) # this is the variance of LIGO noise given the definition of the likelihood function - noise_real = jax.random.normal(rng_keys[1],shape=(len(psd),))*jnp.sqrt(var) - noise_imag = jax.random.normal(rng_keys[2],shape=(len(psd),))*jnp.sqrt(var) - noise_fd_dict[ifo] = noise_real + 1j*noise_imag - - return freqs, psd_dict, noise_fd_dict \ No newline at end of file diff --git a/src/jimgw/PE/heterodyneLikelihood.py b/src/jimgw/PE/heterodyneLikelihood.py deleted file mode 100644 index d7a6ee50..00000000 --- a/src/jimgw/PE/heterodyneLikelihood.py +++ /dev/null @@ -1,130 +0,0 @@ -import wave -import numpy as np -from scipy.interpolate import interp1d - -import jax.numpy as jnp - -def max_phase_diff(f, f_low, f_high, chi=1): - gamma = np.arange(-5,6,1)/3. - f = np.repeat(f[:,None],len(gamma),axis=1) - f_star = np.repeat(f_low, len(gamma)) - f_star[gamma >= 0] = f_high - return 2*np.pi*chi*np.sum((f/f_star)**gamma*np.sign(gamma),axis=1) - - -def make_binning_scheme(freqs, n_bins, chi=1): - phase_diff_array = max_phase_diff(freqs,freqs[0],freqs[-1],chi=1) - bin_f = interp1d(phase_diff_array, freqs) - f_bins = np.array([]) - for i in np.linspace(phase_diff_array[0], phase_diff_array[-1], n_bins): - f_bins = np.append(f_bins,bin_f(i)) - f_bins_center = (f_bins[:-1] + f_bins[1:])/2 - return f_bins, f_bins_center - -def compute_coefficients(data, h_ref, psd, freqs, f_bins, f_bins_center): - A0_array = [] - A1_array = [] - B0_array = [] - B1_array = [] - - df = freqs[1] - freqs[0] - data_prod = np.array(data*h_ref.conj()) - self_prod = np.array(h_ref*h_ref.conj()) - for i in range(len(f_bins)-1): - f_index = np.where((freqs >= f_bins[i]) & (freqs < f_bins[i+1]))[0] - A0_array.append(4*np.sum(data_prod[f_index]/psd[f_index])*df) - A1_array.append(4*np.sum(data_prod[f_index]/psd[f_index]*(freqs[f_index]-f_bins_center[i]))*df) - B0_array.append(4*np.sum(self_prod[f_index]/psd[f_index])*df) - B1_array.append(4*np.sum(self_prod[f_index]/psd[f_index]*(freqs[f_index]-f_bins_center[i]))*df) - - A0_array = jnp.array(A0_array) - A1_array = jnp.array(A1_array) - B0_array = jnp.array(B0_array) - B1_array = jnp.array(B1_array) - return A0_array, A1_array, B0_array, B1_array - -def make_heterodyne_likelihood(data, h_function, ref_theta, psd, freqs, n_bins=101): - f_bins, f_bins_center = make_binning_scheme(freqs, n_bins) - h_ref = h_function(freqs, ref_theta) - h_ref_low = h_function(f_bins[:-1], ref_theta) - h_ref_bincenter = h_function(f_bins_center, ref_theta) - - A0, A1, B0, B1 = compute_coefficients(data, h_ref, psd, freqs, f_bins, f_bins_center) - - def heterodyne_likelihood(params): - waveform_low = h_function(f_bins[:-1], params) - waveform_center = h_function(f_bins_center, params) - - r0 = waveform_center/h_ref_bincenter - r1 = (waveform_low/h_ref_low - r0)/(f_bins[:-1]-f_bins_center) - - match_filter_SNR = jnp.nansum(A0*r0.conj() + A1*r1.conj()) - optimal_SNR = jnp.nansum(B0*jnp.abs(r0)**2 + 2*B1*(r0*r1.conj()).real) - - return (match_filter_SNR - optimal_SNR/2).real - - return heterodyne_likelihood - -def make_heterodyne_likelihood_mutliple_detector(data_list, psd_list, respose_list, h_function, ref_theta, freqs, gmst, epoch, f_ref, n_bins=101): - - num_detector = len(data_list) - theta_waveform = ref_theta - theta_waveform = theta_waveform.at[5].set(0) - raw_hp, raw_hc = h_function(freqs, theta_waveform, f_ref) - index = jnp.where((jnp.abs(raw_hc)+jnp.abs(raw_hp)) > 0) - freqs = freqs[index] - raw_hp = raw_hp[index] - raw_hc = raw_hc[index] - for i in range(num_detector): - data_list[i] = data_list[i][index] - psd_list[i] = psd_list[i][index] - - f_bins, f_bins_center = make_binning_scheme(freqs, n_bins) - ra, dec = ref_theta[9], ref_theta[10] - h_ref = [] - h_ref_low = [] - h_ref_bincenter = [] - raw_hp_bin, raw_hc_bin = h_function(f_bins[:-1], theta_waveform, f_ref) - raw_hp_bincenter, raw_hc_bincenter = h_function(f_bins_center, theta_waveform, f_ref) - for i in range(num_detector): - h_ref.append(respose_list[i](freqs, raw_hp, raw_hc, ra, dec, gmst, ref_theta[8])*jnp.exp(-1j*2*jnp.pi*freqs*(epoch+ref_theta[5]))) - h_ref_low.append(respose_list[i](f_bins[:-1], raw_hp_bin, raw_hc_bin, ra, dec, gmst, ref_theta[8])*jnp.exp(-1j*2*jnp.pi*f_bins[:-1]*(epoch+ref_theta[5]))) - h_ref_bincenter.append(respose_list[i](f_bins_center, raw_hp_bincenter, raw_hc_bincenter, ra, dec, gmst, ref_theta[8])*jnp.exp(-1j*2*jnp.pi*f_bins_center*(epoch+ref_theta[5]))) - - A0_array = [] - A1_array = [] - B0_array = [] - B1_array = [] - - for i in range(num_detector): - A0, A1, B0, B1 = compute_coefficients(data_list[i], h_ref[i], psd_list[i], freqs, f_bins, f_bins_center) - A0_array.append(A0) - A1_array.append(A1) - B0_array.append(B0) - B1_array.append(B1) - - - def hetrodyne_likelihood(params): - theta_waveform = params - theta_waveform = theta_waveform.at[5].set(0) - ra, dec = params[9], params[10] - - output_SNR = 0 - - raw_hp_edge, raw_hc_edge = h_function(f_bins[:-1], theta_waveform, f_ref) - raw_hp_center, raw_hc_center = h_function(f_bins_center, theta_waveform, f_ref) - - for i in range(num_detector): - waveform_low = respose_list[i](f_bins[:-1], raw_hp_edge, raw_hc_edge, ra, dec, gmst, params[8])*jnp.exp(-1j*2*jnp.pi*f_bins[:-1]*(epoch+params[5])) - waveform_center = respose_list[i](f_bins_center, raw_hp_center, raw_hc_center, ra, dec, gmst, params[8])*jnp.exp(-1j*2*jnp.pi*f_bins_center*(epoch+params[5])) - - r0 = waveform_center/h_ref_bincenter[i] - r1 = (waveform_low/h_ref_low[i] - r0)/(f_bins[:-1]-f_bins_center) - match_filter_SNR = jnp.sum(A0_array[i]*r0.conj() + A1_array[i]*r1.conj()) - optimal_SNR = jnp.sum(B0_array[i]*jnp.abs(r0)**2 + 2*B1_array[i]*(r0*r1.conj()).real) - - output_SNR += (match_filter_SNR - optimal_SNR/2).real - - return output_SNR - - return hetrodyne_likelihood diff --git a/src/jimgw/PE/single_event_likelihood.py b/src/jimgw/PE/single_event_likelihood.py deleted file mode 100644 index 327f4e56..00000000 --- a/src/jimgw/PE/single_event_likelihood.py +++ /dev/null @@ -1,13 +0,0 @@ -from jax import jit -from jimgw.PE.detector_projection import get_detector_response -from jimgw.PE.utils import inner_product - -def single_detector_likelihood(waveform_model, params, data, data_f, PSD, detector): - waveform = waveform_model(data_f, params) - waveform = get_detector_response(waveform, params, detector) - match_filter_SNR = inner_product(waveform, data, data_f, PSD) - optimal_SNR = inner_product(waveform, waveform, data_f, PSD) - return (-2*match_filter_SNR + optimal_SNR)/2 - - - diff --git a/src/jimgw/PE/time_and_date.py b/src/jimgw/PE/time_and_date.py deleted file mode 100644 index 9e4f7794..00000000 --- a/src/jimgw/PE/time_and_date.py +++ /dev/null @@ -1,38 +0,0 @@ -import jax.numpy as np - - {2444239.5, -43200, 19}, /* 1980-Jan-01 */ - {2444786.5, 46828800, 20}, /* 1981-Jul-01 */ - {2445151.5, 78364801, 21}, /* 1982-Jul-01 */ - {2445516.5, 109900802, 22}, /* 1983-Jul-01 */ - {2446247.5, 173059203, 23}, /* 1985-Jul-01 */ -#if 0 - /* NOTE: IF THIS WERE A NEGATIVE LEAP SECOND, INSERT AS FOLLOWS */ - {2447161.5, 252028803, 22}, /* 1988-Jan-01 EXAMPLE ONLY! */ -#endif - {2447161.5, 252028804, 24}, /* 1988-Jan-01 */ - {2447892.5, 315187205, 25}, /* 1990-Jan-01 */ - {2448257.5, 346723206, 26}, /* 1991-Jan-01 */ - {2448804.5, 393984007, 27}, /* 1992-Jul-01 */ - {2449169.5, 425520008, 28}, /* 1993-Jul-01 */ - {2449534.5, 457056009, 29}, /* 1994-Jul-01 */ - {2450083.5, 504489610, 30}, /* 1996-Jan-01 */ - {2450630.5, 551750411, 31}, /* 1997-Jul-01 */ - {2451179.5, 599184012, 32}, /* 1999-Jan-01 */ - {2453736.5, 820108813, 33}, /* 2006-Jan-01 */ - {2454832.5, 914803214, 34}, /* 2009-Jan-01 */ - {2456109.5, 1025136015, 35}, /* 2012-Jul-01 */ - {2457204.5, 1119744016, 36}, /* 2015-Jul-01 */ - {2457754.5, 1167264017, 37}, /* 2017-Jan-01 */ - - -def gps_to_utc(gps_time): - -def greenwich_mean_sidereal_time(gps_time): - -def time_delay_geocentric(detector1, detector2, ra, dec, time): - gmst = fmod(greenwich_mean_sidereal_time(time), 2 * np.pi) - theta, phi = ra_dec_to_theta_phi(ra, dec, gmst) - omega = np.array([np.sin(theta) * np.cos(phi), np.sin(theta) * np.sin(phi), np.cos(theta)]) - delta_d = detector2 - detector1 - return np.dot(omega, delta_d) / speed_of_light - diff --git a/src/jimgw/PE/utils.py b/src/jimgw/PE/utils.py deleted file mode 100644 index b521263c..00000000 --- a/src/jimgw/PE/utils.py +++ /dev/null @@ -1,51 +0,0 @@ -import jax.numpy as jnp -from jax import jit - -@jit -def inner_product(h1, h2, frequency, PSD): - """ - Do PSD interpolation outside the inner product loop to speed up the evaluation - """ - #psd_interp = jnp.interp(frequency, PSD_frequency, PSD) - df = frequency[1] - frequency[0] - integrand = jnp.conj(h1)* h2 / PSD - return 4. * jnp.real(jnp.trapz(integrand,dx=df)) - -@jit -def m1m2_to_Mq(m1,m2): - """ - Transforming the primary mass m1 and secondary mass m2 to the Total mass M - and mass ratio q. - - Args: - m1: Primary mass of the binary. - m2: Secondary mass of the binary. - - Returns: - A tuple containing both the total mass M and mass ratio q. - """ - M_tot = jnp.log(m1+m2) - q = jnp.log(m2/m1)-jnp.log(1-m2/m1) - return M_tot, q - -@jit -def Mq_to_m1m2(trans_M_tot,trans_q): - M_tot = jnp.exp(trans_M_tot) - q = 1./(1+jnp.exp(-trans_q)) - m1 = M_tot/(1+q) - m2 = m1*q - return m1, m2 - -@jit -def Mc_q_to_m1m2(Mc,q): - eta = q/(1+q)**2 - M_tot = Mc/eta**(3./5) - m1 = M_tot/(1+q) - m2 = m1*q - return m1, m2 - -def ra_dec_to_theta_phi(ra, dec, gmst): - phi = ra - gmst - theta = jnp.pi / 2 - dec - return theta, phi - From db2b73216fb1de007b5fc125060ae19517c5fd9e Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Wed, 5 Jul 2023 15:30:03 -0400 Subject: [PATCH 193/300] Fetch examples from documentation branch --- example/GW150914.py | 209 +++++++++++++++++++++++++++++++------------- 1 file changed, 147 insertions(+), 62 deletions(-) diff --git a/example/GW150914.py b/example/GW150914.py index dbfd7c0f..a4b78ac6 100644 --- a/example/GW150914.py +++ b/example/GW150914.py @@ -1,32 +1,96 @@ import numpy as np +import matplotlib.pyplot as plt +import time import jax.numpy as jnp import jax from lal import GreenwichMeanSiderealTime from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar from jimgw.PE.detector_preset import * -from jimgw.PE.heterodyneLikelihood import make_heterodyne_likelihood +from jimgw.PE.heterodyneLikelihood import make_heterodyne_likelihood_mutliple_detector from jimgw.PE.detector_projection import make_detector_response -from flowMC.nfmodel.rqSpline import RQSpline +from flowMC.nfmodel.rqSpline import MaskedCouplingRQSpline +from flowMC.nfmodel.common import Gaussian from flowMC.sampler.Sampler import Sampler from flowMC.sampler.MALA import MALA from flowMC.utils.PRNG_keys import initialize_rng_keys from flowMC.nfmodel.utils import * - -data = np.load('./data/GW150914_data.npz',allow_pickle=True) - -minimum_frequency = data['minimum_frequency'] - -H1_frequency = data['frequency'].tolist()['H1'] -H1_data = data['data'].tolist()['H1'][H1_frequency>minimum_frequency] -H1_psd = data['psd'].tolist()['H1'][H1_frequency>minimum_frequency] -H1_frequency = H1_frequency[H1_frequency>minimum_frequency] - -L1_frequency = data['frequency'].tolist()['L1'] -L1_data = data['data'].tolist()['L1'][L1_frequency>minimum_frequency] -L1_psd = data['psd'].tolist()['L1'][L1_frequency>minimum_frequency] -L1_frequency = L1_frequency[L1_frequency>minimum_frequency] +from flowMC.utils.EvolutionaryOptimizer import EvolutionaryOptimizer + +# We only use this to grab the data +from gwpy.timeseries import TimeSeries +from scipy.signal.windows import tukey + +########################################### +########## First we grab data ############# +########################################### + +total_time_start = time.time() + +# first, fetch a 4s segment centered on GW150914 +gps = 1126259462.4 +start = gps - 2 +end = gps + 2 +fmin = 20 +fmax = 1024 + +ifos = ['H1', 'L1'] + +print("Fetching data...") +data_td_dict = {ifo: TimeSeries.fetch_open_data(ifo, start, end) for ifo in ifos} +print("Finished fetching data.") + +# GWpy normalizes the FFT like an instrumentalist would, which is not what we +# want for the likelihoood, so fix this manually +n = len(data_td_dict[ifos[0]]) +delta_t = data_td_dict[ifos[0]].dt.value + +print("Computing the FFTs...") +# For BNS 0.00625 is a good choice for the tukey window +# For BBH 0.2 is a good choice for the tukey window +data_fd_dict = {i: np.fft.rfft(np.array(d)*tukey(n, 0.2))*delta_t + for i, d in data_td_dict.items()} + +freq = np.fft.rfftfreq(n, delta_t) + +# # We take a bit of extra data to compute PSDs +start_psd = int(gps) - 16 +end_psd = int(gps) + 16 + +print("Fetching PSD data...") +psd_data_td_dict = {ifo: TimeSeries.fetch_open_data(ifo, start_psd, end_psd) for ifo in ifos} +psd_dict = {i: d.psd(fftlength=4) for i, d in psd_data_td_dict.items()} +print("Finished generating data.") + +H1_frequency = np.array(freq[(freq>fmin)&(freqfmin)&(freqfmin)&(freqfmin)&(freqfmin)&(freqfmin)&(freq 300.] = 0 +# # go back to time domain and plot +# wd_td = np.fft.irfft(wd_fd.data) +# plt.plot(data_td_dict[i].times, wd_td, label=i) + +# plt.xlim(gps-0.1, gps+0.1) +# plt.xlabel('GPS time (s)') +# plt.ylabel('whitened strain') +# plt.legend() +# plt.show() + +########################################### +######## Set up the likelihood ############ +########################################### H1 = get_H1() H1_response = make_detector_response(H1[0], H1[1]) @@ -40,63 +104,79 @@ gmst = GreenwichMeanSiderealTime(trigger_time) f_ref = 20 -def gen_waveform_H1(f, theta, epoch, gmst, f_ref): - theta_waveform = theta[:8] - theta_waveform = theta_waveform.at[5].set(0) - ra = theta[9] - dec = theta[10] - hp, hc = gen_IMRPhenomD_polar(f, theta_waveform, f_ref) - return H1_response(f, hp, hc, ra, dec, gmst , theta[8]) * jnp.exp(-1j*2*jnp.pi*f*(epoch+theta[5])) - -def gen_waveform_L1(f, theta, epoch, gmst, f_ref): +def LogLikelihood(theta): theta_waveform = theta[:8] theta_waveform = theta_waveform.at[5].set(0) ra = theta[9] dec = theta[10] - hp, hc = gen_IMRPhenomD_polar(f, theta_waveform, f_ref) - return L1_response(f, hp, hc, ra, dec, gmst, theta[8]) * jnp.exp(-1j*2*jnp.pi*f*(epoch+theta[5])) - -def H1_LogLikelihood(theta): - h_test = gen_waveform_H1(H1_frequency, theta, epoch, gmst, f_ref) - df = H1_frequency[1] - H1_frequency[0] - match_filter_SNR = 4*jnp.sum((jnp.conj(h_test)*H1_data)/H1_psd*df).real - optimal_SNR = 4*jnp.sum((jnp.conj(h_test)*h_test)/H1_psd*df).real - return (match_filter_SNR-optimal_SNR/2) - -def L1_LogLikelihood(theta): - h_test = gen_waveform_L1(L1_frequency, theta, epoch, gmst, f_ref) - df = L1_frequency[1] - L1_frequency[0] - match_filter_SNR = 4*jnp.sum((jnp.conj(h_test)*L1_data)/L1_psd*df).real - optimal_SNR = 4*jnp.sum((jnp.conj(h_test)*h_test)/L1_psd*df).real - return (match_filter_SNR-optimal_SNR/2) - -ref_param = jnp.array([ 3.13857132e+01, 2.49301122e-01, 1.31593299e-02, 2.61342217e-03, - 5.37766606e+02, 1.18679090e-02, 1.26153956e+00, 2.61240760e+00, - 1.33131339e+00, 2.33978644e+00, -1.20993116e+00]) - - -from jimgw.PE.heterodyneLikelihood import make_heterodyne_likelihood_mutliple_detector + hp_test, hc_test = gen_IMRPhenomD_polar(H1_frequency, theta_waveform, f_ref) + align_time = jnp.exp(-1j*2*jnp.pi*H1_frequency*(epoch+theta[5])) + h_test_H1 = H1_response(H1_frequency, hp_test, hc_test, ra, dec, gmst, theta[8]) * align_time + h_test_L1 = L1_response(L1_frequency, hp_test, hc_test, ra, dec, gmst, theta[8]) * align_time + df = H1_frequency[1] - H1_frequency[0] + match_filter_SNR_H1 = 4*jnp.sum((jnp.conj(h_test_H1)*H1_data)/H1_psd*df).real + match_filter_SNR_L1 = 4*jnp.sum((jnp.conj(h_test_L1)*L1_data)/L1_psd*df).real + optimal_SNR_H1 = 4*jnp.sum((jnp.conj(h_test_H1)*h_test_H1)/H1_psd*df).real + optimal_SNR_L1 = 4*jnp.sum((jnp.conj(h_test_L1)*h_test_L1)/L1_psd*df).real + + return (match_filter_SNR_H1-optimal_SNR_H1/2) + (match_filter_SNR_L1-optimal_SNR_L1/2) + +# prior on the waveform parameters +# these are Mc, eta, s1, s2, dist, tc, phic, ra, dec, psi +prior_range = jnp.array([[20.,50.],[0.20,0.25],[-0.9,0.9], + [-0.9,0.9],[100,3000],[-1.0,1.0], + [0,2*np.pi],[0.001,np.pi],[0.001,np.pi], + [0.001,2*np.pi],[-jnp.pi/2,jnp.pi/2]]) + + +########################################### +##### Optimize to find high L point ####### +########################################### + +set_nwalkers = 100 +initial_guess = jax.random.uniform(jax.random.PRNGKey(42), (set_nwalkers,11,), + minval=prior_range[:,0], maxval=prior_range[:,1]) + +y = lambda x: -LogLikelihood(x) +y = jax.jit(jax.vmap(y)) +print("Compiling likelihood function") +y(initial_guess) +print("Done compiling") + +print("Starting the optimizer") +optimizer = EvolutionaryOptimizer(11, verbose = True) +state = optimizer.optimize(y, prior_range, n_loops=2000) +best_fit = optimizer.get_result()[0] + +print(best_fit) data_list = [H1_data, L1_data] psd_list = [H1_psd, L1_psd] response_list = [H1_response, L1_response] -logL = make_heterodyne_likelihood_mutliple_detector(data_list, psd_list, response_list, gen_IMRPhenomD_polar, ref_param, H1_frequency, gmst, epoch, f_ref, 301) +print("Constructing the heterodyned likelihood function") +logL = make_heterodyne_likelihood_mutliple_detector(data_list, psd_list, response_list, gen_IMRPhenomD_polar, + best_fit, H1_frequency, gmst, epoch, f_ref, 301) + + +########################################### +####### Finally, we can sample! ########### +########################################### n_dim = 11 -n_chains = 1000 -n_loop_training = 20 +n_chains = 500 +n_loop_training = 10 n_loop_production = 10 n_local_steps = 200 n_global_steps = 200 learning_rate = 0.001 max_samples = 100000 momentum = 0.9 -num_epochs = 60 +num_epochs = 200 batch_size = 50000 -guess_param = ref_param +guess_param = best_fit guess_param = np.array(jnp.repeat(guess_param[None,:],int(n_chains),axis=0)*np.random.normal(loc=1,scale=0.1,size=(int(n_chains),n_dim))) guess_param[guess_param[:,1]>0.25,1] = 0.249 @@ -111,7 +191,9 @@ def L1_LogLikelihood(theta): print("Initializing MCMC model and normalizing flow model.") -prior_range = jnp.array([[10,80],[0.125,1.0],[-1,1],[-1,1],[0,2000],[-0.1,0.1],[0,2*np.pi],[-1,1],[0,np.pi],[0,2*np.pi],[-1,1]]) +prior_range = jnp.array([[10,80],[0.125,1.0],[-1,1],[-1,1], + [0,2000],[-0.05,0.05],[0,2*np.pi],[-1,1], + [0,np.pi],[0,2*np.pi],[-1,1]]) initial_position = jax.random.uniform(rng_key_set[0], shape=(int(n_chains), n_dim)) * 1 @@ -139,6 +221,8 @@ def top_hat(x): def posterior(theta): q = theta[1] + theta = theta.at[7].set(jnp.arcsin(jnp.sin(theta[7]/2*jnp.pi))*2/jnp.pi) + theta = theta.at[10].set(jnp.arcsin(jnp.sin(theta[10]/2*jnp.pi))*2/jnp.pi) iota = jnp.arccos(theta[7]) dec = jnp.arcsin(theta[10]) prior = top_hat(theta) @@ -147,24 +231,24 @@ def posterior(theta): theta = theta.at[10].set(dec) # convert cos dec to dec return logL(theta) + prior -model = RQSpline(n_dim, 10, [128,128], 8) +posterior_new = lambda theta, data: posterior(theta) -print("Initializing sampler class") +model = MaskedCouplingRQSpline(n_dim, 10, [128,128], 8, jax.random.PRNGKey(10)) -posterior = posterior +print("Initializing sampler class") mass_matrix = jnp.eye(n_dim) mass_matrix = mass_matrix.at[1,1].set(1e-3) mass_matrix = mass_matrix.at[5,5].set(1e-3) -local_sampler = MALA(posterior, True, {"step_size": mass_matrix*3e-3}) +local_sampler = MALA(posterior_new, True, {"step_size": mass_matrix*3e-3}) print("Running sampler") nf_sampler = Sampler( n_dim, rng_key_set, + None, local_sampler, - posterior, model, n_loop_training=n_loop_training, n_loop_production = n_loop_production, @@ -177,9 +261,10 @@ def posterior(theta): batch_size=batch_size, use_global=True, keep_quantile=0., - train_thinning = 40 + train_thinning = 40, ) -nf_sampler.sample(initial_position) +nf_sampler.sample(initial_position, None) chains, log_prob, local_accs, global_accs = nf_sampler.get_sampler_state().values() -np.savez('/mnt/home/wwong/ceph/GWProject/JaxGW/RealtimePE/GW150914.npz', chains=chains, log_prob=log_prob, local_accs=local_accs, global_accs=global_accs) \ No newline at end of file +print("Script complete and took: {} minutes".format((time.time()-total_time_start)/60)) +# np.savez('GW150914.npz', chains=chains, log_prob=log_prob, local_accs=local_accs, global_accs=global_accs) \ No newline at end of file From fc71e9e9478fa59c2a98b1606a9948852060efed Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Wed, 5 Jul 2023 15:58:18 -0400 Subject: [PATCH 194/300] More nukes --- example/GW150914.py | 17 ---------------- example/SingleEventExample.py | 15 ++++++++++++++ setup.cfg | 9 ++++----- src/jimgw/time_and_date.py | 38 ----------------------------------- 4 files changed, 19 insertions(+), 60 deletions(-) create mode 100644 example/SingleEventExample.py delete mode 100644 src/jimgw/time_and_date.py diff --git a/example/GW150914.py b/example/GW150914.py index a4b78ac6..809e1262 100644 --- a/example/GW150914.py +++ b/example/GW150914.py @@ -11,7 +11,6 @@ from jimgw.PE.detector_projection import make_detector_response from flowMC.nfmodel.rqSpline import MaskedCouplingRQSpline -from flowMC.nfmodel.common import Gaussian from flowMC.sampler.Sampler import Sampler from flowMC.sampler.MALA import MALA from flowMC.utils.PRNG_keys import initialize_rng_keys @@ -71,22 +70,6 @@ L1_data = np.array(data_fd_dict['L1'].data)[(freq>fmin)&(freqfmin)&(freq 300.] = 0 -# # go back to time domain and plot -# wd_td = np.fft.irfft(wd_fd.data) -# plt.plot(data_td_dict[i].times, wd_td, label=i) - -# plt.xlim(gps-0.1, gps+0.1) -# plt.xlabel('GPS time (s)') -# plt.ylabel('whitened strain') -# plt.legend() -# plt.show() ########################################### ######## Set up the likelihood ############ diff --git a/example/SingleEventExample.py b/example/SingleEventExample.py new file mode 100644 index 00000000..e10ad7cc --- /dev/null +++ b/example/SingleEventExample.py @@ -0,0 +1,15 @@ +# Fetching data + +# Constructing the likelihood + +## Making detectors + +## Getting waveform models info + +## Setting up priors + +# Setting up flowMC sampler + +## Sampling + +# Output diff --git a/setup.cfg b/setup.cfg index d627ccc4..372db06b 100644 --- a/setup.cfg +++ b/setup.cfg @@ -13,14 +13,13 @@ packages_dir= =src packages = find: install_requires = - jax==0.4.1 - jaxlib==0.4.1 - flax==0.6.3 - flowMC + jax>=0.4.12 + jaxlib>=0.4.12 + flowMC>=0.2.1 ripplegw gwpy corner -python_requires = >=3.8,<3.11 +python_requires = >=3.9,<3.11 [options.packages.find] where=src diff --git a/src/jimgw/time_and_date.py b/src/jimgw/time_and_date.py deleted file mode 100644 index 9e4f7794..00000000 --- a/src/jimgw/time_and_date.py +++ /dev/null @@ -1,38 +0,0 @@ -import jax.numpy as np - - {2444239.5, -43200, 19}, /* 1980-Jan-01 */ - {2444786.5, 46828800, 20}, /* 1981-Jul-01 */ - {2445151.5, 78364801, 21}, /* 1982-Jul-01 */ - {2445516.5, 109900802, 22}, /* 1983-Jul-01 */ - {2446247.5, 173059203, 23}, /* 1985-Jul-01 */ -#if 0 - /* NOTE: IF THIS WERE A NEGATIVE LEAP SECOND, INSERT AS FOLLOWS */ - {2447161.5, 252028803, 22}, /* 1988-Jan-01 EXAMPLE ONLY! */ -#endif - {2447161.5, 252028804, 24}, /* 1988-Jan-01 */ - {2447892.5, 315187205, 25}, /* 1990-Jan-01 */ - {2448257.5, 346723206, 26}, /* 1991-Jan-01 */ - {2448804.5, 393984007, 27}, /* 1992-Jul-01 */ - {2449169.5, 425520008, 28}, /* 1993-Jul-01 */ - {2449534.5, 457056009, 29}, /* 1994-Jul-01 */ - {2450083.5, 504489610, 30}, /* 1996-Jan-01 */ - {2450630.5, 551750411, 31}, /* 1997-Jul-01 */ - {2451179.5, 599184012, 32}, /* 1999-Jan-01 */ - {2453736.5, 820108813, 33}, /* 2006-Jan-01 */ - {2454832.5, 914803214, 34}, /* 2009-Jan-01 */ - {2456109.5, 1025136015, 35}, /* 2012-Jul-01 */ - {2457204.5, 1119744016, 36}, /* 2015-Jul-01 */ - {2457754.5, 1167264017, 37}, /* 2017-Jan-01 */ - - -def gps_to_utc(gps_time): - -def greenwich_mean_sidereal_time(gps_time): - -def time_delay_geocentric(detector1, detector2, ra, dec, time): - gmst = fmod(greenwich_mean_sidereal_time(time), 2 * np.pi) - theta, phi = ra_dec_to_theta_phi(ra, dec, gmst) - omega = np.array([np.sin(theta) * np.cos(phi), np.sin(theta) * np.sin(phi), np.cos(theta)]) - delta_d = detector2 - detector1 - return np.dot(omega, delta_d) / speed_of_light - From 0b6464b5af87bedf3eabf657e4c882e90bd176a0 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Wed, 5 Jul 2023 16:23:38 -0400 Subject: [PATCH 195/300] Kick lal out of script --- example/GW150914.py | 11 ++++++----- example/waveform_comparison.py | 0 setup.cfg | 1 + 3 files changed, 7 insertions(+), 5 deletions(-) create mode 100644 example/waveform_comparison.py diff --git a/example/GW150914.py b/example/GW150914.py index 809e1262..aaf4a1dd 100644 --- a/example/GW150914.py +++ b/example/GW150914.py @@ -3,7 +3,6 @@ import time import jax.numpy as jnp import jax -from lal import GreenwichMeanSiderealTime from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar from jimgw.PE.detector_preset import * @@ -17,6 +16,8 @@ from flowMC.nfmodel.utils import * from flowMC.utils.EvolutionaryOptimizer import EvolutionaryOptimizer +from astropy.time import Time + # We only use this to grab the data from gwpy.timeseries import TimeSeries from scipy.signal.windows import tukey @@ -84,7 +85,7 @@ duration = 4 post_trigger_duration = 2 epoch = duration - post_trigger_duration -gmst = GreenwichMeanSiderealTime(trigger_time) +gmst = Time(trigger_time, format='gps').sidereal_time('apparent', 'greenwich').rad f_ref = 20 def LogLikelihood(theta): @@ -149,10 +150,10 @@ def LogLikelihood(theta): n_dim = 11 n_chains = 500 -n_loop_training = 10 +n_loop_training = 15 n_loop_production = 10 -n_local_steps = 200 -n_global_steps = 200 +n_local_steps = 100 +n_global_steps = 100 learning_rate = 0.001 max_samples = 100000 momentum = 0.9 diff --git a/example/waveform_comparison.py b/example/waveform_comparison.py new file mode 100644 index 00000000..e69de29b diff --git a/setup.cfg b/setup.cfg index 372db06b..ab51c9dc 100644 --- a/setup.cfg +++ b/setup.cfg @@ -19,6 +19,7 @@ install_requires = ripplegw gwpy corner + astropy python_requires = >=3.9,<3.11 [options.packages.find] From 6dc016eb18a6ad8554ee20141c1c87eea7aa6b8f Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Wed, 5 Jul 2023 16:24:11 -0400 Subject: [PATCH 196/300] Remove obsolete tests --- test/test_IMRPhenomC.py | 23 ---- test/test_TaylorF2.py | 42 ------- test/test_interferometer.py | 27 ----- test/test_likelihood.py | 104 ----------------- test/test_likelihood_bilby.py | 213 ---------------------------------- test/waveform_test.py | 106 ----------------- 6 files changed, 515 deletions(-) delete mode 100644 test/test_IMRPhenomC.py delete mode 100644 test/test_TaylorF2.py delete mode 100644 test/test_interferometer.py delete mode 100644 test/test_likelihood.py delete mode 100644 test/test_likelihood_bilby.py delete mode 100644 test/waveform_test.py diff --git a/test/test_IMRPhenomC.py b/test/test_IMRPhenomC.py deleted file mode 100644 index dc42d642..00000000 --- a/test/test_IMRPhenomC.py +++ /dev/null @@ -1,23 +0,0 @@ -from lal import MSUN_SI, PC_SI, MTSUN_SI -import lalsimulation as lalsim -import numpy as np - -from jaxgw.gw.waveform.IMRPhenomC import IMRPhenomC -from jaxgw.gw.waveform.TaylorF2 import TaylorF2 - -mass_1 = 30. -mass_2 = 30. -luminosity_distance = 410. -f0 = 20. -max_f = 2048 -delta_f = 1./8 -spin = 0. - -injection_parameters = dict( - mass_1=mass_1, mass_2=mass_2, spin_1=0.0, spin_2=0.0, luminosity_distance=luminosity_distance, phase_c=0, t_c=0, theta_jn=0.4, psi=2.659,) - - -waveform1 = lalsim.SimIMRPhenomCGenerateFD(0., delta_f, mass_1*MSUN_SI, mass_2* MSUN_SI, 0., f0, max_f,luminosity_distance* 1e6*PC_SI,{}) -frequency = waveform1.f0 + np.arange(len(waveform1.data.data)) * waveform1.deltaF -waveform2 = IMRPhenomC(frequency,injection_parameters) -waveform3 = TaylorF2(frequency,injection_parameters) \ No newline at end of file diff --git a/test/test_TaylorF2.py b/test/test_TaylorF2.py deleted file mode 100644 index cd0d371c..00000000 --- a/test/test_TaylorF2.py +++ /dev/null @@ -1,42 +0,0 @@ -from lal import MSUN_SI, PC_SI, MTSUN_SI -import lalsimulation as lalsim -import numpy as np - -from jaxgw.gw.waveform.TaylorF2 import TaylorF2 -from bilby.gw.utils import greenwich_mean_sidereal_time - -mass_1 = 30. -mass_2 = 30. -luminosity_distance = 410. -f0 = 20. -max_f = 2048 -delta_f = 1./8 -spin = 0.02 - -injection_parameters = dict( - mass_1=mass_1, mass_2=mass_2, spin_1=spin, spin_2=spin, luminosity_distance=luminosity_distance, phase_c=0, t_c=0,\ - theta_jn=0.4, psi=2.659,f_ref = 50) - - -waveform1 = lalsim.SimInspiralTaylorF2(0., delta_f, mass_1*MSUN_SI, mass_2* MSUN_SI, 0., 0., f0, max_f, 50,luminosity_distance* 1e6*PC_SI,{}) -waveform2 = lalsim.SimInspiralChooseFDWaveform(mass_1*MSUN_SI, mass_2*MSUN_SI, 0, 0, 0, 0, 0, 0, luminosity_distance*1e6*PC_SI,0.4,0,0,0,0,1./8,40,2048,50,{},5) -frequency = waveform1.f0 + np.arange(len(waveform1.data.data)) * waveform1.deltaF -waveform3 = TaylorF2(frequency,injection_parameters) - -import bilby - -duration = 32 -sampling_frequency = 2 * 1024 - -# Fixed arguments passed into the source model. The analysis starts at 40 Hz. -waveform_arguments = dict(waveform_approximant='TaylorF2', - reference_frequency=50., minimum_frequency=40.0) - -# Create the waveform_generator using a LAL Binary Neutron Star source function -waveform_generator = bilby.gw.WaveformGenerator( - duration=duration, sampling_frequency=sampling_frequency, - frequency_domain_source_model=bilby.gw.source.lal_binary_neutron_star, - parameter_conversion=bilby.gw.conversion.convert_to_lal_binary_neutron_star_parameters, - waveform_arguments=waveform_arguments) - -waveform4 = waveform_generator.frequency_domain_source_model(frequency, mass_1, mass_2, luminosity_distance, 0, 0, 0, 0, 0, 0, 0.4, 0, 0, 0) \ No newline at end of file diff --git a/test/test_interferometer.py b/test/test_interferometer.py deleted file mode 100644 index 718c0d1e..00000000 --- a/test/test_interferometer.py +++ /dev/null @@ -1,27 +0,0 @@ -from jaxgw.likelihood.detector_projection import construct_arm, detector_tensor, antenna_response - - -H1_lat = 46 + 27. / 60 + 18.528 / 3600 -H1_long = -(119 + 24. / 60 + 27.5657 / 3600) -H1_xarm_azimuth = 125.9994 -H1_yarm_azimuth = 215.9994 -H1_xarm_tilt = -6.195e-4 -H1_yarm_tilt = 1.25e-5 - -L1_lat = 30 + 33. / 60 + 46.4196 / 3600 -L1_long = -(90 + 46. / 60 + 27.2654 / 3600) -L1_xarm_azimuth = 197.7165 -L1_yarm_azimuth = 287.7165 -L1_xarm_tilt = 0 -L1_yarm_tilt = 0 - -H1_arm1 = construct_arm(H1_long, H1_lat, H1_xarm_tilt, H1_xarm_azimuth) -H1_arm2 = construct_arm(H1_long, H1_lat, H1_yarm_tilt, H1_yarm_azimuth) - -L1_arm1 = construct_arm(L1_long, L1_lat, L1_xarm_tilt, L1_xarm_azimuth) -L1_arm2 = construct_arm(L1_long, L1_lat, L1_yarm_tilt, L1_yarm_azimuth) - -H1 = detector_tensor(H1_arm1, H1_arm2) -L1 = detector_tensor(L1_arm1, L1_arm2) - -H1_proj = antenna_response(H1, 1, 1, 0, 1,'plus') diff --git a/test/test_likelihood.py b/test/test_likelihood.py deleted file mode 100644 index 40f46ee7..00000000 --- a/test/test_likelihood.py +++ /dev/null @@ -1,104 +0,0 @@ -import numpy as np -import bilby -import jax.numpy as jnp - -from jax.config import config -from jax import grad -config.update("jax_enable_x64", True) - -# Set the duration and sampling frequency of the data segment that we're -# going to inject the signal into -duration = 4. -sampling_frequency = 2048. -minimum_frequency = 20 - -# Specify the output directory and the name of the simulation. -outdir = 'outdir' -label = 'fast_tutorial' -bilby.core.utils.setup_logger(outdir=outdir, label=label) - -# Set up a random seed for result reproducibility. This is optional! -np.random.seed(88170235) - -# We are going to inject a binary black hole waveform. We first establish a -# dictionary of parameters that includes all of the different waveform -# parameters, including masses of the two black holes (mass_1, mass_2), -# spins of both black holes (a, tilt, phi), etc. -injection_parameters = dict( - mass_1=36., mass_2=29., a_1=0.4, a_2=0.3, tilt_1=0.5, tilt_2=1.0, - phi_12=1.7, phi_jl=0.3, luminosity_distance=2000., theta_jn=0.4, psi=2.659, - phase=1.3, geocent_time=1126259642.413, ra=1.375, dec=-1.2108) - -# Fixed arguments passed into the source model -waveform_arguments = dict(waveform_approximant='IMRPhenomPv2', - reference_frequency=50., - minimum_frequency=minimum_frequency) - -# Create the waveform_generator using a LAL BinaryBlackHole source function -waveform_generator = bilby.gw.WaveformGenerator( - duration=duration, sampling_frequency=sampling_frequency, - frequency_domain_source_model=bilby.gw.source.lal_binary_black_hole, - parameter_conversion=bilby.gw.conversion.convert_to_lal_binary_black_hole_parameters, - waveform_arguments=waveform_arguments) - -# Set up interferometers. In this case we'll use two interferometers -# (LIGO-Hanford (H1), LIGO-Livingston (L1). These default to their design -# sensitivity -ifos = bilby.gw.detector.InterferometerList(['H1']) -ifos.set_strain_data_from_power_spectral_densities( - sampling_frequency=sampling_frequency, duration=duration, - start_time=injection_parameters['geocent_time'] - 3) -ifos.inject_signal(waveform_generator=waveform_generator, - parameters=injection_parameters) - -# Initialise the likelihood by passing in the interferometer data (ifos) and -# the waveform generator -likelihood = bilby.gw.GravitationalWaveTransient( - interferometers=ifos, waveform_generator=waveform_generator) - -likelihood.parameters = injection_parameters -snr_bilby = likelihood.calculate_snrs(waveform_generator.frequency_domain_strain(),ifos[0]).optimal_snr_squared - -############################################## -# Jax section -############################################## - - -waveform = waveform_generator.frequency_domain_strain() -waveform_frequency = waveform_generator.frequency_array - -psd = ifos[0].power_spectral_density_array -psd_frequency = ifos[0].frequency_array - -waveform_frequency = waveform_frequency[jnp.isfinite(psd)] -psd_frequency = psd_frequency[jnp.isfinite(psd)] -psd = psd[jnp.isfinite(psd)] - -from jaxgw.likelihood.detector_projection import construct_arm, detector_tensor, antenna_response, get_detector_response -from jaxgw.likelihood.utils import inner_product -from jaxgw.waveform.TaylorF2 import TaylorF2 - -waveform = TaylorF2(waveform_frequency, injection_parameters) -H1_lat = 46 + 27. / 60 + 18.528 / 3600 -H1_long = -(119 + 24. / 60 + 27.5657 / 3600) -H1_xarm_azimuth = 125.9994 -H1_yarm_azimuth = 215.9994 -H1_xarm_tilt = -6.195e-4 -H1_yarm_tilt = 1.25e-5 - -H1_arm1 = construct_arm(H1_long, H1_lat, H1_xarm_tilt, H1_xarm_azimuth) -H1_arm2 = construct_arm(H1_long, H1_lat, H1_yarm_tilt, H1_yarm_azimuth) - -H1 = detector_tensor(H1_arm1, H1_arm2) - -strain = get_detector_response(waveform,injection_parameters,H1) -jaxgw_snr = inner_product(strain, strain, waveform_frequency, psd, psd_frequency) -d_jaxgw_snr = grad(inner_product)(strain, strain, waveform_frequency, psd, psd_frequency) - -def jax_likelihood(params, data, data_f, PSD, PSD_f): - waveform = TaylorF2(data_f, params) - waveform = get_detector_response(waveform, params, H1) - output = inner_product(waveform, data, data_f, PSD, PSD_f) - return output - -dlikelihood = grad(jax_likelihood)(injection_parameters, strain, waveform_frequency, psd, psd_frequency) diff --git a/test/test_likelihood_bilby.py b/test/test_likelihood_bilby.py deleted file mode 100644 index efda70e8..00000000 --- a/test/test_likelihood_bilby.py +++ /dev/null @@ -1,213 +0,0 @@ -from bilby.gw.detector import psd -import numpy as np -import bilby -import jax -import jax.numpy as jnp - -from jax.config import config - -from jaxgw.sampler.NF_proposal import nf_metropolis_kernel, nf_metropolis_sampler -config.update("jax_enable_x64", True) - -from jaxgw.gw.likelihood.detector_projection import construct_arm, detector_tensor, antenna_response, get_detector_response - -from jaxgw.gw.likelihood.utils import inner_product -from jaxgw.gw.likelihood.detector_preset import get_H1, get_L1 -from jaxgw.gw.waveform.TaylorF2 import TaylorF2 -from jaxgw.gw.waveform.IMRPhenomC import IMRPhenomC -from jax import random, grad, jit, vmap, jacfwd, jacrev, value_and_grad, pmap - -from jaxgw.sampler.Gaussian_random_walk import rw_metropolis_sampler -from jaxgw.sampler.maf import MaskedAutoregressiveFlow -from jaxgw.sampler.realNVP import RealNVP -from jax.scipy.stats import multivariate_normal -from flax.training import train_state # Useful dataclass to keep train state -import optax # Optimizers - - -""" -This tutorial includes advanced specifications -for analysing binary neutron star event data. -Here GW170817 is used as an example. -""" -import bilby -from gwpy.timeseries import TimeSeries -from bilby.gw.utils import greenwich_mean_sidereal_time -import lalsimulation as lalsim -from lal import MSUN_SI, PC_SI, MTSUN_SI - -logger = bilby.core.utils.logger - -outdir = 'outdir' -label = 'bns_example' -bilby.core.utils.setup_logger(outdir=outdir, label=label) - -# Set up a random seed for result reproducibility. This is optional! -np.random.seed(88170235) - -# We are going to inject a binary neutron star waveform. We first establish a -# dictionary of parameters that includes all of the different waveform -# parameters, including masses of the two black holes (mass_1, mass_2), -# aligned spins of both black holes (chi_1, chi_2), etc. -injection_parameters = dict( - mass_1=1.5, mass_2=1.3, chi_1=0.0, chi_2=0.0, luminosity_distance=50., - theta_jn=0.4, psi=2.659, phase=1.3, geocent_time=1126259642.413, - ra=1.375, dec=-1.2108, lambda_1=0, lambda_2=0) - - -# Set the duration and sampling frequency of the data segment that we're going -# to inject the signal into. For the -# TaylorF2 waveform, we cut the signal close to the isco frequency -duration = 32 -sampling_frequency = 2 * 1024 -start_time = injection_parameters['geocent_time'] + 2 - duration - -jaxgw_params = dict(mass_1=1.5, mass_2=1.3, spin_1=0.0, spin_2=0.0, luminosity_distance=50, phase_c=1.3, t_c=0,\ - theta_jn=0.4, psi=2.659, ra=1.375, dec=-1.2108,\ - f_ref=50., geocent_time = start_time, start_time=start_time, - greenwich_mean_sidereal_time=greenwich_mean_sidereal_time(start_time)) - - -# Fixed arguments passed into the source model. The analysis starts at 40 Hz. -waveform_arguments = dict(waveform_approximant='TaylorF2', - reference_frequency=50., minimum_frequency=40.0) - -# Create the waveform_generator using a LAL Binary Neutron Star source function -waveform_generator = bilby.gw.WaveformGenerator( - duration=duration, sampling_frequency=sampling_frequency, - frequency_domain_source_model=bilby.gw.source.lal_binary_neutron_star, - parameter_conversion=bilby.gw.conversion.convert_to_lal_binary_neutron_star_parameters, - waveform_arguments=waveform_arguments) - -# Set up interferometers. In this case we'll use three interferometers -# (LIGO-Hanford (H1), LIGO-Livingston (L1), and Virgo (V1)). -# These default to their design sensitivity and start at 40 Hz. -interferometers = bilby.gw.detector.InterferometerList(['H1', 'L1']) -for interferometer in interferometers: - interferometer.minimum_frequency = 40 -interferometers.set_strain_data_from_power_spectral_densities( - sampling_frequency=sampling_frequency, duration=duration, - start_time=start_time) -interferometers.inject_signal(parameters=injection_parameters, - waveform_generator=waveform_generator) - -# Load the default prior for binary neutron stars. -# We're going to sample in chirp_mass, symmetric_mass_ratio, lambda_tilde, and -# delta_lambda rather than mass_1, mass_2, lambda_1, and lambda_2. -# BNS have aligned spins by default, if you want to allow precessing spins -# pass aligned_spin=False to the BNSPriorDict -priors = bilby.gw.prior.BNSPriorDict() -for key in ['psi', 'geocent_time', 'ra', 'dec', 'chi_1', 'chi_2', - 'theta_jn', 'luminosity_distance', 'phase']: - priors[key] = injection_parameters[key] -priors.pop('mass_ratio') -priors.pop('lambda_1') -priors.pop('lambda_2') -priors['chirp_mass'] = bilby.core.prior.Gaussian( - 1.215, 0.1, name='chirp_mass', unit='$M_{\\odot}$') -priors['symmetric_mass_ratio'] = bilby.core.prior.Uniform( - 0.1, 0.25, name='symmetric_mass_ratio') -priors['lambda_tilde'] = bilby.core.prior.Uniform(0, 5000, name='lambda_tilde') -priors['delta_lambda'] = bilby.core.prior.Uniform( - -5000, 5000, name='delta_lambda') - -# Initialise the likelihood by passing in the interferometer data (IFOs) -# and the waveform generator -bilby_likelihood = bilby.gw.GravitationalWaveTransient( - interferometers=interferometers, waveform_generator=waveform_generator, - time_marginalization=False, phase_marginalization=False, - distance_marginalization=False, priors=priors) - -psd_frequency = interferometers[0].frequency_array[1:] -H1, H1_vertex = get_H1() -L1, L1_vertex = get_L1() - -strain_H1 = get_detector_response(psd_frequency,TaylorF2(psd_frequency,jaxgw_params), jaxgw_params,H1,H1_vertex)#interferometers[0].frequency_domain_strain[1:] -strain_L1 = get_detector_response(psd_frequency,TaylorF2(psd_frequency,jaxgw_params), jaxgw_params,L1,L1_vertex)#interferometers[1].frequency_domain_strain[1:] -psd_H1 = interferometers[0].power_spectral_density_array[1:] -psd_L1 = interferometers[1].power_spectral_density_array[1:] - -duration = waveform_generator.duration - -print('SNR of the event in H1: '+str(np.sqrt(inner_product(strain_H1,strain_H1,psd_frequency,psd_H1)))) -print('SNR of the event in L1: '+str(np.sqrt(inner_product(strain_L1,strain_L1,psd_frequency,psd_L1)))) - -@jit -def single_detector_likelihood(params, data, data_f, PSD, detector, detector_vertex): -# waveform = IMRPhenomC(data_f, params) - waveform = TaylorF2(data_f, params) - waveform = get_detector_response(data_f, waveform, params, detector, detector_vertex) -# waveform *= mask - match_filter_SNR = inner_product(waveform, data, data_f, PSD) - optimal_SNR = inner_product(waveform, waveform, data_f, PSD) - return match_filter_SNR, optimal_SNR - -@jit -def single_detector_likelihood_bilby(params, data, data_f, PSD, detector, detector_vertex): -# waveform = IMRPhenomC(data_f, params) - waveform = TaylorF2(data_f, params) - waveform = get_detector_response(data_f, waveform, params, detector, detector_vertex) - log_l = -2 / duration * jnp.vdot(data-waveform, (data-waveform)/PSD) - return log_l.real - -@jit -def logprob_wrap(mass_1, mass_2, luminosity_distance, phase_c, t_c, theta_jn, psi, ra, dec): - params = dict(mass_1=mass_1, mass_2=mass_2, spin_1=0, spin_2=0, luminosity_distance=luminosity_distance, phase_c=phase_c%(2*jnp.pi), t_c=t_c,\ - theta_jn=theta_jn%(jnp.pi), psi=psi%(jnp.pi), ra=ra%(2*jnp.pi), dec=dec%(jnp.pi),\ - f_ref=50., geocent_time = interferometers[0].strain_data.start_time+t_c, start_time=interferometers[0].strain_data.start_time, - greenwich_mean_sidereal_time=greenwich_mean_sidereal_time(interferometers[0].strain_data.start_time)) - H1_SNR = single_detector_likelihood(params, strain_H1, psd_frequency, psd_H1, H1, H1_vertex) - L1_SNR = single_detector_likelihood(params, strain_L1, psd_frequency, psd_L1, L1, L1_vertex) - match_filter_SNR = H1_SNR[0] + L1_SNR[0] - optimal_SNR = H1_SNR[1] + L1_SNR[1] - return match_filter_SNR - optimal_SNR/2 - - -likelihood = lambda x: logprob_wrap(*x) -likelihood = jit(likelihood) -d_likelihood = jit(grad(likelihood)) -para_logp = jit(jax.vmap(likelihood)) - -result = bilby.result.read_in_result(filename='/mnt/home/wwong/GWProject/Tutorial/bilby_tutorial/outdir/bns_example_result.json') - -for i in result.posterior.keys(): - bilby_likelihood.parameters[i] = result.posterior[i].values[-1] - -print("Section where we use bilby waveform generator.") - -waveform = bilby_likelihood.waveform_generator.frequency_domain_strain(bilby_likelihood.parameters) -params = {} -params['mass_1'] = bilby_likelihood.parameters['mass_1'] -params['mass_2'] = bilby_likelihood.parameters['mass_2'] -params['spin_1'] = 0.0#bilby_likelihood.parameters['a_1'] -params['spin_2'] = 0.0#bilby_likelihood.parameters['a_2'] -params['luminosity_distance'] = bilby_likelihood.parameters['luminosity_distance'] -params['phase_c'] = bilby_likelihood.parameters['phase'] -params['t_c'] = 0#bilby_likelihood.parameters['geocent_time'] -params['theta_jn'] = bilby_likelihood.parameters['theta_jn'] -params['psi'] = bilby_likelihood.parameters['psi'] -params['ra'] = bilby_likelihood.parameters['ra'] -params['dec'] = bilby_likelihood.parameters['dec'] -params['f_ref'] = bilby_likelihood.parameters['reference_frequency'] -params['start_time'] = interferometers[0].strain_data.start_time -params['geocent_time'] = bilby_likelihood.parameters['geocent_time'] -params['greenwich_mean_sidereal_time'] = greenwich_mean_sidereal_time(bilby_likelihood.parameters['geocent_time']) - -mask = np.ones(psd_frequency.shape) -mask[psd_frequency Date: Wed, 5 Jul 2023 17:07:22 -0400 Subject: [PATCH 197/300] I think using equinox for some of the classes will simplify the API tremendously --- example/GW150914.py | 6 +++--- src/jimgw/detector.py | 13 +++++++++++++ src/jimgw/likelihood.py | 17 ++++++++++++++++- 3 files changed, 32 insertions(+), 4 deletions(-) diff --git a/example/GW150914.py b/example/GW150914.py index aaf4a1dd..acc05269 100644 --- a/example/GW150914.py +++ b/example/GW150914.py @@ -149,15 +149,15 @@ def LogLikelihood(theta): ########################################### n_dim = 11 -n_chains = 500 -n_loop_training = 15 +n_chains = 1000 +n_loop_training = 20 n_loop_production = 10 n_local_steps = 100 n_global_steps = 100 learning_rate = 0.001 max_samples = 100000 momentum = 0.9 -num_epochs = 200 +num_epochs = 50 batch_size = 50000 guess_param = best_fit diff --git a/src/jimgw/detector.py b/src/jimgw/detector.py index b8920933..c82cd925 100644 --- a/src/jimgw/detector.py +++ b/src/jimgw/detector.py @@ -2,6 +2,8 @@ from .constants import * from .wave import Polarization from scipy.signal.windows import tukey +from abc import abstractmethod +import equinox as eqx DEG_TO_RAD = jnp.pi/180 @@ -13,6 +15,17 @@ def np2(x): p = p << 1 return p +class Detector(ABC): + """ Base class for all detectors. + + + + """ + + @abstractmethod + def fd_response(self, ) + + class Detector(object): """Defines a ground-based gravitational-wave detector. diff --git a/src/jimgw/likelihood.py b/src/jimgw/likelihood.py index cf6a5c00..38926d40 100644 --- a/src/jimgw/likelihood.py +++ b/src/jimgw/likelihood.py @@ -1,6 +1,21 @@ -import numpy as np from gwpy.timeseries import TimeSeries from gwpy.frequencyseries import FrequencySeries +from abc import ABC + +class LikelihoodBase(ABC): + """Base class for likelihoods. + + """ + + def __init__(self, + waveform, + detectors, + heterodyne: bool = False): + self.waveform = waveform + self.heterodyne = heterodyne + # whether to include Earth's rotation in the antenna pattern + + class LogLikelihoodTransientFD(object): """Object to construct a frequency-domain JAX-based log-likelihood function From d7a447853666aebf09e6fa2308717b235210f5ad Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Wed, 5 Jul 2023 17:38:24 -0400 Subject: [PATCH 198/300] Scaffolding classes --- example/GW150914.py | 4 ++-- src/jimgw/detector.py | 14 ++++++++++++-- src/jimgw/likelihood.py | 8 +++++++- src/jimgw/single_event_likelihood.py | 13 ------------- 4 files changed, 21 insertions(+), 18 deletions(-) delete mode 100644 src/jimgw/single_event_likelihood.py diff --git a/example/GW150914.py b/example/GW150914.py index acc05269..6ed9b090 100644 --- a/example/GW150914.py +++ b/example/GW150914.py @@ -150,14 +150,14 @@ def LogLikelihood(theta): n_dim = 11 n_chains = 1000 -n_loop_training = 20 +n_loop_training = 10 n_loop_production = 10 n_local_steps = 100 n_global_steps = 100 learning_rate = 0.001 max_samples = 100000 momentum = 0.9 -num_epochs = 50 +num_epochs = 100 batch_size = 50000 guess_param = best_fit diff --git a/src/jimgw/detector.py b/src/jimgw/detector.py index c82cd925..22b1564f 100644 --- a/src/jimgw/detector.py +++ b/src/jimgw/detector.py @@ -15,15 +15,25 @@ def np2(x): p = p << 1 return p -class Detector(ABC): +class Detector(eqx.Module): """ Base class for all detectors. """ + + + @abstractmethod + def fd_response(self, ): + raise NotImplementedError + @abstractmethod - def fd_response(self, ) + def td_response(self, time: ): + raise NotImplementedError + + @abstractmethod + class Detector(object): diff --git a/src/jimgw/likelihood.py b/src/jimgw/likelihood.py index 38926d40..0c110311 100644 --- a/src/jimgw/likelihood.py +++ b/src/jimgw/likelihood.py @@ -1,9 +1,12 @@ from gwpy.timeseries import TimeSeries from gwpy.frequencyseries import FrequencySeries -from abc import ABC +from abc import ABC, abstractmethod class LikelihoodBase(ABC): """Base class for likelihoods. + Note that this likelihood class should work for a somehwat general class of problems. + In light of that, this class would be somewhat abstract, but the idea behind it is this + handles two main components of a likelihood: the data and the model. """ @@ -15,6 +18,9 @@ def __init__(self, self.heterodyne = heterodyne # whether to include Earth's rotation in the antenna pattern + @abstractmethod + def + class LogLikelihoodTransientFD(object): diff --git a/src/jimgw/single_event_likelihood.py b/src/jimgw/single_event_likelihood.py deleted file mode 100644 index e0fc4c2a..00000000 --- a/src/jimgw/single_event_likelihood.py +++ /dev/null @@ -1,13 +0,0 @@ -from jax import jit -from jaxgw.PE.detector_projection import get_detector_response -from jaxgw.PE.utils import inner_product - -def single_detector_likelihood(waveform_model, params, data, data_f, PSD, detector): - waveform = waveform_model(data_f, params) - waveform = get_detector_response(waveform, params, detector) - match_filter_SNR = inner_product(waveform, data, data_f, PSD) - optimal_SNR = inner_product(waveform, waveform, data_f, PSD) - return (-2*match_filter_SNR + optimal_SNR)/2 - - - From 6a0152c59364ae42b61441fa362ae67279fc8285 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Wed, 5 Jul 2023 17:49:53 -0400 Subject: [PATCH 199/300] Scaffolding --- src/jimgw/base.py | 29 +++++++++++++++++++++++++++++ src/jimgw/detector.py | 10 +++++----- src/jimgw/likelihood.py | 16 +++++++--------- 3 files changed, 41 insertions(+), 14 deletions(-) create mode 100644 src/jimgw/base.py diff --git a/src/jimgw/base.py b/src/jimgw/base.py new file mode 100644 index 00000000..39ae4605 --- /dev/null +++ b/src/jimgw/base.py @@ -0,0 +1,29 @@ +import equinox as eqx +from abc import ABC, abstractmethod +from jaxtyping import Array + +class Data(ABC): + + @abstractmethod + def __init__(self): + raise NotImplementedError + + @abstractmethod + def fetch(self): + raise NotImplementedError + +class Model(eqx.Module): + + params: dict + + @abstractmethod + def __init__(self): + raise NotImplementedError + + def __call__(self, x: Array) -> float: + raise NotImplementedError + +class Jim(object): + """ Master class for interfacing with flowMC + + """ \ No newline at end of file diff --git a/src/jimgw/detector.py b/src/jimgw/detector.py index 22b1564f..5c86653a 100644 --- a/src/jimgw/detector.py +++ b/src/jimgw/detector.py @@ -4,6 +4,7 @@ from scipy.signal.windows import tukey from abc import abstractmethod import equinox as eqx +from jaxtyping import Array DEG_TO_RAD = jnp.pi/180 @@ -22,18 +23,17 @@ class Detector(eqx.Module): """ - - @abstractmethod - def fd_response(self, ): + def load_data(self, data): raise NotImplementedError @abstractmethod - def td_response(self, time: ): + def fd_response(self, frequency: Array) -> Array: raise NotImplementedError @abstractmethod - + def td_response(self, time: Array) -> Array: + raise NotImplementedError class Detector(object): diff --git a/src/jimgw/likelihood.py b/src/jimgw/likelihood.py index 0c110311..bae46471 100644 --- a/src/jimgw/likelihood.py +++ b/src/jimgw/likelihood.py @@ -2,6 +2,7 @@ from gwpy.frequencyseries import FrequencySeries from abc import ABC, abstractmethod + class LikelihoodBase(ABC): """Base class for likelihoods. Note that this likelihood class should work for a somehwat general class of problems. @@ -10,16 +11,13 @@ class LikelihoodBase(ABC): """ - def __init__(self, - waveform, - detectors, - heterodyne: bool = False): - self.waveform = waveform - self.heterodyne = heterodyne - # whether to include Earth's rotation in the antenna pattern - @abstractmethod - def + def evalutate(self, params): + """Evaluate the likelihood for a given set of parameters. + """ + raise NotImplementedError + + From f8f2f39629257d02aaa23d671fd984119ca02424 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Wed, 5 Jul 2023 17:54:18 -0400 Subject: [PATCH 200/300] Scaffolding --- src/jimgw/detector.py | 2 +- src/jimgw/likelihood.py | 37 +++++++++++++++++++++++++++++++++++-- src/jimgw/waveform.py | 6 ++++++ 3 files changed, 42 insertions(+), 3 deletions(-) create mode 100644 src/jimgw/waveform.py diff --git a/src/jimgw/detector.py b/src/jimgw/detector.py index 5c86653a..68adf5ea 100644 --- a/src/jimgw/detector.py +++ b/src/jimgw/detector.py @@ -33,7 +33,7 @@ def fd_response(self, frequency: Array) -> Array: @abstractmethod def td_response(self, time: Array) -> Array: - raise NotImplementedError + raise NotImplementedError class Detector(object): diff --git a/src/jimgw/likelihood.py b/src/jimgw/likelihood.py index bae46471..7eff7e67 100644 --- a/src/jimgw/likelihood.py +++ b/src/jimgw/likelihood.py @@ -1,6 +1,9 @@ from gwpy.timeseries import TimeSeries from gwpy.frequencyseries import FrequencySeries from abc import ABC, abstractmethod +from typing import Tuple +from jimgw.waveform import Waveform +from jimgw.detector import Detector class LikelihoodBase(ABC): @@ -9,15 +12,45 @@ class LikelihoodBase(ABC): In light of that, this class would be somewhat abstract, but the idea behind it is this handles two main components of a likelihood: the data and the model. + It should be able to take the data and model and evaluate the likelihood for a given set of parameters. + """ + @property + def model(self): + """The model for the likelihood. + """ + return self._model + + @property + def data(self): + """The data for the likelihood. + """ + return self._data + @abstractmethod - def evalutate(self, params): + def evalutate(self, params) -> float: """Evaluate the likelihood for a given set of parameters. """ raise NotImplementedError - +class TransientLikelihoodFD(LikelihoodBase): + + detectors: list[Detector] + waveform: Waveform + + def __init__(self, + detectors: list[Detector], + waveform: Waveform + ) -> None: + super().__init__() + self.detectors = detectors + self.waveform = waveform + + def evaluate(self, params) -> float: + """Evaluate the likelihood for a given set of parameters. + """ + raise NotImplementedError diff --git a/src/jimgw/waveform.py b/src/jimgw/waveform.py new file mode 100644 index 00000000..44b9af2b --- /dev/null +++ b/src/jimgw/waveform.py @@ -0,0 +1,6 @@ +import equinox as eqx + +class Waveform(eqx.Module): + + def __init__(self): + return NotImplemented \ No newline at end of file From 4ae03c5289bbbee4c15f2baa6408033a7ca5d2c1 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Wed, 5 Jul 2023 21:32:53 -0400 Subject: [PATCH 201/300] spread structure --- src/jimgw/data.py | 13 +++++++++++++ src/jimgw/detector.py | 3 +-- src/jimgw/jim.py | 12 ++++++++++++ src/jimgw/{base.py => model.py} | 15 --------------- 4 files changed, 26 insertions(+), 17 deletions(-) create mode 100644 src/jimgw/data.py create mode 100644 src/jimgw/jim.py rename src/jimgw/{base.py => model.py} (53%) diff --git a/src/jimgw/data.py b/src/jimgw/data.py new file mode 100644 index 00000000..9bc31efe --- /dev/null +++ b/src/jimgw/data.py @@ -0,0 +1,13 @@ +import equinox as eqx +from abc import ABC, abstractmethod +from jaxtyping import Array + +class Data(ABC): + + @abstractmethod + def __init__(self): + raise NotImplementedError + + @abstractmethod + def fetch(self): + raise NotImplementedError diff --git a/src/jimgw/detector.py b/src/jimgw/detector.py index 68adf5ea..269168ac 100644 --- a/src/jimgw/detector.py +++ b/src/jimgw/detector.py @@ -19,8 +19,6 @@ def np2(x): class Detector(eqx.Module): """ Base class for all detectors. - - """ @abstractmethod @@ -35,6 +33,7 @@ def fd_response(self, frequency: Array) -> Array: def td_response(self, time: Array) -> Array: raise NotImplementedError +class class Detector(object): """Defines a ground-based gravitational-wave detector. diff --git a/src/jimgw/jim.py b/src/jimgw/jim.py new file mode 100644 index 00000000..025f2bd9 --- /dev/null +++ b/src/jimgw/jim.py @@ -0,0 +1,12 @@ +from abc import ABC, abstractmethod +from jaxtyping import Array +from jimgw.likelihood import LikelihoodBase +from flowMC. + +class Jim(object): + """ Master class for interfacing with flowMC + + """ + + def __init__(self, ): + pass \ No newline at end of file diff --git a/src/jimgw/base.py b/src/jimgw/model.py similarity index 53% rename from src/jimgw/base.py rename to src/jimgw/model.py index 39ae4605..e18c86ef 100644 --- a/src/jimgw/base.py +++ b/src/jimgw/model.py @@ -2,16 +2,6 @@ from abc import ABC, abstractmethod from jaxtyping import Array -class Data(ABC): - - @abstractmethod - def __init__(self): - raise NotImplementedError - - @abstractmethod - def fetch(self): - raise NotImplementedError - class Model(eqx.Module): params: dict @@ -22,8 +12,3 @@ def __init__(self): def __call__(self, x: Array) -> float: raise NotImplementedError - -class Jim(object): - """ Master class for interfacing with flowMC - - """ \ No newline at end of file From fa206062a8c52e113ad3f8daadaab766ed786b61 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Wed, 5 Jul 2023 21:41:51 -0400 Subject: [PATCH 202/300] Update jim --- src/jimgw/jim.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/src/jimgw/jim.py b/src/jimgw/jim.py index 025f2bd9..656d69ad 100644 --- a/src/jimgw/jim.py +++ b/src/jimgw/jim.py @@ -1,12 +1,13 @@ from abc import ABC, abstractmethod from jaxtyping import Array from jimgw.likelihood import LikelihoodBase -from flowMC. +from flowMC.sampler.Sampler import Sampler class Jim(object): """ Master class for interfacing with flowMC """ - def __init__(self, ): - pass \ No newline at end of file + def __init__(self, Sampler: Sampler, Likelihood: LikelihoodBase, **kwargs): + pass + From 8a9991658649bd746921cb558729b818be06d734 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Wed, 5 Jul 2023 22:32:12 -0400 Subject: [PATCH 203/300] Refactored the polarization class --- src/jimgw/wave.py | 111 ++++++++++++++++++---------------------------- 1 file changed, 44 insertions(+), 67 deletions(-) diff --git a/src/jimgw/wave.py b/src/jimgw/wave.py index 280f3e0d..3839c968 100644 --- a/src/jimgw/wave.py +++ b/src/jimgw/wave.py @@ -1,11 +1,15 @@ # Credit some part of the source code from bilby import jax.numpy as jnp -from .constants import * +from jimgw.constants import * +import equinox as eqx +from jaxtyping import Array KNOWN_POLS = 'pcxybl' -class Polarization(object): +class Polarization(eqx.Module): + + name: str """Object defining a given polarization mode, with utilities to produce corresponding tensor in an Earth centric frame. @@ -21,89 +25,62 @@ def __init__(self, name): e = f"unknown mode '{self.name}'; must be one of: {KNOWN_POLS}" raise ValueError(e) - @property - def tensor_from_basis_constructor(self): + def tensor_from_basis(self, x: Array, y: Array) -> Array: """Constructor to obtain polarization tensor from waveframe basis defined by orthonormal vectors (x, y) in arbitrary Cartesian coordinates. """ if self.name == 'p': - def kernel(x, y): - """Plus polarization from (x, y) waveframe basis elements. - """ - return jnp.einsum('i,j->ij', x, x) - jnp.einsum('i,j->ij', y, y) + return jnp.einsum('i,j->ij', x, x) - jnp.einsum('i,j->ij', y, y) elif self.name == 'c': - def kernel(x, y): - """Cross polarization from (x, y) waveframe basis elements. - """ - return jnp.einsum('i,j->ij', x, y) + jnp.einsum('i,j->ij', y, x) + return jnp.einsum('i,j->ij', x, y) + jnp.einsum('i,j->ij', y, x) elif self.name == 'x': - def kernel(x, y): - """Vector-x polarization from (x, y) waveframe basis elements. - """ - z = jnp.cross(x, y) - return jnp.einsum('i,j->ij', x, z) + jnp.einsum('i,j->ij', z, x) + z = jnp.cross(x, y) + return jnp.einsum('i,j->ij', x, z) + jnp.einsum('i,j->ij', z, x) elif self.name == 'y': - def kernel(x, y): - """Vector-y polarization from (x, y) waveframe basis elements. - """ - z = jnp.cross(x, y) - return jnp.einsum('i,j->ij', y, z) + jnp.einsum('i,j->ij', z, y) + z = jnp.cross(x, y) + return jnp.einsum('i,j->ij', y, z) + jnp.einsum('i,j->ij', z, y) elif self.name == 'b': - def kernel(x, y): - """Breathing polarization from (x, y) waveframe basis elements. - """ - return jnp.einsum('i,j->ij', x, x) + jnp.einsum('i,j->ij', y, y) + return jnp.einsum('i,j->ij', x, x) + jnp.einsum('i,j->ij', y, y) elif self.name == 'l': - def kernel(x, y): - """Longitudinal polarization from (x, y) waveframe basis elements. - """ - z = jnp.cross(x, y) - return jnp.einsum('i,j->ij', z, z) + z = jnp.cross(x, y) + return jnp.einsum('i,j->ij', z, z) else: raise ValueError(f"unrecognized polarization {self.name}") - return kernel @property - def tensor_from_sky_constructor(self): - """Constructor to obtain polarization tensor from sky location and - orientation parameters. - """ - kernel = self.tensor_from_sky_constructor - def get_pol_tensor(ra, dec, psi, gmst): - """Computes {name} polarization tensor in celestial - coordinates from sky location and orientation parameters. + def tensor_from_sky(self, ra: float, dec: float, psi: float, gmst: float) -> Array: + """Computes {name} polarization tensor in celestial + coordinates from sky location and orientation parameters. - Arguments - --------- - ra : float - right ascension in radians. - dec : float - declination in radians. - psi : float - polarization angle in radians. - gmst : float - Greenwhich mean standard time (GMST) in radians. + Arguments + --------- + ra : float + right ascension in radians. + dec : float + declination in radians. + psi : float + polarization angle in radians. + gmst : float + Greenwhich mean standard time (GMST) in radians. - Returns - ------- - tensor : array - 3x3 polarization tensor. - """ - gmst = jnp.mod(gmst, 2*jnp.pi) - phi = ra - gmst - theta = jnp.pi / 2 - dec + Returns + ------- + tensor : array + 3x3 polarization tensor. + """ + gmst = jnp.mod(gmst, 2*jnp.pi) + phi = ra - gmst + theta = jnp.pi / 2 - dec - u = jnp.array([jnp.cos(phi) * jnp.cos(theta), - jnp.cos(theta) * jnp.sin(phi), - -jnp.sin(theta)]) - v = jnp.array([-jnp.sin(phi), jnp.cos(phi), 0]) - m = -u * jnp.sin(psi) - v * jnp.cos(psi) - n = -u * jnp.cos(psi) + v * jnp.sin(psi) + u = jnp.array([jnp.cos(phi) * jnp.cos(theta), + jnp.cos(theta) * jnp.sin(phi), + -jnp.sin(theta)]) + v = jnp.array([-jnp.sin(phi), jnp.cos(phi), 0]) + m = -u * jnp.sin(psi) - v * jnp.cos(psi) + n = -u * jnp.cos(psi) + v * jnp.sin(psi) - return kernel(m, n) - get_pol_tensor.__doc__ = get_pol_tensor.__doc__.format(name=self.name) - return get_pol_tensor + return self.tensor_from_basis(m, n) From 3f233a0f53ca5e9976a9ff437ffef04100697f4c Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Wed, 5 Jul 2023 22:36:31 -0400 Subject: [PATCH 204/300] Polarization class seems to wrok fine even with jitting --- src/jimgw/detector.py | 200 ++++++++++++++++++++++++------------------ src/jimgw/wave.py | 1 - 2 files changed, 114 insertions(+), 87 deletions(-) diff --git a/src/jimgw/detector.py b/src/jimgw/detector.py index 269168ac..413608d3 100644 --- a/src/jimgw/detector.py +++ b/src/jimgw/detector.py @@ -1,12 +1,11 @@ import jax.numpy as jnp -from .constants import * -from .wave import Polarization +from jimgw.constants import * +from jimgw.wave import Polarization from scipy.signal.windows import tukey from abc import abstractmethod import equinox as eqx from jaxtyping import Array - DEG_TO_RAD = jnp.pi/180 def np2(x): @@ -32,65 +31,40 @@ def fd_response(self, frequency: Array) -> Array: @abstractmethod def td_response(self, time: Array) -> Array: raise NotImplementedError + -class -class Detector(object): - """Defines a ground-based gravitational-wave detector. +class GroundBased2G(Detector): - Argument - -------- - name : str - interferometer name, e.g., 'H1' for LIGO Hanford. - coordinates : dict - optionally, provide custom detector arm and vertex coordinates. - """ - def __init__(self, name, coordinates=None): - self.name = name.upper() - self._coordinates = coordinates or {} + latitude: float + longitude: float + elevation: float + xarm_azimuth: float + yarm_azimuth: float + xarm_tilt: float + yarm_tilt: float + name: str - @property - def coordinates(self): - """Coordinates defining a triangular detector (angles in radians). - """ - if not self._coordinates: - if self.name == 'H1': - # LIGO Hanford - self._coordinates = dict( - lat = (46 + 27. / 60 + 18.528 / 3600) * DEG_TO_RAD, - lon = -(119 + 24. / 60 + 27.5657 / 3600) * DEG_TO_RAD, - xarm_azimuth = 125.9994 * DEG_TO_RAD, - yarm_azimuth = 215.9994 * DEG_TO_RAD, - xarm_tilt = -6.195e-4, - yarm_tilt = 1.25e-5, - elevation = 142.554, - ) - elif self.name == 'L1': - # LIGO Livingston - self._coordinates = dict( - lat = (30 + 33. / 60 + 46.4196 / 3600) * DEG_TO_RAD, - lon= -(90 + 46. / 60 + 27.2654 / 3600) * DEG_TO_RAD, - xarm_azimuth = 197.7165 * DEG_TO_RAD, - yarm_azimuth = 287.7165 * DEG_TO_RAD, - xarm_tilt = 0 , - yarm_tilt = 0, - elevation = -6.574, - ) - elif self.name == 'V1': - # Virgo - self._coordinates = dict( - lat = (43 + 37. / 60 + 53.0921 / 3600) * DEG_TO_RAD, - lon = (10 + 30. / 60 + 16.1878 / 3600) * DEG_TO_RAD, - xarm_azimuth = 70.5674 * DEG_TO_RAD, - yarm_azimuth = 160.5674 * DEG_TO_RAD, - xarm_tilt = 0, - yarm_tilt = 0, - elevation = 51.884, - ) - elif not self._coordinates: - raise ValueError(f"unknown detector {self.name}") - return self._coordinates + def __init__(self, name: str, **kwargs) -> None: + self.name = name + self.latitude = kwargs.get('latitude', 0) + self.longitude = kwargs.get('longitude', 0) + self.elevation = kwargs.get('elevation', 0) + self.xarm_azimuth = kwargs.get('xarm_azimuth', 0) + self.yarm_azimuth = kwargs.get('yarm_azimuth', 0) + self.xarm_tilt = kwargs.get('xarm_tilt', 0) + self.yarm_tilt = kwargs.get('yarm_tilt', 0) + + def load_data(self, data): + pass + + def fd_response(self, frequency: Array) -> Array: + pass + + def td_response(self, time: Array) -> Array: + pass + @staticmethod def _get_arm(lat, lon, tilt, azimuth): """Construct detector-arm vectors in Earth-centric Cartesian coordinates. @@ -153,38 +127,34 @@ def vertex(self): return jnp.array([x, y, z]) @property - def delay_from_geocenter_constructor(self): - """Gives function to compute the delay from geocenter for any sky - location and GMST. + def delay_from_geocenter_constructor(self, ra: float, dec: float, gmst: float) -> float: + """ Calculate time delay between two detectors in geocentric + coordinates based on XLALArrivaTimeDiff in TimeDelay.c + + https://lscsoft.docs.ligo.org/lalsuite/lal/group___time_delay__h.html + + Arguments + --------- + ra : float + right ascension of the source in rad. + dec : float + declination of the source in rad. + gmst : float + Greenwich mean sidereal time in rad. + + Returns + ------- + float: time delay from Earth center. """ delta_d = -self.vertex - def delay(ra, dec, gmst): - """ Calculate time delay between two detectors in geocentric - coordinates based on XLALArrivaTimeDiff in TimeDelay.c - https://lscsoft.docs.ligo.org/lalsuite/lal/group___time_delay__h.html - - Arguments - --------- - ra : float - right ascension of the source in rad. - dec : float - declination of the source in rad. - gmst : float - Greenwich mean sidereal time in rad. - - Returns - ------- - float: time delay from Earth center. - """ - gmst = jnp.mod(gmst, 2 * jnp.pi) - phi = ra - gmst - theta = jnp.pi / 2 - dec - omega = jnp.array([jnp.sin(theta)*jnp.cos(phi), - jnp.sin(theta)*jnp.sin(phi), - jnp.cos(theta)]) - return jnp.dot(omega, delta_d) / C_SI - return delay + gmst = jnp.mod(gmst, 2 * jnp.pi) + phi = ra - gmst + theta = jnp.pi / 2 - dec + omega = jnp.array([jnp.sin(theta)*jnp.cos(phi), + jnp.sin(theta)*jnp.sin(phi), + jnp.cos(theta)]) + return jnp.dot(omega, delta_d) / C_SI def antenna_pattern_constructor(self, modes='pc'): """Gives function to compute antenna patterns for any sky location, @@ -348,3 +318,61 @@ def get_det_h(f, polwaveforms, ra, dec, psi, gmst, tc): n=len(modes), t=epoch) return get_det_h + + + +class Detector(object): + """Defines a ground-based gravitational-wave detector. + + Argument + -------- + name : str + interferometer name, e.g., 'H1' for LIGO Hanford. + coordinates : dict + optionally, provide custom detector arm and vertex coordinates. + """ + def __init__(self, name, coordinates=None): + self.name = name.upper() + self._coordinates = coordinates or {} + + @property + def coordinates(self): + """Coordinates defining a triangular detector (angles in radians). + """ + if not self._coordinates: + if self.name == 'H1': + # LIGO Hanford + self._coordinates = dict( + lat = (46 + 27. / 60 + 18.528 / 3600) * DEG_TO_RAD, + lon = -(119 + 24. / 60 + 27.5657 / 3600) * DEG_TO_RAD, + xarm_azimuth = 125.9994 * DEG_TO_RAD, + yarm_azimuth = 215.9994 * DEG_TO_RAD, + xarm_tilt = -6.195e-4, + yarm_tilt = 1.25e-5, + elevation = 142.554, + ) + elif self.name == 'L1': + # LIGO Livingston + self._coordinates = dict( + lat = (30 + 33. / 60 + 46.4196 / 3600) * DEG_TO_RAD, + lon= -(90 + 46. / 60 + 27.2654 / 3600) * DEG_TO_RAD, + xarm_azimuth = 197.7165 * DEG_TO_RAD, + yarm_azimuth = 287.7165 * DEG_TO_RAD, + xarm_tilt = 0 , + yarm_tilt = 0, + elevation = -6.574, + ) + elif self.name == 'V1': + # Virgo + self._coordinates = dict( + lat = (43 + 37. / 60 + 53.0921 / 3600) * DEG_TO_RAD, + lon = (10 + 30. / 60 + 16.1878 / 3600) * DEG_TO_RAD, + xarm_azimuth = 70.5674 * DEG_TO_RAD, + yarm_azimuth = 160.5674 * DEG_TO_RAD, + xarm_tilt = 0, + yarm_tilt = 0, + elevation = 51.884, + ) + elif not self._coordinates: + raise ValueError(f"unknown detector {self.name}") + return self._coordinates diff --git a/src/jimgw/wave.py b/src/jimgw/wave.py index 3839c968..ddc6cd4c 100644 --- a/src/jimgw/wave.py +++ b/src/jimgw/wave.py @@ -48,7 +48,6 @@ def tensor_from_basis(self, x: Array, y: Array) -> Array: else: raise ValueError(f"unrecognized polarization {self.name}") - @property def tensor_from_sky(self, ra: float, dec: float, psi: float, gmst: float) -> Array: """Computes {name} polarization tensor in celestial coordinates from sky location and orientation parameters. From 2ba175c9d9050e658cfc474a4b7ddd2cafa8da04 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Wed, 5 Jul 2023 22:47:02 -0400 Subject: [PATCH 205/300] The detector script seem pretty incomplete. I am constructing the script from scratch --- src/jimgw/detector.py | 77 +++++++++++++++++------------------------ src/jimgw/likelihood.py | 1 - 2 files changed, 31 insertions(+), 47 deletions(-) diff --git a/src/jimgw/detector.py b/src/jimgw/detector.py index 413608d3..6b42827d 100644 --- a/src/jimgw/detector.py +++ b/src/jimgw/detector.py @@ -126,8 +126,7 @@ def vertex(self): z = ((minor / major)**2 * r + h)*jnp.sin(lat) return jnp.array([x, y, z]) - @property - def delay_from_geocenter_constructor(self, ra: float, dec: float, gmst: float) -> float: + def delay_from_geocenter(self, ra: float, dec: float, gmst: float) -> float: """ Calculate time delay between two detectors in geocentric coordinates based on XLALArrivaTimeDiff in TimeDelay.c @@ -156,54 +155,40 @@ def delay_from_geocenter_constructor(self, ra: float, dec: float, gmst: float) - jnp.cos(theta)]) return jnp.dot(omega, delta_d) / C_SI - def antenna_pattern_constructor(self, modes='pc'): - """Gives function to compute antenna patterns for any sky location, - polarization angle and GMST. The antenna pattern is defined - instantaneously under the long-wavelength approximation. - + def antenna_pattern(self, ra:float, dec:float, psi:float, gmst:float, modes: str='pc'): + """Computes {name} antenna patterns for {modes} polarizations + at the specified sky location, orientation and GMST. + + In the long-wavelength approximation, the antenna pattern for a + given polarization is the dyadic product between the detector + tensor and the corresponding polarization tensor. + Arguments --------- - modes : list,str - list of polarizations to include, defaults to tensor modes: 'pc'. - aps : func - function to compute antenna patterns for any sky location, - polarization angle and GMST. - """ + ra : float + source right ascension in radians. + dec : float + source declination in radians. + psi : float + source polarization angle in radians. + gmst : float + Greenwich mean sidereal time (GMST) in radians. + modes : str + string of polarizations to include, defaults to tensor modes: 'pc'. + + Returns + ------- + result : list + antenna pattern values for {modes}. + """ detector_tensor = self.tensor - wave_tensor_functions = [Polarization(m).tensor_from_sky_constructor + wave_tensor_functions = [Polarization(m).tensor_from_sky for m in modes] - def aps(ra, dec, psi, gmst): - """Computes {name} antenna patterns for {modes} polarizations - at the specified sky location, orientation and GMST. - - In the long-wavelength approximation, the antenna pattern for a - given polarization is the dyadic product between the detector - tensor and the corresponding polarization tensor. - - Arguments - --------- - ra : float - source right ascension in radians. - dec : float - source declination in radians. - psi : float - source polarization angle in radians. - gmst : float - Greenwich mean sidereal time (GMST) in radians. - - Returns - ------- - Fps : list - antenna pattern values for {modes}. - """ - antenna_patterns = [] - for pol_func in wave_tensor_functions: - wave_tensor = pol_func(ra, dec, psi, gmst) - ap = jnp.einsum('ij,ij->', detector_tensor, wave_tensor) - antenna_patterns.append(ap) - return antenna_patterns - aps.__doc__ = aps.__doc__.format(name=self.name, modes=str(modes)) - return aps + antenna_patterns = [] + for pol_func in wave_tensor_functions: + wave_tensor = pol_func(ra, dec, psi, gmst) + ap = jnp.einsum('ij,ij->', detector_tensor, wave_tensor) + antenna_patterns.append(ap) def construct_fd_response(self, modes='pc', epoch=0., earth_rotation=False, earth_rotation_times=None, data_frequencies=None): diff --git a/src/jimgw/likelihood.py b/src/jimgw/likelihood.py index 7eff7e67..14c9687f 100644 --- a/src/jimgw/likelihood.py +++ b/src/jimgw/likelihood.py @@ -43,7 +43,6 @@ def __init__(self, detectors: list[Detector], waveform: Waveform ) -> None: - super().__init__() self.detectors = detectors self.waveform = waveform From 348969b2b0e75bb717b230b0851e49dce26c0555 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Thu, 6 Jul 2023 10:07:26 -0400 Subject: [PATCH 206/300] Update detector --- src/jimgw/detector.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/src/jimgw/detector.py b/src/jimgw/detector.py index 6b42827d..467bc9cb 100644 --- a/src/jimgw/detector.py +++ b/src/jimgw/detector.py @@ -32,8 +32,6 @@ def fd_response(self, frequency: Array) -> Array: def td_response(self, time: Array) -> Array: raise NotImplementedError - - class GroundBased2G(Detector): latitude: float From 6d4d6afb9a10072503b8b03fc2ead6a7ed669539 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Thu, 6 Jul 2023 16:44:45 -0400 Subject: [PATCH 207/300] Detector seems working okay now --- example/GW150914.py | 414 ++++++++++++++++++---------------------- example/GW150914_old.py | 254 ++++++++++++++++++++++++ setup.cfg | 2 +- src/jimgw/detector.py | 316 +++++++++++------------------- src/jimgw/likelihood.py | 98 ++++------ src/jimgw/waveform.py | 25 ++- 6 files changed, 606 insertions(+), 503 deletions(-) create mode 100644 example/GW150914_old.py diff --git a/example/GW150914.py b/example/GW150914.py index 6ed9b090..0a7f1437 100644 --- a/example/GW150914.py +++ b/example/GW150914.py @@ -1,26 +1,8 @@ -import numpy as np -import matplotlib.pyplot as plt import time +from jimgw.detector import H1, L1 +from jimgw.likelihood import TransientLikelihoodFD +from jimgw.waveform import RippleIMRPhenomD import jax.numpy as jnp -import jax - -from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar -from jimgw.PE.detector_preset import * -from jimgw.PE.heterodyneLikelihood import make_heterodyne_likelihood_mutliple_detector -from jimgw.PE.detector_projection import make_detector_response - -from flowMC.nfmodel.rqSpline import MaskedCouplingRQSpline -from flowMC.sampler.Sampler import Sampler -from flowMC.sampler.MALA import MALA -from flowMC.utils.PRNG_keys import initialize_rng_keys -from flowMC.nfmodel.utils import * -from flowMC.utils.EvolutionaryOptimizer import EvolutionaryOptimizer - -from astropy.time import Time - -# We only use this to grab the data -from gwpy.timeseries import TimeSeries -from scipy.signal.windows import tukey ########################################### ########## First we grab data ############# @@ -37,218 +19,190 @@ ifos = ['H1', 'L1'] -print("Fetching data...") -data_td_dict = {ifo: TimeSeries.fetch_open_data(ifo, start, end) for ifo in ifos} -print("Finished fetching data.") - -# GWpy normalizes the FFT like an instrumentalist would, which is not what we -# want for the likelihoood, so fix this manually -n = len(data_td_dict[ifos[0]]) -delta_t = data_td_dict[ifos[0]].dt.value - -print("Computing the FFTs...") -# For BNS 0.00625 is a good choice for the tukey window -# For BBH 0.2 is a good choice for the tukey window -data_fd_dict = {i: np.fft.rfft(np.array(d)*tukey(n, 0.2))*delta_t - for i, d in data_td_dict.items()} - -freq = np.fft.rfftfreq(n, delta_t) - -# # We take a bit of extra data to compute PSDs -start_psd = int(gps) - 16 -end_psd = int(gps) + 16 - -print("Fetching PSD data...") -psd_data_td_dict = {ifo: TimeSeries.fetch_open_data(ifo, start_psd, end_psd) for ifo in ifos} -psd_dict = {i: d.psd(fftlength=4) for i, d in psd_data_td_dict.items()} -print("Finished generating data.") - -H1_frequency = np.array(freq[(freq>fmin)&(freqfmin)&(freqfmin)&(freqfmin)&(freqfmin)&(freqfmin)&(freq0.25,1] = 0.249 -guess_param[:,6] = (guess_param[:,6]%(2*jnp.pi)) -guess_param[:,7] = (guess_param[:,7]%(jnp.pi)) -guess_param[:,8] = (guess_param[:,8]%(jnp.pi)) -guess_param[:,9] = (guess_param[:,9]%(2*jnp.pi)) - - -print("Preparing RNG keys") -rng_key_set = initialize_rng_keys(n_chains, seed=42) - -print("Initializing MCMC model and normalizing flow model.") - -prior_range = jnp.array([[10,80],[0.125,1.0],[-1,1],[-1,1], - [0,2000],[-0.05,0.05],[0,2*np.pi],[-1,1], - [0,np.pi],[0,2*np.pi],[-1,1]]) - - -initial_position = jax.random.uniform(rng_key_set[0], shape=(int(n_chains), n_dim)) * 1 -for i in range(n_dim): - initial_position = initial_position.at[:,i].set(initial_position[:,i]*(prior_range[i,1]-prior_range[i,0])+prior_range[i,0]) - -from ripple import Mc_eta_to_ms -m1,m2 = jax.vmap(Mc_eta_to_ms)(guess_param[:,:2]) -q = m2/m1 - -initial_position = initial_position.at[:,0].set(guess_param[:,0]) - -from astropy.cosmology import Planck18 as cosmo - -z = np.linspace(0.002,3,10000) -dL = cosmo.luminosity_distance(z).value -dVdz = cosmo.differential_comoving_volume(z).value - -def top_hat(x): - output = 0. - for i in range(n_dim): - output = jax.lax.cond(x[i]>=prior_range[i,0], lambda: output, lambda: -jnp.inf) - output = jax.lax.cond(x[i]<=prior_range[i,1], lambda: output, lambda: -jnp.inf) - return output+jnp.log(jnp.interp(x[4],dL,dVdz)) - -def posterior(theta): - q = theta[1] - theta = theta.at[7].set(jnp.arcsin(jnp.sin(theta[7]/2*jnp.pi))*2/jnp.pi) - theta = theta.at[10].set(jnp.arcsin(jnp.sin(theta[10]/2*jnp.pi))*2/jnp.pi) - iota = jnp.arccos(theta[7]) - dec = jnp.arcsin(theta[10]) - prior = top_hat(theta) - theta = theta.at[1].set(q/(1+q)**2) # convert q to eta - theta = theta.at[7].set(iota) # convert cos iota to iota - theta = theta.at[10].set(dec) # convert cos dec to dec - return logL(theta) + prior - -posterior_new = lambda theta, data: posterior(theta) - -model = MaskedCouplingRQSpline(n_dim, 10, [128,128], 8, jax.random.PRNGKey(10)) - -print("Initializing sampler class") - -mass_matrix = jnp.eye(n_dim) -mass_matrix = mass_matrix.at[1,1].set(1e-3) -mass_matrix = mass_matrix.at[5,5].set(1e-3) - -local_sampler = MALA(posterior_new, True, {"step_size": mass_matrix*3e-3}) -print("Running sampler") - -nf_sampler = Sampler( - n_dim, - rng_key_set, - None, - local_sampler, - model, - n_loop_training=n_loop_training, - n_loop_production = n_loop_production, - n_local_steps=n_local_steps, - n_global_steps=n_global_steps, - n_chains=n_chains, - n_epochs=num_epochs, - learning_rate=learning_rate, - momentum=momentum, - batch_size=batch_size, - use_global=True, - keep_quantile=0., - train_thinning = 40, -) - -nf_sampler.sample(initial_position, None) -chains, log_prob, local_accs, global_accs = nf_sampler.get_sampler_state().values() -print("Script complete and took: {} minutes".format((time.time()-total_time_start)/60)) -# np.savez('GW150914.npz', chains=chains, log_prob=log_prob, local_accs=local_accs, global_accs=global_accs) \ No newline at end of file +# H1 = get_H1() +# H1_response = make_detector_response(H1[0], H1[1]) +# L1 = get_L1() +# L1_response = make_detector_response(L1[0], L1[1]) + +# trigger_time = 1126259462.4 +# duration = 4 +# post_trigger_duration = 2 +# epoch = duration - post_trigger_duration +# gmst = Time(trigger_time, format='gps').sidereal_time('apparent', 'greenwich').rad +# f_ref = 20 + +# def LogLikelihood(theta): +# theta_waveform = theta[:8] +# theta_waveform = theta_waveform.at[5].set(0) +# ra = theta[9] +# dec = theta[10] +# hp_test, hc_test = gen_IMRPhenomD_polar(H1_frequency, theta_waveform, f_ref) +# align_time = jnp.exp(-1j*2*jnp.pi*H1_frequency*(epoch+theta[5])) +# h_test_H1 = H1_response(H1_frequency, hp_test, hc_test, ra, dec, gmst, theta[8]) * align_time +# h_test_L1 = L1_response(L1_frequency, hp_test, hc_test, ra, dec, gmst, theta[8]) * align_time +# df = H1_frequency[1] - H1_frequency[0] +# match_filter_SNR_H1 = 4*jnp.sum((jnp.conj(h_test_H1)*H1_data)/H1_psd*df).real +# match_filter_SNR_L1 = 4*jnp.sum((jnp.conj(h_test_L1)*L1_data)/L1_psd*df).real +# optimal_SNR_H1 = 4*jnp.sum((jnp.conj(h_test_H1)*h_test_H1)/H1_psd*df).real +# optimal_SNR_L1 = 4*jnp.sum((jnp.conj(h_test_L1)*h_test_L1)/L1_psd*df).real + +# return (match_filter_SNR_H1-optimal_SNR_H1/2) + (match_filter_SNR_L1-optimal_SNR_L1/2) + +# # prior on the waveform parameters +# # these are Mc, eta, s1, s2, dist, tc, phic, ra, dec, psi +# prior_range = jnp.array([[20.,50.],[0.20,0.25],[-0.9,0.9], +# [-0.9,0.9],[100,3000],[-1.0,1.0], +# [0,2*np.pi],[0.001,np.pi],[0.001,np.pi], +# [0.001,2*np.pi],[-jnp.pi/2,jnp.pi/2]]) + + +# ########################################### +# ##### Optimize to find high L point ####### +# ########################################### + +# set_nwalkers = 100 +# initial_guess = jax.random.uniform(jax.random.PRNGKey(42), (set_nwalkers,11,), +# minval=prior_range[:,0], maxval=prior_range[:,1]) + +# y = lambda x: -LogLikelihood(x) +# y = jax.jit(jax.vmap(y)) +# print("Compiling likelihood function") +# y(initial_guess) +# print("Done compiling") + +# print("Starting the optimizer") +# optimizer = EvolutionaryOptimizer(11, verbose = True) +# state = optimizer.optimize(y, prior_range, n_loops=2000) +# best_fit = optimizer.get_result()[0] + +# print(best_fit) + +# data_list = [H1_data, L1_data] +# psd_list = [H1_psd, L1_psd] +# response_list = [H1_response, L1_response] + + +# print("Constructing the heterodyned likelihood function") +# logL = make_heterodyne_likelihood_mutliple_detector(data_list, psd_list, response_list, gen_IMRPhenomD_polar, +# best_fit, H1_frequency, gmst, epoch, f_ref, 301) + + +# ########################################### +# ####### Finally, we can sample! ########### +# ########################################### + +# n_dim = 11 +# n_chains = 1000 +# n_loop_training = 10 +# n_loop_production = 10 +# n_local_steps = 100 +# n_global_steps = 100 +# learning_rate = 0.001 +# max_samples = 100000 +# momentum = 0.9 +# num_epochs = 100 +# batch_size = 50000 + +# guess_param = best_fit + +# guess_param = np.array(jnp.repeat(guess_param[None,:],int(n_chains),axis=0)*np.random.normal(loc=1,scale=0.1,size=(int(n_chains),n_dim))) +# guess_param[guess_param[:,1]>0.25,1] = 0.249 +# guess_param[:,6] = (guess_param[:,6]%(2*jnp.pi)) +# guess_param[:,7] = (guess_param[:,7]%(jnp.pi)) +# guess_param[:,8] = (guess_param[:,8]%(jnp.pi)) +# guess_param[:,9] = (guess_param[:,9]%(2*jnp.pi)) + + +# print("Preparing RNG keys") +# rng_key_set = initialize_rng_keys(n_chains, seed=42) + +# print("Initializing MCMC model and normalizing flow model.") + +# prior_range = jnp.array([[10,80],[0.125,1.0],[-1,1],[-1,1], +# [0,2000],[-0.05,0.05],[0,2*np.pi],[-1,1], +# [0,np.pi],[0,2*np.pi],[-1,1]]) + + +# initial_position = jax.random.uniform(rng_key_set[0], shape=(int(n_chains), n_dim)) * 1 +# for i in range(n_dim): +# initial_position = initial_position.at[:,i].set(initial_position[:,i]*(prior_range[i,1]-prior_range[i,0])+prior_range[i,0]) + +# from ripple import Mc_eta_to_ms +# m1,m2 = jax.vmap(Mc_eta_to_ms)(guess_param[:,:2]) +# q = m2/m1 + +# initial_position = initial_position.at[:,0].set(guess_param[:,0]) + +# from astropy.cosmology import Planck18 as cosmo + +# z = np.linspace(0.002,3,10000) +# dL = cosmo.luminosity_distance(z).value +# dVdz = cosmo.differential_comoving_volume(z).value + +# def top_hat(x): +# output = 0. +# for i in range(n_dim): +# output = jax.lax.cond(x[i]>=prior_range[i,0], lambda: output, lambda: -jnp.inf) +# output = jax.lax.cond(x[i]<=prior_range[i,1], lambda: output, lambda: -jnp.inf) +# return output+jnp.log(jnp.interp(x[4],dL,dVdz)) + +# def posterior(theta): +# q = theta[1] +# theta = theta.at[7].set(jnp.arcsin(jnp.sin(theta[7]/2*jnp.pi))*2/jnp.pi) +# theta = theta.at[10].set(jnp.arcsin(jnp.sin(theta[10]/2*jnp.pi))*2/jnp.pi) +# iota = jnp.arccos(theta[7]) +# dec = jnp.arcsin(theta[10]) +# prior = top_hat(theta) +# theta = theta.at[1].set(q/(1+q)**2) # convert q to eta +# theta = theta.at[7].set(iota) # convert cos iota to iota +# theta = theta.at[10].set(dec) # convert cos dec to dec +# return logL(theta) + prior + +# posterior_new = lambda theta, data: posterior(theta) + +# model = MaskedCouplingRQSpline(n_dim, 10, [128,128], 8, jax.random.PRNGKey(10)) + +# print("Initializing sampler class") + +# mass_matrix = jnp.eye(n_dim) +# mass_matrix = mass_matrix.at[1,1].set(1e-3) +# mass_matrix = mass_matrix.at[5,5].set(1e-3) + +# local_sampler = MALA(posterior_new, True, {"step_size": mass_matrix*3e-3}) +# print("Running sampler") + +# nf_sampler = Sampler( +# n_dim, +# rng_key_set, +# None, +# local_sampler, +# model, +# n_loop_training=n_loop_training, +# n_loop_production = n_loop_production, +# n_local_steps=n_local_steps, +# n_global_steps=n_global_steps, +# n_chains=n_chains, +# n_epochs=num_epochs, +# learning_rate=learning_rate, +# momentum=momentum, +# batch_size=batch_size, +# use_global=True, +# keep_quantile=0., +# train_thinning = 40, +# ) + +# nf_sampler.sample(initial_position, None) +# chains, log_prob, local_accs, global_accs = nf_sampler.get_sampler_state().values() +# print("Script complete and took: {} minutes".format((time.time()-total_time_start)/60)) +# # np.savez('GW150914.npz', chains=chains, log_prob=log_prob, local_accs=local_accs, global_accs=global_accs) \ No newline at end of file diff --git a/example/GW150914_old.py b/example/GW150914_old.py new file mode 100644 index 00000000..6ed9b090 --- /dev/null +++ b/example/GW150914_old.py @@ -0,0 +1,254 @@ +import numpy as np +import matplotlib.pyplot as plt +import time +import jax.numpy as jnp +import jax + +from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar +from jimgw.PE.detector_preset import * +from jimgw.PE.heterodyneLikelihood import make_heterodyne_likelihood_mutliple_detector +from jimgw.PE.detector_projection import make_detector_response + +from flowMC.nfmodel.rqSpline import MaskedCouplingRQSpline +from flowMC.sampler.Sampler import Sampler +from flowMC.sampler.MALA import MALA +from flowMC.utils.PRNG_keys import initialize_rng_keys +from flowMC.nfmodel.utils import * +from flowMC.utils.EvolutionaryOptimizer import EvolutionaryOptimizer + +from astropy.time import Time + +# We only use this to grab the data +from gwpy.timeseries import TimeSeries +from scipy.signal.windows import tukey + +########################################### +########## First we grab data ############# +########################################### + +total_time_start = time.time() + +# first, fetch a 4s segment centered on GW150914 +gps = 1126259462.4 +start = gps - 2 +end = gps + 2 +fmin = 20 +fmax = 1024 + +ifos = ['H1', 'L1'] + +print("Fetching data...") +data_td_dict = {ifo: TimeSeries.fetch_open_data(ifo, start, end) for ifo in ifos} +print("Finished fetching data.") + +# GWpy normalizes the FFT like an instrumentalist would, which is not what we +# want for the likelihoood, so fix this manually +n = len(data_td_dict[ifos[0]]) +delta_t = data_td_dict[ifos[0]].dt.value + +print("Computing the FFTs...") +# For BNS 0.00625 is a good choice for the tukey window +# For BBH 0.2 is a good choice for the tukey window +data_fd_dict = {i: np.fft.rfft(np.array(d)*tukey(n, 0.2))*delta_t + for i, d in data_td_dict.items()} + +freq = np.fft.rfftfreq(n, delta_t) + +# # We take a bit of extra data to compute PSDs +start_psd = int(gps) - 16 +end_psd = int(gps) + 16 + +print("Fetching PSD data...") +psd_data_td_dict = {ifo: TimeSeries.fetch_open_data(ifo, start_psd, end_psd) for ifo in ifos} +psd_dict = {i: d.psd(fftlength=4) for i, d in psd_data_td_dict.items()} +print("Finished generating data.") + +H1_frequency = np.array(freq[(freq>fmin)&(freqfmin)&(freqfmin)&(freqfmin)&(freqfmin)&(freqfmin)&(freq0.25,1] = 0.249 +guess_param[:,6] = (guess_param[:,6]%(2*jnp.pi)) +guess_param[:,7] = (guess_param[:,7]%(jnp.pi)) +guess_param[:,8] = (guess_param[:,8]%(jnp.pi)) +guess_param[:,9] = (guess_param[:,9]%(2*jnp.pi)) + + +print("Preparing RNG keys") +rng_key_set = initialize_rng_keys(n_chains, seed=42) + +print("Initializing MCMC model and normalizing flow model.") + +prior_range = jnp.array([[10,80],[0.125,1.0],[-1,1],[-1,1], + [0,2000],[-0.05,0.05],[0,2*np.pi],[-1,1], + [0,np.pi],[0,2*np.pi],[-1,1]]) + + +initial_position = jax.random.uniform(rng_key_set[0], shape=(int(n_chains), n_dim)) * 1 +for i in range(n_dim): + initial_position = initial_position.at[:,i].set(initial_position[:,i]*(prior_range[i,1]-prior_range[i,0])+prior_range[i,0]) + +from ripple import Mc_eta_to_ms +m1,m2 = jax.vmap(Mc_eta_to_ms)(guess_param[:,:2]) +q = m2/m1 + +initial_position = initial_position.at[:,0].set(guess_param[:,0]) + +from astropy.cosmology import Planck18 as cosmo + +z = np.linspace(0.002,3,10000) +dL = cosmo.luminosity_distance(z).value +dVdz = cosmo.differential_comoving_volume(z).value + +def top_hat(x): + output = 0. + for i in range(n_dim): + output = jax.lax.cond(x[i]>=prior_range[i,0], lambda: output, lambda: -jnp.inf) + output = jax.lax.cond(x[i]<=prior_range[i,1], lambda: output, lambda: -jnp.inf) + return output+jnp.log(jnp.interp(x[4],dL,dVdz)) + +def posterior(theta): + q = theta[1] + theta = theta.at[7].set(jnp.arcsin(jnp.sin(theta[7]/2*jnp.pi))*2/jnp.pi) + theta = theta.at[10].set(jnp.arcsin(jnp.sin(theta[10]/2*jnp.pi))*2/jnp.pi) + iota = jnp.arccos(theta[7]) + dec = jnp.arcsin(theta[10]) + prior = top_hat(theta) + theta = theta.at[1].set(q/(1+q)**2) # convert q to eta + theta = theta.at[7].set(iota) # convert cos iota to iota + theta = theta.at[10].set(dec) # convert cos dec to dec + return logL(theta) + prior + +posterior_new = lambda theta, data: posterior(theta) + +model = MaskedCouplingRQSpline(n_dim, 10, [128,128], 8, jax.random.PRNGKey(10)) + +print("Initializing sampler class") + +mass_matrix = jnp.eye(n_dim) +mass_matrix = mass_matrix.at[1,1].set(1e-3) +mass_matrix = mass_matrix.at[5,5].set(1e-3) + +local_sampler = MALA(posterior_new, True, {"step_size": mass_matrix*3e-3}) +print("Running sampler") + +nf_sampler = Sampler( + n_dim, + rng_key_set, + None, + local_sampler, + model, + n_loop_training=n_loop_training, + n_loop_production = n_loop_production, + n_local_steps=n_local_steps, + n_global_steps=n_global_steps, + n_chains=n_chains, + n_epochs=num_epochs, + learning_rate=learning_rate, + momentum=momentum, + batch_size=batch_size, + use_global=True, + keep_quantile=0., + train_thinning = 40, +) + +nf_sampler.sample(initial_position, None) +chains, log_prob, local_accs, global_accs = nf_sampler.get_sampler_state().values() +print("Script complete and took: {} minutes".format((time.time()-total_time_start)/60)) +# np.savez('GW150914.npz', chains=chains, log_prob=log_prob, local_accs=local_accs, global_accs=global_accs) \ No newline at end of file diff --git a/setup.cfg b/setup.cfg index ab51c9dc..8504628b 100644 --- a/setup.cfg +++ b/setup.cfg @@ -1,6 +1,6 @@ [metadata] name = jimGW -version = 0.0.4.5 +version = 0.1.0 author = Kaze Wong author_email = kazewong.physics@gmail.com url = https://github.com/kazewong/jim diff --git a/src/jimgw/detector.py b/src/jimgw/detector.py index 467bc9cb..da32b86b 100644 --- a/src/jimgw/detector.py +++ b/src/jimgw/detector.py @@ -2,9 +2,11 @@ from jimgw.constants import * from jimgw.wave import Polarization from scipy.signal.windows import tukey -from abc import abstractmethod +from abc import ABC, abstractmethod import equinox as eqx from jaxtyping import Array +import jax +from gwpy.timeseries import TimeSeries DEG_TO_RAD = jnp.pi/180 @@ -15,7 +17,7 @@ def np2(x): p = p << 1 return p -class Detector(eqx.Module): +class Detector(ABC): """ Base class for all detectors. """ @@ -25,26 +27,34 @@ def load_data(self, data): raise NotImplementedError @abstractmethod - def fd_response(self, frequency: Array) -> Array: - raise NotImplementedError + def fd_response(self, frequency: Array, h: Array, params: dict) -> Array: + """Modulate the waveform in the sky frame by the detector response in the frequency domain.""" + pass @abstractmethod - def td_response(self, time: Array) -> Array: - raise NotImplementedError + def td_response(self, time: Array, h: Array, params: dict) -> Array: + """Modulate the waveform in the sky frame by the detector response in the time domain.""" + pass class GroundBased2G(Detector): - latitude: float - longitude: float - elevation: float - xarm_azimuth: float - yarm_azimuth: float - xarm_tilt: float - yarm_tilt: float name: str + polarization_mode: list[Polarization] + frequencies: Array = None + data : Array = None + psd: Array = None + + latitude: float = 0 + longitude: float = 0 + xarm_azimuth: float = 0 + yarm_azimuth: float = 0 + xarm_tilt: float = 0 + yarm_tilt: float = 0 + elevation: float = 0 def __init__(self, name: str, **kwargs) -> None: self.name = name + self.latitude = kwargs.get('latitude', 0) self.longitude = kwargs.get('longitude', 0) self.elevation = kwargs.get('elevation', 0) @@ -52,17 +62,51 @@ def __init__(self, name: str, **kwargs) -> None: self.yarm_azimuth = kwargs.get('yarm_azimuth', 0) self.xarm_tilt = kwargs.get('xarm_tilt', 0) self.yarm_tilt = kwargs.get('yarm_tilt', 0) - - def load_data(self, data): + modes = kwargs.get('mode', 'pc') + + self.polarization_mode = [Polarization(m) for m in modes] + + + def load_data(self, trigger_time:float, + gps_start_pad: int, + gps_end_pad: int, + f_min: float, + f_max: float, + psd_pad: int = 16, + tukey_alpha: float = 0.2) -> None: + print("Fetching data from {}...".format(self.name)) + data_td = TimeSeries.fetch_open_data(self.name, trigger_time - gps_start_pad, trigger_time + gps_end_pad) + segment_length = data_td.duration.value + n = len(data_td) + delta_t = data_td.dt.value + data = jnp.fft.rfft(jnp.array(data_td.value)*tukey(n, tukey_alpha))*delta_t + freq = jnp.fft.rfftfreq(n, delta_t) + # TODO: Check if this is the right way to fetch PSD + start_psd = trigger_time - gps_start_pad - psd_pad + end_psd = trigger_time + gps_end_pad + psd_pad + + print("Fetching PSD data...") + psd_data_td = TimeSeries.fetch_open_data(self.name, start_psd, end_psd) + psd = psd_data_td.psd(fftlength=segment_length).value # TODO: Check whether this is sright. + + print("Finished generating data.") + + self.frequencies = freq[(freq>f_min)&(freqf_min)&(freqf_min)&(freq Array: + """Modulate the waveform in the sky frame by the detector response in the frequency domain.""" + ra, dec, psi, gmst = params['ra'], params['dec'], params['psi'], params['gmst'] + antenna_pattern = self.antenna_pattern(ra, dec, psi, gmst) + timeshift = self.delay_from_geocenter(ra, dec, gmst) + h_detector = jax.tree_util.tree_map(lambda h, antenna: h * antenna * jnp.exp(-2j * jnp.pi * frequency * timeshift), h_sky, antenna_pattern) + return jnp.sum(jnp.stack(jax.tree_util.tree_leaves(h_detector)),axis=0) + + def td_response(self, time: Array, h: Array, params: Array) -> Array: + """Modulate the waveform in the sky frame by the detector response in the time domain.""" pass - def fd_response(self, frequency: Array) -> Array: - pass - - def td_response(self, time: Array) -> Array: - pass - - @staticmethod def _get_arm(lat, lon, tilt, azimuth): """Construct detector-arm vectors in Earth-centric Cartesian coordinates. @@ -92,9 +136,8 @@ def _get_arm(lat, lon, tilt, azimuth): def arms(self): """Detector arm vectors (x, y). """ - c = self.coordinates - x = self._get_arm(c['lat'], c['lon'], c['xarm_tilt'], c['xarm_azimuth']) - y = self._get_arm(c['lat'], c['lon'], c['yarm_tilt'], c['yarm_azimuth']) + x = self._get_arm(self.latitude, self.longitude, self.xarm_tilt, self.xarm_azimuth) + y = self._get_arm(self.latitude, self.longitude, self.yarm_tilt, self.yarm_azimuth) return x, y @property @@ -113,9 +156,9 @@ def vertex(self): definition of the local radius; see Section 2.1 of LIGO-T980044-10. """ # get detector and Earth parameters - lat = self.coordinates['lat'] - lon = self.coordinates['lon'] - h = self.coordinates['elevation'] + lat = self.latitude + lon = self.longitude + h = self.elevation major, minor = EARTH_SEMI_MAJOR_AXIS, EARTH_SEMI_MINOR_AXIS # compute vertex location r = major**2*(major**2*jnp.cos(lat)**2 + minor**2*jnp.sin(lat)**2)**(-0.5) @@ -144,7 +187,6 @@ def delay_from_geocenter(self, ra: float, dec: float, gmst: float) -> float: float: time delay from Earth center. """ delta_d = -self.vertex - gmst = jnp.mod(gmst, 2 * jnp.pi) phi = ra - gmst theta = jnp.pi / 2 - dec @@ -153,7 +195,7 @@ def delay_from_geocenter(self, ra: float, dec: float, gmst: float) -> float: jnp.cos(theta)]) return jnp.dot(omega, delta_d) / C_SI - def antenna_pattern(self, ra:float, dec:float, psi:float, gmst:float, modes: str='pc'): + def antenna_pattern(self, ra:float, dec:float, psi:float, gmst:float) -> dict: """Computes {name} antenna patterns for {modes} polarizations at the specified sky location, orientation and GMST. @@ -180,182 +222,40 @@ def antenna_pattern(self, ra:float, dec:float, psi:float, gmst:float, modes: str antenna pattern values for {modes}. """ detector_tensor = self.tensor - wave_tensor_functions = [Polarization(m).tensor_from_sky - for m in modes] - antenna_patterns = [] - for pol_func in wave_tensor_functions: - wave_tensor = pol_func(ra, dec, psi, gmst) - ap = jnp.einsum('ij,ij->', detector_tensor, wave_tensor) - antenna_patterns.append(ap) - - def construct_fd_response(self, modes='pc', epoch=0., earth_rotation=False, - earth_rotation_times=None, data_frequencies=None): - """Generates a function to return the Fourier-domain projection of an - arbitrary gravitational wave onto this detector, starting from FD - polarizations defined at geocenter. - Arguments - --------- - modes : str - polarizations to include in response, defaults to tensor modes 'pc' - epoch : float - time corresponding to beginning of segment, def. 0. - earth_rotation : bool - whether to account for Earth rotation in antenna patterns, - def. False. - - Returns - ------- - get_det_h : func - function to produce the detector response for arbitrary input - polarizations in the Fourier domain. - """ - get_delay = self.delay_from_geocenter_constructor - get_aps = self.antenna_pattern_constructor(modes) - if earth_rotation: - if earth_rotation_times is None: - if data_frequencies is None: - raise ValueError("Must provide data frequencies and epch," - "or explicit time grid to evaluate antenna " - "patterns under Earth rotation.") - else: - # TODO: move this to likelihood! construct_fd_response - # should only accept a time grid. - - # construct time grid on which to evaluate antenna patterns - # the time grid should as long as the data segment implied - # by the provided frequency array, i.e, T = 1/df. - seglen = 1/(data_frequencies[1] - data_frequencies[0]) - # the grid spacing should be as coarse as possible while - # still resolving the evolution of the antenna patterns, - # which has characterisitc frequency of up to 2/(sid_day). - dt_sid = np2(DAYSID_SI)/4 - N = len(data_frequencies) - earth_rotation_times = jnp.arange(N)*dt_sid + epoch - w = tukey(N, 0.1) # TODO: do not hard code alpha! - - def get_det_h(f, polwaveforms, ra, dec, psi, gmst, tc): - """Project Fourier-domain '{p}' polarizations onto {i} detector, - taking into account antenna patterns and time of flight from - geocenter. - - The response is defined by - - .. math:: h(f) = \\sum_p h_p(f) F_p(\\alpha, \\delta, \\psi) \\exp(2\\pi i \delta t) - - for polarization functions :math:`h_p(f)` delayed apropriately - relative to geocenter by a time :math:`\\delta t(\\alpha,\\delta)`, - and antenna patterns :math:`F_p(\\alpha, \\delta, \\psi)` for each - included polarization :math:`p`. - - Arguments - --------- - f : array - frequency array over which polarizations are evaluated. - polwaveforms : list - lenght-{n} list of arrays containing '{p}' polarizations, each - assumed to be defined at geocenter and evaluated over the - frequency grid `f`. - ra : float - source right ascension in radians. - dec : float - source declination in radians. - psi : float - source polarization angle in radians. - gmst : float - Greenwich mean sidereal time (GMST) in radians. - tc : float - time of arrival (coalescence) at geocenter in second measured - from epoch {t}. - - Returns - ------- - h : array - Fourier domain detector response. - """ - dt_geo = get_delay(ra, dec, gmst) - if earth_rotation: - # antenna patterns are a function of time to be evaluated at - # sparsely over a grid of times spanning the data segment - # the result will be FFTed and convolved with the waveform - aps = jnp.vectorize(get_aps)(ra, dec, psi, earth_rotation_times) - delta_t = 0.5 / f[-1] - aps_fd = jnp.fft.rfft(aps*w[:,jnp.newaxis], axis=0) * delta_t - # TODO: ^do we want to zero pad? - # now, we convolve the antenna patterns with the polarizations - h = jnp.zeros_like(polwaveforms[0]) - for p in range(len(modes)): - # TODO: do we want jnp.fftconvolve? - h += jnp.convolve(polwaveforms[p], aps_fd[:,p], mode='same') - else: - aps = get_aps(ra, dec, psi, gmst) - h = jnp.sum([aps[i]*polwaveforms[i] for i in len(aps)], axis=0) - # note, under our sign convention for the Fourier transform the - # phase shift below corresponds to a time shift - # ``t -> t - dt_geo - tc + epoch`` - # this makes sense: a waveform tha that peaks at t=0 at geocenter - # will peak at t=dt_geo at the detector, so dt is indeed a delay. - h *= jnp.exp(-2j*jnp.pi*f*(dt_geo + tc - epoch)) - return h - get_det_h.__doc__ = get_det_h.__doc__.format(p=str(modes), i=self.name, - n=len(modes), t=epoch) - return get_det_h - - - - -class Detector(object): - """Defines a ground-based gravitational-wave detector. - - Argument - -------- - name : str - interferometer name, e.g., 'H1' for LIGO Hanford. - coordinates : dict - optionally, provide custom detector arm and vertex coordinates. - """ - def __init__(self, name, coordinates=None): - self.name = name.upper() - self._coordinates = coordinates or {} - - @property - def coordinates(self): - """Coordinates defining a triangular detector (angles in radians). - """ - if not self._coordinates: - if self.name == 'H1': - # LIGO Hanford - self._coordinates = dict( - lat = (46 + 27. / 60 + 18.528 / 3600) * DEG_TO_RAD, - lon = -(119 + 24. / 60 + 27.5657 / 3600) * DEG_TO_RAD, - xarm_azimuth = 125.9994 * DEG_TO_RAD, - yarm_azimuth = 215.9994 * DEG_TO_RAD, - xarm_tilt = -6.195e-4, - yarm_tilt = 1.25e-5, - elevation = 142.554, - ) - elif self.name == 'L1': - # LIGO Livingston - self._coordinates = dict( - lat = (30 + 33. / 60 + 46.4196 / 3600) * DEG_TO_RAD, - lon= -(90 + 46. / 60 + 27.2654 / 3600) * DEG_TO_RAD, - xarm_azimuth = 197.7165 * DEG_TO_RAD, - yarm_azimuth = 287.7165 * DEG_TO_RAD, - xarm_tilt = 0 , - yarm_tilt = 0, - elevation = -6.574, - ) - elif self.name == 'V1': - # Virgo - self._coordinates = dict( - lat = (43 + 37. / 60 + 53.0921 / 3600) * DEG_TO_RAD, - lon = (10 + 30. / 60 + 16.1878 / 3600) * DEG_TO_RAD, - xarm_azimuth = 70.5674 * DEG_TO_RAD, - yarm_azimuth = 160.5674 * DEG_TO_RAD, - xarm_tilt = 0, - yarm_tilt = 0, - elevation = 51.884, - ) - elif not self._coordinates: - raise ValueError(f"unknown detector {self.name}") - return self._coordinates + antenna_patterns = {} + for polarization in self.polarization_mode: + wave_tensor = polarization.tensor_from_sky(ra, dec, psi, gmst) + antenna_patterns[polarization.name] = jnp.einsum('ij,ij->', detector_tensor, wave_tensor) + + return antenna_patterns + +H1 = GroundBased2G('H1', +latitude = (46 + 27. / 60 + 18.528 / 3600) * DEG_TO_RAD, +longitude = -(119 + 24. / 60 + 27.5657 / 3600) * DEG_TO_RAD, +xarm_azimuth = 125.9994 * DEG_TO_RAD, +yarm_azimuth = 215.9994 * DEG_TO_RAD, +xarm_tilt = -6.195e-4, +yarm_tilt = 1.25e-5, +elevation = 142.554, +mode='pc') + +L1 = GroundBased2G('L1', +latitude = (30 + 33. / 60 + 46.4196 / 3600) * DEG_TO_RAD, +longitude = -(90 + 46. / 60 + 27.2654 / 3600) * DEG_TO_RAD, +xarm_azimuth = 197.7165 * DEG_TO_RAD, +yarm_azimuth = 287.7165 * DEG_TO_RAD, +xarm_tilt = 0 , +yarm_tilt = 0, +elevation = -6.574, +mode='pc') + +V1 = GroundBased2G('V1', +latitude = (43 + 37. / 60 + 53.0921 / 3600) * DEG_TO_RAD, +longitude = (10 + 30. / 60 + 16.1887 / 3600) * DEG_TO_RAD, +xarm_azimuth = 243. * DEG_TO_RAD, +yarm_azimuth = 333. * DEG_TO_RAD, +xarm_tilt = 0 , +yarm_tilt = 0, +elevation = 51.884, +mode='pc') \ No newline at end of file diff --git a/src/jimgw/likelihood.py b/src/jimgw/likelihood.py index 14c9687f..243a8f8d 100644 --- a/src/jimgw/likelihood.py +++ b/src/jimgw/likelihood.py @@ -2,8 +2,11 @@ from gwpy.frequencyseries import FrequencySeries from abc import ABC, abstractmethod from typing import Tuple +from jaxtyping import Array from jimgw.waveform import Waveform from jimgw.detector import Detector +import jax.numpy as jnp +from astropy.time import Time class LikelihoodBase(ABC): @@ -29,7 +32,7 @@ def data(self): return self._data @abstractmethod - def evalutate(self, params) -> float: + def evaluate(self, params) -> float: """Evaluate the likelihood for a given set of parameters. """ raise NotImplementedError @@ -41,76 +44,45 @@ class TransientLikelihoodFD(LikelihoodBase): def __init__(self, detectors: list[Detector], - waveform: Waveform + waveform: Waveform, + trigger_time:float = 0, + duration: float = 4, + post_trigger_duration: float = 2, ) -> None: self.detectors = detectors self.waveform = waveform + self.trigger_time = trigger_time + self.gmst = Time(trigger_time, format='gps').sidereal_time('apparent', 'greenwich').rad - def evaluate(self, params) -> float: - """Evaluate the likelihood for a given set of parameters. - """ - raise NotImplementedError - - - -class LogLikelihoodTransientFD(object): - """Object to construct a frequency-domain JAX-based log-likelihood function - for transient gravitational-wave signals detected by ground-based - detectors with stationary Gaussian noise. - - Arguments - --------- - waveform : - frequency-domain waveform function, must accept an array of frequencies - and a set of parameter values, like - `waveform(frequencies, [param1, param2, ...])`. - heterodyne : bool - whether to approximate likelihood through a heteredoyne. - earth_rotation : bool - whether to include Earth's rotation in the antenna pattern. - """ - def __init__(self, waveform, heterodyne=False, earth_rotation=False): - self.waveform = waveform - self.heterodyne = heterodyne - # whether to include Earth's rotation in the antenna pattern - # TODO: implement automatic defaults based on IFO names - self.earth_rotation = earth_rotation - self.data = {} - self.psds = {} + self.trigger_time = trigger_time + self.duration = duration + self.post_trigger_duration = post_trigger_duration @property - def ifos(self): - """Names of interferometers to analyze. + def epoch(self): + """The epoch of the data. """ - return list(self.data.keys()) + return self.trigger_time - self.duration - def add_data(self, ifo, data, **kws): - """Add frequency-domain strain data for a given detector. - - Arguments - --------- - ifo : str - interferometer name, e.g., 'H1' for LIGO Hanford. - data : array,FrequencySeries - frequency-domain strain data. + @property + def ifos(self): + """The interferometers for the likelihood. """ - if isinstance(data, FrequencySeries): - self.data[ifo] = data - else: - self.data[ifo] = FrequencySeries(data, **kws) - - def add_psd(self, ifo, psd, **kws): - """Add power spectral density (PSD) for the noise of a given detector. + return [detector.name for detector in self.detectors] - Arguments - --------- - ifo : str - interferometer name, e.g., 'H1' for LIGO Hanford. - psd : array,FrequencySeries - power spectrum data. + def evaluate(self, params: Array) -> float: + """Evaluate the likelihood for a given set of parameters. """ - if isinstance(psd, FrequencySeries): - self.psd[ifo] = psd - else: - self.psd[ifo] = FrequencySeries(psd, **kws) - + log_likelihood = 0 + frequencies = self.detectors[0].frequencies + df = frequencies[1] - frequencies[0] + source_params = {"Mc": params[0], "eta": params[1], "s1z": params[2], "s2z": params[3], "distance": params[4], "tc": params[5], "phic": params[6], "incl": params[7], "psi": params[8], "ra": params[9], "dec": params[10]} + detector_params = {"ra": params[9], "dec": params[10], "psi": params[8], "gmst": self.gmst} + waveform_sky = self.waveform(frequencies, source_params) + align_time = jnp.exp(-2j*jnp.pi*frequencies*(self.epoch+params[5])) + for detector in self.detectors: + waveform_dec = detector.fd_response(frequencies, waveform_sky, detector_params) * align_time + match_filter_SNR = 4 * jnp.sum(jnp.conj(waveform_dec)*detector.data/detector.psd*df).real + optimal_SNR = 4 * jnp.sum(jnp.conj(waveform_dec)*waveform_dec/detector.psd*df).real + log_likelihood += match_filter_SNR - optimal_SNR/2 + return log_likelihood diff --git a/src/jimgw/waveform.py b/src/jimgw/waveform.py index 44b9af2b..97ce579f 100644 --- a/src/jimgw/waveform.py +++ b/src/jimgw/waveform.py @@ -1,6 +1,29 @@ import equinox as eqx +from jaxtyping import Array +from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar +import jax.numpy as jnp class Waveform(eqx.Module): def __init__(self): - return NotImplemented \ No newline at end of file + return NotImplemented + + def __call__(self, axis: Array, params: Array) -> Array: + return NotImplemented + +class RippleIMRPhenomD(Waveform): + + f_ref: float + + def __init__(self, f_ref: float = 20.0): + self.f_ref = f_ref + + def __call__(self, frequency: Array, params: dict) -> Array: + output = {} + ra = params['ra'] + dec = params['dec'] + theta = [params['Mc'], params['eta'], params['s1z'], params['s2z'], params['distance'], 0, params['phic'], params['incl'], params['psi'], ra, dec] + hp, hc = gen_IMRPhenomD_polar(frequency, theta, self.f_ref) + output['p'] = hp + output['c'] = hc + return output \ No newline at end of file From 01cd89148b8a2a6386355baea09a4e43666bd18c Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Thu, 6 Jul 2023 17:32:51 -0400 Subject: [PATCH 208/300] The detector data seems odd --- example/GW150914.py | 4 ++-- src/jimgw/detector.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/example/GW150914.py b/example/GW150914.py index 0a7f1437..5aae5acd 100644 --- a/example/GW150914.py +++ b/example/GW150914.py @@ -14,8 +14,8 @@ gps = 1126259462.4 start = gps - 2 end = gps + 2 -fmin = 20 -fmax = 1024 +fmin = 20. +fmax = 1024. ifos = ['H1', 'L1'] diff --git a/src/jimgw/detector.py b/src/jimgw/detector.py index da32b86b..62168658 100644 --- a/src/jimgw/detector.py +++ b/src/jimgw/detector.py @@ -82,8 +82,8 @@ def load_data(self, trigger_time:float, data = jnp.fft.rfft(jnp.array(data_td.value)*tukey(n, tukey_alpha))*delta_t freq = jnp.fft.rfftfreq(n, delta_t) # TODO: Check if this is the right way to fetch PSD - start_psd = trigger_time - gps_start_pad - psd_pad - end_psd = trigger_time + gps_end_pad + psd_pad + start_psd = int(trigger_time) - gps_start_pad - psd_pad # What does Int do here? + end_psd = int(trigger_time) + gps_end_pad + psd_pad print("Fetching PSD data...") psd_data_td = TimeSeries.fetch_open_data(self.name, start_psd, end_psd) From caae0a14f23e6b0884df04c32cffca5dae31e271 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Thu, 6 Jul 2023 18:06:14 -0400 Subject: [PATCH 209/300] Had a GPT debug moment. It is the epoch --- example/GW150914.py | 2 -- src/jimgw/likelihood.py | 6 +++--- 2 files changed, 3 insertions(+), 5 deletions(-) diff --git a/example/GW150914.py b/example/GW150914.py index 5aae5acd..3e82268a 100644 --- a/example/GW150914.py +++ b/example/GW150914.py @@ -24,8 +24,6 @@ likelihood = TransientLikelihoodFD([H1, L1], RippleIMRPhenomD(), gps, 4, 2) -data = jnp.array([30,0.249,0.,0., 400., 5., 6., 1., 2., 3.,4.]) -print(likelihood.evaluate(data)) ########################################### ######## Set up the likelihood ############ ########################################### diff --git a/src/jimgw/likelihood.py b/src/jimgw/likelihood.py index 243a8f8d..b4532583 100644 --- a/src/jimgw/likelihood.py +++ b/src/jimgw/likelihood.py @@ -62,7 +62,7 @@ def __init__(self, def epoch(self): """The epoch of the data. """ - return self.trigger_time - self.duration + return self.duration - self.post_trigger_duration @property def ifos(self): @@ -79,10 +79,10 @@ def evaluate(self, params: Array) -> float: source_params = {"Mc": params[0], "eta": params[1], "s1z": params[2], "s2z": params[3], "distance": params[4], "tc": params[5], "phic": params[6], "incl": params[7], "psi": params[8], "ra": params[9], "dec": params[10]} detector_params = {"ra": params[9], "dec": params[10], "psi": params[8], "gmst": self.gmst} waveform_sky = self.waveform(frequencies, source_params) - align_time = jnp.exp(-2j*jnp.pi*frequencies*(self.epoch+params[5])) + align_time = jnp.exp(-1j*2*jnp.pi*frequencies*(self.epoch+params[5])) for detector in self.detectors: waveform_dec = detector.fd_response(frequencies, waveform_sky, detector_params) * align_time - match_filter_SNR = 4 * jnp.sum(jnp.conj(waveform_dec)*detector.data/detector.psd*df).real + match_filter_SNR = 4 * jnp.sum((jnp.conj(waveform_dec)*detector.data)/detector.psd*df).real optimal_SNR = 4 * jnp.sum(jnp.conj(waveform_dec)*waveform_dec/detector.psd*df).real log_likelihood += match_filter_SNR - optimal_SNR/2 return log_likelihood From 1e3feaf8744fb1ecf49fd7fd899261f7864a08a1 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Thu, 6 Jul 2023 18:10:19 -0400 Subject: [PATCH 210/300] Add scaffolding to jim --- src/jimgw/jim.py | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/src/jimgw/jim.py b/src/jimgw/jim.py index 656d69ad..0d27c98a 100644 --- a/src/jimgw/jim.py +++ b/src/jimgw/jim.py @@ -11,3 +11,11 @@ class Jim(object): def __init__(self, Sampler: Sampler, Likelihood: LikelihoodBase, **kwargs): pass + def maximize_likleihood(self): + pass + + def sample(self): + pass + + def plot(self): + pass \ No newline at end of file From 9fa27941684af24022468fd32c680c1d7eee6860 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Fri, 7 Jul 2023 17:00:50 -0400 Subject: [PATCH 211/300] Teach jim how to maixmize likelihood --- src/jimgw/jim.py | 27 +++++++++++++++++++++++---- 1 file changed, 23 insertions(+), 4 deletions(-) diff --git a/src/jimgw/jim.py b/src/jimgw/jim.py index 0d27c98a..29a6552b 100644 --- a/src/jimgw/jim.py +++ b/src/jimgw/jim.py @@ -2,17 +2,36 @@ from jaxtyping import Array from jimgw.likelihood import LikelihoodBase from flowMC.sampler.Sampler import Sampler +from flowMC.nfmodel.base import Distribution +from flowMC.utils.EvolutionaryOptimizer import EvolutionaryOptimizer +import jax class Jim(object): """ Master class for interfacing with flowMC """ - def __init__(self, Sampler: Sampler, Likelihood: LikelihoodBase, **kwargs): - pass + def __init__(self, sampler: Sampler, likelihood: LikelihoodBase, prior: Distribution, **kwargs): + self.Sampler = sampler + self.Likelihood = likelihood + self.Prior = prior + + def maximize_likleihood(self, bounds: tuple[float,float],set_nwalkers: int = 100, n_loops: int = 2000): + set_nwalkers = set_nwalkers + initial_guess = self.Prior.sample(set_nwalkers) + + y = lambda x: -self.Likelihood(x) + y = jax.jit(jax.vmap(y)) + print("Compiling likelihood function") + y(initial_guess) + print("Done compiling") + + print("Starting the optimizer") + optimizer = EvolutionaryOptimizer(self.Prior.n_dim, verbose = True) + state = optimizer.optimize(y, bounds, n_loops=n_loops) + best_fit = optimizer.get_result()[0] + return best_fit - def maximize_likleihood(self): - pass def sample(self): pass From d929b3a54e855e3b0821533d889d1f7fd2be381c Mon Sep 17 00:00:00 2001 From: kazewong Date: Tue, 11 Jul 2023 10:14:15 -0400 Subject: [PATCH 212/300] Nuke useless data --- example/Injection_new.py | 206 ++++++++++++++++ example/Injection_test.py | 213 ----------------- example/Injection_withParserBNS.py | 258 -------------------- example/Injection_withParser_debug.py | 331 -------------------------- src/jimgw/jim.py | 25 +- src/jimgw/prior.py | 19 ++ 6 files changed, 246 insertions(+), 806 deletions(-) create mode 100644 example/Injection_new.py delete mode 100644 example/Injection_test.py delete mode 100644 example/Injection_withParserBNS.py delete mode 100644 example/Injection_withParser_debug.py create mode 100644 src/jimgw/prior.py diff --git a/example/Injection_new.py b/example/Injection_new.py new file mode 100644 index 00000000..3e82268a --- /dev/null +++ b/example/Injection_new.py @@ -0,0 +1,206 @@ +import time +from jimgw.detector import H1, L1 +from jimgw.likelihood import TransientLikelihoodFD +from jimgw.waveform import RippleIMRPhenomD +import jax.numpy as jnp + +########################################### +########## First we grab data ############# +########################################### + +total_time_start = time.time() + +# first, fetch a 4s segment centered on GW150914 +gps = 1126259462.4 +start = gps - 2 +end = gps + 2 +fmin = 20. +fmax = 1024. + +ifos = ['H1', 'L1'] + +H1.load_data(gps, 2, 2, fmin, fmax, psd_pad=16, tukey_alpha=0.2) +L1.load_data(gps, 2, 2, fmin, fmax, psd_pad=16, tukey_alpha=0.2) + +likelihood = TransientLikelihoodFD([H1, L1], RippleIMRPhenomD(), gps, 4, 2) + +########################################### +######## Set up the likelihood ############ +########################################### + +# H1 = get_H1() +# H1_response = make_detector_response(H1[0], H1[1]) +# L1 = get_L1() +# L1_response = make_detector_response(L1[0], L1[1]) + +# trigger_time = 1126259462.4 +# duration = 4 +# post_trigger_duration = 2 +# epoch = duration - post_trigger_duration +# gmst = Time(trigger_time, format='gps').sidereal_time('apparent', 'greenwich').rad +# f_ref = 20 + +# def LogLikelihood(theta): +# theta_waveform = theta[:8] +# theta_waveform = theta_waveform.at[5].set(0) +# ra = theta[9] +# dec = theta[10] +# hp_test, hc_test = gen_IMRPhenomD_polar(H1_frequency, theta_waveform, f_ref) +# align_time = jnp.exp(-1j*2*jnp.pi*H1_frequency*(epoch+theta[5])) +# h_test_H1 = H1_response(H1_frequency, hp_test, hc_test, ra, dec, gmst, theta[8]) * align_time +# h_test_L1 = L1_response(L1_frequency, hp_test, hc_test, ra, dec, gmst, theta[8]) * align_time +# df = H1_frequency[1] - H1_frequency[0] +# match_filter_SNR_H1 = 4*jnp.sum((jnp.conj(h_test_H1)*H1_data)/H1_psd*df).real +# match_filter_SNR_L1 = 4*jnp.sum((jnp.conj(h_test_L1)*L1_data)/L1_psd*df).real +# optimal_SNR_H1 = 4*jnp.sum((jnp.conj(h_test_H1)*h_test_H1)/H1_psd*df).real +# optimal_SNR_L1 = 4*jnp.sum((jnp.conj(h_test_L1)*h_test_L1)/L1_psd*df).real + +# return (match_filter_SNR_H1-optimal_SNR_H1/2) + (match_filter_SNR_L1-optimal_SNR_L1/2) + +# # prior on the waveform parameters +# # these are Mc, eta, s1, s2, dist, tc, phic, ra, dec, psi +# prior_range = jnp.array([[20.,50.],[0.20,0.25],[-0.9,0.9], +# [-0.9,0.9],[100,3000],[-1.0,1.0], +# [0,2*np.pi],[0.001,np.pi],[0.001,np.pi], +# [0.001,2*np.pi],[-jnp.pi/2,jnp.pi/2]]) + + +# ########################################### +# ##### Optimize to find high L point ####### +# ########################################### + +# set_nwalkers = 100 +# initial_guess = jax.random.uniform(jax.random.PRNGKey(42), (set_nwalkers,11,), +# minval=prior_range[:,0], maxval=prior_range[:,1]) + +# y = lambda x: -LogLikelihood(x) +# y = jax.jit(jax.vmap(y)) +# print("Compiling likelihood function") +# y(initial_guess) +# print("Done compiling") + +# print("Starting the optimizer") +# optimizer = EvolutionaryOptimizer(11, verbose = True) +# state = optimizer.optimize(y, prior_range, n_loops=2000) +# best_fit = optimizer.get_result()[0] + +# print(best_fit) + +# data_list = [H1_data, L1_data] +# psd_list = [H1_psd, L1_psd] +# response_list = [H1_response, L1_response] + + +# print("Constructing the heterodyned likelihood function") +# logL = make_heterodyne_likelihood_mutliple_detector(data_list, psd_list, response_list, gen_IMRPhenomD_polar, +# best_fit, H1_frequency, gmst, epoch, f_ref, 301) + + +# ########################################### +# ####### Finally, we can sample! ########### +# ########################################### + +# n_dim = 11 +# n_chains = 1000 +# n_loop_training = 10 +# n_loop_production = 10 +# n_local_steps = 100 +# n_global_steps = 100 +# learning_rate = 0.001 +# max_samples = 100000 +# momentum = 0.9 +# num_epochs = 100 +# batch_size = 50000 + +# guess_param = best_fit + +# guess_param = np.array(jnp.repeat(guess_param[None,:],int(n_chains),axis=0)*np.random.normal(loc=1,scale=0.1,size=(int(n_chains),n_dim))) +# guess_param[guess_param[:,1]>0.25,1] = 0.249 +# guess_param[:,6] = (guess_param[:,6]%(2*jnp.pi)) +# guess_param[:,7] = (guess_param[:,7]%(jnp.pi)) +# guess_param[:,8] = (guess_param[:,8]%(jnp.pi)) +# guess_param[:,9] = (guess_param[:,9]%(2*jnp.pi)) + + +# print("Preparing RNG keys") +# rng_key_set = initialize_rng_keys(n_chains, seed=42) + +# print("Initializing MCMC model and normalizing flow model.") + +# prior_range = jnp.array([[10,80],[0.125,1.0],[-1,1],[-1,1], +# [0,2000],[-0.05,0.05],[0,2*np.pi],[-1,1], +# [0,np.pi],[0,2*np.pi],[-1,1]]) + + +# initial_position = jax.random.uniform(rng_key_set[0], shape=(int(n_chains), n_dim)) * 1 +# for i in range(n_dim): +# initial_position = initial_position.at[:,i].set(initial_position[:,i]*(prior_range[i,1]-prior_range[i,0])+prior_range[i,0]) + +# from ripple import Mc_eta_to_ms +# m1,m2 = jax.vmap(Mc_eta_to_ms)(guess_param[:,:2]) +# q = m2/m1 + +# initial_position = initial_position.at[:,0].set(guess_param[:,0]) + +# from astropy.cosmology import Planck18 as cosmo + +# z = np.linspace(0.002,3,10000) +# dL = cosmo.luminosity_distance(z).value +# dVdz = cosmo.differential_comoving_volume(z).value + +# def top_hat(x): +# output = 0. +# for i in range(n_dim): +# output = jax.lax.cond(x[i]>=prior_range[i,0], lambda: output, lambda: -jnp.inf) +# output = jax.lax.cond(x[i]<=prior_range[i,1], lambda: output, lambda: -jnp.inf) +# return output+jnp.log(jnp.interp(x[4],dL,dVdz)) + +# def posterior(theta): +# q = theta[1] +# theta = theta.at[7].set(jnp.arcsin(jnp.sin(theta[7]/2*jnp.pi))*2/jnp.pi) +# theta = theta.at[10].set(jnp.arcsin(jnp.sin(theta[10]/2*jnp.pi))*2/jnp.pi) +# iota = jnp.arccos(theta[7]) +# dec = jnp.arcsin(theta[10]) +# prior = top_hat(theta) +# theta = theta.at[1].set(q/(1+q)**2) # convert q to eta +# theta = theta.at[7].set(iota) # convert cos iota to iota +# theta = theta.at[10].set(dec) # convert cos dec to dec +# return logL(theta) + prior + +# posterior_new = lambda theta, data: posterior(theta) + +# model = MaskedCouplingRQSpline(n_dim, 10, [128,128], 8, jax.random.PRNGKey(10)) + +# print("Initializing sampler class") + +# mass_matrix = jnp.eye(n_dim) +# mass_matrix = mass_matrix.at[1,1].set(1e-3) +# mass_matrix = mass_matrix.at[5,5].set(1e-3) + +# local_sampler = MALA(posterior_new, True, {"step_size": mass_matrix*3e-3}) +# print("Running sampler") + +# nf_sampler = Sampler( +# n_dim, +# rng_key_set, +# None, +# local_sampler, +# model, +# n_loop_training=n_loop_training, +# n_loop_production = n_loop_production, +# n_local_steps=n_local_steps, +# n_global_steps=n_global_steps, +# n_chains=n_chains, +# n_epochs=num_epochs, +# learning_rate=learning_rate, +# momentum=momentum, +# batch_size=batch_size, +# use_global=True, +# keep_quantile=0., +# train_thinning = 40, +# ) + +# nf_sampler.sample(initial_position, None) +# chains, log_prob, local_accs, global_accs = nf_sampler.get_sampler_state().values() +# print("Script complete and took: {} minutes".format((time.time()-total_time_start)/60)) +# # np.savez('GW150914.npz', chains=chains, log_prob=log_prob, local_accs=local_accs, global_accs=global_accs) \ No newline at end of file diff --git a/example/Injection_test.py b/example/Injection_test.py deleted file mode 100644 index e6364998..00000000 --- a/example/Injection_test.py +++ /dev/null @@ -1,213 +0,0 @@ -# Import packages -from curses import KEY_REPLACE -import lalsimulation as lalsim -import numpy as np -import jax.numpy as jnp -import jax - -# from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar -from ripple import ms_to_Mc_eta -from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar -from jimgw.PE.detector_preset import * -from jimgw.PE.heterodyneLikelihood import make_heterodyne_likelihood_mutliple_detector - -from jimgw.PE.detector_projection import make_detector_response - -from flowMC.nfmodel.rqSpline import RQSpline -from flowMC.sampler.MALA import make_mala_sampler, mala_sampler_autotune -from flowMC.sampler.Sampler import Sampler -from flowMC.utils.PRNG_keys import initialize_rng_keys -from flowMC.nfmodel.utils import * - -psd_func_dict = { - 'H1': lalsim.SimNoisePSDaLIGOZeroDetHighPower, - 'L1': lalsim.SimNoisePSDaLIGOZeroDetHighPower, - 'V1': lalsim.SimNoisePSDAdvVirgo, -} -ifos = list(psd_func_dict.keys()) - -# define sampling rate and duration -fsamp = 2048 -duration = 4 - -delta_t = 1/fsamp -tlen = int(round(duration / delta_t)) - -freqs = np.fft.rfftfreq(tlen, delta_t) -delta_f = freqs[1] - freqs[0] - -# we will want to pad low frequencies; the function below applies a -# prescription to do so smoothly, but this is not really needed: you -# could just set all values below `fmin` to a constant. -fmin = 30 -def pad_low_freqs(f, psd_ref): - return psd_ref + psd_ref*(fmin-f)*np.exp(-(fmin-f))/3 - -psd_dict = {} -for ifo in ifos: - psd = np.zeros(len(freqs)) - for i,f in enumerate(freqs): - if f >= fmin: - psd[i] = psd_func_dict[ifo](f) - else: - psd[i] = pad_low_freqs(f, psd_func_dict[ifo](fmin)) - psd_dict[ifo] = psd - -rng = np.random.default_rng(12345) - -noise_fd_dict = {} -for ifo, psd in psd_dict.items(): - var = psd / (4.*delta_f) # this is the variance of LIGO noise given the definition of the likelihood function - noise_real = rng.normal(size=len(psd), loc=0, scale=np.sqrt(var)) - noise_imag = rng.normal(size=len(psd), loc=0, scale=np.sqrt(var)) - noise_fd_dict[ifo] = noise_real + 1j*noise_imag - -# These are the parameters of the injected signal -m1 = 30.0 -m2 = 30.0 -Mc, eta = ms_to_Mc_eta(jnp.array([m1, m2])) -chi1 = 0.4 -chi2 = -0.3 -dist_mpc = 1000.0 -tc = 2.0 -phic = np.pi/4 -inclination = 1.57*np.pi/8 -polarization_angle = 1.2*np.pi/8 -ra = 0.3 -dec = 0.5 - - - -H1 = get_H1() -H1_response = make_detector_response(H1[0], H1[1]) -L1 = get_L1() -L1_response = make_detector_response(L1[0], L1[1]) - - -def gen_waveform_H1(f, theta): - theta_waveform = theta[:9] - ra = theta[9] - dec = theta[10] - hp, hc = gen_IMRPhenomD_polar(f, theta_waveform) - return H1_response(f, hp, hc, ra, dec, theta[5], theta[8]) - -def gen_waveform_L1(f, theta): - theta_waveform = theta[:9] - ra = theta[9] - dec = theta[10] - hp, hc = gen_IMRPhenomD_polar(f, theta_waveform) - return L1_response(f, hp, hc, ra, dec, theta[5], theta[8]) - -true_param = jnp.array([Mc, eta, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle, ra, dec]) - - -f_list = freqs[freqs>fmin] -H1_signal = gen_waveform_H1(f_list, true_param) -H1_noise_psd = noise_fd_dict['H1'][freqs>fmin] -H1_data = H1_noise_psd + H1_signal - -L1_signal = gen_waveform_L1(f_list, true_param) -L1_noise_psd = noise_fd_dict['L1'][freqs>fmin] -L1_data = L1_noise_psd + L1_signal - -ref_param = jnp.array([Mc, eta, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle, ra, dec]) - - - -data_list = [H1_data, L1_data] -psd_list = [psd_dict['H1'], psd_dict['L1']] -response_list = [H1_response, L1_response] - -logL = make_heterodyne_likelihood_mutliple_detector(data_list, psd_list, response_list, gen_IMRPhenomD_polar, ref_param, f_list, 101) - - -n_dim = 11 -n_chains = 1000 -n_loop_training = 100 -n_loop_production = 10 -n_local_steps = 100 -n_global_steps = 100 -learning_rate = 0.001 -max_samples = 50000 -momentum = 0.9 -num_epochs = 30 -batch_size = 50000 - -guess_param = np.array(jnp.repeat(true_param[None,:],int(n_chains),axis=0)*np.random.normal(loc=1,scale=0.1,size=(int(n_chains),n_dim))) -guess_param[guess_param[:,1]>0.25,1] = 0.249 -guess_param[:,6] = (guess_param[:,6]+np.pi/2)%(np.pi)-np.pi/2 -guess_param[:,7] = (guess_param[:,7]+np.pi/2)%(np.pi)-np.pi/2 - - -print("Preparing RNG keys") -rng_key_set = initialize_rng_keys(n_chains, seed=42) - -print("Initializing MCMC model and normalizing flow model.") - -prior_range = jnp.array([[10,70],[0.0,0.25],[-1,1],[-1,1],[0,2000],[-5,5],[-np.pi/2,np.pi/2],[-np.pi/2,np.pi/2],[0,2*np.pi],[0,2*np.pi],[0,np.pi]]) - -initial_position = jax.random.uniform(rng_key_set[0], shape=(int(n_chains), n_dim)) * 1 -for i in range(n_dim): - initial_position = initial_position.at[:,i].set(initial_position[:,i]*(prior_range[i,1]-prior_range[i,0])+prior_range[i,0]) - -initial_position = initial_position.at[:,0].set(guess_param[:,0]) -initial_position = initial_position.at[:,1].set(guess_param[:,1]) -initial_position = initial_position.at[:,5].set(guess_param[:,5]) - -def top_hat(x): - output = 0. - for i in range(n_dim): - output = jax.lax.cond(x[i]>=prior_range[i,0], lambda: output, lambda: -jnp.inf) - output = jax.lax.cond(x[i]<=prior_range[i,1], lambda: output, lambda: -jnp.inf) - return output - -def posterior(theta): - prior = top_hat(theta) - return logL(theta) + prior - - -# model = RealNVP(10, n_dim, 64, 1) -model = RQSpline(n_dim, 10, [128,128], 8) - - -print("Initializing sampler class") - -posterior = posterior -dposterior = jax.grad(posterior) - -mass_matrix = jnp.eye(n_dim) -mass_matrix = mass_matrix.at[1,1].set(1e-3) -mass_matrix = mass_matrix.at[5,5].set(1e-3) - -local_sampler_caller = lambda x: make_mala_sampler(x, jit=True) -sampler_params = {'dt':mass_matrix*5e-3} -print("Running sampler") - - - -nf_sampler = Sampler( - n_dim, - rng_key_set, - local_sampler_caller, - sampler_params, - posterior, - model, - n_loop_training=n_loop_training, - n_loop_production = n_loop_production, - n_local_steps=n_local_steps, - n_global_steps=n_global_steps, - n_chains=n_chains, - n_epochs=num_epochs, - learning_rate=learning_rate, - momentum=momentum, - batch_size=batch_size, - use_global=True, - keep_quantile=0., -) - - - -nf_sampler.sample(initial_position) - -labels = ['Mc', 'eta', 'chi1', 'chi2', 'dist_mpc', 'tc', 'phic', 'inclination', 'polarization_angle', 'ra', 'dec'] -truths = true_param diff --git a/example/Injection_withParserBNS.py b/example/Injection_withParserBNS.py deleted file mode 100644 index c8f05f07..00000000 --- a/example/Injection_withParserBNS.py +++ /dev/null @@ -1,258 +0,0 @@ -# Import packages -import lalsimulation as lalsim -import numpy as np -import jax.numpy as jnp -import jax - -# from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar -from ripple import ms_to_Mc_eta -from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar -from jimgw.PE.detector_preset import * -from jimgw.PE.heterodyneLikelihood import make_heterodyne_likelihood_mutliple_detector -from jimgw.PE.detector_projection import make_detector_response -from jimgw.PE.generate_noise import generate_noise - -from flowMC.nfmodel.rqSpline import RQSpline -from flowMC.sampler.MALA import make_mala_sampler -from flowMC.sampler.Sampler import Sampler -from flowMC.utils.PRNG_keys import initialize_rng_keys -from flowMC.nfmodel.utils import * - -import argparse -import yaml - -from tqdm import tqdm -from functools import partialmethod - -tqdm.__init__ = partialmethod(tqdm.__init__, disable=True) - - -import sys -sys.path.append('/mnt/home/wwong/GWProject/JaxGW') - -parser = argparse.ArgumentParser(description='Injection test') - -parser.add_argument('--config', type=str, default='config.yaml', help='config file') - -# Add noise parameters to parser -parser.add_argument('--seed', type=int, default=None, help='seed for random number generator') -parser.add_argument('--f_sampling', type=int, default=None, help='sampling frequency') -parser.add_argument('--duration', type=int, default=None, help='duration of the data') -parser.add_argument('--fmin', type=float, default=None, help='minimum frequency') -parser.add_argument('--ifos', nargs='+', default=None, help='list of detectors') - -# Add injection parameters to parser -parser.add_argument('--m1', type=float, default=None, help='mass of the first component') -parser.add_argument('--m2', type=float, default=None, help='mass of the second component') -parser.add_argument('--chi1', type=float, default=None, help='dimensionless spin of the first component') -parser.add_argument('--chi2', type=float, default=None, help='dimensionless spin of the second component') -parser.add_argument('--dist_mpc', type=float, default=None, help='distance in megaparsecs') -parser.add_argument('--tc', type=float, default=None, help='coalescence time') -parser.add_argument('--phic', type=float, default=None, help='phase of coalescence') -parser.add_argument('--inclination', type=float, default=None, help='inclination angle') -parser.add_argument('--polarization_angle', type=float, default=None, help='polarization angle') -parser.add_argument('--ra', type=float, default=None, help='right ascension') -parser.add_argument('--dec', type=float, default=None, help='declination') -parser.add_argument('--heterodyne_bins', type=int, default=101, help='number of bins for heterodyne likelihood') - -# Add sampler parameters to parser - -parser.add_argument('--n_dim', type=int, default=None, help='number of parameters') -parser.add_argument('--n_chains', type=int, default=None, help='number of chains') -parser.add_argument('--n_loop', type=int, default=None, help='number of loops') -parser.add_argument('--n_local_steps', type=int, default=None, help='number of local steps') -parser.add_argument('--n_global_steps', type=int, default=None, help='number of global steps') -parser.add_argument('--learning_rate', type=float, default=None, help='learning rate') -parser.add_argument('--max_samples', type=int, default=None, help='maximum number of samples') -parser.add_argument('--momentum', type=float, default=None, help='momentum during training') -parser.add_argument('--num_epochs', type=int, default=None, help='number of epochs') -parser.add_argument('--batch_size', type=int, default=None, help='batch size') -parser.add_argument('--stepsize', type=float, default=None, help='stepsize for Local sampler') - -# Add output parameters to parser - -parser.add_argument('--output_path', type=str, default=None, help='output file path') -parser.add_argument('--downsample_factor', type=int, default=1, help='downsample factor') - -# parser - -args = parser.parse_args() -opt = vars(args) -args = yaml.load(open(opt['config'], 'r'), Loader=yaml.FullLoader) -opt.update(args) -args = opt - -# Fetch noise parameters - -print("Constructing detectors") -print("Making noises") - -seed = args['seed'] -f_sampling = args['f_sampling'] -duration = args['duration'] -fmin = args['fmin'] -ifos = args['ifos'] - - -freqs, psd_dict, noise_dict = generate_noise(seed+1234, f_sampling, duration, fmin, ifos) - - -# Fetch injection parameters and inject signal - -print("Injection signals") - -m1 = args['m1'] -m2 = args['m2'] -chi1 = args['chi1'] -chi2 = args['chi2'] -dist_mpc = args['dist_mpc'] -tc = args['tc'] -phic = args['phic'] -inclination = args['inclination'] -polarization_angle = args['polarization_angle'] -ra = args['ra'] -dec = args['dec'] - -Mc, eta = ms_to_Mc_eta(jnp.array([m1, m2])) - -heterodyne_bins = args['heterodyne_bins'] - -H1 = get_H1() -H1_response = make_detector_response(H1[0], H1[1]) -L1 = get_L1() -L1_response = make_detector_response(L1[0], L1[1]) - - -def gen_waveform_H1(f, theta): - theta_waveform = theta[:9] - ra = theta[9] - dec = theta[10] - hp, hc = gen_IMRPhenomD_polar(f, theta_waveform) - return H1_response(f, hp, hc, ra, dec, theta[5], theta[8]) - -def gen_waveform_L1(f, theta): - theta_waveform = theta[:9] - ra = theta[9] - dec = theta[10] - hp, hc = gen_IMRPhenomD_polar(f, theta_waveform) - return L1_response(f, hp, hc, ra, dec, theta[5], theta[8]) - -true_param = jnp.array([Mc, eta, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle, ra, dec]) - - -f_list = freqs[freqs>fmin] -H1_signal = gen_waveform_H1(f_list, true_param) -H1_noise_psd = noise_dict['H1'][freqs>fmin] -H1_data = H1_noise_psd + H1_signal - -L1_signal = gen_waveform_L1(f_list, true_param) -L1_noise_psd = noise_dict['L1'][freqs>fmin] -L1_data = L1_noise_psd + L1_signal - -ref_param = jnp.array([Mc, eta, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle, ra, dec]) - -data_list = [H1_data, L1_data] -psd_list = [psd_dict['H1'], psd_dict['L1']] -response_list = [H1_response, L1_response] - -logL = make_heterodyne_likelihood_mutliple_detector(data_list, psd_list, response_list, gen_IMRPhenomD_polar, ref_param, f_list, heterodyne_bins) - -# Fetch sampler parameters, construct sampler and initial guess - -print("Making sampler") - -n_dim = args['n_dim'] -n_chains = args['n_chains'] -n_loop = args['n_loop'] -n_local_steps = args['n_local_steps'] -n_global_steps = args['n_global_steps'] -learning_rate = args['learning_rate'] -max_samples = args['max_samples'] -momentum = args['momentum'] -num_epochs = args['num_epochs'] -batch_size = args['batch_size'] -stepsize = args['stepsize'] - - -guess_param = np.array(jnp.repeat(true_param[None,:],int(n_chains),axis=0)*(1+0.1*jax.random.normal(jax.random.PRNGKey(seed+98127),shape=(int(n_chains),n_dim)))) -guess_param[guess_param[:,1]>0.25,1] = 0.249 -guess_param[:,6] = (guess_param[:,6]+np.pi/2)%(np.pi)-np.pi/2 -guess_param[:,7] = (guess_param[:,7]+np.pi/2)%(np.pi)-np.pi/2 -guess_param[:,8] = (guess_param[:,8]%(2*np.pi)) -guess_param[:,9] = (guess_param[:,9]%(2*np.pi)) -guess_param[:,10] = (guess_param[:,10]%(np.pi)) - -print("Preparing RNG keys") -rng_key_set = initialize_rng_keys(n_chains, seed=seed) - -print("Initializing MCMC model and normalizing flow model.") - -prior_range = jnp.array([[1,2.5],[0.0,0.25],[-0.1,0.1],[-0.1,0.1],[0,400],[-60,60],[-np.pi/2,np.pi/2],[-np.pi/2,np.pi/2],[0,2*np.pi],[0,2*np.pi],[0,np.pi]]) - -initial_position = jax.random.uniform(rng_key_set[0], shape=(int(n_chains), n_dim)) * 1 -for i in range(n_dim): - initial_position = initial_position.at[:,i].set(initial_position[:,i]*(prior_range[i,1]-prior_range[i,0])+prior_range[i,0]) - -initial_position = initial_position.at[:,0].set(guess_param[:,0]) -initial_position = initial_position.at[:,1].set(guess_param[:,1]) -initial_position = initial_position.at[:,5].set(guess_param[:,5]) - -def top_hat(x): - output = 0. - for i in range(n_dim): - output = jax.lax.cond(x[i]>=prior_range[i,0], lambda: output, lambda: -jnp.inf) - output = jax.lax.cond(x[i]<=prior_range[i,1], lambda: output, lambda: -jnp.inf) - return output - -def posterior(theta): - prior = top_hat(theta) - return logL(theta) + prior - - -model = RQSpline(n_dim, 10, [128,128], 8) - - -print("Initializing sampler class") - -posterior = posterior -dposterior = jax.grad(posterior) - -mass_matrix = jnp.eye(n_dim) -mass_matrix = mass_matrix.at[1,1].set(1e-3) -mass_matrix = mass_matrix.at[5,5].set(1e-3) - -local_sampler,updater, kernel, logp, dlogp = make_mala_sampler(posterior, dposterior,2e-3, jit=True, M=mass_matrix) - -print("Running sampler") - -nf_sampler = Sampler(n_dim, rng_key_set, model, local_sampler, - posterior, - d_likelihood=dposterior, - n_loop=n_loop, - n_local_steps=n_local_steps, - n_global_steps=n_global_steps, - n_chains=n_chains, - stepsize=stepsize, - n_nf_samples=100, - learning_rate=learning_rate, - n_epochs= num_epochs, - max_samples = max_samples, - momentum=momentum, - batch_size=batch_size, - use_global=True, - keep_quantile=0.) - -nf_sampler.sample(initial_position) - -labels = ['Mc', 'eta', 'chi1', 'chi2', 'dist_mpc', 'tc', 'phic', 'inclination', 'polarization_angle', 'ra', 'dec'] - -print("Saving to output") - -chains, log_prob, local_accs, global_accs, loss_vals = nf_sampler.get_sampler_state() - -# Fetch output parameters - -output_path = args['output_path'] -downsample_factor = args['downsample_factor'] - -np.savez(args['output_path'], chains=chains[:,::downsample_factor], log_prob=log_prob[:,::downsample_factor], local_accs=local_accs[:,::downsample_factor], global_accs=global_accs[:,::downsample_factor], loss_vals=loss_vals, labels=labels, true_param=true_param) diff --git a/example/Injection_withParser_debug.py b/example/Injection_withParser_debug.py deleted file mode 100644 index f0b32695..00000000 --- a/example/Injection_withParser_debug.py +++ /dev/null @@ -1,331 +0,0 @@ -# Import packages -import lalsimulation as lalsim -import numpy as np -import jax.numpy as jnp -import jax -from lal import GreenwichMeanSiderealTime - - -# from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar -from ripple import ms_to_Mc_eta -from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar -from jimgw.PE.detector_preset import * -from jimgw.PE.heterodyneLikelihood import make_heterodyne_likelihood_mutliple_detector -from jimgw.PE.detector_projection import make_detector_response -from jimgw.PE.generate_noise import generate_noise - -from flowMC.nfmodel.rqSpline import RQSpline -from flowMC.sampler.MALA import MALA, mala_sampler_autotune -from flowMC.sampler.Sampler import Sampler -from flowMC.utils.PRNG_keys import initialize_rng_keys -from flowMC.nfmodel.utils import * - -import argparse -import yaml - -from tqdm import tqdm -from functools import partialmethod - -import sys -sys.path.append('/mnt/home/wwong/GWProject/JaxGW') - -parser = argparse.ArgumentParser(description='Injection test') - -parser.add_argument('--config', type=str, default='config.yaml', help='config file') - -# Add noise parameters to parser -parser.add_argument('--seed', type=int, default=None, help='seed for random number generator') -parser.add_argument('--f_sampling', type=int, default=None, help='sampling frequency') -parser.add_argument('--duration', type=int, default=None, help='duration of the data') -parser.add_argument('--fmin', type=float, default=None, help='minimum frequency') -parser.add_argument('--ifos', nargs='+', default=None, help='list of detectors') - -# Add injection parameters to parser -parser.add_argument('--m1', type=float, default=None, help='mass of the first component') -parser.add_argument('--m2', type=float, default=None, help='mass of the second component') -parser.add_argument('--chi1', type=float, default=None, help='dimensionless spin of the first component') -parser.add_argument('--chi2', type=float, default=None, help='dimensionless spin of the second component') -parser.add_argument('--dist_mpc', type=float, default=None, help='distance in megaparsecs') -parser.add_argument('--tc', type=float, default=None, help='coalescence time') -parser.add_argument('--phic', type=float, default=None, help='phase of coalescence') -parser.add_argument('--inclination', type=float, default=None, help='inclination angle') -parser.add_argument('--polarization_angle', type=float, default=None, help='polarization angle') -parser.add_argument('--ra', type=float, default=None, help='right ascension') -parser.add_argument('--dec', type=float, default=None, help='declination') -parser.add_argument('--heterodyne_bins', type=int, default=101, help='number of bins for heterodyne likelihood') - -# Add sampler parameters to parser - -parser.add_argument('--n_dim', type=int, default=None, help='number of parameters') -parser.add_argument('--n_chains', type=int, default=None, help='number of chains') -parser.add_argument('--n_loop_training', type=int, default=None, help='number of training loops') -parser.add_argument('--n_loop_production', type=int, default=None, help='number of production loops') -parser.add_argument('--n_local_steps', type=int, default=None, help='number of local steps') -parser.add_argument('--n_global_steps', type=int, default=None, help='number of global steps') -parser.add_argument('--learning_rate', type=float, default=None, help='learning rate') -parser.add_argument('--max_samples', type=int, default=None, help='maximum number of samples') -parser.add_argument('--momentum', type=float, default=None, help='momentum during training') -parser.add_argument('--num_epochs', type=int, default=None, help='number of epochs') -parser.add_argument('--batch_size', type=int, default=None, help='batch size') -parser.add_argument('--stepsize', type=float, default=None, help='stepsize for Local sampler') - -# Add output parameters to parser - -parser.add_argument('--output_path', type=str, default=None, help='output file path') -parser.add_argument('--downsample_factor', type=int, default=1, help='downsample factor') - -# parser - -args = parser.parse_args() -opt = vars(args) -args = yaml.load(open(opt['config'], 'r'), Loader=yaml.FullLoader) -opt.update(args) -args = opt - -# Fetch noise parameters - -print("Constructing detectors") -print("Making noises") - -seed = args['seed'] -f_sampling = args['f_sampling'] -duration = args['duration'] -fmin = args['fmin'] -ifos = args['ifos'] - - -freqs, psd_dict, noise_dict = generate_noise(seed+1234, f_sampling, duration, fmin, ifos) - - -# Fetch injection parameters and inject signal - -print("Injection signals") - -m1 = args['m1'] -m2 = args['m2'] -chi1 = args['chi1'] -chi2 = args['chi2'] -dist_mpc = args['dist_mpc'] -tc = args['tc'] -phic = args['phic'] -inclination = args['inclination'] -polarization_angle = args['polarization_angle'] -ra = args['ra'] -dec = args['dec'] - -Mc, eta = ms_to_Mc_eta(jnp.array([m1, m2])) - -heterodyne_bins = args['heterodyne_bins'] - -H1 = get_H1() -H1_response = make_detector_response(H1[0], H1[1]) -L1 = get_L1() -L1_response = make_detector_response(L1[0], L1[1]) -V1 = get_V1() -V1_response = make_detector_response(V1[0], V1[1]) - -f_ref = 30.0 -trigger_time = 1126259462.4 -post_trigger_duration = 2 -epoch = duration - post_trigger_duration -gmst = GreenwichMeanSiderealTime(trigger_time) - - -def gen_waveform_H1(f, theta): - theta_waveform = theta[:8] - theta_waveform = theta_waveform.at[5].set(0) - ra = theta[9] - dec = theta[10] - hp, hc = gen_IMRPhenomD_polar(f, theta_waveform, f_ref) - return H1_response(f, hp, hc, ra, dec, gmst , theta[8]) * jnp.exp(-1j*2*jnp.pi*f*(epoch+theta[5])) - -def gen_waveform_L1(f, theta): - theta_waveform = theta[:8] - theta_waveform = theta_waveform.at[5].set(0) - ra = theta[9] - dec = theta[10] - hp, hc = gen_IMRPhenomD_polar(f, theta_waveform, f_ref) - return L1_response(f, hp, hc, ra, dec, gmst, theta[8]) * jnp.exp(-1j*2*jnp.pi*f*(epoch+theta[5])) - -def gen_waveform_V1(f, theta): - theta_waveform = theta[:8] - theta_waveform = theta_waveform.at[5].set(0) - ra = theta[9] - dec = theta[10] - hp, hc = gen_IMRPhenomD_polar(f, theta_waveform, f_ref) - return V1_response(f, hp, hc, ra, dec, gmst, theta[8]) * jnp.exp(-1j*2*jnp.pi*f*(epoch+theta[5])) - -true_param = jnp.array([Mc, eta, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle, ra, dec]) - -from scipy.interpolate import interp1d -q_axis = np.linspace(0.1, 1.0, 10000) -eta_axis = q_axis/(1+q_axis)**2 -true_q = interp1d(eta_axis, q_axis)(eta) -cos_inclination = np.cos(inclination) -sin_dec = np.sin(dec) -true_param_trans = jnp.array([Mc, true_q, chi1, chi2, dist_mpc, tc, phic, cos_inclination, polarization_angle, ra, sin_dec]) - -f_list = freqs[freqs>fmin] -H1_signal = gen_waveform_H1(f_list, true_param) -H1_noise_psd = noise_dict['H1'][freqs>fmin] -H1_psd = psd_dict['H1'][freqs>fmin] -H1_data = H1_noise_psd + H1_signal - -L1_signal = gen_waveform_L1(f_list, true_param) -L1_noise_psd = noise_dict['L1'][freqs>fmin] -L1_psd = psd_dict['L1'][freqs>fmin] -L1_data = L1_noise_psd + L1_signal - -V1_signal = gen_waveform_V1(f_list, true_param) -V1_noise_psd = noise_dict['V1'][freqs>fmin] -V1_psd = psd_dict['V1'][freqs>fmin] -V1_data = V1_noise_psd + V1_signal - -ref_param = jnp.array([Mc, eta, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle, ra, dec]) - -data_list = [H1_data, L1_data, V1_data] -psd_list = [H1_psd, L1_psd, V1_psd] -response_list = [H1_response, L1_response, V1_response] - -def LogLikelihood(theta): - theta = jnp.array(theta) - # theta = theta.at[1].set(theta[1]/(1+theta[1])**2) # convert q to eta - # theta = theta.at[7].set(jnp.arccos(theta[7])) # convert cos iota to iota - # theta = theta.at[10].set(jnp.arcsin(theta[10])) # convert cos dec to dec - theta_waveform = theta[:8] - theta_waveform = theta_waveform.at[5].set(0) - ra = theta[9] - dec = theta[10] - hp_test, hc_test = gen_IMRPhenomD_polar(f_list, theta_waveform, f_ref) - align_time = jnp.exp(-1j*2*jnp.pi*f_list*(epoch+theta[5])) - h_test_H1 = H1_response(f_list, hp_test, hc_test, ra, dec, gmst, theta[8]) * align_time - h_test_L1 = L1_response(f_list, hp_test, hc_test, ra, dec, gmst, theta[8]) * align_time - h_test_V1 = V1_response(f_list, hp_test, hc_test, ra, dec, gmst, theta[8]) * align_time - df = f_list[1] - f_list[0] - match_filter_SNR_H1 = 4*jnp.sum((jnp.conj(h_test_H1)*H1_data)/H1_psd*df).real - match_filter_SNR_L1 = 4*jnp.sum((jnp.conj(h_test_L1)*L1_data)/L1_psd*df).real - match_filter_SNR_V1 = 4*jnp.sum((jnp.conj(h_test_V1)*V1_data)/V1_psd*df).real - optimal_SNR_H1 = 4*jnp.sum((jnp.conj(h_test_H1)*h_test_H1)/H1_psd*df).real - optimal_SNR_L1 = 4*jnp.sum((jnp.conj(h_test_L1)*h_test_L1)/L1_psd*df).real - optimal_SNR_V1 = 4*jnp.sum((jnp.conj(h_test_V1)*h_test_V1)/V1_psd*df).real - - return (match_filter_SNR_H1-optimal_SNR_H1/2) + (match_filter_SNR_L1-optimal_SNR_L1/2) + (match_filter_SNR_V1-optimal_SNR_V1/2) - - -logL = make_heterodyne_likelihood_mutliple_detector(data_list, psd_list, response_list, gen_IMRPhenomD_polar, ref_param, f_list, gmst, epoch, f_ref, heterodyne_bins) - -# Fetch sampler parameters, construct sampler and initial guess - -print("Making sampler") - -n_dim = args['n_dim'] -n_chains = args['n_chains'] -n_loop_training = args['n_loop_training'] -n_loop_production = args['n_loop_production'] -n_local_steps = args['n_local_steps'] -n_global_steps = args['n_global_steps'] -learning_rate = args['learning_rate'] -max_samples = args['max_samples'] -momentum = args['momentum'] -num_epochs = args['num_epochs'] -batch_size = args['batch_size'] -stepsize = args['stepsize'] - - -guess_param = np.array(jnp.repeat(true_param_trans[None,:],int(n_chains),axis=0)*(1+0.1*jax.random.normal(jax.random.PRNGKey(seed+98127),shape=(int(n_chains),n_dim)))) -guess_param[guess_param[:,1]>1,1] = 1 - -print("Preparing RNG keys") -rng_key_set = initialize_rng_keys(n_chains, seed=seed) - -print("Initializing MCMC model and normalizing flow model.") - -prior_range = jnp.array([[10,80],[0.125,1.0],[-0.5,0.5],[-0.5,0.5],[300,2000],[-0.5,0.5],[0,2*np.pi],[-1,1],[0,np.pi],[0,2*np.pi],[-1,1]]) - - -initial_position = jax.random.uniform(rng_key_set[0], shape=(int(n_chains), n_dim)) * 1 -for i in range(n_dim): - initial_position = initial_position.at[:,i].set(initial_position[:,i]*(prior_range[i,1]-prior_range[i,0])+prior_range[i,0]) - -from ripple import Mc_eta_to_ms -m1,m2 = jax.vmap(Mc_eta_to_ms)(guess_param[:,:2]) -q = m2/m1 -initial_position = initial_position.at[:,0].set(guess_param[:,0]) -initial_position = initial_position.at[:,5].set(guess_param[:,5]) - -from astropy.cosmology import Planck18 as cosmo - -z = np.linspace(0.01,0.4,10000) -dL = cosmo.luminosity_distance(z).value -dVdz = cosmo.differential_comoving_volume(z).value - -def top_hat(x): - output = 0. - for i in range(n_dim): - output = jax.lax.cond(x[i]>=prior_range[i,0], lambda: output, lambda: -jnp.inf) - output = jax.lax.cond(x[i]<=prior_range[i,1], lambda: output, lambda: -jnp.inf) - return output+jnp.log(jnp.interp(x[4],dL,dVdz)) - -def posterior(theta): - q = theta[1] - iota = jnp.arccos(theta[7]) - dec = jnp.arcsin(theta[10]) - prior = top_hat(theta) - theta = theta.at[1].set(q/(1+q)**2) # convert q to eta - theta = theta.at[7].set(iota) # convert cos iota to iota - theta = theta.at[10].set(dec) # convert cos dec to dec - return logL(theta) + prior - - -model = RQSpline(n_dim, 5, [128,128], 8) - - -print("Initializing sampler class") - -posterior = posterior -dposterior = jax.grad(posterior) - - -mass_matrix = np.eye(n_dim) -mass_matrix = np.abs(1./(jax.grad(logL)(true_param)+jax.grad(top_hat)(true_param)))*mass_matrix -mass_matrix = jnp.array(mass_matrix) - -local_sampler = MALA(posterior, True, {"step_size": mass_matrix*3e-1}) -print("Running sampler") - -nf_sampler = Sampler( - n_dim, - rng_key_set, - local_sampler, - posterior, - model, - n_loop_training=n_loop_training, - n_loop_production = n_loop_production, - n_local_steps=n_local_steps, - n_global_steps=n_global_steps, - n_chains=n_chains, - n_epochs=num_epochs, - learning_rate=learning_rate, - momentum=momentum, - batch_size=batch_size, - use_global=True, - keep_quantile=0., - train_thinning = 40, - local_autotune=mala_sampler_autotune -) - -nf_sampler.sample(initial_position) - -labels = ['Mc', 'eta', 'chi1', 'chi2', 'dist_mpc', 'tc', 'phic', 'cos_inclination', 'polarization_angle', 'ra', 'sin_dec'] - -print("Saving to output") - -chains, log_prob, local_accs, global_accs, loss_vals = nf_sampler.get_sampler_state(training=True).values() -chains, log_prob, local_accs, global_accs = nf_sampler.get_sampler_state().values() - -# Fetch output parameters - -output_path = args['output_path'] -downsample_factor = args['downsample_factor'] - -np.savez(args['output_path'], chains=chains[:,::downsample_factor], log_prob=log_prob[:,::downsample_factor], local_accs=local_accs[:,::downsample_factor], global_accs=global_accs[:,::downsample_factor], loss_vals=loss_vals, labels=labels, true_param=true_param, true_log_prob=LogLikelihood(true_param)) diff --git a/src/jimgw/jim.py b/src/jimgw/jim.py index 29a6552b..b97c81d1 100644 --- a/src/jimgw/jim.py +++ b/src/jimgw/jim.py @@ -1,5 +1,3 @@ -from abc import ABC, abstractmethod -from jaxtyping import Array from jimgw.likelihood import LikelihoodBase from flowMC.sampler.Sampler import Sampler from flowMC.nfmodel.base import Distribution @@ -11,11 +9,30 @@ class Jim(object): """ - def __init__(self, sampler: Sampler, likelihood: LikelihoodBase, prior: Distribution, **kwargs): - self.Sampler = sampler + def __init__(self, likelihood: LikelihoodBase, prior: Distribution, sampler_kwargs, **kwargs): self.Likelihood = likelihood self.Prior = prior + nf_sampler = Sampler( + self.Prior.n_dim, + rng_key_set, + None, + local_sampler, + model, + n_loop_training=n_loop_training, + n_loop_production = n_loop_production, + n_local_steps=n_local_steps, + n_global_steps=n_global_steps, + n_chains=n_chains, + n_epochs=num_epochs, + learning_rate=learning_rate, + momentum=momentum, + batch_size=batch_size, + use_global=True, + keep_quantile=0., + train_thinning = 40, + ) + def maximize_likleihood(self, bounds: tuple[float,float],set_nwalkers: int = 100, n_loops: int = 2000): set_nwalkers = set_nwalkers initial_guess = self.Prior.sample(set_nwalkers) diff --git a/src/jimgw/prior.py b/src/jimgw/prior.py new file mode 100644 index 00000000..95ff01f3 --- /dev/null +++ b/src/jimgw/prior.py @@ -0,0 +1,19 @@ +from flowMC.nfmodel.base import Distribution +import jax +from jaxtyping import Array, Float + +class Uniform(Distribution): + + xmin: Array + xmax: Array + + def __init__(self, xmin: float, xmax: float): + self.xmax = xmax + self.xmin = xmin + + def sample(self, rng_key: jax.random.PRNGKey, n_samples: int) -> Array: + return super().sample(rng_key, n_samples) + + def log_prob(self, x: Array) -> Float: + return super().log_prob(x) + From 763f5854415d40fe34257ec439df09489656455a Mon Sep 17 00:00:00 2001 From: kazewong Date: Tue, 11 Jul 2023 10:45:31 -0400 Subject: [PATCH 213/300] Add sampler configuration in JIm --- src/jimgw/jim.py | 46 ++++++++++++++++++++++++++++++---------------- 1 file changed, 30 insertions(+), 16 deletions(-) diff --git a/src/jimgw/jim.py b/src/jimgw/jim.py index b97c81d1..c92fa149 100644 --- a/src/jimgw/jim.py +++ b/src/jimgw/jim.py @@ -1,6 +1,9 @@ from jimgw.likelihood import LikelihoodBase from flowMC.sampler.Sampler import Sampler +from flowMC.sampler.MALA import MALA from flowMC.nfmodel.base import Distribution +from flowMC.nfmodel.rqSpline import MaskedCouplingRQSpline +from flowMC.utils.PRNG_keys import initialize_rng_keys from flowMC.utils.EvolutionaryOptimizer import EvolutionaryOptimizer import jax @@ -9,29 +12,40 @@ class Jim(object): """ - def __init__(self, likelihood: LikelihoodBase, prior: Distribution, sampler_kwargs, **kwargs): + def __init__(self, likelihood: LikelihoodBase, prior: Distribution, sampler_kwargs: dict, **kwargs): self.Likelihood = likelihood self.Prior = prior + seed = sampler_kwargs.get("seed", 0) - nf_sampler = Sampler( + rng_key_set = initialize_rng_keys(seed) + num_layers = sampler_kwargs.get("num_layers", 10) + hidden_size = sampler_kwargs.get("hidden_size", [128,128]) + num_bins = sampler_kwargs.get("hidden_size", 8) + + local_sampler = MALA(self.Likelihood.evaluate, True, 1e-2) # Remember to add routine to find automated mass matrix + + model = MaskedCouplingRQSpline(self.Prior.n_dim, num_layers, hidden_size, num_bins) + self.Sampler = Sampler( self.Prior.n_dim, rng_key_set, None, local_sampler, model, - n_loop_training=n_loop_training, - n_loop_production = n_loop_production, - n_local_steps=n_local_steps, - n_global_steps=n_global_steps, - n_chains=n_chains, - n_epochs=num_epochs, - learning_rate=learning_rate, - momentum=momentum, - batch_size=batch_size, - use_global=True, - keep_quantile=0., - train_thinning = 40, - ) + **sampler_kwargs) + + # n_loop_training=n_loop_training, + # n_loop_production = n_loop_production, + # n_local_steps=n_local_steps, + # n_global_steps=n_global_steps, + # n_chains=n_chains, + # n_epochs=num_epochs, + # learning_rate=learning_rate, + # momentum=momentum, + # batch_size=batch_size, + # use_global=True, + # keep_quantile=0., + # train_thinning = 40, + # ) def maximize_likleihood(self, bounds: tuple[float,float],set_nwalkers: int = 100, n_loops: int = 2000): set_nwalkers = set_nwalkers @@ -51,7 +65,7 @@ def maximize_likleihood(self, bounds: tuple[float,float],set_nwalkers: int = 100 def sample(self): - pass + self.Sampler.sample(self.Prior.sample()) def plot(self): pass \ No newline at end of file From 0a3a5cf308b48131d0725bf3c6bcfbb294822b34 Mon Sep 17 00:00:00 2001 From: kazewong Date: Tue, 11 Jul 2023 11:08:38 -0400 Subject: [PATCH 214/300] Make prior abstract class --- src/jimgw/prior.py | 31 +++++++++++++++++++++++++------ 1 file changed, 25 insertions(+), 6 deletions(-) diff --git a/src/jimgw/prior.py b/src/jimgw/prior.py index 95ff01f3..06126e3d 100644 --- a/src/jimgw/prior.py +++ b/src/jimgw/prior.py @@ -1,19 +1,38 @@ -from flowMC.nfmodel.base import Distribution import jax +from flowMC.nfmodel.base import Distribution from jaxtyping import Array, Float +from typing import Callable + +class Prior(Distribution): + r"""A thin wrapper build on top of flowMC distributions to do book keeping. + + Should not be used directly since it does not implement any of the real method. + """ + + naming: list[str] + transforms: list[Callable] = [] -class Uniform(Distribution): + def __init__(self, naming: list[str], transforms: list[Callable] = []): + pass + + def transform(self, x: Array): + pass + + +class Uniform(Prior): xmin: Array xmax: Array - def __init__(self, xmin: float, xmax: float): + def __init__(self, xmin: float, xmax: float, naming: list[str]): self.xmax = xmax self.xmin = xmin + self.naming = naming def sample(self, rng_key: jax.random.PRNGKey, n_samples: int) -> Array: - return super().sample(rng_key, n_samples) - + samples = jax.random.uniform(rng_key, n_samples, minval=self.xmin, maxval=self.xmax) + return samples # TODO: remember to cast this to a named array + def log_prob(self, x: Array) -> Float: - return super().log_prob(x) + return 1./(self.xmax-self.xmin) From 73b14947de72f9cab8a1c85788bfe49deec05ff5 Mon Sep 17 00:00:00 2001 From: kazewong Date: Tue, 11 Jul 2023 11:25:25 -0400 Subject: [PATCH 215/300] Make jim posterior more consistent with flowMC --- src/jimgw/jim.py | 13 ++++++++++--- src/jimgw/likelihood.py | 2 +- 2 files changed, 11 insertions(+), 4 deletions(-) diff --git a/src/jimgw/jim.py b/src/jimgw/jim.py index c92fa149..1fc773b9 100644 --- a/src/jimgw/jim.py +++ b/src/jimgw/jim.py @@ -1,10 +1,11 @@ from jimgw.likelihood import LikelihoodBase from flowMC.sampler.Sampler import Sampler from flowMC.sampler.MALA import MALA -from flowMC.nfmodel.base import Distribution from flowMC.nfmodel.rqSpline import MaskedCouplingRQSpline from flowMC.utils.PRNG_keys import initialize_rng_keys from flowMC.utils.EvolutionaryOptimizer import EvolutionaryOptimizer +from jimgw.prior import Prior +from jaxtyping import Array import jax class Jim(object): @@ -12,7 +13,7 @@ class Jim(object): """ - def __init__(self, likelihood: LikelihoodBase, prior: Distribution, sampler_kwargs: dict, **kwargs): + def __init__(self, likelihood: LikelihoodBase, prior: Prior, sampler_kwargs: dict, **kwargs): self.Likelihood = likelihood self.Prior = prior seed = sampler_kwargs.get("seed", 0) @@ -22,7 +23,13 @@ def __init__(self, likelihood: LikelihoodBase, prior: Distribution, sampler_kwar hidden_size = sampler_kwargs.get("hidden_size", [128,128]) num_bins = sampler_kwargs.get("hidden_size", 8) - local_sampler = MALA(self.Likelihood.evaluate, True, 1e-2) # Remember to add routine to find automated mass matrix + def posterior(x: Array, data:dict): + prior = self.Prior.log_prob(x) + x = self.Prior.transform(x) + return self.Likelihood.evaluate(x) + prior + + + local_sampler = MALA(posterior, True, 1e-2) # Remember to add routine to find automated mass matrix model = MaskedCouplingRQSpline(self.Prior.n_dim, num_layers, hidden_size, num_bins) self.Sampler = Sampler( diff --git a/src/jimgw/likelihood.py b/src/jimgw/likelihood.py index b4532583..fc163ece 100644 --- a/src/jimgw/likelihood.py +++ b/src/jimgw/likelihood.py @@ -70,7 +70,7 @@ def ifos(self): """ return [detector.name for detector in self.detectors] - def evaluate(self, params: Array) -> float: + def evaluate(self, params: Array, data: dict) -> float: """Evaluate the likelihood for a given set of parameters. """ log_likelihood = 0 From d185323e7b26b92a33f373eda4d2041f08b54647 Mon Sep 17 00:00:00 2001 From: kazewong Date: Tue, 11 Jul 2023 11:25:52 -0400 Subject: [PATCH 216/300] Make it more consistent with flowMC --- example/GW150914.py | 6 ++++++ src/jimgw/jim.py | 2 +- src/jimgw/likelihood.py | 2 +- src/jimgw/prior.py | 4 ++-- 4 files changed, 10 insertions(+), 4 deletions(-) diff --git a/example/GW150914.py b/example/GW150914.py index 3e82268a..ddf4bb99 100644 --- a/example/GW150914.py +++ b/example/GW150914.py @@ -2,6 +2,7 @@ from jimgw.detector import H1, L1 from jimgw.likelihood import TransientLikelihoodFD from jimgw.waveform import RippleIMRPhenomD +from jimgw.prior import Uniform import jax.numpy as jnp ########################################### @@ -23,6 +24,11 @@ L1.load_data(gps, 2, 2, fmin, fmax, psd_pad=16, tukey_alpha=0.2) likelihood = TransientLikelihoodFD([H1, L1], RippleIMRPhenomD(), gps, 4, 2) +prior = Uniform( + xmin = [10, 0.125, -1., -1., 0., -0.05, 0., -1, 0., 0.,-1.], + xmax = [80., 1., 1., 1., 2000., 0.05, 2*jnp.pi, 1., jnp.pi, 2*jnp.pi, 1.], + naming = ["M_c", "q", "s1_z", "s2_z", "d_L", "t_c", "phase_c", "iota", "psi", "ra", "dec"] +) ########################################### ######## Set up the likelihood ############ diff --git a/src/jimgw/jim.py b/src/jimgw/jim.py index 1fc773b9..5b5def47 100644 --- a/src/jimgw/jim.py +++ b/src/jimgw/jim.py @@ -26,7 +26,7 @@ def __init__(self, likelihood: LikelihoodBase, prior: Prior, sampler_kwargs: dic def posterior(x: Array, data:dict): prior = self.Prior.log_prob(x) x = self.Prior.transform(x) - return self.Likelihood.evaluate(x) + prior + return self.Likelihood.evaluate(x, data) + prior local_sampler = MALA(posterior, True, 1e-2) # Remember to add routine to find automated mass matrix diff --git a/src/jimgw/likelihood.py b/src/jimgw/likelihood.py index fc163ece..ac447a45 100644 --- a/src/jimgw/likelihood.py +++ b/src/jimgw/likelihood.py @@ -70,7 +70,7 @@ def ifos(self): """ return [detector.name for detector in self.detectors] - def evaluate(self, params: Array, data: dict) -> float: + def evaluate(self, params: Array, data: dict) -> float: # TODO: Test whether we need to pass data in or with class changes is fine. """Evaluate the likelihood for a given set of parameters. """ log_likelihood = 0 diff --git a/src/jimgw/prior.py b/src/jimgw/prior.py index 06126e3d..220dae49 100644 --- a/src/jimgw/prior.py +++ b/src/jimgw/prior.py @@ -1,4 +1,5 @@ import jax +import jax.numpy as jnp from flowMC.nfmodel.base import Distribution from jaxtyping import Array, Float from typing import Callable @@ -34,5 +35,4 @@ def sample(self, rng_key: jax.random.PRNGKey, n_samples: int) -> Array: return samples # TODO: remember to cast this to a named array def log_prob(self, x: Array) -> Float: - return 1./(self.xmax-self.xmin) - + return jnp.log(1./(self.xmax-self.xmin)) From 5118af33efc98da302cdfa46de4258006691842c Mon Sep 17 00:00:00 2001 From: kazewong Date: Tue, 11 Jul 2023 17:42:38 -0400 Subject: [PATCH 217/300] Change jim parsing --- example/GW150914.py | 8 ++++++++ src/jimgw/jim.py | 12 ++++++------ 2 files changed, 14 insertions(+), 6 deletions(-) diff --git a/example/GW150914.py b/example/GW150914.py index ddf4bb99..cceffe1f 100644 --- a/example/GW150914.py +++ b/example/GW150914.py @@ -1,4 +1,5 @@ import time +from jaxgw.jim import Jim from jimgw.detector import H1, L1 from jimgw.likelihood import TransientLikelihoodFD from jimgw.waveform import RippleIMRPhenomD @@ -30,6 +31,13 @@ naming = ["M_c", "q", "s1_z", "s2_z", "d_L", "t_c", "phase_c", "iota", "psi", "ra", "dec"] ) +jim = Jim(likelihood, + prior, + n_loop_training = 10 + ) + +jim + ########################################### ######## Set up the likelihood ############ ########################################### diff --git a/src/jimgw/jim.py b/src/jimgw/jim.py index 5b5def47..c3746bdd 100644 --- a/src/jimgw/jim.py +++ b/src/jimgw/jim.py @@ -13,15 +13,15 @@ class Jim(object): """ - def __init__(self, likelihood: LikelihoodBase, prior: Prior, sampler_kwargs: dict, **kwargs): + def __init__(self, likelihood: LikelihoodBase, prior: Prior, **kwargs): self.Likelihood = likelihood self.Prior = prior - seed = sampler_kwargs.get("seed", 0) + seed = kwargs.get("seed", 0) rng_key_set = initialize_rng_keys(seed) - num_layers = sampler_kwargs.get("num_layers", 10) - hidden_size = sampler_kwargs.get("hidden_size", [128,128]) - num_bins = sampler_kwargs.get("hidden_size", 8) + num_layers = kwargs.get("num_layers", 10) + hidden_size = kwargs.get("hidden_size", [128,128]) + num_bins = kwargs.get("hidden_size", 8) def posterior(x: Array, data:dict): prior = self.Prior.log_prob(x) @@ -38,7 +38,7 @@ def posterior(x: Array, data:dict): None, local_sampler, model, - **sampler_kwargs) + **kwargs) # n_loop_training=n_loop_training, # n_loop_production = n_loop_production, From 8e7003a1b5e4aa3a6a569fb56111b1695e3c3de5 Mon Sep 17 00:00:00 2001 From: kazewong Date: Tue, 11 Jul 2023 23:53:22 -0400 Subject: [PATCH 218/300] Fixing prior inconsistency --- example/GW150914.py | 2 +- src/jimgw/detector.py | 4 ++-- src/jimgw/jim.py | 4 ++-- src/jimgw/prior.py | 24 +++++++++++++++--------- 4 files changed, 20 insertions(+), 14 deletions(-) diff --git a/example/GW150914.py b/example/GW150914.py index cceffe1f..b20cd56f 100644 --- a/example/GW150914.py +++ b/example/GW150914.py @@ -1,5 +1,5 @@ import time -from jaxgw.jim import Jim +from jimgw.jim import Jim from jimgw.detector import H1, L1 from jimgw.likelihood import TransientLikelihoodFD from jimgw.waveform import RippleIMRPhenomD diff --git a/src/jimgw/detector.py b/src/jimgw/detector.py index 62168658..08309437 100644 --- a/src/jimgw/detector.py +++ b/src/jimgw/detector.py @@ -75,7 +75,7 @@ def load_data(self, trigger_time:float, psd_pad: int = 16, tukey_alpha: float = 0.2) -> None: print("Fetching data from {}...".format(self.name)) - data_td = TimeSeries.fetch_open_data(self.name, trigger_time - gps_start_pad, trigger_time + gps_end_pad) + data_td = TimeSeries.fetch_open_data(self.name, trigger_time - gps_start_pad, trigger_time + gps_end_pad, cache=True) segment_length = data_td.duration.value n = len(data_td) delta_t = data_td.dt.value @@ -86,7 +86,7 @@ def load_data(self, trigger_time:float, end_psd = int(trigger_time) + gps_end_pad + psd_pad print("Fetching PSD data...") - psd_data_td = TimeSeries.fetch_open_data(self.name, start_psd, end_psd) + psd_data_td = TimeSeries.fetch_open_data(self.name, start_psd, end_psd, cache=True) psd = psd_data_td.psd(fftlength=segment_length).value # TODO: Check whether this is sright. print("Finished generating data.") diff --git a/src/jimgw/jim.py b/src/jimgw/jim.py index c3746bdd..2f86bb92 100644 --- a/src/jimgw/jim.py +++ b/src/jimgw/jim.py @@ -29,9 +29,9 @@ def posterior(x: Array, data:dict): return self.Likelihood.evaluate(x, data) + prior - local_sampler = MALA(posterior, True, 1e-2) # Remember to add routine to find automated mass matrix + local_sampler = MALA(posterior, True, {"step_size": 1e-2}) # Remember to add routine to find automated mass matrix - model = MaskedCouplingRQSpline(self.Prior.n_dim, num_layers, hidden_size, num_bins) + model = MaskedCouplingRQSpline(self.Prior.n_dim, num_layers, hidden_size, num_bins, rng_key_set[-1]) self.Sampler = Sampler( self.Prior.n_dim, rng_key_set, diff --git a/src/jimgw/prior.py b/src/jimgw/prior.py index 220dae49..e0fb7e38 100644 --- a/src/jimgw/prior.py +++ b/src/jimgw/prior.py @@ -2,7 +2,8 @@ import jax.numpy as jnp from flowMC.nfmodel.base import Distribution from jaxtyping import Array, Float -from typing import Callable +from typing import Callable, Union +from dataclasses import field class Prior(Distribution): r"""A thin wrapper build on top of flowMC distributions to do book keeping. @@ -11,13 +12,18 @@ class Prior(Distribution): """ naming: list[str] - transforms: list[Callable] = [] + transforms: list[Callable] = field(default_factory=list) + + @property + def n_dim(self): + return len(self.naming) def __init__(self, naming: list[str], transforms: list[Callable] = []): - pass + self.naming = naming + self.transforms = transforms def transform(self, x: Array): - pass + return x class Uniform(Prior): @@ -25,14 +31,14 @@ class Uniform(Prior): xmin: Array xmax: Array - def __init__(self, xmin: float, xmax: float, naming: list[str]): - self.xmax = xmax - self.xmin = xmin - self.naming = naming + def __init__(self, xmin: Union[float,Array], xmax: Union[float,Array], naming: list[str]): + super().__init__(naming) + self.xmax = jnp.array(xmax) + self.xmin = jnp.array(xmin) def sample(self, rng_key: jax.random.PRNGKey, n_samples: int) -> Array: samples = jax.random.uniform(rng_key, n_samples, minval=self.xmin, maxval=self.xmax) return samples # TODO: remember to cast this to a named array def log_prob(self, x: Array) -> Float: - return jnp.log(1./(self.xmax-self.xmin)) + return jnp.sum(jnp.log(1./(self.xmax-self.xmin))) From 8aff12e31271ba1ef241897ff275f56416d0ea29 Mon Sep 17 00:00:00 2001 From: kazewong Date: Wed, 12 Jul 2023 00:28:53 -0400 Subject: [PATCH 219/300] maximization seems working --- example/GW150914.py | 2 +- src/jimgw/jim.py | 10 +++++++--- src/jimgw/prior.py | 2 +- 3 files changed, 9 insertions(+), 5 deletions(-) diff --git a/example/GW150914.py b/example/GW150914.py index b20cd56f..f82b4163 100644 --- a/example/GW150914.py +++ b/example/GW150914.py @@ -36,7 +36,7 @@ n_loop_training = 10 ) -jim +jim.maximize_likleihood([prior.xmin, prior.xmax]) ########################################### ######## Set up the likelihood ############ diff --git a/src/jimgw/jim.py b/src/jimgw/jim.py index 2f86bb92..269700c3 100644 --- a/src/jimgw/jim.py +++ b/src/jimgw/jim.py @@ -7,6 +7,7 @@ from jimgw.prior import Prior from jaxtyping import Array import jax +import jax.numpy as jnp class Jim(object): """ Master class for interfacing with flowMC @@ -28,6 +29,7 @@ def posterior(x: Array, data:dict): x = self.Prior.transform(x) return self.Likelihood.evaluate(x, data) + prior + self.posterior = posterior local_sampler = MALA(posterior, True, {"step_size": 1e-2}) # Remember to add routine to find automated mass matrix @@ -54,11 +56,13 @@ def posterior(x: Array, data:dict): # train_thinning = 40, # ) - def maximize_likleihood(self, bounds: tuple[float,float],set_nwalkers: int = 100, n_loops: int = 2000): + def maximize_likleihood(self, bounds: tuple[Array,Array],set_nwalkers: int = 100, n_loops: int = 2000, seed = 92348): + bounds = jnp.array(bounds).T + key = jax.random.PRNGKey(seed) set_nwalkers = set_nwalkers - initial_guess = self.Prior.sample(set_nwalkers) + initial_guess = self.Prior.sample(key, set_nwalkers) - y = lambda x: -self.Likelihood(x) + y = lambda x: -self.posterior(x, None) y = jax.jit(jax.vmap(y)) print("Compiling likelihood function") y(initial_guess) diff --git a/src/jimgw/prior.py b/src/jimgw/prior.py index e0fb7e38..08d12306 100644 --- a/src/jimgw/prior.py +++ b/src/jimgw/prior.py @@ -37,7 +37,7 @@ def __init__(self, xmin: Union[float,Array], xmax: Union[float,Array], naming: l self.xmin = jnp.array(xmin) def sample(self, rng_key: jax.random.PRNGKey, n_samples: int) -> Array: - samples = jax.random.uniform(rng_key, n_samples, minval=self.xmin, maxval=self.xmax) + samples = jax.random.uniform(rng_key, (n_samples,self.n_dim), minval=self.xmin, maxval=self.xmax) return samples # TODO: remember to cast this to a named array def log_prob(self, x: Array) -> Float: From d302e8c17dff709a84c2eb9e62a19abb5390350f Mon Sep 17 00:00:00 2001 From: kazewong Date: Wed, 12 Jul 2023 01:09:00 -0400 Subject: [PATCH 220/300] It runs now, but it has a lot to fix --- example/GW150914.py | 2 ++ src/jimgw/jim.py | 10 +++++++--- 2 files changed, 9 insertions(+), 3 deletions(-) diff --git a/example/GW150914.py b/example/GW150914.py index f82b4163..f967c15e 100644 --- a/example/GW150914.py +++ b/example/GW150914.py @@ -5,6 +5,7 @@ from jimgw.waveform import RippleIMRPhenomD from jimgw.prior import Uniform import jax.numpy as jnp +import jax ########################################### ########## First we grab data ############# @@ -37,6 +38,7 @@ ) jim.maximize_likleihood([prior.xmin, prior.xmax]) +jim.sample(jax.random.PRNGKey(42)) ########################################### ######## Set up the likelihood ############ diff --git a/src/jimgw/jim.py b/src/jimgw/jim.py index 269700c3..4150ab76 100644 --- a/src/jimgw/jim.py +++ b/src/jimgw/jim.py @@ -18,8 +18,9 @@ def __init__(self, likelihood: LikelihoodBase, prior: Prior, **kwargs): self.Likelihood = likelihood self.Prior = prior seed = kwargs.get("seed", 0) + n_chains = kwargs.get("n_chains", 20) - rng_key_set = initialize_rng_keys(seed) + rng_key_set = initialize_rng_keys(n_chains, seed=seed) num_layers = kwargs.get("num_layers", 10) hidden_size = kwargs.get("hidden_size", [128,128]) num_bins = kwargs.get("hidden_size", 8) @@ -75,8 +76,11 @@ def maximize_likleihood(self, bounds: tuple[Array,Array],set_nwalkers: int = 100 return best_fit - def sample(self): - self.Sampler.sample(self.Prior.sample()) + def sample(self, key: jax.random.PRNGKey, + initial_guess: Array = None): + if initial_guess is None: + initial_guess = self.Prior.sample(key, self.Sampler.n_chains) + self.Sampler.sample(initial_guess, None) def plot(self): pass \ No newline at end of file From a3f5b2612a83344a8af1b18293d902d5d684a610 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Fri, 7 Jul 2023 17:24:10 -0400 Subject: [PATCH 221/300] Update --- src/jimgw/detector.py | 21 +++++++++++++++++++++ src/jimgw/jim.py | 2 ++ src/jimgw/waveform.py | 4 +++- 3 files changed, 26 insertions(+), 1 deletion(-) diff --git a/src/jimgw/detector.py b/src/jimgw/detector.py index 08309437..3aecd750 100644 --- a/src/jimgw/detector.py +++ b/src/jimgw/detector.py @@ -74,6 +74,27 @@ def load_data(self, trigger_time:float, f_max: float, psd_pad: int = 16, tukey_alpha: float = 0.2) -> None: + """Load data from the detector. + + Parameters + ---------- + trigger_time : float + The GPS time of the trigger. + gps_start_pad : int + The amount of time before the trigger to fetch data. + gps_end_pad : int + The amount of time after the trigger to fetch data. + f_min : float + The minimum frequency to fetch data. + f_max : float + The maximum frequency to fetch data. + psd_pad : int + The amount of time to pad the PSD data. + tukey_alpha : float + The alpha parameter for the Tukey window. + + """ + print("Fetching data from {}...".format(self.name)) data_td = TimeSeries.fetch_open_data(self.name, trigger_time - gps_start_pad, trigger_time + gps_end_pad, cache=True) segment_length = data_td.duration.value diff --git a/src/jimgw/jim.py b/src/jimgw/jim.py index 4150ab76..382ed20a 100644 --- a/src/jimgw/jim.py +++ b/src/jimgw/jim.py @@ -75,6 +75,8 @@ def maximize_likleihood(self, bounds: tuple[Array,Array],set_nwalkers: int = 100 best_fit = optimizer.get_result()[0] return best_fit + def posterior(self, params: Array): + return self.Likelihood.evaluate(params) + self.Prior.log_prob(params) def sample(self, key: jax.random.PRNGKey, initial_guess: Array = None): diff --git a/src/jimgw/waveform.py b/src/jimgw/waveform.py index 97ce579f..db0e5598 100644 --- a/src/jimgw/waveform.py +++ b/src/jimgw/waveform.py @@ -26,4 +26,6 @@ def __call__(self, frequency: Array, params: dict) -> Array: hp, hc = gen_IMRPhenomD_polar(frequency, theta, self.f_ref) output['p'] = hp output['c'] = hc - return output \ No newline at end of file + return output + + From 316059503c85a17e67f933c528d8868a58d1ef4a Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Sat, 15 Jul 2023 08:52:42 -0400 Subject: [PATCH 222/300] Put local argument in Jim --- example/GW150914.py | 23 ++++++++++++++++++++--- src/jimgw/jim.py | 4 +++- 2 files changed, 23 insertions(+), 4 deletions(-) diff --git a/example/GW150914.py b/example/GW150914.py index f967c15e..dd3be85a 100644 --- a/example/GW150914.py +++ b/example/GW150914.py @@ -32,10 +32,27 @@ naming = ["M_c", "q", "s1_z", "s2_z", "d_L", "t_c", "phase_c", "iota", "psi", "ra", "dec"] ) +mass_matrix = jnp.eye(11) +mass_matrix = mass_matrix.at[1,1].set(1e-3) +mass_matrix = mass_matrix.at[5,5].set(1e-3) +local_sampler_arg = {"step_size": mass_matrix*3e-3} + jim = Jim(likelihood, - prior, - n_loop_training = 10 - ) + prior, + n_loop_training=10, + n_loop_production = 10, + n_local_steps=200, + n_global_steps=200, + n_chains=1000, + n_epochs=200, + learning_rate = 0.001, + momentum = 0.9, + batch_size = 50000, + use_global=True, + keep_quantile=0., + train_thinning = 40, + local_sampler_arg = local_sampler_arg, + ) jim.maximize_likleihood([prior.xmin, prior.xmax]) jim.sample(jax.random.PRNGKey(42)) diff --git a/src/jimgw/jim.py b/src/jimgw/jim.py index 382ed20a..70fb4fc7 100644 --- a/src/jimgw/jim.py +++ b/src/jimgw/jim.py @@ -32,7 +32,9 @@ def posterior(x: Array, data:dict): self.posterior = posterior - local_sampler = MALA(posterior, True, {"step_size": 1e-2}) # Remember to add routine to find automated mass matrix + local_sampler_arg = kwargs.get("local_sampler_arg", {}) + + local_sampler = MALA(posterior, True, local_sampler_arg) # Remember to add routine to find automated mass matrix model = MaskedCouplingRQSpline(self.Prior.n_dim, num_layers, hidden_size, num_bins, rng_key_set[-1]) self.Sampler = Sampler( From 79efbe08c12141c7ead6a1ef45128db849586d8b Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Sat, 15 Jul 2023 09:24:49 -0400 Subject: [PATCH 223/300] Update --- src/jimgw/jim.py | 49 ++++++++++++++++++++++++++++++----------- src/jimgw/likelihood.py | 1 - 2 files changed, 36 insertions(+), 14 deletions(-) diff --git a/src/jimgw/jim.py b/src/jimgw/jim.py index 70fb4fc7..e65571be 100644 --- a/src/jimgw/jim.py +++ b/src/jimgw/jim.py @@ -45,19 +45,6 @@ def posterior(x: Array, data:dict): model, **kwargs) - # n_loop_training=n_loop_training, - # n_loop_production = n_loop_production, - # n_local_steps=n_local_steps, - # n_global_steps=n_global_steps, - # n_chains=n_chains, - # n_epochs=num_epochs, - # learning_rate=learning_rate, - # momentum=momentum, - # batch_size=batch_size, - # use_global=True, - # keep_quantile=0., - # train_thinning = 40, - # ) def maximize_likleihood(self, bounds: tuple[Array,Array],set_nwalkers: int = 100, n_loops: int = 2000, seed = 92348): bounds = jnp.array(bounds).T @@ -86,5 +73,41 @@ def sample(self, key: jax.random.PRNGKey, initial_guess = self.Prior.sample(key, self.Sampler.n_chains) self.Sampler.sample(initial_guess, None) + def print_summary(self): + r""" Generate summary of the run + + """ + + train_summary = self.Sampler.get_sampler_state(training=True) + production_summary = self.Sampler.get_sampler_state(training=False) + + training_chain: Array = train_summary["chain"] + training_log_prob: Array = train_summary["log_prob"] + training_local_acceptance: Array = train_summary["local_accs"] + training_global_acceptance: Array = train_summary["global_accs"] + training_loss: Array = train_summary["loss"] + + production_chain: Array = production_summary["chain"] + production_log_prob: Array = production_summary["log_prob"] + production_local_acceptance: Array = production_summary["local_accs"] + production_global_acceptance: Array = production_summary["global_accs"] + + print("Training summary") + print('=' * 10) + for index in range(self.Prior.naming.shape[0]): + print(f"{self.Prior.naming[index]}: {training_chain[:, :, index].mean():.3f} +/- {training_chain[:, :, index].std():.3f}") + print(f"Log probability: {training_log_prob.mean():.3f} +/- {training_log_prob.std():.3f}") + print(f"Local acceptance: {training_local_acceptance.mean():.3f} +/- {training_local_acceptance.std():.3f}") + print(f"Global acceptance: {training_global_acceptance.mean():.3f} +/- {training_global_acceptance.std():.3f}") + print(f"Max loss: {training_loss.max():.3f}, Min loss: {training_loss.min():.3f}") + + print("Production summary") + print('=' * 10) + for index in range(self.Prior.naming.shape[0]): + print(f"{self.Prior.naming[index]}: {production_chain[:, :, index].mean():.3f} +/- {production_chain[:, :, index].std():.3f}") + print(f"Log probability: {production_log_prob.mean():.3f} +/- {production_log_prob.std():.3f}") + print(f"Local acceptance: {production_local_acceptance.mean():.3f} +/- {production_local_acceptance.std():.3f}") + print(f"Global acceptance: {production_global_acceptance.mean():.3f} +/- {production_global_acceptance.std():.3f}") + def plot(self): pass \ No newline at end of file diff --git a/src/jimgw/likelihood.py b/src/jimgw/likelihood.py index ac447a45..1f7f8723 100644 --- a/src/jimgw/likelihood.py +++ b/src/jimgw/likelihood.py @@ -8,7 +8,6 @@ import jax.numpy as jnp from astropy.time import Time - class LikelihoodBase(ABC): """Base class for likelihoods. Note that this likelihood class should work for a somehwat general class of problems. From c5d4ade35a8c99377e7fda93fa56e061a7b9b3d5 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Sat, 15 Jul 2023 10:32:50 -0400 Subject: [PATCH 224/300] Tweaked prior transforms --- example/GW150914.py | 7 +++++-- src/jimgw/jim.py | 6 +++--- src/jimgw/prior.py | 21 ++++++++++++++------- 3 files changed, 22 insertions(+), 12 deletions(-) diff --git a/example/GW150914.py b/example/GW150914.py index dd3be85a..5eb3db85 100644 --- a/example/GW150914.py +++ b/example/GW150914.py @@ -29,7 +29,10 @@ prior = Uniform( xmin = [10, 0.125, -1., -1., 0., -0.05, 0., -1, 0., 0.,-1.], xmax = [80., 1., 1., 1., 2000., 0.05, 2*jnp.pi, 1., jnp.pi, 2*jnp.pi, 1.], - naming = ["M_c", "q", "s1_z", "s2_z", "d_L", "t_c", "phase_c", "iota", "psi", "ra", "dec"] + naming = ["M_c", "q", "s1_z", "s2_z", "d_L", "t_c", "phase_c", "iota", "psi", "ra", "dec"], + transforms = {"q": lambda q: q/(1+q)**2, + "iota": lambda iota: jnp.arccos(iota), + "dec": lambda dec: jnp.arcsin(dec)} ) mass_matrix = jnp.eye(11) @@ -39,7 +42,7 @@ jim = Jim(likelihood, prior, - n_loop_training=10, + n_loop_training=15, n_loop_production = 10, n_local_steps=200, n_global_steps=200, diff --git a/src/jimgw/jim.py b/src/jimgw/jim.py index e65571be..2f06d7ea 100644 --- a/src/jimgw/jim.py +++ b/src/jimgw/jim.py @@ -81,13 +81,13 @@ def print_summary(self): train_summary = self.Sampler.get_sampler_state(training=True) production_summary = self.Sampler.get_sampler_state(training=False) - training_chain: Array = train_summary["chain"] + training_chain: Array = train_summary["chains"] training_log_prob: Array = train_summary["log_prob"] training_local_acceptance: Array = train_summary["local_accs"] training_global_acceptance: Array = train_summary["global_accs"] - training_loss: Array = train_summary["loss"] + training_loss: Array = train_summary["loss_vals"] - production_chain: Array = production_summary["chain"] + production_chain: Array = production_summary["chains"] production_log_prob: Array = production_summary["log_prob"] production_local_acceptance: Array = production_summary["local_accs"] production_global_acceptance: Array = production_summary["global_accs"] diff --git a/src/jimgw/prior.py b/src/jimgw/prior.py index 08d12306..043ac345 100644 --- a/src/jimgw/prior.py +++ b/src/jimgw/prior.py @@ -12,17 +12,24 @@ class Prior(Distribution): """ naming: list[str] - transforms: list[Callable] = field(default_factory=list) + transforms: list[Callable] = field(default_factory=dict) @property def n_dim(self): return len(self.naming) - def __init__(self, naming: list[str], transforms: list[Callable] = []): + def __init__(self, naming: list[str], transforms: dict[Callable] = {}): self.naming = naming - self.transforms = transforms - - def transform(self, x: Array): + self.transforms = [] + for name in naming: + if name in transforms: + self.transforms.append(transforms[name]) + else: + self.transforms.append(lambda x: x) + + def transform(self, x: Array) -> Array: + for i,transform in enumerate(self.transforms): + x = x.at[i].set(transform(x[i])) return x @@ -31,8 +38,8 @@ class Uniform(Prior): xmin: Array xmax: Array - def __init__(self, xmin: Union[float,Array], xmax: Union[float,Array], naming: list[str]): - super().__init__(naming) + def __init__(self, xmin: Union[float,Array], xmax: Union[float,Array], **kwargs): + super().__init__(kwargs.get("naming"), kwargs.get("transforms")) self.xmax = jnp.array(xmax) self.xmin = jnp.array(xmin) From 6da5fcba7008982e66b0c5b6ab590c2dd2264202 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Sat, 15 Jul 2023 14:52:21 -0400 Subject: [PATCH 225/300] Update GW150914.py example --- example/GW150914.py | 191 ++------------------------------------------ 1 file changed, 5 insertions(+), 186 deletions(-) diff --git a/example/GW150914.py b/example/GW150914.py index 5eb3db85..71de6d2b 100644 --- a/example/GW150914.py +++ b/example/GW150914.py @@ -42,12 +42,12 @@ jim = Jim(likelihood, prior, - n_loop_training=15, + n_loop_training=10, n_loop_production = 10, - n_local_steps=200, - n_global_steps=200, - n_chains=1000, - n_epochs=200, + n_local_steps=300, + n_global_steps=300, + n_chains=500, + n_epochs=300, learning_rate = 0.001, momentum = 0.9, batch_size = 50000, @@ -59,184 +59,3 @@ jim.maximize_likleihood([prior.xmin, prior.xmax]) jim.sample(jax.random.PRNGKey(42)) - -########################################### -######## Set up the likelihood ############ -########################################### - -# H1 = get_H1() -# H1_response = make_detector_response(H1[0], H1[1]) -# L1 = get_L1() -# L1_response = make_detector_response(L1[0], L1[1]) - -# trigger_time = 1126259462.4 -# duration = 4 -# post_trigger_duration = 2 -# epoch = duration - post_trigger_duration -# gmst = Time(trigger_time, format='gps').sidereal_time('apparent', 'greenwich').rad -# f_ref = 20 - -# def LogLikelihood(theta): -# theta_waveform = theta[:8] -# theta_waveform = theta_waveform.at[5].set(0) -# ra = theta[9] -# dec = theta[10] -# hp_test, hc_test = gen_IMRPhenomD_polar(H1_frequency, theta_waveform, f_ref) -# align_time = jnp.exp(-1j*2*jnp.pi*H1_frequency*(epoch+theta[5])) -# h_test_H1 = H1_response(H1_frequency, hp_test, hc_test, ra, dec, gmst, theta[8]) * align_time -# h_test_L1 = L1_response(L1_frequency, hp_test, hc_test, ra, dec, gmst, theta[8]) * align_time -# df = H1_frequency[1] - H1_frequency[0] -# match_filter_SNR_H1 = 4*jnp.sum((jnp.conj(h_test_H1)*H1_data)/H1_psd*df).real -# match_filter_SNR_L1 = 4*jnp.sum((jnp.conj(h_test_L1)*L1_data)/L1_psd*df).real -# optimal_SNR_H1 = 4*jnp.sum((jnp.conj(h_test_H1)*h_test_H1)/H1_psd*df).real -# optimal_SNR_L1 = 4*jnp.sum((jnp.conj(h_test_L1)*h_test_L1)/L1_psd*df).real - -# return (match_filter_SNR_H1-optimal_SNR_H1/2) + (match_filter_SNR_L1-optimal_SNR_L1/2) - -# # prior on the waveform parameters -# # these are Mc, eta, s1, s2, dist, tc, phic, ra, dec, psi -# prior_range = jnp.array([[20.,50.],[0.20,0.25],[-0.9,0.9], -# [-0.9,0.9],[100,3000],[-1.0,1.0], -# [0,2*np.pi],[0.001,np.pi],[0.001,np.pi], -# [0.001,2*np.pi],[-jnp.pi/2,jnp.pi/2]]) - - -# ########################################### -# ##### Optimize to find high L point ####### -# ########################################### - -# set_nwalkers = 100 -# initial_guess = jax.random.uniform(jax.random.PRNGKey(42), (set_nwalkers,11,), -# minval=prior_range[:,0], maxval=prior_range[:,1]) - -# y = lambda x: -LogLikelihood(x) -# y = jax.jit(jax.vmap(y)) -# print("Compiling likelihood function") -# y(initial_guess) -# print("Done compiling") - -# print("Starting the optimizer") -# optimizer = EvolutionaryOptimizer(11, verbose = True) -# state = optimizer.optimize(y, prior_range, n_loops=2000) -# best_fit = optimizer.get_result()[0] - -# print(best_fit) - -# data_list = [H1_data, L1_data] -# psd_list = [H1_psd, L1_psd] -# response_list = [H1_response, L1_response] - - -# print("Constructing the heterodyned likelihood function") -# logL = make_heterodyne_likelihood_mutliple_detector(data_list, psd_list, response_list, gen_IMRPhenomD_polar, -# best_fit, H1_frequency, gmst, epoch, f_ref, 301) - - -# ########################################### -# ####### Finally, we can sample! ########### -# ########################################### - -# n_dim = 11 -# n_chains = 1000 -# n_loop_training = 10 -# n_loop_production = 10 -# n_local_steps = 100 -# n_global_steps = 100 -# learning_rate = 0.001 -# max_samples = 100000 -# momentum = 0.9 -# num_epochs = 100 -# batch_size = 50000 - -# guess_param = best_fit - -# guess_param = np.array(jnp.repeat(guess_param[None,:],int(n_chains),axis=0)*np.random.normal(loc=1,scale=0.1,size=(int(n_chains),n_dim))) -# guess_param[guess_param[:,1]>0.25,1] = 0.249 -# guess_param[:,6] = (guess_param[:,6]%(2*jnp.pi)) -# guess_param[:,7] = (guess_param[:,7]%(jnp.pi)) -# guess_param[:,8] = (guess_param[:,8]%(jnp.pi)) -# guess_param[:,9] = (guess_param[:,9]%(2*jnp.pi)) - - -# print("Preparing RNG keys") -# rng_key_set = initialize_rng_keys(n_chains, seed=42) - -# print("Initializing MCMC model and normalizing flow model.") - -# prior_range = jnp.array([[10,80],[0.125,1.0],[-1,1],[-1,1], -# [0,2000],[-0.05,0.05],[0,2*np.pi],[-1,1], -# [0,np.pi],[0,2*np.pi],[-1,1]]) - - -# initial_position = jax.random.uniform(rng_key_set[0], shape=(int(n_chains), n_dim)) * 1 -# for i in range(n_dim): -# initial_position = initial_position.at[:,i].set(initial_position[:,i]*(prior_range[i,1]-prior_range[i,0])+prior_range[i,0]) - -# from ripple import Mc_eta_to_ms -# m1,m2 = jax.vmap(Mc_eta_to_ms)(guess_param[:,:2]) -# q = m2/m1 - -# initial_position = initial_position.at[:,0].set(guess_param[:,0]) - -# from astropy.cosmology import Planck18 as cosmo - -# z = np.linspace(0.002,3,10000) -# dL = cosmo.luminosity_distance(z).value -# dVdz = cosmo.differential_comoving_volume(z).value - -# def top_hat(x): -# output = 0. -# for i in range(n_dim): -# output = jax.lax.cond(x[i]>=prior_range[i,0], lambda: output, lambda: -jnp.inf) -# output = jax.lax.cond(x[i]<=prior_range[i,1], lambda: output, lambda: -jnp.inf) -# return output+jnp.log(jnp.interp(x[4],dL,dVdz)) - -# def posterior(theta): -# q = theta[1] -# theta = theta.at[7].set(jnp.arcsin(jnp.sin(theta[7]/2*jnp.pi))*2/jnp.pi) -# theta = theta.at[10].set(jnp.arcsin(jnp.sin(theta[10]/2*jnp.pi))*2/jnp.pi) -# iota = jnp.arccos(theta[7]) -# dec = jnp.arcsin(theta[10]) -# prior = top_hat(theta) -# theta = theta.at[1].set(q/(1+q)**2) # convert q to eta -# theta = theta.at[7].set(iota) # convert cos iota to iota -# theta = theta.at[10].set(dec) # convert cos dec to dec -# return logL(theta) + prior - -# posterior_new = lambda theta, data: posterior(theta) - -# model = MaskedCouplingRQSpline(n_dim, 10, [128,128], 8, jax.random.PRNGKey(10)) - -# print("Initializing sampler class") - -# mass_matrix = jnp.eye(n_dim) -# mass_matrix = mass_matrix.at[1,1].set(1e-3) -# mass_matrix = mass_matrix.at[5,5].set(1e-3) - -# local_sampler = MALA(posterior_new, True, {"step_size": mass_matrix*3e-3}) -# print("Running sampler") - -# nf_sampler = Sampler( -# n_dim, -# rng_key_set, -# None, -# local_sampler, -# model, -# n_loop_training=n_loop_training, -# n_loop_production = n_loop_production, -# n_local_steps=n_local_steps, -# n_global_steps=n_global_steps, -# n_chains=n_chains, -# n_epochs=num_epochs, -# learning_rate=learning_rate, -# momentum=momentum, -# batch_size=batch_size, -# use_global=True, -# keep_quantile=0., -# train_thinning = 40, -# ) - -# nf_sampler.sample(initial_position, None) -# chains, log_prob, local_accs, global_accs = nf_sampler.get_sampler_state().values() -# print("Script complete and took: {} minutes".format((time.time()-total_time_start)/60)) -# # np.savez('GW150914.npz', chains=chains, log_prob=log_prob, local_accs=local_accs, global_accs=global_accs) \ No newline at end of file From cd48fb15f8dcc914c7b84524944239f4300f12b7 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Sat, 15 Jul 2023 15:04:37 -0400 Subject: [PATCH 226/300] Remove empty script --- example/waveform_comparison.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 example/waveform_comparison.py diff --git a/example/waveform_comparison.py b/example/waveform_comparison.py deleted file mode 100644 index e69de29b..00000000 From 2690382dc167c46a0b76a0e668d0316e3f74ab5d Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Sat, 15 Jul 2023 15:12:48 -0400 Subject: [PATCH 227/300] Realign doc string style --- src/jimgw/detector.py | 39 ++++++++++++++++++++++++++------------- src/jimgw/jim.py | 6 ++++-- src/jimgw/likelihood.py | 21 ++++++++++++++------- src/jimgw/prior.py | 3 ++- 4 files changed, 46 insertions(+), 23 deletions(-) diff --git a/src/jimgw/detector.py b/src/jimgw/detector.py index 3aecd750..e72c506b 100644 --- a/src/jimgw/detector.py +++ b/src/jimgw/detector.py @@ -11,14 +11,16 @@ DEG_TO_RAD = jnp.pi/180 def np2(x): - """Returns the next power of two as big as or larger than x.""" + """ + Returns the next power of two as big as or larger than x.""" p = 1 while p < x: p = p << 1 return p class Detector(ABC): - """ Base class for all detectors. + """ + Base class for all detectors. """ @@ -28,12 +30,14 @@ def load_data(self, data): @abstractmethod def fd_response(self, frequency: Array, h: Array, params: dict) -> Array: - """Modulate the waveform in the sky frame by the detector response in the frequency domain.""" + """ + Modulate the waveform in the sky frame by the detector response in the frequency domain.""" pass @abstractmethod def td_response(self, time: Array, h: Array, params: dict) -> Array: - """Modulate the waveform in the sky frame by the detector response in the time domain.""" + """ + Modulate the waveform in the sky frame by the detector response in the time domain.""" pass class GroundBased2G(Detector): @@ -74,7 +78,8 @@ def load_data(self, trigger_time:float, f_max: float, psd_pad: int = 16, tukey_alpha: float = 0.2) -> None: - """Load data from the detector. + """ + Load data from the detector. Parameters ---------- @@ -117,7 +122,8 @@ def load_data(self, trigger_time:float, self.psd = psd[(freq>f_min)&(freq Array: - """Modulate the waveform in the sky frame by the detector response in the frequency domain.""" + """ + Modulate the waveform in the sky frame by the detector response in the frequency domain.""" ra, dec, psi, gmst = params['ra'], params['dec'], params['psi'], params['gmst'] antenna_pattern = self.antenna_pattern(ra, dec, psi, gmst) timeshift = self.delay_from_geocenter(ra, dec, gmst) @@ -125,12 +131,14 @@ def fd_response(self, frequency: Array, h_sky: dict, params: Array) -> Array: return jnp.sum(jnp.stack(jax.tree_util.tree_leaves(h_detector)),axis=0) def td_response(self, time: Array, h: Array, params: Array) -> Array: - """Modulate the waveform in the sky frame by the detector response in the time domain.""" + """ + Modulate the waveform in the sky frame by the detector response in the time domain.""" pass @staticmethod def _get_arm(lat, lon, tilt, azimuth): - """Construct detector-arm vectors in Earth-centric Cartesian coordinates. + """ + Construct detector-arm vectors in Earth-centric Cartesian coordinates. Arguments --------- @@ -155,7 +163,8 @@ def _get_arm(lat, lon, tilt, azimuth): @property def arms(self): - """Detector arm vectors (x, y). + """ + Detector arm vectors (x, y). """ x = self._get_arm(self.latitude, self.longitude, self.xarm_tilt, self.xarm_azimuth) y = self._get_arm(self.latitude, self.longitude, self.yarm_tilt, self.yarm_azimuth) @@ -163,7 +172,8 @@ def arms(self): @property def tensor(self): - """Detector tensor defining the strain measurement. + """ + Detector tensor defining the strain measurement. """ #TODO: this could easily be generalized for other detector geometries arm1, arm2 = self.arms @@ -172,7 +182,8 @@ def tensor(self): @property def vertex(self): - """Detector vertex coordinates in the reference celestial frame. Based + """ + Detector vertex coordinates in the reference celestial frame. Based on arXiv:gr-qc/0008066 Eqs. (B11-B13) except for a typo in the definition of the local radius; see Section 2.1 of LIGO-T980044-10. """ @@ -189,7 +200,8 @@ def vertex(self): return jnp.array([x, y, z]) def delay_from_geocenter(self, ra: float, dec: float, gmst: float) -> float: - """ Calculate time delay between two detectors in geocentric + """ + Calculate time delay between two detectors in geocentric coordinates based on XLALArrivaTimeDiff in TimeDelay.c https://lscsoft.docs.ligo.org/lalsuite/lal/group___time_delay__h.html @@ -217,7 +229,8 @@ def delay_from_geocenter(self, ra: float, dec: float, gmst: float) -> float: return jnp.dot(omega, delta_d) / C_SI def antenna_pattern(self, ra:float, dec:float, psi:float, gmst:float) -> dict: - """Computes {name} antenna patterns for {modes} polarizations + """ + Computes {name} antenna patterns for {modes} polarizations at the specified sky location, orientation and GMST. In the long-wavelength approximation, the antenna pattern for a diff --git a/src/jimgw/jim.py b/src/jimgw/jim.py index 2f06d7ea..b5a306fe 100644 --- a/src/jimgw/jim.py +++ b/src/jimgw/jim.py @@ -10,7 +10,8 @@ import jax.numpy as jnp class Jim(object): - """ Master class for interfacing with flowMC + """ + Master class for interfacing with flowMC """ @@ -74,7 +75,8 @@ def sample(self, key: jax.random.PRNGKey, self.Sampler.sample(initial_guess, None) def print_summary(self): - r""" Generate summary of the run + """ + Generate summary of the run """ diff --git a/src/jimgw/likelihood.py b/src/jimgw/likelihood.py index 1f7f8723..97cd5d5f 100644 --- a/src/jimgw/likelihood.py +++ b/src/jimgw/likelihood.py @@ -9,7 +9,8 @@ from astropy.time import Time class LikelihoodBase(ABC): - """Base class for likelihoods. + """ + Base class for likelihoods. Note that this likelihood class should work for a somehwat general class of problems. In light of that, this class would be somewhat abstract, but the idea behind it is this handles two main components of a likelihood: the data and the model. @@ -20,19 +21,22 @@ class LikelihoodBase(ABC): @property def model(self): - """The model for the likelihood. + """ + The model for the likelihood. """ return self._model @property def data(self): - """The data for the likelihood. + """ + The data for the likelihood. """ return self._data @abstractmethod def evaluate(self, params) -> float: - """Evaluate the likelihood for a given set of parameters. + """ + Evaluate the likelihood for a given set of parameters. """ raise NotImplementedError @@ -59,18 +63,21 @@ def __init__(self, @property def epoch(self): - """The epoch of the data. + """ + The epoch of the data. """ return self.duration - self.post_trigger_duration @property def ifos(self): - """The interferometers for the likelihood. + """ + The interferometers for the likelihood. """ return [detector.name for detector in self.detectors] def evaluate(self, params: Array, data: dict) -> float: # TODO: Test whether we need to pass data in or with class changes is fine. - """Evaluate the likelihood for a given set of parameters. + """ + Evaluate the likelihood for a given set of parameters. """ log_likelihood = 0 frequencies = self.detectors[0].frequencies diff --git a/src/jimgw/prior.py b/src/jimgw/prior.py index 043ac345..d7c31578 100644 --- a/src/jimgw/prior.py +++ b/src/jimgw/prior.py @@ -6,7 +6,8 @@ from dataclasses import field class Prior(Distribution): - r"""A thin wrapper build on top of flowMC distributions to do book keeping. + """ + A thin wrapper build on top of flowMC distributions to do book keeping. Should not be used directly since it does not implement any of the real method. """ From 41113bded6414ebf1748d81587a84a0e6cfdcbe3 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Sat, 15 Jul 2023 15:44:39 -0400 Subject: [PATCH 228/300] Add mkdocs stuff --- docs/index.md | 17 +++++++++++++++++ docs/requirements.txt | 5 +++++ mkdocs.yml | 1 + readthedocs.yml | 27 +++++++++++++++++++++++++++ 4 files changed, 50 insertions(+) create mode 100644 docs/index.md create mode 100644 docs/requirements.txt create mode 100644 mkdocs.yml create mode 100644 readthedocs.yml diff --git a/docs/index.md b/docs/index.md new file mode 100644 index 00000000..000ea345 --- /dev/null +++ b/docs/index.md @@ -0,0 +1,17 @@ +# Welcome to MkDocs + +For full documentation visit [mkdocs.org](https://www.mkdocs.org). + +## Commands + +* `mkdocs new [dir-name]` - Create a new project. +* `mkdocs serve` - Start the live-reloading docs server. +* `mkdocs build` - Build the documentation site. +* `mkdocs -h` - Print help message and exit. + +## Project layout + + mkdocs.yml # The configuration file. + docs/ + index.md # The documentation homepage. + ... # Other markdown pages, images and other files. diff --git a/docs/requirements.txt b/docs/requirements.txt new file mode 100644 index 00000000..baa511ee --- /dev/null +++ b/docs/requirements.txt @@ -0,0 +1,5 @@ +mkdocs==1.3.0 # Main documentation generator. +mkdocs-material==9.1.8 # Theme +pymdown-extensions==9.4 # Markdown extensions e.g. to handle LaTeX. +mkdocstrings==0.17.0 # Autogenerate documentation from docstrings. +mknotebooks==0.7.1 # Turn Jupyter Lab notebooks into webpages. \ No newline at end of file diff --git a/mkdocs.yml b/mkdocs.yml new file mode 100644 index 00000000..0d814838 --- /dev/null +++ b/mkdocs.yml @@ -0,0 +1 @@ +site_name: jim diff --git a/readthedocs.yml b/readthedocs.yml new file mode 100644 index 00000000..7ec731f8 --- /dev/null +++ b/readthedocs.yml @@ -0,0 +1,27 @@ +# Read the Docs configuration file for MkDocs projects +# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details + +# Required +version: 2 + +# Set the version of Python and other tools you might need +build: + os: ubuntu-22.04 + tools: + python: + version: + - "3.9" + - "3.10" + - "3.11" + +mkdocs: + configuration: mkdocs.yml + +# Optionally declare the Python requirements required to build your docs +python: + version: + - "3.9" + - "3.10" + - "3.11" + install: + - requirements: docs/requirements.txt \ No newline at end of file From a0f100e4058bae9ff7a77497fb448d1bdde1f220 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Sat, 15 Jul 2023 16:12:02 -0400 Subject: [PATCH 229/300] Add theme --- docs/requirements.txt | 8 ++--- mkdocs.yml | 77 +++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 81 insertions(+), 4 deletions(-) diff --git a/docs/requirements.txt b/docs/requirements.txt index baa511ee..e3c5cc2c 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -1,5 +1,5 @@ -mkdocs==1.3.0 # Main documentation generator. -mkdocs-material==9.1.8 # Theme -pymdown-extensions==9.4 # Markdown extensions e.g. to handle LaTeX. -mkdocstrings==0.17.0 # Autogenerate documentation from docstrings. +mkdocs==1.4.3 # Main documentation generator. +mkdocs-material==9.1.18 # Theme +pymdown-extensions==10.1 # Markdown extensions e.g. to handle LaTeX. +mkdocstrings==0.22.0 # Autogenerate documentation from docstrings. mknotebooks==0.7.1 # Turn Jupyter Lab notebooks into webpages. \ No newline at end of file diff --git a/mkdocs.yml b/mkdocs.yml index 0d814838..c96ec4b1 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -1 +1,78 @@ site_name: jim +site_description: A JAX-based gravitational-wave inference toolkit +site_author: Kaze Wong +repo_url: https://github.com/kazewong/jim +repo_name: kazewong/jim + +theme: + name: material + features: + - navigation.sections # Sections are included in the navigation on the left. + - toc.integrate # Table of contents is integrated on the left; does not appear separately on the right. + - header.autohide # header disappears as you scroll + palette: + # Light mode / dark mode + # We deliberately don't automatically use `media` to check a user's preferences. We default to light mode as + # (a) it looks more professional, and (b) is more obvious about the fact that it offers a (dark mode) toggle. + - scheme: default + primary: blue + accent: deep purple + toggle: + icon: material/brightness-5 + name: Dark mode + - scheme: slate + primary: black + accent: amber + toggle: + icon: material/brightness-2 + name: Light mode + + twitter_name: "@physicskaze" + twitter_url: "https://twitter.com/physicskaze" + +markdown_extensions: + - pymdownx.arithmatex: # Render LaTeX via MathJax + generic: true + - pymdownx.superfences # Seems to enable syntax highlighting when used with the Material theme. + - pymdownx.details + - pymdownx.snippets: # Include one Markdown file into another + base_path: docs + - admonition + - toc: + permalink: "¤" # Adds a clickable permalink to each section heading + toc_depth: 4 + +plugins: + - search # default search plugin; needs manually re-enabling when using any other plugins + - autorefs # Cross-links to headings + # - mknotebooks # Jupyter notebooks + - mkdocstrings: + handlers: + python: + setup_commands: + - import pytkdocs_tweaks + - pytkdocs_tweaks.main() + - import jaxtyping + - jaxtyping.set_array_name_format("array") + + selection: + inherited_members: true # Allow looking up inherited methods + rendering: + show_root_heading: true # actually display anything at all... + show_root_full_path: true # display "diffrax.asdf" not just "asdf" + show_if_no_docstring: true + show_signature_annotations: true + show_source: false # don't include source code + members_order: source # order methods according to their order of definition in the source code, not alphabetical order + heading_level: 4 + +nav: + - Home: index.md + # Getting Started: + # - Installation: installation.md + # - Tutorial: tutorial.md + # - Examples: examples.md + # API: + # - Reference: reference.md + # - Modules: modules.md + # - Glossary: glossary.md From 742885e61534271cb3bcb1e1da7239f67ca49fb1 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Sat, 15 Jul 2023 16:27:35 -0400 Subject: [PATCH 230/300] Update --- docs/examples | 1 + docs/requirements.txt | 10 +- example/GW150914_old.py | 254 ------------------------------------- example/GW170817.py | 268 --------------------------------------- example/Injection_new.py | 206 ------------------------------ mkdocs.yml | 4 +- 6 files changed, 9 insertions(+), 734 deletions(-) create mode 120000 docs/examples delete mode 100644 example/GW150914_old.py delete mode 100644 example/GW170817.py delete mode 100644 example/Injection_new.py diff --git a/docs/examples b/docs/examples new file mode 120000 index 00000000..09da01e7 --- /dev/null +++ b/docs/examples @@ -0,0 +1 @@ +../example/ \ No newline at end of file diff --git a/docs/requirements.txt b/docs/requirements.txt index e3c5cc2c..02041f27 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -1,5 +1,5 @@ -mkdocs==1.4.3 # Main documentation generator. -mkdocs-material==9.1.18 # Theme -pymdown-extensions==10.1 # Markdown extensions e.g. to handle LaTeX. -mkdocstrings==0.22.0 # Autogenerate documentation from docstrings. -mknotebooks==0.7.1 # Turn Jupyter Lab notebooks into webpages. \ No newline at end of file +mkdocs==1.4.3 # Main documentation generator. +mkdocs-material==9.1.18 # Theme +pymdown-extensions==10.1 # Markdown extensions e.g. to handle LaTeX. +mkdocstrings==0.22.0 # Autogenerate documentation from docstrings. +mkdocs-jupyter-0.24.2 # Turn Jupyter Lab notebooks into webpages. \ No newline at end of file diff --git a/example/GW150914_old.py b/example/GW150914_old.py deleted file mode 100644 index 6ed9b090..00000000 --- a/example/GW150914_old.py +++ /dev/null @@ -1,254 +0,0 @@ -import numpy as np -import matplotlib.pyplot as plt -import time -import jax.numpy as jnp -import jax - -from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar -from jimgw.PE.detector_preset import * -from jimgw.PE.heterodyneLikelihood import make_heterodyne_likelihood_mutliple_detector -from jimgw.PE.detector_projection import make_detector_response - -from flowMC.nfmodel.rqSpline import MaskedCouplingRQSpline -from flowMC.sampler.Sampler import Sampler -from flowMC.sampler.MALA import MALA -from flowMC.utils.PRNG_keys import initialize_rng_keys -from flowMC.nfmodel.utils import * -from flowMC.utils.EvolutionaryOptimizer import EvolutionaryOptimizer - -from astropy.time import Time - -# We only use this to grab the data -from gwpy.timeseries import TimeSeries -from scipy.signal.windows import tukey - -########################################### -########## First we grab data ############# -########################################### - -total_time_start = time.time() - -# first, fetch a 4s segment centered on GW150914 -gps = 1126259462.4 -start = gps - 2 -end = gps + 2 -fmin = 20 -fmax = 1024 - -ifos = ['H1', 'L1'] - -print("Fetching data...") -data_td_dict = {ifo: TimeSeries.fetch_open_data(ifo, start, end) for ifo in ifos} -print("Finished fetching data.") - -# GWpy normalizes the FFT like an instrumentalist would, which is not what we -# want for the likelihoood, so fix this manually -n = len(data_td_dict[ifos[0]]) -delta_t = data_td_dict[ifos[0]].dt.value - -print("Computing the FFTs...") -# For BNS 0.00625 is a good choice for the tukey window -# For BBH 0.2 is a good choice for the tukey window -data_fd_dict = {i: np.fft.rfft(np.array(d)*tukey(n, 0.2))*delta_t - for i, d in data_td_dict.items()} - -freq = np.fft.rfftfreq(n, delta_t) - -# # We take a bit of extra data to compute PSDs -start_psd = int(gps) - 16 -end_psd = int(gps) + 16 - -print("Fetching PSD data...") -psd_data_td_dict = {ifo: TimeSeries.fetch_open_data(ifo, start_psd, end_psd) for ifo in ifos} -psd_dict = {i: d.psd(fftlength=4) for i, d in psd_data_td_dict.items()} -print("Finished generating data.") - -H1_frequency = np.array(freq[(freq>fmin)&(freqfmin)&(freqfmin)&(freqfmin)&(freqfmin)&(freqfmin)&(freq0.25,1] = 0.249 -guess_param[:,6] = (guess_param[:,6]%(2*jnp.pi)) -guess_param[:,7] = (guess_param[:,7]%(jnp.pi)) -guess_param[:,8] = (guess_param[:,8]%(jnp.pi)) -guess_param[:,9] = (guess_param[:,9]%(2*jnp.pi)) - - -print("Preparing RNG keys") -rng_key_set = initialize_rng_keys(n_chains, seed=42) - -print("Initializing MCMC model and normalizing flow model.") - -prior_range = jnp.array([[10,80],[0.125,1.0],[-1,1],[-1,1], - [0,2000],[-0.05,0.05],[0,2*np.pi],[-1,1], - [0,np.pi],[0,2*np.pi],[-1,1]]) - - -initial_position = jax.random.uniform(rng_key_set[0], shape=(int(n_chains), n_dim)) * 1 -for i in range(n_dim): - initial_position = initial_position.at[:,i].set(initial_position[:,i]*(prior_range[i,1]-prior_range[i,0])+prior_range[i,0]) - -from ripple import Mc_eta_to_ms -m1,m2 = jax.vmap(Mc_eta_to_ms)(guess_param[:,:2]) -q = m2/m1 - -initial_position = initial_position.at[:,0].set(guess_param[:,0]) - -from astropy.cosmology import Planck18 as cosmo - -z = np.linspace(0.002,3,10000) -dL = cosmo.luminosity_distance(z).value -dVdz = cosmo.differential_comoving_volume(z).value - -def top_hat(x): - output = 0. - for i in range(n_dim): - output = jax.lax.cond(x[i]>=prior_range[i,0], lambda: output, lambda: -jnp.inf) - output = jax.lax.cond(x[i]<=prior_range[i,1], lambda: output, lambda: -jnp.inf) - return output+jnp.log(jnp.interp(x[4],dL,dVdz)) - -def posterior(theta): - q = theta[1] - theta = theta.at[7].set(jnp.arcsin(jnp.sin(theta[7]/2*jnp.pi))*2/jnp.pi) - theta = theta.at[10].set(jnp.arcsin(jnp.sin(theta[10]/2*jnp.pi))*2/jnp.pi) - iota = jnp.arccos(theta[7]) - dec = jnp.arcsin(theta[10]) - prior = top_hat(theta) - theta = theta.at[1].set(q/(1+q)**2) # convert q to eta - theta = theta.at[7].set(iota) # convert cos iota to iota - theta = theta.at[10].set(dec) # convert cos dec to dec - return logL(theta) + prior - -posterior_new = lambda theta, data: posterior(theta) - -model = MaskedCouplingRQSpline(n_dim, 10, [128,128], 8, jax.random.PRNGKey(10)) - -print("Initializing sampler class") - -mass_matrix = jnp.eye(n_dim) -mass_matrix = mass_matrix.at[1,1].set(1e-3) -mass_matrix = mass_matrix.at[5,5].set(1e-3) - -local_sampler = MALA(posterior_new, True, {"step_size": mass_matrix*3e-3}) -print("Running sampler") - -nf_sampler = Sampler( - n_dim, - rng_key_set, - None, - local_sampler, - model, - n_loop_training=n_loop_training, - n_loop_production = n_loop_production, - n_local_steps=n_local_steps, - n_global_steps=n_global_steps, - n_chains=n_chains, - n_epochs=num_epochs, - learning_rate=learning_rate, - momentum=momentum, - batch_size=batch_size, - use_global=True, - keep_quantile=0., - train_thinning = 40, -) - -nf_sampler.sample(initial_position, None) -chains, log_prob, local_accs, global_accs = nf_sampler.get_sampler_state().values() -print("Script complete and took: {} minutes".format((time.time()-total_time_start)/60)) -# np.savez('GW150914.npz', chains=chains, log_prob=log_prob, local_accs=local_accs, global_accs=global_accs) \ No newline at end of file diff --git a/example/GW170817.py b/example/GW170817.py deleted file mode 100644 index 209c5f8c..00000000 --- a/example/GW170817.py +++ /dev/null @@ -1,268 +0,0 @@ -import numpy as np -import jax.numpy as jnp -import jax - -from lal import GreenwichMeanSiderealTime -from gwosc.datasets import event_gps -from gwpy.timeseries import TimeSeries -from scipy.signal.windows import tukey -from scipy.interpolate import interp1d - - -from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar -from jimgw.PE.detector_preset import * -from jimgw.PE.heterodyneLikelihood import make_heterodyne_likelihood_mutliple_detector -from jimgw.PE.detector_projection import make_detector_response - -from flowMC.nfmodel.rqSpline import RQSpline -from flowMC.sampler.MALA import MALA -from flowMC.sampler.Sampler import Sampler -from flowMC.utils.PRNG_keys import initialize_rng_keys -from flowMC.nfmodel.utils import * - -minimum_frequency = 23 -maximum_frequency = 700 - -trigger_time = event_gps("GW170817") -duration = 128 -post_trigger_duration = 32 -epoch = duration - post_trigger_duration -gmst = GreenwichMeanSiderealTime(trigger_time) -f_ref = minimum_frequency - -# H1_data = TimeSeries.read('/mnt/home/misi/projects/cbc_birefringence/GW170817/raw_data/H-H1_LOSC_CLN_4_V1-1187007040-2048.gwf','H1:LOSC-STRAIN') -# H1_data = H1_data[(H1_data.times.value >= (trigger_time-epoch)) & (H1_data.times.value <= (trigger_time+post_trigger_duration))] -# n = len(H1_data) -# dt = H1_data.dt.value -# H1_data = np.fft.rfft(H1_data.value*tukey(n, 0.2))/4096 -# H1_frequency = np.fft.rfftfreq(n, dt) -# H1_psd = np.genfromtxt('/mnt/home/misi/projects/cbc_birefringence/GW170817/psd_data/h1_psd.txt') -# H1_psd = interp1d(H1_psd[:,0], H1_psd[:,1], fill_value=np.inf,bounds_error=False)(H1_frequency[H1_frequency>minimum_frequency]) -# H1_data = H1_data[H1_frequency>minimum_frequency] -# H1_frequency = H1_frequency[H1_frequency>minimum_frequency] - -# L1_data = TimeSeries.read('/mnt/home/misi/projects/cbc_birefringence/GW170817/raw_data/L-L1_LOSC_CLN_4_V1-1187007040-2048.gwf','L1:LOSC-STRAIN') -# L1_data = L1_data[(L1_data.times.value >= (trigger_time-epoch)) & (L1_data.times.value <= (trigger_time+post_trigger_duration))] -# n = len(L1_data) -# dt = L1_data.dt.value -# L1_data = np.fft.rfft(L1_data.value*tukey(n, 0.2))/4096 -# L1_frequency = np.fft.rfftfreq(n, dt) -# L1_psd = np.genfromtxt('/mnt/home/misi/projects/cbc_birefringence/GW170817/psd_data/l1_psd.txt') -# L1_psd = interp1d(L1_psd[:,0], L1_psd[:,1], fill_value=np.inf,bounds_error=False)(L1_frequency[L1_frequency>minimum_frequency]) -# L1_data = L1_data[L1_frequency>minimum_frequency] -# L1_frequency = L1_frequency[L1_frequency>minimum_frequency] - -# V1_data = TimeSeries.read('/mnt/home/misi/projects/cbc_birefringence/GW170817/raw_data/V-V1_LOSC_CLN_4_V1-1187007040-2048.gwf','V1:LOSC-STRAIN') -# V1_data = V1_data[(V1_data.times.value >= (trigger_time-epoch)) & (V1_data.times.value <= (trigger_time+post_trigger_duration))] -# n = len(V1_data) -# dt = V1_data.dt.value -# V1_data = np.fft.rfft(V1_data.value*tukey(n, 0.2))/4096 -# V1_frequency = np.fft.rfftfreq(n, dt) -# V1_psd = np.genfromtxt('/mnt/home/misi/projects/cbc_birefringence/GW170817/psd_data/v1_psd.txt') -# V1_psd = interp1d(V1_psd[:,0], V1_psd[:,1], fill_value=np.inf,bounds_error=False)(V1_frequency[V1_frequency>minimum_frequency]) -# V1_data = V1_data[V1_frequency>minimum_frequency] -# V1_frequency = V1_frequency[V1_frequency>minimum_frequency] - -data = np.load('./data/GW170817_data.npz',allow_pickle=True) - - -H1_frequency = data['frequency'] -H1_data = data['data_dict'].tolist()['H1'][(H1_frequency>minimum_frequency)*(H1_frequencyminimum_frequency)*(H1_frequencyminimum_frequency)*(H1_frequencyminimum_frequency)*(L1_frequencyminimum_frequency)*(L1_frequencyminimum_frequency)*(L1_frequencyminimum_frequency)*(V1_frequencyminimum_frequency)*(V1_frequencyminimum_frequency)*(V1_frequency0.25,1] = 0.249 - - -print("Preparing RNG keys") -rng_key_set = initialize_rng_keys(n_chains, seed=42) - -print("Initializing MCMC model and normalizing flow model.") - -prior_range = jnp.array([[1.18,1.21],[0.125,1],[-0.3,0.3],[-0.3,0.3],[1,75],[-0.1,0.1],[0,2*np.pi],[-1,1],[0,np.pi],[0,2*np.pi],[-1,1]]) -# prior_range = jnp.array([[1.18,1.21],[0.2,0.25],[0.0,0.3],[0.0,0.3],[1,75],[-0.1,0.1],[0,2*np.pi],[0,np.pi],[0,np.pi],[0,2*np.pi],[-np.pi/2,np.pi/2]]) - -initial_position = jax.random.uniform(rng_key_set[0], shape=(int(n_chains), n_dim)) * 1 -for i in range(n_dim): - initial_position = initial_position.at[:,i].set(initial_position[:,i]*(prior_range[i,1]-prior_range[i,0])+prior_range[i,0]) - -from ripple import Mc_eta_to_ms -m1,m2 = jax.vmap(Mc_eta_to_ms)(guess_param[:,:2]) -q = m2/m1 - -# initial_position = initial_position.at[:,0].set(guess_param[:,0]) -# initial_position = initial_position.at[:,1].set(q) -# initial_position = initial_position.at[:,2].set(guess_param[:,2]) -# initial_position = initial_position.at[:,3].set(guess_param[:,3]) -# initial_position = initial_position.at[:,4].set(guess_param[:,4]) -initial_position = initial_position.at[:,5].set(guess_param[:,5]) -# initial_position = initial_position.at[:,6].set(guess_param[:,6]) -# initial_position = initial_position.at[:,7].set(jnp.cos(guess_param[:,7])) -# initial_position = initial_position.at[:,8].set(guess_param[:,8]) -# initial_position = initial_position.at[:,9].set(guess_param[:,9]) -# initial_position = initial_position.at[:,10].set(jnp.cos(guess_param[:,10])) - -from astropy.cosmology import Planck18 as cosmo - -z = np.linspace(0.0002,0.03,10000) -dL = cosmo.luminosity_distance(z).value -dVdz = cosmo.differential_comoving_volume(z).value - -def top_hat(x): - output = 0. - for i in range(n_dim): - output = jax.lax.cond(x[i]>=prior_range[i,0], lambda: output, lambda: -jnp.inf) - output = jax.lax.cond(x[i]<=prior_range[i,1], lambda: output, lambda: -jnp.inf) - return output+jnp.log(jnp.interp(x[4],dL,dVdz)) - -def log_likelihood(theta): - theta = theta.at[1].set(theta[1]/(1+theta[1])**2) # convert q to eta - theta = theta.at[7].set(jnp.arccos(theta[7])) # convert cos iota to iota - theta = theta.at[10].set(jnp.arcsin(theta[10])) # convert cos dec to dec - return logL(theta) - -def posterior(theta): - q = theta[1] - prior = top_hat(theta) - theta = theta.at[1].set(q/(1+q)**2) # convert q to eta - theta = theta.at[7].set(jnp.arccos(theta[7])) # convert cos iota to iota - theta = theta.at[10].set(jnp.arcsin(theta[10])) # convert cos dec to dec - - return logL(theta) + prior - -model = RQSpline(n_dim, 10, [128,128], 8) - -print("Initializing sampler class") - -posterior = posterior - -mass_matrix = jnp.eye(n_dim) -mass_matrix = mass_matrix.at[0,0].set(1e-5) -mass_matrix = mass_matrix.at[1,1].set(1e-4) -mass_matrix = mass_matrix.at[2,2].set(1e-3) -mass_matrix = mass_matrix.at[3,3].set(1e-3) -mass_matrix = mass_matrix.at[5,5].set(1e-5) -mass_matrix = mass_matrix.at[9,9].set(1e-2) -mass_matrix = mass_matrix.at[10,10].set(1e-2) - -local_sampler = MALA(posterior, True, {"step_size": mass_matrix*3e-3}) -print("Running sampler") - -nf_sampler = Sampler( - n_dim, - rng_key_set, - local_sampler, - posterior, - model, - n_loop_training=n_loop_training, - n_loop_production = n_loop_production, - n_local_steps=n_local_steps, - n_global_steps=n_global_steps, - n_chains=n_chains, - n_epochs=num_epochs, - learning_rate=learning_rate, - momentum=momentum, - batch_size=batch_size, - use_global=True, - keep_quantile=0., - train_thinning = 40, -) - -nf_sampler.sample(initial_position) -chains, log_prob, local_accs, global_accs = nf_sampler.get_sampler_state().values() diff --git a/example/Injection_new.py b/example/Injection_new.py deleted file mode 100644 index 3e82268a..00000000 --- a/example/Injection_new.py +++ /dev/null @@ -1,206 +0,0 @@ -import time -from jimgw.detector import H1, L1 -from jimgw.likelihood import TransientLikelihoodFD -from jimgw.waveform import RippleIMRPhenomD -import jax.numpy as jnp - -########################################### -########## First we grab data ############# -########################################### - -total_time_start = time.time() - -# first, fetch a 4s segment centered on GW150914 -gps = 1126259462.4 -start = gps - 2 -end = gps + 2 -fmin = 20. -fmax = 1024. - -ifos = ['H1', 'L1'] - -H1.load_data(gps, 2, 2, fmin, fmax, psd_pad=16, tukey_alpha=0.2) -L1.load_data(gps, 2, 2, fmin, fmax, psd_pad=16, tukey_alpha=0.2) - -likelihood = TransientLikelihoodFD([H1, L1], RippleIMRPhenomD(), gps, 4, 2) - -########################################### -######## Set up the likelihood ############ -########################################### - -# H1 = get_H1() -# H1_response = make_detector_response(H1[0], H1[1]) -# L1 = get_L1() -# L1_response = make_detector_response(L1[0], L1[1]) - -# trigger_time = 1126259462.4 -# duration = 4 -# post_trigger_duration = 2 -# epoch = duration - post_trigger_duration -# gmst = Time(trigger_time, format='gps').sidereal_time('apparent', 'greenwich').rad -# f_ref = 20 - -# def LogLikelihood(theta): -# theta_waveform = theta[:8] -# theta_waveform = theta_waveform.at[5].set(0) -# ra = theta[9] -# dec = theta[10] -# hp_test, hc_test = gen_IMRPhenomD_polar(H1_frequency, theta_waveform, f_ref) -# align_time = jnp.exp(-1j*2*jnp.pi*H1_frequency*(epoch+theta[5])) -# h_test_H1 = H1_response(H1_frequency, hp_test, hc_test, ra, dec, gmst, theta[8]) * align_time -# h_test_L1 = L1_response(L1_frequency, hp_test, hc_test, ra, dec, gmst, theta[8]) * align_time -# df = H1_frequency[1] - H1_frequency[0] -# match_filter_SNR_H1 = 4*jnp.sum((jnp.conj(h_test_H1)*H1_data)/H1_psd*df).real -# match_filter_SNR_L1 = 4*jnp.sum((jnp.conj(h_test_L1)*L1_data)/L1_psd*df).real -# optimal_SNR_H1 = 4*jnp.sum((jnp.conj(h_test_H1)*h_test_H1)/H1_psd*df).real -# optimal_SNR_L1 = 4*jnp.sum((jnp.conj(h_test_L1)*h_test_L1)/L1_psd*df).real - -# return (match_filter_SNR_H1-optimal_SNR_H1/2) + (match_filter_SNR_L1-optimal_SNR_L1/2) - -# # prior on the waveform parameters -# # these are Mc, eta, s1, s2, dist, tc, phic, ra, dec, psi -# prior_range = jnp.array([[20.,50.],[0.20,0.25],[-0.9,0.9], -# [-0.9,0.9],[100,3000],[-1.0,1.0], -# [0,2*np.pi],[0.001,np.pi],[0.001,np.pi], -# [0.001,2*np.pi],[-jnp.pi/2,jnp.pi/2]]) - - -# ########################################### -# ##### Optimize to find high L point ####### -# ########################################### - -# set_nwalkers = 100 -# initial_guess = jax.random.uniform(jax.random.PRNGKey(42), (set_nwalkers,11,), -# minval=prior_range[:,0], maxval=prior_range[:,1]) - -# y = lambda x: -LogLikelihood(x) -# y = jax.jit(jax.vmap(y)) -# print("Compiling likelihood function") -# y(initial_guess) -# print("Done compiling") - -# print("Starting the optimizer") -# optimizer = EvolutionaryOptimizer(11, verbose = True) -# state = optimizer.optimize(y, prior_range, n_loops=2000) -# best_fit = optimizer.get_result()[0] - -# print(best_fit) - -# data_list = [H1_data, L1_data] -# psd_list = [H1_psd, L1_psd] -# response_list = [H1_response, L1_response] - - -# print("Constructing the heterodyned likelihood function") -# logL = make_heterodyne_likelihood_mutliple_detector(data_list, psd_list, response_list, gen_IMRPhenomD_polar, -# best_fit, H1_frequency, gmst, epoch, f_ref, 301) - - -# ########################################### -# ####### Finally, we can sample! ########### -# ########################################### - -# n_dim = 11 -# n_chains = 1000 -# n_loop_training = 10 -# n_loop_production = 10 -# n_local_steps = 100 -# n_global_steps = 100 -# learning_rate = 0.001 -# max_samples = 100000 -# momentum = 0.9 -# num_epochs = 100 -# batch_size = 50000 - -# guess_param = best_fit - -# guess_param = np.array(jnp.repeat(guess_param[None,:],int(n_chains),axis=0)*np.random.normal(loc=1,scale=0.1,size=(int(n_chains),n_dim))) -# guess_param[guess_param[:,1]>0.25,1] = 0.249 -# guess_param[:,6] = (guess_param[:,6]%(2*jnp.pi)) -# guess_param[:,7] = (guess_param[:,7]%(jnp.pi)) -# guess_param[:,8] = (guess_param[:,8]%(jnp.pi)) -# guess_param[:,9] = (guess_param[:,9]%(2*jnp.pi)) - - -# print("Preparing RNG keys") -# rng_key_set = initialize_rng_keys(n_chains, seed=42) - -# print("Initializing MCMC model and normalizing flow model.") - -# prior_range = jnp.array([[10,80],[0.125,1.0],[-1,1],[-1,1], -# [0,2000],[-0.05,0.05],[0,2*np.pi],[-1,1], -# [0,np.pi],[0,2*np.pi],[-1,1]]) - - -# initial_position = jax.random.uniform(rng_key_set[0], shape=(int(n_chains), n_dim)) * 1 -# for i in range(n_dim): -# initial_position = initial_position.at[:,i].set(initial_position[:,i]*(prior_range[i,1]-prior_range[i,0])+prior_range[i,0]) - -# from ripple import Mc_eta_to_ms -# m1,m2 = jax.vmap(Mc_eta_to_ms)(guess_param[:,:2]) -# q = m2/m1 - -# initial_position = initial_position.at[:,0].set(guess_param[:,0]) - -# from astropy.cosmology import Planck18 as cosmo - -# z = np.linspace(0.002,3,10000) -# dL = cosmo.luminosity_distance(z).value -# dVdz = cosmo.differential_comoving_volume(z).value - -# def top_hat(x): -# output = 0. -# for i in range(n_dim): -# output = jax.lax.cond(x[i]>=prior_range[i,0], lambda: output, lambda: -jnp.inf) -# output = jax.lax.cond(x[i]<=prior_range[i,1], lambda: output, lambda: -jnp.inf) -# return output+jnp.log(jnp.interp(x[4],dL,dVdz)) - -# def posterior(theta): -# q = theta[1] -# theta = theta.at[7].set(jnp.arcsin(jnp.sin(theta[7]/2*jnp.pi))*2/jnp.pi) -# theta = theta.at[10].set(jnp.arcsin(jnp.sin(theta[10]/2*jnp.pi))*2/jnp.pi) -# iota = jnp.arccos(theta[7]) -# dec = jnp.arcsin(theta[10]) -# prior = top_hat(theta) -# theta = theta.at[1].set(q/(1+q)**2) # convert q to eta -# theta = theta.at[7].set(iota) # convert cos iota to iota -# theta = theta.at[10].set(dec) # convert cos dec to dec -# return logL(theta) + prior - -# posterior_new = lambda theta, data: posterior(theta) - -# model = MaskedCouplingRQSpline(n_dim, 10, [128,128], 8, jax.random.PRNGKey(10)) - -# print("Initializing sampler class") - -# mass_matrix = jnp.eye(n_dim) -# mass_matrix = mass_matrix.at[1,1].set(1e-3) -# mass_matrix = mass_matrix.at[5,5].set(1e-3) - -# local_sampler = MALA(posterior_new, True, {"step_size": mass_matrix*3e-3}) -# print("Running sampler") - -# nf_sampler = Sampler( -# n_dim, -# rng_key_set, -# None, -# local_sampler, -# model, -# n_loop_training=n_loop_training, -# n_loop_production = n_loop_production, -# n_local_steps=n_local_steps, -# n_global_steps=n_global_steps, -# n_chains=n_chains, -# n_epochs=num_epochs, -# learning_rate=learning_rate, -# momentum=momentum, -# batch_size=batch_size, -# use_global=True, -# keep_quantile=0., -# train_thinning = 40, -# ) - -# nf_sampler.sample(initial_position, None) -# chains, log_prob, local_accs, global_accs = nf_sampler.get_sampler_state().values() -# print("Script complete and took: {} minutes".format((time.time()-total_time_start)/60)) -# # np.savez('GW150914.npz', chains=chains, log_prob=log_prob, local_accs=local_accs, global_accs=global_accs) \ No newline at end of file diff --git a/mkdocs.yml b/mkdocs.yml index c96ec4b1..e363ddbb 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -45,7 +45,8 @@ markdown_extensions: plugins: - search # default search plugin; needs manually re-enabling when using any other plugins - autorefs # Cross-links to headings - # - mknotebooks # Jupyter notebooks + - mkdocs-jupyter: # Jupyter notebook support + # show_input: False - mkdocstrings: handlers: python: @@ -68,6 +69,7 @@ plugins: nav: - Home: index.md + - GW150914: examples/GW150914.py # Getting Started: # - Installation: installation.md # - Tutorial: tutorial.md From c9c1ae24c5b32aee6ad411903d048efaa9d318a4 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Sat, 15 Jul 2023 16:40:23 -0400 Subject: [PATCH 231/300] autodoc renders but it is extremely ugly --- docs/gen_ref_pages.py | 31 +++++++++++++++++++++++++++++++ docs/requirements.txt | 3 ++- mkdocs.yml | 6 ++++++ 3 files changed, 39 insertions(+), 1 deletion(-) create mode 100644 docs/gen_ref_pages.py diff --git a/docs/gen_ref_pages.py b/docs/gen_ref_pages.py new file mode 100644 index 00000000..8f633f0f --- /dev/null +++ b/docs/gen_ref_pages.py @@ -0,0 +1,31 @@ +"""Generate the code reference pages.""" + +from pathlib import Path + +import mkdocs_gen_files + +nav = mkdocs_gen_files.Nav() + + +for path in sorted(Path("src").rglob("*.py")): # + module_path = path.relative_to("src").with_suffix("") # + doc_path = path.relative_to("src").with_suffix(".md") # + full_doc_path = Path("reference", doc_path) # + + parts = list(module_path.parts) + + if parts[-1] == "__init__": # + parts = parts[:-1] + elif parts[-1] == "__main__": + continue + + nav[parts] = doc_path.as_posix() + + with mkdocs_gen_files.open(full_doc_path, "w") as fd: # + identifier = ".".join(parts) # + print("::: " + identifier, file=fd) # + + mkdocs_gen_files.set_edit_path(full_doc_path, path) # + + with mkdocs_gen_files.open("reference/SUMMARY.md", "w") as nav_file: # + nav_file.writelines(nav.build_literate_nav()) diff --git a/docs/requirements.txt b/docs/requirements.txt index 02041f27..7ba4ca81 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -2,4 +2,5 @@ mkdocs==1.4.3 # Main documentation generator. mkdocs-material==9.1.18 # Theme pymdown-extensions==10.1 # Markdown extensions e.g. to handle LaTeX. mkdocstrings==0.22.0 # Autogenerate documentation from docstrings. -mkdocs-jupyter-0.24.2 # Turn Jupyter Lab notebooks into webpages. \ No newline at end of file +mkdocs-jupyter-0.24.2 # Turn Jupyter Lab notebooks into webpages. +mkdocs-gen-files-0.5.0 \ No newline at end of file diff --git a/mkdocs.yml b/mkdocs.yml index e363ddbb..939e0c21 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -47,6 +47,11 @@ plugins: - autorefs # Cross-links to headings - mkdocs-jupyter: # Jupyter notebook support # show_input: False + - gen-files: + scripts: + - docs/gen_ref_pages.py + - literate-nav: + nav_file: SUMMARY.md - mkdocstrings: handlers: python: @@ -70,6 +75,7 @@ plugins: nav: - Home: index.md - GW150914: examples/GW150914.py + - Code Reference: reference/ # Getting Started: # - Installation: installation.md # - Tutorial: tutorial.md From c64ca372e48af0ff42bd834e8d00d514a44ea057 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Sat, 15 Jul 2023 16:41:04 -0400 Subject: [PATCH 232/300] Update requirement --- docs/requirements.txt | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/requirements.txt b/docs/requirements.txt index 7ba4ca81..9c89561d 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -1,6 +1,6 @@ mkdocs==1.4.3 # Main documentation generator. mkdocs-material==9.1.18 # Theme pymdown-extensions==10.1 # Markdown extensions e.g. to handle LaTeX. -mkdocstrings==0.22.0 # Autogenerate documentation from docstrings. -mkdocs-jupyter-0.24.2 # Turn Jupyter Lab notebooks into webpages. -mkdocs-gen-files-0.5.0 \ No newline at end of file +mkdocstrings[python]==0.22.0 # Autogenerate documentation from docstrings. +mkdocs-jupyter==0.24.2 # Turn Jupyter Lab notebooks into webpages. +mkdocs-gen-files==0.5.0 \ No newline at end of file From fae66efc6e5b5028ebc5fa960321d298adddbd91 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Sat, 15 Jul 2023 16:43:55 -0400 Subject: [PATCH 233/300] Update utils --- mkdocs.yml | 13 ++----------- src/jimgw/utils.py | 2 +- 2 files changed, 3 insertions(+), 12 deletions(-) diff --git a/mkdocs.yml b/mkdocs.yml index 939e0c21..c97ceb83 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -61,9 +61,8 @@ plugins: - import jaxtyping - jaxtyping.set_array_name_format("array") - selection: + optional: inherited_members: true # Allow looking up inherited methods - rendering: show_root_heading: true # actually display anything at all... show_root_full_path: true # display "diffrax.asdf" not just "asdf" show_if_no_docstring: true @@ -75,12 +74,4 @@ plugins: nav: - Home: index.md - GW150914: examples/GW150914.py - - Code Reference: reference/ - # Getting Started: - # - Installation: installation.md - # - Tutorial: tutorial.md - # - Examples: examples.md - # API: - # - Reference: reference.md - # - Modules: modules.md - # - Glossary: glossary.md + - API: reference/ diff --git a/src/jimgw/utils.py b/src/jimgw/utils.py index b521263c..2473ab7b 100644 --- a/src/jimgw/utils.py +++ b/src/jimgw/utils.py @@ -12,7 +12,7 @@ def inner_product(h1, h2, frequency, PSD): return 4. * jnp.real(jnp.trapz(integrand,dx=df)) @jit -def m1m2_to_Mq(m1,m2): +def m1m2_to_Mq(m1: float,m2: float): """ Transforming the primary mass m1 and secondary mass m2 to the Total mass M and mass ratio q. From 95f3612bc3f1a034d8094397abb9a793864e9a9b Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Sat, 15 Jul 2023 17:04:30 -0400 Subject: [PATCH 234/300] Scaffold stuffs --- docs/gotchas.md | 0 docs/jax.md | 0 docs/tutorials/single_event_pe.md | 0 mkdocs.yml | 9 ++++++--- 4 files changed, 6 insertions(+), 3 deletions(-) create mode 100644 docs/gotchas.md create mode 100644 docs/jax.md create mode 100644 docs/tutorials/single_event_pe.md diff --git a/docs/gotchas.md b/docs/gotchas.md new file mode 100644 index 00000000..e69de29b diff --git a/docs/jax.md b/docs/jax.md new file mode 100644 index 00000000..e69de29b diff --git a/docs/tutorials/single_event_pe.md b/docs/tutorials/single_event_pe.md new file mode 100644 index 00000000..e69de29b diff --git a/mkdocs.yml b/mkdocs.yml index c97ceb83..b44dd4b7 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -7,8 +7,8 @@ repo_name: kazewong/jim theme: name: material features: - - navigation.sections # Sections are included in the navigation on the left. - - toc.integrate # Table of contents is integrated on the left; does not appear separately on the right. + - navigation # Sections are included in the navigation on the left. + - toc # Table of contents is integrated on the left; does not appear separately on the right. - header.autohide # header disappears as you scroll palette: # Light mode / dark mode @@ -73,5 +73,8 @@ plugins: nav: - Home: index.md - - GW150914: examples/GW150914.py + - Gotchas: gotchas.md + - Jax: jax.md + - Tutorial: tutorials/ + - Examples: examples/ - API: reference/ From 248a9f4d5160a67f24130a2df73c243237203fb9 Mon Sep 17 00:00:00 2001 From: kazewong Date: Wed, 19 Jul 2023 10:10:23 -0400 Subject: [PATCH 235/300] add doc string --- src/jimgw/prior.py | 26 +++++++++++++++++++++++++- 1 file changed, 25 insertions(+), 1 deletion(-) diff --git a/src/jimgw/prior.py b/src/jimgw/prior.py index d7c31578..1d02cdf6 100644 --- a/src/jimgw/prior.py +++ b/src/jimgw/prior.py @@ -18,8 +18,16 @@ class Prior(Distribution): @property def n_dim(self): return len(self.naming) - + def __init__(self, naming: list[str], transforms: dict[Callable] = {}): + """ + Parameters + ---------- + naming : list[str] + A list of names for the parameters of the prior. + transforms : dict[Callable] + A dictionary of transforms to apply to the parameters. + """ self.naming = naming self.transforms = [] for name in naming: @@ -45,6 +53,22 @@ def __init__(self, xmin: Union[float,Array], xmax: Union[float,Array], **kwargs) self.xmin = jnp.array(xmin) def sample(self, rng_key: jax.random.PRNGKey, n_samples: int) -> Array: + """ + Sample from a uniform distribution. + + Parameters + ---------- + rng_key : jax.random.PRNGKey + A random key to use for sampling. + n_samples : int + The number of samples to draw. + + Returns + ------- + samples : Array + An array of shape (n_samples, n_dim) containing the samples. + + """ samples = jax.random.uniform(rng_key, (n_samples,self.n_dim), minval=self.xmin, maxval=self.xmax) return samples # TODO: remember to cast this to a named array From 6f81c737fa8be9722dc228954390e2b72ec28042 Mon Sep 17 00:00:00 2001 From: kazewong Date: Wed, 19 Jul 2023 13:57:45 -0400 Subject: [PATCH 236/300] add comments --- src/jimgw/prior.py | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/src/jimgw/prior.py b/src/jimgw/prior.py index 1d02cdf6..4a1492d9 100644 --- a/src/jimgw/prior.py +++ b/src/jimgw/prior.py @@ -37,6 +37,19 @@ def __init__(self, naming: list[str], transforms: dict[Callable] = {}): self.transforms.append(lambda x: x) def transform(self, x: Array) -> Array: + """ + Apply the transforms to the parameters. + + Parameters + ---------- + x : Array + The parameters to transform. + + Returns + ------- + x : Array + The transformed parameters. + """ for i,transform in enumerate(self.transforms): x = x.at[i].set(transform(x[i])) return x From 4b666fec7b0c6470565530441e83d03e53b5451b Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Wed, 19 Jul 2023 14:40:24 -0400 Subject: [PATCH 237/300] bug fix --- src/jimgw/jim.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/jimgw/jim.py b/src/jimgw/jim.py index b5a306fe..e58ca9a9 100644 --- a/src/jimgw/jim.py +++ b/src/jimgw/jim.py @@ -24,7 +24,7 @@ def __init__(self, likelihood: LikelihoodBase, prior: Prior, **kwargs): rng_key_set = initialize_rng_keys(n_chains, seed=seed) num_layers = kwargs.get("num_layers", 10) hidden_size = kwargs.get("hidden_size", [128,128]) - num_bins = kwargs.get("hidden_size", 8) + num_bins = kwargs.get("num_bins", 8) def posterior(x: Array, data:dict): prior = self.Prior.log_prob(x) @@ -96,7 +96,7 @@ def print_summary(self): print("Training summary") print('=' * 10) - for index in range(self.Prior.naming.shape[0]): + for index in range(len(self.Prior.naming)): print(f"{self.Prior.naming[index]}: {training_chain[:, :, index].mean():.3f} +/- {training_chain[:, :, index].std():.3f}") print(f"Log probability: {training_log_prob.mean():.3f} +/- {training_log_prob.std():.3f}") print(f"Local acceptance: {training_local_acceptance.mean():.3f} +/- {training_local_acceptance.std():.3f}") @@ -105,7 +105,7 @@ def print_summary(self): print("Production summary") print('=' * 10) - for index in range(self.Prior.naming.shape[0]): + for index in range(len(self.Prior.naming)): print(f"{self.Prior.naming[index]}: {production_chain[:, :, index].mean():.3f} +/- {production_chain[:, :, index].std():.3f}") print(f"Log probability: {production_log_prob.mean():.3f} +/- {production_log_prob.std():.3f}") print(f"Local acceptance: {production_local_acceptance.mean():.3f} +/- {production_local_acceptance.std():.3f}") From 7fed8ebae9a1bd4cf8d376ac9454093d33d696a1 Mon Sep 17 00:00:00 2001 From: tedwards2412 Date: Wed, 19 Jul 2023 20:06:33 -0400 Subject: [PATCH 238/300] Correctly generates noise in both the frequency and time domain --- src/jimgw/generate_noise.py | 77 ++++++++++++++++++++++++++----------- 1 file changed, 55 insertions(+), 22 deletions(-) diff --git a/src/jimgw/generate_noise.py b/src/jimgw/generate_noise.py index 02084c35..727aa4e2 100644 --- a/src/jimgw/generate_noise.py +++ b/src/jimgw/generate_noise.py @@ -1,23 +1,27 @@ # Import packages from typing import List, Tuple -import lalsimulation as lalsim import jax.numpy as jnp import jax import numpy as np -jax.config.update('jax_enable_x64', True) -psd_func_dict = { - 'H1': lalsim.SimNoisePSDaLIGOZeroDetHighPower, - 'L1': lalsim.SimNoisePSDaLIGOZeroDetHighPower, - 'V1': lalsim.SimNoisePSDAdvVirgo, -} - -def generate_noise(seed: int, f_sampling: int = 2048, duration: int = 4, f_min: float = 30., ifos: List = ['H1', 'L1']): +# This is needed for the noise generation to have enough precision to work +jax.config.update("jax_enable_x64", True) +def generate_fd_noise( + seed: int, + f_sampling: int = 2048, + duration: int = 4, + f_min: float = 30.0, + psd_funcs: dict = { + "H1": None, + }, +): + """ + Generate frequency domain noise for a given set of detectors or specific PSD. + """ # define sampling rate and duration - - delta_t = 1/f_sampling + delta_t = 1 / f_sampling tlen = int(round(duration / delta_t)) freqs = np.fft.rfftfreq(tlen, delta_t) @@ -27,17 +31,17 @@ def generate_noise(seed: int, f_sampling: int = 2048, duration: int = 4, f_min: # prescription to do so smoothly, but this is not really needed: you # could just set all values below `fmin` to a constant. def pad_low_freqs(f, psd_ref): - return psd_ref + psd_ref*(f_min-f)*jnp.exp(-(f_min-f))/3 + return psd_ref + psd_ref * (f_min - f) * jnp.exp(-(f_min - f)) / 3 psd_dict = {} - for ifo in ifos: + for ifo in psd_funcs.keys(): psd = np.zeros(len(freqs)) - for i,f in enumerate(freqs): + for i, f in enumerate(freqs): if f >= f_min: - psd[i] = psd_func_dict[ifo](f) + psd[i] = psd_funcs[ifo](f) else: - psd[i] = pad_low_freqs(f, psd_func_dict[ifo](f_min)) - psd_dict[ifo] = jnp.array(psd,dtype=jnp.float64) + psd[i] = pad_low_freqs(f, psd_funcs[ifo](f_min)) + psd_dict[ifo] = jnp.array(psd, dtype=jnp.float64) rng_key = jax.random.PRNGKey(seed) rng_keys = jax.random.split(rng_key) @@ -45,9 +49,38 @@ def pad_low_freqs(f, psd_ref): noise_fd_dict = {} for ifo, psd in psd_dict.items(): rng_keys = jax.random.split(rng_keys[0], 3) - var = psd / (4.*delta_f) # this is the variance of LIGO noise given the definition of the likelihood function - noise_real = jax.random.normal(rng_keys[1],shape=(len(psd),))*jnp.sqrt(var) - noise_imag = jax.random.normal(rng_keys[2],shape=(len(psd),))*jnp.sqrt(var) - noise_fd_dict[ifo] = noise_real + 1j*noise_imag + # this is the variance of LIGO noise given the definition of the likelihood function + var = psd / (4.0 * delta_f) + noise_real = jax.random.normal(rng_keys[1], shape=(len(psd),)) * jnp.sqrt(var) + noise_imag = jax.random.normal(rng_keys[2], shape=(len(psd),)) * jnp.sqrt(var) + noise_fd_dict[ifo] = noise_real + 1j * noise_imag + + return freqs, psd_dict, noise_fd_dict + + +def generate_td_noise( + seed: int, + f_sampling: int = 2048, + duration: int = 4, + f_min: float = 30.0, + psd_funcs: dict = { + "H1": None, + }, +): + """ + Generate time domain noise for a given set of detectors or specific PSD. + """ + + delta_t = 1 / f_sampling + tlen = int(round(duration / delta_t)) + ts = jnp.linspace(0, duration, tlen) + + _, psd_dict, noise_fd_dict = generate_fd_noise( + seed, duration=duration, f_sampling=f_sampling, psd_funcs=psd_funcs, f_min=f_min + ) + + noise_td_dict = {} + for ifo, psd in noise_fd_dict.items(): + noise_td_dict[ifo] = jnp.fft.irfft(noise_fd_dict[ifo]) * f_sampling - return freqs, psd_dict, noise_fd_dict \ No newline at end of file + return ts, noise_td_dict From 204af1ce23746dfd5ff39f6332064ccf1691c917 Mon Sep 17 00:00:00 2001 From: tedwards2412 Date: Thu, 20 Jul 2023 11:49:02 -0400 Subject: [PATCH 239/300] Added function to generate noise psds --- src/jimgw/generate_noise.py | 41 +++++++++++++++++++++++++++++++++++++ 1 file changed, 41 insertions(+) diff --git a/src/jimgw/generate_noise.py b/src/jimgw/generate_noise.py index 727aa4e2..1acf263b 100644 --- a/src/jimgw/generate_noise.py +++ b/src/jimgw/generate_noise.py @@ -3,11 +3,52 @@ import jax.numpy as jnp import jax import numpy as np +import requests +import scipy.interpolate as interpolate + + +# import urllib2 # the lib that handles the url stuff # This is needed for the noise generation to have enough precision to work jax.config.update("jax_enable_x64", True) +def generate_LVK_PSDdict(ifos: List[str] = ["H1", "L1", "V1"]): + psd_dict = {} + for ifo in ifos: + if ifo == "H1": + print("Grabbing GWTC-2 PSD for H1") + url = "https://dcc.ligo.org/public/0169/P2000251/001/O3-H1-C01_CLEAN_SUB60HZ-1251752040.0_sensitivity_strain_asd.txt" + data = requests.get(url) + open("H1.txt", "wb").write(data.content) + f, asd_vals = np.loadtxt("H1.txt", unpack=True) + psd_vals = asd_vals**2 + psd_dict[ifo] = interpolate.interp1d( + f, psd_vals, fill_value=(psd_vals[0], psd_vals[-1]) + ) + if ifo == "L1": + print("Grabbing GWTC-2 PSD for L1") + url = "https://dcc.ligo.org/public/0169/P2000251/001/O3-L1-C01_CLEAN_SUB60HZ-1240573680.0_sensitivity_strain_asd.txt" + data = requests.get(url) + open("L1.txt", "wb").write(data.content) + f, asd_vals = np.loadtxt("L1.txt", unpack=True) + psd_vals = asd_vals**2 + psd_dict[ifo] = interpolate.interp1d( + f, psd_vals, fill_value=(psd_vals[0], psd_vals[-1]) + ) + if ifo == "V1": + print("Grabbing GWTC-2 PSD for V1") + url = "https://dcc.ligo.org/public/0169/P2000251/001/O3-V1_sensitivity_strain_asd.txt" + data = requests.get(url) + open("V1.txt", "wb").write(data.content) + f, asd_vals = np.loadtxt("V1.txt", unpack=True) + psd_vals = asd_vals**2 + psd_dict[ifo] = interpolate.interp1d( + f, psd_vals, fill_value=(psd_vals[0], psd_vals[-1]) + ) + return psd_dict + + def generate_fd_noise( seed: int, f_sampling: int = 2048, From 52140f1f0e28c53fb88881e340921c9633f44f2c Mon Sep 17 00:00:00 2001 From: tedwards2412 Date: Thu, 20 Jul 2023 11:54:18 -0400 Subject: [PATCH 240/300] Removing useless import --- src/jimgw/generate_noise.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/src/jimgw/generate_noise.py b/src/jimgw/generate_noise.py index 1acf263b..20bf2352 100644 --- a/src/jimgw/generate_noise.py +++ b/src/jimgw/generate_noise.py @@ -6,9 +6,6 @@ import requests import scipy.interpolate as interpolate - -# import urllib2 # the lib that handles the url stuff - # This is needed for the noise generation to have enough precision to work jax.config.update("jax_enable_x64", True) From 60c7791eb0530946950675f4157ef0bfe3c94770 Mon Sep 17 00:00:00 2001 From: tedwards2412 Date: Thu, 20 Jul 2023 13:36:09 -0400 Subject: [PATCH 241/300] added check to see if files already there --- src/jimgw/generate_noise.py | 50 ++++++++++++++++++++++++++----------- 1 file changed, 35 insertions(+), 15 deletions(-) diff --git a/src/jimgw/generate_noise.py b/src/jimgw/generate_noise.py index 20bf2352..e2e81630 100644 --- a/src/jimgw/generate_noise.py +++ b/src/jimgw/generate_noise.py @@ -14,35 +14,50 @@ def generate_LVK_PSDdict(ifos: List[str] = ["H1", "L1", "V1"]): psd_dict = {} for ifo in ifos: if ifo == "H1": - print("Grabbing GWTC-2 PSD for H1") - url = "https://dcc.ligo.org/public/0169/P2000251/001/O3-H1-C01_CLEAN_SUB60HZ-1251752040.0_sensitivity_strain_asd.txt" - data = requests.get(url) - open("H1.txt", "wb").write(data.content) - f, asd_vals = np.loadtxt("H1.txt", unpack=True) + try: + f, asd_vals = np.loadtxt("H1.txt", unpack=True) + except: + print("Grabbing GWTC-2 PSD for H1") + url = "https://dcc.ligo.org/public/0169/P2000251/001/O3-H1-C01_CLEAN_SUB60HZ-1251752040.0_sensitivity_strain_asd.txt" + data = requests.get(url) + open("H1.txt", "wb").write(data.content) + f, asd_vals = np.loadtxt("H1.txt", unpack=True) psd_vals = asd_vals**2 psd_dict[ifo] = interpolate.interp1d( f, psd_vals, fill_value=(psd_vals[0], psd_vals[-1]) ) + continue if ifo == "L1": - print("Grabbing GWTC-2 PSD for L1") - url = "https://dcc.ligo.org/public/0169/P2000251/001/O3-L1-C01_CLEAN_SUB60HZ-1240573680.0_sensitivity_strain_asd.txt" - data = requests.get(url) - open("L1.txt", "wb").write(data.content) - f, asd_vals = np.loadtxt("L1.txt", unpack=True) + try: + f, asd_vals = np.loadtxt("L1.txt", unpack=True) + except: + print("Grabbing GWTC-2 PSD for L1") + url = "https://dcc.ligo.org/public/0169/P2000251/001/O3-L1-C01_CLEAN_SUB60HZ-1240573680.0_sensitivity_strain_asd.txt" + data = requests.get(url) + open("L1.txt", "wb").write(data.content) + f, asd_vals = np.loadtxt("L1.txt", unpack=True) psd_vals = asd_vals**2 psd_dict[ifo] = interpolate.interp1d( f, psd_vals, fill_value=(psd_vals[0], psd_vals[-1]) ) + continue if ifo == "V1": - print("Grabbing GWTC-2 PSD for V1") - url = "https://dcc.ligo.org/public/0169/P2000251/001/O3-V1_sensitivity_strain_asd.txt" - data = requests.get(url) - open("V1.txt", "wb").write(data.content) - f, asd_vals = np.loadtxt("V1.txt", unpack=True) + try: + f, asd_vals = np.loadtxt("V1.txt", unpack=True) + except: + print("Grabbing GWTC-2 PSD for V1") + url = "https://dcc.ligo.org/public/0169/P2000251/001/O3-V1_sensitivity_strain_asd.txt" + data = requests.get(url) + open("V1.txt", "wb").write(data.content) + f, asd_vals = np.loadtxt("V1.txt", unpack=True) + psd_vals = asd_vals**2 psd_dict[ifo] = interpolate.interp1d( f, psd_vals, fill_value=(psd_vals[0], psd_vals[-1]) ) + continue + else: + raise ValueError("IFO not supported") return psd_dict @@ -58,6 +73,9 @@ def generate_fd_noise( """ Generate frequency domain noise for a given set of detectors or specific PSD. """ + for ifo in psd_funcs.keys(): + assert psd_funcs[ifo] is not None, "Need a PSD function for each detector." + # define sampling rate and duration delta_t = 1 / f_sampling tlen = int(round(duration / delta_t)) @@ -108,6 +126,8 @@ def generate_td_noise( """ Generate time domain noise for a given set of detectors or specific PSD. """ + for ifo in psd_funcs.keys(): + assert psd_funcs[ifo] is not None, "Need a PSD function for each detector." delta_t = 1 / f_sampling tlen = int(round(duration / delta_t)) From c7d78711a863b2ff6f313278a3c2dd65b716ae2a Mon Sep 17 00:00:00 2001 From: tedwards2412 Date: Thu, 20 Jul 2023 13:37:04 -0400 Subject: [PATCH 242/300] adding FIXME --- src/jimgw/generate_noise.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/jimgw/generate_noise.py b/src/jimgw/generate_noise.py index e2e81630..a6665736 100644 --- a/src/jimgw/generate_noise.py +++ b/src/jimgw/generate_noise.py @@ -138,6 +138,8 @@ def generate_td_noise( ) noise_td_dict = {} + # FIXME: We still need to add filtering to the frequency domain data + # to ensure that the time domain data behaves correctly for ifo, psd in noise_fd_dict.items(): noise_td_dict[ifo] = jnp.fft.irfft(noise_fd_dict[ifo]) * f_sampling From 1900a4931522178a22924915860c72eb8bb98328 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Thu, 27 Jul 2023 15:30:00 -0400 Subject: [PATCH 243/300] arcsin and arccos are use avoid nan in the transformer. There must be a better way to solve this --- example/GW150914.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/example/GW150914.py b/example/GW150914.py index 71de6d2b..ffc0fd06 100644 --- a/example/GW150914.py +++ b/example/GW150914.py @@ -29,10 +29,10 @@ prior = Uniform( xmin = [10, 0.125, -1., -1., 0., -0.05, 0., -1, 0., 0.,-1.], xmax = [80., 1., 1., 1., 2000., 0.05, 2*jnp.pi, 1., jnp.pi, 2*jnp.pi, 1.], - naming = ["M_c", "q", "s1_z", "s2_z", "d_L", "t_c", "phase_c", "iota", "psi", "ra", "dec"], + naming = ["M_c", "q", "s1_z", "s2_z", "d_L", "t_c", "phase_c", "cos_iota", "psi", "ra", "sin_dec"], transforms = {"q": lambda q: q/(1+q)**2, - "iota": lambda iota: jnp.arccos(iota), - "dec": lambda dec: jnp.arcsin(dec)} + "iota": lambda iota: jnp.arccos(jnp.arcsin(jnp.sin(iota/2*jnp.pi))*2/jnp.pi), + "dec": lambda dec: jnp.arcsin(jnp.arcsin(jnp.sin(dec/2*jnp.pi))*2/jnp.pi)} # sin and arcsin are periodize cos_iota and sin_dec ) mass_matrix = jnp.eye(11) From c33b342a5c20643af6b7691087b503d3ee010221 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Thu, 27 Jul 2023 16:26:49 -0400 Subject: [PATCH 244/300] update requirement --- docs/requirements.txt | 3 ++- setup.cfg | 2 +- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/docs/requirements.txt b/docs/requirements.txt index 9c89561d..88b76a83 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -3,4 +3,5 @@ mkdocs-material==9.1.18 # Theme pymdown-extensions==10.1 # Markdown extensions e.g. to handle LaTeX. mkdocstrings[python]==0.22.0 # Autogenerate documentation from docstrings. mkdocs-jupyter==0.24.2 # Turn Jupyter Lab notebooks into webpages. -mkdocs-gen-files==0.5.0 \ No newline at end of file +mkdocs-gen-files==0.5.0 +mkdocs-literate-nav=0.6.0 \ No newline at end of file diff --git a/setup.cfg b/setup.cfg index 8504628b..0d371429 100644 --- a/setup.cfg +++ b/setup.cfg @@ -20,7 +20,7 @@ install_requires = gwpy corner astropy -python_requires = >=3.9,<3.11 +python_requires = >=3.9 [options.packages.find] where=src From 1bc94594e50a4deab9941f577b7f7ac8799dedfe Mon Sep 17 00:00:00 2001 From: kazewong Date: Mon, 31 Jul 2023 10:11:40 -0400 Subject: [PATCH 245/300] Scaffold injection recovery --- example/InjectionRecovery.py | 328 ++++++++++++++++++++++++++++++++++ example/SingleEventExample.py | 15 -- setup.cfg | 1 + 3 files changed, 329 insertions(+), 15 deletions(-) create mode 100644 example/InjectionRecovery.py delete mode 100644 example/SingleEventExample.py diff --git a/example/InjectionRecovery.py b/example/InjectionRecovery.py new file mode 100644 index 00000000..af93f18d --- /dev/null +++ b/example/InjectionRecovery.py @@ -0,0 +1,328 @@ +# Import packages +import numpy as np +import jax.numpy as jnp +import jax + +# from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar +from ripple import ms_to_Mc_eta +from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar +from jimgw.detector_preset import * +from jimgw.heterodyneLikelihood import make_heterodyne_likelihood_mutliple_detector +from jimgw.detector_projection import make_detector_response +from jimgw.generate_noise import generate_noise + +from flowMC.nfmodel.rqSpline import RQSpline +from flowMC.sampler.MALA import MALA, mala_sampler_autotune +from flowMC.sampler.Sampler import Sampler +from flowMC.utils.PRNG_keys import initialize_rng_keys +from flowMC.nfmodel.utils import * + +import argparse +import yaml + +from tqdm import tqdm +from functools import partialmethod + +import sys +sys.path.append('/mnt/home/wwong/GWProject/JaxGW') + +parser = argparse.ArgumentParser(description='Injection test') + +parser.add_argument('--config', type=str, default='config.yaml', help='config file') + +# Add noise parameters to parser +parser.add_argument('--seed', type=int, default=None, help='seed for random number generator') +parser.add_argument('--f_sampling', type=int, default=None, help='sampling frequency') +parser.add_argument('--duration', type=int, default=None, help='duration of the data') +parser.add_argument('--fmin', type=float, default=None, help='minimum frequency') +parser.add_argument('--ifos', nargs='+', default=None, help='list of detectors') + +# Add injection parameters to parser +parser.add_argument('--m1', type=float, default=None, help='mass of the first component') +parser.add_argument('--m2', type=float, default=None, help='mass of the second component') +parser.add_argument('--chi1', type=float, default=None, help='dimensionless spin of the first component') +parser.add_argument('--chi2', type=float, default=None, help='dimensionless spin of the second component') +parser.add_argument('--dist_mpc', type=float, default=None, help='distance in megaparsecs') +parser.add_argument('--tc', type=float, default=None, help='coalescence time') +parser.add_argument('--phic', type=float, default=None, help='phase of coalescence') +parser.add_argument('--inclination', type=float, default=None, help='inclination angle') +parser.add_argument('--polarization_angle', type=float, default=None, help='polarization angle') +parser.add_argument('--ra', type=float, default=None, help='right ascension') +parser.add_argument('--dec', type=float, default=None, help='declination') +parser.add_argument('--heterodyne_bins', type=int, default=101, help='number of bins for heterodyne likelihood') + +# Add sampler parameters to parser + +parser.add_argument('--n_dim', type=int, default=None, help='number of parameters') +parser.add_argument('--n_chains', type=int, default=None, help='number of chains') +parser.add_argument('--n_loop_training', type=int, default=None, help='number of training loops') +parser.add_argument('--n_loop_production', type=int, default=None, help='number of production loops') +parser.add_argument('--n_local_steps', type=int, default=None, help='number of local steps') +parser.add_argument('--n_global_steps', type=int, default=None, help='number of global steps') +parser.add_argument('--learning_rate', type=float, default=None, help='learning rate') +parser.add_argument('--max_samples', type=int, default=None, help='maximum number of samples') +parser.add_argument('--momentum', type=float, default=None, help='momentum during training') +parser.add_argument('--num_epochs', type=int, default=None, help='number of epochs') +parser.add_argument('--batch_size', type=int, default=None, help='batch size') +parser.add_argument('--stepsize', type=float, default=None, help='stepsize for Local sampler') + +# Add output parameters to parser + +parser.add_argument('--output_path', type=str, default=None, help='output file path') +parser.add_argument('--downsample_factor', type=int, default=1, help='downsample factor') + +# parser + +args = parser.parse_args() +opt = vars(args) +args = yaml.load(open(opt['config'], 'r'), Loader=yaml.FullLoader) +opt.update(args) +args = opt + +# Fetch noise parameters + +print("Constructing detectors") +print("Making noises") + +seed = args['seed'] +f_sampling = args['f_sampling'] +duration = args['duration'] +fmin = args['fmin'] +ifos = args['ifos'] + + +freqs, psd_dict, noise_dict = generate_noise(seed+1234, f_sampling, duration, fmin, ifos) + + +# Fetch injection parameters and inject signal + +print("Injection signals") + +m1 = args['m1'] +m2 = args['m2'] +chi1 = args['chi1'] +chi2 = args['chi2'] +dist_mpc = args['dist_mpc'] +tc = args['tc'] +phic = args['phic'] +inclination = args['inclination'] +polarization_angle = args['polarization_angle'] +ra = args['ra'] +dec = args['dec'] + +Mc, eta = ms_to_Mc_eta(jnp.array([m1, m2])) + +heterodyne_bins = args['heterodyne_bins'] + +H1 = get_H1() +H1_response = make_detector_response(H1[0], H1[1]) +L1 = get_L1() +L1_response = make_detector_response(L1[0], L1[1]) +V1 = get_V1() +V1_response = make_detector_response(V1[0], V1[1]) + +f_ref = 30.0 +trigger_time = 1126259462.4 +post_trigger_duration = 2 +epoch = duration - post_trigger_duration +gmst = GreenwichMeanSiderealTime(trigger_time) + + +def gen_waveform_H1(f, theta): + theta_waveform = theta[:8] + theta_waveform = theta_waveform.at[5].set(0) + ra = theta[9] + dec = theta[10] + hp, hc = gen_IMRPhenomD_polar(f, theta_waveform, f_ref) + return H1_response(f, hp, hc, ra, dec, gmst , theta[8]) * jnp.exp(-1j*2*jnp.pi*f*(epoch+theta[5])) + +def gen_waveform_L1(f, theta): + theta_waveform = theta[:8] + theta_waveform = theta_waveform.at[5].set(0) + ra = theta[9] + dec = theta[10] + hp, hc = gen_IMRPhenomD_polar(f, theta_waveform, f_ref) + return L1_response(f, hp, hc, ra, dec, gmst, theta[8]) * jnp.exp(-1j*2*jnp.pi*f*(epoch+theta[5])) + +def gen_waveform_V1(f, theta): + theta_waveform = theta[:8] + theta_waveform = theta_waveform.at[5].set(0) + ra = theta[9] + dec = theta[10] + hp, hc = gen_IMRPhenomD_polar(f, theta_waveform, f_ref) + return V1_response(f, hp, hc, ra, dec, gmst, theta[8]) * jnp.exp(-1j*2*jnp.pi*f*(epoch+theta[5])) + +true_param = jnp.array([Mc, eta, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle, ra, dec]) + +from scipy.interpolate import interp1d +q_axis = np.linspace(0.1, 1.0, 10000) +eta_axis = q_axis/(1+q_axis)**2 +true_q = interp1d(eta_axis, q_axis)(eta) +cos_inclination = np.cos(inclination) +sin_dec = np.sin(dec) +true_param_trans = jnp.array([Mc, true_q, chi1, chi2, dist_mpc, tc, phic, cos_inclination, polarization_angle, ra, sin_dec]) + +f_list = freqs[freqs>fmin] +H1_signal = gen_waveform_H1(f_list, true_param) +H1_noise_psd = noise_dict['H1'][freqs>fmin] +H1_psd = psd_dict['H1'][freqs>fmin] +H1_data = H1_noise_psd + H1_signal + +L1_signal = gen_waveform_L1(f_list, true_param) +L1_noise_psd = noise_dict['L1'][freqs>fmin] +L1_psd = psd_dict['L1'][freqs>fmin] +L1_data = L1_noise_psd + L1_signal + +V1_signal = gen_waveform_V1(f_list, true_param) +V1_noise_psd = noise_dict['V1'][freqs>fmin] +V1_psd = psd_dict['V1'][freqs>fmin] +V1_data = V1_noise_psd + V1_signal + +ref_param = jnp.array([Mc, eta, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle, ra, dec]) + +data_list = [H1_data, L1_data, V1_data] +psd_list = [H1_psd, L1_psd, V1_psd] +response_list = [H1_response, L1_response, V1_response] + +def LogLikelihood(theta): + theta = jnp.array(theta) + # theta = theta.at[1].set(theta[1]/(1+theta[1])**2) # convert q to eta + # theta = theta.at[7].set(jnp.arccos(theta[7])) # convert cos iota to iota + # theta = theta.at[10].set(jnp.arcsin(theta[10])) # convert cos dec to dec + theta_waveform = theta[:8] + theta_waveform = theta_waveform.at[5].set(0) + ra = theta[9] + dec = theta[10] + hp_test, hc_test = gen_IMRPhenomD_polar(f_list, theta_waveform, f_ref) + align_time = jnp.exp(-1j*2*jnp.pi*f_list*(epoch+theta[5])) + h_test_H1 = H1_response(f_list, hp_test, hc_test, ra, dec, gmst, theta[8]) * align_time + h_test_L1 = L1_response(f_list, hp_test, hc_test, ra, dec, gmst, theta[8]) * align_time + h_test_V1 = V1_response(f_list, hp_test, hc_test, ra, dec, gmst, theta[8]) * align_time + df = f_list[1] - f_list[0] + match_filter_SNR_H1 = 4*jnp.sum((jnp.conj(h_test_H1)*H1_data)/H1_psd*df).real + match_filter_SNR_L1 = 4*jnp.sum((jnp.conj(h_test_L1)*L1_data)/L1_psd*df).real + match_filter_SNR_V1 = 4*jnp.sum((jnp.conj(h_test_V1)*V1_data)/V1_psd*df).real + optimal_SNR_H1 = 4*jnp.sum((jnp.conj(h_test_H1)*h_test_H1)/H1_psd*df).real + optimal_SNR_L1 = 4*jnp.sum((jnp.conj(h_test_L1)*h_test_L1)/L1_psd*df).real + optimal_SNR_V1 = 4*jnp.sum((jnp.conj(h_test_V1)*h_test_V1)/V1_psd*df).real + + return (match_filter_SNR_H1-optimal_SNR_H1/2) + (match_filter_SNR_L1-optimal_SNR_L1/2) + (match_filter_SNR_V1-optimal_SNR_V1/2) + + +logL = make_heterodyne_likelihood_mutliple_detector(data_list, psd_list, response_list, gen_IMRPhenomD_polar, ref_param, f_list, gmst, epoch, f_ref, heterodyne_bins) + +# Fetch sampler parameters, construct sampler and initial guess + +print("Making sampler") + +n_dim = args['n_dim'] +n_chains = args['n_chains'] +n_loop_training = args['n_loop_training'] +n_loop_production = args['n_loop_production'] +n_local_steps = args['n_local_steps'] +n_global_steps = args['n_global_steps'] +learning_rate = args['learning_rate'] +max_samples = args['max_samples'] +momentum = args['momentum'] +num_epochs = args['num_epochs'] +batch_size = args['batch_size'] +stepsize = args['stepsize'] + + +guess_param = np.array(jnp.repeat(true_param_trans[None,:],int(n_chains),axis=0)*(1+0.1*jax.random.normal(jax.random.PRNGKey(seed+98127),shape=(int(n_chains),n_dim)))) +guess_param[guess_param[:,1]>1,1] = 1 + +print("Preparing RNG keys") +rng_key_set = initialize_rng_keys(n_chains, seed=seed) + +print("Initializing MCMC model and normalizing flow model.") + +prior_range = jnp.array([[10,50],[0.5,1.0],[-0.5,0.5],[-0.5,0.5],[300,2000],[-0.5,0.5],[0,2*np.pi],[-1,1],[0,np.pi],[0,2*np.pi],[-1,1]]) + + +initial_position = jax.random.uniform(rng_key_set[0], shape=(int(n_chains), n_dim)) * 1 +for i in range(n_dim): + initial_position = initial_position.at[:,i].set(initial_position[:,i]*(prior_range[i,1]-prior_range[i,0])+prior_range[i,0]) + +from ripple import Mc_eta_to_ms +m1,m2 = jax.vmap(Mc_eta_to_ms)(guess_param[:,:2]) +q = m2/m1 +initial_position = initial_position.at[:,0].set(guess_param[:,0]) +initial_position = initial_position.at[:,5].set(guess_param[:,5]) + +from astropy.cosmology import Planck18 as cosmo + +z = np.linspace(0.01,0.4,10000) +dL = cosmo.luminosity_distance(z).value +dVdz = cosmo.differential_comoving_volume(z).value + +def top_hat(x): + output = 0. + for i in range(n_dim): + output = jax.lax.cond(x[i]>=prior_range[i,0], lambda: output, lambda: -jnp.inf) + output = jax.lax.cond(x[i]<=prior_range[i,1], lambda: output, lambda: -jnp.inf) + return output#+jnp.log(jnp.interp(x[4],dL,dVdz)) + +def posterior(theta): + q = theta[1] + iota = jnp.arccos(theta[7]) + dec = jnp.arcsin(theta[10]) + prior = top_hat(theta) + theta = theta.at[1].set(q/(1+q)**2) # convert q to eta + theta = theta.at[7].set(iota) # convert cos iota to iota + theta = theta.at[10].set(dec) # convert cos dec to dec + return logL(theta) + prior + + +model = RQSpline(n_dim, 10, [128,128], 8) + + +print("Initializing sampler class") + +posterior = posterior +dposterior = jax.grad(posterior) + + +mass_matrix = np.eye(n_dim) +mass_matrix = np.abs(1./(jax.grad(logL)(true_param)+jax.grad(top_hat)(true_param)))*mass_matrix +mass_matrix = jnp.array(mass_matrix) + +local_sampler = MALA(posterior, True, {"step_size": mass_matrix*3e-3}) +print("Running sampler") + +nf_sampler = Sampler( + n_dim, + rng_key_set, + local_sampler, + posterior, + model, + n_loop_training=n_loop_training, + n_loop_production = n_loop_production, + n_local_steps=n_local_steps, + n_global_steps=n_global_steps, + n_chains=n_chains, + n_epochs=num_epochs, + learning_rate=learning_rate, + momentum=momentum, + batch_size=batch_size, + use_global=True, + keep_quantile=0., + train_thinning = 40, + local_autotune=mala_sampler_autotune +) + +nf_sampler.sample(initial_position) + +labels = ['Mc', 'eta', 'chi1', 'chi2', 'dist_mpc', 'tc', 'phic', 'cos_inclination', 'polarization_angle', 'ra', 'sin_dec'] + +print("Saving to output") + +chains, log_prob, local_accs, global_accs, loss_vals = nf_sampler.get_sampler_state(training=True).values() +chains, log_prob, local_accs, global_accs = nf_sampler.get_sampler_state().values() + +# Fetch output parameters + +output_path = args['output_path'] +downsample_factor = args['downsample_factor'] + +np.savez(args['output_path'], chains=chains[:,::downsample_factor], log_prob=log_prob[:,::downsample_factor], local_accs=local_accs[:,::downsample_factor], global_accs=global_accs[:,::downsample_factor], loss_vals=loss_vals, labels=labels, true_param=true_param, true_log_prob=LogLikelihood(true_param)) diff --git a/example/SingleEventExample.py b/example/SingleEventExample.py deleted file mode 100644 index e10ad7cc..00000000 --- a/example/SingleEventExample.py +++ /dev/null @@ -1,15 +0,0 @@ -# Fetching data - -# Constructing the likelihood - -## Making detectors - -## Getting waveform models info - -## Setting up priors - -# Setting up flowMC sampler - -## Sampling - -# Output diff --git a/setup.cfg b/setup.cfg index 0d371429..7d8d242c 100644 --- a/setup.cfg +++ b/setup.cfg @@ -20,6 +20,7 @@ install_requires = gwpy corner astropy + typed-argument-parser python_requires = >=3.9 [options.packages.find] From 960bfe7ac5172c4cbb0c58d31102fce9762f2945 Mon Sep 17 00:00:00 2001 From: kazewong Date: Mon, 31 Jul 2023 10:19:18 -0400 Subject: [PATCH 246/300] Set injection parser --- example/InjectionRecovery.py | 118 +++++++++++++++-------------------- 1 file changed, 50 insertions(+), 68 deletions(-) diff --git a/example/InjectionRecovery.py b/example/InjectionRecovery.py index af93f18d..90a0d8f8 100644 --- a/example/InjectionRecovery.py +++ b/example/InjectionRecovery.py @@ -1,79 +1,61 @@ # Import packages -import numpy as np +import time +from jimgw.jim import Jim +from jimgw.detector import H1, L1 +from jimgw.likelihood import TransientLikelihoodFD +from jimgw.waveform import RippleIMRPhenomD +from jimgw.prior import Uniform import jax.numpy as jnp import jax -# from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar -from ripple import ms_to_Mc_eta -from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar -from jimgw.detector_preset import * -from jimgw.heterodyneLikelihood import make_heterodyne_likelihood_mutliple_detector -from jimgw.detector_projection import make_detector_response -from jimgw.generate_noise import generate_noise - -from flowMC.nfmodel.rqSpline import RQSpline -from flowMC.sampler.MALA import MALA, mala_sampler_autotune -from flowMC.sampler.Sampler import Sampler -from flowMC.utils.PRNG_keys import initialize_rng_keys -from flowMC.nfmodel.utils import * - -import argparse +from tap import Tap import yaml - from tqdm import tqdm from functools import partialmethod -import sys -sys.path.append('/mnt/home/wwong/GWProject/JaxGW') - -parser = argparse.ArgumentParser(description='Injection test') - -parser.add_argument('--config', type=str, default='config.yaml', help='config file') - -# Add noise parameters to parser -parser.add_argument('--seed', type=int, default=None, help='seed for random number generator') -parser.add_argument('--f_sampling', type=int, default=None, help='sampling frequency') -parser.add_argument('--duration', type=int, default=None, help='duration of the data') -parser.add_argument('--fmin', type=float, default=None, help='minimum frequency') -parser.add_argument('--ifos', nargs='+', default=None, help='list of detectors') - -# Add injection parameters to parser -parser.add_argument('--m1', type=float, default=None, help='mass of the first component') -parser.add_argument('--m2', type=float, default=None, help='mass of the second component') -parser.add_argument('--chi1', type=float, default=None, help='dimensionless spin of the first component') -parser.add_argument('--chi2', type=float, default=None, help='dimensionless spin of the second component') -parser.add_argument('--dist_mpc', type=float, default=None, help='distance in megaparsecs') -parser.add_argument('--tc', type=float, default=None, help='coalescence time') -parser.add_argument('--phic', type=float, default=None, help='phase of coalescence') -parser.add_argument('--inclination', type=float, default=None, help='inclination angle') -parser.add_argument('--polarization_angle', type=float, default=None, help='polarization angle') -parser.add_argument('--ra', type=float, default=None, help='right ascension') -parser.add_argument('--dec', type=float, default=None, help='declination') -parser.add_argument('--heterodyne_bins', type=int, default=101, help='number of bins for heterodyne likelihood') - -# Add sampler parameters to parser - -parser.add_argument('--n_dim', type=int, default=None, help='number of parameters') -parser.add_argument('--n_chains', type=int, default=None, help='number of chains') -parser.add_argument('--n_loop_training', type=int, default=None, help='number of training loops') -parser.add_argument('--n_loop_production', type=int, default=None, help='number of production loops') -parser.add_argument('--n_local_steps', type=int, default=None, help='number of local steps') -parser.add_argument('--n_global_steps', type=int, default=None, help='number of global steps') -parser.add_argument('--learning_rate', type=float, default=None, help='learning rate') -parser.add_argument('--max_samples', type=int, default=None, help='maximum number of samples') -parser.add_argument('--momentum', type=float, default=None, help='momentum during training') -parser.add_argument('--num_epochs', type=int, default=None, help='number of epochs') -parser.add_argument('--batch_size', type=int, default=None, help='batch size') -parser.add_argument('--stepsize', type=float, default=None, help='stepsize for Local sampler') - -# Add output parameters to parser - -parser.add_argument('--output_path', type=str, default=None, help='output file path') -parser.add_argument('--downsample_factor', type=int, default=1, help='downsample factor') - -# parser - -args = parser.parse_args() +class InjectionRecoveryParser(Tap): + config: str + + # Noise parameters + seed: int + f_sampling: int + duration: int + fmin: float + ifos: list[str] + + # Injection parameters + m1: float + m2: float + chi1: float + chi2: float + dist_mpc: float + tc: float + phic: float + inclination: float + polarization_angle: float + ra: float + dec: float + + # Sampler parameters + n_dim: int + n_chains: int + n_loop_training: int + n_loop_production: int + n_local_steps: int + n_global_steps: int + learning_rate: float + max_samples: int + momentum: float + num_epochs: int + batch_size: int + stepsize: float + + # Output parameters + output_path: str + downsample_factor: int + + +args = InjectionRecoveryParser().parse_args() opt = vars(args) args = yaml.load(open(opt['config'], 'r'), Loader=yaml.FullLoader) opt.update(args) From eda2782238a82c0e269832b65af454d45f8cde6b Mon Sep 17 00:00:00 2001 From: kazewong Date: Mon, 31 Jul 2023 11:32:24 -0400 Subject: [PATCH 247/300] update injection recovery --- example/InjectionRecovery.py | 31 +++++++------------------------ 1 file changed, 7 insertions(+), 24 deletions(-) diff --git a/example/InjectionRecovery.py b/example/InjectionRecovery.py index 90a0d8f8..565ba4f4 100644 --- a/example/InjectionRecovery.py +++ b/example/InjectionRecovery.py @@ -5,6 +5,7 @@ from jimgw.likelihood import TransientLikelihoodFD from jimgw.waveform import RippleIMRPhenomD from jimgw.prior import Uniform +from jimgw.utils import import jax.numpy as jnp import jax @@ -56,43 +57,25 @@ class InjectionRecoveryParser(Tap): args = InjectionRecoveryParser().parse_args() + opt = vars(args) -args = yaml.load(open(opt['config'], 'r'), Loader=yaml.FullLoader) -opt.update(args) -args = opt +yaml_var = yaml.load(open(opt['config'], 'r'), Loader=yaml.FullLoader) +opt.update(yaml_var) # Fetch noise parameters print("Constructing detectors") print("Making noises") -seed = args['seed'] -f_sampling = args['f_sampling'] -duration = args['duration'] -fmin = args['fmin'] -ifos = args['ifos'] - - -freqs, psd_dict, noise_dict = generate_noise(seed+1234, f_sampling, duration, fmin, ifos) +freqs, psd_dict, noise_dict = generate_noise(args.seed+1234, args.f_sampling, args.duration, args.fmin, args.ifos) # Fetch injection parameters and inject signal print("Injection signals") -m1 = args['m1'] -m2 = args['m2'] -chi1 = args['chi1'] -chi2 = args['chi2'] -dist_mpc = args['dist_mpc'] -tc = args['tc'] -phic = args['phic'] -inclination = args['inclination'] -polarization_angle = args['polarization_angle'] -ra = args['ra'] -dec = args['dec'] - -Mc, eta = ms_to_Mc_eta(jnp.array([m1, m2])) + +Mc, eta = ms_to_Mc_eta(jnp.array([args.m1, args.m2])) heterodyne_bins = args['heterodyne_bins'] From 58e2a0d6a31219fad48b8ec07c747780b363766e Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 31 Jul 2023 14:05:42 -0400 Subject: [PATCH 248/300] UIpdate noise generation --- example/InjectionRecovery.py | 341 +++++++++++++++++------------------ src/jimgw/detector.py | 161 ++++++++++------- src/jimgw/generate_noise.py | 11 +- src/jimgw/waveform.py | 2 +- 4 files changed, 269 insertions(+), 246 deletions(-) diff --git a/example/InjectionRecovery.py b/example/InjectionRecovery.py index 565ba4f4..2e1dbfa9 100644 --- a/example/InjectionRecovery.py +++ b/example/InjectionRecovery.py @@ -1,13 +1,14 @@ -# Import packages import time from jimgw.jim import Jim from jimgw.detector import H1, L1 from jimgw.likelihood import TransientLikelihoodFD from jimgw.waveform import RippleIMRPhenomD from jimgw.prior import Uniform -from jimgw.utils import +from jimgw.generate_noise import generate_fd_noise, generate_LVK_PSDdict +from ripple import ms_to_Mc_eta import jax.numpy as jnp import jax +from astropy.time import Time from tap import Tap import yaml @@ -15,82 +16,72 @@ from functools import partialmethod class InjectionRecoveryParser(Tap): - config: str + config: str # Noise parameters - seed: int - f_sampling: int - duration: int - fmin: float - ifos: list[str] + seed: int = None + f_sampling: int = None + duration: int = None + fmin: float = None + ifos: list[str] = None # Injection parameters - m1: float - m2: float - chi1: float - chi2: float - dist_mpc: float - tc: float - phic: float - inclination: float - polarization_angle: float - ra: float - dec: float + m1: float = None + m2: float = None + chi1: float = None + chi2: float = None + dist_mpc: float = None + tc: float = None + phic: float = None + inclination: float = None + polarization_angle: float = None + ra: float = None + dec: float = None # Sampler parameters - n_dim: int - n_chains: int - n_loop_training: int - n_loop_production: int - n_local_steps: int - n_global_steps: int - learning_rate: float - max_samples: int - momentum: float - num_epochs: int - batch_size: int - stepsize: float + n_dim: int = None + n_chains: int = None + n_loop_training: int = None + n_loop_production: int = None + n_local_steps: int = None + n_global_steps: int = None + learning_rate: float = None + max_samples: int = None + momentum: float = None + num_epochs: int = None + batch_size: int = None + stepsize: float = None # Output parameters - output_path: str - downsample_factor: int + output_path: str = None + downsample_factor: int = None args = InjectionRecoveryParser().parse_args() -opt = vars(args) -yaml_var = yaml.load(open(opt['config'], 'r'), Loader=yaml.FullLoader) -opt.update(yaml_var) +# opt = vars(args) +# yaml_var = yaml.load(open(opt['config'], 'r'), Loader=yaml.FullLoader) +# opt.update(yaml_var) # Fetch noise parameters print("Constructing detectors") print("Making noises") -freqs, psd_dict, noise_dict = generate_noise(args.seed+1234, args.f_sampling, args.duration, args.fmin, args.ifos) +psd_dict = generate_LVK_PSDdict(args.ifos) +freqs, psd_dict, noise_dict = generate_fd_noise(args.seed, args.f_sampling, args.duration, args.fmin, psd_dict) - -# Fetch injection parameters and inject signal +#Fetch injection parameters and inject signal print("Injection signals") - Mc, eta = ms_to_Mc_eta(jnp.array([args.m1, args.m2])) - -heterodyne_bins = args['heterodyne_bins'] - -H1 = get_H1() -H1_response = make_detector_response(H1[0], H1[1]) -L1 = get_L1() -L1_response = make_detector_response(L1[0], L1[1]) -V1 = get_V1() -V1_response = make_detector_response(V1[0], V1[1]) - f_ref = 30.0 trigger_time = 1126259462.4 post_trigger_duration = 2 -epoch = duration - post_trigger_duration -gmst = GreenwichMeanSiderealTime(trigger_time) +epoch = args.duration - post_trigger_duration +gmst = Time(trigger_time, format='gps').sidereal_time('apparent', 'greenwich').rad + def gen_waveform_H1(f, theta): @@ -117,7 +108,7 @@ def gen_waveform_V1(f, theta): hp, hc = gen_IMRPhenomD_polar(f, theta_waveform, f_ref) return V1_response(f, hp, hc, ra, dec, gmst, theta[8]) * jnp.exp(-1j*2*jnp.pi*f*(epoch+theta[5])) -true_param = jnp.array([Mc, eta, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle, ra, dec]) +true_param = jnp.array([Mc, eta, args.chi1, args.chi2, args.dist_mpc, args.tc, args.phic, args.inclination, args.polarization_angle, args.ra, args.dec]) from scipy.interpolate import interp1d q_axis = np.linspace(0.1, 1.0, 10000) @@ -143,151 +134,151 @@ def gen_waveform_V1(f, theta): V1_psd = psd_dict['V1'][freqs>fmin] V1_data = V1_noise_psd + V1_signal -ref_param = jnp.array([Mc, eta, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle, ra, dec]) - -data_list = [H1_data, L1_data, V1_data] -psd_list = [H1_psd, L1_psd, V1_psd] -response_list = [H1_response, L1_response, V1_response] - -def LogLikelihood(theta): - theta = jnp.array(theta) - # theta = theta.at[1].set(theta[1]/(1+theta[1])**2) # convert q to eta - # theta = theta.at[7].set(jnp.arccos(theta[7])) # convert cos iota to iota - # theta = theta.at[10].set(jnp.arcsin(theta[10])) # convert cos dec to dec - theta_waveform = theta[:8] - theta_waveform = theta_waveform.at[5].set(0) - ra = theta[9] - dec = theta[10] - hp_test, hc_test = gen_IMRPhenomD_polar(f_list, theta_waveform, f_ref) - align_time = jnp.exp(-1j*2*jnp.pi*f_list*(epoch+theta[5])) - h_test_H1 = H1_response(f_list, hp_test, hc_test, ra, dec, gmst, theta[8]) * align_time - h_test_L1 = L1_response(f_list, hp_test, hc_test, ra, dec, gmst, theta[8]) * align_time - h_test_V1 = V1_response(f_list, hp_test, hc_test, ra, dec, gmst, theta[8]) * align_time - df = f_list[1] - f_list[0] - match_filter_SNR_H1 = 4*jnp.sum((jnp.conj(h_test_H1)*H1_data)/H1_psd*df).real - match_filter_SNR_L1 = 4*jnp.sum((jnp.conj(h_test_L1)*L1_data)/L1_psd*df).real - match_filter_SNR_V1 = 4*jnp.sum((jnp.conj(h_test_V1)*V1_data)/V1_psd*df).real - optimal_SNR_H1 = 4*jnp.sum((jnp.conj(h_test_H1)*h_test_H1)/H1_psd*df).real - optimal_SNR_L1 = 4*jnp.sum((jnp.conj(h_test_L1)*h_test_L1)/L1_psd*df).real - optimal_SNR_V1 = 4*jnp.sum((jnp.conj(h_test_V1)*h_test_V1)/V1_psd*df).real - - return (match_filter_SNR_H1-optimal_SNR_H1/2) + (match_filter_SNR_L1-optimal_SNR_L1/2) + (match_filter_SNR_V1-optimal_SNR_V1/2) - - -logL = make_heterodyne_likelihood_mutliple_detector(data_list, psd_list, response_list, gen_IMRPhenomD_polar, ref_param, f_list, gmst, epoch, f_ref, heterodyne_bins) - -# Fetch sampler parameters, construct sampler and initial guess - -print("Making sampler") - -n_dim = args['n_dim'] -n_chains = args['n_chains'] -n_loop_training = args['n_loop_training'] -n_loop_production = args['n_loop_production'] -n_local_steps = args['n_local_steps'] -n_global_steps = args['n_global_steps'] -learning_rate = args['learning_rate'] -max_samples = args['max_samples'] -momentum = args['momentum'] -num_epochs = args['num_epochs'] -batch_size = args['batch_size'] -stepsize = args['stepsize'] - - -guess_param = np.array(jnp.repeat(true_param_trans[None,:],int(n_chains),axis=0)*(1+0.1*jax.random.normal(jax.random.PRNGKey(seed+98127),shape=(int(n_chains),n_dim)))) -guess_param[guess_param[:,1]>1,1] = 1 +# ref_param = jnp.array([Mc, eta, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle, ra, dec]) -print("Preparing RNG keys") -rng_key_set = initialize_rng_keys(n_chains, seed=seed) +# data_list = [H1_data, L1_data, V1_data] +# psd_list = [H1_psd, L1_psd, V1_psd] +# response_list = [H1_response, L1_response, V1_response] -print("Initializing MCMC model and normalizing flow model.") +# def LogLikelihood(theta): +# theta = jnp.array(theta) +# # theta = theta.at[1].set(theta[1]/(1+theta[1])**2) # convert q to eta +# # theta = theta.at[7].set(jnp.arccos(theta[7])) # convert cos iota to iota +# # theta = theta.at[10].set(jnp.arcsin(theta[10])) # convert cos dec to dec +# theta_waveform = theta[:8] +# theta_waveform = theta_waveform.at[5].set(0) +# ra = theta[9] +# dec = theta[10] +# hp_test, hc_test = gen_IMRPhenomD_polar(f_list, theta_waveform, f_ref) +# align_time = jnp.exp(-1j*2*jnp.pi*f_list*(epoch+theta[5])) +# h_test_H1 = H1_response(f_list, hp_test, hc_test, ra, dec, gmst, theta[8]) * align_time +# h_test_L1 = L1_response(f_list, hp_test, hc_test, ra, dec, gmst, theta[8]) * align_time +# h_test_V1 = V1_response(f_list, hp_test, hc_test, ra, dec, gmst, theta[8]) * align_time +# df = f_list[1] - f_list[0] +# match_filter_SNR_H1 = 4*jnp.sum((jnp.conj(h_test_H1)*H1_data)/H1_psd*df).real +# match_filter_SNR_L1 = 4*jnp.sum((jnp.conj(h_test_L1)*L1_data)/L1_psd*df).real +# match_filter_SNR_V1 = 4*jnp.sum((jnp.conj(h_test_V1)*V1_data)/V1_psd*df).real +# optimal_SNR_H1 = 4*jnp.sum((jnp.conj(h_test_H1)*h_test_H1)/H1_psd*df).real +# optimal_SNR_L1 = 4*jnp.sum((jnp.conj(h_test_L1)*h_test_L1)/L1_psd*df).real +# optimal_SNR_V1 = 4*jnp.sum((jnp.conj(h_test_V1)*h_test_V1)/V1_psd*df).real + +# return (match_filter_SNR_H1-optimal_SNR_H1/2) + (match_filter_SNR_L1-optimal_SNR_L1/2) + (match_filter_SNR_V1-optimal_SNR_V1/2) + + +# logL = make_heterodyne_likelihood_mutliple_detector(data_list, psd_list, response_list, gen_IMRPhenomD_polar, ref_param, f_list, gmst, epoch, f_ref, heterodyne_bins) + +# # Fetch sampler parameters, construct sampler and initial guess + +# print("Making sampler") + +# n_dim = args['n_dim'] +# n_chains = args['n_chains'] +# n_loop_training = args['n_loop_training'] +# n_loop_production = args['n_loop_production'] +# n_local_steps = args['n_local_steps'] +# n_global_steps = args['n_global_steps'] +# learning_rate = args['learning_rate'] +# max_samples = args['max_samples'] +# momentum = args['momentum'] +# num_epochs = args['num_epochs'] +# batch_size = args['batch_size'] +# stepsize = args['stepsize'] -prior_range = jnp.array([[10,50],[0.5,1.0],[-0.5,0.5],[-0.5,0.5],[300,2000],[-0.5,0.5],[0,2*np.pi],[-1,1],[0,np.pi],[0,2*np.pi],[-1,1]]) +# guess_param = np.array(jnp.repeat(true_param_trans[None,:],int(n_chains),axis=0)*(1+0.1*jax.random.normal(jax.random.PRNGKey(seed+98127),shape=(int(n_chains),n_dim)))) +# guess_param[guess_param[:,1]>1,1] = 1 -initial_position = jax.random.uniform(rng_key_set[0], shape=(int(n_chains), n_dim)) * 1 -for i in range(n_dim): - initial_position = initial_position.at[:,i].set(initial_position[:,i]*(prior_range[i,1]-prior_range[i,0])+prior_range[i,0]) +# print("Preparing RNG keys") +# rng_key_set = initialize_rng_keys(n_chains, seed=seed) -from ripple import Mc_eta_to_ms -m1,m2 = jax.vmap(Mc_eta_to_ms)(guess_param[:,:2]) -q = m2/m1 -initial_position = initial_position.at[:,0].set(guess_param[:,0]) -initial_position = initial_position.at[:,5].set(guess_param[:,5]) +# print("Initializing MCMC model and normalizing flow model.") -from astropy.cosmology import Planck18 as cosmo +# prior_range = jnp.array([[10,50],[0.5,1.0],[-0.5,0.5],[-0.5,0.5],[300,2000],[-0.5,0.5],[0,2*np.pi],[-1,1],[0,np.pi],[0,2*np.pi],[-1,1]]) -z = np.linspace(0.01,0.4,10000) -dL = cosmo.luminosity_distance(z).value -dVdz = cosmo.differential_comoving_volume(z).value -def top_hat(x): - output = 0. - for i in range(n_dim): - output = jax.lax.cond(x[i]>=prior_range[i,0], lambda: output, lambda: -jnp.inf) - output = jax.lax.cond(x[i]<=prior_range[i,1], lambda: output, lambda: -jnp.inf) - return output#+jnp.log(jnp.interp(x[4],dL,dVdz)) +# initial_position = jax.random.uniform(rng_key_set[0], shape=(int(n_chains), n_dim)) * 1 +# for i in range(n_dim): +# initial_position = initial_position.at[:,i].set(initial_position[:,i]*(prior_range[i,1]-prior_range[i,0])+prior_range[i,0]) -def posterior(theta): - q = theta[1] - iota = jnp.arccos(theta[7]) - dec = jnp.arcsin(theta[10]) - prior = top_hat(theta) - theta = theta.at[1].set(q/(1+q)**2) # convert q to eta - theta = theta.at[7].set(iota) # convert cos iota to iota - theta = theta.at[10].set(dec) # convert cos dec to dec - return logL(theta) + prior +# from ripple import Mc_eta_to_ms +# m1,m2 = jax.vmap(Mc_eta_to_ms)(guess_param[:,:2]) +# q = m2/m1 +# initial_position = initial_position.at[:,0].set(guess_param[:,0]) +# initial_position = initial_position.at[:,5].set(guess_param[:,5]) +# from astropy.cosmology import Planck18 as cosmo -model = RQSpline(n_dim, 10, [128,128], 8) +# z = np.linspace(0.01,0.4,10000) +# dL = cosmo.luminosity_distance(z).value +# dVdz = cosmo.differential_comoving_volume(z).value +# def top_hat(x): +# output = 0. +# for i in range(n_dim): +# output = jax.lax.cond(x[i]>=prior_range[i,0], lambda: output, lambda: -jnp.inf) +# output = jax.lax.cond(x[i]<=prior_range[i,1], lambda: output, lambda: -jnp.inf) +# return output#+jnp.log(jnp.interp(x[4],dL,dVdz)) -print("Initializing sampler class") +# def posterior(theta): +# q = theta[1] +# iota = jnp.arccos(theta[7]) +# dec = jnp.arcsin(theta[10]) +# prior = top_hat(theta) +# theta = theta.at[1].set(q/(1+q)**2) # convert q to eta +# theta = theta.at[7].set(iota) # convert cos iota to iota +# theta = theta.at[10].set(dec) # convert cos dec to dec +# return logL(theta) + prior -posterior = posterior -dposterior = jax.grad(posterior) +# model = RQSpline(n_dim, 10, [128,128], 8) -mass_matrix = np.eye(n_dim) -mass_matrix = np.abs(1./(jax.grad(logL)(true_param)+jax.grad(top_hat)(true_param)))*mass_matrix -mass_matrix = jnp.array(mass_matrix) -local_sampler = MALA(posterior, True, {"step_size": mass_matrix*3e-3}) -print("Running sampler") +# print("Initializing sampler class") + +# posterior = posterior +# dposterior = jax.grad(posterior) + + +# mass_matrix = np.eye(n_dim) +# mass_matrix = np.abs(1./(jax.grad(logL)(true_param)+jax.grad(top_hat)(true_param)))*mass_matrix +# mass_matrix = jnp.array(mass_matrix) + +# local_sampler = MALA(posterior, True, {"step_size": mass_matrix*3e-3}) +# print("Running sampler") -nf_sampler = Sampler( - n_dim, - rng_key_set, - local_sampler, - posterior, - model, - n_loop_training=n_loop_training, - n_loop_production = n_loop_production, - n_local_steps=n_local_steps, - n_global_steps=n_global_steps, - n_chains=n_chains, - n_epochs=num_epochs, - learning_rate=learning_rate, - momentum=momentum, - batch_size=batch_size, - use_global=True, - keep_quantile=0., - train_thinning = 40, - local_autotune=mala_sampler_autotune -) +# nf_sampler = Sampler( +# n_dim, +# rng_key_set, +# local_sampler, +# posterior, +# model, +# n_loop_training=n_loop_training, +# n_loop_production = n_loop_production, +# n_local_steps=n_local_steps, +# n_global_steps=n_global_steps, +# n_chains=n_chains, +# n_epochs=num_epochs, +# learning_rate=learning_rate, +# momentum=momentum, +# batch_size=batch_size, +# use_global=True, +# keep_quantile=0., +# train_thinning = 40, +# local_autotune=mala_sampler_autotune +# ) -nf_sampler.sample(initial_position) +# nf_sampler.sample(initial_position) -labels = ['Mc', 'eta', 'chi1', 'chi2', 'dist_mpc', 'tc', 'phic', 'cos_inclination', 'polarization_angle', 'ra', 'sin_dec'] +# labels = ['Mc', 'eta', 'chi1', 'chi2', 'dist_mpc', 'tc', 'phic', 'cos_inclination', 'polarization_angle', 'ra', 'sin_dec'] -print("Saving to output") +# print("Saving to output") -chains, log_prob, local_accs, global_accs, loss_vals = nf_sampler.get_sampler_state(training=True).values() -chains, log_prob, local_accs, global_accs = nf_sampler.get_sampler_state().values() +# chains, log_prob, local_accs, global_accs, loss_vals = nf_sampler.get_sampler_state(training=True).values() +# chains, log_prob, local_accs, global_accs = nf_sampler.get_sampler_state().values() -# Fetch output parameters +# # Fetch output parameters -output_path = args['output_path'] -downsample_factor = args['downsample_factor'] +# output_path = args['output_path'] +# downsample_factor = args['downsample_factor'] -np.savez(args['output_path'], chains=chains[:,::downsample_factor], log_prob=log_prob[:,::downsample_factor], local_accs=local_accs[:,::downsample_factor], global_accs=global_accs[:,::downsample_factor], loss_vals=loss_vals, labels=labels, true_param=true_param, true_log_prob=LogLikelihood(true_param)) +# np.savez(args['output_path'], chains=chains[:,::downsample_factor], log_prob=log_prob[:,::downsample_factor], local_accs=local_accs[:,::downsample_factor], global_accs=global_accs[:,::downsample_factor], loss_vals=loss_vals, labels=labels, true_param=true_param, true_log_prob=LogLikelihood(true_param)) diff --git a/src/jimgw/detector.py b/src/jimgw/detector.py index e72c506b..5a28d869 100644 --- a/src/jimgw/detector.py +++ b/src/jimgw/detector.py @@ -7,9 +7,20 @@ from jaxtyping import Array import jax from gwpy.timeseries import TimeSeries +from typing import Callable +import requests +import numpy as np +from scipy.interpolate import interp1d DEG_TO_RAD = jnp.pi/180 +# TODO: Need to expand this list. Currently it is only O3. +psd_file_dict= { + "H1": "https://dcc.ligo.org/public/0169/P2000251/001/O3-H1-C01_CLEAN_SUB60HZ-1251752040.0_sensitivity_strain_asd.txt", + "L1": "https://dcc.ligo.org/public/0169/P2000251/001/O3-L1-C01_CLEAN_SUB60HZ-1240573680.0_sensitivity_strain_asd.txt", + "V1": "https://dcc.ligo.org/public/0169/P2000251/001/O3-V1_sensitivity_strain_asd.txt", +} + def np2(x): """ Returns the next power of two as big as or larger than x.""" @@ -70,6 +81,69 @@ def __init__(self, name: str, **kwargs) -> None: self.polarization_mode = [Polarization(m) for m in modes] + @staticmethod + def _get_arm(lat, lon, tilt, azimuth): + """ + Construct detector-arm vectors in Earth-centric Cartesian coordinates. + + Arguments + --------- + lat : float + vertex latitude in rad. + lon : float + vertex longitude in rad. + tilt : float + arm tilt in rad. + azimuth : float + arm azimuth in rad. + """ + e_lon = jnp.array([-jnp.sin(lon), jnp.cos(lon), 0]) + e_lat = jnp.array([-jnp.sin(lat) * jnp.cos(lon), + -jnp.sin(lat) * jnp.sin(lon), jnp.cos(lat)]) + e_h = jnp.array([jnp.cos(lat) * jnp.cos(lon), + jnp.cos(lat) * jnp.sin(lon), jnp.sin(lat)]) + + return (jnp.cos(tilt) * jnp.cos(azimuth) * e_lon + + jnp.cos(tilt) * jnp.sin(azimuth) * e_lat + + jnp.sin(tilt) * e_h) + + @property + def arms(self): + """ + Detector arm vectors (x, y). + """ + x = self._get_arm(self.latitude, self.longitude, self.xarm_tilt, self.xarm_azimuth) + y = self._get_arm(self.latitude, self.longitude, self.yarm_tilt, self.yarm_azimuth) + return x, y + + @property + def tensor(self): + """ + Detector tensor defining the strain measurement. + """ + #TODO: this could easily be generalized for other detector geometries + arm1, arm2 = self.arms + return 0.5 * (jnp.einsum('i,j->ij', arm1, arm1) - + jnp.einsum('i,j->ij', arm2, arm2)) + + @property + def vertex(self): + """ + Detector vertex coordinates in the reference celestial frame. Based + on arXiv:gr-qc/0008066 Eqs. (B11-B13) except for a typo in the + definition of the local radius; see Section 2.1 of LIGO-T980044-10. + """ + # get detector and Earth parameters + lat = self.latitude + lon = self.longitude + h = self.elevation + major, minor = EARTH_SEMI_MAJOR_AXIS, EARTH_SEMI_MINOR_AXIS + # compute vertex location + r = major**2*(major**2*jnp.cos(lat)**2 + minor**2*jnp.sin(lat)**2)**(-0.5) + x = (r + h) * jnp.cos(lat) * jnp.cos(lon) + y = (r + h) * jnp.cos(lat) * jnp.sin(lon) + z = ((minor / major)**2 * r + h)*jnp.sin(lat) + return jnp.array([x, y, z]) def load_data(self, trigger_time:float, gps_start_pad: int, @@ -121,7 +195,7 @@ def load_data(self, trigger_time:float, self.data = data[(freq>f_min)&(freqf_min)&(freq Array: + def fd_response(self, frequency: Array, h_sky: dict, params: dict) -> Array: """ Modulate the waveform in the sky frame by the detector response in the frequency domain.""" ra, dec, psi, gmst = params['ra'], params['dec'], params['psi'], params['gmst'] @@ -135,69 +209,7 @@ def td_response(self, time: Array, h: Array, params: Array) -> Array: Modulate the waveform in the sky frame by the detector response in the time domain.""" pass - @staticmethod - def _get_arm(lat, lon, tilt, azimuth): - """ - Construct detector-arm vectors in Earth-centric Cartesian coordinates. - - Arguments - --------- - lat : float - vertex latitude in rad. - lon : float - vertex longitude in rad. - tilt : float - arm tilt in rad. - azimuth : float - arm azimuth in rad. - """ - e_lon = jnp.array([-jnp.sin(lon), jnp.cos(lon), 0]) - e_lat = jnp.array([-jnp.sin(lat) * jnp.cos(lon), - -jnp.sin(lat) * jnp.sin(lon), jnp.cos(lat)]) - e_h = jnp.array([jnp.cos(lat) * jnp.cos(lon), - jnp.cos(lat) * jnp.sin(lon), jnp.sin(lat)]) - - return (jnp.cos(tilt) * jnp.cos(azimuth) * e_lon + - jnp.cos(tilt) * jnp.sin(azimuth) * e_lat + - jnp.sin(tilt) * e_h) - - @property - def arms(self): - """ - Detector arm vectors (x, y). - """ - x = self._get_arm(self.latitude, self.longitude, self.xarm_tilt, self.xarm_azimuth) - y = self._get_arm(self.latitude, self.longitude, self.yarm_tilt, self.yarm_azimuth) - return x, y - - @property - def tensor(self): - """ - Detector tensor defining the strain measurement. - """ - #TODO: this could easily be generalized for other detector geometries - arm1, arm2 = self.arms - return 0.5 * (jnp.einsum('i,j->ij', arm1, arm1) - - jnp.einsum('i,j->ij', arm2, arm2)) - @property - def vertex(self): - """ - Detector vertex coordinates in the reference celestial frame. Based - on arXiv:gr-qc/0008066 Eqs. (B11-B13) except for a typo in the - definition of the local radius; see Section 2.1 of LIGO-T980044-10. - """ - # get detector and Earth parameters - lat = self.latitude - lon = self.longitude - h = self.elevation - major, minor = EARTH_SEMI_MAJOR_AXIS, EARTH_SEMI_MINOR_AXIS - # compute vertex location - r = major**2*(major**2*jnp.cos(lat)**2 + minor**2*jnp.sin(lat)**2)**(-0.5) - x = (r + h) * jnp.cos(lat) * jnp.cos(lon) - y = (r + h) * jnp.cos(lat) * jnp.sin(lon) - z = ((minor / major)**2 * r + h)*jnp.sin(lat) - return jnp.array([x, y, z]) def delay_from_geocenter(self, ra: float, dec: float, gmst: float) -> float: """ @@ -264,6 +276,29 @@ def antenna_pattern(self, ra:float, dec:float, psi:float, gmst:float) -> dict: return antenna_patterns + def inject_signal(self, + freqs: Array, + h_sky: dict, + params: dict, + psd_file: str = None) -> None: + """ + """ + self.frequencies = freqs + self.data = self.fd_response(freqs, h_sky, params) + self.psd = self.load_psd(freqs, psd_file) + + def load_psd(self, freqs: Array, psd_file: str = None) -> None: + if psd_file is None: + print("Grabbing GWTC-2 PSD for H1") + url = psd_file_dict[self.name] + data = requests.get(url) + open(self.name+".txt", "wb").write(data.content) + f, asd_vals = np.loadtxt(self.name+".txt", unpack=True) + else: + f, asd_vals = np.loadtxt(psd_file, unpack=True) + psd_vals = asd_vals**2 + self.psd = interp1d(f, psd_vals, fill_value=(psd_vals[0], psd_vals[-1]))(freqs) + H1 = GroundBased2G('H1', latitude = (46 + 27. / 60 + 18.528 / 3600) * DEG_TO_RAD, longitude = -(119 + 24. / 60 + 27.5657 / 3600) * DEG_TO_RAD, diff --git a/src/jimgw/generate_noise.py b/src/jimgw/generate_noise.py index a6665736..5c448fd5 100644 --- a/src/jimgw/generate_noise.py +++ b/src/jimgw/generate_noise.py @@ -87,17 +87,14 @@ def generate_fd_noise( # prescription to do so smoothly, but this is not really needed: you # could just set all values below `fmin` to a constant. def pad_low_freqs(f, psd_ref): - return psd_ref + psd_ref * (f_min - f) * jnp.exp(-(f_min - f)) / 3 + return psd_ref + psd_ref * (f_min - f) * np.exp(-(f_min - f)) / 3 psd_dict = {} for ifo in psd_funcs.keys(): psd = np.zeros(len(freqs)) - for i, f in enumerate(freqs): - if f >= f_min: - psd[i] = psd_funcs[ifo](f) - else: - psd[i] = pad_low_freqs(f, psd_funcs[ifo](f_min)) - psd_dict[ifo] = jnp.array(psd, dtype=jnp.float64) + psd = pad_low_freqs(freqs, psd_funcs[ifo](f_min)) + psd[freqs>=f_min] = psd_funcs[ifo](freqs[freqs>=f_min]) + psd_dict[ifo] = np.array(psd, dtype=np.float64) rng_key = jax.random.PRNGKey(seed) rng_keys = jax.random.split(rng_key) diff --git a/src/jimgw/waveform.py b/src/jimgw/waveform.py index db0e5598..060a9996 100644 --- a/src/jimgw/waveform.py +++ b/src/jimgw/waveform.py @@ -18,7 +18,7 @@ class RippleIMRPhenomD(Waveform): def __init__(self, f_ref: float = 20.0): self.f_ref = f_ref - def __call__(self, frequency: Array, params: dict) -> Array: + def __call__(self, frequency: Array, params: dict) -> dict: output = {} ra = params['ra'] dec = params['dec'] From 39f1b6f751230be923fbbaadd4c8760edc74a613 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 31 Jul 2023 14:24:23 -0400 Subject: [PATCH 249/300] Add detector injection function --- src/jimgw/detector.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/src/jimgw/detector.py b/src/jimgw/detector.py index 5a28d869..80be4d58 100644 --- a/src/jimgw/detector.py +++ b/src/jimgw/detector.py @@ -4,7 +4,7 @@ from scipy.signal.windows import tukey from abc import ABC, abstractmethod import equinox as eqx -from jaxtyping import Array +from jaxtyping import Array, PRNGKeyArray import jax from gwpy.timeseries import TimeSeries from typing import Callable @@ -277,6 +277,7 @@ def antenna_pattern(self, ra:float, dec:float, psi:float, gmst:float) -> dict: return antenna_patterns def inject_signal(self, + key: PRNGKeyArray, freqs: Array, h_sky: dict, params: dict, @@ -284,8 +285,13 @@ def inject_signal(self, """ """ self.frequencies = freqs - self.data = self.fd_response(freqs, h_sky, params) self.psd = self.load_psd(freqs, psd_file) + key, subkey = jax.random.split(key, 2) + vars = self.psd / (freqs[1] - freqs[0]) + noise_real = jax.random.normal(subkey, shape=freqs.shape)*jnp.sqrt(vars) + noise_imag = jax.random.normal(subkey, shape=freqs.shape)*jnp.sqrt(vars) + signal = self.fd_response(freqs, h_sky, params) + self.data = signal + noise_real + 1j*noise_imag def load_psd(self, freqs: Array, psd_file: str = None) -> None: if psd_file is None: From f88824a574d5d363f6069efa196d5f1987b36ed3 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 31 Jul 2023 14:45:14 -0400 Subject: [PATCH 250/300] Change waveform into ABC --- example/InjectionRecovery.py | 55 +++++++++++++++++++++--------------- src/jimgw/waveform.py | 4 +-- 2 files changed, 34 insertions(+), 25 deletions(-) diff --git a/example/InjectionRecovery.py b/example/InjectionRecovery.py index 2e1dbfa9..6be45860 100644 --- a/example/InjectionRecovery.py +++ b/example/InjectionRecovery.py @@ -83,33 +83,42 @@ class InjectionRecoveryParser(Tap): gmst = Time(trigger_time, format='gps').sidereal_time('apparent', 'greenwich').rad +# def gen_waveform_H1(f, theta): +# theta_waveform = theta[:8] +# theta_waveform = theta_waveform.at[5].set(0) +# ra = theta[9] +# dec = theta[10] +# hp, hc = gen_IMRPhenomD_polar(f, theta_waveform, f_ref) +# return H1_response(f, hp, hc, ra, dec, gmst , theta[8]) * jnp.exp(-1j*2*jnp.pi*f*(epoch+theta[5])) -def gen_waveform_H1(f, theta): - theta_waveform = theta[:8] - theta_waveform = theta_waveform.at[5].set(0) - ra = theta[9] - dec = theta[10] - hp, hc = gen_IMRPhenomD_polar(f, theta_waveform, f_ref) - return H1_response(f, hp, hc, ra, dec, gmst , theta[8]) * jnp.exp(-1j*2*jnp.pi*f*(epoch+theta[5])) - -def gen_waveform_L1(f, theta): - theta_waveform = theta[:8] - theta_waveform = theta_waveform.at[5].set(0) - ra = theta[9] - dec = theta[10] - hp, hc = gen_IMRPhenomD_polar(f, theta_waveform, f_ref) - return L1_response(f, hp, hc, ra, dec, gmst, theta[8]) * jnp.exp(-1j*2*jnp.pi*f*(epoch+theta[5])) - -def gen_waveform_V1(f, theta): - theta_waveform = theta[:8] - theta_waveform = theta_waveform.at[5].set(0) - ra = theta[9] - dec = theta[10] - hp, hc = gen_IMRPhenomD_polar(f, theta_waveform, f_ref) - return V1_response(f, hp, hc, ra, dec, gmst, theta[8]) * jnp.exp(-1j*2*jnp.pi*f*(epoch+theta[5])) +# def gen_waveform_L1(f, theta): +# theta_waveform = theta[:8] +# theta_waveform = theta_waveform.at[5].set(0) +# ra = theta[9] +# dec = theta[10] +# hp, hc = gen_IMRPhenomD_polar(f, theta_waveform, f_ref) +# return L1_response(f, hp, hc, ra, dec, gmst, theta[8]) * jnp.exp(-1j*2*jnp.pi*f*(epoch+theta[5])) +# def gen_waveform_V1(f, theta): +# theta_waveform = theta[:8] +# theta_waveform = theta_waveform.at[5].set(0) +# ra = theta[9] +# dec = theta[10] +# hp, hc = gen_IMRPhenomD_polar(f, theta_waveform, f_ref) +# return V1_response(f, hp, hc, ra, dec, gmst, theta[8]) * jnp.exp(-1j*2*jnp.pi*f*(epoch+theta[5])) + +waveform = RippleIMRPhenomD(f_ref=f_ref) +prior = Uniform( + xmin = [10, 0.125, -1., -1., 0., -0.05, 0., -1, 0., 0.,-1.], + xmax = [80., 1., 1., 1., 2000., 0.05, 2*jnp.pi, 1., jnp.pi, 2*jnp.pi, 1.], + naming = ["M_c", "q", "s1_z", "s2_z", "d_L", "t_c", "phase_c", "cos_iota", "psi", "ra", "sin_dec"], + transforms = {"q": lambda q: q/(1+q)**2, + "iota": lambda iota: jnp.arccos(jnp.arcsin(jnp.sin(iota/2*jnp.pi))*2/jnp.pi), + "dec": lambda dec: jnp.arcsin(jnp.arcsin(jnp.sin(dec/2*jnp.pi))*2/jnp.pi)} # sin and arcsin are periodize cos_iota and sin_dec +) true_param = jnp.array([Mc, eta, args.chi1, args.chi2, args.dist_mpc, args.tc, args.phic, args.inclination, args.polarization_angle, args.ra, args.dec]) + from scipy.interpolate import interp1d q_axis = np.linspace(0.1, 1.0, 10000) eta_axis = q_axis/(1+q_axis)**2 diff --git a/src/jimgw/waveform.py b/src/jimgw/waveform.py index 060a9996..789daeb7 100644 --- a/src/jimgw/waveform.py +++ b/src/jimgw/waveform.py @@ -1,9 +1,9 @@ -import equinox as eqx from jaxtyping import Array from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar import jax.numpy as jnp +from abc import ABC -class Waveform(eqx.Module): +class Waveform(ABC): def __init__(self): return NotImplemented From 8cbf8a0b6c6af02916364f556212cc53413be503 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 31 Jul 2023 15:47:28 -0400 Subject: [PATCH 251/300] InjectionRecovery now runs. Nuking old script --- example/InjectionRecovery.py | 233 ++++------------------ example/Injection_withParser.py | 331 -------------------------------- src/jimgw/detector.py | 5 +- src/jimgw/likelihood.py | 2 +- src/jimgw/prior.py | 26 ++- src/jimgw/waveform.py | 2 +- 6 files changed, 66 insertions(+), 533 deletions(-) delete mode 100644 example/Injection_withParser.py diff --git a/example/InjectionRecovery.py b/example/InjectionRecovery.py index 6be45860..976dc0ac 100644 --- a/example/InjectionRecovery.py +++ b/example/InjectionRecovery.py @@ -1,6 +1,6 @@ import time from jimgw.jim import Jim -from jimgw.detector import H1, L1 +from jimgw.detector import H1, L1, V1 from jimgw.likelihood import TransientLikelihoodFD from jimgw.waveform import RippleIMRPhenomD from jimgw.prior import Uniform @@ -68,13 +68,12 @@ class InjectionRecoveryParser(Tap): print("Constructing detectors") print("Making noises") -psd_dict = generate_LVK_PSDdict(args.ifos) -freqs, psd_dict, noise_dict = generate_fd_noise(args.seed, args.f_sampling, args.duration, args.fmin, psd_dict) - #Fetch injection parameters and inject signal print("Injection signals") +freqs = jnp.arange(args.fmin, args.f_sampling/2+1./args.f_sampling, 1./args.f_sampling) + Mc, eta = ms_to_Mc_eta(jnp.array([args.m1, args.m2])) f_ref = 30.0 trigger_time = 1126259462.4 @@ -82,201 +81,51 @@ class InjectionRecoveryParser(Tap): epoch = args.duration - post_trigger_duration gmst = Time(trigger_time, format='gps').sidereal_time('apparent', 'greenwich').rad - -# def gen_waveform_H1(f, theta): -# theta_waveform = theta[:8] -# theta_waveform = theta_waveform.at[5].set(0) -# ra = theta[9] -# dec = theta[10] -# hp, hc = gen_IMRPhenomD_polar(f, theta_waveform, f_ref) -# return H1_response(f, hp, hc, ra, dec, gmst , theta[8]) * jnp.exp(-1j*2*jnp.pi*f*(epoch+theta[5])) - -# def gen_waveform_L1(f, theta): -# theta_waveform = theta[:8] -# theta_waveform = theta_waveform.at[5].set(0) -# ra = theta[9] -# dec = theta[10] -# hp, hc = gen_IMRPhenomD_polar(f, theta_waveform, f_ref) -# return L1_response(f, hp, hc, ra, dec, gmst, theta[8]) * jnp.exp(-1j*2*jnp.pi*f*(epoch+theta[5])) - -# def gen_waveform_V1(f, theta): -# theta_waveform = theta[:8] -# theta_waveform = theta_waveform.at[5].set(0) -# ra = theta[9] -# dec = theta[10] -# hp, hc = gen_IMRPhenomD_polar(f, theta_waveform, f_ref) -# return V1_response(f, hp, hc, ra, dec, gmst, theta[8]) * jnp.exp(-1j*2*jnp.pi*f*(epoch+theta[5])) - waveform = RippleIMRPhenomD(f_ref=f_ref) prior = Uniform( xmin = [10, 0.125, -1., -1., 0., -0.05, 0., -1, 0., 0.,-1.], xmax = [80., 1., 1., 1., 2000., 0.05, 2*jnp.pi, 1., jnp.pi, 2*jnp.pi, 1.], naming = ["M_c", "q", "s1_z", "s2_z", "d_L", "t_c", "phase_c", "cos_iota", "psi", "ra", "sin_dec"], - transforms = {"q": lambda q: q/(1+q)**2, - "iota": lambda iota: jnp.arccos(jnp.arcsin(jnp.sin(iota/2*jnp.pi))*2/jnp.pi), - "dec": lambda dec: jnp.arcsin(jnp.arcsin(jnp.sin(dec/2*jnp.pi))*2/jnp.pi)} # sin and arcsin are periodize cos_iota and sin_dec + transforms = {"q": ("eta", lambda q: q/(1+q)**2), + "cos_iota": ("iota",lambda cos_iota: jnp.arccos(jnp.arcsin(jnp.sin(cos_iota/2*jnp.pi))*2/jnp.pi)), + "sin_dec": ("dec",lambda sin_dec: jnp.arcsin(jnp.arcsin(jnp.sin(sin_dec/2*jnp.pi))*2/jnp.pi))} # sin and arcsin are periodize cos_iota and sin_dec ) true_param = jnp.array([Mc, eta, args.chi1, args.chi2, args.dist_mpc, args.tc, args.phic, args.inclination, args.polarization_angle, args.ra, args.dec]) - - -from scipy.interpolate import interp1d -q_axis = np.linspace(0.1, 1.0, 10000) -eta_axis = q_axis/(1+q_axis)**2 -true_q = interp1d(eta_axis, q_axis)(eta) -cos_inclination = np.cos(inclination) -sin_dec = np.sin(dec) -true_param_trans = jnp.array([Mc, true_q, chi1, chi2, dist_mpc, tc, phic, cos_inclination, polarization_angle, ra, sin_dec]) - -f_list = freqs[freqs>fmin] -H1_signal = gen_waveform_H1(f_list, true_param) -H1_noise_psd = noise_dict['H1'][freqs>fmin] -H1_psd = psd_dict['H1'][freqs>fmin] -H1_data = H1_noise_psd + H1_signal - -L1_signal = gen_waveform_L1(f_list, true_param) -L1_noise_psd = noise_dict['L1'][freqs>fmin] -L1_psd = psd_dict['L1'][freqs>fmin] -L1_data = L1_noise_psd + L1_signal - -V1_signal = gen_waveform_V1(f_list, true_param) -V1_noise_psd = noise_dict['V1'][freqs>fmin] -V1_psd = psd_dict['V1'][freqs>fmin] -V1_data = V1_noise_psd + V1_signal - -# ref_param = jnp.array([Mc, eta, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle, ra, dec]) - -# data_list = [H1_data, L1_data, V1_data] -# psd_list = [H1_psd, L1_psd, V1_psd] -# response_list = [H1_response, L1_response, V1_response] - -# def LogLikelihood(theta): -# theta = jnp.array(theta) -# # theta = theta.at[1].set(theta[1]/(1+theta[1])**2) # convert q to eta -# # theta = theta.at[7].set(jnp.arccos(theta[7])) # convert cos iota to iota -# # theta = theta.at[10].set(jnp.arcsin(theta[10])) # convert cos dec to dec -# theta_waveform = theta[:8] -# theta_waveform = theta_waveform.at[5].set(0) -# ra = theta[9] -# dec = theta[10] -# hp_test, hc_test = gen_IMRPhenomD_polar(f_list, theta_waveform, f_ref) -# align_time = jnp.exp(-1j*2*jnp.pi*f_list*(epoch+theta[5])) -# h_test_H1 = H1_response(f_list, hp_test, hc_test, ra, dec, gmst, theta[8]) * align_time -# h_test_L1 = L1_response(f_list, hp_test, hc_test, ra, dec, gmst, theta[8]) * align_time -# h_test_V1 = V1_response(f_list, hp_test, hc_test, ra, dec, gmst, theta[8]) * align_time -# df = f_list[1] - f_list[0] -# match_filter_SNR_H1 = 4*jnp.sum((jnp.conj(h_test_H1)*H1_data)/H1_psd*df).real -# match_filter_SNR_L1 = 4*jnp.sum((jnp.conj(h_test_L1)*L1_data)/L1_psd*df).real -# match_filter_SNR_V1 = 4*jnp.sum((jnp.conj(h_test_V1)*V1_data)/V1_psd*df).real -# optimal_SNR_H1 = 4*jnp.sum((jnp.conj(h_test_H1)*h_test_H1)/H1_psd*df).real -# optimal_SNR_L1 = 4*jnp.sum((jnp.conj(h_test_L1)*h_test_L1)/L1_psd*df).real -# optimal_SNR_V1 = 4*jnp.sum((jnp.conj(h_test_V1)*h_test_V1)/V1_psd*df).real - -# return (match_filter_SNR_H1-optimal_SNR_H1/2) + (match_filter_SNR_L1-optimal_SNR_L1/2) + (match_filter_SNR_V1-optimal_SNR_V1/2) - - -# logL = make_heterodyne_likelihood_mutliple_detector(data_list, psd_list, response_list, gen_IMRPhenomD_polar, ref_param, f_list, gmst, epoch, f_ref, heterodyne_bins) - -# # Fetch sampler parameters, construct sampler and initial guess - -# print("Making sampler") - -# n_dim = args['n_dim'] -# n_chains = args['n_chains'] -# n_loop_training = args['n_loop_training'] -# n_loop_production = args['n_loop_production'] -# n_local_steps = args['n_local_steps'] -# n_global_steps = args['n_global_steps'] -# learning_rate = args['learning_rate'] -# max_samples = args['max_samples'] -# momentum = args['momentum'] -# num_epochs = args['num_epochs'] -# batch_size = args['batch_size'] -# stepsize = args['stepsize'] - - -# guess_param = np.array(jnp.repeat(true_param_trans[None,:],int(n_chains),axis=0)*(1+0.1*jax.random.normal(jax.random.PRNGKey(seed+98127),shape=(int(n_chains),n_dim)))) -# guess_param[guess_param[:,1]>1,1] = 1 - -# print("Preparing RNG keys") -# rng_key_set = initialize_rng_keys(n_chains, seed=seed) - -# print("Initializing MCMC model and normalizing flow model.") - -# prior_range = jnp.array([[10,50],[0.5,1.0],[-0.5,0.5],[-0.5,0.5],[300,2000],[-0.5,0.5],[0,2*np.pi],[-1,1],[0,np.pi],[0,2*np.pi],[-1,1]]) - - -# initial_position = jax.random.uniform(rng_key_set[0], shape=(int(n_chains), n_dim)) * 1 -# for i in range(n_dim): -# initial_position = initial_position.at[:,i].set(initial_position[:,i]*(prior_range[i,1]-prior_range[i,0])+prior_range[i,0]) - -# from ripple import Mc_eta_to_ms -# m1,m2 = jax.vmap(Mc_eta_to_ms)(guess_param[:,:2]) -# q = m2/m1 -# initial_position = initial_position.at[:,0].set(guess_param[:,0]) -# initial_position = initial_position.at[:,5].set(guess_param[:,5]) - -# from astropy.cosmology import Planck18 as cosmo - -# z = np.linspace(0.01,0.4,10000) -# dL = cosmo.luminosity_distance(z).value -# dVdz = cosmo.differential_comoving_volume(z).value - -# def top_hat(x): -# output = 0. -# for i in range(n_dim): -# output = jax.lax.cond(x[i]>=prior_range[i,0], lambda: output, lambda: -jnp.inf) -# output = jax.lax.cond(x[i]<=prior_range[i,1], lambda: output, lambda: -jnp.inf) -# return output#+jnp.log(jnp.interp(x[4],dL,dVdz)) - -# def posterior(theta): -# q = theta[1] -# iota = jnp.arccos(theta[7]) -# dec = jnp.arcsin(theta[10]) -# prior = top_hat(theta) -# theta = theta.at[1].set(q/(1+q)**2) # convert q to eta -# theta = theta.at[7].set(iota) # convert cos iota to iota -# theta = theta.at[10].set(dec) # convert cos dec to dec -# return logL(theta) + prior - - -# model = RQSpline(n_dim, 10, [128,128], 8) - - -# print("Initializing sampler class") - -# posterior = posterior -# dposterior = jax.grad(posterior) - - -# mass_matrix = np.eye(n_dim) -# mass_matrix = np.abs(1./(jax.grad(logL)(true_param)+jax.grad(top_hat)(true_param)))*mass_matrix -# mass_matrix = jnp.array(mass_matrix) - -# local_sampler = MALA(posterior, True, {"step_size": mass_matrix*3e-3}) -# print("Running sampler") - -# nf_sampler = Sampler( -# n_dim, -# rng_key_set, -# local_sampler, -# posterior, -# model, -# n_loop_training=n_loop_training, -# n_loop_production = n_loop_production, -# n_local_steps=n_local_steps, -# n_global_steps=n_global_steps, -# n_chains=n_chains, -# n_epochs=num_epochs, -# learning_rate=learning_rate, -# momentum=momentum, -# batch_size=batch_size, -# use_global=True, -# keep_quantile=0., -# train_thinning = 40, -# local_autotune=mala_sampler_autotune -# ) - -# nf_sampler.sample(initial_position) +true_param = prior.add_name(true_param, with_transform=True) +detector_param = {"ra": args.ra, "dec": args.dec, "gmst": gmst, "psi": args.polarization_angle} +h_sky = waveform(freqs, true_param) +key, subkey = jax.random.split(jax.random.PRNGKey(args.seed+1234)) +H1.inject_signal(subkey, freqs, h_sky, detector_param) +key, subkey = jax.random.split(key) +L1.inject_signal(subkey, freqs, h_sky, detector_param) +key, subkey = jax.random.split(key) +V1.inject_signal(subkey, freqs, h_sky, detector_param) + +likelihood = TransientLikelihoodFD([H1, L1], waveform, trigger_time, args.duration, post_trigger_duration) +mass_matrix = jnp.eye(11) +local_sampler_arg = {"step_size": mass_matrix*3e-3} + +jim = Jim(likelihood, + prior, + n_loop_training=10, + n_loop_production = 10, + n_local_steps=300, + n_global_steps=300, + n_chains=10, + n_epochs=300, + learning_rate = 0.001, + momentum = 0.9, + batch_size = 50000, + use_global=True, + keep_quantile=0., + train_thinning = 40, + local_sampler_arg = local_sampler_arg, + seed = args.seed, + ) + +jim.maximize_likleihood([prior.xmin, prior.xmax]) +key, subkey = jax.random.split(key) +jim.sample(subkey) # labels = ['Mc', 'eta', 'chi1', 'chi2', 'dist_mpc', 'tc', 'phic', 'cos_inclination', 'polarization_angle', 'ra', 'sin_dec'] diff --git a/example/Injection_withParser.py b/example/Injection_withParser.py deleted file mode 100644 index 26295a61..00000000 --- a/example/Injection_withParser.py +++ /dev/null @@ -1,331 +0,0 @@ -# Import packages -import lalsimulation as lalsim -import numpy as np -import jax.numpy as jnp -import jax -from lal import GreenwichMeanSiderealTime - - -# from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar -from ripple import ms_to_Mc_eta -from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar -from jimgw.PE.detector_preset import * -from jimgw.PE.heterodyneLikelihood import make_heterodyne_likelihood_mutliple_detector -from jimgw.PE.detector_projection import make_detector_response -from jimgw.PE.generate_noise import generate_noise - -from flowMC.nfmodel.rqSpline import RQSpline -from flowMC.sampler.MALA import MALA, mala_sampler_autotune -from flowMC.sampler.Sampler import Sampler -from flowMC.utils.PRNG_keys import initialize_rng_keys -from flowMC.nfmodel.utils import * - -import argparse -import yaml - -from tqdm import tqdm -from functools import partialmethod - -import sys -sys.path.append('/mnt/home/wwong/GWProject/JaxGW') - -parser = argparse.ArgumentParser(description='Injection test') - -parser.add_argument('--config', type=str, default='config.yaml', help='config file') - -# Add noise parameters to parser -parser.add_argument('--seed', type=int, default=None, help='seed for random number generator') -parser.add_argument('--f_sampling', type=int, default=None, help='sampling frequency') -parser.add_argument('--duration', type=int, default=None, help='duration of the data') -parser.add_argument('--fmin', type=float, default=None, help='minimum frequency') -parser.add_argument('--ifos', nargs='+', default=None, help='list of detectors') - -# Add injection parameters to parser -parser.add_argument('--m1', type=float, default=None, help='mass of the first component') -parser.add_argument('--m2', type=float, default=None, help='mass of the second component') -parser.add_argument('--chi1', type=float, default=None, help='dimensionless spin of the first component') -parser.add_argument('--chi2', type=float, default=None, help='dimensionless spin of the second component') -parser.add_argument('--dist_mpc', type=float, default=None, help='distance in megaparsecs') -parser.add_argument('--tc', type=float, default=None, help='coalescence time') -parser.add_argument('--phic', type=float, default=None, help='phase of coalescence') -parser.add_argument('--inclination', type=float, default=None, help='inclination angle') -parser.add_argument('--polarization_angle', type=float, default=None, help='polarization angle') -parser.add_argument('--ra', type=float, default=None, help='right ascension') -parser.add_argument('--dec', type=float, default=None, help='declination') -parser.add_argument('--heterodyne_bins', type=int, default=101, help='number of bins for heterodyne likelihood') - -# Add sampler parameters to parser - -parser.add_argument('--n_dim', type=int, default=None, help='number of parameters') -parser.add_argument('--n_chains', type=int, default=None, help='number of chains') -parser.add_argument('--n_loop_training', type=int, default=None, help='number of training loops') -parser.add_argument('--n_loop_production', type=int, default=None, help='number of production loops') -parser.add_argument('--n_local_steps', type=int, default=None, help='number of local steps') -parser.add_argument('--n_global_steps', type=int, default=None, help='number of global steps') -parser.add_argument('--learning_rate', type=float, default=None, help='learning rate') -parser.add_argument('--max_samples', type=int, default=None, help='maximum number of samples') -parser.add_argument('--momentum', type=float, default=None, help='momentum during training') -parser.add_argument('--num_epochs', type=int, default=None, help='number of epochs') -parser.add_argument('--batch_size', type=int, default=None, help='batch size') -parser.add_argument('--stepsize', type=float, default=None, help='stepsize for Local sampler') - -# Add output parameters to parser - -parser.add_argument('--output_path', type=str, default=None, help='output file path') -parser.add_argument('--downsample_factor', type=int, default=1, help='downsample factor') - -# parser - -args = parser.parse_args() -opt = vars(args) -args = yaml.load(open(opt['config'], 'r'), Loader=yaml.FullLoader) -opt.update(args) -args = opt - -# Fetch noise parameters - -print("Constructing detectors") -print("Making noises") - -seed = args['seed'] -f_sampling = args['f_sampling'] -duration = args['duration'] -fmin = args['fmin'] -ifos = args['ifos'] - - -freqs, psd_dict, noise_dict = generate_noise(seed+1234, f_sampling, duration, fmin, ifos) - - -# Fetch injection parameters and inject signal - -print("Injection signals") - -m1 = args['m1'] -m2 = args['m2'] -chi1 = args['chi1'] -chi2 = args['chi2'] -dist_mpc = args['dist_mpc'] -tc = args['tc'] -phic = args['phic'] -inclination = args['inclination'] -polarization_angle = args['polarization_angle'] -ra = args['ra'] -dec = args['dec'] - -Mc, eta = ms_to_Mc_eta(jnp.array([m1, m2])) - -heterodyne_bins = args['heterodyne_bins'] - -H1 = get_H1() -H1_response = make_detector_response(H1[0], H1[1]) -L1 = get_L1() -L1_response = make_detector_response(L1[0], L1[1]) -V1 = get_V1() -V1_response = make_detector_response(V1[0], V1[1]) - -f_ref = 30.0 -trigger_time = 1126259462.4 -post_trigger_duration = 2 -epoch = duration - post_trigger_duration -gmst = GreenwichMeanSiderealTime(trigger_time) - - -def gen_waveform_H1(f, theta): - theta_waveform = theta[:8] - theta_waveform = theta_waveform.at[5].set(0) - ra = theta[9] - dec = theta[10] - hp, hc = gen_IMRPhenomD_polar(f, theta_waveform, f_ref) - return H1_response(f, hp, hc, ra, dec, gmst , theta[8]) * jnp.exp(-1j*2*jnp.pi*f*(epoch+theta[5])) - -def gen_waveform_L1(f, theta): - theta_waveform = theta[:8] - theta_waveform = theta_waveform.at[5].set(0) - ra = theta[9] - dec = theta[10] - hp, hc = gen_IMRPhenomD_polar(f, theta_waveform, f_ref) - return L1_response(f, hp, hc, ra, dec, gmst, theta[8]) * jnp.exp(-1j*2*jnp.pi*f*(epoch+theta[5])) - -def gen_waveform_V1(f, theta): - theta_waveform = theta[:8] - theta_waveform = theta_waveform.at[5].set(0) - ra = theta[9] - dec = theta[10] - hp, hc = gen_IMRPhenomD_polar(f, theta_waveform, f_ref) - return V1_response(f, hp, hc, ra, dec, gmst, theta[8]) * jnp.exp(-1j*2*jnp.pi*f*(epoch+theta[5])) - -true_param = jnp.array([Mc, eta, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle, ra, dec]) - -from scipy.interpolate import interp1d -q_axis = np.linspace(0.1, 1.0, 10000) -eta_axis = q_axis/(1+q_axis)**2 -true_q = interp1d(eta_axis, q_axis)(eta) -cos_inclination = np.cos(inclination) -sin_dec = np.sin(dec) -true_param_trans = jnp.array([Mc, true_q, chi1, chi2, dist_mpc, tc, phic, cos_inclination, polarization_angle, ra, sin_dec]) - -f_list = freqs[freqs>fmin] -H1_signal = gen_waveform_H1(f_list, true_param) -H1_noise_psd = noise_dict['H1'][freqs>fmin] -H1_psd = psd_dict['H1'][freqs>fmin] -H1_data = H1_noise_psd + H1_signal - -L1_signal = gen_waveform_L1(f_list, true_param) -L1_noise_psd = noise_dict['L1'][freqs>fmin] -L1_psd = psd_dict['L1'][freqs>fmin] -L1_data = L1_noise_psd + L1_signal - -V1_signal = gen_waveform_V1(f_list, true_param) -V1_noise_psd = noise_dict['V1'][freqs>fmin] -V1_psd = psd_dict['V1'][freqs>fmin] -V1_data = V1_noise_psd + V1_signal - -ref_param = jnp.array([Mc, eta, chi1, chi2, dist_mpc, tc, phic, inclination, polarization_angle, ra, dec]) - -data_list = [H1_data, L1_data, V1_data] -psd_list = [H1_psd, L1_psd, V1_psd] -response_list = [H1_response, L1_response, V1_response] - -def LogLikelihood(theta): - theta = jnp.array(theta) - # theta = theta.at[1].set(theta[1]/(1+theta[1])**2) # convert q to eta - # theta = theta.at[7].set(jnp.arccos(theta[7])) # convert cos iota to iota - # theta = theta.at[10].set(jnp.arcsin(theta[10])) # convert cos dec to dec - theta_waveform = theta[:8] - theta_waveform = theta_waveform.at[5].set(0) - ra = theta[9] - dec = theta[10] - hp_test, hc_test = gen_IMRPhenomD_polar(f_list, theta_waveform, f_ref) - align_time = jnp.exp(-1j*2*jnp.pi*f_list*(epoch+theta[5])) - h_test_H1 = H1_response(f_list, hp_test, hc_test, ra, dec, gmst, theta[8]) * align_time - h_test_L1 = L1_response(f_list, hp_test, hc_test, ra, dec, gmst, theta[8]) * align_time - h_test_V1 = V1_response(f_list, hp_test, hc_test, ra, dec, gmst, theta[8]) * align_time - df = f_list[1] - f_list[0] - match_filter_SNR_H1 = 4*jnp.sum((jnp.conj(h_test_H1)*H1_data)/H1_psd*df).real - match_filter_SNR_L1 = 4*jnp.sum((jnp.conj(h_test_L1)*L1_data)/L1_psd*df).real - match_filter_SNR_V1 = 4*jnp.sum((jnp.conj(h_test_V1)*V1_data)/V1_psd*df).real - optimal_SNR_H1 = 4*jnp.sum((jnp.conj(h_test_H1)*h_test_H1)/H1_psd*df).real - optimal_SNR_L1 = 4*jnp.sum((jnp.conj(h_test_L1)*h_test_L1)/L1_psd*df).real - optimal_SNR_V1 = 4*jnp.sum((jnp.conj(h_test_V1)*h_test_V1)/V1_psd*df).real - - return (match_filter_SNR_H1-optimal_SNR_H1/2) + (match_filter_SNR_L1-optimal_SNR_L1/2) + (match_filter_SNR_V1-optimal_SNR_V1/2) - - -logL = make_heterodyne_likelihood_mutliple_detector(data_list, psd_list, response_list, gen_IMRPhenomD_polar, ref_param, f_list, gmst, epoch, f_ref, heterodyne_bins) - -# Fetch sampler parameters, construct sampler and initial guess - -print("Making sampler") - -n_dim = args['n_dim'] -n_chains = args['n_chains'] -n_loop_training = args['n_loop_training'] -n_loop_production = args['n_loop_production'] -n_local_steps = args['n_local_steps'] -n_global_steps = args['n_global_steps'] -learning_rate = args['learning_rate'] -max_samples = args['max_samples'] -momentum = args['momentum'] -num_epochs = args['num_epochs'] -batch_size = args['batch_size'] -stepsize = args['stepsize'] - - -guess_param = np.array(jnp.repeat(true_param_trans[None,:],int(n_chains),axis=0)*(1+0.1*jax.random.normal(jax.random.PRNGKey(seed+98127),shape=(int(n_chains),n_dim)))) -guess_param[guess_param[:,1]>1,1] = 1 - -print("Preparing RNG keys") -rng_key_set = initialize_rng_keys(n_chains, seed=seed) - -print("Initializing MCMC model and normalizing flow model.") - -prior_range = jnp.array([[10,50],[0.5,1.0],[-0.5,0.5],[-0.5,0.5],[300,2000],[-0.5,0.5],[0,2*np.pi],[-1,1],[0,np.pi],[0,2*np.pi],[-1,1]]) - - -initial_position = jax.random.uniform(rng_key_set[0], shape=(int(n_chains), n_dim)) * 1 -for i in range(n_dim): - initial_position = initial_position.at[:,i].set(initial_position[:,i]*(prior_range[i,1]-prior_range[i,0])+prior_range[i,0]) - -from ripple import Mc_eta_to_ms -m1,m2 = jax.vmap(Mc_eta_to_ms)(guess_param[:,:2]) -q = m2/m1 -initial_position = initial_position.at[:,0].set(guess_param[:,0]) -initial_position = initial_position.at[:,5].set(guess_param[:,5]) - -from astropy.cosmology import Planck18 as cosmo - -z = np.linspace(0.01,0.4,10000) -dL = cosmo.luminosity_distance(z).value -dVdz = cosmo.differential_comoving_volume(z).value - -def top_hat(x): - output = 0. - for i in range(n_dim): - output = jax.lax.cond(x[i]>=prior_range[i,0], lambda: output, lambda: -jnp.inf) - output = jax.lax.cond(x[i]<=prior_range[i,1], lambda: output, lambda: -jnp.inf) - return output#+jnp.log(jnp.interp(x[4],dL,dVdz)) - -def posterior(theta): - q = theta[1] - iota = jnp.arccos(theta[7]) - dec = jnp.arcsin(theta[10]) - prior = top_hat(theta) - theta = theta.at[1].set(q/(1+q)**2) # convert q to eta - theta = theta.at[7].set(iota) # convert cos iota to iota - theta = theta.at[10].set(dec) # convert cos dec to dec - return logL(theta) + prior - - -model = RQSpline(n_dim, 10, [128,128], 8) - - -print("Initializing sampler class") - -posterior = posterior -dposterior = jax.grad(posterior) - - -mass_matrix = np.eye(n_dim) -mass_matrix = np.abs(1./(jax.grad(logL)(true_param)+jax.grad(top_hat)(true_param)))*mass_matrix -mass_matrix = jnp.array(mass_matrix) - -local_sampler = MALA(posterior, True, {"step_size": mass_matrix*3e-3}) -print("Running sampler") - -nf_sampler = Sampler( - n_dim, - rng_key_set, - local_sampler, - posterior, - model, - n_loop_training=n_loop_training, - n_loop_production = n_loop_production, - n_local_steps=n_local_steps, - n_global_steps=n_global_steps, - n_chains=n_chains, - n_epochs=num_epochs, - learning_rate=learning_rate, - momentum=momentum, - batch_size=batch_size, - use_global=True, - keep_quantile=0., - train_thinning = 40, - local_autotune=mala_sampler_autotune -) - -nf_sampler.sample(initial_position) - -labels = ['Mc', 'eta', 'chi1', 'chi2', 'dist_mpc', 'tc', 'phic', 'cos_inclination', 'polarization_angle', 'ra', 'sin_dec'] - -print("Saving to output") - -chains, log_prob, local_accs, global_accs, loss_vals = nf_sampler.get_sampler_state(training=True).values() -chains, log_prob, local_accs, global_accs = nf_sampler.get_sampler_state().values() - -# Fetch output parameters - -output_path = args['output_path'] -downsample_factor = args['downsample_factor'] - -np.savez(args['output_path'], chains=chains[:,::downsample_factor], log_prob=log_prob[:,::downsample_factor], local_accs=local_accs[:,::downsample_factor], global_accs=global_accs[:,::downsample_factor], loss_vals=loss_vals, labels=labels, true_param=true_param, true_log_prob=LogLikelihood(true_param)) diff --git a/src/jimgw/detector.py b/src/jimgw/detector.py index 80be4d58..7cec72f4 100644 --- a/src/jimgw/detector.py +++ b/src/jimgw/detector.py @@ -295,7 +295,7 @@ def inject_signal(self, def load_psd(self, freqs: Array, psd_file: str = None) -> None: if psd_file is None: - print("Grabbing GWTC-2 PSD for H1") + print("Grabbing GWTC-2 PSD for "+self.name) url = psd_file_dict[self.name] data = requests.get(url) open(self.name+".txt", "wb").write(data.content) @@ -303,7 +303,8 @@ def load_psd(self, freqs: Array, psd_file: str = None) -> None: else: f, asd_vals = np.loadtxt(psd_file, unpack=True) psd_vals = asd_vals**2 - self.psd = interp1d(f, psd_vals, fill_value=(psd_vals[0], psd_vals[-1]))(freqs) + psd = interp1d(f, psd_vals, fill_value=(psd_vals[0], psd_vals[-1]))(freqs) + return psd H1 = GroundBased2G('H1', latitude = (46 + 27. / 60 + 18.528 / 3600) * DEG_TO_RAD, diff --git a/src/jimgw/likelihood.py b/src/jimgw/likelihood.py index 97cd5d5f..e4e11f42 100644 --- a/src/jimgw/likelihood.py +++ b/src/jimgw/likelihood.py @@ -82,7 +82,7 @@ def evaluate(self, params: Array, data: dict) -> float: # TODO: Test whether we log_likelihood = 0 frequencies = self.detectors[0].frequencies df = frequencies[1] - frequencies[0] - source_params = {"Mc": params[0], "eta": params[1], "s1z": params[2], "s2z": params[3], "distance": params[4], "tc": params[5], "phic": params[6], "incl": params[7], "psi": params[8], "ra": params[9], "dec": params[10]} + source_params = {"M_c": params[0], "eta": params[1], "s1_z": params[2], "s2_z": params[3], "d_L": params[4], "t_c": params[5], "phase_c": params[6], "iota": params[7], "psi": params[8], "ra": params[9], "dec": params[10]} detector_params = {"ra": params[9], "dec": params[10], "psi": params[8], "gmst": self.gmst} waveform_sky = self.waveform(frequencies, source_params) align_time = jnp.exp(-1j*2*jnp.pi*frequencies*(self.epoch+params[5])) diff --git a/src/jimgw/prior.py b/src/jimgw/prior.py index 4a1492d9..83680f82 100644 --- a/src/jimgw/prior.py +++ b/src/jimgw/prior.py @@ -13,20 +13,22 @@ class Prior(Distribution): """ naming: list[str] - transforms: list[Callable] = field(default_factory=dict) + transforms: dict[tuple[str,Callable]] = field(default_factory=dict) @property def n_dim(self): return len(self.naming) - def __init__(self, naming: list[str], transforms: dict[Callable] = {}): + def __init__(self, naming: list[str], transforms: dict[tuple[str,Callable]] = {}): """ Parameters ---------- naming : list[str] A list of names for the parameters of the prior. - transforms : dict[Callable] - A dictionary of transforms to apply to the parameters. + transforms : dict[tuple[str,Callable]] + A dictionary of transforms to apply to the parameters. The keys are + the names of the parameters and the values are a tuple of the name + of the transform and the transform itself. """ self.naming = naming self.transforms = [] @@ -34,7 +36,7 @@ def __init__(self, naming: list[str], transforms: dict[Callable] = {}): if name in transforms: self.transforms.append(transforms[name]) else: - self.transforms.append(lambda x: x) + self.transforms.append((name,lambda x: x)) def transform(self, x: Array) -> Array: """ @@ -51,9 +53,21 @@ def transform(self, x: Array) -> Array: The transformed parameters. """ for i,transform in enumerate(self.transforms): - x = x.at[i].set(transform(x[i])) + x = x.at[i].set(transform[1](x[i])) return x + def add_name(self, x: Array, with_transform: bool = False) -> dict: + """ + Turn an array into a dictionary + """ + if with_transform: + naming = [] + for i,transform in enumerate(self.transforms): + naming.append(transform[0]) + else: + naming = self.naming + return dict(zip(naming, x)) + class Uniform(Prior): diff --git a/src/jimgw/waveform.py b/src/jimgw/waveform.py index 789daeb7..1b8852ca 100644 --- a/src/jimgw/waveform.py +++ b/src/jimgw/waveform.py @@ -22,7 +22,7 @@ def __call__(self, frequency: Array, params: dict) -> dict: output = {} ra = params['ra'] dec = params['dec'] - theta = [params['Mc'], params['eta'], params['s1z'], params['s2z'], params['distance'], 0, params['phic'], params['incl'], params['psi'], ra, dec] + theta = [params['M_c'], params['eta'], params['s1_z'], params['s2_z'], params['d_L'], 0, params['phase_c'], params['iota'], params['psi'], ra, dec] hp, hc = gen_IMRPhenomD_polar(frequency, theta, self.f_ref) output['p'] = hp output['c'] = hc From 9dd8391b87cc358c944feb2637de17976744a526 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 31 Jul 2023 17:43:15 -0400 Subject: [PATCH 252/300] It runs, but we need auto tune or good initialization --- example/InjectionRecovery.py | 28 +++++++--------------------- src/jimgw/detector.py | 9 +++++---- src/jimgw/jim.py | 15 +++++++++++++++ 3 files changed, 27 insertions(+), 25 deletions(-) diff --git a/example/InjectionRecovery.py b/example/InjectionRecovery.py index 976dc0ac..4e171d19 100644 --- a/example/InjectionRecovery.py +++ b/example/InjectionRecovery.py @@ -1,10 +1,8 @@ -import time from jimgw.jim import Jim from jimgw.detector import H1, L1, V1 from jimgw.likelihood import TransientLikelihoodFD from jimgw.waveform import RippleIMRPhenomD from jimgw.prior import Uniform -from jimgw.generate_noise import generate_fd_noise, generate_LVK_PSDdict from ripple import ms_to_Mc_eta import jax.numpy as jnp import jax @@ -13,7 +11,6 @@ from tap import Tap import yaml from tqdm import tqdm -from functools import partialmethod class InjectionRecoveryParser(Tap): config: str @@ -72,7 +69,7 @@ class InjectionRecoveryParser(Tap): print("Injection signals") -freqs = jnp.arange(args.fmin, args.f_sampling/2+1./args.f_sampling, 1./args.f_sampling) +freqs = jnp.linspace(args.fmin, args.f_sampling/2, args.duration*args.f_sampling//2) Mc, eta = ms_to_Mc_eta(jnp.array([args.m1, args.m2])) f_ref = 30.0 @@ -92,7 +89,7 @@ class InjectionRecoveryParser(Tap): ) true_param = jnp.array([Mc, eta, args.chi1, args.chi2, args.dist_mpc, args.tc, args.phic, args.inclination, args.polarization_angle, args.ra, args.dec]) true_param = prior.add_name(true_param, with_transform=True) -detector_param = {"ra": args.ra, "dec": args.dec, "gmst": gmst, "psi": args.polarization_angle} +detector_param = {"ra": args.ra, "dec": args.dec, "gmst": gmst, "psi": args.polarization_angle, "epoch": epoch, "t_c": args.tc} h_sky = waveform(freqs, true_param) key, subkey = jax.random.split(jax.random.PRNGKey(args.seed+1234)) H1.inject_signal(subkey, freqs, h_sky, detector_param) @@ -103,6 +100,8 @@ class InjectionRecoveryParser(Tap): likelihood = TransientLikelihoodFD([H1, L1], waveform, trigger_time, args.duration, post_trigger_duration) mass_matrix = jnp.eye(11) +mass_matrix = mass_matrix.at[1,1].set(1e-3) +mass_matrix = mass_matrix.at[5,5].set(1e-3) local_sampler_arg = {"step_size": mass_matrix*3e-3} jim = Jim(likelihood, @@ -111,7 +110,7 @@ class InjectionRecoveryParser(Tap): n_loop_production = 10, n_local_steps=300, n_global_steps=300, - n_chains=10, + n_chains=500, n_epochs=300, learning_rate = 0.001, momentum = 0.9, @@ -123,20 +122,7 @@ class InjectionRecoveryParser(Tap): seed = args.seed, ) -jim.maximize_likleihood([prior.xmin, prior.xmax]) +sample = jim.maximize_likleihood([prior.xmin, prior.xmax], n_loops=2000) key, subkey = jax.random.split(key) jim.sample(subkey) - -# labels = ['Mc', 'eta', 'chi1', 'chi2', 'dist_mpc', 'tc', 'phic', 'cos_inclination', 'polarization_angle', 'ra', 'sin_dec'] - -# print("Saving to output") - -# chains, log_prob, local_accs, global_accs, loss_vals = nf_sampler.get_sampler_state(training=True).values() -# chains, log_prob, local_accs, global_accs = nf_sampler.get_sampler_state().values() - -# # Fetch output parameters - -# output_path = args['output_path'] -# downsample_factor = args['downsample_factor'] - -# np.savez(args['output_path'], chains=chains[:,::downsample_factor], log_prob=log_prob[:,::downsample_factor], local_accs=local_accs[:,::downsample_factor], global_accs=global_accs[:,::downsample_factor], loss_vals=loss_vals, labels=labels, true_param=true_param, true_log_prob=LogLikelihood(true_param)) +samples = jim.get_samples() \ No newline at end of file diff --git a/src/jimgw/detector.py b/src/jimgw/detector.py index 7cec72f4..7fe057a6 100644 --- a/src/jimgw/detector.py +++ b/src/jimgw/detector.py @@ -287,10 +287,11 @@ def inject_signal(self, self.frequencies = freqs self.psd = self.load_psd(freqs, psd_file) key, subkey = jax.random.split(key, 2) - vars = self.psd / (freqs[1] - freqs[0]) - noise_real = jax.random.normal(subkey, shape=freqs.shape)*jnp.sqrt(vars) - noise_imag = jax.random.normal(subkey, shape=freqs.shape)*jnp.sqrt(vars) - signal = self.fd_response(freqs, h_sky, params) + var = self.psd / (4 * (freqs[1] - freqs[0])) + noise_real = jax.random.normal(key, shape=freqs.shape)*jnp.sqrt(var) + noise_imag = jax.random.normal(subkey, shape=freqs.shape)*jnp.sqrt(var) + align_time = jnp.exp(-1j*2*jnp.pi*freqs*(params['epoch']+params['t_c'])) + signal = self.fd_response(freqs, h_sky, params) * align_time self.data = signal + noise_real + 1j*noise_imag def load_psd(self, freqs: Array, psd_file: str = None) -> None: diff --git a/src/jimgw/jim.py b/src/jimgw/jim.py index e58ca9a9..1f1bc96f 100644 --- a/src/jimgw/jim.py +++ b/src/jimgw/jim.py @@ -111,5 +111,20 @@ def print_summary(self): print(f"Local acceptance: {production_local_acceptance.mean():.3f} +/- {production_local_acceptance.std():.3f}") print(f"Global acceptance: {production_global_acceptance.mean():.3f} +/- {production_global_acceptance.std():.3f}") + def get_samples(self, training: bool = False): + """ + Get the samples from the sampler + + Args: + training (bool, optional): If True, return the training samples. Defaults to False. + + Returns: + Array: Samples + """ + if training: + return self.Sampler.get_sampler_state(training=True)["chains"] + else: + return self.Sampler.get_sampler_state(training=False)["chains"] + def plot(self): pass \ No newline at end of file From ff3a4297e077b0e4d78c9060dcfc1571d542c51a Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Wed, 2 Aug 2023 10:56:52 -0400 Subject: [PATCH 253/300] Fix typo --- example/GW150914.py | 2 +- example/InjectionRecovery.py | 2 +- src/jimgw/jim.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/example/GW150914.py b/example/GW150914.py index ffc0fd06..b3a79cc9 100644 --- a/example/GW150914.py +++ b/example/GW150914.py @@ -57,5 +57,5 @@ local_sampler_arg = local_sampler_arg, ) -jim.maximize_likleihood([prior.xmin, prior.xmax]) +jim.maximize_likelihood([prior.xmin, prior.xmax]) jim.sample(jax.random.PRNGKey(42)) diff --git a/example/InjectionRecovery.py b/example/InjectionRecovery.py index 4e171d19..186cac55 100644 --- a/example/InjectionRecovery.py +++ b/example/InjectionRecovery.py @@ -122,7 +122,7 @@ class InjectionRecoveryParser(Tap): seed = args.seed, ) -sample = jim.maximize_likleihood([prior.xmin, prior.xmax], n_loops=2000) +sample = jim.maximize_likelihood([prior.xmin, prior.xmax], n_loops=2000) key, subkey = jax.random.split(key) jim.sample(subkey) samples = jim.get_samples() \ No newline at end of file diff --git a/src/jimgw/jim.py b/src/jimgw/jim.py index 1f1bc96f..a7f1f87b 100644 --- a/src/jimgw/jim.py +++ b/src/jimgw/jim.py @@ -47,7 +47,7 @@ def posterior(x: Array, data:dict): **kwargs) - def maximize_likleihood(self, bounds: tuple[Array,Array],set_nwalkers: int = 100, n_loops: int = 2000, seed = 92348): + def maximize_likelihood(self, bounds: tuple[Array,Array],set_nwalkers: int = 100, n_loops: int = 2000, seed = 92348): bounds = jnp.array(bounds).T key = jax.random.PRNGKey(seed) set_nwalkers = set_nwalkers From d10a5e23a274c4740ea14497a92844ad844f8b4d Mon Sep 17 00:00:00 2001 From: Alexander Verhaeghe <108885867+AlexanderVerhaeghe@users.noreply.github.com> Date: Fri, 11 Aug 2023 17:28:51 -0400 Subject: [PATCH 254/300] Update prior.py --- src/jimgw/prior.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/src/jimgw/prior.py b/src/jimgw/prior.py index 83680f82..94ea11b1 100644 --- a/src/jimgw/prior.py +++ b/src/jimgw/prior.py @@ -100,4 +100,9 @@ def sample(self, rng_key: jax.random.PRNGKey, n_samples: int) -> Array: return samples # TODO: remember to cast this to a named array def log_prob(self, x: Array) -> Float: + output = 0. + n_dim = jnp.shape(x)[0] + for i in range(n_dim): + output = jax.lax.cond(x[i]>=self.xmax, lambda: output, lambda: -jnp.inf) + output = jax.lax.cond(x[i]<=self.xmin, lambda: output, lambda: -jnp.inf) return jnp.sum(jnp.log(1./(self.xmax-self.xmin))) From 31ef12aecda25f6bd1af060bfce21f04dc205d6e Mon Sep 17 00:00:00 2001 From: Alexander Verhaeghe <108885867+AlexanderVerhaeghe@users.noreply.github.com> Date: Fri, 11 Aug 2023 17:30:45 -0400 Subject: [PATCH 255/300] Update prior.py --- src/jimgw/prior.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/jimgw/prior.py b/src/jimgw/prior.py index 94ea11b1..1f43b031 100644 --- a/src/jimgw/prior.py +++ b/src/jimgw/prior.py @@ -105,4 +105,4 @@ def log_prob(self, x: Array) -> Float: for i in range(n_dim): output = jax.lax.cond(x[i]>=self.xmax, lambda: output, lambda: -jnp.inf) output = jax.lax.cond(x[i]<=self.xmin, lambda: output, lambda: -jnp.inf) - return jnp.sum(jnp.log(1./(self.xmax-self.xmin))) + return output + jnp.sum(jnp.log(1./(self.xmax-self.xmin))) From cf994d9bf16bce3036b21eaf67fcd47f389add4d Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Fri, 11 Aug 2023 17:32:53 -0400 Subject: [PATCH 256/300] Update prior.py --- src/jimgw/prior.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/src/jimgw/prior.py b/src/jimgw/prior.py index 1f43b031..34bebbd1 100644 --- a/src/jimgw/prior.py +++ b/src/jimgw/prior.py @@ -101,8 +101,7 @@ def sample(self, rng_key: jax.random.PRNGKey, n_samples: int) -> Array: def log_prob(self, x: Array) -> Float: output = 0. - n_dim = jnp.shape(x)[0] - for i in range(n_dim): + for i in range(self.n_dim): output = jax.lax.cond(x[i]>=self.xmax, lambda: output, lambda: -jnp.inf) output = jax.lax.cond(x[i]<=self.xmin, lambda: output, lambda: -jnp.inf) return output + jnp.sum(jnp.log(1./(self.xmax-self.xmin))) From 019ea76b32cd795cee0ddc33774b53466c8b91fb Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Thu, 17 Aug 2023 02:21:42 -0400 Subject: [PATCH 257/300] Update prior.py Uniform prior adoption has bug in indexing. Fixed and made more concise. --- src/jimgw/prior.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/src/jimgw/prior.py b/src/jimgw/prior.py index 34bebbd1..6f30b11e 100644 --- a/src/jimgw/prior.py +++ b/src/jimgw/prior.py @@ -100,8 +100,5 @@ def sample(self, rng_key: jax.random.PRNGKey, n_samples: int) -> Array: return samples # TODO: remember to cast this to a named array def log_prob(self, x: Array) -> Float: - output = 0. - for i in range(self.n_dim): - output = jax.lax.cond(x[i]>=self.xmax, lambda: output, lambda: -jnp.inf) - output = jax.lax.cond(x[i]<=self.xmin, lambda: output, lambda: -jnp.inf) + output = jax.lax.cond(jnp.where((x>=self.xmax) | (x<=self.xmin))[0], lambda: 0, lambda: -jnp.inf) return output + jnp.sum(jnp.log(1./(self.xmax-self.xmin))) From f9de92ff9c6461b7486aaf3f8d86d65900954d96 Mon Sep 17 00:00:00 2001 From: kazewong Date: Thu, 17 Aug 2023 02:25:50 -0400 Subject: [PATCH 258/300] tested and fixed --- src/jimgw/prior.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/jimgw/prior.py b/src/jimgw/prior.py index 6f30b11e..a6f3d8be 100644 --- a/src/jimgw/prior.py +++ b/src/jimgw/prior.py @@ -100,5 +100,5 @@ def sample(self, rng_key: jax.random.PRNGKey, n_samples: int) -> Array: return samples # TODO: remember to cast this to a named array def log_prob(self, x: Array) -> Float: - output = jax.lax.cond(jnp.where((x>=self.xmax) | (x<=self.xmin))[0], lambda: 0, lambda: -jnp.inf) + output = jax.lax.cond(not jnp.where((x>=self.xmax) | (x<=self.xmin))[0].any(), lambda: 0., lambda: -jnp.inf) return output + jnp.sum(jnp.log(1./(self.xmax-self.xmin))) From 55dbea3ef98c902f1da5e0d96a0e99240094f986 Mon Sep 17 00:00:00 2001 From: tedwards2412 Date: Fri, 18 Aug 2023 11:14:57 -0400 Subject: [PATCH 259/300] Correcting prior and adding pv2 --- src/jimgw/prior.py | 3 ++- src/jimgw/waveform.py | 23 ++++++++++++++++++++--- 2 files changed, 22 insertions(+), 4 deletions(-) diff --git a/src/jimgw/prior.py b/src/jimgw/prior.py index a6f3d8be..bb79258c 100644 --- a/src/jimgw/prior.py +++ b/src/jimgw/prior.py @@ -100,5 +100,6 @@ def sample(self, rng_key: jax.random.PRNGKey, n_samples: int) -> Array: return samples # TODO: remember to cast this to a named array def log_prob(self, x: Array) -> Float: - output = jax.lax.cond(not jnp.where((x>=self.xmax) | (x<=self.xmin))[0].any(), lambda: 0., lambda: -jnp.inf) + # output = jax.lax.cond(not jnp.where((x>=self.xmax) | (x<=self.xmin))[0].any(), lambda: 0., lambda: -jnp.inf) + output = jnp.sum(jnp.where((x>=self.xmax) | (x<=self.xmin), jnp.zeros_like(x)-jnp.inf, jnp.zeros_like(x))) return output + jnp.sum(jnp.log(1./(self.xmax-self.xmin))) diff --git a/src/jimgw/waveform.py b/src/jimgw/waveform.py index 1b8852ca..7a1b23c9 100644 --- a/src/jimgw/waveform.py +++ b/src/jimgw/waveform.py @@ -1,5 +1,6 @@ from jaxtyping import Array -from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_polar +from ripple.waveforms.IMRPhenomD import gen_IMRPhenomD_hphc +from ripple.waveforms.IMRPhenomPv2 import gen_IMRPhenomPv2_hphc import jax.numpy as jnp from abc import ABC @@ -22,10 +23,26 @@ def __call__(self, frequency: Array, params: dict) -> dict: output = {} ra = params['ra'] dec = params['dec'] - theta = [params['M_c'], params['eta'], params['s1_z'], params['s2_z'], params['d_L'], 0, params['phase_c'], params['iota'], params['psi'], ra, dec] - hp, hc = gen_IMRPhenomD_polar(frequency, theta, self.f_ref) + theta = [params['M_c'], params['eta'], params['s1_z'], params['s2_z'], params['d_L'], 0, params['phase_c'], params['iota']] + hp, hc = gen_IMRPhenomD_hphc(frequency, theta, self.f_ref) output['p'] = hp output['c'] = hc return output +class RippleIMRPhenomPv2(Waveform): + + f_ref: float + + def __init__(self, f_ref: float = 20.0): + self.f_ref = f_ref + + def __call__(self, frequency: Array, params: dict) -> Array: + output = {} + theta = [params['M_c'], params['eta'], 0.0, 0.0, params['s1_z'], + 0.0, 0.0, params['s2_z'], + params['d_L'], 0, params['phase_c'], params['iota']] + hp, hc = gen_IMRPhenomPv2_hphc(frequency, theta, self.f_ref) + output['p'] = hp + output['c'] = hc + return output From dc3b4ae2c7968246e8421daf61e6f8be035c9204 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Fri, 18 Aug 2023 22:28:56 -0400 Subject: [PATCH 260/300] Update prior.py --- src/jimgw/prior.py | 1 - 1 file changed, 1 deletion(-) diff --git a/src/jimgw/prior.py b/src/jimgw/prior.py index bb79258c..b8dbce99 100644 --- a/src/jimgw/prior.py +++ b/src/jimgw/prior.py @@ -100,6 +100,5 @@ def sample(self, rng_key: jax.random.PRNGKey, n_samples: int) -> Array: return samples # TODO: remember to cast this to a named array def log_prob(self, x: Array) -> Float: - # output = jax.lax.cond(not jnp.where((x>=self.xmax) | (x<=self.xmin))[0].any(), lambda: 0., lambda: -jnp.inf) output = jnp.sum(jnp.where((x>=self.xmax) | (x<=self.xmin), jnp.zeros_like(x)-jnp.inf, jnp.zeros_like(x))) return output + jnp.sum(jnp.log(1./(self.xmax-self.xmin))) From 4874a9883a49c39ddddbc4ca7a07fa0b107c6da7 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Thu, 24 Aug 2023 17:36:10 -0400 Subject: [PATCH 261/300] Update prior --- example/GW150914.py | 10 ++++++---- example/InjectionRecovery.py | 2 +- 2 files changed, 7 insertions(+), 5 deletions(-) diff --git a/example/GW150914.py b/example/GW150914.py index b3a79cc9..02cdd540 100644 --- a/example/GW150914.py +++ b/example/GW150914.py @@ -7,6 +7,8 @@ import jax.numpy as jnp import jax +jax.config.update("jax_enable_x64", True) + ########################################### ########## First we grab data ############# ########################################### @@ -30,9 +32,9 @@ xmin = [10, 0.125, -1., -1., 0., -0.05, 0., -1, 0., 0.,-1.], xmax = [80., 1., 1., 1., 2000., 0.05, 2*jnp.pi, 1., jnp.pi, 2*jnp.pi, 1.], naming = ["M_c", "q", "s1_z", "s2_z", "d_L", "t_c", "phase_c", "cos_iota", "psi", "ra", "sin_dec"], - transforms = {"q": lambda q: q/(1+q)**2, - "iota": lambda iota: jnp.arccos(jnp.arcsin(jnp.sin(iota/2*jnp.pi))*2/jnp.pi), - "dec": lambda dec: jnp.arcsin(jnp.arcsin(jnp.sin(dec/2*jnp.pi))*2/jnp.pi)} # sin and arcsin are periodize cos_iota and sin_dec + transforms = {"q": ("eta", lambda q: q/(1+q)**2), + "cos_iota": ("iota",lambda cos_iota: jnp.arccos(jnp.arcsin(jnp.sin(cos_iota/2*jnp.pi))*2/jnp.pi)), + "sin_dec": ("dec",lambda sin_dec: jnp.arcsin(jnp.arcsin(jnp.sin(sin_dec/2*jnp.pi))*2/jnp.pi))} # sin and arcsin are periodize cos_iota and sin_dec ) mass_matrix = jnp.eye(11) @@ -57,5 +59,5 @@ local_sampler_arg = local_sampler_arg, ) -jim.maximize_likelihood([prior.xmin, prior.xmax]) +# jim.maximize_likelihood([prior.xmin, prior.xmax]) jim.sample(jax.random.PRNGKey(42)) diff --git a/example/InjectionRecovery.py b/example/InjectionRecovery.py index 186cac55..1467a5ff 100644 --- a/example/InjectionRecovery.py +++ b/example/InjectionRecovery.py @@ -106,7 +106,7 @@ class InjectionRecoveryParser(Tap): jim = Jim(likelihood, prior, - n_loop_training=10, + n_loop_training=20, n_loop_production = 10, n_local_steps=300, n_global_steps=300, From 65cbf93367cd0fced29a33e6f35353eda159bcb7 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Thu, 24 Aug 2023 17:36:28 -0400 Subject: [PATCH 262/300] turn maximize likelihood back on in GW150914. --- example/GW150914.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/example/GW150914.py b/example/GW150914.py index 02cdd540..db5d3b11 100644 --- a/example/GW150914.py +++ b/example/GW150914.py @@ -59,5 +59,5 @@ local_sampler_arg = local_sampler_arg, ) -# jim.maximize_likelihood([prior.xmin, prior.xmax]) +jim.maximize_likelihood([prior.xmin, prior.xmax]) jim.sample(jax.random.PRNGKey(42)) From 1bee22f7123e455fd9e829bb46366369d68c9f48 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Sun, 3 Sep 2023 13:54:16 -0400 Subject: [PATCH 263/300] format with black --- example/gen_injection_config.py | 2 +- src/jimgw/heterodyneLikelihood.py | 125 +++++++++++++++++++++--------- src/jimgw/likelihood.py | 59 +++++++++++--- 3 files changed, 139 insertions(+), 47 deletions(-) diff --git a/example/gen_injection_config.py b/example/gen_injection_config.py index 507151f8..045c7804 100644 --- a/example/gen_injection_config.py +++ b/example/gen_injection_config.py @@ -25,7 +25,7 @@ def Mc_eta_to_ms(m): polarization_angle = np.random.uniform(prior_range[8,0],prior_range[8,1],N_config) ra = np.random.uniform(prior_range[9,0],prior_range[9,1],N_config) sin_dec = np.random.uniform(prior_range[10,0],prior_range[10,1],N_config) -dec = np.arcsin(sin_dec) +#dec = np.arcsin(sin_dec) directory = '/mnt/home/wwong/ceph/GWProject/JaxGW/RealtimePE/ppPlots/configs/' diff --git a/src/jimgw/heterodyneLikelihood.py b/src/jimgw/heterodyneLikelihood.py index 51f03af5..42fa534f 100644 --- a/src/jimgw/heterodyneLikelihood.py +++ b/src/jimgw/heterodyneLikelihood.py @@ -4,23 +4,25 @@ import jax.numpy as jnp + def max_phase_diff(f, f_low, f_high, chi=1): - gamma = np.arange(-5,6,1)/3. - f = np.repeat(f[:,None],len(gamma),axis=1) + gamma = np.arange(-5, 6, 1) / 3.0 + f = np.repeat(f[:, None], len(gamma), axis=1) f_star = np.repeat(f_low, len(gamma)) f_star[gamma >= 0] = f_high - return 2*np.pi*chi*np.sum((f/f_star)**gamma*np.sign(gamma),axis=1) + return 2 * np.pi * chi * np.sum((f / f_star) ** gamma * np.sign(gamma), axis=1) def make_binning_scheme(freqs, n_bins, chi=1): - phase_diff_array = max_phase_diff(freqs,freqs[0],freqs[-1],chi=1) + phase_diff_array = max_phase_diff(freqs, freqs[0], freqs[-1], chi=1) bin_f = interp1d(phase_diff_array, freqs) f_bins = np.array([]) for i in np.linspace(phase_diff_array[0], phase_diff_array[-1], n_bins): - f_bins = np.append(f_bins,bin_f(i)) - f_bins_center = (f_bins[:-1] + f_bins[1:])/2 + f_bins = np.append(f_bins, bin_f(i)) + f_bins_center = (f_bins[:-1] + f_bins[1:]) / 2 return f_bins, f_bins_center + def compute_coefficients(data, h_ref, psd, freqs, f_bins, f_bins_center): A0_array = [] A1_array = [] @@ -28,14 +30,26 @@ def compute_coefficients(data, h_ref, psd, freqs, f_bins, f_bins_center): B1_array = [] df = freqs[1] - freqs[0] - data_prod = np.array(data*h_ref.conj()) - self_prod = np.array(h_ref*h_ref.conj()) - for i in range(len(f_bins)-1): - f_index = np.where((freqs >= f_bins[i]) & (freqs < f_bins[i+1]))[0] - A0_array.append(4*np.sum(data_prod[f_index]/psd[f_index])*df) - A1_array.append(4*np.sum(data_prod[f_index]/psd[f_index]*(freqs[f_index]-f_bins_center[i]))*df) - B0_array.append(4*np.sum(self_prod[f_index]/psd[f_index])*df) - B1_array.append(4*np.sum(self_prod[f_index]/psd[f_index]*(freqs[f_index]-f_bins_center[i]))*df) + data_prod = np.array(data * h_ref.conj()) + self_prod = np.array(h_ref * h_ref.conj()) + for i in range(len(f_bins) - 1): + f_index = np.where((freqs >= f_bins[i]) & (freqs < f_bins[i + 1]))[0] + A0_array.append(4 * np.sum(data_prod[f_index] / psd[f_index]) * df) + A1_array.append( + 4 + * np.sum( + data_prod[f_index] / psd[f_index] * (freqs[f_index] - f_bins_center[i]) + ) + * df + ) + B0_array.append(4 * np.sum(self_prod[f_index] / psd[f_index]) * df) + B1_array.append( + 4 + * np.sum( + self_prod[f_index] / psd[f_index] * (freqs[f_index] - f_bins_center[i]) + ) + * df + ) A0_array = jnp.array(A0_array) A1_array = jnp.array(A1_array) @@ -43,14 +57,25 @@ def compute_coefficients(data, h_ref, psd, freqs, f_bins, f_bins_center): B1_array = jnp.array(B1_array) return A0_array, A1_array, B0_array, B1_array -def make_heterodyned_likelihood_multiple_detectors(data_list, psd_list, - response_list, h_function, ref_theta, freqs, gmst, epoch, f_ref, n_bins=101): + +def make_heterodyned_likelihood_multiple_detectors( + data_list, + psd_list, + response_list, + h_function, + ref_theta, + freqs, + gmst, + epoch, + f_ref, + n_bins=101, +): num_detector = len(data_list) theta_waveform = ref_theta theta_waveform = theta_waveform.at[5].set(0) raw_hp, raw_hc = h_function(freqs, theta_waveform, f_ref) - index = jnp.where((jnp.abs(raw_hc)+jnp.abs(raw_hp)) > 0) + index = jnp.where((jnp.abs(raw_hc) + jnp.abs(raw_hp)) > 0) freqs = freqs[index] raw_hp = raw_hp[index] raw_hc = raw_hc[index] @@ -64,25 +89,47 @@ def make_heterodyned_likelihood_multiple_detectors(data_list, psd_list, h_ref_low = [] h_ref_bincenter = [] raw_hp_bin, raw_hc_bin = h_function(f_bins[:-1], theta_waveform, f_ref) - raw_hp_bincenter, raw_hc_bincenter = h_function(f_bins_center, theta_waveform, f_ref) + raw_hp_bincenter, raw_hc_bincenter = h_function( + f_bins_center, theta_waveform, f_ref + ) for i in range(num_detector): - h_ref.append(response_list[i](freqs, raw_hp, raw_hc, ra, dec, gmst, ref_theta[8])*jnp.exp(-1j*2*jnp.pi*freqs*(epoch+ref_theta[5]))) - h_ref_low.append(response_list[i](f_bins[:-1], raw_hp_bin, raw_hc_bin, ra, dec, gmst, ref_theta[8])*jnp.exp(-1j*2*jnp.pi*f_bins[:-1]*(epoch+ref_theta[5]))) - h_ref_bincenter.append(response_list[i](f_bins_center, raw_hp_bincenter, raw_hc_bincenter, ra, dec, gmst, ref_theta[8])*jnp.exp(-1j*2*jnp.pi*f_bins_center*(epoch+ref_theta[5]))) - + h_ref.append( + response_list[i](freqs, raw_hp, raw_hc, ra, dec, gmst, ref_theta[8]) + * jnp.exp(-1j * 2 * jnp.pi * freqs * (epoch + ref_theta[5])) + ) + h_ref_low.append( + response_list[i]( + f_bins[:-1], raw_hp_bin, raw_hc_bin, ra, dec, gmst, ref_theta[8] + ) + * jnp.exp(-1j * 2 * jnp.pi * f_bins[:-1] * (epoch + ref_theta[5])) + ) + h_ref_bincenter.append( + response_list[i]( + f_bins_center, + raw_hp_bincenter, + raw_hc_bincenter, + ra, + dec, + gmst, + ref_theta[8], + ) + * jnp.exp(-1j * 2 * jnp.pi * f_bins_center * (epoch + ref_theta[5])) + ) + A0_array = [] A1_array = [] B0_array = [] B1_array = [] for i in range(num_detector): - A0, A1, B0, B1 = compute_coefficients(data_list[i], h_ref[i], psd_list[i], freqs, f_bins, f_bins_center) + A0, A1, B0, B1 = compute_coefficients( + data_list[i], h_ref[i], psd_list[i], freqs, f_bins, f_bins_center + ) A0_array.append(A0) A1_array.append(A1) B0_array.append(B0) B1_array.append(B1) - - + def heterodyned_likelihood(params): theta_waveform = params theta_waveform = theta_waveform.at[5].set(0) @@ -94,16 +141,24 @@ def heterodyned_likelihood(params): raw_hp_center, raw_hc_center = h_function(f_bins_center, theta_waveform, f_ref) for i in range(num_detector): - waveform_low = response_list[i](f_bins[:-1], raw_hp_edge, raw_hc_edge, ra, dec, gmst, params[8])*jnp.exp(-1j*2*jnp.pi*f_bins[:-1]*(epoch+params[5])) - waveform_center = response_list[i](f_bins_center, raw_hp_center, raw_hc_center, ra, dec, gmst, params[8])*jnp.exp(-1j*2*jnp.pi*f_bins_center*(epoch+params[5])) - - r0 = waveform_center/h_ref_bincenter[i] - r1 = (waveform_low/h_ref_low[i] - r0)/(f_bins[:-1]-f_bins_center) - match_filter_SNR = jnp.sum(A0_array[i]*r0.conj() + A1_array[i]*r1.conj()) - optimal_SNR = jnp.sum(B0_array[i]*jnp.abs(r0)**2 + 2*B1_array[i]*(r0*r1.conj()).real) + waveform_low = response_list[i]( + f_bins[:-1], raw_hp_edge, raw_hc_edge, ra, dec, gmst, params[8] + ) * jnp.exp(-1j * 2 * jnp.pi * f_bins[:-1] * (epoch + params[5])) + waveform_center = response_list[i]( + f_bins_center, raw_hp_center, raw_hc_center, ra, dec, gmst, params[8] + ) * jnp.exp(-1j * 2 * jnp.pi * f_bins_center * (epoch + params[5])) + + r0 = waveform_center / h_ref_bincenter[i] + r1 = (waveform_low / h_ref_low[i] - r0) / (f_bins[:-1] - f_bins_center) + match_filter_SNR = jnp.sum( + A0_array[i] * r0.conj() + A1_array[i] * r1.conj() + ) + optimal_SNR = jnp.sum( + B0_array[i] * jnp.abs(r0) ** 2 + 2 * B1_array[i] * (r0 * r1.conj()).real + ) + + output_SNR += (match_filter_SNR - optimal_SNR / 2).real - output_SNR += (match_filter_SNR - optimal_SNR/2).real - return output_SNR - + return heterodyned_likelihood diff --git a/src/jimgw/likelihood.py b/src/jimgw/likelihood.py index e4e11f42..95573435 100644 --- a/src/jimgw/likelihood.py +++ b/src/jimgw/likelihood.py @@ -8,6 +8,7 @@ import jax.numpy as jnp from astropy.time import Time + class LikelihoodBase(ABC): """ Base class for likelihoods. @@ -40,22 +41,26 @@ def evaluate(self, params) -> float: """ raise NotImplementedError + class TransientLikelihoodFD(LikelihoodBase): detectors: list[Detector] waveform: Waveform - def __init__(self, + def __init__( + self, detectors: list[Detector], waveform: Waveform, - trigger_time:float = 0, + trigger_time: float = 0, duration: float = 4, post_trigger_duration: float = 2, ) -> None: self.detectors = detectors self.waveform = waveform self.trigger_time = trigger_time - self.gmst = Time(trigger_time, format='gps').sidereal_time('apparent', 'greenwich').rad + self.gmst = ( + Time(trigger_time, format="gps").sidereal_time("apparent", "greenwich").rad + ) self.trigger_time = trigger_time self.duration = duration @@ -75,20 +80,52 @@ def ifos(self): """ return [detector.name for detector in self.detectors] - def evaluate(self, params: Array, data: dict) -> float: # TODO: Test whether we need to pass data in or with class changes is fine. + def evaluate( + self, params: Array, data: dict + ) -> float: # TODO: Test whether we need to pass data in or with class changes is fine. """ Evaluate the likelihood for a given set of parameters. """ log_likelihood = 0 frequencies = self.detectors[0].frequencies df = frequencies[1] - frequencies[0] - source_params = {"M_c": params[0], "eta": params[1], "s1_z": params[2], "s2_z": params[3], "d_L": params[4], "t_c": params[5], "phase_c": params[6], "iota": params[7], "psi": params[8], "ra": params[9], "dec": params[10]} - detector_params = {"ra": params[9], "dec": params[10], "psi": params[8], "gmst": self.gmst} + source_params = { + "M_c": params[0], + "eta": params[1], + "s1_z": params[2], + "s2_z": params[3], + "d_L": params[4], + "t_c": params[5], + "phase_c": params[6], + "iota": params[7], + "psi": params[8], + "ra": params[9], + "dec": params[10], + } + detector_params = { + "ra": params[9], + "dec": params[10], + "psi": params[8], + "gmst": self.gmst, + } waveform_sky = self.waveform(frequencies, source_params) - align_time = jnp.exp(-1j*2*jnp.pi*frequencies*(self.epoch+params[5])) + align_time = jnp.exp(-1j * 2 * jnp.pi * frequencies * (self.epoch + params[5])) for detector in self.detectors: - waveform_dec = detector.fd_response(frequencies, waveform_sky, detector_params) * align_time - match_filter_SNR = 4 * jnp.sum((jnp.conj(waveform_dec)*detector.data)/detector.psd*df).real - optimal_SNR = 4 * jnp.sum(jnp.conj(waveform_dec)*waveform_dec/detector.psd*df).real - log_likelihood += match_filter_SNR - optimal_SNR/2 + waveform_dec = ( + detector.fd_response(frequencies, waveform_sky, detector_params) + * align_time + ) + match_filter_SNR = ( + 4 + * jnp.sum( + (jnp.conj(waveform_dec) * detector.data) / detector.psd * df + ).real + ) + optimal_SNR = ( + 4 + * jnp.sum( + jnp.conj(waveform_dec) * waveform_dec / detector.psd * df + ).real + ) + log_likelihood += match_filter_SNR - optimal_SNR / 2 return log_likelihood From 9dcd9a6dccafd573a2a53e69183c73edff6a07f1 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Sun, 3 Sep 2023 13:55:27 -0400 Subject: [PATCH 264/300] black format --- src/jimgw/likelihood.py | 4 ++-- src/jimgw/waveform.py | 45 ++++++++++++++++++++++++++++++----------- 2 files changed, 35 insertions(+), 14 deletions(-) diff --git a/src/jimgw/likelihood.py b/src/jimgw/likelihood.py index 95573435..c1a1a18f 100644 --- a/src/jimgw/likelihood.py +++ b/src/jimgw/likelihood.py @@ -1,8 +1,7 @@ from gwpy.timeseries import TimeSeries from gwpy.frequencyseries import FrequencySeries from abc import ABC, abstractmethod -from typing import Tuple -from jaxtyping import Array +from jaxtyping import Array, Float from jimgw.waveform import Waveform from jimgw.detector import Detector import jax.numpy as jnp @@ -42,6 +41,7 @@ def evaluate(self, params) -> float: raise NotImplementedError + class TransientLikelihoodFD(LikelihoodBase): detectors: list[Detector] diff --git a/src/jimgw/waveform.py b/src/jimgw/waveform.py index 7a1b23c9..dc14dd45 100644 --- a/src/jimgw/waveform.py +++ b/src/jimgw/waveform.py @@ -4,14 +4,15 @@ import jax.numpy as jnp from abc import ABC -class Waveform(ABC): +class Waveform(ABC): def __init__(self): return NotImplemented def __call__(self, axis: Array, params: Array) -> Array: return NotImplemented + class RippleIMRPhenomD(Waveform): f_ref: float @@ -21,14 +22,24 @@ def __init__(self, f_ref: float = 20.0): def __call__(self, frequency: Array, params: dict) -> dict: output = {} - ra = params['ra'] - dec = params['dec'] - theta = [params['M_c'], params['eta'], params['s1_z'], params['s2_z'], params['d_L'], 0, params['phase_c'], params['iota']] + ra = params["ra"] + dec = params["dec"] + theta = [ + params["M_c"], + params["eta"], + params["s1_z"], + params["s2_z"], + params["d_L"], + 0, + params["phase_c"], + params["iota"], + ] hp, hc = gen_IMRPhenomD_hphc(frequency, theta, self.f_ref) - output['p'] = hp - output['c'] = hc + output["p"] = hp + output["c"] = hc return output + class RippleIMRPhenomPv2(Waveform): f_ref: float @@ -38,11 +49,21 @@ def __init__(self, f_ref: float = 20.0): def __call__(self, frequency: Array, params: dict) -> Array: output = {} - theta = [params['M_c'], params['eta'], 0.0, 0.0, params['s1_z'], - 0.0, 0.0, params['s2_z'], - params['d_L'], 0, params['phase_c'], params['iota']] + theta = [ + params["M_c"], + params["eta"], + 0.0, + 0.0, + params["s1_z"], + 0.0, + 0.0, + params["s2_z"], + params["d_L"], + 0, + params["phase_c"], + params["iota"], + ] hp, hc = gen_IMRPhenomPv2_hphc(frequency, theta, self.f_ref) - output['p'] = hp - output['c'] = hc + output["p"] = hp + output["c"] = hc return output - From e77d00cd3edce20033955813c45311b194969c2a Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Sun, 3 Sep 2023 13:55:43 -0400 Subject: [PATCH 265/300] update gitignore --- .gitignore | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.gitignore b/.gitignore index f62c1c25..59dc8d9c 100644 --- a/.gitignore +++ b/.gitignore @@ -136,3 +136,6 @@ slurm_script* build* log* *.swp +H1.txt +L1.txt +V1.txt From 2a90877c33854cfa72afaf9afdc1392e3d2552db Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Sun, 3 Sep 2023 14:04:20 -0400 Subject: [PATCH 266/300] Add other spin components to Pv2 --- src/jimgw/likelihood.py | 4 ++-- src/jimgw/waveform.py | 8 ++++---- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/src/jimgw/likelihood.py b/src/jimgw/likelihood.py index c1a1a18f..4c30c365 100644 --- a/src/jimgw/likelihood.py +++ b/src/jimgw/likelihood.py @@ -11,8 +11,8 @@ class LikelihoodBase(ABC): """ Base class for likelihoods. - Note that this likelihood class should work for a somehwat general class of problems. - In light of that, this class would be somewhat abstract, but the idea behind it is this + Note that this likelihood class should work for a some what general class of problems. + In light of that, this class would be some what abstract, but the idea behind it is this handles two main components of a likelihood: the data and the model. It should be able to take the data and model and evaluate the likelihood for a given set of parameters. diff --git a/src/jimgw/waveform.py b/src/jimgw/waveform.py index dc14dd45..c94b81ad 100644 --- a/src/jimgw/waveform.py +++ b/src/jimgw/waveform.py @@ -52,11 +52,11 @@ def __call__(self, frequency: Array, params: dict) -> Array: theta = [ params["M_c"], params["eta"], - 0.0, - 0.0, + params['s1_x'], + params['s1_y'], params["s1_z"], - 0.0, - 0.0, + params['s2_x'], + params['s2_y'], params["s2_z"], params["d_L"], 0, From 40783540a15eb5b56b08c27db2f210adb3e1e314 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Sun, 3 Sep 2023 14:55:03 -0400 Subject: [PATCH 267/300] update prior to work with dictionary input --- example/GW150914.py | 83 +++++++++++++++++++++++++++++---------------- src/jimgw/jim.py | 3 +- src/jimgw/prior.py | 19 ++++++----- 3 files changed, 67 insertions(+), 38 deletions(-) diff --git a/example/GW150914.py b/example/GW150914.py index db5d3b11..fa4799db 100644 --- a/example/GW150914.py +++ b/example/GW150914.py @@ -19,45 +19,70 @@ gps = 1126259462.4 start = gps - 2 end = gps + 2 -fmin = 20. -fmax = 1024. +fmin = 20.0 +fmax = 1024.0 -ifos = ['H1', 'L1'] +ifos = ["H1", "L1"] H1.load_data(gps, 2, 2, fmin, fmax, psd_pad=16, tukey_alpha=0.2) L1.load_data(gps, 2, 2, fmin, fmax, psd_pad=16, tukey_alpha=0.2) likelihood = TransientLikelihoodFD([H1, L1], RippleIMRPhenomD(), gps, 4, 2) prior = Uniform( - xmin = [10, 0.125, -1., -1., 0., -0.05, 0., -1, 0., 0.,-1.], - xmax = [80., 1., 1., 1., 2000., 0.05, 2*jnp.pi, 1., jnp.pi, 2*jnp.pi, 1.], - naming = ["M_c", "q", "s1_z", "s2_z", "d_L", "t_c", "phase_c", "cos_iota", "psi", "ra", "sin_dec"], - transforms = {"q": ("eta", lambda q: q/(1+q)**2), - "cos_iota": ("iota",lambda cos_iota: jnp.arccos(jnp.arcsin(jnp.sin(cos_iota/2*jnp.pi))*2/jnp.pi)), - "sin_dec": ("dec",lambda sin_dec: jnp.arcsin(jnp.arcsin(jnp.sin(sin_dec/2*jnp.pi))*2/jnp.pi))} # sin and arcsin are periodize cos_iota and sin_dec + xmin=[10, 0.125, -1.0, -1.0, 0.0, -0.05, 0.0, -1, 0.0, 0.0, -1.0], + xmax=[80.0, 1.0, 1.0, 1.0, 2000.0, 0.05, 2 * jnp.pi, 1.0, jnp.pi, 2 * jnp.pi, 1.0], + naming=[ + "M_c", + "q", + "s1_z", + "s2_z", + "d_L", + "t_c", + "phase_c", + "cos_iota", + "psi", + "ra", + "sin_dec", + ], + transforms={ + "q": ("eta", lambda q: q / (1 + q) ** 2), + "cos_iota": ( + "iota", + lambda cos_iota: jnp.arccos( + jnp.arcsin(jnp.sin(cos_iota / 2 * jnp.pi)) * 2 / jnp.pi + ), + ), + "sin_dec": ( + "dec", + lambda sin_dec: jnp.arcsin( + jnp.arcsin(jnp.sin(sin_dec / 2 * jnp.pi)) * 2 / jnp.pi + ), + ), + }, # sin and arcsin are for periodizing cos_iota and sin_dec, otherwise it might gives some nans because of numpy ) mass_matrix = jnp.eye(11) -mass_matrix = mass_matrix.at[1,1].set(1e-3) -mass_matrix = mass_matrix.at[5,5].set(1e-3) -local_sampler_arg = {"step_size": mass_matrix*3e-3} - -jim = Jim(likelihood, - prior, - n_loop_training=10, - n_loop_production = 10, - n_local_steps=300, - n_global_steps=300, - n_chains=500, - n_epochs=300, - learning_rate = 0.001, - momentum = 0.9, - batch_size = 50000, - use_global=True, - keep_quantile=0., - train_thinning = 40, - local_sampler_arg = local_sampler_arg, - ) +mass_matrix = mass_matrix.at[1, 1].set(1e-3) +mass_matrix = mass_matrix.at[5, 5].set(1e-3) +local_sampler_arg = {"step_size": mass_matrix * 3e-3} + +jim = Jim( + likelihood, + prior, + n_loop_training=10, + n_loop_production=10, + n_local_steps=300, + n_global_steps=300, + n_chains=500, + n_epochs=300, + learning_rate=0.001, + momentum=0.9, + batch_size=50000, + use_global=True, + keep_quantile=0.0, + train_thinning=40, + local_sampler_arg=local_sampler_arg, +) jim.maximize_likelihood([prior.xmin, prior.xmax]) jim.sample(jax.random.PRNGKey(42)) diff --git a/src/jimgw/jim.py b/src/jimgw/jim.py index a7f1f87b..f9aa99de 100644 --- a/src/jimgw/jim.py +++ b/src/jimgw/jim.py @@ -66,7 +66,8 @@ def maximize_likelihood(self, bounds: tuple[Array,Array],set_nwalkers: int = 100 return best_fit def posterior(self, params: Array): - return self.Likelihood.evaluate(params) + self.Prior.log_prob(params) + named_params = self.Prior.add_name(params, with_transform=True) + return self.Likelihood.evaluate(named_params) + self.Prior.log_prob(params) def sample(self, key: jax.random.PRNGKey, initial_guess: Array = None): diff --git a/src/jimgw/prior.py b/src/jimgw/prior.py index b8dbce99..e5ec7eb7 100644 --- a/src/jimgw/prior.py +++ b/src/jimgw/prior.py @@ -10,6 +10,9 @@ class Prior(Distribution): A thin wrapper build on top of flowMC distributions to do book keeping. Should not be used directly since it does not implement any of the real method. + + The rationale behind this is to have a class that can be used to keep track of + the names of the parameters and the transforms that are applied to them. """ naming: list[str] @@ -31,12 +34,12 @@ def __init__(self, naming: list[str], transforms: dict[tuple[str,Callable]] = {} of the transform and the transform itself. """ self.naming = naming - self.transforms = [] + self.transforms = {} for name in naming: if name in transforms: - self.transforms.append(transforms[name]) + self.transforms[name] = transforms[name] else: - self.transforms.append((name,lambda x: x)) + self.transforms[name] = (name,lambda x: x) def transform(self, x: Array) -> Array: """ @@ -61,12 +64,12 @@ def add_name(self, x: Array, with_transform: bool = False) -> dict: Turn an array into a dictionary """ if with_transform: - naming = [] - for i,transform in enumerate(self.transforms): - naming.append(transform[0]) + output = {} + for index, (key, value) in enumerate(self.transforms.items()): + output[value[0]] = value[1](x[index]) + return output else: - naming = self.naming - return dict(zip(naming, x)) + return dict(zip(self.naming, x)) class Uniform(Prior): From 6d05bb1fb632c4893e8b9fca74ad3850baa7f81a Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Sun, 3 Sep 2023 15:16:05 -0400 Subject: [PATCH 268/300] Make likelihood deal with dictionary input instead of array input --- src/jimgw/likelihood.py | 26 ++++---------------------- 1 file changed, 4 insertions(+), 22 deletions(-) diff --git a/src/jimgw/likelihood.py b/src/jimgw/likelihood.py index 4c30c365..7d001dd7 100644 --- a/src/jimgw/likelihood.py +++ b/src/jimgw/likelihood.py @@ -89,30 +89,12 @@ def evaluate( log_likelihood = 0 frequencies = self.detectors[0].frequencies df = frequencies[1] - frequencies[0] - source_params = { - "M_c": params[0], - "eta": params[1], - "s1_z": params[2], - "s2_z": params[3], - "d_L": params[4], - "t_c": params[5], - "phase_c": params[6], - "iota": params[7], - "psi": params[8], - "ra": params[9], - "dec": params[10], - } - detector_params = { - "ra": params[9], - "dec": params[10], - "psi": params[8], - "gmst": self.gmst, - } - waveform_sky = self.waveform(frequencies, source_params) - align_time = jnp.exp(-1j * 2 * jnp.pi * frequencies * (self.epoch + params[5])) + params['gmst'] = self.gmst + waveform_sky = self.waveform(frequencies, params) + align_time = jnp.exp(-1j * 2 * jnp.pi * frequencies * (self.epoch + params['t_c'])) for detector in self.detectors: waveform_dec = ( - detector.fd_response(frequencies, waveform_sky, detector_params) + detector.fd_response(frequencies, waveform_sky, params) * align_time ) match_filter_SNR = ( From fc9690870dcea6e25d07f709bf940c048f885794 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Sun, 3 Sep 2023 16:08:09 -0400 Subject: [PATCH 269/300] Jim should support dictionary naming in the likelihood now --- src/jimgw/jim.py | 21 ++++++++------------- src/jimgw/prior.py | 17 +++++++++-------- 2 files changed, 17 insertions(+), 21 deletions(-) diff --git a/src/jimgw/jim.py b/src/jimgw/jim.py index f9aa99de..8af3b66f 100644 --- a/src/jimgw/jim.py +++ b/src/jimgw/jim.py @@ -26,16 +26,9 @@ def __init__(self, likelihood: LikelihoodBase, prior: Prior, **kwargs): hidden_size = kwargs.get("hidden_size", [128,128]) num_bins = kwargs.get("num_bins", 8) - def posterior(x: Array, data:dict): - prior = self.Prior.log_prob(x) - x = self.Prior.transform(x) - return self.Likelihood.evaluate(x, data) + prior - - self.posterior = posterior - local_sampler_arg = kwargs.get("local_sampler_arg", {}) - local_sampler = MALA(posterior, True, local_sampler_arg) # Remember to add routine to find automated mass matrix + local_sampler = MALA(self.posterior, True, local_sampler_arg) # Remember to add routine to find automated mass matrix model = MaskedCouplingRQSpline(self.Prior.n_dim, num_layers, hidden_size, num_bins, rng_key_set[-1]) self.Sampler = Sampler( @@ -65,9 +58,9 @@ def maximize_likelihood(self, bounds: tuple[Array,Array],set_nwalkers: int = 100 best_fit = optimizer.get_result()[0] return best_fit - def posterior(self, params: Array): - named_params = self.Prior.add_name(params, with_transform=True) - return self.Likelihood.evaluate(named_params) + self.Prior.log_prob(params) + def posterior(self, params: Array, data: dict): + named_params = self.Prior.add_name(params, transform_name=True, transform_value=True) + return self.Likelihood.evaluate(named_params, data) + self.Prior.log_prob(params) def sample(self, key: jax.random.PRNGKey, initial_guess: Array = None): @@ -123,9 +116,11 @@ def get_samples(self, training: bool = False): Array: Samples """ if training: - return self.Sampler.get_sampler_state(training=True)["chains"] + chains = self.Sampler.get_sampler_state(training=True)["chains"] else: - return self.Sampler.get_sampler_state(training=False)["chains"] + chains = self.Sampler.get_sampler_state(training=False)["chains"] + + chains = self.Prior.add_name(chains.transpose(2,0,1), transform_name=True) def plot(self): pass \ No newline at end of file diff --git a/src/jimgw/prior.py b/src/jimgw/prior.py index e5ec7eb7..669bd5d8 100644 --- a/src/jimgw/prior.py +++ b/src/jimgw/prior.py @@ -59,18 +59,19 @@ def transform(self, x: Array) -> Array: x = x.at[i].set(transform[1](x[i])) return x - def add_name(self, x: Array, with_transform: bool = False) -> dict: + def add_name(self, x: Array, transform_name: bool = False, transform_value: bool = False) -> dict: """ Turn an array into a dictionary """ - if with_transform: - output = {} - for index, (key, value) in enumerate(self.transforms.items()): - output[value[0]] = value[1](x[index]) - return output + if transform_name: + naming = [value[0] for value in self.transforms.values()] else: - return dict(zip(self.naming, x)) - + naming = self.naming + if transform_value: + value = [value[1](x[index]) for index, value in enumerate(self.transforms.values())] + else: + value = x + return dict(zip(naming,value)) class Uniform(Prior): From 4f59e67fef6f9cf41561be111cda6db34062b796 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Sun, 3 Sep 2023 18:48:35 -0400 Subject: [PATCH 270/300] Heterodyne working --- src/jimgw/detector.py | 3 +- src/jimgw/jim.py | 5 +- src/jimgw/likelihood.py | 195 +++++++++++++++++++++++++++++++++++++++- 3 files changed, 198 insertions(+), 5 deletions(-) diff --git a/src/jimgw/detector.py b/src/jimgw/detector.py index 7fe057a6..0ff7478a 100644 --- a/src/jimgw/detector.py +++ b/src/jimgw/detector.py @@ -34,6 +34,8 @@ class Detector(ABC): Base class for all detectors. """ + name: str + @abstractmethod def load_data(self, data): @@ -53,7 +55,6 @@ def td_response(self, time: Array, h: Array, params: dict) -> Array: class GroundBased2G(Detector): - name: str polarization_mode: list[Polarization] frequencies: Array = None data : Array = None diff --git a/src/jimgw/jim.py b/src/jimgw/jim.py index 8af3b66f..12aa89c1 100644 --- a/src/jimgw/jim.py +++ b/src/jimgw/jim.py @@ -40,7 +40,7 @@ def __init__(self, likelihood: LikelihoodBase, prior: Prior, **kwargs): **kwargs) - def maximize_likelihood(self, bounds: tuple[Array,Array],set_nwalkers: int = 100, n_loops: int = 2000, seed = 92348): + def maximize_likelihood(self, bounds: tuple[Array,Array], set_nwalkers: int = 100, n_loops: int = 2000, seed = 92348): bounds = jnp.array(bounds).T key = jax.random.PRNGKey(seed) set_nwalkers = set_nwalkers @@ -105,7 +105,7 @@ def print_summary(self): print(f"Local acceptance: {production_local_acceptance.mean():.3f} +/- {production_local_acceptance.std():.3f}") print(f"Global acceptance: {production_global_acceptance.mean():.3f} +/- {production_global_acceptance.std():.3f}") - def get_samples(self, training: bool = False): + def get_samples(self, training: bool = False) -> dict: """ Get the samples from the sampler @@ -121,6 +121,7 @@ def get_samples(self, training: bool = False): chains = self.Sampler.get_sampler_state(training=False)["chains"] chains = self.Prior.add_name(chains.transpose(2,0,1), transform_name=True) + return chains def plot(self): pass \ No newline at end of file diff --git a/src/jimgw/likelihood.py b/src/jimgw/likelihood.py index 7d001dd7..7ac740cb 100644 --- a/src/jimgw/likelihood.py +++ b/src/jimgw/likelihood.py @@ -1,11 +1,14 @@ -from gwpy.timeseries import TimeSeries -from gwpy.frequencyseries import FrequencySeries from abc import ABC, abstractmethod from jaxtyping import Array, Float from jimgw.waveform import Waveform from jimgw.detector import Detector import jax.numpy as jnp from astropy.time import Time +import numpy as np +from scipy.interpolate import interp1d +import jax +from flowMC.utils.EvolutionaryOptimizer import EvolutionaryOptimizer +from jimgw.prior import Prior class LikelihoodBase(ABC): @@ -111,3 +114,191 @@ def evaluate( ) log_likelihood += match_filter_SNR - optimal_SNR / 2 return log_likelihood + +class HeterodynedTransientLikelihoodFD(TransientLikelihoodFD): + + n_bins: int # Number of bins to use for the likelihood + ref_params: dict # Reference parameters for the likelihood + freq_grid_low: Array # Heterodyned frequency grid + freq_grid_center: Array # Heterodyned frequency grid at the center of the bin + waveform_low_ref: dict[Array] # Reference waveform at the low edge of the frequency bin, keyed by detector name + waveform_center_ref: dict[Array] # Reference waveform at the center of the frequency bin, keyed by detector name + A0_array: dict[Array] # A0 array for the likelihood, keyed by detector name + A1_array: dict[Array] # A1 array for the likelihood, keyed by detector name + B0_array: dict[Array] # B0 array for the likelihood, keyed by detector name + B1_array: dict[Array] # B1 array for the likelihood, keyed by detector name + + def __init__( + self, + detectors: list[Detector], + waveform: Waveform, + prior: Prior, + bounds: tuple[Array, Array], + n_bins: int = 101, + trigger_time: float = 0, + duration: float = 4, + post_trigger_duration: float = 2, + n_walkers: int = 100, + n_loops: int = 2000, + ) -> None: + super().__init__(detectors, waveform, trigger_time, duration, post_trigger_duration) + + frequency_original = self.detectors[0].frequencies + freq_grid, self.freq_grid_center = self.make_binning_scheme(np.array(frequency_original), n_bins+1) + self.freq_grid_low = freq_grid[:-1] + + self.ref_params = self.maximize_likelihood(bounds=bounds, prior=prior, set_nwalkers=n_walkers, n_loops=n_loops) + + self.ref_params['gmst'] = self.gmst + + self.waveform_low_ref = {} + self.waveform_center_ref = {} + self.A0_array = {} + self.A1_array = {} + self.B0_array = {} + self.B1_array = {} + + h_sky = self.waveform(frequency_original, self.ref_params) + h_sky_low = self.waveform(self.freq_grid_low, self.ref_params) + h_sky_center = self.waveform(self.freq_grid_center, self.ref_params) + + align_time = jnp.exp(-1j * 2 * jnp.pi * frequency_original * (self.epoch + self.ref_params['t_c'])) + align_time_low = jnp.exp(-1j * 2 * jnp.pi * self.freq_grid_low * (self.epoch + self.ref_params['t_c'])) + align_time_center = jnp.exp(-1j * 2 * jnp.pi * self.freq_grid_center * (self.epoch + self.ref_params['t_c'])) + + for detector in self.detectors: + waveform_ref = detector.fd_response(frequency_original, h_sky, self.ref_params) * align_time + self.waveform_low_ref[detector.name] = detector.fd_response(self.freq_grid_low, h_sky_low, self.ref_params) * align_time_low + self.waveform_center_ref[detector.name] = detector.fd_response(self.freq_grid_center, h_sky_center, self.ref_params) * align_time_center + A0, A1, B0, B1 = self.compute_coefficients(detector.data, waveform_ref, detector.psd, frequency_original, freq_grid, self.freq_grid_center) + self.A0_array[detector.name] = A0 + self.A1_array[detector.name] = A1 + self.B0_array[detector.name] = B0 + self.B1_array[detector.name] = B1 + + def evaluate(self, params: Array, data: dict) -> float: + log_likelihood = 0 + frequencies_low = self.freq_grid_low + frequencies_center = self.freq_grid_center + params['gmst'] = self.gmst + waveform_sky_low = self.waveform(frequencies_low, params) + waveform_sky_center = self.waveform(frequencies_center, params) + align_time_low = jnp.exp(-1j * 2 * jnp.pi * frequencies_low * (self.epoch + params['t_c'])) + align_time_center = jnp.exp(-1j * 2 * jnp.pi * frequencies_center * (self.epoch + params['t_c'])) + for detector in self.detectors: + waveform_low = ( + detector.fd_response(frequencies_low, waveform_sky_low, params) + * align_time_low + ) + waveform_center = ( + detector.fd_response(frequencies_center, waveform_sky_center, params) + * align_time_center + ) + r0 = waveform_center / self.waveform_center_ref[detector.name] + r1 = (waveform_low / self.waveform_low_ref[detector.name] - r0) / (frequencies_low - frequencies_center) + match_filter_SNR = jnp.nansum( + self.A0_array[detector.name] * r0.conj() + self.A1_array[detector.name] * r1.conj() + ) + optimal_SNR = jnp.nansum( + self.B0_array[detector.name] * jnp.abs(r0) ** 2 + 2 * self.B1_array[detector.name] * (r0 * r1.conj()).real + ) + log_likelihood += (match_filter_SNR - optimal_SNR / 2).real + + return log_likelihood + + def evaluate_original( + self, params: Array, data: dict + ) -> float: # TODO: Test whether we need to pass data in or with class changes is fine. + """ + Evaluate the likelihood for a given set of parameters. + """ + log_likelihood = 0 + frequencies = self.detectors[0].frequencies + df = frequencies[1] - frequencies[0] + params['gmst'] = self.gmst + waveform_sky = self.waveform(frequencies, params) + align_time = jnp.exp(-1j * 2 * jnp.pi * frequencies * (self.epoch + params['t_c'])) + for detector in self.detectors: + waveform_dec = ( + detector.fd_response(frequencies, waveform_sky, params) + * align_time + ) + match_filter_SNR = ( + 4 + * jnp.sum( + (jnp.conj(waveform_dec) * detector.data) / detector.psd * df + ).real + ) + optimal_SNR = ( + 4 + * jnp.sum( + jnp.conj(waveform_dec) * waveform_dec / detector.psd * df + ).real + ) + log_likelihood += match_filter_SNR - optimal_SNR / 2 + return log_likelihood + + @staticmethod + def max_phase_diff(f, f_low, f_high, chi=1): + gamma = np.arange(-5, 6, 1) / 3.0 + f = np.repeat(f[:, None], len(gamma), axis=1) + f_star = np.repeat(f_low, len(gamma)) + f_star[gamma >= 0] = f_high + return 2 * np.pi * chi * np.sum((f / f_star) ** gamma * np.sign(gamma), axis=1) + + def make_binning_scheme(self, freqs, n_bins, chi=1): + phase_diff_array = self.max_phase_diff(freqs, freqs[0], freqs[-1], chi=1) + bin_f = interp1d(phase_diff_array, freqs) + f_bins = np.array([]) + for i in np.linspace(phase_diff_array[0], phase_diff_array[-1], n_bins): + f_bins = np.append(f_bins, bin_f(i)) + f_bins_center = (f_bins[:-1] + f_bins[1:]) / 2 + return f_bins, f_bins_center + + @staticmethod + def compute_coefficients(data, h_ref, psd, freqs, f_bins, f_bins_center): + A0_array = [] + A1_array = [] + B0_array = [] + B1_array = [] + + df = freqs[1] - freqs[0] + data_prod = np.array(data * h_ref.conj()) + self_prod = np.array(h_ref * h_ref.conj()) + for i in range(len(f_bins) - 1): + f_index = np.where((freqs >= f_bins[i]) & (freqs < f_bins[i + 1]))[0] + A0_array.append(4 * np.sum(data_prod[f_index] / psd[f_index]) * df) + A1_array.append( + 4 + * np.sum( + data_prod[f_index] / psd[f_index] * (freqs[f_index] - f_bins_center[i]) + ) + * df + ) + B0_array.append(4 * np.sum(self_prod[f_index] / psd[f_index]) * df) + B1_array.append( + 4 + * np.sum( + self_prod[f_index] / psd[f_index] * (freqs[f_index] - f_bins_center[i]) + ) + * df + ) + + A0_array = jnp.array(A0_array) + A1_array = jnp.array(A1_array) + B0_array = jnp.array(B0_array) + B1_array = jnp.array(B1_array) + return A0_array, A1_array, B0_array, B1_array + + def maximize_likelihood(self, bounds: tuple[Array,Array], prior: Prior, set_nwalkers: int = 100, n_loops: int = 2000): + bounds = jnp.array(bounds).T + set_nwalkers = set_nwalkers + + y = lambda x: -self.evaluate_original(prior.add_name(x, transform_name=True, transform_value=True), None) + y = jax.jit(jax.vmap(y)) + + print("Starting the optimizer") + optimizer = EvolutionaryOptimizer(len(bounds), verbose = True) + state = optimizer.optimize(y, bounds, n_loops=n_loops) + best_fit = optimizer.get_result()[0] + return prior.add_name(best_fit, transform_name=True, transform_value=True) From f74579b2705e883dc1a61907fb6b83c4eaa262c8 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Sun, 3 Sep 2023 18:49:18 -0400 Subject: [PATCH 271/300] Add heterodyning line in GW150914 --- example/GW150914.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/example/GW150914.py b/example/GW150914.py index fa4799db..6b1e7c32 100644 --- a/example/GW150914.py +++ b/example/GW150914.py @@ -1,7 +1,7 @@ import time from jimgw.jim import Jim from jimgw.detector import H1, L1 -from jimgw.likelihood import TransientLikelihoodFD +from jimgw.likelihood import HeterodynedTransientLikelihoodFD, TransientLikelihoodFD from jimgw.waveform import RippleIMRPhenomD from jimgw.prior import Uniform import jax.numpy as jnp @@ -27,7 +27,6 @@ H1.load_data(gps, 2, 2, fmin, fmax, psd_pad=16, tukey_alpha=0.2) L1.load_data(gps, 2, 2, fmin, fmax, psd_pad=16, tukey_alpha=0.2) -likelihood = TransientLikelihoodFD([H1, L1], RippleIMRPhenomD(), gps, 4, 2) prior = Uniform( xmin=[10, 0.125, -1.0, -1.0, 0.0, -0.05, 0.0, -1, 0.0, 0.0, -1.0], xmax=[80.0, 1.0, 1.0, 1.0, 2000.0, 0.05, 2 * jnp.pi, 1.0, jnp.pi, 2 * jnp.pi, 1.0], @@ -60,6 +59,9 @@ ), }, # sin and arcsin are for periodizing cos_iota and sin_dec, otherwise it might gives some nans because of numpy ) +likelihood = TransientLikelihoodFD([H1, L1], waveform=RippleIMRPhenomD(), trigger_time=gps, duration=4, post_trigger_duration=2) +# likelihood = HeterodynedTransientLikelihoodFD([H1, L1], prior=prior, bounds=[prior.xmin, prior.xmax], waveform=RippleIMRPhenomD(), trigger_time=gps, duration=4, post_trigger_duration=2) + mass_matrix = jnp.eye(11) mass_matrix = mass_matrix.at[1, 1].set(1e-3) From 33975d79d7b9f81e04bbdcfec6441ba164fbb28b Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Sun, 3 Sep 2023 18:52:46 -0400 Subject: [PATCH 272/300] black formatting --- src/jimgw/likelihood.py | 150 +++++++++++++++++++++++++++++----------- 1 file changed, 109 insertions(+), 41 deletions(-) diff --git a/src/jimgw/likelihood.py b/src/jimgw/likelihood.py index 7ac740cb..896f24ed 100644 --- a/src/jimgw/likelihood.py +++ b/src/jimgw/likelihood.py @@ -44,7 +44,6 @@ def evaluate(self, params) -> float: raise NotImplementedError - class TransientLikelihoodFD(LikelihoodBase): detectors: list[Detector] @@ -92,13 +91,14 @@ def evaluate( log_likelihood = 0 frequencies = self.detectors[0].frequencies df = frequencies[1] - frequencies[0] - params['gmst'] = self.gmst + params["gmst"] = self.gmst waveform_sky = self.waveform(frequencies, params) - align_time = jnp.exp(-1j * 2 * jnp.pi * frequencies * (self.epoch + params['t_c'])) + align_time = jnp.exp( + -1j * 2 * jnp.pi * frequencies * (self.epoch + params["t_c"]) + ) for detector in self.detectors: waveform_dec = ( - detector.fd_response(frequencies, waveform_sky, params) - * align_time + detector.fd_response(frequencies, waveform_sky, params) * align_time ) match_filter_SNR = ( 4 @@ -115,18 +115,23 @@ def evaluate( log_likelihood += match_filter_SNR - optimal_SNR / 2 return log_likelihood + class HeterodynedTransientLikelihoodFD(TransientLikelihoodFD): - n_bins: int # Number of bins to use for the likelihood - ref_params: dict # Reference parameters for the likelihood - freq_grid_low: Array # Heterodyned frequency grid - freq_grid_center: Array # Heterodyned frequency grid at the center of the bin - waveform_low_ref: dict[Array] # Reference waveform at the low edge of the frequency bin, keyed by detector name - waveform_center_ref: dict[Array] # Reference waveform at the center of the frequency bin, keyed by detector name - A0_array: dict[Array] # A0 array for the likelihood, keyed by detector name - A1_array: dict[Array] # A1 array for the likelihood, keyed by detector name - B0_array: dict[Array] # B0 array for the likelihood, keyed by detector name - B1_array: dict[Array] # B1 array for the likelihood, keyed by detector name + n_bins: int # Number of bins to use for the likelihood + ref_params: dict # Reference parameters for the likelihood + freq_grid_low: Array # Heterodyned frequency grid + freq_grid_center: Array # Heterodyned frequency grid at the center of the bin + waveform_low_ref: dict[ + Array + ] # Reference waveform at the low edge of the frequency bin, keyed by detector name + waveform_center_ref: dict[ + Array + ] # Reference waveform at the center of the frequency bin, keyed by detector name + A0_array: dict[Array] # A0 array for the likelihood, keyed by detector name + A1_array: dict[Array] # A1 array for the likelihood, keyed by detector name + B0_array: dict[Array] # B0 array for the likelihood, keyed by detector name + B1_array: dict[Array] # B1 array for the likelihood, keyed by detector name def __init__( self, @@ -141,15 +146,21 @@ def __init__( n_walkers: int = 100, n_loops: int = 2000, ) -> None: - super().__init__(detectors, waveform, trigger_time, duration, post_trigger_duration) + super().__init__( + detectors, waveform, trigger_time, duration, post_trigger_duration + ) frequency_original = self.detectors[0].frequencies - freq_grid, self.freq_grid_center = self.make_binning_scheme(np.array(frequency_original), n_bins+1) + freq_grid, self.freq_grid_center = self.make_binning_scheme( + np.array(frequency_original), n_bins + 1 + ) self.freq_grid_low = freq_grid[:-1] - self.ref_params = self.maximize_likelihood(bounds=bounds, prior=prior, set_nwalkers=n_walkers, n_loops=n_loops) + self.ref_params = self.maximize_likelihood( + bounds=bounds, prior=prior, set_nwalkers=n_walkers, n_loops=n_loops + ) - self.ref_params['gmst'] = self.gmst + self.ref_params["gmst"] = self.gmst self.waveform_low_ref = {} self.waveform_center_ref = {} @@ -162,15 +173,51 @@ def __init__( h_sky_low = self.waveform(self.freq_grid_low, self.ref_params) h_sky_center = self.waveform(self.freq_grid_center, self.ref_params) - align_time = jnp.exp(-1j * 2 * jnp.pi * frequency_original * (self.epoch + self.ref_params['t_c'])) - align_time_low = jnp.exp(-1j * 2 * jnp.pi * self.freq_grid_low * (self.epoch + self.ref_params['t_c'])) - align_time_center = jnp.exp(-1j * 2 * jnp.pi * self.freq_grid_center * (self.epoch + self.ref_params['t_c'])) + align_time = jnp.exp( + -1j + * 2 + * jnp.pi + * frequency_original + * (self.epoch + self.ref_params["t_c"]) + ) + align_time_low = jnp.exp( + -1j + * 2 + * jnp.pi + * self.freq_grid_low + * (self.epoch + self.ref_params["t_c"]) + ) + align_time_center = jnp.exp( + -1j + * 2 + * jnp.pi + * self.freq_grid_center + * (self.epoch + self.ref_params["t_c"]) + ) for detector in self.detectors: - waveform_ref = detector.fd_response(frequency_original, h_sky, self.ref_params) * align_time - self.waveform_low_ref[detector.name] = detector.fd_response(self.freq_grid_low, h_sky_low, self.ref_params) * align_time_low - self.waveform_center_ref[detector.name] = detector.fd_response(self.freq_grid_center, h_sky_center, self.ref_params) * align_time_center - A0, A1, B0, B1 = self.compute_coefficients(detector.data, waveform_ref, detector.psd, frequency_original, freq_grid, self.freq_grid_center) + waveform_ref = ( + detector.fd_response(frequency_original, h_sky, self.ref_params) + * align_time + ) + self.waveform_low_ref[detector.name] = ( + detector.fd_response(self.freq_grid_low, h_sky_low, self.ref_params) + * align_time_low + ) + self.waveform_center_ref[detector.name] = ( + detector.fd_response( + self.freq_grid_center, h_sky_center, self.ref_params + ) + * align_time_center + ) + A0, A1, B0, B1 = self.compute_coefficients( + detector.data, + waveform_ref, + detector.psd, + frequency_original, + freq_grid, + self.freq_grid_center, + ) self.A0_array[detector.name] = A0 self.A1_array[detector.name] = A1 self.B0_array[detector.name] = B0 @@ -180,11 +227,15 @@ def evaluate(self, params: Array, data: dict) -> float: log_likelihood = 0 frequencies_low = self.freq_grid_low frequencies_center = self.freq_grid_center - params['gmst'] = self.gmst + params["gmst"] = self.gmst waveform_sky_low = self.waveform(frequencies_low, params) waveform_sky_center = self.waveform(frequencies_center, params) - align_time_low = jnp.exp(-1j * 2 * jnp.pi * frequencies_low * (self.epoch + params['t_c'])) - align_time_center = jnp.exp(-1j * 2 * jnp.pi * frequencies_center * (self.epoch + params['t_c'])) + align_time_low = jnp.exp( + -1j * 2 * jnp.pi * frequencies_low * (self.epoch + params["t_c"]) + ) + align_time_center = jnp.exp( + -1j * 2 * jnp.pi * frequencies_center * (self.epoch + params["t_c"]) + ) for detector in self.detectors: waveform_low = ( detector.fd_response(frequencies_low, waveform_sky_low, params) @@ -195,12 +246,16 @@ def evaluate(self, params: Array, data: dict) -> float: * align_time_center ) r0 = waveform_center / self.waveform_center_ref[detector.name] - r1 = (waveform_low / self.waveform_low_ref[detector.name] - r0) / (frequencies_low - frequencies_center) + r1 = (waveform_low / self.waveform_low_ref[detector.name] - r0) / ( + frequencies_low - frequencies_center + ) match_filter_SNR = jnp.nansum( - self.A0_array[detector.name] * r0.conj() + self.A1_array[detector.name] * r1.conj() + self.A0_array[detector.name] * r0.conj() + + self.A1_array[detector.name] * r1.conj() ) optimal_SNR = jnp.nansum( - self.B0_array[detector.name] * jnp.abs(r0) ** 2 + 2 * self.B1_array[detector.name] * (r0 * r1.conj()).real + self.B0_array[detector.name] * jnp.abs(r0) ** 2 + + 2 * self.B1_array[detector.name] * (r0 * r1.conj()).real ) log_likelihood += (match_filter_SNR - optimal_SNR / 2).real @@ -215,13 +270,14 @@ def evaluate_original( log_likelihood = 0 frequencies = self.detectors[0].frequencies df = frequencies[1] - frequencies[0] - params['gmst'] = self.gmst + params["gmst"] = self.gmst waveform_sky = self.waveform(frequencies, params) - align_time = jnp.exp(-1j * 2 * jnp.pi * frequencies * (self.epoch + params['t_c'])) + align_time = jnp.exp( + -1j * 2 * jnp.pi * frequencies * (self.epoch + params["t_c"]) + ) for detector in self.detectors: waveform_dec = ( - detector.fd_response(frequencies, waveform_sky, params) - * align_time + detector.fd_response(frequencies, waveform_sky, params) * align_time ) match_filter_SNR = ( 4 @@ -271,7 +327,9 @@ def compute_coefficients(data, h_ref, psd, freqs, f_bins, f_bins_center): A1_array.append( 4 * np.sum( - data_prod[f_index] / psd[f_index] * (freqs[f_index] - f_bins_center[i]) + data_prod[f_index] + / psd[f_index] + * (freqs[f_index] - f_bins_center[i]) ) * df ) @@ -279,7 +337,9 @@ def compute_coefficients(data, h_ref, psd, freqs, f_bins, f_bins_center): B1_array.append( 4 * np.sum( - self_prod[f_index] / psd[f_index] * (freqs[f_index] - f_bins_center[i]) + self_prod[f_index] + / psd[f_index] + * (freqs[f_index] - f_bins_center[i]) ) * df ) @@ -290,15 +350,23 @@ def compute_coefficients(data, h_ref, psd, freqs, f_bins, f_bins_center): B1_array = jnp.array(B1_array) return A0_array, A1_array, B0_array, B1_array - def maximize_likelihood(self, bounds: tuple[Array,Array], prior: Prior, set_nwalkers: int = 100, n_loops: int = 2000): + def maximize_likelihood( + self, + bounds: tuple[Array, Array], + prior: Prior, + set_nwalkers: int = 100, + n_loops: int = 2000, + ): bounds = jnp.array(bounds).T set_nwalkers = set_nwalkers - y = lambda x: -self.evaluate_original(prior.add_name(x, transform_name=True, transform_value=True), None) + y = lambda x: -self.evaluate_original( + prior.add_name(x, transform_name=True, transform_value=True), None + ) y = jax.jit(jax.vmap(y)) print("Starting the optimizer") - optimizer = EvolutionaryOptimizer(len(bounds), verbose = True) + optimizer = EvolutionaryOptimizer(len(bounds), verbose=True) state = optimizer.optimize(y, bounds, n_loops=n_loops) best_fit = optimizer.get_result()[0] return prior.add_name(best_fit, transform_name=True, transform_value=True) From c6392d07bc73c8b1900a50e5543f7ff045fe2427 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 4 Sep 2023 20:51:43 -0400 Subject: [PATCH 273/300] Delete files that are migrated --- src/jimgw/generate_noise.py | 143 -------------------------- src/jimgw/heterodyneLikelihood.py | 164 ------------------------------ 2 files changed, 307 deletions(-) delete mode 100644 src/jimgw/generate_noise.py delete mode 100644 src/jimgw/heterodyneLikelihood.py diff --git a/src/jimgw/generate_noise.py b/src/jimgw/generate_noise.py deleted file mode 100644 index 5c448fd5..00000000 --- a/src/jimgw/generate_noise.py +++ /dev/null @@ -1,143 +0,0 @@ -# Import packages -from typing import List, Tuple -import jax.numpy as jnp -import jax -import numpy as np -import requests -import scipy.interpolate as interpolate - -# This is needed for the noise generation to have enough precision to work -jax.config.update("jax_enable_x64", True) - - -def generate_LVK_PSDdict(ifos: List[str] = ["H1", "L1", "V1"]): - psd_dict = {} - for ifo in ifos: - if ifo == "H1": - try: - f, asd_vals = np.loadtxt("H1.txt", unpack=True) - except: - print("Grabbing GWTC-2 PSD for H1") - url = "https://dcc.ligo.org/public/0169/P2000251/001/O3-H1-C01_CLEAN_SUB60HZ-1251752040.0_sensitivity_strain_asd.txt" - data = requests.get(url) - open("H1.txt", "wb").write(data.content) - f, asd_vals = np.loadtxt("H1.txt", unpack=True) - psd_vals = asd_vals**2 - psd_dict[ifo] = interpolate.interp1d( - f, psd_vals, fill_value=(psd_vals[0], psd_vals[-1]) - ) - continue - if ifo == "L1": - try: - f, asd_vals = np.loadtxt("L1.txt", unpack=True) - except: - print("Grabbing GWTC-2 PSD for L1") - url = "https://dcc.ligo.org/public/0169/P2000251/001/O3-L1-C01_CLEAN_SUB60HZ-1240573680.0_sensitivity_strain_asd.txt" - data = requests.get(url) - open("L1.txt", "wb").write(data.content) - f, asd_vals = np.loadtxt("L1.txt", unpack=True) - psd_vals = asd_vals**2 - psd_dict[ifo] = interpolate.interp1d( - f, psd_vals, fill_value=(psd_vals[0], psd_vals[-1]) - ) - continue - if ifo == "V1": - try: - f, asd_vals = np.loadtxt("V1.txt", unpack=True) - except: - print("Grabbing GWTC-2 PSD for V1") - url = "https://dcc.ligo.org/public/0169/P2000251/001/O3-V1_sensitivity_strain_asd.txt" - data = requests.get(url) - open("V1.txt", "wb").write(data.content) - f, asd_vals = np.loadtxt("V1.txt", unpack=True) - - psd_vals = asd_vals**2 - psd_dict[ifo] = interpolate.interp1d( - f, psd_vals, fill_value=(psd_vals[0], psd_vals[-1]) - ) - continue - else: - raise ValueError("IFO not supported") - return psd_dict - - -def generate_fd_noise( - seed: int, - f_sampling: int = 2048, - duration: int = 4, - f_min: float = 30.0, - psd_funcs: dict = { - "H1": None, - }, -): - """ - Generate frequency domain noise for a given set of detectors or specific PSD. - """ - for ifo in psd_funcs.keys(): - assert psd_funcs[ifo] is not None, "Need a PSD function for each detector." - - # define sampling rate and duration - delta_t = 1 / f_sampling - tlen = int(round(duration / delta_t)) - - freqs = np.fft.rfftfreq(tlen, delta_t) - delta_f = freqs[1] - freqs[0] - - # we will want to pad low frequencies; the function below applies a - # prescription to do so smoothly, but this is not really needed: you - # could just set all values below `fmin` to a constant. - def pad_low_freqs(f, psd_ref): - return psd_ref + psd_ref * (f_min - f) * np.exp(-(f_min - f)) / 3 - - psd_dict = {} - for ifo in psd_funcs.keys(): - psd = np.zeros(len(freqs)) - psd = pad_low_freqs(freqs, psd_funcs[ifo](f_min)) - psd[freqs>=f_min] = psd_funcs[ifo](freqs[freqs>=f_min]) - psd_dict[ifo] = np.array(psd, dtype=np.float64) - - rng_key = jax.random.PRNGKey(seed) - rng_keys = jax.random.split(rng_key) - - noise_fd_dict = {} - for ifo, psd in psd_dict.items(): - rng_keys = jax.random.split(rng_keys[0], 3) - # this is the variance of LIGO noise given the definition of the likelihood function - var = psd / (4.0 * delta_f) - noise_real = jax.random.normal(rng_keys[1], shape=(len(psd),)) * jnp.sqrt(var) - noise_imag = jax.random.normal(rng_keys[2], shape=(len(psd),)) * jnp.sqrt(var) - noise_fd_dict[ifo] = noise_real + 1j * noise_imag - - return freqs, psd_dict, noise_fd_dict - - -def generate_td_noise( - seed: int, - f_sampling: int = 2048, - duration: int = 4, - f_min: float = 30.0, - psd_funcs: dict = { - "H1": None, - }, -): - """ - Generate time domain noise for a given set of detectors or specific PSD. - """ - for ifo in psd_funcs.keys(): - assert psd_funcs[ifo] is not None, "Need a PSD function for each detector." - - delta_t = 1 / f_sampling - tlen = int(round(duration / delta_t)) - ts = jnp.linspace(0, duration, tlen) - - _, psd_dict, noise_fd_dict = generate_fd_noise( - seed, duration=duration, f_sampling=f_sampling, psd_funcs=psd_funcs, f_min=f_min - ) - - noise_td_dict = {} - # FIXME: We still need to add filtering to the frequency domain data - # to ensure that the time domain data behaves correctly - for ifo, psd in noise_fd_dict.items(): - noise_td_dict[ifo] = jnp.fft.irfft(noise_fd_dict[ifo]) * f_sampling - - return ts, noise_td_dict diff --git a/src/jimgw/heterodyneLikelihood.py b/src/jimgw/heterodyneLikelihood.py deleted file mode 100644 index 42fa534f..00000000 --- a/src/jimgw/heterodyneLikelihood.py +++ /dev/null @@ -1,164 +0,0 @@ -import wave -import numpy as np -from scipy.interpolate import interp1d - -import jax.numpy as jnp - - -def max_phase_diff(f, f_low, f_high, chi=1): - gamma = np.arange(-5, 6, 1) / 3.0 - f = np.repeat(f[:, None], len(gamma), axis=1) - f_star = np.repeat(f_low, len(gamma)) - f_star[gamma >= 0] = f_high - return 2 * np.pi * chi * np.sum((f / f_star) ** gamma * np.sign(gamma), axis=1) - - -def make_binning_scheme(freqs, n_bins, chi=1): - phase_diff_array = max_phase_diff(freqs, freqs[0], freqs[-1], chi=1) - bin_f = interp1d(phase_diff_array, freqs) - f_bins = np.array([]) - for i in np.linspace(phase_diff_array[0], phase_diff_array[-1], n_bins): - f_bins = np.append(f_bins, bin_f(i)) - f_bins_center = (f_bins[:-1] + f_bins[1:]) / 2 - return f_bins, f_bins_center - - -def compute_coefficients(data, h_ref, psd, freqs, f_bins, f_bins_center): - A0_array = [] - A1_array = [] - B0_array = [] - B1_array = [] - - df = freqs[1] - freqs[0] - data_prod = np.array(data * h_ref.conj()) - self_prod = np.array(h_ref * h_ref.conj()) - for i in range(len(f_bins) - 1): - f_index = np.where((freqs >= f_bins[i]) & (freqs < f_bins[i + 1]))[0] - A0_array.append(4 * np.sum(data_prod[f_index] / psd[f_index]) * df) - A1_array.append( - 4 - * np.sum( - data_prod[f_index] / psd[f_index] * (freqs[f_index] - f_bins_center[i]) - ) - * df - ) - B0_array.append(4 * np.sum(self_prod[f_index] / psd[f_index]) * df) - B1_array.append( - 4 - * np.sum( - self_prod[f_index] / psd[f_index] * (freqs[f_index] - f_bins_center[i]) - ) - * df - ) - - A0_array = jnp.array(A0_array) - A1_array = jnp.array(A1_array) - B0_array = jnp.array(B0_array) - B1_array = jnp.array(B1_array) - return A0_array, A1_array, B0_array, B1_array - - -def make_heterodyned_likelihood_multiple_detectors( - data_list, - psd_list, - response_list, - h_function, - ref_theta, - freqs, - gmst, - epoch, - f_ref, - n_bins=101, -): - - num_detector = len(data_list) - theta_waveform = ref_theta - theta_waveform = theta_waveform.at[5].set(0) - raw_hp, raw_hc = h_function(freqs, theta_waveform, f_ref) - index = jnp.where((jnp.abs(raw_hc) + jnp.abs(raw_hp)) > 0) - freqs = freqs[index] - raw_hp = raw_hp[index] - raw_hc = raw_hc[index] - for i in range(num_detector): - data_list[i] = data_list[i][index] - psd_list[i] = psd_list[i][index] - - f_bins, f_bins_center = make_binning_scheme(freqs, n_bins) - ra, dec = ref_theta[9], ref_theta[10] - h_ref = [] - h_ref_low = [] - h_ref_bincenter = [] - raw_hp_bin, raw_hc_bin = h_function(f_bins[:-1], theta_waveform, f_ref) - raw_hp_bincenter, raw_hc_bincenter = h_function( - f_bins_center, theta_waveform, f_ref - ) - for i in range(num_detector): - h_ref.append( - response_list[i](freqs, raw_hp, raw_hc, ra, dec, gmst, ref_theta[8]) - * jnp.exp(-1j * 2 * jnp.pi * freqs * (epoch + ref_theta[5])) - ) - h_ref_low.append( - response_list[i]( - f_bins[:-1], raw_hp_bin, raw_hc_bin, ra, dec, gmst, ref_theta[8] - ) - * jnp.exp(-1j * 2 * jnp.pi * f_bins[:-1] * (epoch + ref_theta[5])) - ) - h_ref_bincenter.append( - response_list[i]( - f_bins_center, - raw_hp_bincenter, - raw_hc_bincenter, - ra, - dec, - gmst, - ref_theta[8], - ) - * jnp.exp(-1j * 2 * jnp.pi * f_bins_center * (epoch + ref_theta[5])) - ) - - A0_array = [] - A1_array = [] - B0_array = [] - B1_array = [] - - for i in range(num_detector): - A0, A1, B0, B1 = compute_coefficients( - data_list[i], h_ref[i], psd_list[i], freqs, f_bins, f_bins_center - ) - A0_array.append(A0) - A1_array.append(A1) - B0_array.append(B0) - B1_array.append(B1) - - def heterodyned_likelihood(params): - theta_waveform = params - theta_waveform = theta_waveform.at[5].set(0) - ra, dec = params[9], params[10] - - output_SNR = 0 - - raw_hp_edge, raw_hc_edge = h_function(f_bins[:-1], theta_waveform, f_ref) - raw_hp_center, raw_hc_center = h_function(f_bins_center, theta_waveform, f_ref) - - for i in range(num_detector): - waveform_low = response_list[i]( - f_bins[:-1], raw_hp_edge, raw_hc_edge, ra, dec, gmst, params[8] - ) * jnp.exp(-1j * 2 * jnp.pi * f_bins[:-1] * (epoch + params[5])) - waveform_center = response_list[i]( - f_bins_center, raw_hp_center, raw_hc_center, ra, dec, gmst, params[8] - ) * jnp.exp(-1j * 2 * jnp.pi * f_bins_center * (epoch + params[5])) - - r0 = waveform_center / h_ref_bincenter[i] - r1 = (waveform_low / h_ref_low[i] - r0) / (f_bins[:-1] - f_bins_center) - match_filter_SNR = jnp.sum( - A0_array[i] * r0.conj() + A1_array[i] * r1.conj() - ) - optimal_SNR = jnp.sum( - B0_array[i] * jnp.abs(r0) ** 2 + 2 * B1_array[i] * (r0 * r1.conj()).real - ) - - output_SNR += (match_filter_SNR - optimal_SNR / 2).real - - return output_SNR - - return heterodyned_likelihood From cf7640b0e16e344c19ca0ef88608b248ea0f6eaf Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 4 Sep 2023 21:13:42 -0400 Subject: [PATCH 274/300] Update InjectionRecovery.py --- example/InjectionRecovery.py | 31 +++++++++++++++---------------- 1 file changed, 15 insertions(+), 16 deletions(-) diff --git a/example/InjectionRecovery.py b/example/InjectionRecovery.py index 1467a5ff..95b19622 100644 --- a/example/InjectionRecovery.py +++ b/example/InjectionRecovery.py @@ -48,6 +48,9 @@ class InjectionRecoveryParser(Tap): num_epochs: int = None batch_size: int = None stepsize: float = None + use_global: bool = None + keep_quantile: float = None + train_thinning: int = None # Output parameters output_path: str = None @@ -56,10 +59,6 @@ class InjectionRecoveryParser(Tap): args = InjectionRecoveryParser().parse_args() -# opt = vars(args) -# yaml_var = yaml.load(open(opt['config'], 'r'), Loader=yaml.FullLoader) -# opt.update(yaml_var) - # Fetch noise parameters print("Constructing detectors") @@ -106,18 +105,18 @@ class InjectionRecoveryParser(Tap): jim = Jim(likelihood, prior, - n_loop_training=20, - n_loop_production = 10, - n_local_steps=300, - n_global_steps=300, - n_chains=500, - n_epochs=300, - learning_rate = 0.001, - momentum = 0.9, - batch_size = 50000, - use_global=True, - keep_quantile=0., - train_thinning = 40, + n_loop_training=args.n_loop_training, + n_loop_production = args.n_loop_production, + n_local_steps=args.n_local_steps, + n_global_steps=args.n_global_steps, + n_chains=args.n_chains, + n_epochs=args.num_epochs, + learning_rate = args.learning_rate, + momentum = args.momentum, + batch_size = args.batch_size, + use_global=args.use_global, + keep_quantile= args.keep_quantile, + train_thinning = args, local_sampler_arg = local_sampler_arg, seed = args.seed, ) From 6000dce9f1c8ce980b77ba607fe4a5360f4c33c1 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 4 Sep 2023 21:49:44 -0400 Subject: [PATCH 275/300] Update InjectionRecovery to work with IMRPhenomPv2 and with heterodyning --- example/InjectionRecovery.py | 82 +++++++++++++++++++----------------- 1 file changed, 43 insertions(+), 39 deletions(-) diff --git a/example/InjectionRecovery.py b/example/InjectionRecovery.py index 95b19622..1c2a231d 100644 --- a/example/InjectionRecovery.py +++ b/example/InjectionRecovery.py @@ -1,7 +1,7 @@ from jimgw.jim import Jim from jimgw.detector import H1, L1, V1 from jimgw.likelihood import TransientLikelihoodFD -from jimgw.waveform import RippleIMRPhenomD +from jimgw.waveform import RippleIMRPhenomPv2 from jimgw.prior import Uniform from ripple import ms_to_Mc_eta import jax.numpy as jnp @@ -16,45 +16,49 @@ class InjectionRecoveryParser(Tap): config: str # Noise parameters - seed: int = None - f_sampling: int = None - duration: int = None - fmin: float = None - ifos: list[str] = None + seed: int = 0 + f_sampling: int = 2048 + duration: int = 4 + fmin: float = 20.0 + ifos: list[str] = ["H1", "L1", "V1"] # Injection parameters - m1: float = None - m2: float = None - chi1: float = None - chi2: float = None - dist_mpc: float = None - tc: float = None - phic: float = None - inclination: float = None - polarization_angle: float = None - ra: float = None - dec: float = None + m1: float = 30.0 + m2: float = 29.0 + s1_x: float = 0. + s1_y: float = 0. + s1_z: float = 0. + s2_x: float = 0. + s2_y: float = 0. + s2_z: float = 0. + dist_mpc: float = 400. + tc: float = 0. + phic: float = 0. + inclination: float = 0.3 + polarization_angle: float = 0.7 + ra: float = 1.1 + dec: float = 0.3 # Sampler parameters - n_dim: int = None - n_chains: int = None - n_loop_training: int = None - n_loop_production: int = None - n_local_steps: int = None - n_global_steps: int = None - learning_rate: float = None - max_samples: int = None - momentum: float = None - num_epochs: int = None - batch_size: int = None - stepsize: float = None - use_global: bool = None - keep_quantile: float = None - train_thinning: int = None + n_dim: int = 15 + n_chains: int = 500 + n_loop_training: int = 20 + n_loop_production: int = 10 + n_local_steps: int = 200 + n_global_steps: int = 200 + learning_rate: float = 0.001 + max_samples: int = 50000 + momentum: float = 0.9 + num_epochs: int = 300 + batch_size: int = 50000 + stepsize: float = 0.01 + use_global: bool = True + keep_quantile: float = 0.0 + train_thinning: int = 40 # Output parameters - output_path: str = None - downsample_factor: int = None + output_path: str = "./" + downsample_factor: int = 10 args = InjectionRecoveryParser().parse_args() @@ -77,11 +81,11 @@ class InjectionRecoveryParser(Tap): epoch = args.duration - post_trigger_duration gmst = Time(trigger_time, format='gps').sidereal_time('apparent', 'greenwich').rad -waveform = RippleIMRPhenomD(f_ref=f_ref) +waveform = RippleIMRPhenomPv2(f_ref=f_ref) prior = Uniform( - xmin = [10, 0.125, -1., -1., 0., -0.05, 0., -1, 0., 0.,-1.], - xmax = [80., 1., 1., 1., 2000., 0.05, 2*jnp.pi, 1., jnp.pi, 2*jnp.pi, 1.], - naming = ["M_c", "q", "s1_z", "s2_z", "d_L", "t_c", "phase_c", "cos_iota", "psi", "ra", "sin_dec"], + xmin = [10, 0.125, -1., -1., -1., -1., -1., -1., 0., -0.05, 0., -1, 0., 0.,-1.], + xmax = [80., 1., 1., 1., 1., 1., 1., 1., 2000., 0.05, 2*jnp.pi, 1., jnp.pi, 2*jnp.pi, 1.], + naming = ["M_c", "q", "s1_x", "s1_y", "s1_z", "s2_x", "s2_y", "s2_z", "d_L", "t_c", "phase_c", "cos_iota", "psi", "ra", "sin_dec"], transforms = {"q": ("eta", lambda q: q/(1+q)**2), "cos_iota": ("iota",lambda cos_iota: jnp.arccos(jnp.arcsin(jnp.sin(cos_iota/2*jnp.pi))*2/jnp.pi)), "sin_dec": ("dec",lambda sin_dec: jnp.arcsin(jnp.arcsin(jnp.sin(sin_dec/2*jnp.pi))*2/jnp.pi))} # sin and arcsin are periodize cos_iota and sin_dec @@ -97,7 +101,7 @@ class InjectionRecoveryParser(Tap): key, subkey = jax.random.split(key) V1.inject_signal(subkey, freqs, h_sky, detector_param) -likelihood = TransientLikelihoodFD([H1, L1], waveform, trigger_time, args.duration, post_trigger_duration) +likelihood = TransientLikelihoodFD([H1, L1, V1], waveform, trigger_time, args.duration, post_trigger_duration) mass_matrix = jnp.eye(11) mass_matrix = mass_matrix.at[1,1].set(1e-3) mass_matrix = mass_matrix.at[5,5].set(1e-3) From 3c055c9f5b1dff71b32d008a94a6ac737ee42a3a Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 4 Sep 2023 23:44:00 -0400 Subject: [PATCH 276/300] Prior may need some adjustment, and nansum in heterodyne does not work with MALA --- example/InjectionRecovery.py | 50 +++++++++++++++++++++--------------- src/jimgw/prior.py | 22 ++++++++++------ 2 files changed, 44 insertions(+), 28 deletions(-) diff --git a/example/InjectionRecovery.py b/example/InjectionRecovery.py index 1c2a231d..4bbf97b7 100644 --- a/example/InjectionRecovery.py +++ b/example/InjectionRecovery.py @@ -1,6 +1,6 @@ from jimgw.jim import Jim from jimgw.detector import H1, L1, V1 -from jimgw.likelihood import TransientLikelihoodFD +from jimgw.likelihood import HeterodynedTransientLikelihoodFD, TransientLikelihoodFD from jimgw.waveform import RippleIMRPhenomPv2 from jimgw.prior import Uniform from ripple import ms_to_Mc_eta @@ -12,6 +12,9 @@ import yaml from tqdm import tqdm +jax.config.update("jax_enable_x64", True) + + class InjectionRecoveryParser(Tap): config: str @@ -25,12 +28,12 @@ class InjectionRecoveryParser(Tap): # Injection parameters m1: float = 30.0 m2: float = 29.0 - s1_x: float = 0. - s1_y: float = 0. - s1_z: float = 0. - s2_x: float = 0. - s2_y: float = 0. - s2_z: float = 0. + s1_theta: float = 0. + s1_phi: float = 0. + s1_mag: float = 0. + s2_theta: float = 0. + s2_phi: float = 0. + s2_mag: float = 0. dist_mpc: float = 400. tc: float = 0. phic: float = 0. @@ -83,15 +86,21 @@ class InjectionRecoveryParser(Tap): waveform = RippleIMRPhenomPv2(f_ref=f_ref) prior = Uniform( - xmin = [10, 0.125, -1., -1., -1., -1., -1., -1., 0., -0.05, 0., -1, 0., 0.,-1.], - xmax = [80., 1., 1., 1., 1., 1., 1., 1., 2000., 0.05, 2*jnp.pi, 1., jnp.pi, 2*jnp.pi, 1.], - naming = ["M_c", "q", "s1_x", "s1_y", "s1_z", "s2_x", "s2_y", "s2_z", "d_L", "t_c", "phase_c", "cos_iota", "psi", "ra", "sin_dec"], - transforms = {"q": ("eta", lambda q: q/(1+q)**2), - "cos_iota": ("iota",lambda cos_iota: jnp.arccos(jnp.arcsin(jnp.sin(cos_iota/2*jnp.pi))*2/jnp.pi)), - "sin_dec": ("dec",lambda sin_dec: jnp.arcsin(jnp.arcsin(jnp.sin(sin_dec/2*jnp.pi))*2/jnp.pi))} # sin and arcsin are periodize cos_iota and sin_dec + xmin = [10, 0.125, 0, 0, 0, 0, 0, 0, 0., -0.05, 0., -1, 0., 0.,-1.], + xmax = [80., 1., jnp.pi, 2*jnp.pi, 1., jnp.pi, 2*jnp.pi, 1., 2000., 0.05, 2*jnp.pi, 1., jnp.pi, 2*jnp.pi, 1.], + naming = ["M_c", "q", "s1_theta", "s1_phi", "s1_mag", "s2_theta", "s2_phi", "s2_mag", "d_L", "t_c", "phase_c", "cos_iota", "psi", "ra", "sin_dec"], + transforms = {"q": ("eta", lambda params: params['q']/(1+params['q'])**2), + "s1_theta": ("s1_x", lambda params: jnp.sin(params['s1_theta'])*jnp.cos(params['s1_phi'])*params['s1_mag']), + "s1_phi": ("s1_y", lambda params: jnp.sin(params['s1_theta'])*jnp.sin(params['s1_phi'])*params['s1_mag']), + "s1_mag": ("s1_z", lambda params: jnp.cos(params['s1_theta'])*params['s1_mag']), + "s2_theta": ("s2_x", lambda params: jnp.sin(params['s2_theta'])*jnp.cos(params['s2_phi'])*params['s2_mag']), + "s2_phi": ("s2_y", lambda params: jnp.sin(params['s2_theta'])*jnp.sin(params['s2_phi'])*params['s2_mag']), + "s2_mag": ("s2_z", lambda params: jnp.cos(params['s2_theta'])*params['s2_mag']), + "cos_iota": ("iota",lambda params: jnp.arccos(jnp.arcsin(jnp.sin(params['cos_iota']/2*jnp.pi))*2/jnp.pi)), + "sin_dec": ("dec",lambda params: jnp.arcsin(jnp.arcsin(jnp.sin(params['sin_dec']/2*jnp.pi))*2/jnp.pi))} # sin and arcsin are periodize cos_iota and sin_dec ) -true_param = jnp.array([Mc, eta, args.chi1, args.chi2, args.dist_mpc, args.tc, args.phic, args.inclination, args.polarization_angle, args.ra, args.dec]) -true_param = prior.add_name(true_param, with_transform=True) +true_param = jnp.array([Mc, args.m2/args.m1, args.s1_theta, args.s1_phi, args.s1_mag, args.s2_theta, args.s2_phi, args.s2_mag, args.dist_mpc, args.tc, args.phic, args.inclination, args.polarization_angle, args.ra, args.dec]) +true_param = prior.add_name(true_param, transform_name = True, transform_value = True) detector_param = {"ra": args.ra, "dec": args.dec, "gmst": gmst, "psi": args.polarization_angle, "epoch": epoch, "t_c": args.tc} h_sky = waveform(freqs, true_param) key, subkey = jax.random.split(jax.random.PRNGKey(args.seed+1234)) @@ -102,10 +111,12 @@ class InjectionRecoveryParser(Tap): V1.inject_signal(subkey, freqs, h_sky, detector_param) likelihood = TransientLikelihoodFD([H1, L1, V1], waveform, trigger_time, args.duration, post_trigger_duration) -mass_matrix = jnp.eye(11) +# likelihood = HeterodynedTransientLikelihoodFD([H1, L1, V1], prior=prior, bounds=[prior.xmin, prior.xmax], waveform = waveform, trigger_time = trigger_time, duration = args.duration, post_trigger_duration = post_trigger_duration) + +mass_matrix = jnp.eye(args.n_dim) mass_matrix = mass_matrix.at[1,1].set(1e-3) -mass_matrix = mass_matrix.at[5,5].set(1e-3) -local_sampler_arg = {"step_size": mass_matrix*3e-3} +mass_matrix = mass_matrix.at[9,9].set(1e-3) +local_sampler_arg = {"step_size": mass_matrix*3e-4} jim = Jim(likelihood, prior, @@ -120,12 +131,11 @@ class InjectionRecoveryParser(Tap): batch_size = args.batch_size, use_global=args.use_global, keep_quantile= args.keep_quantile, - train_thinning = args, + train_thinning = args.train_thinning, local_sampler_arg = local_sampler_arg, seed = args.seed, ) -sample = jim.maximize_likelihood([prior.xmin, prior.xmax], n_loops=2000) key, subkey = jax.random.split(key) jim.sample(subkey) samples = jim.get_samples() \ No newline at end of file diff --git a/src/jimgw/prior.py b/src/jimgw/prior.py index 669bd5d8..4baf4298 100644 --- a/src/jimgw/prior.py +++ b/src/jimgw/prior.py @@ -35,11 +35,15 @@ def __init__(self, naming: list[str], transforms: dict[tuple[str,Callable]] = {} """ self.naming = naming self.transforms = {} + + def make_lambda(name): + return lambda x: x[name] + for name in naming: if name in transforms: self.transforms[name] = transforms[name] else: - self.transforms[name] = (name,lambda x: x) + self.transforms[name] = (name, make_lambda(name)) # Without the function, the lambda will refer to the variable name instead of its value, which will make lambda reference the last value of the variable name def transform(self, x: Array) -> Array: """ @@ -47,16 +51,17 @@ def transform(self, x: Array) -> Array: Parameters ---------- - x : Array - The parameters to transform. + x : dict + A dictionary of parameters. Names should match the ones in the prior. Returns ------- - x : Array - The transformed parameters. + x : dict + A dictionary of parameters with the transforms applied. """ - for i,transform in enumerate(self.transforms): - x = x.at[i].set(transform[1](x[i])) + output = self.add_name(x, transform_name = False, transform_value = False) + for i, (key, value) in enumerate(self.transforms.items()): + x = x.at[i].set(value[1](output)) return x def add_name(self, x: Array, transform_name: bool = False, transform_value: bool = False) -> dict: @@ -68,7 +73,8 @@ def add_name(self, x: Array, transform_name: bool = False, transform_value: bool else: naming = self.naming if transform_value: - value = [value[1](x[index]) for index, value in enumerate(self.transforms.values())] + x = self.transform(x) + value = x else: value = x return dict(zip(naming,value)) From 93023114b46b93a9753e7ed7341436a747453830 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Thu, 7 Sep 2023 15:35:31 -0400 Subject: [PATCH 277/300] testing update --- example/GW150914.py | 21 +++------- example/GW150914_PV2.py | 75 ++++++++++++++++++++++++++++++++++++ example/InjectionRecovery.py | 25 ++++++------ src/jimgw/likelihood.py | 18 +++++++-- 4 files changed, 108 insertions(+), 31 deletions(-) create mode 100644 example/GW150914_PV2.py diff --git a/example/GW150914.py b/example/GW150914.py index 6b1e7c32..bc67678e 100644 --- a/example/GW150914.py +++ b/example/GW150914.py @@ -43,21 +43,9 @@ "ra", "sin_dec", ], - transforms={ - "q": ("eta", lambda q: q / (1 + q) ** 2), - "cos_iota": ( - "iota", - lambda cos_iota: jnp.arccos( - jnp.arcsin(jnp.sin(cos_iota / 2 * jnp.pi)) * 2 / jnp.pi - ), - ), - "sin_dec": ( - "dec", - lambda sin_dec: jnp.arcsin( - jnp.arcsin(jnp.sin(sin_dec / 2 * jnp.pi)) * 2 / jnp.pi - ), - ), - }, # sin and arcsin are for periodizing cos_iota and sin_dec, otherwise it might gives some nans because of numpy + transforms = {"q": ("eta", lambda params: params['q']/(1+params['q'])**2), + "cos_iota": ("iota",lambda params: jnp.arccos(jnp.arcsin(jnp.sin(params['cos_iota']/2*jnp.pi))*2/jnp.pi)), + "sin_dec": ("dec",lambda params: jnp.arcsin(jnp.arcsin(jnp.sin(params['sin_dec']/2*jnp.pi))*2/jnp.pi))} # sin and arcsin are periodize cos_iota and sin_dec ) likelihood = TransientLikelihoodFD([H1, L1], waveform=RippleIMRPhenomD(), trigger_time=gps, duration=4, post_trigger_duration=2) # likelihood = HeterodynedTransientLikelihoodFD([H1, L1], prior=prior, bounds=[prior.xmin, prior.xmax], waveform=RippleIMRPhenomD(), trigger_time=gps, duration=4, post_trigger_duration=2) @@ -78,11 +66,12 @@ n_chains=500, n_epochs=300, learning_rate=0.001, + max_samples=30000, momentum=0.9, batch_size=50000, use_global=True, keep_quantile=0.0, - train_thinning=40, + train_thinning=10, local_sampler_arg=local_sampler_arg, ) diff --git a/example/GW150914_PV2.py b/example/GW150914_PV2.py new file mode 100644 index 00000000..85f684dc --- /dev/null +++ b/example/GW150914_PV2.py @@ -0,0 +1,75 @@ +import time +from jimgw.jim import Jim +from jimgw.detector import H1, L1 +from jimgw.likelihood import HeterodynedTransientLikelihoodFD, TransientLikelihoodFD +from jimgw.waveform import RippleIMRPhenomD, RippleIMRPhenomPv2 +from jimgw.prior import Uniform +import jax.numpy as jnp +import jax + +jax.config.update("jax_enable_x64", True) + +########################################### +########## First we grab data ############# +########################################### + +total_time_start = time.time() + +# first, fetch a 4s segment centered on GW150914 +gps = 1126259462.4 +start = gps - 2 +end = gps + 2 +fmin = 20.0 +fmax = 1024.0 + +ifos = ["H1", "L1"] + +H1.load_data(gps, 2, 2, fmin, fmax, psd_pad=16, tukey_alpha=0.2) +L1.load_data(gps, 2, 2, fmin, fmax, psd_pad=16, tukey_alpha=0.2) + +waveform = RippleIMRPhenomPv2(f_ref=20) +prior = Uniform( + xmin = [10, 0.125, 0, 0, 0, 0, 0, 0, 0., -0.05, 0., -1, 0., 0.,-1.], + xmax = [80., 1., jnp.pi, 2*jnp.pi, 1., jnp.pi, 2*jnp.pi, 1., 2000., 0.05, 2*jnp.pi, 1., jnp.pi, 2*jnp.pi, 1.], + naming = ["M_c", "q", "s1_theta", "s1_phi", "s1_mag", "s2_theta", "s2_phi", "s2_mag", "d_L", "t_c", "phase_c", "cos_iota", "psi", "ra", "sin_dec"], + transforms = {"q": ("eta", lambda params: params['q']/(1+params['q'])**2), + "s1_theta": ("s1_x", lambda params: jnp.sin(params['s1_theta'])*jnp.cos(params['s1_phi'])*params['s1_mag']), + "s1_phi": ("s1_y", lambda params: jnp.sin(params['s1_theta'])*jnp.sin(params['s1_phi'])*params['s1_mag']), + "s1_mag": ("s1_z", lambda params: jnp.cos(params['s1_theta'])*params['s1_mag']), + "s2_theta": ("s2_x", lambda params: jnp.sin(params['s2_theta'])*jnp.cos(params['s2_phi'])*params['s2_mag']), + "s2_phi": ("s2_y", lambda params: jnp.sin(params['s2_theta'])*jnp.sin(params['s2_phi'])*params['s2_mag']), + "s2_mag": ("s2_z", lambda params: jnp.cos(params['s2_theta'])*params['s2_mag']), + "cos_iota": ("iota",lambda params: jnp.arccos(jnp.arcsin(jnp.sin(params['cos_iota']/2*jnp.pi))*2/jnp.pi)), + "sin_dec": ("dec",lambda params: jnp.arcsin(jnp.arcsin(jnp.sin(params['sin_dec']/2*jnp.pi))*2/jnp.pi))} # sin and arcsin are periodize cos_iota and sin_dec +) +likelihood = TransientLikelihoodFD([H1, L1], waveform=waveform, trigger_time=gps, duration=4, post_trigger_duration=2) +# likelihood = HeterodynedTransientLikelihoodFD([H1, L1], prior=prior, bounds=[prior.xmin, prior.xmax], waveform=RippleIMRPhenomD(), trigger_time=gps, duration=4, post_trigger_duration=2) + + +mass_matrix = jnp.eye(prior.n_dim) +mass_matrix = mass_matrix.at[1, 1].set(1e-3) +mass_matrix = mass_matrix.at[9, 9].set(1e-3) +local_sampler_arg = {"step_size": mass_matrix * 3e-3} + +jim = Jim( + likelihood, + prior, + n_loop_training=10, + n_loop_production=10, + n_local_steps=200, + n_global_steps=200, + n_chains=500, + n_epochs=200, + learning_rate=0.001, + max_samples = 50000, + momentum=0.9, + batch_size=50000, + use_global=True, + keep_quantile=0., + train_thinning=10, + local_sampler_arg=local_sampler_arg, +) + +jim.maximize_likelihood([prior.xmin, prior.xmax]) +initial_guess = jnp.array(jnp.load('initial.npz')['chain']) +jim.sample(jax.random.PRNGKey(42), initial_guess=initial_guess) diff --git a/example/InjectionRecovery.py b/example/InjectionRecovery.py index 4bbf97b7..22ec3c20 100644 --- a/example/InjectionRecovery.py +++ b/example/InjectionRecovery.py @@ -27,13 +27,13 @@ class InjectionRecoveryParser(Tap): # Injection parameters m1: float = 30.0 - m2: float = 29.0 + m2: float = 25.0 s1_theta: float = 0. s1_phi: float = 0. - s1_mag: float = 0. + s1_mag: float = 0.01 s2_theta: float = 0. s2_phi: float = 0. - s2_mag: float = 0. + s2_mag: float = 0.02 dist_mpc: float = 400. tc: float = 0. phic: float = 0. @@ -45,19 +45,19 @@ class InjectionRecoveryParser(Tap): # Sampler parameters n_dim: int = 15 n_chains: int = 500 - n_loop_training: int = 20 + n_loop_training: int = 10 n_loop_production: int = 10 - n_local_steps: int = 200 - n_global_steps: int = 200 + n_local_steps: int = 300 + n_global_steps: int = 300 learning_rate: float = 0.001 max_samples: int = 50000 momentum: float = 0.9 - num_epochs: int = 300 + num_epochs: int = 500 batch_size: int = 50000 stepsize: float = 0.01 use_global: bool = True - keep_quantile: float = 0.0 - train_thinning: int = 40 + keep_quantile: float = 0.1 + train_thinning: int = 10 # Output parameters output_path: str = "./" @@ -78,7 +78,7 @@ class InjectionRecoveryParser(Tap): freqs = jnp.linspace(args.fmin, args.f_sampling/2, args.duration*args.f_sampling//2) Mc, eta = ms_to_Mc_eta(jnp.array([args.m1, args.m2])) -f_ref = 30.0 +f_ref = args.fmin trigger_time = 1126259462.4 post_trigger_duration = 2 epoch = args.duration - post_trigger_duration @@ -110,13 +110,13 @@ class InjectionRecoveryParser(Tap): key, subkey = jax.random.split(key) V1.inject_signal(subkey, freqs, h_sky, detector_param) -likelihood = TransientLikelihoodFD([H1, L1, V1], waveform, trigger_time, args.duration, post_trigger_duration) +likelihood = TransientLikelihoodFD([H1, L1], waveform, trigger_time, args.duration, post_trigger_duration) # likelihood = HeterodynedTransientLikelihoodFD([H1, L1, V1], prior=prior, bounds=[prior.xmin, prior.xmax], waveform = waveform, trigger_time = trigger_time, duration = args.duration, post_trigger_duration = post_trigger_duration) mass_matrix = jnp.eye(args.n_dim) mass_matrix = mass_matrix.at[1,1].set(1e-3) mass_matrix = mass_matrix.at[9,9].set(1e-3) -local_sampler_arg = {"step_size": mass_matrix*3e-4} +local_sampler_arg = {"step_size": mass_matrix*3e-3} jim = Jim(likelihood, prior, @@ -127,6 +127,7 @@ class InjectionRecoveryParser(Tap): n_chains=args.n_chains, n_epochs=args.num_epochs, learning_rate = args.learning_rate, + max_samples = args.max_samples, momentum = args.momentum, batch_size = args.batch_size, use_global=args.use_global, diff --git a/src/jimgw/likelihood.py b/src/jimgw/likelihood.py index 896f24ed..579eca5b 100644 --- a/src/jimgw/likelihood.py +++ b/src/jimgw/likelihood.py @@ -173,6 +173,18 @@ def __init__( h_sky_low = self.waveform(self.freq_grid_low, self.ref_params) h_sky_center = self.waveform(self.freq_grid_center, self.ref_params) + f_valid = frequency_original[jnp.where((jnp.abs(h_sky['p'])+jnp.abs(h_sky['c']))>0)[0]] + f_max = jnp.max(f_valid) + f_min = jnp.min(f_valid) + + h_sky = h_sky[jnp.where((frequency_original>=f_min) & (frequency_original<=f_max))[0]] + h_sky_low = h_sky_low[jnp.where((self.freq_grid_low>=f_min) & (self.freq_grid_low<=f_max))[0]] + h_sky_center = h_sky_center[jnp.where((self.freq_grid_center>=f_min) & (self.freq_grid_center<=f_max))[0]] + + frequency_original = frequency_original[jnp.where((frequency_original>=f_min) & (frequency_original<=f_max))[0]] + self.freq_grid_low = self.freq_grid_low[jnp.where((self.freq_grid_low>=f_min) & (self.freq_grid_low<=f_max))[0]] + self.freq_grid_center = self.freq_grid_center[jnp.where((self.freq_grid_center>=f_min) & (self.freq_grid_center<=f_max))[0]] + align_time = jnp.exp( -1j * 2 @@ -215,7 +227,7 @@ def __init__( waveform_ref, detector.psd, frequency_original, - freq_grid, + self.freq_grid_low, self.freq_grid_center, ) self.A0_array[detector.name] = A0 @@ -249,11 +261,11 @@ def evaluate(self, params: Array, data: dict) -> float: r1 = (waveform_low / self.waveform_low_ref[detector.name] - r0) / ( frequencies_low - frequencies_center ) - match_filter_SNR = jnp.nansum( + match_filter_SNR = jnp.sum( self.A0_array[detector.name] * r0.conj() + self.A1_array[detector.name] * r1.conj() ) - optimal_SNR = jnp.nansum( + optimal_SNR = jnp.sum( self.B0_array[detector.name] * jnp.abs(r0) ** 2 + 2 * self.B1_array[detector.name] * (r0 * r1.conj()).real ) From 23b6f4ae98faf4e8deae740f94c8f11a1b651fb8 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Thu, 14 Sep 2023 21:25:04 -0400 Subject: [PATCH 278/300] This setting for Pv2 seems to work okay. Finish within an hour. --- example/GW150914.py | 13 +++++++------ example/GW150914_PV2.py | 11 ++++++----- 2 files changed, 13 insertions(+), 11 deletions(-) diff --git a/example/GW150914.py b/example/GW150914.py index bc67678e..2e73ad58 100644 --- a/example/GW150914.py +++ b/example/GW150914.py @@ -59,19 +59,20 @@ jim = Jim( likelihood, prior, - n_loop_training=10, + n_loop_training=200, n_loop_production=10, - n_local_steps=300, - n_global_steps=300, + n_local_steps=150, + n_global_steps=150, n_chains=500, - n_epochs=300, + n_epochs=50, learning_rate=0.001, - max_samples=30000, + max_samples=45000, momentum=0.9, batch_size=50000, use_global=True, keep_quantile=0.0, - train_thinning=10, + train_thinning=1, + output_thinning=10, local_sampler_arg=local_sampler_arg, ) diff --git a/example/GW150914_PV2.py b/example/GW150914_PV2.py index 85f684dc..72d94f26 100644 --- a/example/GW150914_PV2.py +++ b/example/GW150914_PV2.py @@ -54,22 +54,23 @@ jim = Jim( likelihood, prior, - n_loop_training=10, + n_loop_training=200, n_loop_production=10, n_local_steps=200, n_global_steps=200, n_chains=500, n_epochs=200, learning_rate=0.001, - max_samples = 50000, + max_samples = 60000, momentum=0.9, batch_size=50000, use_global=True, keep_quantile=0., - train_thinning=10, + train_thinning=1, + output_thinning=20, local_sampler_arg=local_sampler_arg, ) jim.maximize_likelihood([prior.xmin, prior.xmax]) -initial_guess = jnp.array(jnp.load('initial.npz')['chain']) -jim.sample(jax.random.PRNGKey(42), initial_guess=initial_guess) +# initial_guess = jnp.array(jnp.load('initial.npz')['chain']) +jim.sample(jax.random.PRNGKey(42)) From 76d2cfb045bfcead50da89b7509b15ac374c6ac2 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Fri, 15 Sep 2023 18:52:42 -0400 Subject: [PATCH 279/300] More safe parameters --- example/GW150914_PV2.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/example/GW150914_PV2.py b/example/GW150914_PV2.py index 72d94f26..8966fead 100644 --- a/example/GW150914_PV2.py +++ b/example/GW150914_PV2.py @@ -56,10 +56,10 @@ prior, n_loop_training=200, n_loop_production=10, - n_local_steps=200, - n_global_steps=200, + n_local_steps=300, + n_global_steps=300, n_chains=500, - n_epochs=200, + n_epochs=300, learning_rate=0.001, max_samples = 60000, momentum=0.9, @@ -67,7 +67,7 @@ use_global=True, keep_quantile=0., train_thinning=1, - output_thinning=20, + output_thinning=30, local_sampler_arg=local_sampler_arg, ) From 4c111e806dd2ba19ef49c2591af6ed5744ab8bd5 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Sat, 16 Sep 2023 12:32:58 -0400 Subject: [PATCH 280/300] Smaller network seems to improve quality quite a bit --- example/InjectionRecovery.py | 42 +++++++++++++++++++++--------------- 1 file changed, 25 insertions(+), 17 deletions(-) diff --git a/example/InjectionRecovery.py b/example/InjectionRecovery.py index 22ec3c20..a390b76d 100644 --- a/example/InjectionRecovery.py +++ b/example/InjectionRecovery.py @@ -20,7 +20,7 @@ class InjectionRecoveryParser(Tap): # Noise parameters seed: int = 0 - f_sampling: int = 2048 + f_sampling: int = 4096 duration: int = 4 fmin: float = 20.0 ifos: list[str] = ["H1", "L1", "V1"] @@ -28,36 +28,40 @@ class InjectionRecoveryParser(Tap): # Injection parameters m1: float = 30.0 m2: float = 25.0 - s1_theta: float = 0. - s1_phi: float = 0. - s1_mag: float = 0.01 - s2_theta: float = 0. - s2_phi: float = 0. - s2_mag: float = 0.02 + s1_theta: float = 0.04 + s1_phi: float = 0.02 + s1_mag: float = 0.1 + s2_theta: float = 0.01 + s2_phi: float = 0.03 + s2_mag: float = 0.05 dist_mpc: float = 400. tc: float = 0. - phic: float = 0. - inclination: float = 0.3 + phic: float = 0.1 + inclination: float = 0.5 polarization_angle: float = 0.7 - ra: float = 1.1 + ra: float = 1.2 dec: float = 0.3 # Sampler parameters n_dim: int = 15 n_chains: int = 500 - n_loop_training: int = 10 + n_loop_training: int = 200 n_loop_production: int = 10 n_local_steps: int = 300 n_global_steps: int = 300 learning_rate: float = 0.001 - max_samples: int = 50000 + max_samples: int = 60000 momentum: float = 0.9 - num_epochs: int = 500 - batch_size: int = 50000 + num_epochs: int = 300 + batch_size: int = 60000 stepsize: float = 0.01 use_global: bool = True - keep_quantile: float = 0.1 - train_thinning: int = 10 + keep_quantile: float = 0.0 + train_thinning: int = 1 + output_thinning: int = 30 + num_layers: int = 6 + hidden_size: list[int] = [64,64] + num_bins: int = 8 # Output parameters output_path: str = "./" @@ -75,7 +79,7 @@ class InjectionRecoveryParser(Tap): print("Injection signals") -freqs = jnp.linspace(args.fmin, args.f_sampling/2, args.duration*args.f_sampling//2) +freqs = jnp.linspace(args.fmin, args.f_sampling/2, args.duration*args.f_sampling) Mc, eta = ms_to_Mc_eta(jnp.array([args.m1, args.m2])) f_ref = args.fmin @@ -133,8 +137,12 @@ class InjectionRecoveryParser(Tap): use_global=args.use_global, keep_quantile= args.keep_quantile, train_thinning = args.train_thinning, + output_thinning = args.output_thinning, local_sampler_arg = local_sampler_arg, seed = args.seed, + num_layers = args.num_layers, + hidden_size = args.hidden_size, + num_bins = args.num_bins ) key, subkey = jax.random.split(key) From d69179888efe50ab4af0ecd2d5313ec1af59a992 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Sat, 16 Sep 2023 19:35:32 -0400 Subject: [PATCH 281/300] testing NF parameters --- example/GW150914_PV2.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/example/GW150914_PV2.py b/example/GW150914_PV2.py index 8966fead..bf5de111 100644 --- a/example/GW150914_PV2.py +++ b/example/GW150914_PV2.py @@ -54,7 +54,7 @@ jim = Jim( likelihood, prior, - n_loop_training=200, + n_loop_training=400, n_loop_production=10, n_local_steps=300, n_global_steps=300, @@ -63,12 +63,15 @@ learning_rate=0.001, max_samples = 60000, momentum=0.9, - batch_size=50000, + batch_size=30000, use_global=True, keep_quantile=0., train_thinning=1, output_thinning=30, local_sampler_arg=local_sampler_arg, + num_layers = 4, + hidden_size = [32,32], + num_bins = 8 ) jim.maximize_likelihood([prior.xmin, prior.xmax]) From 1818731fb451874ae69daa02c4be264ed27ae16a Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 18 Sep 2023 15:43:40 -0400 Subject: [PATCH 282/300] Update setup.cfg --- setup.cfg | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.cfg b/setup.cfg index 7d8d242c..ed3e8b82 100644 --- a/setup.cfg +++ b/setup.cfg @@ -1,6 +1,6 @@ [metadata] name = jimGW -version = 0.1.0 +version = 0.1.1 author = Kaze Wong author_email = kazewong.physics@gmail.com url = https://github.com/kazewong/jim From 2a31be01f8c77289e634ea73eea38ac272c72b4a Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 18 Sep 2023 15:50:01 -0400 Subject: [PATCH 283/300] Update --- .gitignore | 1 + setup.cfg | 2 ++ 2 files changed, 3 insertions(+) diff --git a/.gitignore b/.gitignore index 59dc8d9c..5d6606d3 100644 --- a/.gitignore +++ b/.gitignore @@ -139,3 +139,4 @@ log* H1.txt L1.txt V1.txt +test_data diff --git a/setup.cfg b/setup.cfg index 7d8d242c..dae85cec 100644 --- a/setup.cfg +++ b/setup.cfg @@ -5,6 +5,8 @@ author = Kaze Wong author_email = kazewong.physics@gmail.com url = https://github.com/kazewong/jim description = Gravitatioanl wave data analysis tool in Jax +long_description = file: README.md +long_description_content_type = text/markdown keywords = sampling, inference, machine learning, normalizing, autodiff, jax license = MIT From b6114493313e20f7a6fdf4bdf6065d69d61c291d Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 18 Sep 2023 16:02:07 -0400 Subject: [PATCH 284/300] Create python-package.yml --- .github/workflows/python-package.yml | 34 ++++++++++++++++++++++++++++ 1 file changed, 34 insertions(+) create mode 100644 .github/workflows/python-package.yml diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml new file mode 100644 index 00000000..56ce5dd7 --- /dev/null +++ b/.github/workflows/python-package.yml @@ -0,0 +1,34 @@ +# This workflow will install Python dependencies, run tests and lint with a variety of Python versions +# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python + +name: Python package + +on: + push: + branches: [ "main" ] + pull_request: + branches: [ "main" ] + +jobs: + build: + + runs-on: ubuntu-latest + strategy: + fail-fast: false + matrix: + python-version: ["3.9", "3.10", "3.11"] + + steps: + - uses: actions/checkout@v3 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v3 + with: + python-version: ${{ matrix.python-version }} + - name: Install dependencies + run: | + python -m pip install --upgrade pip + python -m pip install pytest + if [ -f requirements.txt ]; then pip install -r requirements.txt; fi + - name: Test with pytest + run: | + pytest From 982335730cfff3452a3e1e666b6d843c34f061d0 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 18 Sep 2023 16:05:38 -0400 Subject: [PATCH 285/300] Add placeholder test --- test/test_detector.py | 4 ++++ 1 file changed, 4 insertions(+) create mode 100644 test/test_detector.py diff --git a/test/test_detector.py b/test/test_detector.py new file mode 100644 index 00000000..245de27c --- /dev/null +++ b/test/test_detector.py @@ -0,0 +1,4 @@ +# placeholder for now + +def test_func(): + assert 1 \ No newline at end of file From 8d5563cfb0f10b5716f169e4c090ebdde2342b4d Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 18 Sep 2023 18:04:27 -0400 Subject: [PATCH 286/300] update doc --- README.md | 6 +++++- docs/gotchas.md | 3 +++ docs/index.md | 18 +----------------- docs/jax.md | 0 mkdocs.yml | 1 - readthedocs.yml | 26 ++++++-------------------- 6 files changed, 15 insertions(+), 39 deletions(-) delete mode 100644 docs/jax.md diff --git a/README.md b/README.md index 1b9d6080..36ccb389 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,9 @@ # Jim jim - A JAX-based gravitational-wave inference toolkit + +doc + + Jim comprises a set of tools for estimating parameters of gravitational-wave sources thorugh Bayesian inference. At its core, Jim relies on the JAX-based sampler [flowMC](https://github.com/kazewong/flowMC), which leverages normalizing flows to enhance the convergence of a gradient-based MCMC sampler. @@ -24,7 +28,7 @@ pip install git+https://github.com/kazewong/jim If you would like to take advantage of CUDA, you will additionally need to install a specific version of JAX by doing ``` -pip install --upgrade "jax[cuda]"==0.4.1 -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html +pip install --upgrade "jax[cuda12_pip]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html ``` _NOTE:_ Jim is only currently compatible with Python 3.10. diff --git a/docs/gotchas.md b/docs/gotchas.md index e69de29b..a2f1faa2 100644 --- a/docs/gotchas.md +++ b/docs/gotchas.md @@ -0,0 +1,3 @@ +## Quality assessment +## Tuning guide +## Jax \ No newline at end of file diff --git a/docs/index.md b/docs/index.md index 000ea345..a2ac8797 100644 --- a/docs/index.md +++ b/docs/index.md @@ -1,17 +1 @@ -# Welcome to MkDocs - -For full documentation visit [mkdocs.org](https://www.mkdocs.org). - -## Commands - -* `mkdocs new [dir-name]` - Create a new project. -* `mkdocs serve` - Start the live-reloading docs server. -* `mkdocs build` - Build the documentation site. -* `mkdocs -h` - Print help message and exit. - -## Project layout - - mkdocs.yml # The configuration file. - docs/ - index.md # The documentation homepage. - ... # Other markdown pages, images and other files. +# Jim jim - A JAX-based gravitational-wave inference toolkit \ No newline at end of file diff --git a/docs/jax.md b/docs/jax.md deleted file mode 100644 index e69de29b..00000000 diff --git a/mkdocs.yml b/mkdocs.yml index b44dd4b7..6ece4db8 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -74,7 +74,6 @@ plugins: nav: - Home: index.md - Gotchas: gotchas.md - - Jax: jax.md - Tutorial: tutorials/ - Examples: examples/ - API: reference/ diff --git a/readthedocs.yml b/readthedocs.yml index 7ec731f8..e4c2d803 100644 --- a/readthedocs.yml +++ b/readthedocs.yml @@ -1,27 +1,13 @@ -# Read the Docs configuration file for MkDocs projects -# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details - -# Required version: 2 -# Set the version of Python and other tools you might need +python: + install: + - requirements: docs/requirements.txt + build: os: ubuntu-22.04 tools: - python: - version: - - "3.9" - - "3.10" - - "3.11" + python: "3.11" mkdocs: - configuration: mkdocs.yml - -# Optionally declare the Python requirements required to build your docs -python: - version: - - "3.9" - - "3.10" - - "3.11" - install: - - requirements: docs/requirements.txt \ No newline at end of file + configuration: mkdocs.yml \ No newline at end of file From e1186d9e98ab65106b717b7314ffc5eb93e5074d Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 18 Sep 2023 18:05:42 -0400 Subject: [PATCH 287/300] WTF --- docs/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/requirements.txt b/docs/requirements.txt index 88b76a83..5a99e967 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -4,4 +4,4 @@ pymdown-extensions==10.1 # Markdown extensions e.g. to handle LaTeX. mkdocstrings[python]==0.22.0 # Autogenerate documentation from docstrings. mkdocs-jupyter==0.24.2 # Turn Jupyter Lab notebooks into webpages. mkdocs-gen-files==0.5.0 -mkdocs-literate-nav=0.6.0 \ No newline at end of file +mkdocs-literate-nav==0.6.0 \ No newline at end of file From 803feedd7816d8e9be95a844adbd0abe53713b47 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 18 Sep 2023 18:11:52 -0400 Subject: [PATCH 288/300] update --- README.md | 2 +- docs/index.md | 14 +++++++++++++- 2 files changed, 14 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 36ccb389..f32af38e 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # Jim jim - A JAX-based gravitational-wave inference toolkit - + doc diff --git a/docs/index.md b/docs/index.md index a2ac8797..c4477fbb 100644 --- a/docs/index.md +++ b/docs/index.md @@ -1 +1,13 @@ -# Jim jim - A JAX-based gravitational-wave inference toolkit \ No newline at end of file +# Jim jim - A JAX-based gravitational-wave inference toolkit + +Jim is a set of tools to solve a number of inference problems in the field of gravitational-wave, including single event parameter estimation and population analysis (coming soon!). + + + + + + + +## Design philosophy + +1. Extensibility over "feature complete" From 61c857b9ed4d2b6f5d29a505a0730d7c03706dc7 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 18 Sep 2023 18:13:42 -0400 Subject: [PATCH 289/300] update requirement --- docs/requirements.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/requirements.txt b/docs/requirements.txt index 5a99e967..cb98f1ff 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -1,3 +1,4 @@ +jimgw mkdocs==1.4.3 # Main documentation generator. mkdocs-material==9.1.18 # Theme pymdown-extensions==10.1 # Markdown extensions e.g. to handle LaTeX. From 58cb00e6bb0503dc4d6222c7e183fc08da7d7ff4 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 18 Sep 2023 18:21:22 -0400 Subject: [PATCH 290/300] update --- docs/index.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/docs/index.md b/docs/index.md index c4477fbb..c45fd4d3 100644 --- a/docs/index.md +++ b/docs/index.md @@ -1,7 +1,8 @@ # Jim jim - A JAX-based gravitational-wave inference toolkit -Jim is a set of tools to solve a number of inference problems in the field of gravitational-wave, including single event parameter estimation and population analysis (coming soon!). +Jim is a set of tools to solve a number of inference problems in the field of gravitational-wave, including single event parameter estimation and population analysis (coming soon!). Jim is written in python, with heavy use of the [JAX](https://github.com/google/jax) and uses [flowMC](https://github.com/kazewong/flowMC) as its sampler. +Here is the list of From 50c256100f87fbad0300f8f441b0a25c26d008b9 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 18 Sep 2023 18:36:21 -0400 Subject: [PATCH 291/300] update --- docs/index.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/docs/index.md b/docs/index.md index c45fd4d3..7db220c2 100644 --- a/docs/index.md +++ b/docs/index.md @@ -7,7 +7,9 @@ Here is the list of - + +doc + ## Design philosophy From 5b1de404e34fe20babc764087532e1867e69a9fb Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 18 Sep 2023 18:38:33 -0400 Subject: [PATCH 292/300] update link --- docs/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/index.md b/docs/index.md index 7db220c2..1df15cf2 100644 --- a/docs/index.md +++ b/docs/index.md @@ -7,7 +7,7 @@ Here is the list of - + doc From 43b714bcc2556de144b77078289b3dd0deac1e87 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 18 Sep 2023 18:48:06 -0400 Subject: [PATCH 293/300] Update_badge --- docs/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/index.md b/docs/index.md index 1df15cf2..9b239c1f 100644 --- a/docs/index.md +++ b/docs/index.md @@ -8,7 +8,7 @@ Here is the list of -doc +doc ## Design philosophy From e07003206e2ff24c756cf803f6ae21ac2aba34e7 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Tue, 19 Sep 2023 11:54:32 -0400 Subject: [PATCH 294/300] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index f32af38e..9967b3aa 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # Jim jim - A JAX-based gravitational-wave inference toolkit - + doc From 87a12ef7fd015be8cad944b50cd0385124650b54 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Tue, 19 Sep 2023 13:20:19 -0400 Subject: [PATCH 295/300] update --- docs/index.md | 12 ++++++------ docs/tutorials/anatomy_of_jim.md | 14 ++++++++++++++ docs/tutorials/single_event_PE.md | 6 ++++++ docs/tutorials/single_event_pe.md | 0 4 files changed, 26 insertions(+), 6 deletions(-) create mode 100644 docs/tutorials/anatomy_of_jim.md create mode 100644 docs/tutorials/single_event_PE.md delete mode 100644 docs/tutorials/single_event_pe.md diff --git a/docs/index.md b/docs/index.md index 9b239c1f..370ec541 100644 --- a/docs/index.md +++ b/docs/index.md @@ -2,15 +2,15 @@ Jim is a set of tools to solve a number of inference problems in the field of gravitational-wave, including single event parameter estimation and population analysis (coming soon!). Jim is written in python, with heavy use of the [JAX](https://github.com/google/jax) and uses [flowMC](https://github.com/kazewong/flowMC) as its sampler. -Here is the list of +!!! warning + **Jim is still in development**: As we are refactoring and continuing the development of the code, the API is subject to change. If you have any questions, please feel free to open an issue. - - -doc - - ## Design philosophy 1. Extensibility over "feature complete" +2. Performance is a feature, lacking performance is a bug +3. We do not do use-case optimization + +# \ No newline at end of file diff --git a/docs/tutorials/anatomy_of_jim.md b/docs/tutorials/anatomy_of_jim.md new file mode 100644 index 00000000..b3b2931b --- /dev/null +++ b/docs/tutorials/anatomy_of_jim.md @@ -0,0 +1,14 @@ +# Anatomy of Jim + + + +## Likelihood + +### Data +### Model + +## Prior + +## Sampler + +## Analysis \ No newline at end of file diff --git a/docs/tutorials/single_event_PE.md b/docs/tutorials/single_event_PE.md new file mode 100644 index 00000000..24e729f7 --- /dev/null +++ b/docs/tutorials/single_event_PE.md @@ -0,0 +1,6 @@ +# Single event Parameter Estimation + + +doc + + diff --git a/docs/tutorials/single_event_pe.md b/docs/tutorials/single_event_pe.md deleted file mode 100644 index e69de29b..00000000 From 7b7df8c57c554484e91ad3f68eb1465ce66aee2d Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Wed, 20 Sep 2023 14:51:18 -0400 Subject: [PATCH 296/300] Update doc --- docs/tutorials/anatomy_of_jim.md | 16 ++++++++++++++-- 1 file changed, 14 insertions(+), 2 deletions(-) diff --git a/docs/tutorials/anatomy_of_jim.md b/docs/tutorials/anatomy_of_jim.md index b3b2931b..38f1532a 100644 --- a/docs/tutorials/anatomy_of_jim.md +++ b/docs/tutorials/anatomy_of_jim.md @@ -1,14 +1,26 @@ # Anatomy of Jim - - +While the actual implementation of classes can be as involve as you like, the top level idea of Jim is rather simple. +We encourage all extension to `jim` follow this pattern, as it make sure your code can interface with the rest of `jim` without a problem. +This guide aims to give you a high level overview of what are the important components of Jim, and how they interact with each other. ## Likelihood ### Data + +There should be two main ways to get your data into `jim`, either you fetch it from some public database, or generate synthetic data. + ### Model ## Prior ## Sampler +The main workhorse under the hood is a machine learning-enhanced sampler named [flowMC](https://flowmc.readthedocs.io/en/main/). +It shares a similar interface +For a detail guide to what are all the knobs in `flowMC`, there is a tuning guide for flowMC [here](https://flowmc.readthedocs.io/en/main/configuration/). +At its core, `flowMC` is still a MCMC algorithm, so the hyperparameter tuning is similar to other popular MCMC samplers such as [emcee](https://emcee.readthedocs.io/en/latest/), namely: + +1. If you can, use more chains, especially on a GPU. Bring the number of chains up until you start to get significant performance hit or run out of memory. +2. Run it longer, in particular the training phase. In fact, most of the computation cost goes into the training part, once you get a reasonably tuned normalizing flow model, the production phase is usually quite cheap. To be concrete, blow `n_loop_training` up until you cannot stand how slow it is. + ## Analysis \ No newline at end of file From 38c9584faaff09dd0cc6c258a1c75815a8c3fe96 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Wed, 20 Sep 2023 14:54:07 -0400 Subject: [PATCH 297/300] update doc --- docs/tutorials/anatomy_of_jim.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/docs/tutorials/anatomy_of_jim.md b/docs/tutorials/anatomy_of_jim.md index 38f1532a..15ebef47 100644 --- a/docs/tutorials/anatomy_of_jim.md +++ b/docs/tutorials/anatomy_of_jim.md @@ -13,6 +13,8 @@ There should be two main ways to get your data into `jim`, either you fetch it f ## Prior +The prior class defined in `jim` takes care of a lot of bookkeeping for you, and it is a subclass to the distribution class in `flowMC`. + ## Sampler The main workhorse under the hood is a machine learning-enhanced sampler named [flowMC](https://flowmc.readthedocs.io/en/main/). From 941a88c9e8884b907684613ee0940d864d86f578 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Mon, 25 Sep 2023 12:12:32 -0400 Subject: [PATCH 298/300] add pre-commit yaml --- .pre-commit-config.yaml | 23 +++++++++++++++++++++++ 1 file changed, 23 insertions(+) create mode 100644 .pre-commit-config.yaml diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 00000000..213d97c8 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,23 @@ +repos: + - repo: https://github.com/ambv/black + rev: 23.9.1 + hooks: + - id: black + - repo: https://github.com/charliermarsh/ruff-pre-commit + rev: 'v0.0.290' + hooks: + - id: ruff + args: ["--fix"] + - repo: https://github.com/RobertCraigie/pyright-python + rev: v1.1.327 + hooks: + - id: pyright + additional_dependencies: [beartype, einops, jax, jaxtyping, pytest, tensorflow, tf2onnx, typing_extensions] + - repo: https://github.com/nbQA-dev/nbQA + rev: 1.7.0 + hooks: + - id: nbqa-black + additional_dependencies: [ipython==8.12, black] + - id: nbqa-ruff + args: ["--ignore=I001"] + additional_dependencies: [ipython==8.12, ruff] \ No newline at end of file From de26a7fd14c4bd89ad2ac37d680ec98b8ddd2a38 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Thu, 26 Oct 2023 14:51:37 -0400 Subject: [PATCH 299/300] 10% acceptance rate on this Pv2 --- example/GW150914_PV2.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/example/GW150914_PV2.py b/example/GW150914_PV2.py index bf5de111..6dc91e79 100644 --- a/example/GW150914_PV2.py +++ b/example/GW150914_PV2.py @@ -54,7 +54,7 @@ jim = Jim( likelihood, prior, - n_loop_training=400, + n_loop_training=200, n_loop_production=10, n_local_steps=300, n_global_steps=300, @@ -69,7 +69,7 @@ train_thinning=1, output_thinning=30, local_sampler_arg=local_sampler_arg, - num_layers = 4, + num_layers = 6, hidden_size = [32,32], num_bins = 8 ) From 88c88c0647405342f6c927d6eb0451a3845491d4 Mon Sep 17 00:00:00 2001 From: Kaze Wong Date: Fri, 24 Nov 2023 13:20:00 -0500 Subject: [PATCH 300/300] Create pre-commit.yml --- .github/workflows/pre-commit.yml | 14 ++++++++++++++ 1 file changed, 14 insertions(+) create mode 100644 .github/workflows/pre-commit.yml diff --git a/.github/workflows/pre-commit.yml b/.github/workflows/pre-commit.yml new file mode 100644 index 00000000..c2f7e71f --- /dev/null +++ b/.github/workflows/pre-commit.yml @@ -0,0 +1,14 @@ +name: pre-commit + +on: + pull_request: + push: + branches: [main] + +jobs: + pre-commit: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + - uses: actions/setup-python@v3 + - uses: pre-commit/action@v3.0.0

    7hQzU(%Bub_SenMm!&wcJ(7J z&NtTnKeFBfp6kBtd( zh6ZU!*7H6(&+EFL`+4ryb>H`OU1#zC|9;=^F+RuV^Et}ULX^g$b(JN@y*#O-);N z=+FUots8*b>!XxK!Gb(M|M;9gW{pF5&%z=BWPzl^Hkjs5`}-ic2`PJ9wj`rw@(_Gy z8jLx`PY>GfZVmD){hjEifDD4m+w`7wiG>=o@crZgFs2Aca*omg;*?DH4l{CK$R^A* zDShc?X*7~vIZtd==-#8k;nxzFjEqr>T&6Mw-EO>!f}E)<${Nh0F+-LGECnZl#Te{B z@#gKsch~ecLH|vaFV-+{KZEHD2!1L53T?@C>HXd4LRk2o zLb_|!${J@@%hz7kn;-7d!N|dv^&Sw>flTVoo%_*e>tZ;wZ=w5wrEK7Q2?)*Y4qh3d zX9}>{Fk_Y9O)f94d7N`~Ev=E1GZaJra5Hl{1-Xk+=79sj#cGt~4I=T>yqg?tlN5A( zSwT5rioQf)&loX@TW?ol^E#A~LE& zBP--7G5h79!%~&$p};Ol=bno6jiODYZ$QBU81W1>V$})FQYX`x!cxH)z))#Q;pO#Q zhmdRpIG|J)e@qLStg)2!f#N^m;cFuvhEAxSTN7Hn)gxkrD{S8ZmeX3Q}ecK zHRmI+1A&x@OPlDGqjIMC|Cd zWNO0(>Bi60vU!y~>UH+0jW+W|a^C;P`OyszW} zijpLMR~_|nNLh;W*aD;*ozwMmKVeWp#-*#F!xl0Dt|@Nj6E+bB7S_&3WCe`Xt>+OP zB)5P^pvna(JcJeuVERop zz%p}zT!kSm-}=(am+f&!NE+4IsSlb%4%awH__7I$VKviGt+or{n$$aem`R!@wJl#p zIPP5Zk!y30B%!&w4IbSJ*91^-!W=r3b-;pR?@%_qyiAP}}vp_00KaxcBMV>CFHX zszKthLG6E?%KTQ~dX(+;@tX5>f_pcN&QP2|q^|t+ef{B=hrcumQ8U)2aMss|6@-$ysfJ1;YwH7U| z!)Ar%n+chmQ6{Q{QS7LkE;bYBJI|aR09Sz}cdJ*g7V?7?2Z>x|xuFkg1fa8!ZTm?fK&)JmhM%oHu8lcd z5;{4o7-$QCPxef3?=u(a|GGU1C(2>LMp~7QT3j2;C(C`~EQYWF$s1!c&RJS%{y=sY zaOLcCnmb7pfa-(7-vio}ugJ2DDh6{(3RE2VNAk&voj(OQQdkYy&~Sa!&*t z^q$*otynY&_ro`rUl{TT=U|yE;=S({vA(D%64DL-b0$k%fTv9hBt>M- zh01@~gNJXAF~hmJr;p*?q%vkJ#Y8f%D09i_v_o}4aE0j2QTb!UFuyPSMTYz#*2Zo3 zW3nPDXtl10Pw|$+c)&sTnWMVTyE3w+>?!cM>WHi0njou*Ll|VmGZz$H97|nCLPOy( zd7L`TfW)%mecp5fMs$P$$0zCkvAQ%gT=iI)hN_7WSp=trH7`h& z;rOqNjphgt?h>Uu3`KVu1?IV}scRW8og*XS|WwTy8 zmOM2>ghy1b(NjxE9NSF2_h{>UevsN++7BXa^vlq4iqR1)OVmy#UTf@5NlcvP<#mVI ziwL=S3UbjgM3c5x1R{Vr`>&20_pi&{fr(cj_=c1tKEsbVGbd*~g%nK1ajyfHqfi4V zWCSfBA(?B+f%RKi6G5|^2s$Ej3xY71Q9EXF(M>WT{T-nt5Kxj1GPs=4uFxqc&G&+D z4zEAG(9-s-v+aAu2DeNiv!S!QA?FTWvnJ-;V0tjiiH^i*dgHLoo3(D{-8g+4 zSPIOE+P5D=;j?)Cflxpw6gkh3%1^zLe%5B#5HZ$A%{d8!k|6zua`NF`k=eu@IHSb~ zvOtR$@-0CJtBUT#r$>)k{K3Wfpo5tt8J>`pyYy>Ee2Q!?Ywk0p3Z0Etp|;9g64drR z#?lG(o@Gvhx~->20|nSmlJ7uu?UPrV`8tI0T(A&5pRb%=nF#-No?^jzBB0bkt6!!j&zH*k@}efD-KJquQg;m zX#t)r#>LcL0;53UP#+b){+j#czh^L)!{1MQqj)pdsJcN+ujl1yVV5RsNL)Wz_pS;h zcHdnu1NF5Fy}d7AyO#7k{{_Yr#Nm3c5G%-Z19X=)%KE0>UmhQQvwG2i2A)cHFRL7Z%ieP+LGK6R2_u^)gc1BNHBRVp!sCJ6IhsVA zGdY7zX}r{+une88HvP+vE%MRoU$NJZ2#5A9l44)`SYS}PGq^J&+GH*>A4_)Rk)AN~ zwG1CZU~R#3%S6sfs^`&kY-HGCslA~Hup!W^td3zpM&?{*&eYP~i5LKtqL^x6yaT|| zjd26Th!{H;R6`|~ju31Z4WCeCNoIaqf@V=KArrd7pvjh$W4CZu+dgwNfEKVlnX>IP z2U;}E#7dE=;{|++=DA*hkfStXx@qm@%F9aKj2?O|VSXmH-BegEk~-Q8^p9*%bm`b) zTV&*3?E{8`vbT==wS-C$@u9j>x(r`%SZLyTO{9IoPNX)`7?x936mxn$-gu$VChoc4 z%e&_8qc{w^f}p@r_%<9aayL2K-PZV(QqibqF)4n34QE^&w0sHUBpw~^$X|c_kX?wA z)BOZAu-G=GC7Pym?80rlhSIeW!GnhS`uSaW<=>o`PR+t8zt&*p`YZSEM-o{7#+PJA z-}%2sOg|0)BqVoqAMUJQ#2;$=rsakX4r>4v8k9Q{iEszt^}pfU3o?kvhasKDu<7)% zkmad4Xf6!aV1SxfLs5HVuDzhEHhO#`)z-;ucn25hy(* z^L<@iwg=J(-pu3+`drmYy$t1>GkRLwa&FrpV(#vLB z=bd~N1pbH1-Gf_UIQN!D%?cD#6Y+e3?4VVDW0B=iF3?s(4WkPiL4weymU7@0^4dB* zSC27y5c2DmDf)MA9t4>sQxi3g zL(ijyGE|yawC4ylMzqV%t^)_|ZvzC6(&sS! zX;njtE`GE;y3Dt-@Ld*fa@1h6s9hza?H+Eue=kNa+)7kb)Zds{ZYF?eW_R~N5O%Xr zwBWOw31x$oJX-Jx4D9FZ(IHWG@7uQvqd1;>r?_X&p52!%9LyF0!$nDU>=rnDa92@B zhUC%olPFc5Ob# zXFi>ck8`@;*?aHC6|6GgG*YH$tBe{u)?~>PNPTUG_-!NgfK_A@(4^5t@U1MnaH>iI zYK_V+-s^d=;hjaWh{Q=BWPDX^PA4N{t9U35p`ps)zSnbd8WI|SVuaZSpf)@gCL);8 zK9N%&ix@Sa1JWZ1w8MxlXY#Z4L{~XvAFOK9tRRVY-PJ_!Gh$O)YaZXtL1sBO{gjsj zB4Bq~z;iZVr81i#7DS+SoMH==WC*>F|JE8(1MlnX%F0uA6AH=T4W86O{%6C~Iv##F z4*0adqC7nttCzOpw0FK?N%ea|y83fv+E+LY4Qf+3F~B8Sv7Lds$G`SB7}z~=-jUM_ zC-sHu#E?QZ!*bz(oVUMHivRRi&dr}twu|_)pPrsRomQ{;*7w;G$0ufKhy2G9 zI9UrU6+Vy0ZQ8+qW)w%A72Tk@y)uOT-M_#&a$p0kr6-raC?I(0KOcI{y$9`_bbah> zEuH1&mJp?;%e|oC7T!cy4_A}s#>2O;kHtXT8f%a^-_Xw2YCyygqP@eIC^@lbsZrE-y%!s+$z{LH?^&F5M-SD5*D zdpCQkLq8;W5z0%`K#I9u2z!Q96gE$K-lAyCBw+PFQ{cfXT5!Yx%`9jeWtj@bkq+gx znfmu#1MjtS{PyjeYQu)I`ZQBVOH?ShRm*PHR({}GSP*d`!xv^S?rL(*nYYjc z#;2rY!3)AD>BK8by^Jr(Nl8ZmVWd<5(w31tS1g${DPiMzQu>WcR19;Ue5_*I6*DcqV$L{NLxlA0k>c@81LV3)n zkthOduYu|f$6bxej8B*3cj;hx{^Ett=P0_i4#?+c3Mfoic}IoAb3Gh3@RXH~y;W7W;pnES~sXmf~|V& zvGTo{<*fiRps5^rnVFGttbF^ir_K1I0*AI}(dA5JwjN8QUM0``V|&w%%1gjdmNB}Q ztf#W(`!ozX3eE!zx^6>b`twE9>&yPCQh7yJ-@&%bcXp*Ps)6`m`5k^#V?O**rEA65 zsDk-%9fT3p-l0wpUGa|;1`(`5764!cc!wE*<}pR1oSiMgG_R9|2o9>7jA%Z=Y_?Iv zMHyX5# zRH! z7#6X}unqS5O)qh4?kTB0BAb&Aj7PKx>&be{)`_h+)Eu`8!&T1N-&W$r*WepA zUOuQ_E~oa;*S9KI0S;i<>LC5tE3mUqo{kN#GU{M>zIg4PS_0pFAu{)#El$Z0Mkl0R zz7%oj2im)t0DQuCK;~UgO&I2_x0H=g2hLbNQPlLN_ znpQYW@O7r=bW<2H_3iMQUqF;JmL&HLnM>|qZOn{ImYl;RnDXgj%9+X{<}qSAs#LB- zxQ5Ewne|7q;qZQd@7;Z?Or;Ii{|J(7)f|p>toz}*NY)xt8 zXrRXCa211&rVMr)Ww6soD_1dgNx}9Ah#^24zd`G z&v=U^;8PclZmOzk;Qj2y^XH}uUj=4X#H)unHkGWDR?of6w;`^#7GPpDo}hx{U8mQk zW4!fOR2d9(9j-LS(SoE#5G(|e5cW0=SWN4=2vCBy7-(yopE2ICZJT(uiF5Rv*eRQi zl{-NRqXLn?LqiVBv?cU!hn`!u=5*b^OFtv)`Nb7ZVVlW*PGQp@f%PymY%u0S$n>#E z37@|1nO9}=Wa*xsSuB5T!jt_gC2RAmjLswMRj?NpR37YqV&muf^zqq&rb zF=w(e10?WV=H z#3SX7PEILkf6$$40wW43fyug(XG?vx6&slegOnlK>3YEgq=rKw--`=kIdeh5(+?On zOoP<|@ZTid%3)4mIj>a6!vy@gtXRnucmSrq(TipQc>~Kz%6>*a=x*=<_d#}~kFRqy z&l)NWMSW)cm5&qM#l;hF2nK0$rt%?+COlcy-DBW;+8qMrPFW%ho-8=qjCSA{;-S&I zcd+&DZr!w!vx%X43`#VE3a*WdG4IMH-m*n4(^ggFk|M-o=|IX9$LkOm3k@j*|RqKrc*KRbV_&- z(U#c5&|Oe@03jjPUFF$Iuf%89tYb*|DxfCmC3=UW$uc7h2Ace9v_h2#N?-#@G7GbE z3IQ8sod!+Y0zn`}ueWSmMG2tC&9KUxg80uKN&DWEP41Gc(|@dRVkyd$tT5e^B{10nL={MnLbv85sW5ML_CV zL%&41KsT`8{N&ivxfDo{Gb68Lqvm6cU^31RDRS;$jy+L5|y zE51dQ53)}VNK_Bgyz{ZrTU7Xa_FzhHw=gsHX8u^?0lrgucUd-_HdS!Q#*H`H=k1J$ z$V5CR&K_XR4LbS2A|g>;W#NDrTb26Hu3N4zn3H}4F(L8&0iUKFUhU=j|Z zMNqE^&3o|$Dx^>WCU;%8voLP^pA|IwORLjbgk9XN)~rQ~HC8%?5xvWQFO3>De3*99 z*WbQ6(KZB&;kiz9tAd&fXhtXvezV|almUszB=bknN{z7S#qBkh4hgC=E}!e5sUA&l zDG0gH5MLR0$s%o1ghlb$4L*1GU{-@CJrIc}HN)hKe>wh=mEmv#{vilxwsm66N?C{J z(O9)W{5cr{8J#@153SW?o#12;k8E8`8QoJB69HOQ8qTOVZ;arl~IR5 zqZ<^MJ9$DZzQ5AR<3^$Gxx%EI)a11tRxE4RVt9Gqg;nQI#Onv?FLj%?Y?+&z5y+Y&#u0i!+D78h8*s)^4iBe^vXY zBB;XQY$s`L`m9+k^`31dsRM3Z2ERs+48oATrDI)c6LBwumUE*79mOgd=Kj-@4Ob+e zK7HC{uh9&C_?XJs5ZfSEZ@xdfZ$d|3irhCIw?iH;8cpOa{5Bf>KvAs+AC24O^KMni zKrQr!ktVJ~DyG8k@&lf%Ep^cY4q@CZlVR?L47p#vd?}?3NT#oq?ZdH-M@mmCUd@j? z%>0acyqThVM!1UFs1?Hkw`r>st0}sh329k~p4)qRFfmy7{O-7me=WHg(``M{)?+zn zWeovZ)_X;X1bMMMasl z#=kqBa!aEa2O!!7KdJh}bFVhVwAQSDzY43t$Y{UmH{A!9K0+CWpCg2$(u}F`#oup? zb8^WDADi=I)Td0Hqt)*r71S;cd|Vy?@#P&?MDTj1C)X-A08#@LBy$(BTXcOhw4-0` zB{2%2Pc^+f&3bn4@u&Us>xZrURk{jj-kaj5zuG4mIsAMHo7EkDoA;)GfJK4M?v>%J z={?rr#>nBr1wrOC*1tsi!=K$;RW7AsS)nzfQU*^pC)_4GIrSU!W`2@Q^m`zn*iZG$ z_IzkRcgL5BHS+@>pH}qDum2cAILJ@O40?>Wq)Se69j>+Z(w)1^0*2t?JExn|l+=fN z$4{AUVBVGQ2`6rG+5egRw@AmEM^hucSOs3>NkKZ#)wLy!nE#D^HC~}d?DRG0tQUd^(E(k7JyJORBD_Fww_tNv#YYP(HGgc~`g8lhw2TItwDp_%Vo_xCpa5)1f=^z|$1#*2hBf+x;>f zYp~+x*fvebowKX+$R>2uBN>~#*>&eZ|1Z`N;?gRH4}`&(!?T%xJ;qOwmkV2Dps>p5 za3Th;Sjzl@i|X;frTnVSk8@)p%SWcR(oWtH0A43Qx`{oGBxF7zp}{Pwck)ZC2QO;; zm+*$~*>Hr0_*dN!bOC8uY@_44qhe;fzng#-qkrzQO1=7Fx~DqKe|)4Mbm$d&%xK#5 zYzugmGp6x}X588q4*lllm2s_Fw-%3S8vD3e=FvLVSk148EFv4L`Y#+gYSenp&9|EB z6(>tZr!xYd){dEUW|=~2^+jKi)OVvvu`8p4yXMHgL?Y&6M3S0G!x zZq%U#+*r?+J;E-E-&NGWp&BMr+XB-bqtW)NfTSm8eoSz4@^a-%K<;5uGvIP%GolqZ z(p8*@Wa}wSyxqs;ve;G5&F;Yikfe^1|l7bnE%B91YP4~O=-e0ERO zFZ;>GQTpS8?2~N9CEK>@zwINZP-N&F<*d2u`}FIV!n{YUXAvrzer`?uB=3l@n57!e zhVT3<&^4ZLp}BP%n?IlB;V~Rs0tVicpk2q+8>)?(C!k(Mfxl95zH80AbP7Zu$hBld zzn9AUoR4-Q_Kr+-xPHF8=4bjqY5;VOH`^7hYthXHe#V1CaVlz+gKO4&{Fyj4kAvCm z?{~eym^HdO+vRE;_`Ut^QO7<3mJUPvq}8#$a^=dKMkOi5EmNO5rRRsHuN*saq~4fm zQHFcdi%%ANAzb#cJ6>#>cGIU~?>l=!cT}%NX=~OoFg)G%!1vEbN4mN7k~>iLUNN+Xn%A+=b=d{rCN(>F`l_SU$A63nsgv z>bf>|>3+Uo;v-o_Y+#?LLcz!2_D$%!))Z23rMNbAX};!onpZA;l0S1pF1s}Ns+7()#FxIKDxuKIUwrXBM$OreK`+Jlb#$JcZW;=BYHD+?;Me=1I5ikTf} zX2zZAgpxn1+oZnPLz^atG;%jg3k`+=9M)XF{?R9=arH8%f_b>W&%V6AbF_UeO%eVX z-Vi)>toMLnU=eU{&+zp+jW0(>xpmh5X;}-WRfZHy6Pr_QSti8OY16nw0~w_`?ml<# z_eMXLLvc5>W6CNVDl5PoL}3VK2?}kPtLB@Q(1>n#Hd{4-W5=GjP*^`K>A{`$KVj1f zlui*W3||RgOqNx1cXk|F|7=!D+GQlT>-ap^o^_~st8nz{&(2F0DETjb9=3L^8(^MK z_a9^kIKA^O+_8$d%KH&RxSBtbi7;8C0?^uj8-}ZI>21aj$xC}Su=`m>cV>tHH`nH# zc|>kZ+xnb{FMTVA7M?*v2IeP2cZ|!9GH6c2u!k@Rt!Y-(8Q#F_QNil&=H=!;ML)TGWQO+3^#upe3yaOQMo}} z#Dv){Hm`WWX0C$wTgQWQ0fZj@b;E>nF*w}fQ+dUtI#<)XnIKLA#5l6_vkQD%{gM(& z$V5T_yluS!w#hdMKQa#Ek3{t{;EP3*&Bv!X7}&&gwMMWD)IaLPCS>{U0PJVZoDu11 zMK$lkjpB`AYId}d7VlUVihvc{MCo6qXH~!TXqQ9nocc$-E(X-@7McUBW0@gjcwucv zTPK$2RDPqr2mDG=3QIktObJUKiZXOu#|GN$Jgw}D`Sy)_ZNGbWW&U_WIbmzS+(km~ zyPGl#)tWLLrvaWHLHQrzT1xSV^-$cjY3t9Q9|%R|9f*PXr`0b=M&i1Jz$@_a=VjDk zvI|SZ+(+~7!mbjxYeJsl(X1GbA9&$U)2x^}^%@9^4(!tw`UhkbKdK2fke0r^-DYZr z|7{X*QGWgf-_>AIY|Gsd_2DseuI{l6XHe#R4$1W-eH}kL3_q*b$Q` z!^mpg9xT{d^LMSaW-Q*XF0{_N%A?xQUZVObesr*nXC-~U(1hlcjD!$bRix++r;?r1 zP2hP}1ne-LG5}{gE5L;DRq-w_s)s6tRkshgvSfw*;g3CM-qQDKnwGmp#tK|JKX`AK zJ@7zcw_)EX8(o{E2=G+Cz?+L~aS&X-b?Y6;>64(+l)byNnayheNT*)9nmM?LJD zxWnN}?TCLQeWJAo02HHHm%J46qO*&O{fSxQd{st2EZt79F0*@=cEBSo-?3z6!02vV zHnac5yI2Y@f+vRtkW>COqb0#(|%K3 z2pGW>-BPBdPKR?rE&@K*8)bR5amH`;XPfPZ&Z*9ic-lRhdX5Wt^z>;xlC_97 zK)Z$a4B#tH6Od2D=UAhgOB9T5G2kc8j0GhtDtj=vN$Rt2gAH`7J7F(Mb5mNBqeVM& zhTt!+g=gix=6vAel6}M0sz(_)ZDNf}cE6WIE!%IEJG*?`>O67n=aFd}24yEDC0!#! z!J+Y_(!@u(tm2y|(Y3|l$q!4#2qGqbd_8*+wvSo3c8;tn=ua<0uo59R-@PK?P_d#H z)tdiPeOb3a9b42RO`{f|0rxq)NvWE&)WxODhLdPdSP_sVe`&^Lpx{(bMBH3+0??K1=ahv2v_#O}TbC*{|$wZ)6w|9pqlLB6juf4T8qyX1jOghpA&lQBUbFGp$#Th&bY(^cNCgk&Nd(EVivdG z=wzMM_OF*c?=aiNr%tTDr*H9)Z4VY#`xlk(fx-h#g=R%+9@4z1Jzrk%y&{sP{)@T@ z;E4K!F1^R)GQ7uzkrl+>gxlgtr1a5SM;6Wb=8+paG55Va!yKt@`LF)P=b|^eQY%T> zh*Xzqo{H}unlo#f2?bN?R?+ORmtJOd?>bKO*(`1#JTfSltvtiNqRLxd!q7fj@fk5? zMs93HXwza*X)i0;J*X;{ltH-r+TI@b`x~u7`P4(9I2uei;2gB^7li2|sygrX+G$fm znIFSy{m0L+CG^h2j*)O*;7Yva^7w3LIum{TciOXr63?38Z{Z;!!(+>db0P;L=P0`m z?Dd2EuiCuJ7TO0>kF=gYmO<;}QOI&1Su8~Se3B7h#%Ng=Nt*Yuxa3h=HwjRJ+Y&gz zYrl~&r+os|6JM5Z?d3T}>`(+ttNhswHcJ8Lm&RaRZiDIb=Cv&;=iR@8?+M+dFuuN{ zX995SzuM0?UH3DoVIB2uPI&6Y^!)a<^cm|mZoJ*}9aY}`s#g_^&+uajBFbj@mbT-o zN=QUH9^?VVV?Ci08)^gB66(oq1SGwR8XGo+|4WR26`(kse_&28%WbsZXV=lUCUNn; zU*XIr>RFw|M+$7O>H8sQkoRz>ZCQ%h07vT zPa(fT^1I{8?iRwtNUlOe??x|55uWii+ ztl>dLgSlj>j^W!y`#rBAGhg^ZIk2I;f+xI%Kv?no`{%PZeNvC@BDFv>O*j4AmG>l} zg9b_1L_FQ!_e*-=t%p#8;a=pDc1aEHrqRhnnC(>Kh`_TPM%WH~-1R1rLmSjCS4X5X zOYa77iK)DV!cg`d@CG%yzI|d?TZP?(&S53v({C?4Z0s1Hq^%O9tzy=NbR_bSC==J+ zfFwcyl+GG^9De#9qOt}rjOQ{PDIEP^(LDEn0T1h%(?p8+hJoht>fe31vYxo-%Mczc z@cKk2{xQ21O;VCq3Mk@J!V|?tg;jd0GB)7%z*5g?db)YBb#=iNWqCzo8OM6J9t176 zXm|bL>ogLq!aGSapty4bL1muLyOphR5-|q%aRfn^!8Njx@pGNkEq?7p-<-X1Bbd9) zabWl>!Syr*wzPfzgKl#9VaD7$ z-dCo@na%lyLr1brw4p<)eTbaFqwd2`w!f~rBgIZ%0A^~mnZz|nvGq|RVOvvne}o$x z#4-xLcNclp3u%got5|Y5TzKiuW;faIRr5j$|6@ZnO_LoAi_DK;%krwIDEigU#G6q< zcI`rw4`)Ld$-s#mhJI5=tfNF`8fT;&`~tA!@}iDccb{*dW!$yt_=KBNJ!j9Zxc~A& zW}Ghh!F$bBOtFhd)Ma+6`3nxKwK-wOsh9GGx;fP$Rh<^xXYY2)1a1CwAp^xR=1Df3 z6|Rc+Uy~5mL{NXhY~5{mF@pr3$(Jl#nU|HNmz&R1wdIH4Vo>oR5ho&B+YQf;_dlBb z;KBEl?SCs-{Q6duyK5(!>K?vjGZ?zxrW%tad{qkPaSWv|v`@5ZhHG#(bxy2u8-hBL zpa^0Y7;-!LdSeNv*mMYp54~yEt1=_Uv|(IUyl^)z8hyaQuoaUFNaBj|oJf$eP7iw; zRDafJ5Ykrv2dKN8THZn?P!!XNX$;<3NZUHB-2ziIJraIs2d-C0nTYW+hKcRueh~b= zPR(G%nX8tPow}cbwr%g^o8Mb$XauumV2`364e=fh06ik5e$l2yTm#OC(hlxwUa zbUclIFSW1t+`qC?g6y;-aUMU(`^jW5VbD^1hMk&P5Vxm6d;5Bo=JQrWqqP-e`mL8^ ziXAaBAFAjTSa#sdw2S3A1E=q=bHl;nqqQ5m7tFLEf`Te7LZ1B@W z+|?Jn5$od+pb~G+scfC40#9{0X$WH*sH@{kwFM1Af~5OpqyhtFxU=)Ns%hP|wH4@s zm^#v*%C8YS2y_uhYVJ^}6$Kt)z;}JW6L*Y4vn~JvJj0-W>=;%r&$$z+*OUk6y~f?1 zzka}Z+?OQ~3o0_)-^Fz9If52YW}b*{D1oEB664GQa!Fi8cQk>xnzYP` z?ca;egHvJA#MbU&&6B#Jh4g>z&iWMX;gkB<;V2q?eSAejgZn&^iiCTBIjXGT;$q0I z)|VI3x?0nSfnf+2K*(tntu*iJdwEf~6@42CTKM{V#P`zDR{#>C!=@CgKH^Udh+f^^ z@!2;Hi79RlOtA|94A}^h{p6=ZoDe1%YRc-(v19KjwMqL{lrxf+Sl=-%D)ypr>JyU? zvpn9@YbB4=H4ZB=up6XviHHw#UhIp@?)H<+$|{bq`K7qPwMDU?NQQ8ryZyUS7^ucJ zD8O^(>^RVVtAS>V7NTk{x}`&DA>wK8HCm89<3Yeh4$G??A8)=&r(5TwCynb_{HhcM z(mzCQ0{9HV9oWSUi)V%;=EB=MwSZOu*u!Wc204-yfXs8grG4q;s5m|CV?)O^nLPX) zi=LbZkVE4-#v?Ks2PUDeLjE)!-TQeFZ8N8uZcmj9+*Weqc2$-G~RTal$=fADo zG~JqCq3d}Yi@r$LfZ7$qh6Tr-WVx6MAiRo-tmNcW>p3r_%r2UDChgM_8BLu|;KCyv zx-QSomWW>V^Y>s>%QS19JPYhbqus$%fNd{^V<}HJmahPmGRgad!vb5@yxgpN?|L-O@u(d zgLg3ndiML7)^kegnq3D_gOS?=fhVaZy`58~PDqh?6OfB=s;Dck z^W?z7*F7)A(R?)Dojc0U%*yHdfGGg&lpHz|xc}qBty?cZtMxVJpq)MgLmfIW%B0o~ zho??S&vr75mT4eRAk8V5VvMV@lG2TfUTn>@CHJ8r zDIjSO-U0be&{?5Rpm4e|aUpXmu+y;W5&i+;b~DJCK4Zo(td@&TJk3u}`Mv$| zT!z)nbV`+H^9p&O4b7!!gr0%+Vhb_IcGW zfjX1Y=h%B}|J7H!EAL0OZ!zy;-ZmXWe3S}V6U zGV7`mq`*0>Gc|DXakUz-GPrIKp>Hr5~dM% zm0QERk`*y+A5@1{mfxp}Da?z|31`?O z!y3A_pu@lzV;IRtRGD`NKBaR)-^M}-&IL^{o(0N)Tb6NymTEs&5d0~e4lQf znd|8#nm3B+zi8%Hh4FRiRolL+VZ&*rhS7ws*NPZz?G!-pOk0az&9P52%qr^#MEYj(4TUHBnnAWJ&u)CmW7 z#Hzj>+3!6c!{MMyG{tdGxQxDMj?gjr;F7{^At*=H%}jGhjd-uNls{M2eH1#Swck#I zAs2l9+^yeZ7L;6SbW7W((2`*Mm(3eVC3@J~pjM|EJ#e6Dd?dKPKBk8(jFvlB8JnFF%U-n}jqv!*M z(1kBQi#44()mL@w-)lw81jm1Q*hW(`M8}$rE9s0BpMpqfZ0)RK`C7U%#x>}GZ5Ow1 zrl#$8nCvXD0Ix55=(X8%=dy<;`qf-Kn0s$E7z3#TJb|SF2Ok_-SBe(rk3lm+H|!Le zm_x;gb-VSC(4DmT#+FBl90!Cz zzfsgx7x?D&V-pq29sJ_ql+W4Qd+9A$xBp`ANLs?7iVD>L6#DXdu=?WIg*Q1}}whyGxFB`s6w zkB?4F?4*(eSw$b*XhWNRn<#O58|K8I2p#WVrEYA|i@Z7ao9B3s8O?cVH4C`M z^xdW>_jgnY&>_47td8OO^|7ByBMiji39{~2^(&mqku&r(xx^sX&Y1fIQnj7o_qB%4 zHJU!6s2LAQHJrAGL6WB4uSwD4iup~#btS_U zEo}kFr|MA&1m)uuCQ%B@eVBK-Lf3*#TlL+=1Uyds3)9jy9 z7@R0=6Tk=@46~E_JE;V1>C$J`-9T>JajZH-c}x@?}!<1pBRPFqi^o_T6^jS~K9|`s4Uk%o5YNI($Q~vj(f?aQcqVtDRmiGDQ zC;#{VMij+0Rg2V-5DiNxleAAICO~{t*NN)XI11Q_U*}@@!kq!=7Gbs&$YWZ$TdDrv zXZGJ8$-lX`BVdTRt>)v0i2iuQGU1ZhI*5Y;!%KJ>L!s};5?(;Eth~H_V*;J$D21j% zbd0d~I2)v0ReklVwp;2FzcC+sU%a@lQn(YdSDx3(&&eO{cSm*9!8Z$B%r@1|eKu;& zF3)?vz55;XDo_0pFy6_fCL`&`bhX5nOItT@zW0$|+L%>lkuH_KkV)>}`>Q&_GQqBD z@k;)N6+@XR#a~7i|IYi8(&u7&>epJU;=XCQ{)vZ9_{Z9Q%!>>v?f3Whuiw4SjLsxxBvf- zvy7-}o_Yjy_E%X^L*5+Ympw^IA@-^K)HY$G2OCl% zB_Q_b+jmS+pZ`WpHKmR{B6MV@CCo@U=yWZqQ*{6D8xMB)mZu!n4lW!|!iPN!2(b4} zy*L11hhIIZNrTmAEZhk;V(A6(FPgC?t0h7x?{U3MqHsyQFyjC9b*gTFpp)&JDA0vn zxpqxAdalm@!i@aEEq~P%D;#B-Am%QzlM2=yg(gaZ!Qw~4r?`39%`m@aYP`8(0}cJa zUAx+;4eXi}^T@1!rQU(_~(lz zre*Xr?@HqX-X@eK@MtTfCSWfXoB(nlmPE{lofy?8FlLo#AGeac2@6OTQh5hm|3ggs z&qJ$frO}`sjCZqLVA2e@7xt>{kThEfm-%8*mi4i6HUHN##0`Q=3p1-C3717VZa zgccH1VI=(+8Y$kYaq5}+0dd3%@#3O&8;SwQ9H)_g0$jo2cb`9x=EP*jbZY}J!OC;( zllwV1TasmS9d&pe*kJW(<3|yiWwJmKk~(?d|2@h|w=0Z7w<+Puuk5};$w;FCrss00 z5nLfstSc*iA)o@8c@ptOU?8X{jllQs-|dh8ZoT)*-E9#%vlpMWNwW9ng-wLF!AlMQ z2&_B=PR=^ehC!MN5Ep*q%P)&9MGFLtq3Cy`x6M}jl$ zjgrcWioa17@_a{fs1#o}N~sx`dcH$-w`a#omyJ4Dc`#%6oR#>w_KXB|#iY**|M(9T52|Y(>?(Qob883d zgWuB6{kZ?Vyv*p-eUu1sQ?vG+Z`4*lyWi`~55J<<$n8 z<-2aT_%tc4;ZDj#_Gg$VhYlY(5?ZMh6_dj2?@ekZ1zoupM0a{0wi$)JzVi`n-1$H( ziOJqemU!?OsQgz`!G;@*??Od{7&WkEed|9br=u#xI$1SIrk3#Vc!u$I`r;!7KZgEe z0|R>T)C5S3d-_mQ$->GJCpK9ktw7xoN-rBuqXswN8c)1OzkXRD795M$2+Oc7FwArQ z^IQIV&N_P3{8l*XmHL;*wX0We90vDV$0A&~2BUAMRY%kK&xYcG1U1Td$_{ZXrwo_+ zjCx4|Hb*Z7WP$#$o^=EOY(1LA1V|-7Pu)&B{qsvYj)o&O9qW1MRbp=rM`9C3Gy=u4 z4u&gwkJAAxe*?A?0H>@90CY}V`cP3=RU+9!OAGf%Hd{Y96rrsWaF&nPW9cVn1q!=O zt5*+=%{V>#etl?~!u6nhqj{5$%Q!oE#1gV&aK}gx~AgQzfxoWP$1FHHQzMAxdva7K1h> zYS2A)K6P9>{ZgPJL>A*GaVEf40@Xf$K6&*?^%`CJxvd>~7v0{ul_CCp2tN$UNL1FP zy(o6CFq&c-CM!$D3l2od-*<|x^=lHHaQ=4#@!?`pKc3eiSBtw=`r*DFliKV2FLJ}q z6g)EK&$zHj%0B^{iAstsd(CV_r(5yC6S)35$%yXq(vv6KIR|eL1%!7Re&pCO1`X@v zH8W;s@Dh6J)X&3H8}rx-n$m8M=02ck9-3gMZLQA3DFOizh>0O=%2Lfg6`efb;DB3Z zTG}mGFTy{^HmfDmkJlt2!68oW|KS8Gw*I-%3JAYa!&}GQ;UmLEk<~o1x1H~E7j6)# zZ2hWLmk;kdrmfPN+K^3!<`)b=U2{uF-^1Z+ZvR8<$>nd;G$*svRp2IJH9%VWTKc&| zDO7ba3gHp6qFCe(EHfdY=>pHxRiF>{B>w0}+aN^oH?8>fORU;4GhoCgBP9U}%XX+_ zJf$t9<%kZzIY=2sgO&GLrmXei34}0W#(dtem|XaVZ~B0(ZIHOY_jkFf2O3zbvtn+V zY&)S`Bm8CUOrZ^=qpF981hoc`xUj?-E8}&a3w7`@qikqd_m@{Q1v@RYbAa@^Qpd8a zr>~7)z`Sf(){7T|bN!^tRaMnLDIFGv^j*9>wvzBiRcI}9N0Za{r?#w*QPAz&0jY*7$72wBF zp^RI6s9CUs%t3jW8z{^XVwlfU-V5%&_LEyssn!-9Rgh!8O%~mVQ1S$~BDa8`+X*u! zrrRhO28HA12NyPEPy;X|ih(41YmOi01T8sZEfl~0Q>0p#-l-R+sA1?JKBV3S``myG zwzhfs`O_z|j>Wo+HxjAfU(rP85)&om6KqJID@3O@Q=}t5O=m~JRGVP?96F| z!zfxP zC}U5WO#=885fleaEH=2Uc~&C~I?nHjVgKrO^w_Zd+?aWGbxn^Y97|aOGdyDQ0ztu8 z|3(*}3yCzl=`r5(Mo9L;h~Xpi*z~iPFpsoSfxq+$X$kN)A!#^&Pv7ZKtY3BVm`B09 z-8u_VIMS}_uUW07)Nvv|Tol)=08b(rvIuZO`?p5@890;bwSiIbFC5|Tn(82H>oIKq zio}IL&YL;z{bmGhY44K*GEK^;dML-PaYGbkvm6LX*43*z(Q{*ZGdAAC!7VG{%Dtn? z5C`$ijh8nMHk-n5M#ymR#p+Ij__XKEe(kCR9cwW_qb$H7uO8U(4 zbIbzP1oSj-q;}b@^-|`eAVSclko~XWfXq4IX-f&QaM2=hXNVoLa)BXiiDLky>#@=A23W(L8IFksUi$?+uoiigknu!Cn$UD3r6s^&{9?+Cl zNgXO(5R+=?LBJVS40$1FJH>Vk=wnS=4LupznTD@h*@avvbR22{@cRpzH0=t&cL65Na!#W%=h;20yAQ7Rk~P5J^5@ z5?_rRbDU?%l0xaGKweu;i|9~v;F$Pu$*eiuum4LG)%<%1>b4(mzL3IMcnjp2RgbSW zg}K~`>QA&Uc)N<63)1O!Q}0fcPRV@rfXQ1j{wDZrX-13*N9nn|Xn`g`NGfAf}}!kNcS zlK!)^wK{;4z{O;otZbhx%!ims3Q>f*aTKDX6(>oyL5RQYDQf! zhKH)Ya@)MQb4O9j{2{#LXNG#Dv~{`llZObUnc1P>;xj!2_U_UUqz8HUa; z?ICm}WsO~+0%TK6!$Y0Hw8dwOGcDbNsc*|a5^?CeCS2ILJ+Z#QtaY;jfA>nQ3>aT- z8vn%)PIN3j@L&XAVOk564r@1T-MZHxiL&{MN`X1)o2u{MzIEn%2-Al@V9!|1RI!)Y zv;2n#^UJfgUiIs6;6B|23ijn}`+)vAo#j|_qWA9kP;K?%$HVVSON}-ugX@@8*QP!D zXl7{0oD_V(jY*i#-fheT$;%NPTvlcbE`%XKIGK*YGMPYzVf4vnQw{+ZN9f2RG4$`t ze}7MP`B8mpH$7=gn_ubGyRo*U5m82uM;7bn-Tq8SXlU(;Pt?HoFRoZduO>m;8L8=8a9;WFbln{?Y{` zPcST`IiNgir#F4$xht`$=P%Csz3uRq@+zaBoeN5kmNYY%Z?^K=+s1(5f;Mo4U{7gP zctLv-o~pPkG*lV?IYT5HlMfkUIsItDp{(*sf6#c|^@O>D%`binPhM%(C|So^ekUBQ zoA>1uODWx0A@#g;KpNpBDzWPWKyvFh*ejyOW6TMT;Js{vpJ*&LcJm zxnC3@{EgSpZ+U=1}l8X0@qz}tpN|6d*It0lH$Nl=lhnmx;zi~Zxi}0MY{VKzq*Y$#Rqq1+n{W5Q& zKB{t4gXt}L{_J!)ek7zx~#Nt;w4qyl|kS0o6e6D;>Q;YN;DUH8j@gpvz z@3}~P^_e*JqzK6DldB5JHw-xg*LN zdZ(M)+n^Pb%@I_9K4ZO@DNx83{wsO{okni=%%{FnK{DZ!Z?jE z+C-L;e}1*bRW>L|9^$*+e4-ihK#O{kB6{LkA6*5W;dF+c-tAi;>Ji9=N8xcCbdeBQ!&l-UT8_q4ql-=O}A(pQRL zP|Opr8Rg;f%)4TVO2qpmDd#{_*8q!9kQf;1^`Z2aJ{l`F`K?qDb5?$#d|_SBdJbu= z_h*Bk(!;+QWa3OCWg`9$rUAXE+vzEVwup*huHPBU5X)vJH@ci05T}<%&)kxs&)9)( zqWxfoRtE1tWQUa0!Z`uqkA4U9+JtDGghEPuoA~b(?U=s-1Y3c+_`7s~%*_0otuO5; zmM&*!uN9F=K&?MHaV#viR@{CQukp*_P7|=+5tpSCNVEz!jZ8==FqxA6&%A2@Z5{d3C5B{#@%c(RP2yIg7h{LjlR+)KX|bb;aU1jPX(!zeFX z?WNUhy}AW2Zx*0qnbR;ee|d${^lSHB|B?qVK6b5cH<=cQ%LMNaK)Qjlaww4f76t1F z2>KUdlQ>qh*@~=_+VKG3j!;lY6TRj=?hb)$=d`*aauFs!Nk`eeR|`Js29QkMmbYT< zhSq=!$}%djXt14N@S7umfTlwWCoj2QS~BPoR70?s&g)1EcKo^&<@ z54+sto|J4*f4}F=_*v10gT9G_DI3=~{#R%U#pwsaT>|eFq`b$=?uo>``e9owx*CW< zH(y;SbleBWfmS`J^5Kuj4qOGke-;fKzGVfpY5-xo`3`j#a^VSu!?Yg(u4`GGOQUF^MGbzD<6xWM($F!aUoA%a^%PnZ8{q12YAx%3l@w4 z>Z93T9~*5Gp~H_mgKvhA8wu}aTRgZh;l1wb$_8J=h4!HE6skA4a6E!6Vu1+N*yhE^ zDE7dT^XzFwN32jqx8JBu->wsjs>TpCuK`vIgATmWbbBB)LG(^-I0NGKSd+hR>Lh1p zwYF_-X>V!#uK_2X7_MU3nx2C{(~ubbnk-}fX{9HRPI#tFw!jWulvZ?sIUL3n$+;6k zS`zXT6CLRapfqo#>bi0GHomwYyJFx7fmyHVX~YQa_1=KXRR%tudjw;pyXTjA(Y*Wb zNccR7r1r03tAFG+5HkR}0ur>4u83i!PdT&DV$RD8XO*lPgjnKY6~R=Ycg>WBUsHc>M((-sR=J+l&GhY$ zH8eGK8?$=rWsGv;);jFRtzH;V=^rrW10>eeM;a|!BtMFJJOuXBU#eTE`#RNAR<12w zQL*&icmM4%IirgGG2+XMYd`7XL06XM$w0$*5*WGLu{)|rN9-Ae>X?JGTtpM!?d%Y~ zQ#6OAtAR!X)h#^wM6hsc9@LnhO9PlJ8jz}dUVxqJ6T`;PV#EL*>X^4?$`M<_T*ze3 z=(GxsuYsaHbE)QoN>Yz^Q?6g%hC3eviqs{q4+vD*M_%_RW-`$%fE)kW<+azA2i5wx z?oZ6?Pa$m&*!cg@bRJ+m_wODLnGXsXWt2yvL}X-_-4qQwBPDxei^?pKXrM@>qRg^0 zG7=4|fsmw<3MHe`Ib=AD$ z)*7lHLOk%Ys-kaZQI$j#ehtqw?D+BHA`0q;!Q z{TY3Wgu8XFY23VnM&+MBQCq$;3e}yiljjB=`}xlC-9H?ild4|*_DLVEUen^$+Mt6o z-bP0B2)ELvfG`W{J8v7d<=xfwS_e^W_CRWCE-!EZGKxh2;Z{aQ?r*AoUrW38>NquR z0H}G@1Kf7y*CR99yMB0$>zj-7wksNQ4KgY!4Rm$CguL}huUX!DsO2(L5i`o(nmA9VQ@3qlvn-?f2ebZ02c-E{9OdI2jMWc<%PKq7 zO}cj5I_t;Hq02(~XfG=&jnf{Tpae2QYg$(Rim8-B>b%9*3S158w$0=zL*0jg|C#}K z9l}ac2mB&_NJ7-xJHNSJil+c>P3ivjrj0}cLFOG`vI%f7w64wCxI>K5;R0)tN|;rU zNAxCoz6WN^!xP`XpB**hN|XabBOgZWq@K-VB=2m&+@h=3mTYpFXzKE`WVu_V_0tL1 zjF<)FFHR!;h$6G;{sbTOx(8JY_`d;(r^a1zg^)g|P)Oan*zJ>LUC8EM;l;jhsyvVd zdpy?AI{k!Bo`-V@=KhjKq?2FTJn7O3 z#9yX}SfB;lk`In+v~FH9v!int$Js6c}i zT|gK3JL$B6@c&q)jriMQ#y$$m`q8S(Y{xjE&6{)QH|y#$=LF9yq2Fbe!HY&B zxn%mZX>%Db;>ZkGXLtV088b>d2Kxp`Zd)@l+)A}%1fxL$SMiw+a|Y;(m<+rGucdfN z&+QqEAbnsN=^gosDGR^>a0X%|#{u2u|;Iu9vI5HFD#ZQrqMKn_(3tvuMt2_JFVT!T_(aU;K z%XCGE`u)=n+DoiU|C=5fyy~MOXCUXKjE4dnJ>vKrK8wY3VpStqKNo zZAsoB8;CP9p%23Ztrr{Ful6{2Sow;x!L(+_TeczsS^RpQNs&rthj8nq&H2fxk=x?Y ztjfSAMThLS{DPnJF*q^yS9cR}rf7~vY)*giVjgss6vV`u9GFz<%vAI3he}uM@$~$_ z-DSSoot~HARzk_#i0K)d^H~@2F`hr=VflIdEMvQ<)l77c`%L`yN>0njaHJeZB_<>v z&^PmaS`0x_XIx_G5)@a+hX(uei~)5}oZWr-@*vP>F2X57NPgVR>C`(q}9T5=kHVz^}Fm(qes-_onZ6DkT&F2;_O}c`Ealzh@KKI zC`PJ(_80RB0yiTk6J3Q%MQ)|B?y*V6_D{TV_O*NADM^x5?a<*lez8Q%ym|i8Mbcu( zU?9B#_{^N#2V#&D{Y_xc3&R1#O!(By+~!zrT%# zAk7N(_F#mhkm`-?FIaGa&K#K6k)qN>umHA%!Am>gOC}@{x4ewDmWJyIN<@LWNRbm$ ze;q{>KsDNfCP5%X9#qn+%Ys2blnJ3m4(icMxr)ESpyfr3Lm14}j+;ZhdE-IX_*zS< z+Rs#2G_ogQT48JS6O+w~0XF_BnwmEk%{Yocj)W2i!T*iz9&y&AV-a8jG4BJ=If12N zxlDiFd(Vf$zp@z$sBylUv<8Ay0_HzL87Le%5!_~lp0!Q@&a^&UR{|%5!adwiVfw}0 zRPos`lM|DA%3>&fhHbCXpHrLR#mHL_5eVP>2bdo8)hI%l?}+A?G^$>GB*a;{7cg z-t*6jD=Zuqs**b8`OB9J{#yo^<8!O-DQGGvg9Gid>^~GZ5aLpjF*j0Nd3=+bw`{q9 zsRjje2B~Q7x^J?JC|VP`xIMFVL!jF~Q2@yw@$Kg>TV{bC982A%=#35STyeLSUX|8Z z#&ofh(06)j4hYe2;6UJLMQiJ{4RMW`&)qsVDuj$^yiyg%JHRhh@EJ`v!~5Ci=-QY9 z?Oyrb6s4H)mVb@RC%t9Ay_iH0HpI5aS?{@_ETWX&oJTJ(I!6L^1%l#JK(Qh(<%%gp zD-CTEI;p3;2$~*yJ|{(jGf`V*#L>1p@_aRSg8_;47yqWa!6pGuU#eSEuN8>Soobuxd;v{$IoftzcK$HOdA;N(&b{STdo8~4WF zkJ6j%#|c%l5aTRFo(aPd%JFVGXgncxFCjmbTTMPO<@>Q1RT-tO@3W1g-y8%8gLA#d zubqBzZavB)IXXs5-OrvlG0!Whn7#I-Y-NvA^0p)-=5ET29lPzAA&C;`x%GL>7h162+2 zq0OuL+e#Y?K17q04-NC*#N$k6_R3}+yv>F=Ad-f%L;7PwkM0TVz@%_QxHla^JQYbk zA$Rv|7l9+GwpIaMYnrsaml=7NtPw&Cd1SpMc%%bW)0g#8QO)KW5n>XKkX!=jyArwv-MkBB*y&FGfsvwmY&C`qI z%iMCmg#XA&$hnP%AfT-&n$lmdKl}!5G~GxKkOJ(}VmOk>*mdegoO>U_FAf4ad4Aq* z7$;nSFhY!1n)Y3Q3}AFwA%EkNDK8#EA!xI%?b`BQcJC_XwJ&$^AK_A$k_GK-!+~l8$e*=vqL`#;QB_ zfY_G6OMA*D^^WAN-)0^ylY$2JqTZ0fvBW>OMxPKKl3yni#-g!BiSuyfg!?PR-B{c| zXd^y0KOp`(@~0Ffb-VA%B9(_tTOQGOG}U>9#YAABYJ*12=b|(b<9iNx#PxZj+BTPM zVa!f+Qt_R3@_GbXi2r#RvdXt2=N17w0;r^{grZc7XVP@!*eN|#7^5?MI?0wSfrwmK zy}xa({=k7Eg#j(KsqN$D-|rmF9a*vwcnH5OfjUOkK2r7FOGwa)oU?tusf=5ShK(ij z1V6^>#&#PO%@nc-T^`>1r)A5QqQ<1SN&`R@yF@TCV{$tG1xxuEV7<~}9MV%q6@o{v zFA7ufJpxqViT)LnlB^{)3SAA|FG@lrQMHfHUW002hvY{n8tG_AgF=WuvXc30m%#v z9AWQRUzguYWsvzS-!$+6Tww(#7h#O3*%+NrFuc>!tR-KA3QjH)t*h^_X0ix5i%WLA<-FOio=&2PflK@U#}s=08wLMRnkA|(?yY?PJ-JrcJ` zjS3>b=U7sM|80@Oib|L9girDBaM|=7xn&b%2SiG6g8E7(QWCqhIX$#djSi`GE)^4@ zv&V~#VgpY34#KYmdMt!F3NBep!AFuf51QM4tNne$hp%5BCa3oWF_fl4erSVnb&Z(d zS(<$(m^byYq{N3hi^)VsBoS_QEB9kBCNmv$sqzhgK~^%3bLCRx3Kl%d?5U#i*vKZB5_JqGW<m#?H&ze_`s8N=QuUof{_)_JuD1>bUndb9$Xl32dGjf;W z48R6M;sHmApCar~@hL| z2}Y`1kUB@DujtE}I71fO39JV{s>5fUnY#Z&$$mhckT(6?gQ3Mv`Qq* z1MZ%rtmeV4=D-cvvy-(=0{Im@VN!r)FSMQmC$5{!Obiil6nP+{b~&F6%X(4QhM z2ZR-9^K58<6}i>{xI}@Nkmu8^rFA0o0~aieOeiG==Y1|c;`#&u8pv*;B$%ePaj3?& zrYCB&!`KlNP0Z$$JCb+p73>|p#@=)c^ob@_ez{fpjB$WR~^wfeTE#YJM(IDZl7p^$PiXHO&8$`bYU6; zO~7MN&Fwu$JiRdU+Qd6PmWvZo=SauA8E-jC#FM8^9SV)1Qrdz458|2}S_WyUAe@r# z)fvhl53(w4NH0>X#pDqR_`uxfcZf6?ou}~2B(tUcyMp`0d@%+Y|t6a)yV&{Fv=u& zSHDpeQ!ole&@l)MrGdTAi=dRDRlP}b$Oe#+cj8?YkbFAfk4+?A#kKBY=qk09;EX68 z%L?ehRanN(gsY z;e2CNLf@V$(HNAn}1 z8!U#Kx^tk!HIw*pVv4Ga;{LTn87B6iBxT%t+lr!z&rXxFi;gnv&Fx5y>OTBUf~VMQ z|OF74pZN=bCfGpuJ*SL zj`QZ-1FfKZYl#!AK&D*61AN!9hGLJ79$op!2y?%!Rs2c`qV0OmDE*c-3?s1$_KlDa zBA9$}p6N8Yo?Ve~(XRj`ERa+p5~k@wgEtxxqTiLJ^#mfp^_yH(nX{P$m-jTd${<5} z_lj4nJ{j<_6Gmh5JeO2m+JhrmK|3NCH>wfM%h6#&(rv7F`b z-ObKcXSXS@R;Az+z9;-Os%;q{W4ecIrG;>Oz@pS0q=!j3z=bSV5`TpE=1fDQThK7L zL*6}1&lZ2iBNk5bTEXSnv#mIlH9xP1e_MCqPu)JXfu>LY#qXgXgEd{8My_?y1cU(I z-1=If-_Frn8FwXWbmOCPHNYlLuiL&`r?ku|eaJqD-L1Ta+=7A$#1Js-$l;2~a2yL6 zNPv$O*)vxl^2*mURku&Oxb9(Ko`x8QW3=n?nxw$x-@l_CetL@+5(*btW`nkC!cE@60buU`(mc2mzGdpIf5>R84^*-vF1?NN>)%>a$ zlD-5Vb?P2C+G?d~+ALK~WDY_u++`AyK&oeSCelnoc{zGXuCIm#pBtuM)Rs!`=sONs^4+z#+Os^R_TB12KIpm^c z#LF#TeV(oWF$yCMNN7Rxm&)3tkb|exbV{7kZ-29Ky1DK3m(@j+%5Oz|+nnw8jFQan zLJju`nNoxFIZA7v7NG8#2L>vK13`iPqD6_^{WeDpw9CDN4j0Vy)vQ@_vkOcJ@UWmU zRM1+nHK%X7k*;meRSw7+^670|2`xJlY5EZU_@uUC;Iy(Fs{-pJ3eXw{LGbS~EJh z%50T)H%;5c%liN4mO-7{nK{;E6w$4lx0_nN0o(0EgX5q}a&vOFoD41m&j$D6`jeP0VIP0Y(5+A099T3$@Hi4C@3K4NViRxsx*Rp% z8(T0pW7St)TUk#TdSC_LVS!vZOInu4&aS&SXLRxt>NlcOC?8rdOj?6gpdMpiJ%lQh ztuMpYF9$6>Gv&zc*slw+XB#kcX|nw7q^2!eG)!Od$Y#A;)r-1i%=H4OQ`Q5UMGy3Y)iR*mEW+?k* zw;biTcySWVIKIi>^0^-><8a@b0VH1~g&c_C3IKn1iPW!Y|Z(NIm2c=+`^q#u^g z3=w#s{|ca*CRdFCRsoZ2Kr<14@7`7pzc_=T+&H~X!(F5L37cyFN)20F1Wr)vRYinz zZWO_w-=r7^6Yl^!C}j|-@th{nXz??h=+Miuk#L!!pa5ZLA@cq=Z)VI-Zs#kL(9Jva z6`WO`2%JjltaW?i?Ci(fdj;G;M=Clj8SKD4hSy@gQ%y_e0W#S$$SDDPmJuOuy)gsh zBO`Wwo8rN;6%vNYpOlgmjdrT4y8-a%KNamSH9QX|a&O;}psU+$Kst0%tt4~2yCQu+(j!Yh%abvjoDSZQO5tE^{cBXS8uCh zGnd*xyg^`gPFY0e2J4u8qqBM{FI&1tU}U5KaO238HTt4F zQ@R|w4h&%XjSK7u3nITBDoC~}Lbz?&K-*f*3Z7}a){o7 z9b5Bm%nK!A{lt-sE8C+mf%Tz>D0kCnWrSdYx00fcJDEcnFYlLt6XTq33O)i7;7F0I zwxmPNdrtqNtk{8E5TIS5#yyQv44+GzO#FcEm)e(qsZ~6!a#^E(ZBaLHT(-x>#YIG& zxr#PVTvk9&`}XfY9}c7GNPiq(RmjDHtBQP=ti^PE-P_`--pI}{Dv4%H8_c1sic2g& z76=<@=N`YKw5%+Y{sI1HH$iyMmPr-4L|j=yg$$bby>p<|K?q0w)_+oLwBlUQ+f zSkr_c<4zwOdf~z|5<_8jT-}Pw*&+$%kSZ{*KGiGv2R0wnUZA(_Sb@kV6mF7=1U619 z^6bTneMOByBZt9)BcFb_<+&pfQ~Hl1O()Q*Ns#8MHl66JnJ5mQeGHYX*T1#yslr52 z2xEw}{M(o=qg$tk&TW6e?o9y&JcKLnDx8RJc=T0DirDD%aOsGDj#V1sxG zFo|i52Mh|m#zKPZo5QEabIw=d3h!d9p1$pvmRoA81DVZ^%;>oU6*BKVZ~V2A?*8|0 zBLE^&wZg80$NB$8%LF1ybJ0lvM124r6Y-Q3QMCWsHm9E}8UDcbKkTRd)Q^$O%`OF{W8C@jGlmR3v7O?oTt3*Ml`0u-B;k?_9 zkdwoYkvI=?9#(i(OW8$Fs7u7Kf=7{q)2C&kDC2pcfz->+rFEnhlpW-NsiuDA594}m ze~k<%ueqM^D(C85Cm(i)niAdYlCcc~h#?4Hr0&g|=Q9&Zm-|S-S4>wgdI^aNW_1w_ z6a8g=`P<8j#-JuwHFVv4w93iz$t;Ac%EUSof{i3S{;t?xQeRA5)1-B+DIeK^h#||3 z$Zb7}yL_R@l^D*)R_Kdj5aFm=$?)?}FRmwF1rS zJg!v-qs2wB)C2LgL}EaNM@<*@;u&z`0oiwQKC#7{2%K8@;$&mup4a+Y))N3YUL$3M zj+@6pA?5c42L`qJ!}nD{R;Nk-sP9&(^VG8vV5aafh!Bb4($~sM zmK2A~R$8)XQ4Ur=*ildchg0KnT=gH8FLB!gixAbe;umFL_N0!2#Tp;I{-BMOm?+Ee zl2C69ydYhadYYFUPPk)ex$C`WU$btq4(2KDuReZ$X?p5G6DbQQ{N(|0#^bpSq7L8$ zBN5plW5sCRh&chF`QlGUg&4{<6s|q99XZ@BwHg29%n=Th!RaRuliFA(fpFI>T6q|3 zg$yS+V_B&ZFW**1;XN&*TuYu=^LkP~-5&!IzT3|q@}+kWg=7mDV+?SK9o z)*H0Z9vqL}-kAy}{rp5>n`E4uytg_zUcZZPr}gsOyVtz*2RM^R z7et^u{eaD=?)XFEI(ewm@HJdzhX9e3(X8|D>oNZ-Q#wx?ZoGh@%X=SQ%2U!T1L~$h zXPidE8;Hjb7D9JnYJod?bhsOahdduf$N`u^kR36mgm3fhe2d&fuRX2aUGg&%bv=RI zI4iz10sFg~wPQhBVCXNiri~lRD7KUal)9toR{?tZ;)MjfFIkN-2O0WgXzTz@z+Vv7 zGUO>CPA1~eCDL@SWU5Nmb#h+wC;nr|T}=k4Ac1R9O^fc=cKj+a{Zi~&nL4}ur2${o zI~G@1v%{q$2cHD_7}{g{<%O*Trl!G=7fHB#7&vF`*5&s0We;?F#hG$H=$iY}z-h6S zjffx>vKZ`qj3NH+K`JGkZoq8K`ytCf{eWKPIXXTB{7x@=(xr3f-sL?Z`gOSJH5nOu z#|)e`6|`}~ucCm2q}!~e72PTM$!KX*3Dv6B#tjX-F5~*El5LHB{m*)g1Z}SWfeKHo zX^>a~G2m(*N;eX8{+m*yTBe1Cg*GTesQ0&QH14u&l1KwpbpH2t8Cy@Riicyj4nH*; zH4j_8x{xIfx!m|?QQEaJwQ8!W;r153efsuQ=3Nkigdc;%1?!bwMRVdFPDX@c8 z(1|i`yYnD%++mpiyvnQZPQqNl4*@&)H+a=`P%kGS)eUvEBY>+Tot1Q}ebCu)(N2*G zVBxl-II4cBYaW$zmLkE2;)GgK)||@rih7l9UJS{@XEin=tP1l0>k!UULZ33=Ua=3y z7E3E^ zuUy&)Y5IAbm*^Y4owix8y{OFu%J^Ly%$sG5*dHCe%B!v*d%@>TRMR-xBz6h4xiSLp zagpprO*zNvmnM`cxs+I7p4C66m7l5qN3rX<{hGT8pN~iNPQ0yUZCB8O-@$WU7_~_) zyl8V6N^xMWy2E?jN#nwc9}ZUW>Nx6z&{z^09A z{t^G9{ex;$r|wuJF=+SRy$~GOb^AuhDjoU{_=C~>FXp@vT%hmzyRR&J%X^GLYSUO~ z1k)?tjtpNL>Q3rWocv4*P$+YSi?hSl_7p3Zsf4JX>L`C{C}0=T&Rgpi@73L@tF!@Ru?ofGlcIc6l<1tm&O@-I=pywDjt-wkE!LIXOh9vbRP5YMuwb zIvf=Av7kVF*4&C?fSdl2nN;bQ(2a;a4z$?S<;OncA>r4U_FA7L&_I^oAoTM~NV1)PUPdNUDQ@_W zJqBBHZ=i-c4O_RDTkFgQ*(Kabn^NJ*TnX)3Jz?Xx zGnU``KH?)4fE36a0Ko4?<3e6e`0024QiRBmtjreyK4{)(4k#qrU9NEc(yJzm`s)uG zbnvYqv{aW<^Y{LpJzsQW!=K|307Ea}v1PpjDRip3fCvu8!<~kIU)EeC01U? zDLK#9BS90ROb8RNUBB4prOind(wWdCF^HHLbuTEY2Mq)zSiNZN>I_x=G-gA1h=8rM z(^gA{F#68alZ~tK!5Q`J>G8Wmu=eV8Mn&m~ZuKT`i|+I*k#^pilUp=wi2(1*V5MFB zlb#03>LWJ~9vIbrf}q=2NYF-!%_rB(gYF>mS0=+ik4GJ(#O3#>#l=Kz+rL0ehuMq- z014$o6V#t(+@Z1gNSHktL}G+PhU!C&E`=d8$e|s%TNe{Ndg*a@ot5z^DPOK-OfY5` zVBgX2^La9HYcu)h_3S-tY!{)Q=A)0pf^FV|M&RDTMPD_GL@J={Vpn^q>I!}0kOyP= zh^~jPNExqCw*S(i=_sB04jd?}!r1C#cG3}kmhLPCWY_)mU1tzx1nw!I7eEKM#~(VS zjh0fLE;VE-Q!Pk%WHl+_)_lNmok&2Ro>ayFJku*(+jGorWX{!yeyj`E~vK zKS)pa=Q#n9u9nIQ`63&GOmD?-%Gqhl6v3F5Lpxxu+{xys3X*MzOWBo7?Z5!b2NE!f zfngpg(9!XBVcm60WO z3O8YH$Yj9DDOLk9T;jGrr!c1#(Oh8|X~Iqp!in@pnkF)Jk6@?g^jF>-8+K1A@PL5L zgTkI&MW8_S^8;Rstn8oX_+L&O8gF+@4S!t=IyhpPii@iEEFKkLA%g`psZz9Kd_QovkAeM%x%TXa6pn*phXw zs6H8ry!7sRuj-+0X|5`)*QINAf1P4#lC{o8DUFx(h(bJ9F}hX z*=t6c{sUZMiCvyx`}Bd|YtI+;K63O+vpB-OEz(!7H$U7^s^APqUm?Q<#K*dwSE~AZ z)_l%Z>D*c7aKs^+TgaSNi0qKRd5hOKiCjba>pAOsU!=;l=TsMl4BvR47~8LJU(eO9 zQ%8J;WdE0N61TL{-q19N%kv$5!N#9W zq2bW$Yz+u#3(O@&JZDBRIgN2NUJNeZImN}v(z)8|FVwbmjn z-qpX~{PFxQHV-!CScao-H$U8WD=7C0N_CEN^awEGEII8iUj z4+fG(Nqs#|3A5CLrbu?jG#f`W{ zhBG+9bc`bG=QC@a*-Js0Jl&QE6LBM{yOFP^cuvtw3!BazNVtFh--r1lWz6zJ$h$_9 zyDoAqcg=%v=dTb9>|)gPQe#j~BaltttL1%}&=f1poz;Dx+`E@^Dxdwmi?*ftXbfv=7#7D)Dl|Kt+H7rU4Cn4}XRr-a^VE+P%c zmT{-pZ~Ifv>aW>Ia^M4AGzVp{u(_!cLW!_?(Dh6#zxnM|Jl1eYwOqOnf;=>v}xOMobt@(OP98~ z*+AKR|Iu=#IlnCx9o$DXniWzUv}fLeM{ityzu9=frAB{G3CB%(ZZgNXIdjwv46e-? zR3FNz6E)aF@)NwP)7Teo+P#1EtnIpP{DPR6Yu5^Rq?Cxw&CHUUAMuGwbkL_vdq$xr zle^A0jsgo#rsi8SC%L)<80B_tI;9lSjyUcDI0@2#;#wFff+}@sH#QMMzQ9FTFwO}A zTc~$)N(M|2$jRe;XKwlvkgPPDL4oGG5U5e1ed5r_ByM63kU_VB@3EQeB=76>>%kU3 zssjgiX=r5!5R*<)aVTNf$WjEW5le9?UYb8s52-Tnqq4c8+ z%vtuX|AK~XBL79S6>xTFHG$4GrFmQDL z{864m10v^&M4E+TU8T`GDeUVoAl|$Yp zlINL>llVhSF^ikOxxI#VWwcnrD&9>^)qnB3bbk9Ik=k>d{j=Vu=Zw}nS-o#lSkbRT z>FY$4LQ`6Q^BgVXx4#=_Uy9-}h?TqXxeW?*ZYR@!LIVe=F_^jW3t%Uhio74( z`ubLpg)mtSvh@})K1$d&EBiQL*otR?-EVBt^h>mQ_pE1?OT5#Kp}73E(K1(@&*FuUX1B)J%5~)sp!JZMOjI zx_*7I$e4H%)+zNrAwj~Ih+1YYBPw^xmyV;6649#FZ!A-Yc||YKrnEjQe(v3qI-Okl&s;~h?24MmsGc(ot&)P;mrVWDbI_nEn~XD{I`BC^ z>afLtZK3d*vH+2XZkqMI+LUdaR{7TJYP4_OGO<{}Igz^s{TVs%T1%dluF5OO@Z#5^TVvFQw<@dI$zhiuIc{$npG1OrYg9lbg^Jw5POT6uSb&bm+Wa zKY#k`oQ$c;Quo-Sqq^R^H$})P@ZGQjOmuR3X!aQ2=1v& zHp%!Y9jvC>w?}_k*R8}z>62;5wJ56@=Y2k23nnz60c+2#`<1*g-s$kRlB%EZFUsxP zPXJ(UlagD|JP}mN!g-USQ@EFx*T3{|FMgl+GQ=Y$(9u8Z8}0=Alk-KrKnD4{iZ?5E z9Bqc6B;bqgWmN?gYi*F7q3qwtT5%As)HNTqk1U&>nP0K7qviR2W2&QdU2B279Jo;c z*d1_l5(Ol!y6@Zd9859xrq7v6MiRstL4%I%9gaet>k23Uv0Z6+1_zb?6nbOdUemAo zp0_WcXwC4}sXcmytaoKlLJVLru#%|q1KoV!W3^j7qe9(4b{WRGWbB@p`9DeW~0ZQ_$-BquzRY zdLr{d$S0!-u*LasLt47J%{A_QPiNyDB_Mq|<=b3908Vvd7CnYvvvxc*CiAONW~Gjc z+wk7yTTp(D1_S_Vrzw-sF754Q)G%&Cf(aE3vi-Drt}f{j`O5$i45`oqp&o6^eb!mz zmxucg=@MCW#iP^(e(E?#OT*GyFFyK=^*4I~ZA~8AC|U&;v;hMbQ0aPE)AkwW08GLx zb>|Mld*3IJS`A#rL&Y$s%`N@hgjXdZd{W@AhkU-tcTNNBRwz92!-tMbs#f}OS*I

    • 7hQzU(%Bub_SenMm!&wcJ(7J z&NtTnKeFBfp6kBtd( zh6ZU!*7H6(&+EFL`+4ryb>H`OU1#zC|9;=^F+RuV^Et}ULX^g$b(JN@y*#O-);N z=+FUots8*b>!XxK!Gb(M|M;9gW{pF5&%z=BWPzl^Hkjs5`}-ic2`PJ9wj`rw@(_Gy z8jLx`PY>GfZVmD){hjEifDD4m+w`7wiG>=o@crZgFs2Aca*omg;*?DH4l{CK$R^A* zDShc?X*7~vIZtd==-#8k;nxzFjEqr>T&6Mw-EO>!f}E)<${Nh0F+-LGECnZl#Te{B z@#gKsch~ecLH|vaFV-+{KZEHD2!1L53T?@C>HXd4LRk2o zLb_|!${J@@%hz7kn;-7d!N|dv^&Sw>flTVoo%_*e>tZ;wZ=w5wrEK7Q2?)*Y4qh3d zX9}>{Fk_Y9O)f94d7N`~Ev=E1GZaJra5Hl{1-Xk+=79sj#cGt~4I=T>yqg?tlN5A( zSwT5rioQf)&loX@TW?ol^E#A~LE& zBP--7G5h79!%~&$p};Ol=bno6jiODYZ$QBU81W1>V$})FQYX`x!cxH)z))#Q;pO#Q zhmdRpIG|J)e@qLStg)2!f#N^m;cFuvhEAxSTN7Hn)gxkrD{S8ZmeX3Q}ecK zHRmI+1A&x@OPlDGqjIMC|Cd zWNO0(>Bi60vU!y~>UH+0jW+W|a^C;P`OyszW} zijpLMR~_|nNLh;W*aD;*ozwMmKVeWp#-*#F!xl0Dt|@Nj6E+bB7S_&3WCe`Xt>+OP zB)5P^pvna(JcJeuVERop zz%p}zT!kSm-}=(am+f&!NE+4IsSlb%4%awH__7I$VKviGt+or{n$$aem`R!@wJl#p zIPP5Zk!y30B%!&w4IbSJ*91^-!W=r3b-;pR?@%_qyiAP}}vp_00KaxcBMV>CFHX zszKthLG6E?%KTQ~dX(+;@tX5>f_pcN&QP2|q^|t+ef{B=hrcumQ8U)2aMss|6@-$ysfJ1;YwH7U| z!)Ar%n+chmQ6{Q{QS7LkE;bYBJI|aR09Sz}cdJ*g7V?7?2Z>x|xuFkg1fa8!ZTm?fK&)JmhM%oHu8lcd z5;{4o7-$QCPxef3?=u(a|GGU1C(2>LMp~7QT3j2;C(C`~EQYWF$s1!c&RJS%{y=sY zaOLcCnmb7pfa-(7-vio}ugJ2DDh6{(3RE2VNAk&voj(OQQdkYy&~Sa!&*t z^q$*otynY&_ro`rUl{TT=U|yE;=S({vA(D%64DL-b0$k%fTv9hBt>M- zh01@~gNJXAF~hmJr;p*?q%vkJ#Y8f%D09i_v_o}4aE0j2QTb!UFuyPSMTYz#*2Zo3 zW3nPDXtl10Pw|$+c)&sTnWMVTyE3w+>?!cM>WHi0njou*Ll|VmGZz$H97|nCLPOy( zd7L`TfW)%mecp5fMs$P$$0zCkvAQ%gT=iI)hN_7WSp=trH7`h& z;rOqNjphgt?h>Uu3`KVu1?IV}scRW8og*XS|WwTy8 zmOM2>ghy1b(NjxE9NSF2_h{>UevsN++7BXa^vlq4iqR1)OVmy#UTf@5NlcvP<#mVI ziwL=S3UbjgM3c5x1R{Vr`>&20_pi&{fr(cj_=c1tKEsbVGbd*~g%nK1ajyfHqfi4V zWCSfBA(?B+f%RKi6G5|^2s$Ej3xY71Q9EXF(M>WT{T-nt5Kxj1GPs=4uFxqc&G&+D z4zEAG(9-s-v+aAu2DeNiv!S!QA?FTWvnJ-;V0tjiiH^i*dgHLoo3(D{-8g+4 zSPIOE+P5D=;j?)Cflxpw6gkh3%1^zLe%5B#5HZ$A%{d8!k|6zua`NF`k=eu@IHSb~ zvOtR$@-0CJtBUT#r$>)k{K3Wfpo5tt8J>`pyYy>Ee2Q!?Ywk0p3Z0Etp|;9g64drR z#?lG(o@Gvhx~->20|nSmlJ7uu?UPrV`8tI0T(A&5pRb%=nF#-No?^jzBB0bkt6!!j&zH*k@}efD-KJquQg;m zX#t)r#>LcL0;53UP#+b){+j#czh^L)!{1MQqj)pdsJcN+ujl1yVV5RsNL)Wz_pS;h zcHdnu1NF5Fy}d7AyO#7k{{_Yr#Nm3c5G%-Z19X=)%KE0>UmhQQvwG2i2A)cHFRL7Z%ieP+LGK6R2_u^)gc1BNHBRVp!sCJ6IhsVA zGdY7zX}r{+une88HvP+vE%MRoU$NJZ2#5A9l44)`SYS}PGq^J&+GH*>A4_)Rk)AN~ zwG1CZU~R#3%S6sfs^`&kY-HGCslA~Hup!W^td3zpM&?{*&eYP~i5LKtqL^x6yaT|| zjd26Th!{H;R6`|~ju31Z4WCeCNoIaqf@V=KArrd7pvjh$W4CZu+dgwNfEKVlnX>IP z2U;}E#7dE=;{|++=DA*hkfStXx@qm@%F9aKj2?O|VSXmH-BegEk~-Q8^p9*%bm`b) zTV&*3?E{8`vbT==wS-C$@u9j>x(r`%SZLyTO{9IoPNX)`7?x936mxn$-gu$VChoc4 z%e&_8qc{w^f}p@r_%<9aayL2K-PZV(QqibqF)4n34QE^&w0sHUBpw~^$X|c_kX?wA z)BOZAu-G=GC7Pym?80rlhSIeW!GnhS`uSaW<=>o`PR+t8zt&*p`YZSEM-o{7#+PJA z-}%2sOg|0)BqVoqAMUJQ#2;$=rsakX4r>4v8k9Q{iEszt^}pfU3o?kvhasKDu<7)% zkmad4Xf6!aV1SxfLs5HVuDzhEHhO#`)z-;ucn25hy(* z^L<@iwg=J(-pu3+`drmYy$t1>GkRLwa&FrpV(#vLB z=bd~N1pbH1-Gf_UIQN!D%?cD#6Y+e3?4VVDW0B=iF3?s(4WkPiL4weymU7@0^4dB* zSC27y5c2DmDf)MA9t4>sQxi3g zL(ijyGE|yawC4ylMzqV%t^)_|ZvzC6(&sS! zX;njtE`GE;y3Dt-@Ld*fa@1h6s9hza?H+Eue=kNa+)7kb)Zds{ZYF?eW_R~N5O%Xr zwBWOw31x$oJX-Jx4D9FZ(IHWG@7uQvqd1;>r?_X&p52!%9LyF0!$nDU>=rnDa92@B zhUC%olPFc5Ob# zXFi>ck8`@;*?aHC6|6GgG*YH$tBe{u)?~>PNPTUG_-!NgfK_A@(4^5t@U1MnaH>iI zYK_V+-s^d=;hjaWh{Q=BWPDX^PA4N{t9U35p`ps)zSnbd8WI|SVuaZSpf)@gCL);8 zK9N%&ix@Sa1JWZ1w8MxlXY#Z4L{~XvAFOK9tRRVY-PJ_!Gh$O)YaZXtL1sBO{gjsj zB4Bq~z;iZVr81i#7DS+SoMH==WC*>F|JE8(1MlnX%F0uA6AH=T4W86O{%6C~Iv##F z4*0adqC7nttCzOpw0FK?N%ea|y83fv+E+LY4Qf+3F~B8Sv7Lds$G`SB7}z~=-jUM_ zC-sHu#E?QZ!*bz(oVUMHivRRi&dr}twu|_)pPrsRomQ{;*7w;G$0ufKhy2G9 zI9UrU6+Vy0ZQ8+qW)w%A72Tk@y)uOT-M_#&a$p0kr6-raC?I(0KOcI{y$9`_bbah> zEuH1&mJp?;%e|oC7T!cy4_A}s#>2O;kHtXT8f%a^-_Xw2YCyygqP@eIC^@lbsZrE-y%!s+$z{LH?^&F5M-SD5*D zdpCQkLq8;W5z0%`K#I9u2z!Q96gE$K-lAyCBw+PFQ{cfXT5!Yx%`9jeWtj@bkq+gx znfmu#1MjtS{PyjeYQu)I`ZQBVOH?ShRm*PHR({}GSP*d`!xv^S?rL(*nYYjc z#;2rY!3)AD>BK8by^Jr(Nl8ZmVWd<5(w31tS1g${DPiMzQu>WcR19;Ue5_*I6*DcqV$L{NLxlA0k>c@81LV3)n zkthOduYu|f$6bxej8B*3cj;hx{^Ett=P0_i4#?+c3Mfoic}IoAb3Gh3@RXH~y;W7W;pnES~sXmf~|V& zvGTo{<*fiRps5^rnVFGttbF^ir_K1I0*AI}(dA5JwjN8QUM0``V|&w%%1gjdmNB}Q ztf#W(`!ozX3eE!zx^6>b`twE9>&yPCQh7yJ-@&%bcXp*Ps)6`m`5k^#V?O**rEA65 zsDk-%9fT3p-l0wpUGa|;1`(`5764!cc!wE*<}pR1oSiMgG_R9|2o9>7jA%Z=Y_?Iv zMHyX5# zRH! z7#6X}unqS5O)qh4?kTB0BAb&Aj7PKx>&be{)`_h+)Eu`8!&T1N-&W$r*WepA zUOuQ_E~oa;*S9KI0S;i<>LC5tE3mUqo{kN#GU{M>zIg4PS_0pFAu{)#El$Z0Mkl0R zz7%oj2im)t0DQuCK;~UgO&I2_x0H=g2hLbNQPlLN_ znpQYW@O7r=bW<2H_3iMQUqF;JmL&HLnM>|qZOn{ImYl;RnDXgj%9+X{<}qSAs#LB- zxQ5Ewne|7q;qZQd@7;Z?Or;Ii{|J(7)f|p>toz}*NY)xt8 zXrRXCa211&rVMr)Ww6soD_1dgNx}9Ah#^24zd`G z&v=U^;8PclZmOzk;Qj2y^XH}uUj=4X#H)unHkGWDR?of6w;`^#7GPpDo}hx{U8mQk zW4!fOR2d9(9j-LS(SoE#5G(|e5cW0=SWN4=2vCBy7-(yopE2ICZJT(uiF5Rv*eRQi zl{-NRqXLn?LqiVBv?cU!hn`!u=5*b^OFtv)`Nb7ZVVlW*PGQp@f%PymY%u0S$n>#E z37@|1nO9}=Wa*xsSuB5T!jt_gC2RAmjLswMRj?NpR37YqV&muf^zqq&rb zF=w(e10?WV=H z#3SX7PEILkf6$$40wW43fyug(XG?vx6&slegOnlK>3YEgq=rKw--`=kIdeh5(+?On zOoP<|@ZTid%3)4mIj>a6!vy@gtXRnucmSrq(TipQc>~Kz%6>*a=x*=<_d#}~kFRqy z&l)NWMSW)cm5&qM#l;hF2nK0$rt%?+COlcy-DBW;+8qMrPFW%ho-8=qjCSA{;-S&I zcd+&DZr!w!vx%X43`#VE3a*WdG4IMH-m*n4(^ggFk|M-o=|IX9$LkOm3k@j*|RqKrc*KRbV_&- z(U#c5&|Oe@03jjPUFF$Iuf%89tYb*|DxfCmC3=UW$uc7h2Ace9v_h2#N?-#@G7GbE z3IQ8sod!+Y0zn`}ueWSmMG2tC&9KUxg80uKN&DWEP41Gc(|@dRVkyd$tT5e^B{10nL={MnLbv85sW5ML_CV zL%&41KsT`8{N&ivxfDo{Gb68Lqvm6cU^31RDRS;$jy+L5|y zE51dQ53)}VNK_Bgyz{ZrTU7Xa_FzhHw=gsHX8u^?0lrgucUd-_HdS!Q#*H`H=k1J$ z$V5CR&K_XR4LbS2A|g>;W#NDrTb26Hu3N4zn3H}4F(L8&0iUKFUhU=j|Z zMNqE^&3o|$Dx^>WCU;%8voLP^pA|IwORLjbgk9XN)~rQ~HC8%?5xvWQFO3>De3*99 z*WbQ6(KZB&;kiz9tAd&fXhtXvezV|almUszB=bknN{z7S#qBkh4hgC=E}!e5sUA&l zDG0gH5MLR0$s%o1ghlb$4L*1GU{-@CJrIc}HN)hKe>wh=mEmv#{vilxwsm66N?C{J z(O9)W{5cr{8J#@153SW?o#12;k8E8`8QoJB69HOQ8qTOVZ;arl~IR5 zqZ<^MJ9$DZzQ5AR<3^$Gxx%EI)a11tRxE4RVt9Gqg;nQI#Onv?FLj%?Y?+&z5y+Y&#u0i!+D78h8*s)^4iBe^vXY zBB;XQY$s`L`m9+k^`31dsRM3Z2ERs+48oATrDI)c6LBwumUE*79mOgd=Kj-@4Ob+e zK7HC{uh9&C_?XJs5ZfSEZ@xdfZ$d|3irhCIw?iH;8cpOa{5Bf>KvAs+AC24O^KMni zKrQr!ktVJ~DyG8k@&lf%Ep^cY4q@CZlVR?L47p#vd?}?3NT#oq?ZdH-M@mmCUd@j? z%>0acyqThVM!1UFs1?Hkw`r>st0}sh329k~p4)qRFfmy7{O-7me=WHg(``M{)?+zn zWeovZ)_X;X1bMMMasl z#=kqBa!aEa2O!!7KdJh}bFVhVwAQSDzY43t$Y{UmH{A!9K0+CWpCg2$(u}F`#oup? zb8^WDADi=I)Td0Hqt)*r71S;cd|Vy?@#P&?MDTj1C)X-A08#@LBy$(BTXcOhw4-0` zB{2%2Pc^+f&3bn4@u&Us>xZrURk{jj-kaj5zuG4mIsAMHo7EkDoA;)GfJK4M?v>%J z={?rr#>nBr1wrOC*1tsi!=K$;RW7AsS)nzfQU*^pC)_4GIrSU!W`2@Q^m`zn*iZG$ z_IzkRcgL5BHS+@>pH}qDum2cAILJ@O40?>Wq)Se69j>+Z(w)1^0*2t?JExn|l+=fN z$4{AUVBVGQ2`6rG+5egRw@AmEM^hucSOs3>NkKZ#)wLy!nE#D^HC~}d?DRG0tQUd^(E(k7JyJORBD_Fww_tNv#YYP(HGgc~`g8lhw2TItwDp_%Vo_xCpa5)1f=^z|$1#*2hBf+x;>f zYp~+x*fvebowKX+$R>2uBN>~#*>&eZ|1Z`N;?gRH4}`&(!?T%xJ;qOwmkV2Dps>p5 za3Th;Sjzl@i|X;frTnVSk8@)p%SWcR(oWtH0A43Qx`{oGBxF7zp}{Pwck)ZC2QO;; zm+*$~*>Hr0_*dN!bOC8uY@_44qhe;fzng#-qkrzQO1=7Fx~DqKe|)4Mbm$d&%xK#5 zYzugmGp6x}X588q4*lllm2s_Fw-%3S8vD3e=FvLVSk148EFv4L`Y#+gYSenp&9|EB z6(>tZr!xYd){dEUW|=~2^+jKi)OVvvu`8p4yXMHgL?Y&6M3S0G!x zZq%U#+*r?+J;E-E-&NGWp&BMr+XB-bqtW)NfTSm8eoSz4@^a-%K<;5uGvIP%GolqZ z(p8*@Wa}wSyxqs;ve;G5&F;Yikfe^1|l7bnE%B91YP4~O=-e0ERO zFZ;>GQTpS8?2~N9CEK>@zwINZP-N&F<*d2u`}FIV!n{YUXAvrzer`?uB=3l@n57!e zhVT3<&^4ZLp}BP%n?IlB;V~Rs0tVicpk2q+8>)?(C!k(Mfxl95zH80AbP7Zu$hBld zzn9AUoR4-Q_Kr+-xPHF8=4bjqY5;VOH`^7hYthXHe#V1CaVlz+gKO4&{Fyj4kAvCm z?{~eym^HdO+vRE;_`Ut^QO7<3mJUPvq}8#$a^=dKMkOi5EmNO5rRRsHuN*saq~4fm zQHFcdi%%ANAzb#cJ6>#>cGIU~?>l=!cT}%NX=~OoFg)G%!1vEbN4mN7k~>iLUNN+Xn%A+=b=d{rCN(>F`l_SU$A63nsgv z>bf>|>3+Uo;v-o_Y+#?LLcz!2_D$%!))Z23rMNbAX};!onpZA;l0S1pF1s}Ns+7()#FxIKDxuKIUwrXBM$OreK`+Jlb#$JcZW;=BYHD+?;Me=1I5ikTf} zX2zZAgpxn1+oZnPLz^atG;%jg3k`+=9M)XF{?R9=arH8%f_b>W&%V6AbF_UeO%eVX z-Vi)>toMLnU=eU{&+zp+jW0(>xpmh5X;}-WRfZHy6Pr_QSti8OY16nw0~w_`?ml<# z_eMXLLvc5>W6CNVDl5PoL}3VK2?}kPtLB@Q(1>n#Hd{4-W5=GjP*^`K>A{`$KVj1f zlui*W3||RgOqNx1cXk|F|7=!D+GQlT>-ap^o^_~st8nz{&(2F0DETjb9=3L^8(^MK z_a9^kIKA^O+_8$d%KH&RxSBtbi7;8C0?^uj8-}ZI>21aj$xC}Su=`m>cV>tHH`nH# zc|>kZ+xnb{FMTVA7M?*v2IeP2cZ|!9GH6c2u!k@Rt!Y-(8Q#F_QNil&=H=!;ML)TGWQO+3^#upe3yaOQMo}} z#Dv){Hm`WWX0C$wTgQWQ0fZj@b;E>nF*w}fQ+dUtI#<)XnIKLA#5l6_vkQD%{gM(& z$V5T_yluS!w#hdMKQa#Ek3{t{;EP3*&Bv!X7}&&gwMMWD)IaLPCS>{U0PJVZoDu11 zMK$lkjpB`AYId}d7VlUVihvc{MCo6qXH~!TXqQ9nocc$-E(X-@7McUBW0@gjcwucv zTPK$2RDPqr2mDG=3QIktObJUKiZXOu#|GN$Jgw}D`Sy)_ZNGbWW&U_WIbmzS+(km~ zyPGl#)tWLLrvaWHLHQrzT1xSV^-$cjY3t9Q9|%R|9f*PXr`0b=M&i1Jz$@_a=VjDk zvI|SZ+(+~7!mbjxYeJsl(X1GbA9&$U)2x^}^%@9^4(!tw`UhkbKdK2fke0r^-DYZr z|7{X*QGWgf-_>AIY|Gsd_2DseuI{l6XHe#R4$1W-eH}kL3_q*b$Q` z!^mpg9xT{d^LMSaW-Q*XF0{_N%A?xQUZVObesr*nXC-~U(1hlcjD!$bRix++r;?r1 zP2hP}1ne-LG5}{gE5L;DRq-w_s)s6tRkshgvSfw*;g3CM-qQDKnwGmp#tK|JKX`AK zJ@7zcw_)EX8(o{E2=G+Cz?+L~aS&X-b?Y6;>64(+l)byNnayheNT*)9nmM?LJD zxWnN}?TCLQeWJAo02HHHm%J46qO*&O{fSxQd{st2EZt79F0*@=cEBSo-?3z6!02vV zHnac5yI2Y@f+vRtkW>COqb0#(|%K3 z2pGW>-BPBdPKR?rE&@K*8)bR5amH`;XPfPZ&Z*9ic-lRhdX5Wt^z>;xlC_97 zK)Z$a4B#tH6Od2D=UAhgOB9T5G2kc8j0GhtDtj=vN$Rt2gAH`7J7F(Mb5mNBqeVM& zhTt!+g=gix=6vAel6}M0sz(_)ZDNf}cE6WIE!%IEJG*?`>O67n=aFd}24yEDC0!#! z!J+Y_(!@u(tm2y|(Y3|l$q!4#2qGqbd_8*+wvSo3c8;tn=ua<0uo59R-@PK?P_d#H z)tdiPeOb3a9b42RO`{f|0rxq)NvWE&)WxODhLdPdSP_sVe`&^Lpx{(bMBH3+0??K1=ahv2v_#O}TbC*{|$wZ)6w|9pqlLB6juf4T8qyX1jOghpA&lQBUbFGp$#Th&bY(^cNCgk&Nd(EVivdG z=wzMM_OF*c?=aiNr%tTDr*H9)Z4VY#`xlk(fx-h#g=R%+9@4z1Jzrk%y&{sP{)@T@ z;E4K!F1^R)GQ7uzkrl+>gxlgtr1a5SM;6Wb=8+paG55Va!yKt@`LF)P=b|^eQY%T> zh*Xzqo{H}unlo#f2?bN?R?+ORmtJOd?>bKO*(`1#JTfSltvtiNqRLxd!q7fj@fk5? zMs93HXwza*X)i0;J*X;{ltH-r+TI@b`x~u7`P4(9I2uei;2gB^7li2|sygrX+G$fm znIFSy{m0L+CG^h2j*)O*;7Yva^7w3LIum{TciOXr63?38Z{Z;!!(+>db0P;L=P0`m z?Dd2EuiCuJ7TO0>kF=gYmO<;}QOI&1Su8~Se3B7h#%Ng=Nt*Yuxa3h=HwjRJ+Y&gz zYrl~&r+os|6JM5Z?d3T}>`(+ttNhswHcJ8Lm&RaRZiDIb=Cv&;=iR@8?+M+dFuuN{ zX995SzuM0?UH3DoVIB2uPI&6Y^!)a<^cm|mZoJ*}9aY}`s#g_^&+uajBFbj@mbT-o zN=QUH9^?VVV?Ci08)^gB66(oq1SGwR8XGo+|4WR26`(kse_&28%WbsZXV=lUCUNn; zU*XIr>RFw|M+$7O>H8sQkoRz>ZCQ%h07vT zPa(fT^1I{8?iRwtNUlOe??x|55uWii+ ztl>dLgSlj>j^W!y`#rBAGhg^ZIk2I;f+xI%Kv?no`{%PZeNvC@BDFv>O*j4AmG>l} zg9b_1L_FQ!_e*-=t%p#8;a=pDc1aEHrqRhnnC(>Kh`_TPM%WH~-1R1rLmSjCS4X5X zOYa77iK)DV!cg`d@CG%yzI|d?TZP?(&S53v({C?4Z0s1Hq^%O9tzy=NbR_bSC==J+ zfFwcyl+GG^9De#9qOt}rjOQ{PDIEP^(LDEn0T1h%(?p8+hJoht>fe31vYxo-%Mczc z@cKk2{xQ21O;VCq3Mk@J!V|?tg;jd0GB)7%z*5g?db)YBb#=iNWqCzo8OM6J9t176 zXm|bL>ogLq!aGSapty4bL1muLyOphR5-|q%aRfn^!8Njx@pGNkEq?7p-<-X1Bbd9) zabWl>!Syr*wzPfzgKl#9VaD7$ z-dCo@na%lyLr1brw4p<)eTbaFqwd2`w!f~rBgIZ%0A^~mnZz|nvGq|RVOvvne}o$x z#4-xLcNclp3u%got5|Y5TzKiuW;faIRr5j$|6@ZnO_LoAi_DK;%krwIDEigU#G6q< zcI`rw4`)Ld$-s#mhJI5=tfNF`8fT;&`~tA!@}iDccb{*dW!$yt_=KBNJ!j9Zxc~A& zW}Ghh!F$bBOtFhd)Ma+6`3nxKwK-wOsh9GGx;fP$Rh<^xXYY2)1a1CwAp^xR=1Df3 z6|Rc+Uy~5mL{NXhY~5{mF@pr3$(Jl#nU|HNmz&R1wdIH4Vo>oR5ho&B+YQf;_dlBb z;KBEl?SCs-{Q6duyK5(!>K?vjGZ?zxrW%tad{qkPaSWv|v`@5ZhHG#(bxy2u8-hBL zpa^0Y7;-!LdSeNv*mMYp54~yEt1=_Uv|(IUyl^)z8hyaQuoaUFNaBj|oJf$eP7iw; zRDafJ5Ykrv2dKN8THZn?P!!XNX$;<3NZUHB-2ziIJraIs2d-C0nTYW+hKcRueh~b= zPR(G%nX8tPow}cbwr%g^o8Mb$XauumV2`364e=fh06ik5e$l2yTm#OC(hlxwUa zbUclIFSW1t+`qC?g6y;-aUMU(`^jW5VbD^1hMk&P5Vxm6d;5Bo=JQrWqqP-e`mL8^ ziXAaBAFAjTSa#sdw2S3A1E=q=bHl;nqqQ5m7tFLEf`Te7LZ1B@W z+|?Jn5$od+pb~G+scfC40#9{0X$WH*sH@{kwFM1Af~5OpqyhtFxU=)Ns%hP|wH4@s zm^#v*%C8YS2y_uhYVJ^}6$Kt)z;}JW6L*Y4vn~JvJj0-W>=;%r&$$z+*OUk6y~f?1 zzka}Z+?OQ~3o0_)-^Fz9If52YW}b*{D1oEB664GQa!Fi8cQk>xnzYP` z?ca;egHvJA#MbU&&6B#Jh4g>z&iWMX;gkB<;V2q?eSAejgZn&^iiCTBIjXGT;$q0I z)|VI3x?0nSfnf+2K*(tntu*iJdwEf~6@42CTKM{V#P`zDR{#>C!=@CgKH^Udh+f^^ z@!2;Hi79RlOtA|94A}^h{p6=ZoDe1%YRc-(v19KjwMqL{lrxf+Sl=-%D)ypr>JyU? zvpn9@YbB4=H4ZB=up6XviHHw#UhIp@?)H<+$|{bq`K7qPwMDU?NQQ8ryZyUS7^ucJ zD8O^(>^RVVtAS>V7NTk{x}`&DA>wK8HCm89<3Yeh4$G??A8)=&r(5TwCynb_{HhcM z(mzCQ0{9HV9oWSUi)V%;=EB=MwSZOu*u!Wc204-yfXs8grG4q;s5m|CV?)O^nLPX) zi=LbZkVE4-#v?Ks2PUDeLjE)!-TQeFZ8N8uZcmj9+*Weqc2$-G~RTal$=fADo zG~JqCq3d}Yi@r$LfZ7$qh6Tr-WVx6MAiRo-tmNcW>p3r_%r2UDChgM_8BLu|;KCyv zx-QSomWW>V^Y>s>%QS19JPYhbqus$%fNd{^V<}HJmahPmGRgad!vb5@yxgpN?|L-O@u(d zgLg3ndiML7)^kegnq3D_gOS?=fhVaZy`58~PDqh?6OfB=s;Dck z^W?z7*F7)A(R?)Dojc0U%*yHdfGGg&lpHz|xc}qBty?cZtMxVJpq)MgLmfIW%B0o~ zho??S&vr75mT4eRAk8V5VvMV@lG2TfUTn>@CHJ8r zDIjSO-U0be&{?5Rpm4e|aUpXmu+y;W5&i+;b~DJCK4Zo(td@&TJk3u}`Mv$| zT!z)nbV`+H^9p&O4b7!!gr0%+Vhb_IcGW zfjX1Y=h%B}|J7H!EAL0OZ!zy;-ZmXWe3S}V6U zGV7`mq`*0>Gc|DXakUz-GPrIKp>Hr5~dM% zm0QERk`*y+A5@1{mfxp}Da?z|31`?O z!y3A_pu@lzV;IRtRGD`NKBaR)-^M}-&IL^{o(0N)Tb6NymTEs&5d0~e4lQf znd|8#nm3B+zi8%Hh4FRiRolL+VZ&*rhS7ws*NPZz?G!-pOk0az&9P52%qr^#MEYj(4TUHBnnAWJ&u)CmW7 z#Hzj>+3!6c!{MMyG{tdGxQxDMj?gjr;F7{^At*=H%}jGhjd-uNls{M2eH1#Swck#I zAs2l9+^yeZ7L;6SbW7W((2`*Mm(3eVC3@J~pjM|EJ#e6Dd?dKPKBk8(jFvlB8JnFF%U-n}jqv!*M z(1kBQi#44()mL@w-)lw81jm1Q*hW(`M8}$rE9s0BpMpqfZ0)RK`C7U%#x>}GZ5Ow1 zrl#$8nCvXD0Ix55=(X8%=dy<;`qf-Kn0s$E7z3#TJb|SF2Ok_-SBe(rk3lm+H|!Le zm_x;gb-VSC(4DmT#+FBl90!Cz zzfsgx7x?D&V-pq29sJ_ql+W4Qd+9A$xBp`ANLs?7iVD>L6#DXdu=?WIg*Q1}}whyGxFB`s6w zkB?4F?4*(eSw$b*XhWNRn<#O58|K8I2p#WVrEYA|i@Z7ao9B3s8O?cVH4C`M z^xdW>_jgnY&>_47td8OO^|7ByBMiji39{~2^(&mqku&r(xx^sX&Y1fIQnj7o_qB%4 zHJU!6s2LAQHJrAGL6WB4uSwD4iup~#btS_U zEo}kFr|MA&1m)uuCQ%B@eVBK-Lf3*#TlL+=1Uyds3)9jy9 z7@R0=6Tk=@46~E_JE;V1>C$J`-9T>JajZH-c}x@?}!<1pBRPFqi^o_T6^jS~K9|`s4Uk%o5YNI($Q~vj(f?aQcqVtDRmiGDQ zC;#{VMij+0Rg2V-5DiNxleAAICO~{t*NN)XI11Q_U*}@@!kq!=7Gbs&$YWZ$TdDrv zXZGJ8$-lX`BVdTRt>)v0i2iuQGU1ZhI*5Y;!%KJ>L!s};5?(;Eth~H_V*;J$D21j% zbd0d~I2)v0ReklVwp;2FzcC+sU%a@lQn(YdSDx3(&&eO{cSm*9!8Z$B%r@1|eKu;& zF3)?vz55;XDo_0pFy6_fCL`&`bhX5nOItT@zW0$|+L%>lkuH_KkV)>}`>Q&_GQqBD z@k;)N6+@XR#a~7i|IYi8(&u7&>epJU;=XCQ{)vZ9_{Z9Q%!>>v?f3Whuiw4SjLsxxBvf- zvy7-}o_Yjy_E%X^L*5+Ympw^IA@-^K)HY$G2OCl% zB_Q_b+jmS+pZ`WpHKmR{B6MV@CCo@U=yWZqQ*{6D8xMB)mZu!n4lW!|!iPN!2(b4} zy*L11hhIIZNrTmAEZhk;V(A6(FPgC?t0h7x?{U3MqHsyQFyjC9b*gTFpp)&JDA0vn zxpqxAdalm@!i@aEEq~P%D;#B-Am%QzlM2=yg(gaZ!Qw~4r?`39%`m@aYP`8(0}cJa zUAx+;4eXi}^T@1!rQU(_~(lz zre*Xr?@HqX-X@eK@MtTfCSWfXoB(nlmPE{lofy?8FlLo#AGeac2@6OTQh5hm|3ggs z&qJ$frO}`sjCZqLVA2e@7xt>{kThEfm-%8*mi4i6HUHN##0`Q=3p1-C3717VZa zgccH1VI=(+8Y$kYaq5}+0dd3%@#3O&8;SwQ9H)_g0$jo2cb`9x=EP*jbZY}J!OC;( zllwV1TasmS9d&pe*kJW(<3|yiWwJmKk~(?d|2@h|w=0Z7w<+Puuk5};$w;FCrss00 z5nLfstSc*iA)o@8c@ptOU?8X{jllQs-|dh8ZoT)*-E9#%vlpMWNwW9ng-wLF!AlMQ z2&_B=PR=^ehC!MN5Ep*q%P)&9MGFLtq3Cy`x6M}jl$ zjgrcWioa17@_a{fs1#o}N~sx`dcH$-w`a#omyJ4Dc`#%6oR#>w_KXB|#iY**|M(9T52|Y(>?(Qob883d zgWuB6{kZ?Vyv*p-eUu1sQ?vG+Z`4*lyWi`~55J<<$n8 z<-2aT_%tc4;ZDj#_Gg$VhYlY(5?ZMh6_dj2?@ekZ1zoupM0a{0wi$)JzVi`n-1$H( ziOJqemU!?OsQgz`!G;@*??Od{7&WkEed|9br=u#xI$1SIrk3#Vc!u$I`r;!7KZgEe z0|R>T)C5S3d-_mQ$->GJCpK9ktw7xoN-rBuqXswN8c)1OzkXRD795M$2+Oc7FwArQ z^IQIV&N_P3{8l*XmHL;*wX0We90vDV$0A&~2BUAMRY%kK&xYcG1U1Td$_{ZXrwo_+ zjCx4|Hb*Z7WP$#$o^=EOY(1LA1V|-7Pu)&B{qsvYj)o&O9qW1MRbp=rM`9C3Gy=u4 z4u&gwkJAAxe*?A?0H>@90CY}V`cP3=RU+9!OAGf%Hd{Y96rrsWaF&nPW9cVn1q!=O zt5*+=%{V>#etl?~!u6nhqj{5$%Q!oE#1gV&aK}gx~AgQzfxoWP$1FHHQzMAxdva7K1h> zYS2A)K6P9>{ZgPJL>A*GaVEf40@Xf$K6&*?^%`CJxvd>~7v0{ul_CCp2tN$UNL1FP zy(o6CFq&c-CM!$D3l2od-*<|x^=lHHaQ=4#@!?`pKc3eiSBtw=`r*DFliKV2FLJ}q z6g)EK&$zHj%0B^{iAstsd(CV_r(5yC6S)35$%yXq(vv6KIR|eL1%!7Re&pCO1`X@v zH8W;s@Dh6J)X&3H8}rx-n$m8M=02ck9-3gMZLQA3DFOizh>0O=%2Lfg6`efb;DB3Z zTG}mGFTy{^HmfDmkJlt2!68oW|KS8Gw*I-%3JAYa!&}GQ;UmLEk<~o1x1H~E7j6)# zZ2hWLmk;kdrmfPN+K^3!<`)b=U2{uF-^1Z+ZvR8<$>nd;G$*svRp2IJH9%VWTKc&| zDO7ba3gHp6qFCe(EHfdY=>pHxRiF>{B>w0}+aN^oH?8>fORU;4GhoCgBP9U}%XX+_ zJf$t9<%kZzIY=2sgO&GLrmXei34}0W#(dtem|XaVZ~B0(ZIHOY_jkFf2O3zbvtn+V zY&)S`Bm8CUOrZ^=qpF981hoc`xUj?-E8}&a3w7`@qikqd_m@{Q1v@RYbAa@^Qpd8a zr>~7)z`Sf(){7T|bN!^tRaMnLDIFGv^j*9>wvzBiRcI}9N0Za{r?#w*QPAz&0jY*7$72wBF zp^RI6s9CUs%t3jW8z{^XVwlfU-V5%&_LEyssn!-9Rgh!8O%~mVQ1S$~BDa8`+X*u! zrrRhO28HA12NyPEPy;X|ih(41YmOi01T8sZEfl~0Q>0p#-l-R+sA1?JKBV3S``myG zwzhfs`O_z|j>Wo+HxjAfU(rP85)&om6KqJID@3O@Q=}t5O=m~JRGVP?96F| z!zfxP zC}U5WO#=885fleaEH=2Uc~&C~I?nHjVgKrO^w_Zd+?aWGbxn^Y97|aOGdyDQ0ztu8 z|3(*}3yCzl=`r5(Mo9L;h~Xpi*z~iPFpsoSfxq+$X$kN)A!#^&Pv7ZKtY3BVm`B09 z-8u_VIMS}_uUW07)Nvv|Tol)=08b(rvIuZO`?p5@890;bwSiIbFC5|Tn(82H>oIKq zio}IL&YL;z{bmGhY44K*GEK^;dML-PaYGbkvm6LX*43*z(Q{*ZGdAAC!7VG{%Dtn? z5C`$ijh8nMHk-n5M#ymR#p+Ij__XKEe(kCR9cwW_qb$H7uO8U(4 zbIbzP1oSj-q;}b@^-|`eAVSclko~XWfXq4IX-f&QaM2=hXNVoLa)BXiiDLky>#@=A23W(L8IFksUi$?+uoiigknu!Cn$UD3r6s^&{9?+Cl zNgXO(5R+=?LBJVS40$1FJH>Vk=wnS=4LupznTD@h*@avvbR22{@cRpzH0=t&cL65Na!#W%=h;20yAQ7Rk~P5J^5@ z5?_rRbDU?%l0xaGKweu;i|9~v;F$Pu$*eiuum4LG)%<%1>b4(mzL3IMcnjp2RgbSW zg}K~`>QA&Uc)N<63)1O!Q}0fcPRV@rfXQ1j{wDZrX-13*N9nn|Xn`g`NGfAf}}!kNcS zlK!)^wK{;4z{O;otZbhx%!ims3Q>f*aTKDX6(>oyL5RQYDQf! zhKH)Ya@)MQb4O9j{2{#LXNG#Dv~{`llZObUnc1P>;xj!2_U_UUqz8HUa; z?ICm}WsO~+0%TK6!$Y0Hw8dwOGcDbNsc*|a5^?CeCS2ILJ+Z#QtaY;jfA>nQ3>aT- z8vn%)PIN3j@L&XAVOk564r@1T-MZHxiL&{MN`X1)o2u{MzIEn%2-Al@V9!|1RI!)Y zv;2n#^UJfgUiIs6;6B|23ijn}`+)vAo#j|_qWA9kP;K?%$HVVSON}-ugX@@8*QP!D zXl7{0oD_V(jY*i#-fheT$;%NPTvlcbE`%XKIGK*YGMPYzVf4vnQw{+ZN9f2RG4$`t ze}7MP`B8mpH$7=gn_ubGyRo*U5m82uM;7bn-Tq8SXlU(;Pt?HoFRoZduO>m;8L8=8a9;WFbln{?Y{` zPcST`IiNgir#F4$xht`$=P%Csz3uRq@+zaBoeN5kmNYY%Z?^K=+s1(5f;Mo4U{7gP zctLv-o~pPkG*lV?IYT5HlMfkUIsItDp{(*sf6#c|^@O>D%`binPhM%(C|So^ekUBQ zoA>1uODWx0A@#g;KpNpBDzWPWKyvFh*ejyOW6TMT;Js{vpJ*&LcJm zxnC3@{EgSpZ+U=1}l8X0@qz}tpN|6d*It0lH$Nl=lhnmx;zi~Zxi}0MY{VKzq*Y$#Rqq1+n{W5Q& zKB{t4gXt}L{_J!)ek7zx~#Nt;w4qyl|kS0o6e6D;>Q;YN;DUH8j@gpvz z@3}~P^_e*JqzK6DldB5JHw-xg*LN zdZ(M)+n^Pb%@I_9K4ZO@DNx83{wsO{okni=%%{FnK{DZ!Z?jE z+C-L;e}1*bRW>L|9^$*+e4-ihK#O{kB6{LkA6*5W;dF+c-tAi;>Ji9=N8xcCbdeBQ!&l-UT8_q4ql-=O}A(pQRL zP|Opr8Rg;f%)4TVO2qpmDd#{_*8q!9kQf;1^`Z2aJ{l`F`K?qDb5?$#d|_SBdJbu= z_h*Bk(!;+QWa3OCWg`9$rUAXE+vzEVwup*huHPBU5X)vJH@ci05T}<%&)kxs&)9)( zqWxfoRtE1tWQUa0!Z`uqkA4U9+JtDGghEPuoA~b(?U=s-1Y3c+_`7s~%*_0otuO5; zmM&*!uN9F=K&?MHaV#viR@{CQukp*_P7|=+5tpSCNVEz!jZ8==FqxA6&%A2@Z5{d3C5B{#@%c(RP2yIg7h{LjlR+)KX|bb;aU1jPX(!zeFX z?WNUhy}AW2Zx*0qnbR;ee|d${^lSHB|B?qVK6b5cH<=cQ%LMNaK)Qjlaww4f76t1F z2>KUdlQ>qh*@~=_+VKG3j!;lY6TRj=?hb)$=d`*aauFs!Nk`eeR|`Js29QkMmbYT< zhSq=!$}%djXt14N@S7umfTlwWCoj2QS~BPoR70?s&g)1EcKo^&<@ z54+sto|J4*f4}F=_*v10gT9G_DI3=~{#R%U#pwsaT>|eFq`b$=?uo>``e9owx*CW< zH(y;SbleBWfmS`J^5Kuj4qOGke-;fKzGVfpY5-xo`3`j#a^VSu!?Yg(u4`GGOQUF^MGbzD<6xWM($F!aUoA%a^%PnZ8{q12YAx%3l@w4 z>Z93T9~*5Gp~H_mgKvhA8wu}aTRgZh;l1wb$_8J=h4!HE6skA4a6E!6Vu1+N*yhE^ zDE7dT^XzFwN32jqx8JBu->wsjs>TpCuK`vIgATmWbbBB)LG(^-I0NGKSd+hR>Lh1p zwYF_-X>V!#uK_2X7_MU3nx2C{(~ubbnk-}fX{9HRPI#tFw!jWulvZ?sIUL3n$+;6k zS`zXT6CLRapfqo#>bi0GHomwYyJFx7fmyHVX~YQa_1=KXRR%tudjw;pyXTjA(Y*Wb zNccR7r1r03tAFG+5HkR}0ur>4u83i!PdT&DV$RD8XO*lPgjnKY6~R=Ycg>WBUsHc>M((-sR=J+l&GhY$ zH8eGK8?$=rWsGv;);jFRtzH;V=^rrW10>eeM;a|!BtMFJJOuXBU#eTE`#RNAR<12w zQL*&icmM4%IirgGG2+XMYd`7XL06XM$w0$*5*WGLu{)|rN9-Ae>X?JGTtpM!?d%Y~ zQ#6OAtAR!X)h#^wM6hsc9@LnhO9PlJ8jz}dUVxqJ6T`;PV#EL*>X^4?$`M<_T*ze3 z=(GxsuYsaHbE)QoN>Yz^Q?6g%hC3eviqs{q4+vD*M_%_RW-`$%fE)kW<+azA2i5wx z?oZ6?Pa$m&*!cg@bRJ+m_wODLnGXsXWt2yvL}X-_-4qQwBPDxei^?pKXrM@>qRg^0 zG7=4|fsmw<3MHe`Ib=AD$ z)*7lHLOk%Ys-kaZQI$j#ehtqw?D+BHA`0q;!Q z{TY3Wgu8XFY23VnM&+MBQCq$;3e}yiljjB=`}xlC-9H?ild4|*_DLVEUen^$+Mt6o z-bP0B2)ELvfG`W{J8v7d<=xfwS_e^W_CRWCE-!EZGKxh2;Z{aQ?r*AoUrW38>NquR z0H}G@1Kf7y*CR99yMB0$>zj-7wksNQ4KgY!4Rm$CguL}huUX!DsO2(L5i`o(nmA9VQ@3qlvn-?f2ebZ02c-E{9OdI2jMWc<%PKq7 zO}cj5I_t;Hq02(~XfG=&jnf{Tpae2QYg$(Rim8-B>b%9*3S158w$0=zL*0jg|C#}K z9l}ac2mB&_NJ7-xJHNSJil+c>P3ivjrj0}cLFOG`vI%f7w64wCxI>K5;R0)tN|;rU zNAxCoz6WN^!xP`XpB**hN|XabBOgZWq@K-VB=2m&+@h=3mTYpFXzKE`WVu_V_0tL1 zjF<)FFHR!;h$6G;{sbTOx(8JY_`d;(r^a1zg^)g|P)Oan*zJ>LUC8EM;l;jhsyvVd zdpy?AI{k!Bo`-V@=KhjKq?2FTJn7O3 z#9yX}SfB;lk`In+v~FH9v!int$Js6c}i zT|gK3JL$B6@c&q)jriMQ#y$$m`q8S(Y{xjE&6{)QH|y#$=LF9yq2Fbe!HY&B zxn%mZX>%Db;>ZkGXLtV088b>d2Kxp`Zd)@l+)A}%1fxL$SMiw+a|Y;(m<+rGucdfN z&+QqEAbnsN=^gosDGR^>a0X%|#{u2u|;Iu9vI5HFD#ZQrqMKn_(3tvuMt2_JFVT!T_(aU;K z%XCGE`u)=n+DoiU|C=5fyy~MOXCUXKjE4dnJ>vKrK8wY3VpStqKNo zZAsoB8;CP9p%23Ztrr{Ful6{2Sow;x!L(+_TeczsS^RpQNs&rthj8nq&H2fxk=x?Y ztjfSAMThLS{DPnJF*q^yS9cR}rf7~vY)*giVjgss6vV`u9GFz<%vAI3he}uM@$~$_ z-DSSoot~HARzk_#i0K)d^H~@2F`hr=VflIdEMvQ<)l77c`%L`yN>0njaHJeZB_<>v z&^PmaS`0x_XIx_G5)@a+hX(uei~)5}oZWr-@*vP>F2X57NPgVR>C`(q}9T5=kHVz^}Fm(qes-_onZ6DkT&F2;_O}c`Ealzh@KKI zC`PJ(_80RB0yiTk6J3Q%MQ)|B?y*V6_D{TV_O*NADM^x5?a<*lez8Q%ym|i8Mbcu( zU?9B#_{^N#2V#&D{Y_xc3&R1#O!(By+~!zrT%# zAk7N(_F#mhkm`-?FIaGa&K#K6k)qN>umHA%!Am>gOC}@{x4ewDmWJyIN<@LWNRbm$ ze;q{>KsDNfCP5%X9#qn+%Ys2blnJ3m4(icMxr)ESpyfr3Lm14}j+;ZhdE-IX_*zS< z+Rs#2G_ogQT48JS6O+w~0XF_BnwmEk%{Yocj)W2i!T*iz9&y&AV-a8jG4BJ=If12N zxlDiFd(Vf$zp@z$sBylUv<8Ay0_HzL87Le%5!_~lp0!Q@&a^&UR{|%5!adwiVfw}0 zRPos`lM|DA%3>&fhHbCXpHrLR#mHL_5eVP>2bdo8)hI%l?}+A?G^$>GB*a;{7cg z-t*6jD=Zuqs**b8`OB9J{#yo^<8!O-DQGGvg9Gid>^~GZ5aLpjF*j0Nd3=+bw`{q9 zsRjje2B~Q7x^J?JC|VP`xIMFVL!jF~Q2@yw@$Kg>TV{bC982A%=#35STyeLSUX|8Z z#&ofh(06)j4hYe2;6UJLMQiJ{4RMW`&)qsVDuj$^yiyg%JHRhh@EJ`v!~5Ci=-QY9 z?Oyrb6s4H)mVb@RC%t9Ay_iH0HpI5aS?{@_ETWX&oJTJ(I!6L^1%l#JK(Qh(<%%gp zD-CTEI;p3;2$~*yJ|{(jGf`V*#L>1p@_aRSg8_;47yqWa!6pGuU#eSEuN8>Soobuxd;v{$IoftzcK$HOdA;N(&b{STdo8~4WF zkJ6j%#|c%l5aTRFo(aPd%JFVGXgncxFCjmbTTMPO<@>Q1RT-tO@3W1g-y8%8gLA#d zubqBzZavB)IXXs5-OrvlG0!Whn7#I-Y-NvA^0p)-=5ET29lPzAA&C;`x%GL>7h162+2 zq0OuL+e#Y?K17q04-NC*#N$k6_R3}+yv>F=Ad-f%L;7PwkM0TVz@%_QxHla^JQYbk zA$Rv|7l9+GwpIaMYnrsaml=7NtPw&Cd1SpMc%%bW)0g#8QO)KW5n>XKkX!=jyArwv-MkBB*y&FGfsvwmY&C`qI z%iMCmg#XA&$hnP%AfT-&n$lmdKl}!5G~GxKkOJ(}VmOk>*mdegoO>U_FAf4ad4Aq* z7$;nSFhY!1n)Y3Q3}AFwA%EkNDK8#EA!xI%?b`BQcJC_XwJ&$^AK_A$k_GK-!+~l8$e*=vqL`#;QB_ zfY_G6OMA*D^^WAN-)0^ylY$2JqTZ0fvBW>OMxPKKl3yni#-g!BiSuyfg!?PR-B{c| zXd^y0KOp`(@~0Ffb-VA%B9(_tTOQGOG}U>9#YAABYJ*12=b|(b<9iNx#PxZj+BTPM zVa!f+Qt_R3@_GbXi2r#RvdXt2=N17w0;r^{grZc7XVP@!*eN|#7^5?MI?0wSfrwmK zy}xa({=k7Eg#j(KsqN$D-|rmF9a*vwcnH5OfjUOkK2r7FOGwa)oU?tusf=5ShK(ij z1V6^>#&#PO%@nc-T^`>1r)A5QqQ<1SN&`R@yF@TCV{$tG1xxuEV7<~}9MV%q6@o{v zFA7ufJpxqViT)LnlB^{)3SAA|FG@lrQMHfHUW002hvY{n8tG_AgF=WuvXc30m%#v z9AWQRUzguYWsvzS-!$+6Tww(#7h#O3*%+NrFuc>!tR-KA3QjH)t*h^_X0ix5i%WLA<-FOio=&2PflK@U#}s=08wLMRnkA|(?yY?PJ-JrcJ` zjS3>b=U7sM|80@Oib|L9girDBaM|=7xn&b%2SiG6g8E7(QWCqhIX$#djSi`GE)^4@ zv&V~#VgpY34#KYmdMt!F3NBep!AFuf51QM4tNne$hp%5BCa3oWF_fl4erSVnb&Z(d zS(<$(m^byYq{N3hi^)VsBoS_QEB9kBCNmv$sqzhgK~^%3bLCRx3Kl%d?5U#i*vKZB5_JqGW<m#?H&ze_`s8N=QuUof{_)_JuD1>bUndb9$Xl32dGjf;W z48R6M;sHmApCar~@hL| z2}Y`1kUB@DujtE}I71fO39JV{s>5fUnY#Z&$$mhckT(6?gQ3Mv`Qq* z1MZ%rtmeV4=D-cvvy-(=0{Im@VN!r)FSMQmC$5{!Obiil6nP+{b~&F6%X(4QhM z2ZR-9^K58<6}i>{xI}@Nkmu8^rFA0o0~aieOeiG==Y1|c;`#&u8pv*;B$%ePaj3?& zrYCB&!`KlNP0Z$$JCb+p73>|p#@=)c^ob@_ez{fpjB$WR~^wfeTE#YJM(IDZl7p^$PiXHO&8$`bYU6; zO~7MN&Fwu$JiRdU+Qd6PmWvZo=SauA8E-jC#FM8^9SV)1Qrdz458|2}S_WyUAe@r# z)fvhl53(w4NH0>X#pDqR_`uxfcZf6?ou}~2B(tUcyMp`0d@%+Y|t6a)yV&{Fv=u& zSHDpeQ!ole&@l)MrGdTAi=dRDRlP}b$Oe#+cj8?YkbFAfk4+?A#kKBY=qk09;EX68 z%L?ehRanN(gsY z;e2CNLf@V$(HNAn}1 z8!U#Kx^tk!HIw*pVv4Ga;{LTn87B6iBxT%t+lr!z&rXxFi;gnv&Fx5y>OTBUf~VMQ z|OF74pZN=bCfGpuJ*SL zj`QZ-1FfKZYl#!AK&D*61AN!9hGLJ79$op!2y?%!Rs2c`qV0OmDE*c-3?s1$_KlDa zBA9$}p6N8Yo?Ve~(XRj`ERa+p5~k@wgEtxxqTiLJ^#mfp^_yH(nX{P$m-jTd${<5} z_lj4nJ{j<_6Gmh5JeO2m+JhrmK|3NCH>wfM%h6#&(rv7F`b z-ObKcXSXS@R;Az+z9;-Os%;q{W4ecIrG;>Oz@pS0q=!j3z=bSV5`TpE=1fDQThK7L zL*6}1&lZ2iBNk5bTEXSnv#mIlH9xP1e_MCqPu)JXfu>LY#qXgXgEd{8My_?y1cU(I z-1=If-_Frn8FwXWbmOCPHNYlLuiL&`r?ku|eaJqD-L1Ta+=7A$#1Js-$l;2~a2yL6 zNPv$O*)vxl^2*mURku&Oxb9(Ko`x8QW3=n?nxw$x-@l_CetL@+5(*btW`nkC!cE@60buU`(mc2mzGdpIf5>R84^*-vF1?NN>)%>a$ zlD-5Vb?P2C+G?d~+ALK~WDY_u++`AyK&oeSCelnoc{zGXuCIm#pBtuM)Rs!`=sONs^4+z#+Os^R_TB12KIpm^c z#LF#TeV(oWF$yCMNN7Rxm&)3tkb|exbV{7kZ-29Ky1DK3m(@j+%5Oz|+nnw8jFQan zLJju`nNoxFIZA7v7NG8#2L>vK13`iPqD6_^{WeDpw9CDN4j0Vy)vQ@_vkOcJ@UWmU zRM1+nHK%X7k*;meRSw7+^670|2`xJlY5EZU_@uUC;Iy(Fs{-pJ3eXw{LGbS~EJh z%50T)H%;5c%liN4mO-7{nK{;E6w$4lx0_nN0o(0EgX5q}a&vOFoD41m&j$D6`jeP0VIP0Y(5+A099T3$@Hi4C@3K4NViRxsx*Rp% z8(T0pW7St)TUk#TdSC_LVS!vZOInu4&aS&SXLRxt>NlcOC?8rdOj?6gpdMpiJ%lQh ztuMpYF9$6>Gv&zc*slw+XB#kcX|nw7q^2!eG)!Od$Y#A;)r-1i%=H4OQ`Q5UMGy3Y)iR*mEW+?k* zw;biTcySWVIKIi>^0^-><8a@b0VH1~g&c_C3IKn1iPW!Y|Z(NIm2c=+`^q#u^g z3=w#s{|ca*CRdFCRsoZ2Kr<14@7`7pzc_=T+&H~X!(F5L37cyFN)20F1Wr)vRYinz zZWO_w-=r7^6Yl^!C}j|-@th{nXz??h=+Miuk#L!!pa5ZLA@cq=Z)VI-Zs#kL(9Jva z6`WO`2%JjltaW?i?Ci(fdj;G;M=Clj8SKD4hSy@gQ%y_e0W#S$$SDDPmJuOuy)gsh zBO`Wwo8rN;6%vNYpOlgmjdrT4y8-a%KNamSH9QX|a&O;}psU+$Kst0%tt4~2yCQu+(j!Yh%abvjoDSZQO5tE^{cBXS8uCh zGnd*xyg^`gPFY0e2J4u8qqBM{FI&1tU}U5KaO238HTt4F zQ@R|w4h&%XjSK7u3nITBDoC~}Lbz?&K-*f*3Z7}a){o7 z9b5Bm%nK!A{lt-sE8C+mf%Tz>D0kCnWrSdYx00fcJDEcnFYlLt6XTq33O)i7;7F0I zwxmPNdrtqNtk{8E5TIS5#yyQv44+GzO#FcEm)e(qsZ~6!a#^E(ZBaLHT(-x>#YIG& zxr#PVTvk9&`}XfY9}c7GNPiq(RmjDHtBQP=ti^PE-P_`--pI}{Dv4%H8_c1sic2g& z76=<@=N`YKw5%+Y{sI1HH$iyMmPr-4L|j=yg$$bby>p<|K?q0w)_+oLwBlUQ+f zSkr_c<4zwOdf~z|5<_8jT-}Pw*&+$%kSZ{*KGiGv2R0wnUZA(_Sb@kV6mF7=1U619 z^6bTneMOByBZt9)BcFb_<+&pfQ~Hl1O()Q*Ns#8MHl66JnJ5mQeGHYX*T1#yslr52 z2xEw}{M(o=qg$tk&TW6e?o9y&JcKLnDx8RJc=T0DirDD%aOsGDj#V1sxG zFo|i52Mh|m#zKPZo5QEabIw=d3h!d9p1$pvmRoA81DVZ^%;>oU6*BKVZ~V2A?*8|0 zBLE^&wZg80$NB$8%LF1ybJ0lvM124r6Y-Q3QMCWsHm9E}8UDcbKkTRd)Q^$O%`OF{W8C@jGlmR3v7O?oTt3*Ml`0u-B;k?_9 zkdwoYkvI=?9#(i(OW8$Fs7u7Kf=7{q)2C&kDC2pcfz->+rFEnhlpW-NsiuDA594}m ze~k<%ueqM^D(C85Cm(i)niAdYlCcc~h#?4Hr0&g|=Q9&Zm-|S-S4>wgdI^aNW_1w_ z6a8g=`P<8j#-JuwHFVv4w93iz$t;Ac%EUSof{i3S{;t?xQeRA5)1-B+DIeK^h#||3 z$Zb7}yL_R@l^D*)R_Kdj5aFm=$?)?}FRmwF1rS zJg!v-qs2wB)C2LgL}EaNM@<*@;u&z`0oiwQKC#7{2%K8@;$&mup4a+Y))N3YUL$3M zj+@6pA?5c42L`qJ!}nD{R;Nk-sP9&(^VG8vV5aafh!Bb4($~sM zmK2A~R$8)XQ4Ur=*ildchg0KnT=gH8FLB!gixAbe;umFL_N0!2#Tp;I{-BMOm?+Ee zl2C69ydYhadYYFUPPk)ex$C`WU$btq4(2KDuReZ$X?p5G6DbQQ{N(|0#^bpSq7L8$ zBN5plW5sCRh&chF`QlGUg&4{<6s|q99XZ@BwHg29%n=Th!RaRuliFA(fpFI>T6q|3 zg$yS+V_B&ZFW**1;XN&*TuYu=^LkP~-5&!IzT3|q@}+kWg=7mDV+?SK9o z)*H0Z9vqL}-kAy}{rp5>n`E4uytg_zUcZZPr}gsOyVtz*2RM^R z7et^u{eaD=?)XFEI(ewm@HJdzhX9e3(X8|D>oNZ-Q#wx?ZoGh@%X=SQ%2U!T1L~$h zXPidE8;Hjb7D9JnYJod?bhsOahdduf$N`u^kR36mgm3fhe2d&fuRX2aUGg&%bv=RI zI4iz10sFg~wPQhBVCXNiri~lRD7KUal)9toR{?tZ;)MjfFIkN-2O0WgXzTz@z+Vv7 zGUO>CPA1~eCDL@SWU5Nmb#h+wC;nr|T}=k4Ac1R9O^fc=cKj+a{Zi~&nL4}ur2${o zI~G@1v%{q$2cHD_7}{g{<%O*Trl!G=7fHB#7&vF`*5&s0We;?F#hG$H=$iY}z-h6S zjffx>vKZ`qj3NH+K`JGkZoq8K`ytCf{eWKPIXXTB{7x@=(xr3f-sL?Z`gOSJH5nOu z#|)e`6|`}~ucCm2q}!~e72PTM$!KX*3Dv6B#tjX-F5~*El5LHB{m*)g1Z}SWfeKHo zX^>a~G2m(*N;eX8{+m*yTBe1Cg*GTesQ0&QH14u&l1KwpbpH2t8Cy@Riicyj4nH*; zH4j_8x{xIfx!m|?QQEaJwQ8!W;r153efsuQ=3Nkigdc;%1?!bwMRVdFPDX@c8 z(1|i`yYnD%++mpiyvnQZPQqNl4*@&)H+a=`P%kGS)eUvEBY>+Tot1Q}ebCu)(N2*G zVBxl-II4cBYaW$zmLkE2;)GgK)||@rih7l9UJS{@XEin=tP1l0>k!UULZ33=Ua=3y z7E3E^ zuUy&)Y5IAbm*^Y4owix8y{OFu%J^Ly%$sG5*dHCe%B!v*d%@>TRMR-xBz6h4xiSLp zagpprO*zNvmnM`cxs+I7p4C66m7l5qN3rX<{hGT8pN~iNPQ0yUZCB8O-@$WU7_~_) zyl8V6N^xMWy2E?jN#nwc9}ZUW>Nx6z&{z^09A z{t^G9{ex;$r|wuJF=+SRy$~GOb^AuhDjoU{_=C~>FXp@vT%hmzyRR&J%X^GLYSUO~ z1k)?tjtpNL>Q3rWocv4*P$+YSi?hSl_7p3Zsf4JX>L`C{C}0=T&Rgpi@73L@tF!@Ru?ofGlcIc6l<1tm&O@-I=pywDjt-wkE!LIXOh9vbRP5YMuwb zIvf=Av7kVF*4&C?fSdl2nN;bQ(2a;a4z$?S<;OncA>r4U_FA7L&_I^oAoTM~NV1)PUPdNUDQ@_W zJqBBHZ=i-c4O_RDTkFgQ*(Kabn^NJ*TnX)3Jz?Xx zGnU``KH?)4fE36a0Ko4?<3e6e`0024QiRBmtjreyK4{)(4k#qrU9NEc(yJzm`s)uG zbnvYqv{aW<^Y{LpJzsQW!=K|307Ea}v1PpjDRip3fCvu8!<~kIU)EeC01U? zDLK#9BS90ROb8RNUBB4prOind(wWdCF^HHLbuTEY2Mq)zSiNZN>I_x=G-gA1h=8rM z(^gA{F#68alZ~tK!5Q`J>G8Wmu=eV8Mn&m~ZuKT`i|+I*k#^pilUp=wi2(1*V5MFB zlb#03>LWJ~9vIbrf}q=2NYF-!%_rB(gYF>mS0=+ik4GJ(#O3#>#l=Kz+rL0ehuMq- z014$o6V#t(+@Z1gNSHktL}G+PhU!C&E`=d8$e|s%TNe{Ndg*a@ot5z^DPOK-OfY5` zVBgX2^La9HYcu)h_3S-tY!{)Q=A)0pf^FV|M&RDTMPD_GL@J={Vpn^q>I!}0kOyP= zh^~jPNExqCw*S(i=_sB04jd?}!r1C#cG3}kmhLPCWY_)mU1tzx1nw!I7eEKM#~(VS zjh0fLE;VE-Q!Pk%WHl+_)_lNmok&2Ro>ayFJku*(+jGorWX{!yeyj`E~vK zKS)pa=Q#n9u9nIQ`63&GOmD?-%Gqhl6v3F5Lpxxu+{xys3X*MzOWBo7?Z5!b2NE!f zfngpg(9!XBVcm60WO z3O8YH$Yj9DDOLk9T;jGrr!c1#(Oh8|X~Iqp!in@pnkF)Jk6@?g^jF>-8+K1A@PL5L zgTkI&MW8_S^8;Rstn8oX_+L&O8gF+@4S!t=IyhpPii@iEEFKkLA%g`psZz9Kd_QovkAeM%x%TXa6pn*phXw zs6H8ry!7sRuj-+0X|5`)*QINAf1P4#lC{o8DUFx(h(bJ9F}hX z*=t6c{sUZMiCvyx`}Bd|YtI+;K63O+vpB-OEz(!7H$U7^s^APqUm?Q<#K*dwSE~AZ z)_l%Z>D*c7aKs^+TgaSNi0qKRd5hOKiCjba>pAOsU!=;l=TsMl4BvR47~8LJU(eO9 zQ%8J;WdE0N61TL{-q19N%kv$5!N#9W zq2bW$Yz+u#3(O@&JZDBRIgN2NUJNeZImN}v(z)8|FVwbmjn z-qpX~{PFxQHV-!CScao-H$U8WD=7C0N_CEN^awEGEII8iUj z4+fG(Nqs#|3A5CLrbu?jG#f`W{ zhBG+9bc`bG=QC@a*-Js0Jl&QE6LBM{yOFP^cuvtw3!BazNVtFh--r1lWz6zJ$h$_9 zyDoAqcg=%v=dTb9>|)gPQe#j~BaltttL1%}&=f1poz;Dx+`E@^Dxdwmi?*ftXbfv=7#7D)Dl|Kt+H7rU4Cn4}XRr-a^VE+P%c zmT{-pZ~Ifv>aW>Ia^M4AGzVp{u(_!cLW!_?(Dh6#zxnM|Jl1eYwOqOnf;=>v}xOMobt@(OP98~ z*+AKR|Iu=#IlnCx9o$DXniWzUv}fLeM{ityzu9=frAB{G3CB%(ZZgNXIdjwv46e-? zR3FNz6E)aF@)NwP)7Teo+P#1EtnIpP{DPR6Yu5^Rq?Cxw&CHUUAMuGwbkL_vdq$xr zle^A0jsgo#rsi8SC%L)<80B_tI;9lSjyUcDI0@2#;#wFff+}@sH#QMMzQ9FTFwO}A zTc~$)N(M|2$jRe;XKwlvkgPPDL4oGG5U5e1ed5r_ByM63kU_VB@3EQeB=76>>%kU3 zssjgiX=r5!5R*<)aVTNf$WjEW5le9?UYb8s52-Tnqq4c8+ z%vtuX|AK~XBL79S6>xTFHG$4GrFmQDL z{864m10v^&M4E+TU8T`GDeUVoAl|$Yp zlINL>llVhSF^ikOxxI#VWwcnrD&9>^)qnB3bbk9Ik=k>d{j=Vu=Zw}nS-o#lSkbRT z>FY$4LQ`6Q^BgVXx4#=_Uy9-}h?TqXxeW?*ZYR@!LIVe=F_^jW3t%Uhio74( z`ubLpg)mtSvh@})K1$d&EBiQL*otR?-EVBt^h>mQ_pE1?OT5#Kp}73E(K1(@&*FuUX1B)J%5~)sp!JZMOjI zx_*7I$e4H%)+zNrAwj~Ih+1YYBPw^xmyV;6649#FZ!A-Yc||YKrnEjQe(v3qI-Okl&s;~h?24MmsGc(ot&)P;mrVWDbI_nEn~XD{I`BC^ z>afLtZK3d*vH+2XZkqMI+LUdaR{7TJYP4_OGO<{}Igz^s{TVs%T1%dluF5OO@Z#5^TVvFQw<@dI$zhiuIc{$npG1OrYg9lbg^Jw5POT6uSb&bm+Wa zKY#k`oQ$c;Quo-Sqq^R^H$})P@ZGQjOmuR3X!aQ2=1v& zHp%!Y9jvC>w?}_k*R8}z>62;5wJ56@=Y2k23nnz60c+2#`<1*g-s$kRlB%EZFUsxP zPXJ(UlagD|JP}mN!g-USQ@EFx*T3{|FMgl+GQ=Y$(9u8Z8}0=Alk-KrKnD4{iZ?5E z9Bqc6B;bqgWmN?gYi*F7q3qwtT5%As)HNTqk1U&>nP0K7qviR2W2&QdU2B279Jo;c z*d1_l5(Ol!y6@Zd9859xrq7v6MiRstL4%I%9gaet>k23Uv0Z6+1_zb?6nbOdUemAo zp0_WcXwC4}sXcmytaoKlLJVLru#%|q1KoV!W3^j7qe9(4b{WRGWbB@p`9DeW~0ZQ_$-BquzRY zdLr{d$S0!-u*LasLt47J%{A_QPiNyDB_Mq|<=b3908Vvd7CnYvvvxc*CiAONW~Gjc z+wk7yTTp(D1_S_Vrzw-sF754Q)G%&Cf(aE3vi-Drt}f{j`O5$i45`oqp&o6^eb!mz zmxucg=@MCW#iP^(e(E?#OT*GyFFyK=^*4I~ZA~8AC|U&;v;hMbQ0aPE)AkwW08GLx zb>|Mld*3IJS`A#rL&Y$s%`N@hgjXdZd{W@AhkU-tcTNNBRwz92!-tMbs#f}OS*I