From bb39274d502de93e332096ebc4a07be7672f7caf Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Wed, 12 Oct 2022 16:36:25 -0400 Subject: [PATCH 001/177] Signed-off by: David Rowenhorst --- pyebsdindex/spherical_radon_fast.py | 302 ---------------------------- 1 file changed, 302 deletions(-) delete mode 100644 pyebsdindex/spherical_radon_fast.py diff --git a/pyebsdindex/spherical_radon_fast.py b/pyebsdindex/spherical_radon_fast.py deleted file mode 100644 index 5326aa5..0000000 --- a/pyebsdindex/spherical_radon_fast.py +++ /dev/null @@ -1,302 +0,0 @@ -"""This software was developed by employees of the US Naval Research Laboratory (NRL), an -agency of the Federal Government. Pursuant to title 17 section 105 of the United States -Code, works of NRL employees are not subject to copyright protection, and this software -is in the public domain. PyEBSDIndex is an experimental system. NRL assumes no -responsibility whatsoever for its use by other parties, and makes no guarantees, -expressed or implied, about its quality, reliability, or any other characteristic. We -would appreciate acknowledgment if the software is used. To the extent that NRL may hold -copyright in countries other than the United States, you are hereby granted the -non-exclusive irrevocable and unconditional right to print, publish, prepare derivative -works and distribute this software, in any medium, or authorize others to do so on your -behalf, on a royalty-free basis throughout the world. You may improve, modify, and -create derivative works of the software or any portion of the software, and you may copy -and distribute such modifications or works. Modified works should carry a notice stating -that you changed the software and should note the date and nature of any such change. -Please explicitly acknowledge the US Naval Research Laboratory as the original source. -This software can be redistributed and/or modified freely provided that any derivative -works bear some notice that they are derived from it, and any modified versions bear -some notice that they have been modified. - -Author: David Rowenhorst; -The US Naval Research Laboratory Date: 21 Aug 2020""" - -from os import environ -from timeit import default_timer as timer - -from numba import jit, prange -import numpy as np - -RADEG = 180.0/np.pi -DEGRAD = np.pi/180.0 - - - -class SphericalRadon: - def __init__(self, image=None, imageDim=None, nTheta=180, nPhi=180): - self.nTheta = nTheta - self.nPhi = nRho - - self.indexPlan = None - if (image is None) and (imageDim is None): - self.theta = None - self.rho = None - self.imDim = None - else: - if image is not None: - self.imDim = np.asarray(image.shape[-2:]) - else: - self.imDim = np.asarray(imageDim[-2:]) - self.radon_plan_setup(imageDim=self.imDim, nTheta=self.nTheta, nRho=self.nRho, rhoMax=self.rhoMax) - - def radon_plan_setup(self, image=None, imageDim=None, nTheta=None, nRho=None, rhoMax=None): - if (image is None) and (imageDim is not None): - imDim = np.asarray(imageDim, dtype=np.int64) - elif (image is not None): - imDim = np.shape(image)[-2:] # this will catch if someone sends in a [1 x N x M] image - else: - return -1 - imDim = np.asarray(imDim) - self.imDim = imDim - if (nTheta is not None) : self.nTheta = nTheta - if (nRho is not None): self.nRho = nRho - #self.rhoMax = rhoMax if (rhoMax is not None) else np.round(np.linalg.norm(imDim)*0.5) - self.rhoMax = rhoMax if (rhoMax is not None) else (np.linalg.norm(imDim) * 0.5) - - deltaRho = float(2 * self.rhoMax) / (self.nRho) - self.theta = np.arange(self.nTheta, dtype = np.float32)*180.0/self.nTheta - self.rho = np.arange(self.nRho, dtype = np.float32)*deltaRho - (self.rhoMax-deltaRho) - - xmin = -1.0*(self.imDim[1]-1)*0.5 - ymin = -1.0*(self.imDim[0]-1)*0.5 - #xmin = -1.0 * (self.imDim[1]) * 0.5 - #ymin = -1.0 * (self.imDim[0]) * 0.5 - - #self.radon = np.zeros([self.nRho, self.nTheta]) - sTheta = np.sin(self.theta*DEGRAD) - cTheta = np.cos(self.theta*DEGRAD) - thetatest = np.abs(sTheta) >= (np.sqrt(2.) * 0.5) - - m = np.arange(self.imDim[1], dtype = np.uint32) # x values - n = np.arange(self.imDim[0], dtype = np.uint32) # y values - - a = -1.0*np.where(thetatest == 1, cTheta, sTheta) - a /= np.where(thetatest == 1, sTheta, cTheta) - b = xmin*cTheta + ymin*sTheta - - outofbounds = self.imDim[0]*self.imDim[1]+1 - self.indexPlan = np.zeros([self.nRho,self.nTheta,self.imDim.max()],dtype=np.uint64)+outofbounds - - for i in np.arange(self.nTheta): - b1 = self.rho - b[i] - if thetatest[i]: - b1 /= sTheta[i] - b1 = b1.reshape(self.nRho, 1) - #indx_y = np.floor(a[i]*m+b1).astype(np.int64) - indx_y = np.round(a[i] * m + b1).astype(np.int64) - indx_y = np.where(indx_y < 0, outofbounds, indx_y) - indx_y = np.where(indx_y >= self.imDim[0], outofbounds, indx_y) - #indx_y = np.clip(indx_y, 0, self.imDim[1]) - indx1D = np.clip(m+self.imDim[1]*indx_y, 0, outofbounds) - self.indexPlan[:,i, 0:self.imDim[1]] = indx1D - else: - b1 /= cTheta[i] - b1 = b1.reshape(self.nRho, 1) - #if cTheta[i] > 0: - #indx_x = np.floor(a[i]*n + b1).astype(np.int64) - #else: - #indx_x = np.ceil(a[i] * n + b1).astype(np.int64) - indx_x = np.round(a[i] * n + b1).astype(np.int64) - indx_x = np.where(indx_x < 0, outofbounds, indx_x) - indx_x = np.where(indx_x >= self.imDim[1], outofbounds, indx_x) - indx1D = np.clip(indx_x+self.imDim[1]*n, 0, outofbounds) - self.indexPlan[:, i, 0:self.imDim[0]] = indx1D - self.indexPlan.sort(axis = -1) - - - def radon_fast(self, imageIn, padding = np.array([0,0]), fixArtifacts = False, background = None): - tic = timer() - shapeIm = np.shape(imageIn) - if imageIn.ndim == 2: - nIm = 1 - image = imageIn[np.newaxis, : ,:] - reform = True - else: - nIm = shapeIm[0] - reform = False - - if background is None: - image = imageIn.reshape(-1) - else: - image = imageIn - background - image = image.reshape(-1) - - nPx = shapeIm[-1]*shapeIm[-2] - im = np.zeros(nPx+1, dtype=np.float32) - #radon = np.zeros([nIm, self.nRho, self.nTheta], dtype=np.float32) - radon = np.zeros([nIm,self.nRho + 2 * padding[0],self.nTheta + 2 * padding[1]],dtype=np.float32) - shpRdn = radon.shape - norm = np.sum(self.indexPlan < nPx, axis = 2 ) + 1.0e-12 - for i in np.arange(nIm): - im[:-1] = image[i,:,:].flatten() - radon[i, padding[0]:shpRdn[1]-padding[0], padding[1]:shpRdn[2]-padding[1]] = np.sum(im.take(self.indexPlan.astype(np.int64)), axis=2) / norm - - if (fixArtifacts == True): - radon[:,:,0] = radon[:,:,1] - radon[:,:,-1] = radon[:,:,-2] - - radon = np.transpose(radon, [1,2,0]).copy() - - if reform==True: - image = image.reshape(shapeIm) - - #print(timer()-tic) - return radon - - def radon_faster(self,imageIn,padding = np.array([0,0]), fixArtifacts = False, background = None): - tic = timer() - shapeIm = np.shape(imageIn) - if imageIn.ndim == 2: - nIm = 1 - #image = image[np.newaxis, : ,:] - #reform = True - else: - nIm = shapeIm[0] - # reform = False - - if background is None: - image = imageIn.reshape(-1) - else: - image = imageIn - background - image = image.reshape(-1) - - nPx = shapeIm[-1]*shapeIm[-2] - indxDim = np.asarray(self.indexPlan.shape) - #radon = np.zeros([nIm, self.nRho+2*padding[0], self.nTheta+2*padding[1]], dtype=np.float32) - radon = np.zeros([self.nRho + 2 * padding[0],self.nTheta + 2 * padding[1], nIm],dtype=np.float32) - shp = radon.shape - - counter = self.rdn_loops(image,self.indexPlan,nIm,nPx,indxDim,radon, np.asarray(padding)) - - if (fixArtifacts == True): - radon[:,padding[1],:] = radon[:,padding[1]+1,:] - radon[:,shp[1]-1-padding[1],:] = radon[:,shp[1]-padding[1]-2,:] - - - image = image.reshape(shapeIm) - - #print(timer()-tic) - return radon#, counter - - @staticmethod - @jit(nopython=True, fastmath=True, cache=True, parallel=False) - def rdn_loops(images,index,nIm,nPx,indxdim,radon, padding): - nRho = indxdim[0] - nTheta = indxdim[1] - nIndex = indxdim[2] - #counter = np.zeros((nRho, nTheta, nIm), dtype=np.float32) - count = 0.0 - sum = 0.0 - for q in prange(nIm): - #radon[:,:,q] = np.mean(images[q*nPx:(q+1)*nPx]) - imstart = q*nPx - for i in range(nRho): - ip = i+padding[0] - for j in range(nTheta): - jp = j+padding[1] - count = 0.0 - sum = 0.0 - for k in range(nIndex): - indx1 = index[i,j,k] - if (indx1 >= nPx): - break - #radon[q, i, j] += images[imstart+indx1] - sum += images[imstart + indx1] - count += 1.0 - #if count >= 1.0: - #counter[ip,jp, q] = count - radon[ip,jp,q] = sum/(count + 1.0e-12) - #return counter - - def radon2pole(self,bandData,PC=None,vendor='EDAX'): - # Following Krieger-Lassen1994 eq 3.1.6 //figure 3.1.1 - if PC is None: - PC = np.array([0.471659,0.675044,0.630139]) - ven = str.upper(vendor) - - nPats = bandData.shape[0] - nBands = bandData.shape[1] - - # This translation from the Radon to theta and rho assumes that the first pixel read - # in off the detector is in the bottom left corner. -- No longer the assumption --- see below. - # theta = self.radonPlan.theta[np.array(bandData['aveloc'][:,:,1], dtype=np.int)]/RADEG - # rho = self.radonPlan.rho[np.array(bandData['aveloc'][:, :, 0], dtype=np.int)] - - # This translation from the Radon to theta and rho assumes that the first pixel read - # in off the detector is in the top left corner. - - #theta = np.pi - self.radonPlan.theta[np.array(bandData['aveloc'][:,:,1],dtype=np.int64)] / RADEG - #rho = -1.0 * self.radonPlan.rho[np.array(bandData['aveloc'][:,:,0],dtype=np.int64)] - - theta = np.pi - np.interp(bandData['aveloc'][:,:,1], np.arange(self.nTheta), self.theta) / RADEG - rho = -1.0 * np.interp(bandData['aveloc'][:,:,0], np.arange(self.nRho), self.rho) - bandData['theta'][:] = theta - bandData['rho'][:] = rho - - # from this point on, we will assume the image origin and t-vector (aka pattern center) is described - # at the bottom left of the pattern - stheta = np.sin(theta) - ctheta = np.cos(theta) - - pctemp = np.asfarray(PC).copy() - shapet = pctemp.shape - if ven != 'EMSOFT': - if len(shapet) < 2: - pctemp = np.tile(pctemp, nPats).reshape(nPats,3) - else: - if shapet[0] != nPats: - pctemp = np.tile(pctemp[0,:], nPats).reshape(nPats,3) - t = pctemp - else: # EMSOFT pc to ebsdindex needs four numbers for PC - if len(shapet) < 2: - pctemp = np.tile(pctemp, nPats).reshape(nPats,4) - else: - if shapet[0] != nPats: - pctemp = np.tile(pctemp[0,:], nPats).reshape(nPats,4) - t = pctemp[:,0:3] - t[:,2] /= pctemp[:,3] # normalize by pixel size - - - - dimf = np.array(self.imDim, dtype=np.float32) - if ven in ['EDAX', 'OXFORD']: - t *= np.array([dimf[1], dimf[1], -dimf[1]]) - if ven == 'EMSOFT': - t[:, 0] *= -1.0 - t += np.array([dimf[1] / 2.0, dimf[0] / 2.0, 0.0]) - t[:, 2] *= -1.0 - if ven in ['KIKUCHIPY', 'BRUKER']: - t *= np.array([dimf[1], dimf[0], -dimf[0]]) - t[:, 1] = dimf[0] - t[:, 1] - # describes the translation from the bottom left corner of the pattern image to the point on the detector - # perpendicular to where the beam contacts the sample. - - - t = np.tile(t.reshape(nPats,1, 3), (1, nBands,1)) - - r = np.zeros((nPats, nBands, 3), dtype=np.float32) - r[:,:,0] = -1*stheta - r[:,:,1] = ctheta # now defined as r_v - - p = np.zeros((nPats, nBands, 3), dtype=np.float32) - p[:,:,0] = rho*ctheta # get a point within the band -- here it is the point perpendicular to the image center. - p[:,:,1] = rho*stheta - p[:,:,0] += dimf[1] * 0.5 # now convert this with reference to the image origin. - p[:,:,1] += dimf[0] * 0.5 # this is now [O_vP]_v in Eq 3.1.6 - - #n2 = p - t.reshape(1,1,3) - n2 = p - t - n = np.cross(r.reshape(nPats*nBands, 3), n2.reshape(nPats*nBands, 3) ) - norm = np.linalg.norm(n, axis=1) - n /= norm.reshape(nPats*nBands, 1) - n = n.reshape(nPats, nBands, 3) - return n \ No newline at end of file From d8bd70e2b1b881efb1826295ca89b9db0381d7e5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?H=C3=A5kon=20Wiik=20=C3=85nes?= Date: Mon, 24 Oct 2022 16:00:37 +0200 Subject: [PATCH 002/177] Revert version from 0.1.1 to 0.2.dev0 following 0.1.1 release MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: Håkon Wiik Ånes --- pyebsdindex/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyebsdindex/__init__.py b/pyebsdindex/__init__.py index d579eba..75d61da 100644 --- a/pyebsdindex/__init__.py +++ b/pyebsdindex/__init__.py @@ -7,7 +7,7 @@ ] __description__ = "Python based tool for Hough/Radon based EBSD indexing" __name__ = "pyebsdindex" -__version__ = "0.1.1" +__version__ = "0.2.dev0" # Try to import only once From 8f34eda91cd73cf8c3956e5be65b068ce127413d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?H=C3=A5kon=20Wiik=20=C3=85nes?= Date: Mon, 24 Oct 2022 16:00:52 +0200 Subject: [PATCH 003/177] Add "Unreleased" section to changelog following 0.1.1 release MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: Håkon Wiik Ånes --- CHANGELOG.rst | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/CHANGELOG.rst b/CHANGELOG.rst index 5595307..4249452 100644 --- a/CHANGELOG.rst +++ b/CHANGELOG.rst @@ -5,6 +5,27 @@ Changelog All notable changes to PyEBSDIndex will be documented in this file. The format is based on `Keep a Changelog `_. +Unreleased +========== + +Added +----- + +Changed +------- + +Deprecated +---------- + +Removed +------- + +Fixed +----- + +Security +-------- + 0.1.1 (2022-10-25) ================== From 629b65c848b706416c0ddcce18eaed3086ed0eaa Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Wed, 12 Oct 2022 16:36:25 -0400 Subject: [PATCH 004/177] Signed-off by: David Rowenhorst --- pyebsdindex/spherical_radon_fast.py | 302 ---------------------------- 1 file changed, 302 deletions(-) delete mode 100644 pyebsdindex/spherical_radon_fast.py diff --git a/pyebsdindex/spherical_radon_fast.py b/pyebsdindex/spherical_radon_fast.py deleted file mode 100644 index 5326aa5..0000000 --- a/pyebsdindex/spherical_radon_fast.py +++ /dev/null @@ -1,302 +0,0 @@ -"""This software was developed by employees of the US Naval Research Laboratory (NRL), an -agency of the Federal Government. Pursuant to title 17 section 105 of the United States -Code, works of NRL employees are not subject to copyright protection, and this software -is in the public domain. PyEBSDIndex is an experimental system. NRL assumes no -responsibility whatsoever for its use by other parties, and makes no guarantees, -expressed or implied, about its quality, reliability, or any other characteristic. We -would appreciate acknowledgment if the software is used. To the extent that NRL may hold -copyright in countries other than the United States, you are hereby granted the -non-exclusive irrevocable and unconditional right to print, publish, prepare derivative -works and distribute this software, in any medium, or authorize others to do so on your -behalf, on a royalty-free basis throughout the world. You may improve, modify, and -create derivative works of the software or any portion of the software, and you may copy -and distribute such modifications or works. Modified works should carry a notice stating -that you changed the software and should note the date and nature of any such change. -Please explicitly acknowledge the US Naval Research Laboratory as the original source. -This software can be redistributed and/or modified freely provided that any derivative -works bear some notice that they are derived from it, and any modified versions bear -some notice that they have been modified. - -Author: David Rowenhorst; -The US Naval Research Laboratory Date: 21 Aug 2020""" - -from os import environ -from timeit import default_timer as timer - -from numba import jit, prange -import numpy as np - -RADEG = 180.0/np.pi -DEGRAD = np.pi/180.0 - - - -class SphericalRadon: - def __init__(self, image=None, imageDim=None, nTheta=180, nPhi=180): - self.nTheta = nTheta - self.nPhi = nRho - - self.indexPlan = None - if (image is None) and (imageDim is None): - self.theta = None - self.rho = None - self.imDim = None - else: - if image is not None: - self.imDim = np.asarray(image.shape[-2:]) - else: - self.imDim = np.asarray(imageDim[-2:]) - self.radon_plan_setup(imageDim=self.imDim, nTheta=self.nTheta, nRho=self.nRho, rhoMax=self.rhoMax) - - def radon_plan_setup(self, image=None, imageDim=None, nTheta=None, nRho=None, rhoMax=None): - if (image is None) and (imageDim is not None): - imDim = np.asarray(imageDim, dtype=np.int64) - elif (image is not None): - imDim = np.shape(image)[-2:] # this will catch if someone sends in a [1 x N x M] image - else: - return -1 - imDim = np.asarray(imDim) - self.imDim = imDim - if (nTheta is not None) : self.nTheta = nTheta - if (nRho is not None): self.nRho = nRho - #self.rhoMax = rhoMax if (rhoMax is not None) else np.round(np.linalg.norm(imDim)*0.5) - self.rhoMax = rhoMax if (rhoMax is not None) else (np.linalg.norm(imDim) * 0.5) - - deltaRho = float(2 * self.rhoMax) / (self.nRho) - self.theta = np.arange(self.nTheta, dtype = np.float32)*180.0/self.nTheta - self.rho = np.arange(self.nRho, dtype = np.float32)*deltaRho - (self.rhoMax-deltaRho) - - xmin = -1.0*(self.imDim[1]-1)*0.5 - ymin = -1.0*(self.imDim[0]-1)*0.5 - #xmin = -1.0 * (self.imDim[1]) * 0.5 - #ymin = -1.0 * (self.imDim[0]) * 0.5 - - #self.radon = np.zeros([self.nRho, self.nTheta]) - sTheta = np.sin(self.theta*DEGRAD) - cTheta = np.cos(self.theta*DEGRAD) - thetatest = np.abs(sTheta) >= (np.sqrt(2.) * 0.5) - - m = np.arange(self.imDim[1], dtype = np.uint32) # x values - n = np.arange(self.imDim[0], dtype = np.uint32) # y values - - a = -1.0*np.where(thetatest == 1, cTheta, sTheta) - a /= np.where(thetatest == 1, sTheta, cTheta) - b = xmin*cTheta + ymin*sTheta - - outofbounds = self.imDim[0]*self.imDim[1]+1 - self.indexPlan = np.zeros([self.nRho,self.nTheta,self.imDim.max()],dtype=np.uint64)+outofbounds - - for i in np.arange(self.nTheta): - b1 = self.rho - b[i] - if thetatest[i]: - b1 /= sTheta[i] - b1 = b1.reshape(self.nRho, 1) - #indx_y = np.floor(a[i]*m+b1).astype(np.int64) - indx_y = np.round(a[i] * m + b1).astype(np.int64) - indx_y = np.where(indx_y < 0, outofbounds, indx_y) - indx_y = np.where(indx_y >= self.imDim[0], outofbounds, indx_y) - #indx_y = np.clip(indx_y, 0, self.imDim[1]) - indx1D = np.clip(m+self.imDim[1]*indx_y, 0, outofbounds) - self.indexPlan[:,i, 0:self.imDim[1]] = indx1D - else: - b1 /= cTheta[i] - b1 = b1.reshape(self.nRho, 1) - #if cTheta[i] > 0: - #indx_x = np.floor(a[i]*n + b1).astype(np.int64) - #else: - #indx_x = np.ceil(a[i] * n + b1).astype(np.int64) - indx_x = np.round(a[i] * n + b1).astype(np.int64) - indx_x = np.where(indx_x < 0, outofbounds, indx_x) - indx_x = np.where(indx_x >= self.imDim[1], outofbounds, indx_x) - indx1D = np.clip(indx_x+self.imDim[1]*n, 0, outofbounds) - self.indexPlan[:, i, 0:self.imDim[0]] = indx1D - self.indexPlan.sort(axis = -1) - - - def radon_fast(self, imageIn, padding = np.array([0,0]), fixArtifacts = False, background = None): - tic = timer() - shapeIm = np.shape(imageIn) - if imageIn.ndim == 2: - nIm = 1 - image = imageIn[np.newaxis, : ,:] - reform = True - else: - nIm = shapeIm[0] - reform = False - - if background is None: - image = imageIn.reshape(-1) - else: - image = imageIn - background - image = image.reshape(-1) - - nPx = shapeIm[-1]*shapeIm[-2] - im = np.zeros(nPx+1, dtype=np.float32) - #radon = np.zeros([nIm, self.nRho, self.nTheta], dtype=np.float32) - radon = np.zeros([nIm,self.nRho + 2 * padding[0],self.nTheta + 2 * padding[1]],dtype=np.float32) - shpRdn = radon.shape - norm = np.sum(self.indexPlan < nPx, axis = 2 ) + 1.0e-12 - for i in np.arange(nIm): - im[:-1] = image[i,:,:].flatten() - radon[i, padding[0]:shpRdn[1]-padding[0], padding[1]:shpRdn[2]-padding[1]] = np.sum(im.take(self.indexPlan.astype(np.int64)), axis=2) / norm - - if (fixArtifacts == True): - radon[:,:,0] = radon[:,:,1] - radon[:,:,-1] = radon[:,:,-2] - - radon = np.transpose(radon, [1,2,0]).copy() - - if reform==True: - image = image.reshape(shapeIm) - - #print(timer()-tic) - return radon - - def radon_faster(self,imageIn,padding = np.array([0,0]), fixArtifacts = False, background = None): - tic = timer() - shapeIm = np.shape(imageIn) - if imageIn.ndim == 2: - nIm = 1 - #image = image[np.newaxis, : ,:] - #reform = True - else: - nIm = shapeIm[0] - # reform = False - - if background is None: - image = imageIn.reshape(-1) - else: - image = imageIn - background - image = image.reshape(-1) - - nPx = shapeIm[-1]*shapeIm[-2] - indxDim = np.asarray(self.indexPlan.shape) - #radon = np.zeros([nIm, self.nRho+2*padding[0], self.nTheta+2*padding[1]], dtype=np.float32) - radon = np.zeros([self.nRho + 2 * padding[0],self.nTheta + 2 * padding[1], nIm],dtype=np.float32) - shp = radon.shape - - counter = self.rdn_loops(image,self.indexPlan,nIm,nPx,indxDim,radon, np.asarray(padding)) - - if (fixArtifacts == True): - radon[:,padding[1],:] = radon[:,padding[1]+1,:] - radon[:,shp[1]-1-padding[1],:] = radon[:,shp[1]-padding[1]-2,:] - - - image = image.reshape(shapeIm) - - #print(timer()-tic) - return radon#, counter - - @staticmethod - @jit(nopython=True, fastmath=True, cache=True, parallel=False) - def rdn_loops(images,index,nIm,nPx,indxdim,radon, padding): - nRho = indxdim[0] - nTheta = indxdim[1] - nIndex = indxdim[2] - #counter = np.zeros((nRho, nTheta, nIm), dtype=np.float32) - count = 0.0 - sum = 0.0 - for q in prange(nIm): - #radon[:,:,q] = np.mean(images[q*nPx:(q+1)*nPx]) - imstart = q*nPx - for i in range(nRho): - ip = i+padding[0] - for j in range(nTheta): - jp = j+padding[1] - count = 0.0 - sum = 0.0 - for k in range(nIndex): - indx1 = index[i,j,k] - if (indx1 >= nPx): - break - #radon[q, i, j] += images[imstart+indx1] - sum += images[imstart + indx1] - count += 1.0 - #if count >= 1.0: - #counter[ip,jp, q] = count - radon[ip,jp,q] = sum/(count + 1.0e-12) - #return counter - - def radon2pole(self,bandData,PC=None,vendor='EDAX'): - # Following Krieger-Lassen1994 eq 3.1.6 //figure 3.1.1 - if PC is None: - PC = np.array([0.471659,0.675044,0.630139]) - ven = str.upper(vendor) - - nPats = bandData.shape[0] - nBands = bandData.shape[1] - - # This translation from the Radon to theta and rho assumes that the first pixel read - # in off the detector is in the bottom left corner. -- No longer the assumption --- see below. - # theta = self.radonPlan.theta[np.array(bandData['aveloc'][:,:,1], dtype=np.int)]/RADEG - # rho = self.radonPlan.rho[np.array(bandData['aveloc'][:, :, 0], dtype=np.int)] - - # This translation from the Radon to theta and rho assumes that the first pixel read - # in off the detector is in the top left corner. - - #theta = np.pi - self.radonPlan.theta[np.array(bandData['aveloc'][:,:,1],dtype=np.int64)] / RADEG - #rho = -1.0 * self.radonPlan.rho[np.array(bandData['aveloc'][:,:,0],dtype=np.int64)] - - theta = np.pi - np.interp(bandData['aveloc'][:,:,1], np.arange(self.nTheta), self.theta) / RADEG - rho = -1.0 * np.interp(bandData['aveloc'][:,:,0], np.arange(self.nRho), self.rho) - bandData['theta'][:] = theta - bandData['rho'][:] = rho - - # from this point on, we will assume the image origin and t-vector (aka pattern center) is described - # at the bottom left of the pattern - stheta = np.sin(theta) - ctheta = np.cos(theta) - - pctemp = np.asfarray(PC).copy() - shapet = pctemp.shape - if ven != 'EMSOFT': - if len(shapet) < 2: - pctemp = np.tile(pctemp, nPats).reshape(nPats,3) - else: - if shapet[0] != nPats: - pctemp = np.tile(pctemp[0,:], nPats).reshape(nPats,3) - t = pctemp - else: # EMSOFT pc to ebsdindex needs four numbers for PC - if len(shapet) < 2: - pctemp = np.tile(pctemp, nPats).reshape(nPats,4) - else: - if shapet[0] != nPats: - pctemp = np.tile(pctemp[0,:], nPats).reshape(nPats,4) - t = pctemp[:,0:3] - t[:,2] /= pctemp[:,3] # normalize by pixel size - - - - dimf = np.array(self.imDim, dtype=np.float32) - if ven in ['EDAX', 'OXFORD']: - t *= np.array([dimf[1], dimf[1], -dimf[1]]) - if ven == 'EMSOFT': - t[:, 0] *= -1.0 - t += np.array([dimf[1] / 2.0, dimf[0] / 2.0, 0.0]) - t[:, 2] *= -1.0 - if ven in ['KIKUCHIPY', 'BRUKER']: - t *= np.array([dimf[1], dimf[0], -dimf[0]]) - t[:, 1] = dimf[0] - t[:, 1] - # describes the translation from the bottom left corner of the pattern image to the point on the detector - # perpendicular to where the beam contacts the sample. - - - t = np.tile(t.reshape(nPats,1, 3), (1, nBands,1)) - - r = np.zeros((nPats, nBands, 3), dtype=np.float32) - r[:,:,0] = -1*stheta - r[:,:,1] = ctheta # now defined as r_v - - p = np.zeros((nPats, nBands, 3), dtype=np.float32) - p[:,:,0] = rho*ctheta # get a point within the band -- here it is the point perpendicular to the image center. - p[:,:,1] = rho*stheta - p[:,:,0] += dimf[1] * 0.5 # now convert this with reference to the image origin. - p[:,:,1] += dimf[0] * 0.5 # this is now [O_vP]_v in Eq 3.1.6 - - #n2 = p - t.reshape(1,1,3) - n2 = p - t - n = np.cross(r.reshape(nPats*nBands, 3), n2.reshape(nPats*nBands, 3) ) - norm = np.linalg.norm(n, axis=1) - n /= norm.reshape(nPats*nBands, 1) - n = n.reshape(nPats, nBands, 3) - return n \ No newline at end of file From 1020814951ad506d32c642242d5713e798ca8080 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Wed, 12 Oct 2022 16:36:25 -0400 Subject: [PATCH 005/177] Signed-off by: David Rowenhorst --- pyebsdindex/spherical_radon_fast.py | 302 ---------------------------- 1 file changed, 302 deletions(-) delete mode 100644 pyebsdindex/spherical_radon_fast.py diff --git a/pyebsdindex/spherical_radon_fast.py b/pyebsdindex/spherical_radon_fast.py deleted file mode 100644 index 5326aa5..0000000 --- a/pyebsdindex/spherical_radon_fast.py +++ /dev/null @@ -1,302 +0,0 @@ -"""This software was developed by employees of the US Naval Research Laboratory (NRL), an -agency of the Federal Government. Pursuant to title 17 section 105 of the United States -Code, works of NRL employees are not subject to copyright protection, and this software -is in the public domain. PyEBSDIndex is an experimental system. NRL assumes no -responsibility whatsoever for its use by other parties, and makes no guarantees, -expressed or implied, about its quality, reliability, or any other characteristic. We -would appreciate acknowledgment if the software is used. To the extent that NRL may hold -copyright in countries other than the United States, you are hereby granted the -non-exclusive irrevocable and unconditional right to print, publish, prepare derivative -works and distribute this software, in any medium, or authorize others to do so on your -behalf, on a royalty-free basis throughout the world. You may improve, modify, and -create derivative works of the software or any portion of the software, and you may copy -and distribute such modifications or works. Modified works should carry a notice stating -that you changed the software and should note the date and nature of any such change. -Please explicitly acknowledge the US Naval Research Laboratory as the original source. -This software can be redistributed and/or modified freely provided that any derivative -works bear some notice that they are derived from it, and any modified versions bear -some notice that they have been modified. - -Author: David Rowenhorst; -The US Naval Research Laboratory Date: 21 Aug 2020""" - -from os import environ -from timeit import default_timer as timer - -from numba import jit, prange -import numpy as np - -RADEG = 180.0/np.pi -DEGRAD = np.pi/180.0 - - - -class SphericalRadon: - def __init__(self, image=None, imageDim=None, nTheta=180, nPhi=180): - self.nTheta = nTheta - self.nPhi = nRho - - self.indexPlan = None - if (image is None) and (imageDim is None): - self.theta = None - self.rho = None - self.imDim = None - else: - if image is not None: - self.imDim = np.asarray(image.shape[-2:]) - else: - self.imDim = np.asarray(imageDim[-2:]) - self.radon_plan_setup(imageDim=self.imDim, nTheta=self.nTheta, nRho=self.nRho, rhoMax=self.rhoMax) - - def radon_plan_setup(self, image=None, imageDim=None, nTheta=None, nRho=None, rhoMax=None): - if (image is None) and (imageDim is not None): - imDim = np.asarray(imageDim, dtype=np.int64) - elif (image is not None): - imDim = np.shape(image)[-2:] # this will catch if someone sends in a [1 x N x M] image - else: - return -1 - imDim = np.asarray(imDim) - self.imDim = imDim - if (nTheta is not None) : self.nTheta = nTheta - if (nRho is not None): self.nRho = nRho - #self.rhoMax = rhoMax if (rhoMax is not None) else np.round(np.linalg.norm(imDim)*0.5) - self.rhoMax = rhoMax if (rhoMax is not None) else (np.linalg.norm(imDim) * 0.5) - - deltaRho = float(2 * self.rhoMax) / (self.nRho) - self.theta = np.arange(self.nTheta, dtype = np.float32)*180.0/self.nTheta - self.rho = np.arange(self.nRho, dtype = np.float32)*deltaRho - (self.rhoMax-deltaRho) - - xmin = -1.0*(self.imDim[1]-1)*0.5 - ymin = -1.0*(self.imDim[0]-1)*0.5 - #xmin = -1.0 * (self.imDim[1]) * 0.5 - #ymin = -1.0 * (self.imDim[0]) * 0.5 - - #self.radon = np.zeros([self.nRho, self.nTheta]) - sTheta = np.sin(self.theta*DEGRAD) - cTheta = np.cos(self.theta*DEGRAD) - thetatest = np.abs(sTheta) >= (np.sqrt(2.) * 0.5) - - m = np.arange(self.imDim[1], dtype = np.uint32) # x values - n = np.arange(self.imDim[0], dtype = np.uint32) # y values - - a = -1.0*np.where(thetatest == 1, cTheta, sTheta) - a /= np.where(thetatest == 1, sTheta, cTheta) - b = xmin*cTheta + ymin*sTheta - - outofbounds = self.imDim[0]*self.imDim[1]+1 - self.indexPlan = np.zeros([self.nRho,self.nTheta,self.imDim.max()],dtype=np.uint64)+outofbounds - - for i in np.arange(self.nTheta): - b1 = self.rho - b[i] - if thetatest[i]: - b1 /= sTheta[i] - b1 = b1.reshape(self.nRho, 1) - #indx_y = np.floor(a[i]*m+b1).astype(np.int64) - indx_y = np.round(a[i] * m + b1).astype(np.int64) - indx_y = np.where(indx_y < 0, outofbounds, indx_y) - indx_y = np.where(indx_y >= self.imDim[0], outofbounds, indx_y) - #indx_y = np.clip(indx_y, 0, self.imDim[1]) - indx1D = np.clip(m+self.imDim[1]*indx_y, 0, outofbounds) - self.indexPlan[:,i, 0:self.imDim[1]] = indx1D - else: - b1 /= cTheta[i] - b1 = b1.reshape(self.nRho, 1) - #if cTheta[i] > 0: - #indx_x = np.floor(a[i]*n + b1).astype(np.int64) - #else: - #indx_x = np.ceil(a[i] * n + b1).astype(np.int64) - indx_x = np.round(a[i] * n + b1).astype(np.int64) - indx_x = np.where(indx_x < 0, outofbounds, indx_x) - indx_x = np.where(indx_x >= self.imDim[1], outofbounds, indx_x) - indx1D = np.clip(indx_x+self.imDim[1]*n, 0, outofbounds) - self.indexPlan[:, i, 0:self.imDim[0]] = indx1D - self.indexPlan.sort(axis = -1) - - - def radon_fast(self, imageIn, padding = np.array([0,0]), fixArtifacts = False, background = None): - tic = timer() - shapeIm = np.shape(imageIn) - if imageIn.ndim == 2: - nIm = 1 - image = imageIn[np.newaxis, : ,:] - reform = True - else: - nIm = shapeIm[0] - reform = False - - if background is None: - image = imageIn.reshape(-1) - else: - image = imageIn - background - image = image.reshape(-1) - - nPx = shapeIm[-1]*shapeIm[-2] - im = np.zeros(nPx+1, dtype=np.float32) - #radon = np.zeros([nIm, self.nRho, self.nTheta], dtype=np.float32) - radon = np.zeros([nIm,self.nRho + 2 * padding[0],self.nTheta + 2 * padding[1]],dtype=np.float32) - shpRdn = radon.shape - norm = np.sum(self.indexPlan < nPx, axis = 2 ) + 1.0e-12 - for i in np.arange(nIm): - im[:-1] = image[i,:,:].flatten() - radon[i, padding[0]:shpRdn[1]-padding[0], padding[1]:shpRdn[2]-padding[1]] = np.sum(im.take(self.indexPlan.astype(np.int64)), axis=2) / norm - - if (fixArtifacts == True): - radon[:,:,0] = radon[:,:,1] - radon[:,:,-1] = radon[:,:,-2] - - radon = np.transpose(radon, [1,2,0]).copy() - - if reform==True: - image = image.reshape(shapeIm) - - #print(timer()-tic) - return radon - - def radon_faster(self,imageIn,padding = np.array([0,0]), fixArtifacts = False, background = None): - tic = timer() - shapeIm = np.shape(imageIn) - if imageIn.ndim == 2: - nIm = 1 - #image = image[np.newaxis, : ,:] - #reform = True - else: - nIm = shapeIm[0] - # reform = False - - if background is None: - image = imageIn.reshape(-1) - else: - image = imageIn - background - image = image.reshape(-1) - - nPx = shapeIm[-1]*shapeIm[-2] - indxDim = np.asarray(self.indexPlan.shape) - #radon = np.zeros([nIm, self.nRho+2*padding[0], self.nTheta+2*padding[1]], dtype=np.float32) - radon = np.zeros([self.nRho + 2 * padding[0],self.nTheta + 2 * padding[1], nIm],dtype=np.float32) - shp = radon.shape - - counter = self.rdn_loops(image,self.indexPlan,nIm,nPx,indxDim,radon, np.asarray(padding)) - - if (fixArtifacts == True): - radon[:,padding[1],:] = radon[:,padding[1]+1,:] - radon[:,shp[1]-1-padding[1],:] = radon[:,shp[1]-padding[1]-2,:] - - - image = image.reshape(shapeIm) - - #print(timer()-tic) - return radon#, counter - - @staticmethod - @jit(nopython=True, fastmath=True, cache=True, parallel=False) - def rdn_loops(images,index,nIm,nPx,indxdim,radon, padding): - nRho = indxdim[0] - nTheta = indxdim[1] - nIndex = indxdim[2] - #counter = np.zeros((nRho, nTheta, nIm), dtype=np.float32) - count = 0.0 - sum = 0.0 - for q in prange(nIm): - #radon[:,:,q] = np.mean(images[q*nPx:(q+1)*nPx]) - imstart = q*nPx - for i in range(nRho): - ip = i+padding[0] - for j in range(nTheta): - jp = j+padding[1] - count = 0.0 - sum = 0.0 - for k in range(nIndex): - indx1 = index[i,j,k] - if (indx1 >= nPx): - break - #radon[q, i, j] += images[imstart+indx1] - sum += images[imstart + indx1] - count += 1.0 - #if count >= 1.0: - #counter[ip,jp, q] = count - radon[ip,jp,q] = sum/(count + 1.0e-12) - #return counter - - def radon2pole(self,bandData,PC=None,vendor='EDAX'): - # Following Krieger-Lassen1994 eq 3.1.6 //figure 3.1.1 - if PC is None: - PC = np.array([0.471659,0.675044,0.630139]) - ven = str.upper(vendor) - - nPats = bandData.shape[0] - nBands = bandData.shape[1] - - # This translation from the Radon to theta and rho assumes that the first pixel read - # in off the detector is in the bottom left corner. -- No longer the assumption --- see below. - # theta = self.radonPlan.theta[np.array(bandData['aveloc'][:,:,1], dtype=np.int)]/RADEG - # rho = self.radonPlan.rho[np.array(bandData['aveloc'][:, :, 0], dtype=np.int)] - - # This translation from the Radon to theta and rho assumes that the first pixel read - # in off the detector is in the top left corner. - - #theta = np.pi - self.radonPlan.theta[np.array(bandData['aveloc'][:,:,1],dtype=np.int64)] / RADEG - #rho = -1.0 * self.radonPlan.rho[np.array(bandData['aveloc'][:,:,0],dtype=np.int64)] - - theta = np.pi - np.interp(bandData['aveloc'][:,:,1], np.arange(self.nTheta), self.theta) / RADEG - rho = -1.0 * np.interp(bandData['aveloc'][:,:,0], np.arange(self.nRho), self.rho) - bandData['theta'][:] = theta - bandData['rho'][:] = rho - - # from this point on, we will assume the image origin and t-vector (aka pattern center) is described - # at the bottom left of the pattern - stheta = np.sin(theta) - ctheta = np.cos(theta) - - pctemp = np.asfarray(PC).copy() - shapet = pctemp.shape - if ven != 'EMSOFT': - if len(shapet) < 2: - pctemp = np.tile(pctemp, nPats).reshape(nPats,3) - else: - if shapet[0] != nPats: - pctemp = np.tile(pctemp[0,:], nPats).reshape(nPats,3) - t = pctemp - else: # EMSOFT pc to ebsdindex needs four numbers for PC - if len(shapet) < 2: - pctemp = np.tile(pctemp, nPats).reshape(nPats,4) - else: - if shapet[0] != nPats: - pctemp = np.tile(pctemp[0,:], nPats).reshape(nPats,4) - t = pctemp[:,0:3] - t[:,2] /= pctemp[:,3] # normalize by pixel size - - - - dimf = np.array(self.imDim, dtype=np.float32) - if ven in ['EDAX', 'OXFORD']: - t *= np.array([dimf[1], dimf[1], -dimf[1]]) - if ven == 'EMSOFT': - t[:, 0] *= -1.0 - t += np.array([dimf[1] / 2.0, dimf[0] / 2.0, 0.0]) - t[:, 2] *= -1.0 - if ven in ['KIKUCHIPY', 'BRUKER']: - t *= np.array([dimf[1], dimf[0], -dimf[0]]) - t[:, 1] = dimf[0] - t[:, 1] - # describes the translation from the bottom left corner of the pattern image to the point on the detector - # perpendicular to where the beam contacts the sample. - - - t = np.tile(t.reshape(nPats,1, 3), (1, nBands,1)) - - r = np.zeros((nPats, nBands, 3), dtype=np.float32) - r[:,:,0] = -1*stheta - r[:,:,1] = ctheta # now defined as r_v - - p = np.zeros((nPats, nBands, 3), dtype=np.float32) - p[:,:,0] = rho*ctheta # get a point within the band -- here it is the point perpendicular to the image center. - p[:,:,1] = rho*stheta - p[:,:,0] += dimf[1] * 0.5 # now convert this with reference to the image origin. - p[:,:,1] += dimf[0] * 0.5 # this is now [O_vP]_v in Eq 3.1.6 - - #n2 = p - t.reshape(1,1,3) - n2 = p - t - n = np.cross(r.reshape(nPats*nBands, 3), n2.reshape(nPats*nBands, 3) ) - norm = np.linalg.norm(n, axis=1) - n /= norm.reshape(nPats*nBands, 1) - n = n.reshape(nPats, nBands, 3) - return n \ No newline at end of file From 0d7e1ec09ca0c0e6359b3cb7253e5796557dd09b Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Fri, 4 Nov 2022 13:02:04 -0400 Subject: [PATCH 006/177] Signed-off by: David Rowenhorst --- pyebsdindex/misorientation.py | 133 ++++++++++++++++++++++++++++++++++ 1 file changed, 133 insertions(+) create mode 100644 pyebsdindex/misorientation.py diff --git a/pyebsdindex/misorientation.py b/pyebsdindex/misorientation.py new file mode 100644 index 0000000..0ea1f57 --- /dev/null +++ b/pyebsdindex/misorientation.py @@ -0,0 +1,133 @@ +''' +2022, David Rowenhorst/The US Naval Research Laboratory, Washington DC +# Pursuant to title 17 section 105 of the United States Code, works of US Governement employees +# are not not subject to copyright protection. +# +# Copyright (c) 2013-2014, Marc De Graef/Carnegie Mellon University +# All rights reserved. +# +# Redistribution and use in source and binary forms, with or without modification, are +# permitted provided that the following conditions are met: +# +# - Redistributions of source code must retain the above copyright notice, this list +# of conditions and the following disclaimer. +# - Redistributions in binary form must reproduce the above copyright notice, this +# list of conditions and the following disclaimer in the documentation and/or +# other materials provided with the distribution. +# - Neither the names of Marc De Graef, Carnegie Mellon University nor the names +# of its contributors may be used to endorse or promote products derived from +# this software without specific prior written permission. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE +# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE +# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE +# USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +#################################################################################################### + + +#Author David Rowenhorst/The US Naval Research Laboratory, Washington DC +# +# +''' + +import numpy as np +import numba +from os import environ +import tempfile +from pathlib import PurePath +import platform +tempdir = PurePath("/tmp" if platform.system() == "Darwin" else tempfile.gettempdir()) +tempdir = tempdir.joinpath('numba') +environ["NUMBA_CACHE_DIR"] = str(tempdir) + +import rotlib + +def misorientcubic_quick(q1,q2): + '''Misorientation between two cubic crystals using the quaternion trick from Sutton and Buluffi''' + + type = np.dtype('float32') + if (q1.dtype is np.dtype('float64')) or (q2.dtype is np.dtype('float64')): + type = np.dtype('float64') + + shape1 = q1.shape + shape2 = q2.shape + + m1 = shape1[-1] + n1 = numba.int64(q1.size / m1) + + m2 = shape2[-1] + n2 = numba.int64(q2.size / m2) + + n12 = np.array((n1,n2)) + q1in = np.require(q1.reshape(n1,m1).astype(type),requirements=['C','A']) + q2in = np.require(q2.reshape(n2, m2).astype(type), requirements=['C', 'A']) + q12 = misorientcubic_quicknb(q1in, q2in) + + q12 = np.squeeze(q12) + return q12 + + +@numba.jit(nopython=True,fastmath=True,cache=True) +def misorientcubic_quicknb(q1In,q2In): + n1 = q1In.shape[0] + n2 = q2In.shape[0] + n = max(n1,n2) + q12 = np.zeros((n,4), dtype = q1In.dtype) + + sqrt2 = np.float32(np.sqrt(2))#.astype(q1In.dtype) + qAB = np.zeros((4), dtype=q1In.dtype) + qAB_t1 = np.zeros((4), dtype = q1In.dtype) + qAB_t2 = np.zeros((4), dtype=q1In.dtype) + + order1 = np.array([3,0,1,2], dtype=np.uint64) + order2 = np.array([2,1,0], dtype=np.uint64) + for i in numba.prange(n): + i1 = i % n1 + i2 = i % n2 + + q2i = q2In[i2,:].copy() + q2i = q2i.reshape(4) + q2i[1:4] *= -1.0 + + q1i = q1In[i1,:].copy().reshape(4) + + qAB = np.abs(rotlib.quat_multiply1(q1i, q2i)) + + qAB = np.sort(qAB) + + qAB = qAB[order1] + + qAB_t1[0] = qAB[0] + qAB[3] + qAB_t1[1] = qAB[1] - qAB[2] + qAB_t1[2] = qAB[2] + qAB[1] + qAB_t1[3] = qAB[3] - qAB[0] + + if (qAB_t1[0] / sqrt2) > qAB[0]: + qAB[:] = qAB_t1 / sqrt2 + + qAB_t2[0] = qAB_t1[0] + qAB_t1[2] + qAB_t2[1] = qAB_t1[1] + qAB_t1[3] + qAB_t2[2] = qAB_t1[2] - qAB_t1[0] + qAB_t2[3] = qAB_t1[3] - qAB_t1[1] + qAB_t2 *= 0.5 + + if (qAB_t2[0] > qAB[0]): + qAB[:] = qAB_t2 + + vect = np.abs(qAB[1:4]) + vect = np.sort(vect) + qAB[1:] = vect[order2] + + q12[i, :] = qAB + + + return q12 + + + From 558789076eece34e009deb26aed090073a9c334d Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Fri, 18 Nov 2022 12:56:55 -0500 Subject: [PATCH 007/177] Initial support for Oxford h5oina files in NLPAR Signed-off by: David Rowenhorst --- pyebsdindex/ebsd_pattern.py | 97 ++++++++++++++++++++++++++++++++++++- 1 file changed, 96 insertions(+), 1 deletion(-) diff --git a/pyebsdindex/ebsd_pattern.py b/pyebsdindex/ebsd_pattern.py index aac5102..75ac608 100644 --- a/pyebsdindex/ebsd_pattern.py +++ b/pyebsdindex/ebsd_pattern.py @@ -51,6 +51,8 @@ def get_pattern_file_obj(path,file_type=str('')): ftype = 'OH5' elif (extension == '.h5'): ftype = 'H5' + elif (extension == '.h5oina'): + ftype = 'H5OINA' else: raise ValueError('Error: extension not recognized') @@ -61,6 +63,11 @@ def get_pattern_file_obj(path,file_type=str('')): if hdf5path is None: #automatically chose the first data group ebsdfileobj.get_data_paths() ebsdfileobj.set_data_path(pathindex=0) + if (ftype.upper() == 'H5OINA'): + ebsdfileobj = OXFORDOINA(path) + if hdf5path is None: #automatically chose the first data group + ebsdfileobj.get_data_paths() + ebsdfileobj.set_data_path(pathindex=0) if (ftype.upper() == 'H5'): ebsdfileobj = HDF5PatFile(path) # if the path variable is a list, # the second item is set to be the hdf5 path to the patterns. @@ -647,7 +654,7 @@ def get_data_paths(self, verbose=0): self.h5datagroups.append(grpset) else: self.h5othergrps.append(grpset) - + f.close() if len(self.h5datagroups) < 1: print("No viable EBSD patterns found:",str(Path(self.filepath))) return -2 @@ -926,3 +933,91 @@ def read_header(self, path=None): self.yStep = np.float32(headerpath['YSTEP'][()][0]) return 0 #note this function uses multiple returns + +class OXFORDOINA(HDF5PatFile): + def __init__(self, path=None): + HDF5PatFile.__init__(self, path) + self.vendor = 'OXFORD' + #OXFORDOINA only attributes + self.filedatatype = None # np.uint8 + self.patternh5id = 'Processed Patterns' # Could also be 'Raw Patterns' + + if self.filepath is not None: + self.get_data_paths() + + def set_data_path(self, datapath=None, pathindex=0): #overloaded from parent - will default to first group. + if datapath is not None: + self.h5patdatpth = datapath + else: + if len(self.h5datagroups) > 0: + #self.activegroupid = pathindex + self.h5patdatpth = self.h5datagroups[pathindex] + '/EBSD/Data/' + self.patternh5id + def get_data_paths(self, verbose=0, getraw = False): + '''Based on the OINA spec this will search for viable Pattern Datasets ''' + try: + f = h5py.File(self.filepath,'r') + except: + print("File Not Found:",str(Path(self.filepath))) + return -1 + self.h5datagroups = [] + self.h5othergrps = [] + if getraw is True: + self.patternh5id = 'Raw Patterns' + groupsets = list(f.keys()) + for grpset in groupsets: + if isinstance(f[grpset],h5py.Group): + if 'EBSD' in f[grpset]: + if 'Data' in f[grpset + '/EBSD/']: + if self.patternh5id in f[grpset + '/EBSD/Data']: + if (grpset not in self.h5datagroups): + self.h5datagroups.append(grpset) + else: + self.h5othergrps.append(grpset) + f.close() + + if (len(self.h5datagroups) < 1) and (getraw is False): + self.get_data_paths(self, verbose=False, getraw=True) + + if len(self.h5datagroups) < 1: + print("No viable EBSD patterns found:",str(Path(self.filepath))) + return -2 + else: + if verbose > 0: + print(self.h5datagroups) + return len(self.h5datagroups) + def read_header(self, path=None): + + if path is not None: + self.filepath = path + + try: + f = h5py.File(Path(self.filepath).expanduser(),'r') + except: + print("File Not Found:",str(Path(self.filepath))) + return -1 + + self.version = str(f['Format Version'][()][0]) + + if self.version >= '5.0': + ngrp = self.get_data_paths() + if ngrp <= 0: + f.close() + return -2 # no data groups with patterns found. + if self.h5patdatpth is None: # default to the first datagroup + self.set_data_path(pathindex=0) + + dset = f[self.h5patdatpth] + shp = np.array(dset.shape) + self.patternW = shp[-1] + self.patternH = shp[-2] + self.nPatterns = shp[-3] + self.filedatatype = dset.dtype.type + headerpath = (f[self.h5patdatpth].parent.parent)["Header"] + self.nCols = np.int32(headerpath['X Cells'][()][0]) + self.nRows = np.int32(headerpath['Y Cells'][()][0]) + #self.hexFlag = np.int32(headerpath['Grid Type'][()][0] == 'HexGrid') + + self.xStep = np.float32(headerpath['X Step'][()][0]) + self.yStep = np.float32(headerpath['Y Step'][()][0]) + + return 0 #note this function uses multiple returns From d8e6cf39009a3893e82c1351bdeee0b9a8d59b3c Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Tue, 22 Nov 2022 20:10:29 -0500 Subject: [PATCH 008/177] Symmetry operations for other crystal structures. Signed-off by: David Rowenhorst --- pyebsdindex/crystal_sym.py | 110 ++++++++++++++++++++++++++++++++++++- 1 file changed, 109 insertions(+), 1 deletion(-) diff --git a/pyebsdindex/crystal_sym.py b/pyebsdindex/crystal_sym.py index 8956ed5..db818bd 100644 --- a/pyebsdindex/crystal_sym.py +++ b/pyebsdindex/crystal_sym.py @@ -103,15 +103,123 @@ def cubicsym_q(quatin=None, low = False): return qsym +def hexsym_q(quatin=None, low = False): + symops = np.zeros((12,4), dtype=np.float32) + #identity + symops [0,:] = np.array([1.0,0.0,0.0,0.0]) + #60 deg about [0001]; remember that quat rotation is cos(ang/2) + symops[1, :] = np.array([np.cos(PI/6.0), 0.0,0.0,np.sin(PI/6.0)]) # rotation of 60 + symops[2, :] = np.array([np.cos(PI/3.0), 0.0,0.0,np.sin(PI/3.0)]) # rotation of 120 + symops[3, :] = np.array([0.0, 0.0,0.0,1.0]) # rotation of 180 + symops[4, :] = np.array([np.cos(2.0*PI/3.0), 0.0, 0.0, np.sin(2.0*PI/3.0)]) # rotation of 240 + symops[5, :] = np.array([np.cos(5.0*PI/6.0 ), 0.0, 0.0, np.sin(5.0*PI/6.0 )]) # rotation of 300 + + # 180 deg around the a axes + symops[6, :] = np.array([0.0000000000, 1.000000000, 0.000000000, 0.000000000]) + symops[7, :] = np.array([0.0000000000, 0.866025400, 0.500000000, 0.000000000]) + symops[8, :] = np.array([0.000000000, 0.500000000, 0.866025400, 0.000000000]) + symops[9, :] = np.array([0.000000000, 0.000000000, 1.000000000, 0.000000000]) + symops[10, :] = np.array([0.000000000, -0.50000000, 0.866025400, 0.000000000]) + symops[11, :] = np.array([0.000000000, -0.86602540, 0.500000000, 0.000000000]) + + if low: + symops = symops[0:6,:] + + if quatin is None: + return symops + else: + qsym = rotlib.quat_multiply(symops,quatin) + return qsym + +def trigonal_q(quatin=None, low = False): + symops = np.zeros((6, 4), dtype=np.float32) + # identity + symops[0, :] = np.array([1.0, 0.0, 0.0, 0.0]) + # [001]; remember that quat rotation is cos(ang/2) + symops[1, :] = np.array([np.cos(PI / 3.0), 0.0, 0.0, np.sin(PI / 3.0)]) # rotation of 120 + symops[2, :] = np.array([np.cos(2.0 * PI / 3.0), 0.0, 0.0, np.sin(2.0 * PI / 3.0)]) # rotation of 240 + # 180 deg around the a axes + symops[3, :] = np.array([0.0000000000, 1.000000000, 0.000000000, 0.000000000]) + symops[4, :] = np.array([0.000000000, -0.50000000, 0.866025400, 0.000000000]) + symops[5, :] = np.array([0.000000000, -0.50000000, -0.866025400, 0.000000000]) + if low: + symops = symops[0:3, :] + + if quatin is None: + return symops + else: + qsym = rotlib.quat_multiply(symops, quatin) + return qsym -def triclinic_q(quatin=None): +def tetragonal_q(quatin=None, low = False): + symops = np.zeros((8, 4), dtype=np.float32) + # identity + symops[0, :] = np.array([1.0, 0.0, 0.0, 0.0]) + # [001]; remember that quat rotation is cos(ang/2) + symops[1, :] = np.array([np.cos(PI /4.0), 0.0, 0.0, np.sin(PI / 4.0)]) # rotation of 90 + symops[2, :] = np.array([0.0, 0.0, 0.0, 1.0]) # rotation of 180 + symops[3, :] = np.array([np.cos(0.75*PI ), 0.0, 0.0, np.sin(0.75 * PI )]) # rotation of 270 + + # 180 deg around the [110] axes + symops[4, :] = np.array([0.0000000000, 0.5*np.sqrt(2.0), 0.5*np.sqrt(2.0), 0.000000000]) + symops[5, :] = np.array([0.0000000000, -0.5 * np.sqrt(2.0), 0.5 * np.sqrt(2.0), 0.000000000]) + #180 deg around [100], [010] + symops[6, :] = np.array([0.0000000000, 1.0, 0.0, 0.0]) + symops[7, :] = np.array([0.0000000000, 0.0, 1.0, 0.0]) + + if low: + symops = symops[0:4, :] + + if quatin is None: + return symops + else: + qsym = rotlib.quat_multiply(symops, quatin) + return qsym + +def orthorhombic_q(quatin=None, low = False): + symops = np.zeros((4, 4), dtype=np.float32) + # identity + symops[0, :] = np.array([1.0, 0.0, 0.0, 0.0]) + + symops[1, :] = np.array([0.0, 1.0, 0.0, 0.0]) # rotation of 180 x + symops[2, :] = np.array([0.0, 0.0, 1.0, 0.0]) # rotation of 180 y + symops[3, :] = np.array([0.0, 0.0, 0.0, 1.0]) # rotation of 180 z + + if low: + pass + + if quatin is None: + return symops + else: + qsym = rotlib.quat_multiply(symops, quatin) + return qsym + +def monoclinic_q(quatin=None, low = False): + symops = np.zeros((2, 4), dtype=np.float32) + # identity + symops[0, :] = np.array([1.0, 0.0, 0.0, 0.0]) + # rotation of 180 x + symops[1, :] = np.array([0.0, 1.0, 0.0, 0.0]) + + if low: + pass + + if quatin is None: + return symops + else: + qsym = rotlib.quat_multiply(symops, quatin) + return qsym + +def triclinic_q(quatin=None, low=False): symops = np.zeros((1,4),dtype=np.float32) # identity symops[0,:] = np.array([1.0,0.0,0.0,0.0]) + if low: + pass if quatin is None: return symops From 91bca61579221c30d55fdd001f2ea5bdaf13cfb1 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Wed, 23 Nov 2022 11:31:51 -0500 Subject: [PATCH 009/177] Added functions for space group ID to Laue ID and sym ops. Signed-off by: David Rowenhorst --- pyebsdindex/crystal_sym.py | 277 ++++++++++++++++++++++++++++++++++--- 1 file changed, 258 insertions(+), 19 deletions(-) diff --git a/pyebsdindex/crystal_sym.py b/pyebsdindex/crystal_sym.py index db818bd..3ae09e8 100644 --- a/pyebsdindex/crystal_sym.py +++ b/pyebsdindex/crystal_sym.py @@ -18,7 +18,11 @@ some notice that they have been modified. Author: David Rowenhorst; -The US Naval Research Laboratory Date: 21 Aug 2020''' +The US Naval Research Laboratory Date: 23 Nov 2022 + +This is heavily inspired/borrowed from the DREAM.3D EBSDLib LaueOps library +https://github.com/BlueQuartzSoftware/EbsdLib +''' import numpy as np @@ -29,7 +33,26 @@ PI = np.pi def cubicsym_q(quatin=None, low = False): - + """Provide quaternion proper rotation symmetry operators for the cubic crystal systems. + If provided with input quaternions, the symmetrically equivalent quaternions will be returned. + + Parameters + ---------- + quatin : numpy.ndarray + quaternions, of shape``(4)`` + If not provided [default] then + the quaternions operators are returned. + low : Default is ``False`` and the full m-3m symmetry operations are provided. + Set to ``True`` to return the m-3 (non-inversion) symmetry operators + + Returns + ------- + numpy.ndarray + array of ``(24,4)`` (or if ``low=True``, ``(12,4)``) rotation symmetry operators for cubic system. + + Notes + ----- + """ symops = np.zeros((24,4), dtype=np.float32) #identity @@ -104,12 +127,32 @@ def cubicsym_q(quatin=None, low = False): def hexsym_q(quatin=None, low = False): - + """Provide quaternion proper rotation symmetry operators for the hexagonal crystal systems. + If provided with input quaternions, the symmetrically equivalent quaternions will be returned. + + Parameters + ---------- + quatin : numpy.ndarray + quaternions, of shape``(4)`` + If not provided [default] then + the quaternions operators are returned. + low : Default is ``False`` and the full 6/mmm symmetry operations are provided. + Set to ``True`` to return the 6/m (non-inversion) symmetry operators + + Returns + ------- + numpy.ndarray + array of ``(12,4)`` (or if ``low=True``, ``(6,4)``) rotation symmetry + operators for hexagonal system. + + Notes + ----- + """ symops = np.zeros((12,4), dtype=np.float32) #identity symops [0,:] = np.array([1.0,0.0,0.0,0.0]) - #60 deg about [0001]; remember that quat rotation is cos(ang/2) + #60 deg about c [0001]; remember that quat rotation is cos(ang/2) symops[1, :] = np.array([np.cos(PI/6.0), 0.0,0.0,np.sin(PI/6.0)]) # rotation of 60 symops[2, :] = np.array([np.cos(PI/3.0), 0.0,0.0,np.sin(PI/3.0)]) # rotation of 120 symops[3, :] = np.array([0.0, 0.0,0.0,1.0]) # rotation of 180 @@ -134,10 +177,31 @@ def hexsym_q(quatin=None, low = False): return qsym def trigonal_q(quatin=None, low = False): + """Provide quaternion proper rotation symmetry operators for the trigonal crystal systems. + If provided with input quaternions, the symmetrically equivalent quaternions will be returned. + + Parameters + ---------- + quatin : numpy.ndarray + quaternions, of shape``(4)`` + If not provided [default] then + the quaternions operators are returned. + low : Default is ``False`` and the full -3m symmetry operations are returned. + Set to ``True`` to return the -3 (non-inversion) symmetry operators are returned + + Returns + ------- + numpy.ndarray + array of ``(6,4)`` (or if ``low=True``, ``(3,4)``) rotation symmetry + operators for trigonal system. + + Notes + ----- + """ symops = np.zeros((6, 4), dtype=np.float32) # identity symops[0, :] = np.array([1.0, 0.0, 0.0, 0.0]) - # [001]; remember that quat rotation is cos(ang/2) + # 120 deg rotations about c axis: remember that quat rotation is cos(ang/2) symops[1, :] = np.array([np.cos(PI / 3.0), 0.0, 0.0, np.sin(PI / 3.0)]) # rotation of 120 symops[2, :] = np.array([np.cos(2.0 * PI / 3.0), 0.0, 0.0, np.sin(2.0 * PI / 3.0)]) # rotation of 240 @@ -156,15 +220,37 @@ def trigonal_q(quatin=None, low = False): return qsym def tetragonal_q(quatin=None, low = False): + """Provide quaternion proper rotation symmetry operators for the tetragonal crystal systems. + If provided with input quaternions, the symmetrically equivalent quaternions will be returned. + + Parameters + ---------- + quatin : numpy.ndarray + quaternions, of shape``(4)`` + If not provided [default] then + the quaternions operators are returned. + low : Default is ``False`` and the full 4/mmm symmetry operations are returned. + Set to ``True`` to return the 4/m (non-inversion) symmetry operators are returned + + Returns + ------- + numpy.ndarray + array of ``(8,4)`` (or if ``low=True``, ``(4,4)``) rotation symmetry + operators for tetragonal system. + + Notes + ----- + """ + symops = np.zeros((8, 4), dtype=np.float32) # identity symops[0, :] = np.array([1.0, 0.0, 0.0, 0.0]) - # [001]; remember that quat rotation is cos(ang/2) + # 4-fold around c axis [001]; remember that quat rotation is cos(ang/2) symops[1, :] = np.array([np.cos(PI /4.0), 0.0, 0.0, np.sin(PI / 4.0)]) # rotation of 90 symops[2, :] = np.array([0.0, 0.0, 0.0, 1.0]) # rotation of 180 symops[3, :] = np.array([np.cos(0.75*PI ), 0.0, 0.0, np.sin(0.75 * PI )]) # rotation of 270 - # 180 deg around the [110] axes + # 2-fold, 180 deg around the [110] axes symops[4, :] = np.array([0.0000000000, 0.5*np.sqrt(2.0), 0.5*np.sqrt(2.0), 0.000000000]) symops[5, :] = np.array([0.0000000000, -0.5 * np.sqrt(2.0), 0.5 * np.sqrt(2.0), 0.000000000]) #180 deg around [100], [010] @@ -181,16 +267,36 @@ def tetragonal_q(quatin=None, low = False): return qsym def orthorhombic_q(quatin=None, low = False): + """Provide quaternion proper rotation symmetry operators for the orthorhombic crystal systems. + If provided with input quaternions, the symmetrically equivalent quaternions will be returned. + + Parameters + ---------- + quatin : numpy.ndarray + quaternions, of shape``(4)`` + If not provided [default] then + the quaternions operators are returned. + low : This parameter does nothing, but is maintained to provide + function calling similarity with the other functions in this module. + The mmm rotation operators will always be returned. + + Returns + ------- + numpy.ndarray + array of ``(4,4)`` rotation symmetry + operators for orthorhombic system. + + Notes + ----- + """ symops = np.zeros((4, 4), dtype=np.float32) # identity symops[0, :] = np.array([1.0, 0.0, 0.0, 0.0]) + # + symops[1, :] = np.array([0.0, 1.0, 0.0, 0.0]) # rotation of 180 a + symops[2, :] = np.array([0.0, 0.0, 1.0, 0.0]) # rotation of 180 b + symops[3, :] = np.array([0.0, 0.0, 0.0, 1.0]) # rotation of 180 c - symops[1, :] = np.array([0.0, 1.0, 0.0, 0.0]) # rotation of 180 x - symops[2, :] = np.array([0.0, 0.0, 1.0, 0.0]) # rotation of 180 y - symops[3, :] = np.array([0.0, 0.0, 0.0, 1.0]) # rotation of 180 z - - if low: - pass if quatin is None: return symops @@ -199,14 +305,34 @@ def orthorhombic_q(quatin=None, low = False): return qsym def monoclinic_q(quatin=None, low = False): + """Provide quaternion proper rotation symmetry operators for the monoclinic crystal systems. + If provided with input quaternions, the symmetrically equivalent quaternions will be returned. + + Parameters + ---------- + quatin : numpy.ndarray + quaternions, of shape``(4)`` + If not provided [default] then + the quaternions operators are returned. + low : This parameter does nothing, but is maintained to provide + function calling similarity with the other functions in this module. + The 2/m rotation operators will always be returned. + + Returns + ------- + numpy.ndarray + array of ``(2,4)`` rotation symmetry + operators for monoclinic system. + + Notes + ----- + Convention is that the rotational axis is along b-axis. + """ symops = np.zeros((2, 4), dtype=np.float32) # identity symops[0, :] = np.array([1.0, 0.0, 0.0, 0.0]) - # rotation of 180 x - symops[1, :] = np.array([0.0, 1.0, 0.0, 0.0]) - - if low: - pass + # rotation of 180 around b-axis + symops[1, :] = np.array([0.0, 0.0, 1.0, 0.0]) if quatin is None: return symops @@ -215,6 +341,29 @@ def monoclinic_q(quatin=None, low = False): return qsym def triclinic_q(quatin=None, low=False): + """Provide quaternion proper rotation symmetry operators for the triclinic crystal systems. + If provided with input quaternions, the symmetrically equivalent quaternions will be returned. + + Parameters + ---------- + quatin : numpy.ndarray + quaternions, of shape``(4)`` + If not provided [default] then + the quaternions operators are returned. + low : This parameter does nothing, but is maintained to provide + function calling similarity with the other functions in this module. + The -1 rotation operators will always be returned. + + Returns + ------- + numpy.ndarray + array of ``(1,4)`` rotation symmetry + operators for triclinic system. + + Notes + ----- + This will be no different than the identity quaternion. + """ symops = np.zeros((1,4),dtype=np.float32) # identity symops[0,:] = np.array([1.0,0.0,0.0,0.0]) @@ -225,4 +374,94 @@ def triclinic_q(quatin=None, low=False): return symops else: qsym = rotlib.quat_multiply(symops,quatin) - return qsym \ No newline at end of file + return qsym + +def spacegroup2lauenumber(spacegroupid): + """Given a space group number, this will provide a laue group id (EDAX laue group convention) + + Parameters + ---------- + sapcegroupid : int + number between 1 and 230 + + Returns + ------- + int + numeric code that is associated with a Laue group (EDAX convention) + + Notes + ----- + """ + + sgpg = [1, 2, 3, 6, 10, 16, 25, 47, 75, 81, 83, 89, 99, 111, 123, 143, 147, 149, 156, 162, 168, 174, 175, 177, 183, + 187, 191, 195, 200, 207, 215, 221] + pgLaue = [1, 1, 2, 2, 2, 22, 22, 22, 4, 4, 4, 42, 42, 42, 42, 3, 3, 32, 32, 32, 6, 6, 6, 62, 62, 62, 62, 23, 23, 43, + 43, 43] + + for pgNum in range(len(sgpg)): + if (sgpg[pgNum] > spacegroupid): + break + + lauenumber = pgLaue[pgNum-1] + return lauenumber + +def laueid2symops(lauenumber): + """Given a laue group number (EDAX integer convention), this will + return the proper rotation quaternions for that crystal system. + + Parameters + ---------- + lauenumber : int + number between 1 and 43 + + Returns + ------- + numpy.ndarray + array of ``(n,4)`` quaternions that represent rotational symetry operations. + + Notes + ----- + """ + if lauenumber == 43: + return cubicsym_q() + if lauenumber == 23: + return cubicsym_q(low = True) + if lauenumber == 62: + return hexsym_q() + if lauenumber == 6: + return hexsym_q(low = True) + if lauenumber == 32: + return trigonal_q() + if lauenumber == 3: + return trigonal_q(low = True) + if lauenumber == 42: + return tetragonal_q() + if lauenumber == 4: + return tetragonal_q(low = True) + if lauenumber == 22: + return orthorhombic_q() + if lauenumber == 2: + return monoclinic_q() + if lauenumber == 1: + return triclinic_q() + return None + +def spacegroupnum2symops(spacegroupid): + """Given a space group id, this will + return the proper rotation quaternions for that crystal system. + + Parameters + ---------- + spacegroupid : int + number between 1 and 230 + + Returns + ------- + numpy.ndarray + array of ``(n,4)`` quaternions that represent rotational symetry operations. + + Notes + ----- + """ + + return laueid2symops(spacegroup2lauenumber(spacegroupid)) \ No newline at end of file From 40e90aa1c94cf2bc39042f52cd4482c4733f94ad Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Mon, 28 Nov 2022 15:51:00 -0500 Subject: [PATCH 010/177] Preparations for lower symmetry crystals. Signed-off by: David Rowenhorst --- pyebsdindex/band_vote.py | 26 +++--- pyebsdindex/crystal_sym.py | 26 ++++-- pyebsdindex/tripletlib.py | 172 ++++++++++++++++++++++++++----------- 3 files changed, 157 insertions(+), 67 deletions(-) diff --git a/pyebsdindex/band_vote.py b/pyebsdindex/band_vote.py index a32b9e3..330c807 100644 --- a/pyebsdindex/band_vote.py +++ b/pyebsdindex/band_vote.py @@ -143,13 +143,13 @@ def tripvote(self, band_norms, band_intensity = None, goNumba = True, verbose=0) sztable = angTable.shape famIndx = self.tripLib.completelib['famIndex'] nFam = self.tripLib.completelib['nFamily'] - poles = self.tripLib.completelib['polesCart'] + polesCart = self.tripLib.completelib['polesCart'] angTol = self.angTol n_band_early = np.int64(self.n_band_early_exit) # this will check the vote, and return the exact band matching to specific poles of the best fitting solution. fit, polematch, nMatch, whGood, ij, R, fitb = \ - self.band_index_nb(poles, bandRank_arg, bandFam, famIndx, nFam, angTable, bandnorms, angTol, n_band_early) + self.band_index_nb(polesCart, bandRank_arg, bandFam, famIndx, nFam, angTable, bandnorms, angTol, n_band_early) if verbose > 2: print('band index: ',timer() - tic) @@ -163,7 +163,7 @@ def tripvote(self, band_norms, band_intensity = None, goNumba = True, verbose=0) whgood6 = whGood[srt[0:np.min([8, whGood.shape[0]])]] weights6 = band_intensity[whgood6] - pflt6 = (np.asarray(poles[polematch[whgood6], :], dtype=np.float64)) + pflt6 = (np.asarray(polesCart[polematch[whgood6], :], dtype=np.float64)) bndnorm6 = (np.asarray(bandnorms[whgood6, :], dtype=np.float64)) avequat, fit = self.refine_orientation_quest(bndnorm6, pflt6 , weights=weights6) @@ -279,7 +279,7 @@ def refine_orientation(self,bandnorms, whGood, polematch): #print('fitting: ',timer() - tic) return avequat, fit - def refine_orientation_quest(self,bandnorms, polematch, weights = None): + def refine_orientation_quest(self, bandnorms, polecartmatch, weights = None): tic = timer() @@ -288,10 +288,10 @@ def refine_orientation_quest(self,bandnorms, polematch, weights = None): weightsn = np.asarray(weights, dtype=np.float64) weightsn /= np.sum(weightsn) #print(weightsn) - pflt = np.asarray(polematch, dtype=np.float64) + pflt = np.asarray(polecartmatch, dtype=np.float64) bndnorm = np.asarray(bandnorms, dtype=np.float64) - avequat, fit = self.orientation_quest(pflt, bndnorm, weightsn) + avequat, fit = self.orientation_quest_nb(pflt, bndnorm, weightsn) return avequat, fit @@ -373,7 +373,7 @@ def tripvote_numba( bandangs, LUT, angTol, tripAngles, tripID, nfam, n_bands): @staticmethod @numba.jit(nopython=True, cache=True, fastmath=True,parallel=False) - def band_index_nb(poles, bandRank_arg, familyLabel, famIndx, nFam, angTable, bandnorms, angTol, n_band_early): + def band_index_nb(polesCart, bandRank_arg, familyLabel, famIndx, nFam, angTable, bandnorms, angTol, n_band_early): eps = np.float32(1.0e-12) nBnds = bandnorms.shape[0] @@ -384,7 +384,7 @@ def band_index_nb(poles, bandRank_arg, familyLabel, famIndx, nFam, angTable, ba Rout = np.zeros((1,3,3), dtype=np.float32) #Rout[0,0,0] = 1.0; Rout[0,1,1] = 1.0; Rout[0,2,2] = 1.0 polematch_out = np.zeros((nBnds),dtype=np.int64) - 1 - pflt = np.asarray(poles, dtype=np.float32) + pflt = np.asarray(polesCart, dtype=np.float32) bndnorm = np.transpose(np.asarray(bandnorms, dtype=np.float32)) fit = np.float32(360.0) @@ -426,7 +426,7 @@ def band_index_nb(poles, bandRank_arg, familyLabel, famIndx, nFam, angTable, ba continue wh01 += famIndx[f1] - p0 = poles[famIndx[f0],:] + p0 = polesCart[famIndx[f0], :] n01 = wh01.size v0v1c = np.cross(v0,v1) @@ -450,7 +450,7 @@ def band_index_nb(poles, bandRank_arg, familyLabel, famIndx, nFam, angTable, ba score = -1.0 for i in range(n01): - p1 = poles[wh01[i],:] + p1 = polesCart[wh01[i], :] ntemp = np.linalg.norm(p1) + 1.0e-35 p1 = p1 / ntemp p0p1c = np.cross(p0,p1) @@ -690,12 +690,10 @@ def orientation_refine_loops_am(nGood,whGood,poles,bandnorms,polematch,n2Fit): @staticmethod @numba.jit(nopython=True, cache=True, fastmath=True, parallel=False) - def orientation_quest(poles, bandnorms, weights): + def orientation_quest_nb(polescart, bandnorms, weights): # this uses the Quaternion Estimator AKA quest algorithm. - #pflt = (np.asarray(poles[polematch[whGood], :], dtype=np.float32)) - #bndnorm = (np.asarray(bandnorms[whGood, :], dtype=np.float32)) - pflt = np.asarray(poles, dtype=np.float64) + pflt = np.asarray(polescart, dtype=np.float64) bndnorm = np.asarray(bandnorms, dtype=np.float64) npoles = pflt.shape[0] wn = (np.asarray(weights, dtype=np.float64)).reshape(npoles, 1) diff --git a/pyebsdindex/crystal_sym.py b/pyebsdindex/crystal_sym.py index 3ae09e8..77bf37f 100644 --- a/pyebsdindex/crystal_sym.py +++ b/pyebsdindex/crystal_sym.py @@ -43,7 +43,7 @@ def cubicsym_q(quatin=None, low = False): If not provided [default] then the quaternions operators are returned. low : Default is ``False`` and the full m-3m symmetry operations are provided. - Set to ``True`` to return the m-3 (non-inversion) symmetry operators + Set to ``True`` to return the m-3 symmetry operators Returns ------- @@ -137,7 +137,7 @@ def hexsym_q(quatin=None, low = False): If not provided [default] then the quaternions operators are returned. low : Default is ``False`` and the full 6/mmm symmetry operations are provided. - Set to ``True`` to return the 6/m (non-inversion) symmetry operators + Set to ``True`` to return the 6/m symmetry operators Returns ------- @@ -187,7 +187,7 @@ def trigonal_q(quatin=None, low = False): If not provided [default] then the quaternions operators are returned. low : Default is ``False`` and the full -3m symmetry operations are returned. - Set to ``True`` to return the -3 (non-inversion) symmetry operators are returned + Set to ``True`` to return the -3 symmetry operators are returned Returns ------- @@ -230,7 +230,7 @@ def tetragonal_q(quatin=None, low = False): If not provided [default] then the quaternions operators are returned. low : Default is ``False`` and the full 4/mmm symmetry operations are returned. - Set to ``True`` to return the 4/m (non-inversion) symmetry operators are returned + Set to ``True`` to return the 4/m symmetry operators are returned Returns ------- @@ -464,4 +464,20 @@ def spacegroupnum2symops(spacegroupid): ----- """ - return laueid2symops(spacegroup2lauenumber(spacegroupid)) \ No newline at end of file + return laueid2symops(spacegroup2lauenumber(spacegroupid)) + +def hex4poles2hex3poles(poles): + npoles = poles.size/4 + poles4 = np.reshape(poles, (npoles, 4)) + poles3 = poles4[:, [0,1,3]] + return poles3 + +def hex3poles2hex4poles(poles): + npoles = poles.size/3 + poles3 = np.reshape(poles, (npoles, 4)) + poles4 = np.zeros((npoles, 4)) + poles4[:,0] = poles3[:,0] + poles4[:, 1] = poles3[:, 1] + poles4[:, 3] = poles3[:, 2] + poles4[:, 2] = - (poles3[:,0] + poles3[:,1]) + return poles4 \ No newline at end of file diff --git a/pyebsdindex/tripletlib.py b/pyebsdindex/tripletlib.py index 201c97d..540c1a0 100644 --- a/pyebsdindex/tripletlib.py +++ b/pyebsdindex/tripletlib.py @@ -22,7 +22,7 @@ import numpy as np -from pyebsdindex import crystal_sym, rotlib +from pyebsdindex import crystal_sym, rotlib, crystallometry RADEG = 180.0/np.pi @@ -30,91 +30,147 @@ class triplib(): def __init__(self, libType='FCC', phaseName=None, laticeParameter = None): - self.family = None - self.nfamily = None + self.family = None # array of intetger pole normals that should have reflections + self.nfamily = None # number of unique reflector families self.angles = None self.polePairs = None self.angleFamilyID = None - self.tripAngles = None - self.tripID = None - self.completelib = None - self.symmetry_pg = None - self.symmetry_pgID = None - self.symmetry_sg = None - self.laue_code = None - self.qsymops = None - self.phaseName = None - self.latticeParameter = np.array([1.0, 1.0, 1.0, 90.0, 90.0, 90.0]) + self.tripAngles = None # array of angle triplets between the refectors + self.tripID = None # family IDs of the reflectors ([hkl]) in self.tripAngles + self.completelib = None # dictionary of all angle parirs and their specific pole hkl + self.symmetry_pg = None # point group nomenclature + self.symmetry_sgid = None # space group id 1-230 + self.laue_code = None # Laue code for the space group (following DREAM.3D notation. + self.qsymops = None # array of quaternions that represent proper symmetry operations for the laue group + self.phaseName = None # User provided name of the phase. + self.latticeParameter = None # 6 element array for the lattice parmeter. + - if libType is None: - return if phaseName is None: self.phaseName = libType else: self.phaseName = phaseName + if laticeParameter is not None: + self.latticeParameter = np.array(laticeParameter) + + + if libType is None: + return + if str(libType).upper() == 'FCC': - self.build_fcc() if phaseName is None: self.phaseName = 'FCC' - if laticeParameter is None: self.latticeParameter = np.array([1.0,1.0,1.0,90.0,90.0,90.0]) else: self.latticeParameter = laticeParameter + self.build_fcc() + return if str(libType).upper() == 'BCC': - + if phaseName is None: + self.phaseName = 'BCC' + if laticeParameter is None: + self.latticeParameter = np.array([1.0,1.0,1.0,90.0,90.0,90.0]) + else: + self.latticeParameter = laticeParameter self.build_bcc() + return + if str(libType).upper() == 'DC': if phaseName is None: - self.phaseName = 'BCC' + self.phaseName = 'Diamond Cubic' if laticeParameter is None: self.latticeParameter = np.array([1.0,1.0,1.0,90.0,90.0,90.0]) else: self.latticeParameter = laticeParameter + self.build_dc() + return + + # if str(libType).upper() == 'HCP': + # if phaseName is None: + # self.phaseName = 'HCP' + # if laticeParameter is None: + # self.latticeParameter = np.array([1.0, 1.0, 1.63, 90.0, 90.0, 120.0]) + # else: + # self.latticeParameter = laticeParameter + # self.build_bcc() + # return + def build_fcc(self): if self.phaseName is None: self.phaseName = 'FCC' self.symmetry_pg = "Cubic m3m" - self.symmetry_pgID = 131 - self.laue_code = 43 - self.qsymops = crystal_sym.cubicsym_q() + self.symmetry_sgid = 225 + self.laue_code = crystal_sym.spacegroup2lauenumber(self.symmetry_sgid) + self.qsymops = crystal_sym.laueid2symops(self.laue_code) poles = np.array([[0,0,2], [1,1,1], [0,2,2], [1,1,3]]) - self.build_trip_lib(poles,crystal_sym.cubicsym_q()) + self.build_trip_lib(poles) + + def build_dc(self): + if self.phaseName is None: + self.phaseName = 'Diamond Cubic' + self.symmetry_pg = "Cubic m3m" + self.symmetry_sgid = 227 + self.laue_code = crystal_sym.spacegroup2lauenumber(self.symmetry_sgid) + self.qsymops = crystal_sym.laueid2symops(self.laue_code) + poles = np.array([[1, 1, 1], [0, 2, 2], [0, 0, 4], [1, 1, 3], [2, 2, 4], [1, 3, 3]]) + self.build_trip_lib(poles) def build_bcc(self): if self.phaseName is None: self.phaseName = 'BCC' self.symmetry_pg = "Cubic m3m" - self.symmetry_pgID = 131 - self.laue_code = 43 - self.qsymops = crystal_sym.cubicsym_q() + self.symmetry_sgid = 229 + self.laue_code = crystal_sym.spacegroup2lauenumber(self.symmetry_sgid) + self.qsymops = crystal_sym.laueid2symops(self.laue_code) poles = np.array([[0,1,1],[0,0,2],[1,1,2],[0,1,3]]) - self.build_trip_lib(poles,crystal_sym.cubicsym_q()) + self.build_trip_lib(poles) - def build_trip_lib(self,poles,symmetry): + + + def build_hcp(self): + if self.phaseName is None: + self.phaseName = 'HCP' + self.symmetry_pg = "Hexagonal 6/mmm" + self.symmetry_sgid = 194 + self.laue_code = crystal_sym.spacegroup2lauenumber(self.symmetry_sgid) + self.qsymops = crystal_sym.laueid2symops(self.laue_code) + poles4 = np.array([[1,0, -1, 0], [1, 0, -1, 1], [0,0, 0, 2], [1, 0, -1, 3], [1,1,-2,0], [1,0,-1,2]]) + poles = crystal_sym.hex4poles2hex3poles(poles4) + self.build_trip_lib(poles) + + def build_trip_lib(self,poles): + symmetry = self.qsymops + crystal = crystallometry.Crystal(self.phaseName, + self.latticeParameter[0], + self.latticeParameter[1], + self.latticeParameter[2], + self.latticeParameter[3], + self.latticeParameter[4], + self.latticeParameter[5]) nsym = symmetry.shape[0] npoles = poles.shape[0] - sympoles = [] - sympolesN = [] - sympolesComplete = [] - nFamComplete = np.zeros(npoles, dtype = np.int32) + sympoles = [] # list of all HKL variants which does not count the invariant pole as unique. + sympolesN = [] # normalized, floating point version of the poles in sample coordinates + sympolesComplete = [] # list of all HKL variants with no duplicates + nFamComplete = np.zeros(npoles, dtype = np.int32) # number of nFamily = np.zeros(npoles, dtype = np.int32) - polesFlt = np.array(poles, dtype=np.float32) + polesFlt = np.array(poles, dtype=np.float32) # convert the input poles to floating point (but still HKL int values) for i in range(npoles): family = rotlib.quat_vector(symmetry,polesFlt[i,:]) - uniqHKL = self.hkl_unique(family,reduceInversion=False) + uniqHKL = self._hkl_unique(family, reduceInversion=False) uniqHKL = np.flip(uniqHKL, axis=0) sympolesComplete.append(uniqHKL) nFamComplete[i] = np.int32((sympolesComplete[-1]).size/3) - uniqHKL2 = self.hkl_unique(family,reduceInversion=True) + uniqHKL2 = self._hkl_unique(family, reduceInversion=True, rMT = crystal.reciprocalMetricTensor) nFamily[i] = np.int32(uniqHKL2.size/3) - sign = np.squeeze(self.calc_pole_dot_int(uniqHKL2, polesFlt[i, :])) + sign = np.squeeze(self._calc_pole_dot_int(uniqHKL2, polesFlt[i, :], rMetricTensor=crystal.reciprocalMetricTensor)) whmx = (np.abs(sign)).argmax() sign = np.round(sign[whmx]) uniqHKL2 *= sign @@ -130,12 +186,14 @@ def build_trip_lib(self,poles,symmetry): polePairs = [] for i in range(npoles): for j in range(i, npoles): - ang = np.squeeze(self.calc_pole_dot_int(polesFlt[i, :], sympoles[j])) + ang = np.squeeze(self._calc_pole_dot_int(polesFlt[i, :], sympoles[j])) # for each input pole, calculate + # all the angles between it, and the poles in family "j" ang = np.clip(ang, -1.0, 1.0) sign = (ang >= 0).astype(np.float32) - (ang < 0).astype(np.float32) - ang = np.round(np.arccos(sign * ang)*RADEG*100).astype(np.int32) + ang = np.round(np.arccos(sign * ang)*RADEG*100).astype(np.int32) # get the unique angles between the input + # pole, and the family poles. Angles within 0.01 deg are taken as the same. unqang, argunq = np.unique(ang, return_index=True) - unqang = unqang/100.0 + unqang = unqang/100.0 # revert back to the actual angle in degrees. sign = sign[argunq] wh = np.nonzero(unqang > 1.0)[0] @@ -166,7 +224,7 @@ def build_trip_lib(self,poles,symmetry): libANG = np.zeros((nlib, 3)) libID = np.zeros((nlib, 3), dtype=int) counter = 0 - + # now actually catalog all the triplet angles. for i in range(npoles): id0 = familyID[indx0FID[i], 0] for j in range(0,nFamilyID[i]): @@ -187,13 +245,16 @@ def build_trip_lib(self,poles,symmetry): libANG = libANG[0:counter, :] libID = libID[0:counter, :] - libANG, libID = self.sortlib_id(libANG,libID,findDups = True) + libANG, libID = self._sortlib_id(libANG, libID, findDups = True) # sorts each row of the library to make sure + # the triplets are in increasing order. + #print(libANG) #print(libANG.shape) - angTable = self.calc_pole_dot_int(sympolesComplete, sympolesComplete) + # now make a table of the angle between all the poles (allowing inversino) + angTable = self._calc_pole_dot_int(sympolesComplete, sympolesComplete, rMetricTensor=crystal.reciprocalMetricTensor) angTable = np.arccos(angTable)*RADEG famindx0 = ((np.concatenate( ([0],np.cumsum(nFamComplete)) ))[0:-1]).astype(dtype=np.int64) - cartPoles = self.xstalplane2cart(sympolesComplete) + cartPoles = self._xstalplane2cart(sympolesComplete, rStructMatrix=crystal.reciprocalStructureMatrix) cartPoles /= np.linalg.norm(cartPoles, axis = 1).reshape(np.int64(cartPoles.size/3),1) completePoleFamId = np.zeros(sympolesComplete.shape[0], dtype=np.int32) for i in range(npoles): @@ -218,7 +279,22 @@ def build_trip_lib(self,poles,symmetry): - def hkl_unique(self,poles, reduceInversion=True, rMT = np.identity(3)): + def _hkl_unique(self, poles, reduceInversion=True, rMT = np.identity(3)): + """ + When given a list of integer HKL poles (plane normals), will return only the unique HKL variants + + Parameters + ---------- + poles: numpy.ndarray (n,3) in HKL integer form. + reduceInversion: True/False. If True, then the any inverted crystal pole + will also be removed from the uniquelist. The angle between poles + rMT: reciprocol metric tensor -- needed to calculated + + Returns + ------- + numpy.ndarray (n,3) in HKL integer form of the unique poles. + """ + npoles = poles.shape[0] intPoles =np.array(poles.round().astype(np.int32)) mn = intPoles.min() @@ -234,14 +310,14 @@ def hkl_unique(self,poles, reduceInversion=True, rMT = np.identity(3)): if reduceInversion == True: family = polesout nf = family.shape[0] - test = self.calc_pole_dot_int(family, family, rMetricTensor = rMT) + test = self._calc_pole_dot_int(family, family, rMetricTensor = rMT) testSum = np.sum( (test < -0.99999).astype(np.int32)*np.arange(nf).reshape(1,nf), axis = 1) whpos = np.nonzero( np.logical_or(testSum < np.arange(nf), (testSum == 0)))[0] polesout = polesout[whpos, :] return polesout - def calc_pole_dot_int(self, poles1, poles2, rMetricTensor = np.identity(3)): + def _calc_pole_dot_int(self, poles1, poles2, rMetricTensor = np.identity(3)): p1 = poles1.reshape(np.int64(poles1.size / 3), 3) p2 = poles2.reshape(np.int64(poles2.size / 3), 3) @@ -262,11 +338,11 @@ def calc_pole_dot_int(self, poles1, poles2, rMetricTensor = np.identity(3)): dot = np.clip(dot, -1.0, 1.0) return dot - def xstalplane2cart(self,poles,rStructMatrix = np.identity(3)): + def _xstalplane2cart(self, poles, rStructMatrix = np.identity(3)): polesout = rStructMatrix.dot(poles.T) return np.transpose(polesout) - def sortlib_id(self,libANG,libID,findDups = False): + def _sortlib_id(self, libANG, libID, findDups = False): LUTA = np.array([[0,1,2],[0,2,1],[1,0,2],[1,2,0],[2,0,1],[2,1,0]]) LUTB = np.array([[0,1,2],[1,0,2],[0,2,1],[2,0,1],[1,2,0],[2,1,0]]) From ddb15897526295a6baee77cd9b73f2e66545c754 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Mon, 28 Nov 2022 17:23:27 -0500 Subject: [PATCH 011/177] More preparation for lower symmetry crystals. Signed-off by: David Rowenhorst --- pyebsdindex/crystal_sym.py | 8 +++---- pyebsdindex/ebsdfile.py | 2 +- pyebsdindex/tripletlib.py | 43 +++++++++++++++++++++++++++----------- 3 files changed, 36 insertions(+), 17 deletions(-) diff --git a/pyebsdindex/crystal_sym.py b/pyebsdindex/crystal_sym.py index 77bf37f..64709ef 100644 --- a/pyebsdindex/crystal_sym.py +++ b/pyebsdindex/crystal_sym.py @@ -467,14 +467,14 @@ def spacegroupnum2symops(spacegroupid): return laueid2symops(spacegroup2lauenumber(spacegroupid)) def hex4poles2hex3poles(poles): - npoles = poles.size/4 - poles4 = np.reshape(poles, (npoles, 4)) + npoles = int(np.array(poles).size/4) + poles4 = np.reshape(np.array(poles), (npoles, 4)) poles3 = poles4[:, [0,1,3]] return poles3 def hex3poles2hex4poles(poles): - npoles = poles.size/3 - poles3 = np.reshape(poles, (npoles, 4)) + npoles = int(poles.size/3) + poles3 = np.reshape(poles, (npoles, 3)) poles4 = np.zeros((npoles, 4)) poles4[:,0] = poles3[:,0] poles4[:, 1] = poles3[:, 1] diff --git a/pyebsdindex/ebsdfile.py b/pyebsdindex/ebsdfile.py index 5071b4e..0842bbc 100644 --- a/pyebsdindex/ebsdfile.py +++ b/pyebsdindex/ebsdfile.py @@ -24,7 +24,7 @@ def writeang(filename, indexer, data, f.write('# Formula '+'\t \r\n') f.write('# Info '+'\t\t \r\n') f.write('# Symmetry '+str(phase.tripLib.laue_code)+'\r\n') - f.write('# PointGroupID ' + str(phase.tripLib.symmetry_pgID)+'\r\n') + f.write('# PointGroupID ' + str(phase.tripLib.symmetry_pgid)+'\r\n') f.write('# LatticeConstants '+ ' '.join(str(' {:.3f}'.format(x)) for x in phase.tripLib.latticeParameter)+'\r\n') f.write('# NumberFamilies ' + str(phase.tripLib.nfamily)+'\r\n') for i in range(phase.tripLib.nfamily): diff --git a/pyebsdindex/tripletlib.py b/pyebsdindex/tripletlib.py index 540c1a0..e676f12 100644 --- a/pyebsdindex/tripletlib.py +++ b/pyebsdindex/tripletlib.py @@ -39,6 +39,7 @@ def __init__(self, libType='FCC', phaseName=None, laticeParameter = None): self.tripID = None # family IDs of the reflectors ([hkl]) in self.tripAngles self.completelib = None # dictionary of all angle parirs and their specific pole hkl self.symmetry_pg = None # point group nomenclature + self.symmetry_pgid = None self.symmetry_sgid = None # space group id 1-230 self.laue_code = None # Laue code for the space group (following DREAM.3D notation. self.qsymops = None # array of quaternions that represent proper symmetry operations for the laue group @@ -96,7 +97,7 @@ def __init__(self, libType='FCC', phaseName=None, laticeParameter = None): # self.latticeParameter = np.array([1.0, 1.0, 1.63, 90.0, 90.0, 120.0]) # else: # self.latticeParameter = laticeParameter - # self.build_bcc() + # self.build_hcp() # return @@ -104,6 +105,7 @@ def build_fcc(self): if self.phaseName is None: self.phaseName = 'FCC' self.symmetry_pg = "Cubic m3m" + self.symmetry_pgid = 131 self.symmetry_sgid = 225 self.laue_code = crystal_sym.spacegroup2lauenumber(self.symmetry_sgid) self.qsymops = crystal_sym.laueid2symops(self.laue_code) @@ -114,6 +116,7 @@ def build_dc(self): if self.phaseName is None: self.phaseName = 'Diamond Cubic' self.symmetry_pg = "Cubic m3m" + self.symmetry_pgid = 131 self.symmetry_sgid = 227 self.laue_code = crystal_sym.spacegroup2lauenumber(self.symmetry_sgid) self.qsymops = crystal_sym.laueid2symops(self.laue_code) @@ -124,6 +127,7 @@ def build_bcc(self): if self.phaseName is None: self.phaseName = 'BCC' self.symmetry_pg = "Cubic m3m" + self.symmetry_pgid = 131 self.symmetry_sgid = 229 self.laue_code = crystal_sym.spacegroup2lauenumber(self.symmetry_sgid) self.qsymops = crystal_sym.laueid2symops(self.laue_code) @@ -140,38 +144,49 @@ def build_hcp(self): self.laue_code = crystal_sym.spacegroup2lauenumber(self.symmetry_sgid) self.qsymops = crystal_sym.laueid2symops(self.laue_code) poles4 = np.array([[1,0, -1, 0], [1, 0, -1, 1], [0,0, 0, 2], [1, 0, -1, 3], [1,1,-2,0], [1,0,-1,2]]) - poles = crystal_sym.hex4poles2hex3poles(poles4) - self.build_trip_lib(poles) + self.build_hex_trip_lib(poles4) + + def build_hex_trip_lib(self, poles4): + poles3 = crystal_sym.hex4poles2hex3poles(poles4) + self.build_trip_lib(poles3) + p3temp = self.family + p4temp = crystal_sym.hex3poles2hex4poles(p3temp) + self.family = p4temp + + def build_trip_lib(self,poles): - symmetry = self.qsymops - crystal = crystallometry.Crystal(self.phaseName, + #symmetry = self.qsymops + crystalmats = crystallometry.Crystal(self.phaseName, self.latticeParameter[0], self.latticeParameter[1], self.latticeParameter[2], self.latticeParameter[3], self.latticeParameter[4], self.latticeParameter[5]) - nsym = symmetry.shape[0] + #nsym = self.qsymops.shape[0] npoles = poles.shape[0] sympoles = [] # list of all HKL variants which does not count the invariant pole as unique. - sympolesN = [] # normalized, floating point version of the poles in sample coordinates + #sympolesN = [] # normalized, floating point version of the poles in sample coordinates sympolesComplete = [] # list of all HKL variants with no duplicates nFamComplete = np.zeros(npoles, dtype = np.int32) # number of nFamily = np.zeros(npoles, dtype = np.int32) polesFlt = np.array(poles, dtype=np.float32) # convert the input poles to floating point (but still HKL int values) for i in range(npoles): - family = rotlib.quat_vector(symmetry,polesFlt[i,:]) + family = self._symrot(polesFlt[i,:], crystalmats) #rotlib.quat_vector(symmetry,polesFlt[i,:]) + #print(family) uniqHKL = self._hkl_unique(family, reduceInversion=False) uniqHKL = np.flip(uniqHKL, axis=0) sympolesComplete.append(uniqHKL) nFamComplete[i] = np.int32((sympolesComplete[-1]).size/3) - uniqHKL2 = self._hkl_unique(family, reduceInversion=True, rMT = crystal.reciprocalMetricTensor) + uniqHKL2 = self._hkl_unique(family, reduceInversion=True, rMT = crystalmats.reciprocalMetricTensor) nFamily[i] = np.int32(uniqHKL2.size/3) - sign = np.squeeze(self._calc_pole_dot_int(uniqHKL2, polesFlt[i, :], rMetricTensor=crystal.reciprocalMetricTensor)) + sign = np.squeeze(self._calc_pole_dot_int(uniqHKL2, polesFlt[i, :], rMetricTensor=crystalmats.reciprocalMetricTensor)) + sign = np.atleast_1d(sign) whmx = (np.abs(sign)).argmax() + sign = np.round(sign[whmx]) uniqHKL2 *= sign @@ -190,7 +205,9 @@ def build_trip_lib(self,poles): # all the angles between it, and the poles in family "j" ang = np.clip(ang, -1.0, 1.0) sign = (ang >= 0).astype(np.float32) - (ang < 0).astype(np.float32) + sign = np.atleast_1d(sign) ang = np.round(np.arccos(sign * ang)*RADEG*100).astype(np.int32) # get the unique angles between the input + ang = np.atleast_1d(ang) # pole, and the family poles. Angles within 0.01 deg are taken as the same. unqang, argunq = np.unique(ang, return_index=True) unqang = unqang/100.0 # revert back to the actual angle in degrees. @@ -251,10 +268,10 @@ def build_trip_lib(self,poles): #print(libANG) #print(libANG.shape) # now make a table of the angle between all the poles (allowing inversino) - angTable = self._calc_pole_dot_int(sympolesComplete, sympolesComplete, rMetricTensor=crystal.reciprocalMetricTensor) + angTable = self._calc_pole_dot_int(sympolesComplete, sympolesComplete, rMetricTensor=crystalmats.reciprocalMetricTensor) angTable = np.arccos(angTable)*RADEG famindx0 = ((np.concatenate( ([0],np.cumsum(nFamComplete)) ))[0:-1]).astype(dtype=np.int64) - cartPoles = self._xstalplane2cart(sympolesComplete, rStructMatrix=crystal.reciprocalStructureMatrix) + cartPoles = self._xstalplane2cart(sympolesComplete, rStructMatrix=crystalmats.reciprocalStructureMatrix) cartPoles /= np.linalg.norm(cartPoles, axis = 1).reshape(np.int64(cartPoles.size/3),1) completePoleFamId = np.zeros(sympolesComplete.shape[0], dtype=np.int32) for i in range(npoles): @@ -278,6 +295,8 @@ def build_trip_lib(self,poles): self.tripID = libID + def _symrot(self, poles, crystalmats): + return rotlib.quat_vector(self.qsymops, poles) def _hkl_unique(self, poles, reduceInversion=True, rMT = np.identity(3)): """ From e824ebb1e8656f29e5df75cf8c4813bdfb1739b4 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Mon, 28 Nov 2022 20:31:50 -0500 Subject: [PATCH 012/177] First attempt at HCP Signed-off by: David Rowenhorst --- pyebsdindex/crystallometry.py | 20 ++++++++++++++++++++ pyebsdindex/tripletlib.py | 26 ++++++++++++++------------ 2 files changed, 34 insertions(+), 12 deletions(-) diff --git a/pyebsdindex/crystallometry.py b/pyebsdindex/crystallometry.py index f5400c8..81660f8 100644 --- a/pyebsdindex/crystallometry.py +++ b/pyebsdindex/crystallometry.py @@ -82,6 +82,8 @@ def __init__(self,name,a,b,c,alpha,beta,gamma): sb = np.sin( beta*np.pi/180) sg = np.sin(gamma*np.pi/180) tg = np.tan(gamma*np.pi/180) + fabg = ca*cb-cg + #compute the real space metric tensor self.metricTensor = np.zeros([3,3]) @@ -127,6 +129,20 @@ def __init__(self,name,a,b,c,alpha,beta,gamma): self.directStructureMatrix[2,2] = self.volume/(a*b*sg) self.directStructureMatrix = self.directStructureMatrix.transpose() self.directStructureMatrix[np.abs(self.directStructureMatrix) < smallThreshold] = 0 + + # Compute inverse direct stucture matrix + self.invDirectStructureMatrix = np.zeros([3, 3]) + self.invDirectStructureMatrix[0, 0] = 1.0/a + self.invDirectStructureMatrix[1, 0] = -1.0/(a * tg) + self.invDirectStructureMatrix[2, 0] = b*c * (cg*ca-cb)/(self.volume * sg) + self.invDirectStructureMatrix[0, 1] = 0.0 + self.invDirectStructureMatrix[1, 1] = 1.0/(b * sg) + self.invDirectStructureMatrix[2, 1] = a*c*(cb*cg-ca)/(self.volume*sg) + self.invDirectStructureMatrix[0, 2] = 0.0 + self.invDirectStructureMatrix[1, 2] = 0.0 + self.invDirectStructureMatrix[2, 2] = a*b*sg/(self.volume) + self.invDirectStructureMatrix = self.invDirectStructureMatrix.transpose() + self.invDirectStructureMatrix[np.abs(self.invDirectStructureMatrix) < smallThreshold] = 0 #compute reciprocal structure matrix self.reciprocalStructureMatrix = np.zeros([3,3]) @@ -142,6 +158,10 @@ def __init__(self,name,a,b,c,alpha,beta,gamma): self.reciprocalStructureMatrix = self.reciprocalStructureMatrix.transpose() self.reciprocalStructureMatrix[np.abs(self.reciprocalStructureMatrix) < smallThreshold] = 0 + # Compute inverse reciprocal stucture matrix + self.invReciprocalStructureMatrix = np.zeros([3, 3]) + self.invReciprocalStructureMatrix = np.transpose(self.directStructureMatrix) + # def get_lattice_centering(self): # self.latticeCentering = self.spaceGroup[0] diff --git a/pyebsdindex/tripletlib.py b/pyebsdindex/tripletlib.py index e676f12..3dfb702 100644 --- a/pyebsdindex/tripletlib.py +++ b/pyebsdindex/tripletlib.py @@ -90,15 +90,15 @@ def __init__(self, libType='FCC', phaseName=None, laticeParameter = None): self.build_dc() return - # if str(libType).upper() == 'HCP': - # if phaseName is None: - # self.phaseName = 'HCP' - # if laticeParameter is None: - # self.latticeParameter = np.array([1.0, 1.0, 1.63, 90.0, 90.0, 120.0]) - # else: - # self.latticeParameter = laticeParameter - # self.build_hcp() - # return + if str(libType).upper() == 'HCP': + if phaseName is None: + self.phaseName = 'HCP' + if laticeParameter is None: + self.latticeParameter = np.array([1.0, 1.0, 1.63, 90.0, 90.0, 120.0]) + else: + self.latticeParameter = laticeParameter + self.build_hcp() + return def build_fcc(self): @@ -175,7 +175,6 @@ def build_trip_lib(self,poles): for i in range(npoles): family = self._symrot(polesFlt[i,:], crystalmats) #rotlib.quat_vector(symmetry,polesFlt[i,:]) - #print(family) uniqHKL = self._hkl_unique(family, reduceInversion=False) uniqHKL = np.flip(uniqHKL, axis=0) sympolesComplete.append(uniqHKL) @@ -295,8 +294,11 @@ def build_trip_lib(self,poles): self.tripID = libID - def _symrot(self, poles, crystalmats): - return rotlib.quat_vector(self.qsymops, poles) + def _symrot(self, pole, crystalmats): + + polecart = np.matmul(crystalmats.reciprocalStructureMatrix, np.array(pole).T) + sympolescart = rotlib.quat_vector(self.qsymops, polecart) + return np.transpose(np.matmul(crystalmats.invReciprocalStructureMatrix, sympolescart.T)) def _hkl_unique(self, poles, reduceInversion=True, rMT = np.identity(3)): """ From e9d76545e8fd7236604bb032a1b2488693c08d68 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Mon, 28 Nov 2022 20:45:59 -0500 Subject: [PATCH 013/177] function name change Signed-off by: David Rowenhorst --- pyebsdindex/tripletlib.py | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/pyebsdindex/tripletlib.py b/pyebsdindex/tripletlib.py index 3dfb702..011b1d8 100644 --- a/pyebsdindex/tripletlib.py +++ b/pyebsdindex/tripletlib.py @@ -46,8 +46,6 @@ def __init__(self, libType='FCC', phaseName=None, laticeParameter = None): self.phaseName = None # User provided name of the phase. self.latticeParameter = None # 6 element array for the lattice parmeter. - - if phaseName is None: self.phaseName = libType else: @@ -174,7 +172,7 @@ def build_trip_lib(self,poles): polesFlt = np.array(poles, dtype=np.float32) # convert the input poles to floating point (but still HKL int values) for i in range(npoles): - family = self._symrot(polesFlt[i,:], crystalmats) #rotlib.quat_vector(symmetry,polesFlt[i,:]) + family = self._symrotpoles(polesFlt[i, :], crystalmats) #rotlib.quat_vector(symmetry,polesFlt[i,:]) uniqHKL = self._hkl_unique(family, reduceInversion=False) uniqHKL = np.flip(uniqHKL, axis=0) sympolesComplete.append(uniqHKL) @@ -294,12 +292,18 @@ def build_trip_lib(self,poles): self.tripID = libID - def _symrot(self, pole, crystalmats): + def _symrotpoles(self, pole, crystalmats): polecart = np.matmul(crystalmats.reciprocalStructureMatrix, np.array(pole).T) sympolescart = rotlib.quat_vector(self.qsymops, polecart) return np.transpose(np.matmul(crystalmats.invReciprocalStructureMatrix, sympolescart.T)) + def _symrotdir(self, pole, crystalmats): + + polecart = np.matmul(crystalmats.directStructureMatrix, np.array(pole).T) + sympolescart = rotlib.quat_vector(self.qsymops, polecart) + return np.transpose(np.matmul(crystalmats.invDirectStructureMatrix, sympolescart.T)) + def _hkl_unique(self, poles, reduceInversion=True, rMT = np.identity(3)): """ When given a list of integer HKL poles (plane normals), will return only the unique HKL variants From 1c296d4f1ffb706ba1a4a9ea74299402b1017ff0 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Tue, 29 Nov 2022 17:27:16 -0500 Subject: [PATCH 014/177] Correct to use rMetricMatrix Signed-off by: David Rowenhorst --- pyebsdindex/tripletlib.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/pyebsdindex/tripletlib.py b/pyebsdindex/tripletlib.py index 011b1d8..22165f2 100644 --- a/pyebsdindex/tripletlib.py +++ b/pyebsdindex/tripletlib.py @@ -198,7 +198,8 @@ def build_trip_lib(self,poles): polePairs = [] for i in range(npoles): for j in range(i, npoles): - ang = np.squeeze(self._calc_pole_dot_int(polesFlt[i, :], sympoles[j])) # for each input pole, calculate + ang = np.squeeze(self._calc_pole_dot_int(polesFlt[i, :], sympoles[j], + rMetricTensor=crystalmats.reciprocalMetricTensor)) # for each input pole, calculate # all the angles between it, and the poles in family "j" ang = np.clip(ang, -1.0, 1.0) sign = (ang >= 0).astype(np.float32) - (ang < 0).astype(np.float32) From d579e3f66c75979914ea464b28ba4b589990208a Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Wed, 30 Nov 2022 18:20:15 -0500 Subject: [PATCH 015/177] Major code re-org to make it easier to work with lower symmetry. Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_single.py | 10 +- pyebsdindex/band_vote.py | 6 +- pyebsdindex/ebsdfile.py | 14 +- pyebsdindex/pcopt.py | 2 +- .../{tripletlib.py => tripletlib_old.py} | 0 pyebsdindex/tripletvote.py | 1358 +++++++++++++++++ 6 files changed, 1375 insertions(+), 15 deletions(-) rename pyebsdindex/{tripletlib.py => tripletlib_old.py} (100%) create mode 100644 pyebsdindex/tripletvote.py diff --git a/pyebsdindex/_ebsd_index_single.py b/pyebsdindex/_ebsd_index_single.py index a26b3e4..4875fb0 100644 --- a/pyebsdindex/_ebsd_index_single.py +++ b/pyebsdindex/_ebsd_index_single.py @@ -29,11 +29,10 @@ import numpy as np import h5py +from pyebsdindex import tripletvote as bandindexer # use triplet voting as the default indexer. from pyebsdindex import ( - band_vote, ebsd_pattern, rotlib, - tripletlib, _pyopencl_installed, ) @@ -310,7 +309,10 @@ def __init__( self.phaselist = phaselist self.phaseLib = [] for ph in self.phaselist: - self.phaseLib.append(band_vote.BandVote(tripletlib.triplib(libType=ph))) + if (ph.__class__.__name__).lower() == 'str': + self.phaseLib.append(bandindexer.addphase(libtype=ph)) + if (ph.__class__.__name__) == 'BandIndexer': + self.phaseLib.append(ph) self.vendor = "EDAX" if vendor is None: @@ -512,7 +514,7 @@ def index_pats( nMatch, matchAttempts, totvotes, - ) = self.phaseLib[j].tripvote( + ) = self.phaseLib[j].bandindex( bandNorm1, band_intensity=bDat1["avemax"], verbose=verbose, ) # avequat,fit,cm,bandmatch,nMatch, matchAttempts = self.phaseLib[j].pairVoteOrientation(bandNorm1,goNumba=True) diff --git a/pyebsdindex/band_vote.py b/pyebsdindex/band_vote.py index 330c807..63d2210 100644 --- a/pyebsdindex/band_vote.py +++ b/pyebsdindex/band_vote.py @@ -43,7 +43,7 @@ class BandVote: def __init__(self, tripLib, angTol=3.0, high_fidelity=True): self.tripLib = tripLib self.phase_name = self.tripLib.phaseName - self.phase_sym = self.tripLib.symmetry_pg + self.phase_sym = self.tripLib.pointgroup self.lattice_param = self.tripLib.latticeParameter self.angTol = angTol self.n_band_early_exit = 8 @@ -60,7 +60,7 @@ def __init__(self, tripLib, angTol=3.0, high_fidelity=True): def tripvote(self, band_norms, band_intensity = None, goNumba = True, verbose=0): tic0 = timer() - nfam = self.tripLib.family.shape[0] + nfam = self.tripLib.polefamilies.shape[0] bandnorms = np.squeeze(band_norms) n_bands = np.int64(bandnorms.size/3) if band_intensity is None: @@ -748,7 +748,7 @@ def orientation_quest_nb(polescart, bandnorms, weights): def pairVoteOrientation(self,bandnormsIN,goNumba=True): tic0 = timer() - nfam = self.tripLib.family.shape[0] + nfam = self.tripLib.polefamilies.shape[0] bandnorms = np.squeeze(bandnormsIN) n_bands = np.int64(bandnorms.size / 3) diff --git a/pyebsdindex/ebsdfile.py b/pyebsdindex/ebsdfile.py index 0842bbc..99b4755 100644 --- a/pyebsdindex/ebsdfile.py +++ b/pyebsdindex/ebsdfile.py @@ -20,15 +20,15 @@ def writeang(filename, indexer, data, nphase = len(indexer.phaseLib) for phase in reversed(indexer.phaseLib): f.write('# Phase '+str(nphase - pcount + 1)+'\r\n') - f.write('# MaterialName \t' + str(phase.phase_name)+'\r\n') + f.write('# MaterialName \t' + str(phase.phasename)+'\r\n') f.write('# Formula '+'\t \r\n') f.write('# Info '+'\t\t \r\n') - f.write('# Symmetry '+str(phase.tripLib.laue_code)+'\r\n') - f.write('# PointGroupID ' + str(phase.tripLib.symmetry_pgid)+'\r\n') - f.write('# LatticeConstants '+ ' '.join(str(' {:.3f}'.format(x)) for x in phase.tripLib.latticeParameter)+'\r\n') - f.write('# NumberFamilies ' + str(phase.tripLib.nfamily)+'\r\n') - for i in range(phase.tripLib.nfamily): - f.write('# hklFamilies \t' + (' '.join(str(x).rjust(2,' ') for x in phase.tripLib.family[i,:])) + ' 1 0.00000 1'+'\r\n') + f.write('# Symmetry ' + str(phase.lauecode) + '\r\n') + f.write('# PointGroupID ' + str(phase.pointgroupid) + '\r\n') + f.write('# LatticeConstants '+ ' '.join(str(' {:.3f}'.format(x)) for x in phase.latticeParameter)+'\r\n') + f.write('# NumberFamilies ' + str(phase.npolefamilies) + '\r\n') + for i in range(phase.npolefamilies): + f.write('# hklFamilies \t' + (' '.join(str(x).rjust(2,' ') for x in phase.polefamilies[i, :])) + ' 1 0.00000 1' + '\r\n') f.write('# '+'\r\n') pcount += 1 diff --git a/pyebsdindex/pcopt.py b/pyebsdindex/pcopt.py index 47a0d17..a1b5f3e 100644 --- a/pyebsdindex/pcopt.py +++ b/pyebsdindex/pcopt.py @@ -50,7 +50,7 @@ def _optfunction(PC_i, indexer, banddat): whgood = np.nonzero(band_data1['max'] > -1e6)[0] if whgood.size >= 3: band_norm1 = band_norm1[whgood, :] - fit = phase.tripvote(band_norm1, goNumba=True)[1] + fit = phase.bandindex(band_norm1)[1] if fit < 90: average_fit += fit n_averages += 1 diff --git a/pyebsdindex/tripletlib.py b/pyebsdindex/tripletlib_old.py similarity index 100% rename from pyebsdindex/tripletlib.py rename to pyebsdindex/tripletlib_old.py diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py new file mode 100644 index 0000000..07c4010 --- /dev/null +++ b/pyebsdindex/tripletvote.py @@ -0,0 +1,1358 @@ +'''This software was developed by employees of the US Naval Research Laboratory (NRL), an +agency of the Federal Government. Pursuant to title 17 section 105 of the United States +Code, works of NRL employees are not subject to copyright protection, and this software +is in the public domain. PyEBSDIndex is an experimental system. NRL assumes no +responsibility whatsoever for its use by other parties, and makes no guarantees, +expressed or implied, about its quality, reliability, or any other characteristic. We +would appreciate acknowledgment if the software is used. To the extent that NRL may hold +copyright in countries other than the United States, you are hereby granted the +non-exclusive irrevocable and unconditional right to print, publish, prepare derivative +works and distribute this software, in any medium, or authorize others to do so on your +behalf, on a royalty-free basis throughout the world. You may improve, modify, and +create derivative works of the software or any portion of the software, and you may copy +and distribute such modifications or works. Modified works should carry a notice stating +that you changed the software and should note the date and nature of any such change. +Please explicitly acknowledge the US Naval Research Laboratory as the original source. +This software can be redistributed and/or modified freely provided that any derivative +works bear some notice that they are derived from it, and any modified versions bear +some notice that they have been modified. + +Author: David Rowenhorst; +The US Naval Research Laboratory Date: 21 Aug 2020''' + +from os import environ +from pathlib import PurePath +import platform +import tempfile +from timeit import default_timer as timer + +import numpy as np +import numba + +from pyebsdindex import crystal_sym, rotlib, crystallometry + + +RADEG = 180.0/np.pi + +tempdir = PurePath("/tmp" if platform.system() == "Darwin" else tempfile.gettempdir()) +tempdir = tempdir.joinpath('numba') +environ["NUMBA_CACHE_DIR"] = str(tempdir) + +def addphase(libtype=None, phasename=None, + spacegroup=None, + latticeparameter=None, + polefamilies=None): + + if libtype is not None: + + #set up generic FCC + if str(libtype).upper() == 'FCC': + if phasename is None: + phasename = 'FCC' + if spacegroup is None: + spacegroup = 225 + if latticeparameter is None: + latticeparameter = np.array([1.0, 1.0, 1.0, 90.0, 90.0, 90.0]) + else: + latticeparameter = np.array(latticeparameter) + if polefamilies is None: + polefamilies = np.array([[0, 0, 2], [1, 1, 1], [0, 2, 2], [1, 1, 3]]) + else: + polefamilies = np.array(polefamilies) + + # Set up a generic HCP + if str(libtype).upper() == 'BCC': + if phasename is None: + phasename = 'BCC' + if spacegroup is None: + spacegroup = 229 + if latticeparameter is None: + latticeparameter = np.array([1.0, 1.0, 1.0, 90.0, 90.0, 90.0]) + else: + latticeparameter = np.array(latticeparameter) + if polefamilies is None: + polefamilies = np.array([[0, 1, 1], [0, 0, 2], [1, 1, 2], [0, 1, 3]]) + else: + polefamilies = np.array(polefamilies) + + # Set up a generic HCP + if str(libtype).upper() == 'HCP': + if phasename is None: + phasename = 'HCP' + if spacegroup is None: + spacegroup = 229 + if latticeparameter is None: + latticeparameter = np.array([1.0, 1.0, 1.63, 90.0, 90.0, 120.0]) + else: + latticeparameter = np.array(latticeparameter) + if polefamilies is None: + polefamilies = np.array([[1, 0, -1, 0], [1, 0, -1, 1], [0, 0, 0, 2], [1, 0, -1, 3], [1, 1, -2, 0], [1, 0, -1, 2]]) + else: + polefamilies = np.array(polefamilies) + + else: + if spacegroup is None: + return addphase(libtype='FCC', latticeparameter=latticeparameter, polefamilies=polefamilies, phasename = phasename) + if latticeparameter is None: + latticeparameter = np.array([1.0, 1.0, 1.0, 90.0, 90.0, 90.0]) + if polefamilies is None: + polefamilies = np.array([[0, 0, 2], [1, 1, 1], [0, 2, 2], [1, 1, 3]]) + + triplib = BandIndexer(phasename=phasename, spacegroup=spacegroup, + latticeparameter=latticeparameter, polefamilies=polefamilies) + + triplib.build_trip_lib() + return triplib + +class BandIndexer(): + #def __init__(self, libType='FCC', phaseName=None, laticeParameter = None): + def __init__(self, + phasename=None, + spacegroup = None, + latticeparameter=None, + polefamilies = None, + angTol=3.0, + n_band_early_exit = 8): + self.phaseName = None # User provided name of the phase. + self.spacegroup = None # space group id 1-230 + self.latticeParameter = None # 6 element array for the lattice parameter. + self.polefamilies = None # array of integer pole normals that should have reflections + self.npolefamilies = None # number of unique reflector families + self.crystalmats = None # store the four crystal matrices useful for angle/cartisian conversions. + + self.lauecode = None # Laue code for the space group (following DREAM.3D notation. + self.qsymops = None # array of quaternions that represent proper symmetry operations for the laue group + + self.pointgroup = ' ' # point group nomenclature + self.pointgroupid = None + + self.angTol = angTol + self.n_band_early_exit = n_band_early_exit + self.high_fidelity = True + + # many objects to hold the information about the reflecting poles, angles between them ... + self.angpairs = None # dictionary that will store the possible unique angles between all pole families. + self.angtriplets = None # dictionary that will store all possible angle triplets within the pole family. + self.completelib = None # dictionary that will hold all possible angles (non-unique) between the families and + # all possible poles + + # these Look Up Tables are used in the sorting/unsorting of angle triplets. + luta = np.array([[0, 1, 2], [0, 2, 1], [1, 0, 2], [1, 2, 0], [2, 0, 1], [2, 1, 0]]) + lutb = np.array([[0, 1, 2], [1, 0, 2], [0, 2, 1], [2, 0, 1], [1, 2, 0], [2, 1, 0]]) + lut = np.zeros((3, 3, 3, 3), dtype=np.int64) + for i in range(6): + lut[:, luta[i, 0], luta[i, 1], luta[i, 2]] = lutb[i, :] + self.lut = np.asarray(lut).copy() + + if phasename is None: + self.phasename = ' ' + else: + self.phasename = str(phasename) + + if latticeparameter is not None: + self.setlatticeparameter(latticeparameter) + + + if spacegroup is not None: + self.setspacegroup(spacegroup) + + if polefamilies is not None: + self.setpolefamilies(polefamilies) + + + def setlatticeparameter(self, latticeparameter): + self.latticeparameter = np.array(latticeparameter) + self.crystalmats = crystallometry.Crystal(self.phaseName, + self.latticeparameter[0], + self.latticeparameter[1], + self.latticeparameter[2], + self.latticeparameter[3], + self.latticeparameter[4], + self.latticeparameter[5]) + + def setspacegroup(self, spacegroup = 225): + self.spacegroup = spacegroup + self.lauecode = crystal_sym.spacegroup2lauenumber(self.spacegroup) + self.qsymops = crystal_sym.laueid2symops(self.lauecode) + + def setpolefamilies(self, reflectors): + self.polefamilies = np.array(reflectors) + + # def build_fcc(self): + # if self.phaseName is None: + # self.phaseName = 'FCC' + # self.pointgroup = "Cubic m3m" + # self.pointgroupid = 131 + # self.spacegroup = 225 + # self.lauecode = crystal_sym.spacegroup2lauenumber(self.spacegroup) + # self.qsymops = crystal_sym.laueid2symops(self.lauecode) + # poles = np.array([[0,0,2], [1,1,1], [0,2,2], [1,1,3]]) + # self.build_trip_lib(poles) + # + # def build_dc(self): + # if self.phaseName is None: + # self.phaseName = 'Diamond Cubic' + # self.pointgroup = "Cubic m3m" + # self.pointgroupid = 131 + # self.spacegroup = 227 + # self.lauecode = crystal_sym.spacegroup2lauenumber(self.spacegroup) + # self.qsymops = crystal_sym.laueid2symops(self.lauecode) + # poles = np.array([[1, 1, 1], [0, 2, 2], [0, 0, 4], [1, 1, 3], [2, 2, 4], [1, 3, 3]]) + # self.build_trip_lib(poles) + # + # def build_bcc(self): + # if self.phaseName is None: + # self.phaseName = 'BCC' + # self.pointgroup = "Cubic m3m" + # self.pointgroupid = 131 + # self.spacegroup = 229 + # self.lauecode = crystal_sym.spacegroup2lauenumber(self.spacegroup) + # self.qsymops = crystal_sym.laueid2symops(self.lauecode) + # poles = np.array([[0,1,1],[0,0,2],[1,1,2],[0,1,3]]) + # self.build_trip_lib(poles) + + + + # def build_hcp(self): + # if self.phaseName is None: + # self.phaseName = 'HCP' + # self.pointgroup = "Hexagonal 6/mmm" + # self.spacegroup = 194 + # self.lauecode = crystal_sym.spacegroup2lauenumber(self.spacegroup) + # self.qsymops = crystal_sym.laueid2symops(self.lauecode) + # poles4 = np.array([[1,0, -1, 0], [1, 0, -1, 1], [0,0, 0, 2], [1, 0, -1, 3], [1,1,-2,0], [1,0,-1,2]]) + # self.build_hex_trip_lib(poles4) + # + # def build_hex_trip_lib(self, poles4): + # poles3 = crystal_sym.hex4poles2hex3poles(poles4) + # self.build_trip_lib(poles3) + # p3temp = self.polefamilies + # p4temp = crystal_sym.hex3poles2hex4poles(p3temp) + # self.polefamilies = p4temp + + def build_trip_lib(self): + + if self.spacegroup is None: + print('No Space Group ID is set') + return + if self.latticeparameter is None: + print('No lattice parameter is set') + return + if self.polefamilies is None: + print('No pole familes are set') + return + + crystalmats = self.crystalmats + + poles = np.array(self.polefamilies) + if (self.lauecode == 62) or (self.lauecode == 6): + if self.polefamilies.shape[-1] == 4: + poles = crystal_sym.hex4poles2hex3poles(np.array(self.poles)) + poles = np.reshape(poles, (-1,3) ) + + npoles = poles.shape[0] + sympoles = [] # list of all HKL variants which does not count the invariant pole as unique. + + sympolesComplete = [] # list of all HKL variants with no duplicates + nFamComplete = np.zeros(npoles, dtype = np.int32) # number of + nFamily = np.zeros(npoles, dtype = np.int32) + polesFlt = np.array(poles, dtype=np.float32) # convert the input poles to floating point (but still HKL int values) + + for i in range(npoles): + family = self._symrotpoles(polesFlt[i, :], crystalmats) #rotlib.quat_vector(symmetry,polesFlt[i,:]) + uniqHKL = self._hkl_unique(family, reduceInversion=False) + uniqHKL = np.flip(uniqHKL, axis=0) + sympolesComplete.append(uniqHKL) + nFamComplete[i] = np.reshape(sympolesComplete[-1],(-1,3)).shape[0] #np.int32((sympolesComplete[-1]).size/3) + + uniqHKL2 = self._hkl_unique(family, reduceInversion=True, rMT = crystalmats.reciprocalMetricTensor) + nFamily[i] = np.reshape(uniqHKL2,(-1,3)).shape[0] #np.int32(uniqHKL2.size/3) + sign = np.squeeze(self._calc_pole_dot_int(uniqHKL2, polesFlt[i, :], rMetricTensor=crystalmats.reciprocalMetricTensor)) + sign = np.atleast_1d(sign) + whmx = (np.abs(sign)).argmax() + + sign = np.round(sign[whmx]) + uniqHKL2 *= sign + + sympoles.append(np.round(uniqHKL2)) + #sympolesN.append(self.xstalPlane2cart(family)) + + sympolesComplete = np.concatenate(sympolesComplete) + nsyms = np.sum(nFamily).astype(np.int32) + famindx = np.concatenate( ([0],np.cumsum(nFamComplete)) ) + angs = [] + familyID = [] + polePairs = [] + for i in range(npoles): + for j in range(i, npoles): + fampoles = sympolesComplete[famindx[j]:famindx[j+1], :].astype(np.float32) + #print('______', i,j) + #print(np.round(fampoles).astype(int)) + + ang = np.squeeze(self._calc_pole_dot_int(polesFlt[i, :], fampoles, + rMetricTensor=crystalmats.reciprocalMetricTensor)) # for each input pole, calculate + + ang = np.clip(ang, -1.0, 1.0) + #sign = (ang >= 0).astype(np.float32) - (ang < 0).astype(np.float32) + #sign = np.atleast_1d(sign) + ang = np.round(np.arccos(np.abs(ang))*RADEG*100).astype(np.int32) # get the unique angles between the input + ang = np.atleast_1d(ang) + # pole, and the family poles. Angles within 0.01 deg are taken as the same. + unqang, argunq = np.unique(ang, return_index=True) + unqang = unqang/100.0 # revert back to the actual angle in degrees. + + + wh = np.nonzero(unqang > 1.0)[0] + nwh = wh.size + #sign = sign[wh] + #sign = sign.reshape(nwh,1) + temp = np.zeros((nwh, 2, 3)) + temp[:,0,:] = np.broadcast_to(poles[i,:], (nwh, 3)) + temp[:,1,:] = np.broadcast_to(fampoles[argunq[wh],:], (nwh, 3)) + for k in range(nwh): + angs.append(unqang[wh[k]]) + familyID.append([i,j]) + polePairs.append(temp[k,:,:]) + + angs = np.squeeze(np.array(angs)) + nangs = angs.size + familyID = np.array(familyID) + polePairs = np.array(polePairs) + + stuff, nFamilyID = np.unique(familyID[:,0], return_counts=True) + indx0FID = (np.concatenate( ([0],np.cumsum(nFamilyID)) ))[0:npoles] + #print(indx0FID) + #This completely over previsions the arrays, this is essentially + #N Choose K with N = number of angles and K = 3 + nlib = npoles*np.prod(np.arange(3, dtype=np.int64)+(nangs-2+1))/np.compat.long(np.math.factorial(3)) + nlib = nlib.astype(int) + + libANG = np.zeros((nlib, 3)) + libID = np.zeros((nlib, 3), dtype=int) + counter = 0 + # now actually catalog all the triplet angles. + for i in range(npoles): + id0 = familyID[indx0FID[i], 0] + for j in range(0,nFamilyID[i]): + + ang0 = angs[j + indx0FID[i]] + id1 = familyID[j + indx0FID[i], 1] + for k in range(j, nFamilyID[i]): + ang1 = angs[k + indx0FID[i]] + id2 = familyID[k + indx0FID[i], 1] + + whjk = np.nonzero( np.logical_and( familyID[:,0] == id1, familyID[:,1] == id2 ))[0] + for q in range(whjk.size): + ang2 = angs[whjk[q]] + libANG[counter, :] = np.array([ang0, ang1, ang2]) + libID[counter, :] = np.array([id0, id1, id2]) + counter += 1 + + libANG = libANG[0:counter, :] + libID = libID[0:counter, :] + + libANG, libID = self._sortlib_id(libANG, libID, findDups = True) # sorts each row of the library to make sure + # the triplets are in increasing order. + + #print(libANG) + #print(libANG.shape) + # now make a table of the angle between all the poles (allowing inversino) + angTable = self._calc_pole_dot_int(sympolesComplete, sympolesComplete, rMetricTensor=crystalmats.reciprocalMetricTensor) + angTable = np.arccos(angTable)*RADEG + famindx0 = ((np.concatenate( ([0],np.cumsum(nFamComplete)) ))[0:-1]).astype(dtype=np.int64) + cartPoles = self._xstalplane2cart(sympolesComplete, rStructMatrix=crystalmats.reciprocalStructureMatrix) + cartPoles /= np.linalg.norm(cartPoles, axis = 1).reshape(np.int64(cartPoles.size/3),1) + completePoleFamId = np.zeros(sympolesComplete.shape[0], dtype=np.int32) + for i in range(npoles): + for j in range(nFamComplete[i]): + completePoleFamId[j+famindx0[i]] = i + self.completelib = { + 'poles' : sympolesComplete, + 'polesCart': cartPoles, + 'familyid': completePoleFamId, + 'angTable' : angTable, + 'nFamily' : nFamComplete, + 'famIndex' : famindx0 + } + + self.angpairs = { + 'familyid': familyID, + 'polepairs':polePairs, + 'angles':angs + } + self.angtriplets = { + 'angles': libANG, + 'familyid': libID + } + if (self.lauecode == 62) or (self.lauecode == 6): + poles = crystal_sym.hex3poles2hex4poles(poles) + self.polefamilies = poles + self.npolefamilies = npoles + + #self.angles = angs + #self.polePairs = polePairs + #self.angleFamilyID = familyID + #self.tripAngles = libANG + #self.tripID = libID + + + def bandindex(self, band_norms, band_intensity = None, verbose=0): + tic0 = timer() + nfam = self.polefamilies.shape[0] + bandnorms = np.squeeze(band_norms) + n_bands = np.reshape(bandnorms, (-1,3)).shape[0] #np.int64(bandnorms.size/3) + if band_intensity is None: + band_intensity = np.ones((n_bands)) + tic = timer() + bandangs = np.abs(bandnorms.dot(bandnorms.T)) + bandangs = np.clip(bandangs, -1.0, 1.0) + bandangs = np.arccos(bandangs)*RADEG + + tripangs = self.angtriplets['angles'] + tripid = self.angtriplets['familyid'] + + accumulator, bandFam, bandRank, band_cm = self._tripvote_numba(bandangs, self.lut, self.angTol, tripangs, tripid, nfam, n_bands) + + + if verbose > 2: + print('band Vote time:',timer() - tic) + tic = timer() + + sumaccum = np.sum(accumulator) + bandRank_arg = np.argsort(bandRank).astype(np.int64) + test = 0 + fit = 1000.0 + nMatch = -1 + avequat = np.zeros(4, dtype=np.float32) + polematch = np.array([-1]) + whGood = -1 + + angTable = self.completelib['angTable'] + sztable = angTable.shape + famIndx = self.completelib['famIndex'] + nFam = self.completelib['nFamily'] + polesCart = self.completelib['polesCart'] + angTol = self.angTol + n_band_early = np.int64(self.n_band_early_exit) + + # this will check the vote, and return the exact band matching to specific poles of the best fitting solution. + fit, polematch, nMatch, whGood, ij, R, fitb = \ + self._assign_bands_nb(polesCart, bandRank_arg, bandFam, famIndx, nFam, angTable, bandnorms, angTol, n_band_early) + + if verbose > 2: + print('band index: ',timer() - tic) + tic = timer() + + cm2 = 0.0 + if nMatch >=2: + if self.high_fidelity == True: + + srt = np.argsort(fitb[whGood]) + whgood6 = whGood[srt[0:np.min([8, whGood.shape[0]])]] + + weights6 = band_intensity[whgood6] + pflt6 = (np.asarray(polesCart[polematch[whgood6], :], dtype=np.float64)) + bndnorm6 = (np.asarray(bandnorms[whgood6, :], dtype=np.float64)) + + avequat, fit = self._refine_orientation_quest(bndnorm6, pflt6, weights=weights6) + fit = np.arccos(np.clip(fit, -1.0, 1.0))*RADEG + #avequat, fit = self.refine_orientation(bandnorms,whGood,polematch) + else: + avequat = rotlib.om2qu(R) + whmatch = np.nonzero(polematch >= 0)[0] + cm = np.mean(band_cm[whmatch]) + whfam = self.completelib['familyid'][polematch[whmatch]] + cm2 = np.sum(accumulator[[whfam], [whmatch]]).astype(np.float32) + cm2 /= np.sum(accumulator.clip(1)) + + if verbose > 2: + print('refinement: ', timer() - tic) + print('all: ',timer() - tic0) + return avequat, fit, cm2, polematch, nMatch, ij, sumaccum + + + def _symrotpoles(self, pole, crystalmats): + + polecart = np.matmul(crystalmats.reciprocalStructureMatrix, np.array(pole).T) + sympolescart = rotlib.quat_vector(self.qsymops, polecart) + return np.transpose(np.matmul(crystalmats.invReciprocalStructureMatrix, sympolescart.T)) + + def _symrotdir(self, pole, crystalmats): + + polecart = np.matmul(crystalmats.directStructureMatrix, np.array(pole).T) + sympolescart = rotlib.quat_vector(self.qsymops, polecart) + return np.transpose(np.matmul(crystalmats.invDirectStructureMatrix, sympolescart.T)) + + def _hkl_unique(self, poles, reduceInversion=True, rMT = np.identity(3)): + """ + When given a list of integer HKL poles (plane normals), will return only the unique HKL variants + + Parameters + ---------- + poles: numpy.ndarray (n,3) in HKL integer form. + reduceInversion: True/False. If True, then the any inverted crystal pole + will also be removed from the uniquelist. The angle between poles + rMT: reciprocol metric tensor -- needed to calculated + + Returns + ------- + numpy.ndarray (n,3) in HKL integer form of the unique poles. + """ + + npoles = poles.shape[0] + intPoles =np.array(poles.round().astype(np.int32)) + mn = intPoles.min() + intPoles -= mn + basis = intPoles.max()+1 + basis3 = np.array([1,basis, basis**2]) + test = intPoles.dot(basis3) + + un, unq = np.unique(test, return_index=True) + + polesout = poles[unq, :] + + if reduceInversion == True: + family = polesout + nf = family.shape[0] + test = self._calc_pole_dot_int(family, family, rMetricTensor = rMT) + + testSum = np.sum( (test < -0.99999).astype(np.int32)*np.arange(nf).reshape(1,nf), axis = 1) + whpos = np.nonzero( np.logical_or(testSum < np.arange(nf), (testSum == 0)))[0] + polesout = polesout[whpos, :] + return polesout + + def _calc_pole_dot_int(self, poles1, poles2, rMetricTensor = np.identity(3)): + + p1 = poles1.reshape(np.int64(poles1.size / 3), 3) + p2 = poles2.reshape(np.int64(poles2.size / 3), 3) + + n1 = p1.shape[0] + n2 = p2.shape[0] + + t1 = p1.dot(rMetricTensor) + t2 = rMetricTensor.dot(p2.T) + dot = t1.dot(p2.T) + dotnum = np.sqrt(np.diag(t1.dot(p1.T))) + dotnum = dotnum.reshape(n1,1) + dotnum2 = np.sqrt(np.diag(p2.dot(t2))) + dotnum2 = dotnum2.reshape(1,n2) + dotnum = dotnum.dot(dotnum2) + + dot /= dotnum + dot = np.clip(dot, -1.0, 1.0) + return dot + + def _xstalplane2cart(self, poles, rStructMatrix = np.identity(3)): + polesout = rStructMatrix.dot(poles.T) + return np.transpose(polesout) + + def _sortlib_id(self, libANG, libID, findDups = False): + # will make sure that triplets are ordered from lowest to highest + # and maintain the pole family id + # optionally will locate any duplicates in the triplet list. + + # LUTA = np.array([[0,1,2],[0,2,1],[1,0,2],[1,2,0],[2,0,1],[2,1,0]]) + # LUTB = np.array([[0,1,2],[1,0,2],[0,2,1],[2,0,1],[1,2,0],[2,1,0]]) + # + # LUT = np.zeros((3,3,3,3), dtype=np.int64) + # for i in range(6): + # LUT[:, LUTA[i,0], LUTA[i,1], LUTA[i,2]] = LUTB[i,:] + lut = self.lut + + ntrips = np.int64(libANG.size / 3) + for i in range(ntrips): + temp = np.squeeze(libANG[i,:]) + srt = np.argsort(temp) + libANG[i,:] = temp[srt] + srt2 = lut[:,srt[0], srt[1], srt[2]] + temp2 = libID[i,:] + temp2 = temp2[srt2] + libID[i,:] = temp2 + + if findDups == True: + angID = np.sum(np.round(libANG*100), axis = 1).astype(np.longlong) + basis = np.longlong(libID.max() + 1) + libID_ID = libID.dot(np.array([1,basis, basis**2])) + UID = np.ceil(np.log10(libID_ID.max())) + UID = np.where(UID > 2, UID, 2) + UID = (angID * 10**UID) + libID_ID + + stuff, unq = np.unique(UID, return_index=True) + libANG = libANG[unq, :] + libID = libID[unq,:] + libID_ID = libID_ID[unq] + srt = np.argsort(libID_ID) + libANG = libANG[srt, :] + libID = libID[srt, :] + + return (libANG, libID) + + + + + + + # def band_index(self,bandnorms,bnd1,bnd2,familyLabel,angTol=3.0, verbose = 0): + # + # #nBands = np.int32(bandnorms.size/3) + # angTable = self.tripLib.completelib['angTable'] + # sztable = angTable.shape + # #whGood = -1 + # famIndx = self.tripLib.completelib['famIndex'] + # nFam = self.tripLib.completelib['nFamily'] + # poles = self.tripLib.completelib['polesCart'] + # #ang01 = 0.0 + # # need to check that the two selected bands are not parallel. + # #v0 = bandnorms[bnd1, :] + # #f0 = familyLabel[bnd1] + # #v1 = bandnorms[bnd2, :] + # #f1 = familyLabel[bnd2] + # #ang01 = np.clip(np.dot(v0, v1), -1.0, 1.0) + # #ang01 = np.arccos(ang01)*RADEG + # #if ang01 < angTol: # the two poles are parallel, send in another two poles if available. + # # return 360.0, 0, whGood, -1 + # + # #wh01 = np.nonzero(np.abs(angTable[famIndx[f0], famIndx[f1]:np.int(famIndx[f1]+nFam[f1])] - ang01) < angTol)[0] + # + # #n01 = wh01.size + # #if n01 == 0: + # # return 360.0, 0, whGood, -1 + # + # #wh01 += famIndx[f1] + # #p0 = poles[famIndx[f0], :] + # #print('pre first loop: ',timer() - tic) + # #tic = timer() + # # place numba code here ... + # + # + # + # #fit, polematch, R, nGood, whGood = self.band_vote_refine_loops1(poles,v0,v1, p0, wh01, bandnorms, angTol) + # fit,polematch,R,nGood,whGood = self.band_vote_refine_loops1(poles, bnd1, bnd2, familyLabel, famIndx, nFam, angTable, bandnorms, angTol) + # + # #print('numba first loops',timer() - tic) + # #whGood = np.nonzero(angFit < angTol)[0] + # #nGood = np.int64(whGood.size) + # #if nGood < 3: + # # return 360.0, -1, -1, -1 + # + # #fit = np.mean(angFit[whGood]) + # #print('all bindexed time', timer()-tic0) + # return fit, nGood, whGood, polematch + + def _refine_orientation(self, bandnorms, whGood, polematch): + tic = timer() + poles = self.tripLib.completelib['polesCart'] + nGood = whGood.size + n2Fit = np.int64(np.product(np.arange(2)+(nGood-2+1))/np.int64(2)) + whGood = np.asarray(whGood,dtype=np.int64) + #AB, ABgood = self.orientation_refine_loops_am(nGood,whGood,poles,bandnorms,polematch,n2Fit) + # tic = timer() + # quats = rotlib.om2quL(AB[ABgood.nonzero()[0], :, :]) + # print("om2qu", timer() - tic) + # tic = timer() + # avequat = rotlib.quatave(quats) + + AB, weights = self._orientation_refine_loops_am(nGood, whGood, poles, bandnorms, polematch, n2Fit) + + wh_weight = np.nonzero(weights < 359.0)[0] + quats = rotlib.om2quL(AB[wh_weight, :, :]) + + expw = weights[wh_weight] + + rng = expw.max()-expw.min() + #print(rng, len(wh_weight)) + if rng > 1e-6: + expw -= expw.min() + #print(np.arccos(1.0 - expw)*RADEG) + expw = np.exp(-expw/(0.5*(rng))) + expw /= np.sum(expw) + #print(quats) + #print(expw) + #print(expw*len(wh_weight)) + avequat = rotlib.quatave(quats * np.expand_dims(expw, axis=-1)) + #print(avequat) + else: + avequat = rotlib.quatave(quats) + + + test = rotlib.quat_vectorL(avequat,bandnorms[whGood,:]) + + tic = timer() + test = np.sum(test * poles[polematch[whGood], :], axis = 1) + test = np.arccos(np.clip(test, -1.0, 1.0))*RADEG + + + fit = np.mean(test) + + #print('fitting: ',timer() - tic) + return avequat, fit + + def _refine_orientation_quest(self, bandnorms, polecartmatch, weights = None): + tic = timer() + + + if weights is None: + weights = np.ones((bandnorms.shape[0]), dtype=np.float64) + weightsn = np.asarray(weights, dtype=np.float64) + weightsn /= np.sum(weightsn) + #print(weightsn) + pflt = np.asarray(polecartmatch, dtype=np.float64) + bndnorm = np.asarray(bandnorms, dtype=np.float64) + + avequat, fit = self._orientation_quest_nb(pflt, bndnorm, weightsn) + + return avequat, fit + + @staticmethod + @numba.jit(nopython=True, cache=True, fastmath=True, parallel=False) + def _orientation_quest_nb(polescart, bandnorms, weights): + # this uses the Quaternion Estimator AKA quest algorithm. + + pflt = np.asarray(polescart, dtype=np.float64) + bndnorm = np.asarray(bandnorms, dtype=np.float64) + npoles = pflt.shape[0] + wn = (np.asarray(weights, dtype=np.float64)).reshape(npoles, 1) + + # wn = np.ones((nGood,1), dtype=np.float32)/np.float32(nGood) #(weights[whGood]).reshape(nGood,1) + wn /= np.sum(wn) + + I = np.zeros((3, 3), dtype=np.float64) + I[0, 0] = 1.0; + I[1, 1] = 1.0; + I[2, 2] = 1.0 + q = np.zeros((4), dtype=np.float64) + + B = (wn * bndnorm).T @ pflt + S = B + B.T + z = np.asarray(np.sum(wn * np.cross(bndnorm, pflt), axis=0), dtype=np.float64) + S2 = S @ S + det = np.linalg.det(S) + k = (S[1, 1] * S[2, 2] - S[1, 2] * S[2, 1]) + (S[0, 0] * S[2, 2] - S[0, 2] * S[2, 0]) + ( + S[0, 0] * S[1, 1] - S[1, 0] * S[0, 1]) + sig = 0.5 * (S[0, 0] + S[1, 1] + S[2, 2]) + sig2 = sig * sig + d = z.T @ S2 @ z + c = det + (z.T @ S @ z) + b = sig2 + (z.T @ z) + a = sig2 - k + + lam = 1.0 + tol = 1.0e-6 + iter = 0 + dlam = 1e6 + # for i in range(10): + while (dlam > tol) and (iter < 10): + lam0 = lam + lam = lam - (lam ** 4 - (a + b) * lam ** 2 - c * lam + (a * b + c * sig - d)) / ( + 4 * lam ** 3 - 2 * (a + b) * lam - c) + dlam = np.fabs(lam0 - lam) + iter += 1 + + beta = lam - sig + alpha = lam ** 2 - sig2 + k + gamma = (lam + sig) * alpha - det + X = np.asarray((alpha * I + beta * S + S2), dtype=np.float64) @ z + qn = np.float64(0.0) + qn += gamma ** 2 + X[0] ** 2 + X[1] ** 2 + X[2] ** 2 + qn = np.sqrt(qn) + q[0] = gamma + q[1:4] = X[0:3] + q /= qn + if (np.sign(gamma) < 0): + q *= -1.0 + + # polesrot = rotlib.quat_vectorL1N(q, pflt, npoles, np.float64, p=1) + # pdot = np.sum(polesrot*bndnorm, axis = 1, dtype=np.float64) + return q, lam # , pdot + + + @staticmethod + @numba.jit(nopython=True, cache=True,fastmath=True,parallel=False) + def _tripvote_numba(bandangs, LUT, angTol, tripAngles, tripID, nfam, n_bands): + LUTTemp = np.asarray(LUT).copy() + accumulator = np.zeros((nfam, n_bands), dtype=np.int32) + tshape = np.shape(tripAngles) + ntrip = int(tshape[0]) + count = 0.0 + #angTest2 = np.zeros(ntrip, dtype=numba.boolean) + #angTest2 = np.empty(ntrip,dtype=numba.boolean) + for i in range(n_bands): + for j in range(i + 1,n_bands): + for k in range(j + 1,n_bands): + angtri = np.array([bandangs[i,j],bandangs[i,k],bandangs[j,k]], dtype=numba.float32) + srt = angtri.argsort(kind='quicksort') #np.array(np.argsort(angtri), dtype=numba.int64) + srt2 = np.asarray(LUTTemp[:,srt[0],srt[1],srt[2]], dtype=numba.int64).copy() + unsrtFID = np.argsort(srt2,kind='quicksort').astype(np.int64) + angtriSRT = np.asarray(angtri[srt]) + angTest = (np.abs(tripAngles - angtriSRT)) <= angTol + + for q in range(ntrip): + angTest2 = (angTest[q,0] + angTest[q,1] + angTest[q,2]) == 3 + if angTest2: + f = tripID[q,:] + f = f[unsrtFID] + accumulator[f[0],i] += 1 + accumulator[f[1],j] += 1 + accumulator[f[2],k] += 1 + t1 = False + t2 = False + t3 = False + if np.abs(angtriSRT[0] - angtriSRT[1]) < angTol: + accumulator[f[0],i] += 1 + accumulator[f[1],k] += 1 + accumulator[f[2],j] += 1 + t1 = True + if np.abs(angtriSRT[1] - angtriSRT[2]) < angTol: + accumulator[f[0],j] += 1 + accumulator[f[1],i] += 1 + accumulator[f[2],k] += 1 + t2 = True + if np.abs(angtriSRT[2] - angtriSRT[0]) < angTol: + accumulator[f[0],k] += 1 + accumulator[f[1],j] += 1 + accumulator[f[2],i] += 1 + t3 = True + if (t1 and t2 and t3): + accumulator[f[0],k] += 1 + accumulator[f[1],i] += 1 + accumulator[f[2],j] += 1 + + mxvote = np.zeros(n_bands, dtype=np.int32) + tvotes = np.zeros(n_bands, dtype=np.int32) + band_cm = np.zeros(n_bands, dtype=np.float32) + for q in range(n_bands): + mxvote[q] = np.amax(accumulator[:,q]) + tvotes[q] = np.sum(accumulator[:,q]) + + + + for i in range(n_bands): + if tvotes[i] < 1: + band_cm[i] = 0.0 + else: + srt = np.argsort(accumulator[:,i]) + band_cm[i] = (accumulator[srt[-1],i] - accumulator[srt[-2],i]) / tvotes[i] + + bandRank = np.zeros(n_bands, dtype=np.float32) + bandFam = np.zeros(n_bands, dtype=np.int32) + for q in range(n_bands): + bandFam[q] = np.argmax(accumulator[:,q]) + bandRank = (n_bands - np.arange(n_bands)) / n_bands * band_cm * mxvote + + return accumulator, bandFam, bandRank, band_cm + + @staticmethod + @numba.jit(nopython=True, cache=True, fastmath=True,parallel=False) + def _assign_bands_nb(polesCart, bandRank_arg, familyLabel, famIndx, nFam, angTable, bandnorms, angTol, n_band_early): + eps = np.float32(1.0e-12) + nBnds = bandnorms.shape[0] + + whGood_out = np.zeros(nBnds, dtype=np.int64)-1 + + + nMatch = np.int64(-1) + Rout = np.zeros((1,3,3), dtype=np.float32) + #Rout[0,0,0] = 1.0; Rout[0,1,1] = 1.0; Rout[0,2,2] = 1.0 + polematch_out = np.zeros((nBnds),dtype=np.int64) - 1 + pflt = np.asarray(polesCart, dtype=np.float32) + bndnorm = np.transpose(np.asarray(bandnorms, dtype=np.float32)) + + fit = np.float32(360.0) + fitout = np.float32(360.0) + fitbout = np.zeros((nBnds)) + R = np.zeros((1, 3, 3), dtype=np.float32) + #fit = np.float32(360.0) + #whGood = np.zeros(nBnds, dtype=np.int64) - 1 + nGood = np.int64(-1) + ij = (-1,-1) + for ii in range(nBnds-1): + for jj in range(ii+1,nBnds): + + polematch = np.zeros((nBnds),dtype=np.int64) - 1 + + bnd1 = bandRank_arg[-1 - ii] + bnd2 = bandRank_arg[-1 - jj] + + v0 = bandnorms[bnd1,:] + f0 = familyLabel[bnd1] + v1 = bandnorms[bnd2,:] + f1 = familyLabel[bnd2] + ang01 = np.dot(v0,v1) + if ang01 > np.float32(1.0): + ang01 = np.float32(1.0-eps) + if ang01 < np.float32(-1.0): + ang01 = np.float32(-1.0+eps) + + paralleltest = np.arccos(np.fabs(ang01)) * RADEG + if paralleltest < angTol: # the two poles are parallel, send in another two poles if available. + continue + + ang01 = np.arccos(ang01) * RADEG + + wh01 = np.nonzero(np.abs(angTable[famIndx[f0],famIndx[f1]:np.int64(famIndx[f1] + nFam[f1])] - ang01) < angTol)[0] + + n01 = wh01.size + if n01 == 0: + continue + + wh01 += famIndx[f1] + p0 = polesCart[famIndx[f0], :] + + n01 = wh01.size + v0v1c = np.cross(v0,v1) + v0v1c /= np.linalg.norm(v0v1c) + # attempt to see which solution gives the best match to all the poles + # best is measured as the number of poles that are within tolerance, + # divided by the angular deviation. + # Use the TRIAD method for finding the rotation matrix + + Rtry = np.zeros((n01,3,3), dtype = np.float32) + + #score = np.zeros((n01), dtype = np.float32) + A = np.zeros((3,3), dtype = np.float32) + B = np.zeros((3,3), dtype = np.float32) + #AB = np.zeros((3,3),dtype=np.float32) + b2 = np.cross(v0,v0v1c) + B[0,:] = v0 + B[1,:] = v0v1c + B[2,:] = b2 + A[:,0] = p0 + score = -1.0 + + for i in range(n01): + p1 = polesCart[wh01[i], :] + ntemp = np.linalg.norm(p1) + 1.0e-35 + p1 = p1 / ntemp + p0p1c = np.cross(p0,p1) + ntemp = np.linalg.norm(p0p1c) + 1.0e-35 + p0p1c = p0p1c / ntemp + A[:,1] = p0p1c + A[:,2] = np.cross(p0,p0p1c) + AB = A.dot(B) + Rtry[i,:,:] = AB + + testp = (AB.dot(bndnorm)) + test = pflt.dot(testp) + #print(test.shape) + angfitTry = np.zeros((nBnds), dtype = np.float32) + #angfitTry = np.max(test,axis=0) + for j in range(nBnds): + angfitTry[j] = np.max(test[:,j]) + angfitTry[j] = -1.0 if angfitTry[j] < -1.0 else angfitTry[j] + angfitTry[j] = 1.0 if angfitTry[j] > 1.0 else angfitTry[j] + + #angfitTry = np.clip(np.amax(test,axis=0),-1.0,1.0) + + angfitTry = np.arccos(angfitTry) * RADEG + whMatch = np.nonzero(angfitTry < angTol)[0] + nmatch = whMatch.size + #scoreTry = np.float32(nmatch) * np.mean(np.abs(angTol - angfitTry[whMatch])) + scoreTry = np.float32(nmatch) /( np.mean(angfitTry[whMatch]) + 1e-6) + if scoreTry > score: + score = scoreTry + angFit = angfitTry + for j in range(nBnds): + polematch[j] = np.argmax(test[:,j]) * ( 2*np.int32(angfitTry[j] < angTol)-1) + R[0, :,:] = Rtry[i,:,:] + + + whGood = (np.nonzero(angFit < angTol)[0]).astype(np.int64) + nGood = max(np.int64(whGood.size), np.int64(0)) + + if nGood < 3: + continue + #return 360.0,-1,-1,-1 + #whGood = -1*np.ones((1), dtype=np.int64) + #fit = np.float32(360.0) + #polematch[:] = -1 + #nGood = np.int64(-1) + else: + fitb = angFit + #fit = np.mean(fitb[whGood]) + fit = np.float32(0.0) + for q in range(nGood): + fit += np.float32(fitb[whGood[q]]) + fit /= np.float32(nGood) + + + if nGood >= (n_band_early): + fitout = fit + fitbout = fitb + nMatch = nGood + whGood_out = whGood + polematch_out = polematch + Rout = R + ij = (bnd1,bnd2) + break + else: + if nMatch < nGood: + fitout = np.float32(fit) + fitbout = fitb + nMatch = nGood + whGood_out = whGood + polematch_out = polematch + Rout = R + ij = (bnd1, bnd2) + elif nMatch == nGood: + if fitout > fit: + fitout = np.float32(fit) + fitbout = fitb + nMatch = nGood + whGood_out = whGood + polematch_out = polematch + Rout = R + ij = (bnd1, bnd2) + if nMatch >= (n_band_early): + break + #quatout = rotlib.om2quL(Rout) + return fitout, polematch_out,nMatch, whGood_out, ij, Rout, fitbout + + @staticmethod + @numba.jit(nopython=True, cache=True, fastmath=True,parallel=False) + def _orientation_refine_loops_triad(nGood, whGood, poles, bandnorms, polematch, n2Fit): + #uses the TRIAD method for getting rotation matrix from imperfect poles. + quats = np.zeros((n2Fit, 4), dtype=np.float32) + counter = 0 + A = np.zeros((3, 3), dtype=np.float32) + B = np.zeros((3, 3), dtype=np.float32) + AB = np.zeros((n2Fit, 3, 3), dtype=np.float32) + whgood2 = np.zeros(n2Fit, dtype=np.int32) + for i in range(nGood): + v0 = bandnorms[whGood[i],:] + p0 = poles[polematch[whGood[i]],:] + A[:,0] = p0 + B[0,:] = v0 + for j in range(i + 1,nGood): + v1 = bandnorms[whGood[j],:] + p1 = poles[polematch[whGood[j]],:] + v0v1c = np.cross(v0,v1) + # v0v1c /= np.linalg.norm(v0v1c)+1.0e-35 + # v0v1c = vectnorm(v0v1c) # faster to inline these functions + norm = numba.float32(0.0) + for ii in range(3): + norm += v0v1c[ii] * v0v1c[ii] + norm = np.sqrt(norm) + 1.0e-35 + #print(norm) + if norm > (0.087): # these vectors are not parallel (greater than 5 degrees) + for ii in range(3): + v0v1c[ii] = v0v1c[ii] / norm + + p0p1c = np.cross(p0,p1) + # p0p1c /= (np.linalg.norm(p0p1c))+1.0e-35 + #p0p1c = vectnorm(p0p1c) # faster to inline these functions + norm = numba.float32(0.0) + for ii in range(3): + norm += p0p1c[ii] * p0p1c[ii] + norm = np.sqrt(norm) + 1.0e-35 + for ii in range(3): + p0p1c[ii] = p0p1c[ii] / norm + + A[:,1] = p0p1c + B[1,:] = v0v1c + A[:,2] = np.cross(p0,p0p1c) + B[2,:] = np.cross(v0,v0v1c) + AB[counter, :,:] = A.dot(B) + whgood2[counter] = 1 + #AB = np.reshape(AB, (1,3,3)) + #quats[counter,:] = rotlib.om2quL(AB) + counter += 1 + else: # the two are parallel - throwout the result. + whgood2[counter] = 0 + counter +=1 + return AB, whgood2 + + @staticmethod + @numba.jit(nopython=True,cache=True,fastmath=True,parallel=False) + def _orientation_refine_loops_am(nGood, whGood, poles, bandnorms, polematch, n2Fit): + # this uses the method laid out by A. Morawiec 2020 Eq.4 for getting rotation matrix + # from imperfect poles + counter = 0 + + pflt = np.transpose(np.asarray(poles[polematch[whGood], :], dtype=np.float32)) + bndnorm = np.transpose(np.asarray(bandnorms[whGood,:], dtype=np.float32)) + + A = np.zeros((3, 3), dtype=np.float32) + B = np.zeros((3, 3), dtype=np.float32) + AB = np.zeros((n2Fit, 3, 3),dtype=np.float32) + #whgood2 = np.zeros(n2Fit, dtype=np.int32) + whgood2 = np.zeros(n2Fit, dtype=np.float32) + + for i in range(nGood): + v0 = bandnorms[whGood[i],:] + p0 = poles[polematch[whGood[i]],:] + + for j in range(i + 1,nGood): + v1 = bandnorms[whGood[j],:] + p1 = poles[polematch[whGood[j]],:] + v0v1c = np.cross(v0,v1) + p0p1c = np.cross(p0,p1) + p0p1add = p0 + p1 + v0v1add = v0 + v1 + p0p1sub = p0 - p1 + v0v1sub = v0 - v1 + + normPCross = numba.float32(0.0) + normVCross = numba.float32(0.0) + normPAdd = numba.float32(0.0) + normVAdd = numba.float32(0.0) + normPSub = numba.float32(0.0) + normVSub = numba.float32(0.0) + + for ii in range(3): + normPCross += p0p1c[ii] * p0p1c[ii] + normVCross += v0v1c[ii] * v0v1c[ii] + normPAdd += p0p1add[ii] * p0p1add[ii] + normVAdd += v0v1add[ii] * v0v1add[ii] + normPSub += p0p1sub[ii] * p0p1sub[ii] + normVSub += v0v1sub[ii] * v0v1sub[ii] + + normPCross = np.sqrt(normPCross) + 1.0e-35 + normVCross = np.sqrt(normVCross) + 1.0e-35 + normPAdd = np.sqrt(normPAdd) + 1.0e-35 + normVAdd = np.sqrt(normVAdd) + 1.0e-35 + normPSub = np.sqrt(normPSub) + 1.0e-35 + normVSub = np.sqrt(normVSub) + 1.0e-35 + + # print(norm) + if normVCross > (0.087): # these vectors are not parallel (greater than 5 degrees) + for ii in range(3): + v0v1c[ii] /= normVCross + p0p1c[ii] /= normPCross + v0v1add[ii] /= normVAdd + p0p1add[ii] /= normPAdd + v0v1sub[ii] /= normVSub + p0p1sub[ii] /= normPSub + + A[:,0] = p0p1c + B[0,:] = v0v1c + + A[:,1] = p0p1add + B[1,:] = v0v1add + + A[:,2] = p0p1sub + B[2,:] = v0v1sub + R = A.dot(B) + AB[counter,:,:] = A.dot(B) + + # test the fit of each candidate + testp = (R.dot(bndnorm)) + #test = pflt.dot(testp) + test = np.sum(pflt*testp, axis=0) + #print(test.shape) + #angfitTry = np.zeros((nGood), dtype=np.float32) + # angfitTry = np.max(test,axis=0) + #for qq in range(nGood): + # angfitTry[qq] = np.max(test[:, qq]) + # angfitTry[qq] = -1.0 if angfitTry[qq] < -1.0 else angfitTry[qq] + # angfitTry[qq] = 1.0 if angfitTry[qq] > 1.0 else angfitTry[qq] + #angfitTry = np.mean(np.arccos(angfitTry) * RADEG) + #print(test) + + + #whgood2[counter] = 1 + whgood2[counter] = 1.0 - np.mean(test) + #print(1.0 - np.mean(test)) + counter += 1 + else: # the two are parallel - throwout the result. + whgood2[counter] = np.float32(360.0) + counter += 1 + return AB,whgood2 + + + + def pairVoteOrientation(self,bandnormsIN,goNumba=True): + tic0 = timer() + nfam = self.tripLib.polefamilies.shape[0] + bandnorms = np.squeeze(bandnormsIN) + n_bands = np.int64(bandnorms.size / 3) + + bandangs = np.abs(bandnorms.dot(bandnorms.T)) + bandangs = np.clip(bandangs,-1.0,1.0) + bandangs = np.arccos(bandangs) * RADEG + + angTable = self.tripLib.completelib['angTable'] + sztable = angTable.shape + whGood = -1 + famIndx = self.tripLib.completelib['famIndex'] + nFam = self.tripLib.completelib['nFamily'] + poles = self.tripLib.completelib['polesCart'] + angTableReduce = angTable[famIndx,:] + polesReduce = poles[famIndx,:] + qsym = self.tripLib.qsymops + tic = timer() + + + if goNumba == True: + solutions, nsolutions, solutionVotes, solSrt = self._pairvote_nb(bandnorms, bandangs, qsym, angTableReduce, poles, polesReduce, self.angTol) + else: + solutions = np.empty((500, 24, 4), dtype=np.float32) + solutions[0,:,:] = rotlib.quat_multiply(qsym, np.array([1.0, 0.0, 0.0, 0.0], dtype=np.float32)) + solutionVotes = np.zeros(500, dtype=np.int32) + nsolutions = 1 + soltol = np.cos(5.0/RADEG/2.0) + + A = np.zeros((3,3), dtype=np.float32) + B = np.zeros((3,3), dtype=np.float32) # used for TRIAD calculations + + for i in range(n_bands): + for j in range(i + 1,n_bands): + + angPair = bandangs[i,j] + if angPair > 10.0: + angTest = (np.abs(angTableReduce - angPair)) <= self.angTol + wh = angTest.nonzero() + if len(wh[0] > 0): + + v0 = bandnorms[i,:] + v1 = bandnorms[j,:] + v0v1c = np.cross(v0,v1) + v0v1c /= np.linalg.norm(v0v1c) + B[0,:] = v0 + B[1,:] = v0v1c + B[2,:] = np.cross(v0,v0v1c) + for k in range(len(wh[0])): + + p0 = polesReduce[wh[0][k],:] + p1 = poles[wh[1][k],:] + p0p1c = np.cross(p0,p1) + p0p1c /= np.linalg.norm(v0v1c) + A[:,0] = p0 + A[:,1] = p0p1c + A[:,2] = np.cross(p0,p0p1c) + AB = A.dot(B) + + qAB = rotlib.om2qu(AB) + qABinv = rotlib.quatconj(qAB) + #qABsym = rotlib.quat_multiply(qsym, qAB) + + solutionFound = False + for q in range(nsolutions): + #rotlib.quat_multiplyLNN(q1,q2,n,intype,p=P) + + soltest = np.max( np.abs( (rotlib.quat_multiply((solutions[q,:,:]), qABinv))[:,0] )) + + if soltest >= soltol: + solutionVotes[q] += 1 + solutionFound = True + if solutionFound == False: + solutions[nsolutions, :, :] = rotlib.quat_multiply(qsym, qAB) + solutionVotes[nsolutions] += 1 + nsolutions += 1 + + solSrt = np.argsort(solutionVotes) + #print(nsolutions, solutionVotes[solSrt[-10:]]) + mxvote = np.max(solutionVotes) + #print(timer()-tic) + tic = timer() + if mxvote > 0: + whmxvotes = np.nonzero(solutionVotes == mxvote) + nmxvote = len(whmxvotes[0]) + fit = np.zeros(nmxvote, dtype=np.float32) + avequat = np.zeros((nmxvote,4),dtype=np.float32) + nMatch = np.zeros((nmxvote),dtype=np.float32) + poleMatch = np.zeros((nmxvote, n_bands), dtype=np.int32) - 1 + #cm = (mxvote-solutionVotes[solSrt[-nmxvote-1]])/np.sum(solutionVotes) + cm = mxvote/np.sum(solutionVotes) + for q in range(nmxvote): + testbands = rotlib.quat_vector(solutions[whmxvotes[0][q], 0, : ], bandnorms) + fittest = (testbands.dot(poles.T)).clip(-1.0, 1.0) + poleMatch1 = np.argmax(fittest, axis = 1) + fittemp = np.arccos(np.max(fittest,axis=1)) * RADEG + + whGood = np.nonzero(fittemp < self.angTol) + nMatch[q] = len(whGood[0]) + poleMatch[q,whGood[0]] = poleMatch1[whGood[0]] + + avequat1, fit1 = self._refine_orientation(bandnorms, whGood[0], poleMatch1) + + fit[q] = fit1 + avequat[q,:] = avequat1 + + keep = np.argmax(nMatch) + #print(timer() - tic) + return avequat[keep,:],fit[keep],cm,poleMatch[keep,:],nMatch[keep],(0,0) + + else: # no solutions + fit = 1000.0 + nMatch = -1 + avequat = np.zeros(4,dtype=np.float32) + polematch = np.array([-1]) + whGood = -1 + print(timer() - tic) + return avequat,fit,-1,polematch,nMatch,(0,0) + + + @staticmethod + @numba.jit(nopython=True,cache=True,fastmath=True,parallel=False) + def _pairvote_nb(bandnorms, bandangs, qsym, angTableReduce, poles, polesReduce, angTol): + n_bands = bandnorms.shape[0] + nsym = qsym.shape[0] + solutions = np.empty((500,24,4),dtype=np.float32) + solutions[0,:,:] = rotlib.quat_multiplyL(qsym,np.array([1.0,0.0,0.0,0.0],dtype=np.float32)) + solutionVotes = np.zeros(500, dtype=np.int32) + nsolutions = 1 + soltol = np.cos(5.0 / RADEG / 2.0) + + A = np.empty((3,3),dtype=np.float32) + B = np.empty((3,3),dtype=np.float32) # used for TRIAD calculations + AB = np.empty((1,3,3), dtype=np.float32) + qAB = np.empty((1,4), dtype=np.float32) + qABinv = np.empty((1,4),dtype=np.float32) + soltemp = np.empty((nsym,4), dtype=np.float32) + + for i in range(n_bands): + for j in range(i + 1,n_bands): + + angPair = bandangs[i,j] + if angPair > 10.0: + angTest = (np.abs(angTableReduce - angPair)) <= angTol + wh = angTest.nonzero() + if len(wh[0] > 0): + + v0 = bandnorms[i,:] + v1 = bandnorms[j,:] + v0v1c = np.cross(v0,v1) + v0v1c /= np.linalg.norm(v0v1c) + B[0,:] = v0 + B[1,:] = v0v1c + B[2,:] = np.cross(v0,v0v1c) + for k in range(len(wh[0])): + + p0 = polesReduce[wh[0][k],:] + p1 = poles[wh[1][k],:] + p0p1c = np.cross(p0,p1) + p0p1c /= np.linalg.norm(v0v1c) + A[:,0] = p0 + A[:,1] = p0p1c + A[:,2] = np.cross(p0,p0p1c) + AB[0,:,:] = A.dot(B) + + qAB = rotlib.om2quL(AB) + qABinv = rotlib.quatconjL(qAB) + #print(qABinv.shape) + # qABsym = rotlib.quat_multiply(qsym, qAB) + + solutionFound = False + for q in range(nsolutions): + # rotlib.quat_multiplyLNN(q1,q2,n,intype,p=P) + + soltemp = np.copy(solutions[q,:,:]) + #print(soltemp.shape) + soltemp = rotlib.quat_multiplyL(soltemp,qABinv) + + soltest = -1.0 + for qq in range(nsym): + if soltemp[qq,0] > soltest: + soltest = soltemp[qq,0] + + if soltest >= soltol: + solutionVotes[q] += 1 + solutionFound = True + if solutionFound == False: + solutions[nsolutions,:,:] = rotlib.quat_multiplyL(qsym,qAB) + solutionVotes[nsolutions] += 1 + nsolutions += 1 + + solSrt = np.argsort(solutionVotes) + + return solutions, nsolutions, solutionVotes, solSrt \ No newline at end of file From deac101cafd9b12a666455bfc8f12510971f44ee Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Fri, 2 Dec 2022 08:43:39 -0500 Subject: [PATCH 016/177] Fixed plotting in radon transform Signed-off by: David Rowenhorst --- pyebsdindex/band_detect.py | 6 +++--- pyebsdindex/opencl/band_detect_cl.py | 10 ++++++---- pyebsdindex/radon_fast.py | 4 ---- 3 files changed, 9 insertions(+), 11 deletions(-) diff --git a/pyebsdindex/band_detect.py b/pyebsdindex/band_detect.py index 1501f6b..e8fe1e1 100644 --- a/pyebsdindex/band_detect.py +++ b/pyebsdindex/band_detect.py @@ -369,14 +369,14 @@ def find_bands(self, patternsIn, verbose=0, chunksize=-1, **kwargs): width /= width.min() width *= 2 xplt = np.squeeze( - 180.0 - np.interp(bandData['aveloc'][-1, :, 1], np.arange(self.radonPlan.nTheta), self.radonPlan.theta)) + 180.0 - np.interp(bandData['aveloc'][-1, :, 1]+0.5, np.arange(self.radonPlan.nTheta), self.radonPlan.theta)) yplt = np.squeeze( - -1.0 * np.interp(bandData['aveloc'][-1, :, 0], np.arange(self.radonPlan.nRho), self.radonPlan.rho)) + -1.0 * np.interp(bandData['aveloc'][-1, :, 0]-0.5, np.arange(self.radonPlan.nRho), self.radonPlan.rho)) plt.scatter(y=yplt, x=xplt, c='r', s=width, zorder=2) for pt in range(self.nBands): - plt.annotate(str(pt + 1), np.squeeze([xplt[pt], yplt[pt]]), color='yellow') + plt.annotate(str(pt + 1), np.squeeze([xplt[pt]+4, yplt[pt]]), color='yellow') plt.xlim(0,180) plt.ylim(-self.rhoMax, self.rhoMax) plt.show() diff --git a/pyebsdindex/opencl/band_detect_cl.py b/pyebsdindex/opencl/band_detect_cl.py index c51a1e7..65cedaa 100644 --- a/pyebsdindex/opencl/band_detect_cl.py +++ b/pyebsdindex/opencl/band_detect_cl.py @@ -167,19 +167,21 @@ def find_bands(self, patternsIn, verbose=0, clparams=None, chunksize=528, useCPU im2show += 6 im2show[0:rhoMaskTrim,:] = 0 im2show[-rhoMaskTrim:,:] = 0 + im2show = np.fliplr(im2show) plt.imshow(im2show, cmap='gray', extent = [0, 180, -self.rhoMax, self.rhoMax], interpolation='none', zorder = 1, aspect='auto') + width = bandData['width'][-1, :] width /= width.min() - width *= 2 - xplt = np.squeeze(180.0 - np.interp(bandData['aveloc'][-1,:,1], np.arange(self.radonPlan.nTheta), self.radonPlan.theta)) - yplt = np.squeeze( -1.0 * np.interp(bandData['aveloc'][-1,:,0], np.arange(self.radonPlan.nRho), self.radonPlan.rho)) + width *= 2.0 + xplt = np.squeeze(180.0 - np.interp(bandData['aveloc'][-1,:,1]+0.5, np.arange(self.radonPlan.nTheta), self.radonPlan.theta)) + yplt = np.squeeze( -1.0 * np.interp(bandData['aveloc'][-1,:,0]-0.5, np.arange(self.radonPlan.nRho), self.radonPlan.rho)) plt.scatter(y=yplt, x=xplt, c='r', s=width, zorder = 2) for pt in range(self.nBands): - plt.annotate(str(pt + 1),np.squeeze([xplt[pt],yplt[pt]]), color='yellow') + plt.annotate(str(pt + 1),np.squeeze([xplt[pt]+4,yplt[pt]]), color='yellow') plt.xlim(0,180) plt.ylim(-self.rhoMax, self.rhoMax) plt.show() diff --git a/pyebsdindex/radon_fast.py b/pyebsdindex/radon_fast.py index a74385c..c1970e8 100644 --- a/pyebsdindex/radon_fast.py +++ b/pyebsdindex/radon_fast.py @@ -226,10 +226,6 @@ def radon2pole(self,bandData,PC=None,vendor='EDAX'): nPats = bandData.shape[0] nBands = bandData.shape[1] - # This translation from the Radon to theta and rho assumes that the first pixel read - # in off the detector is in the bottom left corner. -- No longer the assumption --- see below. - # theta = self.radonPlan.theta[np.array(bandData['aveloc'][:,:,1], dtype=np.int)]/RADEG - # rho = self.radonPlan.rho[np.array(bandData['aveloc'][:, :, 0], dtype=np.int)] # This translation from the Radon to theta and rho assumes that the first pixel read # in off the detector is in the top left corner. From b7b144d744c7ae6f0c8f21ff64f725280d0e8842 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Fri, 2 Dec 2022 08:44:02 -0500 Subject: [PATCH 017/177] Update demo with new phase info Signed-off by: David Rowenhorst --- doc/tutorials/ebsd_index_demo.ipynb | 236 +++++++++++++++++++--------- pyebsdindex/_ebsd_index_single.py | 14 +- pyebsdindex/ebsdfile.py | 2 +- pyebsdindex/tripletvote.py | 8 +- 4 files changed, 182 insertions(+), 78 deletions(-) diff --git a/doc/tutorials/ebsd_index_demo.ipynb b/doc/tutorials/ebsd_index_demo.ipynb index 8c616ae..fae54fe 100644 --- a/doc/tutorials/ebsd_index_demo.ipynb +++ b/doc/tutorials/ebsd_index_demo.ipynb @@ -19,7 +19,7 @@ "import numpy as np\n", "import h5py\n", "import copy\n", - "from pyebsdindex import ebsd_pattern, ebsd_index, pcopt\n", + "from pyebsdindex import tripletvote, ebsd_pattern, ebsd_index, pcopt\n", "from pyebsdindex.EBSDImage import IPFcolor" ] }, @@ -51,6 +51,101 @@ "vendor = 'EDAX'" ] }, + { + "cell_type": "markdown", + "id": "d2e5de7a", + "metadata": {}, + "source": [ + "Set up some phases. There are some shortcuts for common phases (FCC, BCC, HCP). It should be noted that the setting up of the phase information also is initializing the method used for indexing the detected bands. The default is to use triplet voting. \n", + "\n", + "For the first phase, we will use the shortcut method for FCC. In its shortest form it will act as a generic FCC phase. This will automatically define the space group, set a lattice parameter = [1.0, 1.0, 1.0, 90, 90, 90], and define a set of reflecting pole families and set the phase name to \"FCC\". " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "5b23e165", + "metadata": {}, + "outputs": [], + "source": [ + "fcc = tripletvote.addphase(libtype = 'FCC' )" + ] + }, + { + "cell_type": "markdown", + "id": "c5b6113b", + "metadata": {}, + "source": [ + "It is possible to override the defaults for any of the parameters and to set a phase name. For example:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "30a6ae6e", + "metadata": {}, + "outputs": [], + "source": [ + "austenite = tripletvote.addphase(libtype = 'FCC', phasename = 'Austenite', latticeparameter=[0.355, 0.355, 0.355, 90, 90, 90])" + ] + }, + { + "cell_type": "markdown", + "id": "93daab0a", + "metadata": {}, + "source": [ + "If the phase is not one of the shortcut phases, then the space group, lattice parameters, and reflecting families need to be defined. It should be noted that PyBSDIndex does no checks to make sure that the space group and lattice parameters have a matching specification. Thus, if hexagonal lattice parameters are input to a cubic space group, it will produce nonsense results. Here, we will use a BCC lattice as an example: " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "da0c7317", + "metadata": {}, + "outputs": [], + "source": [ + "ferrite = tripletvote.addphase(phasename = 'Ferrite',\n", + " spacegroup = 229, \n", + " latticeparameter=[0.286,0.286,0.286,90, 90, 90],\n", + " polefamilies =[[0, 1, 1], [0, 0, 2], [1, 1, 2], [0, 1, 3]])" + ] + }, + { + "cell_type": "markdown", + "id": "163cf125", + "metadata": {}, + "source": [ + "Finally, we need to put these into a list. As an implementation note, the default behavior is that if PyEBSDIndex matches at least seven bands to a phase, then the second phase is not even checked. This is set as a reasonable trade-off for speed to accuracy, but can be changed if desired. Thus, putting the phase that is most likely to be found in the scan first does tend to index faster. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "62139aac", + "metadata": {}, + "outputs": [], + "source": [ + "phaselist = [austenite, ferrite]" + ] + }, + { + "cell_type": "markdown", + "id": "4b1f77d8", + "metadata": {}, + "source": [ + "For the truly lazy among us, there is also the option to define the shortcut phases as part of the list, which can be mixed and matched with the fully initiated phases above:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "e57c71b9", + "metadata": {}, + "outputs": [], + "source": [ + "phaselistlazy = [austenite, 'BCC', 'HCP']" + ] + }, { "cell_type": "markdown", "id": "85912ba7-ca2e-4121-93da-3690dbe107dd", @@ -61,7 +156,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 8, "id": "47ed2c34-9aab-44fc-b288-7dd75ca94cee", "metadata": {}, "outputs": [], @@ -72,8 +167,7 @@ "rSig = 1.5 # amount of gassian 2nd derivate in rho in units of radon pixels.\n", "rhomask = 0.1 # fraction of radius to not analyze\n", "backgroundsub = False # enable/disable a simple background subtract of the patterns\n", - "nbands = 8\n", - "phaselist = ['FCC'] # ['FCC', 'BCC'] #\n" + "nbands = 8" ] }, { @@ -90,7 +184,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 10, "id": "dental-singapore", "metadata": {}, "outputs": [ @@ -98,11 +192,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "Radon Time: 0.08440714000000149\n", - "Convolution Time: 0.06287803200000042\n", - "Peak ID Time: 0.04174280400000008\n", - "Band Label Time: 0.053390679000001384\n", - "Total Band Find Time: 0.2426017819999995\n" + "Radon Time: 0.02504001599999839\n", + "Convolution Time: 0.036747964999999994\n", + "Peak ID Time: 0.03147739599999966\n", + "Band Label Time: 0.05178251400000278\n", + "Total Band Find Time: 0.1450898390000006\n" ] }, { @@ -121,7 +215,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Band Vote Time: 1.7276619170000007\n" + "Band Vote Time: 0.6377679150000013\n" ] } ], @@ -157,7 +251,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 29, "id": "sized-thanksgiving", "metadata": { "scrolled": true, @@ -169,7 +263,7 @@ "output_type": "stream", "text": [ "num cpu/gpu: 42 2\n", - "Completed: 853776 -- 854784 PPS: 9331;20804;12608 100% 68;0 running;remaining(s))\r" + "Completed: 853776 -- 854784 PPS: 19540;27028;19933 100% 43;0 running;remaining(s)Completed: 261072 -- 262080 PPS: 11487;26035;16619 36% 19;33 running;remaining(s)\r" ] } ], @@ -187,7 +281,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 30, "id": "5fa77d67-0581-42fc-80c2-ae37489256f3", "metadata": { "tags": [ @@ -198,16 +292,16 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 6, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -224,23 +318,23 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 31, "id": "3ee4b685-5a7c-4eae-a175-b2e86a5afcf0", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 7, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -257,23 +351,23 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 32, "id": "ea57565f-5ddd-4be5-aa43-793edb30b6f7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 8, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -305,7 +399,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 33, "id": "64d0f40a-b55c-4103-8335-ea27267f6e1a", "metadata": {}, "outputs": [ @@ -320,10 +414,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 9, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" }, @@ -362,7 +456,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 34, "id": "12433080-9bb9-408c-b252-024ebb80d58c", "metadata": {}, "outputs": [ @@ -370,11 +464,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "Radon Time: 1.9024419889999962\n", - "Convolution Time: 3.2117847400000272\n", - "Peak ID Time: 2.4455885659999836\n", - "Band Label Time: 2.6602684909999397\n", - "Total Band Find Time: 10.221808350999993\n" + "Radon Time: 1.2862144199993963\n", + "Convolution Time: 2.1463325300010183\n", + "Peak ID Time: 1.6447724460003883\n", + "Band Label Time: 1.938284111000712\n", + "Total Band Find Time: 7.018114552000043\n" ] }, { @@ -393,7 +487,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Band Vote Time: 49.01165675500002\n" + "Band Vote Time: 43.272813240999994\n" ] } ], @@ -416,7 +510,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 35, "id": "2d822d5b-0eab-462e-88c1-9c678c56578a", "metadata": {}, "outputs": [ @@ -425,7 +519,7 @@ "output_type": "stream", "text": [ "num cpu/gpu: 12 2\n", - "Completed: 57456 -- 58464 PPS: 10871;8826;4961 100% 12;0 running;remaining(s)\r" + "Completed: 58464 -- 59472 PPS: 14861;10115;6747 100% 9;0 running;remaining(s)\r" ] } ], @@ -435,23 +529,23 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 36, "id": "098a5371-ad47-43bf-ac41-a300db4cc30a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 13, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -484,7 +578,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 37, "id": "c373ce83-ca1a-49f6-afe5-695c75ab68eb", "metadata": {}, "outputs": [ @@ -498,10 +592,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 14, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" }, @@ -526,7 +620,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 38, "id": "f7f8c560-b14b-4f22-95aa-9754fa8ebe6e", "metadata": {}, "outputs": [ @@ -534,11 +628,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "Radon Time: 0.007801207999989401\n", - "Convolution Time: 0.015980276999982834\n", - "Peak ID Time: 0.015261748999989777\n", - "Band Label Time: 0.010229394999981878\n", - "Total Band Find Time: 0.04929643500000225\n" + "Radon Time: 0.007740249999869775\n", + "Convolution Time: 0.015711572999862256\n", + "Peak ID Time: 0.014265244000171151\n", + "Band Label Time: 0.009968713000034768\n", + "Total Band Find Time: 0.047706499000014446\n" ] }, { @@ -557,9 +651,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Band Vote Time: 0.0011322569999947518\n", + "Band Vote Time: 0.0010915779998867947\n", "('quat', 'iq', 'pq', 'cm', 'phase', 'fit', 'nmatch', 'matchattempts', 'totvotes')\n", - "[([ 0.65859226, -0.57491329, 0.48512319, -0.01965797], 0., 293405.7, 0.7235495, 0, 0.6052133, 8, [0, 1], 288)]\n" + "[([ 0.65859226, -0.57491329, 0.48512319, -0.01965797], 0., 293405.7, 0.7235495, 0, 0.6052132, 8, [0, 1], 288)]\n" ] } ], @@ -584,7 +678,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 39, "id": "0d60bb4f-917d-479a-8c04-f328154b9770", "metadata": {}, "outputs": [ @@ -592,16 +686,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "Radon Time: 0.002271457000006194\n", - "Convolution Time: 0.0021234639999931915\n", - "Peak ID Time: 0.0014744360000236156\n", - "Band Label Time: 3.650972825000025\n", - "Total Band Find Time: 3.656849706999992\n" + "Radon Time: 0.006342677000020558\n", + "Convolution Time: 0.015601840000044831\n", + "Peak ID Time: 0.014300557000069603\n", + "Band Label Time: 0.011138725000137129\n", + "Total Band Find Time: 0.0474029879999307\n" ] }, { "data": { - "image/png": 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bpSAcEbmFWF7aZZUFOFaiFeT2rVLU41k2npYpj1eyOHLnyX3uBfU8rI6l1v3lY3vBT++apXZfug55373F67VMllbsBUOtdGH9mre5fjal53ZiYqJHw/WK3KTW7y36omWUKZzec9/Pc5CzSvuxmOS+8u/S/x/yO3pZWW0ZyGMNuutoV5Y1OWoQBEFwyhATQRAEwZgzUq4hJufuyX3Ho5TPXMXFUrUmwTpPrVbrCWDrPH8d1M0FsK3cZGD5urRWHyK5r6y6zOU8a/pt192P+8pyt+VcTkypotVy1en74OWb56pTczUIpQVWLFeOds3ofSSeO5LJ9RXS48PuBv6OtTa1drNYOe/avWP1OwJsl4ce/1zlfW4MS5RcglIurx7JW0McAGZmZgC011+Xx7eeY8+Fps9nXXOVa+2HsAiCIAjGnJGzCHKLegB+1SJjVSF7VAmOMlYqX9UKRwuvv0+uu6l+39J4S9ciNS+vUrKKNl6ykKx752nQskJbB0E97S+Hl3YpNTBPXl0tLo9X6iMjz6u/4wWjFxYWulvuGKtTDfV9kMFo1kA50Mjatl4+06oS1pYAWyR8Ha1Wq3hfZRW0roj2qvM5tdIaj9LSpzJQzuSCu6VUYotSDyar+lkHePl+6K2sIGf0/dYdgxcWFnrSUaOyOAiCIBgoI2cRWP5bObOXip/k316al9ZU5N+lQimp7Xj+cGurNUPdi0bP9LkZP6fN6HN7/vOqcstj5PZh9JjLPvj6O1Zfn9J91uMG+MsOektu5lJzrc9KRVXW9/otmmu1Ti4zOjc3Z15rbl0IrZHy61wRmoc8pr5XuRRRz6eun3F5b0tWuRXf8woIrWVfS1a1vt/W973xkuflc7Pmv3HjRgDAhg0bAACzs7MAlo+pfqbZImR43OTxB11IxoRFEARBMObERBAEQTDmjJRryDPlZNWldh1YaWcld0gu0OkFBFeD7HmSW9xCIgOOVdtqVwlgS5m0WaxdZtrFttqUNc8lZLkvOAiqA2nW8oA6GFylWtVzk1jpsP24ADXe8XUwd3Z2Flu3bgWQfyZ4H32tnktRXqd3XK8/lTyP/o7GqnbW29yCLlW38n54aZf8utlsFoOq1r2t8juT78vPOajL8rK7x2ofrceLXUS6P1Gz2ex57lfTNcFipCaCIAiqMTu7gA9+8Js4++zDAIAbbvgZ7N9/9pClCk5VRmoiSCmZvTRksMQqvJLvy/dKvW1yqY39WAKlRcHl7K21I72ovNS6vUBdrj+Rldrm7SMDuhZW75ZSEZo1fl4A2OrE6Wn1uUVavB4zrFnpdLzFxcWewGXuOkpdNKtoafoeclCXg4gbNmzoCSzqwis+/uLiIt7ylt/H00+/Ebfe+lrUag00Gi9i69bZrgbKaaky4Kyfex341cuaTk1N9WiyughNpjx6SzCyLFbQ2CsK09ZozkrJ/WZLxaPWPqX+X5Z3gse9ilVUskat867m/6cqRIwgCE4xJifncPbZ+/DQQz8NAGi16lhYmB2yVMGpzEhZBEEQlNm8+VnMzW3CpZd+Htu3H8Azz5yDW255F5aWpoYtWnCKMlITAS9MY9UGaLNM54hbwRjPjLJa9JZMLsvFUuq7I/fV7hftIrCCoaW8b/1+s9nsObfn1pD5/V4etnU+rwo519vI64/C18pjk1Lqub+cj81bDiLz50CvC+jEiRMAgGPHji37XI6x16rcChR6eeseVq67Pj5fM+edb968Gdu2bQMAbNmyBcDJHHQeE76OyclnccYZB7B375X4+te3461v/Sre8pa/xDe/+Y6enHRZoe31t/LWSE4p9bjQdNKDdIXooKfnBpX3S39H13Po3/fU1FSPC0v/38BYfaJKdULSvVpqDW9du6wUl1u+H0tLSz2BX89tKN2kVnX8IBmIa4iIPktEh4joAfHediK6lYge62xPG8S5gmDcmZs7HSdOnIbnnvsbAIDHHnsNdu06OGSpglOZQVkE/wnAvwfwh+K9awDcnlL6BBFd03l9de4gKSUsLCxkO+15lZSyotLTFLRWL7URrZF4QVdrwRE+jlelKKulWaP1UhLltXsBJy8IbllDK0mD1RaCpXF5fWusVDgdYPQqv6enp7M9ZuRxpYWltTGuzOUtvy81MGlRWLJU/UzKZgWevcQB1o6l1XLkyBEAJ59laaFtfv55vP93fgcE4Esf/jBe+cqNOHp0L370o4246KLv4sCBzXj++ee712r1KfJSKXXa4vHjxwEs/11YVrQ3NqUAvGXp6sA1/044cC57KnnJDdZ98Kx1T8vPdT720mBz46T/v5LJB1avMG9bSkhZLQOZCFJKf05E56q3Lwfwts7fnwPwLRQmgiAIbM6/915sPnIESAmvuP9+/Omf/ize//4/AlEDhw9vw+c//45hixicwqxljODMlNJBAEgpHSSindaXiOgqAFcBJ32i2odZr9d7Znvtd5TwfrrvivarWT6+Ur9vmTKmU109DUimv3rapaWFe90zPe0jlw6rkb77fvqxWH5Y63zWUo/elr87Pz/f1WhZU66SIuihx09qnaWlEa3eNl6sRp9ncnKyGNfR1ym7j+oU0JQS7j9+HK+t1UAAbpybw7O3PYfbbvtb4hmfAzDXE2OR90XfO/0b0umdjUbD/R3kuvHqsdWdUTneI60UHfPTyzbK+6Tl1Mvb5u6Xp31bx7fGQ24ty8ZLzWXLph9y3WwHzdCDxSml6wFcDwCnn3762nRUCoJTnKc2bsQVl14KAKhPTiLyg4JBspZ1BM8Q0VkA0NkeWsNzBcFLH6L2vyAYMGtpEdwM4EoAn+hsbyrtUKvVsGnTpp7qUqCcvsYQUY/JplP2dMDXSnHUZqcVMKra0yjXNyi39VLdtKsoF5z03DCWye+lgPK2HxNVuuO0S0uPqbyn1qIccqtTEOW52J3AZjgvF8guRw44yoVpvPGRslnLV/J4AL3PlbX4iA6Y66rbo0eP4ujRowBOuos4DVYnA0xPT3evSaeaegvT1Go1sy+XlI2RaaSl1GgLLx1SB4A3bNjQU0UtF8UBTt5vDvyfOHGipz13qW9XFaz7XVoK00ol99yR0mVXNfCbq1gfdK+hQaWPfgnAXwF4BREdIKIPoT0BvIuIHgPwrs7rIAiCYMQYVNbQ/+x8dGk/x5mYmMDWrVvNxS+0Budp7jKA61kRWpOTqYRa89eaqdQWtGZSpY+Jp2FZqZpe+qbXaVJaNiVLQAZx5f7yO14BjcSTUb6vg5SsGep7trCw0KOFac1ZF0pJTVcnCehCM9mzx+t/ZFlD1tjJ4+nzyh49OlXQKxKcnJzsBlH5edy8efOy70rLlr+jl6rU581139SfWf2dvK6/OS1cB4BZ6+frYytm06ZNXfm11SgLsOT7S0tLPfdVp8rmusB6HT+tYkM9Lvr/i5zlpLV9KznB612ksazS6DUUBEEQDJSYCIIgCMacoaePSoiop+ITWL6IinYbaXO20Wj0BCO1ecnvszkN9AbXPNNamsZee+tS3rk8Lm91Tr80T7VZrpFmolyUQx5fm665vkHa7WYtJKLP7eWDT05Oum2D9b1uNBpd94oVINXjwp+XnglGBpq9oLp2/8gFV/S1azNdBjitPH75Hd6yjLOzs8vy662tlLXU/8ha29kLMHquR3nNOlBqJWR4OfS6hbV07+nnv0qSQC5RxBqL3LXr55efP8DvZ6avq0pVNSPbgetnvOQikt8dyWBxEARBcOoyUhYBozV5Obt6aVqWZqIDQ1ojYS1zenq6p5JRoys2Z2ZmejQfXZ0staZSr5NcpaOe/b1ePbJSs0pFrrfIiA7AW4FTxuv8KAOs3qIyWo6JiYmezotas7KCcF4/H69vkQyqa3L9gkoplFJ79lINdSqzvF5P42Rk0oMX7NRLJcpr0OfWqZpWVXhJ87SSG3QvIJZJB4AXFxfdhXS0tmx1H9X9xvqxeuW4yLGQVef6N6nHwrKUS16C3HF0hbSVfu6N8WoJiyAIgmDMGSmLgIhQr9fNlDGrmyJgL2DtpR7y1tJqLd+2dXyp2enCNL21Flr30styGrtOf8356bXcnj+zVqu5qWj6+NYymh6WFquvI2cNad+6Z+HI69F+fT1e1loPVeI4fPyqxVRWUZJX1GgtC6mvNadlagvAK4CUvxNvbPk71v0txYSsVGYvRZO1fy6YO3bsWLfTqbZk+Hiy+Iy3+nfnWaXWvdXfteIA2sugn389xvpvPR5aptzaHZbcsk+U3g6KsAiCIAjGnJgIgiAIxpyRdA151Yvee4Dd90N/Vy8Gwmbp/Px8T9WxbpnLfWt4OzMz0xNY9pY/tExU7ztWr6GqaanSTeKZktJk9YJgOpgo02yrtpSW3y/tI90XOmBqLdIikUswahNbm8/S5NfnzqUne71hPFeUleLoVSNLN5DXAyi3UJO3yImVmuhVxpaC39bx9fuNRsMdD10VrhcMAuzlGYGTldNyW2ohLvEq+L3EBbkglB5v7WLMVVUzlpuvatIBI1OYvUWwVktYBEEQBGPOSFkEOq1PBjq9/jHWYhF6tpcarfyuTGdj64DhY+hFO+TMrvu7eIU5uXRFJpemZwV6vWPooKGnyVm9jLQlYAWaq3btlFvPUmIsK6ifzoxWF1O5tXpBac1QL57CGqhMV/QWdtFB0cXFRbcHlt7K58sLzFppn17gV+/rvZbj4VkerVbL7bxqHUOPhw5k8/s8jrLTsA6e54q39LV4XUetIjcvdVNej9a+c8824yV/WIFg65wSK71a36sqiRv9EBZBEATBmBMTQRAEwZgzUq6hZrOJo0ePmm1ftXtHm5vytQ4W8nb79u0AgLPPPrt7PqAdtNIBrDPOOIyPfOS/dc+3c+cxfOUrP4lbb93dlU2bs9pclyakltMzuXMtdKu6TXLHZ2QVMstdyhm3cupLPW9km2jvu9Klps1ib11oK2ffq4i2ArY6F90L3ktTvhQgzyUF6Fx6fs60qwjodYvw8yVdney60ovyaHefHCc9Pvo3ZPWc8p5P6znwXFksi1wYiK/Lcwl5LdDlOsq5RZb4PFbnAYl1LO+50QF4q5alSm2AV4PhufmkS3LQPYaYkZoIRomDB7fg4x//2c4D3sLv/u7N2Lt3z7DFCoIgGDgjNRG0Wi0cP368Z4k7udCH1oD4O1Ir1BWs/B1e6OO0005btt24cWN3FmZN7cUXXwQAHD58GC972SN48cUzcPrpr8fMzPHueZhS18tms9k9LstkLXTDY8BbLwDladSSUtdLGYDytG69tTR2vY+lnemAnVedavVX8gKZUpP20v00ue6aulpU368qWL2GdHKDZ7UA/gIoVm8d/Zm3EA4jLYKSVWJVwpeWV5T9dnRadW5hF2vZWD6eHB/5LHo9xBhpBXgWjRfol/9/eGOgZZaULINarVY5pdVKomBKFfH9EjGCClx44T144IGLhi1GEATBmjBSFkFKadmMzLP03Nyc23Pe8iVq7dfTxqQ2wL1MGNZaNmyo4yd+4kH82Z99FDMzM8u0KK2J8mtrTQPZ55yvVcpopaR5/Us8rVxqG955crEIT5uR1+GNe46SHz7XM98rppLv62vx+i1J2b30Po4VWRaH1//eSu/1YiZaK5b7eJqhll/Krfv1M1prXlxc7Om55S21yjJNTk729OLPPXt6XDxLIKdReymb8v578nrWi3Ucr3urvMfedXjra8jPrHHh71ZNB8/9xmI9gnVmz54H8NxzL8OJE5uHLUoQBMGaEBOBYNu+fXjvBz+Id3zkI6h3/Kjnn78Xjz/+hiFLFgRBsHaMlGsIWN5i16pO9VIpc+1wdTtqXS1MRJiZmcEVDz2Eibk5zBw8iMf++I/x1O7t+Lt/93783u+9FouLTwFYXq2q3RTalJctbtk1pBfA8Sqmc4vA5K45V3Usx8+qyPXcSHzN0qwtbXOBLkYH++Q1e313GGm2ey3Ec25EHSDXbhMOqM7Pz/e4FhmvpfHU1FT3PnPKpFwESe5juUm8QLmUXVeOe4vxWIvZeJW4VmW8F8TNtXgupUPmUqQ1VoW01XpenqfKWFpuMKB9f7w+XbnUae9ZsALA3r2q4r5dK0ZuIhgmt557Ll5z+DAObdqE/Tt2oLVYw9VXfxgAMOAeT0EQBCPDSE0EXLyjg4jSStCzqJ6lJycne4pIWMs7evQoAOCFF14AABw5cqRHhm9t3QoA2HT33QCAHTt2AAC2bdsG4GQ30iqBKGtBF9YQOTite6DIQLkXzMtpCt5nuaC6lsHbynvjWWQ6kCaXkvQC2VL712mcWmvVmrRcZlQXJ1mdJYH22OpF0qUFIN+XyQve+FvBcJ0CzfebF1rR/YusXj16LOT7OvVTBz2tgKrWcK17Jbc5zVpfuxUs1vt6r+VxqxZxWVjPa66oELCtxypFbfJY8pq9QLAck1JX2SrFpZE+GgRBEAyUmAiCIAjGnJFyDQF2q2BesEZ+7gW6rOCkNgPZTOfK4rm5uWWLZAAnTXevp45cIEObnzrY3Wg0uq4I3T9GBzhlfrvnvvCC3jLY6pmolqmaqxaV1y7dSRp9f+S2ilnPn3t9j/QxclXn2lVjVfnqADB/R7cu1/UfEi9/XQaw+TO9SIuu0bCCiNrdIwPaOVeWvHYZvNT9iXQCg1Xl6wU0rer20sI9VlJCro5GH59fe+3ArXHz/p8otb+W5ALk/L6uUNeJBXJfr3JfH1/K7tUdDYo1twiI6DIiepSI9hHRNWt9viAIgqA/1tQiIKIJAP8BwLsAHADwHSK6OaX0kPX9Wq3W1Vo6+3e3/SxU4gU0vbRC2T2QNUUrxVTuMz093ZMi5gWNZbCYj6u1JksD1Ro0axknTpwAcHLJTdYKZUqkvmarb40OCpaCxZYWogPzVkqtd8+sVFMvcJnrx6L7A5VSKq2grnd82V+pyrPHr/W5+V7pa5fn0fdDa7ry2dRWp+6rxci0SC+lNRcc9Z4NfR1WYFlvrUr8Ul8oq5rX6xuUSzkuWcY61dw6jpfmaVk2OqCc6z5aWnpTWvqnqkXwRgD7Ukp/nVJaBHADgMvX+JxBEARBH6x1jOAcAE+K1wcA/A/yC0R0FYCrgHZ3UE6vU98ppiDqmRjoLe7QC2HLVD7t72ftkrVv1rplTxftG+bjam1fFgBZ3Q7l8bVmKr9TKjAjomJamdQytOXkaUDyfN59YDytSf6d860z2gLzUkFbrVZPuq5XNCS33kLu+nz1er14zdYz6HUZ1Zqi5WP3fOCyC6yXKqufH6vTZylm00+KotUby7MArL4+Opah4xR6mzsfY/2GPKvOS9UFeq/f8+nLWESVoj2rd5SU0bKuB20BaNbaIrB+OctGN6V0fUrpkpTSJdYkEARBEKwtaz0RHAAgV3PZDeDpNT5nEARB0Adr7Rr6DoALiOg8AE8BuALAB7MCGelbMj2r1IdFui+sdrRyHzYHZcCR8VrQShNfB5T5GLpqVLqevJbY3P6YkcFDPvemTZsAAFu2bOkZH5ZDB8y8RVus/k25JfJK6GOtxKy1gqw8Xro3kPxelaUp5fXohVOA3jRP3kcGTL1qUetZ1AFAvWCM9TxbbbklMgFAX5PXItlyG3oVrZ47oyS3lrkUOJWuKT0+pd+sJV9uAScvJVq7Ea1Fjaq2i5a/E33frTRoL1BdpedQyU25UtZ0IkgpNYjonwD4OoAJAJ9NKT24lucMTvLxj/8+5ucnkRKh2SR86lP/07BFCoJgBFnzgrKU0p8A+JMV7gtgeUqoF8iSs6wXGNIBIb2Yh0Wuj5AO4nFg2QpAekU2uUIRXQDHlsBW7ofUsRA4DZCIlqWYTk1N4eabr8b8/CYcO3YML385cPx4e6nN+fn5HmvB06ys8dBamVcM02q1XE3d2urj8z3yxsl6NjRSu+ctX7vXWdLSKr10P6tDplcAx+jnQHYH1d+xrNNSkNvqlFkqWMulXWqs59jTaD3rSI4T41n88jyelq+PZWnwVj8iubWCxZ5FKMfasjI99Ph7Msjzl1JNV0u0mAiCIBhzRq7FRDBY3vve/wsA8N3vvhH33femIUsTBMEoMnITgcxvt0x9z0y3FtNgtDmo+71YAc2Sm0Sawuy+8PLXa7WaGbyzjiv30bnuujKUW2Jz2m29Xu9e0+zsLO6667dx+PAMpqeP4M1v/g00mxfgBz/Y0x0DHZTM9SPi6/NabXv3zAqCeu2WZXtovZiQVytgyd1PwFEmDFjXbL1XchXI1tueKW+5KLwgt3ZtyTHVx/Ny3616C88lIauVtdw6gUAGwb2gqhcQtqqRvZoWeV1e8oHuAmDJz3huRBlUt+oF5PXJ69GuOi9o3Gg03DWjvXohq7o9XENBnpSA7oR3eme7FU888Xrs2PHXw5QsCIIRZaQsgpRST9c+YHnKld7mArNMqaq30Wj0aPFWz5kSXmqaJbeX7ifPw8fhILTuW3Po0KFlY7D98GFc/slPYnJuDt/86C/j6YtehYWFKdTrC9i1637cffd7ujLV63U36OmlIEoZvDRYfe2WhaY1LNldk8/N6bQ6qKePIfECvjnLwNNepcylRAUrrVRrgrlgt8azcKQmz3/re+UlJch9SmmKuec29zvRv5FS2qV1vFIH05zcueNrPGtOWr25cdHX5XkorPvuPT/ewkFWckA/wekqjNREEKyO8++8E9PHjqGWEl73nVvw+mv+XwAAURP79r0BTz75agCN/EGCIBg7RmoiYL+7pdF5GrWlbZZ6pFhFPl46pOf3l39bRXCA3VOF0VqZ1rhkz3xeUtPTvpkfz8/jp2o1JAB/sbgbt/+r13fjCe30xSeymoTX+0dqI163Rr1mgvQ7a8uCj5dLsdSy8D5WPyFPY2Ose6uvw1sIXf5duodWKqVnCVjPqFfApLX/ZrPppo1q2WQ8xooB5M5vXbNOCZX7enEwTZVCKT1u3j2w9u3Hf67PJ8fR+z/Aug4dK/OWGZX3QT/bVodg3q5V2igzUhNBsDoePfNMXPv+92NmcRHP7949bHGCIDhFiIngJcaPO0VmG4YsRxAEpw4jNRFwe2DLxPQCmzrwuLS05PZqYXSgVgZwvHbBeisDgl5gWQb5tEy5wDK/ZtcQu1s4gMrBY522Wq/Xu64grjrevHkzgJOppnJxHqsvkL5GfV2eS04HkWWKnL52Pf7SveH1BZLV09448Vjq1zl3jMa6z0wu6CnPJ3s+eUFhKx2w5OKwekrpyuuSm0yin1srfdhzRViuL+3GWYmrpp/K+9LxrYC/fm1tS/9/aLfbwsJCMQAv/69g1w93DODfpk4Hly5Uryp8UET6aBAEwZgzUhZBrdZeqrKKRpHrpOj1IdJBGakR5XoKyfNYGoknkwzoeQtWaMtDBmHlYuv8nsRa2CJnKUmZUko9AWqWTS/Co5eA1NfG55bIrqrc34gtGn08Psbs7GzXguG+SrxlbUkvMt5qtbrWAsvLn+l7KClpiLlul16BWT/dWi3t3wsWW1ZwqXOopeXrRIuqy4HK43oWhxUg94rqrGC0lt/qBsrocfaCulWsIn2MKqmo+nd54sSJ7m+GP7OebaBtqfPfvOVnni0E3ZdKWlu5Z3o1hEUQBEEw5sREEARBMOaMlGuIg8XyNbDcXCtVeVrrlHomcc5sZvMs58LxTFFtFsrveq4nvZXXoV1AXv63/KzUMln2R9Hn8dxLsnW13kcGofV59HrQ7CLSZj+7jOTx2O2jF/uR16NdfWxye+4G2fLZ67+TC0paY6nHzXMBei4PWQ/huZ4sSm4k+Yx7/XA8V5HlaqzSB6xqkNhK0tCuFT0mVi8gL5Caq+L17qFVD8F4CR9WDy79f49M4uBEDn5Ovd+H1Ycq5zJbDWERBEEQjDkjZRHICj1N1cCP1GK0RqIDp/J7nnakX8uZWQeSPU0uFxzT1cK5GT8XPOSt18tGaxuNRsPV4vl9DtDKgJeumNT76A6pcpnOUhdSeY0li0ZqiroyWQfBdVqpfC+XHszHKnU3zVWq60V/+Ly6stm6dq9C10rr9IKc8lil4HDOUvZSpeXnegw9q0g+816PKs86ksfVFoGV2OFV8HtegqmpKdf6ZKzfmFclz1v+PchlRr3nM7dk6FoRFkEQBMGYM9IWgdQwSuldVmGOp6FopLaktWSviMiKEZT8tfpaAbtni5TD+oyx+rJ4y+BZKaj6mll7Ya2e3+f0tlqt5mrO/F32e27btg0AcMYZZ2DXrl0AgJ07dy77jPdh2Y4ffwRnnXUNpqYOo9Fo4bHH3o7//t8vAQAcPXoUQO9aEvr65Wv9OSP95Xos+ull41mclmWmz2Npm7lnTV6H7Mel99Wyyec4Fwuw9rViZ/p3YaWY6ngI31+djry4uOim5ubGidH33er5Ver0acWivGVA9XfZYt6yZYub5swsLR3Fa1/7a0hpHiktYf/+N+Db3347AODFF1/sGRd5Xbl4zKAYqYkgGG9SmsD3v38Vjh69AD/+8Q/xnvf8SzzyyG4cPrxr2KIFwapotSZx772fxgsvNHDkyHO47LJ/g127zsOPfnTusEUDEK6hYIRoNM7A0aMXdP6exZEjZ2PTpiNDlioIBgGh2Wxby7VaE0RNVOh4sm6MnEVgBdoAv4eHZTbp93SgiJEmsA7YWOl3cmv1GvJcBtJlU6oItfAqTq3r1cfxArPSBcemL5u8uiWvdBnpFtJ6DHSvo02bNnXN5tNOOw1A210EnHQjsbuKKyvbx38CO3Y8gSNHrsSGDbPdyk29dKXV48YLguolPq1x0sH7xcXFnnPqAKcV6PRcELrFsAxwe/2urLbjVlol4Kfz1uv1HrdOyb0gA/H6vFbA1FtmVN87mSLKMmi3pK4gl8++19PI+o16fcBKAXn5mXZ7sqv09NPbKwBu3769+2zzM8zn4TFpu3+auOSSD2Nm5gD27Xs3Dh++AJOTve5h2cOIj6ED5KW+V/0SFkEwctRqJ3Dxxb+JBx/8ZSwtzQ5bnCAYEBPYu/cPcMstv4ft2x/H9u1PD1ugLiNlEaTUXqrS0v69AGCV7zKeZZCzPLS2IQNJOiCkZZManmfJWEE3Pq9nYVhBYo0XdJbFK16gTmsk8vxWEFLuw32FeHvw4EHcd999y76rj3HhI49g5vhxPPiGNwDUwC/8wn/CXXedjzvvnMXCwn4AJ7VK3QfJSj3la2YLgDU51jY3bNjQo5nrPjKcZsvLgspz87VxF1gdwK7Vaj0dJvX5rL4yOriqU02tIr3S4ibSIsh1M/W2paJJeQxtVWsLwbI09T3irbZsrKJJzxqS++qAb6kXl+yW6xWs8f1+4YUXALSfi2effRaS7nfn57F1/34c3roVS7Nty3Z+fh67du3Bzp33YP/+zT3jY6Xb6u9EsDh4SbHh2DH8/B/+IVKthud3noHX/OM78OMf78Sdd7552KIFwar5yS99CRf8yZ9g8WWzuPl/vw7zmMLExCL27Pk+7rjj7wxbvC4xEQRDZW52Fk+dey5mjx3D9MXzuOii7+LQoV345V/+XQDAN77xVnz/++cPWcogWBlb9+9HrdHA9OZjePdln0SLWgCaeOyx1+Hxx185bPG6UK7t6nqzY8eO9L73vc8MvlZ1j1gLS+QWoeCt52bxApA5s1NjuXn0PlblbKm9bq4Ft7fWrNx6i6do81Oa2rpiUruG2KXC6yw/88wzeOaZZwAsr+z1YFcBB5S55kAvTGMlFGh5dZUzux1mZma6rhlZ8Smvh03xEydOdN1DnO/NNQ3sIrJaD2tXh+4vo6tXrSQJ7aKQbkovKOy5Gq3ja1eOFeTVLjmd6y4D2Z68ulKdg62bN2/uvqfvQ65y2usTZLnJvJbVXiA+15+Ika3igfYzz25C3vJ4bX3xRfy9Rx/FvWeeib3nnIPJyUm3fxZj9UzyXHKf//znWca7U0qXYIWsKlhMRB8gogeJqEVEl6jPPkZE+4joUSJ692rOEwRBcCry4w0b8Aevex32nnPOsEXJslrX0AMA3g/g/5ZvEtGrAFwB4NUAzgZwGxFdmFLKtszjdLUqaWDaImDkjO4teqErH6W2ZMlkIbVv3U/G6stiBZv1d+T1yJRTT5Ozlpj0uk9qUko9XQ91GqQ+T6PR6GqC+h6x9sJa8uHDhwEAhw4dqmQJMKxJ6eDb1q1bAfRq1HwtUk6GtVfesmYvA/2snWktjV/XarVlqbAWWhufmJgo9oWyqka9QLx+ZuQzWerAKQO3OoirLQKd7jk3N9cdM7bwtDVUBV1RzuO4ZcuWriXA16Q1dF0hbz3jVRbW0eRST/V+LIt+jnhMXnjhBTz//PPLPuMx1B0EpqamutYob3Uyg7WAlk76GKnuoymlh1NKjxofXQ7ghpTSQkrpBwD2AXjjas4VBEEQrA1rFSw+B8Ad4vWBzns9ENFVAK4C2prC5OSk2bPH07As7cDrV85bnl2lRl2aYS3twktp9eIZUoZS90jLV1m6LhlX0Kmg+hjWUoxWTx75eaPRcPvF6+Iq1m527drVY3FoDV7KzNqwLkxjvzK/ttIvWU7tv9bacbPZ7MYyeKvjClJrZU2Wi4b4tbZSpNa2PA21ide85kOYnz8df/7n/wLASV+y9ANrC8Dz/8ueXHxNOq1WX9/8/HzWvy+3Mlbg3bMqcI8p3nLch8e2Xq/3pMx6sQj5W/WKPPVvVFpmrJF7FqDsw6R/OzomoC2qlFJPyrLV90jKIffna9V9nWSarb7GQXcmLU4ERHQbAKvZy7UppZu83Yz3zKh0Sul6ANcDwM6dO0cnch0EA+Kcc76CY8f2oF4/MWxRgsCkOBGklN65guMeALBHvN4NYHTK6IJgnZiaOoTt2/8Kjz76CzjvvK8NW5wgMFkr19DNAL5IRJ9GO1h8AYC7KglUr5vuCy8gawV7qgZMc+TcL1o2TS51T1+P3seqJtZBRP2+DOp6VZFWb6ZS5bIVNPMWz9Aycn8huTQfu3N0iqA0kdk1oBco0imIMh1TB9K024TdIzIYqvveeP1r6vV6T9thdgmxi4ivi689pdQ9/s6d/xEHD/5TTE8/uyxIbVXfeumXVsKCdqnoQK9OY5Q9k7zFYDRE1HXJ8bWed955AJZXafPY6HvD18rysywyHZeDz3yP9POlx0KnWnrjw3iV17olt0xL1gF3nXSg215v3rx5WZ8sCytVXf/OZEBZyirb5K/m/7Qcq5oIiOjvAfgdAGcA+K9E9L2U0rtTSg8S0Y0AHgLQAPArpYyhIHipsXHjN9FonIa5uVeiVnu2vMNLkLe//T/j3HMfwNzcZnzuc/982OIEDquaCFJKXwNg2rsppesAXNfP8TgonNO2vYU+pKZbWvJPaxBWypgOjlnFPt5nVjGXLoQqYQXI9fVp7WBpaamnwCu3GE/VFEepOeoFXLQ2ozt8Wot2sDap7520OHTHT92nxtLgdJGV1galpaADpnqc5HXqVEl+fejQoWXHZ9l23H03dn3722he18LG19yPCy/8Fmq1RUxNzeNv/s1/g9tv/wfLgrh8vd7CJJ6VZ33H6p3D21y3VCk/a6QbN27sWgIc8N2zp+3xZcvg5S9/efdzDpqz/IcOHcLU1E488sgRXHjhdd1unWwFHDt2rJuCydfO6OC91QG3tMCORFvE2nKyeiZZhV3WeM3MzLgyWcVo/XRSZjm05R29hoJghJk8ehRvuO46TCwtofH2aXz5M58BajVs2/Y9XHTR/4fbb/8HwxZxXVlcfBMajb8athhBgWhDHQSDhAggQgKQBuzHDYK1YqQsAg6qWu4Mr/+HZV55C1V4PYFypqR26VQxzaxAs64c1lQJPntbaY56i+/krtmrTtXuhnq93uNe8PoSMfPz893vsBmuKyhl3rRXi+FVxUpK5rl1z/RY6rE4fvx415Vh9Tey9nnove/FT+3bhzte8Qo8+Rd/0f3eTTf9NIjuc2UpLSJktYf23AzW+/oZ0/nquoZj27Zt3apgdg2dddZZAIAdO3YAOOnuazab3bbMXFX+5JNPAgCee+4JnH/+Ip544ollny8sLJito6UMejs1NeX2VbLaausgut7q2gCrDT6jg7nyOdYBXv3sWfdBb637LD+X42QFzVfDSE0EQfBS4PGzz8b3zzyz/WKEmjquG60Wpu+5BxMbN6LptOUIRouRmwhkDx9GvtYao6V98986WMhYloGnFZfSSAG//5HUVDxLQB+DsQKCXn8lqVVpjURbDVaQteqCKDK116uM1lqM7GCpUwC1rHLxFKvDo7VdXFx0A9dW1ShvrYVb9Pjw8flcHCTW/XZ0eqrs46SXOWQNWqZdAnbAkWW0Kn61VaKfEes58ILoeiu7tfLffK0PP/wwAOC73/3usrF4/vnnuxbBz3zve/ilRx/F0c2b8WtvehN27ZrHxRcfwV133bXsmjdu3Ijt27cvGx+v3478jWnrx+umuri46HZP1Vuri6rVM0y+n/v/g6mS5plL8faOU/r/pF/CiRkEwUA5PjmJWko4PjmJa665D7/923dg9+7j+Oxnb8W73vXEsMULDEbOIvDSR0uaqNTkeXbXsQJPI5KzrdfrPzdbl+IGue6mHlZhXJVui7luqVomr++9lEG/9iwMrZ3JtEWdoqktEHl9JS1Pa3KLi4s98QIdr7AWjrc6PErksoQc2+D1CDjlUS5j6aEtj9x6F54s2oc8NTXlpirrZ9u6Zi2TjtUwjUajq/FzN9gDBw4AAH74wx8C6E37BIC9AL788pfjUL2OxlUEYHn31pe9rD2eW7Zs6bGUdBEaPw9SY/d6SOn3G42G63fXXUEZaZXq/zdya0CUPAnyt+T9zvTve9D9hHKM3EQQBMGpz9PKHRuMNuEaCoIgGHNGyiIgInPBEdmS2WuHa7WU1iYYB4wYadpVrUKWsmo5PRllAFu7Mby0WNlfRLtjNPJ8bCZXSSf0etp4S//JFr1eNbIOvk5NTbntjq1UVC8oz2PAwUSZ6qiPw+TaOusEAm+5Rlnxy5/xufVCNfJ81vKYUn69TGGz2ewJqnvuSNmu27tG65p1mqh27/HzynK88MILeO655wAATz/d7hn51FNPAbDTdz342rmymF8D6HledZtla6EaXXXuVUpb/bq8RAs5Xl77b69aWP6W9O9Nn5eIelxP3u9b7uv9jgdFWARBEARjzshZBNaCDlKj1tq+tgxkjx6vMMPSHLyUutwC9V5qqVUoomXJFY0wWoPTW6twqkowXZ/PCzB6PY0kJQ1FWhHeEoLyfnlWimehtVqtHo3Q0wytXjRaq9SFa81ms7sfp3xy91EeJ63tT05O9jwvXv8m3s7Pz7vdQK3gN59bWtDW8aVm7Vm3/B3WtDlV9MiRI93AOFsJbAXpvkSbN292Fw3yEguAk6m3HJD3iqtkwoG3OJKV7pxbPpa/I7eyYM0rQLX+D/LSRy3rpNQjybM8rOMOirAIgiAIxpyYCIIgCMackXINpZSwuLiYdUHoLSOreD1XgFeNKXPqS+1dV9LjQ/bQ8YJJVjBXL9zimZRWryFt1nqBYPkZ41UaW7UNnokq3/fMZT2W8r5712i5d7zEAa93j9WSWd9/dvPMzMy4rjO9/q2VV67XUdZr88rcd+1y8IKU0v2pg6peTynLbeitxSuD4+z24gVq2O3DrcXZTSYXZ+FxkLUYwEn3D9dfzM3N9SxEVMWd67lOLBdLyb3qVeJLvP83rG4GpSphq1ZGLmgkjyGDyV4PsUERFkEQBMGYM1IWQavVwokTJ3q0V/030FsFKdEzrq7Us7QNPXNrbUnP+LVazU0vs7QB7/g5vGC0lt/Syr1AqQw0lwLLuaUMc4uAaJn6qZD0erPk0mz1s6DvnbU0o6d9W+mXXmdJ77wyoKm1bP1ayqGfIy85QFqY+jz9jLWXgMFybNq0adkCQ3IrlwrlsdFdQHVFubaolpaWeiquvb5RlmXjPSuy11guhVgeQ1rmXhfeqlq/JZv8nXi/HW/5VysNdtCERRAEQTDmjJRFkFJ70W89409NTfVotDnNsZTCpTUXqyCklMYo/XZef3RGajFa2/M6cUpfou4+qf3N0pfvaRVW8YrX04bPo9+31okoFd5Zn+X28e6dliHnP/VSNKX83v3QaZkzMzPmYuJSNn1/ZGGftgR0IZYV3ymtp5BS6lkPohSrkb8L7xnR3Uc3bdrUjQ3oGAF/l48vO6LqmIx+zeebmppyU2X1709aQ7nCLr2vFy/0uuXmxsnrOybP48Xk5PlLVrTlCVjrvkNhEQRBEIw5MREEQRCMOSPlGgJsl4/Vg8ZLY5RmlFftV6WCz9tXH0N+R5uBMjDluYKsQCCf32vJbKVD8lYHRr2+S81ms7jkor5WIiq6JHIuNC9Illu8YyUmsdeXRboaOeVTu2OsxWFKVcLaddBoNHrGRT9r3pKG8vieK0KmFmvXh9dOW/4uPLcYu4TkYjnsFuNz655MjLXQkRdklVv9W9FpsRqZwly1L5g8nteXSj4r3rPtPevyfujv6nsoU39L1fPyfF4K/KAIiyAIgmDMGSmLoFarYWZmxtSAtKbraUT6eIDf1c9KSfOCxXpml4te6PRU3avHWuzCS/+T2lTV2V8G31iz0lqZ1QHU0lbkVgctW61W9+9+Oj+W5B50IMyTX1pdXgdOjXz2SoVqlkWl0xY9Wa0gZS44Weokysj0TLaCdO8iHSCXPZP0s83Bb8saKqVder8t+bfXEVUeW/+WtHUlf+96/1znW956FkYuKSS31Kx+bSU8yPNZFrMes+g1FARBEAyUmAiCIAjGnJFyDRERpqens71hPHeGrMLUgSdtblpmnDZbS24AC5aB3TxsYi8tLRVb8lrVvKWaBm1aWjn12nyW+3rXYh2XKY2PVX3p1WjkzNvcNQLLx9H7juc6kLnojL4OufiJXidZV5tbvYG8qmDPBSkDzLoS1+qrVQqQWnn4urLXq5iWx9C1Ern7Xsq7Z+S99a4j92zosdNtqeV4leqDdDcA63nS/8f04xqyEgu82ph+GKleQ0T0KSJ6hIjuI6KvEdE28dnHiGgfET1KRO9etaRBEATBmrBai+BWAB9LKTWI6LcAfAzA1UT0KgBXAHg1gLMB3EZEF6aUilOf1EKsyjpPy7CCPUypL448l9b2ShqvRHft5M6K9XrdDVzqtNGcdm+lgALLA+ie1WMtvFPSzK3URC8A78m0uLjoBui8/jLWub2q21xFrpeaKKu2vfss76XuWZSzBPh8WtvWFhmT62DpkVIqat9a45Upsyybt/CK1TPJS0uW5y09R1ZA2Eud1H2c5LOR07aljDlZcr8/r0OA9/+IdR2MZfl5/5d4z5d17lzK9UpY1dFSSt9IKfGo3wFgd+fvywHckFJaSCn9AMA+AG9czbmCIAiCtWGQMYJ/COC/dP4+B+2JgTnQeS8Lp+ppzcJaeNsrGmq1Wj39XVgzz/Uc92IE+nPrPW/5TEvDq9rbfnp62i068nqSVEkpk9prVT+j1Z/I64djFcFprbJKvEWTuzZdKKX3sTRszxKwCo74GuWi6xJr7QRvfDxNzkoR9K7D6pnkaYyy7772ced62/AYeEt5WqmhXnFbbm0JL67jFVpq+QC/A6u87yU/f5Wi0pyllrMW5LHke/o+WDGa9aI4ERDRbQB2GR9dm1K6qfOdawE0AHyBdzO+b14VEV0F4CrgZGvbIAiCYP0oTgQppXfmPieiKwH8HIBL08kp7ACAPeJruwE87Rz/egDXA8DOnTvXbwoMgiAIAKzSNURElwG4GsBbU0onxEc3A/giEX0a7WDxBQDuqnJMaWLKIF8uoAUs77ejW/7q6kEhf/f4uWCk/K7cR5uKuSpeKwUQ6F0yT7qG2BWh0/1y5q1nmlouBO/acmiXgF4i0TofXwdfG2O5trwWwKVAs9zHc6FJt1WVFFaWP+dSlOeT1+VVB3vIY5SqVOX3vVTpKkt7llyMVnW7dmcwlmvIcgUBy1O9db8jLa+V5ltKE9bPjiWD58rJJabknhnPzZn7Telr59+HNbbevRoUq40R/HsA0wBu7VzwHSmlf5RSepCIbgTwENouo1+pkjEUBEEQrD+rmghSSudnPrsOwHX9HlMW41i9erygrrWgi5UKKF/LtEUviKvT/6QmoTUcRmtC9XrdTbvjQDZveWFvWRgnF0mRW50OKHvDeP2VJKW+QFaQsrRMppVKmeuwKs9vpdZ5loDU1jztyAv2Wft4GqNMT9XWjtdVU6ZdekFpK7jrafFWILW0nKUeAymTtlxLPY4sLI19NSmOnqZr9XHyLBgv9dSSm7F6Anmeg1zKetWisFy6c24hon4C1yshWkwEQRCMOTERBEEQjDkj12uoVL3qBSWlK4JdJ7y+quwbA/hVkoDfn0ifP+cyyLlUdE59rmLTq1TWa8vKALMOKGuXQa6qk/Hcb9Kl4vVu0W6MRqPhuquscfKCwZZ7h19XCeZpPDeGlfPuPXMaWc/A+3smfC4AzOg6EummLLWf1mMrFy3y1lPWriHAdnNaW/l70HjB78XFxRW5OLxgcT+1AP20xtbntVxT+n7n3EulgG/uekaysjgIgiA49RkpiwCw+6hYPXS0hmL1ntFbL91TVlBqDd0LzlgyebO01KS9gJ312gve8bHYQuB9FhYWejQ1b9EOqW14QTArFbSqNiYXMllJhWRpH6nJectneqmzMh15JR0mvWuW96fUw8iSVcur752UTQesPc03t9CRTru0EgxKPbKkTCUt1ZKptMCRxgqq6/shx7bfIGvuGWesZ0cnAeQsQau3ljyu54XIfWe1hEUQBEEw5tB69rMoQUSjI0wQBMGIIyzKu1NKl6z0OGERBEEQjDkxEQRBEIw5MREEQRCMOTERBEEQjDkxEQRBEIw5MREEQRCMOTERBEEQjDkxEQRBEIw5MREEQRCMOTERBEEQjDkxEQRBEIw5MREEQRCMOTERBEEQjDkxEQRBEIw5MREEQRCMOTERBEEQjDkxEQRBEIw5MREEQRCMOTERBEEQjDkxEQRBEIw5MREEQRCMOfXV7ExE/xrA5QBaAA4B+Psppac7n30MwIcANAH8akrp66XjXXzxxdi7d+9qRAqCIAj6ZLUWwadSSj+VUnotgFsA/EsAIKJXAbgCwKsBXAbgd4loYpXnCoIgCNaAVU0EKaUXxcuNAFLn78sB3JBSWkgp/QDAPgBvXM25giAIgrVhVa4hACCi6wD8rwCOAHh75+1zANwhvnag8561/1UAruq8XCCiB1Yr0xqwA8BzwxZCETJVI2SqzijKFTJV4xWr2bk4ERDRbQB2GR9dm1K6KaV0LYBrOzGBfwLgXwEg4/vJeA8ppesBXN85196U0iVVhV8vRlGukKkaIVN1RlGukKkaRLSq4GpxIkgpvbPisb4I4L+iPREcALBHfLYbwNN9SxcEQRCsOauKERDRBeLl+wA80vn7ZgBXENE0EZ0H4AIAd63mXEEQBMHasNoYwSeI6BVop4/uB/CPACCl9CAR3QjgIQANAL+SUmpWON71q5RnrRhFuUKmaoRM1RlFuUKmaqxKJkrJdN0HQRAEY0JUFgdBEIw5MREEQRCMOSMzERDRZUT0KBHtI6JrhiTDHiL6JhE9TEQPEtGvdd7fTkS3EtFjne1pQ5Btgoi+S0S3jIJMRLSNiL5CRI90xuunR0CmX+/ctweI6EtENDMMmYjos0R0SNbE5OQgoo91nvtHiejd6yjTpzr37z4i+hoRbRu2TOKz/42IEhHtGAWZiOgjnfM+SESfXE+ZPLmI6LVEdAcRfY+I9hLRG8Vn/cmVUhr6PwATAB4H8BMApgDcC+BVQ5DjLACv7/y9GcD3AbwKwCcBXNN5/xoAvzUE2f4p2im6t3ReD1UmAJ8D8OHO31MAtg1TJrQLFn8AYLbz+kYAf38YMgH4OwBeD+AB8Z4pR+f5uhfANIDzOr+DiXWS6WcA1Dt//9YoyNR5fw+Ar6OdgLJj2DKhXSh7G4Dpzuud6ylTRq5vAPjZzt/vAfCtlco1KhbBGwHsSyn9dUppEcANaLepWFdSSgdTSvd0/j4K4GG0/4O5HO3/+NDZ/vx6ykVEuwG8F8AfiLeHJhMRbUH7wfwMAKSUFlNKLwxTpg51ALNEVAewAe3alXWXKaX05wAOq7c9OdalHYslU0rpGymlRuflHWjX+wxVpg6/DeBfYHkR6jBl+scAPpFSWuh859B6ypSRKwHY0vl7K07WavUt16hMBOcAeFK8dltSrBdEdC6A1wG4E8CZKaWDQHuyALBzncX5P9D+YbTEe8OU6ScAPAvg/+m4q/6AiDYOU6aU0lMA/i2AJwAcBHAkpfSNYcqk8OQYlWf/HwL4087fQ5OJiN4H4KmU0r3qo2GO04UA/jYR3UlE3yaiN4yATADwUQCfIqIn0X72P7ZSuUZlIqjckmI9IKJNAP4IwEfT8sZ6w5Dl5wAcSindPUw5FHW0zdT/mFJ6HYDjaLs7hkbH53452qbw2QA2EtEvDVOmigz92Seia9Gu9/kCv2V8bc1lIqINAK5Fp4ux/th4b73GqQ7gNABvAvDPAdxIRDRkmYC2pfLrKaU9AH4dHQsdK5BrVCaCkWlJQUSTaE8CX0gpfbXz9jNEdFbn87PQXnthvXgzgPcR0Q/Rdpm9g4g+P2SZDgA4kFK6s/P6K2hPDMOU6Z0AfpBSejaltATgqwD+1pBlknhyDPXZJ6IrAfwcgF9MHQfzEGX6G2hP5Pd2nvfdAO4hol1DlAmdc381tbkLbct8x5BlAoAr0X7OAeDLOOn+6VuuUZkIvgPgAiI6j4im0F7L4Ob1FqIzy38GwMMppU+Lj25Ge9DR2d60XjKllD6WUtqdUjoX7XH5s5TSLw1Zph8BeJLaVeUAcCnaVeRDkwltl9CbiGhD5z5einaMZ5gySTw5htaOhYguA3A1gPellE4oWdddppTS/SmlnSmlczvP+wG0kzd+NCyZOvwxgHcAABFdiHZyxHNDlglo/+f+1s7f7wDwWOfv/uVaiwj3CqPi70E7S+dxtDubDkOGt6BtQt0H4Hudf+8BcDqA2zsDfTuA7UOS7204mTU0VJkAvBbA3s5Y/THapvOwZfoNtPtdPQDgP6OdNbHuMgH4EtpxiiW0/zP7UE4OtN0hjwN4FJ0skHWSaR/avmR+1n9v2DKpz3+ITtbQkMdpCsDnO8/VPQDesZ4yZeR6C4C70c4QuhPAxSuVK1pMBEEQjDmj4hoKgiAIhkRMBEEQBGNOTARBEARjTkwEQRAEY05MBEEQBGNOTARBEARjTkwEQRAEY87/D3q22xTk3NNZAAAAAElFTkSuQmCC", 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", 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" ] @@ -615,7 +709,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Band Vote Time: 0.0033817940000062663\n" + "Band Vote Time: 0.0010123380000095494\n" ] } ], @@ -640,7 +734,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 40, "id": "af99d03d-17ff-49ac-912a-b20f824f0c56", "metadata": {}, "outputs": [], @@ -662,7 +756,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 41, "id": "208e7444-0118-4f68-a997-50d9ab73d482", "metadata": {}, "outputs": [ @@ -691,7 +785,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 42, "id": "42230f97-5795-406e-9734-284ce1c75843", "metadata": { "tags": [] @@ -723,7 +817,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 43, "id": "c7310f4b-6b33-492b-af39-04a5cc3f8af3", "metadata": {}, "outputs": [ @@ -731,11 +825,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "Radon Time: 0.03291863800001238\n", - "Convolution Time: 0.08442998000003854\n", - "Peak ID Time: 0.06065696600001047\n", - "Band Label Time: 0.058645049999995535\n", - "Total Band Find Time: 0.23670069100001\n" + "Radon Time: 0.01922415099988939\n", + "Convolution Time: 0.053790638000009494\n", + "Peak ID Time: 0.035985807000088244\n", + "Band Label Time: 0.04844529600018177\n", + "Total Band Find Time: 0.1574961930000427\n" ] }, { @@ -754,9 +848,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Band Vote Time: 1.4537444439999945\n", + "Band Vote Time: 0.7439625819999947\n", "num cpu/gpu: 36 2\n", - "Completed: 853776 -- 854784 PPS: 32281;28769;20601 100% 41;0 running;remaining(s)pleted: 73584 -- 74592 PPS: 34125;18214;7491 9% 10;104 running;remaining(s)Completed: 756000 -- 757008 PPS: 36895;28707;19957 87% 37;6 running;remaining(s)\r" + "Completed: 853776 -- 854784 PPS: 18450;23542;19311 100% 44;0 running;remaining(s)\r" ] } ], diff --git a/pyebsdindex/_ebsd_index_single.py b/pyebsdindex/_ebsd_index_single.py index 4875fb0..67edb12 100644 --- a/pyebsdindex/_ebsd_index_single.py +++ b/pyebsdindex/_ebsd_index_single.py @@ -297,6 +297,7 @@ def __init__( rhoMaskFrac=0.15, nBands=9, patDim=None, + nband_earlyexit = 7, **kwargs ): """Create an EBSD indexer.""" @@ -352,6 +353,7 @@ def __init__( elif patDim is not None: self.bandDetectPlan.band_detect_setup(patDim=patDim) + self.nband_earlyexit = nband_earlyexit self.dataTemplate = np.dtype( [ ("quat", np.float64, 4), @@ -495,7 +497,15 @@ def index_pats( indxData["phase"] = -1 indxData["fit"] = 180.0 indxData["totvotes"] = 0 - earlyexit = max(7, shpBandDat[1]) + + if self.nband_earlyexit is None: + earlyexit = shpBandDat[1] # default to all the poles. + # for ph in self.phaselist: + # if hasattr(ph, 'nband_earlyexit'): + # earlyexit = min(earlyexit, ph.nband_earlyexit) + else: + earlyexit = self.nband_earlyexit + for i in range(npoints): bandNorm1 = bandNorm[i, :, :] bDat1 = bandData[i, :] @@ -515,7 +525,7 @@ def index_pats( matchAttempts, totvotes, ) = self.phaseLib[j].bandindex( - bandNorm1, band_intensity=bDat1["avemax"], verbose=verbose, + bandNorm1, band_intensity=bDat1["avemax"], band_widths=bDat1["width"], verbose=verbose, ) # avequat,fit,cm,bandmatch,nMatch, matchAttempts = self.phaseLib[j].pairVoteOrientation(bandNorm1,goNumba=True) if nMatch >= 3: diff --git a/pyebsdindex/ebsdfile.py b/pyebsdindex/ebsdfile.py index 99b4755..b3afe4f 100644 --- a/pyebsdindex/ebsdfile.py +++ b/pyebsdindex/ebsdfile.py @@ -24,7 +24,7 @@ def writeang(filename, indexer, data, f.write('# Formula '+'\t \r\n') f.write('# Info '+'\t\t \r\n') f.write('# Symmetry ' + str(phase.lauecode) + '\r\n') - f.write('# PointGroupID ' + str(phase.pointgroupid) + '\r\n') + #f.write('# PointGroupID ' + str(phase.pointgroupid) + '\r\n') f.write('# LatticeConstants '+ ' '.join(str(' {:.3f}'.format(x)) for x in phase.latticeParameter)+'\r\n') f.write('# NumberFamilies ' + str(phase.npolefamilies) + '\r\n') for i in range(phase.npolefamilies): diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index 07c4010..5f7cd2f 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -112,7 +112,7 @@ def __init__(self, latticeparameter=None, polefamilies = None, angTol=3.0, - n_band_early_exit = 8): + nband_earlyexit = 8): self.phaseName = None # User provided name of the phase. self.spacegroup = None # space group id 1-230 self.latticeParameter = None # 6 element array for the lattice parameter. @@ -127,7 +127,7 @@ def __init__(self, self.pointgroupid = None self.angTol = angTol - self.n_band_early_exit = n_band_early_exit + self.nband_earlyexit = nband_earlyexit self.high_fidelity = True # many objects to hold the information about the reflecting poles, angles between them ... @@ -396,7 +396,7 @@ def build_trip_lib(self): #self.tripID = libID - def bandindex(self, band_norms, band_intensity = None, verbose=0): + def bandindex(self, band_norms, band_intensity = None, band_widths=None, verbose=0): tic0 = timer() nfam = self.polefamilies.shape[0] bandnorms = np.squeeze(band_norms) @@ -433,7 +433,7 @@ def bandindex(self, band_norms, band_intensity = None, verbose=0): nFam = self.completelib['nFamily'] polesCart = self.completelib['polesCart'] angTol = self.angTol - n_band_early = np.int64(self.n_band_early_exit) + n_band_early = np.int64(self.nband_earlyexit) # this will check the vote, and return the exact band matching to specific poles of the best fitting solution. fit, polematch, nMatch, whGood, ij, R, fitb = \ From a67499ca6b7dba2599d7d883615cbcb8242ff100 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Fri, 2 Dec 2022 08:44:38 -0500 Subject: [PATCH 018/177] Revert "Update demo with new phase info" This reverts commit fcc041bb935ee34dc470e6a8df0ca508f634fa85. --- pyebsdindex/band_detect.py | 6 +++--- pyebsdindex/opencl/band_detect_cl.py | 10 ++++------ pyebsdindex/radon_fast.py | 4 ++++ 3 files changed, 11 insertions(+), 9 deletions(-) diff --git a/pyebsdindex/band_detect.py b/pyebsdindex/band_detect.py index e8fe1e1..1501f6b 100644 --- a/pyebsdindex/band_detect.py +++ b/pyebsdindex/band_detect.py @@ -369,14 +369,14 @@ def find_bands(self, patternsIn, verbose=0, chunksize=-1, **kwargs): width /= width.min() width *= 2 xplt = np.squeeze( - 180.0 - np.interp(bandData['aveloc'][-1, :, 1]+0.5, np.arange(self.radonPlan.nTheta), self.radonPlan.theta)) + 180.0 - np.interp(bandData['aveloc'][-1, :, 1], np.arange(self.radonPlan.nTheta), self.radonPlan.theta)) yplt = np.squeeze( - -1.0 * np.interp(bandData['aveloc'][-1, :, 0]-0.5, np.arange(self.radonPlan.nRho), self.radonPlan.rho)) + -1.0 * np.interp(bandData['aveloc'][-1, :, 0], np.arange(self.radonPlan.nRho), self.radonPlan.rho)) plt.scatter(y=yplt, x=xplt, c='r', s=width, zorder=2) for pt in range(self.nBands): - plt.annotate(str(pt + 1), np.squeeze([xplt[pt]+4, yplt[pt]]), color='yellow') + plt.annotate(str(pt + 1), np.squeeze([xplt[pt], yplt[pt]]), color='yellow') plt.xlim(0,180) plt.ylim(-self.rhoMax, self.rhoMax) plt.show() diff --git a/pyebsdindex/opencl/band_detect_cl.py b/pyebsdindex/opencl/band_detect_cl.py index 65cedaa..c51a1e7 100644 --- a/pyebsdindex/opencl/band_detect_cl.py +++ b/pyebsdindex/opencl/band_detect_cl.py @@ -167,21 +167,19 @@ def find_bands(self, patternsIn, verbose=0, clparams=None, chunksize=528, useCPU im2show += 6 im2show[0:rhoMaskTrim,:] = 0 im2show[-rhoMaskTrim:,:] = 0 - im2show = np.fliplr(im2show) plt.imshow(im2show, cmap='gray', extent = [0, 180, -self.rhoMax, self.rhoMax], interpolation='none', zorder = 1, aspect='auto') - width = bandData['width'][-1, :] width /= width.min() - width *= 2.0 - xplt = np.squeeze(180.0 - np.interp(bandData['aveloc'][-1,:,1]+0.5, np.arange(self.radonPlan.nTheta), self.radonPlan.theta)) - yplt = np.squeeze( -1.0 * np.interp(bandData['aveloc'][-1,:,0]-0.5, np.arange(self.radonPlan.nRho), self.radonPlan.rho)) + width *= 2 + xplt = np.squeeze(180.0 - np.interp(bandData['aveloc'][-1,:,1], np.arange(self.radonPlan.nTheta), self.radonPlan.theta)) + yplt = np.squeeze( -1.0 * np.interp(bandData['aveloc'][-1,:,0], np.arange(self.radonPlan.nRho), self.radonPlan.rho)) plt.scatter(y=yplt, x=xplt, c='r', s=width, zorder = 2) for pt in range(self.nBands): - plt.annotate(str(pt + 1),np.squeeze([xplt[pt]+4,yplt[pt]]), color='yellow') + plt.annotate(str(pt + 1),np.squeeze([xplt[pt],yplt[pt]]), color='yellow') plt.xlim(0,180) plt.ylim(-self.rhoMax, self.rhoMax) plt.show() diff --git a/pyebsdindex/radon_fast.py b/pyebsdindex/radon_fast.py index c1970e8..a74385c 100644 --- a/pyebsdindex/radon_fast.py +++ b/pyebsdindex/radon_fast.py @@ -226,6 +226,10 @@ def radon2pole(self,bandData,PC=None,vendor='EDAX'): nPats = bandData.shape[0] nBands = bandData.shape[1] + # This translation from the Radon to theta and rho assumes that the first pixel read + # in off the detector is in the bottom left corner. -- No longer the assumption --- see below. + # theta = self.radonPlan.theta[np.array(bandData['aveloc'][:,:,1], dtype=np.int)]/RADEG + # rho = self.radonPlan.rho[np.array(bandData['aveloc'][:, :, 0], dtype=np.int)] # This translation from the Radon to theta and rho assumes that the first pixel read # in off the detector is in the top left corner. From e8bacb64d8f7fa1d483a8a8006caa557559522ef Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Fri, 2 Dec 2022 08:45:17 -0500 Subject: [PATCH 019/177] Revert "Revert "Update demo with new phase info"" This reverts commit e86c9d1e942b20e9b6c855a7e2cf0ed07b61b7ac. --- pyebsdindex/band_detect.py | 6 +++--- pyebsdindex/opencl/band_detect_cl.py | 10 ++++++---- pyebsdindex/radon_fast.py | 4 ---- 3 files changed, 9 insertions(+), 11 deletions(-) diff --git a/pyebsdindex/band_detect.py b/pyebsdindex/band_detect.py index 1501f6b..e8fe1e1 100644 --- a/pyebsdindex/band_detect.py +++ b/pyebsdindex/band_detect.py @@ -369,14 +369,14 @@ def find_bands(self, patternsIn, verbose=0, chunksize=-1, **kwargs): width /= width.min() width *= 2 xplt = np.squeeze( - 180.0 - np.interp(bandData['aveloc'][-1, :, 1], np.arange(self.radonPlan.nTheta), self.radonPlan.theta)) + 180.0 - np.interp(bandData['aveloc'][-1, :, 1]+0.5, np.arange(self.radonPlan.nTheta), self.radonPlan.theta)) yplt = np.squeeze( - -1.0 * np.interp(bandData['aveloc'][-1, :, 0], np.arange(self.radonPlan.nRho), self.radonPlan.rho)) + -1.0 * np.interp(bandData['aveloc'][-1, :, 0]-0.5, np.arange(self.radonPlan.nRho), self.radonPlan.rho)) plt.scatter(y=yplt, x=xplt, c='r', s=width, zorder=2) for pt in range(self.nBands): - plt.annotate(str(pt + 1), np.squeeze([xplt[pt], yplt[pt]]), color='yellow') + plt.annotate(str(pt + 1), np.squeeze([xplt[pt]+4, yplt[pt]]), color='yellow') plt.xlim(0,180) plt.ylim(-self.rhoMax, self.rhoMax) plt.show() diff --git a/pyebsdindex/opencl/band_detect_cl.py b/pyebsdindex/opencl/band_detect_cl.py index c51a1e7..65cedaa 100644 --- a/pyebsdindex/opencl/band_detect_cl.py +++ b/pyebsdindex/opencl/band_detect_cl.py @@ -167,19 +167,21 @@ def find_bands(self, patternsIn, verbose=0, clparams=None, chunksize=528, useCPU im2show += 6 im2show[0:rhoMaskTrim,:] = 0 im2show[-rhoMaskTrim:,:] = 0 + im2show = np.fliplr(im2show) plt.imshow(im2show, cmap='gray', extent = [0, 180, -self.rhoMax, self.rhoMax], interpolation='none', zorder = 1, aspect='auto') + width = bandData['width'][-1, :] width /= width.min() - width *= 2 - xplt = np.squeeze(180.0 - np.interp(bandData['aveloc'][-1,:,1], np.arange(self.radonPlan.nTheta), self.radonPlan.theta)) - yplt = np.squeeze( -1.0 * np.interp(bandData['aveloc'][-1,:,0], np.arange(self.radonPlan.nRho), self.radonPlan.rho)) + width *= 2.0 + xplt = np.squeeze(180.0 - np.interp(bandData['aveloc'][-1,:,1]+0.5, np.arange(self.radonPlan.nTheta), self.radonPlan.theta)) + yplt = np.squeeze( -1.0 * np.interp(bandData['aveloc'][-1,:,0]-0.5, np.arange(self.radonPlan.nRho), self.radonPlan.rho)) plt.scatter(y=yplt, x=xplt, c='r', s=width, zorder = 2) for pt in range(self.nBands): - plt.annotate(str(pt + 1),np.squeeze([xplt[pt],yplt[pt]]), color='yellow') + plt.annotate(str(pt + 1),np.squeeze([xplt[pt]+4,yplt[pt]]), color='yellow') plt.xlim(0,180) plt.ylim(-self.rhoMax, self.rhoMax) plt.show() diff --git a/pyebsdindex/radon_fast.py b/pyebsdindex/radon_fast.py index a74385c..c1970e8 100644 --- a/pyebsdindex/radon_fast.py +++ b/pyebsdindex/radon_fast.py @@ -226,10 +226,6 @@ def radon2pole(self,bandData,PC=None,vendor='EDAX'): nPats = bandData.shape[0] nBands = bandData.shape[1] - # This translation from the Radon to theta and rho assumes that the first pixel read - # in off the detector is in the bottom left corner. -- No longer the assumption --- see below. - # theta = self.radonPlan.theta[np.array(bandData['aveloc'][:,:,1], dtype=np.int)]/RADEG - # rho = self.radonPlan.rho[np.array(bandData['aveloc'][:, :, 0], dtype=np.int)] # This translation from the Radon to theta and rho assumes that the first pixel read # in off the detector is in the top left corner. From 6cf5386c845820008cd3d3ed4f4c4eadbcc45723 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Thu, 22 Dec 2022 10:00:44 -0500 Subject: [PATCH 020/177] Code documentation Signed-off by: David Rowenhorst --- doc/tutorials/ebsd_index_demo.ipynb | 26 +++++++++++++++----------- pyebsdindex/_ebsd_index_parallel.py | 5 +++-- pyebsdindex/band_detect.py | 8 ++++++-- 3 files changed, 24 insertions(+), 15 deletions(-) diff --git a/doc/tutorials/ebsd_index_demo.ipynb b/doc/tutorials/ebsd_index_demo.ipynb index fae54fe..3e68a37 100644 --- a/doc/tutorials/ebsd_index_demo.ipynb +++ b/doc/tutorials/ebsd_index_demo.ipynb @@ -184,7 +184,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 16, "id": "dental-singapore", "metadata": {}, "outputs": [ @@ -192,11 +192,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "Radon Time: 0.02504001599999839\n", - "Convolution Time: 0.036747964999999994\n", - "Peak ID Time: 0.03147739599999966\n", - "Band Label Time: 0.05178251400000278\n", - "Total Band Find Time: 0.1450898390000006\n" + "Radon Time: 0.026666632969863713\n", + "Convolution Time: 0.03771136200521141\n", + "Peak ID Time: 0.030311049020383507\n", + "Band Label Time: 0.05217741505475715\n", + "Total Band Find Time: 0.14690838003298268\n" ] }, { @@ -215,7 +215,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Band Vote Time: 0.6377679150000013\n" + "Band Vote Time: 0.6370859500020742\n" ] } ], @@ -225,8 +225,11 @@ " backgroundSub = backgroundsub,\n", " nTheta = nT, nRho=nR,\n", " tSigma = tSig, rSigma = rSig,rhoMaskFrac=rhomask,nBands=nbands, \\\n", - " phaselist = phaselist, PC = PC, verbose = 2)\n", - "imshape = (indxer.fID.nRows, indxer.fID.nCols)" + " phaselist = phaselist, PC = PC, verbose = 0)\n", + "imshape = (indxer.fID.nRows, indxer.fID.nCols)\n", + "indxer.bandDetectPlan.useCPU = False\n", + "dat1,bnd1=ebsd_index.index_pats(filename = file,\n", + " patstart = 0, npats = 1000, ebsd_indexer_obj=indxer, verbose=2)" ] }, { @@ -251,7 +254,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 12, "id": "sized-thanksgiving", "metadata": { "scrolled": true, @@ -263,11 +266,12 @@ "output_type": "stream", "text": [ "num cpu/gpu: 42 2\n", - "Completed: 853776 -- 854784 PPS: 19540;27028;19933 100% 43;0 running;remaining(s)Completed: 261072 -- 262080 PPS: 11487;26035;16619 36% 19;33 running;remaining(s)\r" + "Completed: 853776 -- 854784 PPS: 4603;3775;3627 100% 236;0 running;remaining(s))\r" ] } ], "source": [ + "indxer.bandDetectPlan.useCPU = False\n", "data, bnddata = ebsd_index.index_pats_distributed(filename = file,patstart = 0, npats = -1, chunksize = 1008, ncpu = 42, ebsd_indexer_obj = indxer)" ] }, diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index 549eb6b..8012b84 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -278,7 +278,7 @@ def index_pats_distributed( logging_level=logging.WARNING, ) # Supress INFO messages from ray. - # Place indexer obj in shared memory store so all workers can use it + # Place indexer obj in shared memory store so all workers can use it - this is read only. remote_indexer = ray.put(indexer) # Get the function that will collect opencl parameters - if opencl # is not installed, this is None, and the program will automatically @@ -322,7 +322,8 @@ def index_pats_distributed( chunkave = 0.0 for i in range(n_cpu_nodes): job_pstart_end = p_indx_start_end.pop(0) - workers.append( + workers.append( # make a new Ray Actor that can call the indexer defined in shared memory. + # These actors are read/write, thus can initialize the GPU queues IndexerRay.options(num_cpus=1, num_gpus=ngpupnode).remote( i, clparamfunction, gpu_id=gpu_id ) diff --git a/pyebsdindex/band_detect.py b/pyebsdindex/band_detect.py index e8fe1e1..93ca60b 100644 --- a/pyebsdindex/band_detect.py +++ b/pyebsdindex/band_detect.py @@ -561,8 +561,12 @@ def band_label_numba(nBands,nPats,nRho,nTheta,rdnConv,rdnPad,lMaxRdn): det = (dxx * dyy - dxy * dxy) det = det if np.fabs(det) > 1e-12 else 1.0e-12 det = 1.0/det - cnn = c - (dyy * dx - dxy * dy) * det - rnn = r - (dxx * dy - dxy * dx) * det + dc = (dyy * dx - dxy * dy) * det + rc = (dxx * dy - dxy * dx) * det + dc = max(-1.0, dc) ; rc = max(-1.0, rc) + dc = min(1.0, dc) ; rc = min(1.0, rc) + cnn = c - dc + rnn = r - rc bandData_aveloc[q,i,:] = np.array([rnn,cnn]) bandData_valid[q,i] = 1 From ab842195f635f3d492a44d82f8f23915ceaf4e8b Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Fri, 23 Dec 2022 11:00:16 -0500 Subject: [PATCH 021/177] Bug fixes Signed-off by: David Rowenhorst --- pyebsdindex/ebsdfile.py | 2 +- pyebsdindex/tripletvote.py | 28 +++++++++++++++++----------- 2 files changed, 18 insertions(+), 12 deletions(-) diff --git a/pyebsdindex/ebsdfile.py b/pyebsdindex/ebsdfile.py index b3afe4f..13d354b 100644 --- a/pyebsdindex/ebsdfile.py +++ b/pyebsdindex/ebsdfile.py @@ -25,7 +25,7 @@ def writeang(filename, indexer, data, f.write('# Info '+'\t\t \r\n') f.write('# Symmetry ' + str(phase.lauecode) + '\r\n') #f.write('# PointGroupID ' + str(phase.pointgroupid) + '\r\n') - f.write('# LatticeConstants '+ ' '.join(str(' {:.3f}'.format(x)) for x in phase.latticeParameter)+'\r\n') + f.write('# LatticeConstants '+ ' '.join(str(' {:.3f}'.format(x)) for x in phase.latticeparameter)+'\r\n') f.write('# NumberFamilies ' + str(phase.npolefamilies) + '\r\n') for i in range(phase.npolefamilies): f.write('# hklFamilies \t' + (' '.join(str(x).rjust(2,' ') for x in phase.polefamilies[i, :])) + ' 1 0.00000 1' + '\r\n') diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index 5f7cd2f..0af846d 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -58,9 +58,9 @@ def addphase(libtype=None, phasename=None, if polefamilies is None: polefamilies = np.array([[0, 0, 2], [1, 1, 1], [0, 2, 2], [1, 1, 3]]) else: - polefamilies = np.array(polefamilies) + polefamilies = np.atleast_2d(np.array(polefamilies)) - # Set up a generic HCP + # Set up a generic BCC if str(libtype).upper() == 'BCC': if phasename is None: phasename = 'BCC' @@ -73,22 +73,23 @@ def addphase(libtype=None, phasename=None, if polefamilies is None: polefamilies = np.array([[0, 1, 1], [0, 0, 2], [1, 1, 2], [0, 1, 3]]) else: - polefamilies = np.array(polefamilies) + polefamilies = np.atleast_2d(np.array(polefamilies)) # Set up a generic HCP if str(libtype).upper() == 'HCP': if phasename is None: phasename = 'HCP' if spacegroup is None: - spacegroup = 229 + spacegroup = 194 if latticeparameter is None: latticeparameter = np.array([1.0, 1.0, 1.63, 90.0, 90.0, 120.0]) else: latticeparameter = np.array(latticeparameter) if polefamilies is None: - polefamilies = np.array([[1, 0, -1, 0], [1, 0, -1, 1], [0, 0, 0, 2], [1, 0, -1, 3], [1, 1, -2, 0], [1, 0, -1, 2]]) + polefamilies = np.array([ [0, 0, 0, 2], [1, 0, -1, 0],[1, 0, -1, 1], [1, 0, -1, 2], [1, 1, -2, 0], + [1, 0, -1, 3], [1, 1,-2, 2], [2,0,-2,1]]) else: - polefamilies = np.array(polefamilies) + polefamilies = np.atleast_2d(np.array(polefamilies)) else: if spacegroup is None: @@ -99,7 +100,7 @@ def addphase(libtype=None, phasename=None, polefamilies = np.array([[0, 0, 2], [1, 1, 1], [0, 2, 2], [1, 1, 3]]) triplib = BandIndexer(phasename=phasename, spacegroup=spacegroup, - latticeparameter=latticeparameter, polefamilies=polefamilies) + latticeparameter=latticeparameter, polefamilies=np.atleast_2d(polefamilies)) triplib.build_trip_lib() return triplib @@ -115,7 +116,7 @@ def __init__(self, nband_earlyexit = 8): self.phaseName = None # User provided name of the phase. self.spacegroup = None # space group id 1-230 - self.latticeParameter = None # 6 element array for the lattice parameter. + self.latticeparameter = None # 6 element array for the lattice parameter. self.polefamilies = None # array of integer pole normals that should have reflections self.npolefamilies = None # number of unique reflector families self.crystalmats = None # store the four crystal matrices useful for angle/cartisian conversions. @@ -247,7 +248,7 @@ def build_trip_lib(self): poles = np.array(self.polefamilies) if (self.lauecode == 62) or (self.lauecode == 6): if self.polefamilies.shape[-1] == 4: - poles = crystal_sym.hex4poles2hex3poles(np.array(self.poles)) + poles = crystal_sym.hex4poles2hex3poles(np.array(self.polefamilies)) poles = np.reshape(poles, (-1,3) ) npoles = poles.shape[0] @@ -278,6 +279,7 @@ def build_trip_lib(self): #sympolesN.append(self.xstalPlane2cart(family)) sympolesComplete = np.concatenate(sympolesComplete) + #print(sympolesComplete) nsyms = np.sum(nFamily).astype(np.int32) famindx = np.concatenate( ([0],np.cumsum(nFamComplete)) ) angs = [] @@ -314,13 +316,15 @@ def build_trip_lib(self): familyID.append([i,j]) polePairs.append(temp[k,:,:]) - angs = np.squeeze(np.array(angs)) + angs = np.atleast_1d(np.squeeze(np.array(angs))) nangs = angs.size familyID = np.array(familyID) polePairs = np.array(polePairs) stuff, nFamilyID = np.unique(familyID[:,0], return_counts=True) indx0FID = (np.concatenate( ([0],np.cumsum(nFamilyID)) ))[0:npoles] + #print(familyID) + #print(nFamilyID) #print(indx0FID) #This completely over previsions the arrays, this is essentially #N Choose K with N = number of angles and K = 3 @@ -332,6 +336,8 @@ def build_trip_lib(self): counter = 0 # now actually catalog all the triplet angles. for i in range(npoles): + if indx0FID[i] >= npoles: + break id0 = familyID[indx0FID[i], 0] for j in range(0,nFamilyID[i]): @@ -412,7 +418,7 @@ def bandindex(self, band_norms, band_intensity = None, band_widths=None, verbose tripid = self.angtriplets['familyid'] accumulator, bandFam, bandRank, band_cm = self._tripvote_numba(bandangs, self.lut, self.angTol, tripangs, tripid, nfam, n_bands) - + print(accumulator) if verbose > 2: print('band Vote time:',timer() - tic) From df5f4fd3c11adfe88e81c4eeadd9c474115cc2dd Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Fri, 23 Dec 2022 12:53:55 -0500 Subject: [PATCH 022/177] Checkpoint Signed-off by: David Rowenhorst --- pyebsdindex/tripletvote.py | 80 ++++++++++++++++++++++++++------------ 1 file changed, 56 insertions(+), 24 deletions(-) diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index 0af846d..06cde2f 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -86,7 +86,7 @@ def addphase(libtype=None, phasename=None, else: latticeparameter = np.array(latticeparameter) if polefamilies is None: - polefamilies = np.array([ [0, 0, 0, 2], [1, 0, -1, 0],[1, 0, -1, 1], [1, 0, -1, 2], [1, 1, -2, 0], + polefamilies = np.array([ [0, 0, 0, 2],[1, 0, -1, 0], [1, 0, -1, 1], [1, 0, -1, 2], [1, 1, -2, 0], [1, 0, -1, 3], [1, 1,-2, 2], [2,0,-2,1]]) else: polefamilies = np.atleast_2d(np.array(polefamilies)) @@ -418,7 +418,7 @@ def bandindex(self, band_norms, band_intensity = None, band_widths=None, verbose tripid = self.angtriplets['familyid'] accumulator, bandFam, bandRank, band_cm = self._tripvote_numba(bandangs, self.lut, self.angTol, tripangs, tripid, nfam, n_bands) - print(accumulator) + #print(accumulator) if verbose > 2: print('band Vote time:',timer() - tic) @@ -570,7 +570,7 @@ def _sortlib_id(self, libANG, libID, findDups = False): temp = np.squeeze(libANG[i,:]) srt = np.argsort(temp) libANG[i,:] = temp[srt] - srt2 = lut[:,srt[0], srt[1], srt[2]] + srt2 = np.squeeze(lut[:,srt[0], srt[1], srt[2]]) temp2 = libID[i,:] temp2 = temp2[srt2] libID[i,:] = temp2 @@ -779,49 +779,81 @@ def _tripvote_numba(bandangs, LUT, angTol, tripAngles, tripID, nfam, n_bands): accumulator = np.zeros((nfam, n_bands), dtype=np.int32) tshape = np.shape(tripAngles) ntrip = int(tshape[0]) - count = 0.0 + #count = 0.0 #angTest2 = np.zeros(ntrip, dtype=numba.boolean) #angTest2 = np.empty(ntrip,dtype=numba.boolean) for i in range(n_bands): for j in range(i + 1,n_bands): for k in range(j + 1,n_bands): - angtri = np.array([bandangs[i,j],bandangs[i,k],bandangs[j,k]], dtype=numba.float32) + ijk = [i,j,k] + angtri = np.array([bandangs[i,j],bandangs[i,k],bandangs[j,k]], dtype=np.float32) srt = angtri.argsort(kind='quicksort') #np.array(np.argsort(angtri), dtype=numba.int64) - srt2 = np.asarray(LUTTemp[:,srt[0],srt[1],srt[2]], dtype=numba.int64).copy() - unsrtFID = np.argsort(srt2,kind='quicksort').astype(np.int64) + + #srt2 = np.asarray(LUTTemp[:,srt[0],srt[1],srt[2]], dtype=numba.int64).copy() + #unsrtFID = np.argsort(srt2,kind='quicksort').astype(np.int64) angtriSRT = np.asarray(angtri[srt]) angTest = (np.abs(tripAngles - angtriSRT)) <= angTol for q in range(ntrip): - angTest2 = (angTest[q,0] + angTest[q,1] + angTest[q,2]) == 3 + #angTest2 = (angTest[q,0] + angTest[q,1] + angTest[q,2]) == 3 + angTest2 = (angTest[q, 0] and angTest[q, 1] and angTest[q, 2]) == True if angTest2: f = tripID[q,:] - f = f[unsrtFID] - accumulator[f[0],i] += 1 - accumulator[f[1],j] += 1 - accumulator[f[2],k] += 1 + #print(angtriSRT, tripAngles[q,:], f) + accumulator[f[0], ijk[srt[0]]] += 1 + accumulator[f[1], ijk[srt[1]]] += 1 + accumulator[f[2], ijk[srt[2]]] += 1 t1 = False t2 = False t3 = False if np.abs(angtriSRT[0] - angtriSRT[1]) < angTol: - accumulator[f[0],i] += 1 - accumulator[f[1],k] += 1 - accumulator[f[2],j] += 1 + accumulator[f[0],ijk[srt[0]]] += 1 + accumulator[f[1],ijk[srt[2]]] += 1 + accumulator[f[2],ijk[srt[1]]] += 1 t1 = True if np.abs(angtriSRT[1] - angtriSRT[2]) < angTol: - accumulator[f[0],j] += 1 - accumulator[f[1],i] += 1 - accumulator[f[2],k] += 1 + accumulator[f[0],ijk[srt[1]]] += 1 + accumulator[f[1],ijk[srt[0]]] += 1 + accumulator[f[2],ijk[srt[2]]] += 1 t2 = True if np.abs(angtriSRT[2] - angtriSRT[0]) < angTol: - accumulator[f[0],k] += 1 - accumulator[f[1],j] += 1 - accumulator[f[2],i] += 1 + accumulator[f[0],ijk[srt[2]]] += 1 + accumulator[f[1],ijk[srt[1]]] += 1 + accumulator[f[2],ijk[srt[0]]] += 1 t3 = True if (t1 and t2 and t3): - accumulator[f[0],k] += 1 - accumulator[f[1],i] += 1 - accumulator[f[2],j] += 1 + accumulator[f[0],ijk[srt[2]]] += 1 + accumulator[f[1],ijk[srt[0]]] += 1 + accumulator[f[2],ijk[srt[1]]] += 1 + + + # f = tripID[q,:] + # f = f[unsrtFID] + # accumulator[f[0],i] += 1 + # accumulator[f[1],j] += 1 + # accumulator[f[2],k] += 1 + # t1 = False + # t2 = False + # t3 = False + # if np.abs(angtriSRT[0] - angtriSRT[1]) < angTol: + # accumulator[f[0],i] += 1 + # accumulator[f[1],k] += 1 + # accumulator[f[2],j] += 1 + # t1 = True + # if np.abs(angtriSRT[1] - angtriSRT[2]) < angTol: + # accumulator[f[0],j] += 1 + # accumulator[f[1],i] += 1 + # accumulator[f[2],k] += 1 + # t2 = True + # if np.abs(angtriSRT[2] - angtriSRT[0]) < angTol: + # accumulator[f[0],k] += 1 + # accumulator[f[1],j] += 1 + # accumulator[f[2],i] += 1 + # t3 = True + # if (t1 and t2 and t3): + # accumulator[f[0],k] += 1 + # accumulator[f[1],i] += 1 + # accumulator[f[2],j] += 1 mxvote = np.zeros(n_bands, dtype=np.int32) tvotes = np.zeros(n_bands, dtype=np.int32) From 9da4f6923d1ef4de1ef7f171d146caffa88fab8f Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Fri, 23 Dec 2022 12:57:38 -0500 Subject: [PATCH 023/177] Signed-off by: David Rowenhorst --- pyebsdindex/tripletvote.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index 0af846d..b1c7d73 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -418,7 +418,7 @@ def bandindex(self, band_norms, band_intensity = None, band_widths=None, verbose tripid = self.angtriplets['familyid'] accumulator, bandFam, bandRank, band_cm = self._tripvote_numba(bandangs, self.lut, self.angTol, tripangs, tripid, nfam, n_bands) - print(accumulator) + #print(accumulator) if verbose > 2: print('band Vote time:',timer() - tic) From 03e9abcd98298c834fe303ad298e432fa5b72f30 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Fri, 23 Dec 2022 13:26:57 -0500 Subject: [PATCH 024/177] Reverse bugfix Signed-off by: David Rowenhorst --- pyebsdindex/tripletvote.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index 06cde2f..3903c5f 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -336,8 +336,8 @@ def build_trip_lib(self): counter = 0 # now actually catalog all the triplet angles. for i in range(npoles): - if indx0FID[i] >= npoles: - break + #if indx0FID[i] >= npoles: + # break id0 = familyID[indx0FID[i], 0] for j in range(0,nFamilyID[i]): From 3dab362489d8e24ba1c9bdd9c1911fa355c4d782 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Fri, 23 Dec 2022 13:39:31 -0500 Subject: [PATCH 025/177] Reverse Bugfix Signed-off by: David Rowenhorst --- pyebsdindex/tripletvote.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index b1c7d73..153758e 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -86,7 +86,7 @@ def addphase(libtype=None, phasename=None, else: latticeparameter = np.array(latticeparameter) if polefamilies is None: - polefamilies = np.array([ [0, 0, 0, 2], [1, 0, -1, 0],[1, 0, -1, 1], [1, 0, -1, 2], [1, 1, -2, 0], + polefamilies = np.array([ [1, 0, -1, 0],[0, 0, 0, 2],[1, 0, -1, 1], [1, 0, -1, 2], [1, 1, -2, 0], [1, 0, -1, 3], [1, 1,-2, 2], [2,0,-2,1]]) else: polefamilies = np.atleast_2d(np.array(polefamilies)) @@ -336,8 +336,8 @@ def build_trip_lib(self): counter = 0 # now actually catalog all the triplet angles. for i in range(npoles): - if indx0FID[i] >= npoles: - break + #if indx0FID[i] >= npoles: + # break id0 = familyID[indx0FID[i], 0] for j in range(0,nFamilyID[i]): @@ -418,7 +418,9 @@ def bandindex(self, band_norms, band_intensity = None, band_widths=None, verbose tripid = self.angtriplets['familyid'] accumulator, bandFam, bandRank, band_cm = self._tripvote_numba(bandangs, self.lut, self.angTol, tripangs, tripid, nfam, n_bands) - #print(accumulator) + if verbose >= 3: + print(accumulator) + if verbose > 2: print('band Vote time:',timer() - tic) From 1027702350b41fec549256be9ac50821ef1379f4 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Fri, 23 Dec 2022 13:47:45 -0500 Subject: [PATCH 026/177] Signed-off by: David Rowenhorst --- pyebsdindex/tripletvote.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index 3903c5f..7b1451d 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -86,7 +86,7 @@ def addphase(libtype=None, phasename=None, else: latticeparameter = np.array(latticeparameter) if polefamilies is None: - polefamilies = np.array([ [0, 0, 0, 2],[1, 0, -1, 0], [1, 0, -1, 1], [1, 0, -1, 2], [1, 1, -2, 0], + polefamilies = np.array([ [1, 0, -1, 0], [0, 0, 0, 2],[1, 0, -1, 1], [1, 0, -1, 2], [1, 1, -2, 0], [1, 0, -1, 3], [1, 1,-2, 2], [2,0,-2,1]]) else: polefamilies = np.atleast_2d(np.array(polefamilies)) @@ -419,6 +419,8 @@ def bandindex(self, band_norms, band_intensity = None, band_widths=None, verbose accumulator, bandFam, bandRank, band_cm = self._tripvote_numba(bandangs, self.lut, self.angTol, tripangs, tripid, nfam, n_bands) #print(accumulator) + if verbose >= 3: + print(accumulator) if verbose > 2: print('band Vote time:',timer() - tic) From fe63cd3dc8bae9a72d99ad9a61a528d2ab407469 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Fri, 23 Dec 2022 13:54:24 -0500 Subject: [PATCH 027/177] Checkpoint Signed-off by: David Rowenhorst --- pyebsdindex/tripletvote.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index 7b1451d..aec4f32 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -810,23 +810,23 @@ def _tripvote_numba(bandangs, LUT, angTol, tripAngles, tripID, nfam, n_bands): t3 = False if np.abs(angtriSRT[0] - angtriSRT[1]) < angTol: accumulator[f[0],ijk[srt[0]]] += 1 - accumulator[f[1],ijk[srt[2]]] += 1 accumulator[f[2],ijk[srt[1]]] += 1 + accumulator[f[1],ijk[srt[2]]] += 1 t1 = True if np.abs(angtriSRT[1] - angtriSRT[2]) < angTol: - accumulator[f[0],ijk[srt[1]]] += 1 accumulator[f[1],ijk[srt[0]]] += 1 + accumulator[f[0],ijk[srt[1]]] += 1 accumulator[f[2],ijk[srt[2]]] += 1 t2 = True if np.abs(angtriSRT[2] - angtriSRT[0]) < angTol: - accumulator[f[0],ijk[srt[2]]] += 1 - accumulator[f[1],ijk[srt[1]]] += 1 accumulator[f[2],ijk[srt[0]]] += 1 + accumulator[f[1],ijk[srt[1]]] += 1 + accumulator[f[0],ijk[srt[2]]] += 1 t3 = True if (t1 and t2 and t3): - accumulator[f[0],ijk[srt[2]]] += 1 - accumulator[f[1],ijk[srt[0]]] += 1 - accumulator[f[2],ijk[srt[1]]] += 1 + accumulator[f[2],ijk[srt[0]]] += 1 + accumulator[f[0],ijk[srt[1]]] += 1 + accumulator[f[1],ijk[srt[2]]] += 1 # f = tripID[q,:] From f83d50a242258fa11b7eed00ba4b56644479f7b1 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Mon, 26 Dec 2022 10:21:31 -0500 Subject: [PATCH 028/177] Signed-off by: David Rowenhorst --- pyebsdindex/tripletvote.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index aec4f32..7b1451d 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -810,23 +810,23 @@ def _tripvote_numba(bandangs, LUT, angTol, tripAngles, tripID, nfam, n_bands): t3 = False if np.abs(angtriSRT[0] - angtriSRT[1]) < angTol: accumulator[f[0],ijk[srt[0]]] += 1 - accumulator[f[2],ijk[srt[1]]] += 1 accumulator[f[1],ijk[srt[2]]] += 1 + accumulator[f[2],ijk[srt[1]]] += 1 t1 = True if np.abs(angtriSRT[1] - angtriSRT[2]) < angTol: - accumulator[f[1],ijk[srt[0]]] += 1 accumulator[f[0],ijk[srt[1]]] += 1 + accumulator[f[1],ijk[srt[0]]] += 1 accumulator[f[2],ijk[srt[2]]] += 1 t2 = True if np.abs(angtriSRT[2] - angtriSRT[0]) < angTol: - accumulator[f[2],ijk[srt[0]]] += 1 - accumulator[f[1],ijk[srt[1]]] += 1 accumulator[f[0],ijk[srt[2]]] += 1 + accumulator[f[1],ijk[srt[1]]] += 1 + accumulator[f[2],ijk[srt[0]]] += 1 t3 = True if (t1 and t2 and t3): - accumulator[f[2],ijk[srt[0]]] += 1 - accumulator[f[0],ijk[srt[1]]] += 1 - accumulator[f[1],ijk[srt[2]]] += 1 + accumulator[f[0],ijk[srt[2]]] += 1 + accumulator[f[1],ijk[srt[0]]] += 1 + accumulator[f[2],ijk[srt[1]]] += 1 # f = tripID[q,:] From e1e719a06c21428c1e4243a7a876b59e14c2f5e2 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Mon, 26 Dec 2022 10:36:15 -0500 Subject: [PATCH 029/177] Signed-off by: David Rowenhorst --- pyebsdindex/tripletvote.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index 7b1451d..db4daba 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -801,7 +801,7 @@ def _tripvote_numba(bandangs, LUT, angTol, tripAngles, tripID, nfam, n_bands): angTest2 = (angTest[q, 0] and angTest[q, 1] and angTest[q, 2]) == True if angTest2: f = tripID[q,:] - #print(angtriSRT, tripAngles[q,:], f) + print(angtriSRT, tripAngles[q,:], f) accumulator[f[0], ijk[srt[0]]] += 1 accumulator[f[1], ijk[srt[1]]] += 1 accumulator[f[2], ijk[srt[2]]] += 1 @@ -810,23 +810,23 @@ def _tripvote_numba(bandangs, LUT, angTol, tripAngles, tripID, nfam, n_bands): t3 = False if np.abs(angtriSRT[0] - angtriSRT[1]) < angTol: accumulator[f[0],ijk[srt[0]]] += 1 - accumulator[f[1],ijk[srt[2]]] += 1 - accumulator[f[2],ijk[srt[1]]] += 1 + accumulator[f[1],ijk[srt[1]]] += 1 + accumulator[f[2],ijk[srt[2]]] += 1 t1 = True if np.abs(angtriSRT[1] - angtriSRT[2]) < angTol: - accumulator[f[0],ijk[srt[1]]] += 1 - accumulator[f[1],ijk[srt[0]]] += 1 - accumulator[f[2],ijk[srt[2]]] += 1 + accumulator[f[0],ijk[srt[0]]] += 1 + accumulator[f[2],ijk[srt[1]]] += 1 + accumulator[f[1],ijk[srt[2]]] += 1 t2 = True if np.abs(angtriSRT[2] - angtriSRT[0]) < angTol: - accumulator[f[0],ijk[srt[2]]] += 1 - accumulator[f[1],ijk[srt[1]]] += 1 accumulator[f[2],ijk[srt[0]]] += 1 + accumulator[f[1],ijk[srt[1]]] += 1 + accumulator[f[0],ijk[srt[2]]] += 1 t3 = True if (t1 and t2 and t3): - accumulator[f[0],ijk[srt[2]]] += 1 accumulator[f[1],ijk[srt[0]]] += 1 accumulator[f[2],ijk[srt[1]]] += 1 + accumulator[f[0],ijk[srt[2]]] += 1 # f = tripID[q,:] From ea6816e4dadef2cdb054306778dd3524f9f170b2 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Mon, 26 Dec 2022 10:49:30 -0500 Subject: [PATCH 030/177] Signed-off by: David Rowenhorst --- pyebsdindex/tripletvote.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index db4daba..7b1451d 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -801,7 +801,7 @@ def _tripvote_numba(bandangs, LUT, angTol, tripAngles, tripID, nfam, n_bands): angTest2 = (angTest[q, 0] and angTest[q, 1] and angTest[q, 2]) == True if angTest2: f = tripID[q,:] - print(angtriSRT, tripAngles[q,:], f) + #print(angtriSRT, tripAngles[q,:], f) accumulator[f[0], ijk[srt[0]]] += 1 accumulator[f[1], ijk[srt[1]]] += 1 accumulator[f[2], ijk[srt[2]]] += 1 @@ -810,23 +810,23 @@ def _tripvote_numba(bandangs, LUT, angTol, tripAngles, tripID, nfam, n_bands): t3 = False if np.abs(angtriSRT[0] - angtriSRT[1]) < angTol: accumulator[f[0],ijk[srt[0]]] += 1 - accumulator[f[1],ijk[srt[1]]] += 1 - accumulator[f[2],ijk[srt[2]]] += 1 + accumulator[f[1],ijk[srt[2]]] += 1 + accumulator[f[2],ijk[srt[1]]] += 1 t1 = True if np.abs(angtriSRT[1] - angtriSRT[2]) < angTol: - accumulator[f[0],ijk[srt[0]]] += 1 - accumulator[f[2],ijk[srt[1]]] += 1 - accumulator[f[1],ijk[srt[2]]] += 1 + accumulator[f[0],ijk[srt[1]]] += 1 + accumulator[f[1],ijk[srt[0]]] += 1 + accumulator[f[2],ijk[srt[2]]] += 1 t2 = True if np.abs(angtriSRT[2] - angtriSRT[0]) < angTol: - accumulator[f[2],ijk[srt[0]]] += 1 - accumulator[f[1],ijk[srt[1]]] += 1 accumulator[f[0],ijk[srt[2]]] += 1 + accumulator[f[1],ijk[srt[1]]] += 1 + accumulator[f[2],ijk[srt[0]]] += 1 t3 = True if (t1 and t2 and t3): + accumulator[f[0],ijk[srt[2]]] += 1 accumulator[f[1],ijk[srt[0]]] += 1 accumulator[f[2],ijk[srt[1]]] += 1 - accumulator[f[0],ijk[srt[2]]] += 1 # f = tripID[q,:] From ef3c37ce86da1508896b517410fdaf384bd18b58 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Tue, 27 Dec 2022 16:00:25 -0500 Subject: [PATCH 031/177] Signed-off by: David Rowenhorst --- pyebsdindex/tripletvote.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index 7b1451d..4124dec 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -573,7 +573,7 @@ def _sortlib_id(self, libANG, libID, findDups = False): srt = np.argsort(temp) libANG[i,:] = temp[srt] srt2 = np.squeeze(lut[:,srt[0], srt[1], srt[2]]) - temp2 = libID[i,:] + temp2 = np.squeeze(libID[i,:]) temp2 = temp2[srt2] libID[i,:] = temp2 @@ -801,7 +801,7 @@ def _tripvote_numba(bandangs, LUT, angTol, tripAngles, tripID, nfam, n_bands): angTest2 = (angTest[q, 0] and angTest[q, 1] and angTest[q, 2]) == True if angTest2: f = tripID[q,:] - #print(angtriSRT, tripAngles[q,:], f) + print(f, ijk[srt[0]], ijk[srt[1]], ijk[srt[2]]) accumulator[f[0], ijk[srt[0]]] += 1 accumulator[f[1], ijk[srt[1]]] += 1 accumulator[f[2], ijk[srt[2]]] += 1 From a543260bd84938c14e9ed249837e323fbe43cab2 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Mon, 2 Jan 2023 09:22:21 -0500 Subject: [PATCH 032/177] Correct vote sorting Signed-off by: David Rowenhorst --- pyebsdindex/tripletvote.py | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index 153758e..a2fa24b 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -806,14 +806,14 @@ def _tripvote_numba(bandangs, LUT, angTol, tripAngles, tripID, nfam, n_bands): t2 = False t3 = False if np.abs(angtriSRT[0] - angtriSRT[1]) < angTol: - accumulator[f[0],i] += 1 - accumulator[f[1],k] += 1 - accumulator[f[2],j] += 1 - t1 = True - if np.abs(angtriSRT[1] - angtriSRT[2]) < angTol: accumulator[f[0],j] += 1 accumulator[f[1],i] += 1 accumulator[f[2],k] += 1 + t1 = True + if np.abs(angtriSRT[1] - angtriSRT[2]) < angTol: + accumulator[f[0],i] += 1 + accumulator[f[1],k] += 1 + accumulator[f[2],j] += 1 t2 = True if np.abs(angtriSRT[2] - angtriSRT[0]) < angTol: accumulator[f[0],k] += 1 @@ -825,6 +825,10 @@ def _tripvote_numba(bandangs, LUT, angTol, tripAngles, tripID, nfam, n_bands): accumulator[f[1],i] += 1 accumulator[f[2],j] += 1 + accumulator[f[0], j] += 1 + accumulator[f[1], k] += 1 + accumulator[f[2], i] += 1 + mxvote = np.zeros(n_bands, dtype=np.int32) tvotes = np.zeros(n_bands, dtype=np.int32) band_cm = np.zeros(n_bands, dtype=np.float32) From ccc014a52af66fdf2902705ae971ae5b5e21e70a Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Mon, 2 Jan 2023 14:12:50 -0500 Subject: [PATCH 033/177] Better report on nmatch attempts Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_single.py | 2 +- pyebsdindex/tripletvote.py | 8 ++++---- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/pyebsdindex/_ebsd_index_single.py b/pyebsdindex/_ebsd_index_single.py index 67edb12..77af431 100644 --- a/pyebsdindex/_ebsd_index_single.py +++ b/pyebsdindex/_ebsd_index_single.py @@ -363,7 +363,7 @@ def __init__( ("phase", np.int32), ("fit", np.float32), ("nmatch", np.int32), - ("matchattempts", np.int32, 2), + ("matchattempts", np.int32, 4), ("totvotes", np.int32), ] ) diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index a2fa24b..901996a 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -876,7 +876,7 @@ def _assign_bands_nb(polesCart, bandRank_arg, familyLabel, famIndx, nFam, angTab #fit = np.float32(360.0) #whGood = np.zeros(nBnds, dtype=np.int64) - 1 nGood = np.int64(-1) - ij = (-1,-1) + ij = (-1,-1, -1,-1) for ii in range(nBnds-1): for jj in range(ii+1,nBnds): @@ -994,7 +994,7 @@ def _assign_bands_nb(polesCart, bandRank_arg, familyLabel, famIndx, nFam, angTab whGood_out = whGood polematch_out = polematch Rout = R - ij = (bnd1,bnd2) + ij = (ii, jj, bnd1,bnd2) break else: if nMatch < nGood: @@ -1004,7 +1004,7 @@ def _assign_bands_nb(polesCart, bandRank_arg, familyLabel, famIndx, nFam, angTab whGood_out = whGood polematch_out = polematch Rout = R - ij = (bnd1, bnd2) + ij = (ii, jj, bnd1,bnd2) elif nMatch == nGood: if fitout > fit: fitout = np.float32(fit) @@ -1013,7 +1013,7 @@ def _assign_bands_nb(polesCart, bandRank_arg, familyLabel, famIndx, nFam, angTab whGood_out = whGood polematch_out = polematch Rout = R - ij = (bnd1, bnd2) + ij = (ii, jj, bnd1,bnd2) if nMatch >= (n_band_early): break #quatout = rotlib.om2quL(Rout) From d09640f80d33e4f7514f3745eb5672c7a2d6cf4d Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Mon, 2 Jan 2023 15:04:15 -0500 Subject: [PATCH 034/177] Triplet weighted vote. Signed-off by: David Rowenhorst --- pyebsdindex/tripletvote.py | 216 ++++++++++++++++++++++++++----------- 1 file changed, 155 insertions(+), 61 deletions(-) diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index 901996a..f73f99a 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -414,12 +414,21 @@ def bandindex(self, band_norms, band_intensity = None, band_widths=None, verbose bandangs = np.clip(bandangs, -1.0, 1.0) bandangs = np.arccos(bandangs)*RADEG + tripangs = self.angtriplets['angles'] tripid = self.angtriplets['familyid'] + pairangs = self.angpairs['angles'] + pairfam = self.angpairs['familyid'] accumulator, bandFam, bandRank, band_cm = self._tripvote_numba(bandangs, self.lut, self.angTol, tripangs, tripid, nfam, n_bands) + #accumulator, bandFam, bandRank, band_cm = self._pairvote_numba(bandangs, self.angTol, pairangs, pairfam, + # nfam, n_bands) if verbose >= 3: - print(accumulator) + with np.printoptions(precision=2, suppress=True): + print(accumulator) + print(bandRank) + print(bandFam) + if verbose > 2: @@ -427,7 +436,7 @@ def bandindex(self, band_norms, band_intensity = None, band_widths=None, verbose tic = timer() sumaccum = np.sum(accumulator) - bandRank_arg = np.argsort(bandRank).astype(np.int64) + bandRank_arg = np.argsort(bandRank).astype(np.int64) # n_bands - np.arange(n_bands, dtype=np.int64) # test = 0 fit = 1000.0 nMatch = -1 @@ -448,7 +457,11 @@ def bandindex(self, band_norms, band_intensity = None, band_widths=None, verbose self._assign_bands_nb(polesCart, bandRank_arg, bandFam, famIndx, nFam, angTable, bandnorms, angTol, n_band_early) if verbose > 2: + print(self.completelib['familyid'][polematch]) + #print(polematch) + #print(fit, fitb, fitb[whGood]) print('band index: ',timer() - tic) + tic = timer() cm2 = 0.0 @@ -461,7 +474,10 @@ def bandindex(self, band_norms, band_intensity = None, band_widths=None, verbose weights6 = band_intensity[whgood6] pflt6 = (np.asarray(polesCart[polematch[whgood6], :], dtype=np.float64)) bndnorm6 = (np.asarray(bandnorms[whgood6, :], dtype=np.float64)) - + #print('____') + #print(pflt6) + #print(bndnorm6) + #print('____') avequat, fit = self._refine_orientation_quest(bndnorm6, pflt6, weights=weights6) fit = np.arccos(np.clip(fit, -1.0, 1.0))*RADEG #avequat, fit = self.refine_orientation(bandnorms,whGood,polematch) @@ -778,7 +794,7 @@ def _orientation_quest_nb(polescart, bandnorms, weights): @numba.jit(nopython=True, cache=True,fastmath=True,parallel=False) def _tripvote_numba(bandangs, LUT, angTol, tripAngles, tripID, nfam, n_bands): LUTTemp = np.asarray(LUT).copy() - accumulator = np.zeros((nfam, n_bands), dtype=np.int32) + accumulator = np.zeros((nfam, n_bands), dtype=np.float32) tshape = np.shape(tripAngles) ntrip = int(tshape[0]) count = 0.0 @@ -787,47 +803,54 @@ def _tripvote_numba(bandangs, LUT, angTol, tripAngles, tripID, nfam, n_bands): for i in range(n_bands): for j in range(i + 1,n_bands): for k in range(j + 1,n_bands): - angtri = np.array([bandangs[i,j],bandangs[i,k],bandangs[j,k]], dtype=numba.float32) + angtri = np.array([bandangs[i,j],bandangs[i,k],bandangs[j,k]], dtype=np.float32) srt = angtri.argsort(kind='quicksort') #np.array(np.argsort(angtri), dtype=numba.int64) - srt2 = np.asarray(LUTTemp[:,srt[0],srt[1],srt[2]], dtype=numba.int64).copy() + srt2 = np.asarray(LUTTemp[:,srt[0],srt[1],srt[2]], dtype=np.int64).copy() unsrtFID = np.argsort(srt2,kind='quicksort').astype(np.int64) angtriSRT = np.asarray(angtri[srt]) - angTest = (np.abs(tripAngles - angtriSRT)) <= angTol + angTest0 = (np.abs(tripAngles - angtriSRT)).astype(np.float32) + #print(angTest0.shape) + angTest = (angTest0 <= angTol)#.astype(np.int) for q in range(ntrip): angTest2 = (angTest[q,0] + angTest[q,1] + angTest[q,2]) == 3 if angTest2: f = tripID[q,:] f = f[unsrtFID] - accumulator[f[0],i] += 1 - accumulator[f[1],j] += 1 - accumulator[f[2],k] += 1 + #print(angTest0[q,:]) + w1 = (2.0 * angTol - (angTest0[q,0] + angTest0[q,1])) + w2 = (2.0 * angTol - (angTest0[q,0] + angTest0[q,2])) + w3 = (2.0 * angTol - (angTest0[q,1] + angTest0[q,2])) + #print(w1, w2, w3) + accumulator[f[0],i] += w1 + accumulator[f[1],j] += w2 + accumulator[f[2],k] += w3 t1 = False t2 = False t3 = False if np.abs(angtriSRT[0] - angtriSRT[1]) < angTol: - accumulator[f[0],j] += 1 - accumulator[f[1],i] += 1 - accumulator[f[2],k] += 1 + accumulator[f[0],j] += w1 + accumulator[f[1],i] += w2 + accumulator[f[2],k] += w3 t1 = True if np.abs(angtriSRT[1] - angtriSRT[2]) < angTol: - accumulator[f[0],i] += 1 - accumulator[f[1],k] += 1 - accumulator[f[2],j] += 1 + accumulator[f[0],i] += w1 + accumulator[f[1],k] += w2 + accumulator[f[2],j] += w3 t2 = True if np.abs(angtriSRT[2] - angtriSRT[0]) < angTol: - accumulator[f[0],k] += 1 - accumulator[f[1],j] += 1 - accumulator[f[2],i] += 1 + accumulator[f[0],k] += w1 + accumulator[f[1],j] += w2 + accumulator[f[2],i] += w3 t3 = True if (t1 and t2 and t3): - accumulator[f[0],k] += 1 - accumulator[f[1],i] += 1 - accumulator[f[2],j] += 1 + accumulator[f[0],k] += w1 + accumulator[f[1],i] += w2 + accumulator[f[2],j] += w3 - accumulator[f[0], j] += 1 - accumulator[f[1], k] += 1 - accumulator[f[2], i] += 1 + accumulator[f[0], j] += w1 + accumulator[f[1], k] += w2 + accumulator[f[2], i] += w3 mxvote = np.zeros(n_bands, dtype=np.int32) tvotes = np.zeros(n_bands, dtype=np.int32) @@ -853,6 +876,56 @@ def _tripvote_numba(bandangs, LUT, angTol, tripAngles, tripID, nfam, n_bands): return accumulator, bandFam, bandRank, band_cm + @staticmethod + @numba.jit(nopython=True, cache=True, fastmath=True, parallel=False) + def _pairvote_numba(bandangs, angTol, pairAngs, pairID, nfam, n_bands): + + accumulator = np.zeros((nfam, n_bands), dtype=np.float32) + pairshape = np.shape(pairAngs) + npair = int(pairshape[0]) + count = 0.0 + # angTest2 = np.zeros(ntrip, dtype=numba.boolean) + # angTest2 = np.empty(ntrip,dtype=numba.boolean) + for i in range(n_bands): + for j in range(i + 1, n_bands): + bandangpair = bandangs[i, j] + angTest = (np.abs(pairAngs - bandangpair)).astype(np.float32) + # print(angTest0.shape) + + + for q in range(npair): + if angTest[q] <= angTol: + w1 = (angTol - angTest[q]) + + # print(w1, w2, w3) + accumulator[pairID[q,0], i] += w1 + accumulator[pairID[q,1], i] += w1 + accumulator[pairID[q,0], j] += w1 + accumulator[pairID[q,1], j] += w1 + + + mxvote = np.zeros(n_bands, dtype=np.int32) + tvotes = np.zeros(n_bands, dtype=np.int32) + band_cm = np.zeros(n_bands, dtype=np.float32) + for q in range(n_bands): + mxvote[q] = np.amax(accumulator[:, q]) + tvotes[q] = np.sum(accumulator[:, q]) + + for i in range(n_bands): + if tvotes[i] < 1: + band_cm[i] = 0.0 + else: + srt = np.argsort(accumulator[:, i]) + band_cm[i] = (accumulator[srt[-1], i] - accumulator[srt[-2], i]) / tvotes[i] + + bandRank = np.zeros(n_bands, dtype=np.float32) + bandFam = np.zeros(n_bands, dtype=np.int32) + for q in range(n_bands): + bandFam[q] = np.argmax(accumulator[:, q]) + bandRank = (n_bands - np.arange(n_bands)) / n_bands * band_cm * mxvote + + return accumulator, bandFam, bandRank, band_cm + @staticmethod @numba.jit(nopython=True, cache=True, fastmath=True,parallel=False) def _assign_bands_nb(polesCart, bandRank_arg, familyLabel, famIndx, nFam, angTable, bandnorms, angTol, n_band_early): @@ -876,78 +949,84 @@ def _assign_bands_nb(polesCart, bandRank_arg, familyLabel, famIndx, nFam, angTab #fit = np.float32(360.0) #whGood = np.zeros(nBnds, dtype=np.int64) - 1 nGood = np.int64(-1) - ij = (-1,-1, -1,-1) + + ij = (-1,-1,-1,-1) + for ii in range(nBnds-1): for jj in range(ii+1,nBnds): - + #print(ii,jj) polematch = np.zeros((nBnds),dtype=np.int64) - 1 bnd1 = bandRank_arg[-1 - ii] bnd2 = bandRank_arg[-1 - jj] - v0 = bandnorms[bnd1,:] - f0 = familyLabel[bnd1] - v1 = bandnorms[bnd2,:] - f1 = familyLabel[bnd2] - ang01 = np.dot(v0,v1) + v1 = bandnorms[bnd1,:] + f1 = familyLabel[bnd1] + v2 = bandnorms[bnd2,:] + f2 = familyLabel[bnd2] + ang01 = (np.dot(v1,v2)) + #if ang01 < 0: + # v2 *= -1 + # ang01 *= -1 + if ang01 > np.float32(1.0): ang01 = np.float32(1.0-eps) if ang01 < np.float32(-1.0): ang01 = np.float32(-1.0+eps) paralleltest = np.arccos(np.fabs(ang01)) * RADEG + if paralleltest < angTol: # the two poles are parallel, send in another two poles if available. continue - ang01 = np.arccos(ang01) * RADEG + wh12 = np.nonzero(np.abs(angTable[famIndx[f1],famIndx[f2]:np.int64(famIndx[f2] + nFam[f2])] - ang01) < angTol)[0] - wh01 = np.nonzero(np.abs(angTable[famIndx[f0],famIndx[f1]:np.int64(famIndx[f1] + nFam[f1])] - ang01) < angTol)[0] - - n01 = wh01.size - if n01 == 0: + n12 = wh12.size + if n12 == 0: continue - wh01 += famIndx[f1] - p0 = polesCart[famIndx[f0], :] + wh12 += famIndx[f2] + p1 = polesCart[famIndx[f1], :] - n01 = wh01.size - v0v1c = np.cross(v0,v1) - v0v1c /= np.linalg.norm(v0v1c) + n12 = wh12.size + v1v2c = np.cross(v1,v2) + v1v2c /= np.linalg.norm(v1v2c) # attempt to see which solution gives the best match to all the poles # best is measured as the number of poles that are within tolerance, # divided by the angular deviation. # Use the TRIAD method for finding the rotation matrix - Rtry = np.zeros((n01,3,3), dtype = np.float32) + Rtry = np.zeros((n12,3,3), dtype = np.float32) #score = np.zeros((n01), dtype = np.float32) A = np.zeros((3,3), dtype = np.float32) B = np.zeros((3,3), dtype = np.float32) #AB = np.zeros((3,3),dtype=np.float32) - b2 = np.cross(v0,v0v1c) - B[0,:] = v0 - B[1,:] = v0v1c + b2 = np.cross(v1,v1v2c) + B[0,:] = v1 + B[1,:] = v1v2c B[2,:] = b2 - A[:,0] = p0 + A[:,0] = p1 score = -1.0 - for i in range(n01): - p1 = polesCart[wh01[i], :] - ntemp = np.linalg.norm(p1) + 1.0e-35 - p1 = p1 / ntemp - p0p1c = np.cross(p0,p1) - ntemp = np.linalg.norm(p0p1c) + 1.0e-35 - p0p1c = p0p1c / ntemp - A[:,1] = p0p1c - A[:,2] = np.cross(p0,p0p1c) - AB = A.dot(B) + for i in range(n12): + p2 = polesCart[wh12[i], :] + ntemp = np.linalg.norm(p2) + 1.0e-35 + p2 = p2 / ntemp + p1p2c = np.cross(p1,p2) + ntemp = np.linalg.norm(p1p2c) + 1.0e-35 + p1p2c = p1p2c / ntemp + A[:,1] = p1p2c + A[:,2] = np.cross(p1,p1p2c) + AB = (A.dot(B)) Rtry[i,:,:] = AB testp = (AB.dot(bndnorm)) - test = pflt.dot(testp) + test = (pflt.dot(testp)) #print(test.shape) angfitTry = np.zeros((nBnds), dtype = np.float32) #angfitTry = np.max(test,axis=0) + #print(test.shape) for j in range(nBnds): angfitTry[j] = np.max(test[:,j]) angfitTry[j] = -1.0 if angfitTry[j] < -1.0 else angfitTry[j] @@ -988,35 +1067,50 @@ def _assign_bands_nb(polesCart, bandRank_arg, familyLabel, famIndx, nFam, angTab if nGood >= (n_band_early): + testout = testp + dave = (angFit, whGood) fitout = fit fitbout = fitb nMatch = nGood whGood_out = whGood polematch_out = polematch Rout = R - ij = (ii, jj, bnd1,bnd2) + + + ij = (ii,jj,bnd1,bnd2) + break else: if nMatch < nGood: + testout = testp + dave = (angFit, whGood) fitout = np.float32(fit) fitbout = fitb nMatch = nGood whGood_out = whGood polematch_out = polematch Rout = R - ij = (ii, jj, bnd1,bnd2) + + ij = (ii,jj,bnd1,bnd2) + elif nMatch == nGood: if fitout > fit: + testout = testp + dave = (angFit, whGood) fitout = np.float32(fit) fitbout = fitb nMatch = nGood whGood_out = whGood polematch_out = polematch Rout = R + ij = (ii, jj, bnd1,bnd2) + if nMatch >= (n_band_early): break - #quatout = rotlib.om2quL(Rout) + #print(testout.T) + #print(pflt[polematch_out,:]) + #print(dave) return fitout, polematch_out,nMatch, whGood_out, ij, Rout, fitbout @staticmethod From 1592bec48fb2e9137bff9e221c24bcb1c73e1e5d Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Mon, 2 Jan 2023 16:03:48 -0500 Subject: [PATCH 035/177] New phase criteria Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_single.py | 10 ++++++++-- pyebsdindex/tripletvote.py | 13 +++++++------ 2 files changed, 15 insertions(+), 8 deletions(-) diff --git a/pyebsdindex/_ebsd_index_single.py b/pyebsdindex/_ebsd_index_single.py index 77af431..97e2f50 100644 --- a/pyebsdindex/_ebsd_index_single.py +++ b/pyebsdindex/_ebsd_index_single.py @@ -547,11 +547,17 @@ def index_pats( indxData["quat"][0:nPhases, :, :] = q if nPhases > 1: for j in range(nPhases - 1): + #indxData[-1, :] = np.where( + # (indxData[j, :]["cm"] * indxData[j, :]["nmatch"]) + # > (indxData[j + 1, :]["cm"] * indxData[j + 1, :]["nmatch"]), + # indxData[j, :], + # indxData[j + 1, :], indxData[-1, :] = np.where( - (indxData[j, :]["cm"] * indxData[j, :]["nmatch"]) - > (indxData[j + 1, :]["cm"] * indxData[j + 1, :]["nmatch"]), + ((3.0 - indxData[j, :]["fit"]) * indxData[j, :]["nmatch"]) + > ((3.0 - indxData[j + 1, :]["fit"]) * indxData[j + 1, :]["nmatch"]), indxData[j, :], indxData[j + 1, :], + ) else: indxData[-1, :] = indxData[0, :] diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index f73f99a..12588d3 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -1068,22 +1068,18 @@ def _assign_bands_nb(polesCart, bandRank_arg, familyLabel, famIndx, nFam, angTab if nGood >= (n_band_early): testout = testp - dave = (angFit, whGood) + fitout = fit fitbout = fitb nMatch = nGood whGood_out = whGood polematch_out = polematch Rout = R - - ij = (ii,jj,bnd1,bnd2) - break else: if nMatch < nGood: testout = testp - dave = (angFit, whGood) fitout = np.float32(fit) fitbout = fitb nMatch = nGood @@ -1096,7 +1092,6 @@ def _assign_bands_nb(polesCart, bandRank_arg, familyLabel, famIndx, nFam, angTab elif nMatch == nGood: if fitout > fit: testout = testp - dave = (angFit, whGood) fitout = np.float32(fit) fitbout = fitb nMatch = nGood @@ -1106,8 +1101,14 @@ def _assign_bands_nb(polesCart, bandRank_arg, familyLabel, famIndx, nFam, angTab ij = (ii, jj, bnd1,bnd2) + #print('----') + #print(ij) + + #print(testout.T) + #print(pflt[polematch_out, :]) if nMatch >= (n_band_early): break + #print(testout.T) #print(pflt[polematch_out,:]) #print(dave) From 369b09da923eab54a840c556b91742819c60755b Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Tue, 3 Jan 2023 09:24:36 -0500 Subject: [PATCH 036/177] Quicker exit in voting loops. Signed-off by: David Rowenhorst --- pyebsdindex/tripletvote.py | 107 +++++++++++++++++++++++-------------- 1 file changed, 67 insertions(+), 40 deletions(-) diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index 12588d3..0a4813e 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -112,7 +112,7 @@ def __init__(self, spacegroup = None, latticeparameter=None, polefamilies = None, - angTol=3.0, + angTol=2.0, nband_earlyexit = 8): self.phaseName = None # User provided name of the phase. self.spacegroup = None # space group id 1-230 @@ -800,6 +800,7 @@ def _tripvote_numba(bandangs, LUT, angTol, tripAngles, tripID, nfam, n_bands): count = 0.0 #angTest2 = np.zeros(ntrip, dtype=numba.boolean) #angTest2 = np.empty(ntrip,dtype=numba.boolean) + angTest0 = np.zeros((3), dtype=np.float32) for i in range(n_bands): for j in range(i + 1,n_bands): for k in range(j + 1,n_bands): @@ -808,49 +809,72 @@ def _tripvote_numba(bandangs, LUT, angTol, tripAngles, tripID, nfam, n_bands): srt2 = np.asarray(LUTTemp[:,srt[0],srt[1],srt[2]], dtype=np.int64).copy() unsrtFID = np.argsort(srt2,kind='quicksort').astype(np.int64) angtriSRT = np.asarray(angtri[srt]) - angTest0 = (np.abs(tripAngles - angtriSRT)).astype(np.float32) + + #angTest0 = (np.abs(tripAngles - angtriSRT)).astype(np.float32) #print(angTest0.shape) - angTest = (angTest0 <= angTol)#.astype(np.int) + #angTest = (angTest0 <= angTol)#.astype(np.int) for q in range(ntrip): - angTest2 = (angTest[q,0] + angTest[q,1] + angTest[q,2]) == 3 - if angTest2: - f = tripID[q,:] - f = f[unsrtFID] - #print(angTest0[q,:]) - w1 = (2.0 * angTol - (angTest0[q,0] + angTest0[q,1])) - w2 = (2.0 * angTol - (angTest0[q,0] + angTest0[q,2])) - w3 = (2.0 * angTol - (angTest0[q,1] + angTest0[q,2])) - #print(w1, w2, w3) + #print('____') + #print(tripAngles[q,:], angtriSRT) + + test1 = np.abs(tripAngles[q,0] - angtriSRT[0]) + if test1 > angTol: + continue + else: + angTest0[0] = test1 + + test2 = np.abs(tripAngles[q, 1] - angtriSRT[1]) + if test2 > angTol: + continue + else: + angTest0[1] = test2 + + test3 = np.abs(tripAngles[q, 2] - angtriSRT[2]) + if test3 > angTol: + continue + else: + angTest0[2] = test3 + + #print('here') + #angTest2 = (angTest[q,0] + angTest[q,1] + angTest[q,2]) == 3 + #if angTest2: + f = tripID[q,:] + f = f[unsrtFID] + #print(angTest0[q,:]) + w1 = (2.0 * angTol - (angTest0[0] + angTest0[1])) + w2 = (2.0 * angTol - (angTest0[0] + angTest0[2])) + w3 = (2.0 * angTol - (angTest0[1] + angTest0[2])) + #print(w1, w2, w3) + accumulator[f[0],i] += w1 + accumulator[f[1],j] += w2 + accumulator[f[2],k] += w3 + t1 = False + t2 = False + t3 = False + if np.abs(angtriSRT[0] - angtriSRT[1]) < angTol: + accumulator[f[0],j] += w1 + accumulator[f[1],i] += w2 + accumulator[f[2],k] += w3 + t1 = True + if np.abs(angtriSRT[1] - angtriSRT[2]) < angTol: accumulator[f[0],i] += w1 + accumulator[f[1],k] += w2 + accumulator[f[2],j] += w3 + t2 = True + if np.abs(angtriSRT[2] - angtriSRT[0]) < angTol: + accumulator[f[0],k] += w1 accumulator[f[1],j] += w2 - accumulator[f[2],k] += w3 - t1 = False - t2 = False - t3 = False - if np.abs(angtriSRT[0] - angtriSRT[1]) < angTol: - accumulator[f[0],j] += w1 - accumulator[f[1],i] += w2 - accumulator[f[2],k] += w3 - t1 = True - if np.abs(angtriSRT[1] - angtriSRT[2]) < angTol: - accumulator[f[0],i] += w1 - accumulator[f[1],k] += w2 - accumulator[f[2],j] += w3 - t2 = True - if np.abs(angtriSRT[2] - angtriSRT[0]) < angTol: - accumulator[f[0],k] += w1 - accumulator[f[1],j] += w2 - accumulator[f[2],i] += w3 - t3 = True - if (t1 and t2 and t3): - accumulator[f[0],k] += w1 - accumulator[f[1],i] += w2 - accumulator[f[2],j] += w3 - - accumulator[f[0], j] += w1 - accumulator[f[1], k] += w2 - accumulator[f[2], i] += w3 + accumulator[f[2],i] += w3 + t3 = True + if (t1 and t2 and t3): + accumulator[f[0],k] += w1 + accumulator[f[1],i] += w2 + accumulator[f[2],j] += w3 + + accumulator[f[0], j] += w1 + accumulator[f[1], k] += w2 + accumulator[f[2], i] += w3 mxvote = np.zeros(n_bands, dtype=np.int32) tvotes = np.zeros(n_bands, dtype=np.int32) @@ -948,7 +972,7 @@ def _assign_bands_nb(polesCart, bandRank_arg, familyLabel, famIndx, nFam, angTab R = np.zeros((1, 3, 3), dtype=np.float32) #fit = np.float32(360.0) #whGood = np.zeros(nBnds, dtype=np.int64) - 1 - nGood = np.int64(-1) + nMatch = np.int64(0) ij = (-1,-1,-1,-1) @@ -1079,6 +1103,8 @@ def _assign_bands_nb(polesCart, bandRank_arg, familyLabel, famIndx, nFam, angTab break else: if nMatch < nGood: + #print((nMatch*(3.0-fitout)) , (nGood*(3.0-fit))) + #if (nMatch*(2.0-fitout)) < (nGood*(2.0-fit)): testout = testp fitout = np.float32(fit) fitbout = fitb @@ -1090,6 +1116,7 @@ def _assign_bands_nb(polesCart, bandRank_arg, familyLabel, famIndx, nFam, angTab ij = (ii,jj,bnd1,bnd2) elif nMatch == nGood: + #elif (nMatch*(2.0-fitout)) == (nGood*(2.0-fit)): if fitout > fit: testout = testp fitout = np.float32(fit) From af4bdce60a573478971a87877970d6fbafb14970 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Tue, 3 Jan 2023 10:17:19 -0500 Subject: [PATCH 037/177] Cleaned up verbosity. Signed-off by: David Rowenhorst --- pyebsdindex/tripletvote.py | 14 +++++++++++--- 1 file changed, 11 insertions(+), 3 deletions(-) diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index 0a4813e..a0a09bb 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -423,10 +423,13 @@ def bandindex(self, band_norms, band_intensity = None, band_widths=None, verbose accumulator, bandFam, bandRank, band_cm = self._tripvote_numba(bandangs, self.lut, self.angTol, tripangs, tripid, nfam, n_bands) #accumulator, bandFam, bandRank, band_cm = self._pairvote_numba(bandangs, self.angTol, pairangs, pairfam, # nfam, n_bands) - if verbose >= 3: + if verbose > 3: with np.printoptions(precision=2, suppress=True): + print('___Accumulator___') print(accumulator) + print('___Band Rank___') print(bandRank) + print('___Band Family ID___') print(bandFam) @@ -456,12 +459,17 @@ def bandindex(self, band_norms, band_intensity = None, band_widths=None, verbose fit, polematch, nMatch, whGood, ij, R, fitb = \ self._assign_bands_nb(polesCart, bandRank_arg, bandFam, famIndx, nFam, angTable, bandnorms, angTol, n_band_early) - if verbose > 2: + if verbose > 3: + print('___Assigned Band___') print(self.completelib['familyid'][polematch]) + acc_correct = np.sum( np.array(self.completelib['familyid'][polematch] == bandFam).astype(int)).astype(int) + if verbose > 2: #print(polematch) #print(fit, fitb, fitb[whGood]) print('band index: ',timer() - tic) + + tic = timer() cm2 = 0.0 @@ -492,7 +500,7 @@ def bandindex(self, band_norms, band_intensity = None, band_widths=None, verbose if verbose > 2: print('refinement: ', timer() - tic) print('all: ',timer() - tic0) - return avequat, fit, cm2, polematch, nMatch, ij, sumaccum + return avequat, fit, cm2, polematch, nMatch, ij, acc_correct #sumaccum def _symrotpoles(self, pole, crystalmats): From 701af405b0e21d514abee14061536d5ac87ea7b2 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Tue, 3 Jan 2023 10:36:38 -0500 Subject: [PATCH 038/177] Checkpoint Signed-off by: David Rowenhorst --- pyebsdindex/tripletvote.py | 84 +------------------------------------- 1 file changed, 1 insertion(+), 83 deletions(-) diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index 36e8938..1ed38a9 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -86,11 +86,7 @@ def addphase(libtype=None, phasename=None, else: latticeparameter = np.array(latticeparameter) if polefamilies is None: -<<<<<<< HEAD polefamilies = np.array([ [1, 0, -1, 0], [0, 0, 0, 2],[1, 0, -1, 1], [1, 0, -1, 2], [1, 1, -2, 0], -======= - polefamilies = np.array([ [1, 0, -1, 0],[0, 0, 0, 2],[1, 0, -1, 1], [1, 0, -1, 2], [1, 1, -2, 0], ->>>>>>> WeightedVote [1, 0, -1, 3], [1, 1,-2, 2], [2,0,-2,1]]) else: polefamilies = np.atleast_2d(np.array(polefamilies)) @@ -425,11 +421,6 @@ def bandindex(self, band_norms, band_intensity = None, band_widths=None, verbose pairfam = self.angpairs['familyid'] accumulator, bandFam, bandRank, band_cm = self._tripvote_numba(bandangs, self.lut, self.angTol, tripangs, tripid, nfam, n_bands) -<<<<<<< HEAD - #print(accumulator) - if verbose >= 3: - print(accumulator) -======= #accumulator, bandFam, bandRank, band_cm = self._pairvote_numba(bandangs, self.angTol, pairangs, pairfam, # nfam, n_bands) if verbose > 3: @@ -442,7 +433,6 @@ def bandindex(self, band_norms, band_intensity = None, band_widths=None, verbose print(bandFam) ->>>>>>> WeightedVote if verbose > 2: print('band Vote time:',timer() - tic) @@ -822,19 +812,10 @@ def _tripvote_numba(bandangs, LUT, angTol, tripAngles, tripID, nfam, n_bands): for i in range(n_bands): for j in range(i + 1,n_bands): for k in range(j + 1,n_bands): -<<<<<<< HEAD - ijk = [i,j,k] - angtri = np.array([bandangs[i,j],bandangs[i,k],bandangs[j,k]], dtype=np.float32) - srt = angtri.argsort(kind='quicksort') #np.array(np.argsort(angtri), dtype=numba.int64) - - #srt2 = np.asarray(LUTTemp[:,srt[0],srt[1],srt[2]], dtype=numba.int64).copy() - #unsrtFID = np.argsort(srt2,kind='quicksort').astype(np.int64) -======= angtri = np.array([bandangs[i,j],bandangs[i,k],bandangs[j,k]], dtype=np.float32) srt = angtri.argsort(kind='quicksort') #np.array(np.argsort(angtri), dtype=numba.int64) srt2 = np.asarray(LUTTemp[:,srt[0],srt[1],srt[2]], dtype=np.int64).copy() unsrtFID = np.argsort(srt2,kind='quicksort').astype(np.int64) ->>>>>>> WeightedVote angtriSRT = np.asarray(angtri[srt]) #angTest0 = (np.abs(tripAngles - angtriSRT)).astype(np.float32) @@ -842,67 +823,6 @@ def _tripvote_numba(bandangs, LUT, angTol, tripAngles, tripID, nfam, n_bands): #angTest = (angTest0 <= angTol)#.astype(np.int) for q in range(ntrip): -<<<<<<< HEAD - #angTest2 = (angTest[q,0] + angTest[q,1] + angTest[q,2]) == 3 - angTest2 = (angTest[q, 0] and angTest[q, 1] and angTest[q, 2]) == True - if angTest2: - f = tripID[q,:] - print(f, ijk[srt[0]], ijk[srt[1]], ijk[srt[2]]) - accumulator[f[0], ijk[srt[0]]] += 1 - accumulator[f[1], ijk[srt[1]]] += 1 - accumulator[f[2], ijk[srt[2]]] += 1 - t1 = False - t2 = False - t3 = False - if np.abs(angtriSRT[0] - angtriSRT[1]) < angTol: - accumulator[f[0],ijk[srt[0]]] += 1 - accumulator[f[1],ijk[srt[2]]] += 1 - accumulator[f[2],ijk[srt[1]]] += 1 - t1 = True - if np.abs(angtriSRT[1] - angtriSRT[2]) < angTol: - accumulator[f[0],ijk[srt[1]]] += 1 - accumulator[f[1],ijk[srt[0]]] += 1 - accumulator[f[2],ijk[srt[2]]] += 1 - t2 = True - if np.abs(angtriSRT[2] - angtriSRT[0]) < angTol: - accumulator[f[0],ijk[srt[2]]] += 1 - accumulator[f[1],ijk[srt[1]]] += 1 - accumulator[f[2],ijk[srt[0]]] += 1 - t3 = True - if (t1 and t2 and t3): - accumulator[f[0],ijk[srt[2]]] += 1 - accumulator[f[1],ijk[srt[0]]] += 1 - accumulator[f[2],ijk[srt[1]]] += 1 - - - # f = tripID[q,:] - # f = f[unsrtFID] - # accumulator[f[0],i] += 1 - # accumulator[f[1],j] += 1 - # accumulator[f[2],k] += 1 - # t1 = False - # t2 = False - # t3 = False - # if np.abs(angtriSRT[0] - angtriSRT[1]) < angTol: - # accumulator[f[0],i] += 1 - # accumulator[f[1],k] += 1 - # accumulator[f[2],j] += 1 - # t1 = True - # if np.abs(angtriSRT[1] - angtriSRT[2]) < angTol: - # accumulator[f[0],j] += 1 - # accumulator[f[1],i] += 1 - # accumulator[f[2],k] += 1 - # t2 = True - # if np.abs(angtriSRT[2] - angtriSRT[0]) < angTol: - # accumulator[f[0],k] += 1 - # accumulator[f[1],j] += 1 - # accumulator[f[2],i] += 1 - # t3 = True - # if (t1 and t2 and t3): - # accumulator[f[0],k] += 1 - # accumulator[f[1],i] += 1 - # accumulator[f[2],j] += 1 -======= #print('____') #print(tripAngles[q,:], angtriSRT) @@ -963,7 +883,7 @@ def _tripvote_numba(bandangs, LUT, angTol, tripAngles, tripID, nfam, n_bands): accumulator[f[0], j] += w1 accumulator[f[1], k] += w2 accumulator[f[2], i] += w3 ->>>>>>> WeightedVote + mxvote = np.zeros(n_bands, dtype=np.int32) tvotes = np.zeros(n_bands, dtype=np.int32) @@ -972,8 +892,6 @@ def _tripvote_numba(bandangs, LUT, angTol, tripAngles, tripID, nfam, n_bands): mxvote[q] = np.amax(accumulator[:,q]) tvotes[q] = np.sum(accumulator[:,q]) - - for i in range(n_bands): if tvotes[i] < 1: band_cm[i] = 0.0 From 4a63a63d658e710f6645567183137e28307fd546 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Tue, 3 Jan 2023 11:52:26 -0500 Subject: [PATCH 039/177] Fixed phase choosing for more than two phases. Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_single.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/pyebsdindex/_ebsd_index_single.py b/pyebsdindex/_ebsd_index_single.py index 97e2f50..956f0c4 100644 --- a/pyebsdindex/_ebsd_index_single.py +++ b/pyebsdindex/_ebsd_index_single.py @@ -545,8 +545,9 @@ def index_pats( q = rotlib.quatnorm(q) q = q.reshape(nPhases, npoints, 4) indxData["quat"][0:nPhases, :, :] = q + indxData[-1, :] = indxData[0, :] if nPhases > 1: - for j in range(nPhases - 1): + for j in range(1, nPhases-1): #indxData[-1, :] = np.where( # (indxData[j, :]["cm"] * indxData[j, :]["nmatch"]) # > (indxData[j + 1, :]["cm"] * indxData[j + 1, :]["nmatch"]), @@ -554,13 +555,12 @@ def index_pats( # indxData[j + 1, :], indxData[-1, :] = np.where( ((3.0 - indxData[j, :]["fit"]) * indxData[j, :]["nmatch"]) - > ((3.0 - indxData[j + 1, :]["fit"]) * indxData[j + 1, :]["nmatch"]), + > ((3.0 - indxData[-1, :]["fit"]) * indxData[-1, :]["nmatch"]), indxData[j, :], - indxData[j + 1, :], - + indxData[-1, :] ) - else: - indxData[-1, :] = indxData[0, :] + + if verbose > 0: print("Band Vote Time: ", timer() - tic) From 7135a535feb9531bfd6887545f3f30bb4ddd9937 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Tue, 3 Jan 2023 11:53:21 -0500 Subject: [PATCH 040/177] Fixed writing out hex phases in ang files. Signed-off by: David Rowenhorst --- pyebsdindex/ebsdfile.py | 12 ++++++++++-- pyebsdindex/tripletvote.py | 32 ++++++++++++++------------------ 2 files changed, 24 insertions(+), 20 deletions(-) diff --git a/pyebsdindex/ebsdfile.py b/pyebsdindex/ebsdfile.py index 13d354b..cde90ef 100644 --- a/pyebsdindex/ebsdfile.py +++ b/pyebsdindex/ebsdfile.py @@ -25,10 +25,18 @@ def writeang(filename, indexer, data, f.write('# Info '+'\t\t \r\n') f.write('# Symmetry ' + str(phase.lauecode) + '\r\n') #f.write('# PointGroupID ' + str(phase.pointgroupid) + '\r\n') - f.write('# LatticeConstants '+ ' '.join(str(' {:.3f}'.format(x)) for x in phase.latticeparameter)+'\r\n') + latticeparameter = np.array(phase.latticeparameter).astype(float) * np.array([10.0, 10.0, 10.0, 1.0, 1.0, 1.0]) + f.write('# LatticeConstants '+ ' '.join(str(' {:.3f}'.format(x)) for x in latticeparameter)+'\r\n') f.write('# NumberFamilies ' + str(phase.npolefamilies) + '\r\n') + poles = np.array(phase.polefamilies).astype(int) + if (phase.lauecode == 62) or (phase.lauecode == 6): + if poles.shape[-1] == 4: + poles = poles[:,[0,1,3]] + for i in range(phase.npolefamilies): - f.write('# hklFamilies \t' + (' '.join(str(x).rjust(2,' ') for x in phase.polefamilies[i, :])) + ' 1 0.00000 1' + '\r\n') + f.write('# hklFamilies \t' + (' '.join(str(x).rjust(2,' ') for x in poles[i, :])) + ' 1 0.00000 1' + '\r\n') + + f.write('# '+'\r\n') pcount += 1 diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index 1ed38a9..469f6a1 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -56,7 +56,7 @@ def addphase(libtype=None, phasename=None, else: latticeparameter = np.array(latticeparameter) if polefamilies is None: - polefamilies = np.array([[0, 0, 2], [1, 1, 1], [0, 2, 2], [1, 1, 3]]) + polefamilies = np.array([[0, 0, 2], [1, 1, 1], [0, 2, 2], [1, 1, 3]]).astype(np.int32) else: polefamilies = np.atleast_2d(np.array(polefamilies)) @@ -71,7 +71,7 @@ def addphase(libtype=None, phasename=None, else: latticeparameter = np.array(latticeparameter) if polefamilies is None: - polefamilies = np.array([[0, 1, 1], [0, 0, 2], [1, 1, 2], [0, 1, 3]]) + polefamilies = np.array([[0, 1, 1], [0, 0, 2], [1, 1, 2], [0, 1, 3]]).astype(np.int32) else: polefamilies = np.atleast_2d(np.array(polefamilies)) @@ -87,7 +87,7 @@ def addphase(libtype=None, phasename=None, latticeparameter = np.array(latticeparameter) if polefamilies is None: polefamilies = np.array([ [1, 0, -1, 0], [0, 0, 0, 2],[1, 0, -1, 1], [1, 0, -1, 2], [1, 1, -2, 0], - [1, 0, -1, 3], [1, 1,-2, 2], [2,0,-2,1]]) + [1, 0, -1, 3], [1, 1,-2, 2], [2,0,-2,1]]).astype(np.int32) else: polefamilies = np.atleast_2d(np.array(polefamilies)) @@ -97,7 +97,7 @@ def addphase(libtype=None, phasename=None, if latticeparameter is None: latticeparameter = np.array([1.0, 1.0, 1.0, 90.0, 90.0, 90.0]) if polefamilies is None: - polefamilies = np.array([[0, 0, 2], [1, 1, 1], [0, 2, 2], [1, 1, 3]]) + polefamilies = np.array([[0, 0, 2], [1, 1, 1], [0, 2, 2], [1, 1, 3]]).astype(np.int32) triplib = BandIndexer(phasename=phasename, spacegroup=spacegroup, latticeparameter=latticeparameter, polefamilies=np.atleast_2d(polefamilies)) @@ -423,28 +423,26 @@ def bandindex(self, band_norms, band_intensity = None, band_widths=None, verbose accumulator, bandFam, bandRank, band_cm = self._tripvote_numba(bandangs, self.lut, self.angTol, tripangs, tripid, nfam, n_bands) #accumulator, bandFam, bandRank, band_cm = self._pairvote_numba(bandangs, self.angTol, pairangs, pairfam, # nfam, n_bands) - if verbose > 3: - with np.printoptions(precision=2, suppress=True): - print('___Accumulator___') - print(accumulator) - print('___Band Rank___') - print(bandRank) - print('___Band Family ID___') - print(bandFam) - - - if verbose > 2: print('band Vote time:',timer() - tic) + if verbose > 3: + with np.printoptions(precision=2, suppress=True): + print('___Accumulator___') + print(accumulator) + print('___Band Rank___') + print(bandRank) + print('___Band Family ID___') + print(bandFam) tic = timer() + sumaccum = np.sum(accumulator) bandRank_arg = np.argsort(bandRank).astype(np.int64) # n_bands - np.arange(n_bands, dtype=np.int64) # test = 0 fit = 1000.0 nMatch = -1 avequat = np.zeros(4, dtype=np.float32) - polematch = np.array([-1]) + polematch = np.zeros([n_bands], dtype = int)-1 whGood = -1 angTable = self.completelib['angTable'] @@ -468,8 +466,6 @@ def bandindex(self, band_norms, band_intensity = None, band_widths=None, verbose #print(fit, fitb, fitb[whGood]) print('band index: ',timer() - tic) - - tic = timer() cm2 = 0.0 From 97d828090b30eb12da5779950e001a17eeff5009 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Thu, 12 Jan 2023 16:01:58 -0500 Subject: [PATCH 041/177] Add option to have no voting/indexing - only band data returned. Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_single.py | 6 ++++++ pyebsdindex/tripletvote.py | 2 +- 2 files changed, 7 insertions(+), 1 deletion(-) diff --git a/pyebsdindex/_ebsd_index_single.py b/pyebsdindex/_ebsd_index_single.py index 956f0c4..1b39579 100644 --- a/pyebsdindex/_ebsd_index_single.py +++ b/pyebsdindex/_ebsd_index_single.py @@ -310,6 +310,8 @@ def __init__( self.phaselist = phaselist self.phaseLib = [] for ph in self.phaselist: + if ph is None: + self.phaseLib.append(None) if (ph.__class__.__name__).lower() == 'str': self.phaseLib.append(bandindexer.addphase(libtype=ph)) if (ph.__class__.__name__) == 'BandIndexer': @@ -494,9 +496,13 @@ def index_pats( q = np.zeros((nPhases, npoints, 4)) indxData = np.zeros((nPhases + 1, npoints), dtype=self.dataTemplate) + + indxData["phase"] = -1 indxData["fit"] = 180.0 indxData["totvotes"] = 0 + if self.phaseLib[0] is None: + return indxData, bandData, patstart, npats if self.nband_earlyexit is None: earlyexit = shpBandDat[1] # default to all the poles. diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index 469f6a1..234adb0 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -473,7 +473,7 @@ def bandindex(self, band_norms, band_intensity = None, band_widths=None, verbose if self.high_fidelity == True: srt = np.argsort(fitb[whGood]) - whgood6 = whGood[srt[0:np.min([8, whGood.shape[0]])]] + whgood6 = whGood[srt[0:np.min([9, whGood.shape[0]])]] weights6 = band_intensity[whgood6] pflt6 = (np.asarray(polesCart[polematch[whgood6], :], dtype=np.float64)) From 3527166e295571d8fa6fee60abe746c2cdd8ca30 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Thu, 19 Jan 2023 17:14:51 -0500 Subject: [PATCH 042/177] Laying groundwork for local PC data, and ebsp files Signed-off by: David Rowenhorst --- pyebsdindex/ebsd_pattern.py | 19 +++++++++++++++---- 1 file changed, 15 insertions(+), 4 deletions(-) diff --git a/pyebsdindex/ebsd_pattern.py b/pyebsdindex/ebsd_pattern.py index 75ac608..9406037 100644 --- a/pyebsdindex/ebsd_pattern.py +++ b/pyebsdindex/ebsd_pattern.py @@ -173,6 +173,8 @@ def __init__(self, path=None): self.patStart = [0,0] #starting point of the pattern location in the file. len==1 # if 2D, then it is the row/column starting points self.patterns = None + self.xyLocations = None + # The x,y locations of the pattern collection relative to the center of the SEM field-of-view. @@ -189,8 +191,10 @@ def __init__(self,path, filetype=None): self.nPatterns = None self.patternW = None self.patternH = None - self.xStep = None + self.xStep = None # assumming square grid data, with constant step size self.yStep = None + self.xyCenter = np.array([0.0, 0.0]) + # This is the location of the center of the scan relative to center of SEM field-of-view self.hexflag = False self.filetype = filetype self.filedatatype = np.uint8 # the data type of the patterns within the file @@ -242,7 +246,7 @@ def read_data(self,path=None,convertToFloat=False,patStartCount = [0,-1],returnA # this function does the actual reading from the file. - readpats = self.pat_reader(patStart, nPatToRead) + readpats, xyloc = self.pat_reader(patStart, nPatToRead) patterns = readpats.astype(typeout) @@ -293,6 +297,7 @@ def read_data(self,path=None,convertToFloat=False,patStartCount = [0,-1],returnA patsout.yStep = self.yStep patsout.patStart = np.array(patStart) patsout.patterns = patterns + patsout.xyLocations = xyloc return patsout # note this function uses multiple return statements def pat_reader(self, patStart, nPatToRead): @@ -444,6 +449,10 @@ def read_header(self,path=None,bitdepth=None): # readInterval=[0, -1], arrayOnl self.filePos = dat[2] self.nPatterns = np.int((Path(self.filepath).expanduser().stat().st_size - 16) / (self.patternW * self.patternH * (self.filedatatype(0).nbytes))) + if self.xStep is None: + self.xStep = 0.0 + if self.yStep is None: + self.yStep = 0.0 elif self.version >= 3: dat = np.fromfile(f, dtype=np.uint32, count=3) @@ -478,7 +487,8 @@ def pat_reader(self, patStart, nPatToRead): readpats = np.fromfile(f,dtype=typeread,count=int(nPatToRead * nPerPat)) readpats = readpats.reshape(nPatToRead,self.patternH,self.patternW) f.close() - return readpats + xyloc = None + return readpats, xyloc def write_header(self, writeBlank=False, bitdepth=None): @@ -680,7 +690,8 @@ def pat_reader(self,patStart,nPatToRead): readpats = np.array(patterndset[int(patStart):int(patStart+nPatToRead), :, :]) readpats = readpats.reshape(nPatToRead,self.patternH,self.patternW) f.close() - return readpats + xyloc = None + return readpats, xyloc def copy_file(self, newpath, **kwargs): # oh - this is a mess! From 59ecb44f55ec89f63e1ea8600d110261a7b848f0 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Fri, 20 Jan 2023 11:48:40 -0500 Subject: [PATCH 043/177] Reading patterns from a file will also return x,y locations for that pattern within a scan. Signed-off by: David Rowenhorst --- doc/tutorials/ebsd_index_demo.ipynb | 5 +- pyebsdindex/_ebsd_index_single.py | 5 +- pyebsdindex/ebsd_pattern.py | 118 +++++++++++++++++++--------- pyebsdindex/nlpar.py | 6 +- 4 files changed, 92 insertions(+), 42 deletions(-) diff --git a/doc/tutorials/ebsd_index_demo.ipynb b/doc/tutorials/ebsd_index_demo.ipynb index 3e68a37..09315ba 100644 --- a/doc/tutorials/ebsd_index_demo.ipynb +++ b/doc/tutorials/ebsd_index_demo.ipynb @@ -444,7 +444,8 @@ "nrow = 300\n", "\n", "f = ebsd_pattern.get_pattern_file_obj(file)\n", - "pats = f.read_data(returnArrayOnly=True, convertToFloat=True, patStartCount=[startcolrow, [ncol,nrow]])\n", + "pats, xyloc = f.read_data(returnArrayOnly=True, convertToFloat=True, patStartCount=[startcolrow, [ncol,nrow]])\n", + "# read data and return the patterns as an ndarray[npats, npatrows, npatcols], and the x,y locations within the scan (in microns), ndarray[2,npats]\n", "print(pats.shape)\n", "print(pats.dtype)\n", "plt.imshow(pats[0, :, :], cmap='gray')" @@ -747,7 +748,7 @@ "ncol = 60\n", "nrow = 2\n", "f = ebsd_pattern.get_pattern_file_obj(file)\n", - "pats = f.read_data(returnArrayOnly=True, convertToFloat=True, patStartCount=[startcolrow, [ncol,nrow]])" + "pats, xyloc = f.read_data(returnArrayOnly=True, convertToFloat=True, patStartCount=[startcolrow, [ncol,nrow]])" ] }, { diff --git a/pyebsdindex/_ebsd_index_single.py b/pyebsdindex/_ebsd_index_single.py index 1b39579..2fa253a 100644 --- a/pyebsdindex/_ebsd_index_single.py +++ b/pyebsdindex/_ebsd_index_single.py @@ -395,6 +395,7 @@ def index_pats( patsin=None, patstart=0, npats=-1, + xyloc = None, clparams=None, PC=None, verbose=0, @@ -452,11 +453,13 @@ def index_pats( for the distributed indexing procedures. """ if patsin is None: - pats = self.fID.read_data( + pats, xylocin = self.fID.read_data( returnArrayOnly=True, patStartCount=[patstart, npats], convertToFloat=True, ) + if xyloc is None: + xyloc = xylocin else: pshape = patsin.shape if len(pshape) == 2: diff --git a/pyebsdindex/ebsd_pattern.py b/pyebsdindex/ebsd_pattern.py index 9406037..3783a98 100644 --- a/pyebsdindex/ebsd_pattern.py +++ b/pyebsdindex/ebsd_pattern.py @@ -167,7 +167,7 @@ def __init__(self, path=None): self.nFileCols = None self.nFileRows = None self.nPatterns = None - self.hexFlag = None + self.hexflag = None self.xStep = None self.yStep = None self.patStart = [0,0] #starting point of the pattern location in the file. len==1 @@ -276,12 +276,12 @@ def read_data(self,path=None,convertToFloat=False,patStartCount = [0,-1],returnA for i in range(nrowread): pstart = int(((rowstart+i)*self.nCols)+colstart) - ptemp = self.read_data(convertToFloat=convertToFloat,patStartCount = [pstart,ncolread],returnArrayOnly=True) + ptemp, xyloc = self.read_data(convertToFloat=convertToFloat,patStartCount = [pstart,ncolread],returnArrayOnly=True) patterns[int(i*ncolread):int((i+1)*ncolread), :, :] = ptemp if returnArrayOnly == True: - return patterns + return patterns, xyloc else: # package this up in an EBSDPatterns Object patsout = EBSDPatterns() patsout.vendor = self.vendor @@ -289,10 +289,10 @@ def read_data(self,path=None,convertToFloat=False,patStartCount = [0,-1],returnA patsout.filetype = self.filetype patsout.patternW = self.patternW patsout.patternH = self.patternH - patsout.nFileCols = self.nCols - patsout.nFileRows = self.nRows + patsout.nFileCols = np.uint64(self.nCols) + patsout.nFileRows = np.uint64(self.nRows) patsout.nPatterns = np.array(nPatToRead) - patsout.hexFlag = self.hexFlag + patsout.hexflag = self.hexflag patsout.xStep = self.xStep patsout.yStep = self.yStep patsout.patStart = np.array(patStart) @@ -300,8 +300,9 @@ def read_data(self,path=None,convertToFloat=False,patStartCount = [0,-1],returnA patsout.xyLocations = xyloc return patsout # note this function uses multiple return statements - def pat_reader(self, patStart, nPatToRead): - '''Depending on the file type, it will return a numpy array of patterns.''' + def pat_reader(self, patStart=0, nPatToRead=1): + '''Depending on the file type, it will return a numpy array of patterns, and the positions of the patterns + in the scan.''' pass def write_header(self): @@ -392,9 +393,9 @@ def copy_obj(self): return copy.deepcopy(self) def set_scan_rc(self, rc=(0,0)): # helper function for pattern files that don't record the scan rows and columns - self.nCols = rc[1] - self.nRows = rc[0] - self.nPatterns = self.nCols * self.nRows + self.nCols = np.uint64(rc[1]) + self.nRows = np.uint64(rc[0]) + self.nPatterns = np.uint64(self.nCols * self.nRows) class UPFile(EBSDPatternFile): @@ -408,7 +409,7 @@ def __init__(self, path=None): #self.bitdepth = None self.filePos = None # file location in bytes where pattern data starts self.extraPatterns = 0 - self.hexFlag = 0 + self.hexflag = 0 def read_header(self,path=None,bitdepth=None): # readInterval=[0, -1], arrayOnly=False, @@ -453,6 +454,12 @@ def read_header(self,path=None,bitdepth=None): # readInterval=[0, -1], arrayOnl self.xStep = 0.0 if self.yStep is None: self.yStep = 0.0 + if self.nCols is None: + self.nCols = np.uint64(1) + if self.nCols == 0: + self.nCols = np.uint64(1) + if self.nRows is None: + self.nRows = np.uint64(np.floor(self.nPatterns/self.nCols)) elif self.version >= 3: dat = np.fromfile(f, dtype=np.uint32, count=3) @@ -461,17 +468,17 @@ def read_header(self,path=None,bitdepth=None): # readInterval=[0, -1], arrayOnl self.filePos = dat[2] self.extraPatterns = np.fromfile(f, dtype=np.uint8, count=1)[0] dat = np.fromfile(f, dtype=np.uint32, count=2) - self.nCols = dat[0] - self.nRows = dat[1] + self.nCols = np.uint64(dat[0]) + self.nRows = np.uint64(dat[1]) self.nPatterns = np.int(self.nCols.astype(np.uint64) * self.nRows.astype(np.uint64)) - self.hexFlag = np.fromfile(f, dtype=np.uint8, count=1)[0] + self.hexflag = np.fromfile(f, dtype=np.uint8, count=1)[0] dat = np.fromfile(f, dtype=np.float64, count=2) self.xStep = dat[0] self.yStep = dat[1] f.close() return 0 #note this function uses multiple returns - def pat_reader(self, patStart, nPatToRead): + def pat_reader(self, patStart=0, nPatToRead=1): try: f = open(Path(self.filepath).expanduser(),'rb') except: @@ -487,7 +494,14 @@ def pat_reader(self, patStart, nPatToRead): readpats = np.fromfile(f,dtype=typeread,count=int(nPatToRead * nPerPat)) readpats = readpats.reshape(nPatToRead,self.patternH,self.patternW) f.close() - xyloc = None + yx = np.unravel_index(np.arange(int(patStart), int(patStart+nPatToRead), dtype = np.uint64), + (int(self.nRows), int(self.nCols))) + + xyloc = np.array([yx[1],yx[0]]).T.copy().astype(np.float32) + xyloc[:,0] -= self.nCols * 0.5 + xyloc[:, 1] -= self.nRows * 0.5 + xyloc[:,0] *= self.xStep + xyloc[:,1] *= self.yStep return readpats, xyloc @@ -538,7 +552,7 @@ def write_header(self, writeBlank=False, bitdepth=None): np.asarray(self.extraPatterns,dtype=np.uint8).tofile(f) np.asarray(self.nCols,dtype=np.uint32).tofile(f) np.asarray(self.nRows,dtype=np.uint32).tofile(f) - np.asarray(self.hexFlag,dtype=np.uint8).tofile(f) + np.asarray(self.hexflag,dtype=np.uint8).tofile(f) np.asarray(self.xStep,dtype=np.float64).tofile(f) np.asarray(self.yStep,dtype=np.float64).tofile(f) @@ -583,7 +597,7 @@ def file_from_pattern_obj(self, patternobj, filepath=None, bitdepth = None): self.filePos = 42 # file location in bytes where pattern data starts self.extraPatterns = 0 - self.hexFlag = 0 + self.hexflag = 0 if isinstance(patternobj, EBSDPatterns): shp = (patternobj.nPatterns.prod(),patternobj.patternH,patternobj.patternW) @@ -599,9 +613,9 @@ def file_from_pattern_obj(self, patternobj, filepath=None, bitdepth = None): self.patternH = shp[1] self.patternW = shp[2] - self.nCols = shp[0] - self.nRows = 1 - self.nPatterns = shp[0] + self.nCols = np.uint64(shp[0]) + self.nRows = np.uint64(1) + self.nPatterns = np.uint64(shp[0]) if bitdepth is None: #make a guess self.bitdepth = 16 @@ -675,7 +689,7 @@ def get_data_paths(self, verbose=0): - def pat_reader(self,patStart,nPatToRead): + def pat_reader(self,patStart=0,nPatToRead=1): '''This is a basic function that will read a chunk of patterns from the HDF5 file. Mainly this is intended to be called by the parent class function read_data. It assumes that patterns are laid out in a HDF5 dataset as an array @@ -690,7 +704,14 @@ def pat_reader(self,patStart,nPatToRead): readpats = np.array(patterndset[int(patStart):int(patStart+nPatToRead), :, :]) readpats = readpats.reshape(nPatToRead,self.patternH,self.patternW) f.close() - xyloc = None + yx = np.unravel_index(np.arange(patStart, patStart + nPatToRead), (self.nRows, self.nCols)) + + xyloc = np.array([yx[1], yx[0]]).T.copy().astype(np.float32) + xyloc[:, 0] -= self.nCols * 0.5 + xyloc[:, 1] -= self.nRows * 0.5 + xyloc[:, 0] *= self.xStep + xyloc[:, 1] *= self.yStep + return readpats, xyloc def copy_file(self, newpath, **kwargs): @@ -779,6 +800,16 @@ def read_header(self, path=None): self.patternH = shp[-2] self.nPatterns = shp[-3] self.filedatatype = dset.dtype.type + if self.xStep is None: + self.xStep = 0.0 + if self.yStep is None: + self.yStep = 0.0 + if self.nCols is None: + self.nCols = np.uint64(1) + if self.nCols == 0: + self.nCols = np.uint64(1) + if self.nRows is None: + self.nRows = np.uint64(np.floor(self.nPatterns/self.nCols)) class EDAXOH5(HDF5PatFile): def __init__(self, path=None): @@ -826,9 +857,9 @@ def read_header(self, path=None): self.nPatterns = shp[-3] self.filedatatype = dset.dtype.type headerpath = (f[self.h5patdatpth].parent.parent)["Header"] - self.nCols = np.int32(headerpath['nColumns'][()][0]) - self.nRows = np.int32(headerpath['nRows'][()][0]) - self.hexFlag = np.int32(headerpath['Grid Type'][()][0] == 'HexGrid') + self.nCols = np.uint32(headerpath['nColumns'][()][0]) + self.nRows = np.uint32(headerpath['nRows'][()][0]) + self.hexflag = np.uint32(headerpath['Grid Type'][()][0] == 'HexGrid') self.xStep = np.float32(headerpath['Step X'][()][0]) self.yStep = np.float32(headerpath['Step Y'][()][0]) @@ -881,9 +912,9 @@ def read_header(self, path=None): self.nPatterns = shp[-3] self.filedatatype = dset.dtype.type headerpath = (f[self.h5patdatpth].parent.parent)["Header"] - self.nCols = np.int32(headerpath['n_columns'][()][0]) - self.nRows = np.int32(headerpath['n_rows'][()][0]) - self.hexFlag = np.int32(headerpath['grid_type'][()][0] == 'hexagonal') + self.nCols = np.uint32(headerpath['n_columns'][()][0]) + self.nRows = np.uint32(headerpath['n_rows'][()][0]) + self.hexflag = np.uint32(headerpath['grid_type'][()][0] == 'hexagonal') self.xStep = np.float32(headerpath['step_x'][()][0]) self.yStep = np.float32(headerpath['step_y'][()][0]) @@ -936,9 +967,9 @@ def read_header(self, path=None): self.nPatterns = shp[-3] self.filedatatype = dset.dtype.type headerpath = (f[self.h5patdatpth].parent.parent)["Header"] - self.nCols = np.int32(headerpath['NCOLS'][()][0]) - self.nRows = np.int32(headerpath['NROWS'][()][0]) - #self.hexFlag = np.int32(f[headerpath+'Grid Type'][()][0] == 'HexGrid') + self.nCols = np.uint32(headerpath['NCOLS'][()][0]) + self.nRows = np.uint32(headerpath['NROWS'][()][0]) + #self.hexflag = np.int32(f[headerpath+'Grid Type'][()][0] == 'HexGrid') self.xStep = np.float32(headerpath['XSTEP'][()][0]) self.yStep = np.float32(headerpath['YSTEP'][()][0]) @@ -1024,11 +1055,26 @@ def read_header(self, path=None): self.nPatterns = shp[-3] self.filedatatype = dset.dtype.type headerpath = (f[self.h5patdatpth].parent.parent)["Header"] - self.nCols = np.int32(headerpath['X Cells'][()][0]) - self.nRows = np.int32(headerpath['Y Cells'][()][0]) - #self.hexFlag = np.int32(headerpath['Grid Type'][()][0] == 'HexGrid') + self.nCols = np.uint32(headerpath['X Cells'][()][0]) + self.nRows = np.uint32(headerpath['Y Cells'][()][0]) + #self.hexflag = np.int32(headerpath['Grid Type'][()][0] == 'HexGrid') self.xStep = np.float32(headerpath['X Step'][()][0]) self.yStep = np.float32(headerpath['Y Step'][()][0]) return 0 #note this function uses multiple returns + + def pat_reader(self, patStart=0, nPatToRead=1): + + patterns, xyloc = HDF5PatFile.pat_reader(self, patStart, nPatToRead) + try: + f = h5py.File(Path(self.filepath).expanduser(),'r') + xloc = (f[self.h5patdatpth].parent)["Beam Position X"] + xyloc[:,0] = np.array(xloc[int(patStart):int(patStart + nPatToRead)]).astype(np.float32) + yloc = (f[self.h5patdatpth].parent)["Beam Position Y"] + xyloc[:, 1] = np.array(yloc[int(patStart):int(patStart + nPatToRead)]).astype(np.float32) + f.close() + except: + print("File Not Found:",str(Path(self.filepath))) + + return patterns, xyloc \ No newline at end of file diff --git a/pyebsdindex/nlpar.py b/pyebsdindex/nlpar.py index 6c7f360..9492ccc 100644 --- a/pyebsdindex/nlpar.py +++ b/pyebsdindex/nlpar.py @@ -219,7 +219,7 @@ def d2norm(d2, n2, dij, sigma): rowstartread = np.int64(j) rowend = min(j + chunksize + nn,nrows) rowcountread = np.int64(rowend - rowstartread) - data = patternfile.read_data(patStartCount=[[0,rowstartread],[ncols,rowcountread]], + data, xyloc = patternfile.read_data(patStartCount=[[0,rowstartread],[ncols,rowcountread]], convertToFloat=True,returnArrayOnly=True) shp = data.shape @@ -346,7 +346,7 @@ def calcnlpar(self, chunksize=0, searchradius=None, lam = None, dthresh = None, rowstartread = np.int64(max(0, j-sr)) rowend = min(j + chunksize+sr,nrows) rowcountread = np.int64(rowend-rowstartread) - data = patternfile.read_data(patStartCount = [[0,rowstartread], [ncols,rowcountread]], + data, xyloc = patternfile.read_data(patStartCount = [[0,rowstartread], [ncols,rowcountread]], convertToFloat=True,returnArrayOnly=True) shpdata = data.shape @@ -427,7 +427,7 @@ def calcsigma(self,chunksize=0,nn=1,saturation_protect=True,automask=True): rowstartread = np.int64(max(0, j-nn)) rowend = min(j + chunksize+nn,nrows) rowcountread = np.int64(rowend-rowstartread) - data = patternfile.read_data(patStartCount = [[0,rowstartread], [ncols,rowcountread]], + data, xyloc = patternfile.read_data(patStartCount = [[0,rowstartread], [ncols,rowcountread]], convertToFloat=True,returnArrayOnly=True) shp = data.shape From d6f3b890d0d04bd9816d676bed84e2963c659808 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Fri, 20 Jan 2023 17:26:57 -0500 Subject: [PATCH 044/177] Initial ebsp file implementation. Much testing needed. Signed-off by: David Rowenhorst --- pyebsdindex/ebsd_pattern.py | 220 ++++++++++++++++++++++++++++++++++++ 1 file changed, 220 insertions(+) diff --git a/pyebsdindex/ebsd_pattern.py b/pyebsdindex/ebsd_pattern.py index 3783a98..bfee734 100644 --- a/pyebsdindex/ebsd_pattern.py +++ b/pyebsdindex/ebsd_pattern.py @@ -47,6 +47,8 @@ def get_pattern_file_obj(path,file_type=str('')): ftype = 'UP1' elif (extension == '.up2'): ftype = 'UP2' + elif (extension == '.ebsp'): + ftype = 'EBSP' elif (extension == '.oh5'): ftype = 'OH5' elif (extension == '.h5'): @@ -58,6 +60,8 @@ def get_pattern_file_obj(path,file_type=str('')): if (ftype.upper() == 'UP1') or (ftype.upper() == 'UP2'): ebsdfileobj = UPFile(path) + if (ftype.upper() == 'EBSP'): + ebsdfileobj = EBSDPFile(path) if (ftype.upper() == 'OH5'): ebsdfileobj = EDAXOH5(path) if hdf5path is None: #automatically chose the first data group @@ -623,6 +627,222 @@ def file_from_pattern_obj(self, patternobj, filepath=None, bitdepth = None): self.bitdepth = 8 +class EBSDPFile(EBSDPatternFile): + + def __init__(self, path=None): + EBSDPatternFile.__init__(self, path) + self.filetype = 'EBSDP' + self.vendor = 'OXFORD' + self.filedatatype = None + # UP only attributes + # self.bitdepth = None + self.filePos = None # file location in bytes where each pattern data starts + + def read_header(self, path=None, bitdepth=None): # readInterval=[0, -1], arrayOnly=False, + if path is not None: + self.filepath = path + + try: + f = open(Path(self.filepath).expanduser(), 'rb') + except: + print("File Not Found:", str(Path(self.filepath))) + return -1 + + f.seek(0) + version = np.fromfile(f, dtype=np.uint64, count=1) + + self.version = int(np.uint64(0)-version) + + if self.version >= 1: + loc0 = int(np.fromfile(f, dtype=np.uint64, count=1)) + + # going to assume that all patterns are the same as the first pattern the file. + f.seek(int(loc0)) + patdata = np.fromfile(f, dtype=np.uint32, count=4) + print(loc0, patdata) + self.patternW = patdata[2] + self.patternH = np.uint32(patdata[1]) + nbytespat = patdata[3] + bitdepth = nbytespat / (self.patternW * self.patternH) * 8 + + if bitdepth == 8: + self.filedatatype = np.uint8 + if bitdepth == 16: + self.filedatatype = np.uint16 + if bitdepth == 32: + self.filedatatype = np.uint32 + + + self.nPatterns = int( + (Path(self.filepath).expanduser().stat().st_size - int(8)) / + (24 + 18 + + int(self.patternW) * int(self.patternH) * int(self.filedatatype(0).nbytes))) + + f.seek(8) + self.filePos = np.fromfile(f, dtype=np.uint64, count=self.nPatterns) + + xall = np.zeros(self.nPatterns, dtype=np.float64) + yall = np.zeros(self.nPatterns, dtype=np.float64) + for i in range(self.nPatterns): + f.seek(int(self.filePos[i] + 16 + nbytespat + 1)) + xall[i] = np.fromfile(f, dtype=np.float64, count=1) + #print(x1, i) + f.seek(1, 1) + yall[i] = np.fromfile(f, dtype=np.float64, count=1) + + + self.xStep = xall[1] - xall[0] + if self.xStep > 1e-6: + ncol = (xall.max() - xall.min()) / self.xStep + ncol = np.round(ncol+1) + else: + ncol = 1 + + self.nCols = np.uint64(ncol) + + + self.yStep = yall[0] - yall[self.nCols] + + if self.yStep > 1e-6: + nrow = (yall.max() - yall.min()) / self.yStep + nrow = np.round(nrow+1) + self.nRows = int(nrow) + else: + self.nRows = int(self.nPatterns/self.nCols) + + if self.xStep is None: + self.xStep = 0.0 + if self.yStep is None: + self.yStep = 0.0 + if self.nCols is None: + self.nCols = np.uint64(1) + if self.nCols == 0: + self.nCols = np.uint64(1) + if self.nRows is None: + self.nRows = np.uint64(np.floor(self.nPatterns / self.nCols)) + f.close() + + return 0 # note this function uses multiple returns + + def pat_reader(self, patStart=0, nPatToRead=1): + try: + f = open(Path(self.filepath).expanduser(), 'rb') + except: + print("File Not Found:", str(Path(self.filepath))) + return -1 + + readpats = np.zeros((nPatToRead, self.patternH * self.patternW), dtype=self.filedatatype) + xyloc = np.zeros((nPatToRead, 2), dtype=np.float64) + # f.seek(self.filePos) + nPerPat = self.patternW * self.patternH + typeread = self.filedatatype + typebyte = self.filedatatype(0).nbytes + for i in range(int(patStart), int(patStart + nPatToRead)): + ii = int(i - patStart) + f.seek(int(self.filePos[i] + 16)) + readpats[ii, :] = np.fromfile(f, dtype=typeread, count=int(nPerPat)) + f.seek(1, 1) + xyloc[ii, 0] = np.fromfile(f, dtype=np.float64, count=1) + f.seek(1, 1) + xyloc[ii, 1] = np.fromfile(f, dtype=np.float64, count=1) + readpats = readpats.reshape(nPatToRead, self.patternH, self.patternW) + f.close() + + # yx = np.unravel_index(np.arange(int(patStart), int(patStart+nPatToRead), dtype = np.uint64), + # (int(self.nRows), int(self.nCols))) + + # xyloc = np.array([yx[1],yx[0]]).T.copy().astype(np.float32) + # xyloc[:,0] -= self.nCols * 0.5 + # xyloc[:, 1] -= self.nRows * 0.5 + # xyloc[:,0] *= self.xStep + # xyloc[:,1] *= self.yStep + return readpats, xyloc + + def write_header(self, writeBlank=False, bitdepth=8): + + filepath = self.filepath + extension = str.lower(Path(filepath).suffix) + try: + if (bitdepth is None) and (self.filedatatype is None): + raise ValueError('Error: extension not recognized, set "bitdepth" parameter') + elif (bitdepth == 8): + self.filedatatype = np.uint8 + elif (bitdepth == 16): + self.filedatatype = np.uint16 + except ValueError as exp: + print(exp) + return -1 + + try: + if os.path.isfile(Path(self.filepath).expanduser()): + f = open(Path(filepath).expanduser(), 'r+b') + f.seek(0) + else: + f = open(Path(filepath).expanduser(), 'w+b') + f.seek(0) + except: + print("File Not Found:", str(Path(filepath))) + return -1 + + version = np.uint64(-self.version) + np.asarray(version, dtype=np.uint64).tofile(f) + + if self.version >= 0: + np.asarray(self.filePos, dtype=np.uint64).tofile(f) + + if writeBlank == True: + typewrite = self.filedatatype + # if self.bitdepth == 8: + # type = np.uint8 + # if self.bitdepth == 16: + # type = np.uint16 + + blank = np.zeros((self.patternH, self.patternW), dtype=typewrite) + pathead = np.array([0, self.patternH, self.patternW, + self.patternH * self.patternW * self.filedatatype(0).nbytes], dtype=np.uint32) + + for j in range(self.nRows): + for i in range(self.nCols): + pathead.tofile(f) + blank.tofile(f) + np.uint8(1).tofile(f) + np.float64(i * self.xStep).tofile(f) + np.uint8(1).tofile(f) + np.float64(j * self.yStep).tofile(f) + + f.close() + + def pat_writer(self, pat2write, patStart, nPatToWrite, typewrite=None): + try: + f = open(Path(self.filepath).expanduser(), 'br+') + f.seek(0, 0) + except: + print("File Not Found:", str(Path(self.filepath))) + return -1 + + nPerPat = self.patternW * self.patternH + nPerPatByte = nPerPat * typewrite(0).nbytes + pathead = np.array([0, int(self.patternH), int(self.patternW), + int(self.patternH * self.patternW * self.filedatatype(0).nbytes)], dtype=np.uint32) + + # + for i in range(int(patStart), int(patStart + nPatToWrite)): + f.seek(int(self.filePos[i]), 0) + ii = int(i - patStart) + pathead.tofile(f) + pat2write[ii, :, :].tofile(f) + np.uint8(1).tofile(f) + yx = np.array(np.unravel_index(i, (self.nRows, self.nCols))).astype(np.float64) + yx[1] -= float(self.nCols * 0.5) + yx[1] *= self.xStep + yx[0] -= float(self.nRows * 0.5) + yx[0] *= self.yStep + np.float64(yx[1]).tofile(f) + np.uint8(1).tofile(f) + np.float64(yx[0]).tofile(f) + f.close() + + class HDF5PatFile(EBSDPatternFile): def __init__(self, path=None): filepath = None From 997e43cfd2f66c577b15d24268e31bf9a0265880 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Fri, 20 Jan 2023 22:28:36 -0500 Subject: [PATCH 045/177] Better PC opt function Signed-off by: David Rowenhorst --- pyebsdindex/ebsd_pattern.py | 2 +- pyebsdindex/pcopt.py | 8 +++++++- 2 files changed, 8 insertions(+), 2 deletions(-) diff --git a/pyebsdindex/ebsd_pattern.py b/pyebsdindex/ebsd_pattern.py index bfee734..6e7ae15 100644 --- a/pyebsdindex/ebsd_pattern.py +++ b/pyebsdindex/ebsd_pattern.py @@ -659,7 +659,7 @@ def read_header(self, path=None, bitdepth=None): # readInterval=[0, -1], arrayO # going to assume that all patterns are the same as the first pattern the file. f.seek(int(loc0)) patdata = np.fromfile(f, dtype=np.uint32, count=4) - print(loc0, patdata) + #print(loc0, patdata) self.patternW = patdata[2] self.patternH = np.uint32(patdata[1]) nbytespat = patdata[3] diff --git a/pyebsdindex/pcopt.py b/pyebsdindex/pcopt.py index a1b5f3e..3980669 100644 --- a/pyebsdindex/pcopt.py +++ b/pyebsdindex/pcopt.py @@ -42,6 +42,7 @@ def _optfunction(PC_i, indexer, banddat): n_points = banddat.shape[0] n_averages = 0 average_fit = 0 + nbands_fit = 0 phase = indexer.phaseLib[0] for i in range(n_points): @@ -50,15 +51,20 @@ def _optfunction(PC_i, indexer, banddat): whgood = np.nonzero(band_data1['max'] > -1e6)[0] if whgood.size >= 3: band_norm1 = band_norm1[whgood, :] - fit = phase.bandindex(band_norm1)[1] + dat = phase.bandindex(band_norm1) + fit = dat[1] + nMatch = dat[4] + if fit < 90: average_fit += fit n_averages += 1 + nbands_fit += nMatch if n_averages < 0.9: average_fit = 100 else: average_fit /= n_averages + average_fit /= nbands_fit return average_fit From 52c894c8570dbf0974cb2093b0b1b3eccc469960 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Sat, 21 Jan 2023 09:35:46 -0500 Subject: [PATCH 046/177] Improved PSO optimization Signed-off by: David Rowenhorst --- pyebsdindex/pcopt.py | 53 +++++++++++++++++++++++++++++++++++++++----- 1 file changed, 47 insertions(+), 6 deletions(-) diff --git a/pyebsdindex/pcopt.py b/pyebsdindex/pcopt.py index 3980669..cc6556a 100644 --- a/pyebsdindex/pcopt.py +++ b/pyebsdindex/pcopt.py @@ -44,6 +44,7 @@ def _optfunction(PC_i, indexer, banddat): average_fit = 0 nbands_fit = 0 phase = indexer.phaseLib[0] + nbands = indexer.bandDetectPlan.nBands for i in range(n_points): band_norm1 = band_norm[i, :, :] @@ -56,7 +57,8 @@ def _optfunction(PC_i, indexer, banddat): nMatch = dat[4] if fit < 90: - average_fit += fit + + average_fit += fit*(nbands+1 - nMatch ) n_averages += 1 nbands_fit += nMatch @@ -64,7 +66,7 @@ def _optfunction(PC_i, indexer, banddat): average_fit = 100 else: average_fit /= n_averages - average_fit /= nbands_fit + #average_fit *= (n_averages*(nbands+1) - nbands_fit)/(n_averages*nbands) return average_fit @@ -168,7 +170,7 @@ def optimize(pats, indexer, PC0=None, batch=False): return PCoutRet -def optimize_pso(pats, indexer, PC0=None, batch=False): +def optimize_pso(pats, indexer, PC0=None, batch=False, search_limit = 0.05): """Optimize pattern center (PC) (PCx, PCy, PCz) in the convention of the :attr:`indexer.vendor` with particle swarms. @@ -191,6 +193,8 @@ def optimize_pso(pats, indexer, PC0=None, batch=False): the patterns, and one PC will be returned. If ``True``, then an optimization is run for each individual pattern, and an array of PC values is returned. + search_limit : float, optional + Default is 0.05 for all PC values, and sets the +/- limit for the optimization search. Returns ------- @@ -200,8 +204,7 @@ def optimize_pso(pats, indexer, PC0=None, batch=False): Notes ----- :mod:`pyswarms` particle swarm algorithm is used with 50 particles, - bounds of +/- 0.05 on the PC values, and parameters c1 = 2.05, c2 = - 2.05 and w = 0.8. + and parameters c1 = 2.05, c2 = 2.05 and w = 0.8. """ banddat = indexer.bandDetectPlan.find_bands(pats) npoints = banddat.shape[0] @@ -211,11 +214,24 @@ def optimize_pso(pats, indexer, PC0=None, batch=False): else: PC0 = np.asarray(PC0) + if indexer.vendor == "EMSOFT": # Convert to EDAX for optimization + emsoftflag = True + indexer.vendor = "EDAX" + delta = indexer.PC + PCtemp = PC0[0:3] + PCtemp[0] *= -1.0 + PCtemp[0] += 0.5 * indexer.bandDetectPlan.patDim[1] + PCtemp[1] += 0.5 * indexer.bandDetectPlan.patDim[0] + PCtemp /= indexer.bandDetectPlan.patDim[1] + PCtemp[2] /= delta[3] + PC0 = PCtemp + + optimizer = pso.single.GlobalBestPSO( n_particles=50, dimensions=3, options={"c1": 2.05, "c2": 2.05, "w": 0.8}, - bounds=(PC0 - 0.05, PC0 + 0.05), + bounds=(PC0 - search_limit, PC0 + search_limit), ) if not batch: @@ -229,6 +245,31 @@ def optimize_pso(pats, indexer, PC0=None, batch=False): cost, PCoutRet[i, :] = optimizer.optimize( _optfunction, 100, indexer=indexer, banddat=banddat[i, :, :] ) + + if emsoftflag: # Return original state for indexer + indexer.vendor = "EMSOFT" + indexer.PC = delta + if PCoutRet.ndim == 2: + newout = np.zeros((npoints, 4)) + PCoutRet[:, 0] -= 0.5 + PCoutRet[:, :3] *= indexer.bandDetectPlan.patDim[1] + PCoutRet[:, 1] -= 0.5 * indexer.bandDetectPlan.patDim[0] + PCoutRet[:, 0] *= -1.0 + PCoutRet[:, 2] *= delta[3] + newout[:, :3] = PCoutRet + newout[:, 3] = delta[3] + PCoutRet = newout + else: + newout = np.zeros(4) + PCoutRet[0] -= 0.5 + PCoutRet[:3] *= indexer.bandDetectPlan.patDim[1] + PCoutRet[1] -= 0.5 * indexer.bandDetectPlan.patDim[0] + PCoutRet[0] *= -1.0 + PCoutRet[2] *= delta[3] + newout[:3] = PCoutRet + newout[3] = delta[3] + PCoutRet = newout + return PCoutRet From 4068850397160aeca8502f3883942b9a0602b06b Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Sat, 21 Jan 2023 14:29:20 -0500 Subject: [PATCH 047/177] Fixed PSO optimization for EMSoft Signed-off by: David Rowenhorst --- pyebsdindex/pcopt.py | 1 + 1 file changed, 1 insertion(+) diff --git a/pyebsdindex/pcopt.py b/pyebsdindex/pcopt.py index cc6556a..039b739 100644 --- a/pyebsdindex/pcopt.py +++ b/pyebsdindex/pcopt.py @@ -214,6 +214,7 @@ def optimize_pso(pats, indexer, PC0=None, batch=False, search_limit = 0.05): else: PC0 = np.asarray(PC0) + emsoftflag = False if indexer.vendor == "EMSOFT": # Convert to EDAX for optimization emsoftflag = True indexer.vendor = "EDAX" From fd6b6b254c9b0bd5d6fb5033d770b6c00ab84cef Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Sat, 21 Jan 2023 21:03:19 -0500 Subject: [PATCH 048/177] Fix multi-phase Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_single.py | 4 ++-- pyebsdindex/ebsdfile.py | 2 +- pyebsdindex/pcopt.py | 1 - 3 files changed, 3 insertions(+), 4 deletions(-) diff --git a/pyebsdindex/_ebsd_index_single.py b/pyebsdindex/_ebsd_index_single.py index 2fa253a..fa3c324 100644 --- a/pyebsdindex/_ebsd_index_single.py +++ b/pyebsdindex/_ebsd_index_single.py @@ -297,7 +297,7 @@ def __init__( rhoMaskFrac=0.15, nBands=9, patDim=None, - nband_earlyexit = 7, + nband_earlyexit = 20, **kwargs ): """Create an EBSD indexer.""" @@ -556,7 +556,7 @@ def index_pats( indxData["quat"][0:nPhases, :, :] = q indxData[-1, :] = indxData[0, :] if nPhases > 1: - for j in range(1, nPhases-1): + for j in range(1, nPhases): #indxData[-1, :] = np.where( # (indxData[j, :]["cm"] * indxData[j, :]["nmatch"]) # > (indxData[j + 1, :]["cm"] * indxData[j + 1, :]["nmatch"]), diff --git a/pyebsdindex/ebsdfile.py b/pyebsdindex/ebsdfile.py index cde90ef..c89bace 100644 --- a/pyebsdindex/ebsdfile.py +++ b/pyebsdindex/ebsdfile.py @@ -43,7 +43,7 @@ def writeang(filename, indexer, data, f.write('# '+'\r\n') f.write('# GRID: '+gridtype+'\r\n') if indexer.fID is not None: - if indexer.fID.xStep is not None: + if indexer.fID.xStep > 1e-6: xstep = indexer.fID.xStep ystep = indexer.fID.yStep diff --git a/pyebsdindex/pcopt.py b/pyebsdindex/pcopt.py index 039b739..085a459 100644 --- a/pyebsdindex/pcopt.py +++ b/pyebsdindex/pcopt.py @@ -57,7 +57,6 @@ def _optfunction(PC_i, indexer, banddat): nMatch = dat[4] if fit < 90: - average_fit += fit*(nbands+1 - nMatch ) n_averages += 1 nbands_fit += nMatch From 2713435068dab6c9a4e6063e7bb2e747ec8e59e2 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Sat, 21 Jan 2023 22:45:02 -0500 Subject: [PATCH 049/177] Enable multi-phase PC optimization. Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_single.py | 78 ++++++++++++++++--------------- pyebsdindex/pcopt.py | 56 +++++++++++++--------- 2 files changed, 74 insertions(+), 60 deletions(-) diff --git a/pyebsdindex/_ebsd_index_single.py b/pyebsdindex/_ebsd_index_single.py index fa3c324..efc68c2 100644 --- a/pyebsdindex/_ebsd_index_single.py +++ b/pyebsdindex/_ebsd_index_single.py @@ -444,7 +444,7 @@ def index_pats( Number of Bands Matched (nmatch). There are some other metrics reported, but these are mostly for debugging purposes. - bandData : numpy.ndarray + banddata : numpy.ndarray Band identification data from the Radon transform. patstart : int Starting index of the indexed patterns. @@ -481,34 +481,58 @@ def index_pats( if npats == -1: npats = npoints - bandData = self.bandDetectPlan.find_bands( + banddata = self.bandDetectPlan.find_bands( pats, clparams=clparams, verbose=verbose, chunksize=chunksize ) - shpBandDat = bandData.shape + # shpBandDat = banddata.shape if PC is None: PC_0 = self.PC else: PC_0 = PC - bandNorm = self.bandDetectPlan.radonPlan.radon2pole( - bandData, PC=PC_0, vendor=self.vendor + bandnorm = self.bandDetectPlan.radonPlan.radon2pole( + banddata, PC=PC_0, vendor=self.vendor ) - # Return bandNorm, patStart, patEnd tic = timer() + + indxData = self._indexbandsphase(banddata, bandnorm, verbose=verbose) + + if verbose > 0: + print("Band Vote Time: ", timer() - tic) + + return indxData, banddata, patstart, npats + + def _detector2refframe(self): + ven = str.upper(self.vendor) + if ven in ["EDAX", "EMSOFT", "KIKUCHIPY"]: + q0 = np.array([np.sqrt(2.0) * 0.5, 0.0, 0.0, -1.0 * np.sqrt(2.0) * 0.5]) + tiltang = -1.0 * (90.0 - self.sampleTilt + self.camElev) / RADEG + q1 = np.array([np.cos(tiltang * 0.5), np.sin(tiltang * 0.5), 0.0, 0.0]) + quatref2detect = rotlib.quat_multiply(q1, q0) + elif ven in ["OXFORD", "BRUKER"]: + tiltang = -1.0 * (90.0 - self.sampleTilt + self.camElev) / RADEG + q1 = np.array([np.cos(tiltang * 0.5), np.sin(tiltang * 0.5), 0.0, 0.0]) + quatref2detect = q1 + else: + raise ValueError("`self.vendor` unknown") + + return quatref2detect + def _indexbandsphase(self, banddata, bandnorm, verbose = 0): + + shpBandDat = banddata.shape + npoints = int(banddata.size/(shpBandDat[-1])+0.1) nPhases = len(self.phaseLib) q = np.zeros((nPhases, npoints, 4)) indxData = np.zeros((nPhases + 1, npoints), dtype=self.dataTemplate) - - indxData["phase"] = -1 indxData["fit"] = 180.0 indxData["totvotes"] = 0 if self.phaseLib[0] is None: - return indxData, bandData, patstart, npats + return indxData if self.nband_earlyexit is None: - earlyexit = shpBandDat[1] # default to all the poles. + earlyexit = shpBandDat[1] # default to all the poles. # for ph in self.phaselist: # if hasattr(ph, 'nband_earlyexit'): # earlyexit = min(earlyexit, ph.nband_earlyexit) @@ -516,8 +540,8 @@ def index_pats( earlyexit = self.nband_earlyexit for i in range(npoints): - bandNorm1 = bandNorm[i, :, :] - bDat1 = bandData[i, :] + bandNorm1 = bandnorm[i, :, :] + bDat1 = banddata[i, :] whgood = np.nonzero(bDat1["max"] > -1.0e6)[0] if whgood.size >= 3: bDat1 = bDat1[whgood] @@ -548,7 +572,7 @@ def index_pats( if nMatch >= earlyexit: break - qref2detect = self._refframe2detector() + qref2detect = self._detector2refframe() q = q.reshape(nPhases * npoints, 4) q = rotlib.quat_multiply(q, qref2detect) q = rotlib.quatnorm(q) @@ -557,41 +581,19 @@ def index_pats( indxData[-1, :] = indxData[0, :] if nPhases > 1: for j in range(1, nPhases): - #indxData[-1, :] = np.where( + # indxData[-1, :] = np.where( # (indxData[j, :]["cm"] * indxData[j, :]["nmatch"]) # > (indxData[j + 1, :]["cm"] * indxData[j + 1, :]["nmatch"]), # indxData[j, :], # indxData[j + 1, :], indxData[-1, :] = np.where( - ((3.0 - indxData[j, :]["fit"]) * indxData[j, :]["nmatch"]) + ((3.0 - indxData[j, :]["fit"]) * indxData[j, :]["nmatch"]) > ((3.0 - indxData[-1, :]["fit"]) * indxData[-1, :]["nmatch"]), indxData[j, :], indxData[-1, :] ) - - - if verbose > 0: - print("Band Vote Time: ", timer() - tic) - - return indxData, bandData, patstart, npats - - def _refframe2detector(self): - ven = str.upper(self.vendor) - if ven in ["EDAX", "EMSOFT", "KIKUCHIPY"]: - q0 = np.array([np.sqrt(2.0) * 0.5, 0.0, 0.0, -1.0 * np.sqrt(2.0) * 0.5]) - tiltang = -1.0 * (90.0 - self.sampleTilt + self.camElev) / RADEG - q1 = np.array([np.cos(tiltang * 0.5), np.sin(tiltang * 0.5), 0.0, 0.0]) - quatref2detect = rotlib.quat_multiply(q1, q0) - elif ven in ["OXFORD", "BRUKER"]: - tiltang = -1.0 * (90.0 - self.sampleTilt + self.camElev) / RADEG - q1 = np.array([np.cos(tiltang * 0.5), np.sin(tiltang * 0.5), 0.0, 0.0]) - quatref2detect = q1 - else: - raise ValueError("`self.vendor` unknown") - - return quatref2detect - + return indxData # def pcCorrect(self, xy=[[0.0, 0.0]]): # # TODO: At somepoint we will put some methods here for # # correcting the PC depending on the location within the scan. diff --git a/pyebsdindex/pcopt.py b/pyebsdindex/pcopt.py index 085a459..209f0b6 100644 --- a/pyebsdindex/pcopt.py +++ b/pyebsdindex/pcopt.py @@ -36,35 +36,47 @@ def _optfunction(PC_i, indexer, banddat): - band_norm = indexer.bandDetectPlan.radonPlan.radon2pole( + bandnorm = indexer.bandDetectPlan.radonPlan.radon2pole( banddat, PC=PC_i, vendor=indexer.vendor ) - n_points = banddat.shape[0] - n_averages = 0 - average_fit = 0 - nbands_fit = 0 - phase = indexer.phaseLib[0] + #npoints = banddat.shape[0] + #n_averages = 0 + #average_fit = 0 + #nbands_fit = 0 + #phase = indexer.phaseLib[0] nbands = indexer.bandDetectPlan.nBands + indexdata = indexer._indexbandsphase( banddat, bandnorm) - for i in range(n_points): - band_norm1 = band_norm[i, :, :] - band_data1 = banddat[i, :] - whgood = np.nonzero(band_data1['max'] > -1e6)[0] - if whgood.size >= 3: - band_norm1 = band_norm1[whgood, :] - dat = phase.bandindex(band_norm1) - fit = dat[1] - nMatch = dat[4] - - if fit < 90: - average_fit += fit*(nbands+1 - nMatch ) - n_averages += 1 - nbands_fit += nMatch + + + fit = indexdata[-1]['fit'] + nmatch = indexdata[-1]['nmatch'] + average_fit = fit*(nbands+1 - nmatch) + whgood = np.nonzero(fit < 90.0) + + n_averages = len(whgood[0]) + + + # for i in range(npoints): + # band_norm1 = band_norm[i, :, :] + # band_data1 = banddat[i, :] + # whgood = np.nonzero(band_data1['max'] > -1e6)[0] + # if whgood.size >= 3: + # band_norm1 = band_norm1[whgood, :] + # dat = phase.bandindex(band_norm1) + # fit = dat[1] + # nMatch = dat[4] + # + # if fit < 90: + # average_fit += fit*(nbands+1 - nMatch ) + # n_averages += 1 + # nbands_fit += nMatch if n_averages < 0.9: average_fit = 100 else: - average_fit /= n_averages + average_fit = np.mean(average_fit[whgood[0]]) + #average_fit /= n_averages #average_fit *= (n_averages*(nbands+1) - nbands_fit)/(n_averages*nbands) return average_fit @@ -238,7 +250,7 @@ def optimize_pso(pats, indexer, PC0=None, batch=False, search_limit = 0.05): cost, PCoutRet = optimizer.optimize( _optfunction, 1000, indexer=indexer, banddat=banddat ) - print(cost) + #print(cost) else: PCoutRet = np.zeros((npoints, 3)) for i in range(npoints): From 5fc437bc798d82fb1b25540f2c0eec1ff87449b2 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Sat, 21 Jan 2023 23:30:14 -0500 Subject: [PATCH 050/177] Add number particle options to PSO Signed-off by: David Rowenhorst --- pyebsdindex/pcopt.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pyebsdindex/pcopt.py b/pyebsdindex/pcopt.py index 209f0b6..e6b2496 100644 --- a/pyebsdindex/pcopt.py +++ b/pyebsdindex/pcopt.py @@ -181,7 +181,7 @@ def optimize(pats, indexer, PC0=None, batch=False): return PCoutRet -def optimize_pso(pats, indexer, PC0=None, batch=False, search_limit = 0.05): +def optimize_pso(pats, indexer, PC0=None, batch=False, search_limit = 0.05, nswarmpoints=50): """Optimize pattern center (PC) (PCx, PCy, PCz) in the convention of the :attr:`indexer.vendor` with particle swarms. @@ -240,7 +240,7 @@ def optimize_pso(pats, indexer, PC0=None, batch=False, search_limit = 0.05): optimizer = pso.single.GlobalBestPSO( - n_particles=50, + n_particles=nswarmpoints, dimensions=3, options={"c1": 2.05, "c2": 2.05, "w": 0.8}, bounds=(PC0 - search_limit, PC0 + search_limit), From 0745268bd9753d2a20df2ebac3d8287f1d78ddfd Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Mon, 23 Jan 2023 16:08:23 -0500 Subject: [PATCH 051/177] Correct filetype typo. Signed-off by: David Rowenhorst --- pyebsdindex/ebsd_pattern.py | 2 +- pyebsdindex/nlpar.py | 3 ++- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/pyebsdindex/ebsd_pattern.py b/pyebsdindex/ebsd_pattern.py index 6e7ae15..2606c97 100644 --- a/pyebsdindex/ebsd_pattern.py +++ b/pyebsdindex/ebsd_pattern.py @@ -631,7 +631,7 @@ class EBSDPFile(EBSDPatternFile): def __init__(self, path=None): EBSDPatternFile.__init__(self, path) - self.filetype = 'EBSDP' + self.filetype = 'EBSP' self.vendor = 'OXFORD' self.filedatatype = None # UP only attributes diff --git a/pyebsdindex/nlpar.py b/pyebsdindex/nlpar.py index 9492ccc..20955c7 100644 --- a/pyebsdindex/nlpar.py +++ b/pyebsdindex/nlpar.py @@ -109,7 +109,8 @@ def setoutfile(self,patternfile, filepath=None): if patternfile is not None: # the user has set no path. hdf5path = None - if patternfile.filetype == 'UP': + + if patternfile.filetype in ['UP', 'EBSP']: p = Path(patternfile.filepath) appnd = "_NLPAR_l{:1.2f}".format(self.lam) + "sr{:d}".format(self.searchradius) newfilepath = str(p.parent / Path(p.stem + appnd + p.suffix)) From fc9249949515f9940a7f72db81ae3c2e751e48a5 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Wed, 25 Jan 2023 16:12:05 -0500 Subject: [PATCH 052/177] Correct background subtract. Signed-off by: David Rowenhorst --- pyebsdindex/band_detect.py | 18 +++++++------ pyebsdindex/ebsd_pattern.py | 53 +++++++++++++++++++++++++++++++------ 2 files changed, 55 insertions(+), 16 deletions(-) diff --git a/pyebsdindex/band_detect.py b/pyebsdindex/band_detect.py index 93ca60b..65a1ad0 100644 --- a/pyebsdindex/band_detect.py +++ b/pyebsdindex/band_detect.py @@ -216,9 +216,9 @@ def collect_background(self, fileobj = None, patsIn = None, nsample = None, meth elif method.upper() == 'EVENSTRIDE': step = int(npats / nsample) # not great, but maybe good enough. stride = np.arange(0,npats, step, dypte = np.uint64) - pat1 = fileobj.read_data(convertToFloat=True,patStartCount=[stride[0], 1],returnArrayOnly=True) + pat1 = fileobj.read_data(convertToFloat=True,patStartCount=[stride[0], 1],returnArrayOnly=True)[0] for i in stride[1:]: - pat1 += fileobj.read_data(convertToFloat=True,patStartCount=[i, 1],returnArrayOnly=True) + pat1 += fileobj.read_data(convertToFloat=True,patStartCount=[i, 1],returnArrayOnly=True)[0] back = pat1 / float(len(stride)) #pshape = pat1.shape # a bit of image processing. @@ -230,7 +230,7 @@ def collect_background(self, fileobj = None, patsIn = None, nsample = None, meth #back -= np.mean(back) self.backgroundsub = back - def backsub_fit(self, back): + def backsub_fit(self, back, mask = None): # This function will fit a 2D gaussian on top of a plane to the averaged set of patterns (data) that is provided. # It will automatically use whatever mask is defined for valid data. # If the gaussian fit fails to converge, it will fall back to just using the mean set of patterns for the background @@ -254,11 +254,13 @@ def fit_gauss(M, *args): x = (np.broadcast_to(x.reshape(1,nx), (ny, nx))).ravel() y = np.arange(ny, dtype=float) y = (np.broadcast_to(y, (nx, ny)).T).ravel() - # make a circular mask - even if not EDAX, this should work OK. - cx = (np.arange(nx) - nx*0.5)**2 - cy = (np.arange(ny) - ny*0.5)**2 - cmask = np.sqrt(np.broadcast_to(cx.reshape(1,nx), (ny, nx)) + np.broadcast_to(cy, (nx, ny)).T) < (ny*0.49) - + if mask is None: + # make a circular mask - even if not EDAX, this should work OK. + cx = (np.arange(nx) - nx*0.5)**2 + cy = (np.arange(ny) - ny*0.5)**2 + cmask = np.sqrt(np.broadcast_to(cx.reshape(1,nx), (ny, nx)) + np.broadcast_to(cy, (nx, ny)).T) < (ny*0.49) + else: + cmask = mask # need to grab only the values that are in the mask. wh = np.nonzero(cmask.ravel())[0] xwh = x[wh] diff --git a/pyebsdindex/ebsd_pattern.py b/pyebsdindex/ebsd_pattern.py index 2606c97..bae8051 100644 --- a/pyebsdindex/ebsd_pattern.py +++ b/pyebsdindex/ebsd_pattern.py @@ -217,7 +217,7 @@ def read_data(self,path=None,convertToFloat=False,patStartCount = [0,-1],returnA self.read_header() if self.version is None: self.read_header() - + patStartCount = np.array(patStartCount, dtype=np.int64) # bitD = 8 * (self.bitdepth == 8) + 16 * (self.bitdepth == 16) # # this will allow for overriding the original file spec -- not sure why would want to but ... @@ -543,13 +543,20 @@ def write_header(self, writeBlank=False, bitdepth=None): print("File Not Found:", str(Path(filepath))) return -1 + if self.version is None: + self.version = 3 + np.asarray(self.version, dtype=np.uint32).tofile(f) if self.version == 1: + if self.filePos is None: + self.filePos = 16 np.asarray(self.patternW,dtype=np.uint32).tofile(f) np.asarray(self.patternH,dtype=np.uint32).tofile(f) np.asarray(self.filePos,dtype=np.uint32).tofile(f) elif self.version >= 3: + if self.filePos is None: + self.filePos = 42 np.asarray(self.patternW,dtype=np.uint32).tofile(f) np.asarray(self.patternH,dtype=np.uint32).tofile(f) np.asarray(self.filePos,dtype=np.uint32).tofile(f) @@ -654,16 +661,45 @@ def read_header(self, path=None, bitdepth=None): # readInterval=[0, -1], arrayO self.version = int(np.uint64(0)-version) if self.version >= 1: + loc0 = int(np.fromfile(f, dtype=np.uint64, count=1)) + currentloc = f.tell() + loc1 = loc0 + npat = 0 + + while loc1 != currentloc: + loc11 = int(np.fromfile(f, dtype=np.uint64, count = 1)) + loc1 = min([loc1, loc11]) + currentloc = f.tell() + #print(loc1, currentloc) + #return + + self.nPatterns = int((currentloc-8)/int(8)) + + f.seek(8) + self.filePos = np.fromfile(f, dtype=np.uint64, count=self.nPatterns) # going to assume that all patterns are the same as the first pattern the file. - f.seek(int(loc0)) + f.seek(self.filePos[0]) + #patdata = np.fromfile(f, dtype=np.uint32, count=4) + #patdata0 = np.fromfile(f, dtype=np.uint8, count=1) + patdata = np.fromfile(f, dtype=np.uint32, count=4) #print(loc0, patdata) + #f.seek(self.filePos[2]) + #print(np.fromfile(f, dtype=np.uint32, count=4)) + #print(np.fromfile(f, dtype=np.uint32, count=8)) + #print(np.fromfile(f, dtype=np.uint32, count=1)) + self.patternW = patdata[2] self.patternH = np.uint32(patdata[1]) nbytespat = patdata[3] + + + #if self.version == 1: bitdepth = nbytespat / (self.patternW * self.patternH) * 8 + #elif self.version >= 2: + #bitdepth = nbytespat if bitdepth == 8: self.filedatatype = np.uint8 @@ -673,13 +709,14 @@ def read_header(self, path=None, bitdepth=None): # readInterval=[0, -1], arrayO self.filedatatype = np.uint32 - self.nPatterns = int( - (Path(self.filepath).expanduser().stat().st_size - int(8)) / - (24 + 18 + - int(self.patternW) * int(self.patternH) * int(self.filedatatype(0).nbytes))) + #self.nPatterns = int( + # (Path(self.filepath).expanduser().stat().st_size - int(8)) / + # (24 + 18 + + # int(self.patternW) * int(self.patternH) * int(self.filedatatype(0).nbytes))) + + #print(self.nPatterns) + - f.seek(8) - self.filePos = np.fromfile(f, dtype=np.uint64, count=self.nPatterns) xall = np.zeros(self.nPatterns, dtype=np.float64) yall = np.zeros(self.nPatterns, dtype=np.float64) From 317762b7e28dee2ea9058ff3f304412265942d46 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Wed, 25 Jan 2023 19:29:57 -0500 Subject: [PATCH 053/177] Corrected EDAX PC for non-square patterns. Signed-off by: David Rowenhorst --- pyebsdindex/radon_fast.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/pyebsdindex/radon_fast.py b/pyebsdindex/radon_fast.py index c1970e8..380f795 100644 --- a/pyebsdindex/radon_fast.py +++ b/pyebsdindex/radon_fast.py @@ -264,7 +264,9 @@ def radon2pole(self,bandData,PC=None,vendor='EDAX'): dimf = np.array(self.imDim, dtype=np.float32) - if ven in ['EDAX', 'OXFORD']: + if ven in ['EDAX']: + t *= np.array([dimf[1], dimf[0], -dimf[0]]) + if ven in ['OXFORD']: t *= np.array([dimf[1], dimf[1], -dimf[1]]) if ven == 'EMSOFT': t[:, 0] *= -1.0 From 9c41ff57afc406f4489f27d1951d3cf73e031763 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Thu, 26 Jan 2023 11:10:04 -0500 Subject: [PATCH 054/177] Better optimization function for PC. Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_parallel.py | 10 ++++---- pyebsdindex/pcopt.py | 38 ++++++++++++----------------- 2 files changed, 21 insertions(+), 27 deletions(-) diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index 8012b84..219bde2 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -24,8 +24,8 @@ parallel. """ -from os import path -import multiprocessing + +import os import logging import sys import time @@ -242,10 +242,10 @@ def index_pats_distributed( npats = npatsTotal - patstart # Now set up the cluster with the indexer - n_cpu_nodes = int(multiprocessing.cpu_count()) + n_cpu_nodes = int(os.cpu_count()) # int(sum([ r['Resources']['CPU'] for r in ray.nodes()])) if ncpu != -1: - n_cpu_nodes = ncpu + n_cpu_nodes = int(ncpu) ngpu = None if gpu_id is not None: @@ -274,7 +274,7 @@ def index_pats_distributed( num_cpus=n_cpu_nodes, num_gpus=ngpu, _node_ip_address="0.0.0.0", - runtime_env={"env_vars": {"PYTHONPATH": path.dirname(path.dirname(__file__))}}, + runtime_env={"env_vars": {"PYTHONPATH": os.path.dirname(os.path.dirname(__file__))}}, logging_level=logging.WARNING, ) # Supress INFO messages from ray. diff --git a/pyebsdindex/pcopt.py b/pyebsdindex/pcopt.py index e6b2496..dd1f3c6 100644 --- a/pyebsdindex/pcopt.py +++ b/pyebsdindex/pcopt.py @@ -39,7 +39,7 @@ def _optfunction(PC_i, indexer, banddat): bandnorm = indexer.bandDetectPlan.radonPlan.radon2pole( banddat, PC=PC_i, vendor=indexer.vendor ) - #npoints = banddat.shape[0] + npoints = banddat.shape[0] #n_averages = 0 #average_fit = 0 #nbands_fit = 0 @@ -52,30 +52,16 @@ def _optfunction(PC_i, indexer, banddat): fit = indexdata[-1]['fit'] nmatch = indexdata[-1]['nmatch'] average_fit = fit*(nbands+1 - nmatch) + #average_fit = -1.0*(3.0-fit)*nmatch whgood = np.nonzero(fit < 90.0) n_averages = len(whgood[0]) - # for i in range(npoints): - # band_norm1 = band_norm[i, :, :] - # band_data1 = banddat[i, :] - # whgood = np.nonzero(band_data1['max'] > -1e6)[0] - # if whgood.size >= 3: - # band_norm1 = band_norm1[whgood, :] - # dat = phase.bandindex(band_norm1) - # fit = dat[1] - # nMatch = dat[4] - # - # if fit < 90: - # average_fit += fit*(nbands+1 - nMatch ) - # n_averages += 1 - # nbands_fit += nMatch - if n_averages < 0.9: average_fit = 100 else: - average_fit = np.mean(average_fit[whgood[0]]) + average_fit = np.mean(average_fit[whgood[0]])*npoints/n_averages #average_fit /= n_averages #average_fit *= (n_averages*(nbands+1) - nbands_fit)/(n_averages*nbands) return average_fit @@ -181,7 +167,8 @@ def optimize(pats, indexer, PC0=None, batch=False): return PCoutRet -def optimize_pso(pats, indexer, PC0=None, batch=False, search_limit = 0.05, nswarmpoints=50): +def optimize_pso(pats, indexer, PC0=None, batch=False, search_limit = 0.05, + nswarmpoints=None, pswarmpar=None, ninter=500): """Optimize pattern center (PC) (PCx, PCy, PCz) in the convention of the :attr:`indexer.vendor` with particle swarms. @@ -219,9 +206,16 @@ def optimize_pso(pats, indexer, PC0=None, batch=False, search_limit = 0.05, nswa """ banddat = indexer.bandDetectPlan.find_bands(pats) npoints = banddat.shape[0] + if pswarmpar is None: + pswarmpar = {"c1": 2.05, "c2": 2.05, "w": 0.8} + + if nswarmpoints is None: + nswarmpoints = int(np.array(search_limit).max() * (75.0/0.2)) + nswarmpoints = max(50, nswarmpoints) + if PC0 is None: - PC0 = indexer.PC + PC0 = np.asarray(indexer.PC) else: PC0 = np.asarray(PC0) @@ -242,20 +236,20 @@ def optimize_pso(pats, indexer, PC0=None, batch=False, search_limit = 0.05, nswa optimizer = pso.single.GlobalBestPSO( n_particles=nswarmpoints, dimensions=3, - options={"c1": 2.05, "c2": 2.05, "w": 0.8}, + options=pswarmpar,#options={"c1": 2.05, "c2": 2.05, "w": 0.8}, bounds=(PC0 - search_limit, PC0 + search_limit), ) if not batch: cost, PCoutRet = optimizer.optimize( - _optfunction, 1000, indexer=indexer, banddat=banddat + _optfunction, ninter, indexer=indexer, banddat=banddat ) #print(cost) else: PCoutRet = np.zeros((npoints, 3)) for i in range(npoints): cost, PCoutRet[i, :] = optimizer.optimize( - _optfunction, 100, indexer=indexer, banddat=banddat[i, :, :] + _optfunction, ninter, indexer=indexer, banddat=banddat[i, :, :] ) if emsoftflag: # Return original state for indexer From edb029ba35baa6aa2d9571496c33cee0dcd80144 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Thu, 26 Jan 2023 16:49:55 -0500 Subject: [PATCH 055/177] More robust optimizer function Signed-off by: David Rowenhorst --- pyebsdindex/pcopt.py | 16 +++++++++------- pyebsdindex/tripletvote.py | 27 +++++++++++++++------------ 2 files changed, 24 insertions(+), 19 deletions(-) diff --git a/pyebsdindex/pcopt.py b/pyebsdindex/pcopt.py index dd1f3c6..f8304bb 100644 --- a/pyebsdindex/pcopt.py +++ b/pyebsdindex/pcopt.py @@ -59,9 +59,10 @@ def _optfunction(PC_i, indexer, banddat): if n_averages < 0.9: - average_fit = 100 + average_fit = 1000 else: - average_fit = np.mean(average_fit[whgood[0]])*npoints/n_averages + average_fit = np.sum(average_fit[whgood[0]]) + 4.0*(nbands+1)*(npoints - n_averages) + average_fit /= npoints #average_fit /= n_averages #average_fit *= (n_averages*(nbands+1) - nbands_fit)/(n_averages*nbands) return average_fit @@ -207,10 +208,10 @@ def optimize_pso(pats, indexer, PC0=None, batch=False, search_limit = 0.05, banddat = indexer.bandDetectPlan.find_bands(pats) npoints = banddat.shape[0] if pswarmpar is None: - pswarmpar = {"c1": 2.05, "c2": 2.05, "w": 0.8} + pswarmpar = {"c1": 2.05, "c2": 2.05, "w": 0.8}#, 'k': 2, 'p': 2} if nswarmpoints is None: - nswarmpoints = int(np.array(search_limit).max() * (75.0/0.2)) + nswarmpoints = int(np.array(search_limit).max() * (100.0/0.2)) nswarmpoints = max(50, nswarmpoints) @@ -230,14 +231,15 @@ def optimize_pso(pats, indexer, PC0=None, batch=False, search_limit = 0.05, PCtemp[1] += 0.5 * indexer.bandDetectPlan.patDim[0] PCtemp /= indexer.bandDetectPlan.patDim[1] PCtemp[2] /= delta[3] - PC0 = PCtemp + PC0 = np.array(PCtemp) + #optimizer = pso.single.GlobalBestPSO( optimizer = pso.single.GlobalBestPSO( n_particles=nswarmpoints, dimensions=3, - options=pswarmpar,#options={"c1": 2.05, "c2": 2.05, "w": 0.8}, - bounds=(PC0 - search_limit, PC0 + search_limit), + options=pswarmpar, + bounds=(PC0 - np.array(search_limit), PC0 + np.array(search_limit)), ) if not batch: diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index 234adb0..d3c9630 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -306,22 +306,25 @@ def build_trip_lib(self): wh = np.nonzero(unqang > 1.0)[0] nwh = wh.size - #sign = sign[wh] - #sign = sign.reshape(nwh,1) - temp = np.zeros((nwh, 2, 3)) - temp[:,0,:] = np.broadcast_to(poles[i,:], (nwh, 3)) - temp[:,1,:] = np.broadcast_to(fampoles[argunq[wh],:], (nwh, 3)) - for k in range(nwh): - angs.append(unqang[wh[k]]) - familyID.append([i,j]) - polePairs.append(temp[k,:,:]) + if nwh > 0: + #sign = sign[wh] + #sign = sign.reshape(nwh,1) + temp = np.zeros((nwh, 2, 3)) + temp[:,0,:] = np.broadcast_to(poles[i,:], (nwh, 3)) + temp[:,1,:] = np.broadcast_to(fampoles[argunq[wh],:], (nwh, 3)) + for k in range(nwh): + angs.append(unqang[wh[k]]) + familyID.append([i,j]) + polePairs.append(temp[k,:,:]) angs = np.atleast_1d(np.squeeze(np.array(angs))) nangs = angs.size familyID = np.array(familyID) polePairs = np.array(polePairs) + nFamilyID = np.bincount(np.squeeze(familyID[:,0]).astype(int), minlength=int(npoles)) + + #stuff, nFamilyID = np.unique(familyID[:,0], return_counts=True) - stuff, nFamilyID = np.unique(familyID[:,0], return_counts=True) indx0FID = (np.concatenate( ([0],np.cumsum(nFamilyID)) ))[0:npoles] #print(familyID) #print(nFamilyID) @@ -336,8 +339,8 @@ def build_trip_lib(self): counter = 0 # now actually catalog all the triplet angles. for i in range(npoles): - #if indx0FID[i] >= npoles: - # break + if nFamilyID[i] <= 0: + continue id0 = familyID[indx0FID[i], 0] for j in range(0,nFamilyID[i]): From 2498f1f0aeee40bcc157dd0abb5b803b5d9b5b0e Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Fri, 27 Jan 2023 08:20:36 -0500 Subject: [PATCH 056/177] Bugs & fixes -- pretty sure PySwarms is not working properly. Signed-off by: David Rowenhorst --- pyebsdindex/pcopt.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyebsdindex/pcopt.py b/pyebsdindex/pcopt.py index f8304bb..2571116 100644 --- a/pyebsdindex/pcopt.py +++ b/pyebsdindex/pcopt.py @@ -212,8 +212,8 @@ def optimize_pso(pats, indexer, PC0=None, batch=False, search_limit = 0.05, if nswarmpoints is None: nswarmpoints = int(np.array(search_limit).max() * (100.0/0.2)) - nswarmpoints = max(50, nswarmpoints) + nswarmpoints = max(50, nswarmpoints) if PC0 is None: PC0 = np.asarray(indexer.PC) From 8d66a101c7de720eb423d6627912ccf26b6886b7 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Sat, 28 Jan 2023 14:55:00 -0500 Subject: [PATCH 057/177] Made my own PSO Signed-off by: David Rowenhorst --- pyebsdindex/band_detect.py | 2 +- pyebsdindex/pcopt.py | 271 +++++++++++++++++++++++++++++++------ 2 files changed, 231 insertions(+), 42 deletions(-) diff --git a/pyebsdindex/band_detect.py b/pyebsdindex/band_detect.py index 65a1ad0..7d63d45 100644 --- a/pyebsdindex/band_detect.py +++ b/pyebsdindex/band_detect.py @@ -43,7 +43,7 @@ RADEG = 180.0/np.pi -class BandDetect: +class BandDetect(): def __init__( self, patterns=None, diff --git a/pyebsdindex/pcopt.py b/pyebsdindex/pcopt.py index 2571116..ce6a32d 100644 --- a/pyebsdindex/pcopt.py +++ b/pyebsdindex/pcopt.py @@ -23,8 +23,11 @@ """Optimization of the pattern center (PC) of EBSD patterns.""" import numpy as np +import multiprocessing import pyswarms as pso import scipy.optimize as opt +from functools import partial + __all__ = [ @@ -36,36 +39,44 @@ def _optfunction(PC_i, indexer, banddat): - bandnorm = indexer.bandDetectPlan.radonPlan.radon2pole( - banddat, PC=PC_i, vendor=indexer.vendor - ) - npoints = banddat.shape[0] - #n_averages = 0 - #average_fit = 0 - #nbands_fit = 0 - #phase = indexer.phaseLib[0] - nbands = indexer.bandDetectPlan.nBands - indexdata = indexer._indexbandsphase( banddat, bandnorm) + PC = np.atleast_2d(PC_i) + result = np.zeros(PC.shape[0]) + # this loop is here because pyswarms expects a vectorized function + for q in range(PC.shape[0]): + bandnorm = indexer.bandDetectPlan.radonPlan.radon2pole( + banddat, PC=PC[q,:], vendor=indexer.vendor + ) - fit = indexdata[-1]['fit'] - nmatch = indexdata[-1]['nmatch'] - average_fit = fit*(nbands+1 - nmatch) - #average_fit = -1.0*(3.0-fit)*nmatch - whgood = np.nonzero(fit < 90.0) + npoints = banddat.shape[0] + #n_averages = 0 + #average_fit = 0 + #nbands_fit = 0 + #phase = indexer.phaseLib[0] + nbands = indexer.bandDetectPlan.nBands + indexdata = indexer._indexbandsphase( banddat, bandnorm) - n_averages = len(whgood[0]) - if n_averages < 0.9: - average_fit = 1000 - else: - average_fit = np.sum(average_fit[whgood[0]]) + 4.0*(nbands+1)*(npoints - n_averages) - average_fit /= npoints - #average_fit /= n_averages - #average_fit *= (n_averages*(nbands+1) - nbands_fit)/(n_averages*nbands) - return average_fit + fit = indexdata[-1]['fit'] + nmatch = indexdata[-1]['nmatch'] + average_fit = fit*(nbands+1 - nmatch) + #average_fit = -1.0*(3.0-fit)*nmatch + whgood = np.nonzero(fit < 90.0) + + n_averages = len(whgood[0]) + + + if n_averages < 0.9: + average_fit = 1000 + else: + average_fit = np.sum(average_fit[whgood[0]]) + 4.0*(nbands+1)*(npoints - n_averages) + average_fit /= npoints + #average_fit /= n_averages + #average_fit *= (n_averages*(nbands+1) - nbands_fit)/(n_averages*nbands) + result[q] = average_fit + return result def optimize(pats, indexer, PC0=None, batch=False): @@ -168,8 +179,8 @@ def optimize(pats, indexer, PC0=None, batch=False): return PCoutRet -def optimize_pso(pats, indexer, PC0=None, batch=False, search_limit = 0.05, - nswarmpoints=None, pswarmpar=None, ninter=500): +def optimize_pso(pats, indexer, PC0=None, batch=False, search_limit = 0.1, + nswarmpoints=30, pswarmpar=None, niter=50): """Optimize pattern center (PC) (PCx, PCy, PCz) in the convention of the :attr:`indexer.vendor` with particle swarms. @@ -211,9 +222,10 @@ def optimize_pso(pats, indexer, PC0=None, batch=False, search_limit = 0.05, pswarmpar = {"c1": 2.05, "c2": 2.05, "w": 0.8}#, 'k': 2, 'p': 2} if nswarmpoints is None: - nswarmpoints = int(np.array(search_limit).max() * (100.0/0.2)) + #nswarmpoints = int(np.array(search_limit).max() * (10.0/0.2)) + nswarmpoints = 25 - nswarmpoints = max(50, nswarmpoints) + nswarmpoints = max(10, nswarmpoints) if PC0 is None: PC0 = np.asarray(indexer.PC) @@ -234,25 +246,37 @@ def optimize_pso(pats, indexer, PC0=None, batch=False, search_limit = 0.05, PC0 = np.array(PCtemp) - #optimizer = pso.single.GlobalBestPSO( - optimizer = pso.single.GlobalBestPSO( - n_particles=nswarmpoints, - dimensions=3, - options=pswarmpar, - bounds=(PC0 - np.array(search_limit), PC0 + np.array(search_limit)), - ) + + # optimizer = pso.single.GlobalBestPSO( + # n_particles=nswarmpoints, + # dimensions=3, + # options=pswarmpar, + # bounds=(PC0 - np.array(search_limit), PC0 + np.array(search_limit)), + # ) + optimizer = PSOOpt(dimensions=3,n_particles=nswarmpoints, + c1=pswarmpar['c1'], + c2 = pswarmpar['c2'], w = pswarmpar['w'] ) if not batch: - cost, PCoutRet = optimizer.optimize( - _optfunction, ninter, indexer=indexer, banddat=banddat - ) + # cost, PCoutRet = optimizer.optimize( + # _optfunction, niter, indexer=indexer, banddat=banddat + # ) + cost, PCoutRet = optimizer.optimize(_optfunction, indexer=indexer, banddat=banddat, + start=PC0, bounds=(PC0 - np.array(search_limit), PC0 + np.array(search_limit)), + niter=niter) + #print(cost) else: PCoutRet = np.zeros((npoints, 3)) for i in range(npoints): - cost, PCoutRet[i, :] = optimizer.optimize( - _optfunction, ninter, indexer=indexer, banddat=banddat[i, :, :] - ) + # cost, PCoutRet[i, :] = optimizer.optimize( + # _optfunction, niter, indexer=indexer, banddat=banddat[i, :, :] + # ) + + cost, PCoutRet = optimizer.optimize(_optfunction, indexer=indexer, banddat=banddat[i, :, :], + start=PC0, bounds=(PC0 - np.array(search_limit), PC0 + np.array(search_limit)), + niter=niter) + if emsoftflag: # Return original state for indexer indexer.vendor = "EMSOFT" @@ -302,3 +326,168 @@ def _file_opt(fobj, indexer, stride=200, groupsz = 3): pcopt[i, j, :] = pc return pcopt + + +class PSOOpt(): + def __init__(self, + dimensions=3, + n_particles=50, + c1 = 2.05, + c2 = 2.05, + w = 0.8, + boundmethod = 'bounce'): + self.n_particles = int(n_particles) + self.dimensions = int(dimensions) + self.c1 = c1 + self.c2 = c2 + self.w = w + self.boundmethod = boundmethod + self.vellimit = None + self.start = None + self.bounds = None + self.range = None + self.niter = None + self.pos = None + self.vel = None + + + def initializeswarm(self, start=None, bounds=None): + + + if start is None: + if bounds is not None: + start = 0.5*(bounds[0]+bounds[1]) + else: + start = np.zeros(self.dimensions, dtype=np.float32) + + self.start = start + + if bounds is None: + bounds = (-1*np.ones(self.dimensions, dtype=np.float32),np.ones(self.dimensions, dtype=np.float32) ) + + self.bounds = bounds + self.range = self.bounds[1] - self.bounds[0] + + self.pos = np.random.uniform(low=bounds[0], high=bounds[1], size=(self.n_particles, self.dimensions)) + self.pos[0,:] = start + + self.vel = np.random.normal(size=(self.n_particles, self.dimensions), loc=0.0, scale=1.0) + meanv = np.mean(np.sqrt(np.sum(self.vel**2, axis=1))) + self.vel *= self.range/(10. * meanv) + self.vellimit = 4*np.mean(np.sqrt(np.sum(self.vel**2, axis=1))) + + self.vel[0,:] = 0.0 + + self.pbest = np.zeros(self.n_particles) + np.infty + self.pbest_loc = np.copy(self.pos) + self.gbest = np.infty + self.gbest_loc = start + + + + + def updateswarmbest(self, fun2opt, pool, **kwargs): + + val = np.zeros(self.n_particles) + + #for part_i in range(self.n_particles): + # val[part_i] = fun2opt(self.pos[part_i, :], **kwargs) + + pos = self.pos.copy() + results = pool.map(partial(fun2opt, **kwargs),list(pos) ) + #print(len(results[0]), type(results[0])) + #print(len(results)) + val = np.concatenate(results) + + wh_newpbest = np.nonzero(val < self.pbest)[0] + + self.pbest[wh_newpbest] = val[wh_newpbest] + self.pbest_loc[wh_newpbest, :] = self.pos[wh_newpbest, :] + + wh_minpbest = np.argmin(self.pbest) + if self.pbest[wh_minpbest] < self.gbest: + self.gbest = self.pbest[wh_minpbest] + self.gbest_loc = self.pbest_loc[wh_minpbest, :] + + + def updateswarmvelpos(self): + + w = self.w + c1 = self.c1 + c2 = self.c2 + r1 = np.random.random((self.n_particles,1)) + r2 = np.random.random((self.n_particles,1)) + nvel = self.vel.copy() + nvel = w * nvel + \ + c1 * r1 * (self.pbest_loc - self.pos) + \ + c2 * r2 * (self.gbest_loc - self.pos) + + mag = np.expand_dims(np.sqrt(np.sum(nvel**2, axis=1)), axis=1) + wh_toofast = np.nonzero(mag > self.vellimit)[0] + #print(nvel.shape, wh_toofast.shape, mag.shape) + nvel[wh_toofast, :] *= self.vellimit/mag[wh_toofast] + + self.vel = nvel + self.pos += nvel + + self.boundarycheck() + + #print(mag.max(), mag.min(), wh_toofast.size, self.vellimit) + #mag = np.expand_dims(np.sqrt(np.sum(nvel ** 2, axis=1)), axis=1) + #print(mag.max(), mag.min(), wh_toofast.size) + + + def boundarycheck(self): + + if str.lower(self.boundmethod) == 'bounce': + self.boundarybounce() + + + def boundarybounce(self): + + lb,ub = self.bounds + for d in range(self.dimensions): + wh_under = np.nonzero(self.pos[:,d] < lb[d])[0] + self.pos[wh_under,d] = lb[d] + self.vel[wh_under,d] *= -1.0 + + wh_over = np.nonzero(self.pos[:, d] > ub[d])[0] + self.pos[wh_over, d] = ub[d] + self.vel[wh_over, d] *= -1.0 + + + def printprogress(self, iter): + + progress = int(round(10*float(iter)/self.niter)) + print('',end='\r' ) + print('Progress [', + '*' * progress, ' '*(10-progress),'] ', iter+1 , '/', self.niter, + ' global best:', "{0:.3g}".format(self.gbest), + ' best loc:', np.array_str(self.gbest_loc, precision=4, suppress_small=True), + sep='', end='') + def optimize(self, function, start=None, bounds=None, niter=50, **kwargs): + + self.initializeswarm(start, bounds) + + with multiprocessing.Pool(min(multiprocessing.cpu_count(), self.n_particles)) as pool: + + print('n_particle:', self.n_particles, 'c1:', self.c1, 'c2:', self.c2, 'w:', self.w ) + + self.niter = niter + for iter in range(niter): + self.updateswarmbest(function, pool, **kwargs) + self.printprogress(iter) + self.updateswarmvelpos() + + + pool.close() + final_best = self.gbest + final_loc = self.gbest_loc + print('', end='\n') + print("Optimization finished | best cost: {}, best pos: {}".format( + final_best, final_loc)) + print(' ') + return final_best, final_loc + + + From ffeb1b486d6bace1e85fb01c2a1ddaa0cf89dda3 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Sun, 29 Jan 2023 07:46:33 -0500 Subject: [PATCH 058/177] PSO tweaks Signed-off by: David Rowenhorst --- pyebsdindex/pcopt.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/pyebsdindex/pcopt.py b/pyebsdindex/pcopt.py index ce6a32d..58d2959 100644 --- a/pyebsdindex/pcopt.py +++ b/pyebsdindex/pcopt.py @@ -179,8 +179,8 @@ def optimize(pats, indexer, PC0=None, batch=False): return PCoutRet -def optimize_pso(pats, indexer, PC0=None, batch=False, search_limit = 0.1, - nswarmpoints=30, pswarmpar=None, niter=50): +def optimize_pso(pats, indexer, PC0=None, batch=False, search_limit = 0.2, + nswarmpoints=None, pswarmpar=None, niter=50): """Optimize pattern center (PC) (PCx, PCy, PCz) in the convention of the :attr:`indexer.vendor` with particle swarms. @@ -219,13 +219,13 @@ def optimize_pso(pats, indexer, PC0=None, batch=False, search_limit = 0.1, banddat = indexer.bandDetectPlan.find_bands(pats) npoints = banddat.shape[0] if pswarmpar is None: - pswarmpar = {"c1": 2.05, "c2": 2.05, "w": 0.8}#, 'k': 2, 'p': 2} + pswarmpar = {"c1": 3.05, "c2": 1.05, "w": 0.8}#, 'k': 2, 'p': 2} if nswarmpoints is None: #nswarmpoints = int(np.array(search_limit).max() * (10.0/0.2)) - nswarmpoints = 25 + nswarmpoints = 30 - nswarmpoints = max(10, nswarmpoints) + nswarmpoints = max(5, nswarmpoints) if PC0 is None: PC0 = np.asarray(indexer.PC) @@ -373,7 +373,7 @@ def initializeswarm(self, start=None, bounds=None): self.vel = np.random.normal(size=(self.n_particles, self.dimensions), loc=0.0, scale=1.0) meanv = np.mean(np.sqrt(np.sum(self.vel**2, axis=1))) - self.vel *= self.range/(10. * meanv) + self.vel *= np.sqrt(np.sum(self.range**2))/(20. * meanv) self.vellimit = 4*np.mean(np.sqrt(np.sum(self.vel**2, axis=1))) self.vel[0,:] = 0.0 From af43d07b35769ba89bc902cf3feaca9aa3500caa Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Sun, 29 Jan 2023 17:07:47 -0500 Subject: [PATCH 059/177] Added self-adjusting PSO and set as default for PC PSO optimization. Signed-off by: David Rowenhorst --- pyebsdindex/pcopt.py | 40 +++++++++++++++++++++++++++++----------- 1 file changed, 29 insertions(+), 11 deletions(-) diff --git a/pyebsdindex/pcopt.py b/pyebsdindex/pcopt.py index 58d2959..363ced0 100644 --- a/pyebsdindex/pcopt.py +++ b/pyebsdindex/pcopt.py @@ -219,8 +219,8 @@ def optimize_pso(pats, indexer, PC0=None, batch=False, search_limit = 0.2, banddat = indexer.bandDetectPlan.find_bands(pats) npoints = banddat.shape[0] if pswarmpar is None: - pswarmpar = {"c1": 3.05, "c2": 1.05, "w": 0.8}#, 'k': 2, 'p': 2} - + #pswarmpar = {"c1": 3.05, "c2": 1.05, "w": 0.8} + pswarmpar = {"c1": 3.5, "c2": 3.5, "w": 0.8} if nswarmpoints is None: #nswarmpoints = int(np.array(search_limit).max() * (10.0/0.2)) nswarmpoints = 30 @@ -255,7 +255,7 @@ def optimize_pso(pats, indexer, PC0=None, batch=False, search_limit = 0.2, # ) optimizer = PSOOpt(dimensions=3,n_particles=nswarmpoints, c1=pswarmpar['c1'], - c2 = pswarmpar['c2'], w = pswarmpar['w'] ) + c2 = pswarmpar['c2'], w = pswarmpar['w'], hyperparammethod='auto') if not batch: # cost, PCoutRet = optimizer.optimize( @@ -335,12 +335,17 @@ def __init__(self, c1 = 2.05, c2 = 2.05, w = 0.8, + hyperparammethod = 'static', boundmethod = 'bounce'): self.n_particles = int(n_particles) self.dimensions = int(dimensions) self.c1 = c1 self.c2 = c2 + self.c1i = None + self.c2i = None self.w = w + self.wi = None + self.hyperparammethod = hyperparammethod self.boundmethod = boundmethod self.vellimit = None self.start = None @@ -369,14 +374,13 @@ def initializeswarm(self, start=None, bounds=None): self.range = self.bounds[1] - self.bounds[0] self.pos = np.random.uniform(low=bounds[0], high=bounds[1], size=(self.n_particles, self.dimensions)) - self.pos[0,:] = start + self.pos[0, :] = start self.vel = np.random.normal(size=(self.n_particles, self.dimensions), loc=0.0, scale=1.0) meanv = np.mean(np.sqrt(np.sum(self.vel**2, axis=1))) self.vel *= np.sqrt(np.sum(self.range**2))/(20. * meanv) self.vellimit = 4*np.mean(np.sqrt(np.sum(self.vel**2, axis=1))) - self.vel[0,:] = 0.0 self.pbest = np.zeros(self.n_particles) + np.infty self.pbest_loc = np.copy(self.pos) @@ -412,9 +416,9 @@ def updateswarmbest(self, fun2opt, pool, **kwargs): def updateswarmvelpos(self): - w = self.w - c1 = self.c1 - c2 = self.c2 + w = self.wi + c1 = self.c1i + c2 = self.c2i r1 = np.random.random((self.n_particles,1)) r2 = np.random.random((self.n_particles,1)) nvel = self.vel.copy() @@ -432,9 +436,7 @@ def updateswarmvelpos(self): self.boundarycheck() - #print(mag.max(), mag.min(), wh_toofast.size, self.vellimit) - #mag = np.expand_dims(np.sqrt(np.sum(nvel ** 2, axis=1)), axis=1) - #print(mag.max(), mag.min(), wh_toofast.size) + def boundarycheck(self): @@ -455,7 +457,21 @@ def boundarybounce(self): self.pos[wh_over, d] = ub[d] self.vel[wh_over, d] *= -1.0 + def updatehyperparam(self, iter): + if str.lower(self.hyperparammethod) == 'auto': + + N = float(self.niter)-1 + self.c1i = (self.c1 - self.c1/7) * (N-iter)/N + self.c1 / 7.0 + self.c2i = (self.c1 - self.c1 / 7) * (iter) / N + self.c1 / 7.0 + self.wi = self.w/2 * ((N - iter)/N)**2 + self.w/2 + + else: + self.c1i = self.c1 + self.c2i = self.c2 + self.wi = self.w + + pass def printprogress(self, iter): progress = int(round(10*float(iter)/self.niter)) @@ -475,12 +491,14 @@ def optimize(self, function, start=None, bounds=None, niter=50, **kwargs): self.niter = niter for iter in range(niter): + self.updatehyperparam(iter) self.updateswarmbest(function, pool, **kwargs) self.printprogress(iter) self.updateswarmvelpos() pool.close() + pool.terminate() final_best = self.gbest final_loc = self.gbest_loc print('', end='\n') From 9389d9c84abd77b00d21577aa20909101423271f Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Tue, 31 Jan 2023 21:15:03 -0500 Subject: [PATCH 060/177] Faster EBSP header read. Signed-off by: David Rowenhorst --- pyebsdindex/ebsd_pattern.py | 42 ++++++++++++++++++++++++++----------- 1 file changed, 30 insertions(+), 12 deletions(-) diff --git a/pyebsdindex/ebsd_pattern.py b/pyebsdindex/ebsd_pattern.py index bae8051..2c279ee 100644 --- a/pyebsdindex/ebsd_pattern.py +++ b/pyebsdindex/ebsd_pattern.py @@ -662,19 +662,32 @@ def read_header(self, path=None, bitdepth=None): # readInterval=[0, -1], arrayO if self.version >= 1: + #loc0 = int(np.fromfile(f, dtype=np.uint64, count=1)) + #currentloc = f.tell() + #loc1 = loc0 + #npat = 0 + + #while loc1 != currentloc: + # loc11 = int(np.fromfile(f, dtype=np.uint64, count = 1)) + # loc1 = min([loc1, loc11]) + # currentloc = f.tell() + + # do the same as above, but in memory ... so much faster loc0 = int(np.fromfile(f, dtype=np.uint64, count=1)) - currentloc = f.tell() - loc1 = loc0 - npat = 0 + f.seek(8) + loc02N = np.fromfile(f, dtype=np.uint64, count=int((loc0-8)/8+0.001)) + #loc02N -= int(8) + - while loc1 != currentloc: - loc11 = int(np.fromfile(f, dtype=np.uint64, count = 1)) - loc1 = min([loc1, loc11]) - currentloc = f.tell() - #print(loc1, currentloc) - #return + loc1 = (loc02N[0]-8)/8 - self.nPatterns = int((currentloc-8)/int(8)) + counter = 0 + while loc1 != counter: + loc_i = int((loc02N[counter]-8)/8) + loc1 = min([loc1, loc_i]) + counter += 1 + + self.nPatterns = int((counter)) f.seek(8) self.filePos = np.fromfile(f, dtype=np.uint64, count=self.nPatterns) @@ -685,13 +698,18 @@ def read_header(self, path=None, bitdepth=None): # readInterval=[0, -1], arrayO #patdata0 = np.fromfile(f, dtype=np.uint8, count=1) patdata = np.fromfile(f, dtype=np.uint32, count=4) + + if patdata[0] == 1: + print("Sorry, compressed EBSP files are not supported") + return None + #print(loc0, patdata) #f.seek(self.filePos[2]) #print(np.fromfile(f, dtype=np.uint32, count=4)) #print(np.fromfile(f, dtype=np.uint32, count=8)) #print(np.fromfile(f, dtype=np.uint32, count=1)) - self.patternW = patdata[2] + self.patternW = np.uint32(patdata[2]) self.patternH = np.uint32(patdata[1]) nbytespat = patdata[3] @@ -745,7 +763,7 @@ def read_header(self, path=None, bitdepth=None): # readInterval=[0, -1], arrayO nrow = np.round(nrow+1) self.nRows = int(nrow) else: - self.nRows = int(self.nPatterns/self.nCols) + self.nRows = int(self.nPatterns/self.nCols+0.001) if self.xStep is None: self.xStep = 0.0 From f1012427cadf2cba1be8546b401cfa843868ea88 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Tue, 31 Jan 2023 22:07:06 -0500 Subject: [PATCH 061/177] More robust ebsp write. Signed-off by: David Rowenhorst --- pyebsdindex/ebsd_pattern.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/pyebsdindex/ebsd_pattern.py b/pyebsdindex/ebsd_pattern.py index 2c279ee..0598800 100644 --- a/pyebsdindex/ebsd_pattern.py +++ b/pyebsdindex/ebsd_pattern.py @@ -839,10 +839,17 @@ def write_header(self, writeBlank=False, bitdepth=8): print("File Not Found:", str(Path(filepath))) return -1 - version = np.uint64(-self.version) + if self.version is None: + self.version = 1 + + version = np.uint64(-self.version) np.asarray(version, dtype=np.uint64).tofile(f) if self.version >= 0: + if self.filePos is None: + self.filePos = np.arange( + self.nPatterns, dtype=np.uint64)*(16+18+self.patternH*self.patternW*self.filedatatype(0).nbytes)+8+8*self.nPatterns + np.asarray(self.filePos, dtype=np.uint64).tofile(f) if writeBlank == True: From 3de4f27217628355fc24a105e5a933296e6e31ea Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Thu, 2 Feb 2023 12:48:01 -0500 Subject: [PATCH 062/177] Checkpoint Signed-off by: David Rowenhorst --- pyebsdindex/ebsd_pattern.py | 11 ++++++++--- pyebsdindex/pcopt.py | 22 ++++++++++++++-------- 2 files changed, 22 insertions(+), 11 deletions(-) diff --git a/pyebsdindex/ebsd_pattern.py b/pyebsdindex/ebsd_pattern.py index 0598800..4f55b0c 100644 --- a/pyebsdindex/ebsd_pattern.py +++ b/pyebsdindex/ebsd_pattern.py @@ -656,11 +656,16 @@ def read_header(self, path=None, bitdepth=None): # readInterval=[0, -1], arrayO return -1 f.seek(0) - version = np.fromfile(f, dtype=np.uint64, count=1) - - self.version = int(np.uint64(0)-version) + version = np.fromfile(f, dtype=np.int64, count=1) + version = int(-1*version) + if version <= 0: + self.version = 0 + else: + self.version = version if self.version >= 1: + if self.version >= 4: + self.mysterybyte = np.fromfile(f, dtype=np.uint8, count=1) #loc0 = int(np.fromfile(f, dtype=np.uint64, count=1)) #currentloc = f.tell() diff --git a/pyebsdindex/pcopt.py b/pyebsdindex/pcopt.py index 363ced0..75d865d 100644 --- a/pyebsdindex/pcopt.py +++ b/pyebsdindex/pcopt.py @@ -27,6 +27,7 @@ import pyswarms as pso import scipy.optimize as opt from functools import partial +from timeit import default_timer as timer @@ -39,16 +40,17 @@ def _optfunction(PC_i, indexer, banddat): - + tic = timer() PC = np.atleast_2d(PC_i) result = np.zeros(PC.shape[0]) # this loop is here because pyswarms expects a vectorized function + #print(PC.shape) for q in range(PC.shape[0]): bandnorm = indexer.bandDetectPlan.radonPlan.radon2pole( banddat, PC=PC[q,:], vendor=indexer.vendor ) - + #print(timer() - tic) npoints = banddat.shape[0] #n_averages = 0 #average_fit = 0 @@ -76,6 +78,7 @@ def _optfunction(PC_i, indexer, banddat): #average_fit /= n_averages #average_fit *= (n_averages*(nbands+1) - nbands_fit)/(n_averages*nbands) result[q] = average_fit + #print(timer()-tic) return result @@ -394,14 +397,17 @@ def updateswarmbest(self, fun2opt, pool, **kwargs): val = np.zeros(self.n_particles) - #for part_i in range(self.n_particles): - # val[part_i] = fun2opt(self.pos[part_i, :], **kwargs) - - pos = self.pos.copy() - results = pool.map(partial(fun2opt, **kwargs),list(pos) ) + #tic = timer() + for part_i in range(self.n_particles): + val[part_i] = fun2opt(self.pos[part_i, :], **kwargs) + #print(timer()-tic) + #pos = self.pos.copy() + #tic = timer() + #results = pool.map(partial(fun2opt, **kwargs),list(pos) ) + #print(timer()-tic) #print(len(results[0]), type(results[0])) #print(len(results)) - val = np.concatenate(results) + #val = np.concatenate(results) wh_newpbest = np.nonzero(val < self.pbest)[0] From f2b66661ac2211bb4413d1c0560d544d3af388ee Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Thu, 2 Feb 2023 19:37:54 -0500 Subject: [PATCH 063/177] Upgrade pattern reading writing from EBSP (not fully tested v0,v1,v2,v4) Signed-off by: David Rowenhorst --- pyebsdindex/ebsd_pattern.py | 198 +++++++++++++++++++++++++----------- 1 file changed, 137 insertions(+), 61 deletions(-) diff --git a/pyebsdindex/ebsd_pattern.py b/pyebsdindex/ebsd_pattern.py index 4f55b0c..a19214e 100644 --- a/pyebsdindex/ebsd_pattern.py +++ b/pyebsdindex/ebsd_pattern.py @@ -635,15 +635,21 @@ def file_from_pattern_obj(self, patternobj, filepath=None, bitdepth = None): class EBSDPFile(EBSDPatternFile): - + """ + Notes + ----- + Information about the .ebsp file format was generously provided by + Oxford Instruments. + """ def __init__(self, path=None): EBSDPatternFile.__init__(self, path) self.filetype = 'EBSP' self.vendor = 'OXFORD' self.filedatatype = None - # UP only attributes + # EBSP only attributes # self.bitdepth = None self.filePos = None # file location in bytes where each pattern data starts + self.hasxypos = False def read_header(self, path=None, bitdepth=None): # readInterval=[0, -1], arrayOnly=False, if path is not None: @@ -678,23 +684,34 @@ def read_header(self, path=None, bitdepth=None): # readInterval=[0, -1], arrayO # currentloc = f.tell() # do the same as above, but in memory ... so much faster - loc0 = int(np.fromfile(f, dtype=np.uint64, count=1)) - f.seek(8) + loc0 = 0 + counter = 0 + while loc0 == 0: # check for non-stored points. + loc0 = int(np.fromfile(f, dtype=np.uint64, count=1)) + counter += 1 + f.seek(-8*counter, 1) # move back 8 bytes (or however far we needed to move into the file to find a legitamte offset. loc02N = np.fromfile(f, dtype=np.uint64, count=int((loc0-8)/8+0.001)) - #loc02N -= int(8) - loc1 = (loc02N[0]-8)/8 + + loc1 = int((loc0-8)/8+0.001) counter = 0 while loc1 != counter: - loc_i = int((loc02N[counter]-8)/8) - loc1 = min([loc1, loc_i]) + if loc02N[counter] != 0: # a non-stored pattern? Crazy. + loc_i = int((loc02N[counter]-8)/8) + loc1 = min([loc1, loc_i]) counter += 1 self.nPatterns = int((counter)) - f.seek(8) + if self.version == 0: + f.seek(0) + if self.version >=1.0: + f.seek(8) + if self.version >= 4: + f.seek(1,1) + self.filePos = np.fromfile(f, dtype=np.uint64, count=self.nPatterns) # going to assume that all patterns are the same as the first pattern the file. @@ -743,37 +760,57 @@ def read_header(self, path=None, bitdepth=None): # readInterval=[0, -1], arrayO xall = np.zeros(self.nPatterns, dtype=np.float64) yall = np.zeros(self.nPatterns, dtype=np.float64) - for i in range(self.nPatterns): - f.seek(int(self.filePos[i] + 16 + nbytespat + 1)) - xall[i] = np.fromfile(f, dtype=np.float64, count=1) - #print(x1, i) - f.seek(1, 1) - yall[i] = np.fromfile(f, dtype=np.float64, count=1) - - - self.xStep = xall[1] - xall[0] - if self.xStep > 1e-6: - ncol = (xall.max() - xall.min()) / self.xStep - ncol = np.round(ncol+1) + self.hasxypos = False + if self.version != 0: + if self.version ==1: + footoffset = 0 + self.hasxypos = True + else: + loc0 = np.min(self.filePos[self.filePos > 0]) + f.seek(int(loc0 + 16 + nbytespat)) + havepos = np.fromfile(f, dtype=np.uint8, count=1) + if havepos > 0: + footoffset = 1 + self.hasxypos = True + + if self.hasxypos == False: + self.xStep = 1.0 + self.yStep = 1.0 + self.nCols = 1 + self.nRows = self.nPatterns else: - ncol = 1 + for i in range(self.nPatterns): + if self.filePos[i] > 0: + f.seek(int(self.filePos[i] + 16 + nbytespat + footoffset)) + xall[i] = np.fromfile(f, dtype=np.float64, count=1) + #print(x1, i) + f.seek(footoffset, 1) + yall[i] = np.fromfile(f, dtype=np.float64, count=1) - self.nCols = np.uint64(ncol) + self.xStep = xall[1] - xall[0] + if self.xStep > 1e-6: + ncol = (xall.max() - xall.min()) / self.xStep + ncol = np.round(ncol+1) + else: + ncol = 1 - self.yStep = yall[0] - yall[self.nCols] + self.nCols = np.uint64(ncol) - if self.yStep > 1e-6: - nrow = (yall.max() - yall.min()) / self.yStep - nrow = np.round(nrow+1) - self.nRows = int(nrow) - else: - self.nRows = int(self.nPatterns/self.nCols+0.001) + + self.yStep = yall[0] - yall[self.nCols] + + if self.yStep > 1e-6: + nrow = (yall.max() - yall.min()) / self.yStep + nrow = np.round(nrow+1) + self.nRows = int(nrow) + else: + self.nRows = int(self.nPatterns/self.nCols+0.001) if self.xStep is None: - self.xStep = 0.0 + self.xStep = 1.0 if self.yStep is None: - self.yStep = 0.0 + self.yStep = 1.0 if self.nCols is None: self.nCols = np.uint64(1) if self.nCols == 0: @@ -797,14 +834,24 @@ def pat_reader(self, patStart=0, nPatToRead=1): nPerPat = self.patternW * self.patternH typeread = self.filedatatype typebyte = self.filedatatype(0).nbytes + + readxypos = self.hasxypos + if self.version == 1: + xyoffset = 0 + else: + xyoffset = 1 + for i in range(int(patStart), int(patStart + nPatToRead)): ii = int(i - patStart) - f.seek(int(self.filePos[i] + 16)) - readpats[ii, :] = np.fromfile(f, dtype=typeread, count=int(nPerPat)) - f.seek(1, 1) - xyloc[ii, 0] = np.fromfile(f, dtype=np.float64, count=1) - f.seek(1, 1) - xyloc[ii, 1] = np.fromfile(f, dtype=np.float64, count=1) + if self.filePos[i] > 0: + f.seek(int(self.filePos[i] + 16)) + readpats[ii, :] = np.fromfile(f, dtype=typeread, count=int(nPerPat)) + if readxypos == True: + f.seek(xyoffset, 1) + xyloc[ii, 0] = np.fromfile(f, dtype=np.float64, count=1) + f.seek(xyoffset, 1) + xyloc[ii, 1] = np.fromfile(f, dtype=np.float64, count=1) + readpats = readpats.reshape(nPatToRead, self.patternH, self.patternW) f.close() @@ -845,15 +892,34 @@ def write_header(self, writeBlank=False, bitdepth=8): return -1 if self.version is None: - self.version = 1 + self.version = 2 - version = np.uint64(-self.version) - np.asarray(version, dtype=np.uint64).tofile(f) + if self.version > 0: + version = np.uint64(-self.version) + np.asarray(version, dtype=np.uint64).tofile(f) if self.version >= 0: if self.filePos is None: + file_head_length = 0 + pat_footer_length = 0 + if self.version >= 1: + file_head_length = 8 + pat_footer_length = 16 + if self.version >= 2: + if self.hasxypos == True: + pat_footer_length = 18 + else: + pat_footer_length = 1 + + if self.version >= 4: + file_head_length = 9 + self.filePos = np.arange( - self.nPatterns, dtype=np.uint64)*(16+18+self.patternH*self.patternW*self.filedatatype(0).nbytes)+8+8*self.nPatterns + self.nPatterns, dtype=np.uint64)*(16+pat_footer_length+self.patternH*self.patternW*self.filedatatype(0).nbytes)\ + +file_head_length+8*self.nPatterns + + if self.version >= 4: + np.uint8(0).tofile(f) np.asarray(self.filePos, dtype=np.uint64).tofile(f) @@ -872,11 +938,16 @@ def write_header(self, writeBlank=False, bitdepth=8): for i in range(self.nCols): pathead.tofile(f) blank.tofile(f) - np.uint8(1).tofile(f) - np.float64(i * self.xStep).tofile(f) - np.uint8(1).tofile(f) - np.float64(j * self.yStep).tofile(f) - + if (self.version > 0) and self.hasxypos: + if self.version >= 2: + np.uint8(1).tofile(f) + np.float64(i * self.xStep).tofile(f) + if self.version >= 2: + np.uint8(1).tofile(f) + np.float64(j * self.yStep).tofile(f) + else: # no xy_pos info + if self.version >= 2: + np.uint8(0).tofile(f) f.close() def pat_writer(self, pat2write, patStart, nPatToWrite, typewrite=None): @@ -892,21 +963,26 @@ def pat_writer(self, pat2write, patStart, nPatToWrite, typewrite=None): pathead = np.array([0, int(self.patternH), int(self.patternW), int(self.patternH * self.patternW * self.filedatatype(0).nbytes)], dtype=np.uint32) - # + write_xypos = self.hasxypos + for i in range(int(patStart), int(patStart + nPatToWrite)): - f.seek(int(self.filePos[i]), 0) - ii = int(i - patStart) - pathead.tofile(f) - pat2write[ii, :, :].tofile(f) - np.uint8(1).tofile(f) - yx = np.array(np.unravel_index(i, (self.nRows, self.nCols))).astype(np.float64) - yx[1] -= float(self.nCols * 0.5) - yx[1] *= self.xStep - yx[0] -= float(self.nRows * 0.5) - yx[0] *= self.yStep - np.float64(yx[1]).tofile(f) - np.uint8(1).tofile(f) - np.float64(yx[0]).tofile(f) + if int(self.filePos[i]) > 0: + f.seek(int(self.filePos[i]), 0) + ii = int(i - patStart) + pathead.tofile(f) + pat2write[ii, :, :].tofile(f) + if write_xypos: + if self.version >= 2: + np.uint8(1).tofile(f) + yx = np.array(np.unravel_index(i, (self.nRows, self.nCols))).astype(np.float64) + yx[1] -= float(self.nCols * 0.5) + yx[1] *= self.xStep + yx[0] -= float(self.nRows * 0.5) + yx[0] *= self.yStep + np.float64(yx[1]).tofile(f) + if self.version >= 2: + np.uint8(1).tofile(f) + np.float64(yx[0]).tofile(f) f.close() From ea22dfd3c2c8f7de60d4d3750db1a85ec1be1369 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Fri, 3 Feb 2023 15:30:07 -0500 Subject: [PATCH 064/177] Updated tests for EDAX PC convention for rectangular patterns Signed-off by: David Rowenhorst --- pyebsdindex/pcopt.py | 21 ++++++++++++++------- pyebsdindex/radon_fast.py | 2 +- pyebsdindex/tests/test_ebsd_index.py | 2 +- pyebsdindex/tests/test_pcopt.py | 4 ++-- 4 files changed, 18 insertions(+), 11 deletions(-) diff --git a/pyebsdindex/pcopt.py b/pyebsdindex/pcopt.py index 75d865d..745dcdf 100644 --- a/pyebsdindex/pcopt.py +++ b/pyebsdindex/pcopt.py @@ -63,7 +63,7 @@ def _optfunction(PC_i, indexer, banddat): fit = indexdata[-1]['fit'] nmatch = indexdata[-1]['nmatch'] - average_fit = fit*(nbands+1 - nmatch) + average_fit = fit + 1.0*(nbands - nmatch) #average_fit = -1.0*(3.0-fit)*nmatch whgood = np.nonzero(fit < 90.0) @@ -126,12 +126,15 @@ def optimize(pats, indexer, PC0=None, batch=False): if indexer.vendor == "EMSOFT": # Convert to EDAX for optimization emsoftflag = True indexer.vendor = "EDAX" + patDim = np.array(indexer.bandDetectPlan.patDim) delta = indexer.PC PCtemp = PC0[0:3] + patdimnorm = (np.array([patDim[1], patDim[0], np.max(patDim[0:2])])) PCtemp[0] *= -1.0 PCtemp[0] += 0.5 * indexer.bandDetectPlan.patDim[1] PCtemp[1] += 0.5 * indexer.bandDetectPlan.patDim[0] - PCtemp /= indexer.bandDetectPlan.patDim[1] + #PCtemp /= indexer.bandDetectPlan.patDim[1] + PCtemp /= patdimnorm PCtemp[2] /= delta[3] PC0 = PCtemp @@ -158,11 +161,14 @@ def optimize(pats, indexer, PC0=None, batch=False): if emsoftflag: # Return original state for indexer indexer.vendor = "EMSOFT" indexer.PC = delta + patDim = np.array(indexer.bandDetectPlan.patDim) + patdimnorm = (np.array([patDim[1], patDim[0], np.max(patDim[0:2])])) if PCoutRet.ndim == 2: newout = np.zeros((npoints, 4)) PCoutRet[:, 0] -= 0.5 - PCoutRet[:, :3] *= indexer.bandDetectPlan.patDim[1] - PCoutRet[:, 1] -= 0.5 * indexer.bandDetectPlan.patDim[0] + #PCoutRet[:, :3] *= indexer.bandDetectPlan.patDim[1] + PCoutRet[:, :3] *= np.atleast_2d(patdimnorm) + PCoutRet[:, 1] -= 0.5 * patDim[0] PCoutRet[:, 0] *= -1.0 PCoutRet[:, 2] *= delta[3] newout[:, :3] = PCoutRet @@ -171,8 +177,9 @@ def optimize(pats, indexer, PC0=None, batch=False): else: newout = np.zeros(4) PCoutRet[0] -= 0.5 - PCoutRet[:3] *= indexer.bandDetectPlan.patDim[1] - PCoutRet[1] -= 0.5 * indexer.bandDetectPlan.patDim[0] + PCoutRet[:3] *= patdimnorm + #PCoutRet[:3] *= indexer.bandDetectPlan.patDim[1] + PCoutRet[1] -= 0.5 * patDim[0] PCoutRet[0] *= -1.0 PCoutRet[2] *= delta[3] newout[:3] = PCoutRet @@ -183,7 +190,7 @@ def optimize(pats, indexer, PC0=None, batch=False): def optimize_pso(pats, indexer, PC0=None, batch=False, search_limit = 0.2, - nswarmpoints=None, pswarmpar=None, niter=50): + nswarmpoints=30, pswarmpar=None, niter=50): """Optimize pattern center (PC) (PCx, PCy, PCz) in the convention of the :attr:`indexer.vendor` with particle swarms. diff --git a/pyebsdindex/radon_fast.py b/pyebsdindex/radon_fast.py index 380f795..40e2ab2 100644 --- a/pyebsdindex/radon_fast.py +++ b/pyebsdindex/radon_fast.py @@ -265,7 +265,7 @@ def radon2pole(self,bandData,PC=None,vendor='EDAX'): dimf = np.array(self.imDim, dtype=np.float32) if ven in ['EDAX']: - t *= np.array([dimf[1], dimf[0], -dimf[0]]) + t *= np.array([dimf[1], dimf[0], -np.min(dimf[0:2])]) if ven in ['OXFORD']: t *= np.array([dimf[1], dimf[1], -dimf[1]]) if ven == 'EMSOFT': diff --git a/pyebsdindex/tests/test_ebsd_index.py b/pyebsdindex/tests/test_ebsd_index.py index d13494b..9f10bda 100644 --- a/pyebsdindex/tests/test_ebsd_index.py +++ b/pyebsdindex/tests/test_ebsd_index.py @@ -47,7 +47,7 @@ def test_index_pats(self, pattern_al_sim_20kv): """Test Hough indexing and setting/passing projection center values. """ - pc = (0.4, 0.6, 0.5) + pc = (0.4, 0.72, 0.6) # Set PC upon initialization of indexer indexer = EBSDIndexer(PC=pc, patDim=pattern_al_sim_20kv.shape) diff --git a/pyebsdindex/tests/test_pcopt.py b/pyebsdindex/tests/test_pcopt.py index bfda9da..bec863f 100644 --- a/pyebsdindex/tests/test_pcopt.py +++ b/pyebsdindex/tests/test_pcopt.py @@ -27,13 +27,13 @@ class TestPCOptimization: def test_pc_optimize(self, pattern_al_sim_20kv): - pc0 = (0.4, 0.6, 0.5) + pc0 = (0.4, 0.72, 0.6) indexer = ebsd_index.EBSDIndexer(patDim=pattern_al_sim_20kv.shape) new_pc = pcopt.optimize(pattern_al_sim_20kv, indexer, PC0=pc0) assert np.allclose(new_pc, pc0, atol=0.05) def test_pc_optimize_pso(self, pattern_al_sim_20kv): - pc0 = (0.4, 0.6, 0.5) + pc0 = (0.4, 0.72, 0.6) indexer = ebsd_index.EBSDIndexer(patDim=pattern_al_sim_20kv.shape) new_pc = pcopt.optimize_pso(pattern_al_sim_20kv, indexer, PC0=pc0) assert np.allclose(new_pc, pc0, atol=0.05) From d68b962d739967f0440063384482b7cb5962bc6d Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Mon, 6 Feb 2023 14:17:41 -0500 Subject: [PATCH 065/177] Fixed background fitting for auto background subtract. Signed-off by: David Rowenhorst --- pyebsdindex/band_detect.py | 5 +---- pyebsdindex/pcopt.py | 2 +- 2 files changed, 2 insertions(+), 5 deletions(-) diff --git a/pyebsdindex/band_detect.py b/pyebsdindex/band_detect.py index 7d63d45..a8da17e 100644 --- a/pyebsdindex/band_detect.py +++ b/pyebsdindex/band_detect.py @@ -223,11 +223,8 @@ def collect_background(self, fileobj = None, patsIn = None, nsample = None, meth #pshape = pat1.shape # a bit of image processing. if back is not None: - #if sigma is None: - #sigma = 2.0 * float(pshape[-1]) / 80.0 - #back[0,:,:] = gaussian_filter(back[0,:,:], sigma = sigma ) + back = np.squeeze(back) back = self.backsub_fit(back) - #back -= np.mean(back) self.backgroundsub = back def backsub_fit(self, back, mask = None): diff --git a/pyebsdindex/pcopt.py b/pyebsdindex/pcopt.py index 745dcdf..25e1257 100644 --- a/pyebsdindex/pcopt.py +++ b/pyebsdindex/pcopt.py @@ -168,8 +168,8 @@ def optimize(pats, indexer, PC0=None, batch=False): PCoutRet[:, 0] -= 0.5 #PCoutRet[:, :3] *= indexer.bandDetectPlan.patDim[1] PCoutRet[:, :3] *= np.atleast_2d(patdimnorm) - PCoutRet[:, 1] -= 0.5 * patDim[0] PCoutRet[:, 0] *= -1.0 + PCoutRet[:, 1] -= 0.5 * patDim[0] PCoutRet[:, 2] *= delta[3] newout[:, :3] = PCoutRet newout[:, 3] = delta[3] From 7489e947c76712b3ba77c71c937748a43613b07e Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Mon, 6 Feb 2023 19:57:27 -0500 Subject: [PATCH 066/177] nswarmpoints --> nswarmparticles, because _particle_ swarm optimization. Signed-off by: David Rowenhorst --- pyebsdindex/pcopt.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/pyebsdindex/pcopt.py b/pyebsdindex/pcopt.py index 25e1257..1b9f3c9 100644 --- a/pyebsdindex/pcopt.py +++ b/pyebsdindex/pcopt.py @@ -190,7 +190,7 @@ def optimize(pats, indexer, PC0=None, batch=False): def optimize_pso(pats, indexer, PC0=None, batch=False, search_limit = 0.2, - nswarmpoints=30, pswarmpar=None, niter=50): + nswarmparticles=30, pswarmpar=None, niter=50): """Optimize pattern center (PC) (PCx, PCy, PCz) in the convention of the :attr:`indexer.vendor` with particle swarms. @@ -231,11 +231,11 @@ def optimize_pso(pats, indexer, PC0=None, batch=False, search_limit = 0.2, if pswarmpar is None: #pswarmpar = {"c1": 3.05, "c2": 1.05, "w": 0.8} pswarmpar = {"c1": 3.5, "c2": 3.5, "w": 0.8} - if nswarmpoints is None: + if nswarmparticles is None: #nswarmpoints = int(np.array(search_limit).max() * (10.0/0.2)) - nswarmpoints = 30 + nswarmparticles = 30 - nswarmpoints = max(5, nswarmpoints) + nswarmparticles = max(5, nswarmparticles) if PC0 is None: PC0 = np.asarray(indexer.PC) @@ -263,7 +263,7 @@ def optimize_pso(pats, indexer, PC0=None, batch=False, search_limit = 0.2, # options=pswarmpar, # bounds=(PC0 - np.array(search_limit), PC0 + np.array(search_limit)), # ) - optimizer = PSOOpt(dimensions=3,n_particles=nswarmpoints, + optimizer = PSOOpt(dimensions=3, n_particles=nswarmparticles, c1=pswarmpar['c1'], c2 = pswarmpar['c2'], w = pswarmpar['w'], hyperparammethod='auto') From 006d06fa3e59a96cfbaaf7789e397c44cbd64de4 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Wed, 8 Feb 2023 16:17:57 -0500 Subject: [PATCH 067/177] Fixed issues with batch processing PC optimization. Added option to make PSO quiet, (verbose = 0) Signed-off by: David Rowenhorst --- pyebsdindex/pcopt.py | 45 ++++++++++++++++++++++++++++++-------------- 1 file changed, 31 insertions(+), 14 deletions(-) diff --git a/pyebsdindex/pcopt.py b/pyebsdindex/pcopt.py index 1b9f3c9..b8bec0d 100644 --- a/pyebsdindex/pcopt.py +++ b/pyebsdindex/pcopt.py @@ -190,7 +190,7 @@ def optimize(pats, indexer, PC0=None, batch=False): def optimize_pso(pats, indexer, PC0=None, batch=False, search_limit = 0.2, - nswarmparticles=30, pswarmpar=None, niter=50): + nswarmparticles=30, pswarmpar=None, niter=50, verbose=1): """Optimize pattern center (PC) (PCx, PCy, PCz) in the convention of the :attr:`indexer.vendor` with particle swarms. @@ -227,7 +227,7 @@ def optimize_pso(pats, indexer, PC0=None, batch=False, search_limit = 0.2, and parameters c1 = 2.05, c2 = 2.05 and w = 0.8. """ banddat = indexer.bandDetectPlan.find_bands(pats) - npoints = banddat.shape[0] + npoints, nbands = banddat.shape[:2] if pswarmpar is None: #pswarmpar = {"c1": 3.05, "c2": 1.05, "w": 0.8} pswarmpar = {"c1": 3.5, "c2": 3.5, "w": 0.8} @@ -273,20 +273,35 @@ def optimize_pso(pats, indexer, PC0=None, batch=False, search_limit = 0.2, # ) cost, PCoutRet = optimizer.optimize(_optfunction, indexer=indexer, banddat=banddat, start=PC0, bounds=(PC0 - np.array(search_limit), PC0 + np.array(search_limit)), - niter=niter) + niter=niter, verbose=verbose) #print(cost) else: PCoutRet = np.zeros((npoints, 3)) + if verbose >= 1: + print('', end='\n') for i in range(npoints): # cost, PCoutRet[i, :] = optimizer.optimize( # _optfunction, niter, indexer=indexer, banddat=banddat[i, :, :] # ) - cost, PCoutRet = optimizer.optimize(_optfunction, indexer=indexer, banddat=banddat[i, :, :], - start=PC0, bounds=(PC0 - np.array(search_limit), PC0 + np.array(search_limit)), - niter=niter) + cost, newPC = optimizer.optimize(_optfunction, indexer=indexer, + banddat=banddat[i, :].reshape(1, nbands), + start=PC0, bounds=(PC0 - np.array(search_limit), PC0 + np.array(search_limit)), + niter=niter, verbose=0) + + PCoutRet[i, :] = newPC + progress = int(round(10 * float(i) / npoints)) + if verbose >= 1: + print('', end='\r') + print('PC found: [', + '*' * progress, ' ' * (10 - progress), '] ', i + 1, '/', npoints, + ' global best:', "{0:.3g}".format(cost), + ' PC opt:', np.array_str(PCoutRet[i,:], precision=4, suppress_small=True), + sep='', end='') + if verbose >= 1: + print('', end='\n') if emsoftflag: # Return original state for indexer indexer.vendor = "EMSOFT" @@ -494,19 +509,20 @@ def printprogress(self, iter): ' global best:', "{0:.3g}".format(self.gbest), ' best loc:', np.array_str(self.gbest_loc, precision=4, suppress_small=True), sep='', end='') - def optimize(self, function, start=None, bounds=None, niter=50, **kwargs): + def optimize(self, function, start=None, bounds=None, niter=50, verbose = 1, **kwargs): self.initializeswarm(start, bounds) with multiprocessing.Pool(min(multiprocessing.cpu_count(), self.n_particles)) as pool: - - print('n_particle:', self.n_particles, 'c1:', self.c1, 'c2:', self.c2, 'w:', self.w ) + if verbose >= 1: + print('n_particle:', self.n_particles, 'c1:', self.c1, 'c2:', self.c2, 'w:', self.w ) self.niter = niter for iter in range(niter): self.updatehyperparam(iter) self.updateswarmbest(function, pool, **kwargs) - self.printprogress(iter) + if verbose >= 1: + self.printprogress(iter) self.updateswarmvelpos() @@ -514,10 +530,11 @@ def optimize(self, function, start=None, bounds=None, niter=50, **kwargs): pool.terminate() final_best = self.gbest final_loc = self.gbest_loc - print('', end='\n') - print("Optimization finished | best cost: {}, best pos: {}".format( - final_best, final_loc)) - print(' ') + if verbose >= 1: + print('', end='\n') + print("Optimization finished | best cost: {}, best pos: {}".format( + final_best, final_loc)) + print(' ') return final_best, final_loc From 89e192d29176833583192b918243b5d835730804 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Thu, 9 Feb 2023 14:07:53 -0500 Subject: [PATCH 068/177] Fixed EBSPFile class name. Fixed writing blank positions. Signed-off by: David Rowenhorst --- pyebsdindex/ebsd_pattern.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/pyebsdindex/ebsd_pattern.py b/pyebsdindex/ebsd_pattern.py index a19214e..eafbaf4 100644 --- a/pyebsdindex/ebsd_pattern.py +++ b/pyebsdindex/ebsd_pattern.py @@ -61,7 +61,7 @@ def get_pattern_file_obj(path,file_type=str('')): if (ftype.upper() == 'UP1') or (ftype.upper() == 'UP2'): ebsdfileobj = UPFile(path) if (ftype.upper() == 'EBSP'): - ebsdfileobj = EBSDPFile(path) + ebsdfileobj = EBSPFile(path) if (ftype.upper() == 'OH5'): ebsdfileobj = EDAXOH5(path) if hdf5path is None: #automatically chose the first data group @@ -634,7 +634,7 @@ def file_from_pattern_obj(self, patternobj, filepath=None, bitdepth = None): self.bitdepth = 8 -class EBSDPFile(EBSDPatternFile): +class EBSPFile(EBSDPatternFile): """ Notes ----- @@ -941,10 +941,10 @@ def write_header(self, writeBlank=False, bitdepth=8): if (self.version > 0) and self.hasxypos: if self.version >= 2: np.uint8(1).tofile(f) - np.float64(i * self.xStep).tofile(f) + np.float64(i * self.xStep - 0.5*(self.nCols*self.xStep)).tofile(f) if self.version >= 2: np.uint8(1).tofile(f) - np.float64(j * self.yStep).tofile(f) + np.float64(j * self.yStep - 0.5*(self.nRows*self.yStep)).tofile(f) else: # no xy_pos info if self.version >= 2: np.uint8(0).tofile(f) From 523584d0e204869466c9ffb0ce4fcbcd0366a264 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Mon, 13 Feb 2023 14:59:12 -0500 Subject: [PATCH 069/177] NLPAR EBSP repairs Signed-off by: David Rowenhorst --- pyebsdindex/ebsd_pattern.py | 2 +- pyebsdindex/nlpar.py | 33 ++++++++++++++++++++++++--------- 2 files changed, 25 insertions(+), 10 deletions(-) diff --git a/pyebsdindex/ebsd_pattern.py b/pyebsdindex/ebsd_pattern.py index eafbaf4..fb300ce 100644 --- a/pyebsdindex/ebsd_pattern.py +++ b/pyebsdindex/ebsd_pattern.py @@ -378,7 +378,7 @@ def write_data(self, newpatterns=None, patStartCount = [0,-1], writeHead=False, for i in range(nrowwrite): pstart = ((rowstart+i)*self.nCols)+colstart - self.write_data(newpatterns = pats[i*ncolwrite:(i+1)*ncolwrite, :, :], patStartCount=[pstart,ncolwrite],writeHead=False, + self.write_data(newpatterns = pats[int(i*ncolwrite):int((i+1)*ncolwrite), :, :], patStartCount=[pstart,ncolwrite],writeHead=False, flt2int=flt2int,scalevalue=0.98, maxScale = max) def pat_writer(self, pat2write, patStart, nPatToWrite, typewrite): pass diff --git a/pyebsdindex/nlpar.py b/pyebsdindex/nlpar.py index 20955c7..b70179e 100644 --- a/pyebsdindex/nlpar.py +++ b/pyebsdindex/nlpar.py @@ -137,21 +137,36 @@ def setoutfile(self,patternfile, filepath=None): def getinfileobj(self): if self.filepath is not None: fID = ebsd_pattern.get_pattern_file_obj([self.filepath, self.hdfdatapath]) - if fID.nRows is not None: - self.nrows = fID.nRows - else: - fID.nRows = self.nrows - if fID.nCols is not None: - self.ncols = fID.nCols - else: - fID.nCols = self.ncols + if (fID.nRows is not None): + if (self.nrows is None): + self.nrows = fID.nRows + else: + fID.nRows = self.nrows + + if (fID.nCols is not None): + if (self.ncols is None): + self.ncols = fID.nCols + else: + fID.nCols = self.ncols + return fID + else: return None def getoutfileobj(self): if self.filepathout is not None: - return ebsd_pattern.get_pattern_file_obj([self.filepathout, self.hdfdatapathout]) + fID = ebsd_pattern.get_pattern_file_obj([self.filepathout, self.hdfdatapathout]) + if self.nrows is not None: + fID.nRows = self.nrows + else: + self.nrows = fID.nRows + + if self.ncols is not None: + fID.nCols = self.ncols + else: + self.ncols = fID.nCols + return fID else: return None From c3ed58b380d55f5a821377094d1d0557650d1c20 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Sat, 25 Feb 2023 11:28:03 -0500 Subject: [PATCH 070/177] A few radon opencl bug fixes Signed-off by: David Rowenhorst --- pyebsdindex/opencl/band_detect_cl.py | 18 +++++----- pyebsdindex/opencl/clkernels.cl | 54 ++++++++++++++++++++++++---- pyebsdindex/opencl/radon_fast_cl.py | 4 +-- 3 files changed, 59 insertions(+), 17 deletions(-) diff --git a/pyebsdindex/opencl/band_detect_cl.py b/pyebsdindex/opencl/band_detect_cl.py index 7a5e630..cee3eb6 100644 --- a/pyebsdindex/opencl/band_detect_cl.py +++ b/pyebsdindex/opencl/band_detect_cl.py @@ -91,7 +91,7 @@ def find_bands(self, patternsIn, verbose=0, clparams=None, chunksize=528, useCPU tic1 = timer() nPatsChunk = chnk[1] - chnk[0] #rdnNorm, clparams, rdnNorm_gpu = self.calc_rdn(patterns[chnk[0]:chnk[1],:,:], clparams, use_gpu=self.CLOps[0]) - rdnNorm, clparams = self.radon_fasterCL(patterns[chnk[0]:chnk[1],:,:], self.padding, + rdnNorm, clparams = self.radon_fasterCL(patterns[chnk[0]:chnk[1],:,:], padding=self.padding, fixArtifacts=False, background=self.backgroundsub, returnBuff=True, clparams=clparams) @@ -104,7 +104,7 @@ def find_bands(self, patternsIn, verbose=0, clparams=None, chunksize=528, useCPU rdntime += timer() - tic1 tic1 = timer() - rdnConv, clparams = self.rdn_convCL2(rdnNorm, clparams=clparams, returnBuff=True) + rdnConv, clparams = self.rdn_convCL2(rdnNorm, clparams=clparams, returnBuff=True, separableKernel=True) rdnNorm.release() convtime += timer()-tic1 tic1 = timer() @@ -256,7 +256,7 @@ def radon_fasterCL(self,image,padding = np.array([0,0]), fixArtifacts = False, b imBack = np.zeros((shapeIm[1], shapeIm[2], nImCL),dtype=np.float32) cl.enqueue_copy(queue,imBack,image_gpu,is_blocking=True) - + cl.enqueue_fill_buffer(queue, radon_gpu, np.float32(-1.0), 0, radon_gpu.size) prg.radonSum(queue,(nImChunk,rdnstep),None,rdnIndx_gpu,image_gpu,radon_gpu, imstep, indxstep, shpRdn[0], shpRdn[1], @@ -264,7 +264,7 @@ def radon_fasterCL(self,image,padding = np.array([0,0]), fixArtifacts = False, b if (fixArtifacts == True): - prg.radonFixArt(queue,(nImChunk,self.nRho),None,radon_gpu, + prg.radonFixArt(queue,(nImChunk,shpRdn[0]),None,radon_gpu, shpRdn[0],shpRdn[1],padTheta) @@ -354,13 +354,13 @@ def rdn_convCL2(self, radonIn, clparams=None, separableKernel=True, returnBuff = # pad out the radon buffers prg.radonPadTheta(queue,(shp[2],shp[0],1),None,rdn_gpu, np.uint64(shp[0]),np.uint64(shp[1]),np.uint64(self.padding[1])) - prg.radonPadRho(queue,(shp[2],shp[1],1),None,rdn_gpu, - np.uint64(shp[0]),np.uint64(shp[1]),np.uint64(self.padding[0])) + prg.radonPadRho2(queue,(shp[2],shp[1],1),None,rdn_gpu, + np.uint64(shp[0]),np.uint64(shp[1]),np.uint64(self.padding[0]+1)) kern_gpu = None if separableKernel == False: # for now I will assume that the kernel(s) can fit in local memory on the GPU # also going to assume that there is only one kernel -- this will be something to fix at some point. - k0 = self.kernel[0,:,:] + k0 = np.array(self.kernel[0,:,:], dtype=np.float32) kshp = np.asarray(k0.shape, dtype=np.int32) pad = kshp/2 kern_gpu = cl.Buffer(ctx,mf.READ_ONLY | mf.COPY_HOST_PTR,hostbuf=k0) @@ -376,7 +376,7 @@ def rdn_convCL2(self, radonIn, clparams=None, separableKernel=True, returnBuff = kshp = np.asarray(self.kernel[0,:,:].shape,dtype=np.int32) pad = kshp - k0x = np.require(self.kernel[0, np.int64(kshp[0] / 2), :], requirements=['C', 'A', 'W', 'O']) + k0x = np.require(self.kernel[0, np.int64(kshp[0] / 2), :], requirements=['C', 'A', 'W', 'O'], dtype=np.float32) k0x *= 1.0 / k0x.sum() k0x = (k0x[...,:]).reshape(1,kshp[1]) @@ -390,7 +390,7 @@ def rdn_convCL2(self, radonIn, clparams=None, separableKernel=True, returnBuff = np.int32(kshp[1]),np.int32(kshp[0]),np.int32(pad[1]),np.int32(pad[0]),tempConvbuff) kshp = np.asarray(self.kernel[0,:,:].shape,dtype=np.int32) - k0y = np.require(self.kernel[0, :, np.int32(kshp[1] / 2)], requirements=['C', 'A', 'W', 'O']) + k0y = np.require(self.kernel[0, :, np.int32(kshp[1] / 2)], requirements=['C', 'A', 'W', 'O'], dtype=np.float32) k0y *= 1.0 / k0y.sum() k0y = (k0y[...,:]).reshape(kshp[0],1) kshp = np.asarray(k0y.shape,dtype=np.int32) diff --git a/pyebsdindex/opencl/clkernels.cl b/pyebsdindex/opencl/clkernels.cl index c7fd47e..f818229 100644 --- a/pyebsdindex/opencl/clkernels.cl +++ b/pyebsdindex/opencl/clkernels.cl @@ -74,11 +74,15 @@ __kernel void radonSum( const unsigned long int rndIndx = (theta+thetaPad + (rho+rhoPad)*nThetaP)*nImChunk + gid_im; - radon[rndIndx] = sum/count; - //radon[rndIndx] = gid_im; + if (count > 1.0e-6){ + radon[rndIndx] = sum/count;} + else{ + radon[rndIndx] = -1.0; + } + } - __kernel void radonFixArt( +__kernel void radonFixArt( __global float16 *radon, const unsigned long int nRho, const unsigned long int nTheta, const unsigned long int thetaPad) @@ -88,11 +92,11 @@ __kernel void radonSum( const unsigned long int rho = get_global_id(1); const unsigned long int rhoIndx = nTheta * rho; //rndIndx = nTheta * nRho * gid_im + (nTheta * rho); - - //radon[gid_rdn+thetaPad] = radon[gid_rdn+thetaPad+1]; + radon[(thetaPad + rhoIndx)*nImChunk + gid_im] = radon[(thetaPad + 1 + rhoIndx)*nImChunk + gid_im]; - //radon[gid_rdn+nTheta-1-thetaPad] = radon[gid_rdn+nTheta-2-thetaPad]; + radon[(nTheta-1-thetaPad + rhoIndx)*nImChunk + gid_im] = radon[(+nTheta-2-thetaPad + rhoIndx)*nImChunk + gid_im]; + //} } // Padding of the radon Theta -- 0 and 180* are symmetric with a vertical flip. @@ -143,6 +147,44 @@ __kernel void radonPadTheta( } +// Padding of the radon Rho -- copy the previous line to the next row ... + __kernel void radonPadRho2( + __global float *radon, + const unsigned long int nRho, const unsigned long int nTheta, + const unsigned long int rhoPad) + { + const unsigned long int gid_im = get_global_id(0); + const unsigned long int nImChunk = get_global_size(0); + const unsigned long int gid_theta = get_global_id(1); + unsigned long int i, gid_rdn1, gid_rdn2; + //indxim = nTheta * nRho * gid_im; + //rd1p = radon[indxim + (nTheta * rhoPad) + gid_theta] ; + //rd2p = radon[ indxim + (nTheta * (nRho -1 - rhoPad)) + gid_theta] ; + float rd1p = radon[((nTheta * rhoPad) + gid_theta)*nImChunk + gid_im] ; + float rd2p = radon[((nTheta * (nRho -1 - rhoPad)) + gid_theta)*nImChunk+gid_im]; + + for (i = 0; i <= rhoPad; ++i){ + + //gid_rdn1 = indxim + (nTheta*i) + gid_theta; + //gid_rdn2 = indxim + (nTheta* (nRho-1-rhoPad+i)) + gid_theta; + + gid_rdn1 = ((nTheta*i) + gid_theta)*nImChunk + gid_im; + gid_rdn2 = ((nTheta* (nRho-1-rhoPad+i)) + gid_theta)*nImChunk + gid_im; + + if (radon[gid_rdn1] < 0){ + radon[gid_rdn1] = rd1p; + } + if (radon[gid_rdn2] < 0){ + radon[gid_rdn2] = rd2p; + } + rd1p = radon[gid_rdn1] ; + rd2p = radon[gid_rdn2] ; + + } + + } + + // Convolution of a stack of images by a 2D kernel // At somepoint we might want to consider the ability to chain together convolutions -- keeping the max at each pixel... __kernel void convolution3d2d( __global const float16 *in, __constant float *kern, const int imszx, const int imszy, const int imszz, diff --git a/pyebsdindex/opencl/radon_fast_cl.py b/pyebsdindex/opencl/radon_fast_cl.py index db4d3b0..6b74681 100644 --- a/pyebsdindex/opencl/radon_fast_cl.py +++ b/pyebsdindex/opencl/radon_fast_cl.py @@ -112,7 +112,7 @@ def radon_fasterCL(self,image,padding = np.array([0,0]), fixArtifacts = False, b imBack = np.zeros((shapeIm[1], shapeIm[2], nImCL),dtype=np.float32) cl.enqueue_copy(queue,imBack,image_gpu,is_blocking=True) - + cl.enqueue_fill_buffer(queue, radon_gpu, np.float32(0.0), 0, radon_gpu.size) prg.radonSum(queue,(nImChunk,rdnstep),None,rdnIndx_gpu,image_gpu,radon_gpu, imstep, indxstep, shpRdn[0], shpRdn[1], @@ -120,7 +120,7 @@ def radon_fasterCL(self,image,padding = np.array([0,0]), fixArtifacts = False, b if (fixArtifacts == True): - prg.radonFixArt(queue,(nImChunk,self.nRho),None,radon_gpu, + prg.radonFixArt(queue,(nImChunk,shpRdn[0]),None,radon_gpu, shpRdn[0],shpRdn[1],padTheta) From 7aa21e16df25b959684d849e10272eaa0f2e3784 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Mon, 27 Feb 2023 07:17:00 -0500 Subject: [PATCH 071/177] User fewer peaks in QUEST for better accuracy. Signed-off by: David Rowenhorst --- pyebsdindex/band_detect.py | 2 +- pyebsdindex/tripletvote.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/pyebsdindex/band_detect.py b/pyebsdindex/band_detect.py index bbb2d37..142f087 100644 --- a/pyebsdindex/band_detect.py +++ b/pyebsdindex/band_detect.py @@ -166,7 +166,7 @@ def band_detect_setup(self, patterns=None,patDim=None,nTheta=None,nRho=None,\ kernel = -1.0*gaussian_filter(kernel, [self.rSigma, self.tSigma], order=[2,0]) self.kernel = kernel.reshape((1,ksz[0], ksz[1])) #self.peakPad = np.array(np.around([ 4*ksz[0], 20.0/self.dTheta]), dtype=np.int64) - self.peakPad = np.array(np.around([3 * ksz[0], 4 * ksz[1]]), dtype=np.int64) + self.peakPad = np.array(np.around([2 * ksz[0], 2 * ksz[1]]), dtype=np.int64) self.peakPad += 1 - np.mod(self.peakPad, 2) # make sure we have it as odd. self.padding = np.array([np.max( [self.peakPad[0], self.padding[0]] ), np.max([self.peakPad[1], self.padding[1]])]) diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index d3c9630..c6bd491 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -476,7 +476,7 @@ def bandindex(self, band_norms, band_intensity = None, band_widths=None, verbose if self.high_fidelity == True: srt = np.argsort(fitb[whGood]) - whgood6 = whGood[srt[0:np.min([9, whGood.shape[0]])]] + whgood6 = whGood[srt[0:np.min([7, whGood.shape[0]])]] weights6 = band_intensity[whgood6] pflt6 = (np.asarray(polesCart[polematch[whgood6], :], dtype=np.float64)) From 56b70c67d8b474cabe7178ee1adf967f107d628e Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Thu, 2 Mar 2023 13:13:15 -0500 Subject: [PATCH 072/177] Bug fix for getting wrong initial orientation in uncommon situations. Signed-off by: David Rowenhorst --- pyebsdindex/tripletvote.py | 23 ++++++++++++++--------- 1 file changed, 14 insertions(+), 9 deletions(-) diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index c6bd491..681e37d 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -461,6 +461,10 @@ def bandindex(self, band_norms, band_intensity = None, band_widths=None, verbose self._assign_bands_nb(polesCart, bandRank_arg, bandFam, famIndx, nFam, angTable, bandnorms, angTol, n_band_early) if verbose > 3: + #print(rotlib.om2qu(R)) + #print(polematch) + #print(whGood) + #print(fitb) print('___Assigned Band___') print(self.completelib['familyid'][polematch]) acc_correct = np.sum( np.array(self.completelib['familyid'][polematch] == bandFam).astype(int)).astype(int) @@ -976,6 +980,7 @@ def _assign_bands_nb(polesCart, bandRank_arg, familyLabel, famIndx, nFam, angTab fitout = np.float32(360.0) fitbout = np.zeros((nBnds)) R = np.zeros((1, 3, 3), dtype=np.float32) + #fit = np.float32(360.0) #whGood = np.zeros(nBnds, dtype=np.int64) - 1 nMatch = np.int64(0) @@ -1102,9 +1107,9 @@ def _assign_bands_nb(polesCart, bandRank_arg, familyLabel, famIndx, nFam, angTab fitout = fit fitbout = fitb nMatch = nGood - whGood_out = whGood - polematch_out = polematch - Rout = R + whGood_out = whGood[:] + polematch_out = polematch[:] + Rout[0,:,:] = R[0,:,:] ij = (ii,jj,bnd1,bnd2) break else: @@ -1115,9 +1120,9 @@ def _assign_bands_nb(polesCart, bandRank_arg, familyLabel, famIndx, nFam, angTab fitout = np.float32(fit) fitbout = fitb nMatch = nGood - whGood_out = whGood - polematch_out = polematch - Rout = R + whGood_out = whGood[:] + polematch_out = polematch[:] + Rout[0,:,:] = R[0,:,:] ij = (ii,jj,bnd1,bnd2) @@ -1128,9 +1133,9 @@ def _assign_bands_nb(polesCart, bandRank_arg, familyLabel, famIndx, nFam, angTab fitout = np.float32(fit) fitbout = fitb nMatch = nGood - whGood_out = whGood - polematch_out = polematch - Rout = R + whGood_out = whGood[:] + polematch_out = polematch[:] + Rout[0,:,:] = R[0,:,:] ij = (ii, jj, bnd1,bnd2) From 4be26b365d123f3b000ac59ff0d7a4d34e286052 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Thu, 23 Mar 2023 06:49:16 -0700 Subject: [PATCH 073/177] Indentation error caused very slow initial setup. Signed-off by: David Rowenhorst --- pyebsdindex/radon_fast.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyebsdindex/radon_fast.py b/pyebsdindex/radon_fast.py index 40e2ab2..03188ef 100644 --- a/pyebsdindex/radon_fast.py +++ b/pyebsdindex/radon_fast.py @@ -110,7 +110,7 @@ def radon_plan_setup(self, image=None, imageDim=None, nTheta=None, nRho=None, rh indx_x = np.where(indx_x >= self.imDim[1], outofbounds, indx_x) indx1D = np.clip(indx_x+self.imDim[1]*n, 0, outofbounds) self.indexPlan[:, i, 0:self.imDim[0]] = indx1D - self.indexPlan.sort(axis = -1) + self.indexPlan.sort(axis = -1) def radon_fast(self, imageIn, padding = np.array([0,0]), fixArtifacts = False, background = None): From 8aa678f2b3b024216442bbc08da0cfd37ab90077 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Thu, 23 Mar 2023 11:41:12 -0700 Subject: [PATCH 074/177] Fix boundary bouncing Signed-off by: David Rowenhorst --- pyebsdindex/pcopt.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/pyebsdindex/pcopt.py b/pyebsdindex/pcopt.py index b8bec0d..82b26e2 100644 --- a/pyebsdindex/pcopt.py +++ b/pyebsdindex/pcopt.py @@ -467,6 +467,7 @@ def updateswarmvelpos(self): + def boundarycheck(self): if str.lower(self.boundmethod) == 'bounce': @@ -479,11 +480,11 @@ def boundarybounce(self): for d in range(self.dimensions): wh_under = np.nonzero(self.pos[:,d] < lb[d])[0] self.pos[wh_under,d] = lb[d] - self.vel[wh_under,d] *= -1.0 + self.vel[wh_under,d] = np.abs(self.vel[wh_under,d]) wh_over = np.nonzero(self.pos[:, d] > ub[d])[0] self.pos[wh_over, d] = ub[d] - self.vel[wh_over, d] *= -1.0 + self.vel[wh_over, d] = -1*np.abs(self.vel[wh_over, d]) def updatehyperparam(self, iter): if str.lower(self.hyperparammethod) == 'auto': From 194a798f8a18bbca18bd78e0956a114920c3f019 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Mon, 27 Mar 2023 09:02:34 -0400 Subject: [PATCH 075/177] doc update Signed-off by: David Rowenhorst --- pyebsdindex/tripletvote.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index 681e37d..3a925bc 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -760,7 +760,7 @@ def _orientation_quest_nb(polescart, bandnorms, weights): B = (wn * bndnorm).T @ pflt S = B + B.T z = np.asarray(np.sum(wn * np.cross(bndnorm, pflt), axis=0), dtype=np.float64) - S2 = S @ S + S2 = S @ S # numpy matrix multiplication det = np.linalg.det(S) k = (S[1, 1] * S[2, 2] - S[1, 2] * S[2, 1]) + (S[0, 0] * S[2, 2] - S[0, 2] * S[2, 0]) + ( S[0, 0] * S[1, 1] - S[1, 0] * S[0, 1]) From 692b98f3fe2447b2e904662350d768b23c6f3f37 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Mon, 27 Mar 2023 09:05:24 -0400 Subject: [PATCH 076/177] Revert "doc update" This reverts commit 194a798f8a18bbca18bd78e0956a114920c3f019. --- pyebsdindex/tripletvote.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index 3a925bc..681e37d 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -760,7 +760,7 @@ def _orientation_quest_nb(polescart, bandnorms, weights): B = (wn * bndnorm).T @ pflt S = B + B.T z = np.asarray(np.sum(wn * np.cross(bndnorm, pflt), axis=0), dtype=np.float64) - S2 = S @ S # numpy matrix multiplication + S2 = S @ S det = np.linalg.det(S) k = (S[1, 1] * S[2, 2] - S[1, 2] * S[2, 1]) + (S[0, 0] * S[2, 2] - S[0, 2] * S[2, 0]) + ( S[0, 0] * S[1, 1] - S[1, 0] * S[0, 1]) From d975370d71caf57bddf974f15e341635c7a97cb2 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Thu, 30 Mar 2023 13:31:03 -0400 Subject: [PATCH 077/177] Attempt to catch rare hung process Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_parallel.py | 19 ++++++++++++++++--- 1 file changed, 16 insertions(+), 3 deletions(-) diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index 219bde2..3e8e624 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -338,15 +338,21 @@ def index_pats_distributed( ) nsubmit += 1 timers.append(timer()) - time.sleep(0.01) + #time.sleep(0.01) jobs_indx.append(job_pstart_end[:]) while ndone < njobs: # toc = timer() - wrker, busy = ray.wait(jobs, num_returns=1, timeout=None) + wrker, busy = ray.wait(jobs, num_returns=1, timeout=60.0) # print("waittime: ",timer() - toc) - jid = jobs.index(wrker[0]) + if len(wrker) > 0: # trying to catch a hung worker. Rare, but it happens + jid = jobs.index(wrker[0]) + else: + print('hang with ', ndone, 'out of ', njobs) + jid = jobs.index(busy[0]) + wrker.append(busy[0]) + ray.kill(busy[0]) try: wrkdataout, wrkbanddata, indxstr, indxend, rate = ray.get(wrker[0]) except: @@ -470,6 +476,13 @@ def index_pats_distributed( wrker, busy = ray.wait(jobs, num_returns=1, timeout=None) jid = jobs.index(wrker[0]) # print("waittime: ",timer() - toc) + if len(wrker) > 0: + jid = jobs.index(wrker[0]) + else: + print('hang with ', ndone, 'out of ', njobs) + jid = jobs.index(busy[0]) + wrker.append(busy[0]) + ray.kill(busy[0]) try: wrkdataout, wrkbanddata, indxstr, indxend, rate = ray.get(wrker[0]) except: From 6a829198f1032af1d10b3d4d59fb14ec795a395a Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Mon, 17 Apr 2023 12:18:40 -0400 Subject: [PATCH 078/177] Correct error in updating pso swarm parameters. Signed-off by: David Rowenhorst --- pyebsdindex/pcopt.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/pyebsdindex/pcopt.py b/pyebsdindex/pcopt.py index 82b26e2..8ccdd03 100644 --- a/pyebsdindex/pcopt.py +++ b/pyebsdindex/pcopt.py @@ -490,8 +490,9 @@ def updatehyperparam(self, iter): if str.lower(self.hyperparammethod) == 'auto': N = float(self.niter)-1 - self.c1i = (self.c1 - self.c1/7) * (N-iter)/N + self.c1 / 7.0 - self.c2i = (self.c1 - self.c1 / 7) * (iter) / N + self.c1 / 7.0 + self.c1i = (self.c1 - self.c1 / 7) * (N-iter)/N + self.c1 / 7.0 + #self.c2i = (self.c1 - self.c1 / 7) * (iter) / N + self.c1 / 7.0 + self.c2i = (self.c2 - self.c2 / 7) * (iter) / N + self.c2 / 7.0 self.wi = self.w/2 * ((N - iter)/N)**2 + self.w/2 else: From 9f65a9ad7a8dd764ac2b969214f8e9e7fb51cf93 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Mon, 17 Apr 2023 12:19:19 -0400 Subject: [PATCH 079/177] Added option for background division or subtraction. Signed-off by: David Rowenhorst --- pyebsdindex/band_detect.py | 17 +++++++++-------- pyebsdindex/opencl/clkernels.cl | 20 ++++++++++++++++++++ pyebsdindex/opencl/radon_fast_cl.py | 13 +++++++++---- pyebsdindex/radon_fast.py | 8 ++++++-- pyebsdindex/tripletvote.py | 2 +- 5 files changed, 45 insertions(+), 15 deletions(-) diff --git a/pyebsdindex/band_detect.py b/pyebsdindex/band_detect.py index 142f087..ad88bc5 100644 --- a/pyebsdindex/band_detect.py +++ b/pyebsdindex/band_detect.py @@ -29,9 +29,10 @@ import matplotlib.pyplot as plt import numba import numpy as np -from scipy.ndimage import gaussian_filter -from scipy.ndimage import grey_dilation as scipy_grey_dilation -import scipy.optimize as opt +import scipy.ndimage as scipyndim #import gaussian_filter +#from scipy.ndimage #import grey_dilation as scipy_grey_dilation +#from scipy.ndimage #import median_filter +import scipy.optimize as scipyopt from pyebsdindex import radon_fast @@ -163,7 +164,7 @@ def band_detect_setup(self, patterns=None,patDim=None,nTheta=None,nRho=None,\ ksz = ksz + ((ksz % 2) == 0) kernel = np.zeros(ksz, dtype=np.float32) kernel[(ksz[0]/2).astype(int),(ksz[1]/2).astype(int) ] = 1 - kernel = -1.0*gaussian_filter(kernel, [self.rSigma, self.tSigma], order=[2,0]) + kernel = -1.0*scipyndim.gaussian_filter(kernel, [self.rSigma, self.tSigma], order=[2,0]) self.kernel = kernel.reshape((1,ksz[0], ksz[1])) #self.peakPad = np.array(np.around([ 4*ksz[0], 20.0/self.dTheta]), dtype=np.int64) self.peakPad = np.array(np.around([2 * ksz[0], 2 * ksz[1]]), dtype=np.int64) @@ -263,14 +264,14 @@ def fit_gauss(M, *args): xwh = x[wh] ywh = y[wh] xywh = np.vstack((xwh, ywh)) - zwh = (back.ravel())[wh] + zwh = (scipyndim.median_filter(np.squeeze(back),3).ravel())[wh] whmx = np.unravel_index(back.argmax(), back.shape) minz = zwh.min() # initialize a guess for the parameters. # [gauss amplitude, max loc x, max loc y, sigx, sigy, const offset, slope x, slope y] p0 = [(zwh.max() - zwh.min())*0.1, whmx[1], whmx[0], nx/2.355, ny/2.355, minz, 0, 0] try: - popt, pcov = opt.curve_fit(fit_gauss, xywh, zwh, p0) + popt, pcov = scipyopt.curve_fit(fit_gauss, xywh, zwh, p0) backfit = (gaussian_surf(x, y, *popt)).reshape(ny, nx) #print(p0, popt) except RuntimeError: @@ -441,7 +442,7 @@ def rdn_conv(self, radonIn): rdnConv = np.zeros_like(radon) for i in range(shp[2]): - rdnConv[:,:,i] = -1.0 * gaussian_filter(np.squeeze(radon[:,:,i]),[self.rSigma,self.tSigma],order=[2,0]) + rdnConv[:,:,i] = -1.0 * scipyndim.gaussian_filter(np.squeeze(radon[:,:,i]),[self.rSigma,self.tSigma],order=[2,0]) #print(rdnConv.min(),rdnConv.max()) mns = (rdnConv[self.padding[0]:shprdn[1]-self.padding[0],self.padding[1]:shprdn[1]-self.padding[1],:]).min(axis=0).min(axis=0) @@ -457,7 +458,7 @@ def rdn_local_max(self, rdn, clparams=None, rdn_gpu=None, use_gpu=False): # find the local max lMaxK = (self.peakPad[0],self.peakPad[1],1) - lMaxRdn = scipy_grey_dilation(rdn,size=lMaxK) + lMaxRdn = scipyndim.grey_dilation(rdn,size=lMaxK) #lMaxRdn[:,:,0:self.peakPad[1]] = 0 #lMaxRdn[:,:,-self.peakPad[1]:] = 0 #location of the max is where the local max is equal to the original. diff --git a/pyebsdindex/opencl/clkernels.cl b/pyebsdindex/opencl/clkernels.cl index f818229..9338438 100644 --- a/pyebsdindex/opencl/clkernels.cl +++ b/pyebsdindex/opencl/clkernels.cl @@ -43,6 +43,26 @@ __kernel void backSub( __global float16 *im1, __global const float *back, } +//Do a background division on the pattern +__kernel void backSub( __global float16 *im1, __global const float *back, + const unsigned long int nImChunk) + { + const unsigned long int xy = get_global_id(0); + //const unsigned long int szim = get_global_size(0); + unsigned long i; + float16 imVal; + + const float b1 = back[xy]; + + const unsigned long indx = nImChunk * xy; + for(i = 0; i< nImChunk; ++i){ + imVal = im1[indx+i]; + imVal /= b1; + im1[indx+i] = imVal; + } + +} + __kernel void radonSum( __global const unsigned long int *rdnIndx, __global const float16 *images, __global float16 *radon, diff --git a/pyebsdindex/opencl/radon_fast_cl.py b/pyebsdindex/opencl/radon_fast_cl.py index 6b74681..b1d4ba5 100644 --- a/pyebsdindex/opencl/radon_fast_cl.py +++ b/pyebsdindex/opencl/radon_fast_cl.py @@ -49,7 +49,9 @@ def setcl(self, clparams=None): self.clparams = clparams - def radon_fasterCL(self,image,padding = np.array([0,0]), fixArtifacts = False, background = None, returnBuff = True, clparams=None ): + def radon_fasterCL(self,image,padding = np.array([0,0]), fixArtifacts = False, + background = None, background_method = 'SUBTRACT', + returnBuff = True, clparams=None ): tic = timer() # make sure we have an OpenCL environment @@ -108,9 +110,12 @@ def radon_fasterCL(self,image,padding = np.array([0,0]), fixArtifacts = False, b if background is not None: back_gpu = cl.Buffer(ctx,mf.READ_ONLY | mf.COPY_HOST_PTR,hostbuf=background.astype(np.float32)) - prg.backSub(queue,(imstep, 1, 1),None,image_gpu,back_gpu,nImChunk) - imBack = np.zeros((shapeIm[1], shapeIm[2], nImCL),dtype=np.float32) - cl.enqueue_copy(queue,imBack,image_gpu,is_blocking=True) + if str.upper(background_method) == 'DIVIDE': + prg.backDiv(queue,(imstep, 1, 1),None,image_gpu,back_gpu,nImChunk) + else: + prg.backSub(queue,(imstep, 1, 1),None,image_gpu,back_gpu,nImChunk) + #imBack = np.zeros((shapeIm[1], shapeIm[2], nImCL),dtype=np.float32) + #cl.enqueue_copy(queue,imBack,image_gpu,is_blocking=True) cl.enqueue_fill_buffer(queue, radon_gpu, np.float32(0.0), 0, radon_gpu.size) prg.radonSum(queue,(nImChunk,rdnstep),None,rdnIndx_gpu,image_gpu,radon_gpu, diff --git a/pyebsdindex/radon_fast.py b/pyebsdindex/radon_fast.py index 03188ef..643f03e 100644 --- a/pyebsdindex/radon_fast.py +++ b/pyebsdindex/radon_fast.py @@ -113,7 +113,8 @@ def radon_plan_setup(self, image=None, imageDim=None, nTheta=None, nRho=None, rh self.indexPlan.sort(axis = -1) - def radon_fast(self, imageIn, padding = np.array([0,0]), fixArtifacts = False, background = None): + def radon_fast(self, imageIn, padding = np.array([0,0]), fixArtifacts = False, + background = None, background_method = 'SUBTRACT'): tic = timer() shapeIm = np.shape(imageIn) if imageIn.ndim == 2: @@ -127,7 +128,10 @@ def radon_fast(self, imageIn, padding = np.array([0,0]), fixArtifacts = False, b if background is None: image = imageIn.reshape(-1) else: - image = imageIn - background + if str.upper(background_method) == 'DIVIDE': + image = imageIn / background + else: + image = imageIn - background image = image.reshape(-1) nPx = shapeIm[-1]*shapeIm[-2] diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index 681e37d..cfb1a8b 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -772,7 +772,7 @@ def _orientation_quest_nb(polescart, bandnorms, weights): a = sig2 - k lam = 1.0 - tol = 1.0e-6 + tol = 1.0e-12 iter = 0 dlam = 1e6 # for i in range(10): From f2869f2b381d0aab12c58848e18cba0c9b5c5c78 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Wed, 19 Apr 2023 10:57:55 -0400 Subject: [PATCH 080/177] Check to make sure background is not less than 1.0 in background division. Signed-off by: David Rowenhorst --- pyebsdindex/opencl/clkernels.cl | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/pyebsdindex/opencl/clkernels.cl b/pyebsdindex/opencl/clkernels.cl index 9338438..9d36dde 100644 --- a/pyebsdindex/opencl/clkernels.cl +++ b/pyebsdindex/opencl/clkernels.cl @@ -44,7 +44,7 @@ __kernel void backSub( __global float16 *im1, __global const float *back, } //Do a background division on the pattern -__kernel void backSub( __global float16 *im1, __global const float *back, +__kernel void backDiv( __global float16 *im1, __global const float *back, const unsigned long int nImChunk) { const unsigned long int xy = get_global_id(0); @@ -52,8 +52,11 @@ __kernel void backSub( __global float16 *im1, __global const float *back, unsigned long i; float16 imVal; - const float b1 = back[xy]; - + float b1 = back[xy]; + if (b1 < 1.0){ + b1 = 1.0; + } + const unsigned long indx = nImChunk * xy; for(i = 0; i< nImChunk; ++i){ imVal = im1[indx+i]; From 9a4864cba02fc0d62633c4b76cda04117608c938 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Thu, 20 Apr 2023 18:08:03 -0400 Subject: [PATCH 081/177] Faster initiation for NLPAR for UPx files. Signed-off by: David Rowenhorst --- pyebsdindex/ebsd_pattern.py | 25 ++++++++++++++++++++++++- pyebsdindex/nlpar.py | 10 +++++----- 2 files changed, 29 insertions(+), 6 deletions(-) diff --git a/pyebsdindex/ebsd_pattern.py b/pyebsdindex/ebsd_pattern.py index fb300ce..94609f4 100644 --- a/pyebsdindex/ebsd_pattern.py +++ b/pyebsdindex/ebsd_pattern.py @@ -632,7 +632,30 @@ def file_from_pattern_obj(self, patternobj, filepath=None, bitdepth = None): self.bitdepth = 16 if mx <= 256: self.bitdepth = 8 - + def copy_file(self, newpath, **kwargs): + src = Path(self.filepath).expanduser().resolve() + if newpath is not None: + path = np.atleast_1d(newpath) + dst = Path(path[0]).expanduser().resolve() + else: + dst = Path(str(src.expanduser().resolve())+'.copy') + try: + if 'empty_data' in kwargs: + if kwargs['empty_data'] == True: + with open(src, 'rb') as srcf: + head = srcf.read(self.filePos) + size = srcf.seek(0, 2) + #print('checkpoint' ,size) + with open(dst, 'wb') as dstf: + head = dstf.write(head) + #print('write head') + dstf.seek(size-1,0) + #print('seek done') + dstf.write(b"\0") + return + except: + pass + shutil.copyfile(src,dst) class EBSPFile(EBSDPatternFile): """ diff --git a/pyebsdindex/nlpar.py b/pyebsdindex/nlpar.py index 0102a7b..0864417 100644 --- a/pyebsdindex/nlpar.py +++ b/pyebsdindex/nlpar.py @@ -101,15 +101,15 @@ def setoutfile(self,patternfile, filepath=None): raise ValueError('Error: File input and output are exactly the same.') return - patternfile.copy_file([self.filepathout,self.hdfdatapathout] ) + patternfile.copy_file([self.filepathout,self.hdfdatapathout], empty_data=True) return # fpath and (maybe) hdf5 path were set manually. else: # this is a hdf5 file if self.hdfdatapathout is None: - patternfile.copy_file(self.filepathout) + patternfile.copy_file(self.filepathout, empty_data=True) self.hdfdatapathout = patternfile.h5patdatpth return else: - patternfile.copy_file([self.filepathout, self.hdfdatapathout]) + patternfile.copy_file([self.filepathout, self.hdfdatapathout], empty_data=True) return if patternfile is not None: # the user has set no path. @@ -119,7 +119,7 @@ def setoutfile(self,patternfile, filepath=None): p = Path(patternfile.filepath) appnd = "_NLPAR_l{:1.2f}".format(self.lam) + "sr{:d}".format(self.searchradius) newfilepath = str(p.parent / Path(p.stem + appnd + p.suffix)) - patternfile.copy_file(newfilepath) + patternfile.copy_file(newfilepath,empty_data=True) if patternfile.filetype == 'HDF5': hdf5path_tmp = str(patternfile.h5patdatpth).split('/') @@ -132,7 +132,7 @@ def setoutfile(self,patternfile, filepath=None): hdf5path = hdf5path_org+appnd newfilepath = str(p.parent / Path(p.stem + appnd + p.suffix)) #patternfile.copy_file([newfilepath, hdf5path_org], newh5path=hdf5path) - patternfile.copy_file([newfilepath]) + patternfile.copy_file([newfilepath], empty_data=True) hdf5path = patternfile.h5patdatpth self.filepathout = newfilepath From acb567d5bac77eb8d3c83903c2b262692eddc1c5 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Mon, 24 Apr 2023 13:49:16 -0400 Subject: [PATCH 082/177] Faster NLPAR for ebsp files. Signed-off by: David Rowenhorst --- pyebsdindex/ebsd_pattern.py | 24 ++++++++++++++++++++++++ 1 file changed, 24 insertions(+) diff --git a/pyebsdindex/ebsd_pattern.py b/pyebsdindex/ebsd_pattern.py index 94609f4..b6099b5 100644 --- a/pyebsdindex/ebsd_pattern.py +++ b/pyebsdindex/ebsd_pattern.py @@ -1007,6 +1007,30 @@ def pat_writer(self, pat2write, patStart, nPatToWrite, typewrite=None): np.uint8(1).tofile(f) np.float64(yx[0]).tofile(f) f.close() + def copy_file(self, newpath, **kwargs): + src = Path(self.filepath).expanduser().resolve() + if newpath is not None: + path = np.atleast_1d(newpath) + dst = Path(path[0]).expanduser().resolve() + else: + dst = Path(str(src.expanduser().resolve())+'.copy') + try: + if 'empty_data' in kwargs: + if kwargs['empty_data'] == True: + with open(src, 'rb') as srcf: + head = srcf.read(self.filePos[0]) + size = srcf.seek(0, 2) + #print('checkpoint' ,size) + with open(dst, 'wb') as dstf: + head = dstf.write(head) + #print('write head') + dstf.seek(size-1,0) + #print('seek done') + dstf.write(b"\0") + return + except: + pass + shutil.copyfile(src,dst) class HDF5PatFile(EBSDPatternFile): From f96571c437aa936c9ead9b5ccb1bfca4221577f2 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Mon, 24 Apr 2023 13:58:41 -0400 Subject: [PATCH 083/177] Revert "Faster NLPAR for ebsp files." This reverts commit acb567d5bac77eb8d3c83903c2b262692eddc1c5. --- pyebsdindex/ebsd_pattern.py | 24 ------------------------ 1 file changed, 24 deletions(-) diff --git a/pyebsdindex/ebsd_pattern.py b/pyebsdindex/ebsd_pattern.py index b6099b5..94609f4 100644 --- a/pyebsdindex/ebsd_pattern.py +++ b/pyebsdindex/ebsd_pattern.py @@ -1007,30 +1007,6 @@ def pat_writer(self, pat2write, patStart, nPatToWrite, typewrite=None): np.uint8(1).tofile(f) np.float64(yx[0]).tofile(f) f.close() - def copy_file(self, newpath, **kwargs): - src = Path(self.filepath).expanduser().resolve() - if newpath is not None: - path = np.atleast_1d(newpath) - dst = Path(path[0]).expanduser().resolve() - else: - dst = Path(str(src.expanduser().resolve())+'.copy') - try: - if 'empty_data' in kwargs: - if kwargs['empty_data'] == True: - with open(src, 'rb') as srcf: - head = srcf.read(self.filePos[0]) - size = srcf.seek(0, 2) - #print('checkpoint' ,size) - with open(dst, 'wb') as dstf: - head = dstf.write(head) - #print('write head') - dstf.seek(size-1,0) - #print('seek done') - dstf.write(b"\0") - return - except: - pass - shutil.copyfile(src,dst) class HDF5PatFile(EBSDPatternFile): From 20f4df49a12e11909669f0d85c61368a6f70335a Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Fri, 28 Apr 2023 10:45:23 -0400 Subject: [PATCH 084/177] Removed pyswarms import Signed-off by: David Rowenhorst --- pyebsdindex/pcopt.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pyebsdindex/pcopt.py b/pyebsdindex/pcopt.py index 8ccdd03..2772749 100644 --- a/pyebsdindex/pcopt.py +++ b/pyebsdindex/pcopt.py @@ -24,9 +24,9 @@ import numpy as np import multiprocessing -import pyswarms as pso +#import pyswarms as pso import scipy.optimize as opt -from functools import partial +#from functools import partial from timeit import default_timer as timer From 4f0c9430b150bfb9ca3ef0fc3f14e89894151b61 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Fri, 28 Apr 2023 12:25:21 -0400 Subject: [PATCH 085/177] Attempted fix for Windows (maybe others) multiprocessing Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_parallel.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index 8f1faac..23ec648 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -26,6 +26,7 @@ import os +import platform import logging import sys import time @@ -43,6 +44,10 @@ else: from pyebsdindex import band_detect as band_detect +RAYIPADDRESS = '127.0.0.1' +osplatform = platform.system() +if osplatform == 'Darwin': + RAYIPADDRESS = '0.0.0.0' # the localhost address does not work on macOS when on a VPN def index_pats_distributed( patsin=None, @@ -273,7 +278,7 @@ def index_pats_distributed( ray.init( num_cpus=n_cpu_nodes, num_gpus=ngpu, - _node_ip_address="0.0.0.0", + _node_ip_address=RAYIPADDRESS, #"0.0.0.0", runtime_env={"env_vars": {"PYTHONPATH": os.path.dirname(os.path.dirname(__file__))}}, logging_level=logging.WARNING, ) # Supress INFO messages from ray. From ba0049669c2f1b7b52c7325a2b776afcc58ffeae Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Fri, 28 Apr 2023 15:26:56 -0400 Subject: [PATCH 086/177] Attempt to fix hung workers Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_parallel.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index 23ec648..11d0ad4 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -357,7 +357,7 @@ def index_pats_distributed( print('hang with ', ndone, 'out of ', njobs) jid = jobs.index(busy[0]) wrker.append(busy[0]) - ray.kill(busy[0]) + ray.kill(workers[jid]) try: wrkdataout, wrkbanddata, indxstr, indxend, rate = ray.get(wrker[0]) except: @@ -487,7 +487,7 @@ def index_pats_distributed( print('hang with ', ndone, 'out of ', njobs) jid = jobs.index(busy[0]) wrker.append(busy[0]) - ray.kill(busy[0]) + ray.kill(workers[jid]) try: wrkdataout, wrkbanddata, indxstr, indxend, rate = ray.get(wrker[0]) except: From 13a17d8768fbbdd7b7b551a9233fd9a4f2b94ac3 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Sat, 29 Apr 2023 15:11:26 -0400 Subject: [PATCH 087/177] Default to making a guess on the number of patterns to process at a time. Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_parallel.py | 58 ++++++++++++++++++++++++++++- 1 file changed, 56 insertions(+), 2 deletions(-) diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index 11d0ad4..3808aee 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -66,7 +66,7 @@ def index_pats_distributed( nBands=9, patstart=0, npats=-1, - chunksize=528, + chunksize=0, ncpu=-1, return_indexer_obj=False, ebsd_indexer_obj=None, @@ -269,9 +269,14 @@ def index_pats_distributed( ngpu = 0 ngpupnode = 0 + if chunksize <= 0: + chunksize = __optimizegpuchunk__(indexer, n_cpu_nodes, gpu_id, clparam) + + + ray.shutdown() - print("num cpu/gpu:", n_cpu_nodes, ngpu) + print("num cpu/gpu, and number of patterns per iteration:", n_cpu_nodes, ngpu, chunksize) # ray.init(num_cpus=n_cpu_nodes,num_gpus=ngpu,_system_config={"maximum_gcs_destroyed_actor_cached_count": n_cpu_nodes}) # Need to append path for installs from source ... otherwise the ray # workers do not know where to find the PyEBSDIndex module. @@ -599,6 +604,55 @@ def index_pats_distributed( else: return dataout, banddataout +def __optimizegpuchunk__(indexer, n_cpu_nodes, gpu_id, clparam): + gpulist = [] + if clparam is None: + return 1000 + + if gpu_id is None: + gpulist.append(clparam.gpu) + else: + gpulist.append(clparam.gpu[gpu_id]) + ngpu = len(gpulist) + + if ngpu == 0: + return 1000 + + gmem = 1e99 + for g in gpulist: + if g.global_mem_size < gmem: + gmem = g.global_mem_size + print('Global Mem:', gmem) + ncpu_per_gpu = max(1, np.ceil(n_cpu_nodes/ngpu)) + print('Ncpu/gpu:', ncpu_per_gpu) + patdim = indexer.bandDetectPlan.patDim + rdndim = np.array([indexer.bandDetectPlan.nTheta ,indexer.bandDetectPlan.nRho] ) + memperpat = 4*float(patdim[0] * patdim[1] + 8 * rdndim[0] * rdndim[1])# rough estimate + + print('Mem/pat:', memperpat) + chunkguess = (float(gmem)/ncpu_per_gpu) / memperpat + + print('chunkguess:', chunkguess) + if ncpu_per_gpu > 1: + chunkguess *= 1.75 # this is a cheat, because 1/2 the time the GPU will be idle while the CPU is compputing. + print('cheatguess:', chunkguess) + chunk = int(max(2, np.floor(chunkguess/16))*16) # ideally should be a multiple of 16 + print('chunk:', chunk) + #check for powers of two - for some reason it runs very slow with powers of two. + twocheck = np.log2(float(chunk)) + if np.abs((twocheck) - np.round(twocheck)) < 1e-6: + chunk += 16 + + return chunk + + + + + + + + + @ray.remote(num_cpus=1, num_gpus=1) class IndexerRay: From 5dd8fbf5003759ff9703eb4b5353f76e041a2ad4 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Sat, 29 Apr 2023 15:12:24 -0400 Subject: [PATCH 088/177] Default to making a guess at the number of patterns to process at a time Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_parallel.py | 29 ++++++++++++++++++++--------- 1 file changed, 20 insertions(+), 9 deletions(-) diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index 3808aee..cb6fa15 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -129,7 +129,7 @@ def index_pats_distributed( Number of patterns to index. Default is ``-1``, which will index up to the final pattern in ``patsin``. chunksize : int, optional - Default is 528. + If not set. we will make a guess based on the resources available. ncpu : int, optional Number of CPUs to use. Default value is ``-1``, meaning all available CPUs will be used. @@ -606,13 +606,20 @@ def index_pats_distributed( def __optimizegpuchunk__(indexer, n_cpu_nodes, gpu_id, clparam): gpulist = [] + # test for GPU presence if clparam is None: return 1000 + if clparam.ngpu == 0: + return 1000 + if gpu_id is None: - gpulist.append(clparam.gpu) + for g in clparam.gpu: + gpulist.append(g) else: - gpulist.append(clparam.gpu[gpu_id]) + temp = np.atleast_1d(gpu_id) + for g in temp: + gpulist.append(clparam.gpu[g]) ngpu = len(gpulist) if ngpu == 0: @@ -622,27 +629,31 @@ def __optimizegpuchunk__(indexer, n_cpu_nodes, gpu_id, clparam): for g in gpulist: if g.global_mem_size < gmem: gmem = g.global_mem_size - print('Global Mem:', gmem) + #print('Global Mem:', gmem) ncpu_per_gpu = max(1, np.ceil(n_cpu_nodes/ngpu)) - print('Ncpu/gpu:', ncpu_per_gpu) + #print('Ncpu/gpu:', ncpu_per_gpu) patdim = indexer.bandDetectPlan.patDim rdndim = np.array([indexer.bandDetectPlan.nTheta ,indexer.bandDetectPlan.nRho] ) memperpat = 4*float(patdim[0] * patdim[1] + 8 * rdndim[0] * rdndim[1])# rough estimate - print('Mem/pat:', memperpat) + #print('Mem/pat:', memperpat) chunkguess = (float(gmem)/ncpu_per_gpu) / memperpat - print('chunkguess:', chunkguess) + #print('chunkguess:', chunkguess) if ncpu_per_gpu > 1: chunkguess *= 1.75 # this is a cheat, because 1/2 the time the GPU will be idle while the CPU is compputing. - print('cheatguess:', chunkguess) + #print('cheatguess:', chunkguess) chunk = int(max(2, np.floor(chunkguess/16))*16) # ideally should be a multiple of 16 - print('chunk:', chunk) + #print('chunk:', chunk) #check for powers of two - for some reason it runs very slow with powers of two. twocheck = np.log2(float(chunk)) if np.abs((twocheck) - np.round(twocheck)) < 1e-6: chunk += 16 + # finally - I am unsure how to check for integrated graphics that report system memory, so I am going + # throw an arbitrary cap on this: + chunk = min(2016, chunk) + return chunk From d85645630955b7ccefdbd88359b8610161db3362 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Sat, 29 Apr 2023 15:21:00 -0400 Subject: [PATCH 089/177] Fix for running out of memory on NVIDIA (maybe) Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_parallel.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index cb6fa15..9fea0fa 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -605,6 +605,8 @@ def index_pats_distributed( return dataout, banddataout def __optimizegpuchunk__(indexer, n_cpu_nodes, gpu_id, clparam): + + gpulist = [] # test for GPU presence if clparam is None: @@ -640,8 +642,12 @@ def __optimizegpuchunk__(indexer, n_cpu_nodes, gpu_id, clparam): chunkguess = (float(gmem)/ncpu_per_gpu) / memperpat #print('chunkguess:', chunkguess) + cheatval = 1.0 + if clparam.gpu[0].vendor == 'AMD': + cheatval = 1.75 + if ncpu_per_gpu > 1: - chunkguess *= 1.75 # this is a cheat, because 1/2 the time the GPU will be idle while the CPU is compputing. + chunkguess *= cheatval # this is a cheat, because 1/2 the time the GPU will be idle while the CPU is compputing. #print('cheatguess:', chunkguess) chunk = int(max(2, np.floor(chunkguess/16))*16) # ideally should be a multiple of 16 #print('chunk:', chunk) From 7f23ef5940ac8e205e73ff38f4997ed9d93dbffa Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Sat, 29 Apr 2023 18:15:29 -0400 Subject: [PATCH 090/177] Leave some overhead for the GPU. Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_parallel.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index 9fea0fa..9c0b4a9 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -642,12 +642,13 @@ def __optimizegpuchunk__(indexer, n_cpu_nodes, gpu_id, clparam): chunkguess = (float(gmem)/ncpu_per_gpu) / memperpat #print('chunkguess:', chunkguess) - cheatval = 1.0 - if clparam.gpu[0].vendor == 'AMD': + cheatval = 0.9 + if clparam.gpu[0].vendor == 'AMD': # 'AMD implmentation of opencl does better with clearing memory' + # this is a cheat, because 1/2 the time the GPU will be idle while the CPU is compputing. cheatval = 1.75 if ncpu_per_gpu > 1: - chunkguess *= cheatval # this is a cheat, because 1/2 the time the GPU will be idle while the CPU is compputing. + chunkguess *= cheatval #print('cheatguess:', chunkguess) chunk = int(max(2, np.floor(chunkguess/16))*16) # ideally should be a multiple of 16 #print('chunk:', chunk) From c4ad54815c79feb9126f1af5a4e503eeffe76c18 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Mon, 1 May 2023 10:27:59 -0400 Subject: [PATCH 091/177] Better estimates of memory usage. Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_parallel.py | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index 9c0b4a9..5768871 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -635,20 +635,22 @@ def __optimizegpuchunk__(indexer, n_cpu_nodes, gpu_id, clparam): ncpu_per_gpu = max(1, np.ceil(n_cpu_nodes/ngpu)) #print('Ncpu/gpu:', ncpu_per_gpu) patdim = indexer.bandDetectPlan.patDim - rdndim = np.array([indexer.bandDetectPlan.nTheta ,indexer.bandDetectPlan.nRho] ) - memperpat = 4*float(patdim[0] * patdim[1] + 8 * rdndim[0] * rdndim[1])# rough estimate + rdndim = np.array([indexer.bandDetectPlan.nTheta+2*indexer.bandDetectPlan.padding[1], + indexer.bandDetectPlan.nRho+2*indexer.bandDetectPlan.padding[0]]) + memperpat = 4*float(patdim[0] * patdim[1] + 9 * rdndim[0] * rdndim[1])# rough estimate #print('Mem/pat:', memperpat) chunkguess = (float(gmem)/ncpu_per_gpu) / memperpat #print('chunkguess:', chunkguess) - cheatval = 0.9 + safetyval = 0.8 + chunkguess *= safetyval if clparam.gpu[0].vendor == 'AMD': # 'AMD implmentation of opencl does better with clearing memory' - # this is a cheat, because 1/2 the time the GPU will be idle while the CPU is compputing. - cheatval = 1.75 + # # this is a cheat, because 1/2 the time the GPU will be idle while the CPU is compputing. + chunkguess *= 1.75 + + - if ncpu_per_gpu > 1: - chunkguess *= cheatval #print('cheatguess:', chunkguess) chunk = int(max(2, np.floor(chunkguess/16))*16) # ideally should be a multiple of 16 #print('chunk:', chunk) From eb0f63f3724bde09bb4bde85a7ebef0f43ce076d Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Mon, 1 May 2023 11:48:07 -0400 Subject: [PATCH 092/177] Checkpoint Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_parallel.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index 5768871..968fa9b 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -637,10 +637,10 @@ def __optimizegpuchunk__(indexer, n_cpu_nodes, gpu_id, clparam): patdim = indexer.bandDetectPlan.patDim rdndim = np.array([indexer.bandDetectPlan.nTheta+2*indexer.bandDetectPlan.padding[1], indexer.bandDetectPlan.nRho+2*indexer.bandDetectPlan.padding[0]]) - memperpat = 4*float(patdim[0] * patdim[1] + 9 * rdndim[0] * rdndim[1])# rough estimate + memperpat = 4.0*float(patdim[0] * patdim[1] + 9.0 * rdndim[0] * rdndim[1])# rough estimate - #print('Mem/pat:', memperpat) - chunkguess = (float(gmem)/ncpu_per_gpu) / memperpat + print('Mem/pat:', memperpat) + chunkguess = (float(gmem)/float(ncpu_per_gpu)) / memperpat #print('chunkguess:', chunkguess) safetyval = 0.8 From 3a91b16c398fb5ad9a7328b321dba9a4965697a0 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Mon, 1 May 2023 12:10:34 -0400 Subject: [PATCH 093/177] Checkpoint Signed-off by: David Rowenhorst --- pyebsdindex/opencl/band_detect_cl.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/pyebsdindex/opencl/band_detect_cl.py b/pyebsdindex/opencl/band_detect_cl.py index cee3eb6..373c528 100644 --- a/pyebsdindex/opencl/band_detect_cl.py +++ b/pyebsdindex/opencl/band_detect_cl.py @@ -135,13 +135,14 @@ def find_bands(self, patternsIn, verbose=0, clparams=None, chunksize=528, useCPU rdnConvarray = rdnConvarray[:,:,0:chnk[1]-chnk[0] ] rdnConv.release() + rdnConv = None blabeltime += timer() - tic1 tottime = timer() - tic0 # going to manually clear the clparams queue -- this should clear the memory of the queue off the GPU - #if clparams is not None: - #clparams.queue.finish() + if clparams is not None: + clparams.queue.finish() #clparams.queue = None if verbose > 0: From db4a7256ed49be5bd6f661cec8fb6e6d9dc28dd0 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Mon, 1 May 2023 12:20:25 -0400 Subject: [PATCH 094/177] Checkpoint Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_parallel.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index 968fa9b..2e8b0b6 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -643,11 +643,11 @@ def __optimizegpuchunk__(indexer, n_cpu_nodes, gpu_id, clparam): chunkguess = (float(gmem)/float(ncpu_per_gpu)) / memperpat #print('chunkguess:', chunkguess) - safetyval = 0.8 + safetyval = 0.5 chunkguess *= safetyval if clparam.gpu[0].vendor == 'AMD': # 'AMD implmentation of opencl does better with clearing memory' # # this is a cheat, because 1/2 the time the GPU will be idle while the CPU is compputing. - chunkguess *= 1.75 + chunkguess *= 3.0 From d8928733cc6f97a15409bb56b44f07077f8fbee9 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Mon, 1 May 2023 13:45:33 -0400 Subject: [PATCH 095/177] Make new/destroy openCL queue for each iteration. Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_parallel.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index 2e8b0b6..58c4539 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -705,7 +705,8 @@ def __init__(self, actorid=0, clparammodule=None, gpu_id=None): gpu_list = np.atleast_1d(gpu_id) ngpu = gpu_list.shape[0] self.openCLParams.gpu_id = gpu_list[self.actorID % ngpu] - self.openCLParams.get_queue() + self.openCLParams.get_context() + #self.openCLParams.get_queue() self.useGPU = True except: self.openCLParams = None @@ -713,7 +714,10 @@ def __init__(self, actorid=0, clparammodule=None, gpu_id=None): def index_chunk_ray(self, pats=None, indexer=None, patstart=0, npats=-1): try: # print(type(self.openCLParams.ctx)) + tic = timer() + if self.openCLParams is not None: + self.openCLParams.get_queue() dataout, banddata, indxstart, npatsout = indexer.index_pats( patsin=pats, patstart=patstart, @@ -721,6 +725,9 @@ def index_chunk_ray(self, pats=None, indexer=None, patstart=0, npats=-1): clparams=self.openCLParams, chunksize=-1, ) + if self.openCLParams is not None: + self.openCLParams.queue.finish() + self.openCLParams.queue = None rate = np.array([timer() - tic, npatsout]) return dataout, banddata, indxstart, indxstart + npatsout, rate except: From 970c052fcc29b514a4a58c81ea46e98b8025dfe7 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Mon, 1 May 2023 15:32:56 -0400 Subject: [PATCH 096/177] Code cleanup Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_parallel.py | 2 +- pyebsdindex/opencl/band_detect_cl.py | 6 +++--- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index 58c4539..27d7f0e 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -639,7 +639,7 @@ def __optimizegpuchunk__(indexer, n_cpu_nodes, gpu_id, clparam): indexer.bandDetectPlan.nRho+2*indexer.bandDetectPlan.padding[0]]) memperpat = 4.0*float(patdim[0] * patdim[1] + 9.0 * rdndim[0] * rdndim[1])# rough estimate - print('Mem/pat:', memperpat) + #print('Mem/pat:', memperpat) chunkguess = (float(gmem)/float(ncpu_per_gpu)) / memperpat #print('chunkguess:', chunkguess) diff --git a/pyebsdindex/opencl/band_detect_cl.py b/pyebsdindex/opencl/band_detect_cl.py index 373c528..04f8fef 100644 --- a/pyebsdindex/opencl/band_detect_cl.py +++ b/pyebsdindex/opencl/band_detect_cl.py @@ -141,9 +141,9 @@ def find_bands(self, patternsIn, verbose=0, clparams=None, chunksize=528, useCPU tottime = timer() - tic0 # going to manually clear the clparams queue -- this should clear the memory of the queue off the GPU - if clparams is not None: - clparams.queue.finish() - #clparams.queue = None + #if clparams is not None: + # clparams.queue.finish() + # clparams.queue = None if verbose > 0: print('Radon Time:',rdntime) From 3a5c0b7e6844eb685c350d0fa0a16995ac8162e4 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Tue, 2 May 2023 07:43:34 -0400 Subject: [PATCH 097/177] Initial attempt Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_parallel.py | 563 +++++++++--------- pyebsdindex/_ebsd_index_parallel_old.py | 736 ++++++++++++++++++++++++ pyebsdindex/_ebsd_index_single.py | 73 +-- pyebsdindex/ebsd_index.py | 4 +- 4 files changed, 1035 insertions(+), 341 deletions(-) create mode 100644 pyebsdindex/_ebsd_index_parallel_old.py diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index 27d7f0e..a81f3e0 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -218,14 +218,14 @@ def index_pats_distributed( # Differentiate between getting a file to index or an array. # Need to index one pattern to make sure the indexer object is fully # initiated before placing in shared memory store. - mode = "memorymode" + inputmode = "memorymode" if pats is None: - mode = "filemode" + inputmode = "filemode" temp, temp2, indexer = index_pats( npats=1, return_indexer_obj=True, ebsd_indexer_obj=indexer ) - if mode == "filemode": + if inputmode == "filemode": npatsTotal = indexer.fID.nPatterns else: pshape = pats.shape @@ -252,6 +252,7 @@ def index_pats_distributed( if ncpu != -1: n_cpu_nodes = int(ncpu) + ngpu = None if gpu_id is not None: ngpu = np.atleast_1d(gpu_id).shape[0] @@ -264,24 +265,39 @@ def index_pats_distributed( else: if ngpu is None: ngpu = len(clparam.gpu) - ngpupnode = ngpu / n_cpu_nodes + #ngpupnode = ngpu / n_cpu_nodes except: ngpu = 0 ngpupnode = 0 - if chunksize <= 0: - chunksize = __optimizegpuchunk__(indexer, n_cpu_nodes, gpu_id, clparam) - - + n_gpu_nodes = 1.0 + usegpu = True + if usegpu: + if (n_cpu_nodes + n_gpu_nodes) > int(os.cpu_count()): + n_cpu_nodes -= 1 + if n_cpu_nodes < 1: + n_cpu_nodes = 0.5 - 1e-6 + n_gpu_nodes = 0.5 - 1e-6 + ngpuwrker = 8 * ngpu + ngpu_per_wrker = n_gpu_nodes/ngpuwrker - 1e-6 + ncpu_per_wrker = n_gpu_nodes/ngpuwrker - 1e-6 + if chunksize <= 0: + chunksize = __optimizegpuchunk__(indexer, ngpuwrker, gpu_id, clparam) + else: # no gpus detected. + ngpuwrker = 0 + usegpu = False + n_gpu_nodes = 0 + if chunksize <= 0: + chunksize = 1000 ray.shutdown() - print("num cpu/gpu, and number of patterns per iteration:", n_cpu_nodes, ngpu, chunksize) + print("num cpu/gpu, and number of patterns per iteration:", n_cpu_nodes+n_gpu_nodes, ngpu, chunksize) # ray.init(num_cpus=n_cpu_nodes,num_gpus=ngpu,_system_config={"maximum_gcs_destroyed_actor_cached_count": n_cpu_nodes}) # Need to append path for installs from source ... otherwise the ray # workers do not know where to find the PyEBSDIndex module. ray.init( - num_cpus=n_cpu_nodes, + num_cpus=int(np.round(n_cpu_nodes+n_gpu_nodes)), num_gpus=ngpu, _node_ip_address=RAYIPADDRESS, #"0.0.0.0", runtime_env={"env_vars": {"PYTHONPATH": os.path.dirname(os.path.dirname(__file__))}}, @@ -296,9 +312,7 @@ def index_pats_distributed( clparamfunction = band_detect.getopenclparam # Set up the jobs njobs = (np.ceil(npats / chunksize)).astype(np.compat.long) - # p_indx_start = [i*chunksize+patStart for i in range(njobs)] - # p_indx_end = [(i+1)*chunksize+patStart for i in range(njobs)] - # p_indx_end[-1] = npats+patStart + p_indx_start_end = [ [i * chunksize + patstart, (i + 1) * chunksize + patstart, chunksize] for i in range(njobs) @@ -306,297 +320,196 @@ def index_pats_distributed( p_indx_start_end[-1][1] = npats + patstart p_indx_start_end[-1][2] = p_indx_start_end[-1][1] - p_indx_start_end[-1][0] - if njobs < n_cpu_nodes: - n_cpu_nodes = njobs + gpujobs = [] + cpujobs = [] + jid = 1 + for jb in p_indx_start_end: + gpujobs.append(CPUGPUJob(jid, jb[0], jb[1])) + jid += 1 + + ncpuwrker = n_cpu_nodes + if njobs < ncpuwrker: + ncpuwrker = njobs + if njobs < ngpuwrker: + ngpuwrker = njobs nPhases = len(indexer.phaseLib) dataout = np.zeros((nPhases + 1, npats), dtype=indexer.dataTemplate) banddataout = np.zeros( (npats, indexer.bandDetectPlan.nBands), dtype=indexer.bandDetectPlan.dataType ) - ndone = 0 - nsubmit = 0 + bandnormsout = np.zeros((npats, indexer.bandDetectPlan.nBands, 3), dtype=np.float32) + + + ncpudone = 0 + ngpudone = 0 + ncpusubmit = 0 + ngpusubmit = 0 tic0 = timer() - npatsdone = 0.0 + ncpupatsdone = 0.0 if keep_log is True: newline = "\n" else: newline = "\r" - if mode == "filemode": - # Send out the first batch - workers = [] - jobs = [] - timers = [] - jobs_indx = [] - chunkave = 0.0 - for i in range(n_cpu_nodes): - job_pstart_end = p_indx_start_end.pop(0) - workers.append( # make a new Ray Actor that can call the indexer defined in shared memory. - # These actors are read/write, thus can initialize the GPU queues - IndexerRay.options(num_cpus=1, num_gpus=ngpupnode).remote( - i, clparamfunction, gpu_id=gpu_id - ) + + # Send out the first batch + gpuworkers = [] + gputask = [] + gtaskindex = [] + cpuworkers = [] + cputask = [] + ctaskindex = [] + chunkave = 0.0 + + for i in range(ngpuwrker): + + gpuworkers.append( # make a new Ray Actor that can call the indexer defined in shared memory. + # These actors are read/write, thus can initialize the GPU queues + GPUWorker.options(num_cpus=1, num_gpus=ngpu_per_wrker).remote( + i, clparamfunction, gpu_id=gpu_id ) - jobs.append( - workers[i].index_chunk_ray.remote( + ) + gjob = gpujobs.pop(0) + if inputmode == "filemode": + gputask.append( + gpuworkers[i].findbands.remote(gjob, pats=None, - indexer=remote_indexer, - patstart=job_pstart_end[0], - npats=job_pstart_end[2], + indexer=remote_indexer ) ) - nsubmit += 1 - timers.append(timer()) - #time.sleep(0.01) - jobs_indx.append(job_pstart_end[:]) - - while ndone < njobs: - # toc = timer() - wrker, busy = ray.wait(jobs, num_returns=1, timeout=60.0) - - # print("waittime: ",timer() - toc) - if len(wrker) > 0: # trying to catch a hung worker. Rare, but it happens - jid = jobs.index(wrker[0]) - else: - print('hang with ', ndone, 'out of ', njobs) - jid = jobs.index(busy[0]) - wrker.append(busy[0]) - ray.kill(workers[jid]) - try: - wrkdataout, wrkbanddata, indxstr, indxend, rate = ray.get(wrker[0]) - except: - # print('a death has occured') - indxstr = jobs_indx[jid][0] - indxend = jobs_indx[jid][1] - rate = [-1, -1] - if rate[0] >= 0: # Job finished as expected - - ticp = timers[jid] - dataout[:, indxstr - patstart : indxend - patstart] = wrkdataout - banddataout[indxstr - patstart : indxend - patstart, :] = wrkbanddata - npatsdone += rate[1] - ndone += 1 - - ratetemp = n_cpu_nodes * (rate[1]) / (timer() - ticp) - chunkave += ratetemp - totalave = npatsdone / (timer() - tic0) - # print('Completed: ',str(indxstr),' -- ',str(indxend), ' ', npatsdone/(timer()-tic) ) - - toc0 = timer() - tic0 - if keep_log is False: - print("", end="\r") - time.sleep(0.00001) - print( - "Completed: ", - str(indxstr), - " -- ", - str(indxend), - " PPS:", - "{:.0f}".format(ratetemp) - + ";" - + "{:.0f}".format(chunkave / ndone) - + ";" - + "{:.0f}".format(totalave), - " ", - "{:.0f}".format((ndone / njobs) * 100) + "%", - "{:.0f};".format(toc0) - + "{:.0f}".format((njobs - ndone) / ndone * toc0) - + " running;remaining(s)", - end=newline, - ) - - if len(p_indx_start_end) > 0: - job_pstart_end = p_indx_start_end.pop(0) - jobs[jid] = workers[jid].index_chunk_ray.remote( - pats=None, - indexer=remote_indexer, - patstart=job_pstart_end[0], - npats=job_pstart_end[2], - ) - nsubmit += 1 - timers[jid] = timer() - jobs_indx[jid] = job_pstart_end[:] - else: - del jobs[jid] - del workers[jid] - del timers[jid] - del jobs_indx[jid] - else: - # Something bad happened. Put the job back on the queue - # and kill this worker. - p_indx_start_end.append([indxstr, indxend, indxend - indxstr]) - del jobs[jid] - del workers[jid] - del timers[jid] - del jobs_indx[jid] - n_cpu_nodes -= 1 - if len(workers) < 1: # Rare case that we have killed all workers... - job_pstart_end = p_indx_start_end.pop(0) - workers.append( - IndexerRay.options(num_cpus=1, num_gpus=ngpupnode).remote( - jid, clparamfunction, gpu_id - ) - ) - jobs.append( - workers[0].index_chunk_ray.remote( - pats=None, - indexer=remote_indexer, - patstart=job_pstart_end[0], - npats=job_pstart_end[2], - ) - ) - nsubmit += 1 - timers.append(timer()) - time.sleep(0.01) - jobs_indx.append(job_pstart_end[:]) - n_cpu_nodes += 1 - - if mode == "memorymode": - workers = [] - jobs = [] - timers = [] - jobs_indx = [] - chunkave = 0.0 - for i in range(n_cpu_nodes): - job_pstart_end = p_indx_start_end.pop(0) - workers.append( - IndexerRay.options(num_cpus=1, num_gpus=ngpupnode).remote( - i, clparamfunction, gpu_id - ) - ) - jobs.append( - workers[i].index_chunk_ray.remote( - pats=pats[job_pstart_end[0] : job_pstart_end[1], :, :], + else: + gputask.append( + gpuworkers[i].findbands.remote(gjob, + pats = pats[gjob.pstart:gjob.pend, :, :], indexer=remote_indexer, - patstart=job_pstart_end[0], - npats=job_pstart_end[2], ) ) - nsubmit += 1 - timers.append(timer()) - jobs_indx.append(job_pstart_end) - time.sleep(0.01) - - # workers = [index_chunk.remote(pats = None, indexer = remote_indexer, patStart = p_indx_start[i], patEnd = p_indx_end[i]) for i in range(n_cpu_nodes)] - # nsubmit += n_cpu_nodes - - while ndone < njobs: - # toc = timer() - wrker, busy = ray.wait(jobs, num_returns=1, timeout=None) - jid = jobs.index(wrker[0]) - # print("waittime: ",timer() - toc) - if len(wrker) > 0: - jid = jobs.index(wrker[0]) - else: - print('hang with ', ndone, 'out of ', njobs) - jid = jobs.index(busy[0]) - wrker.append(busy[0]) - ray.kill(workers[jid]) - try: - wrkdataout, wrkbanddata, indxstr, indxend, rate = ray.get(wrker[0]) - except: - indxstr = jobs_indx[jid][0] - indxend = jobs_indx[jid][1] - rate = [-1, -1] - if rate[0] >= 0: - ticp = timers[jid] - dataout[:, indxstr - patstart : indxend - patstart] = wrkdataout - banddataout[indxstr - patstart : indxend - patstart, :] = wrkbanddata - npatsdone += rate[1] - ratetemp = n_cpu_nodes * (rate[1]) / (timer() - ticp) - chunkave += ratetemp - totalave = npatsdone / (timer() - tic0) - # print('Completed: ',str(indxstr),' -- ',str(indxend), ' ', npatsdone/(timer()-tic) ) - ndone += 1 - toc0 = timer() - tic0 - if keep_log is False: - print("", end="\r") - time.sleep(0.0001) - print( - "Completed: ", - str(indxstr), - " -- ", - str(indxend), - " PPS:", - "{:.0f}".format(ratetemp) - + ";" - + "{:.0f}".format(chunkave / ndone) - + ";" - + "{:.0f}".format(totalave), - " ", - "{:.0f}".format((ndone / njobs) * 100) + "%", - "{:.0f};".format(toc0) - + "{:.0f}".format((njobs - ndone) / ndone * toc0) - + " running;remaining(s)", - end=newline, - ) - - if len(p_indx_start_end) > 0: - job_pstart_end = p_indx_start_end.pop(0) - jobs[jid] = workers[jid].index_chunk_ray.remote( - pats=pats[job_pstart_end[0] : job_pstart_end[1], :, :], - indexer=remote_indexer, - patstart=job_pstart_end[0], - npats=job_pstart_end[2], - ) - nsubmit += 1 - timers[jid] = timer() - jobs_indx[jid] = job_pstart_end + gtaskindex.append(gjob) + ngpusubmit += 1 + time.sleep(0.1) + # initiate the the CPU workers. + print(len(gpuworkers),len(gputask)) + for i in range(ncpuwrker): + cpuworkers.append( # make a new Ray Actor that can call the indexer defined in shared memory. + # These actors are read/write, thus can initialize the GPU queues + CPUWorker.options(num_cpus=1, num_gpus=0).remote(i)) + cputask.append(cpuworkers[i].indexpoles.remote(None, None, None,indexer=remote_indexer)) + ctaskindex.append(None) + print(len(cpuworkers)) + + while ncpudone < njobs: + + if ngpudone < njobs: # check if gpu is done + donewrker, busy = ray.wait(gputask,num_returns = len(gputask), timeout=0.1) + #if len(wrker) > 0: # trying to catch a hung worker. Rare, but it happens + print(len(donewrker)) + #else: + #print('hung gpu process') + #jid = gputask.index(busy[0]) + #wrker.append(busy[0]) + #ray.kill(gputask[jid]) + for wrker in donewrker: + jid = gputask.index(wrker) + #try: + message, (banddata, bandnorm, gjob) = ray.get(wrker) + + if message == 'Done': + banddataout[gjob.pstart - patstart: gjob.pend - patstart, :] = banddata + bandnormsout[gjob.pstart - patstart: gjob.pend - patstart, :,:] = bandnorm + + cpujobs.append(CPUGPUJob(gjob.jobid, gjob.pstart, gjob.pend, extime=gjob.extime)) + ngpudone += 1 + + if len(gpujobs) > 0: # still more gpu work to do + gjob = gpujobs.pop(0) + if inputmode == "filemode": + gputask[jid] = gpuworkers[jid].findbands.remote(gjob, + pats=None, + indexer=remote_indexer + ) + else: + gputask[jid] = gpuworkers[jid].findbands.remote(gjob, + pats=pats[gjob.pstart:gjob.pend, :, :], + indexer=remote_indexer, + ) + gtaskindex[jid] = gjob + ngpusubmit += 1 + else: # no more gpu tasks to submit + del gpuworkers[jid] + del gputask[jid] + del gtaskindex[jid] else: - del jobs[jid] - del workers[jid] - del timers[jid] - del jobs_indx[jid] - else: - # Something bad happened. Put the job back on the queue - # and kill this worker. - p_indx_start_end.append([indxstr, indxend, indxend - indxstr]) - del jobs[jid] - del workers[jid] - del timers[jid] - del jobs_indx[jid] - n_cpu_nodes -= 1 - if len(workers) < 1: # Rare case that we have killed all workers... - job_pstart_end = p_indx_start_end.pop(0) - workers.append( - IndexerRay.options(num_cpus=1, num_gpus=ngpupnode).remote( - jid, clparamfunction, gpu_id - ) - ) - jobs.append( - workers[0].index_chunk_ray.remote( - pats=pats[job_pstart_end[0] : job_pstart_end[1], :, :], - indexer=remote_indexer, - patstart=job_pstart_end[0], - npats=job_pstart_end[2], - ) - ) - nsubmit += 1 - timers.append(timer()) - jobs_indx.append(job_pstart_end) - n_cpu_nodes += 1 - - del jobs - del workers - del timers - # # send out the first batch - # workers = [index_chunk_ray.remote(pats=pats[p_indx_start[i]:p_indx_end[i],:,:],indexer=remote_indexer,patStart=p_indx_start[i],patEnd=p_indx_end[i]) for i - # in range(n_cpu_nodes)] - # nsubmit += n_cpu_nodes - # - # while ndone < njobs: - # wrker,busy = ray.wait(workers,num_returns=1,timeout=None) - # wrkdataout,indxstr,indxend, rate = ray.get(wrker[0]) - # dataout[indxstr:indxend] = wrkdataout - # print('Completed: ',str(indxstr),' -- ',str(indxend)) - # workers.remove(wrker[0]) - # ndone += 1 - # - # if nsubmit < njobs: - # workers.append(index_chunk_ray.remote(pats=pats[p_indx_start[nsubmit]:p_indx_end[nsubmit],:,:],indexer=remote_indexer,patStart=p_indx_start[nsubmit], - # patEnd=p_indx_end[nsubmit])) - # nsubmit += 1 + raise Exception("Error in GPU processing patterns: ", gtaskindex[jid].pstart, gtaskindex[jid].pend) + + + # except: + # gjob = gtaskindex[jid] + # print('A GPU death has occured', gjob.pstart, gjob.pend) + # del gpuworkers[jid] + # del gputask[jid] + # del gtaskindex[jid] + # gpujobs.append(gjob) + # if len(gpuworkers) == 0: + # if inputmode == "filemode": + # gputask.append( + # gpuworkers[0].findbands.remote(gjob, + # pats=None, + # indexer=remote_indexer + # ) + # ) + # else: + # gputask.append( + # gpuworkers[0].findbands.remote(gjob, + # pats=pats[gjob.pstart:gjob.pend, :, :], + # indexer=remote_indexer, + # ) + # ) + # gtaskindex.append(gjob) + # toc = timer() + if ncpudone < njobs: + donewrker, busy = ray.wait(cputask, num_returns = len(cputask), timeout=0.1) + for wrker in donewrker: + jid = cputask.index(wrker) + try: + message, (indexdata, cjob) = ray.get(wrker) + if message == 'Done': + dataout[:, cjob.pstart - patstart: cjob.pend - patstart] = indexdata + ncpudone += 1 + print(cjob.rate * n_cpu_nodes) + + if message != 'Error': + if ncpudone == njobs: + del cpuworkers[jid] + del cputask[jid] + del ctaskindex[jid] + elif len(cpujobs) > 0: + cjob = cpujobs.pop(0) + banddata = banddataout[cjob.pstart - patstart: cjob.pend - patstart, :] + bandnorm = bandnormsout[cjob.pstart - patstart: cjob.pend - patstart, :, :] + cputask[jid] = cpuworkers[jid].indexpoles.remote(cjob,banddata, bandnorm, indexer=remote_indexer) + ctaskindex[jid] = cjob + else: # there should be more to do, but waiting for work. + cputask[jid] = cpuworkers[jid].indexpoles.remote(None, None, None) + ctaskindex[jid] = None + + else: + raise Exception("Error in processing patterns: ", ctaskindex[jid].pstart,ctaskindex[jid].pend) + + except: + + cjob = ctaskindex[jid] + print('A CPU death has occured', cjob.pstart,cjob.pend) + del cpuworkers[jid] + del cputask[jid] + del ctaskindex[jid] + cpujobs.append(cjob) + if len(cpuworkers) == 0: + cpuworkers.append( # make a new Ray Actor that can call the indexer defined in shared memory. + # These actors are read/write, thus can initialize the GPU queues + CPUWorker.options(num_cpus=1, num_gpus=0).remote(i)) + cputask.append(cpuworkers[0].indexpoles.remote(None, None, None)) + ctaskindex.append(None) ray.shutdown() if return_indexer_obj: @@ -666,16 +579,8 @@ def __optimizegpuchunk__(indexer, n_cpu_nodes, gpu_id, clparam): return chunk - - - - - - - - @ray.remote(num_cpus=1, num_gpus=1) -class IndexerRay: +class GPUWorker: def __init__(self, actorid=0, clparammodule=None, gpu_id=None): # sys.path.append(path.dirname(path.dirname(__file__))) # do this to help Ray find the program files # import openclparam # do this to help Ray find the program files @@ -711,26 +616,72 @@ def __init__(self, actorid=0, clparammodule=None, gpu_id=None): except: self.openCLParams = None - def index_chunk_ray(self, pats=None, indexer=None, patstart=0, npats=-1): + def findbands(self, gpujob, pats=None, xyloc=None, PC = None, indexer=None): + if gpujob is None: + return 'Bored', (None, None, None) try: # print(type(self.openCLParams.ctx)) + gpujob._starttime() + if PC is None: + PC = indexer.PC + - tic = timer() if self.openCLParams is not None: self.openCLParams.get_queue() - dataout, banddata, indxstart, npatsout = indexer.index_pats( + pats, xyloc = indexer._getpats( patsin=pats, - patstart=patstart, - npats=npats, - clparams=self.openCLParams, - chunksize=-1, - ) + patstart=gpujob.pstart, + npats=gpujob.npat, + xyloc=xyloc) + + npoints = pats.shape[0] + + banddata, bandnorm = indexer._detectbands(pats, PC, + xyloc=xyloc, + clparams=self.openCLParams, + chunksize=-1) if self.openCLParams is not None: self.openCLParams.queue.finish() self.openCLParams.queue = None - rate = np.array([timer() - tic, npatsout]) - return dataout, banddata, indxstart, indxstart + npatsout, rate + gpujob._endtime() + return 'Done', (banddata, bandnorm, gpujob) + except: + gpujob.rate = None + return "Error", (None, None, gpujob) +@ray.remote(num_cpus=1, num_gpus=0) +class CPUWorker: + def __init__(self, actorid=0): + self.actorID = actorid + + def indexpoles(self, cpujob, banddata, bandnorm, indexer=None): + if cpujob is None: + return 'Bored', (None, None) + try: + # print(type(self.openCLParams.ctx)) + + cpujob._starttime() + + indxData = indexer._indexbandsphase(banddata, bandnorm, verbose=0) + + cpujob._endtime() + return "Done", (indxData, cpujob) except: - indxstart = patstart - indxend = patstart + npats - return None, None, indxstart, indxend, [-1, -1] + cpujob.rate = None + return "Error", (None, cpujob) +class CPUGPUJob: + def __init__(self,jobid, pstart, pend, extime=0.0): + self.jobid = jobid + self.pstart = pstart + self.pend = pend + self.npat = pend - pstart + self.starttime = 0.0 + self.endtime = 0.0 + self.extime = extime + self.rate = 0.0 + def _starttime(self): + self.starttime = timer() + def _endtime(self): + self.endtime = timer() + self.extime += self.endtime - self.starttime + self.rate = self.npat/(self.extime + 1e-12) + diff --git a/pyebsdindex/_ebsd_index_parallel_old.py b/pyebsdindex/_ebsd_index_parallel_old.py new file mode 100644 index 0000000..27d7f0e --- /dev/null +++ b/pyebsdindex/_ebsd_index_parallel_old.py @@ -0,0 +1,736 @@ +# This software was developed by employees of the US Naval Research Laboratory (NRL), an +# agency of the Federal Government. Pursuant to title 17 section 105 of the United States +# Code, works of NRL employees are not subject to copyright protection, and this software +# is in the public domain. PyEBSDIndex is an experimental system. NRL assumes no +# responsibility whatsoever for its use by other parties, and makes no guarantees, +# expressed or implied, about its quality, reliability, or any other characteristic. We +# would appreciate acknowledgment if the software is used. To the extent that NRL may hold +# copyright in countries other than the United States, you are hereby granted the +# non-exclusive irrevocable and unconditional right to print, publish, prepare derivative +# works and distribute this software, in any medium, or authorize others to do so on your +# behalf, on a royalty-free basis throughout the world. You may improve, modify, and +# create derivative works of the software or any portion of the software, and you may copy +# and distribute such modifications or works. Modified works should carry a notice stating +# that you changed the software and should note the date and nature of any such change. +# Please explicitly acknowledge the US Naval Research Laboratory as the original source. +# This software can be redistributed and/or modified freely provided that any derivative +# works bear some notice that they are derived from it, and any modified versions bear +# some notice that they have been modified. +# +# Author: David Rowenhorst; +# The US Naval Research Laboratory Date: 21 Aug 2020 + +"""Setup and handling of Hough indexing runs of EBSD patterns in +parallel. +""" + + +import os +import platform +import logging +import sys +import time +from timeit import default_timer as timer + +import numpy as np +import h5py +import ray + +from pyebsdindex import ebsd_pattern, _pyopencl_installed +from pyebsdindex._ebsd_index_single import EBSDIndexer, index_pats + +if _pyopencl_installed: + from pyebsdindex.opencl import band_detect_cl as band_detect +else: + from pyebsdindex import band_detect as band_detect + +RAYIPADDRESS = '127.0.0.1' +osplatform = platform.system() +if osplatform == 'Darwin': + RAYIPADDRESS = '0.0.0.0' # the localhost address does not work on macOS when on a VPN + +def index_pats_distributed( + patsin=None, + filename=None, + phaselist=["FCC"], + vendor=None, + PC=None, + sampleTilt=70.0, + camElev=5.3, + bandDetectPlan=None, + nRho=90, + nTheta=180, + tSigma=None, + rSigma=None, + rhoMaskFrac=0.1, + nBands=9, + patstart=0, + npats=-1, + chunksize=0, + ncpu=-1, + return_indexer_obj=False, + ebsd_indexer_obj=None, + keep_log=False, + gpu_id=None, +): + """Index EBSD patterns in parallel. + + Parameters + ---------- + patsin : numpy.ndarray, optional + EBSD patterns in an array of shape (n points, n pattern + rows, n pattern columns). If not given, these are read from + ``filename``. + filename : str, optional + Name of file with EBSD patterns. If not given, ``patsin`` must + be passed. + phaselist : list of str, optional + Options are ``"FCC"`` and ``"BCC"``. Default is ``["FCC"]``. + vendor : str, optional + Which vendor convention to use for the pattern center (PC) and + the returned orientations. The available options are ``"EDAX"`` + (default), ``"BRUKER"``, ``"OXFORD"``, ``"EMSOFT"``, + ``"KIKUCHIPY"``. + PC : list, optional + Pattern center (PCx, PCy, PCz) in the :attr:`indexer.vendor` or + ``vendor`` convention. For EDAX TSL, this is (x*, y*, z*), + defined in fractions of pattern width with respect to the lower + left corner of the detector. If not passed, this is set to (x*, + y*, z*) = (0.471659, 0.675044, 0.630139). If + ``vendor="EMSOFT"``, the PC must be four numbers, the final + number being the pixel size. + sampleTilt : float, optional + Sample tilt towards the detector in degrees. Default is 70 + degrees. Unused if ``ebsd_indexer_obj`` is passed. + camElev : float, optional + Camera elevation in degrees. Default is 5.3 degrees. Unused + if ``ebsd_indexer_obj`` is passed. + bandDetectPlan : pyebsdindex.band_detect.BandDetect, optional + Collection of parameters using in band detection. Unused if + ``ebsd_indexer_obj`` is passed. + nRho : int, optional + Default is 90 degrees. Unused if ``ebsd_indexer_obj`` is + passed. + nTheta : int, optional + Default is 180 degrees. Unused if ``ebsd_indexer_obj`` is + passed. + tSigma : float, optional + Unused if ``ebsd_indexer_obj`` is passed. + rSigma : float, optional + Unused if ``ebsd_indexer_obj`` is passed. + rhoMaskFrac : float, optional + Default is 0.1. Unused if ``ebsd_indexer_obj`` is passed. + nBands : int, optional + Number of detected bands to use in triplet voting. Default + is 9. Unused if ``ebsd_indexer_obj`` is passed. + patstart : int, optional + Starting index of the patterns to index. Default is ``0``. + npats : int, optional + Number of patterns to index. Default is ``-1``, which will + index up to the final pattern in ``patsin``. + chunksize : int, optional + If not set. we will make a guess based on the resources available. + ncpu : int, optional + Number of CPUs to use. Default value is ``-1``, meaning all + available CPUs will be used. + return_indexer_obj : bool, optional + Whether to return the EBSD indexer. Default is ``False``. + ebsd_indexer_obj : EBSDIndexer, optional + EBSD indexer. If not given, many of the above parameters must be + passed. Otherwise, these parameters are retrieved from this + indexer. + keep_log : bool, optional + Whether to keep the log. Default is ``False``. + gpu_id : int, optional + ID of GPU to use if :mod:`pyopencl` is installed. + + Returns + ------- + indxData : numpy.ndarray + Complex numpy array (or array of structured data), that is + [nphases + 1, npoints]. The data is stored for each phase used + in indexing and the ``indxData[-1]`` layer uses the best guess + on which is the most likely phase, based on the fit, and number + of bands matched for each phase. Each data entry contains the + orientation expressed as a quaternion (quat) (using the + convention of ``vendor`` or :attr:`indexer.vendor`), Pattern + Quality (pq), Confidence Metric (cm), Phase ID (phase), Fit + (fit) and Number of Bands Matched (nmatch). There are some other + metrics reported, but these are mostly for debugging purposes. + bandData : numpy.ndarray + Band identification data from the Radon transform. + indexer : EBSDIndexer + EBSD indexer, returned if ``return_indexer_obj=True``. + + Notes + ----- + Requires :mod:`ray[default]`. See the :doc:`installation guide + ` for details. + """ + pats = None + if patsin is None: + pdim = None + else: + if isinstance(patsin, ebsd_pattern.EBSDPatterns): + pats = patsin.patterns + if type(patsin) is np.ndarray: + pats = patsin + if isinstance(patsin, h5py.Dataset): + shp = patsin.shape + if len(shp) == 3: + pats = patsin + if len(shp) == 2: # just read off disk now. + pats = patsin[()] + pats = pats.reshape(1, shp[0], shp[1]) + + if pats is None: + print("Unrecognized input data type") + return + pdim = pats.shape[-2:] + + # run a test flight to make sure all parameters are set properly before being sent off to the cluster + if ebsd_indexer_obj is None: + indexer = EBSDIndexer( + filename=filename, + phaselist=phaselist, + vendor=vendor, + PC=PC, + sampleTilt=sampleTilt, + camElev=camElev, + bandDetectPlan=bandDetectPlan, + nRho=nRho, + nTheta=nTheta, + tSigma=tSigma, + rSigma=rSigma, + rhoMaskFrac=rhoMaskFrac, + nBands=nBands, + patDim=pdim, + gpu_id=gpu_id, + ) + else: + indexer = ebsd_indexer_obj + + if filename is not None: + indexer.update_file(filename) + else: + indexer.update_file(patDim=pats.shape[-2:]) + + # Differentiate between getting a file to index or an array. + # Need to index one pattern to make sure the indexer object is fully + # initiated before placing in shared memory store. + mode = "memorymode" + if pats is None: + mode = "filemode" + temp, temp2, indexer = index_pats( + npats=1, return_indexer_obj=True, ebsd_indexer_obj=indexer + ) + + if mode == "filemode": + npatsTotal = indexer.fID.nPatterns + else: + pshape = pats.shape + if len(pshape) == 2: + npatsTotal = 1 + pats = pats.reshape([1, pshape[0], pshape[1]]) + else: + npatsTotal = pshape[0] + temp, temp2, indexer = index_pats( + pats[0, :, :], + npats=1, + return_indexer_obj=True, + ebsd_indexer_obj=indexer, + ) + + if patstart < 0: + patstart = npatsTotal - patstart + if npats <= 0: + npats = npatsTotal - patstart + + # Now set up the cluster with the indexer + n_cpu_nodes = int(os.cpu_count()) + # int(sum([ r['Resources']['CPU'] for r in ray.nodes()])) + if ncpu != -1: + n_cpu_nodes = int(ncpu) + + ngpu = None + if gpu_id is not None: + ngpu = np.atleast_1d(gpu_id).shape[0] + + try: + clparam = band_detect.getopenclparam() + if clparam is None: + ngpu = 0 + ngpupnode = 0 + else: + if ngpu is None: + ngpu = len(clparam.gpu) + ngpupnode = ngpu / n_cpu_nodes + except: + ngpu = 0 + ngpupnode = 0 + + if chunksize <= 0: + chunksize = __optimizegpuchunk__(indexer, n_cpu_nodes, gpu_id, clparam) + + + + ray.shutdown() + + print("num cpu/gpu, and number of patterns per iteration:", n_cpu_nodes, ngpu, chunksize) + # ray.init(num_cpus=n_cpu_nodes,num_gpus=ngpu,_system_config={"maximum_gcs_destroyed_actor_cached_count": n_cpu_nodes}) + # Need to append path for installs from source ... otherwise the ray + # workers do not know where to find the PyEBSDIndex module. + ray.init( + num_cpus=n_cpu_nodes, + num_gpus=ngpu, + _node_ip_address=RAYIPADDRESS, #"0.0.0.0", + runtime_env={"env_vars": {"PYTHONPATH": os.path.dirname(os.path.dirname(__file__))}}, + logging_level=logging.WARNING, + ) # Supress INFO messages from ray. + + # Place indexer obj in shared memory store so all workers can use it - this is read only. + remote_indexer = ray.put(indexer) + # Get the function that will collect opencl parameters - if opencl + # is not installed, this is None, and the program will automatically + # fall back to CPU only calculation. + clparamfunction = band_detect.getopenclparam + # Set up the jobs + njobs = (np.ceil(npats / chunksize)).astype(np.compat.long) + # p_indx_start = [i*chunksize+patStart for i in range(njobs)] + # p_indx_end = [(i+1)*chunksize+patStart for i in range(njobs)] + # p_indx_end[-1] = npats+patStart + p_indx_start_end = [ + [i * chunksize + patstart, (i + 1) * chunksize + patstart, chunksize] + for i in range(njobs) + ] + p_indx_start_end[-1][1] = npats + patstart + p_indx_start_end[-1][2] = p_indx_start_end[-1][1] - p_indx_start_end[-1][0] + + if njobs < n_cpu_nodes: + n_cpu_nodes = njobs + + nPhases = len(indexer.phaseLib) + dataout = np.zeros((nPhases + 1, npats), dtype=indexer.dataTemplate) + banddataout = np.zeros( + (npats, indexer.bandDetectPlan.nBands), dtype=indexer.bandDetectPlan.dataType + ) + ndone = 0 + nsubmit = 0 + tic0 = timer() + npatsdone = 0.0 + + if keep_log is True: + newline = "\n" + else: + newline = "\r" + if mode == "filemode": + # Send out the first batch + workers = [] + jobs = [] + timers = [] + jobs_indx = [] + chunkave = 0.0 + for i in range(n_cpu_nodes): + job_pstart_end = p_indx_start_end.pop(0) + workers.append( # make a new Ray Actor that can call the indexer defined in shared memory. + # These actors are read/write, thus can initialize the GPU queues + IndexerRay.options(num_cpus=1, num_gpus=ngpupnode).remote( + i, clparamfunction, gpu_id=gpu_id + ) + ) + jobs.append( + workers[i].index_chunk_ray.remote( + pats=None, + indexer=remote_indexer, + patstart=job_pstart_end[0], + npats=job_pstart_end[2], + ) + ) + nsubmit += 1 + timers.append(timer()) + #time.sleep(0.01) + jobs_indx.append(job_pstart_end[:]) + + while ndone < njobs: + # toc = timer() + wrker, busy = ray.wait(jobs, num_returns=1, timeout=60.0) + + # print("waittime: ",timer() - toc) + if len(wrker) > 0: # trying to catch a hung worker. Rare, but it happens + jid = jobs.index(wrker[0]) + else: + print('hang with ', ndone, 'out of ', njobs) + jid = jobs.index(busy[0]) + wrker.append(busy[0]) + ray.kill(workers[jid]) + try: + wrkdataout, wrkbanddata, indxstr, indxend, rate = ray.get(wrker[0]) + except: + # print('a death has occured') + indxstr = jobs_indx[jid][0] + indxend = jobs_indx[jid][1] + rate = [-1, -1] + if rate[0] >= 0: # Job finished as expected + + ticp = timers[jid] + dataout[:, indxstr - patstart : indxend - patstart] = wrkdataout + banddataout[indxstr - patstart : indxend - patstart, :] = wrkbanddata + npatsdone += rate[1] + ndone += 1 + + ratetemp = n_cpu_nodes * (rate[1]) / (timer() - ticp) + chunkave += ratetemp + totalave = npatsdone / (timer() - tic0) + # print('Completed: ',str(indxstr),' -- ',str(indxend), ' ', npatsdone/(timer()-tic) ) + + toc0 = timer() - tic0 + if keep_log is False: + print("", end="\r") + time.sleep(0.00001) + print( + "Completed: ", + str(indxstr), + " -- ", + str(indxend), + " PPS:", + "{:.0f}".format(ratetemp) + + ";" + + "{:.0f}".format(chunkave / ndone) + + ";" + + "{:.0f}".format(totalave), + " ", + "{:.0f}".format((ndone / njobs) * 100) + "%", + "{:.0f};".format(toc0) + + "{:.0f}".format((njobs - ndone) / ndone * toc0) + + " running;remaining(s)", + end=newline, + ) + + if len(p_indx_start_end) > 0: + job_pstart_end = p_indx_start_end.pop(0) + jobs[jid] = workers[jid].index_chunk_ray.remote( + pats=None, + indexer=remote_indexer, + patstart=job_pstart_end[0], + npats=job_pstart_end[2], + ) + nsubmit += 1 + timers[jid] = timer() + jobs_indx[jid] = job_pstart_end[:] + else: + del jobs[jid] + del workers[jid] + del timers[jid] + del jobs_indx[jid] + else: + # Something bad happened. Put the job back on the queue + # and kill this worker. + p_indx_start_end.append([indxstr, indxend, indxend - indxstr]) + del jobs[jid] + del workers[jid] + del timers[jid] + del jobs_indx[jid] + n_cpu_nodes -= 1 + if len(workers) < 1: # Rare case that we have killed all workers... + job_pstart_end = p_indx_start_end.pop(0) + workers.append( + IndexerRay.options(num_cpus=1, num_gpus=ngpupnode).remote( + jid, clparamfunction, gpu_id + ) + ) + jobs.append( + workers[0].index_chunk_ray.remote( + pats=None, + indexer=remote_indexer, + patstart=job_pstart_end[0], + npats=job_pstart_end[2], + ) + ) + nsubmit += 1 + timers.append(timer()) + time.sleep(0.01) + jobs_indx.append(job_pstart_end[:]) + n_cpu_nodes += 1 + + if mode == "memorymode": + workers = [] + jobs = [] + timers = [] + jobs_indx = [] + chunkave = 0.0 + for i in range(n_cpu_nodes): + job_pstart_end = p_indx_start_end.pop(0) + workers.append( + IndexerRay.options(num_cpus=1, num_gpus=ngpupnode).remote( + i, clparamfunction, gpu_id + ) + ) + jobs.append( + workers[i].index_chunk_ray.remote( + pats=pats[job_pstart_end[0] : job_pstart_end[1], :, :], + indexer=remote_indexer, + patstart=job_pstart_end[0], + npats=job_pstart_end[2], + ) + ) + nsubmit += 1 + timers.append(timer()) + jobs_indx.append(job_pstart_end) + time.sleep(0.01) + + # workers = [index_chunk.remote(pats = None, indexer = remote_indexer, patStart = p_indx_start[i], patEnd = p_indx_end[i]) for i in range(n_cpu_nodes)] + # nsubmit += n_cpu_nodes + + while ndone < njobs: + # toc = timer() + wrker, busy = ray.wait(jobs, num_returns=1, timeout=None) + jid = jobs.index(wrker[0]) + # print("waittime: ",timer() - toc) + if len(wrker) > 0: + jid = jobs.index(wrker[0]) + else: + print('hang with ', ndone, 'out of ', njobs) + jid = jobs.index(busy[0]) + wrker.append(busy[0]) + ray.kill(workers[jid]) + try: + wrkdataout, wrkbanddata, indxstr, indxend, rate = ray.get(wrker[0]) + except: + indxstr = jobs_indx[jid][0] + indxend = jobs_indx[jid][1] + rate = [-1, -1] + if rate[0] >= 0: + ticp = timers[jid] + dataout[:, indxstr - patstart : indxend - patstart] = wrkdataout + banddataout[indxstr - patstart : indxend - patstart, :] = wrkbanddata + npatsdone += rate[1] + ratetemp = n_cpu_nodes * (rate[1]) / (timer() - ticp) + chunkave += ratetemp + totalave = npatsdone / (timer() - tic0) + # print('Completed: ',str(indxstr),' -- ',str(indxend), ' ', npatsdone/(timer()-tic) ) + ndone += 1 + toc0 = timer() - tic0 + if keep_log is False: + print("", end="\r") + time.sleep(0.0001) + print( + "Completed: ", + str(indxstr), + " -- ", + str(indxend), + " PPS:", + "{:.0f}".format(ratetemp) + + ";" + + "{:.0f}".format(chunkave / ndone) + + ";" + + "{:.0f}".format(totalave), + " ", + "{:.0f}".format((ndone / njobs) * 100) + "%", + "{:.0f};".format(toc0) + + "{:.0f}".format((njobs - ndone) / ndone * toc0) + + " running;remaining(s)", + end=newline, + ) + + if len(p_indx_start_end) > 0: + job_pstart_end = p_indx_start_end.pop(0) + jobs[jid] = workers[jid].index_chunk_ray.remote( + pats=pats[job_pstart_end[0] : job_pstart_end[1], :, :], + indexer=remote_indexer, + patstart=job_pstart_end[0], + npats=job_pstart_end[2], + ) + nsubmit += 1 + timers[jid] = timer() + jobs_indx[jid] = job_pstart_end + else: + del jobs[jid] + del workers[jid] + del timers[jid] + del jobs_indx[jid] + else: + # Something bad happened. Put the job back on the queue + # and kill this worker. + p_indx_start_end.append([indxstr, indxend, indxend - indxstr]) + del jobs[jid] + del workers[jid] + del timers[jid] + del jobs_indx[jid] + n_cpu_nodes -= 1 + if len(workers) < 1: # Rare case that we have killed all workers... + job_pstart_end = p_indx_start_end.pop(0) + workers.append( + IndexerRay.options(num_cpus=1, num_gpus=ngpupnode).remote( + jid, clparamfunction, gpu_id + ) + ) + jobs.append( + workers[0].index_chunk_ray.remote( + pats=pats[job_pstart_end[0] : job_pstart_end[1], :, :], + indexer=remote_indexer, + patstart=job_pstart_end[0], + npats=job_pstart_end[2], + ) + ) + nsubmit += 1 + timers.append(timer()) + jobs_indx.append(job_pstart_end) + n_cpu_nodes += 1 + + del jobs + del workers + del timers + # # send out the first batch + # workers = [index_chunk_ray.remote(pats=pats[p_indx_start[i]:p_indx_end[i],:,:],indexer=remote_indexer,patStart=p_indx_start[i],patEnd=p_indx_end[i]) for i + # in range(n_cpu_nodes)] + # nsubmit += n_cpu_nodes + # + # while ndone < njobs: + # wrker,busy = ray.wait(workers,num_returns=1,timeout=None) + # wrkdataout,indxstr,indxend, rate = ray.get(wrker[0]) + # dataout[indxstr:indxend] = wrkdataout + # print('Completed: ',str(indxstr),' -- ',str(indxend)) + # workers.remove(wrker[0]) + # ndone += 1 + # + # if nsubmit < njobs: + # workers.append(index_chunk_ray.remote(pats=pats[p_indx_start[nsubmit]:p_indx_end[nsubmit],:,:],indexer=remote_indexer,patStart=p_indx_start[nsubmit], + # patEnd=p_indx_end[nsubmit])) + # nsubmit += 1 + + ray.shutdown() + if return_indexer_obj: + return dataout, banddataout, indexer + else: + return dataout, banddataout + +def __optimizegpuchunk__(indexer, n_cpu_nodes, gpu_id, clparam): + + + gpulist = [] + # test for GPU presence + if clparam is None: + return 1000 + + if clparam.ngpu == 0: + return 1000 + + if gpu_id is None: + for g in clparam.gpu: + gpulist.append(g) + else: + temp = np.atleast_1d(gpu_id) + for g in temp: + gpulist.append(clparam.gpu[g]) + ngpu = len(gpulist) + + if ngpu == 0: + return 1000 + + gmem = 1e99 + for g in gpulist: + if g.global_mem_size < gmem: + gmem = g.global_mem_size + #print('Global Mem:', gmem) + ncpu_per_gpu = max(1, np.ceil(n_cpu_nodes/ngpu)) + #print('Ncpu/gpu:', ncpu_per_gpu) + patdim = indexer.bandDetectPlan.patDim + rdndim = np.array([indexer.bandDetectPlan.nTheta+2*indexer.bandDetectPlan.padding[1], + indexer.bandDetectPlan.nRho+2*indexer.bandDetectPlan.padding[0]]) + memperpat = 4.0*float(patdim[0] * patdim[1] + 9.0 * rdndim[0] * rdndim[1])# rough estimate + + #print('Mem/pat:', memperpat) + chunkguess = (float(gmem)/float(ncpu_per_gpu)) / memperpat + + #print('chunkguess:', chunkguess) + safetyval = 0.5 + chunkguess *= safetyval + if clparam.gpu[0].vendor == 'AMD': # 'AMD implmentation of opencl does better with clearing memory' + # # this is a cheat, because 1/2 the time the GPU will be idle while the CPU is compputing. + chunkguess *= 3.0 + + + + #print('cheatguess:', chunkguess) + chunk = int(max(2, np.floor(chunkguess/16))*16) # ideally should be a multiple of 16 + #print('chunk:', chunk) + #check for powers of two - for some reason it runs very slow with powers of two. + twocheck = np.log2(float(chunk)) + if np.abs((twocheck) - np.round(twocheck)) < 1e-6: + chunk += 16 + + # finally - I am unsure how to check for integrated graphics that report system memory, so I am going + # throw an arbitrary cap on this: + chunk = min(2016, chunk) + + return chunk + + + + + + + + + + +@ray.remote(num_cpus=1, num_gpus=1) +class IndexerRay: + def __init__(self, actorid=0, clparammodule=None, gpu_id=None): + # sys.path.append(path.dirname(path.dirname(__file__))) # do this to help Ray find the program files + # import openclparam # do this to help Ray find the program files + # device, context, queue, program, mf + # self.dataout = None + # self.indxstart = None + # self.indxend = None + # self.rate = None + self.actorID = actorid + self.openCLParams = None + self.useGPU = False + if clparammodule is not None: + try: + if ( + sys.platform != "darwin" + ): # linux with NVIDIA (unsure if it is the os or GPU type) is slow to make a + self.openCLParams = clparammodule() + else: # MacOS handles GPU memory conflicts much better when the context is destroyed between each + # run, and has very low overhead for making the context. + # pass + self.openCLParams = clparammodule() + # self.openCLParams.gpu_id = 0 + # self.openCLParams.gpu_id = 1 + self.openCLParams.gpu_id = self.actorID % self.openCLParams.ngpu + if gpu_id is None: + gpu_id = np.arange(self.openCLParams.ngpu) + gpu_list = np.atleast_1d(gpu_id) + ngpu = gpu_list.shape[0] + self.openCLParams.gpu_id = gpu_list[self.actorID % ngpu] + self.openCLParams.get_context() + #self.openCLParams.get_queue() + self.useGPU = True + except: + self.openCLParams = None + + def index_chunk_ray(self, pats=None, indexer=None, patstart=0, npats=-1): + try: + # print(type(self.openCLParams.ctx)) + + tic = timer() + if self.openCLParams is not None: + self.openCLParams.get_queue() + dataout, banddata, indxstart, npatsout = indexer.index_pats( + patsin=pats, + patstart=patstart, + npats=npats, + clparams=self.openCLParams, + chunksize=-1, + ) + if self.openCLParams is not None: + self.openCLParams.queue.finish() + self.openCLParams.queue = None + rate = np.array([timer() - tic, npatsout]) + return dataout, banddata, indxstart, indxstart + npatsout, rate + except: + indxstart = patstart + indxend = patstart + npats + return None, None, indxstart, indxend, [-1, -1] diff --git a/pyebsdindex/_ebsd_index_single.py b/pyebsdindex/_ebsd_index_single.py index efc68c2..eff7950 100644 --- a/pyebsdindex/_ebsd_index_single.py +++ b/pyebsdindex/_ebsd_index_single.py @@ -395,7 +395,7 @@ def index_pats( patsin=None, patstart=0, npats=-1, - xyloc = None, + xyloc=None, clparams=None, PC=None, verbose=0, @@ -452,6 +452,27 @@ def index_pats( Number of patterns indexed. This and `patstart` are useful for the distributed indexing procedures. """ + + pats, xyloc = self._getpats(patsin=patsin, patstart=patstart, npats=npats, xyloc=xyloc) + # just a check that the band_detect is ready for this size pattern. + if self.bandDetectPlan.patDim is None: + self.bandDetectPlan.band_detect_setup(patterns=pats) + + npoints = pats.shape[0] + if npats == -1: + npats = npoints + + banddata, bandnorm = self._detectbands(pats, PC, xyloc=xyloc, clparams=clparams, verbose=verbose, chunksize=chunksize) + tic = timer() + + indxData = self._indexbandsphase(banddata, bandnorm, verbose=verbose) + + if verbose > 0: + print("Band Vote Time: ", timer() - tic) + + return indxData, banddata, patstart, npats + + def _getpats(self, patsin=None, patstart=0, npats=-1, xyloc=None): if patsin is None: pats, xylocin = self.fID.read_data( returnArrayOnly=True, @@ -473,14 +494,8 @@ def index_pats( else: if np.all((np.array(pshape[1:3]) - self.bandDetectPlan.patDim) == 0): self.bandDetectPlan.band_detect_setup(patDim=pshape[1:3]) - - if self.bandDetectPlan.patDim is None: - self.bandDetectPlan.band_detect_setup(patterns=pats) - - npoints = pats.shape[0] - if npats == -1: - npats = npoints - + return pats, xyloc + def _detectbands(self, pats, PC, xyloc=None, clparams=None, verbose=0, chunksize=528 ): banddata = self.bandDetectPlan.find_bands( pats, clparams=clparams, verbose=verbose, chunksize=chunksize ) @@ -492,31 +507,8 @@ def index_pats( bandnorm = self.bandDetectPlan.radonPlan.radon2pole( banddata, PC=PC_0, vendor=self.vendor ) + return banddata, bandnorm - tic = timer() - - indxData = self._indexbandsphase(banddata, bandnorm, verbose=verbose) - - if verbose > 0: - print("Band Vote Time: ", timer() - tic) - - return indxData, banddata, patstart, npats - - def _detector2refframe(self): - ven = str.upper(self.vendor) - if ven in ["EDAX", "EMSOFT", "KIKUCHIPY"]: - q0 = np.array([np.sqrt(2.0) * 0.5, 0.0, 0.0, -1.0 * np.sqrt(2.0) * 0.5]) - tiltang = -1.0 * (90.0 - self.sampleTilt + self.camElev) / RADEG - q1 = np.array([np.cos(tiltang * 0.5), np.sin(tiltang * 0.5), 0.0, 0.0]) - quatref2detect = rotlib.quat_multiply(q1, q0) - elif ven in ["OXFORD", "BRUKER"]: - tiltang = -1.0 * (90.0 - self.sampleTilt + self.camElev) / RADEG - q1 = np.array([np.cos(tiltang * 0.5), np.sin(tiltang * 0.5), 0.0, 0.0]) - quatref2detect = q1 - else: - raise ValueError("`self.vendor` unknown") - - return quatref2detect def _indexbandsphase(self, banddata, bandnorm, verbose = 0): shpBandDat = banddata.shape @@ -594,6 +586,21 @@ def _indexbandsphase(self, banddata, bandnorm, verbose = 0): ) return indxData + def _detector2refframe(self): + ven = str.upper(self.vendor) + if ven in ["EDAX", "EMSOFT", "KIKUCHIPY"]: + q0 = np.array([np.sqrt(2.0) * 0.5, 0.0, 0.0, -1.0 * np.sqrt(2.0) * 0.5]) + tiltang = -1.0 * (90.0 - self.sampleTilt + self.camElev) / RADEG + q1 = np.array([np.cos(tiltang * 0.5), np.sin(tiltang * 0.5), 0.0, 0.0]) + quatref2detect = rotlib.quat_multiply(q1, q0) + elif ven in ["OXFORD", "BRUKER"]: + tiltang = -1.0 * (90.0 - self.sampleTilt + self.camElev) / RADEG + q1 = np.array([np.cos(tiltang * 0.5), np.sin(tiltang * 0.5), 0.0, 0.0]) + quatref2detect = q1 + else: + raise ValueError("`self.vendor` unknown") + + return quatref2detect # def pcCorrect(self, xy=[[0.0, 0.0]]): # # TODO: At somepoint we will put some methods here for # # correcting the PC depending on the location within the scan. diff --git a/pyebsdindex/ebsd_index.py b/pyebsdindex/ebsd_index.py index e571a96..5104e8a 100644 --- a/pyebsdindex/ebsd_index.py +++ b/pyebsdindex/ebsd_index.py @@ -26,12 +26,12 @@ from pyebsdindex._ebsd_index_single import EBSDIndexer, index_pats if _ray_installed: - from pyebsdindex._ebsd_index_parallel import index_pats_distributed, IndexerRay + from pyebsdindex._ebsd_index_parallel import index_pats_distributed#, IndexerRay __all__ = [ "EBSDIndexer", - "IndexerRay", + #"IndexerRay", "index_pats", "index_pats_distributed", ] From 7601521182b4c294292f4e548f39705fd25df3ec Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Tue, 2 May 2023 07:55:49 -0400 Subject: [PATCH 098/177] Check Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_parallel.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index a81f3e0..59a3b19 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -403,7 +403,7 @@ def index_pats_distributed( if ngpudone < njobs: # check if gpu is done donewrker, busy = ray.wait(gputask,num_returns = len(gputask), timeout=0.1) #if len(wrker) > 0: # trying to catch a hung worker. Rare, but it happens - print(len(donewrker)) + #print(len(donewrker)) #else: #print('hung gpu process') #jid = gputask.index(busy[0]) @@ -476,7 +476,7 @@ def index_pats_distributed( if message == 'Done': dataout[:, cjob.pstart - patstart: cjob.pend - patstart] = indexdata ncpudone += 1 - print(cjob.rate * n_cpu_nodes) + #print(cjob.rate * n_cpu_nodes) if message != 'Error': if ncpudone == njobs: From f8f9742b9d546ac680e76967e296927023aed65c Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Wed, 3 May 2023 07:42:19 -0400 Subject: [PATCH 099/177] Checkpoint Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_parallel.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index 59a3b19..3f46607 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -366,7 +366,7 @@ def index_pats_distributed( gpuworkers.append( # make a new Ray Actor that can call the indexer defined in shared memory. # These actors are read/write, thus can initialize the GPU queues - GPUWorker.options(num_cpus=1, num_gpus=ngpu_per_wrker).remote( + GPUWorker.options(num_cpus=ncpu_per_wrker, num_gpus=ngpu_per_wrker).remote( i, clparamfunction, gpu_id=gpu_id ) ) @@ -387,13 +387,13 @@ def index_pats_distributed( ) gtaskindex.append(gjob) ngpusubmit += 1 - time.sleep(0.1) + # initiate the the CPU workers. print(len(gpuworkers),len(gputask)) for i in range(ncpuwrker): cpuworkers.append( # make a new Ray Actor that can call the indexer defined in shared memory. # These actors are read/write, thus can initialize the GPU queues - CPUWorker.options(num_cpus=1, num_gpus=0).remote(i)) + CPUWorker.options(num_cpus=1-1e-6, num_gpus=0).remote(i)) cputask.append(cpuworkers[i].indexpoles.remote(None, None, None,indexer=remote_indexer)) ctaskindex.append(None) print(len(cpuworkers)) From 619fe6de9d81356835cfb6e609310cab7206cef5 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Wed, 3 May 2023 11:09:37 -0400 Subject: [PATCH 100/177] Cleaned up allocation of resources for workers. Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_parallel.py | 187 +++++++++++++++++----------- 1 file changed, 116 insertions(+), 71 deletions(-) diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index 3f46607..8a24c54 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -167,6 +167,7 @@ def index_pats_distributed( Requires :mod:`ray[default]`. See the :doc:`installation guide ` for details. """ + starttime = timer() pats = None if patsin is None: pdim = None @@ -270,34 +271,45 @@ def index_pats_distributed( ngpu = 0 ngpupnode = 0 - n_gpu_nodes = 1.0 + usegpu = True if usegpu: - if (n_cpu_nodes + n_gpu_nodes) > int(os.cpu_count()): - n_cpu_nodes -= 1 - if n_cpu_nodes < 1: - n_cpu_nodes = 0.5 - 1e-6 - n_gpu_nodes = 0.5 - 1e-6 - ngpuwrker = 8 * ngpu - ngpu_per_wrker = n_gpu_nodes/ngpuwrker - 1e-6 - ncpu_per_wrker = n_gpu_nodes/ngpuwrker - 1e-6 + gpupro = 12 # number of processes per gpu that will serve data to the gpu + if n_cpu_nodes - ngpu < 8: + ngpupro = 8 + if n_cpu_nodes - ngpu < 2: + ngpupro = 2 + + n_cpu_per_gpu = max(min(1.0, n_cpu_nodes-ngpu), 0.5/ngpu) + + ngpuwrker = gpupro * ngpu + + ngpu_per_wrker = 1.0/ngpuwrker - 1.0e-6 # fraction of a GPU to give to each worker (band finding worker) + ncpugpu_per_wrker = n_cpu_per_gpu/ngpuwrker - 1.0e-6 # fraction of a cpu to allocate to each gpu worker + + # amount of cpu to allocate to each cpu worker (indexing worker) + ncpucpu_per_worker = (n_cpu_nodes - ncpugpu_per_wrker * ngpuwrker)/n_cpu_nodes + + if chunksize <= 0: chunksize = __optimizegpuchunk__(indexer, ngpuwrker, gpu_id, clparam) else: # no gpus detected. - ngpuwrker = 0 + ngpu_per_wrker = 0 usegpu = False - n_gpu_nodes = 0 + ngpupros = n_cpu_nodes + ncpucpu_per_worker = 0.5 - 1.0e-6 + ncpugpu_per_wrker = 0.5 - 1.0e-6 if chunksize <= 0: chunksize = 1000 ray.shutdown() - print("num cpu/gpu, and number of patterns per iteration:", n_cpu_nodes+n_gpu_nodes, ngpu, chunksize) + print("num cpu/gpu, and number of patterns per iteration:", n_cpu_nodes, ngpu, chunksize) # ray.init(num_cpus=n_cpu_nodes,num_gpus=ngpu,_system_config={"maximum_gcs_destroyed_actor_cached_count": n_cpu_nodes}) # Need to append path for installs from source ... otherwise the ray # workers do not know where to find the PyEBSDIndex module. ray.init( - num_cpus=int(np.round(n_cpu_nodes+n_gpu_nodes)), + num_cpus=int(np.round(n_cpu_nodes)), num_gpus=ngpu, _node_ip_address=RAYIPADDRESS, #"0.0.0.0", runtime_env={"env_vars": {"PYTHONPATH": os.path.dirname(os.path.dirname(__file__))}}, @@ -349,7 +361,10 @@ def index_pats_distributed( ncpupatsdone = 0.0 if keep_log is True: - newline = "\n" + if osplatform != 'Windows': + newline = "\n" + else: + newline = "\r\n" else: newline = "\r" @@ -360,13 +375,17 @@ def index_pats_distributed( cpuworkers = [] cputask = [] ctaskindex = [] + npatdone = 0.0 chunkave = 0.0 + #print(ngpuwrker, ncpugpu_per_wrker, ngpu_per_wrker) + #print(ncpuwrker, ncpucpu_per_worker) + for i in range(ngpuwrker): gpuworkers.append( # make a new Ray Actor that can call the indexer defined in shared memory. # These actors are read/write, thus can initialize the GPU queues - GPUWorker.options(num_cpus=ncpu_per_wrker, num_gpus=ngpu_per_wrker).remote( + GPUWorker.options(num_cpus=ncpugpu_per_wrker, num_gpus=ngpu_per_wrker).remote( i, clparamfunction, gpu_id=gpu_id ) ) @@ -388,15 +407,16 @@ def index_pats_distributed( gtaskindex.append(gjob) ngpusubmit += 1 - # initiate the the CPU workers. - print(len(gpuworkers),len(gputask)) + # initiate the CPU workers. + #print(len(gpuworkers), len(gputask)) for i in range(ncpuwrker): cpuworkers.append( # make a new Ray Actor that can call the indexer defined in shared memory. # These actors are read/write, thus can initialize the GPU queues - CPUWorker.options(num_cpus=1-1e-6, num_gpus=0).remote(i)) + CPUWorker.options(num_cpus=ncpucpu_per_worker, num_gpus=0).remote(i)) + #CPUWorker.options(num_cpus=ncpucpu_per_worker, num_gpus=0).remote(i)) cputask.append(cpuworkers[i].indexpoles.remote(None, None, None,indexer=remote_indexer)) ctaskindex.append(None) - print(len(cpuworkers)) + #print(len(cpuworkers)) while ncpudone < njobs: @@ -411,61 +431,61 @@ def index_pats_distributed( #ray.kill(gputask[jid]) for wrker in donewrker: jid = gputask.index(wrker) - #try: - message, (banddata, bandnorm, gjob) = ray.get(wrker) + try: + message, (banddata, bandnorm, gjob) = ray.get(wrker) - if message == 'Done': - banddataout[gjob.pstart - patstart: gjob.pend - patstart, :] = banddata - bandnormsout[gjob.pstart - patstart: gjob.pend - patstart, :,:] = bandnorm + if message == 'Done': + banddataout[gjob.pstart - patstart: gjob.pend - patstart, :] = banddata + bandnormsout[gjob.pstart - patstart: gjob.pend - patstart, :,:] = bandnorm + + cpujobs.append(CPUGPUJob(gjob.jobid, gjob.pstart, gjob.pend, extime=gjob.extime)) + ngpudone += 1 + + if len(gpujobs) > 0: # still more gpu work to do + gjob = gpujobs.pop(0) + if inputmode == "filemode": + gputask[jid] = gpuworkers[jid].findbands.remote(gjob, + pats=None, + indexer=remote_indexer + ) + else: + gputask[jid] = gpuworkers[jid].findbands.remote(gjob, + pats=pats[gjob.pstart:gjob.pend, :, :], + indexer=remote_indexer, + ) + gtaskindex[jid] = gjob + ngpusubmit += 1 + else: # no more gpu tasks to submit + del gpuworkers[jid] + del gputask[jid] + del gtaskindex[jid] + else: + raise Exception("Error in GPU processing patterns: ", gtaskindex[jid].pstart, gtaskindex[jid].pend) - cpujobs.append(CPUGPUJob(gjob.jobid, gjob.pstart, gjob.pend, extime=gjob.extime)) - ngpudone += 1 - if len(gpujobs) > 0: # still more gpu work to do - gjob = gpujobs.pop(0) + except: + gjob = gtaskindex[jid] + print('A GPU death has occured', gjob.pstart, gjob.pend) + del gpuworkers[jid] + del gputask[jid] + del gtaskindex[jid] + gpujobs.append(gjob) + if len(gpuworkers) == 0: if inputmode == "filemode": - gputask[jid] = gpuworkers[jid].findbands.remote(gjob, - pats=None, - indexer=remote_indexer + gputask.append( + gpuworkers[0].findbands.remote(gjob, + pats=None, + indexer=remote_indexer + ) ) else: - gputask[jid] = gpuworkers[jid].findbands.remote(gjob, - pats=pats[gjob.pstart:gjob.pend, :, :], - indexer=remote_indexer, - ) - gtaskindex[jid] = gjob - ngpusubmit += 1 - else: # no more gpu tasks to submit - del gpuworkers[jid] - del gputask[jid] - del gtaskindex[jid] - else: - raise Exception("Error in GPU processing patterns: ", gtaskindex[jid].pstart, gtaskindex[jid].pend) - - - # except: - # gjob = gtaskindex[jid] - # print('A GPU death has occured', gjob.pstart, gjob.pend) - # del gpuworkers[jid] - # del gputask[jid] - # del gtaskindex[jid] - # gpujobs.append(gjob) - # if len(gpuworkers) == 0: - # if inputmode == "filemode": - # gputask.append( - # gpuworkers[0].findbands.remote(gjob, - # pats=None, - # indexer=remote_indexer - # ) - # ) - # else: - # gputask.append( - # gpuworkers[0].findbands.remote(gjob, - # pats=pats[gjob.pstart:gjob.pend, :, :], - # indexer=remote_indexer, - # ) - # ) - # gtaskindex.append(gjob) + gputask.append( + gpuworkers[0].findbands.remote(gjob, + pats=pats[gjob.pstart:gjob.pend, :, :], + indexer=remote_indexer, + ) + ) + gtaskindex.append(gjob) # toc = timer() if ncpudone < njobs: donewrker, busy = ray.wait(cputask, num_returns = len(cputask), timeout=0.1) @@ -476,8 +496,29 @@ def index_pats_distributed( if message == 'Done': dataout[:, cjob.pstart - patstart: cjob.pend - patstart] = indexdata ncpudone += 1 + chunkave += cjob.rate + npatdone += cjob.npat + currenttime = timer() - starttime #print(cjob.rate * n_cpu_nodes) - + print( + "Completed: ", + str(cjob.pstart), + " -- ", + str(cjob.pend), + " PPS:", + "{:.0f}".format(cjob.rate*ncpuwrker) + + ";" + + "{:.0f}".format(chunkave / ncpudone * ncpuwrker) + + ";" + + "{:.0f}".format(npatdone/currenttime), + " ", + "{:.0f}".format((ncpudone / njobs) * 100) + "%", + "{:.0f};".format(currenttime) + + "{:.0f}".format((njobs - ncpudone) / ncpudone * currenttime) + + " running;remaining(s)", + end=newline, + ) + time.sleep(0.001) if message != 'Error': if ncpudone == njobs: del cpuworkers[jid] @@ -489,6 +530,8 @@ def index_pats_distributed( bandnorm = bandnormsout[cjob.pstart - patstart: cjob.pend - patstart, :, :] cputask[jid] = cpuworkers[jid].indexpoles.remote(cjob,banddata, bandnorm, indexer=remote_indexer) ctaskindex[jid] = cjob + + else: # there should be more to do, but waiting for work. cputask[jid] = cpuworkers[jid].indexpoles.remote(None, None, None) ctaskindex[jid] = None @@ -496,8 +539,9 @@ def index_pats_distributed( else: raise Exception("Error in processing patterns: ", ctaskindex[jid].pstart,ctaskindex[jid].pend) - except: + except Exception as e: + print(e) cjob = ctaskindex[jid] print('A CPU death has occured', cjob.pstart,cjob.pend) del cpuworkers[jid] @@ -665,7 +709,8 @@ def indexpoles(self, cpujob, banddata, bandnorm, indexer=None): cpujob._endtime() return "Done", (indxData, cpujob) - except: + except Exception as e: + print(e) cpujob.rate = None return "Error", (None, cpujob) class CPUGPUJob: From a4de25fe391f07d9bd61c544ec7816b8d520ac22 Mon Sep 17 00:00:00 2001 From: "BLACKBIRD\\Dave Rowenhorst" Date: Wed, 3 May 2023 11:49:52 -0400 Subject: [PATCH 101/177] Corrected estimates of GPU memory usage. --- pyebsdindex/_ebsd_index_parallel.py | 45 +++++++++++++++-------------- 1 file changed, 24 insertions(+), 21 deletions(-) diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index 8a24c54..2f4d30b 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -72,6 +72,7 @@ def index_pats_distributed( ebsd_indexer_obj=None, keep_log=False, gpu_id=None, + verbose = 0 ): """Index EBSD patterns in parallel. @@ -500,25 +501,26 @@ def index_pats_distributed( npatdone += cjob.npat currenttime = timer() - starttime #print(cjob.rate * n_cpu_nodes) - print( - "Completed: ", - str(cjob.pstart), - " -- ", - str(cjob.pend), - " PPS:", - "{:.0f}".format(cjob.rate*ncpuwrker) - + ";" - + "{:.0f}".format(chunkave / ncpudone * ncpuwrker) - + ";" - + "{:.0f}".format(npatdone/currenttime), - " ", - "{:.0f}".format((ncpudone / njobs) * 100) + "%", - "{:.0f};".format(currenttime) - + "{:.0f}".format((njobs - ncpudone) / ncpudone * currenttime) - + " running;remaining(s)", - end=newline, - ) - time.sleep(0.001) + if verbose > 0: + print( + "Completed: ", + str(cjob.pstart), + " -- ", + str(cjob.pend), + " PPS:", + "{:.0f}".format(cjob.rate*ncpuwrker) + + ";" + + "{:.0f}".format(chunkave / ncpudone * ncpuwrker) + + ";" + + "{:.0f}".format(npatdone/currenttime), + " ", + "{:.0f}".format((ncpudone / njobs) * 100) + "%", + "{:.0f};".format(currenttime) + + "{:.0f}".format((njobs - ncpudone) / ncpudone * currenttime) + + " running;remaining(s)", + end=newline, + ) + #time.sleep(0.001) if message != 'Error': if ncpudone == njobs: del cpuworkers[jid] @@ -556,6 +558,7 @@ def index_pats_distributed( ctaskindex.append(None) ray.shutdown() + print('\n') if return_indexer_obj: return dataout, banddataout, indexer else: @@ -594,13 +597,13 @@ def __optimizegpuchunk__(indexer, n_cpu_nodes, gpu_id, clparam): patdim = indexer.bandDetectPlan.patDim rdndim = np.array([indexer.bandDetectPlan.nTheta+2*indexer.bandDetectPlan.padding[1], indexer.bandDetectPlan.nRho+2*indexer.bandDetectPlan.padding[0]]) - memperpat = 4.0*float(patdim[0] * patdim[1] + 9.0 * rdndim[0] * rdndim[1])# rough estimate + memperpat = 4.0*float(patdim[0] * patdim[1] + 6.0 * rdndim[0] * rdndim[1])# rough estimate #print('Mem/pat:', memperpat) chunkguess = (float(gmem)/float(ncpu_per_gpu)) / memperpat #print('chunkguess:', chunkguess) - safetyval = 0.5 + safetyval = 0.8 chunkguess *= safetyval if clparam.gpu[0].vendor == 'AMD': # 'AMD implmentation of opencl does better with clearing memory' # # this is a cheat, because 1/2 the time the GPU will be idle while the CPU is compputing. From b0d8cc1dcefbc841b5d12c2a0aa4145327141fb7 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Wed, 3 May 2023 13:42:42 -0400 Subject: [PATCH 102/177] Correct scaling of gpu processes Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_parallel.py | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index 2f4d30b..acb6f6d 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -72,7 +72,7 @@ def index_pats_distributed( ebsd_indexer_obj=None, keep_log=False, gpu_id=None, - verbose = 0 + verbose = 1 ): """Index EBSD patterns in parallel. @@ -275,7 +275,7 @@ def index_pats_distributed( usegpu = True if usegpu: - gpupro = 12 # number of processes per gpu that will serve data to the gpu + ngpupro = 12 # number of processes per gpu that will serve data to the gpu if n_cpu_nodes - ngpu < 8: ngpupro = 8 if n_cpu_nodes - ngpu < 2: @@ -283,7 +283,7 @@ def index_pats_distributed( n_cpu_per_gpu = max(min(1.0, n_cpu_nodes-ngpu), 0.5/ngpu) - ngpuwrker = gpupro * ngpu + ngpuwrker = ngpupro * ngpu ngpu_per_wrker = 1.0/ngpuwrker - 1.0e-6 # fraction of a GPU to give to each worker (band finding worker) ncpugpu_per_wrker = n_cpu_per_gpu/ngpuwrker - 1.0e-6 # fraction of a cpu to allocate to each gpu worker @@ -294,7 +294,7 @@ def index_pats_distributed( if chunksize <= 0: chunksize = __optimizegpuchunk__(indexer, ngpuwrker, gpu_id, clparam) - else: # no gpus detected. + else: # no gpus detected. ngpu_per_wrker = 0 usegpu = False ngpupros = n_cpu_nodes @@ -413,8 +413,8 @@ def index_pats_distributed( for i in range(ncpuwrker): cpuworkers.append( # make a new Ray Actor that can call the indexer defined in shared memory. # These actors are read/write, thus can initialize the GPU queues - CPUWorker.options(num_cpus=ncpucpu_per_worker, num_gpus=0).remote(i)) - #CPUWorker.options(num_cpus=ncpucpu_per_worker, num_gpus=0).remote(i)) + CPUWorker.options(num_cpus=ncpucpu_per_worker).remote(i)) + #CPUWorker.options(num_cpus=1.0, num_gpus=0).remote(i)) cputask.append(cpuworkers[i].indexpoles.remote(None, None, None,indexer=remote_indexer)) ctaskindex.append(None) #print(len(cpuworkers)) @@ -488,6 +488,8 @@ def index_pats_distributed( ) gtaskindex.append(gjob) # toc = timer() + if ngpudone >= njobs: + print('\n \n GPU Done') if ncpudone < njobs: donewrker, busy = ray.wait(cputask, num_returns = len(cputask), timeout=0.1) for wrker in donewrker: From e5476247a4c4cf94cc3e0afac2a93102b9f9900f Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Wed, 3 May 2023 18:14:54 -0400 Subject: [PATCH 103/177] Code cleanup Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_parallel.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index acb6f6d..7786a82 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -293,7 +293,7 @@ def index_pats_distributed( if chunksize <= 0: - chunksize = __optimizegpuchunk__(indexer, ngpuwrker, gpu_id, clparam) + chunksize = __optimizegpuchunk__(indexer, ngpupro, gpu_id, clparam) else: # no gpus detected. ngpu_per_wrker = 0 usegpu = False @@ -422,7 +422,7 @@ def index_pats_distributed( while ncpudone < njobs: if ngpudone < njobs: # check if gpu is done - donewrker, busy = ray.wait(gputask,num_returns = len(gputask), timeout=0.1) + donewrker, busy = ray.wait(gputask,num_returns = len(gputask), timeout=0.01) #if len(wrker) > 0: # trying to catch a hung worker. Rare, but it happens #print(len(donewrker)) #else: @@ -488,10 +488,10 @@ def index_pats_distributed( ) gtaskindex.append(gjob) # toc = timer() - if ngpudone >= njobs: - print('\n \n GPU Done') + if (ngpudone >= njobs) and (verbose >1 ): + print('\n GPU Done') if ncpudone < njobs: - donewrker, busy = ray.wait(cputask, num_returns = len(cputask), timeout=0.1) + donewrker, busy = ray.wait(cputask, num_returns = len(cputask), timeout=0.01) for wrker in donewrker: jid = cputask.index(wrker) try: @@ -541,7 +541,7 @@ def index_pats_distributed( ctaskindex[jid] = None else: - raise Exception("Error in processing patterns: ", ctaskindex[jid].pstart,ctaskindex[jid].pend) + raise Exception("Error in indexing bands: ", ctaskindex[jid].pstart,ctaskindex[jid].pend) except Exception as e: @@ -609,7 +609,7 @@ def __optimizegpuchunk__(indexer, n_cpu_nodes, gpu_id, clparam): chunkguess *= safetyval if clparam.gpu[0].vendor == 'AMD': # 'AMD implmentation of opencl does better with clearing memory' # # this is a cheat, because 1/2 the time the GPU will be idle while the CPU is compputing. - chunkguess *= 3.0 + chunkguess *= 1.0 From 654fe6a6dba9059f168b0920b8036d4d335e6336 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Wed, 3 May 2023 21:35:05 -0400 Subject: [PATCH 104/177] Bug fixes Signed-off by: David Rowenhorst --- pyebsdindex/ebsd_pattern.py | 70 ++++++++++++++++++++----------------- pyebsdindex/nlpar.py | 31 ++++++++++------ pyebsdindex/rotlib.py | 24 ++++++------- 3 files changed, 70 insertions(+), 55 deletions(-) diff --git a/pyebsdindex/ebsd_pattern.py b/pyebsdindex/ebsd_pattern.py index 94609f4..6780a3a 100644 --- a/pyebsdindex/ebsd_pattern.py +++ b/pyebsdindex/ebsd_pattern.py @@ -239,14 +239,14 @@ def read_data(self,path=None,convertToFloat=False,patStartCount = [0,-1],returnA pStartEnd = np.asarray(patStartCount, dtype=np.int64) if pStartEnd.ndim == 1: # read a continuous set of patterns. - patStart = patStartCount[0] - nPatToRead = patStartCount[-1] + patStart = int(patStartCount[0]) + nPatToRead = int(patStartCount[-1]) if nPatToRead == -1: - nPatToRead = self.nPatterns - patStart + nPatToRead = int(self.nPatterns - patStart) if nPatToRead == 0: nPatToRead = 1 if (patStart + nPatToRead) > self.nPatterns: - nPatToRead = self.nPatterns - patStart + nPatToRead = int(self.nPatterns - patStart) # this function does the actual reading from the file. @@ -256,22 +256,22 @@ def read_data(self,path=None,convertToFloat=False,patStartCount = [0,-1],returnA elif pStartEnd.ndim == 2: # read a slab of patterns. - colstart = pStartEnd[0,0] - ncolread = pStartEnd[1,0] - rowstart = pStartEnd[0,1] - nrowread = pStartEnd[1,1] + colstart = int(pStartEnd[0,0]) + ncolread = int(pStartEnd[1,0]) + rowstart = int(pStartEnd[0,1]) + nrowread = int(pStartEnd[1,1]) patStart = [colstart, rowstart] if ncolread < 0: - ncolread = self.nCols - colstart + ncolread = int(self.nCols - colstart) if nrowread < 0: - nrowread = self.nRows - rowstart + nrowread = int(self.nRows - rowstart) if (colstart+ncolread) > self.nCols: - ncolread = self.nCols - colstart + ncolread = int(self.nCols - colstart) if (rowstart+nrowread) > self.nRows: - nrowread = self.nRows - rowstart + nrowread = int(self.nRows - rowstart) nrowread = np.uint64(nrowread) ncolread = np.uint64(ncolread) nPatToRead = [ncolread, nrowread] @@ -279,7 +279,7 @@ def read_data(self,path=None,convertToFloat=False,patStartCount = [0,-1],returnA patterns = np.zeros([int(ncolread*nrowread),self.patternH,self.patternW],dtype=typeout) for i in range(nrowread): - pstart = int(((rowstart+i)*self.nCols)+colstart) + pstart = int(int(int(rowstart+i)*self.nCols)+colstart) ptemp, xyloc = self.read_data(convertToFloat=convertToFloat,patStartCount = [pstart,ncolread],returnArrayOnly=True) patterns[int(i*ncolread):int((i+1)*ncolread), :, :] = ptemp @@ -344,8 +344,8 @@ def write_data(self, newpatterns=None, patStartCount = [0,-1], writeHead=False, # self.nPatterns == number of patterns in the file # nPats to write == number of patterns to write out if pStartEnd.ndim == 1: # write a continuous set of patterns. - patStart = patStartCount[0] - nPatToWrite = patStartCount[-1] + patStart = int(patStartCount[0]) + nPatToWrite = int(patStartCount[-1]) if nPatToWrite == -1: nPatToWrite = npats if nPatToWrite == 0: @@ -358,26 +358,26 @@ def write_data(self, newpatterns=None, patStartCount = [0,-1], writeHead=False, self.pat_writer(pat2write,patStart,nPatToWrite, typewrite) elif pStartEnd.ndim == 2: # write a slab of patterns. - colstart = pStartEnd[0,0] - ncolwrite = pStartEnd[1,0] - rowstart = pStartEnd[0,1] - nrowwrite = pStartEnd[1,1] + colstart = int(pStartEnd[0,0]) + ncolwrite = int(pStartEnd[1,0]) + rowstart = int(pStartEnd[0,1]) + nrowwrite = int(pStartEnd[1,1]) patStart = [colstart, rowstart] if ncolwrite < 0: - ncolwrite = self.nCols - colstart + ncolwrite = int(self.nCols - colstart) if nrowwrite < 0: - nrowwrite = self.nRows - rowstart + nrowwrite = int(self.nRows - rowstart) if (colstart+ncolwrite) > self.nCols: - ncolwrite = self.nCols - colstart + ncolwrite = int(self.nCols - colstart) if (rowstart+nrowwrite) > self.nRows: - nrowwrite = self.nRows - rowstart + nrowwrite = int(self.nRows - rowstart) for i in range(nrowwrite): - pstart = ((rowstart+i)*self.nCols)+colstart + pstart = int(int(int(rowstart+i)*self.nCols)+colstart) self.write_data(newpatterns = pats[int(i*ncolwrite):int((i+1)*ncolwrite), :, :], patStartCount=[pstart,ncolwrite],writeHead=False, flt2int=flt2int,scalevalue=0.98, maxScale = max) def pat_writer(self, pat2write, patStart, nPatToWrite, typewrite): @@ -583,17 +583,21 @@ def write_header(self, writeBlank=False, bitdepth=None): def pat_writer(self, pat2write, patStart, nPatToWrite, typewrite): try: - f = open(Path(self.filepath).expanduser(),'br+') - f.seek(0,0) - except: - print("File Not Found:",str(Path(self.filepath))) + with open(Path(self.filepath).expanduser(),'br+') as f: + #print(patStart) + f.seek(0,0) + nPerPat = int(self.patternW * self.patternH) + nPerPatByte = int(nPerPat * typewrite(0).nbytes) + f.seek(int(nPerPatByte * (patStart) + self.filePos), 0) + pat2write[0:nPatToWrite, :, :].tofile(f) + #print(patStart) + except Exception as e: + print(e) + print(str(Path(self.filepath))) return -1 - nPerPat = self.patternW * self.patternH - nPerPatByte = nPerPat * typewrite(0).nbytes - f.seek(int(nPerPatByte * (patStart) + self.filePos),0) - pat2write[0:nPatToWrite,:,:].tofile(f) - f.close() + + diff --git a/pyebsdindex/nlpar.py b/pyebsdindex/nlpar.py index 0864417..02dbdab 100644 --- a/pyebsdindex/nlpar.py +++ b/pyebsdindex/nlpar.py @@ -364,8 +364,13 @@ def calcnlpar(self, chunksize=0, searchradius=None, lam = None, dthresh = None, print(lam, sr, dthresh) for j in range(0,nrows,chunksize): + print('Row start', j) + rowstartread = np.int64(max(0, j-sr)) rowend = min(j + chunksize+sr,nrows) + + if (rowend - rowstartread) < (2*sr+1): + rowstartread = np.int64(max(0, rowend - (2*sr+1))) rowcountread = np.int64(rowend-rowstartread) data, xyloc = patternfile.read_data(patStartCount = [[0,rowstartread], [ncols,rowcountread]], convertToFloat=True,returnArrayOnly=True) @@ -387,7 +392,8 @@ def calcnlpar(self, chunksize=0, searchradius=None, lam = None, dthresh = None, else: sigchunk = sigma[rowstartread:rowend,:] - print('Block', j) + #dataout = data + dataout = self.nlpar_nb(data,lam, sr, dthresh, sigchunk, rowcountread,ncols,indices,saturation_protect) @@ -399,7 +405,7 @@ def calcnlpar(self, chunksize=0, searchradius=None, lam = None, dthresh = None, for i in range(dataout.shape[0]): temp = dataout[i,:,:] temp -= temp.min() - temp *= float(mxval)/temp.max() + temp *= np.float32(mxval)/temp.max() dataout[i,:,:] = temp patternfileout.write_data(newpatterns=dataout,patStartCount = [[0,j], [ncols, shpout[0]]], @@ -409,6 +415,7 @@ def calcnlpar(self, chunksize=0, searchradius=None, lam = None, dthresh = None, #return dataout #sigma[j:j+rowstartcount[1],:] += \ # sigchunk[rowstartcount[0]:rowstartcount[0]+rowstartcount[1],:] + numba.set_num_threads(nthreadpos) @@ -443,10 +450,12 @@ def calcsigma(self,chunksize=0,nn=1,saturation_protect=True,automask=True): sigma = np.zeros((nrows, ncols), dtype=np.float32) #d_nn = np.zeros((nrows, ncols, int((2*nn+1)**2)), dtype=np.float32) colstartcount = np.asarray([0,ncols],dtype=np.int64) - dave = 0.0 + for j in range(0,nrows,chunksize): rowstartread = np.int64(max(0, j-nn)) rowend = min(j + chunksize+nn,nrows) + if (rowend - rowstartread) < (3): + rowstartread = np.int64(max(0, rowend - (3))) rowcountread = np.int64(rowend-rowstartread) data, xyloc = patternfile.read_data(patStartCount = [[0,rowstartread], [ncols,rowcountread]], convertToFloat=True,returnArrayOnly=True) @@ -459,9 +468,9 @@ def calcsigma(self,chunksize=0,nn=1,saturation_protect=True,automask=True): rowstartcount = np.asarray([j-rowstartread,rowcountread - (j-rowstartread) ], dtype=np.int64) else: rowstartcount = np.asarray([j-rowstartread,chunksize ], dtype=np.int64) - dtic = timer() + sigchunk, temp = self.sigma_numba(data,nn, rowcountread,ncols,rowstartcount,colstartcount,indices,saturation_protect) - dave += (timer() - dtic) + sigma[j:j+rowstartcount[1],:] += \ sigchunk[rowstartcount[0]:rowstartcount[0]+rowstartcount[1],:] @@ -573,7 +582,7 @@ def sigma_numba(data, nn, nrows, ncols, rowstartcount, colstartcount, indices, s dij[j,i,count,1] = np.uint64(i_nn) # want to save this for labmda optimization indx_nn = i_nn+ncols*j_nn d2 = np.float32(0.0) - n2 = np.float32(0.0) + n2 = np.float32(1.0e-12) nout[j,i,count] = n0 # want to save this for labmda optimization if not((i == i_nn) and (j == j_nn)): for q in range(shpind[0]): @@ -583,8 +592,9 @@ def sigma_numba(data, nn, nrows, ncols, rowstartcount, colstartcount, indices, s n2 += 1.0 d2 += (d0 - d1)**2 nout[j,i,count] = n2 - s0 = d2 / np.float32(n2 * 2.0) + if d2 >= 1.e-3: #sometimes EDAX collects the same pattern twice + s0 = d2 / np.float32(n2 * 2.0) if s0 < mind: mind = s0 dout[j,i,count] = d2 # want to save this for labmda optimization @@ -592,12 +602,13 @@ def sigma_numba(data, nn, nrows, ncols, rowstartcount, colstartcount, indices, s count += 1 sigma[j,i] = np.sqrt(mind) + #if sigma[j,i] > 1e12: + # print(sigma[j,i], dout[j,i,:], nout[i,j,:]) return sigma,( dout, nout, dij) @staticmethod - @numba.jit(nopython=True,cache=True,fastmath=True,parallel=True) + @numba.jit(nopython=True,cache=True,fastmath=False,parallel=True) def nlpar_nb(data,lam, sr, dthresh, sigma, nrows,ncols,indices,saturation_protect=True): - def getpairid(idx0, idx1): idx0_t = int(idx0) idx1_t = int(idx1) @@ -666,7 +677,7 @@ def getpairid(idx0, idx1): counter += 1 #print('________________') # end of window scanning - sum = np.float(0.0) + sum = np.float32(0.0) for i_nn in range(winsz): weights[i_nn] = np.maximum(weights[i_nn]-dthresh, numba.float32(0.0)) diff --git a/pyebsdindex/rotlib.py b/pyebsdindex/rotlib.py index 4274d9c..8133186 100644 --- a/pyebsdindex/rotlib.py +++ b/pyebsdindex/rotlib.py @@ -844,12 +844,12 @@ def ho2cuL(hoIn,p=P): pf = numba.float32(p > 0) * 2.0 - 1.0 ho,m,n,intype = prepIn(hoIn) cu = np.zeros((n,3),dtype=intype) - LPR1 = np.float(1.33067003949147) # (3pi/4)**(1/3) - LPpref = np.float(1.38197659788534) # sqrt(6/pi) - LPbeta = np.float(0.962874509979126) # pi**(5/6)/6**(1/6)/2 - LPr2 = np.float(1.4142135623731) # sqrt(2) - LPpi12 = np.float(0.261799387799149) # pi/12 - LPsc = np.float(0.897772786961286) # a/ap == (pi**(5/6)/6**(1/6)) / pi**(2/3) + LPR1 = np.float64(1.33067003949147) # (3pi/4)**(1/3) + LPpref = np.float64(1.38197659788534) # sqrt(6/pi) + LPbeta = np.float64(0.962874509979126) # pi**(5/6)/6**(1/6)/2 + LPr2 = np.float64(1.4142135623731) # sqrt(2) + LPpi12 = np.float64(0.261799387799149) # pi/12 + LPsc = np.float64(0.897772786961286) # a/ap == (pi**(5/6)/6**(1/6)) / pi**(2/3) eps_loc = 1e-7 @@ -1344,12 +1344,12 @@ def lambert3DCubeToBall(xyz): @numba.jit(['f8[:](f8[:])','f8[:](f4[:])'], nopython=True,fastmath=nbFastmath, cache=nbcache) def lambert3DBallToCube(xyz): - LPR1 = np.float(1.33067003949147) # (3pi/4)**(1/3) - LPpref = np.float(1.38197659788534) # sqrt(6/pi) - LPbeta = np.float(0.962874509979126) # pi**(5/6)/6**(1/6)/2 - LPr2 = np.float(1.4142135623731) # sqrt(2) - LPpi12 = np.float(0.261799387799149) # pi/12 - LPsc = np.float(0.897772786961286) # a/ap == (pi**(5/6)/6**(1/6)) / pi**(2/3) + LPR1 = np.float64(1.33067003949147) # (3pi/4)**(1/3) + LPpref = np.float64(1.38197659788534) # sqrt(6/pi) + LPbeta = np.float64(0.962874509979126) # pi**(5/6)/6**(1/6)/2 + LPr2 = np.float64(1.4142135623731) # sqrt(2) + LPpi12 = np.float64(0.261799387799149) # pi/12 + LPsc = np.float64(0.897772786961286) # a/ap == (pi**(5/6)/6**(1/6)) / pi**(2/3) eps_loc = 1e-7 xyzcu = np.zeros(3, dtype = np.float64) From 33825b8dbe9ee5dab83f107246cf8e966e127fa6 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Thu, 4 May 2023 07:32:20 -0400 Subject: [PATCH 105/177] Bug fixes Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_parallel.py | 11 ++++++----- pyebsdindex/tests/test_package.py | 10 +++++----- 2 files changed, 11 insertions(+), 10 deletions(-) diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index 7786a82..bdf2df8 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -273,8 +273,8 @@ def index_pats_distributed( ngpupnode = 0 - usegpu = True - if usegpu: + + if ngpu > 0: ngpupro = 12 # number of processes per gpu that will serve data to the gpu if n_cpu_nodes - ngpu < 8: ngpupro = 8 @@ -297,12 +297,13 @@ def index_pats_distributed( else: # no gpus detected. ngpu_per_wrker = 0 usegpu = False - ngpupros = n_cpu_nodes + ngpu_per_wrker = 0 + ngpuwrker = n_cpu_nodes ncpucpu_per_worker = 0.5 - 1.0e-6 ncpugpu_per_wrker = 0.5 - 1.0e-6 if chunksize <= 0: chunksize = 1000 - + ncpuwrker = n_cpu_nodes ray.shutdown() print("num cpu/gpu, and number of patterns per iteration:", n_cpu_nodes, ngpu, chunksize) @@ -340,7 +341,7 @@ def index_pats_distributed( gpujobs.append(CPUGPUJob(jid, jb[0], jb[1])) jid += 1 - ncpuwrker = n_cpu_nodes + if njobs < ncpuwrker: ncpuwrker = njobs if njobs < ngpuwrker: diff --git a/pyebsdindex/tests/test_package.py b/pyebsdindex/tests/test_package.py index 3b5ce47..85244e1 100644 --- a/pyebsdindex/tests/test_package.py +++ b/pyebsdindex/tests/test_package.py @@ -45,15 +45,15 @@ def test_unavailable_functionality_without_pyopencl(): @pytest.mark.skipif(not _ray_installed, reason="ray is not installed") def test_available_functionality_with_ray(): from pyebsdindex.ebsd_index import index_pats_distributed - from pyebsdindex.ebsd_index import IndexerRay + #from pyebsdindex.ebsd_index import IndexerRay - assert callable(index_pats_distributed) - _ = IndexerRay.remote() + #assert callable(index_pats_distributed) + #_ = IndexerRay.remote() @pytest.mark.skipif(_ray_installed, reason="ray is installed") def test_unavailable_functionality_without_ray(): with pytest.raises(ImportError): from pyebsdindex.ebsd_index import index_pats_distributed - with pytest.raises(ImportError): - from pyebsdindex.ebsd_index import IndexerRay + #with pytest.raises(ImportError): + # from pyebsdindex.ebsd_index import IndexerRay From 1a258cd2531832c8481c53d6130ac4f41910832c Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Thu, 4 May 2023 21:19:40 -0400 Subject: [PATCH 106/177] Bug fix Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_parallel.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index bdf2df8..6e274d0 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -272,6 +272,8 @@ def index_pats_distributed( ngpu = 0 ngpupnode = 0 + if indexer.bandDetectPlan.useCPU == True: + ngpu = 0 if ngpu > 0: @@ -304,6 +306,7 @@ def index_pats_distributed( if chunksize <= 0: chunksize = 1000 ncpuwrker = n_cpu_nodes + ray.shutdown() print("num cpu/gpu, and number of patterns per iteration:", n_cpu_nodes, ngpu, chunksize) From 7cc1fabbc20f3edfefb2734443c707ececbc3a3b Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Fri, 5 May 2023 09:23:55 -0400 Subject: [PATCH 107/177] Fixed optimization for multiple GPU Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_parallel.py | 25 ++++++++++++------------- pyebsdindex/opencl/band_detect_cl.py | 5 +---- 2 files changed, 13 insertions(+), 17 deletions(-) diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index 6e274d0..4c7bdf6 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -570,7 +570,7 @@ def index_pats_distributed( else: return dataout, banddataout -def __optimizegpuchunk__(indexer, n_cpu_nodes, gpu_id, clparam): +def __optimizegpuchunk__(indexer, ngpupro, gpu_id, clparam): gpulist = [] @@ -586,6 +586,7 @@ def __optimizegpuchunk__(indexer, n_cpu_nodes, gpu_id, clparam): gpulist.append(g) else: temp = np.atleast_1d(gpu_id) + for g in temp: gpulist.append(clparam.gpu[g]) ngpu = len(gpulist) @@ -595,10 +596,14 @@ def __optimizegpuchunk__(indexer, n_cpu_nodes, gpu_id, clparam): gmem = 1e99 for g in gpulist: + if g.global_mem_size < gmem: gmem = g.global_mem_size + + gmem = 0.8*gmem # Build a margin in. #print('Global Mem:', gmem) - ncpu_per_gpu = max(1, np.ceil(n_cpu_nodes/ngpu)) + #ncpu_per_gpu = max(1, np.ceil(n_cpu_nodes/ngpu)) + #print('Ncpu/gpu:', ncpu_per_gpu) patdim = indexer.bandDetectPlan.patDim rdndim = np.array([indexer.bandDetectPlan.nTheta+2*indexer.bandDetectPlan.padding[1], @@ -606,28 +611,22 @@ def __optimizegpuchunk__(indexer, n_cpu_nodes, gpu_id, clparam): memperpat = 4.0*float(patdim[0] * patdim[1] + 6.0 * rdndim[0] * rdndim[1])# rough estimate #print('Mem/pat:', memperpat) - chunkguess = (float(gmem)/float(ncpu_per_gpu)) / memperpat + chunkguess = (float(gmem)/float(ngpupro)) / memperpat #print('chunkguess:', chunkguess) - safetyval = 0.8 - chunkguess *= safetyval - if clparam.gpu[0].vendor == 'AMD': # 'AMD implmentation of opencl does better with clearing memory' - # # this is a cheat, because 1/2 the time the GPU will be idle while the CPU is compputing. - chunkguess *= 1.0 - #print('cheatguess:', chunkguess) chunk = int(max(2, np.floor(chunkguess/16))*16) # ideally should be a multiple of 16 #print('chunk:', chunk) - #check for powers of two - for some reason it runs very slow with powers of two. + #check for powers of two: it is adventageous to be near those. twocheck = np.log2(float(chunk)) - if np.abs((twocheck) - np.round(twocheck)) < 1e-6: - chunk += 16 + if np.abs((twocheck) - np.round(twocheck)) < 0.2: + chunk = int(2**int(np.round(twocheck))) # finally - I am unsure how to check for integrated graphics that report system memory, so I am going # throw an arbitrary cap on this: - chunk = min(2016, chunk) + chunk = min(2048, chunk) return chunk diff --git a/pyebsdindex/opencl/band_detect_cl.py b/pyebsdindex/opencl/band_detect_cl.py index 04f8fef..008edef 100644 --- a/pyebsdindex/opencl/band_detect_cl.py +++ b/pyebsdindex/opencl/band_detect_cl.py @@ -228,10 +228,7 @@ def radon_fasterCL(self,image,padding = np.array([0,0]), fixArtifacts = False, b clvtypesize = 16 # this is the vector size to be used in the openCL implementation. nImCL = np.int32(clvtypesize * (np.int64(np.ceil(nIm/clvtypesize)))) - # there is something very strange that happens if the number of images - # is a exact multiple of the max group size (typically 256) - mxGroupSz = gpu[gpu_id].get_info(cl.device_info.MAX_WORK_GROUP_SIZE) - #nImCL += np.int64(16 * (1 - np.int64(np.mod(nImCL, mxGroupSz ) > 0))) + image_align = np.ones((shapeIm[1], shapeIm[2], nImCL), dtype = np.float32) image_align[:,:,0:nIm] = np.transpose(image, [1,2,0]).copy() shpRdn = np.asarray( ((self.nRho+2*padding[0]), (self.nTheta+2*padding[1]), nImCL),dtype=np.uint64) From 3455dcbda289ce07ba089c2c64359e38a8a6c928 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Fri, 5 May 2023 21:18:39 -0400 Subject: [PATCH 108/177] Update release notes. Signed-off by: David Rowenhorst --- CHANGELOG.rst | 17 ++++++++++++++++- pyebsdindex/_ebsd_index_single.py | 2 +- pyebsdindex/pcopt.py | 2 -- setup.py | 1 - 4 files changed, 17 insertions(+), 5 deletions(-) diff --git a/CHANGELOG.rst b/CHANGELOG.rst index b98dcbb..c9342a5 100644 --- a/CHANGELOG.rst +++ b/CHANGELOG.rst @@ -10,20 +10,35 @@ Unreleased Added ----- +- Initial support for uncompressed EBSP files from Oxford systems. +- Significant improvement in the particle swarm optimization for pattern center optimization. +- Initial support for non-cubic phases. Hexagonal verified with EDAX convention. Others are untested. +- Significant improvements in phase differentiation. +- NLPAR support for Oxford HDF5 and EBSP. Changed ------- +- CRITICAL! All ``ebsd_pattern.EBSDPatternFiles.read_data()`` calls will now return TWO arguments. + The patterns (same as previous), and an nd.array of the x,y location within the scan of the patterns. The origin is + the center of the scan, and reported in microns. +- ``ebsd_index.index_pats_distributed()`` now will auto optimize the number of patterns processed at a time depending on GPU + capability, and is set as the default. +- Updated tutorials for new features. Deprecated ---------- Removed ------- - +- Removed requirement for installation of pyswarms. +- Removed any references to np.floats and replaced with float() or np.float32/64. Fixed ----- - Hough transform figure when ``verbose=2`` is passed to various indexing methods is now plotted in its own figure. +- Several bug fixes with NLPAR file reading/writing. +- Complete rewrite of the scheduling for ``ebsd_index.index_pats_distributed()`` function to be compatible + with NVIDIA cards. Security -------- diff --git a/pyebsdindex/_ebsd_index_single.py b/pyebsdindex/_ebsd_index_single.py index eff7950..45f41ca 100644 --- a/pyebsdindex/_ebsd_index_single.py +++ b/pyebsdindex/_ebsd_index_single.py @@ -399,7 +399,7 @@ def index_pats( clparams=None, PC=None, verbose=0, - chunksize=528, + chunksize=512, ): """Index EBSD patterns. diff --git a/pyebsdindex/pcopt.py b/pyebsdindex/pcopt.py index 2772749..91138b1 100644 --- a/pyebsdindex/pcopt.py +++ b/pyebsdindex/pcopt.py @@ -24,9 +24,7 @@ import numpy as np import multiprocessing -#import pyswarms as pso import scipy.optimize as opt -#from functools import partial from timeit import default_timer as timer diff --git a/setup.py b/setup.py index 122bb8c..3de464e 100644 --- a/setup.py +++ b/setup.py @@ -94,7 +94,6 @@ "matplotlib", "numpy", "numba", - "pyswarms", "scipy", ], # Files to include when distributing package (see also MANIFEST.in) From b39bed4c4b29dde446d062dbf6e7086908ab42e0 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Sun, 7 May 2023 20:49:31 -0400 Subject: [PATCH 109/177] Improve radon plot Signed-off by: David Rowenhorst --- pyebsdindex/opencl/band_detect_cl.py | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/pyebsdindex/opencl/band_detect_cl.py b/pyebsdindex/opencl/band_detect_cl.py index 008edef..c7ed7ab 100644 --- a/pyebsdindex/opencl/band_detect_cl.py +++ b/pyebsdindex/opencl/band_detect_cl.py @@ -170,8 +170,9 @@ def find_bands(self, patternsIn, verbose=0, clparams=None, chunksize=528, useCPU im2show[-rhoMaskTrim:,:] = 0 im2show = np.fliplr(im2show) - plt.figure() - plt.imshow(im2show, cmap='gray', extent=[0, 180, -self.rhoMax, self.rhoMax], + fig = plt.figure(figsize=(12,4)) + subrdn = fig.add_subplot(1,2,1, xlim = (0,180), ylim = (-self.rhoMax, self.rhoMax) ) + subrdn.imshow(im2show, cmap='gray', extent=[0, 180, -self.rhoMax, self.rhoMax], interpolation='none', zorder=1, aspect='auto') width = bandData['width'][-1, :] width /= width.min() @@ -179,13 +180,14 @@ def find_bands(self, patternsIn, verbose=0, clparams=None, chunksize=528, useCPU xplt = np.squeeze(180.0 - np.interp(bandData['aveloc'][-1,:,1]+0.5, np.arange(self.radonPlan.nTheta), self.radonPlan.theta)) yplt = np.squeeze( -1.0 * np.interp(bandData['aveloc'][-1,:,0]-0.5, np.arange(self.radonPlan.nRho), self.radonPlan.rho)) - plt.scatter(y=yplt, x=xplt, c='r', s=width, zorder=2) + subrdn.scatter(y=yplt, x=xplt, c='r', s=width, zorder=2) for pt in range(self.nBands): - plt.annotate(str(pt + 1),np.squeeze([xplt[pt]+4,yplt[pt]]), color='yellow') - plt.xlim(0,180) - plt.ylim(-self.rhoMax, self.rhoMax) - + subrdn.annotate(str(pt + 1),np.squeeze([xplt[pt]+4,yplt[pt]]), color='yellow') + #subrdn.xlim(0,180) + #subrdn.ylim(-self.rhoMax, self.rhoMax) + subpat = fig.add_subplot(1,2,2) + subpat.imshow(patterns[-1,:,:], cmap='gray') except Exception as e: # something went wrong - try the CPU print(e) From addd4edab3b7f6a29f15999d033159f9e50d569e Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Tue, 9 May 2023 09:44:32 -0400 Subject: [PATCH 110/177] Improved weighting for quest fit. Signed-off by: David Rowenhorst --- pyebsdindex/tripletvote.py | 85 +++++++++++++++++++++++++------------- 1 file changed, 57 insertions(+), 28 deletions(-) diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index cfb1a8b..b991ca3 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -423,7 +423,7 @@ def bandindex(self, band_norms, band_intensity = None, band_widths=None, verbose pairangs = self.angpairs['angles'] pairfam = self.angpairs['familyid'] - accumulator, bandFam, bandRank, band_cm = self._tripvote_numba(bandangs, self.lut, self.angTol, tripangs, tripid, nfam, n_bands) + accumulator, bandFam, bandRank, band_cm, accumulator_nw = self._tripvote_numba(bandangs, self.lut, self.angTol, tripangs, tripid, nfam, n_bands) #accumulator, bandFam, bandRank, band_cm = self._pairvote_numba(bandangs, self.angTol, pairangs, pairfam, # nfam, n_bands) if verbose > 2: @@ -479,9 +479,17 @@ def bandindex(self, band_norms, band_intensity = None, band_widths=None, verbose if nMatch >=2: if self.high_fidelity == True: - srt = np.argsort(fitb[whGood]) - whgood6 = whGood[srt[0:np.min([7, whGood.shape[0]])]] - + score = accumulator[[self.completelib['familyid'][polematch[whGood]]], [whGood]] + score /= accumulator_nw[[self.completelib['familyid'][polematch[whGood]]], [whGood]] + 1.0e-6 + score = np.squeeze(score) + #print(score, whGood.shape[0]) + srt = np.flip(np.argsort(score)) + #srt = np.flip(np.argsort(band_intensity[whGood])) + whgood6 = whGood[srt[0:np.min([6, whGood.shape[0]])]] + #whgood6 = whGood[0:min(6, whGood.shape[0])] + if verbose > 2: + print("Good bands:", whGood+1) + print("Fit Bands: ", whgood6+1) weights6 = band_intensity[whgood6] pflt6 = (np.asarray(polesCart[polematch[whgood6], :], dtype=np.float64)) bndnorm6 = (np.asarray(bandnorms[whgood6, :], dtype=np.float64)) @@ -806,6 +814,7 @@ def _orientation_quest_nb(polescart, bandnorms, weights): def _tripvote_numba(bandangs, LUT, angTol, tripAngles, tripID, nfam, n_bands): LUTTemp = np.asarray(LUT).copy() accumulator = np.zeros((nfam, n_bands), dtype=np.float32) + accumulatorW = np.zeros((nfam, n_bands), dtype=np.float32) tshape = np.shape(tripAngles) ntrip = int(tshape[0]) #count = 0.0 @@ -857,58 +866,78 @@ def _tripvote_numba(bandangs, LUT, angTol, tripAngles, tripID, nfam, n_bands): w2 = (2.0 * angTol - (angTest0[0] + angTest0[2])) w3 = (2.0 * angTol - (angTest0[1] + angTest0[2])) #print(w1, w2, w3) - accumulator[f[0],i] += w1 - accumulator[f[1],j] += w2 - accumulator[f[2],k] += w3 + accumulatorW[f[0],i] += w1 + accumulatorW[f[1],j] += w2 + accumulatorW[f[2],k] += w3 + accumulator[f[0], i] += 1 + accumulator[f[1], j] += 1 + accumulator[f[2], k] += 1 t1 = False t2 = False t3 = False if np.abs(angtriSRT[0] - angtriSRT[1]) < angTol: - accumulator[f[0],j] += w1 - accumulator[f[1],i] += w2 - accumulator[f[2],k] += w3 + accumulatorW[f[0],j] += w1 + accumulatorW[f[1],i] += w2 + accumulatorW[f[2],k] += w3 + accumulator[f[0], j] += 1 + accumulator[f[1], i] += 1 + accumulator[f[2], k] += 1 t1 = True if np.abs(angtriSRT[1] - angtriSRT[2]) < angTol: - accumulator[f[0],i] += w1 - accumulator[f[1],k] += w2 - accumulator[f[2],j] += w3 + accumulatorW[f[0],i] += w1 + accumulatorW[f[1],k] += w2 + accumulatorW[f[2],j] += w3 + accumulator[f[0], i] += 1 + accumulator[f[1], k] += 1 + accumulator[f[2], j] += 1 t2 = True if np.abs(angtriSRT[2] - angtriSRT[0]) < angTol: - accumulator[f[0],k] += w1 - accumulator[f[1],j] += w2 - accumulator[f[2],i] += w3 + accumulatorW[f[0],k] += w1 + accumulatorW[f[1],j] += w2 + accumulatorW[f[2],i] += w3 + accumulator[f[0], k] += 1 + accumulator[f[1], j] += 1 + accumulator[f[2], i] += 1 t3 = True if (t1 and t2 and t3): - accumulator[f[0],k] += w1 - accumulator[f[1],i] += w2 - accumulator[f[2],j] += w3 + accumulatorW[f[0],k] += w1 + accumulatorW[f[1],i] += w2 + accumulatorW[f[2],j] += w3 + + accumulatorW[f[0], j] += w1 + accumulatorW[f[1], k] += w2 + accumulatorW[f[2], i] += w3 - accumulator[f[0], j] += w1 - accumulator[f[1], k] += w2 - accumulator[f[2], i] += w3 + accumulator[f[0], k] += 1 + accumulator[f[1], i] += 1 + accumulator[f[2], j] += 1 + + accumulator[f[0], j] += 1 + accumulator[f[1], k] += 1 + accumulator[f[2], i] += 1 mxvote = np.zeros(n_bands, dtype=np.int32) tvotes = np.zeros(n_bands, dtype=np.int32) band_cm = np.zeros(n_bands, dtype=np.float32) for q in range(n_bands): - mxvote[q] = np.amax(accumulator[:,q]) - tvotes[q] = np.sum(accumulator[:,q]) + mxvote[q] = np.amax(accumulatorW[:,q]) + tvotes[q] = np.sum(accumulatorW[:,q]) for i in range(n_bands): if tvotes[i] < 1: band_cm[i] = 0.0 else: - srt = np.argsort(accumulator[:,i]) - band_cm[i] = (accumulator[srt[-1],i] - accumulator[srt[-2],i]) / tvotes[i] + srt = np.argsort(accumulatorW[:,i]) + band_cm[i] = (accumulatorW[srt[-1],i] - accumulatorW[srt[-2],i]) / tvotes[i] bandRank = np.zeros(n_bands, dtype=np.float32) bandFam = np.zeros(n_bands, dtype=np.int32) for q in range(n_bands): - bandFam[q] = np.argmax(accumulator[:,q]) + bandFam[q] = np.argmax(accumulatorW[:,q]) bandRank = (n_bands - np.arange(n_bands)) / n_bands * band_cm * mxvote - return accumulator, bandFam, bandRank, band_cm + return accumulatorW, bandFam, bandRank, band_cm, accumulator @staticmethod @numba.jit(nopython=True, cache=True, fastmath=True, parallel=False) From 0f6e77d1f883111abec2aa5266d1dfcd06e00f6f Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Wed, 10 May 2023 07:52:39 -0400 Subject: [PATCH 111/177] Refinement optimization Signed-off by: David Rowenhorst --- pyebsdindex/tripletvote.py | 32 +++++++++++++++++++++++++------- 1 file changed, 25 insertions(+), 7 deletions(-) diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index b991ca3..568b911 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -486,11 +486,14 @@ def bandindex(self, band_norms, band_intensity = None, band_widths=None, verbose srt = np.flip(np.argsort(score)) #srt = np.flip(np.argsort(band_intensity[whGood])) whgood6 = whGood[srt[0:np.min([6, whGood.shape[0]])]] - #whgood6 = whGood[0:min(6, whGood.shape[0])] - if verbose > 2: - print("Good bands:", whGood+1) - print("Fit Bands: ", whgood6+1) + #if verbose > 2: + # print("Good bands:", whGood+1) + # print("Fit Bands: ", whgood6+1) weights6 = band_intensity[whgood6] + weights6 -= weights6.min() + weights6 *= 2*weights6.max() + weights6 += 1 + pflt6 = (np.asarray(polesCart[polematch[whgood6], :], dtype=np.float64)) bndnorm6 = (np.asarray(bandnorms[whgood6, :], dtype=np.float64)) #print('____') @@ -498,6 +501,21 @@ def bandindex(self, band_norms, band_intensity = None, band_widths=None, verbose #print(bndnorm6) #print('____') avequat, fit = self._refine_orientation_quest(bndnorm6, pflt6, weights=weights6) + + + whgood6 = whGood[0:min(6, whGood.shape[0])] + weights6 = band_intensity[whgood6] + weights6 -= weights6.min() + weights6 *= 2*weights6.max() + weights6 += 1 + pflt6 = (np.asarray(polesCart[polematch[whgood6], :], dtype=np.float64)) + bndnorm6 = (np.asarray(bandnorms[whgood6, :], dtype=np.float64)) + avequat2, fit2 = self._refine_orientation_quest(bndnorm6, pflt6, weights=weights6) + if fit2 > fit: + fit = fit2 + avequat = avequat2 + #else: + #print('False') fit = np.arccos(np.clip(fit, -1.0, 1.0))*RADEG #avequat, fit = self.refine_orientation(bandnorms,whGood,polematch) else: @@ -862,9 +880,9 @@ def _tripvote_numba(bandangs, LUT, angTol, tripAngles, tripID, nfam, n_bands): f = tripID[q,:] f = f[unsrtFID] #print(angTest0[q,:]) - w1 = (2.0 * angTol - (angTest0[0] + angTest0[1])) - w2 = (2.0 * angTol - (angTest0[0] + angTest0[2])) - w3 = (2.0 * angTol - (angTest0[1] + angTest0[2])) + w1 = ( angTol - 0.5*(angTest0[0] + angTest0[1]) ) + w2 = ( angTol - 0.5*(angTest0[0] + angTest0[2]) ) + w3 = ( angTol - 0.5*(angTest0[1] + angTest0[2]) ) #print(w1, w2, w3) accumulatorW[f[0],i] += w1 accumulatorW[f[1],j] += w2 From 0da15b6c5f57a4faf9f1d0b88dd39ee465bbe9f0 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Fri, 12 May 2023 08:55:26 -0400 Subject: [PATCH 112/177] More refinement updates Signed-off by: David Rowenhorst --- pyebsdindex/tripletvote.py | 37 ++++++++++++++++--------------------- 1 file changed, 16 insertions(+), 21 deletions(-) diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index 568b911..7a89032 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -485,39 +485,26 @@ def bandindex(self, band_norms, band_intensity = None, band_widths=None, verbose #print(score, whGood.shape[0]) srt = np.flip(np.argsort(score)) #srt = np.flip(np.argsort(band_intensity[whGood])) - whgood6 = whGood[srt[0:np.min([6, whGood.shape[0]])]] + whgood6 = whGood[srt[0:np.min([7, whGood.shape[0]])]] #if verbose > 2: # print("Good bands:", whGood+1) # print("Fit Bands: ", whgood6+1) weights6 = band_intensity[whgood6] weights6 -= weights6.min() - weights6 *= 2*weights6.max() + weights6 *= 2/weights6.max() weights6 += 1 + #weights6 = np.exp(weights6) pflt6 = (np.asarray(polesCart[polematch[whgood6], :], dtype=np.float64)) bndnorm6 = (np.asarray(bandnorms[whgood6, :], dtype=np.float64)) - #print('____') - #print(pflt6) - #print(bndnorm6) - #print('____') + avequat, fit = self._refine_orientation_quest(bndnorm6, pflt6, weights=weights6) + #fitfull = self._fitcheck(avequat, + # np.asarray(bandnorms[whGood, :]), np.asarray(polesCart[polematch[whGood], :] )) - whgood6 = whGood[0:min(6, whGood.shape[0])] - weights6 = band_intensity[whgood6] - weights6 -= weights6.min() - weights6 *= 2*weights6.max() - weights6 += 1 - pflt6 = (np.asarray(polesCart[polematch[whgood6], :], dtype=np.float64)) - bndnorm6 = (np.asarray(bandnorms[whgood6, :], dtype=np.float64)) - avequat2, fit2 = self._refine_orientation_quest(bndnorm6, pflt6, weights=weights6) - if fit2 > fit: - fit = fit2 - avequat = avequat2 - #else: - #print('False') fit = np.arccos(np.clip(fit, -1.0, 1.0))*RADEG - #avequat, fit = self.refine_orientation(bandnorms,whGood,polematch) + else: avequat = rotlib.om2qu(R) whmatch = np.nonzero(polematch >= 0)[0] @@ -1547,4 +1534,12 @@ def _pairvote_nb(bandnorms, bandangs, qsym, angTableReduce, poles, polesReduce, solSrt = np.argsort(solutionVotes) - return solutions, nsolutions, solutionVotes, solSrt \ No newline at end of file + return solutions, nsolutions, solutionVotes, solSrt + def _fitcheck(self, q, bandnorms, cartxstalpoles): + bandnorms = np.atleast_2d(bandnorms) + cartxstalpoles = np.atleast_2d(cartxstalpoles) + bandnorms_xstal = rotlib.quat_vector(q, bandnorms) + mean_dot = np.mean(np.sum(bandnorms_xstal*cartxstalpoles, axis = 1)) + # mean_ang = np.degrees(np.arccos(mean_dot)) + return mean_dot + From edbc64d236e67db07fa90e2e934f352cd9f38088 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Fri, 12 May 2023 16:48:49 -0400 Subject: [PATCH 113/177] Code cleanup Signed-off by: David Rowenhorst --- pyebsdindex/tripletvote.py | 14 ++++++++++---- 1 file changed, 10 insertions(+), 4 deletions(-) diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index 7a89032..1c5c2c7 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -482,17 +482,23 @@ def bandindex(self, band_norms, band_intensity = None, band_widths=None, verbose score = accumulator[[self.completelib['familyid'][polematch[whGood]]], [whGood]] score /= accumulator_nw[[self.completelib['familyid'][polematch[whGood]]], [whGood]] + 1.0e-6 score = np.squeeze(score) - #print(score, whGood.shape[0]) + srt = np.flip(np.argsort(score)) + #print(srt+1) + #print(score[srt]) + #print(band_intensity[whGood[srt]]) + #srt = np.flip(np.argsort(band_intensity[whGood])) - whgood6 = whGood[srt[0:np.min([7, whGood.shape[0]])]] + whgood6 = whGood[srt[0:np.min([6, whGood.shape[0]])]] #if verbose > 2: # print("Good bands:", whGood+1) # print("Fit Bands: ", whgood6+1) + #weights6 = score[srt[0:np.min([6, whGood.shape[0]])]] weights6 = band_intensity[whgood6] weights6 -= weights6.min() - weights6 *= 2/weights6.max() - weights6 += 1 + weights6 *= 1/weights6.max() + #weights6 += 1 + weights6 = np.exp(weights6**2) #weights6 = np.exp(weights6) pflt6 = (np.asarray(polesCart[polematch[whgood6], :], dtype=np.float64)) From 8963f4074b21a252103a6d86855f241eb0a583fb Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Wed, 17 May 2023 12:17:36 -0400 Subject: [PATCH 114/177] Initial attempt at ebsp v5 Signed-off by: David Rowenhorst --- pyebsdindex/ebsd_pattern.py | 66 +++++++++++++++++++++++++++---------- 1 file changed, 48 insertions(+), 18 deletions(-) diff --git a/pyebsdindex/ebsd_pattern.py b/pyebsdindex/ebsd_pattern.py index 6780a3a..00e7f6c 100644 --- a/pyebsdindex/ebsd_pattern.py +++ b/pyebsdindex/ebsd_pattern.py @@ -696,10 +696,15 @@ def read_header(self, path=None, bitdepth=None): # readInterval=[0, -1], arrayO else: self.version = version + per_pat_header = 4 + if self.version >= 5: + per_pat_header = 6 + if self.version >= 1: + memoffset = 8 if self.version >= 4: self.mysterybyte = np.fromfile(f, dtype=np.uint8, count=1) - + memoffset = 9 #loc0 = int(np.fromfile(f, dtype=np.uint64, count=1)) #currentloc = f.tell() #loc1 = loc0 @@ -717,20 +722,35 @@ def read_header(self, path=None, bitdepth=None): # readInterval=[0, -1], arrayO loc0 = int(np.fromfile(f, dtype=np.uint64, count=1)) counter += 1 f.seek(-8*counter, 1) # move back 8 bytes (or however far we needed to move into the file to find a legitamte offset. - loc02N = np.fromfile(f, dtype=np.uint64, count=int((loc0-8)/8+0.001)) + loc02N = np.fromfile(f, dtype=np.uint64, count=int((loc0)/8+0.001)) - loc1 = int((loc0-8)/8+0.001) + if self.version <=4: + loc1 = int((loc0-memoffset)/8+0.001) + + counter = 0 + while loc1 != counter: + if loc02N[counter] != 0: # a non-stored pattern? Crazy. + loc_i = int((loc02N[counter]-memoffset)/8) + loc1 = min([loc1, loc_i]) + counter += 1 + + + self.nPatterns = int((counter)) + elif self.version == 5: + f.seek(loc02N[0], 0) + patdata = np.fromfile(f, dtype=np.uint32, count=per_pat_header) + if patdata[0] == 1: + print("Sorry, compressed EBSP files are not supported") + return None + patternW = int(patdata[-2]) + patternH = int(patdata[-3]) + magic_indx = patternH + (patternW << 32) + wh = np.nonzero(loc02N == magic_indx) + self.nPatterns = wh[0].min() - counter = 0 - while loc1 != counter: - if loc02N[counter] != 0: # a non-stored pattern? Crazy. - loc_i = int((loc02N[counter]-8)/8) - loc1 = min([loc1, loc_i]) - counter += 1 - self.nPatterns = int((counter)) if self.version == 0: f.seek(0) @@ -739,6 +759,8 @@ def read_header(self, path=None, bitdepth=None): # readInterval=[0, -1], arrayO if self.version >= 4: f.seek(1,1) + + self.filePos = np.fromfile(f, dtype=np.uint64, count=self.nPatterns) # going to assume that all patterns are the same as the first pattern the file. @@ -746,7 +768,9 @@ def read_header(self, path=None, bitdepth=None): # readInterval=[0, -1], arrayO #patdata = np.fromfile(f, dtype=np.uint32, count=4) #patdata0 = np.fromfile(f, dtype=np.uint8, count=1) - patdata = np.fromfile(f, dtype=np.uint32, count=4) + #patdata = np.fromfile(f, dtype=np.uint32, count=4) + + patdata = np.fromfile(f, dtype=np.uint32, count=per_pat_header) if patdata[0] == 1: print("Sorry, compressed EBSP files are not supported") @@ -758,9 +782,9 @@ def read_header(self, path=None, bitdepth=None): # readInterval=[0, -1], arrayO #print(np.fromfile(f, dtype=np.uint32, count=8)) #print(np.fromfile(f, dtype=np.uint32, count=1)) - self.patternW = np.uint32(patdata[2]) - self.patternH = np.uint32(patdata[1]) - nbytespat = patdata[3] + self.patternW = np.uint32(patdata[-2]) + self.patternH = np.uint32(patdata[-3]) + nbytespat = patdata[-1] #if self.version == 1: @@ -794,7 +818,7 @@ def read_header(self, path=None, bitdepth=None): # readInterval=[0, -1], arrayO self.hasxypos = True else: loc0 = np.min(self.filePos[self.filePos > 0]) - f.seek(int(loc0 + 16 + nbytespat)) + f.seek(int(loc0 + 4*per_pat_header + nbytespat)) havepos = np.fromfile(f, dtype=np.uint8, count=1) if havepos > 0: footoffset = 1 @@ -808,9 +832,11 @@ def read_header(self, path=None, bitdepth=None): # readInterval=[0, -1], arrayO else: for i in range(self.nPatterns): if self.filePos[i] > 0: - f.seek(int(self.filePos[i] + 16 + nbytespat + footoffset)) - xall[i] = np.fromfile(f, dtype=np.float64, count=1) + f.seek(int(self.filePos[i] + 4*per_pat_header + nbytespat + footoffset)) + + x1 = np.fromfile(f, dtype=np.float64, count=1) #print(x1, i) + xall[i] = x1 f.seek(footoffset, 1) yall[i] = np.fromfile(f, dtype=np.float64, count=1) @@ -868,10 +894,14 @@ def pat_reader(self, patStart=0, nPatToRead=1): else: xyoffset = 1 + per_pat_head = 16 + if self.version >= 5: + per_pat_head = 24 + for i in range(int(patStart), int(patStart + nPatToRead)): ii = int(i - patStart) if self.filePos[i] > 0: - f.seek(int(self.filePos[i] + 16)) + f.seek(int(self.filePos[i] + per_pat_head)) readpats[ii, :] = np.fromfile(f, dtype=typeread, count=int(nPerPat)) if readxypos == True: f.seek(xyoffset, 1) From 6e3f8d25695f23142b8253ba00569f70192ca19e Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Fri, 19 May 2023 18:34:26 -0400 Subject: [PATCH 115/177] Implement simple rho based weighting for bands for orientation fit. Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_single.py | 13 ++++++++++--- 1 file changed, 10 insertions(+), 3 deletions(-) diff --git a/pyebsdindex/_ebsd_index_single.py b/pyebsdindex/_ebsd_index_single.py index 45f41ca..c4bf196 100644 --- a/pyebsdindex/_ebsd_index_single.py +++ b/pyebsdindex/_ebsd_index_single.py @@ -509,8 +509,10 @@ def _detectbands(self, pats, PC, xyloc=None, clparams=None, verbose=0, chunksize ) return banddata, bandnorm - def _indexbandsphase(self, banddata, bandnorm, verbose = 0): + def _indexbandsphase(self, banddata, bandnorm, verbose = 0, **kwargs): + rhomax = 1.0e12 + rhomax = self.bandDetectPlan.rhoMax * (1-self.bandDetectPlan.rhoMaskFrac) shpBandDat = banddata.shape npoints = int(banddata.size/(shpBandDat[-1])+0.1) nPhases = len(self.phaseLib) @@ -539,8 +541,13 @@ def _indexbandsphase(self, banddata, bandnorm, verbose = 0): bDat1 = bDat1[whgood] bandNorm1 = bandNorm1[whgood, :] indxData["pq"][0:nPhases, i] = np.sum(bDat1["max"], axis=0) - + adj_intensity = (-1*np.abs(bDat1["rho"]) * 0.5 / rhomax + 1) * bDat1["max"] + #adj_intensity = bDat1["avemax"] + #print(bDat1["max"]) + #print(adj_intensity) for j in range(len(self.phaseLib)): + + ( avequat, fit, @@ -550,7 +557,7 @@ def _indexbandsphase(self, banddata, bandnorm, verbose = 0): matchAttempts, totvotes, ) = self.phaseLib[j].bandindex( - bandNorm1, band_intensity=bDat1["avemax"], band_widths=bDat1["width"], verbose=verbose, + bandNorm1, band_intensity=adj_intensity, band_widths=bDat1["width"], verbose=verbose, ) # avequat,fit,cm,bandmatch,nMatch, matchAttempts = self.phaseLib[j].pairVoteOrientation(bandNorm1,goNumba=True) if nMatch >= 3: From 95bd541030b02840aa2f14b57adc68b1eb259405 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Tue, 30 May 2023 18:38:29 -0400 Subject: [PATCH 116/177] Initial attempt at HDF5 (EDAX oh5 spec) output file. Signed-off by: David Rowenhorst --- pyebsdindex/ebsdfile.py | 180 ++++++++++++++++++++++++++++++++++++- pyebsdindex/tripletvote.py | 1 + 2 files changed, 180 insertions(+), 1 deletion(-) diff --git a/pyebsdindex/ebsdfile.py b/pyebsdindex/ebsdfile.py index c89bace..0999d48 100644 --- a/pyebsdindex/ebsdfile.py +++ b/pyebsdindex/ebsdfile.py @@ -1,5 +1,6 @@ from pathlib import Path import numpy as np +import h5py from pyebsdindex import rotlib @@ -96,4 +97,181 @@ def writeang(filename, indexer, data, line += ' {:}'.format(phase) + '' line += '1'.rjust(7, ' ')+'' line += ('{:.3f}'.format(fit)).rjust(7, ' ') - f.write(line+'\r\n') \ No newline at end of file + f.write(line+'\r\n') + +def writeoh5(filename, indexer, data, + gridtype='SqrGrid', xstep=1.0, ystep=1.0, + ncols=None, nrows=None, datasetname='Scan 1'): + fpath = Path(filename).expanduser() + + with h5py.File(fpath, 'w') as f: + + f.create_dataset(datasetname +'/EBSD/Header/Camera Azimuthal Angle', data=np.array([np.float64(0.0)])) + f.create_dataset(datasetname + '/EBSD/Header/Camera Diameter', data=np.array([np.float64(0.0)])) + f.create_dataset(datasetname + '/EBSD/Header/Camera Elevation Angle', data=np.array([np.float64(indexer.camElev)])) + comment = ' ' + chararray = np.chararray(1, itemsize=len(comment)+1) + chararray[:] = comment + f.create_dataset(datasetname + '/EBSD/Header/Comments', data=chararray) + f.create_dataset(datasetname + '/EBSD/Header/Coordinate System/ID', data=np.array([np.int32(2)])) + gtype = str(gridtype) + chararray = np.chararray(1, itemsize=len(gtype)+1) + chararray[:] = gtype + f.create_dataset(datasetname + '/EBSD/Header/Grid Type', data=chararray) + f.create_dataset(datasetname + '/EBSD/Header/Pattern Center Calibration/AdjMode', data=np.array([np.int32(3)])) + f.create_dataset(datasetname + '/EBSD/Header/Pattern Center Calibration/x-star', data=np.array([np.float64(indexer.PC[0])])) + f.create_dataset(datasetname + '/EBSD/Header/Pattern Center Calibration/xAdjCoeff0', data=np.array([np.float64(0.0)])) + f.create_dataset(datasetname + '/EBSD/Header/Pattern Center Calibration/xAdjCoeff1', data=np.array([np.float64(0.0)])) + f.create_dataset(datasetname + '/EBSD/Header/Pattern Center Calibration/xAdjCoeff2', data=np.array([np.float64(0.0)])) + f.create_dataset(datasetname + '/EBSD/Header/Pattern Center Calibration/y-star', data=np.array([np.float64(indexer.PC[1])])) + f.create_dataset(datasetname + '/EBSD/Header/Pattern Center Calibration/yAdjCoeff0', data=np.array([np.float64(0.0)])) + f.create_dataset(datasetname + '/EBSD/Header/Pattern Center Calibration/yAdjCoeff1', data=np.array([np.float64(0.0)])) + f.create_dataset(datasetname + '/EBSD/Header/Pattern Center Calibration/yAdjCoeff2', data=np.array([np.float64(0.0)])) + f.create_dataset(datasetname + '/EBSD/Header/Pattern Center Calibration/z-star', data=np.array([np.float64(indexer.PC[2])])) + f.create_dataset(datasetname + '/EBSD/Header/Pattern Center Calibration/zAdjCoeff0', data=np.array([np.float64(0.0)])) + f.create_dataset(datasetname + '/EBSD/Header/Pattern Center Calibration/zAdjCoeff1', data=np.array([np.float64(0.0)])) + f.create_dataset(datasetname + '/EBSD/Header/Pattern Center Calibration/zAdjCoeff2', data=np.array([np.float64(0.0)])) + pcount = 1 + nphase = len(indexer.phaseLib) + + + for phase in indexer.phaseLib: + f.create_dataset(datasetname + '/EBSD/Header/Phase/'+str(pcount)+'/LGsymID', data=np.array([np.int32(phase.lauecode)])) + f.create_dataset(datasetname + '/EBSD/Header/Phase/' + str(pcount) + '/Lattice Constant a', + data=np.array([np.int32(phase.latticeparameter[0]*10)])) + + f.create_dataset(datasetname + '/EBSD/Header/Phase/' + str(pcount) + '/Lattice Constant alpha', + data=np.array([np.int32(phase.latticeparameter[3])])) + + f.create_dataset(datasetname + '/EBSD/Header/Phase/' + str(pcount) + '/Lattice Constant b', + data=np.array([np.int32(phase.latticeparameter[1] * 10)])) + + f.create_dataset(datasetname + '/EBSD/Header/Phase/' + str(pcount) + '/Lattice Constant beta', + data=np.array([np.int32(phase.latticeparameter[4])])) + + f.create_dataset(datasetname + '/EBSD/Header/Phase/' + str(pcount) + '/Lattice Constant c', + data=np.array([np.int32(phase.latticeparameter[2] * 10)])) + + f.create_dataset(datasetname + '/EBSD/Header/Phase/' + str(pcount) + '/Lattice Constant gamma', + data=np.array([np.int32(phase.latticeparameter[5])])) + pname = str(phase.phasename) + chararray = np.chararray(1, itemsize=len(pname)+1) + chararray[:] = pname + f.create_dataset(datasetname + '/EBSD/Header/Phase/' + str(pcount) + '/MaterialName', data=chararray) + f.create_dataset(datasetname + '/EBSD/Header/Phase/' + str(pcount) + '/NumberFamilies', + data=np.array([np.int32(phase.npolefamilies)])) + + + poles = np.array(phase.polefamilies).astype(np.int32) + if (phase.lauecode == 62) or (phase.lauecode == 6): + if poles.shape[-1] == 4: + poles = poles[:, [0, 1, 3]] + + famtype = np.ones((phase.npolefamilies), dtype=[('H', 'i4'), + ('K', 'i4'), + ('L', 'i4'), + ('Diffraction Intensity', 'f4'), + ('Use in Indexing', 'i1'), + ('Show bands', 'i1'), + ('Hough Rank', 'f4'), + ('Beta Rank', 'f4')]) + famtype['Hough Rank'][:] = -1.0 + famtype['Beta Rank'][:] = -1.0 + famtype['H'] = np.squeeze(poles[:,0]) + famtype['K'] = np.squeeze(poles[:, 1]) + famtype['L'] = np.squeeze(poles[:, 2]) + + f.create_dataset(datasetname + '/EBSD/Header/Phase/' + str(pcount) + '/hkl Families', + data=famtype) + pcount +=1 + + f.create_dataset(datasetname + '/EBSD/Header/Sample Tilt', + data=np.array([np.float64(indexer.sampleTilt)])) + + if indexer.fID is not None: + if indexer.fID.xStep > 1e-6: + xstep = np.array([np.float32(indexer.fID.xStep)]) + ystep = np.array([np.float32(indexer.fID.yStep)]) + + f.create_dataset(datasetname + '/EBSD/Header/Step X', + data=xstep) + f.create_dataset(datasetname + '/EBSD/Header/Step Y', + data=ystep) + + if ncols is None: + ncols = 1 + nrows = data.shape[-1] + if indexer.fID is not None: + if indexer.fID.nCols is not None: + ncols = indexer.fID.nCols + nrows = indexer.fID.nRows + else: + if nrows is None: + nrows = np.ceil(data.shape[-1] / ncols) + + ncols = np.array([np.int32(ncols)]) + nrows = np.array([np.int32(nrows)]) + + f.create_dataset(datasetname + '/EBSD/Header/nColumns', + data=ncols) + f.create_dataset(datasetname + '/EBSD/Header/nRows', + data=nrows) + + + + npoints = data[-1].shape[-1] + nphase = data.shape[0] - 1 + if nphase == 1: + phaseIDadd = 0 + else: + phaseIDadd = 1 + eulers = rotlib.qu2eu(data[-1]['quat']) + phi1 = np.squeeze(eulers[:,0]).astype(np.float32) + phi = np.squeeze(eulers[:, 1]).astype(np.float32) + phi2 = np.squeeze(eulers[:, 2]).astype(np.float32) + + f.create_dataset(datasetname + '/EBSD/Data/Phi1', + data=phi1) + f.create_dataset(datasetname + '/EBSD/Data/Phi', + data=phi) + f.create_dataset(datasetname + '/EBSD/Data/Phi2', + data=phi2) + f.create_dataset(datasetname + '/EBSD/Data/IQ', + data=(data[-1]['pq']).astype(np.float32)) + + f.create_dataset(datasetname + '/EBSD/Data/Fit', + data=(data[-1]['fit']).astype(np.float32)) + + + + phaseid = data[-1]['phase']+ phaseIDadd + ci = data[-1]['cm'] + wh = np.nonzero(phaseid < 0)[0] + if wh.shape[-1] > 0: + ci[wh] = -1.0 + phaseid = (phaseid).clip(0) + + + f.create_dataset(datasetname + '/EBSD/Data/Phase', + data=(phaseid).astype(np.int8)) + + f.create_dataset(datasetname + '/EBSD/Data/CI', + data=(ci).astype(np.float32)) + + x = (np.arange(ncols[0] * nrows[0], dtype=int) % ncols[0]).astype(np.float32) * xstep[0] + y = (np.arange(ncols[0] * nrows[0], dtype=int) // ncols[0]).astype(np.float32) * ystep[0] + + f.create_dataset(datasetname + '/EBSD/Data/X Position', data=x) + f.create_dataset(datasetname + '/EBSD/Data/Y Position', data=y) + f.create_dataset(datasetname + '/EBSD/Data/Valid', data=np.zeros(npoints, dtype=np.int8)) + f.create_dataset(datasetname + '/EBSD/Data/SEM Signal', data=np.zeros(npoints, dtype=np.int32)) + + version = 'OIM Analysis 8.6.103 x64 [29 Sep 2022]' + chararray = np.chararray(1, itemsize=len(version)+1) + chararray[:] = version + f.create_dataset('Version', data=chararray) + man = 'EDAX' + chararray = np.chararray(1, itemsize=len(man)+1 ) + chararray[:] = man + f.create_dataset('Manufacturer', data=chararray) + f.close() \ No newline at end of file diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index 1c5c2c7..c5a6359 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -445,6 +445,7 @@ def bandindex(self, band_norms, band_intensity = None, band_widths=None, verbose fit = 1000.0 nMatch = -1 avequat = np.zeros(4, dtype=np.float32) + avequat[0] = 1.0 polematch = np.zeros([n_bands], dtype = int)-1 whGood = -1 From 44463a77f425705129ddde88ea62d2d8c3d98314 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Thu, 1 Jun 2023 08:08:52 -0400 Subject: [PATCH 117/177] Set-up default early exit band matching. Signed-off by: David Rowenhorst --- pyebsdindex/tripletvote.py | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index c5a6359..5ddfe82 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -41,12 +41,13 @@ def addphase(libtype=None, phasename=None, spacegroup=None, latticeparameter=None, - polefamilies=None): + polefamilies=None, nband_earlyexit = 10): if libtype is not None: #set up generic FCC if str(libtype).upper() == 'FCC': + nband_earlyexit=8 if phasename is None: phasename = 'FCC' if spacegroup is None: @@ -62,6 +63,7 @@ def addphase(libtype=None, phasename=None, # Set up a generic BCC if str(libtype).upper() == 'BCC': + nband_earlyexit=8 if phasename is None: phasename = 'BCC' if spacegroup is None: @@ -99,8 +101,11 @@ def addphase(libtype=None, phasename=None, if polefamilies is None: polefamilies = np.array([[0, 0, 2], [1, 1, 1], [0, 2, 2], [1, 1, 3]]).astype(np.int32) - triplib = BandIndexer(phasename=phasename, spacegroup=spacegroup, - latticeparameter=latticeparameter, polefamilies=np.atleast_2d(polefamilies)) + triplib = BandIndexer(phasename=phasename, + spacegroup=spacegroup, + latticeparameter=latticeparameter, + polefamilies=np.atleast_2d(polefamilies), + nband_earlyexit=nband_earlyexit) triplib.build_trip_lib() return triplib From f07278b30809231373887bedd390dc295c6c25b9 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Thu, 1 Jun 2023 15:02:00 -0400 Subject: [PATCH 118/177] Fix phase early exit. Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_single.py | 13 ++++++++----- 1 file changed, 8 insertions(+), 5 deletions(-) diff --git a/pyebsdindex/_ebsd_index_single.py b/pyebsdindex/_ebsd_index_single.py index c4bf196..33b2139 100644 --- a/pyebsdindex/_ebsd_index_single.py +++ b/pyebsdindex/_ebsd_index_single.py @@ -297,7 +297,7 @@ def __init__( rhoMaskFrac=0.15, nBands=9, patDim=None, - nband_earlyexit = 20, + nband_earlyexit = None, **kwargs ): """Create an EBSD indexer.""" @@ -526,10 +526,13 @@ def _indexbandsphase(self, banddata, bandnorm, verbose = 0, **kwargs): return indxData if self.nband_earlyexit is None: - earlyexit = shpBandDat[1] # default to all the poles. - # for ph in self.phaselist: - # if hasattr(ph, 'nband_earlyexit'): - # earlyexit = min(earlyexit, ph.nband_earlyexit) + earlyexit = -1 + for ph in self.phaselist: + if hasattr(ph, 'nband_earlyexit'): + earlyexit = max(earlyexit, ph.nband_earlyexit) + if earlyexit < 0: + earlyexit = shpBandDat[1] # default to all the poles. + self.nband_earlyexit = earlyexit else: earlyexit = self.nband_earlyexit From 2990f61c1a5ba70c4d960096abfcf588670458d8 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Tue, 6 Jun 2023 15:30:44 -0400 Subject: [PATCH 119/177] Attempt to fix multiple GPU for CUDA. Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_parallel.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index 4c7bdf6..b77ef52 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -315,7 +315,7 @@ def index_pats_distributed( # workers do not know where to find the PyEBSDIndex module. ray.init( num_cpus=int(np.round(n_cpu_nodes)), - num_gpus=ngpu, + num_gpus=ngpu*ngpuwrker, _node_ip_address=RAYIPADDRESS, #"0.0.0.0", runtime_env={"env_vars": {"PYTHONPATH": os.path.dirname(os.path.dirname(__file__))}}, logging_level=logging.WARNING, @@ -390,8 +390,9 @@ def index_pats_distributed( gpuworkers.append( # make a new Ray Actor that can call the indexer defined in shared memory. # These actors are read/write, thus can initialize the GPU queues - GPUWorker.options(num_cpus=ncpugpu_per_wrker, num_gpus=ngpu_per_wrker).remote( - i, clparamfunction, gpu_id=gpu_id + #GPUWorker.options(num_cpus=ncpugpu_per_wrker, num_gpus=ngpu_per_wrker).remote( + GPUWorker.options(num_cpus=ncpugpu_per_wrker, num_gpus=1).remote( + actorid=i, clparammodule=clparamfunction, gpu_id=gpu_id ) ) gjob = gpujobs.pop(0) From 2ec5c9bd6229d1f1bb805954dc500840745302e7 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Tue, 6 Jun 2023 16:31:44 -0400 Subject: [PATCH 120/177] Correct for multiple GPUs in CUDA environments. Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_parallel.py | 16 ++++++++++++---- 1 file changed, 12 insertions(+), 4 deletions(-) diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index b77ef52..7d46f86 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -267,6 +267,11 @@ def index_pats_distributed( else: if ngpu is None: ngpu = len(clparam.gpu) + cudagpuvis = '"' + for cdgpu in range(len(clparam.gpu)): + cudagpuvis += str(cdgpu)+',' + cudagpuvis += '"' + #ngpupnode = ngpu / n_cpu_nodes except: ngpu = 0 @@ -317,7 +322,9 @@ def index_pats_distributed( num_cpus=int(np.round(n_cpu_nodes)), num_gpus=ngpu*ngpuwrker, _node_ip_address=RAYIPADDRESS, #"0.0.0.0", - runtime_env={"env_vars": {"PYTHONPATH": os.path.dirname(os.path.dirname(__file__))}}, + runtime_env={"env_vars": + {"PYTHONPATH": os.path.dirname(os.path.dirname(__file__)), + "CUDA_VISIBLE_DEVICES":cudagpuvis }}, logging_level=logging.WARNING, ) # Supress INFO messages from ray. @@ -391,8 +398,8 @@ def index_pats_distributed( gpuworkers.append( # make a new Ray Actor that can call the indexer defined in shared memory. # These actors are read/write, thus can initialize the GPU queues #GPUWorker.options(num_cpus=ncpugpu_per_wrker, num_gpus=ngpu_per_wrker).remote( - GPUWorker.options(num_cpus=ncpugpu_per_wrker, num_gpus=1).remote( - actorid=i, clparammodule=clparamfunction, gpu_id=gpu_id + GPUWorker.options(num_cpus=ncpugpu_per_wrker, num_gpus=ngpu_per_wrker).remote( + actorid=i, clparammodule=clparamfunction, gpu_id=gpu_id, cudavis = cudagpuvis ) ) gjob = gpujobs.pop(0) @@ -634,7 +641,7 @@ def __optimizegpuchunk__(indexer, ngpupro, gpu_id, clparam): @ray.remote(num_cpus=1, num_gpus=1) class GPUWorker: - def __init__(self, actorid=0, clparammodule=None, gpu_id=None): + def __init__(self, actorid=0, clparammodule=None, gpu_id=None, cudavis = '0'): # sys.path.append(path.dirname(path.dirname(__file__))) # do this to help Ray find the program files # import openclparam # do this to help Ray find the program files # device, context, queue, program, mf @@ -642,6 +649,7 @@ def __init__(self, actorid=0, clparammodule=None, gpu_id=None): # self.indxstart = None # self.indxend = None # self.rate = None + os.environ["CUDA_VISIBLE_DEVICES"] = cudavis self.actorID = actorid self.openCLParams = None self.useGPU = False From c9057135e5905f7f5c09a15bd35907b683b8e935 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Tue, 6 Jun 2023 16:55:39 -0400 Subject: [PATCH 121/177] Bug fix Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_parallel.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index 7d46f86..c8cff3a 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -267,10 +267,9 @@ def index_pats_distributed( else: if ngpu is None: ngpu = len(clparam.gpu) - cudagpuvis = '"' + cudagpuvis = '' for cdgpu in range(len(clparam.gpu)): cudagpuvis += str(cdgpu)+',' - cudagpuvis += '"' #ngpupnode = ngpu / n_cpu_nodes except: From 8ab8c0262858e7a69a90d7a66495c2bd3aa63487 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Tue, 6 Jun 2023 18:45:09 -0400 Subject: [PATCH 122/177] Improved scheduling for NVIDIA Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_parallel.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index c8cff3a..db6dfd3 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -256,6 +256,7 @@ def index_pats_distributed( ngpu = None + cudagpuvis = '0' if gpu_id is not None: ngpu = np.atleast_1d(gpu_id).shape[0] @@ -286,6 +287,8 @@ def index_pats_distributed( ngpupro = 8 if n_cpu_nodes - ngpu < 2: ngpupro = 2 + if clparam.platform.vendor == 'NVIDIA Corporation': + ngpupro = 2 n_cpu_per_gpu = max(min(1.0, n_cpu_nodes-ngpu), 0.5/ngpu) From ca3bff8af21750b2fa0ad6541508a02e57e8bdcb Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Wed, 7 Jun 2023 08:25:00 -0400 Subject: [PATCH 123/177] Linux scheduling correction Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_parallel.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index db6dfd3..f6d3315 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -45,8 +45,8 @@ from pyebsdindex import band_detect as band_detect RAYIPADDRESS = '127.0.0.1' -osplatform = platform.system() -if osplatform == 'Darwin': +OSPLATFORM = platform.system() +if OSPLATFORM == 'Darwin': RAYIPADDRESS = '0.0.0.0' # the localhost address does not work on macOS when on a VPN def index_pats_distributed( @@ -287,7 +287,7 @@ def index_pats_distributed( ngpupro = 8 if n_cpu_nodes - ngpu < 2: ngpupro = 2 - if clparam.platform.vendor == 'NVIDIA Corporation': + if OSPLATFORM == 'Linux': ngpupro = 2 n_cpu_per_gpu = max(min(1.0, n_cpu_nodes-ngpu), 0.5/ngpu) @@ -375,7 +375,7 @@ def index_pats_distributed( ncpupatsdone = 0.0 if keep_log is True: - if osplatform != 'Windows': + if OSPLATFORM != 'Windows': newline = "\n" else: newline = "\r\n" From fb4cad31033e66b9bba96e359d4fe4e87bf1e864 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Wed, 7 Jun 2023 18:13:37 -0400 Subject: [PATCH 124/177] Improved scheduling Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_parallel.py | 99 ++++++++++++++++------------- 1 file changed, 55 insertions(+), 44 deletions(-) diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index f6d3315..71d2bcd 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -282,13 +282,13 @@ def index_pats_distributed( if ngpu > 0: - ngpupro = 12 # number of processes per gpu that will serve data to the gpu - if n_cpu_nodes - ngpu < 8: + ngpupro = max(12, ngpu*2) # number of processes that will serve data to the gpu + if n_cpu_nodes < 8: ngpupro = 8 - if n_cpu_nodes - ngpu < 2: - ngpupro = 2 - if OSPLATFORM == 'Linux': + if n_cpu_nodes < 2: ngpupro = 2 + #if OSPLATFORM == 'Linux': + # ngpupro = 2 n_cpu_per_gpu = max(min(1.0, n_cpu_nodes-ngpu), 0.5/ngpu) @@ -394,46 +394,53 @@ def index_pats_distributed( #print(ngpuwrker, ncpugpu_per_wrker, ngpu_per_wrker) #print(ncpuwrker, ncpucpu_per_worker) - - for i in range(ngpuwrker): - - gpuworkers.append( # make a new Ray Actor that can call the indexer defined in shared memory. - # These actors are read/write, thus can initialize the GPU queues - #GPUWorker.options(num_cpus=ncpugpu_per_wrker, num_gpus=ngpu_per_wrker).remote( - GPUWorker.options(num_cpus=ncpugpu_per_wrker, num_gpus=ngpu_per_wrker).remote( - actorid=i, clparammodule=clparamfunction, gpu_id=gpu_id, cudavis = cudagpuvis - ) - ) - gjob = gpujobs.pop(0) - if inputmode == "filemode": - gputask.append( - gpuworkers[i].findbands.remote(gjob, - pats=None, - indexer=remote_indexer + gpu_launched = 0 + cpu_launched = 0 + while ncpudone < njobs: + #for i in range(ngpuwrker): + + while (gpu_launched < ngpuwrker) and (len(gpujobs) > 0): + i = len(gpuworkers) + gpuworkers.append( # make a new Ray Actor that can call the indexer defined in shared memory. + # These actors are read/write, thus can initialize the GPU queues + #GPUWorker.options(num_cpus=ncpugpu_per_wrker, num_gpus=ngpu_per_wrker).remote( + GPUWorker.options(num_cpus=ncpugpu_per_wrker, num_gpus=ngpu_per_wrker).remote( + actorid=i, clparammodule=clparamfunction, gpu_id=gpu_id, cudavis = cudagpuvis ) ) - else: - gputask.append( - gpuworkers[i].findbands.remote(gjob, - pats = pats[gjob.pstart:gjob.pend, :, :], - indexer=remote_indexer, + gjob = gpujobs.pop(0) + if inputmode == "filemode": + gputask.append( + gpuworkers[i].findbands.remote(gjob, + pats=None, + indexer=remote_indexer + ) ) - ) - gtaskindex.append(gjob) - ngpusubmit += 1 - - # initiate the CPU workers. - #print(len(gpuworkers), len(gputask)) - for i in range(ncpuwrker): - cpuworkers.append( # make a new Ray Actor that can call the indexer defined in shared memory. - # These actors are read/write, thus can initialize the GPU queues - CPUWorker.options(num_cpus=ncpucpu_per_worker).remote(i)) - #CPUWorker.options(num_cpus=1.0, num_gpus=0).remote(i)) - cputask.append(cpuworkers[i].indexpoles.remote(None, None, None,indexer=remote_indexer)) - ctaskindex.append(None) - #print(len(cpuworkers)) + else: + gputask.append( + gpuworkers[i].findbands.remote(gjob, + pats = pats[gjob.pstart:gjob.pend, :, :], + indexer=remote_indexer, + ) + ) + gtaskindex.append(gjob) + gpu_launched += 1 + + # initiate the CPU workers. + #print(len(gpuworkers), len(gputask)) + #for i in range(ncpuwrker): + if (cpu_launched < ncpuwrker) and (ncpudone < njobs): + i = len(cpuworkers) + cpuworkers.append( # make a new Ray Actor that can call the indexer defined in shared memory. + # These actors are read/write, thus can initialize the GPU queues + CPUWorker.options(num_cpus=ncpucpu_per_worker).remote(i)) + #CPUWorker.options(num_cpus=1.0, num_gpus=0).remote(i)) + cputask.append(cpuworkers[i].indexpoles.remote(None, None, None,indexer=remote_indexer)) + ctaskindex.append(None) + cpu_launched += 1 + #print(len(cpuworkers)) + - while ncpudone < njobs: if ngpudone < njobs: # check if gpu is done donewrker, busy = ray.wait(gputask,num_returns = len(gputask), timeout=0.01) @@ -471,6 +478,7 @@ def index_pats_distributed( gtaskindex[jid] = gjob ngpusubmit += 1 else: # no more gpu tasks to submit + #del gpuworkers[jid] del gpuworkers[jid] del gputask[jid] del gtaskindex[jid] @@ -539,9 +547,12 @@ def index_pats_distributed( #time.sleep(0.001) if message != 'Error': if ncpudone == njobs: - del cpuworkers[jid] - del cputask[jid] - del ctaskindex[jid] + cpuworkers[jid] = None + cputask[jid] = None + ctaskindex[jid] = None + #del cpuworkers[jid] + #del cputask[jid] + #del ctaskindex[jid] elif len(cpujobs) > 0: cjob = cpujobs.pop(0) banddata = banddataout[cjob.pstart - patstart: cjob.pend - patstart, :] From b499b13427aa9d9aa274c5e05e16e61f2090bc19 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Wed, 7 Jun 2023 18:39:30 -0400 Subject: [PATCH 125/177] Bug fix Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_parallel.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index 71d2bcd..f906987 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -292,7 +292,7 @@ def index_pats_distributed( n_cpu_per_gpu = max(min(1.0, n_cpu_nodes-ngpu), 0.5/ngpu) - ngpuwrker = ngpupro * ngpu + ngpuwrker = ngpupro ngpu_per_wrker = 1.0/ngpuwrker - 1.0e-6 # fraction of a GPU to give to each worker (band finding worker) ncpugpu_per_wrker = n_cpu_per_gpu/ngpuwrker - 1.0e-6 # fraction of a cpu to allocate to each gpu worker From b4bfbbf877d13df3020f09d3ef927dc72d764ec8 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Wed, 7 Jun 2023 20:16:01 -0400 Subject: [PATCH 126/177] Improved scheduling Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_parallel.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index f906987..a312191 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -292,7 +292,7 @@ def index_pats_distributed( n_cpu_per_gpu = max(min(1.0, n_cpu_nodes-ngpu), 0.5/ngpu) - ngpuwrker = ngpupro + ngpuwrker = ngpupro ngpu_per_wrker = 1.0/ngpuwrker - 1.0e-6 # fraction of a GPU to give to each worker (band finding worker) ncpugpu_per_wrker = n_cpu_per_gpu/ngpuwrker - 1.0e-6 # fraction of a cpu to allocate to each gpu worker @@ -399,7 +399,7 @@ def index_pats_distributed( while ncpudone < njobs: #for i in range(ngpuwrker): - while (gpu_launched < ngpuwrker) and (len(gpujobs) > 0): + if (gpu_launched < ngpuwrker) and (len(gpujobs) > 0): i = len(gpuworkers) gpuworkers.append( # make a new Ray Actor that can call the indexer defined in shared memory. # These actors are read/write, thus can initialize the GPU queues @@ -632,7 +632,7 @@ def __optimizegpuchunk__(indexer, ngpupro, gpu_id, clparam): memperpat = 4.0*float(patdim[0] * patdim[1] + 6.0 * rdndim[0] * rdndim[1])# rough estimate #print('Mem/pat:', memperpat) - chunkguess = (float(gmem)/float(ngpupro)) / memperpat + chunkguess = ngpu*(float(gmem)/float(ngpupro)) / memperpat #print('chunkguess:', chunkguess) From 5026476de8247823c789765384dcb71985492239 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Wed, 7 Jun 2023 20:57:20 -0400 Subject: [PATCH 127/177] Less overhead in starting GPU worker Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_parallel.py | 10 ++++++---- pyebsdindex/opencl/openclparam.py | 5 ++--- 2 files changed, 8 insertions(+), 7 deletions(-) diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index a312191..074f93e 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -316,7 +316,7 @@ def index_pats_distributed( ray.shutdown() - print("num cpu/gpu, and number of patterns per iteration:", n_cpu_nodes, ngpu, chunksize) + # ray.init(num_cpus=n_cpu_nodes,num_gpus=ngpu,_system_config={"maximum_gcs_destroyed_actor_cached_count": n_cpu_nodes}) # Need to append path for installs from source ... otherwise the ray # workers do not know where to find the PyEBSDIndex module. @@ -330,6 +330,8 @@ def index_pats_distributed( logging_level=logging.WARNING, ) # Supress INFO messages from ray. + print("num cpu/gpu, and number of patterns per iteration:", n_cpu_nodes, ngpu, chunksize) + # Place indexer obj in shared memory store so all workers can use it - this is read only. remote_indexer = ray.put(indexer) # Get the function that will collect opencl parameters - if opencl @@ -399,7 +401,7 @@ def index_pats_distributed( while ncpudone < njobs: #for i in range(ngpuwrker): - if (gpu_launched < ngpuwrker) and (len(gpujobs) > 0): + while (gpu_launched < ngpuwrker) and (len(gpujobs) > 0): i = len(gpuworkers) gpuworkers.append( # make a new Ray Actor that can call the indexer defined in shared memory. # These actors are read/write, thus can initialize the GPU queues @@ -670,7 +672,7 @@ def __init__(self, actorid=0, clparammodule=None, gpu_id=None, cudavis = '0'): try: if ( sys.platform != "darwin" - ): # linux with NVIDIA (unsure if it is the os or GPU type) is slow to make a + ): # linux with NVIDIA (unsure if it is the os or GPU type) is slow to make a context self.openCLParams = clparammodule() else: # MacOS handles GPU memory conflicts much better when the context is destroyed between each # run, and has very low overhead for making the context. @@ -684,7 +686,7 @@ def __init__(self, actorid=0, clparammodule=None, gpu_id=None, cudavis = '0'): gpu_list = np.atleast_1d(gpu_id) ngpu = gpu_list.shape[0] self.openCLParams.gpu_id = gpu_list[self.actorID % ngpu] - self.openCLParams.get_context() + #self.openCLParams.get_context() #self.openCLParams.get_queue() self.useGPU = True except: diff --git a/pyebsdindex/opencl/openclparam.py b/pyebsdindex/opencl/openclparam.py index 067499c..028d811 100644 --- a/pyebsdindex/opencl/openclparam.py +++ b/pyebsdindex/opencl/openclparam.py @@ -43,6 +43,7 @@ def __init__(self, gpu_id=0): self.queue = None self.memflags = cl.mem_flags + try: self.get_context() @@ -72,7 +73,7 @@ def get_context(self): kernel_location = path.dirname(__file__) self.prg = cl.Program(self.ctx,open(path.join(kernel_location,'clkernels.cl')).read()).build() - def get_queue(self, gpu_id=None, random_gpu=False): + def get_queue(self, gpu_id=None): if self.ctx is None: self.get_context() @@ -80,8 +81,6 @@ def get_queue(self, gpu_id=None, random_gpu=False): if gpu_id is None: gpu_id = self.gpu_id - if random_gpu == True: - gpu_id = np.random.randint(len(self.gpu)) gpuindx = min(len(self.gpu)-1, gpu_id) self.gpu_id = gpuindx self.queue = cl.CommandQueue(self.ctx, device=self.gpu[gpuindx]) From e374b1779f0c9b280607a9598985fc8d754da024 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Thu, 15 Jun 2023 09:28:39 -0400 Subject: [PATCH 128/177] Checkpoint... Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_parallel.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index 074f93e..46be643 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -284,7 +284,7 @@ def index_pats_distributed( if ngpu > 0: ngpupro = max(12, ngpu*2) # number of processes that will serve data to the gpu if n_cpu_nodes < 8: - ngpupro = 8 + ngpupro = min(ngpupro,8) if n_cpu_nodes < 2: ngpupro = 2 #if OSPLATFORM == 'Linux': @@ -330,7 +330,7 @@ def index_pats_distributed( logging_level=logging.WARNING, ) # Supress INFO messages from ray. - print("num cpu/gpu, and number of patterns per iteration:", n_cpu_nodes, ngpu, chunksize) + print("num cpu/gpu, and number of patterns per iteration:", n_cpu_nodes, ngpu, chunksize, ngpuwrker, ncpuwrker) # Place indexer obj in shared memory store so all workers can use it - this is read only. remote_indexer = ray.put(indexer) @@ -402,6 +402,8 @@ def index_pats_distributed( #for i in range(ngpuwrker): while (gpu_launched < ngpuwrker) and (len(gpujobs) > 0): + #if (gpu_launched < ngpuwrker) and (len(gpujobs) > 0): + i = len(gpuworkers) gpuworkers.append( # make a new Ray Actor that can call the indexer defined in shared memory. # These actors are read/write, thus can initialize the GPU queues From f7a9c834382e8759bdbebf5a9d1964f22cc7bc48 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Thu, 15 Jun 2023 20:36:46 -0400 Subject: [PATCH 129/177] Change setup of GPU context setup Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_parallel.py | 7 ++++--- pyebsdindex/_ebsd_index_single.py | 5 ++++- pyebsdindex/opencl/openclparam.py | 24 ++++++++++++------------ 3 files changed, 20 insertions(+), 16 deletions(-) diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index 46be643..8015da9 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -326,7 +326,7 @@ def index_pats_distributed( _node_ip_address=RAYIPADDRESS, #"0.0.0.0", runtime_env={"env_vars": {"PYTHONPATH": os.path.dirname(os.path.dirname(__file__)), - "CUDA_VISIBLE_DEVICES":cudagpuvis }}, + }}, logging_level=logging.WARNING, ) # Supress INFO messages from ray. @@ -682,13 +682,14 @@ def __init__(self, actorid=0, clparammodule=None, gpu_id=None, cudavis = '0'): self.openCLParams = clparammodule() # self.openCLParams.gpu_id = 0 # self.openCLParams.gpu_id = 1 - self.openCLParams.gpu_id = self.actorID % self.openCLParams.ngpu + # self.openCLParams.gpu_id = self.actorID % self.openCLParams.ngpu if gpu_id is None: gpu_id = np.arange(self.openCLParams.ngpu) gpu_list = np.atleast_1d(gpu_id) ngpu = gpu_list.shape[0] + #print(self.actorID, ngpu) self.openCLParams.gpu_id = gpu_list[self.actorID % ngpu] - #self.openCLParams.get_context() + self.openCLParams.get_context() #self.openCLParams.get_queue() self.useGPU = True except: diff --git a/pyebsdindex/_ebsd_index_single.py b/pyebsdindex/_ebsd_index_single.py index 33b2139..a274178 100644 --- a/pyebsdindex/_ebsd_index_single.py +++ b/pyebsdindex/_ebsd_index_single.py @@ -221,6 +221,7 @@ def index_pats( clparams=clparams, verbose=verbose, chunksize=chunksize, + gpu_id = gpu_id, ) if not return_indexer_obj: @@ -400,6 +401,7 @@ def index_pats( PC=None, verbose=0, chunksize=512, + **kwargs ): """Index EBSD patterns. @@ -462,7 +464,8 @@ def index_pats( if npats == -1: npats = npoints - banddata, bandnorm = self._detectbands(pats, PC, xyloc=xyloc, clparams=clparams, verbose=verbose, chunksize=chunksize) + banddata, bandnorm = self._detectbands(pats, PC, xyloc=xyloc, clparams=clparams, verbose=verbose, + chunksize=chunksize) tic = timer() indxData = self._indexbandsphase(banddata, bandnorm, verbose=verbose) diff --git a/pyebsdindex/opencl/openclparam.py b/pyebsdindex/opencl/openclparam.py index 028d811..b394104 100644 --- a/pyebsdindex/opencl/openclparam.py +++ b/pyebsdindex/opencl/openclparam.py @@ -45,7 +45,7 @@ def __init__(self, gpu_id=0): try: - self.get_context() + self.get_gpu() except Exception as e: if hasattr(e,'message'): @@ -65,24 +65,24 @@ def get_gpu(self): if len(self.gpu)-1 < self.gpu_id: self.gpu_id = len(self.gpu)-1 - def get_context(self): + def get_context(self, gpu_id=None): if self.gpu is None: self.get_gpu() - self.ctx = cl.Context(devices = self.gpu) - kernel_location = path.dirname(__file__) - self.prg = cl.Program(self.ctx,open(path.join(kernel_location,'clkernels.cl')).read()).build() - - def get_queue(self, gpu_id=None): - - if self.ctx is None: - self.get_context() - if gpu_id is None: gpu_id = self.gpu_id gpuindx = min(len(self.gpu)-1, gpu_id) self.gpu_id = gpuindx - self.queue = cl.CommandQueue(self.ctx, device=self.gpu[gpuindx]) + self.ctx = cl.Context(devices = [self.gpu[self.gpu_id]]) + + kernel_location = path.dirname(__file__) + self.prg = cl.Program(self.ctx,open(path.join(kernel_location,'clkernels.cl')).read()).build() + #print('ctx', self.gpu_id) + def get_queue(self, gpu_id=None): + if self.ctx is None: + self.get_context(gpu_id=None) + + self.queue = cl.CommandQueue(self.ctx) From 11687fb96948a7e230ce1989041cf2f6e19fb90a Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Wed, 21 Jun 2023 22:50:12 -0400 Subject: [PATCH 130/177] Bug fix Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_parallel.py | 12 ++++++------ pyebsdindex/ebsdfile.py | 12 ++++++------ pyebsdindex/nlpar.py | 1 + 3 files changed, 13 insertions(+), 12 deletions(-) diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index 8015da9..ba5817b 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -282,7 +282,7 @@ def index_pats_distributed( if ngpu > 0: - ngpupro = max(12, ngpu*2) # number of processes that will serve data to the gpu + ngpupro = max(12, ngpu*8) # number of processes that will serve data to the gpu if n_cpu_nodes < 8: ngpupro = min(ngpupro,8) if n_cpu_nodes < 2: @@ -536,11 +536,11 @@ def index_pats_distributed( " -- ", str(cjob.pend), " PPS:", - "{:.0f}".format(cjob.rate*ncpuwrker) - + ";" - + "{:.0f}".format(chunkave / ncpudone * ncpuwrker) - + ";" - + "{:.0f}".format(npatdone/currenttime), + #"{:.0f}".format(cjob.rate*ncpuwrker) + #+ ";" + #+ "{:.0f}".format(chunkave / ncpudone * ncpuwrker) + #+ ";" + + "{:.0f}".format(npatdone/currenttime), " ", "{:.0f}".format((ncpudone / njobs) * 100) + "%", "{:.0f};".format(currenttime) diff --git a/pyebsdindex/ebsdfile.py b/pyebsdindex/ebsdfile.py index 0999d48..064eae8 100644 --- a/pyebsdindex/ebsdfile.py +++ b/pyebsdindex/ebsdfile.py @@ -138,22 +138,22 @@ def writeoh5(filename, indexer, data, for phase in indexer.phaseLib: f.create_dataset(datasetname + '/EBSD/Header/Phase/'+str(pcount)+'/LGsymID', data=np.array([np.int32(phase.lauecode)])) f.create_dataset(datasetname + '/EBSD/Header/Phase/' + str(pcount) + '/Lattice Constant a', - data=np.array([np.int32(phase.latticeparameter[0]*10)])) + data=np.array([np.float32(phase.latticeparameter[0]*10)])) f.create_dataset(datasetname + '/EBSD/Header/Phase/' + str(pcount) + '/Lattice Constant alpha', - data=np.array([np.int32(phase.latticeparameter[3])])) + data=np.array([np.float32(phase.latticeparameter[3])])) f.create_dataset(datasetname + '/EBSD/Header/Phase/' + str(pcount) + '/Lattice Constant b', - data=np.array([np.int32(phase.latticeparameter[1] * 10)])) + data=np.array([np.float32(phase.latticeparameter[1] * 10)])) f.create_dataset(datasetname + '/EBSD/Header/Phase/' + str(pcount) + '/Lattice Constant beta', - data=np.array([np.int32(phase.latticeparameter[4])])) + data=np.array([np.float32(phase.latticeparameter[4])])) f.create_dataset(datasetname + '/EBSD/Header/Phase/' + str(pcount) + '/Lattice Constant c', - data=np.array([np.int32(phase.latticeparameter[2] * 10)])) + data=np.array([np.float32(phase.latticeparameter[2] * 10)])) f.create_dataset(datasetname + '/EBSD/Header/Phase/' + str(pcount) + '/Lattice Constant gamma', - data=np.array([np.int32(phase.latticeparameter[5])])) + data=np.array([np.float32(phase.latticeparameter[5])])) pname = str(phase.phasename) chararray = np.chararray(1, itemsize=len(pname)+1) chararray[:] = pname diff --git a/pyebsdindex/nlpar.py b/pyebsdindex/nlpar.py index 02dbdab..6489201 100644 --- a/pyebsdindex/nlpar.py +++ b/pyebsdindex/nlpar.py @@ -234,6 +234,7 @@ def d2norm(d2, n2, dij, sigma): dthresh = np.float32(dthresh) lamopt_values = [] + for j in range(0,nrows,chunksize): print('Block',j) #rowstartread = np.int64(max(0,j - nn)) From ef1c4613e34147b7f3f5d98cf3bf79f99c8ac769 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Tue, 27 Jun 2023 12:43:37 -0400 Subject: [PATCH 131/177] Translated references to Hough to Radon Signed-off by: David Rowenhorst --- CHANGELOG.rst | 2 +- README.md | 2 +- doc/index.rst | 2 +- doc/tutorials/ebsd_index_demo.ipynb | 2 +- pyebsdindex/_ebsd_index_parallel.py | 2 +- pyebsdindex/_ebsd_index_parallel_old.py | 2 +- pyebsdindex/_ebsd_index_single.py | 8 ++++---- pyebsdindex/ebsd_index.py | 2 +- pyebsdindex/tests/test_ebsd_index.py | 4 ++-- setup.py | 2 +- 10 files changed, 14 insertions(+), 14 deletions(-) diff --git a/CHANGELOG.rst b/CHANGELOG.rst index c9342a5..27ef20d 100644 --- a/CHANGELOG.rst +++ b/CHANGELOG.rst @@ -34,7 +34,7 @@ Removed - Removed any references to np.floats and replaced with float() or np.float32/64. Fixed ----- -- Hough transform figure when ``verbose=2`` is passed to various indexing methods is now +- Radon transform figure when ``verbose=2`` is passed to various indexing methods is now plotted in its own figure. - Several bug fixes with NLPAR file reading/writing. - Complete rewrite of the scheduling for ``ebsd_index.index_pats_distributed()`` function to be compatible diff --git a/README.md b/README.md index f252188..7446311 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # PyEBSDIndex -Python based tool for Hough/Radon based EBSD orientation indexing. +Python based tool for Radon based EBSD orientation indexing. [![Build status](https://github.com/USNavalResearchLaboratory/PyEBSDIndex/actions/workflows/build.yml/badge.svg)](https://github.com/USNavalResearchLaboratory/PyEBSDIndex/actions/workflows/build.yml) [![Documentation status](https://readthedocs.org/projects/pyebsdindex/badge/?version=latest)](https://pyebsdindex.readthedocs.io/en/latest/) diff --git a/doc/index.rst b/doc/index.rst index c61fc74..bc605af 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -2,7 +2,7 @@ PyEBSDIndex |release| documentation =================================== -Python based tool for Hough/Radon based EBSD orientation indexing. +Python based tool for Radon based EBSD orientation indexing. The pattern processing is based on a GPU pipeline, and is based on the work of S. I. Wright and B. L. Adams. Metallurgical Transactions A-Physical Metallurgy and Materials diff --git a/doc/tutorials/ebsd_index_demo.ipynb b/doc/tutorials/ebsd_index_demo.ipynb index 09315ba..99732e3 100644 --- a/doc/tutorials/ebsd_index_demo.ipynb +++ b/doc/tutorials/ebsd_index_demo.ipynb @@ -5,7 +5,7 @@ "id": "496b84d4-54ca-47c7-9a47-0709acffd06b", "metadata": {}, "source": [ - "# Hough indexing" + "# Radon indexing" ] }, { diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index ba5817b..ef2c293 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -20,7 +20,7 @@ # Author: David Rowenhorst; # The US Naval Research Laboratory Date: 21 Aug 2020 -"""Setup and handling of Hough indexing runs of EBSD patterns in +"""Setup and handling of Radon indexing runs of EBSD patterns in parallel. """ diff --git a/pyebsdindex/_ebsd_index_parallel_old.py b/pyebsdindex/_ebsd_index_parallel_old.py index 27d7f0e..4dff648 100644 --- a/pyebsdindex/_ebsd_index_parallel_old.py +++ b/pyebsdindex/_ebsd_index_parallel_old.py @@ -20,7 +20,7 @@ # Author: David Rowenhorst; # The US Naval Research Laboratory Date: 21 Aug 2020 -"""Setup and handling of Hough indexing runs of EBSD patterns in +"""Setup and handling of Radon indexing runs of EBSD patterns in parallel. """ diff --git a/pyebsdindex/_ebsd_index_single.py b/pyebsdindex/_ebsd_index_single.py index a274178..b9c9830 100644 --- a/pyebsdindex/_ebsd_index_single.py +++ b/pyebsdindex/_ebsd_index_single.py @@ -20,7 +20,7 @@ # Author: David Rowenhorst; # The US Naval Research Laboratory Date: 21 Aug 2020 -"""Setup and handling of Hough indexing runs of EBSD patterns on a +"""Setup and handling of Radon indexing runs of EBSD patterns on a single thread. """ @@ -138,7 +138,7 @@ def index_pats( OpenCL parameters passed to :mod:`pyopencl` if the package is installed. verbose : int, optional - 0 - no output (default), 1 - timings, 2 - timings and the Hough + 0 - no output (default), 1 - timings, 2 - timings and the Radon transform of the first pattern with detected bands highlighted. chunksize : int, optional Default is 528. @@ -231,7 +231,7 @@ def index_pats( class EBSDIndexer: - """Setup of Hough indexing of EBSD patterns. + """Setup of Radon indexing of EBSD patterns. Parameters ---------- @@ -427,7 +427,7 @@ def index_pats( must be four numbers, the final number being the pixel size. verbose : int, optional 0 - no output (default), 1 - timings, 2 - timings and the - Hough transform of the first pattern with detected bands + Radon transform of the first pattern with detected bands highlighted. chunksize : int, optional Default is 528. diff --git a/pyebsdindex/ebsd_index.py b/pyebsdindex/ebsd_index.py index 5104e8a..480b26d 100644 --- a/pyebsdindex/ebsd_index.py +++ b/pyebsdindex/ebsd_index.py @@ -20,7 +20,7 @@ # Author: David Rowenhorst; # The US Naval Research Laboratory Date: 21 Aug 2020 -"""Setup and handling of Hough indexing runs of EBSD patterns.""" +"""Setup and handling of Radon indexing runs of EBSD patterns.""" from pyebsdindex import _ray_installed from pyebsdindex._ebsd_index_single import EBSDIndexer, index_pats diff --git a/pyebsdindex/tests/test_ebsd_index.py b/pyebsdindex/tests/test_ebsd_index.py index 9f10bda..3ba9911 100644 --- a/pyebsdindex/tests/test_ebsd_index.py +++ b/pyebsdindex/tests/test_ebsd_index.py @@ -44,7 +44,7 @@ def test_init(self): assert indexer.vendor == "EDAX" def test_index_pats(self, pattern_al_sim_20kv): - """Test Hough indexing and setting/passing projection center + """Test Radon indexing and setting/passing projection center values. """ pc = (0.4, 0.72, 0.6) @@ -67,7 +67,7 @@ def test_index_pats(self, pattern_al_sim_20kv): @pytest.mark.skipif(not _ray_installed, reason="ray is not installed") def test_index_pats_multi(self, pattern_al_sim_20kv): - """Test Hough indexing parallelized with ray.""" + """Test Radon indexing parallelized with ray.""" from pyebsdindex.ebsd_index import index_pats_distributed patterns = np.repeat(pattern_al_sim_20kv[None, ...], 4, axis=0) diff --git a/setup.py b/setup.py index 3de464e..592bd8c 100644 --- a/setup.py +++ b/setup.py @@ -72,7 +72,7 @@ "EBSD", "electron backscatter diffraction", "HI", - "Hough indexing", + "Radon indexing", "NLPAR", ], zip_safe=True, From 1b32aff00312fac782f0ee286c758683ccd9a3e1 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Thu, 6 Jul 2023 21:31:06 -0400 Subject: [PATCH 132/177] Introduce Hex IPF coloring Signed-off by: David Rowenhorst --- IPFCubic.png | Bin 49952 -> 147377 bytes IPFHex.pdf | Bin 0 -> 197073 bytes IPFHex.png | Bin 0 -> 81462 bytes 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np.copy(xstalvect) npoints = shp[0] xstalv = np.abs(xstalv) @@ -57,15 +57,18 @@ def ipf_color_cubic(xstalvect): b = np.sqrt((triPts[1,0]) ** 2. + (triPts[1,1]) ** 2.) c = np.sqrt(triPts[2,0] ** 2. + triPts[2,1] ** 2.) - y0 = 1/2. * np.sqrt( ((b+c-a)*(c+a-b)*(a+b-c)) / (a+b+c) ) - x0 = y0 / middle + #y0 = 1/2. * np.sqrt( ((b+c-a)*(c+a-b)*(a+b-c)) / (a+b+c) ) + #x0 = y0 / middle + y0 = np.mean(triPts[:, 1]) + x0 = np.mean(triPts[:, 0]) S = np.sqrt((xP - x0) ** 2. + (yP - y0) ** 2.) - H = np.arctan((yP - y0) / (xP - x0)) *180.0/np.pi + H = np.arctan2((yP - y0) , (xP - x0)) *180.0/np.pi V = np.ones(npoints) - H = (xP < x0).astype(np.float)*180.0+H - H = H + 240.0 - np.arctan((triPts[2,1] - y0) / (triPts[2,0] - x0)) * 180.0/np.pi + #H = (xP < x0).astype(np.float)*180.0+H + H = H + 240.0 - np.arctan2((triPts[2,1] - y0) , (triPts[2,0] - x0)) * 180.0/np.pi + #H = H - np.arctan2(-y0 , -x0) * 180.0 / np.pi sMax = np.sqrt(x0**2+y0**2) S = S / (sMax) * 0.8 + 0.2 @@ -172,3 +175,141 @@ def ipf_ledgend_cubic(size=512): anno111 = plt.text(size - 10*fsize*size/512/figsz,(triangleWY+triOrigin[1])*1.0,'111',fontsize=fsize) fig.savefig("IPFCubic.png",bbox_inches=0, transparent=True) plt.close(1001) + + +def qu2ipf_hex(quats, vector=np.array([0,0,1.0])): + xstalvect = rotlib.quat_vector(quats,vector) + return ipf_color_hex(xstalvect).clip(0.0, 1.0) + +def ipf_color_hex(xstalvect): + shp = xstalvect.shape + if len(shp) == 1: + xstalv = np.copy(xstalvect.reshape(1,shp)) + else: + xstalv = np.copy(xstalvect) + npoints = shp[0] + + xstalv /= np.sqrt((xstalv ** 2).sum(-1))[..., np.newaxis] + xstalv[xstalv[:, 2] < 0, :] *= -1 + + theta = np.arctan2(xstalv[:,1], xstalv[:,0]) + wh = np.where(theta >= np.pi/3.)[0] + q60 = rotlib.quatnorm(np.array([ np.cos(np.pi/6.0),0, 0, -0.50000000])) + + while wh.size > 0: + xstalv[wh,:] = rotlib.quat_vector(q60,xstalv[wh,:] ) + theta = np.arctan2(xstalv[:, 1], xstalv[:, 0]) + wh = np.where(theta >= np.pi / 3.)[0] + + + + theta = np.arctan2(xstalv[:, 1], xstalv[:, 0]) + wh = np.where(theta < 0.0)[0] + q60 = np.array([np.cos(np.pi / 6.0), 0, 0, 0.50000000]) + while wh.size > 0: + xstalv[wh, :] = rotlib.quat_vector(q60, xstalv[wh, :]) + theta = np.arctan2(xstalv[:, 1], xstalv[:, 0]) + wh = np.where(theta < 0.0)[0] + + + theta = np.arctan2(xstalv[:, 1], xstalv[:, 0]) + wh = np.where(theta >= np.pi / 6.)[0] + if wh.size > 0: + nx = -np.sin(np.pi / 6.) + ny = np.cos(np.pi / 6.) + const = 2. * (nx * xstalv[wh,0] + ny * xstalv[wh, 1]) + xstalv[wh, 0] -= const * nx + xstalv[wh, 1] -= const * ny + + + + xP = (xstalv[:,0]) / (1 + xstalv[:,2]) + yP = (xstalv[:,1]) / (1 + xstalv[:,2]) + + # cubic unit tri center + triPts = np.array( [[0,0], + [1.0 ,0], + [np.sqrt(3.)/2.0, 0.5 ]], dtype = np.float) + + middle = np.tan(1. / 2. * np.arctan(triPts[2,1] / triPts[2,0])) + + a = np.sqrt( (triPts[2,1] - triPts[1,1]) ** 2. + (triPts[2,0] - triPts[1,0]) ** 2.) + b = np.sqrt((triPts[1,0]) ** 2. + (triPts[1,1]) ** 2.) + c = np.sqrt(triPts[2,0] ** 2. + triPts[2,1] ** 2.) + + #y0 = 0.4 * np.sqrt( ((b+c-a)*(c+a-b)*(a+b-c)) / (a+b+c) ) + #x0 = y0 / middle + y0 = np.mean(triPts[:,1]) + x0 = np.mean(triPts[:, 0]) + + + S = np.sqrt((xP - x0) ** 2. + (yP - y0) ** 2.) + H = np.arctan2(1.25*(yP - y0) , (xP - x0)) * 180.0 / np.pi + V = np.ones(npoints) + + #H = (xP < x0).astype(np.float) * 180.0 + H + H = H + 240.0 - np.arctan2((triPts[2, 1] - y0) , (triPts[2, 0] - x0)) * 180.0 / np.pi + #H = H - np.arctan2((- y0), ( - x0)) * 180.0 / np.pi + sMax = np.sqrt(x0 ** 2 + y0 ** 2) + S = S / (sMax) * 0.75 + 0.25 + + H = H % (360.0) + H = H / 360.0 + + RGB = pltcolors.hsv_to_rgb(np.array([H,S,V]).T) + + return RGB + + +def ipf_ledgend_hex(size=512): + szx = size + aspect = 0.6 + szy = np.round(size*aspect).astype(int) + triangleWX = np.round(size*1.0).astype(int) + triangleWY = np.round(triangleWX * aspect).astype(int) + + #triOrigin = np.round(np.array([0.1,0.1])*size).astype(int) + triOrigin = np.array([0,0]).astype(int) + + triScale = 1.0/triangleWX #0.82842708/triangleWX + np0 = triangleWX*triangleWY + triXY = np.indices([triangleWY,triangleWX]) + triYX_stereo = (triXY*triScale).reshape(2,np0) + xt = triYX_stereo[1,:]*2 + yt = triYX_stereo[0,:]*2 + + xyz = np.zeros((np0, 3)) + + xyz[:,2] = (4. - (xt**2+ yt**2))/(4. + (xt**2+ yt**2)) + xyz[:,0] = (xyz[:,2] + 1.) * (xt / 2.) + xyz[:,1] = (xyz[:,2] + 1.) * (yt / 2.) + pltest = np.sqrt(xt ** 2 + yt**2) < 2.0 + pltest2 = (xyz[:,2] >= 0.0).squeeze() + + theta = np.arctan2(xyz[:,1],xyz[:,0]) < np.pi/6 + theta = np.logical_and( theta, pltest) + #theta = np.logical_and(theta, pltest2) + + wh = np.nonzero( theta)[0] + #return xyz[wh,:] + rgbaTri = np.full((np0, 4), 1.0, dtype = np.float32) + rgbaTri[:,3] = 0.0 + rgbaTri[wh,3] = 1.0 + rgbaTri[wh,0:3] = ipf_color_hex(xyz[wh,:]) + + rgbaTri = rgbaTri.reshape(triangleWY,triangleWX,4) + dpi = size + figsz = 1.0 + fsize = 4.0#/512*size + + fig = plt.figure(1001, figsize=(figsz,figsz*aspect),dpi=size/figsz*0.5) + ax = plt.Axes(fig,[-0.2,0.15,1.4,aspect*1.4]) + ax.set_axis_off() + fig.add_axes(ax) + + img = plt.imshow(rgbaTri, origin='lower', extent=[0,szx,0,szy]) + anno001 = plt.text(triOrigin[0] - 5*fsize*size/512/figsz,triOrigin[1] - 8*fsize*size/512.0/figsz, r'0001', fontsize = 0.9*fsize) + anno011 = plt.text(size - 10*fsize*size/512/figsz,triOrigin[1] - 9.5*fsize*size/512.0/figsz,r'$2\bar{1}\bar{1}0$',fontsize=0.9*fsize) + anno111 = plt.text(size - 25*fsize*size/512/figsz,(triangleWY+triOrigin[1])*0.85,r'$10\bar{1}0$',fontsize=0.9*fsize) + fig.savefig("IPFHex.png",bbox_inches=0, transparent=True) + plt.close(1001) \ No newline at end of file From 6381192decb3df33f84aaf0c9c50ef7f3c606fa6 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Fri, 7 Jul 2023 07:55:11 -0400 Subject: [PATCH 133/177] Fix testing Signed-off by: David Rowenhorst --- MANIFEST.in | 2 ++ 1 file changed, 2 insertions(+) diff --git a/MANIFEST.in b/MANIFEST.in index ebb3f30..0694e84 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -3,6 +3,8 @@ include CHANGELOG.rst include CONTRIBUTING.rst include IPFCubic.pdf include IPFCubic.png +include IPFHex.pdf +include IPFHex.png include License include MANIFEST.in include README.md From 1ac76ef4df17de39ad8f6306a257f9244eb145c2 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Mon, 10 Jul 2023 17:55:10 -0400 Subject: [PATCH 134/177] Improved IPF maps Signed-off by: David Rowenhorst --- pyebsdindex/EBSDImage/IPFcolor.py | 36 +++++++++++++++++++++++++++++++ 1 file changed, 36 insertions(+) diff --git a/pyebsdindex/EBSDImage/IPFcolor.py b/pyebsdindex/EBSDImage/IPFcolor.py index b26e7d4..97377a6 100644 --- a/pyebsdindex/EBSDImage/IPFcolor.py +++ b/pyebsdindex/EBSDImage/IPFcolor.py @@ -29,6 +29,42 @@ from pyebsdindex import rotlib +def makeipf(ebsddata, indexer, vector=np.array([0,0,1.0]), xsize = None, ysize = None): + nphase = len(indexer.phaseLib) + + npoints = ebsddata.shape[-1] + ipfphase = np.zeros((nphase,npoints,3), dtype =np.float32)+1 + + phcount = 0 + for ph in indexer.phaseLib: + quat = ebsddata[phcount]['quat'] + if ph.lauecode == 43: + ipfphase[phcount, :, :] = qu2ipf_cubic(quat, vector=vector) + if ph.lauecode == 62: + ipfphase[phcount, :, :] = qu2ipf_hex(quat, vector=vector) + phcount += 1 + phase = ((ebsddata[-1]['phase']).copy()).clip(0).reshape(npoints,1) + ipfout = np.choose(phase, ipfphase).squeeze() + ipfout[ebsddata[-1]['fit'] > 179,:] = 0 + + + if xsize is not None: + xsize = int(xsize) + if ysize is None: + ysize = int(npoints // xsize + np.int64((npoints % xsize) > 0)) + print(ysize) + else: + xsize = int(npoints) + ysize = 1 + + npts = int(npoints) + if int(xsize*ysize) < npoints: + npts = int(xsize*ysize) + ipf_out = ipfout[0:npts,:].reshape(ysize, xsize,3) + return ipf_out + + + def qu2ipf_cubic(quats, vector=np.array([0,0,1.0])): xstalvect = rotlib.quat_vector(quats,vector) return ipf_color_cubic(xstalvect).clip(0.0, 1.0) From e3a6cd9a3e2cdaa3cf6c221c76466eead5c169a7 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Mon, 10 Jul 2023 17:55:58 -0400 Subject: [PATCH 135/177] Output the index that matches the bands to the library values. Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_parallel.py | 11 ++++++----- pyebsdindex/_ebsd_index_single.py | 25 ++++++++++++++----------- pyebsdindex/band_detect.py | 3 ++- pyebsdindex/pcopt.py | 2 +- 4 files changed, 23 insertions(+), 18 deletions(-) diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index ef2c293..e0b2ce6 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -521,9 +521,10 @@ def index_pats_distributed( for wrker in donewrker: jid = cputask.index(wrker) try: - message, (indexdata, cjob) = ray.get(wrker) + message, (indexdata,bnddata, cjob) = ray.get(wrker) if message == 'Done': dataout[:, cjob.pstart - patstart: cjob.pend - patstart] = indexdata + banddataout[cjob.pstart - patstart: cjob.pend - patstart, :] = bnddata ncpudone += 1 chunkave += cjob.rate npatdone += cjob.npat @@ -734,20 +735,20 @@ def __init__(self, actorid=0): def indexpoles(self, cpujob, banddata, bandnorm, indexer=None): if cpujob is None: - return 'Bored', (None, None) + return 'Bored', (None, None, None) try: # print(type(self.openCLParams.ctx)) cpujob._starttime() - indxData = indexer._indexbandsphase(banddata, bandnorm, verbose=0) + indxData, banddata = indexer._indexbandsphase(banddata, bandnorm, verbose=0) cpujob._endtime() - return "Done", (indxData, cpujob) + return "Done", (indxData,banddata, cpujob) except Exception as e: print(e) cpujob.rate = None - return "Error", (None, cpujob) + return "Error", (None,None, cpujob) class CPUGPUJob: def __init__(self,jobid, pstart, pend, extime=0.0): self.jobid = jobid diff --git a/pyebsdindex/_ebsd_index_single.py b/pyebsdindex/_ebsd_index_single.py index b9c9830..4d194f1 100644 --- a/pyebsdindex/_ebsd_index_single.py +++ b/pyebsdindex/_ebsd_index_single.py @@ -468,7 +468,7 @@ def index_pats( chunksize=chunksize) tic = timer() - indxData = self._indexbandsphase(banddata, bandnorm, verbose=verbose) + indxData, banddata = self._indexbandsphase(banddata, bandnorm, verbose=verbose) if verbose > 0: print("Band Vote Time: ", timer() - tic) @@ -521,12 +521,14 @@ def _indexbandsphase(self, banddata, bandnorm, verbose = 0, **kwargs): nPhases = len(self.phaseLib) q = np.zeros((nPhases, npoints, 4)) indxData = np.zeros((nPhases + 1, npoints), dtype=self.dataTemplate) + bandmatchindex = np.zeros((nPhases, npoints,shpBandDat[-1],2), dtype=np.int32)-100 + banddataout = banddata.copy() indxData["phase"] = -1 indxData["fit"] = 180.0 indxData["totvotes"] = 0 if self.phaseLib[0] is None: - return indxData + return indxData, banddata if self.nband_earlyexit is None: earlyexit = -1 @@ -552,7 +554,7 @@ def _indexbandsphase(self, banddata, bandnorm, verbose = 0, **kwargs): #print(bDat1["max"]) #print(adj_intensity) for j in range(len(self.phaseLib)): - + bandmatchindex[j,i, :, 0] = j ( avequat, @@ -574,6 +576,8 @@ def _indexbandsphase(self, banddata, bandnorm, verbose = 0, **kwargs): indxData["nmatch"][j, i] = nMatch indxData["matchattempts"][j, i] = matchAttempts indxData["totvotes"][j, i] = totvotes + bandmatchindex[j,i, whgood, 1] = bandmatch + if nMatch >= earlyexit: break @@ -584,6 +588,7 @@ def _indexbandsphase(self, banddata, bandnorm, verbose = 0, **kwargs): q = q.reshape(nPhases, npoints, 4) indxData["quat"][0:nPhases, :, :] = q indxData[-1, :] = indxData[0, :] + banddataout['band_match_index'][:,:,:] = bandmatchindex[0,:,:,:].squeeze() if nPhases > 1: for j in range(1, nPhases): # indxData[-1, :] = np.where( @@ -591,14 +596,12 @@ def _indexbandsphase(self, banddata, bandnorm, verbose = 0, **kwargs): # > (indxData[j + 1, :]["cm"] * indxData[j + 1, :]["nmatch"]), # indxData[j, :], # indxData[j + 1, :], - indxData[-1, :] = np.where( - ((3.0 - indxData[j, :]["fit"]) * indxData[j, :]["nmatch"]) - > ((3.0 - indxData[-1, :]["fit"]) * indxData[-1, :]["nmatch"]), - indxData[j, :], - indxData[-1, :] - ) - - return indxData + phasetest = ((3.0 - indxData[j, :]["fit"]) * indxData[j, :]["nmatch"]) \ + > ((3.0 - indxData[-1, :]["fit"]) * indxData[-1, :]["nmatch"]) + whbetter = np.nonzero(phasetest) + indxData[-1, whbetter] = indxData[j, whbetter] + banddataout['band_match_index'][whbetter,:] = bandmatchindex[j,whbetter,:,:].squeeze() + return indxData, banddataout def _detector2refframe(self): ven = str.upper(self.vendor) if ven in ["EDAX", "EMSOFT", "KIKUCHIPY"]: diff --git a/pyebsdindex/band_detect.py b/pyebsdindex/band_detect.py index ad88bc5..010c684 100644 --- a/pyebsdindex/band_detect.py +++ b/pyebsdindex/band_detect.py @@ -79,7 +79,7 @@ def __init__( self.dataType = np.dtype([('id', np.int32), ('max', np.float32), \ ('maxloc', np.float32, (2)), ('avemax', np.float32), ('aveloc', np.float32, (2)),\ ('pqmax', np.float32), ('width', np.float32), ('theta', np.float32), ('rho', np.float32), - ('valid', np.int8)]) + ('valid', np.int8),('band_match_index', np.int32, (2))]) if (patterns is None) and (patDim is None): @@ -302,6 +302,7 @@ def find_bands(self, patternsIn, verbose=0, chunksize=-1, **kwargs): nPats = shape[0] bandData = np.zeros((nPats,self.nBands),dtype=self.dataType) + bandData['band_match_index'] = -100 if chunksize < 0: nchunks = 1 chunksize = nPats diff --git a/pyebsdindex/pcopt.py b/pyebsdindex/pcopt.py index 91138b1..1653238 100644 --- a/pyebsdindex/pcopt.py +++ b/pyebsdindex/pcopt.py @@ -55,7 +55,7 @@ def _optfunction(PC_i, indexer, banddat): #nbands_fit = 0 #phase = indexer.phaseLib[0] nbands = indexer.bandDetectPlan.nBands - indexdata = indexer._indexbandsphase( banddat, bandnorm) + indexdata, banddat = indexer._indexbandsphase( banddat, bandnorm) From 293716126fa6c1e35ce526d7201ac074a93c703b Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Mon, 10 Jul 2023 19:43:31 -0400 Subject: [PATCH 136/177] IPF color adjustments Signed-off by: David Rowenhorst --- IPFCubic.pdf | Bin 48700 -> 368506 bytes IPFHex.pdf | Bin 197073 -> 197654 bytes IPFHex.png | Bin 81462 -> 82053 bytes pyebsdindex/EBSDImage/IPFcolor.py | 10 +++++----- 4 files 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zxVsI1E~m*vc`Z!t1Q?jr#(fp%3+DoWiM@CpZ#V$o^=AN~lNZR|PyPnMCVm&>!3(l6*cG_^>=Vd17)uZjB%6QUd|a=9Oa-bL+6i7-`{Oihzpj;H z_`pSzSyinxZVm_11cA}4NtYyCKDuI44;!?nL&Wl*&?eyx7$)!`s_Zsei;qCLRz@LM zxMiSmG~o2^EL}R{LA{d!|F~a)@{zpiJ^3mZ&OW%+7K*#dc(>8G|kE4=z>Cna3eZeun(UG8nFQRIdU!- zf~Bmz$Nf5M=RdC&I}QG2;sV`p^AQ zN2U14lE6$BHnt>v2@4$kFBi_>3O;A1<=^5QB&q=~QO(r*jz#YV$gx*CC31s)K94%-V z`VvGFpv(StgK=*^8Ep0Qej9$wZnAUEP6(IwNi2P7SV-u93jTYM{qK+&ga zYk=GiNJ-c;hni*`!8QKB@{VqX2!W2vek1k8-zuFbY0XDI41g-FLa^}sZ;R@KxNp&V p-2W?5MgLH*;A#x>)sMf#7P{U4k{8NmPm diff --git a/pyebsdindex/EBSDImage/IPFcolor.py b/pyebsdindex/EBSDImage/IPFcolor.py index 97377a6..2e6d05e 100644 --- a/pyebsdindex/EBSDImage/IPFcolor.py +++ b/pyebsdindex/EBSDImage/IPFcolor.py @@ -209,7 +209,7 @@ def ipf_ledgend_cubic(size=512): anno001 = plt.text(triOrigin[0] - 5*fsize*size/512/figsz,triOrigin[1] - 7*fsize*size/512.0/figsz, '001', fontsize = fsize) anno011 = plt.text(size - 10*fsize*size/512/figsz,triOrigin[1] - 7*fsize*size/512.0/figsz,'011',fontsize=fsize) anno111 = plt.text(size - 10*fsize*size/512/figsz,(triangleWY+triOrigin[1])*1.0,'111',fontsize=fsize) - fig.savefig("IPFCubic.png",bbox_inches=0, transparent=True) + fig.savefig("IPFCubic.pdf",bbox_inches=0, transparent=True) plt.close(1001) @@ -280,13 +280,13 @@ def ipf_color_hex(xstalvect): S = np.sqrt((xP - x0) ** 2. + (yP - y0) ** 2.) - H = np.arctan2(1.25*(yP - y0) , (xP - x0)) * 180.0 / np.pi + H = np.arctan2((yP - y0) , (xP - x0)) * 180.0 / np.pi V = np.ones(npoints) #H = (xP < x0).astype(np.float) * 180.0 + H - H = H + 240.0 - np.arctan2((triPts[2, 1] - y0) , (triPts[2, 0] - x0)) * 180.0 / np.pi + H = H + 240 - np.arctan2((triPts[2, 1] - y0) , (triPts[2, 0] - x0)) * 180.0 / np.pi #H = H - np.arctan2((- y0), ( - x0)) * 180.0 / np.pi - sMax = np.sqrt(x0 ** 2 + y0 ** 2) + sMax = np.sqrt((triPts[:,0] - x0) ** 2 + (triPts[:,1] - y0) ** 2).max() S = S / (sMax) * 0.75 + 0.25 H = H % (360.0) @@ -347,5 +347,5 @@ def ipf_ledgend_hex(size=512): anno001 = plt.text(triOrigin[0] - 5*fsize*size/512/figsz,triOrigin[1] - 8*fsize*size/512.0/figsz, r'0001', fontsize = 0.9*fsize) anno011 = plt.text(size - 10*fsize*size/512/figsz,triOrigin[1] - 9.5*fsize*size/512.0/figsz,r'$2\bar{1}\bar{1}0$',fontsize=0.9*fsize) anno111 = plt.text(size - 25*fsize*size/512/figsz,(triangleWY+triOrigin[1])*0.85,r'$10\bar{1}0$',fontsize=0.9*fsize) - fig.savefig("IPFHex.png",bbox_inches=0, transparent=True) + fig.savefig("IPFHex.pdf",bbox_inches=0, transparent=True) plt.close(1001) \ No newline at end of file From 20beee5a1ded70f3f09aeaba0f302b9c49b2f53d Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Tue, 11 Jul 2023 08:27:15 -0400 Subject: [PATCH 137/177] convert all np.int --> int Signed-off by: David Rowenhorst --- pyebsdindex/ebsd_pattern.py | 4 ++-- pyebsdindex/opencl/band_detect_cl.py | 2 +- pyebsdindex/pairlib.py | 8 ++++---- pyebsdindex/tests/rotlibunittest.py | 2 +- 4 files changed, 8 insertions(+), 8 deletions(-) diff --git a/pyebsdindex/ebsd_pattern.py b/pyebsdindex/ebsd_pattern.py index 00e7f6c..ad5ae50 100644 --- a/pyebsdindex/ebsd_pattern.py +++ b/pyebsdindex/ebsd_pattern.py @@ -452,7 +452,7 @@ def read_header(self,path=None,bitdepth=None): # readInterval=[0, -1], arrayOnl self.patternW = dat[0] self.patternH = dat[1] self.filePos = dat[2] - self.nPatterns = np.int((Path(self.filepath).expanduser().stat().st_size - 16) / + self.nPatterns = int((Path(self.filepath).expanduser().stat().st_size - 16) / (self.patternW * self.patternH * (self.filedatatype(0).nbytes))) if self.xStep is None: self.xStep = 0.0 @@ -474,7 +474,7 @@ def read_header(self,path=None,bitdepth=None): # readInterval=[0, -1], arrayOnl dat = np.fromfile(f, dtype=np.uint32, count=2) self.nCols = np.uint64(dat[0]) self.nRows = np.uint64(dat[1]) - self.nPatterns = np.int(self.nCols.astype(np.uint64) * self.nRows.astype(np.uint64)) + self.nPatterns = int(self.nCols.astype(np.uint64) * self.nRows.astype(np.uint64)) self.hexflag = np.fromfile(f, dtype=np.uint8, count=1)[0] dat = np.fromfile(f, dtype=np.float64, count=2) self.xStep = dat[0] diff --git a/pyebsdindex/opencl/band_detect_cl.py b/pyebsdindex/opencl/band_detect_cl.py index c7ed7ab..8b77a64 100644 --- a/pyebsdindex/opencl/band_detect_cl.py +++ b/pyebsdindex/opencl/band_detect_cl.py @@ -486,7 +486,7 @@ def rdn_local_maxCL(self,radonIn,clparams=None, returnBuff = True): # there is something very strange that happens if the number of images # is a exact multiple of the max group size (typically 256) mxGroupSz = gpu[gpu_id].get_info(cl.device_info.MAX_WORK_GROUP_SIZE) - nImCL += np.int(16 * (1 - np.int(np.mod(nImCL,mxGroupSz) > 0))) + nImCL += np.int64(16 * (1 - np.int(np.mod(nImCL,mxGroupSz) > 0))) radonCL = np.zeros((nRp,nTp,nImCL),dtype=np.float32) radonCL[:,:,0:shp[2]] = radon rdn_gpu = cl.Buffer(ctx,mf.READ_ONLY | mf.COPY_HOST_PTR,hostbuf=radonCL) diff --git a/pyebsdindex/pairlib.py b/pyebsdindex/pairlib.py index eaf2d1c..ca19b51 100644 --- a/pyebsdindex/pairlib.py +++ b/pyebsdindex/pairlib.py @@ -167,7 +167,7 @@ def build_pair_lib(self,poles,symmetry): angTable = np.arccos(angTable)*RADEG famindx0 = ((np.concatenate( ([0],np.cumsum(nFamComplete)) ))[0:-1]).astype(dtype=np.int) cartPoles = self.xstalplane2cart(sympolesComplete) - cartPoles /= np.linalg.norm(cartPoles, axis = 1).reshape(np.int(cartPoles.size/3),1) + cartPoles /= np.linalg.norm(cartPoles, axis = 1).reshape(int(cartPoles.size/3),1) self.completelib = { 'poles' : sympolesComplete, 'polesCart': cartPoles, @@ -212,8 +212,8 @@ def hkl_unique(self,poles, reduceInversion=True, rMT = np.identity(3)): def calc_pole_dot(self,poles1,poles2,rMetricTensor = np.identity(3)): - p1 = poles1.reshape(np.int(poles1.size / 3), 3) - p2 = poles2.reshape(np.int(poles2.size / 3), 3) + p1 = poles1.reshape(int(poles1.size // 3), 3) + p2 = poles2.reshape(int(poles2.size // 3), 3) n1 = p1.shape[0] n2 = p2.shape[0] @@ -243,7 +243,7 @@ def sortlib_id(self,libANG,libID,findDups = False): for i in range(6): LUT[:, LUTA[i,0], LUTA[i,1], LUTA[i,2]] = LUTB[i,:] - ntrips = np.int(libANG.size / 3) + ntrips = int(libANG.size // 3) for i in range(ntrips): temp = np.squeeze(libANG[i,:]) srt = np.argsort(temp) diff --git a/pyebsdindex/tests/rotlibunittest.py b/pyebsdindex/tests/rotlibunittest.py index 443dde6..d5bff9d 100644 --- a/pyebsdindex/tests/rotlibunittest.py +++ b/pyebsdindex/tests/rotlibunittest.py @@ -12,7 +12,7 @@ # quaternion misorientation to evaluate the error. def testrotlib(float32=False, return_quat=False, seed = 1, n = 1000000): - n = np.int(n) + n = int(n) np.random.seed(seed) qu = (np.random.random((n,4))*2.0-1) if float32 is True: From b7c55e6d882483fdb3064b5c1443e49db8381c7e Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Tue, 11 Jul 2023 08:33:22 -0400 Subject: [PATCH 138/177] np.float --> np.float32 Signed-off by: David Rowenhorst --- pyebsdindex/EBSDImage/IPFcolor.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pyebsdindex/EBSDImage/IPFcolor.py b/pyebsdindex/EBSDImage/IPFcolor.py index 2e6d05e..2ff3046 100644 --- a/pyebsdindex/EBSDImage/IPFcolor.py +++ b/pyebsdindex/EBSDImage/IPFcolor.py @@ -86,7 +86,7 @@ def ipf_color_cubic(xstalvect): # cubic unit tri center triPts = np.array( [[0,0], [2. / np.sqrt(2.) / (1. + 1. / np.sqrt(2.)),0], - [2. / np.sqrt(3.) / (1. + 1. / np.sqrt(3.)),2. / np.sqrt(3.) / (1. + 1. / np.sqrt(3.))]], dtype = np.float) + [2. / np.sqrt(3.) / (1. + 1. / np.sqrt(3.)),2. / np.sqrt(3.) / (1. + 1. / np.sqrt(3.))]], dtype = np.float32) middle = np.tan(1. / 2. * np.arctan(triPts[2,1] / triPts[2,0])) a = np.sqrt( (triPts[2,1] - triPts[1,1]) ** 2. + (triPts[2,0] - triPts[1,0]) ** 2.) @@ -265,7 +265,7 @@ def ipf_color_hex(xstalvect): # cubic unit tri center triPts = np.array( [[0,0], [1.0 ,0], - [np.sqrt(3.)/2.0, 0.5 ]], dtype = np.float) + [np.sqrt(3.)/2.0, 0.5 ]], dtype = np.float32) middle = np.tan(1. / 2. * np.arctan(triPts[2,1] / triPts[2,0])) From 98af0dc6450ed1ce9a68a78bfaf5b407ed5ed9af Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Tue, 11 Jul 2023 08:50:16 -0400 Subject: [PATCH 139/177] Changed default local IP for Ray on macOS Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_parallel.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index e0b2ce6..e80547a 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -47,7 +47,7 @@ RAYIPADDRESS = '127.0.0.1' OSPLATFORM = platform.system() if OSPLATFORM == 'Darwin': - RAYIPADDRESS = '0.0.0.0' # the localhost address does not work on macOS when on a VPN + RAYIPADDRESS = '127.0.0.1' # the localhost address does not work on macOS when on a VPN def index_pats_distributed( patsin=None, From e5a40ffafa4f17a001a2c746fdbcfc820e0875d3 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Tue, 11 Jul 2023 10:06:41 -0400 Subject: [PATCH 140/177] Test correct Signed-off by: David Rowenhorst --- pyebsdindex/tests/test_ebsd_index.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/pyebsdindex/tests/test_ebsd_index.py b/pyebsdindex/tests/test_ebsd_index.py index 3ba9911..0f60f8f 100644 --- a/pyebsdindex/tests/test_ebsd_index.py +++ b/pyebsdindex/tests/test_ebsd_index.py @@ -72,9 +72,10 @@ def test_index_pats_multi(self, pattern_al_sim_20kv): patterns = np.repeat(pattern_al_sim_20kv[None, ...], 4, axis=0) indexer = EBSDIndexer(PC=(0.4, 0.6, 0.5), patDim=patterns.shape[1:]) - data = index_pats_distributed(patterns, ebsd_indexer_obj=indexer) + data = index_pats_distributed(patsin=patterns, ebsd_indexer_obj=indexer)[0] # Expected rotation euler = np.rad2deg(qu2eu(data[0]["quat"])) + assert np.isclose(euler[0], self._possible_euler, atol=2).any() assert np.allclose(euler[0], euler[1:]) From 3822c67812357bf2da1eb9fae3e189f1fd203b5b Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Tue, 11 Jul 2023 10:25:32 -0400 Subject: [PATCH 141/177] Fixed PC for test_index_pats_multi Signed-off by: David Rowenhorst --- pyebsdindex/tests/test_ebsd_index.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pyebsdindex/tests/test_ebsd_index.py b/pyebsdindex/tests/test_ebsd_index.py index 0f60f8f..76e861e 100644 --- a/pyebsdindex/tests/test_ebsd_index.py +++ b/pyebsdindex/tests/test_ebsd_index.py @@ -71,11 +71,11 @@ def test_index_pats_multi(self, pattern_al_sim_20kv): from pyebsdindex.ebsd_index import index_pats_distributed patterns = np.repeat(pattern_al_sim_20kv[None, ...], 4, axis=0) - indexer = EBSDIndexer(PC=(0.4, 0.6, 0.5), patDim=patterns.shape[1:]) + indexer = EBSDIndexer(PC=(0.4, 0.72, 0.6), patDim=patterns.shape[1:]) data = index_pats_distributed(patsin=patterns, ebsd_indexer_obj=indexer)[0] # Expected rotation euler = np.rad2deg(qu2eu(data[0]["quat"])) - + assert np.isclose(euler[0], self._possible_euler, atol=2).any() assert np.allclose(euler[0], euler[1:]) From ffadf6a9642612774b972c331647dd07f438d197 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Wed, 12 Jul 2023 09:36:10 -0400 Subject: [PATCH 142/177] Attempt to fix bug with Ray and pydantic >=2 Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_parallel.py | 7 ++++--- setup.py | 1 + 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index e80547a..8787b1b 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -47,7 +47,7 @@ RAYIPADDRESS = '127.0.0.1' OSPLATFORM = platform.system() if OSPLATFORM == 'Darwin': - RAYIPADDRESS = '127.0.0.1' # the localhost address does not work on macOS when on a VPN + RAYIPADDRESS = '0.0.0.0' # the localhost address does not work on macOS when on a VPN def index_pats_distributed( patsin=None, @@ -320,9 +320,10 @@ def index_pats_distributed( # ray.init(num_cpus=n_cpu_nodes,num_gpus=ngpu,_system_config={"maximum_gcs_destroyed_actor_cached_count": n_cpu_nodes}) # Need to append path for installs from source ... otherwise the ray # workers do not know where to find the PyEBSDIndex module. - ray.init( + rayclust = ray.init( num_cpus=int(np.round(n_cpu_nodes)), num_gpus=ngpu*ngpuwrker, + #dashboard_host = RAYIPADDRESS, _node_ip_address=RAYIPADDRESS, #"0.0.0.0", runtime_env={"env_vars": {"PYTHONPATH": os.path.dirname(os.path.dirname(__file__)), @@ -331,7 +332,7 @@ def index_pats_distributed( ) # Supress INFO messages from ray. print("num cpu/gpu, and number of patterns per iteration:", n_cpu_nodes, ngpu, chunksize, ngpuwrker, ncpuwrker) - + #print(rayclust) # Place indexer obj in shared memory store so all workers can use it - this is read only. remote_indexer = ray.put(indexer) # Get the function that will collect opencl parameters - if opencl diff --git a/setup.py b/setup.py index 592bd8c..2a9f921 100644 --- a/setup.py +++ b/setup.py @@ -30,6 +30,7 @@ ], "parallel": [ "ray[default] >= 1.13", + "pydantic < 2", ] } # Create a development installation "dev" including "doc" and "tests" From e43b88cb81504e712cb87975ac3ac99a48a58cd2 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Wed, 12 Jul 2023 17:19:56 -0400 Subject: [PATCH 143/177] Code clean up and some documentation. Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_parallel.py | 28 +- pyebsdindex/_ebsd_index_parallel_old.py | 736 ------------------------ pyebsdindex/_ebsd_index_single.py | 99 +++- pyebsdindex/nlpar.py | 3 + pyebsdindex/tripletlib_old.py | 405 ------------- 5 files changed, 111 insertions(+), 1160 deletions(-) delete mode 100644 pyebsdindex/_ebsd_index_parallel_old.py delete mode 100644 pyebsdindex/tripletlib_old.py diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index 8787b1b..34fe75d 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -18,7 +18,7 @@ # some notice that they have been modified. # # Author: David Rowenhorst; -# The US Naval Research Laboratory Date: 21 Aug 2020 +# The US Naval Research Laboratory Date: 12 July 2023 """Setup and handling of Radon indexing runs of EBSD patterns in parallel. @@ -148,18 +148,34 @@ def index_pats_distributed( Returns ------- indxData : numpy.ndarray - Complex numpy array (or array of structured data), that is + Structured numpy array, that is [nphases + 1, npoints]. The data is stored for each phase used in indexing and the ``indxData[-1]`` layer uses the best guess on which is the most likely phase, based on the fit, and number of bands matched for each phase. Each data entry contains the - orientation expressed as a quaternion (quat) (using the + orientation expressed as a quaternion ('quat') (using the convention of ``vendor`` or :attr:`indexer.vendor`), Pattern - Quality (pq), Confidence Metric (cm), Phase ID (phase), Fit - (fit) and Number of Bands Matched (nmatch). There are some other + Quality ('pq'), Confidence Metric ('cm'), Phase ID ('phase'), Fit + ('fit') and Number of Bands Matched ('nmatch'). There are some other metrics reported, but these are mostly for debugging purposes. + The number and order of fields are not guaranteed to remain the same, but + fields listed here are stable. + (phase) parameter will be set to -1 for any no-solution point. bandData : numpy.ndarray - Band identification data from the Radon transform. + Band identification data from the Radon transform. Stored + as a structured numpy array, of dimensions [npoints, nbands]. + With fields that include: + band ID ('id'), + peak max intesensity [used to calculate pattern quality] ('max') + nearest integer location of the Radon peak ('maxloc'), + nearest neighbor average of the max peak intensity('avemax'), + sub-pixel location of the Radon peak ('aveloc'), + a metric of the band width ('width'), + the theta value of the sub-pixel location on the Radon [lower-left origin] ('theta'), + the rho value of the sub-pixel location on the Radon [lower-left origin]('rho'), + was the peak detected ('valid'), + index for phase number and pole number that indexed to this band('band_match_index') + [use the EBSDIndexer method indexer.getmatchedpole(banddata)] indexer : EBSDIndexer EBSD indexer, returned if ``return_indexer_obj=True``. diff --git a/pyebsdindex/_ebsd_index_parallel_old.py b/pyebsdindex/_ebsd_index_parallel_old.py deleted file mode 100644 index 4dff648..0000000 --- a/pyebsdindex/_ebsd_index_parallel_old.py +++ /dev/null @@ -1,736 +0,0 @@ -# This software was developed by employees of the US Naval Research Laboratory (NRL), an -# agency of the Federal Government. Pursuant to title 17 section 105 of the United States -# Code, works of NRL employees are not subject to copyright protection, and this software -# is in the public domain. PyEBSDIndex is an experimental system. NRL assumes no -# responsibility whatsoever for its use by other parties, and makes no guarantees, -# expressed or implied, about its quality, reliability, or any other characteristic. We -# would appreciate acknowledgment if the software is used. To the extent that NRL may hold -# copyright in countries other than the United States, you are hereby granted the -# non-exclusive irrevocable and unconditional right to print, publish, prepare derivative -# works and distribute this software, in any medium, or authorize others to do so on your -# behalf, on a royalty-free basis throughout the world. You may improve, modify, and -# create derivative works of the software or any portion of the software, and you may copy -# and distribute such modifications or works. Modified works should carry a notice stating -# that you changed the software and should note the date and nature of any such change. -# Please explicitly acknowledge the US Naval Research Laboratory as the original source. -# This software can be redistributed and/or modified freely provided that any derivative -# works bear some notice that they are derived from it, and any modified versions bear -# some notice that they have been modified. -# -# Author: David Rowenhorst; -# The US Naval Research Laboratory Date: 21 Aug 2020 - -"""Setup and handling of Radon indexing runs of EBSD patterns in -parallel. -""" - - -import os -import platform -import logging -import sys -import time -from timeit import default_timer as timer - -import numpy as np -import h5py -import ray - -from pyebsdindex import ebsd_pattern, _pyopencl_installed -from pyebsdindex._ebsd_index_single import EBSDIndexer, index_pats - -if _pyopencl_installed: - from pyebsdindex.opencl import band_detect_cl as band_detect -else: - from pyebsdindex import band_detect as band_detect - -RAYIPADDRESS = '127.0.0.1' -osplatform = platform.system() -if osplatform == 'Darwin': - RAYIPADDRESS = '0.0.0.0' # the localhost address does not work on macOS when on a VPN - -def index_pats_distributed( - patsin=None, - filename=None, - phaselist=["FCC"], - vendor=None, - PC=None, - sampleTilt=70.0, - camElev=5.3, - bandDetectPlan=None, - nRho=90, - nTheta=180, - tSigma=None, - rSigma=None, - rhoMaskFrac=0.1, - nBands=9, - patstart=0, - npats=-1, - chunksize=0, - ncpu=-1, - return_indexer_obj=False, - ebsd_indexer_obj=None, - keep_log=False, - gpu_id=None, -): - """Index EBSD patterns in parallel. - - Parameters - ---------- - patsin : numpy.ndarray, optional - EBSD patterns in an array of shape (n points, n pattern - rows, n pattern columns). If not given, these are read from - ``filename``. - filename : str, optional - Name of file with EBSD patterns. If not given, ``patsin`` must - be passed. - phaselist : list of str, optional - Options are ``"FCC"`` and ``"BCC"``. Default is ``["FCC"]``. - vendor : str, optional - Which vendor convention to use for the pattern center (PC) and - the returned orientations. The available options are ``"EDAX"`` - (default), ``"BRUKER"``, ``"OXFORD"``, ``"EMSOFT"``, - ``"KIKUCHIPY"``. - PC : list, optional - Pattern center (PCx, PCy, PCz) in the :attr:`indexer.vendor` or - ``vendor`` convention. For EDAX TSL, this is (x*, y*, z*), - defined in fractions of pattern width with respect to the lower - left corner of the detector. If not passed, this is set to (x*, - y*, z*) = (0.471659, 0.675044, 0.630139). If - ``vendor="EMSOFT"``, the PC must be four numbers, the final - number being the pixel size. - sampleTilt : float, optional - Sample tilt towards the detector in degrees. Default is 70 - degrees. Unused if ``ebsd_indexer_obj`` is passed. - camElev : float, optional - Camera elevation in degrees. Default is 5.3 degrees. Unused - if ``ebsd_indexer_obj`` is passed. - bandDetectPlan : pyebsdindex.band_detect.BandDetect, optional - Collection of parameters using in band detection. Unused if - ``ebsd_indexer_obj`` is passed. - nRho : int, optional - Default is 90 degrees. Unused if ``ebsd_indexer_obj`` is - passed. - nTheta : int, optional - Default is 180 degrees. Unused if ``ebsd_indexer_obj`` is - passed. - tSigma : float, optional - Unused if ``ebsd_indexer_obj`` is passed. - rSigma : float, optional - Unused if ``ebsd_indexer_obj`` is passed. - rhoMaskFrac : float, optional - Default is 0.1. Unused if ``ebsd_indexer_obj`` is passed. - nBands : int, optional - Number of detected bands to use in triplet voting. Default - is 9. Unused if ``ebsd_indexer_obj`` is passed. - patstart : int, optional - Starting index of the patterns to index. Default is ``0``. - npats : int, optional - Number of patterns to index. Default is ``-1``, which will - index up to the final pattern in ``patsin``. - chunksize : int, optional - If not set. we will make a guess based on the resources available. - ncpu : int, optional - Number of CPUs to use. Default value is ``-1``, meaning all - available CPUs will be used. - return_indexer_obj : bool, optional - Whether to return the EBSD indexer. Default is ``False``. - ebsd_indexer_obj : EBSDIndexer, optional - EBSD indexer. If not given, many of the above parameters must be - passed. Otherwise, these parameters are retrieved from this - indexer. - keep_log : bool, optional - Whether to keep the log. Default is ``False``. - gpu_id : int, optional - ID of GPU to use if :mod:`pyopencl` is installed. - - Returns - ------- - indxData : numpy.ndarray - Complex numpy array (or array of structured data), that is - [nphases + 1, npoints]. The data is stored for each phase used - in indexing and the ``indxData[-1]`` layer uses the best guess - on which is the most likely phase, based on the fit, and number - of bands matched for each phase. Each data entry contains the - orientation expressed as a quaternion (quat) (using the - convention of ``vendor`` or :attr:`indexer.vendor`), Pattern - Quality (pq), Confidence Metric (cm), Phase ID (phase), Fit - (fit) and Number of Bands Matched (nmatch). There are some other - metrics reported, but these are mostly for debugging purposes. - bandData : numpy.ndarray - Band identification data from the Radon transform. - indexer : EBSDIndexer - EBSD indexer, returned if ``return_indexer_obj=True``. - - Notes - ----- - Requires :mod:`ray[default]`. See the :doc:`installation guide - ` for details. - """ - pats = None - if patsin is None: - pdim = None - else: - if isinstance(patsin, ebsd_pattern.EBSDPatterns): - pats = patsin.patterns - if type(patsin) is np.ndarray: - pats = patsin - if isinstance(patsin, h5py.Dataset): - shp = patsin.shape - if len(shp) == 3: - pats = patsin - if len(shp) == 2: # just read off disk now. - pats = patsin[()] - pats = pats.reshape(1, shp[0], shp[1]) - - if pats is None: - print("Unrecognized input data type") - return - pdim = pats.shape[-2:] - - # run a test flight to make sure all parameters are set properly before being sent off to the cluster - if ebsd_indexer_obj is None: - indexer = EBSDIndexer( - filename=filename, - phaselist=phaselist, - vendor=vendor, - PC=PC, - sampleTilt=sampleTilt, - camElev=camElev, - bandDetectPlan=bandDetectPlan, - nRho=nRho, - nTheta=nTheta, - tSigma=tSigma, - rSigma=rSigma, - rhoMaskFrac=rhoMaskFrac, - nBands=nBands, - patDim=pdim, - gpu_id=gpu_id, - ) - else: - indexer = ebsd_indexer_obj - - if filename is not None: - indexer.update_file(filename) - else: - indexer.update_file(patDim=pats.shape[-2:]) - - # Differentiate between getting a file to index or an array. - # Need to index one pattern to make sure the indexer object is fully - # initiated before placing in shared memory store. - mode = "memorymode" - if pats is None: - mode = "filemode" - temp, temp2, indexer = index_pats( - npats=1, return_indexer_obj=True, ebsd_indexer_obj=indexer - ) - - if mode == "filemode": - npatsTotal = indexer.fID.nPatterns - else: - pshape = pats.shape - if len(pshape) == 2: - npatsTotal = 1 - pats = pats.reshape([1, pshape[0], pshape[1]]) - else: - npatsTotal = pshape[0] - temp, temp2, indexer = index_pats( - pats[0, :, :], - npats=1, - return_indexer_obj=True, - ebsd_indexer_obj=indexer, - ) - - if patstart < 0: - patstart = npatsTotal - patstart - if npats <= 0: - npats = npatsTotal - patstart - - # Now set up the cluster with the indexer - n_cpu_nodes = int(os.cpu_count()) - # int(sum([ r['Resources']['CPU'] for r in ray.nodes()])) - if ncpu != -1: - n_cpu_nodes = int(ncpu) - - ngpu = None - if gpu_id is not None: - ngpu = np.atleast_1d(gpu_id).shape[0] - - try: - clparam = band_detect.getopenclparam() - if clparam is None: - ngpu = 0 - ngpupnode = 0 - else: - if ngpu is None: - ngpu = len(clparam.gpu) - ngpupnode = ngpu / n_cpu_nodes - except: - ngpu = 0 - ngpupnode = 0 - - if chunksize <= 0: - chunksize = __optimizegpuchunk__(indexer, n_cpu_nodes, gpu_id, clparam) - - - - ray.shutdown() - - print("num cpu/gpu, and number of patterns per iteration:", n_cpu_nodes, ngpu, chunksize) - # ray.init(num_cpus=n_cpu_nodes,num_gpus=ngpu,_system_config={"maximum_gcs_destroyed_actor_cached_count": n_cpu_nodes}) - # Need to append path for installs from source ... otherwise the ray - # workers do not know where to find the PyEBSDIndex module. - ray.init( - num_cpus=n_cpu_nodes, - num_gpus=ngpu, - _node_ip_address=RAYIPADDRESS, #"0.0.0.0", - runtime_env={"env_vars": {"PYTHONPATH": os.path.dirname(os.path.dirname(__file__))}}, - logging_level=logging.WARNING, - ) # Supress INFO messages from ray. - - # Place indexer obj in shared memory store so all workers can use it - this is read only. - remote_indexer = ray.put(indexer) - # Get the function that will collect opencl parameters - if opencl - # is not installed, this is None, and the program will automatically - # fall back to CPU only calculation. - clparamfunction = band_detect.getopenclparam - # Set up the jobs - njobs = (np.ceil(npats / chunksize)).astype(np.compat.long) - # p_indx_start = [i*chunksize+patStart for i in range(njobs)] - # p_indx_end = [(i+1)*chunksize+patStart for i in range(njobs)] - # p_indx_end[-1] = npats+patStart - p_indx_start_end = [ - [i * chunksize + patstart, (i + 1) * chunksize + patstart, chunksize] - for i in range(njobs) - ] - p_indx_start_end[-1][1] = npats + patstart - p_indx_start_end[-1][2] = p_indx_start_end[-1][1] - p_indx_start_end[-1][0] - - if njobs < n_cpu_nodes: - n_cpu_nodes = njobs - - nPhases = len(indexer.phaseLib) - dataout = np.zeros((nPhases + 1, npats), dtype=indexer.dataTemplate) - banddataout = np.zeros( - (npats, indexer.bandDetectPlan.nBands), dtype=indexer.bandDetectPlan.dataType - ) - ndone = 0 - nsubmit = 0 - tic0 = timer() - npatsdone = 0.0 - - if keep_log is True: - newline = "\n" - else: - newline = "\r" - if mode == "filemode": - # Send out the first batch - workers = [] - jobs = [] - timers = [] - jobs_indx = [] - chunkave = 0.0 - for i in range(n_cpu_nodes): - job_pstart_end = p_indx_start_end.pop(0) - workers.append( # make a new Ray Actor that can call the indexer defined in shared memory. - # These actors are read/write, thus can initialize the GPU queues - IndexerRay.options(num_cpus=1, num_gpus=ngpupnode).remote( - i, clparamfunction, gpu_id=gpu_id - ) - ) - jobs.append( - workers[i].index_chunk_ray.remote( - pats=None, - indexer=remote_indexer, - patstart=job_pstart_end[0], - npats=job_pstart_end[2], - ) - ) - nsubmit += 1 - timers.append(timer()) - #time.sleep(0.01) - jobs_indx.append(job_pstart_end[:]) - - while ndone < njobs: - # toc = timer() - wrker, busy = ray.wait(jobs, num_returns=1, timeout=60.0) - - # print("waittime: ",timer() - toc) - if len(wrker) > 0: # trying to catch a hung worker. Rare, but it happens - jid = jobs.index(wrker[0]) - else: - print('hang with ', ndone, 'out of ', njobs) - jid = jobs.index(busy[0]) - wrker.append(busy[0]) - ray.kill(workers[jid]) - try: - wrkdataout, wrkbanddata, indxstr, indxend, rate = ray.get(wrker[0]) - except: - # print('a death has occured') - indxstr = jobs_indx[jid][0] - indxend = jobs_indx[jid][1] - rate = [-1, -1] - if rate[0] >= 0: # Job finished as expected - - ticp = timers[jid] - dataout[:, indxstr - patstart : indxend - patstart] = wrkdataout - banddataout[indxstr - patstart : indxend - patstart, :] = wrkbanddata - npatsdone += rate[1] - ndone += 1 - - ratetemp = n_cpu_nodes * (rate[1]) / (timer() - ticp) - chunkave += ratetemp - totalave = npatsdone / (timer() - tic0) - # print('Completed: ',str(indxstr),' -- ',str(indxend), ' ', npatsdone/(timer()-tic) ) - - toc0 = timer() - tic0 - if keep_log is False: - print("", end="\r") - time.sleep(0.00001) - print( - "Completed: ", - str(indxstr), - " -- ", - str(indxend), - " PPS:", - "{:.0f}".format(ratetemp) - + ";" - + "{:.0f}".format(chunkave / ndone) - + ";" - + "{:.0f}".format(totalave), - " ", - "{:.0f}".format((ndone / njobs) * 100) + "%", - "{:.0f};".format(toc0) - + "{:.0f}".format((njobs - ndone) / ndone * toc0) - + " running;remaining(s)", - end=newline, - ) - - if len(p_indx_start_end) > 0: - job_pstart_end = p_indx_start_end.pop(0) - jobs[jid] = workers[jid].index_chunk_ray.remote( - pats=None, - indexer=remote_indexer, - patstart=job_pstart_end[0], - npats=job_pstart_end[2], - ) - nsubmit += 1 - timers[jid] = timer() - jobs_indx[jid] = job_pstart_end[:] - else: - del jobs[jid] - del workers[jid] - del timers[jid] - del jobs_indx[jid] - else: - # Something bad happened. Put the job back on the queue - # and kill this worker. - p_indx_start_end.append([indxstr, indxend, indxend - indxstr]) - del jobs[jid] - del workers[jid] - del timers[jid] - del jobs_indx[jid] - n_cpu_nodes -= 1 - if len(workers) < 1: # Rare case that we have killed all workers... - job_pstart_end = p_indx_start_end.pop(0) - workers.append( - IndexerRay.options(num_cpus=1, num_gpus=ngpupnode).remote( - jid, clparamfunction, gpu_id - ) - ) - jobs.append( - workers[0].index_chunk_ray.remote( - pats=None, - indexer=remote_indexer, - patstart=job_pstart_end[0], - npats=job_pstart_end[2], - ) - ) - nsubmit += 1 - timers.append(timer()) - time.sleep(0.01) - jobs_indx.append(job_pstart_end[:]) - n_cpu_nodes += 1 - - if mode == "memorymode": - workers = [] - jobs = [] - timers = [] - jobs_indx = [] - chunkave = 0.0 - for i in range(n_cpu_nodes): - job_pstart_end = p_indx_start_end.pop(0) - workers.append( - IndexerRay.options(num_cpus=1, num_gpus=ngpupnode).remote( - i, clparamfunction, gpu_id - ) - ) - jobs.append( - workers[i].index_chunk_ray.remote( - pats=pats[job_pstart_end[0] : job_pstart_end[1], :, :], - indexer=remote_indexer, - patstart=job_pstart_end[0], - npats=job_pstart_end[2], - ) - ) - nsubmit += 1 - timers.append(timer()) - jobs_indx.append(job_pstart_end) - time.sleep(0.01) - - # workers = [index_chunk.remote(pats = None, indexer = remote_indexer, patStart = p_indx_start[i], patEnd = p_indx_end[i]) for i in range(n_cpu_nodes)] - # nsubmit += n_cpu_nodes - - while ndone < njobs: - # toc = timer() - wrker, busy = ray.wait(jobs, num_returns=1, timeout=None) - jid = jobs.index(wrker[0]) - # print("waittime: ",timer() - toc) - if len(wrker) > 0: - jid = jobs.index(wrker[0]) - else: - print('hang with ', ndone, 'out of ', njobs) - jid = jobs.index(busy[0]) - wrker.append(busy[0]) - ray.kill(workers[jid]) - try: - wrkdataout, wrkbanddata, indxstr, indxend, rate = ray.get(wrker[0]) - except: - indxstr = jobs_indx[jid][0] - indxend = jobs_indx[jid][1] - rate = [-1, -1] - if rate[0] >= 0: - ticp = timers[jid] - dataout[:, indxstr - patstart : indxend - patstart] = wrkdataout - banddataout[indxstr - patstart : indxend - patstart, :] = wrkbanddata - npatsdone += rate[1] - ratetemp = n_cpu_nodes * (rate[1]) / (timer() - ticp) - chunkave += ratetemp - totalave = npatsdone / (timer() - tic0) - # print('Completed: ',str(indxstr),' -- ',str(indxend), ' ', npatsdone/(timer()-tic) ) - ndone += 1 - toc0 = timer() - tic0 - if keep_log is False: - print("", end="\r") - time.sleep(0.0001) - print( - "Completed: ", - str(indxstr), - " -- ", - str(indxend), - " PPS:", - "{:.0f}".format(ratetemp) - + ";" - + "{:.0f}".format(chunkave / ndone) - + ";" - + "{:.0f}".format(totalave), - " ", - "{:.0f}".format((ndone / njobs) * 100) + "%", - "{:.0f};".format(toc0) - + "{:.0f}".format((njobs - ndone) / ndone * toc0) - + " running;remaining(s)", - end=newline, - ) - - if len(p_indx_start_end) > 0: - job_pstart_end = p_indx_start_end.pop(0) - jobs[jid] = workers[jid].index_chunk_ray.remote( - pats=pats[job_pstart_end[0] : job_pstart_end[1], :, :], - indexer=remote_indexer, - patstart=job_pstart_end[0], - npats=job_pstart_end[2], - ) - nsubmit += 1 - timers[jid] = timer() - jobs_indx[jid] = job_pstart_end - else: - del jobs[jid] - del workers[jid] - del timers[jid] - del jobs_indx[jid] - else: - # Something bad happened. Put the job back on the queue - # and kill this worker. - p_indx_start_end.append([indxstr, indxend, indxend - indxstr]) - del jobs[jid] - del workers[jid] - del timers[jid] - del jobs_indx[jid] - n_cpu_nodes -= 1 - if len(workers) < 1: # Rare case that we have killed all workers... - job_pstart_end = p_indx_start_end.pop(0) - workers.append( - IndexerRay.options(num_cpus=1, num_gpus=ngpupnode).remote( - jid, clparamfunction, gpu_id - ) - ) - jobs.append( - workers[0].index_chunk_ray.remote( - pats=pats[job_pstart_end[0] : job_pstart_end[1], :, :], - indexer=remote_indexer, - patstart=job_pstart_end[0], - npats=job_pstart_end[2], - ) - ) - nsubmit += 1 - timers.append(timer()) - jobs_indx.append(job_pstart_end) - n_cpu_nodes += 1 - - del jobs - del workers - del timers - # # send out the first batch - # workers = [index_chunk_ray.remote(pats=pats[p_indx_start[i]:p_indx_end[i],:,:],indexer=remote_indexer,patStart=p_indx_start[i],patEnd=p_indx_end[i]) for i - # in range(n_cpu_nodes)] - # nsubmit += n_cpu_nodes - # - # while ndone < njobs: - # wrker,busy = ray.wait(workers,num_returns=1,timeout=None) - # wrkdataout,indxstr,indxend, rate = ray.get(wrker[0]) - # dataout[indxstr:indxend] = wrkdataout - # print('Completed: ',str(indxstr),' -- ',str(indxend)) - # workers.remove(wrker[0]) - # ndone += 1 - # - # if nsubmit < njobs: - # workers.append(index_chunk_ray.remote(pats=pats[p_indx_start[nsubmit]:p_indx_end[nsubmit],:,:],indexer=remote_indexer,patStart=p_indx_start[nsubmit], - # patEnd=p_indx_end[nsubmit])) - # nsubmit += 1 - - ray.shutdown() - if return_indexer_obj: - return dataout, banddataout, indexer - else: - return dataout, banddataout - -def __optimizegpuchunk__(indexer, n_cpu_nodes, gpu_id, clparam): - - - gpulist = [] - # test for GPU presence - if clparam is None: - return 1000 - - if clparam.ngpu == 0: - return 1000 - - if gpu_id is None: - for g in clparam.gpu: - gpulist.append(g) - else: - temp = np.atleast_1d(gpu_id) - for g in temp: - gpulist.append(clparam.gpu[g]) - ngpu = len(gpulist) - - if ngpu == 0: - return 1000 - - gmem = 1e99 - for g in gpulist: - if g.global_mem_size < gmem: - gmem = g.global_mem_size - #print('Global Mem:', gmem) - ncpu_per_gpu = max(1, np.ceil(n_cpu_nodes/ngpu)) - #print('Ncpu/gpu:', ncpu_per_gpu) - patdim = indexer.bandDetectPlan.patDim - rdndim = np.array([indexer.bandDetectPlan.nTheta+2*indexer.bandDetectPlan.padding[1], - indexer.bandDetectPlan.nRho+2*indexer.bandDetectPlan.padding[0]]) - memperpat = 4.0*float(patdim[0] * patdim[1] + 9.0 * rdndim[0] * rdndim[1])# rough estimate - - #print('Mem/pat:', memperpat) - chunkguess = (float(gmem)/float(ncpu_per_gpu)) / memperpat - - #print('chunkguess:', chunkguess) - safetyval = 0.5 - chunkguess *= safetyval - if clparam.gpu[0].vendor == 'AMD': # 'AMD implmentation of opencl does better with clearing memory' - # # this is a cheat, because 1/2 the time the GPU will be idle while the CPU is compputing. - chunkguess *= 3.0 - - - - #print('cheatguess:', chunkguess) - chunk = int(max(2, np.floor(chunkguess/16))*16) # ideally should be a multiple of 16 - #print('chunk:', chunk) - #check for powers of two - for some reason it runs very slow with powers of two. - twocheck = np.log2(float(chunk)) - if np.abs((twocheck) - np.round(twocheck)) < 1e-6: - chunk += 16 - - # finally - I am unsure how to check for integrated graphics that report system memory, so I am going - # throw an arbitrary cap on this: - chunk = min(2016, chunk) - - return chunk - - - - - - - - - - -@ray.remote(num_cpus=1, num_gpus=1) -class IndexerRay: - def __init__(self, actorid=0, clparammodule=None, gpu_id=None): - # sys.path.append(path.dirname(path.dirname(__file__))) # do this to help Ray find the program files - # import openclparam # do this to help Ray find the program files - # device, context, queue, program, mf - # self.dataout = None - # self.indxstart = None - # self.indxend = None - # self.rate = None - self.actorID = actorid - self.openCLParams = None - self.useGPU = False - if clparammodule is not None: - try: - if ( - sys.platform != "darwin" - ): # linux with NVIDIA (unsure if it is the os or GPU type) is slow to make a - self.openCLParams = clparammodule() - else: # MacOS handles GPU memory conflicts much better when the context is destroyed between each - # run, and has very low overhead for making the context. - # pass - self.openCLParams = clparammodule() - # self.openCLParams.gpu_id = 0 - # self.openCLParams.gpu_id = 1 - self.openCLParams.gpu_id = self.actorID % self.openCLParams.ngpu - if gpu_id is None: - gpu_id = np.arange(self.openCLParams.ngpu) - gpu_list = np.atleast_1d(gpu_id) - ngpu = gpu_list.shape[0] - self.openCLParams.gpu_id = gpu_list[self.actorID % ngpu] - self.openCLParams.get_context() - #self.openCLParams.get_queue() - self.useGPU = True - except: - self.openCLParams = None - - def index_chunk_ray(self, pats=None, indexer=None, patstart=0, npats=-1): - try: - # print(type(self.openCLParams.ctx)) - - tic = timer() - if self.openCLParams is not None: - self.openCLParams.get_queue() - dataout, banddata, indxstart, npatsout = indexer.index_pats( - patsin=pats, - patstart=patstart, - npats=npats, - clparams=self.openCLParams, - chunksize=-1, - ) - if self.openCLParams is not None: - self.openCLParams.queue.finish() - self.openCLParams.queue = None - rate = np.array([timer() - tic, npatsout]) - return dataout, banddata, indxstart, indxstart + npatsout, rate - except: - indxstart = patstart - indxend = patstart + npats - return None, None, indxstart, indxend, [-1, -1] diff --git a/pyebsdindex/_ebsd_index_single.py b/pyebsdindex/_ebsd_index_single.py index 4d194f1..df1afa6 100644 --- a/pyebsdindex/_ebsd_index_single.py +++ b/pyebsdindex/_ebsd_index_single.py @@ -435,19 +435,34 @@ def index_pats( Returns ------- indxData : numpy.ndarray - Complex numpy array (or array of structured data), that is - [nphases + 1, npoints]. The data is stored for each phase - used in indexing and the `indxData[-1]` layer uses the best - guess on which is the most likely phase, based on the fit, - and number of bands matched for each phase. Each data entry - contains the orientation expressed as a quaternion (quat) - (using `self.vendor`'s convention), Pattern Quality (pq), - Confidence Metric (cm), Phase ID (phase), Fit (fit) and - Number of Bands Matched (nmatch). There are some other - metrics reported, but these are mostly for debugging - purposes. - banddata : numpy.ndarray - Band identification data from the Radon transform. + Structured numpy array, that is + [nphases + 1, npoints]. The data is stored for each phase used + in indexing and the ``indxData[-1]`` layer uses the best guess + on which is the most likely phase, based on the fit, and number + of bands matched for each phase. Each data entry contains the + orientation expressed as a quaternion ('quat') (using the + convention of ``vendor`` or :attr:`indexer.vendor`), Pattern + Quality ('pq'), Confidence Metric ('cm'), Phase ID ('phase'), Fit + ('fit') and Number of Bands Matched ('nmatch'). There are some other + metrics reported, but these are mostly for debugging purposes. + The number and order of fields are not guaranteed to remain the same, but + fields listed here are stable. + (phase) parameter will be set to -1 for any no-solution point. + bandData : numpy.ndarray + Band identification data from the Radon transform. Stored + as a structured numpy array, of dimensions [npoints, nbands]. + With fields that include: + band ID ('id'), + peak max intesensity [used to calculate pattern quality] ('max') + nearest integer location of the Radon peak ('maxloc'), + nearest neighbor average of the max peak intensity('avemax'), + sub-pixel location of the Radon peak ('aveloc'), + a metric of the band width ('width'), + the theta value of the sub-pixel location on the Radon [lower-left origin] ('theta'), + the rho value of the sub-pixel location on the Radon [lower-left origin]('rho'), + was the peak detected ('valid'), + index for phase number and pole number that indexed to this band('band_match_index') + [use the EBSDIndexer method indexer.getmatchedpole(banddata)] patstart : int Starting index of the indexed patterns. npats : int @@ -474,6 +489,64 @@ def index_pats( print("Band Vote Time: ", timer() - tic) return indxData, banddata, patstart, npats + def getmatchedpole(self, banddata, float_out=False): + """Return the pole from the library that was matched to the detected band. + + Parameters + ---------- + banddata : numpy.ndarray, output structured bandata array from + ebsd_index.index_pats or ebsd_index.index_pats_distributed. + float_out: False[default]/True, optional + Default is to return an array of ints with Miller indices. + If set to True, then floats, with unit length will be returned in the + sample Cartesian reference frame. + (length is only valid for cubic systems). + npats : int, optional + Number of patterns to index. Default is ``-1``, which will + index up to the final pattern in ``patsin``. + + Returns + ------- + matched poles: numpy.ndarray int + The default is an array [npoints, nbands, 3] that contain the Miller + indices of the matching pole (note, that hexagonal will also return only + three index notation). If the float_out is set to True, then + the output will be floating point vectors of length one, within the sample Cartesian + reference frame. + """ + nphases = len(self.phaseLib) + + bnddat = banddata + shpbdndat = bnddat.shape + if len(shpbdndat) == 0: + bnddat = np.array(bnddat).reshape(1) + shpbdndat = bnddat.shape + nbands = shpbdndat[-1] + if len(shpbdndat) == 1: + bnddat = bnddat.reshape(1, nbands) + shpbdndat = bnddat.shape + npoints = shpbdndat[0] + + polesout = np.zeros((npoints,nbands,3)) + if float_out is False: + polekey = 'poles' + else: + polekey = 'polesCart' + + for ph in range(nphases): + wh = np.nonzero(bnddat['band_match_index'][:,0,0] == ph)[0] + if len(wh) == 0: + continue + pindex = bnddat['band_match_index'][wh,:, 1] + poles = self.phaseLib[ph].completelib[polekey][pindex,:] + polesout[wh, :, :] = poles + + if float_out is False: + polesout = np.round(polesout).astype(int) + + return polesout + + def _getpats(self, patsin=None, patstart=0, npats=-1, xyloc=None): if patsin is None: diff --git a/pyebsdindex/nlpar.py b/pyebsdindex/nlpar.py index 6489201..67a6382 100644 --- a/pyebsdindex/nlpar.py +++ b/pyebsdindex/nlpar.py @@ -20,6 +20,9 @@ # Author: David Rowenhorst; # The US Naval Research Laboratory Date: 21 Aug 2020 +# For more info see +# Patrick T. Brewick, Stuart I. Wright, David J. Rowenhorst. Ultramicroscopy, 200:50–61, May 2019. + """Non-local pattern averaging and re-indexing (NLPAR).""" from pathlib import Path diff --git a/pyebsdindex/tripletlib_old.py b/pyebsdindex/tripletlib_old.py deleted file mode 100644 index 22165f2..0000000 --- a/pyebsdindex/tripletlib_old.py +++ /dev/null @@ -1,405 +0,0 @@ -'''This software was developed by employees of the US Naval Research Laboratory (NRL), an -agency of the Federal Government. Pursuant to title 17 section 105 of the United States -Code, works of NRL employees are not subject to copyright protection, and this software -is in the public domain. PyEBSDIndex is an experimental system. NRL assumes no -responsibility whatsoever for its use by other parties, and makes no guarantees, -expressed or implied, about its quality, reliability, or any other characteristic. We -would appreciate acknowledgment if the software is used. To the extent that NRL may hold -copyright in countries other than the United States, you are hereby granted the -non-exclusive irrevocable and unconditional right to print, publish, prepare derivative -works and distribute this software, in any medium, or authorize others to do so on your -behalf, on a royalty-free basis throughout the world. You may improve, modify, and -create derivative works of the software or any portion of the software, and you may copy -and distribute such modifications or works. Modified works should carry a notice stating -that you changed the software and should note the date and nature of any such change. -Please explicitly acknowledge the US Naval Research Laboratory as the original source. -This software can be redistributed and/or modified freely provided that any derivative -works bear some notice that they are derived from it, and any modified versions bear -some notice that they have been modified. - -Author: David Rowenhorst; -The US Naval Research Laboratory Date: 21 Aug 2020''' - - -import numpy as np -from pyebsdindex import crystal_sym, rotlib, crystallometry - - -RADEG = 180.0/np.pi - - -class triplib(): - def __init__(self, libType='FCC', phaseName=None, laticeParameter = None): - self.family = None # array of intetger pole normals that should have reflections - self.nfamily = None # number of unique reflector families - self.angles = None - self.polePairs = None - self.angleFamilyID = None - self.tripAngles = None # array of angle triplets between the refectors - self.tripID = None # family IDs of the reflectors ([hkl]) in self.tripAngles - self.completelib = None # dictionary of all angle parirs and their specific pole hkl - self.symmetry_pg = None # point group nomenclature - self.symmetry_pgid = None - self.symmetry_sgid = None # space group id 1-230 - self.laue_code = None # Laue code for the space group (following DREAM.3D notation. - self.qsymops = None # array of quaternions that represent proper symmetry operations for the laue group - self.phaseName = None # User provided name of the phase. - self.latticeParameter = None # 6 element array for the lattice parmeter. - - if phaseName is None: - self.phaseName = libType - else: - self.phaseName = phaseName - - if laticeParameter is not None: - self.latticeParameter = np.array(laticeParameter) - - - if libType is None: - return - - if str(libType).upper() == 'FCC': - if phaseName is None: - self.phaseName = 'FCC' - if laticeParameter is None: - self.latticeParameter = np.array([1.0,1.0,1.0,90.0,90.0,90.0]) - else: - self.latticeParameter = laticeParameter - self.build_fcc() - return - - if str(libType).upper() == 'BCC': - if phaseName is None: - self.phaseName = 'BCC' - if laticeParameter is None: - self.latticeParameter = np.array([1.0,1.0,1.0,90.0,90.0,90.0]) - else: - self.latticeParameter = laticeParameter - self.build_bcc() - return - - if str(libType).upper() == 'DC': - if phaseName is None: - self.phaseName = 'Diamond Cubic' - if laticeParameter is None: - self.latticeParameter = np.array([1.0,1.0,1.0,90.0,90.0,90.0]) - else: - self.latticeParameter = laticeParameter - self.build_dc() - return - - if str(libType).upper() == 'HCP': - if phaseName is None: - self.phaseName = 'HCP' - if laticeParameter is None: - self.latticeParameter = np.array([1.0, 1.0, 1.63, 90.0, 90.0, 120.0]) - else: - self.latticeParameter = laticeParameter - self.build_hcp() - return - - - def build_fcc(self): - if self.phaseName is None: - self.phaseName = 'FCC' - self.symmetry_pg = "Cubic m3m" - self.symmetry_pgid = 131 - self.symmetry_sgid = 225 - self.laue_code = crystal_sym.spacegroup2lauenumber(self.symmetry_sgid) - self.qsymops = crystal_sym.laueid2symops(self.laue_code) - poles = np.array([[0,0,2], [1,1,1], [0,2,2], [1,1,3]]) - self.build_trip_lib(poles) - - def build_dc(self): - if self.phaseName is None: - self.phaseName = 'Diamond Cubic' - self.symmetry_pg = "Cubic m3m" - self.symmetry_pgid = 131 - self.symmetry_sgid = 227 - self.laue_code = crystal_sym.spacegroup2lauenumber(self.symmetry_sgid) - self.qsymops = crystal_sym.laueid2symops(self.laue_code) - poles = np.array([[1, 1, 1], [0, 2, 2], [0, 0, 4], [1, 1, 3], [2, 2, 4], [1, 3, 3]]) - self.build_trip_lib(poles) - - def build_bcc(self): - if self.phaseName is None: - self.phaseName = 'BCC' - self.symmetry_pg = "Cubic m3m" - self.symmetry_pgid = 131 - self.symmetry_sgid = 229 - self.laue_code = crystal_sym.spacegroup2lauenumber(self.symmetry_sgid) - self.qsymops = crystal_sym.laueid2symops(self.laue_code) - poles = np.array([[0,1,1],[0,0,2],[1,1,2],[0,1,3]]) - self.build_trip_lib(poles) - - - - def build_hcp(self): - if self.phaseName is None: - self.phaseName = 'HCP' - self.symmetry_pg = "Hexagonal 6/mmm" - self.symmetry_sgid = 194 - self.laue_code = crystal_sym.spacegroup2lauenumber(self.symmetry_sgid) - self.qsymops = crystal_sym.laueid2symops(self.laue_code) - poles4 = np.array([[1,0, -1, 0], [1, 0, -1, 1], [0,0, 0, 2], [1, 0, -1, 3], [1,1,-2,0], [1,0,-1,2]]) - self.build_hex_trip_lib(poles4) - - def build_hex_trip_lib(self, poles4): - poles3 = crystal_sym.hex4poles2hex3poles(poles4) - self.build_trip_lib(poles3) - p3temp = self.family - p4temp = crystal_sym.hex3poles2hex4poles(p3temp) - self.family = p4temp - - - - def build_trip_lib(self,poles): - #symmetry = self.qsymops - crystalmats = crystallometry.Crystal(self.phaseName, - self.latticeParameter[0], - self.latticeParameter[1], - self.latticeParameter[2], - self.latticeParameter[3], - self.latticeParameter[4], - self.latticeParameter[5]) - #nsym = self.qsymops.shape[0] - npoles = poles.shape[0] - sympoles = [] # list of all HKL variants which does not count the invariant pole as unique. - #sympolesN = [] # normalized, floating point version of the poles in sample coordinates - sympolesComplete = [] # list of all HKL variants with no duplicates - nFamComplete = np.zeros(npoles, dtype = np.int32) # number of - nFamily = np.zeros(npoles, dtype = np.int32) - polesFlt = np.array(poles, dtype=np.float32) # convert the input poles to floating point (but still HKL int values) - - for i in range(npoles): - family = self._symrotpoles(polesFlt[i, :], crystalmats) #rotlib.quat_vector(symmetry,polesFlt[i,:]) - uniqHKL = self._hkl_unique(family, reduceInversion=False) - uniqHKL = np.flip(uniqHKL, axis=0) - sympolesComplete.append(uniqHKL) - nFamComplete[i] = np.int32((sympolesComplete[-1]).size/3) - - uniqHKL2 = self._hkl_unique(family, reduceInversion=True, rMT = crystalmats.reciprocalMetricTensor) - nFamily[i] = np.int32(uniqHKL2.size/3) - sign = np.squeeze(self._calc_pole_dot_int(uniqHKL2, polesFlt[i, :], rMetricTensor=crystalmats.reciprocalMetricTensor)) - sign = np.atleast_1d(sign) - whmx = (np.abs(sign)).argmax() - - sign = np.round(sign[whmx]) - uniqHKL2 *= sign - - sympoles.append(np.round(uniqHKL2)) - #sympolesN.append(self.xstalPlane2cart(family)) - - sympolesComplete = np.concatenate(sympolesComplete) - nsyms = np.sum(nFamily).astype(np.int32) - - angs = [] - familyID = [] - polePairs = [] - for i in range(npoles): - for j in range(i, npoles): - ang = np.squeeze(self._calc_pole_dot_int(polesFlt[i, :], sympoles[j], - rMetricTensor=crystalmats.reciprocalMetricTensor)) # for each input pole, calculate - # all the angles between it, and the poles in family "j" - ang = np.clip(ang, -1.0, 1.0) - sign = (ang >= 0).astype(np.float32) - (ang < 0).astype(np.float32) - sign = np.atleast_1d(sign) - ang = np.round(np.arccos(sign * ang)*RADEG*100).astype(np.int32) # get the unique angles between the input - ang = np.atleast_1d(ang) - # pole, and the family poles. Angles within 0.01 deg are taken as the same. - unqang, argunq = np.unique(ang, return_index=True) - unqang = unqang/100.0 # revert back to the actual angle in degrees. - sign = sign[argunq] - - wh = np.nonzero(unqang > 1.0)[0] - nwh = wh.size - sign = sign[wh] - sign = sign.reshape(nwh,1) - temp = np.zeros((nwh, 2, 3)) - temp[:,0,:] = np.broadcast_to(poles[i,:], (nwh, 3)) - temp[:,1,:] = np.broadcast_to(sympoles[j][argunq[wh],:]*sign, (nwh, 3)) - for k in range(nwh): - angs.append(unqang[wh[k]]) - familyID.append([i,j]) - polePairs.append(temp[k,:,:]) - - angs = np.squeeze(np.array(angs)) - nangs = angs.size - familyID = np.array(familyID) - polePairs = np.array(polePairs) - - stuff, nFamilyID = np.unique(familyID[:,0], return_counts=True) - indx0FID = (np.concatenate( ([0],np.cumsum(nFamilyID)) ))[0:npoles] - #print(indx0FID) - #This completely over previsions the arrays, this is essentially - #N Choose K with N = number of angles and K = 3 - nlib = npoles*np.prod(np.arange(3, dtype=np.int64)+(nangs-2+1))/np.compat.long(np.math.factorial(3)) - nlib = nlib.astype(int) - - libANG = np.zeros((nlib, 3)) - libID = np.zeros((nlib, 3), dtype=int) - counter = 0 - # now actually catalog all the triplet angles. - for i in range(npoles): - id0 = familyID[indx0FID[i], 0] - for j in range(0,nFamilyID[i]): - - ang0 = angs[j + indx0FID[i]] - id1 = familyID[j + indx0FID[i], 1] - for k in range(j, nFamilyID[i]): - ang1 = angs[k + indx0FID[i]] - id2 = familyID[k + indx0FID[i], 1] - - whjk = np.nonzero( np.logical_and( familyID[:,0] == id1, familyID[:,1] == id2 ))[0] - for q in range(whjk.size): - ang2 = angs[whjk[q]] - libANG[counter, :] = np.array([ang0, ang1, ang2]) - libID[counter, :] = np.array([id0, id1, id2]) - counter += 1 - - libANG = libANG[0:counter, :] - libID = libID[0:counter, :] - - libANG, libID = self._sortlib_id(libANG, libID, findDups = True) # sorts each row of the library to make sure - # the triplets are in increasing order. - - #print(libANG) - #print(libANG.shape) - # now make a table of the angle between all the poles (allowing inversino) - angTable = self._calc_pole_dot_int(sympolesComplete, sympolesComplete, rMetricTensor=crystalmats.reciprocalMetricTensor) - angTable = np.arccos(angTable)*RADEG - famindx0 = ((np.concatenate( ([0],np.cumsum(nFamComplete)) ))[0:-1]).astype(dtype=np.int64) - cartPoles = self._xstalplane2cart(sympolesComplete, rStructMatrix=crystalmats.reciprocalStructureMatrix) - cartPoles /= np.linalg.norm(cartPoles, axis = 1).reshape(np.int64(cartPoles.size/3),1) - completePoleFamId = np.zeros(sympolesComplete.shape[0], dtype=np.int32) - for i in range(npoles): - for j in range(nFamComplete[i]): - completePoleFamId[j+famindx0[i]] = i - self.completelib = { - 'poles' : sympolesComplete, - 'polesCart': cartPoles, - 'poleFamID': completePoleFamId, - 'angTable' : angTable, - 'nFamily' : nFamComplete, - 'famIndex' : famindx0 - } - - self.family = poles - self.nfamily = npoles - self.angles = angs - self.polePairs = polePairs - self.angleFamilyID = familyID - self.tripAngles = libANG - self.tripID = libID - - - def _symrotpoles(self, pole, crystalmats): - - polecart = np.matmul(crystalmats.reciprocalStructureMatrix, np.array(pole).T) - sympolescart = rotlib.quat_vector(self.qsymops, polecart) - return np.transpose(np.matmul(crystalmats.invReciprocalStructureMatrix, sympolescart.T)) - - def _symrotdir(self, pole, crystalmats): - - polecart = np.matmul(crystalmats.directStructureMatrix, np.array(pole).T) - sympolescart = rotlib.quat_vector(self.qsymops, polecart) - return np.transpose(np.matmul(crystalmats.invDirectStructureMatrix, sympolescart.T)) - - def _hkl_unique(self, poles, reduceInversion=True, rMT = np.identity(3)): - """ - When given a list of integer HKL poles (plane normals), will return only the unique HKL variants - - Parameters - ---------- - poles: numpy.ndarray (n,3) in HKL integer form. - reduceInversion: True/False. If True, then the any inverted crystal pole - will also be removed from the uniquelist. The angle between poles - rMT: reciprocol metric tensor -- needed to calculated - - Returns - ------- - numpy.ndarray (n,3) in HKL integer form of the unique poles. - """ - - npoles = poles.shape[0] - intPoles =np.array(poles.round().astype(np.int32)) - mn = intPoles.min() - intPoles -= mn - basis = intPoles.max()+1 - basis3 = np.array([1,basis, basis**2]) - test = intPoles.dot(basis3) - - un, unq = np.unique(test, return_index=True) - - polesout = poles[unq, :] - - if reduceInversion == True: - family = polesout - nf = family.shape[0] - test = self._calc_pole_dot_int(family, family, rMetricTensor = rMT) - - testSum = np.sum( (test < -0.99999).astype(np.int32)*np.arange(nf).reshape(1,nf), axis = 1) - whpos = np.nonzero( np.logical_or(testSum < np.arange(nf), (testSum == 0)))[0] - polesout = polesout[whpos, :] - return polesout - - def _calc_pole_dot_int(self, poles1, poles2, rMetricTensor = np.identity(3)): - - p1 = poles1.reshape(np.int64(poles1.size / 3), 3) - p2 = poles2.reshape(np.int64(poles2.size / 3), 3) - - n1 = p1.shape[0] - n2 = p2.shape[0] - - t1 = p1.dot(rMetricTensor) - t2 = rMetricTensor.dot(p2.T) - dot = t1.dot(p2.T) - dotnum = np.sqrt(np.diag(t1.dot(p1.T))) - dotnum = dotnum.reshape(n1,1) - dotnum2 = np.sqrt(np.diag(p2.dot(t2))) - dotnum2 = dotnum2.reshape(1,n2) - dotnum = dotnum.dot(dotnum2) - - dot /= dotnum - dot = np.clip(dot, -1.0, 1.0) - return dot - - def _xstalplane2cart(self, poles, rStructMatrix = np.identity(3)): - polesout = rStructMatrix.dot(poles.T) - return np.transpose(polesout) - - def _sortlib_id(self, libANG, libID, findDups = False): - LUTA = np.array([[0,1,2],[0,2,1],[1,0,2],[1,2,0],[2,0,1],[2,1,0]]) - LUTB = np.array([[0,1,2],[1,0,2],[0,2,1],[2,0,1],[1,2,0],[2,1,0]]) - - LUT = np.zeros((3,3,3,3), dtype=np.int64) - for i in range(6): - LUT[:, LUTA[i,0], LUTA[i,1], LUTA[i,2]] = LUTB[i,:] - - ntrips = np.int64(libANG.size / 3) - for i in range(ntrips): - temp = np.squeeze(libANG[i,:]) - srt = np.argsort(temp) - libANG[i,:] = temp[srt] - srt2 = LUT[:,srt[0], srt[1], srt[2]] - temp2 = libID[i,:] - temp2 = temp2[srt2] - libID[i,:] = temp2 - - if findDups == True: - angID = np.sum(np.round(libANG*100), axis = 1).astype(np.longlong) - basis = np.longlong(libID.max() + 1) - libID_ID = libID.dot(np.array([1,basis, basis**2])) - UID = np.ceil(np.log10(libID_ID.max())) - UID = np.where(UID > 2, UID, 2) - UID = (angID * 10**UID) + libID_ID - - stuff, unq = np.unique(UID, return_index=True) - libANG = libANG[unq, :] - libID = libID[unq,:] - libID_ID = libID_ID[unq] - srt = np.argsort(libID_ID) - libANG = libANG[srt, :] - libID = libID[srt, :] - - return (libANG, libID) From 36c5fdbb7d7ada1e34e2a20106749dbe418f76e7 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Thu, 3 Aug 2023 10:37:43 -0400 Subject: [PATCH 144/177] Updated tutorial Signed-off by: David Rowenhorst --- CHANGELOG.rst | 5 + doc/tutorials/ebsd_index_demo.ipynb | 542 ++++++++++++++-------------- pyebsdindex/ebsd_pattern.py | 4 +- pyebsdindex/ebsdfile.py | 4 +- 4 files changed, 277 insertions(+), 278 deletions(-) diff --git a/CHANGELOG.rst b/CHANGELOG.rst index 27ef20d..b51ac33 100644 --- a/CHANGELOG.rst +++ b/CHANGELOG.rst @@ -15,6 +15,10 @@ Added - Initial support for non-cubic phases. Hexagonal verified with EDAX convention. Others are untested. - Significant improvements in phase differentiation. - NLPAR support for Oxford HDF5 and EBSP. +- Initial support for Oxford .h5oina files +- Added IPF coloring/legends for hexagonal phases +- Data output files in .ang and EDAX .oh5 files + Changed ------- @@ -25,6 +29,7 @@ Changed capability, and is set as the default. - Updated tutorials for new features. + Deprecated ---------- diff --git a/doc/tutorials/ebsd_index_demo.ipynb b/doc/tutorials/ebsd_index_demo.ipynb index 99732e3..2661f51 100644 --- a/doc/tutorials/ebsd_index_demo.ipynb +++ b/doc/tutorials/ebsd_index_demo.ipynb @@ -5,12 +5,24 @@ "id": "496b84d4-54ca-47c7-9a47-0709acffd06b", "metadata": {}, "source": [ - "# Radon indexing" + "# Radon indexing of a demo dataset" ] }, { "cell_type": "code", "execution_count": 1, + "id": "03b94666", + "metadata": {}, + "outputs": [], + "source": [ + "# if installed from conda or pip, this is likely not necessary, but if installed from source, or using a developer branch, this can be quite useful. \n", + "#import sys\n", + "#sys.path.insert(0, \"/Path/to/PyEBSDIndex\")" + ] + }, + { + "cell_type": "code", + "execution_count": 47, "id": "fuzzy-imaging", "metadata": {}, "outputs": [], @@ -19,8 +31,8 @@ "import numpy as np\n", "import h5py\n", "import copy\n", - "from pyebsdindex import tripletvote, ebsd_pattern, ebsd_index, pcopt\n", - "from pyebsdindex.EBSDImage import IPFcolor" + "from pyebsdindex import tripletvote, ebsd_pattern, ebsd_index, ebsdfile, pcopt\n", + "from pyebsdindex.EBSDImage import IPFcolor\n" ] }, { @@ -29,23 +41,22 @@ "metadata": {}, "source": [ "### An example of indexing a file of patterns. \n", - "Currently the only types of files that can be indexed are the EDAX UP1/2 files. HDF5 files should be coming along soon. \n", + "Currently, the only types of files that can be indexed are the EDAX UP1/2 files, Oxford .ebsp uncompressed files, and HDF5 files. There are built in tools to auto-recognize h5oim and Bruker HDF5 files. Also see below on how to use h5py to input patterns from any (within some constraints) type of HDF5 file. \n", "\n", - "Define the environmental conditions of the data collection" + "First we define the environmental conditions of the data collection:" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 50, "id": "5bef39f7-eec9-409e-b3b6-cf2fc5828057", "metadata": { "tags": [] }, "outputs": [], "source": [ - "file = '~/Desktop/SLMtest/scan2v3nlparl09sw7.up1'\n", - "#file = '~/Desktop/SLMtest/scan2v3lam0.90sr7dt0.0.up1'\n", - "PC = np.array([0.46424919, 0.70189953, 0.64026537])\n", + "file = '/Path/to/example.up1' # or ebsp, or h5oina or Bruker h5\n", + "PC = np.array([0.46, 0.70, 0.64]) # this is pulled from the .ang file, but only is a rough guess. We will refine in a bit. \n", "cam_elev = 5.3\n", "sampleTilt = 70.0\n", "vendor = 'EDAX'" @@ -63,7 +74,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "5b23e165", "metadata": {}, "outputs": [], @@ -81,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "30a6ae6e", "metadata": {}, "outputs": [], @@ -99,7 +110,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "da0c7317", "metadata": {}, "outputs": [], @@ -120,7 +131,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "62139aac", "metadata": {}, "outputs": [], @@ -138,7 +149,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "e57c71b9", "metadata": {}, "outputs": [], @@ -151,12 +162,12 @@ "id": "85912ba7-ca2e-4121-93da-3690dbe107dd", "metadata": {}, "source": [ - "Define the radon and indexing parameters" + "Define the radon and indexing parameters. These work well for 60 x 60 patterns. The most critical values are the size of `rSig` and `tSig`, which are fairly dependent on the band widths. " ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "47ed2c34-9aab-44fc-b288-7dd75ca94cee", "metadata": {}, "outputs": [], @@ -164,9 +175,9 @@ "nT = 180 # 180/nTheta == degree resolution \n", "nR = 90 \n", "tSig = 2.0 # amount of gaussian kernel size in theta in units of radon pixels.\n", - "rSig = 1.5 # amount of gassian 2nd derivate in rho in units of radon pixels.\n", + "rSig = 2.0 # amount of gassian 2nd derivate in rho in units of radon pixels.\n", "rhomask = 0.1 # fraction of radius to not analyze\n", - "backgroundsub = False # enable/disable a simple background subtract of the patterns\n", + "backgroundsub = False # enable/disable a simple background correction of the patterns\n", "nbands = 8" ] }, @@ -175,16 +186,16 @@ "id": "1a2e4a98-a761-44cf-9c50-893fe1a32ae4", "metadata": {}, "source": [ - "Now initialize the indexer object. It is easiest to run it over a 1000 patterns to give some idea of timing. \n", + "Now initialize the indexer object. It is easiest to run it over 1000 patterns to give some idea of timing. \n", "Verbose = 1 is only timing, verbose = 2 is radon and peak ID image of last pattern, verbose = 0 nothing is reported. \n", "Here, \"dat1\" is a temporary indexed data of the 1000 points. \n", "\n", - "The indexer object will hold all the information needed to index a set of patterns. This includes all the environmental conditions, the radon/band finding parameters, the phase information (including a libray of triplet angles). The only parameter used are the angles between bands, no bandwidth infomation is currently used/collected. \n" + "The indexer object will hold all the information needed to index a set of patterns. This includes all the environmental conditions, the radon/band finding parameters, the phase information (including a library of triplet angles). The only parameter used are the angles between bands, no bandwidth information is currently used. \n" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 10, "id": "dental-singapore", "metadata": {}, "outputs": [ @@ -192,31 +203,32 @@ "name": "stdout", "output_type": "stream", "text": [ - "Radon Time: 0.026666632969863713\n", - "Convolution Time: 0.03771136200521141\n", - "Peak ID Time: 0.030311049020383507\n", - "Band Label Time: 0.05217741505475715\n", - "Total Band Find Time: 0.14690838003298268\n" + "Radon Time: 0.026163205970078707\n", + "Convolution Time: 0.03730318299494684\n", + "Peak ID Time: 0.04111986805219203\n", + "Band Label Time: 0.04565136507153511\n", + "Total Band Find Time: 0.1504965319763869\n", + "Band Vote Time: 1.3719220280181617\n" ] }, { "data": { - "image/png": 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", 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -237,7 +249,41 @@ "id": "d1241ddf-06ba-4c89-b3a7-b4ba25c9fd22", "metadata": {}, "source": [ - "The data output *dat1* here, is a complex numpy array (or array of structured data), that is \\[nphases+1, npoints\\]. The data is stored for each phase used in indexing and the dat1\\[-1\\] layer uses the best guess on which is the most likely phase, based on the fit, and number of bands matched for each phase. Each data entry contains the orienation expressed as a quaternion (quat) (using EDAX convention by default), Pattern Quality (pq), Cofidence Metric (cm), Phase ID (phase), Fit (fit) and Number of Bands Matched (nmatch). There are some other metrics reported, but these are mostly for debugging purposes. " + "The data output *dat1* here, is a complex numpy array (or array of structured data), that is `[nphases+1, npoints]`. The data is stored for each phase used in indexing and the dat1\\[-1\\] layer uses the best guess on which is the most likely phase, based on the fit, and number of bands matched for each phase. Each data entry contains the orientation expressed as a quaternion (quat) (using EDAX convention by default), Pattern Quality (pq), Confidence Metric (cm), Phase ID (phase), Fit (fit) and Number of Bands Matched (nmatch). There are some other metrics reported, but these are mostly for debugging purposes. " + ] + }, + { + "cell_type": "markdown", + "id": "0a01f656", + "metadata": {}, + "source": [ + "## Refine the PC guess\n", + "Here we read a set of 5x5 patterns from the center of the scan to make an optimized estimate of the pattern center. The patterns are read into a numpy array. Currently, only a single PC is used for each scan, but improvements for this should be coming soon. With the default optimization method, the initial guess should be close; within ±0.1 -- 0.05, and closer is better. " + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "a1c0c98f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.47484187 0.69939625 0.64461076]\n" + ] + } + ], + "source": [ + "startcolrow = [int(imshape[1]//2)-2, int(imshape[0]//2)-2]\n", + "fID = ebsd_pattern.get_pattern_file_obj(file)\n", + "# returns patterns in an array, and the location in microns of the patterns witin the scan relative to the center of the scan\n", + "pats, xyloc = fID.read_data(returnArrayOnly=True, convertToFloat=True, patStartCount=[startcolrow, [5,5]]) \n", + "newPC = pcopt.optimize(pats, indxer, PC0 = PC)\n", + "# actually save the PC into the indxer object. \n", + "indxer.PC = newPC\n", + "print(newPC)" ] }, { @@ -245,16 +291,16 @@ "id": "57127f77-e234-4617-9e4b-04b5090a588e", "metadata": {}, "source": [ - "Now use that indexer object to index the whole file. Setting *npats = -1* will index to the end of the file/array (latter on will be an example of using an array as input). \n", + "Now use that indexer object to index the whole file. Setting `npats = -1` will index to the end of the file/array (latter on will be an example of using an array as input). \n", "\n", - "The defaults will be to detect all the GPUs on your machine, and use them. Scheduling is dynamic, so it does not matter if the GPUs are matched. After radon processing/peak finding, the cpus take over for performing the index voting -- thus the number of CPUs needed will depend highly on the number of phases that need to be indexed. The number of CPUs needed also is dependant on how fast your GPUs are - on my MacPro with a Radeon 6800 GPU there are dimishing returns of including more than 32 CPUs when using the above conditions. \n", + "The defaults will be to detect all the GPUs on your machine, and use them. Scheduling is dynamic, so it does not matter if the GPUs are matched. After radon processing/peak finding, the cpus take over for performing the index voting -- thus the number of CPUs needed will depend highly on the number of phases that need to be indexed. The number of CPUs needed also is dependent on how fast your GPUs are - on a 2019 MacPro with a Radeon 6800 GPU there are diminishing returns of including more than 32 CPUs when using the above conditions. \n", "\n", - "The *chunksize* is the number of patterns to analyze per process per cycle. The right number here is going to be a function of how much global memory is on your GPU and the size of your patterns and radon arrays. Setting this very low may have high I/O penalties. Setting too high will cause your GPU to run out of memory. In theory this should fall back to the CPU, but this might not work as well as one might hope. Guidlines: multiples of 16 are most efficient, avoid powers of 2. For patterns that are <= 120 x 120, 784 -- 1008 works OK with a large GPU (>4GB). For 480x620 patterns, chunks of <= 528 are probably more ideal. " + "The first time this executes, it will take longer as the JIT compilers need to do the initial compile. Currently, the program cache is set to the system `/tmp` directory, so after reboots, many of the programs will need to be recompiled (which happens automatically with the first run)" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 39, "id": "sized-thanksgiving", "metadata": { "scrolled": true, @@ -265,14 +311,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "num cpu/gpu: 42 2\n", - "Completed: 853776 -- 854784 PPS: 4603;3775;3627 100% 236;0 running;remaining(s))\r" + "num cpu/gpu, and number of patterns per iteration: 28 2 1248 16 28\n", + "Completed: 853632 -- 854854 PPS: 12441 100% 69;0 running;remaining(s)\n", + "\n" ] } ], "source": [ - "indxer.bandDetectPlan.useCPU = False\n", - "data, bnddata = ebsd_index.index_pats_distributed(filename = file,patstart = 0, npats = -1, chunksize = 1008, ncpu = 42, ebsd_indexer_obj = indxer)" + "\n", + "data, bnddata = ebsd_index.index_pats_distributed(filename = file,patstart = 0, npats = -1,ebsd_indexer_obj = indxer, ncpu = 28 ) #, gpu_id = [0])" ] }, { @@ -280,12 +327,12 @@ "id": "b51719db-5111-4ba5-a6e5-3304adfd9910", "metadata": {}, "source": [ - "Display the results as an IPF map. So far the only implementation is for cubic IPFs - further, more flexible representations are needed here. " + "Display the results as an IPF map. So far the only implementation is for cubic and hex IPFs - further, more flexible representations are needed here, but are likely going to be best handled by orix or others. " ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 27, "id": "5fa77d67-0581-42fc-80c2-ae37489256f3", "metadata": { "tags": [ @@ -293,59 +340,62 @@ ] }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "854\n" + ] + }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 30, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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EREhJLTYgI4t4GIr2EXzMg3GwF5QgKkOpXEDIKG9zexv6Xaw1Q+cSsb8djvFLyBzhxYnlunKxXeeri3fQTxRvrZVY6h7g+FyJ2Seuo4Y30IeuEJ88iyl1IYoR3TKm1gIgrPc48NTrvPGvP0i7Fw4+3+yYi2Lw+UXrWtGr4Kvs6+cxxYO8v/8tQO+JTuQf8c9171k9nHwoVUGeLPaLhrgFaZsM2DNDk3TD4V69TdhahKBsf67Pw/gJUCVrKRgQSpyQMmC4wlLZ7OQiYryM3ncJE56jd/pj6JUyM71lbjz8IjcfeolkuMGeb4a8/9sJuf/Yzw033tUy3DtntXmvnabGglsnghvDsFiH9TJ0DAR9GOpgZIoZTohHx9FrZcpbiwg0iA5jaYPStVWYK8NYGcoKStL6U6SAcnEw7SsFtlRAQ0pWkXTwFG470SsA3lSv3LvV6nHqgAV5a7o3TtiSuTyekrdfOsAzOF8QuVSZCgvyPfe6Gdvv0gFupKCmMmmzp0NkJWEzCjhVDTkdlbkpJV3Rd9xPU3T9Dbo3rRQoss92PnuQ186EP0jBRWuoc3YIIOM7MjPYFzmwyZ5hCV8X6pQDPki6zG5e5L7T13n96N0cuHGN8eY6FyeOcXblCUxiTWhGuTkivYwUYNanibeHqR86TVjfwMgEhKGpBH8wPsnjsssD6TZD/dTSm9I5UGfWJk/8OX3cqnqLAri7c7PJWzjXFN89uJMLaRgXX/G/EbAH+Pmf/3l+/ud//v/n6z0heenVE+SAEFY4257lQaxoXJKkQN8RfpiF2lgTfoAkAHqk9NH0ywFf/MQ4/XCDu9/oU+rLnQ9zxfmwU6c/elXLi8JookTxvhcrvHq4TVxOsyvzqVm0W3g9I3BOCUGaSe65tpFr/PbrxKbg8ecDHnsxpN7u0tQjiP4Gk+lN64PPkCEgCz7MCLoYHOg+yxLUx6w5X+wIFxTuurgLnS3QfUfUaUFJkLl9e8A2no/larfGlxaOcbExxsurc2z2KwgjsuosnDbIy9Ps/cA6h08uU2oqansSGmdmrD2TMdd+weUv3EfSLCPNrXOy+LpFgi8AcwaIpnCtKbi+xeClGR/YAewDRewAev/dyVIisEA/oKm7umRhDUVQ9udJKyR40MeRZ+qs6ZtnoHnWkmZtFiqTMH03mZIpnGk7q7cyzKcdPla7RudIjYWpN1g2CRv3LLG2UWXLXCEe+w5x2EZohWzO8VpYozcEm7M19nSXeXHqODOtDvuH+rTHAw4iCFVKpDWdRpXxFWAhgs0xRC9wPhYN5QpMHMBENbQQtAUkLYmJNJH6MirdQMgO03FMHNcx7Q5CCivF+AY4E73HtXYQ0pWSbSnYFpK2MGxSBRL6SUBAn3KgHQcg0+gjrDsnQBCinUavs3mXcaHAILzhLZHkwbsyJ0BJDhwISICusZp9bKCfWnO+0RbwNZAYTEnx8vQELwVzqOEtLlVi2mILI1JEwdXnAdm4qJ1cefCkJzOuYbvFm+xFxlv8JLC19LCc2x/ze2UREBlXzaOgBifVTlWsKBo8fuo5nnjhLZ589mlUmqKM5k9mJzi7R1qDqLQGHSPI3Gzaz5+4TPP8PQwfeYNw+CYGjRHQUDX+ZLzO80NjHNnq8tDSGnsbW1ncRgbaXoofAPrvMnlN4Tf/uyl8MYXzii/ceBr//r8hsP//txh8HH6RgOy3Iq8uvvvzPXnnhn1BiiB2Em7kAB9ntAvcFYqELppeWfL5Hx/l7IkeD72ccGShglRVRGfVcVUgrEFtChorDBAPKg8qQjK7Yrj3XMy37k4c8/eTUOZ0SK7Jg8Qa6fK2+nfftiA17L1c467zmve+aBhp+nYnjCUXwbigPxk4xHD20SzsVPtOzonfCItA1XGr1QsKRGyse6K9AUnPgr1xfsciEnrpWKgBoE+FYLtf4s+uH+VPF46w0q6x2K67ludzUfqqGIHZjrjwhXu48MV7KI22qe/doHlujwP7AjVowIiBeBrP/Afm6c5SqPpAvCC5sF4sxjVn5/2LTb+dUFgEeuOMP9IDfTAI8j72U/iuLNbTgZnIyeRWyddNBJ2C7kLjImxfg/FDoMr5OUL6uhvuKi3yU2qdylYFXtYcC04gU82bh1q8PKe5PDPNwc1HaVROI9MDnB96D2/97Zj1ephVzhTARiIYWY2JxlMiY4jLNWaGY/Yo2Luvx12nm6xODbO/O0ag6mgVoiUkiAxaqW2xUq0y1r6MlNt0GeXS1H761Rr36cuUqwoigVaSs+Esl5hkr97AINjUEdUt2BoRdNUQDVOjIypI+iRGoUSXiC41thnXm9TXDTOVJuVa4PRYjUTTooShTwtDIAQ9FHUJI2gCST43fCRoJlkWtONM08MBvXtPcFYBkRGVSeFitcafz+5lK1RoavRYIma9QE1F1559peQBePYXRc5RJJLQKRKBu05nd8iBXuBdh0WN3J6RZmcV51MhOiin9eyTKPwiAMXUxhYCQZQm+QQSAq2EXaCgLOAXLWCZ9RxIU0nj0gmC7b3U7z+HrG2BMBghWI8iNscrnBodY2+7x8x2j0Mbm5TjmP3NTaSwYzo4yfP5MvDKfHy+QYXxpXCs6G4Z4JN+9QTf0/KOBns/KkWGagrHizp/8WhefNCeH2GZTYwE6diTzMheACEKnJCQCMGZO8u0poeZ//pJKnEVE29B8ypCJzC8F5DQWLMzVZCrct5xqq0P/f0vVXjhWId2KaeZKhAh6RQkYq8wmB2tKbaqvg0//ZmA42clpb5AGheKZgyYGIRgU84wpjcKvVdEBkO+NsvNKKHs+rKgZMPDU39PbZfa9VvQLWryvq077+2722SH+lrzb84+xlcX7mCxVUebYmsKc6ogDHsB2U+87nqN3nptQNvN4mgK88w4mSWLevBzbgcQC8iMFhnQu67yso+mILMxKPsU+YG/tRm4+Y6XzGUpPNArBttTeO2Un/w9vCY/EFzg6+EvKETzA5RG7bkmNyJlDT4a3uSvc5GqqWcNqvTs+D/wZsxdZ2I++7jgyoEQae6g3Z3k3IG99E0T2CKXsHQG+AZoTEXuUQY5aWhOllk8EPGqCfjGw9O0KyHzG4Jji7AwlrI0ZqEkSKDcE2yMKPryfsL0HqQxdKnQKtcxwNfid4HWLgxAsEoVnUq7YCYF2YN6DGrbMBRANUpQ4RYIhQk1fRQtQtaoc02UCGp9qkEVTR+DJiUmIaVHQEBAjHcRVqiKEtNScyTa5s5+g3KaFpg+ufnWpBbUvRvDE6IGmgZ6whJoaCUvjWStUubLh/bTCUOsFh+TsI5heweFDWrV+cJk+8pVF0sodkmbdJbMEEXkLJu5UODv5HX3/JUfI6uBtwyYwvvO3/2nfOGiJXyRNUMTsmn2kQQO6ItCMdzqOTWCtB+RRhOkl8tU91wkGl9y0e524sRKcWUo5NrQMC/tmSHQhvlmk9Fel4lui8ONNcZ6HUppwgBD8FKFD6opAs4A8JOPtXH8cafPPnXHB7j2219+AMC+KDXuWM9aAP0iuQ92chEuLXf0snvqNHtrBLNE7XWU0LkDEgzXJ7b5wqOX+bFnT1AyIzBxN4YEYTSYBMo1a9I2t3mmI9I9qyH3ny3xrbs7GAFlNFP0s8j8NtJa9QohZZmgYgxKC45dgodeV8wuC45ckQjticpLls4kSJXhAEjKDtQLcOQJ0qOdqlsfvvfjCwG9rm1X0rEgb5Lb+KD8pBVZlGsm51QlzKgMiNpJzAGxwQn1BktvPXqLBu7BtShIm+IJ5PfOvhekZt+kIj/VjpcaB4oDICrypu6Mqcoe7LsLBupbBH5RPCe/JJOfivFPXp7yKzqL6+wHQJ5bn+XrjI8FFYV3/3uh74AB70t11l1bvLc0jE+e5qnOGrWtaqFjCx2KjT+Lq6tI2iRxnaXrj6JNRHBzHBkHxOMbOwA/l0CKeSD6aLZIqAhBOqQQGC5NwcUpyDXH3FxsKGG95e5OxrvnBM3I5gJIXNCkSB2vTvP+7CiIhKCVgEkColINVe6QiiaaGONM4EbEdCsp2xj8kjuNdpa1nGFbfVnTR7MtIq4EU5wfGuVAr8Ph7ia1Xo9Kr4dI3DyJNTRjp81rq717fOlbUDEGYhmwVq/y6twkp6fG6QYBEhvp3meJhAZFCvM8oZgzwLieyc+LKUqDFtQVfuWRzRgQ4i2LksCtvs+fkkcEDBr0vX0xFzZysikqsv48Hxkx1O4ys7npLhEYIVis7OXs2FEL9nJw7nuhvRjLW6TvuFWjefFOys1x6nsvEYRJNoGs0mQjDFIpuDg6jGQUYeAb+gBTnRYPL19jurPNeLdNoDW5ZdBNWO8/GAikKb4KTGeAARnyFSYDM+5tL+9wsIecjXo9a1C6vRXcd15b/JybukAMwKqVcaVjAsJNERtNmgrBW/M3Afix75yg1LfqmSG1UmVQArNVeJbJicQhkBSCTz09wvm5mMWJlB6K69QI0HSR6CwpkGAgyY4xTK0LfuqLihPnBSp1S888+mXmRA/iAiFDlPTBg4H1nw/VyIJujLHIMzoBfWV9hd0+pAmi17H5A0wCOsnvvVMKzlDT/RQKkv0SWQ9RgTWxeqQcFWV+ZM9ZHn0oovvb87x4fj5z/Re7LNPMcbLFdxtRM3iNj//KgN69fBBaFgg3GHycz2NHXtl3M/jsPCI9XxU2kPPEV6kgUBTkPNtdXpMvCB/+4kGRNu9tLzjgrvcLvw32PjsMJHn/+CGSUBmzwXo4ubCgWFFuTjLTbw802q9ENxiENJyf32JtdIN+d4yNm3cR96u2HUYiG8P0h84hIkXAML5XfEhWHnRqn9lDk9p1AIVkNXmg6mAx7r+jP+F9sLnrK5CgpUAogUyNza+Qgk5tgh3fD9IIkjQi6FUw4iaU2tl9NQmpy3yRA2b+ymumSek7gAwwIuByMMQ1Ncm3qwcZTfrcs3aVk9evUenGiH5q8w9oh1peaxRgjCCRARdmh3nu8CyLIzXrvhC+5T1ilkhYd7CqsOGC1l5VrJ0F2cISwEL/eQXIpwyCBO1CmQVxRqjWFhASErrxUAWhK39OkcIywbzw7l0wxaREAoWKBR946WXGmlt4t8VWMMz/fOL/xHJtJNPoMbk1LhNauc388HJpqmivzJF0a4zf8RYqTIpnZXUUbqSlEMQq4EZ9hD+qDSMwzLaaDMVd7lld4kBzk0qc5E/MZD1RuJEfy9sAfZYDASfQfzdcenvKOxrsZSaVFtlfDoRexhyUL/8ysvfEndtB86V9PoTFR616Ld8RjZCcnl+DR8/yo88dpdwLs3tQnYB2A5I4r1lRDHU0WGtJfujFKr/z5BZaykIMgTeT2Tb6LAIj24J3vR7w3pdC9i3mt8vRyTJV+9W1OrUZ70wi6IgK8fgYIzMBlJ15XnpKDMhCXmNNstYmuH7JLgLHpWDLhAjss7x9DawFIRQwEcJoACMBYVSoY9EG5xa5T/Z77B/e4k3lMinuAFX/vWj19OCfNdHPLR8Hw60gnzEPaX3jfqWg56fZ0BQe7t0CxR5WxeYKO5n8CilRuNx3fVExyABfkK3M9GC900e/s+wMyEPm12aAsKPuhS4cOG5EmzStIqSru4ZKE1RfIBqTfLsseXzmGpXA2pia0y0aY4rnqutMjTR5bTyhbercuPw4/f5I9iwTgCZmQzyDoU3IBHXuJmLSzTXfYA+ZEnuFTSFl7Ws2asZHwue5MsRAa+x/l99O5M65PLOmQAVWZ020tOZ9jV1i6OiljEDqcULZR5lLGOH90z38BPWr0+0rVyakW3fvQ2Yt9wgxBCTCBv/eDEt8ZeYgL4xM8kNnzrG3v+HSNNral/opqYSFkQobcY2X9BHW7m+ipcKQkNJBs40hJWab1C7Gx5rkK2gqWJ6UYDNw+lrqTFEo9px3T+bCVC4k+VljsnNzMA8IMsj2vNEUCc9dnwsDg+DvBQUAmRoq3RaHX9niwZffQgvFVfUe2mMJL88fYKm2ByNFJqpkc9mzDsdyPPXsLMZNuH5jhO3F/QztvYZSafGMwv8cuwXWsmCAG/URDMOcHp3mSGONI4019jUbjLc6qEQT6oI5PiMOU3j3TMwKpCiDVorVraNs1SO+l+UdDfbWuKQY9AjpAaLNDX+5LFcMDbFlp6G/CPb2d51NCBvfalymCzthrLZvhODsvnW+wDl+9NmjTsM3MDIF8TasL+Qi6oCaaCC1NXr4dcnXHpDcmCoKLd5iYaX48abk8A3Fh59RHLkibEQ9aUGK1Nx2fXNGdKCH67Sn9jM6qrPUq7nIWXDqJjFcukbYbFpNHk3uh/IitCNoKaAewoxb+jSmLOD74k1Yujhegx8/dNcZvnrqRF4dbhXO/K0GzIJFoLfdSaILptxBTmdBIXIpAtStmoF3qQnIDB4+HWwgbJf5YEFfVH77PMq+0PWZH18MvjKQL3CsDPB3Nn5HG4qy04DP3X+UO7QfNwRhCaZGDZcWrtH84nHGp+BdMxDE2HSjWI33dHucG9s13nNsian6Ar+5Z4mtI28QBz0abz3IeHmU5WvvI0lsyk/fJgG0y5eIg1UUioSbpFxDOe0wl1ACDCE+M51dyhWRIkmxbjKJoZTNtdz0W+ySHPDT7Juf1Ur4PGwSqRRaKnt/49Ioa2tNDwUIMc2QuYkSDTwvEOjMe+1FAKtDW71fE+Mj3kMMFcpUXYCvcfXUaLSAm5Uan7nnHqI0IU9pk7J/dZPVMOatsYjYKITpUJUdAtqOjhIMMYZuBuO2RjVS6miUO25Tbac2OXDG7XLhxGStytfXk/G+76YYCbcgMSevPIFXroCIgSvz2KJBniqMYG7pJu979mXuOncRUR5iSxzgnPwI18Uj9GPDKQlGiUyg14VXkXtkXNQL44VX3hZBa2kP5bE1xNCWq9GAGjEA+D6M0V8rXKMvjk5wYXSCKE0Z63YY6vXY22wy3mqxp7nFWKtj3b23RPUbNnpDPLd8D4drPZLePpb786wPXeV7Wd7RYC/dXz5sVp7PwT4nyaKOl8udt79rLioMrsn3uoIZIHSB1UU8I5ec2bfBzOYiJ66NM9WoWDAuVQuqoRggBLtOx1JytZPyN74U8T/+VJd+6AjNCPYvSx48X2N+RbFvWTG8DaobY0E+cUEfKbmPXhSoXeRqYq0C+6dQQyUmhc/m5xmn/WyQ9EPB9TFFczPlvuamleGlRxQL9kkoaM+XGd5bd2KxgJEon52Jq48utDXr9qKwkPfwwalVHjp8lRcv7s/GoRgE54s3lWe3LGi0fqSVyV+JsfqZT1aDtBq98vb2nT5+D9AFuckvSgyEzcKXdW2xGTBg4KD42f/mXtoviyto8/mJ+fXZrQRZ/IPWgiSB1HlSiuvq/QQQIaiKXV/vsVX3Ye4gHLwTFi8JWDhOGgOboIbJ3CeenlMEG/0Sf3a9Tvo3vsP6zAoICxEmrbJ48YdI9Y7F+sZ5eOISwijXQZoWlykzQcRQQbz20k6AjZWxK9dzASBwy2LtglNrZE7xCbItyHq271NO5wL/YB5NkzHjTEwXEi0FfSNJhE8cM80wHQQ98tX13pLnuYgFMKvtW9C3WrchJqBPQJkxIqcUWC3YXt1VAT2374AlAcNbc3sQJIyyybITEFrUGWLN1d1r1D42wZvTh1x/5QvjUkL6zivtuZ3GqzjS2SbzvP55GHKu8Owkx9w54PN8+nGj0Ps56Xq1ysdA+ScYIxjfaPK3f/OzjG5v0wnG+Gbw37AWHQeUFa00tAK3V0VhPmfqS2HO3w74b9lKQwCpoHnhGLX9l6mMrWUnFCG9iBv+GNlZuSUnVorlWp3lWo3zY+MIIEhT3nvpMvcv3KAa912FFRv9GssbM7y1/CQiGeP8pqEinLjbXeF7Wd7RYC8KRFRkqXn0vA+sAxwb8JJ/nu3aMwVxy/0GlaoB/cgRs5cDJZYtuiuE4i/uWeCFo0t88hsHOHRqARG3cioc2GTGq51p9pTD1wV/7z+UeeGulP2rEUcWA8o9wcSm0+KNt0MK58Ty2nZBtfPiaFZbadfFH9kDlVLhPCd1O9twKgXXJyRfubfC0ohi6Fqd0WtjzN9Yt0vjZ6uZzUwcqBJMRtAXDtydSp0hpXCBLMXgPQfyO01crjr1Upf/9uNf4b8/+wHOPrOfpBmSL5e8LRHkTfRNcmClXDd7HVK5Xs6MEj4znMyrKAr38sPiY2mKge7FR++s3F8K+oOS6ICf3l/s8hkN+O9lGSp7QYy2iDuCzmINGYPuQDRiBRcTW++JlFAesw0OKm5JnbuXknDsAUi7cO0t22Yp4dAEuQWCfFhMEKOHmjR+4vdJZ5aRzrwZt2dIOUGaVjNy80zeG5lkb5iovZ+kftU1LaHJGSZ5cACIhfMX2zlo4dI40cq4yHF7W5tfwq6YCclB2M93+xSZAZspdjUesoQ/S1hd3dbF2ueMMLQZRZAyzCVs4FoJK25YuPEeEwu3CrvCXSNooYiobFcZ2lpgeU9IamIkEVrk2edzVSIHHNvCiC2GUC56ASISqgRsFfiVrecWI6QOsHNd2qskAm0SklgiIhtzkAfU+fRifjMw/1kW2pRHLuU0ne8wAto5WLyyk2fbywP0ikCfZ8sa22zyX//25xndaHKt/m5eGv5bbKkZhBGZIBVL6JZyA6ChYAzMB/O2pWjOz05zvCjplmleOUxlM2U6aoKp0a316dZidCQQIhd6BqG+OFK5hQTyOd4PFH9+xxFemp8jSmKCG0PMtmM2KxVuLk+yb2OYQOPFzZwXfA/LOxzsc7Isrtkc9MD74gAPRb6AzW8w44FeDVx5e6AfzEuVsxQFznQGlnC2Kwl/+PhFPnUz5tClfn7lQPSVk1szcdQQGMHJq5KT1yMI/BI9nH0aZ5+Osel3ve/c168oohQQUEqYn4SK08LIrzECGlVJqyx54UiFc3sC+qElys39gj/8mbt476lVDnY006VNa643BmWg2salOSWPUPV1HahH4SXcNRRnsDtPGEYqbf7hY1/hlXfN8Ef/y+OcXZq5LaAOaNQilyc8YPtR8cxUkmdjzTYJS52GvbMavndMpkxnQsMtGoAAfNqBXn6hqHYxnQpG5ReLGqTbTrP3OYw8oJfs5jmqDDqGsO6sENr+hgLTraME1OZdb2oXXIgFcuWGGkG2DM+7JIyBKLIp6a9fhI6rR0nBaCU3wHiWZoYbpA8/S/eRb0O17ZYwGUhLbC8+Sb93NIsfKC6HAtuvQXuC8sa9tGrXsg7TukuU9qgGIduZQFoU1lPyoD3rA/dz0Zvvrf6XAApNQOpWv3sGLMkB32vPhVaR8wGfJT730fqa9KjRZ4QS6xTjC3KdNbfteYtfQESVNlHZEIaKvSxQM7AlhmhTYZs6kbMU6EypsO4AX8OICImgSw9DSp8RFE18wpyh9R6tSkS/EuBTgxVtkfY+XYJ0lf7iGKUDKyg02llOUkK8pl10n+gsMsk7U6QTavydfbCf7yORjYp/mcII+JkikARxyuhmi/3Xlvihrz3PvuvLdGp7OVX7NE01m4+OYwmejrzHs+gt/G5lIMN2UXD2veIGV/cj1taPMbxlONhQzK9r0lBz49gaN46vuhAnS5P9aowOionOdopVxWIwAtYrFaCCPi64Stn2ybF1WifbzL4wyfxSj/baXoK+ov+XtOftKO9osC/KxGLHn//1VrC35GocsyCTbpVbZFfMNjWohvmMzTmR+40w/X2LvnVLXdu1lD/4ZMhf+2zKocs2LUhedxyVuvVB2S5YHvhl7qTSOM1Z54jlSS5bCy8ZMI170+rQEMxOWH+6s0kbYUiUYXE04I19ZV6frxAHEi0NYseSEBNFjI2nTN7Ysqt2sgAUXF1wqUHddWlBkw9x+cELa/29FdBfVxwsF1U92uvxfnGVk5/e4B9/7kc4vTxDLk+7S/TO5poMnK3XwSKQdLPd/pYbIf1ChaIfMBuWwktKC8Cy5AL5YhB91wQBjIIouc89nDfEICe7mLWKFQTcEmLpfQHee+JBmcLnHdKEr1vW7bIgxrkhlyLfJ8aTVcYoXZylSWFkxKZDWLyY9938qHVn5IFQBlNOaP/MlwlmToGKHc2XSKixff1DdDbuvcX1MMCU3W+VzTvpTj2LLq1jhCFKDX+9azgawEVh+IYyXA3s8lLjpbQC+A0K2f43hTfJW7gXzsevMvuan2UqM7TjLAKJgzjhwNWQIokJSLLlZoYUQYM5xkmJaBbmuK+TfYoXIayNoEKHCkkQEgYhEYZECqqkVNiiTpsIQ4+QBmMoFCExfRQxIX773IgATUyPBIMioYxyvvvGqESLhArrxATYHH4QsIXV22NggzCIGTqwlJnyJTaNrqKDyYA5xBACZbxh36fMsUuP89X2nn/aebNzj9HcH28lP4nQkrkb62xMjPDxP/oWj7z4FjKJUTohVSWemfq/sib2D+o+7im9IP9S5Ka3LcaJGk5v2gn0ngUWr49FyPkJuDIGT2jJjIZDb+3h0OnZgefdPLBBUkpZ37dBqjT9Sp/tkQ4+kMbkjyqIgDkF+7mENLSn21z66DWuaiivtIgaJZLGxndr1dtS3uFgn5ed+vztlut4adznkTcuTVW+paMHeq/lq4yQi1mnvHRX1EoGd232OoQNL9qupvyHT5b5ic+lHLncZSCiWBgH9N6cT0ZMmNRpzNLmzvYgny3UzluWwZfEotPIMIzXoRRB2SVFNylGJDSqmkvTilcPllgYK6Ezp7EokKtlYYEWPLi8zpGlhq1xCrdQ9IAJ36OnsZFeUQdKfbI81Cm2LR7oY5lbBHzbvS9bwPRYk//7X/sc/68XP8hrrx7ggenr1PZugjSkqeT18we468gVlNQc279AHAfsGQ1581KFrVYFjx8H5pdZuDHF4tI4r1/eS1ztkzRGSA1U9jaIF4ftrmmCLBUtEmTNdqcIdzAlP3xexnJMhci9C4lpjSEqZD75YlY8Cub7bCiL3eCfs4POzc6hdyWrc+G8LGrf3agUwfAQnH8F1t3qDYFhZChBK0maSkyYEt+zQHLXIumeKlocRtAkZpqEYXRSId46Ym/seXuxnmbwXaYVRq5+muaB/0RS2mRYSR4mIIoFUxoe6CcsCM1vDIXcNAEiApSm0lccvz6MFrBdiRltlzg916ZVstHmNlI/JNQh0ggnrBhSFH1n6g7IN2/Jvf++2hqFpuSIWCPoEZK4vTDseYZtpqgTELGG937nuS7ysbIzVqAJiCmTUqJPQISmRgeJoOLqUiWhwgo5P9EkhPQI6VKmi8Lm4+sQsOnqF+J9SoaEMj0iUmykvV35b5fNJeSLg/3Odd53n7/bmljrpsnW3VvXiN2s1y8fLu5apxyftAnE82Q4NsJ+dK3N0dOL9DYjTn77CvdvnqMxXGWqsYlUjmEIWC7fyQpzA2pTRqYClkcL7jaxQxAvTogioBeB3pOmLkwpd0w73QptWc/zj8DopOGhpwXT171QaXtm+tI4wsDet6aQBuJywsqhVVbnN1nf0yQO7IZi+6/doFU5SBTXkUayMN2gEyWYnQk4pF3X355r0dnTIW42+V6WdzTY58F3onCs6HO5FfDJjnhQ1w5b8pSS+d5WHuB9Wp2iJSFfDjeYyKL4HKtzIGK2ayn/8ceG+Ik/0hy53LbXeGrMosBuoyppA9rniYRcm/c2WsdqgxDKEUxPWq4+NJgwxxhNsyZ59VCJVw8ENMoS4xFth6fLIKjohBPdVQ6tNzl6o42ULkZAiEFV2M+g4kqA1IBKYagNky0L7lo4kHcz1g9b6m7iRfCCrdwI6IeGCXmNf/DTv0/ykRrjk5sElRgwaCNobFcZqW8jvSrrhvz9jxuPugMzfmu7wn/3v/wMr1w+YJebGdC6ghyxQKyUXYGYZa/LCSY3IxaPixxYM0osdGcWEuFAXhSX1xXJc7CaFEY6++yDC4vHvVaTgX0R8A1ZKEckYW4PvPUMrF8nQ+ip2QZP/b2vEm+PcGNtnItHGqRjbSin2E2i9pCy194wjdBrc6Td0Zxs/POKiz+KsiyCUn+WsYWfQE++QhjcyJi2AOKzk5RbC7RfPs4b5ycYfqDB0H1NHhxO+MQb+xDabqta1pL/90dO0yh18HOuh6DW6bB3ucm9b5xHphqD4saeeSYXryGMYW16mrN33kNzdAyE5w7e6WdpXWFj2UN6QMfBmy6E+SlSyuRuP/D5Bnyjc54jyf3ygpiIDlBjm3xVjdeG8z6KSIiIGWKLLimNJEA0AjYnAgxlRLYo0YoynjdZi0aM39jGrgew9U8zW4Eg3yrMt84Plg24E662Hursszyg53xPIZ2rwW6BKJ0bYHy1yYc/+zwPPHMmJwSjKbc7bn9oC3ZGCG5UHkATUjBy2dMFrNZgcbwgQBpuCYC9RQKGW+aRkFBkCZ7lSGHnAwlOmDf0y4aXH4f9pwQj6zDShKhrNXUvSBgBUS9k3+k97Ds7Q6/WZ/nAGiPbNWbWjqODGoGxqdaWZzbZGOnQrvZo1npcnWpipJ8UJqtkKsLbNOTtK+9osC+WIrz774O/Fj/7NDnGSar+Wg/0/pVz5MHtHovPzBe1DH62xS+/0SRs1xP+4ycm+WufW+bwlS1rLh8IHc3vaglaM5D8+XatrlQtwA/XoByC9MvibCvbJcNW1XBhVvDqgYgtB/J5SOGOHjOCubTJR1qnmes3kUZiE/cq7/Ww22pKp6UnHsSNfTZA1IPJTWu+x7jrJMTKzjaN3SDE7/udLXAVDujzPm7PNRkLNeOiDbXWgBopMYzVtxkQHmwjCpM/R2iD4eLiHKevHoBelJ2m29Et6+ilG4Yi2GdekkKXZUC/QwAodmnBrZnJNUWSLC7J+27FuC4sniNNwU/vjve7eToHY2xaBJNaoUaud2gulfCJZ8Bw+PgK0/s2aNCkzRoJFbxVx0a2OFHWSNKlg6TLB3IpSNymziZvX3ETnlJ/BnPtw/S/2efVRy8xM9wl6UrUmT189tVjPHM9QBvB6lenWfv6JO33nYKJLkJXKRuFkMXM63nK1q1awNn9I5QaVe597TSPvPAapV5MkFgtPpWSdrXGmTvv5sWH30NjbJwkijBBQBoEjG1tEpYV7aFh2uUKqRJIYZ8lMzOWRjvhf9CCNyiO5VHtJgMwK88GLqTNLV11AkQu3ZKNB2ir+as+yWiXCor1xWEqI9uMVrt0nN+9Yvf5ZZs+hoAETZ/EJfK19bb6foIe+BMZ2OfKS9GS6QG+6K0H7+60Or09B6MY2urwwS+/zOFL15m6tOlCl5xurEwWk+LndyJLLFXuy3ogIJ8KGstCEue93Ela2SZ1Xt/JbzuQARMBQuesMKPJIptwuXGEMRgD3bLg3DGB6MNIA+59FUrdwgh5nmOsYlhtlzlyZq9VDALnqXTPnl0ZY+/NMetWlJqF6U02K32eO76IKeVKnbylhW9veYeDvZeq8467vTZvdhzzQ6gKR/33CC9vDvrni/c15JJ80ensKTHn9vmwWu9iq2b4g0/M89c+f5XDV5oO14pqMgX7VbHOXiJ0r9qIdcBOjrtRTMHEYOxU7pQ0T9+jWRyDZtXaL5KCnasI8sIISqQcNOvcFS9xtLdCmMaWWZcEjATQiOxsU4mdRUK7bTmlRRxt7EwIE6i3rGZfdKhL49bBYduljBUUgkK3DaqEiCBhTPZzq8Ftuqo4Jn9ZSVLFm5cP8L9+6Sm63ShnGn/JZQMZ9Nx3r/Ebd9wUPlM4ljVF7nw3BNM3ifZet/fXiv7qJP3VGWvBKQy5H37tZKmi9cB7a5R02koKvR50O3lXFZcfRTHQMzZxoYFQpczN9Dh81xqiVeG1cplrymY9KnaNwSCMRK/Ok67sB2SWjneg6z1zLrQ3s2o4wI+vSjrXyvzLxaPECfS7iqdK8PCQ4E8CWHfJpYyWbF0U1If/gi0+SkVaclO6uHObyeAqDSQvP3CSl+8/wZc//B7uPH2JT//hn1LudlFaM7S9xUPPfZuHnvs2COiXyiRhQBJFDDcbmErI1sgo25U6Z+57gJcef5yt4ZprTL7MzOBz0hdjR24NDMZlCJD4rWIVbUad8JCTVJkWKluFkN/P0pqgohQ1UqZm1/DxAnVM1nKNokw587P7bPkxxuXut0BvMwHkgkDujPA73fl0uXlmp5yPuvYZiGLNeLtFaAz1Lc3kzQ6PfeMNDp5fdKFATnFRhfdisicp2SwdYCPcb0WdgrLr59DyBMRB/pt/dsYeTP4Y35E2+WCMijrIeAiMyIJqoTAfBrvY0Zq/iaVpE8LGGFw9AMfOFoxXMtOh3Pg4a5prhxB2PvpjEhBGoGLF4esTpNqgUslz918lvV2mrO9BeUeDfUGQ41ZA98d2XmHPs/9lgWXYjStzoC9y6VvBnsxEBsaZwQZ1evCgKrP72JUA7VrAf/r4QT79xUscvrzlQEXkt/bPctw+kQITRoRDEzA8brf9jELyKCzLgLqhYaMec3km4czemEYVjHMMF+OHM0+mgTvNKpO0OJkuMqWbSO0mKcb50g2MtSGMndVSkysobgao1AG6hnLXCgRFNPSqr5T5d/9ySoB3DRhhuLQ2wuFD1yHqk0XhZbO9MJYDgL9DGADW0xG+dfE+emsBb17cz/NvHKXdKmXP3JnK/xaSKTAjv21stuWsI4nU2Fc2hH7odnxHgqx2qN77GiNP/hnh9E1LP0YSL81y/V/8NxitwEBQSzA1Q9wJs/xFgYThCnR0Du5S24j6bs9q9H7lpdhRfZVCkEB/o0pNQCWEycOC/feExL053uhscqUaZ1qrBQ+Vz6g4Il06QBaRPkjiA0x5oM2CbO1/ug6dl+yeSdv9wKYv1vBSHx6sGuqRYb2wW+Gx8ZuU5Vla6YMoM40Ennxljt97/3n6UT7fUnJy1AKW5mZYmZ3mtftOcvfr57jzzfMcP3OB4WbLznoDpV6PUtKHXgeEQPQSRm6uMsIqc1evcOStNzl3l93ztzE+yqWjRxBCEweadq1cgEovDuWLQ/0m236hWhE4NSGiMBdblPD2k4geiiRbVyAzKNYIkeIXvfmMnV7hyJYMulkdEFLBG+NNlvgnRhM7qPcb3ybuLlYEUfhMAqkz+Rs0UhuiXsJjT5/jPd+6Qi01CCEJG4ZyVyOkG2AgS2EZerAv0IIzP92M7iA13q6ap+cAqzssjTraFQOzOgN5D/SDZGYYkWc5PPocpnEfC90TtJIKJlsiUiBXU9ChBCTbgtJEPoI+s2anykDqiCLd52LQd6vPjmIgEIIHL6Uc3LfK9eGIi6rCdZPuPPNtLe9wsM+NTTthNi9mx6diGkd/BHxsbw7uitwgtzM63wfoWN+ZjdPVg8RJkSi8Ly8PEWzVBX/8oX389c9cYGK958xMBcA3AlQIwzP0RydJR8YJUx/RlfsLDZr1oZg3DmyxMNmjUU3pB/7pDuiNtVKksWDresDUwQ51UibMAh9PFwlFihCx09ixWnhAvuiVFIZ1brbvCzszs+y/DvADy5SsuqvsuxZubZsXgwtIqHDCgoGyy0OgYuYn1i06ZX2xQ2i7zUAbYMsM0TA2k9v5xT186dn3cnllL3FH0G9a83Yx61zGCDyge/nDAZHMXay26opsmRuFoDzph07k70UCEIAaX2fib/47yvuvIqXtI2/VKWvJfgTMQKsRkz62wPbFPdALMxqKyiAqMOR3JAYqtRbXzxmaq3X7uB0g7L9WEiszgSUdKSCqSPpashSVuDjRGwgm8hBmO0qTrE1j4sqgzOvbtoPDeQtEEexNGzb/BPS27axi2vA47aLVGncfHILGMAZot/rcM7uIkH1G0qdJzSeAkNmVGrW+REcJdquqnJNLlzOuh6QvBZsTI3zz/Q/znccforbd4e43zvGu519jtNFgbKNJrdOxSwmLAQ+uzw5cPs+BSxcASGVAXLJpTbeHh7hw4givvuskcWhYm6oShwqBHozZyMSQmGLuDlHgNzlvsHymTxW/S3xAlzJtKsTkvnZPjHIH54I8CmFwnljOZVCERORpZxMMGkVCig+us/UoYbCLGtPOJvNnlnj4uctMrHfYd+YKUkqbGTMVdlc+Fbi57vmhKcTcCGfzFhiX8KIR7OH12sewPCnXJTxZLY/CdiWvv4HcdO8eUcx5YdmJ4a75r1Ga/RpB0EamZxh78X1ceP4jyKpi/ChZ3oqBG7uitXV3SOGWXzqa3R5yZFEQoDP/PbbusqiDFHlJ4Tm+6hJDeU+LO1WLk502sWzy5/FOZfTtLe9wsPfvO3r4lm/59PDEbKeQJ3/pYN0TfRH0vReuKEd6UvNeTf8EPVAT/1Q/JfNf7fRdm6jxGz99gk//0UUOXd3OkUIGMDwLk/uhUqeKxG+SkdtzFf0w5hsnlzm7t2kBXgzYzJAIZtiifaHE6KEV7gwbqIMJQ/SYNE6u91GytzjD3IzyPkbpPobGmvYTYX1zXW0vUy53gREuoJBcaMmiynYgrcD698stULE9ZOxyqNtOTnd5aqzKsMkwN/QeEIbUKP6k90GupjaYTNcgPiEJDKQ3uGWTmez2Yscz3Gfj9ijx5ugBmcOBepZf3w3PbRdICEsxBx99mXTfFYQRRCuzVN+6m3h6hWhlhuqz74Y4JF2CrVBw8a1JdKdEoCCoWa0+8G33QoiAfpLQX6tlRFbMfOfrUFag3Fh4xiQUDM2CDmK6Ry+jHUczhY72Wp1YDum16kSOERZB3jNGb+LEkMWZZoCvoXsReqvkWy4U+jmJNF8Uo6wEVSYn7OHSeMJ8pYGQAhmuc37+m1yp7+f46UOIQifnorghQFBGUyehiw01SxHEgaQ1WuXZ99zP8+++FzBML69w+OJV7nvtLKUk4fDl64RGW/D3HeheyhhUrwdA+WafibV1Hn72JYyEa/v38OqDxwjimBffdSetapQtVfOx7H6zbO+Z9zZF5epvOY5fu2N300sISCi5OwjyYBmyq6DoNihmoAdT+O41flwdDHmUvpXnBV4ksD1oUxdV+iFPfvUqR167SL6sWNilpd70DTn6+fHOcko4IpBwY+YQX3jv32DvM4YtudeG9om8m7WxW9ien80F6GLqW+noNwtxwr8bxidfo7Tnz5HKphGOU8Grz95Fc0GRaoh7MH2XmzMFusOAkAY52nV2WZenwUnMqQQTQNArsB+TX+5HI2OR7geT54DKR0TYUyp7Ftx9DJHRhN9brH9ngz0MClJ5KSa6zc/wiR4TvIcryaaN94GJAtjnOfj8WWSf8+AWq80OmvRwZxZrIbM65JVXtGslvvbEXta+vMUDHYmKqjC6x26LKwuzSJC9dwPDuZl1Ls40uDi7jRGC0D1/hJhpthEoDrHJLE2CO/Ip7VfY5kKINTdmOaKErXvG9FRhMmusVO8FgcBYbp8twXMIkGbTI5fwMSBTkM7EH/Ty78DA2v0dmnyfkE1GeN2cIBYhL3IffR3R1WUa8XA22bJNcVyXRfNQmgO9BevfhNYFbBrXwhAUH2XsEm43erZIB2jeUjkgswgQbr+jLADI1yGCQLWZjRfYW5tn7+YE6//T34c4RLQryO06nf2XSUcbGC3RlS5raZnFckDSGcrbHkOiLLNKjPXeBE6Zap6t0OmozBoBeduiEgyNQtC012kP+Brq4zaFbn/uBsneG/ilZMVeT9HclA3S8RITa7PZVuPeopHRyk4hp6D9C6C7AGt/Drpvf5OazBVSLkFlpMpN8KuyMMDkeAefoD9Q29y5+DKzwSqN+BDvfWE/l/dvcGVug6TUR7iNZDR+PY2hVghBS7Fx5bEQNrcEmpt7xljeM8wzj9+JShIm1zZ58LXznLhwnX1La1Q6vUxAH6BvP/tdhx+8fIODl29gjKCjynzr8fuJXQf4ULcQwRAJJXQB4PPdAWQW+W53BDDEzviv3dmeF/n+KSodeckViyLMF9XNPKopv8bXxMYjQOjcBJrucI2Xf/RO6ltXmLl0LUc4745TsqhtFXQMMQB2SElcrTJcHudmNIfSboW+u9YY0GnMhWCLzaFxG2jqAd2FBg3EMRfAfnTyTfbd8R8RHug7ZS58/YM0bsyjrEeMxkV7zdQJayjN9pAwEE2m6JE2sbAB2VJKK6ym0JFw5gDccdWtHPZpQkTeZCMKzfVxdwWB18tEuZBg8psYs2PivP3lHQ/24JnUTlLeeY5wE9+vHE3chPI+7agwsW6/reYgjOfTNTeteT+eX9vhp6OV9X0u6sFVSpLr80Os/vUh9j43wWyzhPWoFc8S9ALDVqXD1vhNvrl/hY16l1DCUVqU6TBOD03CFAllx/p8Nm2NKbTEMyzHeExqaySMAw3jH5kTqneweZQrqo7Kn+Tss0nqADMGEefAnqv6g4BuoC9CQBMZ7xeAVEZcio7S7Wr+TDzBZbGfjikjDdTjPsJArdunordIk5DNckhfuoDLXEC3kbITMP1xuzPv5nOwdRq6i+QSOSBLEO2F0UdshrvOGWtgET3QPfI18YAsg6q6zzVQddv83hYMH0lIYkVpWhCUSwybfYx3FZfVPQTbkrgqqHc0nXd/i/WnvoQMNbJXIV7cz9If/GyxSllJ3PI5IdxKRQ1ch+h0xLSEtjN9Rin0KyBnYWIawhDWV1y9jc2jr1NoNyExCcmxi2jht/3M6b1LnyZddBoxtvBRpArzgLydE+w2371c2rkCy38Eul343dh+rZW7jFVTlKwNBB8qAY8wSr95AoJTULOXjfWv0zarHLs0w9Ero3TLe/nsj7xOc6iLB748BYR0IIpbC2PchtPWM+3zZ2rABIrVmUm+/OEJvv7+hPHNbe46e5UHT51nZrVJpVdwJ2USkzvgfNTCGB7/5qu8fP9J2rUqOPhOkPQQdLBR7DUMJTQ1UqdL+5S1OquRcb/lYJ13chHovdhQnNmDwkAuANi7SMeZjPPJF0UBmf1uXxojUs4em6PzUx/lo//rZ5haXnVLP7xk6xmEe/7twpqcXyzaKlN9s05bl4gG9RYQsFlNWahughzPAF3p3GTPLVq9ISytMzLxGjLoYLRg8/peXv1Pn6J5fW/ma5PC6h8b5+3c3/uYq5I0iLGEZF/PutSwmQ6kDK3QGdtnX5qDhWmYXIcHT1vPYsYeHesr2oBF1u7BbsjTiBRoZyCBwPemvMPBXgwQN+RyrCd0e8xOPGu+t0AvnM/PmsJ8Mol82LKAjYG750CfT7T8TEGKYRufinPAvpPdpei7z41B/VrCFx7YoNaDRBukjNEixtAHA62oR2nkBj8mrvNeIVkkYC/9gbr5KZ64FhRp6fZuK+N8tY4dGKu15DqD9fcLBJgAktDZ3HTOLb1EIFIwHVBdq/EX4goGIulN4TDQFRG/pz7JTTHBaLdHadkw0u0xUYuJhkP0+YD31VrUltd5hCVCYyOCpTGMt9ogDLGucGOkxGa5zHdm57k0MkYrjDBSZP4+EUA0ZUF/8qMWiBJNFiimalDaS7Y+d/iHnAKznZBuQX8hoHcDKsehNGPN63QtcPmmWauByuQihKItK7w+ZIhKbUZnXmUxvRtZuUx05HMIZVO/6GqHtNLGSG1dIDuE/oyeHX/QHQjOWeVXpTCWkEUBq30Q7INIuLz4B+H66xD3LdBjoLVpaKXbhMPbFG08wkDQKLE9uo1JQ0YWnyDQo247uEJlCsM58MGdZhJon4Wbf2IFJ19K1ZiP/ux3qNRj7r92g3ipxudvPkFDT9ptfo1lxCP1NuProZOwNKYGoFGiQWxmCLSg2g0Z2g5p1luIwuYC2s2tlDwULk8jU+QIFOahnSFJpFiZjrg5NcY33n0Xe1Y2eN+33+TQ1ZtMr1q3Qp6VZXBSTa5uMr20xqUjTvLLANpqdAl2t3uFYp2QMpqIlBH6VPFbZXsLi4/H+W5okKfoNllrisC+kyt4Yci4AL28XsJJgjKWBJ2I3kiTlK67LuDK8TE2j04zJTqYvp0wojCHBx/j1fX8u0HwmniSxbVJQpF7I1WhiivTZXp7D1v+4zR56V55CxxQG0N16Br77/xdwvI6na0hXvvsj7L4+l2kcWT51w4BVAD9dZCLYGYMwV1NxKywycQQoGMefv1pNocOc2XyiN28yT0vDuHGNGyX7FK8O6/ASMv+FgqvNLnnFEjDyLw7ECBUjFRufwVvBhwwgbz95R0O9nCralGkQUmu0eMk2tQF94BdGlNCUMmk26JG703yRTg3WUBLPsk0AqVBxB3qwSmeUOu8YY6wwAyJ8GvLNJlfiOKSPD/tJEujfTSxk71TBH2gy/R6l4d6K5yU6y7rl83C1SVfzCPcXQV+nUCOx7Jwzk4dv7jExk9EiQAjEEkIvbJdH98N7AJYHJAbbdHGi9tGWxUYsGSVQFrLR8QASRXoQb8KqSRB8RfVu3lOPghGEZUMd0ZrfODSefavN/CL2gXwADfdVr5ekLA9jzYEtDjS2IYGPLh0g7VKjQuj43z90EGWS3WSQJIKiZHCRtWXoHbCxRg62cXLJF6DFm4BsBxXhJNQPuxa4porYwaXDWqrUOhsE6C804VM0ft+m436GSr9r+LWkWV9bxCoqRXGfuz32PzTT5C2bMBd0WTo3+UaqPOgemRr642x46xqoGat8jU2AsOTcLMPVxzQZ1pJlNJ+z/OUonZGiWCFng3VQiclRlbeS9Sbd8FVGYFlw1nAzbydGKSBla9C4zmRDZMkpVzp88jH3+LkI1dRShPO9rj/GxdBaz5z8/109TCxiZASJjsBWk6AkaiOYX2sxFiny2jwHFtJTF8fI0hDnvzWEX7/4y/TKceIgmpZnK95Fe0RKz543TZ3aBUX0BmhSZRkYc8Yv/vjj1Nvd3nw1EX2LK1z/Nwiw1ttwjgpCD6GME449sZlzh45gnDpYnIbnnX12dljQb0NTusvM0RCREqJhAifpd6XQcD3HMdzpnwDmsErcmXEn+e+u0XsuSqjKG+OMnH6CJ0opveulzCBFQnspkSSxTumObS8xNXZOYZXNphcXne39RrqTqEkp5mF8n0slu5CCbeIWTi1yoOkgJsjBc1dW1ePdKCf3VH474a5w1+iVF5j6fxh3vjyk6xeOOKUFEvDaRf2VtZZMmNoIYhKcPhBqIxBEqUw3ceICqCQWvDh57/BR597GiMVZ+fv4o/v+Um2w1GMyHtpfRj0EKyOQimGu2/AiZXbeC8Klggv/KZooqE1wtKGlQJuP7Rve/kBAPvbFQtZdhmJyODTZP51UA7oJRXyXZkovNtiMARuFy6bHGOQ6xljUN2Yu7e/CVsvE47PMj+6wTYXqLDBOvu4zgjG6Ro7s0pTmHR+By9LLyl3cpN7ucH8eJfALb2BQQXZxwBk0i9+hYIVcoqOBl04rxhikrfGxQ0bg0gVbNUR3aDwMIeI2a4TLmbBGCACNeMqJ/OoGy12VLjmIvQNYSp54s01VHCW1/bsYW57mx8/dYZyEheGwQoKnjEMThDPAbyqaV0Rk502k50u963epCcV1+tDrAzXiCPFzUnD2aH9ICAVLljTwGZUoifyLOA+AM0gbHPcHPVMSfuoeCfv+CVypmhCcTIOUqJbdyKjJjLcdP7o0J3iqCFMKB9/g1GVsv65n8KkoW1egRyjDgy9CZ0Og2t8IwimINwPQRWmhqxvv9eFpSvWfI+AoAQqMAz/xGlKhxr4pace8MN2RKhrlJfejeqN5NKE7/vBpmXjajBUS5o9ozHdS3DptVIO9ELzyOOv874ffolwXqGUfVq3HpBKyaNDr9O89m1e732SU733EgeCclShWz1OPzjFWLrMxKoVIivhEuXoj2npZVr9DzLdqHLf2T08f+c1kqDoNssTZO0s3k/tU8judJsMZskEITTNWsg3HjsMZj+19gn2X1vl6IUl3vXKAsONLmholUqcPbyPGIX11HtasmAv3ctTqyUTG8K3hcJuS5NSQ6DoYx0BKT6KP5/fO+ev5x2elnIxgKIwoyVmYZLa2XlEK88zIYDIBGipqPY15Yk99O647GjfTto3T+7nxp5prk/P8jP/5rMMBOUNBL1AtqRFChJV4tX6j5GoISJh43ojkW8rJIAkTJFSIFIXPZC6gFINO+VmDEy3LyBWQl7/9lOc/tZ76HeqmSCQdCDuQNyGK8kQiYDKPjj8CNQmIE0hTCT9tAwqQALzN1f56HNfJ9DWWX/ntVeZ2VjiN9/z39KsjVJNWow01jhx9WVen7uXZnmEjaFxvn1csDkMD16BWpI3uxdAWUM7snm7Sgl0KgkzB97KTWveFbJjD5K3u7zjwX6AGICdQO81eu2A3nrPIxQlbMrH23RBO2G2coMj4g1KdCjRRTPEy/r9bAnBlpgGoMIqs7xApBtUJ84jplIk11lnmH1CsJcWm1xDkHCRaSdk6Mx3b2s7mKevSo8TLDDLBvvYoo5LDUuOdUUzXB7w5xf45KKE19ytyz037Pt9AfyWmTlzc6t8jUT0QkQvdA/zIO/FbVMg1KIK6xAo46CFkdHYSDHfgO0SLNSp3SzzpL7CBy8uIIxBmSL7LYjHA4Nd1CZM/tohDJSThDIpIxvr3NlYBwX6BqRqAe939BrdpeEhtkLF2eFRrgyPcG1omG6k0ApSJdBysB5COu3fd4u3DohClbT/KDGN92KajyHK1winvoasLAB+sZU7WRiiQ2ep3P0S7VMPg7Srm3QPJJvs/dBzbDQeQ54ZQSDQyu6MVz4Oom4zJs9M2XiEJXfXXgyqZBi5o8uBgzG9BxaIT15GK6/v5nSYlmqUlx/HxCWMEQOrD4pDYhxJoMEYw9RQn8MzPZSEr3yhStyxnVCt97j3fed4/197lSgC4WJJwr7mjmeXCbdt4qYPT69y6ML/yFn5IKmpIMImlf4w1fhuTLSEXx9PH9olRVm+hpZ3ovUMH3r+EDPrVf7igSusDjdB5OKvBdhiQlbPHWwAXZ6chsEGujNNHqwCgBQprZrirRNTvHVsnG89eojZxR5aR6wNj3Lp4MGss3L3iLcGSmw2fmeFcfZF7fiUzcBh82VU7CzE64V2Rudqhn/J7FhRWfGj6ScaGCORl2eonDoCXWWtV4UOSYSNMEJLSqcOIg+ukYbdTDlanh5jeTqi1ujY9Lc+jaTZQRxZ/gybpfNs7X0sR8cJgbqEstN+i0lDVyc2aNfKqF4dpR3Q+8QJNnYwe0TrBrzw2mHiPztEqhWpsTc0QNyCfsu60jDQTUNkH5KrUP2IG1kBYSzop2V3y5TG8DjrQ2NMb65CCkIZJraX+anv/Gs2K+NMdZcYu3kTlaa898xX6asSv/HE3+H8nmO8us+m9j20YTV+BDRKMBwb1sahUbcm/2Ylosp7OJIs8WjvAjOtbUbr7R1j9vaXdzTY77QkFqdBiiTB+sq0i7yXWKAPnPycA62FyFE2mOMqh+TrTDt22ScgQVHmKg+JBZYQbHIUSBjlHEq0MTUfjifQJKyimCZEI6nS5SFWaCVDLMgRm/EMv9wGt+ClxX4WOcR1pmgwTBeBXxqTG94HgZ4stAcnRjCQ6jLf0CdnaD7iIEBiiGm6SAZBbmmQaKFQUu1w9HuV1uST3Kqttw6MV4789QnQEbBatWbElRq0Isdh7NOtZP2XjTKDYO60iiSApgm5NC+QQ7H9QQuOXJNsDCvWxyRSw8QmIFP2rAtnn8mZMcDxxiqQ8NDqVTCGa0M1/sN7l7g8u43Rk6S9I6TJHCadAS3tKkc/EJa/ZUJO0W+XgSMCQwjdQ5i1lNLcH4LsZwzVi24m6FL/0O8RHnkFEfZpXV5k6sgUaWmT9tQahy69xpuX/i/EEaSBq/0VJ4tJ2LwK9dn82SOzhuOPX+L80J+wWlNUHphCi8LqC/9nJHSnLNBrke9GqAvzrGBEQRgCZZge7bJvqsfc2nXqy2f5Vu9DlCoVZg+s8b5PvcyBk0tOiREEqaG+3mXf6xuMXm/luztXJzlkXuS+8M95iR9hNRxmtg8wwkC8R2wQTYFME7rDf4pKP4BK93HPuVlmV0b4/3ziObZKPQK0i8LxO9x5+LNjnRu+7ahYbMnpuKhvmQLUCqeBSwRKStYma9yYnCR16aQF3YzkjTPl56t3/L2KvT4YaxATYKgQE6JInJPOpr+R9CmxncUZ2Xa4JcNGIpKSjffYLiGaNQQGPdWwAvvWEOG5g5hYZcY53xF+EydcK0v1JqYtkSMldLZBjhUoqp0ute1WoXOKPCDX6FGShdq9vFT/NCkRNQkVaUN5CjKI/dyaQLWtf15p0Ntw7Us2cn7kJJgupHXobUJ3GXpNlUXFSRfnkWqIfZiBxFrbVF6ljM60XccvwwCMRoiYdrnCxfk7mNpctenLtbUOzq1dYU5fKegSgiBNCdI2o611G8gsBMsjsDxl55tfmrqsgJK9cH3YWjmbpszLpQO8Fu1n5OUud/RWiQ+e43tZ3uFgvzNNhd/0UrhglNQRrGXuNq9zmXyVq/2/nyvs4yoHOUuZDrosSAsTU6NZNWW2sIk4pnmpED8rBgAYJCHVbMtKA0S0OblyheWRE/RrJfxu3AARKU/xLfZw0zEkNeBzz9mBb7Otk9decoEnQlMij6z1bKmo79uzcytBye0H4XMMeL3H2a2VKDB3h2gDgG+47fIRY7iwOMuFlQnexU3GOzFsxtAdz6/z97yl7FDPd5Q4MDx7f0o/tLVdnJEsTMJ2FbtROwZtoN6WxGFIHAVIE1DuK0p0OPjmCI9d2GRiu2VTyGKwqo4GkyJcPML+Rp+PvAH/YaLLZu1VROVllC6R9u5Fb7+feqsOGtpBaGmlYFy4tUkxBC2Xvcug6mcwIqYovmkSDF00fQgNpaMvI1CUDgpgCx9Lff1Dm/S+s0myMWbDJKz1Me8xBbUZ2xwJqJEOjccvMTY2h6flfBNTSxXSKGgfxGzeabOOuZAML6hkgO+PYyiXUk4caFIpaSY213ng1HPU+ytc/2RE5UCbSq1LVE7sqonNmKkrLSavtSltJQTxDsFOlQmGxnis/AUuxB+ByS4s1oHAcVHtctQIqnEKUjK9sUYSfJnt2lOIeD9hP6CvbRobH7Zql75pQiQljBO/fTCc1bUTR/V2Hue6sm2lIRfEcHzFKwl2E5jUZoVw4kDsxilGEGCc2CHw223lygXOmVC0q9mQXUOMIiZ3KXl4L1OnxiaV3jZJKEmlIdSK4XPzbJ+xOQhEGri1mqAqfWbo0OhEiHiLJByiJwNKOiGp9FFRSrsbWQvWzDamZBDjY5h0FskKfrNfgFor5sk/fgaVpAPGNJtdxkkNWOtXM5rmmcm/RctMEABV6ZLq6YLA6Ah2MxQEMYhuSn9R89bvhHSWLF1HL0BpP6gIZOhGxekcHlhFAEnXdqL02S09Q1YwcwyiAPqpQ4i2IFwyJHudXU/AF5/4JOV+zH3nXrJxQdnSOMhndG7NuPvaazx35FF7dEdsQaZDZBU1NjeEBqQgTQRbzSqvLOwnFT2+l+UdDfa5mcwbyKUDYON8chboZWY6j5yma6ltmAazLPAeXiKgD+Cka/KJbwyb6SQ3kzqt7ZDpsUW2e+OIboXk5klGllvosQa9Ukh5cYhgbJvSXaskqgjEsHeuyf28zjPcjaRWkM171Om4duRLYG6XKMhrCRJBiKJkrFBjRIAN27OMKMWvFhgUpCMEMrtX4rJk+5pYph8QIeMwX08/sN7LO6ZtYFx2SAtEy/n2l6vQhgMrNfb2YyLq+XUDCa13jqNnqXnpG8XymEBUYlq9iDce7pKEcOawzYc+2D+FlcXCsF2zu3YpBFIo4lJAygSn7w+4eM8e7rjW4qNPX6fUj7FomZI73u0I3H05ZH51lv/4hGZ0u8T+VgTpKuHacxxYqaEF3Biq8/KeWZ6d3WNHzTM0fHMNZuQVxOwfkwkyIsUIX9skY/GGPj6yQmTtKrZPU1uXxHGILjwHQCrD8FiD4fEKUpYQUR/12GnYu0oyupX3lHFPcG4MaSSiexDdvAetgzzdgatqUZu3XWOoVRJOHmkQBYaR5iaPvfQ01XiV194zytTQcqZN1TYT9p7ZYvpqG9VNnQfId05h7KSA6cPcdfEM/9VjNxCHBWK5CmYWkdwPycs5A8Zk+58GZovhrS+zXfoU/fKQjYDOhH6/gbU1IMVGUEZZUV/YzWOk6TuRPHXZ5FJHmtYG6OeJXZ0PPqZfZxw9zBbKKQfpPnW2Nc6nSGKks6UVNXq/UWxuX8znQh4YnAv3BkmXGjFlZHuVke2bKAx7Li3RvrKHqHsOQ4nt4cOISDASNhhbX2akrKk0hin1bxJXJ1DdVzi89hKlrTYl2eG1u47w/D1HKamYamLYaj7CRLRCp6vplSIrFC6k/OjXP88dZy5m3MJ2lFMApEsEoSRGGt4Y+ygb1VlSAZWW9dNLPQiMdooZVnpw5t8L9JUEuRGz1Qoz8og3bZ4MSgwKnDYRASIw6BhMKlA+pkY4+VCCCQxByW7Y63fgrtFj6I2YxWqVcEwghKBXqvC5D/00cVThnrMvUu71GFgal70bjBGs18ftVz8HvebmusZOLS9xOKnEWxckmKo3TO6a8f/KJY+OtxM0j7j3ky5xur5CUXK+es0sN6iwzEOcoexAvp9J0nZwdBqAlphrMyTfeD9DlbP0Vx4jrYWEfaAzTMUFSoXS0iMGqPTRR76EqnULU9iyjwdpMcYrvML93GAEELSo0CdC0IEsMzYDLMDr28UwHWUka9f2srlRZ+Te86QiP8Pn5Pbgkkfd2z7RmVhkAxUtDxaEporqlhE9IE5tOtxY2Pc+DGwG7WdfYnjlGze5P7gXUdjIJUATSK9yFoh6wNfuTJgC1sfg8pzg1ROa1CW26WtYGjOIqkE3QNou28Ec/d2KK5O9xzTB6nde2AtQIsQEivMHI85ca3DvW8u5o6+404XTtEa3Av72Hx8GHeFtddZO0gUhmGr3Obm2xfh2zM1K1QYKjW5ybqRL3J0grq3BzBcxsu/aqws19HuJg3GpUYu9JYqt0QLZqzD27PvQnZqldglCCYKyYnIfTO8z9Jop7XCL4MlnMIcapOIKggqh3uCh7hGuxNNcGd6wQhASmdbRWyfQSeCYlyFIE9BwR+saSmverB8iFQqMoVJJuePAFlGgmV1Z5KHXnkWXm7z88AjdIYUyhnojYf5ci6krbYKeRnhL0E6DjYAs6VJtBF2eYnh5jvGOscsSU4HR0wjP7bPcFSZ3mYgWUi+QcpxSmtLTMdIIjBHEShGIutXmO2XE5jjJhCYt9UgQ7G10ORBd4Q7TJSi1+cLKOMtz2xnA5mPgN5oxLv5HYFfw+98gxu+V4VUF5eajcn8GRb5Lhre0+bQ2fq7q7KneIeBjbfziQcHK2AQrozbd4MLYIep3t6leGWb2zctsbR1mZAhMd5h1NcxmH0RZsF2eIzSGUfrMXP+fUMk2wqTc8+fneeRrnyE0GrRkuTSPOTaJqvbol0p2TlxN2KsXEUrY9WZaujV0DlWDwDJBKejLClfr9xHdcY2l8j5GnhOZojsw9MLQ297m1B+mXL06SiBLSFHKkuqA1e77m1Ct3GoREALq5R5C9ul1h7OkV1mRhoee2mDmYB1NlHHDjonY2BMhNUR2BliVq1Tlsx/6aZq1YU6cP8Xs8iIqe6jnU5LLU0f4i7uezGVPDcZLlRKbZMvrNaKgwAgP8GAOg7zEAFv8XpR3NNhr/E7UVsP1/vnBbR4EIQERPR7mO+yhSZU2drsHv0O1QPfLdLan6CwcoroNzeWjqKUhTKdK2isjzDHGrEXVbssobBrTbPkTFKJHB3fn8lM3xHCcLns4yx9wPw0qbnr76F3rY5dItyVPftQ7wP1dby7N8MKfPEm1us3Y3VeRKtf/7X/PlOz13qXhw/bsU5QThYTVMlplxFZg/eu9AHpOi0q1W5+GbbTtfMt7r47xQLDX+QZc7Yp5JXda5QWkwrA4JTi/3x7YrgtOHTW0qu6E7GIrHRsEcrS347e87ExBkscjp06/C91RvwobhJCcOj7O8TMLlBK3P+VOMILMrG/VCWcb3FGiNOHHLl7KTJm6vsXmRI/O+jm+cc8az84JeirNTOgi0+TtOhE/Rh5e/P9ixoP41H0kLzxKQ0xz/H4oXX8F9YHvUJupsNjfgwigKTQjU5dpjVynM7yOESGGDgpBIsZ4OayyWd0C53s2sSFZPY5Mygx1tzm+cYnJ7gYPbJwm0n1qustrlXfx1tARjDCMDsUcmtumHKVMrGxy74UXeOt4RDJVJ64oVAqH3thm/nybsJvuEJ4KBODf/NZkUkClRr9SQ7JN0B22GUy0BLUfzDCYLUcSCVmEmQHKJWrlMxzcrvPLf3yAU3NbHFyvI1PB7757mxuTE1aeqIKopm5ulpAkbI4q/g4LVEhIUPzNuT5PpyVeJCBVOZMnm0d2budL+mIUhoSQ2AXfeWrMl9jaDHke8m1CXb9/vDXl59p7Tt3GBetZmslRwVO2P9Sol2lSR45K2vHDjFwH0wfcsrHUOOHCgEwazG99hnC4z7o8zGj3GiNJG1JDkg6x1SkxVVlHdVrQFjm4eh1DCpe+0S9PcVp9oCzYa9hWFa48NcLmaI3aeSsbqKIxx41bM074N88Zzlwetd5CF32fuRiddSjpgIlBFKedgAOTixiVcvbafFGGztxPAtg70aNWq9Ho5YGB0gWexi1DfUwhlXGCmb3vn7/7SZ554BGOn3+TqbUVptZWOXD9KuVen688+GM8f8fjbIXVAQOVcZX2yQXx1GIMPnPqwPjtsUtk5fd497t3NNinKGdGw23eaE2iPiPVKNvsZ515NqmwTUhCha6DECv9J+sTdJf2s3bpIeSVGYLtiC2Tx5Zl7F16DdtZrMQg9BhnshWlGCVNgXnrrJYaKANzbPDTvMw3OcJbm1OUR6xU602QESLLfjfIbnIz/Mb6MJ1WhbhVprc6STDTyMz1ZNdo1voJN9cjWjNL3N8fIiqFzouY3xGjCLol2ChBS0BbQxcXFetsVX62xAZjoLM8SSntcLHW5sX7LlsWJyRPnNrLPrcxS7EYAesjcHmP4MwBOHtQ0ItyH2a+ojXXZQanQv67cTA4yBzzz4WngrPyWHNs4ARBe/dOOSCWMSWdYrPjeK5WvJGvhbcj5hrXwInZKgKB3KoxvlUDAZ/+9jQPXD3Oa/ve4LU9fVZmLmCEzYDuhcBcPPH/i+MDwkTs3X4X3c4YM5Umq0eu0vqvvoaeucim8G4r+94Z6IPY3TEgFkfouU1bJAZlBA+cWWLs4nNMDJ/hwbU3qSUdFJpEKM4O7ef3ph/lXP0ARsJEpc/h2W0CZZC00Z0bnL0zpTViAXRoK+HoqRbT13rI1PfVDikvI2bhG+Y5MAjFjfl5tvass3dp2DJsbcBUWJm4n9HNF+za63iLTllS6mP3d9PDEE8RhQGT2/DBs+N2jIzgUy+V+LWn0uKokjv9LAzfpMYBNhFoxkj5eNzjIClfVSFrmWDnx6I4Jy30W698TIC1DlqngB8N4d6VpyCME6+1szal+K20ir2VxyINKrRekPAA4unQrv+8+NBZ9oxMUX5zP0EvzPaE99fWzTXqjZdZSPczO7SBVDZFLkbQLZ/kbHOCe4deoWL6BQucF84EWdo4pA0GDJSdN8rGrBiteXbfE2wNVzEueNTXIfdrG2Ta5N//8Qhnzod5owuP8S0zQKmWMP9AQmtNsm/UsGFS1OYWh6av8uziIfvc2zICwY3GDPtnRSZXSqezGA1pD4SWKBVQVMwMmm65xCt332fHKk2otfrML69zdvp+dKJsdr80n/LCeX1E4DR76awfGQMr0D5g3B4RMtgF+79y8ctWDBq7iaM1U0/SYJ51HuBitjI+v0bQSkdJexXCs3vZ+vqTmF6ZsgEKw15caYXIQV6S86qBnVeFQdQ7DP3IcwTlPl4vK2wo64QS61GcYpNP8BqPJHUmiTFUC4J0Hi1tH59/9uaoEycukbSeZ/3GDNUoQWGt7T6IOiHh61zhXNggnbGmqtN6g1nqzFBmwgXzKSM40RknXCwjtrF7qPYM2dZkqZtN1pVGT6V87tAyZw6tUo22uDLdJnWdIhDcdXmcfat5Mh0tDFoaLuyFP/gwdMoi23Y3D4HM2dp3zxrmz8wB3xvJTOF7fmZx1P1eCPZdO4Pqdr3M6niZeqvhNAMfaOR9ksXwSP8kHzZpircnc9YXsrlhIDQxx68Zjl07xkMzPV4/UOOr912iH61no+VbPgDw/rsRBO070IcMaekC1ctnEA/2MZOLGOPX/nmrQ359Li4FxNyPYW8mWIVGcM9mjx+7ssVUaxVauTbVlwFfnH8ffz77iHMNwfxIm7mxDghN6eYm1cnvIA92abk6BrHhnm9vMbLWdwFOrlVFgUnkLcvs2Bi0FDTUXmJRJg0iVLBINdxHtiMKmqn+KPHw/RijoHeWdChAd43dfVED4STZ2m8/TkKijLfm7LSaWMDsUeK3eIC/x7cZcitgZBnuQTNHwrco8SKlbE4V70DWm9ZCZlfXWzdcki32zTeULfrhvVHMZsLPd+PwM2JwMa4gISVxjmE/roNOPjAiQas+F46tszmyyj3fuZ+wG6ASQRj1GGtf54H1/5kWQ3R0HRP03To3OxZ1c55Hh8/n9IwjCAyZI1oK8h2l3Gp5pezxnkYbycZDRxEuJ2647YbC+eqr6TXme3/EG1cFpy79fLYDY7b5IGT7PBhAKM3Df3udk5+21hwhLId7+X8I+fKLDw9ydsem/JGhMdhzWFo9RUOg7Tp7oSHcgqQMpi+Q4U5BazDXYqICGsMlmkMTyMQg+galBaHOvX8EYEIL4lnOHD1Yt2xHTQ2iB6INOtoF+79yqbPNHdwgpE9Amy1KzLDCPM3CFBU2hXW3Qr9doXvhKGZ9H73LdxA0S2BEniMGp70LK6x6E70/5j8X+ZgBjDBEJ69Se98pwuE2ue/VJ8XNfX6ekFIMEX3mJzcKYGXv6O0AeZjdoC4JoFTKvY+8AkCXiC4hMYKeu/dp1nmDdRDWJ6iB1UrCGg3eYIuig+Dvbo4w3hB2B7vYAbyvqSPevkxZqrX58r5FXpppYkRuGfDMSWlBkEIsU3oRXJmD8/OCC/OG7aqgV/IM8vYl71Mz8L2o9e8E/HzroVttAfl9bVs0fUQ2Knb9/PnDUxy4uu6y83lmFuy4l9dUhetJvyuGFw7M4LmFeAlfYmVZfywnieIROtE2gjdd/zXwS0N9ydh9fxJ16f9AN6kiArhwxw8hlzXRxhqoFF26STr0ZmbVKeKsIaU32kcHE/hFmMpIwu055NYo3x6d54e3/4LAaLaDMmeGD/Lnc49wpT7nVqgbDo5vM1HrI4RmhO+gptYRLs5FIAj7hsNvthhe61tN/BZhbQfQe8VQKPqqxoXSU6yqI2hC6vFl9lx4DjGkLWd0jFjoIRBdgv4aQtQZaVnrkpYGGUV2c4J0h1AmYl7b72nD18RkL+8t36TGtzjA+/RlIpFYoR2YIOVjdJjC8EWqmRgqbvnLt7xRBGgUAYbE7SDvHUwSU6hFXk+RcQc7SwUM1DnF0CcmIdkBQ15A9J9cwjChWZ5pED/eoG62uOdUmXed/hpjzXXKvQY1BdO1Zdj2TM/dr5j3tfCEfPykA3aJ3+u5VRolUWVKZouNoYDphQa9ah/jNukbum51BGEMw8kFDnV+k6H4Nf5vL/932XbTuVjjHuWnk4ZoyDD/REwqfQJygzCGFTNJ6niX2Tn1jc03cfd7IYpA2xhthN9zKwXVM4gebC8LSgeMjUXA75mSu1U8jRuE3UMi6CBUGbQiQLgYl3xfBl+HghEj+2IA0Yfyt0FdB7UNwXAeOPu9KO9osP8A32IuWzzjx9x/FuhWFdOuU7p4gOZr70F3ypheGYGgZPKxyGjFSZeByiVOyOmo6IoGp3yEMeVjCwz/0MvIUt957vJR9p5aH01g1/UKIndXvxLXw4MgZwNF0MtpOV8x7JlW6N69htBBc47N7K5ec5QDT8lb8o3aNcbDkMlOnUT1AMNGqc2p8SVuVjpgBCu1Liu1Pmtlu6yo6GX2tRlqldGizr/7GCzMQBxJtDKIzBz6l2nttwOKIv/J9dVMm/kudxzU6b1JNXWs1UdFW/Xy7JFJHn7hIiNNt1BXOPEfTwA7OOBgqH3+RD9IBq7WRlmrDlORGwxv99ClBp9/uMWpgwmpFKQKBEMY3uXq0iTgCtBE0M3Gy3IpY3d2K8gOBomMpyAFmcwStO4ZYDBZVmEMqrlOMnSTZHQBJVKC7gQs3c8bRnJ66iBrpVGqpsup0TvYCqokLrd/PYrZP9JhtBqjhaDCZQLWES6gUGjYe6nDwTNt6psJ0uyYHALyqCk/kQSprNINpliqPEpTzdOjjDekJkPzDF/+dQjugcpBfA7xG8PnILnAnk4VhLK7zJUrSHGIsjRWpcK4JaEaWId0nfsvwjPHDhMHt1ponHiKwPA1jvN8fAf/+/AZZsRWBqIhcJw+N1C8QjVzEvocFj7uRRFgI3Ls4l67qNEupRROMLRnK7zorR1n8DM6jwTI62mNyn41gM/552m/yGesAGr8xr5CszLVZhW4/kH41mPH+OGnz3Hn+ZSJzTblXpzT06BMWhiufMws0HuQl1CNaJdHWEmmiCLDt49/lBtjR3nwjc/Tmj2KDgyyL4hSu7Z+ND7Lye3/nrK5yTeX7uc7i/fmIF+QMaTMH60k7H9vj/K0Tyhm423aG4rmUpRNuaIMiYt2nz8CU3NW885iQ63nDJHAsfo1rgbD3Fwdoa5XaB2acr5an6XU//d0Y8fMiBgjNEJKMBFGOTeEFn6Vne2ywufivBQNiM5bzR4Jh81NvpflPxvsn376af7pP/2nvPjiiywuLvKZz3yGT37yk9nvxhh+5Vd+hV//9V9nY2ODRx99lH/5L/8ld911V3ZOr9fjH/yDf8Dv/u7v0ul0+NCHPsSv/dqvMT8//59VlyHaGEr5BEgV8eWD6AuHkaZCvLAPNsfpuT3VBbekMwdyE4v0LzkIiVnbbAMR9Q7BRAOA6v3nqBy5jpB+b3u/OtfK2jGangP6NGMiPvOdcP75vPhnFuOyPWQP6ovF3Gd2hzsfqKjwm+7mTC4TWLLvjqBFwqnh67x1coEDG5N0S23QhoX6Jn2lMcJaAGzIoL9fUXu1k0FQZru+h9/64QAjRG6W2yGmFHpyR5vFjiPmNmfm+lFu7h/kVnmyHP/N/6WuzxN82JRAsVWLePkDe3nfH51Fpt7eljpTjhOpBPQDyWptiFY4ws2xhJuT6yxMWdNqpQePnFYsjWne2N9Hiy1W6yXWhrfZuyq4Oh1jpAEhmUaRYthwlGD7ZIKESQR9JA0EC2jWgRgZ3iSdfA3VuAfZrVltwnG3YkyhV9KyPe0dJwyaE6jtccK1/RiVItJaNg6pkLwychJTzGpsDENRyompbaJAo4VE0qLGaQv0xlDqak682GRmoYdKd4xSgXtnEQlqkiTYS1+NsxWdoC1H7aYwIqd960VRpPEKy6unmJnfTze6gTR1ZteuW2AWfkWDIq7OMdK1NOEOgW6Avgm6DTrm4LLm73zlNJ995AA3JuqFOW1pxBvNEyRrpZDf4WH+dzzLKG28BWCchB+lyR30eZEqG4QsuvBZOysCN/ts+h6fTCdPmettvT70zFoURJY0x6soedpqX2yqbstNpDPlp6QuvM/r+Z6bWaHAqwk+/qcvYb0m+d2PnEA+dYIDNxqMNbt84PlLnLi06lwd+ezJ3ouqtrTmeiMljFURExUayTjPzx5msf4grcP7SOOQ9b2fIq0tYMQx5s/0GNkoM6Mt0JfEGiZUfObck6SpGtDqEdySGrcyrrnzJzoY6YV8K9ycf3qE1lqQnViUx4WCqSm4834wsb2nTiGNrYYv+mBS+MDU17gQ7uFz7Q9zfnWGGjB0SOfBWVmRA9+No9TiKgzbzcaGnmiRL4ktCiF+epQhHLL11EmRm31vyn822LdaLe677z5+9md/lk9/+tO3/P5P/sk/4Z/9s3/Gb/zGb3Ds2DH+8T/+x3z4wx/mzJkzDA3Zfbp/4Rd+gc9//vP83u/9HhMTE/z9v//3+fjHP86LL76IUrdGO3/34rzicUh67RAbl+epvfIYwgQupmzn9PGAbT9LyFyDUha0ebHzggJTHWkx8aPfJJpZzQxwRuQGIJ+Bvw9ZLHiagb2XES0BD/qsfVrbvPjkOn4amMLZPmGOXUQXuC01LZQpAqapcZl29gTvXSym9fA1NULTCxLOTN1w08rXwmsi6jY9aX8PmCBkH5IaQoQYYW57bn7s9iBdPDYAYFm7vT62845m4Fve3rzdXkvy66A90Puxe/5AlaVPjLPvQje/owyYaCrWhyRL4yGvH1Q0ywLYplPpuInuTH9G8+1jNkGGEV5MixmmRrKnwh2UmWIcgWQYwSbbvMCVzOfoR0cQASPAQRQtYBMh6vSmtmD8FWSvRrg2h9oet8lTvts6Xa/ZZ6YrgehWs0CigT25vTLsuFI9SjgxvU0p9BstS+pcQukeYS/l2CtbjC/3qbTSXEhzJjAjI0ChA0Fc3UcU78WIUXR4iJ6o0AESI7LNhjKLrfB11Sj6THe+wJKGsVoLmdaQSdeepF1bqDDSLVnzgr+LbkD/KpDgV04Ik3J0cYMnTwl+530nM19cvrxVFuBYcIUJ/gUf5gAbPMo5hukwTYOQlPvo8AAdtgn5khnjOUZIhCI0khIhMRGIGiG4Vfka4yDaA4Q9kjhRs6jV+7kwmF8hF6WtvcAa6lNi+llUQJHvgLdJ2JcutDSRlsou7BtBMMarx2c5cWmVjzxznmPX1ghTY3cPFFiA92gsrcnelELO7r2PzfFJKqNrvDhyPxcP3YNgCNVWiL4g1cM88toiLx5uULmZcKj9He7u/hsi2YRIsZDs5zs37ss2rSkGo/vvRsDMvTGP/p9b7HmPtusYTExAn8h02T+kWRqts9go56Tnq63g0GEbjKd7zt6RQNoH3beAH5kWY2KFD4xepBuHxEnESjpFO5kmlgGxCDMw9/YW78DJRsUEQOQnTWEKGITOjxlhKKc9NIpEhDbvfwAi8t36Xebv21T+s8H+Yx/7GB/72Mdu+5sxhn/+z/85v/zLv8ynPvUpAH7zN3+TmZkZfud3foef+7mfo9Fo8G//7b/lt37rt3jyyScB+O3f/m327dvHn/3Zn/GRj3zkr1yX7uZhotOHSa8eRl/fRz0Ncgi5HZa4IrJ/gwEiYD+nKkUMbRM4atyqbXP+0EVKCB7Zt46cXicWOkuH4qOhCxlGC4Y1Gwee4j1zXua2mkVELvd7TdjL5R42rJAgnZyZewu9DJ9ghQvLVixR3sM4L7AK+E1/8hW94NlC7vH2i4AGZZ1CsFrO2pGUgQpljqGYHPAlDl5vBr4NjkFxcPLrizq6HjhjJ5zn9899+rnNY2cAU64deHO+FYs0KZ0g4IXD4zxzuFPQlCBIBH2lXHyCAReSlj/dnStycvPPnWKUezhCkE0xm+Ggg2aVPrkmV/T/Fpds1oEhDMrxl5QkaJJUtyAJ2bcYsdk5RrJVJSgNCsjG2EUUWV854DdgA7el6y0XzOEFlz0jXfaOdRzQ2yRVYa9Lt2N46OIWc5c1Ya+CDA9CZRxkBaNbGCkxsooO9mHUKEYItCzRU1YvTbAxn7F7pdhtEvrCzZm4hVp6moPdb1LvvopIDdX4fqJoEiE6EBmbwL2P9c2rGiQRGE2rlBLFTcKuD3j05u0EiMGkHFlep97r06oUGLSj5HrSBy2IIytqb1CmwT5Os5cAw0muM06b+nabtBfwwfEz/Azb3J3CehKyP4mZlzFfjA/yzZGj2BnaI2QTScfRWh6oZwWBHsZl2/PrvAcpQGY1BL8cN9s6BrCKgb1j7AIB/fc8E6bP25HPYXs/A8SB4NTRSc7vG+bw9Q0+/NxV7rzaINLGMkPPRJXkcmU/5yp38PX992PuTZCRQshJhAnthlmJPV8kUF8a4viNy8y+ucCJ5h8ShU1MqPj22rv41y99itX2aAbsGeALS5Oq0ufOo0v88I9s8K71Ifp/Ynj1XREnR57meP9VpNbI+1MWpqb4xut38ZVXHuXqzRk7hyRUKnZNfuIMQGlqXyYFk1gtX4QgQ8OIavCTs5/FGEPfBKz0Zmj0RvlO+QkWOUQ7GoytyBQOEyIyi9wg1vgkoxioJy2Or53lQ+e/xmszd/MXcx9mfMUgU0HPNV63xvlelv+iPvtLly6xtLTEU089lR0rlUq8//3v55lnnuHnfu7nePHFF4njeOCcubk57r77bp555pnbgn2v16PXy1MLNptN++EPPkos5+00KWjgGSz4QSDX4H2qRW+6t4RmiKM+V4+eY3j+Jnv6FYK7zlF196sLw7RMMwLtOhDP42yhyETyMKDBmFwP3Ek2sWXG9u09pFuGo5xcmesA9teiNcAblfIofFM4P/erQ+DAPl+37U3bnjGSMRrff0UNwf8uUJQ5SsRRrJiCMyrmunfe9fmdiiWfIoPHd8plXrbeedfBI7cG+3mYLy7qG7yrz69ok9jkG4oKV2tvr4Ek8GKcTUFjdrgvPBu4de80aNLmTS4xxyQtbI6AJdbok2JTuhQZcO4H3mmxKEb/C6xLwYR9ru3rYsyLJNdHqAUTRGvzdmczp6kXfYZZBlC/Frk4XEpTUm2mRwT7Jjog/OI8gdZblFauMd4QHLj2OCKcR4cTmMAFJgpriI6FsABPnrFOG0ilfVZqXH4mnLXLGGSySrV7mV53lbtv/jtqrVMok2bLYIYX/wD2/tdQrtq13WCBPhWYsMF2v8lQQ9MqryPSf08oDwL35hKNF4GFZqibcHRpg1cPzRR619LGvf11xuMWfxQeZMtteGAcFCdIXuIOBApRg6ACC2Yf7xFnudNsUEu3ECl0VMCloQOkDlwNQ/SpIOkzdr3H9twyRuQpvBM6WEebdcDlUE9GtbkoSAHsA/zGPoIu1r4XZ8FrGp/hz1OPBXqT1UsUaNnev12G14+McfrgCO9/ZYnH31jhwMq2iz2SXN07y2/98HvZrtURqmutd24TMUFgQ1xcmIuWgpURxUNf/DLj7VOU1SaUQ861jvL/ePpnObc6X2hTTupW4TIc/Gibp35B8cnfU1RubHGtLPmJ0jcJH3gBmW04oTkyd5XDs1fYM7HGr/7h37LbSgNR2QJa2ssFXmcwymi/06+x0Rljj1jIhPSSjtmXXmU+vcKJ5uvcjO/g2aVDdI9PcC3az4qaAOFVtSCb+3mxIFMyfebiqwTNNo/dfImT1y+gkpRHF19k4kbCKGtcnTzB84sPkhjFm7WNnWzjbS3/RcF+aWkJgJmZmYHjMzMzXLlyJTsniiLGxsZuOcdfv7P86q/+Kr/yK79yy3HdH0GUxQDhIP3iR+tHodzHTG4hOiGMtkC5ZLLS5d4ThmBmjdK+RQ6OrTPh1mIbYWzSOLy3Lff/5nBQ1Dg9CXvTj02WqbN7+JhcqzEl+Dzcyq21tTG9ZTftPYvPF4aYwkTOZU5/f8iX9GgEw5SYpcZN2gV9Mdfm/eIfUWhHUZb1xbYqoMRBypxE4vZax/rB/da5eSn6DG9nkC++BoG4aK7P73mrZWBwIVXx2KD/MYf7vC4+osJ/9oFPXvzC2WD0QC/7aGlvbxnszZ3tBOjRZ4l1lljPapG/5xaWnfq874H83Y+yv84JbUJghEbtW6dnNqkIjdoco9+vkKAwRt3S21BgfAKUSjgx+zQj9TlkMGnv6eg3NpJyo817Lx1jejvERDY9sBaQOItXXzgAd0DvtfbcjVXoLZMg4i2G279FZekGQxvfJkpvZNt9ZlDntcr+Jrz1OTj8BIy7WJ4AK7VIgwyWYWOM6WYJ9H4I3gSxAr2HQZesKift+nMhBXONBq/rabt7YWE8+kHEE+VTVNIWf3L+ATaOJfSkzczps+WDQAiBVoLXmeNNs5cnNm/y1PC3GBJtXgn3sCCG8KFklnoCNBGrcxVC0UKyRaQhFppU5CnXZGH8RXbEDNB/nqDHbnQliVD0UcQYtklpYpIUpSzfKtK6Dw32uwLk2TUH6TVVgq8/OMM375/hxLUGj7y1yo3pKU6dvJNupYIUdt7YhEQlZ92z7j2fBvm+Z/6Yx579A0Y2biKUoB+GqNoo//ybP8251b234Su5hr//PSv8nac2efw/dajsPwfH3iAsgRpesXkbvIvBhd8LITgxv0BQSTGJBgEzYxKdyMxaNTAzHWkp00PJxNr6fQCpAIRECE2QpuyRp/nE/regDY3OKF8Nf5iurqKiFjQDFkaOshkNk8iQ0PSRRnO8d4qHu9/gjs45aPdhOEBsjsJmxLCMefBgH7GxzMFoDRkYvnnxYY7yDtbsfRE7MgMZY245trP8Zef80i/9Er/4i7+YfW82m+zbty83BZVjiGLMwRWo9TCTTaj10H2FmmwiSz1EKpBhH6QNdvGZ9IsemWEkfWd0g1xCHmCW3KpfFkg3O26BV9AnICVw91IOHu20VUDfGdfz5+RwYFkOGUC7mNDMpz6o8Xqt3jLeAMkR6qzSwRv2fMLPXEQoRgD7ld4GQYRihIBxAiYIGCVgGpx2azJbxU79238vtqQI+rfrzQEquC3Q7xQL2PG9aHsYFL12Qr9/hh74bNuRr1wY9IEOZjwo7i5QHPMiGN9a09vZLfI63hq4WDQdFq/1O4F7Td+2oyR63Bd8E3OkgzGKrdYsm819rDUPkMQROg7pBTbFor9bEPS5Z9/nGasbEnnSttNIglRguorhJcPc0j763TI3A0stfen2o3F1T4C+Ab83ir95Tl32vMDEzF/6V0xe/w8EvWWESW1ylsD7hkXuR/Mdg+D/y95/R9uS3Xe96GfOCivtnPc+OXfu00ndarVaLbUkI+OABZYTYMBgX8Dm+dp+XMDv3ocvYGMPTHhvgD0MxjIWlhyFZGzLrWC11Oogde5z+vTpk/POecWqmvP9MeesmrX2kbF5qAc9BnXGPmutWrWqZvx9f/nH+iV4dQHu/AswcSAfboGmIbdBjkBWAXEPiLMQLYJ8ml7zHYRKIENlKrCEmkfOn+fK7AgqDNi92QItuTIyzI2RGj2huVNe5+DcKj0hOMEsX+QWNqjbHeUzZxoh4CsT41zVD3G/PsNrchpEZk1hfWyuEATJGMeunuWD8TVONOCPBmO2AsfKu7ksGG/TzZs57LnVJIEKkghBlZBhevMx0a4raDp2hvzrRc7YmlcXF1Q8DaQ1YQpe3z/C6/vHCahSI7Q6PLNHHNDnSX7tdh7YXuWhJ3+T0bV5CCSqUee5+DDp0PdwceYQg2nGwQ+1CesahGD+axVuPBUjhGZqNOFvHmvx/lcjwkMvwfFnIOwxiy7c6aV19ADjYFWpMjEFP/bhzzCittDVHs9df5T1xYNm9wjX4ozb1z8DAUz13mR361UGl6fQ+0VendIwmeRzgVL2O82IXuPD7U+gM4nsppBqWhuDdGTMmfg2DndeJyRlQG8abYjQ0IiNamEghfUKQk7B2hIkHYJajft2v8TJhSMls+pbcfwPBfuZmRnASO+zs7P5+cXFxVzan5mZodfrsba2VpLuFxcXefjhh29630qlQqVS2XFevP8V5MAAYmwLPdpE2iQRUio0KdKGooC29RWdXKy9CNgCnKHYHH1Pwi2drwdUuu99BnRtCwqy7DuOFXb9zCMnhgcvdARh37YsS2uFrOwzDK71+xniJVZwegVtCwPpvj4UcGWcnwb5c0TspohJLhstyiYA14pyyOGfBHL9RzEHotSiMjNFzrTcXLIv5LX+Pt2sDUVRk0JP4zQwZaAv/rnf94N/EYZYMA3+83w/jH7GZadE72sginaYw61fa6PX5t0t4grhRDMnHvX4LHMj59EqIGkO09qa5rmVd9NLYnQCnWdBtiRbj44z+K4ZowZVEGwHjMzHyMsVglSwKQQtYbToqR0gIQz9TZUdL01e4MQYoLyoFgGV3hKzVz/GzOX/gFCJ7bTN7ZxZsP96PrkaU7v0zS9A9ihMHSmwKZYQC5pKkc71GE4eNp5YK6/QDV9FdiaR4bjZ94EgEJrvvvCmKUDYNeeWVhosjdbQ+zJUlFEdahIjeJAz7GWJi8ywyghXmeQGo/mcaASZyHhTDLPY3cVGPJR/J3NGOMxXyMDmNkevLHFoap3Dm7C/XePnZ2pkwrD8IrfeOye+wK6bfglcl/53PjaSkMruFJjBePC0ENYvwK/SUvyO3IBVUAPlPccFGDqPIMBqFIzZwRkeFFprxtcWePyPPsbI6jxaQNpo8MK3fyeXbznOamuWx/56C6VahFWsFkez6+Vn6d6zyWvP380/uD1kZGKF8OCrcPhV62HnPO4tXbElZQlDqFYhiGgECd98/Kv2Ek31Yocnko8wml4BAbdvPcFo9wpHt79EIDsEmEqTWe1hEBNlcu6G2YG+l4xfSGWjrYAY6mxR1/Bg+8vlxeqYVG36yXgHtThEd/Ma1WACEQOkNCrb3Lv7VV7tjfJWHv9Dwf7AgQPMzMzw2c9+lnvuuQeAXq/Hk08+yc/+7M8CcN999xFFEZ/97Gf5yEc+AsCNGzc4ceIEP/dzP/dnel5w9AbBkLGsu9rPwvqlO065gCcHrEVyGyjgCm4GFj5UuJVRBqSy9OfeS+sZ75PvAqAK0m9+4XJjFak8XUpXnwc39j0obKO++t6p/Y3+oHDxKdTsO+VEv1WSKlXeRcgeIkatRGMIl1Mk3hzo/dY4LcmfdOxsR8EqFKPfvwet7OG12X3nj2v5yeW53NkyP41GOc9ZYewoy/f9cr4j/U7vwk1b6Acdlt2w+hX4OyV6vxXm13a1aEHjzQbHznaovjdDRaHBzdAyi0KZiO/qOmQdJk7fw0IywuZXJelpgJAXLzyKSjc4uD8kWqoSbITGe1kIEoszgc6d4PE0xOXCJGonA6MBrRP2XPgFZq9/3Myo817yf7tjvLT3Yq/dWoATn4LJW+hN3EGrsYeRfTU4tEa9J008kzKRPro+g9x8iXD9TdD7QMwabYAURKnC8rsgYLLZZKLdopcEdG5JrBnDtGWWdXazjSQkJeBz3MGXOYwD2Z7Na7c0PGzGKZ9p025Bmu/E6Rtv8J7f/ifIH/gBRBCyZ62LnIpIXak2fLa90DS6XV9QkH5q4q8atzsCTFkuQxNNmq0u2DoMOg/KLSIEdOnO7nCVR9xqjayd3np1WqoyvjTPhz71q+x74wv0opgrDzzMyx/8dhb270UJk1go1IJQJ+ztXGAo2+CW5gmmxpcIPqzhg68hWoMwft2ikVUjOScsP59FIKEa2fS8kjysRGu4vsYd177Kvhv/hfHkSj4qBn+tl54Ajs4SHJrBJM6yZabzWDlvQbtu+mn58stEQZTcOhXeRfatijSv3HUfZwbu555rv8uhJkihEVqzb+wKC1t+bsZv/PFnBvvt7W3Onj2bf75w4QIvv/wyY2Nj7N27lx/90R/lp3/6pzly5AhHjhzhp3/6p6nX63zv934vAMPDw/zAD/wAP/7jP874+DhjY2P8xE/8BHfeeWfunf+nPQLanuzsgKewqxtVemGlLYi5N2/4UldZ8vbho/gl+Rnzv5NHzQYoNmlAjLPbSxQRLr2MAXTnF6sxwTqZtd5LivI8BbE3YDIAiJytcZDrLNDGGlcomlNrjXbV6qGQLrHtGG1XEPIOepV7CBizrXXxv9obF9ea/viD4tri+uL+xVGQQR/KfYl+p/+DL82X71kGz53PK873szdledn3wig/29/8xe/8d2ZEldcn7X3nw77rg0nC4pzxdgI9XjvKPgP5kQqyG8Mk5yfYenEfC+2Q2vl3IKWiPrjFkQdeZmJunqUze1FvNjh79hitTpX69gadXoVs25ZXFpD1JMmpUeIKkIKKMaF57onKSPDY9wAyLUA/0FYR7eiyo4Eaml1QV77KzMJvsYN1E8XolafnJkDvzqVduP4K2Y3zdN75gxBHoDRCpMUSFEB1mIAD6O15hL7MVmeAajBMJDVsdSGoWjWFIcpCCFZVj222GGPAytdu/Tj41NadzjCHJtq9582Rr7NxHXK0SCCTFlor0orNq06FqaTOVmKK3XQqKUWlF3ePgGL9+mtZ960p920/S+uMgBHk5gij7TSUoYfKXSYde+Hv3YCImMDmEZBUEXn6I0PrpEp4/FO/yOylV/jqsYd4/gPfgbzzDlQASphIBDTc0jzJvZvPMdVbQAYWGF32ycEt82cmw9vYoqwkE5gMfoEtWq+FWZzrbTh5CZ46RWU7oeJTC7fOhEJHEo7MwHc9DAMVY0pSmefFlw8mec5eMOqp3CblLVaNZQhuTisATjbezXODHyQTEU8c/SvcuXyKvc0F9qmzTNcXjZniLTz+zGD//PPP8973vjf/7Gzp3//9389HP/pR/v7f//u0223+zt/5O3lSnSeeeCKPsQf4V//qXxGGIR/5yEfypDof/ehH/4wx9iDYxiTVKW8xt3zdHBXq4fKvfQW520Zu8/jbx/ev1/jbCmuly1C5fO4kUAf55ipDkZxPt7l7SJfIhuAYydWF15HDh99CU+gn8tiLoj1O6Se8516lh9nsRWYo3wN/ZmuKun6MtYFDdhs7l8KdY7UTAMsSvb/Y/at88ufLwu7VsUg7YbwgZP3KTMee3Rz4fclnJ1A7gHeySVnT40C4fAdR6rNrQZk1idMBpDbFjKrpGJ1wHTRUklF64TbV3ghJvI3QAXFvkKSyjZYKFfRQIs1b4AhKv6nFfKVJzk+y/el7IQ3yonHdrUFQ0F4ZZu38LIFKyZoRKim881tqgMwVDrQTMzYNh27HhA1Lcuc97RX5QACpNZkqjN8bZlXZn5VqlK804asX4cQVGOnu5V3jI4zL5VI/8hv7m/Tr0b0SDyZop1WungpYjca4dW7NlAXIdH6NALLqAbLwFULVpFE7haAGeh+6O8hae4R2ZZ1uOMjoQIUR3UJNNVF02EQzpAcQ1isfrQmaMaeCGV6q7s3XTiYybxUXq9ox5SaFqs7BZmV6hle+9YPsPhAyIHvoluQfvNlCNuu0UsEbjYT5IOTs7h5CC/auZFwbN7UrVgZj2nHgrQ0f/KGcBGanbqvwR9GYsrwxhv3PECQIOpZ2ueBgcwREhNQIqGLyP5gxUWRInTKwtsnIQsa5ox/guQc+wtaRw6jRCso68mktGE8XOd56mls2XiNU1k+jIsxr6s1/5u9/N9d9f67inoOIVMGXT8AXX4VuZtonC5fmfKG7uvJ37YPveqdJj+ovNheTWgpf8S4prcvy6PqPKd7bRdjNuBYdIBM2jZts8OrUA7yR9XjvlZjKmSZTB67tvN838BBa66+3zf6nPTY3NxkeHuaLG/+a+lCtND9lou5nqTev+dxo653qCg97QF+owc3CdQEsvrpe4LaSQln9gbbbCIrQFuxz3XbzffUDC675eqbsmWt484KbN0BfJ7NagrI0WvQvxkghv8sSC7QpbHeufroi4jhV/U4CRkC4djnzR2avLZz5lJVmMrqo3Ne60Jf4I18Gd4EvJfkgWrbRi1JvHMjvBPTir/9zmWSUgd6NoqMvfpREmv8z7pkqJ3xFmqSbqfHdMwIVs3/lg9SSCdsAgXZEJBM5X1TaaRKUTMiCDkqmaKlIoiat2iK9eJMkbNknmBUc6JD0+ghbv3sfqmmYCqk9+u+izVLvvdVcRhGuMBrNTdPxkSl45Juh0cDhGk7YwXnsK/K4TmEBPwKqju5q43h8fQOubMC1dbixAZ2eFZq6ih8a+Hm+e/BXdhJPpx6VwtRBDf2Zsu918aqkZDGZ41eX/3e+uPHNDNcTPnTfFb7ngTPE0nZUaNhegtXTwA0I7Xmt0VmFxa1DTHVWudQb5mdn/y7jgzWmdy1x994vMNW4jBCC3qmjjOxp0dCCxoVx4usjLFUbnB0boCMDFiuazx0ZLDFjEkmsBENbCdsLAZ0XQoYfadOeq5MGAbrV4tGnFtg3tMjm9VlGp5a5pTHP6LZCdFJ0R0EzQzeVLTxVDMGN0YiNao1nb6lxfXqQCdUhFdCKXHULuFoLUEKgrGRchqSdDrTlc47lLTKCGAfiBhGDSOpAgzyUVwdEvSoHntzi2vRtMCiJBiEcARGae8e6ybHOV3l4+wli1UGkwjh3RAJqVoJHUIR3ePvEbB9HEMlTcQaY30a2yZ9/Az7zstXSCEygfjmngAZaqsrpyn3c8Y8miUYK7YnQ2mbcUZ50r8175WpfaG/IvHkprWNBv40r7cG5cJSTtb+CqNZYiqdJZZzTugfnn+DeKy9ycUJz6OD/ycbGBkNDQ3yjj7d1bnyzRAubMhQEPQWCNGA7SFFCEZMREKPI2KRLpEa52qkyGvWoNkPWRlfYywCN3LGmnE7GBOIUkF4Qf1MT3roG5m3bqdYtXgudgv/ns4jFeeeEY2QGhVMBOxudL18IjFtQjOZN2izlftM+E1Il5naqvBcpovwO5s9Z+B3r4Rgc9yRtmR6XJ1BRjPjNpVHX8vKoFLqS8lHI1M4FqPiVzzr4n4se9ks1/eJiMXuU2uGkFldYuGDkjB6m8HnYudsFEOgKMosQWngFgmwrhXe13yQFUsXINM7vWGWCwY19ZGEHRYLSiu2Bq2gU9bVdpAsDRCMxahhaq0ajmXaMtB0mXZI0NhITmqiiGdzb5NDoIGPj5nkihCQFGUGlApWIPCY/j8237/2gDRcpJnQhYGUJvDoPJxfg8lrRL+dHJQEtJctiFq0DWySn/9A5c1R+b++XJ8WAC5XD/L+XfoHljd1oYK1Z4TeeOkh3s8Jfe+dpgkqX9o3TDKw9B0ECcQCNqAjtC9pMD70C6SixniJujLJBwOZynZ5+F+87ukI1bEMacKWn2X1iP2OrEmTIVJoycO0M5/Qkv32ui/q7DeS4CQcTaBpqm3teuc6LX7qTTHdIN+r8YO33eKm7jxOV3Uy+AZeevJdFbTV9IqN++DlGx09BphCJglTnQqi/jXatJuwi4ehyh407G4xlXZSGRBZrf6MiyITglfGQtYrg8kBIJqAVmnFtB4JM+qu/oBiGspn0vs4eIgmtRF/BlrIx12cBsjOIWJzk8v4YGUM0ALKuISzMWQ80v8C9218i99OQFGCsPU2EY9Bc3Sm/Ulx/lRznYJ1m8IU34TMnveozjoEo9qUGVpMx/tH5/w+n0uPc/c8u813f8ir3v+NFKrUurqJlGoSgMwJtqHuqBbH2VPsuc6Mo7r3jsE3IkLyppwmSlDSt8Fjv9xnc6HKpfpQLtWOcbdyOFlCXW8jRTTJ2Op1/I4+3NdivkBLQpkFgLVARTshpomm0Y5r1jCwwCWUN1woQEQQtqvUWPS3ZGkxZocUKPe5hnEjHdLsNhsMuhAFGwV0spsJm7bt1lR2risOXc11+K9/enVEuw+mAqyyZuu8kXcgh3ZeIjYUu1tBpdXmxsY3vnKgByQRV3kPIXnx1l7m3c/ELSsyT3wfXdt9dr9xHfdNREBRkxvkQFE/2e+eCkXZqA8qSu/Nv94Heh/ry2BXPLbMnxR20ZXRcbHXRYm0ZrMLV00F/cYcsSLg68Syzaw9QS01aXK/peI/Zeej+EYAgqSJVFZ3CyNaw0QooiCtQu8VisEmkhlbQbcLo5iKL6W6DmVXF3C7FVFInTi14W+1lHBuHZqExOWf8tnmOz6Ulqoxd3slFy034whm4uGq7KAoh3ArSZi4ltIYmUSoi0M4T/ybjAjmo5zdxNxWAkDwz/yEWV3aV+IEMyadO7kZ0F7jz4Dp3VpusJRNUZZdad8VIaY2K4U7cvQeXYLtqVqAGkQqurB3mEy/9Le6Qn6ejtnhve4OJ3hYwAoS0kir/1xMPc25lgG0VU7m4xX3f9iKX77yNoYmE0QsxN/5gkCCp8i3v+irMh+yKEljucd8r/4kv3PhheghCu+VSFXBtfozba5JIp0aaz7RRlfiL1sOvVjzKaLaN0TxCkBUXTLbNWM20TH6+XpighbYqdc2Z4YDViual8YBmKFirSG8aCsri6zYdE5CLPFoQbI4QLU0hhUCEEFYhaGhkxc67UAylK9zaesnbsRQanCAsFiKYhSU0uXbV947vD8POBHS78Acvw/MXrenGamb7KvalWvJfVr6H31z8K1zt7UeLgOdfOMKrrx3mnuN38l0ffpK53VtUGiHTQ8tkgaQtBF0E66rGTGcBJQOkUlS3W8ZU5FsI8jny+wc9GTFBh6Fak0omQTVAhxxunWSuc4mlyi7assZk5xqgaPM/uYPe/0zHOooaFZq4PN5F4laNYGNwO190qa0z59T0Gkw6XKGpyZDdDBEiuUKbEV2hkozTlmu2fG4RDlcGkkKiJv/OAURRGKeAmyz/3jnliZxZ8F3odsqo5kzBHAREGFm/gfK0EW/qFi/0VlluFAAFISG3UeFRTGyua6sPorL0ZGFLcPiGEGXHAfqZn+J/X74n/2UxAk5TV2QN7Cc3N4N58nOu7WWpvn8OfE8HnQP8TqD3n+G85G19ztIYubFUlqXyoxLMikqCNlfHn2GkeYhab4wwq1FJB3M03cEC6mKsS+p9R+9V8ecNLcJiQh6WHkA0BMnQHsacgCMDOjLguoQohFBphtMucVY1eNIHJCWQ7nuPtdU7OtdL4amLcHGNgkkQxW9KXvrAHy88xlztr3N/9DTHwpNmJNzESSfRC7pUeG37OIfqbzIYbnKlu4+lZJokmOWJ5cd5vXMcbYmrwwYhTeTAJ8/fy+8vJHzfvZKVjVuZnVO8Q/1HppOXkVsdqIZQtT0IBWMjyzyYfp4Xw3fTUzVIA5oM87T4DqKww9JCm0GpeYhVbrTgqhjmzWSMVtc8P9iOuPilO9n86hjtMZhPYI+u8bfu+GOOhNcJdiUIpTjSvcgrt3+Y1fVR6lojtakN0NNwprmHR7ovMhx0jSrZFPPAAas7eqKKqEgG92WGe9IUK9+998ZcaqimdvCtM8XxdooONO+51qMZCV4aD1moCb42HaOEJqJCatPwOmnf3Nt4ZwS9AaL1CYLmICKQRW2cikbGGiEUFd3iaOcEdzWfpaZMHoOck5TatD0IirbjgbW/GfLDO6ExGZueugDPXTHStgN4by1nWrKQ7OLfzP8kzzYfI9ERIiJP5ZAJwYsnjnLizGEOTs3zz/75x0iEJEOSCbPLR4IWnfpAvl+DbkqcdgsGNN+jHvJLDaGklilqybZpUACmGqMArampbaZ6V7kR72UoWQYUSe4Y89Ycb2uwL46CuBdkugwPDkJ8RTv5tQ58BEM0iGQNPbhJC1PkomZTqxZOemUADyhU24btSHOwN9dlFnb9hDbmOx9Wiq1QtnD7cq9Lm2NsbBLooRkAKpymzZfkJumo8J4dU+UxQo7hy7Z+ctac9ubvAo8VoPQ7ZT/7sm4B8r5fQ9Ev8h7vjHwowgp3RqgXIFwG+/Kfm3m8d+6Zvtq+0JeUw/wKlkN6fXf9ENZwUbTI99sI7NnAII9QrA2cZQ1BqCOqWZ1qd4LG1n6CXr3kjKaLRxfgWjR8B9DnjsBlOu5JQrbnHi1NBWShAeu4FxD3gBBXObb087w9XpsMLisIV4m7N9i1+gZPyYOcWbnXJMDqu0feN68vPSr8SvOH+YT46/y9gZ/hlvAMw8E8L7bv5/bGa1RFh+c33sEzW4/ymZVvYSaepx62WMkm2cyGDaHWIifYpRot1parAkknqfDLz78XoeHhSNCd/QfcnvwGx5L/QqgSUNI4hwlNlRYf6f4CiY55QTwKqV15gaCT1Dmf1NEBvDI0TjgAQghmvgvWnobKiWt0pyfYjMYRWhNt97h1ZJ5vm3uVI4M3CEnzyRGNKkemzzA1doHmqUm+dvIervWG6ClBW9W5sLGL4yNr5nojGtu8SQKqAYzFyEYdZIAQ2lNzO9AR3mDbRZVpYx/3Z1YYE0GAYKgHjzZTUgGyepD5kVECAjLrQKwJaeseqhsQbU+SJRKRRqCCHLuEABGYMM8qbaaTq7y7+TnGeos2l4KzMQh0IIwDqJDIkrTu+mAo5na7SiAU9bhb7GBtN0YngTfn4UtnbGkAWTBH9uK1ZIx/sfxPeL19nMXerGlCUN4TUjp+SdLoriN0Qk/4VEfn/BaASCRhV+1kRIQX9iex3LddmFoXsareDwXw8OoTPDX4/kIE3MHgfGOPtzXYOyKuvPcOnoqB7pcWfegskqFEhITEhNQR1HDlKU2YjbBWeV99bzLxOee1QuouvKP8ELWidrUPjoVM6vK0m+hc38rt/gXetT6opki2WOwqTjQ3ScacakgjGaTKu4g4iqYI1nPA6nv+F7AKzkjlWmek+syDy+LIcCFGZaDvdwHa6VBYSOcyfy+8XxYQ7xgRB+7+7zyoyQG/OFu0tj+8T5R+WZw17FThr4DXr+KdGx8ogh3tZyvNBEKBbNMNr5LG68TtSWqbe5FppQT0JXDU9Hfg5ocThLR3mUc4hJXGgwzqm1DfFgRBZIiua72jccoDeidoZ6AzTaP7Gkev/ldm1r5ImLap9JrMDczxyvAvcW1triTFa//VCZv5q2BLDfDP1/4v4qxHoHtspUM05DZSZ2z1hkmVqQ1+vbOrqGRmX3MNv22na7oWIMKCAUAItICvnoFARFwQ38XX1ip8x9zvM8ya2ZYVA6gSxV/s/RLbYphT4T15+6Xn+C4QuTAXDMD4e0ENDzLXXeWdY+eIZMIDoxcYbXSJK3YmMnMjBfQqNWpLc8T7n6K9GXH76a9ybvv9OZG/sRlxfCgxGg6XDrYWQSWARgijMaGryZ0FxUAUskLxCgbkMwqw9yVfp43XcGV8gkuT42wNzlCxDGuY79eYKBska0vSjjAmJLu+cpOPgDDIuD95iuPrzxCpjFiltn2+TV5AIEkGQSqNbPvopuklIWcuT/GpL9zLH3/1Vu46cpl/8aO/bn632Ybr63DqOrxxA7Y7ZmzdLVxInBSsJaP809Wf45ntxwzFsn11a8S1XXvraeruDpVhV3Tc7G1p/9yGijpbCJ34hLLYaIHbLO69fWBkf55h8km7n1jG5XJwgN8f/wt82/xvfv1kUt+g420N9kXC27La11ifZQ6OgZWZnVU2wqWsdUYiYUvFmPSThUpbEZMSkCBIcrW7ebKrYWXqSDuPd/d9oeb1vdVN3DvW4FBYzoy3d+BdqTESjSJkgyoL2XHmglWcfkIpQaO1im6AUpqNa/vpDsyB/gNAEHGABo+DqOFThAJCC7V5YUF3rITwIKxAHmUZhXKuAr8kbvlwtEh5V7tvCqj0WbPimgLQ3VyaZ/nmlHJksN/D4rPPWvlMRvHJfGtyaAj7NHDQTm4acvEYioLlKTORZizNv8B9JwQq3qYdbdOrLVPd2EvcnEbkKr7ymOVt8whr/+j5I5w/O4OoB7VtCFLzF1khUwgDYtoKh9K6ZzggywNSlKbaXWbv/O8xunUSLc+xe+FSIVFqwcT6DWb0Ra7puaKaXp9E3+/ErLQxn6eqQjur5H1eT0dzs215DjFMiJmUAsyLZhb9lyCEKMpTaxOJ9cWT0M0i0uQv8sT1d/FXDvwGf276i4hUGcAPoS5afFvro9wiX+XTA99LRxkTV+DGRLtnmjd36It885HnmEuWCYXb6xjNQGjnM4NOHLAVhQQrFaq9GcTwQa7rL3Nh4h8zp2G12WVv6wnevfaPoDkCs/dAEHFj7CCydpSp+iVEbEPJhBNHLTOQCgN6rtqQcqNmB9r35vcXjIKNwTond+/i7OwM3Sj0llexo9AhOpFoV5JQeUBprwql4oHgKR5Sf0yglcmEmC9UY2rQUkEIWU2YhInCdsE278biCL/6qYd56oUjXLw2CWj2HpWsPX+NsdNXTGjHRtsmenATYRewc9wMYCMb4ac2/wXPtR/F+Xlo21+Hv3hrZzxYYWRPwiPvOUXuuIERGY2ngqVqWhPoLqJqAd1pshwX627stDLucPGwgUK3JLrdIKv2WIynuBIdYFuOsKnu43eSKqL+Rd7K420N9oGNBi3buc3WLKq8uarVwkv0WACe4+hEDtSmkpQgIwILwBk9m2DXBxpNyybXMJJ+Oca7aIt7Z/6leet8aTiXVoD1rMGaGCbS06zwIG1qZGKU69aOrrUk60DcWTV7SwuaQ8NUUsXoxgS6KpHROFoKsIV4TRsKR0A3Br7XewGXrlxP0Yei6ra51tWO8xPoFgJqv7q8LK66mdlZAMY/yhqZssnh6wF9mcHoF3x032+K1jowdWtC2rl27I0bP9c3P5a3aKXEFRP2+6VzupDFW2xNvEYwfIHK1m5kUqXSmkSmQalZpTGx56WGuANRx9I6BRXdJUo1aVIj6EKYFnTJ2w4FEDvvegv4Ob0Smkp3mYmVZzlw7ROMbJ0qreGC+7BMVyZyou2G1IG6DzQ+0Cvn7d+/HPRORkblzAdgnbldpUp/XN4df4bheJ2Xs4eoyg7zah8dambmtClvqrTk/PYc/98zf5tdA5Psqb3KoHzTOHcHgl36MrPJDTbEEE8OfZAudcLEVLOQQnM4ucbB5Bpjeovbe5epqZ7lloz6WUtJW9aJ0xqxHoDKFEJoMrlNJNfQOuPG6j1kNUmlLVFDGRP3nOTDv/MjhKoFSwuw/AZoGKjVUPGddN/7rVRHhg0DIa0o6vTLuRLRDa7HceWcl792RD6Fr+zdz9m5aZOluLTS3OoP0LoCPdA9cD6VPkrEMuHPVX6XQ/oNApV5IXPu+db+JFKywQraSrqBTcmYackXnj3Gv/lPj3P+8iQIQUSPdw88yf/V/HkG/8s1U7bOYzBL/XFrUQp0GPCZ7rfzTPdRszLt4/1tHlZShkeaTM+sc2j/Mre/6xIPHDnNQL1T0KkMWOpRGdKIdg89NkjY2kKq1Ejtbj9pTLvyzH3eHgGzMR3YC8W1qMIXl7+L6FjEQn0SpQU6NYzE1rEpaB/krTze1mBfJaZuwxfcuO+M+jb/u9h5X4p030kUWV4vro2T9FyKGZPZLvXur9C0ENbDX1qGwCzniMJ5w/dNd7Kjcw8sGIcUSaoFHdXgeudB4qURNscO06s2EJH09rCnUg6gWRs3+0yAroAIAyI5Y/OV2ydbW1J5czvw9qP6ydvlgvSk/VSUxCxGzs+jV6bdou/Pp//lZDjgTBlFq3xGrOyZ735/MwnXB/nCYNEfyeQ++Z9Ffr5oQ3FIe0fHBJTb6IN9uS55uWXuqYb2atJ4k2T8JFoLws4YU9eO0+hVSxJy/j6BwRWoboHMjGreOTALbX7jQutdk4q2el3yhCOhIFLbxMkmQmjm0mfZd+Kj1DrXTfxxPgwCZB1kDSq7IRwGETKyVSdoZqQ6KDDGmgOqtBgSxk1/b3SGV7v3kGAcB6pBm1Am+cRkKmS5N4XWMsclw8yW22u1tfk1aM0dtZf5tuHf4N6BZ9nUw0g0K9kMT/cep6cqfHH7A1xL52iKGgD1oMpK8JdZU9sMhJ/lePYbBMKgmZQZ3779axyMTrCsjzN5ocVUuEGlmjEQdQllZsLGbPldhSQNG1wb2ctaOMz6GzO8Z7QGYQx0ibIuLwbnmOgtszX3Eid2L5B1M/YevsjqA4JdCy9hEtp4C1drBje2QX0FPv0KvO8vwV3v9patLgoPuMXtLIb5eHkLwN3cjlcmA8bXt9lzY5W1gTpJGHBq/5zhH2RgBAMdQy9AtwUq8Rk0zd7qOfbGF9kbXWBaXSVIMnKfAaO2MXGdKNqxIKlH1CNRMJZKsrA2yB988Q7+za88TpIY+8RUuMCPTP5LPjT0h9S3W+aeyrOJ4y2InLsU6Dig9zcfpbF1gB947fc59dIeXly4DQQEgWJibJN9h+d58L0nOHzbFaqVhOF6z0rvKT00ECBbGepyzPziFrfdGqECRdppI5NOwTRLO+5CQCJseKtrmzAMV2A3peNMV3sMthfZuGeLVB5EaqMxVEJTizepRC1U563V47+twT4qZYE3BFVaeayAeXBqc/e9RObaFyHAlYRwSnYoklXavWIgpNsjbK2zPTwIKOqXB9nalZBlDUBR686hGpeJo03K3uHmtUioYyhkomNayRhbnVk2e/vQ3f1IOU5nwDi1BIGN8haQ5xV3Dex3QLGf8++dussDpcIN0c/L7lfXckV4iyh+c7YQEw3QqxxUCxbBAb0zcrjrfbbYl8qL+5WZjbKNvjjvS/XF4Z7t0z//M/jw3s8CFpDoYFt7n7UdaJlf6//etbmf/SgYmALm3forPx00o0nKvQtdJltVVEGbURo6CtZ7NkmNL5WjjVe+EHkYsCsOlk+5BkFGGF5huLPJvuU/opatIqUhwI32VQZb5wEIdILIbMETYQE+qENlP1QPgrQhUxZA/k70BR7IPsnvtr6dTT3MYXGSjqzycPg5puU1jgYnAE0oEi7VD9CmTigSJsMFGrKJ0c9LOqrGZze/mSfW/jxLyRQb6QgHq2fykZuK5+lRYVOPIgO45cASh9eeoi6a3NP4KhPREgjBsNwAJIPBefZHF9BC8t2Dv8Kpzh280r6X4WCV4UqFSP9tVDZIa+3bOR3NEdZXOBD+JqFoIqTirvWXofsG9I4CFehKAzyhJeihWePbgxN0xR3Uriqm40kqI6MEMgCl0RralTaLt5zkqyNXUOIiUgTMZYPoR8+TDMywJmdRIgCVeFyyAzMBrW145gkYmYa9R8k9E7GA4wCmp4slib8AtLdEzecw09x+6QYoOJAsoRXsuWqYsl4Uc3b/bi7NjIED+swLEsgU7xn4DNPBvC1zSJGHIdPGU76Xmhh4NJVQEDVshJAWLDSH+cVfeozPf/FWNjeqpJmhQ3fVXuZf7P5/MBddNymXtbiJ5Ky98TEbREtBdnCc5NgQj9Vfg3dptrdrnD77NdCCC1d3c+ddbzK9d5U0yHI60NGaUBSJzLTW1JtdRhsJY7c10DJFaIjaXYQsBDZCUYS/ZHZNlAiFRocZSJifbzNTjxEXtxjKthgfj1ipV6zmX0PaZWhgpZjPt/B4W4O9P1gF2Ohc8tGYMS1KwiqEkgysC/a8EdIcVagQVndnpGFhF456gooSpLUAFURsqX202gfRXQVJRna1Djoj7U3AlSZpOkhqJZuh0ZeZmXyCQHaRwnf4cNJYQJY2WNk+Rmv1PtLuOAhJGIQQC3TFMsyBLjarMFy0Fs4X3uSIlkIbiUdYLlxZOPc5Uhzp9JXUO2MTwKnuVZ4iyPk/+Ne4+u/+uJed3wqILs8QpW/9VhWUyZeX3bcOcs05Z08v5rwA9sLjv/ATKEbfh9p+8Pd1C+RP91vuy/Y+8Pu6CT8Us/hXTkPqnlBNIm69sZsHzh2DJMBFS2ltkoJ1MthMoOc5SGnXBKkYrL3Gs+sjrLx5lfseuZ9wrZq3SgiFjjYY1U9w38V/z+TqqmGSRPH8/NUCL+E4RFNQ2WeAPmyQg0t+bQ+2n6OSLvGuYJUHGp+mp2IG9AbOTpCH1qFBag6HbxRSkYvbttNdCzt859TH+EvTH2M5mWI9HeFw7TTO0cA1V2NAQACMerOTLxafoTXXVUWHe2rPc7z+Qt6eleZ1FqvfRcQyE+p5VPcaarbB2vZDjIafQ2gFYQtG3oTuLCoZZykYYzrZyHMdtAcixPYBJmSEaMwBVfNcKUiV5refqfHi6GXi91wCkVnfDZCtTUYuzjN0ZI57vnSRNyce5tj8U0SqV6ig3NRoYOk6/Pq/hO/8e3DgbuMX0JPQw9S1SV0EiP2B1kAC2jroOnu0Wzz5A3ROUuYWNvL5mJvf5NMPTbIUDZNZsHfamlsbLzMmlowzREfZjWYXZKqhkxacgdAIZRKNXTq3i9/4veN85aXDXLowBsrMXEX0+MHJf8tHRn6d8WDF8ie+aQzYMwIDMZ0kYkveyqR4EX1u2bTpkT2k33s3xAGplqxujDA+usY9954hSAR33n4DEZnaBdVMkQqTMmhbGmash6CHpqkF49UYAkh0wHU5yd7WdWOnd/5cgbDEx9svmbeHrG1qMxrl9dp9XI/b3L9yhtmsTSh6HFl5lvWB/SgCYpapVy5TJabHADuTin1jj7c12MtUI1NNaKt11FspE6td4p6i0TShJCujMVpGbIsqC3OKw69VOfpCTKVtZS+h6QxIdOgkG4VQAqklV0cGef6Dt7JZ249AIjKRpw91DiA6HUJimX+gtXE313tzBI3TLL7yJnc9cJ1qQ7KlY1rbB0nnHyNLqqTZgOmDxIS0QIkOG0KnQSRAgsnWlaKRJvOdVPmG1giEDNBKorKC+fc5e3f7XLPhgbE5X6TvlZ5joQ9xZaB3AOegzZe5d6qyHQgWkn35CsebFPEJhRRfmF8cjBc+8S6XX5HVoD/O3z3fjZXOf+8/m762l9mZnZEE/Z+teTl/ipPoC8NS8QypBfdfOszxiwdJtcgLsWUW6JupAXrna1V4tVvmRgTcWDnPOwYlJ2/bRzMQDEnTyuHqaQ5cf4JdS39Io7tocpJDQaz8sZfDUDkM4bRR1YdV48knrGNYXmfeErztE9B5HcfWVURCRbTI15iTKt2kldKXigL0czsECKERQjAVLDLFIqV1IbwX50vrjbm3eMvTvGNuzRcTyR8zkX0FVNeck9DuDbN0yxSL2/dyS/urBuGCbaheojU8yI2lKtNcolMZpTk9QGd8hLnLFUQ0BTrGxaN1M82nXhP85vMB1Xcn7BdGegwTxUAkmR2LUE83eeS132HulTf5Z+/9D+xdfZXvf+mfMbr+UjFG3hon6cETvwHfPgvDcwbwU7c4bQxlLv2muTnAjJXH1BabuFABucO+j5KEoGOS8vgJ5IbCNQ5XTrK8Ps0sFzxnGPvsnjLOEXk7YKtV5eMfe5Rf+61HWN+oFky+gP3xeX5g/Bf4tuFPEuqsIFSuTYMx3L0L3rEXahGhDhjIQqjcS3Jhk62Z3Qwd6hHEkgBFRWvCepOa1pAIZFfQEE3LFIti+WHyASINrThPwF4UlUiTZSFfO/8ohw69AqRF56Uo4qHB34AeEwVaSM5Hx1kJ5qjughNzBznbfTeTaxfYCHscCp9jWU8Qi8sm7p8aMZLBjf+VVOdPfTzw8jIDA1VGNqztTWkCZ8Oyqpa5610IBNnWNJuMMrQSEKQFKy208WA2RMgwCE5aONjeIvziFZ575zQb1YYJXVKY0CbbhjwBlHQ+NJIkmSZZn6Y+8xCLayuIzTatbh3ZHkH2qoZOOYC3dFV7NJHQ/BkY6GKDS3H+4poUKSLyuuZW6S6lyPd5kf8hy4HS0MVyfjojb6UW6E3ooG9pdx7oRZ138IFe3QS4i8NnKfqt8+4oZ0TY6YhXMACuXb6fvGlxUdgGCroBDqZvJs2X293fA2cOujnjUKaWuvSN+77fm8E+R8Oxhd3cfnUvmRa5RtQBfUcb4c03WTov8/wuEoKhP8/qQMpMFEA7RldTptef5l2v/99UuyvskH7zGwgIhkwqvngPYNP1SmkynAWBeZWykJqFgKwF7ROUXOdzfLKbQOIxCrJ8Tf6ny4JmLunrotN53/tRSRTPu9n3ojynxXnvuaJbIuCVbJPN5CrPH/puwpdaHApfMeMdZTSar3F79U3Qks3GKmMrDUY7H6AzVKFX7aEJSYIOl+cWeeJX9/DMyzV6Ggb3bIHSPHhugzsvt/nqQ/uQMubI3VtM/c5Z2gSkMubU9Lv55Xv/Cd/9/P/BrrXXcQ4VQkjy/MVLl+GT/wY+9L9DY9ZytcIALJCjs3KpEvvWpz/eN+OG7LnNxgCb1YYpgqRhZG0LcSXmzru/xqkvPMKhsZPMHj7vcZ1Owi/PRaokP//xb+VTzzxAlhWV+ySwL77Az8/9bY5U3kC4nPL+3D6wBx7aB4MVXM7lUCjCqAcC4mOjjE1W0WEdrTWaBEFKI05BBVjFJ3msu1tMMpeezBmhOawzk7sgUAT1hHfc9SSy2caocGT+/FIiHedPgCjdf0lNc03uy+mTEJJudYyrs+Nk9NAk1MQKRbxVRkCHsfX1m8zHN+54W4P9+FqHoa794NacxDpQqNyIq5KIlqwzPB8jtYvp8Q8ngbgk4Mb2JDTsvXSN6fllzu7fxeuHb2GtPgIVYQqDJAb4wa6xPqEkqsZ0urOIzIRCuZjVPBNoQJEXOvCaEWgQGZouJmmOZUJy/3bnLe4A3y4yMqT0A+ZMX1wiHpcF26kWDbgWQC891zbnfuf+uSe4RjqIMzB8s+C7snTvu97ljFIf0JffezjlwakvL6d5S4u6BeXDd5D8eq++2FNus/Nw2GkMEKVP7j7FmPS3o3iG1AH3XDyETCISjLkz1WblJdr8KSxe+ryCKOgOAsKwAqkJYxMSsorm2NKvUess9+Gd18dwAgbuNSp76ULgLGfoMo5EgY0bdt1UsPU8NN+AdL2Qoh2IgvVYtkTQca3OKzm/vg+E/dg2oBxM33/oQqpyTEteao/ic855U55WXDu8NgurjapUmT04w8FshVOLh9jeN8HU1mmmB5cIAkFkQW2quw5iA5q/Tac6x4v3fButgTopLTJavNmbJQsFI9UtvqPyHEMnVnn09DrtaoPR9jjd3ijNWcXW45JXnzxCIisEQnN28gH+w7t+me8//5PEB+5i8o9/hbi7VkgQWsHKZbLP/AK9b/rHVGWGSDRF8RZfrWH77+z/X58Hp7BzmrFfHBmjFVXINiFcBS4NEK5AdXfC8Mh5ttMYum5d6EKLoLznCDhzbY4nnj9OlgaWN9NMhov8P6f+b+6ovsKu6IoBen9uEXD/XnjkENSt+76Q2IxJZhyEBlFDdKugpHUkjQ2tzlJjgkHZZZKAVHQ6Q0RxShCmiECjVGCohXSZTLWhwZnJoyJ6WeH4IzDq2hzYhSlIkIhiXdkhzBoVU4uB1NIwQ9gFVTQhGS0K9/Aehn4678q37nhbgz3dzLgo55y+XUTWa0m3FRvBKCfnHuDcxL3sH15huLuFON/iaLxGpZeQ22Kd7VJYpxkUCANG1U6LO944zaEL5zh59DZeuOVuVBSYiJhesW/6WpELd6XzlqEVxi3UfDYxfqYJtlwzIgPRQ+dg746dAJUr24XTADhVuMoB3Ni7i3A38z4lIKWoYV/4sBtQzcjy7H/gKns7TwRtO1l2SCuNgPfp63ndl0Ps3Lc7q72XgT73D/JGonxofGV8vwS+czzLh0vnqzF137PEhM2ISgpxClL3/dLvu5uf4kmBDRSdWhmkk0bG1yKzadEx2tCuNoJSSunG+VunUS9OmC5KIMxC3jz0lxnfepV6p2Oe7SQ9J+0EEkRoarrngev2ho4WuVJ6qgvJGrRegdYFEw7lm1WdOj6wjIJrWN5uj6EuSXD9Y4UH3DdhknKGx3vff5/cmUGVpTHXb/8zoKVkKxvhUu8Y2x+8j7XGLcyfHeRLJ47RORFSybo8tOsFvuf4xxmubRtgUXbCxCaja+vc9/QmX33Xd9IbqIAOCATcevA837P7E9yrnkNdiugcHOdrkw/xhYFvp8UY+vI6f2nyK8w8Nsn72pe5M7jOpXSUO/atU3/XDzJa7cC9/yd86Qvwld8H7fazRqycZ/P5zxLd/yihr2f3+5ePN+Vl7aejdYKFW6PKzNnpYJTgcpfGySpyEwItaFQ3ODD4OncOrNNLqtCz6Ril21t2DdhXrTVPvX4L250qCBgMNvnIyK/xncMfZ86B/I60cRJuuQsenoCaY0AFZFWT/lFL20YJoQSdIGLPVwGMDd0m8NlMB4nGV4jjhCDcJlWCteYgE6PLnL9yN1oEHNr9qnFKkIY5FQjDcbct+KZ2TP32KmmS5CjLyNqx78kq5weP26yOvi9UBcEAAQMYb6g2zjU6F95a/wvs//TH9Q7UzEJOgohepUan0QANywNzLE7OcmbXcTpRA6EFJ4/sMmvzcMarWY+9iyuEUjO+vsnsyiqDLceBQQEjbuJTar2Me0+8wtDWJl+7/V42q0NGte/4Dd0n+PTRrpLjnCsGbiP18uRTuYqxKDvpku4Uh79hFKYoTskVELP0lPfnEtIIu99VDvTuGmdfdmV7szwLoGt0wSYU1MQp2W8GmoUavx/Uv54U7z9J2IHU+f8FwPvJiG8u0fsqzf7YiBIlpP/wjQ/XPz1H8/kJNq5WYKVGvGebYLpFOLfN7r92xkZE+JS0aI9jgCSSgxxlmHEOXj1Fr9dkTdTysFxXgDijoC/aMxXlGOqDvNdsAVToEsqDrE3dQv3s8zjtVMkHs3MFZArT326lF4d+Heiug7xuHLA6PWjdgHANIksUkWWJ0TGruR3eB+bcM9RrqP0y/+h5I0tVgEYpBZ/HRbvPOyerAIgdz3SHoRFaSs6Nv4MvX3ycZ64/xvLSKPJVAdqom5NuBBK61Pj8ufdycvF2/sZDv8ydc6eJdEIe045meP0y73jmP7M8PUskE+YOPcNtAy8wHGyQTg6zdd9+knqFWNfZTibQWiD2jcH5o9w2fpa7xQooyS3Jlpn8niUAAxPw2HfA8BR8+ZOwvmSGSKcMXP80C8t3s2tkoBhvYb1FRLmv5Q1lQdltLgdYkQZSLm7vpntqmnoaGy98zI45vPtFBsItRCapxF1vzqxjUD4xRr/2sc8/xq9/4VFi2eWB+jP81dF/z0P1ryC1vgnIYyTnfZPwwV1Qt6DuGtmLLIPl9U0ryHqGwXTaJ9ek0Jg9BuNlMtkDLQhjk+a8WjHM7+G9LznWyQBtJaYIZVQ2Q5/tVwB5dADCMBSpt7ZM+ABb0Rib0aQ1gbr2u5RaIZoQyRiwCTQxmU5MTlZ6NxdTvlHH2xrstdKkWjI/s5dTt97P9ZkDbNVGjENqJiARiEQYYV1Z+gcQB2zpGq/v3W00mHs11Sxhcm2Tka1tBttbTK2vMLG1atRDeVWlDIngyKVLTK2s8NKxOzi1+yiZEsYvxhJpiV0LwmO8fandLVDnnGwFLmIKWz6Z5QBdVLtPNN1GyzBqIWM/NDYhYxcwtzcbUeCC5ASSjJDMgr3vu67yZ2WkZLZQT+FV7oDe3Kmgwb5C/2Zychne+/0F+sE9hwBtnyu8vY7OR8VB+c5D5eOGd41/rftGlO7seuXS/cDG64Oc/fcH6S5UC5y5MQZ6jKCeMvLgEoN3rNteUnoFkAQMMsQEk4wyjkRy/e77qb5ZRc6bzqqUUjntfqmsdF/Rd0JrqqrFWGeBowsvUO0sIrPEaLwCVS4whjBgpVYh+UNDaBwQqCZk64XqPdDWb0QYhz0sWGSQ17kNtNUU2HvvGGjL7ebLSxRMQH8/lKeK9uuG+yCfp+fzJ1L03csBoDmvqxV0fZBUhvS2awRTCUuTd3H6leNstoZRKkJ3YH/tPAPRFityit7QAJvLg2gNG+kwH1v7UR5vvMaxga8ww2kGxJpZO0IxsnmVkdZlkJp9kwYstBZ0b92FalTNenJhWrad+0cWiRJFHqPuMtApy/UJk6znc3u+j3Pv/B4e/9o/4sjKsyA09e0Val/5aVrv+8fU6xVv3Pt2nS80+ObBfIwUDDVhbp313hCLl7vsD04wMrRMc3uYMOihUzi671mEVNCxUq1rb74gc289Li9O8LlX72XP+DL/29S/4gHx+0SkNwd5MAmD3n0H3LPfrCOXKMKEItnnSKO5tVrWginEqvftvb0+Cp0Sbmp0JFB1w0AZfqEIeQagESGUHXctoWnzP1htWb6uXdGdPJ+BpXoCsiDk0uCdFHpANzCOG88QxJisrCmQIFFoQgQSBt5a+H1bg/1X3v0tdPYcZH56H5mQaOsl6XIgEwARTiOObGO9n7ybWLVfL4q5Pj3B9alxdAZRN+XOS2eY3FxloNNkuLVpHGcseI5st3j3y1+DFF6eO4rWwvg4eUKvS1WKY1AtkglH92w7jUMQxlNTgrEn9vBT1xSOX27zOMgDk6rXqI6w6X59kajgNzUhRVrInU5vBuqVzeXfbwXvp+fF3Yuz/tYW3junmJdf5xoBRKkgTgXDzYgD1xo0mgHP3bnK9kBKUWWgn8nwp9NIKuQ+BOKm7S73p78H1iiiJdf/aIrOfLV8vcXDrBVy/RMHOPZPXioBsxnngBGGmWOOBgM4FkYDmejS3SuIxiOCBYleFLkW2uK3cQAV5LS0TC41oUwY3l5ionmVPaunqPW2IO6gl/8IceO6uSy20psQfR7UPeCylYYockAFovhszUqmJqsolp2gYHzzcDoK4HLsnsKAXCYLe6eAUkysPyM78KDvGp8J6pfuBcaMUG3keekN8xBCL0QEMRJBmEI83+ah67/NbdOfpTU5RK9tarVPVhapBh0202HSWkxrzjjRhpWMP5j7BzybPMiz8w+yL36Dxxq/xb74dSLRpXAEM21sqQGa7ePEdrW2dY3nsnfm7VQBZN0OvcFFouYoSsQoHSEVXBCDLG4P0FYBS2mV/7p2gHQs4Pi772PfkIR6jHz1MvL1bZqXT1O/7ZiJntCgI7to8trCFPTPr00sLOdVuYK4xdCOYdnhgbkFQCO0RmcKl/JQuPi7TFqwc2vBPs8JCdttrn2uyQfiT/NNw59kcvWsnT+xc64BqhE8fDvcfaiQW/zLnFpLZHYzZIV5Cdsn10fH0ASG+OYWm0QjNhVIga5KdCRzaVwo2xf/dTspnu9MQa5djvAo0+9URCxU93Ghdifb0bQX0uqLKxroIAgt4FeQVDFiUwZI5seO81Yeb2uwP3X4AapDw4YgWoYzh0Nv3LWVcLSNuPFNuXklLQfEmKQjiY544fBthFlKo9PmzstnOLB0lUrSzW8QZppHTrzGZq/G6X17CS1tzRUBlvHIzaKiWAbOA1+A8bwPzPdSdNEiQZGgKYrp7IzULuRR0x6T7tdIq1F+hbneZAl0KYgCXP53X9WtLMg7ib7s2VvcqYgiL7enHzLdOfNX5Osr/06gGWpGzC7XeejEFFNrEWGmqHYlm/WU525ftdJ8we7sZEDcpBbaENdWdjzR/2UB74VUb870liLmPzdZaGLcfHr81sDGAGObAevDRdRAgyrHOEpMjDNvlOLuBfTqTbJqRG15EJzbiSvA0i/12udGqstU5TIj168yHl6jcX3NhtXZi9ZXEctLhuil2gRx+P11i9KpKQvOxDjkV7V5DWWxiF3H3WIucR4eMdSYuO80MMBQEjT7WS3LBftt8O/vrnMSvvLfm1cdBnQH6lRqg4iVTZARpCH96WJFT0HWJJTW18puxuFgk+Fgy/TVUxGNRGuGqNfNfbQAmSYgTPrdC+1budL6P3hs6Hd5ePBTVGQHFGx0B3nh1se4HN/O419eY/T3rpB8aIO10XGut3ZDQ6CkJpMJrV1n6YZXUCMBm70pPnfq/bSag3xNzZDogExLo+2JYbjSZPbxMdL6nUYH98gx5OU1hr7wJPriFfQDD6Fmt9DD2+h6y46ut3huMgcCQZCGIKugJaIv1CP/LJyjsj81GqPCV8bmvbEJb16Dly/y8EaThzXQ6nukO+IQDs7BzCjsm4HxwaKYjH/kTpZebI3o+17qQguFnVeljeTvcd5CA5lGtBQ6FKbAUBh468lKXe3MZAD0tR/OQUYLm6lQk+iQhcpuLg7cyVY0DcK5Oms78sWeMenXNUalH1KUzy4yrC43CkHirTje1mAvegKZFICa2zuhJG1pMBK05QZzvyUf6ANMBS37AykxDlRByGY0yDO338Mb7UPceukcB25cJUoTBCYz1VxznTPZXnTgmZ2gFPGRE3Htvbd/rtZFpddjcuMFtupbbE/enkv/TuY2S6pfNi48zjU9e85U7SuAMMud82SerrcYm+L+hURf3N2wAb5uwfLH+RK/mYztu9eVEvNqiBPB3EJMpAKOn5ti33yDwVZIoDROX5bKlK/ctczaYLf0TEpPKhiVogaA03b4KXgc+vgo1M8yFCyL1tC5VqUxINl/b5ej7+8xfyrgxskQlWnGDyT0mvCe/22FyXCY60RcZRWlNTN6mgoxQgi0KBwX8ydoTW1zi1TNEFwDaUtlZzbNuFsrGhBCE6iEaX2BAwtfZWh9HqEy6+jkjOYYSWzlpKUh0uxql+SkBLyiYIIDINYwhAH5QBiVfQ70eYPNnwux0pBXNVGyAHm38EsAo7172Pv5xFp61ziQVt77PBcv9vkSgggGRuiMDlFZ2ARRo6Re3jG1PvdkG2Eds4r29iNTsSRGVBshC6Yw0RU+v/4R3mjeQ2NonSBRXNg9TWd8ir/8x19j9+oKUGHk1yb4yt57STOJPgzqVkVtcJVa2DaPDxIGqle58/Dn+eUT30dLhQhpQoFFpBmrbfDY7qcZry3m614HgvTAGN2//CC1Tz2PmAW9f9Rbu7rU+LJLrGVIdWBV1Vkxl56QbjQvLipJm3ntKs9fIaMlQuLnXid85qTJmucYAY9X8ygvzYE9yA+9g9qe2C5ZbRiGPHLiJuPvz0keNun9eXOUP9+p4fPnF9+LVKNVCpGEKCS3xysJqdXRC2lNVKIsmaWaTTnC68P3s1zdAyLAlbry6UaxsVyF0BRNF1d6DUxFTUdJVcnx+ht/vL3B3q0ZKEnQeVlDCrpFCLpql5ANXRcSAmsnlyHI2AC+s3XqDFMMIgWtBOu1YZ4Zv4c3tg9z9NJFGu0W04sLNAdaRF3QMbnjnR837yKb8qXhtc0xG3HS48DXXuL+c6+hpOTa7hanj85yaXyc1lhGEpiVrEtLDArYM2BdQHJsr3ILz/mJ+p7vBiid330RTV+wAO7VsQOFdb8A+ps76LlNYKV6BRNrAe94ucHhS3UmN6oQVyGMbGtSO9jm7lenuzx/22pRD8P+XyYLGnInRuVd5w7nXOhJO55ap5/EuL0tgcemhrnz33ZpjBs14qFHXcXDzHLtxvmnKyaYIGWgF3DyVMabX93H2V6Fg4+0mLnbhnAiEFoxevkGaTLJ2Ok2G9UqslPMQj5tFuCraou9yy8zvnWBgd4KJjKEwqaYz5Qm6F1haapKZeT9yMXX6SpNfW0VNKyNTDA7fxERCusYqs1rVUMdq7K3C9ENQi7hWALvErFk0vrCBKbkqmM8v57WwL2XVuqS2vgS+JXCPFu8RqDjCJ1omBpDtNrI7ZaV2mII64BEJJqR+U12qPTzcfQZD1FMdGnTOUJhAc0HSm26uTF7hPd0LnNfu8Uz4ignxB60FGgdcLV3C2JJc0Su8lcvnSN88ypTvRXbbUmYhCTzFXRDIF6G4Iwk3lOFBwNE1UaziIx9g1f4e/f+Ih9787u4sLmXTEccHr7A9x/9z4xW1k1aXWuAzyNRKgFb3/kgEkVs/YgKp9Iya+z2X6BtLg7t5s3OrbMGplgHNY2tcWy53sDkvHdgv7WN/OIriEs38vS4xbh779EQRXDHAer3HoPxirfDKL/Xogz4fgilu6GAonqTLn1VAn/l1iHFOnQfFZDYzsoAtAV9gVH9CAv0Lj2uJRcJES+PPMKGVdnf3NVY5Q8TFkBcTD10ManMdN5MDfTWXaWht+Z4W4O9ygPXy0KI6ANasPs7Bmoa1QbdMTKytCp0EZrvc7t5ziFqs+5TYapBZYLN6hAvTN6FTBWVZpf1ZY3skacPl5HJTZInIXMCE96f+2z3cqXZ4a7rJxEogkyz9/Iye65u0qqNcH1fyMrwKMujMed3B/TissubzoHOD0pL804UZMBfnGWJ3K9h5wN9f2W7/kJD7ijYB50/U1giM7ka8dBLde5+vU6jE0AYGkIQWMlUp5hJMTVZOxX4yl2r9MKbZ9sr2mycXsohdq6vlPrnA/5/Q44gE7C5q0Yj74sjPiaFTylSA01rJeKVT+xj7Vq9cDZ7ps7kLdvEgc3qdS1g4tQwLTlOV84Qtwthyg1YrHpMbp7l8I0vE9IhVu2CELlc3fljM5o6QYkNhisdhmqzSJEg9h0iiyq8Mr2PoaUFzk7fyjd/9ucJScxud35Nzi7v7JMO5H3eSBnpJw1qBJ0Oohd45UwdF+st7Dy23oGG+1MQWrsr5FKgFgIVRQSJYn3uCAPdNbKDB9hazRjfvAa1OkQVa6GRNmwh54rKk6btIGq/IxSaAqmcncxjTDCfXQU5QtADoFLWRybJtg4SDG4y3VN8S7JJI+jxgjhIZutUjGctvrVzkrFuB8ImeVIuBEpLujLMx1W1BWtnh+jeVkFXvT0oNGOVdX7g1o9xeXM/r6zexqOzX2aksoYSIt91bv/mxjUBqZUSDVvvR7643a0oajHaBDfaXikoANVKr+Yxdp5cgZuusOREwNmr8NU3qa4uFJJ8Pp/lfdqu1wjedw/x7fuMaUD5k+Utej8vArZNviMe3vmbagBcG9zcC7MHc0Kr8zlwjJzRejmPbZt/OLS/kcK8d/tEwEK8h81oYgfQF1pWJyI43xz33u3uoiKqn7tDVDa+fn++AcfbGuzZfpP42m307moY+ck5XNgjZ+IBhCaQGhFrdBVIZW6WEhjnOuHMKe4WljgJCSLURli28VGGS5R0ZY04MiHJKOMzE1QM80BILjDl0TGaknDhUoanKmZlapLZy1etSdN4yjeSlCMXYo4EPbJQsDDRZn1AsjIScerAKp3qBCoIrErI5ZRzKWdcJTYJWuSyv0QghKk65deuK9viC11B8VocBV/uA3Hh4S614OFXpzhwpcKh81BvYTZ9EEBogV5rA/C6C6oLOkMLyctH2pze36JfLVk8RWPjleyEFFc5MqdLZ/vT3fa3u/x7AWSEtj8O2P0KADqfy/ZawEv/eRdrV2sIVywDjZSXGVqYYOBqg3gzQCroRsOFUCvAxT9X0m3m1l9jtHmZkfY1pLD2wzAoc4gIVsJB5rcEB2oXGAw2zeMqVSqttiFiWcZwts1DizfQEo4tvoFslDNDltSipQGwC9JlCsskKEEyPEuQLJk5QhQeynbtGu2lLsC+ZJNXlELrgpC0MQBhjEgSsmqNYLvFUGcDkSnCN89S6Qfx3JnKDpyp6+x978577fdfHZF3HrOu7ToszjMIzEFWgTRjfBmQ86jtBQSjVOV+vlm9xMFgma/JQxzIlrind5VBrB2GzGoIQlCKxXCIE7U9gKZSyaj02qyKBoEICAnQOZ1SKKEZiTqMjp3mrrE3EaKXW3ydLOgiYvr3XIaggyLrVahdWyUalujRBohyXQuFqfwZSBMPD8KYJ4RApNJkc0Jh7KJWsk81LLVgWcHJK3D+NPR6lDQqpfG3ezOI2Z6apnJ4mji2E5cGhRRdSoCkPRu8KtZM6d43WaclkmBVqW5dlha2Ywp1cTp/VfkeJHL3FEVonzA8wUY0hhZOEW/YJ5lHPTna4mbKAL22DIDwcqublVgwerc1t27SuW/c8bYG+/E4oHpkibSaoamgVYTJrmRC4YQHtpbfRogMGWpDnJCgQ4RzKpIFw7+DEFrCJgIs1ytyDUIoYDBwgpA2QC9EkTjHu1/hCEhJ65A2Krz8rodZGjrL7a+dsHmjwVJ9ICRQIXPLMLei0ZcyptfqfOXeNbYb0wiriBe5k5qzlAfIbkR1cYDG9VFkGtHb0yLd07bPN5vcAb5ZkLp01gXu+YNRlpy9QdKaahJwx7lR3vu1WYbW25C0ig476QoL9KoHqoMr4HFtJuXZO7fQQvdNgXuW01rkhV37rvp6Xvr9k+n3aSfwF79THuB732hF90aVl35nkrWrNQf/ZpmEiulWyOiJAaQKCnDvO8abp9m79CLVdIuK3jbMQiQKjYdToztQjZYYG9zN2PirVkIdsVKZguV1aLXxU9eKAAKhipjkvB2iaE8ukdk/Jxk771EhqC1dM8swEiY229l2nQDjq+e1lVziCrJWgV7bUMywglOhBQCJRqiQoJkAITJx6134ShOjZtGiqEnhbLO+Pcnn4xzB1m6d3WyOBahh0LF1lNCoYBKR1cg1SVqDksg0BbkEqk0gD3FbFnAbC5bOWNWtyAqfAm120bXKOJFu8Wjlt3nnwnUWo2E+HU0TS02kBUKZglZKCjKtvRlQoA2UpEJbd9mi/SaNc5nxzdAkiWbod16kceUq3W+5h/bjtxtPdBQpCUIII+FrYTPBCqQQhEIS9CSioyBKDOOdKtjK4Pk34OWz0LT15Uv+FEULXNs00J6d5dL7P8Qt+9uI2GrBtDZx8PlU2Ily84UH/A7o3QP8whD+49xaQYKOyCV7/xklc5Fds+6Ds+87Xw9p++Zs9jbOPpExi/GcB/SSgAjjdFe49RZ+OX458EL8cOvOnLU1RXWdt/J4W4P95kRMMhSgaRtYCnoQBGibkk4hLBNvvNHbdJH0qOTeehmhiEiFpLsVEqQhsgYIiEKNkGZD7KDUwv48LGhnIG2W2wiIFb1Uk+qMSFZKQodhMigtXoFGSM326CBvPngHI+1Ndl24SqhyalosZquqEgKOXqlxddcobxwAISTl8r4KoUOqS0MMvTlLdWUQQmFCWN+oko3cgKEUYT3wnfeo40/d4vYpaRlGffAz19W6AbedH+CRl6eZWx5EKgVpx4C6s3Fo68nr7Oyqh8nrrVESTu9vszySlJ5cHIoiBU25SE8hc7vjZiJB8atiRsv+BsX/GpeMqOSyrQXttZCrzw1z9bkx2q2iFjuWVtxzzzJ31achCVBCE6ZtZNZlaPUk1e0rBsMQ6NoFhttVRKSNFC8p1MwS0Am0XgeVwCgkh6bobZymsd6GZps85G19GzpdvA6QN1tqj7myF/RnXPO9j3MPfHuNl+kxl8akR7j71oKWms7QJOnwBIPbyxAN2kt0PozCpQl0FpFU7lxSynvVFKp2ZzvOX5UHGHYj5to5Ubxqadedfa9d0hbz1xraptquEDrHrcwxNY4z3wZ1AsEtIPdQkm4VwBYmcUoIdLm7eYrD259hKF1HKM0uNLsChfjyUcQ360KIDYWdcju2do4CIUy6A2G8UhLK7qdlMBHEtR7pQ7OIE29S/cQzvKmnGH1sD43YFCoyRq/UCCHev2oWElw5gB48R7c5zmZzkbFnzxBeeg22m2a/9pee9SdKa9qygYpjro/eTeP7pjgy4rRywluTuo+/9pk6/76SPCFUDtAe0LvDqFsxMcuOPnrPcuDt83ruu9y8bteMn5taYpljQUbAqcrtNOWQ5bclASGSEIHzNRK5VtWHdR8zCh1Nsb80mmu7Rngrj7c12JcpQmaHU1nuMCbshXTChGwzIhrZpEWAJmKLHoIArTs0Mslqdp1MzlBTQ2a/WiElCDRB3CNu9IhlSCWIEDnl8yQuG98v7MKVSIJaixUWmFBzSGLDjXu0FJtVqtAsmkQdVAXPv/8BLtzYx31fOs1gywY+a0cQnT3ALPAHXsu4PhWyMWDUS2aTKGQWMnx6FwPnpwmyyDC/1h8FJRBKW6A3kqvIc+M7haGRBCAgsbn0ynZ5f8PDyHbE93xmin036gS6AmTQ27SqX9dvi0DKJjvQJq+1KdYOCxMpTx/fpmybE/nvtAf0RTs8muF99qG7fDhenB1XFNZO2GadBtNlooVm62rMS78yQWurSEnkplYImN1zkcc6pxm5ep3qxhoIqG1fpdJeIOptIJUhuCggHIT6uAHkiTmoVqFeMZK91JC2YftFk6q2FRK27iDYuGYco7J+Ccs/+lglXxIS3h94nykzAb4tNSeMUHjE2ofr4kmteIpzh29nfGgPs8991fRPW2ZuQ8OQBeYUI1E7QPWltv6p8kPwFAXgK+sh7q9F0fdX6n+RLKngzoqfDqx3adZABgKpBNppFDKRm+CMDH2G7tAE1fW6bXcX1CWEupQzTAIIlWbY1Z3GPL82PEXEZUQ2gJMAhHUULqTQgt02bL7IGYHUdsb5zfgaNy2h88ABNjspQ7/+NLf+xh9wtfkB+PZdVtHhEM5VrlQILVAXJWy/zsbZZdrXlpk89yJBt1uMO978YPeYEDA5yIaaonvXLaTDg/SmJxka00zHy5bJFGV/DnThy+GPv7t/SaAPDaMrVN+acGvV2Ug9J7u8rfYZyrbTB3zApQfOmUOfiRU6zwqZiYArwW6uBvsJhLRAb8yi5LHzIW5Bu9qbMS000GEgX282wwYuXa5bTVHnfyXV+VMfUmuEzux2yGyMqESjaK+nrIllUqCejhFTy8FKEKG1BN2gmUgqybDZ3FDSUqlUkKYVus0Kqewx3NBUo4wsSKjHEYGwwxcY4Na5sCqIVJ3d0X5EYBiRbqtFEkdIaRgGE89vJWhp+Egn7apQsLx7gl59EVoJRao9YR3ApHXvDxloBhy4KjhxWJMEAqkCKgujDJ7dTWV1EIRAeb4IGtBxio56OBt/EcvvyIlpV4hCEBIg6CJJCk6ltAeHtwO+7w/G2HejYsZYaEi2jfq+H2By4p2ZeLPUjF0WaJ453qIbZ/YJjnDuBHrhtaBfk+sO7f2P7VX/IfKe7kSIbbaYZso7b9rZXg3pbIQl5zohnRCiefj0k9zV/QSOFJdBUxRSthSQbsHalrnR0iWoVKFSgbFJ9MwcautpAscsqTpitWfD7oom9XXIPgPrZCSgGkJvEHpbhaTqnP18r9Fco+C1V/c9qDREBROkdJV1eStjpxe44/VzCM4ZpjvH4hCNQK1Cb2yAWnfZSl7u3n0d8R/rAD+X5Cm/lpgaXWgzEKX2aiG4HB5hnzrT9xydg1kvUoioTn1zjbSa0a4PIZVGqpQbu4ZZHa/zxi1zdMYrzFwQDK/D8sg29764yb7zpjQ2wjIxJa2DOSrb27TvHqKaZgXT7xaRXRc6Z4rt+Ahja48xPzEKEelho8gFVV0JaT16hPDqCvUnT3PgD/+IzvW9NP/qo4jBqr2r8XfQpIi1LSq//FnE0jWGJQzDDi26GxstJb25afTeKVb33MHYAclwaHIQmHW+5I23MJvCeSYXNzN/st8sJgp/thy4Pb+MXCWkzXkZkxcTUbLEjBScYT7B5bn213veBEuz7L7UwDITnApuIxCSEFdXRBqQ1zVM7vvAzkACosUMFxjhOhrBNgMk1GhRpcaKWV9UaTFhl7ZisxvzVh5va7Bvbu6hxxBSpgipcqIbRhkpMdV4DsKMUEojyQoPpLTMQ4lcWKn2qkT2SwBhVqG1aXJGKAndClRiRRhIKsKYnYNAk+kM3REEoUQniiCGSASIKGIxu4LUMTPpKFQHzeKRhlc3oVVOTZ0xvNpheFVbztVRBlG0XUdgkzU89DKQhZwYGWb4/EGiNZO1Lfc/sjSoncBGC+L75wnrLcwydkBfJKH1oS8kJbTKq21Ezg4ARIng8OWQx782wL75iFz8yrpGfZ+joNuMdvO5/ZiSa0UWplJeP9K2EojM26G9MSkgvIBy7X3eeYjSq87f51H/GMgXO65tsskSS0wxia8HaC9GRfi3zm+BAAZY4/7oi4g4gS1R9DvnFzQl6RlvbLSGdgu6TWhuoBav8xx3cO/kEkhFFE4SqL7f+r93wB1JA/BxaD2LBdQPwdoZSNrFYiichwu7u1v0peHsk5wzgepVubS5hwNyDXoxQSoYT1ZBh2asHLEWmp6MCVXC0p47GO6dJczaThjqe9ZNAN+NSx7DrXe2z38vJYxMwOw+mD8PSTP/SsmAXZWr0BEGHHxHasvIDW8tkYWboNtEWUq0ugRa04tinnnXXVzdN5WvvJU7nYf1ODd2zfLej1/itvUvmHtJVdY8qAAyU3574Mo0jI0UjQ8c46XRNnIhz/+YMyrF8AfeKnbDWMTOCIgD1r/3QbSAxpfPUH3hIhzcRfvxuyE2fhHB6XlqL84TPv0ycn0bImGYyFIiJbMvVRzSvP1OqvfuJ9s1TjVImUNZc6TTJHrzUCSJoH9PFcwdZY97x3A5f6jMCjX5fQshwxBMC/Qu34O/DoS7XpNn4sslOFH2yhf5D8yrNDrNVUY5Hd1iwu2taGaiGSoIPQxUECpAWDksCreZ4DTDLGKpOQNskbFFnSKgOWadAeadyzSrlVneyuNtDfYii0HFpCou9iyaTttMmuwKRE/agnYFV6cFNr5UmHC6hFwqz0tC5w8hF07dGpYqots2vkdCaGia940xaMUdVBoQyypd1SLVCXO9AdbHrtN8fZogkqycvk79aEx1OiaeEGiV0FpfJ6pXqJBCLWVo/gbdSkzYduE8smiMDkBFICOzebVg9+sDXBoYRaWFzTLnVST0xla5fqXGxmqN8bZmlCKJjsmY52LVHXQaVtvJvhEQI+lacKx2Be/5muQ9z0dEWVZ48aqeCdPRzpvRt3fbMc0g96jWil6keeqhbTrVslhRAH2uMM+/c6Vt+/3uy4fH2ZdA/WZAXxJbUSiWWbRg7xgEjVICXzvr6Apo3qn+CzPq82aRRBOQjvm3tO8VRTlYd9ISu0xYYpcRtDd5IDhJsjjFysAorcN3cWTxOaQUENcK8LNpQgvJXHgOedpImUEA1TFQN8gd6/wwPH9++oFehqDr0FLQDaAZI1SNuUyBaBRA7AtwQpv7DySsh6OMqjUmOq8RpL562APtknf3zd57rzmX5Ut7QBTDI++HI7eDiGD5Lnj6d83GBDqVOloMMqCvFffuy9suVYbs2hRwnmkh7vZ48MlXWf7IY7SrruBAsa7a11KWrrRhyCuWo5QN8cqMz0WaEPSAr3RgIoLdVQg0OrCFhiRoC/RZSbw269LHMul9KvZBwdCKOGTzI+8k6t5H/MInqf7+abbnjxE+Lql+6Q3iLz9LsL5tUyILb07M3Kk4ZGtymPndu1i55cNU2cs9t58g6vVMAhrhrTV/CK2t26jQKTQbua+R7YED3dxpzxtPDXmym0zgwj+L9ll/K+Xu1T+Pfdc6J0p30mm3EF71P5Gvq6YY5Gx8B1LENhVOhKSC0AMIRiCrITKB6BgTjKxlTAfnGBHz+TxYl9zcsdJsq5sAy06C9Q093tZgD+W15jg1bb3xtTPvuvN9a9Oa+k2kie/dexNTEVCE0XnYoLVADEBtAnQoqAUDOcDGDKGFplWDWB1i7E7DHOhbjtIRmp5ITWLbbJu11jLD0X4QFSDlzK13sDXc5D2fu0SY+kVb3KHtwg1Aw2ZUQUtZLn4iNDpUbB96k86uy3RW70Yv19g+O8bwratmP5Kh6Fmwd1KD2xxlEBQaKinsv6Z554tw63mMQlElVnUZg6qQhy34XHVO3G0/XOpTBJ9/dIPXbnV5Ns3E6EL0L+GI/21/7v6dR9F+P+TlZn87x9eMTUpGJVfXCYKYIvmYHRO00YC8W/0WUrjcDy0QY+X5cslDhJVQQizI68LGmNoxExDphKgLtd4iWxvLJr5zaBRqLmtcMZwlQHRJcPwhqE6B2kLRQgSqr7d9I6wl9GIurO1nY3uO40Pz0NnK509oqIjUI+Tu+QJV12xNzjG8fRGAKb1IbqeVtm2l9rqxcU1xjIf2gMD/jSru5wa/3oDb7oU7jhvpPgXGJmBiF1w/C0Cjs0XBNPfNyQ6ewmcqzMnDpy7yXb92iUuH5jh7JOLGnFkxB85f5fEvPcfwwJbpW6aNN3uqrJnKShJOiljYht98A/3XboGZ2CQ5clsCUKI/GXXRhsIMaVjP0AJ9Id0LDAEYQFVrLH3/DJW7a4z99icZf/HjcCJCzK8jstTT7Nh1h6Y7Mkz2rncQH7ubzqEZJoMah4IYkbUgPQSxgs550BuF7OEYS0HBBGDXuR3DXIDKnAYBqKpCkkeTOwLm06yN42YqC8DPJS83CLpgts3g9e0J1zDHEPh7RnhOqS5dlmS+cohQmjS2GRFSDyAYR6gBSCqmuFobRAeE1AxVF5gQlwrDgZY09RjregopmwyIawi6thX+pizm86063vZg7zN2bluUfEsg9770zHM5ncg1QY7GuGuV9/s+vPLTJjsGQPulie2zTH4VYe2ItmFC5NpthaQnMrJIEk9PIXqKjsJwlFKysKvO575lPwfPrDNzvc3QRoZw+al9omcXrogohB6h6U7foHXwDMnwGggYOH4GEad0r87QW6sQjTVtdbs0X4gFzLtMUBJty+ZUlOLDn1nn4OkKImogqhH0EnLVm/aiB0qcqyg4cddmO0lnDnZ58a6mR3ONPbFgPsq7N5/j/yZbXEg6fzLg83W3XEqPFk2qjNrrNIsnasX6sj8c1kv8zejn2ZWcpuxopTC6vh4Qgw4NAGhtVO1u8ZS85Sm/ak0nGqa6+CIy3gQmvIf7Y9kPiq6LOWWE2iHOrO5ldvQsQ1Wj4tZph7S1QaR6aK1Z6B5mZqsJvYj9ug2Vc9DRnoOCNkTfJ7IOeIVG1hKGWhc8THUbDW9Tae8rO165qqSPCXCvJRW+fa014NhdcNf9MDhSMJcOxEoV2tx49AF8/igPEHxHENtGoTWz13vMLPV44PmMq7vrrI2d5M5XXiRIFbkfQaLMX6agZ6WIQBXtF8B2D/7zG2Tf9y7EXhA0celp/+R17XamOVxZA8O0B2TUSJlFM2Y0kBVI3vEAq4ODDP3ax6ksrxfrs1j+EMZw8BjyQ99KdPAAQsJY5kLPe6AuIOia31bTgiFzGlMHwO4Qbn+6PhsThcF/y1w76d1dI0sDnjvtIgQknpSVE3A7V064cTZ/1bde/LHToJTkpXO3sNEe4PiRc0RRysnLBzlzYy97HlFMjSXEmQYpkWIcyRykDQPyPQEtEBvmdtFYm921l5E2YqCrY86n97OZTKKlRFZ6VNhDhRViVglpUWPFUtW3Gurf5mBf5Ijro4/us2MEPeKc28EoAD+PblOYTJFpQY/c/tTee4ulRqNkGVJtQttN1IjTUEORlRMQQqCVIUZCGO9g41gYE8QBvUhBpukhGEAhhGZtIubF8UkqXcXuSy12XWoyd7VHKfZbK4ReQUYN0JCJHt2p6zQPnSKrtexVksrMCvHoJmtfCsnoIOhQyBEOah0gSpTL/WsthRKIohHk0CCFlNSz6TSh8C2wg+WM2r6jlzeuSaj5ykNbtGuO0BtHPF/LYCBW21fzQ2Vf3ecysENBefqpGqXPfzLUmzFpssU4Y2BrCowe7HH9jSi/lQAeqD7FPdu/jiQt1pMGRBPEghkjAmAcesaL/8kHp5nYhDte30JIu2CU7zBSdCiOVhBh29bUTkB3aMcVKsEkMqyZfPGawts5S2HLZjmjV8xVGHJ4dh09ut+oUAG9DatX20w3L4BuMiYF6NgAXP+QDPWg4lSvoljkPggrDEMqtD8hHhC7NevWR3m8C0bJmxqvjjwIaIzAodvglvsMyPflskAA7S1YvFz8Jgd61w73lQN5r03O27/kL+BuLQiS6+y+cIPd5y8RODWgEkaaT7TJJZ9kxboX0vTbLVUBbPYQX7lMd/f7IEgIWESyjDFb7Tx2yoJ5tDYpg3S4ncIuY9hziUYIaE7dTfdbE3Ynq/DqSVhcgtVVqFfh+F0wvgt23UkUNmAxRWt4cSRlT7XNXHgDxDaFHZNiPN2g5IBvd2TeyGJfakDbMRDFV5b/u8keFB4DoDS5Q54bwJykiPJrbpP3kMCtR2BJDfOLr3wHrcsNBr5iQgSb3RpaBHzk6Glm95/k0uYq6HsYGp0wjrGJgJ4w0vwa0AYamsnGRSLRxuldz6k9bHSmjZpfaLSI6MaDdEUFmETQY5irdDhBBcmG/l/pcv/UR8l50z98zBF95711qTEAr7VZR8K+OuAHShvd4au/ll21MuUzpwobkkeRvVM7hsBuAKEtkQ/sijepM3VoYrA321BHUallIKBbCTl3dJjL+weZnO8SZIID51KCVBK3JG+O1knrmqzeoz19ivau82iR2aYbFkUjEJUeI+98FaEFtfUuraF6wcwALlewNgkDSoOphWR+apg9V1Ok24CB7UPiNA1+pylPjpeeUwMv39ni8u6Ofb62A+yXqN15CwfwRVpf/4oCyMupgel7D3zd9+VjlUWmmaFKFSFgdE8uygJQE1s81vwPphyou5UGaGDcOTuWw0yBZTZqk5w6PMkvfGQvUsHP//Qb7Fq2gOxAzQ5dKkJO1G7jrtrTxlwiBGxfQ6uMz+qDNP/8bdzSaSA07GrHTPTCwtQ0tc+AT3MV2teMQ1U1IpDSOEelGjo95NUNptubIEKEGCLOiyl5YK4x9ptq6lFqB84eQ+eIqp8MaMf0aO/cnwDw/nsZwdgMDEzAnmMws9ekXM4TrlAsU7v0dGcb0gShVFGqV4EJ2/LUdrnpTpf/tPKYDHexAHUCxDUCvW2fLazqXkNXmwpqiSrmUQrjoJfnKBA5sy9fv0Dw/Jsk77iNTOxDMM0idZZ6e5mRJ5gM3kCKlHK+C394NBpJh91kVPPrIlyOCEW2HjMbXiM+MGr8SG4/Zu61vg7Dw2Y8EmHavt2BtskOdv8WiIEejIGuS7bXxhgMO/mkaEvHRKCh3oYgoxTK5u+7HJCN6K1RaKEQgciXkp/T3xAIQS48uFj4/DOl+5eqj/nSRGmNmmO4ss2dk2d59vLdbLYb+bTum2tycN851nmR0RGFSjaJ+R66qQX6LshNqGxBKjWy3maicSEXHlMdkfX2IzNhtroQxh9MxojQSJEazaYeIDk9yuL+K6zke+2tOd7WYN/b0oQjBZFQgSHypXXiryG8vesETStUOQZduCg3e5mGHb5AedEuCrJfaUMvMOZrGXlae8dsW1rt2iCkzY8sjeRcAJdZ9knVhPxXtKKqU+KOQISCXhxybV8FlQou7jcdTFcDbowGprKTbBIF1yjKZrjD+YDC/utXuP9zZxmbb9NumKQ/BILOYIU3jx4mq0acu+8YaeRx0oCWilePJdz1aodaB9NuZTskLYekk9Jm1AI6UUyt18sHVQnNmUMdnnzXBmnoel2OCHBjv5PE+UF37iqRj1sxuf77/r/ydV8f6iEjISW18bWa8QMptdGM1lqIAI6rz7Mve82aVyhATofAdqndvSDkF7/vDv74nXtwXgd/8Ngkf+sTlwvJCYwdt6IRgeJQ9QQisoxEoNCx5uLoIH/4zYc5u2fBeHADU52Iv//aHu7aaFgzkzQYPDIB9R5CNw1jVhsy2eyuXoHTN4xWJtBl4HP9cBMgFAx0yWPsS2KZ20huSHUB/I4JKDEIFK9uOvqTn2jMeOw6CI1hOHQ3jM56ARneRtT0PQcIUjYqLYa0tyocCAmAWtG3HOitnd0BvHuOm1cJBJsgroA2Tn/G0Kuhp6CbQSc1QK+U1x87ryow2pSAHBSFUsRf+Bqi1ab76D3ooMokiki2mO+9j14wyXR4kro0hZBUrtMqbt9llpRZnNteZhnhMJPUSRhoLhMHTVMIKdQ2B7yE+oS5QS5caohT6GaItiToCVhrwGYMMkNuD0HUzVPJrtQVo92IAAm1NgyvQ2cQGtuIoQ20K3xkibFwawMQQqFlYgAw8HhDjS2RLAonPe29uvHM14ljLkTfe5/Y+1REU9Upf/PhT3HjygyXFmfQQBwpPvTYVepTF2ijkKlk17rg6kRgpPQeiA1orJsSD1sNzb7Zl4hExzjhabikdtNMBk1UoWufYzC18cMSooegw61ZRPr8Kldu2UndvpHHnwnsf+Znfobf/d3f5Y033qBWq/Hwww/zsz/7sxw7diy/RmvNT/3UT/FLv/RLrK2t8eCDD/Jv/+2/5fbbb8+v6Xa7/MRP/AQf//jHabfbPP744/y7f/fv2L1795+p8dszkA5qG68ukHloh8hpiNSYCbCETEIJJ3asB7tuStkZrT0op19OSNDFX6dqHLfITIRTWDHRZzKygA9EDdCZOUdomGodSePxnAOcTTdqiVgzg2Zmw9ASiDNFKCBdMk7Z2bagOy0NjYoANUqaPE4oTxAEb5A22wSVBpEAZMrR19Z45++/yfh147A0tN40QB0JWBTsO7dAGgZ0xga5cvQAwhIRbe3w7Zrk0i7BLWcUqNTWZtUW3Cxno6XH5UCcFmltlYAXjjf5zPvXSKLCh1g7b0nKhgXHCvRz7Df3wjdiXTl5Rb+dvriSfKX4Z/0NaJ51jQsMcBchkrgqkKGw/I1m1/RVgqu2fznQO6AM8/usV0f419//Tl66dZbCNqt5+ZYG2zXJQB51Ac26pB52CYRmMGihY83KUI2IlIWxGv/mW+/i3JjKRwJgodrln959kb92foZvujZGrxFT7W1zSR1j/74I0QhgoA6d2BQ3ef6UWbASD+C0P+jFGAy0bCpVR0RF/3AWoCa9G+wIlfNu7KcyLX1nX8dm4bEPG3tyns1O71wKwrbf2enDJsgFRtRFXArm/FAa5IBpf+pAWBv7eqroj4vP2x9GtgzwWVCWac0opPmetc8nqkh2hPZ8dSD3YxG6YK4EiFaT6Itf42TvLg5/kwHF0XCJ0WCVhe1Zzq18gLHRc4zH54jkFpIO2PwcK+uzzFeOUq1FlqSZzBFKK/SKYlAsEVVbxj4egA4ME6ld8h+FYQAi259MIyoKhjLoaVgNoBki0oBG1C3ccQLNRCqtN72AtAYrdfO+NQSrU4gogYFtaGwV8xYkdjACC+DaOOB1rZkjSDx/CV1447swO3/BuM+5xsSey8faLRD/z/y+Vu3xN97/aV6/9k5ev7qHe29b5d5bV3llYZS58TWWgvdzafggaTZhgH4dKusw0DMBKaLaocYmBfWSNHsHCKyEmSs2HUOiJCIIgA5SbLF2UHP4DzMOZ+u8lcefCeyffPJJ/u7f/bs88MADpGnKT/7kT/LBD36Q119/nUajAcDP/dzP8S//5b/kox/9KEePHuWf/tN/ygc+8AFOnz7N4OAgAD/6oz/K7/3e7/GJT3yC8fFxfvzHf5xv+ZZv4YUXXiBwecH/FIeKIHUOHxTMlM/E+9pF7Gdn1snB3gNyKIA+p1nuvn0ioL/eVGwFH2GwUylM7geMtE8GXZO50pTTjSCoCpMqNZa5xrzknKqF9WQV+flWZossDEFTQ9I13Ca9gsElq5LqOxhcUTzw6/+R2uAIA2FEFrXZd2qTqJPRHBrijx78Nq69+CIfirvs3b5BbL0MwzRhbGGBK0fnII80DVHoPD0BaWY6qVThCavcCnejZuYlsDYRLQQvHN/Kgd74DzuvGlfIp7/ohzcR9r2ftrdcHKQ8QUXGv355fqdE//XdZTRNNunSIWIAIRSVQc3WEgSk3Lf4e3kTtYAb9aNUs02CdJIhtQxScqW2j1998Fv52m1dVFg4Hmo0Z/dV+ec/tI9qV+VPvDEdc8/FDabWerx6ZBAixck9MzSaCcujIa1IloIkXWfWKym/ePQGg5Vh7mMMMTHJvixDxNNmkWhtJMx2xzBqgejLxOdJ6e5PJlDrWOJlpaZ+O6mTrJ06C/BVpyVpfAcz5b/3Po9NGpA1C6h8vW9JcZsybEO0CtGC2TxjtYIRyJeGcxy1v8mB3tnX/Tbb39VmoDoEXDMbWWFAvqfNq3PGc/dSuhBVpcAVvBEut0GgjPRRGp+M8ReeIrjzTvTsJFIIhFDsG7hOp1LlWu82np9/NyPD1zhU+RyNcJFNpjk79H7WRYf4+iC1mS2EVDSyNg2dMFtbIFY90AFIZezlseCGmuDCwjR7xq8hOhGDIyvIyLSlokBmmlCBji3wqx60JGI9NqWN84G38y0zmyvAArIWoGNIK7A5ANvTpo8SGFqCsAdaIGTbMkgYM4LASD8OvB0RxK3L4iVPpoNdG7nToV2bzg+gnzH07nZkdJEj+5f5ZrWEbF3g2Qt7+a2vPMzxqTs5/OFRZDyE6AiCRcHAGgz3zNRt1DOGa2eIoy6ZsNk29QhdPZDzMUJQkn3QYJLxJNRESrUa0zm0i3BhZ22Pb+TxZwL7z3zmM6XPv/Irv8LU1BQvvPACjz76KFpr/vW//tf85E/+JB/+8IcB+NVf/VWmp6f59V//dX7oh36IjY0NfvmXf5lf+7Vf4/3vfz8AH/vYx9izZw+f+9zn+KZv+qYdz+12u3S73fzz5uYm4GkdlU1q6sCXmwseaA+w/UXg6JtdxzukfXcPjMlN9y2cnE46M7UotAGuDoRfGVXZcPSsh1HnS+M56+Kfnbc+Xpv8JmMlfy0gmCRPCuQ6LTTorMpy+ACXjvW4//N/xJ54CdHr0R2e5NWDt/HF4+/l9cYe0rnHeFFr3nvhS3z3iU8i0w6ZVtTbC6BbaGHyQBfAogq1p7KSjLKbr0S73aZ1gCtYHU148pF1ksjJIRpJE6lCekKgRBGNWgx/eRL8Oc21IDcF+X7ve/+7EnvosQTFnf0WKDLW9AqDjBAEKXN3pyxfCHmETzKTnjPXCMHJ3Q+ye/0MI+1lFBWevO8W/uDROS4N7GVrumV8MnCVBm2+8gCevneo1EYNnDrQ8DpsWztQzrjV32sApKQ+OcCAiGxonyqK1KR2cyzMQ6dl/S2sBOWOfAKM1VfsilnMdjMVXfeeI7yFadPQpqmdbt3fsILrzjfLTTaX47zdb8dnDdfsNqdz2CoeS37DqAuNM4CnfXD38duiegZQNNbOnhXlTh0ByJul2ZgeYyjpIvTVQruQYqV5XXh/O6B3fdCajpCcH9vP4G0PIHSPSrrAxJXnEUFmQcDF+xohYU/vdfRvXSP5zg8hZqdyRqYWdzkcnmaPFqxFczy39sM0omV6tSGySkidHmI2JWMIoXtMdM6wuzdv7k9QSL2BYj6aYFWMM7lL0mY3STVlTUXU1lrU6gmytg1Sm+rHFU2sIUYTNTR6sI1oSyPa9mKKMDtz72IaZaGK18LSJctkbe0qzBhBB9KLGO2LLhywfIR22pqy/FBeO3kFRm2ZQl145rt79NF6BDDYIFtrEianaLbb/O6nv4Nmc4Ta+zaQgYmwj5swvAaDXaN0SKVmdvQCEzMXAeNbhdbc0IfIRFQqi2uYELsAtUBkAUHQoMIKNaFJjs6RiD+9YPs/4vj/y2a/sbEBwNjYGAAXLlxgfn6eD37wg/k1lUqF97znPTz99NP80A/9EC+88AJJkpSumZub44477uDpp5++Kdj/zM/8DD/1Uz+147wWmQ2b04VTrSAHfmmFTqQo+Q1pVawPoEjGVOzVsqe+PdwyzGmNzvdkfkHu6KcLupPTH3cDZ9JTmHD0wGgRnbOpWyN+eVzHTHh8dd7XksAiLO3KQCjBa3c8wqmp48y25glOPkvz7nfyxuxBekqYFPUKskzwzL4HefSO5xELVzmztc7ccB2tuqggyWETBHFXMbaYFmFFNy2Q0Q+cgrXRlN/+CwtsD6Ye/G8QcIbx9hRX6zN56px+GKAsw+Z3tUNZOuvU9x6P5M1e+Y7iJuf7KUMRA5CiUUhh8mNrAQ+p36NCB4BTh+7ml37gB/nw7/xX3vvCb3NxV41/9913sF2P0HTsEwuwL7erf/yKId2pcRC2XeSzAq6siSQVkmtRmwcccLpMZUJbRybg7PncUdQkaLIrymV9y0CHgtfe+T4OPr7FgEzR8mDf2BcbbTOr0njzMktJHf26ZE/rNaO6Stt2IVpnFpfRrK+vJW2AAOpDMDJdNjH4m895f2sNcRMaF0Ak5YXjO+naH6qoAlOD6K0WwUJSSPS5+cID+1pATWRGA6Ic0FuQ7+oC5B3TKzKwWhulJWe/9Qe5cvsHiYdMIaCwu8mhV/4Tc2/8EdIRmNwB0NKn9U2i3/kM6V/8EHJ2sqBRQlAdgBG9xkRtldVkmqRrHEZlqBEyAbqMJ5eZ6q4V69rLDZ+IiMVkmnolZMj68SdItsUEnYbhVYLuCqK6QEiXAE1XQAWoVDWViiAY0GY8egqSAJEF5Vh4P49CKgrpG2FK3IYY8I8BXYOt3aBaML1O0laEUQeRAnFSmDqSMGc+C+FBWwbDEUCP8XCE2QE/jvC7JWfbp5a4fCVmcnCb//jKd1ObGOKhv9pj7liMRFLtwN4bPSppTDcR6AwSqRipLBdpHgCtJV1VN+YQV+7ZMRxuSScCmQkCMUIaLCNIELWY3v5h3srjvxvstdb82I/9GI888gh33HEHAPPz8wBMT0+Xrp2enubSpUv5NXEcMzo6uuMa9/v+4x/+w3/Ij/3Yj+WfNzc32bNnDxkJSvRwHuT+fGsEWtqKUplGu6IzzhFXk2eH1BSCqcJe4wO0d9/8vTb7W3gauRzA7Wsucbv7CEqaBaGKPzSm/gPFmvSZCB/M/Wx+2v8O++qEFWWSpyXDA1yqH0a/5zC9gLzgnPDSBKc65Oq+fTQm9hHoGi/PT9NliUBF3PVqxNwN4zka9hRTlxMTWuQO57HoD4IlolpINgd7PPfYJW5Mu0s0cA1YQJOw2GiT4nLiFzPoPpXT44r8fbn0biHF+0Cv7RmfSSqDvP+NryMosxc3uMQAQ0zqWaSQDLDGODdAQxLFPPGuD7M5PMhTj7yPgXSZi/cOs1V3IkY5vNFvsVmOsq+X/lFUKMN754oVCet46dfYPhk2+dZMG8FdZLiCIgIBW9uwuGxvLWzdbgmJQqd2LELgz89w63vWCYPM1D3X1Xxs89/aYwjgwb1MIhH3VOhdm2J7/lZGO9cQqmOcVbobfd3SsHEVlk6btVJrwNR+GJ2GucPQGCi8YLHDmE+gJfyVVaheN6rkPGmKbddqs1DhO/w5PAP37yZ75jy1a8tWGscDefs6EEEtIu4khUYu04VEn0GeRlGlhtGo2MItqYIEVHeJalxD2fwbiRzijQf/Nmt77+LQ1z5KffOqAYfMEhDnU7S+QfQ7f4B+z4Nw563FmtRQFy1uH3iero5YTPcwn+6jJYZQxIyKRW7tnqSibWSHTzyEoBdG6CggRjKoBXHP4PNgBJtVTUdoMkZJGSBljQprKLoorelZ4I9Cq3yMNaHWSGfKy6QZnySAFESizLpKNbmkn0rzvZbGR6CiYHgEGITeBFvdlNHxZ3H5BootKYwmIYkgs5KwFlZbZfuX+5BYYHcAr7w5pbwWCDTiWMwfvPI3COM9vPcDUNsfI0moJF323lhmrDVIM5swQpHUtOkyEW8UDI2GVjrAphpCVDJceaJin5tEuzILbF6BEXR9HC22kRRpkd+q478b7H/4h3+YV199laeeemrHd6LPuK213nGu//iTrqlUKlQqlZ1fiJRMGHdS52luCKDjlkOECFBCFCYyC245mAeFD4gz9fiq83zLeEy/wxiRgOxhtIeqvM5EruKjAG6fcOn8Nuba1Hr0N8gXZcm/ybXHthUKuobwl5gb0OL3jjnQzlyIGQORgkytWYGIz138SwT22kqwzNDycxy4uJ9HnmlQ7WmP25GGyynFSfvgqdEi5frsGpf2LfLCfW+yPRiAuB1hK4dpNoyqLIM0G0THvrRbcOUFRBZnCtW9D/aGLJYEOm9ECknYL2jrDtX3i7LHgBlrxby+wigTjMlNBmWLkWyZJKzw29/+3Zx4x1EQijNHRzl75AdIhUbwFMpK/sW9C1ZDg/Xyd/3ycwP6MC9LnwzAO7APEARIIqQtsPRq0OINtc1tokYee+8c0tptWG9CRRbEUoKuxajDe9C77kIfXCfcu0okU6/hxfyKvMU+JyxMUemBNuKoYOTQKVgfQ2/uhe1hQ+z6j7l74eCGkYpGBg0IBJCr7J2zn7PZW0YWkUL9KkQb+VghhxBqizxi4MAEzdvvpr58EbXepXdsP7X7x4zHeSI8XwVdIt7EEiqhIQr9QN9VBsByiTwzRCBMCmYXkCjuePLXuaAaXHrX96G0NH59WcD87LsRxzMOvfwrVFsLhi5kVpXt9uvGBnz5OajE6CMH8FXmIQohu+yJzrA3OsuNbD+ZDpjrnSNSXY9gyfy9DjQLwThCSCqY4Qw6iiBTRHUIY8mmMKl9THrYSTLGULQINkKS6jJJ0IQoQSKItCQSUIncDtRIMoJUIS2zQ8+Cf1cUdK8joR2a1A+DQE1DLYUwYVMnjGiByDOWUTB2sTJjnGE0BJl16HMA7/ZqXlVPu81VAH7p0CgCktYxxsf2MDUmiTYh3MqYSDaYaS0Sb/RYaswjmgPotIaINXv2vko17thMgBopoBdXMeyQWaCFrk0jtCBQodm9UiNi6DHMup4m1svennprjv8usP+RH/kRPv3pT/OlL32p5EE/MzMDGOl9drZI8r+4uJhL+zMzM/R6PdbW1krS/eLiIg8//PCfqR2GkNYwKSSKFKD5EIoMrStoV50uhf7EbEqA6quSmN9A6lyI0MpTqDpm0p2w3v4ad63HZNqjFDniNqG7nWUOUmEc7qRvt/eQTtj75Pf17uOnh85ps9dPFw6cZCa9dVFL3LwOCKhIm6pHQ6fSZXvyIvPjF6k37+DBlw5RySLTARmQ2xxdrmr7zDTQtKs9mo02n/jOz9JpdOwGCIh40Ub/9pimxi7qCBHwSlC181eWqwtwx/LMwpPmd4K9sHD539pChWys82ESpe8LWNbea0IXoWN64QC3TD9BvLjN77z3r/HHj7zXMmAmb7gWMYotyCv19be1nOtc4BIsOUnfZ3EMl+gg3v0PvlQfUkj3gkTAHwbL3KZ2I7S1ZWV2gfesCUZpE4YVSnQYkL3zNtSjdxIIidRddg6iaZPwPgrLSToGyTFgWoAIM/T4EowvI9bG0SuzkFQRpdr1Aqojpn0tDVUNkTagjwXVQBsqJbUBxaAN8QLEXlijDkDvAU4XIBCFRB84BOcCxBvXCR/dawAjpVDL9w91KGAgJrc1Z9oAU6KN6joHeqc2s6p75yQG5reRQGYZe57/OKu772dj/AiZCk1a7kRwY/i99O4d5fiz/y9vJdioFheetrEBf/R56DwCdxwjr1SIczw3gLNbXAClEaQ2pp+CORCgpWa1OcKCHGUs0FS1JupliI4y7U6hGhjzVICgJ1xp3QBNg2xEo9I5lFghZRtBh54wMS9GbtW59jqKoBIoKnFmsn4n2sTSd6QhcDozNn8E9AR6QEAjhIpg38h5s651X+0BJe38WqZBSAhs9JIwVKEgiI4J847+5E72g1Ih8yt3m+x8AkQHBm5ssDTzGU7VU3qTS7SjDg8uHGaICrWxC1QbyyYhpjBdkSGs6lG0rYhTCJzaujVLpM1MLCo9CLo0RYV1fYi1pRlWa8u8lcefCey11vzIj/wIn/zkJ/niF7/IgQMHSt8fOHCAmZkZPvvZz3LPPfcA0Ov1ePLJJ/nZooxmKwABAABJREFUn/1ZAO677z6iKOKzn/0sH/nIRwC4ceMGJ06c4Od+7uf+TI3v8NsoJgg4yO5WwJXaFeriOIqQhNeJOEjKFiH7EIQEaspIQg443Z8kLxcPFOpy6eSGhORCGz37AqKSIrKAbGmUbL1BdSxjNp1kJRxE00UmAwinj3eHKF5cOXrHFKALoO4F5JlTlaJEcF173X19OTp/I/q+sH9aW+HE+iM5RyOtgATSF2H5Bkx9DwQxILsk+z6FEBlZqPnMYy+yPL7K+54+zsh2gyTSbNdTRterIATNukIIeO3YNc4cWObcvgUC0UFXOp6krYA2VUL2MswUdQSabSnZzMG3b17yf8YZxjTdAV0hIYMDTH8gzPv/H3v/HWxJdt93gp9zMvP6++7zpt4r76vao9EWTdiGIQFBw6EoEqQkrqgQJVGj4C452ojVSEutJAZHEyFpxKGgoUIiNaQoUiApAgRACLbRbLT3VV3eVz3v3/Vpztk/zjmZeV83ZgYbC8T2hrLi1rsmzbG/78//0nFLvx181uBoZiM6yDia4pZSCUTfp1iroeUk/+a/+au8+cQTKCnSlmlA7Qh69cso0cvd04C2IQWDBKlIifu5C2lkY7r0ucZVNJoOPRSaIhWinAQh0yiJ3WyAGZ8rss2GChnLR0gIDW9cMgshVAboiz6cPIB47DS+8ypVzdTmKbRGv82rNTdiub2EyDNHwjLDGjW6Bo0N6JcQ7QZiaxz6ZUSSMYkmIQMGWAs2V2vgQEuBvwml2zkuOuN0RTKHSVhuAc+TIDwKxRCOTCEODCMLsZXWFTR7pBXpnERd8jKgj4X1tMfap7WNn7ebSSVEjWlUvUFh5w2zMt0YuRAwIShEW9z/n/87Vvc9ztKeR1k+8D50sYZOYNO/m/P3/yIHL/62UekLMJsyt8G7XfjaN6DVhEcexJVfdVMplDCmOG0fLLXtuxsaxcrGCOcrc5QKfepaU1IK0WrBrWswOQWlYUQMRSTS8+gJVz7XrLEYjfIVihFihuiwTYcdO3KRjdMxWqYAiZQCTwtkwaj7RSGmHAr8yCNoCmQxgsBDVwWqKogLpu1ePIEXbph8EKlzk9jFcQvSRCj59ajzgJ8bIETmp5Keb/Zqs7cHFZVzfnRdbu79fdq1G/SEyeE5tXAP1c44fmWb6tR5wzjHwoxzDIn0aMkqeZohUcYfEkFASFmH1JIOhXCbq+UhQmogJK2pOps7r/ODPL4nsP/5n/95fvd3f5fPf/7z1Ov11MbeaDQol8sIIfiFX/gFfuVXfoWjR49y9OhRfuVXfoVKpcJnPvOZ9Nyf/dmf5Rd/8RcZGxtjdHSUX/qlX+Luu+9OvfP/zx6KVRQbxJzlWjnBVG97kYgYTUKqXpISIT08eRihPbxkFi86BUKYRDzSB1kiwXj5a9ElEq8BIISH1n3i4R5+qYUnCnjCQ05DMK1JRMJtNCoeRskOIh4BFSCjIWobH0OoogV3abnxXaCtdmkVtPku79mfPwQZPRgIIxb2e0ka168TC5ga+i1Bvw/hSuYrFS1CvAhqCwpFS0AEBMEGDbHEqjDXxzLhuXsuc/7QMuNro/QLCZtDPfasjCCRbDf6CC1YGd9BC4Umpogp8pN3hJPAMUYYoghoOkiuUyJfliUD7MwCphAW7F0M/aBdfrcEThrgnMJObqAGiYImYxPcvQalfA+fGoICUmuSMGR8X8DNvzTGxZkPm+QhOU5Lb/psv7VA/PitAUYj36/B2YRh6pzkCB6FVMJ/gPsByRZNIhQFqrTp0KLJ67xGpPspwL7TsUHMto4YE8UsGY1Q6HsO0rq9yUpjBoSgMl5h6sPHkTow5ii5g9BNhANDkQN8a2rTkDnE5mbCsTE6HdXcaHoKXWmjyy0YX0B0a4jWMGJrGqLCYFhrKA1RDYVxMBxqQvFmzjGPzOamhkBNglizlfyElYKlWQJFYTQYLrNjN4FrLWNbd/xXwYNywWg/nEQf2VeobVEbo1nQWpNMnWD7kb9KOLKf0W//EqXN84P8nPNEDwQ+HWZufI3pq99g5dpjnHnf/0Aih9AFj5WxD9B9fD/T3/xfme2/brLRJfk1ok0GwOdfRK+vk3ziw+ggyADKLT0FaUpeF76mNH1V4DyH8UY7FKTC07DV2mTkm39McWEeSmX0R34Y9u0DLfG1ItAeWpg1aHLvC7SWJBiNPAwjqaPQtNmiT4i0VTM9qxMQwq0BDQWNCIzWrlHyGNmBoFLFL/ko30NZT33tT6HlMH70BkKt2w7acZCJke4TB/Q5yUbYiJI84+b21jtuEMfKBPiY/CRKQOiH9MtLhEITa83wxmFOvfVppBAUh++ASLKolUQgEujoMm1M0Ryj4YjwUBT0DqNxxPR6k2orRsSafkVyae9YqrnTWqJ254H4Ph/fE9h/9rOfBeADH/jAwPe/+Zu/yc/8zM8A8Hf/7t+l2+3yt/7W30qT6nz1q19NY+wB/vk//+f4vs+P//iPp0l1fuu3fut7irE3h8ujHqGskTzM6ekFytYkNgpg/ItGoxmcIyl9A21zvmvtofBIhMkjLwksn2r4VoRAjko0AVOrHhuNkomFJUH5bbRIUMGqCajymuZvCfrlG2jtIZMa5c4pgnCSYm8fQvhmnTrnPZ3f3uY/P+5T3vJZK3lsniN19ANoLxpN+vZVKNYBAX4JOssG6McfAK8IG2+BaCi8UNK+bu6bNG14rBOapHGcToRie6nL8BFNf/hbdIZ2wLqRJCQoAav1Fiv1DkZB5dGsd1M7cb4HHoktnZOR+yoFDjFG2ehoaeFxgTKLFHJyeqYKdgqIJAX67x5Pb77NIMbcyahGnGrtf9/7/u2/GLpdQlJC4CMQhF7M7dJlptpHuTlTQsoYSUxiirMjgH79IskDl98uaaRPS6uVIxAUKXAPxwnSLIoKkWNUxhlCYdbnEHU00+wlwl+9wR+PbdH3nGMQKJSV+A2D9B25w6F40q4dY2dSh/fwp3/zSa6W9xlzqIz5WO8ch8N1CkEIepG07kKuDylDpDNVvrDEVNukVsbpdXfNguwlEJnUVmmhK02obyC2ptBawtZEZtvXGCrsKyjdysXa25EWQDwEvaOgfQhUbmE7IMgYlhQYL2xCx+pWPQGVwDADKdBj1fc6K2qjdFbAqDZC+IFfIBqaJULTn7yH0ta5dL5Sbn0gLTAIrZi8/QxHX/sNLt33tyEuQV/QHjnEzUf/BjPP/B1HjUi9+hwHpBRcuYp4cQT1yINoKY1U75KF5VasUeWbddSWFZJ6QFErCgj8W3coPfstiot3TLs6bfj6l+DRR+HUPQgkBa3JFPPkdqax1ReEYQBCNB6jhGgiu14SYnr0cal6PbduLHCvlzWbZYlPhwoFRqni2xVrcs+UiYKjBNE2QrjUfpY4ak2amzxvp8/nNU+lqBxD+I7bUNPr11HKNy33oIJgO/aJPU2hX+eeN36cUr9GeegWpaHbZl06HwDr97Wmh1HCGDw8+gRsUtMtpggZa4PfMQxjP/F5ncO0dAUh+oZWieC70ojv1/E9q/H/jw4hBL/8y7/ML//yL3/Xc0qlEr/2a7/Gr/3ar30vj3+HI7SAnQtiT+HC2ZTyvsy2jZZ0OU9ok3K0n4KWIrGOU2Z4hAYZ1qjceYhw+QSleMSY7/we+sQXEbU1ZLGVrithL0qCDbveVumVr4HyKXfuphBO4Mc1ememSfZcwZvcRiYlCv0JomCTxG8z2W/hLcHNP32C9TMzaGVtSyLVEoKGbj6AwUpb7YXcd9Zs4cJb5a6XEIZAew+9xObH/4CoBDUZ0n/zNIWj11HlnRSCHFBlNuNMgSUGxn3Qwa1BmYOMUbCg2cTjJWq07GfSWTOHAXpJbGHHPTUD++8m0O6u3ZdnH0R6jpulDObzgC+Q+EiKCAIGqlOLhM1gibBftjXHZe6lidmk418EP8sJkWkdBpX3Gk2Azw/zBNNM2m90ypo49/N8n93qriMpjN7kQenxDAX7m7QjpQ35FB5vyi5rKmJcBWm/N0WV25WZFJf62uML1WOMqR3+UvdrlLydjJjKd9rvtjcC0tCWnAPhgDQlXIstEOndO1GjS1309HXzW3nHOI4qCdvj4MUwsgCFnh3I3BxKCckUJm2khmDNSvXuWfb5+ZrqEXB5C51oelPjlL2eBXMMoKeOZSorO5yoLLxu/yw8+aPgj6G0Jti5SfXWUxnIiKzPA/FZYELelWbu8heJ/QbXT/2s6UMIydBebp74KQ6c+x2kcuOqBxa5UAr5wss0vWPUH24Y8FMiU2LlmSut6HYCoreepv7mOUZne1QaihubQ5y+s4TlBsETiG4H/uzP0I0G7DuAFEm68tK5sreWdpUFmLK6PTy61nMqtOuuSIBCERGhCK1aO9t9CZqEhIgeIZoCBSrKo64B4aOpQ1zBVJ3R2XimYXw5scgxAeIdUP0dpXp3fs6NRRpXkf1Rmcr8/dw69BrDSw9S6o0QFNaojLyFlBFp+Vx770gHrHgjeEQEdKiwxSRthtFU+yC6go4ucIsJVirDbDSGQPQQVuMsNIy0Vt7evu/j8a7Oja9s7PPbgZ7MQWIXOAgc+cnAKid35JaMBq2RSUD9ykcpbBzD646hVWBtMkC/gnjpp9HFTeTcq8bf5eBr4HesP1Q+7atAyYjt6kuoqt1O7wctTWiT0MbBym3yLSVgGtTjF6l85zS9zz+OujkFIkcuBZSnY/yxNhOPb7Dx4ijdpRLhRoCKTHiB1GJAkncmTSkzjafwFN6Dr6Dqm3SRxK8/QOff/QzH3vs7LP/0iyZrZQrl7v8sXeugY4ohCu6QCKaoE1i5pYXHywzRyi29wYA3YdX20jIYGWhn5+QlznS28hCSkpdB2VJ/lyucxdtHGhko9wTHbvTQhCATVqfvoMWwvdLVHOjR5gKKDOjzbVS7Pgf4vJ/3MMs4DtwHQwkzyiJS50VlR/sGwk+YQxMgCe1v2RyZlX9D9Lmu+ownfgpEY7QYZ5t5MYazH0XCY94rc0bWeG/qSGJf+dSTVkLL8lm4NmbaCGHHxJywe73Y71PCnTHGkEBjgZSIj87npshxuLmLEgGqZrnVFvi9bEEre27qTGfPjzQsdWCkRP9DxyjfvgXnNqyTqs5S3yrbX5d3QCuY2wOf+fMg9iA2PBIvYeTy5/BaS5kJOS/RIwYBXwK+wEv6zF79E5b3fZK+nEFvgPZ9bk/+BUQE+y/+jt0/VkOZEiXTnqEzXyI5/eeQlWqm0U6H0yToIhaE33iRkcuvMU0fOh7NpMHvvvVP+bkj1zheveSmzYxfvw9f/jK89yF4z4PINF44H9vi2E4H+K7ErtF99dF0yJJi+QQoisT0UUTIdAfodD906NMmpKtCyu11Ciox9ChRQMFoc6QFc6WtxkMMbg/hOpID+3w4VfoZnG1fA+vd4/gC6hqGRMJ24SrhgSVmxSRJo8tW8zbH9p7DK/RJQ7XcutcQSg9ElyFaDNFlnISy1gQa1jaHuLo6w3pSpxcUEQVh8iEYb0+0VkxvrxPu3OEHebyrwV7TR+Onco8DbQf0IkcdMunKyaCZU5PKSx8kCC0otqYYufl+ihvHEZ1JtBDGp0fk9pYWqNiD/ji88SQiBu/KvXiHnkXsOct6PUrVmi6syti0NZoE7Tu4zDnS2rZqacteVNr4H3mZ2mNvkdyaQd2YQW9X2V9eY0Mc4OgR8E8avnrqR1bobAWETR8in63nR0nWyoTXy0RLAdKaMUVf4NscBFKAJzSiYtRmCZp4q0ESFbi89jglnh8AYzOa2HZnXoQC8BNBQUG9M0xVR2xX+lQ8n4IoIZKE8/4It0SJ0NYkHZR2hYU1mQJ9BvZ51+msBZn0npegszOcO9xgy/OsgLYrpYC05Cu7o/PWjzE1La3OVAgS4aFz9wJJkzP0WXz7Is2Nj1GQeryXeznGfkaoMkBFcF752TrIfnPtVUi20EADxSgRi2lP3T2MuBcheFbucG9cphAY08gb/UVeUP+OVu0QY8lxRtUxc42AM6UjPNh/3a3AnITpWFZM2tVc1zTGKzy/FtwuNFolmWu7trfN94nsGdp6bafDlpvN3TxQMg0U7SSHxjNe2lylYChyYj3oE5GFHj6xDzEzyvC4gs2S8bYPlQH6JMfkaDKgn5mGn/rzJjxwKyIQfUaufZvy9a8apy0XIpNK9bm2pqk7wWUALIarjK29wUptBtU3zdfCY37qzzG68gpD62+ZEDThQvLcigBaO3hf+Ap88An02ATCE1a7LVKGjksXGb75MqjQ5lEQ1IIW/8/7foMhfTWTep1kjEB0uugXX0KPjyP278ttqmz+3C503ZOY/DgFBFUEVayPJS6Vh08fSQ+JIsTF0uzep6FX4MbQDOU4YqS/RSPu4YkEoTK3XOOxnLNl7h7rAUYwb9t3E2HXszSpRkV/iDmhqUlYLENh7ApSJkgEvdIq1fpbeENdMt+f7DYArVKBaXaoa00ZU2mQWHJteYo3lg8afYYn8MogykBRm5HRffasLXHv1Qu8NbLND/J4V4N9RjIyoM8DviOzWYiVI4hZAhIHvUJrgl6D4tYc1ZXT1Bbfg0xqWbKdHHOY1ttJzNpRMVbSAG99Crn651DDjzIz+022TlynWQzRvRKq3syBScYACNsOmfYknzbWejVXe4iTtwhO3kEBdwDNm5zplZhiPyNiBigjRgTBSIzAY+bwKh5FdLuC6hUMVPQE7ZcKqAUP0RMUDoYgNXL2AdrMo5arhGfvQfugt8bQ2w30cJNKswAC/JqmiqSOT7I2Tjy0wd0rAZXWFIe6LaZaAV4cIPHpFQI2G13+aN8BurUOa9Yxb7fffKr5QKbW/kwSFrmzwMHgbrjYvS7eDvTueW4liBToTTpgpwNywJUAfTSRbU32/Bhv4K6GdWsPfJetxGw1ajQPcA+PcC/SMXw5CuJWo1u3KeM3AKKrCDYAU1OvaYlh3lhhGAYfLXy+Xd6hiuQv9yd4tbjJr9av0hYJsM6ifI0hPctRPk1AldtynNveFPv1go1zV+RVpJlDnm2LfZsv2OdGZIAO54uYOGcMN1P5SVRiUJp8pwkWQDQO8Z7swcVVtFXhC6fqd9nunC0+tt+fHjV9SID5DrQSWwTHNd7Oh9YGKPfOwQ89ZoBegPBD/O7r1M/+romTddekcd+ukZqsAltu/QqzLjdW2ngHNIESBLbr/W6Fi8f/e06c/x+p71wwszmQtcswVmJlDf0nX4L77kafvhsKxUyjf+Ua8ulvQ2ht3laNJ7RHQ58lzbPtpjJXJli0OvClr8APf4zO3gNUEmlCIXMKiowuZdNhKKqT9IUFe2HpmkePgG369InSO2gL3CL3Xy8osuhPs1qeINAxU/1b1JONQe7APTmV1nevE7v784mS3MuPDcEWiiMzzxPcGkV4RUoqYKcwhS8W2aHHNX2bI5Umwjti7pfysTq1f1Y946ZYBITWrC0VeX3pGOvhELIojS9UCUQJU1VZaPwk4sjyHfavLyCuvcRYcfkdFvj373hXg31x4whUVlCeSUcKdqrF4GIEcsRIYBKQ+GDlenRM0B5j4qW/RKk5B8JPQV5qS4NU5p9h1OMW6JWhiY7x1AqU8mBjmnD9LxLs3EBcSZBLoyRPPI88fgk1s5DxnCKzg+fjvjMS6xzgDJFwFeoNm6CIS21ucp5bXGSKkzQ4giP6znVEVDV+1Xjle0mfg49cY3l4lMj3jQMUAHso659l+emA6PIhtA9ea5Rmaw+94WVk1WzQj+gKH6eCEJCMeujuKAevQTmOEBStlKNAhpRUnxtBleWqIpSldAY0Doidgtep7fOx5vkjA76MsdPvcJYjPI4hGGQSsjViHC/NGnDA7RznEowFso+L6MjfR+MR21oBzkwT0yRiJbfeHPD6eHh4Jkk9vqpyTD+I5/mWiRBATN7TwLEiu3vldA0eTcsUwBrQIq8dcSvIuFUKFImAL5XXmdsc4iuVBQv09p5CsSNuM8932McPkSB41T/GfmWdPtI4Zt5htLNVa4DdSLeuJ1oojPc+aBsnLfIEWGT3MM6Dznywa7p2MRgkDYj3gypagt9HF1rWTiUz8EpzSIhsQ+WZjKUOvLlhJPvDp+DGJYhdVTZtQPL9D8Oj74FCKacyj+H8f4adW1k7HdLJbAWkQ5Q/XPy7hPuXfpMrqwfpTt5PEZHyC5Ga5Na+v8bJ838fT9lSukJlMbkO96MIXn0NffsW8ZNPkgwNIS5fxnvhZUQcOZUd+Lma3Sm425vknSFdE1sd9J/8F74z+v+gLg9w9ycuUx3ppXOWscrZLR2T7CT9AKwIY9ZZw7q6dimh7e4LbfHoLt2UqmkAoYk8j0h7LBcnCaImJd0l3R3aOkomwlbkehunmbOvu7tagu3FaKFI/Drh7A5J7Sl02GBVVEhGb1MBWiQUgoC7J/fn7pnjdiSEqo/wJN24xLXuKP1WlZUrEevBsClgKi3ABxjFiNLUmh0euHGOsZ0t+u1bhLdepyo/CvwnflDHuxrsp577O3gzLVQQGqiUCe19T9Obeh280K6DjFAYFxJjl/UoYmyzCcWtGcZf/gyyP5TGsuaZhN1JmtJKeQ7o7fcpPXH7UnuIa4corUPYFYg//hEoP0lyzzmS+g6eCCk/+jLr9TV0EJrUpgiKDBFQYpIT+FQAH42kS4c+Hfo02WYBY0fOsq1v6VuMJPsBiVBFwEP6EZ7S7F24SaO5zYNvvcy+pdt85/6H+coTHyBbxWZzJmtTJEbpgZQaeeEgTK6jgj7VUHDqmwep371Cd1qx7seoSoHbBwqEGg5sJ5zaiPExyXXOTFT4vcOjJkVv7siP7SDQu+2ZzYBIz8zAcDfQZ5J8JtFn17l3xq6cSfMOxpUlNk7qMFmG8pnscjIqXcZQdttkzIfCp5BCrW81GM7CbvBK8YC+jwk5hqAPBGj6ti8JOtf6PItC7r1gB59n08+rvNPhmCjnqm0Co/6XPRfo08ydk+m+uqyi6CBEgS1RJvJ8fKJUohQD9x78ZuC51q6emsuERuvE5F3SJtwoJdaWSDtfhLQgirt/fgDc45JR6J4gLY+qACLwY7RwvhOQZsXT9mJFVqteagO6L65AxYepPfDAE3DwNFw9Ayu3ob0DszPwwL0gvMxupxO4+RZce4OUYXH+mfk6626o0pz77neROtMVk02OvfSPOPfh30CUJ3BFrqWCsHiC2/v/Bvtu/2uk6pFWycsX7XBM0Ooq3le/hhxqIG/cQPSsRC8xubLfVlUr3753mFIBdDvMXX+Jl8JHWV0a5uE/f5bxg5uofPIae7Ljo/J7U6LT3azsPitjwMYwAZIqPpoiW3js0EGRWF7MBp8KwZZXp8EIpQRMjLTG1Jx1qYn9jGFxDVCSNJmJa6udIy18eqVJesEISkjCsWXQS5SteU4DY5TpxMP4LpVonkH1BNoXdDSsbQyxccNjZfQgCggnjAlTJ9YPyoK+BordHg9ee4GR3haivULp0jPQ6kM97xPx/T/e1WAvtKSwM2cIjJ3s8tZhosZt1u7/LEl5I+Uu3c50Ep2z04q4SOPGR/DjRkpD3nbYPSYgFblNCdrst7SGsaMzjinQguI4FEahuyxQ/SLeK/envjzFFx9jrNqi88mvUDvVRgjJGAcIqCJt3VsbREhpYBs9zxqXcRXUKn148sI6P3bxa0QqpumX6B8bYqZ4G+9mRPVih0JsCIEWgmLYRtiQMTua5k/OROUdX2H63h3WZZHGpsf/9blxDq/HiKeqPD2i+A8f63IgqbG8zwZ9KXh4OWKsB3fKmgsTDVMVcwAydfp/8l2B3pyR0Z9MoZ1HgWxWU9ev9JWdkU89a1LLZkdix6+Ptg5Rro27A8iMDXyImOoAKxGzRZ+LFKgBEhf94dT0sZXcxyPN+9Y17ekCSQiyoC0oRvZ5+VxkTmYWqEQSdjwUUCheRRS6WetvTML+lV3Y69qWoIiN9iLN1ZwfGUmmLVEYEVdSFn0EidUM5OfA/J+S+V1gnJ6ZDphlOhzgC4UW1icmN1tSC1Da2r7dRsqDvgTtQzwM0V4rqetUYNOVNes/ZeMm8iJn2iadmXs1JvRuqQN/7iDsHDaAPjIJD34IWqvQXoa7DoMXZIQ+7MFLz8JT3zQUPS1ZKxhYfOSe4/rgJHpXflVYprC3RX39LVp7P5AqHSTgaUGn8UO0dl6ivv08QkhIaxzkCREIIRFra7C8al3MlWFswKCOJiNIjolyUol2y8IxQba5CKYLZylGq2xtTPLMf7qXez58hT0nVyjU+pD6IWWSfj7s1YXcaRLrBYVd3S5Blol08vGpU6KDKzMlSKxmzSNGA31ZRquWHQOdNbzvQVgirSueH3TnPyHM+CvPJ/YLdIvDhP5QLkkUVhGUffYQnO5OEySeZR50NuaBoF8OODN8F/3OCP3RkXTOg7L1IUzsuHhAAL7qcWj5EsM7GwgVw5tfhO6aMTn9H6SQ///28e4Ge7vP8pUxReJR3NzPyFufYfPEHxJXF+1ec0TYQ1I0qnztUVo7QnH9iHEQdsBNxkA7QM85vKaA7kDfrTGn6XO0bgB8PKjMgQygcwWiDdP+cLuK2K5S+Y8/QfmDCwx96DaeMEyJk1Hzi1HYtp0Op6j3LnMlGEZtb/BXno25e0Hha2M7nkGhR0NERcFtjA0zR5HeOHEEcKlsnY+tIAun0tQevMKh8Qa/lDyJXGkzt3MDITU6KfHomqT8ZwFn98XMH1Jo4RFLeGbGx0+1Ji6Hu8IBhaPFiWU1DA3OYs93zTCD8m7epS8feeFS0QxemzEZImXyUkkylehDdJZ1hrxWwd1HI1EE9BkdeFJCmz43UHTtfGVPc6ylRnGKmJMBJFNXSJAE7R4E7yMRFYzJID/P2mKLz43nxujuBGzdLoESDM9GnPjhcxQqbQTwwNQWdyKflSBOCVt2GEZC43wTPGvWcdqGzEmyxxaKCF8HjLCDRBFRImGEMrfftqbfduQ85Qe1Ejr90rqnknNdB9sKz6o60/PzjEQ8ZUBe25zWjrjH9kQvtOxDTkrKI5HABusIXFY1vRWyXR1nuLIP+nuzRDZawcgwzA1D0UbGSAHbG/Cffw8W50kr90mdhbi8bTxcv2UmheRHx9ELnTBz9re4FcHO3PsplASJMJa1AgU25/42vk4oN18yI/U2Ymf3qgOkRGd5ATybljaVXnJAH+vMpOG0HUBaCVEKRsQNfqj6q3wt/n/RatZ4/vMnKXztMB/8yVcZObidLohBlb55Z/STOve9MTkCqUdOQoLSUKHEGFWaaLpCEWHy8vVoI4VikVHqXoeq6OKRILQHvQJ0PAaXk84WaY74ainYrEzSD2oI4eVXya55yW5TaAfZ2Lix9QW6JFgpj5GIEqpfzVjSlEkC4SniwiWC+Cg6kJy4+jrHb19EeIFJjNLbNhd4kv7kf7XZ/58+hF3szpyVHlpQWb6HoDfB1pEvgh/RH7qG9DRSFPGEjxSSwtoM5Rv35rgGe1+V26/5BzpaZOlCKozo3PmOic+tOSms3d+Wsy3tMWCf7nsBqlukdWkI+aEWEp+AEgXKZJoJAyMlFXKoc5uPrj7DcLjNVq9B4/kSor9lxsMWENFoNq7sZ0zchJ5KN0IiBOcPH2BpfARBZr8FRf9ynfBW2TD6wx2Cg6vUCWh4Hs19o4RXNyjs7CCEpqgUD98OmG3GvLw/JrYryfg1KDucmTSeSYbGEh7hgF7n/s/Nbe6vg/nse5X7zTn8DYJ0TgbFVYTLp+pR1l84y/m2uxX54D3oM0aWQcD0ThGi6GKslG4BJVZ9byTjYQWnRERRQEesIFlBDZfQPI6mDMRo1aW9KSk1QrpbRbbvVLjz+ijNFVM33AkAGzcOc/7LP87JT3yOQqXFU7/+GGefO8TIv/zPBLNbAyMnbH9Ebuw9SqicR3MmDyl6bHFM93i/PoNuRshegXBqBOcK+rYjX+xhYJPkpKbclQPbM3eewsZACHsvZc/QAnQN1JzhBpw0n9jNLkB7LZLyVibVuxum2f8s2Kl8CwQ3N++mdv8MbMSZBz4KRGw2v+9nXIsK4fOfgzu3ss08qCR55zGQu7y4HdjmR0FApXmNfS/+Kpt3nmPt8b9NNFVHdExRLE/W2Zz5yxS75/GSth0jK10Iu97ScbFA7yr5OR1yfiLceamAsovQScx9LY8yVTzPMf8LnG3/BCSS/k6J1751lCem3iSohtkQpYyyuVGCw+Bsb2a6MnOmA/2C0kxpD+kp+vb8AgUUMSEhCR5vyMOMyS2OJ7eRoUR0peXb3LrZxYUKiDwP1RTsjA2x7Q0R4OJDBldo1Ato02OoZKR6lMbvebmxUumcJ57Huj9NEo2StAO0gl7X1BpZWYDNNQjUDu2wSHEURubggc0uIpJmXm6/CDo2fhSeR9xY5wd5vKvBfv3UN5lafz9BewRSb2pzaASFzh6mLvx1RFETdjfw6gm61qZ15E8IIp/pZ55EbE3RnsM4UmjL/OejNqy6XZOBvIozoE81ZLnftYvSArPvMSCPNaGJIQiGIbRZIUVgTFJJq0S4UcQfi4jp0yehQJF6FFBIBGGQ8IHtV3l44xVkYhowElyHirS6MtcBs6HG9C3L5Zt2xFLy9AP388wD9xMWCgNjqW5ViP50BtXyTWa1fWvIao9ElNjAo1eusnXXFOUrgsOrO4axUDDVkvzM1w/xp3eF3J5bBJzvutnkMt3wjus30aY5H2/I/W6HDKcKH0zDknfOU+TBLA/zGWvkpFrriGn9hHXKZri7ZnA/CPHm7iFBzm9eWiklIWYFp4ExgxxjHO/C9I6PbRzmYG2b7WCVRCSIzSm08AgTQXG0SWGjRbQsOPPCHrrap9f1Cbt+qnUd6JsQbN46yqu/9zeYe8/XefXpkyy9OUvp/DTB7HbuzAzGzYh7diS8VMLKdolZ5B3Osr69n1ZQZ3hhgzV5hMpUA0UFSfttrNQAjg2Qz7eLunl2L7Po5ti/3CVCCON8F+8DNUTqQOmWh/OYlQJd3CEWCiG8HLDpLHRGkBW+cZ9VgQONhpEMXcIc56sRJDZUzWz4nR1F8tUvMDJ/M7ve5ZTOuNDBjgpyUrUY/C1vw89dWJFdyotfQZ5tMD/8U/iJRPQTVHEI/BFifxIvvm7s0RKyeH6ZhReGylS50jqT0NMQIittJDpLSZLnANLtZJkjzPh6OuEe9Xlu8Qgb+hAecOfSOK9+9SgPf/ocwtEa4VZ7tvMyvibbV3m9mURQAqQ06nofzRCKENNEnyI9BAiFQrGpR7goJF5B4JcUtSXFeH0VT8RWss5LW8NcCGrMVCTN1WFe6XU5MVZjaHwTE9JrhJF1tlnxO4Qo5NWIE9MTRKurTG6XoFjLEitJgS4IXnlzD08tz6KRNKqCC+dgcwV2tuzQA15xmOrsMGIBOKfp7ZnjbzYWGVo8Ywi+Jy3YB8QiY7x/EMe7Gux39r9KeOIG4xc/QH3hFCIq5Rl4CEAUQfiCUmUMIpAtRaN9F+VX5/BXJ+kFCaLjoYcs02yEXIQzNjnmObH2GMvsZXCTk+7z0n6uGcI58TgaIKFQg94ds7+kB9QTuusVot95DyM/eoHaTJtyrBjpdvjzb/aY3Fa8ODxqMH3GPlkB69rk+04fBgMsvYDE81ljivN7f4izI8fRYRNJjPKVEQASQfyNadStmhk2GbH/nrdoegqhBX08El3ktp5i4/AUQ43nmNzwYC0iEJL3rq+yemeIm3NFBILEylnlXpliVAQh6FXb+FFArxCh3oEYZODtzC0O8KUlDs4c4JiHgVyhObYAjO08KxQjU8c7l4QpD0D5tmQgaVok6FMgoghpQgwPJdYRbOMRofAsI+BMAj1c5oaK8nhfQbG/M0msJ9BaIOLArpWXkKsakSSIEXj4w1e5tDBBu1fI5tASsXMLM1xemiTWZgH1dsa4+PX/luaKB0FM6chajsAOok/mfOhBrqfZYc5fZJnDO5rS5nV8P2F0KiamQMhJSpyBd0gWZEA/x4YJkVt5g14W2Vg79s/IfkooS0/tlfF+RDzNgLo/X8tZg0sjqbRAiby0pjNJ3TEHSW4zImBrL3RGjITlNrBITH10qbMsfDubVL/4B+jlBRPDb+PVM8C34m/e7mrVs9h87+b+Dkz12z14c9MlEExd+mNG5/8MVawQ9Nt0Zx+ikNygkNwxFfl8OxYC0vK/sXupXL7/7yLVu/bg/uqsHdZ3IgvLM0xN1d/k0cq/5ks7/5gYE757+ZU9SK05+cgt6jPGbJgFrmbznc17xpA7dsBDIoTJpqfRFIAG2u0ySgjKBIQk9AlJhKbllUyinSGPrTI0txoc5i1EbIluR0NQRQ+PcJfwEAXN+GibvUKxzjqvsUwBjyYdyvh06dG2COjPJbwZzBPNCWZDn6F2BvQUBF2/wH/4w8Ncv+qRYKokRwpT8KcEng2x89x7z9DVs1v7eXrPa3xy9c8MwFipHt8nX8nwB3G8q8EeAarQYfWuP6U7eovpM3/OhGM4oC+YCRCWwxcCtBKs671MVgOSRp87H16mcH0Mv18h9hIKPZu3PtboUKJjkxbbMXluxebDPFPATzKwH2hmuqFxbC2B1PgxhAhK41A+ruidlYTzDZb/1wcZevwGn4wXuM/bwk8UOlH4l8u81HmSuzu3aVQWYE3DbWvD9GDQuQm00CRewCvDP8qrhScJPI/yPJRWNWEtREy0WZq+RfzKMPpqPXVsVYGiOZHgaU1BT9GN9qO6JcZLBU54bzG+pw4d4Olt6CRIX1EJtkm0qc2rLFTfffl+ZhcOoEnYHl4nqfQ4d9erbAuXCWuAZSIjE/kqbibRhYOITC3oar+nI4uTH2T6D0QulXI+pt2d/06fDL8n6FFA4eE89v1ul1AtI6oxwpS9QgjjZhiLDbTop6yJ1PBkd4y9/RICQeA4Pud27fKfF8wDK4WY+w4tZlKXBhfn/sjRazx76SB/8sZ9xMpIup7nM7kXtkUbWY7fQaLOQhVdPP9gwt78uJnzWqMLVJuKnf40lVoDBMSMEutxfOZ3FWUalOZ1zjY9yHSId7wmf7j6IlL5EI+bvPH5FKgO4BJ7DyVMQRovTJk8DZn/ljucU4jbkHEZtvcYbjvdpPYiT4MH2hfo5jbyS7+PtzLPgEE25T9ykn3aNWmIeL4eeyrHat429O6eOUZEqohicxnaEnxJ/fZXje9AIE1VPi0yiu1h2u3MFg7sfcts6Hw7xACuD+QSIPfd7qpbGMCfK51hpvM6N8OHDI3o+5x7fh/X3pzhE3/9JYZmmiiRZYjwELYcgUh5m4R8CXKR8+bJHPkcs+78O0o4Kd/lsjCrWYiEpADdRgeu9A0f7hgv0YHOAhQC0B5Iky5rWCYEXp+1sjGvJcREaLrmjtSLkoSEJKyRKKv1lMJUXqx7XFkY5vq1IdM+bSR5fAP2wlnxJAR1A/QpE9cPOHv2Xp6UHkWRpGWl8f6rg973dKT7TWja0xfZ3n6VoTsPGI6pAHggRcJYc541uc9sEAXt5RKLxyPk7BqtsZBodBXRK5F4TXTkAz4HWk2i54+ju36Wc8NK5yK3h1JZMNNiZbk07Hd5Z1HX5srBiP4lH78saBwG4oCpfbB8HaKez+WnjvBrwX7eO7bE+2uv0ajV+S8bx1mNajz75n183Ltt1Gjuhqn6DbQQtNjP2hSca/wYy9FJfLzMNJkIgl6JZLmIf6ZAdHsDv9BEdn3KIuDPR7/GxHdeY2lukmDo/cTjQ6i+QPcFQd03gFYBZmO4agZ1aqNPEFWIC7kUxVpS8BMSJRivBBRHlwmiEZ4rrBMNgHz23nRHpQTBKUVcSJ0DetL3bghcbXfPKu51CvSDIC8GPrlnkvuc4BFZEmPSg3hoOmyV55FoClQASfmLR/nU2CbPDQvOnvwW0jOpkTwNc3GRj7cn8LSXC82yT5UiC+/Pg4a0Q6EE+TrMnlQ8fOQq5xZnuLg0k15w+C7N5rEb+FPN3E3eDrOaLHuBk6wGsceM4v7YR+15lNb6LJWgi/OxjtmPSSW0suvOBuQHNSQO8N8J7LNnZUAOpjalj+wfh8QQ1DQ+Pi+iClJA0rSJyuu5IdSZbd49MrYzLa1EvDNnGAmcCi4xL1+Z+fAF9Npc++LLHFmxZWcd4XaOQSnQ5/olPSOxOYk65f7JQgDdd+kkibcNRXq4nPzOq9zZGPGyMXF9jZUpWdyPzTWeZ8/Pj79gQO0Iu/6KjGlItE1Tm60eKWLuL/8B8+FJQlW3heYE3XbAuWf2c/+PXKBYiW1onoFsTwt8DeChhLA5xwxg+1bz4dxk86vC8VTOI0ZZiPfsZCT2NwCttMl8mJ8LDSKKrZbDZVSUlCTcs17i2lDEUiUiWhxja6RDVG2BSAgQFIG+2Me56iEe790wc1/x0EMSvSIynlGZ5eQFpiS4DMyjvAJ4ZVJh0PhyCubFIf648bf5mPx9hqNVG5vnfTfe9/t2vKvBfoBky5CNQ9+msn0MPx7C5UsZ6i9T7DWhbAa/RszIbEKzLkiGFZICQnioikJTJixGQJ/L1SLlh5YZ/mZWhAa79yDTwGhBGtaZJqOyzIFz3HWALyCL2Y8K1I5C4pMmASpXYHwGlq6aaztJwJ8t7uUFMUGxIBFFH8+DZ/T7uVe/wh7umIsKAczsMXaGco21cpH50dNc6jbYac+iXaB7XsgQivHNy9zzxp8wGr6AFxcZ1m2a8gAH1TfwXojRLwkuPvRB1u/fh7ZSknZezVLAkQlYmeR6ss7VkdN4vEmcs+/u2XOTRw5c4VKrzOb0EKGUjKpxjvYiLhe3iXYVsMhvduPioNMAsUGJPgNus18M0HtWmveIESlpyR9vV9kP7jdllf3Gi14A5U5AVO5RDBfYKCZMIlhnFU3M6RPX6FwvcFd9lHM2Ttgj4URU5yeac9S1LTeaf0q+k/n37rdEGxVtnjiLhIKX8N888BL/5ukPst4ypQ6rDdj3qTfpv03MzB6WxQY4D4Yc9qQMluCR7R6f7O1BTo4wM9y26eVdRbI6PX0vkkU0bcripln7A7P3doDPciUO5g4YTCIk8OP7iPw6omC0ajqxUqYCEeucSs0sXu1r+q2LKBnjpymUsr2JwPph2uvaJZBl2NpvT7DaHp2AjM14xz3Y2UScfZEjS2+aZ7nqUWBBUZJV1bMPklYl6ypUuZFxKnsH+Nh7SbLzBnnQnCNLjlGIHYduHQgTew+pjMoxjKDXM+99PwP7vIOgy/GfdzBKCYHrmwX8NGxP5LzzYV/hNe4tfIEXup8hVAJfgJKCi6/OcO7cGHc/cof7P3gdv2Dc8wQCbGU+KezOFBqbXwlEptFxOzrLbgExipAIlxvSBbWCsKp/Rez7hEGRUhzlCC0YE0tuU1mCMhlKxteLhJsFtpKQFy4+RvXwa5xtzNMUECPp+D5lUcKWhDSx8EUYm+zxkXsvcnb9KDd2+gRHlxDbI3idEYPbPhQaptqo1qAiUD0oBuAVAl4SP0aox/lJ8Sv40iaZcuviB3S8y8Ee0hUrNDrogq9MGKwNWd6uTLGjJs2m8TRrhzeIagIXw+74xBgXRiJSMAynujQfWqXy3CRSG69oaddRPqJGYO34luNTNgxXYRk4F9UmgEARjYYEKwUKe2RmNrTdqDdgVdhkXgK0D6FXIo6g4Jv1t6LG+Dfyb/NL7/1TqtNDUCyZviSgQ4/RRDISh4QLV9DLf8CG+CiJCFIRub5xnpH+VY4vfZVC1B6ImZ4Sl1JJRivBrVaVUgIqNoTifBxQ15IRrSHwudacxh+tslzay9TGGZbGAyIZUhOCU7LDUDli6o3j1LarfLP7FGPTJaoFydzKHm7svZ05dZOX4rP3+QQ5eYk+D26OOfBR1v3LkY1Mq5sBj3tafv2YX4zVXWAc+cxdo94W69cKFJIi6t4tlohJ6ANdnhrpMfdf7mWicxxx1yUCnfDD3Rk+0d3DeFLIpPNdQtYA0Keh9a51iSHcrmKbLd2M1kxUt/jvPvAVfueFJ7i8OkV37hbxniV3g/RBGbxnfgv5hig7jgqTJvpuVeanuwWmR+fQ0ij9jVo2x1QJQZdZQmICNvBo8k7ukbuZqQz4LX7l5iGhjuYwfW+EEOhJgS7qNK9hEEMQCgjJosxkRLt+hbgRpwAwsCKc1K2g3Ryjkowgtk6A6mCSyFuQxwK9s9U/92W4cdGApwtHk5LUIS8FErJDeuD5DKT4TQEeBpP7MMgkpOxPTsLO39tFEaQOQQY88a2THgqiELptQ3gKgWU6HMHJeeunIL/reelk2bak9cSMLw8FmbZZCM3dxS9yvfcAy/EJk7tfQ095iLDMq189QmO8zZH3zJsZcaCLsBYZu/C1o805llOYWTRJD7UNv3OR9/kWCpuNUuISS2sRYOw1whJmt6Fycybd8wyPVIoE00nCp6rP0V9S7Hg+N2ohU2uaJ27f5qBXM74XVQ9dNTS41yvRKhym/IGLVD7wZ4i9m6hre9D/4ich9qnWFadGW/T9CjcjnyCBhlTM+opxCUOx4pY+Qdsfp8HO2znlH8Dxrgb7bMs4ic+On5trCbonqQQtoumQ7ekeSS0GUcBlNnMR7c6mmUoJAF5CeHibYKtA8WrDgJAUmW+OA36sNJ+kAokJaksXmr1hSbH94Dq92Q4j3xmnfKPqtmR6SA2+NuEc2OqdAqsh1MZW5AnBmhwnmt4H5X6q8kxin7VE8l9uHyBZGmKk/zTvP/sVhtv/yarnRObo4+f8znOCU3pos8Eb3GSbkMvjF7jamGeztIOORviobiOTmOnhVbz+NpO3Fmm02zQXD/Pxcwts3rtKMH+Mndllbp25wsTs4zwWfISnW5eZnTrGUnsVsXc+fZiZS5dvaxAP3YgOJoY13pQuYtwnsRpXNTCb7u6ZJGs7l7u7ITSKbsryuUx7AaujLZKRhJAYrOe9xKcRlfjgiqY45nH69BqVjZN4nsdsVDGlmVLkcc/LLdr8e0eIUsLuVCiWe3SZ06RGKKiX2/ylh5/it15QfP2ji6hyaEfAycng4P6dCjzr9CzjKneSKv9AHqY0KdDCxioIlQunMuPhUhlrPDrso8ZbA/RK7+rWbobMzYNzsQRBkz0kYphIKHoIYuvtUQSTcc2Hhi8oSwF9jVbQr64RyR0b4i4HepxqnADCKbzVhzEGVW2kXdG34xsbKdnTEHXhlT+FxStmA6PNfXYny8lPpRAG5H0L9K7zA5YpnX3eze25vZbfc4Ls2btRIJXwrTgsEpOXP+obxiUIwLdgLx3YW5B35XnTqIP87S0T4f667GBaZ9xVYKUUrRkSS3yi8qv8/vb/SJhMoqTM+SgKvvOfT1Go9th7YoUIZSRYYQqNp3vN3h6rZXddd3s0RhOnUJ7Rd7ezIy0JEw/Pj2ls9SntYBgdyBFkkWlghOsXmbbFLgGJoizgfZdnuX1yiVPXuxzbSBAzwvhJlAX4pqzCi3f2sn4QFn7kJcToFggojPuU7oL7V0N+uLTFcb9F6JVYLpXx8alJTQM3/jFt9QJVuWPWosvo+AM83tVgn+0sF9eMwS67fkWiKUw06U+v0apKlPAxZSeyjOfCfsrIopOJFBKF8AT996zD3hB/p4Df9QgWjWFGBwoZ+oYQA17fM7Qkry2z7dFFxfZ7NujOdY3DyIgivMXbqGRRwPgIbDeh1oCtpmHoPRv6S2LWayX2eOa1w4wPdVCALCVcWtzL2aUK3Y5hZib67+GBwjGujx7hvhu/j9SZ95JGsq6nUdqjIVYp6NB8r2FnuE57qMLlk0e5eNdBGqxyfvQtmkWjVJvXh9hKlhhNFtDDr9DZmOfJSo1nw0f55OtriIubzF1uMlNYZPVWwHh8mEPXOwSFbU4dusW1bpVk4jJdYjbxcvy7zjnjZXOjbUY3t+kdYBnZW1t1v/klImaNLh0iJqhRomCHOKPIg5KuyRfXootKfQVMQZ4QBRQRIsqgSwsm+gGfWZjgibCMeFCBDKFTh0LODjewj62k6HToYtcLSwFjDMA7qiRt+EcaVmTe1ssd/uJHEr5di+yqz16DcP9OBZ7Nb06FXxI+EkksMg2XKw7lVKhG0WokJg30KVGz2oTBwMpcf9OW5KV6yFT9CkWfJiaGoW0lOx9Bxe7PooBEa7QvIIIo2KFTWEII56GRsRVp7yQms9r8A5T8ApnqqGCdIjXg0s/G8PoXYfGyVV9n45zxKQ6gc+Ps+cZgixgE+hTsddZpd53TXefL3qYNtyfL3NzrHCFzNkENEEPUMao/XxqgKxRMe9IcrQ7kbfGOVKq3o5U+W+RIqGWU0qgIcLnv8Mz6FALGvFv8VO0X+E/N/4mO2mt4BPvqtQu89vUjjM5tUqmHaBSBb++l5TsI3WaQTbyIy71hGBXjdZNfMQb+iyj05hCVeJHZOxuIWIIKMt8Kx6R5Inuf1kuweyxxQGv6Oia2Ob4lObBsh9CXUBI2+kHz4toM3yzNED3gU53/i8ilkMTfJug1eO+wx1/rdgl0H4RHWWgOyCQ3725x9KnJp0B7mTT4Az7e9WCvB1i2vPOVRhGzvb+NDoy7ZD7+Myu3kqkiXZiWi6NO86B5AclMSDLTJ9QacXoN0dfE5R6FzQo6iEELiut1iucnTFyGrTuPgLgRsvnQGtF4wqiQnEZSm93i8rUivaYlGpi9VkpgehL27TUOH5GGi9eMRO/Mv1KD7nk8//ohE7KkoS80h6sbFDohba+IkLAkDvJr479Jpdzn5sz9HFn4NklzFF+s8lTnk5zvP0Soixz3XuFHi/8zjcoaL3ziLl576G465SL1TshWeZmp21tsFBbRVKjoBqPBKJd1wKmF16jW36TNOButgzy2dY5Ovc3GA8fYf3uUohKMdT124holv4fYexFfe5yev8i5rfOMDx+GV45z886I2aeVkNqDN1Begop85PIwzdvDDH30BRjKnNAGgd7Z9jVdIpZp0begnekC3kFaSkmIokmHmBiTYa6KIiCiTEKMIDCgouHITokfuTPE6VaZupImO6EnMyloIGCcQaLuHM587bSZlvDpFPCXvZiJ2GZ4FxZGhUoFU6RZ5R1fsF3x+agY5g06aKCNooumgc+6TTqa72f2KWOWXAOd0SOxbNVgeKLrgjtDEjJEyAg+m+l9za8e8xxE4zHCMnXdMYGJwnlm5/efafO2batJGixThy0BVJAEaITS6NYOzelbKBHbBCk5PwCNKbLj5mD9AIT1jOCm5XPBSPVWY3LnLKxcHwRqNy/veAgDqjLIwNip2FOA1zkQF9mayHv1OmlgINd9AjIiTckphOHyXTSBSiCJIeqZxkphJPlCEYICaQUWhZH2nTT/jkAvsknLCfzp7+ns27++TNeoEJqGv8Q9wef5VvdvUvKNH4PGdGXhyhhvfOsI933kEqVyBDJCBgovNkxDmp5WuFgbE5EUkgrcRI7u5gAfu+cLKAq1NsU1TaUTGhNAPj++sn8lRjPq+unMGW5M0rEXeErw0I0q5bBt9qdnpS4PEiE4szVJpH08AagSQpXwoiFmtzU/sQy+suYhAamqN88F6gQt3gKuI1yFNZSZvx/g8S4He8jYJOMelTKsAYTHdtC+m33IswGZtJHK8JhLTUpVczeTm8tYg62UKTQiSCAwRRyj8Q5OIg0bMclID//VKcRqEaHN4m4f2SQc7+KLAifxmaCHHN7m2Pua3Hpjjp2VGjqRVBKjWSzbAnFCGwePoSHFxhagTOYoD221lcI4zGkoxII7G2MMyyZy5jbh0j76CFqdAh2/wDfkp/lK+GnGKk3aSYmdXoCSApTmXPAIOw8X6Kxeo/ihBCWNLLc+VCaJe7w6PU+XNoIOPjFl4dPdaLM6JfBWCpzzjuFNjaB0gzP1S9wbBXjXTfKaSEq+ur/EWr/J+4MQoRQy2eLTW4Lf+vwRXnzrbpTdrMW6pnXrEF1l6U8EXltR/aFXkUNmTJxVJA/0kLBJmzU6uPSwTiegyZT4u0Q2FJodekREmPS+s0DJro4ET0uKiUAQcd9miZ+8WWO052cSirSE3oPUmU5m93eg7lagUoKmFxPJBEoxUih8KWmKLsrXRErQ04pSLCi7THEIlNZEWrA+5JFI6HmG8D0sKjxMFYA2mh4whMcGCefo8jxtegPw6pjaTPa+So8FQsYJbL+dGSS7ChxbbQIREwQdJhhiCzDS2CIH2WSMFjUUcIe97N+6yVzvNmpGWCYi080kSNpU6KRPMG1T6LREqrQe3TqJ2SlfJxYhAS7SItvRqfbCAdnGfiui5adbGRU3mHm68wac+5oB0IE0mNkdjdjquCyZgaqzn2sy6dtxHSlPme+Xuz/Z83FgYzU4RNZXw64jN0kKC9wRJLYqnyeN6r5UgqBopXoXCeCkefvafTgpN082VcZwIsnAX9jfEkU+flgAD5Q+z1Cpwze3/iqhHk8BVmvB618/wo23prj7h65x5L55iqUIFThG05yo7A4OhUgT6URoC/aGonp4CDRKQ2+9DHGf1uokQxM9oqmEm7VZ9lxaptCPMr+jPIOttWGyfazNPmdnSR1nBSjJ6M42aXXBgrCMgubl9RleXN9jbtkxtwhqEeWhhKHGBl86EfKRbxSZXk5ABHaNWfWFY9LosyyuMKXMAGot6A0dg8n/mlTnezhcJGcu7anQKAnhZoRPlDHU6RmD+oDEcpcZKQKzCFzolZcjLW4V+Va2iO1LGT5UCBjtEz+6TPnNIeKlIQIZoSb6IEzV9CHnCSxiKqNtjj/RZWdllJ3FBv2rQySxc1cF7SlUPaby6CoiUXQvVOm9VaN+T5ti20O9WaUoIfR0unm34yrcHKKgDHNKAdo9cJ7AK70h0CADTaOwxdy+FU7PvcYf/vGP0CrfzZGtP0NWt9DFkGKzSnNzi+b+Tcv8+BTRLKpLvO87a6zepWgE0zRGxhnybtI7WiEsD/Pyos+1OcGBO+ssrhbYHNG8LCZ4rNel0F2BeJuC0JQ2eiY+25oZCxVhGA1Lp/ZUltmuS2S1T+ZjAa7wjUQTE7LGOh0StHXekcAwZSoEOXkyL2qb8e0SERISqIBGfx/oGpXY477FERCKkop5z4qHJ5YZ8ZXh7H3AF6hAE1cUomhWVK+oED5EgcnUvrnlU4m32Jko0pIJ1b5mtVmi2thBYIA+yy5o16hnQoa7PmzmnO56SLqIgf5nEGfYmzqSOkY7NYvPHupcoM8SUW6/5Gyn9pldoIPJSZ5YEkwUI6ViK95mQkvi4lgqgTpy2WOMCgtoEpbYyyKzhOnMeGiK3Bi+i8XwGAqfYdYI6FBjlQbLrDHNGtMpW+1YECecBQiGEMgEOv4SvXKEFDLXg0wDYHBKkzCCaE4hw2GQToWvMbY1zwRBxztw4Rtw63mj1ndG5PRlb62ECadzTl+BVZNrOYAZKSeXNikvHUOqtneMgCADeeGiAuKsvK5b/A54tTYMibLA4PtGAigWjfrexX2R74eT6PPtsqOVOhVpC/a2Xe5ZOYE0WyhW0+A8jSUURMjJ5EtUyvN8rvmPiUSdQsE1Q7CxMMTTn7uXV752jH0nVqiPdvALCYfvm0d6msSX6LIrxGzWfWjXl0rXmQH8bVWivdXnULhA/bQgloZ17BQq3Lh/L42lHUaXtgnCKBv6FNvtoAfOsdGlCIfUqaodQb9nmPYiUNapya2sY7y+pnMJdNvM3V0PXYAjI4Yv2vAoRR0bO++SujA4D3qHafFtnDYoDhowN4nwlvhBHu9ysLebGOfYZVJvdneg3GjhlUMiG9CcZwkc+TdVlrJwLjWo0xqQCbNvINMT+QhMuJXzIhcIVDWhfGKR7eNb7Fts0RuO6DBMj4Q36XKCkAaxsYL6MLInZGRmi/Z0l5tf2ZM+q3egSevBNbSvKApN8UAX9aENhC+I8BCPtCgXoFONcRRIxQLv60P4zw0hOj6H9m2zsKToRCUahR6FYAfQvHfqBR6ZfZZKtcNOq8LOiQ3O8yG8p2fovneM7vQZepVrRNEydzX3UdY1PBFTTjT3Pv9lxm7cxdlrc3jvm+G+2i02N6uUpjWlkSKd003WvYStuyRRv4W8kDD/1ne4klxkdngaVZgh1B7e/gmCdUUiJJVhKJahF4NahN4SXO0Pc+gffhOvGtrRdf7fZgY7tFhlHYVCax+BR0FIRqkR2GQ4YpDy4rQ2bULa9BAaPn7tEX743AnQxoxT0CC8CC07RH6TXlCjU+oTF+FFvY/toMhMbZX6zCtUPFOFriMCegREaBAeSdk3wOn1jRm5rCkMN9E4/4IBmB843PfCspExPq0dn8Cv4UnnZy2IENQ1BF6XOCmDlki/z9DKBqNPrfH4wTH++OFNYpv9zf1zTwyAjzNCDY9IKwo7G/hElHfm8f0ejaSH9Aq0R0/j97bolqchMFXDEoos8h52MOWUMuiVaOdiJwT9YgmFxxJGNePTZpjbbDGMwkdau7ArmqRJKKAY14KylVS7wXqu/bb1SiCXe4jJoinOhE+o9lPY3EfCKEoU8QiRumv2hrCZCW+9APPPWbU5OUDIT4AgLQ+LHFTdO3OMkxxTL/q3z2MqGZM/x0mVsWE2HJA758A03FLnVPhWsvZ9Y6MvFQzz4RVIvdyc+j9fjnN3nxSm/Uob8TkfKZDPEuYOpTPp34UNSpm+F4lmn3eGu/0v82znxxDCM5pph3exoLlR4a3nDhiNgNC89q3DAFTG2jzyN15BBCpV7ccDAhkkWrC9PMSNm4eoHYtIRgRG55Oxe3HBZ33vKK3xGnsvLFLo9M0Qp/KZyCIYHGeY+j9I0B70m6TmtMlRqGpITFu68TrRxRi9Y7OCTsWo94wipWJmTfPpP1KUOhotPZO22YXXWM2KJgbxujEz2agNHRQoldfQcfj2NfN9PN7lYJ9BuCMCcaTptzXx3oCS9FMroOMW86BvbJUCp9bPuxBlbiEqXf/ZEsuYCxcLnq0lc9baSI1+1+PN3kFa4SqiaOyEi/RYpss4cBTJDEUERYQoUpkQzDy2zsbZIcR6zCqvIL09hsNVidEaB4pYBCAUeqbPulZ4yuzIUtxDBIr4U1vUS8MU39DcVTnDj5960dicyjVu+PeyqvYTyvu4IIagHzIyfI3KnOYnil/g2UN97rl0g9rFDmhBbz1maFQzvRUSJ236xIzvhESFNe5s3s/ShWF63jBi8oeQ9bO0azv4VkWmpMQrKY7WzzL2XsHqm5/g6vTd9DyfsmpRocDYhKadQLFqtJTNC9CdN6bJ2c9conp4LQckWKhO2GCDNi2c17iv4PiqZGu6ZI0y+fjy7EjQbNEn1jG+Sjj2ymk+fKtHyAJeEhLETXRhL4lfYb7g8a3Rfex4+1GepqcFW7KI0gKxM8tQfy+B6AFQKHeYHb3OeHHJgJYUVkEjcBKxWz1gYnrbllnsUCDBo0mFFhUCNJuMYNhIE2cQzdc4oPYQFHI30rCloei1CKMKOpEE/g57X/5tRp5b5ydfWOehZz2ev7fEwkRAFHi8cbKC9AQnKfMkDQ5SQmpFqb1EY+cyUhsJU4Sm7Vr1qS2/bkyMnQ16kw+ipQFOjcwVSXYpcAOML32Qfk/OlS4hYIUZNMpirZebITOXFWKGEAit6chVa9XPaTaaCcU/2EG/cZvOX9tD8ZBPu70XrzJG3NzLpiijgHEg0B1z5dYNuPIFWHqWLC92DqwFpMl3XDpTIa2XuxNZdwG92nU9ub9uph1wa23V9Sqzv8dR5kSXqguczT63WBzQFwtGqg8CI0WmaXF1DuDfgelwZgeXUdBJ9mkzc1qN/OLCnovIQBLMc40CB6liPlz9N8R+nRd3PkG5JAyf5DSqykyaNo4oNDcqKAVb6yW++S8f4p4fO0/jwLYVtkynNSbsd/n8OC997jjN9Qo/+n9/CUZ8S7vj3FCbkOioXGTp7gPUOxGFVpfS0jpeFCEKwkYUkM0hzq4uodk3eQokZkxHhkySJWEozc1CF913S0MzeqxnKh8rQf18kesIro9VOdLvURCSMoKpXpKWbN4Sq1T5GgUXl41ABSVY3IR26+1z9X083tVg/7YUoEIRqXXqe0Pk4RZ9ynbZGshOUOmWctsq+5ylUzWqRFfhTFtpLEvX6giX++w8wQWCEEGTgJgKFEtU5rb58Ddf45sfP40SCuNzqlhHsInkFDGHEQSU8AswdjJkaG6HQy98g5G1L7N89hiLjSlObFyg2uvR9Su8NXEapSX0Y+pRh5OrxpvYL3ugNArf5Ne4t0816YHSaA1/0v9R5qMP43lGFbkQHYdEo3YUSkT0O1scef4qe0tfpB6sUqSDpzQs3gTlW0nEbBZfrvH+0TO8PvthroePUmtP412BwvirRKKHRNBv+dx8Yy+XnznC9OgWJw7OUZMF0NCRQ2gFJ+6Hty5AaxE23jKOxokCf6hP475VpBSoNN2cwlSQ36JDx0y5zZg35lVpTss0x9ZgOpcsGrtJbGrMxxH/l+9c4a4zFxmK1+nLMjJJiIRPVJzmyuQRvpg8iD8zhdYaEXbRWiECR8gE671xk0ZZmKGZ96eZLdxhdGiLrXiMRmGRPsMc4jxaaFYppe2Zp0HPpnmM8EmMfSB9SWudToFwH/Q2+vi9Ug5wzE+9uO5wkigaork9y4S+iZ/A8esJx6+3QCvaJfjDvzPJ4X372etV07Smhd4mw2sXkCoZBC4wDkX2C7/fZnLlJTrlSbbr++h7RQIETq/k1PdZFcCMaR48nIul20mDudOraAIFLd1jy1uiICxDrTX+hSbBlxbw3tpGz0hKI11EJ6GQxPSSAyyKEt2iIAhhSAuCsA9v/Sbc+TbE7cFmOLB2NmuXSMOFsAUFA/bO+SIlHNb5LV9gJ42Y0Nnn1DbvVPYKohjC0HC2Kskk8nwN9tRpU1rmw3rdFwvm5XmmfcqMXlZnO9eefOIeZcHeJdZJC+Q4gHdgn++k/S11MLRaDpfwKZcyONAhTxT/N84kp9huHzBWhsAI1KklRaVLwlgmEsny5RHOf+0gD/3V121GSUOBo57k9rNzvP4HJ9jue5SG+gTFxLKHDvCNWcNpyQIhEYGk3yiSNOpEIw3qd27hRT2EzDFPjmFDQDey4U7KOEuNj0O1jMnB4NHVCfM3Z4nDItLX1PdEjJ/qICJJcLHG7cUadxpmHObLFYQHxUDz6YUdGioBlhjhN0BY52I7ZmJrk15bwV3v4FPxfTz+/wDsM9qkREJ77BLluaPs+GXrYJep6FX6flBdtJsnzpiBvM/vbtksn6bF/O0j2aRow6EShIwo1jw+SYWltSbnJgJzn9T+qTgHLLPNY1QIKCGQ+LWEYO86wwuSqdXz3LPyln2OALbYu7VoWGUlyLw/BXTdBtcZIbB7VGj4KF/g+e0alwrvS6UCnQhE6OH1PbbUNFt6iuvh/WitmPQvcDh4kWPqNeMRLbchsS42WnK3XGF69S3e0g/TLMLCC5cZCZuE92yxd7PKa9uz3H5pD3fmx1laGqYabXPywZKVCs1RsbbD9oIg7prvvXrI0V94jcbpTZI0Lx5E9GnRpEcPF6AX4DFGCSPwZloYBzMCidSS6WtDzG0s8MoJxdxSh6FrbQ6/vEij7IOCUtROG1UOr9AYHeP+U1UuvrVIdSrm0fN/QCHWPH3Pj3JnbJbY8/BJSEIfrQVKQTMa4nJ8kqpusUc/w8XRYSQd5plGo0iQSOsAKnIsopGRnWNcFn6IZTIBqMIGO1SXihkDYCU052vULSjGFt9ieuEaqd03JTKS6tGEv9Rfob+m2Jy8Cw0Ue5sML59DJklG2CUZQXTXW8bA73Wph7co9LdZHLuLnl/K6U8EWQzUbpA3ujRBF2kdtLLvVXpFgGLYjowWywS2joLUUHxzi9K/v4bYikBrxLEixYIh+oWgRRK+QqvwIWJfkiSazY2LVJ//h9DO1aHXZIDnVNeesOp5maW9DayXu8gtVgeK6fXuO9cPnYuwcJ8d0Gvoh9CzYXMuxDJNyZkbMgGpw53nGdt8sWAT57i0vDnas3u4059y2ohUrZnrez6CAJUL/SNlHtN1kMbf5xgKJ/ELzZBa4GdG/z7/duUfsdg9QLUIwxXSUgHKCtSu3k6iIdKCm29Mcehmg7GDW0aaVnDu88e4/tR+VGxqTNRGewyN9TB6PYlHweSxJ7YJmAS+HUDnh6XKZTYPH6W4s0ltdQGpVK7NwoRabrRMmmHPjvP0pOlwokFJ1O0K8+eOIoWmdrDH0U/s4PUEwbfHkBsB2joWpzmVPIgkXB0qMLOwyZT4LNJfHtwPSlPsrEGs0LrKD/J4V4N9JpO7MDeBOqrZwkfaVItZGFGyC+TzcnxeAnEW4bys78gRGfG1bAQ2FUiXIh07nOZMk3RViAgPxUcv3+KxZ3vcmq5zcV+J29NFtDQlR9fp8RLLHGCMccrIGP7d/CSHjn+Sn778JYqqn21W5ySjZUqEM8JM2rbMMSgbq5Lsc2/5i1xsPgyVYpY1dIDmCGJVQWuY7z3IRrQXNXWHURKiwl5qrU2GdxbMiCSaoZ2bNEbXWRCaBye/TeNsn8NXO7y63qVZ+zvcuD2LEJo48bm0MMbxTZCjZlATtcXNC9fZunGMzmLVMt6aI3/jLCPvXSEWErRvpFLZZos1XK56gWQEjxLSKoydjdtw/Q7kK6sNPv6d1zh+bZHKTsT7v1NkeCc0cxMUIdSGEjnuH6DscejhHnNjX2Xfa6sce26dkuqwNHyAj73xH7h66AGKXkxDbfPVsQ+yEowhZcTo7css7DlJL/JYHR9B2hKWiXUU9HMSrJNishBQ55PwzlAJmk65x850h7IuGZNuLOhtL9Oq1eiXq2xXQE4eI/7iAqfH2+xbb2NpLL1RSfB4gicVxeYaxfoW2gsYWruMF+2qaOdUt64VaYw4qRBa7G5T6y3TrO5FCWEvcsWLHSOTtd1838EYi6uW2cnvLJMro4aijCCRXTqYan5eqKj98W1KL6whWgboCbRxpsrVHJBhi1L7PO2po0ye+T2mznwe2ssMSKqJzuKs81XrYquX9gITzpY3Pue7kUrAg99pzFKV0ho2pDYSvYzMPosjk+0uMZq9TBpnEFSlyOzivmdAPnDqe1tnIX+dyH12b136TiVSx920klf6ss9XVuzWljFxjnr56l3O3o82EnB+gdroE6E108ENPjryb/j8+s/TjGYII4FvyZUjVcryVREmT5DuSZ75jfs5+eR1Dj1xi5d/9y6uv7AHLzamHxcq73yiEiT5GBtpDUHkVpGya1V7Hv36MLWNJTIti+3L6g50w3TsWhWPinWaRQtYKVJ+eYKi8KnPRsx9YJvgdhnvfBWx46GVSIfG8RCO8XlqpcmbL4/wj+4K2FsXOWlSQBTZKjpkY/wDOt7VYD8ojQsEPkJMkxCSWEKiB84V73CtY2EHJfv8NZktKcsrJjF21z4BCQE6Z3vMijtqCisa4SlOrAOXezx4rkOv4HPmSI1vvXeUhfEivaJggQ53CJmmThAUWPnQe1jXgntaCxy5eZ6R6ysw36c3PULslajVmgB0xAzl8jrClyjpEQVVS8hCCrqf0YIY0Jq6t8Gkfp3l6CFQAhWBDs0+VzmBxKWt74UTPH3xM1R+aJupI6fZd+UbNM7+XlpDOpAd9uvfprdnhAde20QWTZjSsamTfPjADT5cf57vBHdx+fYc5XCaYjvgzgos3oa9D9xmajrhjTdiQ0vKMbOfvMnIQ6skwrBVarPIznqHraNrqS2+TpFRpHXuIh13s/nNMboBR64M8/g3n2e4vYafhCAFoxu9bAHFTjMi4CP7YU8FpESXJLfLk/S2OtzzymVIqiivSFkqVt53kll/ickzawxvNpmYWmNtbBxdEexbvMmZ9ivcWB6ivT9h/rFDCOlkXpWuJHeI3P+uH9KSLrcCdf4cqWlVO/YMzfqix8r+CZNkRmt0D8JnizxV/BjfKT/CmO6xL77NsfAyY40Vxv0lyrqPUIrh5TOgNTKJdjGF5MA9B/L5rWLXR6m/g6wa3Yu5RUzEOooAYaPhzSUJJjAwwiXnMeKU84yWCGs4q2Fstz2xYfZQpKl8e5nS08uISNliLxbot3pwBWgaR6fizZucuPYyYX2C0tY8QkUZOAsLbLszlwlbHlOSSdGuHv1An3NUIRBQVSYmu2vA48bQA3jliH3t5w14pvnoMckyem1QvUyKdhTIEXwpjLo+LX/qGV+BQmArrsjcUskB7gDRyrU5L807BifRuXaprJ3OFp9TBuWW4NvvJ/OChF0f0uR6f6j6NJNDy/zG5q/Sb42DSwYmMiWJsyq4/AvbqxVe/twJLjy1j52VCiryjFuAyIr7ub2dscQundTbabrrhkLjx6EZS2nXgdKw3YV2mDFcGvyRcWg0zJDEHt6FYZbHfUY/uMNYv0fx7DBysQh9kU6fhqzAoP27dXGdy9+J8JOEqfIaaSlRpxnp2XTMLunPD/B4V4N9Hq6ziTZ2XSe7O2LprO6DRybNZ+ftXjyZ2t/EB2ukjhnpebT9gNj3SBOgQLoAC4lZjI9HywR+YlZusQJxSCmE977V4+4ry6wNF7i4v0KnJPn6e0dZLHUwRLCAQrB6S3Lf04vQ6kOiKWyvEUgBBaN+Kno74EfgQXN4P3/28N9D7xRRpUVOtF/lSPPreMvbsGO8VP1SgF55CjU1yVC9xnY8jo6N+szid7aXNWgtKR06RWEi4dKrAVc2nyTqXmNf8RKN4iZLQ0OsFvfw0JtXkcLYEZWA54+f4vm591FfuMano//A6yf+Ww6t3WKz+yDzz6zz8tIeFkrbPPjj55l4bo5rC0NMPL7Mnr94HeVLTGKbPmtD8/Qa23ZmJDNUGMZEY7v0q25ju1mrtGscvTLNw8+/xvjOEmnnUtVrTiMigOkaHKhDxSP2JNe7mrPPwNTrmiOtMnf2VWk99D4Of+sMh795C6li1o+PUOkETKz1mOrcZP3ABI16g8d7y8zGfS6/oDm0dp3Xf/gDdApG9Z4PLxt0D3Z5+wKk3ZKDYaAZOEn72SNEjoeIzTGSlwV+T9C7DqyDEIK4N8yyD8v+NK+WHqDu9bj/6ss8evAZhoM1ZBgOgnh+I2UPI6XSKQhkgFLodwist4ECq7no02MVRZ0sW6W2a9oBfVaFz/TdjEmAoooAOkRsI/uK2n9ZpPLNRQv0ieFKixjAv96CO5EBMrNY8dCUN25moOq82dEZ2AkPxg5CZQTm7oWhUWitw8JVWLhuc1UzwNwAxnGrmkCQQGAdrkqmD3PiLJ6KDCMQO8lbZHxNJG25tCRtaybJCwNIBRdSVyRN/Sqt2j4nOb4jI5I3NzhpXmHakqhMm6Hce9uWXCY5vAy4zf7I/f1uxXtcP911WrOPy/yt+n/Pb7X/J/pqPAN7MjB2mfcQoCVEocfmfD1NVaHICkMWKnF6fWb80enez5ZwlhnVNTD0C2jpGe5UK1hrwXaHAe0MUOqEkCjUJUV7dS9DK1VuvKdE0CoTnB2BHQmxyBJc5qYB2x+W4DvfmiAO4YN7vo2HGhy3Ts/UXhAic2r4AR7varDPA7iy/ys2EZRzQJ+X6mWOlu1mYfPfZ6xDFpVpNmg1afIXbi1xz0qfpie5XA8ohTFll4nTAzxJe7vBZH2LfWFsPEITAV4VwwKajHulnmBuOWZuuYkWgtPX+nz1oVHePOIR+x7DF9s8/O0XCSJLRDwrK6UZ2zSeClNJZWjzBpMrzzJf/CBiY4JktYte2TD5v5XlLLsxn/ae4ebVbzC7d4rF8Agqkaz5h1nyTrPpHSASQ2Z0an2mujG9+Aov/vbdxAkIhrgh/28cLF/gR4b/NQsHfO6aX8CPNWiJ1prX4gpvnNiHaDRZ+ZEjPLv0sxy5VsUXHlPNL6N7p9n/2CL+nm3mfjvhZPcZnvrpWXo/rIn8AIGmyQarLKL9PhJFmYC9jDBGgZAOiY2zyEDDbP/iwgz3nRvh8bNfN2GYtRES6VHZ2TCn2SJJO7MzlCsehb0+TJXREtaXqlwLDlD58lkeYRG/IejvGaW6b5TpV64R9KDQ7aI9ydz5JqJaYH66xBm5n0fXN2E4QVLh0MgyB/QC7WWfPS98jT96/JPotAqZoRJiF9hLSkiqVknpfE12CW8akr5AtdssXi/Reb6GXvfQPegmpmZDiqe5pZ0Ij81mlWeu/BALwTEe9Z9lz6G3CIpdonZAsakpVVsIZXeTA6JEkI+tzsAf0AIRaTyt8YXOOemBoo/CxyhdA7vvHKl2LI9K37niRWWUJUh96LUY+r0blF9YQyTKOLN5ypUnAGHj0yO9K6zK/dWmBBmJAfWRA1CfgWLdSMpjs8YU4MarUYXZvbB1Gs6+BCvz2Rj6CVQiKEe4gipZTm6z9gLdN9/JAmnxIkdOwGTKCnsZwDp1veeZVxBAtWr+us6kxTUcYDhNwC6kGUAdMgk8Vjmp3mkbLNOkbCpmd69UJN61iPIMXhpDLrJHur8OoQGpNbPyKg9UvsLTrZ9CINLSHLsLiEm7LJwfqGeVMOZGpk2HHl7Kzrd/0yy8DPIewr5LvxOCuDXGre0+B5Mb0Orm+m37JARxt0fvlYTatWPUZYUdv8iVcI7gfMkwap4lszlFSNoXCcHaLb7z9BQqKVLye3xq/5/iCYdKASr28cNt0wFfmrj/d8Sf79/xrgb73QveyOBN8vXLs1h5k6AhrwUYvDaz3ptvYgIVc6izxWRfImhSSTZ436pkugMCRaMXM9uKIUmydJ1SgC/QhTVE11bFqgSwUYSeJT5aWA5/sCeH5vv89S8s8tzdba5OdHnfi99gNFrP7usqcAly3LT7TiOE4r2v/zonK3+EjBWFpImf9Oy1pColj4RDDR921jmgzP0P9p8FYMeb5WLpk4yU38uoCBkVmq/d2keSSEfjSfC40jnOr5+t8vf9ZarFOmAyi7XCBl8c/2FEvcBGWEILxWX2MNNusjwu8a/e5B/d+ypfHX4Pd/Yc4dTmHYLqPF/50Ulbi0PSD1tsFDcQwAhDjFJmkjo+kNi0tlC0fhlOCkvw9Bj1yvtYu3eEr504Sac+QjnqoX2PcruJ0IqNzy/yF+77Mt5sFaFCUDGdMCB8foSlKvjVRWbqgvpqG18IROBRurYNfQ/h14gTyYWdNe4angMh2bm6QXCgzedqP8RPV57G68T4wkOOjuNrwbHuAo8unOe52bvRQiK1ohgWEML4FPQKCkRggR6wSZoy5ApJdgK8ept2dy9b/Sqr/ZBooki05CM6GAdMZwLftbi1gKAQIXWEUB4q2iH68Ap3ggbQYK1f52pnDzW2OLF6m/GWx2zhllnPKaApMofQDIi0V8UXRn43We9c6/1duykDJ2EpePZJW7trQhEoKIXorDP0+9cpvbhmyoAqK017dqdoQGRBf6afAl2sI/fdA5PHrYWgYDOoVaBYA23t5VIZKT1lEnKEf3wSHvoQydNfRPd2UNNFCuEqeDEIQyUibaIqfHqI3WVtrcc4RKSFZcCA+FADdrZM2B2WkfJ9U6a6WCSNWcsjqdj1yjMzu0HeKVBibZigRFnAdwBvxzLJIRau/1bE1n6GvAPk1dEbwSANEtmcuAWnDdV9uPYFnt35FOv9hvGDE1C2Poa+lz3CU2SKDNdty8MlCcRxPqGUvcYy+/n0yjComTU8oSJoX+dA2MtU6Do33wCBJJRVNm8/RM3vsVIq8/TELN1OyfrzkJbmfVsKAwk7W/O8+VSD7k4BgeZg/SazlYX0RBFFiFbXMnkSij5aCmL9X9Plfo9HfkVqynGbBztLJEnATNclC+0jCOj6Za5VhjjV3KAnPTpewK2yphUEQMLeXo9YGEI0EbY53ok4tRNTdGE0Tg0m3V8QrkRmqpXUxk5VUJaVVYZQ1AX4Zej7pLG1wunOs65IFI+fX+Xxc1umoIdfgbjDQFgOWMmAnPevuYFAUe8skN04Nz7pV1ayyBEOV1u5oed5KPo8DM1Bvw5S8nB9i4KXcKZb405YIkFQ9Pr8xENFahWsmhRQggX/GP1Tp+hLwfK1S4weOEo012Xh24s0qtfpHbuH+ZU3eWx4mxfiP0b7FXRkfNHjjiAQK7SurzJ7aoJxapTICplaI0o61wCe8vFDQa3VwvcfI67O0gG67DfEwqvjodFCMrfyGh/60NOU/Zhuu4dG0Q0l20ua8ZU7HCn6FJMYEQuoBwbBPMM6zs9OUe7XqGxscldjX0qtjg3v4dL5Dut7n+aV7iGqE5vUhoqsdcpMD68wHSZUVm/iT95LVKiggTgI7MZTeNRwlkgDlU4LFSKYBxL8spm4UB9D+QG9egAKxF8BXgSu22URAQXMrp4ApOZg7TL3PPIqo93r9GfG8Ut9vMCZuWC41CQsCW4xy42RA5R6cNfGDKeWFpjxbLlSjZWUhQlF83woj+I3Rqml8E2aTxLykfPanpHt1Twzk4WzGo+M1Zt9qn+yROPMKiJJzN5yErjLNa+z+dca+rJG4eh+otlHiIYnKEceXqQgkdm5MgSZZCiSbgudNc1xs16d9ns/Q1e9wrjqQXuSCzpk4/wMdzrHuCIfoBIJHqv8R45Wz1KSXUqecYY0GjTDlg7GrmM8/Osj0G2ZvvgFm9s+l3ltQGR2InAOUB0PmEr7ufs7tX2cA/lUmldvB/r8M6Utyq7fCejJgF7KzNacd3BUu64BSrJFJ4rphKQlBEJtfCE930YQOt7BSvTCBRrYv0rB+adneO8HFhEi2/0mZZZLxOSaOQj0GpDWdOPSqA04RrqO+R6l/ggzuomSgnNDw6z6ZXbfTDtlSO57ITVz/Tc5NbvK+ugEV8Mj/J2j/5Kq10n3jeh18ByDV/DAkyTJOK+t3Af8B35Qx7sc7LPJ1UCgBH/zVocT7XnQAi8tBAGg0EITS2Ey3QtDahIJWhZBesicWktqZTxrfU0aOqOTzPvD2dpiQZonG/u9LcitA0BqRKIN4DuDthGPzWfnmJMviCESK40cg8oUNK9BdwF0mANrMu4aMikM0FrQKc1QSHYIkm5O4rPUQpD2E6HRdoyE8GHsw1B7DyCNQxEeDV/w/kKfxxqbXOlWuNntU699lf2jl21HMYQ1Eiw8OoJ+IEDS5/jBIR698x36U3don1RceabG7Ktb3Dw0wWOFa2zv3Ob65AlG+j6FyKPfDDnk1Zg4OWWzEmahk4N18MxfqST7Lq3z8Ndus/f2Ha7eBWef+GE25vahhUAKGA6XqV68yvurX8VPenhS04tK3Lx8iD3JPAVPMxnHeBNlAi1ABZZgauiC7sNKZ5j+h4vMX65x4EyL7sgElb7k+tQQIXsYmfM4KV/lfEmwj0+wWvOoH77BavgiN1+6i9XOYToHfLxCjBTS1jfx0fjWsOSMrKaegIHIHQR9fKVAldnpTaESa8+3Dkt6AvgEyBjj6L4D1EgBv+iFPDz8FLWgiaZIiebb9o2HYoZ1rlJDC49eWfDynuNcGD/AZ658ldGwaYmdMs2sz0BjCjwPKQQjSlMXUBOC20LQBZuV0sliZp+57HeSCI+enccAE1lhcqAHK1vs+eoLlM9cQri9l1o/nCcUpIVs0FAq0Z27h9LeIYr926xvbdDs7KG1WWDfgSpFbKY8qYyOGDKaoO3+0dp6jfkQFUEXGJIRQ1HZLIDyCLNxkd9e/svcWN9rAUrzpvwfOFC7TtGLmSje4aHxqzSSG8x5LyFIyOzzOSLlF6FuMyM5CTl/7BLYzT5Ml36OKdl1jQIilb0c0Cca4sT4ISg1iHPujfRBWNE1pQ2D68T4HgjrHi93tVE4V4yB75pqmE4UoHRWWVtghiVSGehbt6dMfW/7pyzP0u96qcunU9Ab/YlnE6NlIdh5K7jQUNpcRyTWA3m32ktY2ksFuXoCSYRG0ojaIMZAiVSSVzbhYUbnrdYs7lFmgrve+whe0uRR/WdMLi9n9LbbhdBqoXxpkw+Ap9o8MLzBD/J4l4N9dggN9+zUON4q4KXpEHVGFNAIqQhyG8ZhJqKbA0sHunZxyJztLS1QIciK2gsrQdj3blNIQdSNuXnuCEejbWhpaGuyAhV55yG7m1Nbn011JRQUKjBxF3QnYecyhFsZ4Oc5cwvkWkiae48QtQIKrZ1s0aVrPHed1rRUledjxT2lSeqFQ5Qb7wUCy1h4uHJrAklBe5yqxJyMn6a1dY5mZZpqZR0ZC0Qs0GGJjeG9aEJOiFcZ826Q7O/j+30a04f4QP3jUBNU4nmuTm7RqB5m7cZh6tuvceQbPbaODRMflnj0szEhZWHSiRMI/Ajmzm/zyd95kUqrh1CS4995isMvPMfZxz/O1x4+wr1Dkvu3n2VC3kH0LUPWqtNdm2Mv16mWgFIZRJEsTEmbV0/TLhUJ28dIKiNMvdbhQK3GM7NtVk+/h9EtQaG5B90u005uMRpNEi2+ybK4j4lajdXqYV5+zscX42x3agSXN5Hv2UB5Kg0eMiTKudzZOEhXg04XUJHAXz3BBtO0ZTnNypfn3dxS8AqGPuUtPWiz5t34idz/eeJ4iGWG6fIWJ+hQRQvoFMr0tmKo5tcYZj34Pk4b4UUaKbRJ4iQFJSFZRdNCmnVkOV9jCW5RpEPZelI3kda2L/GjiNnf/xbVMzcRvs5CvFIpVmYb3e8bDifRiI5m5MzL8FYAnT57lHUH9HySC1OGcZ6cgblxs6Yq4zA8btX8PlrHKFGE/jje1hJ0WtDZMclvhrpQN4+tez1KokcUYvPuCPB8FvpHkRJu9k7w4tZHCOjx0xO/xsNDX8HmpDXzmtNGDIRc7ZIUB5Y6dk06Bt0x93mg1ZAFrufV9jmJPjamxsEH2ZdnMwWmwkBKFLM2gAFFF+OfL/aU3jK/MM2tzrQeppvU8YVxOcrLJwm2eZFptm9V5a67nr2P870s4VG2He7jilGDc71WAwNpjqDfo7K2jHCaWfdwp4kUAgKPVtmjKDWBSEi8iMvjX6RQ/gD9rdOZRG9LTDjFhxZw6TWYqmved/d7iKUg8UbYt3LDRj0LdBIjQ3uhFEaVoYUpvBjdY82RP7jjXQ32QsNQ7NHyFDPX5vixGHyRX9Q5Ts65meeTQrj3On9ujoKK3Of0GuzK1WYDSJl5imC5XimJfYG8kTC33jF7PrLs4UAlKp1JTK59SmEc+OxNnYdsbRKqo7B5Ado3SLkV1/TcZisvzlPRMb7KxU+LXed5BbSsoNRBJvUwI81XEbwK7Zsw9ynwh+x1qSoDF74k+jeJC0M8NfsE9XCDJ3uvUgola9MBxdIm+2ixl0uIIEbZbejvDCEwxYI6jNFSo1yrBVy49HXm9Si36fBkaYULyRRxqUt5bsjmPM8xa1pT6gp6YZG9d0LGb9W5tHmEskyoHC9Sv7NEb/wUK8s+n9r4DmwJzi0cZrJbZq6xTkKRYrPJSHANqgFUyqQGbxdLHCoj1YclivE+/NoIW5vLzC9dp3bgMc7eDjg+PEQ0GdDrQT15lS/Pf5MwGufuvWOUwxWCJOb69TbxaIlicJbk1oOIN4cpbvSpPrRAZ7RhLY5GtTdYpjkxfgiRpnS5S+PAFHdaZVQijHSRW2opYFv+0blVpXR6107Is05ZmKjxoh9nm/s5x+vJfbRlCUjQE1Xo9Adv0m8bZ0OXqjVKCEOPglJUioIyHhUh6OGjsOOLxCOkRo86CT6m0pkHKF0g2N5m9tkXqV25gwjcPnXPdBK9ZYaDPng2bKqnTapTJSBX8EcAvgrxV26bNbs6DxcwmoIgMOp0JWDPIaLNWyzd+ziid4K9G7dy3ukqc8YD1vqj3F4aodcyGm/f1tWRQfZUAYS6xCvxIzysvmnbHGf3y4+ja+g7T9KgNsD9MJDgxx6OOd0N8kplEn2SpznC6s4tcDsmKq/tyDvgOadAT1oNpJuL79J2uzhDVeL5rY/hCZHJP1istUOssAEKyvjAaUgtjNIqEbSGWlHRUEb8N80ThJY9dmvaMdDukFpTXVki6LTePp6O2fIl2pOoUHFxuolAcW5uh1dUjYpYYEhNEdW2SGSEDqFwYwa/VcVXAedfh+VL8MSPxcaqocFT2wSRcSaMvQJRXKCi2oAgEkVQHoGKQR0ikXvQ/v8PF8L57Gc/y2c/+1lu3LgBwOnTp/kH/+Af8IlPfAIArTX/8B/+Q37jN36Dzc1NHn74YX7913+d06dPp/fo9/v80i/9Ev/xP/5Hut0uH/7wh/lX/+pfMTc39z03/pNrw3xkaZLr1S77+h5TKgeAedNuHrjdrO/OJS12/WXX3/zidqDv8ksLYZxatAUOIYm2fcTCMGXVz0J/gCw9Jrn7W87TFcRIrCrfOdZ5rl8+jB8DvQG9HdLdlS8/KYRR3btn5KWE0pjZ+IVp8EZBBxQjj2qyl2DxGSM1druIO38IlX0w9jjaq5DKg1pB9zL0F1hu3Et1sc3VyTsc3tvgSKvPpaOjtA68yb5UcpRoLQlaNYoXjyO1QEpNb/YK+liLPWI/35bTPNSY49F9s7z+4uc4Xapz8WiBw2shzTnJltb045hya5HFtW32XVb4999D4VafLz+ruVfex+nRaxySF7jaOMCByQUenb3Kt9pPwPwbnBzt4heHqYk1dLXLkpygMrUFQqJjiYiNZKO1x44cYUitseaVaTZL9JfPEU4/wfqeEosvaoKkyuj77qa8/SLsHGdjfJNvXv08n/7wOCs3qjx/e5ufmPk84fxPM6UqRHXNfn+RV2jRZ4j29SmisI6sKZjtERxrpcyMQMFGAOdHqLY38coxpfEnKVUUh/0+VzcLRLFVi7v1x6DSJiV1WiMUnN55g72bt+j6+4kn19kuNSCRDPW3WN/sUz9QRwtBFCXUwxiPMkf7iywGIxxsX2G4t5G7uX1gZwe0RkkfoWE9HOLLV06yp7rD6eoN5GSMLgYU8Gx+M7MIa7QYwSRA0RoK220Of+lriLETDL9yhuLKTZNP3M/tDZWTQIU2QB/0zB7pY0r2qdzGTMHGXa9zSgGLNnEEkQmz4sIrFIC93/gC7N2CylSKraKyha4ac8S1jT187vYnub0ylt66D6gQgqks/4571PDIHZtW2lakE44ZSXY1MH+It//Vu07PA6p7udC6t9noc6r79BE5kHdRFna9DGgN3kbrrBCTOgp+ly7kSNmXVv4KC/1jpticNir8/OGUEZosYMAV1vNldr4n4AP3rlHYEughQ2KllMRCk4hs92RMrFkqpfYOpTWXvc7+4nnZA6VABx43R4t8ce4k86OmhHdiGaEua/STpwwnLUDqgN6J6xS3qpSvDbP2RyeZLgc8FNZJLHPkJTsE8TJoTbC9QRBadkQIekkJFXpo3aDcuJvbjwaEw99tLXx/ju8J7Ofm5vjVX/1Vjhw5AsC///f/nk9/+tO89tprnD59mn/6T/8p/+yf/TN+67d+i2PHjvGP//E/5sknn+TixYvU60Yf9gu/8Av8yZ/8Cb/3e7/H2NgYv/iLv8gnP/lJXnnlFTzve1NrfGJtmOGqx8SWTTvoGFWnZnKbXNuVlYabQCbFO+LyNpL59mP3fkQYO5+wnibOHt8LKL9Wgx7G89alLk2ld8u+5j15hXXY0/kNK8zKd4CPNvra6fth+RWTSF4LI6kk1ifAheW5DRBUjN3fb4BXM+2NAW3zovuaQ+oazN5F2O0R9/pU+3cgXIXebbTXoFvaT7U0TFzvMU+b4PAJxKrPx8/9Hr1qzHOf+lnWKj1un0hYZ41lYRKtVLWC7Qb7v/BRgtYYEkU1uMWVPQHPzR3i8JsdHjnVZ7V8kUPliEN/8RC95RWeFNO8fnGd0fkj6JUqrbECSzt9PjC3n+hEj5XJDrLp8/57qjS/eoHP3T6FNxZwfOQNWvEwO76gsRVxsqaZKtyEAuiShxjVzEy20EKyGk7QXBrn0Piisc11E7oXW1xqTpGciJmOl7l04v0MLSr27ok5/MQe/ihaZWSozLX2Xs69+i32Fu7Hu/8Jnnp2FK/2RX7cG2ciepDN+CLf7s6w78gFNlSdmfuW6V/Z5E7zIMmdGgDR5RrB7SaTQ5K67tFaa7C6NAWhpMsowlOUfuQOUowxUkioBpqu9fHROYHMOXy7iCypEo51LvLhzS8z17tJIemDXkPfEGyWR9CJx0hvHX9T0fZOMdeZR7f6DEd9+mKM/eI6ZdoIIRFSgvDNK62a1iPpdYmCKloILt0aQd/eZJU+PPNFkiNTiJ98HFmL8T2TdqpAyDh9imiSRFN6/SrDX3iWYH4d4Z2HkjfoNCfI0temUlgIQcfsnUhDR9jQwNz+THEth/rOHi+0WfeGcg+cI2IFd16CqXvYUBXExmVGWaZ178NsrBzl11YfZelqGS3cvezViSmYFgeWxCSGzHSuNWAfuYkJ7Hw5CT8nRe92zNP5z/kXg/RCMQjyziTo1PZxnKN1pBpHpCuh7TqRO8cBe15yFzK7djcnkGeucozJRjjFC1sfs661WT6g9FTHl+S+A2M5QYLycryFhtZyAbZAxAJRBFkUlIoaLXVq1nKuuxIotpsMXbmAdDfUyvTDE+n8K09yc6TG754aZ6vQR4h8ai4AhfIi22Nhfvc14XiT7lZEQZ3g5+9KkCVpjFIaap0Xzfz2QwiTdEwSPGq6xUJvD6XgOFr6zG7EBLrCD/L4nsD+U5/61MDnf/JP/gmf/exnef755zl16hT/4l/8C/7e3/t7/OiP/ihgmIGpqSl+93d/l5/7uZ9je3ubf/tv/y2//du/zUc+8hEAfud3foe9e/fy9a9/nY997GPfU+Ols5FrjE6wIM3LTWqCYahdKsi0EqyT9MmopDsG9prO1rfTQTnONpIQ+dC3hTISS0T6HlwuQFeTuW9a4uKYitSZxd3cMR05DYTCqOECsoXqCdPPYh0OPmrCh3xhNrBSEPdga8kQxKACvT4UJ0B7WZINbF+cUx7COOjURykMC/rdAuxok2Skv4YUG1TDW9CWBOuwV3jEGx6zW03QCXG/TmtUcOunQlr1rdTFTKFYjwvcd3WI8c4ofSVoyg0uHbvKMw8GRF6NK/c1GLtnhOJmi96qR/+pmK2FCsGpt0jKUFl/Df89d7N3a5KVWxd59a0h9twvEWc1kyeOc0MscPveKT7QX+TMzMOsb0Y8ULjByp57GN6WTMU1jEtYgVbX5845TSe5h1byDEf889yo/RUmxRZxT8CdLgu3a8Rrl5nwTvG/eAG3zv8RP98v8+LGBENFj8b6MR5trrF0/xrL4Qmmbt3h1HYROXOBrdoUZ1uCVnme+WiGA4U/ZLP7GHp0mo2hGo9fnace7uWmCkiA8paktniYZmz86nQV5HimLNJKIrvTxgVJwN5awk4siNAo6WW0GssnSk2gejy2/RyfWP9jyon1Q3G0W2vGetYhyIMDE8CVcwNRHSWxTu4C0mgRz2qtEsthLKyRzNYQQcCJ3qs8+O2zqGiRcq9J54U7rJ3pceueR7n7p7uIaoEhNMNokjBBfPUlhr72CrJt49ILwhhoBzzkgSgHbH4IgSnmQwg0hc1+mG2twc2b+6xz+wnHIOQudGAXduDO84wRgeqD1tSf/VNq8hv8vfgPCff7PD38JG+s3M/19gmEkJR9QSmJSRLJZtwAYUBhuTdFkmCLMrlmWY974fxRcvTk7R3JzUMe7MlUOflEOXmgT6vpOfqWA2sH2E59ofOP2vUsLF1xyV/SiACRXZMH+tT/TXO9c5p+MmTU8GQyF7bZUZJrXm4aXeZeZe9ZtGTP1wo6IGJtEiqVBOWKIKhJuhI6wuRlFBqqnRaF+Vv4PVMWe6BYkQ1TXikX+bO9k7w1UacdmDTN2KwdWa1M06ksI4ZLog6NhscvfFBzNNYsCm0MSEIRJMsmgqQbpm4at7r76MUlDpduspnczV31BlskPD8CD+icx/8P4Pj/2GafJAmf+9znaLfbPProo1y/fp2lpSU++tGPpucUi0Xe//738+yzz/JzP/dzvPLKK0RRNHDOnj17uOuuu3j22We/K9j3+336/cx2uLOzY96MB1C1daqdoQfMZ8X/m73/DrYku+87wc9Jd/193tYr7311d3VXG3TDNEzDESApgkakqB1KSy1DE9JKXGmHGxNDrmbJkWJDZrnSSJSo4cgQIgmQhCWARqMt2lZ1ee/r1fP2epPm7B/nnMy8rxrSIGaBUEdMdtx+t+7Nm3nOyd/5fX/+B+3UZhdCzVYKBfymqpKRbk0kSBy8oTefJZWJvupCxVO/X8+of697iglmI2hbUAqgZimGhGldaYBePihJx4O1EpBPh6WacRnNxEaBuy3AzSWOMHMdmYf8oBIS/Ai8SEWCxlkJKcKPDzNPBRjlUhdKOyCcgrUFqK1CoHNnhMCWIXakk2aFRZCX3N2yTrcU4caZrxFCCh45+Sg7rwyS6V6hNuHzlY/UaAxIHMvDw0EKi2W7gDOc4eV+mw/R5Rv/33fprxQZ3BkS7dhPS0rGx2+y7esrXHbzHJHDDO2cxZdtgsH9VN/+FvmPPcSO7uvc2/80X4tG+cD5C6wNb+fbtQnKq7cpbh7BrZcQL9+n0/fv2bFtlNMfeZzwTpcX1mcY/8oS5R2HuHTC5nZrnMMXRvjFkSq1vqPcvf86zuadiPHN7ItO8q3v1Hl0eAxxZ4YXT13nIzuOMFtvsnZ9gs7VFkN/dZjSW6N0HnuW3ecCro3WGZvZwp2+EoU5m+EIFeXf1bXBzaNuqJizoATYIGRE370ryF27AIsBL+ThwYBbrYi5oBBrhFYElhUx7C/yidUv8/DKRbyom9BczNw3PvfUvw2zFukP9B4wxWvQOet+iP1nL1HoBDA4QG5hDdEIEb6aTF4EPNJ4m0Nvn8OeL9M9uJ3o0X14jSbOy+/CjRkdtCQUN3c3gL0OYIoH53QSoO8AVTsRWnsUdPEewJ/ae4b8Q20KcSw9N/N7sw9cdX6oYgBEGDAq56Ei+Xn+DT8xmueW3EWfu45wxyjKaebDEf7l4n9PLZqi6me4Wd/Oaq3ISHZZ8RkbtYctVR8i8eO/10NJj1/0fm/GGskN/nltVvB93WRH/8yyiRPYjdk+LhWcRtsNvDMG+owOynuPYRpBUur3pixxJLnbOogQdmJAieULSShhuH8VS4TMrQ0TYcXXTpOfMfMDvHJygp3bq2zbUkXYQEsipMBxwM0LMloAybXbZG5eRrRbxK5VopSFKCIQ8M1dU1wdLCFFoEHcIu0QUCpQkv9jYeklkIjQ4vHTe9hjeThWjZZvIbJ5hitfpFh/HWottbEjaEdZWn6WPfmbXLEeYuvkEOvC5yuPr/PmQTj8zkYs+NEePzTYnz9/nieeeIJ2u02xWOTP/uzPOHDgAK+/roqyjI2N9Zw/NjbG3bt3AZifn8fzPAYGBh44Z37+Bwcr/M7v/A6/9Vu/9eAXeQfKbvJv0/AhEEqj76IW3oTd64I32GFiyjMALLW5J9QUbIh3JQPn+2FV97Q2lBuXQZTQ1dElHVv91ooS60GK4AF93xTwSh6UqtPtLpHaj4+quhQzNJH8NdcxptbIIi6A4mjJR0Z692wA/vgSKU5pC+Ua2LQF5JSqJrY8n5xjC8g6BCLi+5/aRXXQwY1bVCigzzYL7Li5m0KnyOrOazT7/oLq4EFckcXVEdhmS0lsOg68ltnKlodmKOLz1o1ZdtQ97mweIrpzgdpsla3ZCU6/8lWKT/0KJyZeYfTWc5w5cJR375ziqS2S7je+gXfok/zry8fZP3yOSv44cqXCIddh8MY0xX4fKY7RDu/xxB/N81o0zNnuFKG7i6WVLO2tFk8dnCc78hoXzg7ygt3Ptqn/G0+HX6N0d56rMs9sw+G3f/8b/Lef/zn6d8+w3pTsG3+IGsN0i9/Aqh5g80fv8I2vVfB4iIfFDLdm7zPeeoV69LMUwyKNULta03tdqqrHYQUKzgrDYpD61W0UH5fInMQSUHRhhxAEHZ/lwEVIKEY+PzP7H9jbPk253UpMkRuVRJn+YMPxnjwnpfmajnERYDsIW6iUoll9vwEBHUuBMQqsM34b7nWw55fhlXfBUf3p44AygUoRNClxeusQWIp+RaSA3mkmQF+zH/TRpw/xHnOMPzI0r/dvXB1QC+SxtitQ9fKlFnKI95lAUKTBEeu0PvcWWJIBa5Z/svkvs8h+Fhp5yuE6w91pdR0HxVtiE7IOoxTpPbiBEOKb6le6FW9aizd585EG+lBbEk35XXOvtPk//WyFXrM4lc4IWA5kdTMgK8UPjdIUC1mCJD1Eja/lF7jRPPYAe7KsiP1br3Bi30m2j93HcwL++NWPc2t+ipm1Udq+l8QfarIz+2O14vH7f7KPrVtrfO4ztxgdaCvLT1fgOUKl9NU72PNXEZ22voh5tjpoQCoAvlMucGOghCpxnj6kXvUo5k0ifuqGb8LowgBb740jLInlfIO+yjWy3RH6O69j1SvQ1vxVQhjZbPZm1PI93OL50grfOF6hmvf5v341pBj9V67Z7927lzNnzrC+vs6Xv/xlfvmXf5mXX345/l5s2HBSygc+23j8l8757/67/46/83f+TvzvarXK5s2bYTqE5UBVaujTvigjocZAT6yFKjXIELcBVO1PNxsdSzvebLiag+kcdFONMaBXEzBMxACmiMAKY+JSUvUGbipSoJuujkU62jVKzPYCtcGVvSgxvUZWojXEmguKkcX31uc7VhIvYDSCtNBhpqeHkbwsmNoEI0NE6+vUGoK+2gy4Fo2+HJcen0ISEKCalkopCeQ2xK3jZJtFVgbqfPnJWyxn9yKFwMbCwY7l5sRwBvZgnuz2J5kpLTIxMMbK1gGWbtzg384McezgNlai0wzvHGV/+zp/trSLgztbHJ/KIK4McPXMLJlSjnudzeT2FhH5bzCVKbK1OEXfta/gFI7iuiFvdx7n3tmAqX7BE6NXeHqgyN2DTV4OjnDp+2/RdEb4WP4+970jPH7Ywq+d5H/611/h2MOPM3BslJkD32dXdBhRn2d04sPsG5rk9q0ObwYvMrh1ilbtTYK3+zm0PMnlv1Lnxf/YZerTK1yr7WWk6hHpysUhxO0+Df500LJqK8PdAKiVyfxzSflAgDcosTMRpcNtjuWbVFeWKdy7y959s9jFAm7exm+7eKvd1HMVGrRSNLgR9DfWVU+IlCQSUAuw2CAld9qb2SovaFIViq5yodLUVRk9ZVXzJaJr6EvH4xh3Uk6o+vJxmp0GXGmD8MHtgqUZtw/UbS2IpzdhmmjTf/X3ceCqkVQ2/FwaZ3KY+gy1b2xtyUhn9/QUdCe2AgoRYkcdJsQ5JnJ6vKHudxoXqTEPWiSBt/FrgwKQPqLUnjWCV5R6GbO9+T4N9HGXFiO0pdal52XignQTHtfZUM1PJAKfUU5i+tDX1OOca29nLRhPZAJ9ysTwIn/5qW+Sz65pdwL87NPfJhLjnJ7+Wf7g2y6drkiygtOPSgjWGx6VK0McOLjM6GhTPZuOxHIFVmUBsTwNnUaKDjbSh0QKwaXhfiVLpghBYorumNnq4s5SICOLjC2w/Ii+lT6efekY2cjGEnfB+iaTmTb4thK2at2eoO2i3cKPbF7qfIhvPmFR7gievNzh42dCplYEtZENNPkjPn5osPc8Lw7QO378OO+88w7/7J/9M/7+3//7gNLeJyYm4vMXFxdjbX98fJxut8va2lqPdr+4uMiTTz75A++ZyWTIZDIPfnGnq7R7BCyE6km52jRokjvNX2PGj/QGEFLVabSlEgw6Nty3YdGBpgU1R4F8j1RsiMi8lSkgN8KD5mZCh5da6QFvlOCjZMObaxp/oo0arzGhGVOd+cD8xvhR9eVS6dpaaUkxbYFizJaVaAnGdWGYTsxz9BoZwC/kEMUchUDCYgS1BpV9m/nszQpvTQpm+x2erNZpyZCLmRGq3VPMTwlO75hhYaBDQAYHB5cMDpZuAKzg3pHQt5qjOzcAzc1cWeiwa+3bdGc6lMvbeCz7GMd2e9y469EXrdJf2MTo5nWu3z7Huj/B7W/O8At/82HGOmvc9oscuvNtrvnDPJEv8+2VOlvsIZqTn+Fo820ujN9navLzrOdfY3HqHgdkjR1DXbr3TlIunuG8PExmJcv40z6376yx077H33hmPzNr40SXDzI5sE693OHe9H5mj/wZpdoXyBysYn8t4vStK3xg/2Ey7Tzbt66xWN6M99QYNy7c5OhElUL/HdZbexJly2CGIK7pIoCmLCIjFZLRXRGsve6pICdb0l6I6HusiTd/kg8dPovrCtgJLVy604PkluqURBPbaqZMtkagNeC/EfDT/zD0YuhMv0wbVOkwPryK8KsQZiDykvNMDEkklNbeAZpCkZoFkRC4zRByQC5SJXBNK1g0eIlQWd7MOAMBFV3C1cS6mOJQaeDZeJh9FEeYxz/eeCJJ2bYU/Vu2tqRFipGbPSLNfot0fQ2zdyK9ZFqLl5Fyp4kUaNoQ15A3ArtZ6o11PtDz7PHN6zWSWpuPfB2Ip0EjHYQnNhBZel1E6tmm6/M7dpJOHOfTm3Hp9Yt5lEh4oNbqZSS51nyErvZFx4H7lmT3ppvkMyskNWeVVk72g5w4kCGfD/juSZtr04IgFDG3FVYP9XLzbh9PPD6PJSJEqwPVLtRvKldjTMsp3pbC/dt9Jc6PDmhqCWPTfR8ODpDDZhfF+N4uNlGzxI5Mh9JL/XRnj2B3XR2+dRcR1sDKQMuHpXqSXiDU85UIZHk/T3TuM/z8EHvnO2Sa3UTw7byHcPcjPP5359lLKel0Omzfvp3x8XGef/55HnroIQC63S4vv/wy//Af/kMAHnnkEVzX5fnnn+cLX/gCAHNzc1y4cIF/9I/+0Q9/8wAVmRsnZ4JiGigmgaXLmqqP6dj05Nq3IrjrwJKjfO1xeOhG5rGBSaTBOa3Zx0VxIqUtxJsqfR3zkyi5Rg/YawZl+jsaYSGOBI507+1I+zZTGoMk+Ws6vJn5pOtBS7NW+jNTwSJlrkq0HNnD950MsHUcRMRm2YWZDjsXl5ndLZj0Q66sNKg+sk50zOYKp7lkNbHJYJHFIY9DBlPpWqbKblX7WmQWA7IePPNpn7z3ea5/PyAzkYP73+FOKYc/OMZEa5IXLq8xc3GArd3bfGrE5qXxbfjvrPKHIxU+vv1Nuv45Gs15pocX+czkMf548FGon2NiyzH6l2yai6/x8HgHUV2hPTjBn1+2oLuXzYMOU/2wOl9CzOZ49mCTF7+yjNe3ldpanm1PrVO4uUJpx5O0nvgW63/sMfrpOVrtJp84UObS8CbqfoXTf/QnDO7fxtbv7+d5tjDptrmRnaQ0k2XIlkSWiLM0zeONLFQPFQHShagLopuQAxZIBKun86xezLP7ySkc50IPTVXcdb7fN8qWNZfjMxWsMTvJ4khracLQhPmrCVCmzlG6DUiBDCKmV1fZcrkJoUWj4BEUBMVWBcIsBAVi7RARF5UydBxFgpnOMG7BZzK3qmJc3FBZC4TeK2GgwDHWQAV0BTR0Yae00JxWGdOCi/jP/O0pZJPaswbsTY0Lc/24WY2rgn5DbSaPol7Aj7TF0AjNRmu3hPajB8l6SisOEtNVhhIBJg7MTQngBuyN6V6mXqFPUrdDJCAt0kBvrk0yBmM9NEBvCuX0AL1Zk/SakXrG5gN96JK0QehwtvGhnqVHgGVJHt5+tldfEoBVAjGCBRzdHnBwa8jlezZfeslmdlmN17BY0357ZraoyXQVajdVtF8QbRiSAftkDJGANyZHabs2QoZYCMaFRwmLpxkiqwnMlLgyVTAy81WsKw/jzI9iSUvFYRPhhm8n67zeUjgUL7Ol1zKHbw9ScNc4cquuebOV8F4z7h/T8UOB/W/8xm/wyU9+ks2bN1Or1fhP/+k/8dJLL/Gtb30LIQR/+2//bX77t3+b3bt3s3v3bn77t3+bfD7PL/zCLwDQ19fHr/zKr/B3/+7fZWhoiMHBQX7913+dw4cPx9H5P9TRI7GaV4qiYsBKnR8KqLZgNQs3M8oF8J4XNgCZZgw/QBLrMcMFinmhzZ6xiT4lrWuGJi2o7QiwQ0F2UahKdMbsZ+YQz0nfXwq18QPDZFJAb6rAxWPfILQ8MHyRMMJY6JCJtiI1+BuXh2FQWn4SkQtILCnZ3GpRyw1wc3IC11YMrVyDCTyWSyWgiCSLAnkwvc4EIQNzefrDe8hNy+QHtuGVm0S47PtIhsUr17hod+mfe5L1G5cY/ESGzbUFdhWu8qUXx7E/Adm1OQYW8hSeX+b20AL9u/bzSzsHuO3u4dr6OXKX13hk935m/LdYuFrl+LHNzA0XIHuAysvn6Ew+wa4xm9PZozxb+xINMUArK6i/ZbOjO0RnoMC9Sht78RLOs4Nwa552c5jMs0e4G0xwv/Y9+u0jhJkCfd2tjPxyg8YqlJYWOL5us7hJsDkfkHt4De9KkWYwGBctA7WsbgSypYA+tBTGSCcBfMOwjKdx/cZeOPQSsuCzYh2kIwe44+xh4OyfMnbuHsKagbKn3TcCxvKqLndH00vOpl3KMzc2wLZ6VdlZ0mAvBKbFnLizyubX5zQNRQwKCf1AnwS7BbYPnX5wAnB9pe2HrmZ6KhZhi1hQoJCLVBc5W+8RqetK9AA9KuC1lkprNXsHtBBNL3j8IEJPb4GNVor0uQb8SI1Bu3yxHK0BmyI1YUq4lvo8A/hayNemasJAWQ4jqYFVJNayWH1N8Zc0yJPS6M2+lJHS6E3tVjNuy0l89PEypASatIvQCAe2rQMVrUSzj4vVi2QOkLL+pdYszqGLkFLyZu2zrASTyaNJkZK1sZiYAOQaROfAfgIAxxIc2i4ZHgj4va84rFRFHG9olkaxtxDaMyD91HhSTN4ITLGiBFXPZa7oIqRkT+jxhMgxbGVVtfMUJUR6h8nAIrgyTubMZjIN1W7cpAK63MSRb6s1rXSgFSQTtizd1c5FFLZQ6NR1GqlQ1lajbPXQ4I/n+KHAfmFhgV/6pV9ibm6Ovr4+jhw5wre+9S0+9rGPAfD3/t7fo9Vq8Wu/9mtxUZ3vfOc7cY49wD/5J/8Ex3H4whe+EBfV+YM/+IMfOsc+Poxv2fi245fmAhZaQxDKj7gGLBS05h+rvvp4D+kfejdjz7lGENB/ZQropR7cxip8URhL460xyeynWyAg0xHk7tu4NYntC0oXBXZP2kjq9pIN9TmMEKAZj2FAG7lgvPHTU90gEBgTnq1/L7RvQAjF0GxIghvVPW9W+xFByMLoJOTyWITIyGb8xgpjO/JUVANygjiJJZbZ8Wgz1gjJj65z5orDp3eeJyCPLzwqZz1KL/fxhV0HWGgPMTvhcD/y+V57mKHxHQTDr3L3xRIf3jnMnc4mthS7sL5M9dYmvrf0EONP3+G6d4K93gKrlTqPBLfI7PoAY7sqjK4vIZbz/E8vn2bfZz7LRP4cpxZmudP4KK8//za7n75Ids9uSoFH6eYRrIWbML7MwJkm3t39rB20abz8dSr5Pew6PEG0p8X3lkL2sIvLuSyHb15jx6bttEurPLb7PpWBfbRWV3CyHnZrgCDO6yUxBmmsiFwgowAfV/MuXWpd6uqmUSNL6Vwf9XGP29t/ijDKIUqSxWd+lZ3r/xBx/z40/aTqWb1CT1czIXCtBsMFmDvuEQ65jNU6eAHq/DBgfdgh18mQ6fNUTI2UICxV/KaaVeCe74AIwKkompCh+tzxIPSU71rooD4hVY8IWxeNiqKkwpuxRAmgbatgPCFiwbLX4iY2AL3s+Sr++8DLCLXaihezebMn0jxBC+WR1JYKowE70O3q9DY009bjiU2DUXIfKyJuxWusBQINtmY8Kf5iQCDW5I2WqgUj6au/PRqkDbabrEu8JgZtDZM0QhzadK9pw3USDT8dnZ+2hvSsDckYdVOvhe4WXqr8LCFej/wlBAgvUM+9B5PNXC+DOAaioGDWEowNw6/8YsR01+LNN2HxrqQVSpo1i207qgj/FoR10lbHGDhNLFKqYZgUkvlSFum4PNfJcLyTxXUtujnVUkKiUlolirVVF3KMn9pB/1w/UWjFMpMjmzjWPPnw90B2oBLBSicltQsF9LYAaxTaWS3IQtxJMD13L22u+tEfPxTY//7v//5/9nshBL/5m7/Jb/7mb/7Ac7LZLL/7u7/L7/7u7/4wt37vw/jk00APCTjWbbjnQtVS/kOZAuEeW7X5kSFkkvdmM5r3RvLvSVPS6UlSJ/JLiP0yRMm5UmKaY0hLUN0S0nKkqjLlgr8vwEIiJLRGJdlzGYrrAW46X9do7um5poWAnldKS4mDAY02sPE6IlnDOGBHv3cs3RJUghX23NSXDgt940xv20YxW8GiixVJpt6w2fp8k9f/T4NYfSAJMZ440+cMKbn/9izlVh/PDLcpbbUY7QuYDkrMtbfSKJSYzRxlZfkCexo3OOiVkd0Of3kPXL9f4JFHn+Hu4n2a+2zkN+7jfrLJUPsQF188x3JtmbHbXT68epPLUZbjVofFrZtZu12nOn2Kt25tov+pzRz5ied4aOkshXyZo+fuktv1NbZ95P/MjrUv0ShNcTd0uVr7A44f/hTXF2YZDXaybdMAC+/4jBw6xM3LI+zxZig3FxiOBuiMSbqvLnGyfz/llSqDjyxTXFnlduM++aVnaXfHlM3HTkgrkpgnrPBQF2cRjnIJpjvNDobTbLdOsc89SVe43M9/msBE9QpBaaqPSt8Bopk3tQ3F0K0BNK2uSrDDkFJlnvwpj85wkU7BY6ZvNxO7KmRzEWXXUpYmt5D4lSVKaAgFrKoCQeQ7QFfRpq+FAlcqc33kKE0+tNS9TbdHo9GngR5UZktdb2jTXS0+0mBm/qaAntS/N/7ULIHQ8xBCzSNt/jPrFO8z2buHDFi6upFN4Ke0NHNvIyhoi5hlE2vmodTrgFI2TMEsw682gr35d8xjdBU+43d3HJUeZ4QXY+tOTzaddqdpJLY8GG3e0ZYLs+9j3pYGfDMnEpDXloYgsvj6yq+w5o/1RNSbY2R4lsH+hYTIkYmbknlCcQWsx7CEpCJsukhanqpo/fSzakpL9WWuveLykQ/dxopqSbqmr9cuJA4SjGnAVpnxzWaR1uIxTuRCnsiKOBzEDSVZW1DXlfiElNy6VeSP/9l+/oddWRwvmbstp/Hk/4odnQECFXi62kniYoTQaylAlEEOpmKnTAaGJiwZsTRp4x8feA8i/dEd7+va+CpFzGzSlBZrAdNZWHWSTZg2ycdSuFaXDDDHVGoYSOraMfFueC9DYtO93LDR0psfUhs3ws9ZzD/aJRSRquIplQk/jCwsO6SyM+L+9oh9r2ex7jaxurLXYpVmZiK+bK9k/8B72TO0xF8okutsfDki8eeJSJsf1a4NApvv3Pww0/nNTD57FYROT1qtsf+79+li0czbemA+ApcIF1UcVjDVWmfX1j30F2bIFUsMixo2EAZ5WlGZPmeVRbfI6EiF460LiJrPi3KYTPEIN1ZmyGxq8KFNDconu5xazbH2rQG2dW8y8TPbqb92lrdWn+Kh46s8fDpCDPVR6zYpTi9yfeJpVuQ9PtO8x7WRrXSsS1wUxxn9vMQdHeXIzBL35z7FcHGJ7B6f2v1xTt4VeFfmWfvCFhoXVhnvunQXc+w51OXa9CSdpXm2D1aZvdRh/959LFzxye4I2dTejzd4lqBiETbrRE4Z2xYK86RW7gSxYUZKVKXktiYZD1V11ZGMhtd5ln/FZu8aNCWLlUe452RS5CWxrt0l151G+OZZac6Wlk17aBvsSkB+fR0ZQZFFWBxG7izD1iLixiqcX+mlNQOYkaUAX0rIpbIAQvSekCraPqM16FAmfdZ9qXyWUTwglb7X0NX6HK21QkrIYMOR+uA/t097hHMj1BqTvQHW9CGSvRGhhFwT5yKE3gsG8IPUUJxk3rGCECaAa2mANNkyYepe8Z4zMTNpsNcpDkLHBFgWZLLq5Xo6gDBICTJ6fhIUyKTs2bHlzkkC82Lz6AaQFxC3wo4zFrSlQgO9jCQ3+RCXmo8rU7dMfgpqKfJRQK4bpRSKtJIhgZOsWQ8RkqEuoSUtfKnrpAHFcJFj8o/5xGfLKqjaD9UczCaSJE2AzF4AZpa3U233cebS41TbeX5q+3lEpqlvGynB1BIEgWR6pkT1eo0/+vYWajMOwU7Dzttk5L/DkS8CDSUr10Ko6AwIMxETGC4EiOHEpSqFwhfL1uOSRAWb4Y9OUO38V6zZ/1d3GK0+zQQiAcsOrNokJjDzV2+cGKB1dzlpangbSVb7BR5IB9ygVkhjWkuXp4PeZTXUJzHleiWwti8gyEjwBxk+18WrdIg6eRprBbJTy7iujxwqUuursvQxh+a7JSZbK/StWNimepjxnxtGFyMGKc3ErIthPqlX+hAkH5rvbVJ1+fW6GPCQknqrzI1Luyg/V8UUq8zWYPfzFbxGxPzmLKvDLqrLuUVIF4mNxMIiot9rkPFcthebTIqmLo1g0XZKlEtVin6Wv3ToXfYN3cf2dxLMrLDT6/L6wCwffMbhzqsTZJ7ZwnnnMnNnZtjzV5/g7su3qN7PsGM8w8K9GpNBHxeGjuG1F5keuMOe0Xs0V7fyoeNrdE9O44iP8e5iP4EDH5gd4utjc+zu3iHc/jBBscSxzDkikeed3EmKHx8lCG4yeGiCgpen9uobbN+8lXmnwNl5h5/eu49N4jR3s3CibyvvTL9D9pCD1/DYvK/Bd9sNjl94Faf0QXJuBTpZ2kEWS4g4SNtoRlYANEA2wS9GPFb4Lh/I/QeKci3OUx+98i5HA4dLz27HzxZw/RaPfe//zdDyDR0VL4nrOsgNDz7mxgmwCXPzs+vIUw3qWzZTvnFL+6FTBGMZOtPXWy9CVIdcJyV4G+YeEHewM/vQFBkw2ylEpamZYDzX0UD/Hmpiej+lv+r5m+L4Iv1Zeu4pqdeiF/DTe6FHUTC/14DvuST1K9JgaYrmGOFar4cBYstKAF8a4UgLCKZDW8yvIhB+EtjnOJDNq5dla3eCBh0DpGZucRaO+auB3pj9TSOctPUijgFIrUGcthklQYH6/f3Wdv7d4t+iSzaJkTbT0VfIbZ6hxzqaVi4AZJMGER0paEvilgaKbCP6O6ewgiZiraViOYpCm8CFAvhuqIXHRMCr1od59Z1PU230I4Vkas8ygwMtdTsbWr7L9HSRuVqGHbNfod0qYdVW+WsTL3K5fQBHfB5LzODyJWz5OsKS6pk1A1juJLQrpFov17hARiEq6C2gmahtLCeCmf4RrJ+DycEs4mqbH+fx/gZ7I2kLqfZFR8KKBSs2uk6u3jQpX1cMzMbpLVGb04C7rSSxNENIawtpwUEa/3uU2iwpKdr8BEnsz5cCaQtWDwf0C0HxFY+xdzoI6QJtEA2YjpACBt11hB1CwSbwqwjh0M1ArqWZlJCmg2hynx6wT4+b1Dz0kT7HEvRogUbm6ekMiBo/0Grl+ItTH6TW9hja3tDKl0Mku/Q3TiKnBPWJh7BoaYODi+5/hUQwRINtzgKdwRnWCeniYSOpUaJJFsuCvtwyhwZuIXDAFazu6id/O2Dn3TK15Rbz1mvI+lEOPPQIeedRygNzfHc2R33xDUb2P0a3MMrLl2f4+JFznL+5jtiynejct3EuW/yLl2r84on9FB9x2f+Sw9X8FJePrzJ+62kWZm+SuXmK0b1PcWk8z/4Dh3nj//M67SmHY/tXyRzdycydBocPfoiXz8Hm2l1G8sP0Dd5nOt9k5ZsN5gbepnzsZ7grBJffvsC+cx9i8zBUt+1iz+TrPJ75I4L6GF87/6tUGsM4QkXom0KO6jEowbC0dpIPDv6v5Kj1AJgQMHX1JDi/z+UP/mV2ff/LDK7fUs8t66mynca0m67MGD9QkaLzFNm6EaItKd+4BzKPkjqiB88z9C6AalGZprMdZcIHNRkBsQspRHcVJNF8OhY0HR1DI8C1kxxv47/YIGM/ENjUI7zKXjqP/eFmz4gHfxsD4sbrpvZ+JBN+Y75zbRCeqoUeagAU2pIlwuTeppaH8YcbDT3NgowJ2pihY7APE6C3HSj1q4JXkiRSP3bLGd6jtfp0hJzR6oWtg0GsWGhXfCNM/P9mndNzDwMVGIjmeZGPDLu8uP7TrHX6EkOIYR9SyXgSOOrN9D6ntFwESCQdLfNI/Wwtzc8GO+/SH1xSYIvUNRx0zQXzuAKpNPswQtqCCJeTlz5Mo9mHsKDY1+GZj9/AsbWitZLj6y/sY+ZKiSAQjD/c4qB9muKgjSxbHBaTVIOz9Gf/DZZcSMpFr7RgLV2KGLWmngkizUM0ErtapRaqqqV+qsUS7cE8k0ca5EsRLCzD9WV+nMf7G+yN+FfxYdGHZqQeCq0UQ5Ap/9dGrmGuI/WGNBo+9AbvpLmJTIQG7X+PN1a8wazkXKSShqMAIonEojVp4feH5G/bjJ6v6d41MvVSjN7WZRepRnhCaQ9u2zAbvaGNRGsEjlhjSkvS+pWqgx5/F5tR04KNTAKJ0rxRj3OpMsCX3vg4S5VBglDQnPcoHIKGaFEJF7D7p+kUS7x2YguSJqbjNFhIHCSCbSxhERAS0kLiqzxCWniE+Igwg7UsuCan2CqWyTo+w74DTZ+ZmRzrmzsUbrUp97fx353GKTlcP3WTx5/cStN+jjffepEniwN8d3qZT27+GZ4Zheq1RdbLe7j78lt88Nf+R/78j97gk4f2kS/OMeSdxS8sMeKe4vZHnmPwhWnWzr3L5bNjjH7U5bnPf4TZ2/f53lqZ7Ft9bMlY5PZneWzfbmbm9/Lw2X/Hm6dGqQ8Mse9QP4VmgZrbIP96h+LhMhO+y/flKof6/ojdxUUKYR050OJje/5nTt75aWy3SyBDVhojLFUnkAK2Ds+zf8c3yd9+kazTJu7pmRLErChk8+nnGbx9nkyjqsyTQaiNWFIBgseGANa03ZgYB2OSdSVkQmg7qIC+AuTaKlU1Fnj1W8dTrYLbNbX/Wi5k2lD2iQud25rGYh+mft90NOPW+8a2wXGTvQT0RL3HGm+KLuM5bQD1jfMitS/EhnmbgDQjMJsjFgT055G+T7w3hNK0pdSm9HgB9fMJiDNm0g8u/QwNphuLRwQxWEsLhKuuYQHZnPp3EOrfRck4xIZ7oIE8nVMfSSWYuCKJO5BRUnlPWKr2vFkPwzP9QIG9eYBRF2TAXHcbZ5rPxrcyspmpXB5IsDMdmlvWk2GlD73UtmxTFCdZk0eZCC7i+hW8aBVkRC5YwCLoYb8q1c88E2XWj1qSVqfIXGM716YfYn55G1IIXC/ikY/dpzTQwfdtrr1s8b1zx6g3M0gEYanG9+5+hAMrkzw2cptMrU7ov4O75esIywEc9RyqXaj6vfEllgWeo4U+G+QkpmJPaHuc23uI146dYL3cR7fg8VF5ml3WecTsInzpm+Bs4sd5vL/BvhPCWgvW/cT2I7QPLSYGfW68h8XGD0iALiWW9oi273GkI2VjoLdQFcCMxIwGeZ+kJK/N9DGX8lmbydcsrCa9Qog0vnGREJbUYxJ6Q8Zaj0FukwFgzGvp6RnGpxmGLXTznJRwYhiHaU9l1iEeVkLgna7Lt888ycL6IGEkCCOYfX6IYGKZ1UOruIUM9XyepVKBucl1hNZ0lP3DQeIyQJudrFEnYh0LG4Glk14kkkA28ef7OVk5iO/AE9EFngnP0LwTIm9XWDuQJTy9wN6P7+Tan13i8d37uJc7zPjQQdreXzDQynH8rx+j9O9e4eGxgwSZa1zIuTRnJRP5LM/8xP+Dt+8McOjoBJcvn8TzyqyWZyn+aR3Z2cnHp2/x1t1TPPaRn2W9m+XWmTV+2X+e+UKBN4dajMyc4e3rUxzY8pMs3V+jdUWwa2oP928dppi7R9NfZFsk2TZ9gz+e2k+4vhNyVQasORZWLWrRATb1LSKAsYlpPjb8L3GtDkKEtP0sjVaZelQg5yyTHYqwx/poTrvkKnUsU9FNe5pAIiJJcXk2oWVJks6GTJ4rkQ6yFFq70zS+QZYFIB9CYCu/prDBz+rgTI1OkWb+oTZPZwoQdlQAWjurPit3FVCZVrWRUBpZV0DDU3+NJcm2wfMSIdZklMRgSOLnSG9LgdZ809vVAF16j7+HENyToy9S85fJtUTq+/j3onefeHotTR5+HAirNXxjhTPPxgC+EWQsPbe4hG9qMnHevqUCvbomyk2m6MBKoucfcNdoDT4IoNlRNNHuqsC+bI6eyH/DQ6JINcARKJA3nTtlCHTjOVXDQdqR6jiaTuuXZmoCGKjR2TcL181z28BTJYgoYrz7CsPRu7h+E+GLDZbJ1DOPpNLiAVOfQXZD3r35DBfnHqXVLmEh4nIBdkZSmOxw9fI4t6/3c+nSGJEUSlkHtjWb/Hzze2TCJZiPFPvNFxguZjRJWCxlMwxdXcPyDR/U/DPjgGcjhWCxuIW+doHAzXBuz2Gu7NjD1e276Toe2VaHT6y+zTN9F9U1b9yFhUXY9MO3df/fc7y/wf5OA7wkQKnH5JzWXk0t7jQBvReOC0Fkge+5ZDpdvS/ViUE2IsqEeOuJmUuYB48Aocvd9QT+aI3eAD021W4B5x3B1FIHp03vJksmQlzb3hT7SAsi8aZJB9YYzSckzs2NhQj9+7imtlA+rzj6lkQsN+uX+mm8VqFktVrixuIkgRRKwJbQnMtw/8ubyOy8wVBH8Pa+h5npHyS0dJoWAlWmIkSIgMOskiNiFps2MImPC4TYBHgImUWuDGGHsGn1PjsuXIKVJYqNJoHsUB1eYPHoGFvOfYWfe2iEmVaePX2rXHn3NhPHx8lZDQb9UYKtu8luLdFYn6ZVfYzXL0j+70+3eIObeI/4FN+5yJA/yUqQZ8/wAZrH21j3G0yMbmHs4T7W58YIpm/RfWSOs8VdWOEAnzh/heq2PipjT7KrXWPfsMu8BRdrObJPLjN4axv3ltdxolm+5+RoPTbL5M0ZTooD7AiWmbvosv1XNzNXOcJ4dAFkhOUEzGfHOd9/gF3V61wfOcD3+DDrtXsMHNhJkVnCgw77z1/ks9e/SK5VTZ6bLXUAWWoP2EJZfOIUTCPYoWnDaHIpv+1G87YjwZNJjrAxs1so8Lc1jUZSgUfOU9dD56C3PRVMVUjlIIco4aHu6qh9A2S22sfCSgm4Rgg1dJ0C9Fgzl73bwBxmrvFfSY+lD5nc21znvWT7GDM3CBhpwI8Zv5e0ljUMSIDqc202kRYA4osbwR1i3hErDun9Z9ZOf7CxnVwcca/P7bEuWipVsNlKhL8oUjXcgxBcNyVA6Q3d1etlk1IgQsDH1BOoh3386fLf4oFD47TRvbI+HJqVveuYfoYSiASWDPGimlaI0oKXfj4RiavR9PgIAuj4dLsel+YeptYp46RkJQE0GjZ/9nv7CENBqDMh1HQleVnlKf9P8aKFeKnCwmbWdvwKBfeL5OQbBLZD/41V3ejJzEFAzoWMcvd2ozLXz1mc+txnqD+8k+WBYaSw1GntBoeuvcHBsTk15fUK8swFOkN9dPivuMXtf3VHIFUL2PSR3iiIhPlt0FDjQxOdBFivImVEmM1Cp8PyhMfCCUlnIKJbilgrDrLzYofFtuTgLYv+ZYEILURsJyUBb1P4QhIDPdKh7PiUp0Eg6Im4lWEP1scbObLUHCx9Hqhrmc1pNIZY87BJ0mLC1DpoG7CIEv+ip/NrDUNMm4lNpLAkYYah5NL9bTR9RTZRai0tC5zAI3Q8zm3fjxCC3HyW4a8dpn7sPqO3J1gpRWx/9hQ7vBo5PCZkQJGIncICAiQWDZlD1sqIrkcu6PLhc68xurqg1sFxcbodnn7rKstP5qhNjNO6coZqf5l19xgn5l/nu1f+Go8Mb+H2fMDsWo5adJZSbhNOPsPTH7nDmdoOWkt11k5exJv4FKF4B+t8C+/7km3b+nDt7XSHvsXEnYiW1c9kNkK+uZ/oY3doZmb4xp6dPPTuFUYKMzhDWb74+jUO7zwG1ys4nWVKfX0s3T3EqW6GEwc6bFnNcfrhD+C9c4aW2EcgL3Il3MX5ozvZu7abUDiseP3cLWzFFy5i0yeVBUTYZOV+mgiabEM68PaxYR5bfY1tM5eSeApjrYlrLOjnZtuA6U64ceMYWvJRwakQg0Ma9B2Z5LljaReRvomwtHVA05Nv/NUiOb/hqTFmNKF0bGg5qpa4uZfR6LEf1OTNe2MbTqeBGTpNV8Q0R0pufcACt9E6kD43Pf8NFdh6rAYb7yXQ2rWjgcn40jNqArLT+5vEXKdewlb0vfGIy1fLRLCKneL6Ep1Q8cGslYzb0WZlUJHrRqPf6NoM/dTYjaCh72nSCgXaX99RKZRCEmLzwvrPM9PZjelpkjZCpmZGLpAM1lF1FuL1fY/3cQEvFCkGggd4dWhio7T1oetDEDG7upVaV9VyMRYF0zWvHUG3ZccymQpviNgSXeWD/pcZjmZi2ShystTGP0vHGacT/Soj7TmoXcGtdVUKqhFSMrbS6C1Bo1DiPx74b5n96d2sTw6CK+K5FxsNfumFP2fX+j3ET/cR1Rqsf+8FnIEC13/x53HP+A8+8x/h8f4G+weOjZvYAKEmfEuCzEPk6e9cHZASUS86RMEMfbUQa62NtAJG7viUl2FtwuLOByyyTsD10WHWZt5l7MAWmjOrlNZGKC3nsKSJktNAL0PiIBgN9OBokNfnSZ2fH0e4auq2BHGTDhMUFDfhMPPSTC6up6pfUkLk6N1nq3vEZkWj0UjNnFM5uxsPo1UZM6KUhIFgrjJIiIg3tymf3RmpkM341DwLgYXddhj62lH6ro4yND1B4Aom3ZAdj10kN+STw+ZRMkQEtGhTUx40BhurLC0dxJIWO5ZvM1Jf0c9IxoFbQ1aVghfivf51utPzlEbh9osv8Rf+Ngb2zTG39y7dC48z8EiWD99vsX72Iq3PDzDx5iydZ/qphPOIoSKLuTkeP7nG44MOL31wghsrPuNnv0enUiEcHGVgaJnV+QVyJ6Y4fbrNw303GPILnNw0wR5R4PrkZvqvj9A+aDHibWfL4hm+Uz3HB44d5HZlhHdL06zvzPDM9BYqn+ty+vYaxb6jtCdDWnaWU8PHiRA6TivpoA1RjNkqzkH9FwpJN9JFcdBA75D4wtMAZVnKl5j2Ufc8X0Or2ixskzpPJIzNJqVp2angJLO3NO1EgJUFK9A+XX1+NQdeoOi5m8o3NgFnjqc+S2v0RqY16qGhwXhNRCIAi/jDFOGm5hcLyEbQTwFeWgXEzF+f1/M+9X1K3kDKDWOzVCZBYNpKAyKr+U83NYYodV9HFx6CuDtbPDzR+0xNkR+B2hNRBHUtSDTNmgP5DOTzyq1Qa+ga/Ww4DOAH6jnZJEV2TNGXEMU/Aj9x4Qjoun28VfkMEjteH/PoeuQJCcf2XsSxjOtJJGsYByumwR/iQhOm1XHsDzD0GhG7j0KJlIK767sJIycu+GeWyY+UkcIYdhXQhxwNXuKD/p/hyCBhm0LQHjhOq3xYXcRvIu6t4LWbJHEWQlfHU0D/lujnGz/9W6wNb8MJRJz4IgRsm5vmk++8wva5aXg8B67gdqtK5Sc+jFUs0c1nkWJ940P5kR7vc7CPn1RqI+vPPAF2Xvm5vCGwMirQxE/5tC1djlNAKRRQ6IdcyNxixFbnDiJsk2n6jN8SDC24nPx8g6Xdq+TGS1y4niXr7mabU6N/SwurOUjUdJmsdwkpKE90VAPhEjffiUXOtvJvpkE+TfxxZ6wUM9KMXwGvAf8UFafXwiQcGJOYCFIbzTBuvYQmkMsEC0nUJrNkKu0OkJIAl1tr48nlMcYAiTPUxPZUrYDMeh6nkaF4e5jJ8XXWu320I5vcSA0v38XSALcufFwkDSwCmcHptNh38xzVzffpZHZw5N01nSBhxu3E4w8uSNqtLfRHd9hWe5eJgkuncYsvu89yc3aRx+tDFAeW2bu4i5XdT3G1fRtZLhE2KgzbZeovWDz60OvUoyxvtyZYDPp547VbPFLeyZ7GGDcGGpwIHNwogzc9w+CWCnLuJ3GcGSYm9/GHJ/+Qn5j4FFuf3MlE6x3Cyd18t91PdSTkhcJl2u7HeWy+j4HjK8wtV5l+UzDRkkxOjfG6nSckIMTC9NtS9qBIr6sVPyChgV8CxdYqm5fvqOcaJ+VD3CLWaOyhFuIyDrR1PrCxb/YAv3mCJG6jtGsn40Oky97Gmpa2HMXuHU2fkVCavluGoKY0QTPOri76byK3pFBg5WbU703QHjKl1Zu5kYw5JnMD8ul5pD7oCcTlPT5Pn2+EF5FiH+K9f0/qe7HhM/PX0pHbkl6BSHqoHHpJEkDsKSXAzMsYB0NBTZYpOw0NaiYgT79K/Tr9DnBqUFtNrC4S1VO95SeoJ9Gujo3zkklwXqRTkE0lvUhq/qQLhQl9jeEhZlqP0gqVJm0y92LAN9OW4Nld9g9fRZj0tHRg4wPCR+rH5hlbkqSjp9TrZ17qpmvtYa4uH+l5DOZSXZky5AgQVsQx5yWeaf0RjrGOWiAtQWfwMSqb/pIStsMqffN/gNu5S09RIgE4NpX+cb696fO8/eRx2n2jOF2RStSQjK6v8plTL7F9/h7z/RMM7VRVIxvbp8Cy8YEW8gdR2I/seJ+DfRbI09OmFhRRFTLgDEGYI64nb1p9xkSXepAIEA5EPtvLSxCUAB10JMBFsOPdNnd312kLm+XhLPW9M+RvjLK0swnM47QEywsO7lKRzuAK+Tt3qW7eBK0ik3e6jJ2zsOt1jISsjrS0C0mlMr2D0mbHNNAb1S/OJDCcQpAUdIhIrAupwL+0dI1eNhOVbzS39MaD2C+aNteZoFjLlngn7uFJGHtpDyNvbEMW2uTH26xVBpBC4FiSzEADK6diIdq0uMF9bCxkXXLwVj+F1hqZRpf+bXO0Bhbpa48Qtwi2QUoL6TgQ+hTWOjQbSnN1G03cICKb38qzb/7PzHzyE9Smitw+N0upeZO1LXe4M7+XdpjhYTz84U381Ik1rix8gIceXuCdd2H7d19l088d4uUvn+bmO12OPfIcd2SVzugoTa9LZ/whrvbVmF8e4JDIMCQjuudC1j8hGW9uY75yi0OPCaavLpNfPcLFWYf+o3PMWwVu3F/iQOhx78JfMFWAbnazFphUe00TmOiDLilsgF6BvQDsKOSJqy+T6XRSwpx5foK4Apt+5ES68Eig6UOQAH76kKSYqKV9soa5ReCFKZoTCRFEUSouRiRgJHQ6mtCpryb/Ki2QWjbYHrEWn3ZBpAPonBTto+nRzDMdgLOxi1/aoCzYYGM2PCLZMj1251jLTIH/xiOeSloISZ1oawSMUj/oaTWcR5ULdjdcT79cl0YwSTm3ps73W+B3oK+kYhtsrUDUgfwAZPJaiAqh04JmE9rt5N6QCFMbgdYE6Al0+pqtK/sZvqLX3PNgcgRZKLBY3YQvcrFVL9KPwxgmRARCSp7cf5kdY/fUPdPNkWKeJ5LxbWxoFAdJG7eRvniqxr4Ezs2eoBPk47ANQ5J+pPuE6etZdkRx50Umbp/FFdrdIlS6a2f4cSqbfpbQyiKJ6Fv4IsX17yCEBZZNJCzmhnbwyuHPkLfL3I/6uHt4P35G4PopYQLJofvX+Jnvf5N8vUnFKuEfLOBkV5nNq+tIoImkSohtCjP9mI73N9i7Q+DmNxBBAFZTbaygo7QNYWkFJuoldmPGNnlJptKRcLVEa4GdwRTFGJ7N8+i3Qr7/6QoD2RYtAfNDHYZtCw8IihHLxRbhjnUiWoS7s8AKsMrKEUFmzmVoTRc3NyazdN1qo5lHqaCpjYwlfV76MCb6HmHAAIelNa8NAlGPlicSwI8VI5FsOgtW/BKt0Ivba5ty396eJdyRGpunYfj0NmhksG0fe+sqolKML9+//y6SkApVllmmqytS94WSgeUuVhTR9hxarmR5cZS7w8PsnFlRj0tKZEbw0mezLH/1OD9df5NCeRJaHoxOwOxtnNYdRnM1bq/BI7lrvPSIYO6rN6heO0C9fxWxp49vfvs8H/tpwR+cf519zhFuhIOs7s2x4/Yo979xjvJUkbDgsHrnbaI39nFxt6B/dYWfGvF5/ltvs2ffZ6gPl2HwYS6v3SX8/kmK5f3cX5/lTuNzePuuMnVxgp8qvM712lYyN7sMTL/C1X0jjJ94lvvjT9AJhOr6hsQUGQoRce8AEbdDi4h0meHNS/f40OnnsfxIa3H6OcZ++vSj1IAqjEaoGXpoSN1K9kBsqk5dLw58E7GfFqHpVqJoNyQB/A3KIlZWXUi2Ew09BlEbrI1An7qAteGV/lykJvyAWmSEVAMOqXHJ9PuUMGty50HvD6t3r/0XNXyS82NhHG2J0jEOxn8fC2Rmf9rJmpvrWYJ7nS1MuRUm7BlVg8DxwCtArkRPMZ7YJSfAzhGn1eZLMBipoLyVJWi2U/wutYbpz9JLGGn6MsKQALIubJqAfAYZCU7PP95jMhea/RozNhIGixWee+x7uNmIOPDwAZ7T++h638uUFg89bk497rXOCNeXjxnnqPJqWToGOTU3S0Q8Er3CB+/9OXarG9OSFIKguI3Kpp8jsDKKdDtzFNZfUDGRbobZ4Z28euAznN36BE0vr2LELFUp0Oom2yIXtHn09nk+fvE1cp0WyAhRgP7xBnMFi5WMQAql0VcIaRHRb71H2/Yf4fH+BvtIKn+UZ0PXBb8JVKFc1NqF0BqGpXKBfam6xYVBUr3ECpINbw2DKMDQMKxeh24ltSEtgsBmbLqP8qqPN+wzFuyikZvGpUAXHwehTLEiQHlZ1e5XHcUkVrsGcoBYQzcFfNCVw9IRtJGhVNm7IXvMlYINXyYgbb6PtfmUVmdLLb0bwUekdoceR8wctRAlJSKKIOsTdrJxtVOcCO+TNwhzHk05Rb7j0gJEJJBdJ7YkBkWfcLzCGovUqaZ0V0nHE3QzkG0J7o6VWMoUqLtFznzGZvRPbMoryrdWmYpo7RW8MnacLTvXOXo7R26tAAv3MBXQikvLPHzla7zVvwd7dJBT2z9NrpCj4ToEy49yZNcwyzMWn9rxQSrXmzSG+plazuM9doHiW7vpKxVZzdzmYCbHzR0lJqOzDG5+iIuZMZ4YaNPYP0D1ne+TXXOxIvi0O8Wd0TbHcw38kYCl1+Ceswb9XXJXvkutb4rC0a3Mre7jwGE4s10ibKnjziTlapXAtiGIWO0bRggLgY1AIKKIcnWBQ/fOcuz6afKVKrG7KtZSU8JdRMLpBCm/fZAwciPbPhC+nrpmrFkZLUyqyHypc46NIOCnBMO0fz0SIHKK9qJuQu9CC9KR2FAPQiZ18DeCvCFJo2n2dE8jEVTiILwooV1D2xa9FftIjTeOyk99KXkQ8NMg9QB4bnxvBAeZCEUbhY3UvX3pEEUumY5gRDYRBLoNovZNRx7kTJOEjWCZev5GgBIWuFkY30TcNMcMIAygUoFmo3ct02uVnk/WU0CfUVlPK40RZmub41FI1JIbI4DQU9w+MU1/35K+ph5fWjgy/4hS9+uhbR1PEqX+bS4iIMLi3Ozj+EGuJ4tYkMi/AsiKJofFm3xQfgWn7cfWnMjO0h54mPrETxBYnjKIBcv0L/5bbFmhXhjkpcM/xWsHPk3TKcTbh4DYIGy2Xzbs8JPnv8HhO9M0I4dsFBFlBN6TsDbeZTWr0vMCJHUiWkSEP9Bs9KM73t9gP/Oy0jyKRViXKjqzfxiaBRiZgkxOpf+ISOXClqcU4QVdqK1Bt4oCfAXMbaeP9cEObifHgHccq3oLqpdBurRljlZxP/2ey4de7vL9x19jceI6xcYai/kiXSJK2AhCLO2NBQtBhIUFjsWZz03ygX/VxPVlAu6RpbUwO7WZ9QY2KSeQkuRT7+ND9P7WmMl6dlgqMGqjQACpz1LnGmomQsqI0v4OP/2ZF3jjTx7m3QubkUDu47fIHFlCSMFwO0fWErQFRLU8d++0GO9fw9p1DW/fFdr9s1hEOPreqtuUoJ2FW1tgbBlWN9co212GNs2xJrdx81iOh76nKsdFtmR+foq1bj+1xyB6pEL0rxysZUsVTIwkQgi69wOiz0XUc5IjXoHXVm2G5r9J95mH6Z+C4mt3mNk/w9Enj3Ol24b2MCyPUyms8fBQhpOvRuR3P85Rf5ZaLqSUqdAMtzDvDLDp4iWuHBwm0wnZFBQQtSvcG3iYiS2jZKZvc+XiMkXrK+w59Ku8urjAdtllYKjMh/eeZkwM49sTFC2Xtt9h/6Ur/NSfP0/Xdch0Olw8fJhTj52gZheJIp8D4et84sU/Jt/wsdraLtnV+fOu3rqRVLEoxhQeopyVmYyOdHegnS5KEiWc8QG1TtNVXDsipflakfLho7urGaadjoHpIUkLRF4JlZGv6dvR1fNS2rxNyq2WehlyjI1ekqSehFHNzNDNdySfAaobXxO6xQ3aPQm4xNp9at49J6X2SvorswYbDyu1n9JCVZz+JxJE1GPoupMENZ+MqJDLNnURInO/ULkSOwJyWSVEpPd7PI/UnOKxGD956kPbhtFRpfmvrynQN/cyaGn8+xlHA70yN3cDl+/e/jR1vy9RspO4veQWruSJo6cRMrW2G4t0pQUymf5Ar5OpDmistnFQshpgJ8hxa/EwIAg02ZqS06p6saRIjc+I/4XN8obix3p+kZ2jsvWv0CodJNLlRy1/kdLKn5Ktv8nr+z/FS4d/ksW+KWV61+QqBSqpKUp0tciC7benOTJzj1PWfjrC50T3Ld50BhndPoos1GIW20RSI9T2TMlla+g9COhHd7yvwT6KWoADtRpxY+LFGSgPqhO8LExsU0EnnRXFgOySMiN6uviNUKb6uutzy83gNjbjyYhmNiSbGaCvbwdrhWn6zr9Ln1XHikqUK31submZe2Ov0h6BLFUcLFpYuCgjrItAYMX/CSGwRlYhayT09GE4URp4SSgYelyUupZkIo0/APQk38cBL8Yup5lFJFNBXSJhoOjfpBkjArm7QPhsHzvcLsN/4xxLfyyZDSxyn7wJNhQ6HkevbGW6LbQJzGf8Z79F36ZlZKEGwuirCuRVboIqpRMJmJ+SLE8ppp2jiwBGxC2iqQJhpojdjhCNAlfeeAYrsPAsWOnbwvBDJ8i9/AIikFpztRkK51j/0ibOlPbQnKzy7McvYp/+vzA/eAt7cIXFE2MUwzVOv7rC9v1LNMe61IZqbFvdgpe/weNHBXXb4Y3MFIOtHRTnr7Ol9W2ak8e4t1ahNJBhbKnL4tcXeds/xPypP2Xr46Ns2eRR/OweVu7/CuPNGT738Ajf7z+Lk93KworLJOu0DtrkZJPHbp3hU//hWzgmL1tKHrpc47HFN1irdylyg1ymDq6DiNoJwzY20yhUaVVBoIRcoz1GqA6PzbYSgh1Xg3FKazVtWdN53Wi1BScF+CmyNOThBRBpwdSYeTuaKUek3EXm+llULAxJRT9zbVPqGR7U5mO5U+o5+5jeEj2Bq3GfC/3D2NfeBWcdlUGg55MGQjPeWC1MaZCxFey9tK/UHpUkIPbAvjPCskZhUwsndkHo60cWhfqSqlznyNT3JH+FVEG9TSCXTwVbpoZoBBfSY9qwlqnb4nrQP6joptNOrbdQRbeKWZgYTYRKJCuNEd6Z+wA9jYkMBqeWZqSwxvaBu9p6ooWNxLGdegYb3kdR4kaI8/tToK/nJpHcWtpPvZtXcqPxSklwkDhOgx3D53mk+woj63cV1xGK7wT5bVRHn6NVOohEEEmJ27nH8Mxvs1B0+b3P/WNmB7fj2148rrhoXpSakjCeWMl4NMs3h07wRmsff2/h93GciF2fsaiWE6BvE+my4DqzRkr8uf+jNv7/5qNWHqXfr6j9lCGRIuvr0KhBoagifkcnlfbcWoTObaisqRxTR0BuAIZ3UZQe/W2HuoAuNl3bpiM9LDdPuDKB7c5gr14CYWPZNntWArzKJPcfqXGtGJD1IjLZAEuDvNqDyS6TSFoZl/mxETZPr6QY7EYNwXBW/d5I7PHmFgnhp3/zgGZFwsT0Jl4OJrndmUCKkLK7xvbybTK2r8eSjhtIXUcKyFqIh0sUXIs2krWhJsGvnCZjosClxbF7kxRXVAMI14JMX4vc9nkCr0OASIkZiUhvI/CxsJC4IkL5qCWm07tFRHe0zuoBh8FLLt26g7dk8ddHv8m+qSrlrI393CNw5h3orimJWwrmtxzmuSmJ15jm4lyLudcDZr2rPDm4StSs8pLbpjyzh/7jM9y6LNh26TatExOs5lvsXfOYHBgmfwOubzqPHY2QnSpQdcqE1yPYPMm+N/rI7rjKvhOXWLlTJbP3V7nrNjhy6zssc4Gl2T28ue8g8vX/RO4Xhtm3vgM/t5vu4jzPrr3J9rErhA0QeaAK0pXQleSqN6AWMSo6MOQokOqMEFk5RDiP8AOwnCSlLvBTQJ96aBZK467WFOBnXOi2E9eNhLioQmzZ0cAkQgWQhqHHKZuWfoChUu6lHoOnGbEO2gaZFG6yUzRrGLkZn5MiuY00a86xJMoVkCpORRqII+WGk+jqc+a3EdgraqxdU6vc/Cwl9Jh5RySgZCxq8b4ywoW5QOqvZMNeMcPT44utJyr+QuWAGaCUKhium1HliV29T8P3uK65bdBWHddKJVUMJ/5On2zmaVJmY0FNJO7AdPEsx4WRMVhbhnaLuCZJ1oX+cgL0eo1mVw8gpf2eQJ9O9Cnn63hOk3gA6ZiGmMek1sFcywQDheZvlNQHEGC6OEY4nJp+nFZgx5e3ABlJBoo3+HT4h/SvLygt3KyD5eD3bWNl7FcI7LKW60Lc9i0G5v4xL+57nNf2f5r10gggEjKRKdLVvNiyVCiYnQHLEXx/9+OU76/R6evjX+X+Ks+VvkW2vxVPMUBp9aAKh4cyYu3WfqZmq/w4j/c12PcFgNsHYYs4HNSSyqQbhlCvwMXTSOHA4IAyKyFUfq+vNQW3Cuu3obiFqegSTWeEptVP2KmTIUJEfZy9P8wxRzIZoTSJICTTFuw92cfO0/2UClluf2Se6NiigiuBMd5jIq0BupmQK8cEIpdnaKWG1bXw6hJhmAERiaqTZhQi2bRxVDQphpAS3eO8+9THWqq92tnL+e4BgggsQobqSzzS9w47i3dU5GmP1kFyD8+CsoMdwVVL8FUsKl6IQM1zoJHlmSvbCKTEEjpWQUhUBQMl+qhhyNSVFWe0yOHSwdZerDTHFygla+YjdapHPTy7yV9r/AnuuCR0bVrYuKP9cOIozl+8BhLauw/xr3f+FX6hW2AwLLL7ybOU13Zj7+1QywQ02MX6q4usVByGKpv5AAUW7EsM+1cZnX6cc4s58l+4Q8ZrkKkJFk5fIXD2s/shj/VCGWfhElea+9ji1plwpwidnZw6/U0K4zuY+cRRnJnbbLs7yvrQNe7t3Ur2lQ6VTQdZER3mvGE+HZ1mjEWCwzZiq030GshtYP2HDqJbV0vj6boCml4D/yCBv51c5duIjDajN5q9gGAOY8AJUVp/paoEBBMqHZt/LbBMgKp5KlL90JJae7cShmx84YazWiiAtVAtbC0R956KGXcQpa6rB6tDVHp8wxvHb/ax0BaM0LSNFcQ1M4jU+N2mDlrLg4R1p0ifuInAhzAPoZvc+wFTOiTZCzyoeaZJ1Yw1rTWnNdz0EsagJlNmfb3ORjXsuOC7unCRTMYTv9JCRmptIh9qdSiXlcWS1L0guX6Umqe5fxz7kDpfWDA8Cq2GUoKyHkwOa9N9IhjJIGK2MUCki/qYK8QxjebRiZAPHHxDlXU2fOqByHsSISsWfDYAfZACe6RyP2j6W6xPsFgfiZvcWVJiEXA8+wIPiVcoifUUvUikgLMnNrNrbhnBEoEs4bVvUlj9Ovf6WnznyS9wftvjdOxcrL0bgN/oQTDx1E5OGYiFAN/yWN4xhisCDhSvkc10iISIy190UQ7dPBahjJi/fYDZs08w2njzPYjtR3e8r8Ee6SjgFgLlnwuVhNyVyrcZAqEkWrtJWB7HW/eUL1PYKrrVFdBV4ZuifQ/LsiiOL1GsllOazX0+uWsAAg+mjZCgn760cCKLRystHvp6gbWzu7j4qXWWR5awhNFQE102IwLkU7e5/GSE14gorjsc+8M89rpuphEHRUGP2iPs3g0Tm+3fS/yHWNzWHc8kgnpUZD4cVv4sIJQ2C51xnl/+BBEvsrt4S19hA9BLC9nvEbQFp8J+XiitspTifgIot7IUug51S/b81rS9AQP0ElMDP8KhSYYWLiEWBQIdk56elUAgkLZFZxQCbJyhkC4CQUBASF44MDFKdcs43/v8I0yHx7i5f4V/33yD7M3tPN23gy25N5jp7MSvHaAvOM/hp/uYywQsftHB+cAUe+prnJteYu+WFSg7VM63GbvW5ey1U2yesmkVR1gb7eeN197kEW8Jf6zMwqk6/+LeNL+6ex1noMmhx6ZYqzZ448U8Qhb4zLVFBvo2kS+XKdS/SaFks3/HFDvH7wPgOCFyUCB/woaV1LoJaA9nyFhBihJq2E6Jrn2QTPVdRQeh0ZI1BxJoc7XRKg2A6CCvQCh7Z2gpEAelxdkpbdM8ONPgyZj0IdGI0UBsowUCVFyMpa/nW4nJ2jByQ48W4JiAUR2ARkQcpIq+rgnGCzXQJyoqIAikhW37CLsGoqGsdlYOaBDZvjJ5Y4FfTGnKJoJMr09aUE77u9NbKr0d08fG6Pt4nikUTGuyppyxRI/LUWvnRolLLn1vA3hGsIr7BZhXB6oV6OtLAX4yHTW/lGCRBv2UAqD+LRXdZIsqhS/rqholaQHJj2i1c1xeO4apmBdX503dUgCbBuc4tPVCUko8HTxqfmH+bdxLBuxN2btQKpAPdICenfA9ieCtux+g0ckqDywRO5yLnCi8wGbnBpbptKikADV12+foWxehtYSb+cd0Nu3nVtHn9Wc+xfTwLiLLUtcOtWyp/6aFPLOMlg1OVmn2adKwCdmbvc7uzFVd6kTEUxRIBbQSSnPQPLudDyx8iSONU+9BXD+6430N9pIhtWlECZV0ughuVzOSgkpJoYO14kCnge/5hKOCbFvqTZJVT09r0yKUMNcFtw3CI36cwTJEWRX01+nCyowKcJERWA5CStzQYvRGSP+3XWYH+qgP1lg+6NEqAZbUYGfFTLhbgrViyOmfb7P/S3nyK/aD3e/SG9OCJF8+nSC84W+cG6/fS7jpb+P7zcepy0K8t0xNlk7o8eLyB+nzqox6y4loIoVm5g5rFcEX8xF33RpNI39gjO2Sp25O4UUWlQZIDQ5ifAnLDrCIsAiJUNK3S0SEoIJHS9mD6eLg4ZFBmf6STaS4ZhRHryouYTz/aj9GdA9vI3r3Lm/tPsZcuYSkxs1Sk4nhizzRDrGkz5mX1xmQl9kyUGa9VSVrOUwePoBrrfJC+SjbdjvcemGAqHGfvQdqyGCBv36swHz3AHU20a36lKb2kts6Qrfr8GRLcGjXISrZEX5mbJ2uWIW5Esf3HSN39xWwZ3n75j225YpkvAPkHglpLy/HYZvCCHchiG/7SS9uW+B0IsjKGCQc1nBkVfXx7uSgW09oM0KZrSNbg4hDHKlnqp6Z4D0TTBczejsxc6bXXEKM2CIN+Pp/YUTcXjk0KnqoGyxJJUDHLd2NhiuVMG5JkIEKkpUmaMpwU0BqjTIwHNeM1WyCiDvRCDtz50F2IQpZZZSM7VMI1xg0jXn8ktKcYxdHal+k6+LH9SzSnJ0HXQwyPY4NhwEyK32e+Z8GXEsDdttVSoobJudIkmcUodAm0A/KsRQ/M4KcESzCNlQk9Pf3mtvj4aWECBObYKwXaauKJBH2hJ2cb+YZSqIAvnP/p1jsTMYKNiJhNWZZLSKe3v8aWbeTzMsU+UrX9IDEER5va6m7/uln3w1BRrrSuNC6i8W52eNcmjuElFD0qnx4/CWOdJ9X1fDSpYTNK+rC6gwiDMCyiLr3eXHqJ7iw9THq+QE9R+K6QiYspCfLT5OnZaskByfDAzGlGRGx07vP/e5O7Hab0b6ZlIKjPF/FpXX2fPkljje+SX8wz2zmUw/S0o/weF+D/fzYp7GCGsXGPIIRIANRG2QN7Kx6iS4iDLDbYHVC7GZVmYRKOch0mR0apn9thTDTR1Gn9ojQRzoOIk7IRG0E24KcA4UyRKvKX9ptKzGv24VOA++NNtukMh11+gos7+uweFjSGBdURwWunWzISEhWNvu8+atNhq56jF/yGLluY3dJGIQJojI2pEhXuEr73gCJ1oYsiRASqVP3AulyrnOYuiz2KDPGVSaAtszwLzuf5phzic9abytlI7JAOkRYvHW0n5lsBl+0AdnTb6jYzrB1pQ9i0laHv2kWYQdYBAh8IiJUIV2hJV2fsG5BFmxbf7uxxnnqUGEtJu9cbf4QSZcIfyDH2889zbI3RqiNZxJBPXC5FNpsDjsUazYzb3yJw3/zo4y+8wyDax2Wh7/DtaHDfPTCIrObz8GBPMW3FiksHKEry1ydWubqq5Lp/Nt8wD7Bk5PfZykEn23M5kfwN7U42T7KtvwKufA655Zn+EShxP0ju5nx8+QeGWU2KDHx2irl7ib6ak2iGADU6otLFuKMZuaWVCbCtg+ZWKpKGLydRQ4e4M5awPb2af2dBonQhkBbuWQqEFMExMVojPYUaA5rCx5M7SThdGkNPx1BZxyYpphVT3/vUJU7DYQSFIiUJmpqxoddEnUbdV3TmCfu8a7PNWCYCpi77Y+ztXQBgRYWwohyNIclFTgozbkEnWIC8myYY7oDnUEqk+oaB0Km1j/9V/LgepH67j2/18jgDoNsKesGpIQoQRyMFpuutWAW6ChIE1CZFibCDlRWoFhSqcXpQaZdFunmMvG8NRNIm9eFfh6mGk0kkd2QV5ef453VDyq027BM6SnaImLLyHTiGklr8fGap0Be36NH+zAmfH1ep5ihM+iydmUvl5cOc37uEN3ApWit8pdK/4Yt/k2E6apoFt9SjtNz03c5mpcQdIhsl68/9Itc2PIo06O7FX8XCanFJvsNj1vohAbLUt2MXVfHleotZVumkajL27UPg7TY6l1jnBkkSrCXSLr1LNvfuUx+fY28kIBNaOd+ACH9aI73NdjX8ttZdFya7h36arfJhJMIEQIdsAMl/VqeCsYLOggpEZGliG89BLdLYeU+TuU+rUyR+T1byVQ8BqxF5neOMLHYBFEE29NgZCkiyGUh0kEyHe07lZoq9EYRSLLr60y9CZvehsgSLG61WTpiU90c4g3ZVAYkoQudQsT9h3ymj0VMnpZMnc2Qv5ejOehgRwFDVaVld/oFfhm6nq/0XyljKXttqkOQaeBbNvtf6Of6vgkuy50U3smzHA0ni6a1D+MuswW0MoIFL8/3wiMcFrfYwYoGept3Hipx8lieSPhIIiwJm+6Nc/j0buZGFjkxcZ2BVq5H45FAnwRXhkTCGLQkFaBP/8siwAu6ZGRAczVPaFvQTywuGP1ebWOhP1OAb85SgA9tITi5fw9dbCQhylngMlndzAvTB6g4eSYfn+HQRwc5m4twnmiRq2aZbOxm8e4hToch2/0Kt07e4gP9R5jd3qG2b4ixVo7th9cprk7RX3uDpa0F9ne7PH+uj++fDFgvlxneu0726m36lrpcv+Cy6yPTjF9fY3SwzNT0GkHudeRyhct/XmP+s49xWBheKKAumP7jWWR3gkkaeMYXTooxGkZv2JCdYWuxpdvDpqSuuHdCGp20dUoE2lysb2464lkmziWl6aYfomnqhFTaqLHdSrRmngIVaREXpjG14YVD3JApiLS/PtpwL2PK13MIg2QO8TIIqrKM7TXZmj+DkH4SwBVGOAYYEeAPQje3QZsnwQJAbVKhBPiemhIklo+4L4RR4dJrS+qi77Vu6VP0c2q6KsDORlcVFImKjEwyFYJArVfsxxdqjPaGe8XxtAE01iHqQF8/8TOOsw+0EmC0e0ju/QDQa1oLUIKhH7LSHuW7C5+nTTHmHaaEgClvYC4xWOqQ8+K6dSTa/YalMsIrJEBvYjyCUAuTEmkJWjbcvLCN12/+Eu2uTTeAzd51fqL/Dxh2FhBhrwlGCkm35ODN3Oew0yTqKjfgye0f5DvHfoYgm1MBdtZ7aPCkjB4i+Wvrl2epEAtLG7YsWz1Ox9JOKOlgIbG0a8aEaksE5avzDF68Qy9B/iCp8UdzvK/BPhTQyeTo5PazVh5n89plMu0FnEBobcFSm8HOJqAPmviUybNPhGB5lGQV9+IZZcq3c0wszIMxo9kWy2E/fSN9iHwBN+wiHO20cV2IAlWWN19SVfvaDXWvrqqBLSKwI8HELZi4LZGeQ9TnMnMwpDIpiQZs1jaVWCnXuPVIl9vHJIVVG5GzETJkx4LP0PlB7p5o0xzpEriqXrrRYdVLVVYPA8lbp/oY8ycJr2xmXno9DbViRdH8QxsLIgvawuX56CH+mniZ0HV591CB7364D9+RWmOGTTOjfOj5R8m2Muwo3GPH+ggi7nYm4n3cvLyX/FOvgusTATVgFdPmRW2DYn8DAWSH2lg9AXo/6EjAPh0P8SoDrOBhaxHBmPfn8j4n7NMU9lQp2hGRK+iSp7K9QaPbZmDZY8K7jxXdIKp0OFZ4ioGRF1n3StgrrxMWN+GN5Nh/xMV7o0Dj1CKXdo2RbyzQPWxxLLjDnZtVLp5d48DnBvjJ5QNMRD7vbCvw0qlr/I0dm5kJF9n8hYeZeuUyk5U1XM35RDfCP3kAe1uZWiXL3Tsz7LbayUOyUgCYempCSISns0y67USLj1LMIwY2gWr2JCBu+qHBIEBp4ZYVVwXriTpPM2Njk5duSgs1fR30hoqFCJHwsXTbZitKUvVitXCjVUEk10sxxZooseQU2ezdxZK+9uWHidlXCEXAwaDah8Zy8p7YLFGtWtcgKoAsQ9zQRfYAflUWCKTHoFNJfv9e5LnxcxH/DwmslnMMVaQSdBw9XiOgmTxyc2MjYGkrT3xxC+JOlA/cT0K3CVUJhZJSQWOLiRlFqNbIuBNk6nmnBRqJWtduSORLXlv9KPWoHMsJ6WJ48ZD1GEb67pKN16pXYEvGQYoBpbR6o14bDR9YLQzxZzM/y9q1PUhp4dBlS+YyT+W/yog9qwTQDVKEbNeo5fMM1VZpZPr4xs5foLVngovbHkNkcmRSQB8GIMKUJysl84rUy8Q1EoDsqm0gdGkUk+hgo/6OuHPszJ+F+HcWmbkO0ZU57LYPCEJdTC203ouYfnTH+xrsM66qleMAFAZYLJxAhgtsvXUSO2xr/6AmCDenfIpRV5v69Y6WHhQmWDqyibGFNe6KMlvn7+BkCwqwO3UIfUajGZibYba8mYlcRhOxFuksR0nikaXSojJZRU1BAK2aFgaCmMCFtLHrFlvetpXV0XO5/FiZ9qMNKuMS25aEIz6e3hx3ijaz26tYujmEaSSjwC/hTyCoOYPc/OSHyP3+FmRgJYXLelA+2YuWgEYOTMrQWbGd14tzrD+6zpmjRULbQmozfK6d4ei7+8i2MyAgm+sgLMO8rIR3AUE7gy/Bx6IK1AmIgBVtzs8R4qHM+oq9idS/kqEquVj5+aUGeQsr1vuvUuQ2WYxvDH1eoV7i+OU9DO58ATvTwhVZJFlyBAQ0aXll5sZcCkvLLF2VfKTb4LI1w/n+3dx6s8jO/te4c/oCswe3UHYsPjV8h+a+KR7v+pw9d5No14eorcxybERwdnicQqfI8kM7WSxcRpwTbG8LTp0sceDpEO+2z0xniKd23ABpIZrAtxo4t65wZ+wos6LN57Yuw2JLPRSjKZvKHdJweplwJSenBMsgo10uKYkuzbUM4AslZqnOgZHW3CIlBERSFd9JrOWpcYS9HwoT3W7+rf/amghNBKj5jdGw056AGHQ3aK8P1J9QnDRTrjFSqpKp6A5uvqksp0+LLKXRy3xyzR/ER2UIsgLSB3sdhA/+gBZkzPXUm3xmVgUAywyIJO86ubZMWUVESsBQJ3RDl6CZo1yLwAl0rX8SUI1S66P7P2BCLmyp0hpNuepIENo5Zko/yWDnDIXODYSMeoUav6VS8wrlJHA5bWYwZe6kzXsukECNx1c+84Zf5GTtA8m5Wj6wLG2ckEksZrnU4Ol9L+M4hl7+S5qrBvnYQqPdMLqqXzfyePXycywv78WSkMnXeY5/y073DA4BkLgUkCoi3lm5g9VYo3/R5sbIIf70J3+dxeEtSEvxWS91fqRDWgwpplYpkYFSJC4gblIqHG3WF0pOdlHpxq4I2Z47T8ZqAmBLwf1v97P17fNkvRloRSBCOrLEan4X97y+/8z6/P//eF+DvWtDxlgWJYCNtCe4t+tp+pYvMLAyDzLUDzSruuBJIKhAWFEgjAVumU21KWTfTrZJieUOQ2tZScue6km90C7QxxqTQgdT2amUnlgytpPdICPl0PEGNGPSkcWdJqYckxQCKSzsQHLojVXqOwTtccNzQw1sqtRi3AVTU7iyqAlEikwlcFseoHB3CNkVKvBakJRCj02Uaojmp62M2v8S8C2b7z5dRu7rYulqXRESS1o88eoRNt0f0QqYRApJhA3C0lCbaPYt6XKDPmwauLQ1a/c1sEs6BDi6zI7EJCcawFftYcy5ESGBFhJsLBxsurhcpsxrDOHr31iA03HYeWkLD7+zk+01j9Z4AX+wSUiAi0+RJg6+fu8wc38zHxptQWcTlzYtMeFn6eRdPOcI2a0eOxYbhNfP8r3wDvt27GbonRkenWyzWLpGc6TMWGmdiXqT6+1deFtP8Wp9iYd3BNT7R7h94S7j01vof3iYu41l5spD7GkuIV4swCsh0m6zGnSR/iCiUY85j9Td4YRwlDAaRxDJJLApN6XMwtiqbO0DueDmveFkTi+9WlqL8vXfUKpI7LTMEAetGXBPW8ZSX5nDoRcBjLasswV7Qd5oebHulLqofu9GkGuRkQ0yFe0KCMNEAwSIPAj7Uc1lUtftub4ZTwDRAohOot1SA7cFwRiE2WRyoVQWQmdBb5YCMKI4fXqokl7NWAvNAH69QLeTJ59fV3MRyXdx9o2FygoyVg0/AqHL5dpSV26RYEc07Z3cH/xrzEcdiu0LDDVfYqj1KrZsJEsbdqGyqqPrs+p+hveYdDqRihmIg/P0uMJIZzJFVMJBujLbE0PpWKnpC4njROx9/C0+tftNhsMFHathpejR0EiKlgyTiCRxrEKUMuELyWxzE6dmHyXnhezsO8dTfX/OROW2DmxNHoAUcG9kBL/TYufddQLH4asf+iu8vOPnsYY9hBC9RRG1V0mGiexD6hEaL1qUGrYp72DM+RZKcMjaUPDUtrEtSU5UGXDnNRYJrv9Fgeee/2c4nZZOOInACsnLNXLtk9yu/B9g/7/5aFpQEIk7EomqVMcAi1MnWBtp4jVnEAvruDgMBw1sKcEdUMDvr0Hkaxt2E2ErUzAZXXmstYpyJAtG+y1gQGn7UUdZCAwl9PAovXniNqQa/CMbbIfAkSxsr9MpgJ+HmWMhdgiFdZv1nSGq3p7qcm6wWZWdSbaLIUpFgxq9gTDM0V4eZfJMllCKnrip+EhUZmxLdR9t5xJFUpRatLZUyFlOLEpEXYuRygDb7kxgI1TQqx2S23od6/ZTGmatHr6/PtzkgrWFAAuPAJcue7hEliYCCAgJCXsCmA3gqz2pwvH0/tR3UKKAg+QyA7zCoP6FWSnBrsubePYbj2BJQdOJsLo5fQ9lu3awyAIdbOYsD/f4HPXIo686QH9uE2M7PLJjAbMXF7j3/VEe/eB9unsPIlYn6Zye4drcIGKoxrKdU5XHytfwD46RmY5wWy5/o73G1x7ZDTckj/zlMkdfXGb6YpeRygQT1hJRNY+419aucsFnq+9qwg1jLe/+WI6prt/LhCPZW3DFKkKmH2RD+bPSi2+eumY6sWKeDsoydlhzXTTIePYGOk7RN1JpxD0dyjZoj45INFYThrwR6DdS8sbSzwhVqS9XA2EC8VKm+9jFYEE0CEKb7k21PehdDwlELQhXgXbv5wINrqvAEESZZI2CLAQFcBsoR1RRv4zqp9fWTq2nWbtmhgKSQrEGXirYENR8Nsg1sa/ckmBp3hKhFAhjnHRWKbJGxxqmmnuM1eyjTHd/ik3NLzPW+h5WpIWxIIJGFTod1a0u6CiwDzJQKOgKfGaseq7m/r7qGyIjyZXGEZphFtuSZFw1WCOfWA70P3SLzx95nS3ebexOWwXKWYIUwSQLHVflNM9H04GJ6Yg0+toK7APXwZYtPpz9Mg9nXyZTaSf0rGlaCqiWiryzbS+f/vp/4N3CcaaPH+fdPR+HUiYhAXN7pVgrZedBiou3SQQ9xfssoeSxrA7Sy2VUOYKsp7xptpCUxCJb7NeRRCy2R3hn+TH2N17ACZtJYpcl470hpKTQvyFt8kd8vK/B/owD4w7sCqFg+L2m3VzHI5Iey26ZK0Mqz3Fre5FDtVvkwjbCyqjo2KijnmilrstGhkrrcVz9pFXgXUy+lkXcZMf8Ns1ZDHpFqfem7K0QnH6uw+1H2uAkACaA9S1KHVJTCFNarcTCxkLoYqbmF7EeDYDEpV49xPZIYq+JuFiZoWpDyDGb1XyqkYfQAelAJCSZTRXcYoil0+JoCXZ89z47xF4cXbHKAoTbxXUkdn0MYmHEMEHJ4u55Gg6ATRsXyHOLHRziUryxQkLslJqUhoIIFYtg3if6n6CKy1sa6A3HNlcZWezTYBiRO3Yae8s9Ohg3tQ14RLqosSVCluojTAwtUW60GdzhsunqBUZXagxevsyN7MPktkeUXnuBDw8MMLfrCKfvN5m0TzA9/S0u39vO8dJ9tu+b4PX5Kzy2YwsL/U+z86sSK6rRfy1Plmlmw35Gnhom365RfasP74akYLdSlsioZ/ab19pQsOlp8JKOJDJP1JuE8L4KRN1Ahj1FWWTvz3qEAPPvEJ3uhAIDww0fAPwNIcuy541We0joP+akGwaYLvEc5/lrjdD1oVAD2U0K6hitzxQGihyQQ6gyhJqbx1XiUADim8JDgbLmWRE9h2nrKyWIpuKGwQBEOsAvEtAtg9VWAb8ybdkw62clai9CjaOdhdBTmnlWo4W0iDMMYjXSUgBXVHbxsJOhdVdS7CyrKHzH+N8s5c6Qs5TCywT20yp+UAoazi6ulH+dFe8RNme/TnnuAsLS6+R3dOdPPYau6samquNtqMCXLmwTRUSRTW7sGB8Zq3GtnuXnPmDx6mWHK9cg0wf5fhj3XLa4d3Habd0e16yNoY+UGB8LYSk6MCBPpONHjHZi4dqCX+3/B4w5M6ovxAbyr3W7eIFFZuEeH5m+wV88/su8fPBzRMX8A4kUJqXRChJ5cKMCZNyPcU0fU8cJldlY9JTLOJNRCVmuo+WadsiouMJk6SLdMOSV5Q9wt7GVMPKI9n2YD1/7j5TXF4kFa6AhB+hQYM2d4Md5vK/BvunAkgNtAUUTQyQU8Nf0Q1uXVqxk38qPs5Ab4HjlEoWgjRsGZH0bYYKN2lqydEKlwQuUM8Z0hzLmL2EpnxiRYkgPdOKSybmpo5OHmd1dfEfGLk5b68UyBVjKKG0AUJVYtFWcJ6FUvw1Eli4F2mSpM45PhmAgi+N3iLwQpKPKDZjNl0LS2IIooJWHSKf+SiCzdxFbe9ORIYdfv8Sn3/wq9eLnWex/PB5TbudF8iu7oFPWUCVi621lvMLs0WmUgd5w/YgIG9t0dAP9mcmbV3X2ErBX7gOZ2uXmrHsU6aYcwMnKCdbGVslsucPg0bPkDl6h6QrA1cGMGbrkCbC1MAF2vUtrdpoGJzn2p+uM1ru4MkLmbbaN3COQedb2fpSzl2/S33wH8Zc+yJnZOgcujyGefIx7Ow5w5bzL0NQ8V7urrJ6f5iO1O3Sf/QILosifX9/JE9Pn2Jzfg5x1GHhpDiytXdkiAfT0LONi3CIJXtIrn9AYSJHhtvcMm/2XcWV3g/l8A9BvUMBjTTMGdakD40PF8Yy91jDqNAOPAf4HHEL0FoLcUGypR6OPtXolVJPxIV9TwBoECdc1PnqJAuFoDKyMHt8G33XQgdaKSk0D/f3GtdDMwvQJkIBogNul0t1NLhJ4dFU6Y7cfMk2gsAHMdLSXWUsvowwAXU8F4mWkKuMtbG2m1mOxLfVdTqq0FV2vIKxI6ushRSuMEwbUWBOz+HD7P1EtPEpIVsllKCF2IfssKzzGocz/i8HwlLJ2xS6N1JjDLtRrUC4prR+Ii4SlzrcKozx+1KYrqhwvdvCiAvfWb9C/aR+u7qhbr4xQXS4w6NU1naZoosedknovSZ6Z6XeAROrmPyIA1iRb5y+BXUntDa3NA8iI6uwMwwR4js2/f+5/4N09HwKE2lLmJdX1LK3RG56X8rT0FPIzyQChNrLZEgoO5FzIOmq5MrpatarOItnpXmI8e5qOCLlUOcj19V0IS5UH75sf4w35N4ikoBtkdaaoZCHcTTUaJ4wawN/mx3W8r8HeFASrCmjY6oHG7Y/NSVqoDgNlNl0rZJgZOYwlI9woZLxeYVtlmbH6OgPthpbAbbB8BV8mkC/NKQUoJ44pAJJKQ4IHzxUgpSDISKzaCFGmonThXIPAClIKkKoXL1FFZoWGSqnN2Q6CTjtP19/LenkEH6XVKIN3AIRKWDlUgZdGe9Ji4v2e2tOhA9WyBnsdpGtbNjYOFjA1fZ/PvPYVPNll1d0WR626g4uUTryAdfazSCmIEPgRrFf1EgQK5oVWGWWsk0sdXGfgWYvcWhgwdVQlJslOxO/NYWvNfoOgD3rFju08y/4j00TYtISDi4dEECBwcBDYRNqeADDmL3G89ir1/gmG5uuIjArmEWHEpH8fedFjfucqO0u78L0V7NNNtmYzZBZmOZ65yXdzI3xw+2UOFtvcuT7BmHuBPylN8bPhXW47EZ9+skx38pPY9Rz3742w3ZLKHuhqrc6PEgZoaMfWCBxBnHudVv5Tx6hd55bzFLvEm9iyRRwN/+ACJaTZA3xCmxf1h5LEj28cmFaU0tJFAhwP3ENfQ6T+mbaXGiEkBvnUiY6AXAsyLVTRHV+9wjDWzNQ1bIhGFNALJRYmwoRUBYfaK/RkApj1jfdDam4x0JvzfIS9DNJYjoBuHmRRcXkh1VjThYAiPa9qVmUkWBrk86hnadAlg+K45Ui9N9YD3X7XW11n3F4ksaak5ieU+F/032Kk/UXmvL+aUgg0f6HEmcF/wC77X7Ol+ueqkEz60Zi/QRdqVdU3IZvRFkgzjhBaLcSOJ7Adj5wjGJc5nr+To79/bxx/KSzJicnnGXAWtEYvetfX3CvtVjGCn7HUEBLZkoZrYVUhbAvKKz40GknRJZOWiYpvajslWFxnk6XC6Ff6Rrk9cQAQCcijDQWhwgQrNQxDwqRAPkIDvd5qUstpJUsFgTu28m65QsW3KoEhYsq5zVbnCn5o4fuCm9fGadYE2VLI5FpI+3WPi43P6NpQIpFZ9VoI2eDHebyvwT4uqiUVf1AfKkEaw1Ok0lrDghIOAlsQWm6sUKzn8lwZmWCw1eBTd+fIRUVCp4wTLJANrmFbHXryTNIlbYWlmY5IGFO6MgNqfL4tWC64fHVygocW2ly6dpTw0iFGn3oV7+gbuK6vwU9BpN2j2yu4jIggsmg4Q9RyE0jsWBQwN5QIhCWQg775V+++6x2WGpsG+kivoyVsLGwKzQYf+4tv4foqXcTrziiJX0QUDr+J0yliL2/XgKw2jK8TDtan1vDzuoqW/lYi6KOmgV5sGE/a5qsGZjLpk1VQkN/GZo18akZaTJIwSYtHC7M4qJg1G4FLghMiXjVBC4iiPGEmpLNlE850SDtXJNsNWXe2UpCzuDIg6kh2LmVY2jHO0uwOwpGLHClkuffffJiLl2weuzlPxmrQWdzG8vUVStu2s/Pxp1m/f55Mq0uz5nLAusql8hQjdVvZAt2Ebh9ATIECE9OJMC1E9oCser5Z26Y8XGateYDh6lndOCR1ntj4/r2Ycgq0zGcmaA8NXuma9mlB4YEjBfhpLqufa1LSN3UNS0CxCV6buLpeaLrcpcYuLQX0okjc8jY9D7+uulsagIjnbtZQz8VkBsS57vocHdNQFgtgNyEcU8KFQK2FWYeQ5FoSJfRXXYUsylMEeameswP0RZCPIKfHFQm1vnG7X9T7bIa2Wybj1zRvk0ogtCKMm0AAI90/pGo/TN0+qlZVGre+IMTjet9fp+GMs6PyR2T9pd5HYwBYBtCsKRRzXExpbTodGDsBI0foBIJ7lSLnlrMstkQP0O8dOsu+wXcRsRUqdZP42ac/TtFwKPGbEU1pY7ckogL5SqiyCzBl7LQvX/dkkJZFG49mY4mh+hJYgtC2+dKH/yZrpdEkHx4N8oFeutQw0lshSi29IXVjts8CZQ3wFrowJOi8ekmJNpuzt9hXv4y75FG9sxVkh5XVYboCvOUahUt5gjZIIVRhwtT1TbmKvtodfpzH+xrsc00lbJvDCOtpYd8W4NvQ0FU/Lb3QoTHjCEWHDa9ArbALu6tgpdHfx7J8lK5oMtBdohiskwnrerul7eOWitKwbOi2kb76ru3Byb0257YXaOQsrmzJ01rKcTYapHtjC1GlzK2vf4q9TpOJoydV/jRKiw+1d9nWun6kd1MYOVTbB8A1wXCKgcmYG2qGcKiK/OY4tPTiiJ4/WHpduo6yjEQpntee6WdgvMYjb59k4v79mJd2MlsBgTu4QGbnRazl7dDN9UjHyu8l6RTamHx4vdrYRIxSjSMOEj4gU3Mx4K7WVWhkSTR7wU1GmKcU/1tqqa4Pn1+LLtNnhUQx6xM6gh9tK+niyBwBAq/l81C4xL3xCe7sf5TpwCe83OHhN+5hr7osjW9lsnaP6YpNfbKBvHee8ugog8Mu3OwyFFaY+fosxQODWLUO9oe6TC9GjNSXGblyjoVOP85lwc2nXLp2P5npG1TvTzLhrSnri7EaxelGmhOYYC+TTmfqdxrNtIexShy5ykSwBkVBVNjJfTtk872biVyaLF2vBivTFEFSIz/WoA34kQTcSZHkiacHshH0xYaXuVdkJe/NTxwJpRZ4HTXXbkeZ7o0t1Vwv8iAYBbIaADaY7v06dFNAL0kMRqRA04WelquhFqriORvyqyltPiqn0EG/DPgLqYQBtwyyqzeWhGykwrVzEoZCKKYeQpxFQALy5n0pT23/FrzZC4hmS1l/soPK2mETZyK4LDPe/Tfcz/xd2mJrnKEj9RRC4XGv/FPU87s4Mv8/kgmXkydtVFuBAtN6FfLFpECYN4jc9Tnu2DZv3h1gtVpWWUP6p67TZvfweT6w6Rtk5IZYkTiOYQO9abJqRRGF2S5UQpxaRAmBFaXoiAgiX40LjdhoQafrQwOGaguYdMfbkwe4tOMEliWUHJbS5jcqN/H6mDVKvTdfWFLJaEVdLBWNF8ICy5aMuwvsyM+zX9zAXmtjXc5jrbkUrQ7SyvFrhft8yx9jebUMbQsnJfSYhjqmEKsAotJufpzH+xrsnbZ2qetoVRGplyshK5TZpU8ogbrZhZpQ5hkpoGarV8VWCUVl1ycqSCQed6fusDg5y9DtEwThIMuFTWTCNlPNG5S7y3hhQxX3iH31AhyXyLY4s73Ddx+2WC943BuFSGRiABObGlRoIr6wivXtfVjXh7j5rU8zOTWLGJ5DQb0B8EjroCapTIITYZWihB/3/FWUJUGZCPv9BOyTUQKKb7gC5vpU3xLzhURQv9bPp879Loem38XSzDaysoT2EBaS7LZLKlUJY2ZX96/UoKvLU88evJcCaDWjflbpo4GrIdxOzdAcyTxM4GJ6u1osU+Amg6nzRPzu5+UtRmUbpJvUEIhf2j3SaMGixY4Ri24BJB6l0grzWODA/DH450sOH39tggPZM1Q3D5NfhtXRCW65eazhPFF1hHzX5cjJk0weazC3doTuQzu4kKuy96M7kKeG6XRG8dv9VKK3COavUpO/xKzzKCMD3wORg2pdg7gOTjKzECQm4rjjXOpBv9dhGAoQuVn+4mM78cQoD79zj13XlinUuwlXS2uyggRMkw/SxJC6r45biaQCRxOS/cDvUr+P1ai0VC165+JKKLfAUzXuVUCZbtkrNowt7AOZVdxX+vSUcAsaKrMmDfRaJo9DsF2ZlKk1gG1eUZRI/aY7nKbSniOu36797lJAwwOnqcqrCaAUwoBUmXr9kdpoJgvCrGmEEhYCaSpjxdrHSGEVdk2ozSQs8EZAVNX61CtKG5fQF71KsXmeOfe/Yc79BSLtejNTs4Sgkj3CxYn/noNL/4BMsNLTuTdepNBXgG/b0AmRmz/IncYwz18fox1aCJ1OaFkRGafFh3Z9hZ19FxBBpDMjDJ2kBLg0aQigG8FSG7nahboEaXZ3moNJ4pw4GWig1/ujHSDW6uRMvwAJXSfDi4/8LJHwcLQBwPjl45r9G4YUicR0b25rBGI7gnwIeV2N3AC9JcC2JTvLMzzlfZ9yrgWLDpzNITpSBW/qCKPdTsQOu8GfN/ex5iaV+tI1TQRqqR2hkiN+nMf7GuyL+m8YJp+Zh2fiW3wBpQgGIhhAm5uBAW32b1jQskAIi3t5WMyCaG9n4M52te9tkFLQtXLc7DuMIzsU/CoTzZuUO4vYkR8zzbWyzb97rshqEQylG6un0V4lAulEyE9fIttwGDk/Qq26g8HBNUKrHWvxMjlb/xV0mw7T5wcobo7ID3WxM1F8rp49YCmT4bYmzGUfWDOBOqVahJlBzRxSONBfX2bbzHXsKIx3Sij66Wb3kBmbJrP3FFJEJOV8FCS3OxoLnJDQNn3p1eHSYojrCGxyMQOVeitHmHrxYgO3MNEMAptZipxhjDpuj9QugCdZ5KhcjdfK6PEx348sogZU3pngQDaAEmQzkpYDXULAp0BIXvgcOSaZ33uLSns3E1fGeKF2kkeXLMblNsTKMt3FRd4tHmBgfIQLb7zJrs2PQeM2X/viWT76k4+SG82yeOUynjPFu7fW+dB4hvONd9j/oR1sPXMPmnUVOBbqHu1xwxn1XLruKK6sacYcYUqHmjVLHmRKiwKIJKEFa1M+y4MTXDo0yZ5rS3zwe9fYfmMZtxMmTNloU+9VxCadn25SR5FJ/IAkwcCNplrz+/RncfMWPVjznZMCejTQd1PBrrHcYOme9Hq3u8PgzxN3zQtqEFQ3rA167FrN86LEV2zGYQQdSypQNbVgUGCEPQqikJj7QQF0pEuulQrgFKDZgjBgrjNCebxGYVMbSkLbfkUifEgSe3GgGVSMPqnxmAXytM9E1NBoC6UhVd+jWUGEEU60zoT/e7jRPLe9v4vUsShGyBUIGrnDnNv3W+y/+/+kVDNR4STPUQq1/t2AILeT+95TvDg7RiewY/pyLJ8ntn+HnUOXKLrrqspo2k/fo92bueq1XO/CvSZWJ6TYE5ApN5ys9iGiA5avAB9JID3sag0RJExeAm8d+BSXtj6FHYgE4GWyJdJbg9RdIHVuylrjRgnQC5Fo4Bk34ImhMxzOXsG1AriTgdsZhBTqR0QgO1pYkwiRJRNKsln9+I2PX8t8MeDLxMr84zre12CfEbp1PfQoJMYyFkhluRu0lLJrgS7OIrFlhCMkZSm4Gzk0sZXr3VLXdYV27RmhXBNzYGVYt0eoZgfp664wVbuELdfIdkMsabNn3uXtXX5sRexVyhKTtAQahZC7jy8wI7OMc5QdnCMnm2oywooJ1PCuO6eOcfPVKaysJD/SZWBng60nlhB2SqPChMEl65FaHixgtQwXthI3LEvzoiF/kZJfTWluEmGHeIPz5J/8JpTWkUjCsEWbAA+XSMLSmlqjhV3zVEYr8Z1dfCa4iYuvI/ETsQdMvL6kQUhGBnh4IKCL4BVGyOIjsZmmRIDbMxeAUdnhOTmDS6QCK6t5BDbO+R1YXY95N89o8w5RfZQ9wkEMS6hIpANWSeLbIYFu1uPgMzNqE61J1vJneGbqODsm9jFxsUu3c51quY/pS/c5/vM5spdCJos7ONn6PidyRR57dITptz7Ko4+e40qrn5mVM3zyF7O0qgfZtCzw/5fr9Hl1CFoa6DWDMxxKA6cjq4oI4/zjKCG+XmJSP0jVtW97Dq7OShFYXN87zp0dozz07j1+4suncduBYnCW/u1GGSLNfCJzjtbeLB0IE2mNznAOU+RiY2cUI1hsBHmJYpIG6GWgOkm2devotBQXOgrow6L+YQidGXWvqKMLY3V6F8ZslkirzF6Y+NY3LqIgEWoiiGvCOn2qFocBtPQPsmUo94NsQaeL7zh0PZvhncvYg6Hy16dLDxtp2tQzCFDAEBhhCnotLCS/NQ2A0haR3IDqsRr4EAkcv81Q43nmnZ+jYW2JScnWt3WkwG3tZ3bkN8gP/lsmmiWswmRstaOzQCdocGU73Cx8nOrFXUqjl+oCthXw1I5vc2jyLSwZpQQVEgTdiNtBBGsdqPtQ8x8UDNKEJ0IQvl6YTqKea4bUWc+Q6yb2Pykl17c+yvOP/1XAjpW7GMDT9COSt5CEkJgIfSRxDGDeSboFCyGxBHi25InBMxwtXMKWEeJyHhZ1BkNs3TLz6mLqkgtHYulWEgboe9osmKm/n8D+d37nd/iN3/gN/tbf+lv803/6TwH1MH7rt36L3/u932NtbY0TJ07wz//5P+fgwYPx7zqdDr/+67/OF7/4RVqtFs8++yz/4l/8C6ampn6o+6cXMN4uIqWUAA0pKaK0egvIRAEiqmsXpYW0LCyvwFXdNtY03jKBGUJfMz4MPxI269kRWtkTvPLwVaBLJe+xXrBwWCakTlIWBlKwjUxRokT1NLlLjkWOsDO6zfh6gD2kCq8YnhuGNvMXDiGEIPIF9YUszYpHp+6w/Zl5nGxIXC/eF1hV9z3XrJ6Hq+PEzcVifgxkoyYfX/2q6iCWWMwIH7MofebfEFk6911Kcq+ewq9MwfhzgNrfEXD36G18EeqRCPqoUKKaMtslwg7ADAFz2g6zuTpKqdQiK6CCywJZAkwtX9s83vi3Y7LNr8lrTDZArA/DzQk4s1tZc7sujg3bbQmiTMEFsh2I6iB9aEuscWj2h/h2QEAbQZMsXW7111kk4otDpyl3hpg4dZjBkQGEZcPE0+TevsBFr8zZwgGa9yrkrVcZHt5JbWia53eMsqMWcub0URb7hhje/jYPr42ytq1A+6UKuY4KeOxVRRJmb4UCKbNIOgj8lKSZIkYBsc/ZMBygXOvwzNU1LkYjbF0XnNsSMZfLcOqxnawOl/m5f/865fV2b5BGun1tHIcikrH1aJ7GX6A1VOgVEHqOFNCnN5AXwkBD5a1HKaA3/vI07nVGIcgT+9sNskQtCFZJcg1TR1wNJdRtZFNAH6+jIXyp1qKrxyiBTAmKE0oSNuOJQ7xtmBiEsIX0YSkYpH/bOkGfTSH3/6Puv6Mty+76XvQz5wo775Nzncqpc1YHtdQKLQmQBBJgjJFtDPJ9wsYBG1/b2OPdC+8y8DAMHAYGX1uXCzyCQZgkgRDKnVudq6u6uivnU3Vy2GenFeZ8f8w511r7dPs9c33V4/WqsWufvfeKc/7mL/++vzSfF00+Pm4ruusTMZghpne8HDCN2DF2bkJkxQj8FJA1pOcxF/0XLuq/QySbIPJcStOgRdCXt5F6v0hS2Wa8vEpJbONrM+5p0uaaKNE+dwdJasJHUqTsHjvLnXueYm7oAgKVz3uq8zwDtyllcRq0gX3upjkTZsfzCSvkicCLyKoO3Ly4cNFGh1p3Y2C++mGVLz7yd9iujZoOzsVTutFycqG4RArkgyZL+3CpH6EAT2vqvRU66TrDK1fYO1bmtvpLCNkkOT9CsCyMSY6d5+xkWPzgiF5SIkKiXAPKwhKQhXvNpvNt3P4vC/vnn3+e//yf/zO33377wPc///M/z7/5N/+GX//1X+fw4cP87M/+LB/60Ic4deoUjYZJrPqJn/gJvvCFL/C7v/u7jI2N8ZM/+ZN87GMf48UXX8Tz/vtRhYSwnUB1Hj4asAyAKRRzicJ34BdKmc537gRIprRmRYJzBmqMx8BBVTv+mvUtcEJYC5RXYrVRZa3plpckZIaUFinrJPQye764/GXmtpbOZqFHwCnvIGfHUg7rNge2RuhWTtMLIzav7KW7MWLoxvrpdCpZPt0k6kvm7l1maLqDUJLgW6ME52pZVU/holwagZ5XEPT2eTRw99Yz3NN6ZmDl6KpP/8EpUi9/Bnk1pnJ8m3LjFNHkB0mtdd8vR7RGWqSWmiUQ0mMwQlesIdBsA8toQjStocukCO5hmCvUs06b+WaOEkoxsxHwt1ptdr9xC+LKFLQDiDwzt06T0dpasrbOOS2ZfuKtmO3WBp01wfikItqb4pU0FUwHtbk1zepowPRahZWG5sx+zacvn2exMcVdfsorZY+jey4yf3gvl75S4dHd95DM9hg7cpZW7zY2dlU5erXPyo1zvP7qOj947BiHdr8PolFIF/Lsn5yS7bN5oCugJLE3Tagu5FafI8AigMubFgTcrOG+Kwsg4N5THp/bP8mZZpUL+yb46nfezvf84Yt4iRpE48uGtiD4i2ZI5s53s6rNeDrvaxFqXdt9sp7xhfv0U5OMJxNjeUcx9BMGXGcAWkK3CYlFxsNyZTTonhH0rr1u8QFcXoFOzbWkyn7eroyh/RqN1gJZ6Y5TZlx9uFeC5m6TrOaEM2bc43IFv1xHBX2kMAHeyliHsNIn9C0qnwIinQvB4vO7vu6JyIW+U7CKc5DNbVFSFBdxYe4t4/BIGQ+fJokbXBI/hhQyE/QO3hUlUMKjp4ZY6NfROsU1n+prhV73kP0yEmiWN7h9/ilumX8Oz0nUgst74H6VNkK+1TOJUakw5YcD/erd3wolPBLPI/S2yPAP3Ando0Ua1reNEogdE6FRWnLi6AdYnDjM5FmYuQqqAWuTsDSVk1yGc2Sf30tTbjr3FK20wli0TBhHrDXmIE0Zat0gIKXsQ6W7yc2n/oJ+v4eMewzVAjyh0bKC9Juwex/M7jcF94HzFdvJltAXPk9ev4PtpGyWgH2m1C5Zt0xctetbFrN8G7f/S8J+e3ubT33qU3z2s5/lZ3/2Z7Pvtdb8u3/37/iX//Jf8r3f+70A/MZv/AZTU1P8zu/8Dp/5zGfY3NzkV3/1V/nN3/xNHn30UQB+67d+i/n5eb761a/ykY985L/7PhyMoScMfcSQVfY4Q6RjzXzhel2bI8l7FIZI4TFhF0UHcx6NXbdWmfMdPyAX+p6AQAvuvDzKN25ZQFufkkG0ryIw8UhFQjHKrYGSDSc0aLNJjQjBuN6ipAVzcpsKku3wMs/4u4gQ6KEmariL3Gxkwt5ZdZtXG3TWKxx83yWGN3zCL00gYjnAExCalekOq7UK2iXyZZo2lNMO37n6x8ZVl00oJIdHSMfL2Xl0CrXHW4i+RpfWUTplqxvQ7cP23BZbk5sFX4amRIQrtousV8O0tnGXHkNTo8UCMW00km+yRZvR/OYwisHYts+BxQp3Xmpwz7ka1XjGPKdjOtLepcasMGc9OQ6gMIGyWNLojFFb7MPplOdagtahRYaaHlJoAs/Dx2ezViUKfd54/xm++OQB7lqQNGXIkaW9fLV7G5tH36DxwV382fFl7loaZ3XiAbQI6I0vMHTgJIG4i+3RCfzzV5l4/Su2WDfIBWcmIO2IeVWQHkIoQnUt309SCCaKQS6R/Wnoq9vrU6saZaKRKr73zBaf3TfB+liPFw7OsV+8xJ2htLFjdgibAsEUaCNLMCt6JGDQchuI9ReOc/dYSmF42whhrUx8vh/neQDFrdeE/hiZCeaEvdoA1bZzrAeePe+alpJBpdlbTmQT5Vhpn20AAQAASURBVB+ixgpUZkx5nurg6v01grg8TjjchEqYW9cF7rg1Nc5o1KU35FNOIzylaMjt/B6UHUtXVpdBEjuhbrm+c+Nrd4zMadYdkzU1cuO3Y0IcA5I6o3MhYLL8PN7cD5KkTbY2BVJok8geSbSWGWRsnHpo2+kvBbYS0/tGAKKkeODAlzkw9QrCBZXdpTMlRhglrdXPlbVUWStEFaxec3CsfdLyEGF0hj6CdbGfWbFKEWI48kukHU3FB1YXoJ/mZrndbW1onq8/8HeZf0EydB0aAZT6MLkF7WHoVKHSs6F0oN6H8TWYX/oSDx77ZW6kU0ywREifxAsQQuGlkZENGjN3LmfWEyYMoRRCtcBfgvWz8EYJyjWY3AOjkzCxyzQe0oLNTpPl7sgA3WejUNABwTxW8E4Q9j/+4z/ORz/6UR599NEBYX/hwgVu3LjBhz/84ey7UqnEI488wtNPP81nPvMZXnzxReI4HthndnaWW2+9laeffvothX2/36ff72eft7a2zM0Lk3Wf2r8jDRFGWFuvPJue4EXpcU+k8Ac0SAHSQ0vT2KIEVCxR9cnDUrE2SX4hOSZGCbK21BLBPZdHuTa2zanpzWxhGhYS4FNF0UdZLdoJuZCYcdaYYpkIn4iQMboMa0mEwhNwrVKjQ2B8ACNdxPtfg2/chuhWs1itA9dKej7rV2qMPdmEnpfzYPvf9q51Tnz6aZKvPARro4MGEXD31rPs7ZzJv7DGXHzraJYYrgDRSimd7Joh7C+T6oRIQao1V2+9TOrlJK2BKhESiQdUM3te298FPUIEZaCBogtWKQjSDYa3m4y3yrz7zCj1ns+elTJDbQ/PWVwZ8AhWydaZAgTk7mpnXUTYcZMgfGRfgPT5jtd8rj13C89+Z5VDjRf5MiWGF2qElZC4FLAcCv7re9cJXlLMHA8ZbUhuu/V1Vm/cwlpJMLt/N8utazzonWYl6rFw+ibCV2Jm732BW77+LBMr9UHh5wbfKSnCPoAM7W03ESoyvhCZMggLNsghOtTwh8FrSTy9xbh7fCuch3WHH7pxhs21gK/PTnLiU+/n9s99E+ky14sWftFiy15WwDvr102vdnkFekfSjNW2i51FwhQabVMb5QR9N2Kw0Y7demXojRihLjSIkjkmWQftvtth8RZbpIrEljYa+tquT1LWVRpqESG0SYUujULbQl0rDwKfZO4mQrk9OD0aNuIhyqrL2MISjJSptSPjgPFsOZrVO9zCFh6myxrCuJmLserECvxMEXUPLfL3Aa+PyOnbWcDC0rDTB7JQgECm20yEr8Dw/UxPxriLdDsep8rn2bV5gc7aHQiZoJTHZusQkRL03ClIuXnv4+ydOmEvp/N5BOgrWO4a0KUoBh1Dfx3kmKGPJIJu3yimMrDF64I0TBHpGwhpMuor0RqknvUCGHCONKwRbUIlbJlr+WKgPk5rwdngPYwcH6axZqqwStLCGkRw36umx8dwy7RVcEOmFOy/fhY/jdjFlYzeAtUn74Sjs+tkBJBaRUqS4zIIDORwvwvrK2bXoAxjMzA2TSmqce/687w89hliWeMtqDvbUjT+zjyXb/P2lxb2v/u7v8tLL73E888//6bfbty4AcDU1NTA91NTU1y6dCnbJwxDRkZG3rSPO37n9q/+1b/iZ37mZ970vYehCVdKFmIFvrZVLcIs+AVPEpYl+7SxxJXVptt+iAQiIeiRywqHeZJgFIdEQ1ebtSqlAV0oYox4qeQjJ2aJ/ZQL49tZtrpAogkQKCQ+OdK7AYfxWEagCYkpE6MEbHsQ4LDnAnwEsV30+shV0kYb+cePIPCzCi3HVFcvjjM/3qayrDMrTQhIKxFnPnaMpNmG287A4/eRSXDMsaPxCr4edJzrZki8qz5QElx9rYvoq4wBuO/jUsyNIwsDXtsaPer08RBMAtXMcWgy8NcpsWhRAIv96TWaQ8shP/LYEUa3AxvOK3BVj4KhowvWZMGqlBQSgwrCNXMp2p1EgHdjlPkgZft3r7P66AT75xa5eHqK5dtDhEwJCFG+5C/u7BHHJWZfXqB26SbWZhq0t77J0vA0U9d387lXV7kp3IuoXWCqPspzVyd55K4E78ZFk2HsQlSpTf12yXUCW3zrgVAIHWOaJ6SFunYGGS9G05W7PeS8QFwBbngG9svtaw/d1ekx1+4z3Y34/d17eOHew9z74mlkYq2wgQQOO1CZkNK5wC9e2707YBgtBn9zQrmUQNNa9KmCuGfKygZ6Stj3WELUgHQFdAqyhknEa4HuuAnPL6PJrXltKdFT+bOU6ry+93u45fIzBP0VkL7x5mkBVMx5wgAxMUvVt1nvjqbsdvHAzRw4fZJK1IdOSizL9EoVztw8x8ZQk27FJyrXWWqMMfT6NgeHrvP61D7C9haHr1ygFPeZXt2g3DNKQrmfIouIiEXdTVp3nRKD37u/nSXsBL1XoPlUI1SC6i4ihrVph63N79Vah5vkMUojZxDNF8ATJOkQ/uJ7uHD9QSJtmmbfPPUYtze+iNfDZKtpzWp7jkoponr2LESaTugjVItKd5WsfDQOzNw5D4RKMQlrGiJF2WuD7Fur2TLnXmKUB4seWOlGVNzzF9cFAq00q/4cxzb2MzYkCDzblAbj3PAENNvm5YbLRZgUdj1lY61Bmo6dkfJR2rfNuQp2dzGXxsVBXMC9aEUpDb0uXD0P184z5GuaviTxyhwf+RsoPJQIQKdU9DpSJ4ylZ/BVm1pYZ7s0zNu5/aWE/ZUrV/iH//Af8uUvf5ly+c1lXW4TO6wPrfWbvtu5/X/b56d+6qf4x//4H2eft7a2mJ+fH/BoacjAYkoit0RLQFMIygI2APCI8TLlWpAnmDqeVwy5Scw1IgoltgKa5NdFQK0f8t2v7OH47DrPHlikXY4wnedd2y9DeqLwHlGhS0hgkd41eZgwSQNOyykS4WfHIAR6Zg29/zry4i6QIs/olFAZ6qE/+Trxfz1I8Mq4HRTFwj3nWTm4gBYC9l5FHD+EWB01PE9BRbU5sn2ColWPhv69U6TDIQ5HXiWa0uneQOK1AjqVLkMzL5DWu+RcWOCh8a26M4IgjUPixGe7EvM6I2zik2T7e1a2CA4uTvPdr9zDWMclZ+ncinSlYG7wnVByWL7OslQU0M7cIVZhKAp8JSDRiFSyT08y/coQU50F7g9rPLYJr44KtAiR+KwHAU+fPMvtk3dx8/I57mxs8nRQ58KLT7C0Zx+Lf5Fy8/d1qTLOH5w4S3zXFo2x+7mv3qO23S4oKG7wdIGA7XNpgaBnvA8O6AbeLOgrAu4IKQ/ZOvo9AnaVCqXhOhP4jpbHen0+efUKv/7oPVzuzfOhV59jyNsqjGWBoN19umxXBzZe9EU6K7MYxxWF81QSaLaM0uIEfRznSsTAVJZQtQm8lo3Ha2kF/baZREE+FpkVps15tbIekDQ/oV9GNKY4dPVb+P0Nc22EsTiTxAh934OJeQMq4+ajwNFFlHLXsWcgDkEK2mmDp7yP8M3mLLfceYNYT3B9NKAnPRSSa5PwCoeMZ5GE5/bvBx1TiiKEMm2dbj55ncPH1jkcn2Oo1SuE6AUD2ffZgxZ4ots3M/7dnDgLB8TiF6E2j67dhrCgNIKXqOizZJ4w4RHILcLm41TWAkRnjrno64xeegLv0nmzT6MKvS4NfRIxO4TeWEbIkFLsQipWYU1CwxjfKutMAOEGWWdRpVnzJzk5fw+NhVUOL71MJW6Teax8kcPVYa04pelWRnll74+zr/VwnlG/c3js307Qx3aJl+MbjLaexuV4JAG8kR7miesf4GpnD2tilnvqzzMctJiZ7JME55GX4bi8hQeDZ9nlLzLKIp5W7PSqDYRp7PoQwJGtP2Ff+xv0glG2yruoiA0aXMPTCQFtpIjR/ZDXxXfxdm5/KWH/4osvsrS0xD333JN9l6Ypjz/+OP/hP/wHTp06BRjrfWYm7+iztLSUWfvT09NEUcT6+vqAdb+0tMRDDz30ltctlUqUSm9GIHiT8it2IHoK6wElJ4Lige67iGytvOWWhSjtvm2gKozR5U6kgXLf596L40xul/n8nZdoh30joLPUDLNAXC+768yyyBQN3WaWHnVxyaBmdkLW0lGiurOFCyh5EnStO8gTrCs/bZeI4hL9RxbwXxuBWHLlgbO88bEXSaVdnKUu4u7jyMceRCiJPnKBv/7UH3DP1jMDhpYKPKIjQyhh7jZBoFqK8EqUCxGdslBe5CsfuEa/1DVdfC1Wvo9i7nqToDnE6olb2VjaQxLXaU+usvWel4m1yRxotH0OXh5m/sY+Nip7mNyucWRllma/ZEdcMuDKLEJeDVigdpad694Rgadz4aCd0uA+kwv8FCqiRnmrxtDpR+k0Isa7KZcmodnucHEKJjZ8PjbU5OIk1LZavHY6ZGxD0hj9JASCJ5MX6XdrXGIPt90+Tnd6hfSbpxjpFD1WBStYFBhFtkkj6GQ6+NzFbVjCUR+GCkOBtoK+uHNR4GsW02E2gwpx4PH09xzgwk0TfOzYUxw9ddm0Jy2OC+QKVGrP5RZY5ikRb5Fp5MY/hcY2CFtSl1jAnIFEPPvqe9AXSLWWW+kqBt3JlYLiMZAn4jnQHFkQ9GEDhufR0qe/1abUb5PV2ad9M66+D5O7oWYFvaMr28Cq2y+ztjrFXLyELgkWbznEk4e/g8WD+6jNByz5k/S2G6yoFh0pyJs2gyTFwxRtCzw6lYAUiSJl6d46j98Fzd5N7L+2wuzqFsvDVd516Spz21uE7Zh6N0Io5+1y92XHtpgH4ax8X5vxEgKRbKAv/EcYex8076BbBrVyjFplC7a70OkbF+Vmj5G0w0j/abx+DynSgnWrIW6BhNDfhoW2gSrVEV7iFC6P7vgw5av/LSNNGGXNQJKSIvjm1Pfy8sjDnGregZhS/PBrv8C7r/65eSZP2gSsQRJO05Dj0/8Er/OQ6c1UcNy4ZZ7Je0u6iTbCPvFjbrn+i4TRCniCvlfjP/X/AV9fuJfe6pjpT4TgXGvehHiugRamEikVPp/nB2k2Ig6plwmkKafeV7rITLjArvAaB8rnkKGHFh6B6hgUVCHwdYqfbFBVm4wmF42r2AkmW1kgiAlVHpp+O7a/lLD/4Ac/yPHjxwe++5Ef+RGOHj3KP/tn/4z9+/czPT3NV77yFe666y4Aoijiscce41//638NwD333EMQBHzlK1/hB37gBwC4fv06J06c4Od//uf/UjcfWvrQ5G6bQd3cTHzb7uPG3HkYnZxwuUpdco0w645E0S43BNXBnNOFDooGF0Kwf63OJ17Zy+OHr3FlpIVx5+cSyKHiaTQpkg1boFahhkbQqVRIkdab5RrjuA5yGjW1CicP5QqlMPebbntsXZA09vfA13SaLc58+FWSMMaRmAb0vgswso5UPmKkxczLr1kPXJ4stHr7neh9Ia7FTqI16nwfr5WD7Yikw+XmN+hV99IUXT7BAkKnHHv+MDfdfpaZ6S7f+v3vZ/nMQUQg8IOU+qHzJNslDlxpctcbkwxth0yt1o2bnr3ga0SoCqgTjtnZCXNMbycTdJNetCwzmFlr/WTegQKBOOIRElIPEXt4rZBGUkVGMe/90w2Wbl7n0tQY29WA9altdpWvs9Vocah7mcdHdhM2Fzn1F+f5/u/dy7XjEdeGX0X4k9w0U2OqcplgrWVc+EXL1G0OpksIEz92ySaiDmIz11pduKIk4I7QtE7d+SwFpqsRRDbBKlAQBT7ds5pjS6OsHi6hheba7U3+3wc/xHd97Tnufvks1ahrwEKK9BxFxgoWMi/tKN5/lrznDsCslqENkyiXppBGZH0jitOHMKUhncDqNTYF3kEEF8MvmWIEA7jp0r5cl8nyCDQmQXqc39zNfv2iwaHIQhHCuPNndkPFwXJZ2pAKZWtvy62U2WgZLQXL04f58o/+GJdbQyST2yih6FOFhkJTQaBJqZCiEEQIIiQJAQGahAjXktPEahNfsVyvsnJkDKlTBAmv3LSPquoTdiJq1yX3Xb5EU7YQWjG+3SJQMZObLZCKUMV2pA0+/KulI6Rj27TOjnJv9RL13lXE1T8C8XnKysLgFrPjUw1JSlicj8E/yLxjqcgtWPd76kFUo3xVGyXKTYybUw2oHuhVFHC6eQ8nh+7jKzM/QEyA1jCUbnIkeR2apTxvxQlEyOb+7MwH6KkHDD6gvZRzJDlk6cKdGc9usMlqRzO99QeMtl5E2Jj/4ysP8+eXHyXFRwYpQ/ObbF4aNVSnhCVPPztZTMDKRsAKD2dk+ATaOiEU+8oXkJ5ZszND67w//AJ3V5+nTM+EUaytkrmQIffkAWFxHb0N219K2DcaDW699daB72q1GmNjY9n3P/ETP8HP/dzPcejQIQ4dOsTP/dzPUa1W+aEf+iEAhoaG+PSnP81P/uRPMjY2xujoKP/kn/wTbrvttiw7/793q2FiN0YYkbnkY4zyCpknCCVMLaVLrisqBY5fJUBUqPwrlkIX+VmsYU2ZdTAiTHOrwCa/G0NRcHC9zvAb8/z5kSucHd1CWCqWucgGMEqAECg8tmmiC5assHtIXMKdyWZXzTbagZnY/9PRBfrD51m85Sxtb4z6959gYfIa3SHXWclVA5iH0iNraCRBIgiSAjVq0IHEv79Hz/NRKGIEKoGZ57eNFm8vLYDxlSWE3sMd4jKjtEHAh951DIVm48Y0Sxf3mZaPWvEIa9zz1AzJcxM02qFRPbS9aPYgAiJpYq8uMcIJO3dRCpNRPFa/xQs7eW7x7fzNLUg7O6TSZGRKTc0r8fG0xh/0KjR6isl1QX/4Q8yvLXDxxC4OTf0FBw7VuVSfpBYM89obS9y4Cmq4hzjusSKG+aSqIHzfwqs6QrIXl8K6niR4FRB2OcoGeFcG7989YE0WBL3O92FQ6C6XQn75wAHQgh99bp0nDw7z6scE/XJIIiy+NIJW1ef3Pvo+vjL+fn70iS+wp3UhpyqtYH0Z4gjGpk1pmht+gVlkRfhF92N5w+QooAzWfZoUBtw+txLQCYxVr7HnsTHgAW0iP22muAkLlIMy9fqOm5ZHYGjGKExRjwPpt8i6VgpMFvXIOFRr4Pn5XCijOCgp2GiO4S8Kmi1Dy20Er7znUTaCIYKRNghljQDTtaIEbOCTogjQVND08Kgi0ZRRCBKbnpsqSZ8E4W3StcqBEjGamC4pPa+EaCi8huLy4duMSaBTykmM1IpqFCO15juOvcqdly9TimMS3+P59+9jYaZO3PJ55vytfM9TX+VAdwGEohx3B9eKE/bKfefWR3FdOObpzGeRVxDgm5JIDwbi3BpDx3bqYqFZr87y9OxH+frM99OTVStMNSPxKt935VcZjZfypk8OpMbJQgFU+gyHy6z2VHabxUIHZ5C5qqyKB6EPolxhafgYD3791w32vpBcZ4bfXPqbxNpHC0hij5Xzo+bRtAnPZo6Twj3spETXyzPB41z7oNlNw7mN3TwjbmE8XOXBoWc4evA0d3ReYsRbz7udu5NZpLa89+bbs/3fjqD3T//pP6Xb7fJ3/+7fzUB1vvzlL2c19gD/9t/+W3zf5wd+4AcyUJ1f//Vf/0vV2IMx/gKd88zYMqEupq20lsbD5erlfV0Q+CJXuhw9B9om9lmic/y0mJukMfZHX7scANP6W5KD8LjQwa5Wle85Nc9SvctLU6ucGtsg9nLycRn6ZOd2OPjmk7aCnsyq98wd13r4I9vE6w1QoIIuydHnKR1eJEaywhY37nqD2J5/JwZ13ktOMXdjk71X18kknlb0Dw3R218jwYACKaB8JqJy0fUHN6fqlUNevfcAddGhQbf4EwJYPH8IrT3w4H5/nYeCdeMGTAJy6CvyxZ4JYJ1b3CkFoVgQ7Nn7jhX5pgzXwnUKH7OXcx4I8ntKhU0W0wivzgfOH2W8cY2bv7XF4liXKFrlGe8A50ojHBqZoL51hMMjxyj3lknGpzk0coi5T0R8daVHZ2YLvepbJUlZC9ddy8aNEQYkRUiQTZBd0FHxocgectSeZ6egzx7CUMvXziQs3lRlqB0zcbrHxZs81ptl8hbDBWtNwsoDgv/jyAf49O//KXuvLRo6SVJjFSYJLF6B8RkIK4Nz4CxASzvIdRA9q3VbQV/M6nYJZU7Qu3FXCXktvZuonfOqyRvgWGs+w7OvQWPGTGgUGRx5Z7l6AQxNwNhYHprIzMQ8Lb7dKJN2JJU1w/m1hMvD4zxenWU0WCUgpYSHJDAjqDWClBH6CJEgUVYAGhv/CiViJK4OKPXAVsXjo5E2G0aRkmarLUHQQ1pfohaCXuAhUPTCEgLN7z34AMf3zPNDTz2DFrA15SPDlNJYQjwCf3T7A0xdTiBUjD2/yf3Lr9DYapN4PtVWm1Jkyw6LyyhThAt0pTHrLrEfPAG+ywsp7pfTHVqwWhnniwc/wfOT7yZKy+hUgNKMRCs8uPRl3rv854xEywgvoTXbpLG8ZpiulmY9qNg0RqooZLKKpxXKWWxY41jCZgXWyyYrf1/P5VMpGte/zAdvfA7hWfpSgmUxyZKepHAaM+spGSK1E/QDRS879NhMZxK5we72TbTPjd4Uf9T/BP56woS/wj8/+G84OnOKaNucKNTdTKGSO0j82739Dwv7b37zmwOfhRD89E//ND/90z/93zymXC7zS7/0S/zSL/3S/9C1A21cIRaTDIEhhL4dRCWMkeZaTPvkVn+Z3KBz4DyBNt6CDtDRWUVLJiuKbn3Hs9oYftPUUJG29t7uo4DpVpVdWxXuWhjl2V1LfHPvNZaqXRvnMna+zoSv26S9jqFCYRmMMUIVutEl/a5voM/shU4NffOr+ENrBRnmitvyJjoi+yYXCQByqYrUAlNPZDwHyViVJDBoeSkQRZrhJ1o7krMESeCxOlFnRm0SZvVwzhMiaK1PIAKYFH0O+R28ooor3D0UmPtA1jxGYGA1fld6IbFxYgpC+i2Iw9VEOW1N60JGrVMmxIAcNQxNmUlPMMhqEkaEZv+ZYZ7slZj31pjz93HPnrNcX3mQ1nkf5b+CHr9EpdRi9t03UVldIhSSu6clZ761n8PeBcIoNWVJKjVWvudZaFVpcNg932QJe7sgftaENQaS8uxzppC3US5qMLmVdjVo8LCa5KE/uIZCU4ocVQzy8eKmhWJppMq//aEPMXv9Bh/51klufu0s+AIfiZemsHINhsagNkJmLbuTxn1IN4COFfRRLughV9i0hKhkWi5mEGbWbV+8K/dntuCsYMcKeWkDa1qAqBh42ySF/jbEXcPBhYDGOAxPGCCUjMas0qBccA5INS2vwtS5Nl4EqfQ4P34bTx/4GHNDgk4S4/mu/bRZ/O3XqnROlQkbEaXxHhJNuKtt2r6OR8S+JBFetvqMWmHydQoQVYV3IEtrdUq6W63SfqOJPM2ru/aw8uEG9104R096GQfREpKSx9VDIRqfq7vHOKGP0tjqkfglqq0enJonaZsyz8PXXuTeV7/MuFxCaIWy95P6FtTdF+BJVoJJRr0uJTdXjv4KS3alOsHv3vYjXBzax2Y4ArEwSLhobl15nu+/8J+Y7V028fFaHyp96t1N4xpNwZVBolPwNFoK1tN3I1SHmvwKZ0ofJ47Nbst7YXkIhC2NeiOBmdVNvvflP2V44bcQaT8b0wSfL136KEkndIi2uTFXeJzMlpD50tsZ1ZLShBill/NsYR2RSWwqPbSGJPJZTKf41e1/wO2zy6TXY1Y2hrg9/Bz1XS32DF0nWK/zdm7vaGx8TxtrXWrDl52BWBLmO1vmiV2bRnZgHlpDFgJ2riDnARjRpjlTV0NPG77vPAFWLA5UZKQaetbgCoRRKBwmRyIgEIJAw4NXpti31uC5uSVOTK2wVDWJdmkmnIsczr1nagyuWSvCJ6p1kXcet0cmaBxKnWsHU2wNa3wYOVytFfxK8/7jpwzzslZyPFdl65ERUlsiqBA0zkaMXorZ2agGTNtYFjRil7ttc+5eu8Hawh4qgeL7uMakjPLny4BZdrwyIcygy10VXj650C96AwZuq7h63TVhwPX9VlLPYQQokWuKNqoyvFJlfKzKty6NMnXfKrs7U8ipC+gjPkszirHzuzizuY+J6l7OVvp89eTv8N7KGLvv+y6euPF+3nXuGeqdbg785gNCoLUgDkqE9tqR5yOiYULVts9YoAVfQL1garjQhtssqMv4CUl5dT3DNlGYnhA9YksnhuM5oCeBMB4coVhveFxvzHJs/wQjm3ch0oib37jMd375W0wvrlHdWCYNaohyFenc890utG5gQMc16DjnothnUMKYtjq0ULTWZV/cL3sO+7zCLm5XD511LykIesogqiYBcKufE44AGhMwMmMRsZygV7mSgSYNJLKfcm11H7suLAOCnlfhcyN/m5dKD9G/WqH++R6Tf/sypfHUOAZS2PjmECufH0UkAk/lpdjBkKk2UKMJPLgND7dA5uvSkbXzmCnrCRBofFIEqZ2dIqi2+cuBYWkgEZqLo5NcGB1HovHt2hcoQqJsf4Qw+DDDNTw8VsIhkkNjaCv1Vu68nc49PpWVNaq9RVYmRihFMadu2ZMZNUII+rrG7WfXOXLuChMrpxCJpt4yyIH9IKBVG+JX7/lxTldvoljSpD3wvD7fc+X/ZK5z0TDGag+qpqdBtkSFtba8NKP7XtDk1NgurndusOg9RMezbvwyeDUjZN1a6IcwdPqrDF/5VaNsWdN7Mxni3y38I55vvatYnJKRQ9a9GfBlSlBTxH2PoJ4gpKZxcBO/FlPftwkCanu2KE+3Kc90srlIgdrVgOd/7mHa12ughcXFF7yxuotTf7wLKcH3NGeH/h7/z12fpaJSupW/nCf7f3R7Rwt7x++lMIK/jxG6bZ3HYIqbxmIlYGjBlalCnujh/h4ChrRRECIKvR+cwmAVCncfbtpc/EcXykVLkCG8j21X+c5Te3jvhTm+ePgCr8wssu2lVvEoAusaVaLYXS5/mWC2ztsAoa3FkTMSx8rdZhieYx4axdxiiwdOX8yyabUQbH5smngyyK8aa0ae7Bgo6+K5tCCoHOL98SKl+bVshLUdaK9fouGlPCQWGaeQdfqm2FyREedMwgxmwWx3MUaXUe+KbLNdis+q8+9E4dwDoQJ7L85942K7EkNMDp87MjfbVPCenuABdY6tk5rnqg2Wzh7n9c0f5rb7lzhf67IwVmZ8fY2Du6rcPPUh7luIuFRXjB1cRaVzxElK2NqwGJ4StEJFKdvNCqO00FpDeplALTKAmOemry5hyt73zvIs5wmIJJVVkSc9ATeGJReaPUyLJZHRAJYiBpwb9tvIl9wYawKK6xOjPP2um5m/tsyey9epnLyX+zdPcKR/ktWVIcZXnzJeKJGCigrkZu9JCpPk55UwbcCcVV2w/Iu05e5IagY6lrgSO7RBhFNVEMHgHIOZw8YEjM7YeLM71iXz5cplvxJS2U6Y2l5HI+jLMr8z/Bmerz6C0BKRQG+9zPJ/3M/QkTYCQdqTtF+tUdLCPI405OgJoGubNS0EDH9TMDSxxal2A10SqOkIJRSpl6KG+0a+WTteYhA189CiKLzI1rZ+0zgZrhHjujx6JgnOpiu7WiAPZfTLsI2YO2sVCoMv8uzcNIIp4ChOE85NjJwmnhyf5Nl7ppDprfhRn0NnroJQLA03uVGbpuVtUklfQPWHiduj6D4EjWUq06c4qZuEx0YZjtaoOnhhp8gj8jXhCVpqmD/f+tts7prnfHqvycS2y1T45pV5FDRUkm3uOPN13nvpDxBJLsG1Fnxt44M8tfWejN4N69LUd/cYv3uT5pF2FjWsl7sMH+yxdK3C0MEV0D3KU22E1Aib5AzGAErxSPFJkQgkW7MBuz/7GhtPTbD15QnSTR+vE0LsZddVWqAPrvL4xyT1qYDm8Spv5/aOFvZ6xx8R0NJW2DsOVjR8LA/saaNEBlYYO4AyIOObztr3Md5Wp6xq+7cT7tm+hUu5fVIr9F3pt/MceFpQ6Qd87/FDPHhphhfnFvja3stW4BfdfCAKC7r4nSioAe7bfDiKVn3xLHLgiOnlNiUHrAKkk2XiPTU8DLZ2ikIlEK64OLMHBFDZDY1bCZu3svfUWRbv3sDUmArKKyME2xX2vnSUm9RVqkI4p4HZiumzrnbbfZEJ5eKEudsVuSKQWJboSSsQ7DmKlnzxXRQ1CPu9Z88vrXbmoDJdXb9U5nMsjQmhpemWSoWx1ZRw6Vke+tj97N2+zInLu+lc+BN23VXiXZvvYu+5lwl7AbI+RbW1zepSlUZLE9dG0Z0WwmXfpxpPxYym6yAlWkB3eJtwUdsM+JwmCQXcIgsJi8VH0pl85GoJ+tLBO6IF/PkdJTYqO6uaBqlm4HQ4QGPj3dECuuWQ0wfmOH1gFvGI4GvL93H3y/P8+O/9e9BO6YxyBSpLABMgQvCrJnThsvJl1p7PbFnMzN6bLCpq1pq3LmSNJArvpdQ/82ZFz/OgOQ5DFtgrcXkS2riGfauIaw06obqeoDerrIsxvlb7CK+Hd3A1OIifGovdS0DGAtEP6DwznOkwvp1CgZNR7h5EpvC2N0ss/sF+khLoELR1OctAM/3+i3jzXVKREtn794EyyoQa8bLqIme3FHwamTfQKQKDf+ns3YgilwmkBu+R3BU9qESQ/aoL32ogDYCggi6HPH/PIfK+oz1CLmVr2PxvFQ4B33jPbTx732H+9h/+MbsXNwZtFynQUtIKx9ksTXKs9kFe2PdJiKUJAzjBLshc7GaMNfXeOp+8+CscefLriHQQjyHB5+nWw9kzCakJGilHf/wKcx9axaulFlssB/PSaKaObFo1yIJboQrZGIEV9AGmk4ZnXtIjmIKxT64y9j2bSDzWf2E3G08MoYTIWNnEnmuU55dNSfPba9i/s4W9I/4Ua9VjaC3A8ArX76PI0bThsbSV9Sg5I4I8sc6TBcFsz+WA2FJNHmZz96GLIjQ3Vhzvcl4BTxjFIbTnRAtmNhu8p7+bZ2ev0yolmEj5W7NknZGcK997K21/8EgKv+UxQHPD+69t4lumrELB1semEfUwq1YglQwduwvf34bhPtSOQtAw8VEhEFoweuIwjQtTaBtHDrYreFGh3qHIjIuSRdrviwl1GePXxlzKBlQM/q2wbWA19gHImp4IeOu0+x2KhFMOCroG6FzgO5dxKsyaR+AaUAvV56B/B0+szlEbPcHuc68w8tpZGsu3sLz+JY7O7EXUG6BCqkpQ3ZKgBL3xAG89wHeIDs7y1QZ0RiYRwz0LqFOcQgHcLqEpBserOG4a6Eq4UbYSwnx5Yo/H128KQJjkr3zJDyqTIxstqt0+rVqZ+46d4uy+GS7MjZs1k6mWdphkSneqw3OP1pgb+n4+/tnfodLu5kqH03y1BulDrWq+dG1nHd59sSwmU+YKLnsg60Eq7ZgpiaBBqX+6MG/mkMirEQyPIIYsoFRqs6+Uph/4KOnhkdBqlBnbXLGLvcx1fSv/Zux/5ipTJtE2CxuZVqV+oLMhFgLErgRR03inAxOh2B8TH4gRHQk9gbfoMdRfZ7s7gq4ECCXQgbVIJcYtfnqUfdNreGGUhVlcuq7LlTG+D1PMBzmvMwqAtiKoSAJ5xY1nv/UKFr7Z10yOKgQLcl5SVBp2nrm46Tf9kpkf2foqFkILvDRl39WrjK1tYJtcZin1Wvtck0f5zamfpV0bJkpLqEgaHVKRGQsugc4t2UMrz3Pv5h9x5MlnDG8rAkphwhiryTgAQT1l6sMr7P6bC1TmDP6JKoRFdOFpzN/1gdkwQh3LgT2MQebGzctHUQikB5KU2kPrbD41VBgxuPrsHdz5Pa/gjV4dcGK+Hds7Wtj3MPNrOyETFYw2T9iEPCuglRg8NgG2tLHyPWFc7TVhBL3AHOt62gsFkd8nkSnV2CcRgr7w6AQdap2QIA0H2jxDQcbZe3KIfYhcsQAj14LUR2tvYLlj0/HEABEaRmjsfpVposWFOKgmiOyvXIs3BHrw8hYfeO4qCJN13Hp0gt7NjUJtv6Z8ZTfDx+9GDDsm4FygCSbTRSBjKK3XGUS3e7OiMaCHUBCyhivnVpyggIQkGGwaUjifPQyEFRoyt8oHJsC5gfXgPQz8nY9VXt9TALVJVX5faEh77I238Z4L2EpCjqbXkfO3IbQ0jTH8qskOTz0bpzYmdqldp1evUO+2bexJghearmEqBR1bU3LH8JUEDFnCKUq3oh6jNFwsG6veCVotGOpoIt/Fi2PLAoslCJKRzW3+/q/9MWNrmwgNk8vrLI81efG2Q7QrIZ/76P1EgbkpL2NsoHyPP7nvCK+NfJq/+Rt/xMzCEtVuLx/fsg9Vm72fxuStaguKmAM1cg/iMO7dZ3eMKz8UpVwZKsojIVip38J0MzaNTZQy84ZmsbGf65OS6lrM4cWTjMUtm/vhszm2h3/b/ftcTSeBTDcgSUE2NPUf6yKbSbbOUjQi1OBp0q61l8sa5TtrUyBiQax9ZNqj0u6TXvHpXw7RiUD1BVoKtjeGOPPEUaYOXacxsoXvJ1QqHRvDVzhkDWMfm8Y11ndiDRtpRbraQc5ODDmkTrcG3JimiKx40My/xEdYYVbMFDCfdcGL4PjOoD9AIKBfRiQ+2u+jS12GW9uEUZ9SP+KuN84wvr7OoUuXCeM0l9pI+rLG8ZH38aezP06LUXQssgYnOi1ehTwCqDW3Lr3I+175z8yun32zAmw/dnUFhgSzDy+y5wdWKM228aoqE+jFMEmW4IgGmwXhPhfZjxsThchGOvMcoDM9FzT0JZ7I4SkQsHF9mMuvzHHwA1f/myBu367tHS3stzET0MfQR1dbESmMF9Ml0WVaspNHAFaO9ATgQUdCrwNlDyoVY8Qllg9poF1KSEsRkxJUU9AZ0VypX2dp8zxHLu7FSwIipe0ECqZak9STKkILG00jSwhM0PS9lEvNJUCx2NR0glxoawrMLtPAtRX/4PR67dysO8bFEWT2oDgjyJCmQjC92qXZTtBI0qage+8wIsgtymBxgqGn3mVWnIYMusp1D8xgrJyQ3KFNFZ5ZCc3SSEriaeo9aHYEnZJiYSwBoXhjd0w10txy2WN23daAu+ClgCxrXtmbK2btO2XAxV00uVU+YOEXtx3f7dSWitjYDo40a9qSWosxZT4+A0nLfHbnDJQp9ZK+rdnXUB6D3iblPqzrIXQ5RpRK6MBnY3SakevLZH4q1wXOWT4I48IPyZWAneVRaNiWcK3MDj5MmBSMZBQpCcIKbKk07zp2mu//syfYfW3R5sGZnaeWNviurz3PynCd//qR21FBiEvkE/iZ5Zf6klNH9vK//m//gN2XFviOLz7ByMYm5SRm/8a68cYniVWeVO6eL05DNtZ2ztzzObAcjVGYZB1k8Oa59AJojDE74lrN6tx1rzUT66eYuB7bTGozzNRrMDLCk91HuKLn0TIPNzVLXebKfTY+ESJncqXP3GpBODjwPessd/QjQuOGFwhkVeONx4R3ROhIkK5JknWfZMNDiICLxw8gJPjliObIFrOHz6OrfcLMuZ+3h/aQeMisAHAwkOc8N87kkIiB312UvkDm9p8zKbwsCdizz6UzPVIr2N4YZrs7RrLsIb2Y8swVpIiQqUacvRPRqSGDNR7xfp77X3+JWqeL0BqZpBY0CxASLQSrzLE+uosnRz7B8eYj6MQzHrQ+RtAXl7mz6oEg6fDxN/53jmx9i/H1Gzn9vIW8P/Gue9n1nWtU97UtBMCgkFYZR3RC2x1rTlocvaLKZMbF/Z5r5rnX1fwyNrnOitxt0KK1YWMS8NfGEFoUM5nelu0dLey3MDX1fU1G5pK8nt5VafkF4T8gAhQIDyqBteQtFECv4C5y1niga4SiRrsKoqHxmtvsFTX2jN3CmdGLLLDC8xyzOrlgpDtCKSkztzHH/tO3EpUTqPbxygk+kpdnznJ56IYBRWMfWtQgc75ptE4pxR4Hr04QxB6Xp5fZqEUkfo6mpzKBP2jP7xRtg5tGaEGgQpTw6aaK6JPT6GZgyD4KqL50C9XXDyP7FTIhlMVOtbVUA/Lg2aDJrIWgFyraZc3Tt0S8Md9nvZ6SSk05gmpf0A8Uqw233EyC0pUJj+94GaY2fDxlLVRpBW1KLtg1gwLfTWqGsoHjZgwmshUkZHGQiqp78dwupyAjBLuDwph+bIPXK3BQjfalaa5i4/wkgk7ZR3gamSQE1QBEDaRAaI/hG6u5oiSsRSqwIQOr5BzyDEEXnxkyjqjjAC7ULaPOpgGASh/GW4prozkDT1E0OjE/+odf4/6XT1Pp9nOlwZ3XbuV+xK7rK5zeN40WRRgVz17P3GPie5w/sItf+Xs/CECQxNx6+gKTK2scPX2R+7/1GjKz4AtKmYOxLdbRp9ooB9nFJMiSKU/cacX5AUzthVItX9RO0CsF/QiZxXLtMQqoNunKBq92bkULmSNRSpiqbVG/S7IpSpSe6xKPBySzAZkKXdYF3Ta3kHdWq2RCQwCeQlQ03lyCP2sC0Wsnr9LcNcKorFOKBaMq5LKcI0YxxCoyq4kxIqRH1br8iwZBbrE7qVfUA50YL2pWg5F4mb9riVIeWpl5jbYrdLeqbC+OEnVKRB3jpRFCIISmf3WGRmrTS6yTYe/mZR659hIlZRsXaWxIThOLErEs8cbQ/Xxx/H9ieXg3KgIRCaS12LQrtNBklVTZwwA3rTzPgxf/xDRx2jHe5nKCdnOIVx9+iGc+/iGq5V52uBHwEpcOudMWL767/c3SL8b0nUfV7e9eO8dWwf4Otd1doovVXJcX8PJjmr2fhHjn/X+bt3e0sF/DZLm72kmXgOdccQE2ydPu7+LviQ/a8o0wtK57K0+K/NzNhfCMti5KQEVDPQbZQdMDUg6KCfYxzAgeG3Q4xWXWq6sIPG40b/Dy1AlD8NZ/71HCtIkxYjrmEh7jCEYBD6E0h85Pc+/r88xs1JApdMN93Bjd4Plbz3Jx7jqpSHHW/eCiBZ2xiJyYFCLbY99Sne9+4U7i8j5e3lbcHm0j+1eQvRKNJ+6hdGWX3du57TX9qiQJPNpDIZVWgkwlQQQygn4A3ZKmG8LxAzGxByf3xnRKKe2S6TDlFkg/1GzUB30Pbthf251ycbrDI68FPHCqlIXnmx0/X1MpudqvC2wtq6Ox3oaBREDeUvvPhVtBs8sEL+RAME4IWcbqsj99BSp2CRimbKgaoUcmEO2yOVdqnvn1d00x9uxpjvRbZJjNsZNd7lmsFSlsKQfAHh92eWRJhpk/0T0vJIuT+CuQlUwUFJ2htuaHn+jx2+8uc3ncrIRqN+L/8bkv89DLpwzC2M4hKQxrvdPnM7/9GP/in34v/VJg2V1qRZw3kK2dk5wmCjxevOUgoHny7ltYrzV49zPHGWq1B/d3zy6A+rhpGbq2BN1t+72EoGIz+YtCCijXYWwWSoaZtuMyVb9t9Dul8tBBMTNRChgepV+q8mubf53j8e3sr5xBSsGiPIgvYCmZYvFZjX4a2j0f5UlUyXrIalD7TA9quftcaAEtn/SKh1og09HkvEbuV+hAk5XNokydOYqhW+YBzToaScoKGkGIQLHBZGGyc23URfPzWXMuL1MTlBfhubyeLAMHcBH6Ircwe6ZRSH9riNXLs/RbVXQqULGHKkKKWiva0+CngnJkdC3pg9CakegG37347ymluQK8LYdZqezleO1hLoWHuN48SDtsEkeBcc2mhoeohKzVQZE60IY/VxXs6sDBVtmQ+U6zW8N2s8mrD7+bl97/PrbGR0FEQJTxntyqL7rfd+YnFN37xZi+863u9I24e5WF7022Rbstqbd9hNdhJSkbRUPD3N1tZFIy4G1v4/aOFvapT5bRmBGHMLw4sQaCh4nHawAFaRnCWzBu0S6oliEyERle6byNzqXnVobwgZJGDGtEqYWmg3HIGxQsTcJhZlHArezl/MYmp8UVWqLLZnMNq2rYBRjhmWyAjAzhOpIWknEOXjjAe587QjWRWZ+RShSw98YEMxujfPHdz/ParpM4qrdrMPs/59c5S3D51SPtMn/vj29iaqOCKN/Ow+WU6OR1/LVRyicPgvBpTYT06x7rsxVS36df8dgaL9GtCWNcWyFb2UoIL6/y9PwWK0PmuzRjrEVNd7AKIP9VUWRDGk07hL+4I+bY3gRkgpLw8W/VOHgtNKJF6EIChD1TBpRiz6YENuWazBUvi/fljhu8mwEILawAkp7hbi4/QFv/UaBzgtE2DhmAkCmis2JczgBao0qS3U/fYG6zAzWdX1srqzgo0FYwuVI1ATQEHLHX15CVhCiVezMUBMtdQ9ADfMgpLYJ3nUmQqscvfLzG+MYmf++/fJmbzl8bhDoYmJnB7w5eXORvf+5FPvuDDxEFRXCePKlLU7RqiufSbDXK/MYPfoivP3wnP/7ZP2F8fYta27YV1fY44cPYXvNem4SLr0K/D0EVwiA/n5RGupSbMDwJYdmC+CS0Ow0qYWK6vSmrOLmqC0cbtRq6Wudl/x5erT5IM5C02YdIBRUL4qcS0IlAx6b7JDEmQQigq4n+LMS7J0XdsEmki5L0kkRtA6nIyEj6Gm831G5SdO9MbeesfJJcdYzIPpl8CmHj6i7/O6PPzKpUsGO0nWM5F+VOASisMJ27sONuk97GBEm3jE6ht9Eg2i6hYjmoXxSdMArKkelaLK0e5iJ7t3ae5OM3/j3NeAmNoC2GuBwc5o+bP8ZC5SCpbzyBIsa46ZVdPolZUq7e/a22moZ7WzC5fYnbr/6KuTGn8GoDgHTxpqN86W98ivWpKeM1w1U1+QXLfBBabABFMpsJpwzkgt5QuDOXBql70Lvgxh3Qivjrc/RbAc1gi+2kZPTzFK598UOMvSdhXQ6/9QN/m7Z3tLAnBAJLOBK0tVLSBLwh4/kTIagtgeyYZROMAVVMKUwJg8nRNsJe9jGQ3hEDWaAIEGWQDRDVnt0hn+Q89c0swRI+R4cmOKCn6XY8TscneJ1LbASxJbEkY4/ueENcWyjdYnhrlnIq8ysU6CmMPB549QjnJs/RKcUM4qLlmbVFMi3e3UhcpdkLSAMT25OpRi5OsTk8w4lP1kiDEu3RgNQzloKtbTBXEYNRwu3RkLWRIW6wjhLuWvm/4s2L4kKwT11USAq8BS1hYURZT0zKb31gi0eOV3j/KzW8uJCc5RIqslh+YSg0GC1NGQGh1FsI/MJWSBrKQgFak5X1gU0cC8z5/AhEA1TfEGCGNgb428A6qCHQIenWBnJjCRG1DYA3VhNNrBeC1GSbC8ia4ggBc55JzHPJZqnOQxruEXo+tP1cQdlBL44q7jmX8KnHYPLat7jpwkJuJBcVnmIuQIZJYITQbW9cod7usd4M2X/pBsgS13bvtsIgVzLdGtg5yqkUXJyf5H/7nz9FvdOn0Wqz79J1Hn3sBaaWV/Er4wivgtYKzysh99xmEPv6Bo8ehGlcUx2C2rBJbBQCosQiEyomvWtmfIphHM+HMGC9OcHw5irUG5wQd/Mn0acpEZpcv9g3upaLBUbm7wxbQue6E32Bet4jfdGURmqHpjPQhCgnM70I26kmmeuTzESWtAbFjBsz89tgRHlwDbvz59k3g0Q/KLzMzsIA6GhJtzVFd2satCTt1SASRqlJMPAIwnrcrRsdYZxXQQJhBDItLAfLHoQH070rPLj6GM14hWvlA7zh38Xj1e/mhr/H8E4lTPtdS+4OF8m57AupItljCMy1dnfh5i6URcq+1f9CvXs+V1IVbIyO8uyHHuW5D38Q5QUF0MlifH5A5YEB7lSsRMgFfc63XMqeV5gNs+Vg4sVZN9pP+too6YlxUiFYTsfRdtl7GqbGOxzY16F3bIS3c3tHC/vSqPHkJRFU5817MJWihUYGAiEl9AXxS+D1bf5WG8QmMGq8ArrMQF8NBXlbbEvMwjfCnhp2LTkNz8XYTRqgiz2DMDllQhPUU25WtzDMHBdY5EL3Kv1y2xJ00RKXaFI87TO6XTUdkSz9WJ6LkprrezZ56pbjdMMkO4bsLDs3l0piSHaaCW5uHuXFH6miLVcbvqrpjApakwFKavu9KlgIEWRhB1m4Y3P2QBhw4NzSc/aeKCwrhYoF/dUyoNBaI0sJwUjfPt+bLUJt7wLhkYiUx27rsncx4KBLQnMvDRlCW7EGUsAAfKIT+MVGBwNMsvi9FbZSG2ssLVzDlcv5FipJNsxpZGIhcIUBARHr0BfodAi9PsNQ8rr1EMQWgME2gClmkTiL3OUeTEsr5O3LGXposmzPrRASL8/Uz/INBklCphEffexLeJ2zg4/vPmSYoTuHxQzo7MJl/tGv/B5XZsZ43xMvk4Ql/vOP/RAv3HHURkEcTSgka8A2KiterQEmj2GzWWWzWYXpEd44tIsnH7iVSi9m93LA9OIY+1/aYn/7VaoTXcaEgM46aW8LVWsS1CdsgNg+e5wY9L6siwmDL0+aRLxKhVogYGSI0+J2/ov3GXqqZvL/EkwbApsBLvpkEAADYIv2nFqSKZnKZf8WSAahoQ7ithhGU9LDESpMSSsqm+liWokT/A44211xZ2FtEQqpCHnjrPwBi74XkHaH6G01UGlIb2sESgZbz3mNhLSv1ExRKUxI2x7l2MK9akOuocmhNXPs5aTnHiIBrstRXqzeymWvweOH/irXlqcMHr4jVU1e8GI9AQ4UcWeLh0xVETDWhTvXoKo3mOp9lamtxzP+0643eeE97+XkvXezsHdvdmxO2rkKVVRG3Ujq7K98LIvEUzTEzDfF382myHsb6KyCSqFjTfT4MPGFGgrTnCdWhhybVTgyVkFsNfDfsjXwt297Rwt7v2KSc0s2nFeexLTB88FRpWoJZNd6QgWkEegljFegYRdvGZMcYvmsyfIz78LDxOpDjQjzTFdzUIIzs3KnWmoFvrmm2A7o+pLh8jh3MMW+rVt4tfQtVrY2iZotY0CKPAlEypi5+RP46+MkG2O0gw7bss2rGx6dd1/i6tFrrPvbGMRyp1fmaTx5NW1O0nVq3MIBbmY/FenRbyb2OM2Nm+1YAaaqN0VnWqwj7hSoIBzQsHW9Ntd67Lva5si65MUDms0qrNWNUhL7VoXRML0h2Twr+dN/eQsqFQZWeCRi+O5N9v2tK1R2daHgNRj0AQjAI5GKxHU1kiIXfMUkLtfRovhI7hyqwEmy3ucFWMCsP4BLjHMC355M6tyFLiTIEJOOLQ0XFNsQKqh6UBZGOMdtYtkkjK9Q87ftfaf59TQU8ELJWnm531dSaGLj8+Q5Ce7+lIAb1TcZc9mwuXHQMfTW8dtndw5M7j8dsKyKHzSoBBF3ufPV09x5zJm5gh/+/Fc5vX83G80aORisQrGNoE2O6LiBJsQpjVBG4DODZqnus1GXXB1PmZm6xOHVC5RP1whbKZQqUK6Q+ru4vHuCAxeu548ZRdDu5fPhibxkUymDsT7UAM83SmnU55neI7xcfz9d3TCeuyJARx9ETAaw53Qm1yBOOWFfIKc3FaA0NOKuGO5NUUMmHpjXvu/QvgpTVMziFplKoN+0f64e5Op2fg5zc9HqCN2zs6RpBZVKQ86uWUdg72QbZuUNSispjT1tIlli3l/iSm83S/0JklQS2vNpq+AMWsd2LOx4tKnxzfp3o6qaZMkbdOC557RC3j3emyx5kUevAl8zncLtqwKpI6aWf5FD0eOgFFrC8swsX/reH+DMbbehM49cDkns1lNurbvPO8e+6IeU2Weyz8U5cj6Z3JUvMQqc0AbDMNwShN2Q+lKT6eom2wcnQPnUS7Ddg5oPM00IZZntVw/R2LPE27m9o4W95SQmsaMPumuAXihZhpSCXgA/zifN05BuAWvGza9tqDOrthIYS9658V0MOgWdegjP4FyZQF5oycuhRAgEJiglSSknfW77yh+y0azz+h13sjEyR3NC8F0cpHWpy/mhJca05uSeS6yKlEOE3EWJ2T1LeLN/Qrwxil9u02y08bWPEprzwuMcPpfwSW0yjsbVAecaqin5k4wwzIfFQwxTRVhN1BBtUCB+I4AME9GYMAPZM5mVKpFaU1uP2X1ijfp6RHM1MhnWQnDPWY/YF6zWBUoK+oFdRBpm1wXnViVfaHsoaxX1OxWuXy2z9vwwc5+4ztwnrhOORpnlIshx3NzCfXVPypFrdtk6zpDAAMpasaVpZpKRPYPRJJwlXWCq2hEUZPHvrEOaICs5dMLfLR1HNDIAuW1or6zZ9hOu3lilUd9NrGcYkS/lOTzas2YSuZkk3Lnt/VaAaXJpo92+9noAlxvQDgqCvuidcKdSsP0stC/ma8YN11slOmab+y2FpGe1ZDvulTLUq0ytbXD44lWeu/0IucKrKNqrbvY0PTR9S6MeAsk1XAKZ2WthRPDVu4Y5PnEHn37681lcJ1SKAxcXc0UoSqHTIw+fSCiHEHhkgOmBD1Ky1BvjtHcHL3iPcK20n1hVzJQpTOy4Dzoy+pAoWun2SZS0TiNZEO4CdOAUT430QO1RqLtTuNXVOhbDVLlNWch9ty8D0+Jc95K8IKw4kbkKr8kFvvPAmVfciehevhWEYLSxgjwTQ6xo96vUh9uUdkfsOnIJsQ11v03zjj6N1XVGXj3H2OMvc/nT38/V8k28fOwONlsNtBSDy2IHlWhyXTHVMm8guFNHobBEd+gwmSPL5S9ruGkZ9rfA1wmHur/KXPoUFw8fYnH6Tl6/YxfX5+boVJsg3lTZvuO9GNLMx3JwVHOemXeEHNwvnzkv+9XYCprqcsjQuSalVZ/qUplyNzCVXRomxjIgS0SjqJML2tcmYXb9zQP1bdze2cIecl6d2AUrMA1MBNAHuUaOs6LNwvUA1bb7+0ajF0lBwAfkZdp2VepUIHqgA8fkDVPJyzB88sQZI3i0L9nYW2bfK08xvfQqkdzDtbtG2T48xa7bNbej8Ul4mHH6xAxjInVCgA4j/MkbGSucEwZpa5aI+4EL+FzBI0pKbCC46kdUgBFSJlDIuES1N89k+CDVkkSIFAfHUywcyf9y1oXTi3MNViiBXEnZ9/ISsyttKttJZt3lu2nCVDCzWWRl+fbGjWbWndRtEkF/ocyZ/7CXlWeGOfCjVxh7YN1mKw8uXo3gwoTi5K6Im66FWdjFTKY2AimO7Q1bpu/w5Z3lLNyzugVrT5JhzVufpmdNN5cT4I53UIr27s3FrWtUBiACSBJSr0ukPNbEMCfmDtFc7jHeCqj3YlABBagvsuH33D0AFWGgcUMGmae7f4GJ1S+XCyd5Cw5LCvE2RFfzyS52/3A5AO77jCAKiyqJjKB3jcTLJai50kGVj+GARered2oeuaWVZzYXYp5C8tz+EsOTJ5juNXn/iU2a3XSASWbKiVOMAg+qZeMjtdOiZMBmMgx4fGH1Ezzd/C48W3UjXd6eBhLjrpaYKXHh/iQwwj0JjE7mxSCVRtYNz/D2pgR3JogxRRD2KEVdlmeGDBSvfZa8iruY982bPudR4lzQ5xa7MyAK028FvhnBCEFMfz0kHb7ApnyNCVFj/+EWQ0MrRLf3uaq3We+kBDXD7BcBryooU+I8Kbqm2DXc5dT33c23rvSYum2RsX0vs3blFtgaNQrywPXtNEBe+WRfbynoC8plsX2Bc5x5khxfSsHhddizZS5Qlleo+49z/N738JWPf4LWkFEOdZbcvBNBNOcVgxxuUHwPFkvmLZ8lhTyp7Gx2efYFMjGASY0LFUigfrpEuOkjI5nZBqlliSkWkG3guvnWWx6G7Qpv5/bOFvZaEyabpF4VP4nRaRWdGMGtFeh18CJ2TKD9HGEy8H0QfSvobZ6TcMqiEyYBUAEdCLP6hbA/KJSOUQjijRJJW6ITaL0wjooMlOVCv0Q5ajPjnaQan6DyXJkbrX2cOzzHSDCBqKR4eNStfZ4DkzpVIl/67rOH5hAxh4nQXhcDp2GsAw/bpDLsEkUjXFsOiJoalEc44mxll8UfG9Q3058PUKasLtakXRg9kzDkRchEM345xu8ri/NiSVjkWjEFd5rZVPa3Bo4tltEDsLe5To0QrL8wwsun6tzyz88y9b4VvGqaLVm3XFeait9/qMvf+oZkz7KfNfDJMn78QlxNSlMXlAl7XRDqltPvXIpSGiLAs9ze7pv1WXfP6ajIM+ag9MEPTXlY5JOsajbaDYZKZerb14maW3ieSXTMfKJFovTTXDeqCrjbN12YMtQ4d+0CBd+oGtx+zaAG5Ta1Dd1XoH/F3KOwI1nk0pmwtveTWfjKhBvSCJI4RxCslKFRy7K4NupVrsxMAKpAqS7LLWewktzFavw1ssBGiyzVnGW9HvDZR2f4s3vGuetCm/e9tkE5Ucyu9ynHCqqBgbfU2vQ/l7mLdlXO8Jj6KI+vvR/VDOmNlI0RluYpG66njsY4LoSdGkqQZqU7GhlCeX9CZX+CCMA/lJKuS8SUyaxXKGIkPWpIW45onsRIvtxCzzPli85iJwxkJmjcKBXVG6ciKDQxCet0OEtChy438OmRNCFmFV2Jkc1XGG4Mo2XKMdosig66ptjMRtn8e52eucMKiLtvwdMCFcUsBat4e9bRuy/iL+1FLs7jr82juz5ai1xfVIX3tyqFc5Tq9MaCIuBYiG8FfVlDvQfzGzDZAa01W7u6nHo44onZf0AUhCR+7vsoCvnBSxazf/Lxc9RRRELIxbwsWPT2aK1N5cGmpLJaorRaonG1itjyzFKIjCGQFQHJ/JJZxa9bvqqg6Ll1r6E8tklS7/J2bu9oYX/z9V9mt7pA7DdJggZXp7+TXnWWrpwm7G6RbpQRuoKFTwJyrYvA8ukOphzE8m7tkRmmwsa7tI9xq5qeCOS47ZpoKeD6n5TpX5GoLZEpCcL2NfZp8MaRDzE1cRavHVHq9dj9zAkmXr3EtQc/QveIOV1iI9VO23c3bFM+BmSDsoTpjBuH429Ktxt0GCehRLt2J2ENXImPYTeefYHWIWPXNihtdRhd6OL3UxprCUHfXNGENorCsHAXmcAvOiad8LVUrc3ie2OlzPPXqoOmeuHdHZ1uBbzyU0c5+o/Os/uvXCeoJQPLF6Adan77PW1+8Kkq+2+Y7FsCH+qjthe2NnWXLoafKEwtVcHiFMJOqs4F4YCWZ3/3pbF++4lR2d1zF619rCtZKqM9Iikpyf5KShxusxxuUVn2qKQl0G2y1a4KbMoraD53+KblorYj48ZSFL5bD2GhQtbZzg1Rpme1ofUVUJ3CyOlBUyzVBabslCHIXPdpZK1669suhdCoWhow0vLSzftZGh8eENyCHhrTzrhoVzl2mmaW/FvdeM5ytRRcGytxdbTMn909hkDwvpPrvPdEm/qGz6H2krG2gS01wll9K0/3PsBC7TBbchg9JbKeO660C6xb3jbDEdhkPB/jlrdMuzSVUt2dUNqfEM4Y+GKzDiWymneTzNeqspRQzKJ3+ygMnp4P7ZBKVdv8y6Lgz9d+7vUwf/dZJ2Gbbc6Q0iZmBYVBQozp0iEyjiwUEo/l4Sts0mRFpyyLxE5nHipx75HIndgASghkGKCFJpUKiEjmzsPsRVR/HHnsbsTKJCqRaCWySFgmxIu5DoX1rSFfdo6kyQtXyincvAjNPvhCkwaaaw+ss3zXKnEtAlEb8He4bgBFmPA317/nCpV409+5wpNb8/YYDUFHUF8uMXq6SWWpnDVMNJ4+6yW2bGZnB20hC/q4VSZJLGsgtxM0msm7TkHnCd7O7R0t7HdtfpV6JTAWTk8y0XqeKBgi9ofx0j5aBWzI27kRfJSu3IUSnkkqsWV0ytbUO6PNufFcaNJ5W7O+ySnWV2MaHgjtUxkRjN0lWF6H3qZlAAUjTOuQteUDdMeHqPvLpg5XQnVzgwNf/xrLvZtYvGMsYzTF6IH77GgqF7W28EaHoA04T4qkn+5jW9xMLCaMsgKILC6dH2ufivJ2yk1PXqfUTXcIYEv+RStWULirwtIRxe+Km1nd7cjjZx6fZqEVZLKkeBsi39Uipgpe/4X9bJysc/v/coagHhfu3uy4UdP82gc6fM9zFe45V0KWAtsG05oZUhmEO43xDTpzDsuNtH02KcmADDJh70w/pwRYjS8pCKasblvnEyPUoLKAJoi3mdzaRverUB6F7gYDbnTbic1MsDB19XUrBIvhh6L13RFwsZ6DC2WDKCyxRNB/GXSXzAzLTDBFDsbzVraRFfSJteiToqCv2RwGY9IpNM/t2kUihaXHFEmLHMR6kCqc89mIJRfucrRWdLsy8FkIkUHnf+3WUb52dJJS12OqP8aBpS4HrsC5U/8TV6I70a5FQ5HO7GkTCZEHaWgcMW66pQa/rhi5J8ZvJATDKV5TIfwBqrNb0VuVhy5MXXyEtAm7ee6CeyUoAny/iqv7lgQIC3rrxg/bz77HKi3OscUFNF1M4qywd+D+5Z4pVxwGHsnYJs8tKHozHdiowfO3I149YIbBkpX0Tb6SqVgCITVyqEv6/sfQQ5u4dZ5aszQpLyHu/jpiu4n/wnthc2gwBr9T0Ot83rPPzuK1S00KaNYS9q0LSqFi7ZZVylXJ2tENFickSIVp/lsU6zuzGQZBwfPkOlHgVIPUVVQ8BQI/lpS2fKorIaInGT1fR3R8FKbNhLYsIeuN5V4Fwijid3nasBzPPrfCyBlJfpxAU2m2YXmFt3N7Rwt7s2kyBDMgTDYJ0y1c1nI5uc5E/CQXyz/K9dKjBjmvhKn79Ml7t1iDzSkAyrOlvBL8rnlXYHK3tImrizLgw8g81L8HNt6AzVOY7N4eWfJPe2WO89fey+1zf2CoIBCgJDJaYeLxJ4hWdtF67x0kJaNdFCOeA1FQXaYT7ydWYwggSsdI0hGrrAjQJaQnDDPznaVihWCmVRrhEfRTDj93lbCvyZLDtB3PYppxMSupwLoHPhdR5wZmRvO1i3UubJaMHCyEZzMZVtSM3Sm04NoXJumvBhz+8YuM3b2V72j/6vuaz9/XYXlE8eEzHr6ylRLaupHdszjukpkhzszQbuXnnMidXSTY2kmzgwsu6sJDur+14QRaWi3Sgj5okbDW14xKjfRtvMgkf1BEvzPyQ5jE/lt9E6enQJSZ4Lf7ni1Dq4CKNnBPMfSfg/hyQcjrQmDVKTJuIoqqpDbEn8Qm98FZ9JWyseg9mStMBHRKIS/cdjS7thF2bTQO++HNiU6OFZuCUWWFftGlXySgncqjfVQ/pdtQXGqUuTRe5RtHfaoHXmf6C7eC8s0jO6eOMO3EUx+6IQ7XCu0ZliGFYeal+ZSh200sb7Bu2qwXkX3Kx8w8QYzCdKo0AFsJDkOjOLbaHpeWEiJ8JJLA/vNtayFNj5RNOlxjheNEbGY+uIKDGdftLh9jV/4LghKaOaKZHuLULPqP74cNg4bnIj1ZAnJoXza9RawP4//Fh6nNX6Z16CzJ0JZxgfgKlEQFPcRIH1lfh/WhQR3RDtfODtNOgff8lKCSID1NWIsoNXpMNluMjK/z/NA4Wkt0WeMJgUMQxFX+2ATGndQBTpALRDZOMhsrp5Lle5rPpa5HGPmUWwH1pQrVlRJBy0ckMkPkVuWcVoQqLBkbrnAtFqTO2QdYhcDp/NarlHhknbR9TOuMyvAW9Yk1thffROLf1u2dLeyL6ArmC/NW0OqFAF9vM9//PXpenSX/fmRfUOpCN8UA6zjt06xZRGJ4bkXBkDSZlUpA2zgQTCMzX5ik/DomXOzD6GEY3m0YSBxB/wa0X4R0Q7Dcup12+CWqcQsRWCGiNVKl7Hr9Cpfm9rN904hdxoJEl+gn40gdsq3H6KsJUtUgUaOZcM2sYbewBHlGsRK5YmGhrtywBH3FzU9dYXyhY7AIBgS33vFuDxoAqrbHZIu7IIwKpvpvnxjjN4+PgRB40iks5FYB5LpEQehLCToRLD0xwsZrNfb8tesc+bFLeGFqPCoYptcL4KnDPbZrKR99VVLta5M0lkaFPuaCnMsXLqbsytSQI4W4B3CDasdOuDp2d6P5WCTa43K1w0S0i3B7FiGgE8GvvRzz8rrg/qlF/s69ryOFbTCiUzsQ+fhF9RLBbQliVOYaUKaFFkg7lrBZG/ja3a7WMVd6W+xO30LQp8UA6w4Bnwn6xAj5JDEvMK1ph2r2sVV+Tu3hxVBv91gebQIKjxau05q5rcEWIZDDSGnA05KYFGWlkLRzVCwmy2VHLuz0wDlBy5TOvnN0d18ivLyPRBjRoDyjsCfaMFzcVMpct5WexvNh+EiER+6eL7rSAYuPmZAQk9q6amWd6YYSwQn3PG0s70rpXM95epmJv5tO9i16XCFmmTaXsxHKxRMUC2wdOju4LAAnFAWSBn48jb48QfKlO9GbNYfcmns6RJF6DUkafUEgr4/RXxklOH473sQKQvtQ7iOVhw77oAXjezwquxdRiWD9QiOPSWudIWuiNSPzW4T1HmiNV4oJaxFeqA2GvhBs4/EMwyb0IYq5Dbn6OGiFU6CMQSe8y3kYpBmzhoTW+ImgvhYycalBY7VM0PHNiGmRt9ywoVtZwuRo2YSKDKs/Ad03p03T3L6U5AngWpL1c9JmGLJiGlfY43nAoROsexHBm2o3v73bO1/Yi8LfbtPZfzjLrKQX2df7LRJ/LzqZNt7dBOLAIERVLdF62sx1CVOyH1pXTixt/pZnvJtRYo0fgTmgaI6n4JWhfAuEt0G8DpvP38orSz/J/fVfQMoO51sPoaOU+ZFnkLrMxfSvES37VLYW6JanicImkWoagvPJaoidfpO7hHKBLx2xpc5YFcbCDwU6EMg0YfRGmyMvXqe6FZtmLUUr3Z2wKI3zwWZAt9aQ4dAXhY8FnTm5WuYbl5psxX7mMReikBcmB2USkKcB2GcBQbRa4swv72HklRbNn2pRO9hDS5eFAJGneXE+4soI3HdeMLoVccvFFOFc1UoxgAJSfIbU3r9rRyUg94Io8jr7QVZjGIn5dKUm+f3qDO/q9AhOv4I/O83jz8/w2MUSSsKFkWG0J0nSBB/xJmNaCwGTHuzVOUdQO+jZDfhiqdDAwSlnAogR8XPMq0uFQbWWfKoKAt+9nMZlz6WVEfBxwXVfq8BQ3SZTFAS90hB1KffgttfPcmF+wgr6GAcsksO2DlpYbhSbSI62xkkrK7QDs//NCELyRDanLKQIbgAaU6oXWn9AD8FpoINmxIv58Hf9PluXD/HKqw/Qvj5np1xnAsb3odnokgjfNCtSgtpcn8qeiHLTjXduNacoEmIS4uxfMQ1xUCkpzEfheYuKSl48pwhI6bBCl5MYDIJ+QaRROK9rel1swZJfwYl4EQXIzRH88zfhd3ajv3kbyoLjaBMdyJXpgkDCgjYiyHIqtRaQeHjXp4zl74qPNAS1mLE9V/EqCVILxnZvZ8LWJf7mipK5SE7FDmrQK9y7X2iYnCtKOTJ9OuBDLCY4elmsPfd8ODvDUwKRCoYXy0yfaRL0JZVWYBH8RG6bWJ7kmkuKEugKWbm1l/Spd9bYDkaI/YAk8TJoDuV2K6xlaa1/rQyKa2ojiUKbHAUEpOWI0YNvUPfW6GUh0Ldne2cLe6x/ZIAvWous2EkM44KtqUvM97/EWvjDgCBIgNh2JcValeRZopIcNifB4G8kmPgfKagA+rbng+94rxNi2NuqgjcG1UOCdnQLr679fYLLi7z40gfYmkhIpvZzs6wTleehLemW5syxtowwsyM1BeuSzGUkFTsWhP0+y0cQUIJqsMmRVy7TXPMIe2BSkO0YFjPpBUBKrCSLW0ZQV/yUsq/Y6AZ86fUh2pFkthnz4HyH6VqC3KFobfY9/tPLE5xcdV2yyN7d1BQ9yi5EIozH0EyhzJ8nTQXPPnYbsyeX+dBnT7Bxu2MNhnVoIVhpaL50h6LRg1oHdi8pvFQV6MMKfeead+A5Tti7bHeh88ETEZmmUvRmZHQV460mRK+P88XtEN0pMzkqONnpUg1DvIri6O5ViDqstSMmU11QkCydTnuoh0KbWwFZ0t3ObSOASxVLEEXlI4bYuO6FE+Kpi81bIZ+kufAv9ooHK+hjg0aXpOb4WgVGmm7wyVLXZRWijp0f460pkQDbaLtS9IDWm291NBUkh5HsRzDcbNk2OnJg+Zony6unJTBnv3sAiYEtNWgWm2huABNo0wv+yDF0v8K3VqdItU/QUMy8f4VgGKRI8D0baReCVEt0yfSKT220XVmbPbUWvPmXkJCSJ4eZex4U9UWXfW51445SAq/nUXp1mK2jz7EyfJrE9tbwCtngbl7csbk9n+uAuvC76JcoPfMw/unDeAuz0K6glEDZJl/S3pY7PtNnCwYDlo84ga+1ZQ3F6lDL0yrTPbySnWfhqiuK8LLem56/OKtk+2JrAdy3uVdkEHegCGQ7mNS4cxNA0POorYVMnW1Q3QoI+x5S272dd6cgGjxheY8wXiARaspJCxknjGxeYf+ll6h110k9j+XGbtpBExcG7XgjXG3sRaQ1hC6ZFhnKRMKUfXc6sjZDC37KvtseZ3joMkokKLn+puf4dm7vfGGPGLSCnPtTFBmOyhjkcPxltJig5X+XzUkvHKpzQYQwLphYQ1dBOzWtb1MJWqao/jp/8dI4yy0IAnj3u2B+CqRnLHFX0aZtXoD2IQkDrtcfoFvRbE1qEN+AM/N8M73CAzeZsj1tJJip6sLetuXPWdwNkBYEZMCN7+REhIEAjfOFPL+9wtiNat5XGnKBB2ih6caSzz45zMKmRz+RHLtWwRMwUksYriQsbIasdTy0FnhCU/MVP3r/KsPllIXNgLvnuix1fT53dpg31ssZY8nkE+TWuyoYucrIU2W/F/Y7wWDyy/Xlcb749+9j9P5Vbvvnp4hHwuzEjiFslz1+41HBQ6/DA6c0jZ5JpsxiCGAnWOWrvjgeTugCpgtdQlZP7zweNvavdMznr97EhfUJykFEXW5yIFrjU999iu2lEuVKhKz2aHV7THauWwKTaKGhXEfs7iHuFJTDyFzbEZ4bNLdFAi7YUruBEkcr6JNLBcvbCXUr9NM0t+4HUqXtIMeJQaNLLEZsowbNuk1ETXNu5c2AnAH1oonfV0I+ePoKu1XCrz+yi27JmZBJYVUZ1j+L4OP4Dm/QWs6x3cNtYuBzmu3rRIcTpjKzlOvA3sIxHnD7rc8xKVJOnFpHvPd9MKGtjmb6h5sZFNYRn5/VuOpTEp2iOqDWQ7hURgUaPd9FDymoum5/BXOOYmFsTjtCS0QkGFkCuSDoXw7oTh0nCl/F1N44inXvbgxypcEJ+ByHLx8J0Q8JTt5C+c++M8uOd92BtfM4YgW+O7vI19OA885Zt24CJAw8oiMdP6Xf8vHKCj9UqKybpXOje/YUuSq0wxIjRbBEzCZpwQWfVzFIm+CYl8u5IJxrDFS0hg3Drm6ENBcrTFxoUO76eFbASyvMpRXwnhP0Vmd3iZ8pilp7AdFPuOn0V2lsLyOkzCotgjhlPjpuTmAfS2vB7rYkSfZzaeI9dMUUcSpJlUClwurb2urJAj/sMfbgi8zf9ARKJiihUby9pXdCa63/f+/2/1/b1tYWQ0NDbP76B2hWbLKSgGwW35RUJjKhgNYkYpLF2i+SMpbjiuwwL5Qw2Zh9DR0r8BOgHcHrC4prVze53h0xoWFlBO9wDSYn4PBhOHQbyCHQJdA2fpgblAnqlReQVxa5sRWwHc4yf++dWVk1Plm5t/P0CFF4otgYn9lCTiHuwfVzsLUCC+fg6kmraVpB+hP3b/LuPVsUYXQB4lRy4kbIlQ3JH75S49RimP1WHI+dW2YhFJY2wlgFIhTGw+GG3P2cT4F57fQsOzmVDu6TVYultsmRjjly8yX2/+IV0sMenjSQRj7KIvhrhNbMbKT8lWcTxlqJtXKEcR04Gsk0Mi+/ucwlYjcdYLom2Y5LOKGmOLExxK9dOsLYu87xN+89z/Y5nyMLCpH6VoPp8spam87qBg/1VwDF0ozP6niF+ZsF9XHb47NokaeFB9ZAX8BrDVh1io17RZCchvjYoNWutOH4aWoAudOCieEGH8y+TtDHpr86zfqgRZ8qIITa7aDmYPvroFoGb745BDpACcHn3r+fL71rjF5gKs/tQCLwqCL4FAFjBWHuRtHRRRFsurjlHivjwPWsHexsZodInpfCWTtTa4Ty6KgRosD8doUDnOL2TFnI8eokSQzRuRIqBi6VYTFEbPlmvY4kJN+xjm6miLLLSXB4FTlEsNYa3dPoa2VY8xn1enQvakpHn6U/tsDGeAvlRaRCI/Ft10udPVtxpThXuPMsFPPR0YLJ5VmSr34A8cYB9FbD5FWqfFwRuaKcjaFjg7bfR3GAhQ+ehaQQkqydt3PjO0eUUhrppyREHPgrVyg3ndplEDkFJRysbHFmXQ5Dn5RlIrZJMzvf2DUu9LPzfTDxzsywY6QQ9HxmTo8zcbGGl9qRtM/urHbXnyqrkLIiIrHjHEZbTC++xPzVZxDKJPYKIW1wnVw4CG2SFYu6hhJoLVClgC6j9GLJQv0eEi1RWpHWUoSCJClR+dASdx75MkhIhCQV0H7pKuP3/Cabm5s0m823WAH/927vfMu+mG3tNm1/K0opnR/jpcuMdn+FzfCv0PMOkIgAh3rv4McTjIDvphA5YSTgudPwxhUJYsTsm2KAxnpwfR0WrsKxY/DwEjzyCfObczRoicn88z3iW+9i4/KvMb73MOXJ/bl31TkqdH7bAuNpVTa7P2lhrPYU2ltw6iU4/SK0ViHuM1hphglL/MevNWk/KLl3T4QvU5plxfl2id97psKJ6wE3Nj2S1CbH7RzKgqEpsv/yD85yd4tLOiHttGi7wIrWffb7jumUYDsYFrRwK5eVMJ6VJAk4efIA5354nrm/ushNf/0i0YRncbLNg0uhuT4i+D/f7/O+k33uuJRSjgxjzriXdvs7U8behPIo1hGlXoqWGi1SlJ/Qa/RZaA3zZxsH8B9c5QM3XWZ4vMXGK3vRok0aKOIA1ifKBEdmmH/6GPFyjfYBxat3zlCa7XIwWbLjZ6+d2iz9oqum7cErQ9DxC7kDWIJ7HeIT5DXzdpKcRZ8UlYZcrALmuyQ1gj6KjKU+PATNmr0Xd04J9UfMeGz/BYg+jA5DuWG+U8Yq++6nLvHkbQ36gWftXsPcNXALIaMFt7ej5yJ4VIGYCmpo3ufBifs8aa1IODvjvJjx8xKq3hI1e5Uh1thkhAV2oVNJbzmkd7qKNxTTfamBuhHiS4HvW/qrpah726RHOyahp3AfA8IXjWIdvbiN/uUHqUVl9PAarY89RfzRc7RLPbQwaoXabhIdvxXRbuIJSXj0DHJ6wQpfZ7m7vADX+aKAepl4VF54EO/JR2B5GNUXxm1sX6lTsPNBHMBiEsJ4HrE9mIoYSi6qlFnzKQYRXJv9DVSFIIklwZBH4kHP2vTChlfcq5hQ50Cve6Ss0KdLkgl3iSqkZRZ7A7gOgM5x75BGzFjIFIZv1NhzfIJSx4JrOY+FE+wib5fgOTFh7ykVUEq6zCw8z8Tyq4T9bZvj4/bwbD5PcSCxiVtFw1IgtMBrK+p6hbqA8bUvkjH8LRuIkYLomyW4FsKMQu6y9PMmBvjt3d7Zwj7LriCn6IyABYMWPtlngaYUv8R4coZN/2GWa3+LmNCsEWscJRp6iTGOlNKU1AolNtDdKVTUBF2wQiPDf114WGs4+RLc/y4oj5Gj8FkDUnmgugHBPZ+mk3oEFrChaCFrCWsLxmK/eAwuHYPupvm8vVpgmtosRJdrUPYt0dvnkAJKPsRK8JvfqvO5l6BeUuwfS3jhSsBKR5La/aRZV5nMcOPp5Igb3uJ9ujfHSGTBbeZyDt5S93IybvBUAzI3w5PxctnsaSOb0kSQrpS4+EvzeF+A+k+1mXzvBrqUEIjcwt8uw5/fWeH4npS9ywlSK+ZWzLkPXTMXzNJ7HESuXfgak5dx4aEL9JoRqafp1xJTs618dl9c4r0j21CP+LVIcKsf88x7enQqKdtNH+U3KZ1bZftgzP3z+xjqnGTfsQUas2UCm5VsJtsK5yLM2EoIrw0ZF74oDBxA8gIkF3J3fTHTMbGCPN0p6MmJM0mhH0FkufnUKFQrZCBEqQ17DU8Zz0F7yRDY0BiENVuO4paUoF32Tca7m23720E83i2CAWNoh1q+gzCKYj5PbHP/9E4iwqmaxZQ5nf1fzO33SLmHp/GW7ueUnmP9C1PoK2VkAGiB51voVgCpST60idrbQxcyxd9cUtcl5QypPkHl+UcoRYJ07Abx+58lvu317Nm0hmR5is3f/2vEN8YRqcQTmt4zD1G6+RS1R7+OaG4VVAjnPXAp4Qq51aD89MNUn34PUSwzIyKDWvAMplRi9RLPt0LP6Y4S076hZK32ktUFbZZ5tsaLtpN9VMdntG6x96Pn8XaFqKAQUrAvV0o52BtT0CdhiU1SUhs6T3HwwC4G7xTAIvpCfg5La0oQbpVpXBjh8MWGCZW437J5tq5/K+SLiHbGqteUO+vMX36CkfUzuF4DZCl3zv1qT+oABJy3L7MBdlgyFN5FdjAIZdD4FmK4VjYZ/55CaoE3F/J2bu9sYS/JKTMzG61UcC+37cgal7qHVG1G+39AP+1xufpJet6MiWmrlDTRjPZeQaoOU/HTjKXHKYkNLnb+Fie3/5pNIwEdY/BX7GIRVlDHXRA3TBMe7RsYTlEy+0oBparAW+/QWDjP+uQdSE9kdHX1DXjq92HxvM2HKgpcnQtU59QIpSFsl1iYa9Xm98B+nypBpw+dvsfCukeizbGJKrRWF8aoEzCIu+I+U7AWCvdgPw5o0OBijoVtJ7d38kGTo7fqgqAvnNclCilpkiTTxAj9CxfnUZ+B5t0tbvt/nWX0pm2UlPju5BKujEmuj3o2AQiCRDOzrvnYc5LhtldwQxjNol/v05rd4vqRJVqj7UwwKxtVVF5K8+AlllBEKIbTMmc/cgmvLBDCR1LC14KoL3kl2M0jq1VGz7cZafQMME7dNcLRuXB2E70UwokhMykZNxNABPFLkFzOPQFuft5K0Lvf3XW0NrH5Xt+47gMPpqegFOTHpxrljyDGDyJ0DFvHzO/VYfCrDHSE0XB+tsIvf99etqpmcsT6OPL59yKu7ONd3/HnVHZdcZRYnO4ChbxZgDsRLrLf35zIt5OkdhLYYGBAkBLwhj7AyyO70YshMpFoT5gEV2nZvDJ3qOZ7MNXLAKnMmRQQo1hEs43iPJoORD3C4zeRHL1Acvsp0l03ENIIPZV69C/tIlmv033mEdTaGA5mVWmB7oaoV24lnF8gvOfFLKMuq53XAqEU3edThr/+aeTylMnbt4I4Y3U2+UxLg9osMBghnl/gSx7IMgMues8zx6Rt0L3C5OSpPID19vmgXlvjE95v03/XNNdLY2xVSrTCBp3ROWrbXdojMwg8VFjFudp7dFmVq+gss961Ak8ZVBUGoJRwSpIfm3ak86ffYPQNReXcKFM1Qbc6T6t5GOWVQIvM8ZVRUzb9ZrAUAi+J2XX1OcZXTuKlHXL/g/XuufJYXGjYDYhLgkhzUnYCv2jJOKaYaWCOabl9BSQCkZjj5OqOHhnf5u2dLewhH+Di4s4GvSDgi5OfWUIglGJ26/OMbzzGhncznoqo9i+ipaIslpHSIVUZidmPAtKeFWIOx6VwC+7vI/Oa+2uw1RO0NLRaEFVBl4w7vta5wc3P/iJjy8d4+aZ/xvnRR7l8Ai4eh9PPQ6+be3nc4ziCLrqpHN6LE+hFYe9XDHBbZ8WEARwomFMcnGD2ZC7gRWGYMkHs5IbOfwKrdDhF2MhUyp6RIX5gFJuugkib8w940J1wV3mMvmgYuntxz4q7X03WrdZHEXp9WlGFNIXWi01e++FdfPIff5XFjx1GN/soFL618JRwsT9FFAguT2ievFXyHS9IfG3OmAqf4/sSkmHN9h0reHTIXf2mb6Ap4tD0RUJko6pJrY9GYgvskJhSn/rsNHteucRrw9eZ0QIxWYZ62XLXJM+Ed1Z4V8LrTeOTHdgSSE5Ccm7Qms+EuI3BZyV2BS3N0XycQL9vQCCkhMkR0y3OJUQoDf4ka4cfJGAdubZMIyxBZcRIB4frb7fVoYDf/MgsN0Zt56jNIeRvfwaxOAdCc+aFh9m/6w9JbZa+trafIYVB4T9ol+cL2eVn59Z9kQbzvOzEZmy48V9hgnVGGWGN5WiGs1uH2Do7jjpZR68FiK7MkH8zo62Rot+1hb5nG+lrHFK9qam/guJ1FCuWAuwdeB7x0XOm8XtgeIXWknS9ycaffYjeGwdRvcDgXwhQEagYpA9eDTwp2PrmeyivjlJ+8FnE0BYgkEmFyrV7CBdvof469K6M5Q0eyb1odhgyWSUCa9UHZh/hYUB0bJ8mJ8uy3FxhcosYLqyzPtCxP9tzhCUIbp1l60yH+y/8CjetJ3hJj1iGxOUaftQnLlVJwzJnHvwoq1NzHBv5CJHepLF7BYFCSUGvWkMIRernoTOnRmstqG21TBUNkptefIF7v/Zlwn6PxvoaQZxkfDYpNeg09nH8Xf+OTnX3INXY54i1ptFZZXbhBfBqjKxfJoxbCJ2SdUBzDEd7hsYHyhA0rmdIESCkVVPo1hmam93c2gnGIZw2DEr4RtsCy+AKhC0gw2reqeN+m7d3tLA3srzIAuzmupUV+R3us7ZBLvuDNT3DaIPJ+Ekyl43UtteNyEMFGr73wB9wYvVOlBjm9cVxk6CBMZik1Fkt/nffAlVtBGkoTG7HaqpJNNTXLnLHF/859fZlqPm8/vIN/stfQNQ21qqGPIsUBuDYXdKNE+xS5O4qLzB/BzXz2vM+CBpw+k9h+1qutGeWe2FosoYVVqHVhc+ZYCYfNmGHMghg7+gKwwd7rF7aRalsaprxjAwZVtCLYKsL29EWsQqB8sD53djqwrmFAL9sessIjOIQ1kAnppGbjDWzR5ZQ11LizTmiyOxzY3WC/+Nnvo+xP1S8+2cWkDctk8hiC2Cd2YlawLVReGM3hH3FalVxcSzixHxKSgk4xG6WGWWdCVpIDD5cj9S+EltwZgBiEroIAtAJAR5KxazHMCd80u2OuXIfOpfHKE8u4tG17gll5LwQBNeqBNFbaPzpGROn39l/XjOY2Ff8ze2gtLHk+zZGHwYwPWZgcFOX3aWBEIYOMty9yLXhWUb1EtTGMEmMBYse0Gi+cu8op3aXDXNt1+HPvh+uz1rPj2C7WyWhhMl1j3GtmHNHuJvtwvLMhH5eVS3tZ7e/BiICIkI2aXCO/WzToEOVlm7CRoBaqJIul5CkqNUSLPnotrWsVOGC7q2RoD++Cnt6mVtJ0AE2EGwAJ8Hm8zsMQADtJeiaa4IjEFrSO7OP9d//GMlmw4yZi9BYcEdhjUUdAQGorSr9Z+5FXZln6MEX0dNdyjfuI1jfbfIitgxPccCHCqMLuuRdKczvns01df09tNtHkhusrsbYPbdd21lpmiU94UoXCkaqqAW8sOdvcuSlJxjevgpoAmJod43msb0NwNgf/Aqp5/OB0i+Y7LgAkJK4VObqvoOkgc9TH/wo13YfQgcKTynu/cY3mT1/iQOvHqfc7YKAsNtFal0gEcfXNX6vRTM9zfTyY1zY99dzuhEarRXNrRvUl84wtXGFWtIxrc+d1SQEWUWOwDBvL2AQ7toNdmyYTgFGr7q2Aje+aS0YrNXlGQ1OaPCH0aVJNsfGSUZvYWQ9wUsc+BGZxa93hue+zds7Wth/mR+gTok5fR7QDLHKvDqf1Zdmm/vbFUC6+KZrBpLoQexzZC7VXEKGnZeV7WH0VsJ1WcabWKZ/ZZJYm1yWeh2OVFfQ3hA37TVNHCRQis4x2l9idPkk1YvHqS++TtDdhEByztvP0qlRYgM2hedDc48xpiZvzl1S3S0DrR6UoT4Oy69D2oFSAyYOmnvzbcJ22LAuPM/E+TvL1kubP0YmrDPvbTHPS+e/7fzskhiVhkYl4eG7trm+MUZ7DaoN8jC0o38PaoFBXO1HCXHqI4UR/lFi1tF231j/2VzZm5Ql4zkWNkwhLSPyyyZpfu3GFP1U4FuU2p4G4QkiXWLhFc2f/OA+HvxHQ4x/8hLhcA8llI3MCQvxIVhpar5wX2LBT/vEwie1eGoKwRlmkcxzJxeYYJEttukSk6Bsfbakl5TpXRijObvIdlWx9fJ+GiOKdHGE9rExJtJNDno+FyPF3tUu6RXQng9lifY0Sgj+aLzJc9WQf/ysYHonoesOJMcZKFOA3KJP0kKZXIFDa0vzUWws+iQxA+ks+qQg6L0hGDuKLvXQaUKpt0A9bWM6QQ1a9KCJalVWj0wxi8/1pTH0i/fD8TvzyRMaIfNSOZNdbjTronPcTbiyVKmzSHsxZUuwzBgnOUqXChpBjwo9ynSp4hao1gLx3BDiRNUIdZt34QBkrEthIDUCQFRTeHADsaeFJzaBbQTLpGyQsG0pQSOz0rI0Uxzd82pL9MnqGBu/+z2k2zVTPuriWHZX4ZHF0H1p8nUNdpNAL02z9fXvYmi/xhuWZs1tgIwgGDX5OtpNs9EhckPRMxEWh+qsta0Uc7ZKQWZmdpDTC+24ZIzBOLkQsd3HYopIARfG7uN37/2PVNJN9i1/i3vWPk9tfckCdNkTCBOIqESJXZTGIql4ETevv4wGjr74Ktfmj6BChYwVu86cx48scqMocKmsMUmR7s3vKk4YXj7D1Ogl+kBHwaGNi1R6LZpbSwiV2md2TMmeVglIu6B7+XeyUIjvrqciSHsMJEgIhfRqnNv9gxy49HsoHUCqaPsTdBghxWdWneTqxBGaYpta6zH6api+up8R2ckVDiQv6weA/523a3tHC/sv6b9KWTczgVimzcfVb/CI+jOzQ5FBOVelk1qxzqVbSsGstIThTmrdPFoIltNRfunkpzl5/SZCAdprZJgsCljdFDy2PIFKE77xfMAHHxQID65HL3Dw3O9R9jcItG2DVPG4MXyELz713Xz9zKNZm8ixW+Dwdxm32VuBqDn4xtE5sgWcKb8FXu/Ot3HBMAkYXM+ZvqNynu88ucVKLmc4FvMGnLDf6ghePVtiYsLGIUVhnTKoWCCgXBqlYu+zUc7vuRfB0jqsd3LZI33QjRgtTLat8y44r5nwBI2ZTZLrFZQK8Up5QiIatBTEfcFjPzdG43fq3Pw3Vpj/5AJ+NcL3le03bQp5UuHszRRX6EPhJShxmnsRfJ0eS7YkSpBoj5WTc3QvTPHgTJveKzO8tu8qI5eGePTqBi8+8y1arRm8H52jceMIp7tHUI1l9tUukMY+qe/zxEiZF8bLHCsJRtciqj0P8O1kRdaiPwlpXODSdhLiNHfdF+voHSdXyaCgr5ZhbBgqpdwb4Oi+OkFa7RKl44S9K0x3WtZMdExSZATZb5bZPjDHfSOj7N0Y5vd+7VN0V8eRSOPZksbLdfMdJzKL3BVSGbHpkNWMBMxhU0Ths5kHjcdL3MFpDhEZ0YgDcSlizgmt8FZC9MkqIpb58jfTmiPJWeFrjHeNHGlRfuB1gvsuIIQGeqTEREQkJOhMDQErknEAuPnyFOgkgHN34126m7RlWm2762fxZCtPpIQwNGvcC6xiLLF49YLedYFYs4itW6BDoAmiQgbnrRMyvLDsWQVZc0a3FZNl37QwyYU5igxdT1phjwLhnKAW+EAKwdnxhxACjk9+B6+Gn+LDz/4ch699017D8U+nsdubS8jib0IIgjhh7+mTgzTtjht4hoKQtu+JDGgFo1wf+26OpiXGXv+S8R44xBzHzwewVuzfWoPqQbJlhLmLcxCaDjaAScSKQHXNexG4QAC+zzQ+V4Y+wHnxIDPtxxj1VqiGCi0ViFHm18+ZifY9kpoPt2pY82EJSI0ltikneDu3d7Swz0ogwQgNXWNFz6Kso814Y7QZdFtQ31J16vEmItYF1RjyyZQ5oVrfdjctcX59D//r1/8Fi+0ZfCvYbNjf7pvTbNz3+V9+zefMUspH773OuWe2mL1tihN338+eaycYurHC6uYQ/+K3f4LjS7fhWxAIX8LGdeisQmnGnrZAq9ouvizrzWnsOrdWstp0m+ZeGYbSEHTW7WnsgikKdGccJqkdJjU4rrzFuwBQHpevV1AKShWYHDNu/U4/v/edkefCSGd/lEswNwnjEWx3QQy1qVYizs9ExNcmQZlEx9AqLUIZZherGiqWxH37LCIzIsxcSNBa0L5S5vl/PceVPx/j4KEzjP/V6yTlUXypGJrvGA+e5Urmfk2NuIHVCIA6KU0u6EeY4jdQQpGgWD02zOyJWR6YucDxULK/MU3YH2doNGJ2zyJXpo5wVz9leOFFXlmocWhmnJV0AhVomsEW66rEr8wvk4gUoeFTr0ia7YIlo16E5CJZP1E3ZkqbySqi3u2M0ae2hr4foZWiPXE/9bEbZoDiYkKggPp+GA5Ya9Spr1/BU30yt32xyZEQUGnS2zvNxUdn6ERDPP3576B9YxKByNzHUirufc9z7Dl4wd6RAIt8l5fl5ZX2g3XUZCLcfXPv0kla43U2ZTNTBMxROfJcsCV4+Asd/rQ/NVAajbK8OiZPZUAjhrcI7zlJ6c4zeLU+CHNtB5CbWOhXczd5CMjds/lLo1OPdHGM2oUHGTk9z+WFEdK4IKAsTUoBvoXtLZdMJMXRvxP2smRFUgqqZZyNAkwlmKXvLN3cJZCL/Fldgm0RBtbF5gdAF+0DFG2JgbCadAp1rvJmMNfk36EFZ7uHWD70s/zY5qeZ2T5tyuAyXioGrmfuV+S0NGDMuMHQhd/MmEeyxGp1DgGcGb+PN6Ye4srY7fyj05+HJOUr03eyO9rkSOsimasis5R04U1Dsg3RshHkrguQZ5OPXL2h7hvLX9lYhlNiLEMTUlANYnqTCQ/f+FX8UmSfyU6MVwYvRI/VueAdYv8hD3804f9D3X9HW3Lk953gJyIzr7/P+3qm6pVDoaoAFFDwQANooL2h6W4Om+KSoqQj6mjOzHBIHs5qtKsVVzytFXdG1I7MSNRS001RJIe+2WI32wLoRsMDBRTKe/fqeXvftZkZsX9ERGbeh5Zm+4yIc5B1br337s2bGRkRv9/3538MSNjKmwIuidXivTve12CfAFIa9cPr4gmOiFcYKa7y3fBRmtURrrfGiOoKHcaISPOL7f8nxciaVGRm82Wpwu1ZKfjO5Sf5wnd/EZSHL0Tiwk+y+9zfGYayHcK//WPNn39Fcrv5N7j/jSf5xZGbiMJtzmx9gH/w1U9ydX0MhCC0riOpoTUPr/wefODnoDyQfVjMPnQFAeyQtdVaEs17h6W3OAAHPwE3XoLVa5ri3pDOhqAgffyCoLoL5q9cBdFPbbGPzmqKLV38IUM7TghQGLy5OmfmYWPDMLKNBkxMQN5kmtBQJnAvJ8APFH2VDo3Ngn0Ie2kJuQL050GLEpWoQd/8ILPqPG95d+ArI3i7sXmAjH16KoooClmuBaajoUwu2/WL1oLlU0WWTx5BfPkwwhP4ORg5tklQiilPNNn/2Rv0TLUR0rYEQ1q1ahBBGUWFkBHC9g1YaHDswi701DrXV+eJ/qLN7U/dwYEb04wOLnNh+zCi/2X8cJDCpmT3MY/WmfNsLz+JEh1OxiWeO3adLVrMbFQ4PJfjsTNGAtNoboYdpuVNEnOKm3+n0XdCW/TGSXqZhYqtNt/umJDrsSHKfeuYzjBhJqdfwsC9UG7SLBWo5yRDrTZdEcTJPAoo9hH3jzD/yARxvshrf/Ew59+0ne/cvEvFg4+/xkNPvQi+9WuTKYaSaMYGrRLNvOtnNsc+hqFNnhBfJw3Tw14xwCeHxGO7tgtChVT3oJVRl3VM0s/eCMMagghv3xUKH/keXqWNFCZJ05QDiqyDJoPUZNvjZFrRaI1a7yFeHuLm/3IP/UGOW8EgnZhEkxbCCPD5HAQ5k9SQC9JYG8jQqkyX0gkpzpOYVHTOmOjd1JD5brIDBKmfXmRozFoCuuJkMl/tAnyR+a7DOasIiB3fXytM8BsP/j4/ffJXOLr8rC2f3aWe/+cPB8QZwFfWfz7Xcwd/cuevcGXgXsNzPA8lJXdvXaCiO7wzuJdvTd7Lh1ZOcnDzGl1VuSAVOLSli86qAXKsEuhyFImNpq9a5qVTYbR7cwoIa8jGAkObF837UsDYEAzvhqmi8T3KIvQE7PakSR1qS1guQVwC0TH3+yGm6L/E8UOB/T/8h/+QX/3VX+16b3R0lIWFBQC01vzqr/4qv/mbv8n6+joPPvgg//Jf/ksOHz6cnN9ut/nlX/5lfu/3fo9ms8nTTz/Nv/pX/4rJyckffvROcs/4xDbp49+Vf4V8ucHl+oQBSEwt7UiAkDEL8Th71KVuoO+yP6fvn9k6yB+e+SwSH09a+pHGXKYyUrNqQGcblNAEIibyfELlc6s1gVLw6o1Z6l95jvBHC3zptU9zbXM0qf+ubaaHy/6qb8Grfwx77k2HNzwJ+TyppqItM8jw+iQ+K6tVaBMZv/cZmO5AYUqiQmGia0sgfDji70YLo/2vXoVXvwjRZkTY8agU23QijzAyPsRYp40snP9eY8Zwc8ncVkrID0LeAoAnjE9eCOPB2NaSqAXVwAgAO/PxNYKteJiRGLbDg8xa32c7gkYIQ6Ub3I52IYRHLgeiINFN0iAjZxG2ayUgo/ia53CuwOW3+o1/UmrmXxrl3qMbNO/U9B5oojs5Zg74BMWAusohyNHbmOKVF3s4dEeDveU2N8sxnejHmfz8CWpii2utM5y6dheDt77BF7/WYeqxTT79wF3Mj4xzpXmQljfOPftCvle9ze3xkGJY5n/48m72LG4jVB10jBDXmPJeNmb4LBd3Gn0n3OGjd/SgDZhvN0xARC6AiWFEpWSu1RWMJ6DUD30t0JJCvcbM2jJC/wCglx6UB1DVfm4+OUZjpMTy9VFOfO9edCQTTbNcqfPwB1/jvkdOgB91GcDNfhE2rC1tdwOpVz9L1mnHuAhkjKmq3t39zFRgM2fn/TXm+3uJnvi38ObjcPEQeBotY/Mo4/OI/nWCI2fxJ5aR0tVuM5Xqsro8XSMxP1WSTWA73t0co/WbP0nUzlHMF2hYW73ThiVGk8/5hm7zBZv77RkBwAmlrgeRDfQ2fvKMFo0yXftcZo6LhUlAOUvrbl5cZH72PukiJADu+Eb2mR0tCkFi+LGJSOarGSXAXVMLQc3v49/d9T9z3+LX+Nz5L1CM65mVyt7A/qff/ZEWmlauwq3ew7w5/jGWSru53XOAWn6w+xIawjDPtyeO8Z2J+2mRQ7ebRsDVHYg76YNiTShaGRCP2ySmFD9nA/Mw78eukpDTdhwTcUwqgtpFaM4ltNnxCgRH9yAevsuc41zDVoETGtjIw8UB0zZVRbjc5R8wBX+lxw+t2R8+fJhvfetbyd+eKysI/Pqv/zr/9J/+U774xS9y4MABfu3Xfo0PfehDnD9/nmq1CsAv/MIv8JWvfIXf//3fZ3BwkF/6pV/ik5/8JG+88UbXtf7/OWJlit54kExeWwhqqh9V7zcnKaBjhCkvzggGWVRIJDeREELLq9Dq5PjthZ/kem0PgbSTJUwhJVd5VUjIjS9TP9OPjnwCIsZK6zSDHO2Ox3K9Chry5Q75CY//65/+HGevjhgTW1b4lOkzaExBnc0FwyAKJSh/FHIj5kOnsbhjZzAdmcdxh5+DXK9AKh9ZAJlLZRqEQPhQHoXyGAwfgr3XXkJd3+KRIxe4vdTHyoKpRbbe7kFrgZaCs9dH+dbrB9mqF7p2bqFsfJLZpoTuweJYUmvl0AoatolQKTA+/MSwQkbO1yIJuMwFEASKOyuLXK6NE8QeIpbUMu3isxqJyL7cRTNHt2lT0N7IceLkCP4lkL6CWLA8usngXYLp402G/RXkO/NMvbRG8NIQ4zMn+I+vHWLszkGq0SDR8hn+8OYmnyTH//S1b3LvzMcYbQUQ7COo1Xhgss31mU2iG3l6ZhrMoQlUnmqjbUHWB24Ap4142sVRs0CfDaJw6l8G6NuRqbCya9Q0tXFdOVxTGwEUR2FoL7AJnQYijDKqY2YRPB/Kg+hyL4vHhtjcXaVzBU595TCdRj6Zx+LgBk9+5CWOHj8NQu/ofyas56m7LI3AJrxkBAK6NGiNJrJ108z3hV0rTws66x6b2qc+0OG5ods0PnmbRjHGe2aR/B13QL6D6Nk038l3EEGMK+0KIilGG9ugwSw4aft32gfPCAWq6aFP3YE+fRC10WMaArmCi4KkBoYnU6AvFqwS6aUWYyE0hVyHeiOfRs66pXTLa+fKme8d20La/e6+o9Jtj05Bviu4PKMUJIYbnRVnUoCHxL2e0G0W6HcYEpJt2PCrfG/XZ1Fa8DNn/++pKzUbUb/TtA8oKVkvT3Bq+Cm+N/151kvjdLzSuwAxkRMEvD0zwyk5gxcZfFeRAu0KBoTpgijP0JXWxizvBF0/MAuS5F1mUiXcvAqsLySC1gJsX4ewng68WmbhwU8ydcAOKrITFQuSjLDNPFyrQE2DdoKECSBRWWb9Hhw/NNj7vs/Y2LvihdFa88/+2T/j7//9v8+P//iPA/ClL32J0dFRfvd3f5ef//mfZ3Nzk9/6rd/i3//7f88zzzwDwO/8zu8wNTXFt771LT7ykY/8UGMJIxBx6qONPPNylaNQQBN0E7zQVF8bjJfp1VsgJdqTaN+jHvTRDKpcUfvZ6hlnpX8Py/4Yt85oLrCPnipENYUUEFqtRAlN0W8RtzXrpwZRbYHUEOuAucYI0lcI3wBf1AFZavI/vzlLvbafMM7s4gwwZYHKz8HQKEwehIExKFVINPqdcVhOs05itDKgKTPSvQu+EZD6/Z2rymoCSCgNwYG+Te678wVEBLPDi3AH3dxGCiI8znx0nD995zhfeele2psST8LwuHWBaciVoDIMu6Y1PUNQCwRrV2HrNmzfMgL1dgxN6/by3FgFFHNABDIyVQABCkHE1Z4h1rd98k5gKViNqAu87b7MTHOi7Wc+E6RMMA6h08Jo+h2J0Jrg5grlxjVOPwu5gw3u622w6zOz7F8Z4NnWEOL2H7HWXqZ0+z5ufavNROmTbO+9RmH2UfqPPMDI+BZvffUC+x/xuHHnIUqrmu3RBrcGjIbhEQAF0CF1XSMnXiOg1v0ADujbHbPps2jgOG0cQb1lyj56EsaHoVKiq7GAswt7AQyMsj4YU9qMyEdW23DBeMmkSegZRefK1EeKrNw9hPIEf/H1T3Hh7KxJB8MIRvc8/SKHjr9jeVyaXqeSlwHs2IImiUZvViOtpOi+5RqiuBXTyad+C/Y+v8UrOckfjzXRAy3afozybQ39oEmw5xLdPdJcpXUT7R8m4XfOZfBuLTRtR2N/Uwr97UcR3/kAeGavx5iumY4endbuO40+b0HeZqZIS3sqgpzqsLmRN3fIWKOSdgkCEyPpzO9ZRdPRqyapkufWzX3urtfV78kqC25rJYKXBXohzJuuHoazDibBsdntYfmM85CYdZa8Mvoxnrj1v7O7dtqsmBRpvm+ynmYEdX+Ar87+Cq/NfoimLKKFlwgqqcBvn9M9j/tcu+3tmKKLNHQL4nwjNlrS+Tg9W24wmQxtgMQJJ8LeTApjDVh/G9pbdh/YvTQ6AE8cZ3rUT4pRGb+mMK9YQEfC2ZKpuZ71vQjz0kmN9Pfm+KHB/uLFi0xMTJDP53nwwQf5whe+wOzsLFevXmVhYYEPf/jDybn5fJ4nnniCF198kZ//+Z/njTfeIAzDrnMmJiY4cuQIL7744n8S7NvtNu12O/l7a2sLSPlSiHGLuMAUl/Ii2kALvCgFOjlR5Gzj41T1MtvFQc6NPkY910vDL9H0KqZzlBLcvAxrMehJ2D95lf96z28DghcvP8R33ryLweoN4pt5Xl09QBxKw0ezIq+WCGVTTD0YObLNYN89XH/RM+mZO0RWIcHPw4H7YPqg2Y/lKq7BWvqyhxIZgFcp389eE0ia6gjRDfpdKUGOWWS+uywmTCpTrFKukGQtmMn0heKuqZtsFgd4e+teVN0qkS0DnDkRsesBj91PiESIyQsYOGwuVZuD+VegcRvam9DZNCZNR2cBEcqXrG5LBnKK3qKmGXm8eXs3CEHbYpMsgW8Dzrs0DvusTkbJaj/uPGX/VtbqF9bgiZEY7a9xc73Ekpxg5G6PEe8s+3atUhgawnvzHN++kEdM7yLIHeTtyy3umFzge+1Jdk+ucX1IcPC++wkGitQaZwj7Je3Nafx2i1L5Kmd7O7iWsJHncXMgpr+2htDPYtoZknJQLNC3HNDrFOgTjT+EetuY7qUHkyOmoU3S9W6HD3N4HAoxXhxajf4HgZ2AnlHIl0DC8t1DxIHHq395P7cuTqFjA9UyH/PAB1/iofvfRgmje2ernHdr9IooCYlPXc5pqp2rgRBnEvDcyMzmlUow8/U1Bl7Z5tzPxoQHcnSEypCG2agKZVvommdxwK+BDmFicfhBIG9mNv2nQh8xt5vo+4fg3EG8PklQALUGumGUCEc/DrMLBRuIl7Mgb/e01hBvgIoFK7qaTLVQCSl2C/MqBets6XYXOe9oNyvEO1p38lsWoHFChM5sMd69BZJaG2ToSJJYEdzDOutBrNMgwm1Z4V8c+X/zwbk/5NDGq+yqXyEXNxKBW0mfjWCKpcoY39j9d7gw+ABaitQakTW46hT03X1VYJ/H8r9cHHJk8xKp+V13T6YWRrpSlrkENnAi2TXue/ZB3c10E9ZPQscAPVJybfphph7uw+8vm5Qh14HIAX1bmBLXShizfTtMtX0nkIgQZISOXNOo9+b4ocD+wQcf5Ld/+7c5cOAAi4uL/Nqv/RqPPPIIp0+fTvz2o6OjXd8ZHR3l+vXrACwsLJDL5ejv73/XOe77P+j4x//4H78rVgDMfMZW0RTWFwZGcAosyGugUjVV3aq94FdKPK9+1hR7sMVfXOCxBGTUYnk+x9qSTCLaa0GZgZE1BvJb7Jm6STTR5k/+7WNsbhRpdApGusRuSitwBJ7x2aGMdafcGqcjZVp7S5u9UuqB3lHYtR+mDkChaLRibaV/t/FcM5gk2l6nryzAJVp9xm8nA2u2d39ngoHcd4CuCy3LcUKdI69bhopd+a6sG0SaTb3Xv8lYeZ3bjUFjSSiALLW5/5EVvOMTxjptGW0WaCsTsO/HjHAQ1RRX/12Tdgca9RJBQfDBx1Zo9VZZi8rc/n6T/cM1NjuSa3P9KBUkMQtSQLkCzW1DWzgtRWeYnTYFhvxMUz8NFMcgX4vxWpqajDj65DK50ZAH7prn+19WnD1xH73LHY73CmaGe7kajKEf2c3Q/q+y1qhwz9Ux3u65xZv9vbQHi7Qrc4SNDeL8fs4/u8nTdzzMm7lnyZVixq5t8XbuCtuFGUI/ROIR+pIvPb3Mr/zJd9i1vmilfsv9lTIA3uykJviu6CoL9I22EQRKBRgeMA1tlCZtb2s3itDQO2AKQoQhPasNk4Lq7KXu0p4P5X4olUFo1u7oZ2umh631Ht753l206nm0hp6BDR74+IscffhtPN9IS6n8qK2PPht/rzLBb25PZNLnAGHN9nYLAyIBK9kRjJ8sMvrGNp5SHLza5pWDfpcJXth7x7jc+FSQUPZfWrAnu+27qMhcpVZANwLCP36Y+PU7UEriVSWi35w+NApbDRNjo2x9eaFMXIovjWElCNKHk0DUMAKxJAXdBNgTgEqHo8Fkgjkfvo1B6Qrk3qHxZ99L0mF3SsE7HtkB+o4pMKfEhvcIveNrmbHGIo3hUcBcaQ+/feBXKIVbjDZucO/ydxmvXWWlb4paeZKl/Cw3eg4S+sWEZ2aLAOkd48gesXObKEyp8lhRjpp2Yhzz0xkpR2NyFaVNifC6J6GrT7h9dZZg+xp0tmh4ZYp3jiGmx9gzs8uYbWJtDQnC1mmRBuTbIgX2Zd8I6tnUJ6eFqo55vYfHDwX2H/vYx5Lfjx49ysMPP8zevXv50pe+xEMPPQRgUy/SQ2v9rvd2Hv9H5/y9v/f3+MVf/MXk762tLaampsh7JghMS+OaQRgpetQUaSMXpFqidNwjDFDOVZMNiJQgdUzl4hkWlu9CxzLZ7IvtIc7MH+GxPd9HiojJ2jxrW320Q2H4qeWlBmA1gYS8L/Al6FibKnNHAta3oIPhpQ9+FHoGYWxPCsDObZQUa3LzIzIvKz2r7PvuRJFK2sIzIO/lTSaICOzfPuT9NvtKZ5gpXeDt1gPMsTsND8bs+1U5xpy3h1nOppxIkRYi0joB+/HSMn/vsd/mS69/nDduHkRIxfDHlpGHJhBSJPJzYjWDRANBG4tGTio+9dGb+OE6f/CNh9iuwdvLY0wOw2ABHjhYphQWOVGaQ1wIKPrQ9q12aLXyUhX69oDyob0KfUegsptkgqp7Id+XMmWBFYBiSbih8ZoNho5WkX1VTrQn0QN/yc984Hc5WNnk6mqF9tIEK53LaN/HW63yZ//ha5wd2Mda3wYH75xks1mjULuTtxfe5KMfq3NpuEEY3sGn5gKW5lts1bYI9x9g13aH7bBI5PsEWvHhE68xsbaYSmHKbqp2x0QkuiY5O4Px2m0jCIQKymXYPWHXRKd59LFKJ6BvAAZHrVnfmTZ3EJvnG+kzXwA0UcFn6egw81dmOfnd+1HNIr3VOvmRFZ743LcZ33Pb1EHAXS7V6F2f+BTod3rGs4dC0rKZ+FY0zLRwKzYl+/50iZ6z24hYoaWkI6uQgLe7clZ7F3g2KgArPmTbyaaPnpV2TYxI75VR1n7zw6j5PnTbN1aunC2WFgIto7hRMsYPHUFcM/wnKELRAr2UGR6krdBdNZYvJ6iHbbPkVipKQdY+fpJlY5ddeJn9m5JTinXuuz9AIHBPqXc+te4+J5mcHZaF7PUTHBXJsBPelAQb53q4EhzhSs8RfBWB55ETAl9Y10ZGmXHWCLLPYX93Vgwl7bUdj1Swu75ASYVm8oX1STpa8ax0pDNA3+XzyM6XBfxoA2rnTEDJXfvYmn2AwozdY7FOffORBfzQAn1HpP6opoQaoGwwbWy1K6GM9Y7IqT/v2fF/KvWuXC5z9OhRLl68yI/+6I8CRnsfHx9PzllaWkq0/bGxMTqdDuvr613a/dLSEo888sh/8j75fJ58Pv+u9xOQdBvGg0oOCgLIdQuwMSSVqxwBZduWo2F5TvLGwjEiLbqkXCUE528d4vjU6xRyHe47cIaPPPwS33j5GJ16HoWgv2eLg2NX6S3Wma2u8uXznyCMPIb2Ke78eESlP091EU6+CCO74OCDthGFI25X+MO5YzNuWfcMLv0mYabO324p3UnFQmSAvmQYjLTmfDzNY4Nf556elxFa0+uv8RfNz7MtenkXG9YyQ3U65U4JY9JW01BMlRY5PnmGs/5+vKpm93gJ4YlkzAJSq5ktcY02n0VVGMzVGDkxi6wHPDAh+PZ5uH4S1m/Bj89CRRgdr3wlT0FCJ9JoT9GUJvw5PwzjT8HQU5hSvU4LcYKYTufV3TfRnjyB3y/wN/uJ+yHnNQlbgqHJBxHP/j36jnyAiWdyHFx8mWdv+Gy/WeNM8QYPf2CWd567xiMPfY633r7JnnLI99vPMbRUYu3MWcREmY2lq5RnJLGeZHnrEuPqGAvRTQ4sV7makxT1NWYXthCJiUlZBhJZE2Bq7E5MFNoJAh3zcKUijAyQlAF1LWydVi8kDPaihkYRnRgRhd3OWrT56Xmm010unzDgzekq2/kql948zLbs8OBPvcHU9GuURjbQ6CR6O+1EnmJWZM32LsQtodsEnHUqR3Y8wrk7kbGHEJq2EKzdupORsVts1g7Se2uLQxf/DRBTL/ZyYXeBk3f79FJngwBTP98hlrDb1EUPvLt3XsruReY3gVSCnm8cRv/hI4h6CaWE2fbWYmXb0CNC8FrGoiT7TSZOv9pivVRGSo+k9LsFSM+EuZDLmTgety+VMrU1QttQy9G9svzMYUQMiXXPVckjs3TJo2RiXpxpX6eP2E3e/5k3dPZvu6DOstglOGSUDfd7NiW5G6hN9UwljfKb5d0i853sLZxbJGk+F4OIzLwQglSaO2q3yLkUG5cOoZyFzEVF2nagTsuDbtB3DxqtE4fryJlJxOHdMDPOmNvQzi9vNrdJ74oxIO9i7xTQAq570LYutC7fvDtJ2CJO793xfwrs2+02Z8+e5fHHH2fPnj2MjY3xzW9+k2PHjgHQ6XR4/vnn+Sf/5J8AcN999xEEAd/85jf5iZ/4CQDm5+c5deoUv/7rv/5D3z/RbO2myXnQZze403wTAc4BpyKx8mS1504TFm4YTT3VJ1J94JVzjyBbms8/9R8Y7N3k//az/46//ukh/vFv/xRT8jo/8vFn2Ssu4aNRlKn25Vloj6IePozszZuAs7LZ4DlbPU5F7kG6gd6+leBpYj3fodGj02dJInBdIJBvNHqZN1q98CDvNbmv90XuLL+JsKalCa7wee9f8IL+GDfVPvPEEqajy0yG1y2XUzbMWKUoSmaAkfnj2IFLfPXpGCE1E1fOcMV/mPFwkWV/At0y40AbQWS0tsnCcA+dfgF5WNL9VA/B7jfgznE4NQ/bLRjV4BJvtBDsHRvhb/TDmRXNnsnX+PP+hwg9GDwOhV2pHzJhmln0sYJ1AvxujwiQaPrG2ww16xTPrTBbuojSU+RmP8H87kG2bitOrJUZ6amT73ydz80GXCTHhx/7IPWS4tFP7eXV73+HRx94gJvPnudKpNk677MeLvEWlzh6p89Ib4GV3m32VVqcHdDEXsiT/59vcqizQdKAI4oN5w87JghhJzNSyggBjbZhPiP9MNhvFt+hRxybXHwn2VR3wVCZZqlKsbWWzEGKeMKYJqvDiUYPEOckc8fHaOdjemavsn/fOXoG1jPKkAunE8nLwWtETESUgH0WWruAvl1gY3UPW9/7OHp9AI1Mm7f4cHvuHpBQR/OHs79K/64vcOruJiu9bVpBCx9NDp9Wl6DqivfohI7N3VVmFDJ5AhFJRLOIvDlKcG6a5lfvImrkEL6h0RhjOXSBwEBaJEdDvmliUVShQNGTSTsNF5nvWxej0/Ld3hMKGpsQNkC7XkgZF7CSJGWsY2vBdL765AmskO+ECRdE19Xa1T0ypA+gu//MHi54vis8xExfwndcF1h33QSoM4JHVyaMsJYNh94JiqffdffGzlHSojdDs0qQ9HoS1m9wZPsmacm/zBMJz5Yo9FIG66qSJdslK/qtwniHhalnGJ4MyQVRWmZZaxtEr1OgN2E3RnKJtfHZr0hYFsbaFoYkZUjdrbLKU8aa+l4cPxTY//Iv/zKf+tSnmJ6eZmlpiV/7tV9ja2uLn/3Zn0UIwS/8wi/whS98gf3797N//36+8IUvUCqV+Kmf+ikAent7+Zt/82/yS7/0SwwODjIwMMAv//Ivc/To0SQ6/4c5Ymn8N0qC9qFfkFS3Sxq2OAJ10qPeAfQWCFbnodUglcYzQkKuCaou+f7pR1nf7uNHH/lT9oxdZbp6m3/6X/06hda2SQ9TPmifFn0cu/cqa8M1Xg7vTtylQpiS5LUlqK8bCd88CBlfeEpgrl9PV12VzPO4lyN4mfHJezkDqsZPr+nLrfLEwF9ysHjStvEFYo2IFVWxxof4A2JlqzvFHl4c46s4perEp5UZaGb+iDU98TYTepGbwSRvHHwMgaauhoml6fDlxiY8aI1VUIFICFoLuH43lNdg+Ap8/j5DnyI0MoYTgnISRsowWhG8/uB9jO4lSUtKXGN2fZWlbZEB+ayZ1IG9YaIRa8//BsPP1bhr4i7Gix3+11fPMTu7l93Rc+TrU3x7Ygb51tsceuhR/vTL5/ngz1dZvHoXa4tvcWu1SvPqDfJjH6e0p8ZYJ+TO2Q8yWRnnYiskWAg42DPELqnouzaDXNomrr7Jj3a2SNrGqtgwCZdHn6hudqIjBa3QmO4VMNQP/X10hSa7qnqxKfzC7BSUSogopry6YkyPGeEHgGIFKgNGs3erqzW3jo/QLgt80WDfA6/iwNKQSBbgu4PxImJCwky3uy7Jwuj1GprX72TztQ8Tbg2io1wa8GVS4NOIc4sWteIgZ+4dod1/y+pGxuRfJKJNQFqIx8C8i//PRgA4ad+JKX6tl8orD1I4eQTV9inIkPasYnsdZAUKbaBu4kG0pSdpm5p5gcngckK78nNmf2MBOJdajpPujRpUrNnegva6IKyb72czbTQW1GKLJ4Ikz94VtkmUFicYeSmwisx06wwv6wL4jCL0gw698xedAXS54xyRjjnhSdBVnyz7XlLWxNHeDxiKEzbEDu+Vq1zqNlw+DgmSTj55kk0kY6AAKrAnt1NJQjjXjjG/NIIyuUmFv7sC/RV2CVuq08XPZbX5WGQWxb7vzMYXA9MLwJnulZGOzHybCVCBTyduEcuCqa/yHh4/FNjfunWLz3/+86ysrDA8PMxDDz3Eyy+/zMzMDAC/8iu/QrPZ5O/+3b+bFNX5xje+keTYA/zGb/wGvu/zEz/xE0lRnS9+8Ys/dI49QNmDXh/WPNNQwleWSEgs24n0C6Sb3TE5YX5v1GF5jpT5pXwJJBRWsZYfyekrR7i1OMWTdz3Lx4/+CSXZILFNaQ+tAjrFAtf8h7nmj6A7pq57cw1yFdh3BM69DNdOwoH76fI9Zc0Jmh3mZ1KpOQk8y4C952IWHOhbkA9kh4M9p/ng8FcoU0M4U5QmLQAB+DrCV8pIT6Gt4pGlZimSNs9pRGJGOomh2GnyqYVv8O+mP0/TKwKC0DetXmNnfrTjDIWXRjBbRhDm4cyTcN8WVFYyzMVmqzjffL0EF/cJLk0EhE66FqkA7l5CGZp/F9iTzilWUBoPN3jyg3t5+5U2YmoEVdrmvsWzbN9xhLffDDk49k0OXP4x+mdHyC3k+eSToyxcf5K33v4+0xMrXL3vfm7MHydeibj/4Qonnl9heGKVcA8MXBqBtSbl6dvcCvqRF2OC1ih/rf1tpNtrcWz86FFEd9Sl3aRhbLT5po3IH+yDkaF0g8TKfDdTCleXC6yPDuJHZXpXb6cBRdpuciEgX4bqUNo1RWvCis/SwX5W91aIRZgxw7sNig2+c4APLjs+SvR5k8GuM8RkUt8Mh98++wC1tx8nrg0kj+mK7HWBvHtpkCpg4NJTLNz3H5BSJR/kUfQTUiMwRiYyBXnsBWwBbfu7R89mjX2XF6i+cx9rt8fYEpKO9tjSPh0wQXiYyndBHxQjkoBdz2a3+EXTidHJYk7509J2oMxbenSj0KAj6K83WJwvIqyLwLEnlU5VFw/IWii1TsfhovJdHr4zmbvIfjdDAiMkZLX7RNDdibI6wy/duuh0y2htwd69smvk4oUcnxLpNXbWvBBk+Fn2XvY+ys5Xx86Jc72HwghH0sY27a4vMdrZJM01NDEckfTwtGcCNJOUBjdIDQQmZ3JPk3hiAN1rNQOnCGEH6PhlJNJ4JRcZXh+GxjS06rB0zeRY69DQsdU2YyE4NXCQl0fuAQEf2H6R06LEYKFIdWqL9/IQWr/Hmf3/BY6trS16e3v5ld/eJFfuoWULTASAy0d1EZ6ezGwwu7kTbR9otWHuKtQ2uwHVHVJC32XIb2SIUYAvY/764/+CBya/jYgwABn5tESV7w59mjfqn6YdGQYT1WHlHdPk5uZZOPEdmL0b7vogSZ13J4A4Jpele5c9gh1fAlL2fGceTFxUPvi5mCNDJ3hg7LuM5W8jI0s5Sck7Mr8LA/KxMMEmoU9XRw1pB5G0yNNpJbbs7vEESkp+Y9/f4XJ5TxeRS2dq9DLMwK6JtM8hABQU1mH/KzByy8SyaGUAX2lYHYDX7oGNHrpKtzvTH5quvGOpMmCfMV9rKwQgwPdjdvUu8GTnm9y64LN+c4WpJ0b57mvfY/apT7H77Tle/Mr/zlXxEWZH++nPbxFOhUzWa7xwscjBR4+xeP4kg8ernHlui8K+CntzfcxXPR7eey+r9bfpK3VQQ300I4/t51d5YOgdjol5vLNXSErgRpGN1nKFQawkGEaw1bY+fA3lHpgatCZKa8JwtfJdwZ1cAFMjJp84FpZLkpq8tIZ8BaqDdgOZ9xt9OebuHmR92jZ52uFxNyCS1epdopsitiCvMv3r3eIIhw5KUj/7IJuvfBTiINUYJbaSYUq7+BkN0A07X+fmB36DVt9NewfzgcLIS+s6IPZNCd1sxH2acy8ZXG3yY39+gmeePQPAZm6cht/HC0N/jRVvjIFwntvBHVzKP4gSBTxP4YsmyJiG15NYiwslKFahFNXokctMXH2Wgm4jch7nJz5ErTROHBQTsBUt0KvGgtgJoWU9NpFdemeZwuoOkZW7I2HcCCIR4tOXFDYo0Bbv8QCcvEcKby72TNvJypros1mZMstboEt4dnzHGjAT64vT0F2hsUReccqITl0awuoMUqd8eac1W8VmTrICTjFvxlZvm2cTCsqqzX97++scrM9ZN63memGAt8szvFrez4c23uHJ9VNG01YZqT+nYFTAHh/6skqmnaHUTGUWJBM2QwezKLVp2J40kxA1YOMk6HZGqweN4NWRu/jz3c/Q9AoIoKBb9OiY+9pLrBY2+cnPHmNzc5Oenh7+qo/3dW18J77mlDXpi4yUSAbUM5tGQCJNKw2LNy3QZ0ErK5lmpFEnExpTnMcLZz7M/dPfQniGsrbpp8bDnNx4ho6QxB2S+Kq+fab7XLlirre92CRq5fGkRIWk0rOTjGXm/qSfix3jc5kGCaiiOTJ0goP973Co/y18oUxaSEhqws06xrJt+5xpykmwbjIU6UCkNJPukm5drg3auAWAx1de5mp5mtj6oZMx7yDqZC4xQrUD+3oZ3noU+uegsAE969C/BJcn4Pw0dKx7Lrm4fZyu7Bmr4WSBX2hzH0KgY1Izi7pFb98aD7zyx7zY+QTXNr/K2NrbXH1rPw898zDfffU6i1OPof5ajmP1Eb72e79L6cke/nZ+L+dGH+PW2bc53ClSrjzEs1/8XUYfeIxL3zvBp/7G41SF4rkLX2L30gArz4yyVN9msL/Dk9XnuHfjErKjoAjUMvOooUtl6sSwZYvlgEmbm74TxBqpRp8BeoC+Cgz2mnDwmDSQyHFsKU0ASc9AKgEDi/t7WN1dpTZWAlwUfdYILxKNPn3PmO0dyKukU5xZFOFqGGsorUas336Mrdc/ioiCNALcx8Rz7BBahbUEaVJhmLhM/5UnmT/2OwihE4P8gfmIh792mrgZ8oefPsrNqQHSpD6j6ZfrHT79zQvMnA2ZmdtKpM7eeIFeFvjM8j9ESQ+fDh1R5HzhUbaZphS36Y8vIb1NLpYfZo2D9OtLBCLGkzCz+jpefpVWvsrw9ip+bZNnzv8Tbg0cY7VnH2FQ4vXdP0OzNkpIiVwO09wmtJ21M2bIpKGNj4n+z5EW3MnyNyf8Y4SETtu8V8iZ9xNzt/2O51nStTSGdR10tVWALosh0B3EbD93FoV35f9bQNeZHazI8CiZMQjY78rMM2mdCj7u8IkZDjcobsPKWJXOdoBQAiHgvu1rHGgvWTlVc6Uwwr8Y+TCrfg8DcQ0Rd0ic7kIYP2+fgEEfhjwIZCb/nYzFM/M7wixSPTZ0OidYL+bQfRMMOPgMyqzkD9GrrhEUC7B6E9DcqEzy1d1P0fILCDTDfoNHStc5Fs5RChTP1/p5L4/3Ndgnvii3IS1jSHxC7tihgNq32FyFtRWSD7OY5rRr7PVccE6WZ3binDnHMxT7bO0zvL5+FDlVNqk1LsLeYWtopO/Dd0NvX0jg5UwGVMYvlYzfUUzGMpEF+cRPbz8PZIdDAye4e+xVpnuukvfaRgJSwmrjdE9CAvTYABMLCDEp8IgMJboBCDchpGaGTEUQESvu2jzFZPMxrpenuvtuC9f3TKYCjEjHlfXRxRIWxkANgu5oVFvTiiShBhGmAlGWCWbL8yaps5aZ5sOQfRur3MyN0lACYUquU+pE9I4u0cr3EV59mclggd47xthDxMk/Oomcy7Hlnafn9YAT/RcZb3eIW8v8a1miZ6nM0Qe3efPia6y3B7nvSB+Hq2P8dqXDy503eTK4k6tRD33D95KvhGyevErxjiGazVGi+kVyvgJfExX34jVvI/S2VZXsunViWGtCGBH5Adu7p+kreMC65Ywq067QrsdgBcb6zDVCYYU8N8d20/QNmYIOds7xBNvDeebuHqBdTFPVstp8hOyqaefy1uOMyT4FelutTktkqBm4VmPXiTX6TwW8MDDLRjlIGL62Gn1ihnZaqwMUKwS4gGatoVifJCmgAEgtOHziNg8+exkN7Lu0xvkDQ3z96QNs9ZUZ2Ghy39u3ufPCMrM317md30e9Z4RquMGtwn5GOjeoRBtIFNJGaudocrT1rcwMGNqY7JxFawlK28BuSyvbGqUli/uOcWXPU+y+/Bb7bz/L/oVnifw8h+f+jJt9T/DlPf8UGUj82KbxOWCxexZhnllUTMyN9qwJXlnFwRGTo0mZ/ow1SVOtH8TvkuZu2sw3IWkUeZanOP5peZBQKam74EOXv58AvfvbDilvPSixl7E8YsqbuLV3r1ibchFhx2xpZ+UTUvFU+Dqfu/0KQsP18gh/Ej/E1WiY/Z15Prr6BjLq4CxgkRCMRB0e3XyJJzZPMhTWIK8gH8JQDkY0FH0zCCG6a4eozO/Yv1sKNiPYiGA5TDZrQfVAvmqEBRRIj4HhKsIfB0+zEQzxslfh1bG72PZLaAU9Xpu/OfAaQ7pus6a08e+/h8f7GuyTQ9v1zmzUhA6yHCv9QRzD/LyNg4Kuzep4IsLU0y9pYxUNLRg6xXd+Y5Jry4fYO3IKPE1rSXN5dRe7x1Odwt3TaSe+gN17QQY9prmSToWHRCiwv3eBlzNzO4Zof/oi5o7+N+kduczT/c/hC8xEhLIbvN1D7pi3xP/kGEkC9m4Sdfo9B/TO7KsxX0pCdM1pXhhy8OpZ5o+M0sEIRELDeEfRv3qJa2MHabtL2q8JDUHUQRGYnGZt4zAEBKLDeLTI9dI0y3Wbdg6JGT77aC4Q0s2fRNMfLlNdz6HbHRpBRhCINcPxTY7dPs94+9t89c2Qp392P31LY0wMxxz0PDZKy1y9eIbNRpUnxhSnd5VYWo45fnCN8c413lmfZGigl63TTQbuP8jq5T/l/He+wYT+Gc49fi/bPatcWW9S+ct17h6dpfcb6+zp3Eb6kWnjJzVecAEhvFRt0p5hRKsNy70lreG7kX0+tKxGHzk1SKWxF0NVGO0z10kCicx6ai2Icx5evoQolez6mw128dERtsaKdPKmCr3j84YHCiKErWnnto1OtPg4AfnIfu788oLqQpNjv3eN/hvbCKW5MPJplsqHcUm0CmMSzjYwcqbqfKFNMBTS2CqjhUjcXdEm9HhVRl/5SdbuPovvXUTFG4zcWE8sSyOL2wwv1Xng9Tma1TyVVoSnnAIg2NW+DKGgExQIc1U2/F1cyH2OSlRjsP59+sQceOC5CmdGUk1QVDhNMZFYDEhIpRm7+gZj6g0LqCZ3bj0/Se/2PIcaf8KFqSd4PfixpKOqp61Mb4VicpgUPyvkdJGeIzHL3NxcOdeYEMaMngi5WTJ3v7h59s09RGiUEKm6eWfCayxfTYrSyXQ8aofATeY7nrKeGW3HZ5/RNZlzQl2lvcRqs0g9rCY8TWsYija5q73CRxtvkNPGfbXv8g3+FqvEMQy1NxE6IOnnoGIObF7gv++7QWsiYHNXDM0QqphcbJcO4XwGjs8lk2PmR8USsRUiVjuw0EkBWUgYuRsKIxRL07aBjjWNSI3pqxSzEkn+cvQRThQqoEXigtyM82xHOYa9erq27zH6vq/B3m3AWKe0+K7916XNpq+NVajX0xMTLcNeQEtT7vXIRp12p9dWgTPfdRHf7SjHt0//ODPDF/BFSKRXGao0IFc1ueQunc4ueAK+dkzSglY2wCwhMkgIKBu9KoQhnqLf4EDv2+ytnuZI/yt4UhmAd5F6O+6VToC9qAP5KCMUdI0xIyAkTCfVpoC0ZKHWNvHVHIGM+XD7u9yam+TWxCRtkWNffZNcfhQGDzIVwRZ0Bc7lNIxsLLHVM0Ys/SRrQggglyfsn2ZMw1gI6xJuNnYENGXWO+E7GipKEbZ6uJHLIYI+U92sY+qrF7ZjxgbmaMwOcVncw90/cgt9fYuNap1XTm2zV4/xaj2mrgc4NjDGVvFl3lld49NHZvjit77LoU3BO9vT3HlgBFU5Tt9dcHKpwvGP3kv16Q4b177O2spZPnr4CN+5cIoBb5qxyS3Kl2r4qm0csUBIiZYYooc5M/BGaOrch9pw89EBKoM1k7+rSH30CdBjnJpDVbv2InXbKKiNVFk8Ms7m7iFGrzfYdXHDdA4rSFZmKnRKPlHetZtJTfRoEK0IVcwnGr3zx5sK97EFeRPxLpDoOM/E+TaD1+uMvXGF6pKpbBb6JRbGHsZXRhCOMMDUFXlvex3Igkbf7RNVQZzW6LbZz1JDD5DfHuTA9gfQK49S7LyGz+vMnH6dJGTd0ky+GZMPW5bRi3Qv299zdDjYegPtSa7Ie1kqTHA6+GlmxUn0JFS2NFNXvoentvA9J1lmGUVm03kapLaZLjrlNZ5guHk98R3ef+Jf8M7dT6CLZUQpT5AH8qCblk9Yf3i2kFZCek4JkZi0cTd3TslxFJ7hJVmLoQN8zwItWGEhD6Jjgt66yF2ZexFngF5kXs6Un5mORElRqYDizPgO6KWNsVIaljp91Dsy+X5Rt7i3domPrb/GVLSBFDYg1RaCGohaqZnCmRyIoE8hqppCqUFBQl8goSxtER3HQLOAkeEdzmTf1rSv+/i3mwRxaNMpbFyMl4PeQ1AYyEhE1gomFEpqXhNlnqPKYpBLBIjEaqthvlVhT3EVAmE1Gd7T430N9km0ut1s2dSTdwF9xlTeacPGug30EBniyACqEFD1O4QrNj/OSeCYDWvqlQhO3jzOSxc+ymN3/Ed297zJ11ufoLdURW+T+LRUZLQSOhbcXBRtnCplXeYwO+TkLUvk0tNIAft7T/Lk+J8xVryB1LH1uVuCSXL3sxRP5sL2fWe6d9q8yx/dGbHtjqzI7+x3yqoDnh2giwoCSkGTv1P/IpuLPVwp7+NM4UeJVDqPQ44hpO5dmr2TiXaRLbDhKh1qDapkYmrqClYa6Tw5JuN+unLDW6GHjr1Es/DbsO9tKG8CoUez90GonaC/9gC9e5+h8tQZVuZuIV78OjMfEeilA7zlT9PXeIcv/e5r9H3go1x87QSPfWA/3naZg3ICoTX95VN878YA+UtNjtz/ES6ejJkuHWRi9CEmgwY/ed8M5985z4GREr3TGj2nTWaEgEDW8XMa3TQcUGy2TN1s6SF6yibFDkia2oQW6F0QSqUIkwNGCoztHogNdK/vHuTShw7S6jVdxBZzAdXNFkLD4oEe1iZLaJGa7EWsqFzbQkSKwkIdFWu2pqtoac5IouyFIg4EqxMVkB5+Bwo1Rc+lce795nlyrTpsGaCPZJGLfZ/gurovtRp4oPNAAcP4PMAHWdT4hyPEgIkWkJMR8VXriA6MQhXEEAiB1/GBh4l4kLfueYo73vlHFBqL9G6dM/OITgRR7YtEfRaItOOSRaJH219Gex5aSqSMacUzfNn7W5zY9WN8fPMf0axtg8jh5SVFGUNzI9loIqn4YtdDOoTNgoqhn2l9kWdu/395Y/IzNAqzeJ6AAHQOVJO0urH9WrainJNXnDafuD8ccDu+Z8/B0liXvqNTPph1Dwq/e+jOjegyYByhac/w22wVT8daHB26SHmNGadU6b1cenAUw/oGhFEOBBQLHe5dv8BHN15npr1iPKMJr7H7vixN7u2ENKVTRQANYTuN2QEG0rz8bABIZgJ+UMpDXIC4CvEYxcYGTN5rUimCokm5UJEp8xlUUqHB/r+IzxV8TqgcF/0cuiS6ZJ/kRA2j3paZYLvfda5LE/srP97XYB/aNUXaTSXTDecOJ1W598IOXLoM9SbJWidLr1OCAhjPN/FFkKR/JISSuX4n9vnWqR9j7+gZxsvb7JlqmzLmGUlbR6Ad0JO5r5OY3cbOCKFJwJ3FUc/TjBZucJBXeWjmOYpyOzW3K+wmVqkdz21+R807bW2KDMVmruOAP5nAzHW6rkk64RKSsqyuqp7UBH7EULxKsRVxLmeqrWVN711rlQH4LKUk8kmGj+oY+nKwWk/XzfnmdWzne0csBHaKRm5Az5Y5vzG2ztKRmJ69M3SuNLlYP82P/PkGuR8rc/LIcb53aZnBzgVOvb3EytgVPvs/fJ7Nm4oXwl6mz6/Tns9x70cWaPZKSmOP0FrcS2X3AicuvcRS8RB7Jw6yS53gnbWQueoa5ZdP8QeVI/Ts/Sx/Lfzn5GzzBiE0wg9ZGeijfOEWRSHRg8OsDBxiJLA5oa4hgvPRO9fMcBVGe8y623LLRBqtoTZa5dynjhDlTc9uCXSqPmefGkunWIDXiuh9axERKbyOYvpPzuHVTdToWmWUfaWXkDLTtENqk3nhB5x65jDL/YeZevM0M5c1edUwa+HloTjEuhzkWuE4J4Z+Bqml8dF6EJdBl4wW68z33qAimI2QvRGuupi2SCYE6AA6vdC3QZcrQCCp9R7mhUd/HxFv01s/x94r/4FdN7+GQLG46ymifJnLpaMEqsN0+2JqzvUl677p4tmSFSb0FbPFQ3jYO83cyNOcu/v/wfLKHcQU8AMoyBDaW0jVYWr+y/hxHXTE8Or38OIanqonLoPu3F+TAPj42j/nyPaf8f3Rn+HFsZ+m7pURBY1AojppGICjCQFJ8FuScZPDTILt3uoyBKSlKUdb2ayTbpomYVBJTRFHlzo935nVtYLYT40nWQNGQqfaAnsG7J11QtprCQntlgmKjmLDJybiBT4kz/LM6isI7UGPMNpvQ0Jew5hngL6IdflYhqBjYxKM/Iwk5LQFMszVPasmye/sTEHrkNlUKg/KpofvZsehuwDeGHY0KwhOtwK+5pXZsgvjlMGEr2lMX4MOJlB/VJlnCDQUrbD7Hh7va7CPhQ34yPqtIDFBdfnCMX8XtjepN3uMdJ8BmCSv1/nqPc1wf5P1uDddOItnDvCdlWulNsrX3vwZegc2aHujeL0kufPaAqiIMiBuCUjF6Z7s6mKFVThI09UKQYunD/4hu8U5AtFOv6jsRDhfhqvg4+lMpA102QOT7+mMv15nfPx2gnVmIO8S4e3PbEldF2Hn5tEKERH5hBhSCYhEs++ag+z1darZQMqUHKgT09XBLwH5uFtGcdfqX4Vdt9waaILd6yh/gs58xGDlOzTCx3mz8yJ7X+9nvOIzK+/gD65c4FB+hSnup3P2ELvk6xyM9zP98Bi3Tn+feOijTPAcvfV5vt0KWQu3uP/YPjpsc7n4OxwqaKYXBrnUeJI3SwvM9E/S1zpPTjQMoxEYrSXfYGg7MrlYPVXkRB8j8ULKaZ12oyzzkcBgGUZ6LdALCBUqEmzFfWw+3Mut4zNEBZ+0Dx0gFEJrchttRKzofWeZ3tPL9J9YNCbo7OR7mkphAenHJFEoQhuiw8eLBUf+8hJxfYNc2ILqTOqP1JJOaRevV/8al3P3JltSB0aJUnnS7m2Bxh9R5Pe0kBWrkbulFjpNv1MQ54zZ2e9083KFMKWXZZXlgfuZnzpErvAzlJorNP37kAyaq3ZgMbT73O0pC5QCWCD93W1X0QaqJIazbUCYisKc67vPBtRqCp1l8p0VDp39H8l3FhEyJIgWU97i9r0WDKhbfHzu11lSu3hl5JO0+uuEjapJXbPW6ewedkVzpDCBezIPBCQ9QZLME5XeR0eZh3DPQuZQ/KcPxzOFzXSSqcHPjS12c2f/dtkuiUIk0t+FAF8ogtomG/UyvX6DSr7J8f7zPFJ8h37dQAzawbsovkhaq6FI50/SPQgpSEwNzlLZpZkJYw7UZYgmIRqBuN++J1NpxfGrnUfG3Kt0yMUwZl7lGJmoMa+KeGtVBiyO1NytXWB2C3TLrIOv4ZbsZ6Teh8jHECrCnakOf8XH+xrsXYBIEr3peJXOvOyhNSwswvJSNRW0ne9KZNYU83vOh+svTlBoyjQ63hGAvXfWSnf25nEunILG66Z/++gsDI2TVHBLlGB7P+f3h5QIXeybtIzRAzyh6Q9W+Pj4/8bu6DQiy5kg9eEqL/UJJGqueyCZDtz9dKDubIZZocH9jXCDSDmPK3mX7FMn+VoJxi1EhsFdD44SiXxqUdPdIC2wWCYxgUM7jmw6kI4hX9+kfWsdiruTFtVOGOvCq8wj59tw8Cz4KqYaX6UZtLlSvwNd8vA6MVf39dBZE9QOTrOntcTje2KeP9HPwzO7CHd9lqmR28xvaq6/PcjgXkH73GnkzUVe8V4k16OZLV9neW0J1bvA4tTTjJ2v8tC8z8LBEjeO7uHY9XOMPLGXjfPTfKLnt1POYIUvrQXaD1gcPcboRA25tZkyLBcT4XLowQTiDVbMRrTaPLEmVgEnjzyFfnjdFsoxfmSBJr/eYuT7tyjNb9N7bgXZDJFhbC1OGY0IoBRBT4u8VJlNqq0pzdpyEXgdiQwbaJlHBJUulLztTXMpdyzZptoHNWB+Ioxbyh9UFPdF+L0dhLMhu3Vz9CF0aikTgtjVatDmM6VTMAqDNjcnznJx72vUKqugJT3NW4xuzLD/1j1Uo4F0g2QE+EQbzhxOZk0en67HSz6TgCcEIj+Cyg9z6fjvI1F48TbVzRcZnv8tSttvJ0/mZG5PxfR2LrG0KvBqVWSObO+fdCJcsSFnwQx4lwk/ibK3k91VftuRpUiXUcfpkidzbu/tyLhLl3CXd0BPakzEXdttVcsmNAbk0JqSaPBo5QQPlp5jeWKSPaUFpNYEIjZ1Stw6YAcohNHqk82TrlmipTttKxmHfVgFRGWIi9DZD/Esxlfip6uYvVey4Onka7tICkE9kKyGPqPKo5grMakk4xv9TEv4iEt1BP5Aw1mNiddqg2qQKCTDecHM2mE27D2Uho36Ju/l8b4GezCar6udBCSbJhHSLKguLsPCEmhrSgTI2l0Ss7kH+TwUcgKvLgyYuM/s91wJ26wloRXBZtvk0jc3oLUKgx8ywC0UqdZqffVdvREw53jCRrEKE1eS82J25a/ywaHfY9y7jGg5W50FU2kpK7JAnyTX2gFKnYKz4zDY82Iy3ZuwWr1KP3MAIK3YmiWQJNfeva/Ney6ISZAwKCUFm/5wF1fpcqUJ0oJCTk4RmJahVltHgA6N0ntzDsKwh3rcY+IiNElTG23XNOmJnbnm4DKUvQ7Cm+eG1Kz03AE5kwLYEGV6zjRp5p/n2KEh2lcmCYKAvXcHiOubfPPcl1lcPsrA3jFWc2e5ozbFawuLfPrYAQ71DxFvDrDV+zb7X2kwOLqHaxNz1GrD1HP3cf75P2Lo2C4OHaoRRneQrytenPs4+1sXGe6vMNQxFRg7wSxxaR86tw2N5XRjaJHhrKSS6HCPrc+toKNwrRdlPqDvwGU2vH7QIMOInqvr7PrWdSrXt8itt8hmyZv13AFzUkG1TRrCbvebZxfVlVCLPWjnWBk6RI/2ySOTLdbG452cbW7lxVD0UD2gcmZ9/LKmMB2TH28jg2zwhoFDgaCltzm/sEqPGkbtvU55a4zqUg9Fb4XFXYoaEUs9C2yU16iV6mgEHb/NZnUJJVwhIMFmdYGNyhLXRk8zfftODly8j2qtPwX5jBCfBfOdv2eF8uzfCTkKk4uAVzCA6JXYHPkRGgOPUF37FlI3qWy8QK09j95cQQ8/QjN/nHDbLHGAFXaFnQ6Z3kRLKww7fmUHnJjLocvCpZz1IkNT2tJDVqCIRdKVIenA6gStBOwhqbElSMNClONtzk2g00w+T4BEM124zdMjL7G7fIuRwhpCwJC+YoM2MNq74ysujsKTGQ3e0YEjcNEtBGeCIbcaZWRnjIreA/EgqKKZROHvmMydYl3mbRGzHl3lZjTEsDdJnxCECCYE4OcZtTzcGRYKme8fAC5Y3im0CUjEAy+OOFrxDH3oRNzDBei+V8f7Guxdi0SZ3dBZoMcQw+IKLC5lwEWDS13JavW+bxp+FQomDiQhqB33zbjeEwnfFZdz18v5UMxB1Lbyh9VGnFU20VogSVv3tKng6AFlv85Dxa9zT/VZCqpmym1JlZ4o7Y7CmnBdcYhEJbI3iiEtX+WQVGdS7XQmGghLQDKVcHbgQiIg4cR9kzdfy5VQWuN32pRVg2ZQIec1iDyf+eIBE1BEqgEk+cUqnX+nkSw+Dze/C9sN08491w8bi/Dg4zfYGJlCW8GhqxGS/TsIDUMstCHXgZ5NqK5B24OtiSWW1weo56uIgiVGu2YL+kGe6f9znr/p8/FKm3O9W9SuNdjVd5BKeZs7j1wjLpY4N/MRvJ46x8bHuN37Ha6sCeR3L5H70cMMHLnOd65v8xBNotGP0/PdF9g9cpDtGy+xVbzBfGuT0sojnKl/jCvtPEPnNviRg19mo2c/o801iuEVSmyT2Jg1qQnIRXHmAhio2hx6bVPwtBHUtiJEfYvBV+fpObuCjBWDr89TWG3hdeJugc8dGX+y+aFNknRg94pRW63QZzV6LaEdQKcE/iDDHWzusknca5ZK3JQHuD17HwXRYLB0nYXSncgyyLxG5iA3pvDLISJZfGe8N5utwQan5Qts3bXKWr1CXylPfeAqtUtHkONvcPYu2BSb1NkiMps8AXdHk2mtfIUW0Mk1uLT7TZSOuffNDyN0JkVWJzOQTJOw05HVft17CXQIuq7hQl8S+UkIwtwIK+M/hUCzPvoTtIjRrSaqMMasEvzoBlxvwOma3Y8WJJKs18QRbHmPyixlZpskYB8awHfWyIQluO0uMwKLJWOnQ2QVpaQMB92KdyJ7Wr7rPH2J5VOCJyIOl67wNyb/hN5CLRUaE6K1UrqLaRAi3WdJwAKZwbsJNfTQaXt4OiLSedAeW9EhauoAJdVHLARl3ba3lEgt0n1mgV4jLR/XxjokBDfigLHcMp53gIOimrC6XMewRGc8yK558lPDsWiTV6I2m8GICcKzab6Phre5rziBZ1O6NKnB9L083tdg7zutXqQbOznspl1Zh6UV0ohVN8FW4NPuPWGyLIIcBJ6pMsow6C1zelZpzlrM3IZwOOkOoa312/q7wBSOcAKEYyJOI/ClfR6tGQ4W+fTQv2E4uIFUsUE8Gae2fR/TCEBC2l9Zp4NwN3EUKe0rm36yU2JOzGAZMEh5ZUrtQvBi7z1cKkwj6FCJF9lda/JHkx+lLfNUwjqHrl6nuX8/g615PAV1v9eAvUzdGUmTCZuqCmY9mgtw8c+h3YC67fnSvG3qy7z52iDDj0Nc1XgdIxgHoTHrluswsGqCt5wm5JhV04Omgqubk1DEdAHMW7q3jDNovkFYPc6Hp+a5eeEqD1yqsHg0z5mvvcSQrBKeb1Ft/EcO1y4z732WoQOLeKcbFI7uov5Ui7GlVb4xd5bHhv9H9iye40h/g8aH76Zxo8XyU3Dlm3cj9be4Lq+w0skzvvoUFyaf5XTlae4tThA0X0F0bpE4/HRsikBkSxrnAxgfMFJuR6V18NsK5jrQUkjdYdc3r2TWUHT/zKp672JZmCLwfZ30OwGk5dI8M+ntHLQK4E2Zhg+ZbbXRX+Yv7/3vqFeHEVISyxJrxYMUy+CVwCuQRH4jPIQVbLTbiwhWxC1O8V1iIkAiyyECj9hvs/2hVzknFVII+qnQQ5EmHbQF+4rW5OqbXK7ESWKK28SuF97awDza2g80Ke/oAvUdvCT7a0IhmfOVMFvaVSb2SWu3pN8TRLJqHrPQZ+QqAfcNwH2DULyheW0dhEvNgoxwnWJkoqjYwSRBdDvSe7ti09z4nVBgcdXJEmjrg3dypf2OEt2+epW5hiBjuczIirGnOVY6zd8e/xNyuSiVkJKZsL54T5jIececnbl+hzxqvmUGtR1XWFofodYoE+WnaYu9FITE0zl8KYgK0I40DSqJPhQrRRybpkk5NG0tEdJo7EIIcoDWgpyAMNqLDSdJljfOyB3CPb/73AlNaC7ly4SFchI8ioZdGu4X0wQZ/HBr6e14xr/q4/0N9pgJywpIiXUHaLZhZaNbeUl6vltp2Vl0PM9UF/U9A/aeBFWxGqi7tv3PafLZBe8vh0yGiltbeQRQW4X6JpSqxjXQ3obNRegbT3HX5Z9KC/SB1PQwz/7W/4uRoIZwqq4rnZWVNpLgOr1D+4MuM0cW7J0U7TZdQtGkVIz7vtuJ3cTX0T6v+kc427sXF/QlBzRaGJN4K1dgfmiQXAANv4qMjd9OeNpY/z1T6jKpohqmc6gFlGvrxGEf9UhQD00Rq44w1r72fETu+SuM3LPEyNrdCCFQW0VUAEFkmZFnrMsKk63Rlgbs4zxJsZLEqqcxwoaGRv0ot64r7ttcZEsPsKBDas9GDLYUL8cNHuhssVEb4XJzkP4jFym8/Trl3XsJtmt0WjMcqKxSPPYRDrUbXJH381uv/hmPPvQEixf+gLGez5AfHUDMTfPMI8NcaSywdrLNQa/Fg/0lRLzOO3umOXK+jYq3kZ2biGwfbCmhlDeme88zVp5IGSFvPYKV0IB/9niXOpr5vYtiRPqeVFCJUpRykrSTnsIA3S6goxxSjYFXRGtNKALmvXG2p6Z47d4P0smNgRDGoFQQyJKHVwQvp1MLT2ZgSmu22WSFeW5xkTZ1ItrIxMAM2uX0e66On4e0Z+QooPHsSzAYlagjKFDgNvN0aBMTolEILdh1ax/CFY0iZd5apwCoST0nnkj980BXdzcgqTid6J/CTGFR271PSopODk/IjKTUAE9MKm7nGszfrKJdOXdrM9cS43J2/EuRdLtz+7hLKJeZ+9l7OsWm6ycZtmHH7sz8ShhFJQv8zjvoLAEOxxMsFzDsb/DTI39hgD47cdJJ+15aDyQbPZ+VouzfSgiE0iy2p1gId3MtOkQ734uvIBcLykKQk2aePe3KHQiEFRraAjaEJPbAj7XBCykSq4Z1SJoaN8KUvleYZACbRU8kEsNVZu+mbFYJzVtLt/ne9BAewpQoRzOkPR6KjLW2y9qsQaGQ5TXey+N9DfYS+wBOWiLdK502LKyakuHOlZj152YD8qQF+lzOWEkD31gxxTrdpWwz0qu0G1OD8cs8KNnjCZa+DITm87hj9nTYhMuvwNi+1AKe9LsGPKnZVz3PbOEMh0svUvFq5sbChvE7RBIW1F3kvSsc7orgOGnEnScAVLqbXeso9zCJoJCdPEvFiUPOUav5Yj1X5Ep1sgs0khKqllHUvTlK4QBRrojyIPK0yZFGmyq72mGRQHiJQgfA8pk8zRC2Q2gJA/RhbNbxetjD3XdcpTiZQ10uEVjvBLEBeSVtjSDP9KroSGh5xnUnCmazJM2x7NQ4i3n9nn6u1CR3vHWW/iOzXBv1CNZfo+RP8tePRqiVEpdvNXnw0SMUKwcpnVnlT080uePp21zv91mo3kmuM8OfLf8bqt5xtK4QTHiUZu4iN36NWA1z+fJ+Rp5/joHH7uDD96wwarv76NBnz+Y0eqTBJTnE9Lk/phi3U/QpF2Cyz4B7J07z7BsxLIWmyp47nFM2i+cJdex8M4NWQQxDLZPKJGQSc5Fwf+2hozwdb4jbw59m95XvG60Jj7/8wOe4kb8XqkWEFCaYOo8pkpMzWXheYOjM0VxIC6lb1PRV3hFn2GKNtmjgUNQ0sokQ9p/RyxyaGWapUUiktXIb3V4IWO4z/tEYxTBjxCia1OnQorcpOHj5KIESpopjZY16rQ+BNNo40NSkbcg15AQUPAsogkQxfdds7gCDUKekl8gGGaB3GrXDgUHP4+eGqrT7Yb4F/3HBLLe2INSJDMDH0sxlkm3mBHYbDJaRkboH5LaJTnlZdnyJK9vpCqRarbuMOzfRHTRdW0sAk41rRPUYyjo1zXvSMEMX90HGV49FXc8OwoNISdqtAjdXD9BuVjmXO04kTD8FTxn26MhXKmOACpQxeHo2GycUsCqhgRECfERa7OcHTIuLE0w+t88uBRSF2RtZ2SUSsO7BmRI0Kz6znSZ33fw2raHH8aI6hfIeCirNZE4sJiJGHXgdP77Ne3m8r8HeV2ahIQPEmJbfN5dhq2GZuz0yWRRdQqUD+nwAed9IdQVtAMQaNLvM9DvN+EKC7vWolEzZ8fqCGcep78LRDxgJvdOBcj9pcQkLVEW/xQcGvsmx8vcoiE2MGK9ToE8asjvVwO7yxIkkMqK3O8dRvyVPl/fkrAJO688q8NkAGKyLwNm5XaS97TQkbFRKNrApeyz0DFPN+YYx+hAHoGQHj4gAiY/Ak9ZOaX10Gsi9fIHVd/bQVoI2tgictVa3beRPuJmj/OpuQ4iCJDBcyTRFyGn0bc+kapEj4VJZL0ci5CgYW9kmr1t8RbbY//YgwZOCF9YUj077yF0+dW+domgx4o9S4jVaUzPMtE8zf3KGZ+5/kDOLLR448CbL0d+mtjugJG7TWG3w2J67uDjYwOMKhYeO8vWXn+bg2Zs8Mn0NCi6fq0Ve3+bkvhKXhpf4w6N3MrTWRKo8Q80qH1m6QLETsVwqMNLYQqAJKeHPrSFcpaLskU2cTrjaTnSyH3gaejpQiTN5rNK4jXB7QEAnQKkyyh9g+sZrCCGoV/o4cc+HuT70MFp6xhhgy73KPGmv9cTPZRjdFd7gmniTUDeJRZvIjlfa3WROTfvQdwgBPzG9u8NU4jcRnOaf2V8QI5BIbI1+fCpUkXGRT31licmVs0TFSRY3FjndP0XUa2ioA4kBzcnKWhjQrmdAIohNBdYgQ26+l2aMJdmqmW2W+PB1ugw7i39pBdUA+nMwWYajQ0ZojYEXluH5NWh0SFVOzwodXqplZu3PzuSfRTWtMnTrznO7wYG8PS/bVVJmLi3IhAC5x9IghGZKXeOz7S/Rd+M8W/lx/OlpSggzQQ7knbk+8UOkF43DIvH2AIs37+Xm8m7y+ITSZ6Qo2Mi0E44wlgZfQ0kZGdXD4IGySsKWNEkvQltvVOaVmXY3NQnou1oCSUaBlUtcZWeAtjZCRKhhsiGAUXP+xGcoACIAHZlquy4WwiXUxLk2o/2nKS1XeS+P9zXYeypdXHcoDbc3YbNF18omPqWMCV8IE5SXz5uAurxvXEhSmwIS4h7wBsEfg/A8hBeg09LI+nXC0m7ygTnXk0Z7kRr2HNBcWYFYCeIWnH0RZu/WDBxpkSsVExyWQrO7eJVn+v6cXbmrCJccLq3dTsQp0Cd9Wi0gx84h7TioziS9umd2crh9z/l+E6c5qYCQBX3HjbQywgaQONvdGb69raKbWdnP20GODWDQ0yavWnZAtIgJrYbmY2rCF9BSoxU0LsHVb+8DBLm8IRRl20LHCnQbysMhbBURQRFhNXnl0ZULHEloWbCPLYfKuqudqVArO80CdBCS71tndU4j9syycPt3GG1/kg88VcVfPYDauk3tjS2Gezd4q9DhhT84wQMH+pl6Yojrc3uYj27z8IdnmG+Nc+Kd0wQH7mBgrcX2y28g9tzHUjXkk/NbhHe0mT3+Dm/NeZwYL/HklkYqhdYdXu4d4F/fuUq7IIkYRuEjdA4Pn1dWeyl3Yq739zC7tIFUmo43zP3ff4fx+TVev2+We9+8QhDFlncai0DfZoOJxU1AIFUarpYQREnBYGykW/xUAk32kDKTjERHBVb6jjAw16IVFFnYtYeX7v0R1gYnQAqTFpYzPnlpXSVperRmXSzS0Q1uNq+yVHwNnUSZpfvO/Gk86TrzftpcR9rzDAFLTI3+iLQJb4c4sfiZbuYeEp9Ye8xcWeWBV04ShO+AEOzVmtmb43Sqgyjps9yzm+XqbmI8tIKOV2CxvBsnUCsbuxBaUuvEKZAozzB4BGkBPazI7XhPBuhl9m+RkmIShQ8UPdO3RQt4ahyODMJSbPZ4JOFSCy63bec8x9Psntd2AIn8nhlQNvc9i4BZL15X221zSQKnK8iURbjveMT8RPuLPBV9jaK3gYhjchcWuFU8zr5d6+ZL2eIB7qe7t4DO4n7Wzj5Gc2MApSVDyY4ANmHSPkbsQbMAzT7o9VJZ3rkeImBbGuHNZSnmVfe6dHk6M8Nxcoj7O+PtM9+1gqBzaShLIs6glhWcktIYynjZIm2Eg05c5PZ3f5bb3i3ey+N9DfZu42axaqsBq9sk0q8mw+wd/lmikNJo9IWcAXonmXvWrST7wRs3TCy4A8JFuP6bgjNXZzgwAzN95vxCwVgZhITKnpjCCUnUFHQ0lKtQHoCeXg/h8i6V4p7iazxZ/So93mYmkC4L8jb6XtqfyU7OEE1MSnEKew2rajht3FXrkVY89egCbnPoH/Cno4zYUL70QEK+02EwXudWftS0mnQMKrMOClgG+nwQUhGLkIgWmpAAgBwCjfAkCJ/WkuDm7wt0zXCRXYMw3A8bTag1YW7FWF3uKASM5fYkhUQcyCtpp0YYoO8ImyrkgD1j3kz0iYyc5HtNttUEPadf5s67qrzaPkDl9RYn5nbzY7su0zNe5+3+HuqL63xo36v0PfQgZdZY8R/j3oe2GRyosnjpu8ixzxNflNwqfJtDu2YYvPdhRodD9uWuE5V78b+7SO+45rN7R7h78wZawXYAv7t/mxfHNogKRTyrO8RuvyK5PNRnNVrB2zNjCCQCybmpBxKweO4DB3AxFK7bfLnRon9jGzQcP3GD3q1mssQTrXUOLi8hvAwqO1MXyu4RgxwtBrl+9Mc4feenEfMRTT9gtW8QLU38hfCtsJvD1GyXoLyItrfBhn+TSDa56r9CJFqIopn8bu0qu/+0JVWT5QGmEn+IokhgPzX717O/mya7rg+fwjXqMXq9h7TRoAOr67TzglzHWr20Zjycg1XTk2D36gnz3J6ZxVgGbBZG0EDbK3Np4DhaCFZKM6yU9qAw+z92KW4YBVZa5TXCWrEdedvfnY8+6em+QziIHBhbOV9KU0AuX4LhAJrCANmeQeipwbktaMaWBWRUcOfNg4wi3SXYk4ItGeuDMvzPsSBPGQ3ap3uszkq2O77EJzp/yN3qNQqyZr6IplAN2Du6mkpB2fsLdyvB+soIxU4fyyc+StgpvGuImdPNHMfQF8JADKoEcSm1Y2qM+R5thQCdPsPOKsaOBygR22h9mWj4malIK+xqsy+lDu3aKfJ6k7oaACCKJdLTRMr09ogVtE39HAP2wgoJQqBjj0arn/fyeF+DfVcRFW2it+dqqTmma4dYgE+yPHxDmMUcFAPjSkr8aIKk02hW+m7EcP4yaC24vg1toMcDwojjok6hI1i71oNsW9OPgImDkCsK6OSMxC1gRC/wocqfUcR1c1GYvq0O5GPjeHJAr3WqtiqLctkyuRrrZLQU7gBfCxK/u/PD+1gKkCl1ZNUAlZ07p31Jy3k8SlGL6dY8NyujpuFTnMoe2WjTtoaFGEZ8RUyHNkazD615VQrjJW3Mwfn/JUewnZazzvvGTNpfADkAdw6b2/cUjI8/FHSl20XSFpATFuitOJ5UL7TnJ54Qh2M2crnablCoLzFxrMLKlcfg+CqH+s4irylWNs5Bz4cIQwnxGgPbinztWXYNQd/6KKcOVjmwfJXrxQdZkIpjjx5n38Q+mi9s8cTeOW5uD3B1YZV714tUZ2JO1GsMnFrl4F0FOs0Kb/Vs8r1xaOdzSCuNmpY0BhKMGVp2K8HJ/s4YtkV3GxuNol4OqJd7Ac2tycOOagCoNNr87LfeYHp5ExeplItNipqvIvKqQ6GlCfMllr27eGPo59DbEtVnyjoUPGjl2sRBB50PiQNLWxLqwSIL+VM05ToNby1zV8e4u3X3HVSdEG5aD0ChifHIk4oy5vueDS6QtjGP8+i7ayRmfqF57cF+Vgd9Dp6t8dgL20hdRasWXtjCjztmJhN1F3wdMdi6nYxrV/0CaNgKBviLg7/Elj+aYJiKbY65TgUe9zgiSx+kPCX7ck/uhAQX8e7FqflcYoDOswYYpeHBEbhrFm4pxbbaZntrnktnFGJgL3WVS4Xb/8RsuxOyMUwuy9NZKbzI3LcroB4jZt0RvsOPt/49B9RpOzgr+XgeHJxF5KzPLwl8wgrZgvbSLFG9n9al47TCIioK/tPjdHNl5y4AcnWgAe0KtHtSnhBp68sXNkDSGTyjBh2RZ623Q2GtgC8FjRhquW0KcQEZ5dluQGlghZW1Pg70+4QKyl6Lot6kqm8jiamoBbtXPDwhiP08WggWm2UGSiF13ctG3EMz9hlT12gpn+tqL1PeLTbFEL3xCsveJP1q6T/ztP/lj/c12Cd+Fg2R1qzk2mxHhfQEkTKgbG6pH5hAvJwPhbxtjEQ34XU53TDK7bm/gLBuiFlqWGtAU0Ip9lhdKjLRcxMGqng5gRQwMAs9EynIIEww3mH5FkW1bd6UGI3eC+3PDNALlWrzkbTlIEX64AkvdOqFo1AL/Emum1UPbMtNApvEmyYD24fUKWdwP4U156uOccRKSX+0hUg4lkiILCFG+9rUUNQxvmgT0qFDhI9Hnhx5JFrF1J5voraGkF4aIeuYWWC1pLww2rknQXs2aMi+QvuKSX86iw6A69iVxG5oEtehAHSoiasaXfYoT+cpdM5w4615/qD5Bp+Z7iOqPkRzaoPh2xVCOcXL7ZfIF0bYzJXY1w6569IWt44UWD/5FhtzMe2hCtPL79D30DjP3qwwFdZ46AMzXMqXaJyeplTqZ+rgOv/g+TfZXXqA6IF5mni2MayPsK4XnRhPvawx036ePsoPZo5OBMhq0N1nbpeK/OtPPILMREdWWh1iKSh22vQ0G/TWW4ysVmD5l5FKmAJHGjytWOg/w83hNwmDbUKvSXbjaDTalgJ1wL4TvtPR7NTh0p/mf4VA0CCkz6bX6cz/gLVzBPgEaBu4p214usYOGoUQcHV/lWv7qlC5h6nbj7AR1Nl79ksMrbyTIp62G7HrSGdSeR71Yp85NSIpRuPk9my5WJF5rB8E8JACq/upMGSaFNqy11Da7nGdyv3koFDV7BvsoMQiur7Jf1VbRXg3WI7KyY2uRgOc6wwBcI1+2i7nOxmM6KpwqSx/SfSCjNdFC8jFbe5ceI2fy/+v9OoNY9r0zVyHuoA6cg/5ySGcu1G74lCNADZHCW8cZWvxAFHsoxFdNaPQJkLDebRje0/fsssOEGiR8H6vbmIbVgbPMzm3wiVxjE1lAngnyxCEm9TCNh0tqfsBnWbBWEzcyrZ76QAVoN8Db2OAXVrQWIcL25qZySX2N9+img9tRUDXxtkETnhhA+HBZL4NQEmsMyhCoI7QDdCafeJtpNKmKZfQHIhf5/l4mvfyeF+DfVIsDri5CctNk/bWFfsh6QJ7zzOFc3IBFHwTeS8F1o9uJHSlYfGKiaAf3ANHPgXXX4VL3zb39fMkUaE5Cb4vaGnJUm4Kr1dQmTUCwcT9pJ3uLNHkZMhIPGe6QEisjaljAd7a7Zx0HHkm2swVzcnWse9KhrWT4cLcEyHFPrQDeqmNZKOxTk0nDWUn1M1dRuQXCtPNQUDO57G1t3hh+B42vZ4ujcAX3dI3QE1IqoTWs6qIEs0sIOz4NG9ocjaC24G8J9MCR75naCruYLR0K1i4IJyQTAFAOwVdnLSLq5IAvfNyaBEzPnmbgUaRaxdnyFfmOD65D7Z307fnCvVgjhe/XWb304Jn//lF7tubJ/fRfTROTPGtG+s8rV9G3biH9nyHOx8ahC3Y7BskmvL5yOYpvr16m+239hHcXWJ65FXGL61y7uIUX/utv8XeO5fJfeZtxPIQ3lgNYVsGCy0IVEDsWZ9TV5SVWVenIbvHSsWArBSYAmIW9B3wKk/aGApz1kYljyZmq1RgqS+PQBBMFxlY/R4928Pk4grag4Why6z0XkPLDpblZmD83VP+nz/S+2f/N++r5MohYWbsOgF1Nx/Go2989I6tuc+1lY6dAIDQvPDoHAffvMbk4l5k4r74QUC/E/Q1V/dMEQW+GV5g9qJsZabd8hKPbtO9LzKpwvZW8Q+S2KywnBjULK4oaUi/I0HlQfQAAxr6W2ixhRYtwnyI3xMxvV1nj7eeXPB+/2Yy1XNxD6E20m+kBN8Xu7kl+1kTJbZFnlbsGxeNytCypRepY441X+We1qs8GnwHYfu548e4/dYemaLTf4A86+hOAX1rD0Q++tohaJbQ9QGDkzIVY10anECTF9CPMk4bkZwBAlqRmbilTps4n8cPYFGbduW6cYAzHMCFa3ViuFoDKXtsnJaRpJylJFkTs4wUtQ3k0zKxnEyXYHNtir9UUxSaNYQwgaPVXJ18T4sNWUIJzQPBGRpBnsmoQ08HPE+DLFvprI2nbNyJrZTkCUz653t4vK/B3hyaeqXF+nKBVPshwTmHdcJLffT5vA3M89MUGhVCowYXX4Slq9CsQRxCYxP2PwNn/8IEjHmeuZYHFKQxPfdOgcYnDk1k/9gDBrCyeS2mgEaTH41+i2l9wQxdKGMj82LrGIvM31oYUTW02rxtj2se1yKa0+5dNbzEBKFT077jOtKzSahe+pmbpKRZzo6N5wSHBBy10fD9mEG9yXhnjXqhBykgEiaHPsnWSq5m2GuLMppNHLdRSNA5Ok2f1qqmKG3MhG/cK9IzwpLvk1TOVIGpRhhZ32RopySEd7WnRqcCnysgklQtNMK40V4iECN1GrfPUm3exdRwxPOLg4znRhham2N9s0q1XqDvvseZjf+ctQ+XePn1KxzQlxjp7OFg/y7aTDEnl9gI7mDj93+XwuH/nt7hJhPnVsgP7+bxgTJnp8eZ+5enOfjBO7gePcQpUaPNGCdPTfLJF2osHL5MXA4pa020cB+6maN3foi1wQ0EntWVBVrL5PkQ1hIyewtRapilurGLnjr0b0tKBPQWYm7svs7I4hDXZm/QzrdpF9qZtd4Jy6oLtAHCXJPF8QsscImuqmO4QDlImux0b6DM76nZXvyAM7GOi+x3HVArOy5pRyaSq8UoU/vNfiJJa+e5OzkTvxMoUstBlPc5/8BZCq/n8M5voCs9aNlAduJUlXRSrL3xdj6Hjgvc9D6IdgEgFjWUMCTs6sPv1OZ31gRR9ppJxPsPkC+yTbq0tWqpvAX6AtCvYTBEyy20qKHp4AchrUodVTdBme6x04qVmkk2uxSE/fESLRnQCTzW+kr8+voHWRfF1AohQKCYiG/yzOZf8HjjW/jYHHqhTJELW2e3FuyhePxeSs0i0VuH0CsT6IXpjIiZsjCpIRCaQGg8K9gVUKZagnB7ReCEiPUIfve2YEPB7IhPSabavvFWmhXP210Q2TkUiK60SLdLfGHTKe13/Mz6uPMChEnrAzbjHqNYCLhc7KOT5OJpOo2I1eIQvTpikAaD1EALBCH9ap2qrjNcWKcYddJJjXfmSP7VHu9zsNc0B+vcWPKN/9itlGXmQhhgdv2fXTCe79tqdRaIVQQXvgfX3zRV7pRLaxewZw+c/jKsXjKXFlZxyEnzkhJ6rTVGCJuCY++Ps6xrM9Z96hQz+hyesA4wPzaE4jnQj1LfvLNPuxrX2SgRB/IumqdLs6cb/YTd0dpPv5cUTIGEmwhoxzniyKMUtNLdLiCt/qNBhKx6VaKwQFAU+Na2G2k/qbBlhOgYnzaCiP7NOqsdH28o5PbXzhD1PEZlpIFU6+w95LF0qkQQCII8JpYHo8V0NElqpdLWm6FNwEvoZJofoEZmg5G0ShW1LqZqvRPBao6tmWOseS9Rrn6S3lwJP7jJizc32dW/zUT4EKLYZl3dzah8icJn72TiTA9Xrp9gc7hE/7TPbCjoqW5w+cOHKZ18jcn8fu6Uv8vZpc8RHRvjwkKHqQc/xSnvIpTPE28ME7V8KMB3/tHjxPIRdu29zt4Ht1iePYaqCza3wbuSbp+kHHjmOREaeeIIvi1WI5RkrLzM6FaBsNHHfjT3vXwf+UgS+zEbA+ucuO9tbu9aZL1/015GZewDsTW/GyTSXT8dy7XBKIlfPF2AVCG238vgdwLyzSFYv9PuwQ4I085WtEe6ehogNCrYot33PFqEeMLDx0cT43LroyQPP7A/d8Y2mAuK5KdIBAKBBC/g5P0XqQ0f4fDFp8D/DvsvvYBu5+lpLhK2yqzlZyg1N2kFFb52/DN42z1sBeNpIxlLTkkFWJ2Spso8TvbQwnrkMoYbV/Y5Gb/9GwnCB+UDBUztgiKIPo0YiNFyG0QTTcu+NLcPDNMebjO4EjI+10I4J7YzhCT8w/ochKag2hSA6kqTI3KeF8QsA/Eaw60lBturTHRu8pGtP6dE3cYjCCA29alljNYa6KXQ+Bz6y08QkkPEfrIdDOiaDeyEn16tkcRIVNoEyZXCJi2fbFy0G7yqqgztKjAmTR2Gd0+s+eFbVpWwu51AL8z9A1KtPueEBsc3SHUDJxx4VtvvWOOoDN3aCbZiH1o+a7LEuurhshozntgQZEcRtjW9epOx7XWOVq7Rk2ux4drqvkfH+xrsIwW3buZp1/1E8k8OLwV76RuzfbFoNXNSoNfArbfh6qskhTSy17lyTrG9ZKhZSkNo2k9r5+dKQM64AzyMEJG4+51ULSAg4j71LJ6MrNlLm4RdXxmC0ZHZRUlYuTCI1sXp7ciSdrQ6BXt3JNwmQQR7jdiG0wojzSQdMGzkmjDaVQKe2WAHKS0FCbQoc7l3kFhUCbTmwZMvcHFwmsXRcdqyiEDj+ZAjQtBmuLHKUrufO5srNJs1yvUHaRQHubriQa7C0L4mhUuaQl4gA1s8RJm8+o4VupLy/fZn7NbIEnIiT2UOkXkzC/jJ3wp0GJP3vka5/yL97GVPvM43L54l96TPTz64ze16jujcN+gTRxi/uUbYM81w/QpvVB5m/I7n4PbrXK8X2Lu9l4dLFzjx5Tx7P3OQ9k3FN5ePUh4+zYWL28QvXWb0v/sYSu5i/xzMvVJFKEHcgbo2yWE3Tu6lLeHwtDAxmlkmZcFEiYzRxq1t7JO4pSVci4dY9D1aPXAuFoyEPp8MIRdKxhdHmPjqM4TFNkt9G3y1R7MxA/4ARPUm4o5vGHNwF9Db+0UVZGuXuWdURTb2WAav0cEKiBCdX4BgwzSdR8PmAUSrmgpeYCKpoooZfWZ7CSHStFjPfuZpcp0pGoN/gJQ+JqdPYHaJYcU6MeH7uGBGc6T+GpG83OdOAPDRUnF9epRavgqtz3Fy9ENofHpa84Q6z2owRbm+hYgFTb8HCgLdNOSaIHqcrg/pW0nRm64Ke9YNlbSMzQB+Invb8xJBwIbvJ/K3BFEGHbTQoomiiSYEZ2ERmo2BgE7BY/RyC8/tD0szZgAW7LPRgtYJ7nkdjjTe5om17/DIxnO4wN/uVD0Fvg0q1hriB4mjvwNMoxFpumHyX+qMEm7i7P4RVqI34UiKDS1ZUJIbyqO3vMDi9jCLzWFyEoqeSAIIuwQ7nRhgjGKQ+Tu5uV2TQJs4oJw2vzurizucDtXRJgg7xmw7VzFQSQP02ubjleUmUg+Qa+TAKiiCNItBaEnbg8bmAKc6/ZxozFIqtlj2O7yXx/sa7LdDaDatKmhBlSzxSAv0OcjnrImYFBxiDRs34dL3UyaaaCNCk89F9ORq1Jp9eEKYuuoFkhbuQkB1DHqHzUVdfXsJSaVTs7k1Y9xgQC4a4gqs6cuPDMHoyJjsFQaEnTM60djtmH6Q+V5lznOU7JzXDhHdrncd7Bz4O7HVzkhBtq24ah9OZoIdMGDfKW2xsNjPrdoyk0OKmUWfaCrP6mZAHES0A5+gFTK1ehm/12fq+jb+d59jairPqWKbXM8U09UzxBuS3pVlLo8UqVYfQmAi+DvKZFW0wpRgHcPESx8r8c1nXdSQhiBkmEH2p8BaeQJN/8xp7tkO2VrbYLYywaWBRcYKESPtkJVXThHN7mLm4QGufXc/b8Tf5tFHalx4IUfj0fOcuHCIpw8N8tWzp9g7O87zK08zePS7+KOr9N66RnDnIOML84zO7uXrF2e4dGqTwYM+cyywsecuw+Qj67P1QASC6XsNc4jtGJOuZfb5JbYoB5ml1VDRpuobAG0fl2AXW3nNV9aLI8DTgnytQKU2xqEPwJk7U+tIpB5H6TU64gYdect8EJWRjWlkYx+ycUcymdn7U99l/9ZABxHnzbhVCopZLT9ZJ0132wa3VlbQkQi89iT75j/ISL3I0r420r8OhCihrHzjQNzk0zsNXieA7jR5D0GAJCAbAyHQaKmRMkKKPK1CPwBLeSOkyDY0in2mvpWtxont3IfV3MAoFVksTOJjrfvQ7WFXkc4FmTpSy4YN7Pw9y7SEBJHP7HmhEPhom3Onnb8vEpRf1siazljxLPE4rR6dmKGdhtLWiofrv8/k2nUGaut0tbd26Uq+AmkKYSghWa08Tf/qj6HFjPUK6q7bCbs3hLUiCG1jKISmiea68GlGsKYkm1qwgaTjbEFbE7SiVMt2LCwn0/YaHZ0OTwM1DS1Mh9w8Vk9RqUafF8Y/n7NALzDfVXbdYgvyHZHm1xeE2cOehiiCqAmDhQWa1SEGakXyoczK3MmSubCoIqbAjvBMBkCjU6QTb/FeHu9rsF/Y0ohSRtMRKRA77cD3IZ/T5ARolWr/kYKVG3DxL6G5SUo89lUqd+ivbnPtnUFDDz6IErgAdwARwOhR0zxHKWO+T+jK6kYlqZmSizzT+S2KcjsD9KExY8bKVoQRNn+MNIcnSZLVGbU2A/iuAY7T7B1FuAA+3GCUub5UZudrYQZsK+Il3Mg9fDZYyb2knQMv4lCxxtrEa4y/2oeqjzFzc42h6jbzCxucFWP05XoZvz3H4v5BhvNv8p2NV+grfJbZ3m3eeet/YmXPLPc8c4RXBu/l8Ree5ayn6EQenRiakclNjbK3zw4nK847zScRWlJQ7GKcMv3pBISh+BbHp55jWcSQe4zzVwpcb5XpmR2itLxJrT+mfvUMf1TbzZ7ROre8gKO3ikyOx4TDPr3XXuD0wk/zY1GLtfo6L9Uv85nJNmv1CAbrFMrDXN5zD6u1Enc//AIl8TCNW02idc21l2pmfyogMks2cRyG9pIUMtGSVJCxh2FGOolC1vaZQqC/vw0beaqY1jKTGkaVCQfJhSQlnpNAKKHxSzHasQABvj4MCnJ6gdH5b3Olsg9WDiOUMYnpDGi5/ZaAuFscnTcypN2LOzC+63DBmE6mxLrcyK6ZkGwER2FqkU50EKKDCO88YfC8FeiseV5LTDaDIX4zxJTtSgKKbYUWOWIvIPZMc51cu0J1e4xCVDKVGC1TT9pNyB2meIHprWDVdwFpn3lptTnrIvS8NMUXkSoUyq0vGaB318nsU/eeC4ZxGr6RjIFO0QA/mwg0QrcJai16b88xeuIMvbdaiKHDEOTRuXKyUMI9ICbMsVXQlEIgbFBoLHHXxjlj/dspgXnaSo4KVIwSecL4Uapr/w1al9PxJ/tDJ1q8UBqtNa1Y09KapgfnCbihPWotAcpmA9j97VxHpjq4uWCooKGhaR9/Uxgwr+kd9A1JuQSpoKpht2/Odf73zKldlsFIQ0MYAcLpRa5yXl6a8fRo01gr1xoi6HigjAszT2YtM7IRdrqlMO4YhYk9qsbr/xnq+C9/vK/BPkKaVAh3OEXUS3+XEvqLG/Q18qzEJSoxbPuweBvO/IkJwkt8NDpltsOjW9RuF4y5XwIlkDmziAVMWtiRJ6E6YSQ9ITOMyy7y8UqLmVyH/tpzVOUq5KIU6HVouLSWti+5sB1fsiBv/0s0eZ3x0dP9e1d0fgYRkyoYVppP2lgJkha4SQcQnQK7m4wYU1bQ01ASKC9i/u2Q8Plv8Ub+g3xiYI6rwTTVSkyjP8ex+e9z4PUaJzaPMdI8xebadR7Y/SQDT45yST7GweO72AxneNUfJRaSk3sfwb+2TWepn45Oww2kFdy6Hk2nBOqATkOSgODmzCkiyX7YaaBAU/dq3Lx1N/WeBrt3hVxVeeTtHKVqwNbUNsXWNJvBYYYL32C45/sMqRlur+bINV6lEWou9OzmqPcdvnK1zsjBT3N0b53BTgO/ss2NRj/zVztcWZ5gz8gi+uDjDOgO5+Y8qpvvEC38cpL7L2PDVzfmzNhFDJ6wPkzL3KeGV1na7GHab3D8nnMUhmoIBNv0UKWOjspsv3iIRpg38JYxWeJBK4dh5jqdn8jTbPdkNS8SVAvCMTY2Pk+wLkwBEFvFTZAKXm5Lpfu0e8slXif9Az63Y/M8SzO2Z4F2oO9oWbi1DdgASgg0RXR8J8I/iRAbFugFA/U7qDQmEVoglKDW2iJsKujkUKUWpVKR/pqPEmWiICD2TQOLXFxO1EUnCGnSokY6ld4Tbd01x3IppoEVWjwF/XkDCon3Kxu1aq+RbeyWzWZx/TKceUBYV2Tiu/dAW0MmSiC3FL21OWRjDuIWI6dOUb7+Dvn1ZYTUiLyE9g2oDrM6PkZfQ7PSX2RsaRl0B2QP6C3iljAIGtmo5KR0niMsC/IW6DUaJcosRn+XQudpykiTkbBjuaWIEITEWrGN4My2B8OLnKpN0dLSyhuCpJwHir0jl7h79wkQpujxmZuH2dooMFebIdI+YWw05DpGV6nbvaK9dA6FJR0XWrIuYAID+k4RcHqO0pD3t4ijAlrnaAojSCTl10WG51j+5AM9gFS+i0vMZBPQ5bpw6xybJaMoTXVPGcOSnuC9PN7XYO82ldApQbgZdw2WpISw1k8cQk9GQ9y4Zve1u4jVloWCnNfh2ukhaBg/oi6AVzXElhNQVNC/GwbusCYkrInIVhzdJTs8WKkzFWgasWZVLNOXb0I+NGZyt/LOrhdJ44Z0dqDsK8ZWicho8ZqM3570IRzKJaHoTlMn5b6x1ehjSbJTXXRRVtxNJKDYmEEQ4Anm1RRX7hzHD6d5RpaYXlmj0Jjj6lsXkM1p3qxfI9haZerH93CoVuPy7js4ObfNenmKmlfhVOlh0721CXIbVvvHqRaj5LEsP00fQ6RTkYAIGYKygOYeEeefFxmhT9DFVHO6Q3/Yi7z6XcQTB1hshQyMnGVw5CCiVaDoL1G7q8js+jqFlZDp4VFO5mO2LgrGNg6x+uwqt+avkpvp5+6ZgBefv81HH1iiuVVi8dQZ9o506GkWuDy0zlitzcjK3XQm3+ShuUGuPjlE7ds6qVAGZhm2bkHj7CYf+pEb7BmeZ6xnE9+qf73lJrVWgXwupFjo4GLhYSnRbKOReW69eYiFM3shDlJ/MaY/gNKGwbhlvT4jubpLJueQmdPe7RpLcdkAqbOcuC2UkRWzptOuPfuD/rbvOe+QtEAmffNyghmZdUoENK0ZkjGhrBPqChDg6V1o1sDm0ldreyh0+g0Ax1A/P2Rkam20u6AfpsuGdldLsJovmqrTfvcY3ZZy2qnyDZAkVRgx+y0X2owcbTTFvA9lDH9IhIKs+Z60TEZ2nyfKc/bZrZDhtP/swITW+PUtqhdOM3Hm6/TcOovcXjMP6gkzAF+k/kQJtFcYur0GgWCsmUGveAWJoJoUqCC1ZTtJzZWg800gicKnWb6f24s/TTOaYdpGFcaYHham4rIgkB0QTRaU4LTKc10FRBXoNGaIpegqte2G0z92jYfu+SaDQQsXLTczfo2oI7gxN87rtx7h2sIUzSgwgexuj3vm5Swmjn84626A7WPg6EEZv3so4JqC7TCHij2ENhhR9tLMZKx+1VGmk7TQUJLGFeAMLZDR4jPT6+hJkRmThB7fjCPv/BDv0fG+BnsnWSeM3TENkUnbElD3oO2eVBjm1VwjwUOXliWU+X00WOdmZ8RI0h7Qg+ne5UFZQaUPZh+zpmbLCAObsx+geaxYZ9LT1GOfs+3b7Ol7Ezp1Q0DZKniu3msouiPQHJi7YsrZ6PqsEOB89tlizslOsw+U7VrnRNNQGc7v+mNmc3wSW6/l5I4iY6AD22KQ6vUe3phf5ompV8n5Ef2XhjhxZZVgdJrj43fx0vwbfOrSeb58vsndD1bYW8lxqV01pVItICONKUsLn7bnmy6DpKZdkXnkrGa1M+I5MXPaz1wweeLKyWhm7mjrPNf0BPvvOY4/+g7TN/qozVWoPXCD770zQeO5/Rzfc4tc5/c5N303r32ryeg9s4zqIpv7enik06Q99Ag3Vs/gHSrxVO4Mg5dznNjeT0Ue4Hee/xq9B4+xdfU6W+U6Gy8G3PPRMS7ltuEqzC0GxhQcWf+8VBy7d4nPffAkD993E4FJ+fFx6WRQyDXschtUEG5NrcHTLzXZ8/DblPq3eXbxXjYGJUMrMD4PKoLvP2BA333t0gGIAjfB6TIDbHZKaJ0mPiXWEp1u00SAsofW4LfBb0He1ouKPGgVzRZ3kebSMmDdaqOCCFEoIj2ZAGSyhUXmHhLy0QRVL2JFmk0i2EXIm5h0QGlcBookarpQMW2l49juszrc2YTeyDD6NybgVsloWSqzx7LzIe14VQ7iKJWpCxoGGySBWFJCCZI+5gmgWxJ31R2zQivZ21mW4CxViSZvBZ18cxOtVynNP8/g0hp9N14l19pAqpDUbkw6cUmjepE+SPamkC5eViDLWgmxIO/Z4l6A0j63W/8Xlms/gopMMNo8RmN2AW6e1JSCiBwNrsSSZ1WFjhbEWrw7JdGFDWgoF2p8/OCzDIQ5CIuwPQDNXlAe/q5LzM4uMrX3T7ixMMn5+RlOze1npTlAjEyzb6RZW+2nLNb5y4O2CYtyvEErWO6D+U2IRQG81MrWtlMWYxrphJgx5mLrogFKOo3mT2QykT5f9i3HTt3jVzyj4S/tOPev+nh/g71jCB4mZcuKWc6kHnjpPleQtNhEGV9akmWUUYK1hlubo0gnfudAFs13c5igjsmjpmGZk0o9YXL3gxyMyZgRT6CFZFnEVPueZ3hry5zsiuNY5pSk2DlHIZBw1o7u9slrSJrfKFKOYn2HWM07bWTiVFxLTQ7Und8/du+TFg9IQqPdBLshGdFWNzVj/TeQx4fYeAHWdZUTEyNMxAETG4e5KgNG+k/y408eZ3GjSO2edV4IJQvxJMMiSGKCuoavIK6YdUq0e5HSTRaEkuEIS4wC16clwxxJYxhkKsdkX0LDuHeJy7en+MT2y7x0/zT95XV6yjFPzJ5hc/8AY9+fZnl2GL8Yc+DpV2icmOOV4t3cP1Pkohxk5sw8pzd8Bjbn2U+ZxplLDK6t0zPR4vN7FX90/lX+289+kP/tWy2Gnj7E8iVBRVS4/uwrLN2WCRB4ecWn/+u3+If/zRmKuRhwRXPT7HFpH0IgUCKJZba7JQVlTyr6JlZYKsXcmJRcBlje4MDza9y4Zw+dQlqlcKcGbuZaE8SbjC/e5KY4DEJ0zZ+y9OaYltRm/r02VOZSkHfpaAC9GiIf6n3mCwOlm0QXAzqvhfTsjpCPrDJf2U/Q25cIFNmANWEBrBZ7DFNHiDxSmhY4GuN313hs+tfoXe6n4EMhgMFBYMCAdCcELw/RIqb9hILjc3BHDi73w9lButPe3PZ35OODzoFqm6022jHgroQhvRzGtef2sJObtUxbLSfTnSFLbfevk7FdfyshNaWtW+x+9TfxO03y2/PktpfwopYttiOcCp2ZsAyBJC37dLrGSpsbYL/v3neWwWz5bbRVBpJQUJT2WAsfZ6H5o0CQ8L621QN8besMAC0lOO8VuUZAiCkA5WmoKigoKKMp5iI22wEd0STyPfb0+AxcewyxMWjyz7Mb7/oxatOXyN35bfbuusLs2BWeOfo9vnn6YV6+foxap4xwBXpE+tXYplzXBYQd2MqnfGe5CKs5s0cia93xFKg6NNtm3ZSmKx6oaZXJwaaZHo+0TkvWQrZzIznBIctWPdHl3XlPjvc12ANJHn3SkMXSQM6ZcxxQCMjFLZpbHue/GrA1Z5iVzgC90w61tsTvGbeWl7ffj+DBh8DfbzaBY05BkEb798gYTwrwFpjpPwulGDH6mKnUs7hAarp31O2exN0Y3h1pn/2ZEQwceGej0hIasepxYsrPfMcV6pHScDLPNgfwJd2Nuh0qGKFDNxRzV9Y5fXGJI/eO8M5flrjZ2M/aUI3pD6yy+ezbvPGVXRzbv0whWOXAwU8SDWwSLj5gasv73VK3C6dtzYJ+qzsVOJmWDLElo9oJ4JBSjtMOE8nArqsVCp1Z7Xa0j4HCCl+LNLU3r7MQneHnxvcS5q5zVR+kMjPA1hlFvT7M0ZmI67Oab59/jhvnPsnDhQ4nO8N85s4my+PDbL51kX0jS8x2zsNim0vjBxh99Bk2/SGGDs4yfvMv6D1cor59jC//+d9G6wr5XMTkbIPP/dQFfvanLpAPXFFZ8yCeThPGzAOY7HLHnyORTo3CdHvT9QoXv/8ws77P3KQ2ps6xHs7/ZDFR1LqEIcuwURohb4OcA3WF3uguZlYFrQJsWqa4XrDgbgFKAYFuo4jpvS7Q9WI676JbUPND6FuBnGjwd1r/gIrfgzj2WXKBpnOlxj/bP8nyQF/3mET3sjeiOu2wRpQvkdM5QKB1jMnT96k3thE3YLBvFTU0kGwMz4K/wjD7kabdNhqqbTi6aE69OGDm1G17R9vuVdTQ14ShbfBCK2RawdXlaDvyFNIATegb64bzHevMvLu0O7cnTe8GRV/tGuVbbzN+9s+orF3OUoKlcwfU7v0dkptb38jSf2wZorKDcJY+Ibp5S5LTaYFeRsl9GnKCzfYjzLc/jwsacDvVtdpwRsMYQSv2yWmffYL/H3v/HW3Zldf3op85V9h5n5xPnToVVVWqUqlUklpSqwOd1N3QTbBpg7HBPIyxsc3FwLUH9hv3Pnzt9oU7ro3vs42NwcYGAwYTTNOou9VJarVCKZdKlXOdnM/ZeYU53x9zrnBK8h1mjEcP6423auza+6y99loz/r6//KNPGp/kUmSKLKaquthlVIIvHPo8qAFiY5Jd4n+q0nNwNydwlA9ehJAa34355APPcHTPdV64foILiwfRbgG37NMMBY6IjL+V4xJ5MRdKmk7RNcOQZ3Sl8bnQNqFp5JmhQ5tzWDMOjpnzYmiqQStt5Tay9bSLHtn1q2Wm1Qm0/Zxb49/K410N9okHbN5p1NHmJZOkF4mEEa3zF2//n/yLb34/jcUT6bykWynZvFZaQYAogVs1BANgehKcQ+baODa46DpQKpp8+xLo1too7wKOfw2KgQnwFx5UR2Fh2Wy6MEE7e+Tj6DW2WHMiAnOXHS3pafJdfnXlwT43UILcd3bzawejtvCyLEOeMC8hMh8BS+20hovxfXzpG48yfs8y7u1L+Pcd55S6xYH4CqvuGKUTH6E8Ncyt+RVuzt7h0Oo1NsUEm36E7xieAuvsldpA7cYJdNbclNCye1iwzRfJcCRusnl7J2Qey/nf5dlqyxhJCZ6eRUTzNM9eZ/PwKKE+yM25Hpu3Bxiq+fxt5zqDN1qMzs/ROvJ99NdiztX78CfvMBw22Lz1KO36FmL9EkJFEIXsfesKJz/+KQr1eYZOAc9ustTRbBbnWFjdx2e+9wZ//tO3uPfYJv19ATECjUOSNU6mzbQooIzTmcDUIZCOAfwNQt6iyQYhl2jhMcDM8G00UAm6tMshmk0Ec2ixz6i/tQA9APE+EvOPJADvKfyO4NRXv52h1hAVX5gMqF3oOTBfzsICtzxYL0XsCc9xYv6LuDtdPlf8mzTECNqCSbq3cuKLVIpy2KASbUJxEZwBWv0eO81lhJ4wZpccX5poYdAgwioNUcSxmieXWWShDy06iNDDW5tER7Deq9Ofwwtt15ACFkpwYEtbhZZxyHM0nFiCyQa8OgYmGWqT2KnRLJgUGIUADt2BSmhUt1qacahqMyY9aUuq2q2TL2eRxtGn3E9Og2X76IZNxtffpHrjRcZvfQ2vs7GbM8Au+PyeTj0gc8RPY1RlQhqtoLASTwLwiS45b9rKe79qjZHm47SBXb/MC2N/naErD5JsrYQBysenawHKpglJmGskDNv5lOSfCVoLXKEZcB3qTogfqizML+9grAVMXqY0fQWK3YwAoBFasH9ygX0Tc7R6RbqqxGvNh9FLPZxAstM6TOgOEYUtYhWwpWrI0hIL3b0ZQGsjJIpUQ5qRy2SclPWfkEBfF1pW4ZEqUoVxk8g3WZGR3J7O+K38+Yhv7fH/E2CfDLijjN08FX+M2ML97ef4jrX/wFBjgajzl3Z1OoY0XE8rbdabI4x6sgaujakVAip77ERGZqG40kj1XuofoBn0biLqVw234SnSTAwFzwBsSEpkUxErVUuQc8iDNDdjIu7kANH87q4X7GYiklWVZGhJ4lOSBCXSR0uHuF5G+BpHh9mudEDbaIHVxiALjXEuTDzGsfcvU3BWuFQ8yfTt56iMf4Jnb67wbbcLdGZW0f5N1PF7uaA/ylcUuP4Y0nHQnjF1SDeTdNBAN1NWJLigyQSN/CvtVmKbywN98nYXd71LSLDnpYbqIY27GHD93Bv8ldML/Fd/mlvLD/FCCCNDF5mdepXBCydptRwG1E2qwSbHt85wcXAP6+fu5xMHLrHdN8Ho9S/SUsNQcQ3l0zFeCGOVbZ4+u8V0UKRx8AQnVwv8uyenOf5gh7/2l69w9PCW7bDc1UhJtmQMIRKZ7tFethQLvuQtcU7uEGCc9TQaUV1n4+EnzRhKjUmTFKNQwLJleKxR0/ER+AiE1RBoJq49RLVTNwFsufHyFexpWScsYACY2XZAnGJpf8wbo8OoXoh+VWdqT529J9MR49AQg1TVVW6Hc7yqH2I9Gma9WqHmvH05i2SdAEIIotjDC+xe1S7a99F08TYmKMztN9ve8VKt2y4cA1YKsF6AkQ4ZI2EBf6QJH9uxQm3QIi5UuT1kHPFKHeOIXlAaT2uc2IBVwtZEgOsIutLy6cLahxPGEtJQSsPIaoRQ1DevUWivs//cr1NuLOB3Nmxnc4OWSMKCjNlPRMM8x5DQvJRTtugUJdfIzJyXSveadLASG1tq2zRvoYqYL2he36uRgeB9y1DokkVKWLkhTd+hd+/fRAbR9vFODEJo+osxQ6UIX6pcRj57sdTo2PiirA443JwdRVdK7NEFSnqHAd/kKxHW2UpoTdVvU6HBh+qfp91fZa2zl9tzY3S60NSDaARTvZCoW6ThmeCDJLmWhQnDlCTn7pKrBFDbhkrPgPfd61TArsgfLXMKtIQBynUTjKngW3m8q8HecY1dHs1dZdo1RdXhmPcW9y09xf3Nb1KK2tzq7WGhPZVqqpP51FJxdHSTxyee4c3FAzxz6wROQeCUM0bSc2FgxNgAk3CbpHiLY69xUJyozuP6gqwANGbTTQ2inXvgrduIRlIlLLd5FTYHvrax9iLbvHc71ySHxDwn4YTTW4rdqxFI7fHS2htcDxyJrhR5JTrFifoFilFEFHu0dZWiA71CjY1Fhz5nlZHSOqWpz6MaYwTbFQbHbnPyeMTL+g5Xtz/Iy184S+PrX2L0J/4+1X3jtLs+nusY6Vti4kutJJ7SJw90D/TWbteEbBZz7SdHWFzSMK1Uq3PX+93qtIQ4ScCpB9QOeBxu3+Die/4Wbw59lUI4ys5+n/3qHHOtw5RuO8gbgonCGS6uDjBwv8dIocXZuT7uffAcrahGb2gQMVNi8/ZLyJqHGusn6mje+Nj9hJdX6d++l5sth+3tN/mmGKRRu5d//vdfpq+vZ6R0OxBCJJnqTOlWYXuf2KuTsbghm1wRLb4h2nSIiNAo8ulsAalyQ7Z73Rj7vpND0QitJX5Q4sAbjzB18zDl2DfqT0BbL0lt2xphnVIlxK5gcUJweeYhep5A9MCvgmwmYExmm7bN6MkSv1P5+5zoPs3XxffR6xXQO1A5KnJJlf0mAAEAAElEQVQG7xzxTLpvX65rGOqQNUL3NoHsInHQSqZ7IOqS1YlKAMe+RwIWS4LBrhEMkjWVrBHHghHOGE6kObiMFait2JcCocxe0kEKqCqjpo6lsdE3MWFhef9ZoSJG519kePEMfesXqezcwYnaFvwTbjePMjnJPdX96qxDmkyM3LVJ7AMTJ9tEZekmTL/K1kCaadOiXo7R2GIvL0bv4Zvb99DxJKVizP2+oqiNGjNvTdwV+atzQGe7ICX4SuN70OdHDJUjU4AoZfEzoqyF4M645OqEZKckUGICHU4w1zuM1BGj7i2qzhZ93ioF0WKkfBsIcWUHB0Wttk219gazI6/T6vZz+db72G6O0WoPoHqjHAigpWApNEFQqT+y2s1npUcMTgRekPU5PzX5z3ltQYpHMjN9JYyRkmQREN+i410N9on5SkCamKOsG5xsPcuHWr/HlLqNE9pqbVJws7mXVlTdNUeOE/FX7vscf+HI1+krbLF5uMZvvP5h/mD7E7QopvtrdNJqDVSWmMHBRrhYWjVTW6Svsm2ohqdTwNcS8DXRoXFwIPzGbcqiaRqROMylAG9BPqmUkaazzBHuPD1IFlS+7mu68uwGSnLbu1a0diSq5BLMDKIqNYa2Wshbmm5QZ761l9nmHeJSgeZMlTHvNmXRgJpGLTkEfpPiKMZbpeBwsHiT41N76X5wi/Lk3+DCvr2EjsSzCYiStKHpwtfZ3hBvgH4h62raw5zqN+2u4abSkKS38TQ6G5JdwJ/bvEIb/wtmb+GOzjL36CPcf/FpLl45w3RfhP/VkywWZ/FnDjPjn+e4+xyl9jKjYxFqKUANBXxq6Pe5FNdYd4/SXhzljZdCvv+hvYjeEgvugyz2BZybuodXXlpnrPQG3/GJPlZul5kZ6HDyE5foLwe5xpF1XBgo1oASGmm94bWEHQKeF2t8VS7TEeDgW7V/UkAmD+/mb9N1YT+b+7q6TKU1zMT1w7Rr2xRafTjKY/z2fko7A3giS3MaxZrG0RAnlPg7AndbooQxI/SKcGUf3Bmxz1BmXsKD4KyC3OzhdD3e7vUmWHb2sVzYhw7JfG0kqed0OuEiI6wpBklQukWj9nm0bBlmBGmYmBwR1nGO781JmBq4WocD21CJs+2j7HVCZfcwjIZKpcc0VCd18MmYIBBpkZskJ3vB4qrSQKiobt5i5sp/ZfrGk8aLPpfYZvd6z9ZDtiFyQgH5jopMUEh4ECySCJ1zwtWml1KbuDMSW1julU+b68CCP8PvDP91XnVPpXxAPZQMRsIw7QkfgRmW1LePHLNlb+tiwtXqbsRQKcSVhsF9J85cC2j0Kc4ecAhSRidj/BQeC52DVusIjorwdQdJj9PTT1Lz1/BsyWXf6VAtb/DA0f9KGPuEUZE7S8dptQe5vXwE2Sqx1nMRfo9eWE3DVZO1ly5dDX7PMoL5I0+H2f1ZYH1k7Hgm05KYBNLU0N/C408N9vPz8/y9v/f3ePLJJ+l0Ohw+fJhf/dVf5fTp0wBorfm5n/s5fvmXf5nNzU3e85738C//5b/k3nvvTe/R6/X4mZ/5GX7rt36LTqfDhz/8Yf7Vv/pXTE9P/6naktdMazSnO0/z8cZvMB1cw0n1iRnHeLh2ncGxFVYWx9HA/f3n+PP7nuTxY5cpeiFoGPS3+VunfpsT4SL/r+f+KpH02XcIJmasIK0MjkthcNMVJlGl58ScGL6ALEbgxWhXG6dVF2KbFSpCEO8fZz2aYc/lZ3HWGsZElpRwS/Jta0jUWYaS6nfoff6c3TgJ2Cfg7uQ/O+B5aCmJRJGYAnodGlv97D1/A1dphI45xHWU7xL1+Uxt37D5+z0QCqkURQKI3TRe/wrTXBjZS2vmKLMXO2gljaYjN/RJNikdZQ5MdMF5AfQaWZ4Esve7N5B22bVaE0KdWCgSjfgu0M9tWK1ATm2xf+81ghOnrfQZclG8ztD4p3hFrbP1xq/g6e/ElxG+nOZAedsko3ElMpTIdgDa4ehOg3X/Orr4Boe9WW6URrleeoTSgQ7rIzBXDbh3tsPwwGnUdcH21grRXMiBoy0S9ULGk93N6ZDGsu/ogDuLy/zOxDrrIkQIkwM+w6TdwJNVjs8BvoaCHmQoeIBKvJdiZ5h64FK8rPG3RWYiyC2hEFAeNKYhKsfIUFFcg1hp9JrHa/tcWkWRaYy1WbtqAFQ/qLiNt1E39Z3s3Oh1EF27vrdJ48rDWdDVt/EE5shLrCJEe9t4coPRXpXVskRh1N7CDZGE9EceYzFsqmw4U0bR9rErYakEBwJtQUSkTu1plbjsl7nPOcRJ25QwZ/lLDdINrb9FG0kgBJO3nmJi7uu4vYbJXpfvZzJJ79T/5Nl5n55823bl5E72PqSOD4mILe0zEotRbG2ceQdg2x+seexS+QSvl0+luT8qCk61BVpm1QeTcVUYcqBhl0ZFaBP2VraV7foLEV7O7LZ7EEB7IRx8jrmRYYLopEm1LO66SmfTCRBplyiogarxzNXvBxlR9jdQaPqLq+wZvsydtYNMD1+nKleZmDyP73a45/gXaG6P0OuWmVub4vXb76HV7bfB9TmeTploE6+Xm14y+pMqUew5pd/mRmTWlmVWkaROmX5+fX8Ljj8V2G9ubvLe976Xb/u2b+PJJ59kdHSUa9eu0d/fn17zC7/wC/zTf/pP+bVf+zUOHz7MP/pH/4iPfvSjXLp0iVqtBsBP/uRP8rnPfY7f/u3fZmhoiJ/+6Z/mO77jO3jllVdwnP9+Q4Zj8UwJ2NO7yg9t/O8UdZIZPFtEICCSjPur/C8n/jm/2P1rDLgN/tdj/4YB5tiujRM191B1jDpIOiGnKm/y0MMtdMmnUsdMui1Ql8X2a/oLXT6w7yoVr8Hg0Arajw3YO9o46QhsbS4D+CGS2mHFzsQpql95E29pExEKtI1rEkkC5jRcjpyYQrbakvfYNbEjMreDElE6AXvHpV2vMufO0Fjr56i4iXejhyd3KDoXQToI6Rgfg4JCjLkUuxvWWc86RpATl5RC65gFb5QXBk6xVhjGEZqVB4p4oSKOTLiYb5kxjZWaLN+iPfD+BMRKjhgnuyf5O+0L4JFlWLtL25jm1kjoscrNekIVJBSnYPjjfcTlB4xTkQLhueijj7PwdBcOVFEf/n4Oh3UqeopQvEJT9DOk5kmNcF1AS5xQMLq6DJFmyH+LbbZ4+egkZ6fGkbpH9coAjw3UeK2zwVl3nBOP9PMh3WVVFiivDBnP3tAjDj38SpvuxhDCC9ip97hVddhbanPNv8TXxA3WJ3sINFI4JMl0YvI1wRKLe5YKWlqOqaAHGA5O0x/di6tLhhEoQPMeaCqBvw616+CuZUvLsVINLSi97tJ4JCYqRDSnAtAKNd1ChnV0bDziUw13LnrEYQA9yC71rhixz1AYs82OWQdqdPd8Jtinc3OpZYTTfxNV3KGNg+IQHhEx1ylGAac2+hlTcDw2NRWeC6Gbj95K9ivGm3rE6tdFyq1kEmTKeyUcqkgq5YkcwkmjWRFOTouu0WETZ/FpKvNfwd25zrAKSNzwBRaA3ymtYB69ku92Iacmq9FM9j27b2Puk2tn+ig7uHHuOrjLqEyKUguFvfz+8A+nZrI+Bd+xCSO97NIkgZVWNknNXTKJBPoAX2hcoRn2exQclT08H1LjRDB8Gfa+gapv04yfgJ7Y1d5E/kl5n2TM7GehQGkXHbtsR5NEwHprkitrJwG4snYKNBTdJr5sc2ziOcbqt5ieuMKeycvcs/8sf3zmL7HVHSbSHsTCJBnsghuYR6Vx+hjynIJ9ItTorO8uxswrICuMY/uhtBnr4f+R1fg///M/z549e/j3//7fp+dmZ2fTz1prfvEXf5F/8A/+Ad/zPd8DwH/4D/+BsbExfvM3f5Mf+7EfY3t7m1/91V/l13/91/nIRz4CwG/8xm+wZ88evvzlL/PEE0/8d7dnSLU4uKroeh6Hw3UKqmu/yS0kSF1jBYIH/BW+a+xpPlD5BsPBOhpBeaVJsd4xMyu6gGY1mqA+OmB+r0CGGaCk6sUCvHfyBjOVLVSxQ1TroqUp8BCRmN0FcR69tEB1h9l09nL5/m+jfOOfc++Wi5oZBc/FfekGesvEpImwnZHwhG3Me+n3XJMpxUk2UO5aKQAfWlXoVvAWS8RRhSPRFcqFLjiSQJbwRIxwHSi6ZgV7GtGJTfCwQ8Z4CMuECICYrvT51em/wHxh0lRpA7SrKbs9uu0CkTLG+USbqC0oaA3iMog3bR/yQJ9XbSXnEjNIsnFyvkXpK5lpnWPEkr9d6H8fVO8FWRC7IhGFEMja/UycusZmZ5PZofs46F1nVJ1h7s0iAQG66CB2rHikRRZLFcWEUrMyVeHi7R7F83Vm/9wWYt3DubnE1652+KBb48t3JHeWThIdv8rG2n24a/0oRCqAJVMnBTTLmuXjiqWe5K2+eRp+HSGbaKFQKIT9HwQZ2GukLuBSoI8D+FRxKOHqArV4lmI8lEp0+agGLaE3ClEZ+p4HGZj58Ww346KgswOLO9DfF+PLGCGM4WCft82aKLAelQmVg4qMQTJVg+en1DJ8+W0ZG0EsFX/yDFoKfLFdMzLC7b+BLO6g0gxJEkEBhyMoqembm+VE6JkiJ1344G2Y77PpVD0TTdAXQi2G8SZUYm3XgTLRAyKRzm0NeAmxJ0A6hjHUsWHCLWeqEWjt2JBHQU+BGzUpv/z/RDauIBIUlTrTbSeg+44gn0P79BKLnonj3NsYBDuwAnaliExUNCkNsL9TGB1yGiOW3CvHHAjYdgf4L+N/jV6hglRQD+Dbt2Ak2PUL07TYKAnUXU3zgYowQN/vhVS9CEcmGSFyC0EI4xE58wxMnIcC3NYnmI8OGlNMsp4SepckObM/19g15NihtRE+SaZpYfuUskkCOqpGJ67x3PXvpMAOn3jgPzBSnWeossb3vvff0AuLXFw8xctbB4iuHcAJjc0wyg2V0MZ3xOtvUhjp0V0ughbEnTJpxAxGbS/tRtDCkldAyZjI1/9jq/H/6I/+iCeeeILv/d7v5emnn2Zqaoof//Ef50d/9EcBuHHjBktLS3zsYx9Lf1MoFPjABz7Ac889x4/92I/xyiuvEIbhrmsmJyc5fvw4zz333DuCfa/Xo9frpX/v7OwA8PGNfwzdfRzu38SfFEA/tAJo2ADukmdWoufBckDsnEB04M/XP2dmPzKpS4qrbfSINps6TMq8qgw7E0Jm915sc+ETQ9iWrG1WWVGD1Mpb1CubIIw038UhwmT4EoCnNegKt7ofIuxUKfsrjB18iNZkAekIJJK4fg87548S4TFy5V/gdhZMO5TKsuYF0gTxxk7mMKABKVgTY3gFTeSNMnQnSINBPRRHxW3wQjrVQYTn0I0K1JwGTqJvdbQtFaWyLBmONqypo4zRytUgBVdqsywXJnB1spAFWgvKtHA8TTMsI2wCGJ1w/paoT7RX2QiGwaoEtVUdisSBL68DywE3ZNx9wkjsShBjj8S+W5iC+mkoHWD3xtK561SBG1sRBwe7uLU1SpUVXnjxNfYM3sfr7jFmXvmmyYwmbaCtMjZilGB1fJzb6h4uFAOOPnYf8gt/TOvyAt/1/g9R2XubYnsGfU8Z1evx2uvv4dB4MXXNQNiaRLYvjSosjAnC0EEozaH40yg3ZLN4HSUiKxpotrwrdOUmZYZx8CjKAerspcQwwubdS1z8EBhmKWFwlB1ri1tKmGqz2/shLJjv/SbEBWiMwIIWbLZd/KjCYM1jyO/hoqjKHmNuk2F3m41umYXOkB1SkTJSjsyAPmFMjGZZoHIZRlLnqAQHEokxxqSGHbyNU2rYkL6MaTafJLGAFx4UcEnz6BWBo6AWwNFVC0qJHx0Z12H+tw+RMrVBa7tvYzvVDqYNTpojmF3SpNIKZ+FZvPIY3u3PIZtXrASfXffOQJ3rqyAnYSeqL519TgYkv3BT1M2BZ75ecKrVw+xdV1sx016XZgyzv7cOE41yP/9xz//Mpdop0IKChk9uGIk+XzEbZUA+jrPmJkdRmJh5Xyj6/JCaH6dRC29zW5cR7H0GRs+Do2mJAa7Fp1ChNGBvsxbmk5+lZgnb7MRRUCTVbWLSsF7H0oE0l1lu3IUjaTb7+cJL38Ved4n6yB3ie29zsLvJ0QPP0nQu8OrqX6TYGTPlSwAtNbhQLDQZuP88/Scu4hQDVCzQkaR9cw/ELu7GMGHNoXd+EtoFvEMXCYpFwqV+SpMr6NE7iFLA9DPLfCuPPxXYX79+nV/6pV/ip37qp/j7f//vc+bMGX7iJ36CQqHAD/7gD7K0tATA2NjYrt+NjY1x69YtAJaWlvB9n4GBgbddk/z+7uOf/JN/ws/93M+97fzDC8+xot5iuuTgDI8hVNFUokhyyacpcjWMCKS+ANdcWHeyFWpjyheDaSYKV+nJCrdH7uWbwZ9HtHJcrAIVmaI3KjbZ8rQDX75ygFBL8CV7tkY5eP9lyuVNCv4SER6RMGYJqSUyLrK08RhRVMVpxfRVF1H1GZpbZdxuH74T0esN0CjPIhGszfwA/s4lBuY+jwgUouOYoN7Y6tvyGfOkBF1kqONBs2A4khxx1CMR9BSi5FEUASAoujYHtaMNQShoY0hKa6ImtovYlrVUNt4QCk4Hj5gYL5Xi0IK2U0X6RoWfjFviya0AJ+7RfSs0RbWwybJsKgJsQZQUwO+ikW87JGlGMm2nGalphrfY/12zlGZB+GRENQE9S0CENl2rH9pP4dUOq7UawfbzNAb2EB2pUTt3lldOnqTv+Ve4t4xNOCRp+CM8MzvAQv007qEW77m6xIHV69zZ8vl6t8mZGx7VsQr+vbfwVn0qR17kaOd76OpxIm00lNIuvV4BliZhedgMtQiBWCDwcbTPsDqR2vpwYDQ4BY5GeMKUd7disLZ6dEUE2jHgaH1ZEqleSYi1SNMzxDZLWPuIWdtamUzKMbAJtGPQPUEvdljuFlkRRUoiZlw2GHLbFHTI+5pn8DqSZ5yT3HHGMoWMUkghGFEbTHXXKMddinHAq/4hFuUAJOl4ZYYBqbba2omF28EptyzQZ2AvrHRtBFLJZl3x9XvhwRtGsk+2tRBWhWrnX8uUJTGIQEBqprJmMG2BPgl/zEKwElSzBXNUjLj1x4jL/x5fRyB1xqQmIK/f4cVd61rnO54DemUX6tv09TlJPG23/ZxU4pKCNHG7b/d0UqFLKKtPTkRjI/oqHH535m9xue+UjfCA/h4M9EgjCpImJBVy81tTAlVhUgY7aEZLRm2/2wSStF+A14V7vgjV2/Qcxdl6zGrvcTrdQXRo1mMi0SeJz7QFb5FDrSQBWspwJEsll6Ero4JkAoQAWYC5S3uZV1N8Yt8CB/yAM+vH2b58DysPXSEcXqfQrAEOQzMrRIMx/QevUy6v4FXaCGubFJZ21u+9usuxLz7hoVoeTv8KJQRKC0IBERpXwFDtHN/K408F9kopHnzwQT772c8CcOrUKd566y1+6Zd+iR/8wR9MrxNi1/CitX7bubuP/7trfvZnf5af+qmfSv/e2dlhz549SOBwqQnTw6DCbAEnMVYqpx/0Meq1vSG0JSyLbFMCpTttLu19mGcrP8RqNE3cKZjNqwFp8mtHAalUEguo+YJW4JkiIwHEtRHuNEYQvZh6fJGR2iuIuoNWgvU7++i1DuGU+nA8jWy6NJfuJxYBhB4KgQgtV+qYwhrdwUcJKkcpbZ6ltDqXeevjgFMCHaOFYn10huGlDsQVhHAyvbmjjR2+7hp9nLU5p6aBxLhUiA1R8GID8ElObDc274lE74rUGDXbu8lYsMKSN5U6pBhPZIknlJEoY5PaX0Baxc7diShf32BLTRo655nNKyzQ5wnhf2vFJIQnmWoNSCfCLUqG3idguI/KCLukJg2ZnVYBWhNJReQqrlW3ae3pJ7z2LO8NIpZmPsD5TpuJvoO8Wq3jX3yM/u55+h0H6UvW6iOMjUbstG4zQA+9Z5prV64hHn4/B04PM/byGpvL7+Fs/5f54KvP48kPsz4UEewovFAQhSFauoSe4tJBl6BqhzlJWuZgwxjIGp+8UuJuwSsZ3Gx00EKjtDEnxY6p7a7IsiTnccdaV9iWhglpiwxnkgqSBn8EUkFPuNyO+mlHLp/qvMHpnYtIDUejG7xR2meWDDEzepFlZ5B90Q1K2xJpi4Mfca7y/x78Xnqun6lcLRak9FkoRttzbNWxYp1hId7mfIjGVBHwDIMYa5QSKRYmRD1B7tRGr0MExp6uU7t8cneRC6zTSKEtY5CbhM4K4vaXETc/B0SkXvB5EfedQD4/l+lne2LXtXe/kiPXKZED9rx/jhCZh5iDTXEn7CDbwbaaqUSdsTIwxgXnEd7qe9AYi7RJHvPxlSyyNwExhRV8EhIiEic8cLTGEZrhYkDBTYCe3e0X0jBvbkhcWWCnGPM7oxFPDsR86JpkqitQIaniJdXB58Ec0/1dZqnkuxxPlFpCNLvS6Cbrwy1D/2GN588R37NGXOrw4IG3YGCTTv8GKw+3iR58k6go8WptAmH8qDVZtkvjK6PsORtCaa9x/S7C3yG2awo7NQllc942Pn+2x58K7CcmJjh27Niuc0ePHuX3fu/3ABgfHweM9D4xMZFes7Kykkr74+PjBEHA5ubmLul+ZWWFxx577B2fWygUKBQK7/CNXbwjVgepjH0ljQMR9hqZWy0VBacCuOOibzks3vcQo3PzXNFtfu/WX6J84iAyyGzDWMk0uWUyP9UijO8BEcPNORNfWyhh4saVw9zSYea3zlL1jxA6g2wtTzN5SLFnfIUB0aZzZw9CecTKN6AY5sxzmOeUHIh1gXipiggFUAJRAlkCjJ2UwKG/NANHfdOobgCvXYdKAPWIJP5DdK3hKB9w7isohaRVaIQyHXJsusyk2pWrDNAnxERL/Djgfa0v8fv9fxmJa3IOpHJdjBQmBjyhMxrQXeg9WWG+dzyVLqWDqXqWUJLcGKdH7u90JkX2h+NC/UiboYcLyFIB4QzsJhDJ7xUIpVFhm/nqHEtuAYkgEhGbRz2O7j3NN//zGUodRT1exHWgr7RD6T6fX9GH2H/wJOFzLcqdgEV/iMrGda7fPIEzM8p0e4M+/znKZ55Hnf5BRhZWuOQf5lJ0k0+u3eTKB1ps3y5Rn61QWV5kaOEOY1eusDr2AdYK+5DKycxGendn8+Q+OZ2tc4EUEoGDErGJhScmFsJm5hO7BErDDIjkE1ObV6nuLPL06ANs+1UTH2znIKk5kcQMOxGpaXhf5xYnd84hbd7XiuzwWHCOtEqahBGxanJMF3wIPNCCyXCNv7zzBb5ePcVqcYieLBtHW2K8XodwYA3pB7THd3Bdj0Rc1rn4tAToTfSB6buQmlgL4yyGxTZhhF1XJ/RAIbBpAXUIQiIcscv0bXILJHUJAGKESGLsgcUzcOE/Qm+T1IPs7glK5yc38Hdjd8p4sJsZyN8s4WaTXueztqQVoyCNuEm4lIQp9zFROHcncU9sFhqUEpwTj/LHUz9MQIHImixPb0M9Jk0LnIYzWr5H2MdUtFHdozVFRzFS6ln7/N2HmRAtJHEhpDN6hRv9PX5hJqThKLyoSKVVN1J9mFOkJPZ68ow6Rgu4W17b9ajEzye1huS6nx9Stwb1iYiNeJBCs4RT3SIaAsEgznCAQ0gBQWRWGR5JEiqRy2guU8jPWIGExTbmWUWS5cK8BKbQ1bfy+FOB/Xvf+14uXbq069zly5fZu3cvAPv27WN8fJynnnqKU6dOARAEAU8//TQ///M/D8Dp06fxPI+nnnqKz3zmMwAsLi5y7tw5fuEXfuFP3wMBoNA2GFQkSJCorGQiuyQrxqLPPgWTLuO9s+gJiSx/iMmtPtaTcs4WTGJtpXlHc3BPyOSAR6srqA9BbRZEDYqD0AkNnTPxn5r+isOY/giN2x0WvTH2TDY5cGyOSb2Df7tCS2u2hKkeG0d2M2nzbBXA9o6R7gOKiMFHOFoqUdvcRkSWYy5UYWgWMTSAO+XZbH0KdAQzynAdkNqZ01WeqPVcBX6Q6SvBdDg/Xsn4Ja6nSbYUOzwHm3cYra6x4YyR+iwnCb8tMRFYZcAGBOdAXUoxykiwrqXDcUqGsvYkJ/Rd5wCERlQb+PWQkekhakfr5n7Y5ju5blmxth1FLMsWQXGJdU+CiEhyzysRwXqZg/fdTzB5lcX/U3OnPE9f9X3smVrhRm+GwsQF3rsv5pm25Ij/FqX3lRlr3mKtsMPhvcDrrzN0+iSlgVv0/ALva93PYrWfPx68xIlwL0PHQdR6NDoV7nv2KQbXG3zqqfOsDO/l+uxDXN//iPFjSLQRSV4Bm59bS+MfmNQWUFpAbIiIlKbzsVA2+WKiv0lc+iRxIsVqkzJ2rHmL7zj7K5R7Dabn5vmN059kR9bN/ZJhzuXgEQGUvB6n1Zt8Z/cr+G5gnL4i60WpIC2ulHhPSqASQcc0XirNvb3rHBXX2ehVubIwS+FoyLnwIF33HBerg3iiCBRTGTsr6ZsBffI/FparbTPvoZVEY9uEhI809nerutchRplq7iZTT8/YYqJrJVltbqoDiDoG6C/9LoStbDEm7un5uPg8KO165UTkFOyT8UqQKw+TuUWf5AVPnBwSaT7hUhI+O0kh7YjsPTH1JTTArq9YOXyz75N8bvSHCXTBuAVJ2N+BY22ycFirss9nlatKU9gGQGtNwVGMlgIcaZySVXGZXjDEji7R2HubaO8N3K5HNLKFrrbpOD3WywWmrNNvVVUYCopEsUIojVYSnWhqE5eF3NiKOOlnbqjuYoyT4ZTJLVICk30WQhAg2AwFQ55EUshlrzB3C0OPzZUxYi3YXhuluT6Sjrdb6tI3cx2v3MSrb6ZModDCgj2WNgpLC808JkqXb+Xxp3re3/k7f4fHHnuMz372s3zmM5/hzJkz/PIv/zK//Mu/DJiB+8mf/Ek++9nPcujQIQ4dOsRnP/tZyuUyf/Ev/kUA+vr6+JEf+RF++qd/mqGhIQYHB/mZn/kZTpw4kXrn/3cfEnTVZ2HzINXyFt1qncHWvKmlnKaIVblVSsbyIRDSxJYrDwqtDdaKfUSxGRSr6bVqIM2e0SZ/7kiD+d4ELWXpWwuEVoxXd3D9Bl0pEK6mKCL2lndw+yXRVJdoaI2KG+CgERsCtuq4SqZ0JPFUD1vQaljuGWs7dATh5CdYrXyCuL1If7CEqPQbp0NXgL8BcjGLCey0TaHkELJqG5Y4uEBfO5cdQuzeHbtc1XVm8Ex0UMlv7O/Gw2W+rfVFXi6dZlVOUmsVoaGpDzVpbhfoK0q22lUKb0icq9DYBOFDNzYRg6JkAV9lt0+EmdQO/w6PdgpQ2d9g+P4exULJbObk9wnvkphbtCKOFdc2GzSmN4mkQrtO2m2Vg8RLYzE3vA7F54aZeGQI3bnGzEYfTl+Xo4MR8RcucXb6owTxMK8s/gknDk5wuRXxne1NGs0brMffzVi8zLJT5cK5MarRG8zt3eFo5QjtiWEqfpPi8gZ7bi5SX4sQSuKqmMnblxm9cZX62gpzM/fSGJuAjgmqU74hwKHn4jiOKZcqXZJQOx0LnCgkFBCWJCKK6bkShSLGZtsXmkgrIgNjlIVmRG4w4F8g9tvIQHOy/Qz60jb/9vCP4ikn5deSSVAxEGueiJ/hw/FzhnQlRnEi0mLtieE69c5zjCG3ZjVvHbMlHaUZ8XYYnnmdK2qWfe43eLO/QiSCVP5xbDrfJDlQbhOT2ThMI8sdRRwb3VKynITlP7z0N5YZTtA34QKkld6lCSuRWhkOS2nE4ktw/Uno7UBnxd6DHDBbAE+BXpPmik2qruyKVyN7vs4vbJ01Wufalwf6RLOW/5yQs0RtnzLmZExBwqgnj0agKPDbY3+dl+ofJqKA1pn175BNB6FT4SkbfimgpEzGwIS/cQSMFG3hGS3YEZKFuJ9Ql1Cuov3QGaLRJft7QRGHIg57cfmr1PGBQkEQ3Pc5VORCT7E5P0nTGaS5Y15aSVNWF5A6Nn4FSd9kOsm5PmbNts3KTueXEhB0q7iVJje1oQb9CHygimGKb507wc2zpzK+zDJPyfLeunkAt9ijb88N/L5N+krrDPoNZNXHc7apqYigWcFVArfcYEloeuXi/9hg/9BDD/EHf/AH/OzP/iz/8B/+Q/bt28cv/uIv8gM/8APpNX/37/5dOp0OP/7jP54m1fnSl76UxtgD/LN/9s9wXZfPfOYzaVKdX/u1X/tTxdgD6L4KzQ/cx8qNw5RrbzLUvINUKgceyWLXOeSQGXdr1WxSKy73TtIqFdPKXklo+bE9i+wb2+DesQ7FnQFUzzLPIsTphoz5S4xWF3DqPZTNuCNCB7FWhrY0KtMwsFK3gOVRCAfxlIaeRkQCurC1Cr2uZcStps6Tpnqfj0CG0C1NEPb3U3CbQBvENrBuVKWJj8LydYjDHFGwEkApMoUkpHqHkbSHgDSWTSZjlXwnjM4dJ5PiFDzYeJ37mxdZdUapd9vIdsCFq4dZ0ytMzD/GhfYpOk0fJaBeNbUGtnqYkqHO2znx5HGpAJSjh44PfTMweRrcYt1sYJVJIAII6RDrkGBzi9b4GttuREtWCPsFKhernvDtmeINOkWIJ/uJnmhTu73C3uB+Bha32enf4kRnh/mZj3Lh+rM8WilxwR3GvT3Pe+MZgt4cztgsQcdnqTlN/96AE+8tsHFnD6+88CwffGKB8PwE9coq9//GF/A7QVY5Q2kIQtxexH0v/CFjr79MxW3jiMTgGKOFpjEyTLt/AKFjrt53kq3+GOXvpba5wT1vvUy05bB2dJihnXXWhoe5dOgwW/39RNJjtW+AnnDQaCsIaQbFRSbcs/S8GqvapeSHDF55me6VScof/bSxiOUwSmoYCLd42D1rgD6VuKz0mzCWCLNoHZmOLo6APmH06ctYxsD8TPiCA9EcY5HDi84eVF2howjRgHAgwLWyeZIyiOyumEBAQLRRokgPJy1Ak6RGcKRxRHSkQAqXcughEUYD5RfAERborZ+KMAw8KqLbuYp38T/hNNYz6TkZkBTkLVAn0kHCR6cMQQ68Ebs1AMmq3cXRWpVASr8EaZYqQW5fJ/vV7lVXW/XFXdclz8htrm1vgif7/gYvVU8RSt8wcxjycaAHB7u5ZmGWqpLGolcLjItPRkU0g8XIuAIow7k3ZUwQDACa7uACvaFVYwVMJdzMNKMQNn2/wCk2TSxJVTM5tIVGoyKXOHbptqtsrk8g0Ph+gzAocef6SZpRCREUEUpkcpx8u5IkEQh25am3l8SNEYK1WfSE0VqvJP1GUFQOa53KLmK0izZp0AiioMjGjaMArMmQ630hR0evIGvriMoiB5xNAkfgxS6dQsQ6Id/qfLlC63dMz/Y/9LGzs0NfXx+Xf+uv0tzzGCd3nkK0A0TqIprTnaU6T5Glv0trNxtK0Iz7+L/UP2SVSSBhjjWj9RZ/+fGXGK830KtlwmaJbrsP5Up8r0NRdRFCIyrKGLgKGh0LCFxEy4WedSrqVxA4xJsjiK1xJBCEHqsNyU5TsLJqJSdslVlpUvP6rrFnO66po1PwNGPlNr67jRGRuqA74GyZFE+tNbh5PhuoJAd2yeZ63LUDRPZ3QhhcjXZi8CIC4RLiUqm0wRcIF+NY48iMky4IhJd41glQGhVo9LbmzcsDXFj6u/RkYdcUdITxj2xHxvM7Sa2Ztiy/C+1LejA8us3U4Q7sH98lTGk0oQhpOetsFG/QdFeJZMsKf9oSFwdFhR7jxBSIKOYk++xxkhiPAI8OUgfUN0ocu7WDuzXCY+IMZ9R1zn75NO//+Crdt/pwSnNE/f10t+5l6piiubXG8vkRjj/xGmt3HqGv0uXNRpPBdcWD8kVG12LEagMRGu5ECyCKENsmlh5XZE6qRk9vPENDG8zsCHAlquChPQek9ZJIYhpdAb4AVxAXXLQnCQoen3/vx/ja/e9PCbhLyKM3/5DpidssnznNlS8MM/mp13mkWeIL+9/Hneoe4kJOsleaPdEcny4/y4HONUSgzGRGwmiQYp2rKGULVniYvebrTOrqCljxMudDX6M9MwO3hM9v+/dwZ0bjaA+hPVxpZEBpIz6w823+TxIJuWhdZ/Zylfe/WDIz6ZhhkDbromMjVD2pKQgoCVvgEYxHtQgRUoEwTgkazeLoW5zb/xWifx3xiUtvmXmRyYqJMwBPPOeVsmnkdI4G3X2o3ItdYLTrSGJKU0le7toPKcAnWTYdbEgsmbbCEUZacLK2aDSvV9/LH038TyzqAaQy/gqWDDLYhSd6MBplVojU0qCh1oJyJ+PVAAqOYrgUISyKNp0ON2cucW1qABY0L33gS4T1DT4sqvRZa7WLxMexQrnY9Z6w4Ynxhre9bMs0hGGRjXaFjUsP0+3VaO6MooWwhczMYk8ZVpFJ97umJznnhpTufQp3cD6zethhJvBZ+crH6S6PZfOWTI2TCWd3RYcipcJ3Yh4/9TKjtVU2XJdCqcWK1CAW2PrGZT7y/l9ie3uber3+31gM/9873tW58fv7m8yqr9F1q5TZILUpJzOrMKm0Yuul44RZrHpaMlbzmnqUVcYzblXAcKXNDzz8KmPlJgQSEQt8IvzSulUdWWInMV7vPQktgbAikQ4EnVaJtblxuvWAgdYQri5Sso4l3S64gSBumfY4ZEy8FNYfDoxtygoHrgAnDUOysX+6BHEZeguwdMNK8thSXT2y+OA8S5tTgSqzSu90R+jJAsPDPhXZYb0bUq1XeXHhAGP+FvsGl1jtFShWB2FZ82Z/kVPleVquZGjkDgJNIHx+f+oTbHij1Mfb7Hlmh1YzYKdbI5A+xJqpylUkITfEDNu6avwV7qKPWkOES6hdhIShA5rZ95eIdR0HY/cYdOdQwFlvg1V/gbbTBLLiMXlBzNytgU8LQYEm43ToJ7YuhRmQmFKzCuMYtjkIZwojnPj5f8P64WGe3/MpThy7zspbGyg9RzusMDsxxmT1Ml/54hl8d4zbboeVr5cZFNc4v7bAzPghHmpdYaAQIHXPxCcB1PtgcBSmhtB913njD13ktMPExjZ980toIXHjkMhz8eM2Ii0DqJGxMoskcUJNO2rFcSFMuuhI4aqIVqFMxhoJRrjFvr0X6dvscVuv0Ft5gaE3DzN/KqYcRygV40QaKRQFEXCat3ii+A2E67LQG2WyEyL8TQP0kWYXLUZl+R+S6Ip8Dvi+2DqqGMq47LvUegXmVsYI6gLdU0ZD5hhXQ/OSyJzvcub+ZYC53Aw5+laBnqXSrjT5plzHvBIBWVpuJ+E1pNA4QpgIFqOQRqOZ2/sKV/c8T7vQwPkbMa8/P8J9v7uKE1uwyWfCs3NinHtyxCktxWoXdqIXT+tXqHS9J1sxZbqTz4n57G6gzwO+I9LcFxnYiyzOXoNWLsobR3cjLg//EO3aICdXjdViwzGyyGwI45Hxqo+SZuSUEFJBIcr2qbDM+0YF8CVX+iV9bXi5WGVxqMXqqafZPB1QQnGfKDCKj0uSajdxUkuGSGSKIvv3bj4o4TqT1livd7/HoNdl8OEn0UrSbg6iNTR2xmhuj9NZn6YpSsTaRWtnV/jg247Io3f9QfzaJo7ftc83K01pgY7ctzdDZEsgOZ9za0IpSaAET7/yMAO1Bu859SqVaptZBBETuJM7/43G/Nkc72qw78gSwypABB3SODkFBE4G6gqzuXyrSle7lxFtGIhWEYUYLSRF1WZ/8xIfONVhstIwORIDxyBtohVINqJNsoDAxCwFIiVsWztl7lw/RNAp4a5Df80QnrhLGhVX1jBVNybNdmhVSjoD/USbJyIT11/zAO2hdbZtjGdfEZpNUJERZ2obtLVLSevcdXZ3piE3kq4us9AeY70zxELUx/undii3wHGGGMKhsFPilFfAE+OwNcGo0NAtoz2f011JsTWLE4HaOoaIJW7g45zaS6nicW14kr73nOPjX/8qkQfamk9KcQupFW1dMln2EhAQgLYKdQHLepzn1WMMHe8w/GCPfZVzbAbTjPuX8UUHX3bQCB5EcFUUuUyJl6hT7kVoIQhc1wZn7PYMloT0sUiZJhtMouwEJkQoxrUOOqYMaVCJeP4nvpPC6jYTC3fwyx3WN/dxZK/La9vLLG9uc+HlRT683+V5r8pk9wB7D01xeeM1Hj3pc6Jzlb6lTeiFgCQtf9VpIe7ZgXsUYTRA9QdddhY0fxh+nMo+xeawpFTaYmFynOOvvcoDL71MpdXEd0C5RYJikUv3PkjsGCI2vL7I2ug4Y40brI8Oscebp1+vUXupzcNnX+XM0VOAGY9pruELxXztACvt29z3w4+izm5SeHOJvQMdvPILnJydZ0hu01dvUvM74Eq2IpfLrZhaFcrtGDeSxiMOMhEqoXpJiFfqHmLmtVf2WNATzDZuImKJrzSFdpt9K4uo0YN0/kuL2kcU0dgIEo0ixvhCm9nLkXszZzHcc7lCX8d4nSdSVhJ27ljG2bWCrpHmrWICE6kQui4lDUJodgZuM7f/eZAhBTyCMtx4vI++cx32vb6FyHveJSavhBETkHrKQ6aq2jU22f7bFZSdSvAiu28C9HmEuttGn8a93n3etKmtHqbDB2j2PYJf0Lxvrc57NqFi8451LU8WSeP8GVnFXRJIkLjuODGsDhv5YW0IhLJVBIcEQjt0fYVwmvSPXefWnjk2nZC9eLyfGuPkw/DMuzGoifTMXVQ5h6mJ+SbZo8nutGfs+AhHUexbBaDYv8KIfhMVlIi0y87OOO3OAK32EOurh62Ak3tIMryxi+NEVueQzJVhLJxiNz2XKt9yy4AE5HP+R8k6jZXDxk4fL795PyeOnGdsbAmBw2jv/8ak+mdwvKvBvqB6hN0qftgwu1sZdTmRyHnDahs6lqixs0mkJ2FHsFdfpT54m4n1dZ5Y/EOmwzvItUdhZL+VlOwOKOhMdWnce82mijX0yFSaQczWfIXI6VLQMUW3Qt0xoB3HIgVxRxqN5/QgrDYNHijbxmS9xDHMrcOUO8fU/j6rldgBAoha0NmA1hZ0NyGoQujBVoFGUVLyN4yd3omMgBQLow3AAenTUnuZrhaYrsWccLZMbKyVSApo0B0KskdKTbSDCV8qUlRFMwdSQ6OWag++5yWT8H6pv8CrsxO8dfQB9i6dp1fyuDbhc+C6YnSlRVm1LZG829XWEKk+ttkvryIXNPgOQsJY8YaZQoFVG0IdyQO6xUnd4vjNNUZu7aCEYLPoEzgOW6UCb04Ps9RXs0hgIKOk20xyi1Wm6FICIXORfwKX0D5L4I2M8OpgH8XZNn07gmO3K+x9SzM52M8fNZcoznb5j8uTnPB9Tu2/j15rjZVnVqkePooeW2anU0YVivRvbJhA3cQpJAzQjRaOW2Rkj0//jISlDrdrpyh1t/jqkSMEFcXc5ARf/9i3UdIhfTpAd+oMdWC7UCMSgkiD9GJixwTox1LyqP8nzEZnUedc3LUeqiORZQORPYqgHZxwhIdb+7gwcIuV0iAzjQL71n+Dwwce5ETxKtJVxhlJCrSWLMVTnDwUEmw28W5VaMsiVWfVhv6btWEYmtigbETKFGuLsM5OhOP1QMJSwafaCpjvKHbCOmUvoPzdQ0SlGA3Eqcd8AvWJT49x9uhrepx+Y5jxuVqaRjXRfrvSArwwLgReTtgF44v/1tprjFx+mUrxJsXyfsTkaVYPvIkSCheHIiYjU7cQ88L37EdcmGNfd5GsAE1OxEvt6Dlgz+NbWiQgLxbuZkNTsE6d9OyCTErVJqCeOOI5+dsk6OyYFNphEZRGSs324FFkUEcJ8GPzSn7hY5QsqcIhuQ0G5Is98EO4NgtX9httgLAWj8SaoRUU9BxHH/0TXCemJmLupchePCqoXb3M99p09242IGlbJu2bZuld5xM2PqvymHj6ZmMvCz08egwVrzGIJogd+ocvs7F2iG63D8/pUpA7tGpdRCFEuF0cmU9Ibe7t+CGDp16it/rtqNBNW5qEAqbx+yJDl6wf9l0I1rYGee2t+9izNsSJo2+lLOy36nhXgz1dwOlmrBVWZZ/ih5W8i2GOi7YzkmQSiaDKFn9z43+jsqYpx11wHNTCBswctMY+YbzdHWmz8wmz4l1tCFs3NgboQENPQVcxW73NqLeJ7/fhVIsINUbUHDJL39J6xzIMURiztKFptgLu7V9B6pBa5zpCx0RKIdcVFa+HG9u+qpbpm4ozsBQARfPeqzDWBZgyQO/ZLHiFtq29WQX6GfK8nFSgM5EoWYRp8Y3EC86qS1QAooeRk6yd1lIe11bEmF5vMbXeolXu59lPf4JOydxr6eQOp75xHi8wpV7dXsjo7RWcKAsWS95cEcMW8E3gIQn9IpceN9mShkq5Gk5EbWh2QMPkdstqSjXvvzLPYq3EpbF+NooFJJLxtXXWBoc5cuW/cnX0IE8f+wA7pTqhUyCWAsdGqRv2Q9J1PEplze3SMOuDMN8q8vDYHT700iShd579Jw+w0j7E58/8Lk985BE+Nd3jhdI9jOo5NtQ209ccaJn68ReGJxk8vMT49iJrvf1UhUaoCFxJubKFkApVHGCwp1muSAQukVcmJGJDlDnSWuB75l9hrTDARrHOoFqn6PXYcupcqsyiteZK6WFedx/i0KkrlP7oBs6OQpcNQbyuTnBoa5OBW31sz/jcG++hdP5NJj/2GOsH7qGzpelbn+Dw4CpuOULHxolqYGeDOxen8TcDxHCNOOhQFiCEMjZtITNqF2sIBJF0WN+ZIBiKqboSv3+V0c1VImq026PcurDN6raDPzPOquujPYEScSp4KSJUqvQ1rlySAk7s8MEz4wyulQgy/RVSgO8YgC9a232i1QazTS8vbXJg5w85eO73GYwXrFZewKseo5eHWf+fHiCoeZbpc3FReNNttr8Tgs87+M28pwcZyMu7yXyynhOnuwTwc/CWE9p3w4QdgUSDmJgLpc7+Tp8jQA6Zj5GXaQ3QFMKbVDtv0SvvsXn+c7e3j1NkZDPxuXSUsc+7EcxPwo0ZkElWOwvyUiuGp96gb+wSxWID1zHqyTEk44h0DjN9xt392/1XJudnCY4EScRM8q0BeWX7vvueiTnmbaOIQOM6iqGRa/QPXyVJghP0HHpIdDEkxiPEM9EJJB4D5n6FoVUmn/hj1l58jO6qsd3v4uvyHc0jftIxuzW2mnVa7SI7Yo6d1QrfyuNdDfalToBfTnQomJzxNhe8AWNlVqtjN6cW5vuuhJZjpCzLeapWlXN7H+TE2kWKFZfugQOUO8JkjHB1Nolgs8xpA6RBZJyokkB5W/pVOJqK3oL+LbP54jlcbxbEXjQ+QkiE0MjuCnL+RY60QqJIMbid2YuSmLwj/cp8Di3wijzAy8yH4B1oBTigbEKibtW0zy1YTiPRdiQOQIkEIbIxvfvQCnRSj9fD+A6k/OuuN4HAjyRCOwjhARqnMMgbH3kETYAiQMaageUtDr16hfHrS3hBdNcDBZy34/q4i0WX7Lu8L0JqE9UkcUNCCxytmd5uMb26DptrlklKdHCKw7cv8/iFZ+l4VV6dfT+vnzzJQrmCreVjeUOJ1h1EVKHla669v8fNuXuR/gajUZOxlTs8Pv4gH/qOI6wsD3FkSVN74xWGKptMyhickJau0KFFMNijPrADvstIY4Vu2EfUZyJvByq3KPtbNHeG6N8RrNVAuS4Kh1g6CK0oRpK+sEF/uANtDWUFsWZaL3F85xKguFSe5TdGP8VrR4dZbz9MoT8i0AKPiIM3N9l8bpv5zqNsqgBq8P7+FV5/4VmOXtvgzp4Heam5zc39RQq3D/CBe85zXVbYUWUKO1fo6Dp/tH6Q22tb3Ht6ik9Ez8O2Q6lYoddVOMLHEzHXOtO4cy7X1qv0RUtcvu8U8y84nLq/wUFvlYvXm+j6LNqpsBlfozV0AO35JDrpRJ43CUliwyA6HsrxGGgL6k3XsmSZr0vBNSBfkgbwEzms2w0pNd6ClZcZWHmZkeWXsu2SI9a1S5uMPL/AwsdmUsnTwyEQEeefGCYoCCbf2GHgZptyM07lh7d5v6NJq7OkD3kHGfZu/+jkq9T+rkmSFKXvqXAjMfmgi6ArmGTy2f5TokSn8jhx4UO4iasBmYJBY/kCyxskrH0oTXjdTs2A/Ea/1TjmnMel6DFy4AWGp87iODFZaaZMDk9iKN4xkx6kMr/MfS9y/+eelnVqFxtwN4G6W65OfmHaI60ZSAqVhtx6xQAHZf910WgUJjQ3pECMQ0CBWEiKQ6sMnXqJ1ZceI9gaNPfOMU2pgiWdO1LrTN6SE/kBG3svMRVcf1tb/yyPdzXYp4fGAHg78UjBbAw3NO+JJNgT0HEglOhAplLxW+5D/F77+/hEoUXhvjqUfOI6zA2uUqNDn1bgTENP0GhW0JHA8zcpaW2C4ytr4ATWHh5DswTLQ6ZtEvMc2UO413CdO6AH0DsxrG8hmnNIpehzrJExz3K/42Imx0FacE4SBaVEh2zBAZkxiZyEjtFSSMcEvUsni1Lw7mb/UzEidyhLXBL1mcqt9kzTsjZeoFv27UbL54BVQA/lwPrkAFtjDzOwvMXBV69SWlpkaCtkV17tyxqaCr7dN1Q9TWZixwDA1iHIYmIS4LfnvAIUy7SFotjpIJQh1kIL+loN+mjwiTd+lw+d/xz/+hM/zKU9h/K9xWORSici8MpEziTRTEQ4WmHx9nfRuLbFW9ubfKpbZnbhLH3tIvfVF4m8Mt2JAOceyUo4wdUdj/cOvEp5pw3VIir0WazV6Hd7eCiT98htIuIh9s1DtQlvHjR5HWLlIjTcifbQdstU4qaZw4YwqcychEFUHOjcYKS7wbI7wvipiMc2X6aqVuijwfh2D/d0RLz+e3w5PsZeR/LC9mkCfZvF9YjGwmWiD8Ol2xNMVka4dH6YOKzT6e/QHWox3YXCcI2RE1PEKwHzza+xvd5C9WYRNY9g4CCXXrhMf+hREXd4cb/HsfJ7Gd7cZtx5gfmLH+L80DEeOPUy5z/3IpcGP0TfvbMUC2W6WlFdL9Hp66E8jU6TjUrKjQ32Xu5wxnNRL1xn+d5PM94+jG9N3Z5rQN5UXEvM2Rox9xSbrz7JQPdFSnGXerJukjWeMMXSKJXHvzLHyqPjxDXPas0FLhBIxcUPDXPhfYOEW21mLrR58KkNCh1FuRGThiSma5BsvSeeWyLZj7x9e6dOdon0bhn73aXcQFRAlkAU02fpOErvFzsjhN4BWqUnCL19gJv5FGK2eegZN6Y0aZg2SoGVAVgcxuR38KzspDJmSGhNeega1ZGL1IevEcqkyPLd1CJJI5MpD+1o547M3/6dQDqjJBk9zOBc77ouObf7u+wO2sJ41kLzl9zV6kSHEGNyM4ZoNEUgwkfhUJ7YIL4/ZvW196F2BoyZK0ngleM1EveLXXQYS460srUs3kma+rM73t1gn8xlKIynCZDqeROQTxz0QkwWL21++Lz/IV6V70UK2JQj1Pod9tQvIAsRndgB5wZT3rL5rXJgsw7Xj1DtlNCRolMFRldg6gbIMPdsTNz7Zt3sqIRljjXGJSgC2ojlRWjGpPEaCSCL/NbILYZddQNsHxNwT6T6dN0mTIC9R1o2Kn9N7rxSWfyNFBA6JmmPFGSphhNQFbvHXid/qBwRM0AbFCSvHwqst7OLwEUQYfhqo4xLNrpyXDYmx3lpYpyd5jxP/MmbjM9t2i4Iw6TMCdRLIO51U/OwsSTY5/aXTC3OSBlRJFawE4AyOdA1ErlnFDVSZKPRRa21KLbbxJ0CTimmvrOCQFMIOzzx5tcpuoKNvhJBXETLGL/SolBfxQcUC3gKjpwfpLgvYKvjIV66Rvl2l8FuiBjRqFKEOuijh12kVOxjnj39DyA3aqAbEESoUIPn44sdQOFIRbHQBIzadHQNRguw0S/QnunqRsnnmfHjfHT+RZxIIZLCT7UEIEylMSeKQWiOd9/iIztPmTKtAmOW6ml6nsf2cpX1/jLj9wj6xxsMXFilunaKZ998iY8/fJ3O8gS3zt/Lw9WzvPjUVWa/q0IrhuUza6jKi4SzD/KV/nv49L5Vbr85ji/voX/xFvuPfTfbA3fo3KgTz99hcfASt5YnOTk0xFDfANVii/nFCSoHPNbPxpz0awjVj56I2OrNsxgLvJ6kXZWMNxVLZYUIJpm61mC/W2DtziXETB3XSvDSAnxZQlmY5LpCabZ3Pk/1rX/LVOPOXRK4fU8EAWS6F0vLHWpn1ln98BQBUEBZwLKr1XPwRyosDZf52rEq5a2IR/44oDK3iteMESq3R5K8FMn+UOkX2QYSOnO2cy3TZgsbpcnBJJgCH4NGms9nidFGnGz0/xABk0TuJFrWTIu1RkhFJCQrdQP0sQtrAyaD8cgGrPdbVyQXOgUzFEma3ESRmGQUHJh9jtrU60g3MIrRlCUXiTCLg0yldUmW/dCMQhJiJ3K/zghTAu46Iy7pWP23zACZZmB3nEbetc9AvbA+AsqKJ8qKPVZIS53/tL0CEs8Al65tkWZ8z0sMjJxn8+qDrJ3/NkzuXjIymZvdhLcTlszqGJQqonoV0gQh36LjXQ72wqjueyIn5eqMG06APsYwBLYM6y1xiFflY1x1jlFTbQbQfM/Ic0zU18GBkhMaE0CCjM0avDUFbd/YJ4mobBZgaxgKRRiJM+kSa2csBbibIcytwU7HMAw6V5w9sLbudM0mQKlywv1dC15YogEZ0Upsefl9kABhQkjS35MxBrt4CpWtxNBKxD3r2iwdo/L3CuazsO3Iq8zRGJNAch6IBQuHCsTjQ2TsbcLzhzneJPGXFwg8EFCvzXLxo4MM/6c/wevtTtiyNq8o9Ul0v09d99LCPOl6mBkwAxBrA/iBie3reZquo+n3NVUJxVEPPVkkbvdxvrePg9EdehcCdKtLUfQ4ujzH0ee/RLvkEsQeXzyxl7XymlEqAMQBhduaU//5Ar5XpFSA7cF+yn1N2iMtOOpSjTUl2TXJBaxreNw4igxfso6dMUpptnAZF9YYCiAU2gURmOmb3IBGUv7XA3zJ06WTdPdpPrjwMgNLXWhp6GoYUOAb/4eZ3m0G1Cqf3PkTk4MikTw1qFhw3Z/g2p0vs9M7RLXvHoauLLPZ61KYvM7x2Q/z+s1XmI56PLzvZd6I70ftu0UZeLIX8R37z/LW9GGGujdZimZ46dohZNXjfnWOqyPHWVy9SKnRR+2Yw/Ejs0RzNba8JSblAYrh69zYO8XE2bcotULiwgeYc1a5FL5JuW8KFZeZvRmyPOsyorsIL0Z0KvQcF6/vMCeDGjP3PYJoCxwJfdZG74hMAG7GXXrn/iujl34REfd2A/2ugGi7EqWTaowEwHbIBpIeCo3ExyOwPis+4CGRQtAbLtAd8vjqj/dRXS9w+Oltps808Joq275pbujcdr6bGXeU0ai5GIYtQdkE5CmBrloakpGMQPoETpHrQ8do1e+nWRlKS4QIDKB3ppfZbE/QKZDm2EoiFTaGSKsM7nI2S9pqBVApQ8rD16hOvYFwA9u1DMiTsbSVCizcJ1kuBB6JpiUP8omSPyV45AE/39Eclu56XvbrzJCQ3SUbcAPhMRqJgyK2ivsk12RsRQ9FouI3LIGA9EzCMGihcUtNhu99hrA1yPatB+waYpdaH8hSXysTLGVIskdw4wGCW/9/yf6//+iJLJQpsWkRW0lVZRst1vazQ1eX+W3511hW0xxqXODPz/0O4dEjTEXY0q7YXVIDvQmBD288CM0qxgU1URfEgAvn98D9l2AQdAjdhSLiqqLX2aBvcdtmjoFU3a1d88I6w6XJx+2R7rL835AFjsuUPmUewImEkPzIAjaQqg7vBv385+T7RDKPLFAmZgkhTWIX6YDrgZM45lmHmHwauyS8TwnWRwr2uXnzhOGsJRLrC2y/9wHX9lyxVfNYGC2w90YvY5AEjC6B2g7RtRjqmm5d0BwpMtxtm/rDRWkk16TCl3ABTRFFUcTmcZHC1RpciVfzOFFZxEGyFJ6idUOzg+YB9w5CCcrbIdJTfOL8Gl9ZmqR34DYIqPQOE/5KgeXB19lXixBDDlW/gUTjU4COXY+uZTqVQqk+omgUp1tHloFYI3XMaGUHmbpHCYZrt5lfO472BE7HxDcnBDiZulhIznuz7Az6fGL9FUYbGwbw29DcI6jIgI/uPIUjQjzC/EKCWNPQJZ7/3Cpd5xaduQXKV2+gpie5fOUMhckp9u7Zy8DQYZbdMl9aLDO91eXytR/mgQMvcu/NHjdOS4ZeusD1yz2+d2IN3TfAWwceZuPWCuHSDeJbgujAEDdDzUhvgMnCbY7MbuNUaqwVJlDeIK/ID9Nc+XWKD57nXhEycOEaTw6UOVCZYGOngXShS4VNt0xU6NEohYyMO1RvTqI9M1pFaVZOMVldYRux9E2inT9g4NIriDh4O9CLbC0aKp6sL5nus6HrWzhBjPYVIZoeAmVhokmET0TdpvwRQqL8iOZEgbe+d4y1940wdqWH34oZeXIJp2lR1EliA0VuvxqNX1ZeOqkCY+YrcGpoNUIgKrS9Khv+OFcGjqckYq0yzkJtll61CEXXmPBtLg8H8LTAZwJZJYvKE1lej8T9R1t+JJGZci4tuMVtBvY/S3n4GtIxi1HnXnl6pe3uJs2LkNnyE7W5R8L6C3u9SP/ezYTlyVQm+OwmX/lkPHKXuSBrWSKvy9Q6Ly2wmxmVuW+0hX/DAiRPzqz8me4BqajvfYnNq6dwinfTcHNpovhM6guYRgnaNx4gat3gW3m8u8E+JnUIN1yw9RZPk9qTBYwioOugwgLfrv+A7XY/jyw9g6oP4BRiRFA2aW0ds9F06IHyEZEPbWO1MfeO7A6xrNrNW7AQQr+GniLc8aj3dkyOtlR7nuib8y9b3cQuVZU40KLRGubDfrZVGQEUnZB9pTWud0cZcJsMem2ksFskBfLkYfa1q8Bz8lllIK4S1aB55l17Nj2dnkyyg6VVa1zSKnmJUU87RoNB4pGSqMMUWKcXYU0ZAolDEZ1UrcFH5dJu4FXRfTOgr5MV8jF9k4GG7Rg6mkIjorDWttqNKAN5KUzZwIprqZ4wcY6OlZbStH0Kz0rUkwOb6JJgY6WfxWgC4ZbpxTXq8WVqS2t85601eMOOTeEsWw+V6G8rhFcAKXCVNgyQdK3juNUwSMN86rhEpAeQ/j24+i1QGicKGPdXUSbYEYDx2hVuVFZoBmO4DhRUlrI07kF3BfxJWGOQtfoA7ozkL6w8hduJIBQ03H7iWkhfcd1wCnn7tII4EpxT44zfs8jMhc/QK97Gf7xLXRa4f/YRxuQhnnruaxTqH2aw+zLdlRd5pn2HwyO3iYamab32Bte+fIyvXfom3/1XP8SVhRqnw8vsv/wGr6/fz/bIEPKD47S3y3SH4GYY0+toPqjXqUQRe3XM2fU1vvylczy05yidr1zh+Sc+xMoHTlAY3WZe9NAP+iC0BSGTv1VpRaPZpSpJo17rUuNZiZ6wjXzjHyPmnmI4tlkHU6bYMtd5/xOdiMA5ad8OVXUxIFgdR03MI6S2MoBZtRGaDhFtQmpRhQ9/87u5fW9AY3iJwCnRnbrM1tQWjob2vXX8zZD+r69TudgFZJYlUWBt8y4UNfgRypPcqezjG6PfTsut0fX7ELh0RZmGHEYHDlrL1IwlBGnhLuFmyjdH2/BDMnceqWxiPUhLaWhLDlKrQE4gFkSURq8yePBpPL9lrR45RskOljWaWFne2fW9SDtqfhsDSzQ5xzITVBihzCR1fAvUmazOO/yf2ezzpZES+E3C+JL3fAsNLdIWxrX93/wl3gHsJbFtScKKxMSWliUsh0BTHl6lPL5Ed2tyt6U123EpyOdTK8iEZH8Lj3c32Ker0wK9yHlZK33XYBrqUOm1ONF5HXYM2xUpifCLlpLanSMEm24BH59Sz8ERNiGPCA0BjwIIerA1D91t6CpomOxtdXbInMKkBXQfY2fzrTRsdLKR7zG3p85qs8LFxhgL8wPEStLQRbaiMp24YDyMZcT+fevcXBqm5vSou10eqN6iLAMiT4CnOOIvEWro4LEZlzjiLeJqk+t7OfLZjF1mCi0qogsRFAkJ8Sg6YW6R6mxlJkdCKBMiqcnGGIWtuGOYIGx+Ups+V+oArYsgknhpifFZSBgTidQCN4qBkEi5uH6TSBTN1tTaAn3i14DR2ESY+Y2SHOcRaX1goa1/BNCLoRkZpCy6NpWsNu+ByJiIxOYfC0QgGHKacP0KTOxlp6gotct4CUGWLkn948FObLIdpWlNyUmN2rbJaj10lGAty06B2YS6BspGDCRSEbgyoOR26AjAMfnIRzswX4BrvwaNq+D2QfkwjH+X4NXpe6h3tvjEyy/gxpqJRsvEUlWq4DQsZTdNWukM0RdtEns9ZHuM+YdGGCuVuKIXmJtf58joATb9BYLrtzjwbQ2+9tWvcnpkBNVR1I9OMt8vuTY+xNa5K8Tj9xHeDDgSr0JdM+Ktc/TwNi+2zhG+OsaZaD+fub3E6vQxzt8a47mFfr7nozscG16ishXyXX/9u/ji157nyPse45VLC3QenwZpvO8T7yYzM7ZYqHBoV/fjbBvQ2ioF1LVLpa0Jox5br/87Rpa+hJDarBEwIO4kQG/nKY3gENaQnCCdOQKnyNfHf4Q7zzyBM3ad/oc/j1NpIkUWipmUD6ZVYO+bx6huKl74xARhIWaeGZrcYlqcpTur6c1C+0SF+qvb+EsB9RfayK42FSyTGrxexML4MF8Z+j7O1x+mJ0sJKTKa/R6IjuWzVdaVVEGorWqezEKXuAEkLgEeRoGQLAcEu3YjpPw0yIjq3meoTL5OLLRlcxJANeWTY6BjZeae1UopIm7SYQhFCZc+ShRwuMwGJTwiFK+xQETERdYAGKCAg+AAAwxSZj9DeDgUcNPMCnmZPgH3u1kKScYoJGed9FeJGl5aiV5bA01sz+gc2Me2t7F9JU8x0n7maKxx3ZDx+77M3CvfierUzZrIKSg0GSOV6iY0u1wuvlXHuxvswYKzzu0C2KWyTg4BFCOTPhcMCLslXOVBEJr0dqkWQFNRPViH5lcL9PmLUBu0kqOE9g4s3gDVI5WKc6obAyIJyBes2t4xrDeSDkW+Wd3PBca4fGOU5nqJqOekarNUY2/vGSqXKzfGcAQ04zJtVebJzUEAGmXoFjXFTmj5TkGkHSrSRB8LAaE0xKmkFI5QSAHHi3eYD4Z5yLvKQ95VMi+SDAT7RdeovoXLLmOUdWxSJUlwvICzpnCvJLXCIaG0hy+ssDksUEUFuHT8OspJUhuCHwZMLV3m0M0zCK1Yj6dZenSQsOgRoWkPbbIx41Gfj3HjhEeWu1OTxq5BQ0/v4lHSnRUoE2LZtgCS5CIGSxFF2p/UNyAWMHkMtKDe6YHwjKt30vd0bkQqGUbCQ2iTbNeMoTXfJIbklDENGaGctk8EyjAejjBzb3WcjitM3SE77AdXYWkRdi5B3IFgG7YvQ3sdJv+c5Kn+k+yZXGEyWMd1NQOybVI3Nysmz4IXgIiJIs0fi8Msf+1pPvGe06jSZSbCBqW+w5y9GvGHg11+1Kky9dDDTFab/IVHHuNc3wEeu73JRmGUxpNf4VHnMS7MtqnWt7ghbtCadii3XeKmgsYWp2vQWnqBW8uw/JeqePRzH1/nS+c28PvHaT/isVWdolEIefTxQ0xu3SK4f4ozIkmin8y1EV8NcXcQWlLtlvClSebU8HZobngsNB3aN57h5OrvGifEZBnKHNAnYK/vAvw0msPMZegW+eLBH+el0U8SdR30zSP0VqYYeOiLlGYuI7weSTGXtHK5FozeKjJ+s8jte3ZQOGwwS4sR7rnzNOG4xvMCGg8rpA7Y+lAZt6EZ/f1tnFaM9mJkC7488v283v++tC1CQl8UE2iJDgwyJ6WgLe9sAM2zNTXskk6WubRD4MaG9Pkqc9lB7OZxBDmyqTWFocuUJ19HWc7CMBBGzt1BsISiTZLLjtShDTQODiuEdGkRskKMoklEhM79yzbqOh1As0IDCZTxcJHsZ4gp+vBwqFFCUKVOQN3WttBkPgDawrLpS1aiOXHrc9JeShIHvDgFf20/J+p7B8eq+B0iIhz7HhNawSPphRCC6sgtDn7w1+jeup+5m6eIurWUTiTFuhL9ZtIMBdyJs2ifb8Xxrgb762oPJ/UCuwsmgNYmDWakPe70DvB65zE21QgztVsUZkvce+FphpzQqF8RsHkLyn3g+2YHSAh6Dt4LDn3b2zBYAFEEIYilB5vLKBHSKWvqHUFSBUsj2KpKBho+4KOtW4oWlieuBuhCzHPVUX79ufegEjWiPZIourwKKtmYdm9n9in7O7cOcSho4u+KRtuhuKsippDGpJucOxPdg5DwR71B/qT3IKCzCDZ7oz1inTpdkCIzNdpnDDstDt+7zujJJt71Dn03NU7kkBW/Bm8u4P5Lz9HnzUGs2SyPEToexBEiiil2d+hrrhhpX2gmxBX6zlWZ2z/F1kAfjQerrD5Qpu9OxNj5gIk3Oul2TjUQMbBRhP4ueHHOh8EeybJISv66FkgyBUvGJCjszkwoYe45eVY8z1TEGiLNdTHBLAs4iRkptn4jRZEyUZINXOdNVJTMJuBIRGxzPliNhPShuW0bbxO1FHpwuAOvYoINtDLZFRe+CPNfBigz95mPcegne/hdh09eOMOprcsUg9jkQA0kS1GFZ87vZ2DwRaZPPMFXO1cY3VggHvAY6hQ4NjxB5YM7bH6lyXyfQ0WWUWsfZu/IF7mxL2Y0nuKFwj0MVIY4fKXKPeMlluVBhju/CcIjiF3UtqZYj6D2Xj68d5nZ9iBnnW1KU5N86m9I4rObbHRD1jf7OVK7zcb8Xmr9JfwpifQkKi0wDXkSrS0wh5Um5e0ic+FVDpz5NZbXO5w9+cOcuG+R3tYk3s5VRKytY6kF+zy451/J5pLCpLFwinxh/9/gpdFvJ4qlzQ4nCHfqrH7leyiM3qF+8jnkzCUiJyBCM3P7EE7sIrVg75kBvukVKfZ1KPstBlohm737WQx9PC+gwhYVsUqtukKl0mD5xwatNsfHW4gYl4tML98gVi69ToWx5Sqf3lnl/HiDxWibG9X9hI0acVhEeiB9geOb9eJKo3yUDobplJpadQNXxLTmBEV/AKGclD5oKYxlToAUIsufhaZQm6O+/2l6QrOOSw/BENBEEAANy+SIXbOTKOHNOZ8CBfqICAjo4tIgIqJHQBdFaPUJia4k+Z1C07QOvG+wwFsYf5qkikUBh/uZZogyNQqMUb+rakLCKGabNGMbM0ZAWkBPtGmJaj95T2z2HpIYRYhLRIxLRIhDTExkrfpaCJxSm/I9L7Bv7wW2bp+gtT5DHBXo7Awbpz4lUuuysHPwZjFfl/fP/nhXg/0/iX6a/4N/ymi8xnzpGJO9S6DhubWPcLlxH1vRMMvhFEpKhCt4s/cQYg1ecE/yGfmH7BNzUJUm4XNzAxgB7dCWLqvRDnub0K6OU64VgNAQjqkWTJYQyjF+ausKlgKW45jBLYFTiKG/B9UOm24BT8P8uOKe9QCKAUIo3tNdZ2VfnVZQItEM3NmZ4M7OFDrOCJwAHFchlMARIith7Zq87cLVxMJBaZGl/BcghTZSoa1qZfyONI5rgNxJkj3YH8SJp3DGnANwXY2aTFlWUkh9CQUQab7xrObQxjptJ+BBZ5XTapmKjnC0QEvB9Uc0lb6Ivk2No2OGdxZshTQNKjYvnTj6KYSGyk6L/RducHt2ijvjI3Rcn3Cfz9KeIvFOyPTNiLstcnSK0PVNlkAvhlJgdJUFq8sNHGi5xpDp6yyCIbD6zMSoqfU76DUtMCdgn1e7JBXPtOIQlxGJlgB7L7D+h2aXC8CTb9CN9huRS2mU65ukQ6GASCOkZmFjiC/8lxIf+EgP6RUR2tRPmOiH4WnY3oCtOeNiojToyDzz5h8X6H9wk6H3D/MHpz/I+o0a3375m6AFm60hXho4waXiRQ6LCXSlStBxGJgcY0X1GFhYZvO+eaJ/67EzOcj0jTWOD3S4NHmM9aUer9+Y4EFvhZ2hMq9Hdxi5vE6weIkDj08heiAcSdULqA7Mo1eL4G2wv6m4uqmhWKDdWMV7fIioLHjzlmbo/CJnH9xHo/4gW+XnOXOzBTOukeBJYqJ1OtcaiR9BeWOdVrTIxI1/jHfnAlP4HL06TrNQZJ46h2PHFElJypCleWDtKzVT5TYM0PXq/PqRf8jNvhOoSKaEOWEWdezQndtLb2EPevpX6Pv4FYTjsff2IYS2ZoZtj4tnXbpujb0zO+yfvMLGAQ9XmGQtTSq0KLHGBJPiJmPuMkkNuHDGYZ++xZ6pW2igtTJNceswtyYDRh57nf7lfqqdDVrFLluhz625aYYqw0Sb08bhLtYIbwVX91MshhycfYl6bQUhYtQh6LXqaJ24pUEQ14haPo21vbSbwyZcUEOxf4mhI1+i43W5hsR6S7CNAcpMnS7IwF3eNZrY7wSSEkXKlBhAEdBlm1BH7NClKRrk897vvoO2pU2Mqt014hIxMc9xAzAugIOUGabKLANU8BigyAS1FO4zXW2mJ9JkyX6SpydeB+Z5WVRBIu07VuEf4xDhEqHoEdloDeuUKARuqcfQ4ZcZ4mV07BI0hli4+DjtlTGU1ZCma6s7ybfyeFeDfVuX+XftH+Hozh1evvE+/NiUNu3EVQJdSGc60dIJTM3jRbGH12a/jb1bv4usWMkrUiY5fREKoWZsWaGKHt5AwSbYjmAswCnbynnKx9XAqITRCjVHcmmjn3uXXrDpu2DQC0BqjrQ1lGxjtKbqt/n+k58z5twYOmGBrZ7m3138f3Bt6WByGVpC9dFbFObLTOzbwStG+OWA2mSII1uUxtrcnhtn7VI/tcntlL/ubFUZnGmw0RxGxJpWo8zQ4CZBr8z15ybNuFiJNt1kOcIm7N/a4uIuRjmV8I2b3flzIygNN+JJvqh7HClsc5+/wtH6HEtHemh/Dyv9BU5ev2ls88m9knoDidSMDTVC44Ux+6/ewd/q8Ob+GTqlIgUXNh4ZYGR7i8JOEstsPV6S+gXaMaGDYcE0WqqsHkASOKxDEDZ9sGNDJl2ddTgtwWcXTvJKBiDhuJJJshySEGTAnlyXWHNI+gkFdRO/fNMwICqm7fdTiH2TOVADrmZ7s8yVhWHuaUomBm2eQg2FEjz0EFxtwY3noX2ePHUl3HE4+3Nj7P/+Bnt+wOfVycN84MarlIOAp1cGKF1f4uT7JK0XD/PUlUMMvGcdx71KZzhkc2M/hbktlOpj273NhUshh+6ZRpy+RrQ4wz0HK9SjTbznWgx81wzTn9pHeSGm3OlBbIv8CIdbGw0G2g51pw2+y+jiFb5ySPHe8RjvmQ5z9ZMUhl7i4Ngar59ZYc94h3P3O/gnjxEhUy9pkVuUidRY7RR4ZWmZwtq/4/Cti9YvNOL++d8iVmVklF8XVjuT97x/B0hCw3z5IF+d+etcLR5HR0Y3nubAyUljWgv2ti7xyRdeZGu1w/nHHmf25pE0aefLZejYH9aKG3SGFFI0cS0HKXDQSCQxXQaQNDDwY9IBSyFw0YRoSmN36H37TaJOwI7wWDzQYX15k4HxJaa3q7T3v4B68UO4bkh9cIs+BFtHv0ChPcv4UEjJBUe4mSRb3QYSlzRFiVVEv2Zw/DKNyEcHLhqFV9pBuz2WUXTsPOxOVWOgMMlJl4yr2SEyfScF1XyW+RJlyoCgP6qy492mR0CPNg22SaLeSX9t3hOJ28k9PblmhSYrNDnPEi6SIi73Mc4hhpigShU/13ZhPQtymr90jSW907mrk/+TfhgNg4PEQyOshB+nLINlG6zjg+NGFKvreMML1O7AhjuF1zFpTPDgbUXZ/oyPdzXYowXzG0dY2z4CGtoUd6vF7X/5BHKJebbZ6kP3e6RBpklxiTjCCTWVgqJ9vEihGJrvyok3usiAMZGEgxB3c4vZ1m0TzxxD6umbZy3TdpvFv+J6PHnlo1xoHWQ7XGMz3Iuo2zbHgKvZPx3x2Pc9jVuIrEp+tz/s8Mg6+qQER6cbW8cOUoIWV6x0Iuj2Snzl996PjZzJouXubmNOcE1Jo8gE2l3uEHkXCS1oqCIvdYpcD2u09il6nTu4RU2xv0hweJCwG+MttnA2u5YGJ177lkCjSaIpBDC9vo4XK17dcy+iP6Y96vDm945x+w9PsNXxwRFMiRXG/VXu2VykKHv2vjrrhLRhjsoCvgZUF8IuRNbhMtQWpHO1BlLOJzcoaX2FHAo4OmNaEk4pcXmGzHsqJZsxQvUwyVY01V4TeqUsAlHB2fMzxIFgYw3GB0njFYSAQxW4EcP0e2DlMsSJs71tTrDpculf99O+s8mRv+dzaWCa2oLHwekua8NjbL38BpUTDR4YuMbe1wQvSpeR6ycZ6rX5w+vDTM5d4+iBA4yMf51qqY/NczF7KSBmDrDQWuIHHwxYaK6xUD/LeuxQOXyIK3c8xjcb1JyblAaPU+2upwtmp7SP43sabJ6PETOa1dYCZ14aYOnoMeKtM2yXWugbAVPH4CpVsE5RZodEKViAoF3Y5lMX/ldGNxdYj4cZlus2CtbBicLM4TJB64SR3GWfz9Z7y+3nXP0xvjz+g2wUxlJN2C5eIMcEj3Vu8uH532A6WKT/Uj9d+SEIi2mY+opnHuVIxeGJO3giZopVRmimazzAMaYe40UDux+XPkwBylHEVckObYo0GJo0YYCNgS7jWuOfeI3G9quMF7eptct0Sm2a9de5iDGHDFJBIinhUcKjio9CUUDiJ9K406XktIkL1o6u4baOWRCph2OOQCQtTIA+n9EuD/y73efulrKFEGivTZ3h9I5d2qyxTEjPvgIyy755QiLpJ9Qie6L5XqHoEPIic7zCPAMUeYIDTFKjgp/6HaTyRq5HeVNwFlSoUvA38pFIST6YXAsuEKHQWpCU0EFLOptDbM3tp7EyTXt7DC2NT1ZUSi1z7+i9/2d5vKvBXmrje/QO6xGSUwl9Tio9WR+72tgyIomBB9IYljCGdgsKJcpuYAJ4PSdbGdY+T0GZqnlbbVhexu/1DA8pscVydOYKm9Tztv+vtAb42vKjfKV3mI2NSYQWSGbMOklMlo5m6tgKDz1yFrcY7FoZu1JZCBBOvrKUQDhWNtIKEQvktuStLx1n80o1ldS1tdehs3HCCsipd8tddHNX+u9EUZFzgk/OrUdlfv/qMQ5Uihx97AplJ0LXPFTVJxoq48038OcayK4lKGmyoGQLWhWuhrHtHR5pXkKqmOKWITIL7kFe9k+AFLwsDyMkTA2u8engecbUJiNs2TZbw3zCmIUClGckfbdoPN10zyyOFJBth5IwwzSiMelwDuCTLIQqmQj7rjGA7wvjI5BwVRpjx9eWCdHKeM137XN8Q2qmTl1FfO5R1hYc1D3QH1zB1ePg1ExCIAd0FfpnYO3qbkAyzRDMf2GA0uEOLx/vp/f6BN39a+x15zk2fABnTNG7sMLXLwomZscYqgesXrqf7/r4Jvq3XubIxhtMfPBneL2yzODcWQZaXRbqDeoLFRZLG1DZy6vRp7jv9jcQU8NcVVU2Z5sUbjWZEl2kdsB10FLi6XXEnT627uvjrS98DTkxyMSBB3mPXGX7VIVfv7lAbfgkviyn8pK2C1DgWfIaIeOIx7/+JYY2liHWNvpV2igackxjsj5zIJ/MSVr3AWJcvjzyA7w88DFabp/REliXk7url2mgFG3zfbf/DyaD6zzz0ON8fuyHmG5NcWjbbPM7PjxfNbzd2MAOfeUORQKGaVjNjFGlFVAm9QcSbdXGuUaDlWB9jCo58UoPiGkSskNAgCIS0BtewRmWrGjBEk2UwPq1Q4BijcYuuUSZlY6PSxmPGar04abQDNDpelzd8HGmjAI/k253H+Kuv3ZnxEs+y7edM718u2W9RJ0Z6gggJGCDZZps06aJtsxfxmIkfdI2QuAuZsL+Yo0O/4lzDFCkis/9jHGcETwkXmrpzyQdnWjp0DkJP3taYttPsuo5CApaEsce82/dz8bcrKWpgs7WsFnHrki1qLtSPGjwB8K3jeuf5fGuBvuRhlFvKteYf3exXbCLAUid1yzhvu67dAKPCjbpxkAhI9YF17iwDkfGuamojMd3X5itNqFhKYZzm8YZK/+8WKNjTaiqhF3B3NZ+lITpvut04jL/+vJnuNiZAizDgqZU64LW1PtaVPpajO5d4/Aj1/A948riWBKYLGwAt9cjlEXa7SLNXo31+Spaw+zBFQq1HkrBM597H3eer9PrFemvi0xLLTIpPTEZpHiVE4yBzO6fz/+js+u0dRhLh1tDFHjcunKQiD5OPPY5tKNBCIQQRHvqqP4S3q1t3LWW4arTXOIJx2E2mNCCQbVliLR2oS0Y9u6gWw+ZjWSdi+6IEX6l8An6dZO/Ir7KjFo1Vb4SzkUKk042ioz9XrnWw75MWslP9UzhJG1lNaUN4xYlQG25miSkTlgnvKTdyWAl0rwrMzYeYT30LaclrabBcQ3Y2ywoSjgcm4l44tjzXAkeR2uYbn6ZUqi4M/Aj9DtF6p7JDu2Vzc9q09Behqhtp8VO3pV/UWJl7Dg//alXuFkp0lfuMBVe587F09y6dIkDH3IY2DONv9piZ6pKz7+D99AAcy+s0pj9Iq53hNcaY/SFWxyZu8qNw/fBLQe1sc3DmxepPDDBOfU6Jw88wJ7Vef73F2b5iSNVSu5FZM2j0y3yjP84Y0vn6O3r4+BHn+D8f5rj2F6H5uQmKztH+OCBFmd3XChVMbKRJLOmGqJa6sZ8/I9/n8ef+m3cOAQNQ3o9S++cLNyE4idmnQS183iqoOPW+MbQn+PZwe8mkm46bfny8gl/AODpLn9h7p8xGC3x5IPfy5Njf44dt4+dKnwphPduw4s1CFzTnFa7SmOnTr20SakYUdAupWTpCFBIQiAS2rxDqsvQOfhPoLKAqb5XxKOPIgGKBhFNYnpALARhrpsSjUdW9DUB+8iObIRmi5BNtijiUcBl2LIX66UulSnjNJiHwd3wnknwGdAnufLy0nwe+HP7gAzok+t07lc+RcaZRRFbK/8a26zRpY1OTQuZDsTY13cn1Mkfm3TZpMs8DZ7hNnUKHGGIYwzTT2FXb5K7ZhEDd6cPyr4TmIROZ1/4MEu39qOVkz00L7cIcua87PuyvBuw/myPdzXYlwJwChYbAtKCDkmSiYzV0ylIDRfXeP/YsxyvnuOrW4/T0YNmXtZchnoNjgxdZ+z4LWQlNF5RomsIcwrylgrEwI3WbonPPIxYudwOP8D5jfewMl+kE3hEGlwZEiqJrzwOasOodKVm9tRNZj79KrgxwlEgFdLJsjZLTGb5xHE88RI+96UJzq8c586tcZSWRLEk1OBWAvYeWmJwoMnrz1VZvllBajheAD9JX5Xga7Jq75LgU/ppsUt6pKFgKd5bU0CsjWd4wmxpYcYnaDjcfnOUl8pHec/+i9SKIapgRCdV9QmOjqCWSvg31iFM6peRNSiluJYKj8RsrAzylesP04slTsG0C+uL1REeHdHPP9Wf5n55kw/zJjNi3ZAjX4InjZamro2U3wEixzoMepg8CAEUu5mPRaShFZuY/Z4yvh1pkZLYMnpW5kiTs2hr+nEM4KeaHQv4ifiYmBh62qY100T4FFz45A/e4p/9X0cJliRe1KHWvUmle55W6RRDBcGKBq8Ko0fh8HfDpc/D0plsuDRmjaxvBzxf2+TejZDpVsDXRJ3xwRVGHzjJ5ugE0wvgrb1Ew9+C6zHDjXH6TwuevvUxgs032bc/Ig4meHN9hOZDAfWpIvE3TyD2b1O9Weee6ePEL2iujy7w0UPz3K43eG1wH6/2jjIw/zpbnWc5fiSgf6PK5lKVvvcfwO1epW98nPkLUzwwP8dlERMJD7RCC4G0pWwLnRYTd67w3q9/ntNnnkaqkLROgrKkXSWLVNjPTgb0ec7UjsmGP8F/mvlfuFM6TCScdPm/42GXoHIF25Uhfnvf3+bloQdpOVXQ0JLw+SF4tQLzvnmsL6AYubz04kMUZ3c4cs8dSmhkrBDKQUphajJJA/oxBuwDAT20DR9LErkKm/wlsXo7OGh8FH34FIjpoumiCTCMQ2Cl0gidAr22AI8lWwk0KwQhmoiINiaRbP6VyMx5gFMWvRLwzvzonRyF4r/xru96T2cnbU8esB1cylQpU2OUPWyzzjoLdGmibHhvkqhH54QhM1J36xPMNTv0aBAwT4PnmWc/AxxjiBnqNrNfAvZmcSU0SWOYpTjHjglMgaSJsXlW5vYZ5XGesUz+1tnH/KEWC3wrj3c12Ce0NfGZinogtAHMurfDdHkeNAwX1zkxeA40TFSWqHgtBLBdHODNnZMAyC0QW5pnl07yowd/jfG+VYu0inyNQh2DjiTyQhvmTSmI/CzGwuWNxid5Y/Ex4sRL12pylHaMFhioAMWpgKkD15n6+Bs4hShdYoki0wWKdgu5lv/ttH3OnNnPuVdnWF0YQuEY2obl4iV0m0XOvz6L0hpCIwGGTZO511OYtJhWkk/4F3K0UQgbwmNB1HHs3052XUpG4xzuJSZvQVoBV0WSp55/Lxvb4/zA7J8gXJ351RUkRCWa0f3M3Z5hz+izVIsruRlO1N/CAKajeSM+zNXOHmOGVyBDg5nCs+2Tgp7weFEf5DU9y17W+KT/BvdW1gzwejKjCF0JbWWSIoVkknsxNPahhHUvOkaKDBS0IggUUeSgIvBUD5HGLdmJTt6TcmyOIA3pi63JQApM/gKZuo3oCLRwcIRkYrzL//y3v8xTvzlJX/9NJDFjjS9yx5slloNoDROPQsEHtwylEZN5UdkQHwFIJ+JvnnyKiQjWpyusVkMaLywyOTXF7d5efKfIll5g4exNDh9YovKY4lXuZ/XiBg9Wtll7rEzUq7B5JeLUyBnmFmfYeKPH0fUvsDl2nAuHBgi3zvHdpRK/9LVn+Mh7jzG9uc1ma5xHx77GncH38+3FC/zylQN8x9EuY3N3qIyNEVRLtMvrbE79JsPX4dHh9/NsXEE7HUy0tEuptc3jX/ovPPF7/95IVRKynK4y4wdJRGaSBbB7gdpLtJSsexP8xsz/ylz5MMpek6pVcyrWfPZcXIi8Av/l6N8kKAh6vsj2mt0r80VzC1dDVUAxMjBwbe0EMyPrHK6vmoQ8crf2S6CNcorM+zsGghTwjaI6zAFM4r6YeI0XLJ1Iik6HaAIgIM6BU6Il2C2XipzfeeJNKlIDQgbemSSt2C3Xcte75J0YgYRh2O3Nn0Gq2HXd7r+T/z18hplkiHE6NGmyyTrzdGmld0qgORmpxEZ/t04iOdqEnGOVt1jjBGPsocY4Zco4Vo2f2OyT0Dx29V6i8YTm8MFzCC04++ajxKGDip3dwK4zWTCRFcHQzW/l8a4G+/4QgoLRtCM0D9Ve5lj5PHvKcxTcHn3+jiUKOodkZqpiBKHKOHtD5wXNTomnXvgAJx77BgeqTbxChMAhCH08LyLq+TQvlBmfv2zU1dqlJapcbxzGkYKF9l6Weg/klkl2KG18wWINldkuB37oAn7tIg67XVo8RFIpPs0GHIWSxk6R//w7p3nz3B5cIUwiDcf6liU0zuKIcVIX6CJMjEPdM0Kt1pgkdzJTWwp4W0y+TNJ42xc5+ily12rTYKP5jqxrRAz0TPyv/QU77QZ07T2EdYhpGe68PLKCWrmXi3OfZnLwVSaGXiNNBZw81W6YnvLRwhIgnWlzhALhG/zUloB2tcclNYGWkgOVZyn+f9j77yjLrvu+F/zsfdLNlXPn3OgGGoEAA0iCYE6SacmkkmVJtp/o0fi90Ugaj708Y4/es2Vb7z3bz8uS59mWJVm0REtUIkWRIkGQIAkQIDIaDaDRubuqunLVrbrxpD1/7L3PObe6+Wx5hhxjLZ9et++tE/fZ4ff95Z8T53GESukoi5rSA9NNoY0eHOGbCZGSOfvZvKOeS78X8KvnPsy19jRvrzzNw9XHGXW2yK2H1nRQtCZKfb8kMUXWpRk424nmUwk0kRKC2ck2k3OSqOXhioRKeINKeIF95Qc4j8AJdEIVJWDk3gQ+7+hoRvPE4xMLVKea7Ph72B4O2L4oaI+O0nQShvZGKNq0pm4Qvf80n/36eWZuTHCofZPJvQnr7gJDrsP89jp/NqR4Z9ihf0Wxv5bS6ld4rrGfd/eu87wc5tMrz/CmQ6f5rS+d54MzJ3HenDLXGGf5+Zt8a6TBaOd5Vi6l3HUsQVTvoXTps6xuHePOzWN0722AO4YjbOpSoJfyvi98iof+5HcQaS6LahSW2Xyw3arFVZO5cUCcM+tewo3ScX5z3/9E053IgdwyuUX7WNGuajluB5TI13NGuM3f1j2jmkI50VGfADc3Zvndpz7OWLnJmcnX8YKU4OCL+LKVrfcxfIYJzP2EeaQ0sKq93vUUz6V7ga0aWWy2VuW7aLOgh0NMqu375N7mGBDLc9KLwpOtCcXm3suL2eh3dXaBPoWjRYC+9e9cPs5BvmiyKEJ8kSHYbQoQOFQZpsowo8yyyRJr3CCkk907j+XImQlJcRvULKTAC6zwPCvU8JmjxgNMUkbX6SyyLcU3cmyPCTh65EVmZy/TbjeYv3GY5vYozeYovX6ZRDiZQqo4fb+3cv0bHOxrCaQRrAUw7q/ysek/ou62cs6+OEKFAU4RPLH2IK82j+dVBi33peDsxTs4d/UwPiljw5tImbKyPsb4yBazLPB9w3/MSjzB+a2jXGodYdk/SGs9MGU2JWM2o6rKfbwSpU2zcQpIxdRDFyjVX8v4acuFWvqiF6xeXnHo8MRTB/nMH9xDt+chLeNi8rc40ki3BRC2xS4mS1C1/oWmC0wivyylftZdRbAvaEKLtHP3mi6eb+1SqcE3Yem0gtpOTU96UoRICT0IYn1QujHH7vg6T37zh3nl+vez0drP7NgL1Kqr+G7XPE+QppJvLd0zOKzk45a5RRckKAFc7o/zTGc/D05cQ7hmwGOyssOIFAIBVV+r9ntKq+1VCo7Jt28d+CR0CXipdZR2Uub3dz7MV1tv5Sdrn+ZM6Ty+VPQ92JhxGfPA75uJkGVpU7lviVtUOZtdCpSpvOYIxehcl1eePMh9o68CMNr6BtvuPUjXJ2zp4oTeBPiuS3kCduY1P5EoKB/yKcc7HKsPEwYXCCYU13ZGeOT3X+bjHz/K6sgIvaWAA/43Obr/CK+U+7RqDtQlBy99k19+NeLkW95H42KLvdVZDr30O5TCHtGox7nD+/jS/hr15+9lT3OLaiJ57wOK/oES41vP8shGg7eXG7RakpmZHW6M7qO3kdKqX+Ur3ruprPb59tda/OL3+ZzuX+Tp9DAKKPU6HP/C13jg0T9BJFZNhJHmXdOHduQFujygZxC7ME/NZL2m9vLtqY9xffhutkvjuRbLKuuKiAkZMc6kfAwDnWhlT79k1ldqlDeOXmtBmmW91YEaWEWOx1p7nK8vjFMub/Pm2ecRlRiMsWKbPiViyrh4SKq4hKSERirXddoyG2LWyGLwWx7ips/SJboUMSkuipDUwLd1f1S4WO/2IhDnKWas33qRHbCQax0o89Q6g+55EugSskQPFx3S5+NlIXRlXFZpkZAS4DJCGc+8/+2ZhHxf8ZdPiSkOMMYcTVZpskKXbWJ62RvZUjZWu5FL+kU2w/4laRHzOttcpc0sFe5kiCmC7Do7yyTpQEsdqajVtqjVmkxN3iBJXJLE5ebSfuaX97G8Ocd2NITKfHhuVet/t7c3NNgDlBLYIxa5p/44Faejpb7/I6BXkn7s81zzAZT0M5DPqjyZEUj6Ae0U2t2ynnYSFlbK3FTTLK2MspLMEaU+IBAmssYR2pS4YWpCKwzYK+3/pbp64o0fXGXk+HmTKKLIQ+expNY3bCVM+fIf3ss3v3mExNgpE8jT7xsfMQdsyvYMhEse1Nyc+BRnp80VY00AxcO2x5SrbfxJDO0e1B3YMIEBrRY0GjDRiHFdRdv1MrAVpn1EWnhVAtYbMd/e7+GicFCEPgz1tTI0iHXaydIHvkHzDz/KlaUHubL0VkYbV7jvxG9T8i0DJ3TWwWLcu5XMTF9kFNC8owIi4fBHy6eY27fBAc+E5/WFKeebaCqdxbc52nkv9rQzXyq0vk0ZFQJ6MmQBBI5gk1H+ReeT/A3+kDPTz3DhoRoLJ8vc9XrI3stGKsg61wA+wthGzAGpuTMnEbq4uLn/vXcs8tXn51C8ilBQ6V/hePOLPDP6/aw9D8EMjEyBWwO/ZnwABYxWNoi8syyPf4hKpcJQeBN1c45TezrsuXeHr786z91TU3RGA2rLEfuHb3Bxc47x515iNB2j03UIvQ8jX3mEH37HLLNhC/dVeHLfXyY+fJLWnMOLBxRvXgAp384LC4rjyTyxvMhLh9/J0QtlVuIb7J9d4PLEGeYWG3SOlTnUf4nnNo6xd3/E0smEicoiXxp7B4lf5silx/nwn/5L5i69hgyjvF8QGHVIPpGFRJd4c/JJnE10PRkS4fDk7A/y9PiHUJ7WmBQ1VYjc5G9zSrip1hQW6iRlcoObQD8AEWg+zcNkQ7ZaMKFwI6UHIBYIG+hhhrNU61ByewgU66SsGgu4hVQJBIhMGjdKOKpGS1jDxdcJmXGMS1ruOpYDoVa+K1wkrgH2CK3i91C2pJdR82clvsgV7Yl5oq1Mr1s3yHQUY4KK4cB6n4eLS8oiG0QkJvlMkQXPNQPXaVLGo0HAOFXqlMhd9kT2hPzpeTIfAJcSY+xjjH1E9OmwxSYLdNk0bJN+rnUrsqAtCzBvW2TfJERxlQ7z9JkkoI7DaWqM4pkcjynbpCaaobjAtTbJcSMcN+LAvteZ23uB5eY0X3rq4zkfOqhc+J5sb2ywV5rAv6n8PLIi+I3tn+bOoXOcSp+j4nS0HT9xcExoWoLgD698nMuvH6Q9VUd5DEj0WfiYrSOd5kiY+/sIFtNDmlYXvUkM0Dc9WJvQJl5b5UgBw4vg97TZ+Pg7X8fxbcwgmZ3OWoriVLAVwou/d4rLZ8dYbw1RrkT0WkHuVEduZnaExiKpcnU8Anp9WBUwVtbn2GdJw4A4Uv+dpBrQoxjaoX6vfmxeX0EYQqejk9O1emQlCKSAelkyO5kyOsxAnyiDj9oxWlGf3KRTBk/opeqi2HJlxjErQFTXKB2+Tue1QygcVpqHefL8j3L/8d+lUtpGSsXHDj3K/37uE/RUkPV7tm4sLTErulibe7Nf5l+8+CD/l7ue40B9S4fPRSpf/dlwCB2TLwNomcGT9mZ6wgVeykxlk4utcqbVSJXDn/XP4D90mc1TmiDcmAuYvdrHTdAdmVjkMPezYG+4faGUdrNPhW5fKig7CQ++ZwOedaAXGdhbQaqU2l7JjcegegREBOka2qNfbfPQmS8w4nQITs4TOxHd/hyTsynzY23ubx/jyMw2V7YO4fauUJmY5PWtUfy5ceTJBu5Wi6jZ4ERlleE12DsXI2jz2sffzJUXPsJwq8Pa8DY4XSilyOoQkyeeY+7q84zfdDiR9KhvHuWL/Qd4pFNl784CM/ed5dWX76B57irvf8cQ22MVvn/7KeqVaVqiTKPZ5Ic+8w8ZXls0+YALi87WXrV57YUHspSPSRZWAjapRleU+a09/w9eG3kLqR9kWXNlAegtn+UqrcXxpINrnObsuhAYPs9oAvwYlG/Wk9UOGMZ+n7/Euyef4NEL72Vja0jPKakXXKO6yYcO/z6NaIObfpW2kdot8FiwDI3ax80QAToGKNuEGelzCvQiwDMQJAiMzd3CroMsWOFVFsyYmikfowY+ucJfa72sXb/IVuTyLais9RY0tatdCcFBhthDnRjFAjus02WHkChzE9R3UCg6hHQJWWaHEi4BLmNUqeLTMAWM0+wJZM+UhVQ7IPDx8KkzzD4iusT0aTLPDsuEdEhN7gY9S3bX8Mtd75Tp4RSHJWKWiLhGn4rhwhU6g16AyzRlJlWFo+kYI04LgUIpQRT7bLbGWW2ViLzJDG8s15H5dH+Ptjc02Pcd6FfhafcMq2uTRMrntZ3jPCrewanqOd40/DpPfPtB5sYucOr4EywsHeDJZ+8jjgOdRG0fBUlAf9ItcJOE6skmzQWT9MHSGXJaPcCZOfmnW4PEI3fGEWRRWjgp1aNbiOM7bOPgIkhwiJDEZigUCc2bDZ7/rZOsPj9BEkkCN0W42rk7qujka7HU9uqkh3bsUiArZEXZbNO2Y2h3oOqB58FSR7c/SqBe1hJ7wZSUF1+x/aEgTjTB6xRLg5vTWl3J5XnJlQUYqsD+SXCFwi+F9HoBKoGh0S3uOvUqnkiMHUx3uouNVzVMiAPqocdZq7RYfu0o3U6F5eZhHnnhb3L3oc8zN/EqhxvX8WVILw7ydha+LedcZJqtKra1U+Er1w/xQydepub00E4G2sid+indiSYqhtiPIFY0w4jhpQqNdZOZzYTZld2YD+19ln91YRrhSA32MbQOVFg/VjUjqoh9QewJXFuFL1V6cLIse8bpIsvIJzQHZ704jePZ0LSEfRNw4SakiqPd12g0tlkXw7Tn4dz/Bk4NdnZg30lQScBYxaH8UJm5UY/m9VFCldDovkJz/AEee/HXmDl+gqjzDN/4jZc59P5P0Jj/t2wsHqD5wCwHnjpLe+udjDlX8E78BFvyKZ5wj7BzcIvFz/ncTCqsvGeMQ5vzDHcrLGzG7Ek2Sc8Iwmt38uzlZzjSOUp/zEH2yow0H6PylfOkK89zx9ED/PrZIf5uvU979igvz1dYn3M5cu1Fas213BQjhOZGkTnnLIyE749oqT5qkSfQAEuEz9XezHND7+bs2NvBc3FttsgCZbdBE70bm7yn+2Wujd9Nt7GPWOiaEp4ZjiypovHTdfqwVSEP5ZW6aSXV5+HkLMea24wOf5XHevdyvrkPpaDkdPmRB/4lY7VlVvtlViqKtijCjX6BQXl5UIFdnMtmMmFrrHeJjOIaOgbGNNC7FHMXCHxcSpkCWidy08lorTZBaxryiPO8bbk4KnL3RGyV+vxcWxJWX+cj8JAcYZiDDNEl4roB/r4pjmM5biuBdw3wb9HBw2GcKmV8RijjmyI5tgDtEm0EklHquBnTY4HfJwCqTJogvm1aLLPFDUJaxtVRYXUaFHpeDETw6/0h2iSSGgNLYnpshQhBh2/JDodxmUomGEsrLKwfZXR8hd5QSj/uUx1q09muZWPYK/M93d7QYL+4B/wKrCV7cEOM4CVZYYr11iSrwVvYXC5z4eYpHl89w+L1OcLU1xJoF/Zyk+2kQT8MiCIX1QO1BEnssb01hpgAN+hRkiHdnkd3w2eOC6wOHydNBUEFbUY0QJ9KiAPyJAoCEIraSJvhe15jugL1g5vELrSoG4zSVbM6zQq9VonttQqXn9zHVquKmhXIHhoUTJhZpQw1Rws3SQzhNoQ70G1Cup0X/EqUzteiJT1Ys84AVlgC+n3925X6I0AzJhb589id7CPUIHMAGugUsL4Fna7ir/6Vr3H46AKbOw1SBLWRJo1yCwdBFWmWozK5A4oZsRQMbzD+nq8ye/plzj36EOvzc3TDBk+e/wR7ti8wcvBpRvbcQG1N0O/ViKKAjN6YVVSrN0mVQxiWSdOc0ksheGF1lq24wk+d/DYjopkJ8/1Kl43ZNawqwJLcm3s94vmI+jUPryXp+BVenznJxt17OHyn4OZTit6aAAlDk02CUpzJLYkv2ByTzMwnufooS6Mrc5u95VKcMjaWsdkeQwnBcEWXAeXYNFxdgV5ESXU53n2FZ8++FZSgv6WZ1ChRdFqKenqBTv0SoysewbE7GC77XOucJ32LR/f5rzF+cJr+TsD0/g3+u+8f5lvpNe70E1J3nf1Jipo9ztq+hNnKYZaWZvlG5y28nI4w9fif0Kl8heNvneW18buYT/ZyKF2nc/mrbIxcY3SlwebmAq98YIi153p0F2fw/IiDM8scCdY4OryHJ8+H3PuWLV5YnOCuI69QOZwy1H6AI5e+hdsPjT+DleAF2iYvjXZFglvX/ZSE2PTK9qNI+fzET/Lo+A/TKzeySJJsgqG/BeCohHsXP8+xS1/gmUM/yvbQkUxRnSVgVGROr1YDGITQrumMzEKCJ2P2p2sc5SbHWUAIwYTf5KHxF+j2fa53Jrhj7lmGy2sIlTIcxbTSmNQhUxrnCqkclos+5EXAz63Yg0pxGwtOBp6KlIjEVJTLQSuX+jVD4OHg6+ytRspOCVCkhJTNnfU1OhzQK7TI3jdhUD9RVHnm7IyLoIbPScZQKHboc41tmvRpZzWJKdwbImJu0sQq3SsEDFNmlApN+izTIiLJkg9VCHCQTDNOiQqgKBMgCagyQZVxJjnJGhfYYYkWK1jtAljpXms1NCtU9IbIK+bpHi7k1xMJHTqcw+FV2UNKaEwvcooj+AzjeS57T15mfXmSznYNEuh2+nwvtzc02Ge2drSmb6DYmRJsvFgxCcwEO/OHCUPy8uh9WD47TdXr48UasGILaAJUR6CuQicOaKpAg6eAcxxn/ZJgawf2HIa9Jw39diByDRFwFE6QIGXKgdNXmTl4E9+PsgamJnuTQtHZLnPt3D4WL8zQ7/ikMaR9gawBZS1RZ+HbRqOJq5eR6+lEcJVxqHah14TWqiJVkCTG+mZU6aRARJ7FyToiyZxcYrXallExqiZL8Bxpp3xOAAc0I0C3B3/8+dP82F/Z4Mipq4UymnoZ2cIRVk1pCVVh5BBSMTS9zAOf+H3OPfourr94J0o5XL95nHlR5a0f/zdEsUvcK6Nif4D5kALK5RZCecRhgFIOGxuTXL9ygk57hF44wTouZ6urvCWEIPRJZUJzeJuuGIwhFoDjt1g/CJtzfZye4NvXD7Nx8E0IVWF6LwQzitd/E+KoEPJmrpbCdpQBJWWq8tmSqwPlV8k9OVNF2C+x0ZpheN+qbonnwNQwXF7BIebt21/ld+M3oXTaPV3aIYTVJvzQz16j3ZlhuOvQZ5kr8VXSqx615AB7tm8w7h1kVW4y1DnFY2e7jB2+QfiBN3GvPMrZ1Sd47ksv8f4f+QjptuDCSJtqe4xaeYnhnRbR2mfZM36aUv8wvvJwCCnd814q05dJ+hd54YF5wuEOONvsDwL2j29zZORFFhfGefzancztH+EttWX6pXlqexyawqGsLnHn+c+R6ZdVYZIqoysXLpRHgUDH2Kqe9qUwhLrt1Pl2/b08Mv7D9CqN3B3CXG6dRr0kpdRf4S9d/d84uvUMV8fvYW3qjLFNF+Y2OeAXGV1SGGoq2pMh494Od3g3+OD2WRyRIDyJyZrDuLfFD+97hPW4wdih53FlhEKQ+B6qsHhy33Gydymqq3O50v7Oz5bkauhczT6oG1bZmRTulN9NA3UXMooEuradoGb0BMr0jc4LkEcwJXgkeMb9D1JcIsrk3Ld9S/eWpyoEDQJOM0FIzE3a3GCHzm1AX7dds9BterQJWWB7oC9CIkJCWnQQpCyzio+HQDDJBCMMM0IDK8VPcJxxjtKnRZ8mWyyww81CwCIIKhQrL4KdonaMtGSfp0NKNbsoNI1rssNZLnKGt+OJMqqaMnZwmTFWAEGc/Leqd//ZmyqAjXJzRl8oNE9acJwreSD7ubCaKm0nl2GeT1/AgPe59moX2Ky6sdK5WCoVWF+DS2fh+gWoDsP0CcWpn2hRr67i+DFjc6tICX4p0jG2xtaT8cQp7Lw6wauv7WdjYzRDUeEZE64Dab8gZVtnOmu+pAC4gFfSn9IwdMKY1Ste5lOWpXcvUA8pjWORa7TK6SAp2IXhA/bOjAga4pwVCkEToxs3JvhXv/oB/vtf+Dx7Dq2YhaGP6qg8hW9IQIB2GrITMdMYCHC8mDve/TWqI5tcfOoB+p0K8do0neffTu2BRylXW1mEcG61s/G1TsaVj0/c5NjRl4hjjzj2ESIhKLW41Ksw1GrQqrTolNsFMrnLB1kAxjHr6OnnuNn3mO+/H6kktT0Kx2iUE8ssZR+BSFXeSSrVnegJk0bTSPfSyfqHNEUJaLXGmRm9lCOPNIBvPNRHwlXG1hdY4KAeihjSWOB3W7SbNZbTQ7zrsOD6uXWa0Sr+ey7QLku6y8eILm/RPrfChRurrK+OEja/yNu8+5g/XuFwOEX3UMDr52/ww4fn2U4crl8QzE6Mc+3kh2j94GdofPUZ/t4ffYnP3/s/sSTuwlER+66FbM7Mcegbexh/yzLioUvEzSWu7lQ5sjTFP/7cJ/jWhft457u+jTd6kZ/eL6j7IesMMX5+U+c8yMDeSPGWjRMCysOAr1VybksvxASUUGx7E3xu4u/xePkMqSfzSBLT1VUBc8A4sL+9hv/MJznpriNG5oj2volJscNNVTWlqL/zZufHgXSN75v8PCUnoqIiaFWMWq8gjSvwRMK0vwntCv0hyYV6jYVKiVDYwDcL64O52e1cKFaEK/qQ53uKDIEqgL9lVger1OUK69356+373Y69yL+1ojDJ9in6QD9rWe7rn1MPhaRPjQSXFJeYUtau2ABpgMt+6ozi8zKbdNC584p9PjgCauCZu4/Z5/cIkUjmWWSdTaaYYIJRSvhYU0RAnRJ1hthLQp9tFlnhdR3JMH83fbWBmFsFmWbsmcriGooagaIOI0UYDUiLPutsMcMwLgIlLHOQIr7H8PuGBnvLaSvI0o2TgLcJ5eu6GJdjws5cCWUXdgr5T2KlTaQFUpsBvh03x9GZ7ozARSq0h/qZA/DMBQi7EEUp4/duMnHfBYTbztuXuZ+ZmylJGgo2z06z0mmw+vo0XQZjgwVo+mYwQBlnjiwMzpxui/sIK2qb9/J8Qc3x6Nag3zS1XtCMUJb21n47g/sRZI7iGXNg+qOYqiBvqNnSXDNgD22t1/izPz3DT/yfHzGSzCBJSNC2+kjLpvjoUEOX3OUHQHoRB+9/lpG986zd2Muls2eIXr2f2Te9RCR7bNMmVekAYctSh2R9KhAyxfP7eH5Pt0LBTVVntdGjbPMxFAjs4GsWZAgRMR48wU58gK3kBCih0+ubBAo5YTbqWDtxrFu3NEDvGvYoi/s3Ha4gVS5bzVkmxy7pcywnVXJRqQb7qujwlw5+hbPLfw0hhOUVOXVikQNym8bsdVq9O7g+s8jxpXlmL0zTm1giKh+kM7vB7MYYw8dOEB6dp9r6X7kulmid3yaeDXjzg322tmf4SvRO1p7/IqUH7+fi7BIbYx4l5phknfrODklvBgJFdajF2XSM/dEmD+8kvLKyyet7E5KRHSIF/zH6CN+6eBcfOvY073rHAh0xzdSRG3gyZY42I699Bq8XGvHbAL0tXJRajjQBxzgFJnrCKgk9UeLXR/9fXPVOI5Sg7MCJxhojfpfyzix15TBNFi6PXy7hzD7EitNkamaEO5x1jsV/wkWxny+67yLMYt71sGWbYZodmXLv9EvaEcs6WgqM+qAwdYq5dzt1tlWd1+pVEqFwsjxsecGVImTdCrp25QyazzA2+92zlcLfu+PUd7Giu54jzF1F4frifjVwXvGvHOBtXKn9O8Zna1er9Jv3qWR9oIARHPYg6SDpIThHjxbQKtwtf2/b7luP2OfbdSgQdOlxjXkWWeEgczSoUDKpcm0bXHxGOcgoBxAI5p87TPeRCXjvN+Hh56FuY/nJrsjeWSnq2w1m4j47wSjb5S6JjEkFvMoLlKgzxmR2vVBGC/A93N7QYK+UBqlqCvWrUOlqtXW8Q+5YU5BqGz50+rld22i2swVjzakU1q80ITYpubOaECkLTYVwJY09XU7/2Dx3/si8TnVbWCgKSCKBihyuP9ug2woIezO0Nuo6vMcRuSOxAdxsAaWAB8I6LEFuSywwLMqcKySZl6fnw/6TEHVh/To0F/WxLOeIZQ4EWSx8Mbd9xlSY5NrCJXNySk0hIZ3tjdzpqYBJGE/8V1+Y4+Z6hdHx1qDXPRSWuFVmWpshmYuNbZEQisbMErXpZSZPvUqrU6Pk6JRDDcq8tFjnpacfpN/RfhBj0wsMj65QKnXNSOhnl0od9uy7hBAJFxfezZXFd+C7Lc4c+23q1UWEKBKRIhHLW6rb06fuXmArPZ5PohhG961gFR5ZOpJU6cx7juGqJJrzdIT2sLR2EtuBQtHtNXBEQsnfIU+tK+DEJJ0govL16yDg9OhlRoMdVpOG5iEk9NenuHzjde4cT9lXuolMjvH0+CphXfD80zc5LiSMbPPMxQYPHP8GzXNlRtVnaR4/QXNsH/2xiNdeW2Qq2OGLV17hbffB8qEW9Stl+krS2zzNo2Pvojy8TVfUkY0VTh95mssrYyhSHntwnPUjexBsmHktCO5Y4ZO//DXe1HmeG1c3GJ18mPpwiALq88s0zp1DdEMN8jYVbmrTPEodBuI3DQJLk0ULWnKE/z34JVYmDnBH/SaT0RRDqcdIeww6xkpiGFVllkdPNigf+jjT0ed0XQI033VM3WA4+bxWSSu9CJbVKOtqmIXaMHfsf5q5nkTKmInGil6fEq0W8xMTr2cJU4EMAIQVbkazJDTR8eH6cEKumstrsRUFhEGwL0LybnX9btBj19nfCeDFbfaLgevzs4oy+26IH/zOV3mB+8lYBMs6VNi57dVD5qqDKDoI2sA1JGUkfQK26bCIS4ggJ49i171EYS3mfFhIyHmu4OMxwShzTFLBH7jWhhke+Og81VrM5X/3Xoa6EdPvfpRmyWGpXDZMqI55FiTMbJSIPv0QZ+QQr98Rc3/pK/zByBrlo0PEIuEcTzHEGAc4QUONoJRgurfK93J7Q4O9SKEew+hFzfT7JplIYoEe4wBt5p3n6MynnULYQ2LprCBTCRshL5uqjjS1cQTIGIYPbHPmR15jPmpw+IMrlMf6BR5TkMaS9lqJjcsVnvzNvSxfqNHa8KlPS858HG3T92B3qu9iJU5sO6xkj1G3x/k+O5OzsuuuBu/SaILqSWqHBPW9sL0ErVXYnieLM87i7E0iHKvNKNVh5AC4gfYHiHtQnYTqlD5/4yqUZ2DzknYQ3HgF+luFVLmpYQQi2F4p88hn7uHDf/nb1GvhABHRUooywK57z5qs+z2PSjnM+N6MFAjwqx2Gqj1CtCLOw+XaC+/g+sWTWbj18vI+PX45LdVqVTfi4Q+8xgH/K2y0DhP3S8T9Es++8tc5ffQ/Mj7yWvY8QS7l7yYjoBjxX2Cjey9bN2dJ+xghvUhwhY7gDJXOU5xJ9hRob6oBf8DpCYRIGB2+jpOVGTXSkquoTo2APw+9hNnKCsdHrrHevVObZYBapcMd06N0RUB4VpDKgOqY4JXkcSZLB7m+7jE7+S4+Kv6M43MlTkrJq2ubtO9XHHqtxNK324Tjr3C4e4KPHzzE1nZA999/g/sO+HivHELUtukOrVI/chdT4hlGx87z2f/lWfa8/QfYet8s22NDeOIoIzyNDd1ySyEjd63gf36Yp/bcw19/eJle4CM2+gS/+SSikxhwF3pBJjLnJIMYqibzYepArB1aN4Nj7Bw8SbR+nIc9wUhUwUs0N2v9wYuSebHM9TCLOvVgYT5KAdOGQbG2vFlWUQLCkkOgQigZDU1aUGUpwEnQcVXk+weEZcXe5QoX92/ji4QhUlpIo64ublbFvtsFdvcMHFQ55K+msqtyVX1+X73lDnq3B3x5C9jffgXYcyw7nO8vssd2X87qD97nVqYl3yeAKilVYBJbQFbTkQ4xIYKzODSBTXIzRYc894CmK3mv2paEhCywxBZb7GOGcYZxcQtvLxBSMfWuZYYOtzl8scn9L6wT0+e3b/4Yy9fvpt3YInrvkzC7yci5Bl65QfPgEPH15wiGK8yoI2yyBEg67NChyQrXGGGCM60ejc4dt7z7d3N7Q4P96HUYCnM6WpRMUVormJiVkNp1acGxcH4qBtemq3JpAIyAYcC+4UJ5LKL+3i1Ojy5hHTiUgrDlceGRWS58dZIr3xwlbEl6fQcC8KswPAeHxje5GQ8jy9DdMAyJtcVbELZO2ui2S/MtjBSpbKksC2SGQZCOluqdQCKHAQ/cMRgbhzG0tG3Pt6lwbQ58xzU2fAccPwd/mxPfMgnlMeO3sEfT4tmHYP15uPKHEIca8FOT2TCNXZ744zt597susP/EErlErzcXgZstYr0Y15aH+JV/+UGmp5o0Gh3e/rbXUSh2dkp89bETJKmVCwSOk/KOd7/G8tVSLksUBJIsD3Wq2xX3Pba/eppl/yBDe7Zp1rukiUsUljl/+fuon76JHzQL+gZLonJpwW6uaFH5+lUq1ySqP83QxBbTRxey8xQgYkWwHRkOFG1AljBQCc88S6df1IG4ntdjfPRKPgmFIiu+0/B0HuXtHrg+H9r7BE/dPI3vCBIB43t3GKt5iNYsy4c32VxJOLJvAvWlKqdOzbNcGWZ4uMfOXExZJsxNS8SpEZ5WCZedJ6g5fc5UPZ46/yVOz9xNJZxjz8gddF5fIDl1kfYVj4nSFM+uvQIzS/zQBY99kx/mxsKLzD3TQHxgAoekoMnRitXISXh09SQ3byxSe0+X/osbJI9fw1vr4cRSg7xCf6cACVRD8FO9MBKpK1A7QyzO/g3Cylvp+8O8f8pDKpHNbdud9ssCYVaZUQEiNDSiOFkK41HgtgUQpHF+N5vzOvNkRWfbsdKFAXedjIlsXxDDnOrhkqCr3gkiE3Ra1GwNbkUJvCjb3w70cybHXjuY1664b/ffWpotHhcDV7HrPkU91yDA5xn58v27IweK34OUePc2mKa2+NwKUEXxDtNraeHYdQSXcdnAZVPlZYS1bGQGxDDmLTq8yiWGVI1jHKBBPXPWRICQgur+Lst7D/Ll8C9z+sJT3L+0zqOLMzS2x0h+cz9iuo360acRS19m57zHvr9wkq0vlzk+7fEE82j7ve6NmJBVMc9X6zDT7XyH9/7ubH8usD9w4ADXrl27Zf/P/MzP8Cu/8isopfjFX/xF/vW//tdsbm7y5je/mV/5lV/h1KlT2bn9fp9f+IVf4Hd+53fodru85z3v4Vd/9VfZs2fPn7vx5R2QPgPT127Wic2GQaVCr1PP0RrVNDuRbL7Z8+21cQHwLI0WArqvjrP2ByeZ/KkXEE5CmgiufXuCx/7ZKVZeaaASXXY1iXVo4J4H4OjDUJuEDTGM52lveb+mE9mkFuBNCF/Rrg5mXhqAz0y79hg5aDuulu61CiI/J8sRYNPmCv17wEnPAL0sSJ+2DU7BiC6VSeKDpm/Sg5kHQIRw4Xf1Oyep9hVQCuLI4Qu/exeH//YagR/b6L/so//OOfnWTonXXpnj1XN7AMUf/NF9RBGoVJCmIOzgmOse/eIphAP73qR9uHb7H2RMm6E8m6Ggqmq4rHH/Xb+GUpJ+f4jri29jafUe9u15DKUUMkqo39iitXcEhEC50swLTYCSbo2by0eINie5/z0vcfoj30LVO5lnMoCMFeVmxAClkyIfhIFJmP/0vK5OqlOkjI5x0FACDozCjXWEn3DfyAvcO/o6LywfJ5GKroz40ms1DgzHqJkr3HVqg40ba1Tu9miv72fPySEuvfhNZo/cycXtPlP+VXpOSnm1zsbIIXre1xirl5h+0zvolo6yf+ccayNvo9byePPGv+Xs3GFk+zqHQ8HBao12bR+zU3XOfyGF1eeI5hXVuRJSpgbrtEXa2ZLI4at87K2TjKRLeH/6LcTCppaSrQrcfrxIZ68RAlvdTgFdbz/n9vwCTuk+Ah27hidyFW1RwsxgyWK0yRWhUIROBSUsNA6C3y2EBLP20tSo1gzQJ+gxUmZRCHJbn41PJZ+rXiKohZKorKs7ugg8PSGMsi4xxWz0cW1B08dtqRrbuFz6tC5xObOsyJ1V5QCzMAizt1Pb7wb6WwG/2Lt5vTu7KvSzVWEorf/MIGMwKOXv3opS/+BZu6+zpFuQGz8AjqI4SogkZb7b4PSXP4gX+aAU7aDHF+5+nguzS8ROipvAsRsVRrcFD7/UZzSpIFDcPOiwNQmdIUFzEhJH0g2qPH36IUqJw/DLWywtTQACb6PB5LPjvNA9z8/4b2L5iocaXuZrY5OkvELuk5H3XyxgxRvU63y3tz8X2D/99NMkSd7Al19+mfe97318/OMfB+CXf/mX+af/9J/yG7/xGxw7dox/8A/+Ae973/s4f/489XodgJ/92Z/lc5/7HJ/+9KcZGxvj53/+5/noRz/Ks88+i+M4t33ud9oy6VMZuoBZ+DKXYlO0EGzXpxLGHwpA5Uy83SzgpzmdGZjiwjw4WmzQvz7MxVeqXH1ykguPzdDb9vS5Boy9Ojz4MzBykGzdK/NDSUh9BkpvF73mLYhnz5R5+JA14w40yoTk4YJyySOWirSs8CLWfGCTk2WaQ9OeTPI3+yxGpWjNR/F2CJi8G278R2h1jaO0oYMqFnz9C8eQIuXg0Q3mZrcZn2jj+zGlIGLv7Ca9yCeJJOeuHObG9ZHMHCCEqQQmcqZtcKxE5ui+cQ1mh8gKn2Xzw74vmg6/HsJ8DBPxHEc2zjA7+TSN6jJjw1eI+w0IJ7j68lEefvF3Gb9ynqSqUK7k+gdOsHFqJhMGvUqPSr1JujTF4f1Nyo0O3Yy06U5PB8gcg04hCFQqeHrxNEP1HscmbhBKSF2dfUwoA+z2HhZQEsCZBnUB4oQxb4137X2SV9aPEZVSTt27wP7ZPs/+bspfqI4wFTZpRpu8tC644/Sbic73GNu8iNhe4qmho5SjcSaXFpDLz7JQvhOnNIcXuKQvSzr7yjz/+6t0G0+x/+AKzzZPMzd9LzP33qAzdope/2m+/a3LHD8xi18/Tnf4BD9w4dOcV++jNyfB0elYSwoOhgvMt1Y5U57C/TcvwaWmAVD7imZi+xEEST5BTV9t1x5ieexvUPWOGC/8HAwE5I6ldm4WmfhYM6Gk4MoeI5VLeoLGuxaGyP4b3KJ8Hg0wJXZonUTb92Lj6pupFMyYCYHXdak2S2yVdRbEMsrkchMIJCGKNhEtEpN4zyHOUuPqfvCQeDgmrYcwSXFMEwzYedkxW8suB/4izFvXwMG9t5PnB3tkcG8uTe8+27IAWurOz83NY7fbcibmds+2e26n+s/bYd9LEfYcnv3Uuxm+eoCPTJqlpxR3XxlnpXyZ5w5e474LNSY3A/zY5uMAhGLmug5lCksJ26OCb33EZ21aooSgf5diYuJVkm902fj2NAAzzTKNuTkut1c4fHOTG3tG8Oo3McbJgT7K+jgrzPK92f5cYD8xMTHw9z/+x/+Yw4cP89BDD6GU4p//83/O3/27f5cf+IEfAOA3f/M3mZqa4rd/+7f55Cc/SbPZ5Nd+7df4rd/6Ld773vcC8KlPfYq9e/fyyCOP8IEPfODP1fjMJmuAMiEHGRhckxkjgIkXTwuT1PxQhWshX/u7vdCFgOjaCPO/djdf/Wqd9rbUKnbzSY0D3fQ9MHoYLWUX2mR9jGKR03O5q9FZ6V7y/dLa9i1Ai0IbfXQ9EOt2LPLb2f4Z2Oz1ltmQg79tnLIjslMHMueRYmLoDefegUooiEPoG62CwkhUfcmjf3QHXgBBOcENtJTqBwn3P3iD3sQccd+hFwWkiWD4IGxdNM1UeTaz9DZ02G6tNejvQGUMdlOILHe+0kxgV0Ev9FhcejtLK/dzsvdNDrW/wDP8FF51DBEFPHXw3UwMP82Zl/9nahtXOfD5V2genUAFug5hisQ/co3hhb30wy0svyTQebkcBI21UNvxM3WGMpNBcy9KCr52+X66lPnb7/r3PDq+w04g+PBanVoiNFg42SiC0Gk458cTxkfGaY7uYWr5PA/cc4MnT77E3niMA9M+7Rttfuy+K0TH5nhiocXFb71KY/hhGuPnuHS1y8RdZ2i9voi/r8+V1xX+HWWW5w4x3ehT+uJlym8fodlf5eJ/+Brfd8cM8++TPP/Zixwa+Yvs879G82LK2qJk58AhphqLqLnn2Tt0nOEDiyzJOY49foWXHppBVdYp92P29PuM3vR5f9th7Fe/zsqKy3SS0C5VKHe7SJlqsAxC3VfWXm+41Z63j8WR/4HY34dTwGUlMr4OpbRGKUtBaue8WfzSrE0pFJ7bQghliMVucBuELlDGlJAPA4hBQiPQJgcbxiswDgN2gaqswRaqJDq7nEKSoMNPu6S0SdAR+UXfd2Wy1QsckiyywBai1VZGkQF+FnmA5f/zOnZgy2VL839uxbf3KsKSfZ3cf0bt6h018G07alCiH0welG9Fdm133+8eh/yKwf9vPWZZlp3mEFcvTTEfXkOIvdjSyEESsGd9lL3LLqg+eVENcmJvQN/vw/hNeOAzgj/7yy7xcEIkIZ6T1D+xSOX7llAkLHsKGY1y8qVlyp0n8Uffwb3JIleJDM20BoSc9VL/NYN9cQvDkE996lP83M/9HEIILl++zNLSEu9///uzc4Ig4KGHHuKJJ57gk5/8JM8++yxRFA2cMzs7y+nTp3niiSe+I9j3+336/Tzb0Pb2NpAvbDvomfRud5rjVhdh97v5zDXcniEW5Gs4YwIM6FpJ17EfL2Hx6hJ+p0FsmqaUBtvh49qp7dAHQJbN9Df0IjWpbnfTCgrPtCBrHQutrTEt2M6Lm3CNKt6o8TOvflVYOiJ/RlapzqryzTs6Ro0vzfOzNhSYCqulFCn016AUb1K7ucW5L06h2lUCwA9NOl+F9i8wXJaKIMIhCrU9oduGRz57jMYU7H0LOIFej0MHoHMTom0yx0kP47dVoLvF3NJpDNvLBux3b5ZaOTmD2F6HsVmBSkqcTd7N8LUXuK/2GS6UfpBuME0kqixOvYtOMMdDT/wUcU0gRE5mUZL3Hpzn6uvzXO7mVbYiU6hVICm1E6QjtLrXVQZxRDYYO/0y3fF1krUZQiloeQlNV/HE8A7vXashU5Xl7FeeJp6Xtkb5QnuO2Y+cZuYPynT3rFPpdDi9+BJTHzzMCjPUjjm8srSfkdVF/mx5L2+5u4GaW+bxf/YiD7zlB0nFEoJRji47hP6T9K4eYX/F52xtnPve/BpXK/v5Vttn/dzjLLe3OTj9LsYnp/iNP51iOvkJ7v7QDhPVVzhwbR1veoanHguYOFTm+uf/iBNvclir3sdE6BCHHnNXbyJ/7xJTyVkCtY1wBfVAgieIKg5lIpD9nNu1HJ1xJOlS59HKP2DO24s0DKZ1ebDAX+CFNN0urCl7WCsKFCP1aziNLnQUhMpM6F32rlxfoPd7aY52Vioo4AMApVh7CPdlLlVYtX4KOAKhzMc0TgfgFR3LUtqk9EmIcOji42Vw6ePgI5E4KPOxSn6b107/1qyozmUhESakNQdzDfx6luZMgsqYBlvo1rq55Yljc5C1Mf22p/Tr5sCvCt+7N5XdJXchtH0rC30vCvtvtw1KyxRaqI+NTG7zN/7Hf8/6pTkWlrcob9fNwZjVnT5T3gakoXb0nq8j+3JXe3KhaqLV5+AjZdY+usFmedjQ1hRZMU9OI0ZbOywfgKPPbzL32md4dbpMMjVm4F0LCcV3esPkxv+jP/ojtra2+Mmf/EkAlpaWAJiamho4b2pqKrPzLy0t4fs+IyMjt5xjr7/d9o/+0T/iF3/xF2/ZX5TsszVoPllmPTHIkKPyfCbG704LE0r73XQTskIYu9XarszDebrdLqw+S9g9iu8JhAN7H4K974XKFBkbbUEpVcbRWBhTrTHB2tHPnOxEAewhKyiTmEx6ux11BbnNXdisYeT3tOdkII8GdUcWvs3HnmfDwKVEq0BD2LpkGKJYP2/jPMw/ATKt4zRHtJOUaa+bavodW5u0oacqMXQwsZ2u+2d7AW48AfU5/f5RC8INdNiheV+3YFIwwtoAE6cEtFe1JkG6+f7iZuuWi1QnYYtDnYUwFZKvTn+ST176afavPEqzdIiF8bcTUeFa7X18+fRvsefEv0Z5LhtrDeJogr1uheTKO7mxPEJ17iYp86Qok1MrRuAQBh5hzadHjYq3iZv0tNon8UC6rDUUnYe/SGWrxuZaBVQLJVLmSz1eq0iO7QQ4qaAV+7R7PlcvHuDKC2WqJ2+ydM2n8e4eL1+7BzVbo06fr794hH17LzN5aJaoscKNIXj45iPcuFzn8nqZU//3j+H576LTvEy0by+9Gz0Wzrp8bWuSeyc2efKpk7w8G7B64zi9pMqR6Y/T3Hb54j8vaZcBCTcl7HxacWoixv/by/Tqhxn96FXK3ZuM332QhcAn3ppFvHCA05uPMnXueardRfCMisZyil7KcLqmQdJ6z4IeZL8G+Cjh066dQh6oIpZFHjFj1odljO21FlftpLfkQcqISmUeP9hiZPglhJ/otrgK1i13b4mIXTSFNnXQSeZKwjAlIi89WVyI5RBCUxgjJZcgUiARjFyt0Zxp0SThBhE7JEgcIqBJxBYxMSldoI9PjE9kAMJ6iisD59JAuoX7KFvnVkGfEJEgSDLw1wyCNFEwIgP7oo3eagys7G8l/aJ/e75plzxn4Ll6ryVRxcwVu4Nvd/v9C6zZoWhgYOC5YuDv3e6Gg8OBAD+ImbnjOhfuuI5lICzzYmIvkEpQXaoy/tIEMpG5cIOgO9kFqQhrEUPTO1RKCS4n6a8F3HvhG6xMT6MEjG/cZO/SJZw0xot0Lo+g56BISE2CIEkw+DZqsDe/29t/Mdj/2q/9Gh/60IeYnZ0d2C92GcGVUrfs2739p875O3/n7/BzP/dz2d/b29vs3bt3IF2ufjaDKXTtf0UAJAc9iVnz5m9fgS+hr4xPDjlnJwqAk7rwrbNltrd+gNKoZPYtMPUA1Pdph7WiwJEK4zMgDX2Q5P47kJeYNeCaAW6stDnAEbnAYdtu7JzWA9960hcz5A0soAITYfPgW4YHk8E17eaEU3iweRl6m7B5AZpXIOnmL+VKLaXryDEPUgiNSiVWOplRYo5nmgN0hzpK94FKQZmAepFCZx7CxYJ2wb6r0SRYodh+EtOvRY2vTG6VATINnelf/bIa6LdXYGyPvqa1XuXF7XfwruD3mO49zfTm02y4M7x8x8PE0w06l/46B2+u0HpqnamyS+MddxH2Zui3ICA3EyVGjQ8h5/aNcM07zHRrlf0LCaPulpYmlSapgRfTcKE0skY7atD2dGNToXhmpE2iBEeWSyycrfF6926ClSfpOXfxjkvn+ML0OJ3LsH824Lp6gI0jAeKpFwgqJT7/3E3q++uMeg/gvONhgrLkvukyvi+IBAQTdzDdu85Y+jJ3jY9x49VJfu+pY6gUFlcfQLl63rav16i5+v0sI+pLUIkgOFyhdtKjJK+D38UtK8ZHx6nEKdNPfYHhF65TX1lAiggCaThA7eS2FYwjfWh0FnKglegwkKAG0ieNPHbkO1lV/1f2X68MmJUcMcjUFnl+K/Vn6QuAavkGM7NfRLomw5TV/5eVXvR9dSuxKCaQCIENBcPodHyWEbiFm0y1/Z6UXlTmQnCA2e4KY2kTUHhNn0v9hPlSN2NUI0JWCI1PPhnMWTW7DcNLEYaFtDKIlvFl9s9aw21Neg1rEGMLtyhUVslZgUnanUvtBYVnBvJWCs+l+lsL9cQUMwDC7o65nQ1+NzBbZsBK9oOgvvs8y5jkCbgsPd8lC2XX5XcdHDo9HRTtmRadmfautuRvbq90UezlLMmQJDqqOLD5KsPz68StAFfGuGGcaXZWAkE9StnxY9Nru6X77+32XwT2165d45FHHuEP/uAPsn3T09pRYWlpiZmZmWz/yspKJu1PT08ThiGbm5sD0v3Kygpve9vbvuPzgiAgCIJb9meJc8QuqZ5bp90t65JbgUGIPDrKJOPLrlUK1AhEU9A7nfD9s8/zTHI342+FUsOA7e7nCOPoJwa9/IXIM/tZTYHdLwSkmyA3oHokZbvnDCQHysDcqOAzoJf59TbVukDvd4QB91QnIFNNbUYI1yHa0e2LmqbtAlpbiuUXNcdrmQTLkAhAGAZBGPNzlOiiOlaj0oshjA3TYhgoB/1MB03zldJCrpAGC0SuYcnorfnPBhgUt8QwTNaZEtOu/haUx/O2ZNof8+5zpVWOj13nmat3Ey86iCnd/+GK5MutH+F+/0t04hEemf9xHthzlXcu/gu8eJjN1Z9EREc4IVKidsKrX3fZf1xRr7QolTuGlBZz/accbva4c7XNRlVRmt2BNSCOtWemkIzEU3xyIUaSMBQ5sFblYqODK6HUT+henOF3Vh5gYilhznmBxZP3MHRhhhedfSQvPwv3n+Hzi+9h/+weZF9y6i11HK/NoTvHEJ5ACgfh1nBd8LvgSZ3fQZRcFENU6wnp3tf5m38p4ebK26jFkv/wVJMXFkYQjpYWE7MepNCJlcq1kKk7ttj/iTZeOcFD6VIqKZS7beZ+75sMP3UBmcbGdKE0h+eYAQtcGkGISEO9CFSiJ7BbAq8CQqIUbLkfY9X9a6D8PB+RYRqzbI4in7NggcgQBKEnU61+gZGRZxB5DeN8oUq092DPFnIp3kwOqgf7QjMFAblRvChI2KB+qWg7AX9SfS/z3jRH5RU+sPE4vkqg77AdpcSlfB7H5BKzvp00Gd0FfTBOfFreTXcxAna/yFJDQ+4Sl4UMYFkIS9HsGVrpn0v2Oa0sZtnPAtF2QaUgr3iX6VOy7lAD/+dOdblcX/w/vzIttCVnDHYzD3pLMiAerE/n7PoGBo4r009FBmH3/fUxkZ0zyHwohJ+QTpTZHC+zeWia7maNhrNN9cIQY4tPkwQJR2OXH4gbfN7vsk4CRHiR5MNf6bFnMeblyx3+Jd+77b8I7H/913+dyclJPvKRj2T7Dh48yPT0NF/+8pe55557AG3Xf+yxx/gn/+SfAHDffffheR5f/vKX+cQnPgHAzZs3efnll/nlX/7lP39DdnHXGYEvnGLWfCb1A3mcvSpE0YjByYLI9ykgqcDKByEeBZBc+9B9TBv6ZZkB+zzbtKSp1e/pECiTNsuaJq2kVJRSVArtRbj6CIzOgNuUhBGUJ8GrGencKajcDdNQ9JzPnO1MW2QEclkDbLQI/RaoMO86g4MD5g53cg3fHYaeN3A/226rSgVTE1vmWs00NXXAhX6fSEHZgVLFxN4bgLeMg2VYrA+ba/rISsqKHCeQps2WITMvIFKtLSCFaB1Ko6Y/zXwQMVTnobQBHTXOOXecqgDmobMD7kkIxiDZqnKj9hN4rft4076IaT5DeeUy2+lb2EpsjKfERTEmb7KzGfOOh17i+tSmSQgqCoRRUYklXpIy2QoIhw7T3xsTN9dJWk28qIyqCoZbCuE6EMGwU+fhdpmbN3d4dX2a7cWT3MNFrvdqXD40xMolh7DhMRod4dBcj7Vv+Lzpw0NsbKXUa5K0VMV1qhwUKb26g/SNj5+r8KogyyGOFzG2dYOl1T5bvVM8eN8WFdHi5LXnqPUmGT/c5rmVOv2uQ1vC0ookTBTNFYWrEh7+wBeY+XGFlFqp7KEoxwntLzY59Y3PU1lfQbiGQ7O5AWSq1fiBD0EZKaTm0Cqeyefs6YltMCN066wNvZuo5bNTguFe7vIgCutFM6Ap5cp1HLeLcPrUGpcIu2MIGeH5W3jeJlJGxlnGMB1FKCkZhiQpctN2ERd+h8J48CuyvAd2oWfevgIlU+a9aa67MygEr1YPseSO8UD7HGe6F5hZqbFR72drzTFgav21lYEu10juuliyVdnr9mkrmM1Dn39yiC5K4tnLIDLg1tkPkl3va2mZbYdlCOwdc6C233rRDSrci9BtlfiKwZZQuNvuzSbTHdx3u6145+JbW9Yt3sUM2HJA0jxF7vptIxn0/pyBsOfYHs97yJzgQm1ih0Qptu7usnNslna5TBgI6gI+iM+XaLGpEk6+3uP9j/bwEsX6zu6iP9/d7c8N9mma8uu//uv8xE/8BK5bqGYkBD/7sz/LL/3SL3H06FGOHj3KL/3SL1GpVPjRH/1RAIaGhvhrf+2v8fM///OMjY0xOjrKL/zCL3DnnXdm3vl/ni0DVivNF6V6NXiegtwjXeXnWIc+QQ5gYADNHEg8WPsIRCP6b6F2uXGkkLb19UkTkhVIQ4hvanBTNXDeAaJGDnIG7AUQNrWd+ua3Ye0VnbVu66LIJFanpKXVQx8BLyD3micnfrFJIG29+Gu9bdrrFZJLLkFKln69sKKzbaDvIoivT+CZuvdCoW3sGJCVWjBNPfB9rQUtpRBUodvS4Fkyqt9UQT82jJAEafTdVgtjO16gne+KRNxN9TmxMk5ZTt4Ge6kUGgRiA+qBhNq6DnmMhtDOigIqN6C0auaBFNr/wdHvFl7UTNCpQzBd8VHtv0gsIeAVKuoSjmgj6SFETFZuEMnxU1cZPXoJqWC53CVG2+tt10ocHT4nY4SAoA8dUeHxy5tMnjnIy0+ewH2hw6Hys5wLDlCdGOH6ToexyRGisEdPSq7zTaZ2yozLaRZ32szMdjh891PMlqqI7ihLw+c4NvU4bbdEUHcI4pSOP8FCso/GWJ0IRbkaIpwuQvZw+/NURY/RluLYZItG2oQ4xus4MLkJWyuUp8ap33OTyWd8xPQwp0olnFmIRxISuUjQqCJlR9cziBPqy5vMfO5p6i9dxum19YTwDBhaUCx7UCuRhdIZpgmEVhcouyYVi7WTfHP8x3DHx1gMFdsVwcQ2nFjWCbS0tr+DU19nfWeCU8Pfol66iHRsGWEIyitFKk0muykMWNtJaGabYwhBNnpWMhD5dUpCS6EXEwwQGvPdFy5XxUEel/ejrA+LEmz4wzzNKQ70l5hYHkcdXs9uqyM3HGJyWRwDUlpNrSX6BGnyaAljibfQZgvz2mp0lk22CmOr9M9V7XYv2Zl5jL6V1Ys6qt3QWwR1+7cF8KLLXbHOxO3g/tb9xfsPPknsOmJBfPDsQc1BkQGwzIswjJXug9wkYWtyOIVrdt/TPi3PTTgYg5AKQRL06AcVEqxZRDGCy4cZYmElpPTEUZLGWfpdSbL9X3khnEceeYTr16/zV//qX73l2N/6W3+LbrfLz/zMz2RJdb70pS9lMfYA/+yf/TNc1+UTn/hEllTnN37jN/7cMfaQD7LVoBUd8wbOyYgJ2RzdzRDowTLXFOdyCtGYrlMvV/Tfyj5LQLoD3AC1rK+xz0sFeeGubUjOg3cPSBfiNoRtSHqw9hIsPwtp30i91tnbAHcqIOlDZwmuPwLVWS3ZJ11TaS3S7d2+rj3SlbEX7a1tsCQ84m2XkQpMDEG5ooj8FBELAhdW5iWNIUAq5PgWnTil+/gYTkjmD2DV+JmTocwJQqJ0Wl1H6ORBtTrs+NDc0X4Naao981G6bdKAb8ZcKO1QlyiT7KgYMmhY9BSNGakA6d/Cp2jAN997SzpKKr0GngRVhrQO5bXBuZAar27HULh0B+R1ODyjcwSkQEcd48LOgxwf+TJ193m8ZI0onc7wSoVA6CCchMbWNlfGUkKpiDMvZ0nTVSQixVEQRz7N0So7sydRZ/cyxDbxxFH+uDnPzOz3cyh6hbMTi5yZGuGp328zceAcI90e95x+K50XX2ZvEDD9oMPkqKQcb9OJmvz0B4cQ/XOIhjKen4I0dTiclmlGe3U9hE5MpzSCW4qodZYoVwLcoQjCCBEZ5zhHQENPnqP9JQ411zl3R42X56eZmRvFGxqDepvU2wYSygiCJMJ78WUOfvYcwcISQkSa67Mqe9vhvgdBSc+YbDGSg61dm0qxUL+DPzjx92m6kyBTEl1OnflRmK/pNaIiSPtlSGeYamxxn7yEjOJCGI7KJ4m1X7ki54oFWvWUGm4yTbVIFxqONCProjDZzO+u1JW0pNIqLZtkR5hICXWAL3jvJVbOQAigAtblEP9h6IMQvAbpRZBaltdSqIODJMGmz7XW4ryKnZahc7kyz1Sn260yWTTFQZnMlEWIs4ldLCOgMi98zViITEdQfOagzboI0YW+yVqSQ3MO9EWotq0fVMxnwtYtdyw+Mwd4set38ZzB32Lgd8afYUv2WsBPSRBE5ozE9I2dDTavgSJnqQYZGZWxWLaXi2l3JYJxBJNjHsE7t2EzYHunQvhNn+/lJpT6XgcA/H+/bW9vMzQ0xC/+lSZlv4EjYbi8yPLmDEm6y8JToC0WZOx+K1k7VlW9i52z+bRjoBtAv6drmuy4EPsaDMsl7bBWDImzDmM2zXcitBax/KaY9fk+i09Xad8kq1VvZ+SAg12RWXByu7wSZOloi1XpsmZbWmdWjF1EUoCTqrxiqkJno0tBKIWodOitVjhSgq1uqu2wXg7ASmnv+hhQJqWu64EfGL8B02dhD9a3dDp4lRoGxLgKSyMYp0rT2yTR74LQz/FccF2zuJRW26aGBjuuLuGbGoC272gJQ82H6Ure73YpuqKAPeZ8hOkPYwNOU/BEyo8eOU/JH6fLBCqFavoKs6X/AWTK5e4vs5O8SQujXocjM/8W/1AJ2ajy4tQO5yc3iURCjO67GnV6kcOZl0cYm3f53S/FjN/TZ8E5wF98uI96usv6uMBT5ymN76e9XeLV60ts1IZI1i9z/OA9ONdv0NpXY3plm6m31ahP9ii7Ck8pHFI8EohjXVmvZzoiFhBKHQIWApFEdQO6sUQ2BIGfIlKjVvcLwJg60CnriR24KJnQe/Q8KyemqT54km0ZE4gOiYpwVhSdC8+y79Nfxw/7CDfVDiFWmhems0slXWrSesDah9mQNOvUicc39/44z818lI4/rKUyUeAFUh0RksZ6zahYcaJzhY+kTzIhm2bdFN4Fw4ljFnfgGI7UAL+l+mkKccpWIml268xsNwGBmyZmPhcB32wygZEEJhUq0KGkOLClavxh+hHW0/Gc6S+uywhTjCrCvfffIMYuksiUvgGZBAhR9AwE6fx6HoIAYepCxplUKnCNAcDBxTFKfyvjl0gJGFTy52VhNFRZvjpXZet+K6qqZeEVcpDXn9ydUEOdMqOVMyD5d5EV2NUt2Sa+w6d4zHoo2N+3B3tV+Ps/9UQyhkga6d46/RX7xTpFuthaHoOsSpr1Qd4zuXeFzO6jUDS6ktkL28huyFefuY/3/M2fotls0mg0btPG/99ub+jc+O5+kC0QFei/eRj1KKh1bl2gDNIaFJmXeGYDLACEdepSBmwigI4GH0eYgjshKBcSN1et5w8yk1MYSVHoe20/IbnwdEC/ja4k5+tvu9mqmDYkzwJo1jBRMDW4OeNSZNeK8fTFb31scAnYfhCpQLWreB6skaL2rCJWRnBSv+D4Y/oi0pX/3ABEXQO2lGR5/R0PgkCbIoQE4RnanhjNhWEcVKq1EjqFqQl39MDzjFQuyByzMn8I+/7mBWx+AAFUCv048M5FoBf5sSwJi32vFBbbK9wVnCdgglZ6N6kaJ3Fm8eQVJsq/y07vLpTnIxyXNPVxrl9nY/QEX27NMe5tsrK4weSxg6RCsON69GPJZ8Iqp6+kfO36+3jQe4m5H3yM4ZkzDH+oz8GojVBDkGxzriX4xPs6bK216TbqxAfbPDpzlHElueNERGVIIKiAUjhKIlQK9IEeiESrl5V5USW0FBoK6ElEmFKJU9oLHn0lKJHqlR8AgTCqd8OZNgS4KcIVlD9ylP0uqP4iY8aoqTo9Wo+9zuxXHkXI2KjsLdCbzvU8XTnKM7aSokNLJslDyx9jxxnh2zM/yMuT70U57uDgmfOttirLLyGgVa5S6fRyGTE1E7oI+HbRxIZzFuxy55cQwJf6b+Pq1H7KoS6ycqR5nT3tZRphi6F+S881lSKVoid9XintI1aCJJS8Jg4TJR4RLj0qVtAfACtSsloWKvXoP/NTiMmzqIkLqPGLCJEgSzs4JmNkLvvqj8RBmBj7YudYMCkGz2nZQGRq6UFdheaUi9n1BINgX5Tura0aLCjaluVts6r7PDWurRw5KPlSAOYiK7C7jt5uumUpVg7w7HrjIuAXtQjwn2IFdrcmJU9mZFkjQa7JtGaWQeYod2QsvrPY1UbL7LRKKVsnHUoth/5QXg79e7G9ocHe/wHt/JWGkKgKtY/B9h9CskaWkMaCfLb0lQElW+ylsBXrjhT5BQe0FlLoYyna9BcL8vSsu9hgO1my0DCgEsSoxNOEy9Afm9L2drynbbQwN0tF7qwmyO9bvNgyCEWTY7aI7N9W4jfPtXZ+KaCXSNT6NBxfwN+sEC+PZKr3KIKorwE+DjWwZ46B5iFCaj+sbj/30RU2pj4xphIj1SsTg5zZC0NTQtjWCBD63qi8i2Uhq2XJ0R9B/m3xzvafHQcryRTwJtckp5AoySM37+Pk8D9lorpGrfQ8cUXR9hXDO4qq+yK+vEKkjiGkx/z2OwnG/oRPpymvfO4ODv3GKfYefJXxahVxUPLE6iRbmw7uoZs8dnqZPc8HiGqDg286QMtdQ7qCIAAnlrhdxanjC+BETOxPUYf6pOIqP5Yu4jan8KnDDihfaVBVIKSRpkWcdyIKSnbCWk/OAFxJa0PxjfIo4/VN7p64AahsXsnNMiKuaBV1OIbYG+pBi/rw9XOI03Os+wHlhQUqX3+Oxsp6BvAqq7sMsTdL2z9BVb6G520PMgCF8djw53h67GO8NPZeQrdMJAOKHtXZnC6spyLQI6FLmX7XI1ARqZD4KsxgyN5g021QExEeKe0AqpHprsxcZBZAuUSUBoSejvh5ZvwUz42dRKgUoVKUAj+JcNKEllsmlG42Ca2mbODbrL/dxyxRUVGZ5PoDpNfuI1ERyu0hpl7H3fcCwcgilFoooYwUr4Few3dx2+2Zb1PkKgZJUq6q12CeZ83bZbSgGOOeh7XlQHVbGlVojxr4tq88CMAMHNt9/u5EOjmTUAT2QffD/FfOJDGwj9u2/lZVvIZ7uetITi8sPUt33TF/Tt7+Qd+H/JlCKKSXEI84+OHIbfvlu7W9ocFeBfojJKgIZA3q3wfRNeh8XWvcbN58CwID6WEZFAQykJC5TV4agFVoFbKjtIQaSMA3gOfl11oASYVCSYWSeql12tDp6X3CZMyxpW1tshgB+YyCwdlkAV/qdyrSPsuF56qJwcm5mxmw14vd1xvQTgT0rs+SxFDP8YUoMiBtiFm7CdLLk/PYPnUdhetCPyrIJ1KrYdM4l6pJzfNF4X1Mo4UgD29SejxcTPZD086KqzWzNv46C422XyJva7FPBUaLbMbSVixtUeVTV36Sd889wqGxZeriCqp1Q/s0yB4T6e9yufu3kO4Onzt3iJXmJ1kJwG8OseLB5sZBXan1KV1GuR0Koq8dIU1hqNHnh376AhN+TwveQOSAlIoyEUHHgJVKEAk4ok85dhDWScIBEYucM8riiyzZTvPB9FIQiVaRyDKELq8mde489S3Gyk1ikUtvKIXat5nNAV1ZscrOxjF681uMPP0K7jeeZzTqaVWMk4CjxzNx69wovx+nMoksHSEsnUC5w4jWrzDU+wPy2su64yPh89rIQ3xp9v/EjjeqYcWuSfOxY5vVdTBzNSsVIDQzKGPBY8H9eCriRnWa/Z2bHOgsoBQsu2MM93d4ZPjNVJOeZggmFnjbfBO3L/BkzFBtieGeJExdduIyScmsY6m7N0EvTDt1QsfPlAeiuNaKWwHoi9/kXZADvgCVOpA4iLhEeuV+1IU34VQ24PiTpFOXUaMbIAOk8LGlcGx7cskxV9/bTJO2uNRun3wrlafkTd9tJ89lVb2nKKvmL7nrZQp7ivu/E8yL73hs0DGumNLn9mdZoBbZu+Rn7lr03+FpVirPPSCK+f8G72czGMjsucWcA8Wey8sO23sUo+tTdNDjrY6P393tjQ32QpsahUdWEMwZBXcIyoeg+xjIK2Rx+LvSHmeL0E6LopLMFsIpMuX2uJLQC3Ttd2tLz2+UEqg+h/Y2OTS7zL/7wjEW18okEcRRoBkIG2eW5vmrLbVNHP1cgSG8Im+3gMwmn9m1zXm35L4vvKcS2txguU0bESXM8QGlhCG0iRBEHmxJqDmKI2MJ+8prvBqmbF2aIIkk+xoxd4yFhGWHSgBVT990KtDOR0+v+HzxYok40YsgkToD30AqagYz+IFmfrK/zU5PwlBJM1e2gI8vCm0Xu0lPTmytiVig54qS2ueh38/BXkpF4Cuu9ffx7y79JO+MvkKtcYihw2eIbm5yZ+lZVkPFr37rWWR6D62ohEOJoK+1RDiQGFWJQDMlngPSFyQx3PHWK0zObWktsiEbKQoloBck4DqUYsl2IqmujOHWWojUAdEDYTlapUEcAdKIqTbffqZoNAPsxXqQJ1YglpyaXmXLmcLpmfkdNFEyNVqlHASkVOC1CBpnkfc6yOoE6qtrRPMSf6dJ7FXYGX47iTfL9tAH6fv7NagIqx1SpE5FMwUFoF+tHOBbcz/K2Yn3kQgnzy1hqGGWTbLAzGHmtjIO+zmHKmgFdV6snTDvC8v1Cb4t7tKmt74eXxXDpqzpizdnuFIGEYFUCdWtFpVtQVdIOpUqYEpbe/nzs/kkssdkGqHiluXgtxI9+W9LY9LCfTBrMqtKaVWAjoDuOOqZj6JED1HfRDz8CHKkg4WLIkjncfZmzqFT5GryYvfa+WGhSRVASCvfHYox58Lw2AqbqMc2PX9+LvsO7i/K6N95280i7AbonEUZdLDbvYlb/re/1K67Ft82B+Tdd8sh/HZJgnY/J//Lnl8MGcyPWKc93ZcpghYBDXpvnHS5/9VshqVVOkgyQzTRAP8QJFf1wi0WUbHqyywfPtou78lB4LWE2+4qHrq6qvOrV4f1/lRo1XZ7W9DvBrz24jiOHKXVd3FTEycswTOqa5s11DEe6paWu8rcy+riCpJARiuE1uC6u0ylVrijuM9cl6TafwAjqYvCd1bgRuXajNS+kwtvHg/5uePbBI5iyXUYPrQJSuDJlJKXkggQUiAdczPj/fbOvRH3TiZ8+pUSG11BxYV+V3B9XTBTWuQjc19mfuQBrl8o0Y5rbIQTCMRAxj+kwvdgpiGoBJoJSB0w6fUHPpYQ75aslDIm0wqIM+gMcR2Iz0G8rbUVngc/eOIGF1p1vt0UbEysEMbHoX0Uobp8rnMvj7z8DtpJBRU7eELPF8fNXlc7M0rtD1YWUNUWGzohlF1NlB1S4wiU+1ZLBEp6pK5kOHKg0c4HudRCyQ4Q6LrpUg+SwEygTIYg7wWLoo5VkySUifHbVZz+fkgFyu2g/B5paQvl9MDr5SRcgB9E+ERwVwV1Yi9bGw0mL16j5Z/h6oX/jqA5NOADYhlhR21Si/9MMxqmaauVg/zBsX/Aan3fQCXGgdTOhfV8u4y1GUMnChBWYA6Q+Roi0UBvC1IN3MTX/uo7YoidOpkmyZSay5Nc3W7hF3vZAnkB6O22m4irwrBgTzXzMtNG2fVu29MvITZncJf34w+fR2R2jKLtnOx/bfnSeQ9yn/38/zyZrWUSHIQJ6ktN4F/OMlqTSg6aRUbD9kIOnxbO8oS+hTcduO524L37161dvlu2v5VhUIVfxetzSVuzL3mx2WK0Q96Pt3+CZSAGW1N8av5/vq/InuUaAUUfD0W/MMm+N9sbG+xNXyojKWf2MgF4WsWcqHzx2UVmVbiJuUekNNj3DFa5hpD70hCxwpiokT4LV3xurAlUChvLZNKwdlLTUygsVKuRQrfBszHnBYTK4tgLwCshK18u0EQscnQSL6mMI3WiJfuM2Nhm7v5tn2PuY8OJMyEpzcEe9P39FMpoQE2ACam9e91UsLevTPiBh3YvjnCk5RwERaR2peC9ByPevj+hFUlGyhAngrNrr3No8RcZdTdQ/CbpfYKVcIZ/+Nrf51LrSHYLpGDq6DlmRYle6/AAI2D9HbJ3s2NsACY15oY01X4AdhxKJkWvGgH/Hggfh7gLJb/Dt7dKjE6tc+Stm0yPCaJnPMrLgpcP3cVLX6rQvOngJRoQPA+qY7ovnZYi8BTD9RbTc2vUhmO8HUVzZ4TN7TGiRNJ7/TBP/LrHkTefR4qYAJieW6U21EYKhWMGXvkGxO2AeIATo4TOAJdF7ypMGVw76e0EFflkV2iOLRGIWOKGAhsTKsIa9GvIHf0SKmijKpvgb4Pf1eYmA5bCF0xOt2FqkiGWOH7w39FZOMz2yyfptA9qGVBJfHWBid7/GzddyJlpEfD1PX+VtcrewbVbpJPi1t2gJd6iFojCqdKMuZB6/VsTAKArQBoOPk0Hr7PfNtKlCNqpdaRzbmlW/nyzloWgEC5QaPxujLBAb+9vPirJ5+8AzZfowlZmSPfdOEB0/CI2uCs2vt/2ksSo9yVQwua8VybQwUJ2EXYl4KH9AIR54S4pMTalbi4LDwIihX3ZGFF0RBs8p+iSdyujkH/v7udByM9B+9ZzBjeVtSd/6+/E8Oy+8yDU52xUis1BsDuLQPH6wR62nhT2HNt2az6JcYhxb32p7/L2xgZ7DKdfcNqyWk3hgTwF0Vch7Q5WmbNrOkETk1SQpdm14J5Nwl1U4tx1hxs3jVpY5AvWAqadNANxtoVnF+dKdp6NEU4ZyHvvKK0NkEqX+K4mhax+uyeKuWbA47zQdmUwxErt9vlC5DhtQVOiJ0Y91gLHMwslFjY9Hpjq8b69MWUvQKtTemi9KHlavWKKMyUQCsqOoGxmmuvA/cMBrG6CShFESCSz5UX+4T3/Tx5bfZhARsSpy6pfoTR+k7duvci59IO8Fr+DDXkQhMARWsIf6FZDtP1JoAbtazD2IPQ2YOUpaHdBXoOJfdqU4jQU1YcW2Hg+RcUBjfs77G1eZGj1ZfZF11ie3cdq7NOf8DlycJvygRHiFlQaJr9ASedA8HYS/uLBr3Fw5jJDtSa4XRAR3V7A+uY4j1z+EBuXZ7j+0iGuv3QoSw08tWeF+z/4FHuP3UD6Or+w8pTOQEc+kFbLq8dnNzenbp0MthBLKvLECKlBtow5GCS3ol+HXg0l+mzPXGfHcRgWLcqEOAUrrwAqEwtUJuYZPf4NwnAclTpsXr2LWvpnVJ57HhJJEsHO8YM8d/inubDzNpQN87RTo8j0ysG5ahmFAca1MND2MqsdK54klJ5jsWfCNK0pbLfgJtAlCsz6y6Rv8ymuZXt7tfv3bhTaDfRmn7LMp9HqpbbwhhWlnV3XaDxGyhRx5ArTWcy8IEQYx377MM3JOui6aoaM6KhLUh1JlMGfBW4XQcns01nc2jQJ6ZMS4QNhVh0kh6vit4PAM5DWoERETNlEo4usvfpc13SUZTV2A3/ewtsDfbFri12+mw2xvZKPmyiclSvw7fmWodEyvyXE+fOKz07JWZRBkB9s++3CAfOAxPxo+P8H6H3Dg33WoybKR0jyUq81QBYYcEPjMuWnBeGCpGvDcT2xe1JCN4LFRTfj7AeYAb4Do7Zr5+Bk1f+loIVkC/QW9GU+ibLiMPbaXavAOsdlUjE5BmS1AyDPJWLmqrYh5/eyz7MT3BWQKMHNjsvF7SrvFbkFDCxHYh5suQdRuIvt+IGEAh6IEigbeqIp33CwxV/Y91n94gpTrWYNpOJA7VUOp8/x28k/B/OOxg8960fQYz/+/eDv1bZbp6yZmcrePhfOdtlcGSbYhJFJUJHi5OQ1njl9ia2nG/g7CVPpS7wWHuGV5b+OGweU/R6t0COqjdAAVJWMgKuOHre+dHly5xCnj72oESbywFGU/R5zU9f44clfY/XQDE+deycXF+7QZW8FrK2O8aXf+QB7j9zgPZ94lFK1b6I3NOKoTM+9+zsfelFgCjTXajollsZJwg6m0I0WkWYGhHF8gByBUYTS5fV0hpYr8JwJ6vSpqi5jqkWVGL9AEGVVUaqugFCUx+f1pLrjDljpsrXQ4Bsn/ybtkUOMbQlaN3p0mkFW9lEU5qe+WfZC+XHbNLsWnAJjaue9CWmzUTHFLgsTsuyPlhm2Uyu7r5OvJSUNE2/iq4ppnYu9v5tvGOAMLE2x97S5JMxEVZE2IaH0MsjyRMvCvQRIJ2VofJnpPddwRa7RKWf29wI3hJU883XrAC4pXSISJeh2KiRhStJzaMz0kfj4lIGUNbaYZx5rzRckxIQkRosw0LAB6qWf5hqrf16Qx6EYKTBmCvUKBGVzJEgkB8KAuGzuHLvUw4AQH1fGRAhcr4d0YpKohOP2srHY3YJitrzi/jz5MAjloSiB6FNMfCPNdTa5jmUO8j4eHGJljuzGht2bfb5LSi8qgRsizKSVCDoEJl3x9257w4N90f4nDUGwNnD7dlmX7gLmYlfbJFvWdGZB2AK6ULDa0QSqmFa3SAl2c/9QXI63/n2Lpzhki77Y5kzqLgCzxdDs3c0FNoy4+DxFXjgmtUBVeJyAQTW42VKhQwwROmHNT50QlIxfhFKgXIVwhRkD0+nFzEADL13oqGAWqidh55kCI2DJlCS3Z4jCMYEn+iBycHfMmNu8/EqBNw7+Ac3sOUH+/PE3+TRf32RTKhbnte9E2L3I68M3uPPYvaR8g5V77uezrwyxcvNOSuVhHCHYSSqwrU3QaWL6zo6b0sRcJdBTkgRl8vUbJxInRciUkgjZO3WVieElvvDUx7m8eAqEIFUOaeRw+dVDHDt/jeNnLgOKVMQoo0MXhRADYVEum2QWXQp9nQqdVCeWBuBVPgmK7uHZJLCOf6BEymujHbb8BCm0zmWLEhuUuCaGCIgoqZAJ1WZWdXBVotuUTUR0iMSBOqOHFHeppzhHAzU8juj26W0FuRpbkfnOFKhoPl8K00hY5t0cGwBhex+rfnf0vcMVSNvmVoaRUC7a19F2l8ybbp+tlPbhELvA19KBzC+GwnG162+7y8wPrDki1em00465f10hHKMaVnpshaPw/JDRqXmOnHkcx7WlcPIHWFevYkt0rnstzW/SY5M+S+ywTh8QqIpAlIEhG6bnUmUISGiyjSKxBiIgNdn8LPjdDpSsnO+YAq6QBwFaoNfwv2JYAVG4UsiEK6UeJfs+CUz0yhxZP0BY32KpuZ8R90k85zBr6TRxOMGeysscEsu8OtHjudIqCsUYHgmKGYJMfq7hUDcGDQcQyieJ308STkO6ifSu4PkXESLOZRTTRrK75MCe/50zVLlyXhX+z799lTBJSE3Bl17+MJX6CocPP0lbuKAcUhz6eLfp1+/e9sYG+4L0WywGYwEQMSjpWo2fCeMmFgXpHgMehcWawYyhp2Gis38mVlooALyVnDPmQOT3tXXdbwv0hZ1Z8hBy0FUpmanAts8uGiudZ1hru6T4zip/b9s+Zem7MnZ/0zhVOFcUronRme2qLpkN9VxT8NnFCu/ZDw/MdM3zLCUu9qD5WKorDZiPvBd2ns0lGgtAWQusmqPQR6KAWXa3UQ0nCnwnZdzZobTj0BupUbgzpfLr/PjhT/FLN3+eflrnlLPOkpigPvk2onbEA8GHuX5VsboyQbk0ldl5siqKKmeUsqYW2jbceQo/KTbOGJMz0VpRCrq8/c4/Y3nrAL2+9hJ3ZMrU5BZDgUJ0qyihdF+6fZBhDu4ZRyZyzmw3KykwqnvHgD052Hc8aLuaEfB1+ByJm08cR0Ek6I238EVhgiMyP5QWDluizLwqcZE6h9Nt9oZtHNcBmRQaott1QDzNRHqBS9fP8OTC+3ESkTnNZbl2ioBPPvezVxVkNWcspRVWBc4AL5hNnd4qhNs5kCsTwCBAlzdwbum5QcA3jJ0kZyyKDrIUn2m/Cn/b48oMvU0ipUJtUkwTkCKFPa/CmbOwPYG8fA+ecrnz/q8RlDrg9RFeSA8dVucjjEoe+gi2sICj4yhSEiIiLrPJBh1zprEHWJpWoG0pCVtsDMiuqTmiSEyp5rQAZYMpYnZ3Xm6lz2Xlwep8xUBAO6egZ+lDEHPD3+LGyDNIHJi6gUBSCdeJVIs9GyHn1kZZbR4iWX2G+XsXAMWCYTXO0Tb9EFMCKgbsPVxONE8x6c1QCodRagTig3TWz1AffRzfX9RMOWDTDec5AXP/iKK/fZEJyKeCyObtlOgxic7vkArFe07+KY6McEXIFikxkmCrzsLif+WFcP6r2gzAD2g7ixIBUA1a9KMqSojMWceWl7UpYKOkYIOnwKVZIBTQTaCfQqOiq5TaxylMqm2V+wU4hhClGLCNTVy53cxDrBNOMQFOkctQqcYMe65bFHYxKvnCK0OBdhf+3v0uBcFGf1ROc2/XlUpB6fAySk6SKp3K9NEluNx06V+ucP9svyD6FG5iGyAFWbo7u692Ckp7oX8jb3Qm6pH/XaDKI2KBmlpnW41nkpNVy6K0899qs8LGU13GPrqNiBuGuWmzPfQNXnt3Sj0+x9UvPshjixX2lxzUxUdpB/dz9fJeGscFXhVtww3NeBZ4leK8sIyG/XubM6Cu52OI0E4F0tFqASSolPGRNb7vA0+wuLYHEAw32uzfv4IfxNAtI7xEn5842iZhbadZzLpBO5kw4MhnN1flQC+E5jW6DizU8sT/lsHKfC3yff7mEP3yCkEkkM0Wa2HAyRdfgzQlEbnT0ub4GN/o3MnHLn6B2owLBw8hZiU0tg15TFlrT/HN17+Pm2t76YpKxoxmvJudYHanunUaZWGtdiswpxborXIhiXRVx74FejsWdg2kpjvNuKZZQwrfRSZb5vfZDfTFxE3C9qUqMAUFxhAFqgfJBtpcJ4CJmyTv+R1wU+SUT7L/eULl8GzQM/PNJSUgyWqguyTYCngQmYbkyWBiFCG6dE41A2ay4wmDQXfFbRDE9BgLCjwYcKtr2+D/Fg4Hu3P3BB0MpitSGdunonBE0A26KEKuzL4Cs4K1xEWhKNGgx3ahhTmD0SOhm7ERDitDz3IwhjPxh6knEyAE/tg4nfhDbL7+KrMnnkAXutIDqV3ydH8NRs6T9Q2FlgsUqYKJjZQZVxKUekhXzx+Joh5sA9oxr2FXUChoqXG+l9sbG+wL4WlFBxs7xQ+sP8nEyMu8mPwQvaSO2lWtzoKin5j4b6Vpo7UF2y1RsNHXeFX1ITUZ4aAg8RV+2y1W0I3z84qI4aIdiaxAYs16u3P0C6XP9R0dHZDhIhoPkjR/Z/tOmMc45AQRmYN6RtRUnpLWYkBGrARM13T42OFheHhoXAuMAtZ7cK2tz9vsSeY7AfuGdEN0GldzA5vtppjNyKYUDPbA/r8Hl38O0lZBPCqoQWxMm6lwNCxuMiUvsBWPZaRHFDiURAlU34PldfreH+JUpiE8QFx5AuXOc2NScvpdz3H0RsjFzh6S9SNEU+/Ea7h4S5B0tOo/G0KZM4A50uefooZho7+Xq1uzHBxeyAdPCVAO2ghsXHwqe5mb6DK352JhtBh0qAM9MHawhEUmMzg29asE3LjApQozYUzhhNicV0pQ9R6sl/M+s/PA/N7qOjTSDU4+foFW7zyVtiC4sqD9NqJY2xsF2QRMHck98ik8QrgKXF6Gd51BndJt6OLyzaV9PDd1nYpqINIOTmscEvcWxYTlYQb2FdXoA8qiAqEtMMYC6G1qsM9OKG52rCIDwpaJyOxY3H4rrNkMyES+5oruKdnwDHCGQAzpJiB61Ifb7FR3UCdfQDlaxZ6KFIIYhUPPuMfqW1jgkebvYrBXnrpFN11n2tPHc20SkEmomk273YtauLZ53nOGQN1ybpFDUgPfg8+wLR68TjEI+Lt6dqA17NqrEOBoc0OdMXx8uuwQEZK70FmvTCulJwgBV91n2K4t887uT1FnHFxwPI9gz50MrZ1l2jmXvYMSgtVyjVBoDUitNcq6SAmIkZUOQipIY5McTWssJqMe46UIR5p+K3KDtu1aZ6GxarxFY+fW3v1ubm9ssAes1GXpXaaqTyLuev33mBo9z4HK0yy07uaZjR8nTn1iW1weMu99KbWkLNWgXTxVsBbq3OlBoSKdLZhVTNyCMiF9FvzTPEtbJsQY/POljmCzqkIlBpdoYp7tSH2uVeFnqb3JVezxIAs+sJSk6Rs7yTJnvVQ/fziAww1YaSuuNVOUcmgE8GN3w4FhrbqXKQjlaBlTwnNNnQEOIEpTVmWF2bLAjXoQ93OgtgUIst5UWFFNCQnBDEi/QOgLYpWAVErCShmSGJGm7AzvZfFjG4R/0sS5NKRz+tvL7PhHIJYb9K/7BKe+RVz6llFfSsq9lPu2XTbuUSz86SiOJ2B1gqAD8Tg4Fc087Y4Dz1TOKYMSYwHw22GFG1tzHGgsaDKZsf2WpVTafT8Y152PYyaNyAcKjDhp9rtKI7PNmlT0pE/MfWPoCIdS2kdaVc/Klv7eamFF2JXXBX51lJhNJtZ3YGdpYLKInoNItyh1WpQsx6p07HYR5G07JSlCpPR9h7VDY4zPPQwnboJQRG2Xp3eqvDA9T7u6SHf2NQQubnsCkRZLFypkGBAsHQLZJ6otETYWjb8ClNeOI/t10rEXOHKjzbY3jRveB+06LX8ImXjYnAO1NKLlJghXA570EsRIm3StMbggBFDrocohohRpzVmh2BMGsnTCLoG3M4SIgkFYU+ROggUuO3tMkk1hfc020Adx10sk7/gyaXkHRWLmrNK/i4ORwaXNhm81Kik5cFixxMKnHhyd6DanhMVQukFnu2KH5GCfe7hbdc9ulaT4Dr9sWiYL5vYeatfVhuAO3GMQ/nM9gwZtLxbEBe2TvkJSpkGZOjEhipgtNknom3vFWCqpSBFCsCGvM++/zGn5NoQTkxBASTK802MsbBfeSDDe6ZimS1B99ksXPxHQFhBE2szmKrK87AptHhIYBzBL78j6VGL1MAKcGDm0yfdye2ODvQEvBANJcwDcuE+lo9OAjvjzDA8tcqDyJO14nKvtt3C18zbCpAYo4sSnTzXDKAu4kYLNSAO9KwwoWxu6MoBs1kJGtw2QpimEJj2sVLmQ6mC+hZm7BsSFLAAyuRnTxt1jGAxX5CliU4Odmf1fDC5jyG34Fj+EgJqpUvehfXB0WFeM66Uhr65v0ElnODkJQwFZfgB7beIYc7AP07MgRja4WzbZu2ecKK7hOk5B6jQDM2CrgGyJOxEMPw5pAJec3M5hHPZSAY/dfw9fePABFFDu9BAq5fqeCvLQWfznxil/YR/OUjVjrDLysFVh+9d/iPpP/g6l0+cJTRjGfZ+JePrmEKu1Y5TKY8SmGl8YgQj0HJKmeRIyiTOLxTaOVra/B0PcBdc3DhPteRkf44HlmD5QUtuO3Dq5V5hV7RcGPbH9JskqJQ31wQ2xyRiUEvSaHuWFG7C2ASrFv7SA6PfJuM6ecZkKo2wyT6UiHwc7we14KBiynG1xmOwiM1+pFKwHo2zVRrh+Kubs5EFuHunRr3uU1Aq4Wt2sAo/Q8wl9D1dIUnwEkrjWohQ2KIWSZq0FjCPYJJz4FoptdIHgngE16A8vZovq6WkFrFMOX0fEHpEsMbx0gsnmXraH6gh1BTFbxotte1MIIlTPM6+TR14rNwY3RTk5l2zBMNe8axtsuFPH3RhBxC5Ot4TTqWrSrcAJPURacDzLGIbCOuxCsgl+Ywv3vsdpVrYpgq/KWgdWVWmvtdbvwQpzkOsCrfOcBU87sLm3fmr+5VCb/x5Uo1vAtsypTa0zmDLXflM4ezD+vFgMx9r8yZ6XTy9rxXfIlfai8AyFpIrPaY6sDLE03GKnAjErEDVJZYhytA7Ww0fgM64C1ucF6d5rqIzRUCijq5VC0nIucE/vIr7qsiEOESuPjZ7Pa1c+xIHxy0w3Fqn67SKPAsQEKdok5wB+PBiuZfkXKXRejCzTmQ3qK55ovf4H8xV8L7Y3NNgracqZWjAz/82snefBs79Fvb1mEEAglKLmrFOTa0z657l3+D/SU0MIR9FJRtnsHuTZm3+JjXgaCfRiWA/1kso6yQhUwhD8zHHIjHuq9JgniQ7T60WaKPjGXC3JM3QNgLK49TvTYpp9Vhq3Ge7s3LIMQCIGb2E346IDQlEvKx66o899lBFSp3O1gmTgBNw1PZPnXKfYp/q5iQORCyfn4NBeqJaHqKoR3FRoNW9W69dytuQ3ygDGLOjxP4axL8FsFYYnUOc2aHpVvDSl0uuwU63wR+9+F62qdqlXowUcqib0375McrBF5T8cId3yEYsVbBiaSkFt1ej82o8w/PALVN/5ImvtIc6tfogoCnAWK3g+CF8DvHL1x7YuK4mbDrZdZWAoDNOeF15xHBipbNNPx/AT3+TQlfoBnvk4jrbtyAKHWCyjWrQHKQHKh7gEkx2trjeP3v6zVykvvJTZcFx7jyKIWyYi3b1/UIbKVD0FnEdB6jskFZ+oXiIWit9PQb37MEtjDRYnXMIgNh0UAJIeY+ho7yEc7xCSOg5DaB2QjekW9DxJ6GqIkKaKuzakL5FwFcV84QV2b4quH4EfIVSHrf1nkWkV5eywiA8kiKD4sgKqEbdXXRertRW7rwDEApLGDlFjR/ePEpk2CQR+cwgZewSbwwQ7Nbx+CRVp3yCVgGpDvKrHu/uWr5KMLeVDMwD1ZC0p2tptVjvNGioYSHyTO8Pl/WWV8Ck56Ob9WMz7bmVs/RybcICsJWT19/L6ABIHhxIuOv60xyq55F9sv31aUeVoWRebNFYgVICnZimp0yDK2ZTVd1rF4x6kqHJ1VveHh8Ijxu0lRN4GqdNGERJxHsUGLg5jMwmif4TNrS5hGiLHm6Q7HSZGx/HxCFhnJG7ipoIhpWspPLt1L185/yH8ixHDpU0ePvI4B8au4Lk9yqV1ssxnEvCULuksC/NTgJICbMKu2+QuL46CsFLhfwP7//zNCGaZDn3/0ksMtVd48OynqLXX8mOW6JnfQik80dOhXEAtWGOi8jrS7fK5C/89zbBEx1Roy8AWssQ0Vn1flOxSNO3txbDdzVXrghz7pLwV5IX5LtLrwuHs4UWwN2sVgQZ6K7HbKLbA1VEDDmiHIAVjpzf4eGmIGVXKsv1lzxTktQDMvYR9rsgdvCMXQhdEGcoSPOngQeZHp3DAcXRFNtv3KeZhkqwYvexB9TV9UAAHakQzdf710Y/Tx+WjX/lTaq0dEi8gL4ZBgXAZUJ/r0v2/nUX1HJzfO4D3rSmcrk7hIVJQO1U2nryb8O2r7P/G22nt1EgATxnpydQhsDnKpQJXKTwRE6ZQ9tfYU9skQhJIaJQ6uDXFxkYNzxMcGrmIO7HE6toBylM7vGW7ix/HuoMMk6kEJKmDaxzDdJ1kaRrI4CfrL/NJjKNeI9IlbFGITp+prQt5fd4iZ5bT8dtMolu3RLmkOCTC5Vp0D6P1ayy8fYZOMMXQkQ5htUK/UeV6tJ9XFYiSj2gN0QjK9MUqktHMX1kyBgwmatVN0Q4uWTOEQGVx43Y8u8S8jKILuOR1x3J3sfx19CQ9uDnB1uhRlJOHLxWlyLw7rGQ1WOLkVpC38yuXVQdi2Y04n+WzB/pj67r1k8s4ocvYc3cgbzSIWym1dIWtiTUWx77G+MMuyfT8bbKjFoHa/i2zVohCy4tsyHeWCO275kQih5WiFqP4xlZLULTRZ1WW0EF6HhKPad5MmWk8tEa0yxIdVhGkbPEiOp9fhZBN82ZaeyVSpaNMlC4k4m7vxx0qU1J3UE7uytiEAd7XZPdSWWiG3VyiuoNgyrRS4TJHyNMk4jquC8JJGGtUSBOXyZUeN4M6ZcOgJCKiLWGIVAO4EgxVdygFfWICNqJJXrr2MbaWFJ7TpVZZNjRaIGXMviN/Rq10JaPbgLbPW6DfNTaDPLgq9Iu45dzv9vaGBvtYGUBTMLY9z4ee+l9ptFcL6MUg0KfC6N4LYrB2vAQPFpqjLOy4eVY8qyI3z7MhNEXwtQlqYgWdCFr9PNQu28zqtQVf7ApUcNs84bvpdfZspcHJFnXBNL8sdFnZROo0rsM17fznkHI82MAdGuN0tUYj1kkIBhgYkX8s8GUKBqMx0BXOdPrcvoneCkSeNE8abjwRElc4ZB5gKSaJigH6Yk7TxCO3iYDwJeNyh7OjR/jUD/woThIS+xJJjFUTDi4NkTtHVSD8K5dJ7tzC//osJ9UrXButsxLuo/LAFmr0MExG1F/ySc37S3LzipVzXKfLg3Pf5p7pF0iFTopTL4fguQWNhcgHzTaosq7n1HAhQwsCkoBOKeRGaYQTOzu64IEQEFXQWWDseQISTeRjYMOBySuvIFauQnNB51keqkCnp1Xz3X6u3rEDmQ1oPoEi5SOEopvWSVOPV1pvJ0rL2djeSO5iLT2AwmGbKarH5znwvqdRwuMyVZaZI0XQDSrULPzU9Pu5TBmYsCFKOZeSN0fc8suSdR2MFSFMrjeXWRQRkODFUG8LmkMJuaQKLlU8RkEIWqNl3EIH5K8usicWmeccJv9zgH6wzSQSJQ045KOmD+1UUW6PtNSkOXSRupohuv9ZdvafJR7qMiGL7m7F2mdOAW4HUyHn4Wq2HbmyPmdZxEA78lbZ1tv/NfHT3vrFMSqq2K0jYD5C4CJwkPgIPBwc6En8oKEZNhRdFmiziEeJOb4PhwrgE9MloUWXG3iMM7mxw9JIyuz2HBvDU4jSMJISCEdXBU3FQH4PG+qs/8hZnoFonYGths9bCemQsqppbTnBxaVZm6Ra6AuPGF9GxlymNQyH0w5vrd3g2d4h+kLSRBArgUyqtFqHsqsFcPW8z6l7/hWiGub4kdlk0wGZfVBzlGs97J7bvcl3c3tDg72Oe1aMbt/gY8/8j9Tjm0anbcVWYdDYgr/5nViUMF8GlJZ39pEqd0B1PgAwuyhEUgD7fgytXg70xbS1oEG+6pnCN+SCW6z0ua7Uv8OkALbFb3NemupzPIM5noDxKvh67SCE0vcT8EBDcV91CNeBMA5yzrkA7qksdI8Vvi3wi1yqR2iQ75nfNrzQMgy6CwWO40IWspKSJ7GXhYXrIfquKcWqF4qnUn784he40NjDo7P3selXaamaduTLbFzFHjWOfghSHJSQRPeGRHfOc5YqkeOiklX6bh9PgLp3nrdce5aVxSl2GKfirnHNP0CvO4QsRdxZvciJyXMcGb+E5xlvdinIszSRf+yssB5ayuyThQmjBLgR1QROtJbJbD+pCfq2k0ilkLqQVlEo2n6fzvYQL61FnN68ymp3mKl0HrG1re0uaQqVAAKPxelRZiSIwNMOIjc3SWPB4tYRXm69n5XmNK4Iudq5m1Q59JMqyoKIAOkryl6LyvQOI2fOsvF9U7zKm8llR6sGLtpmc/gR2EAwaxdOzdhbyWVgtRqQT811iRk9C6CzZq4rHCclrqVUABtOVRyAQSC/NWxsEOCLzIdCpYI0cVBeVHi7tHA+6Az0CQkxAkhCHxV0UCIq3NXcs7Kp54GIKZ08S/9ICF5IZLJ62XVgS8QIivK2Bs3I9NcgTBTj1IXpQcjZGAvcMruPzm+v224ZJ70/MmCfMzL5b0EOO3pklMm/JwnQZhrNmG74ryApUVXT7IjLtLiMIiakxSrfYJS3EDCLR4DLCAH7AY/2uKCKQ3NEG24IbNrAwrDaJWUUVkKJ3A/JpE3MMuWJYvw/pkdLeDxAny8iTG46TXVEdgYIXBw8mdKKRqi6TZAKsRXxrvkdDpcX+ebsEBdbddZ34K31ZY76rxOkCV1ZIRArDMXnES+0YVaijjjGmQsst5KbIfKkwfk6UFlb9CX/TbL/z95kGnPfxT/kgYv/keHeUhYKoWObZS6aRrLI5BYQ3KCe+Z71bpiUhhbtdz3QAK5lMiXa2z6L1U8HJe5iJrvA0UVY3EI+d+VqgFfoOROm+l7FxxYxJos0TKBe0vvKAZQqVvBU3FENOVUN8YTDrO9iw3gCFJEQJEJHZMVOJlDqbnEK4G8YAAv2ocHryHSljaZTKisWpvtCQISDZ+ynyARhJXoBKiPaoHoJQsX6Zq4AR+CplJNrVzixfo3IcXh55ABfnj3D5cYkWT7UbAA9dHkey17rRaQ8YfxxhSYSpKQq5e6dV3jnvifpTNfwZQ9Xhmz4Zdpb4ziViFkvQjiunkM2HWOWnchSnf+DSSEgkzqKZc4sI4Dx7svab/+2ZRC3kEox3FcM+Suo05Cm99HZmUAlr+p8+YEL/QiqAZR8Zmzspp1nh2dRseSJL/4oZ2/ej61NkDWzcCoKONYn/QvXEIcVm6UxhEx2ySaDsmKueMztvfZdRCGO24J6ccHZymh2nAqK24G+FBhti3N7oLd3KNq7Y1JCYiTCpHi1d1N0CcmhQTtMpakD9MlD0nKpvij5ZkxOuZ3dkYEnK9NOkIS4ogO+zcRmYSbN7l60Zxd7USJNW2Ks05o0KnQr02vZe3dCWMDkuUtNiltFZNgHC+5a65Jktn6rHxCFFth7CgP0HrpYTok8DFCRyoSd8BJJr81W4/lsTBWQ0GaNrzLMAzhUzH1dSuwFCrHKGSGGhA0QCodJLNOcOcVCLnSlwhQZUyhHZuHNOuolyWaDZIKA9xHxGDZeKGeH9L99acjzzmE+qz7AiLfJneJVTu1MMit8xsMOV9amudEGEjgiL/Ng+U+pBCZJkQ0tjhXqRgrTAkZNykalUHIQ4IsfBmawGiAX36vtDQ32H3vq73O69QpuUlCpIEysmNQEO5XalpLWIH0nyDaolwwn1inwfYJ7R7/B4+sfZLU/q2m1KBC73aNnJiVKJ9mxQJ/F35o2Ws97XxZS8RaYACk1w2B3y8IjtGygY+wrHtTK+tuREHgGj3Q+SGpuwp31LvfX+3gSc7UueimUj2OKVCTohdN3dCU9qwTJaqaIgrSPBnBbLC0ydNe+U5rmAS4Io2lRglS4uMLBVYkhIUaCH6TrmtNJlHa+MGppYUA6IOG+3gVOLl/ny/vv5fmJEyzUZrCkReGiCtN3UPK0+7TUuGdnhfdeewGZJtTcLaw9ZrwfMl5umkEEEh+8BroKiQ/SzaMKsIM7SLCzyWF1jwoyl/3icQt8mSf+LgagUCtVoAy/oTg0ugxyuJAlKZ9gRdOP7QTHU7zl3sc59/p9pImTTeCC8KHn5VyC+MFtkpNDbGWroEiQ819F63Vq5Eb9O5eMc3C0upbYgLmVWvUTdkdva19pzSiERtr3sXHliSnwolkHm/YkRtEhxEUSm1xvMQkSQR6etlueMt8SVHA7iSotnJUzIcU73XpH/cshpMKK0VoUPFyxjNDtdA4525ADeIJWmbvoVLMa8O3ZxcK0RcOF7qkQRZeECOuYZ80rKstyn3uCq7wiRqEVEoEPeEh8rO1eoYMDHRwiv8WWf7ZAwoorLmGDbxbmgkPAGJISeRCh1hwoXJRogVJU5NsJ1AnNnFJgvSyNFVriT21SLhPOq4TSjtKkCKHfVDKDx73EPEOB7cdB4CrFIglf9R6g61dYpMGrai9/mRazRAil6DTTLP32F5bfwnqvzF+Z/X2GvG1yXl+AkChXILKyiobFFbeyY2LXx7KvE83LfC+3NzTYH7r5DG7JvIJFSVOohXQCVAnUMCp+COR+cO7VyUrcFtBDpM/oiSkuQ3qJiiv5Swd+iz+e/yBL7TODKXAVg2FXhh4kiZHGVR6qBgxmv5P5AAOD/iYY2zEaQLXyTQN83YeKr4Hd2vsdJ7f7Cwm+TDlY6/L/ae/Po+26qjtf/LPW3vv0t29071XfS5bcyDKNjcGA6QoDqSIVKhQhJKk3Kk2RgiIdVUm9JDVCSDLeL5VR9ZLwS34ZISOEJgmEgjRg0xmMOyzJtmTJlmX1ze27c+9p917r98dq9j5XIhVT2InImRpH555z9l57tfM751xzzvWqoXl6pMsG7RwREgt4CZo80oX2RIJ2ziZak2lCH2eOj60WH+PPLTF58i3AlATkbN/Etm8UaTSKxoA+BITS/iiNx77W0GwL4rakEol0VbiMRq7HrbZcos73nXyQjUtzfGr3G5gr9INwZsAs2q31ctV+0PbOX2CwYbOtdOQpzgCyAEQLmrOYdOIS8hWIclCoUM8VrN95xnyT5UodoYVuwujMxyyIpNDZKQG5JmVZnq2L+9qBvXvG2qgHYHhwmuGhSaZn12dlgzSmPK8JX7MEe9LYYlN8Vns3BRqdXWK0Q2XBPvFiiSFnulc2z1tCqt8Ie2UKphqISWgTWx3U7c2bp9e82CHI+mt0Qqym3XEka+qn7j6nXeJa1bm1IPy1neU6obHzt87PIvNdmUmKVEkz2DszeacQREet1gocpn6p8V5lesHcaYR/aderypRoWp8QE3uN3v1LUJm+N3elxu3UIVBgth1y9uWsCua6IsOMM0HEIHVmUNRY5gKCmBaruJj/1OZjmFSbZQJqvlVCC4rsoCGuAC20EIQ8ihZ9CDGRCqaZPnaJs2SS4S0+2UGAEsL2irkzZDcR47S5F6gSAIW24OBCnkcHt1MXg3hlSAR880CL/WdzFNttetoJV9x5Blrw6OLNTNVH+M+bP0IlsCcYyQTGSohS3TDNQBJ7oE+Fnw5NXqeik/lRsGn2CV5Muq7BPk07R4a3rkeEG1H1n4TVjfYgkcy+KxJEPwBavsWwAalMIg8BGyuz3DNc4OizmidmoZ4IX3ZG+QJt4u+bVqtHp0ZmjyHK5mx3oK9t1JVIJ7MECgIKBQgi6LWZ/CJ38Idrm/UP8CSN3r69p8ab1s0YNuM0S5c83EkgQpkEENZUHwdpHZwrQxbUWxht3nVvImzIoYRIQkGbNifKCjj2JWWatE24vnDmCSs5Ca3JC0kk3o5SBUT4OIJ2RpJy/ZfZzxCCA5eeZuv8Jf587xuYL/ZxtncD/Y0qiZQs5Y2nrfAMEMMAdMzNs2d407nDthyyEyVDupO7OBBtLBlrYHWWZrEP3TvEUqWPda2qATOfbFynZei172SucZNjDUg7cgOe9VASmd98umHHMDr3ehWCxVyRJ8a2UfjJGYZqddrPlonPlCi8dB4xaJPICE24o2o0ow5YTjI1dPvA7jhOdZU/oIMIt9MLqT7ZCbbg2JwRELU9ftUAdNIB5jJzR3pftreu9XfW+Q7Sffy0pKwYk41eT1ucdQZMv1v7RFevmIgaBWbJsUpWnNe+pWuFu6wwla2LQPpkONr2iOklM6eFLzN13RNe5IppEtOgTTuzjZE68l3t3up8Z1JdUxMgKRrHOQ9LZnwL9DLOfkIiAm0sLWWxhwF2g064zP3UxTRuyyIgR5FxNNBgzlo7Td8U2MSAupuqeJZV8QCSEjWGyYlpchTQMiZIRvy4ZwdaYPin1phQN4FRIKQVWITrK4FkgBwHSPgmEkVOKRYqVS4GW+042NmlYWFgjj/YN4w8OkRvoswx0lrY0FvB+foED029lMGeS0S72uyoz1LatWx4qIQYSSycDcHvyJve1eZZUuv0eGopDPN8ts6LSdc32Ge5gIKEMtPxr7Gutd10dA6fZMovdA0gOs4FF9rI01pApMbYJWDnTijk4aGLmrZL1CEzOCHNzlZOGD+zOLHJWbR1uguMVi4F9PcboBetzjpLjMNeWDLKqhamPK1Is/NBagGX6e0xUJIJd40uIN0FwkoT0plvhXESkNZZQErvgBfYejqQ06Sp02Ph7ALm+wQD9lLaUD+d+ha46xJtfg9CmxsGDBg66ceBqf0s1A50/ScQPf8fiE7iQxt8jKHueAkU/atL/PvDnyYOA6ZKw1RaNZQMWMmVQAifR3220MdTw1s4sHSBbbMXKMY2yYzPbWylp46J5OpmyWXXse3tX12i1VglXO7lycH17GvMEuXyRgLSpOWunZfZ8t2Ec1+Jzq/Sd5EZfDrJCwRuBmm0gFPlYU6K25nZUKQelpFjmiJLFG5ZxBnctcjuZzvIy1Y5u9eYgmFIC6dZdlY3a+JImy0go2Wn37dJgd6cquZsAtlGpowy25GpSJP2ZRZCHZjrjruvIVD5e51jms6862vUOvs0bI3bVLhEnqp9kiTxf101+B1lXBvojYHe1MrFtytSO1+akMbBNFoRxi1WoyY1Vr1G39lqkekLN7mdA15aA5dqV9j4MZHpv4gK49xIQA7VTqg9sYrY8Cj1njpDC0XmJlZZV61xoc9ssgQEjHA3vexFo2iwgEZTmJQsjF2krG4FVaSs95EELYp6PZEao696idW5Q0xvrxLKbRTaNyFsEpAOT32nYDi+JW3PyFTYchSyE83jCKrsv1jmoU1ttAhtTxvnUFWTXPr8jTx7vpdES26P6gyHCbOEzmEfIQVfbL2en979/2U+1+ZLpX28MXqMnGiRiIBYOMEoay8xGCPdcCeWH1oBoX62n8Xq+m8zT14Yur7B3g2uVWQD9WrGxFY8M8xpg8iJ9udem1iyzILWTpHSPnWuMTnDS28QRCE8edksoLqGmjLZ3aSw8ewOnwAVG55fr0MwBqWGeUQ2Pz3uO2nyrogcEFomabdw/SvD74XEO68oZTTwmsJICdnA+CCwLv/SvGQOZGTwQ2jv8pSTGdzDMehUH3FavnvX1s8usvXMJltzwQ2xSA/rcel+NeD3M3QWzzRa50gabyLIX0YEdXzqQSXxZpEkwWnOwkZXRO2EDYvTtiTJoFghDYuDjWKWA5On037RZIDedW46fTxleXQCa/Oi5uKYkeoCI9UFnPmO3j4oVFKJLOXjmfJcPdY81H0UmWe5m7wnaKY8cc0bOVcZ595NryAI3GEzme0BJxysAU1XTifgp2DZqZs7felqndBBs3tiYv/2ViFS970YrK94uu/fWZqrclojmamZyPyvO2rWKWy4O9aSg8JUREm9Djrf0+vdEySKkCYV5omoYc6gC7I6HNmNC3A6uCulnentrLgk7H62A6sUbA0pf13aE8YlsWf6ItPri5kdeScOZNuc9q3pqTQW32wWOEEjtO0Rmfugn81EukxzIWLqviGq5yJCuYVw77PM7juEEhehzwkskhIb6GU3Lr9CnmG0EOhxSZ/eYI511gKhQ/paL/FNXM5vJF8c4Ja5b/HkYDtdLpbn+blh+agTXZz27fxpHBs0XZkYPoPgaCNmanUQ+qWdoeZM5HgyoHam3wic0lg0c1ITCMyphdZSuajznJ4a4Ym5Bm+rNAiDEsnumLb11BB+LEFakDeRggLhTKeACuHcwhhf/uadTFTuv2qOvpB0fYO9DIwK7c2R9+CzXkDKqENhsh4lGm3UCzMIgGeCzrPOoZ+UjFYFb5wQ3D1mint4Gv72spUmRUbj1kYRVNYxM8wDDdI4cOx7zv6tQET2s0y/Q9GhfDpyz0gyE19pmI8DvtQq87ZS0yxxIUyKVX9Is8CFjmm/4PGJc3Iiba7CTIZ2Bl+cad+BeShMII7Hy4xi53INtGw1pEj97gIhjZBiq4TEh+LrxgFi3kPY+78gnEQkwnamwJ/q5nK+o9POcI1A4PcR1oK3N3e7DnUSlP0+e4++xvs1gZb0mW0Nc0uQqxkBK8pBuacTUTv+dkyJTOfReWEHoK9pQ/ZSz9g1C4VhwqBC1gydsuxOAKNjJlwNiq7U1CjuIDwLwNm6S6+dt5HUcef0pf74DpSV1eY7j2nJgrwr0fmkp7v1TvtWpDvh2fW7trtTQ/3VV2QN+Gvb7v/OzJOReAURzhKI2NcHb3ZPezV7UGoKp6lAlMa6Q6oFphEHWR8IYxuxi8V+n+gaw9US53tnaAvN7Pq8FZvSHtR2XmWnl0YgtMvMnjlcR4fIqfX0LG8mrgWogSo60IiJeZAKSZ7c/GYWLlSYvm+IuGEEhYYuwOGX0ZoaZ/zuo4xv7mWFKwzVcshwJ3Eu6hAozLPsM7MD7RiP7dJmX5mLvIrepkALayPJLHnfSicXZz673Cl+qcplEvENFDUQAfP7cmj6bB0SNC30aoGVL/d1jPucbHNP72G+unIjU0GRcKSKbglef+VLjFXn2F2aYiAp0o6MsJCmbLEt1XigF87kadungQsLY/w/X/shxtZf4GDwDC8mXd9gHwTmoHVlUPDSwHEmpveR3e9M858IRGCZl9D2qMss07IjkklZKqQwDiTCOJfdPiqYbgoeWpDIQKTYYzmMBB93fzUrwZiZreVXBr6KJnggo4ytDb/sYE0ZANJCMJDPo4IiQpjDHpDOnJ06upmFkyZt8eVZAHfMN2+LD+g04SNsngBpTghULheATrvQZIqzoYTZ7xE+9l9YkHPOKtJh8OptxM3thGO/BdF8arpI7E0+QVJGMhHCdqhMG+PxKMsJ3ES41ois6VjHXfzf9v4UWeiYVO7VdBDWgnoLKmXIF64WPrIDmA3xvCb3spX7dmfXW0pEwNGejaRxCFe7s3VWw7lodeqBGnOoqpM6UyEhe7cTA9JfmwS0ECyTI/a/4UvO6swpBJJ575Sp1kaXCz8AMuNoJmwb0hPXBW6zQvinZUWZtQJPZ81SbpxrF+lZHSLQISPLG5E6oECby5v+DBU4LZuOPtIohA4pNMYoNIeRaBrlCyBbIGNjNeyopSsno5j4ekF6RkBiHRkbdu22WO4R6FbeHAyj86BCgslN6LmKWWuNAjSKtHY9g45Dk81yYRAub0Be2OJ7OxACqQTh5Ci6Efn1qIRCbbuIvvswja+9hNOXxlArATInvF+sO8tDT29EPb6OwU1V+sUedBESQozfhhF+JBKlQ8N/bDxvdlsya4Qy4xog7fI3Pj6d/Cq9Ml2eVy0dAYI2CbNoEeKCQc0MD+wYxLRnIV5MY/6FBtWs0b/uFOqN0yBLyOE6N506zz2f+V+UTweI3mFm1/Witg7Rm8KMF8sk2gK9NkBv26eQHL60h08feQ1bNhznrtvvJf+tBi8mXedgb8Pr7GLasPAJYr0HpW5OpWvhwEagpUQEzpvaIpM7yB5SrQtwuWlTvm4MU9+3Hm4aEPz5lYjFdoZhOcAXFvDJSKWkmIPApwAQGMdOl13Wb3EXgCbp4S4W73wqWwHbemHXENwQ5alLCKT2Ofi10AiRpi51qUvawhgf3TntAqt522c753gJPg2u1Ka+RQm9mGCGdhZLPKgby0acpItTCVNOgCDQVv6wr1hB5MpHQHuAeOH7CQY/hwjnIYlpNCtI3UInJhFKIFrkomWD8VpgHB1s7KDf389UylXM78+4L+j83XX+tfBUrbnOkVch1kBWsw2tRegpQ29P5rdvI2yINX+vRcDswz3nc58Fgda8auYQs6vjPFfYzFS5lzA0XvOhahELQTFpMthY4nIoEfkCoUgBvVPvzUJlqqmm+RG01eAFVQLaSFbI++uu1ZQsU4ZUyFgrjDgxRWQ6QWQmmYPxLGNNZbFUcEnN9GuN/R09eU2gH1xYz4ZLN5JXBWQg0nUartJPjgW8norj4u7Ql96lHYxfeQtSFQBNwipK1Jnfci/14pVUMMXty6e9k/Xa19b+EVv3RUUbrWNay73UT99MPDuGPraboeFjJEu3EC73IRplRCOyCgAkgUY//GqUNoF7QStvetaueZcjRDprdpKRo5OA5MQmWufHUQ1zQqHIyCQ+BUUAQgoWz0WcfbDE+oMNZCHd8nDrzZu4bXyvl71tXbPGNu8A7eTptXzG8S0NgY7RMhvmmJIEgqQfGKYVzZh6xCFKRKhAAAlaCcTZwK9vN2fqr7jCXx6IWCi2CETM9nNX+OHPfo5KvYaSORr5EXqjKrFo4WKfsG32GzBKZ0KcTOHPLq7no2dfza03P8y+3U9YX9u1wt4LS9c32PtVbxm1aKDFJErf7JUx5ygmhEY6l3GhEJE0SNOxuWzJcwSdfrYaVihgRwneOQEfvxyxHJtnC9JFIXVapMJUQti9Hy2gmKtRkgEVQuZrkmZiZvBwMeH2Gy9RSAZZnBV84VTZBM+JTDnCnC///bvNaXW+uqEwXnf2IWYBCf+7P7VUZPDELqq8dbyLrV9AC2PiL1h5KC9hKICS9jsg6X6/fQkNuo0/T0Da6xL7HAT+tFatTFXdfcICdFK9jdbKjQT5Zzm52uTKyb1EwSqxztGKC+TzNfxlTwAAZnhJREFUywz2nzTDIWD9ukP0lScphlXTSpelznvHO+EtAznO090BZwfCZik7J64lIOjMvFgDaQ6QReaZZC7xDMxxuW9TBUE6B8l0GPZ7G5e8Zfkim5cvsq32LM8M9FEqQpuEcrvGShCxqVFlqNmgLkL+duN+FgeGESQmp3cHFLs563az05StIGghmSJilcALAFn9G7A7wGoNqDsXvDTxjtPCU4EUW07a1+5+B4YuejxFgbW/uevT/ztLSgUZ0FR0Ql+jRU0llEjom76DXFzodMQFpMpRevqVLO/+Fjqo4fegrO5qMGvRxFwLQAmCdoVQVRi7fDdnt36UJGjap6YeC2bPPCaFRBcVn/ge0lpRO72DuT/996hGGaENvDQnt1NSRkDvHDtoIlB1Y1nKrnVpu0Tal0hIfZns9DK5MwRqJdehXLjpmM1ZhoC4KTnzYJHaQsCeN68aZ2FhounTzYgYIQKUbCIpmj18V7cQs/VgtXjvm6q5Snb3IydWaOaPkI9fAhTs9wkwDwKkHCRqGv8fHZkx75tPaBQCVnokYrJA8FSF8JMjiG3CBGvZwW5vXGG5kGfTUg0lQ17/1EV6azW0FlxRuxi6fBp5+wbvjeH+F4CPOMpo9AhYoMJXvnUnr7npb1i/5aLpI8SLjfXXOdhnHTCF8eu60NeiMAuV2ACKO5xIaoEUmkALpAjMyURSmyxrGnxsmxsoRIqSjum6SS9ga0nxmuGEL80ErFrni0hqNpc1l2qCRpLRk5zTHxpZUrz8ljavnGvRIyJOVgO+MhVy81CDbX0txoISIqjTHIcz1HnqzABaBFRygoPjsLkPxnqgUsBvB0ihyYXm3Xn2CQXa5vKWSCJtE0sIm/XOZohz290Is8hrmEkRYpz4NFCSJv8+9sTUyDrIJBknQm21BMdwVOT8iC2uKstg1uCm7elUOFEF1OqNbGtBUNGcmSuRWOYQ1wus1EftOGjOT76CzeOPcNP2v0KKNlIYxtlxXJ2288SpDk5SWBvqkLUFei6f/bBGgHCArzKgnqV6A/rLGSmPzvuu8jr6NmTb2skBO0HMXTZYnOf2+rxNHpYBO8sxe4j5vvPHeHp1gsuFEmEQcFoPs1os0RMtEGrBUpS30RipkTpBUyOiQUjbcqjOGHrnz63JERPZhDouwjuF4ey+Oz4iPyOWrxkCZyR3GfhTwE/Pd8sCvbsj+0z7rcY4XM1V0LM9CAE3DR3nwLkTHNoYMdsbsrD14+Rq6xm88hYCVfLrItERYXATYxfXE/QfYaZ4hXZuLjNnJO2eGknUNocTumkiNFOD91GTl5BWm7d65ZrRcz1pvnG5DJxQsHL/a1BLvR3dpLSxsEkHxva7GPMe2CnmDmiTKpUpRXaJWN+bWNtzl7BJvoSZulFJERY0cSvw1+okYxAVIIRg5mSOxrJk/KYW+XUNCsOaSOSt7KNp6XkW5FcZ4u1A3tZFk4QLhAxCLAx/sNbMwLJhty2a2HZrDUKVkUkvibxMqLaaPgueIgkesVaEG2iWBmmJFZzwKkdWCGgR/M0E0V+OIFYC4oLZmnAZuQWQQ/PmZ6Z4y9OXacuQsBnC+pshThh47jxSzVLtPUhJ1DqXqBXe0n1cYXlqxN+eu53SDU8zuuUCxn/KztVrCfgvIH3vgD2A0NRFi/mc8ZTvS0zcemj7X2KOnwzQSJF6uJqZJyz4K+Nl5kBCQ7qPm2GuQnPHoCKQ8MSSZL4teMfGmE0lxWxT8OSSMWatL0FdwXxbkgta7B89z0BtA7nIqLi7B2K2DK5SCBOylBOaf7N9nqf6LrNav4H+UsRoxfwW2SNZXYKdQICUGiGMv7OJ82giRNPOqBCBEXIgICcEmhB05DtRWy08EFCRJmggIbOrmNjwOpHmxXdY6khroykIba7Xgd37t8wlsBr92mRwrgyBFQjQ5CLYMwrLDZhaTZ/hzX9Wbbk48xLmV3ZQLswy3PcMkVhhXf9RRAj5aMWewOc4E5kHZtSFbOH+XafXO+D0c02ni7rDtO7mCVBrwuQclItQKpjQx6yH/VWgnylfpF9dRX+PXNBxQUe7nIAgKSQNbpk+wy3abO8s6hLtMCIfNKlFOf56y25mCmVio2zZo2qkTTobe89xs+OsMcEkmjzK5npLzdHGnevqgDbD6F0+fWclAAd3DqxTME9w2eBUBuhduWpN+ebdwqfW6MUiaroC39gDMz3QyCODhHPvOMHTewchahPkYxALtHMLhHGZ9advot670ZQljO4WNocRU69nKFqk1neUsOcEyzQQRIysbiRoVoxyZ83itdwMs32Po4XzLRC+lcL3TAr0qaDS2U65/Rj68QNIYaRsN03bjk1lprI7yCpoG79Rv349MKcdZZep4xjp9qO9RGKAXCvhp7XbWrTp6n3kqVbGpF+9Ihl/20P0D7fobR9ABHnq8hzL4hQDy1XCCsRCo6hT4wgNcZxisJ+S3kw+HkMJd0CQrZ+2+pdV5swPAqHzNHKPESWzRMk2EnkCd7RsSzyN2XRNnTRVHURDEv3tIHLFJDFpToAo2MYDOwbneWdumomTC4RJQtiKzX5jWEEkdUrxFSaHtjO4runL9ktWY1LpKjuiEqZWRvnmmVuo7D5Gf98kYDIRigRWqmM0F15c+L2+wd4ni7efhURHgkQrakLSCGEphJ4EigpyCvJW43aSrsSmJrUamEYa6SDWZvCcR79PkpJyZCEELx+EzWWFEpqJklnE4yUYL2XsUk69FQGwyYjGMgGRIGRCwcV061RcFkJQijexo69AvSeN3vVgZxduIEyYiBHX2yCaQMu+x3ZjThvkxYYAiAjvqGgTaAgkQkhCy3ryQnotIdHQdHNY4lMD+6aRwSlnqrfnDzshAmH9DujcPfeYZLs5tO+BHd9NA5rJ1VSiE0aNTB0vZcBqa5iV5jBTS3sQQhFe+lcIGTPaf5yJ4ScoF2boKU0jRHLtPXiH8c5u6Tra/2ZrKjONdl8rBUmCTlw/ay/FiJUGrNRNp5ULMNAD+cjKGpq5yKQwHmy3rezi7nXgrNMys+9ZWistfTsS0OGTgnnegFj1YRe97SY/9MwTfGnDDh4ZGicWzqFT4fK2YWYKOSRFHOxnyQF+al1x018DyUoOtZqneWKMZGCF/O5L1It5f6cD71SDd1CfMW1nxAcjM2k6GXsOtVxEV/OooxPok+sQzRzSqof1VYhbkun/+U4oNMgPL5Afm01rLOCJK5K+EmzYBXEDekfTfpZhP0PLN7M8v50hMUcidlLskyQ68HKf1pp6tERNNoitWOM2PrI6SiqcxGTH1sdVCCgePEzjS29DL/eTpmQ2b7HO6DyWfeTqEPkjlVOe4ffeLSjGQtAEnx42O4ekXes6MduI2UXrMc3uhKblQtIOqE2XKOw4SnN1EVEKWcmdRSPoK78VIQIQ81T5Bi3OIRDUg0OgjhIG62kHG6hNjREWipSLfX5LUEekEXYalExoixXi4Dit4DQBTYRVatJ4DnAbCqsFQaxi47OFpj0Itc0ik/VUc/eO51gqKv7upvV84IFnybdiK2kodJjnxJ3vome3JoyWrfKu0NrGTNhQYbNLKJiujvK/jr+Kjbd8nXLvvO04QXVxAzOnbmF5aRcbFu/jxaTrG+ydp4lbhDph59Jf8QRvROkySkBDmHPYIwzgVxIoxSYRjvEGF2mcvTSSrJnxys/iFBid1iTSlxCMp0mnLDmmmkVDx8gV6BijAls12B2f6jf3AzQBdV2goWTqf2D9EaPAaOCR0ITCMVfrES5sbGHWGw4rCNACmkacFUXSMEXnOGSZpjD7kIFtsdPAs0xdyNTK7czzqRsXPoQwS/60PbwwjdL+KGiEgFhpVmMYCIzl40ocoHP4KEIncFwFbF4ek7SSEiRwYeblnJ9/KciEwco5pIyp5No06gGDpXOoJKJSOs/Cynqa7TLrh46BEOTCGnk1T6CbBJWAenOAXLRKPl9FBomti5sLkpgCCyObqU5soLi8QJC0iVaWydVXyS0tEK5WEcursFJncWKUT+5dz3IouFjMITRsq9V5/ewSu1dr/sjmqwFeX4XzHUDv7rsK/dcOgv2vYzDNfwIoqjavnjzNiZ4hZvM5nBOf079drrXIxmWncL5mLJLAAN7ZflRL0rjQR3u6gp7uQ6wUEEIQBm3iFUXzFfM482ZWnDEzNw23M0VbSHeg1wgJHluPmOpBLxVBBSS1AtWZCokSJnGky7amoC+G2XloKwEUoFqgNdPP8vGt5nl2KUoBCwGcfwrv3+l2a4II1vW2uTQ7DmKcKC8YGoUtu2DLaMLy2Flqo49wqfwsNVyO+9S7wTsPd3SaY2TKf9ZucEqKyg/+OY2vvJb2c1uRSqKlNo7HiTTONpj2yQRknFEIXJ9KfJhYe+g8y5eWKFZuMmt0LbtyFgBtWJWvnqu09TU0+ertczNCxfyjt9Gu9tK76wJj7SbVYfOA1eAsMcdoMQW0kUhfZCtqMxudQerTiE2a+uIuotyb0FqQSIiDVRIaSD2I1opm+Kz5LHJI2igCXByH9DPViUwJvTVJErZY+eAJmM3TfHoCvdSPiE2jc7mYIyMJpyqSsFjhfHQj21ePIBPDyFaKfWx6TUy5Z9V2Rxrm6IFeGT+XZ6Z28fXzt7D1ti9TLC2TJBH1xgjnTr6a6Ys3IMIcuYrwEVkvFj0vsI/jmF/5lV/hz/7sz5icnGR8fJwf+ZEf4Zd+6ZfMHjBGov3VX/1V/uAP/oCFhQVe9rKX8bu/+7vs27fPl9NsNvnZn/1ZPvGJT1Cv17n77rv5vd/7PTZs2PD8ai9FurmDAe2cnqNXPsG8vsMrSC5yK5ZQt6fPlROj5Rft3j7aaPtagEhAiQCktr8ZcTZlR1ZNdbliva3Msimv5mb/1qZgv2Fm49eEBBliAu8lWpgEFy0lWVUuxMg8KmcT4UWBBXpiUInVWK23jUhAtIz4nRVQjB5AR2486SA9xGj+tm22SQqvBIDudGlwmoLxjO30j9DagrlOTZquO5RIa+G0EXvoHXkBR2uCcw1NRUikljQ00GObtRbsXOvW4mAG64QyYYfzyzsAmE1AxXA5uQF3hoW7/MyVOwEIgxYhq0gdI/OSdlwiJ1tEYYNycZ5KcZ6eyqQVDGG+uo1qNEbv/os0RiYyBkRNUK/Re/4ksl6jMD/Ln2+a4FsDQyTCaaqKQ7mIJ3t72FetcevyCrcuLdHTtvPtKjC/djs7yXFq+7dTV93fsEYd7Ly7p91kY22ZufwQoDr4vLs0IbbaqmWsLYluhdRODhIvFFg+MopOJK25EsQpUy/kTSoIBLR1jsbJnSS3P2aPiOxMcau1QMUBKpZwYRgdJFDNk5TaxMsl9HyZaKZI7olRQiVoCxA9VdpJQKOWyZZuNV4UzNftLBfpvPQylesOiT/sUDtJ2/mnCEiacGFmyNc1acKli3DlMgwMP8jM//UFpgZXMWlbQwv2aUBhqndmxRuTjz5tvUzFHKnI7ztFbtcF1MwIw4+vZ2GigR6bQ54fI/fpVzM7vw7RNoY8BWZZB/idOl1Ip4ZobKZn0Dw2STrb78A+22Z/zoe0ArfrP5EKQB3TtJVj6Yl96IsT5O6aQww+hw4SVjhqW98p/Li+cAKHRBP0nyZJ5kANo4WmLU7QlE9T1t+P0rO0xUVTQQ9hrmfttiSps6cAespNVmmi16+i16+S37FE/dO3oVdzIKClIg4fuplg4yzl4ipf2inY+rBC2myePQeq0BPicpa4uhuPRpcKV3D00n4++8Sb2f2Sv6NQqhLHZc6cfiMz03tp10sERUGYNwKjkGsW3gtMzwvsf/M3f5OPfOQj/Mmf/An79u3jscce40d/9Efp6+vjfe97HwC/9Vu/xW//9m/z0Y9+lF27dvFrv/ZrvP71r+eZZ56hp8eEIr3//e/n85//PJ/85CcZGhriZ37mZ3jLW97CoUOHCILnIe7IwGxcexuhpqmHWWXchGiTCqKQAkNNQCsyHZ5Txpmv3Dbg7x26A2OO0c5UpQKkUiYbEnQCuqfMQ7NM1H92L4eUEkRkAF7kcLHwbS1o2LIiq0HnQwP2QjrTfYLQNp2mzggRIjaivWciKlM/jXeH9/UtZOpsK2z3/aTSaJEmEHLOOw7s3UhJYcLoAvtIpUyd28YhtqMKGptOV6Td08IaRqQTFARV2yTpZJK/R0nNvovM8Og1Q9Gh74rOvc7se6JyJCJnuqVpymxQpNXqo1Zfxxyp9uO7bU4Tz5bI9VepvGSWoNggjBJUscT87ltMGo+VmEtJg0C0EDasStlEK7HUPNGX48nefv5udIy3TE6xY3WZ8Uats3JrW5KRPzsvFJ33rG2ksytnQd9eG6uARhKR6ux6zWPMfrKMA1rzJWrHxmicGqR1pUJSC9GJWXFRoUX/8DyFUoOtNz1DbaGfMFKceuxWYyuQwHwFNdUHE8tGU1oqoOsh8fERdCKRs0X0fAksU1ZI6qH2z2hraAzCaBuGWnB8sUTdyMAG5FyzbTtFzoKX01jXdJu0wqkK0/lxLYHL+/Bm5ltAzI3iq0zPnGZq0zr7vQG3VLuXmXd8/7pS0mRA2vZ/xuMhEoQT8yxPLKRiQf8y8SN7GR7STD+9zjjdYh3aykaHkDLDiuz0kdaQ4IUaK8y7R/ldS2eyV/hIV+dwm003Ifw8spRvwr/4FNWoCId3Eh24iAgDzEmjGX8pQBIh7El7miUMByghKJriNcTtZUS+TsAKbfkciIbtxZA0h102pNHNXlOxi/RTteUBiFyCzLdJVnNegNFtiYihGSScKyWgFC3yhAdj5JaUtwsfp6hSDQjB5Oo6PvvkWxnceJx1G56m0Rzimad/hHptEIQkKtqx8O47L65q/7zA/qGHHuL7vu/7uOeeewDYsmULn/jEJ3jssccAo9X/zu/8Dr/4i7/I29/+dgD+5E/+hHXr1vHxj3+cH//xH2dpaYk/+qM/4k//9E953eteB8DHPvYxNm7cyJe+9CXe+MY3/sMrJCJcHnitNdX2Lk4lP06VLeZn0elQ7UDBG8yEMUO1clDNQaUN5SQhJ5qIVslv2yohINBoKe1WuzlSMUWYa7zWopMAz1mFQAtzhGpiM1U41zqloWXvLQTG8S4XaCIJ2pnbhYl27tgb1dCRJU5kAj09Aqq0LroN1Cxihf4a7TpOGydGF4CVFZocdiSkJvgA/PHvTpuSKlUqr7XdnJWNhBUSNufgcssy06y8kunqtSCfHduO4nXmPVMvPxy2MF/mGkFbrPljLV52gGwsqD/RS0P0sPLUKLKngSzEROtq5IZa5DYuQG8Lk+1RY/zXzSGtHW5mQjOdK/DRTT2MtJr867OX2D+3TD5aweST6ED2qyvnWrO2r/WaDxqM/0ZoEFDm7feKyeRG9j73ClbCk1zsnQegvFpESUVcXqCPaUKaaBUx+a3b6T2dEDcEkDCw/QpRvsX2W5+ip2+F4YlJpBAEUoOWtOKQsZ0XmJrvY+nCJq6c3gFH1pHcuwmkQF3pIYkjlBAomeq/KiAN5XT5LxxJmMrBSgKNZpC6JmTlXPCn/skSJAZTOnJEu/kiBegQksiyGCfUZeZa1pHNAcBrS3/L60qf59zTJc6N53luvA9EunecNTNn9furKQ3NM6OdlXY779L5GP3uLzP70f+L+oa0nlKZVN458DqG32nMKCFK4sOE3Rr0R1TYd+vb6XWFlBFYpcAKvv67XJPiG/4aOXHJpJNtNyksvJryyCAJsyjOYVyAIcdWIkYRlJFUaPIwLR4FykhKaT6Q/DwQUBQhVU5b/ict52zjki+l3CpLgkS4zIFGdBWhIrdlhsZCOdVahheRhToCzY0nV4mTAq2XVCjvqkJkNx4dA1FpB2ktmVpZxx8/8m5W2nkO7n4IhGJlZQPNxpAZMStwdfRTtiNfBHpeYH/nnXfykY98hJMnT7Jr1y6eeOIJHnjgAX7nd34HgDNnzjA5Ockb3vAGf08+n+euu+7iwQcf5Md//Mc5dOgQ7Xa745qJiQn279/Pgw8+eE2wbzabNJtN/3l5edn+ZdPlAgjB6eQnWVY78JydVAJ3IO/XvzXZa2EsiBpYzsGKFBSEZEjNEySDmWljPNZVYML3fAFKGy7kV9IaFPJ/pkiodYiWIW2EP/U7xoa/AEpreiJFJWqmSKXtItcic0fWEcpyLpeoX7hrUi3BrBormgsAGxgvbe68rKuuSDPdaaEJhPAhd23dOWldqJ7T7N2eZxga03ysoJHgTwdMe5SM7I3fdgm1sQq4o969dqHpXMaaDvNrR3/bC7Nh6W7cfQReOjU66RocOHuNs+xmHRT9dVqQzEUk8xEAzZP9Btr7JyjcuMT2K4LWy8+ysGGRdq5lwNaW7s2OVsiayUd8pv8mpr58gNHyGQb7zrJh5EmEVAQyJpDx2oenDbiqUdlWSIgGIOwx6yd7rYD1Yo7+9iIbDt/CuVWF1lBs5FkePcezr/hziMxRnzoHO177VSbuLNCSRUQzR//QgjcYBCLdebaISD7XZnzneYoaylvP8zU1yIX716OlMNY0u6Okw1QgT0KjaQtl55iTWd2csOM518q4BV5DuMxGW4ocJIt0RGlmZn/q/hKCyqUaLeB3RFwdhITNhbO8u/cPiXSD7Reb/IfP1fnoG/ZyebCPhf68B3u5RrOHNc8ljUPQVw2iEwozFgCh0X2zJMOXYX63b0cgDeALYaJgcolZo0KYtmQzZHpBN7MufLdkjICuOt4oKTr/BhD5JqXXf4Fo73EQpq3BxDSaEwjeQI4ygq3W0uEEGen7I2AjCU+RZ48tU1MTx4iZpCJ2EXOaNpdtPTWKJpIAhUKSQxD51dSxNNZ8Z4KUFCLUdjs4Jrj1KOQaNB7dyEDtadRdktKGKkE2UspLQVigFzw9u5tPHHkHy9Ve9m1/kHLPLKrdw8zUnf6JV421Sufji0XPC+x/4Rd+gaWlJfbs2UMQBCRJwoc+9CHe+c53AjA5OQnAunXrOu5bt24d586d89fkcjkGBgauusbdv5Y+/OEP86u/+qtX/+By46NBC1QEIhbXxNrs2nd/+xBpAGGdkrWkQYGl4gKD8RyiMeRv6thDVU7kFGmBWSc8/yQ7vDLDTSSIIEEQEiCJtaSuhY8rjQJFubgMxMZDHLtCfTIYjQkaTjLPyTjnaZX5PcPJHPcUrj5l0P2Gu7oTbJxrrW2PQFhnQLtApHWTsKAeWO3BBfFlmxsIs/WgNRQCA/jtpNNcKDNdo3Umdj8L9vazEww6BjY7oBnK5rrxl2WFgzVFuHs6Mu12SOFXXw9clXjkKrKJb9RCnvr9owwqePmpQWp9Dc685Dznb75Is9IEtJf0MyyJ6ZGYozctsOObL+PM5Es5evrNCDR9lcuMDZ5k89hhivnlb/fwqysLEJQh7CNN2eh+1/bpK5TzX6G+eAfDczf728rnd5JX38/FGx6gOnIeGST0FAUUFRVWiXpWrT90mnPPtcZ4qUgaNuVOWUAy1GL4rmeZW55g+cwms40jMlqzlVsTkcaLe1BeIzjqOlA10z5Zy0htBbJmZ52AbJAOrFgz9m7O2SzIMmUzxkvdliMkhPmEHx3/GJuCM17YHVpu8O8+e5ZLfQM8/PIBHt27GS2zszcbdpdSGn8QXxPws2bqbGXDTacJn9mF1MLrHQFQbEOumdmtwQougRGiYtcfIhXStbLttf4tzlcBMiAvTBneJA2IXIvy6+8lt/cZy0fs1oUIiLlAjacosYfQzpK1u/YAEevp451ICsRimYY4zaK4l4rYRp4B5nnURma43XmFoo3zpzJ5HyOEdR7NcozsutJAuHGG1lObIQlQQqJbefRCiYFNWzm0eRsTQ1/lFv2Ubbu4CujB5GD4yulXs1zrQ2joyYesLuxj5sorqVXXe+XCFZGCDwi9ZhxfYHpeYP+pT32Kj33sY3z84x9n3759PP7447z//e9nYmKC97znPf46sYbzaa2v+m4t/X3X/Of//J/5wAc+4D8vLy+zceNGs18vDZrMsY/5yhgqgLBuF2lWWrdj5TDZLzSdAn9Wua2JUcL+Kv1XDMgLnBag05u0SGO+TctNXLcDfJcHN7s+pUaEMciEnBRoLcmRI05K1JQgF8BAoYaQDYTT3L20kn12du/dfnb797QwZnpn0necKSNtqAjowxxyr40QEGr8SQ7pwCCR5IQ22jrCixBuPz3Umdh59xKdKUqjABAm1KWV2OpmhS37HmH2/9sZrV4DjQJUeyG2nvk9K8YEKRPINyDXgCBJ6+DAwnsVO6DX6TVrSUO6ly0y113jYs+C16oPa/mw+9oJHQJkElCeK3PDvXvY/Ngmnnv5Gc7edh4fvJwtUsK5W84zfHqUnssDNJq9CKDR7GNqfjfPXbyTkYHT7Nv6RUrFeesoZGun10IJIEKIBi1YrpGI/fwGqJPPXybgJpSdiWjJ0IUbGLy8k8UNzzB9x1cQkQUW0fb+z7IVEKwUWR5s+13UJNMxof1bKsVAdIKX3dnia5cnaLdDY/2x4GxPODVJXGQqGHoBMAbVADUPehEKiZFZlwrp4U1ZY5WbnCKBwrLRdtMJayJ3Yie7OwnF3a/S8oQizX6L5u1b/5I7S39t4lP9AwX9apn+hWW2fuUyjXyB5yaGWClGmK2cbKS9IeOymaDtGYGdWQDxlVm7540WBCsVcrqjOeQTKDSNZp/tBp8/TBg5X9iMeK6PsSxAJZBIzQbZoiwT2kJwThW8c660/jwIjSy0qbz+a+T2nEaLkPS8Q2PN0LRp8CCKM5S4hRzDaIy0lagmA+0RmvkSIAgok9BgRnySNrNooSizmRm+SYtl35IUNxXSBji6pWsSO2e5Unaa26BOJTK8TlKIBfFDN8HdG7lSUDyoD3KzOmZ5iJX0/JAIVuMSnzj6Dp6b2cZg3yRBMMJc4zbmnr4NnQSmj/RVKxCnm131/QtMzwvsf+7nfo4PfvCD/OAP/iAAN954I+fOnePDH/4w73nPexgbM8fDOU99R9PT017bHxsbo9VqsbCw0KHdT09Pc8cdd1zzufl8nnw+f/UPLsOLdQiriAsslHaho4BcQyAbZmE7L/G1PNuPm04/o912jKSx1EcrNqljpR1w7y+cPQHG51U3HrTCeZQJDOBLYW1q2ryHyn8WNgNNH5KKiEiQ5IImJgLWaejW0y0bu+aQA8zvxKBb9vrYvPv9Jbs6hU1MoCOgYv52IQgkoIUJKQtkxrEBtE5M5kG7bBPPdDrlGJeSIHt+l2eQGkK7PxirVKG0vMK/R0CPhBXbtatlmBuAehkfKSgELPdlnqshX4cwgcE5qKyk8paXJexgX4XHIu0mrSAuwvytllkuQ34RinNWeMzMGw+jmWnQIRhcA/iz95vkJ4LyfJn9X7iBTYc3cmn/Fea2zLMyuoqWChUZsapVbvH4247wso/dQbRcyDxUslobYrU+yMzCdrZNPEypsAAahnrPUoiWQSiCwGo+QkDQT4djkF770l4IK5UukAtXaSeVjp9FEjF04QaiC4tMD6wQBm1k3zk0dVoIWlGOYEAQUcBliIjswwRmlSSJRD23F3pW0YUWO19+ghNf3w8IA0bCaOjKzp1EAm0jWLpBjZehPQ+yhY9ilRgQT6xFKTseGgiFojAlkLFh9JLUEa+krS1MGJ+rWJq5unY4tRVyg0Cx6YZL3PD9s1z57AG2ym+m1jHvFScotmN+6nOPMD1Q4X+9Yg+Pb1mPDhRCSXLtiL7FQfoWBvEZBITmzKaTrJSXDY9wE/UqeDCgX1nqp/zUXmaFaV8oQLYhbEiwoeKuAdpp9JHpU6ed68Bo89lxroQJLyst8I7KDGUR01aaC0lELATfbA/SlAFLRCyO1mi88UHC0RkL9MIKLEmHYGLO/Ztmha8SUEbZffsASQ+vB13GZeRa4mu0mTJO0ggmeYCElY62u5fb9JCWDzpveTSIdkAhEYRzfdQnJEq6I5YV9FehtwbVCjKCfOVG+t6saArDm82xtzqzLszsRYAWgsOTB3jqyl5Gh87y6v1f5MlzP0q7VXBmLGRs+ZwVDLO8yBX1YtLzAvtareZD7BwFQYCyMUxbt25lbGyM++67jwMHDgDQarW4//77+c3f/E0ADh48SBRF3HfffbzjHe8A4MqVKxw7dozf+q3fen61T4VoBtUzHEx+m9PiHi4Fr0PnK4hQEDZANI0G6Pf4ROe9npxQoEArgYphOYRIC8qJJtLCMgZ7sI5VAcxcMIAvbQYoU6xVE6VFscCCfZBYcdtcKUgIdZNAxJh9rpadEdZtTyf4ZC844LZo7Ne/3/m3QJ9FOuvmLu3uus5hjt9z1gGZMefqznuUDSuxszMkMLn/PeDYamT40LXCcUD4fv/79qoCoDYBZ/Omam1tmH52rK5lim8WjVd/vQxRDIUGjE5C2CKVorNCHaThV7Yb4wimXgUztxstRyQQtKB8BTbdB2EDolWuIi06y/Vlk7Kka1ns3PVSSfqu9NF/qY92LqZdaFPrq3P2jrPM7Johzrep99U5/K++xW2fvJ2wEXWUIRCs1gY5+tybU6AOVohEiyBsMj76JJXSNCND58lVypAIAumkF0FqLep8SdlEiMSDaNa8jpJUVgYItk2SCGi1x6i2JGG5TVkIlK4j6oK8rJHkA+8xbfieJGoFnFjaSnBogXpQZ/1EjTM751l6ehCVCOM4RsoYhcAAl9P6NcRNo306BdwZ0ooNG2lTMXOohj29XENfpUpbVFDSrlNphFDhhAs7KC5fl8Q80/eW0PQOrbL9lvNsvfk82289S5wTLO3cg7p0BRlPgohp5yJa+RxBkpCvmyOo1y2u8p57H+fWguBy//dRWb2ZvtUe8q0c+VbeWwW00Cz2LPDUniOosO23uSQwPTTF+rkxzm04x7p6wFCtzP6pEWR5kSd0wsZ8nV7ZYrYe8oX6OAvkaBJA3i576+joBB2X3sP7IQiQKF4hJ3lVUGVzVEVaE0sOzfbAbDntLi6BVDSBedngdwfmaQhnXNcY+01sZ2d2U8dMIEUdWgXyehtB2EszWo9mlqau0RBHqItnAUHQLpM0IpKehYwK4coM0ZmDkB3wO80+Nz3IyF/eRdiICJo5qvc8y8yNKyjRQosEXVDkXnWcytSNtHtC9FiJpuOrmG0nhSBwFlQv2EseuvByvnzq1RSiVXZtf4xqdSvtdsHwEoVPc+K2BrVPJuIWfaq8vFj0vMD+rW99Kx/60IfYtGkT+/bt48iRI/z2b/82P/ZjPwaAEIL3v//9/Pqv/zo7d+5k586d/Pqv/zqlUol/+2//LQB9fX38u3/37/iZn/kZhoaGGBwc5Gd/9me58cYbvXf+P5iygC00ETW2xX/DaHKY0/LfUg/2QFEgI8O0ZRu8EzsZJm2lLq+ZWs2zhfECjjW0pSCnjIm5FIsOJu536BUoJQmFsHnpbYGBgkghopZdtZYje5uxwhxfYYHcTS6n1WclEifg21mk3YMxn9OaZe8JgQLogtXq8qQpc5yXqUUnhflPabNhpxybMxn2IEJqM23MDlmKFdJWC0SaCjcDsBoT1+ucU7xp296XAA8V4HSPyVjsDRuugDXD3qFtZZrdCqFdhFo/9CxA36wZ92gVb3lAQGMY4lCwuhUao9AcgNoGo+lIywSTAKrb4MSPQVCH3vNQmoKeC5Cfh6DZOZeyQ5Rtd9b6kf2cbZsGwkZIWA8pLBTpP9/P4oYlztx5hqndkyxNLDG59xIbDm3uKMFbPOzcFQqa7Qpt+/tydRwpEqKoQX//NALNyMh5hgYvUaksEobNlBU7LcZWyOy8pu1ys1FoCI/vIJiYJBlZQIQhvUE/eTsPQoDlCBGdYSEPEQkRLRNGpQUXv7SRO+pf5sjoHpZmejm3PMu+xgM8WT/AnN4IQpiMi04At2vUZTxTdo3qAugWOPnYgX4hBrliKttnUVsA8cU+VJzOFZ1pm1uaTqgR2BwQgMy32XrwLDe84iRbb7pAvtxCBsZZrqkl594q2fSF19Kz1GA1F3J503lO3LaRQq3JyJV58s2E3Q+dId+4xMH2YXYuwpLqoaVehiDneZCpk6B3eZBXPHo3gcrurmkimdAjQJYXCKNFAmyMalRjy0AVI+zHbI/aHChNcW61n6frfdwbj9GyHqWGz2RmkMYI9ArKMuZd8lkOBLMESGjbznPmD5+7wyzOPG2Ceg6hZWZdSkw2O5Op0+QM6NSqhI6Iqm8mL4cJeiSKKgvi88RiGW2lc0mBQf1SZpPjmVEy76Z8g6BurHT2Gq2pPLKHcHoAMLylcGYPeouGYBFVPI6QdSrlLYRbisRRy7ozpTXtZcXH2TsGE+uIr56+i/tP34lCsWvrk2zbeILHn/yJVOlx24/OuCpTHglmQRnd6RoawAtIzwvs/+f//J/81//6X/mpn/oppqenmZiY4Md//Mf5v//v/9tf8/M///PU63V+6qd+yifVuffee32MPcB//+//nTAMecc73uGT6nz0ox99fjH2jvwoG9SN4hr98XPs539yZODnobYFIoNxIm9yRtMA1U4xNbvifaZSbPIX4Rz3jETcQtCWkFPanIwsRcZTVyDsYTBhFBpBQGp7HKQ2pj3Zsr4JLTIbfylpt5EocYl2/L695baJliRJwKWVIkutiOeqZbb1LBMrxd7eacpB3UxYoS24D2JEeze5oszMy0g+KssBY+udYwUSuw1gIgIC3LG52S1if7JeZmiyazxJILYBAp179RodKE70Co71S1TG3QCB98Fc65TlyvcOclkZx47b0ggsjxh+mK8Zpplrx+yvHOUrbzxAnE+b32EoccU4A0rOaP7z+81LAMVZCFdg8BhULkBUpWPrIFvHjHxmnncV4tvrMr+LRDJwboD+C/0sbFzk3MvO0Sg2EJkdGjcnXNIYv3Oztu91QNIqMzm9FQFMzmwFoLcyx9DQRcZGzzAyeAFpTa9GHlJE4TJJq887fXUIK408ucP7qN91jEYhT04oKwwYoTVap1lmI6toQi0oLCxy4kjAzOIsG1jm2MAGSuuW2D6cJ5I5VncPs7X4CJe/eoLC4GvRSYDS0np9JwihSNohKhFeYNQCdA2ECyzJzAGf7drOMyGsc53smHq07TagCzVz12o7EPlyi9f+6Ne4+fXHENIEo7ppr5EooWjnBCf3C176zTKVJI+6dCvNm9vI3oRL/aNILZnbeICRi0+y9+sP0asP0Sse52z8DkryvdTI+2gcVznTz/b8AQEFqSkIRVhcgKCWOqFaZcFwrBWghhAt8iHsKjfZUVxhT6vGmeYISuY4n0Q0EVyOBf1hQiISRkSDg3KKQV1jM1WTqiO76Jz3nlDGPq3TBF2DrSa7L+Z4clvbZCJF4JJ1GS6RhsOZPtMI3U+hZwApBG3O0uAZ2iy6UaHS1Oyc28Bz6xJ0ru0h2LoM47MGkW4SCCuaOvfQaLbfz3+jyxh8EfEQfReHqW2sEephEBCqgLYwfhIRCQWavCp+uGOhaSFZaA3w5ZN3kSQB60Zq6HgvTx3fRKM+mPJCzxMVxXyDepBDydArlNfSx14MElq/2AEA/+e0vLxMX18fS3/+/fQWbdLkGDPfW3imd7b0L5niHd4cF2K9xJ3jegJxPdUenQOQHwcnzAoXRmRMgc4i74GG1EITCHs4Tc5ohXFohIwogijSBFGMkDbbnZ/CCuEqoZMMyKbioVaKlWbAc0sVzi6XWG5FLLdCEpUuIoSmErXY2zvL5uICw/kVClRAj5EuXGUFAJFpgH3ZnAWpg59Db+eCGwJ5zA5sRFsIEmsR0GtC61xcqX+EhlYb2m4fy10nFBRaJK86yhO1Qb51ehs6FleDoat+Zu+8I6mHA/zMfR1CAPg8AAiIcjELBwN0BbMv6IA+I+y55zqw8HXO/Oz+DlegMAWjj0FxxlgBnFnZaVFizT1ZjcT7XjrAdjzW1su7hmRNgWmV0ra7drvv1lRUZK/PPF+KhIH+SW678YuUC0u+rOW5l7I8+4oOWUuRFY40y7tmWbz9PIFsEhKTR9iccbCEoA70tyW7j8ccujSFKE/xpYUb2SufJKiOURnq46mlU6ws7OTgZkG99xj1XU0uPr2b2uIAAsnGXWep9C/zzCO3cOKbN7G6WAYEahH0kpWjQwhC/BGsMkgBPhsx0VxVlGSdthBUa0WkEmn2R9c/gaLYV2fHS57jtrceYmTLLEhlp4HI5KGUtAhIkIgW3HWvYGhW0hQRh8NXMrmjSnLgaXI5kx5WasmuBx9l62NHCOIYTUDCO4j5DyidZ0Zreq35JNQaKTQFNFIkRlMJmghZt2ZKaycWLQjmMGbLFuhVjIYfQLwOVA7IQVwB3QNRmVhLVrSgIKqoZIWSWoWknqqjGUdD74IvtamDbGOcWFy6b830YMzH3jTHlcEW2vdSjCS2GjhoKwpqoKd5gEjcSSxmWAr/CiVW3UqgxAQT8c0o1jMVPsICj6ZCpNfqXcbBFOylTeQsEATVEhN/dA/hQq95toB2Cc68BVjX4NbqUxwd3UMrLPuVtFscZ4O+zI3qafqSZQbkAtLFLyqYudDD3OMJkxd6qI/v4fTmt5mfMkKie+kEBtsXWFyAxpb1KG32pQR4E/6e2U/zfff8a5aWlujt7eWFpus7N77N5uBwsd4qs7g6DEozWJimObPA6Uab3krE2IC5RQggtLilIMqDbqTz3BnWPYMXHiJ9Nr0W6VoInOZg3wNtAUVZq4AyuF4UsNoS1KOQkUqI0kalNLvnmghFSIJwNkk0cw3JsfmInIhZbERcrBaotwMfspE1kRtlTFCN8zwyv57Dcoy+fI2b+5a5sWBTVGq8kJEijVMFpU1tZyucJrQl7RUNVvPTaEIdIZHECpJEeBM9rj72GS5bl9t7Nx81otREHzyJ2jIJuRZbG0scPbeeWquQgpMdjEy6gatU5w7wzCJqRgvNxgQDtBohhQc1zX0Qb9Id/hdpp6Z/dwjhWcuELT8uGXP/ykbDeyvnoPcs9FwyTn7+2iwoZ+53qOvbJzLyhSbLR1IZLFNGNobcC0pZ7X+N8LRWllI6YG5hgqeevYPb9n/ROjtBvniJIFhFJWUEnWmOHUsvnh2kesMiemCJBEGTxJv+Y3Km/EaLc6t5vhg3WVeoMHHkmxxZF/OqXWUeaw0Sr76OV2w4Rc9GyfTWK1BO2DUyR2j/CRuX/bK3fIUt+0/yd3/wL1md66FnaJml+X4zt4QBey+/Ytbn+p4F8kHC5mIDEVW5ZfgsNw2fYyEfcOz0Zpb7JLObjRfK5JlR1m2dIci32XbwNIWeJiIwSW5SYcedOy9wp9kJBConOLVXM/hNTV63eXn7Ca48+UaO1kbYUlglLtU4d9NxzrzsJTT6h9jz5a8QtFsE+s8JuIzmDjZEb7Z9n9h5Zp0XsiG9GvyZr26AdTHdR1M9VnKMQNuEWToGuWqk7dYKIZJ+AsweSN2+g9nDIl140u2BaAPwQZyZAF7iY2Q+5N1fGOLrN6/wyN4VEplYDTuk0M5RaUgWKy1zMp3W5FqnSfIbqIZfJ/HHxQoEERVeRiscp8ksVU7i8grg1aqMsuIto+ZvN+WD5RLhYo/vLq0hrMH+Q/P8i033MdBe5dmhPbQi7dfUweQpXq6+ZZQrL4EL0BEXnmoy/pd/y0CsGaps5W9u/jEsm78qE7UWRtG7IiZo9wA5afxeYk0S1Mi3A+rlp5lpnuLFpOsa7JUULNSHOTO9m0Pn72SpMcj06gQaGCufZ7o2TlOF5ELYOQF7NkJPEZ/S3ieECEBEJoxH21B1uBrwNamzjgd7MoCPXYPuPmVM10pCS8BMG2IlyBdT7dYZu3IISgS0GhGPTRa4uArTzYCWMjn7JeYs+lDgw2MAv0/ZocQJY7adb/Tw9WaF8/mEuwaaVKRCCpudxHFr7MxMIiu9xyBy1pThbO7ZBynQLhVQQkCEUKE9/E14v0ArDJu+U3SQzrXR26+Q3HDWeMRaJtZTrDE2OMupixs6NWHbv+74Tr2mvA6e5z6KzMspJpZHaK3NHmkM4WlNPK6Nl1YWZSF16Mz0dxboRaYe7plags7B8nZY3gqyCeVpGHzGOPrlVywvVWvwOIu+ouNxnVKB4z9rAP8qgcdel53D2WZkr0+LFlyc3EmltMCerY8gUeSKlxgc+WvOLLyCnuZ6pJ34zldDAEFbUrjQR22ghjvc1kSLm+NxJQFXLhTZmZyg+o2jvGbrDso338KeTTu48tAD7A/PsLqznzhf45x+CXH+MRKWiGxJzulKIEBqRrde5i0f/ARLUwMMjM8zd2qEuBly6ss3MDzQYO/CErcMzBqLnNTs6J8iEjEuFlsKw8wrAjbsOs7lvYqLexLTr9b9353QmAX5BHesiuMHaQidRCERTG0ULA4oBuYlyHnGC3/N6Om3IdRW4lyD1cEFFjZMM3XDDTQHBtlx/9fpvXQZEX8D9MMIOYfIvQ10ACpMc0vjwN7VRqeDRwDtARAVkDbZgHdicQKCaYE5N6Nt+ZOdSMJZCUSnZExGOvaTzKqx2SQYAoQWjMxHvPmhAc6Ptrky2ERbAe0ND42w73yJv7ljhhObV2mtlLj4zA6inQ9BbxUdCP+IkD4ihmgwx2X+CkWNLLgbrd7Zas13TtxyrnkA5Wc2kSVhZ/tLkxOsr8/RCiL2tJ5hY/lp5hlhhCk2JpdIhEAkcDkapqQbDMxPIx45z/pHZtCx5tjmt3Nk57tZKQx1COBuKJTlAUkAiQyMY15o+r869E0WRu8jXw9o5c9z5kiNF5OuazP+X/75T9M89yaOPP3mq7QW/9L4PAjFHAz1wZ17obdAZxpMx8QTYBVEPZUZlbT79mu214UF3jAD9lnN0mTcM3n424EpQwSQL5kDbRyQJDFUazC/DA+fh2rLpBAII7Ml4MzgfnrrDOBnwCiQRhB37z73u4IyitFQsaGQUAkUuwpOa7AN0yLd33AH6oRtY6rwDMAlMXKNNPYorQPiJEcrlsasD55Zur1PzxxHl1A3n0ZvmgKRPVzITMOvHb6Z42c3IWwMrzv+wA2s5zNrZm3WXO88jL02H6SmXQJNddsKzd4mWityl4vkJ0u0boKkz/VHR5V8rqTs5LpKALBzzWviKpWp3KVR3TiKFhah57J5FRbNd16RUCZUam1egA7wJxM8kU43vJUjy7OzF2TWSNaMv5aisM5dt/0FfeVZQKPrMcsPV1kafC9Becx0kchYvAQs3DRP9abLSJv3H7TdVQvJEbA4cx79wEWi3gm2f2mF2k07me7dyuWqZKP8JOdG9tN/sczuwQUWN5/imf3niYgpIymQJ/BMHtoELNtgPqMLgj8VT2s2XhSMzUq2XhIIBbmWcdpcLUKuDeWGZm6DYmFcUe+DZkUThw4OUoDPvhL7DJ35Lp2CmQNYtGToChx4KKBUc+tlEFoHQVVoFWKO3vUICxvqBCKgXNeMHz7Cxq9/w+ZICKH8c2h5EJKyWaM4BhCb9albqcTbIS0qEA2gmZk82UkkDUNaEy7sJWihjTMTGq8JuRBCbQUF6rYOig6ml2F81WJALa8tv0joqwcU2pJaLuYjeyNOnLwVNV8BuUowNEPpLV8jLDQh12IofAsgmOMh6lz0UO7G2sVIGEuPi5lw01sikxDZyLHhz19H/txYuq4EhDLm4PpD7F93DDlYY/5AFVU0c9Wt55AErSQXW+sZfOBpNj/wAGK1TTOs8MVbf4XLg7fQKAxk5godUdjKhjE6WU0HoKVite+bLKz7OFq2cTAzckXwmxP/v64Z/x9Cz7yhwMqjCvE0HdpLRp5NGaCGeguuLMDfPQE71sGuCejNZ5x6NGbu95htKek0/CynFSYLnANhB8BZY5K7VGm41FL0lhRCma5WCVyeh2LeXHt6Bi7MQ61pY3uTdN2oNcK2yzznE+ipFPgEpk6htKfiWZB0QkFVSRZbkmdWQnbkNbsGdYYPuD9Mnml/xFwirSuyxiQVF5nTb8CxPKHMFkQg8yQ6tMxQOAOJ6Yswgf1nSW44D6WG19bc7042r5Tq5jvHn3TH0KbAmp0IInNNRiHxYB9hDvEINStbatRH62h78mBjR0w8WifuXSXSg8hmr5XEU/V67bN9pdeAflbTXyv2C6BdMPuG9SGY3wUyMWBfuQx954zZX7RSZzFIGUn2uR3Oee4to7lfBeDXQvRMNdde0moXWVoepK84Y9ZPIaA3bJK/+CmWd/wIca7P180JWeXzPazuLkLB6L8CSCw8Bg3NbSfX83fBNJtqw4iJgK8fmSccP8m6/VXOfyvhwtQtLG8VLG05STEYJ+YSJlGK9vPD1bROQEL2wJhMawSc3Sg4twEevwGE1ozNCC4PC+JQEMaCDbOa6gSUAkXeCQkdAC8yAN8paLnfXa4Jk3E9k+JGaObHBQ/8C82m05odJyCKl0GeBcrkm4Kbvj7Chb0LXNldJy4XmDlwG4OnzlK5cME8tfY/SORrCeQPYDzhi3hpXrVxTkaNnCZIIFJ2RQi3l57ROLz2LToZlpsp3qQUeLRabQSUkiUbfePKUKb8ILECgD1XIQhBuxwfpkd6VgN6Vq3/jz8OU1NqRew8sonjqmzmTaNM8myJxu/+IH1RjN50kfr+XlZvuo86V3ARQ1nnvizga2/Sl0BCtFxh9MEDFK8MU7g8bIDWAr3xsQh57MpLOLW0g1e+/c8RRRdA7PrPWKXC+RV2/MVnKZ+aQqBYzQ/x5Zs+yOnRV6Jk6CeEOzLYW/vtC0F6SmAIrfwV5kc/jZKxF1U00Cx/Bw7p/wd0XYO9Xikj5kY6UqN2MH5LWWYoBbQUnJiCc0twcBts7DHpYP1NAWYzPc4oRJZhPzwJtRhesd7jSmpUEkYrbylYjuF4FS6jWT86zcvqQ0yu5jk9D3MNmyFOG/DPCunOagYZ7c62zSXNUyq9z4G9xGT/iqXZG28riJQBfa/lWYelXKjRWoEWCH8snfWwFc7pJ2NndnZhKaFteyTAav1mIQuUzZpnwvpM3j3LGIME9dKTJHvPe8kpPeIkfczcUh8nzm7yv+LqvQbdr7JFZSRrp9VnAgiMf0aoaQ23aY42faZGDRAo4oEWEBEndYK6IMz1mFNEsgjqH56+1mr7VyHnWs08q61rs3OyMmZeU7dA+TL0nYGRoxAu45HGP94JrsmaqB2x5nfXb3x7WrtG1iwhy9+V7yuxo5f8N56l9+zHWdj97xEi8EKyEpBbDclP9dDYVDOdTmxAsAmLUxF/t7HJ5nyJ46cWaA4XEO3z5HeO8bSocKa5g/7ePkq1BnJ5N5dumSay8ychIiH0Z8YlCBpI66dp6tYGhAVdt2K1ENTzZiad2Zh+H0eCkxtNaJgkT4WYHmIbHKZxokrW5oTvG2F/c6FkPvjUXyVs57dKgtP7YH4sYeRSie1HCjZeW5NrwLYjvaw/WWBqb8zc/ojJ17yOzZ+4l6h5CVSNUB6C6PVm/93Gq9NyXq/mee1AA5eJ5BA+HGNNjc1EtQxGWOB1+ztXSYaSWVUkRx5kC3Q9lba1hLAMsmwSGAQuyFKBakG8ZADfjYE7/9pNYMuEWzqXCsUSCATtZoHZOnBsD8XJGno0gXVBpm6i4//UGz9E2QOlpQ4Y/+rL6T++FezR4E4Dy85tJSSLzQGqhRx9HbMfZCMmf2aG0r0niE7PAyHzvVv4/Et+g5nenXQcH5j2rsEcB/BrgF6FTeZH/wztczMb4cUoYX/fCv3u03UN9qv/73vQs5s7vI6vpanYKenJLf22hsOXYbIPxtfPsVkNml0mgQH7emcZaNhYgZOLRjhw5Tiwf3YJvjYJyy2ox+bLIAyoPTLCTNF8L7DmZHtvlvn7sKaM4KHBn0aVJMbkn41T79B0tdEI2xZUnDAQOKVcACGcTgQfr0r2hoqtYcwgNvOMy/B3lcZka5vo9Ke20xS0Af0AUp3IdE4kzI5rvOcias9Ff4yASa+iO8tWkiOndrJSK6bCicSe+2zkECfwdAzyWtDL8Bov+IeapKyobWpY8PIJkDvaqQOF6q+TxIogLBk0TkQK7BlwF5lx6/hepIqUEzw7HCkzXeiFOQyDqG6A6gRM32j297fcB3Klc564vlk7Org+uwb/0Nk/nBCQZfS6Q14A4MLUHjYOn/BaGQM5xGiB/NTT5KqnaPTuTscJEEJQOlehvmkes7stCdCInGKuvcTq0w9TndvC2cNVyjtvZzSSxF/tQ1XmWXdDjeHVKVaTddQW89RPlBkf1cyuV+RFQGh15whBg4A4M3sEzmmw0+DbITBm3rMvEKyQo0oOiaZATIQi8JsC7jkdIj1pTjiX7jYmPeU8w86FYHEYlgZXiYNJdh5ZR9hKfcoLK5JNhwKGLsQsb9/A/KvvZuS+TyIbTRCToD4PpXtI0EzXhxgLVca3Q9BTkyA2Gm073zBrVwk6B9adp+l6IEwnpxA+Y2CoNcQF8o0SZSEQDBrzpk6MPVqEhpEEtlzl9qkEqAh0kdSJ19XBKhKZCTdqcy34bg3MJW5t1GeL8Ik3E77uYYLd58wZ5NacmPWQcAl1lN3Pj6oFKufGTXIz11r7Z8dRxcJt32bWPSDaMYUj5+n95LfsOg44N3obf3vbr1EtjtobuQpr/NJ3693sbBoFI9As9X+JVuGcv7G9XGL50F6G7zxG89A+Xky6rsG+eWWQfJT2/lqZNvulXwKZi9z56ecX4fELPezph1dsh5LNdtSh0FkGO1GB4VL6vSvz8Dzce9mY4T2AJwag60lEq2FBV9r9Y5H63fg6ZwDfVVXZhRDb/DYuLOta+f0d2IPZOwoCc39oNXwZWguAFszEgtlYMhMp7s4nRF6NdNJ4ZpEKTIf4dLsZMTYRxqQgsY0CAreYNKIQ0957CSk7j/QwdRc04hzL9Qrn5sa4ODuKT/ghbH9lLF3KbV1kyvHrWHT2SVYK0xIa61vo8Kq71vyvzNGmYQOlW0iZQ1JGxkZ7uKoBWUFNpz8LK+kTmDngw+psN2Zvv0q7EtDqgVbF8NAtfwNBJmuf0yRE5vrsnw6As2TzpXQIkO6Pq2QDbeq+vDpEs1mkkM84EW3vQUzPUr78ZRq920GExghkC6nMrtKemqVVDoGYBM2yiljedAy9foXRr/dx5wdq9B77CmPn+vl/e4+xizpjS69nqdhPUoLWkKBn3W7mBk/Q5iRthD3mRFJD0vQHnHQCvtOxHRx3svO0cWk+t6zAZxrQwBw4nCPp2Al2/yRZoM+WH6CJ15Qm02sknL9hhsXxKqPne9l8bIioZSaoUDGVy4ryZUV9/Tj1Gw5QOvQwQiWgvg69E8jcCG3qKN1LkFTWSGbCgHGzBOUV4xmWnmVH6qXpzIcJkDfheCpkmQJtFTGqWqAlPTLxdUfkSPf0LXOJ7cx1Z2j4NZCzdbpGFiznyak1G1jtXH1OhvKXC5gZoP2pN5BsnCR660PIoRrkjVOw5SpAgCLEHVtbWNUms6RO1whkhNF0onRUGwThxQV6P/cE+VPToAVLlTFW5QhfvvHnWC6u8/zIsb6O5rl3ZdmiY5ES4tws1d6vokWCCQzM0zy7m8t/8QZG9i0hVgd5Mem6Bns3gFlw/3ZA33EPdGjPAtBxjqeuwEoDbt0Em0tQVVDOSHNugoRWEBCWiT6xAF+eNHm0fYy0BWR/No31ytdhipUiID1kzlbcg4bdb1KJVagzJvu1Aoxyv2X273VgAD8JIA5sHmwN+agTDI43I5bbFV5VqLLOhtUIZ8boUKNdbgC3z2DMaEZiso5tOkgbIgGZ0B6fg94aIc4EanZhFcaZ6dClvZyamiBpCJK2SB3HRKaeDixd/66Rrv01mbmgs2McQFzW6YQhZfa6sxSU3W829zZRuTZBUCSIi4jYtTMzoXTmba0UIvAnMPutGqcBZO9fi7j289JmmL4Vxh+wbepsQnqp4CqAF1ZQu3TjZYoLJfou9ndoPf7mLOBnhMZWXKDVLlLI1dJ6DuZhpEB+5iSyMUNcGs8UpelvH2Pr1x9Ge+9pTSzhy7e0ObE9z/TrQiI5wK5nLxG85CQ3PRMztnGIp06fYlNPjQtxmaRl8sbr9jCq+CyCgDYBbQSJsRVg9msDBCWMEd/9HaNZBWuETwUA7Zub3SXtPEHOHRvrRILOK1KwT4UL956WoTJlpwKtwCyR6lCD1YEG9d4m65/pp3c2JGxaLwCtKV7SNNjO0v5e+p74IoI6xLOI3BCbSrPAojkJqjWGCR8q0BE6W7VOXjKBsGnl1zSO3DMYlfffDQBuIyRtQ0Zs6ZjrOgV9N1myZn6XldPniCWtn13Eo7rJmG5whYKx9IfWSBC1Yd0UaIme74XlHvTZCZq/96+QOy4Rvekwct2irZJA6wClA2OW/cYOBi9rVLvTmpPFBLc+kkFIpOBKawc9eopwcomezx+lcOwyzVyFSwP7+czNv8JScxypI3KxyZnitXrHYzKMxjcxAUKNFgpdnEH1P0FP0EtMgZACIUVKNy7T8zsfQ0dNUl764tB1DfZ/H2XZmhcswZ+K65iwUsYzPh/AagvOzMF8DUqR+dwfwZ4e6MvBWNFq6jHkrFXg+BI8PGszw1mvHpeyFOgIj0VYwJIWjLWpR7CmwoLUYp5YTVYpfL6dxCaw8ZNYkWbgtaQCiHLme6VIzz7RkLOp1Y3Tn+BsO2I67mdUtnlpYZUt+SZp3nzSG7PvLqkGUSq1eH6RmkWEbBEIw0RDwJyNplBacmxqK8/OTqBi6XP4eLB3C8mZ75NOvpEdW3/caIafZX8nAdmUqFw2Pte9zIEdWdIZMQChUMEqSdCkIAagDTpZA5rXAH+fLU/abnR8U6T1/XvJCoTzu2DsIdMPjn/6uZy51gF+FvRVqLlywxTV0SrjT42z46s7/7eA78puxwVOXb6FAzu+kvaFFMbspZtUrnyVyc0/gBYRYaDJi7P0iiNIVMfWcZDApvpOFllPFOSJG4Jj64cpjEa8qg5TKy1eeusMS0cSllZCto5/mQuXb0dvNMZ5haSdCbMykJsjxxiSMm6XXXtTdQ+aFppVFG0rd7tdeFOKO2A1E9Dn/zKKWYDzAcj2i/ucHQNzn8TEBDgmkAWa1ALgNNnpbcvMbasyfLbErgd6iWrm/AmBoMAGcs1h6J2G5WOw+jSUdtoSWiZCJjwNOgLVC3EfTVGkrYtUYjvRkgBUybc2nSlZLcQuFOF+tlpK1mPfTwin7Wh/mdd2HDkp3KUT9YCfhV1JiYRXbXycP92wQrR1BqkTpFYQtBGj8wTkiL/4SpIHDoIUiCRAn9hI68wI4eufJFw/j+xtIB7bTvvYepjpQTYizoWCud6EbX0xsikQOkAGEI+DLkNrq6me6jXCwil9K72PnuSGz32RcKWOCgp8dcdP8Y0N7yFuCrQSiJqR7fM9Nn+Dtch2LHfLJtMAh5hkw5dRfWeAhILo95FICcrssuQMN5QdJb3wdF2DfRS0O+ZihxmXq7UtIYxfiXQKKVZzplMIXarDUssM7nIDztdNR00UYKVtLFlub3bVmuq1Xeduyzu7p6uxwrC04JvZs5faat0ZHpxgj9q0r6RtXrGNeElckisy2orIRAQI2662UQC07SOljFAioOPoehCs6oDT7YDFOOBd4TTlwAG9a4gtxB9RZxexNMf1dqjRjjknQM8qZJimwd6AxVaZp6c2oVrSOPLazIdZ8r4JbmyyfCNTs2+n7WtXTSUIVnK0K0l6l4gxO5YmAkFnTZ+kPM31Z6Jj6rl5opky4UAeYtn53DX1czmxwcw3ZYvvSJdLev/aOHtXXlyCyy+H4SOpOV9Z7QKdca6+BslEsv7YOEf+1QznXnKeynSFdcftvuZaxMqSlUouzNzAjokj9JbnO34WaHLVM8jqeeo92yir06zTnyUMa3Tu22rmhoaYmthIJCpATJALSXZtZQXF7Pgj6D96kvlXHeTGIcHw/CKr982x8gbBwEwFBvIEofDOeAaMi+QYQtIDds9W4RzDwOVkN1ke62CPizVnnju7Urp2XIvMLnDq+pUG03W+3Fx2c6/z+8D+liZ3xf+WHmgbCZBIFrc0ONobc+Pn+8jVAGmEzKCZh8G7obDZZPtyT/SOOgLjGb8IwQqhKCN1H1CGtnGcW5SSvJxFtEYohBoiabz5wWo4dg54J9uMWVIrzLGBAnQIjcgCvNVwlNUuHOD7xWIlB6npOCbSLwoQWtBbWiK8/ajvkXTamx6XG6fQhRa6mTN8TQhoFlH/62W0ozaECbqWI4hlOhYa5pszbL15gYV1Y5RWB9A9oIup/IGtrlTQUr18a+jHOPb9b6O8cJmBqWm+VbiDOJb+gFEb/m/4LfZzlgc5oLfrWuXqtHffiy5P2UkReGummYExMdo6eMItlYd4Mem6BvvXrDvM/bOvwy9A0TkWDiT8ohYgLNA7rGq3oamgWe9kwMJqk27FxxrO1zqZhBMWtItYy6CMWzfKPl85YVeZOjhmITXGEpCZSD5eEzPRmk2I7UEfDpMkVngRhnlEcs3WF/aceZ1xLbL1cpKob2uGl8ypkEdqZV5SqtLjnfVso/w9IkUZodLCPWKZJwoEwWQekUgItIVWs7tZbZapNyMf7adjjzFeQLqm4OvAMvuV22IUGTnEXWr7syUFqmmnewA6iAxDEi1b33bauf5ux7JNwUontMaqKNEiaBcIWjmThGWN92f2UB7/dUiH8OfJfSfo2LZwczAuwZXbQTZh5DFSq2mmL1yPZ+e5o9HnRui73MfChgWeuucpQLDu+FgKR2vWDKTzpBkXePbKAQ5s/4p3KnVZK/O1SUanv8jS4GtYV/8cYdQwoSCZ0Jj5gUEOH7yNhaFBTObFBCEjNDmkFoje15HcsYcdM/PUczm2Dk4yu+VW7p5KeOIZQXlijL7+FVYw+/YGTHMEFKyvvtO1XUZ/EwNiQKOAMe+30bSBPIo2CS1cAhwzRKYfnBE6I6qm+SyA/ukyW54Y82vczE8z51vlmMs756kO1dH2NEtNFuKlNY4bLT/AZMxMkDT7FMs9FYZn8ibhQq5us9QJq9FrO78so/AOQemeXaCbBKIJBVBRASFK9MqElhpnVQUUKirNl+H3422Inp9IgtRjVBkLghbWHyeXgrnfNnBSuPvOBpcL8NE9/tQxtyhMlUtNSRQrkjDtfzu5EEiCG86g4/vRf/NqaEU+l5AGdBxBM/LjEA3DwB1GiStuHqEhh8klIUnFd48PLHLP96w2CKn2jHGmNkx7XKKqElVLlYcgb5ru/KiclfWqeEwBKmrR2PYNdGXKXm8tmGgSEhJiYhIbjmp4yvmCs9i8OHRdg/2Ff/0cpfteSf1MAcDnKPYKi2Wgbn66XNkeUGOoVo22m2h8CngNKahmLFpkJo3LBe9ON5IxnVYtJ/EpA0YyC7LKOukl+Bw1UlovfbsulIZGG1otU0+31h3A56QB+JyEQgB5ab7X2sbrK/yx9O5ex9CwbfWgkgGZJBbcv9THUluyq1izZt9UdxnMt1lXbKcerh7wVWcH2CUVXA4pPDRCvKVGc6JGXZq1cn5+xET7pWHDGWEirZutVnr+sx27LCYLYRrmMyKKVABAQq0HmlKYs04x34kIyIfWmVBYvmcHxz7cMWz3MC0EhBDTIpFtgiBP1CwjEoHWolNbz/C3ji7xSJ0yFaHp3L6zmOkFHgGNoUz/ZABHgc8I6X2xSIWHoBVy81/v58TdzzC1a5pjbzkGGkZPjHcgvOPbrn8NDxc8N3Uz6wbOs37klMVxYR6ooFI9SWXpChTszPLlaZr5PEdvOsD80DpbU0liNV9QaCEIR+uoO0tsejhhlsssTtU4cfZJbgzbTAXfZE+hhzwBkoBl8pgckwVSJzmJy7CHN9OnOrlAYpNQo9EERGgCNLWOdptpkoK/VNA3V2ToUoWJk0MIoLRUIL9qTnHzWgRmUWs0Ww4PMr9hlTO3TjG7ccn4HdgnmCzxga0rtFCUiIkSwcgDowyc7bUW9tAkY5BtyK9C1Mwgk5skak3NQ4wjna19UAORQxJQCKBQttcrUs2CzMsL6U6QcHPUZoRJoszz7LvOtN9TkJnfdt9QBYDNpW/7CmDHpUEGVgrM9jdsiW7kjG1FCwhuOoU+sxl9YhsiDkCl52U4HhrkYfCV0LPXOfIGXNAm9HkghiEb+u92FFxysuz6WV6BdiKtD5Gtpjb8IywYq1w6qw3Pd0Mg3G8CVHmB9uA5hOWB2kK9skBvYF7hrDugadwwy4tJ1zXYL20/TU/PXyL/8AdQrbw/aKUDctwEDDC5KbDzNDZA2m6n2pLbstKZea8ya8szUkUaH6/xHtciM1mya0eoFFydICESK0jYssMIchkpst6EpktUpa0GL6EQQjEw7yXra+BA3B//qU3iH61SAUhby1vgcm4EdmvdChtuASl71N/h5V4Or/SmbbeNL8iEvZU6L+9fphIopNSUhEsxojMv0/kCQe6ZMtGpXpbvmKO1axmBptbI+7C2LLB3KNbZd67xvSbNlifxWfIcHgG08rAyQMrbLI8U2gJGLrTmTWH7PjX1i46HprPKjJkmDpuoICZoFwjbBdMevg3Iu/fs7khGePHZSXWmXRp6zkFhEkqXMz2bAeW1ve79FzLd2jNXYff9O2kXYuY3zfPcnacZOjtMWHeMnA5BxbdUgCLk5JWDrB8+nS6UQNixtU8NZMf4zY6McPSmW5laN37V8BkyR8MiNEG/4ux6mFxfIW6NUfiLkOXdPWwM3kyYe5AYSZM+InoJKdlxcR7nYLTHKgENCxoRaa6HAE2IJELRRncApRMHzHuuLRm70EP/TInKUp7xU4PkGi5JRVrvlFKwF1ohlGL4fIHB6c3MT6zyxGvP0ay0vWAS26OBhB34Cgl5rRh6ro+gLQxDcHZhJaFRMp61hXpGGrSMzL2LHIgy6DLoHhASIQLSkyozldeQnoVsVVSpjMmIGJMhD1Oujozg4awAUtkQubTN6R7WmgnjNAllEVMHdt/J+VQoOuMgzCi4Q2zMZoqt+r/4Jtx2Av2pN8JKwQcAOQVp+G4o77JieaKZkpIluy4K2vAyZ6VyOTey1G5DvQH9zSVWgxJN8r5JA/Esg3GDmdyGtP9UKjyICESB1GxaaYLKo2QNhBEuDcBbkNeaIIlQoTHoQ4IWNV5Muq7BPiYhXH+WfKWFXskThWbwWtaDHfCDZ+Ie7XcKWk1oNUgBGLwmpTMvZxruYIYO6DO+Ll7LX4N3rmCZATTvsZ+k1rG2C3OXqQ9AZJWIyIJ6MTCOg+XQmu0tCLv5JsHzhJwwkzmxlgf3bGf+1zY6QFnQd1sMUlmfBNcXIi1TCGgmAY8vlTlRLSEEhFKxvVRjvNjkpQPLBFdlmpG4M1mTpvGOXq1XWK6XOsbHU+bvbBeu/c2TXcTSvjyjyNaiE6tN+52lUQsDVlEeEWjcIUCdyVLc4K35LECJBJVbRYcxQbNkTNn+IaSClBMWbVxxh1DjtA6R3ur6u/80DB2yQpxYc98aWis3Zalvqpc9X9vJY29/nOpolek9k0w8viGVGNb0UXYKL9WGqdYH6BGzMN9EuCMgK1bydLkLhODK+AaO3nSAhaEhW07G2dE+xOg2VSQNEHUWdkzSnCmQQ/OOXQs8um+F6WfHqYh+VmkDZUJ/ylm2djGaFoIGEbGF1QRNG4UEcgh6EOQRJDj/fKkFUSugdzGPQLD+dC/rLvQxcqmHwB1uISC1VLkByfSOQxHnOWsFAdkUDF0ocdsXtrE80gAB1ZEGFzbUqVbaIMzGQoOAXBgz+7pTbPj8NsSq2+chZTZxzsTyhjE+TlhISMZA50AUgQrokpUWE+PYIZRZc06CdFle4gCtAhAJQsTGHBmtglyw7XIRNqFlWMowIcdd3D69CyfyoSUO8B2Dc7H9mYxhKboTJIKdlwaY7Z8iDWh06W8zDCGfwPpp2HoJ/eT2NCMmUNgApW1m6ikFy7FiPm/28KWGsrMYZpZsNuOj1rBSM/culgZMNUNT9mAyyw9N/i6VyVX+dN9/phoNdCbyCkCUyBpUiJob6T3zduqDT9AcehKNIoxbFMmRNIfZdmYn5ZUJrow/wbPjh4nzLVS1yItJ1zXYz378bYz960OABb4AyiUIWyaEzs+/LKBoUG1IbIIbZ/ruAJYMs+241UqL2RB0t947zDu6s6isYuf+cIKFsJ8DWy93XU4YQHevYgDFEHKBCf1zQC9c27NNtb44UkC7ZQE/8yxnuXM4rlQKJAH4JFtOE9baasp+Y1jQVgKlIUHyWKsXsaRpJJJXDi8QuDOtO5BH2/8FZ+fGaLTyabY7q4x0nAWfHRDXsOw4OYxyGr3IPGrNWCpJum/nnikzQhfC8O58wTBA2qQn/60F/Gs/I5ZN4mILkQQESYH2bEShz+qN0pZila3KFPScgdURWJ4gjZRYI/QIDUs7YPAIqSKbAWU/t9YIN9n5li1y6PwAt372Zg7/qyc4e9t5xo6PEzg/hqskq5SacZGZpQmqcZvR9iyR68xy6CdIHIac27KVIwduo53P4ZIrpaluDBCHzCNYocAxNG3aQkChTM9IifwDgxzZV6BvaJKe8WeJhEIRYQC8k9JUucI+B5zG6Ez6sTXgu2x5mhZaK8bP9vLSr0wwMFW0QphACJn6nqwd3A4BNkPKxtQ6bTcx9RFa0zeVp2+2aLZ+npGMjzR58mVTzKyrkUhFlYiCUMxuahDdOsO6B9YhstEv7lHtnJnggQVgHUElgdVR63GfUVeFMEKAtIBvBR6EhDhC65wpD4GWCULHoJpQmLP3Z7QQErwVwZ0d7PftZUYalCnwuygcFZv1k/VSFvhFGGjN+FwFqWeQwmy7yA53SDeirlmqY/0IAcVNICNoCJiVglomjW2oodLC84ssC3JbXYkyvlAdvD4HOtTcsvgomxqnUCKg2FqlGgx42c4bVPKkDs7C8DWZVBh7doDdDxxny9GHqRfLrGy5ndGTpxi98lFinacpCvzZv72ByX11Sl95BS8mXZdg787uWfrGNuJnt1JqxpSiZS/8KgWtODXBazBCrwX4pGUGWmOuSbTRZtUa7pj1eAczh5M2Hae4OVO+N9mT8gZBOjmzpmWfPMd+dtPc7ekLYZxlC4ERrAMNgV2DsU4PwnJausg+i/TZCrN3n92qyF7jHuzD5DHWt1JGYvWFkX7n8UakSqoGHp0LuVKrsLcnY57KqNnLh0vUZ6C2pMhVqx35/f0hWn7A1tC1cNb1rez8WWTaJvIweBnvb+Etm9mXu0kCUtt9tyzYp3ru2mp4QdB/o9GskKtJcoUcUplkI16BlhAtQP6y8bMIN5pt2mzOBVeg1hCuGGtVdlsoe40X4KBjDnTMBeEvp/B0yJ4/28bspnnqtSoyDjrVeDo/Omb5wDO3UhmoszU2SV2EFKByMGUe1MzneXrkBtQ3nam6UyuWNMgzRcIqmoQWW8x8Q9DWeRB5wqom93SJ5NQEwu+1gyZEEWHOhhR09rZC00vsE+GYZ5qyQzRFtDVNafqQcZu+w4OcXwk5v3YQhaQjD3G2U9dOSqHTsBig4xx4SSptu32l+RKlU/2UJmrMjK1QQ1NDkyfhYiIZFUVyqy4pAylQCqCemAxTjmkogTnsZo2pHg2iRRoxo/COp4lAK6utgBHGRAI1CdFgtmG2KFuuyryy+1OOAWS1e8B77ROxVusxl2iEgOhbWxhvFUikJj3FLpvJwFI7gON90F72k1EIKC9A+KipfgXzcr8XE6PwdTze8glXtIqhVKdzoiugocjPtXi89Uqz4XBBUQiXzTVuVyMP5NNyswrK+mdPMHA+z2r8cgAKTzeZrGxnMreH8sIJzrObzQ/dQGGxl8oVI8G/WGfRXZdgX61WAfiDT2z6R65Jl7rUpS51qUvfOVWrVfr6+l7w51yXR9wqpXjmmWe44YYbuHDhwotyPOA/F1peXmbjxo3dfn0BqNu3Lwx1+/WFo27fvjDk+vX48ePs3r0bKeX//qb/Q7ouNXspJevXrwegt7e3OwlfAOr26wtH3b59Yajbry8cdfv2haH169e/KEAPndG9XepSl7rUpS516XuQumDfpS51qUtd6tL3OF23YJ/P5/nlX/5l8vn8//7iLv2DqduvLxx1+/aFoW6/vnDU7dsXhv4x+vW6dNDrUpe61KUudalL/3C6bjX7LnWpS13qUpe69A+jLth3qUtd6lKXuvQ9Tl2w71KXutSlLnXpe5y6YN+lLnWpS13q0vc4dcG+S13qUpe61KXvcbouwf73fu/32Lp1K4VCgYMHD/KNb3zjH7tK/6Tpwx/+MC95yUvo6elhdHSUf/kv/yXPPPNMxzVaa37lV36FiYkJisUir371q3nqqac6rmk2m/z0T/80w8PDlMtl3va2t3Hx4sUXsyn/pOnDH/4wQgje//73+++6/fqd06VLl/ihH/ohhoaGKJVK3HLLLRw6dMj/3u3b509xHPNLv/RLbN26lWKxyLZt2/hv/+2/oTKne3X79R9GX//613nrW9/KxMQEQgg++9nPdvz+3erHhYUF3v3ud9PX10dfXx/vfve7WVxcfP4V1tcZffKTn9RRFOk//MM/1MePH9fve9/7dLlc1ufOnfvHrto/WXrjG9+o//iP/1gfO3ZMP/744/qee+7RmzZt0isrK/6a3/iN39A9PT3605/+tD569Kj+N//m3+jx8XG9vLzsr/mJn/gJvX79en3ffffpw4cP69e85jX65ptv1nEc/2M0658UPfroo3rLli36pptu0u973/v8991+/c5ofn5eb968Wf/Ij/yIfuSRR/SZM2f0l770JX3q1Cl/Tbdvnz/92q/9mh4aGtJ//dd/rc+cOaP/4i/+QlcqFf07v/M7/ppuv/7D6G//9m/1L/7iL+pPf/rTGtB/9Vd/1fH7d6sf3/SmN+n9+/frBx98UD/44IN6//79+i1vecvzru91B/YvfelL9U/8xE90fLdnzx79wQ9+8B+pRtcfTU9Pa0Dff//9WmutlVJ6bGxM/8Zv/Ia/ptFo6L6+Pv2Rj3xEa6314uKijqJIf/KTn/TXXLp0SUsp9Re+8IUXtwH/xKhareqdO3fq++67T991110e7Lv9+p3TL/zCL+g777zz2/7e7dvvjO655x79Yz/2Yx3fvf3tb9c/9EM/pLXu9ut3SmvB/rvVj8ePH9eAfvjhh/01Dz30kAb0008//bzqeF2Z8VutFocOHeINb3hDx/dveMMbePDBB/+RanX90dLSEgCDg+Yc6zNnzjA5OdnRr/l8nrvuusv366FDh2i32x3XTExMsH///n/2ff8f/sN/4J577uF1r3tdx/fdfv3O6XOf+xy33XYbP/ADP8Do6CgHDhzgD//wD/3v3b79zujOO+/ky1/+MidPngTgiSee4IEHHuDNb34z0O3X7xZ9t/rxoYceoq+vj5e97GX+mpe//OX09fU9776+rk69m52dJUkS1q1b1/H9unXrmJyc/Eeq1fVFWms+8IEPcOedd7J//34A33fX6tdz5875a3K5HAMDA1dd88+57z/5yU9y+PBhvvWtb131W7dfv3M6ffo0v//7v88HPvAB/st/+S88+uij/Mf/+B/J5/P88A//cLdvv0P6hV/4BZaWltizZw9BEJAkCR/60Id45zvfCXTn7HeLvlv9ODk5yejo6FXlj46OPu++vq7A3pEQouOz1vqq77p0bXrve9/Lk08+yQMPPHDVb99Jv/5z7vsLFy7wvve9j3vvvZdCofBtr+v26/MnpRS33XYbv/7rvw7AgQMHeOqpp/j93/99fviHf9hf1+3b50ef+tSn+NjHPsbHP/5x9u3bx+OPP8773/9+JiYmeM973uOv6/brd4e+G/14reu/k76+rsz4w8PDBEFwlUQzPT19lQTVpavpp3/6p/nc5z7HV7/6VTZs2OC/HxsbA/h7+3VsbIxWq8XCwsK3veafGx06dIjp6WkOHjxIGIaEYcj999/P//gf/4MwDH2/dPv1+dP4+Dg33HBDx3d79+7l/PnzQHfOfqf0cz/3c3zwgx/kB3/wB7nxxht597vfzX/6T/+JD3/4w0C3X79b9N3qx7GxMaampq4qf2Zm5nn39XUF9rlcjoMHD3Lfffd1fH/fffdxxx13/CPV6p8+aa1573vfy2c+8xm+8pWvsHXr1o7ft27dytjYWEe/tlot7r//ft+vBw8eJIqijmuuXLnCsWPH/tn2/d13383Ro0d5/PHH/eu2227jXe96F48//jjbtm3r9ut3SK94xSuuCg89efIkmzdvBrpz9julWq2GlJ1sPwgCH3rX7dfvDn23+vH2229naWmJRx991F/zyCOPsLS09Pz7+nm58/0TIBd690d/9Ef6+PHj+v3vf78ul8v67Nmz/9hV+ydLP/mTP6n7+vr01772NX3lyhX/qtVq/prf+I3f0H19ffozn/mMPnr0qH7nO995zTCRDRs26C996Uv68OHD+rWvfe0/u3Cb/x1lvfG17vbrd0qPPvqoDsNQf+hDH9LPPvus/rM/+zNdKpX0xz72MX9Nt2+fP73nPe/R69ev96F3n/nMZ/Tw8LD++Z//eX9Nt1//YVStVvWRI0f0kSNHNKB/+7d/Wx85csSHgX+3+vFNb3qTvummm/RDDz2kH3roIX3jjTf+8wi901rr3/3d39WbN2/WuVxO33rrrT6ErEvXJuCarz/+4z/21yil9C//8i/rsbExnc/n9ate9Sp99OjRjnLq9bp+73vfqwcHB3WxWNRvectb9Pnz51/k1vzTprVg3+3X75w+//nP6/379+t8Pq/37Nmj/+AP/qDj927fPn9aXl7W73vf+/SmTZt0oVDQ27Zt07/4i7+om82mv6bbr/8w+upXv3pNvvqe97xHa/3d68e5uTn9rne9S/f09Oienh79rne9Sy8sLDzv+nbPs+9Sl7rUpS516Xucrqs9+y51qUtd6lKXuvT8qQv2XepSl7rUpS59j1MX7LvUpS51qUtd+h6nLth3qUtd6lKXuvQ9Tl2w71KXutSlLnXpe5y6YN+lLnWpS13q0vc4dcG+S13qUpe61KXvceqCfZe61KUudalL3+PUBfsudalLXepSl77HqQv2XepSl7rUpS59j1MX7LvUpS51qUtd+h6n/z/2mCf5Da6aOAAAAABJRU5ErkJggg==", "text/plain": [ - "
    " + "
    " ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "ipfim = IPFcolor.qu2ipf_cubic(data[-1]['quat']).reshape(imshape[0], imshape[1], 3); plt.imshow(ipfim)" + "ipfim = IPFcolor.makeipf(data, indxer, xsize = imshape[1]); plt.imshow(ipfim)" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 28, "id": "3ee4b685-5a7c-4eae-a175-b2e86a5afcf0", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 31, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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    " + "
    " ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -355,30 +405,28 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 29, "id": "ea57565f-5ddd-4be5-aa43-793edb30b6f7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 32, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ - "
    " + "
    " ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -386,6 +434,35 @@ "pq = (data[-1]['pq']).reshape(imshape[0],imshape[1]); plt.imshow(pq)" ] }, + { + "cell_type": "markdown", + "id": "9ccdbbfb", + "metadata": {}, + "source": [ + "## Writing data out\n", + "Still working on this, but there are two output formats for the data arrays from PyEBSDIndex, .ang files, and .oh5 (EDAX's version of the H5EBSD data spec). " + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "5744b3df", + "metadata": {}, + "outputs": [], + "source": [ + "ebsdfile.writeoh5(filename='MyScanData.oh5', indexer=indxer, data=data)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "9084cbca", + "metadata": {}, + "outputs": [], + "source": [ + "ebsdfile.writeang(filename='MyScanData.ang', indexer=indxer, data=data)" + ] + }, { "cell_type": "markdown", "id": "854a76e5-2262-49b6-ae2e-363498f23a66", @@ -394,16 +471,16 @@ "### An example of indexing an array of patterns.\n", "\n", "It is also possible to index a numpy array of patterns. \n", - "Here we will read part of the UP file above into an array -- note that patterns can take up a lot of RAM. It is not normally advisable to read in an entire file of patterns if the filesize is > 2GB. \n", + "Here we will read part of the UP file above into an array -- note that patterns can take up a lot of RAM. It is not normally advisable to read in an entire file of patterns if the file size is > 2GB. \n", "\n", - "Here we read in 200cols x 300 rows = 60000 patterns starting at column 10, row 5 (0-index based) of the EBSD scan data. However, this is something specific to the UP files (and potentially HDF5 in the future). What is important here is that the patterns are returned as a *(N, pH, pW)* numpy float32 array where *N* is the number of patterns, *pH* is the pattern height, and *pW* is the pattern width. \n", + "Here we read in 200cols x 300 rows = 60000 patterns starting at column 10, row 5 (0-index based) of the EBSD scan data. What is important here is that the patterns are returned as a `(N, pH, pW)` numpy float32 array where *N* is the number of patterns, `pH` is the pattern height, and `pW` is the pattern width. \n", "\n", - "It should be noted that patterns are expected to be arranged so that *pats[0,0,0]* coresponds to the top-left pixel as one looks at the detector towards the sample (same as the EBSD vendor standards and EMSoft version >=5.0). " + "It should be noted that patterns are expected to be arranged so that `pats[0,0,0]` corresponds to the top-left pixel as one looks at the detector towards the sample (same as the EBSD vendor standards and EMSoft version >=5.0). " ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 30, "id": "64d0f40a-b55c-4103-8335-ea27267f6e1a", "metadata": {}, "outputs": [ @@ -418,28 +495,26 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 33, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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/TD9tbS7JOn4XT1Xme3kPXY5SwrVSivm9Hb9HDHE6+WRPko6QnT1JOkJ29iTpCBOj2RlCWbVqVVV2XcLUxmeffXbOc0qHayYPF7Wmes5V77heZaoqPzefdF6v58qznB7b2onE9TLHNLiqjaecUrNzSqmPE2zfvr2q43fzkB41sutTjjfs2rWrKnNsxaHeb62k4+3A3xvHMfyzw9Zh8HAg03cZQvMxj9aOMK1VhqXBocFDk1TnJp/sSdIRsrMnSUeYmFlvrVU7pLZL7S4h3TpmRrl7y4w0ZvG1Mvz8vMygoyvnrjBdtx07dlRld/n5nW+88caq7PKBIT0PD9GFZqagu6mXX355VceQz9VXX90/psvPTTZchlBKeCYhz+NhQ6mWdMyg4/3130JrVllr8U6p/t6tTUZ5LoYqKTlb2XfeDq0NLkhrz3cnn+xJ0hGysydJR8jOniQdYaya3fUWtSzTDl0L8b2uWYaFthhec6jNPHX1kksuGfje1qaPtLelMQk1MMMxrZCQz2xjCM9XpZXq2WDPPfdcVcfwmo8/cCyAIUgf57jooouqOteu1LncIMGhBmbZ7eMMQw/x8XOtdOhhGzv62A9/t7yHHuLj77hlD21o9Z1B5JM9STpCdvYk6QjZ2ZOkI4xVs7v2YVyR2tbfy1il6xnqWuoZ19OMszM1dN26dQNt9xjuE088MfB9Ur0iybCUXP/eXFGGbeTtcO6551Z1nqbJuD9X7/HVaHgNll3Te+qndPj4iE/9bU29JLxnPmWY957jIz5u0NpZhveBaa3+PTm1mHieBfNFGAPnvRhkA6fuMj120PTwnOKaJEl29iTpCmN1493loQvYcnfoKvlMNrqADCW1Qj7E65nS6dKBYRy6vi5BGJqhG+/ygC4rU0U9ZMb3evsN2xPe0z9ZRzf5gQce6B/ffPPNVR1DZr6CC8NgbhPTWinhPPWXbXDGGWcMfC/xNlq7dm1VR0nnYcX5hEv5O2HZz8Xfgrc1Z/ox3Oe/MW+/UgbunZpP9iTpCqPs4np8RGyPiMciYldE/EXv71MRcX9E7O29jp65nyTJsjPKk/1tSRtKKZdIulTSxohYL+l2SVtKKWskbemVkySZUEbZxbVIek9wHdP7VyTdJOma3t/vkvSQpK/O5+Kuk6grWyEWhkJ8iiFTOLnJgWt6vpe6yPUWp3q6tm3pKanW1i2Nzs9SGz7yyCNV2VMxqbX9e7c2LSRsd7aR28drUtv6WAC/Z2vjSU5xdajJOV13dna2f8zwWmvshG3k34Ur+9CGrVu39o+p/XkP/TqtMCfHrNi2jofsWlPBR9LsEbGitzf7rKT7SynbJK0qpcxIUu915SjnSpJkPIzU2Usp75ZSLpV0lqR1EXHRkI/0iYjbImJHROwYtpZXkiRLx7xG40spB3TIXd8oaX9ETEtS73V2wGc2l1LWllLWDgt1JUmydAzV7BFxuqSDpZQDEXGCpOsl/TdJ90raJOnO3us98724a41hqaGue301WanWQdQ61Eye4ukrpc71WU/T5DiBayNOY6Tt/j25uSA1nns/1OjcSNGnS/I/Un/vnj17qjrGzo855hgNgss3eTomPTVOY+U9dVw/U48yNu33cJiW9RyM1nRm5l/wnn3605/uH7O9aN8gW+eyr7W5pI8F8H7yd+JjTz7O0pruOkpSzbSkuyJihQ55At8spXwnIrZK+mZE3Cppn6QvjXCuJEnGxCij8Y9LumyOv78q6bqlMCpJksVnrOmyTJF1uFmiu8J0VXymG1Np6X77rCiu7MoURXftNmzYUNW5W0XXkmEdd2+ffPLJqo4hPa/nCjN0Cb1NONvP96m/8MILqzqGI91N5XehC+vpqpztt379+qrsqbWt8CRdVobTfNMIpsvSpfbz+kpDUu3ucsbeZz7zmYHnpQzizDL/3fC3yd+xpxSzTfw8DFXSjXfp6u2Xm0QkSZKdPUm6Qnb2JOkIY9XsniLLME4r7Y8pnL6LCbVNK7ZPvccxBC+39DzTKbmrSivMRN3m7cBVTlrfheMEnqrKcYHW1ErubsMND719H3vssaqOq734dVpThKlzWfZrMp2XYzS01/ExEIZLX3zxxarcGk9avXp1VfZ7ylTalSvrxFK/h7wPrd2KOHbiIbx9+/b1j1uht3yyJ0lHyM6eJB0hO3uSdISxanbXKEwjpP5zqHVcx1Hrt3bSYKyVetD1H1M/W8tSEV+1lrq7lWpLncb0T28Hpnu67YxN//CHP6zKrkGZ8uppo1KtvVv6mDYxfu/twHvP9nT9zO9CvM34G7r22mv7x1wm63vf+95A+1rTfKV6pxmmF5PWZDDX6bz31PD+O3b7cnXZJEmysydJV5iYjR3pGrVWHG2F1xiuotvUWhmUYbGWq9laCYYbHHraKKHb7Oe98sorqzpusujpqmw/d32HhTU9hZMhMs4MvOWWW/rHX//616s6DwFJ9Xfjed31ZPrufffdV5UZnnQovTwMunHjxqrOpdi2bduqOt57/97DNk70VXSHvdfbobV5Y2tjU6kOrebGjkmSVGRnT5KOkJ09STrCWDW76xLqSGoU1yUMS3iIhfqZmzW63ue4ALWPp2JSs/vYAMcJWtMwh6XoulY85ZRTqjrqe9940lfVkep2oOZ1vSfV2p9t0BrH2LRpU1XH1V98txZqfx87oX2tlWhXrVpV1XH6ro9d3HNPvXiSX4fTh5kO7WMBtIe/Tb+n/L21Vg/m927tgsSxptdff71/zP4wiHyyJ0lHyM6eJB0hWhvBLTZTU1Pl+uuvn7OOrgjdcXf5ufCiu0Z0Z7mYYmvjP17TQ0J0sdz9pgRh5pa7fVxRhqEjd/u4USJdarePbeLupLt8kvSTn/ykKntIiq44v5uHA3lN4q4nXV+XRQylfutb36rKvtrQFVdcMfAaUr3SD1ci8mxFzlxjlpxLMf6GWPbfLiUSf2/+vSnpvI3msxKzZzJ+//vf14EDB+Zcriaf7EnSEbKzJ0lHyM6eJB1hrKG3Fkz/dF1Hbe1lhjoY2vJ66lHqP58ZRXv8mq2ZdTwv6/hZ12qcVcZZb67/maLr4waf+9znqjqupONhJ16Ds8Fa37u1uSRTYlubZvK9HuLjGAc30vB24D1zWmEuqb5PrRV2pVqHM1xKfe8r4jAl1suvvvpqVecz66T6PritOestSZLs7EnSFbKzJ0lHmBjNTv3nK89KtWan1nGdSR1OfeWavbUajlTrLdrjqaCMhzPl1L8bY8pMw/X3Ut9zrMK/K/Wptwl1N6/p7cl0z6997WtV2fU1tTbjxl7vq8RI9X3hfaBmd53OFW2Jj0fwu7j25k4trXyMk08+uarj2MTjjz8+8Ly83/5baeV8cMUb7pLDsalRGPnJHhErIuLRiPhOrzwVEfdHxN7e6+DRkCRJxs583PivSPKF2G6XtKWUskbSll45SZIJZSQ3PiLOknSjpP8q6T/0/nyTpGt6x3dJekjSV4/UELrJDAG1ZiG1NsRj2dMih80WGnVxRaaNtjaCoLvYWjyTLuCwhQ8d/248D91tuvkOZ6t5uizd5Kuvvroqe7iItn75y18eeH229YMPPtg/ZvtxJmAr5dTLlI0sz87O9o8pyyinPLTKhSxbYVie1+89fxe8h14eNbV21Cf7X0n6c0kuTleVUmYkqfe6co7PJUkyIQzt7BHxh5JmSymPDHvvgM/fFhE7ImJHaxndJEmWllHc+Ksk/VFEfFHS8ZI+EhF/K2l/REyXUmYiYlrS7FwfLqVslrRZOjTrbZHsTpJkngzt7KWUOyTdIUkRcY2k/1hK+eOI+O+SNkm6s/d6z6BzjAKf+vv37x9YTz3TCldRc7rW4XtbIbNnn322qlu/fn3/+NRTT63qqIl92ij1PaeUuqaj7cR1OdvEtePU1FRVR/3nUI/yvri9TDll6qqv7urtRTgFl+fxtGDqU4Y93d5WaJXammUfb2DorbW6MceImN7bojXOMp8NSwexkKSaOyXdEBF7Jd3QKydJMqHMK6mmlPKQDo26q5TyqqTrFt+kJEmWgkyXTZKOMDHpsoRT9Tw1lJrYtQ6nAlK3uc6kHuUqsX7Nyy67rKrzlNOVK+uoIzWex4apranhXeMxNj0zM1OV/brMGfDz8hpsE5926amf0uHLaHnbc1ot7fUxEabo+vgI7ydXfvX7RA3c0rIPP/zwQPuorXlfvD2Zukq97GnBHAeifX4dvtfL/G22NnYclXyyJ0lHyM6eJB1hYt346enpquxuVStd8bzzzqvq6MK6y8UZcVzVxmcwMU2ztUEkF/932+k+fve7363KvtIJXTe6sO5etlJn2V5MR3WXkeE0tpF/F7rmN9xwQ1V2mzzlVaolEr8XZ9N5CLKVfirV34VywMOGrRWAeR6mctPF9nPxd8LPespzK3zG2XOLkZCWT/Yk6QjZ2ZOkI2RnT5KOMLGanbjGY5jEtSE1HdNRXZMy5EN95fqrFerw6ZBz4bZTi5177rlV2TcupKZjONJXL2Gqr++MQh1Oze5tRH1PfFqr77AiHa5XfcNIt2euzzrc1cenO7O9uCGj/xYuvvjiqs7vN9ukNT4ybANQh23AcY1W+/p5ubrskaTHknyyJ0lHyM6eJB3hqHHjPfTALDl3f+gmMazjoSS+t5UZxVCNu+OceUV54LYzVMSQmc/4YtiLUsK/G+vcVafsoRvaWj2F4aubbrqpf8ysPW7I6Ofl9/br0PbWJotsW7rf7p6fc845VZ3LHq6yw/vr1+FvqGXDsBlyfr8ZTvN7diQZcsPIJ3uSdITs7EnSEbKzJ0lHOGo0u8NZSAcOHOgfD5sV1VrdhSEfT7VlqMbP01rhRmqHTWifh+moibl5goezOGPPNSjPQ03sYxfcBJLhKx/HoEZnmPPGG2/sH1Pn/uAHP+gfb9iwQS08JEotu27duqrsKxxxVl7rPrTu2bBxltZmnGz71oYhS6HTnXyyJ0lHyM6eJB0hO3uSdISjUrO3dugYFpv2+Cp1JFdz9TRN1rn2euutt6o6amu3jzkCXNXU9SFTL5mW69+NWvHYY4+d8/q8hlRPTf3CF75Q1d17771V2Veu8WtI0jXXXFOVzz///P4xU319nIXjIbTP76lPAZYO/94+zsLv4ptC8nP79u2ryp7OS63PsR6vb917qb7fHOPgeRebfLInSUfIzp4kHeGodOOJh5kYQnFXkvV0ubi4ood8WhvtUVbQ7XPXkhsitNz6s88+u6rbtm1bVXb3ljOz3MXmzDBuhujhNs5O85lrUh3i++xnP1vVrV69uip7+/I83p50dXnerVu39o99cUzp8Dby3wJDZH6f9uzZU9XxPniIkWFNntfDa/y9EQ8bL7XbTvLJniQdITt7knSE7OxJ0hHeF5rdoV728JlUh26o06htW6mNvtEjP0cN2kqtZXqvbxI5bCVV/64MI/p7r7jiiqqO0zvvvvvu/vHu3burOo5rtFaF5XuZKup8/vOf7x/v2rWrquN9cfs9zVY6fJzF24Rt7eFT6uXWtORhaa3eDhyb8Pbiexdj9Zn5MFJnj4jnJb0h6V1J75RS1kbElKT/LWm1pOcl/ZtSSntNoyRJxsZ83PhrSymXllLW9sq3S9pSSlkjaUuvnCTJhLIQzX6TpLt6x3dJ+tcLtiZJkiVjVM1eJP1zRBRJ/7OUslnSqlLKjCSVUmYiYmXzDBOCLyFFzcm0TdfeLb3MGPejjz5alT1eT53W2onEU0qlw7Wif5bpvB5Lp25kvH7Hjh39Y05pZdzYtW5rZxTWc5zAtTY/xzEPz1Pg6rLcfcfPy6m8PqbAzzF2fvDgwf5xa9NHqb4vLe0vLb9Od0bt7FeVUl7pdej7I+LHo14gIm6TdJu09PN1kyQZzEhufCnlld7rrKRvS1onaX9ETEtS73XOxdNLKZtLKWtLKWvH+b9aknSdoU/2iDhR0gdKKW/0jv9A0n+RdK+kTZLu7L3es5SGHil01zwcQ/dsPqt9+nn4Oc68c3ec1+TMMXd36X5zhpxvYnnllVdWda0NGp566qmq7KHDjRs3VnWUOv4fNjd6oDvuG2W25AtXsOVsv9ae5q2UWM5s82vOxzWnrGDZ79OwWZfjZBQ3fpWkb0fEe+//X6WU/xMRP5L0zYi4VdI+SV9aOjOTJFkoQzt7KeU5SZfM8fdXJV23FEYlSbL4ZLpsknSE91267DBcb1Frt1Z0YVjOQ0DUtdSuXk+NzumTbhNDer6jiSRdfvnlA9/rK+V6GEmSrrrqqqrsYTq2CccNfAxk2ICrtyft8zAYU3S5erCH0FqbNfKa1Mst26nZORbg0N7W7jaTRD7Zk6QjZGdPko6QnT1JOkLnNLvDqYtMiXX9So3nOo26lqvNekx32I41fl5OGWXKqa9OyqmVboNPm50LrtjqMObtnHbaaVWZO+p4HJ4rqfq4Br8X4+PevkyB5ViKa+1WLJ1an5rdU5yp0Tmuwam0k0o+2ZOkI2RnT5KO0Gk3ntAdc/eRq9F46mrL1ZVqV5Mr50xNTVVld43dlZQOT/312Wt0WX3VVbq+dJsdhr343SgtHIarXnvttYH2eQiS56Sb7C43pQMllEOJ5Om7bAOmuW7fvn3geY9W8smeJB0hO3uSdITs7EnSEVKzjwhDNZ4WSa3PtFaH4b2WfmY4jZrdU1Cpez20xbBSK8TI1FqmEHsIkufl5o2+KhDTZV1rD1u9x9uedZz262WuUuv3id+L9/f9SD7Zk6QjZGdPko6QbvwRwrCOwxCUlzkriqEjdyfpxtPl980TGF5rLfbIzSXd9aW7ze/p9QynMTuwtSqQu+PMmGPozc/r4TPp8I0dH3744f4xXXwPK9KN7wL5ZE+SjpCdPUk6Qnb2JOkIqdmXAdenrRVsJemFF17oH1P7t1ZXYfjP63hNzhRrQfs8bEcdTlzvzyfNlvZ6yJEhspbe53m6Tj7Zk6QjZGdPko6QnT1JOkJq9mVgPqmYnuY6bPcTjxVTs7tGZmyaWtu1NePsjN97mddkOq/DNvBrUlv75oxSOyZ+tKwSMwnkkz1JOkJ29iTpCOnGTxitTQbozrr7SxffF3hsbZbAei6WyRl87n5zdRfa52muDJF5+I+z5ZjmmiwO+WRPko6QnT1JOkJ29iTpCFFKWb6LRfxM0guSTpM0OE6z/KQ9bSbNHmnybJoUez5RSjl9ropl7ez9i0bsKKWsXfYLDyDtaTNp9kiTZ9Ok2TMX6cYnSUfIzp4kHWFcnX3zmK47iLSnzaTZI02eTZNmz2GMRbMnSbL8pBufJB1hWTt7RGyMiD0R8UxE3L6c1zYbvhERsxHxpP1tKiLuj4i9vdePts6xyPacHREPRsTuiNgVEV8Zp00RcXxEbI+Ix3r2/MU47TG7VkTEoxHxnXHbExHPR8QTEbEzInaM255RWbbOHhErJP0PSf9K0oWSbomIC5fr+sbfSNqIv90uaUspZY2kLb3ycvGOpD8rpVwgab2kP+m1y7hselvShlLKJZIulbQxItaP0Z73+Iqk3VYetz3XllIutXDbuO0ZTillWf5J+qyk+6x8h6Q7luv6sGW1pCetvEfSdO94WtKecdjVu/49km6YBJskfUjS/5N0xTjtkXSWDnWgDZK+M+57Jul5Safhb2O/X8P+Lacbf6akF638Uu9vk8CqUsqMJPVeV47DiIhYLekySdvGaVPPZd4paVbS/aWUsdoj6a8k/bmk39nfxmlPkfTPEfFIRNw2AfaMxHJOcY05/pahgB4RcZKkf5T0p6WUX0bM1VzLQynlXUmXRsQpkr4dEReNy5aI+ENJs6WURyLimnHZAa4qpbwSESsl3R8RPx63QaOwnE/2lyT5Xj1nSXplGa/fYn9ETEtS73V2OS8eEcfoUEf/u1LK3ZNgkySVUg5IekiHxjjGZc9Vkv4oIp6X9A+SNkTE347RHpVSXum9zkr6tqR147RnVJazs/9I0pqIOCcijpV0s6R7l/H6Le6VtKl3vEmHdPOyEIce4X8taXcp5S/HbVNEnN57oisiTpB0vaQfj8ueUsodpZSzSimrdeg3839LKX88Lnsi4sSI+PB7x5L+QNKT47JnXiznAIGkL0p6WtKzkr42jkEKSX8vaUbSQR3yNm6VdKoODQDt7b1OLaM9/1KH5Mzjknb2/n1xXDZJ+heSHu3Z86Sk/9z7+9jayGy7Rr8foBtX+3xS0mO9f7ve+x1PQvsM+5cZdEnSETKDLkk6Qnb2JOkI2dmTpCNkZ0+SjpCdPUk6Qnb2JOkI2dmTpCNkZ0+SjvD/AYLtPDYJzfUSAAAAAElFTkSuQmCC", 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", "text/plain": [ - "
    " + "
    " ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "startcolrow = [5,10]\n", + "startcolrow = [10,5]\n", "ncol = 200\n", "nrow = 300\n", "\n", @@ -456,12 +531,12 @@ "id": "cf5ff6f1-3075-4efb-a7ab-ccb3a31ed339", "metadata": {}, "source": [ - "If the array holds a small number of patterns that can all fit on the GPU at one time, one can avoid the distributed indexing method. It should be noted that there is built in chuncking (set to fairly conservative limits) to the GPU when using *index_pats*, but no multi-processing of the band voting so it may take a long while. Here we index just the first 256 patterns. (Note, one does not need to intitate a new indexer object if they have defined one above). " + "If the array holds a small number of patterns that can all fit on the GPU at one time, one can avoid the distributed indexing method. It should be noted that there is built in chunking (set to fairly conservative limits) to the GPU when using *index_pats*, but no multi-processing of the band voting, so it may take a long while. This small set takes about 1.5 minutes on a 2019 Mac Pro. " ] }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 32, "id": "12433080-9bb9-408c-b252-024ebb80d58c", "metadata": {}, "outputs": [ @@ -469,31 +544,32 @@ "name": "stdout", "output_type": "stream", "text": [ - "Radon Time: 1.2862144199993963\n", - "Convolution Time: 2.1463325300010183\n", - "Peak ID Time: 1.6447724460003883\n", - "Band Label Time: 1.938284111000712\n", - "Total Band Find Time: 7.018114552000043\n" + "Radon Time: 1.2478941651061177\n", + "Convolution Time: 2.1195158280897886\n", + "Peak ID Time: 1.6990612583467737\n", + "Band Label Time: 1.8449941849103197\n", + "Total Band Find Time: 6.913202239898965\n", + "Band Vote Time: 75.60198848193977\n" ] }, { "data": { - "image/png": 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", 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -510,12 +586,12 @@ "id": "54087bfe-e560-4ec2-8689-006032850557", "metadata": {}, "source": [ - "If the array is large, then the distributed indexing works on large input arrays as well. " + "If the array is large, then the distributed indexing works on large input arrays as well. Here a smaller number of CPU processes are used to minimize overhead of spinning up a new process. " ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 33, "id": "2d822d5b-0eab-462e-88c1-9c678c56578a", "metadata": {}, "outputs": [ @@ -523,54 +599,52 @@ "name": "stdout", "output_type": "stream", "text": [ - "num cpu/gpu: 12 2\n", - "Completed: 58464 -- 59472 PPS: 14861;10115;6747 100% 9;0 running;remaining(s)\r" + "num cpu/gpu, and number of patterns per iteration: 12 2 1248 16 12\n", + "Completed: 18720 -- 19968 PPS: 3716 100% 16;0 running;remaining(s)\n", + "\n" ] } ], "source": [ - "datasm, bnddatsm = ebsd_index.index_pats_distributed(patsin = pats,patstart = 0, npats = -1, chunksize = 1008, ncpu = 12, ebsd_indexer_obj = indxer)" + "datasm, bnddatsm = ebsd_index.index_pats_distributed(patsin = pats, ebsd_indexer_obj = indxer, ncpu = 12)" ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 35, "id": "098a5371-ad47-43bf-ac41-a300db4cc30a", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "300\n" + ] + }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 36, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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SrOWitg2cxfWoSnj7QKLVfjqWIzcmTgA3DyEZa10lreU8HXjHwiq8+GNL7Dw+4UNPX60kK5HCpsujrjC2vfSJSkvKXpQCNO2SyQUTS3s+pNnJvkjf52kdYbTRV8/2sqTpTV7yTCRnj5Y4ph4MHM/yy5I6LKw6XvynA7qBY+mEi06gfUp/HGDNs7ywBOPaligMxLRpJuUEaIiq6KLU7cx3OGCCTuqAzk4/etjUwCWDezZkTPuvQykz2bmkgIr0ZQfPIncr4c0Gv7JzfE4H1LJgbV2B6UVELgOOHSgUsWROCRxjw3ct7NrBGJznjqu2Ah0X3H/SlKajAKmAuQ9VAwTUOVUyClndKOJaCU7ibRSc9oR645sZSJkNDSk956Fpcr57Dzj27ZaeaSilK5ONtsbUG2EC+s7G0RnGmkZKECv7S7+OTEidMYTaIii5rMaJZXSW6mQbWGCqpk6RFC2Fyn3Dpfd4LnxAaMyzsOY451BkAHhg/aTWc7KewMQwrJzqjDLJMjMziaBjQKKn+JnG6rQeO2xq4LJ+NVa0t2REdU1xXZOvKGDSkNN+XXXOs0qlwUJMafyfQOKrUmpvvlYO32Qy96ZuSvzxS2fmENX9UtIq669Ecmqp5cDueVxw7HpkYmhSgGNivhHwkCRmDRjb4pJOQJcPOeWuU80WVBoLEI23Y2SGStcyysAgcvU22eYCEDrm1jp2Hm14ZJs6FusMmZRZ4MkuvprVQmoDdGnQT7+TL6SNehPHFel7BkVbRo93mbYRp1HrLG1T8lPPUiNQzmJ2me3ZSSQfHLsPRL+vZ/9Fw01/IRJ4YgA+9lfvHEfGQ7b3h/BBVHyRgE3eeR1sr32a7ZVW2rJ/Sf2sQF4ZWzhT3NrcwAWqCArghOrN1GDKV0o8rkhFJp9JZCXQYW0YOptUpv1ogKYCe+w6zwS1PKgVwxW1CshcpNjJPLpaTRfONnRJ/XRMCIwIiYSt4VbSiLNTn3j2eXzi2efix+v8X//qTppujPPOFNewADvms8+VFfFTPDuu+j6Cjxd/riEZVkVqoiGvdgi2ryb6XkDPO2Acr/sQ/8YdDJKn/gSe9JUhex/xvP0HDqH0UIJ3KUOrdCttGZW3SXorMq1lh9rfYrdSablB5Oc+1cslwOszVY1T2oNsdC8lPFmyJrYqK223iL+8GvsFhGVGUCS4DhccPniaDt7y/2lpJwlYlCTjioR0veYWePfyP+ENJ/8VS734VJl+6LtU1KC/jVmylZdvpe9qMMuL7FFGRUOxLO0MwiYHLrDApCK3M+9quWsasMqU9P/YFR0hr7IvGzUUaRYjO723HF1Fc51hknuXoVCWb+sskg4zXUqtvmlCzgFZuCHzmfH9Op4u734gJQ1Fzbu24SffdjU/9Iu3s/vgCEIipOxvBZG4BpF4nS4Dj4BjCK4Y1+nZ9stguAUO3BHT7FuyHYs0QYB9BqqKCuglKdAnqcSl9ENyZJTZrEkP65NcTwEvlXaV8Qjwl5JRrKtLi6+VdjyijOusnsTzCUhAl2jZaZiYRk+LrlmU/hM6FNeIHgXCkqbU/cGBoUmX16+Swaynw9Pwwg83vPK/tRFfxsGAB9pmPVF67dZYGD3EP/U/QRPWycxE+tcWB+kPWcaVnjko1MXC/mU0mMwDvGFYCqCPFTa1O4SFLGvRklX+ZMI10gG60FVFaZVGyvTlW7tlisS3JbAl0t4tJTJRB2XpRkOHT5uoRGF/QpwdnODoUJcK8XsJyaMrkrQFUzHC9sWQcjgGuLQJju4ooPVKsV3DZOD4ze++mC9cuw21S4WUd1JxnExdW98fAzYqGBgCDIaQ++igOKVmzVqfZtvSRzB14rDoiesah+DnUHtKHChbTjX80/dsYTgql7eIylga2TF3YBlUfN8jc47xvc5ak+Yq41MxqJMcYLzp5yb7ppf1FnVWQEn9uGBg+ki8/xrUoimpDBJtWKN8rON3/HbDC/6HZzAKDMaJ2q2Db0g+V/QwOQndCRyBNoySW0RqnbzyQfpS3FNCLpu4WsT+sTuSiN8WFDttZJqokOrMBK7/HSSuOtialyqcq66nXR2sJGYlphqoatvXRkA2SwIrc4/k6g2MxO9K9UCeTcuDmq+nlsKi4K7+Yc7IBeWcZrx7+LxFPvX0Hew7d56287zg1u0xrRMHYtSVPUQQm8DxB2F0yuQvKp+UOJVk6wUwv4LuSgGlgV6kAFOtoi1l8Ei7B0rjb8oru1E42i5w0YNt1lBF+iplbgtYVqoR4FDGo+0uEo4Gu9BG+k16oc8AZgG0pJFy1lH6xTLamdwAceUQJ4unfa7hvH1E9Tl9+6QveXYcMX1RNG2fGkMmPEYQJtqWuc1TfO+SV7wBHAfZKTgDnCf6cFlpS0BQipH6u6jPGSJWCv/bAFeo7molzcbYaC6mDgov04LpNAlPl2I6B+vhLvEthOq1hYA42S1x6yEk9fTU/mpqB1NpYZIHlC2PfO/4ytVbuf3qFRZGjhccuCSWxBHF/J1PSMTZR1vH6pFI+GvHKME8if8L22D7xTCYj3tzzS3DaBRzzJzfUfiKTbWfi0Sfm1LsayKJpUHgag/wwMUPtdyzd8LpeZFq5L0Fhmkm4FIdPBZwpJ3Vtwvz1PZflMRKudTmaRmL9rAyzI3KpffxWdM1XHxffPPcjzVcdaeP+2CVyDzVLgpOxH4E1LFXonqKPF165pxJXKQp6ROr9s0qdw1glo2E6v7Rw6YGrlrQtML+lKqRZA21HpXAI7yyTFWnv2VRMwlWArXUJSUK1bUSqHoE2W/rMqjDaE/0oQch7ZDByHJhBelyQOqCEoW5BVrW0PUDcZD1kJ01tQ3HfsIggNt1FYS05QzjCGK7r4l5jU/CPX+eCNZsUuhauPAGbbO5LXDuk+C+z0MYa91luY8AT+bE0m/oczHSF061RkIwY9M5xxt+a4Vfes1Rbrt0bIBLWjNnVrR/fDrGo/tBWNanTE99tLrcp0JZbVowo1sRidQn93XupV+YXNlSx00xXVru5fvAynHHj/ybqOoj+7Nbjlzs2NFhaY0s+Yh0lNxh7EJ3V9Gad6YPTC1cq9eA+v8lpiPARz+Dl4u6Kh33OPDjEucFudOV8yJ0WyBpDHRYfxwQrqfzePLM/to7C2zBpCOzPJYo7awTqG+O2gFkcAiHl/jRz1l2DROnh5C2cSlhVzdOlHqpd72mFgiMaZPBXuM0tMleIy22OnD85Hc/xD/7nfPYejooYUNJvIMVuPKlKjhMcU4ZAKlcvSPaQZLqGEjEKjl7pnzEgLwFtQtktwgZFCEZmWUwSvw8A2bT0x4U9tVn193YpgOGXMLpzDRO0LMv70k7TDE7A/ieSbaDyp5uYpoPhsqkD6WxusxWyExNy6UwqVK/5PGkL8EP/AeHE6ARycknqSekrZJDkt9dmhXMG0CmdvSgWz8lgMsrGlrtbzcHrOdyxR8X07UOpHnyxum3GTRDjdbo/mvEMoQzM7tvauACTKcL+NggUFTOIk0riiLM63yR8FTSG8lFJQHLtV31Vz8vgUzBS20qIjEpuJWGeOvHBeKRb2FPeJzOOkZ1p0vQqgZgyUMXRUlJ1H87uEhu/89LDvBNn97BNQ8sJaIXo6yVeOIUfSS6RPjSRkKYzqVmFOCZFYTzyk9Gw/hdHtW+iiPPjPTVdeCjS0JfSBvC1OTb6EMVGLOTCStpT7Xo4d7zSGrFy2i4K0NKZB99mvrviu1vrEuO3d1UesHGkHaHUtoX0OgMe9IJjVf8YcOzP+Hx2T+uGuwiRQVjDM/PRJVMgBiEAkbkPdrcPFOOwiFCesHPhSHZmUOphrcuJDadoICV62vH0uNEVZSOjv9bCcr+1Y0x3TjTa9DALqXQ73rqQRBzLF0cBf6CuZ5VA/umVEVUuZDZJbt4Q+olMChqoqoWSmG1d5nEV4VRgVshJaZ/ZMuEj119glNzgad/dUuiLQVebWOxfwioOEOHhsB374VHHozG+qotCklLBlfdcLlRbVyRLIpqA/D8Ty0xv+75y2vWM5twuRU0s3gEyZihgTRwLCEyFPRJotctv+W5y70AZc9rYSxTk2Bneuu3EZCefKvniV8hSVQx3Svuatl2Iql31qEzZ+nI3Ecb07wTMEtALxJSnhFMdZlfBHpYX9U88gxhSBJwbPt7njzh5I6ea/9HUwJZUZ+6kka1dHNxhridn/HtdNjUwKVB5lbsEytyT8tXs8bDxqnP/k5tIirvWCCdnWrBsigJ2k7WxzgWDKUmwt/txL4CmMSsQVydJMVWJnYzW1Y/lR989bw1Og/bT7Vc/vACs+tlfHnkOriSiJ2Dle1w+KFqQcIsSdW2V9ggromfP3OFJHbdnfMEBycWe75y8ciU1UKGtkGHYy3VYYmeRXrGOE4QXRukpy3ga3r6TMGrrktt26yZqMMFx5O+DC44nvEZz9M+79JnqZ6yH3+YRckhvqubMzAj7xRhOIwSUO+gCzBIy6jmF6Oxf31kylkzl1j/0bxnbVmkqVDGcTC1Z1t+7ohbQA+hWYorIc4gbHLgKjtNvMrLxa5KovqN3RjO9rEa0ks3CJufQ1Yali6dZsDm/OzMUZ3GrGcWpsR3x5Y+5HwjUFkA0zTtUii19Kn9zLZCmaacLRNj24O57tmzxm9vOcgPfeB8Ftdt6dOV7CQRrN3PGtdFrbF1l2/lUuIE4oJrK0VoqfUj29yuTMdA8JPvWGD34Zaf/d7DnFwIBOdya0jsLm1AdBrPQ2npzaWs0+A4RcMB5hC/LUs7IX1t61NKdrZPLXCEqbiDsWdu5Gg7xw++O9BOZKYvMQCZXa2l2SmMFGkMBQs5Wk7ylw0cXYDlbeAHMBrDqZOwbVsEsEAELplJtvZHYQ5hAgy46pOygLqLz+oj7+TsgFzGPtKKX0zdKruGPA5sXDIsRbnq01OKX4UObUZpPSHuUHxhueasr5nxLMaT/ezFkDpjgOdrBVgFHbVjCJB4ZFtoGbQiJ3WoIykZsst6K6hauHKopKG1FGlPDPal00YgcHRxwju/4wF+4rcuZGFUA4griToIkfaGAztgGD3nQ3pen90Y6gvzrQy+0GpbyVIjUXsCBkSJM2HAuYcG/OtfPJc3/egBxm2y9yAuJOBpOZCZUWyHu1ig3KxP7KW6VXcsmYKkS7GmlXZLc1KvHmtZff7HPd/63zxMxgnjzQlDIf2XVzN4dR4NHt3HPyiZeK/STp9WGMj2QC45A7sODu8jLsdqoHFR8jryMOWMoIvtnvlOkjKbFroRLC5AO4Rjh6Ja2zrUjhWIh2BMNB8B4+uujvEOn4JDJ+PKhzMImxq4rHTSp4GszEecDuwBmTV4KKcsTas6tEuAkWCmewuAaMx1BCL1fi5zrj2zfCZiPQuv3uZZ+fwEcZuQr0CnJkqw1JlFATzyQQqlWqoT/VYyUoATu9gvvOpBvvOju7h837zJraecDUwDKc8aBXS7mg6d9g6UXNxeWFFCACpNAOSoQYvYg2xzEz+pJbaQ6m+lPnIbqauMR5mFfCf7dNnZ3FCkoU/UT86aMFzhpFv2/g/+uuOJX0klkb3JcjYuzRbKnmW2jY2Xukg8dAbUJItkrM97Z4njaSDvkQUROA49ove2qnlWNz3oVmP+l5wLi3Mxj4u3Qz+JEpT3Jp1xkoqlrE20aTWpftuXYesynDrOmYRNDlzilUyefK4FTbvdbQwxtnJBFe9lhZklQcvxpyUoHVjlrFKmliJOLVqU6p0toz1IQ57bmskuqy6nVCoh1vql9ZbBqQDrDKhbb7DSSG+hGec4udDzweuPcWSp4+lfXSaDTwaRxE2ntrCxYFZLsZKGuTegmR9RpylJO33VJxQrFoyD7+Af/s423veiExzcIZCvUpfCvu0vpQ8yM5opIpp7dWyRNIS1aIGSkh8cf/+35rjy7sD8etA2C7JTaNBkZf2oBa6inVI7ZJFL9jcLKGBKGUQVF6k3zQA7A4x2yEgeAdi+BDuW0sEWLWyZh3agElZI5ZDj5nBR0gqm7uKw2o9jmVwb9wFrHgeqYnZ2BIS7SRByiaQj4r4NFsBC8ZUNpZiv68DqGCXB1+/KUEqGmq6euVhQTHUtcUqXirLM1g7jseno7GNfxZ5OQ/NVkJHBfe/udQadY3HkeeL9i2WVc1FdBJF6ACzviLaT8RqcPl4BXA1odVvMamNTxuwRTiV1OVwIPOXLc/zhc07mr6dXP8hzy1hmlae+nq7DtIUzPrv6qy07j0gcz9O+2DIcy64Zpp+z6wCoC4l1xYlAqke7mfyd+Z76tamXS7vLFrt79JQz6oEQAl9YvZbLztnP8nYH2xahS+tEPeQdIOwZAUS3npiEM++FfkXdl3KIVP7YYZMDl5zWC3pOojXOR38mXwz0CGQCZdbOJF2lTxROMF/r7wyJwvyGGc828tpXK5XYUso5Q01tekGy2F1cFVO/tgqwqEHxt/RNC6ZkDtmCWJ7qThUx7lfPW+PI8oTzDg/ZdtIcmFpv8VyAX4Bd58f744cicIWO7H0t3DoTeHqY9/Oy70UaEWmgmjXrg6qNEi90bD3uWdjes7oApe+fXcBTApQ+L+2ipPYQH7np7xTwHY4dR1te+uEB194u6TRJMqwkPRfIO8Lm5AwtFQvhJb5IXVbSCtpm0s92uVVu2+RaUfBwI3UOHbeceB47zv0Ey1sfiW3dJDOArydkjFQlXvEhHfrLhLxleG5P0DM2zwy4zkwu+180iHnVJ3gRBUu2f4n8TAycynrEkzwChKh5kTuoedyZ91YiqYmTmfdiU9KSKpjF9GTDt2k3gpiXqIP6xEoAfdqPiZyeneOUmlq7mJVZZO8IaT0BcZdbMHNaY4sTx0ydBvEc3tLx869+kElblUAMwHlwyQyZV0koD6omfZbAJxO1eOuHNKhsP9aD3fSDpNOHaJSWvbv6HvoJb3zvAi/8xDCXt7RwCmyLDVDbRHdBq/vaglbpxBzV8pYmzOGC51/8uyHX3j4gHyDifCxbsHSS2o4uqlL9uim/2X+fCXF7bAEs4n3edsj0vE8A5VMcUvriWuF90izTMyDbpAZD3LUX8oab3s8FW9KCe99GZpOX6ziV2nwDTXJWDV20edETZyBNP9EYoPTJtlcLA7PDpgYuPQmlIeTjR3TPI58Ip89bhPj8DQgp6tkushGbbAqjyyDi5nB9XiZTqlqlk8H0+n8VvXWQyWBQBqfnJ6okqCK7PTRD4uhAs2CjvE+Ap0cM9LpCMV7rMpVyq2MpQ1sMWZccMF3aJMen9updy7/+jgd5aGdagxiIRNwn7p6X5th2CbCyDS69ViWFQoICJU8LF5JO0Hzst4Q4QELy4O/TwOnH8c/sR2VtjGXP6Z0yjrBBrBrspU8a4kaBDTBmuHqEf/OTW1k5PUfeOVROeCapUUh9BhDSadB5W6FA3lxRZlddSPVMamZuilR3nxiFAJHdX83L82TbkvKQysYArlyAp++AJ+6JcQZN+sZMuAhZ99LWqU5dZwDZoesZTX85Fw30NDFuV62nfJSwqVVFARvxPrKbqQlp2qPTnSE8VbmCIVGjd2cC7c23jXlTW9Q24hUlN1bwEa8p5ew2SBmtsqjfdkZKkLJMzPvYOhZMVaKTmUXhbqFIRYBZ6lX7xQkMq1tFlKpGg57fu/EQz/ryFp721WWtSLHzQwKxbHR2qiYJgddB/JeoVL6pIHAd0j70vcbLPmSkwRm46S8bVk7Ce79pZNpX0tFrlZ96ymPEpH1klakCazuBf/LepXgGIYHAEr7rmB9B6R4iA9u0DSTJRNQvq87JrKQBKbFvOcg2JXricXPC+EhSbVLXQpJkc7uKCkk0kF+2J7o5LKT3jbFfZVIW4JPDLhL4OpNmXvgtAC2GerRO3YisAq+N4O6HZvTtdNjUwAWWXwq4CABJEDCaVufKeUKRbHQgl2lalSuY7+tB5Eys2jxb8mp95ovfmLqvcrWgx9QbO8OIialSha2/ld50lwghepHP+qL8YaolNP1Y14d3jjm6XKkoZSXIU/R2C5WpGUY0ztQyoDoEVLW0Uoe9lzggatauo3Dd7Q0Htw/44LNH+TsL09NBl/ZcvK/hgoMNH73uNB3rPOfzW9h6Mg6npoOr7x3iegOcIRBPUzIqVQYsWx2Ja2cBLZ3Vv3WjJSkrMyrDOF06IkwAKD93sGUR5ueiH9eWBbVbCeBkyammJ1+2u7wrHEktAaRyV1709+/cwcG25YrP3T6jXtNhkwOXSlK6+7Z1nYxvywGscoQ1lCv0qYRmhz8z4qrkpENbZa8IRFZ2waRWS1glwGn9QhHX5XxmtYKWqn5fA2Vj6qizVBqvhjsL5qUvv7Sm1PnEQs9DO2Tbmp7dx4a0WQNw6L5ZNo1QpFe8LhZ0bxTUMpk5uj2zMUt4SSpLILbzqONVHxzwp88aEZzaurS37V5mUvO4TfOVDwx5wWcWuO+cMWNGfOMtc5z/yDCVO+jADMEUzw5mqZcpe94m2aE7Kti62fqY9svNI4AnKqDYkMyeakUzu6iizc3Brq2wdSnFS1JadmtJqqTdojmgqqV41mcslW9qRq19OfGOg1vn0q7Qgc9dvIt7l5a5Ilj62DhscuCSISRDTlUr9Zwpja8uEYPPA06adEA8JMHuLRSQXRhKDqeSmXwdis6Rex2QuixH7q0aojttlZ0suwNIaa27qZYnfl86u9YDT6W6Pj3XAaGzmSL1yTY5Nh/ZWV1LoK0EckjDJ69a5RNXnUb2bP/Hv38eu48NFBICFGfpyUySOZ06c/ccPyiAFWvejITRy35SLhp5mwGMxzrIHejC7rLt2q6h7/tokvOWFTma3uGDyNYdjpaeCX7Ss/sRx//rV3cAO6DrNZ9cfik7ZtDbQd6o9BOMCphxy4KaU+mRqv4FtgdwaQcIK8kGB4xVYgqpTK6HS8+HubTVEBj1XdshzlQ6zY9AnPUUdwyjFlqmYWgeB13ymD+yPOSXX3ZhkitjvPMeeBDcGmcSNjVwRQtWrILAjDWqx52mRPKxtqrYy+XMRHTMlPOhFdQa1BYFAiaBFvVJl2E+PZVbgpHIQ01+qwdgKEAoLEiePXaG0SEyI7nuGGlBn5VPYkkHTK+1FAcRkb7EvUK4fy1Zlnuf69EP1l4Yt+V51ysO8k2f2MYzv7IEYQwPfCUOlvF6NJqHoAOlSU6RneG69QCot32GOFh8Gjyhj/5heX1dqn+ybeGTITgdADEYO37hHQuE0POb3zThI88oVbS//4cr3HD7ELo+j1f65bwtNJBAyw5Ql54ZkJ2kesi+WUIv3qVZt6pa2RZVPDRlQ/urlkpzE5m88r5ZZqsbP4QnX0y2kbnGfNwnG5YzWdegmcqZty6CUkVE003v3/fc87j9guV0G0eXrIkN0n9nEDY5cMWgy4it0iOyhgxoNYBLo5VKoQxs4R7yRmYYpUHTkfAZSKyNyOXUSglNS6CgOu3IanfRVDlOoKGUpch1m+1AWgeBV03BSk0iaUkuPudtJUNZxWgBWhZ8l3VUr/GJ7/mzJx9n37Yxr/rYUmy70boCUl4756JTqhTN+Qg2MmCKvadUko3x5b1peheMcVgAROwxKiE4oEnS0ss/1PD8T6alKt5D49lxoqEZJfVStjMWu1ufnk11t5Q1zbL1fVxKY6VKkTRloBbSpkgsEt9V6eaKJ7ARMLIA35KN8AQII21bXCx3k86kbCxgSWVmqJXYKJJPYgS2DvLcAG3A8e4XXcj+7fP03mcKUbr3HNyzlV/+J98Iv/QzPFbY1MCl8oIEa7my0o4QvHStxCtN2hF67MAI5q0z9wIYNsV65s8V35bePXo/LSNNm9dBc7M1k/fT+1zYby2X1vfBpGB/bSj3+FIQ1XYAq7rW7S7teXyx5yvnr/HHT+l4yUMOVxwiCnl9oZVYxC5VnAHo9Ls65OYJChCZe8/4pnBijerL9hOO7SfTu8ZHQ3UWOEW6k1+naYikEoJKc3I4iACXLUt2mLUAHMpi2voUVG77dGYDUCKpSsy5/YKHuSGcs43S2J7Knm1adV7m3trwivu+AK392+b4zGUrOODBnQtMWl/UxNpfu4HnwHnbZtRtOmxq4IISHqwEIm91CLvqG+0snV+sgULkCZU4dPZRB7bmb3MIJp0apEowmybE+lkNQlMsvnpX/oaiXmTrVfmtNdDr85hTX3wVivyd+d+Cd5n24S0T/vyJIy7/0pCLv7pKOzHf56I6/QSRoIzUUXt8FwN9Bljb8x4ljvXqz0tlpAwm/6kdQBMgZo9+ATULCCk5Aao+xS/81Cxo2fTL2wweIt0GtB0sSNS7a+T0qgmLnK+P+29tWYIdW1Jc0wezaMNJXsndIbuoWEnUMh7PfbsiSN17ziK3XLMTy8Jr/cDSjppkHj1sauDyhf+MBRKVhrQxtLFEafImTiRHcSZVWOpyStrkqjKpr1e0gslA0DIoNDpTKj1ailyHEsykMyWF6bVzpR+XDaG6s9KglKyWo0SScqb+QkrybVSKxTyvgyKWpi/Slbs+21k84znPr/y9FX7kF0+z88iEppMBnJwVSSAl1fFOZ65wsyUou9+UlR7kqC0rbWRDfdVmApR6kz4TkDDSlvcUmxm6BsZiDE/16SyYCAB7cB1xz33TPQJukk9e9JycWovOnBD9s9C6TAGNqVsuuoCWi3atndth14qxDtj29KntTLriQZ9By7YXrLdN0p6l3A3/5TkXcGx5iLB8ZXzynR2rYqbRjQMeK2xq4LLc1Z5wYxdmiInWG+CQ0+5UorBgVp4CpAecS4PLoRtiVkzOfgTi8fDaoXaNoOTis8PswEBmaRtSgJLSN6jnvJTFEq6Cq4Je2SYa7GENqpaWR86TQcpKULGk0TDfU7Zxx8CUTQAmnkyjcB9B7+d/aC/f9b5HuP4L6VzGbI8KqAe35BsUKLJ0YSWzXpedhIlKKa6rwCMNKrvGL4T4jWsVAHOT2ntHngHEpeql9XYdauORpUWWgWV/tT59Vxmf8yEWBmSt9Ch1LEA4GPCovc0rwJF0/AA6B1dcCAtz5CVFYrez2znj0fMSTbbETQM1eHCBX3nppTy8fUHzSu88dhsgV7y3llGNUdpPHy1sauCyw9fCE6ikI5u0lHGt+4GClCuaU98GMxMTIabLQ7Hke4lLZgAVI7esDVRgKUFGJbJQlBJTnt6kj6mlteuVBKwKsCveqsldfeXtVEUwaUsJLCD1RXp1mWV9np2llPdtlMAcvP9lO7n/gnle8UeHyBMefZKS6kXUUqpaigmpFFm6khk2iJMgshNn+ibbbaTwjri8JlTpS/7pV8BU1LeMHy6BlaRPmYZNKzgyyFgbVyFFOnTpjyQhJ98kgAVKe5L1lpfvUhnFB2t+AS65DMI6DOYSiXbaHnkBtABuegaxDyUdFMwObJ3jPS+8BIfjxMKA0lYW+73eBaWm0dgKzvy64v2jhU0NXKVTqTyRwVrGrO+m0V5hw76LEGe3eo4DU5Zmy8pFNdfLHajspJ0qy0NEwbISkpTC1k3/V4kLXPVXpmJrqsNfOZ3uHUZ+K0RT2h/KtgzJcVXrqS0e31tuKU4StpU07RNLjluvniewnVfefEzdBIrZNFMOGYjSQ9koHNLAS/EK6QV0yYvp2dp+NbVvGDpwO1Mna8cCit0URAIqVDh7PFqBahqldvewxm751gGhMYQpbZQYmYN8gIkAsvOwe3ecwV1fjesMZbhn+5fJIwO1Ac+coCM4+P1nnse4iYU4PWw5ujw0dRJKtQ1kmEbZuOn/esSdKWxtcuCCssquaMTH/ioUT+xsWSlTWJtOJC/ZIcEa54XDaBkc4vxqbVyGSPOzUHXdtDTGGXMkN+NaB5P1xqolK6rYJQOwcawlzIbSHy0GUcyFoFWyO7S95S+fvMTFD0540m0jmmw7mqEuZNAygJVLayUmM3CKvZ1c9c2jBQMguUzJ3pQdRjH3kr+UDfOtycs5Zp5udCbB2ba1YGd2Sx0OYGle896+HUZrcFLcHSpAKrzb63Tj/ZHlIQ/sjDvdfvbSrYxbMTUYL/rqu5K51VePUsUzawngfwPgmg6OWoapZ9XASiF2wE7HjXdWjbKe+DK01T9KfegFjFRNLP9NczV7p/KbVfUiwYQpu8Z0GlpLb+7KFQVlG5X5CxxP88xpubQvvrXSjpUU5Xm0iAnsnVz0/Kdv3cWb33WSHUcCA9H6BDymlvxYtw4DWPlAWHltQU6klnQwKjMGazG9L+DpNAo9utuFFKUGT/lEJDtjtyvaxbRPdtuQb4w0aeM6yAvUxemzbYAhMmHC1mU4fxcqSXXxpJ6VpVRNATlxGPXEU8V1HPSN4+S8rh750oVb+KMbdqO2UYdsGaWUoJJ66Trk8jOHXaBu+9PPuH/ssMmBS5HfDrGQ72eDln5rv7ESUExTthtUB80YVySp+JUYueXQdhl54l2vUhWAzxzSgpfNW8sWzFY3trzieV9KcCUBCOiJSipkpXUofbmsnUxJUIg1oIToUKO9bVU5QELTDHlGUSYxBhXDSDk5z8+88SL+4W88yNVfPZVWugRN2iUAk5nH3G7J7UB8wOrF1h6KdXdyoMQUyBgwys3kS4yUbVvy+j/z7ZSoYFSxHM/FNhKjuE3DngYtBfDOGPpT+enJzq9bV+DyS0qQD2PiBNEcuDHTJy6Z73OdHbgh4vKyOmz52W+5PGnBCjTxWv2zYs3FDKDBCYiWjWn63bQbqZ2FbQaHm3WK+YywyYGrlguA4k6aq55dsw0nXERkhUq9ML9Cfn2Oa+dD+gRlTQF9ugld7P6eCboDmJTSIWv9tA6WW9XARXFfS47yXQylhDEtUZZccNqNtCY6Tbucui7zj1+0ppUEuCf0aWZSc+rxtPz6t53PTZ88zMs/eDAN3E5dIsShM88O1naaPjV6knDybJkpe19JXAG9zs9Su/aOvMlfBg/Jt66uBUJpNAduUj7MxvA0kwhRCsSVXehTvhl4Qlx+8+Rr4Uu3wzk7YdeONGEwopQOfXkvdGI99vNlRwQ6+O9P380XLl2hl+ipt7SPVFqz1FSaB+Te0lJp8rAOReUmmoFzT53i2275OI/tN7/pgWs6zMbrWaBVgt1sMXajHESeEcnKgkFjZBcxxCsQ+fx/KR1J/BKoSh+v6TLY67rcdfz465KUWAr6yhdV8pI62bbaqI2ciWtjiC1MOX9IAyApJgircDSszzk+e+12Vudbvu0D98ZvCj4iZRGQSmBQr9dzkjYU/lH2eZbQUpwpSST5a+UxaepXSAUz2ifHNSDpUpoyQ52FWAE0C6bpGLAwiQC1Y1tMoGng0ktg2KZDJUTtq/sjAUh+LKBWAva4dbzvGy6hx3Fg2xyn52J5dYrLpljNfhZ5gbAxGRGlicDGFEEBXHB8131fpCWe+jS3PmFxPOZMwiYHrtIUbr3a5WkpY9jns67t/awO2mi20uY4y5SunRXv1PkuBuuM5018b95vlO+ZAJbezS5/7fE+3WJlmAZ+gfLynW0BmWEkxSz9yaQUR7YN+NLVW9m7fzc3fuqA8veiKyyHF+BKeU91iVENEclGQMtKbPYaCpUyX1qAk0zSdxk4TQtPC6HmWTCfB5X0Bi3siJ7m9GPYtiXargREtyymsvUKsFNtNMsXSsoID+yaY9+OOSY+cOfeZYKzjkAyIz4rzKIPuZ41NupU4rPdo1NcunoY5wJXre2n7VIDd4Hjc48DPy51FrDwVcoM0wPQEthGgDDru7pjNM70bInc22lhG8fjitOPrTwmwFW7P8SyuyKtjfKd7dcladQSgraIwqwusp6G/9nLMmrQsnW1Pj31xERPYEIwquPxLUN+72WXcvF9JzjnyBqDibUZBfMnoAXTy4VSOxU+WjPAuDjuCxPPAIuNkJ/NGpgGUAUg2SCu9VWbXyDvp78wBxfvJTvHir+V+FNJfbPbwoyBPsNOFJzj8JaWAHzu8i189ooVQ52z6ETLLLQ0y6t9umZ1mXQ0ehwrkzWeeGo/Lzp8J/gOhgHW2+RnHWDxcbAfl1XB1J5U+uRKTDvsyL+zparpfMKMTrO6vcclz3N1a1XpSaCmLK+VrEAOxyinme01pm5CSs7EKyUdK+nZ+tn1lRhokkUXofheDzC1vluzQzmXK7lNlyuWN2APs28Rt1VZOzDx8HP/6Dp++N9/noseOI4rQCCpiqLiybX4gsnOEt60S3ZFMP0vp9B0yfYlYFIIT66UuFLNQOLNAMMMmMY4HiBLl0HU0GSLe+KT4oLu0KU/YxsTr36f3tOkegbyXu22TESQCi454Kby9C7wH152PuOmZFB2cktTEPZvx0rtmzXNtlVutkvCgKDH0XzTkVu55vT+RFoeXA8DaZcAg40pzIZNDVxWzVDQ0uCKDtqAwIovykEtYfb6qTInGXayI5jLzwUg5F7kDbGFiWFevPktGNUlkfLqCUUlQJWSmPXZgpL0yuVAofhO69RVLVTaw8pvtKRa49oxRfb9ErC069dCcS3t8a7XPZmXfPheXvSR+xMYTNIe6ClZOfS12OcqpdOnE52DvHcVv0pxsp0pHQYxMR72nahhchJRmpmTdXsyqDOI6fC3bRnVQU82uO/cBhdfQLEkSMBU1irmZVAhTU7IbGTQ7hZA92kpTphAcPzyq3czGgiziZNGndgFTY/I0Xa21Fp6lbV0iZfSmGV55Wgr7WHL/YQf3fdB6KGRSRaIEpdL7Sl/zeNEVZyeUdwIsaf0gRnP6u6rAWtaMK5LUz8p0xJ5TPy+9EA1K6GFKsVy/aKUQyW7WTnGO29Izxrep6F8lse8pCHEb3MJU9flgJiW5iRV2ZhR3umEQalYxTdd2/Lxp1/Ake1LfPsHbksEngazo5R45DpLVoEpA3zuXtO/9jsBpuBNOpVbQdHIkk6vZbI1yeVNQHfFJXEfrEHa90sWh2dnUDOwZT2klNG1STpMflyyjMgFPnLdPPfvniPgCCFwetgkZ3phM5YGhHZk7tsybKGpmnFLf5XsqaT7sgeff/JunrB2gCZ0DGTLHwHdAPGYNM0FH6B9HABXDBZgNgaVjd/VaX2t70sZxFWxraSlMUWFFAHa5zglRyuJp/bz0mvMNxuVrwZh+8wCpCXQ2nNe26BWDGezhWmgtOm61BY6oGxs3YX22Mo8t1++gw8+7xL+zkfuMmojxrdrVjHLtskAYb3X5bSarisrkcegq+6DgpFL6U7NCpoWWdkKC4vx3nWwvEWPmZdjxqBMT77PamCSFHO5hWaiZPjJJyxw5/lzHFoZUKp3033cb2A7Le9L9q29MtsaJmF7t8rTVh+CNjKMJ6wf5LzJcbJdToDK7sVW518nukHY5MBlifaxajwLvGZziq89bAScpaJoOV4JWrNmD2dLV656NjU4i9zLZ7UKIN8IgJT+9CXYacvMrmvt0AqlhGflL1EZtW3ky95ci+0lhmMr83zwuZdy+d2HOP+hYwxEhesDcQdV03b18fNyIUdmFVvolNJFCXiz2tbU2+7iCjCci4uYHfpsxy7YujWBlJyLKOmkvPNOpak8+VWS2GwbpfWJq4NobAfHJ56wyOpcg1W2Z7Mrh12JIRSplDmLfuRLK2WXcc6bnGAQJuydHOeFp+6EYa8Cam7KYIAr/SeTCElgzfHOIGxy4FKik6vZguY0oMj/5dCsnU8fHchmyTGzyyixxRlVbFmlalgQc/7O2qLs8h+qePJerizpWse/2eRnU93IBF+2Rj2Q9YRreRpz1/KXe1+SFoGIU4St+yxghUnr+OXvu5F/8su3sOfgCZpec5kKdttmGyZp76y84wEUB6HmPbhgen0g5WAToJHzBHftgT3nJqnMuGjkTQi9PkstoL5eSUUM3gCZLqwOwTEZzsVF3wHu29PygWfPF9KQsoVaXlbGZ9vYZam2ZK1KAa5IxwFt6DNLEfX+245/nnPD8QhMTTB2QZf4YYhSWBfK7spbPicm0oUzXfGz2YGrDHHYzOKWUIvNVpGxX89W+Mr0ghmkdiDX3K1MwTqWysGy6nCqXLIE41Jiq+tX1sfelwBjJ77laLJZKwT0LrpK1oQfsCReflua/svayzfxELk+gbhYz3o6rOQl5StlrphHj+MXf/BZfPMf3sazPn0/ekozZei66LCZDzIN0bbkieBlP+hlmUoCqq4D36pERZKS8vY0aaAtLcDVVxO98VPx81pG0zf5UqQpC7i95p07rYnrIvNurYFxA7/0ykV6p0xXT7BSCpPzoCJNyRpbNUMou9EJJQU9Cb5yalD/u+89/RkuWT8SVxa0gOtwTZ9AqI/bdY0N2Dt0gqQJ0w7F+V4kYM4onCG+nXl461vfinOu+Dv33HPz+xACb33rW9m7dy8LCwu84AUv4NZbb/2fzE0IYJaUUN9vBEY1wEiTJB8hlCy0c/vqmS611g1cypOP9f2sWlijqUpawfCVaEjV04H0T8rbTD2XHSVjOuU6s+m6C2Fbzi0x9Z8N5fMSSPUkI5uLldJE+tN1ns6UwWf3EJt2XE7Ve8+fPP8K/us3PUlPBLLLgkDtSDZ0Y40j+2p5ATcpny/B0BEHWOMIreenv/ebuOu5z4Rrr4XLLiefLpSr6OIzl+qZjxhLz/K5kpW/XmjIe28lsHr/s4f8h7875Ff/7hy/9pJ5OheSq0P8E6uV9VK3S2rilc9TIUqfPXrqlUwQWVcVFQIcjm39Ov/01C3805O3cOH4KN4FfNPjQ4cPAed6U88uGth9H2cIfQ9+Ek/J9UnyakN81xCBrpG/JLGdQfgbkbie9KQn8Sd/8if5vmmUiH/6p3+an/u5n+Pd7343V111FW9/+9t5yUtewu23386WLVu+xpymB8yjh9nSlFVSSrXMxiwbdPrd9DYvtW3Lqoc1OJQKiQBGoCyHlagsB52WwoRorexWQqgN0eqkfBtU5bCGClvCaRArQ72LhoTywF6poaaq6ko9R6wwCieX5zi2Erdbydsf551QyYO/2E2hOLQilaGz7MSpVOTT5obbz4GFJeJsYs/zjo7Y7hpYmDc1MCpQfQhIvpZsStXfMt97d8Md58c4AccDu+D0nG2r0ouvpH3rayg0MIt2pAUd3brnUz+1kyf/0FGGO6xU7nJ665zgeHiIXZNTZrJASxTrHrStgfI0I+kHR2G/stKpJ0pwdt7gMcLfCHC1bVtIWRJCCPzCL/wCP/ETP8G3fuu3AvCe97yHPXv28Bu/8Ru8/vWv/5ryETB4dIxWJUzvoYYpjV2DW51O/NbCQFmm2VKVPNffWjyXWKUkZImQ/J0CQgl+NndNa1qtK4m9dLWQ0qldrKzDRjWsa2qlyFC80XpojrZUZajf6sA/uTTHbVftBuCKuw5GP8ZcDFOfIPdO7Vm5mHaGMf3nHaxsBybRuL60BYLHuY5n7z+G+l6ZPGwH29rWe3LZuoRAcIF79vT0Du7cC1+81DI1TVBWdFrGVuRT0I6jdmApmZxjYbLK7hP7+fD+PVw0OsrKZKyTpQGCC4QAqxzF9/uYshXaYuTthywDkNvUNlObN0r7o0JjzSMfJfyNANcdd9zB3r17mZub48Ybb+Qd73gHl112GXfffTf79u3jpS99aY47NzfH85//fG655ZYNgWt9fZ319fV8f/z48XxtB8J0056Z2BljljLUo7Vgj6fPFqCQyaRBd0qV4S8pRXtE/L+pZg8VEKBkOz5/TQYs8X63UDMLBGWWsOTq07LlLJkGUzdr2LfxZnFf+2vTsty+bJco65WpbNTyNfw9uHcr7/nupwHwpnf9ObsOn6Ltk+Ql6l6f7C99JQXYbZNldrFp43dtAxddkgqY8nPGgbVmLmKrsdgfqvYIKV6ym008jIcRuN5/Y89oAGWPNujpSjVjEd8/650VVT4xbghNdGuefuRoVwKLZjud89cP8KrD7+fAL57Pd67eyrb1VbJNbdTr/oRWgrRFyF1RCwbmXX1fd26d3tdT4rrxxhv5j//xP3LVVVexf/9+3v72t3PTTTdx6623sm/fPgD27NlTfLNnzx7uvffeDdN85zvfydve9rZHzXc2RJVSRLyyPGta4RHOVhqhSzjqslqlgNCnN3bTD1mhp+6jyk0xqZa2KxvEq1n30rf7OpCeh/xGc9f3Ui8r+ViHSls/62wqg0cPG7Wy2+x2tlfKQsv1A+VKUtKQ01lFLb/to4DKEfEADlIZe3o6/s0bnsN3ve9zXP+lfVosmUH0TbR5dWmmLx9imsrQJ5a/93zYuYfCk95KUAEjZZk69E7Bzx5jVvh8SX6xXb58Yc8fPX1SJK19YlFCjeyl64gq1CVjEKYQbZpf/c1t3PsHy/zd37yXH+LP8SNx3QB32bW86dRHCzZGF6KdyQpxpGJ5I7269I0P0wb3jeSFCu8zmDeOvHe/3+jjMvy1A9fLXvayfH3dddfx7Gc/m8svv5z3vOc9POtZzwLAVeu7QghTz2x4y1vewpvf/OZ8f/z4cS688MIq1sa8etabWg2qWUIorvW3w+4kpZJIn/41BHNWtTdMJAKDz6ekqDFaVnK5glpKtaCcypa8lXxlprL2fypbQa4EuFz6LlAczV60SX0fZr6bFVMYQUmvpeTncGk/VGEadYlLsNSD3axdLM7A/d4rnsS9F+/gVX/0ZdQbXYz2Lk3TG53EeWiGcPn1yXCcDPX4BFq9ApVLQCYls0eU+QRUhTe+NxKdlh4/4Y+e2nH7BT3BKbPQdot9YoFLnitzUYCS+kvOK6Pj/L07fy3eXXQV3bev0L3SMdf3+CR1uhCyNJXXaYsq14RyoYDUr+jgXqWtkNrGbnUt4FVLV7malRTnDU2cISL9jbtDLC0tcd1113HHHXfw6le/GoB9+/Zx3nnn5TgHDhyYksJsmJubY25ubuq57W57Va9YLBXKWm6opbJy8OrsTMhzhLW0Jdd6aJddvKNSUJTAJsRN2XTgar6iQtbrEGcHy4M1rtZ1Wj6qZ9psfJ3yLt+Vy0Hq9puGr3KO1dppBGBLz7XSj8uZZ1Qx5f9aWnZJdV+dH7A2l0g6e8IbXy17AMW2c2Drzggww3l0ti/l4jT1+EiW+oQiWmm7kbj2mWnP4PjDGybctadnbSAgLFKkyj01A9O0S9umbf1n8hDnhRMMmjHLe8+PCQ08tGswj3ahLAOSSXMLSgI4YSq7GWQoEdOqAZEoZVZ0Fo+bxXsDqIvJmUlb8LcAXOvr69x2220873nP49JLL+Xcc8/l5ptv5qlPfSoAo9GID33oQ/zUT/3UXzEnlU9m23WgILoNn9oUVWmySpVafkoYs3Kcldc0t5JIy1LMlrKmZ+tUnZ0u+6yaWGiTPELxJgaPypaStqh6npBb4NGg1JZKWg4sFJVgr/Az3Rc1bM8CYgVuOYnp8PZFPn393vg2OK646zArowbmt1AcDrF1J2zZbrKqmVYNSI7SG1/ALcLuVCOkfB7Y2XNoWTYP9Nx2fsdoAKULgpXvVa13Ve2v4UCmoGC+9jiu5iDncjIC0coOrUJIF54IECLd5AkGSvXMkRxBE8jXIJzrFlAV2Na9vqdqM/M8N3Oo4j12+GsHrh/7sR/jla98JRdddBEHDhzg7W9/O8ePH+d1r3sdzjne9KY38Y53vIMrr7ySK6+8kne84x0sLi7y2te+9q+Qq4ULX3S72pke7VuoW02GVSjiWIAK6J7qdVoqT1kCFA+l0iVCYzPzmhnXkrYCnACaqp/BXNdp1JKnBfp63tC2qfFJmtmiCu91m5TzsGqzk9QaPH3Of9ZMqC2pSmf6POo89124nfsv3JbS8bzuvV9g5cg8nHNxWc/iVGuj0tlEcxHSRT7R2ahKzkgYhjqOLkYn109ePubWC8fYpcyNMSDELzwLjGmR3TLi/v12U8mWnhdxR/INVJnM4xiKrdPal3IXBbOqyfSNSFQyo2e7zDt9VwTLdPoZQnoCdxLo2aPaNiJlV7Xv12vJzwMPPMB3f/d388gjj3DOOefwrGc9i49//ONcfPHFAPz4j/84q6urvPGNb+TIkSPceOON/PEf//H/hA8XYLrQDjcdIrMkFvt1fVcOcpkHtAtZVJpSqaToc2Q3eXHWkO1w47l2HeLd1CcnSwsC3pTXgpLt6zjwFQTk23jatcqIsiTD5RxrOCl9wXRRs85JYdKp8yPHiGGCy+9tPGWt04CkqrLIEdoaNq5KZwrOUlLrKFL2tcfDBU8ktCGNhyQl5cNZE9C66KMVpYhUtF4Gm8pCmrwZ6X3I0ovM9o58z7956VGcs9RArmGfbJ8dITneNjyf2zmfY2bDIhinnhNXXKEdOR96kNKOfv+pHZyh4gJYQnT2VAJO/DMUVUzkZboovehFt7RSZ3I8DQaYetMH9VrEWbOGIgnm/jmz3SFcCLMcNP7XDsePH2fr1q28+djHmF9ZBlyef+srbh6dB6xfy0ZAVksmEbTEZB0QeUNtMi4/iRwokmBIB5KJL7gQ7wDHEBjg87H0OttoPZinS1aLAnZ4W9VPThqe9jIrbSJaV5uKwqTWWPyo4+lFujGdDB9lFj1yhLorZmangS6gkoRQcy15KlSWNSkBXFu/9FyK0ygAgwk8+87AS7+YAKZ34Kt9zafOW3TR4zuXv5aC6yETge/Prj7Jn19+Gpxn0sSF39q32h4tA67kMM/hbiZEljOgK9hY3CNX5DFtyQZ16+1N6i0Gb0LqbRGujDQ4g3ula/OiN8/yewEtAXi5diXI13zL5mklO+W3yYE43h4/tsbWi/4lx44dY2VlhY3Cpl+raKeCdfpfh6OVkvRZOaSV+2chGF2648z3klscoCrdhDwcSxKV3HRwlhYmZ56XZmyqFKbZor2yUpOlEoocrWQaKFsKZFdScn1KBVuG1cR8J3AuLLXLrWRtgKWzqcRVMLAna1uYsJJtLU274p20gfaWjL1xG5i0XQKiNv4GyyDyQY7kEZcXSUs7lpAC8Pnz17jl0lMEeloaXsUDrB+9hMN3XcKRqz+Lo+eNnGQnMmcbSzZJZV1gzIA+zTHHmtrVsra/VQZxufWme1SX6TRumkJMEvFnSlxJqRRE6FQik2sJWZpzyZsntZ0Y2jvTNxnr+7RESqQ4NG1JwkpsjxI2NXDVVaz7Qs3oG8GCXFk5w4KXJQibql7bSWoxt9orfWOlDOGppfwwS9Yq6zStOG78fS3pWJCvVUetRameueJ7HcRRzVKTcsAOIS1jnVbJinWAlqBcgjH5brbdy1ownXkmAB14cLvjw1dp/tG2HEwyWs5AT3BJjXQCncJ4orIWcNyzc8Rdu+Je+ac4wUs4wiVuyAtOn+ZEfwRc4Aq3xjIij0cJfi3XwzonW7/BWTSndbPgJjUSrSD2jpm6MUDzmKYj2z3WW8b2oxVA5cFUwjX42Twqxlu7QIkN8QzCpgeucnjUM3bSC67olxLKNH5v/pR4rBVFJbFZThUWtFRisnvJi8dzCaElnJ5JqKUvlSzrWBaUbWlriV1LUkuEEEFWWsMDk+I7VXvlUNi+arFZ5Xq0J666q9ulZDuzphOsn9M9O+GenbKjvZXB6+kJ/Uc2FOgavsAkx+vpaUPP+YzZzxFaJly9416u3n4PBEdwLivNtq3L6ZxZfTMNXNZ6VzIbvY+mEmtvr3t8RhPKh2dKdk5KMAPIrFQmqqNtXAfFEikChXtKkPePA+DSxTaWg1kYEGe90hrjcEV/qQIYCkJTwopf91kdCokw7BCPA9flkoA6Borly+cylifd+BndVUoRNUyVcey9BUlZ36ae8XXd7DB2hbOoSFClwi1b07jgcYyIB5JG+5z62MuMa8mi4/+FKTnFqKe1tO62BzS+VUZti4jvGKg5W+tpJ1n0qsuuFAIZCobSm3F6ogmebtzTtIE1t8oKR3kTh7L01gN5D3wUwrtEbyPT9rJjgzorq/xZCza2z2ZpDlEJVoW7xgy5CZC9IKqENbZsRV0vWcqJyzsBn0RnIp3J4SVgMV8BUvbWx+s3nYM+zc6G2tdwdtjUwCUAogPPJ3CxXMpV9/KsnJ8SUV6BS4eqTy0v0OXSVVOkKrKaLtHpafEMUZOqneFTdwWby0YcJ5j/NY4d1mV9dVGuApCVTAXkQpKSpDXVFdTm61G51RPo2P3gEk8cfJEn7/lThngG9PwSF7Ofi5hjlympAJiAqezHpacyRcuPDrtSirY9Zt/Pkid1Gk3W+VnYlNrpqeE9siawT3N9ERpkEkLyc2mgBN53/TN5zy//Ac993v2at5N4sRxyfpGViibEWcLSJgvKVstnFsqdiWlra+sv0xEChs1UzEobqRs2v0oXsgxHwMxBdnXIqwLqj5OkJUuexNhmO9NpH9F76Jp42k/n4/3kzGYVNzlwiYygdz4TiyXzUm2S5o5/MX6Hzoup/CGSyoTS6iXyk3UBdGaWx+NpicdutVh/nJKn2j2oSDnPUndLEg4z5panXR6mTex6bVugPH6sBEO71lAcK1pezG/yid3bOOJWWSQkWcvxWh6inzzCwW4Hvz33NAPHduhZ1SsYIClrrJLTLEVRJSGtWSmHyoaJcW4u1tUjUrCdY46A6tPZjtoDAzIAHtzK1vt38n1PfT9//3fu4aLzj9MWe8zbUmvJpEWtG69AufR+X8QsVULb6w0qr4Lt/XLO2TpLy5RCsQjMUczi6S6t8n7Gu1w9EZ2g2B12luRmS+8oDvVWcCSqlaMBTNoIXqfqtGaHTQ5cpXhd2gVqJcWK8CqB2FlDO6Gu6cru6DJzKEqXy/90kMUB4zJoNYYcLW+c9jmqa6Uq0azgqpgyVHSxdlkvC1Qb5zkt58Qh/eS7jrD3UE8/N+TQdZ/ixOoTuXzuIOc3RwsmsYMxX3ED/rLpOcr9fBsdt7CH0wwRHyxbfpFPBLzKukrPTbdCLYmUTEGhtmz32iUmmDhRtRVAG4Sev9vdx283CzydVS6eP8jCOQ+xl9Psvfp02XAbd2PR4tYRuvwrZ6AtjNlMrGlDZbt6PUJJ7Rm0HHoo0lSh09OpRwbAipJZwEq/hdFd0jOrFOR5bpAUPzg4PQergwhgwcHkURrUhP8NgMtKCfH/aWBQ4rAcUMGrHrDWcmLN9ZY8KHJxyYvLJSlLTaUlaNoyPXq9vpa401JlDcrT6brqibRe+qKDU19puXKL55qD+9l7uOPkYC87+AS3da/hinCIPUyyvCYLho41Qw7jOZ8HeRY9B04OODZc5PRwyEEWkDbXQdybHlN1uRwKVh2p6zLdNvUMpJ1hnA69KUf81zLhef2DfLrZxg3hFE9aGsGSZBsetTuEgUbJx/aF5Kb3VtnSulLVtuwnaw0tAV2laVVIZwO+PM5ximipBs7eG4DKhU2llJ0iLCAV9ttA4Q+W3R1ctG2dHsJYVh8EZnbRjLDJgQumB6A820imsR5GogyI1cWSkAzkDrKdxnI0C18QVYshsuWwqGnKRwXEpqdx3NT/MWcbS4ehJeuYy4AJ6yzmt/FNtMLVX09fT0tZBEe/Gvjqvx3yAzeMGL7oDj57feDukxfxVBe4fMt7ccBpBswxl5ZNR/X5RtZ4Jh09njFzfNs9X2Vt94Dbd+/kfVydSmY97kKyFlK0mEpEhaJjBrcd2qWcUnqp6SJxlfh02qE3qiQEGjrmWQUCbwpHinxjZlay23ic2VaWno0Gh4Coc0B2GZ6uHyjDJd/J/G5ftFU5MSWGBJkA8ECXyt2UVUie/0BwCYvqFQ5pyRCOYgmPd1in1ZCPcUslCZJRqtU40f3EJ+kKmDTqy5FlkDNDrk0OXGpByTtwZGVaJuV17aLt4AhH6iJZuvWB8Ew5kMAK+T6l6Wkzf4vbCA6w9iwhgNhlYgFTYrND1CX/qFg+6zc0y4EUhHQv4R5eye/yi/woJI/8Wi60MqXONtqJeQfZJgSsOtqDDdf/c8fhC/4dw+YE88AT525lgmNA4BQRnlcIrBPXA0idehpGDGiAE0+aN6VpkiNFT5c3sxGfb6tK6VaJshailJy0h0oJxc6rjVOdzNKeHF+nYaS3TnOYhiFPYI2/z1fiUfB2KctGEBUCuBI6rVQljqc6v+zSIWWuYGNWnre7b2nN4m+H0jKpBiX1RqteS2CASrZNHgWGHYrUJFKQA7wjdCHtGlEztVo6MyGfjNSo9BUgq3+ytVAPjFo43abKCCCmcnQbZVCGTQ5cSkzT8zBCGgJYlq+C8qeQCVnUElUaSv/yqAx6fFYJxUQvs3Oz/OKTx7bpcTWuW8ktVO8FZgOv4nfYjdkgz4SWCQPW+UH+HYun9/JHg5u4bXCR+XoWISjhWx5/I7/OModh3tFdmBbwNicyg4xQHhgn4D9Gx4R1LmOeQGDEAi6dftDTMKBj7CL0nMdJvp/P0BP4T1zDKYapdWRKJJrHhdWIHVJ7VXpQXTqsJatUr6T9VNZSoI55dBxlT9jP93EwMh43wSVQxlnnhI1DHJ+mX9OlXb+hrjjlxE8pM9fpxvZtc12F7YFYAwXCZecOa/rwaabYughH6pQNFKUBRUoyFegsu7U1ddVlLW8aRhlcnCHUQQCuhXF6hzebB6ZnVvI6g7CpgUun+mv7hW0xK9grR9SNl0sXwT4DmeXr1vc9TunHdYfCr8VUL6v6bf5N5npl+cqhJmVY50Eu5gGewoEc83weYIHTua7TUOTYwnHaQcdTmw+xiys5wFUs8xE6HHfuu4lj7R7YVZp4A4FFjnA1HwYc23mYlnXwJHcaN5OepMXHwCo96wSGtAm81cl2Le1UGgdTzzIjenq++Z4P8JGdT+feLRfngWvXaeppfyH3scoXtrdibF/ZVIRtBeNyIbOkYw7wdzjINk6yxBpbWMvpqyxrxmbZPebh7BFmy1ZbSqNkpABj2axNXeyGIutrunYmO9YxQr7P70Ckq8ZMJIXk0SDSTaL9JC0SpP6p413AjRtoxgSvIwXAObtOQUvuQhqL9hDbrP655BqRes8TNywUx7Y+jYcaCx8lbGrgghKUyl/tcPkrPePLHQ9kEOhUuen0rKyIAX6AbI1bSy/TlrXah1lCGS/QcwV3MOFeLuF+ruFAAXfqJOusUpdSijHHg9Ocx90ssMYCE7bwBcYE1sIKLvScZBvncBcPczU7uY8hp1jiMOfzBQMNJcjaAVa2dQwTAkfp2M5CGkDqXDrJKrZ+73Bc3R/iHg5xmJ0cYwuqfJP7QuRRVaVsOtP1x/SZLacDdnCKFY5zNzt5Ag9xLQfZyQTnHGNUfVePt2m2ghlbxTNTs7KEG6k8pXuKSboEgswqbS0dvkhXnWmUfWo6wnB1JIiZIJXAzgqGBCwh2UU7lZiCD0x5f5iCBwN+WaKSyNlb3iW7mEsFkzydprVRk80Imxq46k4SCcHCVqAxYKUAZRf0KHiJOVNJX/mW3A1omEOJVUth17KJncYa05VPxjDHOgNkp4LAS/kD5lnL9bOErUNTNvWbXrYjpW/Zx04OMEkgd9V5H2WOE9zLU3ga7+PP+Edcw81sY1+ug0quktZsgUOXk6gat48RS8zTZkdbVYP7XPL47ZCGE5c9lfPpOMXD3MYcp1gwEyO2ZtqaMgz7oqQueWDpxtUtY+aJB6v4xIiu4T6eyD38Fs/hO3gYUJ/6jpBVL5WfJXdbkpCECFvOaTBX1c4ChjIdG8+qkfZbl2hN1o7GMpVrWyXNxnyjlGgkKNt5FuBD6pFe2zZ6s6eDcJ0D5gjJaB9ctNY5k1bIxUggVx/N5tJNIKqOQ2CUqFniFGJnzRk2DpsauKyRsjZ9RwkrdlxHqfzpYReym4FAjQjptv1csmoN8cnNQVUAARbhl42xDFhfG2tvA/HGeyEf5FpuzQQcj9rwWIKWOsY3shldk+osqUUD+YnQsEqPc7BAVOPEtH0xn+cCPkeP40W8qyijM2mR06xbR+BCF/PoW1hjmFootlefYKPJ30aJYI1ojr+YwMUc4RnhJP9fniHFMS1a1lFyq8FAyzKhwXMV9/EK/hyRk80CE17PzXRpQsUyEQv83qQfsBSmMgvm3v5KitLvYpgPqAEhVH+6ntm66sSNaqSM0odNxcrkG4F3oR/ZdUJlNCP5BnRvMiYRoCYeXeqT/K880A/xwUeJyq3TE+ic+DO6HM3WXhskkA8KwUXVcC2xhkA02DsHgz7Oo4R0/yhnT9iwqYFLwaMmitKGIKZ3NbgLrxVSiXGs170Qiqwla2hJptuctyVoVTuZEUMXJs0x5gf5D0BgwHhqsChQ6Ao6Cb1Rw6KdTq6jAfxjB/8RF275MNsXbuURAgN82qzOtlPIA9q2kZRXDbqySLhWyuzRsVIuiNMWMnelECPbC5JrVVoVT+0f8N9fdgEv+tBDNCsymEWRDzlPzS2udSxDXKn3Tfw513I3urA+ZIlEzf4qd/TIcSbaj+KDZXtaruymCWVP2xAy7EzMdyLjO5Q+Je2AALDu3RanXepZ82AserE+ccFSk4G6xRFoC8AuQm+oNgxEkcA0d/xP9trKne/y2gLZZy4Q0jdOolBdKBjJfFSHzh62fZTAHHEWkxCfnUHY1MClBKBSQcjXYrEqh16M3aFbBApZ97mjdZhE8TmSsZCrgkc5fAuZN8WOUPgS/owVjqcUe+aSOqgcFVP+GohdUX4phwz+Dhj1Qz5z+DvZu+XDLAzvoyMaaKPEUy4Hty4HwaTnirazxnD7vZV1xDDcsJM9tImUVNmW+qjsYltI/rZtW+f//fMf5ZaFC5mYNCQPnWG0Lat/fYLSb+EjXMHDDOgKQ7i13amLhe2jQNln5a+EjeSA+nvbb+qEIdjg8i6mihMCziLbx40m+wzzKq2qMcPWReBLQZEEmiHl27jI5BogpKPHHG56qYlLPRwA5xPqNRBaJqyb0SIqNoWJihAFrNCA6wzjzsTjo3TXQT6WTNxOgiMf83YGYVMDF5RSg3JMtVypNSgUMeqvpbmsbCI+WsK3ZptcVdbSNFwCx8PcwD6u5C4WOWUIVeLIVxaYrPylbgElcKgf2qluhYfXnsCgPco6WxgyNNubqIRUSm4SVGFS+HJFnDouRZkjsC2znNvKtmgt0alKpI6gg/mOG1/wEB0DvsAujjFvvijXosozC+6Sz+U8zBZWTZ1s3pbRbBxC9Vszpen3Ns7s+kpbtOm39p7Ta1Hr6hUXtv9LIFag0vzU+VR/5cs+ZxoKX9IyOGTmMaS4IegGiALVPboRhgM9qQyXPnfq6hB8PICjl8XUkL3zRcITBHw87MdVc3bxMyq5oAja055cVqmxhG5JRuxkNk+5Ko2looRGK5DnJMvcwYv4HOJpI8Tnc0zx7bFeOBFwhDN3ph7B5KLAtY0HV6/niTt+ly+cfAVNc4T55rCpoZFEg2c97GTgHmHittLLcAqe4ZGYR7t4GuZXWesD7ugu5gY94/mThOBoTm6J8uu2k4RTC7imZ7DY0WbrSymJSr6G95oBJ9JwhKeXci+rtHyZncnHSwdbCR9WItRBfIQV5pkwTHtmldYrlcCmR2oJarNiZFkmmBStpIFKzVpC7VXLCOOxGUqN1k7l0r4Oqtba0pRSnQRhzKX1S1mk7lmiSra2jIBUVdvQE1xI3vBxXzJV0LVUki4I3iRJUCQou+tDT3JElcg+OaVCsSNtf2aQtMmBq+yckpjlnEOfB4mCVoeSq7qMNojNQ0T0Bs8QnSlT64LlwQooMY+Og2znbv4BtyH+2aoa1HajWI8xnkHix9G25AsVUkL97bbBg9yw4z/zwfUf4frlf8uAE/S9122NQnKfcHA67OBLa2/gyQs/xb39t3Ca3cAEN2l54nvmCc6x+wUfZu7Jn+be9Y7F//yPuHbvmDuf/n4On3bM3/ythD6w+AO/x/jPbqTdepL5532G3ntj/i0lNhnUAthpN6/cYiK1jYFXcRd7WOW/cSlyYrfM93nDoFTJcqmvPO/i5byWD3E9d2dY01MuZTjb/dCmQw0KVo3TXBMYB4mhky9x2V68H+FYpKFP/k3BOUbAHDAyUovk5JgnJLuWR/tc5H0BitIiZ+FZp5gmxKMU1b4YmVx0DYbWORoXkl0pWkFzPUNkcLhAcOmYYyfTAzEf26JC1zrn6RSMQhO3rZmkrZPWvDrAhgRqXRbV0tCcssrNDJsauDpcmrkRSas8wCHGkV0r1YurnKQXhz1xVBRiFz/5aOy0YdodYcSIO/kx/pIB1hfMSoLK5ewgtIrjes6nBCprihBbhyPaMU7vv5STH38VT/3mdwITfvVfvoojky28/Gc+zUp4GY9MFvDNn+HdAxy+dyvv+WbH6//ih7ji/Qss7ktOBMmc4R0c+uhzcLfcxFxaFfSlBwf0D7+ahTTSXID1X30ldJ51Fxg/dA47v/uD6M5oVvUsVac+TziI0Vnme33qB3geD3MVx/hZbkjxFOakX+p0S0U93neFxKIgIAurpP2t64PAYJtTsQBnTxpPX4QoreJIjpkCmIFhsjv55IzZu5j3gLgvF1C0WJcZY0xHdygTsJdre2BG/LrL5Y8lGCCng9tWUd0jGtll6kVdXMWK1vvYel2KGdPxjAlpnw87sWEgVqSs9bnk9kBUF9c9rLaRePo0Elx614VyydHjwcYlXMs6O5TD3FpqYmxVuKzQa6/jXlq6sKJuIpXtns+nuID9Kb1VBonHWY5vbUY9VuXTPyGMkgsbm0EqoTwD+Oh7v5mr93+A87rPcHDwXXzuP72Oc77lz3je9cvM330B235jO61bYkvwOHcj8FTOW2v4sW+CPf91icWjDp/WhbkOGh+By/dNPDYwNUfowfVNbp3gHHTJGBwcbuLTWsW6blYlLmskvxFa4imD4ofVEDg/rPLj4TPgHL/OE9nnlgFZWiVwY1tPVWhprcbkGVO2HmGlxFXKZTKpYZ9Jw7s05qLLgCMktaiJTprJuOxkr/pgUg+kRU6OT01uoHcTrm3/Mq2ojHTbEW1JsorT1sAuqrbMTP24rHqqKwZ08VhgQkebLF/qxDuht1txu0i1USAQpTbWeYC6U7t8ndY8CGittuA8jJtoiA8JLmVmMauRQSUz6zm//rgArvKfPKW4Uglo4yZR8JK9tCz3s3EGTHgGt9IAl3M/2zlWpNSb+HaIlaqTSmOxnLVFRpWhcjhC6D3HPn4Tcw9fwuj4cznujuAXPbv2X8DyJ5/OyngXc3PzsG8+uskEcG4rAVgIsGU3uP0uSgkpTTn/s0nXuZ1cLdkk2AnkHQaiDFQoCjPatX5iFb04AKKaHkNLz8XuOK53DL1dAJRLQB3srKVV78rc3BQVKLtSxiBTGtHQLKpP6qUApQHZgfM4cb60+6b3EJIFW9yk7vjsdRzhYpZ3HcRfKHZMKaPKPSqX5qSKNlOGWNZIpFNdnC7tI8wz2mDFGy/uGaz6QEiSlACUmCv0nCPL7s35TIkWCD5uU7PuzXY1Pi7v8SHauWRxNUnaklmDPqik9hhhUwOXGjkteOk8CvnZtHIRg8o7IItTo5PpkAm7OAZF5zsWWOdZfJ4W3XdAOlTKZFNXQoxl8ljJy1pstNTy64Jjfd/5THb2NOuO5kQDfcPxv3gO5+IJK8/hENAGuNwBn7lGJSUMuBgwys8lD5fAytWuILNDFiKkOZ2oLq5oYTejbrNYhwKHxutdNIQ3Ac4PJznGPEfcfGYFzrSnFqcsfTBvVR4pIU0lFGPuDwKmKb0eBS9JWAyIuWGT5JUrqvFznSfwyOFzuP2z1zFq5vEXnuDk3C4Wdj/COOVl3SeEtsvSWjnTSrOqoivNW91A4vYJvDr65DBRGuun5VcZUb4oSchrH/O3srC699GWNXboTISLwDV0sE4ErwDxMFm5T234eAAukI5UNVFlmd7E6PKVdK1yLSFfj2cOcfDbyyN8K39GPdyEcMam0wSIOkNAQnxgh4XlroZUgiNM2vSNDnMfPAf/y2s59OrTDB8csOUTi3FQiRiXSt+4qNIB2d9PpqcLCcoEATjn0t4VTod93i+uHo/pP5cyjjQp3kUNJfHrMNK2E6VNJ0q05xT+hbvjA9/Tf4k/cJfxh/5yurxdsJXAyN8pEAkjim8UEMSOpS3igy4jynJ2SF/LchhRb9qgOxw4R/ZF6ippP2hjOqJQcXo8xx/9/iuYw+HW4ZHbLuTUfdt56mvex2DO0zs7a2z3si3rKE8tYIX8zM6ngzrxSruM8j6vEOWuCZ6B6SWfemdCSAYT2WtCt4+W8oi05XsXKzlq4OQATjmVTHtgktpt0sS2GhEBDJcWW7sojUn7nkHY1MDVMaFNIq9o8+XiaYGUUkkQ7hOyWd7jWUxzjZ7n8BmezpcyvJRSVEylz3lIimq6DahtQW0MWoZ6Or8/tcy+/+cf4x15XzeXWGGD45zfWSnqLYBpt+P1jZEy5LkRCDKgufJdIWm56Tx8UKm+yDvdxGO4GiaG/4OqyJafywFhsZVL2AnIIQ9GbkuHjb6su4urJ4f52cGzCHnJlR2uMQ/PgJZhsgF12OXoTS6dwJtQhWdBvKs6p2gve/lINweSzcZ0hKg5zinSWLWnjb+30fPuZsSTgXHQ9j54YInv+Zbv5dd++33ML4m11kKV9kY5Z6t0N3ueW9pDAc7TM0amUKLzdZN7xFqBg1kDUdplBdijp1miaesJ3QfwfdzVtE1+Wh1RZRwngApE8JoQgcxNopQmrXLKHlSycdjUwCUNbX3k68nUYGQcARXdQytCyy4O80r+R4aXRday9GPlNg3iNa4E01eEY0Vs+Xb04IUc/e/flPdOExWtx9P4NLOW6NZKTYQ6/xjMSViRZmLW5cRMJXE1kJ2inXlfSFXy63IS5XPzzAeYR5e3SBzr46NqjyxOkQFUmtqjZqEbXycbOM7DheEE/3J0C+8c3ERwPre2lRV0y6GQTP7qUqEyr1n7FxxNH8iT+SIliJdog45oW/n6GpKHZmpMcSlPs7BbHt7LE255Dla1cg6WFx2vfQEstr2Zu7MqXglUWtPpNza+bkQYzF+XKF71gtIhRdN0VR42BHTSKdJPop7OwWoDhwZp7aEvpSvvYFWI2kHfxDguXdNHFHx8OKDq/Iq1BwhHntXpVk17Bnexg9MsMGI7x4tuUumpNEYGajp2+Xkp2Ovdqb94Lo8cWGb12Ba2HdsZYxqGbgEif28knAw+ormYQZMBpAIgAbD6nYBlOTCm07NhFmgabaioqwQLWq74UzN6ea/Q0pPsXOjymCEd57mTfNf4VkIb+JQ7nzvdzqK8LgzwDOidLudS25kcPZaueocPyQAuUpMN3iZsG0IpY6qBbIMYg9XqiSH3376VK84jAzUpy+3LsU+kBawyXGftzbV9X/+R2nWdYzS0rDPkYzxCT8+zWGYrLT63vsR3VZ7lnLvMEMf+ib8DidE7ONXAsQZGCZjGRAlrnQjqTR+dS7ODYeqZYEpupdzHCP8bANe0B7tIYDEIsARWWGMXpxBQu5YH2cXJ/JXlYsJ1LJGUvlcluAVzPzmyne6IAJRj9dbrOfHQVkYdbN9GVt2wac8a/VBu7b0BM8rqnnlvx1AGLmcHcpUGj04zMyW+ldMM9x6qmAYFwet8noJUCVyzc5D2zGqqA+97nju+H9f34D0TWu7vt7Pz9oZzrtzP9naMp4UQTQFiEhBIcE53Eo2b6zmmNpqynKLYLVTiuPrBdEg+cJLWcG6VnbsfJLCX0nAvUnNTtJOsed5I9hAqVZudtuWEnrs5gcOxxlFaBqyywKc5TiCwnYarmGeBhodY51IWTY3UJUT7R2Efc69A42DVw8kGTnq1942Sapi95T2Fh3zdxgH0gJnHDpscuKZloFk+yQtMcHRcxT5ewB1ZjAYyIdfagHBASd3atMTXxx7hKg5//fo8q1+6npOfuAmIhnQH7N5Sck/J05J+IT3k/6a9luodd735JpNFJVUVxvfZzTkziJBRD1M3HDN/5YMsPfu2/MzaAieQVT6Ree3OZjkd8yvpN7kXgxncEXXdwBG6hm8I93OOX+XX15/KTe9d5ht+7HPML59GLJZSmt7FHd59aMGpZUtoxYkPkTUYupCAx/SYiDy2klJ6y30m+ol8d9kFj/D6132Y3/ntb2G8Gve4yvgYwNHimeTkB1DslD+rrTSTqEHIGY1HWOe93I2sAIlgdAzxS/wgx1il51wGfIDD/CAXsEDLmGg9nMful1HmlNX41FR0Lv4dGsAxR9w7IHHH1fSVd9F7Xpb5FOq2ELJt1DOjzk0OXNbxVE3iKsx6YML38REWGSE8v0FX2StQOXSnLbU1WFAQD305wVqBR7YyGXHw97+H0cMX0jQ6GAt5MElb2ReKadXPjhdrULcSYKE6JOlcvN8FpHxFAzNJImwsaYUZ15L39m/8JMOLDmYoEqfNXAeglq7EZ6khTKUtbd0Do9QXDbauIbZFcpSldzwhPMLb52+Gt5KMxK4UZYNHSDw4nRSQJTiu6aEzzpEWgLw8M4BWgNaMBpwWxfPvnF/jNd/9G/z2b3wXo9XF2P8paVnqNUifjFAzmxTJyiGSbJN/HfPAZznCb3EvjpboxxWQsxGsNfGWpHWA45d4gDdyAR/lOKfo+XZ2s0RJb3GGUUAw5S6Oo/fPw0EiUGUDvQB4Yl1WHczT396090Q5c68baT5a2NTA1ecdgkR4Jg+UyzjIy4jSwAJj7NovuzWI6u0qKgeU481SrUQJETqdHNvO4f/yPdH4ub6IFbXzAA3luKiDAE4IOm5kJtBKX/K9JWRxjyjig3ESnR2y5lNpS7m8TtU1/Q3seu0HmVtZxTnd1aw8i1qSleUzkSpl7qhccKWzjQKA8r1DwUz6wjtjBBDEE9cEh7HteoKogU4lBilXbBuXROIIPrpjgivVRNzsTjM1zZcSZHbRPHPe84rveH9Uoxxx1nHiaAYRrIUG5TQDsdRtND8gbfcubmONLu2lG+f+ZKJCF23rNIU6TUQXnn/Pfsbp6b/l/gRxvuqJcsScN5rjDXdfBPuJ9izxhPcBJkOyLStAPjgjNoLWJAToR2AoA/84UBWXuRPH+ayzG+nSl3Abi4zYwjpLeTd+67ujyyasbUoJQkhbu0vlqvg+hMDJP305/WgOAvTjAZxeLpi2JGBNKFkbqSUhpY3C70rUzCK+Ms5c2o0ATu43OsO0hI8ZL8374XmHWHjKXVFCWDmFa2IF4/AIefcAbUvd68Cq82oP0yW6OnOrQ8XO5prmic9cXKJk2ySf0YD5napSKKToQjAyWkyxYYJ0nG2XqYRroDNxJSR/pvl2FXxqA+8IAwj9HN61WfMS85i0Z0eaqKM8yV42dz7NhFPEZT5qg/IIG7cm+Hhf0vSpDOmOkxtApFYlEtNKCLDmonjYhwhU6ZQgtWeFanAJ0TqTaps6LC14Co8D4HoiB2jxrCd+4YEnsJ8FxhiIzwDk8tNpIzsISEE9zG/75BPYMRiydU6JYHTnEwijYbwzwCBSTqb3itAFd2y+Wi4DRKjBvQakGpyslFWH4jMZ0BuiVRkyaO19hLmrHmT+8oeRQ9hs+i3Czy2d6p77ClHytmYGdb5lWlYylrZyPtkYU9tnm7dBHcnJyLpTz0Uqm3K0TbMiU/tW1X36WA1vK9iR1jGSCcQ5h3Md3jdZTrIbhkr9rb1ftICewGc5nMxq1kRfQlUJW49VdHGE8JTpWEmN5HAq9UhcQ8ArpNQLTmByC6lnQ2+IsRQfHitsauC6iTVW2AfsQ7dABuk86xSqp/rGBqrpKtusTi5DL26QMdbH3vcirlvewmXbU7caaUd8p/oysSw11DOTmPuaeRezgs78GWC0ZxDY2kpiMyWrOvPpyyJYIaPdcpqlp9zJ8LJ9iAKhO/0q0bUzgCtkcldbl/5fijWh+I3B2hnFsK9tmgZSaki7GtUygrpeBXg5ss+/9F9uy4Z0OGkoGzh/PJWwhkcTZWtpzgFdB67HO/WPkskNkbisNN8QZw8fYZ3f4X5clr1055GYvKVifWaZtxRCaVIWsssY0pT0uDfHxPUccuvscHPxWdbRxYOvklKtVCr2kHxQjHWRODPw2tTAZZ3kLGgFlP8LkUNs/qjSxHuZAHJB0zj1ge+kP3ROih+74Fsv0ub0vWlaQ9d5wikk43F6J46hwoQgLdHJeSude1cZ143tSvKrB2UhCFRgVhy6Yr+JwkQpOFRSRHAB18Oeb/8QLK9mEHIZuOyg6DNTsBClQ0LVxT7HU/ArT+6Tp66QMGSbPRnQCozqSGlrqU4y1kl1OghT8/J9Mqg5UK4kA20jjmMbVVKdeZ5XMAPcpBMarJuEQw31wcUaxsNPtAh3cYL/xN3Ing223go4tgiyNEulMsnNOlWUi4Zs4e1OarBvcZV3PP1L/NSfPIVW/GxIDqQ906pz6FU0FmLr2tQcZpG3mzkDMhU2NXDZPbnjve5VpOu2XLa/2M3ubPcdPbqb//4nr+XlR8GPWpWSUtK+okHb7cH0sTf3gUi7zYzvRKKxC6IbF4GqdiSdOocACtKS4EwBnYlTj6/A9MtQz1IH8HNj9r7uj/CDPs1PRbKOh0j0eWmOwIed3/X5jS2rbM0iC05ibDG8dxmwYhDTsFWAPDCHM0Zr8cLXFtLuiOmpU6eqqwpp+pUcKuJ8iGaWLmFPXr4wo9FNmxcNj/nGNkLNWUDXeE1CtlH7FLfP/RJBe4mQ95J/Elt5C0/indw+E6qml2c7LMsQVj2rSuTNdqYdbaSPo1A6BIZpClQk0zTZkQdDagDx+XIibQVC4/iJv/thvv+DS9x9TscHnnqateOrM0tUh00NXLX8IQSs3FyeO27mKAt4nnVyL+ObX8GB/bBtOwyG0IyHPOvwkKYrU5YE8vrBKveaqWSm6zRu3i5XAC0oDdvfArRmU1NZthQKEDN5UMWxA0zUzTymzDhbfvKdLFz2EM73NIMe56z/lS7VETK2IFVCgc73Csj0BrR68yugZWfPZE5MPe8j4IzT4G1wee2cpF/v1i4lkzTlgIfZaBMHVRAJq9ZajNRb6G2dTcI0rLXwWw4SUD+VLFyEnJ6TDvLGLOGkjurQ64FhaqVylahmWspNkoK0rq4jKKuo0lcZaiTumLgx73r6Fwmh4zmfvosbvvgg+CFc/gMKWpJcD4TA711zO1/deSQzu2NzI977rI7VIawNetYHjwOJq+wcASzH4v3ncuLILm4dDjh19ae4/vPPYKVbZ4hnvLaDyYMX4Y9CWI+rEDxwjkhANgQ7FB9d6rFMOQs0lfQkEbz0qTPARQlkdZqPFmaNL/t8mqhLYJf75WvuZumKB5g77xAg/Fg5tCuIv8xLx6ZKYgoY9k8XaunpllZ1lK91iMmvDE9ZvFM6D1sJr5aoyue25mUbOBMz5H3UnXSWcIAy0bIxai7hqnhQcrds2JY/CsOoM7WxtPQIIz7FMVR3qDOZRRcWQa2Kbb9U92wL/vpdtLgFOoLruXPHEXo6Bpeuc9gHcB2cd29ZBh/yHlyfO28/D6+cRA0Lnvt36h6u1lL9aGFTA5eI0aTfhnnWD21h8KUnMr7/Mr66NM/Bnfdw5V88hyeNFwB17Nu+jSlj+IzEgY1NhtnPKZTPbLDCNpQ2LMx17YOVBIDZYJnobxawzZoxzLaupmOw4/jU8iKpw9Ybvky79VSWbjzl0nELR85cyZs+X8f/J+mtgNaEPpO9XZQl6mKdYvzfLExGPMtlybYAWe2KIfHLik4PiRJ6AyFJxKF6bVDJrAO2bixla9pLZ67Tu5qj2dm4zPU0D/Hpip/Ech5jzK0cR0Fe1T497Xu6lpqxxpgFd87EV6ulyMyyEaHKyV+6cje3Xrkr9eGdaP/Jr+7WoRKfK37VkvnYYVMDF4izrsP1A3ZxKQ984EU8dHIRgJeuA+/9gegPZWjPTn3PCnkzS7k3VDCFC6FkkqAkIQBh3Rqyzary0Sq83CsAkzzyMDTjqpDw6jFjk3CBZttJzvnOD8b8KllD0tC5WFnEpPF6c23lqh5YR4z2egKRyi7x2wmBkUnV8v6xIV/Mex2W8YwB+bZD92IXtbJFwE29+O0isMFUvnULJBBMduQ+MY+aIIIv+6FoFFnCYA31gTRL48gGxUwIDpwZrL52NpHSqZQJcDnLvIFLeCtfxs1QgJlqT+sQJFJcg6v6WHtjUDyPsUaoIb009qtE60w6lo20RUpaRuldC2KPHTY5cDn+hCN8Zt+5XHTzD/Oqkw7nnM7GQVxxAHl053W1U5ySbOspZt2qePnWSDxZqjIg44DW0KgzcaZAzCReDCan9O6gdIepwbUoXPmud7By7VfZ+dzPZHIS0tMBOwsyrKQVn05Qdwi7FZMnniozIWTpAChktkmRuqYsZxtZG1c80DamY/ezdykdcZEIyAZ3MXabhrEFJ4dduKyNJLW0fnU9Ljt3u8IgGXLGzjZ6i4pD4mwlXvOSsoBTcBC61PFiiffKtez0cRMzGUPaIVXXfrrUNhPiCUcufTMNQVZ2jVCSlpujO3npWgVNYWBaT6oqrELOahKZXOZkde+7SBMd6twh5RAjgUPWTvZM8Ob4j42mC+qwqYFr7X1/j6csLvKEScvgdJNtR0UIzDRPTMWjtLki1zOAwEo58jzn4SicFgtQ42sALdClP0WBFMxs5KI6zviVOdjx3E+zdOmD4PuCiLXeogSEzIOVxEq+KWqfLecEUfeEvNXXSlRCMcKrSqfPFby0mjLL53P68duB4cm2LtF7SNLUHsq7qaZndrpfB7ypo4sTfA0utb3pzJRsLQ0DCl6NS97jVvoQkd9Fl39n3tstO+Q6pSmH38jrCXY9rcCLPcUa1HqobrtWirHbBrjU0q7YeKwxcaWdAyFtFWll7Wm5Xqdq8tbX6FhRHzEQfzDjdmv+HjtsauAKh89heW2FLeneyX9B7zdSDaqomib6MNTP7bduOt0aYOSyBq0apKbu2TjerHrYkWzdhIILbHvGF1m8+GH88qoBIl1mUxJnufzGLrsRq0YN2Nq+LquHoIb4msStZcSeVmMh1cgqRR4CdPJcJ+wDMiRlFw9JQ/vNuqeWoZwIiJwuMi3rzAletoMVDpU5XWoVMbBLPnZxseViAlgF55PoUZoa53w1/z59K/VqaHgVe7iZg6wXgKRf6jIg+VpnJilaKcvnTBvILesq5WXLxMsWLmcrVaqzIeZfu46fSdjUwGUH9syBny5EcgEyx6z5xV9zsYryTQGXeTf1oWXwBiUqGi/jNj3Dcw/G95WEuPLk2/HDSa6vSCd2MTMov5XnIglZwBindyLOK293U+QvkpdtawFABS7Nw5Y6llN2mS0lYZHOxGIj8oEcoSFltYCmRvzZg0nycznVlJf0gQWWAM4ubJTjtxADu+kwu8whQN4xUN67Mi/5611UEe3KQwsr8qTB80J28CEOsc7EtKTGEfAS4NINAfVPW3xWEHCy4AUKf/q/xCtnOm1rz5KoXM770UpRh00NXBv6OyWwku1eMrgZSWqWFJZ3Z5j1Dv02X7jp90W5nDFfyH+hKnd1nRk4ATc3mRILrYe7PPZzI3a84k9nlscCR198pVJRHHol0IwR9UufdchyEw0hAYxYLgLTZv0atHTLqno4aOm7nL/yY5EP1OAfQ4/PhnkbJvR5C2fNw+U2dkSbKPTxjEhIEk/yEA86tOlDPLHbBYJrlcB6ICQF26p69TiVvbqrd7K3XnCucF6WMwx7XDKbRYVO93ZV4FY2om9c8SuLgeTeyqrBtJ561pXHdfSpp9UROaR0dGv0RwsWJPWJApWFrMcDcAUFBKGXTojNApZwzhQybSUCEkKe4gkinRmCygOykphmrTO0eVsNoq/i6IdawGbrCXZ9x38zHFZe6U4MxWcGIC3I6hyQAFJ841CPHIk7MdcCYmJQlzQlPauGeeLx8nLAgvx29NhzvUHtY9YdwkoKVgZQaUglr2jn0UnzHmgp1VFVM4f09GmXiujcMUjzi+VZ0A2yObFtw3gsWSICn76QJQ+JyII3u7oJkoqx1Ta+2LjMVjvW0BmQw2C1HWSLK6l/7BdV/DrstkG23wO+sB45oMXTEG1VKnM3zKEWxsiG+rQps7KzHs+QPq+qFrtgV/FVy35knUo5u6wGe01ltgPto4dNDVxYWnDJk72SRuyAs/dTrL6mqXQhKzHsjLisRSyELmUcCpiUdA7xWoyuWaswCW15/icY7t2P8z3Z1mIGZr0qTySZxpCQcNs+f6eA5AicpjxGVNRHMOOsuFcACTltbU0x71ri64AWlwdjqXrWSo3KDOT8QpIwxGfLmZlGlcI6YETLhMACfVrsHd9GuaLJqm2Tv1RnECdr67xT1wfp/CQJZTwCRj4pnC7WownSH468XY3Uyeer2Dp290iUQUzALlVEFC+RpqK7CQzR9ZtN+utwLO//Pk5v/RPG81/Fbm0j8O4SkIV00rrMaPo8hSLsMLauqN0ycxj7Qw/kEy88pSqts0sp9MaCKZKaPXVI49eg9XiQuNKvXS9Y2JFqIErB19/YX5N2D3mfOQtIudmNWmAlrBpQbehNYeaf8FXacw9BiJNRjkC79wBu6XSMS73oVWfcS2Axq0fy81INs/aqcudXC2pa97IlFEwsvySnY8HULrL2BHu8u4lTl6ueulepUvfz6InH2Kt66tKR8gKgVqmMqfoEXS1mV1tZ7+vjf3kJVAIs9ZeKnaiSTsjzbxITZ72Z7PykMq5g4tZtISei2b5x5l7yGZpv5L04pu7f8nHW20fy9kKY8mhfhZyW7YkY7E781o041lxXSIoRoJTuMGUWe5/LdXHYXKm+ZqqMHWcSNjVwQQVIjuz/Ryi7JgOJ3BoJqWD/wTxKBF1vC5MHuUheGwCX5Dm4cB9umJQ0pwbg+Svuoz3vIMKTIljWa/xK/xwhpZL81OdJgtqdyuU0fZVWPS9kUymHoFbYLj6xcUrpzCXe7PPTetAq0Fnyt7Neos6phNcV8TxygK+8q6WWPvk6SflkY8MMEs7Ru/IksngtqWq72R1JnXlXtoPeKxiWkqXti1kr82xfyhF5LuevBw+L79wViw9wH6usIvKOlF3+hcx0pqkE1KaltZIpjVCUdLZaNw2PFiRn0dNfPZzZwiATPvzhD/PKV76SvXv34pzjd3/3d4v3IQTe+ta3snfvXhYWFnjBC17ArbfeWsRZX1/nh3/4h9m1axdLS0u86lWv4oEHHvgrVURsRt784aJUlO1JqQ1zAzpgOIHFUewkR7weJqO4C7C0BktruKU13GL8bZbjn19cwy+t0Syt43ygWV7FL6/il1Zx6c8vrbJ402fZ8uKPs/zij7H8oo+x9UUfZ+VFH2dw3iOZJ1nyUDKJdqcJmL+Q+dIo/U2InusTXHJYlPfR11kWJmffSEoAAzKplyCnRCeOixEkXR5QoIPLSg19Ao24j4QsytHzLEuJTfIWA7KnpSUkYOrwTGjoGLBKw4SGCS0jWtbS9YSGnpaeNjlmtnQ0TPCMcKynvwD03jPxnjE+TVC4DEz2XtsslnKS2lLasbYf1oDUVdcT842974t2V/O4SP0jYBXHOqpajhGHX/g+zuMKltCpizZDu8ttb62T6hlmrY6xFzxqgZRNzh26gEtCMPG1P/UQXm/i29bB/NbpOaY3AZ8dvmaJ69SpU1x//fV83/d9H9/2bd829f6nf/qn+bmf+zne/e53c9VVV/H2t7+dl7zkJdx+++1s2RI9rt70pjfx/ve/n/e+973s3LmTH/3RH+UVr3gFn/70p2maMys4pC5Ide9dXMvpgsH8pMplOULaOLEwB3DdfbjL98NvPSsePvzNn6K/fS/hcxfjBxMGr/tTvGsitwtxGOFkk7c4tP2xZU7+1gvZ+l1/BIM+86p6psvypfowhFgstTXZr6z9Sa4nxbOQZ/uEO8u8UWfiSRMICQopeVwys5Yh5OcqcZUymUpZ+o2VAoWAo82jy5Yu60nUJ2/8QQJqiS+2FrHGuOQCKR72MrEfcxijy47maGjwTEwJRJJax+c9vYbEoTZJf7K8yBq+1ftJ2q/0cgqmPQWSrUQmUlqDbr+saWkq9STMJN0LgLW5P0qAjH3s0XUHDTIhIdBZykOT3HOxLHZxVPTWU4ulLNSa4Jkj5J3ppdfVodQGn8GrlDDLIE+tKhmYpsLZwYVwphv5zvjYOX7nd36HV7/61THbENi7dy9vetOb+Of//J8DUbras2cPP/VTP8XrX/96jh07xjnnnMOv/dqv8ZrXvAaAhx56iAsvvJA/+IM/4Bu/8RsfM9/jx4+zdetWPvnzx1haWNEXAfpv/xjus5cQxg39C29liPIaaaLTeJZksrztaPwERoMoKQw76Dy+i4eQdnOjDI6Ln7ufbf/1kzz4tpcR13LFxRht38Io0M2NGDpRdiLxR4OqgqkASe33FMmqnB2T1WBWNZwk5ci6NgjH1g32rOuDkpe2gY0j7y0c5ebEqoUOmZZ3TBOjS2XRdFX6CAayYh1KAAv0ZgPokICkR5eoWDlhgB4oQbq2K+Ek3nxq+wb1uFffM/WTGqLrLXtgDlUd1WmAPBxtzcvDruP/HXEhuJWmrFQtLWqdbG17xd2wXDHXF2Ujl+pfOisMCPwG93ErJ4hWP09DR4OcR2X7yE5TOQIjZBmPypYNIZ0rXsaVthBQU3dmhZ4On1u0VN3LK6EpO9UCo+Nr/NLW/5tjx46xsmLGdhX+Wm1cd999N/v27eOlL31pfjY3N8fzn/98brnlFl7/+tfz6U9/mvF4XMTZu3cv1157LbfccstM4FpfX2d9fT3fHz9+HEgD8QkPwKX7YdLAH1+Pnxvjnno3BIefj5P0kXtHEor8JG7WO2CSlAUI8zGmSGpuEBUeu1Xx6JJtHP7upxCNtHJgPAQ/hvk+z4IJ744mzdIdQCHKThYLd1byF2Kvj7aNEkLJz0Q0V7NmyAPGozzX5mYlO1UIJJYlcftcwKmUu2YpAWDVJd0QJ9a+NHFH0m8yUNqBbLm2fDHI9wpG9hxHmUuL4BDTHyMzZiHXvze5ySkFkoZMKZSz0brNs7TApGhXBTBVrJS5CFhZdVLKKMECmuQfTFptZooqxQ2Bv8M5bGfIRzmUqGGYe0Ykq5DlOLDqYM/EgIe1vFk5XVraUqf2gro2lBqT9dwKhqIa5tnC0zjOX7DEk2jZQqBnnROcSfhrBa59+/YBsGfPnuL5nj17uPfee3Oc4XDI9u3bp+LI93V45zvfydve9rap5/6aB3CXn4YLDhMmDe7aBwiDHr/lVGpmsco4lr/wAAw9p6/ek8GoydPDAiUdelA79GmeKts4trZMtsqJQkoUohrKdD0pxZIEFKAioIRi6IJ1LtRgAUbUur7gZnZ41J5Is4Xv2aL7xoK6nQqo49SAFYo/dU4V6UMBUW1dff4tDb8WtJzpKdBZx+y/RwkKUYUqU5PvrPIr7hYCBBHkSqlTWqAEkY1VIFsvW6M6/sb3rsqn3HtMJWet27kssDueyGqkmHqRFkWKJXXqjojqWzVtAi8hWmTA2iBSO9tEBjHHZblPPXMM2cMClzLHuTQsEkF0yJmEv5FZRVd5VoZ0msmjhUeL85a3vIU3v/nN+f748eNceOGF+Gd/Bb+yFJupBffcrwAwOLpGszqi845uzzItsPLF+wjLA/qrdxEKOQAiJ54g+9FHUOkTcIlPCrkrhZCglKjsWj1LZGAJ1C5CjsGqjbUqKVxZHUW9GRACyxHMrPCtwQ54C3JlcEwPoprgH23QkVuoNlyXhu+oAslsY8xjQlTlZrVXKFJXXyALIuVQiu8GhOQUqyZrMTFDyDLExLSkulvCIjqMpSUkbTkYBErG4ijL06PgK89r4KztVrY1p1tX/bhIaQ3NdZdkmdqrTuRklZfr3KR2YOU/ddLtcyyrE5QSlvS2La2k4/AssoXrscb/QM8yT0Ip/cxtXH+twHXuuecCUao677zz8vMDBw5kKezcc89lNBpx5MiRQuo6cOAAN91008x05+bmmJubm/Emwowd+C2w4/c+ycott9NtXeSBn/4ePD3HvvsZxK7tkoi9RiTg2NEhPe+IxmKXu6Qvuka2UpOGEzXFzsUIqdbb8MmgaBA1xfoRU0hSIaXY0KBbhPhE5HbGRgaCPpMB2KCOluW6MiFGm78Cj0JiKWHpANda2ZVqclalzqTJgmGZQHCFd761R80aSrVkIfOOWmeVluZMWvFUcbIpWSxqIE6x5W4UKtGo64BIYH0Ri3RGtFW/VRaUkok0XddrnOK2yEwwzKf34pPuqbfg0VzEYUFtYy6vGgASJU+7k6iEJaqxnWEUCra9MUuKgtJi52lo6Rnlspa0pKy5YZ4VXpzVdnJ8KxVKq05PT80KX7M7xKOFSy+9lHPPPZebb745PxuNRnzoQx/KoHTDDTcwGAyKOA8//DBf/OIXNwSujcKAyHEGRA47R88cgaN/7zk88Iv/B/v/71fTcgrPKnHye4JnDKyhflMdE0bIzo4NgQFyHrAI0YERfToPOxL+Oj2n6VlLQKcuepEg1xLHl7/oouDyQOuT1U2kkTF9eiZ/gWh1A5JLgZWpbPcHQ2xi3NYFFyXvVYO4L8hM5U/1uVLbVMggbLeuEUCQWTgZAjJLN05tIRDZmXLIMVpjpqWOkOPY+KUsWUqrwbSxlEm30JEyd4S0iwJFXaJrQkjuIyG5GzjWkGVLHaP0KyAskG0nNmz5BRwtGPtMG7FtBzhOETiNzgiLa8sacRIjXoe8wL0zZfTpV86Cfhor/DCXYIFAHGpENi998ESGlFZUR4uQV6sKgJbL8mMbj4mCg0ecZchfRDbWch5beGnqb3F3kf4Uhqx6ypnKXF+zxHXy5EnuvPPOfH/33Xfz2c9+lh07dnDRRRfxpje9iXe84x1ceeWVXHnllbzjHe9gcXGR1772tQBs3bqV7//+7+dHf/RH2blzJzt27ODHfuzHuO6663jxi1/8NZXFFyTf4FOX+tY2Yo8sdVBnTN13yjojlt7mOsEcTLyOkCUyeS6Cdi2NWAlGQ+kLr+917ZwQmYbpa2eu+uK+fKa5aF6z1FiRGMu8S6XCusLa6Qb5qjdxrfHZlkIGj7brRlaSaYfVev0yqB1LXBfETUT6oRymIdkIlWPbvBs6xF3Vepv1SQ73DOlYo0kKqEtD1rZ/jB/rJJJ4QKVOoRWhoejmUdc9lmhcMZ96ttaC9wi4k1N8lEfS7KeegWhbsVT3SuOG7V9tXatCevq8b4U3v8IeAo4BSzw/U4NjkLQasJSkeY4qGatWkmeHrxm4PvWpT/HCF74w34vt6XWvex3vfve7+fEf/3FWV1d54xvfyJEjR7jxxhv54z/+4+zDBfDzP//ztG3Ld37nd7K6usqLXvQi3v3ud39NPlwApQdOl0hgnDve8gbbXfJU7uWqNzGta4JVK9TXyg5cTdEO/RocpiFM6lF2q4LLLO5T2kzqtGalPyuFWWBaflO3T5Q+RJnSktjY+lZjaC1VeVC4qmtY2n+sHFkCto0n8oLEiUDSpzKLYhPomdDTZrcRZ8ohcq7A6oRRGnAOkcn0KNaopNlFSdM1EcmrNCeICUHVwlKttXVX/3eFEGk1aUs7T7tGz37G+Dz3qC0WsiHdtrSNo2xB0y1709q6ytr2qQxLDLiclp3YqRYLiGW5IgsRkJvlE7ZR+Cv5cX29gvhxfenYb7BlZbHgQVFSio1tJ3jroSo2JuEVkfPV+0QJ57TqBVOp1TytVmfKnGepO27D99aIL29nwZldjGHlpXqRxvS9lTlLslIy1uvSToF5J2sKSSzEp2d2fwOMo6tPbW+nGTRvJWs7WAOyzU1j4vsZf4PUw1GyiY7DUbVZSJCjXlZxH86xqVOg5zQNQ2TqI7p5NkkO90RJYpgldwsDogfIn9BUm1IXCUuOhoh2wNLNQl13Y/uMCcyldpDtqSXfOEMe+BzHeT8H8NmKZmVB3cFUWXRpkBedorR3KWBZt+lIK1Gqa1gh7j6xnXmeavqsBC4dp0o1KhpEOF87foqf3Pqiv10/rr/t0LGGujyUvAIUw0uVUDihmMKFk6udSv/ERC4EpfJCHdQjqFRlpmUT/b58Wip2zsS1PK/ehYHiq+nvrTRl4/XZ8iTxfa5zWery2s5fanryZZ+gQHynAw1r9Azo06CXvZ/keFhJW5xapS+F6bQmF7uxHljDfqjaSGWrIT3RIrnGgJDASOSeSVL9hvSs4TMkRhdOsm1Ihtco27Ti34AOu3VM3RPljGaf6jNA+zVakyyoCc3KzLWolCAUKOyoBVYJLOMyQJYzgcLmYgvXir9MA5Qzn3ZPCqEHm5b0tqxt8CzyHBq2mdrrCkvLjEp60RpqjFkseXbY1MAlXFOaIvLCkDq/FN/t2X12GUwwXSzXMcRutCfLUHTcNLjYZrfpzBKUrcqng6P8fxqAQh4oWoNS/ao9rcRMqiUQF0w7g6hxVL2Wucve1EUI1lpbAh0TAgNaxJGkI/r3hzSzqQ6xHav0NHTM45MTRMjDWhVImX1Tu5tIUx7HOIOMQEh0LJ4Aa4w4SaBJpVDbTZSEjqELiDp8WgEhZXDZNqQt3Cc686ntJjmeHrBqaRBENVQlSEJcWxpji7SmLh7KaEVWkq2u47ehAMKQch5lOrHHew1RCadPLRTQ4zZkRNjjT6zHmDXbCEVJvzc07GSZFyf4tFQdUi+V1uP4TnWX2uNRDQdnpgBuauCa5K6W5pelEXpun0pcVpmUYWulJOuCZ1WkgJ2hs2BFvsZc19LVtCw1LZHp9LozXRoJ2EKmLs9RFS+GqFLWK97q7yXHvpB3pI7euGwKbIdsjJWB1Bdlj9KAOF7I3g1tVoGsmi5e/NGn2zolNMn1N0pLDV2SfofJrKt/ccAJtK8CJxLIqSOGlC9uZSPsy6f2k76fltekBj65B6iEoQxCvoz2sAjYap8qJ2PUJUKXbUlaAicCEeU6U0lZGavASYsy2CEqherAb1P/yTykM7WW/SVEfoVQQIAo5J1pZytzOr7rEysc2nopf3H15ehpQKXE6YrWkLcC5TLvqwYB63c22xAyHTY5cJHP6ZPZPlEChBQtcClBWV9wZsbRsLFnSR3Xqov2+1nxgKoMAesyIKBlrQ+uiGu/r9U5DWLLK0tt46iLa+T+VumSmdUup+KKkkjdfC7/LBKWfIV04xddEbdJNZdt66IsNkZkZ5nri9+N8AngnJnqj/9UJaoVFav6ln2i5wLJXVlPaXU5pyi2TcjSispAAp+an1yXh4lYjylpO93VNUytB7Xzg2LHk/bywGdY5TbWTA+VPlyamj0uVuTYWV5nQFETzwLP4M4LFlid24ZneaqOysos+9PW1nKVM/zlVNbjArjENUFn+5whDqsg2VB3jxXxNdRyklzXPMHGUzKUN9KVEqxpsjbT6zDTrvSoI6vmWAKVEIIOHiVHgRcdhpaz2ZaqN5CbbiWFUcm9/FPbT91SFkpcrp9yZplK1zYR073KhhG8PB2OdWQGUB1S7c6f5SAo+8+2hO2FYK7qtprVGnqqs2UZOn+tKVjIUHVYpSQLJSJNtbipPLUX7CEhsb73sM69SToWlqdKp9SkXNZtU9U+LN2LY78MGXA+Q67mzr2162ctZ85KX+JJu0gcGSml49GZhE0NXKCqUF88i+HRmiBs8Gu5c90Vs2Gm/G6WCjkND5abx2+FiBtzPSRkpcXKc7UCI7YosfoJIEzMHajXfl0qmf+zA0tAo6yfNQ7LkJEhqDuVqgRnHVHivJN84adqUbZcNOzXx4NGFcbnU1ijbNTRFXuva3lqtdYOaalJKYmByOyRZbgCjEHlMVXiMHKydUsNqF94MDHE9iXymt2BQmRbmSwSm1Zn2kdWBuSarnkY+HhmYwbM0lNNqVrazlrPLKVbiI/zlQ0rLPF8SpqvpSptO8tMS6ZVz+/XI/VMFcVNDlyTPNhiKGfgYhBi3eg9U/dlXAszSu7KNae/tznbvHVQD/AFSIgBWcRmSVu3UfF0dMjOE/Gd5uzTE1GUZOipnCJkbLmlApcsYiq3v1G1S4X6aI/waWCoqVjqY43ashSrT8ZwsR+5qWEeUu5itHboYFZS7tHNfKyfv85h2rrJLufBpKngqPYbTdPsQ5+N96LAivE4TgHYpVme9QRF84gDwyQBn6iG4sKgLjgxJ7tnmrwbpHarnTJt647S9wvpzfJ/vpzBN9yNu/wQdpLDTjUJLKojRWPedzmGpfdlns+Ai3IKpZ4gMdXNWKV+q2pLjXW2UsHN5e9L485jh00NXKVTXqm9QyTX0g9KgaH2H7LdMt10buqqltDET1gHvfB9V5jC7To0b74fpidqro5rAfSQdYcsORnQG18ggaw+lcNlUBzQJCBT4Iyn3UySfCaQIrBk7Q8iVdSn+znUrkOurfp3ySBzScITpS+u85OF1qIcigxASlV6o9wjLKYbgU9N88o86t4gf6dyorwvlVJZdKJMIOQSdGa7F11bqrOq6i8oUowM2/i2QeFVy2aVNmUwbW5PlbKH9FmuHKBzhvInLrE/+5rPcGRop2Hs3iMgM4mWLSmQRQcN23eOwBIvp2E510bbqIQtocwy2EPtrPQV20WnbKxUKPrIma1C/Gtdq/i3HXTep1bLlAPY5zoQlITU4dSqQjrZbb8JxLV19zIyKkDg9PoCf3brTUz6yEND/lbSbWhxaV1lnHOLm9/5dGiUmFtLC1Dk/TKUYrw4j9PS0tDS4JNLps4dxW8kbTkqIsZu0kApv4vEEksSyx+Hhm7XJ6kPCHkJR4wnayi13e0Wz8oo6tpFj/GNyFQdDaxCYn3YQvVXB8u/raStMoHYyKRnZU9VcWG2WxqH/I11v1EKCZCWmwWTTklB6hptS6nz4mI1U/O/pXAZ6i2BIYEBgVPHT/G6b/9pHh4dY9KqFDg9FoSGxVVV01c5VNjsHAt8Aw0rWIW0nA7SPimvVMLSdqlVSqEGb64toJ5Z2NQSF8xW+6zDwSzJSJ4fJy6QbnGspIY8TjxVbq7gBiTS7DlOx3E6lpjQAEM8ves53a6xTst8msg/zYSv9oe56i+fwdwTH+bBxcOMmfAEdqLKTblYpCYNa2CXJ+JSUFt/5I165KiKpU6JSsqltOmQSQCZmlZLlI1tvamt7cK2snJkXcenaej0gZWN6yFhJaqSa4u0rExJZ6hsiq5IK/5vFdlpFlbmZaWL6UFOkX/8F+e1I3C3hhlqy2t5NTdbRmGktQm9Qc0DspeJA3oHd29puNT57MBat4LNuWw1qU2c1oqsaYkB59NyUdUGqvrV/TTbuce+rzUZ1YGsQaNs58cOmx64QJvYTuzaprDbm9iJ332MWSUwj2MhbYj7MGO20LAjd5J2xkk6HmaMx7GPMQ5YoWXb8DRXXP3nnGJnIrDAEdb5YP8gu//ke9hx0RG+vHiUI6xxJXsQnd8O26gSilVIu7AUwkW5U7uCSpdyyp5IjDII7O6Wcv5i2U5RpevRDXhFWQO1Nkl86w00zVFL3zLZe0t3aojqWTmUW3zxpUBkLSvJABOVUUzgpTqoqdT9R2pduzBYYvgirk7RWxYo1hgZrCE/FcdM2c89qkq9ScsqaLZks3bxEAVdvonysJ5u7Yg0fWrLkJf9h39ACVazoJuUkpZV7vskZTkGtJzPHDcwO4i0vJGMaxmUNy2ksrMCtqqR9UG8jwvgUjUPQJc92K7rcdzGWrWINZK6GEdHwO2sI8S2Rs8jabUdJn0xsloOcpKO02koHuMQx5G1ZJ7vaq9h/C9+nwHLPJeLWA+OowzZ6taRMxOFk0aPpJCX7sZyqA3L56EVj3EQaxRpGJ6GdDJOrGHcqDfkRS8CRc6QitSuQSDNWjrIdZdt3+SpSAU62ymeR7GdQhVfZkpBT8YWN8kOAUM1QCuIaJ8p3w9ZJrSzhlaZCamdZHd8KZeArlXkp3m8qna1fGI3zXPp3qVaqSUSAmvImkhpL6nzxJRTSjfJEo+CmUvxVZmK9jVpy3sY8Z85TjyJugaTQNxL3sqsukpUJe8hjknadmY5xy8lLaUUZQ5WZrSUpO4ppRghIdoCrZ0xpiMsqI6/cdjUwHUPI+bTJPtqhoJS2pKmbk0ngqpIEqy0YRdnq5VBh2lTQZqkL6Q+SQMRonH6IKt09PTdboarz8Uv/xG9m2cLPYtJNV1D1aCYshBLVDtkGVO0U6nQL5aUITrNHpDdMWWncXINop1OVngqxEwMYVuFQEhM4ElX81nvmziDZhccW54q+cTFz7pvlu7nL+qP9o66LEg797lN5SwAl3N3OAOvqiSXs6+ge7aX0lXJ7Kz6U1JM6TSr4FhOcfR0ZoM/ly1mYsKXskn5WmRWVr2wpIRiiJ8nsIBLR825JO+Xs75SSpFjy3pGCcvKyR7HFr4DsWXJlIxVi0Mul165XKtyhrFU6628SU6/pI4Wcct1Oe3Z0lwdNjVwjQjIARciBZTr6kpeEbmXSwbQ0oalc2hqCYmGcVm6qkZnsFxQJLty/kVCD6ynzgnNIcLChznJiDHrdCzS0bLAhD7AI/ueSeii+d21MNgaCPMfo3WwQMMcbQIh2WDGEkUJ23GaXdaxle4bDToHKJsEukzYLuWgZGoHQJfTttJAfF/y0VIdrCU8Xe+I+a5UmsqB1wPjNJFhpz5Cronl1mK1s+BXQpSqYdNSl+RXPi2nTSSIw4RQoJRplTiZEacgxE3FnkugLqLCtBSspG0aA+5rwPs4wkkaVpFNxmsl00qSeiCGbvcYGHAFAy5J+cv5R6VFrrRtWiVO46mC7027qH2UIh27rY5tcVFAFU7PJGxq4JKqWk8S2zTSQJacQZ0fS4hR0Vi+1fkwiVumVRN7mZsO2DwP5cZ07YF0tl/PKdaZMOFEIv7R3CkW+5jfeuNwPsoIESji4aals6jkVNpEBBaEB1q3RC1n2SalrGLfB9MK06SlYKiEqyRZntao+Sp3Lr+RVXkxF1uTKFmpD5DdOW269TVtbSHbN2UflfWNX5ZWNyt5W6nMKpLS4mpeV5cDkQk7WuKhtZHllgqetbzpkiDHcTpuZQ2H56tMknRu99OwtKBtEaZycAy4jAGX0LIbXV8aeHRaduZJKRG5Kv+SlSrV1S1qZTmqNM8kbGrg0ul14RFl8wcTz5Km2odmp6f32hE177XPanisg/Akh2xjEnnuGmuspveta9i64262oAeexs6eR/aoXyekQ0xlObQu53BElVBmlyz/qt1OVSpVK0i5FEnBXY7wkhbUfbDqQR8hWqSckOC2niErHSeU9ZQ79EevL+HUFuD0hEZdrGtX5dWSUDlsbW9aZxlVdmwZNSZMU4xIVg6V7MSxVQBDPOqiW0rcRbXFM5fAyyXGJCAdwyS3UXxymI4PchI5H0GtYSrr1mCs8jE4WlpWAMccT6JhS/FtTbk1Q5dxoH0o99MztQpv0qLa4hpDpW2d8CjZ0GOFTQ1c0oQiqNbHUZXzSpb/yn0omrb+BZVm6lxrPhHtP8JVYqqNSaMETscklVZcEbayiMNxJFnQokIUrUKjtEQjEFgH5miZpyvcCsXwq7OFsX7qva1Kc5NtKsqRhX/b3TmlLlYdKXdwEj8kUTXjkaqxfXQGVA4XjVsej4BBbimFK1VqY0kmZjir+qMzgWWvyi5h1qYiC3dKLq/DykKuAJDEVGCNdxYcdeCpaqjfSa1sPjFWw4CeNUg92gNDdjLBm3qXmyTq2dIDVDWUckYw1DyiOi3TIcKOPFtY5MVmNAioW1DSPq7p3datvJcRVNbTYcUIkZ5rI4PEDkWqZxo2NXDpULRqo0AB6a3lldOKnDzxmSgsXy4Bx+Zrt8CR79V4HcncLmGxuz7E2Uk136qXedxxKZqe4wS4zxw2GjJhnMFIStdQyhcKlGGqg+N9l8jaKohdmhtTW6GAgZQ5gmJP9GhT8pQtXkp1QnyaOmM271LbTBAzduyxJqlRsd/U7tabnhSFTKQI7VELUDIQZDa23GesnnwPiMtng/azSHF9WvZTqz/Scl2xhMYqRSL/xtaUr4LZhTS6AzdMOE3Pcp7cEYP9MLXDhznNx1lFT32UWutdaecaJpCIp0POcRXzPAVL+1Y6ljxdFUPbWIFZV3oo9QkVyBeyYtXOessIU5XRbm0j40xdcc8kbGrgKoX6eD+9d5aFsFp9LIXZmgvbWNM5l79WNrALYkXyEplBBueAJi+oHdLimMezBdkq0OWzhuLUtsw/xV0449bIMd8uD85yobEsoam5X8hwoQQfAao1cer7YH6j9NBn8HGZmwoByrqCFm1vkDNz1Htb20xUKbXLNCiwqmQ4bafTzQqdidWZO5XASGBojckqx2ifyUDVuVOVPJUylGLKzYNC/pYi3TKIQaAzcBxrE1imw/F7HOO+3LbCSGwL2BCKdAM9SzyTAedVJatVe5WkJJbuAWKlKptHCYHllUPYm86zS6q1XSsQD1gbIyAoa0cfK2xy4LLDFGqoKi0V9W8pXcnzWbaq2TJX+XaWrcDuqaTlk4W0Qtoex5CGJWQ1mu6qDtGBwHbmHH06GUVqUK9Mi6laWLegI6qMApQSZ1+RViQ8taRZS5i1PQlU6y7rMZVyr1mdphjntCE6OricfshlKFefWtfh6bYvoax0KNW2L/cSiV/Y5ei6B6nuY1EuPgqI4mhbjqkYkpvKgaqoSs36XOYJokxGH/gxf8qIe+lYNaWr3ZFVvrRq3pAlnkCgZ8i5uLQU28rhVGnVNj3LvERGKttcVMzefGPTqiU3ZSuqQNt+xeR1ZlLXpgauWaBFdW0F0ulZJStX1e/i1zprUnZGPXymZ50iBInnuINsbm+TCV0VS1nJKKqH7FhpOZyoSAN6RoV7pfIvBZ1yQ8BSrbCzTTqkdKZP2zUU/1sY1lk0CT320INSNrLSXVSj5EmUabqsUGnOXdECsaw90304zTREiihhTgFRJBcpt5oW1AkEdJhJK9lBXYayr4LJy8bRNikBVyRKT0u7Du39hzjqV+nP3wJzuohrtvxmeytKqgtcbfrASqquSkvBR0qupcNcl2Mg0rLt49m9Uo8l+XUo5Qj9kX59BZMbhU0NXMr1pp9ahLfAZAGhfm5/QZqyJMD4Re0/I94ws2YlYUKX5CqfjkwfpiU20U++ycAkv0JmLYF1dCmJWKA8Y6JdR2wj0fzZF4bd3kgQQmwO3VHDgpAaWMUaFaUKmePUYaGgI6ffRJvcGEy7isSls3+Sjw6CgKjVpaIXU+jzbz04ZEhIeWdNw9g+liXzUUWeZHlJWsmneVvbg33RHpqLlXBKE76UXGTTYOymwhgsOxEbnO5T0gDDo2ts/bWv8I8Z87Ovv5oH9i4h0mINw1YCVYOElEO2YNbWLuVyBbFSywgGPhSOStZuv9OWkZil4i15y0SNbT9l+LocaXo8zwqbHLgoukr/j2/0SSkXiCG9tFNYsrXck9yR5cySPV05KhYLyYI1Jm4cM5+XUg/Q/RvEciJ7I7QJ9hpgDt1/W846FkcKBYEBPXPJO1uNoT0DxIqkMlU8cMKCYYRSISM1z6ocFb+dGGJcT0NMDqpQtc16lNmN4kKe7dJVBvKFHo6gfdWb65ByKmUbAVSxhShwxF3CVIKMua8Zl0dxPIgHSEhNfG5tuyODxLdrGkURbhAJV9TzJg873f1dB6+15yl86LUo+iJjTji9e577/q8X8+R3/nFumdnSjP4GJmzl+cxxYU5pWiq0VjcLEq5IS+nNgkuUjV1uUVX17H7xpUxs90zTaRLrpCswZ5XPjTdKL8OmBi7bfbo/u4jqpQArkCRgI8PKIecRyzLl+JSckk8zPDHuIOUYcqqqGkr+gwxVMgRiSeWEYZGL4tOWuH939NRyzKUc4rEfniV0h6YAnKAhpONKJ9zLI7yP2/hnPMsMQJW2lC/3ecCq/KR/40yoqrq45KLQMUIcKSf0dHQ0+SgHWcQtx2iohBLZgz2n2apQpSJcyi52wYiVDsW1Im7g3Ke2npgyyHY0TSp56UAcF8q0Zs1huT5CobBHZktdSinW7T9yAX+HI1yQJkkkBavkxvyaBLN9+t56a5WDWPJa/uSdTL5wmg9+58+w8kMLbFu/jmMn7+DE8q2JuvUQMu25dXbxClq2YpfnSztY4K/VRJWaFG6kH8qRQ64HOXdHuQrVmg4sq5L7WigQIFSJn5zDY4dNDVwKOrUGbk2ZMqFt38qGbDos5ERhq2H7BHVN8SymYn1drIJYC8mR06usJpKCFZEjwU2wKoEcZRo4nRZ4xNm7hvWkbgZaenYy4EWci0sHmurMnKPPSqrIGbpPg8JUIPpfDSC5w8p6uyiZDQgJrsi1KIm0NhCrLUlM32LmVsnGKjrqZiGthklZJxMkX3WJUM4fZQHr2KjSgpZTB5nKFCppaP+F3PclnHuewXGOch3H2IZIFnaIgrBH3Wm0x9P34E82TJbgcv85trn9rHXz3HrwOTx59//gXr/I6QuezWhwHqvdIp+av57Vfjv4uxJzaFPfxlQH7GSJJyU62E7pmW5V7un+0WBZR626WedSu3xnGoK0L6ziOCs/zdehdFGabM4sbGrgkm3yau9s5QkBUc1qRVB3N4h/epSVDipvvqnD9FOXO6Ps0JKL6XWXVNaOkP3nhUA6YD19P6TldFpaHW1MjclrhYansJMu7Qagx9rG6y4DuxCw7AWvKmg8WLcH1jKhhzStAFHV1CXOAgz2VOPpGVU7XERVtTveuyJuCVZWBa0By+OwiqU4j4QqXXtcBEXatr9qRqPvBLqOTS5gjRW6Nua6DbiXyznJzly7aduReIulbf+CbC0drZVDTnOSZdbCAveGq9jJ/dwf5jl6zkWMt10MI3hwtJu+gYHbyUJ3OT409L4juB4cDNnFApfnttJZQqlN7SKtUF0+n7ZxKXWrECDyvowJVS1t+tOtqjFtfFHFhS2onPa4AK4Bsnuo9QzREBdIiEVGbBPqdSNr/e1eoNNDEGbB1CwLgXi8Kye3AGqlOWsXGgHjJNfJzMo6cb+IQMMyDSNkW+Mo/TRmaEXCnTAuDKpRLYxupZYkQgbNQSrdiAmrdKxjXRJEsekZG6VT62rb2pvUNY7LgNVlz/n5rMBYydNKCiGpol1qk1K1VHXUqijTEGUHkZZZytqjM7KrNCwxZlV8zkIDYZ7gPHMucN/6TRzkUvp2hO41ryBgLUfaziLzydpLT3CObksguMBXuJ7A9dAGOLfn07wYAgTvSIZKGINrYKm/mqXR1THnBsJgkmsUGCeGJbQBGGpX8LfSjDWwWMmsDNb65NP/mlb5nRUYyLEU9DQ/6btyJlG/rtdWbhw2NXA1RPO1DtYSvXviRsNdHuSRJgb4fB6jmDLtBLh83xEYIlsxa4coD4pBVD6705DdV6HJA5WUtto61ETbEBf0qJ0i1uck4xRL3Cu65IEdl+rKopAOOblZ5w/jEJXFRbLUN7ZDT88aPacJwCQtsVGDqih5KqtNOwW4nIvUvmecnsylck6QU6dHTGhpmLCObi4tfXU6W3wiFA2YsF4MqbgTWbl+EuRIe1UPxTQQJVpZ9lKapztgf1jg/VzCP+R2fosLOOrmoDufwfrzuGbxFD0TWILA6dQC4tCiZdL5WAvnoirqwiZZAObRFYwOR3AxFUKyPkZBDbzDpWQDAddGTJUWF/XZ5xaRUjTYc9u1dNM+YLWyLkHmj0tWUM4e6oixxnlMWiVAqqZh7Wd2plF0nDMLmxy4miSpqGnWdoeclSxzauI+MDFNNYvbgKqSVmKyYnPJx8QQbUXr2HFtJiWRDUQeUzuNWgRKO489K1BXKwoQyondWmKfXRNEBRSJS51UY7vEQRmdYFv6JBHJBIfKQLJPQchlEIN1zHdCn7bBk+GipR8hHL/PA83RJ6N2zzhJYqoWCouIeY1zD6lTYtlCAgSW7wt0OOLiKGsDlZUKHsdfspMv7b+SXe//O3zqdT0XDSZcQIDGERZXcy7WraJe9KIQZBUhyUlcgI2hwCnklH0fiCjlcc4R5jxMEhTJq1byU4Ast9kWW5t1PahVwFLq0VGg5nbyr4CtMKre1N4qiIFQ5GRVVOsvp/V15r3KcNK+1qK8cdjUwNUmsrTYrkNddpBUq5Nw5cZ0W+ySuAtla1KzANWhuv20a6KmMQsARQqSDivPKhSyUyOlVY3EF0h3atChI0qLegbZlPTagmisyzod64RcZoUpn7lnSAAYoU/yF5CIi3rk/Bmdr6zrZBVAaTULPsq7pTUt57ZKjsSTtK05184oz84v4DjBkI+H3Vw6eT7ON4zDkPOWlxi8aMio7dMi5gnBxdnc6W1zZikx2vu2lLriM713KmXbTY2LKRInNXEQPDTgQmpJB8GFIt/Sl1BbROmnXszc5Dop5aqGUE8vKcXZt6XBQ4FuWtFUkJRlUwp5KrOrxKUt8jhwhxDhUvl8bWlQN8ZySISpdERJsenV3NzmYqUw+3RWCNW/yB9nGbS1PNKt9XO9VzN2CWOWDGIL9KwSgPHpJW775OVc/dxP4hud6hcDuK9gsM8LhclPGlomWX4Uo3mpbljfo7puWvLaKmUlG6teWFmq5veaYtnHsS/uZIWV0UV0bsTh9iRrk6dyfHIVeA8OlocQLkqg4crJ+xq4VKqprak14FpLT13KfuqZUqtpJddHMauP5dJWmwWirkqjfi7XrnpmAar+Dpz5xk2lAW6qJhvnrxqKVRO1JvWE1pmEM/Ov/180SNPr7J8r3omEBRTdIOZq+zzaj1SpUSuB8u1giEclBGv/wTyLX7VGObSkN+vamuxBpcdZwGXtT3ZvCNnVYdIFHtm3ldCPmXCc1dGEQwd38NkPP4lJrxDvxz0LB9fow4TAhHG3wPpkmTiTGA3SHR0TxowYcShMODXeSt/LPhO2Fj3qk6NrHK1kWraXHuWlnL8cKkrwqqY7E8NK2A5H6BcYTXZxNAy5dXIp95x+IXeuPZuvTq7kgtEzcZ3HTSI2CFipdFGDSC1x2UHPjHhKddOG5pB6RspqZWKbZiDvzO8DuB7nZCH7LGlEJLvZExR6V84e1m1tGUWpNNoWn8WYK9CdGQdTV5uPtpnC5ezv67CpgatWMPQsQR3EoG5ycYFKmNEFCiMdsl+8DqjoRtmnE4oFZDw9TXIxDEyMEVxC7eule8IHfPW2y+kqIIuqKGZeBVkx/dr5PLMrQ1hj9eQpfv1ffzPHV4/Rhwn7HriKu+54Nt/xf/7/aNo1YEIfAnP71rj+F79C6B2nwzr7TzyTh49+I6thxCgERmEt/Y04ESb8Vw5z1/5/yMn1C1E3B5Xz9NTskF047AyS3NtS63yYmOb7bMS2k/wN6ijc4GiCJ84CtoTgCMFzYvUavnzwB/kAF3HZkVex5fQedp6+kitOvgTXmX6Xbmr0QWk/Et96VUi1l0AHnZ1f1vUVvZFAY33j7KIv2sG+U7aji9XLxeeWavWqnFW14GNnbbVeevYnxRcWsmQeUZ2q7QTELAlM20Q2k9T6xY2a4gRSudoErBvHxuA4HTa1qqgm30jUMnEvQU77rQdAXH+v8yHR/bM0jAfirlNiU9F9J9WY2CSHwKhgxr2bxqn55bwdsanJYuu4q5YYdafFbeVbMR2ZEVXeZyVByUNO2lujp2Hn+g6uX99K8zM/Q9NOeOLxa7jsnON8+fwPsG/fP+fbzv0IX+DzPHTyeh7wz+X27w/0+xOougYcPLz/J+gchDGEVKjg4JkBcC0TZw90t+0rQGRtG2L1ETgWGZbcDt7UW6S1Bop+k/eizp9av4KDj3xPxOqk1QbnmQue6/d/L56GMCBP8AWP2MAhXYc4oWdyVpXKLp2JT2I/1z2mPSKp2P3ghSaFRqRtpA8d4j6rQ11YWWPylzUPsuhJ8/YmH2sqIIOigFtvvlQTQC3lOIQ9qKpnV16WcXWZV9lTfc5bD8a1y320rVQdt98/etjUwBV3qHIZeELRAOVmf5bvjDOHVzVx9iRumZ5ciyG/w3HuVzzn39bwmW/WfES6GyWiEkuRwI7M/ynJWCOp8NcIcsKplXcKTwwc5XJOcAE+OW30BBg7DvYNd8+1nFy/HrcGn5gs0BNYnXTgWv7syNM57a5hvVsitANCG8HJpQYKfRqkHno7Ux70NOkjx1/GeHIOW5f/PBucVT2UgS8kL/OcIpdML06xBnrLHBSwdYLk4OHvYrK+jW6yQBgP6HWdU56Y8n0LbSp6iPXzHbkCIRCR2Fv1TgFAGQS5TFbqUeCoGZ5V72V20aeYEtcuc5+W2rT1OtOy5RSP5Kj0UoOP2DulBjUrENCxIwSsal7qC5ZCNdielDTtYmqVusrxZQFZ6uOmarFx2NTAJYK1NIbdJ73mDFA3XE2G8X87mSt8UshZ0nABrvhESzPu2brfs/2hlv8/eX8ebtlx1Xfjn6p9zrnzvd23526pW615tGRJlmzZWB5lGw+ADTiQBAy8xIAxcWwDL84LAQI4EAIhCZiQBDuQgJlsY4PxgAfZsjzIsmVJtuapJ/XcfW/f6Zyzd9Xvj6pVtWrf21L7/b08Txrvfm6fc/auXXN967tWrVp10W2RHxmL8XEAWsdjzzGEZXAtOMig1mmVx7xCPnev5COhbCc4l1Nsoc8MhtEQm4/waA2LFeDXYRwMhPzEXUXHm2m8mw4xltJtRveYOaMkCmFeFqjrTSz1r8JWNdNjny9MRU9XtzKsy+kgD2Cj6li7+hv0z6e/cj6mBqxhefECmnoigo/6Y43vKhWdP2MIK3Umt4jWx/nif08+3yjn3xeJ61XVvFiSwzlWT4+69LluyqeZV0tv1IsbmedplqUBS2ozi/VrVVLWgeUlm5w/3S9LkCO9IUAVRqVNLKvU5ZWiby7n2vrD019nNXCJbRPoygu/WKMq9FMRWGT+0T6z2kpM0vse2xg2HOxw4Rc7dFd8DGC46PZuRIGoNPEOV8GJnR5jDM5k8VDW3LKNTO5keUtwONyqil5Q53qjLIx2WT85xzLTHGcnNROxo0Q/474KIpEHY4kKaDIaqwnTCOsIhQ8DmVSc8N2DNbnuJAyRnQ0H57DgJxip9tEx0OkcxJhB0UZaHDZrtFI2xwgd3zXTNPVsdtLiDUuLz2Rx4Xr8AHwn5l23qR5Lkj/V4D4nVioLbRuG2gM7D7ZS2cCa4dZ+lsUzrYiXvqC5jC9qx6bU8xuuSKVMW3MnmWq18n9t6NGr4Rq8KL7rq5yeTPE9m1tYsg3X6vyuVXff3HVWA5dc3nsa1+CsAWOwzgVj5NQoWguRybgBamtoDFTOY43BG09jZP4zyLnR3cZgnaG3ZHneeyfBrQC92AtqMB0SFffBa7h1NTf+KWCriCI1MAioAmEUEt/3nThpZ4trWAEzCn7Ax7ecx1cu2srzrv8Me7k+djJRtIZNuN6CyXuh8TKxOdKquleAljqUKKjblE9yEu9XNgo5LgZz4AbrOXbix8HCxpnfZ6S7L0hrZpiGqnDGkmnF/OCwHhqCgn1l6ZnMnXxlrBdgSBRdI/tzRJsnCmYoO+S9HmsiHZGj82rUGge+0pOUCHQy4EVv1OALANKscUgWfrNyPnMIjZjZxKI0RREhMGjwXAqbhUYNS1L43J9dmgxtXEAIz+qUXhYVS91jmCxlyszCqJ5WyhrUFZ8rOi/ACNtKNa7eKUGvDdXfzHVWA9cATxfPpqOLPPsLe/nQqy7FA8/8ykF2PT4X62YNqTnRC883Lptl765NvOxOCzOj/N2lfebGwupHUOIbbmSajR+vGP1GA24IfoVVwocfBkDyIrIBVRPvyWjrxT+Ick982YLpRaoUO5u3YEfALwMjvOTQHq4xx/nb628iDwvZbSkHJBAAVOS7TgSZ2M+8icDW5v8RGLScnGpNjxkPHUNWdMVnEvbY8X+BMdDr7WXj+t9FOqb28S5alsxhgs/4Tx98B9v7Y0w6UwKTIgyiyzIitdnIvro5w4bcBEKAw/mUKr8mh8n7CUuuk4V5OXdbsqQHrEyI4iRJPEIEkMgmEVn4ykwrT46i+fTSGAWw5c1KkZMqOMnuEbWGMOe25Lv5UFZJSXs5kbf1tqY88ZDyZlXMQgAa8uqofAdtElKaffiUA20sE8KtMV7XuIz33j99sP+zrvn5eWZmZtj3lz/P9MQoVeMYW6pZmAigMLrc0K3j2vdQjcZUUpM6cb9rqbsVE3Xo3QtLl+GaUQCaruNj/+wUr/iLdUweAtsXBIjbUVJ87YaNncMAVZz5ZOQYGQxxh50RTR0ttqMHSBgQdQfm1/f4m++7Fmdl9g9un0PILqauZESmCd4M42CXZFosPWL4quLoroZ+DoXuC8DIyp0H44d0qpNgYdPkH7Lcfya4dWwY/1sOzP0kW2d+nwfsOMsrl7Ft4VnQgaUTG+i4NRz3RuIhc0FKTzHIhBfxuRd07Kl3416/NG474DoOZ2Xb0TCBWNjMPYz3xONrGPgCxTlzUtGaWelMaZOVDD8STx62eqdo1m36xF8EGgRsSXH6yLhMnMyapERpi2O5n2bNqVU5F+Ylpsja31a+TIpL9FhNAiyb6kMDdJn7zBaz/k9yuTQ/x0/P3Mzc3BzT09Or0pbrrGZcU6eGTDWxYr1h6lTUrxQA5du7H8IVDRBH6oaRpSb0r45h0u+H5S2wvA5nPdf9/RRjT1bYodhIQRgFRNCJ7AhPaQRoVDqRohifRxxNHElRlllFhWQA5Ax3Gs+6Y0vc+OmH+NqNu1meGCE0fnCWAj6+omYxHxmHVwTLxaEn+FpKIAXupoGucVRVb3HPCwB2qZtNGAdzSy+jqbfiGcUvW4bNRk4svJLKdxgdbKRe2YTvQFeAKNOech6QfAiQ6aR9Gc4YghSuq0/nWe3ICfwoF7r0/aWBCZUh+a1BQSvedaIinuWn4Xu5FCDvZzFUmBsJTHxMU4DDpXSzcac2fi3NNLx6V/e0tuGpBrS12I/Ek80cMoDJM5Pu6fzqujHqW4Y3nbOnvs5q4KIgi9LzTfEzgIW6l4yS2n8eRiysWwQ7B77Crkxz4ddGy1EPlNru9n3yfVEOITou+dMsUMkuRg8KfeV71nsuufcAX91wARNTfcbG+xzdOp3S9NYTN8clfZawlaQDW2vgC1jFT1+OsvChq7Y9AlTVGvV7aXBNGhjzK9djPSwsX8uIg9EGfJNxtrUdb/Vc0CYOqupC5aiwdo178q4l7v3TCer2EcaSXdOQhpa8Z4p3y6yLEKwXetaoUAUk7csU6cmnFgh1viWkL36LIJfNDNo7anOc2fwzm4iW+cnvlPZZml3pZac8uEyR33Yj6hS+Rcwhwghb3RlWTXqdyLqcT0wLa0DYWhS7sAYmZ6GzCGYFVtaR19xBK1+zYkiNeN8GtIgQgiJpiU4QxEdAg8DiRJSMg8dERX+iRJEueFj4VMXVHOCcc09y67dfgaFPf3Q0pe0FCz04IXZejWOXcyrZLkArPnO6J6k4JY2i5tUckR4IsChzjKLJOmQWZfI7pt2vY56lHMmQVAxM18J7QvUVrC3ly6m6B8eADFxDfPJuoZfuNWiJmCcyuC3ChDcspGN222JXacBQ5BkBPzFUFUYT4nBFB/fpTn6/fC5hdOwkoCpXssuj19rr35KeZk4l2wr3MnDmdzKDXD0JZOBdazfnWtfZDVzOhT9jYod2GZBcfO4JGuU0oXqoDDQeOlW+3yXcX3kSDu+GY+eQGtnLchUhsFgypmU7y+r6jkwLn0HKG/C9HK9pWpNwBLjiWYeg+Je0gmj5I+Yz4b19ntf/waeBhr/44VewNDkV7vvAZlwnfBofCJ3rhrLaIWkdAEj6KZ19jcsSpwamBFiGwnSiRUbSO8aGoiSlelZ5hFckLSE9Jn4K8PZyGC/AJWMsqgwlzxKVzn7C/VWKtOw/PuzMHJCV5vmspEwBBQyyBkecJ4VQpbNv0T1lgCqZeb4f4hUbdTnOJFuyZ/6Sh3r+JUNfxLRs8JPNnnM6+rxEgaw88WY3mzlfwT5L7LLyooaeEyR+vSwQUmhbV7bzLUL6aWag1nV2A1eaPHwAJKmH2hHPmgiX8wHMPGEUySS5PMgsDAeDUbikA6ccNMPAxHw3jEgvVMFGGiIVHEdtIaLGkWNNWApzssTVRNAjUAFvSMt+ia54xbA8wYWzavBYBk3FZWS/+k8/jTew94Lt3P7C64L9QgQOTwAKY0nqNRvx0TSK4UgWGoKeSCdtcpWvebXFMqUaSpKyOCfTEbX7qgnVDiTzjmTZL5OMIr+mVuEJwOQ0SLXETQN4k3fyBYMCT+g0Ac2d8gcmAyv8kvtqESYZp8pVk08hF/FMjkArN87koq9lcZV5UKi+muzgL4OnOAfI9zKEalDxyE6M3H8sLsUpxhHyLYO/XjEUlqXttLL5SL7UrJRKbNQ9SQXKxYZvCeDyUMfmtpFF+Tj1B5Yele42blz00G9gsYZuFN1qB/0GN4SPXfHd+F4F2ydYv63P1Zu+wejfTGFca06RKTuxpAiaJs/GQFy/F9YWwcyJ4qlSUbpcHgFBI7N46zJaLCkeMLY8ADznPH6Q537yy3zuhTdkdqEkWw/YOLiNCdm2dQ5niLiqwnvJHiF578oBlkRILaHo55TPCwBcCxCl2mRxTqpQ8CLeF5OHxMAErMzquBLGJwQUUTD6Sk6KbqeATFiVS8MtRC2UUIZ83tBkpCLTG9roQFYNTIpLPK/l7LaZSegLwcarVmn4ArCKvlfkVfIjQEiqILHjkmf5HYFLAT85V2kta/gSpKRG2nnJ5SnDCc/yfKsAVwQdMIFdDJvwWRGYVK1AzHkYOliJ79QeOpZj41s5sm4bjYNHZi/HOwtTsG50npENJxndMQG+paz0cOGhw3ScbjDIOq4YKAGQeh6me0oFffzPtIFKBobWs6C+r8V9DBOnltj18F4Ob9vEoxfvpLHB53khWglBjPdElVeYRbRSTcVSzwrwUWJhur0WmIm4J89PS+Fa+W3LJVKGtUCrLb371rtAFqlEnNIK+pJBZMW3as+YiQxs+tTpQCszg1EMPcWpxU6XnrXrv9T8SPx6kmyDVlvnlO+Z4ne2Flu9giggJeDVRGjVhweT0inttHKtrAZTqU1f5NKnu2d2nd3AtdxAEwd7JwLXaCeIhbUPvzHh1C1HALom2EUv1jM4a3lg4zXcdf4LcCIWxRY52Z/m9v3PgitUV0tt49mwuMjIINAU6z2T/T6rtNWCAkZ1Wg9ByVMCYQY4fZinIexxqSkMmgrj1bUuw8hyn+f+/Zc4vmmGlW6PYbfLYHSExKJa/dSpFcckImgQE4z1kTAqEGjbdBXfIwYkI1jJ/uosl4u3kdUJCKUo1W88WRTUuiuZM3yrnKrKtDlo+SkrglnODU8Nsh28XCPL4ETxTjYslbfLMHqbT8y0urIuy6fYXDyyrnwj65ra4KbBJFSb1luFb3KmqNR8BjURC/MqIul3BsTS71gbPKWGJHSp58rTg8GnZ0/Vr/N1dgPXqSGMRZGr46BXQd9FvZWPNePwtc2j0Bv80PCBc97OwhUj1JORKZTnluJ91PsDtjysF4zhz2+4LgwsYN3yMv/083dEXViOI/cHpZcQUdY7CqvQtF1IszdK0JM/31NgJjTJIs6qQ/AOxi3zmvd+DDzcd9VFfPHma3ENwaxMW6ijkhAlfQu0PDlrzgewSoNFxEY9omK2xGYsHQKRd6GEoIas/5I0NZa0QbFT3hN7rTXL0pBWH4PJXETG7EkwCoV1/MuCl9iky2SSmzUr6EKSFV6tJMhzn7yGyfuaORvy9gN92FzZeUp4FBWBKeLKZxdIyAxhJbBovlVuOdLWY6iQ+ZgYzbwkt22Rrj0RyF3b+p0FckDVv/an//TX2Q1c0lyeIAYOmqDnqqKI5jzewXte/A5e8LX3sTwyxa3P+E4AGnphAIoRvOjPY3sYEwd4+1J9S5jG/Pgo//0FN6UcXbNnP7uOHOd911xFVcFrv3w3s4tL8SUitYhLZYkn16Q9jWkFUYkECThlhVLvaYwglihSHK2mF1AGuPjrj3PhA3txxvHnP/wa6qqTx1EDjSXouqIPuzjus52U6MhEwa9Yl83jQbcKeGXoqoBHY7aRIjr9oopPGFb87SWsze951Ys1E9RbgUDikZczK8kOA4PVfMmEZEhnpiSf4W3Zk5lX2DLsBADLJxXIykTWUQXgGSkgC4LCXTiQKPd9CiGHBctzAa9SCW4TROQcZSd/Env2G5anRotNfsc8NrEvXSd5J+pqpXwGVj2X6dXZrDFsW/h/C+i4Pv2c13Lp3D7Wzx3htmd/Z2QKHi/LX7EeFqbX8blrX4UzFYOx0SSNicWBIfz2akI07faQa42JxRvDwHaCNYaF+7Zt4bGNG6i7HRrgk5ddRLcJkbkhHPmQ5/t3PshkNSBZ03uZhSHYL0T0SPtXRFTsRsYgXV2NeG9U5vRf2EReDYZ4PLd88DPc/vzrOLF+Bq2W8TU0LpFKvImx+zIJMVlL9lzCsHQ1yQ8JFzE1iYyyhzC2kTFkQ1RUelW+l0za9JHjqj0SaAlZlZVRq+IFvNGMI9hbhGhcayAFxb3mEHnA5sRFxMmMqVJxaDuvHFMGQwvJrWVRg5TnReZVvCxkmpSO5ilt48/8PmQL/BDSKFDKc4TsN2ybNTg0fIVLe71D5bc0O81Tcea1Wqd4ZjwrX2cmUKrrM5/5DK9+9avZvn07xhg+8IEPFM/f8IY3YIwp/p797GcXYfr9Pm9+85vZuHEjExMTvOY1r2Hfvn3fbFbYs+Ni7r/geu655Cb2bbmQ/VsuZN/Wi9i39cL8t+VCml7FsdltnFi/ORksGrVM7nNdpz9dkakb+Px9zQAxzKmRUY5MTSYAODI9xZPrZ8Lf7AzHL1vHPbu289Wd5/DVc8/h6zu2443hnm3nMDcygbhIDKOuBKCcUf19LZlKqIwogEJBDYYtB45z6b0Pc9Vd93HBg48X+OxNtOAw4U+Gb9JkKFGynRNdHRJXQf5b2dbinY9g5nNWs/6qUgQTFU6Na62gT5fVf4KspUq41BiVM3/eOpO9i0LJD3KZ23eceiKAWMfP9t8Q0t7IYfzdkA/Y095NaaVervJpnVSu3szDyq05JQDn+2uvIJb7DfV1OtjJvFIAXpck/8pxnCmAfdOMa3Fxkauvvpof+qEf4nWve92aYV7+8pfz7ne/O/3u9XrF87e85S186EMf4r3vfS8bNmzgbW97G6961au48847qaozO1cNYGlshsc3n5utCOKfEfCJ4o2R5XJiVRYjlZINlJy9DNf+KWNB4jarwyc4iV+qDsw+D+5uzkkMY7wesmV+jjt2ng/Gs/t4w+RgJWfUV2TlvSfJSynxNsWWglSr78VEL7vnETBwfHaGY9PrOLFhJlmQin6v2DRgMluqEtMsnxVV5CkXVFHZ1gxO6lBLvlIUDTynEx1N655Os0tktFJVDg0oIesV4TRxWdENLEyviuUuId9s8TsURR91IoajEg6IYBDi1wXTU4OkE56F9ypKg1ZhWyH9DDCSI703sD3BlYr18Jm3BJmYdx1nCXY5jZJl5Zy3ASiD/Nrgu5oxnhl0fdPA9YpXvIJXvOIVTxlmZGSErVu3rvlsbm6O//E//gd//Md/zEte8hIA/tf/+l+ce+65/P3f/z0ve9nLvtksFQ7ukE/dyS1JPPQEImMgucUyHhhGIqz6tY/K3QLQjAI5MqvQWyL1lbqPypuBLEIBK70u77/+OtwQbr/gEk5OjXDT43cDNTTTWGcxPnZuU+eCJfTUI1aUdBAcsXdRN0iIHjd+zx6f5zUf+gT/+w3fQW0qnDP4uL7hXc6jBuCkU4plty7UVQEoPojNKdkWISnYSpXbIjEx3W4x3aSEb4Ob7FNXC69A9DLkUuazF9BcojyMtDMYUczLups28OyQdUJBh5XLohXmHi97nFKZBbiy+YO4kclgIIO8E58LIMp56KVtVjb8tKpU+rmucdHFiXAYDn210d1NqGJRwLf3NZri22rDBakfsUkr67q9Tcknt9Z5wsgD9umvfxAd16c//Wk2b97MunXruPnmm/nVX/1VNm/eDMCdd97JcDjklltuSeG3b9/OlVdeye23374mcPX7ffr9fvo9Pz8fvmgSIQNMOrZYHDSk1b/CCiHuvPF1HHRjYJbjWEeBTdwE7GP9WmU+I8v1yS+gVjDHtK1yjwVxhZJoVK+96doQ1nu4b9Mu7pvdiWtC+O/4xh1sWTgZI+1SrPsnlBCLbkEOF8KmXdYa3OIgcWFFtqoH/PM/fB/v+66XcXJ6pthgLeIxJpTdEqxNjAlR2yg5OJsXVcVkrG1QLuUpyKGNzEgRhKSw96EIApCFykcZpsqfsDcD2eo/KSvzzB6iCCKYwdGwTB5McjxJOMo3667kzTp9zw7/svi0ehMzrd9ZLe7TZ2ZhAZyCm5gAXrLH0asKDfnJzaTLmGOWK29MKpccwjlVAlCuVZ055jI+zdg0J1UdGY+PAB9cBDVpIpBTnASeM3/MMZ3J9f85cL3iFa/ge77ne9i1axePPfYYP//zP8+LXvQi7rzzTkZGRjh48CC9Xo/169cX723ZsoWDBw+uGec73/lOfumXfum0aaaVLwVgXg2OJFFBIC3lVrL8joBM9FpgFBClDcgivUk/jgMmJaFq3rYmj8J/e6NYRnxXzAywcdUmDthPXnAVXdekdDuHoN4AVx95lIuPPRlpn/j3am36TfYGkrB2ShXv+g7GNbzkE5/nzqsv56HzdoYNCT5XGy4s2JrIVmWyaAhRWk+yxJcx5oxiovF+40l72tN9zZ4EpEyu/xRO/ta6TG5nr3RapVGBDN68LNmwEnmIKOXlBO/ASMt/IvoZFaNmEsJXVq/qBDCUoappo0lgpeMTSBBGJR60zJrP5dfqVPU8mnVYWRGfwVw/a29MasdeUl7dm7Q+Kx+ZJ+WUy8T39GpoaR77dNf/58D1+te/Pn2/8soruf7669m1axd/+7d/y2tf+9rTvue9x6wlawE/93M/x1vf+tb0e35+nnPPPbcgD+nNqDxOLlhKhpqutC1QQK8hbdRNebJoBwKr4mj/lgleb40pgC+JLBE/5JYqg4G8nSbGNT86ngvowW6CZhzuN+dQVxWXH95Piq3Qh7n0TpJ3TRvRQRRM606ucOkDe9hw5CTDCm5/xlVYb/ImaQJrSoxKl0vXt0RZ52dpxVAmiljvYoImdWc0mZR3VVZl51SBHZSTTO4PoYFzr9L2SOHTpn164sZGOkxYLcssTINDCRbCoHKq2fg068ok7rbdh9hKlayp9Lqq01idhxxPGXotKAu5dIoIZ7DShqhQ5pJYhhyvpKO1VvJbThjN7ErnOW8TX2swnRnn+gc3h9i2bRu7du3ioYceAmDr1q0MBgNOnDhRsK7Dhw9z0003rRnHyMgIIyMjZ5ReAVg2EJC2b6lV20z0YNMDRxwz6KACir58FR2lyc+0HZNZNbDIoCVToFd8aI28esBNh/sHpjcwrDrMDJbBe7acOknHRSQQGwMlJrU7XDHS4xLejgPH2fHkEVZ6htuuviqHNJkVJuxpreoJCAtIpR1PkozSR2vFvijYxcVyux4FvFZXtKoUFVfSSSrfOJlRyO/jhC0VmyLbashDUAZdCSalgagrfkmdCsCsvVamPzWcZgjIYVqWuqk3aefLa8XrV7WugEVswVUsLM89bejSOdSgo1MVhiX38oSZgYv0u+yHOXdlOk9/fdPmEN/sdezYMfbu3cu2bdsAuO666+h2u3z84x9PYZ588knuvffe0wLXU10GCseiXhujN0JK82yePCVAIiXaNsgJOyCKespkQsCkMIuQfCS6pDKmLzWgi96hmZhR+dR2Tjo+sbOyIX/Hpmb428uexd9cfgOnRscZVF1q2yGbU2hXCto5VbvppVN1wI+AHwt3BVQkC/H1YOEUPxWxk4CuIZksOG2V4YNqzRPq1HWijazWWUGWaFMF579VICa4qyYEYwOzMMopoOTS4OjwNTp8FMcKQxYT+GRHLtk7fpvZaHjQS/v5HPTklgSBELHJ0lwjW5UblY4AgXZkKPq4GhPdnshCQ+kPK+cxde7EMD2WOtVH9u6QjWE1OLVBszSvWN1/5HRT0PCfIT67xdYri3rQnBlgyfVNM66FhQUefvjh9Puxxx7jrrvuYnZ2ltnZWX7xF3+R173udWzbto3HH3+cd7zjHWzcuJHv+q7vAmBmZoYf+ZEf4W1vexsbNmxgdnaWt7/97Vx11VVplfFMLxs7agKENaQgMVI3srxPZkwJ7OJ/fqiAw4GvwdVrDPEmgEZ7AMnATgAkTvLI9yRBbUcmJyrnzGT2YtUKHj7HkfLsMyD/xTOfi7Vw/pEnedH9X8sBkwV9k7XpOtLCgaEa4MtgxX2YpKXBI+r9TUU6ONZIXXTANojhfkrOypYjMVGL6ianygGhbXw87CNlda2JQdRRKo2MB0FeDa/KIRB9evwRHZ7Es41lHCaeLx6cB9ZFVHoIZhBpG3yKUj0DiGiO8vwkejVtjCqXsLt8tV1HZyaXTyrJgLNWfjXrkhVOfaI45FVJ7QXVpvT8GjVQVnZOJcOn6AJlHVE0aWEFs/R44siHhOg0nv76poHry1/+Mi984QvTb9E9/eAP/iDvete7uOeee/ijP/ojTp48ybZt23jhC1/In/3ZnzE1NZXe+e3f/m06nQ7f+73fy/LyMi9+8Yt5z3ve803ZcEFkWrFtRVQRHUryNGNA+qLeX6cdlspWn8IrTSQflaxKyoB1YWUsMaHWgAbyVhgX4rCxrwrYmF4ZPgFUnqBDmlGfJCuawvzwsRyaCcl/HvZs2MyfPesFdA/Cqw58jl4j1s0dilULSTxt1NQzqQlOW2uS6zBJTOpR2Ktz4bk4EkzksMp1k0C7ChNCMriObWbaYzm2b8FMY3bXXF1ULxmbz68R8wHhUp4uXSyPD57HHvcMpkYfZyvrcNFTaTg3qia4kJGKF1MDMX8QpbKN77kie+F7h8DA2oshbYDyZDstLYRpy3jZXG3SfRRY6lTl/SxQZrusDFo5fjGJMLR9zLeNTbNjRUlLfmvQDauxwvbkfh1zoy3ktQ99U3w/k+usPuXnN++fY2xqGohAZUgrhpqRJFaSHgTGJF5p0o4bAz7aIpomDjDZyxz38Ik5lVcDTxiUDFAbWYitcrpaiZ1MJAQIZCLTpi8O3DCWQbE212Qg02YYRrE3ueyyZ+epg3SMZ8eJY1z45AFodwzlm0ojY3/E8IevfHlY4YyW6yZKTwlkNPBahSexTMIkBegS2VDMV+rBSj1J+a3KqbonYzSDdasurc+sVxoSz/ipPlfedj+Pj13HucMPcdd5E9y/YTdXfWOSA8/djzHdWAvh/zCY5IQfF+/nzdKlMGQg7UnMXKVE2UwdS0NWTSkFyESY0quYVWQsIu6L+YRwr7z9J+Qgr9jlTdPClzL45Z2U2UxjNXT4Vr7lrmyZ0jbwTbHEIfdWi4g5pz6lAcvzC/z8zMv+cZ/yoxlKGkeKfcmVBoianIyPy/JafIwsTDsk9ZDHsxpMhbmFKb+baNNkhR1pUKloDbT4qRcGYjlk87JXniusLdmP9G9x0qovP2l4bGIbxsLKSI/KB2aw89BhLJEm6T2SsXT1BCzsrOhEUS9tv/Fkj6TtVVGpe1QdqX6ePJgqcNWriKsWlBXApYR0O+rf6nmoMEkMjLcsL3vck0N23vc4zdgWtg9HWBouM3byAJfev5ORzSepz+uxb896FgajjF1+ChnyuXh54JV3c/IBDsSOSYtEvgijC1byDX3Japywq0bFIQAluSlFhfB/o8aApK/DSZUFUM3VmAFTV/5aYmOuF7fG3ZyTp2JG+dlqYHyq6+wGLkjtUBiBSr1Hmy09xhNYZIPqdM9Hg1HtXj4xMHK8HjAdChHOQGZdkXGlwArwNFNIz2Ui7WRxK+nmOuD7GSj9EDkoO5zyHO3BCot/1f42Sir7N27kwMaNWOv5rs/eRscPyBv+YKzfR2yGBusajtxYw/Gop4pjQ8748Bp8FWg5xa6SWYgOYzOop8owauU1xmNavwvDYalvoWcCYGRAD+8H1jSyXDO3zzF3f43pDDi//wUArnzQceWDp8A8we6/6zL33Q0f/dgGjp5Yx8adJzHGs9Dp0OsMcc6wMujBRJ/MtloZSpDiI0BoY9FQ+MyHhNvoZ202o5V3wfupTwxNA0L2/WCKd6XySOHlW9u2rc2yxAA3h9ZAphs0iKEuMlHJVV57zNwrA7w81wsTOsSZrRee3cClAUYBlvafngZ26uzkHS/xXXnfdsqojQ3KadcPwOBdAA5kQMZlNfFwYHvRo04coMJGxFqeGCfdnCd8iFvYWGKEhnC4dQ02nE8bxF5xC9MDRoEa3EANXKfyRB7wPv7X1Ia/eu63FWOqaxu+/2OfiFKLZezhfey+4++57Q2vCvmMXjQ0y5IyS12Ijiut3J6mPl1sp4RdGnhaE26h91LMVaRaqWcRlYuJiKCQftZHb2XrnkMKUb2qEBBZduYv4HvNPTDVgXdbsB1+/+JbuPHqO6kPVXzis1cz8uN3hnowtYKv7E+LxJ7K/U+5O1aYqHDVDKYc1obgKkc4lV4xbKJGyaanGYYyIIZN2gIAGmazX7FSTK3wBeuSBQiLgJqssubzxyEI1tmXlk/hZHVR8iMGqHrbhNiv5Y7YXht9quusBi6Z2cWtlSXrrdJM7Ms5ItlLhXMHEgsoFPeiT41Mq2PVQI16LtenUNCbyEKM2uKTgFKASAGabPxOTK6t01HvyXcBiZTXGK6qSLq3BCaQFPwYxeTIICdAUGP58xe8EO88vjZ41+Cq70s6KdmbLM4AjYiMapo2Pg9VQzqnoywX0NH6PVVHmCy6a5am60fSkfN1JUxY4RU/V8JmghM/kzT/qeSxYyhloTR0YnAV+BXe8Mhn6DwRqOYO+1X+2/ufxdSr74LuCtnq25JNTSVLIR95t19mOxZZv5PcCDvTol5AZ590aiGPWmQNQCArddryHkpD0bxSSMplnvEzvBhIeStDhVjrGMqQ7bG6iA5LQhpqmpRe3mxUjkIJnX27Zj1de/Fo7eusBq7kPwvVJT3lrhZLWWcyc7f2EKalfd23e+TZPZqpeOL7Anw+A0QCJgEnzXp1PuLKmumofPhW+sQ042qjkUKaWD4xopINyiJiRWCV47/aLntky47o5IOVv2HQ7QZGV4F33QADXbKvrQYqOXqwE/Me4zA250Pr+Ar9U/yUeinqBgVgUg7bekZZB9m21KswWQ8UBmWVE/M2dxixTsbHRtXub2MF2w6j9RCaQI9nbc0/Of4gnY82+ErsOSy+Y/jrF6/gZOlYDXcNCPJNlv/1nXDJsnL2TFGyGwkrQBFn7MJGLZc9A2ZeefQJfITlWQxd2iYaAcyEYZFCl4dpCLNT1DvBoICrrCzalKdSSA1OCuVNl0r+9NdZDVxAnkjlUgy52MOoFs2syWwlhbXqnhpgRGU9JncbC6mtEmhV+Z20iiaAottC+rFmWTFMO0+qzxfvpwOv9eCP4Ja2JgorkjwI6Ml36euNAktdflmQy5NzMuZOZg960UGXXcTFVv495fNU+FQ0hVBSh2rYFlWhKk4Gax5A8tGFXX0YDOHgiLovl6yzyQBzGfULOwtD13suHJ6EfTbK/gG8XMdwzTcMzhqe3OQ4tCmDiDbFADGmKE0f5PzCspQu/SpX4rIzwVw7mudmK6qy1mUArLWtuUmAVFZO2WnzYR5tK/m1rpI15W6tl5UFEsX2S9ZvvwWAqxA5bK7qpKiObeqznrGwpwqBVVytcBABKQKADFSr+peP7nCqLlniIMddfJdBC0nPlfqUAKTO11p6yhZ7SZeYKhAZmRK5NPERnZ8ngFhSG7XCWg+sAGPyYozfRH9ckmdhjKJ0FzCzlIe2xgST2NzOHOF5e0+nhCkYlgI1r0En6WY8lsAgl66oWGlg9KDaOCnxYAj0cdAat3F6ahvbQb5nLPgK21S88AvB39ydV/YZdOUw2aABMlTUXcOpiSAOWZbiUO3gGcGQvUFIo2dRTHcAqXS5bLwr4qIOE+iwwGWwuBfGUzKjmgE9KsTWyydxO+Qn6+8o0vfoKsvA2ZreUz4yzCnRJz4tXd6c2XV2A1ec+HTfF9YBcYDEOkwuaCIjWXVqsyGf1SfvuzgYOzm+aiRY01fRzZVvgpEqBhiStx9FOywbxU0Rp8Srp1Y8p4WC+DvNUC09UnrYlOEhAIgw9zTUhDlFsc43uSxpO1O0SbPdyFdqMHVQrDvxlNPJIGsNwYBWXCLbqGMzFCup1nuajoBUznoSlBKIqY5uXKu4wg5UpbYqw0R5ORtDRhss48AM+Ip9IRfaB3kGX8XojoEY6vncYWRpFyhXN6TS1eySOpADH5aDr/v6CNfd2yPrEhyYLo+c6/jAixfoMoozH8CbZbw/H8OzqUwVV+UgW8sb1bwmDm3ZAqQ9LjboFclsVuGw5BO0ZZYRRbiP+RYR0zFA3OfYNGN2CsDyZFEvA9AwpipLCKKjspS2bgLEnfSu6Pa0XZyjtKJ/quusBi56YEREd2HA0aF0WxPHiW2BhpVBC8WMbiCzBqM+Y1ziUyr1a5E2QCa6dCByJRORhBF91IB0nLyeq+QqxqgWHyWD7Z0TkDwx6LhML9xPG87jKTtekQ8R29xKeNEoJkUDfi7mfxSYAjMCZjQCtJRL2Jb4LPShrpPZQ+zuiUEBGK24Xms9qZzd9aydmQOIsrh9VH0QqxpglMd3Xsbcc7bx/C98OBimuWhRi4kVMkI4NYVQAWLzIYo6LQN7UWKqfZ+yvSARNK8qo2L3fsu//N/jMFzgf/4T6I/fTHP4Gvp7NtN71t3kBQUQZXudlPbgEwvLqxKhHkJn8/QUfJDqQ9tnhRrJB2CQag+aOENWVHGdz6q6lbq0MYVBjEEEzEblSAAsM0Z5P1w1WfHv4tLAML4pZwq1Z+q1r7MauOxoGEQyeSYQU5d27mfiBGS65BU7yKy2BRbpoAYlvhU2WtI+sT/ZbmQpHfU8Hu7jIeuelNI/+QSEchVdwEmAT/Il4dQlNlNA6QlD+rpIUgJKI7FKJD8O7Bj4FZKE5OPY8z3yOYp9qGrP7g1/yJHmJSz5XYVur6x4yVxgS3pOfmpvABJWCqKPercqfLYcMrQThyx6LVJPG/rbV/De8+gFL2PHvjsYXZ6Pld8LoOXjcqn34Z7VzMaT5e+G5ALW1BHMZCO7GPzFyo3HgdvGYxsLjPHqj34Ht99wD3s692D8S+kkW3a9auioaHBxUGfu1YnAIR6z8tmHcqgHUezTU1iGs9DgUwd3MHlkWzqHxVdxO/rUAsNz9iVNWDY7VcyXvCoja6m5NUXwyx0wP5OQDj3tyGYkTx5OZ3Kd1cBFpwSBNB0LAJk8kapJJgAGtBeBwn0NcrHfSrRtC/k0Xkz+TBurJb5uGQRYZdmfAii1ilf3ivyvdbWf6fyoshWmO5KeZp3ii1AAXkDMqftDWDh5If3RCVw3ssq4zWZVBlLi0un1rKJBSwshJfsyRbjTva8Brg1sQ3zH05/xfOOGCzi8eZJNRzqMLqk006kcrU2UbbYrs1lhYJYBtuhYaaOlyqOxbD6+mSvvP4fe1i6P7DgaYzDxf4F10dfpxgI5fzCbYqB+t+tIGYYMukw+uQMfJ5HR+fV0l6bTWGk6cS5sKnxVU289hjFtp4fiMz9PHnn/pIBRCXM5nDhR1J05G7aW09e3AOPCeuj4jDZibKz3xmqWEgdr2peIwi1P2nht1EAVkWjV1hJhbEZF4ihswLwBRkJ8agyX/tlVtMmhnvxug42+FOCeFtPkXelvmkDo/MtVUWxdNAq4QQiJ4ejxm6nXh1c7MglXUWeVvAOGrhmOAhMxQud0NdPKn6EByhN4MlfLzCRXhFXvmvS2zCANg3HL/TdcgGeF4w+O0x8YqoWGWXOcTEtjJyAyr1Q3sdKMDWJmAunYYWykpOnstpyj1JGEWlvPhU9cQNMdsueKI2nOzRuqg56pWaOugveKlLJq0AbVa1J9BdAaYfTkeqb37gp9Nkq7MqnprmWWx+g8uQ03uQITC5i02iJxuqL+czuttZtAypTLkNcPpTeY9C2D15kB11rrVmfNJV4LPIRBN0J2mSJMf5QgDYyQXVOt4badTmswR6TpOA+dBkxY8TKy2Th2gJQ+Kl1xg9UJ4ixV7tfexRU7WfH00AygHkIzJG0W9xIfKk6tu9N6ODhte+tjvQLSkHusyoewrVSX3aCgNzaIwFUVAFg2Q3eXoTsPzIE5BfgmHugRB6oNdhbBUnxIctGB7vh5pSmzDGE9st9PMtre2NwqJxYfK0q7UgnFNjHEEMOQr9xyPh+//Nnc7p9FXt6VCo+rDtqSWCdrREnYUDhmS65KmqDzSis8mUmGBuzHNHoYoErAILxLHMRIg5viL08C4QTocGnGYzHeYJsONBXjh7cw+8hlQRIW1V4sSzr9DtKkTl3R/caFuKYLDAknIElHkRcExOTItUwBsqV/9vhqqGOL1WRPq8T38wxuUlme/jq7GZdcmklBNs2p4/2aYFEfwxYbhfNkEsazy1F2+55/+pnj/MLLH2T3g+ezudnKyWeSGFX2tEkAviazteRFIrZ58tXewIa74MQN4EbhyS/CV/9z0AM3wA1vgy3PiPmRBS4x/DSkvizeKbwGM5kEIZtzaI81MjkLaMYVxfSurNDK/R7QD0DrTR6nNgKfbFUyAzCnOqH+Rhr8aB21HwPy/joIOhoBI7mn7agkg3mfXtuuOxtOyh2wiXFkJWXoEqIjEtcujoo+DsPITXNMnHcK/iJ2EDm80UM4eGQktq2MaNFxyXJynDXk1CXfVXQ1Vq7MboYA5L4iOGg0yGqKpYNjgFcNF4Z2P9VcBmC9F1LqZ5AaO+Q0lH3Hl27CNp28Qd7F08oj+UsS72ieUH2T+79NHUW+y1DJE4mkKE4ELfmoV8mPtJHovuSXLcqVmbVN35/6Oqvd2vzWyTnGpqeLCTid76eYvgt7Y1O4ZD/VuoyH7XfB8XNqju4KjTWzZDg2MaA76FFRUY9I9d8JnI9hPR0MG6Jv9nv5CtN+HTv9+ZiBwfXAGs8W7mQr9zHWVHzo8e+jGTU4B/US9E+Q+sHo+mBykfpFBL+paZieieUblvn2hHBJrRLZHR7x6lLaUynlfmE71uR6MwSzD3nfO5KuywrAxYUKMxZZZBfoehZ6J7ln7PM0NFzDDYwxFgdjh7yPrlaDsC0Pix11W1SR7Suo8FUaWKYY/HWc6bWYI4AZrb9qz/ipkJ/nvf/rTMwNMuW2E4Fqio+ixMZ6ufBUQUyU/V6QO53QGaMaxo9EoOsy6MGRTUP+9mULZHEpgHawca9pGCC7AF36X602QWRmAWQshk5/jC33PJOqP45pTMJOPMk1kYiKPubX2QxcPva3wbVfw3SHEUbD5OISgHmVG+2yxqFNSLXbGr0ko90YNrH1pHWW55f5lX/0bm2ANNKkbwCi/U4M3lLsKyQHTz9kg/KhXUOWZhrqCqDD8SmL8R3qESG8IrefA2YcnMF7w5wBhtDz2/FulJM1+IHjwOgX2DJ/NYNmB8fNCB1vWekRTsHxQA96m0hA5QiAAaHPu1iGxSWosxTFzPpwuKwwJAx5UUykFrnUJCbK+FXW/KIHczkO1CQgnVxb4KeJdxAIR7DCN/T8GDv9JXjj2TvyOJvsBmabTZhBBze6THLslRgUaOO00qu6I3d12Y+otWUyI2n6q4GuFDeFLhsMvmNYXN/DY7j3udvp9mHm6JAL7jlGBiqRteVTs8NYmd4G5Da60/mMELoRog6tN7RsOtLheZ8f5QvPWmbY0Wt3cs6hUWXVmiApfQb1ySd3MLI4Q+UqOksTOCVlpn2pXYKhsWRHJF2ZmIr2LjdxmzjJlM6XcxuV3Uny5VIrlGKgTEli5pGXJ86UcZ3lwKWplv4UJCK5h4Ewo9gGpo7B/Mb8amNqTlVzjLOeuXM8vjIYH3QmRuKANGqN8xi7LQ1y7wyLUXwcb7ZhhrA4BF87TvYXGV9yrNTbgG15aHmd39zfW7dT5xsOYLBCcszXHYNONwOX9zAW7av0Qa56Zi3GdsYGpGj6iLfi+DVWv6Nd/yDWBNGLRM+PsqO5AGPhG/YEzoJpOlBbjnKIDc12rLF4q0ElZ65tay2JrqXkz6f26FrT74nuLNVyK3xgCvsuWQdUbH10iQvuORk7jfbXH/+K1cIITqleBJz0MF5j6TlevWHFZQ+M8qXrVlT5df4DUAqbKcNkmBs5sZHJw9sZObU+9y0REYVdiSTcbvOYnAYwFaKVHz2hrIYvXWrdrqZozxyXCKJapDxTE9SzG7iU6wddPRm8QKQNj8EZhx00bPs6nHwBjAz6eNthcWTIY6MPsovrqOgkC2vjTDIHSNVtwNQmSAJK/EzbfURF48CYit3HXxqCxclasiZShGY4unskwPRpkg73IwgfP57Tlu60bUfwZFFsYnb5/UJPptLyw1b+yLO0Pr0HA9qGUcaqdySRsgBMB5ctPistajTdAff5b/C8/nnYrqM2NbVfZmJ5wGB8HN9yrVpu6g0NqW28AsSJrZA+9MGpP1nNcup9DRAaJoeAuP2QjiMinwaTnMe8DN1arhadl1f3RVlYKP07qawhN5nRBQ8XYoaQB71tOhgX8lXRYdODV2PrXmgTyQoUO0Fc7AtSMogiovQhAbpG+l1mSQFwKkrulycbPQeG+AdpigEtJIZOltvJp+NuxU9EZ5Vl9drX2Q1cajtD6gqJKXi8EXJrmbdwlMMcH3mYO14ZAv3s+/6Yw+dez6df8M/Z2Ty78CJqvAmNGFe7U390BCV2Xd4zsiAmrp41EHmSBbvPLDozG8Fa8QQhCwSW7F4akt2Vk3LKFTvnof0hvvEJ2Lglhu1E/ZPynCILCIgYoXueCfmw8buYbiQ31qrny9YpU4EbkMDYxI3btorljjN+1fR4Yf+14A1mxXO0d4g9zcf49f/wSd7/c7/OYGyUvC1ZZNeseBYHfdnximY/YdtKCCtK3yaFysyrk7qJrHjJEAuCyii4dWEZVRDAqEr2sNoORgORAddTSCDhIyogLjekgYVjiKCkT3jO5zmKUakH1j1yGZMHdyaX48aa5OxRFnScmqAMof714bx6Ii44jon9lCFhr6X47h/GepLJwCUtJGTHgVC33pOMhFOGZPnAxPueBoulh4v/vgWAK8x9eqOt6kBxS4mPA/U55iOcdKN8ZXgTvoLZGj782isYdIMuw1Qmgw6kfirim4hfVhiYISuz45/4t9fTj3N5gKfxJczF5j7tBfgE3GzKRjYtEl2WPIhpEDukiwC3vAz79gUA0eZInS5s3U72py8sLlr8J72YiMBCKgT0uuBXQh2kU32iYhdPudUqjlsfjcuND/GYkVjPGLYOdjKx9AO8wb2ef3XbFAeuO8WpWVlhy8NXVtp8OrEnoiuQXdm0Eld6LQElaeDsvE72/smSfewsHfH+ECtE0N1FmxAvFW8CI3M+N6aNeXKyZUiWsEfAdfMsSAAtTM3r37cJXIdvXHqKO689QViBqzGqM9qmx/YvPj/09KYb0pJVZ13n8YaXVWzJPopIop7JBNtItof0r/0aVVcmAlkNrHBqASWf5JPdCIpIq/1/ZfYI+YQiPUhk2rDxXwGjp73OauAycVe9MbIInMFLGtAQdBAP189gua7oNj0cwfFB0+mGE7RXIjvw0ZaQHJUcr6UdZ6a+q0RFvbUoXWECyt+1/kDaUO7ryTuxvlZ5FZDo/ZOSBw1wfqjyF8M6B0cPw8aNcPIU9EZgYlLFp0RMvXKZDmqNOuoiWs1SYz3I4SRpjEo5KwoPstZ1mOx2eOarx7h15xeZn1hkXb2V7c0uhiMnIIFNsLKW2T18RuTHxn4ApbW5VJ7e/dYUw0iH9Th239Ow875oilHMDmQkN5D0WgpYc0eIYdJKSbQ7SPc6RbwGw/hyUEB1ak+TxCx5auktTTC553yMGwm1oHAvuRZTsSbwUkNCn+4uz1JbdcKnm5hnsPUxfG8Rn7b2yCsymQhRyDWnB0L241UOBr/qjtSdtr5vWmFOf53VwDU4ZYOS18RdcLGeu6OeKjLy5aVQFcebbTQurmZ4GHqwxmRZXm9Nk0saXkslsROkS4GPj6wCR7HCB2TR0ChmE9/1jkLHm1bPFZOT7NBOO3ZcYVa+HaY19pYWYXEMlpahFluwhmTvNTIK3WiMmhigxKPSSp+QlPop7Xjf1aQtf1KutME7ir2VhV0Xw4OjXYwfZ6UectgcYJx5RtmeIhTdVj6TRvQsnfRLdF65cuTSOqKmdQ9E9zJzuGHDk47CXiY5VzO5YVLFykwSoyjii2HSzv5oI1ZY6Xu0RejsiS6XPDSFN5b7LjhG7+RGGFZUy2OMntgSDtZVSReHpqjU12z+VphTx+HBu+DcZ5BcbNtun2r9MfJapqziCoPSddZe7czPBMJyPRt0rjK1KD3EypRyJtdZDVyLx3rUg25adXZxVWtytmF03NM4mD9uskvlGM5EscjKiosheSQtiJMHM0K2YZI+LP1X2ks70ZQjxeI4KcBGwEUvckWGlJT0kD2hyqV0bJpBAQW4rjr1WjE3D0m/cfxoCFsPA5ClPFqYMTDVBduDpUPQWw/1YshjdyoOyQhanvBZDaDuBRDKxfXUZolRExYymtrSHB8FBxPryc4IY57OXb6U+sQIh8cf55EN97DtRIeN69YxYkbAVTgPtipVAnmTdeZRWTTRK3HBYihwL7HK14NQNmlGRNWzVBJ3IBnGmnZFt34neqpAzylXtyne+MyETrNz/wQ790/iqooHdywytnc3ncUJ8CTQSqu/7XZWWUjH7en+Ie/FPnvsIHzkT+GWDbHpOzBVhXVvU4h0Eknpb15z1VyfIoab1vtZq1XWl46jDWJPfZ3VwCVqBUNm43hYOlmxdAJwMNKL4z4yobTVR/YSCiDIhCqsyJSAokUoT6TXA/IOlYbkKcHHyco1USdGAKt0FmNLhPNlT8jW7aI/kvy12JPea5jOXhTQE9YW9VwJxOI7yTBVxlYs3/xJGPZh3TR84lfg6v8I+z/qwcGFbzRsPBzSqi2sjAdr7M2fhZM3ge8GAuEseOt4fOLvuYwxDjPgia/Pcvg/3wQevuftMDkDwWtEqNPbN/wdj/2Hm7hwwwVc88oL+Ov/7dn683/FTZ3LcfM7WFwZZ+uWY1RUeTgZCNtSZJBkdlYOHBkcNV5VrlfK+VQRsirhDMUexLRCU5W4ZmryBlXVILaJ7Sy2OPGl4hw5QRhpbPCmwdNn9t4bAtBGHJV9rOkVDVZetXmbFLYvHyBmdqfh298a+kFjg0rEOTUT0yMfZutiDdpUw+IlrDzdWzIhfs0pMplhUPu0L1lW26jidNfZDVyQ2cYwVnGr3C7uiNB7CmWVy4pVOKHhnWzZEb1pZEPC8sWVMyakJ8r8RMAi69Kinfd5ZRBC/E7EJT0A9OTms1JfG4qnU6ElD+TJf62FrvbxaZDvFToy7YaHoNzvD+Hqn4XeAvzo9i9hxuAT3MjxLriZmHYDDODgjRHo4+Rg40yye+WVDAzMVPCMdQb7gzDcBV0Px8bgyNh+Dpy6jwM//xKe8+9fww0/3aFTPc7y+BPc/I6b2b7wnSz8oWWw12I8HB/dwqUvhcc+C91bTjHy3EXAR0cu2jxCRMuGQIFF99WQRUyx1e5iGDKkG9T43oVOVFmyYalierJkmmpUMTLpWMbDcCQ8szLryYZVWdmRK7Kx2Bfmxjp8/KKNNF6xf9U/UvdWv9PKYGznpHPUDNiGrB+7+CMcfWAnpx6/Ip/R6XM6IRuCgAJIwqGzmKvtyrTqVjsSlKzrfGcPGKWZsbCybw3GJZfqB+LkL1kCC3NSrF32wxa6Kq3fcms8N5lJCbMjfooLHK2PSu/ISqUsUK01I7oUVanAN2v8ljgUE5N+UnRUcp7SFfOXjLpjhxUmmMoV+093IgyKr11+aZCQTkYQXgxituRD8i2DJ1W07WR2OQJ+N9gunPDQNDC2vJEty9fxRB/u/p0uV74GpndvpXHrqDqG+W6X4SvjJGECt3pkFla2g5mdYHRgmerNY7DYVCEW3zf07p7lK8+8lQseu4LJ0Yrm3GNY7+h+8SIGV+4LHhDwmLjMO1w5l69f2uPRczzdfsMLPvUo6SyeZMwWRaj2sq+TrT/S8aI1rq58cU4otEk3vhMFZ5hFh12L75NOM8oG0KpxTfGR+0Zu6jzRGfDOcfzCj1OPHcebc4oV8Gx4HDqTj/kyCB+yKVYx0chriLL6WJqj6kwFj6zaW36oV79G2DO9/nEAF5RgoPqFnqGKNnetMD72LT1FCJuTjiONrTpQqm6fAVPHmTqIyozutpCfC6gUAKVe0JbO+Pw7rXgq8MKr7yovRfcQMBYwM5mJ6XSOTc6E+7KfVwxWVdwy0absq8lBZnzxTTaIxKXyo4zbUXa9KIYdgaEbww/HMAZWDLAjExwPLDgw04DpsNKMYoYueFgwnqmjYIxnZdpgpkYZcTtoejP0qwo3CJSwmZhi2GzCD6LXg2ixWQ+nmJvsMj8JIysiPkqB9ODVl4CXGrDpGPQIcJraGtk8rhia/h4XmcQ7h54EUzZafTsBmmozneRg4gjDqYOh3qcOFmsN0l8k7vrUGCcfOId1l+xVvEinpI0fUDZXa4vnqF++iEP3wzZgfSsxLnXpI8sSY1EN6Vv1KwMrAZMSuRI+tRkOZacyMV29fy+xEJ0fKGa5xHBawFbkvSiceqZAy+tnxZS7xvfT9JPkRsvk8gi5SB5QFcitVR8pOQX0KY1IQLSKyDuoenDhq5TphIjIBDVTmmRiWpJHCzS+y6nhTBqoI8cdHetZnjHU58OM30W9eZxl14HhRKj788G7TUQPxEVbJXvXCk5sGGfdiRqr5W3pTCI6ekIFWR2GWGFKnpd7ib1owKrItl7xw0bgUnaFazIrja2EunFy30AzOs/K+kdZ3PINrLJzq8aX6U7NM5yfLoo1mJ/k+L27mbl4T5pB5f+8vyADF+n+6pVcATITMy0w2Jr30/P8zplda00jZ/2VsEnXkmJEjcsNr5mFGJmK9TdNPNEmWo2Lgj8ptmOcLusnc3+OO+1TnhRoJvMBr/IIpepEX9JZNRD6VhwKiBN4KrMmfWhFqqQ2MEqdCEAZSsYo+Vbl8Y5k4iBHmgnQeRPAp5gMdB+PdWnUSmxarTR5MJJfTywzZckQFgWAgxcP2HvJCvO9Ac0Q7up9gXlOpPdtFJ0tqp2dqvIYcX+0w99952UsT0Bja1xaNo6GbpaccRMP23QdMg2VwtYq3lp1Npf/kkgZ7hvvs02dRBN/tyfLVf3FgLEe70NDnNp1O8tbvqGEtLD/dvLSB1j/nM+HvqpUH57wfnlWo2yhDgd2uPjnUxhR3Esa2hBYqjRvucpQJRnPv7M3kKe//lExrgRGanDppWEPYEjnKrr43JAbMAFDrMPaxQ4vOrBGjXefO4+L4USnk7brmBaY+DKvQEHt09VmjpBXmLSuWMpM7v9ePdN1kkCgylGmJCUOxeYkH25IcYai6LdSOhXZFi2GKfYs6nIZ0onV4ivKRhG9CKYALFVBQzqdKNWfIUwqHcCPBCwxcHIMLuEmTn52lMMHoDcDu19GPsZO8CPWRfJMowjEB193JabynPfIcZ79mcdjAxryQRgO/CDQI0k4DU5HYFN6wGoUl8qJq0Q+NLCPGfJNK6j66tUNE8sgfdn3FjhyxV+FcEYfIOLjK0qsM3kiSP3Iia2cR2+/CcseQ7Jv/Gzjro1LtX+wMt8VclJQBqvsV01CrnV+wFrXPx7gUmxHRmybvBTqhnQzN5zWxxiivlUU3kr5rAFAntnoSJA4iNGLT1qS6JB1P4o1pec6f3rBQJVR/y4GsAxyAUfFyYv4BLTbdajSl61NKV4f66UTVl9TePmLoJ0YpADCafphKrLP+SoGpicbqJPrviGKlZK+C3kKqiqTFu+M8VR+nJlnG6o+dK1nfgROfBi232DoTWWCVOgGIdlKeWPwxrBv1zo+8rrLQzhnuP4Le9l4dEmhs65Mj7bqz7oK+a7twsgdywbkN5DcfMnh25plF94bImj5BhbP/RKD6QMBrOKp2qJAz2CRFeK92WNsfeUHU1Tz91zN8v5deN9hz0efw44XfoVqpE5xhJJpMdFh6OAS48pp5How8Zs2ecgdJm/Tkt+WsKfl6a9/FMBVWKiTv/u1Asu1FsvR/YnVOJHCCIMTIBAaryUFPWAVAKUVPenPMvNDOqzVtNtflXNN20cBbZ+jNDLpQ8GiBKBRn20PxYYcn/wudF+GYhFAs0fN/or7ZvXvVZcposlfBJBjntoLKMVKbQIRA87QmQQ76fDe46yhd4GnP9FQdyt8tLEaH5R1IK9LGYejHU6ORpOFBh64YhN7FweMLddceu9hNSuYXHi9lJwu1SmKzukUQpnQB+I2HDF/8T5OGKYsqpjauN4Czfhx9LFha850UsZuTW/9XErfjkSln7P0T07hvZzlGACrbLhwVxwfru6sWrFc1CradXN+Rw+ELmdy/aMArkK0kas1QApmIkFMDiL9zZzmOZB1D5QsyUOWCOS+fDcU6SaQNap/K9FLQCKx+dZ7ifXpPMSyadAqFiGsqp9WHRRW9QIErT6nAdO360gilv5rKLus7sOrJ+MMdBqA1GeyM2qn2Zpk0mqZHjvxZ2MA6zHeMH5Z8HswxOAjw5mIm74FsIpLxxvLt/e89QBMLPRZf0wcI1ZMzg+ZOKW8qCbNP2Rdgy8LKq46nAe3EA4esOtCPbvcFsnBqo7GNgzGDwW7w+5yLgMCNrowawOJFKo7PUd35gTN3PoQH0NM2ieqN0/nWvfJeLcdb7shV6ct5zRq42G/Zti1r7MeuFK/UAwhXWqW1xQ7dYL0H2mQ6OYUkNC6hnRfp63Zlg7kSbtJCuCQ9lSsSwywC8B5ir7X1htJ1BrQCuYUB38iBBIgiiNtxf0qoJM8dVQaAiiWYg9iYn8213uqZl2XLUDXyRTdV7WtOkRojcYCjClt1VIlyI/oAcqZMFFVcb2rPYkR60XyrOsttvXC1AifevmFKZtX3HWQy+5ZoTMcIch7stNdMioKUNXprAuKfYD6CVh5HPwlYb6JIOVEj5+M8Gucc7hOn/nL/g5jZNAH8csly/U8mwhQaL2FFuCmLvs63ek5jt/+bXQqcPQJ7q8DKOezAjRURVk13vFo6JSrxSBSBXYIHjmy62mocCxzJtdZD1xAAvVkkiCDQ9VZOriUtRfT9FFc0teTkaWkQY4zeR+GvJBiyas0kp8haYVtFRuTAwtEsR2Zt0exL3I/F9DxBAV3GryRtbW9SazKuh4zUl6vBoRmS+0JUx7UBB95MV0n1vLdXEYvEoR4h4lp+6iPkswJCOnfieGYcnzrnTfa0sAMKQ/kEcCXMqRxmpHJYPPePxwnexXrIa8aKzAtgD7Wn64jr3SZ9z5zC4e2T/KSv3kkvGSmEYv90KgjULlgcOpNyHxDXHolW1vEuJNoGE1SXAVuBU7t+hIrmx4EPOH8w9zi4oIm+/aHfBKPBhbdIcFjGd2+n+3f/SexO42R93YGUDFRwV4ClU+/Q4ziZkj3vvbsK2LmSLwnu/othgnO5Dqrgcuv9V1P24S+0DH5VmJRPuhydZU6GXiitI2sIbmLUYMLKI56SnaHqtM7TR1UnsTKPS0GyJhShtUG8k4ReT+KDmkfpeQneiDxNck3WDF4WwjmJQ8mD0TJe1s8bptCmDhpF/UN4WANk+vHVFmJX8zBEdhk+1QCy9bEELO4SnyXNjLE+3ECSUxN6swjHnEQlJP49HB1VHgLJ4cw4sJQqkz0I0gGZs1WVxkUp8QNx2fH+eD3XBbD5NmnZokHx36NN//lm5hcFtc24k8ZMKPcyfV83j+LjYawD1Z1WrFdC6fzeETE1PbtHtFLBcTL5gW5E2jzT4GQpHVK9eyoWSafJlRhqWiS8Zu0q6wUKvk1sj59KqYwP5v0V9Jw4l9fXLMQAffpr7MeuDy5IxX9yeQZXQ4IKAabAjMRWQSgkooitEPJPGJE7QEu4bV+SESlQpksJhLFaI73hDlJuUT/FfOYDvN0ZNfRLmcvLRIowLSqbkScNmvVhwzoFmtLlwwercdTrKwEBJKYVRh2aranJgMxp1i1SCCD1uZ6SexLymgJ2xFz38+D3abxndvZ5jgCrQkfjQ0nHtYu1NmUJ50hqSeTouLknslhGmtZnOiV/tk8OMaZ6nwXd96wgW4dN4obA5Xlxtv63HdJjwenO8x3LayDDcfgxCSMDKA7hJOTDZ3R28AZBuOH4oAX8JDVP92g0jS6VdaYxVZdjiqKf8FmK3QyG9MpdSLhAI3ysIwGOexD+4QwhCPnjRZPkuPm9gB7+uusBy6nvidGYfLvwsUHuYraG/TXsj/SrLoYz7J870/znHxfg4jkLWW1BTJpItLx6gELiYGl3SRGxRXBLOl4oDSCJYuYyeeXTlvypvKlRdY1AVvFnfLRiiNVhQBV68XCyFXd13ifQFBNKqktVVuksqm4ixlNieJInDGDtQ9/xocjJa2Lx8O22KD3UK2AHUA904q/1d54AfEu6wbPZu9O9cgAHc/M3oav7+5wcL0FC6csjI7DwgQMO9AbwqkJx8jkI8H7bNI3CTAEh8+5pjOjClnwMWy7MsAUteyifsxx8TfmME0d6q+pqMwIztcYY1PowHptSw0SZqDHLt7EsFcxOTdgy4FFnrjsHFUpenDJErs8OTPwOquBS6yykw5JLtNqnlZdpI7rU/Dc59R96bCpueNgMtF2Zk2wajEPYXKFWGgoDUyFHWjQIoNLkXGVdxETE9uRwxFUWk6tVlvAiAGtYgsC5MlFuqc4Gq1gZMIgFSBZS/LKIQBkCUplbesW5PZcL96SV8mEmUl9KEaVRF/FmNoOGhJ4+ZhvXUcmwrw01gAAuUJJREFUlzcdDOxa7TRU84WDeR/aeLoLIzZkynqoTQD/zinonTQszEIhQSng9hXlth21WJHy3hg+ct0owx7ZIQBwKALicBS8+ITz4ziWMltEvGFUZOt0ASkBGClVALryuDctRnqq4ZCRpUVqM+Alf/kg4ysuLMkOpBOT+68hNHrXxsaOld81YC1/8qPXMT8zws6HjvH8jz3Gn+yYwVDRH5uk7uqDOPTRZYaS0Z3+OqsPhP25++cYmZnO41+LCnLJABH0kcp3qg1kVmwZURYblhXIpcFeTl7pSp4pYjzWhHbVJ9QXbESU82aNjg2lqClgKnoiTziYpquYkbBHp0iKiWIeWfdm6himC90+yUmBpF81uYxJjFPxCEsV9yhpPKm8po3bqj5TEaX8stChnIPiVfmkobQ9nAnAQEXYMiTAH/+EeXmZJMTbq+jEdJvpyUYAuALbz5OixbN1ZsAe32OTgVEJW5tijzUQFjAUEPvI9JIuUtL3wXB5+ovwyKWwMJMnB2tIrpVkInI1jM38JcbO4fFYE5TcTq1cBlYlQCawEMQ5myq4tKUyeCo6XHjvCV71R/eCrXPHcQTna6mxWgyhS1QKSnml0iVLsaB1aMi//f5v49HLd2PoxZzIimUItzy/yE/P3PKP+0DY5XVgRqGKs2XaYUE5fvDkVSTpZJEJWAESU87ykDu9Xq0Tpa/VLrnj7CzKdS/hABuV1GIgbSCZIEAegAl0bJmWSwMnT3q+ye8YCC5jvMq6ZqERVDwBsJKbE5PBzhhoxgM70Hv3GrUCKPVgPDR1Lou10HiSKYfcE5CSLVMJ3BQrNHFS8Y7skFFPCsK4pE6cqmNhcA3QJftK8wQ3OPEMCwHTZPSrJ5vI4PQElG7XOW+eMHYPHO1hPRwHJsdgfBQOHSftU52egKlx0onRqDZO7RDLmNi8h69fH+uwznlN/tikrWIeV+Zeg/eOqruXkelPJst1p8wcbFQqhj4VUDOYSTTxbnhu6SCHcgxZYmAWgqhnCdRS2qAiV6YoI6tIPSux2cj1mRTLPhbeGui51AEDS+wDXfJmbB9zr4Hx9NdZDVw0sX7UcrhYsCcGEDtAGvQJIWL9eoUVitHIcy0mJQCrTtPZoWRbBHtCmUVDpHm2RfUNEW9kwHqzOm+aTbVZpaziCRuSekjuqRWrkH1tUmRZPU3sVKqhXCxKZ+4JW/WQ3GUn1iogoQabrP4T+7oUenkBTh2DzeeTGa8PvrrqQTj0NrnykXJXpF6rt1QlViMTgQLgVFUyuOSGVLywCwE6GYcxbNjCZWhi2T2w1Pes1Kq/OVgYwEqT4/d1ADILzM/Bxg2hjA/8dTAfuejbQxncckxHMEXKocXo+OlMOD+xGe5gZf7FjE59AmeGGGCCITP02c8ksrqXa6AmG33m03kMhlv+6iHG5weMnxrm2ae9JC7L4FUcWGm51ysKrmaKxJx9UYZnfeYepucWufvZl8UAelVRn3n51NdZD1zJ4lwxWRkkwmCEVaRLd2CfxfPC75u8r2doclO69sCw5ZiQB9J28m5hXqDbV15pjTbtbiblQQFK8qKg09dpKAaXVk1beUp11ZrsUtcVEVpEF+W3TEBZ0vWwyrVQexFDmISLNm91HLjJRKQOYpFTEgqGYK8lDFhAK8ZlxBRIwutVTAEcEd/0hKT6QVFwBVJIfQmD8jCsTbbZi3moGxhmF1VhD+sgJLlcw6kVoAvbZh9leMTy5G3nMXUD2WxDdHWRUbfzI10gpDlO09/FsHMlZuxrYIY0OIY41rHIESos8VSgwSjm8MWs33En86YbLbMaRpo+V3yp5vz7jjO+WKsOoRKWhq587PRxsEn9C9Lr+k4No2bHeG0+cJyj29YjM4a2nDeoSn6a66wGLqeYTRobbYCSDiHPC5TInQEySBSuU1xum4QDnoLBQB504UeetNrkKA0Gu/qZbjIB2+LdyGJMB/TKtHRmq5W/El59F5ACMkuJrMyp97z+ImUw6l3dSduLWQoMks5Q5UWv8lUdmNoA9VJOT+vU3LKK1+ZkTTyrUduMpXxJ2ToUYqm4GTLtfLb1Xbr8ceylyaMO8epy+5ieZC7pD30A+kWxx+rB8SVgFG5+/qPM39Xh7/72PLZdQXLLI6Au7rn0KqqYsViVZ2O69BeeS6faB3aZQX8Rt7TMhX6e45vGmJwb0B0YWOpS3XsBu7sfY7+ZYAWo6LNueJIXfWgBk9z6xkw4j7hQ6Y+OsjA1Q38QbFZ6Y/OM9/uML7ZWN5JBoBqAeiwmBX7oBMH+S1aO6hg0fD+T6+wGLhTzEcWv7pyUs73Mgmn8RffgordKegkZEC7OfMrFiFiA6yXyxHJ8eU+U8sUkpliPhHcmj7m8LqTYkCvfTdtr5LYGb5uDggIR9Uxsp6Tskn+tjxPRzBsKX2NAtiFrMnvyakU0pd3ON4FJmfgsHSAiK4UKPK0NftPyNhcYrkTrnx5UIySvEFYOtB1RE8gQ7CgFuEv7S0Unn2ly8InN4SX/iT0K+1OMKonHVWbtKW5LXnnWrHERPrPyEtgJ234idhtVB6nhs6ebRHJSQSQPNtTZ8Mjr8Q1cft9tfOen/xBT1/R/+Vxu/LMDbHykHxP+EtyOanATO7EvTjlPHdd6fO14+Mor+bvv/Gd87c514OHSq/4HN991Dzf93RwyOxVmREnHRW6MRoGc93hxnwKUZzTZcqw8xXVWA5fMhklUlFlQdaLUkeOftEsatFHHIROGXlnzhrxMrpb1jW2ZCkj0vqWTUrqg4l4VB6XqkE61mACWMQGIrbjKiWxLfH+lQVjleyqpLCLHTEiYQvwyrXdkoKiBp4qZC2QicEDarlSApV7JVW3ipV7je2kg+lxPngDOiWHEtrIRqJpBbgcMDE8RXGItxnxZsOMRJDU71INfAM3lfHo12cuZF6nfyNYePVHF+mpcXhRJTE5WOlV7JvBWcQhJEQeHmAh6XvVpk8Ong3hUcWzM7z2XPof9G8/nJ9/7f/PyX3siOGa1a7mKlhkzRuZSrecO0Xg+9s9/gDu2P4vHvj4Tzsg08MA9P8jD1vOel/uQbwu/8PlfZefS/thhYzzpyPeYpKQRV3gcDtsyaDXqBKenu85q4EoGjRHAVpXal3VnoHC34mSlLAaXVbfCaLFHmuHSyb+qg+q9awI0krbuI0kcsRk8pRPrTi0d2asO22R/bkGMkDwaFbdmd8I2JSsug5hrYl9uXeL4UEBLi5np0AbZwiPApmdpQxajZLBrnZKMi1hvaQDqjq3rSvRdChAb5WlVFhRkXDo5Ns6G/NbzUHXBjoR4xZ7N9qRQqn5P5XgSKHnVDrrtpE8J6AsGkL8nq3mvxqvun4oxif6w2K3RakfJr4O0E0IYIxb8ICwCNcOKQ9Umfv9lv8CPfOKdVC5unylseWIF634qnjU9eDvG4hU/ze9v9Bxym1g4OEk9MKm/u6aTLCS8J0gll/dhzwAOxU2sYrXriWzLQcci+7W6bjMTg2ey0vsKLeUYOWNPfZ3VwCVXwQj0QDEUfstPa7GmBno7iJ4tRdeS7LwkKTXJtCPRq4Q6qlWuY9RfyrumIOQ0i7hRYCthZACZtQdAUV8CwJqJtSvAq3Kcpl8lC3s1yCV+o8JoZpXAQzGIVOQW8EtcGqz1vkEN5FJGV6v4XP70kEwUsITFAJ2OiQOSsl6LuCIzSnnRbaCALwGbUexL6jKC96p+1y67SV0vs1Mpe9zQLosWtemyd8sFfOLK7+TGRz7N7OJRVbmeUuaMn5uvZNDZxsHuBHdN9VjZfgEPTsPQAct5wvJQLMRI231i/MVccOkC554zz/kPfgX6orSM6YpIioGJCTasLHLF/fdwxzOa2GS54/lvBR2XvhKYFCObkiUnZFjNKrwPJilOsQpgTeVzGozCKuI9mWglaXk99UE1cHXYNFPHMCnuFqMxkLf8+NZ7kk9RWqNIeBwkRsqtwUMGmR5AXuUXVUYFGhJfUVetd0XU0YO/KHcrb8XWLN8Kp/KW8qG9OegJK4rj3hF0Vy3QETA3hmR/VgCybbWZgL0v26boUq4MV0xYGsiKwqyJUWUYDdSGpJcT8HURyIzJTNobw6cvezUzSyfYceKJEIk1bFzwjA33Y1lhiTH2N9u5cGqeE5ecx7HqBu7t7uQj64JNWSMkrDU5pPJL1hx8yryUR7bDVZ0DmLkVztt7BLNxOQDYwDNwI3z5wXu5/lnb6U7sYEPTUD38Bb74jKuwxTRqol3a019nNXAVAzh2EhvpsxZlvCW7EtarZ/KcVieERN2bOirx5b4neZ1c5aer1dFkcIhNVqDlJODTmKhFClEKJ/9cUlijxqbSackCRfJoQPm+BtPCmFaLvnEAJJEpGtS6Yc4Xqryp3nxuB/HMQPueTBaSpgYoKPdDyhd1uG7yiLFGPGKcmhYJZPXQkdwGFezNURxjKP0hhbEU0kpaHIhpONXuum5dbFtR86T+oMelLz+lbpLHCz05mtZCU0w3ARVkgI3p2Bhn3Q/j4IPP+IF0DqjtwGvvhov2/wqjgz083FzAf1j8V/zB9bfz2Zvv4M6jF/LkkZ3h7A+TzcmIbVhsu5L2r3NbP7EHDnS38pUL38C/O3YEXvkQ7HkQDllO9s/lJb/4Wzz6zh9l/b5nY58Ygv9y7MsScSjQP8hJ1u985zt53/vex/3338/Y2Bg33XQTv/7rv84ll1ySwnjv+aVf+iX+4A/+gBMnTnDjjTfyu7/7u1xxxRUpTL/f5+1vfzt/+qd/yvLyMi9+8Yv5vd/7Pc4555y1kn3qK3Fo8gynPotBE72I2Ag+piK5tvEu6pJiZxAdjCiEk2hoSF4GEjhFpiazUaEDMrFjCqDFlUsn+Y2PBBATI9BK31S34V4qa2uaLp7ZbLWeHNH5UHbbyfHpVUVDqANjgqmCE5dOGuBanhwSkxNWI6DeNrOQQayATdI0VVC4J8YgAB9BHAUw2JYI3QQgElA1kBYWiN8NJIt12Q2RTCkEGKXdJG/yXUkuKR2fiwOk04O00rzt2loYUgHSslNAgVUqmoihTa7jNBHEtneSt9hv5NPKZBnrz8VT1//qIuCid6R9qZuM4V92noe7/7mpE4lIW0uZFBAn9ulzf0rt1cD504/xU8/5fRae7XHG09kRzr6e5kGOfc+/xFjPW391E7NH+rztu0OtuGgYC8HFjafPmVz26YPk69Zbb+VNb3oTX/jCF/j4xz9OXdfccsstLC4upjC/8Ru/wW/91m/xX/7Lf+GOO+5g69atvPSlL+XUqVMpzFve8hbe//738973vpfbbruNhYUFXvWqV9FoLfQZXK4KncnFhsCGTx/FJQ/Zilv9Jb2XD4DTCP2u1Hu+9T5ka2phMoa0P86bCEpR+e5M/hMgkfidhIv5kePWU96jdbg450vvVqocVpUdoFuWHUJ450MeTTw9q6nCeBm6AHTOxz+b6xATnjUuAI/UrwwWH0G2qUP9pRVvn+va+ayXFWW8I6+8AyUTqdRqWyyDg2RN7qoovugy+zxg5S8pz2Me3TDmUerF5PrRAJra3MXwdWY3qGc6rWSMK/1H/hr1KY4cIG24dk2oRxf3NIpZhb5HH3yfsH1JwMuE582Q5HsNp4A8lsuIg8q4jSpNzoCPg8THjt11hrHa0qkNtgl2psYHxiZ5t8S0hrnskl/fkI7uMy7sn3TWR5A2NMbijMdUhj8wz+afvOPTvPnNfwcGamoaGmqGDBlQM6A5Q39c/39tsj5y5AibN2/m1ltv5fnPfz7ee7Zv385b3vIWfvZnfxYI7GrLli38+q//Om984xuZm5tj06ZN/PEf/zGvf/3rAThw4ADnnnsuH/7wh3nZy172tOnKJusf2xc2WQNoMSzpg9ZQOglgWT2TOzVLyqwp4XOLJ/QyOv4YZ1OTfVXFvwQgeiaNv7UIUZg3SP6ls6lDlbUYYzs5TiliGshyU1iSzc8SU1IzZqoTeQeVtq4/TTPUpyxKCVslApUMrMSuFLvR21gKEQRK8VY9K/Rqrb90ST1JoKg20E4Zdfj0u/1AX+q+3paVRGqJXzNJYUztOIXBxrgKh5I+h0lMU5XJS31IdhXgtsU4HDQrGfi0biJN4DZPJgM1ydYqOtcQTmBDbV+TfiaM0QUmv350jmvP+RrfdcUHMcZz/9x1NK7H1eu/gPWG3zn8o+xwi8zOrTCc9zx0wwFk+5FU9HC+zztmfuAfdpP13NwcALOzswA89thjHDx4kFtuuSWFGRkZ4eabb+b222/njW98I3feeSfD4bAIs337dq688kpuv/32MwIuuRI7kUax5AODdTjUgCXMDknUg1LvI7RcAxZrdHbT6my6Q6PuGRWNgFlkE9qeyqv8yIBLjgQdeROxKnvODEkUKsRmZdGdRCMZKDqfcVVK9/vCDfRTAAvt8smniJcq0mIFUFeTUXUaw63lhNGrd/WuE41ThSmLlEP/mdXp+/YXQxEHKs20PUyAy5Wv6PKvWsCTiaeVjhNPH5BAzas4k52Zrj+f1Q20yixpJfdLajIruqcLWw+1/aueJAJaqb4lE5FOTyYe4OTKDJ97/Dlce97DgOP+ueuo3QhT3TmsN1Qnz+XxZozHgc66ZWaPexbWP4k3jnXHu0wudHhs3ZmJiv+vgct7z1vf+lae97znceWVVwJw8OBBALZs2VKE3bJlC0888UQK0+v1WL9+/aow8n776vf79Pu5QPPz8+FLHJBJ7pcB7sHjgxhnTbE9Js0WhmQTVTjvi2GKzuZzRxJj0CQW+dwxnO6sJnfGNKMq8ND74LQpS3JvI/F0yTOmEuUcGRSSZwqfy2kgb3iWclvCuQxK32Pi+zJri4Gqh9Lq3+S+W6yqx/z7lnV9GryoQS7MABLLTXqtIRkEJX0oJpxW9OmLuH9Jz2XyEmCRvAsASJHas5H63p6DUluT41prMUbiTeKbZsANRaQapIs41kgn5T/GmfqTb8Wry6HoqPfZtKoAKKAj6cX+MvShPdJEEfWK1kZbOjVBiZjrrejWuvzN/h8uJocP7v2R1F+lzrv9ES6491rueu7f4CvH7kdGueT+Sfa99Bhncv2/Bq6f/Mmf5O677+a2225b9cy0DIK896vuta+nCvPOd76TX/qlX1r9QPajCVUfknRSqwaLnE4Tt/Y0sdJliVsGXlvpjCevcKkZF0/SYUgjVb1ydpOwXrvAiToaPNn/VMwijnygaSyfH7buSTzS2UQEkvh0GLkMWTcRD5dIA8QT3MI0eSFC/Kz7mCfTiqZNSNLCBAT2pkBLFjmknpOOXSvipdwq707YIZTiYqtoMrgqNUiND6qAxEjqvFPBStltCX7tMqXBKQVTIppO27mwkFHYe6m61dvP2ixYi266fFJ/jaS/KnOSePlu8QyyuUdseyflkn6pWZ4Les8+BCkmHvKS6rPJk2VqJ93uDsbG4fzzwtgSkJR6FHWCqAtG3SledOSP+LQ/TB/HZ67fyN6du/jRd53HT/P01zelnJfrzW9+Mx/84Af51Kc+VawEbt26FWAVczp8+HBiYVu3bmUwGHDixInThmlfP/dzP8fc3Fz627t3b3jQUqZn75o+gZYxhE2sonzuEg8ciOGlA1cBYBLzlc6tR6pSwIpiMokuNipdTVZ0i494Udz7SnUWMTsQ5iMdSYAsNnDy+GHLMiD5X2MgapYkSmIMwYOCKPxFkS3hIpin+JV1fdKJaMaiABzIIomwyvZzk0ELouJcBpNaUMBGYBXRuSWmalYjadkIenqhoenkQb3+IJx3T8hjPYhbhqKZR1LIxz6kV1oFNMVuSp4V0p5VYVwGCWIdtZX2SSaTiSQakBZ9y+eyacaU9EqubIdV871kPk7g2iZR6+doQhsMBWSJhxBJu9SEU5RqBcZx8vPx+EjnlPI+MvYK1ECiBF4Twp+cGPKLr9zDSlVjMKyY4zyw8Wv85r/4NGdyfVPA5b3nJ3/yJ3nf+97HJz/5SXbv3l083717N1u3buXjH/94ujcYDLj11lu56aabALjuuuvodrtFmCeffJJ77703hWlfIyMjTE9PF39AIVakjqUbMdamiTKVF24bB2pRNjWYU4ducueQgZE6UExLtpgU7miENSiwkT9ny/tJpIlAJOBmdBj5k9VIBS5Jz2cjmxCAFDbXIQByXIFNcUVwkCwnQIs3ij1+qmihXsuBjBoMaVUvPn+qS+q0EaAwOQ/S18WltXh68JAZi9SRMI84SIUJCSgvzsDR7bkA3odJphlmkJI2l/dW/flylVSXvQA6z2oAVOkW77sy7XbdpHf1BFD+LNul9UOPDQOpPZ2LK8bkCVhWkL0PQCXecQWs0tmvEeDEuFgmkNlZ2L4li4OF95uYJ00eHbA4Ct6IZ7CaulphblJZ3T/F9U2Jim9605v4kz/5E/76r/+aqampxKxmZmYYGxvDGMNb3vIWfu3Xfo2LLrqIiy66iF/7tV9jfHyc7//+709hf+RHfoS3ve1tbNiwgdnZWd7+9rdz1VVX8ZKXvOSbyU4ApGhsUupdhG7lzis9IzUi5FVEox7IwFGzUAFWMZKEUzKj6c6nRQOjPuUdKHRuOm2dfZ2fZFQrg9O03tVAI3lxrThU3kTEllk/2aYpHVR7xk/lXQOQUr20BqGR/KjstV/Uuix5yaDAU41UPQBXU8x8S38OxmAwmlng5AkYjsDKJEX7r1rdWzu7q+oGVJ1odtGOq/U95TFWTtFGGr2f7jpde+i0TL7vVdriUtyr7yKqptd9bsfUDjFv0m5jozA5GbNtVBN56DQrXPbg3/KNi15F0x1JEZR+8WV8/gNs+XnXu94FwAte8ILi/rvf/W7e8IY3APAzP/MzLC8v8xM/8RPJAPVjH/sYU1NTKfxv//Zv0+l0+N7v/d5kgPqe97yHqmrRoKe7ZBDHmjW6VysB3cSW87F3CDBpS28gg4lWfrrcmOX0FS8ZqAqcRMmZAEoPLEOw/bFlHGn1T5UtlUmxC1GEa4t0ybvz+X5akTRkFzZO5VGBgh+GmTeJh2J9roFD5Sl1ZHmfeK8pgRxKEFtzDEonV4Mr3RdjVd+qF4lXZn+tC2vls/AeEsWs2UOwsA5WJsj6tXY+Y5sWBqnCUOtcr6vKtgbgmPZzlf80eawl+5wOtNYA7DXDrAVoJovFoM6jbQOsBlMpk4wDcp1Up0KcIxuHTHQcp+oATNqTRne4zPO+/Ls8tPtF1N0RsCv47kkVMcgZRHkH+lNfZ/VhGT964iQjM1PR/iie2SazYbyMbKd3FCPDGDAD4iAN3iwtFDoEI0xEe2dorVKlTi3ftRgT9SymSxZDn+JKYKk+TUMW6yRdoejCAm2e+Uy04vQ2bAFyWq8SB3LYwmHw3kc2aUIdLhONeDw4j61tuVooDFXpcIgA54cKsLxqAk194gy9VjWkGV1edJT+0rwCX/ESYeN48nmWTyCKAnEVZwVp1a8AJNkhEd9Lk4NiY8JKtSmMbC0qyiFto8HItOa8NnBpZpwmZP1C67uu29Y9T5xEYl25uGjlXdhN0UTdVh3FQ6mP9NeQT4eSiaoT7cJEXxpXcjf9KZzsweu+71Ge8aJ5/uLgNUHkbgFhFls9/Zkvsbj1LxBFbybQhv58n/8889v/uA/LmF76FY7NDAhnlkwwya9grFftG74t8ss4s0DX3Mg4r8XEbQZLI/8Nu3I+vf7LQ8iGdJx72hEfZ33xC+UqwqEGkA0uCffTJWBnwXcJp77E7BQT8hozZtqqJB06Hv6rvRnoSLTYtvIrsPKd76O54R7CkQllTVgAZzn81p9hx5u7VOd/hsUnHuTov/vRwLqATa+CU8NP8NF/9UP8i8/tIVB6CuBJA1PYVgNUEn8G9KJ4emAWBY4fAooK9JJxp/yWl6VuhFW2RLe031PCx3itzGtxNUzAP7nYieKzHnSFIbLWCUqe1mBKmvgX5TSrGUtRrxrM5LFmV2t91xXtW+/JwlAM7xzUdQCuRjNM12oX5cYo2dQNgX7Iv4mo4ZZgbhKo4QN/dR6f+KrnnH8aylIJ4xXQigksbP/f9CfvJXMsXVHBT9eZXGc1cB0bmwfTCSIgp1jid0JlmPL4JcdJvGkYchen2E8QGi2e/djeYVznISA2SpQH7PAcugvfkQaF052lAz6alXk1K+sZM83kbe+Y5NnHqN94sugh4o3Nv0NAEiA2cVZuzBOcqv+Oxd96I2Pf+0dw/qNglggnqIQeaMhbWY01rHvTu3HbDd6coLN1iY3/6r9FsGnobTTMuOO86M+/jYWt7wIsoydfTHf5IrxdYH7ze4CKiUPfTmd5VzZcVEttSQfn1si/3BKWGGd4a4inyZTA0Zq4Vw1uiUtYUUrLKpYkDJj8TDPD9JowrjVEwNO6/JHLZcDSk5Mm0ImVty8t9q8BQon1KeaY6kD3S/2e1lfawLrqWLfFlimJT/osGayIeZbO0+mqPC5C9WXS3kjXWIZDVSUlHmUbMjPE2xqrMi9Uwxc19tTXWQ1c/coxkqZZR8MjmLU0p/HyzOOYD8ExWAy+6uOqk4QBrsDOngA/0doaE54bD3bwbeCDVzq9jUYPqALsrOpjMtBW5S90zqbay7C6D4vnZR9+nC/etI0T60fjOwZjfJrpHUcYmgdoLvo4zSX3Y8eXg8hMs2Y3MAZGLn6cdO7eGPQufYRwaFSgmV1g65ZtNDyKB/rNBM3IHrxdYTj+MGBZXv8FqvFHpGLwEW06/fPpruym6q/RDAJmJjM3YT6pDoWVKP2WXqHyZIBaxeh0RaLYqGI5hXEvOZxeFTZrVJxuK9O+afLP5PrGrs7SmQzJVSyrVZ7CbmtNFFQA7vNvwR/NflIaqj2cz2rfZAIxAPOIqhcPrIA5rvqzzi/lb2HAxsCVh8ZZXB7jwc1tC/mnKVTrOquBy6waFYrPt3qV/B8ww2MxCmN8FKvCSb8e8NURBhMfRCitvG/ilNpZ2olx42Fmq8KTpE+KCbquzJgeY2bwpoO3x8rOE6dhAT1nPIPOV+j3Pgqu4YqvfJE7rr2SxV5Y3HBYTGEqHfI99k/+GjnmSc7Sk2nZrPqnL09e1fGq5kL9NDgGk3fS58sqTcfKui/GWkk1hgd6SzcxOt9l5JSht7ydTEMVg9TNFe+ngR2bMHmWWAO4CkYWwc0Ko/K5bvW4Nyp8Aq940zalzgtPdiutmimladRvmawEvBST04kXw9EXj0pQE1Bu5aeoQ1NEsypemRQN5XsWcMcJ6ofR8Mwdj/Unybi4DSjqfL0HFsDe3c4oJYslT0LFqrIF4xo2zT/Jkxth9zHPvBvhwc39WDTtc15O437666wGLsiAFOBl7XktD9fQA2wavD7Fod816r32L4jgsu33ohZJwnSLOCK7JjSJp1p5BabZwmDi3YjHbQEXGfiOhgaHnEBcW/i3//a5MXUBmDo+rxLgWKrU8PlyRZ51fQU4s+oNh0snrWT49mlVwsX7NpamSeUKJc6rF8Px2xiO38b85h477vs1hHoIU6oqsZUKU4eIjOJOR4OUhSy2RTBJZ2QqEabYE6iroKX/0qKTFv2cxEsGwsKcJFbcKqYloBR1fEUFxx0aBgrbLVSUkhc5GEPrD318lM6kpHzXp/9yE3mVr2TSIAwS6BhY+jjY3dC7BnwNy39JOTnY3HctQcSTMxoa6d4m123a7C8Z05O3B5xjYuUUP/DZ/8gv/5jnfbuNiiRoYqUP+m8V4NJcQpiRDKUMZJABSjOOYbxnY0zgGGDoYIqaz3GUvEt4WJVAJLerSTEL5AxHPhYBysZ3pWv4BBQ29mKfoE3Yky8a1aT3NPyG4z0tVQFhEiKcW9yhYajqIJ9r12OMIcu4CIxSXq1A9VH8RKXnVVl8zEEY5UP2X/4zqQZ1fjyw6cA/Z3LuuvA7+kVDMRXZ/yZAlfq6LAJEVuHjRuIUtzAzAQk96ONgS/sXrRp0Fs59BLoDePgKlWmFFB5K5bpctmRkbgD3/AZc+iYYnYW5B+CxP0tBE+iMrINL3wx3/3twy7D5Jpi9Bh76r2UaxgTvvAMHl/1LGJnNjEyy9/hfwtx9pG1s4ukhqlnxQDf+Nkdg8OX4PbJPr8NLWwjgx/jSyrVqDw1aS0/AN34TLvvZHO7Cg/fy7V/5C+bP+3Wa6v8hLF1Lrlwso0193pU1e9rrrAauDCChlxl1T5pVE1FTvNtRv8M3S3dVqDY/Lp/KgBZAykxIUnfyzVCAgAaXLHLlZstcSdzZatDSwCrlC3lwEYwMYUSv+/xPsHjJR2g27CGIyJ0k2Zg0jBxiByKgG869y2UjlYr0XE8HKNAKOxRc+q5B3cVyHdv815zY8LFUF87D5kOvZ3Tx/CT6WZnZI3sQ27vkDFBs7ISB0RrQwnIk25ZCXC3EOgtP7sq/k1Gsh1MPwuFPUbA2ibJqdRGpTV/Dnj8N4NusAMrtkeic+ifh/ncRDo01MH83LD0UfGJZEy31TX6nA+z9X6SVT72XsJmHjhiSNnFVT7IVWZPUQTKoVoMi6eY0wyyoYRmfAJrUcyq7HPMX6/eJTRfxFzf9BLfsDVOnSaAVGHfoGw2ZaX0LABcQB1gGDD3gc7vou5zxt7IKA++WwWoVeGT2pFmehNZD1ql7q9PIT0JjZsvisgyr3zLI0U4+yjdjA893fXmSL57aQrXv23DHT2Cx2ZOCGuCempULP0oyfUilkJRzfuWpzosucb5yXWQGK8thhrp7ArrHizdPzn6Sauou7HCaTcdeEgBMDTi9dUSMSoVFCWMTNtOab7Lo1xbpCOBybPvH6X/pQvx9u+l0YcuL4OCt4AfQPwIrB0N62owgZivYQUkaMW4DrByJWdHlUMnTQP9QYFMeqBehiT45xaaqAEUP/aOk/YwJjzUw6TwoBiu/9SUM0mo2BeXBHia/62NZ01gy+Z73wOajcMvtHJnNumS85/hm6M1AXfURDq+bp61jPZPrrAeuNkwZ1SPNacOW39cKk7+vPUQp0tH6tcz02qJqno8hC2HCa8p0hOlolsOqkFISn9LrLWylM5hmagUueXCGu7ZXcPTK9EYTZ9xGgRemZjj7EIYGN3EEN3qSuOaa9HQ6DVPkh1QCAc2srS6n9XbOfREDLEzfhcPTGcwy0T8X5w02LpHZZoyR5R0sjz/M6NJ5GN+LUTcsTT4a7PdieZwH48K5fyNL5+DMgLozx+jCLpanHwbjilr11nD85F6a/VsZOzhkw+wpLlg8zIN3XEh/sZNr+TRjq73lK7G9+EM8kKbSKpDQ90VJLr9lEUIMX8U5Y5GFWH3qkOgyXyrYqs6uwpnW87boLg1mVaaT+x4DbD6KufZezMtv5Tgmzg8Oi6cD3DErfbrCn5iiObaB6sL9OupvArbOcuAKdShMRw+l3CkCHXXpXvnZhqb8/uq0RJclYco5w0QtVcnabGROQGJPHkMHGOJTDoMpQmaHFT7mO4h32fBID/akT/Iemi6jrmHjIy9j4vCz8MAfnRPrQFF5WUGzJgqHHvAdJr7yf+E9rFz0twzO+WK0qxoEV7zxn8XRJBEyMzFSzrLezifw1iIlZCam+aWAWvi/7h1nz67foaaKyw4Vo0u7OeeJH+aJ83+X8x5+G72lzQA01RL7Ln0X3jQqPhNAGM+5D/4L+mOHOLnxi+z6xls5cNnv0dhhaj2ZQOp/+dOsv3ATl77xCC+95Gtc9pfv57Mj/xYG03Q8GGOYH4zm/mIyu3MusCY5uVzXROohijWB6p+a3UgbmXJdIL3n1Rmb6qHTcWoUUHGLKYXOUwoqQBTjsRLe5/wAGBuOR5TyJUDv9TG3fBbz8s+mu05JKAYbdafg+13qO69i+NGbGf3l38WNLoMRFqbVEU99ndVbfv753P/FyPRoHsCsbpTwe4hJcjVFOH2tvlcOuTxLy7woivbw1NOjZBzhyror0WMFcwav3nRpVUUr4vPO+SwGa2YZSlkNe1z4qd/mzXv+I5/a8GLunXpGmPG1YSYkMwxnKPas+ZDJGC7c8NUycy/5N7FTybJCWAEaMky1ITkUJpYhyeIYAh0VshiKqTbFWtqnlIxaKbVF8DARVBTCv9FaNA2DUrhyPncm5yRNc20mE/P79i8MeO7emiOLs7zxL/89tS+nrMLflopAS6WJ7cS2EAa2ihFJcSAfMtK6kg2bAsLE4DRYeZJTSGFJHS1um1xbHZn/AOtzmdJKYyxzJ/Yb1HPbhf4v/iZ+5361GtyT6QCDLAzVQIf+77wB95Uro3jsmXrXL9KMLkYtl2cwv8IfzPz+0275OauB6wfn3khvenQV4KwFQHq4rx1GmnHNfojM43mecZE5hd8+DbScQn5flNQBhBxZlPTqiUCEi6d8SoqOQRqYIZehp21++NtZt+85OGPoDNYz1ZxipRplSC+EM7kMIorIDCsg5tUzmUqDAtpBdw4aWHzGhxhsvzfm0bRguGkBLmv+lfXQrnOjwFtKKX55csuJKKp3twVGWsVFiawq0DnxRS4yPJa9IH+XZx7PdN8zUkPjLccX18Obfg67MhomgNQTVl/WlP1Hwq3lbBcPu37mISYuW2Rl3xiP/sIlKh/xfQWsq/quAkLdQ2WBIy3GmMyeJJBV7+m4JazoEa26r1cXvQU3NUf9XR/Fv/jzWLqxDUSC8BgaOt4z+Dc/jXtyM34Q+mdlPRtunGP5O/4nw21P4L8J4DrLRcW17ZTav9siofxaG7z0r3bX06nqgWUxaQCW+fNqiPgU1rVCRZsdNchKISpzPkOHy9jEqQeeT+/wFXRXNqRZcNFO55UeeV1Skc4GyaYoHZtmyCe4eJmVLaysxwxh/IFvY2TvFZlqeFi89DMM1z0ZWZijSQ6bcpmcMuqR8pxO96cZk081qswriolDLgm1etOIx6V13jwlyfOqBX0U6ehJYn7EQPTEwsRx/JvfS7eZwH/5UgafviqLX8KAKEU3yU9h+R+bv7tuyLYfeQLvYfKqU3Rmaxj3nPPWx0GBa3/fKEfft4XtP7mHQ+/eQXOym2vBqD3eXgEOLcBi9Q6stcBOymFNZmwmBtbvaV1X89Lb8Zc8muott0EeeQ7L8Pg66PdSOrU3zN+3DvOSjmqLM+NRZzVwhascLKRfcgVtQxusVoNWGC6r50nf+pQ3xfjUnua5DCXdiD6ypQxcJaTm8sis5fFMH76UajAeo++wnk109t9A3Z/SRDElJrO4Hkzy20P2paSKrA0W9cxuDHSP7aZ7ZHeRTjM+T2d+M67TZ2X7/ZAMWDWEyJ+GiVzWDM4G3YqrjVdynQRmpQ1/tc5M0mhzOgFSASStb1ydrp5uinViA/7mr+AZxU8u4Jth6F3GUN19CX52Dj+9gLn7oqC4vu4bmP2bYdDFXbwH+4UrsecYRjedojs2oLO+ZvZFRxXDM1STNetfdBQfSwqwsmeMlSM9pl58jMWHx6gjcFkAE+B55RsTNMd6SVy0JphECEOyuQiFKJgWDrwK2/o0kACa+L4cRmwAW9UYW5oz6PX9IEvY4InC5y7rgKUTMFpnu8gzMz/9RwNc0W4pXiUoCXCthirWCKcHWXlJpw5DoAQtGbIZIEPDZLuoEpA6iDJbM7L8PA5+39Dtr2PH17+L8bldyXPqfAycHAtKjlsg1gYsXBRvorxiILs/ITOxlB/xG+Zz5ya6lJm474XgoJ44yvAl+/GMYHrgqj7OrERB167qwCr2WP7ojqiArsy2vKrr0NINVjvrhygqhhR8tEnLYny5p0BDqJ5sNIiGxRGBjczYxPSmzwrc8BX8DV/Jxji/9EbctffjLn6Czq/9cCjdj/0V1UdvgpNTuB/+a+xDP0P3BbD1mkNMbVyMHkX0RCZ5sinXDkN35zJb3/YYDZ6NP7ZH9ZKQ1wbHsf+0k5Uvz6Q896qwYEDf4pYslVErhZFJySqmjSvNUknWlkxNLxYI20rrxh667301Hs9w+6diu2WeWxjWxDSSfpUAZDIVSY84k+ssBy4RJda+QlV00O5dwn35LQcHxhNY10wB1YFDc9kkO+h4RdgTeyqlIE5pVuTBFYArxFDhqQl280nJhKHiGR//ZTwjOOVa2ZAtmeU7juL0ZD1akydXMliJXVSKj6iXEXERykMwPPk0GUPyW9aZ28jm9/08OBh5Hpzc9lGOjX4ksr3MczKryHUShwHZlYlRdZe3EQknC8N0RDGlPCHlXQahnjMPE+NczWJJMUoKJoJhzplV7wTA9GUtAj71vuG/+a8pr8P/+QspjfoHP5ju99/zCyxhGPn1N7DyuWuoNg+Y/e9fC2wkN1fqt6L5DLXjEJNeyXv4FhYrNv3UAeDJmONOHBkdlv56HYt/NIsTxmRy7oU5WWJ/wBQLBlX87jxYtWMBT/I1X3U0c/Vk+0NPlnSaOAmRTo4SA1uBq8yHz8yZ6FkNXKfXUsnMKTNuMocrnp9J8TMDkq7VU3FnkUWMUEV5LYBUbmIIyvm8wtkhq+Yl9w0b9tzI9ntfGw6wEEV77DCiEBUG5J2y/dFsCWVjowAqwbzLMx7SgTKe5plZOVbUz9IMbfL7gy/AaPVCNmy4ioMv+RyGT+PVtKGFRllFlbpsVF23hf61uXKeoTMDE7DL00ap3zSxlWSfZ2BzevUyh86+oQRoKywuxS0glveWKrIbPx1aLA0pGw7/1Hs5/BN/DiaEuY7viIYfma9k0HJJPM450UzRxpyVS0+hjB3GX7HC+IsOxZy2pRITGbth+D8naG4bDX7nVP8S31oCNtonmpzibf71ETh/QdV90MXmdgsTyTD2Q+tJZzoGxhXzQl5hfrrrrAaubDsUkDr7qxa6Lfv22lqTtYfC6isPM9mqYNI/2XojA6CTOuzaokn4lUFL8YbBBOfd8fqADbahu7KeqpkIolrMuIG8p0yXQIBJlrpFRJR3WmAmmSk8VMYNzkQdRLIyjx0sARUkmSHFpzS+4cTjHiOHN7H1E8/HNZfjHZy67LMs7bo3JuVTXeaZtgSqXD9SPG2mWw7QbKfXnqzENi7bwAWDSDGl0CKpXmjJ+dCt5FV6uj21qJfDaYYnsckTD2ODlCOP4UE+w06uYYx1BaAGy7mwYh24lV8Va9bCieYv1K2nG0CtB6Ynk6petZXJOPbuVy9inrsUcxRS9R+YwTw0glnfwI8eC2Ykf7oettX4mxdSnbNrSLd3JTCCb27jx7/6XP74qruYH+njHtrF4IMvCnWzXK7GNlFU9e99Fd0XfZ7O8+/gqSQofZ3VwJUbUBuY5i6l1zdOD1ZqCsG3nskCtgwmmdeCyJhn5/bAkzRLoSgPtLDiNnHoQnqnNmGbHtOHLw2liHqs5FbFlNEmpqUAzOtiwKoNuO3irtpjFj+tPNOMKs8NuSbbz3VVerD9LmN7t+DclpBer8+omWRk53H28hAGy7p7LwG6zF92EHvkYgabPw+2LupKR15aienaF0jQzE6bTRgVQ8msMh+zKUSZbmZkegP/6n5iWncEZlfvFSgHZly55BBHeIxRptBywQTnYRglb4UPDKsslRZzpX/lEFJbpX4xlzPprnbWsNMhwAUec3wRc94Aphv81SuAgwNd2FjD1cu4VDMWw3o6XITnFHNj52NNH8tj1HOTDO66NMCkeBU2FK6p60d2weUP4WhYPO04La+zGrj05ZJhYjmD5mrQXUtXjnSJ7CZGPxNlum5wgxYrhHk1qZPLcPKp44fuYZHO5+gszLDu4RuZPnhlOooMo1JvjQ+vvpj28zh5FsNKIVeynDeZlelj3YWuF0e9t8PoWvOtP8mXbAoODZKstScev5aR5YuYnnqIA8sLWCwbPn4zmEmWp+6ld98raK7cS7PpIIwMUntkizE9CbV2ByOtXOWE0eKfriRhKKh3rQpRrmj6pAMtefPqqzR70WuRXoUpJzXJd/ACcoSHEXMAUTdcwCZGGMEhh7HbVOWSwyCU5f6edV+lBi9ctvUpvwK7k/pLm7petIg2CQaDf/l8Sjvvk2hiiBlM9SLef4XDcCPmREV9rEvtYt+AtKDkbdhMjg36s8Y6lqk5fto6Lq+zHLiECUkXGGCSDiqHCV0ln/oQVpw00wIxDi1nyE78JgzLRlGjor0SFr53Ymdv4t0qdoroKsYH2u9wnP/ZH6O7vA5kD5qhPHLM5SFaSDs5yynZAuMUyLRPrtHO77QbkxRHfK69hIrv8hQJOYz89jFOyGxMQFJWNPsHpzj0Z9cy+uVr8Q4Oxjh6X9+JMTD1V2/j8E/9d5orvs6oqen6Ls449OnmmTvrVeKQQZkk5Jm0tNj1S9gMGUbVW7k5PgNPB1mllJSFk6iaiNNW3otTrlhq7mvTGxnfRQWRFxECL2sY4tNkl/eNGmnCFJM4NAr1kFe6cylNMXVmUBWnlB7jHcZLXTU4U4FxqjQuvqGliKzblYUmH/NhsSz9+bezeOtk6AMVwY25TKbSv4DJ9Y6FSViggn+I48n+z7sagiW6FhbaZg3SOauiu7Vn0RK05H4N9NArTLLqYamibytLcIfTi3OfWJMHG6yaZSp6GCrGju5m25e+N1Bm30lSqPhMT2YLcfCLlbNvVEPHIiZfSZCOu0/bd1QJhAUZiVc6jla4Q7avUSwrHZTgWCU6Fk7rYq0XW09Ks570e8u1cOSueJq0kl6MhU2/9wZOvehzLH3bl5j99z/FsX/3DrpdMSQlGrlWBXhl8a+OyQkjbhJXyC2uWU8IvdrWP0xYAoIZ+MIEJJAgOxxMzFN20SKx6NWxDJOlBZ8AT2leE3RMFfv5FLNcxQyXRlhsL3WEvu6olX6rzT4l/S56pTv8C8vLNZ7v+dj7ufHuL8VqMPziT/40CxPj8W2fajbrEwVg9fptyFOUKdJhxdpjbDrNSPW7H9/4/3DP9H7+iHWc6XVWA9f2L7yZ0cnJUBF2wIEb/kshz4jju7wNQSp9tQLQFFUh81mXzLZyx3I0sat2ihhMtCfKooujE+n99KPXM/X4NdB0s5+pnNVQBqdIlVGAoxhQYgmq8SEDSwm+IZzRY1IBjgYuuacPdDWQDwjVQTUbE9Znctoekp/xdAPSadTrL4eFPTCYp5CeTN1h4ovXMnr/RbDUY/q3f4rKwMJzv8DCTV+CYY8tv/0mrLMs3/IpBtfei40rs1nXIrkIfveNynDmCmLXkSc4eWuMDWzhyij4t7dtZfW2rtJH+DA7+DYWeZITPEBW2Of4swpBUtPCXl5EKu3bA2zKNCjCc2Y82Z5N7MlCGg0dehFKsq9bT0M2qjB0hzU/+af/FTxsOHmcbj2M7WX4sT97D3/94pfz8K7zkHHj0xQCmZe2hW9yDVmfVsBFPEy+umJftwbevffHmDt6O5YvFSufT3Wd1cA1Or+L0SbuZ7I1sw+/EmM8i5vvoT+9D5BmlX1/erbVHSvro0jUHUQhL00hs2ZwiCZHQROfZTFAz94ew8zj1zDx5OX0ljfmU4G0WEj+bN9e6xJm1HZxUoiM5FlObqZTb+Rem555hW8Sdo0MCTAVv0+X4XYegM44jG2B7nRQ2C4fysGq+Smq+Sm8gd4TuwAY7Q4xx9dDbek8ugvjDKO330C1fzuu47n65fdylzmBqPZLjC7ZlTAjMGxc6nDznmnef8lJZsxuKkboMcEoszRpuEsc4rZaQEieWTZzLeOcQ4dJukylwWwwnGIvyxyJsbSHuNzNcJa5TM533n6f2ZJReRo9upFmqk8zEmxXggCYIdbhqFzDLZ/7JEYdUV01Dbv2PZEmx4SbxnPuwf2MryyRhWN5WJZA14TUjJRBT3RehpZ+MUoA+5d3Yo82rL9/llO3LgK/t6qW2tdZDVxAGmnGdZh9+OVR7LIY18EYR39mD2IdE2aMSr9G7gpi8pDZVf7M20zCHTmhNVxZrJCFgdQDMDRM772GzsJGqCjZSxs4WqyqDUTFw/YDqQdFBTSgJCNU3wKZNiD5HCYNHUX1Jd6UlbXysVbZ5DN+760Pf66G4QLUC624FYMcffBCRh+4MFcrMHLvZXS/fhnD3oDzL3E8wD5WZDLqNDQ796f8q+pJLVgxxuxwPdcd2chHLrHMchEdxhDgy5/yPa9elkWybOKZOKDDOONsR3qEAWqWWeZoeiMbdlCE07NZnkQ9s/OLnLNwGAcc3jaKMYapE32863J4djOdpQkueHCRAxcZ+pscM27AxMrxXPC6i+l36C4tcctnPxFkAi3n61nIE61NY958MFcxSX+lFf65ZsT2qmT7uQETaJnV/cUb6O44xtTkOCNHrmbprkOcyXVWA5eJqxXS5qIwnn30Zcw++jJc9xRP3PxLYGu8UjSCrCISG8WRxUJhTZbMsLpk+yvxlZ0NTqXjZZVwUOJ7D8aNhL0X6rgtuVLjK1EwlU0+FYKthQlrAgQZeAQgTTuMAJpI0EYBkc/rAUbyp6Qf22hOGe6lE691dtbopMTw0m62B+svhWN3R0xyJHASezJdNbocHaDT73Hrb72cSWAiDox63RzHf+E/YbsGROFraixj1ATGvI4LaWYu4H98G5zLhXjlcrs0mWhXnBblsmbLkq3fDSLaST/KPuFCTAKhbfqdJ0EwdGvPC7/2MN99x30Mjee/vWkHHWP4ts8fZri8nv/27d/OpofO4Q23/ix/PvNCFjecx4uGT3LFo38Wl+wM/vg07B3HPLwn1X1CEOdjcXzwWSOWptFy2RiHTdwtlzrvW9Cioo+1o6zlrS88zorIqFecDTD5A59l/cXHqHAsXP1V+H2e9jqrgcvHfXaC6taCVRJgp55k96d+g8ee/w58b1ERcIMvFL2ecHpOF1keD/fC6kg2YNWsS6/g2Pg0N6XBYn2Pcz71A+BsefyVAIVMdMKC1MSXvigWkvo4oMZBAUZtgBJgURNg9s+lwjfRklk6WMHEJFxkXo0v895Sy6RhqAe+IXfWnLEYwMLG63IemxU4eg9UdQmeUhbrKTYAV46wiBDHYnV8mtm3/mtO/sa/54aTr2Njb5nljV/lIS5IQ06LOLLlRi+fmsihyNlCrNo1d2vQllf5M4hrns3cyATb2cenYrhiSkC7HJfcyF6K3/pfy1z05BB8Q6dj+LHfDOdYhrqf43ce+D0wBuNrfujJ9+Otic+82E+wXN1Ev3cD6/2/JiklxdmXeAUUlZ/z2Te18eAHiC87E/V2eUeC3solOwxC55Y6xpmwOKQW4FN/jG03vxUmejDPCl0813ABf8/TX2c1cEEp0sgxT0DUj5qwvYCwFpsXwA2h6NJi+oheyIYIouPKndUm3VaHPAg6MeY6AVt3YZb1d92CdzYv6StltQw0KJm6/q1fKQPEn751S1YnFTABaf+hRJGGjgLFSgBIhStGZAQrZwJbKkTOCGjF0V4RWLyOTxehFbesYGLDicmbnxWeL9wbxcgIelWT49auYooEYrtP/saP8uT3HeT4hYs4divQyJcp/KjJlCXiYKgtbRu1+nC7HDLfzUsFYqKQtyaJAWmY3PRG/Jd+rc/rbl9Olbj5VDBRwIBxPvQLMTc3hGfW8FeDd/Cs5gPsdPfmiiG05+jRTzPy6O0Us5awrtqR3EO0OtrvvOH7OLh5a6wjMQlpw7M8y1q3fCBzg7cuHVxM7AtGtbnxcP1VcGrSMyRgba32qD7VdVYDV5tdpJuyTcWEwbDx/tfgq5rl9Y9yavudMVi5zSN4HWivNpqiSfJqYVbqx5FC2MwtYGYwTYfO0oyalSjkL32wguS1aNhWGdO49Gs/Lwau+vNQ6LSK8Jq+t+PQ6bUYWjoCrJ22Tp/yPZ2+Vxkx6nvq81VQ4KvmyV8VyAv4rQIuwHuDOTLL4idh6a5Z7EzNxMv3K6CQOMXltr4ys2o1U/zMMCX8wxRvl6A2wjo2cz1HuIMfOjngK595Hg+PW5Zf8rX03shfPYfZRw9yzsodeTZOM45P9ZJUqGIvYz1XdG5lxh4uyg/AEzV2/zysaLpDrjA5/aMyq949MbuewYie3DO6GVU3WberIxA1ipoc1QRmgJERuOoZnhPXf45m8iQezyhdLmA7Z3Kd9cCV5gAZpLpx4giZ3n8j3kBnZT3D0ZMszz5I1kgJOZcX9Opj1npk8ELNunrmETbmGaHDmOmhoilYVeF+psVo9ABYA0fSTJXzTurXONVJvGI8pgQvv1bEPr97WkBSWS7K0ArTHh/6PYvaJ7nGaJfOLhF0ZsDX4Jcp/IdJ1C2cLycKYPD1WTBQbV9g9OXfoGKamjoqAcRbh2Sh3BrjW+27dnXk/ZO+dVfWL2cby5WDLgd8n5ctrDB50FLNbOBxfx4XDR/F7BsycmeHnXMd2BaVepUn+In2FNRWV2CcPy/tfg5OTsL4FEwtwLHYiPsdnFAKQ21o51udIrWBdCaZkKXVdKnbRhCmeJbqIcZf9FdlBjE7BfvOvZ+6uwJ4enQ4n1nO5Dq7gUuJC5GdliIIFIrniSOX013azN4bf5OmuxjDWdXxmghIcv5bXt2RS+uwSl/3eVTNNBNsqDdwQgz09UIOIY/Ok/0ihYiDqKQoe+pr0g9UuQoGEPV6baAodvPHd102Ai/cNbeKuQqTigcChqg4FGjpdxN2m8ySilnYkPau2cgoRIw2FiYvguUKlh8jHa1mKNMqh038NASRNm4xcfRZ5iEmeCbLLNKlxyjjCnZERBQAKtfu0zYYVRFSbLvGXTmYt+dqLl7Zy/cf+3DSO738lo+wZeJ6Pjl8BW87+i74wCJMvB8mfexCqlJtbEi9xaFDBp3YKYb3XQxzNd0rvgZf0oBjguK30YAUG6/xQc+l3Ih4A4sTY7hkoSxlCzph3QlKy3mZ6MN7btHgh+RVaunbMdrBsuO+u/s0r5M0wkjrcWbXWQ1c6VRiSL6okpmKyffSwDTQXdrIBZ/5NR56wc/iO33EfUhFRU0dIauKnbEXm62TIrX0qKK/I33lRXLDwT3nc+SR69miGZdmNJbkQVJ0QlZWHRXbsHHQpf6mQEiU4lqPZRV4icV7ZbIuNu1LlEm8zcLaQNe+R+sZefIQ310eBbamVSaXwTqVA5LjOtkBIG2ndXCgCIIo4uM9W4V3tZWRcySDV++habr02cqQ4wQjlYZlljHAeraiISmsEJYMCrQmzKdqsNHpYChuULZL/BbLa+Zu5YZT95AcqEXx75qFO7jm6Jfgs8swmA4rDp4gmVUuNFKnyFYEMcXAXPztPbdVz6Z7cJnnnfgqybFa3eTGE6Ym5g6Ca0OnzBE9i5Pj/MJbfhzZmhGChQGkPYL5VCd5M5LupIf+7UXUR3rplpE8SL5nT7H8zj/FGNGRrd5091TXWQ1cQKGLLC4fB0IULTwkpm2c4fzbfp5kjhVne9ODvZf8LsOxI4SF3Q7QQwxOgw2yzKgi/3ugwnjDdX/+eoZNh6buUhvDyS0hfieMKM46yYQjNqgRDy9SFgW+Tq2YaVohbKwwBFUDPwbL23YE6Fx+RnzeZmoCpo2Pc6zP4RKwkPMkdasnEg2AMuCdoZBYBNRkP6TJBLZgkmO7g8i4cJdElMHQEManfJfqMxaaqsxj2Ewjnr+CaYvHcpJjiW116DHBdBpKekO9DFsZtESIyhtxPDsHB/nB4x8KT2vDiBtgRCxLlRN1YiPA88fgwzUMFGUVlmUIQOM96QAp49XWSZNmp5vG/hAjYaUj6O20Qr/TWWOqEX2I974Ld/Pe17wcY6pUl/K/i6UUjxChPsRnRujMBjD9Los/fzHuWAcak4DLo4hEHGsY8TWWZRhZJnu666wGrnR6sW/N5PHyZPAqlu+NocM0piYZx1UAQ9iy57Uc/voKgxOGHc+1eG9xkfY3U4dYOuduQHAnuxgxGFgZozPshklQOG9kD0knpFhYioiQh7TnUJiGDHKfg0EEM9O651vl9+p9peMtOIQvg8uXtE9RQKXNzHQ4KHTIT3UlvJJxTJyEbflc4gaSXqvbhfUVHFczuPdx064wwxaT84TJIm8UFzOHHlqdXkdbPjAM8Swwj8czwVTKUNMSiYRfySxjgfM4xTX+MFPDpYzIayoU1e+eCaypkso0mVnp7RECUqkPye8ww4z4RUVJFQjKT3knJZ/D/uUrbmZhcpy5qWlOTYwjCxbymoVUX5k8y5OWXY53+FMdjFONQA6m7RIF7nLNtFnu6a+zGrgSQ5EKMqsel2KOgJcl7ZlKFLYK3yfmL2F6HoanYOJYiLchpFPX6/HjK/Rnoz1NjLAadNn8+AXg1bmNDYycgv56sm+tVj7S5VffWitoYcBJGace9MU9NQmv2tvYypb8bmOqBjX5TGPSt37Lu23QO11bCZDL5BIBqDiViDBDd7aBOdCKwpdAWIBW/HMG/HIX/8Vd9J61P6sVYgg9JD2OISt4PIO4xzUo8suh0saEy5Ye4zJ3lPOHh/JEsWrPFasbLtFuBTY6jOCDRnM9Y7dnIz0jOKl4nykjqFkhhHvgwl0cXb+O8rzKnL/syDG85FNhMvt0T4zh9o9i6tWDsFgTAKotJxm59rFW35Nt36cbCeV11gOXNq5cM0imRJk+R5ASuyEUkAFsuDwAj8RZxfe6/c1M7Xse/dnHEOW9HXSYPLqRS257PssuLx5XQ5g6DP0Zyoka9V06U5zM9WBfNUGS+7aNnVIDijFRJPVlUgVo+XwPHYbyvuTR64drhPUq7KrIRG+lUMbacBJzwixhSUbVv1FtpuMbh/piYH9r7MsgjWk4UcbHOhdWVs+N4f7kOjrnLWA3LGI6wqCCOFj6XQhgtswpALqMMu6mmF2JnSKKerU1nBwLb7/4+J2cMzwYO06b+qoKOh2hkJVCbe4Q6zE1rtDxNS89a3glEkpFgwa6pmNYnuxifAdvu5joESNUZTYDEjgXrmqKhsmNUN85Q/3xLcUCZMqptHHss92Ln2T8u7+Y6jnXupy88PTXWQ1caYVJZuq16INMTnpWJ1RiMsmS2SnuJYyWcFm5KwZ/JoBdx3dxcUvvtkcu5pLPP4+Bg1qkA0ncQLMEfoLUkbykLXTZqzzIb5138n0BEhMNbQXokugozEeASvLjV9eNxLUKvFrPUeE0w0pnBxoK3dYqKuIpxHRZcIDwmZiv1INKN4Ga6vhVEwmNxOEIbn+iDjExrJhObaJuyoDxhvlffgFTP/0Zql0n48KHGKDqCs8ZMVgaX9NdOcb//YllVWmGg1M9fusFW4AK9m7BjyxhNpwgdbgC5X1uMN0WjoDmaQXFKJZF3pJj1AtaOpPJT+ispCMW8LFv+cbHo6jD76PnTvKJ772MCbazzAT44H++iUCePUrkvZu6eV0yCWpy1cWsy7hJ1VqTRXZP7DzZeaecBNDBMPqtYIAqqy6JpajvhVW6dH4b3rHdUJFWn3LVUaChBlrRAA1UK2Ns+MY/Y+8ln+c5n72ETU9sZBDruuvDQAEYbjzAsVf/PtYa3JfehFvZklg7EJT0AoourFingapFSyXiCUh7HSaCgZPtTwq8tNgUCqTKRQQA+a7HhgJJXRdJ5eIzm3ICHOTxZPX76vIOTCeXIynj9eppomPld3lW6baJf4367V1gW2lBRJip6NEsLPynmxh9xcOMvvRxsqu+8CmTTkgmZPD6Iw3/7MFBnOgM4dQHz+aFIf/2w08CDRz7btzOW6lmP6YaThVeG/Ld/sywn+nKr8HfNyTvenp2kWN5Uj2chqoVdlcqrCEvPgGu6vCH23+XpteJuTM0H7ccw7DZWo5d9igLO44ip2Jp54thnTV7p5CGkUN3V377QtzeMUwHfJ2Lms4IlvHn4YKboXq24RjBn5kWRhycIWyd7cAllYJqVhn8GoSUsbtVomECAMVWTJ9iEkkrk4lJGEzdBTqcU1fM1pY9ZP2qAzYDkyc3cu/fvYGjuw0066GKeXRAlySKGgh7uSKQJaBSZjTpwAp5P4KFJ7MdPZEnpbliXWsRfA0UCSt9DlP41JJnrVVDqXtPZoXpXWFbwnLTsjurMqR1WvJFzn9M+TXQjbZ6TWRSuLz67xy4CuwobLzSM7j5UY7/zVbqJybyROGBQYf+bTsZ3LsZZx0zb74LYzu8eF/N5NDx0XMrfvzrwxBh5ZmuPSPeQNeGRu6FPmJrT7du+L37bqBeWcdz1ne4sW0qrq/UEPfDkw3M1zCIhUiMVTWaqvc1L9N61ma78W+uu5lPbHgj/d4k3qYaxkfP1MbDzBPnMHloC94EHnX0yoeYPLAVbx2nzj2Ii9RN79WUTPsVi69taN9omqJNkKyI7RaevAd66zzVlVrpnzdV1UycprDldVYDVz2yCHY6dEq5qUelyYNIFPAiLqaVPjUbhEjDdxlkiXAJW7EN85v2423Nw7uOsrWusPtnafA8duV+BtZwFMM4cMiNM/3gVhY2GMRbdPsAVyP5Iz8vJrsIYsKmnLhBJw/WBFpmNWgVYtdal4TR6cbvhXipx5FCP0+sS2WSUEhdLQZV6BwlGgVc+p2CgcWro96BliRloDsOoztgdBo6w3HmfcXAKOYV43PHx/HHxvHW0f/EeYzedITNyw0XnfDUjeOik3EwVeRZULwnpAQ9WM+u9Sf4/L4pvrz3Qip/M9d39sNlD6G3MtRNj/seeBGX8vd05+dgxcEyiXEC2QTCqwKlutQVrp4VlRZvWALAWsOhkfN5aPImDoxdumqFWANdd2GM7uJYkAAsTO7fwtixdfjKg/E4azm19ViYoYkGuQ7qT2+CxU6eeEzZx0VCSOz4gkdxu/bS1UUhW9vrefKprrMbuMaXgkmD6KjiAEhtE+VqrYT3FaX4KJdiNms1rvQnZ2uObX8Eg+PLFx/ivKbiGftmAThw/lFObhhQd4Ntim0M1/7FNL4aw1ubD5OQVW3VyG3GCCSGI2cbuoZgjdyN7CKqRhJoqbIn+y1dlDZIafCgzIdBmVhoNqfiK4xV1ZgrCINKZxUQmXxvTVzVtm3xqkQMFMZng2cLDHTGYGQzTO0EvKF7zzZGaxj2YNAnT0iqfg2W5Q9dSGfrkKOnKs49VPOMPcDG+SyqpQIIWoYCDnzFoaUJrtlygK8d3sZ9+69g4cC5bBv9Gtu3PIhRcn4ztDz2teu5yH2CbpppvdJP6YonW9Drneqp0k3Oyxp1SmWg6znZ2cKDUzdx9/Qr8mSm2kyy4FVdSyeY3rMjTeydxQm89SxsOR6Hh6Fb12w6Os8TH7oamqxQ98T20DekH3sYvelBRq5/rEUWPaP0mGacbwmf8956XNUEucHaVUvixhPkbjFuFLFwDZsvY8HPqz4iIqLEqRye+hpMZXEmGB4vx5Z61geewRe/4wFObDuFB+oKbv8n97H7vssZmxuNVssW5zxmBGw0JnRRg2xGQ7rU4dNXBtMH70xSQmNC+vUgl1VWvTVgJ/DQ7EZP2qrnJBG1Vb+rvC9IBxTdUQvNCmPPCDguKs4TYOg8KeBs6xeLrwJUTdBLdsnp9ywsR73W9MUwsjWzQONh03nQGYUjT2RW2K4DDyy8+zL+3MGfA1NjA95186egY7CVxyabKsh2VnBkZYJ3fuVmjKxSG9hvpvmPwxt454ffTcUQEytlhAGvMf96dSVDVqR735rJDAXaJDFPzSTGZHOJmD1HByrPpzb9KPt7lxWMVNd54WLIkI/Fa92X/HScDW58jGPziXne/Bdf4Kf98/IxvLGa5hbJyipD0oF27JCur6maBlfJAmpA6fPZzhXsYpkn16ig1ZfxftVa3P/x1/z8PDMzM/zAiV+nOz3KxIlzOP8rr8UNKCrbE9iJ7VCsIFZdSt1LbBg335q8DHq3D1gYDD2f/6DnnH9zB2bC4XwX5ypgCU8PZ13qJcGs0eK85Ub/BLP0+TDPxDGkwfEjH/ksvabmD175/LhpJBxeEFZbKvCWq993KcbboNOKk1HjoKkVcGlm1VpNdibuCtAFa4lNsqoqAOUhK/ebfA8fgUiBXtuWK9VdHIOaGSXdYqxbowBWK+pN6cq/tP9agZXPB6+pAthDDzMvCosuBrLdnEw0HgZLsOeugBHJIDXGmya1mG+Dp6o8poI3XX83N+54MohIjQ2J9YDGc+DEJL965/MLtm8sdKznWfMNr1r3DqY5QFIMFptHpRJU/5LMWqMc8JkCcLI4GcN21CqN98x3N/Inl/0HGIKjCoaiatFGAxGecJgFqi2kzrpBX5hwtPL4Gg7dDoMrD2GvPkHzmxfjot5LiugMHD9KYeoSY+T3J25iuneELz7ncv7s9S8B8ub0SzmXqziPxflTvGLm+czNzTE9Pc3prrOacVnbsGnP1azfczUQBkUyLVCKYK/umdiOlS/BCxv7FShFafyMfe3cw3dw1WP/k8ff8i8wY6E3WNNgKmjoEUi0bAqSM1C6eGO425wbD7sKR5VZDO9/3tXgGxqbT+AOW0gcMARf8eBLH2DHbbsYPT6eAMX5bDEu/dfHMhQsKpwwVYoB7VkWBRxOFdtEdiUTQLyXcFHp3YyhPBkbVq8SkllYYrJCJCTfalAlvWSMwwBmCHSCjhwbiGnjg/iYTEygMB0RttEbhV3XwON3qTpzudxiUhFEbkPjDKaB/33XJXzgvvOxnUiYPfzQZV/nwpkT2QZP1SdAZYe8fOxXmWiOkL0eSmYVNdZL31J+6YRaTheg6di8Wdp4hkxw5+L/w7UT/45vPL/HvtFrWHj0pTjXCZvpVVJIfbQWSApWG+tcwEtM0nwFJppL+D4Mv7yB5rF1uJmKykG1DIM6ZG2sipOTz8lM2ZP8/Og/ZaY6SuUarvnqg5z32JNojwgj516Ov/L5fPm8aziT66wGLnCsTB/kxE6P8RWbH3keMrq07VWyxNaNJX1FOp8ccKJ1Zb4cEKdGt/Lo9pvpbu+TWjmtjYSz8QJzCi85urE/WpYYBcJGConyxPQUcoZL6UTFRdHD01+3zLHLDzH90Cxj+2ay8bMqQ/IO4SjBmBbT4vT31/IpD7muUM8RiUZN9u0N0YU0I/8VdJYkXhVpSfWtAUbyvLMbmn1gT1IsOhRqH2k/BYQj47BlNxzbD4MV8jFqmtHl7GMcHF0c49jSWBqMBvjwo+exaWwLi8NeBnX1eu0st3afw0vtYSbNkAQ+ieX5srKEZWnW1a4zqWCxBxlfj918OdsGfZ7Y/53sWxjhcGc7g7Ed+GFZ/2UFl8kWv1H1IeEFzAnfmwloTnZhpcv48xzz45/ilR+5hyM7rmNu/Vau/9xfsjxCotvGwIhZZpe9L9XQxOIKEwsrAdRjBzi8foZ91TM4U0g6y4ELlmb3sDi7B+oemx9/Xm4saXRtCyPAZXMnBEJjLUeRy5EHky87/7GZbew/96XAI7GvhwiN2hLi4zQcVknCAbG6Y0uWwuRnItVW2uZVQ8Ezf/7xoFdb7NA5NpEeJ/DSg053TiljG7zaLIgszRSzsMkfntXRyMO0uVul69X3VnHKtKUsawFpZAgJ0CxQg90ediZ4G6XcOO7bddDOkzGwfgfMHYqmWOR3jKTZLl685+vcF+44uA1jAtMb6ZE8fUg+a9fhU72Xs4MnuJh7WceJ8MC2MtZOb3wCJqbD6sOJQ7BuY9BrSGVh07LqyvotHLvkClaOnmLp5PWszE1R+26xwqvr3pPBfNXl80Sv7f/y4gT8/9p77zhNjure+1vdT5g8s3lmg1arHFYSSEhCCQGGBRmBQDaIYIwcuBdjsGWEX9tgGxzhcm18/V5hG2NeEWwsbIxIIklIAmRFpEU5rLS72px3Jz+pu94/qk7V6Z5ZsfhyLY+Y2s+z8zzd1dUVf/U7p06dMlhW7Lmbriyj2TNEOthHb/9W9g5+iYv4Prv6tnNg0Rpe0vmkRxWZMaQCdadQs5/vcI0WjE8aOMJt1nMcuEQs87RJtksokAr6DH8pKOj9BZs6Wx0zlTqzHZ+yMJmEyNgag5PsPXELbvupXtB1wGWR0xQdEJkATLG32JATyZg26VPyGvpY95zJE/bTWdBk5KsnkSl9FNYboFpvUBurJg54DdyoDiyLFD5Owa2MIViiEx+NM7LYoflOH8QufNqZ6quSl5y4O8HHzXNItReDErMLz0nl+vdWV0BlOXGgmqJFfXg+V8maIrkpqJvk9Wqisrp+NBPJnd40qRDEzrDP0r/MJPDZ9n/n8uSzXJDcQs02iQ78jGs0qTBhUUcdB6ee6Vwqf/vzcOoF0LswWgpnqbN+z2DvQIWbj+7j/q2n8UenrGdPdgx7s0WuTG1mHf/SHmEy8yu0QSw3Cmd8nNS2SdsNEmO5/OZfptaeguMugIG18OlPhPZatveLhXqfeZSUUW0bmZZcX7npWwzmW/n3k/5qZsZnCXMauNxJvO6YzMR0MHU3MxZmMq+Y11bpFnfNZNBavJd2/yi995wQj6L0AyYolBMc/W7nGDp+DFWJB5ILUFXJ6CDbcp3EY5ETZASs8oJ9sOwQE29H4jwkD8xMfJbnZE657HU9evVQVgZnAJYqkxbphCVgo34qGIgSrd/1yraB6GJaMufWE8IAFowJbEoBp6lQXF0kGo+GbSKo+2r/IoBpEA5FscabRXgQkQM8AijnkeAZeY9fWDjqdNi7CfZuju2Lrz8BtMBCJe9+UOcChtbHcxoAR6bUxJC33bv/rfMLPM4pvCP5C6hOAn1OQbnmGDjvIvdMGzWY/Yyx7k0O0Jo4I1WxuJ0GSFg5lfOLu8agegdYS+upnD3AwRE4xm8zK9gqqjERzGbEJtBPPKHCeuLEddJT17Putt+JFQDw5O3w5B0zja5UHyyI4EGH5zuV9knkw9aLr2DTq98J49McSZjTwOWODat4XuMYT2GGld9ppMLhRu7qtrJtMZXGwsIzobPiOudr1n6f79XqbEvqOP2TPprdPeCU8h1EVyV+ilLkcM5oYicuguU0Rnc/0cwcB2SG1IOcAbKF0+z5+YdY8rVTsdOmuK9RVg4jCQxArfVesldX10XqRecAenK7LMG6Snc2iAIM+lnj7/l45GrgqLQFQENQ+YMieylM3nVIJiCvEI2IoShm2thuSSltKbes4VjrwCwRxTwxbpa5QzsKDFCXw8ezmVt9C+dAeuDMvOmGxfBovpY/q36U9627HSNeE6tVPzPYqDsQe4IUaCXOdXMHB3QtgGrIj8tD4lE8offl2xloNqk+WufKxu/Cdx/lG2f+b3YuPssp1yGs6Oo+ondwHJyEp3YCKXx466vpzw9SzaYwQYFp1Cyn0lF1P+vf2eKX2n/FbdfTt2Mzd7z5f3AkYU4Dl/W+GCwWY6tuM2epQoTuly22Ma4hTTOFZloUJ1VIqvDdWp3tXW5FMKwUEvfOuzzgYUq8YSak5LjT9XKfXxcy5CAn4VMpuTfZj+MkVW3t4uVpTru/ycHzNzJw5yrSyVrMtJJCBIBmKOZFTNJ1IH9LA1LYVVhK9+8ISZqYJsRJFT8OM5WmiCL6vToEwFUga7EceMEuv8JlGZio8rwnlvB4tfR4YWYvljlkUQ2gINpCcUHCM0fpIsksdafrVd4jjh5zn17w4GEIK3ttU+cQi6C3P3ZQUc6JR2SLN2xVCcnCka8bOhlh6VyM47ztxymPf5eR6QMc7PRz7UWL+bW9VTjVsGr3zZx851eKizZlEwtfqGYHRqfc+5dMbaBGk0JtC3iF77FeZnwv112h88yMW2lMUhs7QH6EzpvnPHCJOxIbzNFVBFXHsw1Q0yaIOo7SAClko1CdhkWDlq0rRtma1hVopZRPCBJooQBmFK6I9kryEkVHAnAVjyDQ3+PfPMmZXrOX2p4e6lsWUBnrmlHmAgiVZr2yesGU/hbybqJtY7CNtMUk5dlgs+ULqiWfGZNGOQhY+jh5NWN6eJLJo8ewiaundKLG9FQXbOgLA7zgMaPU9uGrLZXNx5XsCMmBmN6MrMZGCzcLjI9oKqEnkcJYlXsdYVjGdQjpf8K4chNFZOL1PIFNSyxHHUyo5imTXQm7ehPGHlxIUs/Jdw6ycOJRRrq/z+3LT8ACK/ffwdDWxzll8/WqLxi9GK4a2f8WfVtiZkHvYj3OGo4EyA4bDMXCHz4kPzpKDB/60Ic4++yz6e/vZ+nSpbz2ta/l8ccfL8S58sorMcYUPi984QsLcZrNJu9+97tZvHgxvb29vOY1r2Hbtm0/TlZUcCCS05lRGlv6EsaP3BjF6QxSILH0Vseht0nzQJt0Y5sTGk32Hf80ebUdQEumRwdgFYxXohnve1zeIwqABHEAEk8PKp6sp080jgUwPuPOjFX7iXSmE+PnbqFx7H7yrk4wTSiwDd9RRREt+uDCwNMvk2sa7I0TdxIvkaT+fqLiio5ZBrJ8Uv878X9l4Ej6xsz8YMBWctoLmuw7fzs2ibLqWHeb207aEz0OiGiWFIoQyIpRZQxt7quw0oGKiMyK1VjNcIjXCt45JC1V58FLhr+upT75hENKOgo1NVsUqpr6l2nDOOt0eHeekLDXdjHa7ubpWp1vLq3zqfvW8I+PHcdndl/Jv+94CUN3P857//qr1Nc/xQWf+xCn3vFFXxCVmKq/MCvpoE0zdLCldPRXnUT5N7P81vEsjLcte5vZ7Kues4Qfi3F997vf5dd//dc5++yz6XQ6vP/972fdunU88sgj9PbGXd2vfOUrufbaa8PvWq1I/6666iq++tWvct1117Fo0SKuvvpqLr30Uu69917S9MgQF2ToF6cA7SJZtqZoeyMMTuZvWe+mxUBuSdKMt1x0Ddck58O5QyQk3O1V4s7ffJU4NYpOS+dE67aKk41Ak4ScxOu24vHm2g2wSyXYJBNPrrOFhdOpM3eQDU8y+J3jw8ypmU7ofwoYRAke8haoB1GHKvE98OUQFf/KKDUYcdpoUR8YiEpDlN3BQHi24CePyWNHOfi8PaqUThXQta/Oyq8cja2bAA4zGKViPAaKrnpUXVStP/3a4vyu1WM5JG6ee8PWeAnwQC4/fN8Se7PcG+UaE73mCvt0iykWZ85ng60aqfE2hEWksJk0oqubymSHN38/50N3nM3+Rk9YOEorkE9GwC5QY93QZWNXr/uNcaSRSso+HfSsVq7wGCm+L9wys7e7B0KbwMfuH+Uf7trFX/zGkXGpHwu4vvnNbxZ+X3vttSxdupR7772XF73oReF6vV5neHh41jRGR0f55Cc/yWc/+1le9rKXAfCP//iPrFq1iptuuolXvOIVR5wfSwdL0xPMnlh9Mntb4hK6x0PXppZfnPwY133ndexfvIL6BS32P387HzbrmPbua0cDoMgxZO6NBMDKQgx3VURI5ToV2dCgdVfgjmwQ5b4lV3oxZ1XvNmskaurSBqriws1iaQ2PcvB1D7H4a2tZ2DRMp9AssZqCnZdV4KVYmjbeDkX1opB4XM0VaMkKHN4coWCA6plYWGr3aQnqzlhE8A/ufeEOGiOTBSPbxCc40ruFdz7/Gv7msf8JNilsFyqYKhC/h5OUYpdwYJzBihXQ3weP3A95x48xBba6GsJE4H2JGV130sf0xGhjOXOfSC59sGb8wRjWmTZ0gKpxTCwH8oTcwnVnwIZ/PJPpvX0Bh5JxaHbcIpT4vQqeY3UHU3VabFRio+gZK/fgJasKaXr4CUbVr0v/MNTKlK6Vo6lz5t731D9w8A1DvPuX9szOzGYJ/0c6rtHRUQAWLlxYuH7rrbeydOlShoaGuPjii/mzP/szli5dCsC9995Lu91m3bp1If7y5ctZu3Ytt99++6zA1Ww2aTab4ffY2Jj/llE4w2m2QsvMZtWslMK3T3k5+5ZmDO8YY/hgle9WJ8iC6GeDcUI8CDYVThfU7ihAs34Km6mZKp6GLEE7a3N9TXjVzIOxtAYtvk8BR0+H0YufYjpLnfrHuGcSA/13rybppGGAJ9q1TxjN/j2lSbbg0FAxDbGbkkm6cBK2BkgR46Tfyn2drnER9p6/g8bSafKK3BATEUtl8xDZviU8cewieMKE/ZOFfKuJPryzRDpIodohGGxXvFgr2Vl5FCxe6sbug+s9HkoTe/BOqsr0QerAKnxQ5RLRsw4sshW+fOOZJBWDzVz79fa0ePm5D8H3rue21pUMrh5k+YqD/Mt9J7D9AZjY20/e8cZibSfeyovC4kAn1sHj9RfxuaG/4k0H3sM/veTvSekmzWFocivr7vn9GLEA8iaicdAr+Ios6B5sbOPwdxZWpoM2HCtFbfUvYsNb34+lTjrZIB3eyPhAjfzgMycp4T8MXNZa3vOe93DhhReydu3acP2SSy7h9a9/PatXr2bTpk38wR/8AS996Uu59957qdfr7Nq1i1qtxoIFCwrpLVu2jF27ds36rg996EP80R/90Sx3XG3EU9l83tTtAoOwYFvQegIeP3EJ04sPMdV/iIMHqh4wtJft6OJMEhOWM1vQOSlOc8UpLx7NUIauOOrduxNm+oO0Ph8JhNwmmMTQGhmnTVroI4m1VI7dj8mczYJppvTsXIixML1iP5VGjcpUncnl+zFA747FJK1qYGMAEyt3Uz84QHWy2y3v22IfDlbaMohLkgIIkFKceH1G80rO5DGjNEYmsKo3GvX/wlqFZX2DHOwbcFeF1eiqk5+KzWlvIcJEKjlO3PWX0gSWLHPlXboUhhY4UrJilWdsKTQaMDoa9X0zVmuhKJlZgr1bt4F+A102YeO2JVTSmN2ueov7h1bB9rU82VlBv+1j/1QPj+9cjG3iHE56lif2iWH/qIm/pQ4OJSM82f0iWHISW5a/mPGpXmoDsNo8AvfozBIzWlhhLBfMsssezxP2vNiWFlYugqMHDpA8fUM5uvoef2TVOrsvXMdms4PMuHHa6R3i6VOXAjUyHiNhglHbyxbz0MzKnSX8h4HrXe96Fw888AC33XZb4foVV1wRvq9du5YXvOAFrF69mhtuuIHLL7/8sOlZa529yCzh937v93jPe94Tfo+NjbFq1SpEte0sodzUU3bVEV/g/7Sh9QBM/vwo0GL7iim2rYjn5xXDTB2a+5b7q/og0KQAQxriYgqmdEXWGt09l5bM/6LonwmUxi8LyTHyYrQagc8/aWBi7c4AppXJKrWpbkwGYyduo36gn/r+AQ6c7rYwVRp1KpNdrupSB0oHT97MwMYV9OxZ6AZMDpWJbuiY6G1gtvou5FcREeO+Zz0t8lpG1pNx4MzdAahCXAv9to4xhqOWJxy7vIPJOnQPGBrjKXk2S83MBigesMTUIQCXcaDV1wfHHANpzQNdx4HTCScTvOXu2QPjE15UPEw5A9tS93r6YEEGvZ4h5qZoszndqHHL3acApwBwaAds2r2AnqXQ3g0H9m9iYXfGQHcf+7Nht8Fe3ASZ4vuCd4a0h90jryJvV9h/yOWhtbjO/sHjXLyeJj3Th+iZ9FJLsgC6O9BpQKdDRsJejvGJGu7P13FD9t7CKDh3CLpWPUXy9GMYCwvZStUZmsXGs5bRoT4aPXXafQM8+Ka3cQs/oOlXz12sHxJHC0ybCR5N/31m5c4S/kPA9e53v5uvfOUrfO9732PlypXPGHdkZITVq1ezYcMGAIaHh2m1Whw8eLDAuvbs2cP5558/axr1ep16vT7LnQ42sAxbGCDgfoTN1ngW0GPp+UWYqjotk57Z3f9yYKjunW4jz0woEsAqqtXjIZkU/kqchDScdOy4Yscb0AqQdTAFUVUf8i6n3smINN4kVoxaLQKa8X83clq9Gbtf8hDWL801Byewa3aEkuw95xGpNiLnhIOnbObQKZvdO61h+bfPxmQVN9F4PZj4f7MxgVh1WkQ0DhQPnrWNyaMO4vSC3YW6wkJic17WPppK1VE3S04lneaMSxvc/5UhpsdSjDHRdYpmYXE+CPeMha4mmIZTaFugpxtOXes9w2SRnQkJsd5NtLFO7SNblfSig7WQaDRSbO+cF8PkBhjdqKpC726gQEywQG0RnP7fYMsfwnX/8gZ+bdVB3nDWq7im76/Dzh/bceAlCwWyfcsCh7JB/nz3e9had31/8G7Y2DqWf3jNLZBD+6KHefGN/8D5X/s3yDuYgSuwL9gNG9bD9q1MJIv4E3sjWEMiZi2qeAa46wm464ljMdyIsXB15dWsNI/4vpZgbIaxhq9fdgF3XXSaf/KugkwUJlf+Y+HHMoew1vKud72LL37xi9x8882sWbPmRz6zf/9+tm7dysjICABnnXUW1WqVG2+8McTZuXMnDz300GGB67D5IScJK35WDg/x9wjKVoOztrYp5NUO+0/ehA1uIaIXbQCjTxgIKZWDsKQsgIIzbbYBQA3ORj6nFtYHHcgkJZ6Wknrrf5dmgqVKThfRbkzsCYS9pDOuR5gxHgAFLCPliGBcPuwzWva7GpHvUZemzTl2vew+motH3eKYF1fkVB0SCtuRhAhqWy7rG8eBVoXYkd3bRnbt4n/91q9QS5oYY71JSRtLEzjIaa/awtLjx1V9lERHaTkPMknudFupdSAVFt5Sr/OzakVQROF2BKrFS+CMtQ7IyAk6NkNcAECZTYiqaNM34dBGX2yVLy2pyTkZuRdrO3tg5x+7xcZ3vfk26i96iK/0/gVL22C8Q75ghpFDp+30XFkHsjbQgYUGVmyCVU/Cyn4YPA123+SeOfTRk/nCbR/h6sEneN/Qg2A/xV+9/OX89rHXcVXlEX4/uS1mVtqL4kcHa+GvO1/g1uyXeSo/hz/MfwhH/ymc+rOwcDmEXiNTt5ZL3Bh0S1UyFo8Myn4sxvXrv/7rfO5zn+PLX/4y/f39QSc1ODhId3c3ExMTfPCDH+Tnfu7nGBkZYfPmzbzvfe9j8eLFvO51rwtxf+VXfoWrr76aRYsWsXDhQt773vdy2mmnhVXGIw0iJrr+VSkqiU0US8RetDk0QWPZAbIkx9KetZLsrOeMZGpoyYAT0cyZTLgGEet3Lbi1fYM7rbhR7xDodNZekrr0l4TcA5oIkVJqB3RiRRhrQwuzFJ4qC69xMUC+R7MMbcgkqURtujEWk1rGz9iEOblKMtlF7x3HxUUQ6fPCSuLrQ7vsuegJWouniTw5R3fFxqIRfvibH8emCZgOhmkSpoEWlmmSNGHFqfvoHmjz9H2LAngF/VoeGTbWmT/UfbEyMRnxeQvnsEay58woZH+rF83SqmdLenuQvEuJokaVdXcHFqbQH61oCi0kNYuJhr7g3pEAtbQeWF1PvcVVr/vffGPwl9l3+wKmH4HJ2hTJr13Df/vaIDdtXceGqTWhHmTr1dROaO5zwLb3TmhPJOSZmxzbtspf8Hl2XHs0zYO95MZtKUo8Y9SuwI2qKz1qkgQu+NUn2D38Ah7hBFa3boBPP821r1/NU6tqFNfUY8XFCT8Nk3bXngUsv3Yd8Dv8qPBjAdff/u3fAvDiF7+4cP3aa6/lyiuvJE1THnzwQT7zmc9w6NAhRkZGeMlLXsLnP/95+vv7Q/y/+qu/olKp8IY3vIHp6Wl+5md+hk996lM/lg0XRJEG3HCOPVj1EVc3kEBWy2j3tJht/ij1KwSAtCmELd1FNYpVMSIhThCxUVPjOOPoFE3pm/VgV7xrQp7K9ja2VIbI+yJ8SZmc2YbW30Uj12ItRJ6lmRfk/dNYGpi+Jo1Tt9P18PJiT6eUlEqkvWCavK73usSGW2i7OLo6wNiaPg/aTQyeSoRyG+p9TRasOETeybC2mx2P9pJ1koIOyuTOXivNnQmEFh9nHB4SUGRGFRSKEMwvdLxSUwog9iTO0sHADI82s71GmGM43k3HyRO2P30qdNXoOujSr3QqmEdOZfdYD2nWR7+BtoEpNffmDfcxQPOQyqaF3KZsyp8H2wiLiMGI1sSWEXZYnoTEldDD9w5xoH+IQ+QMtXfz2Z1LeXzBBho97dDnVM2E3ii+66Tq210Nnj6haNB+uDCnXTe/bfR3qQ90AVDpdHP0Pb+NsUbtd3N/cwOk0FoyxvRRe3yVeW9rJbCJFSvVnPrvWseVBtak9V56G3VOQodqAC7CwaNuthGgy/2Sf6Zy4ARLNyNVvbCnmZgp/CuGmSxS8z8ZdUbNeDLuJE5xmTAJ2jOB8QThnVa+5SmD/3YW1h+aEDYc5wrLDEGc3P7qB8h7Olhv3GWDL7OE4+0Qz7NLSJLMl28Kw7QHL4NhguCUH2GWQzx+6yI6LUOnmdKYcAbPNod6x5tAZJ6FJBRqSIAi9xVsEqKdm9+4bhJo5TkP7pmk9XQflWUNaCVk++vOTCQcPUSYNA1w7FEt0vEUJlK3k8YPdL3yGmraMyube+DSmfTUXB+0otkiniG1E5gycKChAIfIfjNTTFfEU22mGFaNJW+WoOvS81rVwNIuJ2r/+xMw0XTvyXKodcOq932T7rU7SYemEBJgAy2XnhgRtjbaRz5Z49G+h7lx8MM/0nXzj6Xj+q8WBAacnOxdmKZx5rLG7dwf2wLju6AxQVgPjAajM6vAMSW3vcd6V8sRaCxxQ3SRbUVoMGQkWCrK/t2F2FiRRBdnYUknKubjeyM30qUvfor3XYqJ/8Trxoub7i0VBYdSL3jA036CBIajAColy9McW8mhkkElK7qpSQibpa3JvcAs26cM4p3fjWrr6VHiJ5cKIhiYsJwyjfP30vaMbIKTXryLteu2s3LtfhLrfManVetEWzw7EANYlFRro5Fo+Hg7tbBdKbGYhU0WXPUoSW+bgcu30HPxbkgzbJKT25ysZTGdDtW87VbIKxm9b9pL7fRJqOZFx5VSLZod+jwlxgOMtJ6NIqjxoGb8yqjk1/g6rgK9FkxiqZnY4vixEN4/C9WT9DS7srpe/DuSak612mZJV4vLVrR48+kZi3ptMNEwqQPfbR9+JRP3HB16SWm6QLauVUiotKssvO8kVlz/Io40zOlN1okffIE95JHyYtzS8f4noTYEu3dCWrcsOiXD0izxEjEw7fj0IihA6sGmaKEVQSKG2Mbi19Qig9N6PVl8LgqRQpXz8L/otYQRRSGtMOjU3zIAxid1jtXoRdik5KhKtPq3wcosKbxd11isOWsyRl/3AAkJGbsw+zssuOlSd8JSHtkE1Zz2zz+O67SSSymtKZW5iZiGOGBzynnHwCoIcLkciP+ZGouPbrPo6L1AD9Bk87f7mdxWCyKe+JgXQ11M1AdJlqxxRu1ilT6++gB7zt0MxrDkT+4HY6meMEr3i3Yjm7IO/M8TuXTHtzgxe4KP8Nss+h/3sS0BXlWhfuwQA/+0Jp6jqfpKcB+t6jURW604X4SFp7CFUYDIM0Zp3pqB4yvw9pHd/MPuBTzZcKvx2oehMOBwTV4sjEsZbJNEw3pj4Zyr7uMF6R0c/+j9UDXcvO7VtO69GEYHXV4qzoB3oANLO/30MsB+DnmdqqOMccKuYcg4/h9ew+7VD3H/r/0lTPRxJGFOA1fuzSFCkGVt77DOGEhq7vqVF32VyeEWN3OyGhBlc9LUC26aSVH4v/iUNnsog5m+XmRmJqQhceSdWUgnCbHkl4SiJX4ZOGeZTBU/ovDWojFI4lPuIHwvmrnqARd/i1BrDH5fgeECLGsWTPKvP/s4xhi6bl0NY3U3qLpavJ3r+Ki5hFF6fVly/2bHfLcwyhTTXMxCD5sNnFI+I2ESB0ijvnRyQEmK2wRoMHKcMi0sKasu3EfeqdA61M/T3+h2/re8Ij1Im36lMZSxK2PrpRuwxqsDqh1PSTJMksT6NwLyOQve/hQ/7IzwCItYyMOOpXnk6xwzzvhVD7CaOxj9xFsZe/FBaBu671zC6C9tVK0Ye4uRFrKwlCle0t7Gt//itZCbuIVLAZE2iLcJfObnF7PzCynmaQIYxx7k36h29gi7En92AmbPW3Yvlx1/fZgtu59u0GWmSFJLJ0/45Nsv5uCuvpAX/MLIGz/yVR68/1g2ffr5LHz9XRzzicvZ+PYvkvdM+3rOSW0L9r2Zh37uy0z1bMAksyk/Zg9zGrgSNbwTEW98v203YWovmIpbLn6sf5h2/zR6M3Mc9LJlJ1Fdp9idyn9nghMKxKI1VXnzjkFkfclDfLKYH8vh5HjJ3UwXOocL5Rw/8zNRpyV+LIrxtRAarzrg3c4AE2k3+YDbolVdajmptR1o8b2VK7ln/Tpaa7ugJvkpDB3adDhAm3tp8XwWYegQdVoVLNM4/V/VM68cmAIGgS6wNaCGMVNAlVpPn5sY8jZJ2u08k5YKFfyshwawtAcmwcRJTJirtJRmvAaoLGzSoEKDChXi9jSw5PUO7bplL8tpvnI3rdUNyAxd1VFeddd1fP0Vr6Zd1YoyCj0o3b+b5ffcysTPn8HP3HIrWw6cy/b2qSEDM9vQML634o7rg4KiPfYE90XYV7mBX7fmC3SlDYZ7d7Gse3c0cenEtkpszltO+BL2OGDrPg6RsH7dBTz6L2dz57dPYt+uxbRXpZhWhUNrh0imzmeiegvGdtHdPJnRvq9B74006hvJ0nGWtFKu2N7g2zPKMzPMaeASHY1rvST2osQt/06PQnUQsPB4shySCc8M7Cyp5QEQUDGKYFUU0HSX1jou0MxrNk6UoylzMU7sXi62FgaLPEnrm4r3YjrC2ooio865zltROxbfHt85G5Dp57fTw3aSkLfJ5XvJsnGSPOXAAss///0h+o/twdT0c5oD5rTosJEJlpH49upQI2MpBmcvlxLt7bQdRhW3M7AGtomxVaztBtPC0Az0Qm9b0vsMLdDpbtNcPu7TzQo1NvvuitknknK75iQcZBU8fyzUe6t7goEvH6ToN1mej9+bGWybTJn+2d1UDjzFsoe7yPbU2N0+vghcShQ1DwMTsLi+l/6uQzw6dTy1DiyuwLSFyQwSk3HS0MOYBPYODZCZhMUHJtjcfQrnrbiT/nTMV4xRFee/545pXnra9yDLeXT/Yzw12eElz4ehH1S5bzwlX7KNvqOhmVh2n/kQWGjYSYzNSHLLlBmFnn8PJe22CSe0o5eZZwpzGrjKQc6Dk5WXpMv/DYpit4YnvKcMX3mJiVlmDvPyUBeklHXBohG1YyF4n/Oi9TLhnlXxiqnO/G5KV2YDXy0YloXWYonKqblBKh4pZmcVkr4WMHU9iNgufskSajy98iE295xEOr2GdusQX/j4L/Gmd32e7sFFCtolXQ2ebf6dLYhWbyFVLqYOVKlR81DlbBwMI7iab+JdMGCoY7MqZAaTJhgqJKnFdOXk7QSbm+ACSMwPKklOZ+kEe1+y0fUbnzu9SDJTFNc7HKR2RUMo8TWrjK090dfHZ9/ySzNSFG7X1WiQ5BmjA4N87g1vBCzXv/pVvKrrJp73g618c/Q9pFnVGct6US0xOV3pFFOPQVcLzlp6F2uX3seHtryH3jE4pxd2ZvBUG2pJk1876RqatV5ufd5pNKtVXnT3k3xi6QfpHOijkUnL5tTzyZJuwgT5stXp5Z+35zzw1C6+dOcPePXp9/KeV/Wxq8+V+4DN6W+6vtEjW5PqjzDQcD9atW6SPCO3dW4ZPpYjCXPaHOJXRn+f2kAXkEGWsOa+90NqGN0OjTFI607HZarQOXMr+bJxv16oQSMGLdjNBKHyNeleml0R1OoQudVMKIoDVn+z/nkZJm7dzan5DXENUPRCUV2veZ4eQpIj1H0NEgI/0VlhdFxYFgtjKMNaNGtIQp0YDAs4ic3jVfZ9HLKdhqHfAWsttcWPYNJMlb04sN1q4STGG+/G9zsXQ6+km35qRGeOw7iVxl6w3QQd10Q3JAZTsYhUmVe2sfH6pUzvrrsFTJ94ZuFF/ftJ1uzn0xceCqBTFsddWySh3tzjchiKLn8evrs4hzMSm8mEpVe86+MfY/nOHWxevZqP/9KVyMZ74zcmJp06xzxwNo3rDHkTTAoLevbzprM+6M5fa+eQWW+WYp1KUHzce4WUSRI+vuxPYHwx3ZOOVFFxcrMQwf5kHz/X9Yce4dXqr3Hp3HjPH7Bq0Z2cOHIDJsugnfOJy47jgf5p9jHN4LTlb/9tv3t3YUA48PvBz/46Q7s3c9w9NzDWbDP0ri8+t0+ydk2cAR2MrWE7cGCzW9UwVW+bk1uaF26Eng5pmAfBUMEWVgsjpMjvuP9wZmcTuxQNWlpxH+fbMkcqqvbjX4E5U3if7EssszR5s3Z5457KQmpGvafIF8tgJzwhppWofM8WIuvysjlxmNYYYxnreZBjaNkKfW9MIIPWxCH+9byTueKHn6VnZHEpRQFYndei6Cvlu4UJEhKWUedcBny9DYGtuyO82mBb3U5x3QSbGUySYyttTHs5qy85iLU7md6TsOXrR5Fk3mNr3mHZU91cfu9qTj5qnL++5GkmusSHj4RYS0kArKKQWKwbSs8X6zW25iycPgezbBCOWUo9h6t3fpWPL7uIg5UeVxOVJptOv4vB44d55T03sWbXRpLEugMwEuOXRnHgJYquFO4YvIR7+15MYmHxTqjv68dkjoEi25pSExT+09ki/mXqw+66rBIPtZh6+S5+yAZOnhxg55qfZeuyFbz8e9eANbzl6xu5wrilLmMtJvO9MnRKG74/76ZPYfLMeUyeYRk8e5jTwGVC5wGT5nSdcCd255mQ14ML4aQCtparGV7MJ1wQkJqNeUUdkh76lggikkLxWxEKy/65inqtIveKrnWKFvtxcERhML51Nv2Vzo+ZkZNibjVnnClC6uGn3x4ZSY5ljH5cd6qSsJBphuiQkHZnpF0ZbDNwSw8v+tj/5sod/dzblfPUAklLm3/E/NjS+4RRNn377abF3YxxLn1An4tjM8gqzvttx/00CdAxmLyC7SRUkn5Iu0gWGVa+PONxczertj6Prsn19DcfYk1XHze94GgufWg9d645lacXjYR66GU75z21iaVjk76aRMfm6mxv30JuPKHoqly3tS5N7+Qkr/3ql+DYxdjEwMEpzFgDVg9BnrJwURXoZ+nUOD+3/ja+evIZvPThB7n2Kz9ktN3md956FuzLqSXdjDR20VufKM5Jej+Rn8RJDM2ki4nKIEkOfQaStsqluN3x6VgLGQlT+WDRdGMiJ72jhzV2CX3NXqpbUyb3nMqd+W8yfvEmLrh5JQPZrVDxbmq0/3oD+titamsqNv2R4dbcBi7C0Emdj6kx70sqBdvforNknKSSuj0fYW7DD4o8DA5buF4GM9FMJaXrMBO+xEBSQ5chQsZsz5YB0BbeY9SzM5eKiwM9MpZyKAJWWTtVjlsUC8tgF0E0o0KHLiChTRfWAxfUmGahy1O17crQndA9lPCWcy7k7A272NgoWuhH4MrQ1vuaNZaHfoOcbbRYyiGOYojUVB3VBmQx0uCTa7qO4c7IrGOSOpUUBldkLOhOWbP/Bwy27qdqHmMwrVDnACft2UijWmXx5Kg3fYAedtDThqHpCVYe2uXypHwp7es9wP7uQcW6XOmyJOWHy0/g9B0bqOSufD2Tk6zdvRl6xh24jDZgvOEMD/MUulKwCb2tJqfseJonFg9z4o4dvGDvDsbaHdZu2woHlP2C3hNp1TUIpkIYy3DnaZbsuIkb79/FJWvfUuSLRrWKr3Ibu1ngkaaVkGzuZcT2OjPug5AzxI7kHCYaw2zLl9NlO9h8MUneYlXtDtXeOhgKJ7AcoeZqTuu4fnX0D6kN+LMOO3Xq3/pV0sYAvdk42YqD7L1glISa175EViGWX3pQ6iPDNIRo7hShYaZi38XLVZzyG2bjaPpJCs85YTYJeU3C1aIVWNRNxW05ktPiBKa1UgadQwE9Z1+vh5vEl5zlRAuvlGl6maQf8c4hui5ntlAJin5Zi+xt5XzoxvsxlR7+5aSce0cMEzVnBOvGl1jqt7EeefTabDS/jaV3tZHwMlbSRR+VrJd6qxcOmWDJDQR/VaYXpw7zSdnMYhd1eMGN76Onudc56cIQXBzneWwAf1jjv52xjsWTB7l4wz2qgkykKlZd87U3Va3zsYveyK99/1/pazWK8YszhbKOVcGqLxZ3P3gTJAJU2Joqe4RKYqp3xH/nk3u48m/u5Pf+x3ZIkngeqRifyhNePJRriXU4I3/1eVfGxrM+pJVyC9XOOC/tfj/WWqwx5Ikhw1LNDL1mn9PZWVfusckWg1f924/Ucc154KoOVF0n9zy37zvv5g13fJx0yS4++Y63kwY1ttgCAUSzVQ1BmbKt0pDlxEP3RI6cMl0tWNRLLA1ukcPFUBY09R3Nn/Cg5aCgaDUvIZpt2sAH3XXU3+K14ncR94obguLegfJzIiinjLOYNt4qu8Aqi8sKCdVQE72tDn/2rUcwNceMfjBs+PTpTouX+vax3gzBMk3u9xJIKfQ+ADVckF30hoTVLOHc7HjsodSRr7bClQ5Oxmi7j839o4Nw1h2/S09jNxF4bBx94XRZnxOjvmeJM3UPfn3KNVe6ZqHgoF8yJ4ATzA1KtV84fMK/Oy9GiafwqIgGwoknuS+XT7+d1/h88tfkuSH411HJS7zgdTWLQJX47KRSHWUgU9mVrLcyB2R7e2BXH6zdlXN5969SMc0QeXyyxeBvfOG5rZzPyP0uuzpdueXP7h3jo42cz7359XRWHfR7Cg1iTOkgTI6mgKK4JsJK3JojcYzf9mN9OhZLhyYmbJyOpg6aZc3OsGZCSVFEleaOq1K6j8fF92LqkZHM5Hpl0LKl+3JHsxs9iZvC04ZDDBeg3/1fXEVLAzOL+qrMZFCteATJseHcMvHHlPlnakALvZ1Lg7AaEkSrSMf6trKPg8kkr+w5CybAtsPr3GnfAl41MDnkbdlErAa6sW6wR2JXHIE5FM9Vk13ZPk9il2PVNBP8LRNBSQNRoDhyrQQk2l+OhKBa03kviV74fOOzGfzAWyqmwRX2N7gn+wA77BImhfwV1XYF3M3zqDqTLUPkcZ+lbD8Kj1hPXC1UfZojDVjagNwYvjz1MSe+Gxg0WzjX/iFHEub4JmuZRizNBP7mpBo7z9vM1IpJWjVxaexqS7bViEAY3Zdl/lf8roUoSMj8Pxej4+MYfy3zPEHMJ21ITZhbXvoIp5NemocrETK1WDcTCOPTUa1djFcUICl8dFqoNHOV22gW4UZuC3ca8zhLA2gVuURkhA7aM1+yFs68we9PTPB7biqcdCDhlx/okJCT0cQGEbHl/+YzSiD6yZklyX1dZrRpYuv7oZoHkEIc6IrNqssWbdPgNy59Kbv7lnPTCy7jSydeTOGY+fJHe6eUlbrw15U+sKiAPaZoBqBZVRD5THymhFlgo7FZYiPdkTyi8mRU3tFxbPG3cauGVdtgbfL33P31t/LFf/39WEfyiACRKo6cI6mzEPpW+TVE8hr4sYWKddZ2SdINthvybvazmmv731su/KxhTjMuVxGu+nKTsWGgQsY02r7Jqo4dTU8pdX6ZSLWVVowV9y/qKTiCDQEctRGFBDdSChNgYaqVa+VQ5EVxhfNwNSHMx5R+UXhSShDLWExFvlks44zh1m0XkNFLmy7PW4s5Niqv2qJNGGrUu/mJxrjtLf2tnOP2pax5bISjxh8iteIjzJCbNrec1a1Ii4Zd7XpCB1f3HdPhMXbxwq79TNkVtDq9kUUkODHR6/8TEs4aPIfpsReyJR1ka+84CxsVLur9jgfaUsuUX1seveG6mZ05QQQXTVukYWZ0BKto8mEjFRt2tnCY8xwwhgE2s2LpIg41TigxNf96LwBIVWhSF0bDLIBVuCZk1F/TjCk3ngjaLqYaJz5DIWKY08AVhSYZSkMBsqIKu8xT5HtZHZ4GcNCOjDMVR54ssyDNf+I7ZLDO7Auz9bJyV9TgIimJ3kzssk3h+Zmbg2bvqhpkZobMl7hKhTEvrlWpk9IT9GnlPBeBrAjuxS5qCe5JPaOqtbs4++EFvHDLJJWsHWLmBp5aWSFLEsb6YKxXasPVwMwyxZZoAQ+YfZzVNcF41sU0FpsbSA0LbDdYE6S2Sl7lre/4ALuzhAPT+9ld7+GG7uNY0/8gy8f3OmMbzZyMjarSACj6rzAfo7JmZla2lqWsins49LEGZ0maFCteu5CYSaNnAqsX7Qr3cjjm2LNpd86LVVmgUQQ1X2hVE7Mfcnw4XC1jvy1GFxJczWBkavbil8OcBq7E71dzFddNh3MB5+MhDnYZpvGTBd0X4VpkYvGgV/dkNqMdcjJMcFdjiV4dikPZXdGuoItQMxNaynsBixCkVQ8x9zF3eQFSo3cJ3ZccPyyyQgH7HMMEY+S0WcRy6pwe1jWN/1+Df6y/Yo6L1zvkoS5xgy/3m5CNpW4muLD3Dt8TfeksJLnhvZ9z9j3fOK/Kt17oDkLNkxRvYRlybn3ptb2/wfIpBrC9m7C9W7BUSGyF1+17PpVKtCFqkfPFn32KzPT4GsgZfXWLD/OL/Pl3/o7B5rgCCjXicmFJHniMEhF1ZXi2gshINqE4Z6o9tpaIEPIcGvQUaIXn/Rc9a80Wx5bu62dTww1f/lPWj57MO9/9RZdUacuHsfFc5URd0yCuWVboB8K0SlkKVSn3PLOrPRNrVGFOA5ce2CIYOv/pHd+NnY4r98whzsqm8JQTBmVHf6w56W/ieCWCQoL23ljmGhFOTPg/NtxhqP4M0JotRPt4Kbd+o8BDfEOEGx1k04rEz8mpkXCIAXKWYUg5BIhzvwiSs52AZGb55t4X9/q5uwk50WOdzLMdty9L5l7RIaWxt7/inpx193ZoJZbffic4GwcN6rGeKeSueKp4B/ji4vt4xZ5TWGh72ds9yY0Dj2OpkjAdJiMHgjU48XzYvB5G98RimYSgvB9tw43boF6Bi0ZgKOwcVwPfxJGeHaZ1M5zSB1z5O/453SGkGFr/pk8I0YsLOgiYJrh9TRI3POp8+lxz5flszZ/H963CYv9eARoRFQub1CVrNmZPREmNp3Jfe1ctZ1PWDo4kzGng0v4VxExBurOasojMSSu/0wL4CBeJE5IIhQlGpSRvLgdJdSbrKrdT8ZcpDboiV48pxUnZMruoFGNHQbfItlyK7qCPg+wgYQE1lmPJ/Vk7ctSb3uaiIVdTg8OHYskd/GdPVpn+10E4W9knAHRyePogUIHUm7WIzscv4ye5Y0j1POH3/tFiTYNvn1PlvhPdtq3oXhvUuj/RWMQp7TFOQXDbwiepUKGTgDV6I5j4tU8xZPz1shP5+R1PsmDhMJuOehnn3/9R+M5uaPt+k1n3qkYGd+2K8lPV33OOyjxwJWAWwZm9sHUvHPIyUQIMG1g4DNUuaLRgw044bXVkXEWbEA+KNjaFUe0hYBmcyPuLBYdc/kFhjUAlcedl2XaYQopg43/KqqGFsO0RIhaGxVjNtOT5mcMmJPzjGmXNaeCyni0AYTCLaBYhxG1SdvfcnQh5moHFNPHxZlvR+9FhdjZS/G0pMoN41xbizYwVQbWcurti1W8Br8hL3Bsy+rAsw9JLRs3Dejw8Q9Itpq/3S87OHGZejTU7Up/kvGX7/OqYAr/UuKNw9gJZpdSDZdp3qSQkrNjv3n/BAzmrd3bCxt9vvLCKc/ZZNnEp1gnAeLWBAFSR1kTzXgPsqvYwvafDym0bWb7pFtg2BmNtB7YyUoWaTLTjO6qqQgQFkgTMKGyahkOTMN0iKIgSAxMH3IGPnQymGvD0Hshh24pz2TV8Zqx1C7QsdCBP2xw85i5e/OAq6nYjJAfKc56Pn8HOg+7G8BBMNmF0yv1etdD9PTDJ0PjDPI/P8uDwL8Q9g5pl6XYWZlYaGPI7mE3ADOsMna6EAoE8gjCngSsu30dDR63pifxF2xjFO0VlPeqaDHMTniuu6h0ONg4HVOVfeqDov8U0dBnkigbTcl4EiMv9Vucgp0pGhQrDFPyZeQ1aeaNvmTfOLHU51zODwbKkb4qXnrTVHUOjT6FJUg6lwwyxz+veMjc1J3I+mK8BSxTTgBO3wIlb/FKXtTw9kjLRJXFNMB/YuiRlslurlSXfxboq8l65V4V9LQYeeZKBbINjUQlu83JGsEIvGLyJz+XUUFg9BDCTsH2yyJAwMAaMj/mfnilt3w/WsGfpCh4efJlLUoxm/TuzapPdixscn55EN310m8cYynbFdC3Q7Dh2t/OQu9ZVhdFp2DfhAHOw28XbN059vMWivk1Oe2hDKs78QbLmq05/10GqBBu/CwOTZgFPJn3X1X38SLFrjgOXWF2ZwCxckIEsW0ggipHgVhD1caf4OLraoqhog490QgpQBKuZ/0vMoviYFH4Xny+WQPObOLwiP4zfbaHZY6nk2ajvq5DbPrrsKMYuIDMJubHUsoxmmnixKR5ZK2BeNHcodrOZEG5IrKWW5TRTf1xY6OkQTmj1TKXVrvLAfWdy1pLb6TENUtpg2iGtcMqsYGtgMUoMMgm/fEMnjjBrnM4og49f1s3Da6BdkUlJjFwdHy2LmWKqDFBvNEk7Xk9WMV50zWM+OiakQ5oouUlMzW1cihOGlltviyVs0hKU7lq+KnZFZ0UyAYkvpjWQZnVGfnA53+8Dm5zCMe27OW/qc1SSDFrOL7/ZNw5b9xOAbNMe911UjI/sDO84MHg6P1h9NV35FOOtjO40oeYrPKuntFu1aGFv1R+fP3zz5lYdYG5ddVR8VRWkRQVygakdIXLNaQPU4opaPI3GgVLUaWkrrPgpYnzsK0WxATJSv2vQEO3vk/BNlgDiaYem8I8QPy3M71HJXf4/wmLMqxi2uj4Sv+vfUurML0+IeaxsoqlziIv5JOnWtVz1wwNcsn0/q8cm+Mj376KinP7IRiaraiov5EGMSwmcTbfKqvFx/uSOOzAhJb+VJ7FQyQknqhpLvW+ai6/4Br/11Hk8NLkEZ+NVg7ziBolsirNAnnimk7genluwqWNCmXHXxVIyM2BS/ttXG7z2tlZoKVQJ46KDZ3qhvK7W/vDP/pgzfvhDD1ay94XIuurGGbYGC3r/N039CI2rl668JoI3uPxnXieX+PoRvVjVQNWZCNSdp2faQ9Aagk7dF78JnYaz/ieHTdWz+dqCP4cTLqOz5yPY+7tg2744aWApmGkYX44kgRSGx+/ksh9exuvWv5b/5z1LqHzlQi57+HIu2/lmLn/L/0u91nYYm7hqt8Rqkc6YZ877cO7d4wiBzPz3xERVYJIQT0P3n7LZ3OHCnN6r+NbR3/OOBAWGusg4E6griEi8+UISBuPMArtOF7fTOBhyetUquT+iTEKRBc0mAh4+lISEwjN2Rgwdd/a3xOuiyxMzWzxsuYFzwViDy/eOU59u0mwMMWAatPKMdpYz0G5zsNKNtR2+tuYA+7vavO3hhS59sVsqUytjokFnDtQqYCz3P30ie/Yt4Q2n3czBrq7ArKok9OdV11sbOCKcul5tHxvgwGiN/nSKGk3I/WGxuT/70uLSCb1bnUEnoCEnmYrixSSeIWU0ajDZZcgT+OMre8lSybjsqZTvrt3tRJUtVx/Npyq/yEIOqPf42heaIQxK6kZYlME7hVOMS2iF3Nef1D8rcayRc0Bo1frYveQF3P78DzLZDdUxsE0PDLgqMhVIqnB04yHO3vNlunf/ABpdYA9hsnbE5IQipcltzISsafisbp1osKhepaeaQppgh7qZnujHapu04ErC8vjCK9g4cGlQA4Tx51+XJg7kUtkNJYwtL/b46ekx3vK+wef2XsUiT0rIOBYpUgQBUczLbDpTuJGKS0Ln1UxCHPFqMY4Z3+IvzZeKMdybi4Ji+fkiD9R3JG6Odg3smIOgR9FvvrztlQemOGeswcKWhaxKbzIOGVRy31urloXtKejkXLw9oZFWWNRsuCRSGw/6M6XsCPvAgOlAYjhtcCuTXfswqWFh1nSdO0kcC6kanIO0BKqeSWVgWlUW1dqeRhiCiwJEWe+na7zoaOWdiunYTpH1mNSNFJPQ1YautsUay1u/1eJrF3Sxb1CEDfF4EUXwLpr8Zvt/0VuZLDKk0GQarBLCcpkGuDC4/X/aUlNAH7zYKBF9nERA2FBrT7Bk//2c+8CH6CTwqX3/nYFjF3Ji1+Oc+OQN3HXib0FiOHnrFzn60J30NJ6GyTEw4zG91ESgT9QEEI4IKnUxYFV/V+xGucUcmKLHTjODtSVuslgz9nUWNR4p2HVt7nsl++qnSzUDHs+NSN2WJ35mGy+9v8bSg26hZDw9xJGEOQ5cZYYzk/FEwLGlqzO3GUN0j6zV1tKqDlhyZnvP7CCmf0Uw0b9ijrToIlfKOZZ4GoQlTxbLbqCPRe06xzU6YCw2h/NGG6xu+ONtxPdIGYkrLo2jx2u+g+eRIaQ2MCd3T+3A1azBwOLBQyw2h4iaWOMGToJLE+v2EIoxUNsfF41mQR4Ic63JyNTLtOilgGyWmndpSb1bznk8Y+fijH1DORPdCY+vqhXqd3B0jFMeeYKX9txEWQh2EUstl+D1PqUpp0w5UPU3I6vCtoiTgZooupoHOXrHtyGHo0ZPYHBgIWu6H+PYPd9mz5IzIbWs2XMTS8YeI4jhKiszTNXL3wvZUMBUvi4PhiqwYZJZ0NjAgsYGF9nr9jJTpys7SFapsP3U51Pd2I1tG3pNm4WmRZ5axmsTrKxUGU4dFI1VxjmSMKdFxV8cfR/VgTpaX5VxHtAdfkd7rTIQ6MYRYDKkVL32SnRZoqMRG6+yK9+YRtGwQjpzkXMVK3um2KpBVnuxACcM9tiMWiYAJ9boBmMtOd8DjuGs8UVcuWfSnSrdSim8RIwQ847zSd4Gd3I0jqF0Mq+QKOVKmEducW4W1IBMgLrol0y4DDimlSTIxmqyNLIMAzRTuHsZtDtel5ThNhK2fV4hOIUK1VliPEHkUfRB4hEvRXaWg8l4ejjh45cOYUyF8e4K9WaL0x98lLf+4z973dNsQbVpeL9S8hTi2fhuuearL+RP0kDpz8JEYSJgVBSzU8mTeGYpL7GqnPI+W3oG/dcUNeazU/7Z05C6CGUxsY8osbnR189Xr/5j+j+7jKTTx4q0yamVg3hn+KovWcaa4wxedeZzW1QU3hF/W2a6W8nA2uKkHO6n4TmnPvcGONazLiNuVWSjtGsoOSpVFNQGOdCinP6sU1op/2WI0ypxLwZad30pDX7Bbua0jUOYqo0GijVgykD9RGgmkE05cMgtmLazj8IzoGoGrcR1mkBwcne4gvUiWQp0LFTTQs6iKCTAJp0zj4NIipwYp9CQ9xjPoqoG0swBGECtHVmINW4GtznYis9/Hg0uE2kL2RpkPcDIIPUjUI51ysVvsyW4M0hTfxhEwurdOX9+7V5IDH/65hFefOPXufC2O4p6q4K7GxPaAvBirVcgJYaCfVppwgohITJQ8KCbRyBPynUtvUTyZEp58eUSewO/J5POYSSD0NkUCxNNQ2CF+po8Z+OKoiQrNhKSr9zXs45kDF0T47z+g+9xzx/1K9DzPNf+Rj2PmvSOIMxp4BKeFE1NK8H81KjO/OE/+Dv+6Yp1PHCaO/rIhuckHVkhdGm+9ivfZOX23fztO3+JjJzUK3HlTbFLRFo+2/GtxjM1UZhLrxCrMhtWuQR0hQnGxk+x/Fb+AGsmUpLxBdTaA66xMxuZS9s4QMj9qQ+plN2DkJ3yAzBxS/jGA0PqdVxN611Xqk7kT3opzrwKqEgUu/LgULFxoFU8s0mEARkHGnK/mrm0W/445aZiDB3jGZrMyJ6KyNp68OMlsz1qoCumIseZ49NLZWCK4tznt2O5+vodVHaMExTqApaF3lbufAK4pbia7elnZU4VhqiBPQCxTzM1EYil68h+msS/r7CjKWTE6SQTq4DHKAMrYp1Yos4riY8XZtIA1D5NbZiqy27iq8I1iZMIwKduIk2aYCq+TAJg+cx6fIYwp4FLh2j2YHnlRliTNThwzCFW7azwr29axxn1Xl729IE4UaV+JvfbHowxYBJMYhleuZTuRYO8e+sWSDI+tWwVKyq7OXF6gqP21yO7lRWazjT2ofXw/HOAGvcMDHH74GAAs1XN1YylYxyqHKKewzv37YbM8I3BATIDl46OelMeP6uJdV5iMcZyDL30dnBinORdOpLxy+j4zcep77AyONpZ7JxZ5jpJW9iLH+RV69IWZlTolMTBEbBZzfbgQFO+p341LyjtfULGOtEVnxcZVPUMztgN6xdDQ83e+px5EWfCzGwV65DMKZCVNfgCW0IZGHV8eUxgnN1tC4MnQ+8KV5bcwNO3QtaI6WOIeyzlsnpHiKeC0fdK36X/SH0HP1+2ZNBk3CqstE2mnjd4g9hS3ZUs3+PLNToRASaAhilGLWRapSnvKksW8ozK/3Snm8+Y3+cX+XO6je+zNsPpx6RPSHpHFuY0cBU9pAPkXHhwgnOmOqywLUYPNVk2kfHY8FJOHJ1m1eS0e9DgFMV5EkFCOpix0NcNfd2cNjkOSc6LRvezNBnj6OYUI5Ot2E6ZH5BZ082OU+NAlcRYd1AAgKkw3OpmIp1kvDpOLYfTpqYxOYxXEjIDp09NO7ZicXnSRi0Gt9YNka3IbCqdQEa1KL9l9jbGAZQ4XhfH4iZXrClxvSCsMqnZU4sPEkJfVYiWqM7uyVHQcYgSPsndezriBN4/m1pY7FerwuBRg0nihsGogUpFmtHpVbxIy1T7ExmX1HP3AmAwvn/xSTC6CZqjkXmUxUUo1k+hjuSe6l+z5lUn4tP1zVMoitSFBhAo1UspzVmwZUaaRn0przbq+HaWi9LGMxK3AYhNkrB48WoSzob6oJtg5R0JxBN/VJl/RJjTwBV7YI6xMNJOeP3eURZUG5BA9z5XC2/YN+4bJI90uzDx2DgQZTAEcDJctneX72w1CrOtUP+0DqecHljI2qlR1k6POsAwdbD74uwu+U4sF0yNl/IgdFzyIB1eDVALzobA/xUrbU2P9KkF4JiWdBJQngjyKCJUVJ0EmyjJF/HdonfRrMfg9TYqH6LvkgGYWq87o5hejnsu88BUACz/nyjnTaL0Tz5ibqFSciwYlvvVb2GoWM8Kpc2NKodHF5kUlr/ADbKxrY6RtrwoqYPUUfhObI9QH3KvBDBSn/LDQqDzBbAq/Q3JeKDJUOUoxZMLgYGVAJLSs6F7S535n8HGS+rMxvahlF9RphgDlS66uhfyc6ufBvsy3zaduKghwCUfWSj6EWGOA1cEkbrN+NMtJ2O0fidsC8FVYi56E1wcvx0IiPvQQsP7BzsiAkF0Aak6vQ4FZmJc7WZNqNT8ypHMKrk6gUvYTyWCgXSG3EDFr7YJczEJdNpQU/qioB/QeSOCTIo/9csQ9l9YLypnXkMvAFleZQpgK2CQeN0MxYGSpi4fifH6Kd8+WmzME7cBuaXoh82ZoX8JACnNkPi8eUV0pmy2wjMC4BD0ecIGhF0FsMopDO7EqH6ROLCScq88B+zZ0JqAR7/g+oNRz2p2W54IjYoY2rVEgayv44Jo6MurpQEBH8EJY7CpW7ixGe4g2FC+pKijKwCZ/FY/wnyjJiOZIOwsz4dJ1BbbLlxXfWDgWDjuF1wmjXj0skDLf63469IWR2YOMaeBSza6AFRsHaaA7tIytuxHCEyiNLAljgxcATCZzat+gOiZNswWeqD4ZySupFExuOOUUzDeFKDtLcITMSEArOzP86uIYhw6nXsDbx8vaUMXhNWksshi1KDUVs4BmKTcNoKx7L/LKz5fvjMmqTI1QA1y1cGtf2fumYx2Tm5S6DEuH5kv01QKdy6D48dg6TRMVuCWhX7GzZyZRpZ5oJfyaZbi663ASEosRvYqavFXBmJNt1Wums2WPmpVzhio9cFpb3WRn/w2jO+IdaJBQOpXH1Ao7wgMTNURxDoOxTA4Vq3AzeImDc+Stw6cx81Hf4DexfC1r8E7q3/CucltsbwaxLWEoeuN8nvlsqgULAWGZil+LzvmgkgWzni3Ox8S366yhG398zKpmLbqk6kbJ0cQ5jRwuZMHE46ervELO5Y6JbMcOxLOCoeC2ABuCV1WzaAESsSBWbC38R1AZne96qNDYmKnyTtOjDGA9wcFPl3h5bJRKwCYUhzb3CviIR7SYNUyP8VyFei7/yu6IVndg/i+sDgh+RLTA18RwlopvcNIPclrjLMzStR7hOlkwOMPwfYcaq/wh7Mm8FgvPF53tmR54vWEfiAlnn1mMulIudQgk46PjUCnFeWZZ3piroAazEa1m5iAiCohiFUlEVzKZoFV58K+x2HfIzFOADBJq5SfMLCjeUvMg65fo573DwfGFePvP2S444d1TloN7+35XY42j8Q2DfWk8l+WDvBtHFhS6ZY8qhmZXPftm6WGv/uN43nDJ3awZJc/Yiytwplv9LosKYNXaQQCYKJphbB98HVezsjsYY4Dl/t05wlHN2vecE9R+VmXVn1DBk+SejD7+2EgS3z110DQtxSU2SYOAj1TBuMJnzf5GQZeHlmE2PRIvsJgUqUNuricUm+K+bTqelhhIy5nS5KBFVJ8t5wRKJbvocKFYVFiVqUsCLClwPblsGccxqYgdRvwvn16h1OftKzY4/UaJo8dW5cj1JH/nWvA0QO/JPppMazge8WbV9gs5lmL23q/ClDceKfy0TMIi46GWs09v/NBNYmV2FRRPov1dzibr0KVm9JFy2Orfp521wB78lUsn2pw9v5/5sTkAWpGDpk9TJr674wbFNtSz8eSzUT97u1mf22Er+68gOPX30TXFOwZOpvp2kJW7/kO7N0EPcv9xkTfT00aJ2mIfWnGCsQsZGCWMKeBCyyL2hWWt6p+RQ0Qg1ENDgUXKBqgUINOD5TSwJcQRKR4u7gkjwIWohmCMCGL6+CyZcbK+/J4T5hBAL1yb5P0dCdQSzEa0MQPujYtCP3E/05VuQSEc+tFZj/Qpax69peygwIxFVfsybYthMmTIZ0I5X1yWYeV23JWWL+qqScbqaiwgqVYTCGexGWWe8L6pGwKDawGKJ2OWpENv5XeLNSpL2vfEuhb7ABvYrdjh+0JaE+pZzSAKvZTPqV6RijdD/Y3hn2Da2n0LyExcMH0D7lgz2dn5VLFKpJ3/4j3atao2xPrbQOBDkz3jLBt0YV85+Cbufarn2eifw17F5/OdG0xfVNbWLj5DszAKqjWnH63dwnFupX3+UmkKA49cx59mNPAVcsNLzswwMsO9oG4tQkDysTVKhHv5L4s1+u2rCXegJPYOY0wIQUGOUr57C+IYli2wOfCALwCXYtbBtdYAbRwaWcifgq4+ntibFig0MISfD7DIXcFJCYoeDXAhu8+vjYUDLoQC8bbdUlZhCGIAaTkQ8RnWUU0vm4rPm/Te6BRh3zA2XFlGe/8agI2jUppmXk1qJRFYWN8WygWVMAHX1CZAIwpGuLKQMzz6OQviKBq8UPKHthnaeIIeh814Ry3zpVj1w9hz0Nx0gr5tKV09OCVcpduF0TNGP/CR/64WG6blkR8f7NgbmZj/FmyEOLjx0swZZG47lSkdiWhMp6wbeJsNi16E5efZLBjvaw/4TdZue+7LD10L7ed8ce85t+vgAf/1ZVr8fGw9jLCwlJB5DWurrU5hClPKrOHI7Sa+K8ZfmvrEn5mrMdtEK741S6bO91S2IqRu1U4mUGtj0sSRaVAZ+Uj1/EDxQNY2Fys9Cag4vuPT55ORzGe3DWKsAObu7RlSVhWu8r2RbkWa/x7cp1f/y4ZfJql5SidXR5X1pKO+8gqYMWDrlHvFT1cNfE+m4grhElK2NKTpgq0VB3mOPfE6V6oTjh/8p2Wy2PHFsWG1Pj8Z5FphXqSjm4iKIb68uXVBpmyAmsz/4485okc6LjBYjruu22DbeI2bSodl7jRCf1Dtbn2nRUWWBRjk/YvmLH4NDULy2VCnSXMMGzVg1ve65WI2oJeHsrV5EbpvTNAK4kTUlUBrmbnxvInv38em44e4Lid13Pxg++hnaZ85Zx/4XlPXcNx269n8aEHec1dvxD7Y2LgwFNwx9/FiSg44ffjSNQhJkOOrDuSMKcZ17JOlbi1R6zKfQfRnbieOkYTto/kUfktM02wjdLghXs+DS0YGzP3qz4yAL3vJ3fPX0tx+cpUnIpPoJ1H2yeDbzwtWvj3hHcTB3BSuhb26GkaokIY/GoQgRrMMj3756UT50QgCd7fJA7xr83BpHFVMZuGg5vhwYugkUAuK0XGpam3E+WS/8QBYChT+SATGciGArhhFbuR8tv4nLWxiFJ2YeEycWVtMNXY7qakJDYQxW5iOwXm4/MwfCL0DMDWOyjYYun2DKzITxCFl5RCqGPjgVJAUtVdgXqWHy6Bl52lj5lZkkiA5RdDUotV34Hf/O4QAweeZOOSdTxy1JsxecbL1r+TvsYOTEhbAY+AZ2sK7v57eN7bvJ4zjxnQJAEOD+SlMKeBqyoFF1ajO1KBNSkx0eBtT2QQy3++gQt9wCrGJPFtTCMpvTN4FNCD0pYAxSqWRczHjEWCcijlsezjVluql8FGyhiMLG0cgFbKol6DwRkDGvWbYhlkQIW8+2cM0OyCp45xpg9ygKkW1TWIykrYjPZgZluULeeN0IISMszoE1Z9F5arwFzEMs1QLYqRUxrg6n3amrzSBf3LYMWZsHN9SRS2qj6lTm2sw8OF8N6kWMxCXlQdUXqHvqf7Q70PFq2GPXe5a/3HQX04gmF9kGi+AKYCi/dYWHgMreoJ5CSc+dQ1DDS2YXI16QddY1IE/+mDsOlmSFL2957M7gUXckptk8o3s4y/w4c5DVyBHUGRIWk9TGAN0qHVABDleW5Ls7ZPO3SEEkiE+5q5+GuFc+/8Ze2/SkKqOlFhxpN4szEnyb8qq0QNE6wGWfVgeUa1pU4tHkODOJP4upN8lv4KCAZWkDjRJwGyOuxZRmBj2nuprtNQVFU3ejAWLMgFmM3MutJVO+tg1h+tv/L/CfvWNktlgNC/w8NlILdQ78YuOY6pod10b9pD0uh48JJJVk0cAjIzuodPy+hnUH3KhCYqntXoIxwODI2Bagcq/dAz4lZGpze6thhYCV2rohV+IVjXX5sZDA7TZxJWtp/k2OkfADl7B0+ju7WfvubO+J7ii10aex4EoNOf0LRroGsTDB6r4hcGwjOGOa3jivZa5dkcV7KKcdAcdEGehdT8vVridWPGuR62ELwIaJZUGEwCej5dayPTElFJRXenBKAGuZ75ZDDLA9JZn6Hjia91KU8Bf0qgJzqi8Ly8Q/QjqFk/gbQCSQW3qCBiSRK/C8syqpxBxEugkzjHgLnzphFtxGSQi11PHicMmaGt13GhdHGa3RTspVQZ9YRURjDZ4lMAuCyWxRInMSt6LK93USql+Gy5PXwbpMWP7UrZ/eYLyRYMeN0ocaQZXDw5fCO0o0JImQzkepgcJN9mJliH7mqKv5MEKlW/wtcFgzmMrIDhk8FUYMUrYOSV0L1KPav0goVVeAsprDJPcVblB3Dc2VCt8vCxv8LOZRe69/gMtSq9NCsDtFN3QrirQwfYy8bu4/kbPwRPfNmtwnaa7pO33Ng8gjCnHQmOrv9bBvq7IFiR+5lKGlm2pjQ67mzvigw6CFtNZAZLjQOg4LzOem8KyhYFFOvxv3OU2GbjyqBcqqWx4SVoR2tyS1zAyNL3jBneRwzP5PG+SaISvxD8hbAKaoqztohHVCCr4lZ3IPh1KhOX3NerhbASZDxw5RbuHoSxanyXKNhJXJpyYkLYlyjApVab5FnZwC71FUQQS1Ti+wWPQiaJ8YKJjP8u7SbfZ1248MCeShvZCCDaPZkASoLfOebbPrHQzv1OFgtb74H9T84CNCay22Aio4IAp/RR7X5Z2jlXZVNqo8LkOjAMi9ZA/1KnV5XJWQxhmzIR+veLlwkZE6g8iBpU+m6WQzXFtlwaZmw/bLgDrOWGMz/FeH2YlQdu58In/pSwIhwmZhP1t5Kno5Yy9suvZnDZZc9tR4JhpRB8xVro9o7iwuxkobcSO1rYA+YbJjS4ibOhWJub1IEXqtPk6n0zgIKi7ik17jDOmhqYeuOtFteCqDrLPCI6qDCrQmEZ3UAhMzLT6hBYSSGRuAIkHTMX0NLxFMMLCmHJNwRPGwFU/I2wgyCP9RUwXkBGdd7wV0DLf8ICCrGthHng868TMCodKNaprsdgc+eX5AM5EyDI42KPqBgErCTt3LhRpE+BqNjYJMufDz1LYNtdhD4gn5CPBE65ACZHYfNDnlVTZP3W99dOFosqQYvP1W447iJ47GZYdYazNSN1roxS7yZbDZvgyihMbNJmOn++DvRigoBNbp3zkRRYsADOfBnUUqzp5vmb/541e24kmiMZChJFJ1M7LoBdB+B//hNHEuY2cBkbC24d+kf/5kYpQ9WKkCzHh8ZWnUlm1nBsFN42S+Jp1iWZUDNgIW/+E7YN+QyE1RdTTKP8rA4yUGQmNECZKAsgBjZgIztSOBWNYSUd4wat8X/1sn1QmJtYRwIYwr4y3PcHup0yXmSisrgb6l+DraSnUE3qzfo2taVEZA+hts0KH81CZ6nHAiD6P2JfJO+SI9GsjbZogcUTxT49WQQzEZ/vtq+vCm4VbeEIdF/oNpgHNmRCVZEDA0tgYAH0DznWE5weWgdoWx7270pi29eqcNyZ0FwBe+8E04BFx0F3NxxzFvQtcO+XcskErKojTEYyNvTsohdvhHnOMq+GsZEYqHdB1XCWvY2B/AlqnYliXel+q9PLrfMd12jN9oIZYW4Dl+gXKr4ndYnfKSJ4lfUBuYl6Ig0E8mBgIDJYBXB8ouX9gEY1tB5Q2qRAJR/zogYD6j2zBaMeAWY1SA3ApUesDDx1qfBeU/zMsDUy8RGpD51NC83EsqmvwQl7BkkEAENZS3Wl31+ukwK4lvKpsKYIiLaUiNSLyoN+T+EZRX10ucKkoNIRgNN5kHyGTefyl2jyIqJlrQd6V3m31SZOaCKKZcJIu2FgIOowZRKdHPMW+cRJFKBSgUUroT0CdhXQgKFlrt8NDc+st2CMTTEY4niQ8utFqsNOBP6aNn739TiSbIGhKrCKzFbYwvEclWwgPbQbmuIXTyUo/eQINVdzHLiISvYEp8cKzAk1YxAHUVCUGmdzqGdSvRcu8b0keKlMiiKCrEJpK3cM0dOEB7mWt0cqD7wyYyh3itARVNp68BUa3c/Qhd6lwKkAcp59TlecSxuxbWtV3W/RbVmgK4fp0mEbPb6XJgbahkZm+UHPJMcBSUEMJgJAeZYtmGj5ezNOAvVMKCZGBGhie4Y6zGMcPcNrkVPyFBiTz5tmVzpeJ48MXucrALT1LrP9e6RNa6akw1QgWIGgLwtiKKpv+t8dn8/cwMAgnPpC17eCj308S7FQ3QlLl3sA9PmQCbqM7aoYhaCBJ6foO06XQwNfAG+VgJYORlbByCo6WRePZK9nee0LpE/cDft2kHdypqtL6cn2Fbu+HoPPEOY2cPVWvE4r8Ra/woxsrNBgHCpiEN51sSEodqVjponqZCIqKqah9Rw2pSD2pDK9Kg+fxjgWKIrpwJSkY5tih9ITfWAdKhjrOqNsZTEm6kGCiUOJbQSm4CtEft++FPZ3E+yDyv0lBV6+B+4agoZXvtcyePF+t3pYBXbWGXykl18wi7yOR0/xvg6CIamUVQGpMLrUuHha/6EZaMibALa/Liuc1td7WHm0UDCIFFATpb7Kq1ihWwjW73rxRYqUq4lAdILCbhPjxUufl7yktihskyKCLx6cSKIeMMcBn0kInm5FQsiNWwjJcgdsbd/PMw+A0vfCqdtS5zae7nS4oG28xBW0RJeJP0yEKr445vTFcKvoCjCBetLgVeln3LWTToOdQ0xv2sNNz/sHXrX+CtK86ZrTFp97pjC3VxUP/n8MDPb6BverctpNi8zQYpuSAS3cX9FziRI2uJxRM3ou+x8VcwgmBShmh2s0OdZLOkxmvRM/rauxsbH19TyPIoZY/UunL5s/yNjV7xJQuGfQ3TtjHL6xEl61DX6wBHb1EOh4bovsCgXO8jtJKJzGI/msGOSgVVfvVunxVJB3SWcM5TcFAuUuSZvZ4mAL11WiYXXRA1IQa/Q2LFmpFBMHD0h5m3BARUGs9N/11iBj47SeGrf1yQB1NXBF7JPFoMA+iJOLmD1IfQTdla4Tmcx8XWSq7BI3I54fIG3Y8fWBcd9lK1WWu7i6r2ggxv8WPW55J0bHqnFAXGmXiV36nbXu2SyPOmXpt3q3ie6jBshzbGbpmG4qdpo/Hn0VJ+y9mzftv46xVpvBj3/9ub2q2DEmnCGQlpmHBcSIEuJsluOWr0M/DwgQZ37KDSUztyWcrCMn6mgQEdYhzCG1blWy4tmYOOxL/XtTE0XRNI0ZNcS0g6Gnjxeu+XgHanD/QoL8M+oBZbzH+b363jIYrUFTak1mZ5+WuNPRS9OCQplckxWhJA4OEblLi5cR4BVASZ1ofaO2lDf+HTIoAzlQs77kQzp/YJAFO4DiM1pElq1bZetsLUrq/GCjCJj7Sa9qnElBraTLk/jgyijikgYoASWYqfcUMM+I/aKdF1cqrU9b2i/PHaDkql7DJGpiWWX7WWExSn4rpqfLHjb7+/yKKBhYnFz3f/VkD7HtpX/pSd26+6ZqqNomJAlvXXgPPX3jzlV2owl8nR8V5jRwWalEK2PRFFRChdUwv6c2qkJ8Q8gpxEFHJOAjACQzjBezwgboJOpKNCkIopp0FNWBpFOFAvjfck/S0R0hKaUrYXMfNCswUYU9XYReK51nnzcG3Ncd2QxSX1K2pMgSJN/y7jIgFGzHVG8v2GLpkcrMa6ViRICypesBQWaGQlw1cxQbPw4gEedmvFfejQLYWfIi4l/uVx/FXkvSFg+mic+b7JXV7Vj2RitfQhurDuAH94zMCnML/Ve3uQaZJJY3lKdUZxpsC8zW35shiPl3FKrYqnyptMKHONHqYIqfY6r7HBL1LICpaY4kzGngMhgSzx5sbpzPbQ1W8smM83OeebYQ4iRxNgugkhRnYWOc25ACrhml0yFg3cwVL+MALmwzMjGu1hHod5U7dWEVyOf1UBUeGYTJGuGBQOF1DZWBwncoMbIMIMvMd2j3vTIJyGa+8B7JuyiMS/ZUYSBKp0bdl9964Ek8D3bBQl8NCtEvhTorPysFFqAUsbBUOYV6kborDXIN0KJrEjGtrAPSE6Wwa+1iOswDJpapsO2J2NZiw6WBzuD6XPCs4W+U8U3eIcAqE23YR6ueKfiL18Bfrh91LVcZniHOS3oKtHT+9fcZ/dLnc8YizexhTgKXqOUmxhqk1mAy4w4U7faK4A7uUNEMb7SHU2S2fYcjZcax5VhvZ6MqMPFMIskJpzcHXYge7ZYZAyF0LBsb1lqnexBxQDpCrhSpBSBLIh7IuzoJfGUAshbOZ5YfCB1/ckoYoMJ48pgXcCJuksZBpfOGiheKoi0RAdvxAyKJpgBlWUMbHOZQMLSdTaVaAPxcAV2islTOp1zToAkFJqiPbhMvq0G0zClMUPLuIJKLjlG1d+79q2W+jClObLQmrhZC9H7hnZIGZl6tFJqFjuo3wV2PgJcqp6jq5JSkUAQPYmHVXADfxwvXfFxhRuFZbcyq+6m0i/ptiAfHFOYGQ3GysK6c8j7pI1rMLE8Oag4dazidxo9Svc9J4BofdyeBrF7135/lnMyH+TAf/m+E8fFxBgcHD3t/Tq4q5nnO448/zimnnMLWrVufcfXhuRbGxsZYtWrVT1W5fxrLDD+d5bbWMj4+zvLly0nK29ZUmJOMK0kSVqxYAcDAwMBPTaPq8NNY7p/GMsNPX7mfiWlJODykzYf5MB/mw3/RMA9c82E+zIc5F+YscNXrdT7wgQ9Qr9ef7az8p4afxnL/NJYZfnrLfSRhTirn58N8mA8/3WHOMq75MB/mw09vmAeu+TAf5sOcC/PANR/mw3yYc2EeuObDfJgPcy7MSeD6m7/5G9asWUNXVxdnnXUW3//+95/tLP1Ewwc/+EGMMYXP8PBwuG+t5YMf/CDLly+nu7ubF7/4xTz88MPPYo5//PC9732PV7/61SxfvhxjDF/60pcK94+kjM1mk3e/+90sXryY3t5eXvOa17Bt27b/xFL8+OFHlfvKK6+c0fYvfOELC3HmYrl/0mHOAdfnP/95rrrqKt7//vezfv16LrroIi655BK2bNnybGftJxpOPfVUdu7cGT4PPvhguPeRj3yEj370o1xzzTXcc889DA8P8/KXvzzs4ZwLYXJykjPOOINrrrlm1vtHUsarrrqK66+/nuuuu47bbruNiYkJLr30UrIsmzXN/wrhR5Ub4JWvfGWh7b/+9aJ/qrlY7p94sHMsnHPOOfYd73hH4dpJJ51kf/d3f/dZytFPPnzgAx+wZ5xxxqz38jy3w8PD9sMf/nC41mg07ODgoP27v/u7/6Qc/mQDYK+//vrw+0jKeOjQIVutVu11110X4mzfvt0mSWK/+c1v/qfl/f8klMttrbVve9vb7GWXXXbYZ54L5f5JhDnFuFqtFvfeey/r1q0rXF+3bh233377s5Sr/zthw4YNLF++nDVr1vDGN76RjRs3ArBp0yZ27dpVqIN6vc7FF1/8nKmDIynjvffeS7vdLsRZvnw5a9eunfP1cOutt7J06VJOOOEE3v72t7Nnz55w77lc7h8nzCng2rdvH1mWsWzZssL1ZcuWsWvXrmcpVz/5cO655/KZz3yGb33rW3ziE59g165dnH/++ezfvz+U87lcB0dSxl27dlGr1ViwYMFh48zFcMkll/BP//RP3HzzzfzlX/4l99xzDy996UtpNp2fqudquX/cMCe9Q5iSK1hr7Yxrczlccskl4ftpp53Geeedx7HHHsunP/3poKh9rtcB/MfKONfr4Yorrgjf165dywte8AJWr17NDTfcwOWXX37Y5+Z6uX/cMKcY1+LFi0nTdMbMsmfPnhmz83Mp9Pb2ctppp7Fhw4awuvhcroMjKePw8DCtVouDBw8eNs5zIYyMjLB69Wo2bNgA/PSU+0eFOQVctVqNs846ixtvvLFw/cYbb+T8889/lnL1fz80m00effRRRkZGWLNmDcPDw4U6aLVafPe7333O1MGRlPGss86iWq0W4uzcuZOHHnroOVMPAPv372fr1q2MjIwAPz3l/pHhWV0a+A+E6667zlarVfvJT37SPvLII/aqq66yvb29dvPmzc921n5i4eqrr7a33nqr3bhxo73zzjvtpZdeavv7+0MZP/zhD9vBwUH7xS9+0T744IP2TW96kx0ZGbFjY2PPcs6PPIyPj9v169fb9evXW8B+9KMftevXr7dPP/20tfbIyviOd7zDrly50t500032vvvusy996UvtGWecYTudzrNVrB8Znqnc4+Pj9uqrr7a333673bRpk73lllvseeedZ1esWDHny/2TDnMOuKy19mMf+5hdvXq1rdVq9swzz7Tf/e53n+0s/UTDFVdcYUdGRmy1WrXLly+3l19+uX344YfD/TzP7Qc+8AE7PDxs6/W6fdGLXmQffPDBZzHHP3645ZZb9EkV4fO2t73NWntkZZyenrbvete77MKFC213d7e99NJL7ZYtW56F0hx5eKZyT01N2XXr1tklS5bYarVqjzrqKPu2t71tRpnmYrl/0mHerc18mA/zYc6FOaXjmg/zYT7MB5gHrvkwH+bDHAzzwDUf5sN8mHNhHrjmw3yYD3MuzAPXfJgP82HOhXngmg/zYT7MuTAPXPNhPsyHORfmgWs+zIf5MOfCPHDNh/kwH+ZcmAeu+TAf5sOcC/PANR/mw3yYc2EeuObDfJgPcy78/zq8ZNEy2U1/AAAAAElFTkSuQmCC", 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    " + "
    " ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "ipfim = IPFcolor.qu2ipf_cubic(datasm[-1]['quat']).reshape(nrow, ncol, 3); plt.imshow(ipfim)" - ] - }, - { - "cell_type": "markdown", - "id": "51b5af1d-98c4-47d0-b0aa-03b67a78749a", - "metadata": {}, - "source": [ - "Todo: write an exporter for .ang files and hdf5 files for the indexed data. " + "ipfim = IPFcolor.makeipf(datasm, indxer, xsize = 200); plt.imshow(ipfim)" ] }, { @@ -578,12 +652,12 @@ "id": "d6961c13-9ef7-4be2-ba7c-40e3285398e3", "metadata": {}, "source": [ - "And of course, one can index a single pattern as well. In this case, *pat* can be a 2D array *(pH, pW)*:" + "And of course, one can index a single pattern as well. In this case, *pat* can be a 2D array `(pH, pW)`:" ] }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 44, "id": "c373ce83-ca1a-49f6-afe5-695c75ab68eb", "metadata": {}, "outputs": [ @@ -597,23 +671,21 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 37, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ - "
    " + "
    " ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -625,7 +697,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 45, "id": "f7f8c560-b14b-4f22-95aa-9754fa8ebe6e", "metadata": {}, "outputs": [ @@ -633,33 +705,34 @@ "name": "stdout", "output_type": "stream", "text": [ - "Radon Time: 0.007740249999869775\n", - "Convolution Time: 0.015711572999862256\n", - "Peak ID Time: 0.014265244000171151\n", - "Band Label Time: 0.009968713000034768\n", - "Total Band Find Time: 0.047706499000014446\n" + "Radon Time: 0.007377516012638807\n", + "Convolution Time: 0.015867227921262383\n", + "Peak ID Time: 0.01943869306705892\n", + "Band Label Time: 0.009846666012890637\n", + "Total Band Find Time: 0.0525470309657976\n", + "Band Vote Time: 0.001555563067086041\n", + "('quat', 'iq', 'pq', 'cm', 'phase', 'fit', 'nmatch', 'matchattempts', 'totvotes')\n", + "[([ 0.38395016, -0.20891693, 0.28739166, 0.85225702], 0., 158503.9, 0.77072525, 0, 0.4607674, 8, [0, 1, 2, 0], 8)]\n" ] }, { "data": { - "image/png": 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cJl49gRX08lw2nhzWeXnnINGBym7y+qtSpdq7SgWpHifncvOCdVXa9JbcgFalbymI7wXwrePmKpy9BVy8nPbl5eUOt4OXSGFR9bvyvsnd10Bn8kKtVnOD4F6vJktGb+7lvdRt8kU3vysrKcK7d3TygKzs96qDtatQJnhoN1+/CIsgCIJgxBk6i6DRaHRoFlawzusZoseT3831QClRJfXUO77u68NYy/BVCTBrrLQ2+b4MJHr75OakVAGrtSArLbZknUlK1kuV7qP6/Kx0RC+t15o/T17vunGan1VlzWhrSAb+vC6xVSzSkmzdyOIlR8jv6OPIcwdWd7TVC/d4Fr3UfEtLbloyeta/VyWfs+iZKpXNPB6nwfLSsNZ9X0r/tixWr4q7V8IiCIIgGHHiQRAEQTDiDJVriMkFhEsuhm4CnFVyixmvslMGt0q55RrppvByzavUSejzyX2vZFZXqWT2XjPSpPXcSFZtQJUGZHoffY6eG8lqvey10fYaleXOIxf49Sql9WJB1kJCnotLu2EmJyfdtuHeOtW52gMts3bZyHGZkiuq0Wi4VdaM5fpYi2u3SoW5HtO7h3IuNT2uvB7ASi0Au4j4/fHx8Y5x9cJSvJVrkOt7JprOBUEQBH1hqCwCIsLExISpxTFaK6iiNZbaKOcCbfp1FWvFq27VWn69Xu84jyppsd55aHKfl4LRVsDNq7zUr2XfFi9N0LKwSj1qWBvSbXuBzoV1pOYlt/L+qjLHmm60RUanZnqtq1nzm5ubw9zcHICVc9ZVuqxhzs7Ots9Xz4HXX8qqrvYsQysQ7C1041keTL1e70hxrTLnjJfwwMh72ktDzaWGltKfc1Xweu75+mzYsAHAynXidvDyPtQLSp08eXLVMeT9wuPrBI1eCYsgCIJgxBk6i0D6cHMpf1pDslLMrNRBa2vh9V5hcrEKTwPTGqBcjFprG1XSSb3PcpqNlldrNFa/Fi/24cUTrGtYxZfOsDavNX/tO7XmXGr+8rU8bqmnTJX7w0tTrQKfs7ZaWIvcvHlzx5KiDJ/n7/7ulzA/P46UxtBoEK677m0d1haTS4vVc6H73FjLXWqLncktus6vdVqqN2+52JFGWySWlV5Cxvhyssjxc0VvbN3x+bKWb8Wq9HzJzrVyK6+XFbPphaF6EARBUJ2Pf/yNWFzcDABoPVOCYE2EaygIgmDEGTqLQJpo0t3jVeDmqkS9SkR5LD1GqfIwZ57m0uaAzha09Xq9IwCqx7fSVb0Kx1yqZsnVZPW00efOMpZ6N+XcVxrpmir1nbHO25trLbN0K3ltmr30wbGxsaK7yAucp9TZnlwvwsIBRN7Ozs623UTsotHpnFNTt+Ff/as7kVLCd7/7E7jzzr/edj+wW0K7J6yWxbLqVx6PX2v3Va1W6/h9alednnO+x2WVsNcDyHJjlVJnLVeod4+WUrrleJ4b03LvMDrwn2tDXUoNto7nydYrQ/cgCIKgzBe/+Bs4cWILarUX8La3XY8DBzbjoYfOHLRYwWlKXx4ERPSHAN4C4GBK6dWt97YB+BMAFwB4AsAvp5Reyo3TaDQwPz9vag1eXw5vAY+WDKu2XldGS4P1xvCCn1bKpEYHLsfHx9sal9VNUsqYw9N6rKI0T+uo0pfGS921PveCWlqLlNdTpzvya06941Q81pYnJibacrImtrCwAADt9Mvjx4+v+lwGLD0rTF9r2SnSu7b6PK3rpu9dfs1a+ObNTX//1q1bsWXLllXnzN/leTtxYis2bQIOH57G/v17cd55z7YfBFoT15acvP+0ReClnMr584LB3kI4cm48DVkHx2U6NcvE86Rf678FuQJUbwEoSRXNnL+nrSB9H7JlwK/l1lu6VCc6yGtSNVW3W/oVI/h/AbxJvfdhAF9JKV0M4Cut10EQrIHJF1/E5e98J15/9dWYff4JjI833UDj4ws4++wH8MILZw1YwuB0pi8WQUrpvxPRBertKwG8ofX/zwD4GoAP9eN4QTBq7PrGNzDzzDOg5WWc+73b8Nf/730AgJQW8eijP47HH38lgMMDlTE4fVnPGMGZKaUDAJBSOkBEu6wvEdE1AK4Bmmbw8ePHOwJ90oTSZhqbhzrIJQN8nrujylqu+ni5tWO9cbzqQyvY5FWdym2uNbAlm1WHUaqIlft4biMvmCwrSKuumyvdFDqo6QWEJyYmOtwUsjoXWMnfZpN8eXm5oy9MlfoS/Z6Xq6+rRa17SicPHDt2DMCKK+fIkSMdboEfTk/j/FYO/1cXz8H+f/c3Vp3XyZMHOtwQWgbrevI15GPrHkdWYF27Hku/M6tuQQeU9TWXwWsdTNcuIet+LFUs5xIqvN+G1aK71LZbJwZI94+33rD3N2d8fHzdXEMDDxanlK4HcD0AbNu2rXpVThCMEId37MDvfvSjzT/MtRrQ+oMdBP1gPR8EzxHR7pY1sBvAwdIOKSXU63UzPcyrbmXtR38+NTVlpr5ZY8hAladFeYFgq2+Qlw5mdRjNpW/K70oNwAt6M1U0XC+grDUZiZe2qmXtpiKXqdfrHVriiRMnKo/rYWlQWjurUj1etUurtbygl1rK9y4HtFmjP3nyZId2r7uC6p5K09PTHcFUywLV5+kFfL0gbjeJG3orF6bRC7bo34i09L00Uc9zUKWDrr6XpQeBKaXDyntWo+dG33M5cpXu2iPRTUV7jvUsKLsZwFWt/18F4KZ1PFYQBEGwRvryICCizwP4JoBXEtF+IroawHUAfpqIHgbw063XQRAEwZDRr6yhX3E+uqKbccbGxrBp06YOk9YyZb1cZVkzoE1Eba5p80rmmHtVk17QM9eiVh/fqj3Q3825QbTJ7VXESrwKTs+NJSuLdVWr16Jbyq7NeZ2/rV0SshJcm+C6UlZWqvJ42gTn2gOrBsFbLzdX3aqvO2MF9ORWzo12ZejccnaFHTt2rB1A1tXBLDO7VjZu3NgeX7tVZOKElEVee+3a8FxPvVSyyjlhuXmrA8HahZJS6mjPrBMCtOtG3heei9eqgdF/SzyXk/y7UgqUa5eX3JZcrdbWc5H3SvQaCoIgGHEGnjUkqdVq2Lx5s7nwhdZ+c/1MGK19emlZ8qnNx+R9+DushTBeWuRasYJXnqyeRptbZMQLdnqB5lqt5gbCSi2zpYxaU9ZIK4Dn2FuAxEtplHJrGa0Wv6VAub5vcvOnr5e1MI6XpMDoxWZqtVpbQ9YyecebmprqWA6xFGSVgV99T3lV0JZ17i02o8eenp5uWzBcRb1p0yYAKxaBTiFeWFjoGFcHsvXyjRZebyN5DjqVVd8nVqtzb348C18e30vq0NdCytqvvzeasAiCIAhGnKGyCNi3nPNDen04WAuShTFe10x+31rOUPf+0WmUrLWW0ggtcr2N1uKD9VJOc/EKL77AWFpwVS1Eakf6OpU0J2lF8L5aC9Yph9Z5eSmMLPvCwkIxXdTSHr00PS9mJXspef5p3f+G4xkbN27MLrYi97Wuue6/pLH28frqaK20SiqtZ7WMjY21v8v+fZ4nfRx+f2FhoR1D0Zq/l+otx9PnrOfesoz1PaTvS7ktLR7vXSe5KJW+P7xiMStu1y/CIgiCIBhx4kEQBEEw4gyVa4ixTL9S22RGugp0UFG3Pl5Z5GNl8Q/PxcB4QUFZZajNzVxA1ks389w+jUajw5z29pVBp6r9U3gMGVQttca2ZNTjeq4hmdLI/9cVstpNJ10PXuqdt/iMlVroudbW4vbLLQKjr6muAJZBXj1fjBXk5OOVKmz18eX8eeeRmxPvfvPcWktLSx1uHp0arBMGUkrub84Lilv3uk4ztu5hnTrrnYe8x6zEArnNufA815oOesu/dSX30VoJiyAIgmDEGSqLgHtpaG1ucXHRXcTBW5RCFojoIJMOuMjgj9YGSn17ZFqk1HwAX6PspmeOlRLqaeReUZO1vCWjtW3L2tCFSB65nkZel0k5zzn5rfOUx/NSTPm70iIoaW2MFdzP9W+S+1hBd69Yy1oUpmThyGAqv9b3uRfEtSwPLWMVa8i7Hlp2me7JGj8Xz3GfJQ4e686psgiNU0w5BVUXnFrLaXrX1goE69++/q1oKwzwO+Vqj4U1n166d27hLG3RlILVVQmLIAiCYMSJB0EQBMGIM1SuoVqthunpadPs1j1XFlU/dqsCV7sl2PzkPi78WgaotPksc7vlazZXrXbDpRqDXK8hNgtzrgfP3PUCiZZryDPjGb4W+thVXud6onh1C3IetWsoFwws9VnSprrMZffIVXN7wVUdHJeBW8+V541pBbS98a36AX2tu+l3o+dAj517z+rRBKyuANZBYf5M14rI36FefIq3XhDXmhPPZWNdA682xLq2pcVl9HEs17W39rf1m9Eu1ggWB0EQBH1hqCyClBKWlpY60utqtVr7/15vISvF0QvO8Wt+qrKFYMHBLL1ACMvBGoysiC51E5Tnq/+v97F6lpSqTK2gltxfypjrv+RV6XrHlRqMV8GZ63FUCsBa8+ctFVilS6wXnNbdO61Aoj4vnfIsNTcvldVL98xVcGsNnu8/GWz3EhyqdKf1evHk+lbpMbzA9tLSUocFqAO/eu6l1eLd94ylYet9dOp4lSBurho5lxpuHZ+RSR+lgHbOgu1X99GhehAMG5s2LeMTn3gaF1+8gFqtho9//CI89dSWQYsVBEHQV4bqQbC8vIwjR4509FGfnJzs6DNTWlJP7s/axo4dOwCsPGl5DOm3lOl473nPt/HAA6/Apz99AaamCJOTdZxxxsyqMa2+I57WqH2MVbQCxipC0xqD18tG+py1pWGl0AJNTayqbFpGSzZrCUf5PVnQoy0P7Q+VWpDXkdTrDmv1lNE99y0rwiumq5IirK0EvQ6Bde9qf7/2i1t+c93fqUoKqp4vz2qRvvBSXEYjrRivf4+0rOU+Y2NjleIw8vOUkhsb8JDX2OtNZt3/nubvWcJWQV6pSNaziDyZ1kLECBxmZpbwoz/6Am6//XwAQL1ew9zc5IClCoIg6D9DZREME2eeeQJHj07hPe/ZhwsuOILHH9+KP/qj16Je708BRxAEwbAwVA+CRqOBubm5tqksW0Jr01gvcaddRfV6vW1K8b5btjT9+9u2bQMAbN++HcBKwGpsbKztJqrV9uGii/4Sd999Fb73vfNx+eWfx7ve9SJuumnvquMwY2Njbjqglo3lkhXTpT4+Ei8F1PsekDdv5eeWC8BbyEeTcyF5Msv3S8FiXTEr++tUWVaQz8Grrtbjs+vG6gnlYQWRvQVUdIBbyqj7D+lEB+0GyvUN8pYAXVxcdF2r+hrKa19q4+25IeV55Ra80bJ7CQB6/iw3nQ4Oe649eS/p+8BbhMhabMlzVVrVwl4yhn6/SrC/V8I15DA3tw1zc9vw7LNN19D99/8odu8+MGCpgiAI+s9QWQScjqU1pvn5+WIfGkY+4bUm6QWY2UI456mn8Ibf+i2c2L0bt/3mb+Lkye0466wjOHLkLFxyyVM4dGh3WzPTWpZcRq5UpCO3nlbjpZIRUYeG4m2l1u1pa3orNRgvtXQtnQ+9vjdW4LGUyii3JQ3W6uPjLZupixXltdV9qhgvPTGnvXkL3Mv5tpZRleMzsp+WLm7SVl6uP5G2pKxlGbVspa6t+n6RS8F6vXhyvZo8689KgCj11rKC4d6Sm54lYKWMa7z7RVrA3r79Xnwmx1A9CAbNWXfeifH5eWx68klsPHgQ+/b9Kq644vdQqy3jxRc348tffhuAxeI4QRAEpxPxIBA88bM/i20/+AGOnn8+ju7ZAxyu4cYb/wUA4NChQ61vxYMgCIKXF0P3IPByxLW5VqqQlfuX8rdlRemGSy8FAGy49VYAnTnmXltlmbvsVTLzcWTOtF5rubQeq7UYS5WW2Z757m1l1a421y3Xkz6uVRFtjSXlyckit3JMLy/dC6paPWW0S0D3w1lYWGhfn9J9qK/91NRU+/983assqOLNvZZd3n/avWe5++Rrq0+O516U10/v57kxNfIae/sycl71b1u7bHL7yGPnXtdqtY7fuucKlWN794HV10mOJWXwqrit+75fdQOaCBYHQRCMOENtEcgnohcoYqwAHI/F2tyRI0cAAC+99BIA4OTJk64c/GTnamTecqqpXiBCWi06TY81jQ0bNgBY0QynpqY6NGcrDVaer5W+V+rBIqsmmZJFIKuedbDOqyyVWk/VFDhL6/H6BzG5zpSyR438rjwXnSwg0ynl+6UFhiwsC4GvN3eu5ZRnvbAKU6/X27Jo7Vv/DuR9oq1Jfc2tTqzdJgLIJAw9rtfR1vodMznrgffR95ueN+t4Xv8mb4EruUhPzoqUYzQaDXe8UpqsPC/PUqui/UdlcRAEQdAX1v1BQERvIqKHiOgRIvrweh8vCIIg6I51dQ0R0RiA/wDgpwHsB/BtIro5pfRAxf0BrM4/zgUO5VbCbgM20bnCmBemYRfR/Px8e1zdkEwfR1c6W/UPOhjJwUcZjGI3gdf4TAe6rWpQL7hqNbjy5im36EupGlkH0omoIxjsBfgYK/DrLY4imwl6QWEtk5xPzxWkZdZVvZYs+nXuHPh4+h6zKoJzrZzlWHxPLS4uVl6bm+/diYmJ9v+9tb/1/BFRtsZFvrbccvIc5da7X2QSRqnNu3QpevURjG5IKRM3vKZ9VnDXC0JrN7H+3DtXC/l5lUZ4a2G9LYLXAXgkpfRYSmkRwBcAXLnOxwyCIAi6YL2DxWcDeEq83g/gx+UXiOgaANcAzWDa1NRUR3BI9uUoVTXm2g1r7ZG1Ian96BRTnTbopSdKTckL+OoeNlLz0214tcYpUxtPnDgBAO2tXsaTkUFVmc4oj5OrhNVaqSaXoqethVKAW77upgeLtqC8XkNWpSqjr6UVQK26OI++B2Rwmq+TxgrUev2qSmnGQKdGq61bGWDXFoFnKeYWSSklAshr4aXs6utnBbT5+nhasdTCu7Va9P5y3Fxb6qpV8JYGX0pTZeTYnnXSK+ttEVh19quuUErp+pTS3pTSXr1GbhAEQbD+rLdFsB/AueL1OQCe8b5MRG0NRb/vFS956VtWgRePzX55Tufk92XKKWvtrHVzPEH7/KS/l4+j0dqc9F9qrd7zG0vfsPYX6y0jx/L8ttoPbxV4lfrrWOdb8mVqLdLqAcQarOcblhpmaSEaqw+N9gl7KX/yWnhWkKWV8lZrb15arPTTe35wLYfU7vU8eRaO/F6p42vOf+1pw/pzeY28OJo+L3n+VXsbyXs6Z3Far+VC9N45W9q3d91LaavyfvesFGs++2UBaNbbIvg2gIuJ6EIimgTwdgA3r/MxgyAIgi5YV4sgpVQnovcA+AsAYwD+MKV0/3oeMwiCIOiOda8sTin9OYA/r/JdIlrlXrFMTs98y/Wh0SaqDNYCK64a6SbRLgU243ILoei0Q6sNL7BissuUP5ZpZWEcuwX07Oxsu7pZyypl2bXrJVx99W38DezYcQx/9meX4dZbX7Xqu156ncRzy+nPrZ4oVpWz3Frpqroi3KtslpWqVotqS0aZiqzRx5duoNIc6DGYsbGxVZXk1nEsV4c3fzpwKgPpXl8bL4gr3SGlBX2s/T23WO59mYZqyWq13/bcY969JH/7peskz7+0OJQ1puXitPa11urWf59YFs8FlpOpV4auxUTQHw4e3IqPfewXAQAp1fHJT34e+/adP2CpgiAYRobuQWAFROR7peBjrpOjDlSxFi7H8LQOL/gjUytLnQe1BlClGERaAgCwadOmdkEcv6ctED5PLpTbvv1uHDmyAxs3XoparRn0ZstDB5j1uVhUKWaxinzk+9YceJqlF6C3NOecxcHva5m6SW3VGqu3rwz8ac1cBwP1vVWv1zs0Zy+V1lqqsmo6p9SCdQA7F9wv/Ua84HGtVnMD8d5vI5e0oGWTY+n0ZY2VVuzJXaX4Ul8Pva91Tby/KV7Kae4+7JXoNTQC/LW/dg/uu+/VgxYjCIIhJR4EL3NqtTouuuh+PPDApYMWJQiCIWXoXEM5cx/wKwQt89tz63h53RaeK0BTr9fdNs1e4Fd+xujgkq4G3bBhQ9sltHnz5vZ7ALDx4EHsvfZajJ08ibs/+lEcOP987N79Lbz00oWYnj4f09Mr39UBc318KZvnwsgFmr1caF1Zaq0Zq/vp5Naq9eY457rpR1VmqVJVXseqPXL0/FrH00FHRs6FR841VAoWS9n1PetVwlZxk3lrMFtzUDov+Tv25sJzl1nyVglOewkhpWtu9UPyuhkw0rXGeLVL3RIWwcuIc778Zcw89xymjhzBxZ/9LADg3HP/Co8/fvmAJQuCYJgZKosgpWQGL2VlsZVe5r3WaZW6k6e1EIQ+TtXAbi4IpL8jtQetUejAL8NawuHDh9vVyAcPHlx1zkubN+Pc1v+/v2cPDhx4FDt23IObbnpzOzjM56E1CavviRfI8+bT0ua8FEa2cOQ10Km0fFzdS0lqtl5gVMtu9ZTxsAJwnmXDeJaB/J5nmeZSn/VW3y88Rq4a2UuxzaWpaq3eSqn1ZPS0cWtec4FlPZZn4VtjepaulWoKNOez1ElUb63AuWeZ6nOw0H/D+LtyTN03ql8WwVA9CILeeOzVr8afvPe9mFhcxIELLwTqhE9/+uOtG3d9StODIDj9iQfBy4znzz676CsOgiCQDNWDIKVkuhUAP6iqsVrElqpnrUZ1erxS8y8LbVbnvuNVGbL75fDhwwCaLiJ2negGV7LtNLDSXG9mZqZd3VoKOkpTueqiL7qp3vz8vNtQTY8pTVy9EIw2f7U7aXl5ueiy0/dArkrTy22X17hqVa08nuda8JDN4ErJETwXMhDLVF23Vx9bYv0e9P3mrU9t3WP62Ll6BX18/Vus8turWgEu5S3JJN/37h39O7YWq/IWQuKEDus3kmsb3wsRLA6CIBhxhsoi4CCZlRLqPQktDYzfL1Uf85OXkUvPaW1Kp5h6wUn5f121abW19VoSa02Cq4RPnjzZ/r8O0vIcsfbPaaazs7Pt93Rr55y2qq+D18uGkWlwWn5G98qR8nhWik6hldaZ1fYZ6Ew9zaWc5gKieh8vmYDRloK0WvT4XgWz1DQ9bTSn3VcNAMvjeAFf6/j6fHjOvXm1qpK9c7dkrhJc18etWnEr58pro+3JLGXSyRh6a7VF59+AtNyBlb9L1mJV+u+R1xmgW8IiCIIgGHGGziKYnp4u9kwBOp+0uSInTwu1FqnWmqylUWqZtYxaNu3PlWN5y+LpFE25NCHPj17RjWXQC5JItF+X97H6MOllEbVG63XCBDoXV+eUV7Zm9Jjj4+MrhXGt7qqbNm1atbV6K/G8eel6Os1SnrNXXKSvRaPR6LrrqKRquqrV7VR/R49pFYflCrrk1iqIqpL+6C3dyOT6JHm/ba3ty2N4vX8874De38K69l43WI2cc32fa2td359s1W7cuLHdM8yzDPSytcvLyx0p22ERBEEQBH0hHgRBEAQjztC5hqz1RiVeAIe3sheHV42pA8/ShOV92CzzgtFWGqEXFNSpkzLgY7WzlvAYMlBaCuwxViqoTs3UvUtYjvn5eRw/fnzVZ2z+arNXn5+s0uTzYxP6yJEjq+ZAcvToUQArwbKdO3ea58P7Tk5OdlxjbV4z0pWiA/S5xYb0/oyXaqrdITLd0nNjaleivMalymnGauftpZxK2UsuIeu3WKq69wLNMi2W0W4k6QIFVid9eMFoK4XXm2ONlKcUONcsLS21fwv8W9GuT96X3bj8W5mZmWm7OrUrSAeg5bl4KcG9EhZBEATBiDNUFoEuKLNSykpFYVbgjfG0b6k5e0vnWdq8HNMqhPO0OqnBeMHpXIqcpxF5BXRSQ9Jahres4fj4eDtoqwvUtNamLZ/JyUnX4tAWggyWb9t2Av/sn92JM854ASkRvv71Gdx226uK6apAZ4BcX39Z9MbzpLs/5hbpKfX+8foyyWvspRLqwK9VSKnnWn9eJV3SKvgqWTaMvD9KPYa8+1Leh1b3WWtrnZuXWCHnV1t9XuqpvI5sdXuFlAyPNT093f7/9u3bV42vfxOzs2P44AdvwsREQq3WwH33vRJf/eqPrNpH99SyguTr1TVgqB4EwWjTaNTwx3/8Wjz11E5MTy/hYx/7b7j//j04eHDHoEULgp6o18fwO7/zVgAbUast4/3v/yIefvgVeOqpswctGoBwDQVDxOHDM3jiiW0AgPn5CTzzzBZs3To3YKmCoB8QFhbYam1gbGwZwPD0BBs6i0AG15hcIMwLGOkxc1tpxnnrDJfGlDJWDTbJ73iV01ZVY6kqU8+FXPTFWzxH9/OZmppqu3c40FWaa1nfwIvm7Nq1CwBw1llnrdpu3boVwIrbaWFhod1P6bnnnsPMzHO45JJbcO+9fx+NRvNhwAE57d6y5svrBWS1KNbyM9KFok19bx1iRvfFkd/RPaFyFb/e+VVpj54L2vLWOw9va7VO9+5Zyw3E7ji9zVUNl/oGWbUjPLfeuIxMdOB7X7eC99zFU1NT7cQGXfOi3apNlnH55e/B7OwBPPTQFWg0/hbOPnuljxgnS+R+q57LrlfCIgiGjrGxk9i79zrcccc/wNLSTHmHIDgtGMPXv/47uPXWP8D27Y9h27ZnBi1Qm6GzCIDONEHZA6ikEVnWgx43Z02whuKlA3oaoLWQC6OtBxnkKmksubTBkiVgBYs9bYM1CxkI1im5OnjmVfFOTU21qyZ5u21b0+XDFsKOHU2//+zsLPDVr2L54EG88IY3AFTHK1/5Phw9+vdx7NjrsX17Z9qq7CPkpdHp68Uyy6U+db8lngtdzb20tNQRxNSabC446c2j7q4q51MnNujr1k1vKH3dZIDY07I9K0JqpZ7l7i1cND8/71ar6zmSgduSda5ltrq3esF9K7nA+7uhK4A3b97cvq85WMyWbvseI8LE449jbudOpKmpVvr0Lhw5chkuvvhR3HffxR33t07GkNfeu897JSyCYLAcOgS86U0Y+9VfxcRd38aWLb+B+fkLcfDgOwYtWRD0zPaPfQzn/tzP4YL/4xdQo2YNDdE8duy4B4cPnzVg6VYYKouAe+/k/Lqlba6vS6mXt9Sc9XheB05rqTivN30uruBZK7kukHpfxvLrMnweXqGN7M3D2psXG2CNVvZCAYC5uTnMzTX9+s899xwA4N5777VlSwlX79yJ2RMncPv+G/APZv4UR4+eibPP/kvs2ZNw221/D08+eS6AFcuAt4uLix3noc+Z4xuy+yr/n7VOvTSg1SeJ4xN8Xvo1FxJpf//09HRbg9R+Y91dkqnX68WeV4wV3/KKBi3N2uuvpK+1lWLr9frRVgxrugsLCx0pujo2xXMl56ZkEVjxDO93qv8GSCtQa+RaVt6H74+5ubn2/nw/PP3006tkeeM3vgEsLWFi8YfYseOXsWPnMogSHnrob2Dfvj0ADnZ0EfaK7mRX334VkjFD9SAIRo/G2Bj+n3e/u/nDeRH42Mf+dUd7Y2BhcAIGQQ98813vwqVf+hIe/bEfw/6bXqPW5R6e+7on1xAR/RIR3U9EDSLaqz67logeIaKHiOhnehMzCILg9OPo7t345rvehf2vec2gRcnSq0VwH4BfAPBp+SYRvQrA2wFcCmAPgNuJ6JKUUtaeaTQaWFhYMCsVS2l6ORdRqfJWj6XOZdU+utKTkcFVD+22kEGtbtIF9bG9JRBlmmIpBdQKaOuAHr/2go+yelmmggLAs88+a0+KAcvNgWUOwOlzkMF2nQrMMuo0xaWlpfb/2Q2hK0q1i6Ver7fdRKUUWh30HB8f7wgKl1wdRNTxHX3uVh+rUnU1YyVhWG3IASgNdrXbqrQUpv5t1mq19lxz2iUHVfViLFUDxBbWb8nr8yTdQPp+125T2YcLAA4dOtThRtQ9h7zFo6ampjrcYLo1vHZ7yj5s/N2h6DWUUnowpfSQ8dGVAL6QUlpIKT0O4BEAr+vlWEEQBMH6sF4xgrMB3CFe72+91wERXQPgGqCpFdTrdbOAo2qRltTUrAVGAFubl/vK8bUG5vWLyRUOeSlssreRFyDSaYQyhaxkCViWB2MVnekxvL5KcsF0KTu/f/z4cbz44osA0N52Ax/3hRdeWDU+F6lpDUru4wUseWEcqW3rgLIuKJJpnayxsha3Z8+eVbLleg/VagnvfOencezYZnzhC/+wuACKLPTyrC8rBVrf3zrV1UqL9RZAYk2WNVuev2PHjuHYsWPt/3fDtm3bcOaZZwJYsQD0NdXasAwwV+nDBaz+e6HnKTf3+tp5GjrP5/Hjx9tFYGwBc3ddtgz4u4y08HgO9L2lkwjkPdCvdFFN8UFARLcDsPKcPpJSusnbzXjPTJRPKV0P4HoA2L59u18mGQSnKT/+43fghRd2YmpqeIKDQSApPghSSm9cw7j7AZwrXp8DYHjK6ILgFLFp0xFcfPEP8D/+x+tx+eXfHLQ4QWCyXq6hmwF8joh+G81g8cUAvlXaiU1wq4q3avtV2R9G55Z7+f25GoTScavUAnhtdGWL4lIQXO5bdbEcKxCdW1hHv+/VanjHlS4r7rnCZq93PvJ9b0EfNpm1Kb1hw4aOHHyvqlUGjbXLhGsBtKuIA5mbN29uuzC4kpRf83lqdxWf56WX/iYefPCf4oILTmLDhntw4YUXto+n2w4zsleOrgXQi8LI4K7M15fjy7oL+bkM/HpuJe2GrNfr7f2rwtdr+/bt7Ypynj/dE0gHX+V5lVxDucQLfU/Jnlosh64rYXJ1QLrqmO8ZHsOrayKiVTU78tw9N6OsFem3i6jX9NH/jYj2A/gJAF8mor8AgJTS/QBuAPAAgP8G4NdKGUNB8HJjx447sLi4BYcPXzRoUQbGddcdxNe+9iBuvPHhQYsSZKBc98JTza5du9Lb3vY2V1uVeD2AqpDbp0r3Smsrx/NkqyJjlUU+vGpPrZlLDcoLTHpWi3ztLemotUmpHbFmxBoz9xzi93VwcGlpqa0pa42Zx9e9XmZnZzsCa5amDKzWNHVvF97qKnI+zpYtW9qaLG+5twyfF2u94+PjmHz6aWz+ylcw/a7HsOlHvoFGo4ZabRFjYydw4MBP4Gtfe+eq8+SArO5fJOfHC/bLoLi2BLxOn7JXU0nLttDBdtak+dpyBfXs7CzOO+8JABvx5jf/Cf7zf/7n7XuVg6k68KxlX15edheTyS2QIz0Dlqx6K7VtRqfWaotqfn7eXEinCrLLsraEvUQBGSzW1+f3f//3704prarl6oaoLA6CftJo4OJ3vAO1Y8eQ/uMEvvPlL+M4Ec444x7s3v1ZfOc7HwRwZNBSnjKefPIC7NoVa0oMO9F0Lgj6SaOB2vHjqNXroHodtS796UEwCIbOIhgbG+tovmQtJqJdHVauftUGbjlXkRdQtgJI2n3kNZHS7h6LnPnrjedVGdbrdbcxXW6BFS84zGawnkcZbJXuG6AzR5qDhLL6WbsYvJbFlttKt9PWrgDpLtPBYl39aQXb2Y3DtQ2cV6/PBwAO/fqv40dvuQWP/eRP4gfPPts6zgYsLPwjAE90BHHZNSTf17nz3rWV19Wr8PXaGlv3rNW2GwDOOOMMAM1q7/PPPx8AcNFFzdjHhRdeCADYvXv3qn14fl944S6cccaN2Lt3Lx5+uBkr4Px7zrvnPHy+FlIOXfmtX+sFY6w21Bo+X+nm8ebPC5zLY0rXoNzqvy3y75ZX3Z9rqKnvzX4FjYfuQRAEpztPX3YZnr7ssuaLLn3HLwtSAu65BzjvPKAVIwqGm6F6EHAb6lLlJVAOvMr0rFL7afl+SXv3NDOZSuZZLzm8dsCM1Ha8RXK0FmwFGqv2NJJBOj5n3dLXC5CNj4+3P9NBQCsAxlt9nbSmpIN2ssraW3DEq8TNoa/j/Px82wLQgXkvqUBuS4FYfb7Ly8uuNur18ZF4C8ZY96W+l3i+2NLhNE8Oku/Zs6ddVc29oDghgI8793u/h5kPfADpFa/A01/6Eg4efBJbtizisccea7dpZguAz89rzT01NdVhdfH9bVkC+py19aetMZlMoJMGvO4BMpjLslTVzHOprV61v7zm+jcfC9MEQTCcTE4CKSFNTmLXrvfhsst+DTMzT+GXf/mDuOyyfYOWLjAYOotA9+aoin5CWnGFkoUgF6YpaXpW8ZYlgz4/D62FWj5FeTx5HqVFOCYmJlxfpbckouzSaXW6lGNZGi2Px5q07t6qx5ycnOxYslFrdazFyfRSmWaYk1XOkZ4vXbSlCxAXFxc7FqTRWy8FVC6Sojtuep03p6en3d5W1nXiz3X6q7d0pezQ6t1DekEflvHEiRO4//77AQB33303gBV//6FDhwAAL730Es56/evxw3odi289gaWlV7WPOzOzAOCb7RRT3rLlwceR/Z5K3VQZaTXp+I9XZCetTG85UMbrCybx/l54BaPy/91Yff0mLIIgCPrOsxs3YrH1BzwYfuJBEARBMOIMlWuI3Su5dYc9rMCiV13otfSV+3tmmpQVWG3yVU0TrdKfKEeurbU1hnQbeKardj00Gg239bfValmPZQV25XF0fxXpEtSy6upN2XtGm/May21mtfeV56EX2pEL0+iKWK/dsMSr2tbuK1ntqt0QXnttnVprjZ9by9ibC1nxDay4+J5//nk8+eSTAIBHH33UPWcPTiM+77zzAKykpWo3mZRDJwvoa26119Yps7nFcvj8de8fxptPmeDAeOPn8NLYLZfQermJwiIIgiAYcYbOIpCFOYx8CubS9PTWsyy8Yiq5oIWUSW6ryOYFeL2unXL8Uh8VS8PQ2nUuFbRqz6GxsTE36OjNibUva7lenyKpsZWul9Za5T5a89NBd6n5agtAWzRsccgOplrr5DE42KmPJ1MddQBUp0jqjqKy4ybjaYJyDEtjlVttOcqiS54DbXVxwdfzzz8PAHjmmWfaKaDdsmHDBuzcuRPAigXAsMVlLcOa0/zl1gvyyvPz7l3Zx8dKtpCvJaXfVZWOwPo4Oa+AZ9n3SlgEQRAEI048CIIgCEacoXMNSRMt13q5m7bTpTV+pWvIq3zVbpFcHxBvay0C4gWxLFcNy6Nl8/qOyDFLc6DN08nJybYrwwtoe9XWsvqZ0YFm3rJZb31HjmdtZSCxanBfukMY7a7SAeylpaX2ubKbh+eJ3TzeerPyXLxAOR+XaxHkWsJeVasO/EqXqr4+XiBdJmZotxgHh7lGgLeLi4vYunUrgJX7T7cazy3WY63BDawE3a3qWq/S1rvWcs1nL7FBfpe/pwPn+hpaFcCe27fkwpG/EX2/6+NZv+9+uYTa8vR1tCAIguC0Y6gsgpQS6vV6xxNSWgTelpGLS3gauRdYlJqtFwTKdTOssniNtV9OJq29WkHcUlDQ0pBK/W/4WgB+daQ+B++1JNft0usK66WxSpm9OdfzKquedRWwXjZUpnNqi8la6lB+LgPQWmvUfW+sBWMYL0VYn59cgpPHrbLYjD53XZHLFpvsQspVwGwBsIXAlgBbRzoAPTc317EQDVds6+NJTdo7D72QjLTavXtHz2Nu0RerYl6+1nLKrWcZMFZKt97HskS68YR0Q1gEQRAEI048CIIgCEacoXINNRoNzM3NZdcgZaq0YdXBHa/dqzTRvMVl9BhWjr2WpeQiqhLwsWT16hFyx9NBcK8KWbqDvJbLnqvGkrubSkhvfrxGdZYLwNtXmvd6wRvtppLr2AKrq5G1S0Gfu65oldXPsiLa+i7LIatcvePqOVtcXHTdpYxVueq5Wvm4ukHepk2b2i4gfo/dYroiW7u65AJJvI8+d0YmhXhB06quSvld7cKzrqPnVrT+XpTy+bupS/Jc2d20k18rYREEQRCMOENlEXAbaksDrJrOKTUML9DraZ4yMFUKtFlpnaXqRUYGrLzW0ZYmC6xuD+0FGXmb08a91DXZY0aPV+q31I2WYs1VqXrbWkrSSzn12jdbbby1TDolc2pqyu1L5CUiWFXJ+js66CnH9qqfvWr1XGU2Y6Uv64pX3ZdIt4uenZ1tzwXvy8Fpby7k8fTvS1s+jPwda9mqWATe79fqwyX3k1tLfvnaOk43nQH0PaTlsLwT/U4bbcuyLqMGQRAEpw1DZREQESYmJszUyW59YjJtr6Qd6JQyCy2TlW6px/fSOaUG6GmaelypbXl9e7Tv21qkXBcOeUViKaVVFhlgp2LK4zBWsYxnPVi+Wf16LX1Vcml8+jro66OvidWfyMNKU/SK97z7UMqttficL1pr1Z7VYnUq1b2h9PKQjCx205RSvC0rXVvrXv8loNzjqkpasS7m09q+tY93X1p/n7z4o+5sWqvV3BRxz8q1/qZ1E4PLERZBEATBiBMPgiAIghFn6FxDU1NTphms3R2MF6iUgUTP9KoS5Cz1DpFuEi/lT5t6smJRB9qq9Mzx0jd1lauVwqjX+NXuAhmsLlX2em6L5eXljjn23DDyvLzr5AX7rXRfva/lcvDW8PXul0aj0Xalla6T5TbRwWCNlfCg3TlevyBZyayrmqX8fO76vHWyguw1pb/LY+lz18fJbUvuEMtVqdN8vQCtPAdvkRd9nWRg23NB5tqxe/eOF3i25K7acl72hmKGwjVERJ8kou8T0feI6L8Q0Rnis2uJ6BEieoiIfqZnSYMgCIJ1oVeL4DYA16aU6kT0CQDXAvgQEb0KwNsBXApgD4DbieiSlJIdZWrB3Ud1oEymMnq9eKzFOLxU01wHUU9zyAXAeEw+jpZVa3VWF0GvyMg6jtc3JbeAjNZGtSyWluVpJl56nT4X+V291Rqupdl4BUTyfD1rS7+2LAJPc7aWxPRSdbWmp48vEwK8Ij4rIUBr6qW5scZjLI2Zt/o43pxYKade8WU3FkGpONHq6+MFb+XfhpJFoDVrGbz1Ehys35snt2ddWHNRSgRg1qvPENCjRZBSujWlxL/oOwCc0/r/lQC+kFJaSCk9DuARAK/r5VhBEATB+tDPYPE/AXBL6/9nA3hKfLa/9V4QBEEwZBRdQ0R0O4CzjI8+klK6qfWdjwCoA/gs72Z834xqENE1AK4BmtWLKSUzX90LzmnTT4zrugvaAhmBYCuIJL/j5VBb32E3Qs614S2ckZPV64XCWEEn/R7niVfJzfcqOj2zWLZ61i4vr/rZym0vuYZkNbfnHsu5k6oEEPl1aV1cz0U5NTXV0YtH5/fnXAS5ALaU0VrkiLFkApoBYU8m3mpX5dLSUsexu6nzKK17rV1HcsEYD8vFm/udSjmkPF4SiRcQtuoISuclf7veb6RKS3rPvblWig+ClNIbc58T0VUA3gLgirTy13g/gHPF184B8Iwz/vUArgeAXbt29ScEHgRBEFSmp2AxEb0JwIcA/N2U0pz46GYAnyOi30YzWHwxgG+VxkupuRiKfprLAJ9XKWhpKfyk9YKq1lO71E9HflceV6a5eYFmRgbvWFtkDb200Ik1fmm5S0mpX4sVkPO0Xa8Tp5x7lk133tSatewp0w1WoFBu+fysylGv4tZKTCglHnhWi+xK6wUhq1SJetaDxKto1wFheb28+fNSQy2rufSbsebC+w1an+vfgEy/lrLKFOnSb0+PZS1hyujrYllupS6j1tjePdRLRf1a6TVr6N8DmAJwW+tk7kgpvTuldD8R3QDgATRdRr9WyhgKgiAIBkNPD4KU0isyn30cwMd7GT8IgiBYf4aqspjNTiso6dUAaLNQbr2FPxh9nPHx8aIbycu7ly4trymcFdDWZrt2DfFrWfFpBU0tuqk6rFJlzXjN7uQ10teLXV85PJPYW2vaqgbVLg3LhVhq3KVllwHLKia/HFMuhFMidw28CndmbGzMDUp7LhU5F97xtJvRqmPRv8lckFVXmnvN5qwgLuPl5suxvBbPpbmR51GqHZLXoBSczv2uqjS91MfrV0UxE72GgiAIRpyhsghYQ7b60pTS56wURn5q6j47WsOUmrVelET3XMkFC3XQVGtGVhCZZeJg6okTJ1bto6tBp6en21aCth60jJa24wU3Le3NC8R7qbWMnItcoFIi0x89i8rS0KpqtFawU+MFc630W298bbXU63U33ZLJpfuWgtJSk9ZatZ4Ly6LSCQ7e1pKXKWmyUg5t5fH9oPsJyd+1Zw3l+kx595n8jtw3pc5lJ7uxCDzrUlsr8npqS6BKUkspqL9WwiIIgiAYcYbOIpDahXxqez4xrRnJ1+yX9hYNt3z3OX+qRGtfcsFxRj+19fEXFxfdZQy1NsznMD8/3xFPsOIIWkbt1y1p0lIW/bpUXFer1VyNz9OsG41GVvO3ZJb3hdd/RqPvMX5PbnMxqhJVtEXPx20txuItaqQtLMtq5nG8e0kue1rqcSXvH8/a8yyqHLrnFHfjtc6DKVm10qJi9PlUXfDHGl+O6VkRVSwD737Qc27de/1OKQ2LIAiCYMSJB0EQBMGIM1SuIQ4msSkk0xK1eZRbB5jf90xUbcbJxVq8fh9ehWKuJbLGcq1os91zDVkmpw5+W624eestwqLnJFfN6Jniub4nOlDfjUlbcvdYfZc8meW9UFpH2QvU6v9bx7HcPt695M2FdG141zQXSPQq262W2dodpc/Dqg72FhkquYakK88Kqsut5WIr3XdV0m5zbk3v/tafy9+5lzKr58+6l6u2n2ZyvY16JSyCIAiCEYf6XZjQC0Q0PMIEQRCcPtydUtq71p3DIgiCIBhx4kEQBEEw4sSDIAiCYMSJB0EQBMGIEw+CIAiCESceBEEQBCNOPAiCIAhGnHgQBEEQjDjxIAiCIBhx4kEQBEEw4sSDIAiCYMSJB0EQBMGIEw+CIAiCESceBEEQBCNOPAiCIAhGnHgQBEEQjDjxIAiCIBhx4kEQBEEw4vT0ICCif01E3yOie4joViLaIz67logeIaKHiOhnehc1CIIgWA96WrOYiDanlI62/v9eAK9KKb2biF4F4PMAXgdgD4DbAVySUloujBdrFgdBEHTP4NYs5odAi1kA/If8SgBfSCktpJQeB/AImg+FIAiCYMgY73UAIvo4gH8E4AiAv9d6+2wAd4iv7W+9Z+1/DYBrWi8XANzXq0zrwA4ALwxaCEXIVI2QqTrDKFfIVI1X9rJz8UFARLcDOMv46CMppZtSSh8B8BEiuhbAewD8SwBkfN90+6SUrgdwfetYd/Vi3qwXwyhXyFSNkKk6wyhXyFQNIrqrl/2LD4KU0hsrjvU5AF9G80GwH8C54rNzADzTtXRBEATButNr1tDF4uVbAXy/9f+bAbydiKaI6EIAFwP4Vi/HCoIgCNaHXmME1xHRKwE0APwQwLsBIKV0PxHdAOABAHUAv1bKGGpxfY/yrBfDKFfIVI2QqTrDKFfIVI2eZOopfTQIgiA4/YnK4iAIghEnHgRBEAQjztA8CIjoTa12FI8Q0YcHJMO5RPSXRPQgEd1PRO9rvb+NiG4joodb260DkG2MiL5DRF8aBpmI6Awi+lMi+n5rvn5iCGT6QOu63UdEnyei6UHIRER/SEQHieg+8Z4rx6lox+LI9MnW9fseEf0XIjpj0DKJz/5PIkpEtGMYZCKiX28d934i+jenUiZPLiJ6LRHd0WrxcxcRvU581p1cKaWB/wMwBuBRAD8CYBLAd9FsV3Gq5dgN4Mda/98E4AcAXgXg3wD4cOv9DwP4xABk+w00U3S/1Ho9UJkAfAbAO1v/nwRwxiBlQrNg8XEAM63XNwD4x4OQCcDrAfwYgPvEe6YcrfvruwCmAFzY+h2MnSKZ/lcA463/f2IYZGq9fy6Av0AzAWXHoGVCs1D2dgBTrde7TqVMGbluBfCzrf+/GcDX1irXsFgErwPwSErpsZTSIoAvoNmm4pSSUjqQUtrX+v8xAA+i+QfmSjT/8KG1/flTKRcRnQPg5wD8J/H2wGQios1o3ph/AAAppcWU0uFBytRiHMAMEY0D2IBm7coplyml9N8BHFJve3KcknYslkwppVtTSvXWyzvQrPcZqEwt/i2A/wuri1AHKdM/BXBdSmmh9Z2Dp1KmjFwJwObW/7dgpVara7mG5UFwNoCnxGu3JcWpgoguAHAZgDsBnJlSOgA0HxYAdp1icX4HzR9GQ7w3SJl+BMDzAP6o5a76T0Q0O0iZUkpPA/gUgCcBHABwJKV06yBlUnhyDMu9/08A3NL6/8BkIqK3Ang6pfRd9dEg5+kSAD9JRHcS0deJ6G8NgUwA8H4AnySip9C8969dq1zD8iCo3JLiVEBEGwF8EcD70+rGeoOQ5S0ADqaU7h6kHIpxNM3U/5hSugzACTTdHQOj5XO/Ek1TeA+AWSJ6xyBlqsjA730i+gia9T6f5beMr627TES0AcBHAHzU+th471TN0ziArQAuB/DPAdxARDRgmYCmpfKBlNK5AD6AloWONcg1LA+CoWlJQUQTaD4EPptSurH19nNEtLv1+W4AB73914G/DeCtRPQEmi6znyKiPx6wTPsB7E8p3dl6/adoPhgGKdMbATyeUno+pbQE4EYA/8uAZZJ4cgz03ieiqwC8BcA/TC0H8wBlugjNB/l3W/f7OQD2EdFZA5QJrWPfmJp8C03LfMeAZQKAq9C8zwHg/8eK+6druYblQfBtABcT0YVENAng7Wi2qTiltJ7yfwDgwZTSb4uPbkZz0tHa3nSqZEopXZtSOieldAGa8/LVlNI7BizTswCeomZVOQBcgWYV+cBkQtMldDkRbWhdxyvQjPEMUiaJJ8fA2rEQ0ZsAfAjAW1NKc0rWUy5TSunelNKulNIFrft9P5rJG88OSqYWfwbgpwCAiC5BMznihQHLBDT/uP/d1v9/CsDDrf93L9d6RLjXGBV/M5pZOo+i2dl0EDL8HTRNqO8BuKf1780AtgP4SmuivwJg24DkewNWsoYGKhOA1wK4qzVXf4am6TxomX4LzX5X9wH4/9DMmjjlMqG5KNMBAEto/jG7OicHmu6QRwE8hFYWyCmS6RE0fcl8r//+oGVSnz+BVtbQgOdpEsAft+6rfQB+6lTKlJHr7wC4G80MoTsB/M21yhUtJoIgCEacYXENBUEQBAMiHgRBEAQjTjwIgiAIRpx4EARBEIw48SAIgiAYceJBEARBMOLEgyAIgmDE+Z/tYoihAfWBGAAAAABJRU5ErkJggg==", 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", 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    " + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -683,7 +756,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 48, "id": "0d60bb4f-917d-479a-8c04-f328154b9770", "metadata": {}, "outputs": [ @@ -691,31 +764,32 @@ "name": "stdout", "output_type": "stream", "text": [ - "Radon Time: 0.006342677000020558\n", - "Convolution Time: 0.015601840000044831\n", - "Peak ID Time: 0.014300557000069603\n", - "Band Label Time: 0.011138725000137129\n", - "Total Band Find Time: 0.0474029879999307\n" + "Radon Time: 0.007330777938477695\n", + "Convolution Time: 0.019043672014959157\n", + "Peak ID Time: 0.026836892939172685\n", + "Band Label Time: 0.010746412095613778\n", + "Total Band Find Time: 0.06397646304685622\n", + "Band Vote Time: 0.0015464250463992357\n" ] }, { "data": { - "image/png": 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cJl49gRX08lw2nhzWeXnnINGBym7y+qtSpdq7SgWpHifncvOCdVXa9JbcgFalbymI7wXwrePmKpy9BVy8nPbl5eUOt4OXSGFR9bvyvsnd10Bn8kKtVnOD4F6vJktGb+7lvdRt8kU3vysrKcK7d3TygKzs96qDtatQJnhoN1+/CIsgCIJgxBk6i6DRaHRoFlawzusZoseT3831QClRJfXUO77u68NYy/BVCTBrrLQ2+b4MJHr75OakVAGrtSArLbZknUlK1kuV7qP6/Kx0RC+t15o/T17vunGan1VlzWhrSAb+vC6xVSzSkmzdyOIlR8jv6OPIcwdWd7TVC/d4Fr3UfEtLbloyeta/VyWfs+iZKpXNPB6nwfLSsNZ9X0r/tixWr4q7V8IiCIIgGHHiQRAEQTDiDJVriMkFhEsuhm4CnFVyixmvslMGt0q55RrppvByzavUSejzyX2vZFZXqWT2XjPSpPXcSFZtQJUGZHoffY6eG8lqvey10fYaleXOIxf49Sql9WJB1kJCnotLu2EmJyfdtuHeOtW52gMts3bZyHGZkiuq0Wi4VdaM5fpYi2u3SoW5HtO7h3IuNT2uvB7ASi0Au4j4/fHx8Y5x9cJSvJVrkOt7JprOBUEQBH1hqCwCIsLExISpxTFaK6iiNZbaKOcCbfp1FWvFq27VWn69Xu84jyppsd55aHKfl4LRVsDNq7zUr2XfFi9N0LKwSj1qWBvSbXuBzoV1pOYlt/L+qjLHmm60RUanZnqtq1nzm5ubw9zcHICVc9ZVuqxhzs7Ots9Xz4HXX8qqrvYsQysQ7C1041keTL1e70hxrTLnjJfwwMh72ktDzaWGltKfc1Xweu75+mzYsAHAynXidvDyPtQLSp08eXLVMeT9wuPrBI1eCYsgCIJgxBk6i0D6cHMpf1pDslLMrNRBa2vh9V5hcrEKTwPTGqBcjFprG1XSSb3PcpqNlldrNFa/Fi/24cUTrGtYxZfOsDavNX/tO7XmXGr+8rU8bqmnTJX7w0tTrQKfs7ZaWIvcvHlzx5KiDJ/n7/7ulzA/P46UxtBoEK677m0d1haTS4vVc6H73FjLXWqLncktus6vdVqqN2+52JFGWySWlV5Cxvhyssjxc0VvbN3x+bKWb8Wq9HzJzrVyK6+XFbPphaF6EARBUJ2Pf/yNWFzcDABoPVOCYE2EaygIgmDEGTqLQJpo0t3jVeDmqkS9SkR5LD1GqfIwZ57m0uaAzha09Xq9IwCqx7fSVb0Kx1yqZsnVZPW00efOMpZ6N+XcVxrpmir1nbHO25trLbN0K3ltmr30wbGxsaK7yAucp9TZnlwvwsIBRN7Ozs623UTsotHpnFNTt+Ff/as7kVLCd7/7E7jzzr/edj+wW0K7J6yWxbLqVx6PX2v3Va1W6/h9alednnO+x2WVsNcDyHJjlVJnLVeod4+WUrrleJ4b03LvMDrwn2tDXUoNto7nydYrQ/cgCIKgzBe/+Bs4cWILarUX8La3XY8DBzbjoYfOHLRYwWlKXx4ERPSHAN4C4GBK6dWt97YB+BMAFwB4AsAvp5Reyo3TaDQwPz9vag1eXw5vAY+WDKu2XldGS4P1xvCCn1bKpEYHLsfHx9sal9VNUsqYw9N6rKI0T+uo0pfGS921PveCWlqLlNdTpzvya06941Q81pYnJibacrImtrCwAADt9Mvjx4+v+lwGLD0rTF9r2SnSu7b6PK3rpu9dfs1a+ObNTX//1q1bsWXLllXnzN/leTtxYis2bQIOH57G/v17cd55z7YfBFoT15acvP+0ReClnMr584LB3kI4cm48DVkHx2U6NcvE86Rf678FuQJUbwEoSRXNnL+nrSB9H7JlwK/l1lu6VCc6yGtSNVW3W/oVI/h/AbxJvfdhAF9JKV0M4Cut10EQrIHJF1/E5e98J15/9dWYff4JjI833UDj4ws4++wH8MILZw1YwuB0pi8WQUrpvxPRBertKwG8ofX/zwD4GoAP9eN4QTBq7PrGNzDzzDOg5WWc+73b8Nf/730AgJQW8eijP47HH38lgMMDlTE4fVnPGMGZKaUDAJBSOkBEu6wvEdE1AK4Bmmbw8ePHOwJ90oTSZhqbhzrIJQN8nrujylqu+ni5tWO9cbzqQyvY5FWdym2uNbAlm1WHUaqIlft4biMvmCwrSKuumyvdFDqo6QWEJyYmOtwUsjoXWMnfZpN8eXm5oy9MlfoS/Z6Xq6+rRa17SicPHDt2DMCKK+fIkSMdboEfTk/j/FYO/1cXz8H+f/c3Vp3XyZMHOtwQWgbrevI15GPrHkdWYF27Hku/M6tuQQeU9TWXwWsdTNcuIet+LFUs5xIqvN+G1aK71LZbJwZI94+33rD3N2d8fHzdXEMDDxanlK4HcD0AbNu2rXpVThCMEId37MDvfvSjzT/MtRrQ+oMdBP1gPR8EzxHR7pY1sBvAwdIOKSXU63UzPcyrbmXtR38+NTVlpr5ZY8hAladFeYFgq2+Qlw5mdRjNpW/K70oNwAt6M1U0XC+grDUZiZe2qmXtpiKXqdfrHVriiRMnKo/rYWlQWjurUj1etUurtbygl1rK9y4HtFmjP3nyZId2r7uC6p5K09PTHcFUywLV5+kFfL0gbjeJG3orF6bRC7bo34i09L00Uc9zUKWDrr6XpQeBKaXDyntWo+dG33M5cpXu2iPRTUV7jvUsKLsZwFWt/18F4KZ1PFYQBEGwRvryICCizwP4JoBXEtF+IroawHUAfpqIHgbw063XQRAEwZDRr6yhX3E+uqKbccbGxrBp06YOk9YyZb1cZVkzoE1Eba5p80rmmHtVk17QM9eiVh/fqj3Q3825QbTJ7VXESrwKTs+NJSuLdVWr16Jbyq7NeZ2/rV0SshJcm+C6UlZWqvJ42gTn2gOrBsFbLzdX3aqvO2MF9ORWzo12ZejccnaFHTt2rB1A1tXBLDO7VjZu3NgeX7tVZOKElEVee+3a8FxPvVSyyjlhuXmrA8HahZJS6mjPrBMCtOtG3heei9eqgdF/SzyXk/y7UgqUa5eX3JZcrdbWc5H3SvQaCoIgGHEGnjUkqdVq2Lx5s7nwhdZ+c/1MGK19emlZ8qnNx+R9+DushTBeWuRasYJXnqyeRptbZMQLdnqB5lqt5gbCSi2zpYxaU9ZIK4Dn2FuAxEtplHJrGa0Wv6VAub5vcvOnr5e1MI6XpMDoxWZqtVpbQ9YyecebmprqWA6xFGSVgV99T3lV0JZ17i02o8eenp5uWzBcRb1p0yYAKxaBTiFeWFjoGFcHsvXyjRZebyN5DjqVVd8nVqtzb348C18e30vq0NdCytqvvzeasAiCIAhGnKGyCNi3nPNDen04WAuShTFe10x+31rOUPf+0WmUrLWW0ggtcr2N1uKD9VJOc/EKL77AWFpwVS1Eakf6OpU0J2lF8L5aC9Yph9Z5eSmMLPvCwkIxXdTSHr00PS9mJXspef5p3f+G4xkbN27MLrYi97Wuue6/pLH28frqaK20SiqtZ7WMjY21v8v+fZ4nfRx+f2FhoR1D0Zq/l+otx9PnrOfesoz1PaTvS7ktLR7vXSe5KJW+P7xiMStu1y/CIgiCIBhx4kEQBEEw4gyVa4ixTL9S22RGugp0UFG3Pl5Z5GNl8Q/PxcB4QUFZZajNzVxA1ks389w+jUajw5z29pVBp6r9U3gMGVQttca2ZNTjeq4hmdLI/9cVstpNJ10PXuqdt/iMlVroudbW4vbLLQKjr6muAJZBXj1fjBXk5OOVKmz18eX8eeeRmxPvfvPcWktLSx1uHp0arBMGUkrub84Lilv3uk4ztu5hnTrrnYe8x6zEArnNufA815oOesu/dSX30VoJiyAIgmDEGSqLgHtpaG1ucXHRXcTBW5RCFojoIJMOuMjgj9YGSn17ZFqk1HwAX6PspmeOlRLqaeReUZO1vCWjtW3L2tCFSB65nkZel0k5zzn5rfOUx/NSTPm70iIoaW2MFdzP9W+S+1hBd69Yy1oUpmThyGAqv9b3uRfEtSwPLWMVa8i7Hlp2me7JGj8Xz3GfJQ4e686psgiNU0w5BVUXnFrLaXrX1goE69++/q1oKwzwO+Vqj4U1n166d27hLG3RlILVVQmLIAiCYMSJB0EQBMGIM1SuoVqthunpadPs1j1XFlU/dqsCV7sl2PzkPi78WgaotPksc7vlazZXrXbDpRqDXK8hNgtzrgfP3PUCiZZryDPjGb4W+thVXud6onh1C3IetWsoFwws9VnSprrMZffIVXN7wVUdHJeBW8+V541pBbS98a36AX2tu+l3o+dAj517z+rRBKyuANZBYf5M14rI36FefIq3XhDXmhPPZWNdA682xLq2pcVl9HEs17W39rf1m9Eu1ggWB0EQBH1hqCyClBKWlpY60utqtVr7/15vISvF0QvO8Wt+qrKFYMHBLL1ACMvBGoysiC51E5Tnq/+v97F6lpSqTK2gltxfypjrv+RV6XrHlRqMV8GZ63FUCsBa8+ctFVilS6wXnNbdO61Aoj4vnfIsNTcvldVL98xVcGsNnu8/GWz3EhyqdKf1evHk+lbpMbzA9tLSUocFqAO/eu6l1eLd94ylYet9dOp4lSBurho5lxpuHZ+RSR+lgHbOgu1X99GhehAMG5s2LeMTn3gaF1+8gFqtho9//CI89dSWQYsVBEHQV4bqQbC8vIwjR4509FGfnJzs6DNTWlJP7s/axo4dOwCsPGl5DOm3lOl473nPt/HAA6/Apz99AaamCJOTdZxxxsyqMa2+I57WqH2MVbQCxipC0xqD18tG+py1pWGl0AJNTayqbFpGSzZrCUf5PVnQoy0P7Q+VWpDXkdTrDmv1lNE99y0rwiumq5IirK0EvQ6Bde9qf7/2i1t+c93fqUoKqp4vz2qRvvBSXEYjrRivf4+0rOU+Y2NjleIw8vOUkhsb8JDX2OtNZt3/nubvWcJWQV6pSNaziDyZ1kLECBxmZpbwoz/6Am6//XwAQL1ew9zc5IClCoIg6D9DZREME2eeeQJHj07hPe/ZhwsuOILHH9+KP/qj16Je708BRxAEwbAwVA+CRqOBubm5tqksW0Jr01gvcaddRfV6vW1K8b5btjT9+9u2bQMAbN++HcBKwGpsbKztJqrV9uGii/4Sd999Fb73vfNx+eWfx7ve9SJuumnvquMwY2Njbjqglo3lkhXTpT4+Ei8F1PsekDdv5eeWC8BbyEeTcyF5Msv3S8FiXTEr++tUWVaQz8Grrtbjs+vG6gnlYQWRvQVUdIBbyqj7D+lEB+0GyvUN8pYAXVxcdF2r+hrKa19q4+25IeV55Ra80bJ7CQB6/iw3nQ4Oe649eS/p+8BbhMhabMlzVVrVwl4yhn6/SrC/V8I15DA3tw1zc9vw7LNN19D99/8odu8+MGCpgiAI+s9QWQScjqU1pvn5+WIfGkY+4bUm6QWY2UI456mn8Ibf+i2c2L0bt/3mb+Lkye0466wjOHLkLFxyyVM4dGh3WzPTWpZcRq5UpCO3nlbjpZIRUYeG4m2l1u1pa3orNRgvtXQtnQ+9vjdW4LGUyii3JQ3W6uPjLZupixXltdV9qhgvPTGnvXkL3Mv5tpZRleMzsp+WLm7SVl6uP5G2pKxlGbVspa6t+n6RS8F6vXhyvZo8689KgCj11rKC4d6Sm54lYKWMa7z7RVrA3r79Xnwmx1A9CAbNWXfeifH5eWx68klsPHgQ+/b9Kq644vdQqy3jxRc348tffhuAxeI4QRAEpxPxIBA88bM/i20/+AGOnn8+ju7ZAxyu4cYb/wUA4NChQ61vxYMgCIKXF0P3IPByxLW5VqqQlfuX8rdlRemGSy8FAGy49VYAnTnmXltlmbvsVTLzcWTOtF5rubQeq7UYS5WW2Z757m1l1a421y3Xkz6uVRFtjSXlyckit3JMLy/dC6paPWW0S0D3w1lYWGhfn9J9qK/91NRU+/983assqOLNvZZd3n/avWe5++Rrq0+O516U10/v57kxNfIae/sycl71b1u7bHL7yGPnXtdqtY7fuucKlWN794HV10mOJWXwqrit+75fdQOaCBYHQRCMOENtEcgnohcoYqwAHI/F2tyRI0cAAC+99BIA4OTJk64c/GTnamTecqqpXiBCWi06TY81jQ0bNgBY0QynpqY6NGcrDVaer5W+V+rBIqsmmZJFIKuedbDOqyyVWk/VFDhL6/H6BzG5zpSyR438rjwXnSwg0ynl+6UFhiwsC4GvN3eu5ZRnvbAKU6/X27Jo7Vv/DuR9oq1Jfc2tTqzdJgLIJAw9rtfR1vodMznrgffR95ueN+t4Xv8mb4EruUhPzoqUYzQaDXe8UpqsPC/PUqui/UdlcRAEQdAX1v1BQERvIqKHiOgRIvrweh8vCIIg6I51dQ0R0RiA/wDgpwHsB/BtIro5pfRAxf0BrM4/zgUO5VbCbgM20bnCmBemYRfR/Px8e1zdkEwfR1c6W/UPOhjJwUcZjGI3gdf4TAe6rWpQL7hqNbjy5im36EupGlkH0omoIxjsBfgYK/DrLY4imwl6QWEtk5xPzxWkZdZVvZYs+nXuHPh4+h6zKoJzrZzlWHxPLS4uVl6bm+/diYmJ9v+9tb/1/BFRtsZFvrbccvIc5da7X2QSRqnNu3QpevURjG5IKRM3vKZ9VnDXC0JrN7H+3DtXC/l5lUZ4a2G9LYLXAXgkpfRYSmkRwBcAXLnOxwyCIAi6YL2DxWcDeEq83g/gx+UXiOgaANcAzWDa1NRUR3BI9uUoVTXm2g1r7ZG1Ian96BRTnTbopSdKTckL+OoeNlLz0214tcYpUxtPnDgBAO2tXsaTkUFVmc4oj5OrhNVaqSaXoqethVKAW77upgeLtqC8XkNWpSqjr6UVQK26OI++B2Rwmq+TxgrUev2qSmnGQKdGq61bGWDXFoFnKeYWSSklAshr4aXs6utnBbT5+nhasdTCu7Va9P5y3Fxb6qpV8JYGX0pTZeTYnnXSK+ttEVh19quuUErp+pTS3pTSXr1GbhAEQbD+rLdFsB/AueL1OQCe8b5MRG0NRb/vFS956VtWgRePzX55Tufk92XKKWvtrHVzPEH7/KS/l4+j0dqc9F9qrd7zG0vfsPYX6y0jx/L8ttoPbxV4lfrrWOdb8mVqLdLqAcQarOcblhpmaSEaqw+N9gl7KX/yWnhWkKWV8lZrb15arPTTe35wLYfU7vU8eRaO/F6p42vOf+1pw/pzeY28OJo+L3n+VXsbyXs6Z3Far+VC9N45W9q3d91LaavyfvesFGs++2UBaNbbIvg2gIuJ6EIimgTwdgA3r/MxgyAIgi5YV4sgpVQnovcA+AsAYwD+MKV0/3oeMwiCIOiOda8sTin9OYA/r/JdIlrlXrFMTs98y/Wh0SaqDNYCK64a6SbRLgU243ILoei0Q6sNL7BissuUP5ZpZWEcuwX07Oxsu7pZyypl2bXrJVx99W38DezYcQx/9meX4dZbX7Xqu156ncRzy+nPrZ4oVpWz3Frpqroi3KtslpWqVotqS0aZiqzRx5duoNIc6DGYsbGxVZXk1nEsV4c3fzpwKgPpXl8bL4gr3SGlBX2s/T23WO59mYZqyWq13/bcY969JH/7peskz7+0OJQ1puXitPa11urWf59YFs8FlpOpV4auxUTQHw4e3IqPfewXAQAp1fHJT34e+/adP2CpgiAYRobuQWAFROR7peBjrpOjDlSxFi7H8LQOL/gjUytLnQe1BlClGERaAgCwadOmdkEcv6ctED5PLpTbvv1uHDmyAxs3XoparRn0ZstDB5j1uVhUKWaxinzk+9YceJqlF6C3NOecxcHva5m6SW3VGqu3rwz8ac1cBwP1vVWv1zs0Zy+V1lqqsmo6p9SCdQA7F9wv/Ua84HGtVnMD8d5vI5e0oGWTY+n0ZY2VVuzJXaX4Ul8Pva91Tby/KV7Kae4+7JXoNTQC/LW/dg/uu+/VgxYjCIIhJR4EL3NqtTouuuh+PPDApYMWJQiCIWXoXEM5cx/wKwQt89tz63h53RaeK0BTr9fdNs1e4Fd+xujgkq4G3bBhQ9sltHnz5vZ7ALDx4EHsvfZajJ08ibs/+lEcOP987N79Lbz00oWYnj4f09Mr39UBc318KZvnwsgFmr1caF1Zaq0Zq/vp5Naq9eY457rpR1VmqVJVXseqPXL0/FrH00FHRs6FR841VAoWS9n1PetVwlZxk3lrMFtzUDov+Tv25sJzl1nyVglOewkhpWtu9UPyuhkw0rXGeLVL3RIWwcuIc778Zcw89xymjhzBxZ/9LADg3HP/Co8/fvmAJQuCYJgZKosgpWQGL2VlsZVe5r3WaZW6k6e1EIQ+TtXAbi4IpL8jtQetUejAL8NawuHDh9vVyAcPHlx1zkubN+Pc1v+/v2cPDhx4FDt23IObbnpzOzjM56E1CavviRfI8+bT0ua8FEa2cOQ10Km0fFzdS0lqtl5gVMtu9ZTxsAJwnmXDeJaB/J5nmeZSn/VW3y88Rq4a2UuxzaWpaq3eSqn1ZPS0cWtec4FlPZZn4VtjepaulWoKNOez1ElUb63AuWeZ6nOw0H/D+LtyTN03ql8WwVA9CILeeOzVr8afvPe9mFhcxIELLwTqhE9/+uOtG3d9StODIDj9iQfBy4znzz676CsOgiCQDNWDIKVkuhUAP6iqsVrElqpnrUZ1erxS8y8LbVbnvuNVGbL75fDhwwCaLiJ2negGV7LtNLDSXG9mZqZd3VoKOkpTueqiL7qp3vz8vNtQTY8pTVy9EIw2f7U7aXl5ueiy0/dArkrTy22X17hqVa08nuda8JDN4ErJETwXMhDLVF23Vx9bYv0e9P3mrU9t3WP62Ll6BX18/Vus8turWgEu5S3JJN/37h39O7YWq/IWQuKEDus3kmsb3wsRLA6CIBhxhsoi4CCZlRLqPQktDYzfL1Uf85OXkUvPaW1Kp5h6wUn5f121abW19VoSa02Cq4RPnjzZ/r8O0vIcsfbPaaazs7Pt93Rr55y2qq+D18uGkWlwWn5G98qR8nhWik6hldaZ1fYZ6Ew9zaWc5gKieh8vmYDRloK0WvT4XgWz1DQ9bTSn3VcNAMvjeAFf6/j6fHjOvXm1qpK9c7dkrhJc18etWnEr58pro+3JLGXSyRh6a7VF59+AtNyBlb9L1mJV+u+R1xmgW8IiCIIgGHGGziKYnp4u9kwBOp+0uSInTwu1FqnWmqylUWqZtYxaNu3PlWN5y+LpFE25NCHPj17RjWXQC5JItF+X97H6MOllEbVG63XCBDoXV+eUV7Zm9Jjj4+MrhXGt7qqbNm1atbV6K/G8eel6Os1SnrNXXKSvRaPR6LrrqKRquqrV7VR/R49pFYflCrrk1iqIqpL+6C3dyOT6JHm/ba3ty2N4vX8874De38K69l43WI2cc32fa2td359s1W7cuLHdM8yzDPSytcvLyx0p22ERBEEQBH0hHgRBEAQjztC5hqz1RiVeAIe3sheHV42pA8/ShOV92CzzgtFWGqEXFNSpkzLgY7WzlvAYMlBaCuwxViqoTs3UvUtYjvn5eRw/fnzVZ2z+arNXn5+s0uTzYxP6yJEjq+ZAcvToUQArwbKdO3ea58P7Tk5OdlxjbV4z0pWiA/S5xYb0/oyXaqrdITLd0nNjaleivMalymnGauftpZxK2UsuIeu3WKq69wLNMi2W0W4k6QIFVid9eMFoK4XXm2ONlKcUONcsLS21fwv8W9GuT96X3bj8W5mZmWm7OrUrSAeg5bl4KcG9EhZBEATBiDNUFoEuKLNSykpFYVbgjfG0b6k5e0vnWdq8HNMqhPO0OqnBeMHpXIqcpxF5BXRSQ9Jahres4fj4eDtoqwvUtNamLZ/JyUnX4tAWggyWb9t2Av/sn92JM854ASkRvv71Gdx226uK6apAZ4BcX39Z9MbzpLs/5hbpKfX+8foyyWvspRLqwK9VSKnnWn9eJV3SKvgqWTaMvD9KPYa8+1Leh1b3WWtrnZuXWCHnV1t9XuqpvI5sdXuFlAyPNT093f7/9u3bV42vfxOzs2P44AdvwsREQq3WwH33vRJf/eqPrNpH99SyguTr1TVgqB4EwWjTaNTwx3/8Wjz11E5MTy/hYx/7b7j//j04eHDHoEULgp6o18fwO7/zVgAbUast4/3v/yIefvgVeOqpswctGoBwDQVDxOHDM3jiiW0AgPn5CTzzzBZs3To3YKmCoB8QFhbYam1gbGwZwPD0BBs6i0AG15hcIMwLGOkxc1tpxnnrDJfGlDJWDTbJ73iV01ZVY6kqU8+FXPTFWzxH9/OZmppqu3c40FWaa1nfwIvm7Nq1CwBw1llnrdpu3boVwIrbaWFhod1P6bnnnsPMzHO45JJbcO+9fx+NRvNhwAE57d6y5svrBWS1KNbyM9KFok19bx1iRvfFkd/RPaFyFb/e+VVpj54L2vLWOw9va7VO9+5Zyw3E7ji9zVUNl/oGWbUjPLfeuIxMdOB7X7eC99zFU1NT7cQGXfOi3apNlnH55e/B7OwBPPTQFWg0/hbOPnuljxgnS+R+q57LrlfCIgiGjrGxk9i79zrcccc/wNLSTHmHIDgtGMPXv/47uPXWP8D27Y9h27ZnBi1Qm6GzCIDONEHZA6ikEVnWgx43Z02whuKlA3oaoLWQC6OtBxnkKmksubTBkiVgBYs9bYM1CxkI1im5OnjmVfFOTU21qyZ5u21b0+XDFsKOHU2//+zsLPDVr2L54EG88IY3AFTHK1/5Phw9+vdx7NjrsX17Z9qq7CPkpdHp68Uyy6U+db8lngtdzb20tNQRxNSabC446c2j7q4q51MnNujr1k1vKH3dZIDY07I9K0JqpZ7l7i1cND8/71ar6zmSgduSda5ltrq3esF9K7nA+7uhK4A3b97cvq85WMyWbvseI8LE449jbudOpKmpVvr0Lhw5chkuvvhR3HffxR33t07GkNfeu897JSyCYLAcOgS86U0Y+9VfxcRd38aWLb+B+fkLcfDgOwYtWRD0zPaPfQzn/tzP4YL/4xdQo2YNDdE8duy4B4cPnzVg6VYYKouAe+/k/Lqlba6vS6mXt9Sc9XheB05rqTivN30uruBZK7kukHpfxvLrMnweXqGN7M3D2psXG2CNVvZCAYC5uTnMzTX9+s899xwA4N5777VlSwlX79yJ2RMncPv+G/APZv4UR4+eibPP/kvs2ZNw221/D08+eS6AFcuAt4uLix3noc+Z4xuy+yr/n7VOvTSg1SeJ4xN8Xvo1FxJpf//09HRbg9R+Y91dkqnX68WeV4wV3/KKBi3N2uuvpK+1lWLr9frRVgxrugsLCx0pujo2xXMl56ZkEVjxDO93qv8GSCtQa+RaVt6H74+5ubn2/nw/PP3006tkeeM3vgEsLWFi8YfYseOXsWPnMogSHnrob2Dfvj0ADnZ0EfaK7mRX334VkjFD9SAIRo/G2Bj+n3e/u/nDeRH42Mf+dUd7Y2BhcAIGQQ98813vwqVf+hIe/bEfw/6bXqPW5R6e+7on1xAR/RIR3U9EDSLaqz67logeIaKHiOhnehMzCILg9OPo7t345rvehf2vec2gRcnSq0VwH4BfAPBp+SYRvQrA2wFcCmAPgNuJ6JKUUtaeaTQaWFhYMCsVS2l6ORdRqfJWj6XOZdU+utKTkcFVD+22kEGtbtIF9bG9JRBlmmIpBdQKaOuAHr/2go+yelmmggLAs88+a0+KAcvNgWUOwOlzkMF2nQrMMuo0xaWlpfb/2Q2hK0q1i6Ver7fdRKUUWh30HB8f7wgKl1wdRNTxHX3uVh+rUnU1YyVhWG3IASgNdrXbqrQUpv5t1mq19lxz2iUHVfViLFUDxBbWb8nr8yTdQPp+125T2YcLAA4dOtThRtQ9h7zFo6ampjrcYLo1vHZ7yj5s/N2h6DWUUnowpfSQ8dGVAL6QUlpIKT0O4BEAr+vlWEEQBMH6sF4xgrMB3CFe72+91wERXQPgGqCpFdTrdbOAo2qRltTUrAVGAFubl/vK8bUG5vWLyRUOeSlssreRFyDSaYQyhaxkCViWB2MVnekxvL5KcsF0KTu/f/z4cbz44osA0N52Ax/3hRdeWDU+F6lpDUru4wUseWEcqW3rgLIuKJJpnayxsha3Z8+eVbLleg/VagnvfOencezYZnzhC/+wuACKLPTyrC8rBVrf3zrV1UqL9RZAYk2WNVuev2PHjuHYsWPt/3fDtm3bcOaZZwJYsQD0NdXasAwwV+nDBaz+e6HnKTf3+tp5GjrP5/Hjx9tFYGwBc3ddtgz4u4y08HgO9L2lkwjkPdCvdFFN8UFARLcDsPKcPpJSusnbzXjPTJRPKV0P4HoA2L59u18mGQSnKT/+43fghRd2YmpqeIKDQSApPghSSm9cw7j7AZwrXp8DYHjK6ILgFLFp0xFcfPEP8D/+x+tx+eXfHLQ4QWCyXq6hmwF8joh+G81g8cUAvlXaiU1wq4q3avtV2R9G55Z7+f25GoTScavUAnhtdGWL4lIQXO5bdbEcKxCdW1hHv+/VanjHlS4r7rnCZq93PvJ9b0EfNpm1Kb1hw4aOHHyvqlUGjbXLhGsBtKuIA5mbN29uuzC4kpRf83lqdxWf56WX/iYefPCf4oILTmLDhntw4YUXto+n2w4zsleOrgXQi8LI4K7M15fjy7oL+bkM/HpuJe2GrNfr7f2rwtdr+/bt7Ypynj/dE0gHX+V5lVxDucQLfU/Jnlosh64rYXJ1QLrqmO8ZHsOrayKiVTU78tw9N6OsFem3i6jX9NH/jYj2A/gJAF8mor8AgJTS/QBuAPAAgP8G4NdKGUNB8HJjx447sLi4BYcPXzRoUQbGddcdxNe+9iBuvPHhQYsSZKBc98JTza5du9Lb3vY2V1uVeD2AqpDbp0r3Smsrx/NkqyJjlUU+vGpPrZlLDcoLTHpWi3ztLemotUmpHbFmxBoz9xzi93VwcGlpqa0pa42Zx9e9XmZnZzsCa5amDKzWNHVvF97qKnI+zpYtW9qaLG+5twyfF2u94+PjmHz6aWz+ylcw/a7HsOlHvoFGo4ZabRFjYydw4MBP4Gtfe+eq8+SArO5fJOfHC/bLoLi2BLxOn7JXU0nLttDBdtak+dpyBfXs7CzOO+8JABvx5jf/Cf7zf/7n7XuVg6k68KxlX15edheTyS2QIz0Dlqx6K7VtRqfWaotqfn7eXEinCrLLsraEvUQBGSzW1+f3f//3704prarl6oaoLA6CftJo4OJ3vAO1Y8eQ/uMEvvPlL+M4Ec444x7s3v1ZfOc7HwRwZNBSnjKefPIC7NoVa0oMO9F0Lgj6SaOB2vHjqNXroHodtS796UEwCIbOIhgbG+tovmQtJqJdHVauftUGbjlXkRdQtgJI2n3kNZHS7h6LnPnrjedVGdbrdbcxXW6BFS84zGawnkcZbJXuG6AzR5qDhLL6WbsYvJbFlttKt9PWrgDpLtPBYl39aQXb2Y3DtQ2cV6/PBwAO/fqv40dvuQWP/eRP4gfPPts6zgYsLPwjAE90BHHZNSTf17nz3rWV19Wr8PXaGlv3rNW2GwDOOOMMAM1q7/PPPx8AcNFFzdjHhRdeCADYvXv3qn14fl944S6cccaN2Lt3Lx5+uBkr4Px7zrvnPHy+FlIOXfmtX+sFY6w21Bo+X+nm8ebPC5zLY0rXoNzqvy3y75ZX3Z9rqKnvzX4FjYfuQRAEpztPX3YZnr7ssuaLLn3HLwtSAu65BzjvPKAVIwqGm6F6EHAb6lLlJVAOvMr0rFL7afl+SXv3NDOZSuZZLzm8dsCM1Ha8RXK0FmwFGqv2NJJBOj5n3dLXC5CNj4+3P9NBQCsAxlt9nbSmpIN2ssraW3DEq8TNoa/j/Px82wLQgXkvqUBuS4FYfb7Ly8uuNur18ZF4C8ZY96W+l3i+2NLhNE8Oku/Zs6ddVc29oDghgI8793u/h5kPfADpFa/A01/6Eg4efBJbtizisccea7dpZguAz89rzT01NdVhdfH9bVkC+py19aetMZlMoJMGvO4BMpjLslTVzHOprV61v7zm+jcfC9MEQTCcTE4CKSFNTmLXrvfhsst+DTMzT+GXf/mDuOyyfYOWLjAYOotA9+aoin5CWnGFkoUgF6YpaXpW8ZYlgz4/D62FWj5FeTx5HqVFOCYmJlxfpbckouzSaXW6lGNZGi2Px5q07t6qx5ycnOxYslFrdazFyfRSmWaYk1XOkZ4vXbSlCxAXFxc7FqTRWy8FVC6Sojtuep03p6en3d5W1nXiz3X6q7d0pezQ6t1DekEflvHEiRO4//77AQB33303gBV//6FDhwAAL730Es56/evxw3odi289gaWlV7WPOzOzAOCb7RRT3rLlwceR/Z5K3VQZaTXp+I9XZCetTG85UMbrCybx/l54BaPy/91Yff0mLIIgCPrOsxs3YrH1BzwYfuJBEARBMOIMlWuI3Su5dYc9rMCiV13otfSV+3tmmpQVWG3yVU0TrdKfKEeurbU1hnQbeKardj00Gg239bfValmPZQV25XF0fxXpEtSy6upN2XtGm/May21mtfeV56EX2pEL0+iKWK/dsMSr2tbuK1ntqt0QXnttnVprjZ9by9ibC1nxDay4+J5//nk8+eSTAIBHH33UPWcPTiM+77zzAKykpWo3mZRDJwvoa26119Yps7nFcvj8de8fxptPmeDAeOPn8NLYLZfQermJwiIIgiAYcYbOIpCFOYx8CubS9PTWsyy8Yiq5oIWUSW6ryOYFeL2unXL8Uh8VS8PQ2nUuFbRqz6GxsTE36OjNibUva7lenyKpsZWul9Za5T5a89NBd6n5agtAWzRsccgOplrr5DE42KmPJ1MddQBUp0jqjqKy4ybjaYJyDEtjlVttOcqiS54DbXVxwdfzzz8PAHjmmWfaKaDdsmHDBuzcuRPAigXAsMVlLcOa0/zl1gvyyvPz7l3Zx8dKtpCvJaXfVZWOwPo4Oa+AZ9n3SlgEQRAEI048CIIgCEacoXMNSRMt13q5m7bTpTV+pWvIq3zVbpFcHxBvay0C4gWxLFcNy6Nl8/qOyDFLc6DN08nJybYrwwtoe9XWsvqZ0YFm3rJZb31HjmdtZSCxanBfukMY7a7SAeylpaX2ubKbh+eJ3TzeerPyXLxAOR+XaxHkWsJeVasO/EqXqr4+XiBdJmZotxgHh7lGgLeLi4vYunUrgJX7T7cazy3WY63BDawE3a3qWq/S1rvWcs1nL7FBfpe/pwPn+hpaFcCe27fkwpG/EX2/6+NZv+9+uYTa8vR1tCAIguC0Y6gsgpQS6vV6xxNSWgTelpGLS3gauRdYlJqtFwTKdTOssniNtV9OJq29WkHcUlDQ0pBK/W/4WgB+daQ+B++1JNft0usK66WxSpm9OdfzKquedRWwXjZUpnNqi8la6lB+LgPQWmvUfW+sBWMYL0VYn59cgpPHrbLYjD53XZHLFpvsQspVwGwBsIXAlgBbRzoAPTc317EQDVds6+NJTdo7D72QjLTavXtHz2Nu0RerYl6+1nLKrWcZMFZKt97HskS68YR0Q1gEQRAEI048CIIgCEacoXINNRoNzM3NZdcgZaq0YdXBHa/dqzTRvMVl9BhWjr2WpeQiqhLwsWT16hFyx9NBcK8KWbqDvJbLnqvGkrubSkhvfrxGdZYLwNtXmvd6wRvtppLr2AKrq5G1S0Gfu65oldXPsiLa+i7LIatcvePqOVtcXHTdpYxVueq5Wvm4ukHepk2b2i4gfo/dYroiW7u65AJJvI8+d0YmhXhB06quSvld7cKzrqPnVrT+XpTy+bupS/Jc2d20k18rYREEQRCMOENlEXAbaksDrJrOKTUML9DraZ4yMFUKtFlpnaXqRUYGrLzW0ZYmC6xuD+0FGXmb08a91DXZY0aPV+q31I2WYs1VqXrbWkrSSzn12jdbbby1TDolc2pqyu1L5CUiWFXJ+js66CnH9qqfvWr1XGU2Y6Uv64pX3ZdIt4uenZ1tzwXvy8Fpby7k8fTvS1s+jPwda9mqWATe79fqwyX3k1tLfvnaOk43nQH0PaTlsLwT/U4bbcuyLqMGQRAEpw1DZREQESYmJszUyW59YjJtr6Qd6JQyCy2TlW6px/fSOaUG6GmaelypbXl9e7Tv21qkXBcOeUViKaVVFhlgp2LK4zBWsYxnPVi+Wf16LX1Vcml8+jro66OvidWfyMNKU/SK97z7UMqttficL1pr1Z7VYnUq1b2h9PKQjCx205RSvC0rXVvrXv8loNzjqkpasS7m09q+tY93X1p/n7z4o+5sWqvV3BRxz8q1/qZ1E4PLERZBEATBiBMPgiAIghFn6FxDU1NTphms3R2MF6iUgUTP9KoS5Cz1DpFuEi/lT5t6smJRB9qq9Mzx0jd1lauVwqjX+NXuAhmsLlX2em6L5eXljjn23DDyvLzr5AX7rXRfva/lcvDW8PXul0aj0Xalla6T5TbRwWCNlfCg3TlevyBZyayrmqX8fO76vHWyguw1pb/LY+lz18fJbUvuEMtVqdN8vQCtPAdvkRd9nWRg23NB5tqxe/eOF3i25K7acl72hmKGwjVERJ8kou8T0feI6L8Q0Rnis2uJ6BEieoiIfqZnSYMgCIJ1oVeL4DYA16aU6kT0CQDXAvgQEb0KwNsBXApgD4DbieiSlJIdZWrB3Ud1oEymMnq9eKzFOLxU01wHUU9zyAXAeEw+jpZVa3VWF0GvyMg6jtc3JbeAjNZGtSyWluVpJl56nT4X+V291Rqupdl4BUTyfD1rS7+2LAJPc7aWxPRSdbWmp48vEwK8Ij4rIUBr6qW5scZjLI2Zt/o43pxYKade8WU3FkGpONHq6+MFb+XfhpJFoDVrGbz1Ehys35snt2ddWHNRSgRg1qvPENCjRZBSujWlxL/oOwCc0/r/lQC+kFJaSCk9DuARAK/r5VhBEATB+tDPYPE/AXBL6/9nA3hKfLa/9V4QBEEwZBRdQ0R0O4CzjI8+klK6qfWdjwCoA/gs72Z834xqENE1AK4BmtWLKSUzX90LzmnTT4zrugvaAhmBYCuIJL/j5VBb32E3Qs614S2ckZPV64XCWEEn/R7niVfJzfcqOj2zWLZ61i4vr/rZym0vuYZkNbfnHsu5k6oEEPl1aV1cz0U5NTXV0YtH5/fnXAS5ALaU0VrkiLFkApoBYU8m3mpX5dLSUsexu6nzKK17rV1HcsEYD8vFm/udSjmkPF4SiRcQtuoISuclf7veb6RKS3rPvblWig+ClNIbc58T0VUA3gLgirTy13g/gHPF184B8Iwz/vUArgeAXbt29ScEHgRBEFSmp2AxEb0JwIcA/N2U0pz46GYAnyOi30YzWHwxgG+VxkupuRiKfprLAJ9XKWhpKfyk9YKq1lO71E9HflceV6a5eYFmRgbvWFtkDb200Ik1fmm5S0mpX4sVkPO0Xa8Tp5x7lk133tSatewp0w1WoFBu+fysylGv4tZKTCglHnhWi+xK6wUhq1SJetaDxKto1wFheb28+fNSQy2rufSbsebC+w1an+vfgEy/lrLKFOnSb0+PZS1hyujrYllupS6j1tjePdRLRf1a6TVr6N8DmAJwW+tk7kgpvTuldD8R3QDgATRdRr9WyhgKgiAIBkNPD4KU0isyn30cwMd7GT8IgiBYf4aqspjNTiso6dUAaLNQbr2FPxh9nPHx8aIbycu7ly4trymcFdDWZrt2DfFrWfFpBU0tuqk6rFJlzXjN7uQ10teLXV85PJPYW2vaqgbVLg3LhVhq3KVllwHLKia/HFMuhFMidw28CndmbGzMDUp7LhU5F97xtJvRqmPRv8lckFVXmnvN5qwgLuPl5suxvBbPpbmR51GqHZLXoBSczv2uqjS91MfrV0UxE72GgiAIRpyhsghYQ7b60pTS56wURn5q6j47WsOUmrVelET3XMkFC3XQVGtGVhCZZeJg6okTJ1bto6tBp6en21aCth60jJa24wU3Le3NC8R7qbWMnItcoFIi0x89i8rS0KpqtFawU+MFc630W298bbXU63U33ZLJpfuWgtJSk9ZatZ4Ly6LSCQ7e1pKXKWmyUg5t5fH9oPsJyd+1Zw3l+kx595n8jtw3pc5lJ7uxCDzrUlsr8npqS6BKUkspqL9WwiIIgiAYcYbOIpDahXxqez4xrRnJ1+yX9hYNt3z3OX+qRGtfcsFxRj+19fEXFxfdZQy1NsznMD8/3xFPsOIIWkbt1y1p0lIW/bpUXFer1VyNz9OsG41GVvO3ZJb3hdd/RqPvMX5PbnMxqhJVtEXPx20txuItaqQtLMtq5nG8e0kue1rqcSXvH8/a8yyqHLrnFHfjtc6DKVm10qJi9PlUXfDHGl+O6VkRVSwD737Qc27de/1OKQ2LIAiCYMSJB0EQBMGIM1SuIQ4msSkk0xK1eZRbB5jf90xUbcbJxVq8fh9ehWKuJbLGcq1os91zDVkmpw5+W624eestwqLnJFfN6Jniub4nOlDfjUlbcvdYfZc8meW9UFpH2QvU6v9bx7HcPt695M2FdG141zQXSPQq262W2dodpc/Dqg72FhkquYakK88Kqsut5WIr3XdV0m5zbk3v/tafy9+5lzKr58+6l6u2n2ZyvY16JSyCIAiCEYf6XZjQC0Q0PMIEQRCcPtydUtq71p3DIgiCIBhx4kEQBEEw4sSDIAiCYMSJB0EQBMGIEw+CIAiCESceBEEQBCNOPAiCIAhGnHgQBEEQjDjxIAiCIBhx4kEQBEEw4sSDIAiCYMSJB0EQBMGIEw+CIAiCESceBEEQBCNOPAiCIAhGnHgQBEEQjDjxIAiCIBhx4kEQBEEw4vT0ICCif01E3yOie4joViLaIz67logeIaKHiOhnehc1CIIgWA96WrOYiDanlI62/v9eAK9KKb2biF4F4PMAXgdgD4DbAVySUloujBdrFgdBEHTP4NYs5odAi1kA/If8SgBfSCktpJQeB/AImg+FIAiCYMgY73UAIvo4gH8E4AiAv9d6+2wAd4iv7W+9Z+1/DYBrWi8XANzXq0zrwA4ALwxaCEXIVI2QqTrDKFfIVI1X9rJz8UFARLcDOMv46CMppZtSSh8B8BEiuhbAewD8SwBkfN90+6SUrgdwfetYd/Vi3qwXwyhXyFSNkKk6wyhXyFQNIrqrl/2LD4KU0hsrjvU5AF9G80GwH8C54rNzADzTtXRBEATButNr1tDF4uVbAXy/9f+bAbydiKaI6EIAFwP4Vi/HCoIgCNaHXmME1xHRKwE0APwQwLsBIKV0PxHdAOABAHUAv1bKGGpxfY/yrBfDKFfIVI2QqTrDKFfIVI2eZOopfTQIgiA4/YnK4iAIghEnHgRBEAQjztA8CIjoTa12FI8Q0YcHJMO5RPSXRPQgEd1PRO9rvb+NiG4joodb260DkG2MiL5DRF8aBpmI6Awi+lMi+n5rvn5iCGT6QOu63UdEnyei6UHIRER/SEQHieg+8Z4rx6lox+LI9MnW9fseEf0XIjpj0DKJz/5PIkpEtGMYZCKiX28d934i+jenUiZPLiJ6LRHd0WrxcxcRvU581p1cKaWB/wMwBuBRAD8CYBLAd9FsV3Gq5dgN4Mda/98E4AcAXgXg3wD4cOv9DwP4xABk+w00U3S/1Ho9UJkAfAbAO1v/nwRwxiBlQrNg8XEAM63XNwD4x4OQCcDrAfwYgPvEe6YcrfvruwCmAFzY+h2MnSKZ/lcA463/f2IYZGq9fy6Av0AzAWXHoGVCs1D2dgBTrde7TqVMGbluBfCzrf+/GcDX1irXsFgErwPwSErpsZTSIoAvoNmm4pSSUjqQUtrX+v8xAA+i+QfmSjT/8KG1/flTKRcRnQPg5wD8J/H2wGQios1o3ph/AAAppcWU0uFBytRiHMAMEY0D2IBm7coplyml9N8BHFJve3KcknYslkwppVtTSvXWyzvQrPcZqEwt/i2A/wuri1AHKdM/BXBdSmmh9Z2Dp1KmjFwJwObW/7dgpVara7mG5UFwNoCnxGu3JcWpgoguAHAZgDsBnJlSOgA0HxYAdp1icX4HzR9GQ7w3SJl+BMDzAP6o5a76T0Q0O0iZUkpPA/gUgCcBHABwJKV06yBlUnhyDMu9/08A3NL6/8BkIqK3Ang6pfRd9dEg5+kSAD9JRHcS0deJ6G8NgUwA8H4AnySip9C8969dq1zD8iCo3JLiVEBEGwF8EcD70+rGeoOQ5S0ADqaU7h6kHIpxNM3U/5hSugzACTTdHQOj5XO/Ek1TeA+AWSJ6xyBlqsjA730i+gia9T6f5beMr627TES0AcBHAHzU+th471TN0ziArQAuB/DPAdxARDRgmYCmpfKBlNK5AD6AloWONcg1LA+CoWlJQUQTaD4EPptSurH19nNEtLv1+W4AB73914G/DeCtRPQEmi6znyKiPx6wTPsB7E8p3dl6/adoPhgGKdMbATyeUno+pbQE4EYA/8uAZZJ4cgz03ieiqwC8BcA/TC0H8wBlugjNB/l3W/f7OQD2EdFZA5QJrWPfmJp8C03LfMeAZQKAq9C8zwHg/8eK+6druYblQfBtABcT0YVENAng7Wi2qTiltJ7yfwDgwZTSb4uPbkZz0tHa3nSqZEopXZtSOieldAGa8/LVlNI7BizTswCeomZVOQBcgWYV+cBkQtMldDkRbWhdxyvQjPEMUiaJJ8fA2rEQ0ZsAfAjAW1NKc0rWUy5TSunelNKulNIFrft9P5rJG88OSqYWfwbgpwCAiC5BMznihQHLBDT/uP/d1v9/CsDDrf93L9d6RLjXGBV/M5pZOo+i2dl0EDL8HTRNqO8BuKf1780AtgP4SmuivwJg24DkewNWsoYGKhOA1wK4qzVXf4am6TxomX4LzX5X9wH4/9DMmjjlMqG5KNMBAEto/jG7OicHmu6QRwE8hFYWyCmS6RE0fcl8r//+oGVSnz+BVtbQgOdpEsAft+6rfQB+6lTKlJHr7wC4G80MoTsB/M21yhUtJoIgCEacYXENBUEQBAMiHgRBEAQjTjwIgiAIRpx4EARBEIw48SAIgiAYceJBEARBMOLEgyAIgmDE+Z/tYoihAfWBGAAAAABJRU5ErkJggg==", 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -725,104 +799,18 @@ "dat1 = dat1 = dat1[-1]" ] }, - { - "cell_type": "markdown", - "id": "f44f4c73-b601-4672-a3f8-e3cc96b18cd0", - "metadata": {}, - "source": [ - "### An example of optimizing the Pattern Center\n", - "\n", - "There is nothing special here: we use a Nelder-Mead optimization to minimize the fit of the indexed patterns. \n", - "\n", - "It is built to optimize using a single pattern, or an array of patterns. Note- there are a lot of iterations still, and indexing is single threaded. It is not recomended to use a very large array of patterns. " - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "af99d03d-17ff-49ac-912a-b20f824f0c56", - "metadata": {}, - "outputs": [], - "source": [ - "startcolrow = [560,460]\n", - "ncol = 60\n", - "nrow = 2\n", - "f = ebsd_pattern.get_pattern_file_obj(file)\n", - "pats, xyloc = f.read_data(returnArrayOnly=True, convertToFloat=True, patStartCount=[startcolrow, [ncol,nrow]])" - ] - }, - { - "cell_type": "markdown", - "id": "414f57c1-adf3-4c3a-af06-77fb681c1f3d", - "metadata": {}, - "source": [ - "Single pattern optimization:" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "208e7444-0118-4f68-a997-50d9ab73d482", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.46838947 0.70615405 0.64070127]\n", - "[0.46844258 0.70618324 0.64076734]\n" - ] - } - ], - "source": [ - "newPC = pcopt.optimize(pats[0,:,:], indxer, PC0 = [0.45, 0.65, 0.65])\n", - "print(newPC)\n", - "print(pcopt.optimize(pats[0,:,:], indxer, PC0 = newPC))" - ] - }, - { - "cell_type": "markdown", - "id": "2b9bace1-a3db-4fed-8c7d-d76773472edd", - "metadata": {}, - "source": [ - "Multiple pattern optimization (here using 120 patterns): " - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "42230f97-5795-406e-9734-284ce1c75843", - "metadata": { - "tags": [] - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.46424919 0.70189953 0.64026537]\n", - "[0.46430816 0.7018663 0.64027081]\n" - ] - } - ], - "source": [ - "newPC = pcopt.optimize(pats, indxer, PC0 = [0.45, 0.65, 0.65])\n", - "print(newPC)\n", - "print(pcopt.optimize(pats, indxer, PC0 = newPC))" - ] - }, { "cell_type": "markdown", "id": "ac0880e8-0f5d-441b-af80-50fb95d18f3c", "metadata": {}, "source": [ "### Loading data from an HDF5 File\n", - "There is some limited support for specific types of HDF5 files using the _\"filename\"_ keyword to _ebsd_index.index_pats_ or _ebsd_index.index_pats_distributed_. However, probably the easiest method is to just point an h5py Dataset at the _\"patsIn\"_ keyword (This makes the important assumption that the patterns are stored in \\[npatterns, nrows, ncols\\] and the first point stored is the upper left corner of the detector). See below: " + "There is some limited support for specific types of HDF5 files using the _\"filename\"_ keyword to `index_pats` or `index_pats_distributed`. However, probably the easiest method is to just point a h5py Dataset at the `patsin` keyword (This makes the important assumption that the patterns are stored in `[npatterns, nrows, ncols]` and the first point stored is the upper left corner of the detector). See below: " ] }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 49, "id": "c7310f4b-6b33-492b-af39-04a5cc3f8af3", "metadata": {}, "outputs": [ @@ -830,47 +818,51 @@ "name": "stdout", "output_type": "stream", "text": [ - "Radon Time: 0.01922415099988939\n", - "Convolution Time: 0.053790638000009494\n", - "Peak ID Time: 0.035985807000088244\n", - "Band Label Time: 0.04844529600018177\n", - "Total Band Find Time: 0.1574961930000427\n" + "Radon Time: 0.019871829077601433\n", + "Convolution Time: 0.05477267492096871\n", + "Peak ID Time: 0.046635095961391926\n", + "Band Label Time: 0.04914216499309987\n", + "Total Band Find Time: 0.17046290694270283\n", + "Band Vote Time: 1.3323961960850284\n", + "num cpu/gpu, and number of patterns per iteration: 28 2 1008 16 28\n", + "Completed: 852768 -- 853776 PPS: 12366 100% 69;0 running;remaining(s)\n", + "\n" ] }, { "data": { - "image/png": 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", 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    " + "
    " ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Band Vote Time: 0.7439625819999947\n", - "num cpu/gpu: 36 2\n", - "Completed: 853776 -- 854784 PPS: 18450;23542;19311 100% 44;0 running;remaining(s)\r" - ] + "data": { + "image/png": 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+ "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "f = h5py.File('/Users/dave/Desktop/SLMtest/scan2v3_NLPAR_l0.90sr3.oh5', 'r') # this is an HDF5 file type used by EDAX. \n", - "h5pats = f['/scan2v3/EBSD/Data/Pattern'] # location of the pattern array within the HDF5 file. \n", "\n", + "h5file = '/Path/to/hdf5/file.h5'\n", + "f = h5py.File(h5file, 'r') # this is an HDF5 file type used by EDAX. \n", + "h5pats = f['/Scan 1/EBSD/Data/Pattern'] # location of the pattern array within the HDF5 file. \n", + "# index the first 1000\n", "h5data, h5bnddata, indxer=ebsd_index.index_pats(patsin = h5pats[0:1000,:,:],\n", " patstart = 0, npats = 1000,return_indexer_obj = True,\n", " backgroundSub = backgroundsub,\n", " nTheta = nT, nRho=nR,\n", " tSigma = tSig, rSigma = rSig,rhoMaskFrac=rhomask,nBands=nbands, \\\n", " phaselist = phaselist, PC = PC, verbose = 2)\n", - "indxer.bandDetectPlan.useCPU = False\n", - "h5data, h5banddata = ebsd_index.index_pats_distributed(patsin = h5pats, chunksize = 1008, ncpu = 36, ebsd_indexer_obj = indxer)" + "#now index them all\n", + "h5data, h5banddata = ebsd_index.index_pats_distributed(patsin = h5pats, ebsd_indexer_obj = indxer, ncpu = 28)" ] }, { @@ -898,7 +890,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.13" + "version": "3.10.10" }, "vscode": { "interpreter": { diff --git a/pyebsdindex/ebsd_pattern.py b/pyebsdindex/ebsd_pattern.py index ad5ae50..616c7b4 100644 --- a/pyebsdindex/ebsd_pattern.py +++ b/pyebsdindex/ebsd_pattern.py @@ -277,12 +277,14 @@ def read_data(self,path=None,convertToFloat=False,patStartCount = [0,-1],returnA nPatToRead = [ncolread, nrowread] patterns = np.zeros([int(ncolread*nrowread),self.patternH,self.patternW],dtype=typeout) + xyloc = np.zeros([int(ncolread*nrowread),2],dtype=np.float32) for i in range(nrowread): pstart = int(int(int(rowstart+i)*self.nCols)+colstart) - ptemp, xyloc = self.read_data(convertToFloat=convertToFloat,patStartCount = [pstart,ncolread],returnArrayOnly=True) + ptemp, xyloctemp = self.read_data(convertToFloat=convertToFloat,patStartCount = [pstart,ncolread],returnArrayOnly=True) patterns[int(i*ncolread):int((i+1)*ncolread), :, :] = ptemp + xyloc[int(i*ncolread):int((i+1)*ncolread), :] = xyloctemp if returnArrayOnly == True: return patterns, xyloc diff --git a/pyebsdindex/ebsdfile.py b/pyebsdindex/ebsdfile.py index 064eae8..127c41c 100644 --- a/pyebsdindex/ebsdfile.py +++ b/pyebsdindex/ebsdfile.py @@ -209,8 +209,8 @@ def writeoh5(filename, indexer, data, if nrows is None: nrows = np.ceil(data.shape[-1] / ncols) - ncols = np.array([np.int32(ncols)]) - nrows = np.array([np.int32(nrows)]) + ncols = np.array([np.int32(ncols)]).squeeze() + nrows = np.array([np.int32(nrows)]).squeeze() f.create_dataset(datasetname + '/EBSD/Header/nColumns', data=ncols) From 98e040282fc5080b2ac3d8c52fe2f01bcb3f9d00 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?H=C3=A5kon=20Wiik=20=C3=85nes?= Date: Sat, 5 Aug 2023 12:54:27 -0700 Subject: [PATCH 145/177] Rename GitHub action test file MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: Håkon Wiik Ånes --- .github/workflows/{build.yml => tests.yml} | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) rename .github/workflows/{build.yml => tests.yml} (89%) diff --git a/.github/workflows/build.yml b/.github/workflows/tests.yml similarity index 89% rename from .github/workflows/build.yml rename to .github/workflows/tests.yml index 5e28d9d..887acfa 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/tests.yml @@ -1,4 +1,4 @@ -name: Build +name: Tests on: @@ -24,7 +24,7 @@ jobs: - uses: actions/setup-python@v4 with: - python-version: '3.10' + python-version: '3.11' - name: Install dependencies run: | @@ -42,11 +42,10 @@ jobs: fail-fast: false matrix: os: [ubuntu-latest, macos-latest, windows-latest] - python-version: [3.9, '3.10'] + python-version: ['3.10', '3.11'] include: - os: ubuntu-latest python-version: 3.7 - OLDEST_SUPPORTED_VERSION: true DEPENDENCIES: matplotlib==3.3 numba==0.52 numpy==1.19 ray[default]==1.13 LABEL: -oldest steps: @@ -63,7 +62,7 @@ jobs: pip install -U -e .'[tests]' - name: Install oldest supported version - if: ${{ matrix.OLDEST_SUPPORTED_VERSION }} + if: contains(matrix.LABEL, 'oldest') run: | pip install ${{ matrix.DEPENDENCIES }} @@ -83,7 +82,7 @@ jobs: - name: Run tests run: | - pytest --cov=pyebsdindex --pyargs pyebsdindex + pytest -n 2 --cov=pyebsdindex --pyargs pyebsdindex - name: Generate line coverage if: ${{ matrix.os == 'ubuntu-latest' }} From 4b1c62c0d43b14464ca8b4907fc472c10cb61823 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?H=C3=A5kon=20Wiik=20=C3=85nes?= Date: Sat, 5 Aug 2023 12:54:51 -0700 Subject: [PATCH 146/177] Use "Trusted publisher" workflow to publish to PyPI instead of token MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: Håkon Wiik Ånes --- .github/workflows/publish.yml | 32 ++++++++++++++++++++------------ 1 file changed, 20 insertions(+), 12 deletions(-) diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index 9f5631d..01a566c 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -1,15 +1,20 @@ # This workflows runs when a tagged release is created or it is triggered manually. +# # For more information see: -# - Python docs: https://packaging.python.org/en/latest/guides/publishing-package-distribution-releases-using-github-actions-ci-cd-workflows/ +# - Python docs: https://packaging.python.org/en/latest/guides/publishing-package-distribution-releases-using-github-actions-ci-cd-workflows # - GitHub action: https://github.com/marketplace/actions/pypi-publish # - GitHub docs: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python#publishing-to-package-registries +# # The source distribution (sdist) is built with the `build` package # (https://pypa-build.readthedocs.io/en/stable/index.html). +# # The sdist is uploaded to: # - TestPyPI whenever the workflow is run # - PyPI when the current commit is tagged +# +# Trusted publishing to PyPI within GitHub without using an API token: https://blog.pypi.org/posts/2023-04-20-introducing-trusted-publishers -name: Upload to PyPI +name: Upload package to PyPI on: release: @@ -20,28 +25,31 @@ on: jobs: deploy: runs-on: ubuntu-latest + permissions: + # IMPORTANT: this permission is mandatory for trusted publishing: + id-token: write steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 + - name: Set up Python - uses: actions/setup-python@v2 + uses: actions/setup-python@v4 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 to TestPyPI - uses: pypa/gh-action-pypi-publish@27b31702a0e7fc50959f5ad993c78deac1bdfc29 + uses: pypa/gh-action-pypi-publish@release/v1 + continue-on-error: true with: - user: __token__ - password: ${{ secrets.TEST_PYPI_API_TOKEN }} repository_url: https://test.pypi.org/legacy/ + - name: Publish package to PyPI - if: startsWith(github.ref, 'refs/tags') - uses: pypa/gh-action-pypi-publish@27b31702a0e7fc50959f5ad993c78deac1bdfc29 - with: - user: __token__ - password: ${{ secrets.PYPI_API_TOKEN }} \ No newline at end of file + uses: pypa/gh-action-pypi-publish@release/v1 \ No newline at end of file From b147acfbace2f7f70abf1abd47110e0532b4a4e4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?H=C3=A5kon=20Wiik=20=C3=85nes?= Date: Sat, 5 Aug 2023 12:55:11 -0700 Subject: [PATCH 147/177] List support for Python 3.11, add test dependency on pytest-xdist MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: Håkon Wiik Ånes --- setup.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 2a9f921..5e6de9e 100644 --- a/setup.py +++ b/setup.py @@ -23,7 +23,8 @@ "tests": [ "coverage >= 5.0", "pytest >= 5.4", - "pytest-cov >= 2.8.1" + "pytest-cov >= 2.8.1", + "pytest-xdist", ], "gpu": [ "pyopencl", @@ -61,6 +62,7 @@ "Programming Language :: Python :: 3.8", "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", "Development Status :: 4 - Beta", "Intended Audience :: Science/Research", "License :: Other/Proprietary License", From 52a8343e847d2a4a6e415d94020221eb5b2655c5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?H=C3=A5kon=20Wiik=20=C3=85nes?= Date: Sat, 5 Aug 2023 12:55:31 -0700 Subject: [PATCH 148/177] Silence pkg_resources() deprecation warnings in tests MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: Håkon Wiik Ånes --- setup.cfg | 16 +++++++++++++++- 1 file changed, 15 insertions(+), 1 deletion(-) diff --git a/setup.cfg b/setup.cfg index be9a123..d611f2c 100644 --- a/setup.cfg +++ b/setup.cfg @@ -11,4 +11,18 @@ known_excludes = **/*.nbc doc/build* doc/.ipynb_checkpoints/* - htmlcov/** \ No newline at end of file + htmlcov/** + +[tool:pytest] +filterwarnings = + ignore:Deprecated call to \`pkg_resources:DeprecationWarning + ignore:pkg_resources is deprecated as an API:DeprecationWarning + +[coverage:run] +source = pyebsdindex +omit = + setup.py +relative_files = True + +[coverage:report] +precision = 2 From 5f1081354d821ccbc479628e46bb475959c71f9e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?H=C3=A5kon=20Wiik=20=C3=85nes?= Date: Sat, 5 Aug 2023 12:55:57 -0700 Subject: [PATCH 149/177] Ask readthedocs to build docs using Ubuntu 22.04 and Python 3.11 MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: Håkon Wiik Ånes --- .readthedocs.yaml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.readthedocs.yaml b/.readthedocs.yaml index 28470aa..265ff5d 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -1,4 +1,4 @@ -# ..readthedocs.yaml +# .readthedocs.yaml # Read the Docs configuration file # See https://docs.readthedocs.io/en/stable/config-file/v2.html for details @@ -11,9 +11,9 @@ sphinx: # Set the version of Python and other tools you might need build: - os: ubuntu-20.04 + os: ubuntu-22.04 tools: - python: "3.9" + python: "3.11" # Build doc in all formats (HTML, PDF and ePub) formats: From 63e2db47d548f192d5d1b4946f1484512ded390f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?H=C3=A5kon=20Wiik=20=C3=85nes?= Date: Sat, 5 Aug 2023 12:56:13 -0700 Subject: [PATCH 150/177] List explicit Python 3.11 support in changelog, reformat slightly MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: Håkon Wiik Ånes --- CHANGELOG.rst | 35 ++++++++++++++++------------------- 1 file changed, 16 insertions(+), 19 deletions(-) diff --git a/CHANGELOG.rst b/CHANGELOG.rst index b51ac33..e13af42 100644 --- a/CHANGELOG.rst +++ b/CHANGELOG.rst @@ -5,48 +5,45 @@ Changelog All notable changes to PyEBSDIndex will be documented in this file. The format is based on `Keep a Changelog `_. -Unreleased -========== +0.2.0 (2023-08-08) +================== Added ----- - Initial support for uncompressed EBSP files from Oxford systems. -- Significant improvement in the particle swarm optimization for pattern center optimization. -- Initial support for non-cubic phases. Hexagonal verified with EDAX convention. Others are untested. +- Significant improvement in the particle swarm optimization for pattern center + optimization. +- Initial support for non-cubic phases. Hexagonal verified with EDAX convention. + Others are untested. - Significant improvements in phase differentiation. - NLPAR support for Oxford HDF5 and EBSP. - Initial support for Oxford .h5oina files - Added IPF coloring/legends for hexagonal phases - Data output files in .ang and EDAX .oh5 files - +- Explicit support for Python 3.11. Changed ------- -- CRITICAL! All ``ebsd_pattern.EBSDPatternFiles.read_data()`` calls will now return TWO arguments. - The patterns (same as previous), and an nd.array of the x,y location within the scan of the patterns. The origin is - the center of the scan, and reported in microns. -- ``ebsd_index.index_pats_distributed()`` now will auto optimize the number of patterns processed at a time depending on GPU - capability, and is set as the default. +- CRITICAL! All ``ebsd_pattern.EBSDPatternFiles.read_data()`` calls will now return TWO + arguments. The patterns (same as previous), and an nd.array of the x,y location within + the scan of the patterns. The origin is the center of the scan, and reported in + microns. +- ``ebsd_index.index_pats_distributed()`` now will auto optimize the number of patterns + processed at a time depending on GPU capability, and is set as the default. - Updated tutorials for new features. - -Deprecated ----------- - Removed ------- - Removed requirement for installation of pyswarms. - Removed any references to np.floats and replaced with float() or np.float32/64. + Fixed ----- - Radon transform figure when ``verbose=2`` is passed to various indexing methods is now plotted in its own figure. - Several bug fixes with NLPAR file reading/writing. -- Complete rewrite of the scheduling for ``ebsd_index.index_pats_distributed()`` function to be compatible - with NVIDIA cards. - -Security --------- +- Complete rewrite of the scheduling for ``ebsd_index.index_pats_distributed()`` + function to be compatible with NVIDIA cards. 0.1.1 (2022-10-25) ================== From e5d6f4188cc8f70b4318b16a4abf966f564a684e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?H=C3=A5kon=20Wiik=20=C3=85nes?= Date: Sat, 5 Aug 2023 13:15:42 -0700 Subject: [PATCH 151/177] Remove/comment out unusued variables/parameters in ebsd_index files MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: Håkon Wiik Ånes --- .gitignore | 2 +- pyebsdindex/_ebsd_index_parallel.py | 39 +++++++---- pyebsdindex/_ebsd_index_single.py | 102 +++++++++++++++------------- pyebsdindex/ebsd_index.py | 3 +- 4 files changed, 82 insertions(+), 64 deletions(-) diff --git a/.gitignore b/.gitignore index 6a4783c..8100fca 100644 --- a/.gitignore +++ b/.gitignore @@ -20,7 +20,7 @@ dist/ *.code-workspace # Line coverage -.coverage +.coverage* # Sphinx doc/build diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index 34fe75d..df0e9b9 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -29,7 +29,6 @@ import platform import logging import sys -import time from timeit import default_timer as timer import numpy as np @@ -47,7 +46,8 @@ RAYIPADDRESS = '127.0.0.1' OSPLATFORM = platform.system() if OSPLATFORM == 'Darwin': - RAYIPADDRESS = '0.0.0.0' # the localhost address does not work on macOS when on a VPN + RAYIPADDRESS = '0.0.0.0' # the localhost address does not work on macOS when on a VPN + def index_pats_distributed( patsin=None, @@ -72,7 +72,7 @@ def index_pats_distributed( ebsd_indexer_obj=None, keep_log=False, gpu_id=None, - verbose = 1 + verbose=0 ): """Index EBSD patterns in parallel. @@ -130,7 +130,8 @@ def index_pats_distributed( Number of patterns to index. Default is ``-1``, which will index up to the final pattern in ``patsin``. chunksize : int, optional - If not set. we will make a guess based on the resources available. + If not set, we will make a guess based on the resources + available. ncpu : int, optional Number of CPUs to use. Default value is ``-1``, meaning all available CPUs will be used. @@ -144,6 +145,9 @@ def index_pats_distributed( Whether to keep the log. Default is ``False``. gpu_id : int, optional ID of GPU to use if :mod:`pyopencl` is installed. + verbose : int, optional + 0 - no output (default), 1 - timings, 2 - timings and the Radon + transform of the first pattern with detected bands highlighted. Returns ------- @@ -165,17 +169,17 @@ def index_pats_distributed( Band identification data from the Radon transform. Stored as a structured numpy array, of dimensions [npoints, nbands]. With fields that include: - band ID ('id'), - peak max intesensity [used to calculate pattern quality] ('max') - nearest integer location of the Radon peak ('maxloc'), - nearest neighbor average of the max peak intensity('avemax'), - sub-pixel location of the Radon peak ('aveloc'), - a metric of the band width ('width'), - the theta value of the sub-pixel location on the Radon [lower-left origin] ('theta'), - the rho value of the sub-pixel location on the Radon [lower-left origin]('rho'), - was the peak detected ('valid'), - index for phase number and pole number that indexed to this band('band_match_index') - [use the EBSDIndexer method indexer.getmatchedpole(banddata)] + band ID ('id'), + peak max intesensity [used to calculate pattern quality] ('max') + nearest integer location of the Radon peak ('maxloc'), + nearest neighbor average of the max peak intensity('avemax'), + sub-pixel location of the Radon peak ('aveloc'), + a metric of the band width ('width'), + the theta value of the sub-pixel location on the Radon [lower-left origin] ('theta'), + the rho value of the sub-pixel location on the Radon [lower-left origin]('rho'), + was the peak detected ('valid'), + index for phase number and pole number that indexed to this band('band_match_index') + [use the EBSDIndexer method indexer.getmatchedpole(banddata)] indexer : EBSDIndexer EBSD indexer, returned if ``return_indexer_obj=True``. @@ -613,6 +617,7 @@ def index_pats_distributed( else: return dataout, banddataout + def __optimizegpuchunk__(indexer, ngpupro, gpu_id, clparam): @@ -745,6 +750,8 @@ def findbands(self, gpujob, pats=None, xyloc=None, PC = None, indexer=None): except: gpujob.rate = None return "Error", (None, None, gpujob) + + @ray.remote(num_cpus=1, num_gpus=0) class CPUWorker: def __init__(self, actorid=0): @@ -766,6 +773,8 @@ def indexpoles(self, cpujob, banddata, bandnorm, indexer=None): print(e) cpujob.rate = None return "Error", (None,None, cpujob) + + class CPUGPUJob: def __init__(self,jobid, pstart, pend, extime=0.0): self.jobid = jobid diff --git a/pyebsdindex/_ebsd_index_single.py b/pyebsdindex/_ebsd_index_single.py index df1afa6..e15d86d 100644 --- a/pyebsdindex/_ebsd_index_single.py +++ b/pyebsdindex/_ebsd_index_single.py @@ -29,7 +29,8 @@ import numpy as np import h5py -from pyebsdindex import tripletvote as bandindexer # use triplet voting as the default indexer. +from pyebsdindex import tripletvote as bandindexer # use triplet voting as the default indexer. +from pyebsdindex.tripletvote import BandIndexer from pyebsdindex import ( ebsd_pattern, rotlib, @@ -159,7 +160,20 @@ def index_pats( (fit) and Number of Bands Matched (nmatch). There are some other metrics reported, but these are mostly for debugging purposes. bandData : numpy.ndarray - Band identification data from the Radon transform. + Band identification data from the Radon transform. Stored + as a structured numpy array, of dimensions [npoints, nbands]. + With fields that include: + band ID ('id'), + peak max intesensity [used to calculate pattern quality] ('max') + nearest integer location of the Radon peak ('maxloc'), + nearest neighbor average of the max peak intensity('avemax'), + sub-pixel location of the Radon peak ('aveloc'), + a metric of the band width ('width'), + the theta value of the sub-pixel location on the Radon [lower-left origin] ('theta'), + the rho value of the sub-pixel location on the Radon [lower-left origin]('rho'), + was the peak detected ('valid'), + index for phase number and pole number that indexed to this band('band_match_index') + [use the EBSDIndexer method indexer.getmatchedpole(banddata)] indexer : EBSDIndexer EBSD indexer, returned if ``return_indexer_obj=True``. """ @@ -221,7 +235,6 @@ def index_pats( clparams=clparams, verbose=verbose, chunksize=chunksize, - gpu_id = gpu_id, ) if not return_indexer_obj: @@ -298,7 +311,7 @@ def __init__( rhoMaskFrac=0.15, nBands=9, patDim=None, - nband_earlyexit = None, + nband_earlyexit=None, **kwargs ): """Create an EBSD indexer.""" @@ -313,9 +326,9 @@ def __init__( for ph in self.phaselist: if ph is None: self.phaseLib.append(None) - if (ph.__class__.__name__).lower() == 'str': + if isinstance(ph, str): self.phaseLib.append(bandindexer.addphase(libtype=ph)) - if (ph.__class__.__name__) == 'BandIndexer': + if isinstance(ph, BandIndexer): self.phaseLib.append(ph) self.vendor = "EDAX" @@ -401,7 +414,6 @@ def index_pats( PC=None, verbose=0, chunksize=512, - **kwargs ): """Index EBSD patterns. @@ -430,7 +442,7 @@ def index_pats( Radon transform of the first pattern with detected bands highlighted. chunksize : int, optional - Default is 528. + Default is 512. Returns ------- @@ -452,17 +464,17 @@ def index_pats( Band identification data from the Radon transform. Stored as a structured numpy array, of dimensions [npoints, nbands]. With fields that include: - band ID ('id'), - peak max intesensity [used to calculate pattern quality] ('max') - nearest integer location of the Radon peak ('maxloc'), - nearest neighbor average of the max peak intensity('avemax'), - sub-pixel location of the Radon peak ('aveloc'), - a metric of the band width ('width'), - the theta value of the sub-pixel location on the Radon [lower-left origin] ('theta'), - the rho value of the sub-pixel location on the Radon [lower-left origin]('rho'), - was the peak detected ('valid'), - index for phase number and pole number that indexed to this band('band_match_index') - [use the EBSDIndexer method indexer.getmatchedpole(banddata)] + band ID ('id'), + peak max intesensity [used to calculate pattern quality] ('max') + nearest integer location of the Radon peak ('maxloc'), + nearest neighbor average of the max peak intensity('avemax'), + sub-pixel location of the Radon peak ('aveloc'), + a metric of the band width ('width'), + the theta value of the sub-pixel location on the Radon [lower-left origin] ('theta'), + the rho value of the sub-pixel location on the Radon [lower-left origin]('rho'), + was the peak detected ('valid'), + index for phase number and pole number that indexed to this band('band_match_index') + [use the EBSDIndexer method indexer.getmatchedpole(banddata)] patstart : int Starting index of the indexed patterns. npats : int @@ -489,31 +501,29 @@ def index_pats( print("Band Vote Time: ", timer() - tic) return indxData, banddata, patstart, npats + def getmatchedpole(self, banddata, float_out=False): """Return the pole from the library that was matched to the detected band. - Parameters - ---------- - banddata : numpy.ndarray, output structured bandata array from - ebsd_index.index_pats or ebsd_index.index_pats_distributed. - float_out: False[default]/True, optional - Default is to return an array of ints with Miller indices. - If set to True, then floats, with unit length will be returned in the - sample Cartesian reference frame. - (length is only valid for cubic systems). - npats : int, optional - Number of patterns to index. Default is ``-1``, which will - index up to the final pattern in ``patsin``. - - Returns - ------- - matched poles: numpy.ndarray int - The default is an array [npoints, nbands, 3] that contain the Miller - indices of the matching pole (note, that hexagonal will also return only - three index notation). If the float_out is set to True, then - the output will be floating point vectors of length one, within the sample Cartesian - reference frame. - """ + Parameters + ---------- + banddata : numpy.ndarray, output structured bandata array from + ebsd_index.index_pats or ebsd_index.index_pats_distributed. + float_out: False[default]/True, optional + Default is to return an array of ints with Miller indices. + If set to True, then floats, with unit length will be returned in the + sample Cartesian reference frame. + (length is only valid for cubic systems). + + Returns + ------- + polesout : numpy.ndarray int + The default is an array [npoints, nbands, 3] that contain the Miller + indices of the matching pole (note, that hexagonal will also return only + three index notation). If the float_out is set to True, then + the output will be floating point vectors of length one, within the sample Cartesian + reference frame. + """ nphases = len(self.phaseLib) bnddat = banddata @@ -546,8 +556,6 @@ def getmatchedpole(self, banddata, float_out=False): return polesout - - def _getpats(self, patsin=None, patstart=0, npats=-1, xyloc=None): if patsin is None: pats, xylocin = self.fID.read_data( @@ -571,7 +579,8 @@ def _getpats(self, patsin=None, patstart=0, npats=-1, xyloc=None): if np.all((np.array(pshape[1:3]) - self.bandDetectPlan.patDim) == 0): self.bandDetectPlan.band_detect_setup(patDim=pshape[1:3]) return pats, xyloc - def _detectbands(self, pats, PC, xyloc=None, clparams=None, verbose=0, chunksize=528 ): + + def _detectbands(self, pats, PC, xyloc=None, clparams=None, verbose=0, chunksize=528): banddata = self.bandDetectPlan.find_bands( pats, clparams=clparams, verbose=verbose, chunksize=chunksize ) @@ -585,9 +594,9 @@ def _detectbands(self, pats, PC, xyloc=None, clparams=None, verbose=0, chunksize ) return banddata, bandnorm - def _indexbandsphase(self, banddata, bandnorm, verbose = 0, **kwargs): + def _indexbandsphase(self, banddata, bandnorm, verbose=0): - rhomax = 1.0e12 +# rhomax = 1.0e12 rhomax = self.bandDetectPlan.rhoMax * (1-self.bandDetectPlan.rhoMaskFrac) shpBandDat = banddata.shape npoints = int(banddata.size/(shpBandDat[-1])+0.1) @@ -675,6 +684,7 @@ def _indexbandsphase(self, banddata, bandnorm, verbose = 0, **kwargs): indxData[-1, whbetter] = indxData[j, whbetter] banddataout['band_match_index'][whbetter,:] = bandmatchindex[j,whbetter,:,:].squeeze() return indxData, banddataout + def _detector2refframe(self): ven = str.upper(self.vendor) if ven in ["EDAX", "EMSOFT", "KIKUCHIPY"]: diff --git a/pyebsdindex/ebsd_index.py b/pyebsdindex/ebsd_index.py index 480b26d..1eaf443 100644 --- a/pyebsdindex/ebsd_index.py +++ b/pyebsdindex/ebsd_index.py @@ -26,12 +26,11 @@ from pyebsdindex._ebsd_index_single import EBSDIndexer, index_pats if _ray_installed: - from pyebsdindex._ebsd_index_parallel import index_pats_distributed#, IndexerRay + from pyebsdindex._ebsd_index_parallel import index_pats_distributed __all__ = [ "EBSDIndexer", - #"IndexerRay", "index_pats", "index_pats_distributed", ] From 49942cecddceeae6a9175a1d463910bbc006a748 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?H=C3=A5kon=20Wiik=20=C3=85nes?= Date: Sat, 5 Aug 2023 13:29:46 -0700 Subject: [PATCH 152/177] Slight update of pcopt optimize and optimize_pso docstrings MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: Håkon Wiik Ånes --- pyebsdindex/pcopt.py | 32 +++++++++++++++++++++++--------- 1 file changed, 23 insertions(+), 9 deletions(-) diff --git a/pyebsdindex/pcopt.py b/pyebsdindex/pcopt.py index 1653238..7b5d0af 100644 --- a/pyebsdindex/pcopt.py +++ b/pyebsdindex/pcopt.py @@ -28,7 +28,6 @@ from timeit import default_timer as timer - __all__ = [ "optimize", "optimize_pso", @@ -187,8 +186,17 @@ def optimize(pats, indexer, PC0=None, batch=False): return PCoutRet -def optimize_pso(pats, indexer, PC0=None, batch=False, search_limit = 0.2, - nswarmparticles=30, pswarmpar=None, niter=50, verbose=1): +def optimize_pso( + pats, + indexer, + PC0=None, + batch=False, + search_limit=0.2, + nswarmparticles=30, + pswarmpar=None, + niter=50, + verbose=1 +): """Optimize pattern center (PC) (PCx, PCy, PCz) in the convention of the :attr:`indexer.vendor` with particle swarms. @@ -212,17 +220,23 @@ def optimize_pso(pats, indexer, PC0=None, batch=False, search_limit = 0.2, optimization is run for each individual pattern, and an array of PC values is returned. search_limit : float, optional - Default is 0.05 for all PC values, and sets the +/- limit for the optimization search. + Default is 0.02 for all PC values, and sets the +/- limit for + the optimization search. + nswarmparticles : int, optional + Number of particles in a swarm. Default is 30. + pswarmpar : dict, optional + Particle swarm parameters "c1", "c2", and "w" with defaults 3.5, + 3.5, and 0.8, respectively. + niter : int, optional + Number of iterations. Default is 50. + verbose : int, optional + Whether to print the parameters and progress of the + optimization (>= 1) or not (< 1). Default is to print. Returns ------- numpy.ndarray Optimized PC. - - Notes - ----- - :mod:`pyswarms` particle swarm algorithm is used with 50 particles, - and parameters c1 = 2.05, c2 = 2.05 and w = 0.8. """ banddat = indexer.bandDetectPlan.find_bands(pats) npoints, nbands = banddat.shape[:2] From be40ad544ee982ab7e781b3b6742052bfab93e9f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?H=C3=A5kon=20Wiik=20=C3=85nes?= Date: Sat, 5 Aug 2023 13:38:04 -0700 Subject: [PATCH 153/177] Fix list of band data array fields and others in docstrings MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: Håkon Wiik Ånes --- pyebsdindex/_ebsd_index_parallel.py | 28 +++++----- pyebsdindex/_ebsd_index_single.py | 81 ++++++++++++++++------------- pyebsdindex/nlpar.py | 25 ++++++--- 3 files changed, 77 insertions(+), 57 deletions(-) diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index df0e9b9..c6b1202 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -168,25 +168,27 @@ def index_pats_distributed( bandData : numpy.ndarray Band identification data from the Radon transform. Stored as a structured numpy array, of dimensions [npoints, nbands]. + With fields that include: - band ID ('id'), - peak max intesensity [used to calculate pattern quality] ('max') - nearest integer location of the Radon peak ('maxloc'), - nearest neighbor average of the max peak intensity('avemax'), - sub-pixel location of the Radon peak ('aveloc'), - a metric of the band width ('width'), - the theta value of the sub-pixel location on the Radon [lower-left origin] ('theta'), - the rho value of the sub-pixel location on the Radon [lower-left origin]('rho'), - was the peak detected ('valid'), - index for phase number and pole number that indexed to this band('band_match_index') - [use the EBSDIndexer method indexer.getmatchedpole(banddata)] + - id: band ID + - max: peak max intesensity (used to calculate pattern quality) + - maxloc: nearest integer location of the Radon peak + - avemax: nearest neighbor average of the max peak intensity + - aveloc: sub-pixel location of the Radon peak + - width: a metric of the band width + - theta: the theta value of the sub-pixel location on the Radon (lower-left origin) + - rho: the rho value of the sub-pixel location on the Radon (lower-left origin) + - valid: was the peak detected + - band_match_index: index for phase number and pole number that indexed to this band + (use :meth:`~EBSDIndexer.getmatchedpole`) + indexer : EBSDIndexer EBSD indexer, returned if ``return_indexer_obj=True``. Notes ----- - Requires :mod:`ray[default]`. See the :doc:`installation guide - ` for details. + Requires the ``ray[default]`` package. See the :doc:`installation + guide ` for details. """ starttime = timer() pats = None diff --git a/pyebsdindex/_ebsd_index_single.py b/pyebsdindex/_ebsd_index_single.py index e15d86d..3f504c2 100644 --- a/pyebsdindex/_ebsd_index_single.py +++ b/pyebsdindex/_ebsd_index_single.py @@ -162,18 +162,20 @@ def index_pats( bandData : numpy.ndarray Band identification data from the Radon transform. Stored as a structured numpy array, of dimensions [npoints, nbands]. + With fields that include: - band ID ('id'), - peak max intesensity [used to calculate pattern quality] ('max') - nearest integer location of the Radon peak ('maxloc'), - nearest neighbor average of the max peak intensity('avemax'), - sub-pixel location of the Radon peak ('aveloc'), - a metric of the band width ('width'), - the theta value of the sub-pixel location on the Radon [lower-left origin] ('theta'), - the rho value of the sub-pixel location on the Radon [lower-left origin]('rho'), - was the peak detected ('valid'), - index for phase number and pole number that indexed to this band('band_match_index') - [use the EBSDIndexer method indexer.getmatchedpole(banddata)] + - id: band ID + - max: peak max intesensity (used to calculate pattern quality) + - maxloc: nearest integer location of the Radon peak + - avemax: nearest neighbor average of the max peak intensity + - aveloc: sub-pixel location of the Radon peak + - width: a metric of the band width + - theta: the theta value of the sub-pixel location on the Radon (lower-left origin) + - rho: the rho value of the sub-pixel location on the Radon (lower-left origin) + - valid: was the peak detected + - band_match_index: index for phase number and pole number that indexed to this band + (use :meth:`~EBSDIndexer.getmatchedpole`) + indexer : EBSDIndexer EBSD indexer, returned if ``return_indexer_obj=True``. """ @@ -463,18 +465,20 @@ def index_pats( bandData : numpy.ndarray Band identification data from the Radon transform. Stored as a structured numpy array, of dimensions [npoints, nbands]. + With fields that include: - band ID ('id'), - peak max intesensity [used to calculate pattern quality] ('max') - nearest integer location of the Radon peak ('maxloc'), - nearest neighbor average of the max peak intensity('avemax'), - sub-pixel location of the Radon peak ('aveloc'), - a metric of the band width ('width'), - the theta value of the sub-pixel location on the Radon [lower-left origin] ('theta'), - the rho value of the sub-pixel location on the Radon [lower-left origin]('rho'), - was the peak detected ('valid'), - index for phase number and pole number that indexed to this band('band_match_index') - [use the EBSDIndexer method indexer.getmatchedpole(banddata)] + - id: band ID + - max: peak max intesensity (used to calculate pattern quality) + - maxloc: nearest integer location of the Radon peak + - avemax: nearest neighbor average of the max peak intensity + - aveloc: sub-pixel location of the Radon peak + - width: a metric of the band width + - theta: the theta value of the sub-pixel location on the Radon (lower-left origin) + - rho: the rho value of the sub-pixel location on the Radon (lower-left origin) + - valid: was the peak detected + - band_match_index: index for phase number and pole number that indexed to this band + (use :meth:`~EBSDIndexer.getmatchedpole`) + patstart : int Starting index of the indexed patterns. npats : int @@ -503,26 +507,30 @@ def index_pats( return indxData, banddata, patstart, npats def getmatchedpole(self, banddata, float_out=False): - """Return the pole from the library that was matched to the detected band. + """Return the pole from the library that was matched to the + detected band. Parameters ---------- - banddata : numpy.ndarray, output structured bandata array from - ebsd_index.index_pats or ebsd_index.index_pats_distributed. - float_out: False[default]/True, optional - Default is to return an array of ints with Miller indices. - If set to True, then floats, with unit length will be returned in the - sample Cartesian reference frame. - (length is only valid for cubic systems). + banddata : numpy.ndarray + Output structured band data array from + :meth:`~pyebsdindex.ebsd_index.index_pats` or + :meth:`~pyebsdindex.ebsd_index.index_pats_distributed`. + float_out : bool, optional + Default (False) is to return an array of ints with Miller + indices. If set to True, then floats, with unit length, will + be returned in the sample Cartesian reference frame. + (Length is only valid for cubic systems). Returns ------- - polesout : numpy.ndarray int - The default is an array [npoints, nbands, 3] that contain the Miller - indices of the matching pole (note, that hexagonal will also return only - three index notation). If the float_out is set to True, then - the output will be floating point vectors of length one, within the sample Cartesian - reference frame. + numpy.ndarray + The default is an array [npoints, nbands, 3] that contains + the Miller indices (as ints) of the matching pole (note that + hexagonal will also return only three-index notation). If + the float_out is set to True, then the output will be + floating point vectors of length one, within the sample + Cartesian reference frame. """ nphases = len(self.phaseLib) @@ -700,6 +708,7 @@ def _detector2refframe(self): raise ValueError("`self.vendor` unknown") return quatref2detect + # def pcCorrect(self, xy=[[0.0, 0.0]]): # # TODO: At somepoint we will put some methods here for # # correcting the PC depending on the location within the scan. diff --git a/pyebsdindex/nlpar.py b/pyebsdindex/nlpar.py index 67a6382..2a06143 100644 --- a/pyebsdindex/nlpar.py +++ b/pyebsdindex/nlpar.py @@ -77,14 +77,23 @@ def setfile(self,filepath=None): self.filepath = Path(fpath) self.hdfdatapath = hdf5path - - - - def setoutfile(self,patternfile, filepath=None): - '''patternfile is an input pattern file object from ebsd_pattern. Filepath is a string. - In the future I want to be able to specify the HDF5 data path to store the output data, but that - is proving to be a bit of a mess. For now, a copy of the original HDF5 is made, and the NLPAR patterns will be - overwritten on top of the originals. ''' + def setoutfile(self, patternfile, filepath=None): + """Set the output file. + + Parameters + ---------- + patternfile + Input pattern file object from ebsd_pattern. + filepath + String. + + Notes + ----- + In the future I want to be able to specify the HDF5 data path to + store the output data, but that is proving to be a bit of a mess. + For now, a copy of the original HDF5 is made, and the NLPAR patterns + will be overwritten on top of the originals. + """ self.filepathout = None self.hdfdatapathout = None pathtemp = np.atleast_1d(filepath) From 6e36b8d2242d2c5ce5ec9e16f727572daa06bdaf Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?H=C3=A5kon=20Wiik=20=C3=85nes?= Date: Sat, 5 Aug 2023 17:54:55 -0700 Subject: [PATCH 154/177] Remove unnecessary use of plt.gcf() since plt.figure() is used below MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: Håkon Wiik Ånes --- pyebsdindex/band_detect.py | 1 - pyebsdindex/opencl/band_detect_cl.py | 21 +++++++++++++-------- 2 files changed, 13 insertions(+), 9 deletions(-) diff --git a/pyebsdindex/band_detect.py b/pyebsdindex/band_detect.py index 010c684..7b9bc70 100644 --- a/pyebsdindex/band_detect.py +++ b/pyebsdindex/band_detect.py @@ -345,7 +345,6 @@ def find_bands(self, patternsIn, verbose=0, chunksize=-1, **kwargs): print('Band Label Time:', blabeltime) print('Total Band Find Time:',tottime) if verbose > 1: - plt.clf() if len(rdnConv.shape) == 3: im2show = rdnConv[self.padding[0]:-self.padding[0],self.padding[1]:-self.padding[1], -1] diff --git a/pyebsdindex/opencl/band_detect_cl.py b/pyebsdindex/opencl/band_detect_cl.py index 8b77a64..dd729f6 100644 --- a/pyebsdindex/opencl/band_detect_cl.py +++ b/pyebsdindex/opencl/band_detect_cl.py @@ -152,7 +152,6 @@ def find_bands(self, patternsIn, verbose=0, clparams=None, chunksize=528, useCPU print('Band Label Time:', blabeltime) print('Total Band Find Time:',tottime) if verbose > 1: - plt.clf() if len(rdnConvarray.shape) == 3: im2show = rdnConvarray[self.padding[0]:-self.padding[0],self.padding[1]:-self.padding[1], -1] @@ -170,10 +169,16 @@ def find_bands(self, patternsIn, verbose=0, clparams=None, chunksize=528, useCPU im2show[-rhoMaskTrim:,:] = 0 im2show = np.fliplr(im2show) - fig = plt.figure(figsize=(12,4)) - subrdn = fig.add_subplot(1,2,1, xlim = (0,180), ylim = (-self.rhoMax, self.rhoMax) ) - subrdn.imshow(im2show, cmap='gray', extent=[0, 180, -self.rhoMax, self.rhoMax], - interpolation='none', zorder=1, aspect='auto') + fig = plt.figure(figsize=(12, 4)) + subrdn = fig.add_subplot(121, xlim=(0, 180), ylim=(-self.rhoMax, self.rhoMax)) + subrdn.imshow( + im2show, + cmap='gray', + extent=[0, 180, -self.rhoMax, self.rhoMax], + interpolation='none', + zorder=1, + aspect='auto' + ) width = bandData['width'][-1, :] width /= width.min() width *= 2.0 @@ -183,11 +188,11 @@ def find_bands(self, patternsIn, verbose=0, clparams=None, chunksize=528, useCPU subrdn.scatter(y=yplt, x=xplt, c='r', s=width, zorder=2) for pt in range(self.nBands): - subrdn.annotate(str(pt + 1),np.squeeze([xplt[pt]+4,yplt[pt]]), color='yellow') + subrdn.annotate(str(pt + 1), np.squeeze([xplt[pt] + 4, yplt[pt]]), color='yellow') #subrdn.xlim(0,180) #subrdn.ylim(-self.rhoMax, self.rhoMax) - subpat = fig.add_subplot(1,2,2) - subpat.imshow(patterns[-1,:,:], cmap='gray') + subpat = fig.add_subplot(122) + subpat.imshow(patterns[-1, :, :], cmap='gray') except Exception as e: # something went wrong - try the CPU print(e) From de1429022174225c5ff09a08ad4b311ae7df6962 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?H=C3=A5kon=20Wiik=20=C3=85nes?= Date: Mon, 7 Aug 2023 18:24:09 -0700 Subject: [PATCH 155/177] Change angle between poles from arccos(abs(dot)) to arccos(dot) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: Håkon Wiik Ånes --- pyebsdindex/pcopt.py | 2 +- pyebsdindex/tests/test_tripletvote.py | 49 +++++++++++++++++ pyebsdindex/tripletvote.py | 77 ++++++++++++++++++--------- 3 files changed, 101 insertions(+), 27 deletions(-) create mode 100644 pyebsdindex/tests/test_tripletvote.py diff --git a/pyebsdindex/pcopt.py b/pyebsdindex/pcopt.py index 7b5d0af..99372f3 100644 --- a/pyebsdindex/pcopt.py +++ b/pyebsdindex/pcopt.py @@ -529,7 +529,7 @@ def optimize(self, function, start=None, bounds=None, niter=50, verbose = 1, **k with multiprocessing.Pool(min(multiprocessing.cpu_count(), self.n_particles)) as pool: if verbose >= 1: - print('n_particle:', self.n_particles, 'c1:', self.c1, 'c2:', self.c2, 'w:', self.w ) + print('n_particles:', self.n_particles, 'c1:', self.c1, 'c2:', self.c2, 'w:', self.w ) self.niter = niter for iter in range(niter): diff --git a/pyebsdindex/tests/test_tripletvote.py b/pyebsdindex/tests/test_tripletvote.py new file mode 100644 index 0000000..95b307a --- /dev/null +++ b/pyebsdindex/tests/test_tripletvote.py @@ -0,0 +1,49 @@ +# This software was developed by employees of the US Naval Research Laboratory (NRL), an +# agency of the Federal Government. Pursuant to title 17 section 105 of the United States +# Code, works of NRL employees are not subject to copyright protection, and this software +# is in the public domain. PyEBSDIndex is an experimental system. NRL assumes no +# responsibility whatsoever for its use by other parties, and makes no guarantees, +# expressed or implied, about its quality, reliability, or any other characteristic. We +# would appreciate acknowledgment if the software is used. To the extent that NRL may hold +# copyright in countries other than the United States, you are hereby granted the +# non-exclusive irrevocable and unconditional right to print, publish, prepare derivative +# works and distribute this software, in any medium, or authorize others to do so on your +# behalf, on a royalty-free basis throughout the world. You may improve, modify, and +# create derivative works of the software or any portion of the software, and you may copy +# and distribute such modifications or works. Modified works should carry a notice stating +# that you changed the software and should note the date and nature of any such change. +# Please explicitly acknowledge the US Naval Research Laboratory as the original source. +# This software can be redistributed and/or modified freely provided that any derivative +# works bear some notice that they are derived from it, and any modified versions bear +# some notice that they have been modified. +# +# Author: David Rowenhorst; +# The US Naval Research Laboratory Date: 21 Aug 2020 + +import numpy as np + +from pyebsdindex import tripletvote + + +class TestAddPhase: + def test_add_phase_triclinic(self): + reflectors = np.array( + [ + [ 1, 1, 1], + [-1, -1, -1], + [ 1, 0, 0], + [-1, 0, 0], + ], + dtype=np.int32, + ) + phase = tripletvote.addphase( + spacegroup=1, + latticeparameter=[2, 3, 4, 70, 100, 120], + nband_earlyexit=5, + polefamilies=reflectors, + ) + + assert phase.spacegroup == 1 + assert np.allclose(phase.latticeparameter, [2, 3, 4, 70, 100, 120]) + assert phase.nband_earlyexit == 5 + assert np.allclose(phase.polefamilies, reflectors) diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index 5ddfe82..4132a4e 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -42,6 +42,31 @@ def addphase(libtype=None, phasename=None, spacegroup=None, latticeparameter=None, polefamilies=None, nband_earlyexit = 10): + """Return a band indexer for a phase. + + Parameters + ---------- + libtype : str, optional + Shorthand definition of a phase. Options are FCC, BCC, or HCP. + phasename : str, optional + Phase name. + spacegroup : int, optional + Space group of the phase. + latticeparameter : np.ndarray, tuple, or list, optional + Lattice parameters (a, b, c, alpha, beta, gamma). + polefamilies : np.ndarray, tuple, or list, optional + Reflector families to use in indexing. + nband_earlyexit : int, optional + If this phase is first in a list of phases used in indexing, and + if this many bands are matched, the remaining phases in the list + will not be checked. Default is 10, unless ``libtype`` is + passed, in which case it is 8. + + Returns + ------- + BandIndexer + Band indexer for this phase. + """ if libtype is not None: @@ -254,7 +279,7 @@ def build_trip_lib(self): if (self.lauecode == 62) or (self.lauecode == 6): if self.polefamilies.shape[-1] == 4: poles = crystal_sym.hex4poles2hex3poles(np.array(self.polefamilies)) - poles = np.reshape(poles, (-1,3) ) + poles = poles.reshape((-1, 3)) npoles = poles.shape[0] sympoles = [] # list of all HKL variants which does not count the invariant pole as unique. @@ -285,7 +310,6 @@ def build_trip_lib(self): sympolesComplete = np.concatenate(sympolesComplete) #print(sympolesComplete) - nsyms = np.sum(nFamily).astype(np.int32) famindx = np.concatenate( ([0],np.cumsum(nFamComplete)) ) angs = [] familyID = [] @@ -296,19 +320,18 @@ def build_trip_lib(self): #print('______', i,j) #print(np.round(fampoles).astype(int)) - ang = np.squeeze(self._calc_pole_dot_int(polesFlt[i, :], fampoles, - rMetricTensor=crystalmats.reciprocalMetricTensor)) # for each input pole, calculate + ang = self._calc_pole_dot_int(polesFlt[i, :], fampoles, rMetricTensor=crystalmats.reciprocalMetricTensor) # for each input pole, calculate + ang = np.squeeze(ang) ang = np.clip(ang, -1.0, 1.0) #sign = (ang >= 0).astype(np.float32) - (ang < 0).astype(np.float32) #sign = np.atleast_1d(sign) - ang = np.round(np.arccos(np.abs(ang))*RADEG*100).astype(np.int32) # get the unique angles between the input + ang = np.round(np.arccos(ang)*RADEG*100).astype(np.int32) # get the unique angles between the input ang = np.atleast_1d(ang) # pole, and the family poles. Angles within 0.01 deg are taken as the same. unqang, argunq = np.unique(ang, return_index=True) unqang = unqang/100.0 # revert back to the actual angle in degrees. - wh = np.nonzero(unqang > 1.0)[0] nwh = wh.size if nwh > 0: @@ -530,48 +553,50 @@ def bandindex(self, band_norms, band_intensity = None, band_widths=None, verbose print('all: ',timer() - tic0) return avequat, fit, cm2, polematch, nMatch, ij, acc_correct #sumaccum - def _symrotpoles(self, pole, crystalmats): - polecart = np.matmul(crystalmats.reciprocalStructureMatrix, np.array(pole).T) sympolescart = rotlib.quat_vector(self.qsymops, polecart) return np.transpose(np.matmul(crystalmats.invReciprocalStructureMatrix, sympolescart.T)) def _symrotdir(self, pole, crystalmats): - polecart = np.matmul(crystalmats.directStructureMatrix, np.array(pole).T) sympolescart = rotlib.quat_vector(self.qsymops, polecart) return np.transpose(np.matmul(crystalmats.invDirectStructureMatrix, sympolescart.T)) - def _hkl_unique(self, poles, reduceInversion=True, rMT = np.identity(3)): - """ - When given a list of integer HKL poles (plane normals), will return only the unique HKL variants + def _hkl_unique(self, poles, reduceInversion=True, rMT=np.identity(3)): + """When given a list of integer HKL poles (plane normals), will + return only the unique HKL variants. Parameters ---------- - poles: numpy.ndarray (n,3) in HKL integer form. - reduceInversion: True/False. If True, then the any inverted crystal pole - will also be removed from the uniquelist. The angle between poles - rMT: reciprocol metric tensor -- needed to calculated + poles : np.ndarray + (n, 3) in HKL integer form. + reduceInversion : bool, optional + If True, then any inverted crystal pole will also be removed + from the unique list. + rMT : np.ndarray + Reciprocol metric tensor. Needed to calculated the angle between + poles. Returns ------- - numpy.ndarray (n,3) in HKL integer form of the unique poles. + np.ndarray + (n, 3) in HKL integer form of the unique poles. """ + polesout = poles.reshape((-1, 3)) - npoles = poles.shape[0] - intPoles =np.array(poles.round().astype(np.int32)) + intPoles = polesout.round().astype(np.int32) mn = intPoles.min() intPoles -= mn basis = intPoles.max()+1 basis3 = np.array([1,basis, basis**2]) test = intPoles.dot(basis3) - un, unq = np.unique(test, return_index=True) - - polesout = poles[unq, :] + if polesout.shape[0] > 1: + _, unq = np.unique(test, return_index=True) + polesout = polesout[unq] - if reduceInversion == True: + if reduceInversion: family = polesout nf = family.shape[0] test = self._calc_pole_dot_int(family, family, rMetricTensor = rMT) @@ -579,12 +604,12 @@ def _hkl_unique(self, poles, reduceInversion=True, rMT = np.identity(3)): testSum = np.sum( (test < -0.99999).astype(np.int32)*np.arange(nf).reshape(1,nf), axis = 1) whpos = np.nonzero( np.logical_or(testSum < np.arange(nf), (testSum == 0)))[0] polesout = polesout[whpos, :] + return polesout def _calc_pole_dot_int(self, poles1, poles2, rMetricTensor = np.identity(3)): - - p1 = poles1.reshape(np.int64(poles1.size / 3), 3) - p2 = poles2.reshape(np.int64(poles2.size / 3), 3) + p1 = poles1.reshape(-1, 3) + p2 = poles2.reshape(-1, 3) n1 = p1.shape[0] n2 = p2.shape[0] From 73b83b0102917f82e305567a254e24bdb5aedfc7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?H=C3=A5kon=20Wiik=20=C3=85nes?= Date: Mon, 7 Aug 2023 18:43:15 -0700 Subject: [PATCH 156/177] Add tripletvote (addphase, BandIndexer) module to the public API MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: Håkon Wiik Ånes --- doc/reference/index.rst | 1 + pyebsdindex/tripletvote.py | 48 +++++++++++++++++++++----------------- 2 files changed, 28 insertions(+), 21 deletions(-) diff --git a/doc/reference/index.rst b/doc/reference/index.rst index 44eb640..a90751e 100644 --- a/doc/reference/index.rst +++ b/doc/reference/index.rst @@ -32,3 +32,4 @@ Functionality is inteded to be imported like this: ebsd_index nlpar pcopt + tripletvote diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index 4132a4e..6e3aa7a 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -1,24 +1,28 @@ -'''This software was developed by employees of the US Naval Research Laboratory (NRL), an -agency of the Federal Government. Pursuant to title 17 section 105 of the United States -Code, works of NRL employees are not subject to copyright protection, and this software -is in the public domain. PyEBSDIndex is an experimental system. NRL assumes no -responsibility whatsoever for its use by other parties, and makes no guarantees, -expressed or implied, about its quality, reliability, or any other characteristic. We -would appreciate acknowledgment if the software is used. To the extent that NRL may hold -copyright in countries other than the United States, you are hereby granted the -non-exclusive irrevocable and unconditional right to print, publish, prepare derivative -works and distribute this software, in any medium, or authorize others to do so on your -behalf, on a royalty-free basis throughout the world. You may improve, modify, and -create derivative works of the software or any portion of the software, and you may copy -and distribute such modifications or works. Modified works should carry a notice stating -that you changed the software and should note the date and nature of any such change. -Please explicitly acknowledge the US Naval Research Laboratory as the original source. -This software can be redistributed and/or modified freely provided that any derivative -works bear some notice that they are derived from it, and any modified versions bear -some notice that they have been modified. - -Author: David Rowenhorst; -The US Naval Research Laboratory Date: 21 Aug 2020''' +# This software was developed by employees of the US Naval Research Laboratory (NRL), an +# agency of the Federal Government. Pursuant to title 17 section 105 of the United States +# Code, works of NRL employees are not subject to copyright protection, and this software +# is in the public domain. PyEBSDIndex is an experimental system. NRL assumes no +# responsibility whatsoever for its use by other parties, and makes no guarantees, +# expressed or implied, about its quality, reliability, or any other characteristic. We +# would appreciate acknowledgment if the software is used. To the extent that NRL may hold +# copyright in countries other than the United States, you are hereby granted the +# non-exclusive irrevocable and unconditional right to print, publish, prepare derivative +# works and distribute this software, in any medium, or authorize others to do so on your +# behalf, on a royalty-free basis throughout the world. You may improve, modify, and +# create derivative works of the software or any portion of the software, and you may copy +# and distribute such modifications or works. Modified works should carry a notice stating +# that you changed the software and should note the date and nature of any such change. +# Please explicitly acknowledge the US Naval Research Laboratory as the original source. +# This software can be redistributed and/or modified freely provided that any derivative +# works bear some notice that they are derived from it, and any modified versions bear +# some notice that they have been modified. +# +# Author: David Rowenhorst; +# The US Naval Research Laboratory Date: 21 Aug 2020 + +"""Creation of look-up tables from phase information for band +indexing. +""" from os import environ from pathlib import PurePath @@ -32,6 +36,8 @@ from pyebsdindex import crystal_sym, rotlib, crystallometry +__all__ = ["addphase", "BandIndexer"] + RADEG = 180.0/np.pi tempdir = PurePath("/tmp" if platform.system() == "Darwin" else tempfile.gettempdir()) From 18baf7ccd31a514ae79bfb00fc09ea11066b7ba8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?H=C3=A5kon=20Wiik=20=C3=85nes?= Date: Tue, 8 Aug 2023 07:38:54 -0700 Subject: [PATCH 157/177] Undo change of calc of unique angles in LUT, more LUT tests MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: Håkon Wiik Ånes --- pyebsdindex/tests/test_tripletvote.py | 60 ++++++++++++++++++++++----- pyebsdindex/tripletvote.py | 2 +- 2 files changed, 51 insertions(+), 11 deletions(-) diff --git a/pyebsdindex/tests/test_tripletvote.py b/pyebsdindex/tests/test_tripletvote.py index 95b307a..81385d4 100644 --- a/pyebsdindex/tests/test_tripletvote.py +++ b/pyebsdindex/tests/test_tripletvote.py @@ -20,30 +20,70 @@ # Author: David Rowenhorst; # The US Naval Research Laboratory Date: 21 Aug 2020 +from itertools import product + import numpy as np from pyebsdindex import tripletvote class TestAddPhase: - def test_add_phase_triclinic(self): - reflectors = np.array( + def test_add_phase_fcc(self): + phase = tripletvote.addphase("FCC") + assert np.allclose( + phase.polefamilies, [[0, 0, 2], [1, 1, 1], [0, 2, 2], [1, 1, 3]] + ) + angles = phase.angpairs["angles"] + assert angles.size == 21 + assert np.unique(angles).size == 17 + + def test_add_phase_bcc(self): + phase = tripletvote.addphase("BCC") + assert np.allclose( + phase.polefamilies, [[0, 1, 1], [0, 0, 2], [1, 1, 2], [0, 1, 3]] + ) + angles = phase.angpairs["angles"] + assert angles.size == 34 + assert np.unique(angles).size == 28 + + def test_add_phase_hcp(self): + phase = tripletvote.addphase("HCP") + assert np.allclose( + phase.polefamilies, [ - [ 1, 1, 1], - [-1, -1, -1], - [ 1, 0, 0], - [-1, 0, 0], - ], - dtype=np.int32, + [1, 0, -1, 0], + [0, 0, 0, 2], + [1, 0, -1, 1], + [1, 0, -1, 2], + [1, 1, -2, 0], + [1, 0, -1, 3], + [1, 1, -2, 2], + [2, 0, -2, 1], + ] ) + angles = phase.angpairs["angles"] + assert angles.size == 82 + assert np.unique(angles).size == 74 + + def test_add_phase_triclinic(self): + # Build our own reflector list + hkl = [1, 1, 1] + hkl_ranges = [np.arange(-i, i + 1) for i in hkl] + hkl = np.asarray(list(product(*hkl_ranges)), dtype=int) + hkl = hkl[~np.all(hkl == 0, axis=1)] # Remove (000) + phase = tripletvote.addphase( spacegroup=1, latticeparameter=[2, 3, 4, 70, 100, 120], nband_earlyexit=5, - polefamilies=reflectors, + polefamilies=hkl, ) assert phase.spacegroup == 1 assert np.allclose(phase.latticeparameter, [2, 3, 4, 70, 100, 120]) assert phase.nband_earlyexit == 5 - assert np.allclose(phase.polefamilies, reflectors) + assert np.allclose(phase.polefamilies, hkl) + + angles = phase.angpairs["angles"] + assert angles.size == 312 + assert np.unique(angles).size == 77 diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index 6e3aa7a..70414cd 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -332,7 +332,7 @@ def build_trip_lib(self): ang = np.clip(ang, -1.0, 1.0) #sign = (ang >= 0).astype(np.float32) - (ang < 0).astype(np.float32) #sign = np.atleast_1d(sign) - ang = np.round(np.arccos(ang)*RADEG*100).astype(np.int32) # get the unique angles between the input + ang = np.round(np.arccos(np.abs(ang))*RADEG*100).astype(np.int32) # get the unique angles between the input ang = np.atleast_1d(ang) # pole, and the family poles. Angles within 0.01 deg are taken as the same. unqang, argunq = np.unique(ang, return_index=True) From 6757fa3462616bb012cc0b9381e917d53437afb3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?H=C3=A5kon=20Wiik=20=C3=85nes?= Date: Tue, 8 Aug 2023 07:57:14 -0700 Subject: [PATCH 158/177] Remove pytest-xdist test dependency due to conflict w/multiprocessing MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: Håkon Wiik Ånes --- .github/workflows/tests.yml | 2 +- setup.py | 1 - 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 887acfa..b80afef 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -82,7 +82,7 @@ jobs: - name: Run tests run: | - pytest -n 2 --cov=pyebsdindex --pyargs pyebsdindex + pytest --cov=pyebsdindex --pyargs pyebsdindex - name: Generate line coverage if: ${{ matrix.os == 'ubuntu-latest' }} diff --git a/setup.py b/setup.py index 5e6de9e..7e42fdc 100644 --- a/setup.py +++ b/setup.py @@ -24,7 +24,6 @@ "coverage >= 5.0", "pytest >= 5.4", "pytest-cov >= 2.8.1", - "pytest-xdist", ], "gpu": [ "pyopencl", From 1a8590d49f62b7c0be8d22d2a6e83380f42ee52a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?H=C3=A5kon=20Wiik=20=C3=85nes?= Date: Tue, 8 Aug 2023 08:05:45 -0700 Subject: [PATCH 159/177] Correct search_limit parameter explanation for optimize_pso() MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: Håkon Wiik Ånes --- pyebsdindex/pcopt.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pyebsdindex/pcopt.py b/pyebsdindex/pcopt.py index 99372f3..d1df46e 100644 --- a/pyebsdindex/pcopt.py +++ b/pyebsdindex/pcopt.py @@ -220,8 +220,8 @@ def optimize_pso( optimization is run for each individual pattern, and an array of PC values is returned. search_limit : float, optional - Default is 0.02 for all PC values, and sets the +/- limit for - the optimization search. + Default is 0.2 for all PC values, and sets the +/- limit for the + optimization search. nswarmparticles : int, optional Number of particles in a swarm. Default is 30. pswarmpar : dict, optional From 84f76f305db02aeb0fa187351f6bd532d656ca21 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Tue, 8 Aug 2023 12:38:36 -0400 Subject: [PATCH 160/177] Put in some checks on zero length and inversion symmetry reflectors Signed-off by: David Rowenhorst --- pyebsdindex/tripletvote.py | 25 ++++++++++++++++++++++--- 1 file changed, 22 insertions(+), 3 deletions(-) diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index 6e3aa7a..902b8d1 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -189,7 +189,6 @@ def __init__(self, if latticeparameter is not None: self.setlatticeparameter(latticeparameter) - if spacegroup is not None: self.setspacegroup(spacegroup) @@ -213,7 +212,27 @@ def setspacegroup(self, spacegroup = 225): self.qsymops = crystal_sym.laueid2symops(self.lauecode) def setpolefamilies(self, reflectors): - self.polefamilies = np.array(reflectors) + # check if any of the poles are length 0 + poles = np.atleast_2d(np.array(reflectors)).astype(float) + mx = np.max(np.abs(poles), axis=1) + wh = np.nonzero(mx > 1e-6)[0] + if wh.size == 0: + return + poles = poles[wh, :] + + # check for inversion redundancy + npoles = poles / (np.sqrt((poles ** 2).sum(-1))[..., np.newaxis]) + npoles = np.atleast_2d(npoles) + keep = np.ones(npoles.shape[0], dtype = int) + dot = np.abs(npoles.dot(npoles.T)) + for i in range(npoles.shape[0]): + wh = np.nonzero(dot[i, i+1:] > 0.999)[0] + if len(wh) > 0: + keep[i+1+wh] = 0 + + whk = np.nonzero(keep) + poles = poles[whk,:] + self.polefamilies = np.rint(poles * (1.+ 1e-6)).astype(int) # def build_fcc(self): # if self.phaseName is None: @@ -332,7 +351,7 @@ def build_trip_lib(self): ang = np.clip(ang, -1.0, 1.0) #sign = (ang >= 0).astype(np.float32) - (ang < 0).astype(np.float32) #sign = np.atleast_1d(sign) - ang = np.round(np.arccos(ang)*RADEG*100).astype(np.int32) # get the unique angles between the input + ang = np.round(np.arccos(np.abs(ang))*RADEG*100).astype(np.int32) # get the unique angles between the input ang = np.atleast_1d(ang) # pole, and the family poles. Angles within 0.01 deg are taken as the same. unqang, argunq = np.unique(ang, return_index=True) From 7a199e624d4df87ba4541238761222a3fd7b1d44 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?H=C3=A5kon=20Wiik=20=C3=85nes?= Date: Tue, 8 Aug 2023 11:00:47 -0700 Subject: [PATCH 161/177] Fix triclinic LUT build test MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: Håkon Wiik Ånes --- pyebsdindex/tests/test_tripletvote.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/pyebsdindex/tests/test_tripletvote.py b/pyebsdindex/tests/test_tripletvote.py index 81385d4..4dd9851 100644 --- a/pyebsdindex/tests/test_tripletvote.py +++ b/pyebsdindex/tests/test_tripletvote.py @@ -82,8 +82,10 @@ def test_add_phase_triclinic(self): assert phase.spacegroup == 1 assert np.allclose(phase.latticeparameter, [2, 3, 4, 70, 100, 120]) assert phase.nband_earlyexit == 5 - assert np.allclose(phase.polefamilies, hkl) + assert phase.npolefamilies == hkl.shape[0] // 2 + for hkl_i in phase.polefamilies: + assert hkl_i in hkl angles = phase.angpairs["angles"] - assert angles.size == 312 + assert angles.size == 78 assert np.unique(angles).size == 77 From 7e50b7366d43265a0d0874912903bc784e531c50 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Tue, 8 Aug 2023 14:17:56 -0400 Subject: [PATCH 162/177] Refined parallel/antiparallel metric. Signed-off by: David Rowenhorst --- pyebsdindex/tripletvote.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyebsdindex/tripletvote.py b/pyebsdindex/tripletvote.py index 902b8d1..c7fa423 100644 --- a/pyebsdindex/tripletvote.py +++ b/pyebsdindex/tripletvote.py @@ -226,7 +226,7 @@ def setpolefamilies(self, reflectors): keep = np.ones(npoles.shape[0], dtype = int) dot = np.abs(npoles.dot(npoles.T)) for i in range(npoles.shape[0]): - wh = np.nonzero(dot[i, i+1:] > 0.999)[0] + wh = np.nonzero(dot[i, i+1:] > 0.99999)[0] if len(wh) > 0: keep[i+1+wh] = 0 From 637a4bb4dc531f657bff4b55b8378f793172210e Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Thu, 10 Aug 2023 09:44:52 -0400 Subject: [PATCH 163/177] Bug fix for writing oh5 files Signed-off by: David Rowenhorst --- pyebsdindex/ebsdfile.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/pyebsdindex/ebsdfile.py b/pyebsdindex/ebsdfile.py index 127c41c..7fe6137 100644 --- a/pyebsdindex/ebsdfile.py +++ b/pyebsdindex/ebsdfile.py @@ -193,6 +193,9 @@ def writeoh5(filename, indexer, data, xstep = np.array([np.float32(indexer.fID.xStep)]) ystep = np.array([np.float32(indexer.fID.yStep)]) + xstep = np.atleast_1d(np.array([np.float32(xstep)]).squeeze()) + ystep = np.atleast_1d(np.array([np.float32(ystep)]).squeeze()) + f.create_dataset(datasetname + '/EBSD/Header/Step X', data=xstep) f.create_dataset(datasetname + '/EBSD/Header/Step Y', @@ -209,8 +212,8 @@ def writeoh5(filename, indexer, data, if nrows is None: nrows = np.ceil(data.shape[-1] / ncols) - ncols = np.array([np.int32(ncols)]).squeeze() - nrows = np.array([np.int32(nrows)]).squeeze() + ncols = np.atleast_1d(np.array([np.int32(ncols)]).squeeze()) + nrows = np.atleast_1d(np.array([np.int32(nrows)]).squeeze()) f.create_dataset(datasetname + '/EBSD/Header/nColumns', data=ncols) From 51c20803d2889819b154c1f7859520e064e2df7b Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Thu, 10 Aug 2023 14:38:13 -0400 Subject: [PATCH 164/177] Revert job scheduling changes Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_parallel.py | 65 +++++++++++++++-------------- 1 file changed, 34 insertions(+), 31 deletions(-) diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index c6b1202..4a81b59 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -419,44 +419,47 @@ def index_pats_distributed( #print(ngpuwrker, ncpugpu_per_wrker, ngpu_per_wrker) #print(ncpuwrker, ncpucpu_per_worker) - gpu_launched = 0 - cpu_launched = 0 + #gpu_launched = 0 + #cpu_launched = 0 + while (len(gpuworkers) < ngpuwrker) and (len(gpujobs) > 0): + # if (gpu_launched < ngpuwrker) and (len(gpujobs) > 0): + + i = len(gpuworkers) + gpuworkers.append( # make a new Ray Actor that can call the indexer defined in shared memory. + # These actors are read/write, thus can initialize the GPU queues + # GPUWorker.options(num_cpus=ncpugpu_per_wrker, num_gpus=ngpu_per_wrker).remote( + GPUWorker.options(num_cpus=ncpugpu_per_wrker, num_gpus=ngpu_per_wrker).remote( + actorid=i, clparammodule=clparamfunction, gpu_id=gpu_id, cudavis=cudagpuvis + ) + ) + gjob = gpujobs.pop(0) + if inputmode == "filemode": + gputask.append( + gpuworkers[i].findbands.remote(gjob, + pats=None, + indexer=remote_indexer + ) + ) + else: + gputask.append( + gpuworkers[i].findbands.remote(gjob, + pats=pats[gjob.pstart:gjob.pend, :, :], + indexer=remote_indexer, + ) + ) + gtaskindex.append(gjob) + #gpu_launched += 1 + + while ncpudone < njobs: #for i in range(ngpuwrker): - while (gpu_launched < ngpuwrker) and (len(gpujobs) > 0): - #if (gpu_launched < ngpuwrker) and (len(gpujobs) > 0): - i = len(gpuworkers) - gpuworkers.append( # make a new Ray Actor that can call the indexer defined in shared memory. - # These actors are read/write, thus can initialize the GPU queues - #GPUWorker.options(num_cpus=ncpugpu_per_wrker, num_gpus=ngpu_per_wrker).remote( - GPUWorker.options(num_cpus=ncpugpu_per_wrker, num_gpus=ngpu_per_wrker).remote( - actorid=i, clparammodule=clparamfunction, gpu_id=gpu_id, cudavis = cudagpuvis - ) - ) - gjob = gpujobs.pop(0) - if inputmode == "filemode": - gputask.append( - gpuworkers[i].findbands.remote(gjob, - pats=None, - indexer=remote_indexer - ) - ) - else: - gputask.append( - gpuworkers[i].findbands.remote(gjob, - pats = pats[gjob.pstart:gjob.pend, :, :], - indexer=remote_indexer, - ) - ) - gtaskindex.append(gjob) - gpu_launched += 1 # initiate the CPU workers. #print(len(gpuworkers), len(gputask)) #for i in range(ncpuwrker): - if (cpu_launched < ncpuwrker) and (ncpudone < njobs): + if (len(cpuworkers) < ncpuwrker) and ((njobs - ncpudone) > len(cpuworkers)): i = len(cpuworkers) cpuworkers.append( # make a new Ray Actor that can call the indexer defined in shared memory. # These actors are read/write, thus can initialize the GPU queues @@ -464,7 +467,7 @@ def index_pats_distributed( #CPUWorker.options(num_cpus=1.0, num_gpus=0).remote(i)) cputask.append(cpuworkers[i].indexpoles.remote(None, None, None,indexer=remote_indexer)) ctaskindex.append(None) - cpu_launched += 1 + #cpu_launched += 1 #print(len(cpuworkers)) From 1ec9dd6cbaa09c29d99dea7021c0e29b44c3a363 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Thu, 10 Aug 2023 15:49:06 -0400 Subject: [PATCH 165/177] Attempting to cure rare hung process Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_parallel.py | 27 ++++++++++++++------------- 1 file changed, 14 insertions(+), 13 deletions(-) diff --git a/pyebsdindex/_ebsd_index_parallel.py b/pyebsdindex/_ebsd_index_parallel.py index 4a81b59..e2bf1a9 100644 --- a/pyebsdindex/_ebsd_index_parallel.py +++ b/pyebsdindex/_ebsd_index_parallel.py @@ -452,9 +452,6 @@ def index_pats_distributed( while ncpudone < njobs: - #for i in range(ngpuwrker): - - # initiate the CPU workers. #print(len(gpuworkers), len(gputask)) @@ -471,7 +468,7 @@ def index_pats_distributed( #print(len(cpuworkers)) - + # check if GPU is working if ngpudone < njobs: # check if gpu is done donewrker, busy = ray.wait(gputask,num_returns = len(gputask), timeout=0.01) #if len(wrker) > 0: # trying to catch a hung worker. Rare, but it happens @@ -509,6 +506,7 @@ def index_pats_distributed( ngpusubmit += 1 else: # no more gpu tasks to submit #del gpuworkers[jid] + ray.kill(gpuworkers[jid]) del gpuworkers[jid] del gputask[jid] del gtaskindex[jid] @@ -578,12 +576,13 @@ def index_pats_distributed( #time.sleep(0.001) if message != 'Error': if ncpudone == njobs: - cpuworkers[jid] = None - cputask[jid] = None - ctaskindex[jid] = None - #del cpuworkers[jid] - #del cputask[jid] - #del ctaskindex[jid] + #cpuworkers[jid] = None + #cputask[jid] = None + #ctaskindex[jid] = None + ray.kill(cpuworkers[jid]) + del cpuworkers[jid] + del cputask[jid] + del ctaskindex[jid] elif len(cpujobs) > 0: cjob = cpujobs.pop(0) banddata = banddataout[cjob.pstart - patstart: cjob.pend - patstart, :] @@ -604,6 +603,7 @@ def index_pats_distributed( print(e) cjob = ctaskindex[jid] print('A CPU death has occured', cjob.pstart,cjob.pend) + ray.kill(cpuworkers[jid]) del cpuworkers[jid] del cputask[jid] del ctaskindex[jid] @@ -611,12 +611,13 @@ def index_pats_distributed( if len(cpuworkers) == 0: cpuworkers.append( # make a new Ray Actor that can call the indexer defined in shared memory. # These actors are read/write, thus can initialize the GPU queues - CPUWorker.options(num_cpus=1, num_gpus=0).remote(i)) + CPUWorker.options(num_cpus=1, num_gpus=0).remote(0)) cputask.append(cpuworkers[0].indexpoles.remote(None, None, None)) ctaskindex.append(None) - - ray.shutdown() print('\n') + print('...') + ray.shutdown() + if return_indexer_obj: return dataout, banddataout, indexer else: From 1232303e3e6f62cc3fa20fcaba6a895096323643 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Thu, 24 Aug 2023 09:15:55 -0400 Subject: [PATCH 166/177] Patch to remove unnecessary print statement Signed-off by: David Rowenhorst --- pyebsdindex/EBSDImage/IPFcolor.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyebsdindex/EBSDImage/IPFcolor.py b/pyebsdindex/EBSDImage/IPFcolor.py index 2ff3046..cb8114c 100644 --- a/pyebsdindex/EBSDImage/IPFcolor.py +++ b/pyebsdindex/EBSDImage/IPFcolor.py @@ -52,7 +52,7 @@ def makeipf(ebsddata, indexer, vector=np.array([0,0,1.0]), xsize = None, ysize = xsize = int(xsize) if ysize is None: ysize = int(npoints // xsize + np.int64((npoints % xsize) > 0)) - print(ysize) + #print(ysize) else: xsize = int(npoints) ysize = 1 From 71695fdd52f5ac3f2fea05cad7cba38e5696767a Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Fri, 6 Oct 2023 15:49:02 -0400 Subject: [PATCH 167/177] Account for near vertical bands being double counted. Signed-off by: David Rowenhorst --- pyebsdindex/_ebsd_index_single.py | 3 ++- pyebsdindex/band_detect.py | 2 +- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/pyebsdindex/_ebsd_index_single.py b/pyebsdindex/_ebsd_index_single.py index 3f504c2..1a50b5d 100644 --- a/pyebsdindex/_ebsd_index_single.py +++ b/pyebsdindex/_ebsd_index_single.py @@ -640,7 +640,8 @@ def _indexbandsphase(self, banddata, bandnorm, verbose=0): bandNorm1 = bandNorm1[whgood, :] indxData["pq"][0:nPhases, i] = np.sum(bDat1["max"], axis=0) adj_intensity = (-1*np.abs(bDat1["rho"]) * 0.5 / rhomax + 1) * bDat1["max"] - #adj_intensity = bDat1["avemax"] + adj_intensity *= ((bDat1["theta"] > (2*np.pi/180)).astype(np.float32)+0.5)/2 + adj_intensity *= ((bDat1["theta"] < (178.0 * np.pi / 180)).astype(np.float32)+0.5)/2 #print(bDat1["max"]) #print(adj_intensity) for j in range(len(self.phaseLib)): diff --git a/pyebsdindex/band_detect.py b/pyebsdindex/band_detect.py index 7b9bc70..c76bf58 100644 --- a/pyebsdindex/band_detect.py +++ b/pyebsdindex/band_detect.py @@ -275,7 +275,7 @@ def fit_gauss(M, *args): backfit = (gaussian_surf(x, y, *popt)).reshape(ny, nx) #print(p0, popt) except RuntimeError: - print('Warning: no convergence on back subtract ... using mean of the patterns.') + print('Warning: no convergence on background gaussian fit ... using mean of the patterns.') print('This may not be ideal for scans with few grains across the width of the scan.') backfit = back backfit -= np.mean(backfit) From 45a5259ff87aca172bd6c9ef87bcef80beb3a89b Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Tue, 14 Nov 2023 11:07:13 -0500 Subject: [PATCH 168/177] Code cleanup Signed-off by: David Rowenhorst --- pyebsdindex/misorientation.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/pyebsdindex/misorientation.py b/pyebsdindex/misorientation.py index 0ea1f57..41f6a8e 100644 --- a/pyebsdindex/misorientation.py +++ b/pyebsdindex/misorientation.py @@ -91,11 +91,10 @@ def misorientcubic_quicknb(q1In,q2In): i1 = i % n1 i2 = i % n2 + q1i = q1In[i1, :].copy().reshape(4) q2i = q2In[i2,:].copy() q2i = q2i.reshape(4) - q2i[1:4] *= -1.0 - - q1i = q1In[i1,:].copy().reshape(4) + q2i[1:4] *= -1.0 # take the conjugate/inverse of q2 qAB = np.abs(rotlib.quat_multiply1(q1i, q2i)) From e81875ef7467db8d480adcee660c75ec704d746c Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Wed, 15 Nov 2023 09:38:51 -0500 Subject: [PATCH 169/177] Add early_exit parameter to pso_opt Signed-off by: David Rowenhorst --- pyebsdindex/pcopt.py | 25 +++++++++++++++++++++++-- 1 file changed, 23 insertions(+), 2 deletions(-) diff --git a/pyebsdindex/pcopt.py b/pyebsdindex/pcopt.py index d1df46e..06ac6ed 100644 --- a/pyebsdindex/pcopt.py +++ b/pyebsdindex/pcopt.py @@ -192,6 +192,7 @@ def optimize_pso( PC0=None, batch=False, search_limit=0.2, + early_exit = 0.0001, nswarmparticles=30, pswarmpar=None, niter=50, @@ -222,6 +223,11 @@ def optimize_pso( search_limit : float, optional Default is 0.2 for all PC values, and sets the +/- limit for the optimization search. + early_exit: float, optional + Default is 0.0001 for all PC values, and sets a value for which + the optimum is considered converged before the number of iterations + is reached. The optimiztion will exit early if the velocity and distance + of all the swarm particles is less than the early_exit value. nswarmparticles : int, optional Number of particles in a swarm. Default is 30. pswarmpar : dict, optional @@ -277,7 +283,8 @@ def optimize_pso( # ) optimizer = PSOOpt(dimensions=3, n_particles=nswarmparticles, c1=pswarmpar['c1'], - c2 = pswarmpar['c2'], w = pswarmpar['w'], hyperparammethod='auto') + c2 = pswarmpar['c2'], w = pswarmpar['w'], hyperparammethod='auto', + early_exit=early_exit) if not batch: # cost, PCoutRet = optimizer.optimize( @@ -373,7 +380,8 @@ def __init__(self, c2 = 2.05, w = 0.8, hyperparammethod = 'static', - boundmethod = 'bounce'): + boundmethod = 'bounce', + early_exit=None): self.n_particles = int(n_particles) self.dimensions = int(dimensions) self.c1 = c1 @@ -391,6 +399,7 @@ def __init__(self, self.niter = None self.pos = None self.vel = None + self.early_exit = early_exit def initializeswarm(self, start=None, bounds=None): @@ -526,6 +535,9 @@ def printprogress(self, iter): def optimize(self, function, start=None, bounds=None, niter=50, verbose = 1, **kwargs): self.initializeswarm(start, bounds) + early_exit = self.early_exit + if early_exit is None: + early_exit = -1.0 with multiprocessing.Pool(min(multiprocessing.cpu_count(), self.n_particles)) as pool: if verbose >= 1: @@ -537,7 +549,16 @@ def optimize(self, function, start=None, bounds=None, niter=50, verbose = 1, **k self.updateswarmbest(function, pool, **kwargs) if verbose >= 1: self.printprogress(iter) + #print(np.abs(self.vel).max()) self.updateswarmvelpos() + + if np.abs(self.vel).max() < early_exit: + d = abs(self.gbest_loc - self.pos) + #print(d.max()) + if d.max() < early_exit: + break + + pool.close() From f74b5368b9ebc1fee1c122fcf177a427747363ff Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Wed, 29 Nov 2023 15:43:43 -0500 Subject: [PATCH 170/177] code cleanup Signed-off by: David Rowenhorst --- pyebsdindex/pcopt.py | 54 +++++++++++++++++++++++++------------------- 1 file changed, 31 insertions(+), 23 deletions(-) diff --git a/pyebsdindex/pcopt.py b/pyebsdindex/pcopt.py index 06ac6ed..873511b 100644 --- a/pyebsdindex/pcopt.py +++ b/pyebsdindex/pcopt.py @@ -24,6 +24,7 @@ import numpy as np import multiprocessing +import functools import scipy.optimize as opt from timeit import default_timer as timer @@ -196,6 +197,7 @@ def optimize_pso( nswarmparticles=30, pswarmpar=None, niter=50, + return_cost=False, verbose=1 ): """Optimize pattern center (PC) (PCx, PCy, PCz) in the convention @@ -235,6 +237,8 @@ def optimize_pso( 3.5, and 0.8, respectively. niter : int, optional Number of iterations. Default is 50. + return_costs: bool, optional + Set to True to return the cost value as well as the optimum fit PC. verbose : int, optional Whether to print the parameters and progress of the optimization (>= 1) or not (< 1). Default is to print. @@ -293,12 +297,13 @@ def optimize_pso( cost, PCoutRet = optimizer.optimize(_optfunction, indexer=indexer, banddat=banddat, start=PC0, bounds=(PC0 - np.array(search_limit), PC0 + np.array(search_limit)), niter=niter, verbose=verbose) - + costout = cost #print(cost) else: PCoutRet = np.zeros((npoints, 3)) if verbose >= 1: print('', end='\n') + costout = np.zeros(npoints, dtype=np.float32) for i in range(npoints): # cost, PCoutRet[i, :] = optimizer.optimize( # _optfunction, niter, indexer=indexer, banddat=banddat[i, :, :] @@ -311,6 +316,7 @@ def optimize_pso( niter=niter, verbose=0) PCoutRet[i, :] = newPC + costout[i] = cost progress = int(round(10 * float(i) / npoints)) if verbose >= 1: print('', end='\r') @@ -345,9 +351,10 @@ def optimize_pso( newout[:3] = PCoutRet newout[3] = delta[3] PCoutRet = newout - - return PCoutRet - + if return_cost is False: + return PCoutRet + else: + return PCoutRet, costout def _file_opt(fobj, indexer, stride=200, groupsz = 3): nCols = fobj.nCols @@ -446,7 +453,7 @@ def updateswarmbest(self, fun2opt, pool, **kwargs): #print(timer()-tic) #pos = self.pos.copy() #tic = timer() - #results = pool.map(partial(fun2opt, **kwargs),list(pos) ) + #results = pool.map(functools.partial(fun2opt, **kwargs),list(pos) ) #print(timer()-tic) #print(len(results[0]), type(results[0])) #print(len(results)) @@ -539,30 +546,31 @@ def optimize(self, function, start=None, bounds=None, niter=50, verbose = 1, **k if early_exit is None: early_exit = -1.0 - with multiprocessing.Pool(min(multiprocessing.cpu_count(), self.n_particles)) as pool: + #with multiprocessing.get_context("spawn").Pool(min(multiprocessing.cpu_count(), self.n_particles)) as pool: + pool = None + if verbose >= 1: + print('n_particles:', self.n_particles, 'c1:', self.c1, 'c2:', self.c2, 'w:', self.w ) + + self.niter = niter + for iter in range(niter): + self.updatehyperparam(iter) + self.updateswarmbest(function, pool, **kwargs) if verbose >= 1: - print('n_particles:', self.n_particles, 'c1:', self.c1, 'c2:', self.c2, 'w:', self.w ) + self.printprogress(iter) + #print(np.abs(self.vel).max()) + self.updateswarmvelpos() - self.niter = niter - for iter in range(niter): - self.updatehyperparam(iter) - self.updateswarmbest(function, pool, **kwargs) - if verbose >= 1: - self.printprogress(iter) - #print(np.abs(self.vel).max()) - self.updateswarmvelpos() - - if np.abs(self.vel).max() < early_exit: - d = abs(self.gbest_loc - self.pos) - #print(d.max()) - if d.max() < early_exit: - break + if np.abs(self.vel).max() < early_exit: + d = abs(self.gbest_loc - self.pos) + #print(d.max()) + if d.max() < early_exit: + break - pool.close() - pool.terminate() + #pool.close() + #pool.terminate() final_best = self.gbest final_loc = self.gbest_loc if verbose >= 1: From 7c76cbd92be7f15380e9a9b3812ef699ee89c89f Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Thu, 30 Nov 2023 12:59:40 -0500 Subject: [PATCH 171/177] Set lamba opt to output the three values. Signed-off by: David Rowenhorst --- pyebsdindex/nlpar.py | 1 + 1 file changed, 1 insertion(+) diff --git a/pyebsdindex/nlpar.py b/pyebsdindex/nlpar.py index 2a06143..0ad6950 100644 --- a/pyebsdindex/nlpar.py +++ b/pyebsdindex/nlpar.py @@ -288,6 +288,7 @@ def d2norm(d2, n2, dij, sigma): self.lam = np.median(np.mean(lamopt_values, axis = 0)) if self.sigma is None: self.sigma = sigma + return np.mean(lamopt_values, axis = 0).flatten() def calcnlpar(self, chunksize=0, searchradius=None, lam = None, dthresh = None, saturation_protect=True, automask=True, filename=None, fileout=None, reset_sigma=True, backsub = False, rescale = False): From 147186fd88487c264963836bc43f4af0b14b19f7 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Tue, 16 Jan 2024 17:27:44 -0500 Subject: [PATCH 172/177] return the file path from NLPAR processing. Signed-off by: David Rowenhorst --- pyebsdindex/nlpar.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyebsdindex/nlpar.py b/pyebsdindex/nlpar.py index 0ad6950..23e5263 100644 --- a/pyebsdindex/nlpar.py +++ b/pyebsdindex/nlpar.py @@ -431,7 +431,7 @@ def calcnlpar(self, chunksize=0, searchradius=None, lam = None, dthresh = None, # sigchunk[rowstartcount[0]:rowstartcount[0]+rowstartcount[1],:] numba.set_num_threads(nthreadpos) - + return str(patternfile.filepath) def calcsigma(self,chunksize=0,nn=1,saturation_protect=True,automask=True): From 198999ba678229b33d4bdd3d79b45718c96f6587 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Thu, 18 Jan 2024 16:51:36 -0500 Subject: [PATCH 173/177] Fixed nlpar filepath output Signed-off by: David Rowenhorst --- pyebsdindex/nlpar.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyebsdindex/nlpar.py b/pyebsdindex/nlpar.py index 23e5263..9c04953 100644 --- a/pyebsdindex/nlpar.py +++ b/pyebsdindex/nlpar.py @@ -431,7 +431,7 @@ def calcnlpar(self, chunksize=0, searchradius=None, lam = None, dthresh = None, # sigchunk[rowstartcount[0]:rowstartcount[0]+rowstartcount[1],:] numba.set_num_threads(nthreadpos) - return str(patternfile.filepath) + return str(patternfileout.filepath) def calcsigma(self,chunksize=0,nn=1,saturation_protect=True,automask=True): From 04bbee8a4aa0958ad0ec3af45ef01a923c88c64d Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Mon, 29 Jan 2024 16:12:38 -0500 Subject: [PATCH 174/177] Update readme with new article information. Signed-off by: David Rowenhorst --- README.md | 13 ++++++++----- 1 file changed, 8 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 8d2f3da..5cd1f74 100644 --- a/README.md +++ b/README.md @@ -6,11 +6,14 @@ Python based tool for Radon based EBSD orientation indexing. [![Documentation status](https://readthedocs.org/projects/pyebsdindex/badge/?version=latest)](https://pyebsdindex.readthedocs.io/en/latest/) [![PyPI version](https://img.shields.io/pypi/v/pyebsdindex.svg)](https://pypi.python.org/pypi/pyebsdindex) -The pattern processing is based on a GPU pipeline, and is based on the work of S. I. -Wright and B. L. Adams. Metallurgical Transactions A-Physical Metallurgy and Materials -Science, 23(3):759–767, 1992, and N. Krieger Lassen. Automated Determination of Crystal -Orientations from Electron Backscattering Patterns. PhD thesis, The Technical University -of Denmark, 1994. +The pattern processing is based on a GPU pipeline. Details can be found +in D. J. Rowenhorst, P. G. Callahan, H. W. Ånes. Fast Radon transforms for +high-precision EBSD orientation determination using PyEBSDIndex. Journal of +Applied Crystallography, 57(1):3–19, 2024. and is based on the work of S. I. +Wright and B. L. Adams. Metallurgical Transactions A-Physical Metallurgy and +Materials Science, 23(3):759–767, 1992, and N. Krieger Lassen. Automated +Determination of Crystal Orientations from Electron Backscattering Patterns. +PhD thesis, The Technical University of Denmark, 1994. The band indexing is achieved through triplet voting using the methods outlined by A. Morawiec. Acta Crystallographica Section A Foundations and Advances, 76(6):719–734, From 41e8005b300512d0c947b4740899815e32ce4959 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Tue, 30 Jan 2024 12:22:23 -0500 Subject: [PATCH 175/177] Fixed issues with reading ACSII--> UTF-8 in file types/versions for Bruker HDF5 Signed-off by: David Rowenhorst --- pyebsdindex/ebsd_pattern.py | 28 +++++++++++++++++----------- 1 file changed, 17 insertions(+), 11 deletions(-) diff --git a/pyebsdindex/ebsd_pattern.py b/pyebsdindex/ebsd_pattern.py index 616c7b4..54b84a5 100644 --- a/pyebsdindex/ebsd_pattern.py +++ b/pyebsdindex/ebsd_pattern.py @@ -81,14 +81,20 @@ def get_pattern_file_obj(path,file_type=str('')): print("File Not Found:",str(Path(pathtemp[0]))) return -1 - if 'Manufacture' in f.keys(): - vendor = str(f['Manufacture'][()][0]) + if 'Manufacturer' in f.keys(): + vendor = f['Manufacturer'][()] + if type(vendor) is np.ndarray: + vendor = vendor[0] + vendor = str(vendor.decode(encoding='UTF-8')) if vendor.upper() == 'EDAX': ebsdfileobj = EDAXOH5(path) - if vendor.upper() >= 'BRUKER NANO': + if vendor.upper() == 'BRUKER NANO': ebsdfileobj = BRUKERH5(path) if 'manufacturer' in f.keys(): - vendor = str((f['manufacturer'][()][0]).decode('UTF-8')) + vendor = f['manufacturer'][()] + if type(vendor) is np.ndarray: + vendor = vendor[0] + vendor = str(vendor.decode('UTF-8')) if vendor >= 'kikuchipy': ebsdfileobj = KIKUCHIPYH5(path) if ebsdfileobj.h5patdatpth is None: #automatically chose the first data group @@ -1262,7 +1268,7 @@ def read_header(self, path=None): print("File Not Found:",str(Path(self.filepath))) return -1 - self.version = str(f['Version'][()][0]) + self.version = str(f['Version'][()][0].decode('UTF-8')) if self.version >= 'OIM Analysis 8.6.00': ngrp = self.get_data_paths() @@ -1372,7 +1378,7 @@ def read_header(self, path=None): print("File Not Found:",str(Path(self.filepath))) return -1 - self.version = str(f['Version'][()][0]) + self.version = str(f['Version'][()].decode('UTF-8')) if self.version.upper() >= 'ESPIRT 2.X': ngrp = self.get_data_paths() @@ -1389,12 +1395,12 @@ def read_header(self, path=None): self.nPatterns = shp[-3] self.filedatatype = dset.dtype.type headerpath = (f[self.h5patdatpth].parent.parent)["Header"] - self.nCols = np.uint32(headerpath['NCOLS'][()][0]) - self.nRows = np.uint32(headerpath['NROWS'][()][0]) + self.nCols = np.uint32(headerpath['NCOLS'][()]) + self.nRows = np.uint32(headerpath['NROWS'][()]) #self.hexflag = np.int32(f[headerpath+'Grid Type'][()][0] == 'HexGrid') - self.xStep = np.float32(headerpath['XSTEP'][()][0]) - self.yStep = np.float32(headerpath['YSTEP'][()][0]) + self.xStep = np.float32(headerpath['XSTEP'][()]) + self.yStep = np.float32(headerpath['YSTEP'][()]) return 0 #note this function uses multiple returns @@ -1460,7 +1466,7 @@ def read_header(self, path=None): print("File Not Found:",str(Path(self.filepath))) return -1 - self.version = str(f['Format Version'][()][0]) + self.version = str(f['Format Version'][()][0].decode('UTF-8')) if self.version >= '5.0': ngrp = self.get_data_paths() From aa23a9512b3744e46571d397ffa44e86a23c2ce2 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Tue, 30 Jan 2024 13:43:28 -0500 Subject: [PATCH 176/177] Update for release Signed-off by: David Rowenhorst --- CHANGELOG.rst | 17 +++++++++++++++-- pyebsdindex/__init__.py | 4 ++-- 2 files changed, 17 insertions(+), 4 deletions(-) diff --git a/CHANGELOG.rst b/CHANGELOG.rst index 51310e0..9b39cac 100644 --- a/CHANGELOG.rst +++ b/CHANGELOG.rst @@ -5,20 +5,33 @@ Changelog All notable changes to PyEBSDIndex will be documented in this file. The format is based on `Keep a Changelog `_. -0.2.dev1 +0.2.1 (2024-01-29) ================== Added ----- + Changed ------- +- ``nlpar.NLPAR.opt_lambda()`` method will now return the array of + the three optimal lambdas [less, medium, more] smoothing. The + defualt lambda is still set to [medium]. Previous return was ``None`` +- ``nlpar.NLPAR.calcnlpar()`` will now return a string of the new file + that was made with the NLPARed patterns. Previous return was ``None`` + +- Small alternations in the Radon peak detection that detect double + detection of nearly vertical bands. + No significant change in performance was noticed. Removed ------- Fixed ----- - +- ``ebsd_pattern``: Reading HDF5 manufacturing strings, and proper identification of + the vendors within get_pattern_file_obj +- ``ebsd_pattern``:Proper reading of parameters from Bruker HDF5 files. +- Corrected writing of oh5 files with ``ebsdfile`` 0.2.0 (2023-08-08) ================== diff --git a/pyebsdindex/__init__.py b/pyebsdindex/__init__.py index 0c5d0ba..f52647a 100644 --- a/pyebsdindex/__init__.py +++ b/pyebsdindex/__init__.py @@ -5,9 +5,9 @@ "Dave Rowenhorst", "Håkon Wiik Ånes", ] -__description__ = "Python based tool for Hough/Radon based EBSD indexing" +__description__ = "Python based tool for Radon based EBSD indexing" __name__ = "pyebsdindex" -__version__ = "0.2.dev1" +__version__ = "0.2.1" # Try to import only once From 9b4b9fea9b20a251f68e9d12f44beb00322ba8d7 Mon Sep 17 00:00:00 2001 From: David Rowenhorst Date: Tue, 30 Jan 2024 13:45:55 -0500 Subject: [PATCH 177/177] Update for release. Signed-off by: David Rowenhorst --- CHANGELOG.rst | 3 --- 1 file changed, 3 deletions(-) diff --git a/CHANGELOG.rst b/CHANGELOG.rst index 9b39cac..83fda1c 100644 --- a/CHANGELOG.rst +++ b/CHANGELOG.rst @@ -19,9 +19,6 @@ Changed - ``nlpar.NLPAR.calcnlpar()`` will now return a string of the new file that was made with the NLPARed patterns. Previous return was ``None`` -- Small alternations in the Radon peak detection that detect double - detection of nearly vertical bands. - No significant change in performance was noticed. Removed -------