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plot_ann_activity_as_quiver_plot_one_stimulus.py
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import sys
import pylab
import numpy as np
import simulation_parameters as sp
import matplotlib
from matplotlib import cm
import time
import utils
PS = sp.parameter_storage()
params = PS.load_params()
tp = np.loadtxt(params['tuning_prop_means_fn'])
input_params = np.loadtxt(params['parameters_folder'] + 'input_params.txt')
try:
# stimuli = range(0, int(sys.argv[1]))
stimuli = [int(sys.argv[1])]
except:
stimuli = [0]
print 'Stimuli number:', stimuli
scale = 1
#network_activity_fn = 'Abstract/Parameters/all_output_activity.dat'
#output_folder_fig = params['figures_folder'] + 'Test/'
#network_activity_fn = 'Abstract/Parameters/all_output_activity_test_minus_training.dat'
#output_folder_fig = params['figures_folder'] + 'Test_minus_training/'
network_activity_fn = '%sall_inputs_scaled.dat' % (params['parameters_folder'])
output_folder_fig = params['figures_folder']
#iteration = 0
#network_activity_fn = 'Abstract/ANNActivity/output_activity_%d.dat' % iteration
#output_folder_fig = params['figures_folder'] + 'Test/'
print 'Loading ', network_activity_fn
network_activity = np.loadtxt(network_activity_fn)
#network_activity = np.exp(network_activity)
n_cells = params['n_exc']
#n_time_steps = d[:, 0].size
n_time_steps_per_stimulus = int(params['t_sim'] / params['dt_rate'])
n_time_steps_for_averaging = int(n_time_steps_per_stimulus / 20)
n_steps_offset = 0
n_steps = n_time_steps_per_stimulus / n_time_steps_for_averaging
for stimulus_number in stimuli:
mp = input_params[stimulus_number, :]
t0 = stimulus_number * n_time_steps_per_stimulus
t1 = (stimulus_number + 1) * n_time_steps_per_stimulus
network_activity_during_stim = network_activity[t0:t1, :]
max_activities = np.zeros(n_steps)
min_activities = np.zeros(n_steps)
avg_activities = np.zeros(n_steps)
# for different colorscales
for step in xrange(n_steps_offset, n_steps):
t1 = step * n_time_steps_for_averaging
t2 = (step + 1) * n_time_steps_for_averaging
summed_activities = np.zeros(n_cells)
for cell in xrange(n_cells):
activity = network_activity_during_stim[t1:t2, cell].sum()
summed_activities[cell] = activity
max_activities[step] = summed_activities.max()
min_activities[step] = summed_activities.min()
avg_activities[step] = summed_activities.mean()
act_cnt, act_bins = np.histogram(summed_activities, bins=20)
# print 'act_cnt', act_cnt
# print 'act_bins', act_bins
print '%d max activity %.6f\tmin activity %.6f\tmean activitiy %.6f' % (step, max_activities[step], min_activities[step], avg_activities[step])
print 'Average max activity:', max_activities.mean(), max_activities.std()
print 'Average min activity:', min_activities.mean(), min_activities.std()
print 'Average mean activity:', avg_activities.mean(), avg_activities.std()
o_max = max_activities.max()
o_min = avg_activities.mean()
# o_min = min_activities.min()
for step in xrange(n_steps_offset, n_steps):
# print 'Step', step
fig = pylab.figure()
ax = fig.add_subplot(111)
# if seperate colorscales:
# o_max = max_activities[step]
# o_min = min_activities[step]
norm = matplotlib.mpl.colors.Normalize(vmin=o_min, vmax=o_max)
m = matplotlib.cm.ScalarMappable(norm=norm, cmap=cm.Greys)#jet)
m.set_array(np.arange(o_min, o_max, 0.01))
fig.colorbar(m)
t_ = (float(step) / n_steps) * params['t_sim'] / params['t_stimulus']
stim_pos_x = mp[0] + mp[2] * t_
stim_pos_y = mp[1] + mp[3] * t_
t1 = step * n_time_steps_for_averaging
t2 = (step + 1) * n_time_steps_for_averaging
# cells_closest_to_stim, x_dist = utils.sort_gids_by_distance_to_stimulus(tp, mp)
# cells_closest_to_stim.tolist().reverse()
data = np.zeros((n_cells+1, 4), dtype=np.double)
data[:n_cells,:] = tp
# data[:n_cells,:] = tp[cells_closest_to_stim, :]
data[-1,:] = stim_pos_x, stim_pos_y, mp[2], mp[3]
rgba_colors = []
for cell in xrange(n_cells):
# for cell in cells_closest_to_stim:
activity = network_activity_during_stim[t1:t2, cell].sum()
rgba_colors.append(m.to_rgba(activity))
rgba_colors.append('r')
ax.quiver(data[:, 0], data[:, 1], data[:, 2], data[:, 3], \
angles='xy', scale_units='xy', scale=scale, color=rgba_colors, headwidth=4, pivot='middle')
ax.annotate('Stimulus', (stim_pos_x+0.02, stim_pos_y+0.02), fontsize=12, color='r')
ax.set_xlim((-0.2, 1.2))
ax.set_ylim((-0.2, 1.2))
output_fn = output_folder_fig + 'network_activity_%03d.png' % (stimulus_number * n_steps + step)
print 'output_fig', step, output_fn
# print 'o_max o_min', o_max, o_min
pylab.savefig(output_fn)
print 'Average max activity:', max_activities.mean(), max_activities.std()
#pylab.show()