# Plotting
############
if save_figures:
symmetric = 0
colorbar = False
closest_square_to_kernel = int(np.sqrt(kernel_size))**2
directory = './figures/'
formating = '.pdf'
title = 'real_regresion_h1'
save_filename = directory + title + formating
plot_mutliplot_bilinear(closest_square_to_kernel,
h1,
colorbar=colorbar,
symmetric=symmetric)
figure = plt.gcf() # get current figure
if remove_axis:
# Remove axis
for i in xrange(closest_square_to_kernel):
figure.get_axes()[i].get_xaxis().set_visible(False)
figure.get_axes()[i].get_yaxis().set_visible(False)
figure.set_size_inches(16, 12)
plt.savefig(save_filename, dpi=100)
os.system("pdfcrop %s %s" % (save_filename, save_filename))
plt.show()
n1 = 7.0
n2 = 8.0
t1 = -6.0
t2 = -6.0
td = 6.0
p1 = K1 * ((c1*(t - t1))**n1 * np.exp(-c1*(t - t1))) / ((n1**n1) * np.exp(-n1))
p2 = K2 * ((c2*(t - t2))**n2 * np.exp(-c2*(t - t2))) / ((n2**n2) * np.exp(-n2))
p3 = p1 - p2
plt.plot(t, p3, label='temporal kernel')
plt.xlabel('time (ms)')
plt.legend()
plt.show()
## Now create the spatio-temporal filter
# Initialize and fill the spatio-temporal kernel
kernel = np.zeros((kernel_size, int(lx/dx), int(ly/dy)))
for k, p in enumerate(p3):
kernel[k,...] = p * Z
plot_mutliplot_bilinear(25,kernel, colorbar=True, symmetric=2)
p
plt.show()
############
# Plotting
############
if save_figures:
symmetric = 0
colorbar = False
closest_square_to_kernel = int(np.sqrt(kernel_size)) ** 2
directory = './figures/'
formating='.pdf'
title = 'real_regresion_h1'
save_filename = directory + title + formating
plot_mutliplot_bilinear(closest_square_to_kernel, h1, colorbar=colorbar, symmetric=symmetric)
figure = plt.gcf() # get current figure
if remove_axis:
# Remove axis
for i in xrange(closest_square_to_kernel):
figure.get_axes()[i].get_xaxis().set_visible(False)
figure.get_axes()[i].get_yaxis().set_visible(False)
figure.set_size_inches(16, 12)
plt.savefig(save_filename, dpi = 100)
os.system("pdfcrop %s %s" % (save_filename, save_filename))
plt.show()
ims,
training_indexes,
delay_indexes,
image_indexes,
kernel_to_input,
input_to_image,
kernel_times,
verbose=verbose)
############
# Plotting and saving
############
symmetric = 1
colorbar = True
closest_square_to_kernel = int(np.sqrt(kernel_size))**2
plot_mutliplot_bilinear(closest_square_to_kernel, sta)
directory = './figures/'
formating = '.pdf'
title = 'STA' + quality + stimuli_type
save_filename = directory + title + formating
figure = plt.gcf() # get current figure
if remove_axis:
#Remove axis
for i in xrange(closest_square_to_kernel):
figure.get_axes()[i].get_xaxis().set_visible(False)
figure.get_axes()[i].get_yaxis().set_visible(False)
figure.set_size_inches(16, 12)
############
# Plotting
############
symmetric = 1
colorbar = True
closest_square_to_kernel = int(np.sqrt(kernel_size))**2
# Plot dense
directory = './figures/'
formating = '.pdf'
title = 'simulation_regresion_h1' + quality + stimuli_type_dense
save_filename = directory + title + formating
plot_mutliplot_bilinear(closest_square_to_kernel,
h1_dense,
colorbar=colorbar,
symmetric=symmetric)
figure = plt.gcf() # get current figure
if remove_axis:
#Remove axis
for i in xrange(closest_square_to_kernel):
figure.get_axes()[i].get_xaxis().set_visible(False)
figure.get_axes()[i].get_yaxis().set_visible(False)
figure.set_size_inches(16, 12)
plt.savefig(save_filename, dpi=100)
os.system("pdfcrop %s %s" % (save_filename, save_filename))
if show_plot:
plt.show()
# Calculate STA
############
print 'file name:', quality+ stimuli_type
print 'Examples used to calculate it :', training_indexes.size
print 'kernel size', kernel_size
verbose = True # Whether we want the delays to be display or not
sta = sta_v(V, ims, training_indexes, delay_indexes, image_indexes, kernel_to_input, input_to_image, kernel_times, verbose=verbose)
############
# Plotting and saving
############
symmetric = 1
colorbar = True
closest_square_to_kernel = int(np.sqrt(kernel_size)) ** 2
plot_mutliplot_bilinear(closest_square_to_kernel, sta)
directory = './figures/'
formating='.pdf'
title = 'STA' + quality + stimuli_type
save_filename = directory + title + formating
figure = plt.gcf() # get current figure
if remove_axis:
#Remove axis
for i in xrange(closest_square_to_kernel):
figure.get_axes()[i].get_xaxis().set_visible(False)
figure.get_axes()[i].get_yaxis().set_visible(False)
figure.set_size_inches(16, 12)
#symmetric = 1
symmetric = 0
colorbar = True
closest_square_to_kernel = int(np.sqrt(kernel_size)) ** 2
aux1=-0.70
aux2=0.45
# Plot dense
directory = './figures/'
formating='.pdf'
title = 'simulation_regresion_h1' + quality + 'cell' + cell_number + '_'+ stimuli_type_dense
save_filename = directory + title + formating
#plot_mutliplot_bilinear(closest_square_to_kernel, h1_dense, colorbar=colorbar, symmetric=symmetric)
plot_mutliplot_bilinear(closest_square_to_kernel, h1_dense, colorbar=colorbar, symmetric=symmetric, aux1=aux1, aux2=aux2)
figure = plt.gcf() # get current figure
if remove_axis:
#Remove axis
for i in xrange(closest_square_to_kernel):
figure.get_axes()[i].get_xaxis().set_visible(False)
figure.get_axes()[i].get_yaxis().set_visible(False)
figure.set_size_inches(16, 12)
plt.savefig(save_filename, dpi = 100)
os.system("pdfcrop %s %s" % (save_filename, save_filename))
if show_plot:
plt.show()
