def plot_coverage(position_heat_map, prm):
print('I will plot a heat map of the position of the animal to show coverage.')
save_path = prm.get_output_path() + '/Figures/session'
if os.path.exists(save_path) is False:
os.makedirs(save_path)
coverage = plt.figure()
coverage.set_size_inches(5, 5, forward=True)
ax = coverage.add_subplot(1, 1, 1) # specify (nrows, ncols, axnum)
ax = plot_utility.style_open_field_plot(ax)
ax.imshow(position_heat_map, cmap=cmocean.cm.thermal, interpolation='nearest')
plt.title('coverage', y=1.08)
plt.savefig(save_path + '/heatmap.png', dpi=300)
plt.savefig(save_path + '/heatmap.pdf')
plt.close()
def plot_firing_rate_maps(spatial_firing, prm):
print('I will make rate map plots.')
save_path = prm.get_output_path() + '/Figures/rate_maps'
if os.path.exists(save_path) is False:
os.makedirs(save_path)
for cluster in range(len(spatial_firing)):
cluster = spatial_firing.cluster_id.values[cluster] - 1
firing_rate_map_original = spatial_firing.firing_maps[cluster]
firing_rate_map = np.rot90(firing_rate_map_original)
firing_rate_map_fig = plt.figure()
firing_rate_map_fig.set_size_inches(5, 5, forward=True)
ax = firing_rate_map_fig.add_subplot(1, 1, 1) # specify (nrows, ncols, axnum)
ax = plot_utility.style_open_field_plot(ax)
ax.imshow(firing_rate_map, cmap='jet', interpolation='nearest')
plt.title('max fr: ' + str(round(spatial_firing.max_firing_rate[cluster], 2)) + ' Hz', y=1.08)
plt.savefig(save_path + '/' + spatial_firing.session_id[cluster] + '_rate_map_' + str(cluster + 1) + '.png', dpi=300)
# plt.savefig(save_path + '/' + spatial_firing.session_id[cluster] + '_rate_map_' + str(cluster + 1) + '.pdf')
plt.close()
def plot_rate_map_autocorrelogram(spatial_firing, prm):
print('I will make the rate map autocorrelogram grid plots now.')
save_path = prm.get_output_path() + '/Figures/rate_map_autocorrelogram'
if os.path.exists(save_path) is False:
os.makedirs(save_path)
for cluster in range(len(spatial_firing)):
cluster = spatial_firing.cluster_id.values[cluster] - 1
rate_map_autocorr_fig = plt.figure()
rate_map_autocorr_fig.set_size_inches(5, 5, forward=True)
ax = rate_map_autocorr_fig.add_subplot(1, 1, 1) # specify (nrows, ncols, axnum)
rate_map_autocorr = spatial_firing.rate_map_autocorrelogram[cluster]
if rate_map_autocorr.size:
ax = plt.subplot(1, 1, 1)
ax = plot_utility.style_open_field_plot(ax)
ax.imshow(rate_map_autocorr, cmap='jet', interpolation='nearest')
plt.tight_layout()
plt.title('grid score: ' + str(round(spatial_firing.grid_score[cluster], 2)))
plt.savefig(save_path + '/' + spatial_firing.session_id[cluster] + '_rate_map_autocorrelogram_' + str(cluster + 1) + '.png', dpi=300, bbox_inches="tight")
# plt.savefig(save_path + '/' + spatial_firing.session_id[cluster] + '_rate_map_autocorrelogram_' + str(cluster + 1) + '.pdf', bbox_inches="tight")
plt.close()
def plot_hd(spatial_firing, position_data, prm):
print('I will plot HD on open field maps as a scatter plot for each cluster.')
save_path = prm.get_output_path() + '/Figures/head_direction_plots_2d'
if os.path.exists(save_path) is False:
os.makedirs(save_path)
for cluster in range(len(spatial_firing)):
cluster = spatial_firing.cluster_id.values[cluster] - 1
x_positions = spatial_firing.position_x[cluster]
y_positions = spatial_firing.position_y[cluster]
hd = spatial_firing.hd[cluster]
hd_map_fig = plt.figure()
hd_map_fig.set_size_inches(5, 5, forward=True)
ax = hd_map_fig.add_subplot(1, 1, 1) # specify (nrows, ncols, axnum)
ax = plot_utility.style_open_field_plot(ax)
ax.plot(position_data['position_x'], position_data['position_y'], color='black', linewidth=2, zorder=1,
alpha=0.2)
hd_plot = ax.scatter(x_positions, y_positions, s=20, c=hd, vmin=-180, vmax=180, marker='o', cmap=cmocean.cm.phase)
plt.colorbar(hd_plot, fraction=0.046, pad=0.04)
plt.title('Head direction at spikes', y=1.08, fontsize=24)
plt.savefig(save_path + '/' + spatial_firing.session_id[cluster] + '_hd_map_' + str(cluster + 1) + '.png', dpi=300, bbox_inches='tight', pad_inches=0)
# plt.savefig(save_path + '/' + spatial_firing.session_id[cluster] + '_hd_map_' + str(cluster + 1) + '.pdf', bbox_inches='tight', pad_inches=0)
plt.close()
def plot_rate_map_comparison(grid_data, rate_map_1, rate_map_2, iterator,
path):
# corr = scipy.signal.correlate2d(rate_map_2, rate_map_2)
corr = PostSorting.open_field_grid_cells.get_rate_map_autocorrelogram(
rate_map_2)
firing_rate_map_fig = plt.figure()
firing_rate_map_fig.set_size_inches(5, 5, forward=True)
ax = firing_rate_map_fig.add_subplot(1, 1,
1) # specify (nrows, ncols, axnum)
ax = plot_utility.style_open_field_plot(ax)
rate_map_img = ax.imshow(corr, cmap='jet', interpolation='nearest')
# firing_rate_map_fig.colorbar(rate_map_img)
ax.axvline(x=(corr.shape[1]) / 2, color='black', linewidth=4)
ax.axhline(y=(corr.shape[0]) / 2, color='black', linewidth=4)
plt.savefig(path + grid_data.iloc[iterator].session_id +
str(grid_data.iloc[iterator].session_id) + 'auto_corr2.png')
plt.cla()
corr = PostSorting.open_field_grid_cells.get_rate_map_autocorrelogram(
rate_map_1)
firing_rate_map_fig = plt.figure()
firing_rate_map_fig.set_size_inches(5, 5, forward=True)
ax = firing_rate_map_fig.add_subplot(1, 1,
1) # specify (nrows, ncols, axnum)
ax = plot_utility.style_open_field_plot(ax)
rate_map_img = ax.imshow(corr, cmap='jet', interpolation='nearest')
# firing_rate_map_fig.colorbar(rate_map_img)
ax.axvline(x=(corr.shape[1]) / 2, color='black', linewidth=4)
ax.axhline(y=(corr.shape[0]) / 2, color='black', linewidth=4)
plt.savefig(path + grid_data.iloc[iterator].session_id +
str(grid_data.iloc[iterator].session_id) + 'auto_corr1.png')
plt.cla()
corr = PostSorting.open_field_grid_cells.get_rate_map_crosscorrelogram(
rate_map_1, rate_map_2)
firing_rate_map_fig = plt.figure()
firing_rate_map_fig.set_size_inches(5, 5, forward=True)
ax = firing_rate_map_fig.add_subplot(1, 1,
1) # specify (nrows, ncols, axnum)
ax = plot_utility.style_open_field_plot(ax)
rate_map_img = ax.imshow(corr, cmap='jet', interpolation='nearest')
# firing_rate_map_fig.colorbar(rate_map_img)
ax.axvline(x=(corr.shape[1]) / 2, color='black', linewidth=4)
ax.axhline(y=(corr.shape[0]) / 2, color='black', linewidth=4)
plt.savefig(path + grid_data.iloc[iterator].session_id +
str(grid_data.iloc[iterator].session_id) + 'cross_corr.png')
plt.cla()
firing_rate_map_fig = plt.figure()
firing_rate_map_fig.set_size_inches(5, 5, forward=True)
ax = firing_rate_map_fig.add_subplot(1, 1,
1) # specify (nrows, ncols, axnum)
ax = plot_utility.style_open_field_plot(ax)
rate_map_img = ax.imshow(rate_map_2 - rate_map_1,
cmap='jet',
interpolation='nearest')
firing_rate_map_fig.colorbar(rate_map_img)
plt.savefig(path + grid_data.iloc[iterator].session_id +
str(grid_data.iloc[iterator].session_id) + 'subtract.png')
plt.cla()
plt.cla()
firing_rate_map_fig = plt.figure()
firing_rate_map_fig.set_size_inches(5, 5, forward=True)
ax = firing_rate_map_fig.add_subplot(1, 1,
1) # specify (nrows, ncols, axnum)
ax = plot_utility.style_open_field_plot(ax)
rate_map_img = ax.imshow(rate_map_1 - rate_map_2,
cmap='jet',
interpolation='nearest')
firing_rate_map_fig.colorbar(rate_map_img)
plt.savefig(path + grid_data.iloc[iterator].session_id +
str(grid_data.iloc[iterator].session_id) + 'subtract2.png')
plt.cla()