def plot_spikes_for_channel_centered(grid, spike_data, cluster, channel,
snippet_column_name):
max_channel = spike_data.primary_channel[cluster]
sd = np.std(spike_data.random_snippets[cluster][max_channel - 1, :, :] *
-1)
highest_value = np.median(
spike_data.random_snippets[cluster][max_channel - 1, :, :] *
-1) + (sd * 4)
lowest_value = np.median(
spike_data.random_snippets[cluster][max_channel - 1, :, :] *
-1) - (sd * 4)
snippet_plot = plt.subplot(grid[int(channel / 2), channel % 2])
plt.ylim(lowest_value - 10, highest_value + 30)
plot_utility.style_plot(snippet_plot)
snippet_plot.plot(spike_data[snippet_column_name][cluster][channel, :, :] *
-1,
color='lightslategray')
snippet_plot.plot(
np.mean(spike_data[snippet_column_name][cluster][channel, :, :], 1) *
-1,
color='red')
plt.xticks([0, 30], [0, 1])
plt.xticks(fontsize=14)
plt.yticks(fontsize=14)
plt.xlabel('Time (ms)', fontsize=14)
plt.ylabel('Voltage (µV)', fontsize=14)
def plot_interesting_cells(cluster_id, spike_index, save_output_path):
cluster5 = np.where(cluster_id == 5)
cluster5_firings = np.take(spike_index, cluster5[0])
cluster6 = np.where(cluster_id == 6)
cluster6_firings = np.take(spike_index, cluster6[0])
c5 = np.array(cluster5_firings, dtype=int)
c6 = np.array(cluster6_firings, dtype=int)
spikes_fig_whole_recording = plt.figure()
ax1 = spikes_fig_whole_recording.add_subplot(1, 1, 1)
spikes_fig_whole_recording, ax1 = plot_utility.style_plot(ax1)
ax1.get_yaxis().set_visible(False)
ax1.spines['left'].set_visible(False)
ax1.vlines(c5, 0.5, 0.95, color='navy')
ax1.vlines(c6, 1, 1.45, color='black')
plt.title('C5 and C6 from M5, 6th of March')
plt.xlabel('sample points')
plt.ylabel('cluster')
plt.savefig(save_output_path + 'M5_conjunctive_vs_grid_cluster')
spikes_small_section = plt.figure()
ax2 = spikes_small_section.add_subplot(1, 1, 1)
spikes_small_section, ax2 = plot_utility.style_plot(ax2)
ax2.get_yaxis().set_visible(False)
ax2.spines['left'].set_visible(False)
ax2.vlines(c5, 0.5, 0.95, color='navy')
ax2.vlines(c6, 1, 1.45, color='black')
plt.title('C5 and C6 from M5, 6th of March')
plt.xlabel('sample points')
plt.ylabel('cluster')
plt.xlim(3050000, 3150000)
plt.savefig(save_output_path + 'M5_conjunctive_vs_grid_cluster_section')
def plot_spikes_for_channel(grid, highest_value, lowest_value, spike_data,
cluster, channel, snippet_column_name):
snippet_plot = plt.subplot(grid[int(channel / 2), channel % 2])
plt.ylim(lowest_value - 10, highest_value + 30)
plot_utility.style_plot(snippet_plot)
snippet_plot.plot(spike_data[snippet_column_name][cluster][channel, :, :] *
-1,
color='lightslategray')
snippet_plot.plot(
np.mean(spike_data[snippet_column_name][cluster][channel, :, :], 1) *
-1,
color='red')
plt.xticks([0, 10, 30], [-10, 0, 20])
def plot_firing_rate_vs_speed(spatial_firing, spatial_data, prm):
sampling_rate = 30
print(
'I will plot spikes vs speed for the whole session excluding opto tagging.'
)
save_path = prm.get_output_path() + '/Figures/firing_properties'
if os.path.exists(save_path) is False:
os.makedirs(save_path)
speed = spatial_data.speed[~np.isnan(spatial_data.speed)]
number_of_bins = math.ceil(max(speed)) - math.floor(min(speed))
session_hist, bins_s = np.histogram(speed,
bins=number_of_bins,
range=(math.floor(min(speed)),
math.ceil(max(speed))))
for cluster in range(len(spatial_firing)):
cluster = spatial_firing.cluster_id.values[cluster] - 1
speed_cluster = spatial_firing.speed[cluster]
speed_cluster = sorted(speed_cluster)
spike_hist = plt.figure()
spike_hist.set_size_inches(5, 5, forward=True)
ax = spike_hist.add_subplot(1, 1, 1)
speed_hist, ax = plot_utility.style_plot(ax)
if number_of_bins > 0:
hist, bins = np.histogram(speed_cluster[1:],
bins=number_of_bins,
range=(math.floor(min(speed)),
math.ceil(max(speed))))
width = bins[1] - bins[0]
center_bin = (bins[:-1] + bins[1:]) / 2
center = center_bin[tuple(
[np.where(session_hist > sum(session_hist) * 0.005)])]
hist = np.array(hist, dtype=float)
session_hist = np.array(session_hist, dtype=float)
rate = np.divide(hist,
session_hist,
out=np.zeros_like(hist),
where=session_hist != 0)
rate = rate[tuple([
np.where(
session_hist[~np.isnan(session_hist)] > sum(session_hist) *
0.005)
])]
plt.bar(center[0],
rate[0] * sampling_rate,
align='center',
width=width,
color='black')
plt.xlabel('speed [cm/s]')
plt.ylabel('firing rate [Hz]')
plt.xlim(0, 30)
plt.savefig(save_path + '/' + spatial_firing.session_id[cluster] +
'_' + str(cluster + 1) + '_speed_histogram.png',
dpi=300,
bbox_inches='tight',
pad_inches=0)
plt.savefig(save_path + '/' + spatial_firing.session_id[cluster] +
'_' + str(cluster + 1) + '_speed_histogram.pdf',
bbox_inches='tight',
pad_inches=0)
plt.close()
def plot_spike_histogram(spatial_firing, prm):
sampling_rate = prm.get_sampling_rate()
print(
'I will plot spikes vs time for the whole session excluding opto tagging.'
)
save_path = prm.get_output_path() + '/Figures/firing_properties'
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
number_of_bins = int(
(spatial_firing.firing_times[cluster][-1] -
spatial_firing.firing_times[cluster][0]) / (5 * sampling_rate))
firings_cluster = spatial_firing.firing_times[
cluster] / sampling_rate / 60
spike_hist = plt.figure()
spike_hist.set_size_inches(5, 5, forward=True)
ax = spike_hist.add_subplot(1, 1, 1)
spike_hist, ax = plot_utility.style_plot(ax)
if number_of_bins > 0:
hist, bins = np.histogram(firings_cluster, bins=number_of_bins)
width = bins[1] - bins[0]
center = (bins[:-1] + bins[1:]) / 2
plt.bar(center, hist, align='center', width=width, color='black')
plt.title(
'Spike histogram \n total spikes = ' +
str(spatial_firing.number_of_spikes[cluster]) + ', \n mean fr = ' +
str(round(spatial_firing.mean_firing_rate[cluster], 0)) + ' Hz',
y=1.08,
fontsize=24)
plt.xlabel('Time (min)', fontsize=25)
plt.ylabel('Number of spikes', fontsize=25)
plt.xticks(fontsize=20)
plt.yticks(fontsize=20)
plt.savefig(save_path + '/' + spatial_firing.session_id[cluster] +
'_' + str(cluster + 1) + '_spike_histogram.png',
dpi=300,
bbox_inches='tight',
pad_inches=0)
# plt.savefig(save_path + '/' + spatial_firing.session_id[cluster] + '_' + str(cluster + 1) + '_spike_histogram.pdf', bbox_inches='tight', pad_inches=0)
plt.close()
def plot_correlation_coef_hist(correlation_coefs,
save_path,
y_axis_label='Number of fields'):
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1) # specify (nrows, ncols, axnum)
fig, ax = plot_utility.style_plot(ax)
ax.hist(correlation_coefs, color='navy')
ax.xaxis.set_tick_params(labelsize=20)
ax.yaxis.set_tick_params(labelsize=20)
plt.xlabel('Correlation coefficient', fontsize=30)
plt.ylabel(y_axis_label, fontsize=30)
plt.xlim(-1, 1)
plt.axvline(x=0, color='red', linewidth=5)
plt.gcf().subplots_adjust(bottom=0.15)
plt.savefig(save_path)
plt.close()
plt.cla()
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1) # specify (nrows, ncols, axnum)
fig, ax = plot_utility.style_plot(ax)
# ax.hist(correlation_coefs, color='navy')
plot_utility.plot_cumulative_histogram(correlation_coefs, ax)
ax.xaxis.set_tick_params(labelsize=20)
ax.yaxis.set_tick_params(labelsize=20)
plt.xlabel('Correlation coefficient', fontsize=25)
plt.ylabel(y_axis_label, fontsize=25)
plt.xlim(-1, 1)
plt.axvline(x=0, color='red', linewidth=5)
plt.gcf().subplots_adjust(bottom=0.15)
plt.savefig(save_path + 'cumulative.png')
plt.close()
fields = pd.read_excel(path)
print(fields.head())
significant = (fields.p_value < 0.001)
correlation_coefs = fields[significant]['correlation coef'].values
save_path = path + 'correlation_coef_hist.png'
plot_correlation_coef_hist(correlation_coefs, save_path)
grid_cells = fields['cell type'] == 'grid'
hd_cells = fields['cell type'] == 'hd'
conjunctive_cells = fields['cell type'] == 'conjunctive'
not_classified = fields['cell type'] == 'na'
fields[grid_cells & significant]['correlation coef'].std()
grid_coeffs = fields[grid_cells & significant]['correlation coef'].values
save_path = path + 'correlation_coef_hist_grid.png'
plot_correlation_coef_hist(grid_coeffs, save_path)
grid_coeffs = fields[hd_cells & significant]['correlation coef'].values
save_path = path + 'correlation_coef_hist_hd.png'
plot_correlation_coef_hist(grid_coeffs, save_path)
grid_coeffs = fields[not_classified
& significant]['correlation coef'].values
save_path = path + 'correlation_coef_hist_nc.png'
plot_correlation_coef_hist(grid_coeffs, save_path)
grid_coeffs = fields[conjunctive_cells
& significant]['correlation coef'].values
save_path = path + 'correlation_coef_hist_conj.png'
plot_correlation_coef_hist(grid_coeffs, save_path)