def plot_results_of_watson_test(df_all_animals, cell_type='grid', animal='mouse', xlim=False):
if xlim is True:
tag = 'zoomed'
else:
tag = ''
good_cluster = df_all_animals.false_positive == False
if cell_type == 'grid':
grid = df_all_animals.grid_score >= 0.4
not_hd = df_all_animals.hd_score < 0.5
cells_to_analyze = grid & not_hd
elif cell_type == 'hd':
not_grid = df_all_animals.grid_score < 0.4
hd = df_all_animals.hd_score >= 0.5
cells_to_analyze = not_grid & hd
else:
not_grid = df_all_animals.grid_score < 0.4
not_hd = df_all_animals.hd_score < 0.5
cells_to_analyze = not_grid & not_hd
watson_test_stats = df_all_animals.watson_test_hd[good_cluster & cells_to_analyze]
watson_test_stats = watson_test_stats[~np.isnan(watson_test_stats)]
print('\n' + animal)
print(cell_type)
print('all cells: ' + str(len(watson_test_stats)))
print('significant: ' + str(len(watson_test_stats > 0.268)))
fig, ax = plt.subplots()
if xlim == True:
plt.xlim(0, 1)
plt.hist(watson_test_stats, bins=30, color='navy', normed=True, alpha=0.7)
ax.xaxis.set_tick_params(labelsize=20)
ax.yaxis.set_tick_params(labelsize=20)
plt.axvline(x=0.268, linewidth=3, color='red') # p < 0.01 based on r docs for watson two test
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
ax.set_xlabel('Watson $U^2$', size=30)
ax.set_ylabel('Proportion', size=30)
plt.savefig(save_output_path + animal + '_two_sample_watson_stats_hist_all_spikes_' + cell_type + '_cells_' + tag + '.png', bbox_inches="tight")
plt.close()
fig, ax = plt.subplots()
ax = plot_utility.format_bar_chart(ax, 'Watson $U^2$', 'Cumulative probability')
plt.xscale('log')
plt.yticks([0, 1])
plt.xlim(0.1, 100)
plt.axvline(x=0.268, linewidth=3, color='red') # p < 0.01 based on r docs for watson two test
values, base = np.histogram(watson_test_stats, bins=40)
# evaluate the cumulative
cumulative = np.cumsum(values / len(watson_test_stats))
# plot the cumulative function
plt.plot(base[:-1], cumulative, c='navy', linewidth=5)
plt.savefig(save_output_path + animal + '_two_sample_watson_stats_hist_all_spikes_' + cell_type + '_cells_cumulative' + tag + '.png', bbox_inches="tight")
plt.close()
def plot_speed_dependence(spatial_firing, animal, tag, color='navy'):
# plt.hist(spatial_firing.speed_score, alpha=0.5, normed=True, color='gray')
plt.cla()
fig, ax = plt.subplots()
ax = plot_utility.format_bar_chart(ax, 'Speed score',
'Number of ' + tag + ' cells')
plt.hist(spatial_firing.speed_score, alpha=0.8, color=color)
plt.xlim(-1, 1)
plt.savefig(save_output_path + animal + '_' + tag +
'_cell_speed_scores.png')
plt.close()
def plot_speed_vs_firing_rate_grid(position: pd.DataFrame,
spatial_firing: pd.DataFrame,
sampling_rate_conversion: int,
video_sampling_rate: int,
save_path: str) -> None:
sigma = 250 / video_sampling_rate
speed = scipy.ndimage.filters.gaussian_filter(position.speed, sigma)
spatial_firing = OverallAnalysis.analyze_speed.add_cell_types_to_data_frame(
spatial_firing)
for index, cell in spatial_firing.iterrows():
if cell['cell type'] == 'grid':
firing_times = cell.firing_times
firing_hist, edges = np.histogram(
firing_times,
bins=len(speed),
range=(0,
max(position.synced_time) * sampling_rate_conversion))
firing_hist *= video_sampling_rate
smooth_hist = scipy.ndimage.filters.gaussian_filter(
firing_hist.astype(float), sigma)
speed, smooth_hist = array_utility.remove_nans_from_both_arrays(
speed, smooth_hist)
median_x, median_y, percentile_25, percentile_75 = calculate_median_for_scatter_binned(
speed, smooth_hist)
plt.cla()
fig, ax = plt.subplots()
ax = plot_utility.format_bar_chart(ax, 'Speed (cm/s)',
'Firing rate (Hz)')
plt.scatter(speed[::10],
smooth_hist[::10],
color='gray',
alpha=0.7)
plt.plot(median_x, percentile_25, color='black', linewidth=5)
plt.plot(median_x, percentile_75, color='black', linewidth=5)
plt.scatter(median_x, median_y, color='black', s=100)
plt.title('speed score: ' + str(np.round(cell.speed_score, 4)))
plt.xlim(0, 50)
plt.ylim(0, None)
plt.savefig(save_path + cell.session_id + str(cell.cluster_id) +
'_speed.png')
plt.close()
def plot_speed_vs_firing_rate(position: pd.DataFrame,
spatial_firing: pd.DataFrame,
sampling_rate_conversion: int, gauss_sd: float,
prm: object) -> None:
sampling_rate_video = int(1 / position['synced_time'].diff().mean())
sigma = gauss_sd / sampling_rate_video
speed = scipy.ndimage.filters.gaussian_filter(position.speed, sigma)
save_path = prm.get_output_path() + '/Figures/firing_properties'
for index, cell in spatial_firing.iterrows():
firing_times = cell.firing_times
firing_hist, edges = np.histogram(firing_times,
bins=len(speed),
range=(0, max(position.synced_time) *
sampling_rate_conversion))
firing_hist *= sampling_rate_video
smooth_hist = scipy.ndimage.filters.gaussian_filter(
firing_hist.astype(float), sigma)
speed, smooth_hist = array_utility.remove_nans_from_both_arrays(
speed, smooth_hist)
median_x, median_y, percentile_25, percentile_75 = calculate_median_for_scatter_binned(
speed, smooth_hist)
plt.cla()
fig, ax = plt.subplots()
ax = plot_utility.format_bar_chart(ax, 'Speed (cm/s)',
'Firing rate (Hz)')
plt.scatter(speed[::10], smooth_hist[::10], color='gray', alpha=0.7)
plt.plot(median_x, percentile_25, color='black', linewidth=5)
plt.plot(median_x, percentile_75, color='black', linewidth=5)
plt.scatter(median_x, median_y, color='black', s=100)
plt.title('Speed score: ' + str(np.round(cell.speed_score, 4)),
fontsize=24)
plt.xlim(0, 50)
plt.ylim(0, None)
plt.savefig(save_path + '/' + cell.session_id + '_' +
str(cell.cluster_id) + '_speed_vs_firing_rate.png',
dpi=300,
bbox_inches='tight',
pad_inches=0)
plt.close()
def make_combined_plot_of_distributions(shuffled_field_data,
tag='grid',
shuffle_type='occupancy'):
tail, percentile_95, percentile_99 = find_tail_of_shuffled_distribution_of_rejects(
shuffled_field_data)
number_of_rejects_shuffled = shuffled_field_data.number_of_different_bins_shuffled
flat_shuffled = []
for field in number_of_rejects_shuffled:
flat_shuffled.extend(field)
fig, ax = plt.subplots()
plt.hist(flat_shuffled, normed=True, color='black', alpha=0.5)
number_of_rejects_real = shuffled_field_data.number_of_different_bins
plt.hist(number_of_rejects_real, normed=True, color='navy', alpha=0.5)
# plt.axvline(x=tail, color='red', alpha=0.5, linestyle='dashed')
# plt.axvline(x=percentile_95, color='red', alpha=0.5, linestyle='dashed')
# plt.axvline(x=percentile_99, color='red', alpha=0.5, linestyle='dashed')
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_tick_params(labelsize=20)
ax.yaxis.set_tick_params(labelsize=20)
ax.set_xlabel('Rejected bars / field', size=30)
ax.set_ylabel('Proportion', size=30)
ax.set_xlim(0, 20)
plt.savefig(analysis_path + 'distribution_of_rejects_combined_all_' +
shuffle_type + tag + '.png',
bbox_inches="tight")
plt.close()
fig, ax = plt.subplots()
plt.yticks([0, 1])
plt.ylim(0, 1.01)
ax = plot_utility.format_bar_chart(ax, 'Pearson correlation coef.',
'Cumulative probability')
ax.set_xlim(0, 20)
values, base = np.histogram(flat_shuffled, bins=40)
# evaluate the cumulative
cumulative = np.cumsum(values / len(flat_shuffled))
# plot the cumulative function
plt.plot(base[:-1], cumulative, c='gray', linewidth=5)
values, base = np.histogram(number_of_rejects_real, bins=40)
# evaluate the cumulative
cumulative = np.cumsum(values / len(number_of_rejects_real))
# plot the cumulative function
plt.plot(base[:-1], cumulative, c='navy', linewidth=5)
# plt.axvline(x=tail, color='red', alpha=0.5, linestyle='dashed')
# plt.axvline(x=percentile_95, color='red', alpha=0.5, linestyle='dashed')
# plt.axvline(x=percentile_99, color='red', alpha=0.5, linestyle='dashed')
ax.set_xlabel('Rejected bars / field', size=30)
ax.set_ylabel('Cumulative probability', size=30)
plt.savefig(analysis_path + 'distribution_of_rejects_combined_all_' +
shuffle_type + tag + '_cumulative.png',
bbox_inches="tight")
plt.close()
