def plot_all_waterfalls(df, savepath, scale='blank_subtracted_NLL'):
areas, cres = dataset_params()
for area in areas:
for cre in cres:
session_IDs = get_sessions(df, area, cre)
if len(session_IDs) > 0:
#sort by direction using event magnitude
direction_order = get_cell_order_direction_sorted(
df, area, cre, savepath)
#display response significance
resp, blank, p_all = pool_sessions(session_IDs,
area + '_' + cre,
savepath,
scale=scale)
resp = center_direction_zero(resp)
condition_responses = resp[p_all < SIG_THRESH]
dirXcon_mat = concatenate_contrasts(condition_responses)
dirXcon_mat = dirXcon_mat[direction_order]
dirXcon_mat = move_all_negative_to_bottom(dirXcon_mat)
plot_full_waterfall(
dirXcon_mat, cre,
shorthand(area) + '_' + shorthand(cre) + '_full', scale,
savepath)
def plot_pooled_mat(pooled_mat, area, cre, pass_str, savepath):
max_resp = 80.0
cre_colors = get_cre_colors()
x_tick_labels = ['-135', '-90', '-45', '0', '45', '90', '135', '180']
directions, contrasts = grating_params()
plt.figure(figsize=(4.2, 4))
ax = plt.subplot(111)
current_cmap = matplotlib.cm.get_cmap(name='RdBu_r')
current_cmap.set_bad(color=[0.8, 0.8, 0.8])
im = ax.imshow(pooled_mat.T,
vmin=-max_resp,
vmax=max_resp,
interpolation='nearest',
aspect='auto',
cmap='RdBu_r',
origin='lower')
ax.set_xlabel('Direction (deg)', fontsize=14)
ax.set_ylabel('Contrast (%)', fontsize=14)
ax.set_yticks(np.arange(len(contrasts)))
ax.set_yticklabels([str(int(100 * x)) for x in contrasts], fontsize=10)
ax.set_xticks(np.arange(len(directions)))
ax.set_xticklabels(x_tick_labels, fontsize=10)
ax.set_title(shorthand(cre) + ' population',
fontsize=16,
color=cre_colors[cre])
cbar = plt.colorbar(
im,
ax=ax,
ticks=[-max_resp, -max_resp / 2.0, 0.0, max_resp / 2.0, max_resp])
cbar.set_label('Event magnitude per second (%), blank subtracted',
rotation=270,
labelpad=15.0)
plt.savefig(savepath + shorthand(area) + '_' + shorthand(cre) + '_' +
pass_str + '_summed_tuning.svg',
format='svg')
plt.close()
def plot_SbC_stats(df, savepath):
SbC_THRESH = 0.05
cre_colors = get_cre_colors()
directions, contrasts = grating_params()
areas, cres = dataset_params()
percent_SbC = []
labels = []
colors = []
sample_size = []
for area in areas:
for cre in cres:
session_IDs = get_sessions(df, area, cre)
if len(session_IDs) > 0:
num_cells = 0
num_SbC = 0
for session_ID in session_IDs:
SbC_pval = test_SbC(session_ID, savepath)
num_cells += len(SbC_pval)
num_SbC += (SbC_pval < SbC_THRESH).sum()
labels.append(shorthand(cre))
colors.append(cre_colors[cre])
percent_SbC.append(100.0 * num_SbC / num_cells)
sample_size.append(num_cells)
plt.figure(figsize=(6, 4))
ax = plt.subplot(111)
for x, group in enumerate(labels):
ax.bar(x, percent_SbC[x], color=colors[x])
ax.text(x,
max(percent_SbC[x], 5) + 1,
'(' + str(sample_size[x]) + ')',
horizontalalignment='center',
fontsize=8)
ax.plot([-1, len(labels)], [100 * SbC_THRESH, 100 * SbC_THRESH],
'--k',
linewidth=2.0)
ax.set_ylim(0, 30)
ax.set_xlim(-1, 14)
ax.set_xticks(np.arange(len(labels)))
ax.set_xticklabels(labels, fontsize=10, rotation=45)
ax.set_ylabel('% Suppressed by Contrast', fontsize=14)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
plt.savefig(savepath + 'SbC_stats.svg', format='svg')
plt.close()
def plot_direction_vector_sum_by_contrast(df, savepath):
areas, cres = dataset_params()
directions, contrasts = grating_params()
for area in areas:
for cre in cres:
session_IDs = get_sessions(df, area, cre)
if len(session_IDs) > 0:
resp, blank, p_all = pool_sessions(session_IDs,
area + '_' + cre,
savepath,
scale='event')
sig_resp = resp[p_all < SIG_THRESH]
pref_dir_mat = calc_pref_direction_dist_by_contrast(sig_resp)
pref_dir_mat = pref_dir_mat / np.sum(
pref_dir_mat, axis=0, keepdims=True)
resultant_mag = []
resultant_theta = []
for i_con, contrast in enumerate(contrasts):
mag, theta = calc_vector_sum(pref_dir_mat[:, i_con])
resultant_mag.append(mag)
resultant_theta.append(theta)
#bootstrap CI for distribution at 5% contrast
num_cells = len(sig_resp)
uniform_LB, uniform_UB = uniform_direction_vector_sum(
num_cells)
radial_direction_figure(
np.zeros((len(directions), )), np.zeros(
(len(directions), )), resultant_mag, resultant_theta,
uniform_LB, uniform_UB, cre, num_cells,
shorthand(area) + '_' + shorthand(cre) + '_combined',
savepath)
def plot_contrast_CoM(df, savepath, curve='cdf'):
areas, cres = dataset_params()
cre_colors = get_cre_colors()
area = 'VISp'
pooled_resp = []
colors = []
alphas = []
cre_labels = []
for cre in cres:
session_IDs = get_sessions(df, area, cre)
resp, blank, p_all = pool_sessions(session_IDs,
area + '_' + cre,
savepath,
scale='event')
pooled_resp.append(resp[p_all < SIG_THRESH])
colors.append(cre_colors[cre])
alphas.append(1.0)
cre_labels.append(shorthand(cre))
center_of_mass = center_of_mass_for_list(pooled_resp)
contrasts = [5, 10, 20, 40, 60, 80]
plot_cdf(metric=center_of_mass,
metric_labels=cre_labels,
colors=colors,
alphas=alphas,
hist_range=(np.log(5.0), np.log(70.0)),
hist_bins=200,
x_label='Contrast (CoM)',
x_ticks=np.log(contrasts),
x_tick_labels=[str(x) for x in contrasts],
save_name=shorthand(area) + '_contrast_' + curve,
savepath=savepath,
do_legend=True)
def model_GLM(df, savepath):
for area in ['VISp']:
for cre in ['Vip-IRES-Cre', 'Sst-IRES-Cre', 'Cux2-CreERT2']:
session_IDs = get_sessions(df, area, cre)
savename = shorthand(area) + '_' + shorthand(cre)
if len(session_IDs) > 0:
lamb = K_session_cross_validation(session_IDs, savename,
savepath)
print(shorthand(cre) + ' lambda used: ' + str(lamb))
X, y = construct_pooled_Xy(session_IDs, savepath)
glm = sm.GLM(y, X, family=sm.families.Poisson())
res = glm.fit_regularized(
method='elastic_net',
alpha=lamb,
maxiter=200,
L1_wt=1.0, # 1.0:all L1, 0.0: all L2
refit=True)
model_params = np.array(res.params)
y_hat = np.exp(
np.sum(model_params.reshape(1, len(model_params)) * X,
axis=1))
plot_y(X, y_hat, area, cre, 'predicted', savepath)
plot_param_CI(res, area, cre, savepath)
plot_param_heatmaps(model_params, cre,
shorthand(area) + '_' + shorthand(cre),
savepath)
def plot_DSI_distribution(df, savepath, curve='cdf'):
areas, cres = dataset_params()
cre_colors = get_cre_colors()
area = 'VISp'
pooled_DSI = []
colors = []
alphas = []
cre_labels = []
for cre in cres:
session_IDs = get_sessions(df, area, cre)
resp, blank, p_all = pool_sessions(session_IDs,
area + '_' + cre,
savepath,
scale='event')
dsi = calc_DSI(resp[p_all < SIG_THRESH])
pooled_DSI.append(dsi)
colors.append(cre_colors[cre])
alphas.append(1.0)
cre_labels.append(shorthand(cre))
xticks = [0.0, 0.2, 0.4, 0.6, 0.8, 1.0]
plot_cdf(metric=pooled_DSI,
metric_labels=cre_labels,
colors=colors,
alphas=alphas,
hist_range=(0.0, 1.0),
hist_bins=200,
x_label='DSI',
x_ticks=xticks,
x_tick_labels=[str(x) for x in xticks],
save_name='V1_DSI_' + curve,
savepath=savepath,
do_legend=False)
def plot_from_curve_dict(curve_dict, pass_type, area, cre, num_sessions,
savepath):
plot_pooled_mat(
np.nanmean(get_from_curve_dict(curve_dict, 'conXdir',
pass_type + '_run', ''),
axis=0), area, cre, pass_type + '_run', savepath)
plot_pooled_mat(
np.nanmean(get_from_curve_dict(curve_dict, 'conXdir',
pass_type + '_stat', ''),
axis=0), area, cre, pass_type + '_stat', savepath)
run_contrast_pooled = get_from_curve_dict(curve_dict, 'contrast',
pass_type + '_run', '')
stat_contrast_pooled = get_from_curve_dict(curve_dict, 'contrast',
pass_type + '_stat', '')
run_direction_pooled_low = get_from_curve_dict(curve_dict, 'direction',
pass_type + '_run', 'low')
stat_direction_pooled_low = get_from_curve_dict(curve_dict, 'direction',
pass_type + '_stat', 'low')
run_aligned_pooled_low = get_from_curve_dict(curve_dict, 'aligned',
pass_type + '_run', 'low')
stat_aligned_pooled_low = get_from_curve_dict(curve_dict, 'aligned',
pass_type + '_stat', 'low')
run_direction_pooled_high = get_from_curve_dict(curve_dict, 'direction',
pass_type + '_run', 'high')
stat_direction_pooled_high = get_from_curve_dict(curve_dict, 'direction',
pass_type + '_stat',
'high')
run_aligned_pooled_high = get_from_curve_dict(curve_dict, 'aligned',
pass_type + '_run', 'high')
stat_aligned_pooled_high = get_from_curve_dict(curve_dict, 'aligned',
pass_type + '_stat', 'high')
x_con = np.log([0.025, 0.05, 0.1, 0.2, 0.4, 0.6, 0.8])
contrast_labels = ['blank', '5', '10', '20', '40', '60', '80']
x_dir = [-45, 0, 45, 90, 135, 180, 225, 270, 315]
direction_labels = [
'blank', '-135', '-90', '-45', '0', '45', '90', '135', '180'
]
pref_labels = [
'blank', '-135', '-90', '-45', '0', '45', '90', '135', '180'
]
inset_x_dir = [-45, 45, 135, 225, 315]
inset_direction_labels = ['blank', '-90', '0', '90', '180']
inset_x_pref = [-45, 45, 135, 225, 315]
inset_pref_labels = ['blank', '-90', '0', '90', '180']
make_errorbars_plot_running(
run_contrast_pooled, stat_contrast_pooled, x_con, contrast_labels,
'Contrast', area, cre, num_sessions,
shorthand(area) + '_' + shorthand(cre) + '_' + pass_type, 'contrast',
savepath)
make_errorbars_plot_running(
run_direction_pooled_low, stat_direction_pooled_low, x_dir,
direction_labels, 'Direction', area, cre, num_sessions,
shorthand(area) + '_' + shorthand(cre) + '_' + pass_type + '_low',
'direction', savepath)
make_errorbars_plot_running(
run_aligned_pooled_low, stat_aligned_pooled_low, x_dir, pref_labels,
'Direction - Peak', area, cre, num_sessions,
shorthand(area) + '_' + shorthand(cre) + '_' + pass_type + '_low',
'preferred_direction', savepath)
make_errorbars_plot_running(run_direction_pooled_high,
stat_direction_pooled_high,
x_dir,
inset_direction_labels,
'Direction',
area,
cre,
num_sessions,
shorthand(area) + '_' + shorthand(cre) + '_' +
pass_type + '_high',
'direction',
savepath,
as_inset=True,
inset_x_ticks=inset_x_dir)
make_errorbars_plot_running(run_aligned_pooled_high,
stat_aligned_pooled_high,
x_dir,
inset_pref_labels,
'Direction - Peak',
area,
cre,
num_sessions,
shorthand(area) + '_' + shorthand(cre) + '_' +
pass_type + '_high',
'preferred_direction',
savepath,
as_inset=True,
inset_x_ticks=inset_x_pref)
def make_errorbars_plot_running(run_responses,
stat_responses,
x_values,
x_tick_labels,
x_label,
area,
cre,
num_sessions,
savename,
plot_type,
savepath,
as_inset=False,
inset_x_ticks=None):
cre_colors = get_cre_colors()
if as_inset:
num_y_ticks = 3
x_tick_loc = inset_x_ticks
label_font_size = 22
tick_font_size = 17
else:
num_y_ticks = 5
x_tick_loc = x_values
label_font_size = 14
tick_font_size = 10
min_y = 0.0
max_y = 0.04
y_ticks = np.linspace(min_y, max_y, num=num_y_ticks)
y_ticks = np.round(y_ticks, decimals=3)
(num_cells, num_conditions) = np.shape(run_responses)
run_means, run_errors = compute_error_curve(run_responses)
stat_means, stat_errors = compute_error_curve(stat_responses)
min_x = x_values[0] - 0.5 * (x_values[1] - x_values[0])
max_x = x_values[-1] + 0.5 * (x_values[1] - x_values[0])
plt.figure(figsize=(4.2, 4))
ax = plt.subplot(111)
ax.plot([x_values[0], x_values[-1]], [run_means[0], run_means[0]],
color=cre_colors[cre],
linestyle='dotted',
linewidth=2.0)
ax.plot([x_values[0], x_values[-1]], [stat_means[0], stat_means[0]],
color=cre_colors[cre],
linestyle='dotted',
linewidth=2.0,
alpha=0.5)
ax.errorbar([x_values[0]], [run_means[0]],
yerr=run_errors[:, 0].reshape(2, 1),
color=cre_colors[cre],
linewidth=3,
capsize=5,
elinewidth=2,
markeredgewidth=2)
ax.errorbar([x_values[0]], [stat_means[0]],
yerr=stat_errors[:, 0].reshape(2, 1),
color=cre_colors[cre],
linewidth=3,
capsize=5,
elinewidth=2,
markeredgewidth=2,
alpha=0.5)
ax.errorbar(x_values[1:],
run_means[1:],
yerr=run_errors[:, 1:],
color=cre_colors[cre],
linewidth=3,
capsize=5,
elinewidth=2,
markeredgewidth=2)
ax.errorbar(x_values[1:],
stat_means[1:],
yerr=stat_errors[:, 1:],
color=cre_colors[cre],
linewidth=3,
capsize=5,
elinewidth=2,
markeredgewidth=2,
alpha=0.5)
if x_label == 'Contrast':
x_label = 'Contrast (%)'
elif x_label.find('Direction') != -1:
x_label = x_label + ' (deg)'
if not as_inset:
ax.set_ylabel('Mean event magnitude (a.u.)', fontsize=label_font_size)
ax.set_xlabel(x_label, fontsize=label_font_size)
ax.set_xticks(x_tick_loc)
ax.set_xticklabels(x_tick_labels, fontsize=tick_font_size)
ax.set_xlim(min_x, max_x)
ax.set_yticks(y_ticks)
ax.set_yticklabels([str(x) for x in y_ticks], fontsize=tick_font_size)
ax.set_ylim(min_y, max_y)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
if plot_type == 'contrast':
ax.text(x_values[4],
0.9 * max_y,
'n = ' + str(num_cells) + ' (' + str(num_sessions) + ')',
fontsize=10,
horizontalalignment='center')
if shorthand(cre) == 'Sst':
ax.text(x_values[0],
0.024,
'run',
fontsize=14,
color=cre_colors[cre])
ax.text(x_values[0],
0.020,
'stat',
fontsize=14,
color=cre_colors[cre],
alpha=0.5)
else:
ax.text(x_values[-2],
0.024,
'run',
fontsize=14,
color=cre_colors[cre])
ax.text(x_values[-2],
0.020,
'stat',
fontsize=14,
color=cre_colors[cre],
alpha=0.5)
ax.set_aspect('auto')
plt.tight_layout()
plt.savefig(savepath + savename + '_' + plot_type + '_errorbars.svg',
format='svg')
plt.close()
def plot_single_cell_example(df,
savepath,
cre,
example_cell,
example_session_idx=0):
directions, contrasts = grating_params()
session_IDs = get_sessions(df, 'VISp', cre)
session_ID = session_IDs[example_session_idx]
mse = load_mean_sweep_events(savepath, session_ID)
sweep_table = load_sweep_table(savepath, session_ID)
condition_responses, __ = compute_mean_condition_responses(
sweep_table, mse)
condition_SEM, __ = compute_SEM_condition_responses(sweep_table, mse)
p_all = chi_square_all_conditions(sweep_table, mse, session_ID, savepath)
sig_resp = condition_responses[p_all < SIG_THRESH]
sig_SEM = condition_SEM[p_all < SIG_THRESH]
#shift zero to center:
directions = [-135, -90, -45, 0, 45, 90, 135, 180]
sig_resp = center_direction_zero(sig_resp)
sig_SEM = center_direction_zero(sig_SEM)
#full direction by contrast response heatmap
plt.figure(figsize=(6, 4))
ax = plt.subplot2grid((5, 5), (0, 0), rowspan=5, colspan=2)
ax.imshow(sig_resp[example_cell],
vmin=0.0,
interpolation='nearest',
aspect='auto',
cmap='plasma')
ax.set_ylabel('Direction (deg)', fontsize=14)
ax.set_xlabel('Contrast (%)', fontsize=14)
ax.set_xticks(np.arange(len(contrasts)))
ax.set_xticklabels([str(int(100 * x)) for x in contrasts], fontsize=10)
ax.set_yticks(np.arange(len(directions)))
ax.set_yticklabels([str(x) for x in directions], fontsize=10)
peak_dir_idx, peak_con_idx = get_peak_conditions(sig_resp)
#contrast tuning at peak direction
contrast_means = sig_resp[example_cell, peak_dir_idx[example_cell], :]
contrast_SEMs = sig_SEM[example_cell, peak_dir_idx[example_cell], :]
ax = plt.subplot2grid((5, 5), (0, 3), rowspan=2, colspan=2)
ax.errorbar(np.log(contrasts), contrast_means, contrast_SEMs)
ax.set_xticks(np.log(contrasts))
ax.set_xticklabels([str(int(100 * x)) for x in contrasts], fontsize=10)
ax.set_xlabel('Contrast (%)', fontsize=14)
ax.set_ylabel('Response', fontsize=14)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
#direction tuning at peak contrast
direction_means = sig_resp[example_cell, :, peak_con_idx[example_cell]]
direction_SEMs = sig_SEM[example_cell, :, peak_con_idx[example_cell]]
ax = plt.subplot2grid((5, 5), (3, 3), rowspan=2, colspan=2)
ax.errorbar(np.arange(len(directions)), direction_means, direction_SEMs)
ax.set_xlim(-0.07, 7.07)
ax.set_xticks(np.arange(len(directions)))
ax.set_xticklabels([str(x) for x in directions], fontsize=10)
ax.set_xlabel('Direction (deg)', fontsize=14)
ax.set_ylabel('Response', fontsize=14)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
plt.tight_layout(w_pad=-5.5, h_pad=0.1)
plt.savefig(savepath + shorthand(cre) + '_example_cell.svg', format='svg')
plt.close()
def radial_direction_figure(x_coor,
y_coor,
resultant_mag,
resultant_theta,
CI_LB,
CI_UB,
cre,
num_cells,
savename,
savepath,
max_radius=0.75):
color = get_cre_colors()[cre]
directions, contrasts = grating_params()
unit_circle_x = np.linspace(-1.0, 1.0, 100)
unit_circle_y = np.sqrt(1.0 - unit_circle_x**2)
plt.figure(figsize=(4, 4))
ax = plt.subplot(111)
outer_CI = Circle((0, 0), CI_UB / max_radius, facecolor=[0.6, 0.6, 0.6])
inner_CI = Circle((0, 0), CI_LB / max_radius, facecolor=[1.0, 1.0, 1.0])
ax.add_patch(outer_CI)
ax.add_patch(inner_CI)
#spokes
for i, direction in enumerate(directions):
ax.plot([0, np.cos(np.pi * direction / 180.0)],
[0, np.sin(np.pi * direction / 180.0)],
'k--',
linewidth=1.0)
#outer ring
ax.plot(unit_circle_x, unit_circle_y, 'k', linewidth=2.0)
ax.plot(unit_circle_x, -unit_circle_y, 'k', linewidth=2.0)
ax.plot(0.25 * unit_circle_x / max_radius,
0.25 * unit_circle_y / max_radius,
'--k',
linewidth=1.0)
ax.plot(0.25 * unit_circle_x / max_radius,
-0.25 * unit_circle_y / max_radius,
'--k',
linewidth=1.0)
ax.plot(0.5 * unit_circle_x / max_radius,
0.5 * unit_circle_y / max_radius,
'--k',
linewidth=1.0)
ax.plot(0.5 * unit_circle_x / max_radius,
-0.5 * unit_circle_y / max_radius,
'--k',
linewidth=1.0)
ax.plot(unit_circle_x, unit_circle_y, 'k', linewidth=2.0)
ax.plot(unit_circle_x, -unit_circle_y, 'k', linewidth=2.0)
#center
ax.plot(unit_circle_x / 200.0, unit_circle_y / 200.0, 'k', linewidth=2.0)
ax.plot(unit_circle_x / 200.0, -unit_circle_y / 200.0, 'k', linewidth=2.0)
ax.plot(np.array(x_coor) / max_radius,
np.array(y_coor) / max_radius,
color=color,
linewidth=2.0)
contrast_colors = get_contrast_colors()
for i, mag in enumerate(resultant_mag[::-1]):
ax.arrow(0,
0,
mag * np.cos(-resultant_theta[len(contrasts) - i - 1]) /
(max_radius),
mag * np.sin(-resultant_theta[len(contrasts) - i - 1]) /
(max_radius),
color=contrast_colors[i],
linewidth=2.0,
head_width=0.03)
#labels
ax.text(0,
1.02,
'U',
fontsize=12,
horizontalalignment='center',
verticalalignment='bottom')
ax.text(0,
-1.02,
'D',
fontsize=12,
horizontalalignment='center',
verticalalignment='top')
ax.text(1.02,
0,
'T',
fontsize=12,
verticalalignment='center',
horizontalalignment='left')
ax.text(-1.02,
0,
'N',
fontsize=12,
verticalalignment='center',
horizontalalignment='right')
ax.text(-1, 0.99, shorthand(cre), fontsize=16, horizontalalignment='left')
ax.text(0.73,
0.99,
'(n=' + str(num_cells) + ')',
fontsize=10,
horizontalalignment='left')
ax.text(.73,
-.73,
'45',
fontsize=10,
horizontalalignment='left',
verticalalignment='top')
ax.text(-.78,
-.75,
'135',
fontsize=10,
horizontalalignment='right',
verticalalignment='top')
ax.text(-.73,
.73,
'-135',
fontsize=10,
verticalalignment='bottom',
horizontalalignment='right')
ax.text(.73,
.73,
'-45',
fontsize=10,
verticalalignment='bottom',
horizontalalignment='left')
ax.text(.81,
-.71,
'$^\circ$',
fontsize=18,
horizontalalignment='left',
verticalalignment='top')
ax.text(-.69,
-.73,
'$^\circ$',
fontsize=18,
horizontalalignment='right',
verticalalignment='top')
ax.text(-.64,
.69,
'$^\circ$',
fontsize=18,
verticalalignment='bottom',
horizontalalignment='right')
ax.text(.85,
.69,
'$^\circ$',
fontsize=18,
verticalalignment='bottom',
horizontalalignment='left')
ax.set_xlim(-1.2, 1.2)
ax.set_ylim(-1.2, 1.2)
plt.axis('equal')
plt.axis('off')
plt.savefig(savepath + savename + '_radial_direction_tuning.svg',
format='svg')
plt.close()
def plot_full_waterfall(cells_by_condition,
cre,
save_name,
scale,
savepath,
do_colorbar=False):
resp_max = 4.0
resp_min = -4.0
directions, contrasts = grating_params()
num_contrasts = len(contrasts)
num_directions = len(directions)
(num_cells, num_conditions) = np.shape(cells_by_condition)
cre_colors = get_cre_colors()
plt.figure(figsize=(10, 4))
ax = plt.subplot(111)
im = ax.imshow(cells_by_condition,
vmin=resp_min,
vmax=resp_max,
interpolation='nearest',
aspect='auto',
cmap='RdBu_r')
#dividing lines between contrasts
for i_con in range(num_contrasts - 1):
ax.plot([(i_con + 1) * num_directions - 0.5,
(i_con + 1) * num_directions - 0.5], [0, num_cells - 1],
'k',
linewidth=2.0)
ax.set_ylabel(shorthand(cre) + ' cell number',
fontsize=14,
color=cre_colors[cre],
labelpad=-6)
ax.set_xlabel('Contrast (%)', fontsize=14, labelpad=-5)
ax.set_xticks(num_directions * np.arange(num_contrasts) +
(num_directions / 2) - 0.5)
ax.set_xticklabels([str(int(100 * x)) for x in contrasts], fontsize=12)
ax.set_yticks([0, num_cells - 1])
ax.set_yticklabels(['0', str(num_cells - 1)], fontsize=12)
if do_colorbar:
percentile_ticks = [
0.0001, 0.001, 0.01, 0.1, 0.5, 0.9, 0.99, 0.999, 0.9999
]
NLL_ticks = percentile_to_NLL(percentile_ticks, num_shuffles=200000)
cbar = plt.colorbar(im,
ax=ax,
ticks=NLL_ticks,
orientation='horizontal')
cbar.ax.set_xticklabels([str(100 * x) for x in percentile_ticks],
fontsize=12)
cbar.set_label('Response Percentile',
fontsize=16,
rotation=0,
labelpad=15.0)
plt.tight_layout()
plt.savefig(savepath + save_name + '_' + scale + '.svg', format='svg')
plt.close()
def decode_direction_from_running(df, savepath, save_format='svg'):
directions, contrasts = grating_params()
running_dict = {}
areas, cres = dataset_params()
for area in ['VISp']:
for cre in cres:
celltype = shorthand(area) + ' ' + shorthand(cre)
session_IDs = get_sessions(df, area, cre)
num_sessions = len(session_IDs)
if num_sessions > 0:
savename = shorthand(area) + '_' + shorthand(
cre) + '_running_direction_decoder.npy'
if os.path.isfile(savepath + savename):
#decoder_performance = np.load(savepath+savename)
running_performance = np.load(
savepath + shorthand(area) + '_' + shorthand(cre) +
'_running_direction_decoder.npy')
else:
#decoder_performance = []
running_performance = []
for i_session, session_ID in enumerate(session_IDs):
#mean_sweep_events = load_mean_sweep_events(savepath,session_ID)
mean_sweep_running = load_mean_sweep_running(
session_ID, savepath)
sweep_table = load_sweep_table(savepath, session_ID)
#(num_sweeps,num_cells) = np.shape(mean_sweep_events)
is_blank = sweep_table['Ori'].isnull().values
blank_sweeps = np.argwhere(is_blank)[:, 0]
sweep_directions = sweep_table['Ori'].values
sweep_categories = sweep_directions.copy()
sweep_categories[blank_sweeps] = 360
sweep_categories = sweep_categories.astype(np.int) / 45
is_low = sweep_table['Contrast'].values < 0.2
sweeps_included = np.argwhere(is_low)[:, 0]
sweep_categories = sweep_categories[sweeps_included]
#mean_sweep_events = mean_sweep_events[sweeps_included]
mean_sweep_running = mean_sweep_running[
sweeps_included]
#decode front-to-back motion
# is_front_to_back = (sweep_categories==0) | (sweep_categories==7)
# front_to_back_sweeps = np.argwhere(is_front_to_back)[:,0]
# rest_sweeps = np.argwhere(~is_front_to_back)[:,0]
# sweep_categories[front_to_back_sweeps] = 0
# sweep_categories[rest_sweeps] = 1
running_performance.append(
decode_direction(
mean_sweep_running.reshape(
len(sweeps_included), 1),
sweep_categories))
#for nc in range(num_cells):
#decoder_performance.append(decode_direction(mean_sweep_events,sweep_categories))
#decoder_performance = np.array(decoder_performance)
running_performance = np.array(running_performance)
#np.save(savepath+savename,decoder_performance)
np.save(
savepath + shorthand(area) + '_' + shorthand(cre) +
'_running_direction_decoder.npy', running_performance)
#print celltype + ': ' + str(np.mean(decoder_performance))
print(celltype + ': ' + str(np.mean(running_performance)))
running_dict[shorthand(cre)] = running_performance
cre_colors = get_cre_colors()
plt.figure(figsize=(6, 4))
ax = plt.subplot(111)
ax.plot([-1, 6], [12.5, 12.5], 'k--')
label_loc = []
labels = []
for i, cre in enumerate(cres):
session_performance = running_dict[shorthand(cre)]
ax.plot(i * np.ones((len(session_performance), )),
100.0 * session_performance,
'.',
markersize=4.0,
color=cre_colors[cre])
ax.plot([i - 0.4, i + 0.4], [
100.0 * session_performance.mean(),
100.0 * session_performance.mean()
],
color=cre_colors[cre],
linewidth=3)
label_loc.append(i)
labels.append(shorthand(cre))
ax.set_xticks(label_loc)
ax.set_xticklabels(labels, rotation=45, fontsize=10)
ax.set_ylim(0, 25)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.set_xlim(-1, 14)
#ax.text(3,20,'Predict direction from running',fontsize=14,horizontalalignment='center')
ax.set_ylabel('Decoding performance (%)', fontsize=14)
if save_format == 'svg':
plt.savefig(savepath + 'running_decoder.svg', format='svg')
else:
plt.savefig(savepath + 'running_decoder.png', dpi=300)
plt.close()
def plot_param_CI(res, area, cre, savepath, PARAM_TOL=1E-2, save_format='svg'):
model_params = np.array(res.params)
terms = unpack_params(model_params)
CI = np.array(res.conf_int(alpha=0.05))
CI_lb = unpack_params(CI[:, 0])
CI_ub = unpack_params(CI[:, 1])
plot_order = [
'blank', 'run', 'dir', 'con', 'dirXrun', 'conXrun', 'dirXcon',
'dirXconXrun'
]
directions, contrasts = grating_params()
directions = [-135, -90, -45, 0, 45, 90, 135, 180]
x_labels = {}
x_labels['blank'] = ['blank']
x_labels['blankXrun'] = ['blank X run']
x_labels['run'] = ['run']
x_labels['dir'] = [str(x) for x in directions]
x_labels['con'] = [str(int(100 * x)) for x in contrasts]
x_labels['dirXrun'] = [str(x) for x in directions]
x_labels['conXrun'] = [str(int(100 * x)) for x in contrasts]
plt.figure(figsize=(20, 4.5))
savename = shorthand(area) + '_' + shorthand(cre)
cre_colors = get_cre_colors()
ax = plt.subplot(111)
curr_x = 0
x_ticks = []
x_ticklabels = []
for i, param_name in enumerate(plot_order):
param_means = terms[param_name]
param_CI_lb = CI_lb[param_name]
param_CI_ub = CI_ub[param_name]
#center directions on zero
if param_name == 'dirXcon' or param_name == 'dirXconXrun' or param_name == 'dir' or param_name == 'dirXrun':
param_means = center_dir_on_zero(param_means)
param_CI_lb = center_dir_on_zero(param_CI_lb)
param_CI_ub = center_dir_on_zero(param_CI_ub)
#handle parameters that are not 1D arrays
if type(param_means) == np.float64:
param_means = np.array([param_means])
param_CI_lb = np.array([param_CI_lb])
param_CI_ub = np.array([param_CI_ub])
elif param_name == 'dirXcon' or param_name == 'dirXconXrun':
param_means = param_means.flatten()
param_CI_lb = param_CI_lb.flatten()
param_CI_ub = param_CI_ub.flatten()
param_errs = CI_to_errorbars(param_means, param_CI_lb, param_CI_ub)
num_params = np.shape(param_errs)[1]
#for dirXcon terms, only plot non-zero values
if param_name == 'dirXcon' or param_name == 'dirXconXrun':
non_zero_idx = np.argwhere((param_CI_ub < -PARAM_TOL)
| (param_CI_lb > PARAM_TOL))[:, 0]
num_params = len(non_zero_idx)
cond_tick_labels = []
if num_params > 0:
param_means = param_means[non_zero_idx]
param_errs = param_errs[:, non_zero_idx]
for i_cond, idx in enumerate(non_zero_idx):
i_dir = int(idx / 6)
i_con = int(idx % 6)
cond_tick_labels.append(
str(int(100 * contrasts[i_con])) + '%,' +
str(int(directions[i_dir])))
# pad params to make all plots equal size
ticks_to_plot = 6
num_to_pad = ticks_to_plot - num_params
for i_pad in range(num_to_pad):
cond_tick_labels.append('')
x_labels[param_name] = cond_tick_labels
x_values = np.arange(curr_x, curr_x + 2 * num_params,
2) #double spacing
else:
ticks_to_plot = num_params
x_values = np.arange(curr_x, curr_x + num_params)
if num_params > 0:
ax.errorbar(x_values,
param_means,
yerr=param_errs,
fmt='o',
color=cre_colors[cre],
linewidth=3,
capsize=5,
elinewidth=2,
markeredgewidth=2)
for i_x, x in enumerate(x_values):
x_ticks.append(x)
x_ticklabels.append(x_labels[param_name][i_x])
curr_x += ticks_to_plot + 1
ax.plot([-1, curr_x], [0, 0], 'k', linewidth=1.0)
ax.set_ylabel('Weight', fontsize=14)
ax.set_xlim([-1, curr_x])
ax.set_xticks(x_ticks)
ax.set_xticklabels(x_ticklabels)
y_max = 2.5
y_min = -1.5
y_ticks = [-1, 0, 1, 2]
ax.set_yticks(y_ticks)
ax.set_yticklabels([str(int(y)) for y in y_ticks])
ax.set_ylim([y_min, y_max])
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
if save_format == 'svg':
plt.savefig(savepath + savename + '_param_CI.svg', format='svg')
else:
plt.savefig(savepath + savename + '_param_CI.png', dpi=300)
plt.close()
def plot_peak_response_distribution(run_aligned_pooled_low,
stat_aligned_pooled_low,
run_aligned_pooled_high,
stat_aligned_pooled_high, area, cre,
savename, savepath):
directions, contrasts = grating_params()
plt.figure(figsize=(7, 4))
ax = plt.subplot(111)
MAX_CELLS = 15000
cre_colors = get_cre_colors()
resp_dict = {}
BLANK_IDX = 0
resp_dict = add_group_to_dict(resp_dict, run_aligned_pooled_low, BLANK_IDX,
'run blank')
resp_dict = add_group_to_dict(resp_dict, stat_aligned_pooled_low,
BLANK_IDX, 'stat blank')
directions = [-135, -90, -45, 0, 45, 90, 135, 180]
contrasts = [0.05, 0.8]
for run_state in ['run', 'stat']:
for i_con, contrast in enumerate(contrasts):
if run_state == 'run' and contrast == 0.05:
resps = run_aligned_pooled_low
elif run_state == 'run' and contrast == 0.8:
resps = run_aligned_pooled_high
elif run_state == 'stat' and contrast == 0.05:
resps = stat_aligned_pooled_low
else:
resps = stat_aligned_pooled_high
for i_dir, direction in enumerate(directions):
group_name = run_state + ' ' + str(direction) + ' ' + str(
int(100 * contrast)) + '%'
resp_dict = add_group_to_dict(resp_dict, resps, 1 + i_dir,
group_name)
plot_order = [('space1', ''), ('run blank', ''), ('stat blank', ''),
('space2', '')]
curr_space = 3
for run_state in ['run', 'stat']:
for i_con, contrast in enumerate(contrasts):
for i_dir, direction in enumerate(directions):
plot_order.append((run_state + ' ' + str(direction) + ' ' +
str(int(100 * contrast)) + '%', ''))
plot_order.append(('space' + str(curr_space), ''))
curr_space += 1
colors = ['#9f9f9f'] #blanks
for i in range(len(plot_order)):
colors.append(cre_colors[cre])
cre_palette = sns.color_palette(colors)
resp_df = pd.DataFrame(np.zeros((MAX_CELLS, 3)),
columns=('Response to Preferred Direction',
'cell_type', 'cre'))
curr_cell = 0
labels = []
x_pos = []
dist = []
dir_idx = 0
for line, (group, cre_name) in enumerate(plot_order):
if group.find('space') == -1:
resp_mag = resp_dict[group]
resp_mag = resp_mag[np.argwhere(np.isfinite(resp_mag))[:, 0]]
num_cells = len(resp_mag)
resp_df['Response to Preferred Direction'][curr_cell:(
curr_cell + num_cells)] = resp_mag
resp_df['cre'][curr_cell:(curr_cell + num_cells)] = cre_name
resp_df['cell_type'][curr_cell:(curr_cell + num_cells)] = group
curr_cell += num_cells
x_pos.append(line)
dist.append(resp_mag)
if group.find('blank') != -1:
if group.find('run') != -1:
labels.append('run')
else:
labels.append('stat')
else:
labels.append(str(directions[dir_idx]))
dir_idx += 1
if dir_idx == len(directions):
dir_idx = 0
else:
resp_df['Response to Preferred Direction'][curr_cell] = np.NaN
resp_df['cre'][curr_cell] = 'blank'
resp_df['cell_type'][curr_cell] = group
curr_cell += 1
resp_df = resp_df.drop(index=np.arange(curr_cell, MAX_CELLS))
ax = sns.swarmplot(x='cell_type',
y='Response to Preferred Direction',
hue='cre',
size=1.0,
palette=cre_palette,
data=resp_df)
ax.set_xticks(np.array(x_pos))
ax.set_xticklabels(labels, fontsize=4.5, rotation=0)
ax.legend_.remove()
for i, d in enumerate(dist):
plot_quartiles(ax, d, x_pos[i])
ax.set_ylim(-20, 400)
ax.set_ylabel('Event magnitude per second (%)', fontsize=12)
ax.set_xlabel(
'Blank run 5% contrast run 80% contrast stat 5% contrast stat 80% contrast ',
fontsize=9)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
plt.tight_layout()
plt.savefig(savepath + shorthand(area) + '_' + shorthand(cre) + '_' +
savename + '_cell_response_distribution.svg',
format='svg')
plt.close()