def compute_blank_subtracted_NLL(session_ID,savepath,num_shuffles=200000):
if os.path.isfile(savepath+str(session_ID)+'_blank_subtracted_NLL.npy'):
condition_NLL = np.load(savepath+str(session_ID)+'_blank_subtracted_NLL.npy')
blank_NLL = np.load(savepath+str(session_ID)+'_blank_subtracted_blank_NLL.npy')
else:
sweep_table = load_sweep_table(savepath,session_ID)
mean_sweep_events = load_mean_sweep_events(savepath,session_ID)
(num_sweeps,num_cells) = np.shape(mean_sweep_events)
condition_responses, blank_sweep_responses = compute_mean_condition_responses(sweep_table,mean_sweep_events)
condition_responses = np.swapaxes(condition_responses,0,2)
condition_responses = np.swapaxes(condition_responses,0,1)
directions, contrasts = grating_params()
# different conditions can have different number of trials...
trials_per_condition, num_blanks = compute_trials_per_condition(sweep_table)
unique_trial_counts = np.unique(trials_per_condition.flatten())
trial_count_mat = np.tile(trials_per_condition,reps=(num_cells,1,1))
trial_count_mat = np.swapaxes(trial_count_mat,0,2)
trial_count_mat = np.swapaxes(trial_count_mat,0,1)
blank_shuffle_sweeps = np.random.choice(num_sweeps,size=(num_shuffles*num_blanks,))
blank_shuffle_responses = mean_sweep_events[blank_shuffle_sweeps].reshape(num_shuffles,num_blanks,num_cells)
blank_null_dist = blank_shuffle_responses.mean(axis=1)
condition_NLL = np.zeros((len(directions),len(contrasts),num_cells))
for trial_count in unique_trial_counts:
#create null distribution and compute condition NLL
shuffle_sweeps = np.random.choice(num_sweeps,size=(num_shuffles*trial_count,))
shuffle_responses = mean_sweep_events[shuffle_sweeps].reshape(num_shuffles,trial_count,num_cells)
null_diff_dist = shuffle_responses.mean(axis=1) - blank_null_dist
actual_diffs = condition_responses.reshape(len(directions),len(contrasts),1,num_cells) - blank_sweep_responses.reshape(1,1,1,num_cells)
resp_above_null = null_diff_dist.reshape(1,1,num_shuffles,num_cells) < actual_diffs
percentile = resp_above_null.mean(axis=2)
NLL = percentile_to_NLL(percentile,num_shuffles)
has_count = trial_count_mat == trial_count
condition_NLL = np.where(has_count,NLL,condition_NLL)
#repeat for blank sweeps
blank_null_dist_2 = blank_null_dist[np.random.choice(num_shuffles,size=num_shuffles),:]
blank_null_diff_dist = blank_null_dist_2 - blank_null_dist
actual_diffs = 0.0
resp_above_null = blank_null_diff_dist < actual_diffs
percentile = resp_above_null.mean(axis=0)
blank_NLL = percentile_to_NLL(percentile,num_shuffles)
np.save(savepath+str(session_ID)+'_blank_subtracted_NLL.npy',condition_NLL)
np.save(savepath+str(session_ID)+'_blank_subtracted_blank_NLL.npy',blank_NLL)
condition_NLL = np.swapaxes(condition_NLL,0,2)
condition_NLL = np.swapaxes(condition_NLL,1,2)
return condition_NLL, blank_NLL
def construct_pooled_Xy(session_IDs, savepath, RUN_THRESH=1.0):
y = None
for session_ID in session_IDs:
sweep_table = load_sweep_table(savepath, session_ID)
mean_sweep_events = load_mean_sweep_events(savepath, session_ID)
pvals = chi_square_all_conditions(sweep_table, mean_sweep_events,
session_ID, savepath)
sig_cells = pvals < 0.01
mean_sweep_events = mean_sweep_events[:, sig_cells]
(num_sweeps, session_cells) = np.shape(mean_sweep_events)
mean_sweep_running = load_mean_sweep_running(session_ID, savepath)
is_run = mean_sweep_running >= RUN_THRESH
X, session_y = construct_session_Xy(sweep_table, is_run,
mean_sweep_events)
if y is not None:
y = np.append(y, session_y, axis=1)
else:
y = session_y
y = np.nanmean(y, axis=1)
conditions_sampled = np.argwhere(np.isfinite(y))[:, 0]
y *= 3000
return X[conditions_sampled], y[conditions_sampled]
def plot_tuning_split_by_run_state(df, savepath):
running_threshold = 1.0 # cm/s
directions, contrasts = grating_params()
MIN_SESSIONS = 3
MIN_CELLS = 3 #per session
areas, cres = dataset_params()
for area in areas:
for cre in cres:
session_IDs = get_sessions(df, area, cre)
num_sessions = len(session_IDs)
if num_sessions >= MIN_SESSIONS:
curve_dict = {}
num_sessions_included = 0
for i_session, session_ID in enumerate(session_IDs):
sweep_table = load_sweep_table(savepath, session_ID)
mse = load_mean_sweep_events(savepath, session_ID)
condition_responses, blank_responses = compute_mean_condition_responses(
sweep_table, mse)
p_all = chi_square_all_conditions(sweep_table, mse,
session_ID, savepath)
all_idx = np.argwhere(p_all < SIG_THRESH)[:, 0]
mean_sweep_running = load_mean_sweep_running(
session_ID, savepath)
is_run = mean_sweep_running >= running_threshold
run_responses, stat_responses, run_blank, stat_blank = condition_response_running(
sweep_table, mse, is_run)
condition_responses = center_direction_zero(
condition_responses)
run_responses = center_direction_zero(run_responses)
stat_responses = center_direction_zero(stat_responses)
peak_dir, __ = get_peak_conditions(condition_responses)
if len(all_idx) >= MIN_CELLS:
curve_dict = populate_curve_dict(
curve_dict, run_responses, run_blank, all_idx,
'all_run', peak_dir)
curve_dict = populate_curve_dict(
curve_dict, stat_responses, stat_blank, all_idx,
'all_stat', peak_dir)
num_sessions_included += 1
if num_sessions_included >= MIN_SESSIONS:
plot_from_curve_dict(curve_dict, 'all', area, cre,
num_sessions_included, savepath)
def pool_sessions(session_IDs,pool_name,savepath,scale='blank_subtracted_NLL'):
if os.path.isfile(savepath+pool_name+'_condition_responses_'+scale+'.npy'):
pooled_condition_responses = np.load(savepath+pool_name+'_condition_responses_'+scale+'.npy')
pooled_blank_responses = np.load(savepath+pool_name+'_blank_responses_'+scale+'.npy')
p_vals_all = np.load(savepath+pool_name+'_chisq_all.npy')
else:
print('Pooling sessions for ' + pool_name)
directions, contrasts = grating_params()
MAX_CELLS = 5000
pooled_condition_responses = np.zeros((MAX_CELLS,len(directions),len(contrasts)))
pooled_blank_responses = np.zeros((MAX_CELLS,))
p_vals_all = np.zeros((MAX_CELLS,))
curr_cell = 0
for session_ID in session_IDs:
print(str(session_ID))
mse = load_mean_sweep_events(savepath,session_ID)
sweep_table = load_sweep_table(savepath,session_ID)
if scale=='event':
condition_responses, blank_responses = compute_mean_condition_responses(sweep_table,mse)
elif scale=='blank_subtracted_NLL':
condition_responses, blank_responses = compute_blank_subtracted_NLL(session_ID,savepath)
p_all = chi_square_all_conditions(sweep_table,mse,session_ID,savepath)
session_cells = len(p_all)
pooled_condition_responses[curr_cell:(curr_cell+session_cells)] = condition_responses
pooled_blank_responses[curr_cell:(curr_cell+session_cells)] = blank_responses
p_vals_all[curr_cell:(curr_cell+session_cells)] = p_all
curr_cell += session_cells
pooled_condition_responses = pooled_condition_responses[:curr_cell]
pooled_blank_responses = pooled_blank_responses[:curr_cell]
p_vals_all = p_vals_all[:curr_cell]
np.save(savepath+pool_name+'_condition_responses_'+scale+'.npy',pooled_condition_responses)
np.save(savepath+pool_name+'_blank_responses_'+scale+'.npy',pooled_blank_responses)
np.save(savepath+pool_name+'_chisq_all.npy',p_vals_all)
print('Done.')
return pooled_condition_responses, pooled_blank_responses, p_vals_all
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 plot_single_cell_tuning_curves(session_ID, savepath, cre, example_cell,
plot_path, figure_format):
directions, contrasts = cu.grating_params()
mse = 3000.0 * cu.load_mean_sweep_events(savepath, session_ID)
sweep_table = cu.load_sweep_table(savepath, session_ID)
condition_responses, blank_responses = cm.compute_mean_condition_responses(
sweep_table, mse)
condition_SEM, __ = cm.compute_SEM_condition_responses(sweep_table, mse)
#shift zero to center:
directions = [-135, -90, -45, 0, 45, 90, 135, 180]
condition_resp = cu.center_direction_zero(condition_responses)
condition_SEM = cu.center_direction_zero(condition_SEM)
#full direction by contrast response heatmap
plt.figure(figsize=(7, 4))
ax = plt.subplot2grid((5, 5), (0, 3), rowspan=5, colspan=2)
im = ax.imshow(condition_resp[example_cell],
vmin=0.0,
interpolation='nearest',
aspect='auto',
cmap='plasma')
ax.set_ylabel('Direction (deg)', fontsize=12)
ax.set_xlabel('Contrast (%)', fontsize=12)
ax.set_xticks(np.arange(len(contrasts)))
ax.set_xticklabels([str(int(100 * x)) for x in contrasts], fontsize=12)
ax.set_yticks(np.arange(len(directions)))
ax.set_yticklabels([str(x) for x in directions], fontsize=12)
cbar = plt.colorbar(im, ax=ax)
cbar.set_label('Event magnitude per second (%)', fontsize=12)
peak_dir_idx, peak_con_idx = cm.get_peak_conditions(condition_resp)
#contrast tuning at peak direction
contrast_means = condition_resp[example_cell,
peak_dir_idx[example_cell], :]
contrast_SEMs = condition_SEM[example_cell, peak_dir_idx[example_cell], :]
y_max = 1.1 * np.max(contrast_means + contrast_SEMs)
ax = plt.subplot2grid((5, 5), (0, 0), rowspan=2, colspan=2)
ax.errorbar(np.log(contrasts),
contrast_means,
contrast_SEMs,
linewidth=0.7,
color='b')
ax.plot([np.log(contrasts[0]), np.log(contrasts[-1])],
[blank_responses[example_cell], blank_responses[example_cell]],
linewidth=0.7,
linestyle='--',
color='b')
ax.set_xticks(np.log(contrasts))
ax.set_xticklabels([str(int(100 * x)) for x in contrasts], fontsize=12)
ax.tick_params(axis='y', labelsize=12)
ax.set_xlabel('Contrast (%)', fontsize=12)
ax.set_ylabel('Event magnitude per second (%) ', fontsize=12)
ax.set_ylim([0, y_max])
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.set_title('@ ' + str(directions[peak_dir_idx[example_cell]]) +
' degrees',
fontsize=12)
#direction tuning at peak contrast
direction_means = condition_resp[example_cell, :,
peak_con_idx[example_cell]]
direction_SEMs = condition_SEM[example_cell, :, peak_con_idx[example_cell]]
ax = plt.subplot2grid((5, 5), (3, 0), rowspan=2, colspan=2)
ax.errorbar(np.arange(len(directions)),
direction_means,
direction_SEMs,
linewidth=0.7,
color='b')
ax.plot([0, len(directions) - 1],
[blank_responses[example_cell], blank_responses[example_cell]],
linestyle='--',
color='b',
linewidth=0.7)
ax.set_xlim(-0.07, 7.07)
ax.set_xticks(np.arange(len(directions)))
ax.set_xticklabels([str(x) for x in directions], fontsize=12)
ax.tick_params(axis='y', labelsize=12)
ax.set_xlabel('Direction (deg)', fontsize=12)
#ax.set_ylabel('Response',fontsize=14)
ax.set_ylim([0, y_max])
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.set_title('@ ' + str(int(100 * contrasts[peak_con_idx[example_cell]])) +
'% contrast',
fontsize=12)
plt.tight_layout(w_pad=-5.5, h_pad=0.1)
if figure_format == 'svg':
plt.savefig(plot_path + cre + '_' + str(session_ID) + '_cell_' +
str(example_cell) + '_tuning_curves.svg',
format='svg')
else:
plt.savefig(plot_path + cre + '_' + str(session_ID) + '_cell_' +
str(example_cell) + '_tuning_curves.png',
dpi=300)
plt.close()
def LP_HP_model_selection(session_ID,savepath,do_plot=False):
if os.path.isfile(savepath+str(session_ID)+'_model_AIC.npy'):
model_AIC = np.load(savepath+str(session_ID)+'_model_AIC.npy')
low_pass_params = np.load(savepath+str(session_ID)+'_LP_params.npy')
high_pass_params = np.load(savepath+str(session_ID)+'_HP_params.npy')
band_pass_params = np.load(savepath+str(session_ID)+'_BP_params.npy')
else:
directions, __ = grating_params()
sweep_table = load_sweep_table(savepath,session_ID)
mean_sweep_events = load_mean_sweep_events(savepath,session_ID)
(num_sweeps,num_cells) = np.shape(mean_sweep_events)
condition_responses, __ = compute_mean_condition_responses(sweep_table,mean_sweep_events)
p_all = chi_square_all_conditions(sweep_table,mean_sweep_events,session_ID,savepath)
sig_cells = p_all < SIG_THRESH
peak_dir_idx, __ = get_peak_conditions(condition_responses)
peak_directions = directions[peak_dir_idx]
high_pass_params = np.zeros((num_cells,3))
high_pass_params[:] = np.NaN
low_pass_params = np.zeros((num_cells,3))
low_pass_params[:] = np.NaN
band_pass_params = np.zeros((num_cells,4))
band_pass_params[:] = np.NaN
model_AIC = np.zeros((num_cells,3))
model_AIC[:] = np.NaN
for i_dir,direction in enumerate(directions):
cells_pref_dir = (peak_directions == direction) & sig_cells
not_60_contrast = sweep_table['Contrast'].values != 0.6
is_direction = (sweep_table['Ori'] == direction).values
sweeps_with_dir = np.argwhere(is_direction & not_60_contrast)[:,0]
sweep_contrasts = 100 * sweep_table['Contrast'][sweeps_with_dir].values
cell_idx = np.argwhere(cells_pref_dir)[:,0]
for cell in cell_idx:
sweep_responses = mean_sweep_events[sweeps_with_dir,cell]
lp_params, lp_aic = select_over_initial_conditions(sweep_responses,sweep_contrasts,'LP')
hp_params, hp_aic = select_over_initial_conditions(sweep_responses,sweep_contrasts,'HP')
bp_params, bp_aic = select_over_initial_conditions(sweep_responses,sweep_contrasts,'BP')
low_pass_params[cell,:] = lp_params
model_AIC[cell,0] = lp_aic
high_pass_params[cell,:] = hp_params
model_AIC[cell,1] = hp_aic
band_pass_params[cell,:] = bp_params
model_AIC[cell,2] = bp_aic
if do_plot:
x_sample = np.linspace(np.log(5.0),np.log(80.0))
plt.figure()
plt.plot(np.log(sweep_contrasts),sweep_responses,'ko')
plt.plot(x_sample,high_pass(x_sample,high_pass_params[cell,0],high_pass_params[cell,1],high_pass_params[cell,2]),'r')
plt.plot(x_sample,low_pass(x_sample,low_pass_params[cell,0],low_pass_params[cell,1],low_pass_params[cell,2]),'b')
plt.plot(x_sample,band_pass(x_sample,band_pass_params[cell,0],band_pass_params[cell,1],band_pass_params[cell,2],band_pass_params[cell,3]),'g')
plt.show()
np.save(savepath+str(session_ID)+'_model_AIC.npy',model_AIC)
np.save(savepath+str(session_ID)+'_LP_params.npy',low_pass_params)
np.save(savepath+str(session_ID)+'_HP_params.npy',high_pass_params)
np.save(savepath+str(session_ID)+'_BP_params.npy',band_pass_params)
LP_c50 = low_pass_params[:,0]
HP_c50 = high_pass_params[:,0]
BP_rise_c50 = band_pass_params[:,0]
BP_fall_c50 = band_pass_params[:,1]
return LP_c50, HP_c50, BP_rise_c50, BP_fall_c50, model_AIC