def C_analysis_pdf(datafile, iprotocol=0, Nmax=1000000, roi=0):
data = MultimodalData(datafile)
if not os.path.isdir(summary_pdf_folder(datafile)):
os.mkdir(summary_pdf_folder(datafile))
pdf_filename = os.path.join(
summary_pdf_folder(datafile),
'%s-gratings-analysis.pdf' % data.protocols[iprotocol])
if roi == 0:
ROIs = np.arange(data.iscell.sum())[:Nmax]
else:
ROIs = [roi]
CELL_RESPS = []
with PdfPages(pdf_filename) as pdf:
for roi in ROIs:
fig, cell_resp = C_ROI_analysis(data,
roiIndex=roi,
iprotocol=iprotocol)
CELL_RESPS.append(cell_resp)
pdf.savefig(fig) # saves the current figure into a pdf page
plt.close(fig)
fig = summary_fig(CELL_RESPS)
pdf.savefig(fig)
plt.close(fig)
print('[ok] moving dot analysis saved as: "%s" ' % pdf_filename)
def DS_analysis_pdf(datafile, iprotocol=0, Nmax=1000000):
data = MultimodalData(datafile)
pdf_filename = os.path.join(
summary_pdf_folder(datafile),
'%s-direction_selectivity.pdf' % data.protocols[iprotocol])
Nresp, SIs = 0, []
with PdfPages(pdf_filename) as pdf:
for roi in np.arange(data.iscell.sum())[:Nmax]:
print(' - direction-selectivity analysis for ROI #%i / %i' %
(roi + 1, data.iscell.sum()))
fig, SI, responsive = DS_ROI_analysis(data,
roiIndex=roi,
iprotocol=iprotocol)
pdf.savefig() # saves the current figure into a pdf page
plt.close()
if responsive:
Nresp += 1
SIs.append(SI)
summary_fig2(Nresp,
data.iscell.sum(),
SIs,
label='Direct. Select. Index')
pdf.savefig() # saves the current figure into a pdf page
plt.close()
print('[ok] direction selectivity analysis saved as: "%s" ' % pdf_filename)
def analysis_pdf(datafile, iprotocol=0, Nmax=1000000):
data = Data(datafile)
pdf_filename = os.path.join(
summary_pdf_folder(datafile),
'%s-behavioral-modulation.pdf' % data.protocols[iprotocol])
full_resp = compute_population_resp(datafile,
protocol_id=iprotocol,
Nmax=Nmax)
with PdfPages(pdf_filename) as pdf:
# print(' - behavioral-modulation analysis for ROI #%i / %i' % (roi+1, data.iscell.sum()))
fig = population_tuning_fig(full_resp)
pdf.savefig(fig) # saves the current figure into a pdf page
plt.close()
fig1, fig2 = behavior_mod_population_tuning_fig(
full_resp, running_speed_threshold=0.1, pupil_threshold=2.5)
pdf.savefig(fig1) # saves the current figure into a pdf page
plt.close(fig1)
pdf.savefig(fig2) # saves the current figure into a pdf page
plt.close(fig2)
print('[ok] behavioral-modulation analysis saved as: "%s" ' % pdf_filename)
def analysis_pdf(datafile, iprotocol=0, Nmax=1000000):
data = MultimodalData(datafile)
pdf_filename = os.path.join(summary_pdf_folder(datafile), '%s-secondary_RF.pdf' % data.protocols[iprotocol])
# find center and surround
icenter = np.argwhere([('center' in p) for p in data.protocols])[0][0]
isurround = np.argwhere([('surround' in p) for p in data.protocols])[0][0]
curves, results = [], {'Ntot':data.iscell.sum()}
with PdfPages(pdf_filename) as pdf:
for roi in np.arange(data.iscell.sum())[:Nmax]:
print(' - secondary-RF analysis for ROI #%i / %i' % (roi+1, data.iscell.sum()))
# fig, fig2, radii, max_response_curve, imax_response_curve, full_resp = ROI_analysis(data, roiIndex=roi,
# iprotocol_center=icenter,
# iprotocol_surround=isurround)
FIGS = ROI_analysis(data, roiIndex=roi,
iprotocol_center=icenter,
iprotocol_surround=isurround)
for fig in FIGS:
pdf.savefig(fig)
plt.close(fig)
# pdf.savefig(fig2)
# plt.close(fig2)
# if max_response_curve is not None:
# curves.append(max_response_curve)
# # initialize if not done
# for key in full_resp:
# if ('-bins' in key) and (key not in results):
# results[key] = full_resp[key]
# elif (key not in results):
# results[key] = []
# significant_cond = (full_resp['significant']==True)
# if np.sum(significant_cond)>0:
# imax = np.argmax(full_resp['value'][significant_cond])
# for key in full_resp:
# if ('-bins' not in key):
# results[key].append(full_resp[key][significant_cond][imax])
# if len(results['value'])>0:
# fig = SS_summary_fig(results)
# pdf.savefig(fig)
# plt.close(fig)
# fig = summary_fig(radii, curves, data.iscell.sum())
# pdf.savefig(fig)
# plt.close(fig)
print('[ok] secondary RF analysis saved as: "%s" ' % pdf_filename)
def analysis_pdf(datafile,
iprotocol=0,
response_significance_threshold=0.01,
roi=0,
Nmax=1000000):
data = MultimodalData(datafile)
pdf_filename = os.path.join(
summary_pdf_folder(datafile),
'%s-moving-dots_selectivity.pdf' % data.protocols[iprotocol])
if roi == 0:
ROIs = np.arange(data.iscell.sum())[:Nmax]
else:
ROIs = [roi]
CELL_RESPS = []
with PdfPages(pdf_filename) as pdf:
for roi in ROIs:
fig, cell_resp = ROI_analysis(
data,
roiIndex=roi,
iprotocol=iprotocol,
response_significance_threshold=response_significance_threshold
)
CELL_RESPS.append(cell_resp)
pdf.savefig(fig) # saves the current figure into a pdf page
plt.close(fig)
fig = summary_fig(CELL_RESPS)
pdf.savefig(fig)
plt.close(fig)
np.save(
os.path.join(summary_pdf_folder(datafile),
'summary-moving-dot.npy'), CELL_RESPS)
print('[ok] moving dot analysis saved as: "%s" ' % pdf_filename)
def analysis_pdf(datafile, iprotocol=0, Nmax=1000000):
data = MultimodalData(datafile)
pdf_filename = os.path.join(
summary_pdf_folder(datafile),
'%s-surround_suppression.pdf' % data.protocols[iprotocol])
curves, results = [], {'Ntot': data.iscell.sum()}
with PdfPages(pdf_filename) as pdf:
for roi in np.arange(data.iscell.sum())[:Nmax]:
print(' - surround-suppression analysis for ROI #%i / %i' %
(roi + 1, data.iscell.sum()))
fig, fig2, radii, max_response_curve, imax_response_curve, full_resp = ROI_analysis(
data, roiIndex=roi, iprotocol=iprotocol)
pdf.savefig(fig)
pdf.savefig(fig2)
plt.close(fig)
plt.close(fig2)
if max_response_curve is not None:
curves.append(max_response_curve)
# initialize if not done
for key in full_resp:
if ('-bins' in key) and (key not in results):
results[key] = full_resp[key]
elif (key not in results):
results[key] = []
significant_cond = (full_resp['significant'] == True)
if np.sum(significant_cond) > 0:
imax = np.argmax(full_resp['value'][significant_cond])
for key in full_resp:
if ('-bins' not in key):
results[key].append(
full_resp[key][significant_cond][imax])
if len(results['value']) > 0:
fig = SS_summary_fig(results)
pdf.savefig(fig)
plt.close(fig)
fig = summary_fig(radii, curves, data.iscell.sum())
pdf.savefig(fig)
plt.close(fig)
print('[ok] direction selectivity analysis saved as: "%s" ' % pdf_filename)
def analysis_pdf(datafile, iprotocol=0, Nmax=1000000):
data = MultimodalData(datafile, with_visual_stim=True)
pdf_filename = os.path.join(
summary_pdf_folder(datafile),
'%s-RF_mapping.pdf' % data.protocols[iprotocol])
curves, results = [], {'Ntot': data.iscell.sum()}
with PdfPages(pdf_filename) as pdf:
for roi in np.arange(data.iscell.sum())[:Nmax]:
print(' - RF mapping analysis for ROI #%i / %i' %
(roi + 1, data.iscell.sum()))
fig = ROI_analysis(data, roiIndex=roi, iprotocol=iprotocol)
pdf.savefig(fig)
plt.close(fig)
print('[ok] RF mapping analysis saved as: "%s" ' % pdf_filename)
def analysis_pdf(datafile, iprotocol=0, Nmax=1000000):
data = MultimodalData(datafile)
pdf_filename = os.path.join(
summary_pdf_folder(datafile),
'%s-contrast_curves.pdf' % data.protocols[iprotocol])
if data.metadata['CaImaging']:
results = {'Ntot': data.iscell.sum()}
with PdfPages(pdf_filename) as pdf:
CURVES = []
for roi in np.arange(data.iscell.sum())[:Nmax]:
print(' - contrast-curves analysis for ROI #%i / %i' %
(roi + 1, data.iscell.sum()))
fig, contrasts, max_response_curve = ROI_analysis(
data, roiIndex=roi, iprotocol=iprotocol)
pdf.savefig(fig)
plt.close(fig)
if np.max(max_response_curve) > 0:
CURVES.append(max_response_curve)
#
fig = summary_fig(contrasts, CURVES, data.iscell.sum())
pdf.savefig(fig)
plt.close(fig)
elif data.metadata['Electrophy']:
with PdfPages(pdf_filename) as pdf:
fig, fig2 = Ephys_analysis(data, iprotocol=iprotocol)
pdf.savefig(fig)
plt.close(fig)
pdf.savefig(fig2)
plt.close(fig2)
print('[ok] contrast-curves analysis saved as: "%s" ' % pdf_filename)
def analysis_pdf(datafile,
iprotocol=-1,
Nmax=2,
verbose=False):
data = MultimodalData(datafile)
if iprotocol<0:
iprotocol = np.argwhere([('gaussian-blobs' in p) for p in data.protocols])[0][0]
print('gaussian-blob analysis for protocol #', iprotocol)
pdf_filename = os.path.join(summary_pdf_folder(datafile), '%s-flashes.pdf' % data.protocols[iprotocol])
if data.metadata['CaImaging']:
# results = {'Ntot':data.iscell.sum()}
with PdfPages(pdf_filename) as pdf:
fig = Ophys_analysis(data,
Nmax=Nmax,
verbose=verbose)
pdf.savefig(fig)
plt.close(fig)
elif data.metadata['Electrophy']:
with PdfPages(pdf_filename) as pdf:
fig, fig2 = Ephys_analysis(data, iprotocol=iprotocol)
pdf.savefig(fig)
plt.close(fig)
pdf.savefig(fig2)
plt.close(fig2)
print('[ok] gaussian-blobs analysis saved as: "%s" ' % pdf_filename)
def run_analysis_and_save_figs(datafile, suffix='', folder='./', Ybar=0.1):
pdf_filename = os.path.join(summary_pdf_folder(datafile),
'motion-contour-interaction.pdf')
data = MCI_data(datafile)
if len(data.episode_static_patch.varied_parameters.keys()) == 2:
contour_key = [
k for k in data.episode_static_patch.varied_parameters.keys()
][0]
elif len(data.episode_static_patch.varied_parameters.keys()) == 1:
contour_key = ''
else:
print(
'\n\n /!\ MORE THAN ONE CONTOUR KEY /!\ \n --> needs special analysis \n\n '
)
if len(data.episode_moving_dots.varied_parameters.keys()) == 2:
motion_key = [
k for k in data.episode_moving_dots.varied_parameters.keys()
][0]
elif len(data.episode_moving_dots.varied_parameters.keys()) == 1:
motion_key = ''
else:
print(
'\n\n /!\ MORE THAN ONE MOTION KEY /!\ \n --> needs special analysis \n\n '
)
if 'patch-delay' in data.episode_mixed.varied_parameters.keys():
mixed_only_key = 'patch-delay'
else:
mixed_only_key = ''
with PdfPages(pdf_filename) as pdf:
# static patches
fig, AX = data.episode_static_patch.plot_trial_average(
roiIndices='mean',
column_key=contour_key,
with_annotation=True,
with_std=False,
ybar=Ybar,
ybarlabel='%.1fdF/F' % Ybar,
xbar=1,
xbarlabel='1s')
fig.suptitle('static patches\n\n', fontsize=9)
pdf.savefig(fig)
plt.close(fig)
# moving dots
fig, AX = data.episode_moving_dots.plot_trial_average(
roiIndices='mean',
column_key=motion_key,
with_annotation=True,
with_std=False,
ybar=Ybar,
ybarlabel='%.1fdF/F' % Ybar,
xbar=1,
xbarlabel='1s')
fig.suptitle('moving dots\n\n', fontsize=9)
pdf.savefig(fig)
plt.close(fig)
# mixed stimuli
fig, AX = data.episode_mixed.plot_trial_average(
roiIndices='mean',
column_key=mixed_only_key,
row_key=motion_key,
color_key=('patch-%s' % contour_key if
(contour_key != '') else ''),
with_annotation=True,
with_std=False,
ybar=Ybar,
ybarlabel='%.1fdF/F' % Ybar,
xbar=1,
xbarlabel='1s')
fig.suptitle('mixed static-patch + moving-dots\n\n', fontsize=9)
pdf.savefig(fig)
plt.close(fig)
if data.episode_random_dots is not None:
fig, AX = data.episode_random_dots.plot_trial_average(
roiIndices='mean',
column_key=motion_key,
with_annotation=True,
with_std=False,
ybar=Ybar,
ybarlabel='%.1fdF/F' % Ybar,
xbar=1,
xbarlabel='1s')
fig.suptitle('random line moving-dots\n\n', fontsize=9)
pdf.savefig(fig)
plt.close(fig)
if data.episode_mixed_random_dots is not None:
fig, AX = data.episode_mixed_random_dots.plot_trial_average(
roiIndices='mean',
column_key=motion_key,
color_key=('patch-%s' % contour_key if
(contour_key != '') else ''),
with_annotation=True,
with_std=False,
ybar=Ybar,
ybarlabel='%.1fdF/F' % Ybar,
xbar=1,
xbarlabel='1s')
fig.suptitle('mixed static-patch + random line moving-dots\n\n',
fontsize=9)
pdf.savefig(fig)
plt.close(fig)
## Focusing on cells responding to contour features
rois_of_interest_contour = find_responsive_cells(
data.episode_static_patch,
param_key=contour_key,
interval_pre=[-1, 0],
interval_post=[0.5, 1.5])
rois_of_interest_motion = find_responsive_cells(
data.episode_moving_dots,
param_key=motion_key,
interval_pre=[-2, 0],
interval_post=[1, 3])
rois_of_interest_contour_only = exclude_motion_sensitive_cells(
rois_of_interest_contour, rois_of_interest_motion)
fig, ax = make_proportion_fig(data, rois_of_interest_contour,
rois_of_interest_motion,
rois_of_interest_contour_only)
pdf.savefig(fig)
plt.close(fig)
for key in [
key for key in rois_of_interest_contour_only
if 'resp' not in key
]:
print('- %s patch --> %i significantly modulated cells (%.1f%%) ' %
(key, len(rois_of_interest_contour_only[key]),
100 * len(rois_of_interest_contour_only[key]) / data.nROIs))
if len(rois_of_interest_contour_only[key]) > 0:
fig, AX = data.episode_static_patch.plot_trial_average(
roiIndices=rois_of_interest_contour_only[key],
column_key=contour_key,
with_annotation=True,
with_std=False,
ybar=Ybar,
ybarlabel='%.1fdF/F' % Ybar,
xbar=1,
xbarlabel='1s')
fig.suptitle('static patches --> cells resp. to %s\n\n\n' %
key,
fontsize=8)
pdf.savefig(fig)
plt.close(fig)
fig, AX = data.episode_moving_dots.plot_trial_average(
roiIndices=rois_of_interest_contour_only[key],
column_key=motion_key,
with_annotation=True,
with_std=False,
ybar=Ybar,
ybarlabel='%.1fdF/F' % Ybar,
xbar=1,
xbarlabel='1s')
fig.suptitle('moving-dots --> cells resp. to %s\n\n\n' % key,
fontsize=8)
pdf.savefig(fig)
plt.close(fig)
fig, AX = data.episode_mixed.plot_trial_average(
roiIndices=rois_of_interest_contour_only[key],
column_key=mixed_only_key,
row_key=motion_key,
color_key=('patch-%s' % contour_key if
(contour_key != '') else ''),
with_annotation=True,
with_std=False,
ybar=Ybar,
ybarlabel='%.1fdF/F' % Ybar,
xbar=1,
xbarlabel='1s')
fig.suptitle('mixed-stim --> cells resp. to %s\n\n\n' % key,
fontsize=8)
pdf.savefig(fig)
plt.close(fig)
for contour_index in range(
len(data.episode_static_patch.
varied_parameters[contour_key])):
for motion_index in range(
len(data.episode_moving_dots.
varied_parameters[motion_key])):
for mixed_index in range(
len(data.episode_mixed.
varied_parameters[mixed_only_key])):
mixed_indices = [
motion_index, contour_index, mixed_index
]
mixed_keys = [
motion_key,
'patch-%s' % contour_key, mixed_only_key
]
fig, _, _ = interaction_fig(
data.get_responses(
data.episode_static_patch.
find_episode_cond(contour_key,
contour_index),
data.episode_moving_dots.find_episode_cond(
motion_key, motion_index),
data.episode_mixed.find_episode_cond(
mixed_keys, mixed_indices),
roiIndices=rois_of_interest_contour_only[
key]),
static_patch_label='patch\n (%s=%i)' %
(contour_key[:3], data.episode_static_patch.
varied_parameters[contour_key][contour_index]
),
moving_dots_label='mv-dots\n (%s=%i)' %
(motion_key[:3], data.episode_moving_dots.
varied_parameters[motion_key][motion_index]),
mixed_label='mixed\n (%s=%i)' %
(mixed_only_key.replace('patch-', '')[:3],
data.episode_mixed.varied_parameters[
mixed_only_key][mixed_index]),
Ybar=Ybar)
pdf.savefig(fig)
plt.close(fig)
print('[ok] motion-contour-interaction analysis saved as: "%s" ' %
pdf_filename)
def analysis_pdf(datafile, Nmax=1000000):
data = MultimodalData(datafile)
stim_duration = data.metadata['Protocol-1-presentation-duration']
interval_post = [1. / 2. * stim_duration, stim_duration]
interval_pre = [interval_post[0] - interval_post[1], 0]
try:
# find the protocol with the many-standards
iprotocol_MS = np.argwhere([('many-standards' in p)
for p in data.protocols])[0][0]
# find the protocol with the oddball-1
iprotocol_O1 = np.argwhere([('oddball-1' in p)
for p in data.protocols])[0][0]
# find the protocol with the oddball-2
iprotocol_O2 = np.argwhere([('oddball-2' in p)
for p in data.protocols])[0][0]
# mismatch negativity angles
MM_angles = [
data.metadata['Protocol-%i-angle-redundant (deg)' %
(1 + iprotocol_O1)],
data.metadata['Protocol-%i-angle-deviant (deg)' %
(1 + iprotocol_O1)]
]
#
DATA = {'stim_duration': stim_duration}
DATA[str(int(MM_angles[0]))] = {
'iprotocol_control': iprotocol_MS,
'control': [],
'redundant': [],
'deviant': [],
'responsive': []
}
DATA[str(int(MM_angles[1]))] = {
'iprotocol_control': iprotocol_MS,
'control': [],
'redundant': [],
'deviant': [],
'responsive': []
}
DATA[str(
int(data.metadata[
'Protocol-%i-angle-redundant (deg)' %
(1 + iprotocol_O1)]))]['iprotocol_redundant'] = iprotocol_O1
DATA[str(
int(data.metadata[
'Protocol-%i-angle-deviant (deg)' %
(1 + iprotocol_O1)]))]['iprotocol_deviant'] = iprotocol_O1
DATA[str(
int(data.metadata[
'Protocol-%i-angle-redundant (deg)' %
(1 + iprotocol_O2)]))]['iprotocol_redundant'] = iprotocol_O2
DATA[str(
int(data.metadata[
'Protocol-%i-angle-deviant (deg)' %
(1 + iprotocol_O2)]))]['iprotocol_deviant'] = iprotocol_O2
# find the angle for the redundant and deviant conditions
print(data.metadata['Protocol-3-angle-redundant (deg)'],
data.metadata['Protocol-3-angle-deviant (deg)'])
Nresp, Nresp_selective, SIs = 0, 0, []
pdf_OS = PdfPages(
os.path.join(
summary_pdf_folder(datafile),
'%s-orientation_selectivity.pdf' %
data.protocols[iprotocol_MS]))
pdf_MSO = PdfPages(
os.path.join(
summary_pdf_folder(datafile),
'%s-mismatch_selective_only.pdf' %
data.protocols[iprotocol_MS]))
pdf_MA = PdfPages(
os.path.join(summary_pdf_folder(datafile),
'%s-mismatch_all.pdf' % data.protocols[iprotocol_MS]))
for roi in np.arange(data.iscell.sum())[:Nmax]:
print(' - MMN analysis for ROI #%i / %i' %
(roi + 1, data.iscell.sum()))
## ORIENTATION SELECTIVITY ANALYSIS
fig, SI, responsive, responsive_angles = ODS.OS_ROI_analysis(
data,
roiIndex=roi,
iprotocol=iprotocol_MS,
stat_test_props=dict(interval_pre=interval_pre,
interval_post=interval_post,
test='wilcoxon',
positive=True),
with_responsive_angles=True)
pdf_OS.savefig() # saves the current figure into a pdf page
plt.close()
if responsive:
Nresp += 1
SIs.append(SI)
EPISODES = EpisodeResponse(
data,
protocol_id=None, # means all
quantity='CaImaging',
subquantity='dF/F',
roiIndex=roi)
fig, AX = ge.figure(axes=(2, 1), wspace=3., right=10.)
responsive_for_at_least_one = False
ge.annotate(fig, 'ROI #%i' % (roi + 1), (0.02, 0.98), va='top')
for angle, ax in zip(MM_angles, AX):
DATA[str(int(angle))]['responsive'].append(
False) # False by default
ge.title(ax, '$\\theta$=%.1f$^{o}$' % angle)
for ik, key, color in zip(range(3),
['control', 'redundant', 'deviant'],
['k', ge.blue, ge.red]):
cond = data.get_stimulus_conditions([
np.array(DATA[str(int(angle))]['iprotocol_%s' % key]),
np.array([float(angle)])
], ['protocol_id', 'angle'], None)[0]
ge.plot(EPISODES.t,
EPISODES.resp[cond, :].mean(axis=0),
sy=EPISODES.resp[cond, :].std(axis=0),
color=color,
ax=ax,
no_set=True)
ge.annotate(ax,
ik * '\n' + '%s, n=%i' % (key, np.sum(cond)),
(0.98, 1.),
color=color,
va='top',
size='small')
# storing for population analysis:
DATA[str(int(angle))][key].append(
EPISODES.resp[cond, :].mean(axis=0))
if angle in responsive_angles:
responsive_for_at_least_one = True
DATA[str(int(
angle))]['responsive'][-1] = True # shift to True
ge.set_plot(ax, xlabel='time (s)', ylabel='dF/F')
pdf_MA.savefig()
if responsive_for_at_least_one:
pdf_MSO.savefig()
plt.close()
for angle in MM_angles:
fig, AX = modulation_summary_panel(EPISODES.t,
DATA[str(int(angle))],
title='$\\theta$=%.1f$^{o}$' %
angle)
pdf_MA.savefig()
plt.close()
fig, AX = modulation_summary_panel(EPISODES.t,
DATA[str(int(angle))],
title='$\\theta$=%.1f$^{o}$' %
angle,
responsive_only=True)
pdf_MSO.savefig()
plt.close()
# orientation selectivity summary
ODS.summary_fig(Nresp,
data.iscell.sum(),
SIs,
label='Orient. Select. Index')
pdf_OS.savefig() # saves the current figure into a pdf page
plt.close()
# modulation summary
for pdf in [pdf_OS, pdf_MSO, pdf_MA]:
pdf.close()
print('[ok] mismatch negativity analysis saved in: "%s" ' %
summary_pdf_folder(datafile))
except BaseException as be:
print('\n', be)
print('---------------------------------------')
print(' /!\ Pb with mismatch negativity analysis /!\ ')
