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 population_analysis(FILES,
min_time_minutes=2,
exclude_subjects=[],
ax=None,
running_speed_threshold=0.1):
times, fracs_running, subjects = [], [], []
if ax is None:
fig, ax = ge.figure(figsize=(5, 1), top=5)
else:
fig = None
for f in FILES:
data = MultimodalData(f)
if (data.nwbfile is not None) and ('Running-Speed'
in data.nwbfile.acquisition):
speed = data.nwbfile.acquisition['Running-Speed'].data[:]
max_time = len(
speed) / data.nwbfile.acquisition['Running-Speed'].rate
if (max_time > 60 * min_time_minutes) and (
data.metadata['subject_ID'] not in exclude_subjects):
times.append(max_time)
fracs_running.append(
100 * np.sum(speed > running_speed_threshold) / len(speed))
subjects.append(data.metadata['subject_ID'])
i = -1
for c, s in enumerate(np.unique(subjects)):
s_cond = np.array(subjects) == s
ax.bar(np.arange(1 + i, i + 1 + np.sum(s_cond)),
np.array(fracs_running)[s_cond] + 1,
width=.75,
color=plt.cm.tab10(c % 10))
i += np.sum(s_cond) + 1
ax.bar([i + 2], [np.mean(fracs_running)],
yerr=[np.std(fracs_running)],
width=1.5,
color='grey')
ge.annotate(ax,
'frac. running:\n%.1f+/-%.1f %%' %
(np.mean(fracs_running), np.std(fracs_running)),
(i + 3, np.mean(fracs_running)),
xycoords='data')
ge.set_plot(ax,
xticks=[],
xlabel='\nrecording',
ylabel=' frac. running (%)')
ymax, i = ax.get_ylim()[1], -1
for c, s in enumerate(np.unique(subjects)):
s_cond = np.array(subjects) == s
ge.annotate(ax,
s, (1 + i, ymax),
rotation=90,
color=plt.cm.tab10(c % 10),
xycoords='data',
size='x-small')
i += np.sum(s_cond) + 1
return fig, ax
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, 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, Tzoom=120, NzoomPlot=3):
pdf_filename = os.path.join(summary_pdf_folder(datafile), 'behavior.pdf')
data = MultimodalData(datafile)
with PdfPages(pdf_filename) as pdf:
print('* plotting behavioral data as "behavior.pdf" [...]')
print(' - raw behavior plot ')
fig, ax = plt.subplots(1, figsize=(11.4, 3.5))
fig.subplots_adjust(top=0.8, bottom=0.05)
subsampling = max(
[int((data.tlim[1] - data.tlim[0]) / data.CaImaging_dt / 1000), 1])
data.plot_raw_data(
data.tlim,
settings=raw_data_plot_settings(
data,
subsampling_factor=int((data.tlim[1] - data.tlim[0]) / 60. +
1)),
ax=ax)
axT = add_inset_with_time_sample(data.tlim, data.tlim, plt)
pdf.savefig() # saves the current figure into a pdf page
plt.close()
# plot raw data sample
for t0 in np.linspace(Tzoom, data.tlim[1], NzoomPlot):
TLIM = [np.max([10, t0 - Tzoom]), t0]
print(' - plotting raw data sample at times ', TLIM)
fig, ax = plt.subplots(1, figsize=(11.4, 5))
fig.subplots_adjust(top=0.8, bottom=0.05)
data.plot_raw_data(TLIM,
settings=raw_data_plot_settings(
data, subsampling_factor=1),
ax=ax)
axT = add_inset_with_time_sample(TLIM, data.tlim, plt)
pdf.savefig() # saves the current figure into a pdf page
plt.close()
print(' - behavior analysis ')
fig = analysis_fig(data)
pdf.savefig() # saves the current figure into a pdf page
plt.close()
print('[ok] behavioral data saved as: "%s" ' % pdf_filename)
def analysis_pdf(datafile,
NzoomPlot=5,
Nroi=10):
pdf_filename = os.path.join(summary_pdf_folder(datafile), 'raw.pdf')
data = MultimodalData(datafile)
if data.metadata['CaImaging']:
Tzoom=60
else:
Tzoom=20
with PdfPages(pdf_filename) as pdf:
print('* plotting raw data as "raw.pdf" [...]')
fig, ax = plt.subplots(1, figsize=(11.4, 5))
fig.subplots_adjust(top=0.8, bottom=0.05)
print(' - plotting full data view')
if data.metadata['CaImaging']:
subsampling = max([int((data.tlim[1]-data.tlim[0])/data.CaImaging_dt/1000), 1])
else:
subsampling = 1
data.plot_raw_data(data.tlim,
settings=raw_data_plot_settings(data,
Nroi=Nroi,
subsampling_factor=int((data.tlim[1]-data.tlim[0])/60.+1)),
ax=ax)
axT = add_inset_with_time_sample(data.tlim, data.tlim, plt)
pdf.savefig() # saves the current figure into a pdf page
plt.close()
# plot raw data sample
for t0 in np.linspace(Tzoom, data.tlim[1], NzoomPlot):
TLIM = [np.max([10,t0-Tzoom]),t0]
print(' - plotting raw data sample at times ', TLIM)
fig, ax = plt.subplots(1, figsize=(11.4, 5))
fig.subplots_adjust(top=0.8, bottom=0.05)
data.plot_raw_data(TLIM, settings=raw_data_plot_settings(data, Nroi=Nroi), ax=ax)
axT = add_inset_with_time_sample(TLIM, data.tlim, plt)
pdf.savefig() # saves the current figure into a pdf page
plt.close()
print('[ok] raw data plot saved as: "%s" ' % pdf_filename)
def analysis_pdf(datafile, Nmax=1000000):
pdf_filename = os.path.join(summary_pdf_folder(datafile), 'rois.pdf')
data = MultimodalData(datafile)
data.correctedFluo, data.F0 = data.build_dFoF(
return_corrected_F_and_F0=True)
with PdfPages(pdf_filename) as pdf:
print('* plotting ROI analysis as "rois.pdf" [...]')
print(' - plotting imaging FOV')
fig, AX = plt.subplots(1, 5, figsize=(11.4, 2.5))
plt.subplots_adjust(left=0.03, right=0.97)
data.show_CaImaging_FOV(key='meanImg', NL=1, cmap='viridis', ax=AX[0])
data.show_CaImaging_FOV(key='meanImg', NL=2, cmap='viridis', ax=AX[1])
data.show_CaImaging_FOV(key='meanImgE', NL=2, cmap='viridis', ax=AX[2])
data.show_CaImaging_FOV(key='max_proj', NL=2, cmap='viridis', ax=AX[3])
try:
data.show_CaImaging_FOV(key='meanImg_chan2',
NL=2,
cmap='viridis',
ax=AX[4])
except KeyError:
AX[4].annotate('no red channel', (0.5, 0.5),
xycoords='axes fraction',
fontsize=9,
va='center',
ha='center')
AX[4].axis('off')
pdf.savefig() # saves the current figure into a pdf page
plt.close()
for i in np.arange(data.nROIs)[:Nmax]:
print(' - plotting analysis of ROI #%i' % (i + 1))
try:
fig = raw_fluo_fig(data, roiIndex=i)
pdf.savefig(fig) # saves the current figure into a pdf page
plt.close(fig)
fig = analysis_fig(data, roiIndex=i)
pdf.savefig(fig) # saves the current figure into a pdf page
plt.close(fig)
except BaseException as be:
print(be)
print(' /!\ Pb with ROI #%i /!\ ' % (i + 1))
print('[ok] roi 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, 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 make_sumary_pdf(filename,
Nmax=1000000,
include=['exp', 'rois', 'raw', 'protocols'],
T_raw_data=60,
N_raw_data=5,
ROI_raw_data=20,
Tbar_raw_data=5):
data = MultimodalData(filename)
data.roiIndices = np.sort(
np.random.choice(np.arange(data.iscell.sum()),
size=min([data.iscell.sum(), ROI_raw_data]),
replace=False))
folder = filename.replace('.nwb', '')
if not os.path.isdir(folder):
os.mkdir(folder)
if 'exp' in include:
with PdfPages(os.path.join(folder, 'exp.pdf')) as pdf:
print('writing experimental metadata ')
fig = metadata_fig(data)
pdf.savefig() # saves the current figure into a pdf page
plt.close()
print('plotting behavior analysis ')
fig = behavior_analysis_fig(data)
pdf.savefig() # saves the current figure into a pdf page
plt.close()
if 'exp' in include:
with PdfPages(os.path.join(folder, 'rois.pdf')) as pdf:
print('plotting imaging FOV ')
fig, AX = plt.subplots(1, 4, figsize=(11.4, 2))
data.show_CaImaging_FOV(key='meanImg',
NL=1,
cmap='viridis',
ax=AX[0])
data.show_CaImaging_FOV(key='meanImg',
NL=2,
cmap='viridis',
ax=AX[1])
data.show_CaImaging_FOV(key='meanImgE',
NL=2,
cmap='viridis',
ax=AX[2])
data.show_CaImaging_FOV(key='max_proj',
NL=2,
cmap='viridis',
ax=AX[3])
pdf.savefig() # saves the current figure into a pdf page
plt.close()
if 'raw' in include:
with PdfPages(os.path.join(folder, 'raw.pdf')) as pdf:
print('plotting full data view [...]')
fig, ax = plt.subplots(1, figsize=(11.4, 5))
fig.subplots_adjust(top=0.8, bottom=0.05)
data.plot(data.tlim,
settings=raw_data_plot_settings(data,
subsampling_factor=1000),
ax=ax,
Tbar=Tbar_raw_data)
pdf.savefig() # saves the current figure into a pdf page
plt.close()
# # plot raw data sample
# for t0 in np.linspace(T_raw_data, data.tlim[1], N_raw_data):
# TLIM = [np.max([10,t0-T_raw_data]),t0]
# print('plotting raw data sample at times ', TLIM)
# fig, ax = plt.subplots(1, figsize=(11.4, 5))
# fig.subplots_adjust(top=0.8, bottom=0.05)
# data.plot(TLIM, settings=raw_data_plot_settings(data),
# ax=ax, Tbar=Tbar_raw_data)
# # inset with time sample
# axT = plt.axes([0.6, 0.9, 0.3, 0.05])
# axT.axis('off')
# axT.plot(data.tlim, [0,0], 'k-', lw=2)
# axT.plot(TLIM, [0,0], '-', color=plt.cm.tab10(3), lw=5)
# axT.annotate('0 ', (0,0), xycoords='data', ha='right', fontsize=9)
# axT.annotate(' %.1fmin' % (data.tlim[1]/60.), (data.tlim[1],0), xycoords='data', fontsize=9)
# pdf.savefig() # saves the current figure into a pdf page
# plt.close()
print('looping over protocols for analysis [...]')
if 'protocols' in include:
# looping over protocols
for p, protocol in enumerate(data.protocols):
print('plotting protocol "%s" [...]' % protocol)
with PdfPages(os.path.join(folder, '%s.pdf' % protocol)) as pdf:
# finding protocol type
protocol_type = (data.metadata['Protocol-%i-Stimulus' %
(p + 1)] if
(len(data.protocols) > 1) else
data.metadata['Stimulus'])
print(protocol_type)
# then protocol-dependent analysis
if protocol_type == 'full-field-grating':
from analysis.orientation_direction_selectivity import orientation_selectivity_analysis
Nresp, SIs = 0, []
for i in range(data.iscell.sum())[:Nmax]:
fig, SI, responsive = orientation_selectivity_analysis(
data, roiIndex=i, verbose=False)
pdf.savefig(
) # saves the current figure into a pdf page
plt.close()
if responsive:
Nresp += 1
SIs.append(SI)
# summary figure for this protocol
fig, AX = summary_fig(Nresp, data.iscell.sum(),
np.array(SIs))
pdf.savefig() # saves the current figure into a pdf page
plt.close()
elif protocol_type == 'drifting-full-field-grating':
from analysis.orientation_direction_selectivity import direction_selectivity_analysis
Nresp, SIs = 0, []
for i in range(data.iscell.sum())[:Nmax]:
fig, SI, responsive = direction_selectivity_analysis(
data, roiIndex=i, verbose=False)
pdf.savefig(
) # saves the current figure into a pdf page
plt.close()
if responsive:
Nresp += 1
SIs.append(SI)
fig, AX = summary_fig(Nresp,
data.iscell.sum(),
np.array(SIs),
label='Direction Select. Index')
pdf.savefig() # saves the current figure into a pdf page
plt.close()
elif protocol_type in [
'center-grating', 'drifting-center-grating'
]:
from surround_suppression import orientation_size_selectivity_analysis
Nresp, SIs = 0, []
for i in range(data.iscell.sum())[:Nmax]:
fig, responsive = orientation_size_selectivity_analysis(
data, roiIndex=i, verbose=False)
pdf.savefig(
) # saves the current figure into a pdf page
plt.close()
if responsive:
Nresp += 1
SIs.append(0) # TO BE FILLED
fig, AX = summary_fig(Nresp,
data.iscell.sum(),
np.array(SIs),
label='none')
pdf.savefig() # saves the current figure into a pdf page
plt.close()
elif 'noise' in protocol_type:
from receptive_field_mapping import RF_analysis
Nresp, SIs = 0, []
for i in range(data.iscell.sum())[:Nmax]:
fig, SI, responsive = RF_analysis(data,
roiIndex=i,
verbose=False)
pdf.savefig(
) # saves the current figure into a pdf page
plt.close()
if responsive:
Nresp += 1
SIs.append(0) # TO BE FILLED
fig, AX = summary_fig(Nresp,
data.iscell.sum(),
np.array(SIs),
label='none')
pdf.savefig() # saves the current figure into a pdf page
plt.close()
elif 'spatial-location' in protocol_type:
from surround_suppression import orientation_size_selectivity_analysis
Nresp, SIs = 0, []
for i in range(data.iscell.sum())[:Nmax]:
fig, responsive = orientation_size_selectivity_analysis(
data, roiIndex=i, verbose=False)
pdf.savefig(
) # saves the current figure into a pdf page
plt.close()
if responsive:
Nresp += 1
SIs.append(0) # TO BE FILLED
fig, AX = summary_fig(Nresp,
data.iscell.sum(),
np.array(SIs),
label='none')
pdf.savefig() # saves the current figure into a pdf page
plt.close()
print('[ok] pdfs succesfully saved in "%s" !' % folder)
labels=labels,
autopct='%1.1f%%',
shadow=True,
startangle=90)
AX[1].axis(
'equal') # Equal aspect ratio ensures that pie is drawn as a circle.
AX[3].hist(quantity)
AX[3].set_xlabel(label, fontsize=9)
AX[3].set_ylabel('count', fontsize=9)
for ax in [AX[0], AX[2]]:
ax.axis('off')
return fig, AX
if __name__ == '__main__':
# filename = '/home/yann/DATA/Wild_Type/2021_03_11-17-13-03.nwb'
filename = sys.argv[-1]
# pdf_dir = os.path.join(os.path.dirname(filename), 'summary', os.path.basename(filename))
data = MultimodalData(filename)
# fig1 = metadata_fig(data)
# fig2 = behavior_analysis_fig(data)
# fig3 = roi_analysis_fig(data, roiIndex=0)
# plt.show()
# make_sumary_pdf(filename, include=['raw', 'protocols'])
make_sumary_pdf(filename, include=['protocols'])
def make_summary_pdf(filename,
Nmax=1000000,
include=['exp', 'raw', 'behavior', 'rois', 'protocols'],
verbose=True):
data = MultimodalData(filename)
folder = summary_pdf_folder(filename)
if 'exp' in include:
with PdfPages(os.path.join(folder, 'exp.pdf')) as pdf:
print('* writing experimental metadata as "exp.pdf" [...] ')
print(' - notes')
fig = metadata_fig(data)
pdf.savefig() # saves the current figure into a pdf page
plt.close()
print('[ok] notes saved as: "%s" ' % os.path.join(folder, 'exp.pdf'))
if 'behavior' in include:
process_script = os.path.join(
str(pathlib.Path(__file__).resolve().parents[0]), 'behavior.py')
p = subprocess.Popen('%s %s %s' %
(python_path, process_script, filename),
shell=True)
if 'raw' in include:
process_script = os.path.join(
str(pathlib.Path(__file__).resolve().parents[0]), 'raw_data.py')
p = subprocess.Popen('%s %s %s' %
(python_path, process_script, filename),
shell=True)
if 'rois' in include:
process_script = os.path.join(
str(pathlib.Path(__file__).resolve().parents[0]), 'rois.py')
p = subprocess.Popen('%s %s %s --Nmax %i' %
(python_path, process_script, filename, Nmax),
shell=True)
if 'protocols' in include:
print(data.metadata['protocol'])
print('* looping over protocols for analysis [...]')
# --- analysis of multi-protocols ---
if data.metadata['protocol'] == 'NDNF-protocol':
process_script = os.path.join(
str(pathlib.Path(__file__).resolve().parents[0]),
'protocol_scripts', 'ndnf_protocol.py')
p = subprocess.Popen('%s %s %s --Nmax %i' %
(python_path, process_script, filename, Nmax),
shell=True)
elif data.metadata['protocol'] == 'mismatch-negativity':
process_script = os.path.join(
str(pathlib.Path(__file__).resolve().parents[0]),
'protocol_scripts', 'mismatch_negativity.py')
p = subprocess.Popen('%s %s %s --Nmax %i' %
(python_path, process_script, filename, Nmax),
shell=True)
elif ('surround-suppression'
in data.metadata['protocol']) or ('size-tuning'
in data.metadata['protocol']):
process_script = os.path.join(
str(pathlib.Path(__file__).resolve().parents[0]),
'protocol_scripts', 'surround_suppression.py')
p = subprocess.Popen('%s %s %s --Nmax %i' %
(python_path, process_script, filename, Nmax),
shell=True)
elif ('spatial-location'
in data.metadata['protocol']) or ('spatial-mapping'
in data.metadata['protocol']):
process_script = os.path.join(
str(pathlib.Path(__file__).resolve().parents[0]),
'protocol_scripts', 'spatial_selectivity.py')
p = subprocess.Popen('%s %s %s --Nmax %i' %
(python_path, process_script, filename, Nmax),
shell=True)
elif 'contrast-curve' in data.metadata['protocol']:
process_script = os.path.join(
str(pathlib.Path(__file__).resolve().parents[0]),
'protocol_scripts', 'contrast_curves.py')
p = subprocess.Popen('%s %s %s --Nmax %i' %
(python_path, process_script, filename, Nmax),
shell=True)
elif ('secondary' in data.metadata['protocol']):
process_script = os.path.join(
str(pathlib.Path(__file__).resolve().parents[0]),
'protocol_scripts', 'secondary_RF.py')
p = subprocess.Popen('%s %s %s --Nmax %i' %
(python_path, process_script, filename, Nmax),
shell=True)
elif ('motion-contour-interaction' in data.metadata['protocol']):
process_script = os.path.join(
str(pathlib.Path(__file__).resolve().parents[0]),
'protocol_scripts', 'motion_contour_interaction.py')
p = subprocess.Popen('%s %s %s' %
(python_path, process_script, filename),
shell=True)
else:
# --- looping over protocols individually ---
for ip, protocol in enumerate(data.protocols):
print('* * analyzing protocol #%i: "%s" [...]' %
(ip + 1, protocol))
protocol_type = (data.metadata['Protocol-%i-Stimulus' %
(ip + 1)] if
(len(data.protocols) > 1) else
data.metadata['Stimulus'])
# orientation selectivity analyis
if protocol in ['Pakan-et-al-static']:
process_script = os.path.join(
str(pathlib.Path(__file__).resolve().parents[0]),
'protocol_scripts',
'orientation_direction_selectivity.py')
p = subprocess.Popen(
'%s %s %s orientation --iprotocol %i --Nmax %i' %
(python_path, process_script, filename, ip, Nmax),
shell=True)
if protocol in ['Pakan-et-al-drifting']:
process_script = os.path.join(
str(pathlib.Path(__file__).resolve().parents[0]),
'protocol_scripts',
'orientation_direction_selectivity.py')
p = subprocess.Popen(
'%s %s %s direction --iprotocol %i --Nmax %i' %
(python_path, process_script, filename, ip, Nmax),
shell=True)
if 'dg-' in protocol:
process_script = os.path.join(
str(pathlib.Path(__file__).resolve().parents[0]),
'protocol_scripts',
'orientation_direction_selectivity.py')
p = subprocess.Popen(
'%s %s %s gratings --iprotocol %i --Nmax %i' %
(python_path, process_script, filename, ip, Nmax),
shell=True)
if 'looming-' in protocol:
process_script = os.path.join(
str(pathlib.Path(__file__).resolve().parents[0]),
'protocol_scripts', 'looming_stim.py')
p = subprocess.Popen(
'%s %s %s --iprotocol %i --Nmax %i' %
(python_path, process_script, filename, ip, Nmax),
shell=True)
if 'gaussian-blobs' in protocol:
process_script = os.path.join(
str(pathlib.Path(__file__).resolve().parents[0]),
'protocol_scripts', 'gaussian_blobs.py')
p = subprocess.Popen(
'%s %s %s --iprotocol %i' %
(python_path, process_script, filename, ip),
shell=True)
if 'noise' in protocol:
process_script = os.path.join(
str(pathlib.Path(__file__).resolve().parents[0]),
'protocol_scripts', 'receptive_field_mapping.py')
p = subprocess.Popen(
'%s %s %s --iprotocol %i' %
(python_path, process_script, filename, ip),
shell=True)
if ('dot-stim' in protocol) or ('moving-dot' in protocol):
process_script = os.path.join(
str(pathlib.Path(__file__).resolve().parents[0]),
'protocol_scripts', 'moving_dot_selectivity.py')
p = subprocess.Popen(
'%s %s %s --iprotocol %i' %
(python_path, process_script, filename, ip),
shell=True)
print('subprocesses to analyze "%s" were launched !' % filename)
def __init__(
self,
parent=None,
dt_sampling=10, # ms
fig_name=os.path.join(os.path.expanduser('~'), 'Desktop',
'fig.svg'),
title='Figures'):
super(FiguresWindow, self).__init__(parent=parent.app,
title=title,
i=-10,
size=(800, 800))
self.modalities = []
for key1, key2 in zip([
'Photodiode-Signal', 'Electrophysiological-Signal',
'Running-Speed', 'Pupil', 'CaImaging-TimeSeries',
'CaImaging-TimeSeries', 'Photodiode-Signal'
], [
'Photodiode', 'Electrophy', 'Locomotion', 'Pupil',
'CaImagingSum', 'CaImaging', 'VisualStim'
]):
if key1 in parent.nwbfile.acquisition:
self.modalities.append(key2)
self.parent = parent
self.get_varied_parameters()
self.fig_name = fig_name
self.data = MultimodalData(self.parent.datafile)
self.EPISODES = None
self.xlim, self.ylim = [-10, 10], [-10, 10]
mainLayout, Layouts = QtWidgets.QVBoxLayout(self.cwidget), []
# -- protocol
Layouts.append(QtWidgets.QHBoxLayout())
# Layouts[-1].addWidget(QtWidgets.QLabel('-- Protocol: ', self))
self.pbox = QtWidgets.QComboBox(self)
self.pbox.setFixedWidth(400)
self.pbox.addItem(' [PROTOCOL] ')
self.pbox.addItems(self.parent.protocols)
Layouts[-1].addWidget(self.pbox)
# -- quantity and subquantity
Layouts.append(QtWidgets.QHBoxLayout())
Layouts[-1].addWidget(QtWidgets.QLabel('-- Quantity: ', self))
self.qbox = QtWidgets.QComboBox(self)
self.qbox.addItem('')
if 'ophys' in self.parent.nwbfile.processing:
self.qbox.addItem('CaImaging')
for key in parent.nwbfile.acquisition:
if len(parent.nwbfile.acquisition[key].data.shape) == 1:
self.qbox.addItem(key) # only for scalar variables
self.qbox.setFixedWidth(400)
Layouts[-1].addWidget(self.qbox)
Layouts[-1].addWidget(
QtWidgets.QLabel(' -- Sub-quantity: ', self))
self.sqbox = QtWidgets.QLineEdit(self)
self.sqbox.setText(35 * ' ' + 'e.g. "dF/F", "Neuropil", "pLFP", ...')
self.sqbox.setMinimumWidth(400)
Layouts[-1].addWidget(self.sqbox)
# -- roi
if 'ophys' in self.parent.nwbfile.processing:
Layouts[-1].addWidget(QtWidgets.QLabel(' -- ROI: ', self))
self.roiPick = QtGui.QLineEdit()
self.roiPick.setText(
' [select ROI] e.g.: "1", "10-20", "3, 45, 7", ... ')
self.roiPick.setMinimumWidth(400)
self.roiPick.returnPressed.connect(self.select_ROI)
Layouts[-1].addWidget(self.roiPick)
### PLOT RAW DATA
Layouts.append(QtWidgets.QHBoxLayout())
self.rawPlotBtn = QtWidgets.QPushButton(' plot raw data ', self)
self.rawPlotBtn.setFixedWidth(200)
self.rawPlotBtn.clicked.connect(self.plot_raw_data)
Layouts[-1].addWidget(self.rawPlotBtn)
for mod in self.modalities:
Layouts[-1].addWidget(QtWidgets.QLabel(' ' + mod + ':', self))
setattr(self, mod + 'Plot', QtWidgets.QDoubleSpinBox(self))
getattr(self, mod + 'Plot').setValue(1)
getattr(self, mod + 'Plot').setSuffix(' (%-fig)')
Layouts[-1].addWidget(getattr(self, mod + 'Plot'))
### PLOT FIELD OF VIEW
if 'ophys' in self.parent.nwbfile.processing:
Layouts.append(QtWidgets.QHBoxLayout())
self.fovPlotBtn = QtWidgets.QPushButton(
'plot imaging field of view ', self)
self.fovPlotBtn.setFixedWidth(250)
self.fovPlotBtn.clicked.connect(self.plot_FOV)
Layouts[-1].addWidget(self.fovPlotBtn)
Layouts[-1].addWidget(
QtWidgets.QLabel(10 * ' ' + 'FOV type:', self)) # SEPARATOR
self.fovType = QtWidgets.QComboBox(self)
self.fovType.addItems(['meanImg', 'meanImgE', 'max_proj'])
Layouts[-1].addWidget(self.fovType)
Layouts[-1].addWidget(QtWidgets.QLabel(10 * ' ' + 'NL:',
self)) # SEPARATOR
self.fovNL = QtWidgets.QSpinBox(self)
self.fovNL.setValue(1)
Layouts[-1].addWidget(self.fovNL)
Layouts[-1].addWidget(
QtWidgets.QLabel(10 * ' ' + 'colormap:', self)) # SEPARATOR
self.fovMap = QtWidgets.QComboBox(self)
self.fovMap.addItems(
['viridis', 'grey', 'white_to_green', 'black_to_green'])
Layouts[-1].addWidget(self.fovMap)
Layouts[-1].addWidget(QtWidgets.QLabel(' label:',
self)) # SEPARATOR
self.fovLabel = QtGui.QLineEdit()
Layouts[-1].addWidget(self.fovLabel)
### PLOT CORRELATIONS
Layouts.append(QtWidgets.QHBoxLayout())
Layouts[-1].addWidget(QtWidgets.QLabel(50 * '<->', self)) # SEPARATOR
Layouts[-1].addWidget(QtWidgets.QLabel(' -* CORRELATIONS *-', self))
Layouts.append(QtWidgets.QHBoxLayout())
Layouts[-1].addWidget(QtWidgets.QLabel(50 * '<->', self)) # SEPARATOR
# varied keys
Layouts.append(QtWidgets.QHBoxLayout())
Layouts[-1].addWidget(
QtWidgets.QLabel(' -* STIMULI PARAMETERS *-', self))
for key in self.varied_parameters:
Layouts.append(QtWidgets.QHBoxLayout())
Layouts[-1].addWidget(QtWidgets.QLabel('--- %s ' % key, self))
setattr(self, '%s_plot' % key, QtWidgets.QComboBox(self))
getattr(self, '%s_plot' % key).addItems([
'merged', 'single-value', 'column-panels', 'row-panels',
'color-coded', 'x-axis', 'N/A'
])
Layouts[-1].addWidget(getattr(self, '%s_plot' % key))
setattr(self, '%s_values' % key, QtWidgets.QComboBox(self))
getattr(
self, '%s_values' %
key).addItems(['full', 'custom'] +
[str(s) for s in self.varied_parameters[key]])
Layouts[-1].addWidget(getattr(self, '%s_values' % key))
Layouts[-1].addWidget(QtWidgets.QLabel(10 * ' ', self))
Layouts[-1].addWidget(QtWidgets.QLabel(' custom values : ', self))
setattr(self, '%s_customvalues' % key, QtWidgets.QLineEdit(self))
getattr(self, '%s_customvalues' % key).setMaximumWidth(150)
Layouts[-1].addWidget(getattr(self, '%s_customvalues' % key))
Layouts[-1].addWidget(QtWidgets.QLabel(30 * ' ', self))
Layouts.append(QtWidgets.QHBoxLayout())
Layouts[-1].addWidget(QtWidgets.QLabel(50 * '<->', self)) # SEPARATOR
# figure props type
Layouts.append(QtWidgets.QHBoxLayout())
Layouts[-1].addWidget(
QtWidgets.QLabel(' -* RESPONSES *- ', self))
self.responseType = QtWidgets.QComboBox(self)
self.responseType.addItems(
['stim-evoked-traces', 'mean-stim-evoked', 'integral-stim-evoked'])
Layouts[-1].addWidget(self.responseType)
Layouts[-1].addWidget(QtWidgets.QLabel(' color: ', self))
self.color = QtGui.QLineEdit()
Layouts[-1].addWidget(self.color)
Layouts[-1].addWidget(QtWidgets.QLabel(' label: ', self))
self.label = QtGui.QLineEdit()
Layouts[-1].addWidget(self.label)
Layouts[-1].addWidget(QtWidgets.QLabel(' n-label: ', self))
self.nlabel = QtGui.QLineEdit()
self.nlabel.setText('1')
Layouts[-1].addWidget(self.nlabel)
Layouts.append(QtWidgets.QHBoxLayout())
Layouts[-1].addWidget(
QtWidgets.QLabel(' pre-stimulus window (s):', self))
self.preWindow = QtGui.QLineEdit()
self.preWindow.setText('[-1,0]')
Layouts[-1].addWidget(self.preWindow)
Layouts[-1].addWidget(
QtWidgets.QLabel(' post-stimulus window (s):', self))
self.postWindow = QtGui.QLineEdit()
self.postWindow.setText('[1,4]')
Layouts[-1].addWidget(self.postWindow)
Layouts.append(QtWidgets.QHBoxLayout())
self.baseline = QtGui.QCheckBox("baseline substraction")
Layouts[-1].addWidget(self.baseline)
Layouts[-1].addWidget(QtWidgets.QLabel(10 * ' ', self))
self.withStatTest = QtGui.QCheckBox("with stat. test")
Layouts[-1].addWidget(self.withStatTest)
Layouts.append(QtWidgets.QHBoxLayout())
self.compute = QtWidgets.QPushButton('compute episodes', self)
self.compute.setFixedWidth(200)
self.compute.clicked.connect(self.compute_episodes)
Layouts[-1].addWidget(self.compute)
self.plotBtn = QtWidgets.QPushButton(' -* PLOT *- ', self)
self.plotBtn.setFixedWidth(200)
self.plotBtn.clicked.connect(self.plot)
Layouts[-1].addWidget(self.plotBtn)
Layouts.append(QtWidgets.QHBoxLayout())
Layouts[-1].addWidget(QtWidgets.QLabel(50 * '<->', self)) # SEPARATOR
Layouts.append(QtWidgets.QHBoxLayout())
Layouts[-1].addWidget(
QtWidgets.QLabel(' -* Figure Properties *- ', self))
self.fig_presets = QtWidgets.QComboBox(self)
self.fig_presets.setFixedWidth(400)
self.fig_presets.addItems(
['', 'raw-traces-preset', 'IO-curves-preset'])
Layouts[-1].addWidget(self.fig_presets)
Layouts[-1].addWidget(QtWidgets.QLabel('Panel size: ', self))
self.panelsize = QtGui.QLineEdit()
self.panelsize.setText('(1,1)')
Layouts[-1].addWidget(self.panelsize)
#
self.samplingBox = QtWidgets.QDoubleSpinBox(self)
self.samplingBox.setValue(dt_sampling)
self.samplingBox.setMaximum(500)
self.samplingBox.setMinimum(0.1)
self.samplingBox.setSuffix(' (ms) sampling')
Layouts[-1].addWidget(self.samplingBox)
# plot type
Layouts.append(QtWidgets.QHBoxLayout())
Layouts[-1].addWidget(QtWidgets.QLabel('Plot type: ', self))
self.plotbox = QtWidgets.QComboBox(self)
self.plotbox.addItems(['2d-plot', 'polar-plot', 'bar-plot'])
Layouts[-1].addWidget(self.plotbox)
Layouts[-1].addWidget(QtWidgets.QLabel(10 * ' ', self))
self.plotbox2 = QtWidgets.QComboBox(self)
self.plotbox2.addItems(['line', 'dot'])
Layouts[-1].addWidget(self.plotbox2)
Layouts[-1].addWidget(QtWidgets.QLabel(10 * ' ', self))
self.withSTDbox = QtGui.QCheckBox("with s.d.")
Layouts[-1].addWidget(self.withSTDbox)
Layouts[-1].addWidget(QtWidgets.QLabel(10 * ' ', self))
self.stim = QtGui.QCheckBox("with stim. ")
Layouts[-1].addWidget(self.stim)
Layouts[-1].addWidget(QtWidgets.QLabel(10 * ' ', self))
self.screen = QtGui.QCheckBox("with screen inset ")
Layouts[-1].addWidget(self.screen)
Layouts[-1].addWidget(QtWidgets.QLabel(10 * ' ', self))
self.annot = QtGui.QCheckBox("with annot. ")
Layouts[-1].addWidget(self.annot)
Layouts[-1].addWidget(QtWidgets.QLabel(10 * ' ', self))
self.axis = QtGui.QCheckBox("with axis")
Layouts[-1].addWidget(self.axis)
Layouts[-1].addWidget(QtWidgets.QLabel(10 * ' ', self))
self.grid = QtGui.QCheckBox("with grid")
Layouts[-1].addWidget(self.grid)
# X-scales
Layouts.append(QtWidgets.QHBoxLayout())
Layouts[-1].addWidget(QtWidgets.QLabel('X-SCALE --- ', self))
Layouts[-1].addWidget(QtWidgets.QLabel('x-min:', self))
self.xmin = QtGui.QLineEdit()
self.xmin.setText('')
Layouts[-1].addWidget(self.xmin)
Layouts[-1].addWidget(QtWidgets.QLabel('x-max:', self))
self.xmax = QtGui.QLineEdit()
self.xmax.setText('')
Layouts[-1].addWidget(self.xmax)
Layouts[-1].addWidget(QtWidgets.QLabel('x-bar:', self))
self.xbar = QtGui.QLineEdit()
self.xbar.setText('')
Layouts[-1].addWidget(self.xbar)
Layouts[-1].addWidget(QtWidgets.QLabel('x-barlabel:', self))
self.xbarlabel = QtGui.QLineEdit()
self.xbarlabel.setText('')
Layouts[-1].addWidget(self.xbarlabel)
# Y-scales
Layouts.append(QtWidgets.QHBoxLayout())
Layouts[-1].addWidget(QtWidgets.QLabel('Y-SCALE --- ', self))
Layouts[-1].addWidget(QtWidgets.QLabel('y-min:', self))
self.ymin = QtGui.QLineEdit()
self.ymin.setText('')
Layouts[-1].addWidget(self.ymin)
Layouts[-1].addWidget(QtWidgets.QLabel('y-max:', self))
self.ymax = QtGui.QLineEdit()
self.ymax.setText('')
Layouts[-1].addWidget(self.ymax)
Layouts[-1].addWidget(QtWidgets.QLabel('y-bar:', self))
self.ybar = QtGui.QLineEdit()
self.ybar.setText('')
Layouts[-1].addWidget(self.ybar)
Layouts[-1].addWidget(QtWidgets.QLabel('y-barlabel:', self))
self.ybarlabel = QtGui.QLineEdit()
self.ybarlabel.setText('')
Layouts[-1].addWidget(self.ybarlabel)
# curve
Layouts.append(QtWidgets.QHBoxLayout())
# self.Layout12.addWidget(self.baselineCB)
Layouts.append(QtWidgets.QHBoxLayout())
Layouts[-1].addWidget(QtWidgets.QLabel(50 * '<->', self)) # SEPARATOR
# BUTTONS
Layouts.append(QtWidgets.QHBoxLayout())
self.setBtn = QtWidgets.QPushButton('extract settings', self)
self.setBtn.setFixedWidth(200)
self.setBtn.clicked.connect(self.set_settings)
Layouts[-1].addWidget(self.setBtn)
self.rstBtn = QtWidgets.QPushButton('reset settings', self)
self.rstBtn.setFixedWidth(200)
self.rstBtn.clicked.connect(self.reset_settings)
Layouts[-1].addWidget(self.rstBtn)
Layouts.append(QtWidgets.QHBoxLayout())
self.newFig = QtWidgets.QPushButton('save as new figure', self)
self.newFig.setFixedWidth(200)
self.newFig.clicked.connect(self.new_fig)
Layouts[-1].addWidget(self.newFig)
self.append2Fig = QtWidgets.QPushButton('append to figure', self)
self.append2Fig.setFixedWidth(200)
self.append2Fig.clicked.connect(self.append)
Layouts[-1].addWidget(self.append2Fig)
self.exportBtn = QtWidgets.QPushButton('export to png', self)
self.exportBtn.setFixedWidth(200)
self.exportBtn.clicked.connect(self.export)
Layouts[-1].addWidget(self.exportBtn)
self.locBtn = QtWidgets.QComboBox(self)
self.locBtn.addItems(['Desktop', 'summary'])
self.locBtn.setFixedWidth(200)
Layouts[-1].addWidget(self.locBtn)
self.nameBtn = QtWidgets.QLineEdit(self)
self.nameBtn.setText('fig')
self.nameBtn.setFixedWidth(200)
Layouts[-1].addWidget(self.nameBtn)
self.dpi = QtWidgets.QSpinBox(self)
self.dpi.setValue(300)
self.dpi.setRange(50, 600)
self.dpi.setSuffix(' (dpi)')
self.dpi.setFixedWidth(80)
Layouts[-1].addWidget(self.dpi)
for l in Layouts:
mainLayout.addLayout(l)
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 /!\ ')