def plot_ephys_ophys_trace(key_cell,time_to_plot=None,trace_window = 1,show_stimulus = False,show_e_ap_peaks = False, show_o_ap_peaks = False):
#%%
# =============================================================================
# key_cell = {'session': 1,
# 'subject_id': 456462,
# 'cell_number': 3,
# 'motion_correction_method': 'VolPy',
# 'roi_type': 'VolPy'}
# time_to_plot=None
# trace_window = 100
# show_stimulus = False
# show_e_ap_peaks = True
# show_o_ap_peaks = True
# =============================================================================
fig=plt.figure()
ax_ophys = fig.add_axes([0,0,2,.8])
ax_ephys = fig.add_axes([0,-1,2,.8])
if show_stimulus:
ax_ephys_stim = fig.add_axes([0,-1.5,2,.4])
#%
session_time, cell_recording_start = (experiment.Session()*ephys_patch.Cell()&key_cell).fetch1('session_time','cell_recording_start')
first_movie_start_time = np.min(np.asarray(((imaging.Movie()*imaging_gt.GroundTruthROI())&key_cell).fetch('movie_start_time'),float))
first_movie_start_time_real = first_movie_start_time + session_time.total_seconds()
if not time_to_plot:
time_to_plot = trace_window/2#ephys_matched_ap_times[0]
session_time_to_plot = time_to_plot+first_movie_start_time # time relative to session start
cell_time_to_plot= session_time_to_plot + session_time.total_seconds() -cell_recording_start.total_seconds() # time relative to recording start
#%
sweep_start_times,sweep_end_times,sweep_nums = (ephys_patch.Sweep()&key_cell).fetch('sweep_start_time','sweep_end_time','sweep_number')
needed_start_time = cell_time_to_plot - trace_window/2
needed_end_time = cell_time_to_plot + trace_window/2
#%
sweep_nums = sweep_nums[((sweep_start_times > needed_start_time) & (sweep_start_times < needed_end_time)) |
((sweep_end_times > needed_start_time) & (sweep_end_times < needed_end_time)) |
((sweep_end_times > needed_end_time) & (sweep_start_times < needed_start_time)) ]
ephys_traces = list()
ephys_trace_times = list()
ephys_sweep_start_times = list()
ephys_traces_stim = list()
for sweep_num in sweep_nums:
sweep = ephys_patch.Sweep()&key_cell&'sweep_number = %d' % sweep_num
trace,sr= (ephys_patch.SweepMetadata()*ephys_patch.SweepResponse()&sweep).fetch1('response_trace','sample_rate')
trace = trace*1000
sweep_start_time = float(sweep.fetch('sweep_start_time'))
trace_time = np.arange(len(trace))/sr + sweep_start_time + cell_recording_start.total_seconds() - first_movie_start_time_real
trace_idx = (time_to_plot-trace_window/2 < trace_time) & (time_to_plot+trace_window/2 > trace_time)
ax_ephys.plot(trace_time[trace_idx],trace[trace_idx],'k-')
ephys_traces.append(trace)
ephys_trace_times.append(trace_time)
ephys_sweep_start_times.append(sweep_start_time)
if show_e_ap_peaks:
ap_max_index = (ephysanal.ActionPotential()&sweep).fetch('ap_max_index')
aptimes = trace_time[np.asarray(ap_max_index,int)]
apVs = trace[np.asarray(ap_max_index,int)]
ap_needed = (time_to_plot-trace_window/2 < aptimes) & (time_to_plot+trace_window/2 > aptimes)
aptimes = aptimes[ap_needed]
apVs = apVs[ap_needed]
ax_ephys.plot(aptimes,apVs,'ro')
if show_stimulus:
trace_stim= (ephys_patch.SweepMetadata()*ephys_patch.SweepStimulus()&sweep).fetch1('stimulus_trace')
trace_stim = trace_stim*10**12
ax_ephys_stim.plot(trace_time[trace_idx],trace_stim[trace_idx],'k-')
ephys_traces_stim.append(trace_stim)
ephysdata = {'ephys_traces':ephys_traces,'ephys_trace_times':ephys_trace_times}
if show_stimulus:
ephysdata['ephys_traces_stimulus'] = ephys_traces_stim
movie_nums, movie_start_times,movie_frame_rates,movie_frame_nums = ((imaging.Movie()*imaging_gt.GroundTruthROI())&key_cell).fetch('movie_number','movie_start_time','movie_frame_rate','movie_frame_num')
movie_start_times = np.asarray(movie_start_times, float)
movie_end_times = np.asarray(movie_start_times, float)+np.asarray(movie_frame_nums, float)/np.asarray(movie_frame_rates, float)
needed_start_time = session_time_to_plot - trace_window/2
needed_end_time = session_time_to_plot + trace_window/2
movie_nums = movie_nums[((movie_start_times >= needed_start_time) & (movie_start_times <= needed_end_time)) |
((movie_end_times >= needed_start_time) & (movie_end_times <= needed_end_time)) |
((movie_end_times >= needed_end_time) & (movie_start_times <= needed_start_time)) ]
dffs=list()
frametimes = list()
for movie_num in movie_nums:
#movie_num = movie_nums[(session_time_to_plot>movie_start_times)&(session_time_to_plot<movie_end_times)][0]
key_movie = key_cell.copy()
key_movie['movie_number'] = movie_num
dff,gt_roi_number = ((imaging.ROI()*imaging_gt.GroundTruthROI())&key_movie).fetch1('roi_dff','roi_number')
dff_all,roi_number_all = (imaging.ROI()&key_movie).fetch('roi_dff','roi_number')
dff_all =dff_all[roi_number_all!=gt_roi_number]
frame_times = ((imaging.MovieFrameTimes()*imaging_gt.GroundTruthROI())&key_movie).fetch1('frame_times') + (session_time).total_seconds() - first_movie_start_time_real #modified to cell time
frame_idx = (time_to_plot-trace_window/2 < frame_times) & (time_to_plot+trace_window/2 > frame_times)
dff_list = [dff]
dff_list.extend(dff_all)
prevminval = 0
for dff_now,alpha_now in zip(dff_list,np.arange(1,1/(len(dff_list)+1),-1/(len(dff_list)+1))):
dfftoplotnow = dff_now[frame_idx] + prevminval
ax_ophys.plot(frame_times[frame_idx],dfftoplotnow,'g-',alpha=alpha_now)
prevminval = np.min(dfftoplotnow) -.005
#ax_ophys.plot(frame_times[frame_idx],dff[frame_idx],'g-')
dffs.append(dff)
frametimes.append(frame_times)
if show_o_ap_peaks:
if 'raw' in key_cell['roi_type']:
apidxes = ((imaging.ROI()*imaging_gt.GroundTruthROI())&key_movie).fetch1('roi_spike_indices')
else:
apidxes = ((imaging.ROI()*imaging_gt.GroundTruthROI())&key_movie).fetch1('roi_spike_indices')-1
oap_times = frame_times[apidxes]
oap_vals = dff[apidxes]
oap_needed = (time_to_plot-trace_window/2 < oap_times) & (time_to_plot+trace_window/2 > oap_times)
oap_times = oap_times[oap_needed]
oap_vals = oap_vals[oap_needed]
ax_ophys.plot(oap_times,oap_vals,'ro')
ophysdata = {'ophys_traces':dffs,'ophys_trace_times':frametimes}
ax_ophys.autoscale(tight = True)
# =============================================================================
# dfff = np.concatenate(dffs)
# dfff[np.argmin(dfff)]=0
# ax_ophys.set_ylim([min(dfff),max(dfff)])
# =============================================================================
ax_ophys.set_xlim([time_to_plot-trace_window/2,time_to_plot+trace_window/2])
ax_ophys.set_ylabel('dF/F')
ax_ophys.spines["top"].set_visible(False)
ax_ophys.spines["right"].set_visible(False)
ax_ophys.invert_yaxis()
ax_ephys.autoscale(tight = True)
ax_ephys.set_xlim([time_to_plot-trace_window/2,time_to_plot+trace_window/2])
ax_ephys.set_ylabel('Vm (mV))')
ax_ephys.spines["top"].set_visible(False)
ax_ephys.spines["right"].set_visible(False)
if show_stimulus:
# ax_ephys_stim.autoscale(tight = True)
ax_ephys_stim.set_xlim([time_to_plot-trace_window/2,time_to_plot+trace_window/2])
ax_ephys_stim.set_ylabel('Injected current (pA))')
ax_ephys_stim.set_xlabel('time from first movie start (s)')
ax_ephys_stim.spines["top"].set_visible(False)
ax_ephys_stim.spines["right"].set_visible(False)
else:
ax_ephys.set_xlabel('time from first movie start (s)')
outdict = {'ephys':ephysdata,'ophys':ophysdata,'figure_handle':fig}
#%%
return outdict
def make(self, key):
#%
#key = {'subject_id': 454597, 'session': 1, 'cell_number': 0, 'motion_correction_method': 'Matlab', 'roi_type': 'SpikePursuit', 'roi_number': 1}
#key = {'subject_id': 456462, 'session': 1, 'cell_number': 5, 'movie_number': 3, 'motion_correction_method': 'VolPy', 'roi_type': 'VolPy', 'roi_number': 1}
if len(
ROIEphysCorrelation & key
) > 0: # and key['roi_type'] == 'SpikePursuit' #only spikepursuit for now..
key_to_compare = key.copy()
del key_to_compare['roi_number']
#print(key)
#%
roinums = np.unique(
(ROIEphysCorrelation() & key_to_compare).fetch('roi_number'))
snratios_mean = list()
snratios_median = list()
snratios_first50 = list()
for roinum_now in roinums:
snratios = (ROIAPWave() & key_to_compare
& 'roi_number = {}'.format(roinum_now)
).fetch('apwave_snratio')
snratios_mean.append(np.mean(snratios))
snratios_median.append(np.median(snratios))
snratios_first50.append(np.mean(snratios[:50]))
#%%
if np.max(
(ROIEphysCorrelation() & key).fetch('roi_number')
) == roinums[np.argmax(
snratios_first50
)]: #np.max((imaging_gt.ROIEphysCorrelation()&key).fetch('roi_number')) == np.min((imaging_gt.ROIEphysCorrelation&key_to_compare).fetch('roi_number')):#np.max(np.abs((ROIEphysCorrelation&key).fetch('corr_coeff'))) == np.max(np.abs((ROIEphysCorrelation&key_to_compare).fetch('corr_coeff'))):
print('this is it')
print(key['roi_type'])
cellstarttime = (ephys_patch.Cell()
& key).fetch1('cell_recording_start')
sessionstarttime = (experiment.Session()
& key).fetch1('session_time')
aptimes = np.asarray(
(ephysanal.ActionPotential() & key).fetch('ap_max_time'),
float) + (cellstarttime -
sessionstarttime).total_seconds()
sweep_start_times, sweep_end_times = (ephys_patch.Sweep()
& key).fetch(
'sweep_start_time',
'sweep_end_time')
sweep_start_times = np.asarray(sweep_start_times, float) + (
cellstarttime - sessionstarttime).total_seconds()
sweep_end_times = np.asarray(sweep_end_times, float) + (
cellstarttime - sessionstarttime).total_seconds()
frame_timess, roi_spike_indicess = (
imaging.MovieFrameTimes() * imaging.Movie() *
imaging.ROI() & key).fetch('frame_times',
'roi_spike_indices')
movie_start_times = list()
movie_end_times = list()
roi_ap_times = list()
for frame_times, roi_spike_indices in zip(
frame_timess, roi_spike_indicess):
movie_start_times.append(frame_times[0])
movie_end_times.append(frame_times[-1])
roi_ap_times.append(frame_times[roi_spike_indices])
movie_start_times = np.sort(movie_start_times)
movie_end_times = np.sort(movie_end_times)
roi_ap_times = np.sort(np.concatenate(roi_ap_times))
#%
##delete spikes in optical traces where there was no ephys recording
for start_t, end_t in zip(
np.concatenate([sweep_start_times, [np.inf]]),
np.concatenate([[0], sweep_end_times])):
idxtodel = np.where((roi_ap_times > end_t)
& (roi_ap_times < start_t))[0]
if len(idxtodel) > 0:
roi_ap_times = np.delete(roi_ap_times, idxtodel)
##delete spikes in ephys traces where there was no imaging
for start_t, end_t in zip(
np.concatenate([movie_start_times, [np.inf]]),
np.concatenate([[0], movie_end_times])):
idxtodel = np.where((aptimes > end_t)
& (aptimes < start_t))[0]
if len(idxtodel) > 0:
#print(idxtodel)
aptimes = np.delete(aptimes, idxtodel)
#%
D = np.zeros([len(aptimes), len(roi_ap_times)])
for idx, apt in enumerate(aptimes):
D[idx, :] = (roi_ap_times - apt) * 1000
D_test = np.abs(D)
D_test[D_test > 15] = 1000
D_test[D < -1] = 1000
X = scipy.optimize.linear_sum_assignment(D_test)
#%
cost = D_test[X[0], X[1]]
unmatched = np.where(cost == 1000)[0]
X0_final = np.delete(X[0], unmatched)
X1_final = np.delete(X[1], unmatched)
ephys_ap_times = aptimes[X0_final]
ophys_ap_times = roi_ap_times[X1_final]
false_positive_time_imaging = list()
for roi_ap_time in roi_ap_times:
if roi_ap_time not in ophys_ap_times:
false_positive_time_imaging.append(roi_ap_time)
false_negative_time_ephys = list()
for aptime in aptimes:
if aptime not in ephys_ap_times:
false_negative_time_ephys.append(aptime)
key['ephys_matched_ap_times'] = ephys_ap_times
key['ophys_matched_ap_times'] = ophys_ap_times
key['ephys_unmatched_ap_times'] = false_negative_time_ephys
key['ophys_unmatched_ap_times'] = false_positive_time_imaging
#print(imaging.ROI()&key)
#print([len(aptimes),'vs',len(roi_ap_times)])
#%%
self.insert1(key, skip_duplicates=True)
for keynow in movie_dict.keys():
if type(movie_dict[keynow]) == decimal.Decimal:
movie_dict[keynow] = float(movie_dict[keynow])
elif type(movie_dict[keynow]) == datetime.timedelta:
movie_dict[keynow] = str(movie_dict[keynow])
elif type(movie_dict[keynow]) == np.ndarray:
movie_dict[keynow] = movie_dict[keynow].tolist()
save_dir_base = os.path.join(gt_package_directory,str(key['subject_id']),'Cell_{}'.format(key['cell_number']),movie_dict['movie_name'])
if ((os.path.isdir(save_dir_base) and (len(os.listdir(save_dir_base))>3 and not overwrite))):
print('already exported, skipped')
else:
session_time, cell_recording_start = (experiment.Session()*ephys_patch.Cell()&key).fetch1('session_time','cell_recording_start')
first_movie_start_time = np.min(np.asarray(((imaging.Movie()*imaging_gt.GroundTruthROI())&key).fetch('movie_start_time'),float))
first_movie_start_time_real = first_movie_start_time + session_time.total_seconds()
frame_times = (imaging.MovieFrameTimes()&movie).fetch1('frame_times') - cell_recording_start.total_seconds() + session_time.total_seconds()
motion_corr_vectors = np.asarray((imaging.MotionCorrection()*imaging.RegisteredMovie()&movie&'motion_correction_method = "VolPy"'&'motion_corr_description= "rigid motion correction done with VolPy"').fetch1('motion_corr_vectors'))
movie_dict['movie_start_time'] = frame_times[0]
movie_files = list()
repositories , directories , fnames = (imaging.MovieFile() & movie).fetch('movie_file_repository','movie_file_directory','movie_file_name')
for repository,directory,fname in zip(repositories,directories,fnames):
movie_files.append(os.path.join(dj.config['locations.{}'.format(repository)],directory,fname))
sweepdata_out = list()
sweepmetadata_out = list()
for sweep_number in sweep_numbers:
#%
key_sweep = key.copy()
key_sweep['sweep_number'] = sweep_number
neutralizationenable,e_sr= (ephys_patch.SweepMetadata()&key_sweep).fetch1('neutralizationenable','sample_rate')
try:
key ={'subject_id':456462}
key['movie_number'] = 3 #3
key = (imaging_gt.CellMovieCorrespondance()&key).fetch(as_dict = True)[0]
key['roi_type'] = "VolPy"
sweep_numbers = key['sweep_numbers']
del key['sweep_numbers']
#%
motion_corr_vectors = np.asarray((imaging.MotionCorrection()*imaging.RegisteredMovie()&key&'motion_correction_method = "VolPy"'&'motion_corr_description= "rigid motion correction done with VolPy"').fetch1('motion_corr_vectors'))
#% ophys related stuff
session_time, cell_recording_start = (experiment.Session()*ephys_patch.Cell()&key).fetch1('session_time','cell_recording_start')
first_movie_start_time = np.min(np.asarray(((imaging.Movie()*imaging_gt.GroundTruthROI())&key).fetch('movie_start_time'),float))
first_movie_start_time_real = first_movie_start_time + session_time.total_seconds()
frame_times = (imaging.MovieFrameTimes()&key).fetch1('frame_times') - cell_recording_start.total_seconds() + session_time.total_seconds()
roi_dff,roi_f0,roi_spike_indices,framerate = (imaging.Movie()*imaging.ROI*imaging_gt.GroundTruthROI()&key).fetch1('roi_dff','roi_f0','roi_spike_indices','movie_frame_rate')
roi_spike_indices = roi_spike_indices-1
roi_f = (roi_dff*roi_f0)+roi_f0
xvals = frame_times-frame_times[0]
yvals = roi_f
out = scipy.optimize.curve_fit(lambda t,a,b,c,d,e: a*np.exp(-t/b) + c + d*np.exp(-t/e), xvals, yvals,bounds=((0,0,-np.inf,0,0),(np.inf,np.inf,np.inf,np.inf,np.inf)))
f0_fit_f = out[0][0]*np.exp(-xvals/out[0][1])+out[0][2] +out[0][3]*np.exp(-xvals/out[0][4])
dff_fit = (roi_f-f0_fit_f)/f0_fit_f
#%
# calculating F0s
aptimes = (ephysanal.ActionPotential()*imaging_gt.ROIAPWave()*imaging_gt.GroundTruthROI()&key).fetch('ap_max_time')
movie_numbers, famerates = ((imaging_gt.GroundTruthROI() * imaging.Movie())
& key).fetch('movie_number', 'movie_frame_rate')
session_time = (experiment.Session() & key_cell).fetch('session_time')[0]
cell_time = (ephys_patch.Cell() & key_cell).fetch('cell_recording_start')[0]
for movie_number in movie_numbers:
frame_rate = (
(imaging.Movie()) & key_cell
& 'movie_number = ' + str(movie_number)).fetch('movie_frame_rate')[0]
#frame_num = ((imaging.Movie())&key_cell & 'movie_number = '+str(movie_number)).fetch('movie_frame_num')[0]
movie_start_time = float(
((imaging.Movie()) & key_cell
& 'movie_number = ' + str(movie_number)).fetch('movie_start_time')[0])
movie_start_time = session_time.total_seconds(
) + movie_start_time - cell_time.total_seconds()
movie_time = (imaging.MovieFrameTimes() & key_cell
& 'movie_number = ' + str(movie_number)
).fetch('frame_times')[0] - cell_time.total_seconds(
) + session_time.total_seconds()
movie_end_time = movie_time[-1]
sweeps_needed = ephys_patch.Sweep(
) & key_cell & 'sweep_start_time < ' + str(
movie_end_time) & 'sweep_end_time > ' + str(movie_start_time)
sweep_start_ts, sweep_end_ts, traces, sweep_nums, sample_rates = (
sweeps_needed * ephys_patch.SweepResponse() *
ephys_patch.SweepMetadata()).fetch('sweep_start_time',
'sweep_end_time', 'response_trace',
'sweep_number', 'sample_rate')
trace_times = list()
for sweep_start_t, sweep_end_t, trace, sample_rate in zip(
