def populatemytables_core_paralel(arguments, runround):
if runround == 1:
ephysanal.SquarePulse().populate(**arguments)
ephysanal.SweepFrameTimes().populate(**arguments)
elif runround == 2:
ephysanal.SquarePulseSeriesResistance().populate(**arguments)
ephysanal.SweepSeriesResistance().populate(**arguments)
elif runround == 3:
ephysanal.SweepResponseCorrected().populate(**arguments)
elif runround == 4:
ephysanal.ActionPotential().populate(**arguments)
ephysanal.ActionPotentialDetails().populate(**arguments)
def plot_cell_SN_ratio_APwise(roi_type = 'VolPy',v0_max = -35,holding_min = -600,frame_rate_min =300, frame_rate_max = 800 ,bin_num = 10 ):
#%% Show S/N ratios for each AP
cmap = cm.get_cmap('jet')
# =============================================================================
# bin_num = 10
# holding_min = -600 #pA
# v0_max = -35 #mV
# roi_type = 'Spikepursuit'#'Spikepursuit'#'VolPy_denoised'#'SpikePursuit'#'VolPy_dexpF0'#'VolPy'#'SpikePursuit_dexpF0'#'VolPy_dexpF0'#''Spikepursuit'#'VolPy'#
# =============================================================================
key = {'roi_type':roi_type}
gtdata = pd.DataFrame((imaging_gt.GroundTruthROI()&key))
cells = gtdata.groupby(['session', 'subject_id','cell_number','motion_correction_method','roi_type']).size().reset_index(name='Freq')
snratio = list()
v0s = list()
holdings = list()
rss = list()
threshs =list()
mintreshs = list()
f0s_all = list()
snratios_all = list()
peakamplitudes_all = list()
noise_all = list()
for cell in cells.iterrows():
cell = cell[1]
key_cell = dict(cell)
del key_cell['Freq']
snratios,f0,peakamplitudes,noises = (imaging.Movie()*imaging_gt.GroundTruthROI()*imaging_gt.ROIAPWave()*ephysanal.ActionPotentialDetails()&key_cell&'ap_real = 1'&'movie_frame_rate > {}'.format(frame_rate_min)&'movie_frame_rate < {}'.format(frame_rate_max)).fetch('apwave_snratio','apwave_f0','apwave_peak_amplitude','apwave_noise')
#f0 = (imaging_gt.GroundTruthROI()*imaging.ROI()&key_cell).fetch('roi_f0')
sweep = (imaging_gt.GroundTruthROI()*imaging_gt.ROIAPWave()&key_cell).fetch('sweep_number')[0]
thresh = (imaging_gt.GroundTruthROI()*imaging_gt.ROIAPWave()*ephysanal.ActionPotentialDetails()&key_cell&'ap_real = 1').fetch('ap_threshold')
trace = (ephys_patch.SweepResponse()*imaging_gt.GroundTruthROI()&key_cell&'sweep_number = {}'.format(sweep)).fetch('response_trace')
trace =trace[0]
stimulus = (ephys_patch.SweepStimulus()*imaging_gt.GroundTruthROI()&key_cell&'sweep_number = {}'.format(sweep)).fetch('stimulus_trace')
stimulus =stimulus[0]
RS = (ephysanal.SweepSeriesResistance()*imaging_gt.GroundTruthROI()&key_cell&'sweep_number = {}'.format(sweep)).fetch('series_resistance')
RS =RS[0]
medianvoltage = np.median(trace)*1000
holding = np.median(stimulus)*10**12
#print(np.mean(snratios[:100]))
snratio.append(np.mean(snratios[:50]))
v0s.append(medianvoltage)
holdings.append(holding)
rss.append(RS)
threshs.append(thresh)
mintreshs.append(np.min(thresh))
f0s_all.append(f0)
snratios_all.append(snratios)
peakamplitudes_all.append(peakamplitudes)
noise_all.append(noises)
#plot_AP_waveforms(key_cell,AP_tlimits)
#%% for each AP
fig=plt.figure()
ax_sn_f0 = fig.add_axes([0,0,1,1])
ax_sn_f0_binned = fig.add_axes([0,-1.2,1,1])
ax_noise_f0 = fig.add_axes([2.6,0,1,1])
ax_noise_f0_binned = fig.add_axes([2.6,-1.2,1,1])
ax_peakampl_f0 = fig.add_axes([1.3,0,1,1])
ax_peakampl_f0_binned = fig.add_axes([1.3,-1.2,1,1])
for loopidx, (f0,snratio_now,noise_now,peakampl_now,cell_now) in enumerate(zip(f0s_all,snratios_all,noise_all,peakamplitudes_all,cells.iterrows())):
if len(f0)>0:
coloridx = loopidx/len(cells)
cell_now = cell_now[1]
label_now = 'Subject:{}'.format(cell_now['subject_id'])+' Cell:{}'.format(cell_now['cell_number'])
ax_sn_f0.plot(f0,snratio_now,'o',ms=1, color = cmap(coloridx), label= label_now)
ax_noise_f0.plot(f0,noise_now,'o',ms=1, color = cmap(coloridx), label= label_now)
ax_peakampl_f0.plot(f0,peakampl_now,'o',ms=1, color = cmap(coloridx), label= label_now)
lows = np.arange(np.min(f0),np.max(f0),(np.max(f0)-np.min(f0))/(bin_num+1))
highs = lows + (np.max(f0)-np.min(f0))/(bin_num+1)
mean_f0 = list()
sd_f0 = list()
mean_sn = list()
sd_sn =list()
mean_noise = list()
sd_noise =list()
mean_ampl = list()
sd_ampl =list()
for low,high in zip(lows,highs):
idx = (f0 >= low) & (f0 < high)
if len(idx)>10:
mean_f0.append(np.mean(f0[idx]))
sd_f0.append(np.std(f0[idx]))
mean_sn.append(np.mean(snratio_now[idx]))
sd_sn.append(np.std(snratio_now[idx]))
mean_noise.append(np.mean(noise_now[idx]))
sd_noise.append(np.std(noise_now[idx]))
mean_ampl.append(np.mean(peakampl_now[idx]))
sd_ampl.append(np.std(peakampl_now[idx]))
ax_sn_f0_binned.errorbar(mean_f0,mean_sn,sd_sn,sd_f0,'o-', color = cmap(coloridx), label= label_now)
ax_noise_f0_binned.errorbar(mean_f0,mean_noise,sd_noise,sd_f0,'o-', color = cmap(coloridx), label= label_now)
ax_peakampl_f0_binned.errorbar(mean_f0,mean_ampl,sd_ampl,sd_f0,'o-', color = cmap(coloridx), label= label_now)
ax_sn_f0.set_xlabel('F0')
ax_sn_f0.set_ylabel('S/N ratio')
ax_sn_f0_binned.set_xlabel('F0')
ax_sn_f0_binned.set_ylabel('S/N ratio')
#ax_sn_f0_binned.legend()
ax_sn_f0_binned.legend(loc='upper center', bbox_to_anchor=(-.45, 1.5), shadow=True, ncol=1)
ax_noise_f0.set_xlabel('F0')
ax_noise_f0.set_ylabel('Noise (std(dF/F))')
ax_noise_f0_binned.set_xlabel('F0')
ax_noise_f0_binned.set_ylabel('Noise (std(dF/F))')
ax_peakampl_f0.set_xlabel('F0')
ax_peakampl_f0.set_ylabel('Peak amplitude (dF/F)')
ax_peakampl_f0_binned.set_xlabel('F0')
ax_peakampl_f0_binned.set_ylabel('Peak amplitude (dF/F)')
#%%
cells['SN']=snratio
cells['V0']=v0s
cells['holding']=holdings
cells['RS']=np.asarray(rss,float)
print(cells)
cells = cells[cells['V0']<v0_max]
cells = cells[cells['holding']>holding_min]
print(cells)
#% S/N ratio histogram
fig=plt.figure()
ax_hist = fig.add_axes([0,0,1,1])
ax_hist.hist(cells['SN'].values)
ax_hist.set_xlabel('S/N ratio of first 50 spikes')
ax_hist.set_ylabel('# of cells')
ax_hist.set_title(roi_type.replace('_',' '))
ax_hist.set_xlim([0,15])
def plot_AP_waveforms(key,
AP_tlimits = [-.005,.01],
bin_step = .00001,
bin_size = .00025,
save_image = False):
#%
select_high_sn_APs = False
# =============================================================================
# bin_step = .00001
# bin_size = .00025
# =============================================================================
#%
tau_1_on = .64/1000
tau_2_on = 4.1/1000
tau_1_ratio_on = .61
tau_1_off = .78/1000
tau_2_off = 3.9/1000
tau_1_ratio_off = 55
#%
movie_numbers,sweep_numbers,apwavetimes,apwaves,famerates,snratio,apnums,ap_threshold = ((imaging_gt.GroundTruthROI()*imaging.Movie()*imaging_gt.ROIAPWave()*ephysanal.ActionPotentialDetails())&key&'ap_real = 1').fetch('movie_number','sweep_number','apwave_time','apwave_dff','movie_frame_rate','apwave_snratio','ap_num','ap_threshold')
uniquemovienumbers = np.unique(movie_numbers)
for movie_number in uniquemovienumbers:
fig=plt.figure()
ax_ephys=fig.add_axes([0,0,1,1])
ax_raw=fig.add_axes([0,1.1,1,1])
aps_now = movie_numbers == movie_number
ax_bin=fig.add_axes([1.3,1.1,1,1])
ax_e_convolved = fig.add_axes([1.3,0,1,1])
ax_bin.set_title('{} ms binning'.format(bin_size*1000))
if select_high_sn_APs :
medsn = np.median(snratio[aps_now])
aps_now = (movie_numbers == movie_number) & (snratio>medsn)
framerate = famerates[aps_now][0]
apwavetimes_conc = np.concatenate(apwavetimes[aps_now])
apwaves_conc = np.concatenate(apwaves[aps_now])
prev_sweep = None
ephys_vs = list()
for apwavetime,apwave,sweep_number,ap_num in zip(apwavetimes[aps_now],apwaves[aps_now],sweep_numbers[aps_now],apnums[aps_now]):
wave_needed_idx = (apwavetime>=AP_tlimits[0]-1/framerate) & (apwavetime<=AP_tlimits[1]+1/framerate)
ax_raw.plot(apwavetime[wave_needed_idx ]*1000,apwave[wave_needed_idx ])
if prev_sweep != sweep_number:
#%
trace = (ephys_patch.SweepResponse()&key&'sweep_number = {}'.format(sweep_number)).fetch1('response_trace')*1000
e_sr = (ephys_patch.SweepMetadata()&key&'sweep_number = {}'.format(sweep_number)).fetch1('sample_rate')
stepback = int(np.abs(np.round(AP_tlimits[0]*e_sr)))
stepforward = int(np.abs(np.round(AP_tlimits[1]*e_sr)))
ephys_t = np.arange(-stepback,stepforward)/e_sr * 1000
prev_sweep = sweep_number
#%
apmaxindex = (ephysanal.ActionPotential()&key & 'sweep_number = {}'.format(sweep_number) & 'ap_num = {}'.format(ap_num)).fetch1('ap_max_index')
ephys_v = trace[apmaxindex-stepback:apmaxindex+stepforward]
ephys_vs.append(ephys_v)
ax_ephys.plot(ephys_t,ephys_v)
#break
#%
mean_ephys_v = np.mean(np.asarray(ephys_vs),0)
#%
t = np.arange(0,.01,1/e_sr)
f_on = tau_1_ratio_on*np.exp(t/tau_1_on) + (1-tau_1_ratio_on)*np.exp(-t/tau_2_on)
f_off = tau_1_ratio_off*np.exp(t[::-1]/tau_1_off) + (1-tau_1_ratio_off)*np.exp(-t[::-1]/tau_2_off)
f_on = f_on/np.max(f_on)
f_off = f_off/np.max(f_off)
kernel = np.concatenate([f_on,np.zeros(len(f_off))])[::-1]
kernel = kernel /sum(kernel )
trace_conv0 = np.convolve(np.concatenate([mean_ephys_v[::-1],mean_ephys_v,mean_ephys_v[::-1]]),kernel,mode = 'same')
trace_conv0 = trace_conv0[len(mean_ephys_v):2*len(mean_ephys_v)]
kernel = np.ones(int(np.round(e_sr/framerate)))
kernel = kernel /sum(kernel )
trace_conv = np.convolve(np.concatenate([trace_conv0[::-1],trace_conv0,trace_conv0[::-1]]),kernel,mode = 'same')
trace_conv = trace_conv[len(mean_ephys_v):2*len(mean_ephys_v)]
bin_centers = np.arange(np.min(apwavetime),np.max(apwavetime),bin_step)
bin_mean = list()
for bin_center in bin_centers:
bin_mean.append(np.mean(apwaves_conc[(apwavetimes_conc>bin_center-bin_size/2) & (apwavetimes_conc<bin_center+bin_size/2)]))
ax_bin.plot(bin_centers*1000,np.asarray(bin_mean),'g-')
ax_bin.invert_yaxis()
ax_bin.set_xlim(np.asarray(AP_tlimits)*1000)
ax_raw.invert_yaxis()
ax_raw.autoscale(tight = True)
ax_raw.set_xlim(np.asarray(AP_tlimits)*1000)
ax_raw.set_ylabel('dF/F')
ax_raw.set_title('subject: {} cell: {} movie: {} apnum: {}'.format(key['subject_id'],key['cell_number'],movie_number,sum(aps_now)))
ax_ephys.set_xlim(np.asarray(AP_tlimits)*1000)
ax_ephys.set_xlabel('ms')
ax_ephys.set_ylabel('mV')
ax_e_convolved.plot(ephys_t,mean_ephys_v,'k-',label = 'mean')
ax_e_convolved.plot(ephys_t,trace_conv0,'g--',label = 'convolved mean')
ax_e_convolved.plot(ephys_t,trace_conv,'g-',label = 'convolved & binned mean')
ax_e_convolved.legend()
ax_e_convolved.set_xlim(np.asarray(AP_tlimits)*1000)
ax_e_convolved.set_xlabel('ms')
plt.show()
imaging_gt.ROIEphysCorrelation()
if save_image:
fig.savefig('./figures/APwaveforms_subject_{}_cell_{}_movie_{}.png'.format(key['subject_id'],key['cell_number'],movie_number), bbox_inches = 'tight')
step_back = int(integration_window*sr)
if step_back<squarepulse['square_pulse_start_idx'] and np.abs(np.median(stim[:step_back]))<=max_baseline_current and np.median(trace[:step_back])<max_v0:
RS = float((ephysanal.SweepSeriesResistance()&squarepulse).fetch1('series_resistance'))
RS_residual = float((ephysanal.SweepSeriesResistance()&squarepulse).fetch1('series_resistance_residual'))
baseline_v = np.median(trace[squarepulse['square_pulse_start_idx']-step_back:squarepulse['square_pulse_start_idx']])
Rin_v = np.median(trace[squarepulse['square_pulse_end_idx']-step_back:squarepulse['square_pulse_end_idx']])
dv = Rin_v-baseline_v
di = squarepulse['square_pulse_amplitude']
Rin = dv/di/1000000 - RS_residual
Rins.append(Rin)
RSs.append(RS)
v0s.append(baseline_v*1000)
#break
if len(Rins)>1:
rs,threshold,baseline,hw,amplitude = (ephysanal.SweepSeriesResistance()*ephysanal.ActionPotential()*ephysanal.ActionPotentialDetails()&cell&'ap_real=1').fetch('series_resistance','ap_threshold','ap_baseline_value','ap_halfwidth','ap_amplitude')
needed = (rs<AP_max_RS) & (baseline<AP_max_baseline)
if sum(needed)>=100:
ap_order = np.argsort(hw[needed])#[::-1]
AP_ampl = np.median(amplitude[needed][ap_order][:APs_needed])
AP_hw = np.median(hw[needed][ap_order][:APs_needed])
AP_threshold = np.median(threshold[needed][ap_order][:APs_needed])
RS = np.median(RSs)
Rin = np.median(Rins)
v0 = np.median(v0s)
ephys_data['cell_dict'].append(cell)
ephys_data['virus'].append(virus)
ephys_data['expression_time'].append(expression_time)
ephys_data['RS'].append(RS)
ephys_data['Rin'].append(Rin)
ephys_data['AP_amplitude'].append(AP_ampl)
# data['figure_handle'].savefig('./figures/{}_cell_{}_roi_type_{}_long.png'.format(key_cell['subject_id'],key_cell['cell_number'],key_cell['roi_type']), bbox_inches = 'tight')
# print(cell)
# =============================================================================
#%% #%%
data = plot_ephys_ophys_trace(key_cell,time_to_plot=25,trace_window = 1,show_e_ap_peaks = True,show_o_ap_peaks = True)
#%%
session = 1
subject_id = 456462
cell_number = 5
roi_type = 'Spikepursuit'#'Spikepursuit'#'VolPy_denoised'#'SpikePursuit'#'VolPy_dexpF0'#'VolPy'#'SpikePursuit_dexpF0'#'VolPy_dexpF0'#''Spikepursuit'#'VolPy'#
key_cell = {'session':session,'subject_id':subject_id,'cell_number':cell_number,'roi_type':roi_type }
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()
threshold,apmaxtime = (imaging_gt.ROIAPWave()*ephysanal.ActionPotential()*ephysanal.ActionPotentialDetails()&key_cell&'ap_real=1').fetch('ap_threshold','ap_max_time')
threshold=np.asarray(threshold,float)
apmaxtime=np.asarray(apmaxtime,float)
# =============================================================================
# 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
# =============================================================================
#%
time_to_plot = apmaxtime[np.argmin(threshold)]+cell_recording_start.total_seconds() - first_movie_start_time_real
data = plot_ephys_ophys_trace(key_cell,
time_to_plot=time_to_plot,
trace_window = .5,
show_stimulus = False,
show_e_ap_peaks = False,
fig=plt.figure()
ax_ephys = fig.add_axes([0,0,2,.8])
ax_stim = fig.add_axes([0,-.3,2,.2])
ax_ap1 = fig.add_axes([0,-.8,2,.4])
ax_ap2 = ax_ap1.twinx()
ax_snr = fig.add_axes([0,-1.3,2,.4])
for t,response,stimulus,metadata_now in zip(sweep_time,sweep_response,sweep_stimulus,sweep_metadata):
ax_ephys.plot(t,response,'k-')
ax_stim.plot(t,stimulus,'k-')
#%
key_cell ={'subject_id': metadata_now['subject_id'],
'session': metadata_now['session'],
'cell_number':metadata_now['cell_number'],
'sweep_number':metadata_now['sweep_number']}
ap_max_time, ap_amplitude,ap_halfwidth,ap_threshold,snratio = (imaging_gt.GroundTruthROI()*imaging_gt.ROIAPWave()*ephysanal.ActionPotential()*ephysanal.ActionPotentialDetails()&key_cell&'ap_real=1'&'roi_type="VolPy"').fetch('ap_max_time','ap_amplitude','ap_halfwidth','ap_threshold','apwave_snratio')
ap_max_time = np.asarray(ap_max_time,float)
ax_ap2.plot(ap_max_time,ap_threshold-junction_potential,'ro')
ax_ap1.plot(ap_max_time,ap_amplitude,'ko')
ax_snr.plot(ap_max_time,snratio,'go')
#%
if dff is not None:
ax_ophys = fig.add_axes([0,1,2,.8])
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 + prevminval
ax_ophys.plot(frame_times,dfftoplotnow,'g-',alpha=alpha_now)
prevminval = np.min(dfftoplotnow) -.01
#ax_ophys.plot(frame_times,dff,'g-')
def plot_cell_SN_ratio_APwise(roi_type='VolPy',
v0_max=-35,
holding_min=-600,
frame_rate_min=300,
frame_rate_max=1800,
F0_min=50,
bin_num=10):
#%% Show S/N ratios for each AP
bin_num = 10
holding_min = -600 #pA
v0_max = -35 #mV
roi_type = 'VolPy_raw' #'Spikepursuit'#'VolPy_denoised'#'SpikePursuit'#'VolPy_dexpF0'#'VolPy'#'SpikePursuit_dexpF0'#'VolPy_dexpF0'#''Spikepursuit'#'VolPy'#
F0_min = 50
frame_rate_min = 200
frame_rate_max = 800
cmap = cm.get_cmap('jet')
key = {'roi_type': roi_type}
gtdata = pd.DataFrame((imaging_gt.GroundTruthROI() & key))
cells = gtdata.groupby([
'session', 'subject_id', 'cell_number', 'motion_correction_method',
'roi_type'
]).size().reset_index(name='Freq')
snratio = list()
v0s = list()
holdings = list()
rss = list()
threshs = list()
mintreshs = list()
f0s_all = list()
snratios_all = list()
peakamplitudes_all = list()
noise_all = list()
for cell in cells.iterrows():
cell = cell[1]
key_cell = dict(cell)
del key_cell['Freq']
snratios, f0, peakamplitudes, noises = (
imaging.Movie() * imaging_gt.GroundTruthROI() *
imaging_gt.ROIAPWave() * ephysanal.ActionPotentialDetails()
& key_cell & 'ap_real = 1'
& 'movie_frame_rate > {}'.format(frame_rate_min)
& 'movie_frame_rate < {}'.format(frame_rate_max)
& 'apwave_f0 > {}'.format(F0_min)).fetch('apwave_snratio',
'apwave_f0',
'apwave_peak_amplitude',
'apwave_noise')
#f0 = (imaging_gt.GroundTruthROI()*imaging.ROI()&key_cell).fetch('roi_f0')
sweep = (imaging_gt.GroundTruthROI() * imaging_gt.ROIAPWave()
& key_cell).fetch('sweep_number')[0]
thresh = (imaging_gt.GroundTruthROI() * imaging_gt.ROIAPWave() *
ephysanal.ActionPotentialDetails() & key_cell
& 'ap_real = 1').fetch('ap_threshold')
trace = (ephys_patch.SweepResponse() * imaging_gt.GroundTruthROI()
& key_cell
& 'sweep_number = {}'.format(sweep)).fetch('response_trace')
trace = trace[0]
stimulus = (ephys_patch.SweepStimulus() * imaging_gt.GroundTruthROI()
& key_cell &
'sweep_number = {}'.format(sweep)).fetch('stimulus_trace')
stimulus = stimulus[0]
RS = (ephysanal.SweepSeriesResistance() * imaging_gt.GroundTruthROI()
& key_cell
& 'sweep_number = {}'.format(sweep)).fetch('series_resistance')
RS = RS[0]
medianvoltage = np.median(trace) * 1000
holding = np.median(stimulus) * 10**12
#print(np.mean(snratios[:100]))
snratio.append(np.mean(snratios[:50]))
v0s.append(medianvoltage)
holdings.append(holding)
rss.append(RS)
threshs.append(thresh)
mintreshs.append(np.min(thresh))
f0s_all.append(f0)
snratios_all.append(snratios)
peakamplitudes_all.append(peakamplitudes)
noise_all.append(noises)
#plot_AP_waveforms(key_cell,AP_tlimits)
#%%
# =============================================================================
# virus_list=list()
# subject_ids = list()
# for cell in cells.iterrows():
# cell = cell[1]
# key_cell = dict(cell)
# del key_cell['Freq']
# virus_id = (lab.Surgery.VirusInjection()&'subject_id = {}'.format(key_cell['subject_id'])).fetch('virus_id')[0]
# if virus_id == 238:
# virus = 'Voltron 1'
# elif virus_id == 240:
# virus = 'Voltron 2'
# virus_list.append(virus)
# subject_ids.append(key_cell['subject_id'])
# # order = np.argsort(virus_list)
# order = np.lexsort((virus_list, subject_ids))
# snratio = np.asarray(snratio)[order]
# v0s = np.asarray(v0s)[order]
# holdings = np.asarray(holdings)[order]
# rss = np.asarray(rss)[order]
# threshs =np.asarray(threshs)[order]
# mintreshs = np.asarray(mintreshs)[order]
# f0s_all = np.asarray(f0s_all)[order]
# snratios_all = np.asarray(snratios_all)[order]
# peakamplitudes_all = np.asarray(peakamplitudes_all)[order]
# noise_all = np.asarray(noise_all)[order]
# virus_list = np.asarray(virus_list)[order]
# cells = cells.set_index(order, append=True).sort_index(level=1).reset_index(1, drop=True)
# =============================================================================
#%%
apnum = 50
fig = plt.figure(figsize=[10, 10])
ax_exptime_f0 = fig.add_subplot(221)
ax_exptime_dff = fig.add_subplot(222)
ax_exptime_noise = fig.add_subplot(223)
ax_exptime_snration = fig.add_subplot(224)
for loopidx, (f0, snratio_now, noise_now, peakampl_now,
cell_now) in enumerate(
zip(f0s_all, snratios_all, noise_all, peakamplitudes_all,
cells.iterrows())):
if len(
f0
) > 0: # and cell_now[1]['subject_id']==466774:# and cell_now[1]['cell_number']==1:
coloridx = loopidx / len(cells)
cell_now = cell_now[1]
virus_id = (lab.Surgery.VirusInjection()
& 'subject_id = {}'.format(
cell_now['subject_id'])).fetch('virus_id')[0]
if virus_id == 238:
virus = 'Voltron 1'
elif virus_id == 240:
virus = 'Voltron 2'
else:
virus = '??'
label_now = 'Subject:{}'.format(
cell_now['subject_id']) + ' Cell:{} - {}'.format(
cell_now['cell_number'], virus)
expression_time = np.diff(
(lab.Surgery() & 'subject_id = {}'.format(
cell_now['subject_id'])).fetch('start_time'))[0].days
ax_exptime_f0.plot(expression_time,
np.mean(f0[:apnum]),
'o',
ms=10,
color=cmap(coloridx),
label=label_now)
ax_exptime_f0.errorbar(expression_time,
np.mean(f0[:apnum]),
np.std(f0[:apnum]),
ecolor=cmap(coloridx))
ax_exptime_f0.set_xlabel('expression time (days)')
ax_exptime_f0.set_ylabel('F0 (pixel intensity)')
ax_exptime_dff.plot(expression_time,
np.mean(peakampl_now[:apnum]),
'o',
ms=10,
color=cmap(coloridx),
label=label_now)
ax_exptime_dff.errorbar(expression_time,
np.mean(peakampl_now[:apnum]),
np.std(peakampl_now[:apnum]),
ecolor=cmap(coloridx))
ax_exptime_dff.set_xlabel('expression time (days)')
ax_exptime_dff.set_ylabel('AP peak amplitude (dF/F)')
ax_exptime_noise.plot(expression_time,
np.mean(noise_now[:apnum]),
'o',
ms=10,
color=cmap(coloridx),
label=label_now)
ax_exptime_noise.errorbar(expression_time,
np.mean(noise_now[:apnum]),
np.std(noise_now[:apnum]),
ecolor=cmap(coloridx))
ax_exptime_noise.set_xlabel('expression time (days)')
ax_exptime_noise.set_ylabel('noise (dF/F)')
ax_exptime_snration.plot(expression_time,
np.mean(snratio_now[:apnum]),
'o',
ms=10,
color=cmap(coloridx),
label=label_now)
ax_exptime_snration.errorbar(expression_time,
np.mean(snratio_now[:apnum]),
np.std(snratio_now[:apnum]),
ecolor=cmap(coloridx))
ax_exptime_snration.set_xlabel('expression time (days)')
ax_exptime_snration.set_ylabel('S/N ratio')
#%% for each AP
fig = plt.figure()
ax_sn_f0 = fig.add_axes([0, 0, 1, 1])
ax_sn_f0_binned = fig.add_axes([0, -1.2, 1, 1])
ax_noise_f0 = fig.add_axes([2.6, 0, 1, 1])
ax_noise_f0_binned = fig.add_axes([2.6, -1.2, 1, 1])
ax_peakampl_f0 = fig.add_axes([1.3, 0, 1, 1])
ax_peakampl_f0_binned = fig.add_axes([1.3, -1.2, 1, 1])
for loopidx, (f0, snratio_now, noise_now, peakampl_now,
cell_now) in enumerate(
zip(f0s_all, snratios_all, noise_all, peakamplitudes_all,
cells.iterrows())):
if len(
f0
) > 0: # and cell_now[1]['subject_id']==466774:# and cell_now[1]['cell_number']==1:
coloridx = loopidx / len(cells)
cell_now = cell_now[1]
virus_id = (lab.Surgery.VirusInjection()
& 'subject_id = {}'.format(
cell_now['subject_id'])).fetch('virus_id')[0]
if virus_id == 238:
virus = 'Voltron 1'
elif virus_id == 240:
virus = 'Voltron 2'
else:
virus = '??'
label_now = 'Subject:{}'.format(
cell_now['subject_id']) + ' Cell:{} - {}'.format(
cell_now['cell_number'], virus)
ax_sn_f0.plot(f0,
snratio_now,
'o',
ms=1,
color=cmap(coloridx),
label=label_now)
ax_noise_f0.plot(f0,
noise_now,
'o',
ms=1,
color=cmap(coloridx),
label=label_now)
ax_peakampl_f0.plot(f0,
peakampl_now,
'o',
ms=1,
color=cmap(coloridx),
label=label_now)
lows = np.arange(np.min(f0), np.max(f0),
(np.max(f0) - np.min(f0)) / (bin_num + 1))
highs = lows + (np.max(f0) - np.min(f0)) / (bin_num + 1)
mean_f0 = list()
sd_f0 = list()
mean_sn = list()
sd_sn = list()
mean_noise = list()
sd_noise = list()
mean_ampl = list()
sd_ampl = list()
for low, high in zip(lows, highs):
idx = (f0 >= low) & (f0 < high)
if len(idx) > 10:
mean_f0.append(np.mean(f0[idx]))
sd_f0.append(np.std(f0[idx]))
mean_sn.append(np.mean(snratio_now[idx]))
sd_sn.append(np.std(snratio_now[idx]))
mean_noise.append(np.mean(noise_now[idx]))
sd_noise.append(np.std(noise_now[idx]))
mean_ampl.append(np.mean(peakampl_now[idx]))
sd_ampl.append(np.std(peakampl_now[idx]))
ax_sn_f0_binned.errorbar(mean_f0,
mean_sn,
sd_sn,
sd_f0,
'o-',
color=cmap(coloridx),
label=label_now)
ax_noise_f0_binned.errorbar(mean_f0,
mean_noise,
sd_noise,
sd_f0,
'o-',
color=cmap(coloridx),
label=label_now)
ax_peakampl_f0_binned.errorbar(mean_f0,
mean_ampl,
sd_ampl,
sd_f0,
'o-',
color=cmap(coloridx),
label=label_now)
ax_sn_f0.set_xlabel('F0')
ax_sn_f0.set_ylabel('S/N ratio')
ax_sn_f0_binned.set_xlabel('F0')
ax_sn_f0_binned.set_ylabel('S/N ratio')
#ax_sn_f0_binned.legend()
ax_sn_f0_binned.legend(loc='upper center',
bbox_to_anchor=(-.45, 1.5),
shadow=True,
ncol=1)
ax_noise_f0.set_xlabel('F0')
ax_noise_f0.set_ylabel('Noise (std(dF/F))')
ax_noise_f0_binned.set_xlabel('F0')
ax_noise_f0_binned.set_ylabel('Noise (std(dF/F))')
ax_peakampl_f0.set_xlabel('F0')
ax_peakampl_f0.set_ylabel('Peak amplitude (dF/F)')
ax_peakampl_f0_binned.set_xlabel('F0')
ax_peakampl_f0_binned.set_ylabel('Peak amplitude (dF/F)')
#%%
cells['SN'] = snratio
cells['V0'] = v0s
cells['holding'] = holdings
cells['RS'] = np.asarray(rss, float)
print(cells)
cells = cells[cells['V0'] < v0_max]
cells = cells[cells['holding'] > holding_min]
print(cells)
#% S/N ratio histogram
fig = plt.figure()
ax_hist = fig.add_axes([0, 0, 1, 1])
ax_hist.hist(cells['SN'].values)
ax_hist.set_xlabel('S/N ratio of first 50 spikes')
ax_hist.set_ylabel('# of cells')
ax_hist.set_title(roi_type.replace('_', ' '))
ax_hist.set_xlim([0, 15])