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_time_interval(wr_name='FOR04',
cellnum=1,
timeedges=[0, 10],
ylimits_response=None,
plotRS=False):
subject_id = (lab.WaterRestriction() & 'water_restriction_number = "' +
wr_name + '"').fetch('subject_id')[0]
key = {'subject_id': subject_id, 'cell_number': cellnum}
allsweeps = pd.DataFrame((ephys_patch.Sweep() & key))
sweepstoplot = np.where(
np.logical_and(
allsweeps['sweep_end_time'] > float(np.min(timeedges)),
allsweeps['sweep_start_time'] < float(np.max(timeedges))))[0]
df_iv = pd.DataFrame()
for sweepnum in sweepstoplot:
key['sweep_number'] = sweepnum
df_iv = pd.concat([
df_iv,
pd.DataFrame((ephys_patch.Sweep() & key) *
(ephys_patch.SweepResponse() & key) *
(ephys_patch.SweepStimulus() & key) *
(ephys_patch.SweepMetadata() & key))
])
df_IV = pd.DataFrame()
for line in df_iv.iterrows():
linenow = line[1]
time = np.arange(0, len(
linenow['response_trace'])) / linenow['sample_rate']
linenow['time'] = time + float(linenow['sweep_start_time'])
df_IV = pd.concat([df_IV, linenow.to_frame().transpose()])
fig = plt.figure()
ax_IV = fig.add_axes([0, 0, 2, .8])
ax_stim = fig.add_axes([0, -.6, 2, .4])
for line in df_IV.iterrows():
ax_IV.plot(line[1]['time'], line[1]['response_trace'] * 1000, 'k-')
ax_stim.plot(line[1]['time'], line[1]['stimulus_trace'] * 10**12, 'k-')
ax_IV.set_xlabel('Time (s)')
ax_IV.set_xlim([np.min(timeedges), np.max(timeedges)])
if ylimits_response:
ax_IV.set_ylim([np.min(ylimits_response), np.max(ylimits_response)])
ax_IV.set_ylabel('mV')
ax_IV.set_title('Response')
ax_stim.set_xlabel('Time (s)')
ax_stim.set_xlim([np.min(timeedges), np.max(timeedges)])
ax_stim.set_ylabel('pA')
ax_stim.set_title('Stimulus')
if plotRS:
del key['sweep_number']
df_RS = pd.DataFrame((ephysanal.SeriesResistance() *
ephysanal.SquarePulse()) & key)
needed = (df_RS['square_pulse_start_time'].values >
np.min(timeedges)) & (df_RS['square_pulse_start_time'].values
< np.max(timeedges))
ax_RS = fig.add_axes([0, -1.2, 2, .4])
ax_RS.plot(df_RS[needed]['square_pulse_start_time'].values,
df_RS[needed]['series_resistance'].values, 'ko')
ax_RS.set_xlabel('Time (s)')
ax_RS.set_ylabel('RS (MOhm)')
ax_RS.set_xlim([np.min(timeedges), np.max(timeedges)])
min_current = -40*10**-12 #A
min_pulse_time = 0.3 #s
integration_window = .04 #sec
for cell in cells:
if len((lab.Surgery()&'subject_id = {}'.format(cell['subject_id'])).fetch('start_time'))==1:
continue # if there is only one surgery, we don't care
expression_time = np.diff((lab.Surgery()&'subject_id = {}'.format(cell['subject_id'])).fetch('start_time'))[0].days
virus_id = (lab.Surgery.VirusInjection()&'subject_id = {}'.format(cell['subject_id'])).fetch('virus_id')[0]
if virus_id == 238:
virus = 'Voltron 1'
elif virus_id == 240:
virus = 'Voltron 2'
else:
virus = '??'
squarepulses = ephysanal.SquarePulse()&cell&'square_pulse_amplitude < {}'.format(min_current)&'square_pulse_length > {}'.format(min_pulse_time)
Rins = list()
RSs = list()
v0s = list()
for squarepulse in squarepulses:
trace = (ephys_patch.SweepResponse()&squarepulse).fetch1('response_trace')
stim = (ephys_patch.SweepStimulus()&squarepulse).fetch1('stimulus_trace')
sr = (ephys_patch.SweepMetadata()&squarepulse).fetch1('sample_rate')
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']