prestim = 0
poststim = 0
duration = .75
#soundpath = '/auto/users/hellerc/code/baphy/Config/lbhb/SoundObjects/@NaturalSounds/sounds_set4/'
soundpath = '/auto/users/hellerc/code/baphy/Config/lbhb/SoundObjects/@NaturalSounds/sounds_set4/'
manager = BAPHYExperiment(cellid=siteid, batch=batch)
rec = manager.get_recording(**{
'rasterfs': rasterfs,
'stim': False,
'pupil': True,
'resp': True
})
rec['resp'] = rec['resp'].extract_channels(rec.meta['cells_to_extract'])
rec['resp'] = rec['resp'].rasterize()
rec = fix_cpn_epochs(rec)
epochs = rec['resp'].epochs[rec['resp'].epochs.name.str.contains('STIM_')]
epochs = epochs[(epochs.end <= (twin[1] / rasterfs)) & \
(epochs.end >= (twin[0] / rasterfs))]
stims = epochs.name
t = epochs[['start', 'end']].values.tolist()
stimulus = []
spk_times = []
# for saving the highlighted data
stim1 = []
stim2 = []
stim3 = []
r1 = []
r2 = []
df = ld.load_noise_correlation('rsc_ev')
win = 15 # total window size (non overlapping across data)
subwin = 0.25 # sub window size (mean rate across all / sd across all)
# CV = sd of spike counts across all subwindows divided by the mean across all sub windows
# If all neurons are Poisson and statistically independent, then the CV of the population rate will approach zero
site = 'TAR010c'
batch = 289
manager = BAPHYExperiment(cellid=site, batch=batch)
options = {'rasterfs': 4, 'resp': True, 'stim': False, 'pupil': True}
rec = manager.get_recording(**options)
rec['resp'] = rec['resp'].rasterize()
if batch == 331:
rec = nems_preproc.fix_cpn_epochs(rec)
else:
rec = nems_preproc.mask_high_repetion_stims(rec)
rec = generate_psth_from_resp(rec)
# extract continuous data (subtract psth?)
data = rec.apply_mask()['resp']._data #- rec.apply_mask()['psth_sp']._data
pupil = rec.apply_mask()['pupil']._data
# divide into bins
win_bin = int(rec['resp'].fs * win)
subwin_bin = int(rec['resp'].fs * subwin)
CV = []
bpupil = []
i = 0
while ((i * win_bin) <= data.shape[-1]):
manager = BAPHYExperiment(cellid='ARM029a', batch=331)
#manager = BAPHYExperiment(cellid='ARM033a', batch=331)
#manager = BAPHYExperiment(cellid='AMT026a', batch=331)
#manager = BAPHYExperiment(cellid='CRD018d', batch=331)
#manager = BAPHYExperiment(cellid='AMT020a', batch=331)
#manager = BAPHYExperiment(cellid='ARM031a', batch=331)
r = manager.get_recording(recache=True,
**{
'rasterfs': 4,
'resp': True,
'pupil': True,
'stim': False,
'pupil_variable_name': 'area'
})
r['resp'] = r['resp'].rasterize()
r = fix_cpn_epochs(r)
r = generate_psth_from_resp(r)
epochs = [e for e in r['resp'].epochs.name.unique() if e.startswith('STIM')]
r = r.and_mask(epochs)
binsizes = [0.5, 1, 2] #, 3, 4]
thresh = 1
significant_pairs = False
for binsize in binsizes:
rec = r.copy()
# before masking, take raw signals and compute variance of an eyelid over a sliding window
binsize = int(binsize * rec['resp'].fs)
signal = rec['pupil_extras'].extract_channels(['eyelid_top_y'])._data
signal2 = rec['pupil_extras'].extract_channels(['eyelid_bottom_y'])._data
varsig = np.zeros(signal.shape)