datafolder = sys.argv[-1]
appendix = sys.argv[-2]
os.environ.keys()
ii = int(os.environ['SLURM_ARRAY_TASK_ID'])
print('ii = %d' % (ii)) # null hypothesis run index
import numpy as np
data = np.load(datafolder + appendix + '/param.npy', allow_pickle=True)
param = data.item()
for key, val in param.items():
exec(key + '=val')
runs = 400 # runjob series number
STA = np.zeros(L)
for kk in range(1, runs + 1):
datafile = datafolder + appendix + '/Series' + str(
kk) + '/STA_null_run%d.npy' % (ii)
data = np.load(datafile, allow_pickle=True)
dic = data.item()
STA_tmp = dic['STA']
STA = STA + STA_tmp
STA = STA / float(runs)
from transferit import gain, firing_rate_estimate
fr_estimated = firing_rate_estimate(range(1, runs + 1), datafolder, appendix)
[f, gain_filt] = gain(fr_estimated, STA, datafolder, appendix)
transferdata = {'f': f, 'gain': gain_filt}
np.save(
datafolder + appendix +
'/nullhypothesis/transferdata_nullhypothesis_run%d' % (ii), transferdata)
datafolder = sys.argv[-1]
stim_type = sys.argv[-2]
os.environ.keys()
ii = int(os.environ['SLURM_ARRAY_TASK_ID'])
print('ii = %d' % (ii))
for tau in (50, ):
for posNa in (47, ):
for fr in (5192, ):
if stim_type == 'OU':
from transferit import STA_average, gain, firing_rate_estimate
appendix = 'tau%sfr%sposNa%s_spikedistance50_v3' % (tau, fr,
posNa)
# Randomly select STA with replacement.
List = [randint(1, runs) for i in range(runs)]
fr_estimated = firing_rate_estimate(List, datafolder, appendix)
print('Firing rate is estimated to be %sHz.' % (fr_estimated))
STA = STA_average(List, datafolder, appendix) # averaged STA
[f, gain_filt] = gain(fr_estimated, STA, datafolder, appendix)
transferdata = {'f': f, 'gain': gain_filt}
if stim_type == 'step':
amplitude = 0.1
appendix = 'tau%sfr%sposNa%samplitude%s' % (tau, fr, posNa,
amplitude)
from transferit import gain_step, firing_rate_step
List = [randint(1, runs) for i in range(runs)]
print('length of list is %d' % (len(List)))
[f, gain,
firing_rate_smooth] = gain_step(List, datafolder, appendix,
tau, amplitude)