def compute_acov_each(fname): import pandas as pd import data_model as dm import data_model.timewindow as tw from numpy import dot index = pd.HDFStore(fname)['spec'].index[0] x = pd.HDFStore(fname)['spiketimes'] y = dm.PSTH(x) ccg = y.acf(max_shift=2) ccv = dot(ccg, tw.triangle(200, 401)) return (index, ccv)
def compute_acov_each(fname): import pandas as pd import data_model as dm import data_model.timewindow as tw from numpy import dot index = pd.HDFStore(fname)['spec'].index[0] x = pd.HDFStore(fname)['spiketimes'] y = dm.PSTH(x) ccg = y.acf(max_shift=2) ccv = dot(ccg, tw.triangle(200,401)) return (index, ccv)
def compute_pcov_each(fname): import pandas as pd import data_model as dm reload(dm) import data_model.timewindow as tw from numpy import dot, convolve import os from scipy.signal import resample index = pd.HDFStore(fname)['spec'].index[0] Fs = pd.HDFStore(fname)['spec'].iloc[0]['Fs'] c = pd.HDFStore(fname)['spec'].iloc[0]['c'] dname = os.path.dirname(fname) x = pd.HDFStore(fname)['spiketimes'] y = dm.PSTH(x) s = pd.HDFStore(os.path.join(dname, 'noises.h5'))['noise'] sta = y.spike_triggered(s, Fs) ccx = convolve(sta[::-1], sta) ccx = resample(ccx, 401) ccx = ccx * y.rate * y.rate * 1e-6 * c pcv = dot(ccx, tw.triangle(200, 401)) return (index, pcv)
def compute_pcov_each(fname): import pandas as pd import data_model as dm reload(dm) import data_model.timewindow as tw from numpy import dot, convolve import os from scipy.signal import resample index = pd.HDFStore(fname)['spec'].index[0] Fs = pd.HDFStore(fname)['spec'].iloc[0]['Fs'] c = pd.HDFStore(fname)['spec'].iloc[0]['c'] dname = os.path.dirname(fname) x = pd.HDFStore(fname)['spiketimes'] y = dm.PSTH(x) s = pd.HDFStore(os.path.join(dname, 'noises.h5'))['noise'] sta = y.spike_triggered(s, Fs) ccx = convolve(sta[::-1],sta) ccx = resample(ccx, 401) ccx = ccx*y.rate*y.rate*1e-6*c pcv = dot(ccx, tw.triangle(200,401)) return (index, pcv)