Example #1
0
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)
Example #2
0
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)
Example #3
0
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)
Example #4
0
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)