neuroprob/utils/stats.py
def ISI_KS_method(interval_dist, sample_bin, spike_ind, rate):
    """
    Overdispersion analysis using time-rescaled ISIs and Kolmogorov-Smirnov statistics
    
    :param ISI_class interval_dist: the interval dist
    :param float sample_bin: 
    :param numpy.array spike_ind: indices of the spike times
    :param numpy.array rate: 1D rate array, sampled at sample_bin
    :param float alpha: significance level of the statistical test (double tailed for DS)
    :returns: quantiles, q_order, ks_y, T_DS, T_KS, sign_DS, sign_KS
    :rtype: tuple
    """
    rtime = discrete_rate_rescale(sample_bin, rate, spike_ind)
    rISI = rtime[1:] - rtime[:-1]

    quantiles = interval_dist.cum_prob(rISI)
    return quantiles

    Overdispersion analysis using time-rescaled ISIs and Kolmogorov-Smirnov statistics
    
    :param ISI_class interval_dist: the interval dist
    :param float sample_bin: 
    :param numpy.array spike_ind: indices of the spike times
    :param numpy.array rate: 1D rate array, sampled at sample_bin
    :param float alpha: significance level of the statistical test (double tailed for DS)
    :returns: quantiles, q_order, ks_y, T_DS, T_KS, sign_DS, sign_KS
    :rtype: tuple
    
Overdispersion analysis using time-rescaled ISIs and Kolmogorov-Smirnov statistics
Overdispersion analysis using time - rescaled ISIs and Kolmogorov - Smirnov statistics
def ISI_KS_method(interval_dist, sample_bin, spike_ind, rate):
    rtime = discrete_rate_rescale(sample_bin, rate, spike_ind)
    rISI = rtime[1:] - rtime[:-1]
    quantiles = interval_dist.cum_prob(rISI)
    return quantiles
def ISI_KS_method ( interval_dist , sample_bin , spike_ind , rate ) : rtime = discrete_rate_rescale ( sample_bin , rate , spike_ind ) rISI = rtime [ 1 : ] - rtime [ : - 1 ] quantiles = interval_dist . cum_prob ( rISI ) return quantiles
Overdispersion analysis using time-rescaled ISIs and Kolmogorov-Smirnov statistics
Overdispersion analysis using time - rescaled ISIs and Kolmogorov - Smirnov statistics
"""
    Overdispersion analysis using time-rescaled ISIs and Kolmogorov-Smirnov statistics
    
    :param ISI_class interval_dist: the interval dist
    :param float sample_bin: 
    :param numpy.array spike_ind: indices of the spike times
    :param numpy.array rate: 1D rate array, sampled at sample_bin
    :param float alpha: significance level of the statistical test (double tailed for DS)
    :returns: quantiles, q_order, ks_y, T_DS, T_KS, sign_DS, sign_KS
    :rtype: tuple
    """
quantiles, q_order, ks_y, T_DS, T_KS, sign_DS, sign_KS
quantiles q_order ks_y T_DS T_KS sign_DS sign_KS
tuple
interval_dist
the interval dist
the interval dist
sample_bin
None
spike_ind
indices of the spike times
indices of the spike times
rate
1D rate array, sampled at sample_bin
1D rate array sampled at sample_bin
alpha
significance level of the statistical test (double tailed for DS)
significance level of the statistical test ( double tailed for DS )