neuroprob/utils/stats.py
def invISI_KS_method(inv_interval_dist, sample_bin, spike_ind, rate):
    """
    Overdispersion analysis using time-rescaled ISIs.
    
    :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 = inv_interval_dist.cum_prob(1/(rISI+1e-12)) # stabilized
    return quantiles

    Overdispersion analysis using time-rescaled ISIs.
    
    :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.
Overdispersion analysis using time - rescaled ISIs .
def invISI_KS_method(inv_interval_dist, sample_bin, spike_ind, rate):
    rtime = discrete_rate_rescale(sample_bin, rate, spike_ind)
    rISI = rtime[1:] - rtime[:-1]
    quantiles = inv_interval_dist.cum_prob(1/(rISI+1e-12)) 
    return quantiles
def invISI_KS_method ( inv_interval_dist , sample_bin , spike_ind , rate ) : rtime = discrete_rate_rescale ( sample_bin , rate , spike_ind ) rISI = rtime [ 1 : ] - rtime [ : - 1 ] quantiles = inv_interval_dist . cum_prob ( 1 / ( rISI + 1e-12 ) ) return quantiles
Overdispersion analysis using time-rescaled ISIs.
Overdispersion analysis using time - rescaled ISIs .
"""
    Overdispersion analysis using time-rescaled ISIs.
    
    :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
    """ # stabilized
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
inv_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
interval_dist
the interval dist
the interval dist
alpha
significance level of the statistical test (double tailed for DS)
significance level of the statistical test ( double tailed for DS )