def circ_psth(rast_, rr, npips, onset = 0.05, bins = 20, color = 'b', remove_first = False, ax = None): ''' Input: rast_ : subset of block raster corresponding to only the stimuli you want plotted rr : the repetition rate npips : number of pips presented at this rate onset : in seconds, onset time of first pip bins : number of bins to include remove_first : exclude the respose to the first pip ''' spktimes_ = Spikes.rast2spktimes(rast_) period = 1. / rr train_end = float(npips) / rr spktimes_chunk = spktimes_ - onset spktimes_chunk = spktimes_chunk[spktimes_chunk > 0] spktimes_chunk = spktimes_chunk[spktimes_chunk < train_end] if remove_first: spktimes_chunk = spktimes_chunk[spktimes_chunk > period] spktimes_pol = spktimes_chunk * 2*np.pi * rr # convert to radians spktimes_pol = np.mod(spktimes_pol, 2*np.pi) # force range 0-2pi nspks = spktimes_pol.size r, V, theta = circ_stat(spktimes_pol) ax, fig = misc.axis_check(ax, polar = True) if spktimes_pol.size > 0: ax.hist(spktimes_pol, bins = bins, color = color, histtype = 'stepfilled') ax.plot([0, theta], [0, V], 'r.-') ax.plot([0, 0], [0, V*np.cos(theta)], 'r.-') return r, V, theta
def calc_vs(rast_, rr, npips, onset = 0.05, analysistype = 'mean', remove_first = False): ix_times = np.arange(rast_.shape[1]) / 1000. spktimes_ = Spikes.rast2spktimes(rast_, ix_times) spktimes_ = spktimes_ - onset train_end = npips / rr # end at last pip if remove_first: train_start = 1./rr else: train_start = 0. spktimes_chunk = spktimes_[np.logical_and(train_start<spktimes_, spktimes_<train_end)] v = np.exp(1j * 2*np.pi * rr * spktimes_chunk) # v = np.cos(2 * np.pi * rr * spktimes_chunk) + 1j * np.sin(2 * np.pi * rr * spktimes_chunk) if analysistype == 'mean': vs = np.abs(v.mean()) #/ duration if analysistype == 'sum': vs = np.abs(v.sum()) alpha = np.angle(v) p = rayleigh_test(np.angle(v)) return vs, p
