def estimate_bias_in_uncorrected_ppc(signal,times,window=50,Fs=1000,nrand=100):
tried = []
for i in range(nrand):
ff,ppc = uncorrectedppc(phase_randomize(signal),times,window,Fs)
tried.append(ppc)
bias = mean(tried,0)
return ff,bias
def ppc_phase_randomize_chance_level_sample(
signal,times,window=50,Fs=1000,k=4,multitaper=True,
biased=False,delta=100,taper=None):
'''
signal: 1D real valued signal
times: Times of events relative to signal
window: Time around event to examine
Fs: sample rate for computing freqs
k: number of tapers
Also accepts lists of signals / times
Uses phase randomization to sample from the null hypothesis distribution.
Returns the actual PPC samples rather than any summary statistics.
You can do what you want with the distribution returned.
'''
if multitaper:
print("Warning: multitaper can introduce a bias into PPC that depends on the number of tapers!")
print("For a fixed number of tapers, the bias is constant, but be careful")
if not taper is None and multitaper:
print("A windowing taper was specified, but multitaper mode was also selected.")
print("The taper argument is for providing a windowing function when not using multitaper estimation.")
assert 0
if type(taper) is types.FunctionType:
taper = taper(window*2+1)
if biased: warn('skipping bias correction entirely')
assert window>0
# need to compute PPC from phase randomized samples
# We can't phase randomize the snippits because there may be some
# correlation between them, we have to randomize the underlying LFP
if len(shape(signal))==1:
# only a single trial provided
usetimes = discard_spikes_closer_than_delta(signal,times,delta)
signal = phase_randomize(signal)
snippits = array([nodc(signal[t-window:t+window+1]) for t in usetimes])
elif len(shape(signal))==2:
warn('assuming first dimension is trials / repititions')
signals,alltimes = signal,times
snippits = []
for signal,times in zip(signals,alltimes):
signal = phase_randomize(signal)
N = len(signal)
times = array(times)
times = times[times>=window]
times = times[times<N-window]
t_o = -inf
for t in times:
if t-t_o>delta:
t_o = t
snippits.append(nodc(signal[t-window:t+window+1]))
else: assert 0
if biased:
if multitaper: return fftppc_biased_multitaper(snippits,Fs,k),snippits
else: return fftppc_biased(snippits,Fs,taper=taper),snippits
else:
if multitaper: return fftppc_multitaper(snippits,Fs,k),snippits
else: return fftppc(snippits,Fs,taper=taper),snippits
assert 0
