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beta.py
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/
beta.py
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#!/usr/bin/python
# -*- coding: UTF-8 -*-
'''
Routines related to extracting and processing beta
'''
from __future__ import absolute_import
from __future__ import with_statement
from __future__ import division
from __future__ import nested_scopes
from __future__ import generators
from __future__ import unicode_literals
from __future__ import print_function
import sys
from neurotools.system import *
if sys.version_info<(3,):
from itertools import imap as map
from matplotlib.pyplot import *
from numpy import *
import numpy as np # TODO namespace
from neurotools import nlab
from neurotools.graphics.plot import *
import numpy as np
from numpy.core.numeric import convolve
from numpy.lib.function_base import diff
from matplotlib.pyplot import clf,axvspan,plot,ylim,axhline,draw
from scipy.signal.signaltools import *
from neurotools.signal.signal import bandfilter, get_edges
from neurotools.tools import wait
from neurotools.tools import memoize
from neurotools.signal.ppc import *
from cgid.setup import areas
from cgid.lfp import *
from cgid.spikes import get_spikes_session_filtered_by_epoch,get_unit_channel
from cgid.data_loader import get_trial_event,get_good_trials,get_good_channels
from neurotools.signal.multitaper import multitaper_spectrum
@memoize
def get_beta_peak(session,area,epoch,fa,fb):
'''
Parameters
----------
Returns
-------
'''
# determine beta peak
Fs=1000
allspec=[]
for trial in get_good_trials(session):
x = get_all_raw_lfp(session, area, trial, epoch)
f,mts = multitaper_spectrum(x,5,Fs)
allspec.append(mts)
allspec = np.array(allspec)
Ntr,Nch,Nti = np.shape(allspec)
meanspec = np.mean(allspec,axis=(0,1))
peaks,vals = nlab.local_maxima(meanspec)
betapeaks = peaks[(f[peaks]>fa)&(f[peaks]<fb)]
betapeak = f[betapeaks][np.argmax(meanspec[betapeaks])]
return betapeak
def estimate_beta_band(session,area,bw=8,epoch=None,doplot=False):
'''
return betapeak-0.5*bw,betapeak+0.5*bw
Parameters
----------
Returns
-------
'''
print('THIS IS NOT THE ONE YOU WANT TO USE')
print('IT IS EXPERIMENTAL COHERENCE BASED IDENTIFICATION OF BETA')
assert 0
if epoch is None: epoch = (6,-1000,3000)
allco = []
if not area is None:
chs = get_good_channels(session,area)[:2]
for a in chs:
for b in chs:
if a==b: continue
for tr in get_good_trials(session):
x = get_filtered_lfp(session,area,a,tr,epoch,None,300)
y = get_filtered_lfp(session,area,b,tr,epoch,None,300)
co,fr = cohere(x,y,Fs=1000,NFFT=256)
allco.append(co)
else:
for area in areas:
chs = get_good_channels(session,area)[:2]
for a in chs:
for b in chs:
if a==b: continue
for tr in get_good_trials(session):
x = get_filtered_lfp(session,area,a,tr,epoch,None,300)
y = get_filtered_lfp(session,area,b,tr,epoch,None,300)
co,fr = cohere(x,y,Fs=1000,NFFT=256)
allco.append(co)
allco = array(allco)
m = mean(allco,0)
sem = std(allco,0)/sqrt(shape(allco)[0])
# temporary in lieu of multitaper
smooth = ceil(float(bw)/(np.diff(fr)[0]))
smoothed = convolve(m,np.ones(smooth)/smooth,'same')
use = (fr<=56)&(fr>=5)
betafr = (fr<=30-0.5*bw)&(fr>=15+0.5*bw)
betapeak = fr[betafr][np.argmax(smoothed[betafr])]
if doplot:
clf()
plot(fr[use],m[use],lw=2,color='k')
plot(fr[use],smoothed[use],lw=1,color='r')
plot(fr[use],(m+sem)[use],lw=1,color='k')
plot(fr[use],(m-sem)[use],lw=1,color='k')
positivey()
xlim(*rangeover(fr[use]))
shade([[betapeak-0.5*bw],[betapeak+0.5*bw]])
draw()
return betapeak-0.5*bw,betapeak+0.5*bw
def get_stored_beta_peak(session,area,epoch):
'''
Parameters
----------
Returns
-------
'''
epochs = [(6, -1000, 0),(8, -1000, 0)]
if epoch not in epochs:
print('supporting onle the 1s pre-obj and pre go')
print('epoch',epoch,'not available')
assert 0
beta_peaks = {\
('RUS120518', 'M1' , (6, -1000, 0)): 16.0,
('RUS120518', 'M1' , (8, -1000, 0)): 19.0,
('RUS120518', 'PMd', (6, -1000, 0)): 18.0, # changed 26 to 18
('RUS120518', 'PMd', (8, -1000, 0)): 18.0,
('RUS120518', 'PMv', (6, -1000, 0)): 19.0,
('RUS120518', 'PMv', (8, -1000, 0)): 19.0,
('RUS120521', 'M1' , (6, -1000, 0)): 16.0,
('RUS120521', 'M1' , (8, -1000, 0)): 18.0,
('RUS120521', 'PMd', (6, -1000, 0)): 18.0, # changed 24 to 18
('RUS120521', 'PMd', (8, -1000, 0)): 18.0,
('RUS120521', 'PMv', (6, -1000, 0)): 18.0,
('RUS120521', 'PMv', (8, -1000, 0)): 18.0,
('RUS120523', 'M1' , (6, -1000, 0)): 16.0,
('RUS120523', 'M1' , (8, -1000, 0)): 18.0,
('RUS120523', 'PMd', (6, -1000, 0)): 18.0, # changed 23 to 18
('RUS120523', 'PMd', (8, -1000, 0)): 18.0,
('RUS120523', 'PMv', (6, -1000, 0)): 19.0,
('RUS120523', 'PMv', (8, -1000, 0)): 18.0,
('SPK120918', 'M1' , (6, -1000, 0)): 20.0,
('SPK120918', 'M1' , (8, -1000, 0)): 23.0,
('SPK120918', 'PMd', (6, -1000, 0)): 20.0,
('SPK120918', 'PMd', (8, -1000, 0)): 23.0,
('SPK120918', 'PMv', (6, -1000, 0)): 21.0,
('SPK120918', 'PMv', (8, -1000, 0)): 24.0,
('SPK120924', 'M1' , (6, -1000, 0)): 21.0,
('SPK120924', 'M1' , (8, -1000, 0)): 22.0,
('SPK120924', 'PMd', (6, -1000, 0)): 21.0,
('SPK120924', 'PMd', (8, -1000, 0)): 23.0,
('SPK120924', 'PMv', (6, -1000, 0)): 20.0,
('SPK120924', 'PMv', (8, -1000, 0)): 25.0,
('SPK120925', 'M1' , (6, -1000, 0)): 20.0,
('SPK120925', 'M1' , (8, -1000, 0)): 24.0,
('SPK120925', 'PMd', (6, -1000, 0)): 20.0,
('SPK120925', 'PMd', (8, -1000, 0)): 24.0,
('SPK120925', 'PMv', (6, -1000, 0)): 21.0,
('SPK120925', 'PMv', (8, -1000, 0)): 24.0
}
return beta_peaks[session,area,epoch]
def get_mean_beta_peak(session,epoch):
'''
Parameters
----------
Returns
-------
'''
return np.mean([get_stored_beta_peak(session,a,epoch) for a in areas])
def get_mean_beta_peak_full_trial(session):
'''
Parameters
----------
Returns
-------
'''
return np.mean([get_stored_beta_peak(session,a,epoch) for a in areas for epoch in [(6,-1000,0),(8,-1000,0)]])
@memoize
def get_high_beta_events(session,area,channel,epoch,
MINLEN = 40, # ms
BOXLEN = 50, # ms
THSCALE = 1.5, # sigma (standard deviations)
lowf = 10.0, # Hz
highf = 45.0, # Hz
pad = 200, # ms
clip = True,
audit = False
):
'''
get_high_beta_events(session,area,channel,epoch) will identify periods of
elevated beta-frequency power for the given channel.
Thresholds are selected per-channel based on all available trials.
The entire trial time is used when estimating the average beta power.
To avoid recomputing, we extract beta events for all trials at once.
By default events that extend past the edge of the specified epoch will
be clipped. Passing clip=False will discard these events.
returns the event threshold, and a list of event start and stop
times relative to session time (not per-trial or epoch time)
passing audit=True will enable previewing each trial and the isolated
beta events.
>>> thr,events = get_high_beta_events('SPK120925','PMd',50,(6,-1000,0))
Parameters
----------
Returns
-------
'''
# get LFP data
signal = get_raw_lfp_session(session,area,channel)
# esimate threshold for beta events
beta_trials = [get_filtered_lfp(session,area,channel,t,(6,-1000,0),lowf,highf) for t in get_good_trials(session)]
threshold = np.std(beta_trials)*THSCALE
print('threshold=',threshold)
N = len(signal)
event,start,stop = epoch
all_events = []
all_high_beta_times = []
for trial in get_good_trials(session):
evt = get_trial_event(session,area,trial,event)
trialstart = get_trial_event(session,area,trial,4)
epochstart = evt + start + trialstart
epochstop = evt + stop + trialstart
tstart = max(0,epochstart-pad)
tstop = min(N,epochstop +pad)
filtered = bandfilter(signal[tstart:tstop],lowf,highf)
envelope = abs(hilbert(filtered))
smoothed = convolve(envelope,ones(BOXLEN)/float(BOXLEN),'same')
E = array(get_edges(smoothed>threshold))+tstart
E = E[:,(diff(E,1,0)[0]>=MINLEN)
& (E[0,:]<epochstop )
& (E[1,:]>epochstart)]
if audit: print(E)
if clip:
E[0,:] = np.maximum(E[0,:],epochstart)
E[1,:] = np.minimum(E[1,:],epochstop )
else:
E = E[:,(E[1,:]<=epochstop)&(E[0,:]>=epochstart)]
if audit:
clf()
axvspan(epochstart,epochstop,color=(0,0,0,0.25))
plot(arange(tstart,tstop),filtered,lw=0.7,color='k')
plot(arange(tstart,tstop),envelope,lw=0.7,color='r')
plot(arange(tstart,tstop),smoothed,lw=0.7,color='b')
ylim(-80,80)
for a,b in E.T:
axvspan(a,b,color=(1,0,0,0.5))
axhline(threshold,color='k',lw=1.5)
xlim(tstart,tstop)
draw()
wait()
all_events.extend(E.T)
assert all(diff(E,0,1)>=MINLEN)
return threshold, all_events
@memoize
def get_high_and_low_beta_spikes(session,area,unit,epoch,fa,fb):
'''
threshold, event_spikes, nonevent_spikes = get_high_and_low_beta_spikes(session,area,unit,epoch,ishighbeta)
Parameters
----------
Returns
-------
'''
threshold,events = get_high_beta_events(session,area,get_unit_channel(session,area,unit),epoch,lowf=fa,highf=fb)
spikes = get_spikes_session_filtered_by_epoch(session,area,unit,epoch)
n_total_spikes = len(spikes)
n_total_times = len(get_good_trials(session))*(epoch[-1]-epoch[-2])
events = array(events)
if shape(events)[0]==0:
print('NO EVENTS!!!!!!!')
return threshold, NaN, Fs*float(n_total_spikes)/n_total_times
event_spikes = (events[:,1][:,None]>=spikes[None,:])\
&(events[:,0][:,None]<=spikes[None,:])
is_in_event = sum(event_spikes,0)
return threshold, spikes[is_in_event==1], spikes[is_in_event==0]
@memoize
def get_high_low_ppc_unit(s,a,u,e,fa,fb):
'''
High/low beta PPC for a given band.
The band is used only to identify beta events,
PPC itself is broad-band.
freqs, event_ppc, nonevent_ppc, threshold = get_high_low_ppc_unit(s,a,u,e,fa,fb)
Parameters
----------
Returns
-------
'''
# get beta LFP.
# No need to restrict this to high or low beta,
# restricting the spikes will accomplish that
ch = get_channel(s,a,u)
lfp = get_raw_lfp_session(s,a,ch)
# get beta events around peak (bottleneck)
# get spikes separated into high and low beta groups
threshold, event_spikes, nonevent_spikes = get_high_and_low_beta_spikes(s,a,u,e,fa,fb)
(freqs,event_ppc ,_),_ = pairwise_phase_consistancy(lfp,event_spikes ,window=100,multitaper=False,biased=False,delta=200,taper=hanning)
(freqs,nonevent_ppc,_),_ = pairwise_phase_consistancy(lfp,nonevent_spikes,window=100,multitaper=False,biased=False,delta=200,taper=hanning)
return freqs, event_ppc, nonevent_ppc, threshold
@memoize
def get_high_low_beta_firing_rates(session,area,unit,epoch,fa,fb):
Fs=1000
'''
Computes the unit firing rates during high and low beta events for
the given task epoch. Good trials only. Defaults to Fs = 1000
returns threshold, event_rate, nonevent_rate
Parameters
----------
Returns
-------
'''
threshold,events = get_high_beta_events(session,area,get_unit_channel(session,area,unit),epoch,lowf=fa,highf=fb)
spikes = get_spikes_session_filtered_by_epoch(session,area,unit,epoch)
n_total_spikes = len(spikes)
n_total_times = len(get_good_trials(session))*(epoch[-1]-epoch[-2])
events = array(events)
if shape(events)[0]==0:
print('NO EVENTS!!!!!!!')
return threshold, NaN, Fs*float(n_total_spikes)/n_total_times
# linear time solution exists but quadratic time solution is quicker to code.
n_event_spikes = sum((events[:,1][:,None]>=spikes[None,:])
&(events[:,0][:,None]<=spikes[None,:]))
n_nonevent_spikes = n_total_spikes-n_event_spikes
n_event_times = sum(events[:,1]-events[:,0])
n_nonevent_times = n_total_times - n_event_times
event_rate = Fs*float(n_event_spikes)/n_event_times
nonevent_rate = Fs*float(n_nonevent_spikes)/n_nonevent_times
print(session,area,unit,epoch,event_rate,nonevent_rate, threshold)
return threshold, event_rate, nonevent_rate
@memoize
def get_amplitude_noise_cutoff(session,area,epoch,fa,fb,SKIP):
'''
get amplitude noise cutoff
Parameters
----------
Returns
-------
'''
x = [get_array_packed_lfp_analytic(session,area,trial,epoch,fa,fb)[...,::SKIP] \
for trial in get_good_trials(session)]
s = std(arr(x).real)
return s