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spikesdetection.py
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spikesdetection.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
'''Detection of spikes from continuous data.
'''
from extracellpy import settings
from extracellpy import loadneuralynx
import numpy as np
import os
__author__ = 'Santiago Jaramillo'
__version__ = '0.1'
EPHYSPATH = settings.EPHYS_PATH
def filter_continuous(data,samplingRate,highPassCut=600,order=6):
'''
Filter continuous signal to extract spikes
'''
import scipy.signal as signal
Wn = highPassCut*2/samplingRate # Normalized
[b,a]=signal.butter(order, Wn, btype='high')
print('Filtering continuous data...')
filteredData = signal.lfilter(b,a,data)
print('Done!')
return filteredData
def find_threshold_crossing(data,threshold=None,refractorySamples=8,dual=False):
'''dual defines if detection is done for both positive and negative crossings'''
if not threshold:
threshold = np.std(data)
if threshold>0:
crossesBool = data>threshold
if threshold<0:
crossesBool = data<threshold
if dual:
crossesBool = crossesBool | (data<-threshold)
crosses = np.flatnonzero(crossesBool)
beyondRefraction = np.diff(np.r_[0,crosses])>refractorySamples
crossesCleaned = crosses[beyondRefraction]
params = {'threshold':threshold,'refractorySamples':refractorySamples,
'dual':dual}
return crossesCleaned,params
def define_threshold(animalName,ephysSession,electrode,samples=np.arange(100000)):
import pylab as plt
# -- Load continuous data --
dataDir = os.path.join(EPHYSPATH,'%s/%s/'%(animalName,ephysSession))
contDataFile = os.path.join(dataDir,'CSC%d.ncs'%electrode)
dataLFP = loadneuralynx.DataCont(contDataFile)
# -- Extract spikes --
filtLFP = filter_continuous(dataLFP.samples[samples],dataLFP.samplingRate)
plt.clf()
plt.plot(filtLFP,'b-')
plt.draw()
plt.show()
class ExtractedSpikes(object):
#def __init__(self):
def __init__(self,spikesFileName=None,contFileName=None):
self.timestamps = None
self.detectionParams = {}
if spikesFileName:
try:
self.from_file(spikesFileName)
except IOError:
# TODO: Ask if want to generate
raise
def from_file(self,fileName):
import h5py
# TODO: read also 'header' info.
dataFile = h5py.File(fileName,'r')
self.timestamps = dataFile['timestamps'][...]
dataFile.close()
def extract_spikes(self,animalName,ephysSession,electrode,
threshold=None,refractorySamples=8,dual=False,
waveforms=False,showSignal=False):
import h5py
# -- Load continuous data --
dataDir = os.path.join(EPHYSPATH,'%s/%s/'%(animalName,ephysSession))
contDataFile = os.path.join(dataDir,'CSC%d.ncs'%electrode)
dataLFP = loadneuralynx.DataCont(contDataFile)
# -- Extract spikes --
filtLFP = filter_continuous(dataLFP.samples,dataLFP.samplingRate)
params = {'threshold':threshold,'refractorySamples':refractorySamples,
'dual':dual}
(crosses,params) = find_threshold_crossing(filtLFP,**params)
spikeTimes = dataLFP.time_of_sample(crosses)
self.detectionParams = params
self.timestamps = spikeTimes
# -- Save spikes --
spikesDataDir = settings.EXTRACTED_SPIKES_PATH%animalName
spikesFileName = '%s_%s_e%02d_spikes.h5'%(animalName,ephysSession,electrode)
spikesFileFull = os.path.join(spikesDataDir,spikesFileName)
print 'Saving data to %s'%spikesFileFull
dataFile = h5py.File(spikesFileFull,'w')
try:
dataFile.create_dataset('timestamps', data=self.timestamps)
# FIXME: save parameters as well
except:
dataFile.close() # Close before raising error
raise
dataFile.close()
if showSignal:
import pylab as plt
plt.clf()
plt.plot(filtLFP[:10000],'b-')
plt.hold(True)
plt.axhline(threshold,color='0.75')
#plt.plot(crosses[crosses<10000],2*np.tile(threshold,len(crosses)),'.k')
plt.hold(False)
plt.draw()
plt.show()
'''
'''
return spikesFileFull
if __name__ == "__main__":
CASE = 4
if CASE==1:
from pylab import *
import loadneuralynx
import scipy.signal as signal
#import lfilter, bessel
fileName = '/var/data/neuralynx/saja125/2012-02-03_18-32-48/CSC8.ncs' # Large spikes
#fileName = '/var/data/neuralynx/saja125/2012-02-05_17-34-15/CSC2.ncs' # Very large spikes
dataLFP = loadneuralynx.DataCont(fileName)
# -- Filter signal (high-pass) --
highpassFreq = 100 # Hz
Wn = highpassFreq*2/dataLFP.samplingRate # Normalized
[b,a]=signal.butter(6, Wn, btype='high')
filtLFP = signal.lfilter(b,a,dataLFP.samples[:500000])
#filtLFP = signal.filtfilt(b,a,dataLFP.samples[:500000])
highpassFreq = 600 # Hz
Wn = highpassFreq*2/dataLFP.samplingRate # Normalized
[b,a]=signal.butter(6, Wn, btype='high')
filtLFP2 = signal.lfilter(b,a,dataLFP.samples[:500000])
rangeToPlot = np.arange(100000)+200000
plot(dataLFP.samples[rangeToPlot],'-')
hold(True)
plot(filtLFP[rangeToPlot],'g-')
plot(filtLFP2[rangeToPlot],'r-')
hold(False)
#xlim([72000,84000])
#xlim([90000,92000])
#xlim([85400,86600]) # Good for 2012-02-03_18-32-48/CSC8.ncs'
xlim([82300,83000]) # Good for 2012-02-03_18-32-48/CSC8.ncs'
ylim([-6000,8000])
draw()
show()
if CASE==2:
from pylab import *
import loadneuralynx
reload(loadneuralynx)
import scipy.signal as signal
#import line_profiler as lprof
#profile = lprof.LineProfiler(find_threshold_crossing)
#profile.enable()
fileName = '/var/data/neuralynx/saja125/2012-02-03_18-32-48/CSC8.ncs' # Large spikes
dataLFP = loadneuralynx.DataCont(fileName)
dataChunk = dataLFP.samples[arange(10000)+200000]
#dataChunk = dataLFP.samples
# -- Filter signal (high-pass) --
filtLFP = filter_continuous(dataChunk,dataLFP.samplingRate)
# -- Find crossings --
threshold = 2000
refractorySamples = 8
crosses,params = find_threshold_crossing(filtLFP,threshold,refractorySamples,dual=True)
spikeTimes = dataLFP.time_of_sample(crosses)
#profile.print_stats()
#rangeToPlot = np.arange(10000)
plot(filtLFP,'b-')
hold(True)
axhline(threshold,color='0.75')
plot(crosses,2*tile(threshold,len(crosses)),'.k')
hold(False)
draw()
show()
'''
'''
if CASE==3:
animalName = 'saja125'
ephysSession = '2012-02-03_18-32-48'
electrode = 8
spikes = ExtractedSpikes()
dataFile = spikes.extract_spikes(animalName,ephysSession,electrode,threshold=2000,dual=True)
#newspikes = ExtractedSpikes(dataFile)
if CASE==4:
from pylab import *
animalName = 'saja125'
ephysSession = '2012-02-03_18-32-48'
electrode = 8
spikesDataDir = settings.EXTRACTED_SPIKES_PATH%animalName
spikesFileName = '%s_%s_e%02d_spikes.h5'%(animalName,ephysSession,electrode)
spikesFileFull = os.path.join(spikesDataDir,spikesFileName)
newspikes = ExtractedSpikes(spikesFileFull)
'''
# -- Filter signal (high-pass) --
filtLFP = filter_continuous(dataChunk,dataLFP.samplingRate)
# -- Find crossings --
threshold = 2000
refractorySamples = 8
crosses = find_threshold_crossing(filtLFP,threshold,refractorySamples,dual=True)
spikeTimes = dataLFP.time_of_sample(crosses)
#profile.print_stats()
#rangeToPlot = np.arange(10000)
plot(filtLFP,'b-')
hold(True)
axhline(threshold,color='0.75')
plot(crosses,2*tile(threshold,len(crosses)),'.k')
hold(False)
draw()
show()
'''