/
frequencytuning.py
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frequencytuning.py
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'''
Module for analyzing frequency-tuning sessions.
'''
from extracellpy import settings
reload(settings) # Force reload
from extracellpy import loadneuralynx
reload(loadneuralynx) # Force reload
from extracellpy import loadbehavior
from extracellpy import spikesanalysis
from extracellpy import extrafuncs
import os, sys
import numpy as np
import matplotlib.pyplot as plt
__author__ = 'Santiago Jaramillo'
__version__ = '0.1'
BEHAVIORPATH = settings.BEHAVIOR_PATH
EPHYSPATH = settings.EPHYS_PATH
bitTRIALIND = 3 # TrialIndicator (bitID starting from 0)
bitPHOTOSTIMIND = 4 # PhotoStimIndicator (bitID starting from 0)
bitTARGETIND = 5 # TargetIndicator (bitID starting from 0)
RASTER_MARKERSIZE = 1
FONTSIZE = 14
def plot_frequency_tuning_raster(spikeTimesFromEventOnset,trialIndexForEachSpike,freqEachTrial,timeRange):
xLims = 1e3*timeRange
yLims = [0,trialIndexForEachSpike[-1]-1]
xLabel = 'Time from sound onset (ms)'
plt.clf()
plt.gcf().set_facecolor('w')
ax = plt.axes([0.2,0.2,0.7,0.7])
pR = plt.plot(1e3*spikeTimesFromEventOnset,trialIndexForEachSpike,'.k')
pR[0].set_markersize(RASTER_MARKERSIZE)
plt.xlim(xLims)
plt.ylim(yLims)
plt.xlabel(xLabel,fontsize=FONTSIZE)
#ax.set_yticklabels('')
plt.ylabel('Trial',fontsize=FONTSIZE)
# -- Draw background for each frequency --
#FreqEachTrial = behavData['SoundFreq'][trialsOfInterest]
#possibleFreqs = np.unique(behavData['SoundFreq'])
possibleFreqs = np.unique(freqEachTrial)
lastTrialEachFreq = np.flatnonzero(np.diff(freqEachTrial))
firstTrialEachFreq = np.hstack((0,lastTrialEachFreq+1))
lastTrialEachFreq = np.hstack((lastTrialEachFreq, len(freqEachTrial)))
# NOTE: indexes are from 0 to N+1 (python style)
nFreq = len(firstTrialEachFreq)
plt.hold(True)
possibleColors = ['0.95','0.9']
for indf in range(nFreq):
xpos = np.hstack((xLims,xLims[::-1]))
ypos = np.hstack((np.tile(firstTrialEachFreq[indf]-0.5,2),np.tile(lastTrialEachFreq[indf]+0.5,2)))
thisColor = possibleColors[np.mod(indf,2)]
bg = plt.fill(xpos,ypos,color=thisColor,ec='none')
ht = plt.text(0.99*xLims[1],firstTrialEachFreq[indf],'%d'%possibleFreqs[indf],color='b',
backgroundcolor='w',horizontalalignment='right')
plt.hold(False)
plt.ylim(yLims)
plt.draw()
plt.show()
def align_data(onemu):
rasterDeltaT = 0.1e-3 # sec
timeRange = np.array([-0.2,0.6])
animalName = onemu.animalName
behavSession = onemu.behavSession
ephysSession = onemu.ephysSession
tetrode = onemu.tetrode
clusters = onemu.clusters
# -- Load events from Neuralynx --
dataDir = os.path.join(settings.EPHYS_PATH,'%s/%s/'%(animalName,ephysSession))
clustersDir = os.path.join(settings.EPHYS_PATH,'%s/%s_kk/'%(animalName,ephysSession))
eventsFile = os.path.join(dataDir,'Events.nev')
events = loadneuralynx.DataEvents(eventsFile)
trialEvents = (events.valueTTL & (1<<bitTRIALIND)) != 0
trialStartTimeNL = 1e-6*events.timestamps[trialEvents]
targetEvents = (events.valueTTL & (1<<bitTARGETIND)) != 0
targetTimeNL = 1e-6*events.timestamps[targetEvents]
#targetFreqInd = events.valueTTL[trialEvents]>>8 #
# -- Load events from behavior --
behavDataDir = os.path.join(settings.BEHAVIOR_PATH,'%s/'%(animalName))
behavFileName = 'data_saja_tuningcurve_santiago_%s_%s.h5'%(animalName,behavSession)
behavFile = os.path.join(behavDataDir,behavFileName)
behavData = loadbehavior.TuningBehaviorData(behavFile)
behavData.extract_event_times()
behavData.align_to_ephys(trialStartTimeNL)
# FIXME: add line to check that number of trials of ephys & behavior are consistent
# -- Check is alignment ephys/behavior is correct --
#behavData.check_clock_drift()
#waitforbuttonpress()
# -- Remove first empty trial from data --
behavData['nTrials'] = behavData['nTrials']-1
nTrials = behavData['nTrials']
behavData.trialStartTime = behavData.trialStartTime[1:]
behavData.targetOnsetTime = behavData.targetOnsetTime[1:]
behavData['SoundFreq'] = behavData['SoundFreq'][1:]
# -- Remove incomplete trial from ephys data --
#targetTimeNL = targetTimeNL[:nTrials]
behavData.trialStartTimeEphys = behavData.trialStartTimeEphys[1:] # Remove first empty trial
trialsOfInterest = np.argsort(behavData['SoundFreq'])
eventOfInterest = behavData.targetOnsetTime[trialsOfInterest] - \
behavData.trialStartTime[trialsOfInterest] + \
behavData.trialStartTimeEphys[trialsOfInterest]
timeVec = np.arange(timeRange[0],timeRange[-1]+rasterDeltaT,rasterDeltaT)
freqEachTrial = behavData['SoundFreq'][trialsOfInterest]
# -- Load spikes --
tetrodeFile = os.path.join(dataDir,'TT%d.ntt'%tetrode)
dataTT = loadneuralynx.DataTetrode(tetrodeFile)
dataTT.timestamps = dataTT.timestamps.astype(np.float64)*1e-6 # in sec
# -- Load clusters if required --
#if (clustersEachTetrode is not None) and clustersEachTetrode.has_key(tetrode):
########## BUG: clustersEachTetrode is not defined anywhere !!! #############
if len(clusters)>0:
clustersFile = os.path.join(clustersDir,'TT%d.clu.1'%tetrode)
dataTT.set_clusters(clustersFile)
spikeInds = extrafuncs.ismember(dataTT.clusters,clustersEachTetrode[tetrode])
(spikeTimesFromEventOnset,trialIndexForEachSpike,indexLimitsEachTrial) = \
spikesanalysis.eventlocked_spiketimes(dataTT.timestamps[spikeInds],eventOfInterest,timeRange)
else:
(spikeTimesFromEventOnset,trialIndexForEachSpike,indexLimitsEachTrial) = \
spikesanalysis.eventlocked_spiketimes(dataTT.timestamps,eventOfInterest,timeRange)
return (spikeTimesFromEventOnset,trialIndexForEachSpike,indexLimitsEachTrial,freqEachTrial,timeRange)
def find_trials_each_freq(freqEachTrial):
possibleFreq = np.unique(freqEachTrial)
trialsEachFreq = []
for indf,freq in enumerate(possibleFreq):
trialsEachFreq.append(np.flatnonzero(freqEachTrial==freq))
return (possibleFreq,trialsEachFreq)
def estimate_frequency_tuning(spikeTimesFromEventOnset,indexLimitsEachTrial,freqEachTrial,responseRange=None):
if responseRange is None:
responseRange = np.array([0,0.150])
else:
responseRange = np.array(responseRange)
baselineRange = responseRange-0.200
(possibleFreq,trialsEachFreq) = find_trials_each_freq(freqEachTrial)
meanRespEachFreq = np.empty(len(possibleFreq))
semRespEachFreq = np.empty(len(possibleFreq))
# -- Response --
for indf,freq in enumerate(possibleFreq):
theseTrials = trialsEachFreq[indf]
nSpikes=spikesanalysis.count_spikes_in_range(spikeTimesFromEventOnset,
indexLimitsEachTrial[:,theseTrials],
responseRange)
spikesPerSec = nSpikes/np.diff(responseRange)
meanRespEachFreq[indf] = np.mean(spikesPerSec)
semRespEachFreq[indf] = np.std(spikesPerSec)/np.sqrt(len(theseTrials))
# -- Baseline --
nSpikesBaseline=spikesanalysis.count_spikes_in_range(spikeTimesFromEventOnset,
indexLimitsEachTrial,
baselineRange)
spikesPerSecBaseline = nSpikesBaseline/np.diff(baselineRange)
meanBaseline = np.mean(spikesPerSecBaseline)
semBaseline = np.mean(spikesPerSecBaseline)
return (possibleFreq,meanRespEachFreq,semRespEachFreq,meanBaseline,semBaseline)
def plot_frequency_tuning(possibleFreq,meanRespEachFreq,semRespEachFreq=None,
meanBaseline=None,semBaseline=None):
pcolor='k'
plt.clf()
plt.plot(np.log10(possibleFreq),meanRespEachFreq,'o-',ms=5,mew=2,mec=pcolor,mfc='w',color=pcolor)
if meanBaseline is not None:
plt.axhline(meanBaseline,ls='--',color='k')
plt.draw()
plt.show()
if __name__ == "__main__":
'''
animalName = 'saja125'
# -- Load list of cells --
sys.path.append(settings.CELL_LIST_PATH)
dataModule = 'alltuning_%s'%(animalName)
allMU = __import__(dataModule)
reload(allMU)
muDB = allMU.muDB
'''
'''
animalName = 'saja125'
ephysSession = '2012-01-30_14-31-32'
behavSession = '20120130a'
tetrodes = [2]
clustersEachTetrode = None
'''
from extracellpy import celldatabase
reload(celldatabase)
muDB = celldatabase.MultiUnitDatabase()
animalName = 'saja100'
#ephysSession = '2011-11-27_14-33-15'; behavSession = '20111127a'
#clustersEachTetrode = {7:[13]}
#clustersEachTetrode = {7:range(2,14)}
ephysSession = '2011-12-05_16-35-53'; behavSession = '20111205a'
clustersEachTetrode = {8:range(2,30)}
#clustersEachTetrode = {8:[3]}
for tetrode,clusters in sorted(clustersEachTetrode.items()):
oneCell = celldatabase.MultiUnitInfo(animalName = animalName,
ephysSession = ephysSession,
behavSession = behavSession,
tetrode = tetrode,
clusters = clusters)
muDB.append(oneCell)
outputPath = settings.PROCESSED_REVERSAL_PATH%(animalName)
outputDir = os.path.join(outputPath,'freqtuning')
CASE = 2
if CASE==1:
for indmu,onemu in enumerate(muDB):
#muStr = '%s_%s_T%dmu'%(onemu.animalName, onemu.ephysSession,onemu.tetrode)
titleString = '%s [%s] T%d'%(onemu.animalName,onemu.ephysSession,onemu.tetrode)
(spikeTimesFromEventOnset,trialIndexForEachSpike,indexLimitsEachTrial,
freqEachTrial,timeRange) = align_data(onemu)
print titleString,
plot_frequency_tuning_raster(spikeTimesFromEventOnset,trialIndexForEachSpike,
freqEachTrial,timeRange)
import matplotlib.pyplot as plt
plt.title(titleString,fontsize=FONTSIZE-2)
plt.draw()
plt.show()
#plt.waitforbuttonpress()
'''
plt.gcf().set_size_inches((8,6))
figFormat = 'png' #'png' #'pdf' #'svg'
figName = 'freqtuning_%s_T%d.%s'%(onemu.behavSession,onemu.tetrode,figFormat)
#plt.savefig(os.path.join(outputDir,figName),format=figFormat)
plt.savefig(os.path.join('/tmp/',figName),format=figFormat)
print '... figure saved.'
'''
elif CASE==2:
#rRange=[0,0.025]
rRange=[0.025,0.150]
#rRange=[0,0.150]
onemu = muDB[0]
(spikeTimesFromEventOnset,trialIndexForEachSpike,indexLimitsEachTrial,
freqEachTrial,timeRange) = align_data(onemu)
(possibleFreq,meanRespEachFreq,semRespEachFreq,meanBaseline,semBaseline) = \
estimate_frequency_tuning(spikeTimesFromEventOnset,
indexLimitsEachTrial,freqEachTrial,
responseRange=rRange)
plt.figure(1)
plot_frequency_tuning_raster(spikeTimesFromEventOnset,trialIndexForEachSpike,freqEachTrial,timeRange)
plt.figure(2)
plot_frequency_tuning(possibleFreq,meanRespEachFreq,meanBaseline=meanBaseline)