def evaluate_modulation_each_cell(cellDB,responseRange):
'''
'''
############# FINISH THIS (see spikesanalysis.evaluate_modulation() ##########
timeRange = np.array([baseRange[0], baseRange[-1]+np.diff(baseRange)])
for indcell,onecell in enumerate(cellDB):
print '[%d/%d] %s'%(indcell+1,nCells,onecell)
(behavData,trialEvents,dataTT,spikeInds) = sessionanalysis.load_cell_reversal(onecell)
(eventOfInterest,xLabelStr) = sessionanalysis.align_to_event(behavData,1)
(trialsEachCond,condInfo) = sessionanalysis.trials_by_condition(behavData,1,outcome='correct')
trialsOfInterest = np.hstack(trialsEachCond)
(spikeTimesFromEventOnset,trialIndexForEachSpike,indexLimitsEachTrial) = \
spikesanalysis.eventlocked_spiketimes(dataTT.timestamps[spikeInds],
eventOfInterest[trialsOfInterest],
timeRange)
nCond = len(trialsEachCond)
zStats = np.empty((len(rangeStart),nCond))
for indCond in range(nCond):
toUse = np.arange(condInfo['firstTrialEachCond'][indCond],
condInfo['lastTrialEachCond'][indCond])
(zStats[:,indCond],pValues) = spikesanalysis.evaluate_responsiveness(spikeTimesFromEventOnset,
indexLimitsEachTrial[:,toUse],baseRange,
rangeStart)
zStatsEachCell[:,:,indcell] = zStats
return zStatsEachCell
def save_data_each_cell(cellDB,outputDir,timeRange=np.array([-0.3,0.9]),lockTo=1):
allPostfix = {1:'SoundOn',2:'Cout',3:'SideIn',4:'Cin'} ### FIXME: HARDCODED !!!
for indcell,onecell in enumerate(cellDB):
#cellStr = '%s_%s_T%dc%d'%(onecell.animalName, onecell.ephysSession,
# onecell.tetrode, onecell.cluster)
cellStr = str(onecell).replace(' ','_')
try:
(behavData,trialEvents,dataTT,spikeInds) = load_cell_reversal(onecell)
except IOError:
print 'WARNING: File not found for cell %s'%cellStr
continue
(eventOfInterest,xLabelStr) = align_to_event(behavData,lockTo)
# -- Ignore trialsToExclude --
eventOfInterest[onecell.trialsToExclude] = np.nan
##if len(onecell.trialsToExclude)>0:
(spikeTimesFromEventOnset,trialIndexForEachSpike,indexLimitsEachTrial) = \
spikesanalysis.eventlocked_spiketimes(dataTT.timestamps[spikeInds],
eventOfInterest,timeRange)
filePostfix = allPostfix[lockTo]
if not os.path.exists(outputDir):
os.makedirs(outputDir)
fileName = os.path.join(outputDir,cellStr+'_'+filePostfix+'.npz')
print fileName
np.savez(fileName, spikeTimesFromEventOnset=spikeTimesFromEventOnset,
trialIndexForEachSpike=trialIndexForEachSpike,
indexLimitsEachTrial=indexLimitsEachTrial, xLabelStr=xLabelStr,
timeRange=timeRange,animalName=onecell.animalName,
behavSession=onecell.behavSession)
def save_data_each_mu(muDB,outputDir,timeRange=np.array([-0.3,0.9]),lockTo=1):
for indmu,onemu in enumerate(muDB):
muStr = '%s_%s_T%dmu'%(onemu.animalName, onemu.ephysSession,
onemu.tetrode)
(behavData,trialEvents,dataTT,spikeInds) = load_mu_reversal(onemu)
(eventOfInterest,xLabelStr) = align_to_event(behavData,lockTo)
(spikeTimesFromEventOnset,trialIndexForEachSpike,indexLimitsEachTrial) = \
spikesanalysis.eventlocked_spiketimes(dataTT.timestamps[spikeInds],
eventOfInterest,timeRange)
fileName = os.path.join(outputDir,muStr+'_'+filePostfix+'.npz')
print fileName
np.savez(fileName, spikeTimesFromEventOnset=spikeTimesFromEventOnset,
trialIndexForEachSpike=trialIndexForEachSpike,
indexLimitsEachTrial=indexLimitsEachTrial,
timeRange=timeRange,animalName=onemu.animalName,
behavSession=onemu.behavSession)
if CASE == 1:
import pylab as plt
animalName = 'saja099'
ephysSession = '2011-04-18_20-44-49'
behavSession = '20110418a'
tetrode = 1
cluster = 3
onecell = celldatabase.CellInfo(animalName,ephysSession,behavSession,tetrode,cluster)
print 'Loading %s ...'%(onecell)
(behavData,trialEvents,dataTT,spikeInds) = load_cell_reversal(onecell)
timeRange = np.array([-0.3,0.9])
trialsOfInterest = range(300)
(eventOfInterest,xLabelStr) = align_to_event(behavData,1)
(spikeTimesFromEventOnset,trialIndexForEachSpike,indexLimitsEachTrial) = \
spikesanalysis.eventlocked_spiketimes(dataTT.timestamps[spikeInds],
eventOfInterest,timeRange)
pR, = plt.plot(1e3*spikeTimesFromEventOnset,trialIndexForEachSpike,'.k')
pR.set_markersize(2)
plt.draw()
plt.show()
elif CASE==2:
animalName = 'saja100'
ephysSession = '2011-10-26_11-57-31'
behavSession = '20111026a'
tetrode = 1
clusters = [2,3,4,5,6,7,9,10,11]
oneSite = celldatabase.MultiUnitInfo(animalName = animalName,
ephysSession = ephysSession,
behavSession = behavSession,
tetrode = tetrode,
clusters = clusters)
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)
