def laser_response(spikeTimeStamps, eventOnsetTimes, timeRange=[0.0, 0.1], baseRange=[0.5, 0.6]):
spikeTimesFromEventOnset, trialIndexForEachSpike, indexLimitsEachTrial = spikesanalysis.eventlocked_spiketimes(
spikeTimeStamps, eventOnsetTimes, [min(timeRange),max(baseRange)])
zStatsEachRange,pValueEachRange,maxZvalue = spikesanalysis.response_score(spikeTimesFromEventOnset, indexLimitsEachTrial, baseRange, timeRange)
baseSpikeCountMat = spikesanalysis.spiketimes_to_spikecounts(spikeTimesFromEventOnset, indexLimitsEachTrial, baseRange)
baselineSpikeRate = np.mean(baseSpikeCountMat)/(baseRange[1]-baseRange[0])
laserSpikeCountMat = spikesanalysis.spiketimes_to_spikecounts(spikeTimesFromEventOnset, indexLimitsEachTrial, timeRange)
laserSpikeRate = np.mean(laserSpikeCountMat)/(timeRange[1]-timeRange[0])
return (laserSpikeRate > baselineSpikeRate and pValueEachRange[0] < 0.00001)
def laser_response(cell, timeRange=[0.0, 0.1], baseRange=[0.5, 0.6]):
cellInfo = get_cell_info(cell)
loader = dataloader.DataLoader(cell['subject'])
if len(cellInfo['laserIndex'])>0:
eventData = loader.get_session_events(cellInfo['ephysDirs'][cellInfo['laserIndex'][-1]])
spikeData = loader.get_session_spikes(cellInfo['ephysDirs'][cellInfo['laserIndex'][-1]], cellInfo['tetrode'], cluster=cellInfo['cluster'])
eventOnsetTimes = loader.get_event_onset_times(eventData)
spikeTimestamps = spikeData.timestamps
spikeTimesFromEventOnset, trialIndexForEachSpike, indexLimitsEachTrial = spikesanalysis.eventlocked_spiketimes(
spikeTimestamps, eventOnsetTimes, [min(timeRange),max(baseRange)])
zStatsEachRange,pValueEachRange,maxZvalue = spikesanalysis.response_score(spikeTimesFromEventOnset, indexLimitsEachTrial, baseRange, timeRange)
baseSpikeCountMat = spikesanalysis.spiketimes_to_spikecounts(spikeTimesFromEventOnset, indexLimitsEachTrial, baseRange)
baselineSpikeRate = np.mean(baseSpikeCountMat)/(baseRange[1]-baseRange[0])
laserSpikeCountMat = spikesanalysis.spiketimes_to_spikecounts(spikeTimesFromEventOnset, indexLimitsEachTrial, timeRange)
laserSpikeRate = np.mean(laserSpikeCountMat)/(timeRange[1]-timeRange[0])
laserResponse = (laserSpikeRate > baselineSpikeRate and pValueEachRange[0] < 0.00001)
else:
laserResponse = False
return laserResponse
(spikeTimesFromEventOnset,trialIndexForEachSpike,indexLimitsEachTrial) = \
spikesanalysis.eventlocked_spiketimes(spkTimeStamps,eventOnsetTimes,timeRange)
plt.clf()
ax1=plt.subplot(2,1,1)
plt.plot(spikeTimesFromEventOnset,trialIndexForEachSpike,'.')
plt.show()
# -- Calculate sound responsiveness --
baseRange = [-0.050,-0.025] # Baseline range (in seconds)
rangeLength = np.diff(baseRange) # Time-bin size
binEdges = np.arange(-8,24)*rangeLength # Edges of bins to calculate response (in seconds)
#baseRange = [-0.1, 0] # Baseline range (in seconds)
#binEdges = [-0.1,0, 0.1, 0.2]
[zStat,pValue,maxZ] = spikesanalysis.response_score(spikeTimesFromEventOnset,indexLimitsEachTrial,baseRange,binEdges)
print 'Max absolute z-score: {0}'.format(maxZ)
ax2=plt.subplot(2,1,2,sharex=ax1)
plt.axhline(0,ls='-',color='0.5')
plt.axhline(+3,ls='--',color='0.5')
plt.axhline(-3,ls='--',color='0.5')
plt.step(binEdges[:-1],zStat,where='post',lw=2)
plt.ylabel('z-score')
plt.xlabel('time (sec)')
plt.show()
# -- Load Spike Data From Certain Cluster --
for Frequency in range(numberOfFrequencies):
Freq = possibleFreq[Frequency]
oneFreqTrials = bdata['targetFrequency'] == Freq
oneFreqEventOnsetTimes = eventOnsetTimes[oneFreqTrials] #Choose only the trials with this frequency
spkData = ephyscore.CellData(oneCell)
spkTimeStamps = spkData.spikes.timestamps
(spikeTimesFromEventOnset,trialIndexForEachSpike,indexLimitsEachTrial) = \
spikesanalysis.eventlocked_spiketimes(spkTimeStamps,oneFreqEventOnsetTimes,timeRange)
[zStat,pValue,maxZ] = spikesanalysis.response_score(spikeTimesFromEventOnset,indexLimitsEachTrial,baseRange,binEdges) #computes z score for each bin. zStat is array of z scores. maxZ is maximum value of z in timeRange
clusterNumber = (tetrode-1)*clusNum+(cluster-1)
maxZDict[behavSession][Freq][clusterNumber] = maxZ
bSessionList = []
for bSession in maxZDict:
bSessionList.append(bSession)
bSessionList.sort()
for bSession in bSessionList:
text_file.write("\nBehavior Session:%s" % bSession)
for freq in maxZDict[bSession]:
text_file.write("\n%s " % freq)
for ZVal in maxZDict[bSession][freq]:
text_file.write("%s," % ZVal)
#Use a longer base timerange so that we can determine baseline firing rate
timeRange = [-0.5, 1]
spikeTimesFromEventOnset, trialIndexForEachSpike, indexLimitsEachTrial = spikesanalysis.eventlocked_spiketimes(
spikeTimestamps, eventOnsetTimes, timeRange)
maxZarray = np.zeros([len(possibleIntensity), len(possibleFreq)])
for indInt, intensity in enumerate(possibleIntensity):
for indFreq, freq in enumerate(possibleFreq):
trialsThisCombo = np.flatnonzero((currentIntensity==inten) & (currentFreq==freq))
indexLimitsThisCombo = indexLimitsEachTrial[:, trialsThisCombo]
spikeTimesThisCombo= spikeTimesFromEventOnset[np.in1d(trialIndexForEachSpike, trialsThisCombo)]
[zStat,pValue,maxZ] = spikesanalysis.response_score(spikeTimesThisCombo,indexLimitsThisCombo,baseRange,binEdges) #computes z score for each bin. zStat is array of z scores. maxZ is maximum value of z in timeRange
maxZarray[indInt, indFreq] = maxZ
#Testing the above method
testfreq = possibleFreq[6]
testinten = 60
trialsThisCombo = np.flatnonzero((currentFreq==testfreq)&(currentIntensity==testinten))
timeRange = [-0.5, 1]
spikeTimesFromEventOnset, trialIndexForEachSpike, indexLimitsEachTrial = spikesanalysis.eventlocked_spiketimes(
spikeTimestamps, eventOnsetTimes, timeRange)
for Frequency in range(numberOfFrequencies):
Freq = possibleFreq[Frequency]
oneFreqTrials = bdata['targetFrequency'] == Freq #only use a certain frequency
trialsToUse = (oneFreqTrials & validTrials)
oneFreqEventOnsetTimes = eventOnsetTimes[trialsToUse] #Choose only the trials with this frequency
spkData = ephyscore.CellData(oneCell)
spkTimeStamps = spkData.spikes.timestamps
(spikeTimesFromEventOnset,trialIndexForEachSpike,indexLimitsEachTrial) = \
spikesanalysis.eventlocked_spiketimes(spkTimeStamps,oneFreqEventOnsetTimes,timeRange)
[zStat,pValue,maxZ] = spikesanalysis.response_score(spikeTimesFromEventOnset,indexLimitsEachTrial,baseRange,binEdges) #computes z score for each bin. zStat is array of z scores. maxZ is maximum value of z in timeRange
clusterNumber = (tetrode-1)*clusNum+(cluster-1)
#maxZArray[clusterNumber].append(maxZ)
maxZDict[behavSession][Freq][clusterNumber] = maxZ
#if abs(maxZ)=<Zthreshold & onecell.soundResponsive!=True:
#oneCell.soundResponsive=False
#elif abs(maxZ)>Zthreshold:
#oneCell.soundResponsive=True
#ZscoreArray[:,Frequency,cellID] = zStat
except:
#print "error with session "+oneCell.behavSession
if (oneCell.behavSession not in badSessionList):
badSessionList.append(oneCell.behavSession)
spikesanalysis.eventlocked_spiketimes(spkTimeStamps,eventOnsetTimes,timeRange)
plt.clf()
ax1 = plt.subplot(2, 1, 1)
plt.plot(spikeTimesFromEventOnset, trialIndexForEachSpike, '.')
plt.show()
# -- Calculate sound responsiveness --
baseRange = [-0.050, -0.025] # Baseline range (in seconds)
rangeLength = np.diff(baseRange) # Time-bin size
binEdges = np.arange(
-8, 24) * rangeLength # Edges of bins to calculate response (in seconds)
#baseRange = [-0.1, 0] # Baseline range (in seconds)
#binEdges = [-0.1,0, 0.1, 0.2]
[zStat, pValue, maxZ] = spikesanalysis.response_score(spikeTimesFromEventOnset,
indexLimitsEachTrial,
baseRange, binEdges)
print 'Max absolute z-score: {0}'.format(maxZ)
ax2 = plt.subplot(2, 1, 2, sharex=ax1)
plt.axhline(0, ls='-', color='0.5')
plt.axhline(+3, ls='--', color='0.5')
plt.axhline(-3, ls='--', color='0.5')
plt.step(binEdges[:-1], zStat, where='post', lw=2)
plt.ylabel('z-score')
plt.xlabel('time (sec)')
plt.show()
def tuning_curve_response(cell):
'''
Calculate max Z-score during the response period for each freq-inten combo in a TC
'''
try:
sessiontypeIndex = cell['sessiontype'].index('TuningCurve')
except ValueError: #The cell does not have this session type
return None
#Initialize a data loader for this animal
loader = dataloader.DataLoader(cell['subject'])
#Get the behavior data
behavData = loader.get_session_behavior(cell['behavior'][sessiontypeIndex])
intensityEachTrial = behavData['currentIntensity']
freqEachTrial = behavData['currentFreq']
possibleFreq = np.unique(freqEachTrial)
possibleIntensity = np.unique(intensityEachTrial)
#Return the cluster spike data for this ephys session
ephysDir = cell['ephys'][sessiontypeIndex]
clusterSpikeData = loader.get_session_spikes(ephysDir,
int(cell['tetrode']),
cluster=int(cell['cluster']))
clusterSpikeTimes = clusterSpikeData.timestamps
#Get the events for this session and calculate onset times
eventData = loader.get_session_events(ephysDir)
eventOnsetTimes = loader.get_event_onset_times(eventData,
minEventOnsetDiff=None)
timeRange = [0, 0.1]
baseRange = [-0.050, -0.025] # Baseline range (in seconds)
binTime = baseRange[1] - baseRange[0] # Time-bin size
responseTimeRange = timeRange #Time range to calculate z value for (should be divisible by binTime
responseTime = responseTimeRange[1] - responseTimeRange[0]
numBins = responseTime / binTime
binEdges = np.arange(responseTimeRange[0], responseTimeRange[1], binTime)
zvalArray = np.zeros((len(possibleIntensity), len(possibleFreq)))
for indfreq, freq in enumerate(possibleFreq):
for indinten, inten in enumerate(possibleIntensity):
selectinds = np.flatnonzero((freqEachTrial == freq)
& (intensityEachTrial == inten))
selectedOnsetTimes = eventOnsetTimes[selectinds]
(spikeTimesFromEventOnset,trialIndexForEachSpike,indexLimitsEachTrial) = \
spikesanalysis.eventlocked_spiketimes(clusterSpikeTimes,selectedOnsetTimes,timeRange)
[zStat, pValue, maxZ] = spikesanalysis.response_score(
spikeTimesFromEventOnset, indexLimitsEachTrial, baseRange,
binEdges
) #computes z score for each bin. zStat is array of z scores. maxZ is maximum value of z in timeRange
zvalArray[indinten, indfreq] = maxZ
# zvalArray = np.flipud(zvalArray)
return zvalArray
