def load_ephys_per_cell(oneCell):
'''
Load spike and event data from one cell given a CellInfo object (from celldatabase)
'''
###0301 should TRY spkData = ephyscore.CellData(oneCell)
ephysDir = settings.EPHYS_PATH
ephysData = ephyscore.CellData(
oneCell
) #ephyscore's CellData object uses loadopenephys.DataSpikes method, gets back a DataSpikes object that is stored in CellData.spikes, with fields: nSpikes, samples, timestamps(ephyscore already divides this by sampling rate),gain,samplingRate.
spikeData = ephysData.spikes #so this is the DataSpikes object from loadopenephys
spikeTimestamps = spikeData.timestamps #ephyscore already divided this by sampling rate so unit is in seconds
waveforms = spikeData.samples.astype(
float) - 2**15 #This is specific to open Ephys
waveforms = (1000.0 / spikeData.gain[0, 0]
) * waveforms #converting to microvolt,specific to open Ephys
fullEventFilename = os.path.join(ephysDir, oneCell.animalName,
oneCell.ephysSession,
'all_channels.events')
eventData = loadopenephys.Events(fullEventFilename)
eventData.timestamps = np.array(eventData.timestamps) / SAMPLING_RATE
eventOnsetTimes = np.array(
eventData.timestamps) #all events not just soundonset
return (spikeTimestamps, waveforms, eventOnsetTimes, eventData)
def load_one_cell_waveform(oneCell):
'''
This function takes a cell of the class CellInfo (from jaratoolbox.celldatabase) and gets the waveform data for that cell.
'''
ephysData = ephyscore.CellData(
oneCell
) #ephyscore's CellData object uses loadopenephys.DataSpikes method, gets back a DataSpikes object that is stored in CellData.spikes, with fields: nSpikes, samples, timestamps(ephyscore already divides this by sampling rate),gain,samplingRate.
spikeData = ephysData.spikes #so this is the DataSpikes object from loadopenephys
waveforms = spikeData.samples.astype(
float) - 2**15 #This is specific to open Ephys
waveforms = (1000.0 / spikeData.gain[0, 0]
) * waveforms #converting to microvolt,specific to open Ephys
return waveforms
leftward = bdata['choice']==bdata.labels['choice']['left']
valid = (bdata['outcome']==bdata.labels['outcome']['correct'])|(bdata['outcome']==bdata.labels['outcome']['error'])
correct = bdata['outcome']==bdata.labels['outcome']['correct']
correctRightward = rightward & correct
correctLeftward = leftward & correct
possibleFreq = np.unique(bdata['targetFrequency'])
numberOfFrequencies = len(possibleFreq)
numberOfTrials = len(bdata['choice'])
targetFreqs = bdata['targetFrequency']
for possFreq in possibleFreq:
modIDict[behavSession][possFreq] = np.empty([clusNum*numTetrodes])
# -- Load Spike Data From Certain Cluster --
spkData = ephyscore.CellData(oneCell)
spkTimeStamps = spkData.spikes.timestamps
clusterNumber = (oneCell.tetrode-1)*clusNum+(oneCell.cluster-1)
for Freq in possibleFreq:
oneFreq = targetFreqs == Freq
trialsToUseRight = rightward & oneFreq
trialsToUseLeft = leftward & oneFreq
#print 'behavior ',behavSession,' tetrode ',oneCell.tetrode,' cluster ',oneCell.cluster,'freq',Freq
(spikeTimesFromEventOnset,trialIndexForEachSpike,indexLimitsEachTrial) = \
spikesanalysis.eventlocked_spiketimes(spkTimeStamps,eventOnsetTimes,timeRange)
def raster_tuning(ax):
fullbehaviorDir = behaviorDir + subject + '/'
behavName = subject + '_tuning_curve_' + tuningBehavior + '.h5'
tuningBehavFileName = os.path.join(fullbehaviorDir, behavName)
tuning_bdata = loadbehavior.BehaviorData(tuningBehavFileName,
readmode='full')
freqEachTrial = tuning_bdata['currentFreq']
possibleFreq = np.unique(freqEachTrial)
numberOfTrials = len(freqEachTrial)
# -- The old way of sorting (useful for plotting sorted raster) --
sortedTrials = []
numTrialsEachFreq = [
] #Used to plot lines after each group of sorted trials
for indf, oneFreq in enumerate(
possibleFreq
): #indf is index of this freq and oneFreq is the frequency
indsThisFreq = np.flatnonzero(
freqEachTrial == oneFreq) #this gives indices of this frequency
sortedTrials = np.concatenate(
(sortedTrials,
indsThisFreq)) #adds all indices to a list called sortedTrials
numTrialsEachFreq.append(
len(indsThisFreq)) #finds number of trials each frequency has
sortingInds = argsort(
sortedTrials) #gives array of indices that would sort the sortedTrials
# -- Load event data and convert event timestamps to ms --
tuning_ephysDir = os.path.join(settings.EPHYS_PATH, subject, tuningEphys)
tuning_eventFilename = os.path.join(tuning_ephysDir, 'all_channels.events')
tuning_ev = loadopenephys.Events(
tuning_eventFilename) #load ephys data (like bdata structure)
tuning_eventTimes = np.array(
tuning_ev.timestamps
) / SAMPLING_RATE #get array of timestamps for each event and convert to seconds by dividing by sampling rate (Hz). matches with eventID and
tuning_evID = np.array(
tuning_ev.eventID
) #loads the onset times of events (matches up with eventID to say if event 1 went on (1) or off (0)
tuning_eventOnsetTimes = tuning_eventTimes[
tuning_evID ==
1] #array that is a time stamp for when the chosen event happens.
#ev.eventChannel woul load array of events like trial start and sound start and finish times (sound event is 0 and trial start is 1 for example). There is only one event though and its sound start
while (numberOfTrials < len(tuning_eventOnsetTimes)):
tuning_eventOnsetTimes = tuning_eventOnsetTimes[:-1]
#######################################################################################################
###################THIS IS SUCH A HACK TO GET SPKDATA FROM EPHYSCORE###################################
#######################################################################################################
thisCell = celldatabase.CellInfo(
animalName=subject, ############################################
ephysSession=tuningEphys,
tuningSession='DO NOT NEED THIS',
tetrode=tetrode,
cluster=cluster,
quality=1,
depth=0,
tuningBehavior='DO NOT NEED THIS',
behavSession=tuningBehavior)
tuning_spkData = ephyscore.CellData(thisCell)
tuning_spkTimeStamps = tuning_spkData.spikes.timestamps
(tuning_spikeTimesFromEventOnset, tuning_trialIndexForEachSpike,
tuning_indexLimitsEachTrial) = spikesanalysis.eventlocked_spiketimes(
tuning_spkTimeStamps, tuning_eventOnsetTimes, tuning_timeRange)
#print 'numTrials ',max(tuning_trialIndexForEachSpike)#####################################
'''
Create a vector with the spike timestamps w.r.t. events onset.
(spikeTimesFromEventOnset,trialIndexForEachSpike,indexLimitsEachTrial) =
eventlocked_spiketimes(timeStamps,eventOnsetTimes,timeRange)
timeStamps: (np.array) the time of each spike.
eventOnsetTimes: (np.array) the time of each instance of the event to lock to.
timeRange: (list or np.array) two-element array specifying time-range to extract around event.
spikeTimesFromEventOnset: 1D array with time of spikes locked to event.
o trialIndexForEachSpike: 1D array with the trial corresponding to each spike.
The first spike index is 0.
indexLimitsEachTrial: [2,nTrials] range of spikes for each trial. Note that
the range is from firstSpike to lastSpike+1 (like in python slices)
spikeIndices
'''
tuning_sortedIndexForEachSpike = sortingInds[
tuning_trialIndexForEachSpike] #Takes values of trialIndexForEachSpike and finds value of sortingInds at that index and makes array. This array gives an array with the sorted index of each trial for each spike
# -- Calculate tuning --
#nSpikes = spikesanalysis.spiketimes_to_spikecounts(spikeTimesFromEventOnset,indexLimitsEachTrial,responseRange) #array of the number of spikes in range for each trial
'''Count number of spikes on each trial in a given time range.
spikeTimesFromEventOnset: vector of spikes timestamps with respect
to the onset of the event.
indexLimitsEachTrial: each column contains [firstInd,lastInd+1] of the spikes on a trial.
timeRange: time range to evaluate. Spike times exactly at the limits are not counted.
returns nSpikes
'''
'''
meanSpikesEachFrequency = np.empty(len(possibleFreq)) #make empty array of same size as possibleFreq
# -- This part will be replace by something like behavioranalysis.find_trials_each_type --
trialsEachFreq = []
for indf,oneFreq in enumerate(possibleFreq):
trialsEachFreq.append(np.flatnonzero(freqEachTrial==oneFreq)) #finds indices of each frequency. Appends them to get an array of indices of trials sorted by freq
# -- Calculate average firing for each freq --
for indf,oneFreq in enumerate(possibleFreq):
meanSpikesEachFrequency[indf] = np.mean(nSpikes[trialsEachFreq[indf]])
'''
#clf()
#if (len(tuning_spkTimeStamps)>0):
#ax1 = plt.subplot2grid((4,4), (3, 0), colspan=1)
#spikesorting.plot_isi_loghist(spkData.spikes.timestamps)
#ax3 = plt.subplot2grid((4,4), (3, 3), colspan=1)
#spikesorting.plot_events_in_time(tuning_spkTimeStamps)
#samples = tuning_spkData.spikes.samples.astype(float)-2**15
#samples = (1000.0/tuning_spkData.spikes.gain[0,0]) *samples
#ax2 = plt.subplot2grid((4,4), (3, 1), colspan=2)
#spikesorting.plot_waveforms(samples)
#ax4 = plt.subplot2grid((4,4), (0, 0), colspan=3,rowspan = 3)
plot(tuning_spikeTimesFromEventOnset,
tuning_sortedIndexForEachSpike,
'.',
ms=3)
#axvline(x=0, ymin=0, ymax=1, color='r')
#The cumulative sum of the list of specific frequency presentations,
#used below for plotting the lines across the figure.
numTrials = cumsum(numTrialsEachFreq)
#Plot the lines across the figure in between each group of sorted trials
for indf, num in enumerate(numTrials):
ax.axhline(y=num, xmin=0, xmax=1, color='0.90', zorder=0)
tickPositions = numTrials - mean(numTrialsEachFreq) / 2
tickLabels = [
"%0.2f" % (possibleFreq[indf] / 1000)
for indf in range(len(possibleFreq))
]
ax.set_yticks(tickPositions)
ax.set_yticklabels(tickLabels)
ax.set_ylim([-1, numberOfTrials])
ylabel('Frequency Presented (kHz), {} total trials'.format(numTrials[-1]))
#title(ephysSession+' T{}c{}'.format(tetrodeID,clusterID))
xlabel('Time (sec)')
'''
ax5 = plt.subplot2grid((4,4), (0, 3), colspan=1,rowspan=3)
ax5.set_xscale('log')
plot(possibleFreq,meanSpikesEachFrequency,'o-')
ylabel('Avg spikes in window {0}-{1} sec'.format(*responseRange))
xlabel('Frequency')
'''
#show()
'''
def main():
global behavSession
global subject
global tetrode
global cluster
global tuningBehavior #behavior file name of tuning curve
global tuningEphys #ephys session name of tuning curve
global bdata
global eventOnsetTimes
global spikeTimesFromEventOnset
global indexLimitsEachTrial
global spikeTimesFromMovementOnset
global indexLimitsEachMovementTrial
global titleText
print "switch_tuning_block_allfreq_report"
for cellID in range(0, numOfCells):
oneCell = allcells.cellDB[cellID]
try:
if (behavSession != oneCell.behavSession):
subject = oneCell.animalName
behavSession = oneCell.behavSession
ephysSession = oneCell.ephysSession
tuningSession = oneCell.tuningSession
ephysRoot = os.path.join(ephysRootDir, subject)
tuningBehavior = oneCell.tuningBehavior
tuningEphys = oneCell.tuningSession
print behavSession
# -- Load Behavior Data --
behaviorFilename = loadbehavior.path_to_behavior_data(
subject=subject,
paradigm=paradigm,
sessionstr=behavSession)
bdata = loadbehavior.FlexCategBehaviorData(behaviorFilename)
#bdata = loadbehavior.BehaviorData(behaviorFilename)
numberOfTrials = len(bdata['choice'])
# -- Load event data and convert event timestamps to ms --
ephysDir = os.path.join(ephysRoot, ephysSession)
eventFilename = os.path.join(ephysDir, 'all_channels.events')
events = loadopenephys.Events(
eventFilename) # Load events data
eventTimes = np.array(
events.timestamps
) / SAMPLING_RATE #get array of timestamps for each event and convert to seconds by dividing by sampling rate (Hz). matches with eventID and
soundOnsetEvents = (events.eventID == 1) & (
events.eventChannel == soundTriggerChannel)
eventOnsetTimes = eventTimes[soundOnsetEvents]
soundOnsetTimeBehav = bdata['timeTarget']
# Find missing trials
missingTrials = behavioranalysis.find_missing_trials(
eventOnsetTimes, soundOnsetTimeBehav)
# Remove missing trials
bdata.remove_trials(missingTrials)
bdata.find_trials_each_block()
###############################################################################################
centerOutTimes = bdata[
'timeCenterOut'] #This is the times that the mouse goes out of the center port
soundStartTimes = bdata[
'timeTarget'] #This gives an array with the times in seconds from the start of the behavior paradigm of when the sound was presented for each trial
timeDiff = centerOutTimes - soundStartTimes
if (len(eventOnsetTimes) < len(timeDiff)):
timeDiff = timeDiff[:-1]
eventOnsetTimesCenter = eventOnsetTimes + timeDiff
elif (len(eventOnsetTimes) > len(timeDiff)):
eventOnsetTimesCenter = eventOnsetTimes[:-1] + timeDiff
else:
eventOnsetTimesCenter = eventOnsetTimes + timeDiff
###############################################################################################
tetrode = oneCell.tetrode
cluster = oneCell.cluster
# -- Load Spike Data From Certain Cluster --
spkData = ephyscore.CellData(oneCell)
spkTimeStamps = spkData.spikes.timestamps
(spikeTimesFromEventOnset,trialIndexForEachSpike,indexLimitsEachTrial) = \
spikesanalysis.eventlocked_spiketimes(spkTimeStamps,eventOnsetTimes,timeRange)
(spikeTimesFromMovementOnset,movementTrialIndexForEachSpike,indexLimitsEachMovementTrial) = \
spikesanalysis.eventlocked_spiketimes(spkTimeStamps,eventOnsetTimesCenter,timeRange)
plt.clf()
if (len(spkTimeStamps) > 0):
ax1 = plt.subplot2grid((numRows, numCols),
((numRows - sizeClusterPlot), 0),
colspan=(numCols / 3))
spikesorting.plot_isi_loghist(spkData.spikes.timestamps)
ax3 = plt.subplot2grid(
(numRows, numCols),
((numRows - sizeClusterPlot), (numCols / 3) * 2),
colspan=(numCols / 3))
spikesorting.plot_events_in_time(spkTimeStamps)
samples = spkData.spikes.samples.astype(float) - 2**15
samples = (1000.0 / spkData.spikes.gain[0, 0]) * samples
ax2 = plt.subplot2grid(
(numRows, numCols),
((numRows - sizeClusterPlot), (numCols / 3)),
colspan=(numCols / 3))
spikesorting.plot_waveforms(samples)
###############################################################################
ax4 = plt.subplot2grid((numRows, numCols), (0, 0),
colspan=(numCols / 2),
rowspan=3 * sizeRasters)
#plt.setp(ax4.get_xticklabels(), visible=False)
#fig.axes.get_xaxis().set_visible(False)
raster_tuning(ax4)
axvline(x=0, ymin=0, ymax=1, color='r')
plt.gca().set_xlim(tuning_timeRange)
ax6 = plt.subplot2grid((numRows, numCols), (0, (numCols / 2)),
colspan=(numCols / 2),
rowspan=sizeRasters)
plt.setp(ax6.get_xticklabels(), visible=False)
plt.setp(ax6.get_yticklabels(), visible=False)
raster_sound_block_switching()
plt.title(
'sound aligned, Top: middle freq in blocks, Bottom: all freqs')
ax7 = plt.subplot2grid((numRows, numCols),
(sizeRasters, (numCols / 2)),
colspan=(numCols / 2),
rowspan=sizeHists,
sharex=ax6)
hist_sound_block_switching(ax7)
#plt.setp(ax7.get_yticklabels(), visible=False)
ax7.yaxis.tick_right()
ax7.yaxis.set_ticks_position('both')
plt.setp(ax7.get_xticklabels(), visible=False)
plt.gca().set_xlim(timeRange)
ax10 = plt.subplot2grid((numRows, numCols),
((sizeRasters + sizeHists), (numCols / 2)),
colspan=(numCols / 2),
rowspan=sizeRasters)
plt.setp(ax10.get_xticklabels(), visible=False)
plt.setp(ax10.get_yticklabels(), visible=False)
raster_sound_allFreq_switching()
ax11 = plt.subplot2grid(
(numRows, numCols),
((2 * sizeRasters + sizeHists), (numCols / 2)),
colspan=(numCols / 2),
rowspan=sizeHists,
sharex=ax10)
hist_sound_allFreq_switching(ax11)
ax11.yaxis.tick_right()
ax11.yaxis.set_ticks_position('both')
ax11.set_xlabel('Time (sec)')
#plt.setp(ax11.get_yticklabels(), visible=False)
plt.gca().set_xlim(timeRange)
###############################################################################
#plt.tight_layout()
modulation_index_switching()
plt.suptitle(titleText)
tetrodeClusterName = 'T' + str(oneCell.tetrode) + 'c' + str(
oneCell.cluster)
plt.gcf().set_size_inches((8.5, 11))
figformat = 'png' #'png' #'pdf' #'svg'
filename = reportname + '_%s_%s_%s.%s' % (
subject, behavSession, tetrodeClusterName, figformat)
fulloutputDir = outputDir + subject + '/'
fullFileName = os.path.join(fulloutputDir, filename)
directory = os.path.dirname(fulloutputDir)
if not os.path.exists(directory): #makes sure output folder exists
os.makedirs(directory)
#print 'saving figure to %s'%fullFileName
plt.gcf().savefig(fullFileName, format=figformat)
except:
if (oneCell.behavSession not in badSessionList):
badSessionList.append(oneCell.behavSession)
print 'error with sessions: '
for badSes in badSessionList:
print badSes
def main(oneCell):
oneCell = allcells.cellDB[cellID]
if (behavSession != oneCell.behavSession):
subject = oneCell.animalName
behavSession = oneCell.behavSession
ephysSession = oneCell.ephysSession
ephysRoot = os.path.join(ephysRootDir,subject)
# -- Load Behavior Data --
behaviorFilename = loadbehavior.path_to_behavior_data(subject,experimenter,paradigm,behavSession)
bdata = loadbehavior.BehaviorData(behaviorFilename)
numberOfTrials = len(bdata['choice'])
# -- Load event data and convert event timestamps to ms --
ephysDir = os.path.join(ephysRoot, ephysSession)
eventFilename=os.path.join(ephysDir, 'all_channels.events')
events = loadopenephys.Events(eventFilename) # Load events data
eventTimes=np.array(events.timestamps)/SAMPLING_RATE #get array of timestamps for each event and convert to seconds by dividing by sampling rate (Hz). matches with eventID and
soundOnsetEvents = (events.eventID==1) & (events.eventChannel==soundTriggerChannel)
eventOnsetTimes = eventTimes[soundOnsetEvents]
rightward = bdata['choice']==bdata.labels['choice']['right']
leftward = bdata['choice']==bdata.labels['choice']['left']
invalid = bdata['outcome']==bdata.labels['outcome']['invalid']
correct = bdata['outcome']==bdata.labels['outcome']['correct']
correctRightward = rightward & correct
correctLeftward = leftward & correct
possibleFreq = np.unique(bdata['targetFrequency'])
Freq = possibleFreq[Frequency]
oneFreq = bdata['targetFrequency'] == possibleFreq[Frequency]
trialsToUseRight = correctRightward & oneFreq
trialsToUseLeft = correctLeftward & oneFreq
trialsEachCond = np.c_[trialsToUseLeft,trialsToUseRight]; colorEachCond = ['g','r']
# -- Load Spike Data From Certain Cluster --
spkData = ephyscore.CellData(oneCell)
spkTimeStamps = spkData.spikes.timestamps
(spikeTimesFromEventOnset,trialIndexForEachSpike,indexLimitsEachTrial) = \
spikesanalysis.eventlocked_spiketimes(spkTimeStamps,eventOnsetTimes,timeRange)
plt.clf()
ax1 = plt.subplot2grid((3,1), (0, 0), rowspan=2)
extraplots.raster_plot(spikeTimesFromEventOnset,indexLimitsEachTrial,timeRange,trialsEachCond=trialsEachCond,colorEachCond=colorEachCond,fillWidth=None,labels=None)
plt.ylabel('Trials')
timeVec = np.arange(timeRange[0],timeRange[-1],binWidth)
spikeCountMat = spikesanalysis.spiketimes_to_spikecounts(spikeTimesFromEventOnset,indexLimitsEachTrial,timeVec)
smoothWinSize = 3
ax2 = plt.subplot2grid((3,1), (2, 0), sharex=ax1)
extraplots.plot_psth(spikeCountMat/binWidth,smoothWinSize,timeVec,trialsEachCond=trialsEachCond,
colorEachCond=colorEachCond,linestyle=None,linewidth=3,downsamplefactor=1)
plt.xlabel('Time from sound onset (s)')
plt.ylabel('Firing rate (spk/sec)')
nameFreq = str(Freq)
tetrodeClusterName = 'T'+str(oneCell.tetrode)+'c'+str(oneCell.cluster)
plt.gcf().set_size_inches((8.5,11))
figformat = 'png' #'png' #'pdf' #'svg'
filename = 'rast_%s_%s_%s_%s.%s'%(subject,behavSession,nameFreq,tetrodeClusterName,figformat)
fulloutputDir = outputDir+subject+'/'+ nameFreq +'/'
fullFileName = os.path.join(fulloutputDir,filename)
directory = os.path.dirname(fulloutputDir)
if not os.path.exists(directory):
os.makedirs(directory)
print 'saving figure to %s'%fullFileName
plt.gcf().savefig(fullFileName,format=figformat)
def switch_report(mouseName, behavSession, tetrode, cluster):
#global behavSession
#global subject
global bdata
global eventOnsetTimes
global spikeTimesFromEventOnset
global indexLimitsEachTrial
global spikeTimesFromMovementOnset
global indexLimitsEachMovementTrial
allcellsFileName = 'allcells_' + mouseName
sys.path.append(settings.ALLCELLS_PATH)
allcells = importlib.import_module(allcellsFileName)
cellID = allcells.cellDB.findcell(mouseName, behavSession, tetrode,
cluster)
oneCell = allcells.cellDB[cellID]
subject = oneCell.animalName
behavSession = oneCell.behavSession
ephysSession = oneCell.ephysSession
ephysRoot = os.path.join(ephysRootDir, subject)
# -- Load Behavior Data --
behaviorFilename = loadbehavior.path_to_behavior_data(
subject, experimenter, paradigm, behavSession)
bdata = loadbehavior.FlexCategBehaviorData(behaviorFilename)
#bdata = loadbehavior.BehaviorData(behaviorFilename)
bdata.find_trials_each_block()
numberOfTrials = len(bdata['choice'])
# -- Load event data and convert event timestamps to ms --
ephysDir = os.path.join(ephysRoot, ephysSession)
eventFilename = os.path.join(ephysDir, 'all_channels.events')
events = loadopenephys.Events(eventFilename) # Load events data
eventTimes = np.array(
events.timestamps
) / SAMPLING_RATE #get array of timestamps for each event and convert to seconds by dividing by sampling rate (Hz). matches with eventID and
soundOnsetEvents = (events.eventID == 1) & (events.eventChannel
== soundTriggerChannel)
eventOnsetTimes = eventTimes[soundOnsetEvents]
#################################################################################################
centerOutTimes = bdata[
'timeCenterOut'] #This is the times that the mouse goes out of the center port
soundStartTimes = bdata[
'timeTarget'] #This gives an array with the times in seconds from the start of the behavior paradigm of when the sound was presented for each trial
timeDiff = centerOutTimes - soundStartTimes
if (len(eventOnsetTimes) < len(timeDiff)):
timeDiff = timeDiff[:-1]
eventOnsetTimesCenter = eventOnsetTimes + timeDiff
#################################################################################################
# -- Load Spike Data From Certain Cluster --
spkData = ephyscore.CellData(oneCell)
spkTimeStamps = spkData.spikes.timestamps
(spikeTimesFromEventOnset,trialIndexForEachSpike,indexLimitsEachTrial) = \
spikesanalysis.eventlocked_spiketimes(spkTimeStamps,eventOnsetTimes,timeRange)
(spikeTimesFromMovementOnset,movementTrialIndexForEachSpike,indexLimitsEachMovementTrial) = \
spikesanalysis.eventlocked_spiketimes(spkTimeStamps,eventOnsetTimesCenter,timeRange)
plt.clf()
if (len(spkTimeStamps) > 0):
ax1 = plt.subplot2grid((numRows, numCols),
((numRows - sizeClusterPlot), 0),
colspan=(numCols / 3))
spikesorting.plot_isi_loghist(spkData.spikes.timestamps)
ax3 = plt.subplot2grid(
(numRows, numCols),
((numRows - sizeClusterPlot), (numCols / 3) * 2),
colspan=(numCols / 3))
spikesorting.plot_events_in_time(spkTimeStamps)
samples = spkData.spikes.samples.astype(float) - 2**15
samples = (1000.0 / spkData.spikes.gain[0, 0]) * samples
ax2 = plt.subplot2grid((numRows, numCols),
((numRows - sizeClusterPlot), (numCols / 3)),
colspan=(numCols / 3))
spikesorting.plot_waveforms(samples)
###############################################################################
ax4 = plt.subplot2grid((numRows, numCols), (0, 0),
colspan=(numCols / 2),
rowspan=sizeRasters)
raster_sound_block_switching()
ax5 = plt.subplot2grid((numRows, numCols), (sizeRasters, 0),
colspan=(numCols / 2),
rowspan=sizeHists)
hist_sound_block_switching()
ax6 = plt.subplot2grid((numRows, numCols), (0, (numCols / 2)),
colspan=(numCols / 2),
rowspan=sizeRasters)
raster_movement_block_switching()
ax7 = plt.subplot2grid((numRows, numCols), (sizeRasters, (numCols / 2)),
colspan=(numCols / 2),
rowspan=sizeHists)
hist_movement_block_switching()
ax8 = plt.subplot2grid((numRows, numCols), ((sizeRasters + sizeHists), 0),
colspan=(numCols / 2),
rowspan=sizeRasters)
raster_sound_allFreq_switching()
ax9 = plt.subplot2grid((numRows, numCols),
((2 * sizeRasters + sizeHists), 0),
colspan=(numCols / 2),
rowspan=sizeHists)
hist_sound_allFreq_switching()
ax10 = plt.subplot2grid((numRows, numCols),
((sizeRasters + sizeHists), (numCols / 2)),
colspan=(numCols / 2),
rowspan=sizeRasters)
raster_sound_switching()
ax11 = plt.subplot2grid((numRows, numCols),
((2 * sizeRasters + sizeHists), (numCols / 2)),
colspan=(numCols / 2),
rowspan=sizeHists)
hist_sound_switching()
###############################################################################
#plt.tight_layout()
tetrodeClusterName = 'T' + str(oneCell.tetrode) + 'c' + str(
oneCell.cluster)
plt.suptitle(mouseName + ' ' + behavSession + ' ' + tetrodeClusterName)
plt.gcf().set_size_inches((8.5, 11))
#figformat = 'png' #'png' #'pdf' #'svg'
#filename = 'report_%s_%s_%s.%s'%(subject,behavSession,tetrodeClusterName,figformat)
#fulloutputDir = outputDir+subject +'/'
#fullFileName = os.path.join(fulloutputDir,filename)
#directory = os.path.dirname(fulloutputDir)
#if not os.path.exists(directory): #makes sure output folder exists
#os.makedirs(directory)
#print 'saving figure to %s'%fullFileName
#plt.gcf().savefig(fullFileName,format=figformat)
plt.show()
from jaratoolbox import celldatabase
import pandas as pd
import os
from jaratoolbox import settings
from jaratoolbox import ephyscore
reload(ephyscore)
dbPath = os.path.join(settings.FIGURES_DATA_PATH, '2018thstr',
'celldatabase.h5')
db = pd.read_hdf(dbPath, key='dataframe')
#Select a cell
cell = db.ix[1033]
#Convert the cell to a dict. Or, you can make a dict yourself that has the right fields
cellDict = cell.to_dict()
cellData = ephyscore.CellData(**cellDict)
spikeData, events = cellData.load_ephys('am')
bdata = cellData.load_bdata('am')
def main():
global behavSession
global subject
global ephysSession
global tetrodeID
global bdata
global eventOnsetTimes
global spikeTimesFromEventOnset
global indexLimitsEachTrial
global spikeTimesFromMovementOnset
global indexLimitsEachMovementTrial
for cellID in range(0, numOfCells):
oneCell = allcells.cellDB[cellID]
try:
if (behavSession != oneCell.behavSession):
subject = oneCell.animalName
behavSession = oneCell.behavSession
ephysSession = oneCell.ephysSession
ephysRoot = os.path.join(ephysRootDir, subject)
print behavSession
# -- Load Behavior Data --
behaviorFilename = loadbehavior.path_to_behavior_data(
subject, experimenter, paradigm, behavSession)
bdata = loadbehavior.BehaviorData(behaviorFilename)
numberOfTrials = len(bdata['choice'])
# -- Load event data and convert event timestamps to ms --
ephysDir = os.path.join(ephysRoot, ephysSession)
eventFilename = os.path.join(ephysDir, 'all_channels.events')
events = loadopenephys.Events(
eventFilename) # Load events data
eventTimes = np.array(
events.timestamps
) / SAMPLING_RATE #get array of timestamps for each event and convert to seconds by dividing by sampling rate (Hz). matches with eventID and
soundOnsetEvents = (events.eventID == 1) & (
events.eventChannel == soundTriggerChannel)
eventOnsetTimes = eventTimes[soundOnsetEvents]
possibleFreq = np.unique(bdata['targetFrequency'])
numberOfFrequencies = len(possibleFreq)
centerFrequencies = [(numberOfFrequencies / 2 - 1),
numberOfFrequencies / 2]
#################################################################################################
centerOutTimes = bdata[
'timeCenterOut'] #This is the times that the mouse goes out of the center port
soundStartTimes = bdata[
'timeTarget'] #This gives an array with the times in seconds from the start of the behavior paradigm of when the sound was presented for each trial
timeDiff = centerOutTimes - soundStartTimes
if (len(eventOnsetTimes) < len(timeDiff)):
timeDiff = timeDiff[:-1]
eventOnsetTimesCenter = eventOnsetTimes + timeDiff
#################################################################################################
# -- Load Spike Data From Certain Cluster --
spkData = ephyscore.CellData(oneCell)
spkTimeStamps = spkData.spikes.timestamps
(spikeTimesFromEventOnset,trialIndexForEachSpike,indexLimitsEachTrial) = \
spikesanalysis.eventlocked_spiketimes(spkTimeStamps,eventOnsetTimes,timeRange)
(spikeTimesFromMovementOnset,movementTrialIndexForEachSpike,indexLimitsEachMovementTrial) = \
spikesanalysis.eventlocked_spiketimes(spkTimeStamps,eventOnsetTimesCenter,timeRange)
plt.clf()
if (len(spkTimeStamps) > 0):
ax1 = plt.subplot2grid((7, 6), (6, 0), colspan=2)
spikesorting.plot_isi_loghist(spkData.spikes.timestamps)
ax3 = plt.subplot2grid((7, 6), (6, 4), colspan=2)
spikesorting.plot_events_in_time(spkData.spikes.timestamps)
samples = spkData.spikes.samples.astype(float) - 2**15
samples = (1000.0 / spkData.spikes.gain[0, 0]) * samples
ax2 = plt.subplot2grid((7, 6), (6, 2), colspan=2)
spikesorting.plot_waveforms(samples)
###############################################################################
ax4 = plt.subplot2grid((7, 6), (0, 0), colspan=3, rowspan=2)
raster_sound_psycurve(centerFrequencies[0])
ax5 = plt.subplot2grid((7, 6), (2, 0), colspan=3)
hist_sound_psycurve(centerFrequencies[0])
ax6 = plt.subplot2grid((7, 6), (0, 3), colspan=3, rowspan=2)
raster_sound_psycurve(centerFrequencies[1])
ax7 = plt.subplot2grid((7, 6), (2, 3), colspan=3)
hist_sound_psycurve(centerFrequencies[1])
ax8 = plt.subplot2grid((7, 6), (3, 0), colspan=3, rowspan=2)
raster_movement_psycurve(centerFrequencies[0])
ax9 = plt.subplot2grid((7, 6), (5, 0), colspan=3)
hist_movement_psycurve(centerFrequencies[0])
ax10 = plt.subplot2grid((7, 6), (3, 3), colspan=3, rowspan=2)
raster_movement_psycurve(centerFrequencies[1])
ax11 = plt.subplot2grid((7, 6), (5, 3), colspan=3)
hist_movement_psycurve(centerFrequencies[1])
###############################################################################
#plt.tight_layout()
tetrodeClusterName = 'T' + str(oneCell.tetrode) + 'c' + str(
oneCell.cluster)
plt.gcf().set_size_inches((8.5, 11))
figformat = 'png' #'png' #'pdf' #'svg'
filename = 'report_centerFreq_%s_%s_%s.%s' % (
subject, behavSession, tetrodeClusterName, figformat)
fulloutputDir = outputDir + subject + '/'
fullFileName = os.path.join(fulloutputDir, filename)
directory = os.path.dirname(fulloutputDir)
if not os.path.exists(directory):
os.makedirs(directory)
#print 'saving figure to %s'%fullFileName
plt.gcf().savefig(fullFileName, format=figformat)
#plt.show()
except:
#print "error with session "+oneCell.behavSession
if (oneCell.behavSession not in badSessionList):
badSessionList.append(oneCell.behavSession)
print 'error with sessions: '
for badSes in badSessionList:
print badSes
def rasterBlock(oneCell):
subject = oneCell.animalName
behavSession = oneCell.behavSession
ephysSession = oneCell.ephysSession
ephysRoot = os.path.join(ephysRootDir, subject)
# -- Load Behavior Data --
behaviorFilename = loadbehavior.path_to_behavior_data(
subject, experimenter, paradigm, behavSession)
bdata = loadbehavior.FlexCategBehaviorData(behaviorFilename)
bdata.find_trials_each_block()
# -- Load event data and convert event timestamps to ms --
ephysDir = os.path.join(ephysRoot, ephysSession)
eventFilename = os.path.join(ephysDir, 'all_channels.events')
events = loadopenephys.Events(eventFilename) # Load events data
eventTimes = np.array(events.timestamps) / SAMPLING_RATE
soundOnsetEvents = (events.eventID == 1) & (events.eventChannel
== soundTriggerChannel)
# -- Load Spike Data From Certain Cluster --
spkData = ephyscore.CellData(oneCell)
spkTimeStamps = spkData.spikes.timestamps
eventOnsetTimes = eventTimes[soundOnsetEvents]
correct = bdata['outcome'] == bdata.labels['outcome']['correct']
possibleFreq = np.unique(bdata['targetFrequency'])
oneFreq = bdata['targetFrequency'] == possibleFreq[middleFreq]
correctOneFreq = oneFreq & correct
correctTrialsEachBlock = bdata.blocks[
'trialsEachBlock'] & correctOneFreq[:, np.newaxis]
#trialsEachCond = np.c_[invalid,leftward,rightward]; colorEachCond = ['0.75','g','r']
#trialsEachCond = np.c_[leftward,rightward]; colorEachCond = ['0.5','0.7','0']
trialsEachCond = correctTrialsEachBlock
if bdata['currentBlock'][0] == bdata.labels['currentBlock'][
'low_boundary']:
colorEachBlock = 3 * ['g', 'r']
else:
colorEachBlock = 3 * ['r', 'g']
(spikeTimesFromEventOnset,trialIndexForEachSpike,indexLimitsEachTrial) = \
spikesanalysis.eventlocked_spiketimes(spkTimeStamps,eventOnsetTimes,timeRange)
#plot(spikeTimesFromEventOnset,trialIndexForEachSpike,'.')
plt.clf()
ax1 = plt.subplot2grid((3, 1), (0, 0), rowspan=2)
extraplots.raster_plot(spikeTimesFromEventOnset,
indexLimitsEachTrial,
timeRange,
trialsEachCond=correctTrialsEachBlock,
colorEachCond=colorEachBlock,
fillWidth=None,
labels=None)
#plt.yticks([0,trialsEachCond.sum()])
#ax1.set_xticklabels([])
plt.ylabel('Trials')
timeVec = np.arange(timeRange[0], timeRange[-1], binWidth)
spikeCountMat = spikesanalysis.spiketimes_to_spikecounts(
spikeTimesFromEventOnset, indexLimitsEachTrial, timeVec)
smoothWinSize = 3
ax2 = plt.subplot2grid((3, 1), (2, 0), sharex=ax1)
extraplots.plot_psth(spikeCountMat / binWidth,
smoothWinSize,
timeVec,
trialsEachCond=correctTrialsEachBlock,
colorEachCond=colorEachBlock,
linestyle=None,
linewidth=3,
downsamplefactor=1)
plt.xlabel('Time from sound onset (s)')
plt.ylabel('Firing rate (spk/sec)')
#plt.show()
nameFreq = str(possibleFreq[middleFreq])
tetrodeClusterName = 'T' + str(oneCell.tetrode) + 'c' + str(
oneCell.cluster)
plt.gcf().set_size_inches((8.5, 11))
figformat = 'png' #'png' #'pdf' #'svg'
filename = 'block_%s_%s_%s_%s.%s' % (
subject, behavSession, tetrodeClusterName, nameFreq, figformat)
fulloutputDir = outputDir + subject + '/'
fullFileName = os.path.join(fulloutputDir, filename)
directory = os.path.dirname(fulloutputDir)
if not os.path.exists(directory):
os.makedirs(directory)
print 'saving figure to %s' % fullFileName
plt.gcf().savefig(fullFileName, format=figformat)
def raster_tuning(ax):
fullbehaviorDir = behaviorDir + subject + '/'
behavName = subject + '_tuning_curve_' + tuningBehavior + '.h5'
tuningBehavFileName = os.path.join(fullbehaviorDir, behavName)
tuning_bdata = loadbehavior.BehaviorData(tuningBehavFileName,
readmode='full')
freqEachTrial = tuning_bdata['currentFreq']
possibleFreq = np.unique(freqEachTrial)
numberOfTrials = len(freqEachTrial)
# -- The old way of sorting (useful for plotting sorted raster) --
sortedTrials = []
numTrialsEachFreq = [
] #Used to plot lines after each group of sorted trials
for indf, oneFreq in enumerate(
possibleFreq
): #indf is index of this freq and oneFreq is the frequency
indsThisFreq = np.flatnonzero(
freqEachTrial == oneFreq) #this gives indices of this frequency
sortedTrials = np.concatenate(
(sortedTrials,
indsThisFreq)) #adds all indices to a list called sortedTrials
numTrialsEachFreq.append(
len(indsThisFreq)) #finds number of trials each frequency has
sortingInds = argsort(
sortedTrials) #gives array of indices that would sort the sortedTrials
# -- Load event data and convert event timestamps to ms --
tuning_ephysDir = os.path.join(settings.EPHYS_PATH, subject, tuningEphys)
tuning_eventFilename = os.path.join(tuning_ephysDir, 'all_channels.events')
tuning_ev = loadopenephys.Events(
tuning_eventFilename) #load ephys data (like bdata structure)
tuning_eventTimes = np.array(
tuning_ev.timestamps
) / SAMPLING_RATE #get array of timestamps for each event and convert to seconds by dividing by sampling rate (Hz). matches with eventID and
tuning_evID = np.array(
tuning_ev.eventID
) #loads the onset times of events (matches up with eventID to say if event 1 went on (1) or off (0)
tuning_eventOnsetTimes = tuning_eventTimes[
tuning_evID ==
1] #array that is a time stamp for when the chosen event happens.
#ev.eventChannel woul load array of events like trial start and sound start and finish times (sound event is 0 and trial start is 1 for example). There is only one event though and its sound start
while (numberOfTrials < len(tuning_eventOnsetTimes)):
tuning_eventOnsetTimes = tuning_eventOnsetTimes[:-1]
#######################################################################################################
###################THIS IS SUCH A HACK TO GET SPKDATA FROM EPHYSCORE###################################
#######################################################################################################
thisCell = celldatabase.CellInfo(
animalName=subject, ############################################
ephysSession=tuningEphys,
tuningSession='DO NOT NEED THIS',
tetrode=tetrode,
cluster=cluster,
quality=1,
depth=0,
tuningBehavior='DO NOT NEED THIS',
behavSession=tuningBehavior)
tuning_spkData = ephyscore.CellData(thisCell)
tuning_spkTimeStamps = tuning_spkData.spikes.timestamps
(tuning_spikeTimesFromEventOnset, tuning_trialIndexForEachSpike,
tuning_indexLimitsEachTrial) = spikesanalysis.eventlocked_spiketimes(
tuning_spkTimeStamps, tuning_eventOnsetTimes, tuning_timeRange)
#print 'numTrials ',max(tuning_trialIndexForEachSpike)#####################################
'''
Create a vector with the spike timestamps w.r.t. events onset.
(spikeTimesFromEventOnset,trialIndexForEachSpike,indexLimitsEachTrial) =
eventlocked_spiketimes(timeStamps,eventOnsetTimes,timeRange)
timeStamps: (np.array) the time of each spike.
eventOnsetTimes: (np.array) the time of each instance of the event to lock to.
timeRange: (list or np.array) two-element array specifying time-range to extract around event.
spikeTimesFromEventOnset: 1D array with time of spikes locked to event.
o trialIndexForEachSpike: 1D array with the trial corresponding to each spike.
The first spike index is 0.
indexLimitsEachTrial: [2,nTrials] range of spikes for each trial. Note that
the range is from firstSpike to lastSpike+1 (like in python slices)
spikeIndices
'''
tuning_sortedIndexForEachSpike = sortingInds[
tuning_trialIndexForEachSpike] #Takes values of trialIndexForEachSpike and finds value of sortingInds at that index and makes array. This array gives an array with the sorted index of each trial for each spike
# -- Calculate tuning --
plot(tuning_spikeTimesFromEventOnset,
tuning_sortedIndexForEachSpike,
'.',
ms=3)
#axvline(x=0, ymin=0, ymax=1, color='r')
#The cumulative sum of the list of specific frequency presentations,
#used below for plotting the lines across the figure.
numTrials = cumsum(numTrialsEachFreq)
#Plot the lines across the figure in between each group of sorted trials
for indf, num in enumerate(numTrials):
ax.axhline(y=num, xmin=0, xmax=1, color='0.90', zorder=0)
tickPositions = numTrials - mean(numTrialsEachFreq) / 2
tickLabels = [
"%0.2f" % (possibleFreq[indf] / 1000)
for indf in range(len(possibleFreq))
]
ax.set_yticks(tickPositions)
ax.set_yticklabels(tickLabels)
ax.set_ylim([-1, numberOfTrials])
ylabel('Frequency Presented (kHz), {} total trials'.format(numTrials[-1]))
#title(ephysSession+' T{}c{}'.format(tetrodeID,clusterID))
xlabel('Time (sec)')
soundeventData = loadopenephys.Events(
soundeventFilename) # Load events data
soundeventTimes = np.array(
soundeventData.timestamps) / SAMPLING_RATE
soundOnsetEvents = (soundeventData.eventID == 1) & (
soundeventData.eventChannel == soundTriggerChannel)
soundOnsetTimes = soundeventTimes[soundOnsetEvents]
print "number of laser trials ", len(
laserOnsetTimes), "number of sound trials ", len(
soundOnsetTimes)
maxZLaserDict[behavSession] = np.zeros([clusNum * numTetrodes])
maxZSoundDict[behavSession] = np.zeros([clusNum * numTetrodes])
# -- Load Spike Data From Certain Cluster --
soundSpkData = ephyscore.CellData(
oneCell
) #cannot use this methodfor laser data since it only loads ephys session not laser session
soundSpkTimeStamps = soundSpkData.spikes.timestamps
print len(soundSpkTimeStamps), len(soundOnsetTimes)
laserSpkFullPath = os.path.join(
laserephysDir, 'Tetrode{0}.spikes'.format(tetrode))
laserSpkData = loadopenephys.DataSpikes(laserSpkFullPath)
laserSpkData.timestamps = laserSpkData.timestamps / SAMPLING_RATE
kkDataDir = os.path.dirname(laserSpkFullPath) + '_kk'
clusterFilename = 'Tetrode{0}.clu.1'.format(tetrode)
clusterFullPath = os.path.join(kkDataDir, clusterFilename)
clusters = np.fromfile(clusterFullPath, dtype='int32', sep=' ')[1:]
spikesMaskThisCluster = clusters == cluster
laserSpkTimeStamps = laserSpkData.timestamps[spikesMaskThisCluster]
print len(laserSpkTimeStamps), len(laserOnsetTimes)
