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():
global behavSession
global subject
global tetrode
global cluster
global bdata
global eventOnsetTimes
global spikeTimesFromEventOnset
global indexLimitsEachTrial
global spikeTimesFromMovementOnset
global indexLimitsEachMovementTrial
global titleText
print "SwitchingReport"
for cellID in range(0,numOfCells):
oneCell = allcells.cellDB[cellID]
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.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
###############################################################################################
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 = 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()
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 = '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)
'''
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(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(spkTimeStamps)
samples = spkData.spikes.samples.astype(float)-2**15
samples = (1000.0/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(spikeTimesFromEventOnset, 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
def main():
global behavSession
global subject
global tetrode
global cluster
global bdata
global eventOnsetTimes
global spikeTimesFromEventOnset
global indexLimitsEachTrial
global spikeTimesFromMovementOnset
global indexLimitsEachMovementTrial
global titleText
print "SwitchingReport"
for cellID in range(0, numOfCells):
oneCell = allcells.cellDB[cellID]
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.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
###############################################################################################
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=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()
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 = '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)
def main():
global behavSession
global subject
global ephysSession
global tetrode
global cluster
global bdata
global eventOnsetTimes
global spikeTimesFromEventOnset
global indexLimitsEachTrial
global spikeTimesFromMovementOnset
global indexLimitsEachMovementTrial
global tuningBehavior#behavior file name of tuning curve
global tuningEphys#ephys session name of tuning curve
print 'psycurve_tuning_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
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.BehaviorData(behaviorFilename)
numberOfTrials = len(bdata['choice'])
#print 'numberTrials ',numberOfTrials############################!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
# -- 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)
#print 'missing ',len(missingTrials)############################!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
# Remove missing trials
bdata.remove_trials(missingTrials)
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
#print 'timeDiff ',len(timeDiff)############################!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
#print 'eventOnsetTimes ',len(eventOnsetTimes)############################!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
if (len(eventOnsetTimes) < len(timeDiff)):
eventOnsetTimesCenter = eventOnsetTimes + timeDiff[:-1]
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(spkData.spikes.timestamps)
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)
#raster_sound_psycurve(centerFrequencies[0])
raster_tuning(ax4)
axvline(x=0, ymin=0, ymax=1, color='r')
plt.gca().set_xlim(tuning_timeRange)
#ax5 = plt.subplot2grid((7,6), (2,0), colspan = 3, sharex=ax4)
#hist_sound_psycurve(centerFrequencies[0])
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_psycurve(centerFrequencies[0])
plt.title('Frequency Top: '+str(possibleFreq[centerFrequencies[0]])+' Bottom: '+str(possibleFreq[centerFrequencies[1]]))
ax7 = plt.subplot2grid((numRows,numCols), (sizeRasters,(numCols/2)), colspan = (numCols/2), rowspan = sizeHists, sharex=ax6)
hist_sound_psycurve(centerFrequencies[0])
ax7.yaxis.tick_right()
ax7.yaxis.set_ticks_position('both')
plt.setp(ax7.get_xticklabels(), visible=False)
plt.gca().set_xlim(timeRange)
#ax8 = plt.subplot2grid((7,6), (3,0), colspan = 3, rowspan = 2)
#plt.setp(ax8.get_xticklabels(), visible=False)
#raster_movement_psycurve(centerFrequencies[0])
#ax9 = plt.subplot2grid((7,6), (5,0), colspan = 3, sharex=ax8)
#hist_movement_psycurve(centerFrequencies[0])
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_psycurve(centerFrequencies[1])
ax11 = plt.subplot2grid((numRows,numCols), ((2*sizeRasters+sizeHists),(numCols/2)), colspan = (numCols/2), rowspan = sizeHists, sharex=ax10)
hist_sound_psycurve(centerFrequencies[1])
ax11.yaxis.tick_right()
ax11.yaxis.set_ticks_position('both')
#plt.setp(ax11.get_yticklabels(), visible=False)
plt.gca().set_xlim(timeRange)
###############################################################################
#plt.tight_layout()
modulation_index_psycurve(centerFrequencies)
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 = '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
# -- 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(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(spkTimeStamps)
samples = spkData.spikes.samples.astype(float) - 2**15
samples = (1000.0 / 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(spikeTimesFromEventOnset, 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