def __init__(self,
animalName,
date,
experimenter,
defaultParadigm=None,
defaultTetrodes=[3, 4, 5, 6],
serverUser='******',
serverName='jarahub',
serverBehavPathBase='/data/behavior'):
self.animalName = animalName
self.date = date
self.experimenter = experimenter
self.defaultParadigm = defaultParadigm
self.defaultTetrodes = defaultTetrodes
self.loader = dataloader.DataLoader('online', animalName, date,
experimenter, defaultParadigm)
self.serverUser = serverUser
self.serverName = serverName
self.serverBehavPathBase = serverBehavPathBase
self.experimenter = experimenter
self.serverBehavPath = os.path.join(self.serverBehavPathBase,
self.experimenter, self.animalName)
self.remoteBehavLocation = '{0}@{1}:{2}'.format(
self.serverUser, self.serverName, self.serverBehavPath)
def plot_cluster_tuning(clusterObj, indTC, experimenter='nick', *args, **kwargs):
loader = dataloader.DataLoader('offline', experimenter=experimenter)
spikeData, eventData, behavData = loader.get_cluster_data(clusterObj, indTC)
spikeTimestamps = spikeData.timestamps
eventOnsetTimes = loader.get_event_onset_times(eventData)
freqEachTrial = behavData['currentFreq']
intensityEachTrial = behavData['currentIntensity']
possibleFreq = np.unique(freqEachTrial)
possibleIntensity = np.unique(intensityEachTrial)
xlabel='Frequency (kHz)'
ylabel='Intensity (dB SPL)'
freqLabels = ["%.1f" % freq for freq in possibleFreq/1000.0]
intenLabels = ["%.1f" % inten for inten in possibleIntensity]
# plt.clf()
ax, cax, cbar = dataplotter.two_axis_heatmap(spikeTimestamps,
eventOnsetTimes,
firstSortArray=intensityEachTrial,
secondSortArray=freqEachTrial,
firstSortLabels=intenLabels,
secondSortLabels=freqLabels,
timeRange=[0, 0.1],
*args,
**kwargs)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
return ax, cax, cbar
def calculate_site_response(site, siteName, sessionInd, maxZonly=False):
from jaratoolbox import spikesanalysis
#Zscore settings from billy
baseRange = [-0.050,-0.025] # Baseline range (in seconds)
binTime = baseRange[1]-baseRange[0] # Time-bin size
responseTimeRange = [-0.5,1] #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)
timeRange = [-0.5, 1]
loader = dataloader.DataLoader('offline', experimenter=site.experimenter)
sessionEphys = site.get_mouse_relative_ephys_filenames()[sessionInd]
siteClusterMaxZ = {}
siteClusterPval = {}
siteClusterZstat = {}
for tetrode in site.tetrodes:
oneTT = cluster_site(site, siteName, tetrode, report=False)
possibleClusters=np.unique(oneTT.clusters)
for indClust, cluster in enumerate(possibleClusters):
rasterSpikes = loader.get_session_spikes(sessionEphys, tetrode)
spikeTimes = rasterSpikes.timestamps[rasterSpikes.clusters==cluster]
rasterEvents = loader.get_session_events(sessionEphys)
eventOnsetTimes = loader.get_event_onset_times(rasterEvents)
(spikeTimesFromEventOnset,trialIndexForEachSpike,indexLimitsEachTrial) = \
spikesanalysis.eventlocked_spiketimes(spikeTimes,eventOnsetTimes,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
tetClustName = '{0}T{1}c{2}'.format(siteName, tetrode, cluster)
siteClusterMaxZ[tetClustName] = maxZ
siteClusterPval[tetClustName] = pValue
siteClusterZstat[tetClustName] = zStat
if maxZonly:
return siteClusterMaxZ
else:
return siteClusterZstat, siteClusterPval, siteClusterMaxZ
def am_mod_report(site, siteName, amSessionInd):
'''
'''
loader = dataloader.DataLoader('offline', experimenter=site.experimenter)
for tetrode in site.tetrodes:
try:
oneTT = cluster_site(site, siteName, tetrode)
except AttributeError:
print "There was an attribute error for tetrode {} at {}".format(tetrode, siteName)
continue
possibleClusters=np.unique(oneTT.clusters)
for indClust, cluster in enumerate(possibleClusters):
amFilename = site.get_mouse_relative_ephys_filenames()[amSessionInd]
amBehav = site.get_mouse_relative_behav_filenames()[amSessionInd]
plt.clf()
spikeData = loader.get_session_spikes(amFilename, tetrode, cluster=cluster)
spikeTimes = spikeData.timestamps
eventData = loader.get_session_events(amFilename)
eventOnsetTimes = loader.get_event_onset_times(eventData)
bdata = loader.get_session_behavior(amBehav)
currentFreq = bdata['currentFreq']
dataplotter.plot_raster(spikeTimes, eventOnsetTimes, sortArray=currentFreq)
fig_path = oneTT.clustersDir
fig_name = 'TT{0}Cluster{1}_Amp_Mod.png'.format(tetrode, cluster)
full_fig_path = os.path.join(fig_path, fig_name)
print full_fig_path
plt.savefig(full_fig_path, format = 'png')
reload(simple_spike_selector)
from jaratoolbox.test.nick.database import dataloader
reload(dataloader)
from jaratoolbox.test.nick.database import cellDB
reload(cellDB)
from jaratoolbox.test.nick.database import dataplotter
from jaratoolbox import spikesorting
from jaratoolbox.test.nick import clustercutting
from pylab import *
loader = dataloader.DataLoader('offline', experimenter='nick')
dbFn = '/home/nick/data/database/nick_thalamus_cells.json'
db = cellDB.CellDB()
db.load_from_json(dbFn)
c = db[0]
spikeData, eventData, behavData = loader.get_cluster_data(c, 'tc_heatmap')
spikeTimes = spikeData.timestamps
eventOnsetTimes = loader.get_event_onset_times(eventData)
currentFreq = behavData['currentFreq']
currentIntensity = behavData['currentIntensity']
trialsToPlot = currentIntensity==70
eventOnsetTimes=eventOnsetTimes[trialsToPlot]
def compare_session_spike_waveforms(spikeSamples, spikeTimes, eventOnsetTimes,
rasterTimeRange, timeRangeList):
fig = plt.figure()
plt.subplot2grid((3, 2), (0, 0), rowspan=2, colspan=2)
dataplotter.plot_raster(spikeTimes,
eventOnsetTimes,
timeRange=rasterTimeRange)
for ind, tr in enumerate(timeRangeList):
plt.subplot2grid((3, 2), (2, ind), rowspan=1, colspan=1)
dataplotter.plot_waveforms_in_event_locked_timerange(
spikeSamples, spikeTimes, eventOnsetTimes, tr)
plt.subplots_adjust(hspace=0.7)
if __name__ == "__main__":
loader = dataloader.DataLoader('online', 'pinp005', '2015-07-30',
'laser_tuning_curve')
spikeData = loader.get_session_spikes('22-10-33', 4, cluster=8)
events = loader.get_session_events('22-10-33')
eventOnsetTimes = loader.get_event_onset_times(events)
spikeTimes = spikeData.timestamps
waveforms = spikeData.samples
compare_session_spike_waveforms(waveforms, spikeTimes, eventOnsetTimes,
[-0.5, 1], [[-0.2, 0], [0, 0.1]])
#TODO: It would be awesome if we could show the spikes on the raster in a color that corresponds to the waveforms
##Getting the data from a cluster to make a plot
# -----------------------
#Sessions with no behav data return just the ephys
cell1NoisePhys = cell1.get_data_filenames('noiseBurst')
#Sessions with behav data return the tuple (ephysFilename, behavFilename)
cell1TuningPhys, cell1TuningBehavior = cell1.get_data_filenames('tcHeatmap')
# -----------------------
##Initialize an offline data loader
# -----------------------
#For now we still need to specify the experimenter for offline data analysis
#since the behavior data is broken up by experimenter
loader = dataloader.DataLoader('offline', experimenter='nick')
# -----------------------
##Get ephys and behavior data by passing the filenames from the cluster
# -----------------------
cell1NoiseSpikesTT6 = loader.get_session_spikes(cell1NoisePhys,
6,
cluster=cell1.cluster)
cell1NoiseEvents = loader.get_session_events(cell1NoisePhys)
cell1TuningBdata = loader.get_session_behavior(cell1TuningBehavior)
eventOnsetTimes = loader.get_event_onset_times(cell1NoiseEvents)
spikeTimes = cell1NoiseSpikesTT6.timestamps
# -----------------------
##Make a raster plot
def nick_lan_daily_report(site, siteName, mainRasterInds, mainTCind, pcasort=True):
'''
'''
loader = dataloader.DataLoader('offline', experimenter=site.experimenter)
for tetrode in site.tetrodes:
#Tetrodes with no spikes will cause an error when clustering
try:
if pcasort:
oneTT = cluster_site_PCA(site, siteName, tetrode)
else:
oneTT = cluster_site(site, siteName, tetrode)
except AttributeError:
print "There was an attribute error for tetrode {} at {}".format(tetrode, siteName)
continue
possibleClusters=np.unique(oneTT.clusters)
#Iterate through the clusters, making a new figure for each cluster.
#for indClust, cluster in enumerate([3]):
for indClust, cluster in enumerate(possibleClusters):
mainRasterEphysFilenames = [site.get_mouse_relative_ephys_filenames()[i] for i in mainRasterInds]
mainRasterTypes = [site.get_session_types()[i] for i in mainRasterInds]
if mainTCind:
mainTCsession = site.get_mouse_relative_ephys_filenames()[mainTCind]
mainTCbehavFilename = site.get_mouse_relative_behav_filenames()[mainTCind]
mainTCtype = site.get_session_types()[mainTCind]
else:
mainTCsession=None
# plt.figure() #The main report for this cluster/tetrode/session
plt.clf()
for indRaster, rasterSession in enumerate(mainRasterEphysFilenames):
plt.subplot2grid((6, 6), (indRaster, 0), rowspan = 1, colspan = 3)
rasterSpikes = loader.get_session_spikes(rasterSession, tetrode)
spikeTimestamps = rasterSpikes.timestamps[rasterSpikes.clusters==cluster]
rasterEvents = loader.get_session_events(rasterSession)
eventOnsetTimes = loader.get_event_onset_times(rasterEvents)
dataplotter.plot_raster(spikeTimestamps, eventOnsetTimes, ms=1)
plt.ylabel('{}\n{}'.format(mainRasterTypes[indRaster], rasterSession.split('_')[1]), fontsize = 10)
ax=plt.gca()
extraplots.set_ticks_fontsize(ax,6) #Should this go in dataplotter?
#We can only do one main TC for now.
if mainTCsession:
plt.subplot2grid((6, 6), (0, 3), rowspan = 3, colspan = 3)
bdata = loader.get_session_behavior(mainTCbehavFilename)
plotTitle = loader.get_session_filename(mainTCsession)
eventData = loader.get_session_events(mainTCsession)
spikeData = loader.get_session_spikes(mainTCsession, tetrode)
spikeTimestamps = spikeData.timestamps[spikeData.clusters==cluster]
eventOnsetTimes = loader.get_event_onset_times(eventData)
freqEachTrial = bdata['currentFreq']
intensityEachTrial = bdata['currentIntensity']
possibleFreq = np.unique(freqEachTrial)
possibleIntensity = np.unique(intensityEachTrial)
xlabel='Frequency (kHz)'
ylabel='Intensity (dB SPL)'
# firstSortLabels = ["%.1f" % freq for freq in possibleFreq/1000.0]
# secondSortLabels = ['{}'.format(inten) for inten in possibleIntensity]
# dataplotter.two_axis_heatmap(spikeTimestamps,
# eventOnsetTimes,
# freqEachTrial,
# intensityEachTrial,
# firstSortLabels,
# secondSortLabels,
# xlabel,
# ylabel,
# plotTitle=plotTitle,
# flipFirstAxis=False,
# flipSecondAxis=True,
# timeRange=[0, 0.1])
freqLabels = ["%.1f" % freq for freq in possibleFreq/1000.0]
intenLabels = ["%.1f" % inten for inten in possibleIntensity]
dataplotter.two_axis_heatmap(spikeTimestamps=spikeTimestamps,
eventOnsetTimes=eventOnsetTimes,
firstSortArray=intensityEachTrial,
secondSortArray=freqEachTrial,
firstSortLabels=intenLabels,
secondSortLabels=freqLabels,
xlabel=xlabel,
ylabel=ylabel,
plotTitle=plotTitle,
flipFirstAxis=True,
flipSecondAxis=False,
timeRange=[0, 0.1])
plt.title("{0}\n{1}".format(mainTCsession, mainTCbehavFilename), fontsize = 10)
plt.show()
nSpikes = len(oneTT.timestamps)
nClusters = len(possibleClusters)
tsThisCluster = oneTT.timestamps[oneTT.clusters==cluster]
wavesThisCluster = oneTT.samples[oneTT.clusters==cluster]
# -- Plot ISI histogram --
plt.subplot2grid((6,6), (4,0), rowspan=1, colspan=3)
spikesorting.plot_isi_loghist(tsThisCluster)
plt.ylabel('c%d'%cluster,rotation=0,va='center',ha='center')
plt.xlabel('')
# -- Plot waveforms --
plt.subplot2grid((6,6), (5,0), rowspan=1, colspan=3)
spikesorting.plot_waveforms(wavesThisCluster)
# -- Plot projections --
plt.subplot2grid((6,6), (4,3), rowspan=1, colspan=3)
spikesorting.plot_projections(wavesThisCluster)
# -- Plot events in time --
plt.subplot2grid((6,6), (5,3), rowspan=1, colspan=3)
spikesorting.plot_events_in_time(tsThisCluster)
plt.subplots_adjust(wspace = 0.7)
fig_path = oneTT.clustersDir
fig_name = 'TT{0}Cluster{1}.png'.format(tetrode, cluster)
full_fig_path = os.path.join(fig_path, fig_name)
print full_fig_path
#plt.tight_layout()
plt.savefig(full_fig_path, format = 'png')
