'depth': 1275,
'tetrode': 4,
'cluster': 2
}
] #another AC cell
args = sys.argv[1:]
if len(args):
cellsToGenerate = [int(x) for x in args]
else:
cellsToGenerate = range(len(cellList))
print cellsToGenerate
for indCell in cellsToGenerate:
# -- find the cell we want based on dictionary --
cellInd, dbRow = celldatabase.find_cell(dbase, **cellList[indCell])
cell = ephyscore.Cell(dbRow, useModifiedClusters=True)
# --- loads spike and event data for frequency tuning ephys sessions ---
tuningEphysData, tuningBData = cell.load('tuningCurve')
tuningEventOnsetTimes = tuningEphysData['events']['soundDetectorOn']
if len(tuningEventOnsetTimes
) == 0: #some cells recorded before sound detector installed
tuningEventOnsetTimes = tuningEphysData['events'][
'stimOn'] + 0.0095 #correction for bandwidth trials, determined by comparing sound detector onset to stim event onset
tuningEventOnsetTimes = spikesanalysis.minimum_event_onset_diff(
tuningEventOnsetTimes, minEventOnsetDiff=0.2)
tuningSpikeTimestamps = tuningEphysData['spikeTimes']
freqEachTrial = tuningBData['currentFreq']
numFreqs = np.unique(freqEachTrial)
'cluster': 5
}, #suppressed cell in PV-ArchT mouse
{
'subject': 'band073',
'date': '2018-09-14',
'depth': 1300,
'tetrode': 4,
'cluster': 4
} #suppressed cell in SOM-ArchT mouse
]
cellTypes = ['PV', 'SOM']
for indCell, oneCell in enumerate(cellList):
# -- find the cell we want based on dictionary --
cellInd, dbRow = celldatabase.find_cell(db, **oneCell)
cell = ephyscore.Cell(dbRow)
bandEphysData, bandBData = cell.load_by_index(int(
dbRow['bestBandSession']))
bandEventOnsetTimes = bandEphysData['events']['soundDetectorOn']
bandEventOnsetTimes = spikesanalysis.minimum_event_onset_diff(
bandEventOnsetTimes, minEventOnsetDiff=0.2)
bandSpikeTimestamps = bandEphysData['spikeTimes']
bandEachTrial = bandBData['currentBand']
numBands = np.unique(bandEachTrial)
LaserEachTrial = bandBData['laserTrial']
numLaser = np.unique(LaserEachTrial)
bandTrialsEachCond = behavioranalysis.find_trials_each_combination(
acExample = {
'cluster': 5,
'date': '2017-02-02',
'depth': 1275.0,
'subject': 'pinp015',
'tetrode': 1
}
#This example is noisey and not as good
# acExample = {'cluster': 6,
# 'date': '2017-03-09',
# 'depth': 1904.0,
# 'subject': 'pinp016',
# 'tetrode': 6}
indRowThal, rowThal = celldatabase.find_cell(db, **thalExample)
indRowAC, rowAC = celldatabase.find_cell(db, **acExample)
## -- Plot colors -- ##
colorNoise = figparams.colp['sound']
colorLaser = figparams.colp['blueLaser']
colorPSTH = 'k'
colorRaster = 'k'
stimLineWidth = 4
psthLineWidth = 2
rasterMS = figparams.rasterMS
## -- Raster/PSTH parameters --##
stimLineOffsetFrac = 0.2
alignmentRange = [-0.2, 0.6]
displayRange = [-0.1, 0.3]
'subject': 'band060',
'date': '2018-04-04',
'depth': 1225,
'tetrode': 3,
'cluster': 4
}
] #SOM cell
cellTypes = [
'AC', 'AC', 'AC', 'AC', 'AC', 'PV', 'PV', 'PV', 'PV', 'SOM', 'SOM', 'SOM',
'SOM'
]
for indCell, thisCell in enumerate(cellList):
# -- find the cell we want based on dictionary --
cellInd, dbRow = celldatabase.find_cell(db, **thisCell)
cell = ephyscore.Cell(dbRow, useModifiedClusters=True)
# --- loads spike and event data for laser pulse sessions ---
laserEphysData, noBehav = cell.load('laserPulse')
laserEventOnsetTimes = laserEphysData['events']['laserOn']
laserSpikeTimestamps = laserEphysData['spikeTimes']
timeRange = [-0.1, 0.3]
laserSpikeTimesFromEventOnset, trialIndexForEachSpike, laserIndexLimitsEachTrial = spikesanalysis.eventlocked_spiketimes(
laserSpikeTimestamps, laserEventOnsetTimes, timeRange)
# --- loads waveform for this specific cell ---
peaks = dbRow['spikePeakAmplitudes']
normSpikeShape = np.array(dbRow['spikeShape'] / np.abs(peaks[1]))
cellsToTest = [
'pinp017_2017-03-22_1338.0_TT3c4',
'pinp017_2017-03-23_1604.0_TT2c6',
'pinp017_2017-03-23_1518.0_TT8c2',
'pinp016_2017-03-16_3707.0_TT2c6',
'pinp015_2017-02-15_2902.0_TT8c4',
'pinp016_2017-03-15_3797.0_TT1c3',
'pinp026_2017-11-16_3256.0_TT6c3'
]
for cellName in cellsToTest:
(subject, date, depth, tetrodeCluster) = cellName.split('_')
depth = float(depth)
tetrode = int(tetrodeCluster[2])
cluster = int(tetrodeCluster[4:])
indRow, dbRow = celldatabase.find_cell(db, subject, date, depth, tetrode, cluster)
# indRow, dbRow = celldatabase.find_cell(db, **cellDict)
cell = ephyscore.Cell(dbRow)
ephysData, bdata = cell.load('tc')
# eventOnsetTimes = ephysData['events']['soundDetectorOn']
eventOnsetTimes = ephysData['events']['stimOn']
eventOnsetTimes = spikesanalysis.minimum_event_onset_diff(eventOnsetTimes, minEventOnsetDiff=0.2)
spikeTimes = ephysData['spikeTimes']
freqEachTrial = bdata['currentFreq']
possibleFreq = np.unique(freqEachTrial)
intensityEachTrial = bdata['currentIntensity']
possibleIntensity = np.unique(intensityEachTrial)
color=color)
labelPosX = [0.01, 0.23, 0.5, 0.775] # Horiz position for panel labels
labelPosY = [0.94] # Vert position for panel labels
plt.clf()
# Criterion 1: Responds to laser pulse
labelHeightFactor = 0.8
indCriterion = 0
for indExample in range(nExamples):
indRow, dbRow = celldatabase.find_cell(database, **examples[indExample])
cell = ephyscore.Cell(dbRow)
### -- Pre -- ###
sessiontype = 'laserpulse_pre'
axPSTH = plt.subplot(examplesAllCriteria[indCriterion][indExample][0])
ephysData, bdata = cell.load(sessiontype)
eventOnsetTimes = ephysData['events']['stimOn']
eventOnsetTimes = spikesanalysis.minimum_event_onset_diff(
eventOnsetTimes, minEventOnsetDiff=0.5)
if not indCriterion == 2:
timeRange = [-0.3, 1.0]
else:
timeRange = [-0.01, 0.05]
dataframe = goodFitToUse
### -- Example plot -- ###
threshold = 0.2
cellDict = {
'subject': 'pinp017',
'date': '2017-03-22',
'depth': 1143,
'tetrode': 2,
'cluster': 4
}
cellInd, dbRow = celldatabase.find_cell(dbase, **cellDict)
cell = ephyscore.Cell(dbRow)
try:
ephysData, bdata = cell.load('tc')
except (IndexError, ValueError
): #The cell does not have a tc or the tc session has no spikes
print "No tc for cell {}".format(indRow)
sys.exit()
eventOnsetTimes = ephysData['events']['soundDetectorOn']
eventOnsetTimes = spikesanalysis.minimum_event_onset_diff(
eventOnsetTimes, minEventOnsetDiff=0.2)
spikeTimes = ephysData['spikeTimes']
freqEachTrial = bdata['currentFreq']
possibleFreq = np.unique(freqEachTrial)