def inactivation_base_stats(db, filename=''):
laserTestStatistic = np.empty(len(db))
laserPVal = np.empty(len(db))
soundResponseTestStatistic = np.empty(len(db))
soundResponsePVal = np.empty(len(db))
onsetSoundResponseTestStatistic = np.empty(len(db))
onsetSoundResponsePVal = np.empty(len(db))
sustainedSoundResponseTestStatistic = np.empty(len(db))
sustainedSoundResponsePVal = np.empty(len(db))
gaussFit = []
tuningTimeRange = []
Rsquared = np.empty(len(db))
prefFreq = np.empty(len(db))
octavesFromPrefFreq = np.empty(len(db))
bestBandSession = np.empty(len(db))
for indRow, (dbIndex, dbRow) in enumerate(db.iterrows()):
cellObj = ephyscore.Cell(dbRow)
print "Now processing", dbRow['subject'], dbRow['date'], dbRow[
'depth'], dbRow['tetrode'], dbRow['cluster']
# --- Determine laser responsiveness of each cell (using first 100 ms of noise-in-laser trials) ---
try:
laserEphysData, noBehav = cellObj.load('lasernoisebursts')
except IndexError:
print "No laser pulse session for this cell"
testStatistic = np.nan
pVal = np.nan
changeFR = np.nan
else:
testStatistic, pVal, changeFR = funcs.laser_response(
laserEphysData,
baseRange=[-0.3, -0.2],
responseRange=[0.0, 0.1])
laserTestStatistic[indRow] = testStatistic
laserPVal[indRow] = pVal
# --- Determine sound responsiveness during bandwidth sessions and calculate baseline firing rates with and without laser---
#done in a kind of stupid way because regular and control sessions are handled the same way
if any(session in dbRow['sessionType']
for session in ['laserBandwidth', 'laserBandwidthControl']):
if 'laserBandwidth' in dbRow['sessionType']:
bandEphysData, bandBehavData = cellObj.load('laserBandwidth')
behavSession = 'laserBandwidth'
db.at[dbIndex, 'controlSession'] = 0
elif 'laserBandwidthControl' in dbRow['sessionType']:
bandEphysData, bandBehavData = cellObj.load(
'laserBandwidthControl')
behavSession = 'laserBandwidthControl'
db.at[dbIndex, 'controlSession'] = 1
bandEventOnsetTimes = funcs.get_sound_onset_times(
bandEphysData, 'bandwidth')
bandSpikeTimestamps = bandEphysData['spikeTimes']
bandEachTrial = bandBehavData['currentBand']
secondSort = bandBehavData['laserTrial']
numBands = np.unique(bandEachTrial)
numSec = np.unique(secondSort)
trialsEachComb = behavioranalysis.find_trials_each_combination(
bandEachTrial, numBands, secondSort, numSec)
trialsEachBaseCond = trialsEachComb[:, :,
0] #using no laser trials to determine sound responsiveness
testStatistic, pVal = funcs.sound_response_any_stimulus(
bandEventOnsetTimes, bandSpikeTimestamps, trialsEachBaseCond,
[0.0, 1.0], [-1.2, -0.2])
onsetTestStatistic, onsetpVal = funcs.sound_response_any_stimulus(
bandEventOnsetTimes, bandSpikeTimestamps, trialsEachBaseCond,
[0.0, 0.05], [-0.25, 0.2])
sustainedTestStatistic, sustainedpVal = funcs.sound_response_any_stimulus(
bandEventOnsetTimes, bandSpikeTimestamps, trialsEachBaseCond,
[0.2, 1.0], [-1.0, 0.2])
pVal *= len(numBands) #correction for multiple comparisons
onsetpVal *= len(numBands)
sustainedpVal *= len(numBands)
#pdb.set_trace()
#find baselines with and without laser
baselineRange = [-0.05, 0.0]
baselineRates, baselineSEMs = funcs.inactivated_cells_baselines(
bandSpikeTimestamps, bandEventOnsetTimes, secondSort,
baselineRange)
db.at[dbIndex, 'baselineFRnoLaser'] = baselineRates[0]
db.at[dbIndex, 'baselineFRLaser'] = baselineRates[1]
db.at[dbIndex, 'baselineFRnoLaserSEM'] = baselineSEMs[0]
db.at[dbIndex, 'baselineFRLaserSEM'] = baselineSEMs[1]
db.at[dbIndex,
'baselineChangeFR'] = baselineRates[1] - baselineRates[0]
else:
print "No bandwidth session for this cell"
testStatistic = np.nan
pVal = np.nan
onsetTestStatistic = np.nan
onsetpVal = np.nan
sustainedTestStatistic = np.nan
sustainedpVal = np.nan
#pdb.set_trace()
soundResponseTestStatistic[indRow] = testStatistic
soundResponsePVal[indRow] = pVal
onsetSoundResponseTestStatistic[indRow] = onsetTestStatistic
onsetSoundResponsePVal[indRow] = onsetpVal
sustainedSoundResponseTestStatistic[indRow] = sustainedTestStatistic
sustainedSoundResponsePVal[indRow] = sustainedpVal
# --- Determine frequency tuning of cells ---
try:
tuningEphysData, tuningBehavData = cellObj.load('tuningCurve')
except IndexError:
print "No tuning session for this cell"
freqFit = np.full(4, np.nan)
thisRsquared = np.nan
bestFreq = np.nan
tuningWindow = np.full(2, np.nan)
octavesFromBest = np.nan
bandIndex = np.nan
else:
tuningEventOnsetTimes = funcs.get_sound_onset_times(
tuningEphysData, 'tuningCurve')
tuningSpikeTimestamps = tuningEphysData['spikeTimes']
freqEachTrial = tuningBehavData['currentFreq']
intensityEachTrial = tuningBehavData['currentIntensity']
numFreqs = np.unique(freqEachTrial)
numIntensities = np.unique(intensityEachTrial)
timeRange = [-0.2, 0.2]
spikeTimesFromEventOnset, trialIndexForEachSpike, indexLimitsEachTrial = spikesanalysis.eventlocked_spiketimes(
tuningSpikeTimestamps, tuningEventOnsetTimes, timeRange)
trialsEachType = behavioranalysis.find_trials_each_type(
intensityEachTrial, numIntensities)
trialsHighInt = trialsEachType[:, -1]
trialsEachComb = behavioranalysis.find_trials_each_combination(
freqEachTrial, numFreqs, intensityEachTrial, numIntensities)
trialsEachFreqHighInt = trialsEachComb[:, :, -1]
tuningWindow = funcs.best_window_freq_tuning(
spikeTimesFromEventOnset, indexLimitsEachTrial,
trialsEachFreqHighInt)
tuningWindow = np.array(tuningWindow)
spikeCountMat = spikesanalysis.spiketimes_to_spikecounts(
spikeTimesFromEventOnset, indexLimitsEachTrial, tuningWindow)
tuningSpikeRates = (spikeCountMat[trialsHighInt].flatten()) / (
tuningWindow[1] - tuningWindow[0])
freqsThisIntensity = freqEachTrial[trialsHighInt]
freqFit, thisRsquared = funcs.gaussian_tuning_fit(
np.log2(freqsThisIntensity), tuningSpikeRates)
if freqFit is not None:
bestFreq = 2**freqFit[0]
bandIndex, octavesFromBest = funcs.best_index(
cellObj, bestFreq, behavSession)
else:
freqFit = np.full(4, np.nan)
bestFreq = np.nan
bandIndex = np.nan
octavesFromBest = np.nan
gaussFit.append(freqFit)
tuningTimeRange.append(tuningWindow)
Rsquared[indRow] = thisRsquared
prefFreq[indRow] = bestFreq
octavesFromPrefFreq[indRow] = octavesFromBest
bestBandSession[indRow] = bandIndex
db['laserPVal'] = laserPVal
db['laserUStat'] = laserTestStatistic
db['soundResponseUStat'] = soundResponseTestStatistic
db['soundResponsePVal'] = soundResponsePVal
db['onsetSoundResponseUStat'] = onsetSoundResponseTestStatistic
db['onsetSoundResponsePVal'] = onsetSoundResponsePVal
db['sustainedSoundResponseUStat'] = sustainedSoundResponseTestStatistic
db['sustainedSoundResponsePVal'] = sustainedSoundResponsePVal
db['gaussFit'] = gaussFit
db['tuningTimeRange'] = tuningTimeRange
db['tuningFitR2'] = Rsquared
db['prefFreq'] = prefFreq
db['octavesFromPrefFreq'] = octavesFromPrefFreq
db['bestBandSession'] = bestBandSession
if len(filename) != 0:
celldatabase.save_hdf(db, filename)
print filename + " saved"
return db
def inactivation_indices(db, filename=''):
bestCells = db.query("isiViolations<0.02") # or modifiedISI<0.02")
bestCells = bestCells.loc[bestCells['spikeShapeQuality'] > 2]
bestCells = bestCells.query(
'soundResponsePVal<0.05 or onsetSoundResponsePVal<0.05 or sustainedSoundResponsePVal<0.05'
)
bestCells = bestCells.loc[bestCells['tuningFitR2'] > 0.05]
bestCells = bestCells.loc[bestCells['octavesFromPrefFreq'] < 0.5]
for dbIndex, dbRow in bestCells.iterrows():
cell = ephyscore.Cell(dbRow)
bandEphysData, bandBehavData = cell.load_by_index(
int(dbRow['bestBandSession']))
bandEventOnsetTimes = funcs.get_sound_onset_times(
bandEphysData, 'bandwidth')
bandSpikeTimestamps = bandEphysData['spikeTimes']
bandEachTrial = bandBehavData['currentBand']
secondSort = bandBehavData['laserTrial']
propOnset, propSustained = funcs.onset_sustained_spike_proportion(
bandSpikeTimestamps, bandEventOnsetTimes)
db.at[dbIndex, 'proportionSpikesOnset'] = propOnset
db.at[dbIndex, 'proportionSpikesSustained'] = propSustained
#by default: not subtracting baseline, but are replacing pure tone response with baseline for 0 bw condition
onsetSupInds, onsetSupIndpVals, onsetFacInds, onsetFacIndpVals, onsetPeakInds, onsetSpikeArray = funcs.bandwidth_suppression_from_peak(
bandSpikeTimestamps,
bandEventOnsetTimes,
bandEachTrial,
secondSort,
timeRange=[0.0, 0.05],
baseRange=[-0.05, 0.0])
db.at[dbIndex, 'onsetSuppressionIndexLaser'] = onsetSupInds[-1]
db.at[dbIndex, 'onsetSuppressionpValLaser'] = onsetSupIndpVals[-1]
db.at[dbIndex, 'onsetFacilitationIndexLaser'] = onsetFacInds[-1]
db.at[dbIndex, 'onsetFacilitationpValLaser'] = onsetFacIndpVals[-1]
db.at[dbIndex,
'onsetPrefBandwidthLaser'] = bandEachTrial[onsetPeakInds[-1]]
db.at[dbIndex, 'onsetSuppressionIndexNoLaser'] = onsetSupInds[0]
db.at[dbIndex, 'onsetSuppressionpValNoLaser'] = onsetSupIndpVals[0]
db.at[dbIndex, 'onsetFacilitationIndexNoLaser'] = onsetFacInds[0]
db.at[dbIndex, 'onsetFacilitationpValNoLaser'] = onsetFacIndpVals[0]
db.at[dbIndex,
'onsetPrefBandwidthNoLaser'] = bandEachTrial[onsetPeakInds[0]]
#base range is right before sound onset so we get estimate for laser baseline
sustainedSupInds, sustainedSupIndpVals, sustainedFacInds, sustainedFacIndpVals, sustainedPeakInds, sustainedSpikeArray = funcs.bandwidth_suppression_from_peak(
bandSpikeTimestamps,
bandEventOnsetTimes,
bandEachTrial,
secondSort,
timeRange=[0.2, 1.0],
baseRange=[-0.05, 0.0])
db.at[dbIndex, 'sustainedSuppressionIndexLaser'] = sustainedSupInds[-1]
db.at[dbIndex,
'sustainedSuppressionpValLaser'] = sustainedSupIndpVals[-1]
db.at[dbIndex,
'sustainedFacilitationIndexLaser'] = sustainedFacInds[-1]
db.at[dbIndex,
'sustainedFacilitationpValLaser'] = sustainedFacIndpVals[-1]
db.at[dbIndex, 'sustainedPrefBandwidthLaser'] = bandEachTrial[
sustainedPeakInds[-1]]
db.at[dbIndex,
'sustainedSuppressionIndexNoLaser'] = sustainedSupInds[0]
db.at[dbIndex,
'sustainedSuppressionpValNoLaser'] = sustainedSupIndpVals[0]
db.at[dbIndex,
'sustainedFacilitationIndexNoLaser'] = sustainedFacInds[0]
db.at[dbIndex,
'sustainedFacilitationpValNoLaser'] = sustainedFacIndpVals[0]
db.at[dbIndex, 'sustainedPrefBandwidthNoLaser'] = bandEachTrial[
sustainedPeakInds[0]]
#no laser fit
sustainedResponseNoLaser = sustainedSpikeArray[:, 0]
bandsForFit = np.unique(bandEachTrial)
bandsForFit[-1] = 6
mFixed = 1
fitParams, R2 = fitfuncs.diff_of_gauss_fit(bandsForFit,
sustainedResponseNoLaser,
mFixed=mFixed)
#fit params
db.at[dbIndex, 'R0noLaser'] = fitParams[0]
db.at[dbIndex, 'RDnoLaser'] = fitParams[3]
db.at[dbIndex, 'RSnoLaser'] = fitParams[4]
db.at[dbIndex, 'mnoLaser'] = mFixed
db.at[dbIndex, 'sigmaDnoLaser'] = fitParams[1]
db.at[dbIndex, 'sigmaSnoLaser'] = fitParams[2]
db.at[dbIndex, 'bandwidthTuningR2noLaser'] = R2
testBands = np.linspace(bandsForFit[0], bandsForFit[-1], 500)
allFitParams = [mFixed]
allFitParams.extend(fitParams)
suppInd, prefBW = fitfuncs.extract_stats_from_fit(
allFitParams, testBands)
db.at[dbIndex, 'fitSustainedSuppressionIndexNoLaser'] = suppInd
db.at[dbIndex, 'fitSustainedPrefBandwidthNoLaser'] = prefBW
#laser fit
sustainedResponseLaser = sustainedSpikeArray[:, 1]
fitParamsLaser, R2Laser = fitfuncs.diff_of_gauss_fit(
bandsForFit, sustainedResponseLaser, mFixed=mFixed)
#fit params
db.at[dbIndex, 'R0laser'] = fitParamsLaser[0]
db.at[dbIndex, 'RDlaser'] = fitParamsLaser[3]
db.at[dbIndex, 'RSlaser'] = fitParamsLaser[4]
db.at[dbIndex, 'mlaser'] = mFixed
db.at[dbIndex, 'sigmaDlaser'] = fitParamsLaser[1]
db.at[dbIndex, 'sigmaSlaser'] = fitParamsLaser[2]
db.at[dbIndex, 'bandwidthTuningR2laser'] = R2Laser
allFitParamsLaser = [mFixed]
allFitParamsLaser.extend(fitParamsLaser)
suppIndLaser, prefBWLaser = fitfuncs.extract_stats_from_fit(
allFitParamsLaser, testBands)
db.at[dbIndex, 'fitSustainedSuppressionIndexLaser'] = suppIndLaser
db.at[dbIndex, 'fitSustainedPrefBandwidthLaser'] = prefBWLaser
meanLaserDiff = np.mean(sustainedResponseLaser -
sustainedResponseNoLaser)
db.at[dbIndex, 'laserChangeResponse'] = meanLaserDiff
laserDiff = sustainedResponseLaser - sustainedResponseNoLaser
peakInd = np.argmax(sustainedResponseNoLaser)
db.at[dbIndex, 'peakChangeFR'] = laserDiff[peakInd]
db.at[dbIndex, 'WNChangeFR'] = laserDiff[-1]
testRespsNoLaser = fitfuncs.diff_gauss_form(testBands, *allFitParams)
testRespsLaser = fitfuncs.diff_gauss_form(testBands,
*allFitParamsLaser)
laserDiffModel = testRespsLaser - testRespsNoLaser
peakIndModel = np.argmax(testRespsNoLaser)
db.at[dbIndex, 'fitPeakChangeFR'] = laserDiffModel[peakIndModel]
db.at[dbIndex, 'fitWNChangeFR'] = laserDiffModel[-1]
#also calculating fits and suppression with pure tone being 0 bw condition
toneSustainedSupInds, toneSustainedSupIndpVals, toneSustainedFacInds, toneSustainedFacIndpVals, toneSustainedPeakInds, toneSustainedSpikeArray = funcs.bandwidth_suppression_from_peak(
bandSpikeTimestamps,
bandEventOnsetTimes,
bandEachTrial,
secondSort,
timeRange=[0.2, 1.0],
baseRange=[-0.05, 0.0],
zeroBWBaseline=False)
db.at[
dbIndex,
'sustainedSuppressionIndexNoLaserPureTone'] = toneSustainedSupInds[
0]
db.at[
dbIndex,
'sustainedSuppressionpValNoLaserPureTone'] = toneSustainedSupIndpVals[
0]
db.at[
dbIndex,
'sustainedFacilitationIndexNoLaserPureTone'] = toneSustainedFacInds[
0]
db.at[
dbIndex,
'sustainedFacilitationpValNoLaserPureTone'] = toneSustainedFacIndpVals[
0]
db.at[dbIndex,
'sustainedPrefBandwidthNoLaserPureTone'] = bandEachTrial[
toneSustainedPeakInds[0]]
db.at[dbIndex,
'sustainedSuppressionIndexLaserPureTone'] = toneSustainedSupInds[
-1]
db.at[
dbIndex,
'sustainedSuppressionpValLaserPureTone'] = toneSustainedSupIndpVals[
-1]
db.at[
dbIndex,
'sustainedFacilitationIndexLaserPureTone'] = toneSustainedFacInds[
-1]
db.at[
dbIndex,
'sustainedFacilitationpValLaserPureTone'] = toneSustainedFacIndpVals[
-1]
db.at[dbIndex, 'sustainedPrefBandwidthLaserPureTone'] = bandEachTrial[
toneSustainedPeakInds[-1]]
toneSustainedResponseNoLaser = toneSustainedSpikeArray[:, 0]
toneFitParamsNoLaser, toneR2 = fitfuncs.diff_of_gauss_fit(
bandsForFit, toneSustainedResponseNoLaser, mFixed=mFixed)
#fit params
db.at[dbIndex, 'R0PureToneNoLaser'] = toneFitParamsNoLaser[0]
db.at[dbIndex, 'RDPureToneNoLaser'] = toneFitParamsNoLaser[3]
db.at[dbIndex, 'RSPureToneNoLaser'] = toneFitParamsNoLaser[4]
db.at[dbIndex, 'mPureToneNoLaser'] = mFixed
db.at[dbIndex, 'sigmaDPureToneNoLaser'] = toneFitParamsNoLaser[1]
db.at[dbIndex, 'sigmaSPureToneNoLaser'] = toneFitParamsNoLaser[2]
db.at[dbIndex, 'bandwidthTuningR2PureToneNoLaser'] = toneR2
allFitParamsToneNoLaser = [mFixed]
allFitParamsToneNoLaser.extend(toneFitParamsNoLaser)
suppIndTone, prefBWTone = fitfuncs.extract_stats_from_fit(
allFitParamsToneNoLaser, testBands)
db.at[dbIndex,
'fitSustainedSuppressionIndexPureToneNoLaser'] = suppIndTone
db.at[dbIndex, 'fitSustainedPrefBandwidthPureToneNoLaser'] = prefBWTone
toneSustainedResponseLaser = toneSustainedSpikeArray[:, 1]
toneFitParamsLaser, toneR2Laser = fitfuncs.diff_of_gauss_fit(
bandsForFit, toneSustainedResponseLaser, mFixed=mFixed)
#fit params
db.at[dbIndex, 'R0PureToneLaser'] = toneFitParamsLaser[0]
db.at[dbIndex, 'RDPureToneLaser'] = toneFitParamsLaser[3]
db.at[dbIndex, 'RSPureToneLaser'] = toneFitParamsLaser[4]
db.at[dbIndex, 'mPureToneLaser'] = mFixed
db.at[dbIndex, 'sigmaDPureToneLaser'] = toneFitParamsLaser[1]
db.at[dbIndex, 'sigmaSPureToneLaser'] = toneFitParamsLaser[2]
db.at[dbIndex, 'bandwidthTuningR2PureToneLaser'] = toneR2Laser
allFitParamsToneLaser = [mFixed]
allFitParamsToneLaser.extend(toneFitParamsLaser)
suppIndToneLaser, prefBWToneLaser = fitfuncs.extract_stats_from_fit(
allFitParamsToneLaser, testBands)
db.at[dbIndex,
'fitSustainedSuppressionIndexPureToneLaser'] = suppIndToneLaser
db.at[dbIndex,
'fitSustainedPrefBandwidthPureToneLaser'] = prefBWToneLaser
testRespsNoLaser = fitfuncs.diff_gauss_form(testBands,
*allFitParamsToneNoLaser)
testRespsLaser = fitfuncs.diff_gauss_form(testBands,
*allFitParamsToneLaser)
laserDiffModel = testRespsLaser - testRespsNoLaser
peakIndModel = np.argmax(testRespsNoLaser)
db.at[dbIndex,
'fitPeakChangeFRPureTone'] = laserDiffModel[peakIndModel]
db.at[dbIndex, 'fitWNChangeFRPureTone'] = laserDiffModel[-1]
#also calculating fits and suppression with nothing being fit for bw 0
noZeroSustainedResponseNoLaser = sustainedSpikeArray[1:, 0]
bandsForFitNoZero = bandsForFit[1:]
noZeroFitParamsNoLaser, noZeroR2 = fitfuncs.diff_of_gauss_fit(
bandsForFitNoZero, noZeroSustainedResponseNoLaser, mFixed=mFixed)
#fit params
db.at[dbIndex, 'R0noZeroNoLaser'] = noZeroFitParamsNoLaser[0]
db.at[dbIndex, 'RDnoZeroNoLaser'] = noZeroFitParamsNoLaser[3]
db.at[dbIndex, 'RSnoZeroNoLaser'] = noZeroFitParamsNoLaser[4]
db.at[dbIndex, 'mnoZeroNoLaser'] = mFixed
db.at[dbIndex, 'sigmaDnoZeroNoLaser'] = noZeroFitParamsNoLaser[1]
db.at[dbIndex, 'sigmaSnoZeroNoLaser'] = noZeroFitParamsNoLaser[2]
db.at[dbIndex, 'bandwidthTuningR2noZeroNoLaser'] = noZeroR2
allFitParamsNoZero = [mFixed]
allFitParamsNoZero.extend(noZeroFitParamsNoLaser)
testBandsNoZero = np.linspace(bandsForFitNoZero[0],
bandsForFitNoZero[-1], 500)
suppIndNoZero, prefBWNoZero = fitfuncs.extract_stats_from_fit(
allFitParamsNoZero, testBandsNoZero)
db.at[dbIndex,
'fitSustainedSuppressionIndexNoZeroNoLaser'] = suppIndNoZero
db.at[dbIndex, 'fitSustainedPrefBandwidthNoZeroNoLaser'] = prefBWNoZero
noZeroSustainedResponseLaser = sustainedSpikeArray[1:, 1]
bandsForFitNoZero = bandsForFit[1:]
noZeroFitParamsLaser, noZeroR2Laser = fitfuncs.diff_of_gauss_fit(
bandsForFitNoZero, noZeroSustainedResponseLaser, mFixed=mFixed)
#fit params
db.at[dbIndex, 'R0noZeroLaser'] = noZeroFitParamsLaser[0]
db.at[dbIndex, 'RDnoZeroLaser'] = noZeroFitParamsLaser[3]
db.at[dbIndex, 'RSnoZeroLaser'] = noZeroFitParamsLaser[4]
db.at[dbIndex, 'mnoZeroLaser'] = mFixed
db.at[dbIndex, 'sigmaDnoZeroLaser'] = noZeroFitParamsLaser[1]
db.at[dbIndex, 'sigmaSnoZeroLaser'] = noZeroFitParamsLaser[2]
db.at[dbIndex, 'bandwidthTuningR2noZeroLaser'] = noZeroR2Laser
allFitParamsNoZeroLaser = [mFixed]
allFitParamsNoZeroLaser.extend(noZeroFitParamsLaser)
suppIndNoZeroLaser, prefBWNoZeroLaser = fitfuncs.extract_stats_from_fit(
allFitParamsNoZeroLaser, testBandsNoZero)
db.at[dbIndex,
'fitSustainedSuppressionIndexNoZeroLaser'] = suppIndNoZeroLaser
db.at[dbIndex,
'fitSustainedPrefBandwidthNoZeroLaser'] = prefBWNoZeroLaser
testRespsNoLaser = fitfuncs.diff_gauss_form(testBandsNoZero,
*allFitParamsNoZero)
testRespsLaser = fitfuncs.diff_gauss_form(testBandsNoZero,
*allFitParamsNoZeroLaser)
laserDiffModel = testRespsLaser - testRespsNoLaser
peakIndModel = np.argmax(testRespsNoLaser)
db.at[dbIndex, 'fitPeakChangeFRNoZero'] = laserDiffModel[peakIndModel]
db.at[dbIndex, 'fitWNChangeFRNoZero'] = laserDiffModel[-1]
if len(filename) != 0:
celldatabase.save_hdf(db, filename)
print filename + " saved"
return db
def photoID_indices(db, filename=''):
'''
This function takes as argument a pandas DataFrame and adds new columns.
The filename should be the full path to where the database will be saved. If a filename is not specified, the database will not be saved.
This function computes indices like Suppression Index for all cells passing (generous) thresholds for good cells.
The thresholds used here are more generous than the ones in studyparams, in case we want to relax our criteria in the future.
'''
bestCells = db.query("isiViolations<0.02 or modifiedISI<0.02")
bestCells = bestCells.loc[bestCells['spikeShapeQuality'] > 2]
bestCells = bestCells.query(
'soundResponsePVal<0.05 or onsetSoundResponsePVal<0.05 or sustainedSoundResponsePVal<0.05'
)
bestCells = bestCells.loc[bestCells['tuningFitR2'] > 0.05]
bestCells = bestCells.loc[bestCells['octavesFromPrefFreq'] < 0.5]
#prepare arrays of NaNs for when we save arrays of data
bandwidthSpikeArraysHighAmp = np.empty((len(db), 7))
bandwidthSpikeArraysHighAmp[:] = np.nan
bandwidthSpikeArraysLowAmp = np.empty((len(db), 7))
bandwidthSpikeArraysLowAmp[:] = np.nan
bandwidthSpikeArraysOnset = np.empty((len(db), 6))
bandwidthSpikeArraysOnset[:] = np.nan
for dbIndex, dbRow in bestCells.iterrows():
cell = ephyscore.Cell(dbRow, useModifiedClusters=True)
bandEphysData, bandBehavData = cell.load_by_index(
int(dbRow['bestBandSession']))
bandEventOnsetTimes = funcs.get_sound_onset_times(
bandEphysData, 'bandwidth')
bandSpikeTimestamps = bandEphysData['spikeTimes']
bandEachTrial = bandBehavData['currentBand']
secondSort = bandBehavData['currentAmp']
propOnset, propSustained = funcs.onset_sustained_spike_proportion(
bandSpikeTimestamps, bandEventOnsetTimes)
db.at[dbIndex, 'proportionSpikesOnset'] = propOnset
db.at[dbIndex, 'proportionSpikesSustained'] = propSustained
#by default: not subtracting baseline, but are replacing pure tone response with baseline for 0 bw condition
onsetSupInds, onsetSupIndpVals, onsetFacInds, onsetFacIndpVals, onsetPeakInds, onsetSpikeArray = funcs.bandwidth_suppression_from_peak(
bandSpikeTimestamps,
bandEventOnsetTimes,
bandEachTrial,
secondSort,
timeRange=[0.0, 0.05],
baseRange=[-0.05, 0.0])
db.at[dbIndex, 'onsetSuppressionIndex'] = onsetSupInds[-1]
db.at[dbIndex, 'onsetSuppressionpVal'] = onsetSupIndpVals[-1]
db.at[dbIndex, 'onsetFacilitationIndex'] = onsetFacInds[-1]
db.at[dbIndex, 'onsetFacilitationpVal'] = onsetFacIndpVals[-1]
db.at[dbIndex, 'onsetPrefBandwidth'] = bandEachTrial[onsetPeakInds[-1]]
sustainedSupInds, sustainedSupIndpVals, sustainedFacInds, sustainedFacIndpVals, sustainedPeakInds, sustainedSpikeArray = funcs.bandwidth_suppression_from_peak(
bandSpikeTimestamps,
bandEventOnsetTimes,
bandEachTrial,
secondSort,
timeRange=[0.2, 1.0],
baseRange=[-1.0, -0.2])
db.at[dbIndex, 'sustainedSuppressionIndex'] = sustainedSupInds[-1]
db.at[dbIndex, 'sustainedSuppressionpVal'] = sustainedSupIndpVals[-1]
db.at[dbIndex, 'sustainedFacilitationIndex'] = sustainedFacInds[-1]
db.at[dbIndex, 'sustainedFacilitationpVal'] = sustainedFacIndpVals[-1]
db.at[dbIndex,
'sustainedPrefBandwidth'] = bandEachTrial[sustainedPeakInds[-1]]
#only interested in high amp responses
sustainedResponse = sustainedSpikeArray[:, -1]
bandsForFit = np.unique(bandEachTrial)
bandsForFit[-1] = 6
mFixed = 1
fitParams, R2 = fitfuncs.diff_of_gauss_fit(bandsForFit,
sustainedResponse,
mFixed=mFixed)
#fit params
db.at[dbIndex, 'R0'] = fitParams[0]
db.at[dbIndex, 'RD'] = fitParams[3]
db.at[dbIndex, 'RS'] = fitParams[4]
db.at[dbIndex, 'm'] = mFixed
db.at[dbIndex, 'sigmaD'] = fitParams[1]
db.at[dbIndex, 'sigmaS'] = fitParams[2]
db.at[dbIndex, 'bandwidthTuningR2'] = R2
testBands = np.linspace(bandsForFit[0], bandsForFit[-1], 500)
allFitParams = [mFixed]
allFitParams.extend(fitParams)
suppInd, prefBW = fitfuncs.extract_stats_from_fit(
allFitParams, testBands)
db.at[dbIndex, 'fitSustainedSuppressionIndex'] = suppInd
db.at[dbIndex, 'fitSustainedPrefBandwidth'] = prefBW
#also calculating fits and suppression with pure tone being 0 bw condition
toneSustainedSupInds, toneSustainedSupIndpVals, toneSustainedFacInds, toneSustainedFacIndpVals, toneSustainedPeakInds, toneSustainedSpikeArray = funcs.bandwidth_suppression_from_peak(
bandSpikeTimestamps,
bandEventOnsetTimes,
bandEachTrial,
secondSort,
timeRange=[0.2, 1.0],
baseRange=[-1.0, -0.2],
zeroBWBaseline=False)
db.at[dbIndex,
'sustainedSuppressionIndexPureTone'] = toneSustainedSupInds[-1]
db.at[
dbIndex,
'sustainedSuppressionpValPureTone'] = toneSustainedSupIndpVals[-1]
db.at[dbIndex,
'sustainedFacilitationIndexPureTone'] = toneSustainedFacInds[-1]
db.at[
dbIndex,
'sustainedFacilitationpValPureTone'] = toneSustainedFacIndpVals[-1]
db.at[dbIndex, 'sustainedPrefBandwidthPureTone'] = bandEachTrial[
toneSustainedPeakInds[-1]]
#only interested in high amp responses
toneSustainedResponse = toneSustainedSpikeArray[:, -1]
toneFitParams, toneR2 = fitfuncs.diff_of_gauss_fit(
bandsForFit, toneSustainedResponse, mFixed=mFixed)
#fit params
db.at[dbIndex, 'R0PureTone'] = toneFitParams[0]
db.at[dbIndex, 'RDPureTone'] = toneFitParams[3]
db.at[dbIndex, 'RSPureTone'] = toneFitParams[4]
db.at[dbIndex, 'mPureTone'] = mFixed
db.at[dbIndex, 'sigmaDPureTone'] = toneFitParams[1]
db.at[dbIndex, 'sigmaSPureTone'] = toneFitParams[2]
db.at[dbIndex, 'bandwidthTuningR2PureTone'] = toneR2
allFitParamsTone = [mFixed]
allFitParamsTone.extend(toneFitParams)
suppIndTone, prefBWTone = fitfuncs.extract_stats_from_fit(
allFitParamsTone, testBands)
db.at[dbIndex, 'fitSustainedSuppressionIndexPureTone'] = suppIndTone
db.at[dbIndex, 'fitSustainedPrefBandwidthPureTone'] = prefBWTone
#also calculating fits and suppression with nothing being fit for bw 0, want to do both intensities here
#doing this for onset responses as well
noZeroSustainedResponseHighAmp = sustainedSpikeArray[1:, -1]
noZeroOnsetResponseHighAmp = onsetSpikeArray[1:, -1]
bandsForFitNoZero = bandsForFit[1:]
noZeroFitParamsHigh, noZeroR2High = fitfuncs.diff_of_gauss_fit(
bandsForFitNoZero, noZeroSustainedResponseHighAmp, mFixed=mFixed)
#fit params
db.at[dbIndex, 'R0noZeroHighAmp'] = noZeroFitParamsHigh[0]
db.at[dbIndex, 'RDnoZeroHighAmp'] = noZeroFitParamsHigh[3]
db.at[dbIndex, 'RSnoZeroHighAmp'] = noZeroFitParamsHigh[4]
db.at[dbIndex, 'mnoZeroHighAmp'] = mFixed
db.at[dbIndex, 'sigmaDnoZeroHighAmp'] = noZeroFitParamsHigh[1]
db.at[dbIndex, 'sigmaSnoZeroHighAmp'] = noZeroFitParamsHigh[2]
db.at[dbIndex, 'bandwidthTuningR2noZeroHighAmp'] = noZeroR2High
allFitParamsNoZeroHigh = [mFixed]
allFitParamsNoZeroHigh.extend(noZeroFitParamsHigh)
testBandsNoZero = np.linspace(bandsForFitNoZero[0],
bandsForFitNoZero[-1], 500)
suppIndNoZero, prefBWNoZero = fitfuncs.extract_stats_from_fit(
allFitParamsNoZeroHigh, testBandsNoZero)
db.at[dbIndex,
'fitSustainedSuppressionIndexNoZeroHighAmp'] = suppIndNoZero
db.at[dbIndex, 'fitSustainedPrefBandwidthNoZeroHighAmp'] = prefBWNoZero
noZeroFitParamsOnset, noZeroR2Onset = fitfuncs.diff_of_gauss_fit(
bandsForFitNoZero, noZeroOnsetResponseHighAmp, mFixed=mFixed)
db.at[dbIndex, 'R0noZeroOnset'] = noZeroFitParamsOnset[0]
db.at[dbIndex, 'RDnoZeroOnset'] = noZeroFitParamsOnset[3]
db.at[dbIndex, 'RSnoZeroOnset'] = noZeroFitParamsOnset[4]
db.at[dbIndex, 'mnoZeroOnset'] = mFixed
db.at[dbIndex, 'sigmaDnoZeroOnset'] = noZeroFitParamsOnset[1]
db.at[dbIndex, 'sigmaSnoZeroOnset'] = noZeroFitParamsOnset[2]
db.at[dbIndex, 'bandwidthTuningR2noZeroOnset'] = noZeroR2Onset
allFitParamsNoZeroOnset = [mFixed]
allFitParamsNoZeroOnset.extend(noZeroFitParamsOnset)
suppIndNoZero, prefBWNoZero = fitfuncs.extract_stats_from_fit(
allFitParamsNoZeroOnset, testBandsNoZero)
db.at[dbIndex, 'fitOnsetSuppressionIndexNoZero'] = suppIndNoZero
db.at[dbIndex, 'fitOnsetPrefBandwidthNoZero'] = prefBWNoZero
if sustainedSpikeArray.shape[1] > 1:
noZeroSustainedResponseLowAmp = sustainedSpikeArray[1:, 0]
if all(noZeroSustainedResponseLowAmp):
noZeroFitParamsLow, noZeroR2Low = fitfuncs.diff_of_gauss_fit(
bandsForFitNoZero,
noZeroSustainedResponseLowAmp,
mFixed=mFixed)
#fit params
db.at[dbIndex, 'R0noZeroLowAmp'] = noZeroFitParamsLow[0]
db.at[dbIndex, 'RDnoZeroLowAmp'] = noZeroFitParamsLow[3]
db.at[dbIndex, 'RSnoZeroLowAmp'] = noZeroFitParamsLow[4]
db.at[dbIndex, 'mnoZeroLowAmp'] = mFixed
db.at[dbIndex, 'sigmaDnoZeroLowAmp'] = noZeroFitParamsLow[1]
db.at[dbIndex, 'sigmaSnoZeroLowAmp'] = noZeroFitParamsLow[2]
db.at[dbIndex, 'bandwidthTuningR2noZeroLowAmp'] = noZeroR2Low
allFitParamsNoZeroLow = [mFixed]
allFitParamsNoZeroLow.extend(noZeroFitParamsLow)
suppIndNoZero, prefBWNoZero = fitfuncs.extract_stats_from_fit(
allFitParamsNoZeroLow, testBandsNoZero)
db.at[
dbIndex,
'fitSustainedSuppressionIndexNoZeroLowAmp'] = suppIndNoZero
db.at[dbIndex,
'fitSustainedPrefBandwidthNoZeroLowAmp'] = prefBWNoZero
#save the spike array and baseline rate for each cell in case needed for future calculations
bandwidthSpikeArraysHighAmp[dbIndex, :] = toneSustainedSpikeArray[:,
-1]
bandwidthSpikeArraysLowAmp[dbIndex, :] = toneSustainedSpikeArray[:, 0]
bandwidthSpikeArraysOnset[dbIndex, :] = noZeroOnsetResponseHighAmp
db.at[dbIndex, 'bandwidthBaselineRate'] = sustainedResponse[0]
db['bandwidthOnsetSpikeArrayHighAmp'] = list(bandwidthSpikeArraysOnset)
db['bandwidthSustainedSpikeArrayHighAmp'] = list(
bandwidthSpikeArraysHighAmp)
db['bandwidthSustainedSpikeArrayLowAmp'] = list(bandwidthSpikeArraysLowAmp)
if len(filename) != 0:
celldatabase.save_hdf(db, filename)
print filename + " saved"
return db
def photoID_base_stats(db, filename=''):
'''
This function takes as argument a pandas DataFrame and adds new columns.
The filename should be the full path to where the database will be saved. If a filename is not specified, the database will not be saved.
This function computed basic statistics for all clusters (e.g. laser responsiveness, sound responsiveness, preferred frequency).
'''
soundLoc = []
NaKpeakLatency = np.empty(len(db))
laserTestStatistic = np.empty(len(db))
laserPVal = np.empty(len(db))
laserTrainTestStatistic = np.empty(len(db))
laserTrainPVal = np.empty(len(db))
laserChangeFR = np.empty(len(db))
soundResponseTestStatistic = np.empty(len(db))
soundResponsePVal = np.empty(len(db))
onsetSoundResponseTestStatistic = np.empty(len(db))
onsetSoundResponsePVal = np.empty(len(db))
sustainedSoundResponseTestStatistic = np.empty(len(db))
sustainedSoundResponsePVal = np.empty(len(db))
AMRate = np.empty(len(db))
gaussFit = []
tuningTimeRange = []
Rsquared = np.empty(len(db))
prefFreq = np.empty(len(db))
octavesFromPrefFreq = np.empty(len(db))
bestBandSession = np.empty(len(db))
for indRow, (dbIndex, dbRow) in enumerate(db.iterrows()):
cellObj = ephyscore.Cell(dbRow, useModifiedClusters=True)
print "Now processing", dbRow['subject'], dbRow['date'], dbRow[
'depth'], dbRow['tetrode'], dbRow['cluster']
# --- Determine if sound presentation was ipsi or contra to recording location ---
soundSide = dbRow['info'][2]
recordingSide = dbRow['brainArea']
if (soundSide == 'sound_left' and recordingSide == 'left_AC') or (
soundSide == 'sound_right' and recordingSide == 'right_AC'):
soundLoc.append('ipsi')
else:
soundLoc.append('contra')
# --- Determine time difference between Na and K peak (spike width) ---
peakTimes = dbRow['spikePeakTimes']
latency = peakTimes[2] - peakTimes[1]
NaKpeakLatency[indRow] = latency
# --- Determine laser responsiveness of each cell (using laser pulse) ---
try:
laserEphysData, noBehav = cellObj.load('laserPulse')
except IndexError:
print "No laser pulse session for this cell"
testStatistic = np.nan
pVal = np.nan
changeFR = np.nan
else:
testStatistic, pVal, changeFR = funcs.laser_response(
laserEphysData)
laserTestStatistic[indRow] = testStatistic
laserPVal[indRow] = pVal
laserChangeFR[indRow] = changeFR
# --- Determine laser responsiveness of each cell (using laser train) ---
try:
laserTrainEphysData, noBehav = cellObj.load('laserTrain')
except IndexError:
print "No laser train session for this cell"
testStatistic = np.nan
pVal = np.nan
else:
testStatistic, pVal, changeFR = funcs.laser_response(
laserTrainEphysData)
laserTrainTestStatistic[indRow] = testStatistic
laserTrainPVal[indRow] = pVal
# --- Determine sound responsiveness during bandwidth sessions and other statistics about bandwidth session---
try:
bandEphysData, bandBehavData = cellObj.load('bandwidth')
except IndexError:
print "No bandwidth session for this cell"
testStatistic = np.nan
pVal = np.nan
onsetTestStatistic = np.nan
onsetpVal = np.nan
sustainedTestStatistic = np.nan
sustainedpVal = np.nan
AM = np.nan
else:
bandEventOnsetTimes = funcs.get_sound_onset_times(
bandEphysData, 'bandwidth')
bandSpikeTimestamps = bandEphysData['spikeTimes']
bandEachTrial = bandBehavData['currentBand']
secondSort = bandBehavData['currentAmp']
numBands = np.unique(bandEachTrial)
numSec = np.unique(secondSort)
AM = np.unique(bandBehavData['modRate'])
trialsEachComb = behavioranalysis.find_trials_each_combination(
bandEachTrial, numBands, secondSort, numSec)
trialsEachBaseCond = trialsEachComb[:, :,
-1] #using high amp trials for photoidentified, no laser for inactivation
testStatistic, pVal = funcs.sound_response_any_stimulus(
bandEventOnsetTimes, bandSpikeTimestamps, trialsEachBaseCond,
[0.0, 1.0], [-1.2, -0.2])
onsetTestStatistic, onsetpVal = funcs.sound_response_any_stimulus(
bandEventOnsetTimes, bandSpikeTimestamps, trialsEachBaseCond,
[0.0, 0.05], [-0.25, 0.2])
sustainedTestStatistic, sustainedpVal = funcs.sound_response_any_stimulus(
bandEventOnsetTimes, bandSpikeTimestamps, trialsEachBaseCond,
[0.2, 1.0], [-1.0, 0.2])
pVal *= len(numSec) #correction for multiple comparisons
onsetpVal *= len(numSec)
sustainedpVal *= len(numSec)
soundResponseTestStatistic[indRow] = testStatistic
soundResponsePVal[indRow] = pVal
onsetSoundResponseTestStatistic[indRow] = onsetTestStatistic
onsetSoundResponsePVal[indRow] = onsetpVal
sustainedSoundResponseTestStatistic[indRow] = sustainedTestStatistic
sustainedSoundResponsePVal[indRow] = sustainedpVal
AMRate[indRow] = AM
# --- Determine frequency tuning of cells ---
try:
tuningEphysData, tuningBehavData = cellObj.load('tuningCurve')
except IndexError:
print "No tuning session for this cell"
freqFit = np.full(4, np.nan)
thisRsquared = np.nan
bestFreq = np.nan
tuningWindow = [np.nan, np.nan]
octavesFromBest = np.nan
bandIndex = np.nan
else:
tuningEventOnsetTimes = funcs.get_sound_onset_times(
tuningEphysData, 'tuningCurve')
tuningSpikeTimestamps = tuningEphysData['spikeTimes']
freqEachTrial = tuningBehavData['currentFreq']
intensityEachTrial = tuningBehavData['currentIntensity']
numFreqs = np.unique(freqEachTrial)
numIntensities = np.unique(intensityEachTrial)
timeRange = [-0.2, 0.2]
spikeTimesFromEventOnset, trialIndexForEachSpike, indexLimitsEachTrial = spikesanalysis.eventlocked_spiketimes(
tuningSpikeTimestamps, tuningEventOnsetTimes, timeRange)
trialsEachType = behavioranalysis.find_trials_each_type(
intensityEachTrial, numIntensities)
trialsHighInt = trialsEachType[:, -1]
trialsEachComb = behavioranalysis.find_trials_each_combination(
freqEachTrial, numFreqs, intensityEachTrial, numIntensities)
trialsEachFreqHighInt = trialsEachComb[:, :, -1]
tuningWindow = funcs.best_window_freq_tuning(
spikeTimesFromEventOnset, indexLimitsEachTrial,
trialsEachFreqHighInt)
spikeCountMat = spikesanalysis.spiketimes_to_spikecounts(
spikeTimesFromEventOnset, indexLimitsEachTrial, tuningWindow)
tuningSpikeRates = (spikeCountMat[trialsHighInt].flatten()) / (
tuningWindow[1] - tuningWindow[0])
freqsThisIntensity = freqEachTrial[trialsHighInt]
freqFit, thisRsquared = funcs.gaussian_tuning_fit(
np.log2(freqsThisIntensity), tuningSpikeRates)
if freqFit is not None:
bestFreq = 2**freqFit[0]
bandIndex, octavesFromBest = funcs.best_index(
cellObj, bestFreq, 'bandwidth')
else:
freqFit = np.full(4, np.nan)
bestFreq = np.nan
bandIndex = np.nan
octavesFromBest = np.nan
gaussFit.append(freqFit)
tuningTimeRange.append(tuningWindow)
Rsquared[indRow] = thisRsquared
prefFreq[indRow] = bestFreq
octavesFromPrefFreq[indRow] = octavesFromBest
bestBandSession[indRow] = bandIndex
db['soundLocation'] = soundLoc
db['spikeWidth'] = NaKpeakLatency
db['AMRate'] = AMRate
db['laserPVal'] = laserPVal
db['laserUStat'] = laserTestStatistic
db['laserTrainPVal'] = laserTrainPVal
db['laserTrainUStat'] = laserTrainTestStatistic
db['laserChangeFR'] = laserChangeFR
db['soundResponseUStat'] = soundResponseTestStatistic
db['soundResponsePVal'] = soundResponsePVal
db['onsetSoundResponseUStat'] = onsetSoundResponseTestStatistic
db['onsetSoundResponsePVal'] = onsetSoundResponsePVal
db['sustainedSoundResponseUStat'] = sustainedSoundResponseTestStatistic
db['sustainedSoundResponsePVal'] = sustainedSoundResponsePVal
db['gaussFit'] = gaussFit
db['tuningTimeRange'] = tuningTimeRange
db['tuningFitR2'] = Rsquared
db['prefFreq'] = prefFreq
db['octavesFromPrefFreq'] = octavesFromPrefFreq
db['bestBandSession'] = bestBandSession
if len(filename) != 0:
celldatabase.save_hdf(db, filename)
print filename + " saved"
return db
def inactivation_indices(db):
'''
This function takes as argument a pandas DataFrame and adds new columns.
The filename should be the full path to where the database will be saved. If a filename is not specified, the database will not be saved.
This function computes indices like Suppression Index with and without laser for all cells passing (generous) thresholds for good cells.
The thresholds used here are more generous than the ones in studyparams, in case we want to relax our criteria in the future.
'''
bestCells = db.query("isiViolations<0.02") # or modifiedISI<0.02")
bestCells = bestCells.loc[bestCells['spikeShapeQuality'] > 2]
bestCells = bestCells.query(
'soundResponsePVal<0.05 or onsetSoundResponsePVal<0.05 or sustainedSoundResponsePVal<0.05'
)
bestCells = bestCells.loc[bestCells['tuningFitR2'] > 0.05]
bestCells = bestCells.loc[bestCells['octavesFromPrefFreq'] < 0.5]
for dbIndex, dbRow in bestCells.iterrows():
cell = ephyscore.Cell(dbRow)
print "Now processing", dbRow['subject'], dbRow['date'], dbRow[
'depth'], dbRow['tetrode'], dbRow['cluster']
bandEphysData, bandBehavData = cell.load_by_index(
int(dbRow['bestBandSession']))
bandEventOnsetTimes = funcs.get_sound_onset_times(
bandEphysData, 'bandwidth')
bandSpikeTimestamps = bandEphysData['spikeTimes']
bandEachTrial = bandBehavData['currentBand']
secondSort = bandBehavData['laserTrial']
propOnset, propSustained = funcs.onset_sustained_spike_proportion(
bandSpikeTimestamps, bandEventOnsetTimes)
db.at[dbIndex, 'proportionSpikesOnset'] = propOnset
db.at[dbIndex, 'proportionSpikesSustained'] = propSustained
# by default: not subtracting baseline, but are replacing pure tone response with baseline for 0 bw condition
onsetSupInds, onsetSupIndpVals, onsetFacInds, onsetFacIndpVals, onsetPeakInds, onsetSpikeArray = funcs.bandwidth_suppression_from_peak(
bandSpikeTimestamps,
bandEventOnsetTimes,
bandEachTrial,
secondSort,
timeRange=[0.0, 0.05],
baseRange=[-0.05, 0.0])
#print onsetSupInds
db.at[dbIndex, 'onsetSuppressionIndexLaser'] = onsetSupInds[-1]
db.at[dbIndex, 'onsetSuppressionpValLaser'] = onsetSupIndpVals[-1]
db.at[dbIndex, 'onsetFacilitationIndexLaser'] = onsetFacInds[-1]
db.at[dbIndex, 'onsetFacilitationpValLaser'] = onsetFacIndpVals[-1]
db.at[dbIndex, 'onsetPrefBandwidthLaser'] = bandEachTrial[int(
onsetPeakInds[-1])]
db.at[dbIndex, 'onsetSuppressionIndexNoLaser'] = onsetSupInds[0]
db.at[dbIndex, 'onsetSuppressionpValNoLaser'] = onsetSupIndpVals[0]
db.at[dbIndex, 'onsetFacilitationIndexNoLaser'] = onsetFacInds[0]
db.at[dbIndex, 'onsetFacilitationpValNoLaser'] = onsetFacIndpVals[0]
db.at[dbIndex, 'onsetPrefBandwidthNoLaser'] = bandEachTrial[int(
onsetPeakInds[0])]
# base range is right before sound onset so we get estimate for laser baseline
sustainedSupInds, sustainedSupIndpVals, sustainedFacInds, sustainedFacIndpVals, sustainedPeakInds, sustainedSpikeArray = funcs.bandwidth_suppression_from_peak(
bandSpikeTimestamps,
bandEventOnsetTimes,
bandEachTrial,
secondSort,
timeRange=[0.2, 1.0],
baseRange=[-0.05, 0.0])
db.at[dbIndex, 'sustainedSuppressionIndexLaser'] = sustainedSupInds[-1]
db.at[dbIndex,
'sustainedSuppressionpValLaser'] = sustainedSupIndpVals[-1]
db.at[dbIndex,
'sustainedFacilitationIndexLaser'] = sustainedFacInds[-1]
db.at[dbIndex,
'sustainedFacilitationpValLaser'] = sustainedFacIndpVals[-1]
db.at[dbIndex, 'sustainedPrefBandwidthLaser'] = bandEachTrial[int(
sustainedPeakInds[-1])]
db.at[dbIndex,
'sustainedSuppressionIndexNoLaser'] = sustainedSupInds[0]
db.at[dbIndex,
'sustainedSuppressionpValNoLaser'] = sustainedSupIndpVals[0]
db.at[dbIndex,
'sustainedFacilitationIndexNoLaser'] = sustainedFacInds[0]
db.at[dbIndex,
'sustainedFacilitationpValNoLaser'] = sustainedFacIndpVals[0]
db.at[dbIndex, 'sustainedPrefBandwidthNoLaser'] = bandEachTrial[int(
sustainedPeakInds[0])]
# no laser fit
sustainedResponseNoLaser = sustainedSpikeArray[:, 0]
bandsForFit = np.unique(bandEachTrial)
bandsForFit[-1] = 6
mFixed = 1
fitParams, R2 = fitfuncs.diff_of_gauss_fit(bandsForFit,
sustainedResponseNoLaser,
mFixed=mFixed)
# fit params
db.at[dbIndex, 'R0noLaser'] = fitParams[0]
db.at[dbIndex, 'RDnoLaser'] = fitParams[3]
db.at[dbIndex, 'RSnoLaser'] = fitParams[4]
db.at[dbIndex, 'mnoLaser'] = mFixed
db.at[dbIndex, 'sigmaDnoLaser'] = fitParams[1]
db.at[dbIndex, 'sigmaSnoLaser'] = fitParams[2]
db.at[dbIndex, 'bandwidthTuningR2noLaser'] = R2
testBands = np.linspace(bandsForFit[0], bandsForFit[-1], 500)
allFitParams = [mFixed]
allFitParams.extend(fitParams)
suppInd, prefBW = fitfuncs.extract_stats_from_fit(
allFitParams, testBands)
db.at[dbIndex, 'fitSustainedSuppressionIndexNoLaser'] = suppInd
db.at[dbIndex, 'fitSustainedPrefBandwidthNoLaser'] = prefBW
# laser fit
sustainedResponseLaser = sustainedSpikeArray[:, 1]
fitParamsLaser, R2Laser = fitfuncs.diff_of_gauss_fit(
bandsForFit, sustainedResponseLaser, mFixed=mFixed)
# fit params
db.at[dbIndex, 'R0laser'] = fitParamsLaser[0]
db.at[dbIndex, 'RDlaser'] = fitParamsLaser[3]
db.at[dbIndex, 'RSlaser'] = fitParamsLaser[4]
db.at[dbIndex, 'mlaser'] = mFixed
db.at[dbIndex, 'sigmaDlaser'] = fitParamsLaser[1]
db.at[dbIndex, 'sigmaSlaser'] = fitParamsLaser[2]
db.at[dbIndex, 'bandwidthTuningR2laser'] = R2Laser
allFitParamsLaser = [mFixed]
allFitParamsLaser.extend(fitParamsLaser)
suppIndLaser, prefBWLaser = fitfuncs.extract_stats_from_fit(
allFitParamsLaser, testBands)
db.at[dbIndex, 'fitSustainedSuppressionIndexLaser'] = suppIndLaser
db.at[dbIndex, 'fitSustainedPrefBandwidthLaser'] = prefBWLaser
meanLaserDiff = np.mean(sustainedResponseLaser -
sustainedResponseNoLaser)
db.at[dbIndex, 'laserChangeResponse'] = meanLaserDiff
laserDiff = sustainedResponseLaser - sustainedResponseNoLaser
peakInd = np.argmax(sustainedResponseNoLaser)
db.at[dbIndex, 'peakChangeFR'] = laserDiff[peakInd]
db.at[dbIndex, 'WNChangeFR'] = laserDiff[-1]
testRespsNoLaser = fitfuncs.diff_gauss_form(testBands, *allFitParams)
testRespsLaser = fitfuncs.diff_gauss_form(testBands,
*allFitParamsLaser)
laserDiffModel = testRespsLaser - testRespsNoLaser
peakIndModel = np.argmax(testRespsNoLaser)
db.at[dbIndex, 'fitPeakFRNoLaser'] = testRespsNoLaser[peakIndModel]
db.at[dbIndex, 'fitPeakFRLaser'] = testRespsLaser[peakIndModel]
db.at[dbIndex, 'fitPeakChangeFR'] = laserDiffModel[peakIndModel]
db.at[dbIndex, 'fitWNFRNoLaser'] = testRespsNoLaser[-1]
db.at[dbIndex, 'fitWNFRLaser'] = testRespsLaser[-1]
db.at[dbIndex, 'fitWNChangeFR'] = laserDiffModel[-1]
# also calculating fits and suppression with pure tone being 0 bw condition
# toneSustainedSupInds, toneSustainedSupIndpVals, toneSustainedFacInds, toneSustainedFacIndpVals, toneSustainedPeakInds, toneSustainedSpikeArray = funcs.bandwidth_suppression_from_peak(bandSpikeTimestamps, bandEventOnsetTimes, bandEachTrial, secondSort, timeRange=[0.2,1.0], baseRange=[-0.05,0.0], zeroBWBaseline=False)
# db.at[dbIndex, 'sustainedSuppressionIndexNoLaserPureTone'] = toneSustainedSupInds[0]
# db.at[dbIndex, 'sustainedSuppressionpValNoLaserPureTone'] = toneSustainedSupIndpVals[0]
# db.at[dbIndex, 'sustainedFacilitationIndexNoLaserPureTone'] = toneSustainedFacInds[0]
# db.at[dbIndex, 'sustainedFacilitationpValNoLaserPureTone'] = toneSustainedFacIndpVals[0]
# db.at[dbIndex, 'sustainedPrefBandwidthNoLaserPureTone'] = bandEachTrial[int(toneSustainedPeakInds[0])]
#
# db.at[dbIndex, 'sustainedSuppressionIndexLaserPureTone'] = toneSustainedSupInds[-1]
# db.at[dbIndex, 'sustainedSuppressionpValLaserPureTone'] = toneSustainedSupIndpVals[-1]
# db.at[dbIndex, 'sustainedFacilitationIndexLaserPureTone'] = toneSustainedFacInds[-1]
# db.at[dbIndex, 'sustainedFacilitationpValLaserPureTone'] = toneSustainedFacIndpVals[-1]
# db.at[dbIndex, 'sustainedPrefBandwidthLaserPureTone'] = bandEachTrial[int(toneSustainedPeakInds[-1])]
#
# toneSustainedResponseNoLaser = toneSustainedSpikeArray[:,0]
#
# toneFitParamsNoLaser, toneR2 = fitfuncs.diff_of_gauss_fit(bandsForFit, toneSustainedResponseNoLaser, mFixed=mFixed)
#
# # fit params
# db.at[dbIndex, 'R0PureToneNoLaser'] = toneFitParamsNoLaser[0]
# db.at[dbIndex, 'RDPureToneNoLaser'] = toneFitParamsNoLaser[3]
# db.at[dbIndex, 'RSPureToneNoLaser'] = toneFitParamsNoLaser[4]
# db.at[dbIndex, 'mPureToneNoLaser'] = mFixed
# db.at[dbIndex, 'sigmaDPureToneNoLaser'] = toneFitParamsNoLaser[1]
# db.at[dbIndex, 'sigmaSPureToneNoLaser'] = toneFitParamsNoLaser[2]
# db.at[dbIndex, 'bandwidthTuningR2PureToneNoLaser'] = toneR2
#
# allFitParamsToneNoLaser = [mFixed]
# allFitParamsToneNoLaser.extend(toneFitParamsNoLaser)
# suppIndTone, prefBWTone = fitfuncs.extract_stats_from_fit(allFitParamsToneNoLaser, testBands)
#
# db.at[dbIndex, 'fitSustainedSuppressionIndexPureToneNoLaser'] = suppIndTone
# db.at[dbIndex, 'fitSustainedPrefBandwidthPureToneNoLaser'] = prefBWTone
#
#
# toneSustainedResponseLaser = toneSustainedSpikeArray[:,1]
#
# toneFitParamsLaser, toneR2Laser = fitfuncs.diff_of_gauss_fit(bandsForFit, toneSustainedResponseLaser, mFixed=mFixed)
#
# # fit params
# db.at[dbIndex, 'R0PureToneLaser'] = toneFitParamsLaser[0]
# db.at[dbIndex, 'RDPureToneLaser'] = toneFitParamsLaser[3]
# db.at[dbIndex, 'RSPureToneLaser'] = toneFitParamsLaser[4]
# db.at[dbIndex, 'mPureToneLaser'] = mFixed
# db.at[dbIndex, 'sigmaDPureToneLaser'] = toneFitParamsLaser[1]
# db.at[dbIndex, 'sigmaSPureToneLaser'] = toneFitParamsLaser[2]
# db.at[dbIndex, 'bandwidthTuningR2PureToneLaser'] = toneR2Laser
#
# allFitParamsToneLaser = [mFixed]
# allFitParamsToneLaser.extend(toneFitParamsLaser)
# suppIndToneLaser, prefBWToneLaser = fitfuncs.extract_stats_from_fit(allFitParamsToneLaser, testBands)
#
# db.at[dbIndex, 'fitSustainedSuppressionIndexPureToneLaser'] = suppIndToneLaser
# db.at[dbIndex, 'fitSustainedPrefBandwidthPureToneLaser'] = prefBWToneLaser
#
# testRespsNoLaser = fitfuncs.diff_gauss_form(testBands, *allFitParamsToneNoLaser)
# testRespsLaser = fitfuncs.diff_gauss_form(testBands, *allFitParamsToneLaser)
#
# laserDiffModel = testRespsLaser-testRespsNoLaser
# peakIndModel = np.argmax(testRespsNoLaser)
#
# db.at[dbIndex, 'fitPeakChangeFRPureTone'] = laserDiffModel[peakIndModel]
# db.at[dbIndex, 'fitWNChangeFRPureTone'] = laserDiffModel[-1]
# also calculating fits and suppression with nothing being fit for bw 0
noZeroSustainedResponseNoLaser = sustainedSpikeArray[1:, 0]
bandsForFitNoZero = bandsForFit[1:]
noZeroFitParamsNoLaser, noZeroR2 = fitfuncs.diff_of_gauss_fit(
bandsForFitNoZero, noZeroSustainedResponseNoLaser, mFixed=mFixed)
# fit params
db.at[dbIndex, 'R0noZeroNoLaser'] = noZeroFitParamsNoLaser[0]
db.at[dbIndex, 'RDnoZeroNoLaser'] = noZeroFitParamsNoLaser[3]
db.at[dbIndex, 'RSnoZeroNoLaser'] = noZeroFitParamsNoLaser[4]
db.at[dbIndex, 'mnoZeroNoLaser'] = mFixed
db.at[dbIndex, 'sigmaDnoZeroNoLaser'] = noZeroFitParamsNoLaser[1]
db.at[dbIndex, 'sigmaSnoZeroNoLaser'] = noZeroFitParamsNoLaser[2]
db.at[dbIndex, 'bandwidthTuningR2noZeroNoLaser'] = noZeroR2
allFitParamsNoZero = [mFixed]
allFitParamsNoZero.extend(noZeroFitParamsNoLaser)
testBandsNoZero = np.linspace(bandsForFitNoZero[0],
bandsForFitNoZero[-1], 500)
suppIndNoZero, prefBWNoZero = fitfuncs.extract_stats_from_fit(
allFitParamsNoZero, testBandsNoZero)
db.at[dbIndex,
'fitSustainedSuppressionIndexNoZeroNoLaser'] = suppIndNoZero
db.at[dbIndex, 'fitSustainedPrefBandwidthNoZeroNoLaser'] = prefBWNoZero
noZeroSustainedResponseLaser = sustainedSpikeArray[1:, 1]
bandsForFitNoZero = bandsForFit[1:]
noZeroFitParamsLaser, noZeroR2Laser = fitfuncs.diff_of_gauss_fit(
bandsForFitNoZero, noZeroSustainedResponseLaser, mFixed=mFixed)
# fit params
db.at[dbIndex, 'R0noZeroLaser'] = noZeroFitParamsLaser[0]
db.at[dbIndex, 'RDnoZeroLaser'] = noZeroFitParamsLaser[3]
db.at[dbIndex, 'RSnoZeroLaser'] = noZeroFitParamsLaser[4]
db.at[dbIndex, 'mnoZeroLaser'] = mFixed
db.at[dbIndex, 'sigmaDnoZeroLaser'] = noZeroFitParamsLaser[1]
db.at[dbIndex, 'sigmaSnoZeroLaser'] = noZeroFitParamsLaser[2]
db.at[dbIndex, 'bandwidthTuningR2noZeroLaser'] = noZeroR2Laser
allFitParamsNoZeroLaser = [mFixed]
allFitParamsNoZeroLaser.extend(noZeroFitParamsLaser)
suppIndNoZeroLaser, prefBWNoZeroLaser = fitfuncs.extract_stats_from_fit(
allFitParamsNoZeroLaser, testBandsNoZero)
db.at[dbIndex,
'fitSustainedSuppressionIndexNoZeroLaser'] = suppIndNoZeroLaser
db.at[dbIndex,
'fitSustainedPrefBandwidthNoZeroLaser'] = prefBWNoZeroLaser
testRespsNoLaser = fitfuncs.diff_gauss_form(testBandsNoZero,
*allFitParamsNoZero)
testRespsLaser = fitfuncs.diff_gauss_form(testBandsNoZero,
*allFitParamsNoZeroLaser)
laserDiffModel = testRespsLaser - testRespsNoLaser
peakIndModel = np.argmax(testRespsNoLaser)
db.at[dbIndex, 'fitPeakChangeFRNoZero'] = laserDiffModel[peakIndModel]
db.at[dbIndex, 'fitWNChangeFRNoZero'] = laserDiffModel[-1]
# onset change
noZeroOnsetResponseNoLaser = onsetSpikeArray[1:, 0]
bandsForFitNoZero = bandsForFit[1:]
noZeroOnsetFitParamsNoLaser, noZeroOnsetR2 = fitfuncs.diff_of_gauss_fit(
bandsForFitNoZero, noZeroOnsetResponseNoLaser, mFixed=mFixed)
# fit params
db.at[dbIndex, 'onsetR0noZeroNoLaser'] = noZeroOnsetFitParamsNoLaser[0]
db.at[dbIndex, 'onsetRDnoZeroNoLaser'] = noZeroOnsetFitParamsNoLaser[3]
db.at[dbIndex, 'onsetRSnoZeroNoLaser'] = noZeroOnsetFitParamsNoLaser[4]
db.at[dbIndex, 'onsetmnoZeroNoLaser'] = mFixed
db.at[dbIndex,
'onsetsigmaDnoZeroNoLaser'] = noZeroOnsetFitParamsNoLaser[1]
db.at[dbIndex,
'onsetsigmaSnoZeroNoLaser'] = noZeroOnsetFitParamsNoLaser[2]
db.at[dbIndex, 'onsetbandwidthTuningR2noZeroNoLaser'] = noZeroOnsetR2
allFitParamsOnsetNoZero = [mFixed]
allFitParamsOnsetNoZero.extend(noZeroOnsetFitParamsNoLaser)
testBandsNoZero = np.linspace(bandsForFitNoZero[0],
bandsForFitNoZero[-1], 500)
suppIndOnsetNoZero, prefBWOnsetNoZero = fitfuncs.extract_stats_from_fit(
allFitParamsOnsetNoZero, testBandsNoZero)
db.at[dbIndex,
'fitOnsetSuppressionIndexNoZeroNoLaser'] = suppIndOnsetNoZero
db.at[dbIndex,
'fitOnsetPrefBandwidthNoZeroNoLaser'] = prefBWOnsetNoZero
noZeroOnsetResponseLaser = onsetSpikeArray[1:, 1]
bandsForFitNoZero = bandsForFit[1:]
noZeroOnsetFitParamsLaser, noZeroOnsetR2Laser = fitfuncs.diff_of_gauss_fit(
bandsForFitNoZero, noZeroOnsetResponseLaser, mFixed=mFixed)
# fit params
db.at[dbIndex, 'onsetR0noZeroLaser'] = noZeroOnsetFitParamsLaser[0]
db.at[dbIndex, 'onsetRDnoZeroLaser'] = noZeroOnsetFitParamsLaser[3]
db.at[dbIndex, 'onsetRSnoZeroLaser'] = noZeroOnsetFitParamsLaser[4]
db.at[dbIndex, 'onsetmnoZeroLaser'] = mFixed
db.at[dbIndex, 'onsetsigmaDnoZeroLaser'] = noZeroOnsetFitParamsLaser[1]
db.at[dbIndex, 'onsetsigmaSnoZeroLaser'] = noZeroOnsetFitParamsLaser[2]
db.at[dbIndex,
'onsetbandwidthTuningR2noZeroLaser'] = noZeroOnsetR2Laser
allFitParamsOnsetNoZeroLaser = [mFixed]
allFitParamsOnsetNoZeroLaser.extend(noZeroOnsetFitParamsLaser)
suppIndOnsetNoZeroLaser, prefBWOnsetNoZeroLaser = fitfuncs.extract_stats_from_fit(
allFitParamsOnsetNoZeroLaser, testBandsNoZero)
db.at[dbIndex,
'fitOnsetSuppressionIndexNoZeroLaser'] = suppIndOnsetNoZeroLaser
db.at[dbIndex,
'fitOnsetPrefBandwidthNoZeroLaser'] = prefBWOnsetNoZeroLaser
testRespsNoLaser = fitfuncs.diff_gauss_form(testBandsNoZero,
*allFitParamsOnsetNoZero)
testRespsLaser = fitfuncs.diff_gauss_form(
testBandsNoZero, *allFitParamsOnsetNoZeroLaser)
laserDiffModel = testRespsLaser - testRespsNoLaser
peakIndModel = np.argmax(testRespsNoLaser)
db.at[dbIndex,
'fitOnsetPeakChangeFRNoZero'] = laserDiffModel[peakIndModel]
db.at[dbIndex, 'fitOnsetWNChangeFRNoZero'] = laserDiffModel[-1]
return db
def inactivation_database(db,
baseStats=False,
computeIndices=True,
filename='inactivation_cells.h5'):
if type(db) == str:
dbPath = os.path.join(settings.DATABASE_PATH, db)
db = celldatabase.load_hdf(dbPath)
if baseStats:
soundResponseTestStatistic = np.empty(len(db))
soundResponsePVal = np.empty(len(db))
onsetSoundResponseTestStatistic = np.empty(len(db))
onsetSoundResponsePVal = np.empty(len(db))
sustainedSoundResponseTestStatistic = np.empty(len(db))
sustainedSoundResponsePVal = np.empty(len(db))
gaussFit = []
tuningTimeRange = []
Rsquared = np.empty(len(db))
prefFreq = np.empty(len(db))
octavesFromPrefFreq = np.empty(len(db))
bestBandSession = np.empty(len(db))
for indRow, (dbIndex, dbRow) in enumerate(db.iterrows()):
cellObj = ephyscore.Cell(dbRow)
print "Now processing", dbRow['subject'], dbRow['date'], dbRow[
'depth'], dbRow['tetrode'], dbRow['cluster']
# --- Determine sound responsiveness during bandwidth sessions and calculate baseline firing rates with and without laser---
#done in a kind of stupid way because regular and control sessions are handled the same way
if any(session in dbRow['sessionType']
for session in ['laserBandwidth', 'laserBandwidthControl']):
if 'laserBandwidth' in dbRow['sessionType']:
bandEphysData, bandBehavData = cellObj.load(
'laserBandwidth')
behavSession = 'laserBandwidth'
db.at[dbIndex, 'controlSession'] = 0
elif 'laserBandwidthControl' in dbRow['sessionType']:
bandEphysData, bandBehavData = cellObj.load(
'laserBandwidthControl')
behavSession = 'laserBandwidthControl'
db.at[dbIndex, 'controlSession'] = 1
bandEventOnsetTimes = funcs.get_sound_onset_times(
bandEphysData, 'bandwidth')
bandSpikeTimestamps = bandEphysData['spikeTimes']
bandEachTrial = bandBehavData['currentBand']
secondSort = bandBehavData['laserTrial']
numBands = np.unique(bandEachTrial)
numSec = np.unique(secondSort)
trialsEachComb = behavioranalysis.find_trials_each_combination(
bandEachTrial, numBands, secondSort, numSec)
trialsEachBaseCond = trialsEachComb[:, :,
0] #using no laser trials to determine sound responsiveness
testStatistic, pVal = funcs.sound_response_any_stimulus(
bandEventOnsetTimes, bandSpikeTimestamps,
trialsEachBaseCond, [0.0, 1.0], [-1.2, -0.2])
onsetTestStatistic, onsetpVal = funcs.sound_response_any_stimulus(
bandEventOnsetTimes, bandSpikeTimestamps,
trialsEachBaseCond, [0.0, 0.05], [-0.25, 0.2])
sustainedTestStatistic, sustainedpVal = funcs.sound_response_any_stimulus(
bandEventOnsetTimes, bandSpikeTimestamps,
trialsEachBaseCond, [0.2, 1.0], [-1.0, 0.2])
pVal *= len(numBands) #correction for multiple comparisons
onsetpVal *= len(numBands)
sustainedpVal *= len(numBands)
#pdb.set_trace()
#find baselines with and without laser
baselineRange = [-0.05, 0.0]
baselineRates, baselineSEMs = funcs.inactivated_cells_baselines(
bandSpikeTimestamps, bandEventOnsetTimes, secondSort,
baselineRange)
db.at[dbIndex, 'baselineFRnoLaser'] = baselineRates[0]
db.at[dbIndex, 'baselineFRLaser'] = baselineRates[1]
db.at[dbIndex, 'baselineFRnoLaserSEM'] = baselineSEMs[0]
db.at[dbIndex, 'baselineFRLaserSEM'] = baselineSEMs[1]
db.at[dbIndex,
'baselineChangeFR'] = baselineRates[1] - baselineRates[0]
else:
print "No bandwidth session for this cell"
testStatistic = np.nan
pVal = np.nan
onsetTestStatistic = np.nan
onsetpVal = np.nan
sustainedTestStatistic = np.nan
sustainedpVal = np.nan
#pdb.set_trace()
soundResponseTestStatistic[indRow] = testStatistic
soundResponsePVal[indRow] = pVal
onsetSoundResponseTestStatistic[indRow] = onsetTestStatistic
onsetSoundResponsePVal[indRow] = onsetpVal
sustainedSoundResponseTestStatistic[
indRow] = sustainedTestStatistic
sustainedSoundResponsePVal[indRow] = sustainedpVal
# --- Determine frequency tuning of cells ---
try:
tuningEphysData, tuningBehavData = cellObj.load('tuningCurve')
except IndexError:
print "No tuning session for this cell"
freqFit = np.full(4, np.nan)
thisRsquared = np.nan
bestFreq = np.nan
tuningWindow = np.full(2, np.nan)
octavesFromBest = np.nan
bandIndex = np.nan
else:
tuningEventOnsetTimes = funcs.get_sound_onset_times(
tuningEphysData, 'tuningCurve')
tuningSpikeTimestamps = tuningEphysData['spikeTimes']
freqEachTrial = tuningBehavData['currentFreq']
intensityEachTrial = tuningBehavData['currentIntensity']
numFreqs = np.unique(freqEachTrial)
numIntensities = np.unique(intensityEachTrial)
timeRange = [-0.2, 0.2]
spikeTimesFromEventOnset, trialIndexForEachSpike, indexLimitsEachTrial = spikesanalysis.eventlocked_spiketimes(
tuningSpikeTimestamps, tuningEventOnsetTimes, timeRange)
trialsEachType = behavioranalysis.find_trials_each_type(
intensityEachTrial, numIntensities)
trialsHighInt = trialsEachType[:, -1]
trialsEachComb = behavioranalysis.find_trials_each_combination(
freqEachTrial, numFreqs, intensityEachTrial,
numIntensities)
trialsEachFreqHighInt = trialsEachComb[:, :, -1]
tuningWindow = funcs.best_window_freq_tuning(
spikeTimesFromEventOnset, indexLimitsEachTrial,
trialsEachFreqHighInt)
tuningWindow = np.array(tuningWindow)
spikeCountMat = spikesanalysis.spiketimes_to_spikecounts(
spikeTimesFromEventOnset, indexLimitsEachTrial,
tuningWindow)
tuningSpikeRates = (spikeCountMat[trialsHighInt].flatten()) / (
tuningWindow[1] - tuningWindow[0])
freqsThisIntensity = freqEachTrial[trialsHighInt]
freqFit, thisRsquared = funcs.gaussian_tuning_fit(
np.log2(freqsThisIntensity), tuningSpikeRates)
if freqFit is not None:
bestFreq = 2**freqFit[0]
bandIndex, octavesFromBest = funcs.best_index(
cellObj, bestFreq, behavSession)
else:
freqFit = np.full(4, np.nan)
bestFreq = np.nan
bandIndex = np.nan
octavesFromBest = np.nan
gaussFit.append(freqFit)
tuningTimeRange.append(tuningWindow)
Rsquared[indRow] = thisRsquared
prefFreq[indRow] = bestFreq
octavesFromPrefFreq[indRow] = octavesFromBest
bestBandSession[indRow] = bandIndex
db['soundResponseUStat'] = soundResponseTestStatistic
db['soundResponsePVal'] = soundResponsePVal
db['onsetSoundResponseUStat'] = onsetSoundResponseTestStatistic
db['onsetSoundResponsePVal'] = onsetSoundResponsePVal
db['sustainedSoundResponseUStat'] = sustainedSoundResponseTestStatistic
db['sustainedSoundResponsePVal'] = sustainedSoundResponsePVal
db['gaussFit'] = gaussFit
db['tuningTimeRange'] = tuningTimeRange
db['tuningFitR2'] = Rsquared
db['prefFreq'] = prefFreq
db['octavesFromPrefFreq'] = octavesFromPrefFreq
db['bestBandSession'] = bestBandSession
if computeIndices:
bestCells = db.query("isiViolations<0.02") # or modifiedISI<0.02")
bestCells = bestCells.loc[bestCells['spikeShapeQuality'] > 2]
bestCells = bestCells.query(
'soundResponsePVal<0.05 or onsetSoundResponsePVal<0.05 or sustainedSoundResponsePVal<0.05'
)
bestCells = bestCells.loc[bestCells['tuningFitR2'] > R2CUTOFF]
bestCells = bestCells.loc[
bestCells['octavesFromPrefFreq'] < OCTAVESCUTOFF]
for dbIndex, dbRow in bestCells.iterrows():
cell = ephyscore.Cell(dbRow)
bandEphysData, bandBehavData = cell.load_by_index(
int(dbRow['bestBandSession']))
bandEventOnsetTimes = funcs.get_sound_onset_times(
bandEphysData, 'bandwidth')
bandSpikeTimestamps = bandEphysData['spikeTimes']
bandEachTrial = bandBehavData['currentBand']
secondSort = bandBehavData['laserTrial']
propOnset, propSustained = funcs.onset_sustained_spike_proportion(
bandSpikeTimestamps, bandEventOnsetTimes)
db.at[dbIndex, 'proportionSpikesOnset'] = propOnset
db.at[dbIndex, 'proportionSpikesSustained'] = propSustained
#by default: not subtracting baseline, but are replacing pure tone response with baseline for 0 bw condition
onsetSupInds, onsetSupIndpVals, onsetFacInds, onsetFacIndpVals, onsetPeakInds, onsetSpikeArray = funcs.bandwidth_suppression_from_peak(
bandSpikeTimestamps,
bandEventOnsetTimes,
bandEachTrial,
secondSort,
timeRange=[0.0, 0.05],
baseRange=[-0.05, 0.0])
db.at[dbIndex, 'onsetSuppressionIndexLaser'] = onsetSupInds[-1]
db.at[dbIndex, 'onsetSuppressionpValLaser'] = onsetSupIndpVals[-1]
db.at[dbIndex, 'onsetFacilitationIndexLaser'] = onsetFacInds[-1]
db.at[dbIndex, 'onsetFacilitationpValLaser'] = onsetFacIndpVals[-1]
db.at[dbIndex,
'onsetPrefBandwidthLaser'] = bandEachTrial[onsetPeakInds[-1]]
db.at[dbIndex, 'onsetSuppressionIndexNoLaser'] = onsetSupInds[0]
db.at[dbIndex, 'onsetSuppressionpValNoLaser'] = onsetSupIndpVals[0]
db.at[dbIndex, 'onsetFacilitationIndexNoLaser'] = onsetFacInds[0]
db.at[dbIndex,
'onsetFacilitationpValNoLaser'] = onsetFacIndpVals[0]
db.at[dbIndex, 'onsetPrefBandwidthNoLaser'] = bandEachTrial[
onsetPeakInds[0]]
#base range is right before sound onset so we get estimate for laser baseline
sustainedSupInds, sustainedSupIndpVals, sustainedFacInds, sustainedFacIndpVals, sustainedPeakInds, sustainedSpikeArray = funcs.bandwidth_suppression_from_peak(
bandSpikeTimestamps,
bandEventOnsetTimes,
bandEachTrial,
secondSort,
timeRange=[0.2, 1.0],
baseRange=[-0.05, 0.0])
db.at[dbIndex,
'sustainedSuppressionIndexLaser'] = sustainedSupInds[-1]
db.at[dbIndex,
'sustainedSuppressionpValLaser'] = sustainedSupIndpVals[-1]
db.at[dbIndex,
'sustainedFacilitationIndexLaser'] = sustainedFacInds[-1]
db.at[dbIndex,
'sustainedFacilitationpValLaser'] = sustainedFacIndpVals[-1]
db.at[dbIndex, 'sustainedPrefBandwidthLaser'] = bandEachTrial[
sustainedPeakInds[-1]]
db.at[dbIndex,
'sustainedSuppressionIndexNoLaser'] = sustainedSupInds[0]
db.at[dbIndex,
'sustainedSuppressionpValNoLaser'] = sustainedSupIndpVals[0]
db.at[dbIndex,
'sustainedFacilitationIndexNoLaser'] = sustainedFacInds[0]
db.at[dbIndex,
'sustainedFacilitationpValNoLaser'] = sustainedFacIndpVals[0]
db.at[dbIndex, 'sustainedPrefBandwidthNoLaser'] = bandEachTrial[
sustainedPeakInds[0]]
#no laser fit
sustainedResponseNoLaser = sustainedSpikeArray[:, 0]
bandsForFit = np.unique(bandEachTrial)
bandsForFit[-1] = 6
mFixed = 1
fitParams, R2 = fitfuncs.diff_of_gauss_fit(
bandsForFit, sustainedResponseNoLaser, mFixed=mFixed)
#fit params
db.at[dbIndex, 'R0noLaser'] = fitParams[0]
db.at[dbIndex, 'RDnoLaser'] = fitParams[3]
db.at[dbIndex, 'RSnoLaser'] = fitParams[4]
db.at[dbIndex, 'mnoLaser'] = mFixed
db.at[dbIndex, 'sigmaDnoLaser'] = fitParams[1]
db.at[dbIndex, 'sigmaSnoLaser'] = fitParams[2]
db.at[dbIndex, 'bandwidthTuningR2noLaser'] = R2
testBands = np.linspace(bandsForFit[0], bandsForFit[-1], 500)
allFitParams = [mFixed]
allFitParams.extend(fitParams)
suppInd, prefBW = fitfuncs.extract_stats_from_fit(
allFitParams, testBands)
db.at[dbIndex, 'fitSustainedSuppressionIndexNoLaser'] = suppInd
db.at[dbIndex, 'fitSustainedPrefBandwidthNoLaser'] = prefBW
#laser fit
sustainedResponseLaser = sustainedSpikeArray[:, 1]
fitParamsLaser, R2Laser = fitfuncs.diff_of_gauss_fit(
bandsForFit, sustainedResponseLaser, mFixed=mFixed)
#fit params
db.at[dbIndex, 'R0laser'] = fitParamsLaser[0]
db.at[dbIndex, 'RDlaser'] = fitParamsLaser[3]
db.at[dbIndex, 'RSlaser'] = fitParamsLaser[4]
db.at[dbIndex, 'mlaser'] = mFixed
db.at[dbIndex, 'sigmaDlaser'] = fitParamsLaser[1]
db.at[dbIndex, 'sigmaSlaser'] = fitParamsLaser[2]
db.at[dbIndex, 'bandwidthTuningR2laser'] = R2Laser
allFitParamsLaser = [mFixed]
allFitParamsLaser.extend(fitParamsLaser)
suppIndLaser, prefBWLaser = fitfuncs.extract_stats_from_fit(
allFitParamsLaser, testBands)
db.at[dbIndex, 'fitSustainedSuppressionIndexLaser'] = suppIndLaser
db.at[dbIndex, 'fitSustainedPrefBandwidthLaser'] = prefBWLaser
meanLaserDiff = np.mean(sustainedResponseLaser -
sustainedResponseNoLaser)
db.at[dbIndex, 'laserChangeResponse'] = meanLaserDiff
laserDiff = sustainedResponseLaser - sustainedResponseNoLaser
peakInd = np.argmax(sustainedResponseNoLaser)
db.at[dbIndex, 'peakChangeFR'] = laserDiff[peakInd]
db.at[dbIndex, 'WNChangeFR'] = laserDiff[-1]
testRespsNoLaser = fitfuncs.diff_gauss_form(
testBands, *allFitParams)
testRespsLaser = fitfuncs.diff_gauss_form(testBands,
*allFitParamsLaser)
laserDiffModel = testRespsLaser - testRespsNoLaser
peakIndModel = np.argmax(testRespsNoLaser)
db.at[dbIndex, 'fitPeakChangeFR'] = laserDiffModel[peakIndModel]
db.at[dbIndex, 'fitWNChangeFR'] = laserDiffModel[-1]
#also calculating fits and suppression with pure tone being 0 bw condition
toneSustainedSupInds, toneSustainedSupIndpVals, toneSustainedFacInds, toneSustainedFacIndpVals, toneSustainedPeakInds, toneSustainedSpikeArray = funcs.bandwidth_suppression_from_peak(
bandSpikeTimestamps,
bandEventOnsetTimes,
bandEachTrial,
secondSort,
timeRange=[0.2, 1.0],
baseRange=[-0.05, 0.0],
zeroBWBaseline=False)
db.at[
dbIndex,
'sustainedSuppressionIndexNoLaserPureTone'] = toneSustainedSupInds[
0]
db.at[
dbIndex,
'sustainedSuppressionpValNoLaserPureTone'] = toneSustainedSupIndpVals[
0]
db.at[
dbIndex,
'sustainedFacilitationIndexNoLaserPureTone'] = toneSustainedFacInds[
0]
db.at[
dbIndex,
'sustainedFacilitationpValNoLaserPureTone'] = toneSustainedFacIndpVals[
0]
db.at[dbIndex,
'sustainedPrefBandwidthNoLaserPureTone'] = bandEachTrial[
toneSustainedPeakInds[0]]
db.at[
dbIndex,
'sustainedSuppressionIndexLaserPureTone'] = toneSustainedSupInds[
-1]
db.at[
dbIndex,
'sustainedSuppressionpValLaserPureTone'] = toneSustainedSupIndpVals[
-1]
db.at[
dbIndex,
'sustainedFacilitationIndexLaserPureTone'] = toneSustainedFacInds[
-1]
db.at[
dbIndex,
'sustainedFacilitationpValLaserPureTone'] = toneSustainedFacIndpVals[
-1]
db.at[dbIndex,
'sustainedPrefBandwidthLaserPureTone'] = bandEachTrial[
toneSustainedPeakInds[-1]]
toneSustainedResponseNoLaser = toneSustainedSpikeArray[:, 0]
toneFitParamsNoLaser, toneR2 = fitfuncs.diff_of_gauss_fit(
bandsForFit, toneSustainedResponseNoLaser, mFixed=mFixed)
#fit params
db.at[dbIndex, 'R0PureToneNoLaser'] = toneFitParamsNoLaser[0]
db.at[dbIndex, 'RDPureToneNoLaser'] = toneFitParamsNoLaser[3]
db.at[dbIndex, 'RSPureToneNoLaser'] = toneFitParamsNoLaser[4]
db.at[dbIndex, 'mPureToneNoLaser'] = mFixed
db.at[dbIndex, 'sigmaDPureToneNoLaser'] = toneFitParamsNoLaser[1]
db.at[dbIndex, 'sigmaSPureToneNoLaser'] = toneFitParamsNoLaser[2]
db.at[dbIndex, 'bandwidthTuningR2PureToneNoLaser'] = toneR2
allFitParamsToneNoLaser = [mFixed]
allFitParamsToneNoLaser.extend(toneFitParamsNoLaser)
suppIndTone, prefBWTone = fitfuncs.extract_stats_from_fit(
allFitParamsToneNoLaser, testBands)
db.at[dbIndex,
'fitSustainedSuppressionIndexPureToneNoLaser'] = suppIndTone
db.at[dbIndex,
'fitSustainedPrefBandwidthPureToneNoLaser'] = prefBWTone
toneSustainedResponseLaser = toneSustainedSpikeArray[:, 1]
toneFitParamsLaser, toneR2Laser = fitfuncs.diff_of_gauss_fit(
bandsForFit, toneSustainedResponseLaser, mFixed=mFixed)
#fit params
db.at[dbIndex, 'R0PureToneLaser'] = toneFitParamsLaser[0]
db.at[dbIndex, 'RDPureToneLaser'] = toneFitParamsLaser[3]
db.at[dbIndex, 'RSPureToneLaser'] = toneFitParamsLaser[4]
db.at[dbIndex, 'mPureToneLaser'] = mFixed
db.at[dbIndex, 'sigmaDPureToneLaser'] = toneFitParamsLaser[1]
db.at[dbIndex, 'sigmaSPureToneLaser'] = toneFitParamsLaser[2]
db.at[dbIndex, 'bandwidthTuningR2PureToneLaser'] = toneR2Laser
allFitParamsToneLaser = [mFixed]
allFitParamsToneLaser.extend(toneFitParamsLaser)
suppIndToneLaser, prefBWToneLaser = fitfuncs.extract_stats_from_fit(
allFitParamsToneLaser, testBands)
db.at[
dbIndex,
'fitSustainedSuppressionIndexPureToneLaser'] = suppIndToneLaser
db.at[dbIndex,
'fitSustainedPrefBandwidthPureToneLaser'] = prefBWToneLaser
testRespsNoLaser = fitfuncs.diff_gauss_form(
testBands, *allFitParamsToneNoLaser)
testRespsLaser = fitfuncs.diff_gauss_form(testBands,
*allFitParamsToneLaser)
laserDiffModel = testRespsLaser - testRespsNoLaser
peakIndModel = np.argmax(testRespsNoLaser)
db.at[dbIndex,
'fitPeakChangeFRPureTone'] = laserDiffModel[peakIndModel]
db.at[dbIndex, 'fitWNChangeFRPureTone'] = laserDiffModel[-1]
#also calculating fits and suppression with nothing being fit for bw 0
noZeroSustainedResponseNoLaser = sustainedSpikeArray[1:, 0]
bandsForFitNoZero = bandsForFit[1:]
noZeroFitParamsNoLaser, noZeroR2 = fitfuncs.diff_of_gauss_fit(
bandsForFitNoZero,
noZeroSustainedResponseNoLaser,
mFixed=mFixed)
#fit params
db.at[dbIndex, 'R0noZeroNoLaser'] = noZeroFitParamsNoLaser[0]
db.at[dbIndex, 'RDnoZeroNoLaser'] = noZeroFitParamsNoLaser[3]
db.at[dbIndex, 'RSnoZeroNoLaser'] = noZeroFitParamsNoLaser[4]
db.at[dbIndex, 'mnoZeroNoLaser'] = mFixed
db.at[dbIndex, 'sigmaDnoZeroNoLaser'] = noZeroFitParamsNoLaser[1]
db.at[dbIndex, 'sigmaSnoZeroNoLaser'] = noZeroFitParamsNoLaser[2]
db.at[dbIndex, 'bandwidthTuningR2noZeroNoLaser'] = noZeroR2
allFitParamsNoZero = [mFixed]
allFitParamsNoZero.extend(noZeroFitParamsNoLaser)
testBandsNoZero = np.linspace(bandsForFitNoZero[0],
bandsForFitNoZero[-1], 500)
suppIndNoZero, prefBWNoZero = fitfuncs.extract_stats_from_fit(
allFitParamsNoZero, testBandsNoZero)
db.at[dbIndex,
'fitSustainedSuppressionIndexNoZeroNoLaser'] = suppIndNoZero
db.at[dbIndex,
'fitSustainedPrefBandwidthNoZeroNoLaser'] = prefBWNoZero
noZeroSustainedResponseLaser = sustainedSpikeArray[1:, 1]
bandsForFitNoZero = bandsForFit[1:]
noZeroFitParamsLaser, noZeroR2Laser = fitfuncs.diff_of_gauss_fit(
bandsForFitNoZero, noZeroSustainedResponseLaser, mFixed=mFixed)
#fit params
db.at[dbIndex, 'R0noZeroLaser'] = noZeroFitParamsLaser[0]
db.at[dbIndex, 'RDnoZeroLaser'] = noZeroFitParamsLaser[3]
db.at[dbIndex, 'RSnoZeroLaser'] = noZeroFitParamsLaser[4]
db.at[dbIndex, 'mnoZeroLaser'] = mFixed
db.at[dbIndex, 'sigmaDnoZeroLaser'] = noZeroFitParamsLaser[1]
db.at[dbIndex, 'sigmaSnoZeroLaser'] = noZeroFitParamsLaser[2]
db.at[dbIndex, 'bandwidthTuningR2noZeroLaser'] = noZeroR2Laser
allFitParamsNoZeroLaser = [mFixed]
allFitParamsNoZeroLaser.extend(noZeroFitParamsLaser)
suppIndNoZeroLaser, prefBWNoZeroLaser = fitfuncs.extract_stats_from_fit(
allFitParamsNoZeroLaser, testBandsNoZero)
db.at[
dbIndex,
'fitSustainedSuppressionIndexNoZeroLaser'] = suppIndNoZeroLaser
db.at[dbIndex,
'fitSustainedPrefBandwidthNoZeroLaser'] = prefBWNoZeroLaser
testRespsNoLaser = fitfuncs.diff_gauss_form(
testBandsNoZero, *allFitParamsNoZero)
testRespsLaser = fitfuncs.diff_gauss_form(testBandsNoZero,
*allFitParamsNoZeroLaser)
laserDiffModel = testRespsLaser - testRespsNoLaser
peakIndModel = np.argmax(testRespsNoLaser)
db.at[dbIndex,
'fitPeakChangeFRNoZero'] = laserDiffModel[peakIndModel]
db.at[dbIndex, 'fitWNChangeFRNoZero'] = laserDiffModel[-1]
if len(filename) != 0:
celldatabase.save_hdf(db, dbFilename)