def normalized_hist_dataframe(data_column,
bin_number=50,
output_dir='/var/tmp/'):
"""
Create a histogram using 2019AStrpi database with just the column name as an input
Args:
data_column (str): Name of the column to index
bin_number (int): Number of bins for the histogram
output_dir (str): Directory to save figure to
Returns:
fig: The figure object the hisotgram was plotted on
ax: The axis of the figure the histogram was plotted on
"""
db = celldatabase.load_hdf(
"/var/tmp/figuresdata/2019astrpi/direct_and_indirect_cells.h5")
# dbTuned = db.query(studyparams.TUNING_FILTER)
D1DB = db.query(studyparams.D1_CELLS)
nD1DB = db.query(studyparams.nD1_CELLS)
D1DB = D1DB.replace([np.inf, -np.inf], np.nan)
nD1DB = nD1DB.replace([np.inf, -np.inf], np.nan)
D1DB = D1DB[D1DB[data_column].notnull()]
nD1DB = nD1DB[nD1DB[data_column].notnull()]
D1Hist, D1bins = np.histogram(D1DB[data_column],
bins=bin_number,
density=True)
nD1Hist, nD1bins = np.histogram(nD1DB[data_column],
bins=bin_number,
density=True)
center = (D1bins[:-1] + D1bins[1:]) / 2
width = 0.7 * (D1bins[1] - D1bins[0])
D1Median = np.median(D1DB[data_column])
nD1Median = np.median(nD1DB[data_column])
fig = plt.gcf()
fig.clf()
figFilename = "{}".format(data_column) # Do not include extension
figFormat = 'png' # 'pdf' or 'svg'
figSize = [5, 5]
ax = fig.add_subplot()
ax.bar(center, D1Hist, width=width, align='center', label='D1', alpha=0.5)
ax.bar(center,
nD1Hist,
width=width,
align='center',
label='nD1',
alpha=0.5)
ax.legend()
ax.set_xlabel('{} value'.format(data_column))
ax.set_ylabel('Frequency')
ax.set_title(data_column)
ymin, ymax = ax.get_ybound()
ax.vlines(D1Median, ymin, ymax, color="Green")
ax.vlines(nD1Median, ymin, ymax, color="Red")
extraplots.save_figure(figFilename, figFormat, figSize, output_dir, 'w')
plt.show()
return fig, ax
def merge_dataframes(df1, df2):
"""
Takes two dataframes and concatenates them so we can process one mouse at a time
instead of having to regenerate an entire database when we add on a new mouse
Args:
df1 (list): List of strings containing full paths to dataframe(s).
df2 (string): Full path to second dataframe to which the list of dataframes will be appended to the end of
Returns:
new_df (pandas.DataFrame): Two given dataframes appended through index value
"""
df2 = celldatabase.load_hdf(df2)
for frame in df1:
appendedFrame = celldatabase.load_hdf(frame)
df2 = pd.concat([df2, appendedFrame],
axis=0,
ignore_index=True,
sort=False)
return df2
from __future__ import division
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from jaratoolbox import settings
from jaratoolbox import celldatabase
import studyparams
reload(studyparams)
dbPath = os.path.join(settings.FIGURES_DATA_PATH, studyparams.STUDY_NAME)
dbFilename = os.path.join(
dbPath, 'signRespCellsdb_{}.h5'.format(studyparams.STUDY_NAME))
# -- Load the database of responsive cells --
signRespCells = celldatabase.load_hdf(dbFilename)
suppressedCells = []
for indRow, dbRow in signRespCells.iterrows():
highBaseA = signRespCells['meanBaseHighA'][indRow]
highEvokedA = signRespCells['meanEvokedHighA'][indRow]
midBaseA = signRespCells['meanBaseMidA'][indRow]
midEvokedA = signRespCells['meanEvokedMidA'][indRow]
lowBaseA = signRespCells['meanBaseLowA'][indRow]
lowEvokedA = signRespCells['meanEvokedLowA'][indRow]
if signRespCells['mostRespFreqPValueOddStdA'][indRow] == signRespCells[
'pValHighFRA'][indRow]:
if highBaseA > highEvokedA:
suppressedCells.append(signRespCells.iloc[indRow])
#dbFilenameNew = os.path.join(settings.DATABASE_PATH,'{0}_new.h5'.format(subject))
dbFilenameNew = '/home/jarauser/data/databases/tmp/{}_new.h5'.format(
subject)
celldatabase.save_hdf(db, dbFilenameNew)
print('Saved database to: {}'.format(dbFilenameNew))
# -- To load the HDF5 --
# df = celldatabase.load_hdf('/tmp/band045_new.h5')
#allSubjects = subjects_info.PV_ARCHT_MICE + subjects_info.SOM_ARCHT_MICE
#allSubjects = ['dapa012', 'dapa013', 'dapa014', 'dapa015']
allSubjects = []
fulldb = pd.DataFrame()
for subject in allSubjects:
db = celldatabase.load_hdf('/tmp/{}_new.h5'.format(subject))
fulldb = fulldb.append(db, ignore_index=True)
fulldbFilename = os.path.join(settings.DATABASE_PATH, 'dapa_cells.h5')
celldatabase.save_hdf(fulldb, fulldbFilename)
####################################################################################
####################################################################################
####################################################################################
# import os, sys
# from jaratoolbox import celldatabase
# from jaratoolbox import extraplots
# from jaratoolbox import spikesanalysis
# from jaratoolbox import spikesorting
# from jaratoolbox import behavioranalysis
# from jaratoolbox import loadbehavior
if not os.path.exists(dataDir):
os.mkdir(dataDir)
####################################################################################
scriptFullPath = os.path.realpath(__file__)
brainAreas = ['rightAC', 'rightAStr']
maxZThreshold = 3
alphaLevel = 0.05
movementWindow = [0.0, 0.3] #[0.05, 0.15] # in seconds
removeSideIn = True
###################################################################################
#dbKey = 'reward_change'
dbFolder = os.path.join(settings.FIGURES_DATA_PATH, STUDY_NAME)
celldbPath = os.path.join(dbFolder, 'rc_database.h5')
celldb = celldatabase.load_hdf(celldbPath)
for brainArea in brainAreas:
#goodQualCells = celldb.query("keepAfterDupTest==1 and brainArea=='{}'".format(brainArea))
goodQualCells = celldb.query(
"keepAfterDupTest==1 and cellInTargetArea==1 and brainArea=='{}'".
format(brainArea))
if removeSideIn:
movementModI = goodQualCells['movementModI_{}_removedsidein'.format(
movementWindow)]
movementModS = goodQualCells['movementModS_{}_removedsidein'.format(
movementWindow)]
encodeMv = (
goodQualCells['movementSelective_moredif_Mv'] +
goodQualCells['movementSelective_samedif_MvSd']).astype(bool)
Plotting histogram of number of cells vs expectation index along with which of those are significantly responsive for each of the three frequencies and for both sessions for five auditory regions (primary AC(0-1300um), ventral AC(1300-1800um), TeA(1800-2175um), Ectorhinal(2175-2675um), and Perirhinal(2675-3050um)).
"""
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from jaratoolbox import settings
from jaratoolbox import celldatabase
import studyparams
reload(studyparams)
dbPath = os.path.join(settings.FIGURES_DATA_PATH, studyparams.STUDY_NAME)
# -- Loading responsive cell database --
dbFilename = os.path.join(dbPath,'responsivedb_{}.h5'.format(studyparams.STUDY_NAME))
responsivedb = celldatabase.load_hdf(dbFilename)
# -- Loading suppressed cell database --
dbFilename = os.path.join(dbPath,'suppressedRespCellsdb_{}.h5'.format(studyparams.STUDY_NAME))
suppresseddb = celldatabase.load_hdf(dbFilename)
def expectation_index_hist_by_region(database, region, title):
bins = 30
plt.figure(figsize=(10,4.5)).suptitle(region[0] + ' ' + title, fontsize=9, y=1.01)
ax = plt.subplot2grid((2,3), (0,0))
area = database.query(region[1])
# -- Reindexing the responsive database --
area = area.reset_index(drop=True)
# -- Ascending --
highFreqRespCellsA = []
import numpy as np
from jaratoolbox import celldatabase
from jaratoolbox import ephyscore
from jaratoolbox import spikesanalysis
import pandas as pd
import figparams #TODO: Remove this once we move plotting code to a figure file
import matplotlib.pyplot as plt
from scipy import stats
db = celldatabase.load_hdf('/tmp/database_with_normResponse.h5')
db['noiseOnsetivityIndex'] = (
db['sustainedRateNoise'] -
db['onsetRateNoise']) / (db['sustainedRateNoise'] + db['onsetRateNoise'])
plt.clf()
def jitter(arr, frac):
jitter = (np.random.random(len(arr)) - 0.5) * 2 * frac
jitteredArr = arr + jitter
return jitteredArr
def medline(ax, yval, midline, width, color='k', linewidth=3):
start = midline - (width / 2)
end = midline + (width / 2)
ax.plot([start, end], [yval, yval], color=color, lw=linewidth)
goodFit = db.query('rsquaredFit > 0.04')
import numpy as np
import pandas as pd
import matplotlib.gridspec as gridspec
from scipy import stats
import studyparams
from jaratoolbox import behavioranalysis
from jaratoolbox import celldatabase
from jaratoolbox import ephyscore
from jaratoolbox import extraplots
from jaratoolbox import spikesanalysis
import database_generation_funcs as funcs
import figparams
from extras import figure_R2_comparison as histDraw
#%% Calculations of various data to plot
db = celldatabase.load_hdf("/var/tmp/figuresdata/2019astrpi/ttDBR2.h5")
D1DB = db.query(studyparams.D1_CELLS)
nD1DB = db.query(studyparams.nD1_CELLS)
emptyD1 = np.zeros(len(D1DB))
emptyND1 = np.zeros(len(nD1DB))
D1Results = pd.DataFrame({
"noiseburstBase": emptyD1,
"noiseburstResp": emptyD1,
"tuningTestBase": emptyD1,
"tuningTestResp": emptyD1,
"tuningCurveBase": emptyD1,
"tuningCurveResp": emptyD1,
"amBase": emptyD1,
"amResp": emptyD1,
})
from jaratoolbox import behavioranalysis
from jaratoolbox import spikesanalysis
from jaratoolbox import celldatabase
from scipy import stats
import pandas as pd
import figparams
import studyparams
FIGNAME = 'figure_am'
d1mice = studyparams.ASTR_D1_CHR2_MICE
nameDB = '_'.join(d1mice) + '.h5'
# pathtoDB = os.path.join(settings.FIGURES_DATA_PATH, studyparams.STUDY_NAME, nameDB)
pathtoDB = os.path.join(settings.FIGURES_DATA_PATH, studyparams.STUDY_NAME,
'{}.h5'.format('temp'))
# os.path.join(studyparams.PATH_TO_TEST,nameDB)
db = celldatabase.load_hdf(pathtoDB)
outputDir = '/var/tmp/figuresdata/2019astrpi/output'
# # db = pd.read_hdf(dbPath, key='dataframe')
# db = celldatabase.load_hdf(dbPath)
# # db = db.query("subject=='pinp015'")
# # goodLaser = db.query('pulsePval<0.05 and pulseZscore>0 and trainRatio>0.8')
# # goodLaser = db[db['taggedCond']==0]
# goodISI = db.query('isiViolations<0.02 or modifiedISI<0.02')
# goodShape = goodISI.query('spikeShapeQuality > 2')
# goodLaser = goodShape.query("autoTagged==1 and subject != 'pinp018'")
# # goodLaser = goodShape.query("autoTagged==1 and subject != 'pinp018' and summaryPulseLatency < 0.01")
# goodNSpikes = goodLaser.query('nSpikes>2000')
# goodPulseLatency = goodNSpikes.query('summaryPulseLatency<0.006')
import numpy as np
from jaratoolbox import ephyscore
from jaratoolbox import celldatabase
db = celldatabase.load_hdf('/tmp/inactivation_cells.h5')
import time
ticTime = time.time()
anArray = np.empty(len(db))
for indRow, (dbIndex, dbRow) in enumerate(db.iterrows()):
cellObj = ephyscore.Cell(dbRow)
try:
laserEphysData, noBehav = cellObj.load('lasernoisebursts')
except IndexError:
pass
anArray[indRow] = 0
db['trash'] = anArray
print 'Elapsed Time: ' + str(time.time()-ticTime)
ticTime = time.time()
for indRow, (dbIndex, dbRow) in enumerate(db.iterrows()):
cellObj = ephyscore.Cell(dbRow)
try:
laserEphysData, noBehav = cellObj.load('lasernoisebursts')
from jaratoolbox import ephyscore
from jaratoolbox import celldatabase
db = celldatabase.load_hdf('/home/jarauser/data/database/inactivation_cells2.h5')
import time
ticTime = time.time()
for indRow, (dbIndex, dbRow) in enumerate(db.iterrows()):
cellObj = ephyscore.Cell(dbRow)
try:
laserEphysData, noBehav = cellObj.load('laserPulse')
except IndexError:
pass
try:
bandEphysData, bandBehavData = cellObj.load('bandwidth')
except IndexError:
pass
print 'Elapsed Time: ' + str(time.time()-ticTime)
ticTime = time.time()
for indRow, (dbIndex, dbRow) in enumerate(db.iterrows()):
cellObj = ephyscore.Cell(dbRow)
try:
laserEphysData, noBehav = cellObj.load('laserPulse')
except IndexError:
pass
for indRow, (dbIndex, dbRow) in enumerate(db.iterrows()):
fullDb = celldatabase.generate_cell_database(inforecFn)
fullDbFullPath = os.path.join(
dbFolder, '{}_database_all_clusters.h5'.format(animal))
if SAVE_FULL_DB:
print 'Saving database to {}'.format(fullDbFullPath)
#fullDb.to_hdf(fullDbFullPath, key=dbKey)
celldatabase.save_hdf(fullDb, fullDbFullPath)
if CASE == 2:
# -- check behavior criteria, cell depth is inside target region, consistent firing during behavior(2afc) session, generate a good quality cell db, save only subset of good qual cells on disk -- #
for animal in animals:
fullDbFullPath = os.path.join(
dbFolder, '{}_database_all_clusters.h5'.format(animal))
#fullDb = pd.read_hdf(fullDbFullPath, key=dbKey)
fullDb = celldatabase.load_hdf(fullDbFullPath)
# -- check if cell meets behavior criteria -- #
print 'Checking behavior criteria'
metBehavCriteria = behavCriteria.ensure_behav_criteria_celldb(
fullDb,
strict=useStrictBehavCriterionWhenSaving,
sessiontype='behavior',
minBlockNum=minBlockNum,
minTrialNumEndBlock=minTrialNumEndBlock,
performanceThreshold=performanceThreshold)
fullDb['metBehavCriteria'] = metBehavCriteria
# -- check if cell depth is inside target region range -- #
print 'Checking whether cell in target range'
actualDepthEachCell, inTargetArea = inTargetRangeCheck.celldb_in_target_range_check(
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)
from jaratoolbox import spikesorting from jaratoolbox import ephyscore from jaratoolbox import extraplots from jaratoolbox import colorpalette as cp from jaratoolbox import settings from scipy import stats from scipy import signal STUDY_NAME = '2018thstr' SAVE=0 # dbPath = os.path.join(settings.FIGURES_DATA_PATH, STUDY_NAME, 'celldatabase_ALLCELLS_MODIFIED_CLU.h5') # dbPath = os.path.join(settings.FIGURES_DATA_PATH, STUDY_NAME, 'celldatabase_ALLCELLS_MODIFIED_CLU_newtagged.h5') dbPath = os.path.join(settings.FIGURES_DATA_PATH, STUDY_NAME, 'celldatabase_calculated_columns.h5') # database = pd.read_hdf(dbPath, key='dataframe') database = celldatabase.load_hdf(dbPath) # dbPath = os.path.join(settings.FIGURES_DATA_PATH, STUDY_NAME, 'celldatabase_NBQX.h5') # database = celldatabase.load_hdf(dbPath) ### Init new database columns ### database['summaryPulsePval'] = 1 # Whether or not the cell responds to the laser pulse database['summaryTrainResponses'] = np.nan # How many of the pulses in the train the cell responds to. database['summaryTrainLatency'] = np.nan database['summaryPulseLatency'] = np.nan #DEBUG import ipdb #Neurons with 4 laser pulse responses that are not tagged # database = database.loc[[136, 1034]]
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
# save db as h5
dbFilenameNew = os.path.join(settings.DATABASE_PATH,'{0}_clusters.h5'.format(subject))
celldatabase.save_hdf(db, dbFilenameNew)
print('Saved database to: {}'.format(dbFilenameNew))
fulldb = pd.DataFrame()
for subject in allSubjects:
dbFilename = os.path.join(settings.DATABASE_PATH,'{0}_clusters.h5'.format(subject))
db = celldatabase.load_hdf(dbFilename)
fulldb = fulldb.append(db, ignore_index=True)
if subType == 'chr2':
filename = 'photoidentification_cells.h5'
elif subType == 'archt':
filename = 'inactivation_cells.h5'
fulldbFilename = os.path.join(settings.DATABASE_PATH,filename)
celldatabase.save_hdf(fulldb, fulldbFilename)
import os
import numpy as np
import scipy.stats
import matplotlib.pyplot as plt
import matplotlib.colors
from jaratoolbox import celldatabase
from jaratoolbox import extraplots
from jaratoolbox import settings
FONTSIZE = 14
dbFilename = os.path.join(settings.FIGURES_DATA_PATH, '2018acsup',
'photoidentification_cells.h5')
#db = celldatabase.load_hdf(dbFilename)
db = celldatabase.load_hdf('/tmp/photoidentification_cells_0.h5')
bestCells = db.query('isiViolations<0.02 or modifiedISI<0.02')
#bestCells = bestCells.query('spikeShapeQuality>2.5 and tuningFitR2>0.1 and octavesFromPrefFreq<0.3 and sustainedSoundResponsePVal<0.05')
bestCells = bestCells.query(
'spikeShapeQuality>2.5 and tuningFitR2>0.1 and octavesFromPrefFreq<0.3 and onsetSoundResponsePVal<0.05'
)
LASER_RESPONSE_PVAL = 0.001 #want to be EXTRA sure not to include false positives
EXC_LASER_RESPONSE_PVAL = 0.5 #for selecting putative excitatory cells NOT responsive to laser
EXC_SPIKE_WIDTH = 0.0004
PV_CHR2_MICE = ['band004', 'band026', 'band032', 'band033']
SOM_CHR2_MICE = [
'band005', 'band015', 'band016', 'band027', 'band028', 'band029',
'band030', 'band031', 'band034', 'band037', 'band038', 'band044',
#databaseFullPath = os.path.join(settings.DATABASE_PATH, '{}_database.h5'.format(mouseName))
databaseFullPath = os.path.join(settings.DATABASE_PATH, NEW_DATABASE_FOLDER, '{}_database.h5'.format(mouseName))
outFilename = '/var/tmp/{}_reward_change_modulation_{}.h5'.format(mouseName,processedDate)
if os.path.isfile(outFilename):
print 'Analysis for this mouse was saved before.'
processed = pd.read_hdf(outFilename, key='reward_change')
processedSessions = np.unique(processed['date'])
else:
processed = pd.DataFrame({'date':[]})
processedSessions = []
modulationDict = {'subject': [],
'date': [],
'tetrode': [],
'cluster': []}
cellDb = celldatabase.load_hdf(databaseFullPath) #pd.read_hdf(databaseFullPath, key=dbKey)
newTime=time.time(); print 'Elapsed time: {0:0.2f} ALLCELLS'.format(newTime-zeroTime); zeroTime=newTime; sys.stdout.flush() ### PROFILER
# Process each mouse in chunks
chunkSize = 20
#chunks = [allcells.cellDB[ind:ind+chunkSize] for ind in range(0, len(allcells.cellDB), chunkSize)]
chunks = [cellDb.iloc[ind:ind+chunkSize] for ind in range(0, len(cellDb), chunkSize)]
for chunk in chunks:
modulationDict = {'subject': [],
'date': [],
'tetrode': [],
'cluster': []}
import pandas as pd
import numpy as np
from jaratoolbox import celldatabase
db = pd.DataFrame()
rand = np.random.random(100)
goodColumn = []
stupidColumn = []
for num in rand:
if num > 0.5:
goodColumn.append(np.array([0.0,1.0]))
stupidColumn.append([0.0,1.0])
else:
goodColumn.append(np.full(2, np.nan))
stupidColumn.append([np.nan, np.nan])
db['goodColumn'] = goodColumn
db['stupidColumn'] = stupidColumn
celldatabase.save_hdf(db, '/tmp/test_db.h5')
loadDB = celldatabase.load_hdf('/tmp/test_db.h5')
# '''
#dbFilenameNew = os.path.join(settings.DATABASE_PATH,'{0}_new.h5'.format(subject))
dbFilenameNew = '/home/jarauser/data/databases/tmp/{}_new.h5'.format(subject)
celldatabase.save_hdf(db, dbFilenameNew)
print('Saved database to: {}'.format(dbFilenameNew))
# -- To load the HDF5 --
# df = celldatabase.load_hdf('/tmp/band045_new.h5')
#allSubjects = subjects_info.PV_ARCHT_MICE + subjects_info.SOM_ARCHT_MICE
#allSubjects = ['dapa012', 'dapa013', 'dapa014', 'dapa015']
allSubjects = []
fulldb = pd.DataFrame()
for subject in allSubjects:
db = celldatabase.load_hdf('/tmp/{}_new.h5'.format(subject))
fulldb = fulldb.append(db, ignore_index=True)
fulldbFilename = os.path.join(settings.DATABASE_PATH,'dapa_cells.h5')
celldatabase.save_hdf(fulldb, fulldbFilename)
####################################################################################
####################################################################################
####################################################################################
# import os, sys
# from jaratoolbox import celldatabase
import os
import importlib
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from jaratoolbox import settings
from jaratoolbox import celldatabase
import studyparams
importlib.reload(studyparams)
#dbPath = os.path.join(settings.FIGURES_DATA_PATH, studyparams.STUDY_NAME)
dbPath = settings.FIGURES_DATA_PATH
dbFilename = os.path.join(dbPath,
'responsivedb_{}.h5'.format(studyparams.STUDY_NAME))
# -- Load the database of responsive cells --
responsivedb = celldatabase.load_hdf(dbFilename)
dbFilenamex = os.path.join(dbPath,
'celldb_{}.h5'.format(studyparams.STUDY_NAME))
# -- Load the database of cells --
celldb = celldatabase.load_hdf(dbFilenamex)
def comparing_odd_std_significance(responsivedb):
"""
Counts how many of the responsive cells show a significant difference between oddball and standard firing rates for their most responsive frequency.
Inputs:
responsivedb: Cell database that contains only the cells that are sound responsive and that allows for loading of ephys and behavior data and calculated base stats and indices.
Outputs:
from jaratoolbox import ephyscore
from jaratoolbox import celldatabase
db = celldatabase.load_hdf(
'/home/jarauser/data/database/inactivation_cells2.h5')
import time
ticTime = time.time()
for indRow, (dbIndex, dbRow) in enumerate(db.iterrows()):
cellObj = ephyscore.Cell(dbRow)
try:
laserEphysData, noBehav = cellObj.load('laserPulse')
except IndexError:
pass
try:
bandEphysData, bandBehavData = cellObj.load('bandwidth')
except IndexError:
pass
print 'Elapsed Time: ' + str(time.time() - ticTime)
ticTime = time.time()
for indRow, (dbIndex, dbRow) in enumerate(db.iterrows()):
cellObj = ephyscore.Cell(dbRow)
try:
laserEphysData, noBehav = cellObj.load('laserPulse')
except IndexError:
pass
for indRow, (dbIndex, dbRow) in enumerate(db.iterrows()):
import numpy as np
from jaratoolbox import celldatabase
from jaratoolbox import ephyscore
from jaratoolbox import spikesanalysis
import pandas as pd
import figparams
import matplotlib.pyplot as plt
from scipy import stats
db = celldatabase.load_hdf('/tmp/database_with_cf_onsetivity.h5')
db['cfOnsetivityIndex'] = (db['sustainedRateCF'] - db['onsetRateCF']) / (
db['sustainedRateCF'] + db['onsetRateCF'])
goodFit = db.query('rsquaredFit > 0.04')
plt.clf()
def jitter(arr, frac):
jitter = (np.random.random(len(arr)) - 0.5) * 2 * frac
jitteredArr = arr + jitter
return jitteredArr
def medline(ax, yval, midline, width, color='k', linewidth=3):
start = midline - (width / 2)
end = midline + (width / 2)
ax.plot([start, end], [yval, yval], color=color, lw=linewidth)
def medline(ax, yval, midline, width, color='k', linewidth=3):
start = midline-(width/2)
end = midline+(width/2)
ax.plot([start, end], [yval, yval], color=color, lw=linewidth)
# ==========================parameters==========================================
FIGNAME = 'figure_frequency_tuning'
d1mice = studyparams.ASTR_D1_CHR2_MICE
nameDB = studyparams.DATABASE_NAME + '.h5'
pathtoDB = os.path.join(settings.FIGURES_DATA_PATH, studyparams.STUDY_NAME, nameDB)
# pathtoDB = os.path.join(settings.FIGURES_DATA_PATH, studyparams.STUDY_NAME, '{}.h5'.format('ttDBR2'))
# os.path.join(studyparams.PATH_TO_TEST,nameDB)
db = celldatabase.load_hdf(pathtoDB)
db = db.query(studyparams.TUNING_FILTER)
exampleDataPath = os.path.join(settings.FIGURES_DATA_PATH, studyparams.STUDY_NAME, FIGNAME, 'data_freq_tuning_examples.npz')
# =======================================================================
exData = np.load(exampleDataPath) # npz data generated from generate_example_freq_tuning
np.random.seed(8)
D1 = db.query(studyparams.D1_CELLS) # laser activation response
nD1 = db.query(studyparams.nD1_CELLS) # no laser repsonse or laser inactivation response
PANELS = [1, 1, 1, 1, 1, 1, 1] # Plot panel i if PANELS[i]==1
SAVE_FIGURE = 1
outputDir = figparams.FIGURE_OUTPUT_DIR
import os
import numpy as np
import copy
from jaratoolbox import celldatabase
from jaratoolbox import ephyscore
from jaratoolbox import spikesanalysis
from jaratoolbox import behavioranalysis
from jaratoolbox import settings
db = celldatabase.load_hdf(
'/mnt/jarahubdata/figuresdata/2018acsup/photoidentification_cells.h5')
bestCells = db.query('isiViolations<0.02 or modifiedISI<0.02')
bestCells = bestCells.query(
'spikeShapeQuality>2.5 and sustainedSoundResponsePVal<0.05')
LASER_RESPONSE_PVAL = 0.001 # want to be EXTRA sure not to include false positives
EXC_LASER_RESPONSE_PVAL = 0.5 # for selecting putative excitatory cells NOT responsive to laser
EXC_SPIKE_WIDTH = 0.0004
PV_CHR2_MICE = ['band004', 'band026', 'band032', 'band033']
SOM_CHR2_MICE = [
'band005', 'band015', 'band016', 'band027', 'band028', 'band029',
'band030', 'band031', 'band034', 'band037', 'band038', 'band044',
'band045', 'band054', 'band059', 'band060'
]
PV_CELLS = bestCells.query(
'laserPVal<{} and laserUStat>0 and subject=={}'.format(
LASER_RESPONSE_PVAL, PV_CHR2_MICE))
from jaratoolbox import celldatabase
from jaratoolbox import spikesorting # For clustering
from jaratoolbox import spikesanalysis
from jaratoolbox import settings
from jaratoolbox import behavioranalysis
# import database_generation_funcs as funcs
from jaratoolbox import ephyscore
import studyparams
reload(studyparams)
dbPath = os.path.join(settings.FIGURES_DATA_PATH, studyparams.STUDY_NAME)
dbFilename = os.path.join(dbPath,
'celldb_{}.h5'.format(studyparams.STUDY_NAME))
# -- Load the database of cells --
celldb = celldatabase.load_hdf(dbFilename)
'''
def calculate_pvalues(celldb):
Generates p-Values between oddball and standard firing rates during sound presentation and between the high frequency sound 100ms before sound presentation and during sound presentation.
Inputs:
celldb: Database that allows for loading of ephys and behavior data.
'''
ratioFR = 1.01
timeRange = [-0.1, 0.3] # seconds
binWidth = 0.01
responseRange = [0, 0.1] # sec
spikeTimesFromEventOnset=spikeTimesFromEventOnset,
trialIndexForEachSpike=trialIndexForEachSpike,
indexLimitsEachTrial=indexLimitsEachTrial,
timeRange=timeRange,
alignment=alignment)
print 'Saved event-locked data to {0}'.format(newOutputFullPath)
sessiontype = 'behavior' #2afc behavior
recalculate = False
timeRange = [
-0.5, 0.5
] # In seconds. Time range for to calculate spikeTimesFromEventOnset, this time window has to span all the possible count time ranges for generating spike count matrix
newOutputDir = '/home/languo/data/ephys/evlock_spktimes'
oldOutputDir = '/var/tmp/processed_data'
#dbKey = 'reward_change'
NEW_DATABASE_FOLDER = 'new_celldb'
databaseFullPath = os.path.join(settings.DATABASE_PATH, NEW_DATABASE_FOLDER,
'rc_database.h5')
cellDb = celldatabase.load_hdf(databaseFullPath)
alignments = ['sound', 'center-out', 'side-in']
for ind, cell in cellDb.iterrows():
cellObj = ephyscore.Cell(cell)
for alignment in alignments:
save_evlock_spktimes_cell(cellObj, sessiontype, alignment, timeRange,
recalculate, oldOutputDir, newOutputDir)
import numpy as np
from jaratoolbox import celldatabase
from jaratoolbox import ephyscore
from jaratoolbox import spikesanalysis
import pandas as pd
import figparams #TODO: Remove this once we move plotting code to a figure file
import matplotlib.pyplot as plt
from scipy import stats
PLOT=1
SAVE = 1
dbPath = '/mnt/jarahubdata/figuresdata/2018thstr/celldatabase_calculated_columns.h5'
database = celldatabase.load_hdf(dbPath)
# goodISI = database.query('isiViolations<0.02 or modifiedISI<0.02')
# goodShape = goodISI.query('spikeShapeQuality > 2')
# goodLaser = goodShape.query("autoTagged==1 and subject != 'pinp018'")
# goodNSpikes = goodLaser.query('nSpikes>2000')
# db = goodNSpikes
plt.clf()
# ax0 = plt.subplot(121)
ax0 = plt.subplot(111)
# ax1 = plt.subplot(122)
mono = []
maxSpikes = []
import numpy as np
import pandas as pd
from jaratoolbox import celldatabase
from matplotlib import pyplot as plt
databaseFn = '/tmp/database_with_pulse_responses.h5'
databaseNBQXFn = '/tmp/nbqx_database_with_pulse_responses.h5'
database = celldatabase.load_hdf(databaseFn)
databaseNBQX = celldatabase.load_hdf(databaseNBQXFn)
def jitter(arr, frac):
jitter = (np.random.random(len(arr))-0.5)*2*frac
jitteredArr = arr + jitter
return jitteredArr
plt.clf()
fig = plt.gcf()
# ax = fig.add_subplot(111, projection='3d')
ax0 = fig.add_subplot(121)
ax1 = fig.add_subplot(122)
axes = [ax0, ax1]
for ax in axes:
ax.hold(1)
# lasercells = database.query('isiViolations<0.02 and spikeShapeQuality>2 and autoTagged==1')
# allcells = database.query('isiViolations<0.02 and spikeShapeQuality>2 and summaryPulsePval<0.05')
#Accept as tagged only cells with latency less than 10ms
CASE=0