def new_db(self, mice):
'''
Sets self.db to a new database built from a list of mice (uses their inforec files)
'''
dbs = []
for mouse in mice:
db = celldatabase.generate_cell_database(
self.inforecFolderFormat.format(mouse, mouse))
dbs.append(db)
self.db = pandas.concat(dbs, ignore_index=True)
from jaratoolbox import celldatabase
from jaratoolbox import spikesanalysis
import numpy as np
from numpy import inf
import pandas
from jaratest.nick.reports import pinp_report
from jaratest.nick.database import dataloader_v3 as dataloader
from jaratest.nick.stats import am_funcs
from scipy import stats
inforecFn = '/home/nick/src/jaratest/common/inforecordings/pinp024_inforec.py'
db = celldatabase.generate_cell_database(inforecFn)
#Calculate shape quality
allShapeQuality = np.empty(len(db))
for indCell, cell in db.iterrows():
peakAmplitudes = cell['clusterPeakAmplitudes']
spikeShapeSD = cell['clusterSpikeSD']
shapeQuality = abs(peakAmplitudes[1]/spikeShapeSD.mean())
allShapeQuality[indCell] = shapeQuality
allShapeQuality[allShapeQuality==inf]=0
db['shapeQuality'] = allShapeQuality
#Calculate noiseburst response
#TODO: Response to things other than noise as well??
noiseZscore = np.empty(len(db))
noisePval = np.empty(len(db))
baseRange = [-0.2,0]
responseRange = [0, 0.2]
for indCell, cell in db.iterrows():
spikeData, eventData = dataloader.get_session_ephys(cell, 'noiseburst')
animal = 'gosi008'
inforecFullPath = os.path.join(settings.INFOREC_PATH,
'{}_inforec.py'.format(animal))
databaseFullPath = os.path.join(settings.DATABASE_PATH, animal,
'{}_database.h5'.format(animal))
key = 'reward_change'
baseRange = [-0.1, 0] #Range of baseline period, in sec
respRange = [0, 0.1] #Range of sound response period, in sec
timeRange = [-0.5, 1]
movementTimeRange = [0.05, 0.15]
soundTriggerChannel = 0
if not os.path.isfile(databaseFullPath):
gosi008db = celldatabase.generate_cell_database(inforecFullPath)
gosi008db['animalName'] = animal
gosi008db.to_hdf(databaseFullPath, key=key)
else:
gosi008db = pd.read_hdf(databaseFullPath, key=key)
if not ('shapeQaulity' in gosi008db.columns):
#Waveform analysis
allShapeQuality = np.empty(len(gosi008db))
for indCell, cell in gosi008db.iterrows():
peakAmplitudes = cell['clusterPeakAmplitudes']
spikeShapeSD = cell['clusterSpikeSD']
shapeQuality = abs(peakAmplitudes[1] / spikeShapeSD.mean())
allShapeQuality[indCell] = shapeQuality
allShapeQuality[allShapeQuality == np.inf] = 0
from jaratoolbox import behavioranalysis
from jaratoolbox import loadbehavior
from jaratoolbox import loadopenephys
reload(loadopenephys)
from jaratoolbox import settings
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import numpy as np
from scipy import stats
import os
import pdb
subject = 'dapa003'
#Generate cell database
db = celldatabase.generate_cell_database(
'/home/jarauser/src/jaratest/common/inforecordings/dapa003_inforec.py')
#Calculate shape quality
allShapeQuality = np.empty(len(db))
for indCell, cell in db.iterrows():
peakAmplitudes = cell['clusterPeakAmplitudes']
spikeShapeSD = cell['clusterSpikeSD']
thisShapeQuality = abs(peakAmplitudes[1] / spikeShapeSD.mean())
allShapeQuality[indCell] = thisShapeQuality
allShapeQuality[allShapeQuality == np.inf] = 0
db['shapeQuality'] = allShapeQuality
#Select good cells
goodCells = db.query('isiViolations<0.02 and shapeQuality>2')
'''
#Cell 15
bestBehavIndex = None
octavesFromBest = None
return bestBehavIndex, octavesFromBest
if __name__=='__main__':
if len(sys.argv[1:]):
subjects = sys.argv[1:]
else:
subjects = subjects_info.PV_ARCHT_MICE + subjects_info.SOM_ARCHT_MICE
for subject in subjects:
inforec = '/home/jarauser/src/jaratest/common/inforecordings/{0}_inforec.py'.format(subject)
#inforec = os.path.join(settings.INFOREC_PATH,'{0}_inforec.py'.format(subject))
db = celldatabase.generate_cell_database(inforec)
# # NOTE: this way to remove bad cells seems too convoluted
# #db = db.drop(db[(db['isiViolations'] > 0.05) | (db['spikeShapeQuality'] < 2)].index).reset_index(drop=True)
# # --- Keep only good cells ---
# db = db[(db['isiViolations'] < 0.05) | (db['spikeShapeQuality'] > 2)]
# # --- compute statistics used for selection for each cell ---
# laserTestStatistic = np.empty(len(db))
# laserPVal = np.empty(len(db))
# laserTrainTestStatistic = np.empty(len(db))
# laserTrainPVal = np.empty(len(db))
# soundResponseTestStatistic = np.empty(len(db))
# soundResponsePVal = np.empty(len(db))
from jaratoolbox import celldatabase
import pandas
import subprocess
from jaratest.nick.analysis import pinp_report
reload(pinp_report)
from jaratest.nick.analysis.pinp_report import *
from numpy import inf
from jaratest.nick.stats import eventresponse
from jaratest.nick.stats import am_funcs
pinp016db = celldatabase.generate_cell_database('/home/nick/src/jaratest/nick/inforecordings/pinp016/pinp016_inforec.py')
#Waveform analysis
allShapeQuality = np.empty(len(pinp016db))
for indCell, cell in pinp016db.iterrows():
peakAmplitudes = cell['clusterPeakAmplitudes']
spikeShapeSD = cell['clusterSpikeSD']
shapeQuality = abs(peakAmplitudes[1]/spikeShapeSD.mean())
allShapeQuality[indCell] = shapeQuality
allShapeQuality[allShapeQuality==inf]=0
pinp016db['shapeQuality'] = allShapeQuality
#Noise response, new version
noiseZscore = np.empty(len(pinp016db))
noisePval = np.empty(len(pinp016db))
baseRange = [-0.2,0]
responseRange = [0, 0.2]
for indCell, cell in pinp016db.iterrows():
zScore, pVal = eventresponse.event_response_score(cell, 'noiseburst',
responseRange=responseRange,
baseRange=baseRange)
from jaratoolbox import celldatabase
from jaratoolbox import spikesanalysis
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
STUDY_NAME = '2018thstr'
SAVE = 1
inforecPath = '/home/nick/src/jaratest/common/inforecordings/pinp031_inforec.py'
dbPath = os.path.join(settings.FIGURES_DATA_PATH, STUDY_NAME,
'celldatabase_NBQX.h5')
dataframe = celldatabase.generate_cell_database(inforecPath)
dataframe['autoTagged'] = 0
dataframe['autoTagged'] = dataframe['autoTagged'].astype(int)
dataframe['survivedNBQX'] = 0
dataframe['washout'] = 0
for indIter, (indRow, dbRow) in enumerate(dataframe.iterrows()):
cell = ephyscore.Cell(dbRow)
try:
pulseData, _ = cell.load('laserpulse_pre')
except (IndexError, ValueError):
print "Cell has no laserpulse session, loading laser train session for pulse data"
try:
from jaratoolbox import behavioranalysis
from jaratoolbox import loadbehavior
from jaratoolbox import loadopenephys
reload(loadopenephys)
from jaratoolbox import settings
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import numpy as np
from scipy import stats
import os
import pdb
subject = 'dapa003'
#Generate cell database
db = celldatabase.generate_cell_database('/home/jarauser/src/jaratest/common/inforecordings/dapa003_inforec.py')
#Calculate shape quality
allShapeQuality = np.empty(len(db))
for indCell, cell in db.iterrows():
peakAmplitudes = cell['clusterPeakAmplitudes']
spikeShapeSD = cell['clusterSpikeSD']
thisShapeQuality = abs(peakAmplitudes[1]/spikeShapeSD.mean())
allShapeQuality[indCell] = thisShapeQuality
allShapeQuality[allShapeQuality==np.inf]=0
db['shapeQuality'] = allShapeQuality
#Select good cells
goodCells = db.query('isiViolations<0.02 and shapeQuality>2')
'''
import pandas as pd
from jaratoolbox import ephyscore
from jaratoolbox import celldatabase
reload(ephyscore)
reload(celldatabase)
# dbPath = '/home/nick/data/jarahubdata/figuresdata/2018thstr/celldatabase.h5'
# db = pd.read_hdf(dbPath, key='dataframe')
db = celldatabase.generate_cell_database(
'/home/nick/src/jaratest/common/inforecordings/pinp025_inforec.py')
dbRow = db.ix[0]
oneCell = ephyscore.Cell(dbRow)
amInd = oneCell.get_session_inds('am')
behavData = oneCell.load_behavior_by_index(amInd[0])
ephysData = oneCell.load_ephys_by_index(amInd[0])
allData = oneCell.load('am')
from jaratoolbox import celldatabase
from jaratoolbox import spikesorting
import band_ephys_analysis as bandan
reload(bandan)
CASE=1
if CASE==1:
#currentdb = pd.read_hdf('/home/jarauser/src/jaratest/anna/analysis/all_clusters.h5','database',index_col=0)
currentdb = pd.DataFrame()
db = pd.DataFrame()
subjects = ['band002','band003','band004','band005','band015','band016','band022','band023','band025','band026','band027','band028','band029','band030','band031','band033','band034']
for subject in subjects:
inforec = '/home/jarauser/src/jaratest/common/inforecordings/{0}_inforec.py'.format(subject)
db = db.append(celldatabase.generate_cell_database(inforec),ignore_index=True)
db = db.reindex(columns=np.concatenate((db.columns.values,['clusterQuality','bestFreq','bestBandIndex','laserResponse','nBandSpikes','suppressionIndex','facilitationIndex','preferedBandwidth','gaussFit'])))
db['suppressionIndex'] = db['suppressionIndex'].astype('object')
db['facilitationIndex'] = db['facilitationIndex'].astype('object')
db['preferedBandwidth'] = db['preferedBandwidth'].astype('object')
db['gaussFit'] = db['gaussFit'].astype('object')
for indCell, cell in db.iterrows():
peakAmplitudes = cell['clusterPeakAmplitudes']
spikeShapeSD = cell['clusterSpikeSD']
shapeQuality = abs(peakAmplitudes[1]/spikeShapeSD.mean())
db.set_value(indCell, 'clusterQuality', shapeQuality)
laserResponse = bandan.laser_response(cell)
db.set_value(indCell, 'laserResponse', laserResponse)
CASE = 1
if CASE == 1:
#currentdb = pd.read_hdf('/home/jarauser/src/jaratest/anna/analysis/all_clusters.h5','database',index_col=0)
currentdb = pd.DataFrame()
db = pd.DataFrame()
subjects = [
'band002', 'band003', 'band004', 'band005', 'band015', 'band016',
'band022', 'band023', 'band025', 'band026', 'band027', 'band028',
'band029', 'band030', 'band031', 'band033', 'band034'
]
for subject in subjects:
inforec = '/home/jarauser/src/jaratest/common/inforecordings/{0}_inforec.py'.format(
subject)
db = db.append(celldatabase.generate_cell_database(inforec),
ignore_index=True)
db = db.reindex(columns=np.concatenate((db.columns.values, [
'clusterQuality', 'bestFreq', 'bestBandIndex', 'laserResponse',
'nBandSpikes', 'suppressionIndex', 'facilitationIndex',
'preferedBandwidth', 'gaussFit'
])))
db['suppressionIndex'] = db['suppressionIndex'].astype('object')
db['facilitationIndex'] = db['facilitationIndex'].astype('object')
db['preferedBandwidth'] = db['preferedBandwidth'].astype('object')
db['gaussFit'] = db['gaussFit'].astype('object')
for indCell, cell in db.iterrows():
peakAmplitudes = cell['clusterPeakAmplitudes']
spikeShapeSD = cell['clusterSpikeSD']
shapeQuality = abs(peakAmplitudes[1] / spikeShapeSD.mean())
from jaratoolbox import celldatabase
from jaratoolbox import spikesorting
reload(celldatabase)
import bandwidths_analysis_v2 as bandan
reload(bandan)
#subjects = ['band022','band023']
currentdb = db = pd.read_hdf('/home/jarauser/src/jaratest/anna/analysis/all_clusters.h5','database',index_col=0)
db = pd.DataFrame()
subjects = ['band025']
for subject in subjects:
inforec = '/home/jarauser/src/jaratest/common/inforecordings/{0}_inforec.py'.format(subject)
ci = spikesorting.ClusterInforec(inforec)
ci.cluster_all_experiments()
db = db.append(celldatabase.generate_cell_database(inforec),ignore_index=True)
#db = db[db['isiViolations']<2.0]
db = db.reindex(columns=np.concatenate((db.columns.values,['clusterQuality','atBestFreq','bestFreq'])))
for indCell, cell in db.iterrows():
peakAmplitudes = cell['clusterPeakAmplitudes']
spikeShapeSD = cell['clusterSpikeSD']
shapeQuality = abs(peakAmplitudes[1]/spikeShapeSD.mean())
db.set_value(indCell, 'clusterQuality', shapeQuality)
bestBand, atBestFreq, bestFreq = bandan.best_band_index(cell)
db.set_value(indCell, 'atBestFreq', atBestFreq)
if atBestFreq:
db.set_value(indCell, 'bestFreq', bestFreq)
suppressionStats, laserResponse = bandan.suppression_stats(cell, bestBand)
if suppressionStats is not None:
db.set_value(indCell, 'laserResponse', laserResponse)
