def calculate_cell_locations(
db,
filename=''): # to be filled after complete collecting histology data
pass
if __name__ == "__main__":
# Cluster your data
CLUSTER_DATA = 0 # We don't generally run this code. We kept this for documentation
# d1mice = studyparams.ASTR_D1_CHR2_MICE
d1mice = studyparams.SINGLE_MOUSE
if CLUSTER_DATA: # SPIKE SORTING
inforecFile = os.path.join(settings.INFOREC_PATH,
'{}_inforec.py'.format(d1mice))
clusteringObj = spikesorting.ClusterInforec(inforecFile)
clusteringObj.process_all_experiments()
pass
# Generate_cell_database_filters cells with the followings: isi < 0.05, spike quality > 2
basicDB = celldatabase.generate_cell_database_from_subjects(d1mice)
d1DBFilename = os.path.join(settings.FIGURES_DATA_PATH,
'{}_d1mice.h5'.format(studyparams.STUDY_NAME))
# Create and save a database, computing first the base stats and then the indices
firstDB = calculate_base_stats(basicDB, filename=d1DBFilename)
# bestCells = calculate_indices(firstDB, filename = d1DBFilename)
if SAVE:
dbpath = os.path.join(settings.FIGURES_DATA_PATH,
studyparams.STUDY_NAME,
STUDY_NAME = '2018thstr'
SAVE = 1
RECALCULATE_TETRODESTATS=0
FIND_TETRODES_WITH_NO_SPIKES=0
#We need access to ALL of the neurons from all animals that have been recorded from.
animals = ['pinp0'+s for s in map(str, [15, 16, 17, 18, 19, 20, 21, 25, 26, 29])] #22 not done yet
inforecFolder = '/home/nick/src/jaratest/common/inforecordings'
dbList = []
for animal in animals:
inforecFn = os.path.join(inforecFolder, '{}_inforec.py'.format(animal))
#If we need to regenerate the tetrodeStats files
if RECALCULATE_TETRODESTATS:
ci = spikesorting.ClusterInforec(inforecFn)
ci.process_all_experiments(recluster=False)
if FIND_TETRODES_WITH_NO_SPIKES:
ci = spikesorting.ClusterInforec(inforecFn)
ci.find_tetrodes_with_no_spikes()
continue
db = celldatabase.generate_cell_database(inforecFn)
#Calculate noiseburst response
#TODO: Instead of using alternative session types if 'noiseburst' is not available, should we
#just calculate the onset response to each sound stimulus type and see if any of them are significant?
noiseZscore = np.empty(len(db))
noisePval = np.empty(len(db))
baseRange = [-0.1,0]
def cluster_spike_data(subjects):
for subject in subjects:
inforec = '/home/jarauser/src/jaratest/common/inforecordings/{0}_inforec.py'.format(
subject)
ci = spikesorting.ClusterInforec(inforec)
ci.process_all_experiments()
import os
import pandas as pd
import subprocess
from jaratest.nick.database import dataloader_v2 as dataloader
import numpy as np
from scipy import stats
import imp
animal = 'adap041'
inforecFullPath = os.path.join(settings.INFOREC_PATH,
'{}_inforec.py'.format(animal))
CASE = 3
if CASE == 1:
# -- Cluster inforec and store stats, if have not already done so -- #
ci = spikesorting.ClusterInforec(inforecFullPath)
ci.cluster_all_experiments(maxClusters=6,
maxPossibleClusters=6,
recluster=False)
##############################################################
if CASE == 2:
databaseFullPath = os.path.join(settings.DATABASE_PATH,
'{}_database.h5'.format(animal))
key = 'head_fixed'
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
