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,
'{}.h5'.format('_'.join(d1mice)))
# dbpath = os.path.join(settings.FIGURES_DATA_PATH, studyparams.STUDY_NAME, '{}.h5'.format('temp'))
if os.path.isdir(
os.path.join(settings.FIGURES_DATA_PATH,
studyparams.STUDY_NAME)):
''' Test generating databases for multiple subjects. ''' from jaratoolbox import celldatabase reload(celldatabase) #subjects = ['band062','band072'] subjects = ['pinp019', 'pinp026'] celldb = celldatabase.generate_cell_database_from_subjects(subjects) #dd = celldatabase.generate_cell_database_from_subjects(subjects,removeBadCells=False)
SAVE = 1 # 1: Save database
startTime = time.time()
# -- Cluster your data --
CLUSTER_DATA = 0 # We don't generally run this code. We kept this for documentation
miceList = studyparams.MICE_LIST
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 --
celldb = celldatabase.generate_cell_database_from_subjects(miceList)
# Computing first the base stats and then the indices
if SAVE:
dbPath = os.path.join(settings.FIGURES_DATA_PATH,
studyparams.STUDY_NAME)
dbFilename = os.path.join(
dbPath, 'celldb_{}.h5'.format(studyparams.STUDY_NAME))
if os.path.isdir(dbPath):
celldatabase.save_hdf(celldb, dbFilename)
print('Saved database to {}'.format(dbFilename))
else:
print(
'{} does not exist. Please create this folder.'.format(dbPath))
def calculate_cell_locations(db):
pass
if __name__ == "__main__":
# -- Spike sort the data (code is left here for reference) --
'''
subject = 'testXXX'
inforecFile = os.path.join(settings.INFOREC_PATH,'{}_inforec.py'.format(subject))
clusteringObj = spikesorting.ClusterInforec(inforecFile)
clusteringObj.process_all_experiments()
'''
# -- Generate cell database (this function excludes clusters with isi>0.05, spikeQuality<2 --
celldb = celldatabase.generate_cell_database_from_subjects(studyparams.MICE_LIST)
# -- Compute the base stats and indices for each cell --
celldb = calculate_base_stats(celldb) # Calculated for all cells
celldb = calculate_indices(celldb) # Calculated for a selected subset of cells
dbPath = os.path.join(settings.FIGURES_DATA_PATH, studyparams.STUDY_NAME)
dbFilename = os.path.join(dbPath,'celldb_{}.h5'.format(studyparams.STUDY_NAME))
if os.path.isdir(dbPath):
celldatabase.save_hdf(celldb, dbFilename)
print('Saved database to {}'.format(dbFilename))
else:
print('{} does not exist. Please create this folder.'.format(dbPath))
# -- select which database to generate --
args = sys.argv[1:]
if len(args):
dbsToGenerate = np.zeros(2)
indsToGenerate = [int(x) for x in args]
dbsToGenerate[indsToGenerate] = 1
else:
print("Please select a database to generate (0: photoID, 1: inactivation)")
if dbsToGenerate[0]:
# cluster your data
chr2mice = studyparams.PV_CHR2_MICE + studyparams.SOM_CHR2_MICE
cluster_ephys_data.cluster_spike_data(chr2mice)
# creates a basic database and performs cluster rescue
basicDB = celldatabase.generate_cell_database_from_subjects(chr2mice)
basicDB = cluster_ephys_data.cluster_rescue(basicDB, isiThreshold=studyparams.ISI_THRESHOLD)
# creates and saves a database for photoidentified cells, computing first the base stats and then the indices
photoDBFilename = os.path.join(settings.FIGURES_DATA_PATH, figparams.STUDY_NAME,'photoidentification_cells.h5')
photoIDDB = database_photoidentification.photoID_base_stats(basicDB, filename = photoDBFilename)
photoIDDB = database_photoidentification.photoID_indices(basicDB, filename = photoDBFilename)
if dbsToGenerate[1]:
# cluster your data
archTmice = studyparams.PV_ARCHT_MICE + studyparams.SOM_ARCHT_MICE
cluster_ephys_data.cluster_spike_data(archTmice)
# creates and saves a database for inactivation
inactivationDBFilename = os.path.join(settings.FIGURES_DATA_PATH, figparams.STUDY_NAME,'inactivation_cells.h5')
basicDB = celldatabase.generate_cell_database_from_subjects(archTmice)