def main():
import argparse
qid = os.environ.get('QUEUEID', None)
parser = argparse.ArgumentParser(
description='Fit cell from batch to model')
parser.add_argument('cell', type=str, help='Batch ID containing data')
parser.add_argument('batch', type=str, help='Cell ID to fit')
parser.add_argument('--wcg_n', type=int, help='wcg rank', default=2)
parser.add_argument('--fir_n', type=int, help='FIR ntaps', default=15)
parser.add_argument('--shuffle-phase',
action='store_true',
help='Shuffle phase')
parser.add_argument('--shuffle-stream',
action='store_true',
help='Shuffle stream')
parser.add_argument('model',
type=str,
help='Model name (ignored)',
nargs='?')
args = parser.parse_args()
if qid is not None:
db.update_job_start(qid)
nems.utils.progress_fun = db.update_job_tick
do_fit(args.batch, args.cell, args.wcg_n, args.fir_n, args.shuffle_phase,
args.shuffle_stream)
if qid is not None:
db.update_job_complete(qid)
print("Problem importing nems.db, can't update tQueue")
print(e)
db_exists = False
if __name__ == '__main__':
if 'QUEUEID' in os.environ:
queueid = os.environ['QUEUEID']
nems.utils.progress_fun = nd.update_job_tick
else:
queueid = 0
if queueid:
log.info("Starting QUEUEID={}".format(queueid))
nd.update_job_start(queueid)
# perform pupil fit
video_file = sys.argv[1]
modelname = sys.argv[2]
species, animal = sys.argv[3].split('_')
# load the keras model
project_dir = os.path.join(ps.ROOT_DIRECTORY, species+'/')
if (modelname == 'current') | (modelname == 'Current'):
if (animal != '') & (animal != 'None') & (animal != 'All') & (animal != None):
this_model_dir = 'animal_specific_fits/{}/'.format(animal)
default_date = os.listdir(project_dir + this_model_dir + 'default_trained_model/')[0]
name = os.listdir(project_dir + this_model_dir + 'default_trained_model/{0}'.format(default_date))[0]
modelpath = project_dir + this_model_dir + 'default_trained_model/{0}/{1}'.format(default_date, name)
else:
