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: