jgrewe/chirp_probing
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add argparse for easier using

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Jan Grewe 2020-09-30 10:16:34 +02:00
parent 59153c860e
commit 2665e166f9
1 changed files with 26 additions and 7 deletions
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punit_responses.py
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@ -1,5 +1,6 @@
import numpy as np
import nixio as nix
import argparse
import os
from numpy.core.fromnumeric import repeat
from traitlets.traitlets import Instance
@ -183,11 +184,11 @@ def simulate_responses(stimulus_params, model_params, repeats=10, deltaf=20):
print("\n")
def simulate_cell(cell_id, models):
deltafs = [-200, -100, -50, -20, -10, -5, 5, 10, 20, 50, 100, 200] # Hz, difference frequency between self and other
chirp_sizes = [40, 60, 100]
def simulate_cell(cell_id, models, args):
deltafs = args.deltafs # Hz, difference frequency between self and other
chirp_sizes = args.chirpsizes # Hz, the chirp size, i.e. the frequency excursion
stimulus_params = { "eodfs": {"self": 0.0, "other": 0.0}, # eod frequency in Hz, to be overwritten
"contrasts": [20, 10, 5, 2.5, 1.25, 0.625, 0.3125],
"contrasts": args.contrasts,
"chirp_size": 100, # Hz, frequency excursion
"chirp_duration": 0.015, # s, chirp duration
"chirp_amplitude_dip": 0.05, # %, amplitude drop during chirp
@ -212,14 +213,32 @@ def simulate_cell(cell_id, models):
stimulus_params["duration"] - stimulus_params["chirp_duration"],
1./stimulus_params["chirp_frequency"])
stimulus_params["chirp_times"] = chirp_times
simulate_responses(stimulus_params, model_params, repeats=25, deltaf=deltaf)
simulate_responses(stimulus_params, model_params, repeats=args.trials, deltaf=deltaf)
def main():
parser = argparse.ArgumentParser(description="Simulate P-unit responses using the model parameters from the models.csv file. Calling it without any arguments works with the defaults, may need some time.")
parser.add_argument("-n", "--number", type=int, default=20, help="Number of simulated neurons. Randomly chosen from model list. Defaults to 20")
parser.add_argument("-t", "--trials", type=int, default=25, help="Number of stimulus repetitions, trials. Defaults to 25")
parser.add_argument("-dfs", "--deltafs", type=float, nargs="+", default=[-200, -100, -50, -20, -10, -5, 5, 10, 20, 50, 100, 200],
help="List of difference frequencies. Defaults to [-200, -100, -50, -20, -10, -5, 5, 10, 20, 50, 100, 200]")
parser.add_argument("-cs", "--chirpsizes", type=float, nargs="+", default=[40, 60, 100],
help="List of chirp sizes. Defaults to [40, 60, 100]")
parser.add_argument("-ct", "--contrasts", type=float, nargs="+", default=[20, 10, 5, 2.5, 1.25, 0.625, 0.3125],
help="List of foreign fish contrasts. Defaults to [20, 10, 5, 2.5, 1.25, 0.625, 0.3125].")
parser.add_argument("-o", "--output_folder", type=str, default=data_folder, help="Where to store the data. Defaults to %s"%os.path.join(".", data_folder))
parser.add_argument("-j", "--jobs", type=int, default=max(1, int(np.floor(multiprocessing.cpu_count() * 0.5))), help="Number of parallel processes (simulations) defaults to half of the available cores.")
args = parser.parse_args()
models = load_models("models.csv")
num_cores = multiprocessing.cpu_count() - 6
num_models = args.number
if args.number > len(models):
print("INFO: number of cells larger than number of available models. Reset to max number of models.")
num_models = len(models)
indices = list(range(len(models)))
np.random.shuffle(indices)
Parallel(n_jobs=num_cores)(delayed(simulate_cell)(cell_id, models) for cell_id in range(len(models[:20])))
Parallel(n_jobs=args.jobs)(delayed(simulate_cell)(cell_id, models, args) for cell_id in indices)
if __name__ == "__main__":