optimization.py
import os
import warnings
import csv
import argparse
import numpy as np
import matplotlib.pyplot as plt
from scipy import signal as sig
import scipy.spatial.distance as dst
import parameters
from optlib import cost_func
# Main code
if __name__ == "__main__":
""" Exhibition of the functions to be used """
# Parse arguments
parser = argparse.ArgumentParser(description='Parameter optimization based on simulated spike data')
parser.add_argument('-d', '--directory',
nargs='?',
metavar='-d',
type=str,
default='results_opt',
help='Default directory to load the data from')
parser.add_argument('-t', '--target',
nargs='+',
metavar='-t',
type=float,
default=[6,6,6,6,6],
help='Target vector for the optimization; mean FR per area, max FR @ CA1, output frequency')
parser.add_argument('-o', '--output',
nargs='?',
metavar='-o',
type=str,
default='optimization_new.csv',
help='Output file name')
args = parser.parse_args()
if len(args.target) != 5:
print("Wrong target length. Try again.")
exit(-1)
# Base directory
basedir = os.path.join(args.directory, 'None')
print('Base directory "{0}"'.format(basedir))
# Parameters
# ----------
fnames = ["EC_pyCAN", "EC_inh", "DG_py", "DG_inh", "CA3_pyCAN", "CA3_inh", "CA1_pyCAN", "CA1_inh"]
fs = 10e3
winsize_FR = 10/1e3
overlap_FR = 0.9 # percentage
winstep_FR = winsize_FR*round(1-overlap_FR,4)
fs_FR = int(1/winstep_FR)
settling_time = 3 # s
ending_time = 4 # s
# target for EC firing rate w/ noise
# target_vals = [6., 60., 6., 60., 6., 60., 6., 60., 10., 6.]
# target_vals = [int(args.area == "EC")]*2 + [int(args.area == "DG")]*2 + [int(args.area == "CA3")]*2 + [int(args.area == "CA1")]*2 + [int(args.area == "CA1")] + [6.]
target_vals = args.target
with open(args.output, 'w', encoding='UTF8', newline='') as fout:
writer = csv.writer(fout)
# Write the header to the CSV file
csv_header = ['fname', 'J', 'input' ,'a', 'b', 'c', 'd', 'vector']
writer.writerow(csv_header)
for item in os.listdir(basedir):
if os.path.isdir(os.path.join(basedir, item)):
currdir = os.path.join(basedir, item)
print()
print('Current directory:', currdir)
datadir = os.path.join(currdir, 'data')
spikesdir = os.path.join(datadir, 'spikes')
# print('Data/Spikes directory:', datadir)
# Load parameters file for later
params = parameters.load(os.path.join(currdir, 'parameters_bak.json'))
data = {}
for f in fnames:
tokens = f.split('_')
area = tokens[0]
pop = tokens[1]
if area not in data:
data[area] = {}
data[area]["E"] = {}
data[area]["I"] = {}
# Ignore empty txt file warnings
with warnings.catch_warnings():
warnings.simplefilter("ignore")
if tokens[1] == "inh":
t = np.loadtxt(spikesdir + '/' + f + '_spikemon_t.txt', ndmin=1)/1000
i = np.loadtxt(spikesdir + '/' + f + '_spikemon_i.txt', ndmin=1)
idx_crop = np.where(t <= settling_time)
t_tmp = np.delete(t, idx_crop)
i_tmp = np.delete(i, idx_crop)
data[area]["I"]["t"] = t_tmp
data[area]["I"]["i"] = i_tmp
idx_crop = np.where(t_tmp >= ending_time)
t_tmp = np.delete(t_tmp, idx_crop)
i_tmp = np.delete(i_tmp, idx_crop)
data[area]["I"]["t"] = t_tmp
data[area]["I"]["i"] = i_tmp
else:
t = np.loadtxt(spikesdir + '/' + f + '_spikemon_t.txt', ndmin=1)/1000
i = np.loadtxt(spikesdir + '/' + f + '_spikemon_i.txt', ndmin=1)
idx_crop = np.where(t <= settling_time)
t_tmp = np.delete(t, idx_crop)
i_tmp = np.delete(i, idx_crop)
data[area]["E"]["t"] = t_tmp
data[area]["E"]["i"] = i_tmp
idx_crop = np.where(t_tmp >= ending_time)
t_tmp = np.delete(t_tmp, idx_crop)
i_tmp = np.delete(i_tmp, idx_crop)
data[area]["E"]["t"] = t_tmp
data[area]["E"]["i"] = i_tmp
# Output rhythm
r = np.loadtxt(datadir + '/' + 'order_param_mon_rhythm.txt')
data["rhythm"] = r[int(settling_time*fs):int(ending_time*fs)]
duration = len(data["rhythm"])/fs
duration0 = (ending_time-settling_time)
# exit()
# Run the cost function
params_FR = {"winsize":winsize_FR, "overlap":overlap_FR}
J, vec = cost_func(data, target_vals, duration, fs, params_FR=params_FR)
print("Euclidean distance:", J)
print(target_vals)
print(vec)
# Write to a CSV file
# csv_data = [os.path.join(currdir, 'parameters_bak.json'), params['areas'][args.area]["E"]["noise"], params['areas'][args.area]["I"]["noise"], J] + vec
inp_val = params["Kuramoto"]["gain_rhythm"]
a = params["connectivity"]["inter_custom"]["EC"]["E"][1][0]
b = params["connectivity"]["inter_custom"]["EC"]["E"][2][0]
c = params["connectivity"]["inter_custom"]["EC"]["E"][3][0]
d = params["connectivity"]["inter_custom"]["CA1"]["E"][0][0]
# noise_EC_exc = params["areas"]["EC"]["E"]["noise"]
# noise_EC_inh = params["areas"]["EC"]["I"]["noise"]
# noise_DG_exc = params["areas"]["DG"]["E"]["noise"]
# noise_DG_inh = params["areas"]["DG"]["I"]["noise"]
# noise_CA3_exc = params["areas"]["CA3"]["E"]["noise"]
# noise_CA3_inh = params["areas"]["CA3"]["I"]["noise"]
# noise_CA1_exc = params["areas"]["CA1"]["E"]["noise"]
# noise_CA1_inh = params["areas"]["CA1"]["I"]["noise"]
# csv_data = [os.path.join(currdir, 'parameters_bak.json'), J, inp_val, a, b, c, d, noise_EC_exc, noise_EC_inh, noise_DG_exc, noise_DG_inh, noise_CA3_exc, noise_CA3_inh, noise_CA1_exc, noise_CA1_inh] + vec
csv_data = [os.path.join(currdir, 'parameters_bak.json'), J, inp_val, a, b, c, d,] + vec
print(csv_data)
# write the data
writer.writerow(csv_data)
# continue
# print("Not reached!")
exit(0)
