structured_inhibition_spatial_network_model/scripts/spatial_maps/orientation_maps/orientation_map_generator_pypet.py

import numpy as np
import pandas as pd

from scripts.spatial_maps.orientation_maps.orientation_map import OrientationMap
from pypet import Environment, cartesian_product

DATA_FOLDER = "../../../data/"
LOG_FOLDER = "../../../logs/"

TRAJ_NAME_ORIENTATION_MAPS = "precalculated_orientation_maps_test"

def generate_and_save_seeded_map(traj):
    corr_len = traj.corr_len
    seed = traj.seed
    print(corr_len,seed)

    dim = 60
    size = 900

    map = OrientationMap(dim, dim, corr_len, size, size, seed)

    loaded = False
    try:
        map.load_orientation_map()
        loaded = True
    except:
        print(
            'No map yet with {}x{} pixels and {} pixel correllation length and {} seed'.format(map.x_dim, map.y_dim, map.corr_len, map.rnd_seed))
    if not loaded:
        map.improve(10)
        traj.f_add_result('map', map.angle_grid, comment='map with {}x{} pixels and {} pixel correllation length and seed {}'.format(map.x_dim, map.y_dim, map.corr_len, map.rnd_seed))

        print('map_{}x{}_pixels_{}_corr_pix_{}_seed is saved'.format(map.x_dim, map.y_dim,map.corr_len,map.rnd_seed))

    return map.angle_grid


def create_seeded_maps_in_scale_range():
    env = Environment(trajectory=TRAJ_NAME_ORIENTATION_MAPS, filename=DATA_FOLDER,
                      file_title='orientation_map_set',
                      comment='A number of precalculated orientation maps!',
                      large_overview_tables=True,
                      overwrite_file=True,
                      multiproc=True,
                      log_folder=LOG_FOLDER,
                      ncores=0)
    traj = env.trajectory

    traj.f_add_parameter('corr_len', 200.0, comment='Correlation length')
    traj.f_add_parameter('seed', 1, comment='Seed for random')

    correlation_length_range = np.linspace(1.0, 650.0, 24, endpoint=True).tolist()
    # correlation_length_range = [200.0, 400.0, 800.0]
    seed_range = range(10)
    # seed_range = [1]
    
    traj.f_explore(cartesian_product({'corr_len': correlation_length_range, 'seed': seed_range}))

    env.run(generate_and_save_seeded_map)

    env.disable_logging()


if __name__ == "__main__":
    create_seeded_maps_in_scale_range()