structured_inhibition_spatial_network_model/scripts/spatial_maps/placement_jitter_test.py

import numpy as np
import matplotlib.pyplot as plt

from scripts.interneuron_placement import create_grid_of_excitatory_neurons, \
    create_interneuron_sheet_entropy_max_orientation, get_correct_position_mesh, \
    get_excitatory_neurons_in_inhibitory_axonal_clouds
from scripts.spatial_maps.uniform_perlin_map import get_orientation_map

dim = 30
size = 450


n_steps = 5
placement_jitters = np.linspace(0.0, 40.0, n_steps)

fig, axes = plt.subplots(2, n_steps, figsize=(13.5, 4.5))
orientation_map = get_orientation_map(200.0, 1, size, dim*dim)

for pj, ax1, ax2 in zip(placement_jitters, *axes):

    ex_positions, ex_tunings = create_grid_of_excitatory_neurons(size, size, dim, orientation_map.tuning)

    # inhibitory_axon_long_axis = 100.
    # inhibitory_axon_short_axis = 25.
    inhibitory_axon_long_axis = np.sqrt(2500.)
    inhibitory_axon_short_axis = np.sqrt(2500.)

    entropy_maximisation_steps = 30 if inhibitory_axon_long_axis != inhibitory_axon_short_axis else 1

    inhibitory_axonal_clouds, ellipse_single_trial_entropy = create_interneuron_sheet_entropy_max_orientation(
        ex_positions, ex_tunings, 100, inhibitory_axon_long_axis,
        inhibitory_axon_short_axis, size, size, trial_orientations=entropy_maximisation_steps, placement_jitter=pj)

    X, Y = get_correct_position_mesh(ex_positions)

    c = ax1.pcolor(X, Y, np.array(ex_tunings).reshape((dim, dim)), vmin=-np.pi, vmax=np.pi, cmap='hsv')
    ax1.set_title(pj)
    # fig.colorbar(c, ax=ax, label="Tuning")

    for i, p in enumerate(inhibitory_axonal_clouds):
        ell = p.get_ellipse()
        ax1.add_artist(ell)

    ie_connections = get_excitatory_neurons_in_inhibitory_axonal_clouds(ex_positions, inhibitory_axonal_clouds)
    ie_adjacency = np.zeros((100, 900))
    for i_idx, ie_conn in enumerate(ie_connections):
        for e_idx in ie_conn:
            ie_adjacency[i_idx, e_idx] = 1
    exc_in_degree = np.sum(ie_adjacency, axis=0)

    ax2.hist(exc_in_degree, bins=np.arange(12)-0.5)
    ax2.set_ylim(0,300)

plt.show()