structured_inhibition_spatial_network_model/scripts/spatial_maps/supplement_max_entropy_rule/figure_max_entropy_rule.py

import matplotlib.pyplot as plt
import numpy as np
# from scripts.spatial_network.perlin_map.paper_figures_spatial_head_direction_network_perlin_map import FIGURE_SAVE_PATH
FIGURE_SAVE_PATH = '../../../figures/figure_4_paper_perlin/'

from scripts.spatial_network.perlin_map.run_simulation_perlin_map import get_perlin_map
from scripts.spatial_maps.supplement_max_entropy_rule.spatial_layout import SpatialLayout
from scripts.spatial_maps.spatial_network_layout import Interneuron, get_excitatory_phases_in_inhibitory_axon, get_position_mesh, plot_neural_sheet
from scripts.spatial_maps.supplement_max_entropy_rule.spatial_layout import get_entropy
# Get input map

seed = 1
correlation_length = 200
map_size = 900
long_half = 100
short_half = 25

Ne = 3600
Ni = 400

Ne_per_row = int(np.sqrt(Ne))
Ni_per_row = int(np.sqrt(Ni))

input_map = get_perlin_map(correlation_length, seed, map_size, Ne)
spatial_layout = SpatialLayout(input_map, Ne, Ni, long_half, short_half, map_size)

plot_neural_sheet(spatial_layout.ex_positions, spatial_layout.ex_tunings)

# Choose interneuron and get entropies

in_idx = 144

pos_x = spatial_layout.in_positions[in_idx][0]
pos_y = spatial_layout.in_positions[in_idx][1]

equivalent_radius = np.sqrt(long_half * short_half)
circular_axon = Interneuron(pos_x, pos_y, equivalent_radius, equivalent_radius, 0)

neuron_positions = spatial_layout.ex_positions
neuron_tunings = spatial_layout.ex_tunings

number_of_phases = 36
phases = np.linspace(-np.pi / 2, np.pi / 2, number_of_phases, endpoint=False)

number_of_bins = 10
tuning_bins = np.linspace(-np.pi, np.pi, number_of_bins + 1)
tunings = []
entropies = []
for phase in phases:
    axonal_cloud = Interneuron(pos_x, pos_y, long_half, short_half, phase)
    tunings.append(get_excitatory_phases_in_inhibitory_axon(neuron_positions, neuron_tunings, axonal_cloud))
    entropies.append(get_entropy(axonal_cloud, neuron_positions, neuron_tunings, tuning_bins))

circular_entropy = get_entropy(circular_axon, neuron_positions, neuron_tunings, tuning_bins)

max_entropy = max(entropies)
max_phase = phases[np.argmax(entropies)]
max_id = np.argmax(entropies)

min_entropy = min(entropies)
min_phase = phases[np.argmin(entropies)]
min_id = np.argmin(entropies)

circular_tuning = get_excitatory_phases_in_inhibitory_axon(neuron_positions, neuron_tunings, circular_axon)

# Figures

import matplotlib

# In[20]:


plt.style.use('../../spatial_network/perlin_map/figures.mplstyle')

# In[21]:


width = 4.0 / 2.54
height = 3.2 / 2.54

detail_size = 2.5 * long_half

detail_x_low = pos_x - detail_size / 2.0
detail_x_high = pos_x + detail_size / 2.0
detail_y_low = pos_y - detail_size / 2.0
detail_y_high = pos_y + detail_size / 2.0

colormap = matplotlib.cm.get_cmap('hsv')

bar_colors = [colormap(value) for value in np.linspace(0, 1, number_of_bins)]

X, Y = get_position_mesh(spatial_layout.ex_positions)
head_dir_preference = np.array(spatial_layout.ex_tunings).reshape((Ne_per_row, Ne_per_row))

# ### Polar Interneuron

# In[22]:


fig = plt.figure(figsize=(width, height))

ax_map = fig.add_axes([0.05, 0.1, 0.7 * height / width, 0.7])

ax_map.pcolor(X, Y, head_dir_preference, vmin=-np.pi, vmax=np.pi, cmap=colormap)

line_length = 1.2 * detail_size
max_entropy_ell = Interneuron(pos_x, pos_y, long_half, short_half, max_phase).get_ellipse()

max_entropy_line = plt.Line2D(
    [pos_x - np.cos(max_phase) * line_length / 2.0, pos_x + np.cos(max_phase) * line_length / 2.0],
    [pos_y - np.sin(max_phase) * line_length / 2.0, pos_y + np.sin(max_phase) * line_length / 2.0])
max_entropy_line.set_linestyle('dotted')
max_entropy_line.set_clip_on(False)

min_entropy_ell = Interneuron(pos_x, pos_y, long_half, short_half, min_phase).get_ellipse()
min_entropy_ell.set_linestyle('dashed')

min_entropy_line = plt.Line2D(
    [pos_x - np.cos(min_phase) * line_length / 2.0, pos_x + np.cos(min_phase) * line_length / 2.0],
    [pos_y - np.sin(min_phase) * line_length / 2.0, pos_y + np.sin(min_phase) * line_length / 2.0])
min_entropy_line.set_linestyle('dotted')
min_entropy_line.set_clip_on(False)

ax_map.add_artist(max_entropy_ell)
ax_map.add_artist(max_entropy_line)
ax_map.add_artist(min_entropy_ell)
ax_map.add_artist(min_entropy_line)

ax_map.set_xlim(detail_x_low, detail_x_high)
ax_map.set_ylim(detail_y_low, detail_y_high)

ax_map.spines["right"].set_visible(False)
ax_map.spines["left"].set_visible(False)
ax_map.spines["top"].set_visible(False)
ax_map.spines["bottom"].set_visible(False)
ax_map.xaxis.set_ticks([])
ax_map.yaxis.set_ticks([])

ax_map.text(pos_x - np.cos(max_phase) * line_length / 2.0, pos_y - np.sin(max_phase) * line_length / 2.0,
            "$\\phi_{max}$", va='bottom', ha='left')
ax_map.text(pos_x + np.cos(min_phase) * line_length / 2.0, pos_y + np.sin(min_phase) * line_length / 2.0,
            "$\\phi_{min}$", va='center', ha='left')

ax_dist_max = fig.add_axes(
    [0.05 + 0.7 * height / width + 0.03, 0.1, 1 - 0.75 * height / width - 0.05 - 0.05 - 0.1, 0.27])
n_max_entropy, _, bars = ax_dist_max.hist(tunings[max_id], bins=number_of_bins, range=(-np.pi, np.pi))
for bar, bar_color in zip(bars, bar_colors):
    bar.set_color(bar_color)
ax_dist_max.text(0, 1.4, "$\\phi_{max}$\n" + "S={:.2f}".format(entropies[max_id]), transform=ax_dist_max.transAxes,
                 va='top')

ax_dist_min = fig.add_axes(
    [0.05 + 0.7 * height / width + 0.03, 0.48, 1 - 0.75 * height / width - 0.05 - 0.05 - 0.1, 0.27])
n_min_entropy, _, bars = ax_dist_min.hist(tunings[min_id], bins=number_of_bins, range=(-np.pi, np.pi))
for bar, bar_color in zip(bars, bar_colors):
    bar.set_color(bar_color)
ax_dist_min.text(0, 1.6, "$\\phi_{min}$\n" + "S={:.2f}".format(entropies[min_id]), transform=ax_dist_min.transAxes,
                 va='top')

for ax in [ax_dist_max, ax_dist_min]:
    ax.set_ylim(0, max(max(n_max_entropy), max(n_min_entropy)))
    ax.spines["right"].set_visible(False)
    ax.spines["left"].set_visible(False)
    ax.spines["top"].set_visible(False)
    ax.spines["bottom"].set_linewidth(0.5)
    ax.xaxis.set_ticks([])
    ax.yaxis.tick_right()

fig.savefig(FIGURE_SAVE_PATH + "B_ii_max_entropy_rule_polar_ins.png")

# ### Circular Interneuron

# In[25]:


from scripts.model_figure.figure_utils import add_length_scale

# In[36]:


fig = plt.figure(figsize=(width, height))

ax_map = fig.add_axes([0.05, 0.1, 0.7 * height / width, 0.7])

ax_map.pcolor(X, Y, head_dir_preference, vmin=-np.pi, vmax=np.pi, cmap=colormap)

circle = Interneuron(pos_x, pos_y, equivalent_radius, equivalent_radius, 0).get_ellipse()

ax_map.add_artist(circle)

ax_map.set_xlim(detail_x_low, detail_x_high)
ax_map.set_ylim(detail_y_low, detail_y_high)

ax_map.spines["right"].set_visible(False)
ax_map.spines["left"].set_visible(False)
ax_map.spines["top"].set_visible(False)
ax_map.spines["bottom"].set_visible(False)
ax_map.xaxis.set_ticks([])
ax_map.yaxis.set_ticks([])

add_length_scale(ax_map, 100, detail_x_high + 30, detail_x_high + 10 + 100, detail_y_low + 40, detail_y_low + 40)

ax_dist = fig.add_axes([0.05 + 0.7 * height / width + 0.03, 0.375, 1 - 0.75 * height / width - 0.05 - 0.05 - 0.1, 0.27])
n_circle, _, bars = ax_dist.hist(circular_tuning, bins=number_of_bins, range=(-np.pi, np.pi))
for bar, bar_color in zip(bars, bar_colors):
    bar.set_color(bar_color)

ax_dist.text(0, 1.6, "circ. \nS={:.2f}".format(circular_entropy), transform=ax_dist.transAxes, va='top')

for ax in [ax_dist]:
    ax.set_ylim(0, max(max(n_max_entropy), max(n_min_entropy)))
    ax.spines["right"].set_visible(False)
    ax.spines["left"].set_visible(False)
    ax.spines["top"].set_visible(False)
    ax.spines["bottom"].set_linewidth(0.5)
    ax.xaxis.set_ticks([])
    ax.yaxis.tick_right()

fig.savefig(FIGURE_SAVE_PATH + "B_ii_max_entropy_rule_circular_in.png")