Put max entropy figure in a script because I can't get notebooks working. Also added the correlation length analysis for circular values.

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graphics/fig4.sla

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@@ -27,7 +27,6 @@
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notebooks/2020-08-12-max-entropy-rule-schematics-with-real-input-map.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -29,9 +29,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'scripts'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-3-eec7f6728c0b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mscripts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspatial_network\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mperlin\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_entropy_maximisation_perlin_map\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mget_perlin_map\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mscripts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspatial_layout\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSpatialLayout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mscripts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minterneuron_placement\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mplot_neural_sheet\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'scripts'"
+     ]
+    }
+   ],
    "source": [
     "from scripts.spatial_network.perlin.run_entropy_maximisation_perlin_map import get_perlin_map\n",
     "from scripts.spatial_layout import SpatialLayout\n",
@@ -40,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -438,9 +450,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "subiculum_polar_interneurons_from_paul",
    "language": "python",
-   "name": "python3"
+   "name": "subiculum_polar_interneurons_from_paul"
   },
   "language_info": {
    "codemirror_mode": {

scripts/spatial_maps/interneuron_orientation_correlation/orientation_difference_per_distance_perlin.py

@@ -18,8 +18,10 @@ n_inh = 100
 inhibitory_axon_long_axis = 100
 inhibitory_axon_short_axis = 25
 # corr_len = 800
+# corr_len_list = [50, 200, 800]
 corr_len_list = [50, 200, 800]
 
+
 fig, axes = plt.subplots(3, 4)
 
 for corr_len, axes_line in zip(corr_len_list, axes):

scripts/spatial_maps/max_entropy_supplementary/figure_max_entropy_rule.py

@@ -0,0 +1,284 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+# get_ipython().run_line_magic('load_ext', 'autoreload')
+# get_ipython().run_line_magic('autoreload', '2')
+
+# In[2]:
+
+
+import matplotlib.pyplot as plt
+import numpy as np
+# from scripts.spatial_network.perlin.paper_figures_spatial_head_direction_network_perlin_map import FIGURE_SAVE_PATH
+FIGURE_SAVE_PATH = '../../../figures/figure_4_paper_perlin/'
+# ## Get input map
+
+# In[3]:
+
+
+from scripts.spatial_network.perlin.run_entropy_maximisation_perlin_map import get_perlin_map
+from scripts.spatial_layout import SpatialLayout
+from scripts.interneuron_placement import plot_neural_sheet
+
+# In[4]:
+
+
+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))
+
+# In[6]:
+
+
+input_map = get_perlin_map(correlation_length, seed, map_size, Ne)
+spatial_layout = SpatialLayout(input_map, Ne, Ni, long_half, short_half, map_size)
+
+# In[7]:
+
+
+plot_neural_sheet(spatial_layout.ex_positions, spatial_layout.ex_tunings)
+
+# ## Choose interneuron and get entropies
+
+# In[8]:
+
+
+from scripts.interneuron_placement import Pickle, get_excitatory_phases_in_inhibitory_axon, get_correct_position_mesh
+
+# In[9]:
+
+
+from scripts.spatial_layout import get_entropy
+
+# In[10]:
+
+
+in_idx = 144
+
+# In[11]:
+
+
+pos_x = spatial_layout.in_positions[in_idx][0]
+pos_y = spatial_layout.in_positions[in_idx][1]
+
+# In[12]:
+
+
+equivalent_radius = np.sqrt(long_half * short_half)
+circular_axon = Pickle(pos_x, pos_y, equivalent_radius, equivalent_radius, 0)
+
+# In[13]:
+
+
+neuron_positions = spatial_layout.ex_positions
+neuron_tunings = spatial_layout.ex_tunings
+
+# In[14]:
+
+
+number_of_phases = 36
+phases = np.linspace(-np.pi / 2, np.pi / 2, number_of_phases, endpoint=False)
+
+# In[15]:
+
+
+number_of_bins = 10
+tuning_bins = np.linspace(-np.pi, np.pi, number_of_bins + 1)
+tunings = []
+entropies = []
+for phase in phases:
+    axonal_cloud = Pickle(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))
+
+# In[16]:
+
+
+circular_entropy = get_entropy(circular_axon, neuron_positions, neuron_tunings, tuning_bins)
+
+# In[17]:
+
+
+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)
+
+# In[18]:
+
+
+circular_tuning = get_excitatory_phases_in_inhibitory_axon(neuron_positions, neuron_tunings, circular_axon)
+
+# ## Figures
+
+# In[19]:
+
+
+import matplotlib
+
+# In[20]:
+
+
+plt.style.use('../../spatial_network/perlin/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_correct_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 = Pickle(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 = Pickle(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,
+            "$\\theta_{max}$", 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,
+            "$\\theta_{min}$", 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, 1.2, "$\\theta_{max}$\n" + "{:.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, 1.2, "$\\theta_{min}$\n" + "{:.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.spatial_network.perlin.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 = Pickle(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, 1.2, "circ. \n{:.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")
+
+

scripts/spatial_maps/simplex_algorithm_deconstruction.py

@@ -173,17 +173,17 @@ class OpenSimplex(object):
         dy1 = dy0 - 0 - SQUISH_CONSTANT_2D
         attn1 = 2 - dx1 * dx1 - dy1 * dy1
         extrapolate = self._extrapolate2d
-        # if attn1 > 0:
-        #     attn1 *= attn1
-        #     value += attn1 * attn1 * extrapolate(xsb + 1, ysb + 0, dx1, dy1)
+        if attn1 > 0:
+            attn1 *= attn1
+            value += attn1 * attn1 * extrapolate(xsb + 1, ysb + 0, dx1, dy1)
 
         # Contribution (0,1)
         dx2 = dx0 - 0 - SQUISH_CONSTANT_2D
         dy2 = dy0 - 1 - SQUISH_CONSTANT_2D
         attn2 = 2 - dx2 * dx2 - dy2 * dy2
-        # if attn2 > 0:
-        #     attn2 *= attn2
-        #     value += attn2 * attn2 * extrapolate(xsb + 0, ysb + 1, dx2, dy2)
+        if attn2 > 0:
+            attn2 *= attn2
+            value += attn2 * attn2 * extrapolate(xsb + 0, ysb + 1, dx2, dy2)
 
         if in_sum <= 1: # We're inside the triangle (2-Simplex) at (0,0)
             zins = 1 - in_sum

scripts/spatial_maps/simplex_input_tuning_correlation/correlation_length_distance_perlin.py

@@ -0,0 +1,89 @@
+import os
+import matplotlib.pyplot as plt
+import numpy as np
+from pypet import Trajectory
+from pypet.brian2 import Brian2MonitorResult
+from scripts.spatial_network.perlin.run_entropy_maximisation_perlin_map import DATA_FOLDER
+from scripts.interneuron_placement import create_grid_of_excitatory_neurons
+from scripts.spatial_maps.uniform_perlin_map import UniformPerlinMap
+from scipy.optimize import curve_fit
+
+
+def filter_run_names_by_par_dict(traj, par_dict):
+    run_name_list = []
+    for run_idx, run_name in enumerate(traj.f_get_run_names()):
+        traj.f_set_crun(run_name)
+        paramters_equal = True
+        for key, val in par_dict.items():
+            if (traj.par[key] != val):
+                paramters_equal = False
+        if paramters_equal:
+            run_name_list.append(run_name)
+    traj.f_restore_default()
+
+    return run_name_list
+
+
+def correlation_length_fit_dict(traj, load=True):
+    def phi_diff(d_phi):
+        return (d_phi + np.pi) % (2 * np.pi) - np.pi
+
+    def exp_fit_func(x, corr_len):
+        return np.pi / 2. * np.exp(-corr_len * x)
+
+    corr_len_expl = traj.f_get('correlation_length').f_get_range()
+    corr_len_list = np.unique(corr_len_expl)
+
+    dim = 60
+    size = 900
+    n_exc = dim * dim
+
+    if not load:
+        fit_correlation_lengths_dict = {}
+
+        ex_positions = traj.results.runs[traj.f_get_run_names()[0]].ex_positions
+        for corr_len in corr_len_list:
+
+            par_dict = {'seed': 1, 'correlation_length': corr_len, 'long_axis': 100.0}
+
+            run_name = filter_run_names_by_par_dict(traj, par_dict)
+
+            ex_tunings = traj.results.runs[run_name].ex_tunings
+
+            bins = np.linspace(0.0, 800, 301)
+
+            dist_list = []
+            phi_list = []
+            d_phi_list = []
+            for i in range(n_exc):
+                phi_list.append(ex_tunings[i])
+                for j in range(n_exc):
+                    xi, yi = ex_positions[i]
+                    xj, yj = ex_positions[j]
+                    dist = np.sqrt((xi - xj) ** 2 + (yi - yj) ** 2)
+                    dist_list.append(dist)
+                    d_phi = np.abs(phi_diff(ex_tunings[i] - ex_tunings[j]))
+                    d_phi_list.append(d_phi)
+
+            binned_ids = np.digitize(dist_list, bins)
+            binned_d_phi = [[] for _ in range(len(bins))]
+            for id, bin_id in enumerate(binned_ids):
+                current_d_phi = d_phi_list[id]
+                binned_d_phi[bin_id - 1].append(current_d_phi)
+
+            binned_mean = np.array([np.mean(bin) for bin in binned_d_phi])
+
+            nan_ids = np.isnan(binned_mean)
+            binned_mean = binned_mean[~nan_ids]
+            bins = bins[~nan_ids]
+
+            params, _ = curve_fit(exp_fit_func, bins, np.pi / 2 - np.array(binned_mean), [1])
+            fit_corr_len = 1. / params[0]
+            print(corr_len, fit_corr_len)
+            fit_correlation_lengths_dict[corr_len] = fit_corr_len
+
+        np.save(DATA_FOLDER + 'fit_correlation_lengths_dict.npy', fit_correlation_lengths_dict)
+        return fit_correlation_lengths_dict
+    else:
+        fit_correlation_lengths_dict = np.load(DATA_FOLDER + 'fit_correlation_lengths_dict.npy')
+        return fit_correlation_lengths_dict.item()

scripts/spatial_maps/simplex_input_tuning_correlation/correlation_length_with_circular_correlation_formula.py

@@ -0,0 +1,94 @@
+import os
+import matplotlib.pyplot as plt
+import numpy as np
+from pypet import Trajectory
+from pypet.brian2 import Brian2MonitorResult
+
+from scripts.interneuron_placement import create_grid_of_excitatory_neurons, get_correct_position_mesh
+from scripts.spatial_maps.uniform_perlin_map import UniformPerlinMap
+from scipy.optimize import curve_fit
+import pandas as pd
+
+def phi_diff(d_phi):
+    return (d_phi + np.pi) % (2*np.pi) - np.pi
+
+def exp_fit_func(x, corr_len):
+    return np.pi / 2. * np.exp(-corr_len * x)
+
+use_saved_array = False
+dim = 60
+size = 900
+n_exc = dim * dim
+
+corr_len_list = [200, 400, 800]
+
+fig, axes = plt.subplots(3, 2)
+
+for idx, (corr_len, axes_line) in enumerate(zip(corr_len_list, axes)):
+
+    bins = np.linspace(0.0, 800, 301)
+
+    perlin_map = UniformPerlinMap(dim, dim, corr_len, size, size, 3)
+
+    ex_positions, ex_tunings = create_grid_of_excitatory_neurons(perlin_map.sheet_x, perlin_map.sheet_y, perlin_map.x_dim,
+                                                                 perlin_map.get_tuning)
+
+    ex_tun_array = np.array(ex_tunings).reshape((dim, dim))
+    # TODO: Why was this transposed for plotting? (now changed)
+
+    x_index = [pos[0] for pos in ex_positions]
+    y_index = [pos[1] for pos in ex_positions]
+
+    x_displacements = range(-dim+1,dim)
+    y_displacements = range(-dim+1,dim)
+    r_c_array = np.ndarray((len(x_displacements),len(y_displacements)))
+
+    # For test purposes:
+    # for i in range(dim):
+    #     for j in range(dim):
+    #         ex_tun_array[i,j] = np.sin(2*np.pi*i/dim)
+
+    for id_dx in x_displacements:
+        for id_dy in y_displacements:
+            a_1 = ex_tun_array[max(0, id_dx): min(dim, dim + id_dx), max(0, id_dy): min(dim, dim + id_dy)]
+            C_11 = np.sum(np.cos(a_1)) / len(a_1)
+            S_11 = np.sum(np.sin(a_1)) / len(a_1)
+            T_11 = np.arctan2(S_11, C_11)
+
+            a_2 = ex_tun_array[max(0, -id_dx): min(dim, dim - id_dx), max(0, -id_dy): min(dim, dim - id_dy)]
+            C_21 = np.sum(np.cos(a_2)) / len(a_2)
+            S_21 = np.sum(np.sin(a_2)) / len(a_2)
+            T_21 = np.arctan2(S_21, C_21)
+
+            r_c_denominator = np.sin(a_1 - T_11) * np.sin(a_2 - T_21)
+            r_c_norm = np.sqrt(np.sum(np.sin(a_1 - T_11) ** 2) * np.sum(np.sin(a_2 - T_21) ** 2))
+            if r_c_norm != 0:
+                r_c = np.sum(r_c_denominator) / r_c_norm
+            else:
+                # TODO: Not sure which value to put here.
+                r_c = 0
+            r_c_array[id_dx + dim - 1, id_dy + dim - 1] = r_c
+    print(r_c_array)
+
+    X, Y = get_correct_position_mesh(ex_positions)
+
+    head_dir_preference = np.array(ex_tunings).reshape((dim, dim))
+    c = axes_line[0].pcolor(X, Y, head_dir_preference, vmin=-np.pi, vmax=np.pi, cmap='hsv')
+    axes_line[0].set_title('Scale factor: {}'.format(corr_len))
+    axes_line[0].set_aspect('equal')
+
+    xmin = np.min(x_displacements)
+    xmax = np.max(x_displacements)
+    dx = (xmax - xmin) / (len(x_displacements) - 1)
+
+    ymin = np.min(y_displacements)
+    ymax = np.max(y_displacements)
+    dy = (ymax - ymin) / (len(x_displacements) - 1)
+
+    DX, DY = np.meshgrid(np.arange(xmin, xmax + 2 * dx, dx) - dx / 2., np.arange(ymin, ymax + 2 * dy, dy) - dy / 2.)
+    d = axes_line[1].pcolor(DX, DY, r_c_array)
+    axes_line[1].set_aspect('equal')
+
+    fig.colorbar(d, ax=axes_line[1], label="correlation")
+
+plt.show()

scripts/spatial_network/perlin/paper_figures_spatial_head_direction_network_perlin_map.py

@@ -13,6 +13,8 @@ from pypet.brian2 import Brian2MonitorResult
 
 from scripts.interneuron_placement import get_position_mesh, Pickle, get_correct_position_mesh
 from scripts.model_figure.plot_circular_colorbar import plot_colorbar
+from scripts.spatial_maps.simplex_input_tuning_correlation.correlation_length_distance_perlin import \
+    correlation_length_fit_dict
 from scripts.spatial_network.figures_spatial_head_direction_network_orientation_map import plot_hdi_in_space
 from scripts.spatial_network.perlin.figure_utils import remove_frame, remove_ticks, add_length_scale, cm_per_inch, \
     panel_size, head_direction_input_colormap
@@ -1117,7 +1119,7 @@ def filter_run_names_by_par_dict(traj, par_dict):
     return run_name_list
 
 
-def plot_exc_and_inh_hdi_over_corr_len(traj, plot_run_names):
+def plot_exc_and_inh_hdi_over_simplex_grid_scale(traj, plot_run_names):
     corr_len_expl = traj.f_get('correlation_length').f_get_range()
     seed_expl = traj.f_get('seed').f_get_range()
     label_expl = [traj.derived_parameters.runs[run_name].morphology.morph_label for run_name in traj.f_get_run_names()]
@@ -1161,6 +1163,8 @@ def plot_exc_and_inh_hdi_over_corr_len(traj, plot_run_names):
     # linestyles = ['solid', 'dashed', 'solid']
     # markers = [verts, '', 'o']
 
+    # corr_len_fit_dict = correlation_length_fit_dict(traj, load=True)
+
     width = 2 * panel_size
     height = 1.2 * panel_size
     fig, ax = plt.subplots(1, 1, figsize=(width, height))
@@ -1191,18 +1195,20 @@ def plot_exc_and_inh_hdi_over_corr_len(traj, plot_run_names):
         exc_col, exc_lin, exc_mar, exc_mar_size = exc_style_dict[label]
         inh_col, inh_lin, inh_mar, inh_mar_size = inh_style_dict[label]
 
-        ax.plot(corr_len_range, exc_hdi_mean, label='exc., ' + label, marker=exc_mar, color=exc_col, linestyle=exc_lin,
+        simplex_grid_scale = corr_len_range * np.sqrt(2)
+
+        ax.plot(simplex_grid_scale, exc_hdi_mean, label='exc., ' + label, marker=exc_mar, color=exc_col, linestyle=exc_lin,
                 markersize=exc_mar_size, alpha=0.5)
-        plt.fill_between(corr_len_range, exc_hdi_mean - exc_hdi_std,
+        plt.fill_between(simplex_grid_scale, exc_hdi_mean - exc_hdi_std,
                          exc_hdi_mean + exc_hdi_std, alpha=0.3, color=exc_col)
-        ax.plot(corr_len_range, inh_hdi_mean, label='inh., ' + label, marker=inh_mar, color=inh_col, linestyle=inh_lin,
+        ax.plot(simplex_grid_scale, inh_hdi_mean, label='inh., ' + label, marker=inh_mar, color=inh_col, linestyle=inh_lin,
                 markersize=inh_mar_size, alpha=0.5)
-        plt.fill_between(corr_len_range, inh_hdi_mean - inh_hdi_std,
+        plt.fill_between(simplex_grid_scale, inh_hdi_mean - inh_hdi_std,
                          inh_hdi_mean + inh_hdi_std, alpha=0.3, color=inh_col)
 
-    ax.set_xlabel('correlation length')
+    ax.set_xlabel('simplex grid scale')
     ax.set_ylabel('head direction index')
-    ax.axvline(get_closest_correlation_length(traj, 200.0), color='k', linewidth=0.5, zorder=0)
+    ax.axvline(get_closest_correlation_length(traj, 200.0) * np.sqrt(2), color='k', linewidth=0.5, zorder=0)
     ax.set_ylim(0.0, 1.0)
     # ax.set_xlim(0.0, np.max(corr_len_range))
     remove_frame(ax, ["right", "top"])
@@ -1218,6 +1224,111 @@ def plot_exc_and_inh_hdi_over_corr_len(traj, plot_run_names):
         plt.savefig(FIGURE_SAVE_PATH + 'F_hdi_over_corr_len_scaled.png')
         plt.close(fig)
 
+def plot_exc_and_inh_hdi_over_fit_corr_len(traj, plot_run_names):
+    corr_len_expl = traj.f_get('correlation_length').f_get_range()
+    seed_expl = traj.f_get('seed').f_get_range()
+    label_expl = [traj.derived_parameters.runs[run_name].morphology.morph_label for run_name in traj.f_get_run_names()]
+    label_range = set(label_expl)
+
+    exc_hdi_frame = pd.Series(index=[corr_len_expl, seed_expl, label_expl])
+    exc_hdi_frame.index.names = ["corr_len", "seed", "label"]
+    inh_hdi_frame = pd.Series(index=[corr_len_expl, seed_expl, label_expl])
+    inh_hdi_frame.index.names = ["corr_len", "seed", "label"]
+
+    for run_name, corr_len, seed, label in zip(plot_run_names, corr_len_expl, seed_expl, label_expl):
+        ex_tunings = traj.results.runs[run_name].ex_tunings
+        head_direction_indices = traj.results[run_name].head_direction_indices
+        # TODO: Actual correlation lengths
+        # actual_corr_len = get_correlation_length(ex_tunings.reshape((30,30)), 450, 30)
+        exc_hdi_frame[corr_len, seed, label] = np.mean(head_direction_indices)
+
+        inh_head_direction_indices = traj.results[run_name].inh_head_direction_indices
+        inh_hdi_frame[corr_len, seed, label] = np.mean(inh_head_direction_indices)
+
+    # TODO: Standard deviation also for the population
+    exc_hdi_n_and_seed_mean = exc_hdi_frame.groupby(level=[0, 2]).mean()
+    exc_hdi_n_and_seed_std_dev = exc_hdi_frame.groupby(level=[0, 2]).std()
+
+    inh_hdi_n_and_seed_mean = inh_hdi_frame.groupby(level=[0, 2]).mean()
+    inh_hdi_n_and_seed_std_dev = inh_hdi_frame.groupby(level=[0, 2]).std()
+
+    markersize = 4.
+    exc_style_dict = {
+        NO_SYNAPSES: ['dimgrey', 'dashed', '', 0],
+        POLARIZED: ['red', 'solid', '^', markersize],
+        CIRCULAR: ['lightsalmon', 'solid', '^', markersize]
+    }
+    inh_style_dict = {
+        NO_SYNAPSES: ['dimgrey', 'dashed', '', 0],
+        POLARIZED: ['blue', 'solid', 'o', markersize],
+        CIRCULAR: ['lightblue', 'solid', 'o', markersize]
+
+    }
+    # colors = ['blue', 'grey', 'lightblue']
+    # linestyles = ['solid', 'dashed', 'solid']
+    # markers = [verts, '', 'o']
+
+    corr_len_fit_dict = correlation_length_fit_dict(traj, load=True)
+
+    width = 2 * panel_size
+    height = 1.2 * panel_size
+    fig, ax = plt.subplots(1, 1, figsize=(width, height))
+
+    for label in sorted(label_range, reverse=True):
+        if label == NO_SYNAPSES:
+            no_conn_hdi = exc_hdi_n_and_seed_mean[1, label]
+            ax.axhline(no_conn_hdi, color='grey', linestyle='--')
+
+            ax.annotate(short_labels(label), xy=(1.0, no_conn_hdi), xytext=(0, -2), xycoords='axes fraction',
+                        textcoords="offset points",
+                        va="top", \
+                        ha="right",
+                        color="dimgrey")
+
+            continue
+        exc_hdi_mean = exc_hdi_n_and_seed_mean[:, label]
+        exc_hdi_std = exc_hdi_n_and_seed_std_dev[:, label]
+
+        inh_hdi_mean = inh_hdi_n_and_seed_mean[:, label]
+        inh_hdi_std = inh_hdi_n_and_seed_std_dev[:, label]
+
+        corr_len_range = exc_hdi_mean.keys().to_numpy()
+        print(label)
+        for corr_len, ex_hdi, in_hdi in zip(corr_len_range, exc_hdi_mean, inh_hdi_mean):
+            print("length: {:.2f} um, ex hdi: {:.2f} and in hdi {:.2f}".format(corr_len, ex_hdi, in_hdi))
+
+        exc_col, exc_lin, exc_mar, exc_mar_size = exc_style_dict[label]
+        inh_col, inh_lin, inh_mar, inh_mar_size = inh_style_dict[label]
+
+        fit_corr_len = [corr_len_fit_dict[corr_len] for corr_len in corr_len_range]
+        print(corr_len_range)
+        print(fit_corr_len)
+        ax.plot(fit_corr_len, exc_hdi_mean, label='exc., ' + label, marker=exc_mar, color=exc_col, linestyle=exc_lin,
+                markersize=exc_mar_size, alpha=0.5)
+        plt.fill_between(fit_corr_len, exc_hdi_mean - exc_hdi_std,
+                         exc_hdi_mean + exc_hdi_std, alpha=0.3, color=exc_col)
+        ax.plot(fit_corr_len, inh_hdi_mean, label='inh., ' + label, marker=inh_mar, color=inh_col, linestyle=inh_lin,
+                markersize=inh_mar_size, alpha=0.5)
+        plt.fill_between(fit_corr_len, inh_hdi_mean - inh_hdi_std,
+                         inh_hdi_mean + inh_hdi_std, alpha=0.3, color=inh_col)
+
+    ax.set_xlabel('correlation length')
+    ax.set_ylabel('head direction index')
+    ax.axvline(corr_len_fit_dict[get_closest_correlation_length(traj, 200.0)], color='k', linewidth=0.5, zorder=0)
+    ax.set_ylim(0.0, 1.0)
+    # ax.set_xlim(0.0, np.max(corr_len_range))
+    remove_frame(ax, ["right", "top"])
+
+    tablelegend(ax, ncol=2, bbox_to_anchor=(1.1, 1.1), loc="upper right",
+                row_labels=None,
+                col_labels=[short_labels(label) for label in sorted(label_range - {"no conn"}, reverse=True)],
+                title_label='', borderaxespad=0, handlelength=2, edgecolor='white')
+    fig.subplots_adjust(bottom=0.2, left=0.2)
+    # plt.legend()
+
+    if save_figs:
+        plt.savefig(FIGURE_SAVE_PATH + 'hdi_over_fit_corr_len.png')
+        plt.close(fig)
 
 def plot_in_degree_map(traj, plot_run_names):
     n_ex = int(np.sqrt(traj.N_E))
@@ -1436,6 +1547,12 @@ if __name__ == "__main__":
     map_seed = 1
     exemplary_head_direction = 0
 
+    # corr_len_fit_dict = correlation_length_fit_dict(traj, load=True)
+    # plt.plot(corr_len_fit_dict.keys(),corr_len_fit_dict.values())
+    # plt.show()
+    # abbrechen
+
+
     print("## Map specifications")
     print("\tcorrelation length: {:.1f} um".format(map_length_scale))
     print("\tmap seed: {:d}".format(map_seed))
@@ -1485,7 +1602,7 @@ if __name__ == "__main__":
         selected_inhibitory_neuron,
         cut_off_dist=100.)
     #
-    number_of_suggestions = 30
+    number_of_suggestions = 20
     representative_excitatory_neuron_indices = get_neurons_with_given_hdi(hdi_means["polar_exc"], hdi_means[
         "circular_exc"],
                                                                           number_of_suggestions, plot_run_names,
@@ -1527,7 +1644,9 @@ if __name__ == "__main__":
 
     plot_orientation_maps_diff_scales_with_ellipse(traj)
 
-    plot_exc_and_inh_hdi_over_corr_len(traj, traj.f_get_run_names())
+    plot_exc_and_inh_hdi_over_simplex_grid_scale(traj, traj.f_get_run_names())
+
+    plot_exc_and_inh_hdi_over_fit_corr_len(traj, traj.f_get_run_names())
 
     if not save_figs:
         plt.show()

scripts/spatial_network/perlin/run_entropy_maximisation_perlin_map.py

@@ -31,7 +31,7 @@ def get_local_data_folder():
     return data_folder
 
 
-DATA_FOLDER = "/groups/susanne/moritz/share/"
+DATA_FOLDER = "../../../data/"
 LOG_FOLDER = "../../../logs/"
 
 TRAJ_NAME = "full_figure_perlin_map"