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@@ -0,0 +1,751 @@
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+import matplotlib.pyplot as plt
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+import numpy as np
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+import pandas as pd
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+from brian2.units import *
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+from mpl_toolkits.axes_grid1 import make_axes_locatable
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+from pypet import Trajectory
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+from pypet.brian2 import Brian2MonitorResult
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+from scipy.optimize import curve_fit
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+from matplotlib.patches import Ellipse
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+from matplotlib.patches import Polygon
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+
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+from scripts.interneuron_placement import get_position_mesh, Pickle, get_correct_position_mesh
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+from scripts.spatial_network.run_entropy_maximisation_orientation_map import DATA_FOLDER, TRAJ_NAME
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+
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+FIGURE_SAVE_PATH = '../../figures/figure_4_paper_draft/'
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+
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+def get_closest_correlation_length(traj, correlation_length):
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+ available_lengths = sorted(list(set(traj.f_get("correlation_length").f_get_range())))
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+ closest_length = available_lengths[np.argmin(np.abs(np.array(available_lengths) - correlation_length))]
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+ if closest_length != correlation_length:
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+ print("Warning: desired correlation length {:.1f} not available. Taking {:.1f} instead".format(
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+ correlation_length, closest_length))
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+ corr_len = closest_length
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+ return corr_len
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+
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+def gauss(x, *p):
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+ A, mu, sigma, B = p
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+ return A * np.exp(-(x - mu) ** 2 / (2. * sigma ** 2)) + B
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+
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+
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+def plot_tuning_curve(traj, direction_idx, plot_run_names):
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+ # p0 is the initial guess for the fitting coefficients (A, mu and sigma above)
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+ p0 = [30., 0., 1., 10.]
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+
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+ fig, ax = plt.subplots(1, 1)
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+ for run_idx, run_name in enumerate(plot_run_names):
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+ label = traj.derived_parameters.runs[run_name].morphology.morph_label
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+
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+ ex_tunings = traj.results.runs[run_name].ex_tunings
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+
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+ coeff_list = []
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+
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+ ex_tunings_plt = np.array(ex_tunings)
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+ sort_ids = ex_tunings_plt.argsort()
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+ ex_tunings_plt = ex_tunings_plt[sort_ids]
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+
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+ firing_rate_array = traj.results[run_name].firing_rate_array
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+ # firing_rate_array = traj.f_get('firing_rate_array')
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+
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+ rates_plt = firing_rate_array[:, direction_idx]
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+ rates_plt = rates_plt[sort_ids]
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+ coeff, var_matrix = curve_fit(gauss, ex_tunings_plt, rates_plt / hertz, p0=p0)
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+ coeff_list.append(coeff)
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+ ax.scatter(ex_tunings_plt, rates_plt / hertz, label=label, alpha=0.3)
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+ ax.plot(ex_tunings_plt, gauss(ex_tunings_plt, *coeff), label=label + '-fit')
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+
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+ ax.legend()
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+ ax.set_xlabel("Angles (rad)")
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+ ax.set_ylabel("f (Hz)")
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+ ax.set_title('tuning curves', fontsize=16)
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+
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+ if save_figs:
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+ plt.savefig(FIGURE_SAVE_PATH + 'tuning_curve.png', dpi=200)
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+
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+def colorbar(mappable):
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+ from mpl_toolkits.axes_grid1 import make_axes_locatable
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+ import matplotlib.pyplot as plt
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+ last_axes = plt.gca()
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+ ax = mappable.axes
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+ fig = ax.figure
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+ divider = make_axes_locatable(ax)
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+ cax = divider.append_axes("right", size="5%", pad=0.05)
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+ cbar = fig.colorbar(mappable, cax=cax)
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+ plt.sca(last_axes)
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+ return cbar
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+
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+
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+def plot_firing_rate_map_excitatory(traj, direction_idx, plot_run_names):
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+ max_val = 0
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+ for run_name in plot_run_names:
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+ fr_array = traj.results.runs[run_name].firing_rate_array
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+ f_rates = fr_array[:, direction_idx]
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+ run_max_val = np.max(f_rates)
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+ if run_max_val > max_val:
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+ # if traj.derived_parameters.runs[run_name].morphology.morph_label == 'ellipsoid':
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+ # n_id_max_rate = np.argmax(f_rates)
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+ max_val = run_max_val
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+
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+ n_id_polar_plot = 609
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+
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+ # Mark the neuron that is shown in Polar plot
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+ ex_positions = traj.results.runs[plot_run_names[0]].ex_positions
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+ polar_plot_x, polar_plot_y = ex_positions[n_id_polar_plot]
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+
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+ # Vertices for the plotted triangle
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+ tr_scale = 13.
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+ tr_x = tr_scale * np.cos(2. * np.pi / 3. + np.pi / 2.)
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+ tr_y = tr_scale * np.sin(2. * np.pi / 3. + np.pi / 2.) + polar_plot_y
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+ tr_vertices = np.array([[polar_plot_x, polar_plot_y + tr_scale], [tr_x + polar_plot_x, tr_y], [-tr_x + polar_plot_x, tr_y]])
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+
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+ height = 4.5
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+ # color_bar_size = 0.05 * height + 0.05
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+ # width = 3 * height + color_bar_size
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+ width = 13.5
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+
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+ fig, axes = plt.subplots(1, 3, figsize=(width, height))
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+ for ax, run_name in zip(axes, plot_run_names[::-1]):
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+ label = traj.derived_parameters.runs[run_name].morphology.morph_label
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+
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+ X, Y = get_correct_position_mesh(traj.results.runs[run_name].ex_positions)
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+ firing_rate_array = traj.results[run_name].firing_rate_array
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+ number_of_excitatory_neurons_per_row = int(np.sqrt(traj.N_E))
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+ c = ax.pcolor(X, Y, np.reshape(firing_rate_array[:, direction_idx], (number_of_excitatory_neurons_per_row,
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+ number_of_excitatory_neurons_per_row)),
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+ vmin=0, vmax=max_val, cmap='Reds')
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+ ax.set_title(label)
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+ # ax.add_artist(Ellipse((polar_plot_x, polar_plot_y), 20., 20., color='k', fill=False, lw=2.))
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+ # ax.add_artist(Ellipse((polar_plot_x, polar_plot_y), 20., 20., color='w', fill=False, lw=1.))
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+ ax.add_artist(Polygon(tr_vertices, closed=True, fill=False, lw=2.5, color='k'))
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+ ax.add_artist(Polygon(tr_vertices, closed=True, fill=False, lw=1.5, color='w'))
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+ # fig.suptitle('spatial firing rate map', fontsize=16)
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+ colorbar(c)
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+
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+
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+ fig.tight_layout()
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+ if save_figs:
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+ plt.savefig(FIGURE_SAVE_PATH + 'firing_rate_map.png', dpi=200)
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+
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+ return n_id_polar_plot
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+
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+def plot_firing_rate_map_inhibitory(traj, direction_idx, plot_run_names):
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+ max_val = 0
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+ for run_name in plot_run_names:
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+ fr_array = traj.results.runs[run_name].inh_firing_rate_array
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+ f_rates = fr_array[:, direction_idx]
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+ run_max_val = np.max(f_rates)
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+ if run_max_val > max_val:
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+ max_val = run_max_val
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+
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+ n_id_polar_plot = 52
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+
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+ # Mark the neuron that is shown in Polar plot
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+ inhibitory_axonal_cloud_array = traj.results.runs[plot_run_names[1]].inhibitory_axonal_cloud_array
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+ polar_plot_x = inhibitory_axonal_cloud_array[n_id_polar_plot, 0]
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+ polar_plot_y = inhibitory_axonal_cloud_array[n_id_polar_plot, 1]
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+
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+ plot_run_names_sorted = [plot_run_names[1], plot_run_names[0]]
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+
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+ fig, axes = plt.subplots(1, 2, figsize=(9.0, 4.5))
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+ for ax, run_name in zip(axes, plot_run_names_sorted):
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+ label = traj.derived_parameters.runs[run_name].morphology.morph_label
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+
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+ inhibitory_axonal_cloud_array = traj.results.runs[run_name].inhibitory_axonal_cloud_array
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+
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+ inh_positions = [[p[0], p[1]] for p in inhibitory_axonal_cloud_array]
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+
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+ X, Y = get_correct_position_mesh(inh_positions)
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+ inh_firing_rate_array = traj.results[run_name].inh_firing_rate_array
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+ number_of_inhibitory_neurons_per_row = int(np.sqrt(traj.N_I))
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+ c = ax.pcolor(X, Y, np.reshape(inh_firing_rate_array[:, direction_idx], (number_of_inhibitory_neurons_per_row,
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+ number_of_inhibitory_neurons_per_row)),
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+ vmin=0, vmax=max_val, cmap='Blues')
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+ ax.set_title(label)
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+
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+ circle_r = 40.
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+ ax.add_artist(Ellipse((polar_plot_x, polar_plot_y), circle_r, circle_r, color='k', fill=False, lw=4.5))
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+ ax.add_artist(Ellipse((polar_plot_x, polar_plot_y), circle_r, circle_r, color='w', fill=False, lw=3))
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+ # fig.colorbar(c, ax=ax, label="f (Hz)")
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+ # fig.suptitle('spatial firing rate map', fontsize=16)
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+ colorbar(c)
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+ fig.tight_layout()
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+ if save_figs:
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+ plt.savefig(FIGURE_SAVE_PATH + 'inh_firing_rate_map.png', dpi=200)
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+ return n_id_polar_plot, max_val
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+
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+def plot_hdi_over_tuning(traj, plot_run_names):
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+ fig, ax = plt.subplots(1, 1)
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+ for run_idx, run_name in enumerate(plot_run_names):
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+ label = traj.derived_parameters.runs[run_name].morphology.morph_label
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+
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+ ex_tunings = traj.results.runs[run_name].ex_tunings
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+
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+ ex_tunings_plt = np.array(ex_tunings)
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+ sort_ids = ex_tunings_plt.argsort()
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+ ex_tunings_plt = ex_tunings_plt[sort_ids]
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+
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+ head_direction_indices = traj.results[run_name].head_direction_indices
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+
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+ hdi_plt = head_direction_indices
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+ hdi_plt = hdi_plt[sort_ids]
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+ ax.scatter(ex_tunings_plt, hdi_plt, label=label, alpha=0.3)
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+
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+ ax.legend()
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+ ax.set_xlabel("Angles (rad)")
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+ ax.set_ylabel("head direction index")
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+ ax.set_title('hdi over input tuning', fontsize=16)
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+
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+ if save_figs:
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+ plt.savefig(FIGURE_SAVE_PATH + 'hdi_over_tuning.png', dpi=200)
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+
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+
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+def plot_axonal_clouds(traj, plot_run_names):
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+ n_ex = int(np.sqrt(traj.N_E))
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+
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+ fig, axes = plt.subplots(1, 3, figsize=(13.5, 4.5))
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+ for ax, run_name in zip(axes, plot_run_names[::-1]):
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+ traj.f_set_crun(run_name)
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+
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+ label = traj.derived_parameters.runs[run_name].morphology.morph_label
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+
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+ X, Y = get_correct_position_mesh(traj.results.runs[run_name].ex_positions)
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+
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+ inhibitory_axonal_cloud_array = traj.results.runs[run_name].inhibitory_axonal_cloud_array
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+ axonal_clouds = [Pickle(p[0], p[1], traj.morphology.long_axis, traj.morphology.short_axis, p[2]) for p in
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+ inhibitory_axonal_cloud_array]
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+
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+ head_dir_preference = np.array(traj.results.runs[run_name].ex_tunings).reshape((n_ex, n_ex))
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+ # TODO: Why was this transposed for plotting? (now changed)
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+ c = ax.pcolor(X, Y, head_dir_preference, vmin=-np.pi, vmax=np.pi, cmap='hsv')
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+ ax.set_title(label)
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+ # fig.colorbar(c, ax=ax, label="Tuning")
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+
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+ if label != 'no conn' and axonal_clouds is not None:
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+ for i, p in enumerate(axonal_clouds):
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+ ell = p.get_ellipse()
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+ ax.add_artist(ell)
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+ # fig.suptitle('axonal cloud', fontsize=16)
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+ traj.f_restore_default()
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+
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+ fig.tight_layout()
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+
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+ if save_figs:
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+ plt.savefig(FIGURE_SAVE_PATH + 'axonal_clouds.png', dpi=200)
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+
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+def plot_orientation_maps_diff_scales(traj):
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+
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+ n_ex = int(np.sqrt(traj.N_E))
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+
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+ scale_run_names = []
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+ plot_scales = [0.0, 100.0, 200.0, 300.0]
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+ for scale in plot_scales:
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+ par_dict = {'seed': 1, 'correlation_length': get_closest_correlation_length(traj,scale), 'long_axis': 100.}
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+ scale_run_names.append(*filter_run_names_by_par_dict(traj, par_dict))
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+
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+ fig, axes = plt.subplots(1, 4, figsize=(18., 4.5))
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+ for ax, run_name, scale in zip(axes, scale_run_names, plot_scales):
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+ traj.f_set_crun(run_name)
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+
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+ X, Y = get_position_mesh(traj.results.runs[run_name].ex_positions)
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+
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+ head_dir_preference = np.array(traj.results.runs[run_name].ex_tunings).reshape((n_ex, n_ex))
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+ # TODO: Why was this transposed for plotting? (now changed)
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+ c = ax.pcolor(X, Y, head_dir_preference, vmin=-np.pi, vmax=np.pi, cmap='twilight')
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+ ax.set_title('Correlation length: {}'.format(scale))
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+ fig.colorbar(c, ax=ax, label="Tuning")
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+
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+ # fig.suptitle('axonal cloud', fontsize=16)
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+ traj.f_restore_default()
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+
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+ if save_figs:
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+ plt.savefig(FIGURE_SAVE_PATH + 'orientation_maps_diff_scales.png', dpi=200)
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+
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+
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+def plot_orientation_maps_diff_scales_with_ellipse(traj):
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+ n_ex = int(np.sqrt(traj.N_E))
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+
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+ scale_run_names = []
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+ plot_scales = [0.0, 100.0, 200.0, 300.0, 400.0]
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+ for scale in plot_scales:
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+ par_dict = {'seed': 1, 'correlation_length': get_closest_correlation_length(traj,scale), 'long_axis': 100.}
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+ scale_run_names.append(*filter_run_names_by_par_dict(traj, par_dict))
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+ print(scale_run_names)
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+
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+ fig, axes = plt.subplots(1, 5, figsize=(18., 4.5))
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+ for ax, run_name, scale in zip(axes, scale_run_names, plot_scales):
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+ traj.f_set_crun(run_name)
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+
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+ X, Y = get_position_mesh(traj.results.runs[run_name].ex_positions)
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+
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+ inhibitory_axonal_cloud_array = traj.results.runs[run_name].inhibitory_axonal_cloud_array
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+ axonal_clouds = [Pickle(p[0], p[1], traj.morphology.long_axis, traj.morphology.short_axis, p[2]) for p in
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+ inhibitory_axonal_cloud_array]
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+
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+ head_dir_preference = np.array(traj.results.runs[run_name].ex_tunings).reshape((n_ex, n_ex))
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+ # TODO: Why was this transposed for plotting? (now changed)
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+ c = ax.pcolor(X, Y, head_dir_preference, vmin=-np.pi, vmax=np.pi, cmap='hsv')
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+ # ax.set_title('Correlation length: {}'.format(scale))
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+ # fig.colorbar(c, ax=ax, label="Tuning")
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+ ax.set_xticks([])
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+ ax.set_yticks([])
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+
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+ p1 = axonal_clouds[44]
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+ ell = p1.get_ellipse()
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+ ell._linewidth = 5.
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+ ax.add_artist(ell)
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+
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+ p2 = axonal_clouds[77]
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+ circ_r = 2 * np.sqrt(2500.)
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+ circ = Ellipse((p2.x, p2.y), circ_r, circ_r, fill=False, zorder=2, edgecolor='k')
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+ circ._linewidth = 5.
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+
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+ ax.add_artist(circ)
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+
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+ # fig.suptitle('axonal cloud', fontsize=16)
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+ traj.f_restore_default()
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+
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+ fig.tight_layout()
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+
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+ if save_figs:
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+ plt.savefig(FIGURE_SAVE_PATH + 'orientation_maps_diff_scales_with_ellipse.png', dpi=200)
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+
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+def plot_excitatory_condensed_polar_plot(traj, plot_run_names, polar_plot_id):
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+ directions = np.linspace(-np.pi, np.pi, traj.input.number_of_directions, endpoint=False)
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+ directions_plt = list(directions)
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+ directions_plt.append(directions[0])
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+ fig, ax = plt.subplots(1, 1, figsize=(3.5, 3.5), subplot_kw=dict(projection='polar'))
|
|
|
+ # head_direction_indices = traj.results.runs[plot_run_names[0]].head_direction_indices
|
|
|
+ # sorted_ids = np.argsort(head_direction_indices)
|
|
|
+ # plot_n_idx = sorted_ids[-75]
|
|
|
+ plot_n_idx = polar_plot_id
|
|
|
+
|
|
|
+ line_styles = ['dotted', 'solid', 'dashed']
|
|
|
+ colors = ['r', 'lightsalmon', 'grey']
|
|
|
+
|
|
|
+ max_rate = 0.0
|
|
|
+ for run_idx, run_name in enumerate(plot_run_names):
|
|
|
+ label = traj.derived_parameters.runs[run_name].morphology.morph_label
|
|
|
+
|
|
|
+ tuning_vectors = traj.results.runs[run_name].tuning_vectors
|
|
|
+ rate_plot = [np.linalg.norm(v) for v in tuning_vectors[plot_n_idx]]
|
|
|
+ run_max_rate = np.max(rate_plot)
|
|
|
+ if run_max_rate > max_rate:
|
|
|
+ max_rate = run_max_rate
|
|
|
+ rate_plot.append(rate_plot[0])
|
|
|
+ ax.plot(directions_plt, rate_plot, label=label, color=colors[run_idx], linestyle=line_styles[run_idx])
|
|
|
+ # ax.set_title('Firing Rate')
|
|
|
+ ax.plot([0.0, 0.0], [0.0, 1.05 * max_rate], color='red', alpha=0.25, linewidth=4.)
|
|
|
+ # TODO: Set ticks for polar
|
|
|
+ ticks = [30., 60., 90.]
|
|
|
+ ax.set_rticks(ticks)
|
|
|
+ ax.set_rlabel_position(230)
|
|
|
+ ax.legend(loc='upper center', bbox_to_anchor=(0.2, 1.05),
|
|
|
+ fancybox=True, shadow=True)
|
|
|
+ plt.tight_layout()
|
|
|
+
|
|
|
+ if save_figs:
|
|
|
+ plt.savefig(FIGURE_SAVE_PATH + 'condensed_polar_plot.png', dpi=200)
|
|
|
+
|
|
|
+def plot_inhibitory_condensed_polar_plot(traj, plot_run_names, polar_plot_id, max_rate):
|
|
|
+ directions = np.linspace(-np.pi, np.pi, traj.input.number_of_directions, endpoint=False)
|
|
|
+ directions_plt = list(directions)
|
|
|
+ directions_plt.append(directions[0])
|
|
|
+ fig, ax = plt.subplots(1, 1, figsize=(3.5, 3.5), subplot_kw=dict(projection='polar'))
|
|
|
+ # head_direction_indices = traj.results.runs[plot_run_names[0]].inh_head_direction_indices
|
|
|
+ # sorted_ids = np.argsort(head_direction_indices)
|
|
|
+ # plot_n_idx = sorted_ids[-75]
|
|
|
+ plot_n_idx = polar_plot_id
|
|
|
+
|
|
|
+ line_styles = ['dotted', 'solid']
|
|
|
+ colors = ['b', 'lightblue']
|
|
|
+
|
|
|
+ for run_idx, run_name in enumerate(plot_run_names[:2]):
|
|
|
+ # ax = axes[max_hdi_idx, run_idx]
|
|
|
+ label = traj.derived_parameters.runs[run_name].morphology.morph_label
|
|
|
+
|
|
|
+ tuning_vectors = traj.results.runs[run_name].inh_tuning_vectors
|
|
|
+ rate_plot = [np.linalg.norm(v) for v in tuning_vectors[plot_n_idx]]
|
|
|
+ rate_plot.append(rate_plot[0])
|
|
|
+ ax.plot(directions_plt, rate_plot, label=label, color=colors[run_idx], linestyle=line_styles[run_idx])
|
|
|
+ # ax.set_title('Inh. Firing Rate')
|
|
|
+ # TODO: Set ticks for polar
|
|
|
+ # ticks = [np.round(max_rate / 3.), np.round(max_rate * 2. / 3.), np.round(max_rate)]
|
|
|
+ ticks = [40., 80., 120.]
|
|
|
+ ax.set_rticks(ticks)
|
|
|
+ ax.set_rlabel_position(230)
|
|
|
+ ax.legend(loc='upper center', bbox_to_anchor=(0.2, 1.05),
|
|
|
+ fancybox=True, shadow=True)
|
|
|
+ plt.tight_layout()
|
|
|
+
|
|
|
+ if save_figs:
|
|
|
+ plt.savefig(FIGURE_SAVE_PATH + 'condensed_inhibitory_polar_plot.png', dpi=200)
|
|
|
+
|
|
|
+def plot_hdi_over_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)
|
|
|
+
|
|
|
+ hdi_frame = pd.Series(index=[corr_len_expl, seed_expl, label_expl])
|
|
|
+ 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
|
|
|
+ hdi_frame[corr_len, seed, label] = np.mean(head_direction_indices)
|
|
|
+
|
|
|
+ # TODO: Standart deviation also for the population
|
|
|
+ hdi_exc_n_and_seed_mean = hdi_frame.groupby(level=[0, 2]).mean()
|
|
|
+ hdi_exc_n_and_seed_std_dev = hdi_frame.groupby(level=[0, 2]).std()
|
|
|
+
|
|
|
+ # Ellipsoid markers
|
|
|
+ rx, ry = 5., 12.
|
|
|
+ # area = rx * ry * np.pi * 2.
|
|
|
+ area = 1.
|
|
|
+ theta = np.arange(0, 2 * np.pi + 0.01, 0.1)
|
|
|
+ verts = np.column_stack([rx / area * np.cos(theta), ry / area * np.sin(theta)])
|
|
|
+
|
|
|
+ style_dict = {
|
|
|
+ 'no conn': ['grey', 'dashed', '', 0],
|
|
|
+ 'ellipsoid': ['blue', 'solid', verts, 10.],
|
|
|
+ 'circular': ['lightblue', 'solid', 'o', 8.]
|
|
|
+
|
|
|
+ }
|
|
|
+ # colors = ['blue', 'grey', 'lightblue']
|
|
|
+ # linestyles = ['solid', 'dashed', 'solid']
|
|
|
+ # markers = [verts, '', 'o']
|
|
|
+
|
|
|
+
|
|
|
+ fig, ax = plt.subplots(1, 1)
|
|
|
+
|
|
|
+ for label in label_range:
|
|
|
+ hdi_mean = hdi_exc_n_and_seed_mean[:, label]
|
|
|
+ hdi_std = hdi_exc_n_and_seed_std_dev[:, label]
|
|
|
+ corr_len_range = hdi_mean.keys().to_numpy()
|
|
|
+
|
|
|
+ col, lin, mar, mar_size = style_dict[label]
|
|
|
+
|
|
|
+ ax.plot(corr_len_range, hdi_mean, label=label, marker=mar, color=col, linestyle=lin, markersize=mar_size)
|
|
|
+ plt.fill_between(corr_len_range, hdi_mean - hdi_std,
|
|
|
+ hdi_mean + hdi_std, alpha=0.4, color=col)
|
|
|
+ ax.set_xlabel('Correlation length')
|
|
|
+ ax.set_ylabel('Head Direction Index')
|
|
|
+ ax.axvline(206.9, color='k', linewidth=0.5)
|
|
|
+ ax.set_ylim(0.0,1.0)
|
|
|
+ ax.set_xlim(0.0,400.)
|
|
|
+ ax.legend()
|
|
|
+ if save_figs:
|
|
|
+ plt.savefig(FIGURE_SAVE_PATH + 'hdi_over_corr_len_scaled.png', dpi=200)
|
|
|
+
|
|
|
+
|
|
|
+def plot_hdi_histogram_excitatory(traj, plot_run_names):
|
|
|
+ labels = []
|
|
|
+ hdis = []
|
|
|
+ colors = ['black', 'red', 'green']
|
|
|
+ for run_idx, run_name in enumerate(plot_run_names):
|
|
|
+ label = traj.derived_parameters.runs[run_name].morphology.morph_label
|
|
|
+ labels.append(label)
|
|
|
+
|
|
|
+ head_direction_indices = traj.results.runs[run_name].head_direction_indices
|
|
|
+ hdis.append(head_direction_indices)
|
|
|
+
|
|
|
+ fig, ax = plt.subplots(1, 1, figsize=(6, 3))
|
|
|
+ ax.hist(hdis, color=colors, label=labels, bins=30)
|
|
|
+
|
|
|
+ for hdi, color in zip(hdis, colors):
|
|
|
+ mean_hdi = np.mean(hdi)
|
|
|
+ ax.axvline(mean_hdi, 0, 1, color=color, linestyle='--')
|
|
|
+ ax.set_xlabel("HDI")
|
|
|
+ ax.legend()
|
|
|
+
|
|
|
+ fig.tight_layout()
|
|
|
+
|
|
|
+ if save_figs:
|
|
|
+ plt.savefig(FIGURE_SAVE_PATH + 'hdi_histogram_excitatory.png', dpi=200)
|
|
|
+
|
|
|
+
|
|
|
+def plot_hdi_violin_excitatory(traj, plot_run_names):
|
|
|
+ labels = []
|
|
|
+ hdis = []
|
|
|
+ colors = ['black', 'red', 'green']
|
|
|
+ no_conn_hdi = 0.
|
|
|
+ for run_idx, run_name in enumerate(plot_run_names):
|
|
|
+ label = traj.derived_parameters.runs[run_name].morphology.morph_label
|
|
|
+ head_direction_indices = traj.results.runs[run_name].head_direction_indices
|
|
|
+
|
|
|
+ if label == 'no conn':
|
|
|
+ no_conn_hdi = np.mean(head_direction_indices)
|
|
|
+ else:
|
|
|
+ labels.append(label)
|
|
|
+ hdis.append(sorted(head_direction_indices))
|
|
|
+
|
|
|
+ fig, ax = plt.subplots(1, 1, figsize=(6, 3))
|
|
|
+ # hdis = np.array(hdis)
|
|
|
+ viol_plt = ax.violinplot(hdis, showmeans=True, showextrema=False)
|
|
|
+
|
|
|
+ viol_plt['cmeans'].set_color('black')
|
|
|
+
|
|
|
+ for pc in viol_plt['bodies']:
|
|
|
+ pc.set_facecolor('red')
|
|
|
+ pc.set_edgecolor('black')
|
|
|
+ pc.set_alpha(0.7)
|
|
|
+
|
|
|
+ ax.axhline(no_conn_hdi, color='black', linestyle='--')
|
|
|
+ ax.annotate('no conn', xy=(0.45,0.48), xycoords='axes fraction')
|
|
|
+
|
|
|
+ ax.set_xticks(np.arange(1, len(labels) + 1))
|
|
|
+ ax.set_xticklabels(labels)
|
|
|
+ ax.set_ylabel('HDI')
|
|
|
+
|
|
|
+ fig.tight_layout()
|
|
|
+
|
|
|
+ if save_figs:
|
|
|
+ plt.savefig(FIGURE_SAVE_PATH + 'hdi_violin_excitatory.png', dpi=200)
|
|
|
+
|
|
|
+
|
|
|
+def plot_hdi_violin_inhibitory(traj, plot_run_names):
|
|
|
+ labels = []
|
|
|
+ hdis = []
|
|
|
+ colors = ['black', 'red']
|
|
|
+ for run_idx, run_name in enumerate(plot_run_names):
|
|
|
+ label = traj.derived_parameters.runs[run_name].morphology.morph_label
|
|
|
+ if label != 'no conn':
|
|
|
+ labels.append(label)
|
|
|
+
|
|
|
+ head_direction_indices = traj.results.runs[run_name].inh_head_direction_indices
|
|
|
+ hdis.append(sorted(head_direction_indices))
|
|
|
+
|
|
|
+ fig, ax = plt.subplots(1, 1, figsize=(6, 3))
|
|
|
+ viol_plt = ax.violinplot(hdis, showmeans=True, showextrema=False)
|
|
|
+
|
|
|
+ viol_plt['cmeans'].set_color('black')
|
|
|
+
|
|
|
+ for pc in viol_plt['bodies']:
|
|
|
+ pc.set_facecolor('blue')
|
|
|
+ pc.set_edgecolor('black')
|
|
|
+ pc.set_alpha(0.7)
|
|
|
+
|
|
|
+ ax.set_xticks(np.arange(1, len(labels) + 1))
|
|
|
+ ax.set_xticklabels(labels)
|
|
|
+ ax.set_ylabel('HDI')
|
|
|
+
|
|
|
+
|
|
|
+ fig.tight_layout()
|
|
|
+
|
|
|
+ if save_figs:
|
|
|
+ plt.savefig(FIGURE_SAVE_PATH + 'hdi_violin_inhibitory.png', dpi=200)
|
|
|
+
|
|
|
+def plot_hdi_violin_combined(traj, plot_run_names):
|
|
|
+ labels = []
|
|
|
+ inh_hdis = []
|
|
|
+ exc_hdis = []
|
|
|
+ no_conn_hdi = 0.
|
|
|
+
|
|
|
+ colors = ['black', 'red']
|
|
|
+ for run_idx, run_name in enumerate(plot_run_names):
|
|
|
+ label = traj.derived_parameters.runs[run_name].morphology.morph_label
|
|
|
+ if label != 'no conn':
|
|
|
+ labels.append(label)
|
|
|
+
|
|
|
+ inh_head_direction_indices = traj.results.runs[run_name].inh_head_direction_indices
|
|
|
+ inh_hdis.append(sorted(inh_head_direction_indices))
|
|
|
+
|
|
|
+ exc_head_direction_indices = traj.results.runs[run_name].head_direction_indices
|
|
|
+ exc_hdis.append(sorted(exc_head_direction_indices))
|
|
|
+ else:
|
|
|
+ exc_head_direction_indices = traj.results.runs[run_name].head_direction_indices
|
|
|
+ no_conn_hdi = np.mean(exc_head_direction_indices)
|
|
|
+
|
|
|
+ fig, ax = plt.subplots(1, 1, figsize=(6, 3))
|
|
|
+ inh_viol_plt = ax.violinplot(inh_hdis, showmeans=True, showextrema=False)
|
|
|
+
|
|
|
+ # viol_plt['cmeans'].set_color('black')
|
|
|
+ #
|
|
|
+ # for pc in viol_plt['bodies']:
|
|
|
+ # pc.set_facecolor('blue')
|
|
|
+ # pc.set_edgecolor('black')
|
|
|
+ # pc.set_alpha(0.7)
|
|
|
+
|
|
|
+ for b in inh_viol_plt['bodies']:
|
|
|
+ m = np.mean(b.get_paths()[0].vertices[:, 0])
|
|
|
+ b.get_paths()[0].vertices[:, 0] = np.clip(b.get_paths()[0].vertices[:, 0], m, np.inf)
|
|
|
+ b.set_color('b')
|
|
|
+
|
|
|
+ exc_viol_plt = ax.violinplot(exc_hdis, showmeans=True, showextrema=False)
|
|
|
+
|
|
|
+ for b in exc_viol_plt['bodies']:
|
|
|
+ m = np.mean(b.get_paths()[0].vertices[:, 0])
|
|
|
+ b.get_paths()[0].vertices[:, 0] = np.clip(b.get_paths()[0].vertices[:, 0], -np.inf, m)
|
|
|
+ b.set_color('r')
|
|
|
+
|
|
|
+ ax.axhline(no_conn_hdi, color='black', linestyle='--')
|
|
|
+ ax.annotate('no conn', xy=(0.45, 0.48), xycoords='axes fraction')
|
|
|
+
|
|
|
+ ax.set_xticks(np.arange(1, len(labels) + 1))
|
|
|
+ ax.set_xticklabels(labels)
|
|
|
+ ax.set_ylabel('HDI')
|
|
|
+
|
|
|
+
|
|
|
+ fig.tight_layout()
|
|
|
+
|
|
|
+ if save_figs:
|
|
|
+ plt.savefig(FIGURE_SAVE_PATH + 'hdi_violin_combined.svg', dpi=200)
|
|
|
+
|
|
|
+def plot_hdi_violin_combined_and_overlayed(traj, plot_run_names):
|
|
|
+ labels = []
|
|
|
+ inh_hdis = []
|
|
|
+ exc_hdis = []
|
|
|
+ no_conn_hdi = 0.
|
|
|
+
|
|
|
+ colors = ['black', 'red']
|
|
|
+ for run_idx, run_name in enumerate(plot_run_names):
|
|
|
+ label = traj.derived_parameters.runs[run_name].morphology.morph_label
|
|
|
+ if label != 'no conn':
|
|
|
+ labels.append(label)
|
|
|
+
|
|
|
+ inh_head_direction_indices = traj.results.runs[run_name].inh_head_direction_indices
|
|
|
+ inh_hdis.append(sorted(inh_head_direction_indices))
|
|
|
+
|
|
|
+ exc_head_direction_indices = traj.results.runs[run_name].head_direction_indices
|
|
|
+ exc_hdis.append(sorted(exc_head_direction_indices))
|
|
|
+ else:
|
|
|
+ exc_head_direction_indices = traj.results.runs[run_name].head_direction_indices
|
|
|
+ no_conn_hdi = np.mean(exc_head_direction_indices)
|
|
|
+
|
|
|
+ fig, ax = plt.subplots(1, 1, figsize=(3.5, 4.5))
|
|
|
+
|
|
|
+ inh_ell_viol_plt = ax.violinplot(inh_hdis[0], showmeans=True, showextrema=False)
|
|
|
+ for b in inh_ell_viol_plt['bodies']:
|
|
|
+ m = np.mean(b.get_paths()[0].vertices[:, 0])
|
|
|
+ b.get_paths()[0].vertices[:, 0] = np.clip(b.get_paths()[0].vertices[:, 0], m, np.inf)
|
|
|
+ b.set_color('b')
|
|
|
+ mean_line = inh_ell_viol_plt['cmeans']
|
|
|
+ mean_line.set_color('b')
|
|
|
+ mean_line.get_paths()[0].vertices[:, 0] = np.clip(mean_line.get_paths()[0].vertices[:, 0], m, np.inf)
|
|
|
+
|
|
|
+ exc_ell_viol_plt = ax.violinplot(exc_hdis[0], showmeans=True, showextrema=False)
|
|
|
+ for b in exc_ell_viol_plt['bodies']:
|
|
|
+ m = np.mean(b.get_paths()[0].vertices[:, 0])
|
|
|
+ b.get_paths()[0].vertices[:, 0] = np.clip(b.get_paths()[0].vertices[:, 0], m, np.inf)
|
|
|
+ b.set_color('r')
|
|
|
+ mean_line = exc_ell_viol_plt['cmeans']
|
|
|
+ mean_line.set_color('r')
|
|
|
+ mean_line.get_paths()[0].vertices[:, 0] = np.clip(mean_line.get_paths()[0].vertices[:, 0], m, np.inf)
|
|
|
+
|
|
|
+ inh_cir_viol_plt = ax.violinplot(inh_hdis[1], showmeans=True, showextrema=False)
|
|
|
+ for b in inh_cir_viol_plt['bodies']:
|
|
|
+ m = np.mean(b.get_paths()[0].vertices[:, 0])
|
|
|
+ b.get_paths()[0].vertices[:, 0] = np.clip(b.get_paths()[0].vertices[:, 0], -np.inf, m)
|
|
|
+ b.set_color('b')
|
|
|
+ mean_line = inh_cir_viol_plt['cmeans']
|
|
|
+ mean_line.set_color('b')
|
|
|
+ mean_line.get_paths()[0].vertices[:, 0] = np.clip(mean_line.get_paths()[0].vertices[:, 0], -np.inf, m)
|
|
|
+
|
|
|
+ exc_cir_viol_plt = ax.violinplot(exc_hdis[1], showmeans=True, showextrema=False)
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|
|
+ for b in exc_cir_viol_plt['bodies']:
|
|
|
+ m = np.mean(b.get_paths()[0].vertices[:, 0])
|
|
|
+ b.get_paths()[0].vertices[:, 0] = np.clip(b.get_paths()[0].vertices[:, 0], -np.inf, m)
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|
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+ b.set_color('r')
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|
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+ mean_line = exc_cir_viol_plt['cmeans']
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|
|
+ mean_line.set_color('r')
|
|
|
+ mean_line.get_paths()[0].vertices[:, 0] = np.clip(mean_line.get_paths()[0].vertices[:, 0], -np.inf, m)
|
|
|
+
|
|
|
+ ax.axhline(no_conn_hdi, 0.5, 1., color='black', linestyle='--')
|
|
|
+ ax.axvline(1.0, color='k')
|
|
|
+ ax.annotate('no conn', xy=(0.75, 0.415), xycoords='axes fraction')
|
|
|
+ ax.set_xlim(0.5, 1.5)
|
|
|
+ ax.set_ylim(0.0, 1.0)
|
|
|
+ ax.set_xticks([0.75, 1.25])
|
|
|
+ ax.set_xticklabels(['circular', 'ellipsoid'])
|
|
|
+ ax.set_ylabel('HDI')
|
|
|
+
|
|
|
+
|
|
|
+ fig.tight_layout()
|
|
|
+
|
|
|
+ if save_figs:
|
|
|
+ plt.savefig(FIGURE_SAVE_PATH + 'hdi_violin_combined_and_overlayed.svg', dpi=200)
|
|
|
+
|
|
|
+def plot_hdi_histogram_inhibitory(traj, plot_run_names):
|
|
|
+ labels = []
|
|
|
+ hdis = []
|
|
|
+ colors = ['black', 'red']
|
|
|
+ for run_idx, run_name in enumerate(plot_run_names):
|
|
|
+ label = traj.derived_parameters.runs[run_name].morphology.morph_label
|
|
|
+ if label != 'no conn':
|
|
|
+ labels.append(label)
|
|
|
+
|
|
|
+ head_direction_indices = traj.results.runs[run_name].inh_head_direction_indices
|
|
|
+ hdis.append(head_direction_indices)
|
|
|
+
|
|
|
+ fig, ax = plt.subplots(1, 1, figsize=(6, 3))
|
|
|
+ ax.hist(hdis, color=colors, label=labels, bins=30)
|
|
|
+
|
|
|
+ for hdi, color in zip(hdis, colors):
|
|
|
+ mean_hdi = np.mean(hdi)
|
|
|
+ ax.axvline(mean_hdi, 0, 1, color=color, linestyle='--')
|
|
|
+ ax.set_xlabel("HDI")
|
|
|
+ ax.legend()
|
|
|
+
|
|
|
+ fig.tight_layout()
|
|
|
+
|
|
|
+ if save_figs:
|
|
|
+ plt.savefig(FIGURE_SAVE_PATH + 'hdi_histogram_inhibitory.png', dpi=200)
|
|
|
+
|
|
|
+
|
|
|
+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
|
|
|
+
|
|
|
+
|
|
|
+if __name__ == "__main__":
|
|
|
+ traj = Trajectory(TRAJ_NAME, add_time=False, dynamic_imports=Brian2MonitorResult)
|
|
|
+ NO_LOADING = 0
|
|
|
+ FULL_LOAD = 2
|
|
|
+ traj.f_load(filename=DATA_FOLDER + TRAJ_NAME + ".hdf5", load_parameters=FULL_LOAD, load_results=NO_LOADING)
|
|
|
+ traj.v_auto_load = True
|
|
|
+ save_figs = True
|
|
|
+
|
|
|
+ plot_corr_len = get_closest_correlation_length(traj, 200.0)
|
|
|
+ par_dict = {'seed': 1, 'correlation_length': plot_corr_len}
|
|
|
+ plot_run_names = filter_run_names_by_par_dict(traj, par_dict)
|
|
|
+ print(plot_run_names)
|
|
|
+
|
|
|
+ direction_idx = 6
|
|
|
+ dir_indices = [0, 3, 6, 9]
|
|
|
+
|
|
|
+ # plot_axonal_clouds(traj, plot_run_names)
|
|
|
+ # #
|
|
|
+ # ex_polar_plot_id = plot_firing_rate_map_excitatory(traj, direction_idx, plot_run_names)
|
|
|
+ # #
|
|
|
+ # in_polar_plot_id, in_max_rate = plot_firing_rate_map_inhibitory(traj, direction_idx, plot_run_names)
|
|
|
+ #
|
|
|
+ # plot_orientation_maps_diff_scales_with_ellipse(traj)
|
|
|
+ #
|
|
|
+ # plot_hdi_histogram_inhibitory(traj, plot_run_names)
|
|
|
+ #
|
|
|
+ # plot_hdi_histogram_excitatory(traj, plot_run_names)
|
|
|
+ #
|
|
|
+ # plot_hdi_over_corr_len(traj, traj.f_get_run_names())
|
|
|
+
|
|
|
+ # plot_excitatory_condensed_polar_plot(traj, plot_run_names, ex_polar_plot_id)
|
|
|
+ #
|
|
|
+ # plot_inhibitory_condensed_polar_plot(traj, plot_run_names, in_polar_plot_id, in_max_rate)
|
|
|
+
|
|
|
+ # plot_hdi_violin_combined(traj, plot_run_names)
|
|
|
+ #
|
|
|
+ plot_hdi_violin_combined_and_overlayed(traj, plot_run_names)
|
|
|
+
|
|
|
+ if not save_figs:
|
|
|
+ plt.show()
|
|
|
+
|
|
|
+ traj.f_restore_default()
|
moritz