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graph_rewiring_v2.py
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graph_rewiring_v2.py
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#!/usr/bin/env python
import os
import subprocess
import ruamel_yaml as yaml
import configure_seaborn as cs
import matplotlib.lines as mlines
import matplotlib.pyplot as plt
import networkx as nx
import numpy as np
import seaborn as sns
import utils as utils
from matplotlib.gridspec import GridSpec
from matplotlib.patches import Ellipse, FancyArrowPatch, Polygon
sns.set(context='paper', style='ticks', rc=cs.rc_params)
def load_configuration(filepath):
with open(filepath, "r") as f:
config = yaml.safe_load(f)
return config
def draw_connections(G, pos, node_colors, ax, edge_weights=None):
alpha = 1.0
for n in G:
c = Ellipse(pos[n], width=0.015, height=0.015,
alpha=alpha, color=node_colors[n], clip_on=False)
ax.add_patch(c)
G.nodes[n]["patch"] = c
x, y = pos[n]
seen = {}
alpha = 1.0 # 0.8
for (u, v, d) in G.edges(data=True):
n1 = G.nodes[u]["patch"]
n2 = G.nodes[v]["patch"]
rad = 0.1
if (u, v) in seen:
rad = seen.get((u, v))
rad = (rad + np.sign(rad) * 0.1) * -1
color = node_colors[u]
e = FancyArrowPatch(n1.center,
n2.center,
patchA=n1,
patchB=n2,
shrinkA=0,
shrinkB=0,
arrowstyle='-',
linewidth=0.5,
connectionstyle="arc3, rad=%s" % rad,
mutation_scale=10.0,
alpha=alpha,
color=color,
clip_on=False)
ax.add_patch(e)
seen[(u, v)] = rad
def draw_assemblies(G, assemblies, colors):
node_colors = []
for i, assembly in enumerate(assemblies):
G.add_nodes_from(assembly)
node_colors += [colors[i]] * len(assembly)
return node_colors
def draw_neuron(G, ax, branch_nodes, center_assemblies):
x_c = center_assemblies
x_branch_end = [x_c - 0.75, x_c - 0.2, x_c + 0.6, x_c + 1.0]
def b1(x): return -0.2666667 * x - 0.573 # noqa
def b2(x): return -3.18 * x - 1.35 # noqa
def b3(x): return 0.666667 * x - 0.423 # noqa
def b4(x): return 0.201299 * x - 0.516 # noqa
b_fun = [b1, b2, b3, b4]
# Branch nodes
pos_branch_nodes = []
for i, (xe, bf, branch_node) in enumerate(zip(x_branch_end, b_fun, branch_nodes)):
dx = (x_c - xe) / 4
pos_branch_nodes += [(x_c - dx, bf(x_c - dx)),
(x_c - 2 * dx, bf(x_c - 2 * dx)),
(x_c - 3 * dx, bf(x_c - 3 * dx))]
G.add_nodes_from(branch_node)
node_colors = ["w"] * len(pos_branch_nodes)
# Neuron
xy = np.array([[x_c, -1.6], [x_c + 0.07, -1 + y_offset],
[x_c - 0.07, -1 + y_offset]])
nrn = Polygon(xy, clip_on=False, fill=False, color="k", lw=1)
ax.add_patch(nrn)
trunk = mlines.Line2D([x_c, x_c], [-1.6, -0.5], clip_on=False, color="k", linewidth=1)
ax.add_line(trunk)
branch1 = mlines.Line2D([x_c - 0.01, x_c - 0.75], [-0.5, -0.3], clip_on=False, color="k", linewidth=1)
ax.add_line(branch1)
branch2 = mlines.Line2D([x_c - 0.003, x_c - 0.2], [-0.486, 0.15], clip_on=False, color="k", linewidth=1)
ax.add_line(branch2)
branch3 = mlines.Line2D([x_c + 0.01, x_c + 0.7], [-0.6, -0.11], clip_on=False, color="k", linewidth=1)
ax.add_line(branch3)
branch4 = mlines.Line2D([x_c + 0.08, x_c + 0.85], [-0.555, -0.4], clip_on=False, color="k", linewidth=1)
ax.add_line(branch4)
return pos_branch_nodes, node_colors
def add_connections(experiment, weights, assemblies, assembly_idc, assembly_map, idc_other_assemblies,
num_neurons_per_assembly, idc_branch_nodes, min_plot_weight):
conn = []
for i, w in enumerate(weights):
nrns = np.where(w > min_plot_weight)[0]
print(len(nrns))
map_idx_a_idx_nb = {}
last_idx_nb = 0
for nrn in nrns:
idx_a = np.random.choice(map_neuron_id_to_assembly_id(nrn, assembly_idc))
if experiment == "rewiring_ex5":
if idx_a in map_idx_a_idx_nb:
idx_nb = map_idx_a_idx_nb[idx_a]
else:
if last_idx_nb == 0:
idx_nb = 2
last_idx_nb = 2
else:
idx_nb = 0
last_idx_nb = 0
map_idx_a_idx_nb[idx_a] = idx_nb
else:
idx_nb = np.random.choice(idc_branch_nodes)
idx_nrn = np.random.choice(num_neurons_per_assembly)
if idx_a not in assembly_map.keys():
idx_a = np.random.choice(idc_other_assemblies)
conn.append((assemblies[idx_a][idx_nrn], branch_nodes[i][idx_nb]))
else:
conn.append((assemblies[assembly_map[idx_a]][idx_nrn], branch_nodes[i][idx_nb]))
G.add_edges_from(conn)
def plot_input_spikes(input_spike_times, input_size, ax, xlim, pattern_labels, pattern_colors):
idc = np.where((input_spike_times[:, 0] >= xlim[0]) & (input_spike_times[:, 0] <= xlim[1]))
ax.scatter(input_spike_times[idc, 0], input_spike_times[idc, 1], s=1.0, color="k", edgecolor="none")
ax.set_xlim(xlim)
ax.set_ylim([None, input_size + 20])
ax.set_xticks([])
ax.set_yticks([1, input_size])
ax.set_yticklabels("%d" % f for f in ax.get_yticks())
ax.set_ylabel("Input", labelpad=3.2)
ax.yaxis.set_tick_params(width=0.8, length=2.5)
for p, (pl, pc) in enumerate(zip(pattern_labels, pattern_colors)):
ax.plot([xlim[0] + p * 0.5 + 0.2, xlim[0] + (p + 1) * 0.5],
[355, 355], color=pc, alpha=1.0, linestyle='-',
linewidth=1, clip_on=False)
ax.text(xlim[0] + (p + 1) * 0.5 - 0.15, 400, pl, ha="center", color=pc, alpha=1.0)
def plot_soma_potential(mem_soma, ax, xlim):
idc = np.where(mem_soma[:, 0] <= xlim[0] + 0.01)
mem_soma[:, 1][idc] = -70
idc = np.where((mem_soma[:, 0] >= xlim[0]) & (mem_soma[:, 0] <= xlim[1]))
ax.plot(mem_soma[idc][:, 0], mem_soma[idc][:, 1], color="k", linewidth=0.6)
ax.set_ylabel(r"$V^{\mathrm{soma}}$", rotation=0, va="baseline")
ax.yaxis.set_label_coords(0.003, 0.86)
ax.set_yticks([])
ax.set_ylim([-72, -25])
ax.set_xlim(xlim)
ax.set_xticks([])
for spine in ["left", "bottom"]:
ax.spines[spine].set_visible(False)
def plot_branch_potential(mem_branch, branch_id, ax, xlim):
idc = np.where((mem_branch[:, 0] >= xlim[0]) & (mem_branch[:, 0] <= xlim[1]))
ax.plot(mem_branch[idc][:, 0] - 0 * xlim[0], mem_branch[idc][:, 1], color="k", linewidth=0.6)
ax.set_xlim(xlim)
ax.set_ylim([-72, -25])
xticks = ax.get_xticks()
ax.set_xticks([])
ax.set_yticks([])
ax.set_ylabel(r"$V^{\mathrm{b}}_{" + str(branch_id) + "}$", rotation=0, va="center")
if branch_id >= 10:
ax.yaxis.set_label_coords(-0.0101, 0.75)
else:
ax.yaxis.set_label_coords(-0.012, 0.75)
for spine in ["left", "bottom"]:
ax.spines[spine].set_visible(False)
return xticks
def plot_scale(xlim):
line = mlines.Line2D([xlim[0] - 0.02, xlim[0] - 0.02], [-68.9, -48.9], clip_on=False, color=".15",
linewidth=0.7)
ax.add_line(line)
ax.text(xlim[0] - 0.19, -64, r"20 mV", fontsize=8)
def plot_scale_soma(xlim):
line = mlines.Line2D([xlim[0] + 0.006, xlim[0] + 0.306], [-76.0 - 2, -76.0 - 2],
clip_on=False, color=".15", linewidth=0.7)
ax.add_line(line)
ax.text(np.mean([xlim[0] + 0.006, xlim[0] + 0.306]), -93 - 2, r"0.3 s", ha="center", fontsize=8)
line = mlines.Line2D([xlim[0] - 0.025, xlim[0] - 0.025], [-68.9 + 3, -44.5 + 3], clip_on=False,
color=".15", linewidth=0.7)
ax.add_line(line)
ax.text(xlim[0] - 0.19, -63 + 3, r"25 mV", fontsize=8)
def map_neuron_id(gid, old_min=0, old_max=319, new_min=0, new_max=35):
old_range = (old_max - old_min)
new_range = (new_max - new_min)
return int(((((gid - old_min) * new_range) / old_range) + new_min))
def map_neuron_id_to_assembly_id(gid, assembly_idc):
if not any(gid in x for x in assembly_idc):
return [-1]
return np.where(assembly_idc == gid)[0]
# ------------------------------------------------------------------------------
experiment = "rewiring_ex2"
sim_date = "191125_114900/22"
patterns = [0, 1, 2, 3, 4, 5]
branches = [1, 2, 3, 4]
branch_names = [2, 3, 4, 5]
xlim = [3.0, 6.2]
cs.set_figure_size(176 + 39, 68 + 23)
# ------------------------------------------------------------------------------
# Directory of simulation results and log files.
if experiment == "rewiring_ex4":
input_directory = os.path.join("results", experiment, "4", sim_date, "data")
else:
input_directory = os.path.join("results", experiment, sim_date, "data")
# Directory for plots.
plots_directory = os.path.join(input_directory, "..", "plots")
if not os.path.exists(plots_directory):
os.makedirs(plots_directory)
# Colors of patters.
# c = plt.rcParams['axes.prop_cycle'].by_key()['color']
# c[6] = c[8]
# c[7] = c[9]
c = sns.color_palette().as_hex()
c2 = sns.hls_palette(8, l=.3, s=.8)[-3:] # noqa
colors = [c[7]]
colors += [c[p] for i, p in enumerate(patterns[0:3])]
colors += [c[7]]
pattern_labels = [r"$\mathrm{A}_%d$" % (p + 1) for p in patterns[0:3]]
pattern_labels += [r"$\mathrm{R}_{%d}$" % (p + 1) for p in range(3)]
pattern_colors = [c[p] for p in patterns[0:3]]
pattern_colors += [c2[i] for i, p in enumerate(patterns[3:])]
assembly_map = {p: i + 1 for i, p in enumerate(patterns[0:3])}
# branches.sort()
np.random.seed(18) # 14, 16, 18
num_rows = 3
node_colors = []
num_branches = 3
num_assemblies = 5
min_plot_weight = 0.9
idc_other_assemblies = [0, 4]
num_assemblies_real = 3
num_branch_nodes = 3
num_neurons_per_row = 7
num_neurons_per_assembly = num_rows * num_neurons_per_row
pos_assemblies = []
x_offset = -0.54
y_offset = -0.79
for i in range(num_rows):
for x in np.linspace(-1 + x_offset, 1, 35):
pos_assemblies.append((x, 1.0 - i * 0.1))
np.random.shuffle(pos_assemblies)
assemblies = np.split(np.arange(num_neurons_per_assembly * num_assemblies), num_assemblies)
branch1_nodes = np.max(assemblies) + 1 + np.arange(num_branch_nodes)
branch2_nodes = np.max(branch1_nodes) + 1 + np.arange(num_branch_nodes)
branch3_nodes = np.max(branch2_nodes) + 1 + np.arange(num_branch_nodes)
branch4_nodes = np.max(branch3_nodes) + 1 + np.arange(num_branch_nodes)
# branch_nodes = [branch4_nodes, branch2_nodes, branch1_nodes, branch3_nodes]
branch_nodes = [branch1_nodes, branch2_nodes, branch3_nodes, branch4_nodes]
if experiment == "rewiring_ex3":
idc_branch_nodes = [0, 2]
else:
idc_branch_nodes = range(num_branch_nodes)
# Load the configuration file.
config = utils.load_configuration(os.path.join(input_directory, "..", "config_" + experiment + ".yaml"))
sim_simulation_time = config["simulation_time"]
sim_w_max = config["connection_parameters"]["w_max"]
sim_input_size = config["input_parameters"]["num_inputs"]
sim_num_assemblies = config["input_parameters"]["num_assemblies"]
sim_assembly_size = config["input_parameters"]["assembly_size"]
sim_num_branches = config["neuron_parameters"]["num_branches"]
sim_sampling_interval_weights = config["sampling_interval_weights"]
input_size = config["input_parameters"]["num_inputs"]
if experiment == "rewiring_ex3":
assembly_idc = []
assembly_idc_low = np.loadtxt(os.path.join(input_directory, "assembly_idc_low"), dtype=np.int)
for assembly_idx_low in np.sort(assembly_idc_low):
assembly_idc.append(np.arange(assembly_idx_low, assembly_idx_low + sim_assembly_size))
else:
assembly_idc = np.split(np.arange(sim_num_assemblies * sim_assembly_size), sim_num_assemblies)
# Load the simulation results.
mem_branch = []
for b in branches:
mem_branch.append(np.loadtxt(os.path.join(
input_directory, "test_branch" + str(b) + ".0.mem")))
mem_soma = np.loadtxt(os.path.join(input_directory, 'test_soma.0.mem'))
input_spike_times = np.loadtxt(os.path.join(input_directory, 'test_input.0.ras'))
input_spike_times[:, 1] = np.random.randint(0, sim_input_size, len(input_spike_times))
assembly_neurons_idc_random = np.load(os.path.join(
input_directory, 'assembly_neurons_idc.npy'))[3:]
assembly_neurons_idc = [i * 40 + np.arange(40) for i in range(8)]
overlaps = np.zeros((len(assembly_neurons_idc_random), len(assembly_neurons_idc)))
for i in range(len(assembly_neurons_idc_random)):
for j in range(len(assembly_neurons_idc)):
overlaps[i, j] = np.intersect1d(assembly_neurons_idc_random[i], assembly_neurons_idc[j]).size / 40
header_lenght = 3
with open(os.path.join(input_directory, "weights.0.dat"), "rb") as f:
lines = f.readlines()
weights_post = np.loadtxt(lines[-sim_num_branches:])
weights_train_end = [weights_post[b] for b in branches]
# After training.
# ------------------------------------------------------------------------------
fig = plt.figure()
gs = GridSpec(7, 4)
# Input spikes.
ax = plt.subplot(gs[0, 1:])
plot_input_spikes(input_spike_times, input_size, ax, xlim, pattern_labels, pattern_colors)
# Create graph.
node_colors = []
G = nx.MultiGraph()
ax = plt.subplot(gs[0:-2, 0])
# Draw the assemblies.
node_colors += draw_assemblies(G, assemblies, colors)
# Draw the neuron.
xc = np.mean(pos_assemblies, axis=0)[0]
pos_branch_nodes, nc = draw_neuron(G, ax, branch_nodes, xc)
node_colors += nc
# Add connections to graph.
add_connections(experiment, weights_train_end, assemblies, assembly_idc, assembly_map, idc_other_assemblies,
num_neurons_per_assembly, idc_branch_nodes, min_plot_weight)
# Draw connections.
draw_connections(G, (pos_assemblies + pos_branch_nodes), node_colors, ax)
plt.axis('off')
ax.set_xlim([-1, 1])
ax.set_ylim([-1 + y_offset + 0.00, 1.125])
# Branch 4 potential.
ax = plt.subplot(gs[1, 1:])
xticks = plot_branch_potential(mem_branch[3], branch_names[3], ax, xlim)
# Scale
# plot_scale(xlim)
# Branch 3 potential.
ax = plt.subplot(gs[2, 1:])
xticks = plot_branch_potential(mem_branch[2], branch_names[2], ax, xlim)
# Branch 2 potential.
ax = plt.subplot(gs[3, 1:])
xticks = plot_branch_potential(mem_branch[1], branch_names[1], ax, xlim)
# Branch 1 potential.
ax = plt.subplot(gs[4, 1:])
xticks = plot_branch_potential(mem_branch[0], branch_names[0], ax, xlim)
# Soma potential.
ax = plt.subplot(gs[5, 1:])
plot_soma_potential(mem_soma, ax, xlim)
plot_scale_soma(xlim)
plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.4, hspace=0.4)
fname = os.path.join(plots_directory, "graph-v2")
fig.savefig(fname + ".pdf", pad_inches=0.01)
subprocess.call(["pdftops", "-eps", fname + ".pdf", fname + ".eps"])
plt.close(fig)