-
54fab295
M2E_visualize_interareal_connectivity-checkpoint.py 12.35 KiB
import json
import numpy as np
import matplotlib.pyplot as pl
import os
import sys
sys.path.append('./figures/Schmidt2018')
from helpers import area_list, datapath
from matplotlib import gridspec
from matplotlib.colors import LogNorm
from matplotlib.ticker import FixedLocator
from matplotlib import rc_file
from multiarea_model import MultiAreaModel
from plotcolors import myblue
from scipy import stats
# rc_file('plotstyle.rc')
def visualize_interareal_connectivity(M):
# full-scale model
M_full_scale = MultiAreaModel({})
"""
Figure layout
"""
# nrows = 2
nrows = 1
ncols = 3
# width = 6.8556
width = 15
panel_wh_ratio = 0.7 * (1. + np.sqrt(5)) / 2. # golden ratio
height = width / panel_wh_ratio * float(nrows) / ncols
# print(width, height)
pl.rcParams['figure.figsize'] = (width, height)
fig = pl.figure()
fig.suptitle('Interareal connectivity for full-scale (left) and down-scale (right) multi-area model', fontsize=14, y=1.1)
axes = {}
# gs1 = gridspec.GridSpec(2, 2)
gs1 = gridspec.GridSpec(1, 3)
# gs1.update(left=0.06, right=0.95, top=0.95, bottom=0.1, wspace=0.1, hspace=0.3)
gs1.update(left=0.06, right=0.95, top=0.95, bottom=0.1, wspace=0.3, hspace=0.3)
axes['A'] = pl.subplot(gs1[:1, :1])
axes['B'] = pl.subplot(gs1[:1, 1:2])
axes['D'] = pl.subplot(gs1[:1, 2:])
# pos = axes['A'].get_position()
pos2 = axes['D'].get_position()
# axes['C'] = pl.axes([pos.x0 + 0.01, pos2.y0, pos.x1 - pos.x0 - 0.025, 0.23])
# print(pos.x1 - pos.x0 - 0.025)
# labels = ['A', 'B', 'C', 'D']
labels = ['A','B', 'D']
labels_display = ['Binary connectivity from CoCoMac', 'Full-scale model', 'Down-scale model']
# for label in labels:
for i in range(len(labels)):
label = labels[i]
label_display = labels_display[i]
if label in ['C']:
label_pos = [-0.045, 1.18]
else:
label_pos = [-0.2, 1.04]
# pl.text(label_pos[0], label_pos[1], r'\bfseries{}' + label,
# fontdict={'fontsize': 10, 'weight': 'bold',
# 'horizontalalignment': 'left', 'verticalalignment':
# 'bottom'}, transform=axes[label].transAxes)
pl.text(label_pos[0], label_pos[1], label_display,
fontdict={'fontsize': 10, 'weight': 'bold',
'horizontalalignment': 'left', 'verticalalignment':
'bottom'}, transform=axes[label].transAxes)
"""
Load data
"""
# M = MultiAreaModel({})
M_full_scale = MultiAreaModel({})
datapath = './multiarea_model/data_multiarea/'
with open(os.path.join(datapath, 'viscortex_processed_data.json'), 'r') as f:
proc = json.load(f)
with open(os.path.join(datapath, 'viscortex_raw_data.json'), 'r') as f:
raw = json.load(f)
FLN_Data_FV91 = proc['FLN_Data_FV91']
cocomac_data = raw['cocomac_data']
median_distance_data = raw['median_distance_data']
cocomac = np.zeros((32, 32))
conn_matrix = np.zeros((32, 32))
for i, area1 in enumerate(area_list[::-1]):
for j, area2 in enumerate(area_list):
# if M.K_areas[area1][area2] > 0. and area2 in cocomac_data[area1]:
if M_full_scale.K_areas[area1][area2] > 0. and area2 in cocomac_data[area1]:
cocomac[i][j] = 1.
if area2 in FLN_Data_FV91[area1]:
conn_matrix[i][j] = FLN_Data_FV91[area1][area2]
"""
Panel A: CoCoMac Data
"""
ax = axes['A']
ax.yaxis.set_ticks_position("left")
ax.xaxis.set_ticks_position("bottom")
ax.set_aspect(1. / ax.get_data_ratio())
ax.yaxis.set_ticks_position("none")
ax.xaxis.set_ticks_position("none")
masked_matrix = np.ma.masked_values(cocomac, 0.0)
cmap = pl.cm.binary
cmap.set_bad('w', 1.0)
x = np.arange(0, len(area_list) + 1)
y = np.arange(0, len(area_list[::-1]) + 1)
X, Y = np.meshgrid(x, y)
ax.set_xticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
ax.set_xticks([i + 0.5 for i in np.arange(0, len(area_list), 1)])
# ax.set_xticklabels(area_list, rotation=90, size=6.)
ax.set_xticklabels(area_list, rotation=90, size=10.)
# ax.set_yticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
ax.set_yticks([i + 0.5 for i in np.arange(0, len(area_list), 1)])
# ax.set_yticklabels(area_list[::-1], size=6.)
ax.set_yticklabels(area_list[::-1], size=8.)
ax.set_ylabel('Target area')
ax.set_xlabel('Source area')
im = ax.pcolormesh(masked_matrix, cmap=cmap,
edgecolors='None', vmin=0., vmax=1.)
t = FixedLocator([])
cbar = pl.colorbar(im, ticks=t, fraction=0.046, ax=ax)
cbar.set_alpha(0.)
cbar.remove()
# """
# Panel B: Data from Markov et al. (2014) "A weighted and directed
# interareal connectivity matrix for macaque cerebral cortex."
# Cerebral Cortex, 24(1), 17–36.
# """
# ax = axes['B']
# ax.set_aspect(1. / ax.get_data_ratio())
# ax.yaxis.set_ticks_position("none")
# ax.xaxis.set_ticks_position("none")
# masked_matrix = np.ma.masked_values(conn_matrix, 0.0)
# cmap = pl.get_cmap('inferno')
# cmap.set_bad('w', 1.0)
# x = np.arange(0, len(area_list) + 1)
# y = np.arange(0, len(area_list[::-1]) + 1)
# X, Y = np.meshgrid(x, y)
# ax.set_xticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
# ax.set_xticklabels(area_list, rotation=90, size=6.)
# ax.set_yticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
# ax.set_yticklabels(area_list[::-1], size=6.)
# im = ax.pcolormesh(masked_matrix, cmap=cmap,
# edgecolors='None', norm=LogNorm(vmin=1e-6, vmax=1.))
# t = FixedLocator([1e-6, 1e-4, 1e-2, 1])
# cbar = pl.colorbar(im, ticks=t, fraction=0.046, ax=ax)
# cbar.set_alpha(0.)
"""
Panel B: Interareal connectivity of full-scaling multi-area model
"""
conn_matrix_full_scale = np.zeros((32, 32))
for i, area1 in enumerate(area_list[::-1]):
for j, area2 in enumerate(area_list):
conn_matrix_full_scale[i][j] = M_full_scale.K_areas[area1][
area2] / np.sum(list(M_full_scale.K_areas[area1].values()))
ax = axes['B']
ax.yaxis.set_ticks_position("none")
ax.xaxis.set_ticks_position("none")
ax.set_aspect(1. / ax.get_data_ratio())
masked_matrix_full_scale = np.ma.masked_values(conn_matrix_full_scale, 0.0)
cmap = pl.get_cmap('inferno')
cmap.set_bad('w', 1.0)
x = np.arange(0, len(area_list) + 1)
y = np.arange(0, len(area_list[::-1]) + 1)
X, Y = np.meshgrid(x, y)
# ax.set_xticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
ax.set_xticks([i + 0.5 for i in np.arange(0, len(area_list), 1)])
# ax.set_xticklabels(area_list, rotation=90, size=6.)
ax.set_xticklabels(area_list, rotation=90, size=8.)
# ax.set_yticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
ax.set_yticks([i + 0.5 for i in np.arange(0, len(area_list), 1)])
# ax.set_yticklabels(area_list[::-1], size=6.)
ax.set_yticklabels(area_list[::-1], size=8.)
ax.set_ylabel('Target area')
ax.set_xlabel('Source area')
im = ax.pcolormesh(masked_matrix_full_scale, cmap=cmap,
edgecolors='None', norm=LogNorm(vmin=1e-6, vmax=1.))
t = FixedLocator([1e-6, 1e-4, 1e-2, 1])
cbar = pl.colorbar(im, ticks=t, fraction=0.046, ax=ax)
cbar.set_alpha(0.)
# """
# Panel C: Exponential decay of FLN with distance
# """
# FLN_values_FV91 = np.array([])
# distances_FV91 = np.array([])
# for target_area in FLN_Data_FV91:
# for source_area in FLN_Data_FV91[target_area]:
# if target_area in median_distance_data and source_area in median_distance_data:
# if FLN_Data_FV91[target_area][source_area]:
# FLN_values_FV91 = np.append(FLN_values_FV91, FLN_Data_FV91[
# target_area][source_area])
# distances_FV91 = np.append(distances_FV91, median_distance_data[
# target_area][source_area])
# # Linear fit of distances vs. log FLN
# print("\n \n Linear fit to logarithmic values")
# gradient, intercept, r_value, p_value, std_err = stats.linregress(
# distances_FV91, np.log(FLN_values_FV91))
# print("Raw parameters: ", gradient, intercept)
# print("Transformed parameters: ", -gradient, np.exp(intercept))
# print('r_value**2', r_value ** 2)
# print('p_value', p_value)
# print('std_err', std_err)
# ax = axes['C']
# ax.yaxis.set_ticks_position("left")
# ax.xaxis.set_ticks_position("bottom")
# ax.yaxis.set_ticks_position("left")
# ax.xaxis.set_ticks_position("bottom")
# ax.spines['right'].set_color('none')
# ax.spines['top'].set_color('none')
# ax.yaxis.set_ticks_position("left")
# ax.xaxis.set_ticks_position("bottom")
# ax.plot(distances_FV91, np.log10(FLN_values_FV91), '.', color=myblue)
# x = np.arange(np.min(distances_FV91), np.max(distances_FV91), 1)
# ax.plot(x, (intercept + gradient * x) / np.log(10), linewidth=2.0,
# color='Black', label='Linear regression fit')
# ax.set_xlabel('Distance (mm)', labelpad=7)
# ax.set_ylabel(r'$\log(FLN)$')
# ax.set_yticks([-6, -4, -2, 0])
# print("log fit")
# print(np.corrcoef(gradient * distances_FV91 + intercept, np.log(FLN_values_FV91))[0][1])
# """
# Panel D: Resulting connectivity matrix
# """
# conn_matrix = np.zeros((32, 32))
# for i, area1 in enumerate(area_list[::-1]):
# for j, area2 in enumerate(area_list):
# conn_matrix[i][j] = M.K_areas[area1][
# area2] / np.sum(list(M.K_areas[area1].values()))
# ax = axes['D']
# ax.yaxis.set_ticks_position("none")
# ax.xaxis.set_ticks_position("none")
# ax.set_aspect(1. / ax.get_data_ratio())
# masked_matrix = np.ma.masked_values(conn_matrix, 0.0)
# cmap = pl.get_cmap('inferno')
# cmap.set_bad('w', 1.0)
# x = np.arange(0, len(area_list) + 1)
# y = np.arange(0, len(area_list[::-1]) + 1)
# X, Y = np.meshgrid(x, y)
# ax.set_xticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
# ax.set_xticklabels(area_list, rotation=90, size=6.)
# ax.set_yticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
# ax.set_yticklabels(area_list[::-1], size=6.)
# ax.set_ylabel('Target area')
# ax.set_xlabel('Source area')
# im = ax.pcolormesh(masked_matrix, cmap=cmap,
# edgecolors='None', norm=LogNorm(vmin=1e-6, vmax=1.))
# t = FixedLocator([1e-6, 1e-4, 1e-2, 1])
# cbar = pl.colorbar(im, ticks=t, fraction=0.046, ax=ax)
# cbar.set_alpha(0.)
"""
Panel D: Interareal connectivity of down-scaling multi-area model
"""
conn_matrix_down_scale = np.zeros((32, 32))
for i, area1 in enumerate(area_list[::-1]):
for j, area2 in enumerate(area_list):
conn_matrix_down_scale[i][j] = M.K_areas[area1][
area2] / np.sum(list(M.K_areas[area1].values()))
ax = axes['D']
ax.yaxis.set_ticks_position("none")
ax.xaxis.set_ticks_position("none")
ax.set_aspect(1. / ax.get_data_ratio())
masked_matrix_down_scale = np.ma.masked_values(conn_matrix_down_scale, 0.0)
cmap = pl.get_cmap('inferno')
cmap.set_bad('w', 1.0)
x = np.arange(0, len(area_list) + 1)
y = np.arange(0, len(area_list[::-1]) + 1)
X, Y = np.meshgrid(x, y)
# ax.set_xticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
ax.set_xticks([i + 0.5 for i in np.arange(0, len(area_list), 1)])
# ax.set_xticklabels(area_list, rotation=90, size=6.)
ax.set_xticklabels(area_list, rotation=90, size=8.)
# ax.set_yticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
ax.set_yticks([i + 0.5 for i in np.arange(0, len(area_list), 1)])
# ax.set_yticklabels(area_list[::-1], size=6.)
ax.set_yticklabels(area_list[::-1], size=8.)
ax.set_ylabel('Target area')
ax.set_xlabel('Source area')
im = ax.pcolormesh(masked_matrix_down_scale, cmap=cmap,
edgecolors='None', norm=LogNorm(vmin=1e-6, vmax=1.))
t = FixedLocator([1e-6, 1e-4, 1e-2, 1])
cbar = pl.colorbar(im, ticks=t, fraction=0.046, ax=ax)
cbar.set_alpha(0.)
# """
# Save figure
# """
# pl.savefig('Fig4_connectivity.eps')