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shimoura authored295edf8f
M2E_visualize_interareal_connectivity.py 4.54 KiB
import numpy as np
import matplotlib.pyplot as pl
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 multiarea_model import MultiAreaModel
def visualize_interareal_connectivity(M):
"""
Visualize inter-area connectivity for a comparison of the full-scale model and the downscaled model
Parameters:
- M ((MultiAreaModel)): Object containing simulation data.
Returns:
None
"""
# Full-scale model
M_full_scale = MultiAreaModel({})
"""
Figure layout
"""
nrows = 1
ncols = 2
width = 12
panel_wh_ratio = 0.7 * (1. + np.sqrt(5)) / 2. # golden ratio
height = width / panel_wh_ratio * float(nrows) / ncols
pl.rcParams['figure.figsize'] = (width, height)
fig = pl.figure()
fig.suptitle('Area-level connectivity of the full-scale and downscaled MAM expressed as relative indegrees for each target area', fontsize=15, x=0.5, y=1.05)
axes = {}
gs1 = gridspec.GridSpec(1, 2)
gs1.update(left=0.1, right=0.95, top=0.95, bottom=0.1, wspace=0.3, hspace=0.3)
axes['B'] = pl.subplot(gs1[:1, :1])
axes['D'] = pl.subplot(gs1[:1, 1:2])
pos2 = axes['D'].get_position()
labels = ['B', 'D']
labels_display = ['Full-scale model', 'Downscaled model']
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], label_display,
fontdict={'fontsize': 12, 'weight': 'bold',
'horizontalalignment': 'left', 'verticalalignment':
'bottom'}, transform=axes[label].transAxes)
"""
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('YlOrBr')
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)])
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)])
ax.set_yticklabels(area_list[::-1], size=10.)
ax.set_ylabel('Target area', fontsize=15)
ax.set_xlabel('Source area', fontsize=15)
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 D: Interareal connectivity of downscaling 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.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)])
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)])
ax.set_yticklabels(area_list[::-1], size=10.)
ax.set_ylabel('Target area', fontsize=15)
ax.set_xlabel('Source area', fontsize=15)
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.)