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Commit 18cb5e6f authored by Didi Hou's avatar Didi Hou Committed by Administrator
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1 merge request!35Pre-release MAM v1.1.0
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figures/MAM2EBRAINS/.ipynb_checkpoints/M2E_visualize_interareal_connectivity-checkpoint.py
+ 65
− 63
View file @ 18cb5e6f
......@@ -26,7 +26,7 @@ def visualize_interareal_connectivity(M):
"""
# nrows = 2
nrows = 1
ncols = 3
ncols = 2
# width = 6.8556
width = 15
panel_wh_ratio = 0.7 * (1. + np.sqrt(5)) / 2. # golden ratio
......@@ -40,23 +40,25 @@ def visualize_interareal_connectivity(M):
axes = {}
# gs1 = gridspec.GridSpec(2, 2)
gs1 = gridspec.GridSpec(1, 3)
gs1 = gridspec.GridSpec(1, 2)
# 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:])
# axes['A'] = pl.subplot(gs1[:1, :1])
# axes['B'] = pl.subplot(gs1[:1, 1:2])
axes['B'] = pl.subplot(gs1[:1, :1])
# axes['D'] = pl.subplot(gs1[:1, 2:])
axes['D'] = pl.subplot(gs1[:1, 1:2])
pos = axes['A'].get_position()
# 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']
labels = ['B', 'D']
labels_display = ['Full-scale model', 'Down-scale model']
# for label in labels:
for i in range(len(labels)):
label = labels[i]
......@@ -74,71 +76,71 @@ def visualize_interareal_connectivity(M):
'horizontalalignment': 'left', 'verticalalignment':
'bottom'}, transform=axes[label].transAxes)
"""
Load data
"""
# M = MultiAreaModel({})
M_full_scale = MultiAreaModel({})
# """
# 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']
# 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]
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")
# """
# 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")
# 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)
# 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)
# 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_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=6.)
# # 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=6.)
ax.set_ylabel('Target area')
ax.set_xlabel('Source area')
# ax.set_ylabel('Target area')
# ax.set_xlabel('Source area')
im = ax.pcolormesh(masked_matrix, cmap=cmap,
edgecolors='None', vmin=0., vmax=1.)
# 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.)
# t = FixedLocator([])
# cbar = pl.colorbar(im, ticks=t, fraction=0.046, ax=ax)
# cbar.set_alpha(0.)
# cbar.remove()
# """
......
figures/MAM2EBRAINS/M2E_visualize_interareal_connectivity.py
+ 65
− 63
View file @ 18cb5e6f
......@@ -26,7 +26,7 @@ def visualize_interareal_connectivity(M):
"""
# nrows = 2
nrows = 1
ncols = 3
ncols = 2
# width = 6.8556
width = 15
panel_wh_ratio = 0.7 * (1. + np.sqrt(5)) / 2. # golden ratio
......@@ -40,23 +40,25 @@ def visualize_interareal_connectivity(M):
axes = {}
# gs1 = gridspec.GridSpec(2, 2)
gs1 = gridspec.GridSpec(1, 3)
gs1 = gridspec.GridSpec(1, 2)
# 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:])
# axes['A'] = pl.subplot(gs1[:1, :1])
# axes['B'] = pl.subplot(gs1[:1, 1:2])
axes['B'] = pl.subplot(gs1[:1, :1])
# axes['D'] = pl.subplot(gs1[:1, 2:])
axes['D'] = pl.subplot(gs1[:1, 1:2])
pos = axes['A'].get_position()
# 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']
labels = ['B', 'D']
labels_display = ['Full-scale model', 'Down-scale model']
# for label in labels:
for i in range(len(labels)):
label = labels[i]
......@@ -74,71 +76,71 @@ def visualize_interareal_connectivity(M):
'horizontalalignment': 'left', 'verticalalignment':
'bottom'}, transform=axes[label].transAxes)
"""
Load data
"""
# M = MultiAreaModel({})
M_full_scale = MultiAreaModel({})
# """
# 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']
# 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]
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")
# """
# 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")
# 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)
# 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)
# 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_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=6.)
# # 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=6.)
ax.set_ylabel('Target area')
ax.set_xlabel('Source area')
# ax.set_ylabel('Target area')
# ax.set_xlabel('Source area')
im = ax.pcolormesh(masked_matrix, cmap=cmap,
edgecolors='None', vmin=0., vmax=1.)
# 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.)
# t = FixedLocator([])
# cbar = pl.colorbar(im, ticks=t, fraction=0.046, ax=ax)
# cbar.set_alpha(0.)
# cbar.remove()
# """
......
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