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Delete figures/Schmidt2018/.ipynb_checkpoints directory

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figures/Schmidt2018/.ipynb_checkpoints/Fig4_connectivity-checkpoint.py deleted 100644 → 0
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import json
import numpy as np
import matplotlib.pyplot as pl
import os
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')
"""
Figure layout
"""
nrows = 2
ncols = 2
width = 6.8556
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()
axes = {}
gs1 = gridspec.GridSpec(2, 2)
gs1.update(left=0.06, right=0.95, top=0.95, bottom=0.1, wspace=0.1, 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, 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']
for label in labels:
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)
"""
Load data
"""
M = MultiAreaModel({})
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]:
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_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', 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 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.)
"""
Save figure
"""
pl.savefig('Fig4_connectivity.eps')
figures/Schmidt2018/.ipynb_checkpoints/helpers-checkpoint.py deleted 100644 → 0
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import numpy as np
"""
Helper file collecting a number of necessary
imports for the plot scripts
"""
area_list = ['V1', 'V2', 'VP', 'V3', 'V3A', 'MT', 'V4t', 'V4', 'VOT', 'MSTd',
'PIP', 'PO', 'DP', 'MIP', 'MDP', 'VIP', 'LIP', 'PITv', 'PITd',
'MSTl', 'CITv', 'CITd', 'FEF', 'TF', 'AITv', 'FST', '7a', 'STPp',
'STPa', '46', 'AITd', 'TH']
population_list = ['23E', '23I', '4E', '4I', '5E', '5I', '6E', '6I']
datapath = '../../multiarea_model/data_multiarea'
raw_datapath = '../../multiarea_model/data_multiarea/raw_data/'
population_labels = ['2/3E', '2/3I', '4E', '4I', '5E', '5I', '6E', '6I']
layer_labels = ['L1', 'L2', 'L3', 'L4', 'L5', 'L6']
tex_names = {'23': 'twothree', '4': 'four', '5': 'five', '6': 'six'}
# This path determines the location of the infomap
# installation and needs to be provided to execute the script for Fig. 7
infomap_path = None
def hierarchical_relation(target_area, source_area, SLN_completed, thresh=(0.35, 0.65)):
"""
Returns the hierarchical relation between
two areas based on their SLN value (data + estimated).
Parameters
----------
target_area : str
Name of target area.
source_area : str
Name of source area.
SLN_completed : dict
Dictionary of SLN values for pairs of areas.
thresh : tuple of floats
Threshold values to classify connections
as FF/FB/lateral.
Returns
-------
hierarchical_relation : str
Hierarchical relation between source
and target area.
"""
if (target_area != source_area and
source_area in SLN_completed[target_area]):
if SLN_completed[target_area][source_area] > thresh[1]:
return 'FF'
elif SLN_completed[target_area][source_area] < thresh[0]:
return 'FB'
else:
return 'lateral'
else:
return 'same-area'
def structural_gradient(target_area, source_area, arch_types):
"""
Returns the structural gradient between two areas
See Schmidt, M., Bakker, R., Hilgetag, C.C. et al.
Brain Structure and Function (2018), 223:1409,
for a definition.
Parameters
----------
target_area : str
Name of target area.
source_area : str
Name of source area.
arch_types : dict
Dictionary containing the architectural type for each area.
"""
if target_area != source_area:
if arch_types[target_area] < arch_types[source_area]:
return 'HL'
elif arch_types[target_area] > arch_types[source_area]:
return 'LH'
else:
return 'HZ'
else:
return 'same-area'
def write_out_lw(fn, C, std=False):
"""
Stores line widths for arrows in path figures
generated by pstricks to a txt file.
Parameters
----------
fn : str
Filename of output file.
C : dict
Dictionary with line width values.
std : bool
Whether to write out mean or std values.
"""
if not std:
max_lw = 0.3 # This is an empirically determined value
scale_factor = max_lw / np.max(list(C.values()))
with open(fn, 'w') as f:
for pair, count in list(C.items()):
s = '\setboolean{{DRAW{}{}{}{}}}{{true}}'.format(tex_names[pair[0][:-1]],
pair[0][-1],
tex_names[pair[1][:-1]],
pair[1][-1])
f.write(s)
f.write('\n')
s = '\def\{}{}{}{}{{{}}}'.format(tex_names[pair[0][:-1]],
pair[0][-1],
tex_names[pair[1][:-1]],
pair[1][-1],
float(count) * scale_factor)
f.write(s)
f.write('\n')
else:
max_lw = 0.3
scale_factor = max_lw / np.max(list(C['mean'].values()))
with open(fn, 'w') as f:
for pair, count in list(C['mean'].items()):
s = '\setboolean{{DRAW\{}{}{}{}}}{{true}}'.format(tex_names[pair[0][:-1]],
pair[0][-1],
tex_names[pair[1][:-1]],
pair[1][-1])
f.write('\n')
s = '\def\{}{}{}{}{{{}}}'.format(tex_names[pair[0][:-1]],
pair[0][-1],
tex_names[pair[1][:-1]],
pair[1][-1],
float(count) * scale_factor)
f.write('\n')
for pair, count in list(C['1sigma'].items()):
f.write('\n')
s = '\def\{}{}{}{}sigma{{{}}}'.format(tex_names[pair[0][:-1]],
pair[0][-1],
tex_names[pair[1][:-1]],
pair[1][-1],
float(count) * scale_factor)
f.write('\n')
def area_population_list(structure, area):
"""
Construct list of all populations in an area.
Parameters
----------
structure : dict
Dictionary defining the structure of each area.
area : str
Area to construct list for.
"""
complete = []
for pop in structure[area]:
complete.append(area + '-' + pop)
return complete
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