diff --git a/README.md b/README.md
index 8bf8bc5063b536c52b9766bc0ae87b90f54f95b9..669f8312838845b2e9f4ee4706cb2f0bebabf732 100644
--- a/README.md
+++ b/README.md
@@ -82,7 +82,10 @@ basic analysis and plotting.
The `figures` folder contains a subfolder with all scripts necessary to produce
the figures from [1]. The scripts for [2] and [3] will follow soon.
If snakemake is installed, the figures can be produced by executing
-`snakemake` in the respective folder.
+`snakemake` in the respective folder:
+
+ cd figures/Schmidt2017/
+ snakemake
## Running a simulation
diff --git a/config_template.py b/config_template.py
index a5db4e4b72dfbc931bd68e1b9dfa3c8013f53a1a..4f3f7ad5cde91703b5a42f9fbb1021e0ade2d84a 100644
--- a/config_template.py
+++ b/config_template.py
@@ -1,8 +1,8 @@
-# Absolut path of repository
+# Absolute path of repository
base_path = None
# Place to store simulations
data_path = None
-# Template for jobscripts
+# Template for job scripts
jobscript_template = """
# Instruction for the queuing system
diff --git a/figures/Schmidt2017/Fig2_anatomy.py b/figures/Schmidt2017/Fig2_anatomy.py
index 786966e02c7c7605b0ce024e339f84c7377c6f14..5f97204271e42e7693ed636427ae126b5ac30913 100644
--- a/figures/Schmidt2017/Fig2_anatomy.py
+++ b/figures/Schmidt2017/Fig2_anatomy.py
@@ -27,8 +27,6 @@ thicknesses = raw['laminar_thicknesses']
total_thickness_data = raw['total_thickness_data']
# calculate average relative layer thicknesses for each area
-# where possible, compare with total thickness, with total thickness
-# without L1, and compare L23-L4, L23-L5, L23-L6, L4-L5, L4-L6, and L5-L6
frac_of_total = {}
for area in list(thicknesses.keys()):
area_dict_total = {}
@@ -168,7 +166,6 @@ for l in layers:
else:
rho[l] = np.zeros(8)
-# ax.bar(x - 0.4, y)
bottom = np.zeros(8)
for l in layers[:]:
bottom += rho[l]
@@ -196,12 +193,6 @@ ax.legend(loc=(0.035, 0.45), edgecolor='k')
##################################################
-# total thicknesses from Barbas lab vs architectural type
-
-
-# total cortical thicknesses from Barbas lab
-
-
barbas_array = np.zeros(len(area_list))
for i, area in enumerate(area_list):
barbas_array[i] = total_thickness_data[area] / 1000.
@@ -224,7 +215,6 @@ line = gradient * log_density_array + intercept
ax.plot(log_density_array, line, '-', linewidth=1.5, color='k')
ax.set_xlabel('Log neuron density', labelpad=0.3)
ax.set_ylabel('Total thickness (mm)')
-# ax.set_xlim((0, 9))
ax.set_xticks([4.7, 5.0])
ax.set_yticks(np.arange(1., 3., 0.5))
@@ -246,7 +236,7 @@ for i, data in enumerate([frac1_of_total, frac23_of_total,
ax.plot(log_density_array, line, '-', linewidth=2.0, c=colors[i])
ax.set_xlabel('Log neuron density', labelpad=0.3)
-ax.set_ylabel('Proportion of \n total thickness') # ,size=16.5)
+ax.set_ylabel('Proportion of \n total thickness')
ax.set_xlim((4.6, 5.3))
ax.set_xticks([4.7, 5.0])
ax.set_yticks(np.arange(0., 0.7, 0.2))
diff --git a/figures/Schmidt2017/Fig3_construction.py b/figures/Schmidt2017/Fig3_construction.py
index 2ef7db5284e70caf936c4df70987ca49d0312d27..3c6d4f0f2254396cb508e76f8603139b02a20380 100644
--- a/figures/Schmidt2017/Fig3_construction.py
+++ b/figures/Schmidt2017/Fig3_construction.py
@@ -10,7 +10,7 @@ from multiarea_model import MultiAreaModel
from plotfuncs import create_fig
"""
-Loading and procesing of data
+Loading and processing of data
"""
M = MultiAreaModel({})
with open(os.path.join(datapath, 'viscortex_processed_data.json'), 'r') as f:
diff --git a/figures/Schmidt2017/Fig4_connectivity.py b/figures/Schmidt2017/Fig4_connectivity.py
index b4a4c91dedaeaa139c743e4d09363aa018c9e3a0..586e7fe25b8568657bea24c588dd068ca961f87e 100644
--- a/figures/Schmidt2017/Fig4_connectivity.py
+++ b/figures/Schmidt2017/Fig4_connectivity.py
@@ -114,7 +114,9 @@ cbar.set_alpha(0.)
cbar.remove()
"""
-Panel B: Data from Markov et al. (2014)
+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())
@@ -143,7 +145,7 @@ cbar = pl.colorbar(im, ticks=t, fraction=0.046, ax=ax)
cbar.set_alpha(0.)
"""
-Panel C: Exponential distance rule of FLN
+Panel C: Exponential decay of FLN with distance
"""
FLN_values_FV91 = np.array([])
distances_FV91 = np.array([])
@@ -157,7 +159,7 @@ for target_area in FLN_Data_FV91:
distances_FV91 = np.append(distances_FV91, median_distance_data[
target_area][source_area])
-# Linear Fit to log values"
+# 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))
@@ -221,7 +223,7 @@ 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') # , labelpad=-0.01)
+ax.set_xlabel('Source area')
im = ax.pcolormesh(masked_matrix, cmap=cmap,
edgecolors='None', norm=LogNorm(vmin=1e-6, vmax=1.))
diff --git a/figures/Schmidt2017/Fig5_cc_laminar_pattern.py b/figures/Schmidt2017/Fig5_cc_laminar_pattern.py
index 4897ee593767222a1eb8220b0238d54242a82500..ac09cdb67f77cc9f017d8b1f1d7d7a434dd4b007 100644
--- a/figures/Schmidt2017/Fig5_cc_laminar_pattern.py
+++ b/figures/Schmidt2017/Fig5_cc_laminar_pattern.py
@@ -176,7 +176,7 @@ corr_bb = round(np.corrcoef(
print("SLN Fit: R={}, Chi2={}".format(corr_bb, goodness_bb))
-# Target - source relationship
+# Target-source relationship
target_low_SLN_unweighted = np.zeros(6)
target_medium_SLN_unweighted = np.zeros(6)
diff --git a/figures/Schmidt2017/Fig6_connectivity_measures.py b/figures/Schmidt2017/Fig6_connectivity_measures.py
index 81348bda44425f8a8f194657e46d2079a4709a9c..daa9600f2d403e6c2e3c8ac7df302eb98f67abdd 100644
--- a/figures/Schmidt2017/Fig6_connectivity_measures.py
+++ b/figures/Schmidt2017/Fig6_connectivity_measures.py
@@ -80,7 +80,7 @@ Nsyn = M.K_matrix[:, :-1] * Npost
outdegree = Nsyn / Npre
indegree = M.K_matrix[:, :-1]
-plot_areas = ['V1', 'V2'] # , 'V2', 'MT', 'FEF', '46', 'TH']
+plot_areas = ['V1', 'V2']
mask = create_mask(M.structure, target_areas=plot_areas,
source_areas=plot_areas,
extern=False)[:, :-1]
@@ -124,8 +124,6 @@ ax.set_ylim((0, new_size))
ax.set_xticks(np.arange(16) + 0.5)
ax.set_xticklabels(2 * population_labels, rotation=90, size=7.)
-# ax.set_xticks(ticks)
-# ax.set_xticklabels(plot_areas, rotation=90, size=7.)
ax.set_yticks(np.arange(16)[::-1] + 0.5)
ax.set_yticklabels(2 * population_labels, size=7.)
@@ -155,9 +153,6 @@ ax_hist.set_xlim(cb.get_clim())
ax_hist.set_xticks([])
ax_hist.set_yticks([])
-# ax.set_yticks(ticks_r)
-# ax.set_yticklabels(plot_areas, size=7.)
-
ax = axes['B']
matrix = C_plot
diff --git a/figures/Schmidt2017/Fig7_community_structure.py b/figures/Schmidt2017/Fig7_community_structure.py
index adc1996f5953ff22049e2606e2fd45f1c50bc9ea..e121ca4a0f19b5dc95753f8f2f831681954aee0c 100644
--- a/figures/Schmidt2017/Fig7_community_structure.py
+++ b/figures/Schmidt2017/Fig7_community_structure.py
@@ -3,7 +3,7 @@ import copy
from helpers import area_list
import numpy as np
from config import base_path
-from graph_helpers import perform_map_equation, modularity
+from graph_helpers import apply_map_equation, modularity
from graph_helpers import create_graph, plot_clustered_graph
from multiarea_model import MultiAreaModel
from multiarea_model.multiarea_helpers import load_degree_data
@@ -62,7 +62,7 @@ g_abs = create_graph(conn_matrix_abs, area_list)
"""
Determine clusters using the map equation.
"""
-modules, modules_areas, index = perform_map_equation(
+modules, modules_areas, index = apply_map_equation(
conn_matrix, area_list, filename='Model')
f = open('Model.map', 'r')
@@ -108,11 +108,11 @@ mod_list = []
# In the connectivity matrix, rows == targets, columns == sources
# For each column, we shuffle the rows and therefore conserve the total outdegree of each area.
-for ii in range(10):
+for ii in range(1000):
for jj in range(32):
ind = np.extract(np.arange(32) != jj, np.arange(32))
null_model[:, jj][ind] = null_model[:, jj][ind][np.random.shuffle(ind)]
- modules, modules_areas, index = perform_map_equation(null_model, area_list, filename='null')
+ modules, modules_areas, index = apply_map_equation(null_model, area_list, filename='null')
g_null = create_graph(null_model, area_list)
mod_list.append(modularity(g_null, modules[index]))
diff --git a/figures/Schmidt2017/Fig8_laminar_paths.py b/figures/Schmidt2017/Fig8_laminar_paths.py
index 6199ec28dfe24143861ced814b245412a3afed82..75c3e3bb7dbc8deb22c16ea5535dc8a72ec19dd8 100644
--- a/figures/Schmidt2017/Fig8_laminar_paths.py
+++ b/figures/Schmidt2017/Fig8_laminar_paths.py
@@ -115,7 +115,6 @@ height = 5.25
print(width, height)
pl.rcParams['figure.figsize'] = (width, height)
-# ################### Figure layout ####################
fine_tune_cols = 2
fig = pl.figure()
@@ -154,8 +153,9 @@ and write out the linewidths for the corresponding tex plots to file.
1. Distinguish using SLN
"""
-# Differential analysis of hierarchically directed connections (hierarchy_criterion=SLN)
-hierarchy_criterion = 'SLN'
+# Differential analysis of hierarchically directed connections
+# FF = feedforward
+# FB = feedback
FF_path_pairs = []
FB_path_pairs = []
lateral_path_pairs = []
@@ -228,9 +228,8 @@ write_out_lw(fn, C)
"""
2. Use architectural types
"""
-hierarchy_criterion = 'arch_types'
-# HL = early to late
-# LH = late to early
+# HL = high to low type
+# LH = low to high type
# HZ = equal arch. types
HL_path_pairs = []
LH_path_pairs = []
@@ -344,7 +343,7 @@ layout_barplot_axes(axes['HL'])
C = Counter(HL_single_area_patterns)
counts = list(C.values())
-# Manually define order of pairs to stick to originally published figure
+# Define order of pairs consistently across panels
pairs = ['5E', '23E', '4I+23E', '6E']
counts = [C[p] for p in pairs]
# Add 0 value for last pair
@@ -362,7 +361,7 @@ layout_barplot_axes(axes['HZ'])
C = Counter(HZ_single_area_patterns)
counts = list(C.values())
-# Manually define order of pairs to stick to originally published figure
+# Define order of pairs consistently across panels
pairs = ['5E', '23E', '4I+23E', '6E']
counts = [C[p] for p in pairs]
# Add 0 value for last pair
@@ -380,7 +379,7 @@ layout_barplot_axes(axes['LH'])
C = Counter(LH_single_area_patterns)
counts = list(C.values())
-# Manually define order of pairs to stick to originally published figure
+# Define order of pairs consistently across panels
pairs = ['5E', '23E', '4I+23E', '6E']
counts = [C[p] for p in pairs]
@@ -412,9 +411,6 @@ Finally, merge the tex-created figures into the main figure.
import pyx
c = pyx.canvas.canvas()
-# c.text(0.4, 12.3,r'\textbf{\textsf{A}}')
-# c.text(6., 12.3,r'\textbf{\textsf{B}}')
-
c.insert(pyx.epsfile.epsfile(0., 0., "Fig8_laminar_paths_mpl.eps", width=17.3))
diff --git a/figures/Schmidt2017/README.md b/figures/Schmidt2017/README.md
index f0c8ae445577d291f2d7e6200421f177081f84bc..762f2b9e3ec46057724d5b863713ab2d93149fc7 100644
--- a/figures/Schmidt2017/README.md
+++ b/figures/Schmidt2017/README.md
@@ -1,4 +1,11 @@
+## Figures of Schmidt M, Bakker R, Hilgetag C-C, Diesmann M and van Albada SJ: "Multi-scale account of the network structure of macaque visual cortex"
+
This folder contains the scripts to reproduce all figures of Schmidt M, Bakker R, Hilgetag C-C, Diesmann M and van Albada SJ: "Multi-scale account of the network structure of macaque visual cortex", Brain Structure and Function (2017) [https://doi.org/10.1007/s00429-017-1554-4](https://doi.org/10.1007/s00429-017-1554-4)
-Please note: Figures 2, 5, and 8 show slight deviations from the published figures in the paper. The published figures 2 and 5 lack few data points. This error was unintentionally introduced during the review process. Importantly, the presented fits are identical in the correct figures in this repository and in the manuscript. These deviations thus do not affect the scientific conclusions.
+Please note: Figures 2, 5, and 8 show slight deviations from the published figures in the paper. Published Figures 2 and 5 miss a few data points. This error slipped in during the review process. Importantly, the presented fits are identical in the (correct) figures in this repository and in the manuscript. These deviations thus do not affect the scientific conclusions.
+
+Note that the placement of areas in Figure 7 will deviate from the published figure, because their location depends on the force-directed algorithm implemented in `igraph` and `python-igraph` does not allow manual setting of the random seed for the algorithm. This is a mere visual issue and does not affect the scientific content.
+
+If snakemake is installed, the figures can be produced by executing
+`snakemake`
diff --git a/figures/Schmidt2017/graph_helpers.py b/figures/Schmidt2017/graph_helpers.py
index 304c3af263df20d099284dcd0f01161826885057..3e6ff8802d2b03721ca8660699db094609ebbb11 100644
--- a/figures/Schmidt2017/graph_helpers.py
+++ b/figures/Schmidt2017/graph_helpers.py
@@ -5,9 +5,9 @@ import numpy as np
import os
-def perform_map_equation(graph_matrix, node_list, filename='', infomap_path=None):
+def apply_map_equation(graph_matrix, node_list, filename='', infomap_path=None):
"""
- Perform the map equation of
+ Apply the map equation algorithm of
Rosvall M, Axelsson D, Bergstrom CT (2009) The map equation.
Eur Phys J Spec Top 178(1):13–23
@@ -63,7 +63,7 @@ def perform_map_equation(graph_matrix, node_list, filename='', infomap_path=None
"""
if infomap_path:
os.chdir(infomap_path)
- os.system('./Infomap --directed --clu --map --verbose ' +
+ os.system('Infomap --directed --clu --map --verbose ' +
base_dir + '/' + net_fn + ' ' + base_dir)
os.chdir(base_dir)
diff --git a/figures/Schmidt2017/plotfuncs.py b/figures/Schmidt2017/plotfuncs.py
index 6a167f8c70f07282fdafdea99a7ef1938ec921eb..d112cb87f8b47a9776d0cb61902708e7cbce4ef0 100644
--- a/figures/Schmidt2017/plotfuncs.py
+++ b/figures/Schmidt2017/plotfuncs.py
@@ -5,8 +5,9 @@ mp.use('GTKAgg')
class panel_factory():
- """Class that generates a subpanel in the PloS figure"""
-
+ """
+ Class for generating subpanels
+ """
def __init__(self, scale, figure, n_pan_x, n_pan_y, hoffset,
voffset, hspace, vspace, panel_width, panel_height):
self.scale = scale