From d6d868536b36e22fe165291e5f3f75653f2765ed Mon Sep 17 00:00:00 2001
From: Maximilian Schmidt <max.schmidt@fz-juelich.de>
Date: Thu, 29 Mar 2018 11:00:39 +0900
Subject: [PATCH] Fix issues raised by albada
---
README.md | 5 ++++-
config_template.py | 4 ++--
figures/Schmidt2017/Fig2_anatomy.py | 12 +----------
figures/Schmidt2017/Fig3_construction.py | 2 +-
figures/Schmidt2017/Fig4_connectivity.py | 10 ++++++----
.../Schmidt2017/Fig5_cc_laminar_pattern.py | 2 +-
.../Schmidt2017/Fig6_connectivity_measures.py | 7 +------
.../Schmidt2017/Fig7_community_structure.py | 8 ++++----
figures/Schmidt2017/Fig8_laminar_paths.py | 20 ++++++++-----------
figures/Schmidt2017/README.md | 9 ++++++++-
figures/Schmidt2017/graph_helpers.py | 6 +++---
figures/Schmidt2017/plotfuncs.py | 5 +++--
12 files changed, 42 insertions(+), 48 deletions(-)
diff --git a/README.md b/README.md
index 8bf8bc5..669f831 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 a5db4e4..4f3f7ad 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 786966e..5f97204 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 2ef7db5..3c6d4f0 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 b4a4c91..586e7fe 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 4897ee5..ac09cdb 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 81348bd..daa9600 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 adc1996..e121ca4 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 6199ec2..75c3e3b 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 f0c8ae4..762f2b9 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 304c3af..3e6ff88 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 6a167f8..d112cb8 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
--
GitLab