Fix issues of figure scripts

figures/Schmidt2018/Fig3_construction.py

@@ -17,11 +17,21 @@ with open(os.path.join(datapath, 'viscortex_processed_data.json'), 'r') as f:
     proc = json.load(f)
 density = proc['neuronal_densities']
 
+if density is None:
+    print("Since we cannot publish the underlying density data, we"
+          "here hard-code the list of areas sorted by their overall"
+          "density.")
+    areas_sorted_density = ['V1', 'V2', 'V3', 'VP', 'V4', 'MT', 'V4t',
+                            'PITv', 'PITd', 'VOT', 'V3A', 'LIP', 'MIP', 'MDP', 'PO', 'PIP',
+                            'MSTl', 'VIP', 'MSTd', 'DP', 'TF', 'FEF', 'CITv', 'CITd', 'AITv',
+                            'AITd', 'STPa', 'STPp', 'FST', '46', '7a', 'TH']
+else:
     ordered_density = OrderedDict(
         sorted(list(density.items()), key=lambda t: t[1]['overall'], reverse=True))
+    areas_sorted_density = list(ordered_density.keys())
 
 average_indegree = {}
-for area in list(ordered_density.keys()):
+for area in areas_sorted_density:
     s = 0
     for pop in population_list:
         for source_area in list(area_list):
@@ -32,7 +42,7 @@ for area in list(ordered_density.keys()):
 
 indegrees = []
 num_list = []
-for area in list(ordered_density.keys()):
+for area in areas_sorted_density:
     indegrees.append(average_indegree[area])
     num_list.append(M.N[area]['total'])
 

figures/Schmidt2018/Fig5_cc_laminar_pattern.py

@@ -13,6 +13,9 @@ from plotcolors import myred, myblue
 from multiarea_model import MultiAreaModel
 from multiarea_model.multiarea_helpers import create_mask
 
+NEURON_DENSITIES_AVAILABLE = False
+
+if NEURON_DENSITIES_AVAILABLE:
     """
     Layout
     """
@@ -146,18 +149,13 @@ SLN_array = np.array(data[:, 10], dtype=np.float)
     densities = np.array(data[:, 7], dtype=np.float)
 
     # Call R script to perform SLN fit
-try:
-    proc = subprocess.Popen(["Rscript",
-                             os.path.join(datapath, 'SLN_logdensities.R')],
-                            stdout=subprocess.PIPE)
-    out = proc.communicate()[0].decode('utf-8')
-    R_fit = [float(out.split('\n')[1].split(' ')[1]),
-             float(out.split('\n')[1].split(' ')[3])]
-except OSError:
-    print("No R installation, taking hard-coded fit parameters.")
+    print("We currently cannot publish the R code because of "
+          "copyright issues, there taking hard-coded fit parameters. "
+          "See Schmidt et al. (2018) for a full explanation "
+          "of the procedure.")
     R_fit = [-0.1516142, -1.5343200]
-
     print(R_fit)
+
     ax = axes['A']
     ax.plot(densities, SLN_array, '.', linewidth=1.5, color=myblue)
     x = np.arange(-2., 2., 0.1)
@@ -307,3 +305,7 @@ c.insert(pyx.epsfile.epsfile(
     c.insert(pyx.epsfile.epsfile(
         9.3, 12., "Fig5_syn_illustration.eps", width=7.))
     c.writeEPSfile("Fig5_cc_laminar_pattern.eps")
+
+else:
+    print("Figure 5 can currently not be produced because "
+          "we cannot publish the underlying raw data.")

figures/Schmidt2018/Fig7_community_structure.py

@@ -62,8 +62,11 @@ g_abs = create_graph(conn_matrix_abs, area_list)
 """
 Determine clusters using the map equation.
 """
+# This path determines the location of the infomap
+# installation and needs to be provided to execute the script
+infomap_path = None
 modules, modules_areas, index = apply_map_equation(
-    conn_matrix, area_list, filename='Model')
+    conn_matrix, area_list, filename='Model', infomap_path=infomap_path)
 
 f = open('Model.map', 'r')
 line = ''
@@ -113,7 +116,9 @@ 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 = apply_map_equation(null_model, area_list, filename='null')
+    modules, modules_areas, index = apply_map_equation(null_model, area_list,
+                                                       filename='null',
+                                                       infomap_path=infomap_path)
     g_null = create_graph(null_model, area_list)
     mod_list.append(modularity(g_null, modules[index]))
 

figures/Schmidt2018/Fig8_laminar_paths.py

@@ -251,7 +251,7 @@ for target_area in area_list:
                               source_areas=[source_area],
                               complete_area_list=area_list,
                               external=False)
-        pM = path_length_matrix[indices]
+        pM = path_length_matrix[indices[:, :-1]]
         pM = pM.reshape(
             (len(M.structure[target_area]), len(M.structure[source_area])))
         imin = np.unravel_index(np.argmin(pM), pM.shape)

figures/Schmidt2018/graph_helpers.py

@@ -63,8 +63,12 @@ def apply_map_equation(graph_matrix, node_list, filename='', infomap_path=None):
     """
     if infomap_path:
         os.chdir(infomap_path)
-    os.system('Infomap --directed --clu --map --verbose ' +
+    ret = os.system('./Infomap --directed --clu --map --verbose ' +
                     base_dir + '/' + net_fn + ' ' + base_dir)
+    if ret != 0:
+        raise OSError("Executing infomap failed. Did you install "
+                      "infomap and provide the correct path by "
+                      "defining the variable infomap_path?")
 
     os.chdir(base_dir)