Implementation of anatomical network visualisations

.gitignore

@@ -1,3 +1,8 @@
+# Ignore the figures directory that is created when you run
+# some of the tutorials. We don't want those outputs to be
+# brought up to the github repository.
+tutorials/figures
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]

docs/source/scona.rst

@@ -62,3 +62,11 @@ scona.stats_functions module
     :members:
     :undoc-members:
     :show-inheritance:
+
+scona.visualisations module
+--------------------------------------------
+
+.. automodule:: scona.visualisations
+    :members:
+    :undoc-members:
+    :show-inheritance:

scona/__init__.py

@@ -42,6 +42,8 @@
 
 from scona.wrappers import *
 
+from scona.visualisations_helpers import *
+
 import scona.datasets
 from scona import *
 

scona/classes.py

@@ -483,8 +483,13 @@ def set_anatomical_graph_attributes(self, names):
 
 class GraphBundle(dict):
     '''
-    GraphBundle is a subclass of :class:`dict` containing
-    :class:`str`: :class:`BrainNetwork` pairs.
+    The GraphBundle class (after instantiating - object) is the scona way to
+    handle across-network comparisons.
+    What is it?
+    Essentially it's a python dictionary with BrainNetwork objects as values
+    (:class:`str`: :class:`BrainNetwork` pairs).
+
+    Mainly used to create random graphs for comparison with your real network data.
 
     Parameters
     ----------
@@ -553,6 +558,9 @@ def report_global_measures(self, as_dict=False, partition=True):
         Calculate global_measures for each BrainNetwork object and report as a
         :class:`pandas.DataFrame` or nested dict.
 
+        Note: Global measures **will not** be calculated again if they have already been calculated.
+        So it is only needed to calculate them once and then they aren't calculated again.
+
         Parameters
         ----------
         as_dict : bool

scona/graph_measures.py

@@ -346,15 +346,12 @@ def calculate_nodal_measures(
 
     # ==== CALCULATE MEASURES ====================
 
-    def calc_nodal_measure(G, measure, method, force=False):
+    for measure, method in nodal_measure_dict.items():
         if (not nx.get_node_attributes(G, name=measure)) or force:
             nx.set_node_attributes(G,
                                    name=measure,
                                    values=method(G))
 
-    for measure, method in nodal_measure_dict.items():
-        calc_nodal_measure(G, measure, method, force=force)
-
 
 # ============= Global measures =============
 
@@ -452,6 +449,9 @@ def calculate_global_measures(G,
     `average_shortest_path_length`, `assortativity`, `modularity`, and
     `efficiency` of G.
 
+    Note: Global measures **will not** be calculated again if they have already been calculated.
+    So it is only needed to calculate them once and then they aren't calculated again.
+
     Parameters
     ----------
     G : :class:`networkx.Graph`