code styles

teneto/networkmeasures/shortest_temporal_path.py

@@ -19,11 +19,11 @@ def seqpath_to_path(pairseq, source):
         # Get all remaining possible paths in sequence
         iterset = set(np.where((pairrows == pairrows[node, 0]) | (
             pairrows == pairrows[node, 1]))[0]) - set(range(node+1))
-        for next in iterset:
-            if source in pairrows[next]:
-                yield list(reversed(pairrows[path + [next]].tolist()))
+        for nextset in iterset:
+            if source in pairrows[nextset]:
+                yield list(reversed(pairrows[path + [nextset]].tolist()))
             else:
-                queue.append((next, path + [next]))
+                queue.append((nextset, path + [nextset]))
 
 
 def shortest_path_from_pairseq(pairseq, source):

teneto/networkmeasures/temporal_degree_centrality.py

@@ -17,24 +17,24 @@ def temporal_degree_centrality(tnet, axis=0, calc='avg', communities=None, decay
 
     net : array, dict
         Temporal network input (graphlet or contact). Can have nettype: 'bu', 'bd', 'wu', 'wd'
-    axis : int 
+    axis : int
         Dimension that is returned 0 or 1 (default 0).
         Note, only relevant for directed networks.
         i.e. if 0, node i has Aijt summed over j and t.
         and if 1, node j has Aijt summed over i and t.
     calc : str
-        Can be following alternatives: 
+        Can be following alternatives:
 
         'avg' : (returns temporal degree centrality (a 1xnode vector))
 
         'time' : (returns a node x time matrix),
 
-        'module_degree_zscore' : returns the Z-scored within community degree centrality 
+        'module_degree_zscore' : returns the Z-scored within community degree centrality
         (communities argument required). This is done for each time-point
         i.e. 'time' returns static degree centrality per time-point.
 
     ignorediagonal: bool
-        if true, diagonal is made to 0. 
+        if true, diagonal is made to 0.
     communities : array (Nx1)
         Vector of community assignment.
         If this is given and calc='time', then the strength within and between each communities is returned (technically not degree centrality).

teneto/networkmeasures/temporal_participation_coeff.py

@@ -15,7 +15,7 @@ def temporal_participation_coeff(tnet, communities=None, decay=None, removeneg=F
     communities : array
         community vector. Either 1D (node) community index or 2D (node,time).
     removeneg : bool (default false)
-        If true, all values < 0 are made to be 0. 
+        If true, all values < 0 are made to be 0.
 
 
     Returns
@@ -29,7 +29,7 @@ def temporal_participation_coeff(tnet, communities=None, decay=None, removeneg=F
 
     Static participatoin coefficient is:
 
-    .. math:: P_i = 1 - \sum_s^{N_M}({{k_{is}}\over{k_i}})^2 
+    .. math:: P_i = 1 - \sum_s^{N_M}({{k_{is}}\over{k_i}})^2
 
     Where s is the index of each community (:math:`N_M`). :math:`k_i` is total degree of node. And :math:`k_{is}` is degree of connections within community.[part-1]_
 

teneto/utils/__init__.py

@@ -2,6 +2,6 @@
 Many helper functions for Teneto
 """
 
-from .bidsutils import * 
+from .bidsutils import *
 from .utils import *
 from .io import *