fixed pep8ing

ndmg/stats/graph_qc.py

@@ -57,50 +57,50 @@ def compute_metrics(fs, outdir, atlas, verb=False):
     #  Number of non-zero edges (i.e. binary edge count)
     print("Computing: NNZ")
     nnz = OrderedDict((subj, len(nx.edges(graphs[subj]))) for subj in graphs)
-    write(outdir, 'number_non_zeros', nnz, atlas) 
+    write(outdir, 'number_non_zeros', nnz, atlas)
 
     #  Degree sequence
     print("Computing: Degree Seuqence")
     temp_deg = OrderedDict((subj, np.array(nx.degree(graphs[subj]).values()))
-                               for subj in graphs)
+                           for subj in graphs)
     deg = density(temp_deg)
-    write(outdir, 'degree_distribution', deg, atlas) 
+    write(outdir, 'degree_distribution', deg, atlas)
 
     #  Edge Weights
     print("Computing: Edge Weight Sequence")
-    temp_ew = OrderedDict((subj, [graphs[subj].get_edge_data(e[0], e[1])['weight']
-                           for e in graphs[subj].edges()]) for subj in graphs)
+    temp_ew = OrderedDict((s, [graphs[s].get_edge_data(e[0], e[1])['weight']
+                           for e in graphs[s].edges()]) for s in graphs)
     ew = density(temp_ew)
-    write(outdir, 'edge_weight_distribution', ew, atlas) 
+    write(outdir, 'edge_weight_distribution', ew, atlas)
 
     #   Clustering Coefficients
     print("Computing: Clustering Coefficient Sequence")
     temp_cc = OrderedDict((subj, nx.clustering(graphs[subj]).values())
-                           for subj in graphs)
+                          for subj in graphs)
     ccoefs = density(temp_cc)
-    write(outdir, 'clustering_coefficients', ccoefs, atlas) 
+    write(outdir, 'clustering_coefficients', ccoefs, atlas)
 
     # Scan Statistic-1
     print("Computing: Scan Statistic-1 Sequence")
     temp_ss1 = scan_statistic(graphs, 1)
     ss1 = density(temp_ss1)
-    write(outdir, 'scan_statistic_1', ss1, atlas) 
+    write(outdir, 'scan_statistic_1', ss1, atlas)
 
     # Eigen Values
     print("Computing: Eigen Value Sequence")
     laplac = OrderedDict((subj, nx.normalized_laplacian_matrix(graphs[subj]))
-                             for subj in graphs)
+                         for subj in graphs)
     eigs = OrderedDict((subj, np.sort(np.linalg.eigvals(laplac[subj].A))[::-1])
-                                 for subj in graphs)
-    write(outdir, 'eigen_sequence', eigs, atlas) 
+                       for subj in graphs)
+    write(outdir, 'eigen_sequence', eigs, atlas)
 
     # Betweenness Centrality
     print("Computing: Betweenness Centrality Sequence")
     nxbc = nx.algorithms.betweenness_centrality  # For PEP8 line length...
     temp_bc = OrderedDict((subj, nxbc(graphs[subj]).values())
-                           for subj in graphs)
+                          for subj in graphs)
     centrality = density(temp_bc)
-    write(outdir, 'betweenness_centrality', centrality, atlas) 
+    write(outdir, 'betweenness_centrality', centrality, atlas)
 
     outf = outdir + '/' + atlas + '_summary.png'
 

ndmg/stats/plot_metrics.py

@@ -19,24 +19,23 @@
 # Created by Greg Kiar on 2016-05-11.
 # Email: gkiar@jhu.edu
 
-import numpy as np
-import networkx as nx
-import matplotlib
-matplotlib.use('Agg') #very important this is above pyplot import
-
-import matplotlib.pyplot as plt
-import matplotlib.ticker as mtick
 import time
 import json
 import sys
 import os
+import numpy as np
+import networkx as nx
+import matplotlib; matplotlib.use('Agg')  # very important above pyplot import
+import matplotlib.pyplot as plt
+import matplotlib.ticker as mtick
 
-font = {'weight' : 'bold',
-        'size'   : 14}
+font = {'weight': 'bold',
+        'size': 14}
 matplotlib.rc('font', **font)
 
 cols = '#000000'
 
+
 class plot_metrics():
 
     def __init__(self, nnz, deg, ew, ccoefs, ss1, eigs, centrality, outf):
@@ -93,7 +92,7 @@ def plotting(self):
         plt.savefig(self.outf, bbox_inches='tight')
         # plt.show()
 
-        metadata = {"subjects" : self.nnz.keys(),
+        metadata = {"subjects": self.nnz.keys(),
                     "date": time.asctime(time.localtime())}
         with open(os.path.splitext(self.outf)[0]+'_info.json', 'w') as fp:
             json.dump(metadata, fp)
@@ -135,8 +134,8 @@ def plot_helper(self, data, tit, typ='hi'):
     def rand_jitter(self, arr):
         stdev = .03*(max(arr)-min(arr)+2)
         return arr + np.random.randn(len(arr)) * stdev
-        
-    def factors(self, N): 
+
+    def factors(self, N):
         factors = [subitem for subitem in [(i, N//i)
                    for i in range(1, int(N**0.5) + 1)
                    if N % i == 0 and i > 1]]

ndmg/utils/bids_s3.py

@@ -34,7 +34,7 @@ def get_data(bucket, remote_path, local, subj=None):
     if bucket not in bkts:
         sys.exit("Error: could not locate bucket. Available buckets: " +
                  ", ".join(bkts))
-    
+
     cmd = "".join(['aws s3 cp --recursive s3://', bucket, '/',
                    remote_path])
     if subj is not None:

ndmg/utils/loadGraphs.py

@@ -26,6 +26,7 @@
 import sys
 import os
 
+
 def loadGraphs(filenames, verb=False):
     """
     Given a list of files, returns a dictionary of graphs
@@ -38,7 +39,8 @@ def loadGraphs(filenames, verb=False):
             - Toggles verbose output statements
     """
     #  Initializes empty dictionary
-    if type(filenames) is not list: filenames = [filenames]
+    if type(filenames) is not list:
+        filenames = [filenames]
     gstruct = OrderedDict()
     for idx, files in enumerate(filenames):
         if verb: