[STY] Fixed style of rich_feeder_peripheral

Author: justinehansen

netneurotools/metrics.py

@@ -9,6 +9,7 @@
 from scipy.stats import ttest_ind
 from bct import degrees_und
 
+
 def communicability_bin(adjacency, normalize=False):
     """
     Computes the communicability of pairs of nodes in `adjacency`
@@ -101,79 +102,86 @@ def communicability_wei(adjacency):
 
     return cmc
 
-def rich_feeder_peripheral(x, sc, stat = 'median'):
+
+def rich_feeder_peripheral(x, sc, stat='median'):
     """
-    Calculates median or mean "connectivity" in rich (hub to hub), feeder (hub to non-hub),
-    and peripheral (non-hub to non-hub) links.
-    
-    This function takes a square symmetric correlation/connectivity matrix `x` 
-    and computes the median or mean value within rich, feeder, and peripheral links.
-    Hubs are defined againt a threshold (from 0 to the maximum degree `k`,
-    defined on the structural connectivity matrix, `sc`). 
-    
+    Calculates median or mean "connectivity" in rich (hub to hub), feeder
+    (hub to non-hub), and peripheral (non-hub to non-hub) links.
+
+    This function takes a square symmetric correlation/connectivity matrix `x`
+    and computes the median or mean value within rich, feeder, and peripheral
+    links. Hubs are defined againt a threshold (from 0 to the maximum degree
+    `k`, defined on the structural connectivity matrix, `sc`).
 
     Parameters
     ----------
-    x : (N, N) symmetric numpy.ndarray.
-    sc : (N, N) symmetric numpy.ndarray
-                binary structural connectivity matrix 
-    stat : 'median' (default) or 'mean'.
-            statistic to use over rich/feeder/peripheral links.
-
+    x : (N, N) numpy.ndarray
+        Symmetric correlation or connectivity matrix
+    sc : (N, N) numpy.ndarray
+        binary structural connectivity matrix
+    stat : str
+        statistic to use over rich/feeder/peripheral links
+        'mean' or 'median' (default)
+   
     Returns
     -------
-    rfp : (3, k) numpy.ndarray of median rich (0), feeder (1), and peripheral (2)
-          values, defined by `x`. `k` is the maximum degree defined on `sc`.
-    pvals : (3, k) numpy.ndarray p-value for each link, computed using Welch's t-test.
-            Rich links are compared against non-rich links. Feeder links are
-            compared against peripheral links. Peripheral links are compared
-            against feeder links. T-test is two-sided.
-            
+    rfp : (3, k) numpy.ndarray
+        Array of median rich (0), feeder (1), and peripheral (2)
+        values, defined by `x`. `k` is the maximum degree defined on `sc`.
+    pvals : (3, k) numpy.ndarray
+        p-value for each link, computed using Welch's t-test.
+        Rich links are compared against non-rich links. Feeder links are
+        compared against peripheral links. Peripheral links are compared
+        against feeder links. T-test is two-sided.
+
     Author
     ------
     This code was written by Justine Hansen who promises to fix and even
     optimize the code should any issues arise, provided you let her know.
-
     """
+    
     nnodes = len(sc)
-    mask = np.triu(np.ones(nnodes),1) > 0
+    mask = np.triu(np.ones(nnodes), 1) > 0
     node_degree = degrees_und(sc)
     k = np.max(node_degree).astype(np.int64)
-    rfp_label = np.zeros((len(sc[mask]),k))
-    
+    rfp_label = np.zeros((len(sc[mask]), k))
+
     for i in range(k):                        # for each degree threshold
         hub_idx = np.where(node_degree >= i)  # find the hubs
-        hub = np.zeros([nnodes,1])
-        hub[hub_idx,:] = 1
-        
-        rfp = np.zeros([nnodes,nnodes])       # for each link, define rfp
+        hub = np.zeros([nnodes, 1])
+        hub[hub_idx, :] = 1
+
+        rfp = np.zeros([nnodes, nnodes])       # for each link, define rfp
         for ii in range(nnodes):
             for iii in range(nnodes):
                 if hub[ii] + hub[iii] == 2:
-                    rfp[ii,iii] = 1 # rich
+                    rfp[ii, iii] = 1  # rich
                 if hub[ii] + hub[iii] == 1:
-                    rfp[ii,iii] = 2 # feeder
+                    rfp[ii, iii] = 2  # feeder
                 if hub[ii] + hub[iii] == 0:
-                    rfp[ii,iii] = 3 # peripheral
-        rfp_label[:,i] = rfp[mask]
-    
-    rfp = np.zeros([3,k])
-    pvals = np.zeros([3,k])
+                    rfp[ii, iii] = 3  # peripheral
+        rfp_label[:, i] = rfp[mask]
+
+    rfp = np.zeros([3, k])
+    pvals = np.zeros([3, k])
     for i in range(k):
 
         if stat == 'median':
-            rfp[0,i] = np.median(x[rfp_label[:,i] == 1]) # rich
-            rfp[1,i] = np.median(x[rfp_label[:,i] == 2]) # feeder
-            rfp[2,i] = np.median(x[rfp_label[:,i] == 3]) # peripheral
-            
+            rfp[0, i] = np.median(x[rfp_label[:, i] == 1])  # rich
+            rfp[1, i] = np.median(x[rfp_label[:, i] == 2])  # feeder
+            rfp[2, i] = np.median(x[rfp_label[:, i] == 3])  # peripheral
+
         if stat == 'mean':
-            rfp[0,i] = np.mean(x[rfp_label[:,i] == 1]) # rich
-            rfp[1,i] = np.mean(x[rfp_label[:,i] == 2]) # feeder
-            rfp[2,i] = np.mean(x[rfp_label[:,i] == 3]) # peripheral
-        
-        # p-value
-        _, pvals[0,i] = ttest_ind(x[rfp_label[:,i] == 1], x[rfp_label[:,i] != 1], equal_var=False) # Welch's t-test
-        _, pvals[1,i] = ttest_ind(x[rfp_label[:,i] == 2], x[rfp_label[:,i] == 3], equal_var=False) 
-        _, pvals[2,i] = ttest_ind(x[rfp_label[:,i] == 3], x[rfp_label[:,i] == 2], equal_var=False) 
-        
-    return rfp, pvals
+            rfp[0, i] = np.mean(x[rfp_label[:, i] == 1])  # rich
+            rfp[1, i] = np.mean(x[rfp_label[:, i] == 2])  # feeder
+            rfp[2, i] = np.mean(x[rfp_label[:, i] == 3])  # peripheral
+
+        # p-value (Welch's t-test)
+        _, pvals[0,i] = ttest_ind(x[rfp_label[:, i] == 1], \
+                                  x[rfp_label[:, i] != 1], equal_var=False)
+        _, pvals[1,i] = ttest_ind(x[rfp_label[:, i] == 2], \
+                                  x[rfp_label[:, i] == 3], equal_var=False)
+        _, pvals[2,i] = ttest_ind(x[rfp_label[:, i] == 3], \
+                                  x[rfp_label[:, i] == 2], equal_var=False)
+
+    return rfp, pvals