added functions std, path_average_weight, remove_paths

debruijn/debruijn.py

 Small assembly module based on de bruijn graphs
 """
 import os
+import statistics
 import networkx as nx
 from networkx import algorithms
 
 
     return starting_nodes
 
-def std():
-    pass
+def std(values):
+    """
+    Arguments:
+        values, list: list of values
+
+    Returns :
+        standard deviation of the 'values' data list
+    """
+    return stdev(float(values))
 
 
 def get_sink_nodes(graph):
     return sink_nodes
 
 
-def path_average_weight():
-    pass
+def path_average_weight(graph, path):
+    """
+    Arguments:
+        graph, nx.DiGraph: a de bruijn graph
+        path, list of str(nodes): list of nodes constituing a path
 
+    Return:
+        mean weight, float: the mean weight of the path
+    """
+    weight = 0
+    for i in range(len(path)-1):
+        weight += graph[path[i][i+i][weight]
+    
+    return weight/(len(path)-1)
 
-def remove_paths():
-    pass
+
+
+def remove_paths(graph, paths, delete_entry_node=False, delete_sink_node=False):
+    """
+    Removes paths in 'paths' list from 'graph' object. Don't delete entry and
+    sink nodes unless specified.
+
+    Arguments:
+        graph, nd.DiGraph(): de bruijn graph
+        paths, list of string lists: list of paths composed of nodes
+        delete_entry_node, boolean: Delete entry if True
+        delete_sink_node, boolean: Delete sink node if True
+
+    Return:
+        graph, nd.DiGraph(): de bruijn graph with deletes paths.
+    """
+    entry = 1
+    sink = -1
+    if delete_entry_node == True:
+        entry = 0
+    if delete_sink_node == True:
+        sink = None
+    for path in paths:
+        graph.remove_nodes_from(path[entry:sink]) 
+
+    return graph
 
 
 def select_best_path():
                 for i in range(len(path)-1):
                     contig += path[i+1][-1]
                 contigs.append((contig, len(contig)))
-
+    
     return contigs
 
 def solve_bubble():