copy() added to dataset. Does deepcopy. deepcopy issue fixed in ReverseDict

system/dataset.py

@@ -2,6 +2,7 @@ from scipy import ndarray,atleast_2d,asarray
 from scipy import sort as array_sort
 from itertools import izip
 import shelve
+import copy
 
 class Dataset:
     """The Dataset base class.
@@ -40,12 +41,16 @@ class Dataset:
         self._name = name
         self._identifiers = identifiers
         self._type = 'n'
-        if isinstance(array,ndarray):
+        try:
             array = atleast_2d(asarray(array))
+        except:
+            print "Cant cast array as numpy-array"
+            return
         # vectors are column vectors 
         if array.shape[0]==1:
             array = array.T
         self.shape = array.shape
+
         if identifiers!=None:
             self._set_identifiers(identifiers,self._all_dims)
         else:
@@ -54,9 +59,6 @@ class Dataset:
             
         self._array = array
 
-        else:
-            raise ValueError, "Array input must be of type ndarray"
-
     def __iter__(self):
         """Returns an iterator over dimensions of dataset."""
         return self._dims.__iter__()
@@ -189,6 +191,9 @@ class Dataset:
                      for key in idents if self._map[dim].has_key(key)]
         return asarray(index)        
 
+    def copy(self):
+        return copy.deepcopy(self)
+
 
 class CategoryDataset(Dataset):
     """The category dataset class.
@@ -245,8 +250,9 @@ class CategoryDataset(Dataset):
 class GraphDataset(Dataset):
     """The graph dataset class.
 
-    A dataset class for representing graphs using an adjacency matrix
-    (aka. restricted to square symmetric signed integers matrices)
+    A dataset class for representing graphs using an (weighted)
+    adjacency matrix
+    (aka. restricted to square symmetric matrices)
     
     If the library NetworkX is installed, there is support for
     representing the graph as a NetworkX.Graph, or NetworkX.XGraph structure.
@@ -254,7 +260,7 @@ class GraphDataset(Dataset):
     
     def __init__(self,array=None,identifiers=None,shape=None,all_dims=[],**kwds):
         Dataset.__init__(self,array=array,identifiers=identifiers,name='A')
-        self.has_graph = False
+        self._graph = None
         self._type = 'g'
         
     def asnetworkx(self,nx_type='graph'):
@@ -262,34 +268,41 @@ class GraphDataset(Dataset):
         ids = self.get_identifiers(dim,sorted=True)
         adj_mat = self.asarray()
         G = self._graph_from_adj_matrix(adj_mat,labels=ids)
-        self.has_graph = True
+        self._graph = G
         return G
     
-    def _graph_from_adj_matrix(self,A,labels=None,nx_type='graph'):
-        """Creates a networkx graph class from adjacency matrix and
-        ordered labels.  nx_type = ['graph',['xgraph']] labels = None,
-        results in string-numbered labels
+    def _graph_from_adj_matrix(self,A,labels=None):
+        """Creates a networkx graph class from adjacency
+        (possibly weighted) matrix and ordered labels.
         
+        nx_type = ['graph',['xgraph']]
+        labels = None, results in string-numbered labels
         """
+        
+        try:
             import networkx as nx
+        except:
+            print "Failed in import of NetworkX"
+            return
         m,n = A.shape# adjacency matrix must be of type that evals to true/false for neigbours
         if m!=n:
             raise IOError, "Adjacency matrix must be square"
-        if nx_type=='graph':
+
+        if A[A[:,0].nonzero()[0],0]==1: #unweighted graph
             G = nx.Graph()
-        elif nx_type=='x_graph':
-            G = nx.XGraph()
         else:
-            raise IOError, "Unknown graph type: %s" %nx_type
+            G = nx.XGraph()
 
         if labels==None: # if labels not provided mark vertices with numbers
             labels = [str(i) for i in range(m)]
 
-	
         for nbrs,head in izip(A,labels):
             for i,nbr in enumerate(nbrs):
                 if nbr:
                     tail = labels[i]
+                    if type(G)==nx.XGraph:
+                        G.add_edge(head,tail,nbr)
+                    else:
                         G.add_edge(head,tail)
         return G
         
@@ -310,7 +323,10 @@ class ReverseDict(dict):
 
     def __setitem__(self, key, value):
         dict.__setitem__(self, key, value)
+        try:
             self.reverse[value] = key
+        except:
+            self.reverse = {value:key}
 
 def to_file(filepath,dataset,name=None):
     """Write dataset to file. A file may contain multiple datasets.