Workflow updates

workflows/demo.py

@@ -445,21 +445,23 @@ class SubgraphQuery(workflow.Function):
 
         # 2.) Pairwise goodness in loading space
         indices = self._pairsim(Dw)
-        print indices
-        print indices.shape
-        
         idents1 = Dw.get_identifiers(self._dim, indices[:,0])
         idents2 = Dw.get_identifiers(self._dim, indices[:,1])
         idents = zip(idents1, idents2)
         
         # 3.) Identify close subgraphs
-        
+        G = DA.asnetworkx()
+        for edge in G.edges():
+            if edge not in idents:
+                G.delete_edge(edge)
+        S = nx.connected_component_subgraphs(G)
+        print map(len, S)
         # 4.) Rank subgraphs
         
         main.project.set_selection('gene_ids', idents1)
         #main.project.set_sele
         logger.log("notice", "Gene ids updated")
-        #plt = GraphQueryScatterPlot(SS, Dw)
+        plt = GraphQueryScatterPlot(S, Dw)
         #return [plt]
 
     def _pairsim(self, Dw, ptype='cosine',cut_rat=.2):
@@ -505,38 +507,75 @@ class SubgraphQuery(workflow.Function):
             pass
         
 class GraphQueryScatterPlot(plots.ScatterPlot):
-    def __init__(self, S, Dw, *args, **kw):
-        self.S = S
-        self.Dw = Dw
+    def __init__(self, subgraphs, Dw, *args, **kw):
+        self._subgraphs = subgraphs
+        self._nx_nodes = []
+        self._nx_edges = []
+        self._init_scatter(Dw)
+        self.overlay_subgraphs()
+
+    def _init_scatter(self, Dw):
+        self._Dw = Dw
         id_dim, sel_dim = Dw.get_dim_name()
         self._dim = id_dim
         id_1, = Dw.get_identifiers(sel_dim, [0])
         id_2, = Dw.get_identifiers(sel_dim, [1])
-        print id_1
-        print id_2
-
-        plots.ScatterPlot.__init__(self, Dw, Dw, id_dim, sel_dim, id_1, id_2, c='g', s=50,name="Kegg test", alpha=.5)
-        self.overlay_subgraphs()
+        plots.ScatterPlot.__init__(self, Dw, Dw, id_dim, sel_dim, id_1, id_2, c='g', s=50,name="Hypo", alpha=.5)
     
     def overlay_subgraphs(self):
-        for subgraph in self.S:
+        all_nodes = self._Dw.get_identifiers(self._dim, sorted=True)
+        for subgraph in self._subgraphs:
+            # get xy positions from 
             nodes = subgraph.nodes()
-            print self._dim
-            
-            sub_dw = self.Dw.subdata(self._dim, nodes)
-            nodes = sub_dw.get_identifiers(self._dim, sorted=True)
-            xy = sub_dw.asarray()[:,:2]
-            pos = {}
-            for i, node in enumerate(nodes):
-                pos[node] = xy[i]
-            nx.draw_networkx_nodes(subgraph, pos, node_size=200, ax=self.axes, zorder=3)
-            nx.draw_networkx_edges(subgraph, pos, ax=self.axes, zorder=3)
-            
-            
+            for i, node in enumerate(all_nodes):
+                pos[node] = (self.xaxis_data[i], self.yaxis_data[i])
+            nn = nx.draw_networkx_nodes(subgraph, pos, node_size=200, ax=self.axes, zorder=10)
+            ee = nx.draw_networkx_edges(subgraph, pos, ax=self.axes, zorder=9)
+            self._nx_nodes.append(nn)
+            self._nx_edges.append(ee)
+
+    def _delete_networks(self):
+        if len(self._nx_nodes) > 0:
+            for n in self._nx_nodes:
+                self._nx_nodes.remove(n)
+                self.axes.collections.remove(n)
+        if len(self._nx_edges) > 0:
+            for e in self._nx_edges:
+                self._nx_edges.remove(e)
+                self.axes.collections.remove(e)
     
-    def on_update(self, selection, graph):
-        g = nx.subgraph(graph, selection)
-        S = nx.connected_components_subgraphs(g)
-    
-    def _subgraph_score(self, subgraph):
+    def set_ordinate(self, sb):
+        self._delete_networks()
+        self.overlay_subgraphs()
+        plots.ScatterPlot.set_ordinate(self, sb)
+
+    def set_absicca(self, sb):
+        self._delete_networks()
+        self.overlay_subgraphs()
+        plots.ScatterPlot.set_absicca(self, sb)
+
+
+class CAsinglesel(workflow.Function):
+    """ Modified non-symmetric correpsondence analysis.
+
+    Setup multiple selections:
+
+    Input : - a subset(s) along a dimension (selection) of `interesting` identifiers.
+            - Predefined subsets (categories) along the same dimension.
+
+    1.) The cooccurence matrix of interesting identifers and categories is made.
+    2.) The variables are scaled to represent the relative frequencies.
+
+    """
+
+    def run(X, Ckegg):
         pass
+
+
+    class CASingleSelDouble(workflow.Function):
+        """
+        """
+
+    def run(X, Ckegg):
+        pass
+