Improved drag'n'drop of data into scatter plots so that it no longer requires

matching identifiers along dimensions.

fluents/lib/blmplots.py

@@ -65,39 +65,6 @@ class BlmScatterPlot(plots.ScatterPlot):
         self.sc = self._mappable
         self.add_pc_spin_buttons(self._T.shape[1], absi, ordi)
 
-    def _update_color_from_dataset(self, data):
-        """Overriding scatter for testing of colormaps.
-        """
-        is_category = False
-        array = data.asarray()
-        #only support for 2d-arrays:
-        try:
-            m, n = array.shape
-        except:
-            raise ValueError, "No support for more than 2 dimensions."
-        # is dataset a vector or matrix?
-        if not n==1:
-            # we have a category dataset
-            if isinstance(data, fluents.dataset.CategoryDataset):
-                is_category = True
-                map_vec = scipy.dot(array, scipy.diag(scipy.arange(n))).sum(1)
-            else:
-                map_vec = array.sum(1)
-        else:
-            map_vec = array.ravel()
-
-        # update facecolors
-        self.sc.set_array(map_vec)
-        self.sc.set_clim(map_vec.min(), map_vec.max())
-        if is_category:
-            cmap = cm.Paired
-        else:
-            cmap = cm.jet
-
-        self.sc.set_cmap(cmap)
-        self.sc.update_scalarmappable() #sets facecolors from array
-        self.canvas.draw()
-        
     def set_facecolor(self, colors):
         """Set patch facecolors.
         """

fluents/plots.py

@@ -441,41 +441,59 @@ class ScatterPlot(Plot):
     def is_mappable_with(self, obj):
         """Returns True if dataset/selection is mappable with this plot.
         """
+        print "is_mappable_with"
         if isinstance(obj, fluents.dataset.Dataset):
-            if self.current_dim in obj.get_dim_name() \
-                   and obj.asarray().shape[0] == self.xaxis_data.shape[0]:
+            if self.current_dim in obj.get_dim_name():
+                print "is_mappable_with: True"
                 return True
         
         elif isinstance(obj, fluents.dataset.Selection):
             if self.current_dim in obj.get_dim_name():
-                print "Selection is mappable"
+                print "is_mappable_with: True"
                 return True
 
         else:
+            print "is_mappable_with: False"
             return False
         
     def _update_color_from_dataset(self, data):
         """Updates the facecolors from a dataset.
         """
+        print "_update_color_from_dataset"
         array = data.asarray()
         #only support for 2d-arrays:
         try:
             m, n = array.shape
         except:
             raise ValueError, "No support for more than 2 dimensions."
+
         # is dataset a vector or matrix?
         if not n==1:
             # we have a category dataset
             if isinstance(data, fluents.dataset.CategoryDataset):
-                map_vec = scipy.dot(array, scipy.diag(scipy.arange(n))).sum(1)
+                vec = dot(array, diag(arange(n))).sum(1)
             else:
-                map_vec = array.sum(1)
+                vec = array.sum(1)
         else:
-            map_vec = array.ravel()
+            vec = array.ravel()
+
+        identifiers = self.dataset_1.get_identifiers(self.current_dim, sorted=True)
+        indices = data.get_indices(self.current_dim, identifiers)
+        existing_ids = data.existing_identifiers(self.current_dim, identifiers)
+
+        v = vec[indices]
+        vec_min = min(vec[vec > -scipy.inf])
+        vec_max = max(vec[vec < scipy.inf])
+        v[v==scipy.inf] = vec_max
+        v[v==-scipy.inf] = vec_min
+        
+        indices = self.dataset_1.get_indices(self.current_dim, existing_ids)
+        map_vec = vec_min*scipy.ones(len(identifiers))
+        map_vec[indices] = v
 
         # update facecolors
         self.sc.set_array(map_vec)
-        self.sc.set_clim(map_vec.min(), map_vec.max())
+        self.sc.set_clim(vec_min, vec_max)
         self.sc.update_scalarmappable() #sets facecolors from array
         self.canvas.draw()
                     

workflows/gobrowser.py

@@ -726,8 +726,8 @@ class DagPlot(plots.Plot):
         nodes = ds.existing_identifiers(self.current_dim, self.nodes)
 
         v = vec.take(indices, 0)
-        vec_min = min(v[v > -inf])
-        vec_max = max(v[v < inf])
+        vec_min = min(vec[vec > -inf])
+        vec_max = max(vec[vec < inf])
         v[v==inf] = vec_max
         v[v==-inf] = vec_min
 
@@ -739,7 +739,7 @@ class DagPlot(plots.Plot):
 
         # update facecolors
         self.node_collection.set_array(map_vec)
-        self.node_collection.set_clim(map_vec.min(), map_vec.max())
+        self.node_collection.set_clim(vec_min, vec_max)
         self.node_collection.update_scalarmappable() #sets facecolors from array
         self.canvas.draw()