Changeset 436 for inundation/ga/storm_surge/pyvolution/least_squares.py

Timestamp:

Oct 21, 2004, 5:54:28 PM (20 years ago)

Author:

duncan

Message:

reducing the # of errors when testing.
Adding fit_points function that doesn't rely on broadcasting, since cg_solve doesn't broadcast.

File:

• inundation/ga/storm_surge/pyvolution/least_squares.py (modified) (4 diffs)

inundation/ga/storm_surge/pyvolution/least_squares.py

@@ -25 +25 @@
 from LinearAlgebra import solve_linear_equations
 from scipy import sparse
-from cg_solve import conjugate_gradient
+from cg_solve import conjugate_gradient, VectorShapeError
 
 try:
@@ -130 +130 @@
                            alpha = alpha)
     
-    vertex_attributes = interp.fit(point_attributes)
+    vertex_attributes = interp.fit_points(point_attributes)
     return vertex_attributes
 
@@ -212 +212 @@
         #Build n x m interpolation matrix        
         m = self.mesh.coordinates.shape[0] #Nbr of basis functions (1/vertex)
-        n = point_coordinates.shape[0]     #Nbr of data points         
-
+        n = point_coordinates.shape[0]     #Nbr of data points
+        
         #self.A = zeros((n,m), Float)
         self.A = sparse.dok_matrix()
@@ -394 +394 @@
         #FIXME: Should we store the result here for later use? (ON)
     
-
+    def fit_points(self, z):
+        """
+        Like fit, but more robust when each point has two or more attributes
+        """
+        try:
+            return self.fit(z)
+        except VectorShapeError, e:
+            # broadcasting is not supported.
+
+            #Convert input to Numeric arrays
+            z = array(z).astype(Float)
+            
+            #Build n x m interpolation matrix        
+            m = self.mesh.coordinates.shape[0] #Number of vertices
+            n = z.shape[1]               #Number of data points         
+
+            f = zeros((m,n), Float)
+            #f = sparse.dok_matrix() # even though it wont be sparse?
+            
+            for i in range(z.shape[1]):
+                f[:,i] = self.fit(z[:,i])
+            return f
+            
+        
     def interpolate(self, f):
         """Compute predicted values at data points implied in self.A.