Changeset 5010

Timestamp:

Feb 8, 2008, 5:11:28 PM (17 years ago)

Author:

nick

Message:

update 'clip' and clip_outside' with verbose and added some comment statments if verbose

File:

• anuga_core/source/anuga/geospatial_data/geospatial_data.py (modified) (13 diffs)

anuga_core/source/anuga/geospatial_data/geospatial_data.py

@@ -261 +261 @@
             raise Exception, msg
 
-    def clip(self, polygon, closed=True):
+    def clip(self, polygon, closed=True, verbose=False):
         """Clip geospatial data by a polygon
 
@@ -282 +282 @@
             # Polygon is an object - extract points
             polygon = polygon.get_data_points()
+            #print 'polygon',polygon
 
         points = self.get_data_points()    
-        inside_indices = inside_polygon(points, polygon, closed)
+        #print '%s points:%s' %(verbose,points)
+        inside_indices = inside_polygon(points, polygon, closed, verbose)
 
         clipped_G = self.get_sample(inside_indices)
@@ -301 +303 @@
         
         
-    def clip_outside(self, polygon, closed=True):
+    def clip_outside(self, polygon, closed=True,verbose=False):
         """Clip geospatial date by a polygon, keeping data OUTSIDE of polygon
 
@@ -323 +325 @@
 
         points = self.get_data_points()    
-        outside_indices = outside_polygon(points, polygon, closed)
+        outside_indices = outside_polygon(points, polygon, closed,verbose)
 
         clipped_G = self.get_sample(outside_indices)
@@ -630 +632 @@
     
     def split(self, factor=0.5,seed_num=None, verbose=False):
-        """Returns two
-        
+        """Returns two        
         geospatial_data object, first is the size of the 'factor'
         smaller the original and the second is the remainder. The two
@@ -637 +638 @@
         
         Points of the two new object have selected RANDOMLY. 
-        AND if factor is a decimal it will round (2.25 to 2 and 2.5 to 3)
         
         This method create two lists of indices which are passed into
@@ -679 +679 @@
         if verbose: print "seed:", get_seed()
         
+        #print 'size',self_size, new_size
         random_num = randint(0,self_size-1,(int(new_size),))
+        #print 'random num',random_num
         random_num = random_num.tolist()
 
@@ -1447 +1449 @@
 
     if mesh_file is None:
+        if verbose: print "building mesh"
         mesh_file='temp.msh'
 
@@ -1475 +1478 @@
     else: # if mesh file provided
         #test mesh file exists?
+        if verbose: "reading from file: %s" %mesh_file 
         if access(mesh_file,F_OK) == 0:
             msg="file %s doesn't exist!" %mesh_file
@@ -1480 +1484 @@
 
     #split topo data
+    if verbose: print 'Reading elevation file: %s' %data_file
     G = Geospatial_data(file_name = data_file)
-    if verbose: print 'start split'
+    if verbose: print 'Start split'
     G_small, G_other = G.split(split_factor,seed_num, verbose=verbose)
-    if verbose: print 'finish split'
+    if verbose: print 'Finish split'
     points=G_small.get_data_points()
 
@@ -1552 +1557 @@
         #returns the predicted elevation of the points that were "split" out 
         #of the original data set for one particular alpha
-        if verbose: print 'get predicted elevation for location to be compared'
+        if verbose: print 'Get predicted elevation for location to be compared'
         elevation_predicted=domain.quantities[attribute_smoothed].\
                             get_values(interpolation_points=points_geo)
@@ -1565 +1570 @@
         #print 'memory usage during compare',mem_usage()
         
-        if verbose: print'covariance for alpha ',normal_cov[i][0],'= ',normal_cov[i][1]
-
+        if verbose: print'Covariance for alpha ',normal_cov[i][0],'= ',normal_cov[i][1]
+        if verbose: print'-------------------------------------------- \n'
 #    if verbose: print 'Determine difference between predicted results and actual data'
 #    for i,alpha in enumerate(domains):
@@ -1602 +1607 @@
     if verbose: 
         print 'Final results:'
-        for i in len(alphas):
+        for i,alpha in enumerate(alphas):
             print'covariance for alpha %s = %s ' %(normal_cov[i][0],normal_cov[i][1])
         print '\n Optimal alpha is: %s ' % normal_cov_new[(argmin(normal_cov_new,axis=0))[1],0]