granular-channel-hydro

commit afff4e386f601b61e0e2a0323d80d16b3074fe7b
parent 4516a5f45c72fc1a57aa0d5da689b2225948ebbf
Author: Anders Damsgaard Christensen <adc@geo.au.dk>
Date:   Fri, 13 Jan 2017 22:44:10 -0800

increase entropy in grid

Diffstat:
M
granular_channel_drainage/model.py
 | 
28
++++++++++++++++++++++------

1 file changed, 22 insertions(+), 6 deletions(-)
diff --git a/granular_channel_drainage/model.py b/granular_channel_drainage/model.py
@@ -15,7 +15,8 @@ class model:
         self.name = name
 
     def generateVoronoiDelaunayGrid(self, Lx, Ly, Nx, Ny,
-                                    structure='pseudorandom'):
+                                    structure='pseudorandom',
+                                    distribution='uniform'):
         '''
         Generate a Voronoi Delaunay grid with randomly positioned nodes using
         Landlab.
@@ -35,6 +36,10 @@ class model:
             grid points, while ``pseudorandom`` (default) will add random noise
             to a regular grid.
         :type structure: str
+        :name distribution: Type of random numeric distribution adding noise to
+            the pseudorandom structured grid.  Accepted values are 'uniform'
+            (default) or 'normal'.
+        :type distribution: str
         '''
         self.grid_type = 'VoronoiDelaunay'
 
@@ -50,13 +55,24 @@ class model:
                 #numpy.random.normal(0., noise_amplitude, N)
             #numpy.random.shuffle(x)
             #numpy.random.shuffle(y)
-            xPoints = numpy.linspace(Lx/Nx, Lx - Lx/Nx, Nx)
-            yPoints = numpy.linspace(Ly/Ny, Ly - Ly/Ny, Ny)
+            dx = Lx/Nx
+            dy = Ly/Ny
+            xPoints = numpy.linspace(dx*.5, Lx - dx*.5, Nx)
+            yPoints = numpy.linspace(dy*.5, Ly - dy*.5, Ny)
             gridPoints = numpy.array([[x,y] for y in yPoints for x in xPoints])
-            gridPoints[::2, 1] = gridPoints[::2, 1] + Lx/Nx*0.5
+            gridPoints[::2, 1] = gridPoints[::2, 1] + dy*0.5
             N = len(gridPoints[:, 0])
-            x = gridPoints[:, 0] + numpy.random.normal(0, Lx/Nx*0.10, N)
-            y = gridPoints[:, 1] + numpy.random.normal(0, Ly/Ny*0.10, N)
+            if distribution == 'normal':
+                x = gridPoints[:, 0] + numpy.random.normal(0, dx*0.10, N)
+                y = gridPoints[:, 1] + numpy.random.normal(0, dy*0.10, N)
+            elif distribution == 'uniform':
+                x = gridPoints[:, 0] + numpy.random.uniform(-dx*.5, dx*.5, N)
+                y = gridPoints[:, 1] + numpy.random.uniform(-dy*.5, dy*.5, N)
+
+            else:
+                raise Exception('generateVoronoiDelaunayGrid: ' +
+                                'distribution type ' + distribution +
+                                ' not understood.')
 
         self.grid = landlab.grid.VoronoiDelaunayGrid(x, y)