tincrease entropy in grid - granular-channel-hydro - subglacial hydrology model for sedimentary channels
(HTM) git clone git://src.adamsgaard.dk/granular-channel-hydro
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---
(DIR) commit afff4e386f601b61e0e2a0323d80d16b3074fe7b
(DIR) parent 4516a5f45c72fc1a57aa0d5da689b2225948ebbf
(HTM) 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(-)
---
(DIR) diff --git a/granular_channel_drainage/model.py b/granular_channel_drainage/model.py
t@@ -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.
t@@ -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'
t@@ -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)