commit 8983440e41f994aedaf6b34648e690dedb03818a
parent 6ce83970ccaba7dd7c4f0ad316ce4f25f1e38e30
Author: Anders Damsgaard Christensen <adc@geo.au.dk>
Date: Fri, 13 Jan 2017 22:35:01 -0800
fix grid generation, increase random variability of point positioning
Diffstat:
1 file changed, 32 insertions(+), 21 deletions(-)
diff --git a/granular_channel_drainage/model.py b/granular_channel_drainage/model.py
@@ -14,7 +14,8 @@ class model:
'''
self.name = name
- def generateVoronoiDelaunayGrid(self, Lx, Ly, N, structure='pseudorandom'):
+ def generateVoronoiDelaunayGrid(self, Lx, Ly, Nx, Ny,
+ structure='pseudorandom'):
'''
Generate a Voronoi Delaunay grid with randomly positioned nodes using
Landlab.
@@ -25,8 +26,10 @@ class model:
:param Ly: A tuple containing the length along y of the model
domain.
:type Ly: float
- :param N: The number of random model nodes in the model.
- :type N: int
+ :param Nx: The number of random model nodes along ``x`` in the model.
+ :type Nx: int
+ :param Ny: The number of random model nodes along ``y`` in the model.
+ :type Ny: int
:param structure: The type of numeric distribution used to seed the
grid. A ``random`` grid will produce uniformly random-distributed
grid points, while ``pseudorandom`` (default) will add random noise
@@ -36,36 +39,44 @@ class model:
self.grid_type = 'VoronoiDelaunay'
if structure == 'random':
- x = numpy.random.rand(N)*Lx
- y = numpy.random.rand(N)*Ly
+ x = numpy.random.rand(Nx*Ny)*Lx
+ y = numpy.random.rand(Nx*Ny)*Ly
elif structure == 'pseudorandom':
- noise_amplitude = Lx/(N**0.5)*0.01
- x = numpy.linspace(0, Lx, N) + \
- numpy.random.normal(0., noise_amplitude, N)
- y = numpy.linspace(0, Ly, N) + \
- numpy.random.normal(0., noise_amplitude, N)
- print x
- print y
- x = numpy.random.shuffle(x) # why does this return an empty array?
- y = numpy.random.shuffle(y)
- print x
- print y
+ #noise_amplitude = Lx/(N**0.5)*0.001
+ #noise_amplitude = 1.
+ #x = numpy.linspace(0, Lx, N) #+ \
+ #numpy.random.normal(0., noise_amplitude, N)
+ #y = numpy.linspace(0, Ly, N) #+ \
+ #numpy.random.normal(0., noise_amplitude, N)
+ #numpy.random.shuffle(x)
+ #numpy.random.shuffle(y)
+ xPoints = numpy.linspace(0., Lx, Nx)
+ yPoints = numpy.linspace(0., Ly, Ny)
+ gridPoints = numpy.array([[x,y] for y in yPoints for x in xPoints])
+ gridPoints[::2, 1] = gridPoints[::2, 1] + Lx/Nx*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)
self.grid = landlab.grid.VoronoiDelaunayGrid(x, y)
self.grid.add_zeros('node', 'bed__topographic__elevation')
self.grid.add_zeros('node', 'surface__topographic__elevation')
- def plotGrid(self, save=False, saveformat='pdf'):
- #landlab.plot.imshow_grid(self.grid, self.grid.node_x)
+ def plotGrid(self, field='nodes',
+ save=False, saveformat='pdf'):
fig = plt.figure()
- plt.plot(self.grid.x_of_node, self.grid.y_of_node, '.')
- plt.xlabel('$x$ [m]')
- plt.ylabel('$y$ [m]')
+ if field == 'nodes':
+ plt.plot(self.grid.node_x, self.grid.node_y, '.')
+ plt.axis('equal')
+ else:
+ landlab.plot.imshow_grid(self.grid, field)
+ plt.tight_layout()
if save:
plt.savefig('grid.' + saveformat)
else:
plt.show()
+ plt.clf()
plt.close()
