granular-channel-hydro

commit 6ce83970ccaba7dd7c4f0ad316ce4f25f1e38e30
Author: Anders Damsgaard Christensen <adc@geo.au.dk>
Date:   Fri, 13 Jan 2017 21:02:16 -0800

first commit

Diffstat:
A
README.md
 | 
2
++
A
granular_channel_drainage/__init__.py
 | 
5
+++++
A
granular_channel_drainage/model.py
 | 
73
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
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requirements.txt
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4
++++

4 files changed, 84 insertions(+), 0 deletions(-)
diff --git a/README.md b/README.md
@@ -0,0 +1,2 @@
+# README
+
diff --git a/granular_channel_drainage/__init__.py b/granular_channel_drainage/__init__.py
@@ -0,0 +1,5 @@
+#!/usr/bin/env python
+__all__ = ['model']
+__version__ = '0.01'
+
+from model import model
diff --git a/granular_channel_drainage/model.py b/granular_channel_drainage/model.py
@@ -0,0 +1,73 @@
+#!/usr/bin/env python
+import numpy
+import matplotlib.pyplot as plt
+import landlab
+
+class model:
+    def __init__(self, name='unnamed'):
+        '''
+        Initialize a blank hydrology model object and optionally assign a
+        simulation name to it.
+
+        :param name: A uniquely identifying simulation name
+        :type name: str
+        '''
+        self.name = name
+
+    def generateVoronoiDelaunayGrid(self, Lx, Ly, N, structure='pseudorandom'):
+        '''
+        Generate a Voronoi Delaunay grid with randomly positioned nodes using
+        Landlab.
+
+        :param Lx: A tuple containing the length along x of the model
+            domain.
+        :type Lx: float
+        :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 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
+            to a regular grid.
+        :type structure: str
+        '''
+        self.grid_type = 'VoronoiDelaunay'
+
+        if structure == 'random':
+            x = numpy.random.rand(N)*Lx
+            y = numpy.random.rand(N)*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
+
+        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)
+        fig = plt.figure()
+        plt.plot(self.grid.x_of_node, self.grid.y_of_node, '.')
+        plt.xlabel('$x$ [m]')
+        plt.ylabel('$y$ [m]')
+        if save:
+            plt.savefig('grid.' + saveformat)
+        else:
+            plt.show()
+        plt.close()
+
+
+
+
diff --git a/requirements.txt b/requirements.txt
@@ -0,0 +1,4 @@
+scipy>=0.14
+numpy
+landlab
+matplotlib