def test_diffusion():
inputs = ModelParameterDictionary(os.path.join(_THIS_DIR,
'diffusion_params.txt'))
nrows = inputs.read_int('nrows')
ncols = inputs.read_int('ncols')
dx = inputs.read_float('dx')
dt = inputs.read_float('dt')
time_to_run = inputs.read_float('run_time')
init_elev = inputs.read_float('init_elev')
mg = RasterModelGrid((nrows, ncols), (dx, dx))
uplift_rate = mg.node_y[mg.core_cells] / 100000.
# create the fields in the grid
mg.add_zeros('topographic__elevation', at='node')
z = mg.zeros(at='node') + init_elev
np.random.seed(0)
mg['node']['topographic__elevation'] = z + np.random.rand(len(z)) / 1000.
mg.set_fixed_value_boundaries_at_grid_edges(True, True, True, True)
# instantiate:
dfn = LinearDiffuser(mg, inputs)
# perform the loop:
elapsed_time = 0. # total time in simulation
while elapsed_time < time_to_run:
if elapsed_time + dt > time_to_run:
dt = time_to_run - elapsed_time
dfn.diffuse(dt)
mg.at_node['topographic__elevation'][mg.core_nodes] += uplift_rate * dt
elapsed_time += dt
z_target = np.array([5.48813504e-04, 7.15189366e-04, 6.02763376e-04,
5.44883183e-04, 4.23654799e-04, 6.45894113e-04,
4.37587211e-04, 8.91773001e-04, 9.63662761e-04,
3.83441519e-04, 7.91725038e-04, 9.17712569e-04,
1.02071232e-03, 1.10556005e-03, 1.14946096e-03,
1.20022436e-03, 1.12938983e-03, 1.12734463e-03,
1.00699946e-03, 8.70012148e-04, 9.78618342e-04,
1.12639399e-03, 1.41449092e-03, 2.66410106e-03,
2.80054364e-03, 2.82446032e-03, 2.68730913e-03,
2.44468915e-03, 2.03932919e-03, 4.14661940e-04,
2.64555612e-04, 2.14829293e-03, 2.77874345e-03,
3.21568734e-03, 3.45767578e-03, 4.46407381e-03,
4.25087300e-03, 3.82133955e-03, 3.24905386e-03,
6.81820299e-04, 3.59507901e-04, 3.36368061e-03,
4.19756389e-03, 4.81101469e-03, 5.12991298e-03,
5.14551834e-03, 4.82212362e-03, 5.21733341e-03,
4.36590155e-03, 3.63710771e-04, 5.70196770e-04,
4.64503306e-03, 5.67212626e-03, 6.43364397e-03,
6.85196170e-03, 6.84719792e-03, 6.44199511e-03,
5.63718526e-03, 4.53705235e-03, 2.44425592e-04,
1.58969584e-04, 5.85390586e-03, 7.15167925e-03,
8.09808225e-03, 8.58987393e-03, 8.60040722e-03,
8.08353544e-03, 7.11357773e-03, 5.74363061e-03,
9.60984079e-05, 9.76459465e-04, 6.28409752e-03,
7.69302638e-03, 9.77162122e-03, 1.03665461e-02,
1.03596559e-02, 9.77581391e-03, 8.63184402e-03,
7.06221620e-03, 1.18727719e-04, 3.17983179e-04,
7.42141185e-03, 9.16089943e-03, 1.04492602e-02,
1.11235269e-02, 1.21428645e-02, 1.14619409e-02,
1.01991149e-02, 8.52209581e-03, 9.29296198e-04,
3.18568952e-04, 8.66507665e-03, 1.06513243e-02,
1.20945739e-02, 1.28805314e-02, 1.28817803e-02,
1.21355585e-02, 1.16763419e-02, 9.65364442e-03,
4.69547619e-06, 6.77816537e-04, 9.99969455e-03,
1.21210512e-02, 1.37222525e-02, 1.45639747e-02,
1.45899214e-02, 1.37503063e-02, 1.21879324e-02,
1.00970967e-02, 9.52749012e-04, 4.47125379e-04,
1.11861039e-02, 1.35273547e-02, 1.52388381e-02,
1.61710069e-02, 1.61700373e-02, 1.52797248e-02,
1.35608698e-02, 1.12630871e-02, 6.92531590e-04,
7.25254280e-04, 1.14112447e-02, 1.38209264e-02,
1.66345746e-02, 1.75854796e-02, 1.76037603e-02,
1.66317633e-02, 1.48117588e-02, 1.22941311e-02,
2.90077607e-04, 6.18015429e-04, 1.24651035e-02,
1.49479969e-02, 1.67762718e-02, 1.77682020e-02,
1.87567995e-02, 1.77826641e-02, 1.59106401e-02,
1.33841504e-02, 4.31418435e-04, 8.96546596e-04,
1.34298871e-02, 1.58121125e-02, 1.76176213e-02,
1.85627061e-02, 1.85694232e-02, 1.75911811e-02,
1.67878955e-02, 1.43993331e-02, 9.98847007e-04,
1.49448305e-04, 1.40269701e-02, 1.63483655e-02,
1.80226762e-02, 1.89156586e-02, 1.88916976e-02,
1.79880403e-02, 1.62672916e-02, 1.39254090e-02,
6.91669955e-05, 6.97428773e-04, 1.46967049e-02,
1.65718319e-02, 1.79957330e-02, 1.87279193e-02,
1.87059832e-02, 1.79052307e-02, 1.64399258e-02,
1.44435378e-02, 1.71629677e-04, 5.21036606e-04,
1.40296771e-02, 1.54293506e-02, 1.75066749e-02,
1.80476077e-02, 1.79866098e-02, 1.73732550e-02,
1.62714602e-02, 1.47877073e-02, 3.18389295e-05,
1.64694156e-04, 1.41367998e-02, 1.49517470e-02,
1.57129817e-02, 1.59700260e-02, 1.68585204e-02,
1.64421520e-02, 1.58441873e-02, 1.50473253e-02,
3.11944995e-04, 3.98221062e-04, 2.09843749e-04,
1.86193006e-04, 9.44372390e-04, 7.39550795e-04,
4.90458809e-04, 2.27414628e-04, 2.54356482e-04,
5.80291603e-05, 4.34416626e-04])
assert_array_almost_equal(mg.at_node['topographic__elevation'], z_target)
