def test_fastscape_new():
input_str = os.path.join(_THIS_DIR, 'drive_sp_params.txt')
inputs = ModelParameterDictionary(input_str, auto_type=True)
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')
uplift = inputs.read_float('uplift_rate')
init_elev = inputs.read_float('init_elev')
mg = RasterModelGrid(nrows, ncols, dx)
mg.set_closed_boundaries_at_grid_edges(False, False, True, True)
mg.add_zeros('topographic__elevation', at='node')
z = mg.zeros(at='node') + init_elev
numpy.random.seed(0)
mg['node']['topographic__elevation'] = z + \
numpy.random.rand(len(z)) / 1000.
fr = FlowAccumulator(mg, flow_director='D8')
fsp = Fsc(mg, **inputs) # here's the diff from the above
elapsed_time = 0.
while elapsed_time < time_to_run:
if elapsed_time + dt > time_to_run:
dt = time_to_run - elapsed_time
fr.run_one_step()
fsp.run_one_step(dt) # new style
mg.at_node['topographic__elevation'][mg.core_nodes] += uplift * dt
elapsed_time += dt
z_trg = numpy.array([5.48813504e-04, 7.15189366e-04, 6.02763376e-04,
5.44883183e-04, 4.23654799e-04, 6.45894113e-04,
1.01830760e-02, 9.58036770e-03, 6.55865452e-03,
3.83441519e-04, 7.91725038e-04, 1.00142749e-02,
8.80798884e-03, 5.78387585e-03, 7.10360582e-05,
8.71292997e-05, 9.81911417e-03, 9.52243406e-03,
7.55093226e-03, 8.70012148e-04, 9.78618342e-04,
1.00629755e-02, 8.49253798e-03, 5.33216680e-03,
1.18274426e-04, 6.39921021e-04, 9.88956320e-03,
9.47119567e-03, 6.43790696e-03, 4.14661940e-04,
2.64555612e-04, 1.00450743e-02, 8.37262908e-03,
5.21540904e-03, 1.87898004e-05, 6.17635497e-04,
9.21286940e-03, 9.34022513e-03, 7.51114450e-03,
6.81820299e-04, 3.59507901e-04, 6.19166921e-03,
7.10456176e-03, 6.62585507e-03, 6.66766715e-04,
6.70637870e-04, 2.10382561e-04, 1.28926298e-04,
3.15428351e-04, 3.63710771e-04])
assert_array_almost_equal(mg.at_node['topographic__elevation'], z_trg)
def test_fastscape():
uplift = 0.001
dt = 1.0
time_to_run = 10.0
mg = RasterModelGrid((10, 5))
mg.set_closed_boundaries_at_grid_edges(False, False, True, True)
mg.add_zeros("topographic__elevation", at="node")
z = mg.zeros(at="node")
numpy.random.seed(0)
mg["node"]["topographic__elevation"] = z + numpy.random.rand(
len(z)) / 1000.0
fr = FlowAccumulator(mg, flow_director="D8")
fsp = Fsc(mg, K_sp=0.1, m_sp=0.5, n_sp=1.0, threshold_sp=0.0)
elapsed_time = 0.0
while elapsed_time < time_to_run:
if elapsed_time + dt > time_to_run:
dt = time_to_run - elapsed_time
fr.run_one_step()
fsp.run_one_step(dt=dt)
mg.at_node["topographic__elevation"][mg.core_nodes] += uplift * dt
elapsed_time += dt
z_trg = numpy.array([
5.48813504e-04,
7.15189366e-04,
6.02763376e-04,
5.44883183e-04,
4.23654799e-04,
6.45894113e-04,
1.01830760e-02,
9.58036770e-03,
6.55865452e-03,
3.83441519e-04,
7.91725038e-04,
1.00142749e-02,
8.80798884e-03,
5.78387585e-03,
7.10360582e-05,
8.71292997e-05,
9.81911417e-03,
9.52243406e-03,
7.55093226e-03,
8.70012148e-04,
9.78618342e-04,
1.00629755e-02,
8.49253798e-03,
5.33216680e-03,
1.18274426e-04,
6.39921021e-04,
9.88956320e-03,
9.47119567e-03,
6.43790696e-03,
4.14661940e-04,
2.64555612e-04,
1.00450743e-02,
8.37262908e-03,
5.21540904e-03,
1.87898004e-05,
6.17635497e-04,
9.21286940e-03,
9.34022513e-03,
7.51114450e-03,
6.81820299e-04,
3.59507901e-04,
6.19166921e-03,
7.10456176e-03,
6.62585507e-03,
6.66766715e-04,
6.70637870e-04,
2.10382561e-04,
1.28926298e-04,
3.15428351e-04,
3.63710771e-04,
])
assert_array_almost_equal(mg.at_node["topographic__elevation"], z_trg)
