def test_diffusion_no_deposit():
# Make an array with three core nodes, in one column.
# Because there is zero slope between two of the nodes, there
# would be deposition. However, with the deposit flag as 'False',
# the elevation of the node with zero downslope gradient will not change.
# Use closed boundaries all around because this is a simpler scenario.
mg = RasterModelGrid((5, 3), (10, 10))
z = mg.zeros(at="node")
z[4] = 3.
z[7] = 3.
z[10] = 4.
mg["node"]["topographic__elevation"] = z
# The gradient at node 7 should be zero, so the elevation here would
# go up if deposition was allowed. Maker sure it doesn't change with
# deposit set to 'False'
z_7_before = z[7]
mg.set_closed_boundaries_at_grid_edges(True, True, True, True)
# instantiate:
dfn = LinearDiffuser(mg,
linear_diffusivity=1.,
method="simple",
deposit=False)
dfn.run_one_step(100)
z_7_after = z[7]
assert_equal(z_7_before, z_7_after)
def main():
# INITIALIZE
# Name of parameter input file
input_file_name = 'test_inputs_for_diffusion_model.txt'
# Open input file and read run-control parameters
mpd = ModelParameterDictionary(input_file_name)
run_duration = mpd.get('RUN_DURATION', ptype=float)
# Create and initialize a grid
mg = create_and_initialize_grid(mpd)
# Create and initialize a diffusion component
dc = LinearDiffuser(mg)
dc.initialize(mpd)
# RUN
# Run the diffusion component until it's time for the next output
dc.run_until(run_duration)
# FINALIZE
# Display results to screen
mg.imshow('node', 'landscape_surface__elevation')
import pylab
pylab.show()
from landlab.io.netcdf import write_netcdf
write_netcdf('diffusion_example.nc', mg)
def __init__(self, input_file=None, params=None):
"""Initialize the LinearDiffusionModel."""
# Call ErosionModel's init
super(LinearDiffusionModel, self).__init__(input_file=input_file,
params=params)
# Instantiate a LinearDiffuser component
self.diffuser = LinearDiffuser(self.grid, **self.params)
def test_diffusion():
infile = os.path.join(_THIS_DIR, 'diffusion_params.txt')
inputs = ModelParameterDictionary(infile, 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')
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.run_one_step(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, 9.18166135e-04,
1.02015039e-03, 1.10666198e-03, 1.14866514e-03, 1.20224288e-03,
1.12953135e-03, 1.12966219e-03, 1.00745155e-03, 8.70012148e-04,
9.78618342e-04, 1.12628772e-03, 1.41663596e-03, 2.66338249e-03,
2.80420703e-03, 2.82445061e-03, 2.69263914e-03, 2.44620140e-03,
2.04237613e-03, 4.14661940e-04, 2.64555612e-04, 2.15073330e-03,
2.77965579e-03, 3.22134736e-03, 3.45859244e-03, 4.47224671e-03,
4.25371135e-03, 3.82941648e-03, 3.25127747e-03, 6.81820299e-04,
3.59507901e-04, 3.36577718e-03, 4.20490812e-03, 4.81467159e-03,
5.14099588e-03, 5.15029835e-03, 4.83533539e-03, 5.22312276e-03,
4.37284689e-03, 3.63710771e-04, 5.70196770e-04, 4.65122535e-03,
5.67854747e-03, 6.44757828e-03, 6.85985389e-03, 6.86464781e-03,
6.45159799e-03, 5.65255723e-03, 4.54258827e-03, 2.44425592e-04,
1.58969584e-04, 5.85971567e-03, 7.16648352e-03, 8.10954246e-03,
8.61082386e-03, 8.61350727e-03, 8.10597021e-03, 7.12594182e-03,
5.75483957e-03, 9.60984079e-05, 9.76459465e-04, 6.29476234e-03,
7.70594852e-03, 9.79504842e-03, 1.03829367e-02, 1.03869062e-02,
9.79374998e-03, 8.65447904e-03, 7.07179252e-03, 1.18727719e-04,
3.17983179e-04, 7.43078552e-03, 9.18353155e-03, 1.04682910e-02,
1.11542648e-02, 1.21643980e-02, 1.14930584e-02, 1.02184219e-02,
8.53727126e-03, 9.29296198e-04, 3.18568952e-04, 8.68034110e-03,
1.06702554e-02, 1.21275181e-02, 1.29049224e-02, 1.29184938e-02,
1.21616788e-02, 1.17059081e-02, 9.66728348e-03, 4.69547619e-06,
6.77816537e-04, 1.00128306e-02, 1.21521279e-02, 1.37494046e-02,
1.46053573e-02, 1.46205669e-02, 1.37908840e-02, 1.22146332e-02,
1.01165765e-02, 9.52749012e-04, 4.47125379e-04, 1.12069867e-02,
1.35547122e-02, 1.52840440e-02, 1.62069802e-02, 1.62196380e-02,
1.53169489e-02, 1.35997836e-02, 1.12818577e-02, 6.92531590e-04,
7.25254280e-04, 1.14310516e-02, 1.38647655e-02, 1.66771925e-02,
1.76447108e-02, 1.76515649e-02, 1.66885162e-02, 1.48507549e-02,
1.23206170e-02, 2.90077607e-04, 6.18015429e-04, 1.24952067e-02,
1.49924260e-02, 1.68435913e-02, 1.78291009e-02, 1.88311310e-02,
1.78422046e-02, 1.59665841e-02, 1.34122052e-02, 4.31418435e-04,
8.96546596e-04, 1.34612553e-02, 1.58763600e-02, 1.76887976e-02,
1.86526609e-02, 1.86492669e-02, 1.76752679e-02, 1.68480793e-02,
1.44368883e-02, 9.98847007e-04, 1.49448305e-04, 1.40672989e-02,
1.64140227e-02, 1.81162514e-02, 1.90091351e-02, 1.89959971e-02,
1.80757625e-02, 1.63425116e-02, 1.39643530e-02, 6.91669955e-05,
6.97428773e-04, 1.47340964e-02, 1.66453353e-02, 1.80835612e-02,
1.88335770e-02, 1.88048458e-02, 1.80022362e-02, 1.65110248e-02,
1.44854151e-02, 1.71629677e-04, 5.21036606e-04, 1.40633664e-02,
1.54867652e-02, 1.75865008e-02, 1.81309867e-02, 1.80760242e-02,
1.74501109e-02, 1.63343931e-02, 1.48208186e-02, 3.18389295e-05,
1.64694156e-04, 1.41550038e-02, 1.49870334e-02, 1.57563641e-02,
1.60213295e-02, 1.69074625e-02, 1.64888825e-02, 1.58787450e-02,
1.50671910e-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)
