def test_deAlm_analytical():
from landlab import RasterModelGrid
grid = RasterModelGrid((32, 240), spacing = 25)
grid.add_zeros('node', 'surface_water__depth')
grid.add_zeros('node', 'topographic__elevation')
grid.set_closed_boundaries_at_grid_edges(True, True, True, True)
left_inactive_ids = left_edge_horizontal_ids(grid.shape)
deAlm = OverlandFlow(grid, mannings_n=0.01, h_init=0.001)
time = 0.0
while time < 500.:
grid['link']['surface_water__discharge'][left_inactive_ids] = (
grid['link']['surface_water__discharge'][left_inactive_ids + 1])
dt = deAlm.calc_time_step()
deAlm.overland_flow(dt)
h_boundary = (((7./3.) * (0.01**2) * (0.4**3) *
time) ** (3./7.))
grid.at_node['surface_water__depth'][grid.nodes[1: -1, 1]] = h_boundary
time += dt
x = np.arange(0, ((grid.shape[1]) * grid.dx), grid.dx)
h_analytical = (-(7./3.) * (0.01**2) * (0.4**2) * (x - (0.4 * 500)))
h_analytical[np.where(h_analytical > 0)] = (h_analytical[np.where(
h_analytical > 0)] ** (3./7.))
h_analytical[np.where(h_analytical < 0)] = 0.0
hdeAlm = deAlm.h.reshape(grid.shape)
hdeAlm = hdeAlm[1][1:]
hdeAlm = np.append(hdeAlm, [0])
np.testing.assert_almost_equal(h_analytical, hdeAlm, decimal=1)
def test_thickness_ids_wrong_shape():
"""Test wrong size thickness and id shapes."""
# first with thicknesses and IDs both as ndim = 1 arrays
mg = RasterModelGrid((3, 3))
mg.add_zeros("node", "topographic__elevation")
thicknesses = [1, 2, 4, 1, 5]
ids = [1, 2, 1, 2]
attrs = {"K_sp": {1: 0.001, 2: 0.0001}}
with pytest.raises(ValueError):
Lithology(mg, thicknesses, ids, attrs)
# next as both as ndim = 2 arrays
ones = np.ones(mg.number_of_nodes)
mg = RasterModelGrid((3, 3))
mg.add_zeros("node", "topographic__elevation")
thicknesses = [1 * ones, 2 * ones, 4 * ones, 1 * ones, 5 * ones]
ids = [1 * ones, 2 * ones, 1 * ones, 2 * ones]
attrs = {"K_sp": {1: 0.001, 2: 0.0001}}
with pytest.raises(ValueError):
Lithology(mg, thicknesses, ids, attrs)
# now with thickness as ndim 2 and id as ndim 1
ones = np.ones(mg.number_of_nodes)
mg = RasterModelGrid((3, 3))
thicknesses = [1 * ones, 2 * ones, 4 * ones, 1 * ones, 5 * ones]
ids = [1, 2, 1, 2]
attrs = {"K_sp": {1: 0.001, 2: 0.0001}}
with pytest.raises(ValueError):
Lithology(mg, thicknesses, ids, attrs)
def test_run_one_step():
from landlab import RasterModelGrid
import numpy as np
from landlab.components.overland_flow import KinwaveOverlandFlowModel
grid = RasterModelGrid((10, 10), spacing=0.5)
grid.add_zeros('node', 'topographic__elevation', dtype=float)
grid.add_zeros('node', 'topographic__gradient')
topo_arr = np.ones(100).reshape(10, 10)
i=0
while i <= 9:
topo_arr[:, i] = 5 + (0.002*i)
i+=1
topo_arr = topo_arr.flatten()
grid['node']['topographic__elevation'] = topo_arr
KinWaveOF = KinwaveOverlandFlowModel(grid, precip_rate=100.,
precip_duration=1.0, roughness=0.02)
KinWaveOF.run_one_step(60)
# I'll admit this is very non-robust. Solution roughly based on plot #9
# from Heng et. al, (2009): "Modeling overland flow and soil eroion on
# non uniform hillslopes: A finite volume scheme." They do not provide the
# numerical solution but the plots match...
max_h_mm = max(grid['node']['surface_water__depth']) * 1000.
np.testing.assert_almost_equal(max_h_mm, 1.66666666667)
def test_storms():
input_file_string = os.path.join(_THIS_DIR, 'drive_sp_params_storms.txt')
inputs = ModelParameterDictionary(input_file_string)
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')
mg = RasterModelGrid(nrows, ncols, dx)
mg.add_zeros('topographic__elevation', at='node')
z = mg.zeros(at='node')
mg['node']['topographic__elevation'] = z + np.random.rand(len(z)) / 1000.
mg.add_zeros('water__unit_flux_in', at='node')
precip = PrecipitationDistribution(input_file=input_file_string)
fr = FlowRouter(mg)
sp = StreamPowerEroder(mg, **inputs)
for (interval_duration, rainfall_rate) in \
precip.yield_storm_interstorm_duration_intensity():
if rainfall_rate != 0.:
mg.at_node['water__unit_flux_in'].fill(rainfall_rate)
mg = fr.route_flow()
sp.run_one_step(dt)
mg.at_node['topographic__elevation'][
mg.core_nodes] += uplift * interval_duration
def test_mask_is_stable():
mg = RasterModelGrid((10, 10))
mg.add_zeros("node", "topographic__elevation")
np.random.seed(3542)
noise = np.random.rand(mg.size("node"))
mg.at_node["topographic__elevation"] += noise
fr = FlowAccumulator(mg, flow_director="D8")
fsc = FastscapeEroder(mg, K_sp=0.01, m_sp=0.5, n_sp=1)
for x in range(2):
fr.run_one_step()
fsc.run_one_step(dt=10.0)
mg.at_node["topographic__elevation"][mg.core_nodes] += 0.01
mask = np.zeros(len(mg.at_node["topographic__elevation"]), dtype=np.uint8)
mask[np.where(mg.at_node["drainage_area"] > 0)] = 1
mask0 = mask.copy()
dd = DrainageDensity(mg, channel__mask=mask)
mask1 = mask.copy()
dd.calc_drainage_density()
mask2 = mask.copy()
assert_array_equal(mask0, mask1)
assert_array_equal(mask0[mg.core_nodes], mask2[mg.core_nodes])
def test_sp_discharges_new():
input_str = os.path.join(_THIS_DIR, 'test_sp_params_discharge_new.txt')
inputs = ModelParameterDictionary(input_str, auto_type=True)
nrows = 5
ncols = 5
dx = inputs.read_float('dx')
dt = inputs.read_float('dt')
mg = RasterModelGrid(nrows, ncols, dx)
mg.add_zeros('topographic__elevation', at='node')
z = np.array([5., 5., 0., 5., 5.,
5., 2., 1., 2., 5.,
5., 3., 2., 3., 5.,
5., 4., 4., 4., 5.,
5., 5., 5., 5., 5.])
mg['node']['topographic__elevation'] = z
fr = FlowRouter(mg)
sp = StreamPowerEroder(mg, **inputs)
# perform the loop (once!)
for i in range(1):
fr.route_flow()
sp.run_one_step(dt)
z_tg = np.array([5. , 5. , 0. , 5. ,
5. , 5. , 1.47759225, 0.43050087,
1.47759225, 5. , 5. , 2.32883687,
1.21525044, 2.32883687, 5. , 5. ,
3.27261262, 3.07175015, 3.27261262, 5. ,
5. , 5. , 5. , 5. ,
5. ])
assert_array_almost_equal(mg.at_node['topographic__elevation'], z_tg)
def test_rock_block_xarray():
"""Test that the xarray method works as expected."""
sample_depths = np.arange(0, 10, 1)
mg = RasterModelGrid((3, 3))
mg.add_zeros("node", "topographic__elevation")
layer_ids = np.tile([0, 1, 2, 3], 5)
layer_elevations = 3.0 * np.arange(-10, 10)
layer_elevations[-1] = layer_elevations[-2] + 100
attrs = {"K_sp": {0: 0.0003, 1: 0.0001, 2: 0.0002, 3: 0.0004}}
lith = LithoLayers(
mg, layer_elevations, layer_ids, function=lambda x, y: x + y, attrs=attrs
)
ds = lith.rock_cube_to_xarray(sample_depths)
expected_array = np.array(
[
[[3.0, 2.0, 2.0], [2.0, 2.0, 2.0], [2.0, 2.0, 1.0]],
[[3.0, 3.0, 2.0], [3.0, 2.0, 2.0], [2.0, 2.0, 2.0]],
[[3.0, 3.0, 3.0], [3.0, 3.0, 2.0], [3.0, 2.0, 2.0]],
[[0.0, 3.0, 3.0], [3.0, 3.0, 3.0], [3.0, 3.0, 2.0]],
[[0.0, 0.0, 3.0], [0.0, 3.0, 3.0], [3.0, 3.0, 3.0]],
[[0.0, 0.0, 0.0], [0.0, 0.0, 3.0], [0.0, 3.0, 3.0]],
[[1.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 3.0]],
[[1.0, 1.0, 0.0], [1.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
[[1.0, 1.0, 1.0], [1.0, 1.0, 0.0], [1.0, 0.0, 0.0]],
[[2.0, 1.0, 1.0], [1.0, 1.0, 1.0], [1.0, 1.0, 0.0]],
]
)
assert_array_equal(ds.rock_type__id.values, expected_array)
def test_bad_solver_name():
"""
Test that any solver name besides 'basic' and 'adaptive' raises an error.
"""
#set up a 5x5 grid with one open outlet node and low initial elevations.
nr = 5
nc = 5
mg = RasterModelGrid((nr, nc), 10.0)
mg.add_zeros('node', 'topographic__elevation')
mg['node']['topographic__elevation'] += mg.node_y / 10000 \
+ mg.node_x / 10000 \
+ np.random.rand(len(mg.node_y)) / 10000
mg.set_closed_boundaries_at_grid_edges(bottom_is_closed=True,
left_is_closed=True,
right_is_closed=True,
top_is_closed=True)
mg.set_watershed_boundary_condition_outlet_id(0,
mg['node']['topographic__elevation'],
-9999.)
# Create a D8 flow handler
fa = FlowAccumulator(mg, flow_director='D8')
#try to instantiate ErodionDeposition using a wrong solver name
with pytest.raises(ValueError):
ErosionDeposition(mg, K=0.01, phi=0.0, v_s=0.001, m_sp=0.5, n_sp=1.0,
sp_crit=0, F_f=0.0, solver='something_else')
def test_Ff_bad_vals():
"""
Test that instantiating ErosionDeposition with a F_f value > 1 throws a
ValueError.
"""
#set up a 5x5 grid with one open outlet node and low initial elevations.
nr = 5
nc = 5
mg = RasterModelGrid((nr, nc), 10.0)
mg.add_zeros('node', 'topographic__elevation')
mg['node']['topographic__elevation'] += mg.node_y / 100000 \
+ mg.node_x / 100000 \
+ np.random.rand(len(mg.node_y)) / 10000
mg.set_closed_boundaries_at_grid_edges(bottom_is_closed=True,
left_is_closed=True,
right_is_closed=True,
top_is_closed=True)
mg.set_watershed_boundary_condition_outlet_id(0,
mg['node']['topographic__elevation'],
-9999.)
# Create a D8 flow handler
fa = FlowAccumulator(mg, flow_director='D8',
depression_finder='DepressionFinderAndRouter')
# Instantiate the ErosionDeposition component...
with pytest.raises(ValueError):
ErosionDeposition(mg, K=0.01, F_f=2.0, phi=0.5, v_s=0.001, m_sp=0.5,
n_sp=1.0, sp_crit=0.0, solver='basic')
def test_tl_fluvial():
input_file = os.path.join(_THIS_DIR, 'stream_power_params_ideal.txt')
inputs = ModelParameterDictionary(input_file)
nrows = inputs.read_int('nrows')
ncols = inputs.read_int('ncols')
dx = inputs.read_float('dx')
leftmost_elev = inputs.read_float('leftmost_elevation')
initial_slope = inputs.read_float('initial_slope')
uplift_rate = inputs.read_float('uplift_rate')
runtime = inputs.read_float('total_time')
dt = inputs.read_float('dt')
nt = int(runtime // dt)
uplift_per_step = uplift_rate * dt
mg = RasterModelGrid(nrows, ncols, dx)
mg.add_zeros('node', 'topographic__elevation')
z = np.loadtxt(os.path.join(_THIS_DIR, 'tl_init.txt'))
mg['node']['topographic__elevation'] = z
mg.set_closed_boundaries_at_grid_edges(True, False, True, False)
mg.set_fixed_value_boundaries_at_grid_edges(
False, True, False, True, value_of='topographic__elevation')
fr = FlowRouter(mg)
tl = TransportLimitedEroder(mg, input_file)
for i in range(nt):
mg.at_node['topographic__elevation'][mg.core_nodes] += uplift_per_step
mg = fr.route_flow()
mg, _ = tl.erode(mg, dt, stability_condition='loose')
z_tg = np.loadtxt(os.path.join(_THIS_DIR, 'tlz_tg.txt'))
assert_array_almost_equal(mg.at_node['topographic__elevation'], z_tg)
def test_deAlm_analytical_imposed_dt_short():
grid = RasterModelGrid((32, 240), xy_spacing=25)
grid.add_zeros("node", "surface_water__depth")
grid.add_zeros("node", "topographic__elevation")
grid.set_closed_boundaries_at_grid_edges(True, True, True, True)
left_inactive_ids = left_edge_horizontal_ids(grid.shape)
deAlm = OverlandFlow(grid, mannings_n=0.01, h_init=0.001)
time = 0.0
while time < 500.0:
grid.at_link["surface_water__discharge"][left_inactive_ids] = grid.at_link[
"surface_water__discharge"
][left_inactive_ids + 1]
dt = 10.0
deAlm.overland_flow(dt)
h_boundary = ((7.0 / 3.0) * (0.01 ** 2) * (0.4 ** 3) * time) ** (3.0 / 7.0)
grid.at_node["surface_water__depth"][grid.nodes[1:-1, 1]] = h_boundary
time += dt
x = np.arange(0, ((grid.shape[1]) * grid.dx), grid.dx)
h_analytical = -(7.0 / 3.0) * (0.01 ** 2) * (0.4 ** 2) * (x - (0.4 * 500))
h_analytical[np.where(h_analytical > 0)] = h_analytical[
np.where(h_analytical > 0)
] ** (3.0 / 7.0)
h_analytical[np.where(h_analytical < 0)] = 0.0
hdeAlm = deAlm.h.reshape(grid.shape)
hdeAlm = hdeAlm[1][1:]
hdeAlm = np.append(hdeAlm, [0])
np.testing.assert_almost_equal(h_analytical, hdeAlm, decimal=1)
def test_sed_dep():
input_file = os.path.join(_THIS_DIR, "sed_dep_params.txt")
inputs = ModelParameterDictionary(input_file, auto_type=True)
nrows = inputs.read_int("nrows")
ncols = inputs.read_int("ncols")
dx = inputs.read_float("dx")
uplift_rate = inputs.read_float("uplift_rate")
runtime = inputs.read_float("total_time")
dt = inputs.read_float("dt")
nt = int(runtime // dt)
uplift_per_step = uplift_rate * dt
mg = RasterModelGrid((nrows, ncols), xy_spacing=(dx, dx))
mg.add_zeros("topographic__elevation", at="node")
z = np.loadtxt(os.path.join(_THIS_DIR, "seddepinit.txt"))
mg["node"]["topographic__elevation"] = z
mg.set_closed_boundaries_at_grid_edges(True, False, True, False)
fr = FlowAccumulator(mg, flow_director="D8")
sde = SedDepEroder(mg, **inputs)
for i in range(nt):
mg.at_node["topographic__elevation"][mg.core_nodes] += uplift_per_step
mg = fr.run_one_step()
mg, _ = sde.erode(dt)
z_tg = np.loadtxt(os.path.join(_THIS_DIR, "seddepz_tg.txt"))
assert_array_almost_equal(
mg.at_node["topographic__elevation"][mg.core_nodes], z_tg[mg.core_nodes]
)
def test_closed_up_grid():
mg = RasterModelGrid((5, 5))
for edge in ("left", "right", "top", "bottom"):
mg.status_at_node[mg.nodes_at_edge(edge)] = CLOSED_BOUNDARY
mg.add_zeros("node", "topographic__elevation", dtype=float)
with pytest.raises(ValueError):
LakeMapperBarnes(mg)
def test_dx_equals_zero():
"""Test a vertical fault trace."""
grid = RasterModelGrid((6, 6), xy_spacing=10)
grid.add_zeros("node", "topographic__elevation")
param_dict = {
"faulted_surface": "topographic__elevation",
"fault_dip_angle": 90.0,
"fault_throw_rate_through_time": {"time": [0, 9, 10], "rate": [0, 0, 0.05]},
"fault_trace": {"y1": 0, "x1": 30, "y2": 30, "x2": 30},
"include_boundaries": True,
}
nf = NormalFault(grid, **param_dict)
out = np.array(
[
[True, True, True, False, False, False],
[True, True, True, False, False, False],
[True, True, True, False, False, False],
[True, True, True, False, False, False],
[True, True, True, False, False, False],
[True, True, True, False, False, False],
],
dtype=bool,
)
assert_array_equal(nf.faulted_nodes.reshape(grid.shape), out)
def test_Bates_analytical():
from landlab import RasterModelGrid
grid = RasterModelGrid((32, 240), xy_spacing=25)
grid.add_zeros("node", "surface_water__depth")
grid.add_zeros("node", "topographic__elevation")
grid.set_closed_boundaries_at_grid_edges(True, True, True, True)
bates = OverlandFlowBates(grid, mannings_n=0.01, h_init=0.001)
time = 0.0
bates.dt = 1.0
while time < 500:
bates.overland_flow(grid)
h_boundary = ((7. / 3.) * (0.01 ** 2) * (0.4 ** 3) * time) ** (3. / 7.)
grid.at_node["surface_water__depth"][grid.nodes[1:-1, 1]] = h_boundary
time += bates.dt
x = np.arange(0, ((grid.shape[1]) * grid.dx), grid.dx)
h_analytical = -(7. / 3.) * (0.01 ** 2) * (0.4 ** 2) * (x - (0.4 * 500))
h_analytical[np.where(h_analytical > 0)] = h_analytical[
np.where(h_analytical > 0)
] ** (3. / 7.)
h_analytical[np.where(h_analytical < 0)] = 0.0
hBates = bates.h.reshape(grid.shape)
hBates = hBates[1][1:]
hBates = np.append(hBates, [0])
np.testing.assert_almost_equal(h_analytical, hBates, decimal=1)
def setup_grid():
from landlab import RasterModelGrid
grid = RasterModelGrid((32, 240), spacing = 25)
grid.add_zeros('node', 'surface_water__depth')
grid.add_zeros('node', 'topographic__elevation')
bates = OverlandFlowBates(grid, mannings_n = 0.01, h_init=0.001)
globals().update({
'bates': bates})
def test_route_to_many():
mg = RasterModelGrid((5, 5))
mg.add_zeros("node", "topographic__elevation", dtype=float)
fd = FlowDirectorDINF(mg, "topographic__elevation")
fd.run_one_step()
assert mg.at_node["flow__receiver_node"].shape == (mg.number_of_nodes, 2)
with pytest.raises(NotImplementedError):
LakeMapperBarnes(mg, method="D8", redirect_flow_steepest_descent=True)
def test_dip_geq_90():
"""Test dip angles of >90 degrees."""
grid = RasterModelGrid((6, 6), xy_spacing=10)
grid.add_zeros("node", "topographic__elevation")
with pytest.raises(ValueError):
NormalFault(grid, fault_dip_angle=90.001)
def setup_grid():
from landlab import RasterModelGrid
grid = RasterModelGrid((10, 10), spacing=0.5)
grid.add_zeros('node', 'topographic__elevation', dtype=float)
grid.add_zeros('node', 'topographic__gradient')
globals().update({
'KinWaveOF': KinwaveOverlandFlowModel(grid)})
def test_atts_lack_ids():
"""Test Lithology missing ID."""
mg = RasterModelGrid((3, 3))
mg.add_zeros("node", "topographic__elevation")
thicknesses = [1, 2, 4, 1, 5]
ids = [1, 2, 1, 2, 1]
attrs = {"K_sp": {2: 0.0001}, "age": {1: 100, 2: 300}}
with pytest.raises(ValueError):
Lithology(mg, thicknesses, ids, attrs)
def test_add_ones_zeros_empty_to_at_grid():
"""Test different add methods for keyword at='grid'"""
grid = RasterModelGrid((4, 5))
with pytest.raises(ValueError):
grid.add_zeros('value', at='grid')
with pytest.raises(ValueError):
grid.add_empty('value', at='grid')
with pytest.raises(ValueError):
grid.add_ones('value', at='grid')
def test_raise_kwargs_error():
mg = RasterModelGrid((5, 5))
soilTh = mg.add_zeros('node', 'soil__depth')
z = mg.add_zeros('node', 'topographic__elevation')
BRz = mg.add_zeros('node', 'bedrock__elevation')
z += mg.node_x.copy()**2
BRz = z.copy() - 1.0
soilTh[:] = z - BRz
assert_raises(TypeError, DepthDependentTaylorDiffuser, mg, diffusivity=1)
def test_bad_layer_method():
"""Test passing a bad name for the layer method."""
mg = RasterModelGrid((3, 3))
mg.add_zeros("node", "topographic__elevation")
thicknesses = [1, 2, 4, 1]
ids = [1, 2, 1, 2]
attrs = {"K_sp": {1: 0.001, 2: 0.0001}}
with pytest.raises(ValueError):
Lithology(mg, thicknesses, ids, attrs, layer_type="spam")
def test_thickness_nodes_wrong_shape():
"""Test wrong size thickness and id shapes."""
mg = RasterModelGrid((3, 3))
mg.add_zeros("node", "topographic__elevation")
ones = np.ones(mg.number_of_nodes + 1)
thicknesses = [1 * ones, 2 * ones, 4 * ones, 1 * ones, 5 * ones]
ids = [1 * ones, 2 * ones, 1 * ones, 2 * ones, 1 * ones]
attrs = {"K_sp": {1: 0.001, 2: 0.0001}}
with pytest.raises(ValueError):
Lithology(mg, thicknesses, ids, attrs)
def test_updating_rock_type_that_doesnt_exist():
"""Test adding an new rock type with an extra attribute."""
mg = RasterModelGrid((3, 3))
mg.add_zeros("node", "topographic__elevation")
thicknesses = [1, 2, 4, 1, 5]
ids = [1, 2, 1, 2, 1]
attrs = {"K_sp": {1: 0.001, 2: 0.0001}}
lith = Lithology(mg, thicknesses, ids, attrs)
with pytest.raises(ValueError):
lith.update_rock_properties("K_sp", 3, 4)
def test_deposit_with_no_rock_id():
"""Test that adding a deposit to Lithology with no id raises an error."""
mg = RasterModelGrid((3, 3))
mg.add_zeros("node", "topographic__elevation")
thicknesses = [1, 2, 4, 1, 5]
ids = [1, 2, 1, 2, 1]
attrs = {"K_sp": {1: 0.001, 2: 0.0001}}
lith = Lithology(mg, thicknesses, ids, attrs)
with pytest.raises(ValueError):
lith.add_layer(100)
def test_erode_to_zero_thickness():
"""Test that eroding Lithology to zero thickness raises an error."""
mg = RasterModelGrid((3, 3))
mg.add_zeros("node", "topographic__elevation")
thicknesses = [1, 2, 4, 1, 5]
ids = [1, 2, 1, 2, 1]
attrs = {"K_sp": {1: 0.001, 2: 0.0001}}
lith = Lithology(mg, thicknesses, ids, attrs)
with pytest.raises(ValueError):
lith.add_layer(-100)
def test_raise_kwargs_error():
mg = RasterModelGrid((5, 5))
soilTh = mg.add_zeros("node", "soil__depth")
z = mg.add_zeros("node", "topographic__elevation")
BRz = mg.add_zeros("node", "bedrock__elevation")
z += mg.node_x.copy() ** 2
BRz = z.copy() - 1.0
soilTh[:] = z - BRz
with pytest.raises(TypeError):
DepthDependentTaylorDiffuser(mg, diffusivity=1)
def test_updating_attribute_that_doesnt_exist():
"""Test updating an attribute that doesn't exist."""
mg = RasterModelGrid(3, 3)
mg.add_zeros("node", "topographic__elevation")
thicknesses = [1, 2, 4, 1, 5]
ids = [1, 2, 1, 2, 1]
attrs = {"K_sp": {1: 0.001, 2: 0.0001}}
lith = Lithology(mg, thicknesses, ids, attrs)
with pytest.raises(ValueError):
lith.update_rock_properties("spam", 1, 4)
def test_soil_field_already_on_grid():
"""
Test that an existing soil grid field is not changed by instantiating
SPACE.
"""
# set up a 5x5 grid with one open outlet node and low initial elevations.
nr = 5
nc = 5
mg = RasterModelGrid((nr, nc), xy_spacing=10.0)
z = mg.add_zeros("node", "topographic__elevation")
br = mg.add_zeros("node", "bedrock__elevation")
soil = mg.add_zeros("node", "soil__depth")
soil += 1. # add 1m of soil everywehre
mg["node"]["topographic__elevation"] += (
mg.node_y / 10000 + mg.node_x / 10000 + np.random.rand(len(mg.node_y)) / 10000
)
mg.set_closed_boundaries_at_grid_edges(
bottom_is_closed=True,
left_is_closed=True,
right_is_closed=True,
top_is_closed=True,
)
mg.set_watershed_boundary_condition_outlet_id(
0, mg["node"]["topographic__elevation"], -9999.
)
br[:] = z[:] - soil[:]
# Create a D8 flow handler
FlowAccumulator(mg, flow_director="D8")
# Instantiate SPACE
sp = Space(
mg,
K_sed=0.01,
K_br=0.01,
F_f=0.0,
phi=0.0,
v_s=0.001,
m_sp=0.5,
n_sp=1.0,
sp_crit_sed=0,
sp_crit_br=0,
solver="basic",
)
# ensure that 'soil__depth' field is everywhere equal to 1.0 m.
testing.assert_array_equal(
np.ones(mg.number_of_nodes),
sp.soil__depth,
err_msg="SPACE soil depth field test failed",
verbose=True,
)
def main():
# INITIALIZE
# User-defined parameters
nr = 5 # number of rows in grid
nc = 5 # number of columns in grid
plot_interval = 10.0 # time interval for plotting, sec
run_duration = 10.0 # duration of run, sec
report_interval = 10.0 # report interval, in real-time seconds
# Remember the clock time, and calculate when we next want to report
# progress.
current_real_time = time.time()
next_report = current_real_time + report_interval
# Create grid
mg = RasterModelGrid(nr, nc, 1.0)
# Make the boundaries be walls
mg.set_closed_boundaries_at_grid_edges(True, True, True, True)
# Set up the states and pair transitions.
ns_dict = {0: "black", 1: "white"}
xn_list = setup_transition_list()
# Create the node-state array and attach it to the grid
node_state_grid = mg.add_zeros("node", "node_state_map", dtype=int)
# For visual display purposes, set all boundary nodes to fluid
node_state_grid[mg.closed_boundary_nodes] = 0
# Create the CA model
ca = RasterCTS(mg, ns_dict, xn_list, node_state_grid)
# Create a CAPlotter object for handling screen display
ca_plotter = CAPlotter(ca)
# Plot the initial grid
ca_plotter.update_plot()
# RUN
current_time = 0.0
while current_time < run_duration:
# Once in a while, print out simulation and real time to let the user
# know that the sim is running ok
current_real_time = time.time()
if current_real_time >= next_report:
print(
"Current sim time",
current_time,
"(",
100 * current_time / run_duration,
"%)",
)
next_report = current_real_time + report_interval
# Run the model forward in time until the next output step
ca.run(
current_time + plot_interval,
ca.node_state,
plot_each_transition=True,
plotter=ca_plotter,
)
current_time += plot_interval
# Plot the current grid
ca_plotter.update_plot()
# FINALIZE
# Plot
ca_plotter.finalize()
print("ok, here are the keys")
print(ca.__dict__.keys())
def test_raster_cts():
"""
Tests instantiation of a RasterCTS and implementation of one transition,
with a callback function.
"""
# Set up a small grid with no events scheduled
mg = RasterModelGrid(4, 4)
mg.set_closed_boundaries_at_grid_edges(True, True, True, True)
node_state_grid = mg.add_ones("node", "node_state_map", dtype=int)
node_state_grid[6] = 0
ns_dict = {0: "black", 1: "white"}
xn_list = []
xn_list.append(Transition((1, 0, 0), (0, 1, 0), 0.1, "", True, callback_function))
pd = mg.add_zeros("node", "property_data", dtype=int)
pd[5] = 50
ca = RasterCTS(
mg, ns_dict, xn_list, node_state_grid, prop_data=pd, prop_reset_value=0
)
# Test the data structures
assert ca.num_link_states == 4, "wrong number of link states"
assert ca.prop_data[5] == 50, "error in property data"
assert ca.num_node_states == 2, "error in num_node_states"
assert ca.link_orientation[-1] == 0, "error in link orientation array"
assert ca.link_state_dict[(1, 0, 0)] == 2, "error in link state dict"
assert ca.n_trn[2] == 1, "error in n_trn"
assert ca.node_pair[1] == (0, 1, 0), "error in cell_pair list"
assert len(ca.priority_queue._queue) == 1, "event queue has wrong size"
assert ca.next_trn_id.size == 24, "wrong size next_trn_id"
assert ca.trn_id.shape == (4, 1), "wrong size for trn_to"
assert ca.trn_id[2][0] == 0, "wrong value in trn_to"
assert ca.trn_to[0] == 1, "wrong trn_to state"
assert ca.trn_rate[0] == 0.1, "wrong trn rate"
assert ca.trn_propswap[0] == 1, "wrong trn propswap"
assert ca.trn_prop_update_fn == callback_function, "wrong prop upd"
# Manipulate the data in the event queue for testing:
# pop the scheduled event off the queue
(event_time, index, event_link) = ca.priority_queue.pop()
assert (
ca.priority_queue._queue == []
), "event queue should now be empty but is not"
# engineer an event
ca.priority_queue.push(8, 1.0)
ca.next_update[8] = 1.0
ca.next_trn_id[8] = 0
# run the CA
ca.run(2.0)
# some more tests.
# Is current time advancing correctly? (should only go to 1.0, not 2.0)
# Did the two nodes (5 and 6) correctly exchange states?
# Did the property ID and data arrays get updated? Note that the "propswap"
# should switch propids between nodes 5 and 6, and the callback function
# should increase the value of prop_data in the "swap" node from 50 to 150.
assert ca.current_time == 1.0, "current time incorrect"
assert ca.node_state[5] == 0, "error in node state 5"
assert ca.node_state[6] == 1, "error in node state 6"
def test_raise_stability_error():
mg = RasterModelGrid((5, 5))
z = mg.add_zeros('node', 'topographic__elevation')
z += mg.node_x.copy()**2
Cdiff = TaylorNonLinearDiffuser(mg)
assert_raises(RuntimeError, Cdiff.soilflux, 10, if_unstable='raise')
def test_infinite_taylor_error():
mg = RasterModelGrid((5, 5))
z = mg.add_zeros('node', 'topographic__elevation')
z += mg.node_x.copy()**4
Cdiff = TaylorNonLinearDiffuser(mg, nterms=400)
assert_raises(RuntimeError, Cdiff.soilflux, 10)
def extend_perturbed_runs(total_iters_to_reach=0):
"""Load all perturbed runs in current folder, and extend them.
Function should be called from within an experiment folder
(extend all perturbations for all starting uplift rates), an
'uplift_rate_XXXX' folder (extend all perturbations for this rate) or an
'accel_XXX' folder (extend this accel only).
Does NOT create a new expt or run ID, just extends the old ones. Adds a
text file annotating what has happened.
"""
# look for the params to use. Also tells us where we are in the hierarchy
level = 0 # 0: top, 1: uplift, 2: accel:
cwd = os.getcwd()
while True:
try:
paramdict = np.load('expt_ID_paramdict.npy').item()
except IOError:
os.chdir('..')
level += 1
else:
break
# now back to where we started in the dir str:
os.chdir(cwd)
if level == 2: # in accel_ folder
# get the accel that this is:
accel_factors = [
get_float_of_folder_name(),
]
# get the U of the host folder:
uplift_rates = [
get_float_of_folder_name(directory=(cwd + '/..')),
]
wd_stub = os.path.abspath(os.getcwd() + '/../..')
elif level == 1: # in uplift_ folder
accel_fnames = [
filename for filename in os.listdir('.')
if filename.startswith('accel_')
]
accel_factors = [
get_float_of_folder_name(directory=(cwd + '/' + filename))
for filename in accel_fnames
]
uplift_rates = [
get_float_of_folder_name(),
]
wd_stub = os.path.abspath(os.getcwd() + '/..')
elif level == 0: # in top folder
uplift_fnames = [
filename for filename in os.listdir('.')
if filename.startswith('uplift_rate_')
]
uplift_rates = [
get_float_of_folder_name(directory=(cwd + '/' + filename))
for filename in uplift_fnames
]
accel_factors = paramdict['accel_factors']
wd_stub = os.path.abspath(os.getcwd())
for uplift_rate in uplift_rates:
for accel_factor in accel_factors:
wd = (wd_stub + '/uplift_rate_' + str(uplift_rate) + '/accel_' +
str(accel_factor))
# get the saved filenames that already exist in this folder:
runnames = [
filename for filename in os.listdir(wd)
if filename.startswith('topographic__elevation')
]
seddepthnames = [
filename for filename in os.listdir(wd)
if filename.startswith('channel_sediment__depth')
]
# as elsewhere, the final entry is the last run, so --
# establish the loop number of that run:
run_ID = runnames[-1][-14:-4] # is a str
_format = 0
while True:
char = runnames[-1][-16 - _format]
try:
num = int(char)
except ValueError: # was a str
break
else:
_format += 1
finaliter = int(runnames[-1][(-15 - _format):-15])
finalsediter = int(seddepthnames[-1][(-15 - _format):-15])
assert finaliter == finalsediter # ...just in case
# test we need to actually do more runs:
if total_iters_to_reach < finaliter + paramdict['out_interval']:
continue
# check we aren't going to have a "zero problem"; correct if we do
max_zeros = len(str(total_iters_to_reach))
if max_zeros + 1 > _format: # less won't be possible from continue
extra_zeros = max_zeros + 1 - _format
for allfile in os.listdir(wd):
if allfile[-14:-4] == run_ID:
os.rename(
wd + '/' + allfile,
(wd + '/' + allfile[:(-15 - _format)] +
'0' * extra_zeros + allfile[(-15 - _format):]))
runnames = [
filename for filename in os.listdir(wd)
if filename.startswith('topographic__elevation')
]
seddepthnames = [
filename for filename in os.listdir(wd)
if filename.startswith('channel_sediment__depth')
]
if max_zeros + 1 < _format:
max_zeros = _format - 1 # in case of any bonus 0s from old run
# build the structures:
mg = RasterModelGrid(paramdict['shape'], paramdict['dx'])
for edge in (mg.nodes_at_left_edge, mg.nodes_at_top_edge,
mg.nodes_at_right_edge):
mg.status_at_node[edge] = CLOSED_BOUNDARY
z = mg.add_zeros('node', 'topographic__elevation')
seddepth = mg.add_zeros('node', 'channel_sediment__depth')
fr = FlowRouter(mg)
eroder = SedDepEroder(mg, **paramdict)
ld = LinearDiffuser(mg, **paramdict)
# load the last available elev data:
z[:] = np.loadtxt(wd + '/' + runnames[-1])
seddepth[:] = np.loadtxt(wd + '/' + seddepthnames[-1])
# save a note
try:
appendfile = open(wd + '/appended_run_readme.txt', 'a')
except IOError:
appendfile = open(wd + '/appended_run_readme.txt', 'w')
appendfile.write('This run was appended at timestamp ' +
str(int(time.time())) + '.\n')
appendfile.write('New loops were added from iteration ' +
str(finaliter) + ' and terminated at iteration ' +
str(total_iters_to_reach) + '.\n\n')
appendfile.close()
# get runnin'
print('Extending uplift ' + str(uplift_rate) + ' accel ' +
str(accel_factor) + ' from iter number ' + str(finaliter))
dt = paramdict['dt']
for i in xrange(finaliter + 1, total_iters_to_reach):
fr.route_flow()
eroder.run_one_step(dt)
ld.run_one_step(dt)
z[mg.core_nodes] += accel_factor * uplift_rate * dt
print(i)
if i % out_interval == 0:
zeros_to_add = max_zeros - len(str(i)) + 1
# note an OoM buffer! Just to be safe
if zeros_to_add < 0:
# ...just in case, though should never happen
print('Problem allocating zeros on savefiles')
ilabel = '0' * zeros_to_add + str(i)
identifier = ilabel + '_' + str(run_ID)
for field in out_fields:
np.savetxt(
wd + '/' + field + '_' + identifier + '.txt',
mg.at_node[field])
def test_bad_init_method2():
rmg = RasterModelGrid((5, 5), xy_spacing=2.)
rmg.add_zeros("node", "topographic__elevation", dtype=float)
with pytest.raises(ValueError):
LakeMapperBarnes(rmg, method="d8")
m_sp = 0.5
n_sp = 1.
#5)model run
total_t = 150 #number of years (000)
dt = 0.4 #number of years (000)
nt = int(total_t // dt) #number of time steps
#6) random seed
randno = 23456
#GRID SET-UP
#create model x-y grid
mg = RasterModelGrid((ncols, nrows), nodeint)
#initialize with zero elevation values and random noise
z = mg.add_zeros('node', 'topographic__elevation')
np.random.seed(randno)
initial_roughness = np.random.rand(z.size) / 100000.
z += initial_roughness
#set southcenter pixel to zero elevation
#outlet=26
#
#z[outlet]=0
#set boundary conditions of model grid (open only (fixed value) on south (bottom) edge)
for edge in (mg.nodes_at_left_edge, mg.nodes_at_right_edge,
mg.nodes_at_top_edge):
mg.status_at_node[edge] = CLOSED_BOUNDARY
##set southwest pixel to FIXED VALUE
#mg.status_at_node[outlet]=FIXED_VALUE_BOUNDARY
#mg.status_at_node[outlet]=FIXED_VALUE_BOUNDARY
three_over_seven = 3. / 7.
ten_thirds = 10. / 3.
# Elapsed time starts at 1 second. This prevents errors when setting our
# boundary conditions
elapsed_time = 1.0
# Now we create our grid using the parameters set above.
rmg = RasterModelGrid((numrows, numcols), xy_spacing=dx)
# Set our boundaries to closed to prevent water from flowing out of the plane
rmg.set_closed_boundaries_at_grid_edges(True, True, True, True)
# Create fields in the grid for topographic elevation, water depth, discharge.
rmg.add_zeros("topographic__elevation", at="node") # topographic elevation (m)
rmg.add_zeros("surface_water__depth", at="node") # water depth (m)
# Now we'll identify our leftmost, but interior, column and the IDs of those
# nodes. One column in to prevent issues with BC.
inside_left_edge = rmg.nodes[1:-1, 1]
# Initializing our class...
of = OverlandFlowBates(rmg, mannings_n=n, h_init=h_init)
# Let's see how long this run takes...
starttime = time()
while elapsed_time < run_time:
def main():
# INITIALIZE
# User-defined parameters
nr = 200 # number of rows in grid
nc = 200 # number of columns in grid
plot_interval = 0.05 # time interval for plotting (unscaled)
run_duration = 5.0 # duration of run (unscaled)
report_interval = 10.0 # report interval, in real-time seconds
frac_spacing = 10 # average fracture spacing, nodes
outfilename = "wx" # name for netCDF files
# Remember the clock time, and calculate when we next want to report
# progress.
current_real_time = time.time()
next_report = current_real_time + report_interval
# Counter for output files
time_slice = 0
# Create grid
mg = RasterModelGrid(nr, nc, 1.0)
# Make the boundaries be walls
mg.set_closed_boundaries_at_grid_edges(True, True, True, True)
# Set up the states and pair transitions.
ns_dict = {0: "rock", 1: "saprolite"}
xn_list = setup_transition_list()
# Create the node-state array and attach it to the grid.
# (Note use of numpy's uint8 data type. This saves memory AND allows us
# to write output to a netCDF3 file; netCDF3 does not handle the default
# 64-bit integer type)
node_state_grid = mg.add_zeros("node", "node_state_map", dtype=np.uint8)
node_state_grid[:] = make_frac_grid(frac_spacing, model_grid=mg)
# Create the CA model
ca = RasterCTS(mg, ns_dict, xn_list, node_state_grid)
# Set up the color map
rock_color = (0.8, 0.8, 0.8)
sap_color = (0.4, 0.2, 0)
clist = [rock_color, sap_color]
my_cmap = matplotlib.colors.ListedColormap(clist)
# Create a CAPlotter object for handling screen display
ca_plotter = CAPlotter(ca, cmap=my_cmap)
# Plot the initial grid
ca_plotter.update_plot()
# Output the initial grid to file
write_netcdf(
(outfilename + str(time_slice) + ".nc"),
mg,
# format='NETCDF3_64BIT',
names="node_state_map",
)
# RUN
current_time = 0.0
while current_time < run_duration:
# Once in a while, print out simulation and real time to let the user
# know that the sim is running ok
current_real_time = time.time()
if current_real_time >= next_report:
print(
"Current sim time",
current_time,
"(",
100 * current_time / run_duration,
"%)",
)
next_report = current_real_time + report_interval
# Run the model forward in time until the next output step
ca.run(current_time + plot_interval,
ca.node_state,
plot_each_transition=False)
current_time += plot_interval
# Plot the current grid
ca_plotter.update_plot()
# Output the current grid to a netCDF file
time_slice += 1
write_netcdf(
(outfilename + str(time_slice) + ".nc"),
mg,
# format='NETCDF3_64BIT',
names="node_state_map",
)
# FINALIZE
# Plot
ca_plotter.finalize()
from landlab import RasterModelGrid, imshow_grid_at_node
from landlab.components import FlowAccumulator
from landlab.components import SpatialPrecipitationDistribution
from landlab.components import SoilInfiltrationGreenAmpt
from matplotlib.pyplot import show, figure
mg = RasterModelGrid((100, 100), 100.)
z = mg.add_zeros('node', 'topographic__elevation', dtype=float)
z[:] = mg.node_x / 100000.
STORM = SpatialPrecipitationDistribution(mg)
WUFI = mg.add_field('node', 'water__unit_flux_in',
mg.at_node['rainfall__flux'])
fa = FlowAccumulator(mg)
count = 0
for storm in STORM.yield_storms():
fa.run_one_step()
print storm
count += 1
if count % 10 == 0:
figure(count)
imshow_grid_at_node(mg, 'rainfall__flux')
figure(count + 1)
imshow_grid_at_node(mg, 'surface_water__discharge')
show()
from landlab.grid.mappers import map_link_end_node_max_value_to_link
inputs = ModelParameterDictionary('./pot_fr_params.txt')
nrows = 50 #inputs.read_int('nrows')
ncols = 50 #inputs.read_int('ncols')
dx = inputs.read_float('dx')
init_elev = inputs.read_float('init_elev')
mg = RasterModelGrid(nrows, ncols, dx)
# attempt to implement diffusion with flow routing...
#modify the fields in the grid
z = mg.zeros(at='node') + init_elev
mg.at_node['topographic__elevation'] = z + np.random.rand(len(z)) / 1000.
mg.add_zeros('water__unit_flux_in', at='node')
#Set boundary conditions
mg.set_closed_boundaries_at_grid_edges(False, True, True, True)
mg.set_fixed_value_boundaries_at_grid_edges(False, False, False, True)
inlet_node = np.array((mg.number_of_node_columns + 1))
mg.at_node['water__unit_flux_in'].fill(0.)
mg.at_node['water__unit_flux_in'][inlet_node] = 1.
pfr = PotentialityFlowRouter(mg, 'pot_fr_params.txt')
interior_nodes = mg.core_nodes
# do the loop
for i in range(2000):
if i % 50 == 0:
print('loop ' + str(i))
def main():
# INITIALIZE
# User-defined parameters
nr = 80
nc = 80
plot_interval = 2
run_duration = 200
report_interval = 5.0 # report interval, in real-time seconds
# Remember the clock time, and calculate when we next want to report
# progress.
current_real_time = time.time()
next_report = current_real_time + report_interval
# Create grid
mg = RasterModelGrid(nr, nc, 1.0)
# Make the boundaries be walls
mg.set_closed_boundaries_at_grid_edges(True, True, True, True)
# Set up the states and pair transitions.
ns_dict = {0: 'fluid', 1: 'particle'}
xn_list = setup_transition_list()
# Create the node-state array and attach it to the grid
node_state_grid = mg.add_zeros('node', 'node_state_map', dtype=int)
# Initialize the node-state array
middle_rows = where(
bitwise_and(mg.node_y > 0.45 * nr, mg.node_y < 0.55 * nr))[0]
node_state_grid[middle_rows] = 1
# Create the CA model
ca = OrientedRasterCTS(mg, ns_dict, xn_list, node_state_grid)
# Debug output if needed
if _DEBUG:
n = ca.grid.number_of_nodes
for r in range(ca.grid.number_of_node_rows):
for c in range(ca.grid.number_of_node_columns):
n -= 1
print('{0:.0f}'.format(ca.node_state[n]), end=' ')
print()
# Create a CAPlotter object for handling screen display
ca_plotter = CAPlotter(ca)
# Plot the initial grid
ca_plotter.update_plot()
# RUN
current_time = 0.0
while current_time < run_duration:
# Once in a while, print out simulation and real time to let the user
# know that the sim is running ok
current_real_time = time.time()
if current_real_time >= next_report:
print('Current sim time', current_time, '(',
100 * current_time / run_duration, '%)')
next_report = current_real_time + report_interval
# Run the model forward in time until the next output step
ca.run(current_time + plot_interval,
ca.node_state,
plot_each_transition=False) #, plotter=ca_plotter)
current_time += plot_interval
# Plot the current grid
ca_plotter.update_plot()
# for debugging
if _DEBUG:
n = ca.grid.number_of_nodes
for r in range(ca.grid.number_of_node_rows):
for c in range(ca.grid.number_of_node_columns):
n -= 1
print('{0:.0f}'.format(ca.node_state[n]), end=' ')
print()
# FINALIZE
# Plot
ca_plotter.finalize()
Srange = df_params['Srange'][ID]
b = df_params['b'][ID]
ds = df_params['ds'][ID]
tr = df_params['tr'][ID]
tb = df_params['tb'][ID]
Lh = df_params['Lh'][ID]
D = df_params['D'][ID]
U = df_params['U'][ID]
sc = df_params['sc'][ID]
Lh = df_params['Lh'][ID]
# initialize grid
grid = RasterModelGrid((Ny, Nx), xy_spacing=Lh / Nx)
grid.set_status_at_node_on_edges(right=grid.BC_NODE_IS_CLOSED, top=grid.BC_NODE_IS_CLOSED, \
left=grid.BC_NODE_IS_FIXED_VALUE, bottom=grid.BC_NODE_IS_CLOSED)
elev = grid.add_zeros('node', 'topographic__elevation')
x = grid.x_of_node
z = calc_z(x, sc, U, D) - calc_z(x[-1], sc, U, D)
z = np.fliplr(z.reshape(grid.shape))
elev[:] = z.flatten()
base = grid.add_zeros('node', 'aquifer_base__elevation')
base[:] = elev - b
wt = grid.add_zeros('node', 'water_table__elevation')
wt[:] = elev
# initialize landlab and DupuitLEM components
gdp = GroundwaterDupuitPercolator(
grid,
porosity=n,
hydraulic_conductivity=ks,
recharge_rate=0.0,
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)
def test_neighbor_shaping_no_fldir():
mg = RasterModelGrid((5, 5))
mg.add_zeros("node", "topographic__elevation", dtype=float)
with pytest.raises(FieldError):
LakeMapperBarnes(mg, method="D8", redirect_flow_steepest_descent=True)
import time
inputs = ModelParameterDictionary('./drive_sp_params_discharge.txt')
nrows = 5
ncols = 5
dx = inputs.read_float('dx')
dt = inputs.read_float('dt')
time_to_run = inputs.read_float('run_time')
# nt needs defining
uplift = inputs.read_float('uplift_rate')
init_elev = inputs.read_float('init_elev')
mg = RasterModelGrid(nrows, ncols, dx)
# create the fields in the grid
mg.add_zeros('topographic__elevation', at='node')
z = np.array([
5., 5., 0., 5., 5., 5., 2., 1., 2., 5., 5., 3., 2., 3., 5., 5., 4., 4., 4.,
5., 5., 5., 5., 5., 5.
])
mg['node']['topographic__elevation'] = z
print('Running ...')
# instantiate the components:
fr = FlowAccumulator(mg, flow_director='D8')
sp = StreamPowerEroder(mg, './drive_sp_params_discharge.txt')
# load the Fastscape module too, to allow direct comparison
fsp = Fsc(mg, './drive_sp_params_discharge.txt')
# perform the loop (once!)
elif direction == "S":
dir_sed_flux = self._Qsed_s
dir_water_flux = self._Qs
thisslice = (slice(1, -1, 1), slice(0, -1, 1))
deadedge = (slice(0, 1, 1), slice(0, -1, 1))
else:
raise NameError("direction must be {'E', 'N', 'S', 'W'}")
slope_diff = (S_val - S_thresh).clip(0.0)
dir_sed_flux[thisslice] = dir_water_flux[thisslice] * slope_diff[thisslice]
dir_sed_flux[deadedge] = 0.0
def diffuse_sediment(self, Qw_in, Qsed_in):
""""""
pass
if __name__ == "__main__":
from landlab import imshow_grid_at_node
S_crit = 0.25
mg = RasterModelGrid((20, 20), 0.5)
mg.add_zeros("topographic__elevation", at="node")
Qw_in = mg.add_zeros("water__discharge_in", at="node")
Qs_in = mg.add_zeros("sediment__discharge_in", at="node")
Qw_in[0] = 0.5 * np.pi
Qs_in[0] = (1.0 - S_crit) * 0.5 * np.pi
dd = DischargeDiffuser(mg, S_crit)
for i in range(5): # 501
dd.run_one_step(0.01) # 0.08
imshow_grid_at_node(mg, "topographic__elevation")
# Sai Nudurupati and Erkan Istanbulluoglu- 16May2014 :
# Example to use potential_evapotranspiration_field.py
#import landlab
from landlab import RasterModelGrid
from landlab.components.radiation.radiation_field import Radiation
from landlab.components.PET.potential_evapotranspiration_field import PotentialEvapotranspiration
import numpy as np
import matplotlib.pyplot as plt
from landlab.plot.imshow import imshow_field
grid = RasterModelGrid( 100, 100, 20. )
elevation = np.random.rand(grid.number_of_nodes) * 1000
grid.add_zeros('node','Elevation',units = 'm')
grid['node']['Elevation'] = elevation
rad = Radiation( grid )
PET = PotentialEvapotranspiration( grid )
current_time = 0.56
rad.update( current_time )
PET.update( ConstantPotentialEvapotranspiration = 10.0 )
plt.figure(0)
imshow_field(grid,'RadiationFactor',
values_at = 'cell', grid_units = ('m','m'))
plt.figure(1)
imshow_field(grid,'PotentialEvapotranspiration',
values_at = 'cell', grid_units = ('m','m'))
plt.savefig('PET_test')
plt.show()
out_interval = 25
color = 'gnuplot2' # 'winter'
out_fields = ['topographic__elevation', 'channel_sediment__volumetric_flux']
# build the structures:
mg = RasterModelGrid(raster_params['shape'], raster_params['dx'])
for edge in (mg.nodes_at_left_edge, mg.nodes_at_top_edge,
mg.nodes_at_right_edge):
mg.status_at_node[edge] = CLOSED_BOUNDARY
z = mg.add_field('node', 'topographic__elevation',
np.loadtxt(raster_params['initcond']))
sed = mg.add_zeros('node', 'channel_sediment__volumetric_flux', dtype=float)
fr = FlowRouter(mg)
eroder = FastscapeEroder(mg, **inputs_sp)
ld = LinearDiffuser(mg, **inputs_ld)
def build_master_dict(expt_ID):
total_dict = inputs_sp.copy()
total_dict.update(inputs_ld)
total_dict.update(raster_params)
total_dict['expt_ID'] = expt_ID
total_dict['dt'] = dt
total_dict['max_loops'] = max_loops
total_dict['multiplierforstab'] = multiplierforstab
total_dict['out_interval'] = out_interval
def test_steady_state_with_basic_solver_option():
"""
Test that model matches the transport-limited analytical solution
for slope/area relationship at steady state: S=((U * v_s) / (K * A^m)
+ U / (K * A^m))^(1/n).
Also test that model matches the analytical solution for steady-state
sediment flux: Qs = U * A * (1 - phi).
"""
# set up a 5x5 grid with one open outlet node and low initial elevations.
nr = 5
nc = 5
mg = RasterModelGrid((nr, nc), xy_spacing=10.0)
z = mg.add_zeros("node", "topographic__elevation")
mg["node"]["topographic__elevation"] += (
mg.node_y / 100000 + mg.node_x / 100000 +
np.random.rand(len(mg.node_y)) / 10000)
mg.set_closed_boundaries_at_grid_edges(
bottom_is_closed=True,
left_is_closed=True,
right_is_closed=True,
top_is_closed=True,
)
mg.set_watershed_boundary_condition_outlet_id(
0, mg["node"]["topographic__elevation"], -9999.0)
# Instantiate DepressionFinderAndRouter
df = DepressionFinderAndRouter(mg)
# Create a D8 flow handler
fa = FlowAccumulator(mg,
flow_director="D8",
depression_finder="DepressionFinderAndRouter")
# Parameter values for detachment-limited test
K = 0.01
U = 0.0001
dt = 1.0
F_f = 0.0 # all sediment is considered coarse bedload
m_sp = 0.5
n_sp = 1.0
v_s = 0.5
phi = 0.5
# Instantiate the ErosionDeposition component...
ed = ErosionDeposition(
mg,
K=K,
F_f=F_f,
phi=phi,
v_s=v_s,
m_sp=m_sp,
n_sp=n_sp,
sp_crit=0,
solver="basic",
)
# ... and run it to steady state (5000x1-year timesteps).
for i in range(5000):
fa.run_one_step()
flooded = np.where(df.flood_status == 3)[0]
ed.run_one_step(dt=dt, flooded_nodes=flooded)
z[mg.core_nodes] += U * dt # m
# compare numerical and analytical slope solutions
num_slope = mg.at_node["topographic__steepest_slope"][mg.core_nodes]
analytical_slope = np.power(
((U * v_s) /
(K * np.power(mg.at_node["drainage_area"][mg.core_nodes], m_sp))) +
(U / (K * np.power(mg.at_node["drainage_area"][mg.core_nodes], m_sp))),
1.0 / n_sp,
)
# test for match with analytical slope-area relationship
testing.assert_array_almost_equal(
num_slope,
analytical_slope,
decimal=8,
err_msg="E/D slope-area test failed",
verbose=True,
)
# compare numerical and analytical sediment flux solutions
num_sedflux = mg.at_node["sediment__flux"][mg.core_nodes]
analytical_sedflux = U * mg.at_node["drainage_area"][mg.core_nodes] * (1 -
phi)
# test for match with anakytical sediment flux
testing.assert_array_almost_equal(
num_sedflux,
analytical_sedflux,
decimal=8,
err_msg="E/D sediment flux test failed",
verbose=True,
)
def test_raise_kwargs_error():
mg = RasterModelGrid((5, 5))
z = mg.add_zeros('node', 'topographic__elevation')
z += mg.node_x.copy()**2
assert_raises(TypeError, TaylorNonLinearDiffuser, mg, bad_name='true')
def test_fastscape():
input_str = os.path.join(_THIS_DIR, "drive_sp_params.txt")
inputs = ModelParameterDictionary(input_str)
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, xy_spacing=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)) / 1000.0
fr = FlowAccumulator(mg, flow_director="D8")
fsp = Fsc(mg, input_str, method="D8")
elapsed_time = 0.0
while elapsed_time < time_to_run:
if elapsed_time + dt > time_to_run:
dt = time_to_run - elapsed_time
mg = fr.run_one_step()
mg = fsp.erode(mg, 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)
#########################################################
##
## Example for soil_moisture_field.py
##
## Sai Nudurupati and Erkan Istanbulluoglu - 15May2014
##
#########################################################
from landlab import RasterModelGrid
from landlab.components.soil_moisture import SoilMoisture
import numpy as np
grid = RasterModelGrid(10, 10, 1.)
grid.add_zeros('cell', 'VegetationCover', units='None')
grid.add_zeros('cell', 'LiveLeafAreaIndex', units='None')
grid.add_zeros('cell', 'PotentialEvapotranspiration', units='mm')
grid['cell']['InitialSaturationFraction'] = np.random.rand(
grid.number_of_cells)
grid['cell']['VegetationsCover'] = np.random.rand(grid.number_of_cells)
grid['cell']['LiveLeafAreaIndex'] = np.ones(grid.number_of_cells) * 3
grid['cell']['PotentialEvapotranspiration'] = np.ones(grid.number_of_cells) * 6
current_time = 0.56
SM = SoilMoisture(grid)
#PD = PrecipitationDistribution()
#PD.update()
#precip = PD.get_storm_depth()
#Tb = PD.get_interstorm_event_duration()
#Tr = PD.get_precipitation_event_duration()
current_time = SM.update(current_time)
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")
# nt needs defining
uplift = inputs.read_float("uplift_rate")
init_elev = inputs.read_float("init_elev")
mg = RasterModelGrid(nrows, ncols, dx)
# mg.set_inactive_boundaries(False, False, False, False)
# mg.set_inactive_boundaries(True,True,True,True)
mg.set_looped_boundaries(True, True)
# create the fields in the grid
mg.add_zeros("topographic__elevation", at="node")
z = mg.zeros(at="node") + init_elev
mg["node"]["topographic__elevation"] = z + numpy.random.rand(len(z)) / 1000.
# Now add a step to diffuse out:
# mg.at_node['topographic__elevation'][mg.active_nodes[:(mg.active_nodes.shape[0]//2.)]]
# += 0.05 #half block uplift
# pylab.figure(1)
# pylab.close()
# elev = mg['node']['topographic__elevation']
# elev_r = mg.node_vector_to_raster(elev)
# pylab.figure(1)
# im = pylab.imshow(elev_r, cmap=pylab.cm.RdBu)
# pylab.show()
def sm():
grid = RasterModelGrid((20, 20), spacing=10e0)
grid.add_zeros("vegetation__plant_functional_type", at="cell", dtype=int)
return SoilMoisture(grid)
# progress.
current_real_time = time.time()
next_report = current_real_time + report_interval
# Create grid
mg = RasterModelGrid(nr, nc, 1.0)
# Make the boundaries be walls #right, bottom, left, top
mg.set_closed_boundaries_at_grid_edges(False, True, True, True)
# Set up the states and pair transitions.
ns_dict = { 0 : 'fluid', 1 : 'particle' }
xn_list = setup_transition_list()
# Create the node-state array and attach it to the grid
node_state_grid = mg.add_zeros('node', 'node_state_map', dtype=int)
# Initialize the node-state array: here, the initial condition is a pile of
# resting grains at the bottom of a container.
left_cols = np.where(mg.node_x<0.05*nc)[0]
node_state_grid[left_cols] = 1
# For visual display purposes, set all boundary nodes to fluid
node_state_grid[mg.closed_boundary_nodes] = 0
# Create the CA model
ca = OrientedRasterCTS(mg, ns_dict, xn_list, node_state_grid)
grain = '#5F594D'
fluid = '#D0E4F2'
clist = [fluid,grain]
"at_node:aquifer_base__elevation",
"at_node:water_table__elevation",
]
output["base_output_path"] = './data/steady_sp_gam_hi_'
output["run_id"] = ID #make this task_id if multiple runs
#initialize grid
np.random.seed(12345)
grid = RasterModelGrid((125, 125), xy_spacing=v0)
grid.set_status_at_node_on_edges(
right=grid.BC_NODE_IS_CLOSED,
top=grid.BC_NODE_IS_CLOSED,
left=grid.BC_NODE_IS_FIXED_VALUE,
bottom=grid.BC_NODE_IS_CLOSED,
)
elev = grid.add_zeros('node', 'topographic__elevation')
elev[:] = b + 0.1 * hg * np.random.rand(len(elev))
base = grid.add_zeros('node', 'aquifer_base__elevation')
wt = grid.add_zeros('node', 'water_table__elevation')
wt[:] = elev.copy()
#initialize components
gdp = GroundwaterDupuitPercolator(
grid,
porosity=n,
hydraulic_conductivity=ksat,
regularization_f=0.01,
recharge_rate=p,
courant_coefficient=0.1,
vn_coefficient=0.1,
)
def main():
# INITIALIZE
# User-defined parameters
nr = 128
nc = 128
fracture_spacing = 10 # fracture spacing, cell widths
plot_interval = 0.25
run_duration = 4.0
report_interval = 5.0 # report interval, in real-time seconds
# Remember the clock time, and calculate when we next want to report
# progress.
current_real_time = time.time()
next_report = current_real_time + report_interval
# Create grid
mg = RasterModelGrid(nr, nc, 1.0)
# Set up the states and pair transitions.
# Transition data here represent a body of fractured rock, with rock
# represented by nodes with state 0, and saprolite (weathered rock)
# represented by nodes with state 1. Node pairs (links) with 0-1 or 1-0
# can undergo a transition to 1-1, representing chemical weathering of the
# rock.
ns_dict = {0: 'rock', 1: 'saprolite'}
xn_list = setup_transition_list()
# Create the node-state array and attach it to the grid
node_state_grid = mg.add_zeros('node', 'node_state_map', dtype=int)
# Initialize the node-state array as a "fracture grid" in which randomly
# oriented fractures are represented as lines of saprolite embedded in
# bedrock.
node_state_grid[:] = make_frac_grid(fracture_spacing, model_grid=mg)
# Create the CA model
ca = RasterLCA(mg, ns_dict, xn_list, node_state_grid)
# Debug output if needed
if _DEBUG:
n = ca.grid.number_of_nodes
for r in range(ca.grid.number_of_node_rows):
for c in range(ca.grid.number_of_node_columns):
n -= 1
print '{0:.0f}'.format(ca.node_state[n]),
print
# Create a CAPlotter object for handling screen display
ca_plotter = CAPlotter(ca)
# Plot the initial grid
ca_plotter.update_plot()
# RUN
current_time = 0.0
while current_time < run_duration:
# Once in a while, print out simulation and real time to let the user
# know that the sim is running ok
current_real_time = time.time()
if current_real_time >= next_report:
print 'Current sim time', current_time, '(', 100 * current_time / run_duration, '%)'
next_report = current_real_time + report_interval
# Run the model forward in time until the next output step
ca.run(current_time + plot_interval,
ca.node_state,
plot_each_transition=False) #, plotter=ca_plotter)
current_time += plot_interval
# Plot the current grid
ca_plotter.update_plot()
# for debugging
if _DEBUG:
n = ca.grid.number_of_nodes
for r in range(ca.grid.number_of_node_rows):
for c in range(ca.grid.number_of_node_columns):
n -= 1
print '{0:.0f}'.format(ca.node_state[n]),
print
# FINALIZE
# Plot
ca_plotter.finalize()
def test_raster_cts():
"""
Tests instantiation of a RasterCTS and implementation of one transition,
with a callback function.
"""
# Set up a small grid with no events scheduled
mg = RasterModelGrid(4, 4, 1.0)
mg.set_closed_boundaries_at_grid_edges(True, True, True, True)
node_state_grid = mg.add_ones('node', 'node_state_map', dtype=int)
node_state_grid[6] = 0
ns_dict = {0: 'black', 1: 'white'}
xn_list = []
xn_list.append(
Transition((1, 0, 0), (0, 1, 0), 0.1, '', True, callback_function))
pd = mg.add_zeros('node', 'property_data', dtype=int)
pd[5] = 50
ca = RasterCTS(mg,
ns_dict,
xn_list,
node_state_grid,
prop_data=pd,
prop_reset_value=0)
# Test the data structures
assert (ca.xn_to.size == 4), 'wrong size for xn_to'
assert (ca.xn_to.shape == (4, 1)), 'wrong size for xn_to'
assert (ca.xn_to[2][0] == 1), 'wrong value in xn_to'
assert (len(ca.event_queue) == 1), 'event queue has wrong size'
assert (ca.num_link_states == 4), 'wrong number of link states'
assert (ca.prop_data[5] == 50), 'error in property data'
assert (ca.xn_rate[2][0] == 0.1), 'error in transition rate array'
assert (ca.active_links_at_node[1][6] == 8), 'error in active link array'
assert (ca.num_node_states == 2), 'error in num_node_states'
assert (ca.link_orientation[-1] == 0), 'error in link orientation array'
assert (ca.link_state_dict[(1, 0, 0)] == 2), 'error in link state dict'
assert (ca.n_xn[2] == 1), 'error in n_xn'
assert (ca.node_pair[1] == (0, 1, 0)), 'error in cell_pair list'
# Manipulate the data in the event queue for testing:
# pop the scheduled event off the queue
ev = heappop(ca.event_queue)
assert (ca.event_queue == []), 'event queue should now be empty but is not'
# engineer an event
ev.time = 1.0
ev.link = 8
ev.xn_to = 1
ev.propswap = True
ev.prop_update_fn = callback_function
ca.next_update[8] = 1.0
# push it onto the event queue
heappush(ca.event_queue, ev)
# run the CA
ca.run(2.0)
# some more tests.
# Is current time advancing correctly? (should only go to 1.0, not 2.0)
# Did the two nodes (5 and 6) correctly exchange states?
# Did the property ID and data arrays get updated? Note that the "propswap"
# should switch propids between nodes 5 and 6, and the callback function
# should increase the value of prop_data in the "swap" node from 50 to 150.
assert (ca.current_time == 1.0), 'current time incorrect'
assert (ca.node_state[5] == 0), 'error in node state 5'
assert (ca.node_state[6] == 1), 'error in node state 6'
# from landlab.grid.mappers import map_link_end_node_max_value_to_link
inputs = ModelParameterDictionary('./pot_fr_params.txt')
nrows = 50 #inputs.read_int('nrows')
ncols = 50 #inputs.read_int('ncols')
dx = inputs.read_float('dx')
init_elev = inputs.read_float('init_elev')
mg = RasterModelGrid(nrows, ncols, dx)
# attempt to implement diffusion with flow routing...
#modify the fields in the grid
z = mg.zeros(at='node') + init_elev
mg.at_node['topographic__elevation'] = z + np.random.rand(len(z)) / 1000.
mg.add_zeros('water__unit_flux_in', at='node')
mg.add_zeros('surface_water__discharge', at='link')
#Set boundary conditions
inlet_node = np.array((int((1.5 * mg.number_of_node_columns) // 1)))
section_col = int((0.5 * mg.number_of_node_columns) // 1)
mg.at_node['topographic__elevation'][section_col] = 1.
mg.set_closed_boundaries_at_grid_edges(False, False, False, True)
mg.set_fixed_value_boundaries_at_grid_edges(False, True, True, True)
mg.status_at_node[section_col] = 2
mg._update_links_nodes_cells_to_new_BCs()
mg.at_node['water__unit_flux_in'].fill(0.)
mg.at_node['water__unit_flux_in'][inlet_node] = 1.
pfr = PotentialityFlowRouter(mg, 'pot_fr_params.txt')
interior_nodes = mg.core_nodes