def test_names_keyword_with_bad_name():
grid = RasterModelGrid((4, 5), spacing=(2., 2.))
grid.add_field('node', 'air__temperature', np.arange(20.))
with cdtemp() as _:
assert_raises(ValueError, write_esri_ascii, 'test.asc', grid,
names='not_a_name')
def test_degenerate_drainage():
"""
This "hourglass" configuration should be one of the hardest to correctly
re-route.
"""
mg = RasterModelGrid(9, 5)
z_init = mg.node_x.copy()*0.0001 + 1.
lake_pits = np.array([7, 11, 12, 13, 17, 27, 31, 32, 33, 37])
z_init[lake_pits] = -1.
z_init[22] = 0. # the common spill pt for both lakes
z_init[21] = 0.1 # an adverse bump in the spillway
z_init[20] = -0.2 # the spillway
z = mg.add_field('node', 'topographic__elevation', z_init)
fr = FlowRouter(mg)
lf = DepressionFinderAndRouter(mg)
fr.route_flow()
lf.map_depressions()
correct_A = np.array([ 0., 0., 0., 0., 0.,
0., 1., 3., 1., 0.,
0., 5., 1., 2., 0.,
0., 1., 10., 1., 0.,
21., 21., 1., 1., 0.,
0., 1., 9., 1., 0.,
0., 3., 1., 2., 0.,
0., 1., 1., 1., 0.,
0., 0., 0., 0., 0.])
thelake = np.concatenate((lake_pits, [22])).sort()
assert_array_almost_equal(mg.at_node['drainage_area'], correct_A)
def read_netcdf(nc_file, reshape=False, just_grid=False):
"""
Reads the NetCDF file *nc_file*, and writes it to the fields of a new
RasterModelGrid, which it then returns.
Check the names of the fields in the returned grid with
grid.at_nodes.keys().
"""
try:
root = nc.netcdf_file(nc_file, 'r', version=2)
except TypeError:
root = nc4.Dataset(nc_file, 'r', format='NETCDF4')
shape = _read_netcdf_grid_shape(root)
spacing = _read_netcdf_grid_spacing(root)
assert(len(shape) == 2)
assert(len(spacing) == 2)
if spacing[0] != spacing[1]:
raise NotRasterGridError()
grid = RasterModelGrid(num_rows=shape[0], num_cols=shape[1], dx=spacing[0])
if not just_grid:
fields = _read_netcdf_structured_data(root)
for (name, values) in fields.items():
grid.add_field('node', name, values)
root.close()
return grid
def setup_dans_grid4():
"""
Create a 10x10 test grid with two well defined holes in it, into an
inclined surface. This time, one of the holes is a stupid shape, which
will require the component to arrange flow back "uphill".
"""
global hf, fr, mg
global z, depr_outlet_target
global lake, lake1, lake2, outlet, outlet_array
lake1 = np.array([34, 35, 36, 44, 45, 46, 54, 55, 56, 65, 74])
lake2 = np.array([78, 87, 88])
guard_nodes = np.array([23, 33, 53, 63, 73, 83])
lake = np.concatenate((lake1, lake2))
outlet = 35 # shouldn't be needed
outlet_array = np.array([outlet])
mg = RasterModelGrid(10, 10, 1.)
z = np.ones(100, dtype=float)
# add slope
z += mg.node_x
z[guard_nodes] += 0.001 # forces the flow out of a particular node
z[lake] = 0.
depr_outlet_target = np.empty(100, dtype=float)
depr_outlet_target.fill(XX)
depr_outlet_target = XX # not well defined in this simplest case...?
mg.add_field('node', 'topographic__elevation', z, units='-')
fr = FlowRouter(mg)
def test_netcdf_write():
"""Test generic write_netcdf."""
if not WITH_NETCDF4:
raise SkipTest('netCDF4 package not installed')
field = RasterModelGrid(4, 3)
field.add_field('node', 'topographic__elevation', np.arange(12.))
with cdtemp() as _:
write_netcdf('test.nc', field, format='NETCDF4')
root = nc.Dataset('test.nc', 'r', format='NETCDF4')
assert_equal(set(root.dimensions), set(['ni', 'nj', 'nt']))
assert_equal(len(root.dimensions['ni']), 3)
assert_equal(len(root.dimensions['nj']), 4)
assert_true(len(root.dimensions['nt']), 1)
assert_true(root.dimensions['nt'].isunlimited())
assert_equal(set(root.variables),
set(['x', 'y', 'topographic__elevation']))
assert_array_equal(root.variables['x'][:].flat,
np.array([0., 1., 2., 0., 1., 2., 0., 1., 2.,
0., 1., 2., ]))
assert_array_equal(root.variables['y'][:].flat,
np.array([0., 0., 0., 1., 1., 1., 2., 2., 2.,
3., 3., 3., ]))
assert_array_equal(root.variables['topographic__elevation'][:].flat,
field.at_node['topographic__elevation'])
root.close()
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 main():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--n-loads', type=int, default=16,
help='Number of loads to apply')
parser.add_argument('--shape', type=int, default=200,
help='Number rows and columns')
parser.add_argument('--spacing', type=int, default=5e3,
help='Spading between rows and columns (m)')
parser.add_argument('--n-procs', type=int, default=1,
help='Number of processors to use')
parser.add_argument('--plot', action='store_true', default=False,
help='Plot an image of the total deflection')
args = parser.parse_args()
shape = (args.shape, args.shape)
spacing = (args.spacing, args.spacing)
load_locs = get_random_load_locations(shape, args.n_loads)
load_sizes = get_random_load_magnitudes(args.n_loads)
grid = RasterModelGrid(shape[0], shape[1], spacing[0])
flex = FlexureComponent(grid, method='flexure')
put_loads_on_grid(grid, load_locs, load_sizes)
flex.update(n_procs=args.n_procs)
grid.imshow('node', 'lithosphere__elevation', symmetric_cbar=False,
cmap='spectral', show=True)
def test_id_as_array_with_one_interior():
rmg = RasterModelGrid(5, 5)
assert_array_equal(
rmg.node_has_boundary_neighbor(np.arange(25)),
np.array(
[
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
False,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
]
),
)
def test_D_infinity_flat_closed_upper():
mg = RasterModelGrid((5, 4), xy_spacing=(1, 1))
z = mg.add_zeros("node", "topographic__elevation")
z[mg.core_nodes] -= 1
mg.set_closed_boundaries_at_grid_edges(
bottom_is_closed=True,
left_is_closed=True,
right_is_closed=True,
top_is_closed=True,
)
fd = FlowDirectorDINF(mg)
fd.run_one_step()
node_ids = np.arange(mg.number_of_nodes)
true_recievers = -1 * np.ones(fd.receivers.shape)
true_recievers[:, 0] = node_ids
true_proportions = np.zeros(fd.proportions.shape)
true_proportions[:, 0] = 1
assert_array_equal(fd.receivers, true_recievers)
assert_array_equal(
np.round(fd.proportions, decimals=6), np.round(true_proportions, decimals=6)
)
def test_calculate_landslide_probability_lognormal_method():
"""Testing the main method 'calculate_landslide_probability()' with
'lognormal' method.
"""
grid_2 = RasterModelGrid((5, 4), spacing=(0.2, 0.2))
gridnum = grid_2.number_of_nodes
np.random.seed(seed=6)
grid_2.at_node['topographic__slope'] = np.random.rand(gridnum)
scatter_dat = np.random.randint(1, 10, gridnum)
grid_2.at_node['topographic__specific_contributing_area']= (
np.sort(np.random.randint(30, 900, gridnum)))
grid_2.at_node['soil__transmissivity']= (
np.sort(np.random.randint(5, 20, gridnum), -1))
grid_2.at_node['soil__mode_total_cohesion']= (
np.sort(np.random.randint(30, 900, gridnum)))
grid_2.at_node['soil__minimum_total_cohesion']= (
grid_2.at_node['soil__mode_total_cohesion'] - scatter_dat)
grid_2.at_node['soil__maximum_total_cohesion']= (
grid_2.at_node['soil__mode_total_cohesion'] + scatter_dat)
grid_2.at_node['soil__internal_friction_angle']= (
np.sort(np.random.randint(26, 37, gridnum)))
grid_2.at_node['soil__thickness']= (
np.sort(np.random.randint(1, 10, gridnum)))
grid_2.at_node['soil__density']= (2000. * np.ones(gridnum))
ls_prob_lognormal = LandslideProbability(grid_2, number_of_iterations=10,
groundwater__recharge_distribution='lognormal',
groundwater__recharge_mean=5.,
groundwater__recharge_standard_deviation=0.25,
seed=6)
ls_prob_lognormal.calculate_landslide_probability()
np.testing.assert_almost_equal(
grid_2.at_node['landslide__probability_of_failure'][5], 0.8)
np.testing.assert_almost_equal(
grid_2.at_node['landslide__probability_of_failure'][9], 0.4)
def test_id_as_array():
rmg = RasterModelGrid((4, 5))
assert_array_equal(
rmg.node_has_boundary_neighbor(np.arange(20)),
np.array(
[
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
True,
]
),
)
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)
with pytest.raises(RuntimeError):
Cdiff.soilflux(10)
def test_run_one_step():
grid = RasterModelGrid((10, 10), xy_spacing=25)
grid.add_ones("node", "soil_water_infiltration__depth", dtype=float)
grid.add_ones("node", "surface_water__depth")
hydraulic_conductivity = 2.5 * (10 ** -6)
grid["node"]["surface_water__depth"] *= 5.0
grid["node"]["soil_water_infiltration__depth"] *= 10 ** -5
SI = SoilInfiltrationGreenAmpt(
grid,
hydraulic_conductivity=hydraulic_conductivity,
soil_bulk_density=1700.,
rock_density=2650.,
initial_soil_moisture_content=0.2,
soil_type="silt loam",
volume_fraction_coarse_fragments=0.6,
coarse_sed_flag=False,
surface_water_minimum_depth=1.e-7,
soil_pore_size_distribution_index=None,
soil_bubbling_pressure=None,
wetting_front_capillary_pressure_head=None,
)
SI.run_one_step(dt=5)
np.testing.assert_almost_equal(
grid["node"]["surface_water__depth"][0], 3.97677483519, decimal=6
)
def test_netcdf_write():
field = RasterModelGrid(4, 3)
#field.new_field_location('node', 12.)
values = np.arange(12.)
field.add_field('node', 'planet_surface__elevation', values)
write_netcdf('test.nc', field)
import netCDF4 as nc
root = nc.Dataset('test.nc', 'r', format='NETCDF4')
assert_equal(set(root.dimensions), set(['ni', 'nj', 'nt']))
assert_equal(len(root.dimensions['ni']), 3)
assert_equal(len(root.dimensions['nj']), 4)
assert_true(len(root.dimensions['nt']), 1)
assert_true(root.dimensions['nt'].isunlimited())
assert_equal(set(root.variables),
set(['x', 'y', 'planet_surface__elevation']))
assert_list_equal(list(root.variables['x'][:].flat),
[0., 1., 2.,
0., 1., 2.,
0., 1., 2.,
0., 1., 2., ])
assert_list_equal(list(root.variables['y'][:].flat),
[0., 0., 0.,
1., 1., 1.,
2., 2., 2.,
3., 3., 3., ])
assert_list_equal(
list(root.variables['planet_surface__elevation'][:].flat),
range(12))
root.close()
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)
with pytest.raises(RuntimeError):
Cdiff.soilflux(10, if_unstable="raise")
def test_run_with_2_fn_args():
mg = RasterModelGrid((3, 5), xy_spacing=(2, 1))
mg.set_closed_boundaries_at_grid_edges(True, True, False, True)
mg.add_field("topographic__elevation", mg.node_x + mg.node_y, at="node")
def mylossfunction(qw, nodeID):
return 0.5 * qw
fa = LossyFlowAccumulator(
mg,
"topographic__elevation",
flow_director=FlowDirectorSteepest,
routing="D4",
loss_function=mylossfunction,
)
fa.run_one_step()
assert np.allclose(
mg.at_node["drainage_area"].reshape(mg.shape),
np.array([[0., 0., 0., 0., 0.], [6., 6., 4., 2., 0.], [0., 0., 0., 0., 0.]]),
)
assert np.allclose(
mg.at_node["surface_water__discharge"].reshape(mg.shape),
np.array(
[
[0.00, 0.00, 0.00, 0.00, 0.00],
[1.75, 3.50, 3.00, 2.00, 0.00],
[0.00, 0.00, 0.00, 0.00, 0.00],
]
),
)
def _test():
rmg = RasterModelGrid(4, 5)
number_of_elements = rmg.number_of_elements(element)
rtn_values = rmg.add_ones(element, 'name')
assert_is(rtn_values, rmg.field_values(element, 'name'))
assert_array_equal(
rtn_values, np.ones(number_of_elements, dtype=np.float))
def test_out_array():
"""Test using the out keyword."""
rmg = RasterModelGrid((4, 5), xy_spacing=(2, 5))
values_at_nodes = np.arange(20)
output_array = np.empty(rmg.number_of_links)
rtn_array = rmg.calc_grad_at_link(values_at_nodes, out=output_array)
assert_array_equal(
rtn_array[rmg.active_links],
np.array(
[
1.0,
1.0,
1.0,
0.5,
0.5,
0.5,
0.5,
1.0,
1.0,
1.0,
0.5,
0.5,
0.5,
0.5,
1.0,
1.0,
1.0,
]
),
)
assert rtn_array is output_array
def test_unit_spacing():
"""Test with a grid with unit spacing."""
rmg = RasterModelGrid((4, 5))
values_at_nodes = np.arange(20)
grads = rmg.calc_grad_at_link(values_at_nodes)[rmg.active_links]
assert_array_equal(
grads,
np.array(
[
5.0,
5.0,
5.0,
1.0,
1.0,
1.0,
1.0,
5.0,
5.0,
5.0,
1.0,
1.0,
1.0,
1.0,
5.0,
5.0,
5.0,
]
),
)
diffs = rmg.calc_diff_at_link(values_at_nodes)[rmg.active_links]
assert_array_equal(grads, diffs)
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_save_esri_ascii(tmpdir):
grid = RasterModelGrid(4, 5, 2.)
grid.add_field('node', 'air__temperature', np.arange(20.))
with tmpdir.as_cwd():
grid.save('test.asc', format='esri-ascii')
assert os.path.isfile('test.asc')
def test_error_for_to_many_with_depression():
"""Check that an error is thrown when to_many methods started DF."""
mg0 = RasterModelGrid((10, 10), spacing=(1, 1))
z0 = mg0.add_field(
"topographic__elevation", mg0.node_x ** 2 + mg0.node_y ** 2, at="node"
)
mg1 = RasterModelGrid((10, 10), spacing=(1, 1))
z1 = mg1.add_field(
"topographic__elevation", mg1.node_x ** 2 + mg1.node_y ** 2, at="node"
)
with pytest.raises(NotImplementedError):
FlowAccumulator(
mg0, flow_director="MFD", depression_finder="DepressionFinderAndRouter"
)
with pytest.raises(NotImplementedError):
FlowAccumulator(
mg0, flow_director="DINF", depression_finder="DepressionFinderAndRouter"
)
fa0 = FlowAccumulator(mg0, flow_director="MFD")
fa0.run_one_step()
with pytest.raises(NotImplementedError):
DepressionFinderAndRouter(mg0)
fa1 = FlowAccumulator(mg1, flow_director="DINF")
fa1.run_one_step()
with pytest.raises(NotImplementedError):
DepressionFinderAndRouter(mg1)
def test_no_reroute():
mg = RasterModelGrid((5, 5), xy_spacing=2.)
z = mg.add_zeros("node", "topographic__elevation", dtype=float)
z[1] = -1.
z[6] = -2.
z[19] = -2.
z[18] = -1.
z[17] = -3.
fd = FlowDirectorSteepest(mg)
fa = FlowAccumulator(mg)
lmb = LakeMapperBarnes(
mg,
method="Steepest",
fill_flat=True,
redirect_flow_steepest_descent=True,
track_lakes=True,
)
lake_dict = {1: deque([6]), 18: deque([17])}
fd.run_one_step() # fill the director fields
fa.run_one_step() # get a drainage_area
orig_surf = lmb._track_original_surface()
lmb._redirect_flowdirs(orig_surf, lake_dict)
assert mg.at_node["flow__receiver_node"][6] == 1
assert mg.at_node["flow__receiver_node"][17] == 18
assert mg.at_node["flow__receiver_node"][18] == 19
def test_sheetflow():
flux = np.array(
[0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000,
0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000,
0.00000000, 0.50000000, 1.35677118, 2.18064899, 2.98289061,
3.77167837, 4.55070068, 5.32231777, 6.08818915, 6.34956448,
0.00000000, 0.50000000, 1.64322882, 2.77444053, 3.90022802,
5.01817926, 6.12943159, 7.23518857, 8.33656075, 8.93446352,
0.00000000, 0.50000000, 1.50000000, 2.54491048, 3.60279934,
4.66824237, 5.73835212, 6.81180077, 7.88776947, 8.46568929,
0.00000000, 0.50000000, 1.50000000, 2.50000000, 3.51408202,
4.54190001, 5.58151561, 6.63069288, 7.68748063, 8.25028271,
0.00000000, 0.50000000, 1.50000000, 2.50000000, 3.51408202,
4.54190001, 5.58151561, 6.63069288, 7.68748063, 8.25028271,
0.00000000, 0.50000000, 1.50000000, 2.54491048, 3.60279934,
4.66824237, 5.73835212, 6.81180077, 7.88776947, 8.46568929,
0.00000000, 0.50000000, 1.64322882, 2.77444053, 3.90022802,
5.01817926, 6.12943159, 7.23518857, 8.33656075, 8.93446352,
0.00000000, 0.50000000, 1.35677118, 2.18064899, 2.98289061,
3.77167837, 4.55070068, 5.32231777, 6.08818915, 6.34956448,
0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000,
0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000]
)
mg = RasterModelGrid((NROWS, NCOLS), (DX, DX))
z = (3000. - mg.node_x) * 0.5
mg.at_node['topographic__elevation'] = z
mg.set_closed_boundaries_at_grid_edges(False, True, True, True)
mg.at_node['water__volume_flux_in'] = np.ones_like(z, dtype=float)
pfr = PotentialityFlowRouter(mg, INPUTS)
pfr.route_flow(route_on_diagonals=True)
assert_array_almost_equal(mg.at_node['water__volume_flux_magnitude'], flux)
def test_MFD_S_slope_w_diag():
mg = RasterModelGrid((10, 10))
mg.add_field("topographic__elevation", mg.node_y, at="node")
fa = FlowAccumulator(mg, flow_director="FlowDirectorMFD", diagonals=True)
fa.run_one_step()
# this one should part to south west, part to south, and part to southeast
sw_diags = mg.diagonal_adjacent_nodes_at_node[:, 2]
se_diags = mg.diagonal_adjacent_nodes_at_node[:, 3]
s_links = mg.adjacent_nodes_at_node[:, 3]
node_ids = np.arange(mg.number_of_nodes)
true_recievers = -1 * np.ones(fa.flow_director.receivers.shape)
true_recievers[mg.core_nodes, 3] = s_links[mg.core_nodes]
true_recievers[mg.core_nodes, 6] = sw_diags[mg.core_nodes]
true_recievers[mg.core_nodes, 7] = se_diags[mg.core_nodes]
true_recievers[mg.boundary_nodes, 0] = node_ids[mg.boundary_nodes]
true_proportions = np.zeros(fa.flow_director.proportions.shape)
true_proportions[mg.boundary_nodes, 0] = 1
total_sum_of_slopes = 1. + 2. * (1. / 2. ** 0.5)
true_proportions[mg.core_nodes, 3] = 1.0 / total_sum_of_slopes
true_proportions[mg.core_nodes, 6] = (1. / 2. ** 0.5) / total_sum_of_slopes
true_proportions[mg.core_nodes, 7] = (1. / 2. ** 0.5) / total_sum_of_slopes
assert_array_equal(true_recievers, fa.flow_director.receivers)
assert_array_almost_equal(true_proportions, fa.flow_director.proportions)
def setup_dans_grid2():
"""
Create a 10x10 test grid with a well defined hole in it, from a flat
surface.
"""
global hf, mg
global z, depr_outlet_target
global lake, outlet, lake_code, outlet_array
lake = np.array([44, 45, 46, 54, 55, 56, 64, 65, 66])
outlet = 35 # shouldn't be needed
lake_code = 44
outlet_array = np.array([outlet])
mg = RasterModelGrid(10, 10, 1.)
z = np.ones(100, dtype=float)
z[lake] = 0.
depr_outlet_target = np.empty(100, dtype=float)
depr_outlet_target.fill(XX)
depr_outlet_target = XX # not well defined in this simplest case...?
mg.add_field('node', 'topographic__elevation', z, units='-')
hf = SinkFiller(mg)
def setup_dans_grid3():
"""
Create a 10x10 test grid with two well defined holes in it, into an
inclined surface.
"""
global hf, fr, mg
global z, depr_outlet_target
global lake, lake1, lake2, outlet, outlet_array, rcvr
lake1 = np.array([34, 35, 36, 44, 45, 46, 54, 55, 56])
lake2 = np.array([77, 78, 87, 88])
guard_nodes = np.array([23, 33, 53, 63])
lake = np.concatenate((lake1, lake2))
outlet = 35 # shouldn't be needed
outlet_array = np.array([outlet])
mg = RasterModelGrid(10, 10, 1.)
z = np.ones(100, dtype=float)
# add slope
z += mg.node_x
z[guard_nodes] += 0.001
z[lake] = 0.
depr_outlet_target = np.empty(100, dtype=float)
depr_outlet_target.fill(XX)
depr_outlet_target = XX # not well defined in this simplest case...?
mg.add_field('node', 'topographic__elevation', z, units='-')
fr = FlowRouter(mg)
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 setup_dans_grid1():
"""
Create a 7x7 test grid with a well defined hole in it.
"""
global hf, mg
global z, r_new, r_old, A_new, A_old, s_new, depr_outlet_target
global lake, outlet, lake_code, outlet_array
mg = RasterModelGrid(7, 7, 1.)
z = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 2.0, 2.0, 2.0, 2.0, 2.0, 0.0,
0.0, 2.0, 1.6, 1.5, 1.6, 2.0, 0.0,
0.0, 2.0, 1.7, 1.6, 1.7, 2.0, 0.0,
0.0, 2.0, 1.8, 2.0, 2.0, 2.0, 0.0,
0.0, 1.0, 0.6, 1.0, 1.0, 1.0, 0.0,
0.0, 0.0, -0.5, 0.0, 0.0, 0.0, 0.0]).flatten()
depr_outlet_target = np.array([XX, XX, XX, XX, XX, XX, XX,
XX, XX, XX, XX, XX, XX, XX,
XX, XX, 30, 30, 30, XX, XX,
XX, XX, 30, 30, 30, XX, XX,
XX, XX, XX, XX, XX, XX, XX,
XX, XX, XX, XX, XX, XX, XX,
XX, XX, XX, XX, XX, XX, XX]).flatten()
lake = np.array([16, 17, 18, 23, 24, 25])
outlet = 30
lake_code = 17
outlet_array = np.array([outlet])
mg.add_field('node', 'topographic__elevation', z, units='-')
hf = SinkFiller(mg)
def test_nodes_around_point():
rmg = RasterModelGrid((4, 5), spacing=1.)
surrounding_ids = rmg.nodes_around_point(2.1, 1.1)
assert_array_equal(surrounding_ids, np.array([7, 12, 13, 8]))
surrounding_ids = rmg.nodes_around_point(2.1, .9)
assert_array_equal(surrounding_ids, np.array([2, 7, 8, 3]))
def test_youngs_attribute():
grid = RasterModelGrid((20, 20), xy_spacing=10e3)
for val in (10e3, 1e3):
assert Flexure(grid, youngs=val).youngs == pytest.approx(val)
def test_sp_old():
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")
sp = StreamPowerEroder(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()
sp.run_one_step(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)
def read_netcdf(nc_file, just_grid=False):
"""Create a :class:`~.RasterModelGrid` from a netcdf file.
Create a new :class:`~.RasterModelGrid` from the netcdf file, *nc_file*.
If the netcdf file also contains data, add that data to the grid's fields.
To create a new grid without any associated data from the netcdf file,
set the *just_grid* keyword to ``True``.
Parameters
----------
nc_file : str
Name of a netcdf file.
just_grid : boolean, optional
Create a new grid but don't add value data.
Returns
-------
:class:`~.RasterModelGrid`
A newly-created :class:`~.RasterModelGrid`.
Examples
--------
Import :func:`read_netcdf` and the path to an example netcdf file included
with landlab.
>>> from landlab.io.netcdf import read_netcdf
>>> from landlab.io.netcdf import NETCDF4_EXAMPLE_FILE
Create a new grid from the netcdf file. The example grid is a uniform
rectilinear grid with 4 rows and 3 columns of nodes with unit spacing.
The data file also contains data defined at the nodes for the grid for
a variable called, *surface__elevation*.
>>> grid = read_netcdf(NETCDF4_EXAMPLE_FILE)
>>> grid.shape == (4, 3)
True
>>> grid.dy, grid.dx
(1.0, 1.0)
>>> [str(k) for k in grid.at_node.keys()]
['surface__elevation']
>>> grid.at_node['surface__elevation']
array([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10.,
11.])
:func:`read_netcdf` will try to determine the format of the netcdf file.
For example, the same call will also work for *netcdf3*-formatted files.
>>> from landlab.io.netcdf import NETCDF3_64BIT_EXAMPLE_FILE
>>> grid = read_netcdf(NETCDF3_64BIT_EXAMPLE_FILE)
>>> grid.shape == (4, 3)
True
>>> grid.dy, grid.dx
(1.0, 1.0)
"""
from landlab import RasterModelGrid
try:
root = nc.netcdf_file(nc_file, 'r', version=2)
except TypeError:
root = nc4.Dataset(nc_file, 'r', format='NETCDF4')
try:
node_coords = _read_netcdf_structured_grid(root)
except ValueError:
if ((len(root.variables['x'].dimensions) == 1)
and (len(root.variables['y'].dimensions) == 1)):
node_coords = _read_netcdf_raster_structured_grid(root)
else:
assert ValueError('x and y dimensions must both either be 2D '
'(nj, ni) or 1D (ni,) and (nj).')
assert len(node_coords) == 2
spacing = _get_raster_spacing(node_coords)
shape = node_coords[0].shape
grid = RasterModelGrid(shape, spacing=spacing)
if not just_grid:
fields = _read_netcdf_structured_data(root)
for (name, values) in fields.items():
grid.add_field('node', name, values)
root.close()
return grid
def veg():
grid = RasterModelGrid((20, 20), xy_spacing=10e0)
grid.add_zeros("vegetation__plant_functional_type", at="cell", dtype=int)
grid.add_zeros("surface__evapotranspiration", at="cell", dtype=float)
grid.add_zeros("vegetation__water_stress", at="cell", dtype=float)
grid.add_zeros("surface__potential_evapotranspiration_rate",
at="cell",
dtype=float)
grid.add_zeros("surface__potential_evapotranspiration_30day_mean",
at="cell",
dtype=float)
return Vegetation(grid)
def test_grid_with_no_fields(tmpdir):
grid = RasterModelGrid((4, 5), spacing=(2., 2.))
with tmpdir.as_cwd():
with pytest.raises(ValueError):
write_esri_ascii('test.asc', grid)
def test_rho_mantle_attribute():
grid = RasterModelGrid((20, 20), xy_spacing=10e3)
for val in (10e3, 1e3):
assert Flexure(grid, rho_mantle=val).rho_mantle == pytest.approx(val)
def test_gravity_attribute():
grid = RasterModelGrid((20, 20), xy_spacing=10e3)
for val in (10e3, 1e3):
assert Flexure(grid, gravity=val).gravity == pytest.approx(val)
def test_method_names():
grid = RasterModelGrid((20, 20), xy_spacing=10e3)
assert Flexure(grid, method="airy").method == "airy"
assert Flexure(grid, method="flexure").method == "flexure"
with pytest.raises(ValueError):
Flexure(grid, method="bad-name")
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) #"//" means "divide and truncate" ("%" means "division remainder")
uplift_per_step = uplift_rate * dt
#Instantiate the grid object
#We know which parameters are needed for input by inspecting the function where it lives, in landlab.grid.raster
#We could also look at the documentation for landlab found online (http://the-landlab.readthedocs.org)
mg = RasterModelGrid(nrows, ncols, dx)
##create the elevation field in the grid:
#create the field
mg.create_node_array_zeros('topographic_elevation')
z = mg.create_node_array_zeros(
) + leftmost_elev #in our case, slope is zero, so the leftmost_elev is the mean elev
#put these values plus roughness into that field
mg['node']['topographic_elevation'] = z + np.random.rand(len(z)) / 100000.
#set up its boundary conditions (bottom, left, top, right)
#The mechanisms for this are all automated within the grid object
mg.set_fixed_value_boundaries_at_grid_edges(True, True, True, True)
# Display a message
print 'Running ...'
def test_eet_attribute():
grid = RasterModelGrid((20, 20), xy_spacing=10e3)
for val in (10e3, 1e3):
assert Flexure(grid, eet=val).eet == pytest.approx(val)
with pytest.raises(ValueError):
assert Flexure(grid, eet=-10e3)
def setup_unit_grid():
from landlab import RasterModelGrid
global _GRID, _VALUES_AT_NODES
_GRID = RasterModelGrid(4, 5)
_VALUES_AT_NODES = np.arange(20.)
import pylab
from pylab import show
from landlab import RasterModelGrid
from landlab import ModelParameterDictionary
from landlab.plot.imshow import imshow_node_grid
inputs = ModelParameterDictionary('./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')
uplift_rate = inputs.read_float('uplift_rate')
mg = RasterModelGrid(nrows, ncols, dx)
#create the fields in the grid
mg.create_node_array_zeros('topographic__elevation')
z = mg.create_node_array_zeros() + init_elev
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, './diffusion_params.txt')
#perform the loop:
elapsed_time = 0. #total time in simulation
while elapsed_time < time_to_run:
print elapsed_time
Ks = df_params['ksat'] #hydraulic conductivity [m/s]
n = df_params['n'] #drainable porosity [-]
b = df_params['b'] #characteristic depth [m]
p = df_params['p'] #average precipitation rate [m/s]
tr = df_params['tr'] #mean storm duration [s]
tb = df_params['tb'] #mean interstorm duration [s]
ds = df_params['ds'] #mean storm depth [m]
T_h = 50 * df_params['Th'] #total hydrological time [s]
K = df_params['K']
Ksp = K / p #see governing equation. If the discharge field is (Q/sqrt(A)) then streampower coeff is K/p
E0 = df_params['E0']
#initialize grid
dx = grid.dx
mg = RasterModelGrid(grid.shape, xy_spacing=dx)
mg.set_status_at_node_on_edges(right=mg.BC_NODE_IS_CLOSED, top=mg.BC_NODE_IS_CLOSED, \
left=mg.BC_NODE_IS_FIXED_VALUE, bottom=mg.BC_NODE_IS_CLOSED)
z = mg.add_zeros('node', 'topographic__elevation')
z[:] = elev
zb = mg.add_zeros('node', 'aquifer_base__elevation')
zb[:] = base
zwt = mg.add_zeros('node', 'water_table__elevation')
zwt[:] = wt
f = open('../post_proc/%s/dt_qs_s_%d.csv' % (base_output_path, ID), 'w')
def write_SQ(grid, r, dt, file=f):
cores = grid.core_nodes
h = grid.at_node["aquifer__thickness"]
def setup_3x3_grid():
from landlab import RasterModelGrid
global rmg_3x3
rmg_3x3 = RasterModelGrid(3, 3)
Tg = df_params['Tg'][ID]
ksf = df_params['ksf'][ID]
output = {}
output["output_interval"] = 1000
output["output_fields"] = [
"at_node:topographic__elevation",
"at_node:aquifer_base__elevation",
"at_node:water_table__elevation",
]
output["base_output_path"] = './data/steady_sp_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,
import random
#get the needed properties to build the grid:
input_file = './drive_sp_params.txt'
inputs = ModelParameterDictionary(input_file)
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('run_time')
dt = inputs.read_float('dt')
nt = int(runtime // dt)
uplift_per_step = uplift_rate * dt
#instantiate the grid object
mg = RasterModelGrid(nrows, ncols, dx)
boundary_node_list = mg.boundary_nodes
#set up its boundary conditions (bottom, right, top, left is inactive)
mg.set_inactive_boundaries(True, True, True, True)
mg.set_closed_boundaries_at_grid_edges(False, False, False, False)
mg.status_at_node[20] = FIXED_VALUE_BOUNDARY
##create the elevation field in the grid:
#create the field
mg.add_zeros('topographic__elevation', at='node')
z = mg.zeros(at='node') + init_elev
#put these values plus roughness into that field
mg['node']['topographic__elevation'] = z + np.random.rand(len(z)) / 100000.
def test_director_adding_methods_are_equivalent_D8():
"""Check that different methods to specifying the director are the same."""
mg0 = RasterModelGrid((10, 10), spacing=(1, 1))
z0 = mg0.add_field('topographic__elevation',
mg0.node_x**2 + mg0.node_y**2,
at='node')
fa0 = FlowAccumulator(mg0, flow_director='D8')
fa0.run_one_step()
mg1 = RasterModelGrid((10, 10), spacing=(1, 1))
z1 = mg1.add_field('topographic__elevation',
mg1.node_x**2 + mg1.node_y**2,
at='node')
fa1 = FlowAccumulator(mg1, flow_director='FlowDirectorD8')
fa1.run_one_step()
mg2 = RasterModelGrid((10, 10), spacing=(1, 1))
z2 = mg2.add_field('topographic__elevation',
mg2.node_x**2 + mg2.node_y**2,
at='node')
fa2 = FlowAccumulator(mg2, flow_director=FlowDirectorD8)
fa2.run_one_step()
mg3 = RasterModelGrid((10, 10), spacing=(1, 1))
z3 = mg3.add_field('topographic__elevation',
mg3.node_x**2 + mg3.node_y**2,
at='node')
fd = FlowDirectorD8(mg3)
fa3 = FlowAccumulator(mg3, flow_director=fd)
fa3.run_one_step()
for key in mg0.at_node.keys():
assert_array_equal(mg0.at_node[key], mg1.at_node[key])
assert_array_equal(mg1.at_node[key], mg2.at_node[key])
assert_array_equal(mg2.at_node[key], mg3.at_node[key])
def test_misc():
grid = RasterModelGrid((3, 3))
# test bad dimension:
with pytest.raises(ValueError):
DataRecord(
grid,
time=[0.0],
data_vars={"mean_elev": (["time"], [100.0]), "test": (["bad_dim"], [12])},
)
# should return ValueError('Data variable dimensions must be time and/or'
# 'item_id')
# test bad time format:
with pytest.raises(TypeError):
DataRecord(grid=grid, time="bad_time")
# should return TypeError: Time must be a list or an array of length 1
# test bad datavars format:
with pytest.raises(TypeError):
DataRecord(grid, time=[0.0], data_vars=["not a dict"])
# should return TypeError(('Data variables (data_vars) passed to'
# ' DataRecord must be a dictionary (see '
# 'documentation for valid structure)'))
# test bad items format:
with pytest.raises(TypeError):
DataRecord(
grid,
time=[0.0],
items=["not a dict"],
data_vars={"mean_elevation": (["time"], np.array([100]))},
attrs={"time_units": "y"},
)
# Should return TypeError(('You must provide an ''items'' dictionary '
# '(see documentation for required format)'))
#
with pytest.raises(TypeError):
"""Test bad items keys"""
DataRecord(
grid,
time=[0.0],
items={
"grid_element": np.array([["node"], ["link"]]),
"bad_key": np.array([[1], [3]]),
},
data_vars={"mean_elevation": (["time"], np.array([100]))},
attrs={"time_units": "y"},
)
# Should return TypeError(('You must provide an ''items'' dictionary '
# '(see documentation for required format)'))
with pytest.raises(TypeError):
"""Test bad attrs"""
DataRecord(
grid,
time=[0.0],
items={
"grid_element": np.array([["node"], ["link"]]),
"element_id": np.array([[1], [3]]),
},
data_vars={"mean_elevation": (["time"], np.array([100]))},
attrs=["not a dict"],
)
# Should return except AttributeError:
# raise TypeError(('Attributes (attrs) passed to DataRecord'
# 'must be a dictionary'))
#
# test bad loc and id:
rmg = RasterModelGrid((3, 3))
hmg = HexModelGrid((3, 2), spacing=1.0)
radmg = RadialModelGrid(n_rings=1, nodes_in_first_ring=5, xy_of_center=(0.0, 0.0))
vdmg = VoronoiDelaunayGrid(np.random.rand(25), np.random.rand(25))
my_items_bad_loc = {
"grid_element": np.array(["node", "bad_loc"]),
"element_id": np.array([1, 3]),
}
my_items_bad_loc2 = {"grid_element": "bad_loc", "element_id": np.array([1, 3])}
my_items_bad_loc3 = {
"grid_element": np.array(["node", "node", "node"]),
"element_id": np.array([1, 3]),
}
my_items_bad_id = {
"grid_element": np.array(["node", "link"]),
"element_id": np.array([1, 300]),
}
my_items_bad_id2 = {
"grid_element": np.array(["node", "link"]),
"element_id": np.array([1, -300]),
}
my_items_bad_id3 = {
"grid_element": np.array(["node", "link"]),
"element_id": np.array([1, 2.0]),
}
my_items_bad_id4 = {
"grid_element": np.array(["node", "link"]),
"element_id": np.array([1, 2, 3]),
}
with pytest.raises(ValueError):
DataRecord(rmg, items=my_items_bad_loc)
with pytest.raises(ValueError):
DataRecord(hmg, items=my_items_bad_loc)
with pytest.raises(ValueError):
DataRecord(radmg, items=my_items_bad_loc)
with pytest.raises(ValueError):
DataRecord(vdmg, items=my_items_bad_loc)
# should return ValueError(('One or more of the grid elements provided is/are'
# ' not permitted location for this grid type.'))
with pytest.raises(ValueError):
DataRecord(rmg, items=my_items_bad_loc2)
# should return ValueError: Location provided: bad_loc is not a permitted
# location for this grid type.
with pytest.raises(ValueError):
DataRecord(rmg, items=my_items_bad_loc3)
# should return ValueError(('grid_element passed to DataRecord must be '
# ' the same length as the number of items or 1.'))
with pytest.raises(ValueError):
DataRecord(rmg, items=my_items_bad_id)
with pytest.raises(ValueError):
DataRecord(hmg, items=my_items_bad_id)
with pytest.raises(ValueError):
DataRecord(radmg, items=my_items_bad_id)
with pytest.raises(ValueError):
DataRecord(vdmg, items=my_items_bad_id)
# should return ValueError(('An item residing at ' + at + ' has an '
# 'element_id larger than the number of'+ at + 'on the grid.'))
with pytest.raises(ValueError):
DataRecord(rmg, items=my_items_bad_id2)
# should return ValueError(('An item residing at ' + at + ' has '
# 'an element id below zero. This is not permitted.'))
with pytest.raises(ValueError):
DataRecord(rmg, items=my_items_bad_id3)
# should return ValueError(('You have passed a non-integer element_id to '
# 'DataRecord, this is not permitted.'))
with pytest.raises(ValueError):
DataRecord(rmg, items=my_items_bad_id4)
def dans_grid3():
"""
Create a 7x7 test grid with a well defined hole in it.
"""
mg = RasterModelGrid(7, 7, 1.)
z = np.array([
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 2.0, 2.0, 2.0, 2.0, 2.0, 0.0],
[0.0, 2.0, 1.6, 1.5, 1.6, 2.0, 0.0],
[0.0, 2.0, 1.7, 1.6, 1.7, 2.0, 0.0],
[0.0, 2.0, 1.8, 2.0, 2.0, 2.0, 0.0],
[0.0, 1.0, 0.6, 1.0, 1.0, 1.0, 0.0],
[0.0, 0.0, -0.5, 0.0, 0.0, 0.0, 0.0],
]).flatten()
r_old = np.array([
[0, 1, 2, 3, 4, 5, 6],
[7, 1, 2, 3, 4, 5, 13],
[14, 14, 17, 17, 17, 20, 20],
[21, 21, 17, 17, 17, 27, 27],
[28, 28, 37, 38, 39, 34, 34],
[35, 44, 44, 44, 46, 41, 41],
[42, 43, 44, 45, 46, 47, 48],
]).flatten()
r_new = np.array([
[0, 1, 2, 3, 4, 5, 6],
[7, 1, 2, 3, 4, 5, 13],
[14, 14, 23, 24, 24, 20, 20],
[21, 21, 30, 30, 24, 27, 27],
[28, 28, 37, 38, 39, 34, 34],
[35, 44, 44, 44, 46, 41, 41],
[42, 43, 44, 45, 46, 47, 48],
]).flatten()
A_old = np.array([
[0., 1., 1., 1., 1., 1., 0.],
[0., 1., 1., 1., 1., 1., 0.],
[1., 1., 1., 6., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1.],
[0., 1., 2., 2., 2., 1., 1.],
[0., 0., 5., 0., 2., 0., 0.],
]).flatten()
A_new = np.array([
[0., 1., 1., 1., 1., 1., 0.],
[0., 1., 1., 1., 1., 1., 0.],
[1., 1., 1., 1., 1., 1., 1.],
[1., 1., 2., 4., 1., 1., 1.],
[1., 1., 7., 1., 1., 1., 1.],
[0., 1., 8., 2., 2., 1., 1.],
[0., 0., 11., 0., 2., 0., 0.],
]).flatten()
s_new = np.array([
[0, 1, 8, 2, 9, 3, 10],
[4, 11, 5, 12, 6, 7, 13],
[14, 15, 20, 19, 21, 22, 27],
[26, 28, 29, 34, 33, 35, 41],
[40, 42, 43, 44, 36, 37, 30],
[23, 16, 24, 17, 18, 25, 38],
[31, 45, 46, 39, 32, 47, 48],
]).flatten()
links_old = np.array([
[-1, -1, -1, -1, -1, -1, -1],
[-1, 7, 8, 9, 10, 11, -1],
[-1, 26, 28, -1, 29, 31, -1],
[-1, 39, 113, 35, 114, 44, -1],
[-1, 52, 60, 61, 62, 57, -1],
[-1, 146, 73, 149, 75, 70, -1],
[-1, -1, -1, -1, -1, -1, -1],
]).flatten()
links_new = np.array([
[-1, -1, -1, -1, -1, -1, -1],
[-1, 7, 8, 9, 10, 11, -1],
[-1, 26, 34, 35, 115, 31, -1],
[-1, 39, 47, 125, 42, 44, -1],
[-1, 52, 60, 61, 62, 57, -1],
[-1, 146, 73, 149, 75, 70, -1],
[-1, -1, -1, -1, -1, -1, -1],
]).flatten()
depr_outlet_target = np.array([
[XX, XX, XX, XX, XX, XX, XX],
[XX, XX, XX, XX, XX, XX, XX],
[XX, XX, 30, 30, 30, XX, XX],
[XX, XX, 30, 30, 30, XX, XX],
[XX, XX, XX, XX, XX, XX, XX],
[XX, XX, XX, XX, XX, XX, XX],
[XX, XX, XX, XX, XX, XX, XX],
]).flatten()
mg.add_field("node", "topographic__elevation", z, units="-")
fr = FlowAccumulator(mg, flow_director="D8")
lf = DepressionFinderAndRouter(mg)
class DansGrid(object):
pass
dans_grid = DansGrid()
dans_grid.mg = mg
dans_grid.fr = fr
dans_grid.lf = lf
dans_grid.z = z
dans_grid.r_new = r_new
dans_grid.r_old = r_old
dans_grid.A_new = A_new
dans_grid.A_old = A_old
dans_grid.s_new = s_new
dans_grid.depr_outlet_target = depr_outlet_target
dans_grid.links_old = links_old
dans_grid.links_new = links_new
return dans_grid
num_cols = len(spacearray)
#Time Array
t_min = 0 #s
t_max = 18000 #s
dt = 0.3 #s
timearray = np.arange(t_min, t_max + dt, dt)
time_interval = [
timearray[i:i + int((round(len(timearray) / 4)))]
for i in range(0, len(timearray), int(round(len(timearray) / 4)))
]
#2D grid dimensions
x_ncells = num_cols
y_ncells = num_rows
mg = RasterModelGrid(x_ncells, y_ncells, dx) #make 2D grid
#Create data fields
zb = mg.add_empty('node', 'land_surface_elevation')
h = mg.add_zeros('node', 'water_thickness')
zw = mg.add_empty('node', 'water_elevation')
w_slope = mg.add_zeros('link', 'water_slope') #make all links have zero slope
q = mg.add_zeros('link', 'flux')
#Initialize elevation
zb[:] = zmax - valley_s * mg.node_x
zb += side_s * np.abs(mg.node_y - mg.dx * ((num_rows - 1) / 2.0))
zw[:] = zb + h
#Define constants
n = 0.045 #roughness of bed surface
def dans_grid1():
"""
Create a 5x5 test grid.
This is a sheet flow test.
"""
mg = RasterModelGrid((5, 5), spacing=(10., 10.))
this_dir = os.path.abspath(os.path.dirname(__file__))
infile = os.path.join(this_dir, "test_fr_input.txt")
z = mg.node_x.copy()
A_target = (np.array([
[0., 0., 0., 0., 0.],
[3., 3., 2., 1., 0.],
[3., 3., 2., 1., 0.],
[3., 3., 2., 1., 0.],
[0., 0., 0., 0., 0.],
]).flatten() * 100.)
frcvr_target = np.array([
[0, 1, 2, 3, 4],
[5, 5, 6, 7, 9],
[10, 10, 11, 12, 14],
[15, 15, 16, 17, 19],
[20, 21, 22, 23, 24],
]).flatten()
upids_target = np.array([
[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9],
[10, 11, 12, 13, 14],
[15, 16, 17, 18, 19],
[20, 21, 22, 23, 24],
]).flatten()
links2rcvr_target = np.full(25, XX)
links2rcvr_target[mg.core_nodes] = np.array(
[9, 10, 11, 18, 19, 20, 27, 28, 29])
Q_target = A_target * 2. # only once Q_in is used
steepest_target = np.array([
[0., 0., 0., 0., 0.],
[0., 1., 1., 1., 0.],
[0., 1., 1., 1., 0.],
[0., 1., 1., 1., 0.],
[0., 0., 0., 0., 0.],
]).flatten()
mg.add_field("node", "topographic__elevation", z, units="-")
class DansGrid(object):
pass
dans_grid = DansGrid()
dans_grid.mg = mg
dans_grid.z = z
dans_grid.infile = infile
dans_grid.A_target = A_target
dans_grid.frcvr_target = frcvr_target
dans_grid.upids_target = upids_target
dans_grid.Q_target = Q_target
dans_grid.steepest_target = steepest_target
dans_grid.links2rcvr_target = links2rcvr_target
return dans_grid
def internal_closed():
"""
Create a 6x5 test grid, but with two internal nodes closed.
This is a sheet flow test.
"""
mg = RasterModelGrid((6, 5), spacing=(10., 10.))
mg.set_closed_boundaries_at_grid_edges(True, True, False, True)
mg.status_at_node[7] = CLOSED_BOUNDARY
mg.status_at_node[16] = CLOSED_BOUNDARY
z = mg.node_x.copy()
Q_in = np.full(25, 2.)
A_target = (np.array([
[0., 0., 0., 0., 0.],
[1., 1., 0., 1., 0.],
[6., 6., 3., 1., 0.],
[0., 0., 2., 1., 0.],
[3., 3., 2., 1., 0.],
[0., 0., 0., 0., 0.],
]).flatten() * 100.)
frcvr_target = np.array([
[0, 1, 2, 3, 4],
[5, 5, 7, 12, 9],
[10, 10, 11, 12, 14],
[15, 16, 11, 17, 19],
[20, 20, 21, 22, 24],
[25, 26, 27, 28, 29],
]).flatten()
links2rcvr_target = np.full(mg.number_of_nodes, XX)
links2rcvr_target[mg.core_nodes] = np.array(
[9, 62, 18, 19, 20, 67, 29, 36, 37, 38])
steepest_target = np.array([
[0., 0., 0., 0., 0.],
[0., 1., 0., 0., 0.],
[0., 1., 1., 1., 0.],
[0., 0., 0., 1., 0.],
[0., 1., 1., 1., 0.],
[0., 0., 0., 0., 0.],
]).flatten()
steepest_target[np.array([8, 17])] = 1. / np.sqrt(2.)
mg.add_field("node", "topographic__elevation", z, units="-")
class DansGrid(object):
pass
dans_grid = DansGrid()
dans_grid.mg = mg
dans_grid.z = z
dans_grid.Q_in = Q_in
dans_grid.A_target = A_target
dans_grid.frcvr_target = frcvr_target
dans_grid.steepest_target = steepest_target
dans_grid.links2rcvr_target = links2rcvr_target
return dans_grid
def test_add_field_at_node():
"""Add field at nodes."""
grid = RasterModelGrid((4, 5))
grid.add_field('z', np.arange(20), at='node')
assert_array_equal(grid.at_node['z'], np.arange(20))
def dans_grid2():
"""
Create a 5x5 test grid.
This tests more complex routing, with diffs between D4 & D8.
"""
mg = RasterModelGrid((5, 5), spacing=(10., 10.))
this_dir = os.path.abspath(os.path.dirname(__file__))
infile = os.path.join(this_dir, "test_fr_input.txt")
z = np.array([
[7., 7., 7., 7., 7.],
[7., 5., 3.2, 6., 7.],
[7., 2., 3., 5., 7.],
[7., 1., 1.9, 4., 7.],
[7., 0., 7., 7., 7.],
]).flatten()
A_target_D8 = np.array([
[0., 0., 0., 0., 0.],
[0., 100., 200., 100., 0.],
[0., 400., 100., 100., 0.],
[0., 600., 300., 100., 0.],
[0., 900., 0., 0., 0.],
]).flatten()
A_target_D4 = np.array([
[0., 0., 0., 0., 0.],
[0., 100., 200., 100., 0.],
[0., 200., 400., 100., 0.],
[0., 900., 600., 100., 0.],
[0., 900., 0., 0., 0.],
]).flatten()
frcvr_target_D8 = np.array([
[0, 1, 2, 3, 4],
[5, 11, 11, 7, 9],
[10, 16, 16, 17, 14],
[15, 21, 21, 17, 19],
[20, 21, 22, 23, 24],
]).flatten()
frcvr_target_D4 = np.array([
[0, 1, 2, 3, 4],
[5, 11, 12, 7, 9],
[10, 16, 17, 12, 14],
[15, 21, 16, 17, 19],
[20, 21, 22, 23, 24],
]).flatten()
upids_target_D8 = np.array([
[0, 1, 2, 3, 4],
[5, 9, 10, 14, 15],
[19, 20, 21, 16, 11],
[6, 7, 8, 12, 17],
[13, 18, 22, 23, 24],
]).flatten()
upids_target_D4 = np.array([
[0, 1, 2, 3, 4],
[5, 9, 10, 14, 15],
[19, 20, 21, 16, 11],
[6, 17, 12, 7, 8],
[13, 18, 22, 23, 24],
]).flatten()
links2rcvr_target_D8 = np.full(25, XX)
links2rcvr_target_D8[mg.core_nodes] = np.array(
[14, 51, 11, 23, 59, 61, 32, 67, 29])
links2rcvr_target_D4 = np.full(25, XX)
links2rcvr_target_D4[mg.core_nodes] = np.array(
[14, 15, 11, 23, 24, 20, 32, 28, 29])
steepest_target_D8 = np.array([
[0., 0., 0., 0., 0.],
[0., 0.3, 0.08485281, 0.28, 0.],
[0., 0.1, 0.14142136, 0.21920310, 0.],
[0., 0.1, 0.13435029, 0.21, 0.],
[0., 0., 0., 0., 0.],
]).flatten()
steepest_target_D4 = np.array([
[0., 0., 0., 0., 0.],
[0., 0.3, 0.02, 0.28, 0.],
[0., 0.1, 0.11, 0.2, 0.],
[0., 0.1, 0.09, 0.21, 0.],
[0., 0., 0., 0., 0.],
]).flatten()
mg.add_field("node", "topographic__elevation", z, units="-")
class DansGrid(object):
pass
dans_grid = DansGrid()
dans_grid.mg = mg
dans_grid.z = z
dans_grid.infile = infile
dans_grid.A_target_D8 = A_target_D8
dans_grid.A_target_D4 = A_target_D4
dans_grid.frcvr_target_D8 = frcvr_target_D8
dans_grid.frcvr_target_D4 = frcvr_target_D4
dans_grid.upids_target_D8 = upids_target_D8
dans_grid.upids_target_D4 = upids_target_D4
dans_grid.steepest_target_D8 = steepest_target_D8
dans_grid.steepest_target_D4 = steepest_target_D4
dans_grid.links2rcvr_target_D8 = links2rcvr_target_D8
dans_grid.links2rcvr_target_D4 = links2rcvr_target_D4
return dans_grid
def test_add_ones_zeros_empty_to_at_grid():
"""Test different add methods for keyword at='grid'"""
grid = RasterModelGrid((4, 5))
assert_raises(ValueError, grid.add_zeros, 'value', at='grid')
assert_raises(ValueError, grid.add_empty, 'value', at='grid')
assert_raises(ValueError, grid.add_ones, 'value', at='grid')
import numpy as np
import time
import pylab
#get the needed properties to build the grid:
input_file = './craters_params_init.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')
nt = inputs.read_int('number_of_craters_per_loop')
loops = inputs.read_int('number_of_loops')
mg = RasterModelGrid(nrows, ncols, dx)
mg.set_looped_boundaries(True, True)
#create the fields in the grid
mg.create_node_array_zeros('topographic_elevation')
z = mg.create_node_array_zeros() + leftmost_elev
z += initial_slope * np.amax(mg.node_y) - initial_slope * mg.node_y
mg['node']['topographic_elevation'] = z #+ np.random.rand(len(z))/10000.
# Display a message
print('Running ...')
start_time = time.time()
#instantiate the component:
craters_component = impactor(mg, input_file)
def test_add_field_at_grid():
"""Test add field at grid."""
grid = RasterModelGrid((4, 5))
grid.at_grid['value']=1
assert_array_equal(1, grid.at_grid['value'].size)
def test_ones_zeros_empty_to_at_grid():
"""Test get array with field size methods for keyword at='grid'"""
grid = RasterModelGrid((4, 5))
assert_raises(ValueError, grid.zeros, at='grid')
assert_raises(ValueError, grid.empty, at='grid')
assert_raises(ValueError, grid.ones, at='grid')
def test_add_field_without_at():
"""Test raises error with wrong size array."""
grid = RasterModelGrid((4, 5))
assert_raises(ValueError, grid.add_field, 'z', np.arange(21))
def test_adding_field_at_grid_two_ways():
"""Test add field at grid two ways."""
grid = RasterModelGrid((4, 5))
grid.at_grid['value_1']=1
grid.add_field('value_2', 1, at='grid')
assert_array_equal(grid.at_grid['value_1'], grid.at_grid['value_2'])
