def test_symmetry_of_solution():
"""test that water table is symmetric under constant recharge
Notes:
----
Under constant recharge with radially symmetric aquifer base elevation,
the model should produce a water table that is radially symmetric. This test
demonstrates this is the case where topographic elevation and aquifer
base elevation are radially symmetric parabolas on a hexagonal grid.
"""
hmg = HexModelGrid(shape=(7, 4), spacing=10.0)
x = hmg.x_of_node
y = hmg.y_of_node
elev = hmg.add_zeros("topographic__elevation", at="node")
elev[:] = 1e-3 * (x * (max(x) - x) + y * (max(y) - y)) + 2
base = hmg.add_zeros("aquifer_base__elevation", at="node")
base[:] = elev - 2
wt = hmg.add_zeros("water_table__elevation", at="node")
wt[:] = elev
wt[hmg.open_boundary_nodes] = 0.0
gdp = GroundwaterDupuitPercolator(hmg,
recharge_rate=1e-7,
hydraulic_conductivity=1e-4)
for _ in range(1000):
gdp.run_one_step(1e3)
tc = hmg.at_node["aquifer__thickness"]
assert_almost_equal(tc[5], tc[31]) # SW-NE
assert_almost_equal(tc[29], tc[7]) # NW-SE
assert_almost_equal(tc[16], tc[20]) # W-E
def test_hex_grid():
mg = HexModelGrid((5, 5))
mg.add_zeros("soil__depth", at="node")
dt = 1000.0
expw3 = ExponentialWeathererIntegrated(
mg,
soil_production__maximum_rate=1.0,
soil_production__decay_depth=1.0,
soil_production__expansion_factor=1.3,
)
expw3.run_one_step(dt)
errors = []
# replace assertions by conditions
if not np.allclose(
mg.at_node["soil_production__dt_weathered_depth"][mg.core_nodes], 5.5161
):
errors.append("error in weathered depth")
if not np.allclose(
mg.at_node["soil_production__dt_produced_depth"][mg.core_nodes], 7.1709
):
errors.append("error in produced depth")
if not np.allclose(dt * mg.at_node["soil_production__rate"][mg.core_nodes], 1000.0):
errors.append("error in basic euler integration")
# assert no error message has been registered, else print messages
assert not errors, "errors occured:\n{}".format("\n".join(errors))
def test_with_lake_mapper_barnes():
hmg_hole = HexModelGrid((9, 5))
z = hmg_hole.add_field(
"topographic__elevation",
hmg_hole.x_of_node + np.round(hmg_hole.y_of_node),
at="node",
)
hole_nodes = [21, 22, 23, 30, 31, 39, 40]
z[hole_nodes] = z[hole_nodes] * 0.1
hmg_hole.add_zeros("filled__elevation", at="node")
fa = FlowAccumulator(
hmg_hole,
flow_director="Steepest",
depression_finder="LakeMapperBarnes",
fill_flat=False,
redirect_flow_steepest_descent=True,
reaccumulate_flow=True,
fill_surface="filled__elevation",
)
fa.run_one_step()
rcvrs = hmg_hole.at_node["flow__receiver_node"]
assert_array_equal(rcvrs[[13, 21, 46]], [6, 13, 39])
from landlab.components import LakeMapperBarnes
fa = FlowAccumulator(hmg_hole, depression_finder=LakeMapperBarnes)
lmb = LakeMapperBarnes(hmg_hole)
fa = FlowAccumulator(hmg_hole, depression_finder=lmb)
def test_outlet_lowering_object_bad_file():
"""Test using an outlet lowering object with a bad file."""
mg = HexModelGrid((5, 5))
mg.add_zeros("node", "topographic__elevation")
with pytest.raises(ValueError):
NotCoreNodeBaselevelHandler(mg, lowering_file_path="foo.txt")
def test_function_of_four_variables():
mg = HexModelGrid((5, 5))
mg.add_zeros("node", "topographic__elevation")
with pytest.raises(ValueError):
GenericFuncBaselevelHandler(mg,
function=lambda x, y, t, q:
(10 * x + 10 * y + 10 * t, +10 * q))
def test_neighbor_shaping_hex():
hmg = HexModelGrid((6, 5), spacing=1.0)
hmg.add_zeros("topographic__elevation", at="node", dtype=float)
_ = FlowAccumulator(hmg)
lmb = LakeMapperBarnes(hmg, redirect_flow_steepest_descent=True)
for arr in (lmb._neighbor_arrays, lmb._link_arrays):
assert len(arr) == 1
assert arr[0].shape == (hmg.number_of_nodes, 6)
assert len(lmb._neighbor_lengths) == hmg.number_of_links
def test_with_hex_grid():
grid = HexModelGrid((5, 5), node_layout="rect")
grid.add_zeros("topographic__elevation", at="node")
ListricKinematicExtender(grid)
ListricKinematicExtender(grid, fault_location=2.0)
grid = HexModelGrid((5, 5), node_layout="rect", orientation="vertical")
grid.add_zeros("topographic__elevation", at="node")
assert_raises(NotImplementedError, ListricKinematicExtender, grid)
def test_save_and_load_hex():
"""Test saving and loading of a HexModelGrid."""
mg1 = HexModelGrid(3, 3, 1.0)
mg1.add_zeros("node", "topographic__elevation")
save_grid(mg1, "testsavedgrid.grid")
mg2 = load_grid("testsavedgrid.grid")
assert mg1.x_of_node[0] == mg2.x_of_node[0]
assert_array_equal(mg1.status_at_node, mg2.status_at_node)
for name in mg1.at_node:
assert_array_equal(mg1.at_node[name], mg2.at_node[name])
def test_function_that_returns_wrong_size():
mg = HexModelGrid((5, 5))
mg.add_zeros("node", "topographic__elevation")
with pytest.raises(ValueError):
GenericFuncBaselevelHandler(
mg,
function=lambda mg, t: np.mean(10 * mg.x_of_node + 10 * mg.
y_of_node + 10 * t),
)
def test_save_and_load_hex():
"""Test saving and loading of a HexModelGrid."""
mg1 = HexModelGrid(3, 3, 1.0)
mg1.add_zeros("node", "topographic__elevation")
save_grid(mg1, "testsavedgrid.grid")
mg2 = load_grid("testsavedgrid.grid")
assert mg1.x_of_node[0] == mg2.x_of_node[0]
assert_array_equal(mg1.status_at_node, mg2.status_at_node)
for name in mg1.at_node:
assert_array_equal(mg1.at_node[name], mg2.at_node[name])
def test_stream_power_save_output_hex(tmpdir):
mg = HexModelGrid((3, 3), node_layout="rect", spacing=10.0)
mg.status_at_node[mg.status_at_node == 1] = 4
mg.status_at_node[1] = 1
mg.add_ones("node", "topographic__elevation")
mg.add_zeros("node", "aquifer_base__elevation")
mg.add_ones("node", "water_table__elevation")
gdp = GroundwaterDupuitPercolator(mg, recharge_rate=1e-4)
hm = HydrologySteadyStreamPower(mg,
groundwater_model=gdp,
routing_method="Steepest")
sp = FastscapeEroder(
mg,
K_sp=1e-10,
m_sp=1,
n_sp=1,
discharge_field="surface_water_area_norm__discharge",
)
ld = LinearDiffuser(mg, linear_diffusivity=1e-10)
rm = RegolithConstantThickness(mg, uplift_rate=0.0)
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"] = tmpdir.strpath + "/"
output["run_id"] = 0 # make this task_id if multiple runs
mdl = StreamPowerModel(
mg,
hydrology_model=hm,
diffusion_model=ld,
erosion_model=sp,
regolith_model=rm,
total_morphological_time=1e8,
output_dict=output,
)
mdl.run_model()
file = tmpdir.join("0_grid_0.nc")
mg1 = from_netcdf(file.strpath)
keys = [
"topographic__elevation",
"aquifer_base__elevation",
"water_table__elevation",
]
assert isinstance(mg1, HexModelGrid)
assert set(mg1.at_node.keys()) == set(keys)
assert_equal(mg1.status_at_node, mg.status_at_node)
def test_outlet_lowering_modify_other_nodes():
mg = HexModelGrid(5, 5)
mg.add_zeros("node", "topographic__elevation")
node_id = 27
file = os.path.join(_TEST_DATA_DIR, "outlet_history.txt")
with pytest.raises(ValueError):
SingleNodeBaselevelHandler(
mg,
outlet_id=node_id,
lowering_file_path=file,
modify_outlet_id=False,
)
def test_hex():
"""Test using a hex grid."""
mg = HexModelGrid((5, 5))
mg.add_zeros("node", "topographic__elevation")
bh = CaptureNodeBaselevelHandler(
mg,
capture_node=3,
capture_incision_rate=-3.0,
capture_start_time=10,
capture_stop_time=20,
post_capture_incision_rate=-0.1,
)
bh.run_one_step(10)
def test_non_raster():
"""Test a hex model grid."""
grid = HexModelGrid(7, 3, dx=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': 30.0,
'x1': 30.0,
'y2': 20.0,
'x2': 0.0},
'include_boundaries': True}
nf = NormalFault(grid, **param_dict)
# plotting, to test this. it works!
#import matplotlib.pyplot as plt
#plt.figure()
#imshow_grid(grid, nf.faulted_nodes, color_for_background='y')
#plt.plot(grid.x_of_node, grid.y_of_node, 'c.')
#plt.plot([param_dict['fault_trace']['x1'], param_dict['fault_trace']['x2']],
# [param_dict['fault_trace']['y1'], param_dict['fault_trace']['y2']], 'r')
#plt.show()
out = np.array([ True, True, True, True, True, True, True, False, True,
True, True, True, False, False, False, False, True, True,
False, False, False, False, False, False, False, False, False,
False, False, False], dtype=bool)
assert_array_equal(nf.faulted_nodes, out)
def test_initial_state_grid():
"""Test passing in an initial array of node states."""
nr = 4
nc = 3
hg = HexModelGrid(shape=(nr, nc),
node_layout='rect',
orientation='vertical')
ins = hg.add_zeros('node', 'node_state', dtype=np.int)
ins[hg.y_of_node < 2] = 8
params = {
'grid_size': (nr, nc),
'report_interval': 5.0,
'output_interval': 1.0e99,
'disturbance_rate': 0.0,
'weathering_rate': 0.0,
'dissolution_rate': 1.0,
'run_duration': 3.0e3,
'plot_interval': 1.0e99,
'uplift_interval': 1.0e7,
'friction_coef': 1.0,
'fault_x': -0.001,
'cell_width': 1.0,
'grav_accel': 9.8,
'plot_file_name': 'test_column_dissolution',
'init_state_grid': ins,
'seed': 0,
}
gfs = GrainFacetSimulator(**params)
assert_equal(np.count_nonzero(gfs.ca.node_state), 6)
def test_non_raster():
"""Test a hex model grid."""
grid = HexModelGrid(7, 3, dx=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': 30.0,
'x1': 30.0,
'y2': 20.0,
'x2': 0.0},
'include_boundaries': True}
nf = NormalFault(grid, **param_dict)
# plotting, to test this. it works!
#import matplotlib.pyplot as plt
#plt.figure()
#imshow_grid(grid, nf.faulted_nodes, color_for_background='y')
#plt.plot(grid.x_of_node, grid.y_of_node, 'c.')
#plt.plot([param_dict['fault_trace']['x1'], param_dict['fault_trace']['x2']],
# [param_dict['fault_trace']['y1'], param_dict['fault_trace']['y2']], 'r')
#plt.show()
out = np.array([ True, True, True, True, True, True, True, False, True,
True, True, True, False, False, False, False, True, True,
False, False, False, False, False, False, False, False, False,
False, False, False], dtype=bool)
assert_array_equal(nf.faulted_nodes, out)
def test_can_run_with_hex():
"""Test that model can run with hex model grid."""
# Set up a 5x5 grid with open boundaries and low initial elevations.
mg = HexModelGrid(7, 7)
z = mg.add_zeros('node', 'topographic__elevation')
z[:] = 0.01 * mg.x_of_node
# Create a D8 flow handler
fa = FlowAccumulator(mg, flow_director='FlowDirectorSteepest')
# Parameter values for test 1
U = 0.001
dt = 10.0
# Create the Space component...
sp = Space(mg,
K_sed=0.00001,
K_br=0.00000000001,
F_f=0.5,
phi=0.1,
H_star=1.,
v_s=0.001,
m_sp=0.5,
n_sp=1.0,
sp_crit_sed=0,
sp_crit_br=0)
# ... and run it to steady state.
for i in range(2000):
fa.run_one_step()
sp.run_one_step(dt=dt)
z[mg.core_nodes] += U * dt
def test_can_run_with_hex():
"""Test that model can run with hex model grid."""
# Set up a 5x5 grid with open boundaries and low initial elevations.
mg = HexModelGrid(7, 7)
z = mg.add_zeros("node", "topographic__elevation")
z[:] = 0.01 * mg.x_of_node
# Create a D8 flow handler
fa = FlowAccumulator(mg, flow_director="FlowDirectorSteepest")
# Parameter values for test 1
U = 0.001
dt = 10.0
# Create the Space component...
sp = Space(
mg,
K_sed=0.00001,
K_br=0.00000000001,
F_f=0.5,
phi=0.1,
H_star=1.,
v_s=0.001,
m_sp=0.5,
n_sp=1.0,
sp_crit_sed=0,
sp_crit_br=0,
)
# ... and run it to steady state.
for i in range(2000):
fa.run_one_step()
sp.run_one_step(dt=dt)
z[mg.core_nodes] += U * dt
class HexLatticeTectonicizer(object):
"""Base class from which classes to represent particular baselevel/fault
geometries are derived.
"""
def __init__(self, grid=None, node_state=None):
# If needed, create grid
if grid is None:
num_rows = _DEFAULT_NUM_ROWS
num_cols = _DEFAULT_NUM_COLS
self.grid = HexModelGrid(num_rows,
num_cols,
dx=1.0,
orientation='vertical',
shape='rect',
reorient_links=True)
else:
# Make sure caller passed the right type of grid
assert (grid.orientation=='vertical'), \
'Grid must have vertical orientation'
# Keep a reference to the grid
self.grid = grid
# If needed, create node-state grid
if node_state is None:
self.node_state = self.grid.add_zeros('node', 'node_state_map')
else:
print 'setting node state'
self.node_state = node_state
# Remember the # of rows and cols
self.nr = self.grid.number_of_node_rows
self.nc = self.grid.number_of_node_columns
class HexLatticeTectonicizer(object):
"""Base class from which classes to represent particular baselevel/fault
geometries are derived.
"""
def __init__(self, grid=None, node_state=None):
# If needed, create grid
if grid is None:
num_rows = _DEFAULT_NUM_ROWS
num_cols = _DEFAULT_NUM_COLS
self.grid = HexModelGrid(num_rows, num_cols, dx=1.0,
orientation='vertical',
shape='rect', reorient_links=True)
else:
# Make sure caller passed the right type of grid
assert (grid.orientation == 'vertical'), \
'Grid must have vertical orientation'
# Keep a reference to the grid
self.grid = grid
# If needed, create node-state grid
if node_state is None:
self.node_state = self.grid.add_zeros('node', 'node_state_map')
else:
self.node_state = node_state
# Remember the # of rows and cols
self.nr = self.grid.number_of_node_rows
self.nc = self.grid.number_of_node_columns
def test_functions_with_Hex():
mg = HexModelGrid(10, 10)
z = mg.add_zeros("node", "topographic__elevation")
z += mg.x_of_node + mg.y_of_node
fa = FlowAccumulator(mg)
fa.run_one_step()
ch = ChiFinder(mg, min_drainage_area=1.0, reference_concavity=1.0)
ch.calculate_chi()
def test_neighbor_shaping_hex():
hmg = HexModelGrid(6, 5, dx=1.)
hmg.add_zeros("node", "topographic__elevation", dtype=float)
hmg.add_zeros("node", "topographic__steepest_slope", dtype=float)
hmg.add_zeros("node", "flow__receiver_node", dtype=int)
hmg.add_zeros("node", "flow__link_to_receiver_node", dtype=int)
lmb = LakeMapperBarnes(hmg, redirect_flow_steepest_descent=True)
for arr in (lmb._neighbor_arrays, lmb._link_arrays):
assert len(arr) == 1
assert arr[0].shape == (hmg.number_of_nodes, 6)
assert len(lmb._neighbor_lengths) == hmg.number_of_links
def test_neighbor_shaping_hex():
hmg = HexModelGrid(6, 5, dx=1.)
hmg.add_zeros("node", "topographic__elevation", dtype=float)
hmg.add_zeros("node", "topographic__steepest_slope", dtype=float)
hmg.add_zeros("node", "flow__receiver_node", dtype=int)
hmg.add_zeros("node", "flow__link_to_receiver_node", dtype=int)
lmb = LakeMapperBarnes(hmg, redirect_flow_steepest_descent=True)
for arr in (lmb._neighbor_arrays, lmb._link_arrays):
assert len(arr) == 1
assert arr[0].shape == (hmg.number_of_nodes, 6)
assert len(lmb._neighbor_lengths) == hmg.number_of_links
def test_transitions_as_ids():
"""Test passing from-state and to-state IDs instead of tuples """
mg = HexModelGrid(3, 2, 1.0, orientation="vertical", reorient_links=True)
nsd = {0: "zero", 1: "one"}
xnlist = []
xnlist.append(Transition(2, 3, 1.0, "transitioning"))
nsg = mg.add_zeros("node", "node_state_grid")
cts = HexCTS(mg, nsd, xnlist, nsg)
assert cts.num_link_states == 4, "wrong number of transitions"
def test_write_hex_to_path(tmpdir, fname):
grid = HexModelGrid((3, 2), spacing=2.0)
z = grid.add_zeros("topographic__elevation", at="node")
z[3] = 1.0
with tmpdir.as_cwd():
actual = write_obj(fname, grid)
assert actual == fname
with open(fname, "r") as fp:
assert fp.read() == LITTLE_HEX_OBJ
def test_transitions_as_ids():
"""Test passing from-state and to-state IDs instead of tuples """
mg = HexModelGrid(3, 2, 1.0, orientation="vertical", reorient_links=True)
nsd = {0: "zero", 1: "one"}
xnlist = []
xnlist.append(Transition(2, 3, 1.0, "transitioning"))
nsg = mg.add_zeros("node", "node_state_grid")
cts = HexCTS(mg, nsd, xnlist, nsg)
assert cts.num_link_states == 4, "wrong number of transitions"
def test_oriented_hex_cts():
"""Tests instantiation of an OrientedHexCTS() object"""
mg = HexModelGrid(3, 2, 1.0, orientation="vertical", reorient_links=True)
nsd = {0: "zero", 1: "one"}
xnlist = []
xnlist.append(Transition((0, 1, 0), (1, 1, 0), 1.0, "transitioning"))
nsg = mg.add_zeros("node", "node_state_grid")
ohcts = OrientedHexCTS(mg, nsd, xnlist, nsg)
assert_equal(ohcts.num_link_states, 12)
assert_array_equal(ohcts.link_orientation, [2, 1, 0, 0, 0, 2, 1, 0, 2, 1, 0])
def test_hex():
"""Test using a hex grid."""
mg = HexModelGrid(5, 5)
z = mg.add_zeros("node", "topographic__elevation")
bh = SingleNodeBaselevelHandler(mg, outlet_id=0, lowering_rate=-0.1)
bh.run_one_step(10.0)
assert z[1] == 0.0
assert z[0] == -1.0
def test_hex_cts():
"""Tests instantiation of a HexCTS() object"""
mg = HexModelGrid(3, 2, 1.0, orientation='vertical', reorient_links=True)
nsd = {0 : 'zero', 1 : 'one'}
xnlist = []
xnlist.append(Transition((0,1,0), (1,1,0), 1.0, 'transitioning'))
nsg = mg.add_zeros('node', 'node_state_grid')
hcts = HexCTS(mg, nsd, xnlist, nsg)
assert_equal(hcts.num_link_states, 4)
assert_array_equal(hcts.link_orientation, [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
def test_hex_cts():
"""Tests instantiation of a HexCTS() object"""
mg = HexModelGrid(3, 2, 1.0, orientation="vertical", reorient_links=True)
nsd = {0: "zero", 1: "one"}
xnlist = []
xnlist.append(Transition((0, 1, 0), (1, 1, 0), 1.0, "transitioning"))
nsg = mg.add_zeros("node", "node_state_grid")
hcts = HexCTS(mg, nsd, xnlist, nsg)
assert hcts.num_link_states == 4
assert_array_equal(hcts.link_orientation, [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
def test_stoch_sp_threshold_hex():
"""
Initialize HydrologyEventStreamPower on a hex grid.
Use single storm-interstorm pair and make sure it returns the
quantity calculated. This is not an analytical solution, just
the value that is returned when using gdp and adaptive
timestep solver. Confirms that hex grid returns the same value
as raster grid, adjusted for cell area. Confirms that when streampower
threshold is zero (Default), returns the same values as
HydrologyEventStreamPower.
"""
mg = HexModelGrid((3, 3), node_layout="rect", spacing=10.0)
mg.status_at_node[mg.status_at_node == 1] = 4
mg.status_at_node[0] = 1
mg.add_ones("node", "topographic__elevation")
mg.add_zeros("node", "aquifer_base__elevation")
mg.add_ones("node", "water_table__elevation")
gdp = GroundwaterDupuitPercolator(mg, recharge_rate=1e-6)
pd = PrecipitationDistribution(
mg,
mean_storm_duration=10,
mean_interstorm_duration=100,
mean_storm_depth=1e-3,
total_t=100,
)
pd.seed_generator(seedval=1)
hm = HydrologyEventThresholdStreamPower(mg,
precip_generator=pd,
groundwater_model=gdp,
routing_method="Steepest")
hm.run_step()
assert_almost_equal(hm.q_eff[4], 0.00017614 * np.sqrt(3) / 2)
assert_almost_equal(
hm.q_an[4],
0.00017614 * np.sqrt(3) / 2 / np.sqrt(np.sqrt(3) / 2 * 100))
def test_handle_grid_mismatch():
"""Test error handling when user passes wrong grid type."""
mg = HexModelGrid(3, 2, 1.0, orientation="vertical", reorient_links=True)
nsd = {0: "zero", 1: "one"}
xnlist = []
xnlist.append(Transition(2, 3, 1.0, "transitioning"))
nsg = mg.add_zeros("node", "node_state_grid")
assert_raises(TypeError, RasterCTS, mg, nsd, xnlist, nsg)
assert_raises(TypeError, OrientedRasterCTS, mg, nsd, xnlist, nsg)
mg = RasterModelGrid((3, 3))
assert_raises(TypeError, HexCTS, mg, nsd, xnlist, nsg)
assert_raises(TypeError, OrientedHexCTS, mg, nsd, xnlist, nsg)
def test_handle_grid_mismatch():
"""Test error handling when user passes wrong grid type."""
mg = HexModelGrid(3, 2, 1.0, orientation="vertical", reorient_links=True)
nsd = {0: "zero", 1: "one"}
xnlist = []
xnlist.append(Transition(2, 3, 1.0, "transitioning"))
nsg = mg.add_zeros("node", "node_state_grid")
assert_raises(TypeError, RasterCTS, mg, nsd, xnlist, nsg)
assert_raises(TypeError, OrientedRasterCTS, mg, nsd, xnlist, nsg)
mg = RasterModelGrid((3, 3))
assert_raises(TypeError, HexCTS, mg, nsd, xnlist, nsg)
assert_raises(TypeError, OrientedHexCTS, mg, nsd, xnlist, nsg)
def test_steady_sp_hex():
"""
Initialize HydrologySteadyStreamPower on a hex grid.
After one timestep it returns all recharge as discharge.
"""
mg = HexModelGrid((3, 3), node_layout="rect", spacing=10.0)
mg.status_at_node[mg.status_at_node == 1] = 4
mg.status_at_node[0] = 1
mg.add_ones("node", "topographic__elevation")
mg.add_zeros("node", "aquifer_base__elevation")
mg.add_ones("node", "water_table__elevation")
gdp = GroundwaterDupuitPercolator(mg, recharge_rate=1e-6)
hm = HydrologySteadyStreamPower(mg,
groundwater_model=gdp,
routing_method="Steepest")
hm.run_step()
assert_almost_equal(hm.q[4], (np.sqrt(3) / 2) * 10**(-4))
assert_almost_equal(hm.q_an[4], np.sqrt((np.sqrt(3) / 2)) * 10**(-5))
def test_outlet_lowering_object_no_scaling():
"""Test using an outlet lowering object with no scaling."""
mg = HexModelGrid(5, 5)
z = mg.add_zeros("node", "topographic__elevation")
node_id = 27
file = os.path.join(_TEST_DATA_DIR, "outlet_history.txt")
bh = SingleNodeBaselevelHandler(
mg, outlet_id=node_id, lowering_file_path=file
)
for _ in range(241):
bh.run_one_step(10)
assert z[1] == 0.0
assert bh.z[node_id] == -47.5
def test_outlet_lowering_rate_no_scaling_bedrock():
"""Test using an rate lowering object with no scaling and bedrock."""
mg = HexModelGrid(5, 5)
z = mg.add_ones("node", "topographic__elevation")
b = mg.add_zeros("node", "bedrock__elevation")
node_id = 27
bh = SingleNodeBaselevelHandler(mg, outlet_id=node_id, lowering_rate=-0.1)
for _ in range(240):
bh.run_one_step(10)
assert z[1] == 1.0
assert b[1] == 0.0
assert z[node_id] == -239.0
assert b[node_id] == -240.0
def test_with_hex_grid():
"""Test mass balance with a hex grid.
The test here is based on a simple mass balance: the computed velocity at
the open boundaries, when multiplied by the depth and the width of all open
cell faces, should equal the total inflow or outflow rate, which is the
inundation rate times the total area.
The test configuration is a hex grid with 5 rows and a maximum width of 5
columns. The bottom 3 nodes are open (fixed value) boundaries; the rest are
closed. The tidal range is 1 meter, the mean depth is 5 meters, and the
tidal period is 40,000 seconds. Node spacing will be 2 meters.
Inundation rate = I = tidal range / tidal half period
= 1 / 20,000 = 5 x 10^-5 m/s
Area of one cell = (3^0.5 / 2) dx^2 ~ 3.4641
Width of one face = dx / 3^0.5
Inundation volume rate = I x cell area x 7 cells
= ~0.0012124 m3/s
Outflow volume = velocity at one the edge of any one of the lower active
links x (solved) depth at that link x width of face x 4 faces. Call the
velocity-depth product q. Then the predicted q should be:
q = inundation volume rate / (face width x 4 faces)
= (7 I (3^0.5 / 2) dx^2) / (4 dx / 3^0.5)
= (21/8) I dx = (21/4) r dx / T = 0.0002625
"""
grid = HexModelGrid((5, 3), spacing=2.0)
z = grid.add_zeros("topographic__elevation", at="node")
z[:] = -5.0
# Close all boundary nodes except the bottom row
grid.status_at_node[
grid.status_at_node != grid.BC_NODE_IS_CORE
] = grid.BC_NODE_IS_CLOSED
grid.status_at_node[0:3] = grid.BC_NODE_IS_FIXED_VALUE
tfc = TidalFlowCalculator(grid, tidal_period=4.0e4)
tfc.run_one_step()
q = grid.at_link["flood_tide_flow__velocity"] * tfc._water_depth_at_links
assert_array_almost_equal(q[3:7], [0.0002625, 0.0002625, 0.0002625, 0.0002625])
def test_can_run_with_hex():
"""Test that model can run with hex model grid."""
# Set up a 5x5 grid with open boundaries and low initial elevations.
mg = HexModelGrid(7, 7)
z = mg.add_zeros('node', 'topographic__elevation')
z[:] = 0.01 * mg.x_of_node
# Create a D8 flow handler
fa = FlowAccumulator(mg, flow_director='FlowDirectorSteepest')
# Parameter values for test 1
K = 0.001
vs = 0.0001
U = 0.001
dt = 10.0
# Create the ErosionDeposition component...
ed = ErosionDeposition(mg,
K=K,
phi=0.0,
v_s=vs,
m_sp=0.5,
n_sp=1.0,
method='simple_stream_power',
discharge_method='drainage_area',
area_field='drainage_area',
solver='adaptive')
# ... and run it to steady state.
for i in range(2000):
fa.run_one_step()
ed.run_one_step(dt=dt)
z[mg.core_nodes] += U * dt
# Test the results
s = mg.at_node['topographic__steepest_slope']
sa_factor = (1.0 + vs) * U / K
a18 = mg.at_node['drainage_area'][18]
a28 = mg.at_node['drainage_area'][28]
s = mg.at_node['topographic__steepest_slope']
s18 = sa_factor * (a18**-0.5)
s28 = sa_factor * (a28**-0.5)
assert_equal(np.round(s[18], 3), np.round(s18, 3))
assert_equal(np.round(s[28], 3), np.round(s28, 3))
def test_shift_link_and_transition_data_upward():
"""Test the LatticeUplifter method that uplifts link data and tr'ns."""
mg = HexModelGrid(4, 3, 1.0, orientation="vertical", shape="rect")
nsd = {0: "yes", 1: "no"}
xnlist = []
xnlist.append(Transition((0, 0, 0), (1, 1, 0), 1.0, "frogging"))
xnlist.append(Transition((0, 0, 1), (1, 1, 1), 1.0, "frogging"))
xnlist.append(Transition((0, 0, 2), (1, 1, 2), 1.0, "frogging"))
nsg = mg.add_zeros("node", "node_state_grid")
ohcts = OrientedHexCTS(mg, nsd, xnlist, nsg)
assert_array_equal(
ohcts.link_state[mg.active_links], [0, 4, 8, 8, 4, 0, 4, 8, 8, 4, 0]
)
assert_array_equal(
ohcts.next_trn_id[mg.active_links], [0, 1, 2, 2, 1, 0, 1, 2, 2, 1, 0]
)
assert_array_equal(
np.round(ohcts.next_update[mg.active_links], 2),
[0.8, 1.26, 0.92, 0.79, 0.55, 1.04, 0.58, 2.22, 3.31, 0.48, 1.57],
)
pq = ohcts.priority_queue
assert_equal(pq._queue[0][2], 20) # link for first event = 20, not shifted
assert_equal(round(pq._queue[0][0], 2), 0.48) # trn scheduled for t = 0.48
assert_equal(pq._queue[2][2], 15) # this event scheduled for link 15...
assert_equal(round(pq._queue[2][0], 2), 0.58) # ...trn sched for t = 0.58
lu = LatticeUplifter(grid=mg)
lu.shift_link_and_transition_data_upward(ohcts, 0.0)
# note new events lowest 5 links
assert_array_equal(
np.round(ohcts.next_update[mg.active_links], 2),
[0.75, 0.84, 2.6, 0.07, 0.09, 0.8, 0.02, 1.79, 1.51, 2.04, 3.85],
)
assert_equal(pq._queue[0][2], 15) # new soonest event
assert_equal(pq._queue[9][2], 14) # was previously 7, now shifted up...
assert_equal(round(pq._queue[9][0], 2), 0.8) # ...still sched for t = 0.80
def test_can_run_with_hex():
"""Test that model can run with hex model grid."""
# Set up a 5x5 grid with open boundaries and low initial elevations.
mg = HexModelGrid(7, 7)
z = mg.add_zeros('node', 'topographic__elevation')
z[:] = 0.01 * mg.x_of_node
# Create a D8 flow handler
fa = FlowAccumulator(mg, flow_director='FlowDirectorSteepest')
# Parameter values for test 1
K = 0.001
vs = 0.0001
U = 0.001
dt = 10.0
# Create the ErosionDeposition component...
ed = ErosionDeposition(mg, K=K, phi=0.0, v_s=vs, m_sp=0.5, n_sp=1.0,
method='simple_stream_power',
discharge_method='drainage_area',
area_field='drainage_area',
solver='adaptive')
# ... and run it to steady state.
for i in range(2000):
fa.run_one_step()
ed.run_one_step(dt=dt)
z[mg.core_nodes] += U * dt
# Test the results
s = mg.at_node['topographic__steepest_slope']
sa_factor = (1.0 + vs) * U / K
a18 = mg.at_node['drainage_area'][18]
a28 = mg.at_node['drainage_area'][28]
s = mg.at_node['topographic__steepest_slope']
s18 = sa_factor * (a18 ** -0.5)
s28 = sa_factor * (a28 ** -0.5)
assert_equal(np.round(s[18], 3), np.round(s18, 3))
assert_equal(np.round(s[28], 3), np.round(s28, 3))
def test_non_raster():
"""Test a hex model grid."""
grid = HexModelGrid(7, 3, dx=10, xy_of_lower_left=(-15.0, 0.0))
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": 30.0, "x1": 30.0, "y2": 20.0, "x2": 0.0},
"include_boundaries": True,
}
nf = NormalFault(grid, **param_dict)
# plotting, to test this. it works!
# import matplotlib.pyplot as plt
# plt.figure()
# imshow_grid(grid, nf.faulted_nodes, color_for_background='y')
# plt.plot(grid.x_of_node, grid.y_of_node, 'c.')
# plt.plot([param_dict['fault_trace']['x1'], param_dict['fault_trace']['x2']],
# [param_dict['fault_trace']['y1'], param_dict['fault_trace']['y2']], 'r')
# plt.show()
out = np.array(
[
True,
True,
True,
True,
True,
True,
True,
False,
True,
True,
True,
True,
False,
False,
False,
False,
True,
True,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
False,
],
dtype=bool,
)
assert_array_equal(nf.faulted_nodes, out)
def main():
# INITIALIZE
# User-defined parameters
nr = 80 # number of rows in grid
nc = 50 # number of columns in grid
plot_interval = 0.5 # time interval for plotting, sec
run_duration = 20.0 # 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 = HexModelGrid(nr, nc, 1.0)
# Make the boundaries be walls
# mg.set_closed_boundaries_at_grid_edges(True, True, True, True)<--I am not sure what the equivalent is for hexgrid
#Create a node-state dictionary
ns_dict = { 0 : 'fluid', 1 : 'particle' }
#Create the transition list
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.
bottom_rows = where(mg.node_y<0.1*nr)[0]
node_state_grid[bottom_rows] = 1
# For visual display purposes, set all boundary nodes to fluid
node_state_grid[mg.closed_boundary_nodes] = 0
# Create the CA model
ca = OrientedHexCTS(mg, ns_dict, xn_list, node_state_grid)
# Set up colors for plotting
grain = '#5F594D'
fluid = '#D0E4F2'
clist = [fluid,grain]
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()
# RUN
current_time = 0.0
while current_time < run_duration:
# Once in a while, print out simulation 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 simulation time '+str(current_time)+' \
('+str(int(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()
ca_plotter.finalize()
def main():
# INITIALIZE
# User-defined parameters
nr = 41
nc = 61
g = 0.8
f = 1.0
silo_y0 = 30.0
silo_opening_half_width = 6
plot_interval = 1.0
run_duration = 80.0
report_interval = 5.0 # report interval, in real-time seconds
p_init = 0.4 # probability that a cell is occupied at start
plot_every_transition = False
# 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 a grid
hmg = HexModelGrid(nr, nc, 1.0, orientation='vertical', reorient_links=True)
# Set up the states and pair transitions.
# Transition data here represent particles moving on a lattice: one state
# per direction (for 6 directions), plus an empty state, a stationary
# state, and a wall state.
ns_dict = { 0 : 'empty',
1 : 'moving up',
2 : 'moving right and up',
3 : 'moving right and down',
4 : 'moving down',
5 : 'moving left and down',
6 : 'moving left and up',
7 : 'rest',
8 : 'wall'}
xn_list = setup_transition_list(g, f)
# Create data and initialize values.
node_state_grid = hmg.add_zeros('node', 'node_state_grid')
# Make the grid boundary all wall particles
node_state_grid[hmg.boundary_nodes] = 8
# Place wall particles to form the base of the silo, initially closed
tan30deg = numpy.tan(numpy.pi/6.)
rampy1 = silo_y0-hmg.node_x*tan30deg
rampy2 = silo_y0-((nc*0.866-1.)-hmg.node_x)*tan30deg
rampy = numpy.maximum(rampy1, rampy2)
(ramp_nodes, ) = numpy.where(numpy.logical_and(hmg.node_y>rampy-0.5, \
hmg.node_y<rampy+0.5))
node_state_grid[ramp_nodes] = 8
# Seed the grid interior with randomly oriented particles
for i in hmg.core_nodes:
if hmg.node_y[i]>rampy[i] and random.random()<p_init:
node_state_grid[i] = random.randint(1, 7)
# Create the CA model
ca = OrientedHexLCA(hmg, 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
# Run with closed silo
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=plot_every_transition, plotter=ca_plotter)
current_time += plot_interval
# Plot the current grid
ca_plotter.update_plot()
# Open the silo
xmid = nc*0.866*0.5
for i in range(hmg.number_of_nodes):
if node_state_grid[i]==8 and hmg.node_x[i]>(xmid-silo_opening_half_width) \
and hmg.node_x[i]<(xmid+silo_opening_half_width) \
and hmg.node_y[i]>0:
node_state_grid[i]=0
# Create the CA model
ca = OrientedHexLCA(hmg, 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()
# Re-run with open silo
current_time = 0.0
while current_time < 5*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=plot_every_transition, plotter=ca_plotter)
current_time += plot_interval
# Plot the current grid
ca_plotter.update_plot()
# FINALIZE
# Plot
ca_plotter.finalize()
Created on Sun Nov 16 09:25:04 2014
@author: gtucker
"""
from landlab import HexModelGrid
from numpy import arange
from pylab import figure, show, title
# Case 1: Make and display a hex-shaped grid with horizontal rows of nodes
# Create the grid
hg1 = HexModelGrid(5, 3, 1.0, orientation='horizontal', shape='hex')
# Make some data
d = hg1.add_zeros('node', 'mydata')
d[:] = arange(hg1.number_of_nodes)
# Display the grid
figure(1)
hg1.hexplot(d)
title('hexagon shape, horizontal orientation')
show()
# Case 2: Make and display a hex-shaped grid with vertical columns of nodes
# Create the grid
hg2 = HexModelGrid(3, 5, 1.0, orientation='vertical', shape='hex')
# Make some data
def main():
# INITIALIZE
# User-defined parameters
nr = 52
nc = 120
plot_interval = 1.0
run_duration = 100.0
report_interval = 5.0 # report interval, in real-time seconds
p_init = 0.1 # probability that a cell is occupied at start
plot_every_transition = False
# 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 a grid
hmg = HexModelGrid(nr, nc, 1.0, orientation='vertical', reorient_links=True)
# Close the grid boundaries
#hmg.set_closed_nodes(hmg.open_boundary_nodes)
# Set up the states and pair transitions.
# Transition data here represent particles moving on a lattice: one state
# per direction (for 6 directions), plus an empty state, a stationary
# state, and a wall state.
ns_dict = { 0 : 'empty',
1 : 'moving up',
2 : 'moving right and up',
3 : 'moving right and down',
4 : 'moving down',
5 : 'moving left and down',
6 : 'moving left and up',
7 : 'rest',
8 : 'wall'}
xn_list = setup_transition_list()
# Create data and initialize values.
node_state_grid = hmg.add_zeros('node', 'node_state_grid', dtype=int)
# Make the grid boundary all wall particles
node_state_grid[hmg.boundary_nodes] = 8
# Seed the grid interior with randomly oriented particles
for i in hmg.core_nodes:
if random.random()<p_init:
node_state_grid[i] = random.randint(1, 7)
# Create the CA model
ca = OrientedHexCTS(hmg, ns_dict, xn_list, node_state_grid)
# Set up a color map for plotting
import matplotlib
clist = [ (1.0, 1.0, 1.0), # empty = white
(1.0, 0.0, 0.0), # up = red
(1.0, 1.0, 0.0), # right-up = yellow
(0.0, 1.0, 0.0), # down-up = green
(0.0, 1.0, 1.0), # down = cyan
(0.0, 0.0, 1.0), # left-down = blue
(1.0, 0.0, 1.0), # left-up = magenta
(0.5, 0.5, 0.5), # resting = gray
(0.0, 0.0, 0.0) ] # wall = black
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()
# Create an array to store the numbers of states at each plot interval
nstates = zeros((9, int(run_duration/plot_interval)))
k = 0
# 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=plot_every_transition, plotter=ca_plotter)
current_time += plot_interval
# Plot the current grid
ca_plotter.update_plot()
axis('off')
# Record numbers in each state
nstates[:,k] = bincount(node_state_grid)
k += 1
# FINALIZE
# Plot
ca_plotter.finalize()
# Display the numbers of each state
fig, ax = subplots()
for i in range(1, 8):
plot(arange(plot_interval, run_duration+plot_interval, plot_interval), nstates[i,:], label=ns_dict[i], color=clist[i])
ax.legend()
xlabel('Time')
ylabel('Number of particles in state')
title('Particle distribution by state')
axis([0, run_duration, 0, 2*nstates[7,0]])
show()
class CTSModel(object):
"""
Implement a generic CellLab-CTS model.
This is the base class from which models should inherit.
"""
def __init__(self, grid_size=(5, 5), report_interval=5.0,
grid_orientation='vertical', grid_shape='rect',
show_plots=False, cts_type='oriented_hex',
run_duration=1.0, output_interval=1.0e99,
plot_every_transition=False, **kwds):
self.initialize(grid_size, report_interval, grid_orientation,
grid_shape, show_plots, cts_type, run_duration,
output_interval, plot_every_transition, **kwds)
def initialize(self, grid_size=(5, 5), report_interval=5.0,
grid_orientation='vertical', grid_shape='rect',
show_plots=False, cts_type='oriented_hex',
run_duration=1.0, output_interval=1.0e99,
plot_every_transition=False, **kwds):
# Remember the clock time, and calculate when we next want to report
# progress.
self.current_real_time = time.time()
self.next_report = self.current_real_time + report_interval
self.report_interval = report_interval
# Interval for output
self.output_interval = output_interval
# Duration for run
self.run_duration = run_duration
# Create a grid
self.create_grid_and_node_state_field(grid_size[0], grid_size[1],
grid_orientation, grid_shape,
cts_type)
# Create the node-state dictionary
ns_dict = self.node_state_dictionary()
# Initialize values of the node-state grid
nsg = self.initialize_node_state_grid()
# Create the transition list
xn_list = self.transition_list()
# Create the CA object
if cts_type == 'raster':
from landlab.ca.raster_cts import RasterCTS
self.ca = RasterCTS(self.grid, ns_dict, xn_list, nsg)
elif cts_type == 'oriented_raster':
from landlab.ca.oriented_raster_cts import OrientedRasterCTS
self.ca = OrientedRasterCTS(self.grid, ns_dict, xn_list, nsg)
elif cts_type == 'hex':
from landlab.ca.hex_cts import HexCTS
self.ca = HexCTS(self.grid, ns_dict, xn_list, nsg)
else:
from landlab.ca.oriented_hex_cts import OrientedHexCTS
self.ca = OrientedHexCTS(self.grid, ns_dict, xn_list, nsg)
# Initialize graphics
self._show_plots = show_plots
if show_plots == True:
self.initialize_plotting(**kwds)
def create_grid_and_node_state_field(self, num_rows, num_cols,
grid_orientation, grid_shape,
cts_type):
"""Create the grid and the field containing node states."""
if cts_type == 'raster' or cts_type == 'oriented_raster':
from landlab import RasterModelGrid
self.grid = RasterModelGrid(shape=(num_rows, num_cols),
spacing=1.0)
else:
from landlab import HexModelGrid
self.grid = HexModelGrid(num_rows, num_cols, 1.0,
orientation=grid_orientation,
shape=grid_shape)
self.grid.add_zeros('node', 'node_state', dtype=int)
def node_state_dictionary(self):
"""Create and return a dictionary of all possible node (cell) states.
This method creates a default set of states (just two); it is a
template meant to be overridden.
"""
ns_dict = { 0 : 'on',
1 : 'off'}
return ns_dict
def transition_list(self):
"""Create and return a list of transition objects.
This method creates a default set of transitions (just two); it is a
template meant to be overridden.
"""
xn_list = []
xn_list.append(Transition((0, 1, 0), (1, 0, 0), 1.0))
xn_list.append(Transition((1, 0, 0), (0, 1, 0), 1.0))
return xn_list
def write_output(self, grid, outfilename, iteration):
"""Write output to file (currently netCDF)."""
filename = outfilename + str(iteration).zfill(4) + '.nc'
save_grid(grid, filename)
def initialize_node_state_grid(self):
"""Initialize values in the node-state grid.
This method should be overridden. The default is random "on" and "off".
"""
num_states = 2
for i in range(self.grid.number_of_nodes):
self.grid.at_node['node_state'][i] = random.randint(num_states)
return self.grid.at_node['node_state']
def initialize_plotting(self, **kwds):
"""Create and configure CAPlotter object."""
self.ca_plotter = CAPlotter(self.ca, **kwds)
self.ca_plotter.update_plot()
axis('off')
def run_for(self, dt):
self.ca.run(self.ca.current_time + dt, self.ca.node_state)
def main():
# INITIALIZE
# User-defined parameters
nr = 21
nc = 21
plot_interval = 0.5
run_duration = 25.0
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 a grid
hmg = HexModelGrid(nr, nc, 1.0, orientation='vertical', reorient_links=True)
# Close the grid boundaries
hmg.set_closed_nodes(hmg.open_boundary_nodes)
# Set up the states and pair transitions.
# Transition data here represent the disease status of a population.
ns_dict = { 0 : 'fluid', 1 : 'grain' }
xn_list = setup_transition_list()
# Create data and initialize values. We start with the 3 middle columns full
# of grains, and the others empty.
node_state_grid = hmg.add_zeros('node', 'node_state_grid')
middle = 0.25*(nc-1)*sqrt(3)
is_middle_cols = logical_and(hmg.node_x<middle+1., hmg.node_x>middle-1.)
node_state_grid[where(is_middle_cols)[0]] = 1
# Create the CA model
ca = OrientedHexCTS(hmg, 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=False)
current_time += plot_interval
# Plot the current grid
ca_plotter.update_plot()
# FINALIZE
# Plot
ca_plotter.finalize()
for j in range(self.grid.number_of_active_links):
i = self.grid.active_links[j]
dy = self.grid.node_y[self.grid.link_tonode[i]]-self.grid.node_y[self.grid.link_fromnode[i]]
dx = self.grid.node_x[self.grid.link_tonode[i]]-self.grid.node_x[self.grid.link_fromnode[i]]
if dx <= 0.:
self.active_link_orientation[j] = 0
elif dy<=0.:
self.active_link_orientation[j] = 2
elif dx>0. and dy>0.:
self.active_link_orientation[j] = 1
else:
assert (False), 'Non-handled link orientation case'
if __name__=='__main__':
print 'main here'
from landlab import HexModelGrid
mg = HexModelGrid(2, 3, 1.0, orientation='vertical', reorient_links=True)
print mg.number_of_active_links
nsd = {0 : 'yes', 1 : 'no'}
xnlist = []
xnlist.append( Transition( (0,1,0), (1,0,0), 1.0, 'falling' ) )
nsg = mg.add_zeros('node', 'node_state_grid')
ohlca = OrientedHexLCA(mg, nsd, xnlist, nsg)
for i in range(mg.number_of_active_links):
j = mg.active_links[i]
print i,j,'(',mg.node_x[mg.link_fromnode[j]],',',mg.node_y[mg.link_fromnode[j]],\
')','(',mg.node_x[mg.link_tonode[j]],',',mg.node_y[mg.link_tonode[j]],\
')',ohlca.active_link_orientation[i]
class CTSModel(object):
"""
Implement a generic CellLab-CTS model.
This is the base class from which models should inherit.
"""
def __init__(self, grid_size=(5, 5), report_interval=5.0,
grid_orientation='vertical', grid_shape='rect',
show_plots=False, cts_type='oriented_hex',
run_duration=1.0, output_interval=1.0e99,
plot_every_transition=False, initial_state_grid=None,
prop_data=None, prop_reset_value=None,
closed_boundaries=(False, False, False, False), **kwds):
self.initialize(grid_size, report_interval, grid_orientation,
grid_shape, show_plots, cts_type, run_duration,
output_interval, plot_every_transition,
initial_state_grid, prop_data, prop_reset_value,
closed_boundaries, **kwds)
def initialize(self, grid_size=(5, 5), report_interval=5.0,
grid_orientation='vertical', grid_shape='rect',
show_plots=False, cts_type='oriented_hex',
run_duration=1.0, output_interval=1.0e99,
plot_every_transition=False, initial_state_grid=None,
prop_data=None, prop_reset_value=None,
closed_boundaries=(False, False, False, False), **kwds):
"""Initialize CTSModel."""
# Remember the clock time, and calculate when we next want to report
# progress.
self.current_real_time = time.time()
self.next_report = self.current_real_time + report_interval
self.report_interval = report_interval
# Interval for output
self.output_interval = output_interval
# Duration for run
self.run_duration = run_duration
# Create a grid
self.create_grid_and_node_state_field(grid_size[0], grid_size[1],
grid_orientation, grid_shape,
cts_type, closed_boundaries)
# If prop_data is a string, we assume it is a field name
if isinstance(prop_data, string_types):
prop_data = self.grid.add_zeros('node', prop_data)
# Create the node-state dictionary
ns_dict = self.node_state_dictionary()
# Initialize values of the node-state grid
if initial_state_grid is None:
nsg = self.initialize_node_state_grid()
else:
try:
nsg = initial_state_grid
self.grid.at_node['node_state'][:] = nsg
except:
#TODO: use new Messaging capability
print('If initial_state_grid given, must be array of int')
raise
# Create the transition list
xn_list = self.transition_list()
# Create the CA object
if cts_type == 'raster':
from landlab.ca.raster_cts import RasterCTS
self.ca = RasterCTS(self.grid, ns_dict, xn_list, nsg, prop_data,
prop_reset_value)
elif cts_type == 'oriented_raster':
from landlab.ca.oriented_raster_cts import OrientedRasterCTS
self.ca = OrientedRasterCTS(self.grid, ns_dict, xn_list, nsg,
prop_data, prop_reset_value)
elif cts_type == 'hex':
from landlab.ca.hex_cts import HexCTS
self.ca = HexCTS(self.grid, ns_dict, xn_list, nsg, prop_data,
prop_reset_value)
else:
from landlab.ca.oriented_hex_cts import OrientedHexCTS
self.ca = OrientedHexCTS(self.grid, ns_dict, xn_list, nsg,
prop_data, prop_reset_value)
# Initialize graphics
self._show_plots = show_plots
if show_plots == True:
self.initialize_plotting(**kwds)
def _set_closed_boundaries_for_hex_grid(self, closed_boundaries):
"""Setup one or more closed boundaries for a hex grid.
Parameters
----------
closed_boundaries : 4-element tuple of bool\
Whether right, top, left, and bottom edges have closed nodes
Examples
--------
>>> from grainhill import CTSModel
>>> cm = CTSModel(closed_boundaries=(True, True, True, True))
>>> cm.grid.status_at_node
array([4, 4, 4, 4, 4, 4, 0, 4, 0, 0, 4, 0, 4, 0, 0, 4, 0, 4, 0, 0, 4, 4,
4, 4, 4], dtype=uint8)
"""
g = self.grid
if closed_boundaries[0]:
g.status_at_node[g.nodes_at_right_edge] = CLOSED_BOUNDARY
if closed_boundaries[1]:
g.status_at_node[g.nodes_at_top_edge] = CLOSED_BOUNDARY
if closed_boundaries[2]:
g.status_at_node[g.nodes_at_left_edge] = CLOSED_BOUNDARY
if closed_boundaries[3]:
g.status_at_node[g.nodes_at_bottom_edge] = CLOSED_BOUNDARY
def create_grid_and_node_state_field(self, num_rows, num_cols,
grid_orientation, grid_shape,
cts_type, closed_bounds):
"""Create the grid and the field containing node states."""
if cts_type == 'raster' or cts_type == 'oriented_raster':
from landlab import RasterModelGrid
self.grid = RasterModelGrid(shape=(num_rows, num_cols),
spacing=1.0)
self.grid.set_closed_boundaries_at_grid_edges(closed_bounds[0],
closed_bounds[1],
closed_bounds[2],
closed_bounds[3])
else:
from landlab import HexModelGrid
self.grid = HexModelGrid(num_rows, num_cols, 1.0,
orientation=grid_orientation,
shape=grid_shape)
if True in closed_bounds:
self._set_closed_boundaries_for_hex_grid(closed_bounds)
self.grid.add_zeros('node', 'node_state', dtype=int)
def node_state_dictionary(self):
"""Create and return a dictionary of all possible node (cell) states.
This method creates a default set of states (just two); it is a
template meant to be overridden.
"""
ns_dict = { 0 : 'on',
1 : 'off'}
return ns_dict
def transition_list(self):
"""Create and return a list of transition objects.
This method creates a default set of transitions (just two); it is a
template meant to be overridden.
"""
xn_list = []
xn_list.append(Transition((0, 1, 0), (1, 0, 0), 1.0))
xn_list.append(Transition((1, 0, 0), (0, 1, 0), 1.0))
return xn_list
def write_output(self, grid, outfilename, iteration):
"""Write output to file (currently netCDF)."""
filename = outfilename + str(iteration).zfill(4) + '.nc'
save_grid(grid, filename)
def initialize_node_state_grid(self):
"""Initialize values in the node-state grid.
This method should be overridden. The default is random "on" and "off".
"""
num_states = 2
for i in range(self.grid.number_of_nodes):
self.grid.at_node['node_state'][i] = random.randint(num_states)
return self.grid.at_node['node_state']
def initialize_plotting(self, **kwds):
"""Create and configure CAPlotter object."""
self.ca_plotter = CAPlotter(self.ca, **kwds)
self.ca_plotter.update_plot()
axis('off')
def run_for(self, dt):
self.ca.run(self.ca.current_time + dt, self.ca.node_state)
def main():
# INITIALIZE
# User-defined parameters
nr = 41
nc = 61
g = 1.0
f = 0.7
silo_y0 = 30.0
silo_opening_half_width = 6
plot_interval = 10.0
run_duration = 240.0
report_interval = 300.0 # report interval, in real-time seconds
p_init = 0.4 # probability that a cell is occupied at start
plot_every_transition = False
# 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 a grid
hmg = HexModelGrid(
nr, nc, 1.0, orientation="vertical", shape="rect", reorient_links=True
)
# Set up the states and pair transitions.
# Transition data here represent particles moving on a lattice: one state
# per direction (for 6 directions), plus an empty state, a stationary
# state, and a wall state.
ns_dict = {
0: "empty",
1: "moving up",
2: "moving right and up",
3: "moving right and down",
4: "moving down",
5: "moving left and down",
6: "moving left and up",
7: "rest",
8: "wall",
}
xn_list = setup_transition_list(g, f)
# Create data and initialize values.
node_state_grid = hmg.add_zeros("node", "node_state_grid")
# Make the grid boundary all wall particles
node_state_grid[hmg.boundary_nodes] = 8
# Place wall particles to form the base of the silo, initially closed
tan30deg = numpy.tan(numpy.pi / 6.)
rampy1 = silo_y0 - hmg.node_x * tan30deg
rampy2 = silo_y0 - ((nc * 0.866 - 1.) - hmg.node_x) * tan30deg
rampy = numpy.maximum(rampy1, rampy2)
(ramp_nodes,) = numpy.where(
numpy.logical_and(hmg.node_y > rampy - 0.5, hmg.node_y < rampy + 0.5)
)
node_state_grid[ramp_nodes] = 8
# Seed the grid interior with randomly oriented particles
for i in hmg.core_nodes:
if hmg.node_y[i] > rampy[i] and random.random() < p_init:
node_state_grid[i] = random.randint(1, 7)
# Create the CA model
ca = OrientedHexCTS(hmg, ns_dict, xn_list, node_state_grid)
import matplotlib
rock = (0.0, 0.0, 0.0) # '#5F594D'
sed = (0.6, 0.6, 0.6) # '#A4874B'
# sky = '#CBD5E1'
# sky = '#85A5CC'
sky = (1.0, 1.0, 1.0) # '#D0E4F2'
mob = (0.3, 0.3, 0.3) # '#D98859'
# mob = '#DB764F'
# mob = '#FFFF00'
# sed = '#CAAE98'
# clist = [(0.5, 0.9, 0.9),mob, mob, mob, mob, mob, mob,'#CD6839',(0.3,0.3,0.3)]
clist = [sky, mob, mob, mob, mob, mob, mob, sed, rock]
my_cmap = matplotlib.colors.ListedColormap(clist)
# Create a CAPlotter object for handling screen display
ca_plotter = CAPlotter(ca, cmap=my_cmap)
k = 0
# Plot the initial grid
ca_plotter.update_plot()
# RUN
# Run with closed silo
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=plot_every_transition,
plotter=ca_plotter,
)
current_time += plot_interval
# Plot the current grid
ca_plotter.update_plot()
# Open the silo
xmid = nc * 0.866 * 0.5
for i in range(hmg.number_of_nodes):
if (
node_state_grid[i] == 8
and hmg.node_x[i] > (xmid - silo_opening_half_width)
and hmg.node_x[i] < (xmid + silo_opening_half_width)
and hmg.node_y[i] > 0
and hmg.node_y[i] < 38.0
):
node_state_grid[i] = 0
# Create the CA model
ca = OrientedHexCTS(hmg, 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()
# Re-run with open silo
savefig("silo" + str(k) + ".png")
k += 1
current_time = 0.0
while current_time < 5 * 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=plot_every_transition,
plotter=ca_plotter,
)
current_time += plot_interval
# Plot the current grid
ca_plotter.update_plot()
savefig("silo" + str(k) + ".png")
k += 1
# FINALIZE
# Plot
ca_plotter.finalize()
def main():
# INITIALIZE
# User-defined parameters
nr = 41
nc = 61
g = 0.05
plot_interval = 1.0
run_duration = 100.0
report_interval = 5.0 # report interval, in real-time seconds
p_init = 0.1 # probability that a cell is occupied at start
plot_every_transition = False
# 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 a grid
hmg = HexModelGrid(nr, nc, 1.0, orientation='vertical', reorient_links=True)
# Close the grid boundaries
#hmg.set_closed_nodes(hmg.open_boundary_nodes)
# Set up the states and pair transitions.
# Transition data here represent particles moving on a lattice: one state
# per direction (for 6 directions), plus an empty state, a stationary
# state, and a wall state.
ns_dict = { 0 : 'empty',
1 : 'moving up',
2 : 'moving right and up',
3 : 'moving right and down',
4 : 'moving down',
5 : 'moving left and down',
6 : 'moving left and up',
7 : 'rest',
8 : 'wall'}
xn_list = setup_transition_list(g)
# Create data and initialize values.
node_state_grid = hmg.add_zeros('node', 'node_state_grid')
# Make the grid boundary all wall particles
node_state_grid[hmg.boundary_nodes] = 8
# Seed the grid interior with randomly oriented particles
for i in hmg.core_nodes:
if random.random()<p_init:
node_state_grid[i] = random.randint(1, 7)
# Create the CA model
ca = OrientedHexCTS(hmg, 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=plot_every_transition, plotter=ca_plotter)
current_time += plot_interval
# Plot the current grid
ca_plotter.update_plot()
# FINALIZE
# Plot
ca_plotter.finalize()
class HexLatticeTectonicizer(object):
"""Handles tectonics and baselevel for CellLab-CTS models.
This is the base class from which classes to represent particular
baselevel/fault geometries are derived.
Examples
--------
>>> hlt = HexLatticeTectonicizer()
>>> hlt.grid.number_of_nodes
25
>>> hlt.nr
5
>>> hlt.nc
5
"""
def __init__(self, grid=None, node_state=None, propid=None, prop_data=None,
prop_reset_value=None):
"""
Create and initialize a HexLatticeTectonicizer.
Examples
--------
>>> from landlab import HexModelGrid
>>> hg = HexModelGrid(6, 6, shape='rect')
>>> hlt = HexLatticeTectonicizer()
>>> hlt.grid.number_of_nodes
25
>>> hlt.nr
5
>>> hlt.nc
5
"""
# If needed, create grid
if grid is None:
num_rows = _DEFAULT_NUM_ROWS
num_cols = _DEFAULT_NUM_COLS
self.grid = HexModelGrid(num_rows, num_cols, dx=1.0,
orientation='vertical',
shape='rect', reorient_links=True)
else:
# Make sure caller passed the right type of grid
assert (grid.orientation == 'vertical'), \
'Grid must have vertical orientation'
# Keep a reference to the grid
self.grid = grid
# If needed, create node-state grid
if node_state is None:
self.node_state = self.grid.add_zeros('node', 'node_state_map',
dtype=int)
else:
self.node_state = node_state
# Remember the # of rows and cols
self.nr = self.grid.number_of_node_rows
self.nc = self.grid.number_of_node_columns
# propid should be either a reference to a CA model's "property id"
# array, or None
self.propid = propid
self.prop_data = prop_data
self.prop_reset_value = prop_reset_value
def main():
# INITIALIZE
# User-defined parameters
nr = 80
nc = 41
plot_interval = 0.25
run_duration = 5.0
report_interval = 5.0 # report interval, in real-time seconds
infection_rate = 8.0
outfilename = 'sirmodel'+str(int(infection_rate))+'ir'
# 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
time_slice = 0
# Create a grid
hmg = HexModelGrid(nr, nc, 1.0)
# Set up the states and pair transitions.
# Transition data here represent the disease status of a population.
ns_dict = { 0 : 'susceptible', 1 : 'infectious', 2: 'recovered' }
xn_list = setup_transition_list(infection_rate)
# Create data and initialize values
node_state_grid = hmg.add_zeros('node', 'node_state_grid')
wid = nc-1.0
ht = (nr-1.0)*0.866
is_middle_rows = logical_and(hmg.node_y>=0.4*ht, hmg.node_y<=0.5*ht)
is_middle_cols = logical_and(hmg.node_x>=0.4*wid, hmg.node_x<=0.6*wid)
middle_area = where(logical_and(is_middle_rows, is_middle_cols))[0]
node_state_grid[middle_area] = 1
node_state_grid[0] = 2 # to force full color range, set lower left to 'recovered'
# Create the CA model
ca = HexCTS(hmg, ns_dict, xn_list, node_state_grid)
# Set up the color map
import matplotlib
susceptible_color = (0.5, 0.5, 0.5) # gray
infectious_color = (0.5, 0.0, 0.0) # dark red
recovered_color = (0.0, 0.0, 1.0) # blue
clist = [susceptible_color, infectious_color, recovered_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()
pylab.axis('off')
savename = outfilename+'0'
pylab.savefig(savename+'.pdf', format='pdf')
# 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) #True, plotter=ca_plotter)
current_time += plot_interval
# Plot the current grid
ca_plotter.update_plot()
pylab.axis('off')
time_slice += 1
savename = outfilename+str(time_slice)
pylab.savefig(savename+'.pdf', format='pdf')
# FINALIZE
# Plot
ca_plotter.finalize()
def test_bad_init_gridmethod():
hmg = HexModelGrid(30, 29, dx=3.)
hmg.add_zeros("node", "topographic__elevation", dtype=float)
with pytest.raises(ValueError):
LakeMapperBarnes(hmg, method="D8")
def main():
"""
In this simple tutorial example, the main function does all the work:
it sets the parameter values, creates and initializes a grid, sets up
the state variables, runs the main loop, and cleans up.
"""
# INITIALIZE
# User-defined parameter values
numrows = 7 # number of rows in the grid
basenumcols = 6 # number of columns in the grid
dx = 10.0 # grid cell spacing
kd = 0.01 # diffusivity coefficient, in m2/yr
uplift_rate = 0.001 # baselevel/uplift rate, in m/yr
num_time_steps = 1000 # number of time steps in run
# Derived parameters
dt = 0.1*dx**2 / kd # time-step size set by CFL condition
# Create and initialize a raster model grid
mg = HexModelGrid(numrows, basenumcols, dx)
# Set up scalar values: elevation and elevation time derivative
z = mg.add_zeros('node', 'Land_surface__elevation')
dzdt = mg.add_zeros('node', 'Land_surface__time_derivative_of_elevation')
# Get a list of the core nodes
core_nodes = mg.core_nodes
# Display a message
print( 'Running diffusion_with_model_hex_grid.py' )
print( 'Time-step size has been set to ' + str( dt ) + ' years.' )
start_time = time.time()
# RUN
# Main loop
for i in range(0, num_time_steps):
# Calculate the gradients and sediment fluxes
g = mg.calculate_gradients_at_active_links(z)
qs = -kd*g
# Calculate the net deposition/erosion rate at each node
dqsds = mg.calculate_flux_divergence_at_nodes(qs)
# Calculate the total rate of elevation change
dzdt = uplift_rate - dqsds
# Update the elevations
z[core_nodes] = z[core_nodes] + dzdt[core_nodes] * dt
# FINALIZE
import numpy
# Test solution for a 1-cell hex grid
if mg.number_of_nodes==7: # single cell with 6 boundaries
perimeter = dx*6*(numpy.sqrt(3.)/3.)
flux = kd*(numpy.amax(z)/dx)
total_outflux = perimeter*flux
total_influx = mg.cell_areas*uplift_rate # just one cell ...
print 'total influx=',total_influx,'total outflux=',total_outflux
print('Run time = '+str(time.time()-start_time)+' seconds')
# Plot the points, colored by elevation
pylab.figure()
maxelev = numpy.amax(z)
for i in range(mg.number_of_nodes):
mycolor = str(z[i]/maxelev)
pylab.plot(mg.node_x[i], mg.node_y[i], 'o', color=mycolor, ms=50)
pylab.show()
mg.display_grid()
def main():
# INITIALIZE
# User-defined parameters
nr = 20
nc = 20
plot_interval = 2.0
run_duration = 2.0
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 a grid
hmg = HexModelGrid(nr, nc, 1.0)
# Set up the states and pair transitions.
# Transition data here represent the disease status of a population.
ns_dict = { 0 : 'susceptible', 1 : 'infectious', 2: 'recovered' }
xn_list = setup_transition_list()
# Create data and initialize values
node_state_grid = hmg.add_zeros('node', 'node_state_grid')
wid = nc-1.0
ht = (nr-1.0)*0.866
is_middle_rows = logical_and(hmg.node_y>=0.4*ht, hmg.node_y<=0.5*ht)
is_middle_cols = logical_and(hmg.node_x>=0.4*wid, hmg.node_x<=0.6*wid)
middle_area = where(logical_and(is_middle_rows, is_middle_cols))[0]
node_state_grid[middle_area] = 1
node_state_grid[0] = 2 # to force full color range, set lower left to 'recovered'
# Create the CA model
ca = HexLCA(hmg, 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()
def testing_flux_divergence_with_hex():
"""Test flux divergence function(s).
Notes
-----
Test grid looks like this:
(7)-17-.(8)-18-.(9)
. . . . . .
11 12 13 14 15 16
/ \ / \ / \
(3)--8-.(4)--9-.(5)-10-.(6)
. . . . . .
2 3 4 5 6 7
\ / \ / \ /
(0)--0-.(1)--1-.(2)
Node numbers in parentheses; others are link numbers; period indicates
link head.
"""
hmg = HexModelGrid(3, 3, reorient_links=True)
f = hmg.add_zeros('link', 'test_flux')
f[:] = np.arange(hmg.number_of_links)
make_links_at_node_array(hmg)
assert_array_equal(hmg.gt_num_links_at_node,
[3, 4, 3, 3, 6, 6, 3, 3, 4, 3])
assert_array_equal(hmg.gt_links_at_node,
[[ 0, 0, 1, 2, 3, 5, 7, 11, 13, 15],
[ 2, 1, 6, 8, 4, 6, 10, 12, 14, 16],
[ 3, 4, 7, 11, 8, 9, 16, 17, 17, 18],
[ -1, 5, -1, -1, 9, 10, -1, -1, 18, -1],
[ -1, -1, -1, -1, 12, 14, -1, -1, -1, -1],
[ -1, -1, -1, -1, 13, 15, -1, -1, -1, -1]])
assert_array_equal(hmg.gt_link_dirs_at_node,
[[ -1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[ -1, -1, -1, -1, 1, 1, 1, 1, 1, 1],
[ -1, -1, -1, -1, 1, 1, -1, -1, 1, 1],
[ 0, -1, 0, 0, -1, -1, 0, 0, -1, 0],
[ 0, 0, 0, 0, -1, -1, 0, 0, 0, 0],
[ 0, 0, 0, 0, -1, -1, 0, 0, 0, 0]])
raw_net_flux = np.zeros(hmg.number_of_nodes)
for r in range(MAX_NUM_LINKS):
raw_net_flux += f[hmg.gt_links_at_node[r,:]]*hmg.gt_link_dirs_at_node[r,:]
assert_array_equal(raw_net_flux,
[ -5., -10., -12., -17., -19., -19., 1., 6., 26., 49.])
nv = np.arange(hmg.number_of_nodes)
#gt_calc_gradients_at_faces(hmg, nv)
#gt_link_flux_divergence_at_cells_with_2darray(hmg, f)
# Some time trials
start = time.time()
for i in range(1000):
gt_grads_at_faces1(hmg, nv)
endtime = time.time()
@author: gtucker
"""
from numpy import arange
from pylab import figure, show, title
from landlab import HexModelGrid
# Case 1: Make and display a hex-shaped grid with horizontal rows of nodes
# Create the grid
hg1 = HexModelGrid(5, 3, 1.0, orientation="horizontal", shape="hex")
# Make some data
d = hg1.add_zeros("node", "mydata")
d[:] = arange(hg1.number_of_nodes)
# Display the grid
figure(1)
hg1.hexplot(d)
title("hexagon shape, horizontal orientation")
show()
# Case 2: Make and display a hex-shaped grid with vertical columns of nodes
# Create the grid
hg2 = HexModelGrid(3, 5, 1.0, orientation="vertical", shape="hex")
# Make some data
