def test_bad_field_location():
dict_like = {
"grid": {"RasterModelGrid": [(4, 5)]},
"fields": {"at_bad_location": {"foo": "bar"}},
}
with pytest.raises(ValueError):
create_grid(dict_like)
def test_bad_field_function():
dict_like = {
"grid": {"RasterModelGrid": [(4, 5)]},
"fields": {"at_node": {"new_field_name": {"not_a_function": ["bar", "spam"]}}},
}
with pytest.raises(ValueError):
create_grid(dict_like)
def test_bad_field_function():
dict_like = {
"grid": {"RasterModelGrid": [(4, 5)]},
"fields": {"at_node": {"new_field_name": {"not_a_function": ["bar", "spam"]}}},
}
with pytest.raises(ValueError):
create_grid(dict_like)
def test_bad_field_location():
dict_like = {
"grid": {"RasterModelGrid": [(4, 5)]},
"fields": {"at_bad_location": {"foo": "bar"}},
}
with pytest.raises(ValueError):
create_grid(dict_like)
def test_two_boundary_condition_dicts():
dict_like = {
"grid": {"RasterModelGrid": [(4, 3)]},
"boundary_conditions": [
{
"set_closed_boundaries_at_grid_edges": [True, True, True, True],
"not_a_function": [True, False],
}
],
}
with pytest.raises(ValueError):
create_grid(dict_like)
def test_two_boundary_condition_dicts():
dict_like = {
"grid": {"RasterModelGrid": [(4, 3)]},
"boundary_conditions": [
{
"set_closed_boundaries_at_grid_edges": [True, True, True, True],
"not_a_function": [True, False],
}
],
}
with pytest.raises(ValueError):
create_grid(dict_like)
def test_esri_ascii_create():
filename = os.path.join(_TEST_DATA_DIR, "4_x_3_no_nodata_value.asc")
dict_like = {
"grid": {
"RasterModelGrid": [(4, 3), {"xy_spacing": 10, "xy_of_lower_left": (1, 2)}]
},
"fields": {
"at_node": {"topographic__elevation": {"read_esri_ascii": [filename]}}
},
}
mg = create_grid(dict_like)
x_of_node = np.array(
[1.0, 11.0, 21.0, 1.0, 11.0, 21.0, 1.0, 11.0, 21.0, 1.0, 11.0, 21.0]
)
y_of_node = np.array(
[2.0, 2.0, 2.0, 12.0, 12.0, 12.0, 22.0, 22.0, 22.0, 32.0, 32.0, 32.0]
)
status_at_node = np.array([1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1], dtype=np.uint8)
topographic__elevation = np.array(
[9.0, 10.0, 11.0, 6.0, 7.0, 8.0, 3.0, 4.0, 5.0, 0.0, 1.0, 2.0]
)
assert_array_equal(mg.x_of_node, x_of_node)
assert_array_equal(mg.y_of_node, y_of_node)
assert_array_equal(status_at_node, mg.status_at_node)
assert_array_equal(topographic__elevation, mg.at_node["topographic__elevation"])
def test_read_netcdf_create():
filename = os.path.join(_TEST_DATA_DIR, "test-netcdf4.nc")
dict_like = {
"grid": {
"RasterModelGrid": [(4, 3)]
},
"fields": {
"at_node": {
"surface__elevation": {
"read_netcdf": [filename]
}
}
},
}
mg = create_grid(dict_like)
x_of_node = np.array([0., 1., 2., 0., 1., 2., 0., 1., 2., 0., 1., 2.])
y_of_node = np.array([0., 0., 0., 1., 1., 1., 2., 2., 2., 3., 3., 3.])
status_at_node = np.array([1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1],
dtype=np.uint8)
surface__elevation = np.array(
[0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11.])
assert_array_equal(mg.x_of_node, x_of_node)
assert_array_equal(mg.y_of_node, y_of_node)
assert_array_equal(status_at_node, mg.status_at_node)
assert_array_equal(surface__elevation, mg.at_node["surface__elevation"])
def test_simple_create(tmpdir):
"""Load parameters from YAML-formatted file."""
with tmpdir.as_cwd():
with open("params.yaml", "w") as fp:
fp.write(SIMPLE_PARAMS_STR)
mg = create_grid("./params.yaml")
assert mg.number_of_nodes == 20
assert "topographic__elevation" in mg.at_node
x_of_node = np.array(
[
0.,
3.,
6.,
9.,
12.,
0.,
3.,
6.,
9.,
12.,
0.,
3.,
6.,
9.,
12.,
0.,
3.,
6.,
9.,
12.,
]
)
status_at_node = np.array(
[4, 4, 4, 4, 4, 4, 0, 0, 0, 4, 4, 0, 0, 0, 4, 4, 4, 4, 4, 4], dtype=np.uint8
)
topographic__elevation = np.array(
[
[-2., 4., 10., 16., 22.],
[2., 8., 14., 20., 26.],
[6., 12., 18., 24., 30.],
[10., 16., 22., 28., 34.],
]
)
assert_array_equal(mg.x_of_node, x_of_node)
assert_array_equal(status_at_node, mg.status_at_node)
assert_array_equal(
topographic__elevation,
np.round(mg.at_node["topographic__elevation"].reshape(mg.shape), decimals=2),
)
def test_simple_create(tmpdir):
"""Load parameters from YAML-formatted file."""
with tmpdir.as_cwd():
with open("params.yaml", "w") as fp:
fp.write(SIMPLE_PARAMS_STR)
mg = create_grid("./params.yaml")
assert mg.number_of_nodes == 20
assert "topographic__elevation" in mg.at_node
x_of_node = np.array(
[
0.0,
3.0,
6.0,
9.0,
12.0,
0.0,
3.0,
6.0,
9.0,
12.0,
0.0,
3.0,
6.0,
9.0,
12.0,
0.0,
3.0,
6.0,
9.0,
12.0,
]
)
status_at_node = np.array(
[4, 4, 4, 4, 4, 4, 0, 0, 0, 4, 4, 0, 0, 0, 4, 4, 4, 4, 4, 4], dtype=np.uint8
)
topographic__elevation = np.array(
[
[-2.0, 4.0, 10.0, 16.0, 22.0],
[2.0, 8.0, 14.0, 20.0, 26.0],
[6.0, 12.0, 18.0, 24.0, 30.0],
[10.0, 16.0, 22.0, 28.0, 34.0],
]
)
assert_array_equal(mg.x_of_node, x_of_node)
assert_array_equal(status_at_node, mg.status_at_node)
assert_array_equal(
topographic__elevation,
np.round(mg.at_node["topographic__elevation"].reshape(mg.shape), decimals=2),
)
def test_read_netcdf_create():
filename = os.path.join(_TEST_DATA_DIR, "test-netcdf4.nc")
dict_like = {
"grid": {"RasterModelGrid": [(4, 3)]},
"fields": {"at_node": {"surface__elevation": {"read_netcdf": [filename]}}},
}
mg = create_grid(dict_like)
x_of_node = np.array([0., 1., 2., 0., 1., 2., 0., 1., 2., 0., 1., 2.])
y_of_node = np.array([0., 0., 0., 1., 1., 1., 2., 2., 2., 3., 3., 3.])
status_at_node = np.array([1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1], dtype=np.uint8)
surface__elevation = np.array([0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11.])
assert_array_equal(mg.x_of_node, x_of_node)
assert_array_equal(mg.y_of_node, y_of_node)
assert_array_equal(status_at_node, mg.status_at_node)
assert_array_equal(surface__elevation, mg.at_node["surface__elevation"])
def test_esri_ascii_create():
filename = os.path.join(_TEST_DATA_DIR, "4_x_3_no_nodata_value.asc")
dict_like = {
"grid": {
"RasterModelGrid": [(4, 3), {"xy_spacing": 10, "xy_of_lower_left": (1, 2)}]
},
"fields": {
"at_node": {"topographic__elevation": {"read_esri_ascii": [filename]}}
},
}
mg = create_grid(dict_like)
x_of_node = np.array([1., 11., 21., 1., 11., 21., 1., 11., 21., 1., 11., 21.])
y_of_node = np.array([2., 2., 2., 12., 12., 12., 22., 22., 22., 32., 32., 32.])
status_at_node = np.array([1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1], dtype=np.uint8)
topographic__elevation = np.array(
[9., 10., 11., 6., 7., 8., 3., 4., 5., 0., 1., 2.]
)
assert_array_equal(mg.x_of_node, x_of_node)
assert_array_equal(mg.y_of_node, y_of_node)
assert_array_equal(status_at_node, mg.status_at_node)
assert_array_equal(topographic__elevation, mg.at_node["topographic__elevation"])
def from_dict(cls, params):
"""Create an umami ``Residual`` from a dictionary.
Parameters
----------
params : dict or OrderedDict
This dict must contain a key *grid*, the values of which will be
passed to the `Landlab` function ``create_grid`` to create the
model grid. It will be convereted to an OrderedDict before residuals
are added so as to preserve residual order.
Examples
--------
>>> import numpy as np
>>> from io import StringIO
>>> from umami import Residual
>>> np.random.seed(42)
>>> params = {
... "model": {
... "RasterModelGrid": [
... [10, 10],
... {
... "fields": {
... "node": {
... "topographic__elevation": {
... "plane": [
... {"point": [0, 0, 0]},
... {"normal": [-1, -1, 1]},
... ]
... }
... }
... }
... },
... ]
... },
... "data": {
... "RasterModelGrid": [
... [10, 10],
... {
... "fields": {
... "node": {
... "topographic__elevation": {
... "plane": [
... {"point": [0, 0, 0]},
... {"normal": [-1, -1, 1]},
... ],
... "random" : [
... {"where": "CORE_NODE"},
... {"distribution": "standard_normal"},
... ]
... }
... }
... }
... },
... ]
... },
... "residuals": {
... "me": {
... "_func": "aggregate",
... "method": "mean",
... "field": "topographic__elevation",
... },
... "ep10": {
... "_func": "aggregate",
... "method": "percentile",
... "field": "topographic__elevation",
... "q": 10,
... },
... "oid1_mean": {
... "_func": "watershed_aggregation",
... "field": "topographic__elevation",
... "method": "mean",
... "outlet_id": 1,
... },
... "sn1": {
... "_func": "count_equal",
... "field": "drainage_area",
... "value": 1,
... },
... },
... }
>>> residual = Residual.from_dict(params)
>>> residual.names
['me', 'ep10', 'oid1_mean', 'sn1']
>>> residual.calculate()
>>> np.testing.assert_array_almost_equal(
... residual.value("me"),
... 0.158,
... decimal=3)
>>> np.testing.assert_array_almost_equal(
... residual.values,
... np.array([ 0.158, 0.67 , 4.138, -20. ]),
... decimal=3)
"""
model = create_grid(params.pop("model"))
data = create_grid(params.pop("data"))
return cls(model, data, **params)
def from_dict(cls, params, output_writers=None):
"""Construct a terrainbento model from an input parameter dictionary.
The input parameter dictionary portion associated with the "grid"
keword will be passed directly to the Landlab
`create_grid <https://landlab.readthedocs.io/en/master/reference/grid/create.html#landlab.grid.create.create_grid>`_.
function.
Parameters
----------
params : dict
Dictionary of input parameters.
output_writers : dictionary of output writers.
Classes or functions used to write incremental output (e.g. make a
diagnostic plot). There are two formats for the dictionary entries:
1) Items can have a key of "class" or "function" and a value of
a list of simple output classes (uninstantiated) or
functions, respectively. All output writers defined this way
will use the `output_interval` provided to the ErosionModel
constructor.
2) Items can have a key with any unique string representing the
output writer's name and a value containing a dict with the
uninstantiated class and arguments. The value follows the
format:
.. code-block:: python
{
'class' : MyWriter,
'args' : [], # optional
'kwargs' : {}, # optional
}
where `args` and `kwargs` are passed to the constructor for
`MyWriter`. `MyWriter` must be a child class of
GenericOutputWriter.
The two formats can be present simultaneously. See the Jupyter
notebook examples for more details.
Examples
--------
>>> params = {
... "grid": {
... "RasterModelGrid": [
... (4, 5),
... {
... "fields": {
... "node": {
... "topographic__elevation": {
... "constant": [{"value": 0.0}]
... }
... }
... }
... },
... ]
... },
... "clock": {"step": 1, "stop": 200},
... }
>>> model = ErosionModel.from_dict(params)
>>> model.clock.step
1.0
>>> model.clock.stop
200.0
>>> model.grid.shape
(4, 5)
"""
cls._validate(params)
# grid, clock
grid = create_grid(params.pop("grid"))
clock = Clock.from_dict(params.pop("clock"))
# precipitator
precip_params = params.pop("precipitator", _DEFAULT_PRECIPITATOR)
precipitator = _setup_precipitator_or_runoff(grid, precip_params,
_SUPPORTED_PRECIPITATORS)
# runoff_generator
runoff_params = params.pop("runoff_generator",
_DEFAULT_RUNOFF_GENERATOR)
runoff_generator = _setup_precipitator_or_runoff(
grid, runoff_params, _SUPPORTED_RUNOFF_GENERATORS)
# boundary_handlers
boundary_handlers = params.pop("boundary_handlers", {})
bh_dict = {}
for name in boundary_handlers:
bh_params = boundary_handlers[name]
bh_dict[name] = _setup_boundary_handlers(grid, name, bh_params)
# create instance
return cls(
clock,
grid,
precipitator=precipitator,
runoff_generator=runoff_generator,
boundary_handlers=bh_dict,
output_writers=output_writers,
**params,
)
def from_dict(cls, params, output_writers=None):
"""Construct a terrainbento model from an input parameter dictionary.
The input parameter dictionary portion associated with the "grid"
keword will be passed directly to the Landlab
`create_grid <https://landlab.readthedocs.io/en/latest/landlab.grid.create.html>`_.
function.
Parameters
----------
params : dict
Dictionary of input parameters.
output_writers : dictionary of output writers.
Classes or functions used to write incremental output (e.g. make a
diagnostic plot). These should be passed in a dictionary with two
keys: "class" and "function". The value associated with each of
these should be a list containing the uninstantiated output
writers. See the Jupyter notebook examples for more details.
Examples
--------
>>> params = {
... "grid": {
... "RasterModelGrid": [
... (4, 5),
... {
... "fields": {
... "node": {
... "topographic__elevation": {
... "constant": [{"value": 0}]
... }
... }
... }
... },
... ]
... },
... "clock": {"step": 1, "stop": 200},
... }
>>> model = ErosionModel.from_dict(params)
>>> model.clock.step
1.0
>>> model.clock.stop
200.0
>>> model.grid.shape
(4, 5)
"""
cls._validate(params)
# grid, clock
grid = create_grid(params.pop("grid"))
clock = Clock.from_dict(params.pop("clock"))
# precipitator
precip_params = params.pop("precipitator", _DEFAULT_PRECIPITATOR)
precipitator = _setup_precipitator_or_runoff(grid, precip_params,
_SUPPORTED_PRECIPITATORS)
# runoff_generator
runoff_params = params.pop("runoff_generator",
_DEFAULT_RUNOFF_GENERATOR)
runoff_generator = _setup_precipitator_or_runoff(
grid, runoff_params, _SUPPORTED_RUNOFF_GENERATORS)
# boundary_handlers
boundary_handlers = params.pop("boundary_handlers", {})
bh_dict = {}
for name in boundary_handlers:
bh_params = boundary_handlers[name]
bh_dict[name] = _setup_boundary_handlers(grid, name, bh_params)
# create instance
return cls(clock,
grid,
precipitator=precipitator,
runoff_generator=runoff_generator,
boundary_handlers=bh_dict,
output_writers=output_writers,
**params)
def test_two_grid_types():
dict_like = {"grid": {"RasterModelGrid": [(4, 5)], "HexModelGrid": [6, 7]}}
with pytest.raises(ValueError):
create_grid(dict_like)
def test_bad_grid_name():
dict_like = {"grid": "MagicModelGrid"}
with pytest.raises(ValueError):
create_grid(dict_like)
def test_no_grid_value():
dict_like = {"foo": "bar"}
with pytest.raises(ValueError):
create_grid(dict_like)
def test_bad_boundary_condition_functions():
filename = os.path.join(_TEST_DATA_DIR, "bad_boundary.yaml")
with pytest.raises(ValueError):
create_grid(filename)
def test_bad_boundary_condition_functions():
filename = os.path.join(_TEST_DATA_DIR, "bad_boundary.yaml")
with pytest.raises(ValueError):
create_grid(filename)
def from_dict(cls, params):
"""Create an umami ``Metric`` from a dictionary.
Parameters
----------
params : dict or OrderedDict
This dict must contain a key *grid*, the values of which will be
passed to the `Landlab` function ``create_grid`` to create the
model grid. It will be convereted to an OrderedDict before metrics
are added so as to preserve metric order.
Examples
--------
>>> from io import StringIO
>>> from umami import Metric
>>> params = {
... "grid": {
... "RasterModelGrid": [
... [10, 10],
... {
... "fields": {
... "node": {
... "topographic__elevation": {
... "plane": [
... {"point": [0, 0, 0]},
... {"normal": [-1, -1, 1]},
... ]
... }
... }
... }
... },
... ]
... },
... "metrics": {
... "me": {
... "_func": "aggregate",
... "method": "mean",
... "field": "topographic__elevation",
... },
... "ep10": {
... "_func": "aggregate",
... "method": "percentile",
... "field": "topographic__elevation",
... "q": 10,
... },
... "oid1_mean": {
... "_func": "watershed_aggregation",
... "field": "topographic__elevation",
... "method": "mean",
... "outlet_id": 1,
... },
... "sn1": {
... "_func": "count_equal",
... "field": "drainage_area",
... "value": 1,
... },
... },
... }
>>> metric = Metric.from_dict(params)
>>> metric.names
['me', 'ep10', 'oid1_mean', 'sn1']
>>> metric.calculate()
>>> metric.value('me')
9.0
>>> metric.values
[9.0, 5.0, 5.0, 8]
"""
# create grid
grid = create_grid(params.pop("grid"))
return cls(grid, **params)
def test_no_grid_value():
dict_like = {"foo": "bar"}
with pytest.raises(ValueError):
create_grid(dict_like)
def test_bad_grid_name():
dict_like = {"grid": "MagicModelGrid"}
with pytest.raises(ValueError):
create_grid(dict_like)
def test_two_grid_types():
dict_like = {"grid": {"RasterModelGrid": [(4, 5)], "HexModelGrid": [6, 7]}}
with pytest.raises(ValueError):
create_grid(dict_like)
