def test_ignored_interpolation_params_issue340(self, caplog):
node = Array(
source=[0, 1, 2],
coordinates=Coordinates([[0, 2, 1]], dims=["time"]),
interpolation={"method": "nearest", "params": {"fake_param": 1.1, "spatial_tolerance": 1}},
)
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=DeprecationWarning)
node.eval(Coordinates([[0.5, 1.5]], ["time"]))
assert "interpolation parameter 'fake_param' was ignored" in caplog.text
assert "interpolation parameter 'spatial_tolerance' was ignored" not in caplog.text
def test_get_data(self):
""" defined get_data function"""
node = Array(source=self.data, coordinates=self.coordinates)
output = node.eval(self.coordinates)
assert isinstance(output, UnitsDataArray)
assert output.values[0, 0] == self.data[0, 0]
assert output.values[4, 5] == self.data[4, 5]
def test_stacked_coords_with_partial_dims_issue123(self):
node = Array(
source=[0, 1, 2],
coordinates=Coordinates(
[[[0, 2, 1], [10, 12, 11], ["2018-01-01", "2018-01-02", "2018-01-03"]]], dims=["lat_lon_time"]
),
interpolation="nearest",
)
# unstacked or and stacked requests without time
o1 = node.eval(Coordinates([[0.5, 1.5], [10.5, 11.5]], dims=["lat", "lon"]))
o2 = node.eval(Coordinates([[[0.5, 1.5], [10.5, 11.5]]], dims=["lat_lon"]))
assert_array_equal(o1.data, [[0, 2], [2, 1]])
assert_array_equal(o2.data, [0, 1])
# request without lat or lon
o3 = node.eval(Coordinates(["2018-01-01"], dims=["time"]))
assert o3.data[0] == 0
def test_get_data_multiple(self):
data = np.random.rand(11, 11, 2)
node = Array(source=data,
coordinates=self.coordinates,
outputs=["a", "b"])
output = node.eval(self.coordinates)
assert isinstance(output, UnitsDataArray)
assert output.dims == ("lat", "lon", "output")
np.testing.assert_array_equal(output["output"], ["a", "b"])
np.testing.assert_array_equal(output.sel(output="a"), data[:, :, 0])
np.testing.assert_array_equal(output.sel(output="b"), data[:, :, 1])
node = Array(source=data,
coordinates=self.coordinates,
outputs=["a", "b"],
output="b")
output = node.eval(self.coordinates)
assert isinstance(output, UnitsDataArray)
assert output.dims == ("lat", "lon")
np.testing.assert_array_equal(output, data[:, :, 1])
def test_interpolation_mixin(self):
class InterpArray(InterpolationMixin, ArrayBase):
pass
data = np.random.rand(4, 5)
native_coords = Coordinates([np.linspace(0, 3, 4), np.linspace(0, 4, 5)], ["lat", "lon"])
coords = Coordinates([np.linspace(0, 3, 7), np.linspace(0, 4, 9)], ["lat", "lon"])
iarr_src = InterpArray(source=data, coordinates=native_coords, interpolation="bilinear")
arr_src = Array(source=data, coordinates=native_coords, interpolation="bilinear")
arrb_src = ArrayBase(source=data, coordinates=native_coords)
iaso = iarr_src.eval(coords)
aso = arr_src.eval(coords)
abso = arrb_src.eval(coords)
np.testing.assert_array_equal(iaso.data, aso.data)
np.testing.assert_array_equal(abso.data, data)
def setup_module():
global coords, source, data, multisource, bdata
coords = podpac.Coordinates(
[
podpac.clinspace(0, 1, 10),
podpac.clinspace(0, 1, 10),
podpac.crange("2018-01-01", "2018-01-10", "1,D")
],
dims=["lat", "lon", "time"],
)
a = np.random.random(coords.shape)
a[3, 0, 0] = np.nan
a[0, 3, 0] = np.nan
a[0, 0, 3] = np.nan
source = Array(source=a, coordinates=coords)
data = source.eval(coords)
ab = np.stack([a, 2 * a], -1)
multisource = Array(source=ab, coordinates=coords, outputs=["a", "b"])
bdata = 2 * data
