def test_xr_percentile(nodata):
band_1 = np.random.randint(0, 100, size=(10, 100, 200)).astype(type(nodata))
band_2 = np.random.randint(0, 100, size=(10, 100, 200)).astype(type(nodata))
band_1[np.random.random(size=band_1.shape) > 0.5] = nodata
band_2[np.random.random(size=band_1.shape) > 0.5] = nodata
true_results = dict()
true_results["band_1_pc_20"] = np_percentile(band_1, 0.2, nodata)
true_results["band_2_pc_20"] = np_percentile(band_2, 0.2, nodata)
true_results["band_1_pc_60"] = np_percentile(band_1, 0.6, nodata)
true_results["band_2_pc_60"] = np_percentile(band_2, 0.6, nodata)
band_1 = da.from_array(band_1, chunks=(2, 20, 20))
band_2 = da.from_array(band_2, chunks=(2, 20, 20))
attrs = {"test": "attrs"}
coords = {
"x": np.linspace(10, 20, band_1.shape[2]),
"y": np.linspace(0, 5, band_1.shape[1]),
"t": np.linspace(0, 5, band_1.shape[0])
}
data_vars = {"band_1": (("t", "y", "x"), band_1), "band_2": (("t", "y", "x"), band_2)}
dataset = xr.Dataset(data_vars=data_vars, coords=coords, attrs=attrs)
output = xr_percentile(dataset, [0.2, 0.6], nodata).compute()
for key in output.keys():
np.testing.assert_equal(output[key], true_results[key])
def test_np_percentile():
arr = np.array([[0, 1, 4, 6, 8, 10, 15, 22, 25, 27],
[3, 5, 6, 8, 9, 11, 15, 28, 31, 50]])
np.random.shuffle(arr[0, :])
np.random.shuffle(arr[1, :])
arr = arr.transpose()
assert (np_percentile(arr, 0.5, 255) == np.array([8, 9])).all()
assert (np_percentile(arr, 0.7, 255) == np.array([15, 15])).all()
assert (np_percentile(arr, 1.0, 255) == np.array([27, 50])).all()
assert (np_percentile(arr, 0.0, 255) == np.array([0, 3])).all()
def test_np_percentile_some_bad_data(nodata):
arr = np.array([[0, 1, 4, 6, 8, nodata, nodata, nodata, nodata, nodata],
[3, 5, 6, 8, 9, 11, 15, 28, 31, 50]])
np.random.shuffle(arr[0, :])
np.random.shuffle(arr[1, :])
arr = arr.transpose()
assert (np_percentile(arr, 0.5, nodata) == np.array([4, 9])).all()
assert (np_percentile(arr, 0.7, nodata) == np.array([6, 15])).all()
assert (np_percentile(arr, 1.0, nodata) == np.array([8, 50])).all()
assert (np_percentile(arr, 0.0, nodata) == np.array([0, 3])).all()
def test_np_percentile_bad_data(nodata):
arr = np.array(
[
[0, 1, nodata, nodata, nodata, nodata, nodata, nodata, nodata, nodata],
[3, 5, 6, 8, 9, 11, 15, 28, 31, 50],
]
)
np.random.shuffle(arr[0, :])
np.random.shuffle(arr[1, :])
arr = arr.transpose()
np.testing.assert_equal(np_percentile(arr, 0.5, nodata), np.array([nodata, 9]))
np.testing.assert_equal(np_percentile(arr, 0.7, nodata), np.array([nodata, 15]))
np.testing.assert_equal(np_percentile(arr, 1.0, nodata), np.array([nodata, 50]))
np.testing.assert_equal(np_percentile(arr, 0.0, nodata), np.array([nodata, 3]))
def test_xr_quantile(nodata, use_dask):
band_1 = np.random.randint(0, 100,
size=(10, 100, 200)).astype(type(nodata))
band_2 = np.random.randint(0, 100,
size=(10, 100, 200)).astype(type(nodata))
band_1[np.random.random(size=band_1.shape) > 0.5] = nodata
band_2[np.random.random(size=band_1.shape) > 0.5] = nodata
true_results = dict()
true_results["band_1"] = np.stack([
np_percentile(band_1, 0.2, nodata),
np_percentile(band_1, 0.6, nodata)
],
axis=0)
true_results["band_2"] = np.stack([
np_percentile(band_2, 0.2, nodata),
np_percentile(band_2, 0.6, nodata)
],
axis=0)
if use_dask:
band_1 = da.from_array(band_1, chunks=(2, 20, 20))
band_2 = da.from_array(band_2, chunks=(2, 20, 20))
attrs = {"test": "attrs"}
coords = {
"x": np.linspace(10, 20, band_1.shape[2]),
"y": np.linspace(0, 5, band_1.shape[1]),
"t": np.linspace(0, 5, band_1.shape[0])
}
data_vars = {
"band_1":
xr.DataArray(band_1, dims=("t", "y", "x"), attrs={"test_attr": 1}),
"band_2":
xr.DataArray(band_2, dims=("t", "y", "x"), attrs={"test_attr": 2}),
}
dataset = xr.Dataset(data_vars=data_vars, coords=coords, attrs=attrs)
output = xr_quantile(dataset, [0.2, 0.6], nodata).compute()
for band in output.keys():
np.testing.assert_equal(output[band], true_results[band])