def test_select_on_transposed_dataarray(self):
x = np.linspace(-3, 7, 53)
y = np.linspace(-5, 8, 89)
z = np.exp(-1 * (x**2 + y[:, np.newaxis]**2))
array = xr.DataArray(z, coords=[y, x], dims=['x', 'y'])
img = Image(array)[1:3]
self.assertEqual(img['z'], Image(array.sel(x=slice(1, 3)))['z'])
def test_read_pattern_path_as_pattern_as_str_with_list_of_urlpaths():
pytest.importorskip('rasterio')
cat = intake.open_catalog(os.path.join(here, 'data', 'catalog.yaml'))
colors = cat.pattern_tiff_source_path_pattern_as_str()
da = colors.read()
assert da.shape == (2, 3, 64, 64)
assert len(da.color) == 2
assert set(da.color.data) == set(['red', 'green'])
assert da.sel(color='red').shape == (3, 64, 64)
rgb = {'red': [204, 17, 17], 'green': [17, 204, 17]}
for color, values in rgb.items():
for i, v in enumerate(values):
assert (da.sel(color=color).sel(band=i + 1).values == v).all()
def test_rasterio_vrt_network(self):
import rasterio
url = "https://storage.googleapis.com/\
gcp-public-data-landsat/LC08/01/047/027/\
LC08_L1TP_047027_20130421_20170310_01_T1/\
LC08_L1TP_047027_20130421_20170310_01_T1_B4.TIF"
env = rasterio.Env(
GDAL_DISABLE_READDIR_ON_OPEN="EMPTY_DIR",
CPL_VSIL_CURL_USE_HEAD=False,
CPL_VSIL_CURL_ALLOWED_EXTENSIONS="TIF",
)
with env:
with rasterio.open(url) as src:
with rasterio.vrt.WarpedVRT(src, crs="epsg:4326") as vrt:
expected_shape = (vrt.width, vrt.height)
expected_crs = vrt.crs
expected_res = vrt.res
# Value of single pixel in center of image
lon, lat = vrt.xy(vrt.width // 2, vrt.height // 2)
expected_val = next(vrt.sample([(lon, lat)]))
with xr.open_rasterio(vrt) as da:
actual_shape = (da.sizes["x"], da.sizes["y"])
actual_crs = da.crs
actual_res = da.res
actual_val = da.sel(dict(x=lon, y=lat), method="nearest").data
assert_equal(actual_shape, expected_shape)
assert_equal(actual_crs, expected_crs)
assert_equal(actual_res, expected_res)
assert_equal(expected_val, actual_val)
def test_select_on_transposed_dataarray(self):
x = np.linspace(-3, 7, 53)
y = np.linspace(-5, 8, 89)
z = np.exp(-1*(x**2 + y[:, np.newaxis]**2))
array = xr.DataArray(z, coords=[y, x], dims=['x', 'y'])
img = Image(array)[1:3]
self.assertEqual(img['z'], Image(array.sel(x=slice(1, 3)))['z'])
def index(da, indexers):
from xarray.core.indexing import remap_label_indexers
if not isinstance(da, xr.DataArray):
raise TypeError(f"Expected DataArray. Received {type(da).__name__}")
pos_indexers, new_indexes = remap_label_indexers(da, indexers)
dask_indexers = list(pos_indexers.values())
# TODO: avoid the sel. That could be slow
indexed = da.sel(**indexers).copy(data=dask_safeslice(da.data, dask_indexers))
return indexed
def test_rasterio_vrt(self):
# tmp_file default crs is UTM: CRS({'init': 'epsg:32618'}
with create_tmp_geotiff() as (tmp_file, expected):
with rasterio.open(tmp_file) as src:
with rasterio.vrt.WarpedVRT(src, crs="epsg:4326") as vrt:
expected_shape = (vrt.width, vrt.height)
expected_crs = vrt.crs
expected_res = vrt.res
# Value of single pixel in center of image
lon, lat = vrt.xy(vrt.width // 2, vrt.height // 2)
expected_val = next(vrt.sample([(lon, lat)]))
with xr.open_rasterio(vrt) as da:
actual_shape = (da.sizes["x"], da.sizes["y"])
actual_crs = da.crs
actual_res = da.res
actual_val = da.sel(dict(x=lon, y=lat), method="nearest").data
assert actual_crs == expected_crs
assert actual_res == expected_res
assert actual_shape == expected_shape
assert expected_val.all() == actual_val.all()
def slice_dataarray(da):
return da.sel(lat=site[1].lat.unique(),
lon=site[1].lon.unique(),
method='nearest')
