def test_region_datashade():
"""
Tests that we can datashade a pandas.DataFrame based on the region's bounds
"""
region = Region("Kitaa, Greenland", -1_600_000, -1_520_000, -1_360_000,
-1_300_000)
atl11_dataset: xr.Dataset = catalog.test_data.atl11_test_case.to_dask()
atl11_dataset["x"], atl11_dataset["y"] = lonlat_to_xy(
longitude=atl11_dataset.longitude,
latitude=atl11_dataset.latitude,
epsg=3995,
)
atl11_dataset = atl11_dataset.set_coords(["x", "y"])
df: pd.DataFrame = atl11_dataset.h_corr.to_dataframe()
agg_grid: xr.DataArray = region.datashade(df=df,
z_dim="h_corr",
plot_width=100)
assert agg_grid.shape == (75, 100
) # check correct aspect ratio is maintained
npt.assert_allclose(agg_grid.min(), 1426.336637)
npt.assert_allclose(agg_grid.mean(), 1668.94741)
npt.assert_allclose(agg_grid.max(), 1798.066285)
def test_lonlat_to_xy_dask_series():
"""
Test that converting from longitude/latitude to x/y in EPSG:3031 works when
passing them in as dask.dataframe.core.Series objects.
"""
atl11_dataset: xr.Dataset = catalog.test_data.atl11_test_case.to_dask()
atl11_dataframe: dask.dataframe.core.DataFrame = atl11_dataset.to_dask_dataframe(
)
x, y = lonlat_to_xy(longitude=atl11_dataframe.longitude,
latitude=atl11_dataframe.latitude)
npt.assert_equal(actual=x.mean(), desired=-56900105.00307033)
npt.assert_equal(actual=y.mean(), desired=48141607.48486084)
def test_lonlat_to_xy_xarray_dataarray():
"""
Test that converting from longitude/latitude to x/y in EPSG:3031 works when
passing them in as xarray.DataArray objects. Ensure that the xarray
dimensions are preserved in the process.
"""
atl11_dataset: xr.Dataset = catalog.test_data.atl11_test_case.to_dask()
x, y = lonlat_to_xy(longitude=atl11_dataset.longitude,
latitude=atl11_dataset.latitude)
assert x.dims == y.dims == ("ref_pt", )
assert x.shape == y.shape == (1404, )
npt.assert_equal(actual=x.mean().data, desired=-56900105.00307034)
npt.assert_equal(actual=y.mean().data, desired=48141607.48486084)
engine="zarr",
combine="nested",
concat_dim="ref_pt",
parallel="True",
backend_kwargs={"consolidated": True},
)
# %% [markdown]
# ## Light pre-processing
#
# - Reproject longitude/latitude to EPSG:3031 x/y
# - Mask out low quality height data
# %%
# Calculate the EPSG:3031 x/y projection coordinates
ds["x"], ds["y"] = deepicedrain.lonlat_to_xy(longitude=ds.longitude,
latitude=ds.latitude)
# Set x, y, x_atc and y_atc as coordinates of the xarray.Dataset
ds: xr.Dataset = ds.set_coords(names=["x", "y", "x_atc", "y_atc"])
# %%
# Mask out low quality height data
ds["h_corr"]: xr.DataArray = ds.h_corr.where(
cond=ds.quality_summary_ref_surf == 0)
# %% [markdown]
# ## Trim out unnecessary values (optional)
#
# There's ~220 million ATL11 points for the whole of Antarctica,
# and not all of them will be needed depending on what you want to do.
# To cut down on the number of data points the computer needs to work on,
# we can:
