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from __future__ import annotations
from numbers import Number
import numpy as np
import pytest
import xarray as xr
from xarray.backends.api import _get_default_engine
from xarray.tests import (
assert_identical,
assert_no_warnings,
requires_dask,
requires_netCDF4,
requires_scipy,
)
@requires_netCDF4
@requires_scipy
def test__get_default_engine() -> None:
engine_remote = _get_default_engine("http://example.org/test.nc", allow_remote=True)
assert engine_remote == "netcdf4"
engine_gz = _get_default_engine("/example.gz")
assert engine_gz == "scipy"
engine_default = _get_default_engine("/example")
assert engine_default == "netcdf4"
def test_custom_engine() -> None:
expected = xr.Dataset(
dict(a=2 * np.arange(5)), coords=dict(x=("x", np.arange(5), dict(units="s")))
)
class CustomBackend(xr.backends.BackendEntrypoint):
def open_dataset(
self,
filename_or_obj,
drop_variables=None,
**kwargs,
) -> xr.Dataset:
return expected.copy(deep=True)
actual = xr.open_dataset("fake_filename", engine=CustomBackend)
assert_identical(expected, actual)
def test_multiindex() -> None:
# GH7139
# Check that we properly handle backends that change index variables
dataset = xr.Dataset(coords={"coord1": ["A", "B"], "coord2": [1, 2]})
dataset = dataset.stack(z=["coord1", "coord2"])
class MultiindexBackend(xr.backends.BackendEntrypoint):
def open_dataset(
self,
filename_or_obj,
drop_variables=None,
**kwargs,
) -> xr.Dataset:
return dataset.copy(deep=True)
loaded = xr.open_dataset("fake_filename", engine=MultiindexBackend)
assert_identical(dataset, loaded)
class PassThroughBackendEntrypoint(xr.backends.BackendEntrypoint):
"""Access an object passed to the `open_dataset` method."""
def open_dataset(self, dataset, *, drop_variables=None):
"""Return the first argument."""
return dataset
def explicit_chunks(chunks, shape):
"""Return explicit chunks, expanding any integer member to a tuple of integers."""
# Emulate `dask.array.core.normalize_chunks` but for simpler inputs.
return tuple(
(
(size // chunk) * (chunk,)
+ ((size % chunk,) if size % chunk or size == 0 else ())
)
if isinstance(chunk, Number)
else chunk
for chunk, size in zip(chunks, shape)
)
@requires_dask
class TestPreferredChunks:
"""Test behaviors related to the backend's preferred chunks."""
var_name = "data"
def create_dataset(self, shape, pref_chunks):
"""Return a dataset with a variable with the given shape and preferred chunks."""
dims = tuple(f"dim_{idx}" for idx in range(len(shape)))
return xr.Dataset(
{
self.var_name: xr.Variable(
dims,
np.empty(shape, dtype=np.dtype("V1")),
encoding={"preferred_chunks": dict(zip(dims, pref_chunks))},
)
}
)
def check_dataset(self, initial, final, expected_chunks):
assert_identical(initial, final)
assert final[self.var_name].chunks == expected_chunks
@pytest.mark.parametrize(
"shape,pref_chunks",
[
# Represent preferred chunking with int.
((5,), (2,)),
# Represent preferred chunking with tuple.
((5,), ((2, 2, 1),)),
# Represent preferred chunking with int in two dims.
((5, 6), (4, 2)),
# Represent preferred chunking with tuple in second dim.
((5, 6), (4, (2, 2, 2))),
],
)
@pytest.mark.parametrize("request_with_empty_map", [False, True])
def test_honor_chunks(self, shape, pref_chunks, request_with_empty_map):
"""Honor the backend's preferred chunks when opening a dataset."""
initial = self.create_dataset(shape, pref_chunks)
# To keep the backend's preferred chunks, the `chunks` argument must be an
# empty mapping or map dimensions to `None`.
chunks = (
{}
if request_with_empty_map
else dict.fromkeys(initial[self.var_name].dims, None)
)
final = xr.open_dataset(
initial, engine=PassThroughBackendEntrypoint, chunks=chunks
)
self.check_dataset(initial, final, explicit_chunks(pref_chunks, shape))
@pytest.mark.parametrize(
"shape,pref_chunks,req_chunks",
[
# Preferred chunking is int; requested chunking is int.
((5,), (2,), (3,)),
# Preferred chunking is int; requested chunking is tuple.
((5,), (2,), ((2, 1, 1, 1),)),
# Preferred chunking is tuple; requested chunking is int.
((5,), ((2, 2, 1),), (3,)),
# Preferred chunking is tuple; requested chunking is tuple.
((5,), ((2, 2, 1),), ((2, 1, 1, 1),)),
# Split chunks along a dimension other than the first.
((1, 5), (1, 2), (1, 3)),
],
)
def test_split_chunks(self, shape, pref_chunks, req_chunks):
"""Warn when the requested chunks separate the backend's preferred chunks."""
initial = self.create_dataset(shape, pref_chunks)
with pytest.warns(UserWarning):
final = xr.open_dataset(
initial,
engine=PassThroughBackendEntrypoint,
chunks=dict(zip(initial[self.var_name].dims, req_chunks)),
)
self.check_dataset(initial, final, explicit_chunks(req_chunks, shape))
@pytest.mark.parametrize(
"shape,pref_chunks,req_chunks",
[
# Keep preferred chunks using int representation.
((5,), (2,), (2,)),
# Keep preferred chunks using tuple representation.
((5,), (2,), ((2, 2, 1),)),
# Join chunks, leaving a final short chunk.
((5,), (2,), (4,)),
# Join all chunks with an int larger than the dimension size.
((5,), (2,), (6,)),
# Join one chunk using tuple representation.
((5,), (1,), ((1, 1, 2, 1),)),
# Join one chunk using int representation.
((5,), ((1, 1, 2, 1),), (2,)),
# Join multiple chunks using tuple representation.
((5,), ((1, 1, 2, 1),), ((2, 3),)),
# Join chunks in multiple dimensions.
((5, 5), (2, (1, 1, 2, 1)), (4, (2, 3))),
],
)
def test_join_chunks(self, shape, pref_chunks, req_chunks):
"""Don't warn when the requested chunks join or keep the preferred chunks."""
initial = self.create_dataset(shape, pref_chunks)
with assert_no_warnings():
final = xr.open_dataset(
initial,
engine=PassThroughBackendEntrypoint,
chunks=dict(zip(initial[self.var_name].dims, req_chunks)),
)
self.check_dataset(initial, final, explicit_chunks(req_chunks, shape))
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