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__init__.py
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__init__.py
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from __future__ import annotations
import importlib
import platform
import string
import warnings
from contextlib import contextmanager, nullcontext
from unittest import mock # noqa: F401
import numpy as np
import pandas as pd
import pytest
from numpy.testing import assert_array_equal # noqa: F401
from packaging.version import Version
from pandas.testing import assert_frame_equal # noqa: F401
import xarray.testing
from xarray import Dataset
from xarray.core import utils
from xarray.core.duck_array_ops import allclose_or_equiv # noqa: F401
from xarray.core.indexing import ExplicitlyIndexed
from xarray.core.options import set_options
from xarray.testing import ( # noqa: F401
assert_chunks_equal,
assert_duckarray_allclose,
assert_duckarray_equal,
)
# import mpl and change the backend before other mpl imports
try:
import matplotlib as mpl
# Order of imports is important here.
# Using a different backend makes Travis CI work
mpl.use("Agg")
except ImportError:
pass
# https://github.com/pydata/xarray/issues/7322
warnings.filterwarnings("ignore", "'urllib3.contrib.pyopenssl' module is deprecated")
warnings.filterwarnings("ignore", "Deprecated call to `pkg_resources.declare_namespace")
warnings.filterwarnings("ignore", "pkg_resources is deprecated as an API")
arm_xfail = pytest.mark.xfail(
platform.machine() == "aarch64" or "arm" in platform.machine(),
reason="expected failure on ARM",
)
def _importorskip(
modname: str, minversion: str | None = None
) -> tuple[bool, pytest.MarkDecorator]:
try:
mod = importlib.import_module(modname)
has = True
if minversion is not None:
v = getattr(mod, "__version__", "999")
if Version(v) < Version(minversion):
raise ImportError("Minimum version not satisfied")
except ImportError:
has = False
func = pytest.mark.skipif(not has, reason=f"requires {modname}")
return has, func
has_matplotlib, requires_matplotlib = _importorskip("matplotlib")
has_scipy, requires_scipy = _importorskip("scipy")
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore",
message="'cgi' is deprecated and slated for removal in Python 3.13",
category=DeprecationWarning,
)
has_pydap, requires_pydap = _importorskip("pydap.client")
has_netCDF4, requires_netCDF4 = _importorskip("netCDF4")
has_h5netcdf, requires_h5netcdf = _importorskip("h5netcdf")
has_pynio, requires_pynio = _importorskip("Nio")
has_cftime, requires_cftime = _importorskip("cftime")
has_dask, requires_dask = _importorskip("dask")
has_bottleneck, requires_bottleneck = _importorskip("bottleneck")
has_rasterio, requires_rasterio = _importorskip("rasterio")
has_zarr, requires_zarr = _importorskip("zarr")
has_fsspec, requires_fsspec = _importorskip("fsspec")
has_iris, requires_iris = _importorskip("iris")
has_numbagg, requires_numbagg = _importorskip("numbagg", "0.4.0")
has_seaborn, requires_seaborn = _importorskip("seaborn")
has_sparse, requires_sparse = _importorskip("sparse")
has_cupy, requires_cupy = _importorskip("cupy")
has_cartopy, requires_cartopy = _importorskip("cartopy")
has_pint, requires_pint = _importorskip("pint")
has_numexpr, requires_numexpr = _importorskip("numexpr")
has_flox, requires_flox = _importorskip("flox")
# some special cases
has_scipy_or_netCDF4 = has_scipy or has_netCDF4
requires_scipy_or_netCDF4 = pytest.mark.skipif(
not has_scipy_or_netCDF4, reason="requires scipy or netCDF4"
)
has_numbagg_or_bottleneck = has_numbagg or has_bottleneck
requires_numbagg_or_bottleneck = pytest.mark.skipif(
not has_scipy_or_netCDF4, reason="requires scipy or netCDF4"
)
# _importorskip does not work for development versions
has_pandas_version_two = Version(pd.__version__).major >= 2
requires_pandas_version_two = pytest.mark.skipif(
not has_pandas_version_two, reason="requires pandas 2.0.0"
)
has_numpy_array_api, requires_numpy_array_api = _importorskip("numpy", "1.26.0")
has_h5netcdf_ros3 = _importorskip("h5netcdf", "1.3.0")
requires_h5netcdf_ros3 = pytest.mark.skipif(
not has_h5netcdf_ros3[0], reason="requires h5netcdf 1.3.0"
)
has_netCDF4_1_6_2_or_above, requires_netCDF4_1_6_2_or_above = _importorskip(
"netCDF4", "1.6.2"
)
# change some global options for tests
set_options(warn_for_unclosed_files=True)
if has_dask:
import dask
class CountingScheduler:
"""Simple dask scheduler counting the number of computes.
Reference: https://stackoverflow.com/questions/53289286/"""
def __init__(self, max_computes=0):
self.total_computes = 0
self.max_computes = max_computes
def __call__(self, dsk, keys, **kwargs):
self.total_computes += 1
if self.total_computes > self.max_computes:
raise RuntimeError(
"Too many computes. Total: %d > max: %d."
% (self.total_computes, self.max_computes)
)
return dask.get(dsk, keys, **kwargs)
def raise_if_dask_computes(max_computes=0):
# return a dummy context manager so that this can be used for non-dask objects
if not has_dask:
return nullcontext()
scheduler = CountingScheduler(max_computes)
return dask.config.set(scheduler=scheduler)
flaky = pytest.mark.flaky
network = pytest.mark.network
class UnexpectedDataAccess(Exception):
pass
class InaccessibleArray(utils.NDArrayMixin, ExplicitlyIndexed):
"""Disallows any loading."""
def __init__(self, array):
self.array = array
def get_duck_array(self):
raise UnexpectedDataAccess("Tried accessing data")
def __array__(self, dtype: np.typing.DTypeLike = None):
raise UnexpectedDataAccess("Tried accessing data")
def __getitem__(self, key):
raise UnexpectedDataAccess("Tried accessing data.")
class FirstElementAccessibleArray(InaccessibleArray):
def __getitem__(self, key):
tuple_idxr = key.tuple
if len(tuple_idxr) > 1:
raise UnexpectedDataAccess("Tried accessing more than one element.")
return self.array[tuple_idxr]
class DuckArrayWrapper(utils.NDArrayMixin):
"""Array-like that prevents casting to array.
Modeled after cupy."""
def __init__(self, array: np.ndarray):
self.array = array
def __getitem__(self, key):
return type(self)(self.array[key])
def __array__(self, dtype: np.typing.DTypeLike = None):
raise UnexpectedDataAccess("Tried accessing data")
def __array_namespace__(self):
"""Present to satisfy is_duck_array test."""
class ReturnItem:
def __getitem__(self, key):
return key
class IndexerMaker:
def __init__(self, indexer_cls):
self._indexer_cls = indexer_cls
def __getitem__(self, key):
if not isinstance(key, tuple):
key = (key,)
return self._indexer_cls(key)
def source_ndarray(array):
"""Given an ndarray, return the base object which holds its memory, or the
object itself.
"""
with warnings.catch_warnings():
warnings.filterwarnings("ignore", "DatetimeIndex.base")
warnings.filterwarnings("ignore", "TimedeltaIndex.base")
base = getattr(array, "base", np.asarray(array).base)
if base is None:
base = array
return base
def format_record(record) -> str:
"""Format warning record like `FutureWarning('Function will be deprecated...')`"""
return f"{str(record.category)[8:-2]}('{record.message}'))"
@contextmanager
def assert_no_warnings():
with warnings.catch_warnings(record=True) as record:
yield record
assert (
len(record) == 0
), f"Got {len(record)} unexpected warning(s): {[format_record(r) for r in record]}"
# Internal versions of xarray's test functions that validate additional
# invariants
def assert_equal(a, b, check_default_indexes=True):
__tracebackhide__ = True
xarray.testing.assert_equal(a, b)
xarray.testing._assert_internal_invariants(a, check_default_indexes)
xarray.testing._assert_internal_invariants(b, check_default_indexes)
def assert_identical(a, b, check_default_indexes=True):
__tracebackhide__ = True
xarray.testing.assert_identical(a, b)
xarray.testing._assert_internal_invariants(a, check_default_indexes)
xarray.testing._assert_internal_invariants(b, check_default_indexes)
def assert_allclose(a, b, check_default_indexes=True, **kwargs):
__tracebackhide__ = True
xarray.testing.assert_allclose(a, b, **kwargs)
xarray.testing._assert_internal_invariants(a, check_default_indexes)
xarray.testing._assert_internal_invariants(b, check_default_indexes)
_DEFAULT_TEST_DIM_SIZES = (8, 9, 10)
def create_test_data(
seed: int | None = None,
add_attrs: bool = True,
dim_sizes: tuple[int, int, int] = _DEFAULT_TEST_DIM_SIZES,
) -> Dataset:
rs = np.random.RandomState(seed)
_vars = {
"var1": ["dim1", "dim2"],
"var2": ["dim1", "dim2"],
"var3": ["dim3", "dim1"],
}
_dims = {"dim1": dim_sizes[0], "dim2": dim_sizes[1], "dim3": dim_sizes[2]}
obj = Dataset()
obj["dim2"] = ("dim2", 0.5 * np.arange(_dims["dim2"]))
if _dims["dim3"] > 26:
raise RuntimeError(
f'Not enough letters for filling this dimension size ({_dims["dim3"]})'
)
obj["dim3"] = ("dim3", list(string.ascii_lowercase[0 : _dims["dim3"]]))
obj["time"] = ("time", pd.date_range("2000-01-01", periods=20))
for v, dims in sorted(_vars.items()):
data = rs.normal(size=tuple(_dims[d] for d in dims))
obj[v] = (dims, data)
if add_attrs:
obj[v].attrs = {"foo": "variable"}
if dim_sizes == _DEFAULT_TEST_DIM_SIZES:
numbers_values = np.array([0, 1, 2, 0, 0, 1, 1, 2, 2, 3], dtype="int64")
else:
numbers_values = np.random.randint(0, 3, _dims["dim3"], dtype="int64")
obj.coords["numbers"] = ("dim3", numbers_values)
obj.encoding = {"foo": "bar"}
assert all(obj.data.flags.writeable for obj in obj.variables.values())
return obj
_CFTIME_CALENDARS = [
"365_day",
"360_day",
"julian",
"all_leap",
"366_day",
"gregorian",
"proleptic_gregorian",
"standard",
]