from __future__ import annotations import contextlib import gzip import itertools import math import os.path import pickle import platform import re import shutil import sys import tempfile import uuid import warnings from contextlib import ExitStack from io import BytesIO from pathlib import Path from typing import TYPE_CHECKING, Any, Final, Iterator, cast import numpy as np import pandas as pd import pytest from packaging.version import Version from pandas.errors import OutOfBoundsDatetime import xarray as xr from xarray import ( DataArray, Dataset, backends, load_dataarray, load_dataset, open_dataarray, open_dataset, open_mfdataset, save_mfdataset, ) from xarray.backends.common import robust_getitem from xarray.backends.h5netcdf_ import H5netcdfBackendEntrypoint from xarray.backends.netcdf3 import _nc3_dtype_coercions from xarray.backends.netCDF4_ import ( NetCDF4BackendEntrypoint, _extract_nc4_variable_encoding, ) from xarray.backends.pydap_ import PydapDataStore from xarray.backends.scipy_ import ScipyBackendEntrypoint from xarray.coding.variables import SerializationWarning from xarray.conventions import encode_dataset_coordinates from xarray.core import indexing from xarray.core.options import set_options from xarray.core.pycompat import array_type from xarray.tests import ( arm_xfail, assert_allclose, assert_array_equal, assert_equal, assert_identical, assert_no_warnings, has_dask, has_h5netcdf_0_12, has_netCDF4, has_scipy, mock, network, requires_cfgrib, requires_cftime, requires_dask, requires_fsspec, requires_h5netcdf, requires_h5netcdf_0_12, requires_iris, requires_netCDF4, requires_pseudonetcdf, requires_pydap, requires_pynio, requires_rasterio, requires_scipy, requires_scipy_or_netCDF4, requires_zarr, ) from xarray.tests.test_coding_times import ( _ALL_CALENDARS, _NON_STANDARD_CALENDARS, _STANDARD_CALENDARS, ) from xarray.tests.test_dataset import ( create_append_string_length_mismatch_test_data, create_append_test_data, create_test_data, ) try: import netCDF4 as nc4 except ImportError: pass try: import dask import dask.array as da except ImportError: pass have_zarr_kvstore = False try: from zarr.storage import KVStore have_zarr_kvstore = True except ImportError: KVStore = None have_zarr_v3 = False try: # as of Zarr v2.13 these imports require environment variable # ZARR_V3_EXPERIMENTAL_API=1 from zarr import DirectoryStoreV3, KVStoreV3 have_zarr_v3 = True except ImportError: KVStoreV3 = None ON_WINDOWS = sys.platform == "win32" default_value = object() dask_array_type = array_type("dask") if TYPE_CHECKING: from xarray.backends.api import T_NetcdfEngine, T_NetcdfTypes def open_example_dataset(name, *args, **kwargs) -> Dataset: return open_dataset( os.path.join(os.path.dirname(__file__), "data", name), *args, **kwargs ) def open_example_mfdataset(names, *args, **kwargs) -> Dataset: return open_mfdataset( [os.path.join(os.path.dirname(__file__), "data", name) for name in names], *args, **kwargs, ) def create_masked_and_scaled_data() -> Dataset: x = np.array([np.nan, np.nan, 10, 10.1, 10.2], dtype=np.float32) encoding = { "_FillValue": -1, "add_offset": 10, "scale_factor": np.float32(0.1), "dtype": "i2", } return Dataset({"x": ("t", x, {}, encoding)}) def create_encoded_masked_and_scaled_data() -> Dataset: attributes = {"_FillValue": -1, "add_offset": 10, "scale_factor": np.float32(0.1)} return Dataset( {"x": ("t", np.array([-1, -1, 0, 1, 2], dtype=np.int16), attributes)} ) def create_unsigned_masked_scaled_data() -> Dataset: encoding = { "_FillValue": 255, "_Unsigned": "true", "dtype": "i1", "add_offset": 10, "scale_factor": np.float32(0.1), } x = np.array([10.0, 10.1, 22.7, 22.8, np.nan], dtype=np.float32) return Dataset({"x": ("t", x, {}, encoding)}) def create_encoded_unsigned_masked_scaled_data() -> Dataset: # These are values as written to the file: the _FillValue will # be represented in the signed form. attributes = { "_FillValue": -1, "_Unsigned": "true", "add_offset": 10, "scale_factor": np.float32(0.1), } # Create unsigned data corresponding to [0, 1, 127, 128, 255] unsigned sb = np.asarray([0, 1, 127, -128, -1], dtype="i1") return Dataset({"x": ("t", sb, attributes)}) def create_bad_unsigned_masked_scaled_data() -> Dataset: encoding = { "_FillValue": 255, "_Unsigned": True, "dtype": "i1", "add_offset": 10, "scale_factor": np.float32(0.1), } x = np.array([10.0, 10.1, 22.7, 22.8, np.nan], dtype=np.float32) return Dataset({"x": ("t", x, {}, encoding)}) def create_bad_encoded_unsigned_masked_scaled_data() -> Dataset: # These are values as written to the file: the _FillValue will # be represented in the signed form. attributes = { "_FillValue": -1, "_Unsigned": True, "add_offset": 10, "scale_factor": np.float32(0.1), } # Create signed data corresponding to [0, 1, 127, 128, 255] unsigned sb = np.asarray([0, 1, 127, -128, -1], dtype="i1") return Dataset({"x": ("t", sb, attributes)}) def create_signed_masked_scaled_data() -> Dataset: encoding = { "_FillValue": -127, "_Unsigned": "false", "dtype": "i1", "add_offset": 10, "scale_factor": np.float32(0.1), } x = np.array([-1.0, 10.1, 22.7, np.nan], dtype=np.float32) return Dataset({"x": ("t", x, {}, encoding)}) def create_encoded_signed_masked_scaled_data() -> Dataset: # These are values as written to the file: the _FillValue will # be represented in the signed form. attributes = { "_FillValue": -127, "_Unsigned": "false", "add_offset": 10, "scale_factor": np.float32(0.1), } # Create signed data corresponding to [0, 1, 127, 128, 255] unsigned sb = np.asarray([-110, 1, 127, -127], dtype="i1") return Dataset({"x": ("t", sb, attributes)}) def create_boolean_data() -> Dataset: attributes = {"units": "-"} return Dataset({"x": ("t", [True, False, False, True], attributes)}) class TestCommon: def test_robust_getitem(self) -> None: class UnreliableArrayFailure(Exception): pass class UnreliableArray: def __init__(self, array, failures=1): self.array = array self.failures = failures def __getitem__(self, key): if self.failures > 0: self.failures -= 1 raise UnreliableArrayFailure return self.array[key] array = UnreliableArray([0]) with pytest.raises(UnreliableArrayFailure): array[0] assert array[0] == 0 actual = robust_getitem(array, 0, catch=UnreliableArrayFailure, initial_delay=0) assert actual == 0 class NetCDF3Only: netcdf3_formats: tuple[T_NetcdfTypes, ...] = ("NETCDF3_CLASSIC", "NETCDF3_64BIT") @requires_scipy def test_dtype_coercion_error(self) -> None: """Failing dtype coercion should lead to an error""" for dtype, format in itertools.product( _nc3_dtype_coercions, self.netcdf3_formats ): if dtype == "bool": # coerced upcast (bool to int8) ==> can never fail continue # Using the largest representable value, create some data that will # no longer compare equal after the coerced downcast maxval = np.iinfo(dtype).max x = np.array([0, 1, 2, maxval], dtype=dtype) ds = Dataset({"x": ("t", x, {})}) with create_tmp_file(allow_cleanup_failure=False) as path: with pytest.raises(ValueError, match="could not safely cast"): ds.to_netcdf(path, format=format) class DatasetIOBase: engine: T_NetcdfEngine | None = None file_format: T_NetcdfTypes | None = None def create_store(self): raise NotImplementedError() @contextlib.contextmanager def roundtrip( self, data, save_kwargs=None, open_kwargs=None, allow_cleanup_failure=False ): if save_kwargs is None: save_kwargs = {} if open_kwargs is None: open_kwargs = {} with create_tmp_file(allow_cleanup_failure=allow_cleanup_failure) as path: self.save(data, path, **save_kwargs) with self.open(path, **open_kwargs) as ds: yield ds @contextlib.contextmanager def roundtrip_append( self, data, save_kwargs=None, open_kwargs=None, allow_cleanup_failure=False ): if save_kwargs is None: save_kwargs = {} if open_kwargs is None: open_kwargs = {} with create_tmp_file(allow_cleanup_failure=allow_cleanup_failure) as path: for i, key in enumerate(data.variables): mode = "a" if i > 0 else "w" self.save(data[[key]], path, mode=mode, **save_kwargs) with self.open(path, **open_kwargs) as ds: yield ds # The save/open methods may be overwritten below def save(self, dataset, path, **kwargs): return dataset.to_netcdf( path, engine=self.engine, format=self.file_format, **kwargs ) @contextlib.contextmanager def open(self, path, **kwargs): with open_dataset(path, engine=self.engine, **kwargs) as ds: yield ds def test_zero_dimensional_variable(self) -> None: expected = create_test_data() expected["float_var"] = ([], 1.0e9, {"units": "units of awesome"}) expected["bytes_var"] = ([], b"foobar") expected["string_var"] = ([], "foobar") with self.roundtrip(expected) as actual: assert_identical(expected, actual) def test_write_store(self) -> None: expected = create_test_data() with self.create_store() as store: expected.dump_to_store(store) # we need to cf decode the store because it has time and # non-dimension coordinates with xr.decode_cf(store) as actual: assert_allclose(expected, actual) def check_dtypes_roundtripped(self, expected, actual): for k in expected.variables: expected_dtype = expected.variables[k].dtype # For NetCDF3, the backend should perform dtype coercion if ( isinstance(self, NetCDF3Only) and str(expected_dtype) in _nc3_dtype_coercions ): expected_dtype = np.dtype(_nc3_dtype_coercions[str(expected_dtype)]) actual_dtype = actual.variables[k].dtype # TODO: check expected behavior for string dtypes more carefully string_kinds = {"O", "S", "U"} assert expected_dtype == actual_dtype or ( expected_dtype.kind in string_kinds and actual_dtype.kind in string_kinds ) def test_roundtrip_test_data(self) -> None: expected = create_test_data() with self.roundtrip(expected) as actual: self.check_dtypes_roundtripped(expected, actual) assert_identical(expected, actual) def test_load(self) -> None: expected = create_test_data() @contextlib.contextmanager def assert_loads(vars=None): if vars is None: vars = expected with self.roundtrip(expected) as actual: for k, v in actual.variables.items(): # IndexVariables are eagerly loaded into memory assert v._in_memory == (k in actual.dims) yield actual for k, v in actual.variables.items(): if k in vars: assert v._in_memory assert_identical(expected, actual) with pytest.raises(AssertionError): # make sure the contextmanager works! with assert_loads() as ds: pass with assert_loads() as ds: ds.load() with assert_loads(["var1", "dim1", "dim2"]) as ds: ds["var1"].load() # verify we can read data even after closing the file with self.roundtrip(expected) as ds: actual = ds.load() assert_identical(expected, actual) def test_dataset_compute(self) -> None: expected = create_test_data() with self.roundtrip(expected) as actual: # Test Dataset.compute() for k, v in actual.variables.items(): # IndexVariables are eagerly cached assert v._in_memory == (k in actual.dims) computed = actual.compute() for k, v in actual.variables.items(): assert v._in_memory == (k in actual.dims) for v in computed.variables.values(): assert v._in_memory assert_identical(expected, actual) assert_identical(expected, computed) def test_pickle(self) -> None: if not has_dask: pytest.xfail("pickling requires dask for SerializableLock") expected = Dataset({"foo": ("x", [42])}) with self.roundtrip(expected, allow_cleanup_failure=ON_WINDOWS) as roundtripped: with roundtripped: # Windows doesn't like reopening an already open file raw_pickle = pickle.dumps(roundtripped) with pickle.loads(raw_pickle) as unpickled_ds: assert_identical(expected, unpickled_ds) @pytest.mark.filterwarnings("ignore:deallocating CachingFileManager") def test_pickle_dataarray(self) -> None: if not has_dask: pytest.xfail("pickling requires dask for SerializableLock") expected = Dataset({"foo": ("x", [42])}) with self.roundtrip(expected, allow_cleanup_failure=ON_WINDOWS) as roundtripped: with roundtripped: raw_pickle = pickle.dumps(roundtripped["foo"]) # TODO: figure out how to explicitly close the file for the # unpickled DataArray? unpickled = pickle.loads(raw_pickle) assert_identical(expected["foo"], unpickled) def test_dataset_caching(self) -> None: expected = Dataset({"foo": ("x", [5, 6, 7])}) with self.roundtrip(expected) as actual: assert isinstance(actual.foo.variable._data, indexing.MemoryCachedArray) assert not actual.foo.variable._in_memory actual.foo.values # cache assert actual.foo.variable._in_memory with self.roundtrip(expected, open_kwargs={"cache": False}) as actual: assert isinstance(actual.foo.variable._data, indexing.CopyOnWriteArray) assert not actual.foo.variable._in_memory actual.foo.values # no caching assert not actual.foo.variable._in_memory @pytest.mark.filterwarnings("ignore:deallocating CachingFileManager") def test_roundtrip_None_variable(self) -> None: expected = Dataset({None: (("x", "y"), [[0, 1], [2, 3]])}) with self.roundtrip(expected) as actual: assert_identical(expected, actual) def test_roundtrip_object_dtype(self) -> None: floats = np.array([0.0, 0.0, 1.0, 2.0, 3.0], dtype=object) floats_nans = np.array([np.nan, np.nan, 1.0, 2.0, 3.0], dtype=object) bytes_ = np.array([b"ab", b"cdef", b"g"], dtype=object) bytes_nans = np.array([b"ab", b"cdef", np.nan], dtype=object) strings = np.array(["ab", "cdef", "g"], dtype=object) strings_nans = np.array(["ab", "cdef", np.nan], dtype=object) all_nans = np.array([np.nan, np.nan], dtype=object) original = Dataset( { "floats": ("a", floats), "floats_nans": ("a", floats_nans), "bytes": ("b", bytes_), "bytes_nans": ("b", bytes_nans), "strings": ("b", strings), "strings_nans": ("b", strings_nans), "all_nans": ("c", all_nans), "nan": ([], np.nan), } ) expected = original.copy(deep=True) with self.roundtrip(original) as actual: try: assert_identical(expected, actual) except AssertionError: # Most stores use '' for nans in strings, but some don't. # First try the ideal case (where the store returns exactly) # the original Dataset), then try a more realistic case. # This currently includes all netCDF files when encoding is not # explicitly set. # https://github.com/pydata/xarray/issues/1647 expected["bytes_nans"][-1] = b"" expected["strings_nans"][-1] = "" assert_identical(expected, actual) def test_roundtrip_string_data(self) -> None: expected = Dataset({"x": ("t", ["ab", "cdef"])}) with self.roundtrip(expected) as actual: assert_identical(expected, actual) def test_roundtrip_string_encoded_characters(self) -> None: expected = Dataset({"x": ("t", ["ab", "cdef"])}) expected["x"].encoding["dtype"] = "S1" with self.roundtrip(expected) as actual: assert_identical(expected, actual) assert actual["x"].encoding["_Encoding"] == "utf-8" expected["x"].encoding["_Encoding"] = "ascii" with self.roundtrip(expected) as actual: assert_identical(expected, actual) assert actual["x"].encoding["_Encoding"] == "ascii" @arm_xfail def test_roundtrip_numpy_datetime_data(self) -> None: times = pd.to_datetime(["2000-01-01", "2000-01-02", "NaT"]) expected = Dataset({"t": ("t", times), "t0": times[0]}) kwargs = {"encoding": {"t0": {"units": "days since 1950-01-01"}}} with self.roundtrip(expected, save_kwargs=kwargs) as actual: assert_identical(expected, actual) assert actual.t0.encoding["units"] == "days since 1950-01-01" @requires_cftime def test_roundtrip_cftime_datetime_data(self) -> None: from xarray.tests.test_coding_times import _all_cftime_date_types date_types = _all_cftime_date_types() for date_type in date_types.values(): times = [date_type(1, 1, 1), date_type(1, 1, 2)] expected = Dataset({"t": ("t", times), "t0": times[0]}) kwargs = {"encoding": {"t0": {"units": "days since 0001-01-01"}}} expected_decoded_t = np.array(times) expected_decoded_t0 = np.array([date_type(1, 1, 1)]) expected_calendar = times[0].calendar with warnings.catch_warnings(): if expected_calendar in {"proleptic_gregorian", "standard"}: warnings.filterwarnings("ignore", "Unable to decode time axis") with self.roundtrip(expected, save_kwargs=kwargs) as actual: abs_diff = abs(actual.t.values - expected_decoded_t) assert (abs_diff <= np.timedelta64(1, "s")).all() assert ( actual.t.encoding["units"] == "days since 0001-01-01 00:00:00.000000" ) assert actual.t.encoding["calendar"] == expected_calendar abs_diff = abs(actual.t0.values - expected_decoded_t0) assert (abs_diff <= np.timedelta64(1, "s")).all() assert actual.t0.encoding["units"] == "days since 0001-01-01" assert actual.t.encoding["calendar"] == expected_calendar def test_roundtrip_timedelta_data(self) -> None: time_deltas = pd.to_timedelta(["1h", "2h", "NaT"]) expected = Dataset({"td": ("td", time_deltas), "td0": time_deltas[0]}) with self.roundtrip(expected) as actual: assert_identical(expected, actual) def test_roundtrip_float64_data(self) -> None: expected = Dataset({"x": ("y", np.array([1.0, 2.0, np.pi], dtype="float64"))}) with self.roundtrip(expected) as actual: assert_identical(expected, actual) def test_roundtrip_example_1_netcdf(self) -> None: with open_example_dataset("example_1.nc") as expected: with self.roundtrip(expected) as actual: # we allow the attributes to differ since that # will depend on the encoding used. For example, # without CF encoding 'actual' will end up with # a dtype attribute. assert_equal(expected, actual) def test_roundtrip_coordinates(self) -> None: original = Dataset( {"foo": ("x", [0, 1])}, {"x": [2, 3], "y": ("a", [42]), "z": ("x", [4, 5])} ) with self.roundtrip(original) as actual: assert_identical(original, actual) original["foo"].encoding["coordinates"] = "y" with self.roundtrip(original, open_kwargs={"decode_coords": False}) as expected: # check roundtripping when decode_coords=False with self.roundtrip( expected, open_kwargs={"decode_coords": False} ) as actual: assert_identical(expected, actual) def test_roundtrip_global_coordinates(self) -> None: original = Dataset( {"foo": ("x", [0, 1])}, {"x": [2, 3], "y": ("a", [42]), "z": ("x", [4, 5])} ) with self.roundtrip(original) as actual: assert_identical(original, actual) # test that global "coordinates" is as expected _, attrs = encode_dataset_coordinates(original) assert attrs["coordinates"] == "y" # test warning when global "coordinates" is already set original.attrs["coordinates"] = "foo" with pytest.warns(SerializationWarning): _, attrs = encode_dataset_coordinates(original) assert attrs["coordinates"] == "foo" def test_roundtrip_coordinates_with_space(self) -> None: original = Dataset(coords={"x": 0, "y z": 1}) expected = Dataset({"y z": 1}, {"x": 0}) with pytest.warns(SerializationWarning): with self.roundtrip(original) as actual: assert_identical(expected, actual) def test_roundtrip_boolean_dtype(self) -> None: original = create_boolean_data() assert original["x"].dtype == "bool" with self.roundtrip(original) as actual: assert_identical(original, actual) assert actual["x"].dtype == "bool" def test_orthogonal_indexing(self) -> None: in_memory = create_test_data() with self.roundtrip(in_memory) as on_disk: indexers = {"dim1": [1, 2, 0], "dim2": [3, 2, 0, 3], "dim3": np.arange(5)} expected = in_memory.isel(indexers) actual = on_disk.isel(**indexers) # make sure the array is not yet loaded into memory assert not actual["var1"].variable._in_memory assert_identical(expected, actual) # do it twice, to make sure we're switched from orthogonal -> numpy # when we cached the values actual = on_disk.isel(**indexers) assert_identical(expected, actual) def test_vectorized_indexing(self) -> None: in_memory = create_test_data() with self.roundtrip(in_memory) as on_disk: indexers = { "dim1": DataArray([0, 2, 0], dims="a"), "dim2": DataArray([0, 2, 3], dims="a"), } expected = in_memory.isel(indexers) actual = on_disk.isel(**indexers) # make sure the array is not yet loaded into memory assert not actual["var1"].variable._in_memory assert_identical(expected, actual.load()) # do it twice, to make sure we're switched from # vectorized -> numpy when we cached the values actual = on_disk.isel(**indexers) assert_identical(expected, actual) def multiple_indexing(indexers): # make sure a sequence of lazy indexings certainly works. with self.roundtrip(in_memory) as on_disk: actual = on_disk["var3"] expected = in_memory["var3"] for ind in indexers: actual = actual.isel(ind) expected = expected.isel(ind) # make sure the array is not yet loaded into memory assert not actual.variable._in_memory assert_identical(expected, actual.load()) # two-staged vectorized-indexing indexers2 = [ { "dim1": DataArray([[0, 7], [2, 6], [3, 5]], dims=["a", "b"]), "dim3": DataArray([[0, 4], [1, 3], [2, 2]], dims=["a", "b"]), }, {"a": DataArray([0, 1], dims=["c"]), "b": DataArray([0, 1], dims=["c"])}, ] multiple_indexing(indexers2) # vectorized-slice mixed indexers3 = [ { "dim1": DataArray([[0, 7], [2, 6], [3, 5]], dims=["a", "b"]), "dim3": slice(None, 10), } ] multiple_indexing(indexers3) # vectorized-integer mixed indexers4 = [ {"dim3": 0}, {"dim1": DataArray([[0, 7], [2, 6], [3, 5]], dims=["a", "b"])}, {"a": slice(None, None, 2)}, ] multiple_indexing(indexers4) # vectorized-integer mixed indexers5 = [ {"dim3": 0}, {"dim1": DataArray([[0, 7], [2, 6], [3, 5]], dims=["a", "b"])}, {"a": 1, "b": 0}, ] multiple_indexing(indexers5) @pytest.mark.xfail( reason="zarr without dask handles negative steps in slices incorrectly", ) def test_vectorized_indexing_negative_step(self) -> None: # use dask explicitly when present open_kwargs: dict[str, Any] | None if has_dask: open_kwargs = {"chunks": {}} else: open_kwargs = None in_memory = create_test_data() def multiple_indexing(indexers): # make sure a sequence of lazy indexings certainly works. with self.roundtrip(in_memory, open_kwargs=open_kwargs) as on_disk: actual = on_disk["var3"] expected = in_memory["var3"] for ind in indexers: actual = actual.isel(ind) expected = expected.isel(ind) # make sure the array is not yet loaded into memory assert not actual.variable._in_memory assert_identical(expected, actual.load()) # with negative step slice. indexers = [ { "dim1": DataArray([[0, 7], [2, 6], [3, 5]], dims=["a", "b"]), "dim3": slice(-1, 1, -1), } ] multiple_indexing(indexers) # with negative step slice. indexers = [ { "dim1": DataArray([[0, 7], [2, 6], [3, 5]], dims=["a", "b"]), "dim3": slice(-1, 1, -2), } ] multiple_indexing(indexers) def test_isel_dataarray(self) -> None: # Make sure isel works lazily. GH:issue:1688 in_memory = create_test_data() with self.roundtrip(in_memory) as on_disk: expected = in_memory.isel(dim2=in_memory["dim2"] < 3) actual = on_disk.isel(dim2=on_disk["dim2"] < 3) assert_identical(expected, actual) def validate_array_type(self, ds): # Make sure that only NumpyIndexingAdapter stores a bare np.ndarray. def find_and_validate_array(obj): # recursively called function. obj: array or array wrapper. if hasattr(obj, "array"): if isinstance(obj.array, indexing.ExplicitlyIndexed): find_and_validate_array(obj.array) else: if isinstance(obj.array, np.ndarray): assert isinstance(obj, indexing.NumpyIndexingAdapter) elif isinstance(obj.array, dask_array_type): assert isinstance(obj, indexing.DaskIndexingAdapter) elif isinstance(obj.array, pd.Index): assert isinstance(obj, indexing.PandasIndexingAdapter) else: raise TypeError(f"{type(obj.array)} is wrapped by {type(obj)}") for k, v in ds.variables.items(): find_and_validate_array(v._data) def test_array_type_after_indexing(self) -> None: in_memory = create_test_data() with self.roundtrip(in_memory) as on_disk: self.validate_array_type(on_disk) indexers = {"dim1": [1, 2, 0], "dim2": [3, 2, 0, 3], "dim3": np.arange(5)} expected = in_memory.isel(indexers) actual = on_disk.isel(**indexers) assert_identical(expected, actual) self.validate_array_type(actual) # do it twice, to make sure we're switched from orthogonal -> numpy # when we cached the values actual = on_disk.isel(**indexers) assert_identical(expected, actual) self.validate_array_type(actual) def test_dropna(self) -> None: # regression test for GH:issue:1694 a = np.random.randn(4, 3) a[1, 1] = np.NaN in_memory = xr.Dataset( {"a": (("y", "x"), a)}, coords={"y": np.arange(4), "x": np.arange(3)} ) assert_identical( in_memory.dropna(dim="x"), in_memory.isel(x=slice(None, None, 2)) ) with self.roundtrip(in_memory) as on_disk: self.validate_array_type(on_disk) expected = in_memory.dropna(dim="x") actual = on_disk.dropna(dim="x") assert_identical(expected, actual) def test_ondisk_after_print(self) -> None: """Make sure print does not load file into memory""" in_memory = create_test_data() with self.roundtrip(in_memory) as on_disk: repr(on_disk) assert not on_disk["var1"]._in_memory class CFEncodedBase(DatasetIOBase): def test_roundtrip_bytes_with_fill_value(self) -> None: values = np.array([b"ab", b"cdef", np.nan], dtype=object) encoding = {"_FillValue": b"X", "dtype": "S1"} original = Dataset({"x": ("t", values, {}, encoding)}) expected = original.copy(deep=True) with self.roundtrip(original) as actual: assert_identical(expected, actual) original = Dataset({"x": ("t", values, {}, {"_FillValue": b""})}) with self.roundtrip(original) as actual: assert_identical(expected, actual) def test_roundtrip_string_with_fill_value_nchar(self) -> None: values = np.array(["ab", "cdef", np.nan], dtype=object) expected = Dataset({"x": ("t", values)}) encoding = {"dtype": "S1", "_FillValue": b"X"} original = Dataset({"x": ("t", values, {}, encoding)}) # Not supported yet. with pytest.raises(NotImplementedError): with self.roundtrip(original) as actual: assert_identical(expected, actual) @pytest.mark.parametrize( "decoded_fn, encoded_fn", [ ( create_unsigned_masked_scaled_data, create_encoded_unsigned_masked_scaled_data, ), pytest.param( create_bad_unsigned_masked_scaled_data, create_bad_encoded_unsigned_masked_scaled_data, marks=pytest.mark.xfail(reason="Bad _Unsigned attribute."), ), ( create_signed_masked_scaled_data, create_encoded_signed_masked_scaled_data, ), (create_masked_and_scaled_data, create_encoded_masked_and_scaled_data), ], ) def test_roundtrip_mask_and_scale(self, decoded_fn, encoded_fn) -> None: decoded = decoded_fn() encoded = encoded_fn() with self.roundtrip(decoded) as actual: for k in decoded.variables: assert decoded.variables[k].dtype == actual.variables[k].dtype assert_allclose(decoded, actual, decode_bytes=False) with self.roundtrip(decoded, open_kwargs=dict(decode_cf=False)) as actual: # TODO: this assumes that all roundtrips will first # encode. Is that something we want to test for? for k in encoded.variables: assert encoded.variables[k].dtype == actual.variables[k].dtype assert_allclose(encoded, actual, decode_bytes=False) with self.roundtrip(encoded, open_kwargs=dict(decode_cf=False)) as actual: for k in encoded.variables: assert encoded.variables[k].dtype == actual.variables[k].dtype assert_allclose(encoded, actual, decode_bytes=False) # make sure roundtrip encoding didn't change the # original dataset. assert_allclose(encoded, encoded_fn(), decode_bytes=False) with self.roundtrip(encoded) as actual: for k in decoded.variables: assert decoded.variables[k].dtype == actual.variables[k].dtype assert_allclose(decoded, actual, decode_bytes=False) @staticmethod def _create_cf_dataset(): original = Dataset( dict( variable=( ("ln_p", "latitude", "longitude"), np.arange(8, dtype="f4").reshape(2, 2, 2), {"ancillary_variables": "std_devs det_lim"}, ), std_devs=( ("ln_p", "latitude", "longitude"), np.arange(0.1, 0.9, 0.1).reshape(2, 2, 2), {"standard_name": "standard_error"}, ), det_lim=( (), 0.1, {"standard_name": "detection_minimum"}, ), ), dict( latitude=("latitude", [0, 1], {"units": "degrees_north"}), longitude=("longitude", [0, 1], {"units": "degrees_east"}), latlon=((), -1, {"grid_mapping_name": "latitude_longitude"}), latitude_bnds=(("latitude", "bnds2"), [[0, 1], [1, 2]]), longitude_bnds=(("longitude", "bnds2"), [[0, 1], [1, 2]]), areas=( ("latitude", "longitude"), [[1, 1], [1, 1]], {"units": "degree^2"}, ), ln_p=( "ln_p", [1.0, 0.5], { "standard_name": "atmosphere_ln_pressure_coordinate", "computed_standard_name": "air_pressure", }, ), P0=((), 1013.25, {"units": "hPa"}), ), ) original["variable"].encoding.update( {"cell_measures": "area: areas", "grid_mapping": "latlon"}, ) original.coords["latitude"].encoding.update( dict(grid_mapping="latlon", bounds="latitude_bnds") ) original.coords["longitude"].encoding.update( dict(grid_mapping="latlon", bounds="longitude_bnds") ) original.coords["ln_p"].encoding.update({"formula_terms": "p0: P0 lev : ln_p"}) return original def test_grid_mapping_and_bounds_are_not_coordinates_in_file(self) -> None: original = self._create_cf_dataset() with create_tmp_file() as tmp_file: original.to_netcdf(tmp_file) with open_dataset(tmp_file, decode_coords=False) as ds: assert ds.coords["latitude"].attrs["bounds"] == "latitude_bnds" assert ds.coords["longitude"].attrs["bounds"] == "longitude_bnds" assert "coordinates" not in ds["variable"].attrs assert "coordinates" not in ds.attrs def test_coordinate_variables_after_dataset_roundtrip(self) -> None: original = self._create_cf_dataset() with self.roundtrip(original, open_kwargs={"decode_coords": "all"}) as actual: assert_identical(actual, original) with self.roundtrip(original) as actual: expected = original.reset_coords( ["latitude_bnds", "longitude_bnds", "areas", "P0", "latlon"] ) # equal checks that coords and data_vars are equal which # should be enough # identical would require resetting a number of attributes # skip that. assert_equal(actual, expected) def test_grid_mapping_and_bounds_are_coordinates_after_dataarray_roundtrip( self, ) -> None: original = self._create_cf_dataset() # The DataArray roundtrip should have the same warnings as the # Dataset, but we already tested for those, so just go for the # new warnings. It would appear that there is no way to tell # pytest "This warning and also this warning should both be # present". # xarray/tests/test_conventions.py::TestCFEncodedDataStore # needs the to_dataset. The other backends should be fine # without it. with pytest.warns( UserWarning, match=( r"Variable\(s\) referenced in bounds not in variables: " r"\['l(at|ong)itude_bnds'\]" ), ): with self.roundtrip( original["variable"].to_dataset(), open_kwargs={"decode_coords": "all"} ) as actual: assert_identical(actual, original["variable"].to_dataset()) @requires_iris def test_coordinate_variables_after_iris_roundtrip(self) -> None: original = self._create_cf_dataset() iris_cube = original["variable"].to_iris() actual = DataArray.from_iris(iris_cube) # Bounds will be missing (xfail) del original.coords["latitude_bnds"], original.coords["longitude_bnds"] # Ancillary vars will be missing # Those are data_vars, and will be dropped when grabbing the variable assert_identical(actual, original["variable"]) def test_coordinates_encoding(self) -> None: def equals_latlon(obj): return obj == "lat lon" or obj == "lon lat" original = Dataset( {"temp": ("x", [0, 1]), "precip": ("x", [0, -1])}, {"lat": ("x", [2, 3]), "lon": ("x", [4, 5])}, ) with self.roundtrip(original) as actual: assert_identical(actual, original) with create_tmp_file() as tmp_file: original.to_netcdf(tmp_file) with open_dataset(tmp_file, decode_coords=False) as ds: assert equals_latlon(ds["temp"].attrs["coordinates"]) assert equals_latlon(ds["precip"].attrs["coordinates"]) assert "coordinates" not in ds.attrs assert "coordinates" not in ds["lat"].attrs assert "coordinates" not in ds["lon"].attrs modified = original.drop_vars(["temp", "precip"]) with self.roundtrip(modified) as actual: assert_identical(actual, modified) with create_tmp_file() as tmp_file: modified.to_netcdf(tmp_file) with open_dataset(tmp_file, decode_coords=False) as ds: assert equals_latlon(ds.attrs["coordinates"]) assert "coordinates" not in ds["lat"].attrs assert "coordinates" not in ds["lon"].attrs original["temp"].encoding["coordinates"] = "lat" with self.roundtrip(original) as actual: assert_identical(actual, original) original["precip"].encoding["coordinates"] = "lat" with create_tmp_file() as tmp_file: original.to_netcdf(tmp_file) with open_dataset(tmp_file, decode_coords=True) as ds: assert "lon" not in ds["temp"].encoding["coordinates"] assert "lon" not in ds["precip"].encoding["coordinates"] assert "coordinates" not in ds["lat"].encoding assert "coordinates" not in ds["lon"].encoding def test_roundtrip_endian(self) -> None: ds = Dataset( { "x": np.arange(3, 10, dtype=">i2"), "y": np.arange(3, 20, dtype=" None: te = (TypeError, "string or None") ve = (ValueError, "string must be length 1 or") data = np.random.random((2, 2)) da = xr.DataArray(data) for name, (error, msg) in zip([0, (4, 5), True, ""], [te, te, te, ve]): ds = Dataset({name: da}) with pytest.raises(error) as excinfo: with self.roundtrip(ds): pass excinfo.match(msg) excinfo.match(repr(name)) def test_encoding_kwarg(self) -> None: ds = Dataset({"x": ("y", np.arange(10.0))}) kwargs: dict[str, Any] = dict(encoding={"x": {"dtype": "f4"}}) with self.roundtrip(ds, save_kwargs=kwargs) as actual: encoded_dtype = actual.x.encoding["dtype"] # On OS X, dtype sometimes switches endianness for unclear reasons assert encoded_dtype.kind == "f" and encoded_dtype.itemsize == 4 assert ds.x.encoding == {} kwargs = dict(encoding={"x": {"foo": "bar"}}) with pytest.raises(ValueError, match=r"unexpected encoding"): with self.roundtrip(ds, save_kwargs=kwargs) as actual: pass kwargs = dict(encoding={"x": "foo"}) with pytest.raises(ValueError, match=r"must be castable"): with self.roundtrip(ds, save_kwargs=kwargs) as actual: pass kwargs = dict(encoding={"invalid": {}}) with pytest.raises(KeyError): with self.roundtrip(ds, save_kwargs=kwargs) as actual: pass def test_encoding_kwarg_dates(self) -> None: ds = Dataset({"t": pd.date_range("2000-01-01", periods=3)}) units = "days since 1900-01-01" kwargs = dict(encoding={"t": {"units": units}}) with self.roundtrip(ds, save_kwargs=kwargs) as actual: assert actual.t.encoding["units"] == units assert_identical(actual, ds) def test_encoding_kwarg_fixed_width_string(self) -> None: # regression test for GH2149 for strings in [[b"foo", b"bar", b"baz"], ["foo", "bar", "baz"]]: ds = Dataset({"x": strings}) kwargs = dict(encoding={"x": {"dtype": "S1"}}) with self.roundtrip(ds, save_kwargs=kwargs) as actual: assert actual["x"].encoding["dtype"] == "S1" assert_identical(actual, ds) def test_default_fill_value(self) -> None: # Test default encoding for float: ds = Dataset({"x": ("y", np.arange(10.0))}) kwargs = dict(encoding={"x": {"dtype": "f4"}}) with self.roundtrip(ds, save_kwargs=kwargs) as actual: assert math.isnan(actual.x.encoding["_FillValue"]) assert ds.x.encoding == {} # Test default encoding for int: ds = Dataset({"x": ("y", np.arange(10.0))}) kwargs = dict(encoding={"x": {"dtype": "int16"}}) with warnings.catch_warnings(): warnings.filterwarnings("ignore", ".*floating point data as an integer") with self.roundtrip(ds, save_kwargs=kwargs) as actual: assert "_FillValue" not in actual.x.encoding assert ds.x.encoding == {} # Test default encoding for implicit int: ds = Dataset({"x": ("y", np.arange(10, dtype="int16"))}) with self.roundtrip(ds) as actual: assert "_FillValue" not in actual.x.encoding assert ds.x.encoding == {} def test_explicitly_omit_fill_value(self) -> None: ds = Dataset({"x": ("y", [np.pi, -np.pi])}) ds.x.encoding["_FillValue"] = None with self.roundtrip(ds) as actual: assert "_FillValue" not in actual.x.encoding def test_explicitly_omit_fill_value_via_encoding_kwarg(self) -> None: ds = Dataset({"x": ("y", [np.pi, -np.pi])}) kwargs = dict(encoding={"x": {"_FillValue": None}}) with self.roundtrip(ds, save_kwargs=kwargs) as actual: assert "_FillValue" not in actual.x.encoding assert ds.y.encoding == {} def test_explicitly_omit_fill_value_in_coord(self) -> None: ds = Dataset({"x": ("y", [np.pi, -np.pi])}, coords={"y": [0.0, 1.0]}) ds.y.encoding["_FillValue"] = None with self.roundtrip(ds) as actual: assert "_FillValue" not in actual.y.encoding def test_explicitly_omit_fill_value_in_coord_via_encoding_kwarg(self) -> None: ds = Dataset({"x": ("y", [np.pi, -np.pi])}, coords={"y": [0.0, 1.0]}) kwargs = dict(encoding={"y": {"_FillValue": None}}) with self.roundtrip(ds, save_kwargs=kwargs) as actual: assert "_FillValue" not in actual.y.encoding assert ds.y.encoding == {} def test_encoding_same_dtype(self) -> None: ds = Dataset({"x": ("y", np.arange(10.0, dtype="f4"))}) kwargs = dict(encoding={"x": {"dtype": "f4"}}) with self.roundtrip(ds, save_kwargs=kwargs) as actual: encoded_dtype = actual.x.encoding["dtype"] # On OS X, dtype sometimes switches endianness for unclear reasons assert encoded_dtype.kind == "f" and encoded_dtype.itemsize == 4 assert ds.x.encoding == {} def test_append_write(self) -> None: # regression for GH1215 data = create_test_data() with self.roundtrip_append(data) as actual: assert_identical(data, actual) def test_append_overwrite_values(self) -> None: # regression for GH1215 data = create_test_data() with create_tmp_file(allow_cleanup_failure=False) as tmp_file: self.save(data, tmp_file, mode="w") data["var2"][:] = -999 data["var9"] = data["var2"] * 3 self.save(data[["var2", "var9"]], tmp_file, mode="a") with self.open(tmp_file) as actual: assert_identical(data, actual) def test_append_with_invalid_dim_raises(self) -> None: data = create_test_data() with create_tmp_file(allow_cleanup_failure=False) as tmp_file: self.save(data, tmp_file, mode="w") data["var9"] = data["var2"] * 3 data = data.isel(dim1=slice(2, 6)) # modify one dimension with pytest.raises( ValueError, match=r"Unable to update size for existing dimension" ): self.save(data, tmp_file, mode="a") def test_multiindex_not_implemented(self) -> None: ds = Dataset(coords={"y": ("x", [1, 2]), "z": ("x", ["a", "b"])}).set_index( x=["y", "z"] ) with pytest.raises(NotImplementedError, match=r"MultiIndex"): with self.roundtrip(ds): pass class NetCDFBase(CFEncodedBase): """Tests for all netCDF3 and netCDF4 backends.""" @pytest.mark.skipif( ON_WINDOWS, reason="Windows does not allow modifying open files" ) def test_refresh_from_disk(self) -> None: # regression test for https://github.com/pydata/xarray/issues/4862 with create_tmp_file() as example_1_path: with create_tmp_file() as example_1_modified_path: with open_example_dataset("example_1.nc") as example_1: self.save(example_1, example_1_path) example_1.rh.values += 100 self.save(example_1, example_1_modified_path) a = open_dataset(example_1_path, engine=self.engine).load() # Simulate external process modifying example_1.nc while this script is running shutil.copy(example_1_modified_path, example_1_path) # Reopen example_1.nc (modified) as `b`; note that `a` has NOT been closed b = open_dataset(example_1_path, engine=self.engine).load() try: assert not np.array_equal(a.rh.values, b.rh.values) finally: a.close() b.close() _counter = itertools.count() @contextlib.contextmanager def create_tmp_file( suffix: str = ".nc", allow_cleanup_failure: bool = False ) -> Iterator[str]: temp_dir = tempfile.mkdtemp() path = os.path.join(temp_dir, f"temp-{next(_counter)}{suffix}") try: yield path finally: try: shutil.rmtree(temp_dir) except OSError: if not allow_cleanup_failure: raise @contextlib.contextmanager def create_tmp_files( nfiles: int, suffix: str = ".nc", allow_cleanup_failure: bool = False ) -> Iterator[list[str]]: with ExitStack() as stack: files = [ stack.enter_context(create_tmp_file(suffix, allow_cleanup_failure)) for _ in range(nfiles) ] yield files class NetCDF4Base(NetCDFBase): """Tests for both netCDF4-python and h5netcdf.""" engine: T_NetcdfEngine = "netcdf4" def test_open_group(self) -> None: # Create a netCDF file with a dataset stored within a group with create_tmp_file() as tmp_file: with nc4.Dataset(tmp_file, "w") as rootgrp: foogrp = rootgrp.createGroup("foo") ds = foogrp ds.createDimension("time", size=10) x = np.arange(10) ds.createVariable("x", np.int32, dimensions=("time",)) ds.variables["x"][:] = x expected = Dataset() expected["x"] = ("time", x) # check equivalent ways to specify group for group in "foo", "/foo", "foo/", "/foo/": with self.open(tmp_file, group=group) as actual: assert_equal(actual["x"], expected["x"]) # check that missing group raises appropriate exception with pytest.raises(OSError): open_dataset(tmp_file, group="bar") with pytest.raises(ValueError, match=r"must be a string"): open_dataset(tmp_file, group=(1, 2, 3)) def test_open_subgroup(self) -> None: # Create a netCDF file with a dataset stored within a group within a # group with create_tmp_file() as tmp_file: rootgrp = nc4.Dataset(tmp_file, "w") foogrp = rootgrp.createGroup("foo") bargrp = foogrp.createGroup("bar") ds = bargrp ds.createDimension("time", size=10) x = np.arange(10) ds.createVariable("x", np.int32, dimensions=("time",)) ds.variables["x"][:] = x rootgrp.close() expected = Dataset() expected["x"] = ("time", x) # check equivalent ways to specify group for group in "foo/bar", "/foo/bar", "foo/bar/", "/foo/bar/": with self.open(tmp_file, group=group) as actual: assert_equal(actual["x"], expected["x"]) def test_write_groups(self) -> None: data1 = create_test_data() data2 = data1 * 2 with create_tmp_file() as tmp_file: self.save(data1, tmp_file, group="data/1") self.save(data2, tmp_file, group="data/2", mode="a") with self.open(tmp_file, group="data/1") as actual1: assert_identical(data1, actual1) with self.open(tmp_file, group="data/2") as actual2: assert_identical(data2, actual2) def test_encoding_kwarg_vlen_string(self) -> None: for input_strings in [[b"foo", b"bar", b"baz"], ["foo", "bar", "baz"]]: original = Dataset({"x": input_strings}) expected = Dataset({"x": ["foo", "bar", "baz"]}) kwargs = dict(encoding={"x": {"dtype": str}}) with self.roundtrip(original, save_kwargs=kwargs) as actual: assert actual["x"].encoding["dtype"] is str assert_identical(actual, expected) def test_roundtrip_string_with_fill_value_vlen(self) -> None: values = np.array(["ab", "cdef", np.nan], dtype=object) expected = Dataset({"x": ("t", values)}) # netCDF4-based backends don't support an explicit fillvalue # for variable length strings yet. # https://github.com/Unidata/netcdf4-python/issues/730 # https://github.com/h5netcdf/h5netcdf/issues/37 original = Dataset({"x": ("t", values, {}, {"_FillValue": "XXX"})}) with pytest.raises(NotImplementedError): with self.roundtrip(original) as actual: assert_identical(expected, actual) original = Dataset({"x": ("t", values, {}, {"_FillValue": ""})}) with pytest.raises(NotImplementedError): with self.roundtrip(original) as actual: assert_identical(expected, actual) def test_roundtrip_character_array(self) -> None: with create_tmp_file() as tmp_file: values = np.array([["a", "b", "c"], ["d", "e", "f"]], dtype="S") with nc4.Dataset(tmp_file, mode="w") as nc: nc.createDimension("x", 2) nc.createDimension("string3", 3) v = nc.createVariable("x", np.dtype("S1"), ("x", "string3")) v[:] = values values = np.array(["abc", "def"], dtype="S") expected = Dataset({"x": ("x", values)}) with open_dataset(tmp_file) as actual: assert_identical(expected, actual) # regression test for #157 with self.roundtrip(actual) as roundtripped: assert_identical(expected, roundtripped) def test_default_to_char_arrays(self) -> None: data = Dataset({"x": np.array(["foo", "zzzz"], dtype="S")}) with self.roundtrip(data) as actual: assert_identical(data, actual) assert actual["x"].dtype == np.dtype("S4") def test_open_encodings(self) -> None: # Create a netCDF file with explicit time units # and make sure it makes it into the encodings # and survives a round trip with create_tmp_file() as tmp_file: with nc4.Dataset(tmp_file, "w") as ds: ds.createDimension("time", size=10) ds.createVariable("time", np.int32, dimensions=("time",)) units = "days since 1999-01-01" ds.variables["time"].setncattr("units", units) ds.variables["time"][:] = np.arange(10) + 4 expected = Dataset() time = pd.date_range("1999-01-05", periods=10) encoding = {"units": units, "dtype": np.dtype("int32")} expected["time"] = ("time", time, {}, encoding) with open_dataset(tmp_file) as actual: assert_equal(actual["time"], expected["time"]) actual_encoding = { k: v for k, v in actual["time"].encoding.items() if k in expected["time"].encoding } assert actual_encoding == expected["time"].encoding def test_dump_encodings(self) -> None: # regression test for #709 ds = Dataset({"x": ("y", np.arange(10.0))}) kwargs = dict(encoding={"x": {"zlib": True}}) with self.roundtrip(ds, save_kwargs=kwargs) as actual: assert actual.x.encoding["zlib"] def test_dump_and_open_encodings(self) -> None: # Create a netCDF file with explicit time units # and make sure it makes it into the encodings # and survives a round trip with create_tmp_file() as tmp_file: with nc4.Dataset(tmp_file, "w") as ds: ds.createDimension("time", size=10) ds.createVariable("time", np.int32, dimensions=("time",)) units = "days since 1999-01-01" ds.variables["time"].setncattr("units", units) ds.variables["time"][:] = np.arange(10) + 4 with open_dataset(tmp_file) as xarray_dataset: with create_tmp_file() as tmp_file2: xarray_dataset.to_netcdf(tmp_file2) with nc4.Dataset(tmp_file2, "r") as ds: assert ds.variables["time"].getncattr("units") == units assert_array_equal(ds.variables["time"], np.arange(10) + 4) def test_compression_encoding(self) -> None: data = create_test_data() data["var2"].encoding.update( { "zlib": True, "chunksizes": (5, 5), "fletcher32": True, "shuffle": True, "original_shape": data.var2.shape, } ) with self.roundtrip(data) as actual: for k, v in data["var2"].encoding.items(): assert v == actual["var2"].encoding[k] # regression test for #156 expected = data.isel(dim1=0) with self.roundtrip(expected) as actual: assert_equal(expected, actual) def test_encoding_kwarg_compression(self) -> None: ds = Dataset({"x": np.arange(10.0)}) encoding = dict( dtype="f4", zlib=True, complevel=9, fletcher32=True, chunksizes=(5,), shuffle=True, ) kwargs = dict(encoding=dict(x=encoding)) with self.roundtrip(ds, save_kwargs=kwargs) as actual: assert_equal(actual, ds) assert actual.x.encoding["dtype"] == "f4" assert actual.x.encoding["zlib"] assert actual.x.encoding["complevel"] == 9 assert actual.x.encoding["fletcher32"] assert actual.x.encoding["chunksizes"] == (5,) assert actual.x.encoding["shuffle"] assert ds.x.encoding == {} def test_keep_chunksizes_if_no_original_shape(self) -> None: ds = Dataset({"x": [1, 2, 3]}) chunksizes = (2,) ds.variables["x"].encoding = {"chunksizes": chunksizes} with self.roundtrip(ds) as actual: assert_identical(ds, actual) assert_array_equal( ds["x"].encoding["chunksizes"], actual["x"].encoding["chunksizes"] ) def test_encoding_chunksizes_unlimited(self) -> None: # regression test for GH1225 ds = Dataset({"x": [1, 2, 3], "y": ("x", [2, 3, 4])}) ds.variables["x"].encoding = { "zlib": False, "shuffle": False, "complevel": 0, "fletcher32": False, "contiguous": False, "chunksizes": (2**20,), "original_shape": (3,), } with self.roundtrip(ds) as actual: assert_equal(ds, actual) def test_mask_and_scale(self) -> None: with create_tmp_file() as tmp_file: with nc4.Dataset(tmp_file, mode="w") as nc: nc.createDimension("t", 5) nc.createVariable("x", "int16", ("t",), fill_value=-1) v = nc.variables["x"] v.set_auto_maskandscale(False) v.add_offset = 10 v.scale_factor = 0.1 v[:] = np.array([-1, -1, 0, 1, 2]) # first make sure netCDF4 reads the masked and scaled data # correctly with nc4.Dataset(tmp_file, mode="r") as nc: expected = np.ma.array( [-1, -1, 10, 10.1, 10.2], mask=[True, True, False, False, False] ) actual = nc.variables["x"][:] assert_array_equal(expected, actual) # now check xarray with open_dataset(tmp_file) as ds: expected = create_masked_and_scaled_data() assert_identical(expected, ds) def test_0dimensional_variable(self) -> None: # This fix verifies our work-around to this netCDF4-python bug: # https://github.com/Unidata/netcdf4-python/pull/220 with create_tmp_file() as tmp_file: with nc4.Dataset(tmp_file, mode="w") as nc: v = nc.createVariable("x", "int16") v[...] = 123 with open_dataset(tmp_file) as ds: expected = Dataset({"x": ((), 123)}) assert_identical(expected, ds) def test_read_variable_len_strings(self) -> None: with create_tmp_file() as tmp_file: values = np.array(["foo", "bar", "baz"], dtype=object) with nc4.Dataset(tmp_file, mode="w") as nc: nc.createDimension("x", 3) v = nc.createVariable("x", str, ("x",)) v[:] = values expected = Dataset({"x": ("x", values)}) for kwargs in [{}, {"decode_cf": True}]: with open_dataset(tmp_file, **cast(dict, kwargs)) as actual: assert_identical(expected, actual) def test_encoding_unlimited_dims(self) -> None: ds = Dataset({"x": ("y", np.arange(10.0))}) with self.roundtrip(ds, save_kwargs=dict(unlimited_dims=["y"])) as actual: assert actual.encoding["unlimited_dims"] == set("y") assert_equal(ds, actual) ds.encoding = {"unlimited_dims": ["y"]} with self.roundtrip(ds) as actual: assert actual.encoding["unlimited_dims"] == set("y") assert_equal(ds, actual) @requires_netCDF4 class TestNetCDF4Data(NetCDF4Base): @contextlib.contextmanager def create_store(self): with create_tmp_file() as tmp_file: with backends.NetCDF4DataStore.open(tmp_file, mode="w") as store: yield store def test_variable_order(self) -> None: # doesn't work with scipy or h5py :( ds = Dataset() ds["a"] = 1 ds["z"] = 2 ds["b"] = 3 ds.coords["c"] = 4 with self.roundtrip(ds) as actual: assert list(ds.variables) == list(actual.variables) def test_unsorted_index_raises(self) -> None: # should be fixed in netcdf4 v1.2.1 random_data = np.random.random(size=(4, 6)) dim0 = [0, 1, 2, 3] dim1 = [0, 2, 1, 3, 5, 4] # We will sort this in a later step da = xr.DataArray( data=random_data, dims=("dim0", "dim1"), coords={"dim0": dim0, "dim1": dim1}, name="randovar", ) ds = da.to_dataset() with self.roundtrip(ds) as ondisk: inds = np.argsort(dim1) ds2 = ondisk.isel(dim1=inds) # Older versions of NetCDF4 raise an exception here, and if so we # want to ensure we improve (that is, replace) the error message try: ds2.randovar.values except IndexError as err: assert "first by calling .load" in str(err) def test_setncattr_string(self) -> None: list_of_strings = ["list", "of", "strings"] one_element_list_of_strings = ["one element"] one_string = "one string" attrs = { "foo": list_of_strings, "bar": one_element_list_of_strings, "baz": one_string, } ds = Dataset({"x": ("y", [1, 2, 3], attrs)}, attrs=attrs) with self.roundtrip(ds) as actual: for totest in [actual, actual["x"]]: assert_array_equal(list_of_strings, totest.attrs["foo"]) assert_array_equal(one_element_list_of_strings, totest.attrs["bar"]) assert one_string == totest.attrs["baz"] @pytest.mark.skip(reason="https://github.com/Unidata/netcdf4-python/issues/1195") def test_refresh_from_disk(self) -> None: super().test_refresh_from_disk() @requires_netCDF4 class TestNetCDF4AlreadyOpen: def test_base_case(self) -> None: with create_tmp_file() as tmp_file: with nc4.Dataset(tmp_file, mode="w") as nc: v = nc.createVariable("x", "int") v[...] = 42 nc = nc4.Dataset(tmp_file, mode="r") store = backends.NetCDF4DataStore(nc) with open_dataset(store) as ds: expected = Dataset({"x": ((), 42)}) assert_identical(expected, ds) def test_group(self) -> None: with create_tmp_file() as tmp_file: with nc4.Dataset(tmp_file, mode="w") as nc: group = nc.createGroup("g") v = group.createVariable("x", "int") v[...] = 42 nc = nc4.Dataset(tmp_file, mode="r") store = backends.NetCDF4DataStore(nc.groups["g"]) with open_dataset(store) as ds: expected = Dataset({"x": ((), 42)}) assert_identical(expected, ds) nc = nc4.Dataset(tmp_file, mode="r") store = backends.NetCDF4DataStore(nc, group="g") with open_dataset(store) as ds: expected = Dataset({"x": ((), 42)}) assert_identical(expected, ds) with nc4.Dataset(tmp_file, mode="r") as nc: with pytest.raises(ValueError, match="must supply a root"): backends.NetCDF4DataStore(nc.groups["g"], group="g") def test_deepcopy(self) -> None: # regression test for https://github.com/pydata/xarray/issues/4425 with create_tmp_file() as tmp_file: with nc4.Dataset(tmp_file, mode="w") as nc: nc.createDimension("x", 10) v = nc.createVariable("y", np.int32, ("x",)) v[:] = np.arange(10) h5 = nc4.Dataset(tmp_file, mode="r") store = backends.NetCDF4DataStore(h5) with open_dataset(store) as ds: copied = ds.copy(deep=True) expected = Dataset({"y": ("x", np.arange(10))}) assert_identical(expected, copied) @requires_netCDF4 @requires_dask @pytest.mark.filterwarnings("ignore:deallocating CachingFileManager") class TestNetCDF4ViaDaskData(TestNetCDF4Data): @contextlib.contextmanager def roundtrip( self, data, save_kwargs=None, open_kwargs=None, allow_cleanup_failure=False ): if open_kwargs is None: open_kwargs = {} if save_kwargs is None: save_kwargs = {} open_kwargs.setdefault("chunks", -1) with TestNetCDF4Data.roundtrip( self, data, save_kwargs, open_kwargs, allow_cleanup_failure ) as ds: yield ds def test_unsorted_index_raises(self) -> None: # Skip when using dask because dask rewrites indexers to getitem, # dask first pulls items by block. pass def test_dataset_caching(self) -> None: # caching behavior differs for dask pass def test_write_inconsistent_chunks(self) -> None: # Construct two variables with the same dimensions, but different # chunk sizes. x = da.zeros((100, 100), dtype="f4", chunks=(50, 100)) x = DataArray(data=x, dims=("lat", "lon"), name="x") x.encoding["chunksizes"] = (50, 100) x.encoding["original_shape"] = (100, 100) y = da.ones((100, 100), dtype="f4", chunks=(100, 50)) y = DataArray(data=y, dims=("lat", "lon"), name="y") y.encoding["chunksizes"] = (100, 50) y.encoding["original_shape"] = (100, 100) # Put them both into the same dataset ds = Dataset({"x": x, "y": y}) with self.roundtrip(ds) as actual: assert actual["x"].encoding["chunksizes"] == (50, 100) assert actual["y"].encoding["chunksizes"] == (100, 50) @requires_zarr class ZarrBase(CFEncodedBase): DIMENSION_KEY = "_ARRAY_DIMENSIONS" zarr_version = 2 version_kwargs: dict[str, Any] = {} def create_zarr_target(self): raise NotImplementedError @contextlib.contextmanager def create_store(self): with self.create_zarr_target() as store_target: yield backends.ZarrStore.open_group( store_target, mode="w", **self.version_kwargs ) def save(self, dataset, store_target, **kwargs): return dataset.to_zarr(store=store_target, **kwargs, **self.version_kwargs) @contextlib.contextmanager def open(self, store_target, **kwargs): with xr.open_dataset( store_target, engine="zarr", **kwargs, **self.version_kwargs ) as ds: yield ds @contextlib.contextmanager def roundtrip( self, data, save_kwargs=None, open_kwargs=None, allow_cleanup_failure=False ): if save_kwargs is None: save_kwargs = {} if open_kwargs is None: open_kwargs = {} with self.create_zarr_target() as store_target: self.save(data, store_target, **save_kwargs) with self.open(store_target, **open_kwargs) as ds: yield ds @pytest.mark.parametrize("consolidated", [False, True, None]) def test_roundtrip_consolidated(self, consolidated) -> None: if consolidated and self.zarr_version > 2: pytest.xfail("consolidated metadata is not supported for zarr v3 yet") expected = create_test_data() with self.roundtrip( expected, save_kwargs={"consolidated": consolidated}, open_kwargs={"backend_kwargs": {"consolidated": consolidated}}, ) as actual: self.check_dtypes_roundtripped(expected, actual) assert_identical(expected, actual) def test_read_non_consolidated_warning(self) -> None: if self.zarr_version > 2: pytest.xfail("consolidated metadata is not supported for zarr v3 yet") expected = create_test_data() with self.create_zarr_target() as store: expected.to_zarr(store, consolidated=False, **self.version_kwargs) with pytest.warns( RuntimeWarning, match="Failed to open Zarr store with consolidated", ): with xr.open_zarr(store, **self.version_kwargs) as ds: assert_identical(ds, expected) def test_non_existent_store(self) -> None: with pytest.raises(FileNotFoundError, match=r"No such file or directory:"): xr.open_zarr(f"{uuid.uuid4()}") def test_with_chunkstore(self) -> None: expected = create_test_data() with self.create_zarr_target() as store_target, self.create_zarr_target() as chunk_store: save_kwargs = {"chunk_store": chunk_store} self.save(expected, store_target, **save_kwargs) open_kwargs = {"backend_kwargs": {"chunk_store": chunk_store}} with self.open(store_target, **open_kwargs) as ds: assert_equal(ds, expected) @requires_dask def test_auto_chunk(self) -> None: original = create_test_data().chunk() with self.roundtrip(original, open_kwargs={"chunks": None}) as actual: for k, v in actual.variables.items(): # only index variables should be in memory assert v._in_memory == (k in actual.dims) # there should be no chunks assert v.chunks is None with self.roundtrip(original, open_kwargs={"chunks": {}}) as actual: for k, v in actual.variables.items(): # only index variables should be in memory assert v._in_memory == (k in actual.dims) # chunk size should be the same as original assert v.chunks == original[k].chunks @requires_dask @pytest.mark.filterwarnings("ignore:The specified Dask chunks separate") def test_manual_chunk(self) -> None: original = create_test_data().chunk({"dim1": 3, "dim2": 4, "dim3": 3}) # Using chunks = None should return non-chunked arrays open_kwargs: dict[str, Any] = {"chunks": None} with self.roundtrip(original, open_kwargs=open_kwargs) as actual: for k, v in actual.variables.items(): # only index variables should be in memory assert v._in_memory == (k in actual.dims) # there should be no chunks assert v.chunks is None # uniform arrays for i in range(2, 6): rechunked = original.chunk(chunks=i) open_kwargs = {"chunks": i} with self.roundtrip(original, open_kwargs=open_kwargs) as actual: for k, v in actual.variables.items(): # only index variables should be in memory assert v._in_memory == (k in actual.dims) # chunk size should be the same as rechunked assert v.chunks == rechunked[k].chunks chunks = {"dim1": 2, "dim2": 3, "dim3": 5} rechunked = original.chunk(chunks=chunks) open_kwargs = { "chunks": chunks, "backend_kwargs": {"overwrite_encoded_chunks": True}, } with self.roundtrip(original, open_kwargs=open_kwargs) as actual: for k, v in actual.variables.items(): assert v.chunks == rechunked[k].chunks with self.roundtrip(actual) as auto: # encoding should have changed for k, v in actual.variables.items(): assert v.chunks == rechunked[k].chunks assert_identical(actual, auto) assert_identical(actual.load(), auto.load()) @requires_dask def test_warning_on_bad_chunks(self) -> None: original = create_test_data().chunk({"dim1": 4, "dim2": 3, "dim3": 3}) bad_chunks = (2, {"dim2": (3, 3, 2, 1)}) for chunks in bad_chunks: kwargs = {"chunks": chunks} with pytest.warns(UserWarning): with self.roundtrip(original, open_kwargs=kwargs) as actual: for k, v in actual.variables.items(): # only index variables should be in memory assert v._in_memory == (k in actual.dims) good_chunks: tuple[dict[str, Any], ...] = ({"dim2": 3}, {"dim3": (6, 4)}, {}) for chunks in good_chunks: kwargs = {"chunks": chunks} with assert_no_warnings(): with self.roundtrip(original, open_kwargs=kwargs) as actual: for k, v in actual.variables.items(): # only index variables should be in memory assert v._in_memory == (k in actual.dims) @requires_dask def test_deprecate_auto_chunk(self) -> None: original = create_test_data().chunk() with pytest.raises(TypeError): with self.roundtrip(original, open_kwargs={"auto_chunk": True}) as actual: for k, v in actual.variables.items(): # only index variables should be in memory assert v._in_memory == (k in actual.dims) # chunk size should be the same as original assert v.chunks == original[k].chunks with pytest.raises(TypeError): with self.roundtrip(original, open_kwargs={"auto_chunk": False}) as actual: for k, v in actual.variables.items(): # only index variables should be in memory assert v._in_memory == (k in actual.dims) # there should be no chunks assert v.chunks is None @requires_dask def test_write_uneven_dask_chunks(self) -> None: # regression for GH#2225 original = create_test_data().chunk({"dim1": 3, "dim2": 4, "dim3": 3}) with self.roundtrip(original, open_kwargs={"chunks": {}}) as actual: for k, v in actual.data_vars.items(): print(k) assert v.chunks == actual[k].chunks def test_chunk_encoding(self) -> None: # These datasets have no dask chunks. All chunking specified in # encoding data = create_test_data() chunks = (5, 5) data["var2"].encoding.update({"chunks": chunks}) with self.roundtrip(data) as actual: assert chunks == actual["var2"].encoding["chunks"] # expect an error with non-integer chunks data["var2"].encoding.update({"chunks": (5, 4.5)}) with pytest.raises(TypeError): with self.roundtrip(data) as actual: pass @requires_dask def test_chunk_encoding_with_dask(self) -> None: # These datasets DO have dask chunks. Need to check for various # interactions between dask and zarr chunks ds = xr.DataArray((np.arange(12)), dims="x", name="var1").to_dataset() # - no encoding specified - # zarr automatically gets chunk information from dask chunks ds_chunk4 = ds.chunk({"x": 4}) with self.roundtrip(ds_chunk4) as actual: assert (4,) == actual["var1"].encoding["chunks"] # should fail if dask_chunks are irregular... ds_chunk_irreg = ds.chunk({"x": (5, 4, 3)}) with pytest.raises(ValueError, match=r"uniform chunk sizes."): with self.roundtrip(ds_chunk_irreg) as actual: pass # should fail if encoding["chunks"] clashes with dask_chunks badenc = ds.chunk({"x": 4}) badenc.var1.encoding["chunks"] = (6,) with pytest.raises(NotImplementedError, match=r"named 'var1' would overlap"): with self.roundtrip(badenc) as actual: pass # unless... with self.roundtrip(badenc, save_kwargs={"safe_chunks": False}) as actual: # don't actually check equality because the data could be corrupted pass # if dask chunks (4) are an integer multiple of zarr chunks (2) it should not fail... goodenc = ds.chunk({"x": 4}) goodenc.var1.encoding["chunks"] = (2,) with self.roundtrip(goodenc) as actual: pass # if initial dask chunks are aligned, size of last dask chunk doesn't matter goodenc = ds.chunk({"x": (3, 3, 6)}) goodenc.var1.encoding["chunks"] = (3,) with self.roundtrip(goodenc) as actual: pass goodenc = ds.chunk({"x": (3, 6, 3)}) goodenc.var1.encoding["chunks"] = (3,) with self.roundtrip(goodenc) as actual: pass # ... also if the last chunk is irregular ds_chunk_irreg = ds.chunk({"x": (5, 5, 2)}) with self.roundtrip(ds_chunk_irreg) as actual: assert (5,) == actual["var1"].encoding["chunks"] # re-save Zarr arrays with self.roundtrip(ds_chunk_irreg) as original: with self.roundtrip(original) as actual: assert_identical(original, actual) # but itermediate unaligned chunks are bad badenc = ds.chunk({"x": (3, 5, 3, 1)}) badenc.var1.encoding["chunks"] = (3,) with pytest.raises( NotImplementedError, match=r"would overlap multiple dask chunks" ): with self.roundtrip(badenc) as actual: pass # - encoding specified - # specify compatible encodings for chunk_enc in 4, (4,): ds_chunk4["var1"].encoding.update({"chunks": chunk_enc}) with self.roundtrip(ds_chunk4) as actual: assert (4,) == actual["var1"].encoding["chunks"] # TODO: remove this failure once synchronized overlapping writes are # supported by xarray ds_chunk4["var1"].encoding.update({"chunks": 5}) with pytest.raises(NotImplementedError, match=r"named 'var1' would overlap"): with self.roundtrip(ds_chunk4) as actual: pass # override option with self.roundtrip(ds_chunk4, save_kwargs={"safe_chunks": False}) as actual: # don't actually check equality because the data could be corrupted pass def test_hidden_zarr_keys(self) -> None: expected = create_test_data() with self.create_store() as store: expected.dump_to_store(store) zarr_group = store.ds # check that a variable hidden attribute is present and correct # JSON only has a single array type, which maps to list in Python. # In contrast, dims in xarray is always a tuple. for var in expected.variables.keys(): dims = zarr_group[var].attrs[self.DIMENSION_KEY] assert dims == list(expected[var].dims) with xr.decode_cf(store): # make sure it is hidden for var in expected.variables.keys(): assert self.DIMENSION_KEY not in expected[var].attrs # put it back and try removing from a variable del zarr_group.var2.attrs[self.DIMENSION_KEY] with pytest.raises(KeyError): with xr.decode_cf(store): pass @pytest.mark.parametrize("group", [None, "group1"]) def test_write_persistence_modes(self, group) -> None: original = create_test_data() # overwrite mode with self.roundtrip( original, save_kwargs={"mode": "w", "group": group}, open_kwargs={"group": group}, ) as actual: assert_identical(original, actual) # don't overwrite mode with self.roundtrip( original, save_kwargs={"mode": "w-", "group": group}, open_kwargs={"group": group}, ) as actual: assert_identical(original, actual) # make sure overwriting works as expected with self.create_zarr_target() as store: self.save(original, store) # should overwrite with no error self.save(original, store, mode="w", group=group) with self.open(store, group=group) as actual: assert_identical(original, actual) with pytest.raises(ValueError): self.save(original, store, mode="w-") # check append mode for normal write with self.roundtrip( original, save_kwargs={"mode": "a", "group": group}, open_kwargs={"group": group}, ) as actual: assert_identical(original, actual) # check append mode for append write ds, ds_to_append, _ = create_append_test_data() with self.create_zarr_target() as store_target: ds.to_zarr(store_target, mode="w", group=group, **self.version_kwargs) ds_to_append.to_zarr( store_target, append_dim="time", group=group, **self.version_kwargs ) original = xr.concat([ds, ds_to_append], dim="time") actual = xr.open_dataset( store_target, group=group, engine="zarr", **self.version_kwargs ) assert_identical(original, actual) def test_compressor_encoding(self) -> None: original = create_test_data() # specify a custom compressor import zarr blosc_comp = zarr.Blosc(cname="zstd", clevel=3, shuffle=2) save_kwargs = dict(encoding={"var1": {"compressor": blosc_comp}}) with self.roundtrip(original, save_kwargs=save_kwargs) as ds: actual = ds["var1"].encoding["compressor"] # get_config returns a dictionary of compressor attributes assert actual.get_config() == blosc_comp.get_config() def test_group(self) -> None: original = create_test_data() group = "some/random/path" with self.roundtrip( original, save_kwargs={"group": group}, open_kwargs={"group": group} ) as actual: assert_identical(original, actual) def test_encoding_kwarg_fixed_width_string(self) -> None: # not relevant for zarr, since we don't use EncodedStringCoder pass # TODO: someone who understand caching figure out whether caching # makes sense for Zarr backend @pytest.mark.xfail(reason="Zarr caching not implemented") def test_dataset_caching(self) -> None: super().test_dataset_caching() def test_append_write(self) -> None: super().test_append_write() def test_append_with_mode_rplus_success(self) -> None: original = Dataset({"foo": ("x", [1])}) modified = Dataset({"foo": ("x", [2])}) with self.create_zarr_target() as store: original.to_zarr(store, **self.version_kwargs) modified.to_zarr(store, mode="r+", **self.version_kwargs) with self.open(store) as actual: assert_identical(actual, modified) def test_append_with_mode_rplus_fails(self) -> None: original = Dataset({"foo": ("x", [1])}) modified = Dataset({"bar": ("x", [2])}) with self.create_zarr_target() as store: original.to_zarr(store, **self.version_kwargs) with pytest.raises( ValueError, match="dataset contains non-pre-existing variables" ): modified.to_zarr(store, mode="r+", **self.version_kwargs) def test_append_with_invalid_dim_raises(self) -> None: ds, ds_to_append, _ = create_append_test_data() with self.create_zarr_target() as store_target: ds.to_zarr(store_target, mode="w", **self.version_kwargs) with pytest.raises( ValueError, match="does not match any existing dataset dimensions" ): ds_to_append.to_zarr( store_target, append_dim="notvalid", **self.version_kwargs ) def test_append_with_no_dims_raises(self) -> None: with self.create_zarr_target() as store_target: Dataset({"foo": ("x", [1])}).to_zarr( store_target, mode="w", **self.version_kwargs ) with pytest.raises(ValueError, match="different dimension names"): Dataset({"foo": ("y", [2])}).to_zarr( store_target, mode="a", **self.version_kwargs ) def test_append_with_append_dim_not_set_raises(self) -> None: ds, ds_to_append, _ = create_append_test_data() with self.create_zarr_target() as store_target: ds.to_zarr(store_target, mode="w", **self.version_kwargs) with pytest.raises(ValueError, match="different dimension sizes"): ds_to_append.to_zarr(store_target, mode="a", **self.version_kwargs) def test_append_with_mode_not_a_raises(self) -> None: ds, ds_to_append, _ = create_append_test_data() with self.create_zarr_target() as store_target: ds.to_zarr(store_target, mode="w", **self.version_kwargs) with pytest.raises(ValueError, match="cannot set append_dim unless"): ds_to_append.to_zarr( store_target, mode="w", append_dim="time", **self.version_kwargs ) def test_append_with_existing_encoding_raises(self) -> None: ds, ds_to_append, _ = create_append_test_data() with self.create_zarr_target() as store_target: ds.to_zarr(store_target, mode="w", **self.version_kwargs) with pytest.raises(ValueError, match="but encoding was provided"): ds_to_append.to_zarr( store_target, append_dim="time", encoding={"da": {"compressor": None}}, **self.version_kwargs, ) @pytest.mark.parametrize("dtype", ["U", "S"]) def test_append_string_length_mismatch_raises(self, dtype) -> None: ds, ds_to_append = create_append_string_length_mismatch_test_data(dtype) with self.create_zarr_target() as store_target: ds.to_zarr(store_target, mode="w", **self.version_kwargs) with pytest.raises(ValueError, match="Mismatched dtypes for variable"): ds_to_append.to_zarr( store_target, append_dim="time", **self.version_kwargs ) def test_check_encoding_is_consistent_after_append(self) -> None: ds, ds_to_append, _ = create_append_test_data() # check encoding consistency with self.create_zarr_target() as store_target: import zarr compressor = zarr.Blosc() encoding = {"da": {"compressor": compressor}} ds.to_zarr(store_target, mode="w", encoding=encoding, **self.version_kwargs) ds_to_append.to_zarr(store_target, append_dim="time", **self.version_kwargs) actual_ds = xr.open_dataset( store_target, engine="zarr", **self.version_kwargs ) actual_encoding = actual_ds["da"].encoding["compressor"] assert actual_encoding.get_config() == compressor.get_config() assert_identical( xr.open_dataset( store_target, engine="zarr", **self.version_kwargs ).compute(), xr.concat([ds, ds_to_append], dim="time"), ) def test_append_with_new_variable(self) -> None: ds, ds_to_append, ds_with_new_var = create_append_test_data() # check append mode for new variable with self.create_zarr_target() as store_target: xr.concat([ds, ds_to_append], dim="time").to_zarr( store_target, mode="w", **self.version_kwargs ) ds_with_new_var.to_zarr(store_target, mode="a", **self.version_kwargs) combined = xr.concat([ds, ds_to_append], dim="time") combined["new_var"] = ds_with_new_var["new_var"] assert_identical( combined, xr.open_dataset(store_target, engine="zarr", **self.version_kwargs), ) @requires_dask def test_to_zarr_compute_false_roundtrip(self) -> None: from dask.delayed import Delayed original = create_test_data().chunk() with self.create_zarr_target() as store: delayed_obj = self.save(original, store, compute=False) assert isinstance(delayed_obj, Delayed) # make sure target store has not been written to yet with pytest.raises(AssertionError): with self.open(store) as actual: assert_identical(original, actual) delayed_obj.compute() with self.open(store) as actual: assert_identical(original, actual) @requires_dask def test_to_zarr_append_compute_false_roundtrip(self) -> None: from dask.delayed import Delayed ds, ds_to_append, _ = create_append_test_data() ds, ds_to_append = ds.chunk(), ds_to_append.chunk() with pytest.warns(SerializationWarning): with self.create_zarr_target() as store: delayed_obj = self.save(ds, store, compute=False, mode="w") assert isinstance(delayed_obj, Delayed) with pytest.raises(AssertionError): with self.open(store) as actual: assert_identical(ds, actual) delayed_obj.compute() with self.open(store) as actual: assert_identical(ds, actual) delayed_obj = self.save( ds_to_append, store, compute=False, append_dim="time" ) assert isinstance(delayed_obj, Delayed) with pytest.raises(AssertionError): with self.open(store) as actual: assert_identical( xr.concat([ds, ds_to_append], dim="time"), actual ) delayed_obj.compute() with self.open(store) as actual: assert_identical(xr.concat([ds, ds_to_append], dim="time"), actual) @pytest.mark.parametrize("chunk", [False, True]) def test_save_emptydim(self, chunk) -> None: if chunk and not has_dask: pytest.skip("requires dask") ds = Dataset({"x": (("a", "b"), np.empty((5, 0))), "y": ("a", [1, 2, 5, 8, 9])}) if chunk: ds = ds.chunk({}) # chunk dataset to save dask array with self.roundtrip(ds) as ds_reload: assert_identical(ds, ds_reload) @requires_dask def test_no_warning_from_open_emptydim_with_chunks(self) -> None: ds = Dataset({"x": (("a", "b"), np.empty((5, 0)))}).chunk({"a": 1}) with assert_no_warnings(): with self.roundtrip(ds, open_kwargs=dict(chunks={"a": 1})) as ds_reload: assert_identical(ds, ds_reload) @pytest.mark.parametrize("consolidated", [False, True, None]) @pytest.mark.parametrize("compute", [False, True]) @pytest.mark.parametrize("use_dask", [False, True]) def test_write_region(self, consolidated, compute, use_dask) -> None: if (use_dask or not compute) and not has_dask: pytest.skip("requires dask") if consolidated and self.zarr_version > 2: pytest.xfail("consolidated metadata is not supported for zarr v3 yet") zeros = Dataset({"u": (("x",), np.zeros(10))}) nonzeros = Dataset({"u": (("x",), np.arange(1, 11))}) if use_dask: zeros = zeros.chunk(2) nonzeros = nonzeros.chunk(2) with self.create_zarr_target() as store: zeros.to_zarr( store, consolidated=consolidated, compute=compute, encoding={"u": dict(chunks=2)}, **self.version_kwargs, ) if compute: with xr.open_zarr( store, consolidated=consolidated, **self.version_kwargs ) as actual: assert_identical(actual, zeros) for i in range(0, 10, 2): region = {"x": slice(i, i + 2)} nonzeros.isel(region).to_zarr( store, region=region, consolidated=consolidated, **self.version_kwargs, ) with xr.open_zarr( store, consolidated=consolidated, **self.version_kwargs ) as actual: assert_identical(actual, nonzeros) @pytest.mark.parametrize("mode", [None, "r+", "a"]) def test_write_region_mode(self, mode) -> None: zeros = Dataset({"u": (("x",), np.zeros(10))}) nonzeros = Dataset({"u": (("x",), np.arange(1, 11))}) with self.create_zarr_target() as store: zeros.to_zarr(store, **self.version_kwargs) for region in [{"x": slice(5)}, {"x": slice(5, 10)}]: nonzeros.isel(region).to_zarr( store, region=region, mode=mode, **self.version_kwargs ) with xr.open_zarr(store, **self.version_kwargs) as actual: assert_identical(actual, nonzeros) @requires_dask def test_write_preexisting_override_metadata(self) -> None: """Metadata should be overridden if mode="a" but not in mode="r+".""" original = Dataset( {"u": (("x",), np.zeros(10), {"variable": "original"})}, attrs={"global": "original"}, ) both_modified = Dataset( {"u": (("x",), np.ones(10), {"variable": "modified"})}, attrs={"global": "modified"}, ) global_modified = Dataset( {"u": (("x",), np.ones(10), {"variable": "original"})}, attrs={"global": "modified"}, ) only_new_data = Dataset( {"u": (("x",), np.ones(10), {"variable": "original"})}, attrs={"global": "original"}, ) with self.create_zarr_target() as store: original.to_zarr(store, compute=False, **self.version_kwargs) both_modified.to_zarr(store, mode="a", **self.version_kwargs) with self.open(store) as actual: # NOTE: this arguably incorrect -- we should probably be # overriding the variable metadata, too. See the TODO note in # ZarrStore.set_variables. assert_identical(actual, global_modified) with self.create_zarr_target() as store: original.to_zarr(store, compute=False, **self.version_kwargs) both_modified.to_zarr(store, mode="r+", **self.version_kwargs) with self.open(store) as actual: assert_identical(actual, only_new_data) with self.create_zarr_target() as store: original.to_zarr(store, compute=False, **self.version_kwargs) # with region, the default mode becomes r+ both_modified.to_zarr( store, region={"x": slice(None)}, **self.version_kwargs ) with self.open(store) as actual: assert_identical(actual, only_new_data) def test_write_region_errors(self) -> None: data = Dataset({"u": (("x",), np.arange(5))}) data2 = Dataset({"u": (("x",), np.array([10, 11]))}) @contextlib.contextmanager def setup_and_verify_store(expected=data): with self.create_zarr_target() as store: data.to_zarr(store, **self.version_kwargs) yield store with self.open(store) as actual: assert_identical(actual, expected) # verify the base case works expected = Dataset({"u": (("x",), np.array([10, 11, 2, 3, 4]))}) with setup_and_verify_store(expected) as store: data2.to_zarr(store, region={"x": slice(2)}, **self.version_kwargs) with setup_and_verify_store() as store: with pytest.raises( ValueError, match=re.escape( "cannot set region unless mode='a', mode='r+' or mode=None" ), ): data.to_zarr( store, region={"x": slice(None)}, mode="w", **self.version_kwargs ) with setup_and_verify_store() as store: with pytest.raises(TypeError, match=r"must be a dict"): data.to_zarr(store, region=slice(None), **self.version_kwargs) # type: ignore[call-overload] with setup_and_verify_store() as store: with pytest.raises(TypeError, match=r"must be slice objects"): data2.to_zarr(store, region={"x": [0, 1]}, **self.version_kwargs) # type: ignore[dict-item] with setup_and_verify_store() as store: with pytest.raises(ValueError, match=r"step on all slices"): data2.to_zarr( store, region={"x": slice(None, None, 2)}, **self.version_kwargs ) with setup_and_verify_store() as store: with pytest.raises( ValueError, match=r"all keys in ``region`` are not in Dataset dimensions", ): data.to_zarr(store, region={"y": slice(None)}, **self.version_kwargs) with setup_and_verify_store() as store: with pytest.raises( ValueError, match=r"all variables in the dataset to write must have at least one dimension in common", ): data2.assign(v=2).to_zarr( store, region={"x": slice(2)}, **self.version_kwargs ) with setup_and_verify_store() as store: with pytest.raises( ValueError, match=r"cannot list the same dimension in both" ): data.to_zarr( store, region={"x": slice(None)}, append_dim="x", **self.version_kwargs, ) with setup_and_verify_store() as store: with pytest.raises( ValueError, match=r"variable 'u' already exists with different dimension sizes", ): data2.to_zarr(store, region={"x": slice(3)}, **self.version_kwargs) @requires_dask def test_encoding_chunksizes(self) -> None: # regression test for GH2278 # see also test_encoding_chunksizes_unlimited nx, ny, nt = 4, 4, 5 original = xr.Dataset( {}, coords={"x": np.arange(nx), "y": np.arange(ny), "t": np.arange(nt)} ) original["v"] = xr.Variable(("x", "y", "t"), np.zeros((nx, ny, nt))) original = original.chunk({"t": 1, "x": 2, "y": 2}) with self.roundtrip(original) as ds1: assert_equal(ds1, original) with self.roundtrip(ds1.isel(t=0)) as ds2: assert_equal(ds2, original.isel(t=0)) @requires_dask def test_chunk_encoding_with_partial_dask_chunks(self) -> None: original = xr.Dataset( {"x": xr.DataArray(np.random.random(size=(6, 8)), dims=("a", "b"))} ).chunk({"a": 3}) with self.roundtrip( original, save_kwargs={"encoding": {"x": {"chunks": [3, 2]}}} ) as ds1: assert_equal(ds1, original) @requires_dask def test_chunk_encoding_with_larger_dask_chunks(self) -> None: original = xr.Dataset({"a": ("x", [1, 2, 3, 4])}).chunk({"x": 2}) with self.roundtrip( original, save_kwargs={"encoding": {"a": {"chunks": [1]}}} ) as ds1: assert_equal(ds1, original) @requires_cftime def test_open_zarr_use_cftime(self) -> None: ds = create_test_data() with self.create_zarr_target() as store_target: ds.to_zarr(store_target, **self.version_kwargs) ds_a = xr.open_zarr(store_target, **self.version_kwargs) assert_identical(ds, ds_a) ds_b = xr.open_zarr(store_target, use_cftime=True, **self.version_kwargs) assert xr.coding.times.contains_cftime_datetimes(ds_b.time) def test_write_read_select_write(self) -> None: # Test for https://github.com/pydata/xarray/issues/4084 ds = create_test_data() # NOTE: using self.roundtrip, which uses open_dataset, will not trigger the bug. with self.create_zarr_target() as initial_store: ds.to_zarr(initial_store, mode="w", **self.version_kwargs) ds1 = xr.open_zarr(initial_store, **self.version_kwargs) # Combination of where+squeeze triggers error on write. ds_sel = ds1.where(ds1.coords["dim3"] == "a", drop=True).squeeze("dim3") with self.create_zarr_target() as final_store: ds_sel.to_zarr(final_store, mode="w", **self.version_kwargs) @pytest.mark.parametrize("obj", [Dataset(), DataArray(name="foo")]) def test_attributes(self, obj) -> None: obj = obj.copy() obj.attrs["good"] = {"key": "value"} ds = obj if isinstance(obj, Dataset) else obj.to_dataset() with self.create_zarr_target() as store_target: ds.to_zarr(store_target, **self.version_kwargs) assert_identical(ds, xr.open_zarr(store_target, **self.version_kwargs)) obj.attrs["bad"] = DataArray() ds = obj if isinstance(obj, Dataset) else obj.to_dataset() with self.create_zarr_target() as store_target: with pytest.raises(TypeError, match=r"Invalid attribute in Dataset.attrs."): ds.to_zarr(store_target, **self.version_kwargs) @requires_zarr class TestZarrDictStore(ZarrBase): @contextlib.contextmanager def create_zarr_target(self): if have_zarr_kvstore: yield KVStore({}) else: yield {} @requires_zarr class TestZarrDirectoryStore(ZarrBase): @contextlib.contextmanager def create_zarr_target(self): with create_tmp_file(suffix=".zarr") as tmp: yield tmp @contextlib.contextmanager def create_store(self): with self.create_zarr_target() as store_target: group = backends.ZarrStore.open_group(store_target, mode="w") # older Zarr versions do not have the _store_version attribute if have_zarr_v3: # verify that a v2 store was created assert group.zarr_group.store._store_version == 2 yield group class ZarrBaseV3(ZarrBase): zarr_version = 3 def test_roundtrip_coordinates_with_space(self): original = Dataset(coords={"x": 0, "y z": 1}) with pytest.warns(SerializationWarning): # v3 stores do not allow spaces in the key name with pytest.raises(ValueError): with self.roundtrip(original): pass @pytest.mark.skipif(not have_zarr_v3, reason="requires zarr version 3") class TestZarrKVStoreV3(ZarrBaseV3): @contextlib.contextmanager def create_zarr_target(self): yield KVStoreV3({}) @pytest.mark.skipif(not have_zarr_v3, reason="requires zarr version 3") class TestZarrDirectoryStoreV3(ZarrBaseV3): @contextlib.contextmanager def create_zarr_target(self): with create_tmp_file(suffix=".zr3") as tmp: yield DirectoryStoreV3(tmp) @pytest.mark.skipif(not have_zarr_v3, reason="requires zarr version 3") class TestZarrDirectoryStoreV3FromPath(TestZarrDirectoryStoreV3): # Must specify zarr_version=3 to get a v3 store because create_zarr_target # is a string path. version_kwargs = {"zarr_version": 3} @contextlib.contextmanager def create_zarr_target(self): with create_tmp_file(suffix=".zr3") as tmp: yield tmp @requires_zarr @requires_fsspec def test_zarr_storage_options() -> None: pytest.importorskip("aiobotocore") ds = create_test_data() store_target = "memory://test.zarr" ds.to_zarr(store_target, storage_options={"test": "zarr_write"}) ds_a = xr.open_zarr(store_target, storage_options={"test": "zarr_read"}) assert_identical(ds, ds_a) @requires_scipy class TestScipyInMemoryData(CFEncodedBase, NetCDF3Only): engine: T_NetcdfEngine = "scipy" @contextlib.contextmanager def create_store(self): fobj = BytesIO() yield backends.ScipyDataStore(fobj, "w") def test_to_netcdf_explicit_engine(self) -> None: # regression test for GH1321 Dataset({"foo": 42}).to_netcdf(engine="scipy") def test_bytes_pickle(self) -> None: data = Dataset({"foo": ("x", [1, 2, 3])}) fobj = data.to_netcdf() with self.open(fobj) as ds: unpickled = pickle.loads(pickle.dumps(ds)) assert_identical(unpickled, data) @requires_scipy class TestScipyFileObject(CFEncodedBase, NetCDF3Only): engine: T_NetcdfEngine = "scipy" @contextlib.contextmanager def create_store(self): fobj = BytesIO() yield backends.ScipyDataStore(fobj, "w") @contextlib.contextmanager def roundtrip( self, data, save_kwargs=None, open_kwargs=None, allow_cleanup_failure=False ): if save_kwargs is None: save_kwargs = {} if open_kwargs is None: open_kwargs = {} with create_tmp_file() as tmp_file: with open(tmp_file, "wb") as f: self.save(data, f, **save_kwargs) with open(tmp_file, "rb") as f: with self.open(f, **open_kwargs) as ds: yield ds @pytest.mark.skip(reason="cannot pickle file objects") def test_pickle(self) -> None: pass @pytest.mark.skip(reason="cannot pickle file objects") def test_pickle_dataarray(self) -> None: pass @requires_scipy class TestScipyFilePath(CFEncodedBase, NetCDF3Only): engine: T_NetcdfEngine = "scipy" @contextlib.contextmanager def create_store(self): with create_tmp_file() as tmp_file: with backends.ScipyDataStore(tmp_file, mode="w") as store: yield store def test_array_attrs(self) -> None: ds = Dataset(attrs={"foo": [[1, 2], [3, 4]]}) with pytest.raises(ValueError, match=r"must be 1-dimensional"): with self.roundtrip(ds): pass def test_roundtrip_example_1_netcdf_gz(self) -> None: with open_example_dataset("example_1.nc.gz") as expected: with open_example_dataset("example_1.nc") as actual: assert_identical(expected, actual) def test_netcdf3_endianness(self) -> None: # regression test for GH416 with open_example_dataset("bears.nc", engine="scipy") as expected: for var in expected.variables.values(): assert var.dtype.isnative @requires_netCDF4 def test_nc4_scipy(self) -> None: with create_tmp_file(allow_cleanup_failure=True) as tmp_file: with nc4.Dataset(tmp_file, "w", format="NETCDF4") as rootgrp: rootgrp.createGroup("foo") with pytest.raises(TypeError, match=r"pip install netcdf4"): open_dataset(tmp_file, engine="scipy") @requires_netCDF4 class TestNetCDF3ViaNetCDF4Data(CFEncodedBase, NetCDF3Only): engine: T_NetcdfEngine = "netcdf4" file_format: T_NetcdfTypes = "NETCDF3_CLASSIC" @contextlib.contextmanager def create_store(self): with create_tmp_file() as tmp_file: with backends.NetCDF4DataStore.open( tmp_file, mode="w", format="NETCDF3_CLASSIC" ) as store: yield store def test_encoding_kwarg_vlen_string(self) -> None: original = Dataset({"x": ["foo", "bar", "baz"]}) kwargs = dict(encoding={"x": {"dtype": str}}) with pytest.raises(ValueError, match=r"encoding dtype=str for vlen"): with self.roundtrip(original, save_kwargs=kwargs): pass @requires_netCDF4 class TestNetCDF4ClassicViaNetCDF4Data(CFEncodedBase, NetCDF3Only): engine: T_NetcdfEngine = "netcdf4" file_format: T_NetcdfTypes = "NETCDF4_CLASSIC" @contextlib.contextmanager def create_store(self): with create_tmp_file() as tmp_file: with backends.NetCDF4DataStore.open( tmp_file, mode="w", format="NETCDF4_CLASSIC" ) as store: yield store @requires_scipy_or_netCDF4 class TestGenericNetCDFData(CFEncodedBase, NetCDF3Only): # verify that we can read and write netCDF3 files as long as we have scipy # or netCDF4-python installed file_format: T_NetcdfTypes = "NETCDF3_64BIT" def test_write_store(self) -> None: # there's no specific store to test here pass @requires_scipy def test_engine(self) -> None: data = create_test_data() with pytest.raises(ValueError, match=r"unrecognized engine"): data.to_netcdf("foo.nc", engine="foobar") # type: ignore[call-overload] with pytest.raises(ValueError, match=r"invalid engine"): data.to_netcdf(engine="netcdf4") with create_tmp_file() as tmp_file: data.to_netcdf(tmp_file) with pytest.raises(ValueError, match=r"unrecognized engine"): open_dataset(tmp_file, engine="foobar") netcdf_bytes = data.to_netcdf() with pytest.raises(ValueError, match=r"unrecognized engine"): open_dataset(BytesIO(netcdf_bytes), engine="foobar") def test_cross_engine_read_write_netcdf3(self) -> None: data = create_test_data() valid_engines: set[T_NetcdfEngine] = set() if has_netCDF4: valid_engines.add("netcdf4") if has_scipy: valid_engines.add("scipy") for write_engine in valid_engines: for format in self.netcdf3_formats: with create_tmp_file() as tmp_file: data.to_netcdf(tmp_file, format=format, engine=write_engine) for read_engine in valid_engines: with open_dataset(tmp_file, engine=read_engine) as actual: # hack to allow test to work: # coord comes back as DataArray rather than coord, # and so need to loop through here rather than in # the test function (or we get recursion) [ assert_allclose(data[k].variable, actual[k].variable) for k in data.variables ] def test_encoding_unlimited_dims(self) -> None: ds = Dataset({"x": ("y", np.arange(10.0))}) with self.roundtrip(ds, save_kwargs=dict(unlimited_dims=["y"])) as actual: assert actual.encoding["unlimited_dims"] == set("y") assert_equal(ds, actual) # Regression test for https://github.com/pydata/xarray/issues/2134 with self.roundtrip(ds, save_kwargs=dict(unlimited_dims="y")) as actual: assert actual.encoding["unlimited_dims"] == set("y") assert_equal(ds, actual) ds.encoding = {"unlimited_dims": ["y"]} with self.roundtrip(ds) as actual: assert actual.encoding["unlimited_dims"] == set("y") assert_equal(ds, actual) # Regression test for https://github.com/pydata/xarray/issues/2134 ds.encoding = {"unlimited_dims": "y"} with self.roundtrip(ds) as actual: assert actual.encoding["unlimited_dims"] == set("y") assert_equal(ds, actual) @requires_h5netcdf @requires_netCDF4 @pytest.mark.filterwarnings("ignore:use make_scale(name) instead") class TestH5NetCDFData(NetCDF4Base): engine: T_NetcdfEngine = "h5netcdf" @contextlib.contextmanager def create_store(self): with create_tmp_file() as tmp_file: yield backends.H5NetCDFStore.open(tmp_file, "w") @pytest.mark.parametrize( "invalid_netcdf, warntype, num_warns", [ pytest.param( None, FutureWarning, 1, marks=pytest.mark.skipif(has_h5netcdf_0_12, reason="raises"), ), pytest.param( False, FutureWarning, 1, marks=pytest.mark.skipif(has_h5netcdf_0_12, reason="raises"), ), (True, None, 0), ], ) def test_complex(self, invalid_netcdf, warntype, num_warns) -> None: expected = Dataset({"x": ("y", np.ones(5) + 1j * np.ones(5))}) save_kwargs = {"invalid_netcdf": invalid_netcdf} with warnings.catch_warnings(record=True) as record: with self.roundtrip(expected, save_kwargs=save_kwargs) as actual: assert_equal(expected, actual) recorded_num_warns = 0 if warntype: for warning in record: if issubclass(warning.category, warntype) and ( "complex dtypes" in str(warning.message) ): recorded_num_warns += 1 assert recorded_num_warns == num_warns @requires_h5netcdf_0_12 @pytest.mark.parametrize("invalid_netcdf", [None, False]) def test_complex_error(self, invalid_netcdf) -> None: import h5netcdf expected = Dataset({"x": ("y", np.ones(5) + 1j * np.ones(5))}) save_kwargs = {"invalid_netcdf": invalid_netcdf} with pytest.raises( h5netcdf.CompatibilityError, match="are not a supported NetCDF feature" ): with self.roundtrip(expected, save_kwargs=save_kwargs) as actual: assert_equal(expected, actual) @pytest.mark.filterwarnings("ignore:You are writing invalid netcdf features") def test_numpy_bool_(self) -> None: # h5netcdf loads booleans as numpy.bool_, this type needs to be supported # when writing invalid_netcdf datasets in order to support a roundtrip expected = Dataset({"x": ("y", np.ones(5), {"numpy_bool": np.bool_(True)})}) save_kwargs = {"invalid_netcdf": True} with self.roundtrip(expected, save_kwargs=save_kwargs) as actual: assert_identical(expected, actual) def test_cross_engine_read_write_netcdf4(self) -> None: # Drop dim3, because its labels include strings. These appear to be # not properly read with python-netCDF4, which converts them into # unicode instead of leaving them as bytes. data = create_test_data().drop_vars("dim3") data.attrs["foo"] = "bar" valid_engines: list[T_NetcdfEngine] = ["netcdf4", "h5netcdf"] for write_engine in valid_engines: with create_tmp_file() as tmp_file: data.to_netcdf(tmp_file, engine=write_engine) for read_engine in valid_engines: with open_dataset(tmp_file, engine=read_engine) as actual: assert_identical(data, actual) def test_read_byte_attrs_as_unicode(self) -> None: with create_tmp_file() as tmp_file: with nc4.Dataset(tmp_file, "w") as nc: nc.foo = b"bar" with open_dataset(tmp_file) as actual: expected = Dataset(attrs={"foo": "bar"}) assert_identical(expected, actual) def test_encoding_unlimited_dims(self) -> None: ds = Dataset({"x": ("y", np.arange(10.0))}) with self.roundtrip(ds, save_kwargs=dict(unlimited_dims=["y"])) as actual: assert actual.encoding["unlimited_dims"] == set("y") assert_equal(ds, actual) ds.encoding = {"unlimited_dims": ["y"]} with self.roundtrip(ds) as actual: assert actual.encoding["unlimited_dims"] == set("y") assert_equal(ds, actual) def test_compression_encoding_h5py(self) -> None: ENCODINGS: tuple[tuple[dict[str, Any], dict[str, Any]], ...] = ( # h5py style compression with gzip codec will be converted to # NetCDF4-Python style on round-trip ( {"compression": "gzip", "compression_opts": 9}, {"zlib": True, "complevel": 9}, ), # What can't be expressed in NetCDF4-Python style is # round-tripped unaltered ( {"compression": "lzf", "compression_opts": None}, {"compression": "lzf", "compression_opts": None}, ), # If both styles are used together, h5py format takes precedence ( { "compression": "lzf", "compression_opts": None, "zlib": True, "complevel": 9, }, {"compression": "lzf", "compression_opts": None}, ), ) for compr_in, compr_out in ENCODINGS: data = create_test_data() compr_common = { "chunksizes": (5, 5), "fletcher32": True, "shuffle": True, "original_shape": data.var2.shape, } data["var2"].encoding.update(compr_in) data["var2"].encoding.update(compr_common) compr_out.update(compr_common) data["scalar"] = ("scalar_dim", np.array([2.0])) data["scalar"] = data["scalar"][0] with self.roundtrip(data) as actual: for k, v in compr_out.items(): assert v == actual["var2"].encoding[k] def test_compression_check_encoding_h5py(self) -> None: """When mismatched h5py and NetCDF4-Python encodings are expressed in to_netcdf(encoding=...), must raise ValueError """ data = Dataset({"x": ("y", np.arange(10.0))}) # Compatible encodings are graciously supported with create_tmp_file() as tmp_file: data.to_netcdf( tmp_file, engine="h5netcdf", encoding={ "x": { "compression": "gzip", "zlib": True, "compression_opts": 6, "complevel": 6, } }, ) with open_dataset(tmp_file, engine="h5netcdf") as actual: assert actual.x.encoding["zlib"] is True assert actual.x.encoding["complevel"] == 6 # Incompatible encodings cause a crash with create_tmp_file() as tmp_file: with pytest.raises( ValueError, match=r"'zlib' and 'compression' encodings mismatch" ): data.to_netcdf( tmp_file, engine="h5netcdf", encoding={"x": {"compression": "lzf", "zlib": True}}, ) with create_tmp_file() as tmp_file: with pytest.raises( ValueError, match=r"'complevel' and 'compression_opts' encodings mismatch", ): data.to_netcdf( tmp_file, engine="h5netcdf", encoding={ "x": { "compression": "gzip", "compression_opts": 5, "complevel": 6, } }, ) def test_dump_encodings_h5py(self) -> None: # regression test for #709 ds = Dataset({"x": ("y", np.arange(10.0))}) kwargs = {"encoding": {"x": {"compression": "gzip", "compression_opts": 9}}} with self.roundtrip(ds, save_kwargs=kwargs) as actual: assert actual.x.encoding["zlib"] assert actual.x.encoding["complevel"] == 9 kwargs = {"encoding": {"x": {"compression": "lzf", "compression_opts": None}}} with self.roundtrip(ds, save_kwargs=kwargs) as actual: assert actual.x.encoding["compression"] == "lzf" assert actual.x.encoding["compression_opts"] is None @requires_h5netcdf @requires_netCDF4 class TestH5NetCDFAlreadyOpen: def test_open_dataset_group(self) -> None: import h5netcdf with create_tmp_file() as tmp_file: with nc4.Dataset(tmp_file, mode="w") as nc: group = nc.createGroup("g") v = group.createVariable("x", "int") v[...] = 42 kwargs = {} if Version(h5netcdf.__version__) >= Version("0.10.0") and Version( h5netcdf.core.h5py.__version__ ) >= Version("3.0.0"): kwargs = dict(decode_vlen_strings=True) h5 = h5netcdf.File(tmp_file, mode="r", **kwargs) store = backends.H5NetCDFStore(h5["g"]) with open_dataset(store) as ds: expected = Dataset({"x": ((), 42)}) assert_identical(expected, ds) h5 = h5netcdf.File(tmp_file, mode="r", **kwargs) store = backends.H5NetCDFStore(h5, group="g") with open_dataset(store) as ds: expected = Dataset({"x": ((), 42)}) assert_identical(expected, ds) def test_deepcopy(self) -> None: import h5netcdf with create_tmp_file() as tmp_file: with nc4.Dataset(tmp_file, mode="w") as nc: nc.createDimension("x", 10) v = nc.createVariable("y", np.int32, ("x",)) v[:] = np.arange(10) kwargs = {} if Version(h5netcdf.__version__) >= Version("0.10.0") and Version( h5netcdf.core.h5py.__version__ ) >= Version("3.0.0"): kwargs = dict(decode_vlen_strings=True) h5 = h5netcdf.File(tmp_file, mode="r", **kwargs) store = backends.H5NetCDFStore(h5) with open_dataset(store) as ds: copied = ds.copy(deep=True) expected = Dataset({"y": ("x", np.arange(10))}) assert_identical(expected, copied) @requires_h5netcdf class TestH5NetCDFFileObject(TestH5NetCDFData): engine: T_NetcdfEngine = "h5netcdf" def test_open_badbytes(self) -> None: with pytest.raises(ValueError, match=r"HDF5 as bytes"): with open_dataset(b"\211HDF\r\n\032\n", engine="h5netcdf"): # type: ignore[arg-type] pass with pytest.raises( ValueError, match=r"match in any of xarray's currently installed IO" ): with open_dataset(b"garbage"): # type: ignore[arg-type] pass with pytest.raises(ValueError, match=r"can only read bytes"): with open_dataset(b"garbage", engine="netcdf4"): # type: ignore[arg-type] pass with pytest.raises( ValueError, match=r"not the signature of a valid netCDF4 file" ): with open_dataset(BytesIO(b"garbage"), engine="h5netcdf"): pass def test_open_twice(self) -> None: expected = create_test_data() expected.attrs["foo"] = "bar" with create_tmp_file() as tmp_file: expected.to_netcdf(tmp_file, engine="h5netcdf") with open(tmp_file, "rb") as f: with open_dataset(f, engine="h5netcdf"): with open_dataset(f, engine="h5netcdf"): pass @requires_scipy def test_open_fileobj(self) -> None: # open in-memory datasets instead of local file paths expected = create_test_data().drop_vars("dim3") expected.attrs["foo"] = "bar" with create_tmp_file() as tmp_file: expected.to_netcdf(tmp_file, engine="h5netcdf") with open(tmp_file, "rb") as f: with open_dataset(f, engine="h5netcdf") as actual: assert_identical(expected, actual) f.seek(0) with open_dataset(f) as actual: assert_identical(expected, actual) f.seek(0) with BytesIO(f.read()) as bio: with open_dataset(bio, engine="h5netcdf") as actual: assert_identical(expected, actual) f.seek(0) with pytest.raises(TypeError, match="not a valid NetCDF 3"): open_dataset(f, engine="scipy") # TODO: this additional open is required since scipy seems to close the file # when it fails on the TypeError (though didn't when we used # `raises_regex`?). Ref https://github.com/pydata/xarray/pull/5191 with open(tmp_file, "rb") as f: f.seek(8) open_dataset(f) @requires_h5netcdf @requires_dask @pytest.mark.filterwarnings("ignore:deallocating CachingFileManager") class TestH5NetCDFViaDaskData(TestH5NetCDFData): @contextlib.contextmanager def roundtrip( self, data, save_kwargs=None, open_kwargs=None, allow_cleanup_failure=False ): if save_kwargs is None: save_kwargs = {} if open_kwargs is None: open_kwargs = {} open_kwargs.setdefault("chunks", -1) with TestH5NetCDFData.roundtrip( self, data, save_kwargs, open_kwargs, allow_cleanup_failure ) as ds: yield ds def test_dataset_caching(self) -> None: # caching behavior differs for dask pass def test_write_inconsistent_chunks(self) -> None: # Construct two variables with the same dimensions, but different # chunk sizes. x = da.zeros((100, 100), dtype="f4", chunks=(50, 100)) x = DataArray(data=x, dims=("lat", "lon"), name="x") x.encoding["chunksizes"] = (50, 100) x.encoding["original_shape"] = (100, 100) y = da.ones((100, 100), dtype="f4", chunks=(100, 50)) y = DataArray(data=y, dims=("lat", "lon"), name="y") y.encoding["chunksizes"] = (100, 50) y.encoding["original_shape"] = (100, 100) # Put them both into the same dataset ds = Dataset({"x": x, "y": y}) with self.roundtrip(ds) as actual: assert actual["x"].encoding["chunksizes"] == (50, 100) assert actual["y"].encoding["chunksizes"] == (100, 50) @pytest.fixture(params=["scipy", "netcdf4", "h5netcdf", "pynio", "zarr"]) def readengine(request): return request.param @pytest.fixture(params=[1, 20]) def nfiles(request): return request.param @pytest.fixture(params=[5, None]) def file_cache_maxsize(request): maxsize = request.param if maxsize is not None: with set_options(file_cache_maxsize=maxsize): yield maxsize else: yield maxsize @pytest.fixture(params=[True, False]) def parallel(request): return request.param @pytest.fixture(params=[None, 5]) def chunks(request): return request.param # using pytest.mark.skipif does not work so this a work around def skip_if_not_engine(engine): if engine == "netcdf4": pytest.importorskip("netCDF4") elif engine == "pynio": pytest.importorskip("Nio") else: pytest.importorskip(engine) @requires_dask @pytest.mark.filterwarnings("ignore:use make_scale(name) instead") def test_open_mfdataset_manyfiles( readengine, nfiles, parallel, chunks, file_cache_maxsize ): # skip certain combinations skip_if_not_engine(readengine) if ON_WINDOWS: pytest.skip("Skipping on Windows") randdata = np.random.randn(nfiles) original = Dataset({"foo": ("x", randdata)}) # test standard open_mfdataset approach with too many files with create_tmp_files(nfiles) as tmpfiles: writeengine = readengine if readengine != "pynio" else "netcdf4" # split into multiple sets of temp files for ii in original.x.values: subds = original.isel(x=slice(ii, ii + 1)) if writeengine != "zarr": subds.to_netcdf(tmpfiles[ii], engine=writeengine) else: # if writeengine == "zarr": subds.to_zarr(store=tmpfiles[ii]) # check that calculation on opened datasets works properly with open_mfdataset( tmpfiles, combine="nested", concat_dim="x", engine=readengine, parallel=parallel, chunks=chunks if (not chunks and readengine != "zarr") else "auto", ) as actual: # check that using open_mfdataset returns dask arrays for variables assert isinstance(actual["foo"].data, dask_array_type) assert_identical(original, actual) @requires_netCDF4 @requires_dask def test_open_mfdataset_can_open_path_objects() -> None: dataset = os.path.join(os.path.dirname(__file__), "data", "example_1.nc") with open_mfdataset(Path(dataset)) as actual: assert isinstance(actual, Dataset) @requires_netCDF4 @requires_dask def test_open_mfdataset_list_attr() -> None: """ Case when an attribute of type list differs across the multiple files """ from netCDF4 import Dataset with create_tmp_files(2) as nfiles: for i in range(2): with Dataset(nfiles[i], "w") as f: f.createDimension("x", 3) vlvar = f.createVariable("test_var", np.int32, ("x")) # here create an attribute as a list vlvar.test_attr = [f"string a {i}", f"string b {i}"] vlvar[:] = np.arange(3) with open_dataset(nfiles[0]) as ds1: with open_dataset(nfiles[1]) as ds2: original = xr.concat([ds1, ds2], dim="x") with xr.open_mfdataset( [nfiles[0], nfiles[1]], combine="nested", concat_dim="x" ) as actual: assert_identical(actual, original) @requires_scipy_or_netCDF4 @requires_dask class TestOpenMFDatasetWithDataVarsAndCoordsKw: coord_name = "lon" var_name = "v1" @contextlib.contextmanager def setup_files_and_datasets(self, fuzz=0): ds1, ds2 = self.gen_datasets_with_common_coord_and_time() # to test join='exact' ds1["x"] = ds1.x + fuzz with create_tmp_file() as tmpfile1: with create_tmp_file() as tmpfile2: # save data to the temporary files ds1.to_netcdf(tmpfile1) ds2.to_netcdf(tmpfile2) yield [tmpfile1, tmpfile2], [ds1, ds2] def gen_datasets_with_common_coord_and_time(self): # create coordinate data nx = 10 nt = 10 x = np.arange(nx) t1 = np.arange(nt) t2 = np.arange(nt, 2 * nt, 1) v1 = np.random.randn(nt, nx) v2 = np.random.randn(nt, nx) ds1 = Dataset( data_vars={self.var_name: (["t", "x"], v1), self.coord_name: ("x", 2 * x)}, coords={"t": (["t"], t1), "x": (["x"], x)}, ) ds2 = Dataset( data_vars={self.var_name: (["t", "x"], v2), self.coord_name: ("x", 2 * x)}, coords={"t": (["t"], t2), "x": (["x"], x)}, ) return ds1, ds2 @pytest.mark.parametrize( "combine, concat_dim", [("nested", "t"), ("by_coords", None)] ) @pytest.mark.parametrize("opt", ["all", "minimal", "different"]) @pytest.mark.parametrize("join", ["outer", "inner", "left", "right"]) def test_open_mfdataset_does_same_as_concat( self, combine, concat_dim, opt, join ) -> None: with self.setup_files_and_datasets() as (files, [ds1, ds2]): if combine == "by_coords": files.reverse() with open_mfdataset( files, data_vars=opt, combine=combine, concat_dim=concat_dim, join=join ) as ds: ds_expect = xr.concat([ds1, ds2], data_vars=opt, dim="t", join=join) assert_identical(ds, ds_expect) @pytest.mark.parametrize( ["combine_attrs", "attrs", "expected", "expect_error"], ( pytest.param("drop", [{"a": 1}, {"a": 2}], {}, False, id="drop"), pytest.param( "override", [{"a": 1}, {"a": 2}], {"a": 1}, False, id="override" ), pytest.param( "no_conflicts", [{"a": 1}, {"a": 2}], None, True, id="no_conflicts" ), pytest.param( "identical", [{"a": 1, "b": 2}, {"a": 1, "c": 3}], None, True, id="identical", ), pytest.param( "drop_conflicts", [{"a": 1, "b": 2}, {"b": -1, "c": 3}], {"a": 1, "c": 3}, False, id="drop_conflicts", ), ), ) def test_open_mfdataset_dataset_combine_attrs( self, combine_attrs, attrs, expected, expect_error ): with self.setup_files_and_datasets() as (files, [ds1, ds2]): # Give the files an inconsistent attribute for i, f in enumerate(files): ds = open_dataset(f).load() ds.attrs = attrs[i] ds.close() ds.to_netcdf(f) if expect_error: with pytest.raises(xr.MergeError): xr.open_mfdataset( files, combine="nested", concat_dim="t", combine_attrs=combine_attrs, ) else: with xr.open_mfdataset( files, combine="nested", concat_dim="t", combine_attrs=combine_attrs, ) as ds: assert ds.attrs == expected def test_open_mfdataset_dataset_attr_by_coords(self) -> None: """ Case when an attribute differs across the multiple files """ with self.setup_files_and_datasets() as (files, [ds1, ds2]): # Give the files an inconsistent attribute for i, f in enumerate(files): ds = open_dataset(f).load() ds.attrs["test_dataset_attr"] = 10 + i ds.close() ds.to_netcdf(f) with xr.open_mfdataset(files, combine="nested", concat_dim="t") as ds: assert ds.test_dataset_attr == 10 def test_open_mfdataset_dataarray_attr_by_coords(self) -> None: """ Case when an attribute of a member DataArray differs across the multiple files """ with self.setup_files_and_datasets() as (files, [ds1, ds2]): # Give the files an inconsistent attribute for i, f in enumerate(files): ds = open_dataset(f).load() ds["v1"].attrs["test_dataarray_attr"] = i ds.close() ds.to_netcdf(f) with xr.open_mfdataset(files, combine="nested", concat_dim="t") as ds: assert ds["v1"].test_dataarray_attr == 0 @pytest.mark.parametrize( "combine, concat_dim", [("nested", "t"), ("by_coords", None)] ) @pytest.mark.parametrize("opt", ["all", "minimal", "different"]) def test_open_mfdataset_exact_join_raises_error( self, combine, concat_dim, opt ) -> None: with self.setup_files_and_datasets(fuzz=0.1) as (files, [ds1, ds2]): if combine == "by_coords": files.reverse() with pytest.raises( ValueError, match=r"cannot align objects.*join.*exact.*" ): open_mfdataset( files, data_vars=opt, combine=combine, concat_dim=concat_dim, join="exact", ) def test_common_coord_when_datavars_all(self) -> None: opt: Final = "all" with self.setup_files_and_datasets() as (files, [ds1, ds2]): # open the files with the data_var option with open_mfdataset( files, data_vars=opt, combine="nested", concat_dim="t" ) as ds: coord_shape = ds[self.coord_name].shape coord_shape1 = ds1[self.coord_name].shape coord_shape2 = ds2[self.coord_name].shape var_shape = ds[self.var_name].shape assert var_shape == coord_shape assert coord_shape1 != coord_shape assert coord_shape2 != coord_shape def test_common_coord_when_datavars_minimal(self) -> None: opt: Final = "minimal" with self.setup_files_and_datasets() as (files, [ds1, ds2]): # open the files using data_vars option with open_mfdataset( files, data_vars=opt, combine="nested", concat_dim="t" ) as ds: coord_shape = ds[self.coord_name].shape coord_shape1 = ds1[self.coord_name].shape coord_shape2 = ds2[self.coord_name].shape var_shape = ds[self.var_name].shape assert var_shape != coord_shape assert coord_shape1 == coord_shape assert coord_shape2 == coord_shape def test_invalid_data_vars_value_should_fail(self) -> None: with self.setup_files_and_datasets() as (files, _): with pytest.raises(ValueError): with open_mfdataset(files, data_vars="minimum", combine="by_coords"): # type: ignore[arg-type] pass # test invalid coord parameter with pytest.raises(ValueError): with open_mfdataset(files, coords="minimum", combine="by_coords"): pass @requires_dask @requires_scipy @requires_netCDF4 class TestDask(DatasetIOBase): @contextlib.contextmanager def create_store(self): yield Dataset() @contextlib.contextmanager def roundtrip( self, data, save_kwargs=None, open_kwargs=None, allow_cleanup_failure=False ): yield data.chunk() # Override methods in DatasetIOBase - not applicable to dask def test_roundtrip_string_encoded_characters(self) -> None: pass def test_roundtrip_coordinates_with_space(self) -> None: pass def test_roundtrip_numpy_datetime_data(self) -> None: # Override method in DatasetIOBase - remove not applicable # save_kwargs times = pd.to_datetime(["2000-01-01", "2000-01-02", "NaT"]) expected = Dataset({"t": ("t", times), "t0": times[0]}) with self.roundtrip(expected) as actual: assert_identical(expected, actual) def test_roundtrip_cftime_datetime_data(self) -> None: # Override method in DatasetIOBase - remove not applicable # save_kwargs from xarray.tests.test_coding_times import _all_cftime_date_types date_types = _all_cftime_date_types() for date_type in date_types.values(): times = [date_type(1, 1, 1), date_type(1, 1, 2)] expected = Dataset({"t": ("t", times), "t0": times[0]}) expected_decoded_t = np.array(times) expected_decoded_t0 = np.array([date_type(1, 1, 1)]) with self.roundtrip(expected) as actual: abs_diff = abs(actual.t.values - expected_decoded_t) assert (abs_diff <= np.timedelta64(1, "s")).all() abs_diff = abs(actual.t0.values - expected_decoded_t0) assert (abs_diff <= np.timedelta64(1, "s")).all() def test_write_store(self) -> None: # Override method in DatasetIOBase - not applicable to dask pass def test_dataset_caching(self) -> None: expected = Dataset({"foo": ("x", [5, 6, 7])}) with self.roundtrip(expected) as actual: assert not actual.foo.variable._in_memory actual.foo.values # no caching assert not actual.foo.variable._in_memory def test_open_mfdataset(self) -> None: original = Dataset({"foo": ("x", np.random.randn(10))}) with create_tmp_file() as tmp1: with create_tmp_file() as tmp2: original.isel(x=slice(5)).to_netcdf(tmp1) original.isel(x=slice(5, 10)).to_netcdf(tmp2) with open_mfdataset( [tmp1, tmp2], concat_dim="x", combine="nested" ) as actual: assert isinstance(actual.foo.variable.data, da.Array) assert actual.foo.variable.data.chunks == ((5, 5),) assert_identical(original, actual) with open_mfdataset( [tmp1, tmp2], concat_dim="x", combine="nested", chunks={"x": 3} ) as actual: assert actual.foo.variable.data.chunks == ((3, 2, 3, 2),) with pytest.raises(OSError, match=r"no files to open"): open_mfdataset("foo-bar-baz-*.nc") with pytest.raises(ValueError, match=r"wild-card"): open_mfdataset("http://some/remote/uri") @requires_fsspec def test_open_mfdataset_no_files(self) -> None: pytest.importorskip("aiobotocore") # glob is attempted as of #4823, but finds no files with pytest.raises(OSError, match=r"no files"): open_mfdataset("http://some/remote/uri", engine="zarr") def test_open_mfdataset_2d(self) -> None: original = Dataset({"foo": (["x", "y"], np.random.randn(10, 8))}) with create_tmp_file() as tmp1: with create_tmp_file() as tmp2: with create_tmp_file() as tmp3: with create_tmp_file() as tmp4: original.isel(x=slice(5), y=slice(4)).to_netcdf(tmp1) original.isel(x=slice(5, 10), y=slice(4)).to_netcdf(tmp2) original.isel(x=slice(5), y=slice(4, 8)).to_netcdf(tmp3) original.isel(x=slice(5, 10), y=slice(4, 8)).to_netcdf(tmp4) with open_mfdataset( [[tmp1, tmp2], [tmp3, tmp4]], combine="nested", concat_dim=["y", "x"], ) as actual: assert isinstance(actual.foo.variable.data, da.Array) assert actual.foo.variable.data.chunks == ((5, 5), (4, 4)) assert_identical(original, actual) with open_mfdataset( [[tmp1, tmp2], [tmp3, tmp4]], combine="nested", concat_dim=["y", "x"], chunks={"x": 3, "y": 2}, ) as actual: assert actual.foo.variable.data.chunks == ( (3, 2, 3, 2), (2, 2, 2, 2), ) def test_open_mfdataset_pathlib(self) -> None: original = Dataset({"foo": ("x", np.random.randn(10))}) with create_tmp_file() as tmps1: with create_tmp_file() as tmps2: tmp1 = Path(tmps1) tmp2 = Path(tmps2) original.isel(x=slice(5)).to_netcdf(tmp1) original.isel(x=slice(5, 10)).to_netcdf(tmp2) with open_mfdataset( [tmp1, tmp2], concat_dim="x", combine="nested" ) as actual: assert_identical(original, actual) def test_open_mfdataset_2d_pathlib(self) -> None: original = Dataset({"foo": (["x", "y"], np.random.randn(10, 8))}) with create_tmp_file() as tmps1: with create_tmp_file() as tmps2: with create_tmp_file() as tmps3: with create_tmp_file() as tmps4: tmp1 = Path(tmps1) tmp2 = Path(tmps2) tmp3 = Path(tmps3) tmp4 = Path(tmps4) original.isel(x=slice(5), y=slice(4)).to_netcdf(tmp1) original.isel(x=slice(5, 10), y=slice(4)).to_netcdf(tmp2) original.isel(x=slice(5), y=slice(4, 8)).to_netcdf(tmp3) original.isel(x=slice(5, 10), y=slice(4, 8)).to_netcdf(tmp4) with open_mfdataset( [[tmp1, tmp2], [tmp3, tmp4]], combine="nested", concat_dim=["y", "x"], ) as actual: assert_identical(original, actual) def test_open_mfdataset_2(self) -> None: original = Dataset({"foo": ("x", np.random.randn(10))}) with create_tmp_file() as tmp1: with create_tmp_file() as tmp2: original.isel(x=slice(5)).to_netcdf(tmp1) original.isel(x=slice(5, 10)).to_netcdf(tmp2) with open_mfdataset( [tmp1, tmp2], concat_dim="x", combine="nested" ) as actual: assert_identical(original, actual) def test_attrs_mfdataset(self) -> None: original = Dataset({"foo": ("x", np.random.randn(10))}) with create_tmp_file() as tmp1: with create_tmp_file() as tmp2: ds1 = original.isel(x=slice(5)) ds2 = original.isel(x=slice(5, 10)) ds1.attrs["test1"] = "foo" ds2.attrs["test2"] = "bar" ds1.to_netcdf(tmp1) ds2.to_netcdf(tmp2) with open_mfdataset( [tmp1, tmp2], concat_dim="x", combine="nested" ) as actual: # presumes that attributes inherited from # first dataset loaded assert actual.test1 == ds1.test1 # attributes from ds2 are not retained, e.g., with pytest.raises(AttributeError, match=r"no attribute"): actual.test2 def test_open_mfdataset_attrs_file(self) -> None: original = Dataset({"foo": ("x", np.random.randn(10))}) with create_tmp_files(2) as (tmp1, tmp2): ds1 = original.isel(x=slice(5)) ds2 = original.isel(x=slice(5, 10)) ds1.attrs["test1"] = "foo" ds2.attrs["test2"] = "bar" ds1.to_netcdf(tmp1) ds2.to_netcdf(tmp2) with open_mfdataset( [tmp1, tmp2], concat_dim="x", combine="nested", attrs_file=tmp2 ) as actual: # attributes are inherited from the master file assert actual.attrs["test2"] == ds2.attrs["test2"] # attributes from ds1 are not retained, e.g., assert "test1" not in actual.attrs def test_open_mfdataset_attrs_file_path(self) -> None: original = Dataset({"foo": ("x", np.random.randn(10))}) with create_tmp_files(2) as (tmps1, tmps2): tmp1 = Path(tmps1) tmp2 = Path(tmps2) ds1 = original.isel(x=slice(5)) ds2 = original.isel(x=slice(5, 10)) ds1.attrs["test1"] = "foo" ds2.attrs["test2"] = "bar" ds1.to_netcdf(tmp1) ds2.to_netcdf(tmp2) with open_mfdataset( [tmp1, tmp2], concat_dim="x", combine="nested", attrs_file=tmp2 ) as actual: # attributes are inherited from the master file assert actual.attrs["test2"] == ds2.attrs["test2"] # attributes from ds1 are not retained, e.g., assert "test1" not in actual.attrs def test_open_mfdataset_auto_combine(self) -> None: original = Dataset({"foo": ("x", np.random.randn(10)), "x": np.arange(10)}) with create_tmp_file() as tmp1: with create_tmp_file() as tmp2: original.isel(x=slice(5)).to_netcdf(tmp1) original.isel(x=slice(5, 10)).to_netcdf(tmp2) with open_mfdataset([tmp2, tmp1], combine="by_coords") as actual: assert_identical(original, actual) def test_open_mfdataset_raise_on_bad_combine_args(self) -> None: # Regression test for unhelpful error shown in #5230 original = Dataset({"foo": ("x", np.random.randn(10)), "x": np.arange(10)}) with create_tmp_file() as tmp1: with create_tmp_file() as tmp2: original.isel(x=slice(5)).to_netcdf(tmp1) original.isel(x=slice(5, 10)).to_netcdf(tmp2) with pytest.raises(ValueError, match="`concat_dim` has no effect"): open_mfdataset([tmp1, tmp2], concat_dim="x") @pytest.mark.xfail(reason="mfdataset loses encoding currently.") def test_encoding_mfdataset(self) -> None: original = Dataset( { "foo": ("t", np.random.randn(10)), "t": ("t", pd.date_range(start="2010-01-01", periods=10, freq="1D")), } ) original.t.encoding["units"] = "days since 2010-01-01" with create_tmp_file() as tmp1: with create_tmp_file() as tmp2: ds1 = original.isel(t=slice(5)) ds2 = original.isel(t=slice(5, 10)) ds1.t.encoding["units"] = "days since 2010-01-01" ds2.t.encoding["units"] = "days since 2000-01-01" ds1.to_netcdf(tmp1) ds2.to_netcdf(tmp2) with open_mfdataset([tmp1, tmp2], combine="nested") as actual: assert actual.t.encoding["units"] == original.t.encoding["units"] assert actual.t.encoding["units"] == ds1.t.encoding["units"] assert actual.t.encoding["units"] != ds2.t.encoding["units"] def test_preprocess_mfdataset(self) -> None: original = Dataset({"foo": ("x", np.random.randn(10))}) with create_tmp_file() as tmp: original.to_netcdf(tmp) def preprocess(ds): return ds.assign_coords(z=0) expected = preprocess(original) with open_mfdataset( tmp, preprocess=preprocess, combine="by_coords" ) as actual: assert_identical(expected, actual) def test_save_mfdataset_roundtrip(self) -> None: original = Dataset({"foo": ("x", np.random.randn(10))}) datasets = [original.isel(x=slice(5)), original.isel(x=slice(5, 10))] with create_tmp_file() as tmp1: with create_tmp_file() as tmp2: save_mfdataset(datasets, [tmp1, tmp2]) with open_mfdataset( [tmp1, tmp2], concat_dim="x", combine="nested" ) as actual: assert_identical(actual, original) def test_save_mfdataset_invalid(self) -> None: ds = Dataset() with pytest.raises(ValueError, match=r"cannot use mode"): save_mfdataset([ds, ds], ["same", "same"]) with pytest.raises(ValueError, match=r"same length"): save_mfdataset([ds, ds], ["only one path"]) def test_save_mfdataset_invalid_dataarray(self) -> None: # regression test for GH1555 da = DataArray([1, 2]) with pytest.raises(TypeError, match=r"supports writing Dataset"): save_mfdataset([da], ["dataarray"]) def test_save_mfdataset_pathlib_roundtrip(self) -> None: original = Dataset({"foo": ("x", np.random.randn(10))}) datasets = [original.isel(x=slice(5)), original.isel(x=slice(5, 10))] with create_tmp_file() as tmps1: with create_tmp_file() as tmps2: tmp1 = Path(tmps1) tmp2 = Path(tmps2) save_mfdataset(datasets, [tmp1, tmp2]) with open_mfdataset( [tmp1, tmp2], concat_dim="x", combine="nested" ) as actual: assert_identical(actual, original) def test_save_mfdataset_pass_kwargs(self) -> None: # create a timeseries to store in a netCDF file times = [0, 1] time = xr.DataArray(times, dims=("time",)) # create a simple dataset to write using save_mfdataset test_ds = xr.Dataset() test_ds["time"] = time # make sure the times are written as double and # turn off fill values encoding = dict(time=dict(dtype="double")) unlimited_dims = ["time"] # set the output file name output_path = "test.nc" # attempt to write the dataset with the encoding and unlimited args # passed through xr.save_mfdataset( [test_ds], [output_path], encoding=encoding, unlimited_dims=unlimited_dims ) def test_open_and_do_math(self) -> None: original = Dataset({"foo": ("x", np.random.randn(10))}) with create_tmp_file() as tmp: original.to_netcdf(tmp) with open_mfdataset(tmp, combine="by_coords") as ds: actual = 1.0 * ds assert_allclose(original, actual, decode_bytes=False) def test_open_mfdataset_concat_dim_none(self) -> None: with create_tmp_file() as tmp1: with create_tmp_file() as tmp2: data = Dataset({"x": 0}) data.to_netcdf(tmp1) Dataset({"x": np.nan}).to_netcdf(tmp2) with open_mfdataset( [tmp1, tmp2], concat_dim=None, combine="nested" ) as actual: assert_identical(data, actual) def test_open_mfdataset_concat_dim_default_none(self) -> None: with create_tmp_file() as tmp1: with create_tmp_file() as tmp2: data = Dataset({"x": 0}) data.to_netcdf(tmp1) Dataset({"x": np.nan}).to_netcdf(tmp2) with open_mfdataset([tmp1, tmp2], combine="nested") as actual: assert_identical(data, actual) def test_open_dataset(self) -> None: original = Dataset({"foo": ("x", np.random.randn(10))}) with create_tmp_file() as tmp: original.to_netcdf(tmp) with open_dataset(tmp, chunks={"x": 5}) as actual: assert isinstance(actual.foo.variable.data, da.Array) assert actual.foo.variable.data.chunks == ((5, 5),) assert_identical(original, actual) with open_dataset(tmp, chunks=5) as actual: assert_identical(original, actual) with open_dataset(tmp) as actual: assert isinstance(actual.foo.variable.data, np.ndarray) assert_identical(original, actual) def test_open_single_dataset(self) -> None: # Test for issue GH #1988. This makes sure that the # concat_dim is utilized when specified in open_mfdataset(). rnddata = np.random.randn(10) original = Dataset({"foo": ("x", rnddata)}) dim = DataArray([100], name="baz", dims="baz") expected = Dataset( {"foo": (("baz", "x"), rnddata[np.newaxis, :])}, {"baz": [100]} ) with create_tmp_file() as tmp: original.to_netcdf(tmp) with open_mfdataset([tmp], concat_dim=dim, combine="nested") as actual: assert_identical(expected, actual) def test_open_multi_dataset(self) -> None: # Test for issue GH #1988 and #2647. This makes sure that the # concat_dim is utilized when specified in open_mfdataset(). # The additional wrinkle is to ensure that a length greater # than one is tested as well due to numpy's implicit casting # of 1-length arrays to booleans in tests, which allowed # #2647 to still pass the test_open_single_dataset(), # which is itself still needed as-is because the original # bug caused one-length arrays to not be used correctly # in concatenation. rnddata = np.random.randn(10) original = Dataset({"foo": ("x", rnddata)}) dim = DataArray([100, 150], name="baz", dims="baz") expected = Dataset( {"foo": (("baz", "x"), np.tile(rnddata[np.newaxis, :], (2, 1)))}, {"baz": [100, 150]}, ) with create_tmp_file() as tmp1, create_tmp_file() as tmp2: original.to_netcdf(tmp1) original.to_netcdf(tmp2) with open_mfdataset( [tmp1, tmp2], concat_dim=dim, combine="nested" ) as actual: assert_identical(expected, actual) def test_dask_roundtrip(self) -> None: with create_tmp_file() as tmp: data = create_test_data() data.to_netcdf(tmp) chunks = {"dim1": 4, "dim2": 4, "dim3": 4, "time": 10} with open_dataset(tmp, chunks=chunks) as dask_ds: assert_identical(data, dask_ds) with create_tmp_file() as tmp2: dask_ds.to_netcdf(tmp2) with open_dataset(tmp2) as on_disk: assert_identical(data, on_disk) def test_deterministic_names(self) -> None: with create_tmp_file() as tmp: data = create_test_data() data.to_netcdf(tmp) with open_mfdataset(tmp, combine="by_coords") as ds: original_names = {k: v.data.name for k, v in ds.data_vars.items()} with open_mfdataset(tmp, combine="by_coords") as ds: repeat_names = {k: v.data.name for k, v in ds.data_vars.items()} for var_name, dask_name in original_names.items(): assert var_name in dask_name assert dask_name[:13] == "open_dataset-" assert original_names == repeat_names def test_dataarray_compute(self) -> None: # Test DataArray.compute() on dask backend. # The test for Dataset.compute() is already in DatasetIOBase; # however dask is the only tested backend which supports DataArrays actual = DataArray([1, 2]).chunk() computed = actual.compute() assert not actual._in_memory assert computed._in_memory assert_allclose(actual, computed, decode_bytes=False) @pytest.mark.xfail def test_save_mfdataset_compute_false_roundtrip(self) -> None: from dask.delayed import Delayed original = Dataset({"foo": ("x", np.random.randn(10))}).chunk() datasets = [original.isel(x=slice(5)), original.isel(x=slice(5, 10))] with create_tmp_file(allow_cleanup_failure=ON_WINDOWS) as tmp1: with create_tmp_file(allow_cleanup_failure=ON_WINDOWS) as tmp2: delayed_obj = save_mfdataset( datasets, [tmp1, tmp2], engine=self.engine, compute=False ) assert isinstance(delayed_obj, Delayed) delayed_obj.compute() with open_mfdataset( [tmp1, tmp2], combine="nested", concat_dim="x" ) as actual: assert_identical(actual, original) def test_load_dataset(self) -> None: with create_tmp_file() as tmp: original = Dataset({"foo": ("x", np.random.randn(10))}) original.to_netcdf(tmp) ds = load_dataset(tmp) # this would fail if we used open_dataset instead of load_dataset ds.to_netcdf(tmp) def test_load_dataarray(self) -> None: with create_tmp_file() as tmp: original = Dataset({"foo": ("x", np.random.randn(10))}) original.to_netcdf(tmp) ds = load_dataarray(tmp) # this would fail if we used open_dataarray instead of # load_dataarray ds.to_netcdf(tmp) @pytest.mark.skipif( ON_WINDOWS, reason="counting number of tasks in graph fails on windows for some reason", ) def test_inline_array(self) -> None: with create_tmp_file() as tmp: original = Dataset({"foo": ("x", np.random.randn(10))}) original.to_netcdf(tmp) chunks = {"time": 10} def num_graph_nodes(obj): return len(obj.__dask_graph__()) not_inlined_ds = open_dataset(tmp, inline_array=False, chunks=chunks) inlined_ds = open_dataset(tmp, inline_array=True, chunks=chunks) assert num_graph_nodes(inlined_ds) < num_graph_nodes(not_inlined_ds) not_inlined_da = open_dataarray(tmp, inline_array=False, chunks=chunks) inlined_da = open_dataarray(tmp, inline_array=True, chunks=chunks) assert num_graph_nodes(inlined_da) < num_graph_nodes(not_inlined_da) @requires_scipy_or_netCDF4 @requires_pydap @pytest.mark.filterwarnings("ignore:The binary mode of fromstring is deprecated") class TestPydap: def convert_to_pydap_dataset(self, original): from pydap.model import BaseType, DatasetType, GridType ds = DatasetType("bears", **original.attrs) for key, var in original.data_vars.items(): v = GridType(key) v[key] = BaseType(key, var.values, dimensions=var.dims, **var.attrs) for d in var.dims: v[d] = BaseType(d, var[d].values) ds[key] = v # check all dims are stored in ds for d in original.coords: ds[d] = BaseType( d, original[d].values, dimensions=(d,), **original[d].attrs ) return ds @contextlib.contextmanager def create_datasets(self, **kwargs): with open_example_dataset("bears.nc") as expected: pydap_ds = self.convert_to_pydap_dataset(expected) actual = open_dataset(PydapDataStore(pydap_ds)) # TODO solve this workaround: # netcdf converts string to byte not unicode expected["bears"] = expected["bears"].astype(str) yield actual, expected def test_cmp_local_file(self) -> None: with self.create_datasets() as (actual, expected): assert_equal(actual, expected) # global attributes should be global attributes on the dataset assert "NC_GLOBAL" not in actual.attrs assert "history" in actual.attrs # we don't check attributes exactly with assertDatasetIdentical() # because the test DAP server seems to insert some extra # attributes not found in the netCDF file. assert actual.attrs.keys() == expected.attrs.keys() with self.create_datasets() as (actual, expected): assert_equal(actual[{"l": 2}], expected[{"l": 2}]) with self.create_datasets() as (actual, expected): assert_equal(actual.isel(i=0, j=-1), expected.isel(i=0, j=-1)) with self.create_datasets() as (actual, expected): assert_equal(actual.isel(j=slice(1, 2)), expected.isel(j=slice(1, 2))) with self.create_datasets() as (actual, expected): indexers = {"i": [1, 0, 0], "j": [1, 2, 0, 1]} assert_equal(actual.isel(**indexers), expected.isel(**indexers)) with self.create_datasets() as (actual, expected): indexers2 = { "i": DataArray([0, 1, 0], dims="a"), "j": DataArray([0, 2, 1], dims="a"), } assert_equal(actual.isel(**indexers2), expected.isel(**indexers2)) def test_compatible_to_netcdf(self) -> None: # make sure it can be saved as a netcdf with self.create_datasets() as (actual, expected): with create_tmp_file() as tmp_file: actual.to_netcdf(tmp_file) with open_dataset(tmp_file) as actual2: actual2["bears"] = actual2["bears"].astype(str) assert_equal(actual2, expected) @requires_dask def test_dask(self) -> None: with self.create_datasets(chunks={"j": 2}) as (actual, expected): assert_equal(actual, expected) @network @requires_scipy_or_netCDF4 @requires_pydap class TestPydapOnline(TestPydap): @contextlib.contextmanager def create_datasets(self, **kwargs): url = "http://test.opendap.org/opendap/hyrax/data/nc/bears.nc" actual = open_dataset(url, engine="pydap", **kwargs) with open_example_dataset("bears.nc") as expected: # workaround to restore string which is converted to byte expected["bears"] = expected["bears"].astype(str) yield actual, expected def test_session(self) -> None: from pydap.cas.urs import setup_session session = setup_session("XarrayTestUser", "Xarray2017") with mock.patch("pydap.client.open_url") as mock_func: xr.backends.PydapDataStore.open("http://test.url", session=session) mock_func.assert_called_with( url="http://test.url", application=None, session=session, output_grid=True, timeout=120, ) @requires_scipy @requires_pynio class TestPyNio(CFEncodedBase, NetCDF3Only): def test_write_store(self) -> None: # pynio is read-only for now pass @contextlib.contextmanager def open(self, path, **kwargs): with open_dataset(path, engine="pynio", **kwargs) as ds: yield ds def test_kwargs(self) -> None: kwargs = {"format": "grib"} path = os.path.join(os.path.dirname(__file__), "data", "example") with backends.NioDataStore(path, **kwargs) as store: assert store._manager._kwargs["format"] == "grib" def save(self, dataset, path, **kwargs): return dataset.to_netcdf(path, engine="scipy", **kwargs) def test_weakrefs(self) -> None: example = Dataset({"foo": ("x", np.arange(5.0))}) expected = example.rename({"foo": "bar", "x": "y"}) with create_tmp_file() as tmp_file: example.to_netcdf(tmp_file, engine="scipy") on_disk = open_dataset(tmp_file, engine="pynio") actual = on_disk.rename({"foo": "bar", "x": "y"}) del on_disk # trigger garbage collection assert_identical(actual, expected) @requires_cfgrib class TestCfGrib: def test_read(self) -> None: expected = { "number": 2, "time": 3, "isobaricInhPa": 2, "latitude": 3, "longitude": 4, } with open_example_dataset("example.grib", engine="cfgrib") as ds: assert ds.dims == expected assert list(ds.data_vars) == ["z", "t"] assert ds["z"].min() == 12660.0 def test_read_filter_by_keys(self) -> None: kwargs = {"filter_by_keys": {"shortName": "t"}} expected = { "number": 2, "time": 3, "isobaricInhPa": 2, "latitude": 3, "longitude": 4, } with open_example_dataset( "example.grib", engine="cfgrib", backend_kwargs=kwargs ) as ds: assert ds.dims == expected assert list(ds.data_vars) == ["t"] assert ds["t"].min() == 231.0 def test_read_outer(self) -> None: expected = { "number": 2, "time": 3, "isobaricInhPa": 2, "latitude": 2, "longitude": 3, } with open_example_dataset("example.grib", engine="cfgrib") as ds: res = ds.isel(latitude=[0, 2], longitude=[0, 1, 2]) assert res.dims == expected assert res["t"].min() == 231.0 @requires_pseudonetcdf @pytest.mark.filterwarnings("ignore:IOAPI_ISPH is assumed to be 6370000") class TestPseudoNetCDFFormat: def open(self, path, **kwargs): return open_dataset(path, engine="pseudonetcdf", **kwargs) @contextlib.contextmanager def roundtrip( self, data, save_kwargs=None, open_kwargs=None, allow_cleanup_failure=False ): if save_kwargs is None: save_kwargs = {} if open_kwargs is None: open_kwargs = {} with create_tmp_file(allow_cleanup_failure=allow_cleanup_failure) as path: self.save(data, path, **save_kwargs) with self.open(path, **open_kwargs) as ds: yield ds def test_ict_format(self) -> None: """ Open a CAMx file and test data variables """ stdattr = { "fill_value": -9999.0, "missing_value": -9999, "scale": 1, "llod_flag": -8888, "llod_value": "N/A", "ulod_flag": -7777, "ulod_value": "N/A", } def myatts(**attrs): outattr = stdattr.copy() outattr.update(attrs) return outattr input = { "coords": {}, "attrs": { "fmt": "1001", "n_header_lines": 29, "PI_NAME": "Henderson, Barron", "ORGANIZATION_NAME": "U.S. EPA", "SOURCE_DESCRIPTION": "Example file with artificial data", "MISSION_NAME": "JUST_A_TEST", "VOLUME_INFO": "1, 1", "SDATE": "2018, 04, 27", "WDATE": "2018, 04, 27", "TIME_INTERVAL": "0", "INDEPENDENT_VARIABLE_DEFINITION": "Start_UTC", "INDEPENDENT_VARIABLE": "Start_UTC", "INDEPENDENT_VARIABLE_UNITS": "Start_UTC", "ULOD_FLAG": "-7777", "ULOD_VALUE": "N/A", "LLOD_FLAG": "-8888", "LLOD_VALUE": ("N/A, N/A, N/A, N/A, 0.025"), "OTHER_COMMENTS": ( "www-air.larc.nasa.gov/missions/etc/" + "IcarttDataFormat.htm" ), "REVISION": "R0", "R0": "No comments for this revision.", "TFLAG": "Start_UTC", }, "dims": {"POINTS": 4}, "data_vars": { "Start_UTC": { "data": [43200.0, 46800.0, 50400.0, 50400.0], "dims": ("POINTS",), "attrs": myatts(units="Start_UTC", standard_name="Start_UTC"), }, "lat": { "data": [41.0, 42.0, 42.0, 42.0], "dims": ("POINTS",), "attrs": myatts(units="degrees_north", standard_name="lat"), }, "lon": { "data": [-71.0, -72.0, -73.0, -74.0], "dims": ("POINTS",), "attrs": myatts(units="degrees_east", standard_name="lon"), }, "elev": { "data": [5.0, 15.0, 20.0, 25.0], "dims": ("POINTS",), "attrs": myatts(units="meters", standard_name="elev"), }, "TEST_ppbv": { "data": [1.2345, 2.3456, 3.4567, 4.5678], "dims": ("POINTS",), "attrs": myatts(units="ppbv", standard_name="TEST_ppbv"), }, "TESTM_ppbv": { "data": [2.22, -9999.0, -7777.0, -8888.0], "dims": ("POINTS",), "attrs": myatts( units="ppbv", standard_name="TESTM_ppbv", llod_value=0.025 ), }, }, } chkfile = Dataset.from_dict(input) with open_example_dataset( "example.ict", engine="pseudonetcdf", backend_kwargs={"format": "ffi1001"} ) as ictfile: assert_identical(ictfile, chkfile) def test_ict_format_write(self) -> None: fmtkw = {"format": "ffi1001"} with open_example_dataset( "example.ict", engine="pseudonetcdf", backend_kwargs=fmtkw ) as expected: with self.roundtrip( expected, save_kwargs=fmtkw, open_kwargs={"backend_kwargs": fmtkw} ) as actual: assert_identical(expected, actual) def test_uamiv_format_read(self) -> None: """ Open a CAMx file and test data variables """ camxfile = open_example_dataset( "example.uamiv", engine="pseudonetcdf", backend_kwargs={"format": "uamiv"} ) data = np.arange(20, dtype="f").reshape(1, 1, 4, 5) expected = xr.Variable( ("TSTEP", "LAY", "ROW", "COL"), data, dict(units="ppm", long_name="O3".ljust(16), var_desc="O3".ljust(80)), ) actual = camxfile.variables["O3"] assert_allclose(expected, actual) data = np.array([[[2002154, 0]]], dtype="i") expected = xr.Variable( ("TSTEP", "VAR", "DATE-TIME"), data, dict( long_name="TFLAG".ljust(16), var_desc="TFLAG".ljust(80), units="DATE-TIME".ljust(16), ), ) actual = camxfile.variables["TFLAG"] assert_allclose(expected, actual) camxfile.close() @requires_dask def test_uamiv_format_mfread(self) -> None: """ Open a CAMx file and test data variables """ camxfile = open_example_mfdataset( ["example.uamiv", "example.uamiv"], engine="pseudonetcdf", concat_dim="TSTEP", combine="nested", backend_kwargs={"format": "uamiv"}, ) data1 = np.arange(20, dtype="f").reshape(1, 1, 4, 5) data = np.concatenate([data1] * 2, axis=0) expected = xr.Variable( ("TSTEP", "LAY", "ROW", "COL"), data, dict(units="ppm", long_name="O3".ljust(16), var_desc="O3".ljust(80)), ) actual = camxfile.variables["O3"] assert_allclose(expected, actual) data = np.array([[[2002154, 0]]], dtype="i").repeat(2, 0) attrs = dict( long_name="TFLAG".ljust(16), var_desc="TFLAG".ljust(80), units="DATE-TIME".ljust(16), ) dims = ("TSTEP", "VAR", "DATE-TIME") expected = xr.Variable(dims, data, attrs) actual = camxfile.variables["TFLAG"] assert_allclose(expected, actual) camxfile.close() @pytest.mark.xfail(reason="Flaky; see GH3711") def test_uamiv_format_write(self) -> None: fmtkw = {"format": "uamiv"} expected = open_example_dataset( "example.uamiv", engine="pseudonetcdf", backend_kwargs=fmtkw ) with self.roundtrip( expected, save_kwargs=fmtkw, open_kwargs={"backend_kwargs": fmtkw}, allow_cleanup_failure=True, ) as actual: assert_identical(expected, actual) expected.close() def save(self, dataset, path, **save_kwargs): import PseudoNetCDF as pnc pncf = pnc.PseudoNetCDFFile() pncf.dimensions = { k: pnc.PseudoNetCDFDimension(pncf, k, v) for k, v in dataset.dims.items() } pncf.variables = { k: pnc.PseudoNetCDFVariable( pncf, k, v.dtype.char, v.dims, values=v.data[...], **v.attrs ) for k, v in dataset.variables.items() } for pk, pv in dataset.attrs.items(): setattr(pncf, pk, pv) pnc.pncwrite(pncf, path, **save_kwargs) @requires_rasterio @contextlib.contextmanager def create_tmp_geotiff( nx=4, ny=3, nz=3, transform=None, transform_args=default_value, crs=default_value, open_kwargs=None, additional_attrs=None, ): if transform_args is default_value: transform_args = [5000, 80000, 1000, 2000.0] if crs is default_value: crs = { "units": "m", "no_defs": True, "ellps": "WGS84", "proj": "utm", "zone": 18, } # yields a temporary geotiff file and a corresponding expected DataArray import rasterio from rasterio.transform import from_origin if open_kwargs is None: open_kwargs = {} with create_tmp_file(suffix=".tif", allow_cleanup_failure=ON_WINDOWS) as tmp_file: # allow 2d or 3d shapes if nz == 1: data_shape = ny, nx write_kwargs = {"indexes": 1} else: data_shape = nz, ny, nx write_kwargs = {} data = np.arange(nz * ny * nx, dtype=rasterio.float32).reshape(*data_shape) if transform is None: transform = from_origin(*transform_args) if additional_attrs is None: additional_attrs = { "descriptions": tuple(f"d{n + 1}" for n in range(nz)), "units": tuple(f"u{n + 1}" for n in range(nz)), } with rasterio.open( tmp_file, "w", driver="GTiff", height=ny, width=nx, count=nz, crs=crs, transform=transform, dtype=rasterio.float32, **open_kwargs, ) as s: for attr, val in additional_attrs.items(): setattr(s, attr, val) s.write(data, **write_kwargs) dx, dy = s.res[0], -s.res[1] a, b, c, d = transform_args data = data[np.newaxis, ...] if nz == 1 else data expected = DataArray( data, dims=("band", "y", "x"), coords={ "band": np.arange(nz) + 1, "y": -np.arange(ny) * d + b + dy / 2, "x": np.arange(nx) * c + a + dx / 2, }, ) yield tmp_file, expected @requires_rasterio class TestRasterio: @requires_scipy_or_netCDF4 def test_serialization(self) -> None: with create_tmp_geotiff(additional_attrs={}) as (tmp_file, expected): # Write it to a netcdf and read again (roundtrip) with pytest.warns(DeprecationWarning), xr.open_rasterio(tmp_file) as rioda: with create_tmp_file(suffix=".nc") as tmp_nc_file: rioda.to_netcdf(tmp_nc_file) with xr.open_dataarray(tmp_nc_file) as ncds: assert_identical(rioda, ncds) def test_utm(self) -> None: with create_tmp_geotiff() as (tmp_file, expected): with pytest.warns(DeprecationWarning), xr.open_rasterio(tmp_file) as rioda: assert_allclose(rioda, expected) assert rioda.attrs["scales"] == (1.0, 1.0, 1.0) assert rioda.attrs["offsets"] == (0.0, 0.0, 0.0) assert rioda.attrs["descriptions"] == ("d1", "d2", "d3") assert rioda.attrs["units"] == ("u1", "u2", "u3") assert isinstance(rioda.attrs["crs"], str) assert isinstance(rioda.attrs["res"], tuple) assert isinstance(rioda.attrs["is_tiled"], np.uint8) assert isinstance(rioda.attrs["transform"], tuple) assert len(rioda.attrs["transform"]) == 6 np.testing.assert_array_equal( rioda.attrs["nodatavals"], [np.NaN, np.NaN, np.NaN] ) # Check no parse coords with pytest.warns(DeprecationWarning), xr.open_rasterio( tmp_file, parse_coordinates=False ) as rioda: assert "x" not in rioda.coords assert "y" not in rioda.coords def test_non_rectilinear(self) -> None: from rasterio.transform import from_origin # Create a geotiff file with 2d coordinates with create_tmp_geotiff( transform=from_origin(0, 3, 1, 1).rotation(45), crs=None ) as (tmp_file, _): # Default is to not parse coords with pytest.warns(DeprecationWarning), xr.open_rasterio(tmp_file) as rioda: assert "x" not in rioda.coords assert "y" not in rioda.coords assert "crs" not in rioda.attrs assert rioda.attrs["scales"] == (1.0, 1.0, 1.0) assert rioda.attrs["offsets"] == (0.0, 0.0, 0.0) assert rioda.attrs["descriptions"] == ("d1", "d2", "d3") assert rioda.attrs["units"] == ("u1", "u2", "u3") assert isinstance(rioda.attrs["res"], tuple) assert isinstance(rioda.attrs["is_tiled"], np.uint8) assert isinstance(rioda.attrs["transform"], tuple) assert len(rioda.attrs["transform"]) == 6 # See if a warning is raised if we force it with pytest.warns(Warning, match="transformation isn't rectilinear"): with xr.open_rasterio(tmp_file, parse_coordinates=True) as rioda: assert "x" not in rioda.coords assert "y" not in rioda.coords def test_platecarree(self) -> None: with create_tmp_geotiff( 8, 10, 1, transform_args=[1, 2, 0.5, 2.0], crs="+proj=latlong", open_kwargs={"nodata": -9765}, ) as (tmp_file, expected): with pytest.warns(DeprecationWarning), xr.open_rasterio(tmp_file) as rioda: assert_allclose(rioda, expected) assert rioda.attrs["scales"] == (1.0,) assert rioda.attrs["offsets"] == (0.0,) assert isinstance(rioda.attrs["descriptions"], tuple) assert isinstance(rioda.attrs["units"], tuple) assert isinstance(rioda.attrs["crs"], str) assert isinstance(rioda.attrs["res"], tuple) assert isinstance(rioda.attrs["is_tiled"], np.uint8) assert isinstance(rioda.attrs["transform"], tuple) assert len(rioda.attrs["transform"]) == 6 np.testing.assert_array_equal(rioda.attrs["nodatavals"], [-9765.0]) # rasterio throws a Warning, which is expected since we test rasterio's defaults @pytest.mark.filterwarnings("ignore:Dataset has no geotransform") def test_notransform(self) -> None: # regression test for https://github.com/pydata/xarray/issues/1686 import rasterio # Create a geotiff file with create_tmp_file(suffix=".tif") as tmp_file: # data nx, ny, nz = 4, 3, 3 data = np.arange(nx * ny * nz, dtype=rasterio.float32).reshape(nz, ny, nx) with rasterio.open( tmp_file, "w", driver="GTiff", height=ny, width=nx, count=nz, dtype=rasterio.float32, ) as s: s.descriptions = ("nx", "ny", "nz") s.units = ("cm", "m", "km") s.write(data) # Tests expected = DataArray( data, dims=("band", "y", "x"), coords={ "band": [1, 2, 3], "y": [0.5, 1.5, 2.5], "x": [0.5, 1.5, 2.5, 3.5], }, ) with pytest.warns(DeprecationWarning), xr.open_rasterio(tmp_file) as rioda: assert_allclose(rioda, expected) assert rioda.attrs["scales"] == (1.0, 1.0, 1.0) assert rioda.attrs["offsets"] == (0.0, 0.0, 0.0) assert rioda.attrs["descriptions"] == ("nx", "ny", "nz") assert rioda.attrs["units"] == ("cm", "m", "km") assert isinstance(rioda.attrs["res"], tuple) assert isinstance(rioda.attrs["is_tiled"], np.uint8) assert isinstance(rioda.attrs["transform"], tuple) assert len(rioda.attrs["transform"]) == 6 def test_indexing(self) -> None: with create_tmp_geotiff( 8, 10, 3, transform_args=[1, 2, 0.5, 2.0], crs="+proj=latlong" ) as (tmp_file, expected): with pytest.warns(DeprecationWarning), xr.open_rasterio( tmp_file, cache=False ) as actual: # tests # assert_allclose checks all data + coordinates assert_allclose(actual, expected) assert not actual.variable._in_memory # Basic indexer ind = {"x": slice(2, 5), "y": slice(5, 7)} assert_allclose(expected.isel(**ind), actual.isel(**ind)) assert not actual.variable._in_memory ind2 = {"band": slice(1, 2), "x": slice(2, 5), "y": slice(5, 7)} assert_allclose(expected.isel(**ind2), actual.isel(**ind2)) assert not actual.variable._in_memory ind3 = {"band": slice(1, 2), "x": slice(2, 5), "y": 0} assert_allclose(expected.isel(**ind3), actual.isel(**ind3)) assert not actual.variable._in_memory # orthogonal indexer ind4 = { "band": np.array([2, 1, 0]), "x": np.array([1, 0]), "y": np.array([0, 2]), } assert_allclose(expected.isel(**ind4), actual.isel(**ind4)) assert not actual.variable._in_memory ind5 = {"band": np.array([2, 1, 0]), "x": np.array([1, 0]), "y": 0} assert_allclose(expected.isel(**ind5), actual.isel(**ind5)) assert not actual.variable._in_memory ind6 = {"band": 0, "x": np.array([0, 0]), "y": np.array([1, 1, 1])} assert_allclose(expected.isel(**ind6), actual.isel(**ind6)) assert not actual.variable._in_memory # minus-stepped slice ind7 = {"band": np.array([2, 1, 0]), "x": slice(-1, None, -1), "y": 0} assert_allclose(expected.isel(**ind7), actual.isel(**ind7)) assert not actual.variable._in_memory ind8 = {"band": np.array([2, 1, 0]), "x": 1, "y": slice(-1, 1, -2)} assert_allclose(expected.isel(**ind8), actual.isel(**ind8)) assert not actual.variable._in_memory # empty selection ind9 = {"band": np.array([2, 1, 0]), "x": 1, "y": slice(2, 2, 1)} assert_allclose(expected.isel(**ind9), actual.isel(**ind9)) assert not actual.variable._in_memory ind10 = {"band": slice(0, 0), "x": 1, "y": 2} assert_allclose(expected.isel(**ind10), actual.isel(**ind10)) assert not actual.variable._in_memory # vectorized indexer ind11 = { "band": DataArray([2, 1, 0], dims="a"), "x": DataArray([1, 0, 0], dims="a"), "y": np.array([0, 2]), } assert_allclose(expected.isel(**ind11), actual.isel(**ind11)) assert not actual.variable._in_memory ind12 = { "band": DataArray([[2, 1, 0], [1, 0, 2]], dims=["a", "b"]), "x": DataArray([[1, 0, 0], [0, 1, 0]], dims=["a", "b"]), "y": 0, } assert_allclose(expected.isel(**ind12), actual.isel(**ind12)) assert not actual.variable._in_memory # Selecting lists of bands is fine ex = expected.isel(band=[1, 2]) ac = actual.isel(band=[1, 2]) assert_allclose(ac, ex) ex = expected.isel(band=[0, 2]) ac = actual.isel(band=[0, 2]) assert_allclose(ac, ex) # Integer indexing ex = expected.isel(band=1) ac = actual.isel(band=1) assert_allclose(ac, ex) ex = expected.isel(x=1, y=2) ac = actual.isel(x=1, y=2) assert_allclose(ac, ex) ex = expected.isel(band=0, x=1, y=2) ac = actual.isel(band=0, x=1, y=2) assert_allclose(ac, ex) # Mixed ex = actual.isel(x=slice(2), y=slice(2)) ac = actual.isel(x=[0, 1], y=[0, 1]) assert_allclose(ac, ex) ex = expected.isel(band=0, x=1, y=slice(5, 7)) ac = actual.isel(band=0, x=1, y=slice(5, 7)) assert_allclose(ac, ex) ex = expected.isel(band=0, x=slice(2, 5), y=2) ac = actual.isel(band=0, x=slice(2, 5), y=2) assert_allclose(ac, ex) # One-element lists ex = expected.isel(band=[0], x=slice(2, 5), y=[2]) ac = actual.isel(band=[0], x=slice(2, 5), y=[2]) assert_allclose(ac, ex) def test_caching(self) -> None: with create_tmp_geotiff( 8, 10, 3, transform_args=[1, 2, 0.5, 2.0], crs="+proj=latlong" ) as (tmp_file, expected): # Cache is the default with pytest.warns(DeprecationWarning), xr.open_rasterio(tmp_file) as actual: # This should cache everything assert_allclose(actual, expected) # once cached, non-windowed indexing should become possible ac = actual.isel(x=[2, 4]) ex = expected.isel(x=[2, 4]) assert_allclose(ac, ex) @requires_dask def test_chunks(self) -> None: with create_tmp_geotiff( 8, 10, 3, transform_args=[1, 2, 0.5, 2.0], crs="+proj=latlong" ) as (tmp_file, expected): # Chunk at open time with pytest.warns(DeprecationWarning), xr.open_rasterio( tmp_file, chunks=(1, 2, 2) ) as actual: import dask.array as da assert isinstance(actual.data, da.Array) assert "open_rasterio" in actual.data.name # do some arithmetic ac = actual.mean() ex = expected.mean() assert_allclose(ac, ex) ac = actual.sel(band=1).mean(dim="x") ex = expected.sel(band=1).mean(dim="x") assert_allclose(ac, ex) @pytest.mark.xfail( not has_dask, reason="without dask, a non-serializable lock is used" ) def test_pickle_rasterio(self) -> None: # regression test for https://github.com/pydata/xarray/issues/2121 with create_tmp_geotiff() as (tmp_file, expected): with pytest.warns(DeprecationWarning), xr.open_rasterio(tmp_file) as rioda: temp = pickle.dumps(rioda) with pickle.loads(temp) as actual: assert_equal(actual, rioda) def test_ENVI_tags(self) -> None: rasterio = pytest.importorskip("rasterio", minversion="1.0a") from rasterio.transform import from_origin # Create an ENVI file with some tags in the ENVI namespace # this test uses a custom driver, so we can't use create_tmp_geotiff with create_tmp_file(suffix=".dat") as tmp_file: # data nx, ny, nz = 4, 3, 3 data = np.arange(nx * ny * nz, dtype=rasterio.float32).reshape(nz, ny, nx) transform = from_origin(5000, 80000, 1000, 2000.0) with rasterio.open( tmp_file, "w", driver="ENVI", height=ny, width=nx, count=nz, crs={ "units": "m", "no_defs": True, "ellps": "WGS84", "proj": "utm", "zone": 18, }, transform=transform, dtype=rasterio.float32, ) as s: s.update_tags( ns="ENVI", description="{Tagged file}", wavelength="{123.000000, 234.234000, 345.345678}", fwhm="{1.000000, 0.234000, 0.000345}", ) s.write(data) dx, dy = s.res[0], -s.res[1] # Tests coords = { "band": [1, 2, 3], "y": -np.arange(ny) * 2000 + 80000 + dy / 2, "x": np.arange(nx) * 1000 + 5000 + dx / 2, "wavelength": ("band", np.array([123, 234.234, 345.345678])), "fwhm": ("band", np.array([1, 0.234, 0.000345])), } expected = DataArray(data, dims=("band", "y", "x"), coords=coords) with pytest.warns(DeprecationWarning), xr.open_rasterio(tmp_file) as rioda: assert_allclose(rioda, expected) assert isinstance(rioda.attrs["crs"], str) assert isinstance(rioda.attrs["res"], tuple) assert isinstance(rioda.attrs["is_tiled"], np.uint8) assert isinstance(rioda.attrs["transform"], tuple) assert len(rioda.attrs["transform"]) == 6 # from ENVI tags assert isinstance(rioda.attrs["description"], str) assert isinstance(rioda.attrs["map_info"], str) assert isinstance(rioda.attrs["samples"], str) def test_geotiff_tags(self) -> None: # Create a geotiff file with some tags with create_tmp_geotiff() as (tmp_file, _): with pytest.warns(DeprecationWarning), xr.open_rasterio(tmp_file) as rioda: assert isinstance(rioda.attrs["AREA_OR_POINT"], str) @requires_dask def test_no_mftime(self) -> None: # rasterio can accept "filename" urguments that are actually urls, # including paths to remote files. # In issue #1816, we found that these caused dask to break, because # the modification time was used to determine the dask token. This # tests ensure we can still chunk such files when reading with # rasterio. with create_tmp_geotiff( 8, 10, 3, transform_args=[1, 2, 0.5, 2.0], crs="+proj=latlong" ) as (tmp_file, expected): with mock.patch("os.path.getmtime", side_effect=OSError): with pytest.warns(DeprecationWarning), xr.open_rasterio( tmp_file, chunks=(1, 2, 2) ) as actual: import dask.array as da assert isinstance(actual.data, da.Array) assert_allclose(actual, expected) @network def test_http_url(self) -> None: # more examples urls here # http://download.osgeo.org/geotiff/samples/ url = "http://download.osgeo.org/geotiff/samples/made_up/ntf_nord.tif" with pytest.warns(DeprecationWarning), xr.open_rasterio(url) as actual: assert actual.shape == (1, 512, 512) # make sure chunking works with pytest.warns(DeprecationWarning), xr.open_rasterio( url, chunks=(1, 256, 256) ) as actual: import dask.array as da assert isinstance(actual.data, da.Array) def test_rasterio_environment(self) -> None: import rasterio with create_tmp_geotiff() as (tmp_file, expected): # Should fail with error since suffix not allowed with pytest.raises(Exception): with rasterio.Env(GDAL_SKIP="GTiff"): with pytest.warns(DeprecationWarning), xr.open_rasterio( tmp_file ) as actual: assert_allclose(actual, expected) @pytest.mark.xfail(reason="rasterio 1.1.1 is broken. GH3573") def test_rasterio_vrt(self) -> None: import rasterio # 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 pytest.warns(DeprecationWarning), 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() @pytest.mark.filterwarnings( "ignore:open_rasterio is Deprecated in favor of rioxarray." ) def test_rasterio_vrt_with_transform_and_size(self) -> None: # Test open_rasterio() support of WarpedVRT with transform, width and # height (issue #2864) # https://github.com/rasterio/rasterio/1768 rasterio = pytest.importorskip("rasterio", minversion="1.0.28") from affine import Affine from rasterio.warp import calculate_default_transform with create_tmp_geotiff() as (tmp_file, expected): with rasterio.open(tmp_file) as src: # Estimate the transform, width and height # for a change of resolution # tmp_file initial res is (1000,2000) (default values) trans, w, h = calculate_default_transform( src.crs, src.crs, src.width, src.height, resolution=500, *src.bounds ) with rasterio.vrt.WarpedVRT( src, transform=trans, width=w, height=h ) as vrt: expected_shape = (vrt.width, vrt.height) expected_res = vrt.res expected_transform = vrt.transform with xr.open_rasterio(vrt) as da: actual_shape = (da.sizes["x"], da.sizes["y"]) actual_res = da.res actual_transform = Affine(*da.transform) assert actual_res == expected_res assert actual_shape == expected_shape assert actual_transform == expected_transform def test_rasterio_vrt_with_src_crs(self) -> None: # Test open_rasterio() support of WarpedVRT with specified src_crs # https://github.com/rasterio/rasterio/1768 rasterio = pytest.importorskip("rasterio", minversion="1.0.28") # create geotiff with no CRS and specify it manually with create_tmp_geotiff(crs=None) as (tmp_file, expected): src_crs = rasterio.crs.CRS({"init": "epsg:32618"}) with rasterio.open(tmp_file) as src: assert src.crs is None with rasterio.vrt.WarpedVRT(src, src_crs=src_crs) as vrt: with pytest.warns(DeprecationWarning), xr.open_rasterio(vrt) as da: assert da.crs == src_crs class TestEncodingInvalid: def test_extract_nc4_variable_encoding(self) -> None: var = xr.Variable(("x",), [1, 2, 3], {}, {"foo": "bar"}) with pytest.raises(ValueError, match=r"unexpected encoding"): _extract_nc4_variable_encoding(var, raise_on_invalid=True) var = xr.Variable(("x",), [1, 2, 3], {}, {"chunking": (2, 1)}) encoding = _extract_nc4_variable_encoding(var) assert {} == encoding # regression test var = xr.Variable(("x",), [1, 2, 3], {}, {"shuffle": True}) encoding = _extract_nc4_variable_encoding(var, raise_on_invalid=True) assert {"shuffle": True} == encoding # Variables with unlim dims must be chunked on output. var = xr.Variable(("x",), [1, 2, 3], {}, {"contiguous": True}) encoding = _extract_nc4_variable_encoding(var, unlimited_dims=("x",)) assert {} == encoding @requires_netCDF4 def test_extract_nc4_variable_encoding_netcdf4(self, monkeypatch): # New netCDF4 1.6.0 compression argument. var = xr.Variable(("x",), [1, 2, 3], {}, {"compression": "szlib"}) _extract_nc4_variable_encoding(var, backend="netCDF4", raise_on_invalid=True) def test_extract_h5nc_encoding(self) -> None: # not supported with h5netcdf (yet) var = xr.Variable(("x",), [1, 2, 3], {}, {"least_sigificant_digit": 2}) with pytest.raises(ValueError, match=r"unexpected encoding"): _extract_nc4_variable_encoding(var, raise_on_invalid=True) class MiscObject: pass @requires_netCDF4 class TestValidateAttrs: def test_validating_attrs(self) -> None: def new_dataset(): return Dataset({"data": ("y", np.arange(10.0))}, {"y": np.arange(10)}) def new_dataset_and_dataset_attrs(): ds = new_dataset() return ds, ds.attrs def new_dataset_and_data_attrs(): ds = new_dataset() return ds, ds.data.attrs def new_dataset_and_coord_attrs(): ds = new_dataset() return ds, ds.coords["y"].attrs for new_dataset_and_attrs in [ new_dataset_and_dataset_attrs, new_dataset_and_data_attrs, new_dataset_and_coord_attrs, ]: ds, attrs = new_dataset_and_attrs() attrs[123] = "test" with pytest.raises(TypeError, match=r"Invalid name for attr: 123"): ds.to_netcdf("test.nc") ds, attrs = new_dataset_and_attrs() attrs[MiscObject()] = "test" with pytest.raises(TypeError, match=r"Invalid name for attr: "): ds.to_netcdf("test.nc") ds, attrs = new_dataset_and_attrs() attrs[""] = "test" with pytest.raises(ValueError, match=r"Invalid name for attr '':"): ds.to_netcdf("test.nc") # This one should work ds, attrs = new_dataset_and_attrs() attrs["test"] = "test" with create_tmp_file() as tmp_file: ds.to_netcdf(tmp_file) ds, attrs = new_dataset_and_attrs() attrs["test"] = {"a": 5} with pytest.raises(TypeError, match=r"Invalid value for attr 'test'"): ds.to_netcdf("test.nc") ds, attrs = new_dataset_and_attrs() attrs["test"] = MiscObject() with pytest.raises(TypeError, match=r"Invalid value for attr 'test'"): ds.to_netcdf("test.nc") ds, attrs = new_dataset_and_attrs() attrs["test"] = 5 with create_tmp_file() as tmp_file: ds.to_netcdf(tmp_file) ds, attrs = new_dataset_and_attrs() attrs["test"] = 3.14 with create_tmp_file() as tmp_file: ds.to_netcdf(tmp_file) ds, attrs = new_dataset_and_attrs() attrs["test"] = [1, 2, 3, 4] with create_tmp_file() as tmp_file: ds.to_netcdf(tmp_file) ds, attrs = new_dataset_and_attrs() attrs["test"] = (1.9, 2.5) with create_tmp_file() as tmp_file: ds.to_netcdf(tmp_file) ds, attrs = new_dataset_and_attrs() attrs["test"] = np.arange(5) with create_tmp_file() as tmp_file: ds.to_netcdf(tmp_file) ds, attrs = new_dataset_and_attrs() attrs["test"] = "This is a string" with create_tmp_file() as tmp_file: ds.to_netcdf(tmp_file) ds, attrs = new_dataset_and_attrs() attrs["test"] = "" with create_tmp_file() as tmp_file: ds.to_netcdf(tmp_file) @requires_scipy_or_netCDF4 class TestDataArrayToNetCDF: def test_dataarray_to_netcdf_no_name(self) -> None: original_da = DataArray(np.arange(12).reshape((3, 4))) with create_tmp_file() as tmp: original_da.to_netcdf(tmp) with open_dataarray(tmp) as loaded_da: assert_identical(original_da, loaded_da) def test_dataarray_to_netcdf_with_name(self) -> None: original_da = DataArray(np.arange(12).reshape((3, 4)), name="test") with create_tmp_file() as tmp: original_da.to_netcdf(tmp) with open_dataarray(tmp) as loaded_da: assert_identical(original_da, loaded_da) def test_dataarray_to_netcdf_coord_name_clash(self) -> None: original_da = DataArray( np.arange(12).reshape((3, 4)), dims=["x", "y"], name="x" ) with create_tmp_file() as tmp: original_da.to_netcdf(tmp) with open_dataarray(tmp) as loaded_da: assert_identical(original_da, loaded_da) def test_open_dataarray_options(self) -> None: data = DataArray(np.arange(5), coords={"y": ("x", range(5))}, dims=["x"]) with create_tmp_file() as tmp: data.to_netcdf(tmp) expected = data.drop_vars("y") with open_dataarray(tmp, drop_variables=["y"]) as loaded: assert_identical(expected, loaded) @requires_scipy def test_dataarray_to_netcdf_return_bytes(self) -> None: # regression test for GH1410 data = xr.DataArray([1, 2, 3]) output = data.to_netcdf() assert isinstance(output, bytes) def test_dataarray_to_netcdf_no_name_pathlib(self) -> None: original_da = DataArray(np.arange(12).reshape((3, 4))) with create_tmp_file() as tmps: tmp = Path(tmps) original_da.to_netcdf(tmp) with open_dataarray(tmp) as loaded_da: assert_identical(original_da, loaded_da) @requires_scipy_or_netCDF4 def test_no_warning_from_dask_effective_get() -> None: with create_tmp_file() as tmpfile: with assert_no_warnings(): ds = Dataset() ds.to_netcdf(tmpfile) @requires_scipy_or_netCDF4 def test_source_encoding_always_present() -> None: # Test for GH issue #2550. rnddata = np.random.randn(10) original = Dataset({"foo": ("x", rnddata)}) with create_tmp_file() as tmp: original.to_netcdf(tmp) with open_dataset(tmp) as ds: assert ds.encoding["source"] == tmp @requires_scipy_or_netCDF4 def test_source_encoding_always_present_with_pathlib() -> None: # Test for GH issue #5888. rnddata = np.random.randn(10) original = Dataset({"foo": ("x", rnddata)}) with create_tmp_file() as tmp: original.to_netcdf(tmp) with open_dataset(Path(tmp)) as ds: assert ds.encoding["source"] == tmp def _assert_no_dates_out_of_range_warning(record): undesired_message = "dates out of range" for warning in record: assert undesired_message not in str(warning.message) @requires_scipy_or_netCDF4 @pytest.mark.parametrize("calendar", _STANDARD_CALENDARS) def test_use_cftime_standard_calendar_default_in_range(calendar) -> None: x = [0, 1] time = [0, 720] units_date = "2000-01-01" units = "days since 2000-01-01" original = DataArray(x, [("time", time)], name="x").to_dataset() for v in ["x", "time"]: original[v].attrs["units"] = units original[v].attrs["calendar"] = calendar x_timedeltas = np.array(x).astype("timedelta64[D]") time_timedeltas = np.array(time).astype("timedelta64[D]") decoded_x = np.datetime64(units_date, "ns") + x_timedeltas decoded_time = np.datetime64(units_date, "ns") + time_timedeltas expected_x = DataArray(decoded_x, [("time", decoded_time)], name="x") expected_time = DataArray(decoded_time, [("time", decoded_time)], name="time") with create_tmp_file() as tmp_file: original.to_netcdf(tmp_file) with warnings.catch_warnings(record=True) as record: with open_dataset(tmp_file) as ds: assert_identical(expected_x, ds.x) assert_identical(expected_time, ds.time) _assert_no_dates_out_of_range_warning(record) @requires_cftime @requires_scipy_or_netCDF4 @pytest.mark.parametrize("calendar", _STANDARD_CALENDARS) @pytest.mark.parametrize("units_year", [1500, 2500]) def test_use_cftime_standard_calendar_default_out_of_range( calendar, units_year ) -> None: import cftime x = [0, 1] time = [0, 720] units = f"days since {units_year}-01-01" original = DataArray(x, [("time", time)], name="x").to_dataset() for v in ["x", "time"]: original[v].attrs["units"] = units original[v].attrs["calendar"] = calendar decoded_x = cftime.num2date(x, units, calendar, only_use_cftime_datetimes=True) decoded_time = cftime.num2date( time, units, calendar, only_use_cftime_datetimes=True ) expected_x = DataArray(decoded_x, [("time", decoded_time)], name="x") expected_time = DataArray(decoded_time, [("time", decoded_time)], name="time") with create_tmp_file() as tmp_file: original.to_netcdf(tmp_file) with pytest.warns(SerializationWarning): with open_dataset(tmp_file) as ds: assert_identical(expected_x, ds.x) assert_identical(expected_time, ds.time) @requires_cftime @requires_scipy_or_netCDF4 @pytest.mark.parametrize("calendar", _ALL_CALENDARS) @pytest.mark.parametrize("units_year", [1500, 2000, 2500]) def test_use_cftime_true(calendar, units_year) -> None: import cftime x = [0, 1] time = [0, 720] units = f"days since {units_year}-01-01" original = DataArray(x, [("time", time)], name="x").to_dataset() for v in ["x", "time"]: original[v].attrs["units"] = units original[v].attrs["calendar"] = calendar decoded_x = cftime.num2date(x, units, calendar, only_use_cftime_datetimes=True) decoded_time = cftime.num2date( time, units, calendar, only_use_cftime_datetimes=True ) expected_x = DataArray(decoded_x, [("time", decoded_time)], name="x") expected_time = DataArray(decoded_time, [("time", decoded_time)], name="time") with create_tmp_file() as tmp_file: original.to_netcdf(tmp_file) with warnings.catch_warnings(record=True) as record: with open_dataset(tmp_file, use_cftime=True) as ds: assert_identical(expected_x, ds.x) assert_identical(expected_time, ds.time) _assert_no_dates_out_of_range_warning(record) @requires_scipy_or_netCDF4 @pytest.mark.parametrize("calendar", _STANDARD_CALENDARS) def test_use_cftime_false_standard_calendar_in_range(calendar) -> None: x = [0, 1] time = [0, 720] units_date = "2000-01-01" units = "days since 2000-01-01" original = DataArray(x, [("time", time)], name="x").to_dataset() for v in ["x", "time"]: original[v].attrs["units"] = units original[v].attrs["calendar"] = calendar x_timedeltas = np.array(x).astype("timedelta64[D]") time_timedeltas = np.array(time).astype("timedelta64[D]") decoded_x = np.datetime64(units_date, "ns") + x_timedeltas decoded_time = np.datetime64(units_date, "ns") + time_timedeltas expected_x = DataArray(decoded_x, [("time", decoded_time)], name="x") expected_time = DataArray(decoded_time, [("time", decoded_time)], name="time") with create_tmp_file() as tmp_file: original.to_netcdf(tmp_file) with warnings.catch_warnings(record=True) as record: with open_dataset(tmp_file, use_cftime=False) as ds: assert_identical(expected_x, ds.x) assert_identical(expected_time, ds.time) _assert_no_dates_out_of_range_warning(record) @requires_scipy_or_netCDF4 @pytest.mark.parametrize("calendar", _STANDARD_CALENDARS) @pytest.mark.parametrize("units_year", [1500, 2500]) def test_use_cftime_false_standard_calendar_out_of_range(calendar, units_year) -> None: x = [0, 1] time = [0, 720] units = f"days since {units_year}-01-01" original = DataArray(x, [("time", time)], name="x").to_dataset() for v in ["x", "time"]: original[v].attrs["units"] = units original[v].attrs["calendar"] = calendar with create_tmp_file() as tmp_file: original.to_netcdf(tmp_file) with pytest.raises((OutOfBoundsDatetime, ValueError)): open_dataset(tmp_file, use_cftime=False) @requires_scipy_or_netCDF4 @pytest.mark.parametrize("calendar", _NON_STANDARD_CALENDARS) @pytest.mark.parametrize("units_year", [1500, 2000, 2500]) def test_use_cftime_false_nonstandard_calendar(calendar, units_year) -> None: x = [0, 1] time = [0, 720] units = f"days since {units_year}" original = DataArray(x, [("time", time)], name="x").to_dataset() for v in ["x", "time"]: original[v].attrs["units"] = units original[v].attrs["calendar"] = calendar with create_tmp_file() as tmp_file: original.to_netcdf(tmp_file) with pytest.raises((OutOfBoundsDatetime, ValueError)): open_dataset(tmp_file, use_cftime=False) @pytest.mark.parametrize("engine", ["netcdf4", "scipy"]) def test_invalid_netcdf_raises(engine) -> None: data = create_test_data() with pytest.raises(ValueError, match=r"unrecognized option 'invalid_netcdf'"): data.to_netcdf("foo.nc", engine=engine, invalid_netcdf=True) @requires_zarr def test_encode_zarr_attr_value() -> None: # array -> list arr = np.array([1, 2, 3]) expected1 = [1, 2, 3] actual1 = backends.zarr.encode_zarr_attr_value(arr) assert isinstance(actual1, list) assert actual1 == expected1 # scalar array -> scalar sarr = np.array(1)[()] expected2 = 1 actual2 = backends.zarr.encode_zarr_attr_value(sarr) assert isinstance(actual2, int) assert actual2 == expected2 # string -> string (no change) expected3 = "foo" actual3 = backends.zarr.encode_zarr_attr_value(expected3) assert isinstance(actual3, str) assert actual3 == expected3 @requires_zarr def test_extract_zarr_variable_encoding() -> None: var = xr.Variable("x", [1, 2]) actual = backends.zarr.extract_zarr_variable_encoding(var) assert "chunks" in actual assert actual["chunks"] is None var = xr.Variable("x", [1, 2], encoding={"chunks": (1,)}) actual = backends.zarr.extract_zarr_variable_encoding(var) assert actual["chunks"] == (1,) # does not raise on invalid var = xr.Variable("x", [1, 2], encoding={"foo": (1,)}) actual = backends.zarr.extract_zarr_variable_encoding(var) # raises on invalid var = xr.Variable("x", [1, 2], encoding={"foo": (1,)}) with pytest.raises(ValueError, match=r"unexpected encoding parameters"): actual = backends.zarr.extract_zarr_variable_encoding( var, raise_on_invalid=True ) @requires_zarr @requires_fsspec @pytest.mark.filterwarnings("ignore:deallocating CachingFileManager") def test_open_fsspec() -> None: import fsspec import zarr if not hasattr(zarr.storage, "FSStore") or not hasattr( zarr.storage.FSStore, "getitems" ): pytest.skip("zarr too old") ds = open_dataset(os.path.join(os.path.dirname(__file__), "data", "example_1.nc")) m = fsspec.filesystem("memory") mm = m.get_mapper("out1.zarr") ds.to_zarr(mm) # old interface ds0 = ds.copy() ds0["time"] = ds.time + pd.to_timedelta("1 day") mm = m.get_mapper("out2.zarr") ds0.to_zarr(mm) # old interface # single dataset url = "memory://out2.zarr" ds2 = open_dataset(url, engine="zarr") xr.testing.assert_equal(ds0, ds2) # single dataset with caching url = "simplecache::memory://out2.zarr" ds2 = open_dataset(url, engine="zarr") xr.testing.assert_equal(ds0, ds2) # multi dataset url = "memory://out*.zarr" ds2 = open_mfdataset(url, engine="zarr") xr.testing.assert_equal(xr.concat([ds, ds0], dim="time"), ds2) # multi dataset with caching url = "simplecache::memory://out*.zarr" ds2 = open_mfdataset(url, engine="zarr") xr.testing.assert_equal(xr.concat([ds, ds0], dim="time"), ds2) @requires_h5netcdf @requires_netCDF4 def test_load_single_value_h5netcdf(tmp_path: Path) -> None: """Test that numeric single-element vector attributes are handled fine. At present (h5netcdf v0.8.1), the h5netcdf exposes single-valued numeric variable attributes as arrays of length 1, as opposed to scalars for the NetCDF4 backend. This was leading to a ValueError upon loading a single value from a file, see #4471. Test that loading causes no failure. """ ds = xr.Dataset( { "test": xr.DataArray( np.array([0]), dims=("x",), attrs={"scale_factor": 1, "add_offset": 0} ) } ) ds.to_netcdf(tmp_path / "test.nc") with xr.open_dataset(tmp_path / "test.nc", engine="h5netcdf") as ds2: ds2["test"][0].load() @requires_zarr @requires_dask @pytest.mark.parametrize( "chunks", ["auto", -1, {}, {"x": "auto"}, {"x": -1}, {"x": "auto", "y": -1}] ) def test_open_dataset_chunking_zarr(chunks, tmp_path: Path) -> None: encoded_chunks = 100 dask_arr = da.from_array( np.ones((500, 500), dtype="float64"), chunks=encoded_chunks ) ds = xr.Dataset( { "test": xr.DataArray( dask_arr, dims=("x", "y"), ) } ) ds["test"].encoding["chunks"] = encoded_chunks ds.to_zarr(tmp_path / "test.zarr") with dask.config.set({"array.chunk-size": "1MiB"}): expected = ds.chunk(chunks) with open_dataset( tmp_path / "test.zarr", engine="zarr", chunks=chunks ) as actual: xr.testing.assert_chunks_equal(actual, expected) @requires_zarr @requires_dask @pytest.mark.parametrize( "chunks", ["auto", -1, {}, {"x": "auto"}, {"x": -1}, {"x": "auto", "y": -1}] ) @pytest.mark.filterwarnings("ignore:The specified Dask chunks separate") def test_chunking_consintency(chunks, tmp_path: Path) -> None: encoded_chunks: dict[str, Any] = {} dask_arr = da.from_array( np.ones((500, 500), dtype="float64"), chunks=encoded_chunks ) ds = xr.Dataset( { "test": xr.DataArray( dask_arr, dims=("x", "y"), ) } ) ds["test"].encoding["chunks"] = encoded_chunks ds.to_zarr(tmp_path / "test.zarr") ds.to_netcdf(tmp_path / "test.nc") with dask.config.set({"array.chunk-size": "1MiB"}): expected = ds.chunk(chunks) with xr.open_dataset( tmp_path / "test.zarr", engine="zarr", chunks=chunks ) as actual: xr.testing.assert_chunks_equal(actual, expected) with xr.open_dataset(tmp_path / "test.nc", chunks=chunks) as actual: xr.testing.assert_chunks_equal(actual, expected) def _check_guess_can_open_and_open(entrypoint, obj, engine, expected): assert entrypoint.guess_can_open(obj) with open_dataset(obj, engine=engine) as actual: assert_identical(expected, actual) @requires_netCDF4 def test_netcdf4_entrypoint(tmp_path: Path) -> None: entrypoint = NetCDF4BackendEntrypoint() ds = create_test_data() path = tmp_path / "foo" ds.to_netcdf(path, format="NETCDF3_CLASSIC") _check_guess_can_open_and_open(entrypoint, path, engine="netcdf4", expected=ds) _check_guess_can_open_and_open(entrypoint, str(path), engine="netcdf4", expected=ds) path = tmp_path / "bar" ds.to_netcdf(path, format="NETCDF4_CLASSIC") _check_guess_can_open_and_open(entrypoint, path, engine="netcdf4", expected=ds) _check_guess_can_open_and_open(entrypoint, str(path), engine="netcdf4", expected=ds) assert entrypoint.guess_can_open("http://something/remote") assert entrypoint.guess_can_open("something-local.nc") assert entrypoint.guess_can_open("something-local.nc4") assert entrypoint.guess_can_open("something-local.cdf") assert not entrypoint.guess_can_open("not-found-and-no-extension") path = tmp_path / "baz" with open(path, "wb") as f: f.write(b"not-a-netcdf-file") assert not entrypoint.guess_can_open(path) @requires_scipy def test_scipy_entrypoint(tmp_path: Path) -> None: entrypoint = ScipyBackendEntrypoint() ds = create_test_data() path = tmp_path / "foo" ds.to_netcdf(path, engine="scipy") _check_guess_can_open_and_open(entrypoint, path, engine="scipy", expected=ds) _check_guess_can_open_and_open(entrypoint, str(path), engine="scipy", expected=ds) with open(path, "rb") as f: _check_guess_can_open_and_open(entrypoint, f, engine="scipy", expected=ds) contents = ds.to_netcdf(engine="scipy") _check_guess_can_open_and_open(entrypoint, contents, engine="scipy", expected=ds) _check_guess_can_open_and_open( entrypoint, BytesIO(contents), engine="scipy", expected=ds ) path = tmp_path / "foo.nc.gz" with gzip.open(path, mode="wb") as f: f.write(contents) _check_guess_can_open_and_open(entrypoint, path, engine="scipy", expected=ds) _check_guess_can_open_and_open(entrypoint, str(path), engine="scipy", expected=ds) assert entrypoint.guess_can_open("something-local.nc") assert entrypoint.guess_can_open("something-local.nc.gz") assert not entrypoint.guess_can_open("not-found-and-no-extension") assert not entrypoint.guess_can_open(b"not-a-netcdf-file") @requires_h5netcdf def test_h5netcdf_entrypoint(tmp_path: Path) -> None: entrypoint = H5netcdfBackendEntrypoint() ds = create_test_data() path = tmp_path / "foo" ds.to_netcdf(path, engine="h5netcdf") _check_guess_can_open_and_open(entrypoint, path, engine="h5netcdf", expected=ds) _check_guess_can_open_and_open( entrypoint, str(path), engine="h5netcdf", expected=ds ) with open(path, "rb") as f: _check_guess_can_open_and_open(entrypoint, f, engine="h5netcdf", expected=ds) assert entrypoint.guess_can_open("something-local.nc") assert entrypoint.guess_can_open("something-local.nc4") assert entrypoint.guess_can_open("something-local.cdf") assert not entrypoint.guess_can_open("not-found-and-no-extension") @requires_netCDF4 @pytest.mark.parametrize("str_type", (str, np.str_)) def test_write_file_from_np_str(str_type, tmpdir) -> None: # https://github.com/pydata/xarray/pull/5264 scenarios = [str_type(v) for v in ["scenario_a", "scenario_b", "scenario_c"]] years = range(2015, 2100 + 1) tdf = pd.DataFrame( data=np.random.random((len(scenarios), len(years))), columns=years, index=scenarios, ) tdf.index.name = "scenario" tdf.columns.name = "year" tdf = tdf.stack() tdf.name = "tas" txr = tdf.to_xarray() txr.to_netcdf(tmpdir.join("test.nc")) @requires_zarr @requires_netCDF4 class TestNCZarr: @staticmethod def _create_nczarr(filename): netcdfc_version = Version(nc4.getlibversion().split()[0]) if netcdfc_version < Version("4.8.1"): pytest.skip("requires netcdf-c>=4.8.1") if (platform.system() == "Windows") and (netcdfc_version == Version("4.8.1")): # Bug in netcdf-c==4.8.1 (typo: Nan instead of NaN) # https://github.com/Unidata/netcdf-c/issues/2265 pytest.skip("netcdf-c==4.8.1 has issues on Windows") ds = create_test_data() # Drop dim3: netcdf-c does not support dtype='4.8.1 will add _ARRAY_DIMENSIONS by default mode = "nczarr" if netcdfc_version == Version("4.8.1") else "nczarr,noxarray" ds.to_netcdf(f"file://{filename}#mode={mode}") return ds def test_open_nczarr(self) -> None: with create_tmp_file(suffix=".zarr") as tmp: expected = self._create_nczarr(tmp) actual = xr.open_zarr(tmp, consolidated=False) assert_identical(expected, actual) def test_overwriting_nczarr(self) -> None: with create_tmp_file(suffix=".zarr") as tmp: ds = self._create_nczarr(tmp) expected = ds[["var1"]] expected.to_zarr(tmp, mode="w") actual = xr.open_zarr(tmp, consolidated=False) assert_identical(expected, actual) @pytest.mark.parametrize("mode", ["a", "r+"]) @pytest.mark.filterwarnings("ignore:.*non-consolidated metadata.*") def test_raise_writing_to_nczarr(self, mode) -> None: with create_tmp_file(suffix=".zarr") as tmp: ds = self._create_nczarr(tmp) with pytest.raises( KeyError, match="missing the attribute `_ARRAY_DIMENSIONS`," ): ds.to_zarr(tmp, mode=mode) @requires_netCDF4 @requires_dask def test_pickle_open_mfdataset_dataset(): ds = open_example_mfdataset(["bears.nc"]) assert_identical(ds, pickle.loads(pickle.dumps(ds)))