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from __future__ import absolute_import, division, print_function
import contextlib
import itertools
import math
import os.path
import pickle
import shutil
import sys
import tempfile
import warnings
from io import BytesIO
import numpy as np
import pandas as pd
import pytest
import xarray as xr
from xarray import (
DataArray, Dataset, backends, open_dataarray, open_dataset, open_mfdataset,
save_mfdataset)
from xarray.backends.common import robust_getitem
from xarray.backends.netCDF4_ import _extract_nc4_variable_encoding
from xarray.backends.pydap_ import PydapDataStore
from xarray.core import indexing
from xarray.core.options import set_options
from xarray.core.pycompat import (
ExitStack, basestring, dask_array_type, iteritems)
from xarray.tests import mock
from . import (
assert_allclose, assert_array_equal, assert_equal, assert_identical,
has_dask, has_netCDF4, has_scipy, network, raises_regex, requires_cfgrib, arm_xfail,
requires_cftime, requires_dask, requires_h5netcdf, requires_netCDF4,
requires_pathlib, requires_pseudonetcdf, requires_pydap, requires_pynio,
requires_rasterio, requires_scipy, requires_scipy_or_netCDF4,
requires_zarr)
from .test_dataset import create_test_data
try:
import netCDF4 as nc4
except ImportError:
pass
try:
import dask.array as da
except ImportError:
pass
try:
from pathlib import Path
except ImportError:
try:
from pathlib2 import Path
except ImportError:
pass
ON_WINDOWS = sys.platform == 'win32'
def open_example_dataset(name, *args, **kwargs):
return open_dataset(os.path.join(os.path.dirname(__file__), 'data', name),
*args, **kwargs)
def open_example_mfdataset(names, *args, **kwargs):
return open_mfdataset(
[os.path.join(os.path.dirname(__file__), 'data', name)
for name in names],
*args, **kwargs)
def create_masked_and_scaled_data():
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():
attributes = {'_FillValue': -1, 'add_offset': 10,
'scale_factor': np.float32(0.1)}
return Dataset({'x': ('t', np.int16([-1, -1, 0, 1, 2]), attributes)})
def create_unsigned_masked_scaled_data():
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():
# 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():
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():
# 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():
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():
# 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():
attributes = {'units': '-'}
return Dataset({'x': ('t', [True, False, False, True], attributes)})
class TestCommon(object):
def test_robust_getitem(self):
class UnreliableArrayFailure(Exception):
pass
class UnreliableArray(object):
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(object):
pass
class DatasetIOBase(object):
engine = None
file_format = None
def create_store(self):
raise NotImplementedError
@contextlib.contextmanager
def roundtrip(self, data, save_kwargs={}, open_kwargs={},
allow_cleanup_failure=False):
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={}, open_kwargs={},
allow_cleanup_failure=False):
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):
expected = create_test_data()
expected['float_var'] = ([], 1.0e9, {'units': 'units of awesome'})
expected['bytes_var'] = ([], b'foobar')
expected['string_var'] = ([], u'foobar')
with self.roundtrip(expected) as actual:
assert_identical(expected, actual)
def test_write_store(self):
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
if (isinstance(self, NetCDF3Only) and expected_dtype == 'int64'):
# downcast
expected_dtype = np.dtype('int32')
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):
expected = create_test_data()
with self.roundtrip(expected) as actual:
self.check_dtypes_roundtripped(expected, actual)
assert_identical(expected, actual)
def test_load(self):
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):
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):
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):
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):
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
def test_roundtrip_None_variable(self):
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):
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([u'ab', u'cdef', u'g'], dtype=object)
strings_nans = np.array([u'ab', u'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] = u''
assert_identical(expected, actual)
def test_roundtrip_string_data(self):
expected = Dataset({'x': ('t', ['ab', 'cdef'])})
with self.roundtrip(expected) as actual:
assert_identical(expected, actual)
def test_roundtrip_string_encoded_characters(self):
expected = Dataset({'x': ('t', [u'ab', u'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):
times = pd.to_datetime(['2000-01-01', '2000-01-02', 'NaT'])
expected = Dataset({'t': ('t', times), 't0': times[0]})
kwds = {'encoding': {'t0': {'units': 'days since 1950-01-01'}}}
with self.roundtrip(expected, save_kwargs=kwds) 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):
from .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]})
kwds = {'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', 'gregorian'}:
warnings.filterwarnings(
'ignore', 'Unable to decode time axis')
with self.roundtrip(expected, save_kwargs=kwds) 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):
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):
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):
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):
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)
def test_roundtrip_global_coordinates(self):
original = Dataset({'x': [2, 3], 'y': ('a', [42]), 'z': ('x', [4, 5])})
with self.roundtrip(original) as actual:
assert_identical(original, actual)
def test_roundtrip_coordinates_with_space(self):
original = Dataset(coords={'x': 0, 'y z': 1})
expected = Dataset({'y z': 1}, {'x': 0})
with pytest.warns(xr.SerializationWarning):
with self.roundtrip(original) as actual:
assert_identical(expected, actual)
def test_roundtrip_boolean_dtype(self):
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):
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):
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
indexers = [
{'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(indexers)
# vectorized-slice mixed
indexers = [
{'dim1': DataArray([[0, 7], [2, 6], [3, 5]], dims=['a', 'b']),
'dim3': slice(None, 10)}
]
multiple_indexing(indexers)
# vectorized-integer mixed
indexers = [
{'dim3': 0},
{'dim1': DataArray([[0, 7], [2, 6], [3, 5]], dims=['a', 'b'])},
{'a': slice(None, None, 2)}
]
multiple_indexing(indexers)
# vectorized-integer mixed
indexers = [
{'dim3': 0},
{'dim1': DataArray([[0, 7], [2, 6], [3, 5]], dims=['a', 'b'])},
{'a': 1, 'b': 0}
]
multiple_indexing(indexers)
# 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):
# 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.PandasIndexAdapter)
else:
raise TypeError('{} is wrapped by {}'.format(
type(obj.array), type(obj)))
for k, v in ds.variables.items():
find_and_validate_array(v._data)
def test_array_type_after_indexing(self):
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):
# 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):
""" 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):
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):
values = np.array([u'ab', u'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):
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)
def test_coordinates_encoding(self):
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(['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
def test_roundtrip_endian(self):
ds = Dataset({'x': np.arange(3, 10, dtype='>i2'),
'y': np.arange(3, 20, dtype='<i4'),
'z': np.arange(3, 30, dtype='=i8'),
'w': ('x', np.arange(3, 10, dtype=np.float))})
with self.roundtrip(ds) as actual:
# technically these datasets are slightly different,
# one hold mixed endian data (ds) the other should be
# all big endian (actual). assertDatasetIdentical
# should still pass though.
assert_identical(ds, actual)
if self.engine == 'netcdf4':
ds['z'].encoding['endian'] = 'big'
with pytest.raises(NotImplementedError):
with self.roundtrip(ds) as actual:
pass
def test_invalid_dataarray_names_raise(self):
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, e in zip([0, (4, 5), True, ''], [te, te, te, ve]):
ds = Dataset({name: da})
with raises_regex(*e):
with self.roundtrip(ds):
pass
def test_encoding_kwarg(self):
ds = Dataset({'x': ('y', np.arange(10.0))})
kwargs = dict(encoding={'x': {'dtype': 'f4'}})
with self.roundtrip(ds, save_kwargs=kwargs) as actual:
assert actual.x.encoding['dtype'] == 'f4'
assert ds.x.encoding == {}
kwargs = dict(encoding={'x': {'foo': 'bar'}})
with raises_regex(ValueError, 'unexpected encoding'):
with self.roundtrip(ds, save_kwargs=kwargs) as actual:
pass
kwargs = dict(encoding={'x': 'foo'})
with raises_regex(ValueError, '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):
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):
# regression test for GH2149
for strings in [
[b'foo', b'bar', b'baz'],
[u'foo', u'bar', u'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):
# 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):
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):
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):
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):
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):
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:
assert actual.x.encoding['dtype'] == 'f4'
assert ds.x.encoding == {}
def test_append_write(self):
# regression for GH1215
data = create_test_data()
with self.roundtrip_append(data) as actual:
assert_identical(data, actual)
def test_append_overwrite_values(self):
# 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):
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 raises_regex(ValueError,
'Unable to update size for existing dimension'):
self.save(data, tmp_file, mode='a')
def test_multiindex_not_implemented(self):
ds = (Dataset(coords={'y': ('x', [1, 2]), 'z': ('x', ['a', 'b'])})
.set_index(x=['y', 'z']))
with raises_regex(NotImplementedError, 'MultiIndex'):
with self.roundtrip(ds):
pass
_counter = itertools.count()
@contextlib.contextmanager
def create_tmp_file(suffix='.nc', allow_cleanup_failure=False):
temp_dir = tempfile.mkdtemp()
path = os.path.join(temp_dir, 'temp-%s%s' % (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, suffix='.nc', allow_cleanup_failure=False):
with ExitStack() as stack:
files = [stack.enter_context(create_tmp_file(suffix,
allow_cleanup_failure))
for apath in np.arange(nfiles)]
yield files
class NetCDF4Base(CFEncodedBase):
"""Tests for both netCDF4-python and h5netcdf."""
engine = 'netcdf4'
def test_open_group(self):
# 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(IOError):
open_dataset(tmp_file, group='bar')
with raises_regex(ValueError, 'must be a string'):
open_dataset(tmp_file, group=(1, 2, 3))
def test_open_subgroup(self):
# 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):
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):
for input_strings in [
[b'foo', b'bar', b'baz'],
[u'foo', u'bar', u'baz'],
]:
original = Dataset({'x': input_strings})
expected = Dataset({'x': [u'foo', u'bar', u'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):
values = np.array([u'ab', u'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/shoyer/h5netcdf/issues/37
original = Dataset({'x': ('t', values, {}, {'_FillValue': u'XXX'})})
with pytest.raises(NotImplementedError):
with self.roundtrip(original) as actual:
assert_identical(expected, actual)
original = Dataset({'x': ('t', values, {}, {'_FillValue': u''})})
with pytest.raises(NotImplementedError):
with self.roundtrip(original) as actual:
assert_identical(expected, actual)
def test_roundtrip_character_array(self):
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):
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):
# 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 = dict((k, v) for k, v in
iteritems(actual['time'].encoding)
if k in expected['time'].encoding)
assert actual_encoding == \
expected['time'].encoding
def test_dump_encodings(self):
# 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):
# 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):
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 iteritems(data['var2'].encoding):
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):
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_encoding_chunksizes_unlimited(self):
# 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):
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):
# 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_already_open_dataset(self):
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_read_variable_len_strings(self):
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, **kwargs) as actual:
assert_identical(expected, 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):
# 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):
# 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_88_character_filename_segmentation_fault(self):
# should be fixed in netcdf4 v1.3.1
with mock.patch('netCDF4.__version__', '1.2.4'):
with warnings.catch_warnings():
message = ('A segmentation fault may occur when the '
'file path has exactly 88 characters')
warnings.filterwarnings('error', message)
with pytest.raises(Warning):
# Need to construct 88 character filepath
xr.Dataset().to_netcdf('a' * (88 - len(os.getcwd()) - 1))
def test_setncattr_string(self):
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']
def test_autoclose_future_warning(self):
data = create_test_data()
with create_tmp_file() as tmp_file:
self.save(data, tmp_file)
with pytest.warns(FutureWarning):
with self.open(tmp_file, autoclose=True) as actual:
assert_identical(data, actual)
@requires_netCDF4
@requires_dask
@pytest.mark.filterwarnings('ignore:deallocating CachingFileManager')
class TestNetCDF4ViaDaskData(TestNetCDF4Data):
@contextlib.contextmanager
def roundtrip(self, data, save_kwargs={}, open_kwargs={},
allow_cleanup_failure=False):
with TestNetCDF4Data.roundtrip(
self, data, save_kwargs, open_kwargs,
allow_cleanup_failure) as ds:
yield ds.chunk()
def test_unsorted_index_raises(self):
# Skip when using dask because dask rewrites indexers to getitem,
# dask first pulls items by block.
pass
def test_dataset_caching(self):
# caching behavior differs for dask
pass
def test_write_inconsistent_chunks(self):
# 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'
@contextlib.contextmanager
def create_store(self):
with self.create_zarr_target() as store_target:
yield backends.ZarrStore.open_group(store_target, mode='w')
def save(self, dataset, store_target, **kwargs):
return dataset.to_zarr(store=store_target, **kwargs)
@contextlib.contextmanager
def open(self, store_target, **kwargs):
with xr.open_zarr(store_target, **kwargs) as ds:
yield ds
@contextlib.contextmanager
def roundtrip(self, data, save_kwargs={}, open_kwargs={},
allow_cleanup_failure=False):
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
@contextlib.contextmanager
def roundtrip_append(self, data, save_kwargs={}, open_kwargs={},
allow_cleanup_failure=False):
pytest.skip("zarr backend does not support appending")
def test_roundtrip_consolidated(self):
pytest.importorskip('zarr', minversion="2.2.1.dev2")
expected = create_test_data()
with self.roundtrip(expected,
save_kwargs={'consolidated': True},
open_kwargs={'consolidated': True}) as actual:
self.check_dtypes_roundtripped(expected, actual)
assert_identical(expected, actual)
def test_auto_chunk(self):
original = create_test_data().chunk()
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
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
def test_write_uneven_dask_chunks(self):
# regression for GH#2225
original = create_test_data().chunk({'dim1': 3, 'dim2': 4, 'dim3': 3})
with self.roundtrip(
original, open_kwargs={'auto_chunk': True}) as actual:
for k, v in actual.data_vars.items():
print(k)
assert v.chunks == actual[k].chunks
def test_chunk_encoding(self):
# 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
def test_chunk_encoding_with_dask(self):
# 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) as e_info:
with self.roundtrip(ds_chunk_irreg) as actual:
pass
# make sure this error message is correct and not some other error
assert e_info.match('chunks')
# ... except if the last chunk is smaller than the first
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)
# - 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 syncronized overlapping writes are
# supported by xarray
ds_chunk4['var1'].encoding.update({'chunks': 5})
with pytest.raises(NotImplementedError):
with self.roundtrip(ds_chunk4) as actual:
pass
def test_hidden_zarr_keys(self):
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
def test_write_persistence_modes(self):
original = create_test_data()
# overwrite mode
with self.roundtrip(original, save_kwargs={'mode': 'w'}) as actual:
assert_identical(original, actual)
# don't overwrite mode
with self.roundtrip(original, save_kwargs={'mode': 'w-'}) 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')
with self.open(store) as actual:
assert_identical(original, actual)
with pytest.raises(ValueError):
self.save(original, store, mode='w-')
# check that we can't use other persistence modes
# TODO: reconsider whether other persistence modes should be supported
with pytest.raises(ValueError):
with self.roundtrip(original, save_kwargs={'mode': 'a'}) as actual:
pass
def test_compressor_encoding(self):
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):
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):
# not relevant for zarr, since we don't use EncodedStringCoder
pass
# TODO: someone who understand caching figure out whether chaching
# makes sense for Zarr backend
@pytest.mark.xfail(reason="Zarr caching not implemented")
def test_dataset_caching(self):
super(CFEncodedBase, self).test_dataset_caching()
@pytest.mark.xfail(reason="Zarr stores can not be appended to")
def test_append_write(self):
super(CFEncodedBase, self).test_append_write()
@pytest.mark.xfail(reason="Zarr stores can not be appended to")
def test_append_overwrite_values(self):
super(CFEncodedBase, self).test_append_overwrite_values()
@pytest.mark.xfail(reason="Zarr stores can not be appended to")
def test_append_with_invalid_dim_raises(self):
super(CFEncodedBase, self).test_append_with_invalid_dim_raises()
def test_to_zarr_compute_false_roundtrip(self):
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)
delayed_obj.compute()
with self.open(store) as actual:
assert_identical(original, actual)
def test_encoding_chunksizes(self):
# 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_zarr
class TestZarrDictStore(ZarrBase):
@contextlib.contextmanager
def create_zarr_target(self):
yield {}
@requires_zarr
class TestZarrDirectoryStore(ZarrBase):
@contextlib.contextmanager
def create_zarr_target(self):
with create_tmp_file(suffix='.zarr') as tmp:
yield tmp
class ScipyWriteBase(CFEncodedBase, NetCDF3Only):
def test_append_write(self):
import scipy
if scipy.__version__ == '1.0.1':
pytest.xfail('https://github.com/scipy/scipy/issues/8625')
super(ScipyWriteBase, self).test_append_write()
def test_append_overwrite_values(self):
import scipy
if scipy.__version__ == '1.0.1':
pytest.xfail('https://github.com/scipy/scipy/issues/8625')
super(ScipyWriteBase, self).test_append_overwrite_values()
@requires_scipy
class TestScipyInMemoryData(ScipyWriteBase):
engine = 'scipy'
@contextlib.contextmanager
def create_store(self):
fobj = BytesIO()
yield backends.ScipyDataStore(fobj, 'w')
def test_to_netcdf_explicit_engine(self):
# regression test for GH1321
Dataset({'foo': 42}).to_netcdf(engine='scipy')
def test_bytes_pickle(self):
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(ScipyWriteBase):
engine = 'scipy'
@contextlib.contextmanager
def create_store(self):
fobj = BytesIO()
yield backends.ScipyDataStore(fobj, 'w')
@contextlib.contextmanager
def roundtrip(self, data, save_kwargs={}, open_kwargs={},
allow_cleanup_failure=False):
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):
pass
@pytest.mark.skip(reason='cannot pickle file objects')
def test_pickle_dataarray(self):
pass
@requires_scipy
class TestScipyFilePath(ScipyWriteBase):
engine = '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):
ds = Dataset(attrs={'foo': [[1, 2], [3, 4]]})
with raises_regex(ValueError, 'must be 1-dimensional'):
with self.roundtrip(ds):
pass
def test_roundtrip_example_1_netcdf_gz(self):
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):
# 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):
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 raises_regex(TypeError, 'pip install netcdf4'):
open_dataset(tmp_file, engine='scipy')
@requires_netCDF4
class TestNetCDF3ViaNetCDF4Data(CFEncodedBase, NetCDF3Only):
engine = 'netcdf4'
file_format = '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):
original = Dataset({'x': [u'foo', u'bar', u'baz']})
kwargs = dict(encoding={'x': {'dtype': str}})
with raises_regex(ValueError, 'encoding dtype=str for vlen'):
with self.roundtrip(original, save_kwargs=kwargs):
pass
@requires_netCDF4
class TestNetCDF4ClassicViaNetCDF4Data(CFEncodedBase, NetCDF3Only):
engine = 'netcdf4'
file_format = '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 = 'netcdf3_64bit'
def test_write_store(self):
# there's no specific store to test here
pass
def test_engine(self):
data = create_test_data()
with raises_regex(ValueError, 'unrecognized engine'):
data.to_netcdf('foo.nc', engine='foobar')
with raises_regex(ValueError, 'invalid engine'):
data.to_netcdf(engine='netcdf4')
with create_tmp_file() as tmp_file:
data.to_netcdf(tmp_file)
with raises_regex(ValueError, 'unrecognized engine'):
open_dataset(tmp_file, engine='foobar')
netcdf_bytes = data.to_netcdf()
with raises_regex(ValueError, 'can only read'):
open_dataset(BytesIO(netcdf_bytes), engine='foobar')
@pytest.mark.xfail(reason='https://github.com/pydata/xarray/issues/2050')
def test_cross_engine_read_write_netcdf3(self):
data = create_test_data()
valid_engines = set()
if has_netCDF4:
valid_engines.add('netcdf4')
if has_scipy:
valid_engines.add('scipy')
for write_engine in valid_engines:
for format in ['NETCDF3_CLASSIC', 'NETCDF3_64BIT']:
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):
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
class TestH5NetCDFData(NetCDF4Base):
engine = 'h5netcdf'
@contextlib.contextmanager
def create_store(self):
with create_tmp_file() as tmp_file:
yield backends.H5NetCDFStore(tmp_file, 'w')
@pytest.mark.filterwarnings('ignore:complex dtypes are supported by h5py')
def test_complex(self):
expected = Dataset({'x': ('y', np.ones(5) + 1j * np.ones(5))})
with pytest.warns(FutureWarning):
# TODO: make it possible to write invalid netCDF files from xarray
# without a warning
with self.roundtrip(expected) as actual:
assert_equal(expected, actual)
@pytest.mark.xfail(reason='https://github.com/pydata/xarray/issues/535')
def test_cross_engine_read_write_netcdf4(self):
# 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('dim3')
data.attrs['foo'] = 'bar'
valid_engines = ['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):
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):
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):
ENCODINGS = (
# 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):
"""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 raises_regex(ValueError,
"'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 raises_regex(
ValueError,
"'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):
# 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
@pytest.fixture(params=['scipy', 'netcdf4', 'h5netcdf', 'pynio'])
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)
def test_open_mfdataset_manyfiles(readengine, nfiles, parallel, chunks,
file_cache_maxsize):
# skip certain combinations
skip_if_not_engine(readengine)
if not has_dask and parallel:
pytest.skip('parallel requires dask')
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))
subds.to_netcdf(tmpfiles[ii], engine=writeengine)
# check that calculation on opened datasets works properly
with open_mfdataset(tmpfiles, engine=readengine, parallel=parallel,
chunks=chunks) 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_scipy_or_netCDF4
class TestOpenMFDatasetWithDataVarsAndCoordsKw(object):
coord_name = 'lon'
var_name = 'v1'
@contextlib.contextmanager
def setup_files_and_datasets(self):
ds1, ds2 = self.gen_datasets_with_common_coord_and_time()
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('opt', ['all', 'minimal', 'different'])
def test_open_mfdataset_does_same_as_concat(self, opt):
with self.setup_files_and_datasets() as (files, [ds1, ds2]):
with open_mfdataset(files, data_vars=opt) as ds:
kwargs = dict(data_vars=opt, dim='t')
ds_expect = xr.concat([ds1, ds2], **kwargs)
assert_identical(ds, ds_expect)
with open_mfdataset(files, coords=opt) as ds:
kwargs = dict(coords=opt, dim='t')
ds_expect = xr.concat([ds1, ds2], **kwargs)
assert_identical(ds, ds_expect)
def test_common_coord_when_datavars_all(self):
opt = '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) 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):
opt = '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) 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):
with self.setup_files_and_datasets() as (files, _):
with pytest.raises(ValueError):
with open_mfdataset(files, data_vars='minimum'):
pass
# test invalid coord parameter
with pytest.raises(ValueError):
with open_mfdataset(files, coords='minimum'):
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={}, open_kwargs={},
allow_cleanup_failure=False):
yield data.chunk()
# Override methods in DatasetIOBase - not applicable to dask
def test_roundtrip_string_encoded_characters(self):
pass
def test_roundtrip_coordinates_with_space(self):
pass
def test_roundtrip_numpy_datetime_data(self):
# Override method in DatasetIOBase - remove not applicable
# save_kwds
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):
# Override method in DatasetIOBase - remove not applicable
# save_kwds
from .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):
# Override method in DatasetIOBase - not applicable to dask
pass
def test_dataset_caching(self):
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):
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]) 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], chunks={'x': 3}) as actual:
assert actual.foo.variable.data.chunks == ((3, 2, 3, 2),)
with raises_regex(IOError, 'no files to open'):
open_mfdataset('foo-bar-baz-*.nc')
with raises_regex(ValueError, 'wild-card'):
open_mfdataset('http://some/remote/uri')
def test_open_mfdataset_2d(self):
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]],
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]],
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),)
@requires_pathlib
def test_open_mfdataset_pathlib(self):
original = Dataset({'foo': ('x', np.random.randn(10))})
with create_tmp_file() as tmp1:
with create_tmp_file() as tmp2:
tmp1 = Path(tmp1)
tmp2 = Path(tmp2)
original.isel(x=slice(5)).to_netcdf(tmp1)
original.isel(x=slice(5, 10)).to_netcdf(tmp2)
with open_mfdataset([tmp1, tmp2]) as actual:
assert_identical(original, actual)
@requires_pathlib
def test_open_mfdataset_2d_pathlib(self):
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:
tmp1 = Path(tmp1)
tmp2 = Path(tmp2)
tmp3 = Path(tmp3)
tmp4 = Path(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]],
concat_dim=['y', 'x']) as actual:
assert_identical(original, actual)
@pytest.mark.xfail(reason="Not yet implemented")
def test_open_mfdataset_2(self):
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 pytest.raises(NotImplementedError):
open_mfdataset([tmp1, tmp2], infer_order_from_coords=True)
# With infer_order_from_coords=True this should pass in future
with open_mfdataset([tmp1, tmp2]) as actual:
assert_identical(original, actual)
def test_attrs_mfdataset(self):
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]) as actual:
# presumes that attributes inherited from
# first dataset loaded
assert actual.test1 == ds1.test1
# attributes from ds2 are not retained, e.g.,
with raises_regex(AttributeError,
'no attribute'):
actual.test2
def test_preprocess_mfdataset(self):
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) as actual:
assert_identical(expected, actual)
def test_save_mfdataset_roundtrip(self):
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]) as actual:
assert_identical(actual, original)
def test_save_mfdataset_invalid(self):
ds = Dataset()
with raises_regex(ValueError, 'cannot use mode'):
save_mfdataset([ds, ds], ['same', 'same'])
with raises_regex(ValueError, 'same length'):
save_mfdataset([ds, ds], ['only one path'])
def test_save_mfdataset_invalid_dataarray(self):
# regression test for GH1555
da = DataArray([1, 2])
with raises_regex(TypeError, 'supports writing Dataset'):
save_mfdataset([da], ['dataarray'])
@requires_pathlib
def test_save_mfdataset_pathlib_roundtrip(self):
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:
tmp1 = Path(tmp1)
tmp2 = Path(tmp2)
save_mfdataset(datasets, [tmp1, tmp2])
with open_mfdataset([tmp1, tmp2]) as actual:
assert_identical(actual, original)
def test_open_and_do_math(self):
original = Dataset({'foo': ('x', np.random.randn(10))})
with create_tmp_file() as tmp:
original.to_netcdf(tmp)
with open_mfdataset(tmp) as ds:
actual = 1.0 * ds
assert_allclose(original, actual, decode_bytes=False)
def test_open_mfdataset_concat_dim_none(self):
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) as actual:
assert_identical(data, actual)
def test_open_dataset(self):
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):
# 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) as actual:
assert_identical(expected, actual)
def test_open_multi_dataset(self):
# 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) as actual:
assert_identical(expected, actual)
def test_dask_roundtrip(self):
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):
with create_tmp_file() as tmp:
data = create_test_data()
data.to_netcdf(tmp)
with open_mfdataset(tmp) as ds:
original_names = dict((k, v.data.name)
for k, v in ds.data_vars.items())
with open_mfdataset(tmp) as ds:
repeat_names = dict((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):
# 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)
def test_save_mfdataset_compute_false_roundtrip(self):
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]) as actual:
assert_identical(actual, original)
@requires_scipy_or_netCDF4
@requires_pydap
class TestPydap(object):
def convert_to_pydap_dataset(self, original):
from pydap.model import GridType, BaseType, DatasetType
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):
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.isel(l=2), expected.isel(l=2)) # noqa
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):
indexers = {'i': DataArray([0, 1, 0], dims='a'),
'j': DataArray([0, 2, 1], dims='a')}
assert_equal(actual.isel(**indexers),
expected.isel(**indexers))
def test_compatible_to_netcdf(self):
# 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)
actual = open_dataset(tmp_file)
actual['bears'] = actual['bears'].astype(str)
assert_equal(actual, expected)
@requires_dask
def test_dask(self):
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):
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('http://test.url', session=session)
@requires_scipy
@requires_pynio
class TestPyNio(ScipyWriteBase):
def test_write_store(self):
# 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 save(self, dataset, path, **kwargs):
return dataset.to_netcdf(path, engine='scipy', **kwargs)
def test_weakrefs(self):
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(object):
def test_read(self):
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.
def test_read_filter_by_keys(self):
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.
@requires_pseudonetcdf
@pytest.mark.filterwarnings('ignore:IOAPI_ISPH is assumed to be 6370000')
class TestPseudoNetCDFFormat(object):
def open(self, path, **kwargs):
return open_dataset(path, engine='pseudonetcdf', **kwargs)
@contextlib.contextmanager
def roundtrip(self, data, save_kwargs={}, open_kwargs={},
allow_cleanup_failure=False):
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):
"""
Open a CAMx file and test data variables
"""
ictfile = open_example_dataset('example.ict',
engine='pseudonetcdf',
backend_kwargs={'format': 'ffi1001'})
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': 27,
'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': '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.],
'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)
assert_identical(ictfile, chkfile)
def test_ict_format_write(self):
fmtkw = {'format': 'ffi1001'}
expected = open_example_dataset('example.ict',
engine='pseudonetcdf',
backend_kwargs=fmtkw)
with self.roundtrip(expected, save_kwargs=fmtkw,
open_kwargs={'backend_kwargs': fmtkw}) as actual:
assert_identical(expected, actual)
def test_uamiv_format_read(self):
"""
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(['2002-06-03'], 'datetime64[ns]')
expected = xr.Variable(('TSTEP',), data,
dict(bounds='time_bounds',
long_name=('synthesized time coordinate ' +
'from SDATE, STIME, STEP '
+ 'global attributes')))
actual = camxfile.variables['time']
assert_allclose(expected, actual)
camxfile.close()
def test_uamiv_format_mfread(self):
"""
Open a CAMx file and test data variables
"""
camxfile = open_example_mfdataset(
['example.uamiv',
'example.uamiv'],
engine='pseudonetcdf',
concat_dim=['TSTEP'],
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)
data1 = np.array(['2002-06-03'], 'datetime64[ns]')
data = np.concatenate([data1] * 2, axis=0)
attrs = dict(bounds='time_bounds',
long_name=('synthesized time coordinate ' +
'from SDATE, STIME, STEP '
+ 'global attributes'))
expected = xr.Variable(('TSTEP',), data, attrs)
actual = camxfile.variables['time']
assert_allclose(expected, actual)
camxfile.close()
def test_uamiv_format_write(self):
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}) 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=[5000, 80000, 1000, 2000.],
crs={'units': 'm', 'no_defs': True, 'ellps': 'WGS84',
'proj': 'utm', 'zone': 18},
open_kwargs={}):
# yields a temporary geotiff file and a corresponding expected DataArray
import rasterio
from rasterio.transform import from_origin
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)
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:
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(object):
@requires_scipy_or_netCDF4
def test_serialization(self):
with create_tmp_geotiff() as (tmp_file, expected):
# Write it to a netcdf and read again (roundtrip)
with 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):
with create_tmp_geotiff() as (tmp_file, expected):
with xr.open_rasterio(tmp_file) as rioda:
assert_allclose(rioda, expected)
assert isinstance(rioda.attrs['crs'], basestring)
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 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):
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 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 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):
with create_tmp_geotiff(8, 10, 1, transform_args=[1, 2, 0.5, 2.],
crs='+proj=latlong',
open_kwargs={'nodata': -9765}) \
as (tmp_file, expected):
with xr.open_rasterio(tmp_file) as rioda:
assert_allclose(rioda, expected)
assert isinstance(rioda.attrs['crs'], basestring)
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.])
def test_notransform(self):
# regression test for https://github.com/pydata/xarray/issues/1686
import rasterio
import warnings
# Create a geotiff file
with warnings.catch_warnings():
# rasterio throws a NotGeoreferencedWarning here, which is
# expected since we test rasterio's defaults in this case.
warnings.filterwarnings('ignore', category=UserWarning,
message='Dataset has no geotransform set')
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.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 xr.open_rasterio(tmp_file) as rioda:
assert_allclose(rioda, expected)
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):
with create_tmp_geotiff(8, 10, 3, transform_args=[1, 2, 0.5, 2.],
crs='+proj=latlong') as (tmp_file, expected):
with 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
ind = {'band': slice(1, 2), 'x': slice(2, 5), 'y': slice(5, 7)}
assert_allclose(expected.isel(**ind), actual.isel(**ind))
assert not actual.variable._in_memory
ind = {'band': slice(1, 2), 'x': slice(2, 5), 'y': 0}
assert_allclose(expected.isel(**ind), actual.isel(**ind))
assert not actual.variable._in_memory
# orthogonal indexer
ind = {'band': np.array([2, 1, 0]),
'x': np.array([1, 0]), 'y': np.array([0, 2])}
assert_allclose(expected.isel(**ind), actual.isel(**ind))
assert not actual.variable._in_memory
ind = {'band': np.array([2, 1, 0]),
'x': np.array([1, 0]), 'y': 0}
assert_allclose(expected.isel(**ind), actual.isel(**ind))
assert not actual.variable._in_memory
ind = {'band': 0, 'x': np.array(
[0, 0]), 'y': np.array([1, 1, 1])}
assert_allclose(expected.isel(**ind), actual.isel(**ind))
assert not actual.variable._in_memory
# minus-stepped slice
ind = {'band': np.array([2, 1, 0]),
'x': slice(-1, None, -1), 'y': 0}
assert_allclose(expected.isel(**ind), actual.isel(**ind))
assert not actual.variable._in_memory
ind = {'band': np.array([2, 1, 0]),
'x': 1, 'y': slice(-1, 1, -2)}
assert_allclose(expected.isel(**ind), actual.isel(**ind))
assert not actual.variable._in_memory
# empty selection
ind = {'band': np.array([2, 1, 0]),
'x': 1, 'y': slice(2, 2, 1)}
assert_allclose(expected.isel(**ind), actual.isel(**ind))
assert not actual.variable._in_memory
ind = {'band': slice(0, 0), 'x': 1, 'y': 2}
assert_allclose(expected.isel(**ind), actual.isel(**ind))
assert not actual.variable._in_memory
# vectorized indexer
ind = {'band': DataArray([2, 1, 0], dims='a'),
'x': DataArray([1, 0, 0], dims='a'),
'y': np.array([0, 2])}
assert_allclose(expected.isel(**ind), actual.isel(**ind))
assert not actual.variable._in_memory
ind = {
'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(**ind), actual.isel(**ind))
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):
with create_tmp_geotiff(8, 10, 3, transform_args=[1, 2, 0.5, 2.],
crs='+proj=latlong') as (tmp_file, expected):
# Cache is the default
with 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):
with create_tmp_geotiff(8, 10, 3, transform_args=[1, 2, 0.5, 2.],
crs='+proj=latlong') as (tmp_file, expected):
# Chunk at open time
with 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)
def test_pickle_rasterio(self):
# regression test for https://github.com/pydata/xarray/issues/2121
with create_tmp_geotiff() as (tmp_file, expected):
with 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):
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.)
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 xr.open_rasterio(tmp_file) as rioda:
assert_allclose(rioda, expected)
assert isinstance(rioda.attrs['crs'], basestring)
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'], basestring)
assert isinstance(rioda.attrs['map_info'], basestring)
assert isinstance(rioda.attrs['samples'], basestring)
def test_no_mftime(self):
# 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.],
crs='+proj=latlong') as (tmp_file, expected):
with mock.patch('os.path.getmtime', side_effect=OSError):
with 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):
# more examples urls here
# http://download.osgeo.org/geotiff/samples/
url = 'http://download.osgeo.org/geotiff/samples/made_up/ntf_nord.tif'
with xr.open_rasterio(url) as actual:
assert actual.shape == (1, 512, 512)
# make sure chunking works
with 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):
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 xr.open_rasterio(tmp_file) as actual:
assert_allclose(actual, expected)
def test_rasterio_vrt(self):
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
print(expected_crs)
expected_res = vrt.res
# Value of single pixel in center of image
lon, lat = vrt.xy(vrt.width // 2, vrt.height // 2)
expected_val = next(vrt.sample([(lon, lat)]))
with xr.open_rasterio(vrt) as da:
actual_shape = (da.sizes['x'], da.sizes['y'])
actual_crs = da.crs
print(actual_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()
@network
def test_rasterio_vrt_network(self):
import rasterio
url = 'https://storage.googleapis.com/\
gcp-public-data-landsat/LC08/01/047/027/\
LC08_L1TP_047027_20130421_20170310_01_T1/\
LC08_L1TP_047027_20130421_20170310_01_T1_B4.TIF'
env = rasterio.Env(GDAL_DISABLE_READDIR_ON_OPEN='EMPTY_DIR',
CPL_VSIL_CURL_USE_HEAD=False,
CPL_VSIL_CURL_ALLOWED_EXTENSIONS='TIF')
with env:
with rasterio.open(url) as src:
with rasterio.vrt.WarpedVRT(src, crs='epsg:4326') as vrt:
expected_shape = (vrt.width, vrt.height)
expected_crs = vrt.crs
expected_res = vrt.res
# Value of single pixel in center of image
lon, lat = vrt.xy(vrt.width // 2, vrt.height // 2)
expected_val = next(vrt.sample([(lon, lat)]))
with xr.open_rasterio(vrt) as da:
actual_shape = (da.sizes['x'], da.sizes['y'])
actual_crs = da.crs
actual_res = da.res
actual_val = da.sel(dict(x=lon, y=lat),
method='nearest').data
assert_equal(actual_shape, expected_shape)
assert_equal(actual_crs, expected_crs)
assert_equal(actual_res, expected_res)
assert_equal(expected_val, actual_val)
class TestEncodingInvalid(object):
def test_extract_nc4_variable_encoding(self):
var = xr.Variable(('x',), [1, 2, 3], {}, {'foo': 'bar'})
with raises_regex(ValueError, '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
def test_extract_h5nc_encoding(self):
# not supported with h5netcdf (yet)
var = xr.Variable(('x',), [1, 2, 3], {},
{'least_sigificant_digit': 2})
with raises_regex(ValueError, 'unexpected encoding'):
_extract_nc4_variable_encoding(var, raise_on_invalid=True)
class MiscObject:
pass
@requires_netCDF4
class TestValidateAttrs(object):
def test_validating_attrs(self):
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 raises_regex(TypeError, 'Invalid name for attr'):
ds.to_netcdf('test.nc')
ds, attrs = new_dataset_and_attrs()
attrs[MiscObject()] = 'test'
with raises_regex(TypeError, 'Invalid name for attr'):
ds.to_netcdf('test.nc')
ds, attrs = new_dataset_and_attrs()
attrs[''] = 'test'
with raises_regex(ValueError, '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 raises_regex(TypeError, 'Invalid value for attr'):
ds.to_netcdf('test.nc')
ds, attrs = new_dataset_and_attrs()
attrs['test'] = MiscObject()
with raises_regex(TypeError, 'Invalid value for attr'):
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'] = np.arange(12).reshape(3, 4)
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)
ds, attrs = new_dataset_and_attrs()
attrs['test'] = np.arange(12).reshape(3, 4)
with create_tmp_file() as tmp_file:
ds.to_netcdf(tmp_file)
@requires_scipy_or_netCDF4
class TestDataArrayToNetCDF(object):
def test_dataarray_to_netcdf_no_name(self):
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):
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):
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):
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('y')
with open_dataarray(tmp, drop_variables=['y']) as loaded:
assert_identical(expected, loaded)
def test_dataarray_to_netcdf_return_bytes(self):
# regression test for GH1410
data = xr.DataArray([1, 2, 3])
output = data.to_netcdf()
assert isinstance(output, bytes)
@requires_pathlib
def test_dataarray_to_netcdf_no_name_pathlib(self):
original_da = DataArray(np.arange(12).reshape((3, 4)))
with create_tmp_file() as tmp:
tmp = Path(tmp)
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():
with create_tmp_file() as tmpfile:
with pytest.warns(None) as record:
ds = Dataset()
ds.to_netcdf(tmpfile)
assert len(record) == 0
@requires_scipy_or_netCDF4
def test_source_encoding_always_present():
# 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
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