diff --git a/.travis.yml b/.travis.yml index bd53edb0029..6df70e92954 100644 --- a/.travis.yml +++ b/.travis.yml @@ -14,8 +14,6 @@ matrix: env: CONDA_ENV=py27-min - python: 2.7 env: CONDA_ENV=py27-cdat+iris+pynio - - python: 3.4 - env: CONDA_ENV=py34 - python: 3.5 env: CONDA_ENV=py35 - python: 3.6 @@ -102,7 +100,7 @@ install: script: # TODO: restore this check once the upstream pandas issue is fixed: - # https://github.com/pandas-dev/pandas/issues/21071 + # https://github.com/pandas-dev/pandas/issues/21071 # - python -OO -c "import xarray" - if [[ "$CONDA_ENV" == "docs" ]]; then conda install -c conda-forge sphinx sphinx_rtd_theme sphinx-gallery numpydoc; diff --git a/ci/requirements-py27-min.yml b/ci/requirements-py27-min.yml index 50f6724ec51..118b629271e 100644 --- a/ci/requirements-py27-min.yml +++ b/ci/requirements-py27-min.yml @@ -4,8 +4,8 @@ dependencies: - pytest - flake8 - mock - - numpy==1.11 - - pandas==0.18.0 + - numpy=1.12 + - pandas=0.19 - pip: - coveralls - pytest-cov diff --git a/ci/requirements-py34.yml b/ci/requirements-py34.yml deleted file mode 100644 index ba79e00bb12..00000000000 --- a/ci/requirements-py34.yml +++ /dev/null @@ -1,10 +0,0 @@ -name: test_env -dependencies: - - python=3.4 - - bottleneck - - flake8 - - pandas - - pip: - - coveralls - - pytest-cov - - pytest diff --git a/ci/requirements-py35.yml b/ci/requirements-py35.yml index d3500bc5d10..9615aeba9aa 100644 --- a/ci/requirements-py35.yml +++ b/ci/requirements-py35.yml @@ -4,11 +4,10 @@ channels: dependencies: - python=3.5 - cftime - - dask - - distributed + - dask=0.16 - h5py - h5netcdf - - matplotlib + - matplotlib=1.5 - netcdf4 - pytest - flake8 diff --git a/doc/installing.rst b/doc/installing.rst index 31fc109ee2e..b3154c3d8bb 100644 --- a/doc/installing.rst +++ b/doc/installing.rst @@ -6,9 +6,9 @@ Installation Required dependencies --------------------- -- Python 2.7 [1]_, 3.4, 3.5, or 3.6 -- `numpy `__ (1.11 or later) -- `pandas `__ (0.18.0 or later) +- Python 2.7 [1]_, 3.5, or 3.6 +- `numpy `__ (1.12 or later) +- `pandas `__ (0.19.2 or later) Optional dependencies --------------------- @@ -45,13 +45,14 @@ For accelerating xarray For parallel computing ~~~~~~~~~~~~~~~~~~~~~~ -- `dask.array `__ (0.9.0 or later): required for +- `dask.array `__ (0.16 or later): required for :ref:`dask`. For plotting ~~~~~~~~~~~~ - `matplotlib `__: required for :ref:`plotting` + (1.5 or later) - `cartopy `__: recommended for :ref:`plot-maps` - `seaborn `__: for better diff --git a/doc/whats-new.rst b/doc/whats-new.rst index bd23386a460..af90ea7f9d3 100644 --- a/doc/whats-new.rst +++ b/doc/whats-new.rst @@ -66,6 +66,18 @@ Bug fixes weren't monotonic (:issue:`2250`). By `Fabien Maussion `_. +Breaking changes +~~~~~~~~~~~~~~~~ + +- Xarray no longer supports python 3.4. Additionally, the minimum supported + versions of the following dependencies has been updated and/or clarified: + + - Pandas: 0.18 -> 0.19 + - NumPy: 1.11 -> 1.12 + - Dask: 0.9 -> 0.16 + - Matplotlib: unspecified -> 1.5 + + (:issue:`2204`). By `Joe Hamman `_. .. _whats-new.0.10.7: diff --git a/setup.py b/setup.py index 77c6083f52c..e35611e01b1 100644 --- a/setup.py +++ b/setup.py @@ -71,4 +71,4 @@ tests_require=TESTS_REQUIRE, url=URL, packages=find_packages(), - package_data={'xarray': ['tests/data/*', 'plot/default_colormap.csv']}) + package_data={'xarray': ['tests/data/*']}) diff --git a/xarray/core/duck_array_ops.py b/xarray/core/duck_array_ops.py index 065ac165a0d..3bd105064da 100644 --- a/xarray/core/duck_array_ops.py +++ b/xarray/core/duck_array_ops.py @@ -363,9 +363,9 @@ def f(values, axis=None, skipna=None, **kwargs): median = _create_nan_agg_method('median', numeric_only=True) prod = _create_nan_agg_method('prod', numeric_only=True, no_bottleneck=True) cumprod_1d = _create_nan_agg_method( - 'cumprod', numeric_only=True, np_compat=True, no_bottleneck=True) + 'cumprod', numeric_only=True, no_bottleneck=True) cumsum_1d = _create_nan_agg_method( - 'cumsum', numeric_only=True, np_compat=True, no_bottleneck=True) + 'cumsum', numeric_only=True, no_bottleneck=True) def _nd_cum_func(cum_func, array, axis, **kwargs): diff --git a/xarray/core/missing.py b/xarray/core/missing.py index 743627bb381..bec9e2e1931 100644 --- a/xarray/core/missing.py +++ b/xarray/core/missing.py @@ -8,7 +8,6 @@ from . import rolling from .computation import apply_ufunc -from .npcompat import flip from .pycompat import iteritems from .utils import is_scalar, OrderedSet from .variable import Variable, broadcast_variables @@ -245,13 +244,13 @@ def _bfill(arr, n=None, axis=-1): '''inverse of ffill''' import bottleneck as bn - arr = flip(arr, axis=axis) + arr = np.flip(arr, axis=axis) # fill arr = bn.push(arr, axis=axis, n=n) # reverse back to original - return flip(arr, axis=axis) + return np.flip(arr, axis=axis) def ffill(arr, dim=None, limit=None): diff --git a/xarray/core/npcompat.py b/xarray/core/npcompat.py index ec8adfffbf8..6d4db063b98 100644 --- a/xarray/core/npcompat.py +++ b/xarray/core/npcompat.py @@ -1,261 +1,7 @@ from __future__ import absolute_import, division, print_function -from distutils.version import LooseVersion - import numpy as np -if LooseVersion(np.__version__) >= LooseVersion('1.12'): - as_strided = np.lib.stride_tricks.as_strided -else: - def as_strided(x, shape=None, strides=None, subok=False, writeable=True): - array = np.lib.stride_tricks.as_strided(x, shape, strides, subok) - array.setflags(write=writeable) - return array - - -try: - from numpy import nancumsum, nancumprod, flip -except ImportError: # pragma: no cover - # Code copied from newer versions of NumPy (v1.12). - # Used under the terms of NumPy's license, see licenses/NUMPY_LICENSE. - - def _replace_nan(a, val): - """ - If `a` is of inexact type, make a copy of `a`, replace NaNs with - the `val` value, and return the copy together with a boolean mask - marking the locations where NaNs were present. If `a` is not of - inexact type, do nothing and return `a` together with a mask of None. - - Note that scalars will end up as array scalars, which is important - for using the result as the value of the out argument in some - operations. - - Parameters - ---------- - a : array-like - Input array. - val : float - NaN values are set to val before doing the operation. - - Returns - ------- - y : ndarray - If `a` is of inexact type, return a copy of `a` with the NaNs - replaced by the fill value, otherwise return `a`. - mask: {bool, None} - If `a` is of inexact type, return a boolean mask marking locations - of NaNs, otherwise return None. - - """ - is_new = not isinstance(a, np.ndarray) - if is_new: - a = np.array(a) - if not issubclass(a.dtype.type, np.inexact): - return a, None - if not is_new: - # need copy - a = np.array(a, subok=True) - - mask = np.isnan(a) - np.copyto(a, val, where=mask) - return a, mask - - def nancumsum(a, axis=None, dtype=None, out=None): - """ - Return the cumulative sum of array elements over a given axis treating - Not a Numbers (NaNs) as zero. The cumulative sum does not change when - NaNs are encountered and leading NaNs are replaced by zeros. - - Zeros are returned for slices that are all-NaN or empty. - - .. versionadded:: 1.12.0 - - Parameters - ---------- - a : array_like - Input array. - axis : int, optional - Axis along which the cumulative sum is computed. The default - (None) is to compute the cumsum over the flattened array. - dtype : dtype, optional - Type of the returned array and of the accumulator in which the - elements are summed. If `dtype` is not specified, it defaults - to the dtype of `a`, unless `a` has an integer dtype with a - precision less than that of the default platform integer. In - that case, the default platform integer is used. - out : ndarray, optional - Alternative output array in which to place the result. It must - have the same shape and buffer length as the expected output - but the type will be cast if necessary. See `doc.ufuncs` - (Section "Output arguments") for more details. - - Returns - ------- - nancumsum : ndarray. - A new array holding the result is returned unless `out` is - specified, in which it is returned. The result has the same - size as `a`, and the same shape as `a` if `axis` is not None - or `a` is a 1-d array. - - See Also - -------- - numpy.cumsum : Cumulative sum across array propagating NaNs. - isnan : Show which elements are NaN. - - Examples - -------- - >>> np.nancumsum(1) - array([1]) - >>> np.nancumsum([1]) - array([1]) - >>> np.nancumsum([1, np.nan]) - array([ 1., 1.]) - >>> a = np.array([[1, 2], [3, np.nan]]) - >>> np.nancumsum(a) - array([ 1., 3., 6., 6.]) - >>> np.nancumsum(a, axis=0) - array([[ 1., 2.], - [ 4., 2.]]) - >>> np.nancumsum(a, axis=1) - array([[ 1., 3.], - [ 3., 3.]]) - - """ - a, mask = _replace_nan(a, 0) - return np.cumsum(a, axis=axis, dtype=dtype, out=out) - - def nancumprod(a, axis=None, dtype=None, out=None): - """ - Return the cumulative product of array elements over a given axis - treating Not a Numbers (NaNs) as one. The cumulative product does not - change when NaNs are encountered and leading NaNs are replaced by ones. - - Ones are returned for slices that are all-NaN or empty. - - .. versionadded:: 1.12.0 - - Parameters - ---------- - a : array_like - Input array. - axis : int, optional - Axis along which the cumulative product is computed. By default - the input is flattened. - dtype : dtype, optional - Type of the returned array, as well as of the accumulator in which - the elements are multiplied. If *dtype* is not specified, it - defaults to the dtype of `a`, unless `a` has an integer dtype with - a precision less than that of the default platform integer. In - that case, the default platform integer is used instead. - out : ndarray, optional - Alternative output array in which to place the result. It must - have the same shape and buffer length as the expected output - but the type of the resulting values will be cast if necessary. - - Returns - ------- - nancumprod : ndarray - A new array holding the result is returned unless `out` is - specified, in which case it is returned. - - See Also - -------- - numpy.cumprod : Cumulative product across array propagating NaNs. - isnan : Show which elements are NaN. - - Examples - -------- - >>> np.nancumprod(1) - array([1]) - >>> np.nancumprod([1]) - array([1]) - >>> np.nancumprod([1, np.nan]) - array([ 1., 1.]) - >>> a = np.array([[1, 2], [3, np.nan]]) - >>> np.nancumprod(a) - array([ 1., 2., 6., 6.]) - >>> np.nancumprod(a, axis=0) - array([[ 1., 2.], - [ 3., 2.]]) - >>> np.nancumprod(a, axis=1) - array([[ 1., 2.], - [ 3., 3.]]) - - """ - a, mask = _replace_nan(a, 1) - return np.cumprod(a, axis=axis, dtype=dtype, out=out) - - def flip(m, axis): - """ - Reverse the order of elements in an array along the given axis. - - The shape of the array is preserved, but the elements are reordered. - - .. versionadded:: 1.12.0 - - Parameters - ---------- - m : array_like - Input array. - axis : integer - Axis in array, which entries are reversed. - - - Returns - ------- - out : array_like - A view of `m` with the entries of axis reversed. Since a view is - returned, this operation is done in constant time. - - See Also - -------- - flipud : Flip an array vertically (axis=0). - fliplr : Flip an array horizontally (axis=1). - - Notes - ----- - flip(m, 0) is equivalent to flipud(m). - flip(m, 1) is equivalent to fliplr(m). - flip(m, n) corresponds to ``m[...,::-1,...]`` with ``::-1`` at index n. - - Examples - -------- - >>> A = np.arange(8).reshape((2,2,2)) - >>> A - array([[[0, 1], - [2, 3]], - - [[4, 5], - [6, 7]]]) - - >>> flip(A, 0) - array([[[4, 5], - [6, 7]], - - [[0, 1], - [2, 3]]]) - - >>> flip(A, 1) - array([[[2, 3], - [0, 1]], - - [[6, 7], - [4, 5]]]) - - >>> A = np.random.randn(3,4,5) - >>> np.all(flip(A,2) == A[:,:,::-1,...]) - True - """ - if not hasattr(m, 'ndim'): - m = np.asarray(m) - indexer = [slice(None)] * m.ndim - try: - indexer[axis] = slice(None, None, -1) - except IndexError: - raise ValueError("axis=%i is invalid for the %i-dimensional " - "input array" % (axis, m.ndim)) - return m[tuple(indexer)] - try: from numpy import isin except ImportError: diff --git a/xarray/core/nputils.py b/xarray/core/nputils.py index 4ca1f9390eb..6df2d34bfe3 100644 --- a/xarray/core/nputils.py +++ b/xarray/core/nputils.py @@ -5,8 +5,6 @@ import numpy as np import pandas as pd -from . import npcompat - def _validate_axis(data, axis): ndim = data.ndim @@ -194,6 +192,6 @@ def _rolling_window(a, window, axis=-1): shape = a.shape[:-1] + (a.shape[-1] - window + 1, window) strides = a.strides + (a.strides[-1],) - rolling = npcompat.as_strided(a, shape=shape, strides=strides, - writeable=False) + rolling = np.lib.stride_tricks.as_strided(a, shape=shape, strides=strides, + writeable=False) return np.swapaxes(rolling, -2, axis) diff --git a/xarray/plot/default_colormap.csv b/xarray/plot/default_colormap.csv deleted file mode 100644 index de9632e3f26..00000000000 --- a/xarray/plot/default_colormap.csv +++ /dev/null @@ -1,256 +0,0 @@ -0.26700401,0.00487433,0.32941519 -0.26851048,0.00960483,0.33542652 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-0.78331535,0.87928545,0.12540538 -0.79375994,0.88067763,0.12000532 -0.80418159,0.88204632,0.11496505 -0.81457634,0.88339329,0.11034678 -0.82494028,0.88472036,0.10621724 -0.83526959,0.88602943,0.1026459 -0.84556056,0.88732243,0.09970219 -0.8558096,0.88860134,0.09745186 -0.86601325,0.88986815,0.09595277 -0.87616824,0.89112487,0.09525046 -0.88627146,0.89237353,0.09537439 -0.89632002,0.89361614,0.09633538 -0.90631121,0.89485467,0.09812496 -0.91624212,0.89609127,0.1007168 -0.92610579,0.89732977,0.10407067 -0.93590444,0.8985704,0.10813094 -0.94563626,0.899815,0.11283773 -0.95529972,0.90106534,0.11812832 -0.96489353,0.90232311,0.12394051 -0.97441665,0.90358991,0.13021494 -0.98386829,0.90486726,0.13689671 -0.99324789,0.90615657,0.1439362 diff --git a/xarray/plot/utils.py b/xarray/plot/utils.py index 6846c553b8b..4b9645e02d5 100644 --- a/xarray/plot/utils.py +++ b/xarray/plot/utils.py @@ -13,20 +13,6 @@ ROBUST_PERCENTILE = 2.0 -def _load_default_cmap(fname='default_colormap.csv'): - """ - Returns viridis color map - """ - from matplotlib.colors import LinearSegmentedColormap - - # Not sure what the first arg here should be - f = pkg_resources.resource_stream(__name__, fname) - cm_data = pd.read_csv(f, header=None).values - f.close() - - return LinearSegmentedColormap.from_list('viridis', cm_data) - - def import_seaborn(): '''import seaborn and handle deprecation of apionly module''' with warnings.catch_warnings(record=True) as w: @@ -226,10 +212,6 @@ def _determine_cmap_params(plot_data, vmin=None, vmax=None, cmap=None, else: cmap = "viridis" - # Allow viridis before matplotlib 1.5 - if cmap == "viridis": - cmap = _load_default_cmap() - # Handle discrete levels if levels is not None: if is_scalar(levels): diff --git a/xarray/tests/__init__.py b/xarray/tests/__init__.py index e93d9a80145..3b4d69a35f7 100644 --- a/xarray/tests/__init__.py +++ b/xarray/tests/__init__.py @@ -54,15 +54,13 @@ def _importorskip(modname, minversion=None): raise ImportError('Minimum version not satisfied') except ImportError: has = False - # TODO: use pytest.skipif instead of unittest.skipUnless - # Using `unittest.skipUnless` is a temporary workaround for pytest#568, - # wherein class decorators stain inherited classes. - # xref: xarray#1531, implemented in xarray #1557. - func = unittest.skipUnless(has, reason='requires {}'.format(modname)) + func = pytest.mark.skipif(not has, reason='requires {}'.format(modname)) return has, func has_matplotlib, requires_matplotlib = _importorskip('matplotlib') +has_matplotlib2, requires_matplotlib2 = _importorskip('matplotlib', + minversion='2') has_scipy, requires_scipy = _importorskip('scipy') has_pydap, requires_pydap = _importorskip('pydap.client') has_netCDF4, requires_netCDF4 = _importorskip('netCDF4') @@ -75,15 +73,15 @@ def _importorskip(modname, minversion=None): has_rasterio, requires_rasterio = _importorskip('rasterio') has_pathlib, requires_pathlib = _importorskip('pathlib') has_zarr, requires_zarr = _importorskip('zarr', minversion='2.2') -has_np112, requires_np112 = _importorskip('numpy', minversion='1.12.0') +has_np113, requires_np113 = _importorskip('numpy', minversion='1.13.0') # some special cases has_scipy_or_netCDF4 = has_scipy or has_netCDF4 -requires_scipy_or_netCDF4 = unittest.skipUnless( - has_scipy_or_netCDF4, reason='requires scipy or netCDF4') +requires_scipy_or_netCDF4 = pytest.mark.skipif( + not has_scipy_or_netCDF4, reason='requires scipy or netCDF4') has_cftime_or_netCDF4 = has_cftime or has_netCDF4 -requires_cftime_or_netCDF4 = unittest.skipUnless( - has_cftime_or_netCDF4, reason='requires cftime or netCDF4') +requires_cftime_or_netCDF4 = pytest.mark.skipif( + not has_cftime_or_netCDF4, reason='requires cftime or netCDF4') if not has_pathlib: has_pathlib, requires_pathlib = _importorskip('pathlib2') if has_dask: @@ -97,7 +95,8 @@ def _importorskip(modname, minversion=None): has_seaborn = True except ImportError: has_seaborn = False -requires_seaborn = unittest.skipUnless(has_seaborn, reason='requires seaborn') +requires_seaborn = pytest.mark.skipif(not has_seaborn, + reason='requires seaborn') try: _SKIP_FLAKY = not pytest.config.getoption("--run-flaky") diff --git a/xarray/tests/test_computation.py b/xarray/tests/test_computation.py index a802b91a3db..e30e7e31390 100644 --- a/xarray/tests/test_computation.py +++ b/xarray/tests/test_computation.py @@ -726,9 +726,6 @@ def pandas_median(x): def test_vectorize(): - if LooseVersion(np.__version__) < LooseVersion('1.12.0'): - pytest.skip('numpy 1.12 or later to support vectorize=True.') - data_array = xr.DataArray([[0, 1, 2], [1, 2, 3]], dims=('x', 'y')) expected = xr.DataArray([1, 2], dims=['x']) actual = apply_ufunc(pandas_median, data_array, @@ -739,9 +736,6 @@ def test_vectorize(): @requires_dask def test_vectorize_dask(): - if LooseVersion(np.__version__) < LooseVersion('1.12.0'): - pytest.skip('numpy 1.12 or later to support vectorize=True.') - data_array = xr.DataArray([[0, 1, 2], [1, 2, 3]], dims=('x', 'y')) expected = xr.DataArray([1, 2], dims=['x']) actual = apply_ufunc(pandas_median, data_array.chunk({'x': 1}), diff --git a/xarray/tests/test_dask.py b/xarray/tests/test_dask.py index ee5b3514348..f6c47cce8d8 100644 --- a/xarray/tests/test_dask.py +++ b/xarray/tests/test_dask.py @@ -1,7 +1,6 @@ from __future__ import absolute_import, division, print_function import pickle -from distutils.version import LooseVersion from textwrap import dedent import numpy as np @@ -208,8 +207,6 @@ def test_bivariate_ufunc(self): self.assertLazyAndAllClose(np.maximum(u, 0), xu.maximum(v, 0)) self.assertLazyAndAllClose(np.maximum(u, 0), xu.maximum(0, v)) - @pytest.mark.skipif(LooseVersion(dask.__version__) <= '0.15.4', - reason='Need dask 0.16 for new interface') def test_compute(self): u = self.eager_var v = self.lazy_var @@ -220,8 +217,6 @@ def test_compute(self): assert ((u + 1).data == v2.data).all() - @pytest.mark.skipif(LooseVersion(dask.__version__) <= '0.15.4', - reason='Need dask 0.16 for new interface') def test_persist(self): u = self.eager_var v = self.lazy_var + 1 @@ -281,8 +276,6 @@ def test_lazy_array(self): actual = xr.concat([v[:2], v[2:]], 'x') self.assertLazyAndAllClose(u, actual) - @pytest.mark.skipif(LooseVersion(dask.__version__) <= '0.15.4', - reason='Need dask 0.16 for new interface') def test_compute(self): u = self.eager_array v = self.lazy_array @@ -293,8 +286,6 @@ def test_compute(self): assert ((u + 1).data == v2.data).all() - @pytest.mark.skipif(LooseVersion(dask.__version__) <= '0.15.4', - reason='Need dask 0.16 for new interface') def test_persist(self): u = self.eager_array v = self.lazy_array + 1 @@ -384,10 +375,6 @@ def test_concat_loads_variables(self): assert ds3['c'].data is c3 def test_groupby(self): - if LooseVersion(dask.__version__) == LooseVersion('0.15.3'): - pytest.xfail('upstream bug in dask: ' - 'https://github.com/dask/dask/issues/2718') - u = self.eager_array v = self.lazy_array @@ -779,12 +766,8 @@ def build_dask_array(name): # test both the perist method and the dask.persist function # the dask.persist function requires a new version of dask -@pytest.mark.parametrize('persist', [ - lambda x: x.persist(), - pytest.mark.skipif(LooseVersion(dask.__version__) <= '0.15.4', - lambda x: dask.persist(x)[0], - reason='Need Dask 0.16') -]) +@pytest.mark.parametrize('persist', [lambda x: x.persist(), + lambda x: dask.persist(x)[0]]) def test_persist_Dataset(persist): ds = Dataset({'foo': ('x', range(5)), 'bar': ('x', range(5))}).chunk() @@ -797,12 +780,8 @@ def test_persist_Dataset(persist): assert len(ds.foo.data.dask) == n # doesn't mutate in place -@pytest.mark.parametrize('persist', [ - lambda x: x.persist(), - pytest.mark.skipif(LooseVersion(dask.__version__) <= '0.15.4', - lambda x: dask.persist(x)[0], - reason='Need Dask 0.16') -]) +@pytest.mark.parametrize('persist', [lambda x: x.persist(), + lambda x: dask.persist(x)[0]]) def test_persist_DataArray(persist): x = da.arange(10, chunks=(5,)) y = DataArray(x) @@ -815,8 +794,6 @@ def test_persist_DataArray(persist): assert len(zz.data.dask) == zz.data.npartitions -@pytest.mark.skipif(LooseVersion(dask.__version__) <= '0.15.4', - reason='Need dask 0.16 for new interface') def test_dataarray_with_dask_coords(): import toolz x = xr.Variable('x', da.arange(8, chunks=(4,))) diff --git a/xarray/tests/test_dataarray.py b/xarray/tests/test_dataarray.py index df09a1e58df..e0b1496c7bf 100644 --- a/xarray/tests/test_dataarray.py +++ b/xarray/tests/test_dataarray.py @@ -2,7 +2,6 @@ import pickle from copy import deepcopy -from distutils.version import LooseVersion from textwrap import dedent import warnings @@ -20,7 +19,7 @@ from xarray.tests import ( ReturnItem, TestCase, assert_allclose, assert_array_equal, assert_equal, assert_identical, raises_regex, requires_bottleneck, requires_cftime, - requires_dask, requires_scipy, source_ndarray, unittest) + requires_dask, requires_np113, requires_scipy, source_ndarray, unittest) class TestDataArray(TestCase): @@ -3379,9 +3378,6 @@ def test_sortby(self): actual = da.sortby([day, dax]) assert_equal(actual, expected) - if LooseVersion(np.__version__) < LooseVersion('1.11.0'): - pytest.skip('numpy 1.11.0 or later to support object data-type.') - expected = sorted1d actual = da.sortby('x') assert_equal(actual, expected) @@ -3647,9 +3643,7 @@ def test_rolling_reduce(da, center, min_periods, window, name): assert actual.dims == expected.dims -@pytest.mark.skipif(LooseVersion(np.__version__) < LooseVersion('1.13'), - reason='Old numpy does not support nansum / nanmax for ' - 'object typed arrays.') +@requires_np113 @pytest.mark.parametrize('center', (True, False)) @pytest.mark.parametrize('min_periods', (None, 1, 2, 3)) @pytest.mark.parametrize('window', (1, 2, 3, 4)) diff --git a/xarray/tests/test_dataset.py b/xarray/tests/test_dataset.py index a0d316d74dc..4aa99b8ee5a 100644 --- a/xarray/tests/test_dataset.py +++ b/xarray/tests/test_dataset.py @@ -2,7 +2,6 @@ from __future__ import absolute_import, division, print_function from copy import copy, deepcopy -from distutils.version import LooseVersion from io import StringIO from textwrap import dedent import warnings @@ -4036,9 +4035,6 @@ def test_sortby(self): actual = ds.sortby(ds['A']) assert "DataArray is not 1-D" in str(excinfo.value) - if LooseVersion(np.__version__) < LooseVersion('1.11.0'): - pytest.skip('numpy 1.11.0 or later to support object data-type.') - expected = sorted1d actual = ds.sortby('x') assert_equal(actual, expected) diff --git a/xarray/tests/test_distributed.py b/xarray/tests/test_distributed.py index 8679e892be4..32035afdc57 100644 --- a/xarray/tests/test_distributed.py +++ b/xarray/tests/test_distributed.py @@ -7,8 +7,8 @@ import pytest -dask = pytest.importorskip('dask') # isort:skip -distributed = pytest.importorskip('distributed') # isort:skip +dask = pytest.importorskip('dask', minversion='0.18') # isort:skip +distributed = pytest.importorskip('distributed', minversion='1.21') # isort:skip from dask import array from dask.distributed import Client, Lock diff --git a/xarray/tests/test_duck_array_ops.py b/xarray/tests/test_duck_array_ops.py index 3f4adee6713..f3f93491822 100644 --- a/xarray/tests/test_duck_array_ops.py +++ b/xarray/tests/test_duck_array_ops.py @@ -1,7 +1,5 @@ from __future__ import absolute_import, division, print_function -from distutils.version import LooseVersion - import numpy as np import pytest from numpy import array, nan @@ -16,7 +14,8 @@ from xarray.testing import assert_allclose, assert_equal from . import ( - TestCase, assert_array_equal, has_dask, raises_regex, requires_dask) + TestCase, assert_array_equal, has_dask, has_np113, raises_regex, + requires_dask) class TestOps(TestCase): @@ -263,8 +262,7 @@ def test_reduce(dim_num, dtype, dask, func, skipna, aggdim): warnings.filterwarnings('ignore', 'All-NaN slice') warnings.filterwarnings('ignore', 'invalid value encountered in') - if (LooseVersion(np.__version__) >= LooseVersion('1.13.0') and - da.dtype.kind == 'O' and skipna): + if has_np113 and da.dtype.kind == 'O' and skipna: # Numpy < 1.13 does not handle object-type array. try: if skipna: diff --git a/xarray/tests/test_missing.py b/xarray/tests/test_missing.py index 1dde95adf42..5c7e384c789 100644 --- a/xarray/tests/test_missing.py +++ b/xarray/tests/test_missing.py @@ -12,7 +12,7 @@ from xarray.core.pycompat import dask_array_type from xarray.tests import ( assert_array_equal, assert_equal, raises_regex, requires_bottleneck, - requires_dask, requires_np112, requires_scipy) + requires_dask, requires_scipy) @pytest.fixture @@ -67,7 +67,6 @@ def make_interpolate_example_data(shape, frac_nan, seed=12345, return da, df -@requires_np112 @requires_scipy def test_interpolate_pd_compat(): shapes = [(8, 8), (1, 20), (20, 1), (100, 100)] @@ -93,7 +92,6 @@ def test_interpolate_pd_compat(): np.testing.assert_allclose(actual.values, expected.values) -@requires_np112 @requires_scipy def test_scipy_methods_function(): for method in ['barycentric', 'krog', 'pchip', 'spline', 'akima']: @@ -105,7 +103,6 @@ def test_scipy_methods_function(): assert (da.count('time') <= actual.count('time')).all() -@requires_np112 @requires_scipy def test_interpolate_pd_compat_non_uniform_index(): shapes = [(8, 8), (1, 20), (20, 1), (100, 100)] @@ -134,7 +131,6 @@ def test_interpolate_pd_compat_non_uniform_index(): np.testing.assert_allclose(actual.values, expected.values) -@requires_np112 @requires_scipy def test_interpolate_pd_compat_polynomial(): shapes = [(8, 8), (1, 20), (20, 1), (100, 100)] @@ -154,7 +150,6 @@ def test_interpolate_pd_compat_polynomial(): np.testing.assert_allclose(actual.values, expected.values) -@requires_np112 @requires_scipy def test_interpolate_unsorted_index_raises(): vals = np.array([1, 2, 3], dtype=np.float64) @@ -195,7 +190,6 @@ def test_interpolate_2d_coord_raises(): da.interpolate_na(dim='a', use_coordinate='x') -@requires_np112 @requires_scipy def test_interpolate_kwargs(): da = xr.DataArray(np.array([4, 5, np.nan], dtype=np.float64), dims='x') @@ -208,7 +202,6 @@ def test_interpolate_kwargs(): assert_equal(actual, expected) -@requires_np112 def test_interpolate(): vals = np.array([1, 2, 3, 4, 5, 6], dtype=np.float64) @@ -222,7 +215,6 @@ def test_interpolate(): assert_equal(actual, expected) -@requires_np112 def test_interpolate_nonans(): vals = np.array([1, 2, 3, 4, 5, 6], dtype=np.float64) @@ -231,7 +223,6 @@ def test_interpolate_nonans(): assert_equal(actual, expected) -@requires_np112 @requires_scipy def test_interpolate_allnans(): vals = np.full(6, np.nan, dtype=np.float64) @@ -241,7 +232,6 @@ def test_interpolate_allnans(): assert_equal(actual, expected) -@requires_np112 @requires_bottleneck def test_interpolate_limits(): da = xr.DataArray(np.array([1, 2, np.nan, np.nan, np.nan, 6], @@ -257,7 +247,6 @@ def test_interpolate_limits(): assert_equal(actual, expected) -@requires_np112 @requires_scipy def test_interpolate_methods(): for method in ['linear', 'nearest', 'zero', 'slinear', 'quadratic', @@ -273,7 +262,6 @@ def test_interpolate_methods(): @requires_scipy -@requires_np112 def test_interpolators(): for method, interpolator in [('linear', NumpyInterpolator), ('linear', ScipyInterpolator), @@ -287,7 +275,6 @@ def test_interpolators(): assert pd.isnull(out).sum() == 0 -@requires_np112 def test_interpolate_use_coordinate(): xc = xr.Variable('x', [100, 200, 300, 400, 500, 600]) da = xr.DataArray(np.array([1, 2, np.nan, np.nan, np.nan, 6], @@ -310,7 +297,6 @@ def test_interpolate_use_coordinate(): assert_equal(actual, expected) -@requires_np112 @requires_dask def test_interpolate_dask(): da, _ = make_interpolate_example_data((40, 40), 0.5) @@ -328,7 +314,6 @@ def test_interpolate_dask(): assert_equal(actual, expected) -@requires_np112 @requires_dask def test_interpolate_dask_raises_for_invalid_chunk_dim(): da, _ = make_interpolate_example_data((40, 40), 0.5) @@ -337,7 +322,6 @@ def test_interpolate_dask_raises_for_invalid_chunk_dim(): da.interpolate_na('time') -@requires_np112 @requires_bottleneck def test_ffill(): da = xr.DataArray(np.array([4, 5, np.nan], dtype=np.float64), dims='x') @@ -346,7 +330,6 @@ def test_ffill(): assert_equal(actual, expected) -@requires_np112 @requires_bottleneck @requires_dask def test_ffill_dask(): @@ -384,7 +367,6 @@ def test_bfill_dask(): @requires_bottleneck -@requires_np112 def test_ffill_bfill_nonans(): vals = np.array([1, 2, 3, 4, 5, 6], dtype=np.float64) @@ -398,7 +380,6 @@ def test_ffill_bfill_nonans(): @requires_bottleneck -@requires_np112 def test_ffill_bfill_allnans(): vals = np.full(6, np.nan, dtype=np.float64) @@ -412,14 +393,12 @@ def test_ffill_bfill_allnans(): @requires_bottleneck -@requires_np112 def test_ffill_functions(da): result = da.ffill('time') assert result.isnull().sum() == 0 @requires_bottleneck -@requires_np112 def test_ffill_limit(): da = xr.DataArray( [0, np.nan, np.nan, np.nan, np.nan, 3, 4, 5, np.nan, 6, 7], @@ -433,7 +412,6 @@ def test_ffill_limit(): [0, 0, np.nan, np.nan, np.nan, 3, 4, 5, 5, 6, 7], dims='time') -@requires_np112 def test_interpolate_dataset(ds): actual = ds.interpolate_na(dim='time') # no missing values in var1 @@ -444,12 +422,10 @@ def test_interpolate_dataset(ds): @requires_bottleneck -@requires_np112 def test_ffill_dataset(ds): ds.ffill(dim='time') @requires_bottleneck -@requires_np112 def test_bfill_dataset(ds): ds.ffill(dim='time') diff --git a/xarray/tests/test_plot.py b/xarray/tests/test_plot.py index 986a2a93380..90d30946c9c 100644 --- a/xarray/tests/test_plot.py +++ b/xarray/tests/test_plot.py @@ -17,7 +17,8 @@ from . import ( TestCase, assert_array_equal, assert_equal, raises_regex, - requires_matplotlib, requires_seaborn, requires_cftime) + requires_matplotlib, requires_matplotlib2, requires_seaborn, + requires_cftime) # import mpl and change the backend before other mpl imports try: @@ -283,6 +284,7 @@ def test_convenient_facetgrid(self): d[0].plot(x='x', y='y', col='z', ax=plt.gca()) @pytest.mark.slow + @requires_matplotlib2 def test_subplot_kws(self): a = easy_array((10, 15, 4)) d = DataArray(a, dims=['y', 'x', 'z']) @@ -295,12 +297,9 @@ def test_subplot_kws(self): cmap='cool', subplot_kws=dict(facecolor='r')) for ax in g.axes.flat: - try: - # mpl V2 - assert ax.get_facecolor()[0:3] == \ - mpl.colors.to_rgb('r') - except AttributeError: - assert ax.get_axis_bgcolor() == 'r' + # mpl V2 + assert ax.get_facecolor()[0:3] == \ + mpl.colors.to_rgb('r') @pytest.mark.slow def test_plot_size(self): @@ -462,7 +461,7 @@ def test_robust(self): cmap_params = _determine_cmap_params(self.data, robust=True) assert cmap_params['vmin'] == np.percentile(self.data, 2) assert cmap_params['vmax'] == np.percentile(self.data, 98) - assert cmap_params['cmap'].name == 'viridis' + assert cmap_params['cmap'] == 'viridis' assert cmap_params['extend'] == 'both' assert cmap_params['levels'] is None assert cmap_params['norm'] is None @@ -546,7 +545,7 @@ def test_divergentcontrol(self): cmap_params = _determine_cmap_params(pos) assert cmap_params['vmin'] == 0 assert cmap_params['vmax'] == 1 - assert cmap_params['cmap'].name == "viridis" + assert cmap_params['cmap'] == "viridis" # Default with negative data will be a divergent cmap cmap_params = _determine_cmap_params(neg) @@ -558,17 +557,17 @@ def test_divergentcontrol(self): cmap_params = _determine_cmap_params(neg, vmin=-0.1, center=False) assert cmap_params['vmin'] == -0.1 assert cmap_params['vmax'] == 0.9 - assert cmap_params['cmap'].name == "viridis" + assert cmap_params['cmap'] == "viridis" cmap_params = _determine_cmap_params(neg, vmax=0.5, center=False) assert cmap_params['vmin'] == -0.1 assert cmap_params['vmax'] == 0.5 - assert cmap_params['cmap'].name == "viridis" + assert cmap_params['cmap'] == "viridis" # Setting center=False too cmap_params = _determine_cmap_params(neg, center=False) assert cmap_params['vmin'] == -0.1 assert cmap_params['vmax'] == 0.9 - assert cmap_params['cmap'].name == "viridis" + assert cmap_params['cmap'] == "viridis" # However, I should still be able to set center and have a div cmap cmap_params = _determine_cmap_params(neg, center=0) @@ -598,17 +597,17 @@ def test_divergentcontrol(self): cmap_params = _determine_cmap_params(pos, vmin=0.1) assert cmap_params['vmin'] == 0.1 assert cmap_params['vmax'] == 1 - assert cmap_params['cmap'].name == "viridis" + assert cmap_params['cmap'] == "viridis" cmap_params = _determine_cmap_params(pos, vmax=0.5) assert cmap_params['vmin'] == 0 assert cmap_params['vmax'] == 0.5 - assert cmap_params['cmap'].name == "viridis" + assert cmap_params['cmap'] == "viridis" # If both vmin and vmax are provided, output is non-divergent cmap_params = _determine_cmap_params(neg, vmin=-0.2, vmax=0.6) assert cmap_params['vmin'] == -0.2 assert cmap_params['vmax'] == 0.6 - assert cmap_params['cmap'].name == "viridis" + assert cmap_params['cmap'] == "viridis" @requires_matplotlib diff --git a/xarray/tests/test_ufuncs.py b/xarray/tests/test_ufuncs.py index 91ec1142950..195bb36e36e 100644 --- a/xarray/tests/test_ufuncs.py +++ b/xarray/tests/test_ufuncs.py @@ -1,6 +1,5 @@ from __future__ import absolute_import, division, print_function -from distutils.version import LooseVersion import pickle import numpy as np @@ -11,12 +10,7 @@ from . import ( assert_array_equal, assert_identical as assert_identical_, mock, - raises_regex, -) - - -requires_numpy113 = pytest.mark.skipif(LooseVersion(np.__version__) < '1.13', - reason='numpy 1.13 or newer required') + raises_regex, requires_np113) def assert_identical(a, b): @@ -27,7 +21,7 @@ def assert_identical(a, b): assert_array_equal(a, b) -@requires_numpy113 +@requires_np113 def test_unary(): args = [0, np.zeros(2), @@ -38,7 +32,7 @@ def test_unary(): assert_identical(a + 1, np.cos(a)) -@requires_numpy113 +@requires_np113 def test_binary(): args = [0, np.zeros(2), @@ -53,7 +47,7 @@ def test_binary(): assert_identical(t2 + 1, np.maximum(t2 + 1, t1)) -@requires_numpy113 +@requires_np113 def test_binary_out(): args = [1, np.ones(2), @@ -66,7 +60,7 @@ def test_binary_out(): assert_identical(actual_exponent, arg) -@requires_numpy113 +@requires_np113 def test_groupby(): ds = xr.Dataset({'a': ('x', [0, 0, 0])}, {'c': ('x', [0, 0, 1])}) ds_grouped = ds.groupby('c') @@ -89,7 +83,7 @@ def test_groupby(): np.maximum(ds.a.variable, ds_grouped) -@requires_numpy113 +@requires_np113 def test_alignment(): ds1 = xr.Dataset({'a': ('x', [1, 2])}, {'x': [0, 1]}) ds2 = xr.Dataset({'a': ('x', [2, 3]), 'b': 4}, {'x': [1, 2]}) @@ -105,14 +99,14 @@ def test_alignment(): assert_identical_(actual, expected) -@requires_numpy113 +@requires_np113 def test_kwargs(): x = xr.DataArray(0) result = np.add(x, 1, dtype=np.float64) assert result.dtype == np.float64 -@requires_numpy113 +@requires_np113 def test_xarray_defers_to_unrecognized_type(): class Other(object): @@ -125,7 +119,7 @@ def __array_ufunc__(self, *args, **kwargs): assert np.sin(xarray_obj, out=other) == 'other' -@requires_numpy113 +@requires_np113 def test_xarray_handles_dask(): da = pytest.importorskip('dask.array') x = xr.DataArray(np.ones((2, 2)), dims=['x', 'y']) @@ -135,7 +129,7 @@ def test_xarray_handles_dask(): assert isinstance(result, xr.DataArray) -@requires_numpy113 +@requires_np113 def test_dask_defers_to_xarray(): da = pytest.importorskip('dask.array') x = xr.DataArray(np.ones((2, 2)), dims=['x', 'y']) @@ -145,14 +139,14 @@ def test_dask_defers_to_xarray(): assert isinstance(result, xr.DataArray) -@requires_numpy113 +@requires_np113 def test_gufunc_methods(): xarray_obj = xr.DataArray([1, 2, 3]) with raises_regex(NotImplementedError, 'reduce method'): np.add.reduce(xarray_obj, 1) -@requires_numpy113 +@requires_np113 def test_out(): xarray_obj = xr.DataArray([1, 2, 3]) @@ -166,7 +160,7 @@ def test_out(): assert_identical(other, np.array([1, 2, 3])) -@requires_numpy113 +@requires_np113 def test_gufuncs(): xarray_obj = xr.DataArray([1, 2, 3]) fake_gufunc = mock.Mock(signature='(n)->()', autospec=np.sin) diff --git a/xarray/tests/test_variable.py b/xarray/tests/test_variable.py index c486a394ae6..290c7a6e308 100644 --- a/xarray/tests/test_variable.py +++ b/xarray/tests/test_variable.py @@ -1496,12 +1496,7 @@ def test_reduce_funcs(self): assert_identical(v.cumprod(axis=0), Variable('x', np.array([1, 1, 2, 6]))) assert_identical(v.var(), Variable([], 2.0 / 3)) - - if LooseVersion(np.__version__) < '1.9': - with pytest.raises(NotImplementedError): - v.median() - else: - assert_identical(v.median(), Variable([], 2)) + assert_identical(v.median(), Variable([], 2)) v = Variable('x', [True, False, False]) assert_identical(v.any(), Variable([], True)) @@ -1665,15 +1660,9 @@ def test_eq_all_dtypes(self): super(TestVariableWithDask, self).test_eq_all_dtypes() def test_getitem_fancy(self): - import dask - if LooseVersion(dask.__version__) <= LooseVersion('0.15.1'): - pytest.xfail("vindex from latest dask is required") super(TestVariableWithDask, self).test_getitem_fancy() def test_getitem_1d_fancy(self): - import dask - if LooseVersion(dask.__version__) <= LooseVersion('0.15.1'): - pytest.xfail("vindex from latest dask is required") super(TestVariableWithDask, self).test_getitem_1d_fancy() def test_getitem_with_mask_nd_indexer(self):