1247 lines
46 KiB
Python
1247 lines
46 KiB
Python
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import warnings
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import pytest
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import inspect
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import numpy as np
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from numpy.core.numeric import normalize_axis_tuple
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from numpy.lib.nanfunctions import _nan_mask, _replace_nan
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from numpy.testing import (
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assert_, assert_equal, assert_almost_equal, assert_raises,
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assert_array_equal, suppress_warnings
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)
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# Test data
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_ndat = np.array([[0.6244, np.nan, 0.2692, 0.0116, np.nan, 0.1170],
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[0.5351, -0.9403, np.nan, 0.2100, 0.4759, 0.2833],
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[np.nan, np.nan, np.nan, 0.1042, np.nan, -0.5954],
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[0.1610, np.nan, np.nan, 0.1859, 0.3146, np.nan]])
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# Rows of _ndat with nans removed
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_rdat = [np.array([0.6244, 0.2692, 0.0116, 0.1170]),
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np.array([0.5351, -0.9403, 0.2100, 0.4759, 0.2833]),
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np.array([0.1042, -0.5954]),
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np.array([0.1610, 0.1859, 0.3146])]
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# Rows of _ndat with nans converted to ones
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_ndat_ones = np.array([[0.6244, 1.0, 0.2692, 0.0116, 1.0, 0.1170],
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[0.5351, -0.9403, 1.0, 0.2100, 0.4759, 0.2833],
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[1.0, 1.0, 1.0, 0.1042, 1.0, -0.5954],
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[0.1610, 1.0, 1.0, 0.1859, 0.3146, 1.0]])
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# Rows of _ndat with nans converted to zeros
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_ndat_zeros = np.array([[0.6244, 0.0, 0.2692, 0.0116, 0.0, 0.1170],
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[0.5351, -0.9403, 0.0, 0.2100, 0.4759, 0.2833],
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[0.0, 0.0, 0.0, 0.1042, 0.0, -0.5954],
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[0.1610, 0.0, 0.0, 0.1859, 0.3146, 0.0]])
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class TestSignatureMatch:
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NANFUNCS = {
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np.nanmin: np.amin,
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np.nanmax: np.amax,
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np.nanargmin: np.argmin,
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np.nanargmax: np.argmax,
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np.nansum: np.sum,
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np.nanprod: np.prod,
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np.nancumsum: np.cumsum,
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np.nancumprod: np.cumprod,
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np.nanmean: np.mean,
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np.nanmedian: np.median,
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np.nanpercentile: np.percentile,
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np.nanquantile: np.quantile,
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np.nanvar: np.var,
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np.nanstd: np.std,
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}
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IDS = [k.__name__ for k in NANFUNCS]
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@staticmethod
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def get_signature(func, default="..."):
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"""Construct a signature and replace all default parameter-values."""
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prm_list = []
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signature = inspect.signature(func)
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for prm in signature.parameters.values():
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if prm.default is inspect.Parameter.empty:
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prm_list.append(prm)
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else:
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prm_list.append(prm.replace(default=default))
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return inspect.Signature(prm_list)
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@pytest.mark.parametrize("nan_func,func", NANFUNCS.items(), ids=IDS)
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def test_signature_match(self, nan_func, func):
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# Ignore the default parameter-values as they can sometimes differ
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# between the two functions (*e.g.* one has `False` while the other
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# has `np._NoValue`)
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signature = self.get_signature(func)
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nan_signature = self.get_signature(nan_func)
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np.testing.assert_equal(signature, nan_signature)
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def test_exhaustiveness(self):
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"""Validate that all nan functions are actually tested."""
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np.testing.assert_equal(
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set(self.IDS), set(np.lib.nanfunctions.__all__)
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)
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class TestNanFunctions_MinMax:
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nanfuncs = [np.nanmin, np.nanmax]
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stdfuncs = [np.min, np.max]
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def test_mutation(self):
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# Check that passed array is not modified.
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ndat = _ndat.copy()
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for f in self.nanfuncs:
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f(ndat)
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assert_equal(ndat, _ndat)
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def test_keepdims(self):
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mat = np.eye(3)
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for nf, rf in zip(self.nanfuncs, self.stdfuncs):
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for axis in [None, 0, 1]:
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tgt = rf(mat, axis=axis, keepdims=True)
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res = nf(mat, axis=axis, keepdims=True)
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assert_(res.ndim == tgt.ndim)
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def test_out(self):
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mat = np.eye(3)
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for nf, rf in zip(self.nanfuncs, self.stdfuncs):
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resout = np.zeros(3)
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tgt = rf(mat, axis=1)
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res = nf(mat, axis=1, out=resout)
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assert_almost_equal(res, resout)
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assert_almost_equal(res, tgt)
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def test_dtype_from_input(self):
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codes = 'efdgFDG'
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for nf, rf in zip(self.nanfuncs, self.stdfuncs):
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for c in codes:
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mat = np.eye(3, dtype=c)
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tgt = rf(mat, axis=1).dtype.type
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res = nf(mat, axis=1).dtype.type
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assert_(res is tgt)
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# scalar case
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tgt = rf(mat, axis=None).dtype.type
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res = nf(mat, axis=None).dtype.type
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assert_(res is tgt)
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def test_result_values(self):
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for nf, rf in zip(self.nanfuncs, self.stdfuncs):
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tgt = [rf(d) for d in _rdat]
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res = nf(_ndat, axis=1)
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assert_almost_equal(res, tgt)
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@pytest.mark.parametrize("axis", [None, 0, 1])
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@pytest.mark.parametrize("dtype", np.typecodes["AllFloat"])
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@pytest.mark.parametrize("array", [
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np.array(np.nan),
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np.full((3, 3), np.nan),
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], ids=["0d", "2d"])
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def test_allnans(self, axis, dtype, array):
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if axis is not None and array.ndim == 0:
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pytest.skip(f"`axis != None` not supported for 0d arrays")
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array = array.astype(dtype)
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match = "All-NaN slice encountered"
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for func in self.nanfuncs:
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with pytest.warns(RuntimeWarning, match=match):
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out = func(array, axis=axis)
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assert np.isnan(out).all()
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assert out.dtype == array.dtype
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def test_masked(self):
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mat = np.ma.fix_invalid(_ndat)
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msk = mat._mask.copy()
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for f in [np.nanmin]:
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res = f(mat, axis=1)
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tgt = f(_ndat, axis=1)
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assert_equal(res, tgt)
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assert_equal(mat._mask, msk)
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assert_(not np.isinf(mat).any())
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def test_scalar(self):
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for f in self.nanfuncs:
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assert_(f(0.) == 0.)
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def test_subclass(self):
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class MyNDArray(np.ndarray):
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pass
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# Check that it works and that type and
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# shape are preserved
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mine = np.eye(3).view(MyNDArray)
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for f in self.nanfuncs:
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res = f(mine, axis=0)
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assert_(isinstance(res, MyNDArray))
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assert_(res.shape == (3,))
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res = f(mine, axis=1)
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assert_(isinstance(res, MyNDArray))
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assert_(res.shape == (3,))
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res = f(mine)
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assert_(res.shape == ())
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# check that rows of nan are dealt with for subclasses (#4628)
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mine[1] = np.nan
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for f in self.nanfuncs:
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with warnings.catch_warnings(record=True) as w:
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warnings.simplefilter('always')
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res = f(mine, axis=0)
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assert_(isinstance(res, MyNDArray))
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assert_(not np.any(np.isnan(res)))
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assert_(len(w) == 0)
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with warnings.catch_warnings(record=True) as w:
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warnings.simplefilter('always')
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res = f(mine, axis=1)
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assert_(isinstance(res, MyNDArray))
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assert_(np.isnan(res[1]) and not np.isnan(res[0])
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and not np.isnan(res[2]))
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assert_(len(w) == 1, 'no warning raised')
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assert_(issubclass(w[0].category, RuntimeWarning))
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with warnings.catch_warnings(record=True) as w:
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warnings.simplefilter('always')
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res = f(mine)
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assert_(res.shape == ())
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assert_(res != np.nan)
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assert_(len(w) == 0)
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def test_object_array(self):
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arr = np.array([[1.0, 2.0], [np.nan, 4.0], [np.nan, np.nan]], dtype=object)
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assert_equal(np.nanmin(arr), 1.0)
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assert_equal(np.nanmin(arr, axis=0), [1.0, 2.0])
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with warnings.catch_warnings(record=True) as w:
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warnings.simplefilter('always')
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# assert_equal does not work on object arrays of nan
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assert_equal(list(np.nanmin(arr, axis=1)), [1.0, 4.0, np.nan])
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assert_(len(w) == 1, 'no warning raised')
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assert_(issubclass(w[0].category, RuntimeWarning))
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@pytest.mark.parametrize("dtype", np.typecodes["AllFloat"])
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def test_initial(self, dtype):
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class MyNDArray(np.ndarray):
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pass
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ar = np.arange(9).astype(dtype)
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ar[:5] = np.nan
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for f in self.nanfuncs:
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initial = 100 if f is np.nanmax else 0
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ret1 = f(ar, initial=initial)
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assert ret1.dtype == dtype
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assert ret1 == initial
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ret2 = f(ar.view(MyNDArray), initial=initial)
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assert ret2.dtype == dtype
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assert ret2 == initial
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@pytest.mark.parametrize("dtype", np.typecodes["AllFloat"])
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def test_where(self, dtype):
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class MyNDArray(np.ndarray):
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pass
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ar = np.arange(9).reshape(3, 3).astype(dtype)
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ar[0, :] = np.nan
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where = np.ones_like(ar, dtype=np.bool_)
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where[:, 0] = False
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for f in self.nanfuncs:
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reference = 4 if f is np.nanmin else 8
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ret1 = f(ar, where=where, initial=5)
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assert ret1.dtype == dtype
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assert ret1 == reference
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ret2 = f(ar.view(MyNDArray), where=where, initial=5)
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assert ret2.dtype == dtype
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assert ret2 == reference
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class TestNanFunctions_ArgminArgmax:
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nanfuncs = [np.nanargmin, np.nanargmax]
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def test_mutation(self):
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# Check that passed array is not modified.
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ndat = _ndat.copy()
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for f in self.nanfuncs:
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f(ndat)
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assert_equal(ndat, _ndat)
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def test_result_values(self):
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for f, fcmp in zip(self.nanfuncs, [np.greater, np.less]):
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for row in _ndat:
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with suppress_warnings() as sup:
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sup.filter(RuntimeWarning, "invalid value encountered in")
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ind = f(row)
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val = row[ind]
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# comparing with NaN is tricky as the result
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# is always false except for NaN != NaN
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assert_(not np.isnan(val))
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assert_(not fcmp(val, row).any())
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assert_(not np.equal(val, row[:ind]).any())
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@pytest.mark.parametrize("axis", [None, 0, 1])
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@pytest.mark.parametrize("dtype", np.typecodes["AllFloat"])
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@pytest.mark.parametrize("array", [
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np.array(np.nan),
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np.full((3, 3), np.nan),
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], ids=["0d", "2d"])
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def test_allnans(self, axis, dtype, array):
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if axis is not None and array.ndim == 0:
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pytest.skip(f"`axis != None` not supported for 0d arrays")
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array = array.astype(dtype)
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for func in self.nanfuncs:
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with pytest.raises(ValueError, match="All-NaN slice encountered"):
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func(array, axis=axis)
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def test_empty(self):
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mat = np.zeros((0, 3))
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for f in self.nanfuncs:
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for axis in [0, None]:
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assert_raises(ValueError, f, mat, axis=axis)
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for axis in [1]:
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res = f(mat, axis=axis)
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assert_equal(res, np.zeros(0))
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def test_scalar(self):
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for f in self.nanfuncs:
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assert_(f(0.) == 0.)
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def test_subclass(self):
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class MyNDArray(np.ndarray):
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pass
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# Check that it works and that type and
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# shape are preserved
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mine = np.eye(3).view(MyNDArray)
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for f in self.nanfuncs:
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res = f(mine, axis=0)
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assert_(isinstance(res, MyNDArray))
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assert_(res.shape == (3,))
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res = f(mine, axis=1)
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assert_(isinstance(res, MyNDArray))
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assert_(res.shape == (3,))
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res = f(mine)
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assert_(res.shape == ())
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@pytest.mark.parametrize("dtype", np.typecodes["AllFloat"])
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def test_keepdims(self, dtype):
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ar = np.arange(9).astype(dtype)
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ar[:5] = np.nan
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for f in self.nanfuncs:
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reference = 5 if f is np.nanargmin else 8
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ret = f(ar, keepdims=True)
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assert ret.ndim == ar.ndim
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assert ret == reference
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|
@pytest.mark.parametrize("dtype", np.typecodes["AllFloat"])
|
||
|
|
def test_out(self, dtype):
|
||
|
|
ar = np.arange(9).astype(dtype)
|
||
|
|
ar[:5] = np.nan
|
||
|
|
|
||
|
|
for f in self.nanfuncs:
|
||
|
|
out = np.zeros((), dtype=np.intp)
|
||
|
|
reference = 5 if f is np.nanargmin else 8
|
||
|
|
ret = f(ar, out=out)
|
||
|
|
assert ret is out
|
||
|
|
assert ret == reference
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
_TEST_ARRAYS = {
|
||
|
|
"0d": np.array(5),
|
||
|
|
"1d": np.array([127, 39, 93, 87, 46])
|
||
|
|
}
|
||
|
|
for _v in _TEST_ARRAYS.values():
|
||
|
|
_v.setflags(write=False)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"dtype",
|
||
|
|
np.typecodes["AllInteger"] + np.typecodes["AllFloat"] + "O",
|
||
|
|
)
|
||
|
|
@pytest.mark.parametrize("mat", _TEST_ARRAYS.values(), ids=_TEST_ARRAYS.keys())
|
||
|
|
class TestNanFunctions_NumberTypes:
|
||
|
|
nanfuncs = {
|
||
|
|
np.nanmin: np.min,
|
||
|
|
np.nanmax: np.max,
|
||
|
|
np.nanargmin: np.argmin,
|
||
|
|
np.nanargmax: np.argmax,
|
||
|
|
np.nansum: np.sum,
|
||
|
|
np.nanprod: np.prod,
|
||
|
|
np.nancumsum: np.cumsum,
|
||
|
|
np.nancumprod: np.cumprod,
|
||
|
|
np.nanmean: np.mean,
|
||
|
|
np.nanmedian: np.median,
|
||
|
|
np.nanvar: np.var,
|
||
|
|
np.nanstd: np.std,
|
||
|
|
}
|
||
|
|
nanfunc_ids = [i.__name__ for i in nanfuncs]
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("nanfunc,func", nanfuncs.items(), ids=nanfunc_ids)
|
||
|
|
@np.errstate(over="ignore")
|
||
|
|
def test_nanfunc(self, mat, dtype, nanfunc, func):
|
||
|
|
mat = mat.astype(dtype)
|
||
|
|
tgt = func(mat)
|
||
|
|
out = nanfunc(mat)
|
||
|
|
|
||
|
|
assert_almost_equal(out, tgt)
|
||
|
|
if dtype == "O":
|
||
|
|
assert type(out) is type(tgt)
|
||
|
|
else:
|
||
|
|
assert out.dtype == tgt.dtype
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"nanfunc,func",
|
||
|
|
[(np.nanquantile, np.quantile), (np.nanpercentile, np.percentile)],
|
||
|
|
ids=["nanquantile", "nanpercentile"],
|
||
|
|
)
|
||
|
|
def test_nanfunc_q(self, mat, dtype, nanfunc, func):
|
||
|
|
mat = mat.astype(dtype)
|
||
|
|
tgt = func(mat, q=1)
|
||
|
|
out = nanfunc(mat, q=1)
|
||
|
|
|
||
|
|
assert_almost_equal(out, tgt)
|
||
|
|
if dtype == "O":
|
||
|
|
assert type(out) is type(tgt)
|
||
|
|
else:
|
||
|
|
assert out.dtype == tgt.dtype
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"nanfunc,func",
|
||
|
|
[(np.nanvar, np.var), (np.nanstd, np.std)],
|
||
|
|
ids=["nanvar", "nanstd"],
|
||
|
|
)
|
||
|
|
def test_nanfunc_ddof(self, mat, dtype, nanfunc, func):
|
||
|
|
mat = mat.astype(dtype)
|
||
|
|
tgt = func(mat, ddof=0.5)
|
||
|
|
out = nanfunc(mat, ddof=0.5)
|
||
|
|
|
||
|
|
assert_almost_equal(out, tgt)
|
||
|
|
if dtype == "O":
|
||
|
|
assert type(out) is type(tgt)
|
||
|
|
else:
|
||
|
|
assert out.dtype == tgt.dtype
|
||
|
|
|
||
|
|
|
||
|
|
class SharedNanFunctionsTestsMixin:
|
||
|
|
def test_mutation(self):
|
||
|
|
# Check that passed array is not modified.
|
||
|
|
ndat = _ndat.copy()
|
||
|
|
for f in self.nanfuncs:
|
||
|
|
f(ndat)
|
||
|
|
assert_equal(ndat, _ndat)
|
||
|
|
|
||
|
|
def test_keepdims(self):
|
||
|
|
mat = np.eye(3)
|
||
|
|
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
|
||
|
|
for axis in [None, 0, 1]:
|
||
|
|
tgt = rf(mat, axis=axis, keepdims=True)
|
||
|
|
res = nf(mat, axis=axis, keepdims=True)
|
||
|
|
assert_(res.ndim == tgt.ndim)
|
||
|
|
|
||
|
|
def test_out(self):
|
||
|
|
mat = np.eye(3)
|
||
|
|
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
|
||
|
|
resout = np.zeros(3)
|
||
|
|
tgt = rf(mat, axis=1)
|
||
|
|
res = nf(mat, axis=1, out=resout)
|
||
|
|
assert_almost_equal(res, resout)
|
||
|
|
assert_almost_equal(res, tgt)
|
||
|
|
|
||
|
|
def test_dtype_from_dtype(self):
|
||
|
|
mat = np.eye(3)
|
||
|
|
codes = 'efdgFDG'
|
||
|
|
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
|
||
|
|
for c in codes:
|
||
|
|
with suppress_warnings() as sup:
|
||
|
|
if nf in {np.nanstd, np.nanvar} and c in 'FDG':
|
||
|
|
# Giving the warning is a small bug, see gh-8000
|
||
|
|
sup.filter(np.ComplexWarning)
|
||
|
|
tgt = rf(mat, dtype=np.dtype(c), axis=1).dtype.type
|
||
|
|
res = nf(mat, dtype=np.dtype(c), axis=1).dtype.type
|
||
|
|
assert_(res is tgt)
|
||
|
|
# scalar case
|
||
|
|
tgt = rf(mat, dtype=np.dtype(c), axis=None).dtype.type
|
||
|
|
res = nf(mat, dtype=np.dtype(c), axis=None).dtype.type
|
||
|
|
assert_(res is tgt)
|
||
|
|
|
||
|
|
def test_dtype_from_char(self):
|
||
|
|
mat = np.eye(3)
|
||
|
|
codes = 'efdgFDG'
|
||
|
|
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
|
||
|
|
for c in codes:
|
||
|
|
with suppress_warnings() as sup:
|
||
|
|
if nf in {np.nanstd, np.nanvar} and c in 'FDG':
|
||
|
|
# Giving the warning is a small bug, see gh-8000
|
||
|
|
sup.filter(np.ComplexWarning)
|
||
|
|
tgt = rf(mat, dtype=c, axis=1).dtype.type
|
||
|
|
res = nf(mat, dtype=c, axis=1).dtype.type
|
||
|
|
assert_(res is tgt)
|
||
|
|
# scalar case
|
||
|
|
tgt = rf(mat, dtype=c, axis=None).dtype.type
|
||
|
|
res = nf(mat, dtype=c, axis=None).dtype.type
|
||
|
|
assert_(res is tgt)
|
||
|
|
|
||
|
|
def test_dtype_from_input(self):
|
||
|
|
codes = 'efdgFDG'
|
||
|
|
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
|
||
|
|
for c in codes:
|
||
|
|
mat = np.eye(3, dtype=c)
|
||
|
|
tgt = rf(mat, axis=1).dtype.type
|
||
|
|
res = nf(mat, axis=1).dtype.type
|
||
|
|
assert_(res is tgt, "res %s, tgt %s" % (res, tgt))
|
||
|
|
# scalar case
|
||
|
|
tgt = rf(mat, axis=None).dtype.type
|
||
|
|
res = nf(mat, axis=None).dtype.type
|
||
|
|
assert_(res is tgt)
|
||
|
|
|
||
|
|
def test_result_values(self):
|
||
|
|
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
|
||
|
|
tgt = [rf(d) for d in _rdat]
|
||
|
|
res = nf(_ndat, axis=1)
|
||
|
|
assert_almost_equal(res, tgt)
|
||
|
|
|
||
|
|
def test_scalar(self):
|
||
|
|
for f in self.nanfuncs:
|
||
|
|
assert_(f(0.) == 0.)
|
||
|
|
|
||
|
|
def test_subclass(self):
|
||
|
|
class MyNDArray(np.ndarray):
|
||
|
|
pass
|
||
|
|
|
||
|
|
# Check that it works and that type and
|
||
|
|
# shape are preserved
|
||
|
|
array = np.eye(3)
|
||
|
|
mine = array.view(MyNDArray)
|
||
|
|
for f in self.nanfuncs:
|
||
|
|
expected_shape = f(array, axis=0).shape
|
||
|
|
res = f(mine, axis=0)
|
||
|
|
assert_(isinstance(res, MyNDArray))
|
||
|
|
assert_(res.shape == expected_shape)
|
||
|
|
expected_shape = f(array, axis=1).shape
|
||
|
|
res = f(mine, axis=1)
|
||
|
|
assert_(isinstance(res, MyNDArray))
|
||
|
|
assert_(res.shape == expected_shape)
|
||
|
|
expected_shape = f(array).shape
|
||
|
|
res = f(mine)
|
||
|
|
assert_(isinstance(res, MyNDArray))
|
||
|
|
assert_(res.shape == expected_shape)
|
||
|
|
|
||
|
|
|
||
|
|
class TestNanFunctions_SumProd(SharedNanFunctionsTestsMixin):
|
||
|
|
|
||
|
|
nanfuncs = [np.nansum, np.nanprod]
|
||
|
|
stdfuncs = [np.sum, np.prod]
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("axis", [None, 0, 1])
|
||
|
|
@pytest.mark.parametrize("dtype", np.typecodes["AllFloat"])
|
||
|
|
@pytest.mark.parametrize("array", [
|
||
|
|
np.array(np.nan),
|
||
|
|
np.full((3, 3), np.nan),
|
||
|
|
], ids=["0d", "2d"])
|
||
|
|
def test_allnans(self, axis, dtype, array):
|
||
|
|
if axis is not None and array.ndim == 0:
|
||
|
|
pytest.skip(f"`axis != None` not supported for 0d arrays")
|
||
|
|
|
||
|
|
array = array.astype(dtype)
|
||
|
|
for func, identity in zip(self.nanfuncs, [0, 1]):
|
||
|
|
out = func(array, axis=axis)
|
||
|
|
assert np.all(out == identity)
|
||
|
|
assert out.dtype == array.dtype
|
||
|
|
|
||
|
|
def test_empty(self):
|
||
|
|
for f, tgt_value in zip([np.nansum, np.nanprod], [0, 1]):
|
||
|
|
mat = np.zeros((0, 3))
|
||
|
|
tgt = [tgt_value]*3
|
||
|
|
res = f(mat, axis=0)
|
||
|
|
assert_equal(res, tgt)
|
||
|
|
tgt = []
|
||
|
|
res = f(mat, axis=1)
|
||
|
|
assert_equal(res, tgt)
|
||
|
|
tgt = tgt_value
|
||
|
|
res = f(mat, axis=None)
|
||
|
|
assert_equal(res, tgt)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("dtype", np.typecodes["AllFloat"])
|
||
|
|
def test_initial(self, dtype):
|
||
|
|
ar = np.arange(9).astype(dtype)
|
||
|
|
ar[:5] = np.nan
|
||
|
|
|
||
|
|
for f in self.nanfuncs:
|
||
|
|
reference = 28 if f is np.nansum else 3360
|
||
|
|
ret = f(ar, initial=2)
|
||
|
|
assert ret.dtype == dtype
|
||
|
|
assert ret == reference
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("dtype", np.typecodes["AllFloat"])
|
||
|
|
def test_where(self, dtype):
|
||
|
|
ar = np.arange(9).reshape(3, 3).astype(dtype)
|
||
|
|
ar[0, :] = np.nan
|
||
|
|
where = np.ones_like(ar, dtype=np.bool_)
|
||
|
|
where[:, 0] = False
|
||
|
|
|
||
|
|
for f in self.nanfuncs:
|
||
|
|
reference = 26 if f is np.nansum else 2240
|
||
|
|
ret = f(ar, where=where, initial=2)
|
||
|
|
assert ret.dtype == dtype
|
||
|
|
assert ret == reference
|
||
|
|
|
||
|
|
|
||
|
|
class TestNanFunctions_CumSumProd(SharedNanFunctionsTestsMixin):
|
||
|
|
|
||
|
|
nanfuncs = [np.nancumsum, np.nancumprod]
|
||
|
|
stdfuncs = [np.cumsum, np.cumprod]
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("axis", [None, 0, 1])
|
||
|
|
@pytest.mark.parametrize("dtype", np.typecodes["AllFloat"])
|
||
|
|
@pytest.mark.parametrize("array", [
|
||
|
|
np.array(np.nan),
|
||
|
|
np.full((3, 3), np.nan)
|
||
|
|
], ids=["0d", "2d"])
|
||
|
|
def test_allnans(self, axis, dtype, array):
|
||
|
|
if axis is not None and array.ndim == 0:
|
||
|
|
pytest.skip(f"`axis != None` not supported for 0d arrays")
|
||
|
|
|
||
|
|
array = array.astype(dtype)
|
||
|
|
for func, identity in zip(self.nanfuncs, [0, 1]):
|
||
|
|
out = func(array)
|
||
|
|
assert np.all(out == identity)
|
||
|
|
assert out.dtype == array.dtype
|
||
|
|
|
||
|
|
def test_empty(self):
|
||
|
|
for f, tgt_value in zip(self.nanfuncs, [0, 1]):
|
||
|
|
mat = np.zeros((0, 3))
|
||
|
|
tgt = tgt_value*np.ones((0, 3))
|
||
|
|
res = f(mat, axis=0)
|
||
|
|
assert_equal(res, tgt)
|
||
|
|
tgt = mat
|
||
|
|
res = f(mat, axis=1)
|
||
|
|
assert_equal(res, tgt)
|
||
|
|
tgt = np.zeros((0))
|
||
|
|
res = f(mat, axis=None)
|
||
|
|
assert_equal(res, tgt)
|
||
|
|
|
||
|
|
def test_keepdims(self):
|
||
|
|
for f, g in zip(self.nanfuncs, self.stdfuncs):
|
||
|
|
mat = np.eye(3)
|
||
|
|
for axis in [None, 0, 1]:
|
||
|
|
tgt = f(mat, axis=axis, out=None)
|
||
|
|
res = g(mat, axis=axis, out=None)
|
||
|
|
assert_(res.ndim == tgt.ndim)
|
||
|
|
|
||
|
|
for f in self.nanfuncs:
|
||
|
|
d = np.ones((3, 5, 7, 11))
|
||
|
|
# Randomly set some elements to NaN:
|
||
|
|
rs = np.random.RandomState(0)
|
||
|
|
d[rs.rand(*d.shape) < 0.5] = np.nan
|
||
|
|
res = f(d, axis=None)
|
||
|
|
assert_equal(res.shape, (1155,))
|
||
|
|
for axis in np.arange(4):
|
||
|
|
res = f(d, axis=axis)
|
||
|
|
assert_equal(res.shape, (3, 5, 7, 11))
|
||
|
|
|
||
|
|
def test_result_values(self):
|
||
|
|
for axis in (-2, -1, 0, 1, None):
|
||
|
|
tgt = np.cumprod(_ndat_ones, axis=axis)
|
||
|
|
res = np.nancumprod(_ndat, axis=axis)
|
||
|
|
assert_almost_equal(res, tgt)
|
||
|
|
tgt = np.cumsum(_ndat_zeros,axis=axis)
|
||
|
|
res = np.nancumsum(_ndat, axis=axis)
|
||
|
|
assert_almost_equal(res, tgt)
|
||
|
|
|
||
|
|
def test_out(self):
|
||
|
|
mat = np.eye(3)
|
||
|
|
for nf, rf in zip(self.nanfuncs, self.stdfuncs):
|
||
|
|
resout = np.eye(3)
|
||
|
|
for axis in (-2, -1, 0, 1):
|
||
|
|
tgt = rf(mat, axis=axis)
|
||
|
|
res = nf(mat, axis=axis, out=resout)
|
||
|
|
assert_almost_equal(res, resout)
|
||
|
|
assert_almost_equal(res, tgt)
|
||
|
|
|
||
|
|
|
||
|
|
class TestNanFunctions_MeanVarStd(SharedNanFunctionsTestsMixin):
|
||
|
|
|
||
|
|
nanfuncs = [np.nanmean, np.nanvar, np.nanstd]
|
||
|
|
stdfuncs = [np.mean, np.var, np.std]
|
||
|
|
|
||
|
|
def test_dtype_error(self):
|
||
|
|
for f in self.nanfuncs:
|
||
|
|
for dtype in [np.bool_, np.int_, np.object_]:
|
||
|
|
assert_raises(TypeError, f, _ndat, axis=1, dtype=dtype)
|
||
|
|
|
||
|
|
def test_out_dtype_error(self):
|
||
|
|
for f in self.nanfuncs:
|
||
|
|
for dtype in [np.bool_, np.int_, np.object_]:
|
||
|
|
out = np.empty(_ndat.shape[0], dtype=dtype)
|
||
|
|
assert_raises(TypeError, f, _ndat, axis=1, out=out)
|
||
|
|
|
||
|
|
def test_ddof(self):
|
||
|
|
nanfuncs = [np.nanvar, np.nanstd]
|
||
|
|
stdfuncs = [np.var, np.std]
|
||
|
|
for nf, rf in zip(nanfuncs, stdfuncs):
|
||
|
|
for ddof in [0, 1]:
|
||
|
|
tgt = [rf(d, ddof=ddof) for d in _rdat]
|
||
|
|
res = nf(_ndat, axis=1, ddof=ddof)
|
||
|
|
assert_almost_equal(res, tgt)
|
||
|
|
|
||
|
|
def test_ddof_too_big(self):
|
||
|
|
nanfuncs = [np.nanvar, np.nanstd]
|
||
|
|
stdfuncs = [np.var, np.std]
|
||
|
|
dsize = [len(d) for d in _rdat]
|
||
|
|
for nf, rf in zip(nanfuncs, stdfuncs):
|
||
|
|
for ddof in range(5):
|
||
|
|
with suppress_warnings() as sup:
|
||
|
|
sup.record(RuntimeWarning)
|
||
|
|
sup.filter(np.ComplexWarning)
|
||
|
|
tgt = [ddof >= d for d in dsize]
|
||
|
|
res = nf(_ndat, axis=1, ddof=ddof)
|
||
|
|
assert_equal(np.isnan(res), tgt)
|
||
|
|
if any(tgt):
|
||
|
|
assert_(len(sup.log) == 1)
|
||
|
|
else:
|
||
|
|
assert_(len(sup.log) == 0)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("axis", [None, 0, 1])
|
||
|
|
@pytest.mark.parametrize("dtype", np.typecodes["AllFloat"])
|
||
|
|
@pytest.mark.parametrize("array", [
|
||
|
|
np.array(np.nan),
|
||
|
|
np.full((3, 3), np.nan),
|
||
|
|
], ids=["0d", "2d"])
|
||
|
|
def test_allnans(self, axis, dtype, array):
|
||
|
|
if axis is not None and array.ndim == 0:
|
||
|
|
pytest.skip(f"`axis != None` not supported for 0d arrays")
|
||
|
|
|
||
|
|
array = array.astype(dtype)
|
||
|
|
match = "(Degrees of freedom <= 0 for slice.)|(Mean of empty slice)"
|
||
|
|
for func in self.nanfuncs:
|
||
|
|
with pytest.warns(RuntimeWarning, match=match):
|
||
|
|
out = func(array, axis=axis)
|
||
|
|
assert np.isnan(out).all()
|
||
|
|
|
||
|
|
# `nanvar` and `nanstd` convert complex inputs to their
|
||
|
|
# corresponding floating dtype
|
||
|
|
if func is np.nanmean:
|
||
|
|
assert out.dtype == array.dtype
|
||
|
|
else:
|
||
|
|
assert out.dtype == np.abs(array).dtype
|
||
|
|
|
||
|
|
def test_empty(self):
|
||
|
|
mat = np.zeros((0, 3))
|
||
|
|
for f in self.nanfuncs:
|
||
|
|
for axis in [0, None]:
|
||
|
|
with warnings.catch_warnings(record=True) as w:
|
||
|
|
warnings.simplefilter('always')
|
||
|
|
assert_(np.isnan(f(mat, axis=axis)).all())
|
||
|
|
assert_(len(w) == 1)
|
||
|
|
assert_(issubclass(w[0].category, RuntimeWarning))
|
||
|
|
for axis in [1]:
|
||
|
|
with warnings.catch_warnings(record=True) as w:
|
||
|
|
warnings.simplefilter('always')
|
||
|
|
assert_equal(f(mat, axis=axis), np.zeros([]))
|
||
|
|
assert_(len(w) == 0)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("dtype", np.typecodes["AllFloat"])
|
||
|
|
def test_where(self, dtype):
|
||
|
|
ar = np.arange(9).reshape(3, 3).astype(dtype)
|
||
|
|
ar[0, :] = np.nan
|
||
|
|
where = np.ones_like(ar, dtype=np.bool_)
|
||
|
|
where[:, 0] = False
|
||
|
|
|
||
|
|
for f, f_std in zip(self.nanfuncs, self.stdfuncs):
|
||
|
|
reference = f_std(ar[where][2:])
|
||
|
|
dtype_reference = dtype if f is np.nanmean else ar.real.dtype
|
||
|
|
|
||
|
|
ret = f(ar, where=where)
|
||
|
|
assert ret.dtype == dtype_reference
|
||
|
|
np.testing.assert_allclose(ret, reference)
|
||
|
|
|
||
|
|
|
||
|
|
_TIME_UNITS = (
|
||
|
|
"Y", "M", "W", "D", "h", "m", "s", "ms", "us", "ns", "ps", "fs", "as"
|
||
|
|
)
|
||
|
|
|
||
|
|
# All `inexact` + `timdelta64` type codes
|
||
|
|
_TYPE_CODES = list(np.typecodes["AllFloat"])
|
||
|
|
_TYPE_CODES += [f"m8[{unit}]" for unit in _TIME_UNITS]
|
||
|
|
|
||
|
|
|
||
|
|
class TestNanFunctions_Median:
|
||
|
|
|
||
|
|
def test_mutation(self):
|
||
|
|
# Check that passed array is not modified.
|
||
|
|
ndat = _ndat.copy()
|
||
|
|
np.nanmedian(ndat)
|
||
|
|
assert_equal(ndat, _ndat)
|
||
|
|
|
||
|
|
def test_keepdims(self):
|
||
|
|
mat = np.eye(3)
|
||
|
|
for axis in [None, 0, 1]:
|
||
|
|
tgt = np.median(mat, axis=axis, out=None, overwrite_input=False)
|
||
|
|
res = np.nanmedian(mat, axis=axis, out=None, overwrite_input=False)
|
||
|
|
assert_(res.ndim == tgt.ndim)
|
||
|
|
|
||
|
|
d = np.ones((3, 5, 7, 11))
|
||
|
|
# Randomly set some elements to NaN:
|
||
|
|
w = np.random.random((4, 200)) * np.array(d.shape)[:, None]
|
||
|
|
w = w.astype(np.intp)
|
||
|
|
d[tuple(w)] = np.nan
|
||
|
|
with suppress_warnings() as sup:
|
||
|
|
sup.filter(RuntimeWarning)
|
||
|
|
res = np.nanmedian(d, axis=None, keepdims=True)
|
||
|
|
assert_equal(res.shape, (1, 1, 1, 1))
|
||
|
|
res = np.nanmedian(d, axis=(0, 1), keepdims=True)
|
||
|
|
assert_equal(res.shape, (1, 1, 7, 11))
|
||
|
|
res = np.nanmedian(d, axis=(0, 3), keepdims=True)
|
||
|
|
assert_equal(res.shape, (1, 5, 7, 1))
|
||
|
|
res = np.nanmedian(d, axis=(1,), keepdims=True)
|
||
|
|
assert_equal(res.shape, (3, 1, 7, 11))
|
||
|
|
res = np.nanmedian(d, axis=(0, 1, 2, 3), keepdims=True)
|
||
|
|
assert_equal(res.shape, (1, 1, 1, 1))
|
||
|
|
res = np.nanmedian(d, axis=(0, 1, 3), keepdims=True)
|
||
|
|
assert_equal(res.shape, (1, 1, 7, 1))
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
argnames='axis',
|
||
|
|
argvalues=[
|
||
|
|
None,
|
||
|
|
1,
|
||
|
|
(1, ),
|
||
|
|
(0, 1),
|
||
|
|
(-3, -1),
|
||
|
|
]
|
||
|
|
)
|
||
|
|
@pytest.mark.filterwarnings("ignore:All-NaN slice:RuntimeWarning")
|
||
|
|
def test_keepdims_out(self, axis):
|
||
|
|
d = np.ones((3, 5, 7, 11))
|
||
|
|
# Randomly set some elements to NaN:
|
||
|
|
w = np.random.random((4, 200)) * np.array(d.shape)[:, None]
|
||
|
|
w = w.astype(np.intp)
|
||
|
|
d[tuple(w)] = np.nan
|
||
|
|
if axis is None:
|
||
|
|
shape_out = (1,) * d.ndim
|
||
|
|
else:
|
||
|
|
axis_norm = normalize_axis_tuple(axis, d.ndim)
|
||
|
|
shape_out = tuple(
|
||
|
|
1 if i in axis_norm else d.shape[i] for i in range(d.ndim))
|
||
|
|
out = np.empty(shape_out)
|
||
|
|
result = np.nanmedian(d, axis=axis, keepdims=True, out=out)
|
||
|
|
assert result is out
|
||
|
|
assert_equal(result.shape, shape_out)
|
||
|
|
|
||
|
|
def test_out(self):
|
||
|
|
mat = np.random.rand(3, 3)
|
||
|
|
nan_mat = np.insert(mat, [0, 2], np.nan, axis=1)
|
||
|
|
resout = np.zeros(3)
|
||
|
|
tgt = np.median(mat, axis=1)
|
||
|
|
res = np.nanmedian(nan_mat, axis=1, out=resout)
|
||
|
|
assert_almost_equal(res, resout)
|
||
|
|
assert_almost_equal(res, tgt)
|
||
|
|
# 0-d output:
|
||
|
|
resout = np.zeros(())
|
||
|
|
tgt = np.median(mat, axis=None)
|
||
|
|
res = np.nanmedian(nan_mat, axis=None, out=resout)
|
||
|
|
assert_almost_equal(res, resout)
|
||
|
|
assert_almost_equal(res, tgt)
|
||
|
|
res = np.nanmedian(nan_mat, axis=(0, 1), out=resout)
|
||
|
|
assert_almost_equal(res, resout)
|
||
|
|
assert_almost_equal(res, tgt)
|
||
|
|
|
||
|
|
def test_small_large(self):
|
||
|
|
# test the small and large code paths, current cutoff 400 elements
|
||
|
|
for s in [5, 20, 51, 200, 1000]:
|
||
|
|
d = np.random.randn(4, s)
|
||
|
|
# Randomly set some elements to NaN:
|
||
|
|
w = np.random.randint(0, d.size, size=d.size // 5)
|
||
|
|
d.ravel()[w] = np.nan
|
||
|
|
d[:,0] = 1. # ensure at least one good value
|
||
|
|
# use normal median without nans to compare
|
||
|
|
tgt = []
|
||
|
|
for x in d:
|
||
|
|
nonan = np.compress(~np.isnan(x), x)
|
||
|
|
tgt.append(np.median(nonan, overwrite_input=True))
|
||
|
|
|
||
|
|
assert_array_equal(np.nanmedian(d, axis=-1), tgt)
|
||
|
|
|
||
|
|
def test_result_values(self):
|
||
|
|
tgt = [np.median(d) for d in _rdat]
|
||
|
|
res = np.nanmedian(_ndat, axis=1)
|
||
|
|
assert_almost_equal(res, tgt)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("axis", [None, 0, 1])
|
||
|
|
@pytest.mark.parametrize("dtype", _TYPE_CODES)
|
||
|
|
def test_allnans(self, dtype, axis):
|
||
|
|
mat = np.full((3, 3), np.nan).astype(dtype)
|
||
|
|
with suppress_warnings() as sup:
|
||
|
|
sup.record(RuntimeWarning)
|
||
|
|
|
||
|
|
output = np.nanmedian(mat, axis=axis)
|
||
|
|
assert output.dtype == mat.dtype
|
||
|
|
assert np.isnan(output).all()
|
||
|
|
|
||
|
|
if axis is None:
|
||
|
|
assert_(len(sup.log) == 1)
|
||
|
|
else:
|
||
|
|
assert_(len(sup.log) == 3)
|
||
|
|
|
||
|
|
# Check scalar
|
||
|
|
scalar = np.array(np.nan).astype(dtype)[()]
|
||
|
|
output_scalar = np.nanmedian(scalar)
|
||
|
|
assert output_scalar.dtype == scalar.dtype
|
||
|
|
assert np.isnan(output_scalar)
|
||
|
|
|
||
|
|
if axis is None:
|
||
|
|
assert_(len(sup.log) == 2)
|
||
|
|
else:
|
||
|
|
assert_(len(sup.log) == 4)
|
||
|
|
|
||
|
|
def test_empty(self):
|
||
|
|
mat = np.zeros((0, 3))
|
||
|
|
for axis in [0, None]:
|
||
|
|
with warnings.catch_warnings(record=True) as w:
|
||
|
|
warnings.simplefilter('always')
|
||
|
|
assert_(np.isnan(np.nanmedian(mat, axis=axis)).all())
|
||
|
|
assert_(len(w) == 1)
|
||
|
|
assert_(issubclass(w[0].category, RuntimeWarning))
|
||
|
|
for axis in [1]:
|
||
|
|
with warnings.catch_warnings(record=True) as w:
|
||
|
|
warnings.simplefilter('always')
|
||
|
|
assert_equal(np.nanmedian(mat, axis=axis), np.zeros([]))
|
||
|
|
assert_(len(w) == 0)
|
||
|
|
|
||
|
|
def test_scalar(self):
|
||
|
|
assert_(np.nanmedian(0.) == 0.)
|
||
|
|
|
||
|
|
def test_extended_axis_invalid(self):
|
||
|
|
d = np.ones((3, 5, 7, 11))
|
||
|
|
assert_raises(np.AxisError, np.nanmedian, d, axis=-5)
|
||
|
|
assert_raises(np.AxisError, np.nanmedian, d, axis=(0, -5))
|
||
|
|
assert_raises(np.AxisError, np.nanmedian, d, axis=4)
|
||
|
|
assert_raises(np.AxisError, np.nanmedian, d, axis=(0, 4))
|
||
|
|
assert_raises(ValueError, np.nanmedian, d, axis=(1, 1))
|
||
|
|
|
||
|
|
def test_float_special(self):
|
||
|
|
with suppress_warnings() as sup:
|
||
|
|
sup.filter(RuntimeWarning)
|
||
|
|
for inf in [np.inf, -np.inf]:
|
||
|
|
a = np.array([[inf, np.nan], [np.nan, np.nan]])
|
||
|
|
assert_equal(np.nanmedian(a, axis=0), [inf, np.nan])
|
||
|
|
assert_equal(np.nanmedian(a, axis=1), [inf, np.nan])
|
||
|
|
assert_equal(np.nanmedian(a), inf)
|
||
|
|
|
||
|
|
# minimum fill value check
|
||
|
|
a = np.array([[np.nan, np.nan, inf],
|
||
|
|
[np.nan, np.nan, inf]])
|
||
|
|
assert_equal(np.nanmedian(a), inf)
|
||
|
|
assert_equal(np.nanmedian(a, axis=0), [np.nan, np.nan, inf])
|
||
|
|
assert_equal(np.nanmedian(a, axis=1), inf)
|
||
|
|
|
||
|
|
# no mask path
|
||
|
|
a = np.array([[inf, inf], [inf, inf]])
|
||
|
|
assert_equal(np.nanmedian(a, axis=1), inf)
|
||
|
|
|
||
|
|
a = np.array([[inf, 7, -inf, -9],
|
||
|
|
[-10, np.nan, np.nan, 5],
|
||
|
|
[4, np.nan, np.nan, inf]],
|
||
|
|
dtype=np.float32)
|
||
|
|
if inf > 0:
|
||
|
|
assert_equal(np.nanmedian(a, axis=0), [4., 7., -inf, 5.])
|
||
|
|
assert_equal(np.nanmedian(a), 4.5)
|
||
|
|
else:
|
||
|
|
assert_equal(np.nanmedian(a, axis=0), [-10., 7., -inf, -9.])
|
||
|
|
assert_equal(np.nanmedian(a), -2.5)
|
||
|
|
assert_equal(np.nanmedian(a, axis=-1), [-1., -2.5, inf])
|
||
|
|
|
||
|
|
for i in range(0, 10):
|
||
|
|
for j in range(1, 10):
|
||
|
|
a = np.array([([np.nan] * i) + ([inf] * j)] * 2)
|
||
|
|
assert_equal(np.nanmedian(a), inf)
|
||
|
|
assert_equal(np.nanmedian(a, axis=1), inf)
|
||
|
|
assert_equal(np.nanmedian(a, axis=0),
|
||
|
|
([np.nan] * i) + [inf] * j)
|
||
|
|
|
||
|
|
a = np.array([([np.nan] * i) + ([-inf] * j)] * 2)
|
||
|
|
assert_equal(np.nanmedian(a), -inf)
|
||
|
|
assert_equal(np.nanmedian(a, axis=1), -inf)
|
||
|
|
assert_equal(np.nanmedian(a, axis=0),
|
||
|
|
([np.nan] * i) + [-inf] * j)
|
||
|
|
|
||
|
|
|
||
|
|
class TestNanFunctions_Percentile:
|
||
|
|
|
||
|
|
def test_mutation(self):
|
||
|
|
# Check that passed array is not modified.
|
||
|
|
ndat = _ndat.copy()
|
||
|
|
np.nanpercentile(ndat, 30)
|
||
|
|
assert_equal(ndat, _ndat)
|
||
|
|
|
||
|
|
def test_keepdims(self):
|
||
|
|
mat = np.eye(3)
|
||
|
|
for axis in [None, 0, 1]:
|
||
|
|
tgt = np.percentile(mat, 70, axis=axis, out=None,
|
||
|
|
overwrite_input=False)
|
||
|
|
res = np.nanpercentile(mat, 70, axis=axis, out=None,
|
||
|
|
overwrite_input=False)
|
||
|
|
assert_(res.ndim == tgt.ndim)
|
||
|
|
|
||
|
|
d = np.ones((3, 5, 7, 11))
|
||
|
|
# Randomly set some elements to NaN:
|
||
|
|
w = np.random.random((4, 200)) * np.array(d.shape)[:, None]
|
||
|
|
w = w.astype(np.intp)
|
||
|
|
d[tuple(w)] = np.nan
|
||
|
|
with suppress_warnings() as sup:
|
||
|
|
sup.filter(RuntimeWarning)
|
||
|
|
res = np.nanpercentile(d, 90, axis=None, keepdims=True)
|
||
|
|
assert_equal(res.shape, (1, 1, 1, 1))
|
||
|
|
res = np.nanpercentile(d, 90, axis=(0, 1), keepdims=True)
|
||
|
|
assert_equal(res.shape, (1, 1, 7, 11))
|
||
|
|
res = np.nanpercentile(d, 90, axis=(0, 3), keepdims=True)
|
||
|
|
assert_equal(res.shape, (1, 5, 7, 1))
|
||
|
|
res = np.nanpercentile(d, 90, axis=(1,), keepdims=True)
|
||
|
|
assert_equal(res.shape, (3, 1, 7, 11))
|
||
|
|
res = np.nanpercentile(d, 90, axis=(0, 1, 2, 3), keepdims=True)
|
||
|
|
assert_equal(res.shape, (1, 1, 1, 1))
|
||
|
|
res = np.nanpercentile(d, 90, axis=(0, 1, 3), keepdims=True)
|
||
|
|
assert_equal(res.shape, (1, 1, 7, 1))
|
||
|
|
|
||
|
|
@pytest.mark.parametrize('q', [7, [1, 7]])
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
argnames='axis',
|
||
|
|
argvalues=[
|
||
|
|
None,
|
||
|
|
1,
|
||
|
|
(1,),
|
||
|
|
(0, 1),
|
||
|
|
(-3, -1),
|
||
|
|
]
|
||
|
|
)
|
||
|
|
@pytest.mark.filterwarnings("ignore:All-NaN slice:RuntimeWarning")
|
||
|
|
def test_keepdims_out(self, q, axis):
|
||
|
|
d = np.ones((3, 5, 7, 11))
|
||
|
|
# Randomly set some elements to NaN:
|
||
|
|
w = np.random.random((4, 200)) * np.array(d.shape)[:, None]
|
||
|
|
w = w.astype(np.intp)
|
||
|
|
d[tuple(w)] = np.nan
|
||
|
|
if axis is None:
|
||
|
|
shape_out = (1,) * d.ndim
|
||
|
|
else:
|
||
|
|
axis_norm = normalize_axis_tuple(axis, d.ndim)
|
||
|
|
shape_out = tuple(
|
||
|
|
1 if i in axis_norm else d.shape[i] for i in range(d.ndim))
|
||
|
|
shape_out = np.shape(q) + shape_out
|
||
|
|
|
||
|
|
out = np.empty(shape_out)
|
||
|
|
result = np.nanpercentile(d, q, axis=axis, keepdims=True, out=out)
|
||
|
|
assert result is out
|
||
|
|
assert_equal(result.shape, shape_out)
|
||
|
|
|
||
|
|
def test_out(self):
|
||
|
|
mat = np.random.rand(3, 3)
|
||
|
|
nan_mat = np.insert(mat, [0, 2], np.nan, axis=1)
|
||
|
|
resout = np.zeros(3)
|
||
|
|
tgt = np.percentile(mat, 42, axis=1)
|
||
|
|
res = np.nanpercentile(nan_mat, 42, axis=1, out=resout)
|
||
|
|
assert_almost_equal(res, resout)
|
||
|
|
assert_almost_equal(res, tgt)
|
||
|
|
# 0-d output:
|
||
|
|
resout = np.zeros(())
|
||
|
|
tgt = np.percentile(mat, 42, axis=None)
|
||
|
|
res = np.nanpercentile(nan_mat, 42, axis=None, out=resout)
|
||
|
|
assert_almost_equal(res, resout)
|
||
|
|
assert_almost_equal(res, tgt)
|
||
|
|
res = np.nanpercentile(nan_mat, 42, axis=(0, 1), out=resout)
|
||
|
|
assert_almost_equal(res, resout)
|
||
|
|
assert_almost_equal(res, tgt)
|
||
|
|
|
||
|
|
def test_result_values(self):
|
||
|
|
tgt = [np.percentile(d, 28) for d in _rdat]
|
||
|
|
res = np.nanpercentile(_ndat, 28, axis=1)
|
||
|
|
assert_almost_equal(res, tgt)
|
||
|
|
# Transpose the array to fit the output convention of numpy.percentile
|
||
|
|
tgt = np.transpose([np.percentile(d, (28, 98)) for d in _rdat])
|
||
|
|
res = np.nanpercentile(_ndat, (28, 98), axis=1)
|
||
|
|
assert_almost_equal(res, tgt)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("axis", [None, 0, 1])
|
||
|
|
@pytest.mark.parametrize("dtype", np.typecodes["AllFloat"])
|
||
|
|
@pytest.mark.parametrize("array", [
|
||
|
|
np.array(np.nan),
|
||
|
|
np.full((3, 3), np.nan),
|
||
|
|
], ids=["0d", "2d"])
|
||
|
|
def test_allnans(self, axis, dtype, array):
|
||
|
|
if axis is not None and array.ndim == 0:
|
||
|
|
pytest.skip(f"`axis != None` not supported for 0d arrays")
|
||
|
|
|
||
|
|
array = array.astype(dtype)
|
||
|
|
with pytest.warns(RuntimeWarning, match="All-NaN slice encountered"):
|
||
|
|
out = np.nanpercentile(array, 60, axis=axis)
|
||
|
|
assert np.isnan(out).all()
|
||
|
|
assert out.dtype == array.dtype
|
||
|
|
|
||
|
|
def test_empty(self):
|
||
|
|
mat = np.zeros((0, 3))
|
||
|
|
for axis in [0, None]:
|
||
|
|
with warnings.catch_warnings(record=True) as w:
|
||
|
|
warnings.simplefilter('always')
|
||
|
|
assert_(np.isnan(np.nanpercentile(mat, 40, axis=axis)).all())
|
||
|
|
assert_(len(w) == 1)
|
||
|
|
assert_(issubclass(w[0].category, RuntimeWarning))
|
||
|
|
for axis in [1]:
|
||
|
|
with warnings.catch_warnings(record=True) as w:
|
||
|
|
warnings.simplefilter('always')
|
||
|
|
assert_equal(np.nanpercentile(mat, 40, axis=axis), np.zeros([]))
|
||
|
|
assert_(len(w) == 0)
|
||
|
|
|
||
|
|
def test_scalar(self):
|
||
|
|
assert_equal(np.nanpercentile(0., 100), 0.)
|
||
|
|
a = np.arange(6)
|
||
|
|
r = np.nanpercentile(a, 50, axis=0)
|
||
|
|
assert_equal(r, 2.5)
|
||
|
|
assert_(np.isscalar(r))
|
||
|
|
|
||
|
|
def test_extended_axis_invalid(self):
|
||
|
|
d = np.ones((3, 5, 7, 11))
|
||
|
|
assert_raises(np.AxisError, np.nanpercentile, d, q=5, axis=-5)
|
||
|
|
assert_raises(np.AxisError, np.nanpercentile, d, q=5, axis=(0, -5))
|
||
|
|
assert_raises(np.AxisError, np.nanpercentile, d, q=5, axis=4)
|
||
|
|
assert_raises(np.AxisError, np.nanpercentile, d, q=5, axis=(0, 4))
|
||
|
|
assert_raises(ValueError, np.nanpercentile, d, q=5, axis=(1, 1))
|
||
|
|
|
||
|
|
def test_multiple_percentiles(self):
|
||
|
|
perc = [50, 100]
|
||
|
|
mat = np.ones((4, 3))
|
||
|
|
nan_mat = np.nan * mat
|
||
|
|
# For checking consistency in higher dimensional case
|
||
|
|
large_mat = np.ones((3, 4, 5))
|
||
|
|
large_mat[:, 0:2:4, :] = 0
|
||
|
|
large_mat[:, :, 3:] *= 2
|
||
|
|
for axis in [None, 0, 1]:
|
||
|
|
for keepdim in [False, True]:
|
||
|
|
with suppress_warnings() as sup:
|
||
|
|
sup.filter(RuntimeWarning, "All-NaN slice encountered")
|
||
|
|
val = np.percentile(mat, perc, axis=axis, keepdims=keepdim)
|
||
|
|
nan_val = np.nanpercentile(nan_mat, perc, axis=axis,
|
||
|
|
keepdims=keepdim)
|
||
|
|
assert_equal(nan_val.shape, val.shape)
|
||
|
|
|
||
|
|
val = np.percentile(large_mat, perc, axis=axis,
|
||
|
|
keepdims=keepdim)
|
||
|
|
nan_val = np.nanpercentile(large_mat, perc, axis=axis,
|
||
|
|
keepdims=keepdim)
|
||
|
|
assert_equal(nan_val, val)
|
||
|
|
|
||
|
|
megamat = np.ones((3, 4, 5, 6))
|
||
|
|
assert_equal(np.nanpercentile(megamat, perc, axis=(1, 2)).shape, (2, 3, 6))
|
||
|
|
|
||
|
|
|
||
|
|
class TestNanFunctions_Quantile:
|
||
|
|
# most of this is already tested by TestPercentile
|
||
|
|
|
||
|
|
def test_regression(self):
|
||
|
|
ar = np.arange(24).reshape(2, 3, 4).astype(float)
|
||
|
|
ar[0][1] = np.nan
|
||
|
|
|
||
|
|
assert_equal(np.nanquantile(ar, q=0.5), np.nanpercentile(ar, q=50))
|
||
|
|
assert_equal(np.nanquantile(ar, q=0.5, axis=0),
|
||
|
|
np.nanpercentile(ar, q=50, axis=0))
|
||
|
|
assert_equal(np.nanquantile(ar, q=0.5, axis=1),
|
||
|
|
np.nanpercentile(ar, q=50, axis=1))
|
||
|
|
assert_equal(np.nanquantile(ar, q=[0.5], axis=1),
|
||
|
|
np.nanpercentile(ar, q=[50], axis=1))
|
||
|
|
assert_equal(np.nanquantile(ar, q=[0.25, 0.5, 0.75], axis=1),
|
||
|
|
np.nanpercentile(ar, q=[25, 50, 75], axis=1))
|
||
|
|
|
||
|
|
def test_basic(self):
|
||
|
|
x = np.arange(8) * 0.5
|
||
|
|
assert_equal(np.nanquantile(x, 0), 0.)
|
||
|
|
assert_equal(np.nanquantile(x, 1), 3.5)
|
||
|
|
assert_equal(np.nanquantile(x, 0.5), 1.75)
|
||
|
|
|
||
|
|
def test_no_p_overwrite(self):
|
||
|
|
# this is worth retesting, because quantile does not make a copy
|
||
|
|
p0 = np.array([0, 0.75, 0.25, 0.5, 1.0])
|
||
|
|
p = p0.copy()
|
||
|
|
np.nanquantile(np.arange(100.), p, method="midpoint")
|
||
|
|
assert_array_equal(p, p0)
|
||
|
|
|
||
|
|
p0 = p0.tolist()
|
||
|
|
p = p.tolist()
|
||
|
|
np.nanquantile(np.arange(100.), p, method="midpoint")
|
||
|
|
assert_array_equal(p, p0)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("axis", [None, 0, 1])
|
||
|
|
@pytest.mark.parametrize("dtype", np.typecodes["AllFloat"])
|
||
|
|
@pytest.mark.parametrize("array", [
|
||
|
|
np.array(np.nan),
|
||
|
|
np.full((3, 3), np.nan),
|
||
|
|
], ids=["0d", "2d"])
|
||
|
|
def test_allnans(self, axis, dtype, array):
|
||
|
|
if axis is not None and array.ndim == 0:
|
||
|
|
pytest.skip(f"`axis != None` not supported for 0d arrays")
|
||
|
|
|
||
|
|
array = array.astype(dtype)
|
||
|
|
with pytest.warns(RuntimeWarning, match="All-NaN slice encountered"):
|
||
|
|
out = np.nanquantile(array, 1, axis=axis)
|
||
|
|
assert np.isnan(out).all()
|
||
|
|
assert out.dtype == array.dtype
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("arr, expected", [
|
||
|
|
# array of floats with some nans
|
||
|
|
(np.array([np.nan, 5.0, np.nan, np.inf]),
|
||
|
|
np.array([False, True, False, True])),
|
||
|
|
# int64 array that can't possibly have nans
|
||
|
|
(np.array([1, 5, 7, 9], dtype=np.int64),
|
||
|
|
True),
|
||
|
|
# bool array that can't possibly have nans
|
||
|
|
(np.array([False, True, False, True]),
|
||
|
|
True),
|
||
|
|
# 2-D complex array with nans
|
||
|
|
(np.array([[np.nan, 5.0],
|
||
|
|
[np.nan, np.inf]], dtype=np.complex64),
|
||
|
|
np.array([[False, True],
|
||
|
|
[False, True]])),
|
||
|
|
])
|
||
|
|
def test__nan_mask(arr, expected):
|
||
|
|
for out in [None, np.empty(arr.shape, dtype=np.bool_)]:
|
||
|
|
actual = _nan_mask(arr, out=out)
|
||
|
|
assert_equal(actual, expected)
|
||
|
|
# the above won't distinguish between True proper
|
||
|
|
# and an array of True values; we want True proper
|
||
|
|
# for types that can't possibly contain NaN
|
||
|
|
if type(expected) is not np.ndarray:
|
||
|
|
assert actual is True
|
||
|
|
|
||
|
|
|
||
|
|
def test__replace_nan():
|
||
|
|
""" Test that _replace_nan returns the original array if there are no
|
||
|
|
NaNs, not a copy.
|
||
|
|
"""
|
||
|
|
for dtype in [np.bool_, np.int32, np.int64]:
|
||
|
|
arr = np.array([0, 1], dtype=dtype)
|
||
|
|
result, mask = _replace_nan(arr, 0)
|
||
|
|
assert mask is None
|
||
|
|
# do not make a copy if there are no nans
|
||
|
|
assert result is arr
|
||
|
|
|
||
|
|
for dtype in [np.float32, np.float64]:
|
||
|
|
arr = np.array([0, 1], dtype=dtype)
|
||
|
|
result, mask = _replace_nan(arr, 2)
|
||
|
|
assert (mask == False).all()
|
||
|
|
# mask is not None, so we make a copy
|
||
|
|
assert result is not arr
|
||
|
|
assert_equal(result, arr)
|
||
|
|
|
||
|
|
arr_nan = np.array([0, 1, np.nan], dtype=dtype)
|
||
|
|
result_nan, mask_nan = _replace_nan(arr_nan, 2)
|
||
|
|
assert_equal(mask_nan, np.array([False, False, True]))
|
||
|
|
assert result_nan is not arr_nan
|
||
|
|
assert_equal(result_nan, np.array([0, 1, 2]))
|
||
|
|
assert np.isnan(arr_nan[-1])
|