diff options
Diffstat (limited to 'numpy/lib')
| -rw-r--r-- | numpy/lib/nanfunctions.py | 24 | ||||
| -rw-r--r-- | numpy/lib/tests/test_nanfunctions.py | 138 |
2 files changed, 87 insertions, 75 deletions
diff --git a/numpy/lib/nanfunctions.py b/numpy/lib/nanfunctions.py index 2c2c3435b..766bf3c82 100644 --- a/numpy/lib/nanfunctions.py +++ b/numpy/lib/nanfunctions.py @@ -160,8 +160,12 @@ def _remove_nan_1d(arr1d, overwrite_input=False): True if `res` can be modified in place, given the constraint on the input """ + if arr1d.dtype == object: + # object arrays do not support `isnan` (gh-9009), so make a guess + c = np.not_equal(arr1d, arr1d, dtype=bool) + else: + c = np.isnan(arr1d) - c = np.isnan(arr1d) s = np.nonzero(c)[0] if s.size == arr1d.size: warnings.warn("All-NaN slice encountered", RuntimeWarning, @@ -214,7 +218,11 @@ def _divide_by_count(a, b, out=None): return np.divide(a, b, out=out, casting='unsafe') else: if out is None: - return a.dtype.type(a / b) + # Precaution against reduced object arrays + try: + return a.dtype.type(a / b) + except AttributeError: + return a / b else: # This is questionable, but currently a numpy scalar can # be output to a zero dimensional array. @@ -1551,7 +1559,13 @@ def nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue): # Compute variance. var = np.sum(sqr, axis=axis, dtype=dtype, out=out, keepdims=keepdims) - if var.ndim < cnt.ndim: + + # Precaution against reduced object arrays + try: + var_ndim = var.ndim + except AttributeError: + var_ndim = np.ndim(var) + if var_ndim < cnt.ndim: # Subclasses of ndarray may ignore keepdims, so check here. cnt = cnt.squeeze(axis) dof = cnt - ddof @@ -1671,6 +1685,8 @@ def nanstd(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue): keepdims=keepdims) if isinstance(var, np.ndarray): std = np.sqrt(var, out=var) - else: + elif hasattr(var, 'dtype'): std = var.dtype.type(np.sqrt(var)) + else: + std = np.sqrt(var) return std diff --git a/numpy/lib/tests/test_nanfunctions.py b/numpy/lib/tests/test_nanfunctions.py index 1f1f5601b..7756f2d79 100644 --- a/numpy/lib/tests/test_nanfunctions.py +++ b/numpy/lib/tests/test_nanfunctions.py @@ -231,79 +231,75 @@ class TestNanFunctions_ArgminArgmax: assert_(res.shape == ()) -class TestNanFunctions_IntTypes: - - int_types = (np.int8, np.int16, np.int32, np.int64, np.uint8, - np.uint16, np.uint32, np.uint64) +@pytest.mark.parametrize( + "dtype", + np.typecodes["AllInteger"] + np.typecodes["AllFloat"] + "O", +) +class TestNanFunctions_NumberTypes: mat = np.array([127, 39, 93, 87, 46]) - - def integer_arrays(self): - for dtype in self.int_types: - yield self.mat.astype(dtype) - - def test_nanmin(self): - tgt = np.min(self.mat) - for mat in self.integer_arrays(): - assert_equal(np.nanmin(mat), tgt) - - def test_nanmax(self): - tgt = np.max(self.mat) - for mat in self.integer_arrays(): - assert_equal(np.nanmax(mat), tgt) - - def test_nanargmin(self): - tgt = np.argmin(self.mat) - for mat in self.integer_arrays(): - assert_equal(np.nanargmin(mat), tgt) - - def test_nanargmax(self): - tgt = np.argmax(self.mat) - for mat in self.integer_arrays(): - assert_equal(np.nanargmax(mat), tgt) - - def test_nansum(self): - tgt = np.sum(self.mat) - for mat in self.integer_arrays(): - assert_equal(np.nansum(mat), tgt) - - def test_nanprod(self): - tgt = np.prod(self.mat) - for mat in self.integer_arrays(): - assert_equal(np.nanprod(mat), tgt) - - def test_nancumsum(self): - tgt = np.cumsum(self.mat) - for mat in self.integer_arrays(): - assert_equal(np.nancumsum(mat), tgt) - - def test_nancumprod(self): - tgt = np.cumprod(self.mat) - for mat in self.integer_arrays(): - assert_equal(np.nancumprod(mat), tgt) - - def test_nanmean(self): - tgt = np.mean(self.mat) - for mat in self.integer_arrays(): - assert_equal(np.nanmean(mat), tgt) - - def test_nanvar(self): - tgt = np.var(self.mat) - for mat in self.integer_arrays(): - assert_equal(np.nanvar(mat), tgt) - - tgt = np.var(mat, ddof=1) - for mat in self.integer_arrays(): - assert_equal(np.nanvar(mat, ddof=1), tgt) - - def test_nanstd(self): - tgt = np.std(self.mat) - for mat in self.integer_arrays(): - assert_equal(np.nanstd(mat), tgt) - - tgt = np.std(self.mat, ddof=1) - for mat in self.integer_arrays(): - assert_equal(np.nanstd(mat, ddof=1), tgt) + mat.setflags(write=False) + + 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, dtype, nanfunc, func): + mat = self.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, dtype, nanfunc, func): + mat = self.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, dtype, nanfunc, func): + mat = self.mat.astype(dtype) + tgt = func(mat, ddof=1) + out = nanfunc(mat, ddof=1) + + assert_almost_equal(out, tgt) + if dtype == "O": + assert type(out) is type(tgt) + else: + assert out.dtype == tgt.dtype class SharedNanFunctionsTestsMixin: |