diff options
Diffstat (limited to 'numpy/lib/nanfunctions.py')
| -rw-r--r-- | numpy/lib/nanfunctions.py | 45 |
1 files changed, 23 insertions, 22 deletions
diff --git a/numpy/lib/nanfunctions.py b/numpy/lib/nanfunctions.py index 3814c0727..b3b570860 100644 --- a/numpy/lib/nanfunctions.py +++ b/numpy/lib/nanfunctions.py @@ -169,7 +169,7 @@ def _remove_nan_1d(arr1d, overwrite_input=False): s = np.nonzero(c)[0] if s.size == arr1d.size: warnings.warn("All-NaN slice encountered", RuntimeWarning, - stacklevel=5) + stacklevel=6) return arr1d[:0], True elif s.size == 0: return arr1d, overwrite_input @@ -343,7 +343,7 @@ def nanmin(a, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue, res = np.fmin.reduce(a, axis=axis, out=out, **kwargs) if np.isnan(res).any(): warnings.warn("All-NaN slice encountered", RuntimeWarning, - stacklevel=3) + stacklevel=2) else: # Slow, but safe for subclasses of ndarray a, mask = _replace_nan(a, +np.inf) @@ -357,7 +357,7 @@ def nanmin(a, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue, if np.any(mask): res = _copyto(res, np.nan, mask) warnings.warn("All-NaN axis encountered", RuntimeWarning, - stacklevel=3) + stacklevel=2) return res @@ -476,7 +476,7 @@ def nanmax(a, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue, res = np.fmax.reduce(a, axis=axis, out=out, **kwargs) if np.isnan(res).any(): warnings.warn("All-NaN slice encountered", RuntimeWarning, - stacklevel=3) + stacklevel=2) else: # Slow, but safe for subclasses of ndarray a, mask = _replace_nan(a, -np.inf) @@ -490,7 +490,7 @@ def nanmax(a, axis=None, out=None, keepdims=np._NoValue, initial=np._NoValue, if np.any(mask): res = _copyto(res, np.nan, mask) warnings.warn("All-NaN axis encountered", RuntimeWarning, - stacklevel=3) + stacklevel=2) return res @@ -1049,7 +1049,7 @@ def nanmean(a, axis=None, dtype=None, out=None, keepdims=np._NoValue, isbad = (cnt == 0) if isbad.any(): - warnings.warn("Mean of empty slice", RuntimeWarning, stacklevel=3) + warnings.warn("Mean of empty slice", RuntimeWarning, stacklevel=2) # NaN is the only possible bad value, so no further # action is needed to handle bad results. return avg @@ -1109,7 +1109,7 @@ def _nanmedian_small(a, axis=None, out=None, overwrite_input=False): m = np.ma.median(a, axis=axis, overwrite_input=overwrite_input) for i in range(np.count_nonzero(m.mask.ravel())): warnings.warn("All-NaN slice encountered", RuntimeWarning, - stacklevel=4) + stacklevel=5) fill_value = np.timedelta64("NaT") if m.dtype.kind == "m" else np.nan if out is not None: @@ -1214,12 +1214,9 @@ def nanmedian(a, axis=None, out=None, overwrite_input=False, keepdims=np._NoValu if a.size == 0: return np.nanmean(a, axis, out=out, keepdims=keepdims) - r, k = function_base._ureduce(a, func=_nanmedian, axis=axis, out=out, + return function_base._ureduce(a, func=_nanmedian, keepdims=keepdims, + axis=axis, out=out, overwrite_input=overwrite_input) - if keepdims and keepdims is not np._NoValue: - return r.reshape(k) - else: - return r def _nanpercentile_dispatcher( @@ -1376,6 +1373,9 @@ def nanpercentile( method, interpolation, "nanpercentile") a = np.asanyarray(a) + if a.dtype.kind == "c": + raise TypeError("a must be an array of real numbers") + q = np.true_divide(q, 100.0) # undo any decay that the ufunc performed (see gh-13105) q = np.asanyarray(q) @@ -1415,8 +1415,8 @@ def nanquantile( Input array or object that can be converted to an array, containing nan values to be ignored q : array_like of float - Quantile or sequence of quantiles to compute, which must be between - 0 and 1 inclusive. + Probability or sequence of probabilities for the quantiles to compute. + Values must be between 0 and 1 inclusive. axis : {int, tuple of int, None}, optional Axis or axes along which the quantiles are computed. The default is to compute the quantile(s) along a flattened @@ -1476,8 +1476,8 @@ def nanquantile( Returns ------- quantile : scalar or ndarray - If `q` is a single percentile and `axis=None`, then the result - is a scalar. If multiple quantiles are given, first axis of + If `q` is a single probability and `axis=None`, then the result + is a scalar. If multiple probability levels are given, first axis of the result corresponds to the quantiles. The other axes are the axes that remain after the reduction of `a`. If the input contains integers or floats smaller than ``float64``, the output @@ -1530,11 +1530,15 @@ def nanquantile( The American Statistician, 50(4), pp. 361-365, 1996 """ + if interpolation is not None: method = function_base._check_interpolation_as_method( method, interpolation, "nanquantile") a = np.asanyarray(a) + if a.dtype.kind == "c": + raise TypeError("a must be an array of real numbers") + q = np.asanyarray(q) if not function_base._quantile_is_valid(q): raise ValueError("Quantiles must be in the range [0, 1]") @@ -1556,17 +1560,14 @@ def _nanquantile_unchecked( # so deal them upfront if a.size == 0: return np.nanmean(a, axis, out=out, keepdims=keepdims) - r, k = function_base._ureduce(a, + return function_base._ureduce(a, func=_nanquantile_ureduce_func, q=q, + keepdims=keepdims, axis=axis, out=out, overwrite_input=overwrite_input, method=method) - if keepdims and keepdims is not np._NoValue: - return r.reshape(q.shape + k) - else: - return r def _nanquantile_ureduce_func(a, q, axis=None, out=None, overwrite_input=False, @@ -1762,7 +1763,7 @@ def nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue, isbad = (dof <= 0) if np.any(isbad): warnings.warn("Degrees of freedom <= 0 for slice.", RuntimeWarning, - stacklevel=3) + stacklevel=2) # NaN, inf, or negative numbers are all possible bad # values, so explicitly replace them with NaN. var = _copyto(var, np.nan, isbad) |