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Diffstat (limited to 'numpy/lib/nanfunctions.py')
| -rw-r--r-- | numpy/lib/nanfunctions.py | 573 |
1 files changed, 419 insertions, 154 deletions
diff --git a/numpy/lib/nanfunctions.py b/numpy/lib/nanfunctions.py index 7260a35b8..c024055ba 100644 --- a/numpy/lib/nanfunctions.py +++ b/numpy/lib/nanfunctions.py @@ -9,9 +9,14 @@ Functions - `nanargmin` -- index of minimum non-NaN value - `nanargmax` -- index of maximum non-NaN value - `nansum` -- sum of non-NaN values +- `nanprod` -- product of non-NaN values +- `nancumsum` -- cumulative sum of non-NaN values +- `nancumprod` -- cumulative product of non-NaN values - `nanmean` -- mean of non-NaN values - `nanvar` -- variance of non-NaN values - `nanstd` -- standard deviation of non-NaN values +- `nanmedian` -- median of non-NaN values +- `nanpercentile` -- qth percentile of non-NaN values """ from __future__ import division, absolute_import, print_function @@ -20,9 +25,11 @@ import warnings import numpy as np from numpy.lib.function_base import _ureduce as _ureduce + __all__ = [ 'nansum', 'nanmax', 'nanmin', 'nanargmax', 'nanargmin', 'nanmean', - 'nanmedian', 'nanpercentile', 'nanvar', 'nanstd' + 'nanmedian', 'nanpercentile', 'nanvar', 'nanstd', 'nanprod', + 'nancumsum', 'nancumprod' ] @@ -123,7 +130,7 @@ def _divide_by_count(a, b, out=None): in place. If `a` is a numpy scalar, the division preserves its type. """ - with np.errstate(invalid='ignore'): + with np.errstate(invalid='ignore', divide='ignore'): if isinstance(a, np.ndarray): if out is None: return np.divide(a, b, out=a, casting='unsafe') @@ -138,7 +145,7 @@ def _divide_by_count(a, b, out=None): return np.divide(a, b, out=out, casting='unsafe') -def nanmin(a, axis=None, out=None, keepdims=False): +def nanmin(a, axis=None, out=None, keepdims=np._NoValue): """ Return minimum of an array or minimum along an axis, ignoring any NaNs. When all-NaN slices are encountered a ``RuntimeWarning`` is raised and @@ -160,9 +167,14 @@ def nanmin(a, axis=None, out=None, keepdims=False): .. versionadded:: 1.8.0 keepdims : bool, optional - If this is set to True, the axes which are reduced are left in the - result as dimensions with size one. With this option, the result - will broadcast correctly against the original `a`. + If this is set to True, the axes which are reduced are left + in the result as dimensions with size one. With this option, + the result will broadcast correctly against the original `a`. + + If the value is anything but the default, then + `keepdims` will be passed through to the `min` method + of sub-classes of `ndarray`. If the sub-classes methods + does not implement `keepdims` any exceptions will be raised. .. versionadded:: 1.8.0 @@ -192,7 +204,7 @@ def nanmin(a, axis=None, out=None, keepdims=False): Notes ----- - Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic + NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). This means that Not a Number is not equivalent to infinity. Positive infinity is treated as a very large number and negative infinity is treated as a very small (i.e. negative) number. @@ -217,27 +229,30 @@ def nanmin(a, axis=None, out=None, keepdims=False): -inf """ + kwargs = {} + if keepdims is not np._NoValue: + kwargs['keepdims'] = keepdims if not isinstance(a, np.ndarray) or type(a) is np.ndarray: # Fast, but not safe for subclasses of ndarray - res = np.fmin.reduce(a, axis=axis, out=out, keepdims=keepdims) + res = np.fmin.reduce(a, axis=axis, out=out, **kwargs) if np.isnan(res).any(): - warnings.warn("All-NaN axis encountered", RuntimeWarning) + warnings.warn("All-NaN axis encountered", RuntimeWarning, stacklevel=2) else: # Slow, but safe for subclasses of ndarray a, mask = _replace_nan(a, +np.inf) - res = np.amin(a, axis=axis, out=out, keepdims=keepdims) + res = np.amin(a, axis=axis, out=out, **kwargs) if mask is None: return res # Check for all-NaN axis - mask = np.all(mask, axis=axis, keepdims=keepdims) + mask = np.all(mask, axis=axis, **kwargs) if np.any(mask): res = _copyto(res, np.nan, mask) - warnings.warn("All-NaN axis encountered", RuntimeWarning) + warnings.warn("All-NaN axis encountered", RuntimeWarning, stacklevel=2) return res -def nanmax(a, axis=None, out=None, keepdims=False): +def nanmax(a, axis=None, out=None, keepdims=np._NoValue): """ Return the maximum of an array or maximum along an axis, ignoring any NaNs. When all-NaN slices are encountered a ``RuntimeWarning`` is @@ -259,9 +274,14 @@ def nanmax(a, axis=None, out=None, keepdims=False): .. versionadded:: 1.8.0 keepdims : bool, optional - If this is set to True, the axes which are reduced are left in the - result as dimensions with size one. With this option, the result - will broadcast correctly against the original `a`. + If this is set to True, the axes which are reduced are left + in the result as dimensions with size one. With this option, + the result will broadcast correctly against the original `a`. + + If the value is anything but the default, then + `keepdims` will be passed through to the `max` method + of sub-classes of `ndarray`. If the sub-classes methods + does not implement `keepdims` any exceptions will be raised. .. versionadded:: 1.8.0 @@ -291,7 +311,7 @@ def nanmax(a, axis=None, out=None, keepdims=False): Notes ----- - Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic + NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). This means that Not a Number is not equivalent to infinity. Positive infinity is treated as a very large number and negative infinity is treated as a very small (i.e. negative) number. @@ -316,23 +336,26 @@ def nanmax(a, axis=None, out=None, keepdims=False): inf """ + kwargs = {} + if keepdims is not np._NoValue: + kwargs['keepdims'] = keepdims if not isinstance(a, np.ndarray) or type(a) is np.ndarray: # Fast, but not safe for subclasses of ndarray - res = np.fmax.reduce(a, axis=axis, out=out, keepdims=keepdims) + res = np.fmax.reduce(a, axis=axis, out=out, **kwargs) if np.isnan(res).any(): - warnings.warn("All-NaN slice encountered", RuntimeWarning) + warnings.warn("All-NaN slice encountered", RuntimeWarning, stacklevel=2) else: # Slow, but safe for subclasses of ndarray a, mask = _replace_nan(a, -np.inf) - res = np.amax(a, axis=axis, out=out, keepdims=keepdims) + res = np.amax(a, axis=axis, out=out, **kwargs) if mask is None: return res # Check for all-NaN axis - mask = np.all(mask, axis=axis, keepdims=keepdims) + mask = np.all(mask, axis=axis, **kwargs) if np.any(mask): res = _copyto(res, np.nan, mask) - warnings.warn("All-NaN axis encountered", RuntimeWarning) + warnings.warn("All-NaN axis encountered", RuntimeWarning, stacklevel=2) return res @@ -425,12 +448,12 @@ def nanargmax(a, axis=None): return res -def nansum(a, axis=None, dtype=None, out=None, keepdims=0): +def nansum(a, axis=None, dtype=None, out=None, keepdims=np._NoValue): """ Return the sum of array elements over a given axis treating Not a Numbers (NaNs) as zero. - In Numpy versions <= 1.8 Nan is returned for slices that are all-NaN or + In NumPy versions <= 1.8.0 Nan is returned for slices that are all-NaN or empty. In later versions zero is returned. Parameters @@ -459,15 +482,25 @@ def nansum(a, axis=None, dtype=None, out=None, keepdims=0): .. versionadded:: 1.8.0 keepdims : bool, optional - If True, the axes which are reduced are left in the result as - dimensions with size one. With this option, the result will - broadcast correctly against the original `arr`. + If this is set to True, the axes which are reduced are left + in the result as dimensions with size one. With this option, + the result will broadcast correctly against the original `a`. + + + If the value is anything but the default, then + `keepdims` will be passed through to the `mean` or `sum` methods + of sub-classes of `ndarray`. If the sub-classes methods + does not implement `keepdims` any exceptions will be raised. .. versionadded:: 1.8.0 Returns ------- - y : ndarray or numpy scalar + nansum : 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 -------- @@ -480,11 +513,6 @@ def nansum(a, axis=None, dtype=None, out=None, keepdims=0): If both positive and negative infinity are present, the sum will be Not A Number (NaN). - Numpy integer arithmetic is modular. If the size of a sum exceeds the - size of an integer accumulator, its value will wrap around and the - result will be incorrect. Specifying ``dtype=double`` can alleviate - that problem. - Examples -------- >>> np.nansum(1) @@ -510,7 +538,199 @@ def nansum(a, axis=None, dtype=None, out=None, keepdims=0): return np.sum(a, axis=axis, dtype=dtype, out=out, keepdims=keepdims) -def nanmean(a, axis=None, dtype=None, out=None, keepdims=False): +def nanprod(a, axis=None, dtype=None, out=None, keepdims=np._NoValue): + """ + Return the product of array elements over a given axis treating Not a + Numbers (NaNs) as ones. + + One is returned for slices that are all-NaN or empty. + + .. versionadded:: 1.10.0 + + Parameters + ---------- + a : array_like + Array containing numbers whose sum is desired. If `a` is not an + array, a conversion is attempted. + axis : int, optional + Axis along which the product is computed. The default is to compute + the product of the flattened array. + dtype : data-type, optional + The type of the returned array and of the accumulator in which the + elements are summed. By default, the dtype of `a` is used. An + exception is when `a` has an integer type with less precision than + the platform (u)intp. In that case, the default will be either + (u)int32 or (u)int64 depending on whether the platform is 32 or 64 + bits. For inexact inputs, dtype must be inexact. + out : ndarray, optional + Alternate output array in which to place the result. The default + is ``None``. If provided, it must have the same shape as the + expected output, but the type will be cast if necessary. See + `doc.ufuncs` for details. The casting of NaN to integer can yield + unexpected results. + keepdims : bool, optional + If True, the axes which are reduced are left in the result as + dimensions with size one. With this option, the result will + broadcast correctly against the original `arr`. + + Returns + ------- + nanprod : ndarray + A new array holding the result is returned unless `out` is + specified, in which case it is returned. + + See Also + -------- + numpy.prod : Product across array propagating NaNs. + isnan : Show which elements are NaN. + + Examples + -------- + >>> np.nanprod(1) + 1 + >>> np.nanprod([1]) + 1 + >>> np.nanprod([1, np.nan]) + 1.0 + >>> a = np.array([[1, 2], [3, np.nan]]) + >>> np.nanprod(a) + 6.0 + >>> np.nanprod(a, axis=0) + array([ 3., 2.]) + + """ + a, mask = _replace_nan(a, 1) + return np.prod(a, axis=axis, dtype=dtype, out=out, keepdims=keepdims) + + +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 nanmean(a, axis=None, dtype=None, out=None, keepdims=np._NoValue): """ Compute the arithmetic mean along the specified axis, ignoring NaNs. @@ -540,9 +760,14 @@ def nanmean(a, axis=None, dtype=None, out=None, keepdims=False): expected output, but the type will be cast if necessary. See `doc.ufuncs` for details. keepdims : bool, optional - If this is set to True, the axes which are reduced are left in the - result as dimensions with size one. With this option, the result - will broadcast correctly against the original `arr`. + If this is set to True, the axes which are reduced are left + in the result as dimensions with size one. With this option, + the result will broadcast correctly against the original `a`. + + If the value is anything but the default, then + `keepdims` will be passed through to the `mean` or `sum` methods + of sub-classes of `ndarray`. If the sub-classes methods + does not implement `keepdims` any exceptions will be raised. Returns ------- @@ -590,16 +815,13 @@ def nanmean(a, axis=None, dtype=None, out=None, keepdims=False): if out is not None and not issubclass(out.dtype.type, np.inexact): raise TypeError("If a is inexact, then out must be inexact") - # The warning context speeds things up. - with warnings.catch_warnings(): - warnings.simplefilter('ignore') - cnt = np.sum(~mask, axis=axis, dtype=np.intp, keepdims=keepdims) - tot = np.sum(arr, axis=axis, dtype=dtype, out=out, keepdims=keepdims) - avg = _divide_by_count(tot, cnt, out=out) + cnt = np.sum(~mask, axis=axis, dtype=np.intp, keepdims=keepdims) + tot = np.sum(arr, axis=axis, dtype=dtype, out=out, keepdims=keepdims) + avg = _divide_by_count(tot, cnt, out=out) isbad = (cnt == 0) if isbad.any(): - warnings.warn("Mean of empty slice", RuntimeWarning) + 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 @@ -613,7 +835,7 @@ def _nanmedian1d(arr1d, overwrite_input=False): c = np.isnan(arr1d) s = np.where(c)[0] if s.size == arr1d.size: - warnings.warn("All-NaN slice encountered", RuntimeWarning) + warnings.warn("All-NaN slice encountered", RuntimeWarning, stacklevel=3) return np.nan elif s.size == 0: return np.median(arr1d, overwrite_input=overwrite_input) @@ -654,22 +876,25 @@ def _nanmedian(a, axis=None, out=None, overwrite_input=False): out[...] = result return result + def _nanmedian_small(a, axis=None, out=None, overwrite_input=False): """ - sort + indexing median, faster for small medians along multiple dimensions - due to the high overhead of apply_along_axis + sort + indexing median, faster for small medians along multiple + dimensions due to the high overhead of apply_along_axis + see nanmedian for parameter usage """ a = np.ma.masked_array(a, np.isnan(a)) 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) + warnings.warn("All-NaN slice encountered", RuntimeWarning, stacklevel=3) if out is not None: out[...] = m.filled(np.nan) return out return m.filled(np.nan) -def nanmedian(a, axis=None, out=None, overwrite_input=False, keepdims=False): + +def nanmedian(a, axis=None, out=None, overwrite_input=False, keepdims=np._NoValue): """ Compute the median along the specified axis, while ignoring NaNs. @@ -681,36 +906,41 @@ def nanmedian(a, axis=None, out=None, overwrite_input=False, keepdims=False): ---------- a : array_like Input array or object that can be converted to an array. - axis : int, optional - Axis along which the medians are computed. The default (axis=None) + axis : {int, sequence of int, None}, optional + Axis or axes along which the medians are computed. The default is to compute the median along a flattened version of the array. A sequence of axes is supported since version 1.9.0. 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 output) will be cast if necessary. + 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 output) will be cast if necessary. overwrite_input : bool, optional - If True, then allow use of memory of input array (a) for + If True, then allow use of memory of input array `a` for calculations. The input array will be modified by the call to - median. This will save memory when you do not need to preserve + `median`. This will save memory when you do not need to preserve the contents of the input array. Treat the input as undefined, but it will probably be fully or partially sorted. Default is - False. Note that, if `overwrite_input` is True and the input - is not already an ndarray, an error will be raised. + False. If `overwrite_input` is ``True`` and `a` is not already an + `ndarray`, an error will be raised. keepdims : bool, optional If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, - the result will broadcast correctly against the original `arr`. - + the result will broadcast correctly against the original `a`. + If this is anything but the default value it will be passed + through (in the special case of an empty array) to the + `mean` function of the underlying array. If the array is + a sub-class and `mean` does not have the kwarg `keepdims` this + will raise a RuntimeError. Returns ------- median : ndarray - A new array holding the result. If the input contains integers, or - floats of smaller precision than 64, then the output data-type is - float64. Otherwise, the output data-type is the same as that of the - input. + A new array holding the result. If the input contains integers + or floats smaller than ``float64``, then the output data-type is + ``np.float64``. Otherwise, the data-type of the output is the + same as that of the input. If `out` is specified, that array is + returned instead. See Also -------- @@ -718,10 +948,10 @@ def nanmedian(a, axis=None, out=None, overwrite_input=False, keepdims=False): Notes ----- - Given a vector V of length N, the median of V is the middle value of - a sorted copy of V, ``V_sorted`` - i.e., ``V_sorted[(N-1)/2]``, when N is - odd. When N is even, it is the average of the two middle values of - ``V_sorted``. + Given a vector ``V`` of length ``N``, the median of ``V`` is the + middle value of a sorted copy of ``V``, ``V_sorted`` - i.e., + ``V_sorted[(N-1)/2]``, when ``N`` is odd and the average of the two + middle values of ``V_sorted`` when ``N`` is even. Examples -------- @@ -756,30 +986,34 @@ def nanmedian(a, axis=None, out=None, overwrite_input=False, keepdims=False): r, k = _ureduce(a, func=_nanmedian, axis=axis, out=out, overwrite_input=overwrite_input) - if keepdims: + if keepdims and keepdims is not np._NoValue: return r.reshape(k) else: return r def nanpercentile(a, q, axis=None, out=None, overwrite_input=False, - interpolation='linear', keepdims=False): + interpolation='linear', keepdims=np._NoValue): """ - Compute the qth percentile of the data along the specified axis, while - ignoring nan values. + Compute the qth percentile of the data along the specified axis, + while ignoring nan values. - Returns the qth percentile of the array elements. + Returns the qth percentile(s) of the array elements. + + .. versionadded:: 1.9.0 Parameters ---------- a : array_like Input array or object that can be converted to an array. q : float in range of [0,100] (or sequence of floats) - Percentile to compute which must be between 0 and 100 inclusive. - axis : int or sequence of int, optional - Axis along which the percentiles are computed. The default (None) - is to compute the percentiles along a flattened version of the array. - A sequence of axes is supported since version 1.9.0. + Percentile to compute, which must be between 0 and 100 + inclusive. + axis : {int, sequence of int, None}, optional + Axis or axes along which the percentiles are computed. The + default is to compute the percentile(s) along a flattened + version of the array. A sequence of axes is supported since + version 1.9.0. 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, @@ -787,39 +1021,46 @@ def nanpercentile(a, q, axis=None, out=None, overwrite_input=False, overwrite_input : bool, optional If True, then allow use of memory of input array `a` for calculations. The input array will be modified by the call to - percentile. This will save memory when you do not need to preserve - the contents of the input array. In this case you should not make - any assumptions about the content of the passed in array `a` after - this function completes -- treat it as undefined. Default is False. - Note that, if the `a` input is not already an array this parameter - will have no effect, `a` will be converted to an array internally - regardless of the value of this parameter. + `percentile`. This will save memory when you do not need to + preserve the contents of the input array. In this case you + should not make any assumptions about the contents of the input + `a` after this function completes -- treat it as undefined. + Default is False. If `a` is not already an array, this parameter + will have no effect as `a` will be converted to an array + internally regardless of the value of this parameter. interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'} - This optional parameter specifies the interpolation method to use, - when the desired quantile lies between two data points `i` and `j`: - * linear: `i + (j - i) * fraction`, where `fraction` is the - fractional part of the index surrounded by `i` and `j`. - * lower: `i`. - * higher: `j`. - * nearest: `i` or `j` whichever is nearest. - * midpoint: (`i` + `j`) / 2. - + This optional parameter specifies the interpolation method to + use when the desired quantile lies between two data points + ``i < j``: + * linear: ``i + (j - i) * fraction``, where ``fraction`` is + the fractional part of the index surrounded by ``i`` and + ``j``. + * lower: ``i``. + * higher: ``j``. + * nearest: ``i`` or ``j``, whichever is nearest. + * midpoint: ``(i + j) / 2``. keepdims : bool, optional - If this is set to True, the axes which are reduced are left - in the result as dimensions with size one. With this option, - the result will broadcast correctly against the original `arr`. + If this is set to True, the axes which are reduced are left in + the result as dimensions with size one. With this option, the + result will broadcast correctly against the original array `a`. + If this is anything but the default value it will be passed + through (in the special case of an empty array) to the + `mean` function of the underlying array. If the array is + a sub-class and `mean` does not have the kwarg `keepdims` this + will raise a RuntimeError. Returns ------- - nanpercentile : scalar or ndarray - If a single percentile `q` is given and axis=None a scalar is - returned. If multiple percentiles `q` are given an array holding - the result is returned. The results are listed in the first axis. - (If `out` is specified, in which case that array is returned - instead). If the input contains integers, or floats of smaller - precision than 64, then the output data-type is float64. Otherwise, - the output data-type is the same as that of the input. + percentile : scalar or ndarray + If `q` is a single percentile and `axis=None`, then the result + is a scalar. If multiple percentiles are given, first axis of + the result corresponds to the percentiles. 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 + data-type is ``float64``. Otherwise, the output data-type is the + same as that of the input. If `out` is specified, that array is + returned instead. See Also -------- @@ -827,12 +1068,14 @@ def nanpercentile(a, q, axis=None, out=None, overwrite_input=False, Notes ----- - Given a vector V of length N, the q-th percentile of V is the q-th ranked - value in a sorted copy of V. The values and distances of the two - nearest neighbors as well as the `interpolation` parameter will - determine the percentile if the normalized ranking does not match q - exactly. This function is the same as the median if ``q=50``, the same - as the minimum if ``q=0``and the same as the maximum if ``q=100``. + Given a vector ``V`` of length ``N``, the ``q``-th percentile of + ``V`` is the value ``q/100`` of the way from the mimumum to the + maximum in in a sorted copy of ``V``. The values and distances of + the two nearest neighbors as well as the `interpolation` parameter + will determine the percentile if the normalized ranking does not + match the location of ``q`` exactly. This function is the same as + the median if ``q=50``, the same as the minimum if ``q=0`` and the + same as the maximum if ``q=100``. Examples -------- @@ -846,24 +1089,21 @@ def nanpercentile(a, q, axis=None, out=None, overwrite_input=False, >>> np.nanpercentile(a, 50) 3.5 >>> np.nanpercentile(a, 50, axis=0) - array([[ 6.5, 4.5, 2.5]]) - >>> np.nanpercentile(a, 50, axis=1) + array([ 6.5, 2., 2.5]) + >>> np.nanpercentile(a, 50, axis=1, keepdims=True) array([[ 7.], [ 2.]]) >>> m = np.nanpercentile(a, 50, axis=0) >>> out = np.zeros_like(m) - >>> np.nanpercentile(a, 50, axis=0, out=m) - array([[ 6.5, 4.5, 2.5]]) + >>> np.nanpercentile(a, 50, axis=0, out=out) + array([ 6.5, 2., 2.5]) >>> m - array([[ 6.5, 4.5, 2.5]]) + array([ 6.5, 2. , 2.5]) + >>> b = a.copy() >>> np.nanpercentile(b, 50, axis=1, overwrite_input=True) - array([[ 7.], - [ 2.]]) + array([ 7., 2.]) >>> assert not np.all(a==b) - >>> b = a.copy() - >>> np.nanpercentile(b, 50, axis=None, overwrite_input=True) - array([ 3.5]) """ @@ -877,7 +1117,7 @@ def nanpercentile(a, q, axis=None, out=None, overwrite_input=False, r, k = _ureduce(a, func=_nanpercentile, q=q, axis=axis, out=out, overwrite_input=overwrite_input, interpolation=interpolation) - if keepdims: + if keepdims and keepdims is not np._NoValue: if q.ndim == 0: return r.reshape(k) else: @@ -887,7 +1127,7 @@ def nanpercentile(a, q, axis=None, out=None, overwrite_input=False, def _nanpercentile(a, q, axis=None, out=None, overwrite_input=False, - interpolation='linear', keepdims=False): + interpolation='linear'): """ Private function that doesn't support extended axis or keepdims. These methods are extended to this function using _ureduce @@ -900,6 +1140,11 @@ def _nanpercentile(a, q, axis=None, out=None, overwrite_input=False, else: result = np.apply_along_axis(_nanpercentile1d, axis, a, q, overwrite_input, interpolation) + # apply_along_axis fills in collapsed axis with results. + # Move that axis to the beginning to match percentile's + # convention. + if q.ndim != 0: + result = np.rollaxis(result, axis) if out is not None: out[...] = result @@ -908,15 +1153,19 @@ def _nanpercentile(a, q, axis=None, out=None, overwrite_input=False, def _nanpercentile1d(arr1d, q, overwrite_input=False, interpolation='linear'): """ - Private function for rank 1 arrays. Compute percentile ignoring NaNs. - See nanpercentile for parameter usage + Private function for rank 1 arrays. Compute percentile ignoring + NaNs. + See nanpercentile for parameter usage """ c = np.isnan(arr1d) s = np.where(c)[0] if s.size == arr1d.size: - warnings.warn("All-NaN slice encountered", RuntimeWarning) - return np.nan + warnings.warn("All-NaN slice encountered", RuntimeWarning, stacklevel=3) + if q.ndim == 0: + return np.nan + else: + return np.nan * np.ones((len(q),)) elif s.size == 0: return np.percentile(arr1d, q, overwrite_input=overwrite_input, interpolation=interpolation) @@ -934,7 +1183,7 @@ def _nanpercentile1d(arr1d, q, overwrite_input=False, interpolation='linear'): interpolation=interpolation) -def nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False): +def nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue): """ Compute the variance along the specified axis, while ignoring NaNs. @@ -970,7 +1219,8 @@ def nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False): keepdims : bool, optional If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, - the result will broadcast correctly against the original `arr`. + the result will broadcast correctly against the original `a`. + Returns ------- @@ -1009,6 +1259,9 @@ def nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False): below). Specifying a higher-accuracy accumulator using the ``dtype`` keyword can alleviate this issue. + For this function to work on sub-classes of ndarray, they must define + `sum` with the kwarg `keepdims` + Examples -------- >>> a = np.array([[1, np.nan], [3, 4]]) @@ -1032,40 +1285,46 @@ def nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False): if out is not None and not issubclass(out.dtype.type, np.inexact): raise TypeError("If a is inexact, then out must be inexact") - with warnings.catch_warnings(): - warnings.simplefilter('ignore') - - # Compute mean - cnt = np.sum(~mask, axis=axis, dtype=np.intp, keepdims=True) - avg = np.sum(arr, axis=axis, dtype=dtype, keepdims=True) - avg = _divide_by_count(avg, cnt) - - # Compute squared deviation from mean. - np.subtract(arr, avg, out=arr, casting='unsafe') - arr = _copyto(arr, 0, mask) - if issubclass(arr.dtype.type, np.complexfloating): - sqr = np.multiply(arr, arr.conj(), out=arr).real - else: - sqr = np.multiply(arr, arr, out=arr) + # Compute mean + if type(arr) is np.matrix: + _keepdims = np._NoValue + else: + _keepdims = True + # we need to special case matrix for reverse compatibility + # in order for this to work, these sums need to be called with + # keepdims=True, however matrix now raises an error in this case, but + # the reason that it drops the keepdims kwarg is to force keepdims=True + # so this used to work by serendipity. + cnt = np.sum(~mask, axis=axis, dtype=np.intp, keepdims=_keepdims) + avg = np.sum(arr, axis=axis, dtype=dtype, keepdims=_keepdims) + avg = _divide_by_count(avg, cnt) + + # Compute squared deviation from mean. + np.subtract(arr, avg, out=arr, casting='unsafe') + arr = _copyto(arr, 0, mask) + if issubclass(arr.dtype.type, np.complexfloating): + sqr = np.multiply(arr, arr.conj(), out=arr).real + else: + sqr = np.multiply(arr, arr, out=arr) - # Compute variance. - var = np.sum(sqr, axis=axis, dtype=dtype, out=out, keepdims=keepdims) - if var.ndim < cnt.ndim: - # Subclasses of ndarray may ignore keepdims, so check here. - cnt = cnt.squeeze(axis) - dof = cnt - ddof - var = _divide_by_count(var, dof) + # Compute variance. + var = np.sum(sqr, axis=axis, dtype=dtype, out=out, keepdims=keepdims) + if var.ndim < cnt.ndim: + # Subclasses of ndarray may ignore keepdims, so check here. + cnt = cnt.squeeze(axis) + dof = cnt - ddof + var = _divide_by_count(var, dof) isbad = (dof <= 0) if np.any(isbad): - warnings.warn("Degrees of freedom <= 0 for slice.", RuntimeWarning) + warnings.warn("Degrees of freedom <= 0 for slice.", RuntimeWarning, stacklevel=2) # NaN, inf, or negative numbers are all possible bad # values, so explicitly replace them with NaN. var = _copyto(var, np.nan, isbad) return var -def nanstd(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False): +def nanstd(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue): """ Compute the standard deviation along the specified axis, while ignoring NaNs. @@ -1099,10 +1358,16 @@ def nanstd(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False): Means Delta Degrees of Freedom. The divisor used in calculations is ``N - ddof``, where ``N`` represents the number of non-NaN elements. By default `ddof` is zero. + keepdims : bool, optional If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, - the result will broadcast correctly against the original `arr`. + the result will broadcast correctly against the original `a`. + + If this value is anything but the default it is passed through + as-is to the relevant functions of the sub-classes. If these + functions do not have a `keepdims` kwarg, a RuntimeError will + be raised. Returns ------- |