diff options
Diffstat (limited to 'numpy/lib/function_base.py')
| -rw-r--r-- | numpy/lib/function_base.py | 69 |
1 files changed, 48 insertions, 21 deletions
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index 4d765bbc1..ae3d3d84c 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -2812,8 +2812,8 @@ def median(a, axis=None, out=None, overwrite_input=False): return mean(part[indexer], axis=axis, out=out) -def percentile(a, q, interpolation='linear', axis=None, out=None, - overwrite_input=False): +def percentile(a, q, axis=None, out=None, + overwrite_input=False, interpolation='linear'): """ Compute the qth percentile of the data along the specified axis. @@ -2825,15 +2825,6 @@ def percentile(a, q, interpolation='linear', axis=None, out=None, 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. - 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. axis : int, optional Axis along which the percentiles are computed. The default (None) is to compute the percentiles along a flattened version of the array. @@ -2851,15 +2842,28 @@ def percentile(a, q, interpolation='linear', axis=None, out=None, 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. + 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. + + .. versionadded:: 1.9.0 Returns ------- - percentile : ndarray - A new array holding the result (unless `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 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. See Also -------- @@ -2906,7 +2910,14 @@ def percentile(a, q, interpolation='linear', axis=None, out=None, """ a = asarray(a) - q = atleast_1d(q) + q = asarray(q) + if q.ndim == 0: + # Do not allow 0-d arrays because following code fails for scalar + zerod = True + q = q[None] + else: + zerod = False + q = q / 100.0 if (q < 0).any() or (q > 1).any(): raise ValueError( @@ -2948,7 +2959,13 @@ def percentile(a, q, interpolation='linear', axis=None, out=None, if indices.dtype == intp: # take the points along axis ap.partition(indices, axis=axis) - return take(ap, indices, axis=axis, out=out) + # ensure axis with qth is first + ap = np.rollaxis(ap, axis, 0) + axis = 0 + + if zerod: + indices = indices[0] + r = take(ap, indices, axis=axis, out=out) else: # weight the points above and below the indices indices_below = floor(indices).astype(intp) indices_above = indices_below + 1 @@ -2966,10 +2983,20 @@ def percentile(a, q, interpolation='linear', axis=None, out=None, x1 = take(ap, indices_below, axis=axis) * weights_below x2 = take(ap, indices_above, axis=axis) * weights_above + # ensure axis with qth is first + x1 = np.rollaxis(x1, axis, 0) + x2 = np.rollaxis(x2, axis, 0) + + if zerod: + x1 = x1.squeeze(0) + x2 = x2.squeeze(0) + if out is not None: - return add(x1, x2, out=out) + r = add(x1, x2, out=out) else: - return add(x1, x2) + r = add(x1, x2) + + return r def trapz(y, x=None, dx=1.0, axis=-1): |