diff options
Diffstat (limited to 'numpy/lib/function_base.py')
| -rw-r--r-- | numpy/lib/function_base.py | 77 |
1 files changed, 34 insertions, 43 deletions
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index 499120630..ef8a26fe3 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -1,5 +1,3 @@ -from __future__ import division, absolute_import, print_function - try: # Accessing collections abstract classes from collections # has been deprecated since Python 3.3 @@ -13,10 +11,10 @@ import warnings import numpy as np import numpy.core.numeric as _nx -from numpy.core import atleast_1d, transpose +from numpy.core import transpose from numpy.core.numeric import ( ones, zeros, arange, concatenate, array, asarray, asanyarray, empty, - empty_like, ndarray, around, floor, ceil, take, dot, where, intp, + ndarray, around, floor, ceil, take, dot, where, intp, integer, isscalar, absolute ) from numpy.core.umath import ( @@ -38,21 +36,16 @@ from numpy.core.multiarray import ( from numpy.core.umath import _add_newdoc_ufunc as add_newdoc_ufunc from numpy.compat import long -if sys.version_info[0] < 3: - # Force range to be a generator, for np.delete's usage. - range = xrange - import __builtin__ as builtins -else: - import builtins +import builtins + +# needed in this module for compatibility +from numpy.lib.histograms import histogram, histogramdd array_function_dispatch = functools.partial( overrides.array_function_dispatch, module='numpy') -# needed in this module for compatibility -from numpy.lib.histograms import histogram, histogramdd - __all__ = [ 'select', 'piecewise', 'trim_zeros', 'copy', 'iterable', 'percentile', 'diff', 'gradient', 'angle', 'unwrap', 'sort_complex', 'disp', 'flip', @@ -70,7 +63,7 @@ def _rot90_dispatcher(m, k=None, axes=None): @array_function_dispatch(_rot90_dispatcher) -def rot90(m, k=1, axes=(0,1)): +def rot90(m, k=1, axes=(0, 1)): """ Rotate an array by 90 degrees in the plane specified by axes. @@ -150,7 +143,7 @@ def rot90(m, k=1, axes=(0,1)): axes_list[axes[0]]) if k == 1: - return transpose(flip(m,axes[1]), axes_list) + return transpose(flip(m, axes[1]), axes_list) else: # k == 3 return flip(transpose(m, axes_list), axes[1]) @@ -777,14 +770,14 @@ def copy(a, order='K'): # Basic operations -def _gradient_dispatcher(f, *varargs, **kwargs): +def _gradient_dispatcher(f, *varargs, axis=None, edge_order=None): yield f for v in varargs: yield v @array_function_dispatch(_gradient_dispatcher) -def gradient(f, *varargs, **kwargs): +def gradient(f, *varargs, axis=None, edge_order=1): """ Return the gradient of an N-dimensional array. @@ -961,11 +954,10 @@ def gradient(f, *varargs, **kwargs): f = np.asanyarray(f) N = f.ndim # number of dimensions - axes = kwargs.pop('axis', None) - if axes is None: + if axis is None: axes = tuple(range(N)) else: - axes = _nx.normalize_axis_tuple(axes, N) + axes = _nx.normalize_axis_tuple(axis, N) len_axes = len(axes) n = len(varargs) @@ -979,13 +971,18 @@ def gradient(f, *varargs, **kwargs): # scalar or 1d array for each axis dx = list(varargs) for i, distances in enumerate(dx): - if np.ndim(distances) == 0: + distances = np.asanyarray(distances) + if distances.ndim == 0: continue - elif np.ndim(distances) != 1: + elif distances.ndim != 1: raise ValueError("distances must be either scalars or 1d") if len(distances) != f.shape[axes[i]]: raise ValueError("when 1d, distances must match " "the length of the corresponding dimension") + if np.issubdtype(distances.dtype, np.integer): + # Convert numpy integer types to float64 to avoid modular + # arithmetic in np.diff(distances). + distances = distances.astype(np.float64) diffx = np.diff(distances) # if distances are constant reduce to the scalar case # since it brings a consistent speedup @@ -995,10 +992,6 @@ def gradient(f, *varargs, **kwargs): else: raise TypeError("invalid number of arguments") - edge_order = kwargs.pop('edge_order', 1) - if kwargs: - raise TypeError('"{}" are not valid keyword arguments.'.format( - '", "'.join(kwargs.keys()))) if edge_order > 2: raise ValueError("'edge_order' greater than 2 not supported") @@ -1024,8 +1017,12 @@ def gradient(f, *varargs, **kwargs): elif np.issubdtype(otype, np.inexact): pass else: - # all other types convert to floating point - otype = np.double + # All other types convert to floating point. + # First check if f is a numpy integer type; if so, convert f to float64 + # to avoid modular arithmetic when computing the changes in f. + if np.issubdtype(otype, np.integer): + f = f.astype(np.float64) + otype = np.float64 for axis, ax_dx in zip(axes, dx): if f.shape[axis] < edge_order + 1: @@ -1612,6 +1609,7 @@ def trim_zeros(filt, trim='fb'): last = last - 1 return filt[first:last] + def _extract_dispatcher(condition, arr): return (condition, arr) @@ -1867,7 +1865,7 @@ def _create_arrays(broadcast_shape, dim_sizes, list_of_core_dims, dtypes): @set_module('numpy') -class vectorize(object): +class vectorize: """ vectorize(pyfunc, otypes=None, doc=None, excluded=None, cache=False, signature=None) @@ -2947,6 +2945,7 @@ def hamming(M): n = arange(0, M) return 0.54 - 0.46*cos(2.0*pi*n/(M-1)) + ## Code from cephes for i0 _i0A = [ @@ -3489,6 +3488,7 @@ def median(a, axis=None, out=None, overwrite_input=False, keepdims=False): else: return r + def _median(a, axis=None, out=None, overwrite_input=False): # can't be reasonably be implemented in terms of percentile as we have to # call mean to not break astropy @@ -3707,7 +3707,7 @@ def quantile(a, q, axis=None, out=None, overwrite_input=False, interpolation='linear', keepdims=False): """ Compute the q-th quantile of the data along the specified axis. - + .. versionadded:: 1.15.0 Parameters @@ -3878,7 +3878,7 @@ def _quantile_ureduce_func(a, q, axis=None, out=None, overwrite_input=False, "interpolation can only be 'linear', 'lower' 'higher', " "'midpoint', or 'nearest'") - n = np.array(False, dtype=bool) # check for nan's flag + n = np.array(False, dtype=bool) # check for nan's flag if indices.dtype == intp: # take the points along axis # Check if the array contains any nan's if np.issubdtype(a.dtype, np.inexact): @@ -3898,7 +3898,6 @@ def _quantile_ureduce_func(a, q, axis=None, out=None, overwrite_input=False, 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 @@ -4059,13 +4058,13 @@ def trapz(y, x=None, dx=1.0, axis=-1): return ret -def _meshgrid_dispatcher(*xi, **kwargs): +def _meshgrid_dispatcher(*xi, copy=None, sparse=None, indexing=None): return xi # Based on scitools meshgrid @array_function_dispatch(_meshgrid_dispatcher) -def meshgrid(*xi, **kwargs): +def meshgrid(*xi, copy=True, sparse=False, indexing='xy'): """ Return coordinate matrices from coordinate vectors. @@ -4171,14 +4170,6 @@ def meshgrid(*xi, **kwargs): """ ndim = len(xi) - copy_ = kwargs.pop('copy', True) - sparse = kwargs.pop('sparse', False) - indexing = kwargs.pop('indexing', 'xy') - - if kwargs: - raise TypeError("meshgrid() got an unexpected keyword argument '%s'" - % (list(kwargs)[0],)) - if indexing not in ['xy', 'ij']: raise ValueError( "Valid values for `indexing` are 'xy' and 'ij'.") @@ -4196,7 +4187,7 @@ def meshgrid(*xi, **kwargs): # Return the full N-D matrix (not only the 1-D vector) output = np.broadcast_arrays(*output, subok=True) - if copy_: + if copy: output = [x.copy() for x in output] return output |