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Diffstat (limited to 'numpy/core/numeric.py')
| -rw-r--r-- | numpy/core/numeric.py | 1656 |
1 files changed, 453 insertions, 1203 deletions
diff --git a/numpy/core/numeric.py b/numpy/core/numeric.py index 01dd46c3c..34705efc7 100644 --- a/numpy/core/numeric.py +++ b/numpy/core/numeric.py @@ -1,18 +1,21 @@ from __future__ import division, absolute_import, print_function -import collections +import functools import itertools import operator import sys import warnings +import numbers +import contextlib import numpy as np +from numpy.compat import pickle, basestring from . import multiarray from .multiarray import ( _fastCopyAndTranspose as fastCopyAndTranspose, ALLOW_THREADS, BUFSIZE, CLIP, MAXDIMS, MAY_SHARE_BOUNDS, MAY_SHARE_EXACT, RAISE, WRAP, arange, array, broadcast, can_cast, compare_chararrays, - concatenate, copyto, count_nonzero, dot, dtype, empty, + concatenate, copyto, dot, dtype, empty, empty_like, flatiter, frombuffer, fromfile, fromiter, fromstring, inner, int_asbuffer, lexsort, matmul, may_share_memory, min_scalar_type, ndarray, nditer, nested_iters, promote_types, @@ -21,58 +24,61 @@ from .multiarray import ( if sys.version_info[0] < 3: from .multiarray import newbuffer, getbuffer +from . import overrides from . import umath -from .umath import (invert, sin, UFUNC_BUFSIZE_DEFAULT, ERR_IGNORE, - ERR_WARN, ERR_RAISE, ERR_CALL, ERR_PRINT, ERR_LOG, - ERR_DEFAULT, PINF, NAN) +from .overrides import set_module +from .umath import (multiply, invert, sin, PINF, NAN) from . import numerictypes from .numerictypes import longlong, intc, int_, float_, complex_, bool_ -from ._internal import TooHardError, AxisError +from ._exceptions import TooHardError, AxisError +from ._asarray import asarray, asanyarray +from ._ufunc_config import errstate bitwise_not = invert ufunc = type(sin) newaxis = None if sys.version_info[0] >= 3: - import pickle - basestring = str import builtins else: - import cPickle as pickle import __builtin__ as builtins -loads = pickle.loads + +array_function_dispatch = functools.partial( + overrides.array_function_dispatch, module='numpy') + + +def loads(*args, **kwargs): + # NumPy 1.15.0, 2017-12-10 + warnings.warn( + "np.core.numeric.loads is deprecated, use pickle.loads instead", + DeprecationWarning, stacklevel=2) + return pickle.loads(*args, **kwargs) __all__ = [ 'newaxis', 'ndarray', 'flatiter', 'nditer', 'nested_iters', 'ufunc', - 'arange', 'array', 'zeros', 'count_nonzero', 'empty', 'broadcast', - 'dtype', 'fromstring', 'fromfile', 'frombuffer', 'int_asbuffer', - 'where', 'argwhere', 'copyto', 'concatenate', 'fastCopyAndTranspose', - 'lexsort', 'set_numeric_ops', 'can_cast', 'promote_types', - 'min_scalar_type', 'result_type', 'asarray', 'asanyarray', - 'ascontiguousarray', 'asfortranarray', 'isfortran', 'empty_like', - 'zeros_like', 'ones_like', 'correlate', 'convolve', 'inner', 'dot', - 'outer', 'vdot', 'alterdot', 'restoredot', 'roll', - 'rollaxis', 'moveaxis', 'cross', 'tensordot', 'array2string', - 'get_printoptions', 'set_printoptions', 'array_repr', 'array_str', - 'set_string_function', 'little_endian', 'require', 'fromiter', - 'array_equal', 'array_equiv', 'indices', 'fromfunction', 'isclose', 'load', - 'loads', 'isscalar', 'binary_repr', 'base_repr', 'ones', 'identity', - 'allclose', 'compare_chararrays', 'putmask', 'seterr', 'geterr', - 'setbufsize', 'getbufsize', 'seterrcall', 'geterrcall', 'errstate', - 'flatnonzero', 'Inf', 'inf', 'infty', 'Infinity', 'nan', 'NaN', 'False_', - 'True_', 'bitwise_not', 'CLIP', 'RAISE', 'WRAP', 'MAXDIMS', 'BUFSIZE', - 'ALLOW_THREADS', 'ComplexWarning', 'full', 'full_like', 'matmul', - 'shares_memory', 'may_share_memory', 'MAY_SHARE_BOUNDS', 'MAY_SHARE_EXACT', - 'TooHardError', 'AxisError' - ] - + 'arange', 'array', 'zeros', 'count_nonzero', 'empty', 'broadcast', 'dtype', + 'fromstring', 'fromfile', 'frombuffer', 'int_asbuffer', 'where', + 'argwhere', 'copyto', 'concatenate', 'fastCopyAndTranspose', 'lexsort', + 'set_numeric_ops', 'can_cast', 'promote_types', 'min_scalar_type', + 'result_type', 'isfortran', 'empty_like', 'zeros_like', 'ones_like', + 'correlate', 'convolve', 'inner', 'dot', 'outer', 'vdot', 'roll', + 'rollaxis', 'moveaxis', 'cross', 'tensordot', 'little_endian', + 'fromiter', 'array_equal', 'array_equiv', 'indices', 'fromfunction', + 'isclose', 'load', 'loads', 'isscalar', 'binary_repr', 'base_repr', 'ones', + 'identity', 'allclose', 'compare_chararrays', 'putmask', + 'flatnonzero', 'Inf', 'inf', 'infty', 'Infinity', 'nan', 'NaN', + 'False_', 'True_', 'bitwise_not', 'CLIP', 'RAISE', 'WRAP', 'MAXDIMS', + 'BUFSIZE', 'ALLOW_THREADS', 'ComplexWarning', 'full', 'full_like', + 'matmul', 'shares_memory', 'may_share_memory', 'MAY_SHARE_BOUNDS', + 'MAY_SHARE_EXACT', 'TooHardError', 'AxisError'] if sys.version_info[0] < 3: __all__.extend(['getbuffer', 'newbuffer']) +@set_module('numpy') class ComplexWarning(RuntimeWarning): """ The warning raised when casting a complex dtype to a real dtype. @@ -84,6 +90,11 @@ class ComplexWarning(RuntimeWarning): pass +def _zeros_like_dispatcher(a, dtype=None, order=None, subok=None): + return (a,) + + +@array_function_dispatch(_zeros_like_dispatcher) def zeros_like(a, dtype=None, order='K', subok=True): """ Return an array of zeros with the same shape and type as a given array. @@ -116,11 +127,10 @@ def zeros_like(a, dtype=None, order='K', subok=True): See Also -------- - ones_like : Return an array of ones with shape and type of input. empty_like : Return an empty array with shape and type of input. + ones_like : Return an array of ones with shape and type of input. + full_like : Return a new array with shape of input filled with value. zeros : Return a new array setting values to zero. - ones : Return a new array setting values to one. - empty : Return a new uninitialized array. Examples -------- @@ -133,11 +143,11 @@ def zeros_like(a, dtype=None, order='K', subok=True): array([[0, 0, 0], [0, 0, 0]]) - >>> y = np.arange(3, dtype=np.float) + >>> y = np.arange(3, dtype=float) >>> y - array([ 0., 1., 2.]) + array([0., 1., 2.]) >>> np.zeros_like(y) - array([ 0., 0., 0.]) + array([0., 0., 0.]) """ res = empty_like(a, dtype=dtype, order=order, subok=subok) @@ -147,6 +157,7 @@ def zeros_like(a, dtype=None, order='K', subok=True): return res +@set_module('numpy') def ones(shape, dtype=None, order='C'): """ Return a new array of given shape and type, filled with ones. @@ -158,9 +169,10 @@ def ones(shape, dtype=None, order='C'): dtype : data-type, optional The desired data-type for the array, e.g., `numpy.int8`. Default is `numpy.float64`. - order : {'C', 'F'}, optional - Whether to store multidimensional data in C- or Fortran-contiguous - (row- or column-wise) order in memory. + order : {'C', 'F'}, optional, default: C + Whether to store multi-dimensional data in row-major + (C-style) or column-major (Fortran-style) order in + memory. Returns ------- @@ -169,24 +181,28 @@ def ones(shape, dtype=None, order='C'): See Also -------- - zeros, ones_like + ones_like : Return an array of ones with shape and type of input. + empty : Return a new uninitialized array. + zeros : Return a new array setting values to zero. + full : Return a new array of given shape filled with value. + Examples -------- >>> np.ones(5) - array([ 1., 1., 1., 1., 1.]) + array([1., 1., 1., 1., 1.]) - >>> np.ones((5,), dtype=np.int) + >>> np.ones((5,), dtype=int) array([1, 1, 1, 1, 1]) >>> np.ones((2, 1)) - array([[ 1.], - [ 1.]]) + array([[1.], + [1.]]) >>> s = (2,2) >>> np.ones(s) - array([[ 1., 1.], - [ 1., 1.]]) + array([[1., 1.], + [1., 1.]]) """ a = empty(shape, dtype, order) @@ -194,6 +210,11 @@ def ones(shape, dtype=None, order='C'): return a +def _ones_like_dispatcher(a, dtype=None, order=None, subok=None): + return (a,) + + +@array_function_dispatch(_ones_like_dispatcher) def ones_like(a, dtype=None, order='K', subok=True): """ Return an array of ones with the same shape and type as a given array. @@ -226,11 +247,10 @@ def ones_like(a, dtype=None, order='K', subok=True): See Also -------- - zeros_like : Return an array of zeros with shape and type of input. empty_like : Return an empty array with shape and type of input. - zeros : Return a new array setting values to zero. + zeros_like : Return an array of zeros with shape and type of input. + full_like : Return a new array with shape of input filled with value. ones : Return a new array setting values to one. - empty : Return a new uninitialized array. Examples -------- @@ -243,11 +263,11 @@ def ones_like(a, dtype=None, order='K', subok=True): array([[1, 1, 1], [1, 1, 1]]) - >>> y = np.arange(3, dtype=np.float) + >>> y = np.arange(3, dtype=float) >>> y - array([ 0., 1., 2.]) + array([0., 1., 2.]) >>> np.ones_like(y) - array([ 1., 1., 1.]) + array([1., 1., 1.]) """ res = empty_like(a, dtype=dtype, order=order, subok=subok) @@ -255,6 +275,7 @@ def ones_like(a, dtype=None, order='K', subok=True): return res +@set_module('numpy') def full(shape, fill_value, dtype=None, order='C'): """ Return a new array of given shape and type, filled with `fill_value`. @@ -279,19 +300,16 @@ def full(shape, fill_value, dtype=None, order='C'): See Also -------- - zeros_like : Return an array of zeros with shape and type of input. - ones_like : Return an array of ones with shape and type of input. - empty_like : Return an empty array with shape and type of input. - full_like : Fill an array with shape and type of input. - zeros : Return a new array setting values to zero. - ones : Return a new array setting values to one. + full_like : Return a new array with shape of input filled with value. empty : Return a new uninitialized array. + ones : Return a new array setting values to one. + zeros : Return a new array setting values to zero. Examples -------- >>> np.full((2, 2), np.inf) - array([[ inf, inf], - [ inf, inf]]) + array([[inf, inf], + [inf, inf]]) >>> np.full((2, 2), 10) array([[10, 10], [10, 10]]) @@ -304,6 +322,11 @@ def full(shape, fill_value, dtype=None, order='C'): return a +def _full_like_dispatcher(a, fill_value, dtype=None, order=None, subok=None): + return (a,) + + +@array_function_dispatch(_full_like_dispatcher) def full_like(a, fill_value, dtype=None, order='K', subok=True): """ Return a full array with the same shape and type as a given array. @@ -334,29 +357,26 @@ def full_like(a, fill_value, dtype=None, order='K', subok=True): See Also -------- - zeros_like : Return an array of zeros with shape and type of input. - ones_like : Return an array of ones with shape and type of input. empty_like : Return an empty array with shape and type of input. - zeros : Return a new array setting values to zero. - ones : Return a new array setting values to one. - empty : Return a new uninitialized array. - full : Fill a new array. + ones_like : Return an array of ones with shape and type of input. + zeros_like : Return an array of zeros with shape and type of input. + full : Return a new array of given shape filled with value. Examples -------- - >>> x = np.arange(6, dtype=np.int) + >>> x = np.arange(6, dtype=int) >>> np.full_like(x, 1) array([1, 1, 1, 1, 1, 1]) >>> np.full_like(x, 0.1) array([0, 0, 0, 0, 0, 0]) >>> np.full_like(x, 0.1, dtype=np.double) - array([ 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]) + array([0.1, 0.1, 0.1, 0.1, 0.1, 0.1]) >>> np.full_like(x, np.nan, dtype=np.double) - array([ nan, nan, nan, nan, nan, nan]) + array([nan, nan, nan, nan, nan, nan]) >>> y = np.arange(6, dtype=np.double) >>> np.full_like(y, 0.1) - array([ 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]) + array([0.1, 0.1, 0.1, 0.1, 0.1, 0.1]) """ res = empty_like(a, dtype=dtype, order=order, subok=subok) @@ -364,19 +384,11 @@ def full_like(a, fill_value, dtype=None, order='K', subok=True): return res -def extend_all(module): - adict = {} - for a in __all__: - adict[a] = 1 - try: - mall = getattr(module, '__all__') - except AttributeError: - mall = [k for k in module.__dict__.keys() if not k.startswith('_')] - for a in mall: - if a not in adict: - __all__.append(a) +def _count_nonzero_dispatcher(a, axis=None): + return (a,) +@array_function_dispatch(_count_nonzero_dispatcher) def count_nonzero(a, axis=None): """ Counts the number of non-zero values in the array ``a``. @@ -425,334 +437,21 @@ def count_nonzero(a, axis=None): array([2, 3]) """ - if axis is None or axis == (): + if axis is None: return multiarray.count_nonzero(a) a = asanyarray(a) - if a.dtype == bool: - return a.sum(axis=axis, dtype=np.intp) - - if issubdtype(a.dtype, np.number): - return (a != 0).sum(axis=axis, dtype=np.intp) - - if (issubdtype(a.dtype, np.string_) or - issubdtype(a.dtype, np.unicode_)): - nullstr = a.dtype.type('') - return (a != nullstr).sum(axis=axis, dtype=np.intp) - - axis = asarray(_validate_axis(axis, a.ndim, 'axis')) - counts = np.apply_along_axis(multiarray.count_nonzero, axis[0], a) - - if axis.size == 1: - return counts + # TODO: this works around .astype(bool) not working properly (gh-9847) + if np.issubdtype(a.dtype, np.character): + a_bool = a != a.dtype.type() else: - # for subsequent axis numbers, that number decreases - # by one in this new 'counts' array if it was larger - # than the first axis upon which 'count_nonzero' was - # applied but remains unchanged if that number was - # smaller than that first axis - # - # this trick enables us to perform counts on object-like - # elements across multiple axes very quickly because integer - # addition is very well optimized - return counts.sum(axis=tuple(axis[1:] - ( - axis[1:] > axis[0])), dtype=np.intp) - - -def asarray(a, dtype=None, order=None): - """Convert the input to an array. + a_bool = a.astype(np.bool_, copy=False) - Parameters - ---------- - a : array_like - Input data, in any form that can be converted to an array. This - includes lists, lists of tuples, tuples, tuples of tuples, tuples - of lists and ndarrays. - dtype : data-type, optional - By default, the data-type is inferred from the input data. - order : {'C', 'F'}, optional - Whether to use row-major (C-style) or - column-major (Fortran-style) memory representation. - Defaults to 'C'. - - Returns - ------- - out : ndarray - Array interpretation of `a`. No copy is performed if the input - is already an ndarray with matching dtype and order. If `a` is a - subclass of ndarray, a base class ndarray is returned. - - See Also - -------- - asanyarray : Similar function which passes through subclasses. - ascontiguousarray : Convert input to a contiguous array. - asfarray : Convert input to a floating point ndarray. - asfortranarray : Convert input to an ndarray with column-major - memory order. - asarray_chkfinite : Similar function which checks input for NaNs and Infs. - fromiter : Create an array from an iterator. - fromfunction : Construct an array by executing a function on grid - positions. - - Examples - -------- - Convert a list into an array: - - >>> a = [1, 2] - >>> np.asarray(a) - array([1, 2]) - - Existing arrays are not copied: - - >>> a = np.array([1, 2]) - >>> np.asarray(a) is a - True - - If `dtype` is set, array is copied only if dtype does not match: - - >>> a = np.array([1, 2], dtype=np.float32) - >>> np.asarray(a, dtype=np.float32) is a - True - >>> np.asarray(a, dtype=np.float64) is a - False - - Contrary to `asanyarray`, ndarray subclasses are not passed through: - - >>> issubclass(np.matrix, np.ndarray) - True - >>> a = np.matrix([[1, 2]]) - >>> np.asarray(a) is a - False - >>> np.asanyarray(a) is a - True - - """ - return array(a, dtype, copy=False, order=order) - - -def asanyarray(a, dtype=None, order=None): - """Convert the input to an ndarray, but pass ndarray subclasses through. - - Parameters - ---------- - a : array_like - Input data, in any form that can be converted to an array. This - includes scalars, lists, lists of tuples, tuples, tuples of tuples, - tuples of lists, and ndarrays. - dtype : data-type, optional - By default, the data-type is inferred from the input data. - order : {'C', 'F'}, optional - Whether to use row-major (C-style) or column-major - (Fortran-style) memory representation. Defaults to 'C'. - - Returns - ------- - out : ndarray or an ndarray subclass - Array interpretation of `a`. If `a` is an ndarray or a subclass - of ndarray, it is returned as-is and no copy is performed. - - See Also - -------- - asarray : Similar function which always returns ndarrays. - ascontiguousarray : Convert input to a contiguous array. - asfarray : Convert input to a floating point ndarray. - asfortranarray : Convert input to an ndarray with column-major - memory order. - asarray_chkfinite : Similar function which checks input for NaNs and - Infs. - fromiter : Create an array from an iterator. - fromfunction : Construct an array by executing a function on grid - positions. - - Examples - -------- - Convert a list into an array: - - >>> a = [1, 2] - >>> np.asanyarray(a) - array([1, 2]) - - Instances of `ndarray` subclasses are passed through as-is: - - >>> a = np.matrix([1, 2]) - >>> np.asanyarray(a) is a - True - - """ - return array(a, dtype, copy=False, order=order, subok=True) - - -def ascontiguousarray(a, dtype=None): - """ - Return a contiguous array in memory (C order). - - Parameters - ---------- - a : array_like - Input array. - dtype : str or dtype object, optional - Data-type of returned array. - - Returns - ------- - out : ndarray - Contiguous array of same shape and content as `a`, with type `dtype` - if specified. - - See Also - -------- - asfortranarray : Convert input to an ndarray with column-major - memory order. - require : Return an ndarray that satisfies requirements. - ndarray.flags : Information about the memory layout of the array. - - Examples - -------- - >>> x = np.arange(6).reshape(2,3) - >>> np.ascontiguousarray(x, dtype=np.float32) - array([[ 0., 1., 2.], - [ 3., 4., 5.]], dtype=float32) - >>> x.flags['C_CONTIGUOUS'] - True - - """ - return array(a, dtype, copy=False, order='C', ndmin=1) - - -def asfortranarray(a, dtype=None): - """ - Return an array laid out in Fortran order in memory. - - Parameters - ---------- - a : array_like - Input array. - dtype : str or dtype object, optional - By default, the data-type is inferred from the input data. - - Returns - ------- - out : ndarray - The input `a` in Fortran, or column-major, order. - - See Also - -------- - ascontiguousarray : Convert input to a contiguous (C order) array. - asanyarray : Convert input to an ndarray with either row or - column-major memory order. - require : Return an ndarray that satisfies requirements. - ndarray.flags : Information about the memory layout of the array. - - Examples - -------- - >>> x = np.arange(6).reshape(2,3) - >>> y = np.asfortranarray(x) - >>> x.flags['F_CONTIGUOUS'] - False - >>> y.flags['F_CONTIGUOUS'] - True - - """ - return array(a, dtype, copy=False, order='F', ndmin=1) - - -def require(a, dtype=None, requirements=None): - """ - Return an ndarray of the provided type that satisfies requirements. - - This function is useful to be sure that an array with the correct flags - is returned for passing to compiled code (perhaps through ctypes). - - Parameters - ---------- - a : array_like - The object to be converted to a type-and-requirement-satisfying array. - dtype : data-type - The required data-type. If None preserve the current dtype. If your - application requires the data to be in native byteorder, include - a byteorder specification as a part of the dtype specification. - requirements : str or list of str - The requirements list can be any of the following - - * 'F_CONTIGUOUS' ('F') - ensure a Fortran-contiguous array - * 'C_CONTIGUOUS' ('C') - ensure a C-contiguous array - * 'ALIGNED' ('A') - ensure a data-type aligned array - * 'WRITEABLE' ('W') - ensure a writable array - * 'OWNDATA' ('O') - ensure an array that owns its own data - * 'ENSUREARRAY', ('E') - ensure a base array, instead of a subclass - - See Also - -------- - asarray : Convert input to an ndarray. - asanyarray : Convert to an ndarray, but pass through ndarray subclasses. - ascontiguousarray : Convert input to a contiguous array. - asfortranarray : Convert input to an ndarray with column-major - memory order. - ndarray.flags : Information about the memory layout of the array. - - Notes - ----- - The returned array will be guaranteed to have the listed requirements - by making a copy if needed. - - Examples - -------- - >>> x = np.arange(6).reshape(2,3) - >>> x.flags - C_CONTIGUOUS : True - F_CONTIGUOUS : False - OWNDATA : False - WRITEABLE : True - ALIGNED : True - UPDATEIFCOPY : False - - >>> y = np.require(x, dtype=np.float32, requirements=['A', 'O', 'W', 'F']) - >>> y.flags - C_CONTIGUOUS : False - F_CONTIGUOUS : True - OWNDATA : True - WRITEABLE : True - ALIGNED : True - UPDATEIFCOPY : False - - """ - possible_flags = {'C':'C', 'C_CONTIGUOUS':'C', 'CONTIGUOUS':'C', - 'F':'F', 'F_CONTIGUOUS':'F', 'FORTRAN':'F', - 'A':'A', 'ALIGNED':'A', - 'W':'W', 'WRITEABLE':'W', - 'O':'O', 'OWNDATA':'O', - 'E':'E', 'ENSUREARRAY':'E'} - if not requirements: - return asanyarray(a, dtype=dtype) - else: - requirements = set(possible_flags[x.upper()] for x in requirements) - - if 'E' in requirements: - requirements.remove('E') - subok = False - else: - subok = True - - order = 'A' - if requirements >= set(['C', 'F']): - raise ValueError('Cannot specify both "C" and "F" order') - elif 'F' in requirements: - order = 'F' - requirements.remove('F') - elif 'C' in requirements: - order = 'C' - requirements.remove('C') - - arr = array(a, dtype=dtype, order=order, copy=False, subok=subok) - - for prop in requirements: - if not arr.flags[prop]: - arr = arr.copy(order) - break - return arr + return a_bool.sum(axis=axis, dtype=np.intp) +@set_module('numpy') def isfortran(a): """ Returns True if the array is Fortran contiguous but *not* C contiguous. @@ -782,7 +481,7 @@ def isfortran(a): >>> np.isfortran(a) False - >>> b = np.array([[1, 2, 3], [4, 5, 6]], order='FORTRAN') + >>> b = np.array([[1, 2, 3], [4, 5, 6]], order='F') >>> b array([[1, 2, 3], [4, 5, 6]]) @@ -815,6 +514,11 @@ def isfortran(a): return a.flags.fnc +def _argwhere_dispatcher(a): + return (a,) + + +@array_function_dispatch(_argwhere_dispatcher) def argwhere(a): """ Find the indices of array elements that are non-zero, grouped by element. @@ -838,7 +542,7 @@ def argwhere(a): ``np.argwhere(a)`` is the same as ``np.transpose(np.nonzero(a))``. The output of ``argwhere`` is not suitable for indexing arrays. - For this purpose use ``where(a)`` instead. + For this purpose use ``nonzero(a)`` instead. Examples -------- @@ -856,16 +560,21 @@ def argwhere(a): return transpose(nonzero(a)) +def _flatnonzero_dispatcher(a): + return (a,) + + +@array_function_dispatch(_flatnonzero_dispatcher) def flatnonzero(a): """ Return indices that are non-zero in the flattened version of a. - This is equivalent to a.ravel().nonzero()[0]. + This is equivalent to np.nonzero(np.ravel(a))[0]. Parameters ---------- - a : ndarray - Input array. + a : array_like + Input data. Returns ------- @@ -893,7 +602,7 @@ def flatnonzero(a): array([-2, -1, 1, 2]) """ - return a.ravel().nonzero()[0] + return np.nonzero(np.ravel(a))[0] _mode_from_name_dict = {'v': 0, @@ -907,6 +616,11 @@ def _mode_from_name(mode): return mode +def _correlate_dispatcher(a, v, mode=None): + return (a, v) + + +@array_function_dispatch(_correlate_dispatcher) def correlate(a, v, mode='valid'): """ Cross-correlation of two 1-dimensional sequences. @@ -952,11 +666,11 @@ def correlate(a, v, mode='valid'): Examples -------- >>> np.correlate([1, 2, 3], [0, 1, 0.5]) - array([ 3.5]) + array([3.5]) >>> np.correlate([1, 2, 3], [0, 1, 0.5], "same") - array([ 2. , 3.5, 3. ]) + array([2. , 3.5, 3. ]) >>> np.correlate([1, 2, 3], [0, 1, 0.5], "full") - array([ 0.5, 2. , 3.5, 3. , 0. ]) + array([0.5, 2. , 3.5, 3. , 0. ]) Using complex sequences: @@ -975,6 +689,11 @@ def correlate(a, v, mode='valid'): return multiarray.correlate2(a, v, mode) +def _convolve_dispatcher(a, v, mode=None): + return (a, v) + + +@array_function_dispatch(_convolve_dispatcher) def convolve(a, v, mode='full'): """ Returns the discrete, linear convolution of two one-dimensional sequences. @@ -1038,7 +757,8 @@ def convolve(a, v, mode='full'): References ---------- - .. [1] Wikipedia, "Convolution", http://en.wikipedia.org/wiki/Convolution. + .. [1] Wikipedia, "Convolution", + https://en.wikipedia.org/wiki/Convolution Examples -------- @@ -1046,20 +766,20 @@ def convolve(a, v, mode='full'): before "sliding" the two across one another: >>> np.convolve([1, 2, 3], [0, 1, 0.5]) - array([ 0. , 1. , 2.5, 4. , 1.5]) + array([0. , 1. , 2.5, 4. , 1.5]) Only return the middle values of the convolution. Contains boundary effects, where zeros are taken into account: >>> np.convolve([1,2,3],[0,1,0.5], 'same') - array([ 1. , 2.5, 4. ]) + array([1. , 2.5, 4. ]) The two arrays are of the same length, so there is only one position where they completely overlap: >>> np.convolve([1,2,3],[0,1,0.5], 'valid') - array([ 2.5]) + array([2.5]) """ a, v = array(a, copy=False, ndmin=1), array(v, copy=False, ndmin=1) @@ -1073,6 +793,11 @@ def convolve(a, v, mode='full'): return multiarray.correlate(a, v[::-1], mode) +def _outer_dispatcher(a, b, out=None): + return (a, b, out) + + +@array_function_dispatch(_outer_dispatcher) def outer(a, b, out=None): """ Compute the outer product of two vectors. @@ -1106,11 +831,14 @@ def outer(a, b, out=None): See also -------- - inner, einsum + inner + einsum : ``einsum('i,j->ij', a.ravel(), b.ravel())`` is the equivalent. + ufunc.outer : A generalization to N dimensions and other operations. + ``np.multiply.outer(a.ravel(), b.ravel())`` is the equivalent. References ---------- - .. [1] : G. H. Golub and C. F. van Loan, *Matrix Computations*, 3rd + .. [1] : G. H. Golub and C. F. Van Loan, *Matrix Computations*, 3rd ed., Baltimore, MD, Johns Hopkins University Press, 1996, pg. 8. @@ -1127,11 +855,11 @@ def outer(a, b, out=None): [-2., -1., 0., 1., 2.]]) >>> im = np.outer(1j*np.linspace(2, -2, 5), np.ones((5,))) >>> im - array([[ 0.+2.j, 0.+2.j, 0.+2.j, 0.+2.j, 0.+2.j], - [ 0.+1.j, 0.+1.j, 0.+1.j, 0.+1.j, 0.+1.j], - [ 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j], - [ 0.-1.j, 0.-1.j, 0.-1.j, 0.-1.j, 0.-1.j], - [ 0.-2.j, 0.-2.j, 0.-2.j, 0.-2.j, 0.-2.j]]) + array([[0.+2.j, 0.+2.j, 0.+2.j, 0.+2.j, 0.+2.j], + [0.+1.j, 0.+1.j, 0.+1.j, 0.+1.j, 0.+1.j], + [0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j], + [0.-1.j, 0.-1.j, 0.-1.j, 0.-1.j, 0.-1.j], + [0.-2.j, 0.-2.j, 0.-2.j, 0.-2.j, 0.-2.j]]) >>> grid = rl + im >>> grid array([[-2.+2.j, -1.+2.j, 0.+2.j, 1.+2.j, 2.+2.j], @@ -1144,88 +872,35 @@ def outer(a, b, out=None): >>> x = np.array(['a', 'b', 'c'], dtype=object) >>> np.outer(x, [1, 2, 3]) - array([[a, aa, aaa], - [b, bb, bbb], - [c, cc, ccc]], dtype=object) + array([['a', 'aa', 'aaa'], + ['b', 'bb', 'bbb'], + ['c', 'cc', 'ccc']], dtype=object) """ a = asarray(a) b = asarray(b) - return multiply(a.ravel()[:, newaxis], b.ravel()[newaxis,:], out) - - -def alterdot(): - """ - Change `dot`, `vdot`, and `inner` to use accelerated BLAS functions. - - Typically, as a user of NumPy, you do not explicitly call this - function. If NumPy is built with an accelerated BLAS, this function is - automatically called when NumPy is imported. - - When NumPy is built with an accelerated BLAS like ATLAS, these - functions are replaced to make use of the faster implementations. The - faster implementations only affect float32, float64, complex64, and - complex128 arrays. Furthermore, the BLAS API only includes - matrix-matrix, matrix-vector, and vector-vector products. Products of - arrays with larger dimensionalities use the built in functions and are - not accelerated. + return multiply(a.ravel()[:, newaxis], b.ravel()[newaxis, :], out) - .. note:: Deprecated in NumPy 1.10.0 - The cblas functions have been integrated into the multarray - module and alterdot now longer does anything. It will be - removed in NumPy 1.11.0. - - See Also - -------- - restoredot : `restoredot` undoes the effects of `alterdot`. - - """ - # 2014-08-13, 1.10 - warnings.warn("alterdot no longer does anything.", - DeprecationWarning, stacklevel=2) - - -def restoredot(): - """ - Restore `dot`, `vdot`, and `innerproduct` to the default non-BLAS - implementations. - Typically, the user will only need to call this when troubleshooting - and installation problem, reproducing the conditions of a build without - an accelerated BLAS, or when being very careful about benchmarking - linear algebra operations. - - .. note:: Deprecated in NumPy 1.10.0 - The cblas functions have been integrated into the multarray - module and restoredot now longer does anything. It will be - removed in NumPy 1.11.0. - - See Also - -------- - alterdot : `restoredot` undoes the effects of `alterdot`. - - """ - # 2014-08-13, 1.10 - warnings.warn("restoredot no longer does anything.", - DeprecationWarning, stacklevel=2) +def _tensordot_dispatcher(a, b, axes=None): + return (a, b) +@array_function_dispatch(_tensordot_dispatcher) def tensordot(a, b, axes=2): """ - Compute tensor dot product along specified axes for arrays >= 1-D. + Compute tensor dot product along specified axes. - Given two tensors (arrays of dimension greater than or equal to one), - `a` and `b`, and an array_like object containing two array_like - objects, ``(a_axes, b_axes)``, sum the products of `a`'s and `b`'s - elements (components) over the axes specified by ``a_axes`` and - ``b_axes``. The third argument can be a single non-negative - integer_like scalar, ``N``; if it is such, then the last ``N`` - dimensions of `a` and the first ``N`` dimensions of `b` are summed - over. + Given two tensors, `a` and `b`, and an array_like object containing + two array_like objects, ``(a_axes, b_axes)``, sum the products of + `a`'s and `b`'s elements (components) over the axes specified by + ``a_axes`` and ``b_axes``. The third argument can be a single non-negative + integer_like scalar, ``N``; if it is such, then the last ``N`` dimensions + of `a` and the first ``N`` dimensions of `b` are summed over. Parameters ---------- - a, b : array_like, len(shape) >= 1 + a, b : array_like Tensors to "dot". axes : int or (2,) array_like @@ -1266,11 +941,11 @@ def tensordot(a, b, axes=2): >>> c.shape (5, 2) >>> c - array([[ 4400., 4730.], - [ 4532., 4874.], - [ 4664., 5018.], - [ 4796., 5162.], - [ 4928., 5306.]]) + array([[4400., 4730.], + [4532., 4874.], + [4664., 5018.], + [4796., 5162.], + [4928., 5306.]]) >>> # A slower but equivalent way of computing the same... >>> d = np.zeros((5,2)) >>> for i in range(5): @@ -1283,7 +958,7 @@ def tensordot(a, b, axes=2): [ True, True], [ True, True], [ True, True], - [ True, True]], dtype=bool) + [ True, True]]) An extended example taking advantage of the overloading of + and \\*: @@ -1296,45 +971,45 @@ def tensordot(a, b, axes=2): [3, 4]], [[5, 6], [7, 8]]]) - array([[a, b], - [c, d]], dtype=object) + array([['a', 'b'], + ['c', 'd']], dtype=object) >>> np.tensordot(a, A) # third argument default is 2 for double-contraction - array([abbcccdddd, aaaaabbbbbbcccccccdddddddd], dtype=object) + array(['abbcccdddd', 'aaaaabbbbbbcccccccdddddddd'], dtype=object) >>> np.tensordot(a, A, 1) - array([[[acc, bdd], - [aaacccc, bbbdddd]], - [[aaaaacccccc, bbbbbdddddd], - [aaaaaaacccccccc, bbbbbbbdddddddd]]], dtype=object) + array([[['acc', 'bdd'], + ['aaacccc', 'bbbdddd']], + [['aaaaacccccc', 'bbbbbdddddd'], + ['aaaaaaacccccccc', 'bbbbbbbdddddddd']]], dtype=object) >>> np.tensordot(a, A, 0) # tensor product (result too long to incl.) - array([[[[[a, b], - [c, d]], + array([[[[['a', 'b'], + ['c', 'd']], ... >>> np.tensordot(a, A, (0, 1)) - array([[[abbbbb, cddddd], - [aabbbbbb, ccdddddd]], - [[aaabbbbbbb, cccddddddd], - [aaaabbbbbbbb, ccccdddddddd]]], dtype=object) + array([[['abbbbb', 'cddddd'], + ['aabbbbbb', 'ccdddddd']], + [['aaabbbbbbb', 'cccddddddd'], + ['aaaabbbbbbbb', 'ccccdddddddd']]], dtype=object) >>> np.tensordot(a, A, (2, 1)) - array([[[abb, cdd], - [aaabbbb, cccdddd]], - [[aaaaabbbbbb, cccccdddddd], - [aaaaaaabbbbbbbb, cccccccdddddddd]]], dtype=object) + array([[['abb', 'cdd'], + ['aaabbbb', 'cccdddd']], + [['aaaaabbbbbb', 'cccccdddddd'], + ['aaaaaaabbbbbbbb', 'cccccccdddddddd']]], dtype=object) >>> np.tensordot(a, A, ((0, 1), (0, 1))) - array([abbbcccccddddddd, aabbbbccccccdddddddd], dtype=object) + array(['abbbcccccddddddd', 'aabbbbccccccdddddddd'], dtype=object) >>> np.tensordot(a, A, ((2, 1), (1, 0))) - array([acccbbdddd, aaaaacccccccbbbbbbdddddddd], dtype=object) + array(['acccbbdddd', 'aaaaacccccccbbbbbbdddddddd'], dtype=object) """ try: iter(axes) - except: + except Exception: axes_a = list(range(-axes, 0)) axes_b = list(range(0, axes)) else: @@ -1379,7 +1054,7 @@ def tensordot(a, b, axes=2): N2 = 1 for axis in axes_a: N2 *= as_[axis] - newshape_a = (-1, N2) + newshape_a = (int(multiply.reduce([as_[ax] for ax in notin])), N2) olda = [as_[axis] for axis in notin] notin = [k for k in range(ndb) if k not in axes_b] @@ -1387,7 +1062,7 @@ def tensordot(a, b, axes=2): N2 = 1 for axis in axes_b: N2 *= bs[axis] - newshape_b = (N2, -1) + newshape_b = (N2, int(multiply.reduce([bs[ax] for ax in notin]))) oldb = [bs[axis] for axis in notin] at = a.transpose(newaxes_a).reshape(newshape_a) @@ -1396,6 +1071,11 @@ def tensordot(a, b, axes=2): return res.reshape(olda + oldb) +def _roll_dispatcher(a, shift, axis=None): + return (a,) + + +@array_function_dispatch(_roll_dispatcher) def roll(a, shift, axis=None): """ Roll array elements along a given axis. @@ -1439,7 +1119,9 @@ def roll(a, shift, axis=None): >>> x = np.arange(10) >>> np.roll(x, 2) array([8, 9, 0, 1, 2, 3, 4, 5, 6, 7]) - + >>> np.roll(x, -2) + array([2, 3, 4, 5, 6, 7, 8, 9, 0, 1]) + >>> x2 = np.reshape(x, (2,5)) >>> x2 array([[0, 1, 2, 3, 4], @@ -1447,12 +1129,21 @@ def roll(a, shift, axis=None): >>> np.roll(x2, 1) array([[9, 0, 1, 2, 3], [4, 5, 6, 7, 8]]) + >>> np.roll(x2, -1) + array([[1, 2, 3, 4, 5], + [6, 7, 8, 9, 0]]) >>> np.roll(x2, 1, axis=0) array([[5, 6, 7, 8, 9], [0, 1, 2, 3, 4]]) + >>> np.roll(x2, -1, axis=0) + array([[5, 6, 7, 8, 9], + [0, 1, 2, 3, 4]]) >>> np.roll(x2, 1, axis=1) array([[4, 0, 1, 2, 3], [9, 5, 6, 7, 8]]) + >>> np.roll(x2, -1, axis=1) + array([[1, 2, 3, 4, 0], + [6, 7, 8, 9, 5]]) """ a = asanyarray(a) @@ -1460,16 +1151,14 @@ def roll(a, shift, axis=None): return roll(a.ravel(), shift, 0).reshape(a.shape) else: + axis = normalize_axis_tuple(axis, a.ndim, allow_duplicate=True) broadcasted = broadcast(shift, axis) if broadcasted.ndim > 1: raise ValueError( "'shift' and 'axis' should be scalars or 1D sequences") shifts = {ax: 0 for ax in range(a.ndim)} for sh, ax in broadcasted: - if -a.ndim <= ax < a.ndim: - shifts[ax % a.ndim] += sh - else: - raise ValueError("'axis' entry is out of bounds") + shifts[ax] += sh rolls = [((slice(None), slice(None)),)] * a.ndim for ax, offset in shifts.items(): @@ -1487,10 +1176,19 @@ def roll(a, shift, axis=None): return result +def _rollaxis_dispatcher(a, axis, start=None): + return (a,) + + +@array_function_dispatch(_rollaxis_dispatcher) def rollaxis(a, axis, start=0): """ Roll the specified axis backwards, until it lies in a given position. + This function continues to be supported for backward compatibility, but you + should prefer `moveaxis`. The `moveaxis` function was added in NumPy + 1.11. + Parameters ---------- a : ndarray @@ -1544,27 +1242,77 @@ def rollaxis(a, axis, start=0): return a.transpose(axes) -def _validate_axis(axis, ndim, argname): - try: - axis = [operator.index(axis)] - except TypeError: - axis = list(axis) - axis = [a + ndim if a < 0 else a for a in axis] - if not builtins.all(0 <= a < ndim for a in axis): - raise AxisError('invalid axis for this array in `%s` argument' % - argname) - if len(set(axis)) != len(axis): - raise ValueError('repeated axis in `%s` argument' % argname) +def normalize_axis_tuple(axis, ndim, argname=None, allow_duplicate=False): + """ + Normalizes an axis argument into a tuple of non-negative integer axes. + + This handles shorthands such as ``1`` and converts them to ``(1,)``, + as well as performing the handling of negative indices covered by + `normalize_axis_index`. + + By default, this forbids axes from being specified multiple times. + + Used internally by multi-axis-checking logic. + + .. versionadded:: 1.13.0 + + Parameters + ---------- + axis : int, iterable of int + The un-normalized index or indices of the axis. + ndim : int + The number of dimensions of the array that `axis` should be normalized + against. + argname : str, optional + A prefix to put before the error message, typically the name of the + argument. + allow_duplicate : bool, optional + If False, the default, disallow an axis from being specified twice. + + Returns + ------- + normalized_axes : tuple of int + The normalized axis index, such that `0 <= normalized_axis < ndim` + + Raises + ------ + AxisError + If any axis provided is out of range + ValueError + If an axis is repeated + + See also + -------- + normalize_axis_index : normalizing a single scalar axis + """ + # Optimization to speed-up the most common cases. + if type(axis) not in (tuple, list): + try: + axis = [operator.index(axis)] + except TypeError: + pass + # Going via an iterator directly is slower than via list comprehension. + axis = tuple([normalize_axis_index(ax, ndim, argname) for ax in axis]) + if not allow_duplicate and len(set(axis)) != len(axis): + if argname: + raise ValueError('repeated axis in `{}` argument'.format(argname)) + else: + raise ValueError('repeated axis') return axis +def _moveaxis_dispatcher(a, source, destination): + return (a,) + + +@array_function_dispatch(_moveaxis_dispatcher) def moveaxis(a, source, destination): """ Move axes of an array to new positions. Other axes remain in their original order. - .. versionadded::1.11.0 + .. versionadded:: 1.11.0 Parameters ---------- @@ -1599,7 +1347,7 @@ def moveaxis(a, source, destination): >>> np.transpose(x).shape (5, 4, 3) - >>> np.swapaxis(x, 0, -1).shape + >>> np.swapaxes(x, 0, -1).shape (5, 4, 3) >>> np.moveaxis(x, [0, 1], [-1, -2]).shape (5, 4, 3) @@ -1614,8 +1362,8 @@ def moveaxis(a, source, destination): a = asarray(a) transpose = a.transpose - source = _validate_axis(source, a.ndim, 'source') - destination = _validate_axis(destination, a.ndim, 'destination') + source = normalize_axis_tuple(source, a.ndim, 'source') + destination = normalize_axis_tuple(destination, a.ndim, 'destination') if len(source) != len(destination): raise ValueError('`source` and `destination` arguments must have ' 'the same number of elements') @@ -1631,9 +1379,14 @@ def moveaxis(a, source, destination): # fix hack in scipy which imports this function def _move_axis_to_0(a, axis): - return rollaxis(a, axis, 0) + return moveaxis(a, axis, 0) +def _cross_dispatcher(a, b, axisa=None, axisb=None, axisc=None, axis=None): + return (a, b) + + +@array_function_dispatch(_cross_dispatcher) def cross(a, b, axisa=-1, axisb=-1, axisc=-1, axis=None): """ Return the cross product of two (arrays of) vectors. @@ -1715,7 +1468,7 @@ def cross(a, b, axisa=-1, axisb=-1, axisc=-1, axis=None): >>> x = [1,2] >>> y = [4,5] >>> np.cross(x, y) - -3 + array(-3) Multiple vector cross-products. Note that the direction of the cross product vector is defined by the `right-hand rule`. @@ -1752,14 +1505,12 @@ def cross(a, b, axisa=-1, axisb=-1, axisc=-1, axis=None): a = asarray(a) b = asarray(b) # Check axisa and axisb are within bounds - axis_msg = "'axis{0}' out of bounds" - if axisa < -a.ndim or axisa >= a.ndim: - raise ValueError(axis_msg.format('a')) - if axisb < -b.ndim or axisb >= b.ndim: - raise ValueError(axis_msg.format('b')) + axisa = normalize_axis_index(axisa, a.ndim, msg_prefix='axisa') + axisb = normalize_axis_index(axisb, b.ndim, msg_prefix='axisb') + # Move working axis to the end of the shape - a = rollaxis(a, axisa, a.ndim) - b = rollaxis(b, axisb, b.ndim) + a = moveaxis(a, axisa, -1) + b = moveaxis(b, axisb, -1) msg = ("incompatible dimensions for cross product\n" "(dimension must be 2 or 3)") if a.shape[-1] not in (2, 3) or b.shape[-1] not in (2, 3): @@ -1770,8 +1521,7 @@ def cross(a, b, axisa=-1, axisb=-1, axisc=-1, axis=None): if a.shape[-1] == 3 or b.shape[-1] == 3: shape += (3,) # Check axisc is within bounds - if axisc < -len(shape) or axisc >= len(shape): - raise ValueError(axis_msg.format('c')) + axisc = normalize_axis_index(axisc, len(shape), msg_prefix='axisc') dtype = promote_types(a.dtype, b.dtype) cp = empty(shape, dtype) @@ -1831,199 +1581,13 @@ def cross(a, b, axisa=-1, axisb=-1, axisc=-1, axis=None): multiply(a0, b1, out=cp2) cp2 -= a1 * b0 - # This works because we are moving the last axis - return rollaxis(cp, -1, axisc) - - -# Use numarray's printing function -from .arrayprint import array2string, get_printoptions, set_printoptions - - -_typelessdata = [int_, float_, complex_] -if issubclass(intc, int): - _typelessdata.append(intc) + return moveaxis(cp, -1, axisc) -if issubclass(longlong, int): - _typelessdata.append(longlong) - - -def array_repr(arr, max_line_width=None, precision=None, suppress_small=None): - """ - Return the string representation of an array. - - Parameters - ---------- - arr : ndarray - Input array. - max_line_width : int, optional - The maximum number of columns the string should span. Newline - characters split the string appropriately after array elements. - precision : int, optional - Floating point precision. Default is the current printing precision - (usually 8), which can be altered using `set_printoptions`. - suppress_small : bool, optional - Represent very small numbers as zero, default is False. Very small - is defined by `precision`, if the precision is 8 then - numbers smaller than 5e-9 are represented as zero. - - Returns - ------- - string : str - The string representation of an array. - - See Also - -------- - array_str, array2string, set_printoptions - - Examples - -------- - >>> np.array_repr(np.array([1,2])) - 'array([1, 2])' - >>> np.array_repr(np.ma.array([0.])) - 'MaskedArray([ 0.])' - >>> np.array_repr(np.array([], np.int32)) - 'array([], dtype=int32)' - - >>> x = np.array([1e-6, 4e-7, 2, 3]) - >>> np.array_repr(x, precision=6, suppress_small=True) - 'array([ 0.000001, 0. , 2. , 3. ])' - - """ - if type(arr) is not ndarray: - class_name = type(arr).__name__ - else: - class_name = "array" - - if arr.size > 0 or arr.shape == (0,): - lst = array2string(arr, max_line_width, precision, suppress_small, - ', ', class_name + "(") - else: # show zero-length shape unless it is (0,) - lst = "[], shape=%s" % (repr(arr.shape),) - - skipdtype = (arr.dtype.type in _typelessdata) and arr.size > 0 - - if skipdtype: - return "%s(%s)" % (class_name, lst) - else: - typename = arr.dtype.name - # Quote typename in the output if it is "complex". - if typename and not (typename[0].isalpha() and typename.isalnum()): - typename = "'%s'" % typename - - lf = ' ' - if issubclass(arr.dtype.type, flexible): - if arr.dtype.names: - typename = "%s" % str(arr.dtype) - else: - typename = "'%s'" % str(arr.dtype) - lf = '\n'+' '*len(class_name + "(") - return "%s(%s,%sdtype=%s)" % (class_name, lst, lf, typename) - - -def array_str(a, max_line_width=None, precision=None, suppress_small=None): - """ - Return a string representation of the data in an array. - - The data in the array is returned as a single string. This function is - similar to `array_repr`, the difference being that `array_repr` also - returns information on the kind of array and its data type. - - Parameters - ---------- - a : ndarray - Input array. - max_line_width : int, optional - Inserts newlines if text is longer than `max_line_width`. The - default is, indirectly, 75. - precision : int, optional - Floating point precision. Default is the current printing precision - (usually 8), which can be altered using `set_printoptions`. - suppress_small : bool, optional - Represent numbers "very close" to zero as zero; default is False. - Very close is defined by precision: if the precision is 8, e.g., - numbers smaller (in absolute value) than 5e-9 are represented as - zero. - - See Also - -------- - array2string, array_repr, set_printoptions - - Examples - -------- - >>> np.array_str(np.arange(3)) - '[0 1 2]' - - """ - return array2string(a, max_line_width, precision, suppress_small, ' ', "", str) - - -def set_string_function(f, repr=True): - """ - Set a Python function to be used when pretty printing arrays. - - Parameters - ---------- - f : function or None - Function to be used to pretty print arrays. The function should expect - a single array argument and return a string of the representation of - the array. If None, the function is reset to the default NumPy function - to print arrays. - repr : bool, optional - If True (default), the function for pretty printing (``__repr__``) - is set, if False the function that returns the default string - representation (``__str__``) is set. - - See Also - -------- - set_printoptions, get_printoptions - - Examples - -------- - >>> def pprint(arr): - ... return 'HA! - What are you going to do now?' - ... - >>> np.set_string_function(pprint) - >>> a = np.arange(10) - >>> a - HA! - What are you going to do now? - >>> print(a) - [0 1 2 3 4 5 6 7 8 9] - - We can reset the function to the default: - - >>> np.set_string_function(None) - >>> a - array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) - - `repr` affects either pretty printing or normal string representation. - Note that ``__repr__`` is still affected by setting ``__str__`` - because the width of each array element in the returned string becomes - equal to the length of the result of ``__str__()``. - - >>> x = np.arange(4) - >>> np.set_string_function(lambda x:'random', repr=False) - >>> x.__str__() - 'random' - >>> x.__repr__() - 'array([ 0, 1, 2, 3])' - - """ - if f is None: - if repr: - return multiarray.set_string_function(array_repr, 1) - else: - return multiarray.set_string_function(array_str, 0) - else: - return multiarray.set_string_function(f, repr) - - -set_string_function(array_str, 0) -set_string_function(array_repr, 1) - little_endian = (sys.byteorder == 'little') +@set_module('numpy') def indices(dimensions, dtype=int): """ Return an array representing the indices of a grid. @@ -2095,6 +1659,7 @@ def indices(dimensions, dtype=int): return res +@set_module('numpy') def fromfunction(function, shape, **kwargs): """ Construct an array by executing a function over each coordinate. @@ -2108,8 +1673,8 @@ def fromfunction(function, shape, **kwargs): The function is called with N parameters, where N is the rank of `shape`. Each parameter represents the coordinates of the array varying along a specific axis. For example, if `shape` - were ``(2, 2)``, then the parameters in turn be (0, 0), (0, 1), - (1, 0), (1, 1). + were ``(2, 2)``, then the parameters would be + ``array([[0, 0], [1, 1]])`` and ``array([[0, 1], [0, 1]])`` shape : (N,) tuple of ints Shape of the output array, which also determines the shape of the coordinate arrays passed to `function`. @@ -2123,7 +1688,7 @@ def fromfunction(function, shape, **kwargs): The result of the call to `function` is passed back directly. Therefore the shape of `fromfunction` is completely determined by `function`. If `function` returns a scalar value, the shape of - `fromfunction` would match the `shape` parameter. + `fromfunction` would not match the `shape` parameter. See Also -------- @@ -2138,7 +1703,7 @@ def fromfunction(function, shape, **kwargs): >>> np.fromfunction(lambda i, j: i == j, (3, 3), dtype=int) array([[ True, False, False], [False, True, False], - [False, False, True]], dtype=bool) + [False, False, True]]) >>> np.fromfunction(lambda i, j: i + j, (3, 3), dtype=int) array([[0, 1, 2], @@ -2151,6 +1716,11 @@ def fromfunction(function, shape, **kwargs): return function(*args, **kwargs) +def _frombuffer(buf, dtype, shape, order): + return frombuffer(buf, dtype=dtype).reshape(shape, order=order) + + +@set_module('numpy') def isscalar(num): """ Returns True if the type of `num` is a scalar type. @@ -2165,22 +1735,69 @@ def isscalar(num): val : bool True if `num` is a scalar type, False if it is not. + See Also + -------- + ndim : Get the number of dimensions of an array + + Notes + ----- + In almost all cases ``np.ndim(x) == 0`` should be used instead of this + function, as that will also return true for 0d arrays. This is how + numpy overloads functions in the style of the ``dx`` arguments to `gradient` + and the ``bins`` argument to `histogram`. Some key differences: + + +--------------------------------------+---------------+-------------------+ + | x |``isscalar(x)``|``np.ndim(x) == 0``| + +======================================+===============+===================+ + | PEP 3141 numeric objects (including | ``True`` | ``True`` | + | builtins) | | | + +--------------------------------------+---------------+-------------------+ + | builtin string and buffer objects | ``True`` | ``True`` | + +--------------------------------------+---------------+-------------------+ + | other builtin objects, like | ``False`` | ``True`` | + | `pathlib.Path`, `Exception`, | | | + | the result of `re.compile` | | | + +--------------------------------------+---------------+-------------------+ + | third-party objects like | ``False`` | ``True`` | + | `matplotlib.figure.Figure` | | | + +--------------------------------------+---------------+-------------------+ + | zero-dimensional numpy arrays | ``False`` | ``True`` | + +--------------------------------------+---------------+-------------------+ + | other numpy arrays | ``False`` | ``False`` | + +--------------------------------------+---------------+-------------------+ + | `list`, `tuple`, and other sequence | ``False`` | ``False`` | + | objects | | | + +--------------------------------------+---------------+-------------------+ + Examples -------- >>> np.isscalar(3.1) True + >>> np.isscalar(np.array(3.1)) + False >>> np.isscalar([3.1]) False >>> np.isscalar(False) True + >>> np.isscalar('numpy') + True + + NumPy supports PEP 3141 numbers: + + >>> from fractions import Fraction + >>> np.isscalar(Fraction(5, 17)) + True + >>> from numbers import Number + >>> np.isscalar(Number()) + True """ - if isinstance(num, generic): - return True - else: - return type(num) in ScalarType + return (isinstance(num, generic) + or type(num) in ScalarType + or isinstance(num, numbers.Number)) +@set_module('numpy') def binary_repr(num, width=None): """ Return the binary representation of the input number as a string. @@ -2232,7 +1849,7 @@ def binary_repr(num, width=None): References ---------- .. [1] Wikipedia, "Two's complement", - http://en.wikipedia.org/wiki/Two's_complement + https://en.wikipedia.org/wiki/Two's_complement Examples -------- @@ -2252,7 +1869,7 @@ def binary_repr(num, width=None): '11101' """ - def warn_if_insufficient(width, binwdith): + def warn_if_insufficient(width, binwidth): if width is not None and width < binwidth: warnings.warn( "Insufficient bit width provided. This behavior " @@ -2291,6 +1908,7 @@ def binary_repr(num, width=None): return '1' * (outwidth - binwidth) + binary +@set_module('numpy') def base_repr(number, base=2, padding=0): """ Return a string representation of a number in the given base system. @@ -2361,6 +1979,10 @@ def load(file): load, save """ + # NumPy 1.15.0, 2017-12-10 + warnings.warn( + "np.core.numeric.load is deprecated, use pickle.load instead", + DeprecationWarning, stacklevel=2) if isinstance(file, type("")): file = open(file, "rb") return pickle.load(file) @@ -2381,6 +2003,7 @@ def _maketup(descr, val): return tuple(res) +@set_module('numpy') def identity(n, dtype=None): """ Return the identity array. @@ -2404,15 +2027,20 @@ def identity(n, dtype=None): Examples -------- >>> np.identity(3) - array([[ 1., 0., 0.], - [ 0., 1., 0.], - [ 0., 0., 1.]]) + array([[1., 0., 0.], + [0., 1., 0.], + [0., 0., 1.]]) """ from numpy import eye return eye(n, dtype=dtype) +def _allclose_dispatcher(a, b, rtol=None, atol=None, equal_nan=None): + return (a, b) + + +@array_function_dispatch(_allclose_dispatcher) def allclose(a, b, rtol=1.e-5, atol=1.e-8, equal_nan=False): """ Returns True if two arrays are element-wise equal within a tolerance. @@ -2448,7 +2076,7 @@ def allclose(a, b, rtol=1.e-5, atol=1.e-8, equal_nan=False): See Also -------- - isclose, all, any + isclose, all, any, equal Notes ----- @@ -2458,9 +2086,14 @@ def allclose(a, b, rtol=1.e-5, atol=1.e-8, equal_nan=False): absolute(`a` - `b`) <= (`atol` + `rtol` * absolute(`b`)) The above equation is not symmetric in `a` and `b`, so that - `allclose(a, b)` might be different from `allclose(b, a)` in + ``allclose(a, b)`` might be different from ``allclose(b, a)`` in some rare cases. + The comparison of `a` and `b` uses standard broadcasting, which + means that `a` and `b` need not have the same shape in order for + ``allclose(a, b)`` to evaluate to True. The same is true for + `equal` but not `array_equal`. + Examples -------- >>> np.allclose([1e10,1e-7], [1.00001e10,1e-8]) @@ -2479,6 +2112,11 @@ def allclose(a, b, rtol=1.e-5, atol=1.e-8, equal_nan=False): return bool(res) +def _isclose_dispatcher(a, b, rtol=None, atol=None, equal_nan=None): + return (a, b) + + +@array_function_dispatch(_isclose_dispatcher) def isclose(a, b, rtol=1.e-5, atol=1.e-8, equal_nan=False): """ Returns a boolean array where two arrays are element-wise equal within a @@ -2489,6 +2127,9 @@ def isclose(a, b, rtol=1.e-5, atol=1.e-8, equal_nan=False): `atol` are added together to compare against the absolute difference between `a` and `b`. + .. warning:: The default `atol` is not appropriate for comparing numbers + that are much smaller than one (see Notes). + Parameters ---------- a, b : array_like @@ -2521,32 +2162,43 @@ def isclose(a, b, rtol=1.e-5, atol=1.e-8, equal_nan=False): absolute(`a` - `b`) <= (`atol` + `rtol` * absolute(`b`)) - The above equation is not symmetric in `a` and `b`, so that - `isclose(a, b)` might be different from `isclose(b, a)` in - some rare cases. + Unlike the built-in `math.isclose`, the above equation is not symmetric + in `a` and `b` -- it assumes `b` is the reference value -- so that + `isclose(a, b)` might be different from `isclose(b, a)`. Furthermore, + the default value of atol is not zero, and is used to determine what + small values should be considered close to zero. The default value is + appropriate for expected values of order unity: if the expected values + are significantly smaller than one, it can result in false positives. + `atol` should be carefully selected for the use case at hand. A zero value + for `atol` will result in `False` if either `a` or `b` is zero. Examples -------- >>> np.isclose([1e10,1e-7], [1.00001e10,1e-8]) - array([True, False]) + array([ True, False]) >>> np.isclose([1e10,1e-8], [1.00001e10,1e-9]) - array([True, True]) + array([ True, True]) >>> np.isclose([1e10,1e-8], [1.0001e10,1e-9]) - array([False, True]) + array([False, True]) >>> np.isclose([1.0, np.nan], [1.0, np.nan]) - array([True, False]) + array([ True, False]) >>> np.isclose([1.0, np.nan], [1.0, np.nan], equal_nan=True) - array([True, True]) + array([ True, True]) + >>> np.isclose([1e-8, 1e-7], [0.0, 0.0]) + array([ True, False]) + >>> np.isclose([1e-100, 1e-7], [0.0, 0.0], atol=0.0) + array([False, False]) + >>> np.isclose([1e-10, 1e-10], [1e-20, 0.0]) + array([ True, True]) + >>> np.isclose([1e-10, 1e-10], [1e-20, 0.999999e-10], atol=0.0) + array([False, True]) """ def within_tol(x, y, atol, rtol): with errstate(invalid='ignore'): - result = less_equal(abs(x-y), atol + rtol * abs(y)) - if isscalar(a) and isscalar(b): - result = bool(result) - return result + return less_equal(abs(x-y), atol + rtol * abs(y)) - x = array(a, copy=False, subok=True, ndmin=1) - y = array(b, copy=False, subok=True, ndmin=1) + x = asanyarray(a) + y = asanyarray(b) # Make sure y is an inexact type to avoid bad behavior on abs(MIN_INT). # This will cause casting of x later. Also, make sure to allow subclasses @@ -2573,14 +2225,18 @@ def isclose(a, b, rtol=1.e-5, atol=1.e-8, equal_nan=False): if equal_nan: # Make NaN == NaN both_nan = isnan(x) & isnan(y) + + # Needed to treat masked arrays correctly. = True would not work. cond[both_nan] = both_nan[both_nan] - if isscalar(a) and isscalar(b): - return bool(cond) - else: - return cond + return cond[()] # Flatten 0d arrays to scalars +def _array_equal_dispatcher(a1, a2): + return (a1, a2) + + +@array_function_dispatch(_array_equal_dispatcher) def array_equal(a1, a2): """ True if two arrays have the same shape and elements, False otherwise. @@ -2616,13 +2272,18 @@ def array_equal(a1, a2): """ try: a1, a2 = asarray(a1), asarray(a2) - except: + except Exception: return False if a1.shape != a2.shape: return False return bool(asarray(a1 == a2).all()) +def _array_equiv_dispatcher(a1, a2): + return (a1, a2) + + +@array_function_dispatch(_array_equiv_dispatcher) def array_equiv(a1, a2): """ Returns True if input arrays are shape consistent and all elements equal. @@ -2660,454 +2321,43 @@ def array_equiv(a1, a2): """ try: a1, a2 = asarray(a1), asarray(a2) - except: + except Exception: return False try: multiarray.broadcast(a1, a2) - except: + except Exception: return False return bool(asarray(a1 == a2).all()) -_errdict = {"ignore":ERR_IGNORE, - "warn":ERR_WARN, - "raise":ERR_RAISE, - "call":ERR_CALL, - "print":ERR_PRINT, - "log":ERR_LOG} - -_errdict_rev = {} -for key in _errdict.keys(): - _errdict_rev[_errdict[key]] = key -del key - - -def seterr(all=None, divide=None, over=None, under=None, invalid=None): - """ - Set how floating-point errors are handled. - - Note that operations on integer scalar types (such as `int16`) are - handled like floating point, and are affected by these settings. - - Parameters - ---------- - all : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional - Set treatment for all types of floating-point errors at once: - - - ignore: Take no action when the exception occurs. - - warn: Print a `RuntimeWarning` (via the Python `warnings` module). - - raise: Raise a `FloatingPointError`. - - call: Call a function specified using the `seterrcall` function. - - print: Print a warning directly to ``stdout``. - - log: Record error in a Log object specified by `seterrcall`. - - The default is not to change the current behavior. - divide : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional - Treatment for division by zero. - over : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional - Treatment for floating-point overflow. - under : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional - Treatment for floating-point underflow. - invalid : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional - Treatment for invalid floating-point operation. - - Returns - ------- - old_settings : dict - Dictionary containing the old settings. - - See also - -------- - seterrcall : Set a callback function for the 'call' mode. - geterr, geterrcall, errstate - - Notes - ----- - The floating-point exceptions are defined in the IEEE 754 standard [1]: - - - Division by zero: infinite result obtained from finite numbers. - - Overflow: result too large to be expressed. - - Underflow: result so close to zero that some precision - was lost. - - Invalid operation: result is not an expressible number, typically - indicates that a NaN was produced. - - .. [1] http://en.wikipedia.org/wiki/IEEE_754 - - Examples - -------- - >>> old_settings = np.seterr(all='ignore') #seterr to known value - >>> np.seterr(over='raise') - {'over': 'ignore', 'divide': 'ignore', 'invalid': 'ignore', - 'under': 'ignore'} - >>> np.seterr(**old_settings) # reset to default - {'over': 'raise', 'divide': 'ignore', 'invalid': 'ignore', 'under': 'ignore'} - - >>> np.int16(32000) * np.int16(3) - 30464 - >>> old_settings = np.seterr(all='warn', over='raise') - >>> np.int16(32000) * np.int16(3) - Traceback (most recent call last): - File "<stdin>", line 1, in <module> - FloatingPointError: overflow encountered in short_scalars - - >>> old_settings = np.seterr(all='print') - >>> np.geterr() - {'over': 'print', 'divide': 'print', 'invalid': 'print', 'under': 'print'} - >>> np.int16(32000) * np.int16(3) - Warning: overflow encountered in short_scalars - 30464 - - """ - - pyvals = umath.geterrobj() - old = geterr() - - if divide is None: - divide = all or old['divide'] - if over is None: - over = all or old['over'] - if under is None: - under = all or old['under'] - if invalid is None: - invalid = all or old['invalid'] - - maskvalue = ((_errdict[divide] << SHIFT_DIVIDEBYZERO) + - (_errdict[over] << SHIFT_OVERFLOW) + - (_errdict[under] << SHIFT_UNDERFLOW) + - (_errdict[invalid] << SHIFT_INVALID)) - - pyvals[1] = maskvalue - umath.seterrobj(pyvals) - return old - - -def geterr(): - """ - Get the current way of handling floating-point errors. - - Returns - ------- - res : dict - A dictionary with keys "divide", "over", "under", and "invalid", - whose values are from the strings "ignore", "print", "log", "warn", - "raise", and "call". The keys represent possible floating-point - exceptions, and the values define how these exceptions are handled. - - See Also - -------- - geterrcall, seterr, seterrcall - - Notes - ----- - For complete documentation of the types of floating-point exceptions and - treatment options, see `seterr`. - - Examples - -------- - >>> np.geterr() - {'over': 'warn', 'divide': 'warn', 'invalid': 'warn', - 'under': 'ignore'} - >>> np.arange(3.) / np.arange(3.) - array([ NaN, 1., 1.]) - - >>> oldsettings = np.seterr(all='warn', over='raise') - >>> np.geterr() - {'over': 'raise', 'divide': 'warn', 'invalid': 'warn', 'under': 'warn'} - >>> np.arange(3.) / np.arange(3.) - __main__:1: RuntimeWarning: invalid value encountered in divide - array([ NaN, 1., 1.]) - - """ - maskvalue = umath.geterrobj()[1] - mask = 7 - res = {} - val = (maskvalue >> SHIFT_DIVIDEBYZERO) & mask - res['divide'] = _errdict_rev[val] - val = (maskvalue >> SHIFT_OVERFLOW) & mask - res['over'] = _errdict_rev[val] - val = (maskvalue >> SHIFT_UNDERFLOW) & mask - res['under'] = _errdict_rev[val] - val = (maskvalue >> SHIFT_INVALID) & mask - res['invalid'] = _errdict_rev[val] - return res - - -def setbufsize(size): - """ - Set the size of the buffer used in ufuncs. - - Parameters - ---------- - size : int - Size of buffer. - - """ - if size > 10e6: - raise ValueError("Buffer size, %s, is too big." % size) - if size < 5: - raise ValueError("Buffer size, %s, is too small." % size) - if size % 16 != 0: - raise ValueError("Buffer size, %s, is not a multiple of 16." % size) - - pyvals = umath.geterrobj() - old = getbufsize() - pyvals[0] = size - umath.seterrobj(pyvals) - return old - - -def getbufsize(): - """ - Return the size of the buffer used in ufuncs. - - Returns - ------- - getbufsize : int - Size of ufunc buffer in bytes. - - """ - return umath.geterrobj()[0] - - -def seterrcall(func): - """ - Set the floating-point error callback function or log object. - - There are two ways to capture floating-point error messages. The first - is to set the error-handler to 'call', using `seterr`. Then, set - the function to call using this function. - - The second is to set the error-handler to 'log', using `seterr`. - Floating-point errors then trigger a call to the 'write' method of - the provided object. - - Parameters - ---------- - func : callable f(err, flag) or object with write method - Function to call upon floating-point errors ('call'-mode) or - object whose 'write' method is used to log such message ('log'-mode). - - The call function takes two arguments. The first is a string describing the - type of error (such as "divide by zero", "overflow", "underflow", or "invalid value"), - and the second is the status flag. The flag is a byte, whose four - least-significant bits indicate the type of error, one of "divide", "over", - "under", "invalid":: - - [0 0 0 0 divide over under invalid] - - In other words, ``flags = divide + 2*over + 4*under + 8*invalid``. - - If an object is provided, its write method should take one argument, - a string. - - Returns - ------- - h : callable, log instance or None - The old error handler. - - See Also - -------- - seterr, geterr, geterrcall - - Examples - -------- - Callback upon error: - - >>> def err_handler(type, flag): - ... print("Floating point error (%s), with flag %s" % (type, flag)) - ... - - >>> saved_handler = np.seterrcall(err_handler) - >>> save_err = np.seterr(all='call') - - >>> np.array([1, 2, 3]) / 0.0 - Floating point error (divide by zero), with flag 1 - array([ Inf, Inf, Inf]) - - >>> np.seterrcall(saved_handler) - <function err_handler at 0x...> - >>> np.seterr(**save_err) - {'over': 'call', 'divide': 'call', 'invalid': 'call', 'under': 'call'} - - Log error message: - - >>> class Log(object): - ... def write(self, msg): - ... print("LOG: %s" % msg) - ... - - >>> log = Log() - >>> saved_handler = np.seterrcall(log) - >>> save_err = np.seterr(all='log') - - >>> np.array([1, 2, 3]) / 0.0 - LOG: Warning: divide by zero encountered in divide - <BLANKLINE> - array([ Inf, Inf, Inf]) - - >>> np.seterrcall(saved_handler) - <__main__.Log object at 0x...> - >>> np.seterr(**save_err) - {'over': 'log', 'divide': 'log', 'invalid': 'log', 'under': 'log'} - - """ - if func is not None and not isinstance(func, collections.Callable): - if not hasattr(func, 'write') or not isinstance(func.write, collections.Callable): - raise ValueError("Only callable can be used as callback") - pyvals = umath.geterrobj() - old = geterrcall() - pyvals[2] = func - umath.seterrobj(pyvals) - return old - - -def geterrcall(): - """ - Return the current callback function used on floating-point errors. - - When the error handling for a floating-point error (one of "divide", - "over", "under", or "invalid") is set to 'call' or 'log', the function - that is called or the log instance that is written to is returned by - `geterrcall`. This function or log instance has been set with - `seterrcall`. - - Returns - ------- - errobj : callable, log instance or None - The current error handler. If no handler was set through `seterrcall`, - ``None`` is returned. - - See Also - -------- - seterrcall, seterr, geterr - - Notes - ----- - For complete documentation of the types of floating-point exceptions and - treatment options, see `seterr`. - - Examples - -------- - >>> np.geterrcall() # we did not yet set a handler, returns None - - >>> oldsettings = np.seterr(all='call') - >>> def err_handler(type, flag): - ... print("Floating point error (%s), with flag %s" % (type, flag)) - >>> oldhandler = np.seterrcall(err_handler) - >>> np.array([1, 2, 3]) / 0.0 - Floating point error (divide by zero), with flag 1 - array([ Inf, Inf, Inf]) - - >>> cur_handler = np.geterrcall() - >>> cur_handler is err_handler - True - - """ - return umath.geterrobj()[2] - - -class _unspecified(object): - pass -_Unspecified = _unspecified() - - -class errstate(object): - """ - errstate(**kwargs) - - Context manager for floating-point error handling. - - Using an instance of `errstate` as a context manager allows statements in - that context to execute with a known error handling behavior. Upon entering - the context the error handling is set with `seterr` and `seterrcall`, and - upon exiting it is reset to what it was before. - - Parameters - ---------- - kwargs : {divide, over, under, invalid} - Keyword arguments. The valid keywords are the possible floating-point - exceptions. Each keyword should have a string value that defines the - treatment for the particular error. Possible values are - {'ignore', 'warn', 'raise', 'call', 'print', 'log'}. - - See Also - -------- - seterr, geterr, seterrcall, geterrcall - - Notes - ----- - The ``with`` statement was introduced in Python 2.5, and can only be used - there by importing it: ``from __future__ import with_statement``. In - earlier Python versions the ``with`` statement is not available. - - For complete documentation of the types of floating-point exceptions and - treatment options, see `seterr`. - - Examples - -------- - >>> from __future__ import with_statement # use 'with' in Python 2.5 - >>> olderr = np.seterr(all='ignore') # Set error handling to known state. - - >>> np.arange(3) / 0. - array([ NaN, Inf, Inf]) - >>> with np.errstate(divide='warn'): - ... np.arange(3) / 0. - ... - __main__:2: RuntimeWarning: divide by zero encountered in divide - array([ NaN, Inf, Inf]) - - >>> np.sqrt(-1) - nan - >>> with np.errstate(invalid='raise'): - ... np.sqrt(-1) - Traceback (most recent call last): - File "<stdin>", line 2, in <module> - FloatingPointError: invalid value encountered in sqrt - - Outside the context the error handling behavior has not changed: - - >>> np.geterr() - {'over': 'warn', 'divide': 'warn', 'invalid': 'warn', - 'under': 'ignore'} - - """ - # Note that we don't want to run the above doctests because they will fail - # without a from __future__ import with_statement - - def __init__(self, **kwargs): - self.call = kwargs.pop('call', _Unspecified) - self.kwargs = kwargs - - def __enter__(self): - self.oldstate = seterr(**self.kwargs) - if self.call is not _Unspecified: - self.oldcall = seterrcall(self.call) - - def __exit__(self, *exc_info): - seterr(**self.oldstate) - if self.call is not _Unspecified: - seterrcall(self.oldcall) - - -def _setdef(): - defval = [UFUNC_BUFSIZE_DEFAULT, ERR_DEFAULT, None] - umath.seterrobj(defval) - - -# set the default values -_setdef() - Inf = inf = infty = Infinity = PINF nan = NaN = NAN False_ = bool_(False) True_ = bool_(True) + +def extend_all(module): + existing = set(__all__) + mall = getattr(module, '__all__') + for a in mall: + if a not in existing: + __all__.append(a) + + from .umath import * from .numerictypes import * from . import fromnumeric from .fromnumeric import * +from . import arrayprint +from .arrayprint import * +from . import _asarray +from ._asarray import * +from . import _ufunc_config +from ._ufunc_config import * extend_all(fromnumeric) extend_all(umath) extend_all(numerictypes) +extend_all(arrayprint) +extend_all(_asarray) +extend_all(_ufunc_config) |