diff options
Diffstat (limited to 'numpy/core/numeric.py')
| -rw-r--r-- | numpy/core/numeric.py | 315 |
1 files changed, 6 insertions, 309 deletions
diff --git a/numpy/core/numeric.py b/numpy/core/numeric.py index b4255e733..c15b021c2 100644 --- a/numpy/core/numeric.py +++ b/numpy/core/numeric.py @@ -39,6 +39,7 @@ from .umath import (multiply, invert, sin, UFUNC_BUFSIZE_DEFAULT, from . import numerictypes from .numerictypes import longlong, intc, int_, float_, complex_, bool_ from ._exceptions import TooHardError, AxisError +from ._asarray import asarray, asanyarray bitwise_not = invert ufunc = type(sin) @@ -68,10 +69,9 @@ __all__ = [ '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', + 'result_type', 'isfortran', 'empty_like', 'zeros_like', 'ones_like', 'correlate', 'convolve', 'inner', 'dot', 'outer', 'vdot', 'roll', - 'rollaxis', 'moveaxis', 'cross', 'tensordot', 'little_endian', 'require', + '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', 'seterr', @@ -460,312 +460,6 @@ def count_nonzero(a, axis=None): @set_module('numpy') -def asarray(a, dtype=None, order=None): - """Convert the input to an array. - - 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.recarray, np.ndarray) - True - >>> a = np.array([(1.0, 2), (3.0, 4)], dtype='f4,i4').view(np.recarray) - >>> np.asarray(a) is a - False - >>> np.asanyarray(a) is a - True - - """ - return array(a, dtype, copy=False, order=order) - - -@set_module('numpy') -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.array([(1.0, 2), (3.0, 4)], dtype='f4,i4').view(np.recarray) - >>> np.asanyarray(a) is a - True - - """ - return array(a, dtype, copy=False, order=order, subok=True) - - -@set_module('numpy') -def ascontiguousarray(a, dtype=None): - """ - Return a contiguous array (ndim >= 1) 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 - - Note: This function returns an array with at least one-dimension (1-d) - so it will not preserve 0-d arrays. - - """ - return array(a, dtype, copy=False, order='C', ndmin=1) - - -@set_module('numpy') -def asfortranarray(a, dtype=None): - """ - Return an array (ndim >= 1) 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 - - Note: This function returns an array with at least one-dimension (1-d) - so it will not preserve 0-d arrays. - - """ - return array(a, dtype, copy=False, order='F', ndmin=1) - - -@set_module('numpy') -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 - WRITEBACKIFCOPY : False - 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 - WRITEBACKIFCOPY : False - 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 = {possible_flags[x.upper()] for x in requirements} - - if 'E' in requirements: - requirements.remove('E') - subok = False - else: - subok = True - - order = 'A' - if requirements >= {'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 - - -@set_module('numpy') def isfortran(a): """ Returns True if the array is Fortran contiguous but *not* C contiguous. @@ -3096,7 +2790,10 @@ from . import fromnumeric from .fromnumeric import * from . import arrayprint from .arrayprint import * +from . import _asarray +from ._asarray import * extend_all(fromnumeric) extend_all(umath) extend_all(numerictypes) extend_all(arrayprint) +extend_all(_asarray) |