""" Create the numpy.core.multiarray namespace for backward compatibility. In v1.16 the multiarray and umath c-extension modules were merged into a single _multiarray_umath extension module. So we replicate the old namespace by importing from the extension module. """ from . import _multiarray_umath from .overrides import array_function_dispatch import numpy as np from numpy.core._multiarray_umath import * from numpy.core._multiarray_umath import ( _fastCopyAndTranspose, _flagdict, _insert, _reconstruct, _vec_string, _ARRAY_API, _monotonicity ) __all__ = [ '_ARRAY_API', 'ALLOW_THREADS', 'BUFSIZE', 'CLIP', 'DATETIMEUNITS', 'ITEM_HASOBJECT', 'ITEM_IS_POINTER', 'LIST_PICKLE', 'MAXDIMS', 'MAY_SHARE_BOUNDS', 'MAY_SHARE_EXACT', 'NEEDS_INIT', 'NEEDS_PYAPI', 'RAISE', 'USE_GETITEM', 'USE_SETITEM', 'WRAP', '_fastCopyAndTranspose', '_flagdict', '_insert', '_reconstruct', '_vec_string', '_monotonicity', 'add_docstring', 'arange', 'array', 'bincount', 'broadcast', 'busday_count', 'busday_offset', 'busdaycalendar', 'can_cast', 'compare_chararrays', 'concatenate', 'copyto', 'correlate', 'correlate2', 'count_nonzero', 'c_einsum', 'datetime_as_string', 'datetime_data', 'digitize', 'dot', 'dragon4_positional', 'dragon4_scientific', 'dtype', 'empty', 'empty_like', 'error', 'flagsobj', 'flatiter', 'format_longfloat', 'frombuffer', 'fromfile', 'fromiter', 'fromstring', 'getbuffer', 'inner', 'int_asbuffer', 'interp', 'interp_complex', 'is_busday', 'lexsort', 'matmul', 'may_share_memory', 'min_scalar_type', 'ndarray', 'nditer', 'nested_iters', 'newbuffer', 'normalize_axis_index', 'packbits', 'promote_types', 'putmask', 'ravel_multi_index', 'result_type', 'scalar', 'set_datetimeparse_function', 'set_legacy_print_mode', 'set_numeric_ops', 'set_string_function', 'set_typeDict', 'shares_memory', 'test_interrupt', 'tracemalloc_domain', 'typeinfo', 'unpackbits', 'unravel_index', 'vdot', 'where', 'zeros'] def _empty_like_dispatcher(prototype, dtype=None, order=None, subok=None): return (prototype,) @array_function_dispatch(_empty_like_dispatcher) def empty_like(prototype, dtype=None, order='K', subok=True): """Return a new array with the same shape and type as a given array. Parameters ---------- prototype : array_like The shape and data-type of `prototype` define these same attributes of the returned array. dtype : data-type, optional Overrides the data type of the result. .. versionadded:: 1.6.0 order : {'C', 'F', 'A', or 'K'}, optional Overrides the memory layout of the result. 'C' means C-order, 'F' means F-order, 'A' means 'F' if ``prototype`` is Fortran contiguous, 'C' otherwise. 'K' means match the layout of ``prototype`` as closely as possible. .. versionadded:: 1.6.0 subok : bool, optional. If True, then the newly created array will use the sub-class type of 'a', otherwise it will be a base-class array. Defaults to True. Returns ------- out : ndarray Array of uninitialized (arbitrary) data with the same shape and type as `prototype`. See Also -------- 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_like : Return a new array with shape of input filled with value. empty : Return a new uninitialized array. Notes ----- This function does *not* initialize the returned array; to do that use `zeros_like` or `ones_like` instead. It may be marginally faster than the functions that do set the array values. Examples -------- >>> a = ([1,2,3], [4,5,6]) # a is array-like >>> np.empty_like(a) array([[-1073741821, -1073741821, 3], #random [ 0, 0, -1073741821]]) >>> a = np.array([[1., 2., 3.],[4.,5.,6.]]) >>> np.empty_like(a) array([[ -2.00000715e+000, 1.48219694e-323, -2.00000572e+000],#random [ 4.38791518e-305, -2.00000715e+000, 4.17269252e-309]]) """ return _multiarray_umath.empty_like(prototype, dtype, order, subok) def _concatenate_dispatcher(arrays, axis=None, out=None): for array in arrays: yield array yield out @array_function_dispatch(_concatenate_dispatcher) def concatenate(arrays, axis=0, out=None): """ concatenate((a1, a2, ...), axis=0, out=None) Join a sequence of arrays along an existing axis. Parameters ---------- a1, a2, ... : sequence of array_like The arrays must have the same shape, except in the dimension corresponding to `axis` (the first, by default). axis : int, optional The axis along which the arrays will be joined. If axis is None, arrays are flattened before use. Default is 0. out : ndarray, optional If provided, the destination to place the result. The shape must be correct, matching that of what concatenate would have returned if no out argument were specified. Returns ------- res : ndarray The concatenated array. See Also -------- ma.concatenate : Concatenate function that preserves input masks. array_split : Split an array into multiple sub-arrays of equal or near-equal size. split : Split array into a list of multiple sub-arrays of equal size. hsplit : Split array into multiple sub-arrays horizontally (column wise) vsplit : Split array into multiple sub-arrays vertically (row wise) dsplit : Split array into multiple sub-arrays along the 3rd axis (depth). stack : Stack a sequence of arrays along a new axis. hstack : Stack arrays in sequence horizontally (column wise) vstack : Stack arrays in sequence vertically (row wise) dstack : Stack arrays in sequence depth wise (along third dimension) block : Assemble arrays from blocks. Notes ----- When one or more of the arrays to be concatenated is a MaskedArray, this function will return a MaskedArray object instead of an ndarray, but the input masks are *not* preserved. In cases where a MaskedArray is expected as input, use the ma.concatenate function from the masked array module instead. Examples -------- >>> a = np.array([[1, 2], [3, 4]]) >>> b = np.array([[5, 6]]) >>> np.concatenate((a, b), axis=0) array([[1, 2], [3, 4], [5, 6]]) >>> np.concatenate((a, b.T), axis=1) array([[1, 2, 5], [3, 4, 6]]) >>> np.concatenate((a, b), axis=None) array([1, 2, 3, 4, 5, 6]) This function will not preserve masking of MaskedArray inputs. >>> a = np.ma.arange(3) >>> a[1] = np.ma.masked >>> b = np.arange(2, 5) >>> a masked_array(data=[0, --, 2], mask=[False, True, False], fill_value=999999) >>> b array([2, 3, 4]) >>> np.concatenate([a, b]) masked_array(data=[0, 1, 2, 2, 3, 4], mask=False, fill_value=999999) >>> np.ma.concatenate([a, b]) masked_array(data=[0, --, 2, 2, 3, 4], mask=[False, True, False, False, False, False], fill_value=999999) """ return _multiarray_umath.concatenate(arrays, axis, out) def _inner_dispatcher(a, b): return (a, b) @array_function_dispatch(_inner_dispatcher) def inner(a, b): """ Inner product of two arrays. Ordinary inner product of vectors for 1-D arrays (without complex conjugation), in higher dimensions a sum product over the last axes. Parameters ---------- a, b : array_like If `a` and `b` are nonscalar, their last dimensions must match. Returns ------- out : ndarray `out.shape = a.shape[:-1] + b.shape[:-1]` Raises ------ ValueError If the last dimension of `a` and `b` has different size. See Also -------- tensordot : Sum products over arbitrary axes. dot : Generalised matrix product, using second last dimension of `b`. einsum : Einstein summation convention. Notes ----- For vectors (1-D arrays) it computes the ordinary inner-product:: np.inner(a, b) = sum(a[:]*b[:]) More generally, if `ndim(a) = r > 0` and `ndim(b) = s > 0`:: np.inner(a, b) = np.tensordot(a, b, axes=(-1,-1)) or explicitly:: np.inner(a, b)[i0,...,ir-1,j0,...,js-1] = sum(a[i0,...,ir-1,:]*b[j0,...,js-1,:]) In addition `a` or `b` may be scalars, in which case:: np.inner(a,b) = a*b Examples -------- Ordinary inner product for vectors: >>> a = np.array([1,2,3]) >>> b = np.array([0,1,0]) >>> np.inner(a, b) 2 A multidimensional example: >>> a = np.arange(24).reshape((2,3,4)) >>> b = np.arange(4) >>> np.inner(a, b) array([[ 14, 38, 62], [ 86, 110, 134]]) An example where `b` is a scalar: >>> np.inner(np.eye(2), 7) array([[ 7., 0.], [ 0., 7.]]) """ return _multiarray_umath.inner(a, b) def _where_dispatcher(condition, x=np._NoValue, y=np._NoValue): return (condition, x, y) @array_function_dispatch(_where_dispatcher) def where(condition, x=np._NoValue, y=np._NoValue): """ where(condition, [x, y]) Return elements chosen from `x` or `y` depending on `condition`. .. note:: When only `condition` is provided, this function is a shorthand for ``np.asarray(condition).nonzero()``. Using `nonzero` directly should be preferred, as it behaves correctly for subclasses. The rest of this documentation covers only the case where all three arguments are provided. Parameters ---------- condition : array_like, bool Where True, yield `x`, otherwise yield `y`. x, y : array_like Values from which to choose. `x`, `y` and `condition` need to be broadcastable to some shape. Returns ------- out : ndarray An array with elements from `x` where `condition` is True, and elements from `y` elsewhere. See Also -------- choose nonzero : The function that is called when x and y are omitted Notes ----- If all the arrays are 1-D, `where` is equivalent to:: [xv if c else yv for c, xv, yv in zip(condition, x, y)] Examples -------- >>> a = np.arange(10) >>> a array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) >>> np.where(a < 5, a, 10*a) array([ 0, 1, 2, 3, 4, 50, 60, 70, 80, 90]) This can be used on multidimensional arrays too: >>> np.where([[True, False], [True, True]], ... [[1, 2], [3, 4]], ... [[9, 8], [7, 6]]) array([[1, 8], [3, 4]]) The shapes of x, y, and the condition are broadcast together: >>> x, y = np.ogrid[:3, :4] >>> np.where(x < y, x, 10 + y) # both x and 10+y are broadcast array([[10, 0, 0, 0], [10, 11, 1, 1], [10, 11, 12, 2]]) >>> a = np.array([[0, 1, 2], ... [0, 2, 4], ... [0, 3, 6]]) >>> np.where(a < 4, a, -1) # -1 is broadcast array([[ 0, 1, 2], [ 0, 2, -1], [ 0, 3, -1]]) """ # _multiarray_umath.where only accepts positional arguments args = tuple(a for a in (x, y) if a is not np._NoValue) return _multiarray_umath.where(condition, *args)