1632 lines
52 KiB
Python
1632 lines
52 KiB
Python
"""
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Create the numpy.core.multiarray namespace for backward compatibility. In v1.16
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the multiarray and umath c-extension modules were merged into a single
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_multiarray_umath extension module. So we replicate the old namespace
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by importing from the extension module.
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"""
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import functools
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import sys
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import warnings
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import sys
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from . import overrides
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from . import _multiarray_umath
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import numpy as np
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from numpy.core._multiarray_umath import *
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from numpy.core._multiarray_umath import (
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_fastCopyAndTranspose, _flagdict, _insert, _reconstruct, _vec_string,
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_ARRAY_API, _monotonicity, _get_ndarray_c_version
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)
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__all__ = [
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'_ARRAY_API', 'ALLOW_THREADS', 'BUFSIZE', 'CLIP', 'DATETIMEUNITS',
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'ITEM_HASOBJECT', 'ITEM_IS_POINTER', 'LIST_PICKLE', 'MAXDIMS',
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'MAY_SHARE_BOUNDS', 'MAY_SHARE_EXACT', 'NEEDS_INIT', 'NEEDS_PYAPI',
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'RAISE', 'USE_GETITEM', 'USE_SETITEM', 'WRAP', '_fastCopyAndTranspose',
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'_flagdict', '_insert', '_reconstruct', '_vec_string', '_monotonicity',
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'add_docstring', 'arange', 'array', 'bincount', 'broadcast',
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'busday_count', 'busday_offset', 'busdaycalendar', 'can_cast',
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'compare_chararrays', 'concatenate', 'copyto', 'correlate', 'correlate2',
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'count_nonzero', 'c_einsum', 'datetime_as_string', 'datetime_data',
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'digitize', 'dot', 'dragon4_positional', 'dragon4_scientific', 'dtype',
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'empty', 'empty_like', 'error', 'flagsobj', 'flatiter', 'format_longfloat',
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'frombuffer', 'fromfile', 'fromiter', 'fromstring', 'inner',
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'int_asbuffer', 'interp', 'interp_complex', 'is_busday', 'lexsort',
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'matmul', 'may_share_memory', 'min_scalar_type', 'ndarray', 'nditer',
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'nested_iters', 'normalize_axis_index', 'packbits',
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'promote_types', 'putmask', 'ravel_multi_index', 'result_type', 'scalar',
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'set_datetimeparse_function', 'set_legacy_print_mode', 'set_numeric_ops',
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'set_string_function', 'set_typeDict', 'shares_memory', 'test_interrupt',
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'tracemalloc_domain', 'typeinfo', 'unpackbits', 'unravel_index', 'vdot',
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'where', 'zeros']
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if sys.version_info.major < 3:
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__all__ += ['newbuffer', 'getbuffer']
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# For backward compatibility, make sure pickle imports these functions from here
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_reconstruct.__module__ = 'numpy.core.multiarray'
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scalar.__module__ = 'numpy.core.multiarray'
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arange.__module__ = 'numpy'
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array.__module__ = 'numpy'
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datetime_data.__module__ = 'numpy'
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empty.__module__ = 'numpy'
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frombuffer.__module__ = 'numpy'
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fromfile.__module__ = 'numpy'
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fromiter.__module__ = 'numpy'
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frompyfunc.__module__ = 'numpy'
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fromstring.__module__ = 'numpy'
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geterrobj.__module__ = 'numpy'
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may_share_memory.__module__ = 'numpy'
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nested_iters.__module__ = 'numpy'
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promote_types.__module__ = 'numpy'
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set_numeric_ops.__module__ = 'numpy'
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seterrobj.__module__ = 'numpy'
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zeros.__module__ = 'numpy'
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# We can't verify dispatcher signatures because NumPy's C functions don't
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# support introspection.
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array_function_from_c_func_and_dispatcher = functools.partial(
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overrides.array_function_from_dispatcher,
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module='numpy', docs_from_dispatcher=True, verify=False)
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@array_function_from_c_func_and_dispatcher(_multiarray_umath.empty_like)
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def empty_like(prototype, dtype=None, order=None, subok=None, shape=None):
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"""
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empty_like(prototype, dtype=None, order='K', subok=True, shape=None)
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Return a new array with the same shape and type as a given array.
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Parameters
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----------
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prototype : array_like
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The shape and data-type of `prototype` define these same attributes
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of the returned array.
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dtype : data-type, optional
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Overrides the data type of the result.
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.. versionadded:: 1.6.0
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order : {'C', 'F', 'A', or 'K'}, optional
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Overrides the memory layout of the result. 'C' means C-order,
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'F' means F-order, 'A' means 'F' if ``prototype`` is Fortran
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contiguous, 'C' otherwise. 'K' means match the layout of ``prototype``
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as closely as possible.
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.. versionadded:: 1.6.0
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subok : bool, optional.
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If True, then the newly created array will use the sub-class
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type of 'a', otherwise it will be a base-class array. Defaults
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to True.
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shape : int or sequence of ints, optional.
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Overrides the shape of the result. If order='K' and the number of
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dimensions is unchanged, will try to keep order, otherwise,
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order='C' is implied.
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.. versionadded:: 1.17.0
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Returns
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-------
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out : ndarray
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Array of uninitialized (arbitrary) data with the same
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shape and type as `prototype`.
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See Also
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--------
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ones_like : Return an array of ones with shape and type of input.
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zeros_like : Return an array of zeros with shape and type of input.
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full_like : Return a new array with shape of input filled with value.
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empty : Return a new uninitialized array.
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Notes
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-----
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This function does *not* initialize the returned array; to do that use
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`zeros_like` or `ones_like` instead. It may be marginally faster than
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the functions that do set the array values.
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Examples
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--------
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>>> a = ([1,2,3], [4,5,6]) # a is array-like
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>>> np.empty_like(a)
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array([[-1073741821, -1073741821, 3], # uninitialized
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[ 0, 0, -1073741821]])
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>>> a = np.array([[1., 2., 3.],[4.,5.,6.]])
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>>> np.empty_like(a)
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array([[ -2.00000715e+000, 1.48219694e-323, -2.00000572e+000], # uninitialized
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[ 4.38791518e-305, -2.00000715e+000, 4.17269252e-309]])
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"""
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return (prototype,)
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@array_function_from_c_func_and_dispatcher(_multiarray_umath.concatenate)
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def concatenate(arrays, axis=None, out=None):
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"""
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concatenate((a1, a2, ...), axis=0, out=None)
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Join a sequence of arrays along an existing axis.
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Parameters
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----------
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a1, a2, ... : sequence of array_like
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The arrays must have the same shape, except in the dimension
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corresponding to `axis` (the first, by default).
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axis : int, optional
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The axis along which the arrays will be joined. If axis is None,
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arrays are flattened before use. Default is 0.
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out : ndarray, optional
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If provided, the destination to place the result. The shape must be
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correct, matching that of what concatenate would have returned if no
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out argument were specified.
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Returns
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-------
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res : ndarray
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The concatenated array.
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See Also
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--------
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ma.concatenate : Concatenate function that preserves input masks.
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array_split : Split an array into multiple sub-arrays of equal or
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near-equal size.
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split : Split array into a list of multiple sub-arrays of equal size.
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hsplit : Split array into multiple sub-arrays horizontally (column wise)
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vsplit : Split array into multiple sub-arrays vertically (row wise)
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dsplit : Split array into multiple sub-arrays along the 3rd axis (depth).
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stack : Stack a sequence of arrays along a new axis.
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hstack : Stack arrays in sequence horizontally (column wise)
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vstack : Stack arrays in sequence vertically (row wise)
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dstack : Stack arrays in sequence depth wise (along third dimension)
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block : Assemble arrays from blocks.
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Notes
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-----
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When one or more of the arrays to be concatenated is a MaskedArray,
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this function will return a MaskedArray object instead of an ndarray,
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but the input masks are *not* preserved. In cases where a MaskedArray
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is expected as input, use the ma.concatenate function from the masked
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array module instead.
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Examples
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--------
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>>> a = np.array([[1, 2], [3, 4]])
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>>> b = np.array([[5, 6]])
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>>> np.concatenate((a, b), axis=0)
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array([[1, 2],
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[3, 4],
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[5, 6]])
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>>> np.concatenate((a, b.T), axis=1)
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array([[1, 2, 5],
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[3, 4, 6]])
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>>> np.concatenate((a, b), axis=None)
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array([1, 2, 3, 4, 5, 6])
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This function will not preserve masking of MaskedArray inputs.
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>>> a = np.ma.arange(3)
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>>> a[1] = np.ma.masked
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>>> b = np.arange(2, 5)
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>>> a
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masked_array(data=[0, --, 2],
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mask=[False, True, False],
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fill_value=999999)
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>>> b
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array([2, 3, 4])
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>>> np.concatenate([a, b])
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masked_array(data=[0, 1, 2, 2, 3, 4],
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mask=False,
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fill_value=999999)
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>>> np.ma.concatenate([a, b])
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masked_array(data=[0, --, 2, 2, 3, 4],
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mask=[False, True, False, False, False, False],
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fill_value=999999)
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"""
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if out is not None:
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# optimize for the typical case where only arrays is provided
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arrays = list(arrays)
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arrays.append(out)
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return arrays
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@array_function_from_c_func_and_dispatcher(_multiarray_umath.inner)
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def inner(a, b):
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"""
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inner(a, b)
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Inner product of two arrays.
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Ordinary inner product of vectors for 1-D arrays (without complex
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conjugation), in higher dimensions a sum product over the last axes.
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Parameters
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----------
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a, b : array_like
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If `a` and `b` are nonscalar, their last dimensions must match.
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Returns
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-------
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out : ndarray
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`out.shape = a.shape[:-1] + b.shape[:-1]`
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Raises
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------
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ValueError
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If the last dimension of `a` and `b` has different size.
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See Also
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--------
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tensordot : Sum products over arbitrary axes.
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dot : Generalised matrix product, using second last dimension of `b`.
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einsum : Einstein summation convention.
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Notes
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-----
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For vectors (1-D arrays) it computes the ordinary inner-product::
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np.inner(a, b) = sum(a[:]*b[:])
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More generally, if `ndim(a) = r > 0` and `ndim(b) = s > 0`::
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np.inner(a, b) = np.tensordot(a, b, axes=(-1,-1))
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or explicitly::
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np.inner(a, b)[i0,...,ir-1,j0,...,js-1]
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= sum(a[i0,...,ir-1,:]*b[j0,...,js-1,:])
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In addition `a` or `b` may be scalars, in which case::
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np.inner(a,b) = a*b
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Examples
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--------
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Ordinary inner product for vectors:
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>>> a = np.array([1,2,3])
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>>> b = np.array([0,1,0])
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>>> np.inner(a, b)
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2
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A multidimensional example:
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>>> a = np.arange(24).reshape((2,3,4))
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>>> b = np.arange(4)
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>>> np.inner(a, b)
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array([[ 14, 38, 62],
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[ 86, 110, 134]])
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An example where `b` is a scalar:
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>>> np.inner(np.eye(2), 7)
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array([[7., 0.],
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[0., 7.]])
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"""
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return (a, b)
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@array_function_from_c_func_and_dispatcher(_multiarray_umath.where)
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def where(condition, x=None, y=None):
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"""
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where(condition, [x, y])
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Return elements chosen from `x` or `y` depending on `condition`.
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.. note::
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When only `condition` is provided, this function is a shorthand for
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``np.asarray(condition).nonzero()``. Using `nonzero` directly should be
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preferred, as it behaves correctly for subclasses. The rest of this
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documentation covers only the case where all three arguments are
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provided.
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Parameters
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----------
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condition : array_like, bool
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Where True, yield `x`, otherwise yield `y`.
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x, y : array_like
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Values from which to choose. `x`, `y` and `condition` need to be
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broadcastable to some shape.
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Returns
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-------
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out : ndarray
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An array with elements from `x` where `condition` is True, and elements
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from `y` elsewhere.
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See Also
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--------
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choose
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nonzero : The function that is called when x and y are omitted
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Notes
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-----
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If all the arrays are 1-D, `where` is equivalent to::
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[xv if c else yv
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for c, xv, yv in zip(condition, x, y)]
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Examples
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--------
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>>> a = np.arange(10)
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>>> a
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array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
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>>> np.where(a < 5, a, 10*a)
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array([ 0, 1, 2, 3, 4, 50, 60, 70, 80, 90])
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This can be used on multidimensional arrays too:
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>>> np.where([[True, False], [True, True]],
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... [[1, 2], [3, 4]],
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... [[9, 8], [7, 6]])
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array([[1, 8],
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[3, 4]])
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The shapes of x, y, and the condition are broadcast together:
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>>> x, y = np.ogrid[:3, :4]
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>>> np.where(x < y, x, 10 + y) # both x and 10+y are broadcast
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array([[10, 0, 0, 0],
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[10, 11, 1, 1],
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[10, 11, 12, 2]])
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>>> a = np.array([[0, 1, 2],
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... [0, 2, 4],
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... [0, 3, 6]])
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>>> np.where(a < 4, a, -1) # -1 is broadcast
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array([[ 0, 1, 2],
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[ 0, 2, -1],
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[ 0, 3, -1]])
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"""
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return (condition, x, y)
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@array_function_from_c_func_and_dispatcher(_multiarray_umath.lexsort)
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def lexsort(keys, axis=None):
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"""
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lexsort(keys, axis=-1)
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Perform an indirect stable sort using a sequence of keys.
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Given multiple sorting keys, which can be interpreted as columns in a
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spreadsheet, lexsort returns an array of integer indices that describes
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the sort order by multiple columns. The last key in the sequence is used
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for the primary sort order, the second-to-last key for the secondary sort
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order, and so on. The keys argument must be a sequence of objects that
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can be converted to arrays of the same shape. If a 2D array is provided
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for the keys argument, it's rows are interpreted as the sorting keys and
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sorting is according to the last row, second last row etc.
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Parameters
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----------
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keys : (k, N) array or tuple containing k (N,)-shaped sequences
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The `k` different "columns" to be sorted. The last column (or row if
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`keys` is a 2D array) is the primary sort key.
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axis : int, optional
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Axis to be indirectly sorted. By default, sort over the last axis.
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Returns
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-------
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indices : (N,) ndarray of ints
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Array of indices that sort the keys along the specified axis.
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See Also
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--------
|
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argsort : Indirect sort.
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ndarray.sort : In-place sort.
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sort : Return a sorted copy of an array.
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|
|
Examples
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--------
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Sort names: first by surname, then by name.
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>>> surnames = ('Hertz', 'Galilei', 'Hertz')
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>>> first_names = ('Heinrich', 'Galileo', 'Gustav')
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>>> ind = np.lexsort((first_names, surnames))
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>>> ind
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array([1, 2, 0])
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>>> [surnames[i] + ", " + first_names[i] for i in ind]
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['Galilei, Galileo', 'Hertz, Gustav', 'Hertz, Heinrich']
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Sort two columns of numbers:
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>>> a = [1,5,1,4,3,4,4] # First column
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>>> b = [9,4,0,4,0,2,1] # Second column
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>>> ind = np.lexsort((b,a)) # Sort by a, then by b
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>>> ind
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array([2, 0, 4, 6, 5, 3, 1])
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>>> [(a[i],b[i]) for i in ind]
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[(1, 0), (1, 9), (3, 0), (4, 1), (4, 2), (4, 4), (5, 4)]
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Note that sorting is first according to the elements of ``a``.
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Secondary sorting is according to the elements of ``b``.
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A normal ``argsort`` would have yielded:
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>>> [(a[i],b[i]) for i in np.argsort(a)]
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[(1, 9), (1, 0), (3, 0), (4, 4), (4, 2), (4, 1), (5, 4)]
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Structured arrays are sorted lexically by ``argsort``:
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>>> x = np.array([(1,9), (5,4), (1,0), (4,4), (3,0), (4,2), (4,1)],
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... dtype=np.dtype([('x', int), ('y', int)]))
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>>> np.argsort(x) # or np.argsort(x, order=('x', 'y'))
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array([2, 0, 4, 6, 5, 3, 1])
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"""
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if isinstance(keys, tuple):
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return keys
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else:
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return (keys,)
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|
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|
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@array_function_from_c_func_and_dispatcher(_multiarray_umath.can_cast)
|
|
def can_cast(from_, to, casting=None):
|
|
"""
|
|
can_cast(from_, to, casting='safe')
|
|
|
|
Returns True if cast between data types can occur according to the
|
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casting rule. If from is a scalar or array scalar, also returns
|
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True if the scalar value can be cast without overflow or truncation
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to an integer.
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|
|
Parameters
|
|
----------
|
|
from_ : dtype, dtype specifier, scalar, or array
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Data type, scalar, or array to cast from.
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to : dtype or dtype specifier
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Data type to cast to.
|
|
casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
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|
Controls what kind of data casting may occur.
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|
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* 'no' means the data types should not be cast at all.
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* 'equiv' means only byte-order changes are allowed.
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* 'safe' means only casts which can preserve values are allowed.
|
|
* 'same_kind' means only safe casts or casts within a kind,
|
|
like float64 to float32, are allowed.
|
|
* 'unsafe' means any data conversions may be done.
|
|
|
|
Returns
|
|
-------
|
|
out : bool
|
|
True if cast can occur according to the casting rule.
|
|
|
|
Notes
|
|
-----
|
|
.. versionchanged:: 1.17.0
|
|
Casting between a simple data type and a structured one is possible only
|
|
for "unsafe" casting. Casting to multiple fields is allowed, but
|
|
casting from multiple fields is not.
|
|
|
|
.. versionchanged:: 1.9.0
|
|
Casting from numeric to string types in 'safe' casting mode requires
|
|
that the string dtype length is long enough to store the maximum
|
|
integer/float value converted.
|
|
|
|
See also
|
|
--------
|
|
dtype, result_type
|
|
|
|
Examples
|
|
--------
|
|
Basic examples
|
|
|
|
>>> np.can_cast(np.int32, np.int64)
|
|
True
|
|
>>> np.can_cast(np.float64, complex)
|
|
True
|
|
>>> np.can_cast(complex, float)
|
|
False
|
|
|
|
>>> np.can_cast('i8', 'f8')
|
|
True
|
|
>>> np.can_cast('i8', 'f4')
|
|
False
|
|
>>> np.can_cast('i4', 'S4')
|
|
False
|
|
|
|
Casting scalars
|
|
|
|
>>> np.can_cast(100, 'i1')
|
|
True
|
|
>>> np.can_cast(150, 'i1')
|
|
False
|
|
>>> np.can_cast(150, 'u1')
|
|
True
|
|
|
|
>>> np.can_cast(3.5e100, np.float32)
|
|
False
|
|
>>> np.can_cast(1000.0, np.float32)
|
|
True
|
|
|
|
Array scalar checks the value, array does not
|
|
|
|
>>> np.can_cast(np.array(1000.0), np.float32)
|
|
True
|
|
>>> np.can_cast(np.array([1000.0]), np.float32)
|
|
False
|
|
|
|
Using the casting rules
|
|
|
|
>>> np.can_cast('i8', 'i8', 'no')
|
|
True
|
|
>>> np.can_cast('<i8', '>i8', 'no')
|
|
False
|
|
|
|
>>> np.can_cast('<i8', '>i8', 'equiv')
|
|
True
|
|
>>> np.can_cast('<i4', '>i8', 'equiv')
|
|
False
|
|
|
|
>>> np.can_cast('<i4', '>i8', 'safe')
|
|
True
|
|
>>> np.can_cast('<i8', '>i4', 'safe')
|
|
False
|
|
|
|
>>> np.can_cast('<i8', '>i4', 'same_kind')
|
|
True
|
|
>>> np.can_cast('<i8', '>u4', 'same_kind')
|
|
False
|
|
|
|
>>> np.can_cast('<i8', '>u4', 'unsafe')
|
|
True
|
|
|
|
"""
|
|
return (from_,)
|
|
|
|
|
|
@array_function_from_c_func_and_dispatcher(_multiarray_umath.min_scalar_type)
|
|
def min_scalar_type(a):
|
|
"""
|
|
min_scalar_type(a)
|
|
|
|
For scalar ``a``, returns the data type with the smallest size
|
|
and smallest scalar kind which can hold its value. For non-scalar
|
|
array ``a``, returns the vector's dtype unmodified.
|
|
|
|
Floating point values are not demoted to integers,
|
|
and complex values are not demoted to floats.
|
|
|
|
Parameters
|
|
----------
|
|
a : scalar or array_like
|
|
The value whose minimal data type is to be found.
|
|
|
|
Returns
|
|
-------
|
|
out : dtype
|
|
The minimal data type.
|
|
|
|
Notes
|
|
-----
|
|
.. versionadded:: 1.6.0
|
|
|
|
See Also
|
|
--------
|
|
result_type, promote_types, dtype, can_cast
|
|
|
|
Examples
|
|
--------
|
|
>>> np.min_scalar_type(10)
|
|
dtype('uint8')
|
|
|
|
>>> np.min_scalar_type(-260)
|
|
dtype('int16')
|
|
|
|
>>> np.min_scalar_type(3.1)
|
|
dtype('float16')
|
|
|
|
>>> np.min_scalar_type(1e50)
|
|
dtype('float64')
|
|
|
|
>>> np.min_scalar_type(np.arange(4,dtype='f8'))
|
|
dtype('float64')
|
|
|
|
"""
|
|
return (a,)
|
|
|
|
|
|
@array_function_from_c_func_and_dispatcher(_multiarray_umath.result_type)
|
|
def result_type(*arrays_and_dtypes):
|
|
"""
|
|
result_type(*arrays_and_dtypes)
|
|
|
|
Returns the type that results from applying the NumPy
|
|
type promotion rules to the arguments.
|
|
|
|
Type promotion in NumPy works similarly to the rules in languages
|
|
like C++, with some slight differences. When both scalars and
|
|
arrays are used, the array's type takes precedence and the actual value
|
|
of the scalar is taken into account.
|
|
|
|
For example, calculating 3*a, where a is an array of 32-bit floats,
|
|
intuitively should result in a 32-bit float output. If the 3 is a
|
|
32-bit integer, the NumPy rules indicate it can't convert losslessly
|
|
into a 32-bit float, so a 64-bit float should be the result type.
|
|
By examining the value of the constant, '3', we see that it fits in
|
|
an 8-bit integer, which can be cast losslessly into the 32-bit float.
|
|
|
|
Parameters
|
|
----------
|
|
arrays_and_dtypes : list of arrays and dtypes
|
|
The operands of some operation whose result type is needed.
|
|
|
|
Returns
|
|
-------
|
|
out : dtype
|
|
The result type.
|
|
|
|
See also
|
|
--------
|
|
dtype, promote_types, min_scalar_type, can_cast
|
|
|
|
Notes
|
|
-----
|
|
.. versionadded:: 1.6.0
|
|
|
|
The specific algorithm used is as follows.
|
|
|
|
Categories are determined by first checking which of boolean,
|
|
integer (int/uint), or floating point (float/complex) the maximum
|
|
kind of all the arrays and the scalars are.
|
|
|
|
If there are only scalars or the maximum category of the scalars
|
|
is higher than the maximum category of the arrays,
|
|
the data types are combined with :func:`promote_types`
|
|
to produce the return value.
|
|
|
|
Otherwise, `min_scalar_type` is called on each array, and
|
|
the resulting data types are all combined with :func:`promote_types`
|
|
to produce the return value.
|
|
|
|
The set of int values is not a subset of the uint values for types
|
|
with the same number of bits, something not reflected in
|
|
:func:`min_scalar_type`, but handled as a special case in `result_type`.
|
|
|
|
Examples
|
|
--------
|
|
>>> np.result_type(3, np.arange(7, dtype='i1'))
|
|
dtype('int8')
|
|
|
|
>>> np.result_type('i4', 'c8')
|
|
dtype('complex128')
|
|
|
|
>>> np.result_type(3.0, -2)
|
|
dtype('float64')
|
|
|
|
"""
|
|
return arrays_and_dtypes
|
|
|
|
|
|
@array_function_from_c_func_and_dispatcher(_multiarray_umath.dot)
|
|
def dot(a, b, out=None):
|
|
"""
|
|
dot(a, b, out=None)
|
|
|
|
Dot product of two arrays. Specifically,
|
|
|
|
- If both `a` and `b` are 1-D arrays, it is inner product of vectors
|
|
(without complex conjugation).
|
|
|
|
- If both `a` and `b` are 2-D arrays, it is matrix multiplication,
|
|
but using :func:`matmul` or ``a @ b`` is preferred.
|
|
|
|
- If either `a` or `b` is 0-D (scalar), it is equivalent to :func:`multiply`
|
|
and using ``numpy.multiply(a, b)`` or ``a * b`` is preferred.
|
|
|
|
- If `a` is an N-D array and `b` is a 1-D array, it is a sum product over
|
|
the last axis of `a` and `b`.
|
|
|
|
- If `a` is an N-D array and `b` is an M-D array (where ``M>=2``), it is a
|
|
sum product over the last axis of `a` and the second-to-last axis of `b`::
|
|
|
|
dot(a, b)[i,j,k,m] = sum(a[i,j,:] * b[k,:,m])
|
|
|
|
Parameters
|
|
----------
|
|
a : array_like
|
|
First argument.
|
|
b : array_like
|
|
Second argument.
|
|
out : ndarray, optional
|
|
Output argument. This must have the exact kind that would be returned
|
|
if it was not used. In particular, it must have the right type, must be
|
|
C-contiguous, and its dtype must be the dtype that would be returned
|
|
for `dot(a,b)`. This is a performance feature. Therefore, if these
|
|
conditions are not met, an exception is raised, instead of attempting
|
|
to be flexible.
|
|
|
|
Returns
|
|
-------
|
|
output : ndarray
|
|
Returns the dot product of `a` and `b`. If `a` and `b` are both
|
|
scalars or both 1-D arrays then a scalar is returned; otherwise
|
|
an array is returned.
|
|
If `out` is given, then it is returned.
|
|
|
|
Raises
|
|
------
|
|
ValueError
|
|
If the last dimension of `a` is not the same size as
|
|
the second-to-last dimension of `b`.
|
|
|
|
See Also
|
|
--------
|
|
vdot : Complex-conjugating dot product.
|
|
tensordot : Sum products over arbitrary axes.
|
|
einsum : Einstein summation convention.
|
|
matmul : '@' operator as method with out parameter.
|
|
|
|
Examples
|
|
--------
|
|
>>> np.dot(3, 4)
|
|
12
|
|
|
|
Neither argument is complex-conjugated:
|
|
|
|
>>> np.dot([2j, 3j], [2j, 3j])
|
|
(-13+0j)
|
|
|
|
For 2-D arrays it is the matrix product:
|
|
|
|
>>> a = [[1, 0], [0, 1]]
|
|
>>> b = [[4, 1], [2, 2]]
|
|
>>> np.dot(a, b)
|
|
array([[4, 1],
|
|
[2, 2]])
|
|
|
|
>>> a = np.arange(3*4*5*6).reshape((3,4,5,6))
|
|
>>> b = np.arange(3*4*5*6)[::-1].reshape((5,4,6,3))
|
|
>>> np.dot(a, b)[2,3,2,1,2,2]
|
|
499128
|
|
>>> sum(a[2,3,2,:] * b[1,2,:,2])
|
|
499128
|
|
|
|
"""
|
|
return (a, b, out)
|
|
|
|
|
|
@array_function_from_c_func_and_dispatcher(_multiarray_umath.vdot)
|
|
def vdot(a, b):
|
|
"""
|
|
vdot(a, b)
|
|
|
|
Return the dot product of two vectors.
|
|
|
|
The vdot(`a`, `b`) function handles complex numbers differently than
|
|
dot(`a`, `b`). If the first argument is complex the complex conjugate
|
|
of the first argument is used for the calculation of the dot product.
|
|
|
|
Note that `vdot` handles multidimensional arrays differently than `dot`:
|
|
it does *not* perform a matrix product, but flattens input arguments
|
|
to 1-D vectors first. Consequently, it should only be used for vectors.
|
|
|
|
Parameters
|
|
----------
|
|
a : array_like
|
|
If `a` is complex the complex conjugate is taken before calculation
|
|
of the dot product.
|
|
b : array_like
|
|
Second argument to the dot product.
|
|
|
|
Returns
|
|
-------
|
|
output : ndarray
|
|
Dot product of `a` and `b`. Can be an int, float, or
|
|
complex depending on the types of `a` and `b`.
|
|
|
|
See Also
|
|
--------
|
|
dot : Return the dot product without using the complex conjugate of the
|
|
first argument.
|
|
|
|
Examples
|
|
--------
|
|
>>> a = np.array([1+2j,3+4j])
|
|
>>> b = np.array([5+6j,7+8j])
|
|
>>> np.vdot(a, b)
|
|
(70-8j)
|
|
>>> np.vdot(b, a)
|
|
(70+8j)
|
|
|
|
Note that higher-dimensional arrays are flattened!
|
|
|
|
>>> a = np.array([[1, 4], [5, 6]])
|
|
>>> b = np.array([[4, 1], [2, 2]])
|
|
>>> np.vdot(a, b)
|
|
30
|
|
>>> np.vdot(b, a)
|
|
30
|
|
>>> 1*4 + 4*1 + 5*2 + 6*2
|
|
30
|
|
|
|
"""
|
|
return (a, b)
|
|
|
|
|
|
@array_function_from_c_func_and_dispatcher(_multiarray_umath.bincount)
|
|
def bincount(x, weights=None, minlength=None):
|
|
"""
|
|
bincount(x, weights=None, minlength=0)
|
|
|
|
Count number of occurrences of each value in array of non-negative ints.
|
|
|
|
The number of bins (of size 1) is one larger than the largest value in
|
|
`x`. If `minlength` is specified, there will be at least this number
|
|
of bins in the output array (though it will be longer if necessary,
|
|
depending on the contents of `x`).
|
|
Each bin gives the number of occurrences of its index value in `x`.
|
|
If `weights` is specified the input array is weighted by it, i.e. if a
|
|
value ``n`` is found at position ``i``, ``out[n] += weight[i]`` instead
|
|
of ``out[n] += 1``.
|
|
|
|
Parameters
|
|
----------
|
|
x : array_like, 1 dimension, nonnegative ints
|
|
Input array.
|
|
weights : array_like, optional
|
|
Weights, array of the same shape as `x`.
|
|
minlength : int, optional
|
|
A minimum number of bins for the output array.
|
|
|
|
.. versionadded:: 1.6.0
|
|
|
|
Returns
|
|
-------
|
|
out : ndarray of ints
|
|
The result of binning the input array.
|
|
The length of `out` is equal to ``np.amax(x)+1``.
|
|
|
|
Raises
|
|
------
|
|
ValueError
|
|
If the input is not 1-dimensional, or contains elements with negative
|
|
values, or if `minlength` is negative.
|
|
TypeError
|
|
If the type of the input is float or complex.
|
|
|
|
See Also
|
|
--------
|
|
histogram, digitize, unique
|
|
|
|
Examples
|
|
--------
|
|
>>> np.bincount(np.arange(5))
|
|
array([1, 1, 1, 1, 1])
|
|
>>> np.bincount(np.array([0, 1, 1, 3, 2, 1, 7]))
|
|
array([1, 3, 1, 1, 0, 0, 0, 1])
|
|
|
|
>>> x = np.array([0, 1, 1, 3, 2, 1, 7, 23])
|
|
>>> np.bincount(x).size == np.amax(x)+1
|
|
True
|
|
|
|
The input array needs to be of integer dtype, otherwise a
|
|
TypeError is raised:
|
|
|
|
>>> np.bincount(np.arange(5, dtype=float))
|
|
Traceback (most recent call last):
|
|
File "<stdin>", line 1, in <module>
|
|
TypeError: array cannot be safely cast to required type
|
|
|
|
A possible use of ``bincount`` is to perform sums over
|
|
variable-size chunks of an array, using the ``weights`` keyword.
|
|
|
|
>>> w = np.array([0.3, 0.5, 0.2, 0.7, 1., -0.6]) # weights
|
|
>>> x = np.array([0, 1, 1, 2, 2, 2])
|
|
>>> np.bincount(x, weights=w)
|
|
array([ 0.3, 0.7, 1.1])
|
|
|
|
"""
|
|
return (x, weights)
|
|
|
|
|
|
@array_function_from_c_func_and_dispatcher(_multiarray_umath.ravel_multi_index)
|
|
def ravel_multi_index(multi_index, dims, mode=None, order=None):
|
|
"""
|
|
ravel_multi_index(multi_index, dims, mode='raise', order='C')
|
|
|
|
Converts a tuple of index arrays into an array of flat
|
|
indices, applying boundary modes to the multi-index.
|
|
|
|
Parameters
|
|
----------
|
|
multi_index : tuple of array_like
|
|
A tuple of integer arrays, one array for each dimension.
|
|
dims : tuple of ints
|
|
The shape of array into which the indices from ``multi_index`` apply.
|
|
mode : {'raise', 'wrap', 'clip'}, optional
|
|
Specifies how out-of-bounds indices are handled. Can specify
|
|
either one mode or a tuple of modes, one mode per index.
|
|
|
|
* 'raise' -- raise an error (default)
|
|
* 'wrap' -- wrap around
|
|
* 'clip' -- clip to the range
|
|
|
|
In 'clip' mode, a negative index which would normally
|
|
wrap will clip to 0 instead.
|
|
order : {'C', 'F'}, optional
|
|
Determines whether the multi-index should be viewed as
|
|
indexing in row-major (C-style) or column-major
|
|
(Fortran-style) order.
|
|
|
|
Returns
|
|
-------
|
|
raveled_indices : ndarray
|
|
An array of indices into the flattened version of an array
|
|
of dimensions ``dims``.
|
|
|
|
See Also
|
|
--------
|
|
unravel_index
|
|
|
|
Notes
|
|
-----
|
|
.. versionadded:: 1.6.0
|
|
|
|
Examples
|
|
--------
|
|
>>> arr = np.array([[3,6,6],[4,5,1]])
|
|
>>> np.ravel_multi_index(arr, (7,6))
|
|
array([22, 41, 37])
|
|
>>> np.ravel_multi_index(arr, (7,6), order='F')
|
|
array([31, 41, 13])
|
|
>>> np.ravel_multi_index(arr, (4,6), mode='clip')
|
|
array([22, 23, 19])
|
|
>>> np.ravel_multi_index(arr, (4,4), mode=('clip','wrap'))
|
|
array([12, 13, 13])
|
|
|
|
>>> np.ravel_multi_index((3,1,4,1), (6,7,8,9))
|
|
1621
|
|
"""
|
|
return multi_index
|
|
|
|
|
|
@array_function_from_c_func_and_dispatcher(_multiarray_umath.unravel_index)
|
|
def unravel_index(indices, shape=None, order=None, dims=None):
|
|
"""
|
|
unravel_index(indices, shape, order='C')
|
|
|
|
Converts a flat index or array of flat indices into a tuple
|
|
of coordinate arrays.
|
|
|
|
Parameters
|
|
----------
|
|
indices : array_like
|
|
An integer array whose elements are indices into the flattened
|
|
version of an array of dimensions ``shape``. Before version 1.6.0,
|
|
this function accepted just one index value.
|
|
shape : tuple of ints
|
|
The shape of the array to use for unraveling ``indices``.
|
|
|
|
.. versionchanged:: 1.16.0
|
|
Renamed from ``dims`` to ``shape``.
|
|
|
|
order : {'C', 'F'}, optional
|
|
Determines whether the indices should be viewed as indexing in
|
|
row-major (C-style) or column-major (Fortran-style) order.
|
|
|
|
.. versionadded:: 1.6.0
|
|
|
|
Returns
|
|
-------
|
|
unraveled_coords : tuple of ndarray
|
|
Each array in the tuple has the same shape as the ``indices``
|
|
array.
|
|
|
|
See Also
|
|
--------
|
|
ravel_multi_index
|
|
|
|
Examples
|
|
--------
|
|
>>> np.unravel_index([22, 41, 37], (7,6))
|
|
(array([3, 6, 6]), array([4, 5, 1]))
|
|
>>> np.unravel_index([31, 41, 13], (7,6), order='F')
|
|
(array([3, 6, 6]), array([4, 5, 1]))
|
|
|
|
>>> np.unravel_index(1621, (6,7,8,9))
|
|
(3, 1, 4, 1)
|
|
|
|
"""
|
|
if dims is not None:
|
|
warnings.warn("'shape' argument should be used instead of 'dims'",
|
|
DeprecationWarning, stacklevel=3)
|
|
return (indices,)
|
|
|
|
|
|
@array_function_from_c_func_and_dispatcher(_multiarray_umath.copyto)
|
|
def copyto(dst, src, casting=None, where=None):
|
|
"""
|
|
copyto(dst, src, casting='same_kind', where=True)
|
|
|
|
Copies values from one array to another, broadcasting as necessary.
|
|
|
|
Raises a TypeError if the `casting` rule is violated, and if
|
|
`where` is provided, it selects which elements to copy.
|
|
|
|
.. versionadded:: 1.7.0
|
|
|
|
Parameters
|
|
----------
|
|
dst : ndarray
|
|
The array into which values are copied.
|
|
src : array_like
|
|
The array from which values are copied.
|
|
casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
|
|
Controls what kind of data casting may occur when copying.
|
|
|
|
* 'no' means the data types should not be cast at all.
|
|
* 'equiv' means only byte-order changes are allowed.
|
|
* 'safe' means only casts which can preserve values are allowed.
|
|
* 'same_kind' means only safe casts or casts within a kind,
|
|
like float64 to float32, are allowed.
|
|
* 'unsafe' means any data conversions may be done.
|
|
where : array_like of bool, optional
|
|
A boolean array which is broadcasted to match the dimensions
|
|
of `dst`, and selects elements to copy from `src` to `dst`
|
|
wherever it contains the value True.
|
|
"""
|
|
return (dst, src, where)
|
|
|
|
|
|
@array_function_from_c_func_and_dispatcher(_multiarray_umath.putmask)
|
|
def putmask(a, mask, values):
|
|
"""
|
|
putmask(a, mask, values)
|
|
|
|
Changes elements of an array based on conditional and input values.
|
|
|
|
Sets ``a.flat[n] = values[n]`` for each n where ``mask.flat[n]==True``.
|
|
|
|
If `values` is not the same size as `a` and `mask` then it will repeat.
|
|
This gives behavior different from ``a[mask] = values``.
|
|
|
|
Parameters
|
|
----------
|
|
a : array_like
|
|
Target array.
|
|
mask : array_like
|
|
Boolean mask array. It has to be the same shape as `a`.
|
|
values : array_like
|
|
Values to put into `a` where `mask` is True. If `values` is smaller
|
|
than `a` it will be repeated.
|
|
|
|
See Also
|
|
--------
|
|
place, put, take, copyto
|
|
|
|
Examples
|
|
--------
|
|
>>> x = np.arange(6).reshape(2, 3)
|
|
>>> np.putmask(x, x>2, x**2)
|
|
>>> x
|
|
array([[ 0, 1, 2],
|
|
[ 9, 16, 25]])
|
|
|
|
If `values` is smaller than `a` it is repeated:
|
|
|
|
>>> x = np.arange(5)
|
|
>>> np.putmask(x, x>1, [-33, -44])
|
|
>>> x
|
|
array([ 0, 1, -33, -44, -33])
|
|
|
|
"""
|
|
return (a, mask, values)
|
|
|
|
|
|
@array_function_from_c_func_and_dispatcher(_multiarray_umath.packbits)
|
|
def packbits(a, axis=None, bitorder='big'):
|
|
"""
|
|
packbits(a, axis=None, bitorder='big')
|
|
|
|
Packs the elements of a binary-valued array into bits in a uint8 array.
|
|
|
|
The result is padded to full bytes by inserting zero bits at the end.
|
|
|
|
Parameters
|
|
----------
|
|
a : array_like
|
|
An array of integers or booleans whose elements should be packed to
|
|
bits.
|
|
axis : int, optional
|
|
The dimension over which bit-packing is done.
|
|
``None`` implies packing the flattened array.
|
|
bitorder : {'big', 'little'}, optional
|
|
The order of the input bits. 'big' will mimic bin(val),
|
|
``[0, 0, 0, 0, 0, 0, 1, 1] => 3 = 0b00000011 => ``, 'little' will
|
|
reverse the order so ``[1, 1, 0, 0, 0, 0, 0, 0] => 3``.
|
|
Defaults to 'big'.
|
|
|
|
.. versionadded:: 1.17.0
|
|
|
|
Returns
|
|
-------
|
|
packed : ndarray
|
|
Array of type uint8 whose elements represent bits corresponding to the
|
|
logical (0 or nonzero) value of the input elements. The shape of
|
|
`packed` has the same number of dimensions as the input (unless `axis`
|
|
is None, in which case the output is 1-D).
|
|
|
|
See Also
|
|
--------
|
|
unpackbits: Unpacks elements of a uint8 array into a binary-valued output
|
|
array.
|
|
|
|
Examples
|
|
--------
|
|
>>> a = np.array([[[1,0,1],
|
|
... [0,1,0]],
|
|
... [[1,1,0],
|
|
... [0,0,1]]])
|
|
>>> b = np.packbits(a, axis=-1)
|
|
>>> b
|
|
array([[[160],
|
|
[ 64]],
|
|
[[192],
|
|
[ 32]]], dtype=uint8)
|
|
|
|
Note that in binary 160 = 1010 0000, 64 = 0100 0000, 192 = 1100 0000,
|
|
and 32 = 0010 0000.
|
|
|
|
"""
|
|
return (a,)
|
|
|
|
|
|
@array_function_from_c_func_and_dispatcher(_multiarray_umath.unpackbits)
|
|
def unpackbits(a, axis=None, count=None, bitorder='big'):
|
|
"""
|
|
unpackbits(a, axis=None, count=None, bitorder='big')
|
|
|
|
Unpacks elements of a uint8 array into a binary-valued output array.
|
|
|
|
Each element of `a` represents a bit-field that should be unpacked
|
|
into a binary-valued output array. The shape of the output array is
|
|
either 1-D (if `axis` is ``None``) or the same shape as the input
|
|
array with unpacking done along the axis specified.
|
|
|
|
Parameters
|
|
----------
|
|
a : ndarray, uint8 type
|
|
Input array.
|
|
axis : int, optional
|
|
The dimension over which bit-unpacking is done.
|
|
``None`` implies unpacking the flattened array.
|
|
count : int or None, optional
|
|
The number of elements to unpack along `axis`, provided as a way
|
|
of undoing the effect of packing a size that is not a multiple
|
|
of eight. A non-negative number means to only unpack `count`
|
|
bits. A negative number means to trim off that many bits from
|
|
the end. ``None`` means to unpack the entire array (the
|
|
default). Counts larger than the available number of bits will
|
|
add zero padding to the output. Negative counts must not
|
|
exceed the available number of bits.
|
|
|
|
.. versionadded:: 1.17.0
|
|
|
|
bitorder : {'big', 'little'}, optional
|
|
The order of the returned bits. 'big' will mimic bin(val),
|
|
``3 = 0b00000011 => [0, 0, 0, 0, 0, 0, 1, 1]``, 'little' will reverse
|
|
the order to ``[1, 1, 0, 0, 0, 0, 0, 0]``.
|
|
Defaults to 'big'.
|
|
|
|
.. versionadded:: 1.17.0
|
|
|
|
Returns
|
|
-------
|
|
unpacked : ndarray, uint8 type
|
|
The elements are binary-valued (0 or 1).
|
|
|
|
See Also
|
|
--------
|
|
packbits : Packs the elements of a binary-valued array into bits in
|
|
a uint8 array.
|
|
|
|
Examples
|
|
--------
|
|
>>> a = np.array([[2], [7], [23]], dtype=np.uint8)
|
|
>>> a
|
|
array([[ 2],
|
|
[ 7],
|
|
[23]], dtype=uint8)
|
|
>>> b = np.unpackbits(a, axis=1)
|
|
>>> b
|
|
array([[0, 0, 0, 0, 0, 0, 1, 0],
|
|
[0, 0, 0, 0, 0, 1, 1, 1],
|
|
[0, 0, 0, 1, 0, 1, 1, 1]], dtype=uint8)
|
|
>>> c = np.unpackbits(a, axis=1, count=-3)
|
|
>>> c
|
|
array([[0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0],
|
|
[0, 0, 0, 1, 0]], dtype=uint8)
|
|
|
|
>>> p = np.packbits(b, axis=0)
|
|
>>> np.unpackbits(p, axis=0)
|
|
array([[0, 0, 0, 0, 0, 0, 1, 0],
|
|
[0, 0, 0, 0, 0, 1, 1, 1],
|
|
[0, 0, 0, 1, 0, 1, 1, 1],
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
|
|
>>> np.array_equal(b, np.unpackbits(p, axis=0, count=b.shape[0]))
|
|
True
|
|
|
|
"""
|
|
return (a,)
|
|
|
|
|
|
@array_function_from_c_func_and_dispatcher(_multiarray_umath.shares_memory)
|
|
def shares_memory(a, b, max_work=None):
|
|
"""
|
|
shares_memory(a, b, max_work=None)
|
|
|
|
Determine if two arrays share memory
|
|
|
|
Parameters
|
|
----------
|
|
a, b : ndarray
|
|
Input arrays
|
|
max_work : int, optional
|
|
Effort to spend on solving the overlap problem (maximum number
|
|
of candidate solutions to consider). The following special
|
|
values are recognized:
|
|
|
|
max_work=MAY_SHARE_EXACT (default)
|
|
The problem is solved exactly. In this case, the function returns
|
|
True only if there is an element shared between the arrays.
|
|
max_work=MAY_SHARE_BOUNDS
|
|
Only the memory bounds of a and b are checked.
|
|
|
|
Raises
|
|
------
|
|
numpy.TooHardError
|
|
Exceeded max_work.
|
|
|
|
Returns
|
|
-------
|
|
out : bool
|
|
|
|
See Also
|
|
--------
|
|
may_share_memory
|
|
|
|
Examples
|
|
--------
|
|
>>> np.may_share_memory(np.array([1,2]), np.array([5,8,9]))
|
|
False
|
|
|
|
"""
|
|
return (a, b)
|
|
|
|
|
|
@array_function_from_c_func_and_dispatcher(_multiarray_umath.may_share_memory)
|
|
def may_share_memory(a, b, max_work=None):
|
|
"""
|
|
may_share_memory(a, b, max_work=None)
|
|
|
|
Determine if two arrays might share memory
|
|
|
|
A return of True does not necessarily mean that the two arrays
|
|
share any element. It just means that they *might*.
|
|
|
|
Only the memory bounds of a and b are checked by default.
|
|
|
|
Parameters
|
|
----------
|
|
a, b : ndarray
|
|
Input arrays
|
|
max_work : int, optional
|
|
Effort to spend on solving the overlap problem. See
|
|
`shares_memory` for details. Default for ``may_share_memory``
|
|
is to do a bounds check.
|
|
|
|
Returns
|
|
-------
|
|
out : bool
|
|
|
|
See Also
|
|
--------
|
|
shares_memory
|
|
|
|
Examples
|
|
--------
|
|
>>> np.may_share_memory(np.array([1,2]), np.array([5,8,9]))
|
|
False
|
|
>>> x = np.zeros([3, 4])
|
|
>>> np.may_share_memory(x[:,0], x[:,1])
|
|
True
|
|
|
|
"""
|
|
return (a, b)
|
|
|
|
|
|
@array_function_from_c_func_and_dispatcher(_multiarray_umath.is_busday)
|
|
def is_busday(dates, weekmask=None, holidays=None, busdaycal=None, out=None):
|
|
"""
|
|
is_busday(dates, weekmask='1111100', holidays=None, busdaycal=None, out=None)
|
|
|
|
Calculates which of the given dates are valid days, and which are not.
|
|
|
|
.. versionadded:: 1.7.0
|
|
|
|
Parameters
|
|
----------
|
|
dates : array_like of datetime64[D]
|
|
The array of dates to process.
|
|
weekmask : str or array_like of bool, optional
|
|
A seven-element array indicating which of Monday through Sunday are
|
|
valid days. May be specified as a length-seven list or array, like
|
|
[1,1,1,1,1,0,0]; a length-seven string, like '1111100'; or a string
|
|
like "Mon Tue Wed Thu Fri", made up of 3-character abbreviations for
|
|
weekdays, optionally separated by white space. Valid abbreviations
|
|
are: Mon Tue Wed Thu Fri Sat Sun
|
|
holidays : array_like of datetime64[D], optional
|
|
An array of dates to consider as invalid dates. They may be
|
|
specified in any order, and NaT (not-a-time) dates are ignored.
|
|
This list is saved in a normalized form that is suited for
|
|
fast calculations of valid days.
|
|
busdaycal : busdaycalendar, optional
|
|
A `busdaycalendar` object which specifies the valid days. If this
|
|
parameter is provided, neither weekmask nor holidays may be
|
|
provided.
|
|
out : array of bool, optional
|
|
If provided, this array is filled with the result.
|
|
|
|
Returns
|
|
-------
|
|
out : array of bool
|
|
An array with the same shape as ``dates``, containing True for
|
|
each valid day, and False for each invalid day.
|
|
|
|
See Also
|
|
--------
|
|
busdaycalendar: An object that specifies a custom set of valid days.
|
|
busday_offset : Applies an offset counted in valid days.
|
|
busday_count : Counts how many valid days are in a half-open date range.
|
|
|
|
Examples
|
|
--------
|
|
>>> # The weekdays are Friday, Saturday, and Monday
|
|
... np.is_busday(['2011-07-01', '2011-07-02', '2011-07-18'],
|
|
... holidays=['2011-07-01', '2011-07-04', '2011-07-17'])
|
|
array([False, False, True])
|
|
"""
|
|
return (dates, weekmask, holidays, out)
|
|
|
|
|
|
@array_function_from_c_func_and_dispatcher(_multiarray_umath.busday_offset)
|
|
def busday_offset(dates, offsets, roll=None, weekmask=None, holidays=None,
|
|
busdaycal=None, out=None):
|
|
"""
|
|
busday_offset(dates, offsets, roll='raise', weekmask='1111100', holidays=None, busdaycal=None, out=None)
|
|
|
|
First adjusts the date to fall on a valid day according to
|
|
the ``roll`` rule, then applies offsets to the given dates
|
|
counted in valid days.
|
|
|
|
.. versionadded:: 1.7.0
|
|
|
|
Parameters
|
|
----------
|
|
dates : array_like of datetime64[D]
|
|
The array of dates to process.
|
|
offsets : array_like of int
|
|
The array of offsets, which is broadcast with ``dates``.
|
|
roll : {'raise', 'nat', 'forward', 'following', 'backward', 'preceding', 'modifiedfollowing', 'modifiedpreceding'}, optional
|
|
How to treat dates that do not fall on a valid day. The default
|
|
is 'raise'.
|
|
|
|
* 'raise' means to raise an exception for an invalid day.
|
|
* 'nat' means to return a NaT (not-a-time) for an invalid day.
|
|
* 'forward' and 'following' mean to take the first valid day
|
|
later in time.
|
|
* 'backward' and 'preceding' mean to take the first valid day
|
|
earlier in time.
|
|
* 'modifiedfollowing' means to take the first valid day
|
|
later in time unless it is across a Month boundary, in which
|
|
case to take the first valid day earlier in time.
|
|
* 'modifiedpreceding' means to take the first valid day
|
|
earlier in time unless it is across a Month boundary, in which
|
|
case to take the first valid day later in time.
|
|
weekmask : str or array_like of bool, optional
|
|
A seven-element array indicating which of Monday through Sunday are
|
|
valid days. May be specified as a length-seven list or array, like
|
|
[1,1,1,1,1,0,0]; a length-seven string, like '1111100'; or a string
|
|
like "Mon Tue Wed Thu Fri", made up of 3-character abbreviations for
|
|
weekdays, optionally separated by white space. Valid abbreviations
|
|
are: Mon Tue Wed Thu Fri Sat Sun
|
|
holidays : array_like of datetime64[D], optional
|
|
An array of dates to consider as invalid dates. They may be
|
|
specified in any order, and NaT (not-a-time) dates are ignored.
|
|
This list is saved in a normalized form that is suited for
|
|
fast calculations of valid days.
|
|
busdaycal : busdaycalendar, optional
|
|
A `busdaycalendar` object which specifies the valid days. If this
|
|
parameter is provided, neither weekmask nor holidays may be
|
|
provided.
|
|
out : array of datetime64[D], optional
|
|
If provided, this array is filled with the result.
|
|
|
|
Returns
|
|
-------
|
|
out : array of datetime64[D]
|
|
An array with a shape from broadcasting ``dates`` and ``offsets``
|
|
together, containing the dates with offsets applied.
|
|
|
|
See Also
|
|
--------
|
|
busdaycalendar: An object that specifies a custom set of valid days.
|
|
is_busday : Returns a boolean array indicating valid days.
|
|
busday_count : Counts how many valid days are in a half-open date range.
|
|
|
|
Examples
|
|
--------
|
|
>>> # First business day in October 2011 (not accounting for holidays)
|
|
... np.busday_offset('2011-10', 0, roll='forward')
|
|
numpy.datetime64('2011-10-03')
|
|
>>> # Last business day in February 2012 (not accounting for holidays)
|
|
... np.busday_offset('2012-03', -1, roll='forward')
|
|
numpy.datetime64('2012-02-29')
|
|
>>> # Third Wednesday in January 2011
|
|
... np.busday_offset('2011-01', 2, roll='forward', weekmask='Wed')
|
|
numpy.datetime64('2011-01-19')
|
|
>>> # 2012 Mother's Day in Canada and the U.S.
|
|
... np.busday_offset('2012-05', 1, roll='forward', weekmask='Sun')
|
|
numpy.datetime64('2012-05-13')
|
|
|
|
>>> # First business day on or after a date
|
|
... np.busday_offset('2011-03-20', 0, roll='forward')
|
|
numpy.datetime64('2011-03-21')
|
|
>>> np.busday_offset('2011-03-22', 0, roll='forward')
|
|
numpy.datetime64('2011-03-22')
|
|
>>> # First business day after a date
|
|
... np.busday_offset('2011-03-20', 1, roll='backward')
|
|
numpy.datetime64('2011-03-21')
|
|
>>> np.busday_offset('2011-03-22', 1, roll='backward')
|
|
numpy.datetime64('2011-03-23')
|
|
"""
|
|
return (dates, offsets, weekmask, holidays, out)
|
|
|
|
|
|
@array_function_from_c_func_and_dispatcher(_multiarray_umath.busday_count)
|
|
def busday_count(begindates, enddates, weekmask=None, holidays=None,
|
|
busdaycal=None, out=None):
|
|
"""
|
|
busday_count(begindates, enddates, weekmask='1111100', holidays=[], busdaycal=None, out=None)
|
|
|
|
Counts the number of valid days between `begindates` and
|
|
`enddates`, not including the day of `enddates`.
|
|
|
|
If ``enddates`` specifies a date value that is earlier than the
|
|
corresponding ``begindates`` date value, the count will be negative.
|
|
|
|
.. versionadded:: 1.7.0
|
|
|
|
Parameters
|
|
----------
|
|
begindates : array_like of datetime64[D]
|
|
The array of the first dates for counting.
|
|
enddates : array_like of datetime64[D]
|
|
The array of the end dates for counting, which are excluded
|
|
from the count themselves.
|
|
weekmask : str or array_like of bool, optional
|
|
A seven-element array indicating which of Monday through Sunday are
|
|
valid days. May be specified as a length-seven list or array, like
|
|
[1,1,1,1,1,0,0]; a length-seven string, like '1111100'; or a string
|
|
like "Mon Tue Wed Thu Fri", made up of 3-character abbreviations for
|
|
weekdays, optionally separated by white space. Valid abbreviations
|
|
are: Mon Tue Wed Thu Fri Sat Sun
|
|
holidays : array_like of datetime64[D], optional
|
|
An array of dates to consider as invalid dates. They may be
|
|
specified in any order, and NaT (not-a-time) dates are ignored.
|
|
This list is saved in a normalized form that is suited for
|
|
fast calculations of valid days.
|
|
busdaycal : busdaycalendar, optional
|
|
A `busdaycalendar` object which specifies the valid days. If this
|
|
parameter is provided, neither weekmask nor holidays may be
|
|
provided.
|
|
out : array of int, optional
|
|
If provided, this array is filled with the result.
|
|
|
|
Returns
|
|
-------
|
|
out : array of int
|
|
An array with a shape from broadcasting ``begindates`` and ``enddates``
|
|
together, containing the number of valid days between
|
|
the begin and end dates.
|
|
|
|
See Also
|
|
--------
|
|
busdaycalendar: An object that specifies a custom set of valid days.
|
|
is_busday : Returns a boolean array indicating valid days.
|
|
busday_offset : Applies an offset counted in valid days.
|
|
|
|
Examples
|
|
--------
|
|
>>> # Number of weekdays in January 2011
|
|
... np.busday_count('2011-01', '2011-02')
|
|
21
|
|
>>> # Number of weekdays in 2011
|
|
>>> np.busday_count('2011', '2012')
|
|
260
|
|
>>> # Number of Saturdays in 2011
|
|
... np.busday_count('2011', '2012', weekmask='Sat')
|
|
53
|
|
"""
|
|
return (begindates, enddates, weekmask, holidays, out)
|
|
|
|
|
|
@array_function_from_c_func_and_dispatcher(
|
|
_multiarray_umath.datetime_as_string)
|
|
def datetime_as_string(arr, unit=None, timezone=None, casting=None):
|
|
"""
|
|
datetime_as_string(arr, unit=None, timezone='naive', casting='same_kind')
|
|
|
|
Convert an array of datetimes into an array of strings.
|
|
|
|
Parameters
|
|
----------
|
|
arr : array_like of datetime64
|
|
The array of UTC timestamps to format.
|
|
unit : str
|
|
One of None, 'auto', or a :ref:`datetime unit <arrays.dtypes.dateunits>`.
|
|
timezone : {'naive', 'UTC', 'local'} or tzinfo
|
|
Timezone information to use when displaying the datetime. If 'UTC', end
|
|
with a Z to indicate UTC time. If 'local', convert to the local timezone
|
|
first, and suffix with a +-#### timezone offset. If a tzinfo object,
|
|
then do as with 'local', but use the specified timezone.
|
|
casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}
|
|
Casting to allow when changing between datetime units.
|
|
|
|
Returns
|
|
-------
|
|
str_arr : ndarray
|
|
An array of strings the same shape as `arr`.
|
|
|
|
Examples
|
|
--------
|
|
>>> import pytz
|
|
>>> d = np.arange('2002-10-27T04:30', 4*60, 60, dtype='M8[m]')
|
|
>>> d
|
|
array(['2002-10-27T04:30', '2002-10-27T05:30', '2002-10-27T06:30',
|
|
'2002-10-27T07:30'], dtype='datetime64[m]')
|
|
|
|
Setting the timezone to UTC shows the same information, but with a Z suffix
|
|
|
|
>>> np.datetime_as_string(d, timezone='UTC')
|
|
array(['2002-10-27T04:30Z', '2002-10-27T05:30Z', '2002-10-27T06:30Z',
|
|
'2002-10-27T07:30Z'], dtype='<U35')
|
|
|
|
Note that we picked datetimes that cross a DST boundary. Passing in a
|
|
``pytz`` timezone object will print the appropriate offset
|
|
|
|
>>> np.datetime_as_string(d, timezone=pytz.timezone('US/Eastern'))
|
|
array(['2002-10-27T00:30-0400', '2002-10-27T01:30-0400',
|
|
'2002-10-27T01:30-0500', '2002-10-27T02:30-0500'], dtype='<U39')
|
|
|
|
Passing in a unit will change the precision
|
|
|
|
>>> np.datetime_as_string(d, unit='h')
|
|
array(['2002-10-27T04', '2002-10-27T05', '2002-10-27T06', '2002-10-27T07'],
|
|
dtype='<U32')
|
|
>>> np.datetime_as_string(d, unit='s')
|
|
array(['2002-10-27T04:30:00', '2002-10-27T05:30:00', '2002-10-27T06:30:00',
|
|
'2002-10-27T07:30:00'], dtype='<U38')
|
|
|
|
'casting' can be used to specify whether precision can be changed
|
|
|
|
>>> np.datetime_as_string(d, unit='h', casting='safe')
|
|
Traceback (most recent call last):
|
|
...
|
|
TypeError: Cannot create a datetime string as units 'h' from a NumPy
|
|
datetime with units 'm' according to the rule 'safe'
|
|
"""
|
|
return (arr,)
|