"""
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.
"""
import functools
from . import overrides
from . import _multiarray_umath
from ._multiarray_umath import * # noqa: F403
# These imports are needed for backward compatibility,
# do not change them. issue gh-15518
# _get_ndarray_c_version is semi-public, on purpose not added to __all__
from ._multiarray_umath import (
_flagdict, from_dlpack, _place, _reconstruct,
_vec_string, _ARRAY_API, _monotonicity, _get_ndarray_c_version,
_get_madvise_hugepage, _set_madvise_hugepage,
_get_promotion_state, _set_promotion_state
)
__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',
'_flagdict', 'from_dlpack', '_place', '_reconstruct', '_vec_string',
'_monotonicity', 'add_docstring', 'arange', 'array', 'asarray',
'asanyarray', 'ascontiguousarray', 'asfortranarray', 'bincount',
'broadcast', 'busday_count', 'busday_offset', 'busdaycalendar', 'can_cast',
'compare_chararrays', 'concatenate', 'copyto', 'correlate', 'correlate2',
'count_nonzero', 'c_einsum', 'datetime_as_string', 'datetime_data',
'dot', 'dragon4_positional', 'dragon4_scientific', 'dtype',
'empty', 'empty_like', 'error', 'flagsobj', 'flatiter', 'format_longfloat',
'frombuffer', 'fromfile', 'fromiter', 'fromstring',
'get_handler_name', 'get_handler_version', 'inner', 'interp',
'interp_complex', 'is_busday', 'lexsort', 'matmul', 'vecdot',
'may_share_memory', 'min_scalar_type', 'ndarray', 'nditer', 'nested_iters',
'normalize_axis_index', 'packbits', 'promote_types', 'putmask',
'ravel_multi_index', 'result_type', 'scalar', 'set_datetimeparse_function',
'set_typeDict', 'shares_memory', 'typeinfo',
'unpackbits', 'unravel_index', 'vdot', 'where', 'zeros',
'_get_promotion_state', '_set_promotion_state']
# For backward compatibility, make sure pickle imports
# these functions from here
_reconstruct.__module__ = 'numpy._core.multiarray'
scalar.__module__ = 'numpy._core.multiarray'
from_dlpack.__module__ = 'numpy'
arange.__module__ = 'numpy'
array.__module__ = 'numpy'
asarray.__module__ = 'numpy'
asanyarray.__module__ = 'numpy'
ascontiguousarray.__module__ = 'numpy'
asfortranarray.__module__ = 'numpy'
datetime_data.__module__ = 'numpy'
empty.__module__ = 'numpy'
frombuffer.__module__ = 'numpy'
fromfile.__module__ = 'numpy'
fromiter.__module__ = 'numpy'
frompyfunc.__module__ = 'numpy'
fromstring.__module__ = 'numpy'
may_share_memory.__module__ = 'numpy'
nested_iters.__module__ = 'numpy'
promote_types.__module__ = 'numpy'
zeros.__module__ = 'numpy'
_get_promotion_state.__module__ = 'numpy'
_set_promotion_state.__module__ = 'numpy'
normalize_axis_index.__module__ = 'numpy.lib.array_utils'
# We can't verify dispatcher signatures because NumPy's C functions don't
# support introspection.
array_function_from_c_func_and_dispatcher = functools.partial(
overrides.array_function_from_dispatcher,
module='numpy', docs_from_dispatcher=True, verify=False)
@array_function_from_c_func_and_dispatcher(_multiarray_umath.empty_like)
def empty_like(
prototype, dtype=None, order=None, subok=None, shape=None, *, device=None
):
"""
empty_like(prototype, dtype=None, order='K', subok=True, shape=None, *,
device=None)
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 `prototype`, otherwise it will be a base-class array. Defaults
to True.
shape : int or sequence of ints, optional.
Overrides the shape of the result. If order='K' and the number of
dimensions is unchanged, will try to keep order, otherwise,
order='C' is implied.
.. versionadded:: 1.17.0
device : str, optional
The device on which to place the created array. Default: None.
For Array-API interoperability only, so must be ``"cpu"`` if passed.
.. versionadded:: 2.0.0
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
-----
Unlike other array creation functions (e.g. `zeros_like`, `ones_like`,
`full_like`), `empty_like` does not initialize the values of the array,
and may therefore be marginally faster. However, the values stored in the
newly allocated array are arbitrary. For reproducible behavior, be sure
to set each element of the array before reading.
Examples
--------
>>> import numpy as np
>>> a = ([1,2,3], [4,5,6]) # a is array-like
>>> np.empty_like(a)
array([[-1073741821, -1073741821, 3], # uninitialized
[ 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], # uninitialized
[ 4.38791518e-305, -2.00000715e+000, 4.17269252e-309]])
""" # NOQA
return (prototype,)
@array_function_from_c_func_and_dispatcher(_multiarray_umath.concatenate)
def concatenate(arrays, axis=None, out=None, *, dtype=None, casting=None):
"""
concatenate(
(a1, a2, ...),
axis=0,
out=None,
dtype=None,
casting="same_kind"
)
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.
dtype : str or dtype
If provided, the destination array will have this dtype. Cannot be
provided together with `out`.
.. versionadded:: 1.20.0
casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
Controls what kind of data casting may occur. Defaults to 'same_kind'.
For a description of the options, please see :term:`casting`.
.. versionadded:: 1.20.0
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.
block : Assemble arrays from blocks.
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).
column_stack : Stack 1-D arrays as columns into a 2-D array.
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
--------
>>> import numpy as np
>>> 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)
"""
if out is not None:
# optimize for the typical case where only arrays is provided
arrays = list(arrays)
arrays.append(out)
return arrays
@array_function_from_c_func_and_dispatcher(_multiarray_umath.inner)
def inner(a, b):
"""
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
If `a` and `b` are both
scalars or both 1-D arrays then a scalar is returned; otherwise
an array is returned.
``out.shape = (*a.shape[:-1], *b.shape[:-1])``
Raises
------
ValueError
If both `a` and `b` are nonscalar and their last dimensions have
different sizes.
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-2,j0,...,js-2]
= sum(a[i0,...,ir-2,:]*b[j0,...,js-2,:])
In addition `a` or `b` may be scalars, in which case::
np.inner(a,b) = a*b
Examples
--------
Ordinary inner product for vectors:
>>> import numpy as np
>>> a = np.array([1,2,3])
>>> b = np.array([0,1,0])
>>> np.inner(a, b)
2
Some multidimensional examples:
>>> a = np.arange(24).reshape((2,3,4))
>>> b = np.arange(4)
>>> c = np.inner(a, b)
>>> c.shape
(2, 3)
>>> c
array([[ 14, 38, 62],
[ 86, 110, 134]])
>>> a = np.arange(2).reshape((1,1,2))
>>> b = np.arange(6).reshape((3,2))
>>> c = np.inner(a, b)
>>> c.shape
(1, 1, 3)
>>> c
array([[[1, 3, 5]]])
An example where `b` is a scalar:
>>> np.inner(np.eye(2), 7)
array([[7., 0.],
[0., 7.]])
"""
return (a, b)
@array_function_from_c_func_and_dispatcher(_multiarray_umath.where)
def where(condition, x=None, y=None):
"""
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
--------
>>> import numpy as np
>>> 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]])
"""
return (condition, x, y)
@array_function_from_c_func_and_dispatcher(_multiarray_umath.lexsort)
def lexsort(keys, axis=None):
"""
lexsort(keys, axis=-1)
Perform an indirect stable sort using a sequence of keys.
Given multiple sorting keys, lexsort returns an array of integer indices
that describes the sort order by multiple keys. The last key in the
sequence is used for the primary sort order, ties are broken by the
second-to-last key, and so on.
Parameters
----------
keys : (k, m, n, ...) array-like
The `k` keys to be sorted. The *last* key (e.g, the last
row if `keys` is a 2D array) is the primary sort key.
Each element of `keys` along the zeroth axis must be
an array-like object of the same shape.
axis : int, optional
Axis to be indirectly sorted. By default, sort over the last axis
of each sequence. Separate slices along `axis` sorted over
independently; see last example.
Returns
-------
indices : (m, n, ...) ndarray of ints
Array of indices that sort the keys along the specified axis.
See Also
--------
argsort : Indirect sort.
ndarray.sort : In-place sort.
sort : Return a sorted copy of an array.
Examples
--------
Sort names: first by surname, then by name.
>>> import numpy as np
>>> surnames = ('Hertz', 'Galilei', 'Hertz')
>>> first_names = ('Heinrich', 'Galileo', 'Gustav')
>>> ind = np.lexsort((first_names, surnames))
>>> ind
array([1, 2, 0])
>>> [surnames[i] + ", " + first_names[i] for i in ind]
['Galilei, Galileo', 'Hertz, Gustav', 'Hertz, Heinrich']
Sort according to two numerical keys, first by elements
of ``a``, then breaking ties according to elements of ``b``:
>>> a = [1, 5, 1, 4, 3, 4, 4] # First sequence
>>> b = [9, 4, 0, 4, 0, 2, 1] # Second sequence
>>> ind = np.lexsort((b, a)) # Sort by `a`, then by `b`
>>> ind
array([2, 0, 4, 6, 5, 3, 1])
>>> [(a[i], b[i]) for i in ind]
[(1, 0), (1, 9), (3, 0), (4, 1), (4, 2), (4, 4), (5, 4)]
Compare against `argsort`, which would sort each key independently.
>>> np.argsort((b, a), kind='stable')
array([[2, 4, 6, 5, 1, 3, 0],
[0, 2, 4, 3, 5, 6, 1]])
To sort lexicographically with `argsort`, we would need to provide a
structured array.
>>> x = np.array([(ai, bi) for ai, bi in zip(a, b)],
... dtype = np.dtype([('x', int), ('y', int)]))
>>> np.argsort(x) # or np.argsort(x, order=('x', 'y'))
array([2, 0, 4, 6, 5, 3, 1])
The zeroth axis of `keys` always corresponds with the sequence of keys,
so 2D arrays are treated just like other sequences of keys.
>>> arr = np.asarray([b, a])
>>> ind2 = np.lexsort(arr)
>>> np.testing.assert_equal(ind2, ind)
Accordingly, the `axis` parameter refers to an axis of *each* key, not of
the `keys` argument itself. For instance, the array ``arr`` is treated as
a sequence of two 1-D keys, so specifying ``axis=0`` is equivalent to
using the default axis, ``axis=-1``.
>>> np.testing.assert_equal(np.lexsort(arr, axis=0),
... np.lexsort(arr, axis=-1))
For higher-dimensional arrays, the axis parameter begins to matter. The
resulting array has the same shape as each key, and the values are what
we would expect if `lexsort` were performed on corresponding slices
of the keys independently. For instance,
>>> x = [[1, 2, 3, 4],
... [4, 3, 2, 1],
... [2, 1, 4, 3]]
>>> y = [[2, 2, 1, 1],
... [1, 2, 1, 2],
... [1, 1, 2, 1]]
>>> np.lexsort((x, y), axis=1)
array([[2, 3, 0, 1],
[2, 0, 3, 1],
[1, 0, 3, 2]])
Each row of the result is what we would expect if we were to perform
`lexsort` on the corresponding row of the keys:
>>> for i in range(3):
... print(np.lexsort((x[i], y[i])))
[2 3 0 1]
[2 0 3 1]
[1 0 3 2]
"""
if isinstance(keys, tuple):
return keys
else:
return (keys,)
@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
casting rule.
Parameters
----------
from_ : dtype, dtype specifier, NumPy scalar, or array
Data type, NumPy scalar, or array to cast from.
to : dtype or dtype specifier
Data type to cast to.
casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
Controls what kind of data casting may occur.
* '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.
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.
.. versionchanged:: 2.0
This function does not support Python scalars anymore and does not
apply any value-based logic for 0-D arrays and NumPy scalars.
See also
--------
dtype, result_type
Examples
--------
Basic examples
>>> import numpy as np
>>> 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
"""
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
--------
>>> import numpy as np
>>> 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 scalar, 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
--------
>>> import numpy as np
>>> 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])
It uses an optimized BLAS library when possible (see `numpy.linalg`).
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.
linalg.multi_dot : Chained dot product.
Examples
--------
>>> import numpy as np
>>> 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
--------
>>> import numpy as np
>>> 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
--------
>>> import numpy as np
>>> 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):
...
TypeError: Cannot cast array data from dtype('float64') to dtype('int64')
according to the rule 'safe'
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
--------
>>> import numpy as np
>>> 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):
"""
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
--------
>>> import numpy as np
>>> 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)
"""
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.
Examples
--------
>>> import numpy as np
>>> A = np.array([4, 5, 6])
>>> B = [1, 2, 3]
>>> np.copyto(A, B)
>>> A
array([1, 2, 3])
>>> A = np.array([[1, 2, 3], [4, 5, 6]])
>>> B = [[4, 5, 6], [7, 8, 9]]
>>> np.copyto(A, B)
>>> A
array([[4, 5, 6],
[7, 8, 9]])
"""
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 : ndarray
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
--------
>>> import numpy as np
>>> 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
--------
>>> import numpy as np
>>> 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
--------
>>> import numpy as np
>>> 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.
.. warning::
This function can be exponentially slow for some inputs, unless
`max_work` is set to zero or a positive integer.
If in doubt, use `numpy.may_share_memory` instead.
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=-1 (default)
The problem is solved exactly. In this case, the function returns
True only if there is an element shared between the arrays. Finding
the exact solution may take extremely long in some cases.
max_work=0
Only the memory bounds of a and b are checked.
This is equivalent to using ``may_share_memory()``.
Raises
------
numpy.exceptions.TooHardError
Exceeded max_work.
Returns
-------
out : bool
See Also
--------
may_share_memory
Examples
--------
>>> import numpy as np
>>> x = np.array([1, 2, 3, 4])
>>> np.shares_memory(x, np.array([5, 6, 7]))
False
>>> np.shares_memory(x[::2], x)
True
>>> np.shares_memory(x[::2], x[1::2])
False
Checking whether two arrays share memory is NP-complete, and
runtime may increase exponentially in the number of
dimensions. Hence, `max_work` should generally be set to a finite
number, as it is possible to construct examples that take
extremely long to run:
>>> from numpy.lib.stride_tricks import as_strided
>>> x = np.zeros([192163377], dtype=np.int8)
>>> x1 = as_strided(
... x, strides=(36674, 61119, 85569), shape=(1049, 1049, 1049))
>>> x2 = as_strided(
... x[64023025:], strides=(12223, 12224, 1), shape=(1049, 1049, 1))
>>> np.shares_memory(x1, x2, max_work=1000)
Traceback (most recent call last):
...
numpy.exceptions.TooHardError: Exceeded max_work
Running ``np.shares_memory(x1, x2)`` without `max_work` set takes
around 1 minute for this case. It is possible to find problems
that take still significantly longer.
"""
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
--------
>>> import numpy as np
>>> 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
--------
>>> import numpy as np
>>> # 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
--------
>>> import numpy as np
>>> # First business day in October 2011 (not accounting for holidays)
... np.busday_offset('2011-10', 0, roll='forward')
np.datetime64('2011-10-03')
>>> # Last business day in February 2012 (not accounting for holidays)
... np.busday_offset('2012-03', -1, roll='forward')
np.datetime64('2012-02-29')
>>> # Third Wednesday in January 2011
... np.busday_offset('2011-01', 2, roll='forward', weekmask='Wed')
np.datetime64('2011-01-19')
>>> # 2012 Mother's Day in Canada and the U.S.
... np.busday_offset('2012-05', 1, roll='forward', weekmask='Sun')
np.datetime64('2012-05-13')
>>> # First business day on or after a date
... np.busday_offset('2011-03-20', 0, roll='forward')
np.datetime64('2011-03-21')
>>> np.busday_offset('2011-03-22', 0, roll='forward')
np.datetime64('2011-03-22')
>>> # First business day after a date
... np.busday_offset('2011-03-20', 1, roll='backward')
np.datetime64('2011-03-21')
>>> np.busday_offset('2011-03-22', 1, roll='backward')
np.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
--------
>>> import numpy as np
>>> # 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 numpy as np
>>> 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,)