import builtins

import collections.abc

import functools

import operator

import re

import warnings

import numpy as np

import numpy._core.numeric as _nx

from numpy._core import overrides, transpose

from numpy._core._multiarray_umath import _array_converter

from numpy._core.fromnumeric import any, mean, nonzero, partition, ravel, sum

from numpy._core.multiarray import (

_monotonicity,

_place,

bincount,

interp as compiled_interp,

interp_complex as compiled_interp_complex,

normalize_axis_index,

)

from numpy._core.numeric import (

absolute,

arange,

array,

asanyarray,

asarray,

concatenate,

dot,

empty,

integer,

intp,

isscalar,

ndarray,

ones,

take,

where,

zeros_like,

)

from numpy._core.numerictypes import typecodes

from numpy._core.umath import (

add,

arctan2,

cos,

exp,

floor,

frompyfunc,

less_equal,

minimum,

mod,

not_equal,

pi,

sin,

sqrt,

subtract,

)

from numpy._utils import set_module

# needed in this module for compatibility

from numpy.lib._histograms_impl import histogram, histogramdd # noqa: F401

from numpy.lib._twodim_base_impl import diag

array_function_dispatch = functools.partial(

overrides.array_function_dispatch, module='numpy')

__all__ = [

'select', 'piecewise', 'trim_zeros', 'copy', 'iterable', 'percentile',

'diff', 'gradient', 'angle', 'unwrap', 'sort_complex', 'flip',

'rot90', 'extract', 'place', 'vectorize', 'asarray_chkfinite', 'average',

'bincount', 'digitize', 'cov', 'corrcoef',

'median', 'sinc', 'hamming', 'hanning', 'bartlett',

'blackman', 'kaiser', 'trapezoid', 'i0',

'meshgrid', 'delete', 'insert', 'append', 'interp',

'quantile'

]

# _QuantileMethods is a dictionary listing all the supported methods to

# compute quantile/percentile.

#

# Below virtual_index refers to the index of the element where the percentile

# would be found in the sorted sample.

# When the sample contains exactly the percentile wanted, the virtual_index is

# an integer to the index of this element.

# When the percentile wanted is in between two elements, the virtual_index

# is made of an integer part (a.k.a 'i' or 'left') and a fractional part

# (a.k.a 'g' or 'gamma')

#

# Each method in _QuantileMethods has two properties

# get_virtual_index : Callable

# The function used to compute the virtual_index.

# fix_gamma : Callable

# A function used for discrete methods to force the index to a specific value.

_QuantileMethods = {

# --- HYNDMAN and FAN METHODS

# Discrete methods

'inverted_cdf': {

'get_virtual_index': lambda n, quantiles: _inverted_cdf(n, quantiles),

'fix_gamma': None, # should never be called

},

'averaged_inverted_cdf': {

'get_virtual_index': lambda n, quantiles: (n * quantiles) - 1,

'fix_gamma': lambda gamma, _: _get_gamma_mask(

shape=gamma.shape,

default_value=1.,

conditioned_value=0.5,

where=gamma == 0),

},

'closest_observation': {

'get_virtual_index': lambda n, quantiles: _closest_observation(n, quantiles),

'fix_gamma': None, # should never be called

},

# Continuous methods

'interpolated_inverted_cdf': {

'get_virtual_index': lambda n, quantiles:

_compute_virtual_index(n, quantiles, 0, 1),

'fix_gamma': lambda gamma, _: gamma,

},

'hazen': {

'get_virtual_index': lambda n, quantiles:

_compute_virtual_index(n, quantiles, 0.5, 0.5),

'fix_gamma': lambda gamma, _: gamma,

},

'weibull': {

'get_virtual_index': lambda n, quantiles:

_compute_virtual_index(n, quantiles, 0, 0),

'fix_gamma': lambda gamma, _: gamma,

},

# Default method.

# To avoid some rounding issues, `(n-1) * quantiles` is preferred to

# `_compute_virtual_index(n, quantiles, 1, 1)`.

# They are mathematically equivalent.

'linear': {

'get_virtual_index': lambda n, quantiles: (n - 1) * quantiles,

'fix_gamma': lambda gamma, _: gamma,

},

'median_unbiased': {

'get_virtual_index': lambda n, quantiles:

_compute_virtual_index(n, quantiles, 1 / 3.0, 1 / 3.0),

'fix_gamma': lambda gamma, _: gamma,

},

'normal_unbiased': {

'get_virtual_index': lambda n, quantiles:

_compute_virtual_index(n, quantiles, 3 / 8.0, 3 / 8.0),

'fix_gamma': lambda gamma, _: gamma,

},

# --- OTHER METHODS

'lower': {

'get_virtual_index': lambda n, quantiles: np.floor(

(n - 1) * quantiles).astype(np.intp),

'fix_gamma': None, # should never be called, index dtype is int

},

'higher': {

'get_virtual_index': lambda n, quantiles: np.ceil(

(n - 1) * quantiles).astype(np.intp),

'fix_gamma': None, # should never be called, index dtype is int

},

'midpoint': {

'get_virtual_index': lambda n, quantiles: 0.5 * (

np.floor((n - 1) * quantiles)

+ np.ceil((n - 1) * quantiles)),

'fix_gamma': lambda gamma, index: _get_gamma_mask(

shape=gamma.shape,

default_value=0.5,

conditioned_value=0.,

where=index % 1 == 0),

},

'nearest': {

'get_virtual_index': lambda n, quantiles: np.around(

(n - 1) * quantiles).astype(np.intp),

'fix_gamma': None,

# should never be called, index dtype is int

}}

def _rot90_dispatcher(m, k=None, axes=None):

return (m,)

@array_function_dispatch(_rot90_dispatcher)

def rot90(m, k=1, axes=(0, 1)):

"""

Rotate an array by 90 degrees in the plane specified by axes.

Rotation direction is from the first towards the second axis.

This means for a 2D array with the default `k` and `axes`, the

rotation will be counterclockwise.

Parameters

----------

m : array_like

Array of two or more dimensions.

k : integer

Number of times the array is rotated by 90 degrees.

axes : (2,) array_like

The array is rotated in the plane defined by the axes.

Axes must be different.

Returns

-------

y : ndarray

A rotated view of `m`.

See Also

--------

flip : Reverse the order of elements in an array along the given axis.

fliplr : Flip an array horizontally.

flipud : Flip an array vertically.

Notes

-----

``rot90(m, k=1, axes=(1,0))`` is the reverse of

``rot90(m, k=1, axes=(0,1))``

``rot90(m, k=1, axes=(1,0))`` is equivalent to

``rot90(m, k=-1, axes=(0,1))``

Examples

--------

>>> import numpy as np

>>> m = np.array([[1,2],[3,4]], int)

>>> m

array([[1, 2],

[3, 4]])

>>> np.rot90(m)

array([[2, 4],

[1, 3]])

>>> np.rot90(m, 2)

array([[4, 3],

[2, 1]])

>>> m = np.arange(8).reshape((2,2,2))

>>> np.rot90(m, 1, (1,2))

array([[[1, 3],

[0, 2]],

[[5, 7],

[4, 6]]])

"""

axes = tuple(axes)

if len(axes) != 2:

raise ValueError("len(axes) must be 2.")

m = asanyarray(m)

if axes[0] == axes[1] or absolute(axes[0] - axes[1]) == m.ndim:

raise ValueError("Axes must be different.")

if (axes[0] >= m.ndim or axes[0] < -m.ndim

or axes[1] >= m.ndim or axes[1] < -m.ndim):

raise ValueError(f"Axes={axes} out of range for array of ndim={m.ndim}.")

try:

k = operator.index(k)

except TypeError:

# DEPRECATED 2026-04-27, NumPy 2.5

msg = (f"Passing a value for k ({k!r}) that is not an integer type has been "

"deprecated in NumPy 2.5. The value will be cast to an integer. In "

"the future this will be an error.")

warnings.warn(msg, DeprecationWarning, stacklevel=2)

k = int(k)

k %= 4

if k == 0:

return m[:]

if k == 2:

return flip(flip(m, axes[0]), axes[1])

axes_list = arange(0, m.ndim)

(axes_list[axes[0]], axes_list[axes[1]]) = (axes_list[axes[1]],

axes_list[axes[0]])

if k == 1:

return transpose(flip(m, axes[1]), axes_list)

else:

# k == 3

return flip(transpose(m, axes_list), axes[1])

def _flip_dispatcher(m, axis=None):

return (m,)

@array_function_dispatch(_flip_dispatcher)

def flip(m, axis=None):

"""

Reverse the order of elements in an array along the given axis.

The shape of the array is preserved, but the elements are reordered.

Parameters

----------

m : array_like

Input array.

axis : None or int or tuple of ints, optional

Axis or axes along which to flip over. The default,

axis=None, will flip over all of the axes of the input array.

If axis is negative it counts from the last to the first axis.

If axis is a tuple of ints, flipping is performed on all of the axes

specified in the tuple.

Returns

-------

out : array_like

A view of `m` with the entries of axis reversed. Since a view is

returned, this operation is done in constant time.

See Also

--------

flipud : Flip an array vertically (axis=0).

fliplr : Flip an array horizontally (axis=1).

Notes

-----

flip(m, 0) is equivalent to flipud(m).

flip(m, 1) is equivalent to fliplr(m).

flip(m, n) corresponds to ``m[...,::-1,...]`` with ``::-1`` at position n.

flip(m) corresponds to ``m[::-1,::-1,...,::-1]`` with ``::-1`` at all

positions.

flip(m, (0, 1)) corresponds to ``m[::-1,::-1,...]`` with ``::-1`` at

position 0 and position 1.

Examples

--------

>>> import numpy as np

>>> A = np.arange(8).reshape((2,2,2))

>>> A

array([[[0, 1],

[2, 3]],

[[4, 5],

[6, 7]]])

>>> np.flip(A, 0)

array([[[4, 5],

[6, 7]],

[[0, 1],

[2, 3]]])

>>> np.flip(A, 1)

array([[[2, 3],

[0, 1]],

[[6, 7],

[4, 5]]])

>>> np.flip(A)

array([[[7, 6],

[5, 4]],

[[3, 2],

[1, 0]]])

>>> np.flip(A, (0, 2))

array([[[5, 4],

[7, 6]],

[[1, 0],

[3, 2]]])

>>> rng = np.random.default_rng()

>>> A = rng.normal(size=(3,4,5))

>>> np.all(np.flip(A,2) == A[:,:,::-1,...])

True

"""

if not hasattr(m, 'ndim'):

m = asarray(m)

if axis is None:

indexer = (np.s_[::-1],) * m.ndim

else:

axis = _nx.normalize_axis_tuple(axis, m.ndim)

indexer = [np.s_[:]] * m.ndim

for ax in axis:

indexer[ax] = np.s_[::-1]

indexer = tuple(indexer)

return m[indexer]

@set_module('numpy')

def iterable(y):

"""

Check whether or not an object can be iterated over.

Parameters

----------

y : object

Input object.

Returns

-------

b : bool

Return ``True`` if the object has an iterator method or is a

sequence and ``False`` otherwise.

Examples

--------

>>> import numpy as np

>>> np.iterable([1, 2, 3])

True

>>> np.iterable(2)

False

Notes

-----

In most cases, the results of ``np.iterable(obj)`` are consistent with

``isinstance(obj, collections.abc.Iterable)``. One notable exception is

the treatment of 0-dimensional arrays::

>>> from collections.abc import Iterable

>>> a = np.array(1.0) # 0-dimensional numpy array

>>> isinstance(a, Iterable)

True

>>> np.iterable(a)

False

"""

try:

iter(y)

except TypeError:

return False

return True

def _weights_are_valid(weights, a, axis):

"""Validate weights array.

We assume, weights is not None.

"""

wgt = np.asanyarray(weights)

# Sanity checks

if a.shape != wgt.shape:

if axis is None:

raise TypeError(

"Axis must be specified when shapes of a and weights "

"differ.")

if wgt.shape != tuple(a.shape[ax] for ax in axis):

raise ValueError(

"Shape of weights must be consistent with "

"shape of a along specified axis.")

# setup wgt to broadcast along axis

wgt = wgt.transpose(np.argsort(axis))

wgt = wgt.reshape(tuple((s if ax in axis else 1)

for ax, s in enumerate(a.shape)))

return wgt

def _average_dispatcher(a, axis=None, weights=None, returned=None, *,

keepdims=None):

return (a, weights)

@array_function_dispatch(_average_dispatcher)

def average(a, axis=None, weights=None, returned=False, *,

keepdims=np._NoValue):

"""

Compute the weighted average along the specified axis.

Parameters

----------

a : array_like

Array containing data to be averaged. If `a` is not an array, a

conversion is attempted.

axis : None or int or tuple of ints, optional

Axis or axes along which to average `a`. The default,

`axis=None`, will average over all of the elements of the input array.

If axis is negative it counts from the last to the first axis.

If axis is a tuple of ints, averaging is performed on all of the axes

specified in the tuple instead of a single axis or all the axes as

before.

weights : array_like, optional

An array of weights associated with the values in `a`. Each value in

`a` contributes to the average according to its associated weight.

The array of weights must be the same shape as `a` if no axis is

specified, otherwise the weights must have dimensions and shape

consistent with `a` along the specified axis.

If `weights=None`, then all data in `a` are assumed to have a

weight equal to one.

The calculation is::

avg = sum(a * weights) / sum(weights)

where the sum is over all included elements.

The only constraint on the values of `weights` is that `sum(weights)`

must not be 0.

returned : bool, optional

Default is `False`. If `True`, the tuple (`average`, `sum_of_weights`)

is returned, otherwise only the average is returned.

If `weights=None`, `sum_of_weights` is equivalent to the number of

elements over which the average is taken.

keepdims : bool, optional

If this is set to True, the axes which are reduced are left

in the result as dimensions with size one. With this option,

the result will broadcast correctly against the original `a`.

*Note:* `keepdims` will not work with instances of `numpy.matrix`

or other classes whose methods do not support `keepdims`.

.. versionadded:: 1.23.0

Returns

-------

retval, [sum_of_weights] : array_type or double

Return the average along the specified axis. When `returned` is `True`,

return a tuple with the average as the first element and the sum

of the weights as the second element. `sum_of_weights` is of the

same type as `retval`. The result dtype follows a general pattern.

If `weights` is None, the result dtype will be that of `a` , or ``float64``

if `a` is integral. Otherwise, if `weights` is not None and `a` is non-

integral, the result type will be the type of lowest precision capable of

representing values of both `a` and `weights`. If `a` happens to be

integral, the previous rules still applies but the result dtype will

at least be ``float64``.

Raises

------

ZeroDivisionError

When all weights along axis are zero. See `numpy.ma.average` for a

version robust to this type of error.

TypeError

When `weights` does not have the same shape as `a`, and `axis=None`.

ValueError

When `weights` does not have dimensions and shape consistent with `a`

along specified `axis`.

See Also

--------

mean

ma.average : average for masked arrays -- useful if your data contains

"missing" values

numpy.result_type : Returns the type that results from applying the

numpy type promotion rules to the arguments.

Examples

--------

>>> import numpy as np

>>> data = np.arange(1, 5)

>>> data

array([1, 2, 3, 4])

>>> np.average(data)

2.5

>>> np.average(np.arange(1, 11), weights=np.arange(10, 0, -1))

4.0

>>> data = np.arange(6).reshape((3, 2))

>>> data

array([[0, 1],

[2, 3],

[4, 5]])

>>> np.average(data, axis=1, weights=[1./4, 3./4])

array([0.75, 2.75, 4.75])

>>> np.average(data, weights=[1./4, 3./4])

Traceback (most recent call last):

...

TypeError: Axis must be specified when shapes of a and weights differ.

With ``keepdims=True``, the following result has shape (3, 1).

>>> np.average(data, axis=1, keepdims=True)

array([[0.5],

[2.5],

[4.5]])

>>> data = np.arange(8).reshape((2, 2, 2))

>>> data

array([[[0, 1],

[2, 3]],

[[4, 5],

[6, 7]]])

>>> np.average(data, axis=(0, 1), weights=[[1./4, 3./4], [1., 1./2]])

array([3.4, 4.4])

>>> np.average(data, axis=0, weights=[[1./4, 3./4], [1., 1./2]])

Traceback (most recent call last):

...

ValueError: Shape of weights must be consistent

with shape of a along specified axis.

"""

a = np.asanyarray(a)

if axis is not None:

axis = _nx.normalize_axis_tuple(axis, a.ndim, argname="axis")

if keepdims is np._NoValue:

# Don't pass on the keepdims argument if one wasn't given.

keepdims_kw = {}

else:

keepdims_kw = {'keepdims': keepdims}

if weights is None:

avg = a.mean(axis, **keepdims_kw)

avg_as_array = np.asanyarray(avg)

scl = avg_as_array.dtype.type(a.size / avg_as_array.size)

else:

wgt = _weights_are_valid(weights=weights, a=a, axis=axis)

if issubclass(a.dtype.type, (np.integer, np.bool)):

result_dtype = np.result_type(a.dtype, wgt.dtype, 'f8')

else:

result_dtype = np.result_type(a.dtype, wgt.dtype)

scl = wgt.sum(axis=axis, dtype=result_dtype, **keepdims_kw)

if np.any(scl == 0.0):

raise ZeroDivisionError(

"Weights sum to zero, can't be normalized")

avg = avg_as_array = np.multiply(a, wgt,

dtype=result_dtype).sum(axis, **keepdims_kw) / scl

if returned:

if scl.shape != avg_as_array.shape:

scl = np.broadcast_to(scl, avg_as_array.shape, subok=True).copy()

return avg, scl

else:

return avg

@set_module('numpy')

def asarray_chkfinite(a, dtype=None, order=None):

"""Convert the input to an array, checking for NaNs or Infs.

Parameters

----------

a : array_like

Input data, in any form that can be converted to an array. This

includes lists, lists of tuples, tuples, tuples of tuples, tuples

of lists and ndarrays. Success requires no NaNs or Infs.

dtype : data-type, optional

By default, the data-type is inferred from the input data.

order : {'C', 'F', 'A', 'K'}, optional

The memory layout of the output.

'C' gives a row-major layout (C-style),

'F' gives a column-major layout (Fortran-style).

'C' and 'F' will copy if needed to ensure the output format.

'A' (any) is equivalent to 'F' if input a is non-contiguous or

Fortran-contiguous, otherwise, it is equivalent to 'C'.

Unlike 'C' or 'F', 'A' does not ensure that the result is contiguous.

'K' (keep) preserves the input order for the output.

'C' is the default.

Returns

-------

out : ndarray

Array interpretation of `a`. No copy is performed if the input

is already an ndarray. If `a` is a subclass of ndarray, a base

class ndarray is returned.

Raises

------

ValueError

Raises ValueError if `a` contains NaN (Not a Number) or Inf (Infinity).

See Also

--------

asarray : Create and array.

asanyarray : Similar function which passes through subclasses.

ascontiguousarray : Convert input to a contiguous array.

asfortranarray : Convert input to an ndarray with column-major

memory order.

fromiter : Create an array from an iterator.

fromfunction : Construct an array by executing a function on grid

positions.

Examples

--------

>>> import numpy as np

Convert a list into an array. If all elements are finite, then

``asarray_chkfinite`` is identical to ``asarray``.

>>> a = [1, 2]

>>> np.asarray_chkfinite(a, dtype=np.float64)

array([1., 2.])

Raises ValueError if array_like contains Nans or Infs.

>>> a = [1, 2, np.inf]

>>> try:

... np.asarray_chkfinite(a)

... except ValueError:

... print('ValueError')

...

ValueError

"""

a = asarray(a, dtype=dtype, order=order)

if a.dtype.char in typecodes['AllFloat'] and not np.isfinite(a).all():

raise ValueError(

"array must not contain infs or NaNs")

return a

def _piecewise_dispatcher(x, condlist, funclist, *args, **kw):

yield x

# support the undocumented behavior of allowing scalars

if np.iterable(condlist):

yield from condlist

@array_function_dispatch(_piecewise_dispatcher)

def piecewise(x, condlist, funclist, *args, **kw):

"""

Evaluate a piecewise-defined function.

Given a set of conditions and corresponding functions, evaluate each

function on the input data wherever its condition is true.

Parameters

----------

x : ndarray or scalar

The input domain.

condlist : list of bool arrays or bool scalars

Each boolean array corresponds to a function in `funclist`. Wherever

`condlist[i]` is True, `funclist[i](x)` is used as the output value.

Each boolean array in `condlist` selects a piece of `x`,

and should therefore be of the same shape as `x`.

The length of `condlist` must correspond to that of `funclist`.

If one extra function is given, i.e. if

``len(funclist) == len(condlist) + 1``, then that extra function

is the default value, used wherever all conditions are false.

funclist : list of callables, f(x,*args,**kw), or scalars

Each function is evaluated over `x` wherever its corresponding

condition is True. It should take a 1d array as input and give a 1d

array or a scalar value as output. If, instead of a callable,

a scalar is provided then a constant function (``lambda x: scalar``) is

assumed.

args : tuple, optional

Any further arguments given to `piecewise` are passed to the functions

upon execution, i.e., if called ``piecewise(..., ..., 1, 'a')``, then

each function is called as ``f(x, 1, 'a')``.

kw : dict, optional

Keyword arguments used in calling `piecewise` are passed to the

functions upon execution, i.e., if called

``piecewise(..., ..., alpha=1)``, then each function is called as

``f(x, alpha=1)``.

Returns

-------

out : ndarray

The output is the same shape and type as x and is found by

calling the functions in `funclist` on the appropriate portions of `x`,

as defined by the boolean arrays in `condlist`. Portions not covered

by any condition have a default value of 0.

See Also

--------

choose, select, where

Notes

-----

This is similar to choose or select, except that functions are

evaluated on elements of `x` that satisfy the corresponding condition from

`condlist`.

The result is::

|--

|funclist[0](x[condlist[0]])

out = |funclist[1](x[condlist[1]])

|...

|funclist[n2](x[condlist[n2]])

|--

Examples

--------

>>> import numpy as np

Define the signum function, which is -1 for ``x < 0`` and +1 for ``x >= 0``.

>>> x = np.linspace(-2.5, 2.5, 6)

>>> np.piecewise(x, [x < 0, x >= 0], [-1, 1])

array([-1., -1., -1., 1., 1., 1.])

Define the absolute value, which is ``-x`` for ``x <0`` and ``x`` for

``x >= 0``.

>>> np.piecewise(x, [x < 0, x >= 0], [lambda x: -x, lambda x: x])

array([2.5, 1.5, 0.5, 0.5, 1.5, 2.5])

Apply the same function to a scalar value.

>>> y = -2

>>> np.piecewise(y, [y < 0, y >= 0], [lambda x: -x, lambda x: x])

array(2)

"""

x = asanyarray(x)

n2 = len(funclist)

# undocumented: single condition is promoted to a list of one condition

if isscalar(condlist) or (

not isinstance(condlist[0], (list, ndarray)) and x.ndim != 0):

condlist = [condlist]

condlist = asarray(condlist, dtype=bool)

n = len(condlist)

if n == n2 - 1: # compute the "otherwise" condition.

condelse = ~np.any(condlist, axis=0, keepdims=True)

condlist = np.concatenate([condlist, condelse], axis=0)

n += 1

elif n != n2:

raise ValueError(

f"with {n} condition(s), either {n} or {n + 1} functions are expected"

)

y = zeros_like(x)

for cond, func in zip(condlist, funclist):

if not isinstance(func, collections.abc.Callable):

y[cond] = func

else:

vals = x[cond]

if vals.size > 0:

y[cond] = func(vals, *args, **kw)

return y

def _select_dispatcher(condlist, choicelist, default=None):

yield from condlist

yield from choicelist

@array_function_dispatch(_select_dispatcher)

def select(condlist, choicelist, default=0):

"""

Return an array drawn from elements in choicelist, depending on conditions.

Parameters

----------

condlist : list of bool ndarrays

The list of conditions which determine from which array in `choicelist`

the output elements are taken. When multiple conditions are satisfied,

the first one encountered in `condlist` is used.

choicelist : list of ndarrays

The list of arrays from which the output elements are taken. It has

to be of the same length as `condlist`.

default : array_like, optional

The element inserted in `output` when all conditions evaluate to False.

Returns

-------

output : ndarray

The output at position m is the m-th element of the array in

`choicelist` where the m-th element of the corresponding array in

`condlist` is True.

See Also

--------

where : Return elements from one of two arrays depending on condition.

take, choose, compress, diag, diagonal

Examples

--------

>>> import numpy as np

Beginning with an array of integers from 0 to 5 (inclusive),

elements less than ``3`` are negated, elements greater than ``3``

are squared, and elements not meeting either of these conditions

(exactly ``3``) are replaced with a `default` value of ``42``.

>>> x = np.arange(6)

>>> condlist = [x<3, x>3]

>>> choicelist = [-x, x**2]

>>> np.select(condlist, choicelist, 42)

array([ 0, -1, -2, 42, 16, 25])

When multiple conditions are satisfied, the first one encountered in

`condlist` is used.

>>> condlist = [x<=4, x>3]

>>> choicelist = [x, x**2]

>>> np.select(condlist, choicelist, 55)

array([ 0, 1, 2, 3, 4, 25])

"""

# Check the size of condlist and choicelist are the same, or abort.

if len(condlist) != len(choicelist):

raise ValueError(

'list of cases must be same length as list of conditions')

# Now that the dtype is known, handle the deprecated select([], []) case

if len(condlist) == 0:

raise ValueError("select with an empty condition list is not possible")

# TODO: This preserves the Python int, float, complex manually to get the

# right `result_type` with NEP 50. Most likely we will grow a better

# way to spell this (and this can be replaced).

choicelist = [

choice if type(choice) in (int, float, complex) else np.asarray(choice)

for choice in choicelist]

choicelist.append(default if type(default) in (int, float, complex)

else np.asarray(default))

try:

dtype = np.result_type(*choicelist)

except TypeError as e:

msg = f'Choicelist and default value do not have a common dtype: {e}'

raise TypeError(msg) from None

# Convert conditions to arrays and broadcast conditions and choices

# as the shape is needed for the result. Doing it separately optimizes

# for example when all choices are scalars.

condlist = np.broadcast_arrays(*condlist)

choicelist = np.broadcast_arrays(*choicelist)

# If cond array is not an ndarray in boolean format or scalar bool, abort.

for i, cond in enumerate(condlist):

if cond.dtype.type is not np.bool:

raise TypeError(

f'invalid entry {i} in condlist: should be boolean ndarray')

if choicelist[0].ndim == 0:

# This may be common, so avoid the call.

result_shape = condlist[0].shape

else:

result_shape = np.broadcast_arrays(condlist[0], choicelist[0])[0].shape

result = np.full(result_shape, choicelist[-1], dtype)

# Use np.copyto to burn each choicelist array onto result, using the

# corresponding condlist as a boolean mask. This is done in reverse

# order since the first choice should take precedence.

choicelist = choicelist[-2::-1]

condlist = condlist[::-1]

for choice, cond in zip(choicelist, condlist):

np.copyto(result, choice, where=cond)

return result

def _copy_dispatcher(a, order=None, subok=None):

return (a,)

@array_function_dispatch(_copy_dispatcher)

def copy(a, order='K', subok=False):

"""

Return an array copy of the given object.

Parameters

----------

a : array_like

Input data.

order : {'C', 'F', 'A', 'K'}, optional

Controls the memory layout of the copy. 'C' means C-order,

'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,

'C' otherwise. 'K' means match the layout of `a` as closely

as possible. (Note that this function and :meth:`ndarray.copy` are very

similar, but have different default values for their order=

arguments.)

subok : bool, optional

If True, then sub-classes will be passed-through, otherwise the

returned array will be forced to be a base-class array (defaults to False).

Returns

-------

arr : ndarray

Array interpretation of `a`.

See Also

--------

ndarray.copy : Preferred method for creating an array copy

Notes

-----

This is equivalent to:

>>> np.array(a, copy=True) #doctest: +SKIP

The copy made of the data is shallow, i.e., for arrays with object dtype,

the new array will point to the same objects.

See Examples from `ndarray.copy`.

Examples

--------

>>> import numpy as np

Create an array x, with a reference y and a copy z:

>>> x = np.array([1, 2, 3])

>>> y = x

>>> z = np.copy(x)

Note that, when we modify x, y changes, but not z:

>>> x[0] = 10

>>> x[0] == y[0]

True

>>> x[0] == z[0]

False

Note that, np.copy clears previously set WRITEABLE=False flag.

>>> a = np.array([1, 2, 3])

>>> a.flags["WRITEABLE"] = False

>>> b = np.copy(a)

>>> b.flags["WRITEABLE"]

True

>>> b[0] = 3

>>> b