import builtins
import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy._core.numeric as _nx
from numpy._core import transpose, overrides
from numpy._core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, isscalar, absolute
)
from numpy._core.umath import (
pi, add, arctan2, frompyfunc, cos, less_equal, sqrt, sin,
mod, exp, not_equal, subtract, minimum
)
from numpy._core.fromnumeric import (
ravel, nonzero, partition, mean, any, sum
)
from numpy._core.numerictypes import typecodes
from numpy.lib._twodim_base_impl import diag
from numpy._core.multiarray import (
_place, bincount, normalize_axis_index, _monotonicity,
interp as compiled_interp, interp_complex as compiled_interp_complex
)
from numpy._core._multiarray_umath import _array_converter
from numpy._utils import set_module
# needed in this module for compatibility
from numpy.lib._histograms_impl import histogram, histogramdd # noqa: F401
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', 'trapz', '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 a 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 discret methods to force the index to a specific value.
_QuantileMethods = dict(
# --- HYNDMAN and FAN METHODS
# Discrete methods
inverted_cdf=dict(
get_virtual_index=lambda n, quantiles: _inverted_cdf(n, quantiles),
fix_gamma=None, # should never be called
),
averaged_inverted_cdf=dict(
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=dict(
get_virtual_index=lambda n, quantiles: _closest_observation(n,
quantiles),
fix_gamma=None, # should never be called
),
# Continuous methods
interpolated_inverted_cdf=dict(
get_virtual_index=lambda n, quantiles:
_compute_virtual_index(n, quantiles, 0, 1),
fix_gamma=lambda gamma, _: gamma,
),
hazen=dict(
get_virtual_index=lambda n, quantiles:
_compute_virtual_index(n, quantiles, 0.5, 0.5),
fix_gamma=lambda gamma, _: gamma,
),
weibull=dict(
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=dict(
get_virtual_index=lambda n, quantiles: (n - 1) * quantiles,
fix_gamma=lambda gamma, _: gamma,
),
median_unbiased=dict(
get_virtual_index=lambda n, quantiles:
_compute_virtual_index(n, quantiles, 1 / 3.0, 1 / 3.0),
fix_gamma=lambda gamma, _: gamma,
),
normal_unbiased=dict(
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=dict(
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=dict(
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=dict(
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=dict(
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.
.. versionadded:: 1.12.0
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("Axes={} out of range for array of ndim={}."
.format(axes, m.ndim))
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.
.. versionadded:: 1.12.0
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.
.. versionchanged:: 1.15.0
None and tuples of axes are supported
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.
.. versionadded:: 1.7.0
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).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
Memory layout. 'A' and 'K' depend on the order of input array a.
'C' row-major (C-style),
'F' column-major (Fortran-style) memory representation.
'A' (any) means 'F' if `a` is Fortran contiguous, 'C' otherwise
'K' (keep) preserve input order
Defaults to 'C'.
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=float)
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 an 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(
"with {} condition(s), either {} or {} functions are expected"
.format(n, n, n+1)
)
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 : scalar, 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(
'invalid entry {} in condlist: should be boolean ndarray'.format(i))
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).
.. versionadded:: 1.19.0
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
array([3, 2, 3])
"""
return array(a, order=order, subok=subok, copy=True)
# Basic operations
def _gradient_dispatcher(f, *varargs, axis=None, edge_order=None):
yield f
yield from varargs
@array_function_dispatch(_gradient_dispatcher)
def gradient(f, *varargs, axis=None, edge_order=1):
"""
Return the gradient of an N-dimensional array.
The gradient is computed using second order accurate central differences
in the interior points and either first or second order accurate one-sides
(forward or backwards) differences at the boundaries.
The returned gradient hence has the same shape as the input array.
Parameters
----------
f : array_like
An N-dimensional array containing samples of a scalar function.
varargs : list of scalar or array, optional
Spacing between f values. Default unitary spacing for all dimensions.
Spacing can be specified using:
1. single scalar to specify a sample distance for all dimensions.
2. N scalars to specify a constant sample distance for each dimension.
i.e. `dx`, `dy`, `dz`, ...
3. N arrays to specify the coordinates of the values along each
dimension of F. The length of the array must match the size of
the corresponding dimension
4. Any combination of N scalars/arrays with the meaning of 2. and 3.
If `axis` is given, the number of varargs must equal the number of axes.
Default: 1. (see Examples below).
edge_order : {1, 2}, optional
Gradient is calculated using N-th order accurate differences
at the boundaries. Default: 1.
.. versionadded:: 1.9.1
axis : None or int or tuple of ints, optional
Gradient is calculated only along the given axis or axes
The default (axis = None) is to calculate the gradient for all the axes
of the input array. axis may be negative, in which case it counts from
the last to the first axis.
.. versionadded:: 1.11.0
Returns
-------
gradient : ndarray or tuple of ndarray
A tuple of ndarrays (or a single ndarray if there is only one
dimension) corresponding to the derivatives of f with respect
to each dimension. Each derivative has the same shape as f.
Examples
--------
>>> import numpy as np
>>> f = np.array([1, 2, 4, 7, 11, 16])
>>> np.gradient(f)
array([1. , 1.5, 2.5, 3.5, 4.5, 5. ])
>>> np.gradient(f, 2)
array([0.5 , 0.75, 1.25, 1.75, 2.25, 2.5 ])
Spacing can be also specified with an array that represents the coordinates
of the values F along the dimensions.
For instance a uniform spacing:
>>> x = np.arange(f.size)
>>> np.gradient(f, x)
array([1. , 1.5, 2.5, 3.5, 4.5, 5. ])
Or a non uniform one:
>>> x = np.array([0., 1., 1.5, 3.5, 4., 6.])
>>> np.gradient(f, x)
array([1. , 3. , 3.5, 6.7, 6.9, 2.5])
For two dimensional arrays, the return will be two arrays ordered by
axis. In this example the first array stands for the gradient in
rows and the second one in columns direction:
>>> np.gradient(np.array([[1, 2, 6], [3, 4, 5]]))
(array([[ 2., 2., -1.],
[ 2., 2., -1.]]),
array([[1. , 2.5, 4. ],
[1. , 1. , 1. ]]))
In this example the spacing is also specified:
uniform for axis=0 and non uniform for axis=1
>>> dx = 2.
>>> y = [1., 1.5, 3.5]
>>> np.gradient(np.array([[1, 2, 6], [3, 4, 5]]), dx, y)
(array([[ 1. , 1. , -0.5],
[ 1. , 1. , -0.5]]),
array([[2. , 2. , 2. ],
[2. , 1.7, 0.5]]))
It is possible to specify how boundaries are treated using `edge_order`
>>> x = np.array([0, 1, 2, 3, 4])
>>> f = x**2
>>> np.gradient(f, edge_order=1)
array([1., 2., 4., 6., 7.])
>>> np.gradient(f, edge_order=2)
array([0., 2., 4., 6., 8.])
The `axis` keyword can be used to specify a subset of axes of which the
gradient is calculated
>>> np.gradient(np.array([[1, 2, 6], [3, 4, 5]]), axis=0)
array([[ 2., 2., -1.],
[ 2., 2., -1.]])
The `varargs` argument defines the spacing between sample points in the
input array. It can take two forms:
1. An array, specifying coordinates, which may be unevenly spaced:
>>> x = np.array([0., 2., 3., 6., 8.])
>>> y = x ** 2
>>> np.gradient(y, x, edge_order=2)
array([ 0., 4., 6., 12., 16.])
2. A scalar, representing the fixed sample distance:
>>> dx = 2
>>> x = np.array([0., 2., 4., 6., 8.])
>>> y = x ** 2
>>> np.gradient(y, dx, edge_order=2)
array([ 0., 4., 8., 12., 16.])
It's possible to provide different data for spacing along each dimension.
The number of arguments must match the number of dimensions in the input
data.
>>> dx = 2
>>> dy = 3
>>> x = np.arange(0, 6, dx)
>>> y = np.arange(0, 9, dy)
>>> xs, ys = np.meshgrid(x, y)
>>> zs = xs + 2 * ys
>>> np.gradient(zs, dy, dx) # Passing two scalars
(array([[2., 2., 2.],
[2., 2., 2.],
[2., 2., 2.]]),
array([[1., 1., 1.],
[1., 1., 1.],
[1., 1., 1.]]))
Mixing scalars and arrays is also allowed:
>>> np.gradient(zs, y, dx) # Passing one array and one scalar
(array([[2., 2., 2.],
[2., 2., 2.],
[2., 2., 2.]]),
array([[1., 1., 1.],
[1., 1., 1.],
[1., 1., 1.]]))
Notes
-----
Assuming that :math:`f\\in C^{3}` (i.e., :math:`f` has at least 3 continuous
derivatives) and let :math:`h_{*}` be a non-homogeneous stepsize, we
minimize the "consistency error" :math:`\\eta_{i}` between the true gradient
and its estimate from a linear combination of the neighboring grid-points:
.. math::
\\eta_{i} = f_{i}^{\\left(1\\right)} -
\\left[ \\alpha f\\left(x_{i}\\right) +
\\beta f\\left(x_{i} + h_{d}\\right) +
\\gamma f\\left(x_{i}-h_{s}\\right)
\\right]
By substituting :math:`f(x_{i} + h_{d})` and :math:`f(x_{i} - h_{s})`
with their Taylor series expansion, this translates into solving
the following the linear system:
.. math::
\\left\\{
\\begin{array}{r}
\\alpha+\\beta+\\gamma=0 \\\\
\\beta h_{d}-\\gamma h_{s}=1 \\\\
\\beta h_{d}^{2}+\\gamma h_{s}^{2}=0
\\end{array}
\\right.
The resulting approximation of :math:`f_{i}^{(1)}` is the following:
.. math::
\\hat f_{i}^{(1)} =
\\frac{
h_{s}^{2}f\\left(x_{i} + h_{d}\\right)
+ \\left(h_{d}^{2} - h_{s}^{2}\\right)f\\left(x_{i}\\right)
- h_{d}^{2}f\\left(x_{i}-h_{s}\\right)}
{ h_{s}h_{d}\\left(h_{d} + h_{s}\\right)}
+ \\mathcal{O}\\left(\\frac{h_{d}h_{s}^{2}
+ h_{s}h_{d}^{2}}{h_{d}
+ h_{s}}\\right)
It is worth noting that if :math:`h_{s}=h_{d}`
(i.e., data are evenly spaced)
we find the standard second order approximation:
.. math::
\\hat f_{i}^{(1)}=
\\frac{f\\left(x_{i+1}\\right) - f\\left(x_{i-1}\\right)}{2h}
+ \\mathcal{O}\\left(h^{2}\\right)
With a similar procedure the forward/backward approximations used for
boundaries can be derived.
References
----------
.. [1] Quarteroni A., Sacco R., Saleri F. (2007) Numerical Mathematics
(Texts in Applied Mathematics). New York: Springer.
.. [2] Durran D. R. (1999) Numerical Methods for Wave Equations
in Geophysical Fluid Dynamics. New York: Springer.
.. [3] Fornberg B. (1988) Generation of Finite Difference Formulas on
Arbitrarily Spaced Grids,
Mathematics of Computation 51, no. 184 : 699-706.
`PDF <https://www.ams.org/journals/mcom/1988-51-184/
S0025-5718-1988-0935077-0/S0025-5718-1988-0935077-0.pdf>`_.
"""
f = np.asanyarray(f)
N = f.ndim # number of dimensions
if axis is None:
axes = tuple(range(N))
else:
axes = _nx.normalize_axis_tuple(axis, N)
len_axes = len(axes)
n = len(varargs)
if n == 0:
# no spacing argument - use 1 in all axes
dx = [1.0] * len_axes
elif n == 1 and np.ndim(varargs[0]) == 0:
# single scalar for all axes
dx = varargs * len_axes
elif n == len_axes:
# scalar or 1d array for each axis
dx = list(varargs)
for i, distances in enumerate(dx):
distances = np.asanyarray(distances)
if distances.ndim == 0:
continue
elif distances.ndim != 1:
raise ValueError("distances must be either scalars or 1d")
if len(distances) != f.shape[axes[i]]:
raise ValueError("when 1d, distances must match "
"the length of the corresponding dimension")
if np.issubdtype(distances.dtype, np.integer):
# Convert numpy integer types to float64 to avoid modular
# arithmetic in np.diff(distances).
distances = distances.astype(np.float64)
diffx = np.diff(distances)
# if distances are constant reduce to the scalar case
# since it brings a consistent speedup
if (diffx == diffx[0]).all():
diffx = diffx[0]
dx[i] = diffx
else:
raise TypeError("invalid number of arguments")
if edge_order > 2:
raise ValueError("'edge_order' greater than 2 not supported")
# use central differences on interior and one-sided differences on the
# endpoints. This preserves second order-accuracy over the full domain.
outvals = []
# create slice objects --- initially all are [:, :, ..., :]
slice1 = [slice(None)]*N
slice2 = [slice(None)]*N
slice3 = [slice(None)]*N
slice4 = [slice(None)]*N
otype = f.dtype
if otype.type is np.datetime64:
# the timedelta dtype with the same unit information
otype = np.dtype(otype.name.replace('datetime', 'timedelta'))
# view as timedelta to allow addition
f = f.view(otype)
elif otype.type is np.timedelta64:
pass
elif np.issubdtype(otype, np.inexact):
pass
else:
# All other types convert to floating point.
# First check if f is a numpy integer type; if so, convert f to float64
# to avoid modular arithmetic when computing the changes in f.
if np.issubdtype(otype, np.integer):
f = f.astype(np.float64)
otype = np.float64
for axis, ax_dx in zip(axes, dx):
if f.shape[axis] < edge_order + 1:
raise ValueError(
"Shape of array too small to calculate a numerical gradient, "
"at least (edge_order + 1) elements are required.")
# result allocation
out = np.empty_like(f, dtype=otype)
# spacing for the current axis
uniform_spacing = np.ndim(ax_dx) == 0
# Numerical differentiation: 2nd order interior
slice1[axis] = slice(1, -1)
slice2[axis] = slice(None, -2)
slice3[axis] = slice(1, -1)
slice4[axis] = slice(2, None)
if uniform_spacing:
out[tuple(slice1)] = (f[tuple(slice4)] - f[tuple(slice2)]) / (2. * ax_dx)
else:
dx1 = ax_dx[0:-1]
dx2 = ax_dx[1:]
a = -(dx2)/(dx1 * (dx1 + dx2))
b = (dx2 - dx1) / (dx1 * dx2)
c = dx1 / (dx2 * (dx1 + dx2))
# fix the shape for broadcasting
shape = np.ones(N, dtype=int)
shape[axis] = -1
a.shape = b.shape = c.shape = shape
# 1D equivalent -- out[1:-1] = a * f[:-2] + b * f[1:-1] + c * f[2:]
out[tuple(slice1)] = a * f[tuple(slice2)] + b * f[tuple(slice3)] + c * f[tuple(slice4)]
# Numerical differentiation: 1st order edges
if edge_order == 1:
slice1[axis] = 0
slice2[axis] = 1
slice3[axis] = 0
dx_0 = ax_dx if uniform_spacing else ax_dx[0]
# 1D equivalent -- out[0] = (f[1] - f[0]) / (x[1] - x[0])
out[tuple(slice1)] = (f[tuple(slice2)] - f[tuple(slice3)]) / dx_0
slice1[axis] = -1
slice2[axis] = -1
slice3[axis] = -2
dx_n = ax_dx if uniform_spacing else ax_dx[-1]
# 1D equivalent -- out[-1] = (f[-1] - f[-2]) / (x[-1] - x[-2])
out[tuple(slice1)] = (f[tuple(slice2)] - f[tuple(slice3)]) / dx_n
# Numerical differentiation: 2nd order edges
else:
slice1[axis] = 0
slice2[axis] = 0
slice3[axis] = 1
slice4[axis] = 2
if uniform_spacing:
a = -1.5 / ax_dx
b = 2. / ax_dx
c = -0.5 / ax_dx
else:
dx1 = ax_dx[0]
dx2 = ax_dx[1]
a = -(2. * dx1 + dx2)/(dx1 * (dx1 + dx2))
b = (dx1 + dx2) / (dx1 * dx2)
c = - dx1 / (dx2 * (dx1 + dx2))
# 1D equivalent -- out[0] = a * f[0] + b * f[1] + c * f[2]
out[tuple(slice1)] = a * f[tuple(slice2)] + b * f[tuple(slice3)] + c * f[tuple(slice4)]
slice1[axis] = -1
slice2[axis] = -3
slice3[axis] = -2
slice4[axis] = -1
if uniform_spacing:
a = 0.5 / ax_dx
b = -2. / ax_dx
c = 1.5 / ax_dx
else:
dx1 = ax_dx[-2]
dx2 = ax_dx[-1]
a = (dx2) / (dx1 * (dx1 + dx2))
b = - (dx2 + dx1) / (dx1 * dx2)
c = (2. * dx2 + dx1) / (dx2 * (dx1 + dx2))
# 1D equivalent -- out[-1] = a * f[-3] + b * f[-2] + c * f[-1]
out[tuple(slice1)] = a * f[tuple(slice2)] + b * f[tuple(slice3)] + c * f[tuple(slice4)]
outvals.append(out)
# reset the slice object in this dimension to ":"
slice1[axis] = slice(None)
slice2[axis] = slice(None)
slice3[axis] = slice(None)
slice4[axis] = slice(None)
if len_axes == 1:
return outvals[0]
return tuple(outvals)
def _diff_dispatcher(a, n=None, axis=None, prepend=None, append=None):
return (a, prepend, append)
@array_function_dispatch(_diff_dispatcher)
def diff(a, n=1, axis=-1, prepend=np._NoValue, append=np._NoValue):
"""
Calculate the n-th discrete difference along the given axis.
The first difference is given by ``out[i] = a[i+1] - a[i]`` along
the given axis, higher differences are calculated by using `diff`
recursively.
Parameters
----------
a : array_like
Input array
n : int, optional
The number of times values are differenced. If zero, the input
is returned as-is.
axis : int, optional
The axis along which the difference is taken, default is the
last axis.
prepend, append : array_like, optional
Values to prepend or append to `a` along axis prior to
performing the difference. Scalar values are expanded to
arrays with length 1 in the direction of axis and the shape
of the input array in along all other axes. Otherwise the
dimension and shape must match `a` except along axis.
.. versionadded:: 1.16.0
Returns
-------
diff : ndarray
The n-th differences. The shape of the output is the same as `a`
except along `axis` where the dimension is smaller by `n`. The
type of the output is the same as the type of the difference
between any two elements of `a`. This is the same as the type of
`a` in most cases. A notable exception is `datetime64`, which
results in a `timedelta64` output array.
See Also
--------
gradient, ediff1d, cumsum
Notes
-----
Type is preserved for boolean arrays, so the result will contain
`False` when consecutive elements are the same and `True` when they
differ.
For unsigned integer arrays, the results will also be unsigned. This
should not be surprising, as the result is consistent with
calculating the difference directly:
>>> u8_arr = np.array([1, 0], dtype=np.uint8)
>>> np.diff(u8_arr)
array([255], dtype=uint8)
>>> u8_arr[1,...] - u8_arr[0,...]
255
If this is not desirable, then the array should be cast to a larger
integer type first:
>>> i16_arr = u8_arr.astype(np.int16)
>>> np.diff(i16_arr)
array([-1], dtype=int16)
Examples
--------
>>> import numpy as np
>>> x = np.array([1, 2, 4, 7, 0])
>>> np.diff(x)
array([ 1, 2, 3, -7])
>>> np.diff(x, n=2)
array([ 1, 1, -10])
>>> x = np.array([[1, 3, 6, 10], [0, 5, 6, 8]])
>>> np.diff(x)
array([[2, 3, 4],
[5, 1, 2]])
>>> np.diff(x, axis=0)
array([[-1, 2, 0, -2]])
>>> x = np.arange('1066-10-13', '1066-10-16', dtype=np.datetime64)
>>> np.diff(x)
array([1, 1], dtype='timedelta64[D]')
"""
if n == 0:
return a
if n < 0:
raise ValueError(
"order must be non-negative but got " + repr(n))
a = asanyarray(a)
nd = a.ndim
if nd == 0:
raise ValueError("diff requires input that is at least one dimensional")
axis = normalize_axis_index(axis, nd)
combined = []
if prepend is not np._NoValue:
prepend = np.asanyarray(prepend)
if prepend.ndim == 0:
shape = list(a.shape)
shape[axis] = 1
prepend = np.broadcast_to(prepend, tuple(shape))
combined.append(prepend)
combined.append(a)
if append is not np._NoValue:
append = np.asanyarray(append)
if append.ndim == 0:
shape = list(a.shape)
shape[axis] = 1
append = np.broadcast_to(append, tuple(shape))
combined.append(append)
if len(combined) > 1:
a = np.concatenate(combined, axis)
slice1 = [slice(None)] * nd
slice2 = [slice(None)] * nd
slice1[axis] = slice(1, None)
slice2[axis] = slice(None, -1)
slice1 = tuple(slice1)
slice2 = tuple(slice2)
op = not_equal if a.dtype == np.bool else subtract
for _ in range(n):
a = op(a[slice1], a[slice2])
return a
def _interp_dispatcher(x, xp, fp, left=None, right=None, period=None):
return (x, xp, fp)
@array_function_dispatch(_interp_dispatcher)
def interp(x, xp, fp, left=None, right=None, period=None):
"""
One-dimensional linear interpolation for monotonically increasing sample points.
Returns the one-dimensional piecewise linear interpolant to a function
with given discrete data points (`xp`, `fp`), evaluated at `x`.
Parameters
----------
x : array_like
The x-coordinates at which to evaluate the interpolated values.
xp : 1-D sequence of floats
The x-coordinates of the data points, must be increasing if argument
`period` is not specified. Otherwise, `xp` is internally sorted after
normalizing the periodic boundaries with ``xp = xp % period``.
fp : 1-D sequence of float or complex
The y-coordinates of the data points, same length as `xp`.
left : optional float or complex corresponding to fp
Value to return for `x < xp[0]`, default is `fp[0]`.
right : optional float or complex corresponding to fp
Value to return for `x > xp[-1]`, default is `fp[-1]`.
period : None or float, optional
A period for the x-coordinates. This parameter allows the proper
interpolation of angular x-coordinates. Parameters `left` and `right`
are ignored if `period` is specified.
.. versionadded:: 1.10.0
Returns
-------
y : float or complex (corresponding to fp) or ndarray
The interpolated values, same shape as `x`.
Raises
------
ValueError
If `xp` and `fp` have different length
If `xp` or `fp` are not 1-D sequences
If `period == 0`
See Also
--------
scipy.interpolate
Warnings
--------
The x-coordinate sequence is expected to be increasing, but this is not
explicitly enforced. However, if the sequence `xp` is non-increasing,
interpolation results are meaningless.
Note that, since NaN is unsortable, `xp` also cannot contain NaNs.
A simple check for `xp` being strictly increasing is::
np.all(np.diff(xp) > 0)
Examples
--------
>>> import numpy as np
>>> xp = [1, 2, 3]
>>> fp = [3, 2, 0]
>>> np.interp(2.5, xp, fp)
1.0
>>> np.interp([0, 1, 1.5, 2.72, 3.14], xp, fp)
array([3. , 3. , 2.5 , 0.56, 0. ])
>>> UNDEF = -99.0
>>> np.interp(3.14, xp, fp, right=UNDEF)
-99.0
Plot an interpolant to the sine function:
>>> x = np.linspace(0, 2*np.pi, 10)
>>> y = np.sin(x)
>>> xvals = np.linspace(0, 2*np.pi, 50)
>>> yinterp = np.interp(xvals, x, y)
>>> import matplotlib.pyplot as plt
>>> plt.plot(x, y, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.plot(xvals, yinterp, '-x')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.show()
Interpolation with periodic x-coordinates:
>>> x = [-180, -170, -185, 185, -10, -5, 0, 365]
>>> xp = [190, -190, 350, -350]
>>> fp = [5, 10, 3, 4]
>>> np.interp(x, xp, fp, period=360)
array([7.5 , 5. , 8.75, 6.25, 3. , 3.25, 3.5 , 3.75])
Complex interpolation:
>>> x = [1.5, 4.0]
>>> xp = [2,3,5]
>>> fp = [1.0j, 0, 2+3j]
>>> np.interp(x, xp, fp)
array([0.+1.j , 1.+1.5j])
"""
fp = np.asarray(fp)
if np.iscomplexobj(fp):
interp_func = compiled_interp_complex
input_dtype = np.complex128
else:
interp_func = compiled_interp
input_dtype = np.float64
if period is not None:
if period == 0:
raise ValueError("period must be a non-zero value")
period = abs(period)
left = None
right = None
x = np.asarray(x, dtype=np.float64)
xp = np.asarray(xp, dtype=np.float64)
fp = np.asarray(fp, dtype=input_dtype)
if xp.ndim != 1 or fp.ndim != 1:
raise ValueError("Data points must be 1-D sequences")
if xp.shape[0] != fp.shape[0]:
raise ValueError("fp and xp are not of the same length")
# normalizing periodic boundaries
x = x % period
xp = xp % period
asort_xp = np.argsort(xp)
xp = xp[asort_xp]
fp = fp[asort_xp]
xp = np.concatenate((xp[-1:]-period, xp, xp[0:1]+period))
fp = np.concatenate((fp[-1:], fp, fp[0:1]))
return interp_func(x, xp, fp, left, right)
def _angle_dispatcher(z, deg=None):
return (z,)
@array_function_dispatch(_angle_dispatcher)
def angle(z, deg=False):
"""
Return the angle of the complex argument.
Parameters
----------
z : array_like
A complex number or sequence of complex numbers.
deg : bool, optional
Return angle in degrees if True, radians if False (default).
Returns
-------
angle : ndarray or scalar
The counterclockwise angle from the positive real axis on the complex
plane in the range ``(-pi, pi]``, with dtype as numpy.float64.
.. versionchanged:: 1.16.0
This function works on subclasses of ndarray like `ma.array`.
See Also
--------
arctan2
absolute
Notes
-----
This function passes the imaginary and real parts of the argument to
`arctan2` to compute the result; consequently, it follows the convention
of `arctan2` when the magnitude of the argument is zero. See example.
Examples
--------
>>> import numpy as np
>>> np.angle([1.0, 1.0j, 1+1j]) # in radians
array([ 0. , 1.57079633, 0.78539816]) # may vary
>>> np.angle(1+1j, deg=True) # in degrees
45.0
>>> np.angle([0., -0., complex(0., -0.), complex(-0., -0.)]) # convention
array([ 0. , 3.14159265, -0. , -3.14159265])
"""
z = asanyarray(z)
if issubclass(z.dtype.type, _nx.complexfloating):
zimag = z.imag
zreal = z.real
else:
zimag = 0
zreal = z
a = arctan2(zimag, zreal)
if deg:
a *= 180/pi
return a
def _unwrap_dispatcher(p, discont=None, axis=None, *, period=None):
return (p,)
@array_function_dispatch(_unwrap_dispatcher)
def unwrap(p, discont=None, axis=-1, *, period=2*pi):
r"""
Unwrap by taking the complement of large deltas with respect to the period.
This unwraps a signal `p` by changing elements which have an absolute
difference from their predecessor of more than ``max(discont, period/2)``
to their `period`-complementary values.
For the default case where `period` is :math:`2\pi` and `discont` is
:math:`\pi`, this unwraps a radian phase `p` such that adjacent differences
are never greater than :math:`\pi` by adding :math:`2k\pi` for some
integer :math:`k`.
Parameters
----------
p : array_like
Input array.
discont : float, optional
Maximum discontinuity between values, default is ``period/2``.
Values below ``period/2`` are treated as if they were ``period/2``.
To have an effect different from the default, `discont` should be
larger than ``period/2``.
axis : int, optional
Axis along which unwrap will operate, default is the last axis.
period : float, optional
Size of the range over which the input wraps. By default, it is
``2 pi``.
.. versionadded:: 1.21.0
Returns
-------
out : ndarray
Output array.
See Also
--------
rad2deg, deg2rad
Notes
-----
If the discontinuity in `p` is smaller than ``period/2``,
but larger than `discont`, no unwrapping is done because taking
the complement would only make the discontinuity larger.
Examples
--------
>>> import numpy as np
>>> phase = np.linspace(0, np.pi, num=5)
>>> phase[3:] += np.pi
>>> phase
array([ 0. , 0.78539816, 1.57079633, 5.49778714, 6.28318531]) # may vary
>>> np.unwrap(phase)
array([ 0. , 0.78539816, 1.57079633, -0.78539816, 0. ]) # may vary
>>> np.unwrap([0, 1, 2, -1, 0], period=4)
array([0, 1, 2, 3, 4])
>>> np.unwrap([ 1, 2, 3, 4, 5, 6, 1, 2, 3], period=6)
array([1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> np.unwrap([2, 3, 4, 5, 2, 3, 4, 5], period=4)
array([2, 3, 4, 5, 6, 7, 8, 9])
>>> phase_deg = np.mod(np.linspace(0 ,720, 19), 360) - 180
>>> np.unwrap(phase_deg, period=360)
array([-180., -140., -100., -60., -20., 20., 60., 100., 140.,
180., 220., 260., 300., 340., 380., 420., 460., 500.,
540.])
"""
p = asarray(p)
nd = p.ndim
dd = diff(p, axis=axis)
if discont is None:
discont = period/2
slice1 = [slice(None, None)]*nd # full slices
slice1[axis] = slice(1, None)
slice1 = tuple(slice1)
dtype = np.result_type(dd, period)
if _nx.issubdtype(dtype, _nx.integer):
interval_high, rem = divmod(period, 2)
boundary_ambiguous = rem == 0
else:
interval_high = period / 2
boundary_ambiguous = True
interval_low = -interval_high
ddmod = mod(dd - interval_low, period) + interval_low
if boundary_ambiguous:
# for `mask = (abs(dd) == period/2)`, the above line made
# `ddmod[mask] == -period/2`. correct these such that
# `ddmod[mask] == sign(dd[mask])*period/2`.
_nx.copyto(ddmod, interval_high,
where=(ddmod == interval_low) & (dd > 0))
ph_correct = ddmod - dd
_nx.copyto(ph_correct, 0, where=abs(dd) < discont)
up = array(p, copy=True, dtype=dtype)
up[slice1] = p[slice1] + ph_correct.cumsum(axis)
return up
def _sort_complex(a):
return (a,)
@array_function_dispatch(_sort_complex)
def sort_complex(a):
"""
Sort a complex array using the real part first, then the imaginary part.
Parameters
----------
a : array_like
Input array
Returns
-------
out : complex ndarray
Always returns a sorted complex array.
Examples
--------
>>> import numpy as np
>>> np.sort_complex([5, 3, 6, 2, 1])
array([1.+0.j, 2.+0.j, 3.+0.j, 5.+0.j, 6.+0.j])
>>> np.sort_complex([1 + 2j, 2 - 1j, 3 - 2j, 3 - 3j, 3 + 5j])
array([1.+2.j, 2.-1.j, 3.-3.j, 3.-2.j, 3.+5.j])
"""
b = array(a, copy=True)
b.sort()
if not issubclass(b.dtype.type, _nx.complexfloating):
if b.dtype.char in 'bhBH':
return b.astype('F')
elif b.dtype.char == 'g':
return b.astype('G')
else:
return b.astype('D')
else:
return b
def _trim_zeros(filt, trim=None):
return (filt,)
@array_function_dispatch(_trim_zeros)
def trim_zeros(filt, trim='fb'):
"""
Trim the leading and/or trailing zeros from a 1-D array or sequence.
Parameters
----------
filt : 1-D array or sequence
Input array.
trim : str, optional
A string with 'f' representing trim from front and 'b' to trim from
back. Default is 'fb', trim zeros from both front and back of the
array.
Returns
-------
trimmed : 1-D array or sequence
The result of trimming the input. The input data type is preserved.
Examples
--------
>>> import numpy as np
>>> a = np.array((0, 0, 0, 1, 2, 3, 0, 2, 1, 0))
>>> np.trim_zeros(a)
array([1, 2, 3, 0, 2, 1])
>>> np.trim_zeros(a, 'b')
array([0, 0, 0, ..., 0, 2, 1])
The input data type is preserved, list/tuple in means list/tuple out.
>>> np.trim_zeros([0, 1, 2, 0])
[1, 2]
"""
first = 0
trim = trim.upper()
if 'F' in trim:
for i in filt:
if i != 0.:
break
else:
first = first + 1
last = len(filt)
if 'B' in trim:
for i in filt[::-1]:
if i != 0.:
break
else:
last = last - 1
return filt[first:last]
def _extract_dispatcher(condition, arr):
return (condition, arr)
@array_function_dispatch(_extract_dispatcher)
def extract(condition, arr):
"""
Return the elements of an array that satisfy some condition.
This is equivalent to ``np.compress(ravel(condition), ravel(arr))``. If
`condition` is boolean ``np.extract`` is equivalent to ``arr[condition]``.
Note that `place` does the exact opposite of `extract`.
Parameters
----------
condition : array_like
An array whose nonzero or True entries indicate the elements of `arr`
to extract.
arr : array_like
Input array of the same size as `condition`.
Returns
-------
extract : ndarray
Rank 1 array of values from `arr` where `condition` is True.
See Also
--------
take, put, copyto, compress, place
Examples
--------
>>> import numpy as np
>>> arr = np.arange(12).reshape((3, 4))
>>> arr
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>>> condition = np.mod(arr, 3)==0
>>> condition
array([[ True, False, False, True],
[False, False, True, False],
[False, True, False, False]])
>>> np.extract(condition, arr)
array([0, 3, 6, 9])
If `condition` is boolean:
>>> arr[condition]
array([0, 3, 6, 9])
"""
return _nx.take(ravel(arr), nonzero(ravel(condition))[0])
def _place_dispatcher(arr, mask, vals):
return (arr, mask, vals)
@array_function_dispatch(_place_dispatcher)
def place(arr, mask, vals):
"""
Change elements of an array based on conditional and input values.
Similar to ``np.copyto(arr, vals, where=mask)``, the difference is that
`place` uses the first N elements of `vals`, where N is the number of
True values in `mask`, while `copyto` uses the elements where `mask`
is True.
Note that `extract` does the exact opposite of `place`.
Parameters
----------
arr : ndarray
Array to put data into.
mask : array_like
Boolean mask array. Must have the same size as `a`.
vals : 1-D sequence
Values to put into `a`. Only the first N elements are used, where
N is the number of True values in `mask`. If `vals` is smaller
than N, it will be repeated, and if elements of `a` are to be masked,
this sequence must be non-empty.
See Also
--------
copyto, put, take, extract
Examples
--------
>>> import numpy as np
>>> arr = np.arange(6).reshape(2, 3)
>>> np.place(arr, arr>2, [44, 55])
>>> arr
array([[ 0, 1, 2],
[44, 55, 44]])
"""
return _place(arr, mask, vals)
def disp(mesg, device=None, linefeed=True):
"""
Display a message on a device.
.. deprecated:: 2.0
Use your own printing function instead.
Parameters
----------
mesg : str
Message to display.
device : object
Device to write message. If None, defaults to ``sys.stdout`` which is
very similar to ``print``. `device` needs to have ``write()`` and
``flush()`` methods.
linefeed : bool, optional
Option whether to print a line feed or not. Defaults to True.
Raises
------
AttributeError
If `device` does not have a ``write()`` or ``flush()`` method.
Examples
--------
>>> import numpy as np
Besides ``sys.stdout``, a file-like object can also be used as it has
both required methods:
>>> from io import StringIO
>>> buf = StringIO()
>>> np.disp('"Display" in a file', device=buf)
>>> buf.getvalue()
'"Display" in a file\\n'
"""
# Deprecated in NumPy 2.0, 2023-07-11
warnings.warn(
"`disp` is deprecated, "
"use your own printing function instead. "
"(deprecated in NumPy 2.0)",
DeprecationWarning,
stacklevel=2
)
if device is None:
device = sys.stdout
if linefeed:
device.write('%s\n' % mesg)
else:
device.write('%s' % mesg)
device.flush()
return
# See https://docs.scipy.org/doc/numpy/reference/c-api.generalized-ufuncs.html
_DIMENSION_NAME = r'\w+'
_CORE_DIMENSION_LIST = '(?:{0:}(?:,{0:})*)?'.format(_DIMENSION_NAME)
_ARGUMENT = r'\({}\)'.format(_CORE_DIMENSION_LIST)
_ARGUMENT_LIST = '{0:}(?:,{0:})*'.format(_ARGUMENT)
_SIGNATURE = '^{0:}->{0:}$'.format(_ARGUMENT_LIST)
def _parse_gufunc_signature(signature):
"""
Parse string signatures for a generalized universal function.
Arguments
---------
signature : string
Generalized universal function signature, e.g., ``(m,n),(n,p)->(m,p)``
for ``np.matmul``.
Returns
-------
Tuple of input and output core dimensions parsed from the signature, each
of the form List[Tuple[str, ...]].
"""
signature = re.sub(r'\s+', '', signature)
if not re.match(_SIGNATURE, signature):
raise ValueError(
'not a valid gufunc signature: {}'.format(signature))
return tuple([tuple(re.findall(_DIMENSION_NAME, arg))
for arg in re.findall(_ARGUMENT, arg_list)]
for arg_list in signature.split('->'))
def _update_dim_sizes(dim_sizes, arg, core_dims):
"""
Incrementally check and update core dimension sizes for a single argument.
Arguments
---------
dim_sizes : Dict[str, int]
Sizes of existing core dimensions. Will be updated in-place.
arg : ndarray
Argument to examine.
core_dims : Tuple[str, ...]
Core dimensions for this argument.
"""
if not core_dims:
return
num_core_dims = len(core_dims)
if arg.ndim < num_core_dims:
raise ValueError(
'%d-dimensional argument does not have enough '
'dimensions for all core dimensions %r'
% (arg.ndim, core_dims))
core_shape = arg.shape[-num_core_dims:]
for dim, size in zip(core_dims, core_shape):
if dim in dim_sizes:
if size != dim_sizes[dim]:
raise ValueError(
'inconsistent size for core dimension %r: %r vs %r'
% (dim, size, dim_sizes[dim]))
else:
dim_sizes[dim] = size
def _parse_input_dimensions(args, input_core_dims):
"""
Parse broadcast and core dimensions for vectorize with a signature.
Arguments
---------
args : Tuple[ndarray, ...]
Tuple of input arguments to examine.
input_core_dims : List[Tuple[str, ...]]
List of core dimensions corresponding to each input.
Returns
-------
broadcast_shape : Tuple[int, ...]
Common shape to broadcast all non-core dimensions to.
dim_sizes : Dict[str, int]
Common sizes for named core dimensions.
"""
broadcast_args = []
dim_sizes = {}
for arg, core_dims in zip(args, input_core_dims):
_update_dim_sizes(dim_sizes, arg, core_dims)
ndim = arg.ndim - len(core_dims)
dummy_array = np.lib.stride_tricks.as_strided(0, arg.shape[:ndim])
broadcast_args.append(dummy_array)
broadcast_shape = np.lib._stride_tricks_impl._broadcast_shape(
*broadcast_args
)
return broadcast_shape, dim_sizes
def _calculate_shapes(broadcast_shape, dim_sizes, list_of_core_dims):
"""Helper for calculating broadcast shapes with core dimensions."""
return [broadcast_shape + tuple(dim_sizes[dim] for dim in core_dims)
for core_dims in list_of_core_dims]
def _create_arrays(broadcast_shape, dim_sizes, list_of_core_dims, dtypes,
results=None):
"""Helper for creating output arrays in vectorize."""
shapes = _calculate_shapes(broadcast_shape, dim_sizes, list_of_core_dims)
if dtypes is None:
dtypes = [None] * len(shapes)
if results is None:
arrays = tuple(np.empty(shape=shape, dtype=dtype)
for shape, dtype in zip(shapes, dtypes))
else:
arrays = tuple(np.empty_like(result, shape=shape, dtype=dtype)
for result, shape, dtype
in zip(results, shapes, dtypes))
return arrays
def _get_vectorize_dtype(dtype):
if dtype.char in "SU":
return dtype.char
return dtype
@set_module('numpy')
class vectorize:
"""
vectorize(pyfunc=np._NoValue, otypes=None, doc=None, excluded=None,
cache=False, signature=None)
Returns an object that acts like pyfunc, but takes arrays as input.
Define a vectorized function which takes a nested sequence of objects or
numpy arrays as inputs and returns a single numpy array or a tuple of numpy
arrays. The vectorized function evaluates `pyfunc` over successive tuples
of the input arrays like the python map function, except it uses the
broadcasting rules of numpy.
The data type of the output of `vectorized` is determined by calling
the function with the first element of the input. This can be avoided
by specifying the `otypes` argument.
Parameters
----------
pyfunc : callable, optional
A python function or method.
Can be omitted to produce a decorator with keyword arguments.
otypes : str or list of dtypes, optional
The output data type. It must be specified as either a string of
typecode characters or a list of data type specifiers. There should
be one data type specifier for each output.
doc : str, optional
The docstring for the function. If None, the docstring will be the
``pyfunc.__doc__``.
excluded : set, optional
Set of strings or integers representing the positional or keyword
arguments for which the function will not be vectorized. These will be
passed directly to `pyfunc` unmodified.
.. versionadded:: 1.7.0
cache : bool, optional
If `True`, then cache the first function call that determines the number
of outputs if `otypes` is not provided.
.. versionadded:: 1.7.0
signature : string, optional
Generalized universal function signature, e.g., ``(m,n),(n)->(m)`` for
vectorized matrix-vector multiplication. If provided, ``pyfunc`` will
be called with (and expected to return) arrays with shapes given by the
size of corresponding core dimensions. By default, ``pyfunc`` is
assumed to take scalars as input and output.
.. versionadded:: 1.12.0
Returns
-------
out : callable
A vectorized function if ``pyfunc`` was provided,
a decorator otherwise.
See Also
--------
frompyfunc : Takes an arbitrary Python function and returns a ufunc
Notes
-----
The `vectorize` function is provided primarily for convenience, not for
performance. The implementation is essentially a for loop.
If `otypes` is not specified, then a call to the function with the
first argument will be used to determine the number of outputs. The
results of this call will be cached if `cache` is `True` to prevent
calling the function twice. However, to implement the cache, the
original function must be wrapped which will slow down subsequent
calls, so only do this if your function is expensive.
The new keyword argument interface and `excluded` argument support
further degrades performance.
References
----------
.. [1] :doc:`/reference/c-api/generalized-ufuncs`
Examples
--------
>>> import numpy as np
>>> def myfunc(a, b):
... "Return a-b if a>b, otherwise return a+b"
... if a > b:
... return a - b
... else:
... return a + b
>>> vfunc = np.vectorize(myfunc)
>>> vfunc([1, 2, 3, 4], 2)
array([3, 4, 1, 2])
The docstring is taken from the input function to `vectorize` unless it
is specified:
>>> vfunc.__doc__
'Return a-b if a>b, otherwise return a+b'
>>> vfunc = np.vectorize(myfunc, doc='Vectorized `myfunc`')
>>> vfunc.__doc__
'Vectorized `myfunc`'
The output type is determined by evaluating the first element of the input,
unless it is specified:
>>> out = vfunc([1, 2, 3, 4], 2)
>>> type(out[0])
<class 'numpy.int64'>
>>> vfunc = np.vectorize(myfunc, otypes=[float])
>>> out = vfunc([1, 2, 3, 4], 2)
>>> type(out[0])
<class 'numpy.float64'>
The `excluded` argument can be used to prevent vectorizing over certain
arguments. This can be useful for array-like arguments of a fixed length
such as the coefficients for a polynomial as in `polyval`:
>>> def mypolyval(p, x):
... _p = list(p)
... res = _p.pop(0)
... while _p:
... res = res*x + _p.pop(0)
... return res
>>> vpolyval = np.vectorize(mypolyval, excluded=['p'])
>>> vpolyval(p=[1, 2, 3], x=[0, 1])
array([3, 6])
Positional arguments may also be excluded by specifying their position:
>>> vpolyval.excluded.add(0)
>>> vpolyval([1, 2, 3], x=[0, 1])
array([3, 6])
The `signature` argument allows for vectorizing functions that act on
non-scalar arrays of fixed length. For example, you can use it for a
vectorized calculation of Pearson correlation coefficient and its p-value:
>>> import scipy.stats
>>> pearsonr = np.vectorize(scipy.stats.pearsonr,
... signature='(n),(n)->(),()')
>>> pearsonr([[0, 1, 2, 3]], [[1, 2, 3, 4], [4, 3, 2, 1]])
(array([ 1., -1.]), array([ 0., 0.]))
Or for a vectorized convolution:
>>> convolve = np.vectorize(np.convolve, signature='(n),(m)->(k)')
>>> convolve(np.eye(4), [1, 2, 1])
array([[1., 2., 1., 0., 0., 0.],
[0., 1., 2., 1., 0., 0.],
[0., 0., 1., 2., 1., 0.],
[0., 0., 0., 1., 2., 1.]])
Decorator syntax is supported. The decorator can be called as
a function to provide keyword arguments:
>>> @np.vectorize
... def identity(x):
... return x
...
>>> identity([0, 1, 2])
array([0, 1, 2])
>>> @np.vectorize(otypes=[float])
... def as_float(x):
... return x
...
>>> as_float([0, 1, 2])
array([0., 1., 2.])
"""
def __init__(self, pyfunc=np._NoValue, otypes=None, doc=None,
excluded=None, cache=False, signature=None):
if (pyfunc != np._NoValue) and (not callable(pyfunc)):
#Splitting the error message to keep
#the length below 79 characters.
part1 = "When used as a decorator, "
part2 = "only accepts keyword arguments."
raise TypeError(part1 + part2)
self.pyfunc = pyfunc
self.cache = cache
self.signature = signature
if pyfunc != np._NoValue and hasattr(pyfunc, '__name__'):
self.__name__ = pyfunc.__name__
self._ufunc = {} # Caching to improve default performance
self._doc = None
self.__doc__ = doc
if doc is None and hasattr(pyfunc, '__doc__'):
self.__doc__ = pyfunc.__doc__
else:
self._doc = doc
if isinstance(otypes, str):
for char in otypes:
if char not in typecodes['All']:
raise ValueError("Invalid otype specified: %s" % (char,))
elif iterable(otypes):
otypes = [_get_vectorize_dtype(_nx.dtype(x)) for x in otypes]
elif otypes is not None:
raise ValueError("Invalid otype specification")
self.otypes = otypes
# Excluded variable support
if excluded is None:
excluded = set()
self.excluded = set(excluded)
if signature is not None:
self._in_and_out_core_dims = _parse_gufunc_signature(signature)
else:
self._in_and_out_core_dims = None
def _init_stage_2(self, pyfunc, *args, **kwargs):
self.__name__ = pyfunc.__name__
self.pyfunc = pyfunc
if self._doc is None:
self.__doc__ = pyfunc.__doc__
else:
self.__doc__ = self._doc
def _call_as_normal(self, *args, **kwargs):
"""
Return arrays with the results of `pyfunc` broadcast (vectorized) over
`args` and `kwargs` not in `excluded`.
"""
excluded = self.excluded
if not kwargs and not excluded:
func = self.pyfunc
vargs = args
else:
# The wrapper accepts only positional arguments: we use `names` and
# `inds` to mutate `the_args` and `kwargs` to pass to the original
# function.
nargs = len(args)
names = [_n for _n in kwargs if _n not in excluded]
inds = [_i for _i in range(nargs) if _i not in excluded]
the_args = list(args)
def func(*vargs):
for _n, _i in enumerate(inds):
the_args[_i] = vargs[_n]
kwargs.update(zip(names, vargs[len(inds):]))
return self.pyfunc(*the_args, **kwargs)
vargs = [args[_i] for _i in inds]
vargs.extend([kwargs[_n] for _n in names])
return self._vectorize_call(func=func, args=vargs)
def __call__(self, *args, **kwargs):
if self.pyfunc is np._NoValue:
self._init_stage_2(*args, **kwargs)
return self
return self._call_as_normal(*args, **kwargs)
def _get_ufunc_and_otypes(self, func, args):
"""Return (ufunc, otypes)."""
# frompyfunc will fail if args is empty
if not args:
raise ValueError('args can not be empty')
if self.otypes is not None:
otypes = self.otypes
# self._ufunc is a dictionary whose keys are the number of
# arguments (i.e. len(args)) and whose values are ufuncs created
# by frompyfunc. len(args) can be different for different calls if
# self.pyfunc has parameters with default values. We only use the
# cache when func is self.pyfunc, which occurs when the call uses
# only positional arguments and no arguments are excluded.
nin = len(args)
nout = len(self.otypes)
if func is not self.pyfunc or nin not in self._ufunc:
ufunc = frompyfunc(func, nin, nout)
else:
ufunc = None # We'll get it from self._ufunc
if func is self.pyfunc:
ufunc = self._ufunc.setdefault(nin, ufunc)
else:
# Get number of outputs and output types by calling the function on
# the first entries of args. We also cache the result to prevent
# the subsequent call when the ufunc is evaluated.
# Assumes that ufunc first evaluates the 0th elements in the input
# arrays (the input values are not checked to ensure this)
args = [asarray(arg) for arg in args]
if builtins.any(arg.size == 0 for arg in args):
raise ValueError('cannot call `vectorize` on size 0 inputs '
'unless `otypes` is set')
inputs = [arg.flat[0] for arg in args]
outputs = func(*inputs)
# Performance note: profiling indicates that -- for simple
# functions at least -- this wrapping can almost double the
# execution time.
# Hence we make it optional.
if self.cache:
_cache = [outputs]
def _func(*vargs):
if _cache:
return _cache.pop()
else:
return func(*vargs)
else:
_func = func
if isinstance(outputs, tuple):
nout = len(outputs)
else:
nout = 1
outputs = (outputs,)
otypes = ''.join([asarray(outputs[_k]).dtype.char
for _k in range(nout)])
# Performance note: profiling indicates that creating the ufunc is
# not a significant cost compared with wrapping so it seems not
# worth trying to cache this.
ufunc = frompyfunc(_func, len(args), nout)
return ufunc, otypes
def _vectorize_call(self, func, args):
"""Vectorized call to `func` over positional `args`."""
if self.signature is not None:
res = self._vectorize_call_with_signature(func, args)
elif not args:
res = func()
else:
ufunc, otypes = self._get_ufunc_and_otypes(func=func, args=args)
# Convert args to object arrays first
inputs = [asanyarray(a, dtype=object) for a in args]
outputs = ufunc(*inputs)
if ufunc.nout == 1:
res = asanyarray(outputs, dtype=otypes[0])
else:
res = tuple([asanyarray(x, dtype=t)
for x, t in zip(outputs, otypes)])
return res
def _vectorize_call_with_signature(self, func, args):
"""Vectorized call over positional arguments with a signature."""
input_core_dims, output_core_dims = self._in_and_out_core_dims
if len(args) != len(input_core_dims):
raise TypeError('wrong number of positional arguments: '
'expected %r, got %r'
% (len(input_core_dims), len(args)))
args = tuple(asanyarray(arg) for arg in args)
broadcast_shape, dim_sizes = _parse_input_dimensions(
args, input_core_dims)
input_shapes = _calculate_shapes(broadcast_shape, dim_sizes,
input_core_dims)
args = [np.broadcast_to(arg, shape, subok=True)
for arg, shape in zip(args, input_shapes)]
outputs = None
otypes = self.otypes
nout = len(output_core_dims)
for index in np.ndindex(*broadcast_shape):
results = func(*(arg[index] for arg in args))
n_results = len(results) if isinstance(results, tuple) else 1
if nout != n_results:
raise ValueError(
'wrong number of outputs from pyfunc: expected %r, got %r'
% (nout, n_results))
if nout == 1:
results = (results,)
if outputs is None:
for result, core_dims in zip(results, output_core_dims):
_update_dim_sizes(dim_sizes, result, core_dims)
outputs = _create_arrays(broadcast_shape, dim_sizes,
output_core_dims, otypes, results)
for output, result in zip(outputs, results):
output[index] = result
if outputs is None:
# did not call the function even once
if otypes is None:
raise ValueError('cannot call `vectorize` on size 0 inputs '
'unless `otypes` is set')
if builtins.any(dim not in dim_sizes
for dims in output_core_dims
for dim in dims):
raise ValueError('cannot call `vectorize` with a signature '
'including new output dimensions on size 0 '
'inputs')
outputs = _create_arrays(broadcast_shape, dim_sizes,
output_core_dims, otypes)
return outputs[0] if nout == 1 else outputs
def _cov_dispatcher(m, y=None, rowvar=None, bias=None, ddof=None,
fweights=None, aweights=None, *, dtype=None):
return (m, y, fweights, aweights)
@array_function_dispatch(_cov_dispatcher)
def cov(m, y=None, rowvar=True, bias=False, ddof=None, fweights=None,
aweights=None, *, dtype=None):
"""
Estimate a covariance matrix, given data and weights.
Covariance indicates the level to which two variables vary together.
If we examine N-dimensional samples, :math:`X = [x_1, x_2, ... x_N]^T`,
then the covariance matrix element :math:`C_{ij}` is the covariance of
:math:`x_i` and :math:`x_j`. The element :math:`C_{ii}` is the variance
of :math:`x_i`.
See the notes for an outline of the algorithm.
Parameters
----------
m : array_like
A 1-D or 2-D array containing multiple variables and observations.
Each row of `m` represents a variable, and each column a single
observation of all those variables. Also see `rowvar` below.
y : array_like, optional
An additional set of variables and observations. `y` has the same form
as that of `m`.
rowvar : bool, optional
If `rowvar` is True (default), then each row represents a
variable, with observations in the columns. Otherwise, the relationship
is transposed: each column represents a variable, while the rows
contain observations.
bias : bool, optional
Default normalization (False) is by ``(N - 1)``, where ``N`` is the
number of observations given (unbiased estimate). If `bias` is True,
then normalization is by ``N``. These values can be overridden by using
the keyword ``ddof`` in numpy versions >= 1.5.
ddof : int, optional
If not ``None`` the default value implied by `bias` is overridden.
Note that ``ddof=1`` will return the unbiased estimate, even if both
`fweights` and `aweights` are specified, and ``ddof=0`` will return
the simple average. See the notes for the details. The default value
is ``None``.
.. versionadded:: 1.5
fweights : array_like, int, optional
1-D array of integer frequency weights; the number of times each
observation vector should be repeated.
.. versionadded:: 1.10
aweights : array_like, optional
1-D array of observation vector weights. These relative weights are
typically large for observations considered "important" and smaller for
observations considered less "important". If ``ddof=0`` the array of
weights can be used to assign probabilities to observation vectors.
.. versionadded:: 1.10
dtype : data-type, optional
Data-type of the result. By default, the return data-type will have
at least `numpy.float64` precision.
.. versionadded:: 1.20
Returns
-------
out : ndarray
The covariance matrix of the variables.
See Also
--------
corrcoef : Normalized covariance matrix
Notes
-----
Assume that the observations are in the columns of the observation
array `m` and let ``f = fweights`` and ``a = aweights`` for brevity. The
steps to compute the weighted covariance are as follows::
>>> m = np.arange(10, dtype=np.float64)
>>> f = np.arange(10) * 2
>>> a = np.arange(10) ** 2.
>>> ddof = 1
>>> w = f * a
>>> v1 = np.sum(w)
>>> v2 = np.sum(w * a)
>>> m -= np.sum(m * w, axis=None, keepdims=True) / v1
>>> cov = np.dot(m * w, m.T) * v1 / (v1**2 - ddof * v2)
Note that when ``a == 1``, the normalization factor
``v1 / (v1**2 - ddof * v2)`` goes over to ``1 / (np.sum(f) - ddof)``
as it should.
Examples
--------
>>> import numpy as np
Consider two variables, :math:`x_0` and :math:`x_1`, which
correlate perfectly, but in opposite directions:
>>> x = np.array([[0, 2], [1, 1], [2, 0]]).T
>>> x
array([[0, 1, 2],
[2, 1, 0]])
Note how :math:`x_0` increases while :math:`x_1` decreases. The covariance
matrix shows this clearly:
>>> np.cov(x)
array([[ 1., -1.],
[-1., 1.]])
Note that element :math:`C_{0,1}`, which shows the correlation between
:math:`x_0` and :math:`x_1`, is negative.
Further, note how `x` and `y` are combined:
>>> x = [-2.1, -1, 4.3]
>>> y = [3, 1.1, 0.12]
>>> X = np.stack((x, y), axis=0)
>>> np.cov(X)
array([[11.71 , -4.286 ], # may vary
[-4.286 , 2.144133]])
>>> np.cov(x, y)
array([[11.71 , -4.286 ], # may vary
[-4.286 , 2.144133]])
>>> np.cov(x)
array(11.71)
"""
# Check inputs
if ddof is not None and ddof != int(ddof):
raise ValueError(
"ddof must be integer")
# Handles complex arrays too
m = np.asarray(m)
if m.ndim > 2:
raise ValueError("m has more than 2 dimensions")
if y is not None:
y = np.asarray(y)
if y.ndim > 2:
raise ValueError("y has more than 2 dimensions")
if dtype is None:
if y is None:
dtype = np.result_type(m, np.float64)
else:
dtype = np.result_type(m, y, np.float64)
X = array(m, ndmin=2, dtype=dtype)
if not rowvar and X.shape[0] != 1:
X = X.T
if X.shape[0] == 0:
return np.array([]).reshape(0, 0)
if y is not None:
y = array(y, copy=None, ndmin=2, dtype=dtype)
if not rowvar and y.shape[0] != 1:
y = y.T
X = np.concatenate((X, y), axis=0)
if ddof is None:
if bias == 0:
ddof = 1
else:
ddof = 0
# Get the product of frequencies and weights
w = None
if fweights is not None:
fweights = np.asarray(fweights, dtype=float)
if not np.all(fweights == np.around(fweights)):
raise TypeError(
"fweights must be integer")
if fweights.ndim > 1:
raise RuntimeError(
"cannot handle multidimensional fweights")
if fweights.shape[0] != X.shape[1]:
raise RuntimeError(
"incompatible numbers of samples and fweights")
if any(fweights < 0):
raise ValueError(
"fweights cannot be negative")
w = fweights
if aweights is not None:
aweights = np.asarray(aweights, dtype=float)
if aweights.ndim > 1:
raise RuntimeError(
"cannot handle multidimensional aweights")
if aweights.shape[0] != X.shape[1]:
raise RuntimeError(
"incompatible numbers of samples and aweights")
if any(aweights < 0):
raise ValueError(
"aweights cannot be negative")
if w is None:
w = aweights
else:
w *= aweights
avg, w_sum = average(X, axis=1, weights=w, returned=True)
w_sum = w_sum[0]
# Determine the normalization
if w is None:
fact = X.shape[1] - ddof
elif ddof == 0:
fact = w_sum
elif aweights is None:
fact = w_sum - ddof
else:
fact = w_sum - ddof*sum(w*aweights)/w_sum
if fact <= 0:
warnings.warn("Degrees of freedom <= 0 for slice",
RuntimeWarning, stacklevel=2)
fact = 0.0
X -= avg[:, None]
if w is None:
X_T = X.T
else:
X_T = (X*w).T
c = dot(X, X_T.conj())
c *= np.true_divide(1, fact)
return c.squeeze()
def _corrcoef_dispatcher(x, y=None, rowvar=None, bias=None, ddof=None, *,
dtype=None):
return (x, y)
@array_function_dispatch(_corrcoef_dispatcher)
def corrcoef(x, y=None, rowvar=True, bias=np._NoValue, ddof=np._NoValue, *,
dtype=None):
"""
Return Pearson product-moment correlation coefficients.
Please refer to the documentation for `cov` for more detail. The
relationship between the correlation coefficient matrix, `R`, and the
covariance matrix, `C`, is
.. math:: R_{ij} = \\frac{ C_{ij} } { \\sqrt{ C_{ii} C_{jj} } }
The values of `R` are between -1 and 1, inclusive.
Parameters
----------
x : array_like
A 1-D or 2-D array containing multiple variables and observations.
Each row of `x` represents a variable, and each column a single
observation of all those variables. Also see `rowvar` below.
y : array_like, optional
An additional set of variables and observations. `y` has the same
shape as `x`.
rowvar : bool, optional
If `rowvar` is True (default), then each row represents a
variable, with observations in the columns. Otherwise, the relationship
is transposed: each column represents a variable, while the rows
contain observations.
bias : _NoValue, optional
Has no effect, do not use.
.. deprecated:: 1.10.0
ddof : _NoValue, optional
Has no effect, do not use.
.. deprecated:: 1.10.0
dtype : data-type, optional
Data-type of the result. By default, the return data-type will have
at least `numpy.float64` precision.
.. versionadded:: 1.20
Returns
-------
R : ndarray
The correlation coefficient matrix of the variables.
See Also
--------
cov : Covariance matrix
Notes
-----
Due to floating point rounding the resulting array may not be Hermitian,
the diagonal elements may not be 1, and the elements may not satisfy the
inequality abs(a) <= 1. The real and imaginary parts are clipped to the
interval [-1, 1] in an attempt to improve on that situation but is not
much help in the complex case.
This function accepts but discards arguments `bias` and `ddof`. This is
for backwards compatibility with previous versions of this function. These
arguments had no effect on the return values of the function and can be
safely ignored in this and previous versions of numpy.
Examples
--------
>>> import numpy as np
In this example we generate two random arrays, ``xarr`` and ``yarr``, and
compute the row-wise and column-wise Pearson correlation coefficients,
``R``. Since ``rowvar`` is true by default, we first find the row-wise
Pearson correlation coefficients between the variables of ``xarr``.
>>> import numpy as np
>>> rng = np.random.default_rng(seed=42)
>>> xarr = rng.random((3, 3))
>>> xarr
array([[0.77395605, 0.43887844, 0.85859792],
[0.69736803, 0.09417735, 0.97562235],
[0.7611397 , 0.78606431, 0.12811363]])
>>> R1 = np.corrcoef(xarr)
>>> R1
array([[ 1. , 0.99256089, -0.68080986],
[ 0.99256089, 1. , -0.76492172],
[-0.68080986, -0.76492172, 1. ]])
If we add another set of variables and observations ``yarr``, we can
compute the row-wise Pearson correlation coefficients between the
variables in ``xarr`` and ``yarr``.
>>> yarr = rng.random((3, 3))
>>> yarr
array([[0.45038594, 0.37079802, 0.92676499],
[0.64386512, 0.82276161, 0.4434142 ],
[0.22723872, 0.55458479, 0.06381726]])
>>> R2 = np.corrcoef(xarr, yarr)
>>> R2
array([[ 1. , 0.99256089, -0.68080986, 0.75008178, -0.934284 ,
-0.99004057],
[ 0.99256089, 1. , -0.76492172, 0.82502011, -0.97074098,
-0.99981569],
[-0.68080986, -0.76492172, 1. , -0.99507202, 0.89721355,
0.77714685],
[ 0.75008178, 0.82502011, -0.99507202, 1. , -0.93657855,
-0.83571711],
[-0.934284 , -0.97074098, 0.89721355, -0.93657855, 1. ,
0.97517215],
[-0.99004057, -0.99981569, 0.77714685, -0.83571711, 0.97517215,
1. ]])
Finally if we use the option ``rowvar=False``, the columns are now
being treated as the variables and we will find the column-wise Pearson
correlation coefficients between variables in ``xarr`` and ``yarr``.
>>> R3 = np.corrcoef(xarr, yarr, rowvar=False)
>>> R3
array([[ 1. , 0.77598074, -0.47458546, -0.75078643, -0.9665554 ,
0.22423734],
[ 0.77598074, 1. , -0.92346708, -0.99923895, -0.58826587,
-0.44069024],
[-0.47458546, -0.92346708, 1. , 0.93773029, 0.23297648,
0.75137473],
[-0.75078643, -0.99923895, 0.93773029, 1. , 0.55627469,
0.47536961],
[-0.9665554 , -0.58826587, 0.23297648, 0.55627469, 1. ,
-0.46666491],
[ 0.22423734, -0.44069024, 0.75137473, 0.47536961, -0.46666491,
1. ]])
"""
if bias is not np._NoValue or ddof is not np._NoValue:
# 2015-03-15, 1.10
warnings.warn('bias and ddof have no effect and are deprecated',
DeprecationWarning, stacklevel=2)
c = cov(x, y, rowvar, dtype=dtype)
try:
d = diag(c)
except ValueError:
# scalar covariance
# nan if incorrect value (nan, inf, 0), 1 otherwise
return c / c
stddev = sqrt(d.real)
c /= stddev[:, None]
c /= stddev[None, :]
# Clip real and imaginary parts to [-1, 1]. This does not guarantee
# abs(a[i,j]) <= 1 for complex arrays, but is the best we can do without
# excessive work.
np.clip(c.real, -1, 1, out=c.real)
if np.iscomplexobj(c):
np.clip(c.imag, -1, 1, out=c.imag)
return c
@set_module('numpy')
def blackman(M):
"""
Return the Blackman window.
The Blackman window is a taper formed by using the first three
terms of a summation of cosines. It was designed to have close to the
minimal leakage possible. It is close to optimal, only slightly worse
than a Kaiser window.
Parameters
----------
M : int
Number of points in the output window. If zero or less, an empty
array is returned.
Returns
-------
out : ndarray
The window, with the maximum value normalized to one (the value one
appears only if the number of samples is odd).
See Also
--------
bartlett, hamming, hanning, kaiser
Notes
-----
The Blackman window is defined as
.. math:: w(n) = 0.42 - 0.5 \\cos(2\\pi n/M) + 0.08 \\cos(4\\pi n/M)
Most references to the Blackman window come from the signal processing
literature, where it is used as one of many windowing functions for
smoothing values. It is also known as an apodization (which means
"removing the foot", i.e. smoothing discontinuities at the beginning
and end of the sampled signal) or tapering function. It is known as a
"near optimal" tapering function, almost as good (by some measures)
as the kaiser window.
References
----------
Blackman, R.B. and Tukey, J.W., (1958) The measurement of power spectra,
Dover Publications, New York.
Oppenheim, A.V., and R.W. Schafer. Discrete-Time Signal Processing.
Upper Saddle River, NJ: Prentice-Hall, 1999, pp. 468-471.
Examples
--------
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> np.blackman(12)
array([-1.38777878e-17, 3.26064346e-02, 1.59903635e-01, # may vary
4.14397981e-01, 7.36045180e-01, 9.67046769e-01,
9.67046769e-01, 7.36045180e-01, 4.14397981e-01,
1.59903635e-01, 3.26064346e-02, -1.38777878e-17])
Plot the window and the frequency response.
.. plot::
:include-source:
import matplotlib.pyplot as plt
from numpy.fft import fft, fftshift
window = np.blackman(51)
plt.plot(window)
plt.title("Blackman window")
plt.ylabel("Amplitude")
plt.xlabel("Sample")
plt.show() # doctest: +SKIP
plt.figure()
A = fft(window, 2048) / 25.5
mag = np.abs(fftshift(A))
freq = np.linspace(-0.5, 0.5, len(A))
with np.errstate(divide='ignore', invalid='ignore'):
response = 20 * np.log10(mag)
response = np.clip(response, -100, 100)
plt.plot(freq, response)
plt.title("Frequency response of Blackman window")
plt.ylabel("Magnitude [dB]")
plt.xlabel("Normalized frequency [cycles per sample]")
plt.axis('tight')
plt.show()
"""
# Ensures at least float64 via 0.0. M should be an integer, but conversion
# to double is safe for a range.
values = np.array([0.0, M])
M = values[1]
if M < 1:
return array([], dtype=values.dtype)
if M == 1:
return ones(1, dtype=values.dtype)
n = arange(1-M, M, 2)
return 0.42 + 0.5*cos(pi*n/(M-1)) + 0.08*cos(2.0*pi*n/(M-1))
@set_module('numpy')
def bartlett(M):
"""
Return the Bartlett window.
The Bartlett window is very similar to a triangular window, except
that the end points are at zero. It is often used in signal
processing for tapering a signal, without generating too much
ripple in the frequency domain.
Parameters
----------
M : int
Number of points in the output window. If zero or less, an
empty array is returned.
Returns
-------
out : array
The triangular window, with the maximum value normalized to one
(the value one appears only if the number of samples is odd), with
the first and last samples equal to zero.
See Also
--------
blackman, hamming, hanning, kaiser
Notes
-----
The Bartlett window is defined as
.. math:: w(n) = \\frac{2}{M-1} \\left(
\\frac{M-1}{2} - \\left|n - \\frac{M-1}{2}\\right|
\\right)
Most references to the Bartlett window come from the signal processing
literature, where it is used as one of many windowing functions for
smoothing values. Note that convolution with this window produces linear
interpolation. It is also known as an apodization (which means "removing
the foot", i.e. smoothing discontinuities at the beginning and end of the
sampled signal) or tapering function. The Fourier transform of the
Bartlett window is the product of two sinc functions. Note the excellent
discussion in Kanasewich [2]_.
References
----------
.. [1] M.S. Bartlett, "Periodogram Analysis and Continuous Spectra",
Biometrika 37, 1-16, 1950.
.. [2] E.R. Kanasewich, "Time Sequence Analysis in Geophysics",
The University of Alberta Press, 1975, pp. 109-110.
.. [3] A.V. Oppenheim and R.W. Schafer, "Discrete-Time Signal
Processing", Prentice-Hall, 1999, pp. 468-471.
.. [4] Wikipedia, "Window function",
https://en.wikipedia.org/wiki/Window_function
.. [5] W.H. Press, B.P. Flannery, S.A. Teukolsky, and W.T. Vetterling,
"Numerical Recipes", Cambridge University Press, 1986, page 429.
Examples
--------
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> np.bartlett(12)
array([ 0. , 0.18181818, 0.36363636, 0.54545455, 0.72727273, # may vary
0.90909091, 0.90909091, 0.72727273, 0.54545455, 0.36363636,
0.18181818, 0. ])
Plot the window and its frequency response (requires SciPy and matplotlib).
.. plot::
:include-source:
import matplotlib.pyplot as plt
from numpy.fft import fft, fftshift
window = np.bartlett(51)
plt.plot(window)
plt.title("Bartlett window")
plt.ylabel("Amplitude")
plt.xlabel("Sample")
plt.show()
plt.figure()
A = fft(window, 2048) / 25.5
mag = np.abs(fftshift(A))
freq = np.linspace(-0.5, 0.5, len(A))
with np.errstate(divide='ignore', invalid='ignore'):
response = 20 * np.log10(mag)
response = np.clip(response, -100, 100)
plt.plot(freq, response)
plt.title("Frequency response of Bartlett window")
plt.ylabel("Magnitude [dB]")
plt.xlabel("Normalized frequency [cycles per sample]")
plt.axis('tight')
plt.show()
"""
# Ensures at least float64 via 0.0. M should be an integer, but conversion
# to double is safe for a range.
values = np.array([0.0, M])
M = values[1]
if M < 1:
return array([], dtype=values.dtype)
if M == 1:
return ones(1, dtype=values.dtype)
n = arange(1-M, M, 2)
return where(less_equal(n, 0), 1 + n/(M-1), 1 - n/(M-1))
@set_module('numpy')
def hanning(M):
"""
Return the Hanning window.
The Hanning window is a taper formed by using a weighted cosine.
Parameters
----------
M : int
Number of points in the output window. If zero or less, an
empty array is returned.
Returns
-------
out : ndarray, shape(M,)
The window, with the maximum value normalized to one (the value
one appears only if `M` is odd).
See Also
--------
bartlett, blackman, hamming, kaiser
Notes
-----
The Hanning window is defined as
.. math:: w(n) = 0.5 - 0.5\\cos\\left(\\frac{2\\pi{n}}{M-1}\\right)
\\qquad 0 \\leq n \\leq M-1
The Hanning was named for Julius von Hann, an Austrian meteorologist.
It is also known as the Cosine Bell. Some authors prefer that it be
called a Hann window, to help avoid confusion with the very similar
Hamming window.
Most references to the Hanning window come from the signal processing
literature, where it is used as one of many windowing functions for
smoothing values. It is also known as an apodization (which means
"removing the foot", i.e. smoothing discontinuities at the beginning
and end of the sampled signal) or tapering function.
References
----------
.. [1] Blackman, R.B. and Tukey, J.W., (1958) The measurement of power
spectra, Dover Publications, New York.
.. [2] E.R. Kanasewich, "Time Sequence Analysis in Geophysics",
The University of Alberta Press, 1975, pp. 106-108.
.. [3] Wikipedia, "Window function",
https://en.wikipedia.org/wiki/Window_function
.. [4] W.H. Press, B.P. Flannery, S.A. Teukolsky, and W.T. Vetterling,
"Numerical Recipes", Cambridge University Press, 1986, page 425.
Examples
--------
>>> import numpy as np
>>> np.hanning(12)
array([0. , 0.07937323, 0.29229249, 0.57115742, 0.82743037,
0.97974649, 0.97974649, 0.82743037, 0.57115742, 0.29229249,
0.07937323, 0. ])
Plot the window and its frequency response.
.. plot::
:include-source:
import matplotlib.pyplot as plt
from numpy.fft import fft, fftshift
window = np.hanning(51)
plt.plot(window)
plt.title("Hann window")
plt.ylabel("Amplitude")
plt.xlabel("Sample")
plt.show()
plt.figure()
A = fft(window, 2048) / 25.5
mag = np.abs(fftshift(A))
freq = np.linspace(-0.5, 0.5, len(A))
with np.errstate(divide='ignore', invalid='ignore'):
response = 20 * np.log10(mag)
response = np.clip(response, -100, 100)
plt.plot(freq, response)
plt.title("Frequency response of the Hann window")
plt.ylabel("Magnitude [dB]")
plt.xlabel("Normalized frequency [cycles per sample]")
plt.axis('tight')
plt.show()
"""
# Ensures at least float64 via 0.0. M should be an integer, but conversion
# to double is safe for a range.
values = np.array([0.0, M])
M = values[1]
if M < 1:
return array([], dtype=values.dtype)
if M == 1:
return ones(1, dtype=values.dtype)
n = arange(1-M, M, 2)
return 0.5 + 0.5*cos(pi*n/(M-1))
@set_module('numpy')
def hamming(M):
"""
Return the Hamming window.
The Hamming window is a taper formed by using a weighted cosine.
Parameters
----------
M : int
Number of points in the output window. If zero or less, an
empty array is returned.
Returns
-------
out : ndarray
The window, with the maximum value normalized to one (the value
one appears only if the number of samples is odd).
See Also
--------
bartlett, blackman, hanning, kaiser
Notes
-----
The Hamming window is defined as
.. math:: w(n) = 0.54 - 0.46\\cos\\left(\\frac{2\\pi{n}}{M-1}\\right)
\\qquad 0 \\leq n \\leq M-1
The Hamming was named for R. W. Hamming, an associate of J. W. Tukey
and is described in Blackman and Tukey. It was recommended for
smoothing the truncated autocovariance function in the time domain.
Most references to the Hamming window come from the signal processing
literature, where it is used as one of many windowing functions for
smoothing values. It is also known as an apodization (which means
"removing the foot", i.e. smoothing discontinuities at the beginning
and end of the sampled signal) or tapering function.
References
----------
.. [1] Blackman, R.B. and Tukey, J.W., (1958) The measurement of power
spectra, Dover Publications, New York.
.. [2] E.R. Kanasewich, "Time Sequence Analysis in Geophysics", The
University of Alberta Press, 1975, pp. 109-110.
.. [3] Wikipedia, "Window function",
https://en.wikipedia.org/wiki/Window_function
.. [4] W.H. Press, B.P. Flannery, S.A. Teukolsky, and W.T. Vetterling,
"Numerical Recipes", Cambridge University Press, 1986, page 425.
Examples
--------
>>> import numpy as np
>>> np.hamming(12)
array([ 0.08 , 0.15302337, 0.34890909, 0.60546483, 0.84123594, # may vary
0.98136677, 0.98136677, 0.84123594, 0.60546483, 0.34890909,
0.15302337, 0.08 ])
Plot the window and the frequency response.
.. plot::
:include-source:
import matplotlib.pyplot as plt
from numpy.fft import fft, fftshift
window = np.hamming(51)
plt.plot(window)
plt.title("Hamming window")
plt.ylabel("Amplitude")
plt.xlabel("Sample")
plt.show()
plt.figure()
A = fft(window, 2048) / 25.5
mag = np.abs(fftshift(A))
freq = np.linspace(-0.5, 0.5, len(A))
response = 20 * np.log10(mag)
response = np.clip(response, -100, 100)
plt.plot(freq, response)
plt.title("Frequency response of Hamming window")
plt.ylabel("Magnitude [dB]")
plt.xlabel("Normalized frequency [cycles per sample]")
plt.axis('tight')
plt.show()
"""
# Ensures at least float64 via 0.0. M should be an integer, but conversion
# to double is safe for a range.
values = np.array([0.0, M])
M = values[1]
if M < 1:
return array([], dtype=values.dtype)
if M == 1:
return ones(1, dtype=values.dtype)
n = arange(1-M, M, 2)
return 0.54 + 0.46*cos(pi*n/(M-1))
## Code from cephes for i0
_i0A = [
-4.41534164647933937950E-18,
3.33079451882223809783E-17,
-2.43127984654795469359E-16,
1.71539128555513303061E-15,
-1.16853328779934516808E-14,
7.67618549860493561688E-14,
-4.85644678311192946090E-13,
2.95505266312963983461E-12,
-1.72682629144155570723E-11,
9.67580903537323691224E-11,
-5.18979560163526290666E-10,
2.65982372468238665035E-9,
-1.30002500998624804212E-8,
6.04699502254191894932E-8,
-2.67079385394061173391E-7,
1.11738753912010371815E-6,
-4.41673835845875056359E-6,
1.64484480707288970893E-5,
-5.75419501008210370398E-5,
1.88502885095841655729E-4,
-5.76375574538582365885E-4,
1.63947561694133579842E-3,
-4.32430999505057594430E-3,
1.05464603945949983183E-2,
-2.37374148058994688156E-2,
4.93052842396707084878E-2,
-9.49010970480476444210E-2,
1.71620901522208775349E-1,
-3.04682672343198398683E-1,
6.76795274409476084995E-1
]
_i0B = [
-7.23318048787475395456E-18,
-4.83050448594418207126E-18,
4.46562142029675999901E-17,
3.46122286769746109310E-17,
-2.82762398051658348494E-16,
-3.42548561967721913462E-16,
1.77256013305652638360E-15,
3.81168066935262242075E-15,
-9.55484669882830764870E-15,
-4.15056934728722208663E-14,
1.54008621752140982691E-14,
3.85277838274214270114E-13,
7.18012445138366623367E-13,
-1.79417853150680611778E-12,
-1.32158118404477131188E-11,
-3.14991652796324136454E-11,
1.18891471078464383424E-11,
4.94060238822496958910E-10,
3.39623202570838634515E-9,
2.26666899049817806459E-8,
2.04891858946906374183E-7,
2.89137052083475648297E-6,
6.88975834691682398426E-5,
3.36911647825569408990E-3,
8.04490411014108831608E-1
]
def _chbevl(x, vals):
b0 = vals[0]
b1 = 0.0
for i in range(1, len(vals)):
b2 = b1
b1 = b0
b0 = x*b1 - b2 + vals[i]
return 0.5*(b0 - b2)
def _i0_1(x):
return exp(x) * _chbevl(x/2.0-2, _i0A)
def _i0_2(x):
return exp(x) * _chbevl(32.0/x - 2.0, _i0B) / sqrt(x)
def _i0_dispatcher(x):
return (x,)
@array_function_dispatch(_i0_dispatcher)
def i0(x):
"""
Modified Bessel function of the first kind, order 0.
Usually denoted :math:`I_0`.
Parameters
----------
x : array_like of float
Argument of the Bessel function.
Returns
-------
out : ndarray, shape = x.shape, dtype = float
The modified Bessel function evaluated at each of the elements of `x`.
See Also
--------
scipy.special.i0, scipy.special.iv, scipy.special.ive
Notes
-----
The scipy implementation is recommended over this function: it is a
proper ufunc written in C, and more than an order of magnitude faster.
We use the algorithm published by Clenshaw [1]_ and referenced by
Abramowitz and Stegun [2]_, for which the function domain is
partitioned into the two intervals [0,8] and (8,inf), and Chebyshev
polynomial expansions are employed in each interval. Relative error on
the domain [0,30] using IEEE arithmetic is documented [3]_ as having a
peak of 5.8e-16 with an rms of 1.4e-16 (n = 30000).
References
----------
.. [1] C. W. Clenshaw, "Chebyshev series for mathematical functions", in
*National Physical Laboratory Mathematical Tables*, vol. 5, London:
Her Majesty's Stationery Office, 1962.
.. [2] M. Abramowitz and I. A. Stegun, *Handbook of Mathematical
Functions*, 10th printing, New York: Dover, 1964, pp. 379.
https://personal.math.ubc.ca/~cbm/aands/page_379.htm
.. [3] https://metacpan.org/pod/distribution/Math-Cephes/lib/Math/Cephes.pod#i0:-Modified-Bessel-function-of-order-zero
Examples
--------
>>> import numpy as np
>>> np.i0(0.)
array(1.0)
>>> np.i0([0, 1, 2, 3])
array([1. , 1.26606588, 2.2795853 , 4.88079259])
"""
x = np.asanyarray(x)
if x.dtype.kind == 'c':
raise TypeError("i0 not supported for complex values")
if x.dtype.kind != 'f':
x = x.astype(float)
x = np.abs(x)
return piecewise(x, [x <= 8.0], [_i0_1, _i0_2])
## End of cephes code for i0
@set_module('numpy')
def kaiser(M, beta):
"""
Return the Kaiser window.
The Kaiser window is a taper formed by using a Bessel function.
Parameters
----------
M : int
Number of points in the output window. If zero or less, an
empty array is returned.
beta : float
Shape parameter for window.
Returns
-------
out : array
The window, with the maximum value normalized to one (the value
one appears only if the number of samples is odd).
See Also
--------
bartlett, blackman, hamming, hanning
Notes
-----
The Kaiser window is defined as
.. math:: w(n) = I_0\\left( \\beta \\sqrt{1-\\frac{4n^2}{(M-1)^2}}
\\right)/I_0(\\beta)
with
.. math:: \\quad -\\frac{M-1}{2} \\leq n \\leq \\frac{M-1}{2},
where :math:`I_0` is the modified zeroth-order Bessel function.
The Kaiser was named for Jim Kaiser, who discovered a simple
approximation to the DPSS window based on Bessel functions. The Kaiser
window is a very good approximation to the Digital Prolate Spheroidal
Sequence, or Slepian window, which is the transform which maximizes the
energy in the main lobe of the window relative to total energy.
The Kaiser can approximate many other windows by varying the beta
parameter.
==== =======================
beta Window shape
==== =======================
0 Rectangular
5 Similar to a Hamming
6 Similar to a Hanning
8.6 Similar to a Blackman
==== =======================
A beta value of 14 is probably a good starting point. Note that as beta
gets large, the window narrows, and so the number of samples needs to be
large enough to sample the increasingly narrow spike, otherwise NaNs will
get returned.
Most references to the Kaiser window come from the signal processing
literature, where it is used as one of many windowing functions for
smoothing values. It is also known as an apodization (which means
"removing the foot", i.e. smoothing discontinuities at the beginning
and end of the sampled signal) or tapering function.
References
----------
.. [1] J. F. Kaiser, "Digital Filters" - Ch 7 in "Systems analysis by
digital computer", Editors: F.F. Kuo and J.F. Kaiser, p 218-285.
John Wiley and Sons, New York, (1966).
.. [2] E.R. Kanasewich, "Time Sequence Analysis in Geophysics", The
University of Alberta Press, 1975, pp. 177-178.
.. [3] Wikipedia, "Window function",
https://en.wikipedia.org/wiki/Window_function
Examples
--------
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> np.kaiser(12, 14)
array([7.72686684e-06, 3.46009194e-03, 4.65200189e-02, # may vary
2.29737120e-01, 5.99885316e-01, 9.45674898e-01,
9.45674898e-01, 5.99885316e-01, 2.29737120e-01,
4.65200189e-02, 3.46009194e-03, 7.72686684e-06])
Plot the window and the frequency response.
.. plot::
:include-source:
import matplotlib.pyplot as plt
from numpy.fft import fft, fftshift
window = np.kaiser(51, 14)
plt.plot(window)
plt.title("Kaiser window")
plt.ylabel("Amplitude")
plt.xlabel("Sample")
plt.show()
plt.figure()
A = fft(window, 2048) / 25.5
mag = np.abs(fftshift(A))
freq = np.linspace(-0.5, 0.5, len(A))
response = 20 * np.log10(mag)
response = np.clip(response, -100, 100)
plt.plot(freq, response)
plt.title("Frequency response of Kaiser window")
plt.ylabel("Magnitude [dB]")
plt.xlabel("Normalized frequency [cycles per sample]")
plt.axis('tight')
plt.show()
"""
# Ensures at least float64 via 0.0. M should be an integer, but conversion
# to double is safe for a range. (Simplified result_type with 0.0
# strongly typed. result-type is not/less order sensitive, but that mainly
# matters for integers anyway.)
values = np.array([0.0, M, beta])
M = values[1]
beta = values[2]
if M == 1:
return np.ones(1, dtype=values.dtype)
n = arange(0, M)
alpha = (M-1)/2.0
return i0(beta * sqrt(1-((n-alpha)/alpha)**2.0))/i0(beta)
def _sinc_dispatcher(x):
return (x,)
@array_function_dispatch(_sinc_dispatcher)
def sinc(x):
r"""
Return the normalized sinc function.
The sinc function is equal to :math:`\sin(\pi x)/(\pi x)` for any argument
:math:`x\ne 0`. ``sinc(0)`` takes the limit value 1, making ``sinc`` not
only everywhere continuous but also infinitely differentiable.
.. note::
Note the normalization factor of ``pi`` used in the definition.
This is the most commonly used definition in signal processing.
Use ``sinc(x / np.pi)`` to obtain the unnormalized sinc function
:math:`\sin(x)/x` that is more common in mathematics.
Parameters
----------
x : ndarray
Array (possibly multi-dimensional) of values for which to calculate
``sinc(x)``.
Returns
-------
out : ndarray
``sinc(x)``, which has the same shape as the input.
Notes
-----
The name sinc is short for "sine cardinal" or "sinus cardinalis".
The sinc function is used in various signal processing applications,
including in anti-aliasing, in the construction of a Lanczos resampling
filter, and in interpolation.
For bandlimited interpolation of discrete-time signals, the ideal
interpolation kernel is proportional to the sinc function.
References
----------
.. [1] Weisstein, Eric W. "Sinc Function." From MathWorld--A Wolfram Web
Resource. https://mathworld.wolfram.com/SincFunction.html
.. [2] Wikipedia, "Sinc function",
https://en.wikipedia.org/wiki/Sinc_function
Examples
--------
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> x = np.linspace(-4, 4, 41)
>>> np.sinc(x)
array([-3.89804309e-17, -4.92362781e-02, -8.40918587e-02, # may vary
-8.90384387e-02, -5.84680802e-02, 3.89804309e-17,
6.68206631e-02, 1.16434881e-01, 1.26137788e-01,
8.50444803e-02, -3.89804309e-17, -1.03943254e-01,
-1.89206682e-01, -2.16236208e-01, -1.55914881e-01,
3.89804309e-17, 2.33872321e-01, 5.04551152e-01,
7.56826729e-01, 9.35489284e-01, 1.00000000e+00,
9.35489284e-01, 7.56826729e-01, 5.04551152e-01,
2.33872321e-01, 3.89804309e-17, -1.55914881e-01,
-2.16236208e-01, -1.89206682e-01, -1.03943254e-01,
-3.89804309e-17, 8.50444803e-02, 1.26137788e-01,
1.16434881e-01, 6.68206631e-02, 3.89804309e-17,
-5.84680802e-02, -8.90384387e-02, -8.40918587e-02,
-4.92362781e-02, -3.89804309e-17])
>>> plt.plot(x, np.sinc(x))
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.title("Sinc Function")
Text(0.5, 1.0, 'Sinc Function')
>>> plt.ylabel("Amplitude")
Text(0, 0.5, 'Amplitude')
>>> plt.xlabel("X")
Text(0.5, 0, 'X')
>>> plt.show()
"""
x = np.asanyarray(x)
y = pi * where(x == 0, 1.0e-20, x)
return sin(y)/y
def _ureduce(a, func, keepdims=False, **kwargs):
"""
Internal Function.
Call `func` with `a` as first argument swapping the axes to use extended
axis on functions that don't support it natively.
Returns result and a.shape with axis dims set to 1.
Parameters
----------
a : array_like
Input array or object that can be converted to an array.
func : callable
Reduction function capable of receiving a single axis argument.
It is called with `a` as first argument followed by `kwargs`.
kwargs : keyword arguments
additional keyword arguments to pass to `func`.
Returns
-------
result : tuple
Result of func(a, **kwargs) and a.shape with axis dims set to 1
which can be used to reshape the result to the same shape a ufunc with
keepdims=True would produce.
"""
a = np.asanyarray(a)
axis = kwargs.get('axis', None)
out = kwargs.get('out', None)
if keepdims is np._NoValue:
keepdims = False
nd = a.ndim
if axis is not None:
axis = _nx.normalize_axis_tuple(axis, nd)
if keepdims:
if out is not None:
index_out = tuple(
0 if i in axis else slice(None) for i in range(nd))
kwargs['out'] = out[(Ellipsis, ) + index_out]
if len(axis) == 1:
kwargs['axis'] = axis[0]
else:
keep = set(range(nd)) - set(axis)
nkeep = len(keep)
# swap axis that should not be reduced to front
for i, s in enumerate(sorted(keep)):
a = a.swapaxes(i, s)
# merge reduced axis
a = a.reshape(a.shape[:nkeep] + (-1,))
kwargs['axis'] = -1
else:
if keepdims:
if out is not None:
index_out = (0, ) * nd
kwargs['out'] = out[(Ellipsis, ) + index_out]
r = func(a, **kwargs)
if out is not None:
return out
if keepdims:
if axis is None:
index_r = (np.newaxis, ) * nd
else:
index_r = tuple(
np.newaxis if i in axis else slice(None)
for i in range(nd))
r = r[(Ellipsis, ) + index_r]
return r
def _median_dispatcher(
a, axis=None, out=None, overwrite_input=None, keepdims=None):
return (a, out)
@array_function_dispatch(_median_dispatcher)
def median(a, axis=None, out=None, overwrite_input=False, keepdims=False):
"""
Compute the median along the specified axis.
Returns the median of the array elements.
Parameters
----------
a : array_like
Input array or object that can be converted to an array.
axis : {int, sequence of int, None}, optional
Axis or axes along which the medians are computed. The default,
axis=None, will compute the median along a flattened version of
the array.
.. versionadded:: 1.9.0
If a sequence of axes, the array is first flattened along the
given axes, then the median is computed along the resulting
flattened axis.
out : ndarray, optional
Alternative output array in which to place the result. It must
have the same shape and buffer length as the expected output,
but the type (of the output) will be cast if necessary.
overwrite_input : bool, optional
If True, then allow use of memory of input array `a` for
calculations. The input array will be modified by the call to
`median`. This will save memory when you do not need to preserve
the contents of the input array. Treat the input as undefined,
but it will probably be fully or partially sorted. Default is
False. If `overwrite_input` is ``True`` and `a` is not already an
`ndarray`, an error will be raised.
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 `arr`.
.. versionadded:: 1.9.0
Returns
-------
median : ndarray
A new array holding the result. If the input contains integers
or floats smaller than ``float64``, then the output data-type is
``np.float64``. Otherwise, the data-type of the output is the
same as that of the input. If `out` is specified, that array is
returned instead.
See Also
--------
mean, percentile
Notes
-----
Given a vector ``V`` of length ``N``, the median of ``V`` is the
middle value of a sorted copy of ``V``, ``V_sorted`` - i
e., ``V_sorted[(N-1)/2]``, when ``N`` is odd, and the average of the
two middle values of ``V_sorted`` when ``N`` is even.
Examples
--------
>>> import numpy as np
>>> a = np.array([[10, 7, 4], [3, 2, 1]])
>>> a
array([[10, 7, 4],
[ 3, 2, 1]])
>>> np.median(a)
np.float64(3.5)
>>> np.median(a, axis=0)
array([6.5, 4.5, 2.5])
>>> np.median(a, axis=1)
array([7., 2.])
>>> np.median(a, axis=(0, 1))
np.float64(3.5)
>>> m = np.median(a, axis=0)
>>> out = np.zeros_like(m)
>>> np.median(a, axis=0, out=m)
array([6.5, 4.5, 2.5])
>>> m
array([6.5, 4.5, 2.5])
>>> b = a.copy()
>>> np.median(b, axis=1, overwrite_input=True)
array([7., 2.])
>>> assert not np.all(a==b)
>>> b = a.copy()
>>> np.median(b, axis=None, overwrite_input=True)
np.float64(3.5)
>>> assert not np.all(a==b)
"""
return _ureduce(a, func=_median, keepdims=keepdims, axis=axis, out=out,
overwrite_input=overwrite_input)
def _median(a, axis=None, out=None, overwrite_input=False):
# can't be reasonably be implemented in terms of percentile as we have to
# call mean to not break astropy
a = np.asanyarray(a)
# Set the partition indexes
if axis is None:
sz = a.size
else:
sz = a.shape[axis]
if sz % 2 == 0:
szh = sz // 2
kth = [szh - 1, szh]
else:
kth = [(sz - 1) // 2]
# We have to check for NaNs (as of writing 'M' doesn't actually work).
supports_nans = np.issubdtype(a.dtype, np.inexact) or a.dtype.kind in 'Mm'
if supports_nans:
kth.append(-1)
if overwrite_input:
if axis is None:
part = a.ravel()
part.partition(kth)
else:
a.partition(kth, axis=axis)
part = a
else:
part = partition(a, kth, axis=axis)
if part.shape == ():
# make 0-D arrays work
return part.item()
if axis is None:
axis = 0
indexer = [slice(None)] * part.ndim
index = part.shape[axis] // 2
if part.shape[axis] % 2 == 1:
# index with slice to allow mean (below) to work
indexer[axis] = slice(index, index+1)
else:
indexer[axis] = slice(index-1, index+1)
indexer = tuple(indexer)
# Use mean in both odd and even case to coerce data type,
# using out array if needed.
rout = mean(part[indexer], axis=axis, out=out)
if supports_nans and sz > 0:
# If nans are possible, warn and replace by nans like mean would.
rout = np.lib._utils_impl._median_nancheck(part, rout, axis)
return rout
def _percentile_dispatcher(a, q, axis=None, out=None, overwrite_input=None,
method=None, keepdims=None, *, weights=None,
interpolation=None):
return (a, q, out, weights)
@array_function_dispatch(_percentile_dispatcher)
def percentile(a,
q,
axis=None,
out=None,
overwrite_input=False,
method="linear",
keepdims=False,
*,
weights=None,
interpolation=None):
"""
Compute the q-th percentile of the data along the specified axis.
Returns the q-th percentile(s) of the array elements.
Parameters
----------
a : array_like of real numbers
Input array or object that can be converted to an array.
q : array_like of float
Percentage or sequence of percentages for the percentiles to compute.
Values must be between 0 and 100 inclusive.
axis : {int, tuple of int, None}, optional
Axis or axes along which the percentiles are computed. The
default is to compute the percentile(s) along a flattened
version of the array.
.. versionchanged:: 1.9.0
A tuple of axes is supported
out : ndarray, optional
Alternative output array in which to place the result. It must
have the same shape and buffer length as the expected output,
but the type (of the output) will be cast if necessary.
overwrite_input : bool, optional
If True, then allow the input array `a` to be modified by intermediate
calculations, to save memory. In this case, the contents of the input
`a` after this function completes is undefined.
method : str, optional
This parameter specifies the method to use for estimating the
percentile. There are many different methods, some unique to NumPy.
See the notes for explanation. The options sorted by their R type
as summarized in the H&F paper [1]_ are:
1. 'inverted_cdf'
2. 'averaged_inverted_cdf'
3. 'closest_observation'
4. 'interpolated_inverted_cdf'
5. 'hazen'
6. 'weibull'
7. 'linear' (default)
8. 'median_unbiased'
9. 'normal_unbiased'
The first three methods are discontinuous. NumPy further defines the
following discontinuous variations of the default 'linear' (7.) option:
* 'lower'
* 'higher',
* 'midpoint'
* 'nearest'
.. versionchanged:: 1.22.0
This argument was previously called "interpolation" and only
offered the "linear" default and last four options.
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 array `a`.
.. versionadded:: 1.9.0
weights : array_like, optional
An array of weights associated with the values in `a`. Each value in
`a` contributes to the percentile according to its associated weight.
The weights array can either be 1-D (in which case its length must be
the size of `a` along the given axis) or of the same shape as `a`.
If `weights=None`, then all data in `a` are assumed to have a
weight equal to one.
Only `method="inverted_cdf"` supports weights.
See the notes for more details.
.. versionadded:: 2.0.0
interpolation : str, optional
Deprecated name for the method keyword argument.
.. deprecated:: 1.22.0
Returns
-------
percentile : scalar or ndarray
If `q` is a single percentile and `axis=None`, then the result
is a scalar. If multiple percentiles are given, first axis of
the result corresponds to the percentiles. The other axes are
the axes that remain after the reduction of `a`. If the input
contains integers or floats smaller than ``float64``, the output
data-type is ``float64``. Otherwise, the output data-type is the
same as that of the input. If `out` is specified, that array is
returned instead.
See Also
--------
mean
median : equivalent to ``percentile(..., 50)``
nanpercentile
quantile : equivalent to percentile, except q in the range [0, 1].
Notes
-----
The behavior of `numpy.percentile` with percentage `q` is
that of `numpy.quantile` with argument ``q/100``.
For more information, please see `numpy.quantile`.
Examples
--------
>>> import numpy as np
>>> a = np.array([[10, 7, 4], [3, 2, 1]])
>>> a
array([[10, 7, 4],
[ 3, 2, 1]])
>>> np.percentile(a, 50)
3.5
>>> np.percentile(a, 50, axis=0)
array([6.5, 4.5, 2.5])
>>> np.percentile(a, 50, axis=1)
array([7., 2.])
>>> np.percentile(a, 50, axis=1, keepdims=True)
array([[7.],
[2.]])
>>> m = np.percentile(a, 50, axis=0)
>>> out = np.zeros_like(m)
>>> np.percentile(a, 50, axis=0, out=out)
array([6.5, 4.5, 2.5])
>>> m
array([6.5, 4.5, 2.5])
>>> b = a.copy()
>>> np.percentile(b, 50, axis=1, overwrite_input=True)
array([7., 2.])
>>> assert not np.all(a == b)
The different methods can be visualized graphically:
.. plot::
import matplotlib.pyplot as plt
a = np.arange(4)
p = np.linspace(0, 100, 6001)
ax = plt.gca()
lines = [
('linear', '-', 'C0'),
('inverted_cdf', ':', 'C1'),
# Almost the same as `inverted_cdf`:
('averaged_inverted_cdf', '-.', 'C1'),
('closest_observation', ':', 'C2'),
('interpolated_inverted_cdf', '--', 'C1'),
('hazen', '--', 'C3'),
('weibull', '-.', 'C4'),
('median_unbiased', '--', 'C5'),
('normal_unbiased', '-.', 'C6'),
]
for method, style, color in lines:
ax.plot(
p, np.percentile(a, p, method=method),
label=method, linestyle=style, color=color)
ax.set(
title='Percentiles for different methods and data: ' + str(a),
xlabel='Percentile',
ylabel='Estimated percentile value',
yticks=a)
ax.legend(bbox_to_anchor=(1.03, 1))
plt.tight_layout()
plt.show()
References
----------
.. [1] R. J. Hyndman and Y. Fan,
"Sample quantiles in statistical packages,"
The American Statistician, 50(4), pp. 361-365, 1996
"""
if interpolation is not None:
method = _check_interpolation_as_method(
method, interpolation, "percentile")
a = np.asanyarray(a)
if a.dtype.kind == "c":
raise TypeError("a must be an array of real numbers")
# Use dtype of array if possible (e.g., if q is a python int or float)
# by making the divisor have the dtype of the data array.
q = np.true_divide(q, a.dtype.type(100) if a.dtype.kind == "f" else 100)
q = asanyarray(q) # undo any decay that the ufunc performed (see gh-13105)
if not _quantile_is_valid(q):
raise ValueError("Percentiles must be in the range [0, 100]")
if weights is not None:
if method != "inverted_cdf":
msg = ("Only method 'inverted_cdf' supports weights. "
f"Got: {method}.")
raise ValueError(msg)
if axis is not None:
axis = _nx.normalize_axis_tuple(axis, a.ndim, argname="axis")
weights = _weights_are_valid(weights=weights, a=a, axis=axis)
if np.any(weights < 0):
raise ValueError("Weights must be non-negative.")
return _quantile_unchecked(
a, q, axis, out, overwrite_input, method, keepdims, weights)
def _quantile_dispatcher(a, q, axis=None, out=None, overwrite_input=None,
method=None, keepdims=None, *, weights=None,
interpolation=None):
return (a, q, out, weights)
@array_function_dispatch(_quantile_dispatcher)
def quantile(a,
q,
axis=None,
out=None,
overwrite_input=False,
method="linear",
keepdims=False,
*,
weights=None,
interpolation=None):
"""
Compute the q-th quantile of the data along the specified axis.
.. versionadded:: 1.15.0
Parameters
----------
a : array_like of real numbers
Input array or object that can be converted to an array.
q : array_like of float
Probability or sequence of probabilities of the quantiles to compute.
Values must be between 0 and 1 inclusive.
axis : {int, tuple of int, None}, optional
Axis or axes along which the quantiles are computed. The default is
to compute the quantile(s) along a flattened version of the array.
out : ndarray, optional
Alternative output array in which to place the result. It must have
the same shape and buffer length as the expected output, but the
type (of the output) will be cast if necessary.
overwrite_input : bool, optional
If True, then allow the input array `a` to be modified by
intermediate calculations, to save memory. In this case, the
contents of the input `a` after this function completes is
undefined.
method : str, optional
This parameter specifies the method to use for estimating the
quantile. There are many different methods, some unique to NumPy.
The recommended options, numbered as they appear in [1]_, are:
1. 'inverted_cdf'
2. 'averaged_inverted_cdf'
3. 'closest_observation'
4. 'interpolated_inverted_cdf'
5. 'hazen'
6. 'weibull'
7. 'linear' (default)
8. 'median_unbiased'
9. 'normal_unbiased'
The first three methods are discontinuous. For backward compatibility
with previous versions of NumPy, the following discontinuous variations
of the default 'linear' (7.) option are available:
* 'lower'
* 'higher',
* 'midpoint'
* 'nearest'
See Notes for details.
.. versionchanged:: 1.22.0
This argument was previously called "interpolation" and only
offered the "linear" default and last four options.
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 array `a`.
weights : array_like, optional
An array of weights associated with the values in `a`. Each value in
`a` contributes to the quantile according to its associated weight.
The weights array can either be 1-D (in which case its length must be
the size of `a` along the given axis) or of the same shape as `a`.
If `weights=None`, then all data in `a` are assumed to have a
weight equal to one.
Only `method="inverted_cdf"` supports weights.
See the notes for more details.
.. versionadded:: 2.0.0
interpolation : str, optional
Deprecated name for the method keyword argument.
.. deprecated:: 1.22.0
Returns
-------
quantile : scalar or ndarray
If `q` is a single probability and `axis=None`, then the result
is a scalar. If multiple probability levels are given, first axis
of the result corresponds to the quantiles. The other axes are
the axes that remain after the reduction of `a`. If the input
contains integers or floats smaller than ``float64``, the output
data-type is ``float64``. Otherwise, the output data-type is the
same as that of the input. If `out` is specified, that array is
returned instead.
See Also
--------
mean
percentile : equivalent to quantile, but with q in the range [0, 100].
median : equivalent to ``quantile(..., 0.5)``
nanquantile
Notes
-----
Given a sample `a` from an underlying distribution, `quantile` provides a
nonparametric estimate of the inverse cumulative distribution function.
By default, this is done by interpolating between adjacent elements in
``y``, a sorted copy of `a`::
(1-g)*y[j] + g*y[j+1]
where the index ``j`` and coefficient ``g`` are the integral and
fractional components of ``q * (n-1)``, and ``n`` is the number of
elements in the sample.
This is a special case of Equation 1 of H&F [1]_. More generally,
- ``j = (q*n + m - 1) // 1``, and
- ``g = (q*n + m - 1) % 1``,
where ``m`` may be defined according to several different conventions.
The preferred convention may be selected using the ``method`` parameter:
=============================== =============== ===============
``method`` number in H&F ``m``
=============================== =============== ===============
``interpolated_inverted_cdf`` 4 ``0``
``hazen`` 5 ``1/2``
``weibull`` 6 ``q``
``linear`` (default) 7 ``1 - q``
``median_unbiased`` 8 ``q/3 + 1/3``
``normal_unbiased`` 9 ``q/4 + 3/8``
=============================== =============== ===============
Note that indices ``j`` and ``j + 1`` are clipped to the range ``0`` to
``n - 1`` when the results of the formula would be outside the allowed
range of non-negative indices. The ``- 1`` in the formulas for ``j`` and
``g`` accounts for Python's 0-based indexing.
The table above includes only the estimators from H&F that are continuous
functions of probability `q` (estimators 4-9). NumPy also provides the
three discontinuous estimators from H&F (estimators 1-3), where ``j`` is
defined as above, ``m`` is defined as follows, and ``g`` is a function
of the real-valued ``index = q*n + m - 1`` and ``j``.
1. ``inverted_cdf``: ``m = 0`` and ``g = int(index - j > 0)``
2. ``averaged_inverted_cdf``: ``m = 0`` and
``g = (1 + int(index - j > 0)) / 2``
3. ``closest_observation``: ``m = -1/2`` and
``g = 1 - int((index == j) & (j%2 == 1))``
For backward compatibility with previous versions of NumPy, `quantile`
provides four additional discontinuous estimators. Like
``method='linear'``, all have ``m = 1 - q`` so that ``j = q*(n-1) // 1``,
but ``g`` is defined as follows.
- ``lower``: ``g = 0``
- ``midpoint``: ``g = 0.5``
- ``higher``: ``g = 1``
- ``nearest``: ``g = (q*(n-1) % 1) > 0.5``
**Weighted quantiles:**
More formally, the quantile at probability level :math:`q` of a cumulative
distribution function :math:`F(y)=P(Y \\leq y)` with probability measure
:math:`P` is defined as any number :math:`x` that fulfills the
*coverage conditions*
.. math:: P(Y < x) \\leq q \\quad\\text{and}\\quad P(Y \\leq x) \\geq q
with random variable :math:`Y\\sim P`.
Sample quantiles, the result of `quantile`, provide nonparametric
estimation of the underlying population counterparts, represented by the
unknown :math:`F`, given a data vector `a` of length ``n``.
Some of the estimators above arise when one considers :math:`F` as the
empirical distribution function of the data, i.e.
:math:`F(y) = \\frac{1}{n} \\sum_i 1_{a_i \\leq y}`.
Then, different methods correspond to different choices of :math:`x` that
fulfill the above coverage conditions. Methods that follow this approach
are ``inverted_cdf`` and ``averaged_inverted_cdf``.
For weighted quantiles, the coverage conditions still hold. The
empirical cumulative distribution is simply replaced by its weighted
version, i.e.
:math:`P(Y \\leq t) = \\frac{1}{\\sum_i w_i} \\sum_i w_i 1_{x_i \\leq t}`.
Only ``method="inverted_cdf"`` supports weights.
Examples
--------
>>> import numpy as np
>>> a = np.array([[10, 7, 4], [3, 2, 1]])
>>> a
array([[10, 7, 4],
[ 3, 2, 1]])
>>> np.quantile(a, 0.5)
3.5
>>> np.quantile(a, 0.5, axis=0)
array([6.5, 4.5, 2.5])
>>> np.quantile(a, 0.5, axis=1)
array([7., 2.])
>>> np.quantile(a, 0.5, axis=1, keepdims=True)
array([[7.],
[2.]])
>>> m = np.quantile(a, 0.5, axis=0)
>>> out = np.zeros_like(m)
>>> np.quantile(a, 0.5, axis=0, out=out)
array([6.5, 4.5, 2.5])
>>> m
array([6.5, 4.5, 2.5])
>>> b = a.copy()
>>> np.quantile(b, 0.5, axis=1, overwrite_input=True)
array([7., 2.])
>>> assert not np.all(a == b)
See also `numpy.percentile` for a visualization of most methods.
References
----------
.. [1] R. J. Hyndman and Y. Fan,
"Sample quantiles in statistical packages,"
The American Statistician, 50(4), pp. 361-365, 1996
"""
if interpolation is not None:
method = _check_interpolation_as_method(
method, interpolation, "quantile")
a = np.asanyarray(a)
if a.dtype.kind == "c":
raise TypeError("a must be an array of real numbers")
# Use dtype of array if possible (e.g., if q is a python int or float).
if isinstance(q, (int, float)) and a.dtype.kind == "f":
q = np.asanyarray(q, dtype=a.dtype)
else:
q = np.asanyarray(q)
if not _quantile_is_valid(q):
raise ValueError("Quantiles must be in the range [0, 1]")
if weights is not None:
if method != "inverted_cdf":
msg = ("Only method 'inverted_cdf' supports weights. "
f"Got: {method}.")
raise ValueError(msg)
if axis is not None:
axis = _nx.normalize_axis_tuple(axis, a.ndim, argname="axis")
weights = _weights_are_valid(weights=weights, a=a, axis=axis)
if np.any(weights < 0):
raise ValueError("Weights must be non-negative.")
return _quantile_unchecked(
a, q, axis, out, overwrite_input, method, keepdims, weights)
def _quantile_unchecked(a,
q,
axis=None,
out=None,
overwrite_input=False,
method="linear",
keepdims=False,
weights=None):
"""Assumes that q is in [0, 1], and is an ndarray"""
return _ureduce(a,
func=_quantile_ureduce_func,
q=q,
weights=weights,
keepdims=keepdims,
axis=axis,
out=out,
overwrite_input=overwrite_input,
method=method)
def _quantile_is_valid(q):
# avoid expensive reductions, relevant for arrays with < O(1000) elements
if q.ndim == 1 and q.size < 10:
for i in range(q.size):
if not (0.0 <= q[i] <= 1.0):
return False
else:
if not (q.min() >= 0 and q.max() <= 1):
return False
return True
def _check_interpolation_as_method(method, interpolation, fname):
# Deprecated NumPy 1.22, 2021-11-08
warnings.warn(
f"the `interpolation=` argument to {fname} was renamed to "
"`method=`, which has additional options.\n"
"Users of the modes 'nearest', 'lower', 'higher', or "
"'midpoint' are encouraged to review the method they used. "
"(Deprecated NumPy 1.22)",
DeprecationWarning, stacklevel=4)
if method != "linear":
# sanity check, we assume this basically never happens
raise TypeError(
"You shall not pass both `method` and `interpolation`!\n"
"(`interpolation` is Deprecated in favor of `method`)")
return interpolation
def _compute_virtual_index(n, quantiles, alpha: float, beta: float):
"""
Compute the floating point indexes of an array for the linear
interpolation of quantiles.
n : array_like
The sample sizes.
quantiles : array_like
The quantiles values.
alpha : float
A constant used to correct the index computed.
beta : float
A constant used to correct the index computed.
alpha and beta values depend on the chosen method
(see quantile documentation)
Reference:
Hyndman&Fan paper "Sample Quantiles in Statistical Packages",
DOI: 10.1080/00031305.1996.10473566
"""
return n * quantiles + (
alpha + quantiles * (1 - alpha - beta)
) - 1
def _get_gamma(virtual_indexes, previous_indexes, method):
"""
Compute gamma (a.k.a 'm' or 'weight') for the linear interpolation
of quantiles.
virtual_indexes : array_like
The indexes where the percentile is supposed to be found in the sorted
sample.
previous_indexes : array_like
The floor values of virtual_indexes.
interpolation : dict
The interpolation method chosen, which may have a specific rule
modifying gamma.
gamma is usually the fractional part of virtual_indexes but can be modified
by the interpolation method.
"""
gamma = np.asanyarray(virtual_indexes - previous_indexes)
gamma = method["fix_gamma"](gamma, virtual_indexes)
# Ensure both that we have an array, and that we keep the dtype
# (which may have been matched to the input array).
return np.asanyarray(gamma, dtype=virtual_indexes.dtype)
def _lerp(a, b, t, out=None):
"""
Compute the linear interpolation weighted by gamma on each point of
two same shape array.
a : array_like
Left bound.
b : array_like
Right bound.
t : array_like
The interpolation weight.
out : array_like
Output array.
"""
diff_b_a = subtract(b, a)
# asanyarray is a stop-gap until gh-13105
lerp_interpolation = asanyarray(add(a, diff_b_a * t, out=out))
subtract(b, diff_b_a * (1 - t), out=lerp_interpolation, where=t >= 0.5,
casting='unsafe', dtype=type(lerp_interpolation.dtype))
if lerp_interpolation.ndim == 0 and out is None:
lerp_interpolation = lerp_interpolation[()] # unpack 0d arrays
return lerp_interpolation
def _get_gamma_mask(shape, default_value, conditioned_value, where):
out = np.full(shape, default_value)
np.copyto(out, conditioned_value, where=where, casting="unsafe")
return out
def _discret_interpolation_to_boundaries(index, gamma_condition_fun):
previous = np.floor(index)
next = previous + 1
gamma = index - previous
res = _get_gamma_mask(shape=index.shape,
default_value=next,
conditioned_value=previous,
where=gamma_condition_fun(gamma, index)
).astype(np.intp)
# Some methods can lead to out-of-bound integers, clip them:
res[res < 0] = 0
return res
def _closest_observation(n, quantiles):
# "choose the nearest even order statistic at g=0" (H&F (1996) pp. 362).
# Order is 1-based so for zero-based indexing round to nearest odd index.
gamma_fun = lambda gamma, index: (gamma == 0) & (np.floor(index) % 2 == 1)
return _discret_interpolation_to_boundaries((n * quantiles) - 1 - 0.5,
gamma_fun)
def _inverted_cdf(n, quantiles):
gamma_fun = lambda gamma, _: (gamma == 0)
return _discret_interpolation_to_boundaries((n * quantiles) - 1,
gamma_fun)
def _quantile_ureduce_func(
a: np.array,
q: np.array,
weights: np.array,
axis: int = None,
out=None,
overwrite_input: bool = False,
method="linear",
) -> np.array:
if q.ndim > 2:
# The code below works fine for nd, but it might not have useful
# semantics. For now, keep the supported dimensions the same as it was
# before.
raise ValueError("q must be a scalar or 1d")
if overwrite_input:
if axis is None:
axis = 0
arr = a.ravel()
wgt = None if weights is None else weights.ravel()
else:
arr = a
wgt = weights
else:
if axis is None:
axis = 0
arr = a.flatten()
wgt = None if weights is None else weights.flatten()
else:
arr = a.copy()
wgt = weights
result = _quantile(arr,
quantiles=q,
axis=axis,
method=method,
out=out,
weights=wgt)
return result
def _get_indexes(arr, virtual_indexes, valid_values_count):
"""
Get the valid indexes of arr neighbouring virtual_indexes.
Note
This is a companion function to linear interpolation of
Quantiles
Returns
-------
(previous_indexes, next_indexes): Tuple
A Tuple of virtual_indexes neighbouring indexes
"""
previous_indexes = np.asanyarray(np.floor(virtual_indexes))
next_indexes = np.asanyarray(previous_indexes + 1)
indexes_above_bounds = virtual_indexes >= valid_values_count - 1
# When indexes is above max index, take the max value of the array
if indexes_above_bounds.any():
previous_indexes[indexes_above_bounds] = -1
next_indexes[indexes_above_bounds] = -1
# When indexes is below min index, take the min value of the array
indexes_below_bounds = virtual_indexes < 0
if indexes_below_bounds.any():
previous_indexes[indexes_below_bounds] = 0
next_indexes[indexes_below_bounds] = 0
if np.issubdtype(arr.dtype, np.inexact):
# After the sort, slices having NaNs will have for last element a NaN
virtual_indexes_nans = np.isnan(virtual_indexes)
if virtual_indexes_nans.any():
previous_indexes[virtual_indexes_nans] = -1
next_indexes[virtual_indexes_nans] = -1
previous_indexes = previous_indexes.astype(np.intp)
next_indexes = next_indexes.astype(np.intp)
return previous_indexes, next_indexes
def _quantile(
arr: np.array,
quantiles: np.array,
axis: int = -1,
method="linear",
out=None,
weights=None,
):
"""
Private function that doesn't support extended axis or keepdims.
These methods are extended to this function using _ureduce
See nanpercentile for parameter usage
It computes the quantiles of the array for the given axis.
A linear interpolation is performed based on the `interpolation`.
By default, the method is "linear" where alpha == beta == 1 which
performs the 7th method of Hyndman&Fan.
With "median_unbiased" we get alpha == beta == 1/3
thus the 8th method of Hyndman&Fan.
"""
# --- Setup
arr = np.asanyarray(arr)
values_count = arr.shape[axis]
# The dimensions of `q` are prepended to the output shape, so we need the
# axis being sampled from `arr` to be last.
if axis != 0: # But moveaxis is slow, so only call it if necessary.
arr = np.moveaxis(arr, axis, destination=0)
supports_nans = (
np.issubdtype(arr.dtype, np.inexact) or arr.dtype.kind in 'Mm'
)
if weights is None:
# --- Computation of indexes
# Index where to find the value in the sorted array.
# Virtual because it is a floating point value, not an valid index.
# The nearest neighbours are used for interpolation
try:
method_props = _QuantileMethods[method]
except KeyError:
raise ValueError(
f"{method!r} is not a valid method. Use one of: "
f"{_QuantileMethods.keys()}") from None
virtual_indexes = method_props["get_virtual_index"](values_count,
quantiles)
virtual_indexes = np.asanyarray(virtual_indexes)
if method_props["fix_gamma"] is None:
supports_integers = True
else:
int_virtual_indices = np.issubdtype(virtual_indexes.dtype,
np.integer)
supports_integers = method == 'linear' and int_virtual_indices
if supports_integers:
# No interpolation needed, take the points along axis
if supports_nans:
# may contain nan, which would sort to the end
arr.partition(
concatenate((virtual_indexes.ravel(), [-1])), axis=0,
)
slices_having_nans = np.isnan(arr[-1, ...])
else:
# cannot contain nan
arr.partition(virtual_indexes.ravel(), axis=0)
slices_having_nans = np.array(False, dtype=bool)
result = take(arr, virtual_indexes, axis=0, out=out)
else:
previous_indexes, next_indexes = _get_indexes(arr,
virtual_indexes,
values_count)
# --- Sorting
arr.partition(
np.unique(np.concatenate(([0, -1],
previous_indexes.ravel(),
next_indexes.ravel(),
))),
axis=0)
if supports_nans:
slices_having_nans = np.isnan(arr[-1, ...])
else:
slices_having_nans = None
# --- Get values from indexes
previous = arr[previous_indexes]
next = arr[next_indexes]
# --- Linear interpolation
gamma = _get_gamma(virtual_indexes, previous_indexes, method_props)
result_shape = virtual_indexes.shape + (1,) * (arr.ndim - 1)
gamma = gamma.reshape(result_shape)
result = _lerp(previous,
next,
gamma,
out=out)
else:
# Weighted case
# This implements method="inverted_cdf", the only supported weighted
# method, which needs to sort anyway.
weights = np.asanyarray(weights)
if axis != 0:
weights = np.moveaxis(weights, axis, destination=0)
index_array = np.argsort(arr, axis=0, kind="stable")
# arr = arr[index_array, ...] # but this adds trailing dimensions of
# 1.
arr = np.take_along_axis(arr, index_array, axis=0)
if weights.shape == arr.shape:
weights = np.take_along_axis(weights, index_array, axis=0)
else:
# weights is 1d
weights = weights.reshape(-1)[index_array, ...]
if supports_nans:
# may contain nan, which would sort to the end
slices_having_nans = np.isnan(arr[-1, ...])
else:
# cannot contain nan
slices_having_nans = np.array(False, dtype=bool)
# We use the weights to calculate the empirical cumulative
# distribution function cdf
cdf = weights.cumsum(axis=0, dtype=np.float64)
cdf /= cdf[-1, ...] # normalization to 1
# Search index i such that
# sum(weights[j], j=0..i-1) < quantile <= sum(weights[j], j=0..i)
# is then equivalent to
# cdf[i-1] < quantile <= cdf[i]
# Unfortunately, searchsorted only accepts 1-d arrays as first
# argument, so we will need to iterate over dimensions.
# Without the following cast, searchsorted can return surprising
# results, e.g.
# np.searchsorted(np.array([0.2, 0.4, 0.6, 0.8, 1.]),
# np.array(0.4, dtype=np.float32), side="left")
# returns 2 instead of 1 because 0.4 is not binary representable.
if quantiles.dtype.kind == "f":
cdf = cdf.astype(quantiles.dtype)
# Weights must be non-negative, so we might have zero weights at the
# beginning leading to some leading zeros in cdf. The call to
# np.searchsorted for quantiles=0 will then pick the first element,
# but should pick the first one larger than zero. We
# therefore simply set 0 values in cdf to -1.
if np.any(cdf[0, ...] == 0):
cdf[cdf == 0] = -1
def find_cdf_1d(arr, cdf):
indices = np.searchsorted(cdf, quantiles, side="left")
# We might have reached the maximum with i = len(arr), e.g. for
# quantiles = 1, and need to cut it to len(arr) - 1.
indices = minimum(indices, values_count - 1)
result = take(arr, indices, axis=0)
return result
r_shape = arr.shape[1:]
if quantiles.ndim > 0:
r_shape = quantiles.shape + r_shape
if out is None:
result = np.empty_like(arr, shape=r_shape)
else:
if out.shape != r_shape:
msg = (f"Wrong shape of argument 'out', shape={r_shape} is "
f"required; got shape={out.shape}.")
raise ValueError(msg)
result = out
# See apply_along_axis, which we do for axis=0. Note that Ni = (,)
# always, so we remove it here.
Nk = arr.shape[1:]
for kk in np.ndindex(Nk):
result[(...,) + kk] = find_cdf_1d(
arr[np.s_[:, ] + kk], cdf[np.s_[:, ] + kk]
)
# Make result the same as in unweighted inverted_cdf.
if result.shape == () and result.dtype == np.dtype("O"):
result = result.item()
if np.any(slices_having_nans):
if result.ndim == 0 and out is None:
# can't write to a scalar, but indexing will be correct
result = arr[-1]
else:
np.copyto(result, arr[-1, ...], where=slices_having_nans)
return result
def _trapezoid_dispatcher(y, x=None, dx=None, axis=None):
return (y, x)
@array_function_dispatch(_trapezoid_dispatcher)
def trapezoid(y, x=None, dx=1.0, axis=-1):
r"""
Integrate along the given axis using the composite trapezoidal rule.
If `x` is provided, the integration happens in sequence along its
elements - they are not sorted.
Integrate `y` (`x`) along each 1d slice on the given axis, compute
:math:`\int y(x) dx`.
When `x` is specified, this integrates along the parametric curve,
computing :math:`\int_t y(t) dt =
\int_t y(t) \left.\frac{dx}{dt}\right|_{x=x(t)} dt`.
.. versionadded:: 2.0.0
Parameters
----------
y : array_like
Input array to integrate.
x : array_like, optional
The sample points corresponding to the `y` values. If `x` is None,
the sample points are assumed to be evenly spaced `dx` apart. The
default is None.
dx : scalar, optional
The spacing between sample points when `x` is None. The default is 1.
axis : int, optional
The axis along which to integrate.
Returns
-------
trapezoid : float or ndarray
Definite integral of `y` = n-dimensional array as approximated along
a single axis by the trapezoidal rule. If `y` is a 1-dimensional array,
then the result is a float. If `n` is greater than 1, then the result
is an `n`-1 dimensional array.
See Also
--------
sum, cumsum
Notes
-----
Image [2]_ illustrates trapezoidal rule -- y-axis locations of points
will be taken from `y` array, by default x-axis distances between
points will be 1.0, alternatively they can be provided with `x` array
or with `dx` scalar. Return value will be equal to combined area under
the red lines.
References
----------
.. [1] Wikipedia page: https://en.wikipedia.org/wiki/Trapezoidal_rule
.. [2] Illustration image:
https://en.wikipedia.org/wiki/File:Composite_trapezoidal_rule_illustration.png
Examples
--------
>>> import numpy as np
Use the trapezoidal rule on evenly spaced points:
>>> np.trapezoid([1, 2, 3])
4.0
The spacing between sample points can be selected by either the
``x`` or ``dx`` arguments:
>>> np.trapezoid([1, 2, 3], x=[4, 6, 8])
8.0
>>> np.trapezoid([1, 2, 3], dx=2)
8.0
Using a decreasing ``x`` corresponds to integrating in reverse:
>>> np.trapezoid([1, 2, 3], x=[8, 6, 4])
-8.0
More generally ``x`` is used to integrate along a parametric curve. We can
estimate the integral :math:`\int_0^1 x^2 = 1/3` using:
>>> x = np.linspace(0, 1, num=50)
>>> y = x**2
>>> np.trapezoid(y, x)
0.33340274885464394
Or estimate the area of a circle, noting we repeat the sample which closes
the curve:
>>> theta = np.linspace(0, 2 * np.pi, num=1000, endpoint=True)
>>> np.trapezoid(np.cos(theta), x=np.sin(theta))
3.141571941375841
``np.trapezoid`` can be applied along a specified axis to do multiple
computations in one call:
>>> a = np.arange(6).reshape(2, 3)
>>> a
array([[0, 1, 2],
[3, 4, 5]])
>>> np.trapezoid(a, axis=0)
array([1.5, 2.5, 3.5])
>>> np.trapezoid(a, axis=1)
array([2., 8.])
"""
y = asanyarray(y)
if x is None:
d = dx
else:
x = asanyarray(x)
if x.ndim == 1:
d = diff(x)
# reshape to correct shape
shape = [1]*y.ndim
shape[axis] = d.shape[0]
d = d.reshape(shape)
else:
d = diff(x, axis=axis)
nd = y.ndim
slice1 = [slice(None)]*nd
slice2 = [slice(None)]*nd
slice1[axis] = slice(1, None)
slice2[axis] = slice(None, -1)
try:
ret = (d * (y[tuple(slice1)] + y[tuple(slice2)]) / 2.0).sum(axis)
except ValueError:
# Operations didn't work, cast to ndarray
d = np.asarray(d)
y = np.asarray(y)
ret = add.reduce(d * (y[tuple(slice1)]+y[tuple(slice2)])/2.0, axis)
return ret
@set_module('numpy')
def trapz(y, x=None, dx=1.0, axis=-1):
"""
`trapz` is deprecated in NumPy 2.0.
Please use `trapezoid` instead, or one of the numerical integration
functions in `scipy.integrate`.
"""
# Deprecated in NumPy 2.0, 2023-08-18
warnings.warn(
"`trapz` is deprecated. Use `trapezoid` instead, or one of the "
"numerical integration functions in `scipy.integrate`.",
DeprecationWarning,
stacklevel=2
)
return trapezoid(y, x=x, dx=dx, axis=axis)
def _meshgrid_dispatcher(*xi, copy=None, sparse=None, indexing=None):
return xi
# Based on scitools meshgrid
@array_function_dispatch(_meshgrid_dispatcher)
def meshgrid(*xi, copy=True, sparse=False, indexing='xy'):
"""
Return a tuple of coordinate matrices from coordinate vectors.
Make N-D coordinate arrays for vectorized evaluations of
N-D scalar/vector fields over N-D grids, given
one-dimensional coordinate arrays x1, x2,..., xn.
.. versionchanged:: 1.9
1-D and 0-D cases are allowed.
Parameters
----------
x1, x2,..., xn : array_like
1-D arrays representing the coordinates of a grid.
indexing : {'xy', 'ij'}, optional
Cartesian ('xy', default) or matrix ('ij') indexing of output.
See Notes for more details.
.. versionadded:: 1.7.0
sparse : bool, optional
If True the shape of the returned coordinate array for dimension *i*
is reduced from ``(N1, ..., Ni, ... Nn)`` to
``(1, ..., 1, Ni, 1, ..., 1)``. These sparse coordinate grids are
intended to be use with :ref:`basics.broadcasting`. When all
coordinates are used in an expression, broadcasting still leads to a
fully-dimensonal result array.
Default is False.
.. versionadded:: 1.7.0
copy : bool, optional
If False, a view into the original arrays are returned in order to
conserve memory. Default is True. Please note that
``sparse=False, copy=False`` will likely return non-contiguous
arrays. Furthermore, more than one element of a broadcast array
may refer to a single memory location. If you need to write to the
arrays, make copies first.
.. versionadded:: 1.7.0
Returns
-------
X1, X2,..., XN : tuple of ndarrays
For vectors `x1`, `x2`,..., `xn` with lengths ``Ni=len(xi)``,
returns ``(N1, N2, N3,..., Nn)`` shaped arrays if indexing='ij'
or ``(N2, N1, N3,..., Nn)`` shaped arrays if indexing='xy'
with the elements of `xi` repeated to fill the matrix along
the first dimension for `x1`, the second for `x2` and so on.
Notes
-----
This function supports both indexing conventions through the indexing
keyword argument. Giving the string 'ij' returns a meshgrid with
matrix indexing, while 'xy' returns a meshgrid with Cartesian indexing.
In the 2-D case with inputs of length M and N, the outputs are of shape
(N, M) for 'xy' indexing and (M, N) for 'ij' indexing. In the 3-D case
with inputs of length M, N and P, outputs are of shape (N, M, P) for
'xy' indexing and (M, N, P) for 'ij' indexing. The difference is
illustrated by the following code snippet::
xv, yv = np.meshgrid(x, y, indexing='ij')
for i in range(nx):
for j in range(ny):
# treat xv[i,j], yv[i,j]
xv, yv = np.meshgrid(x, y, indexing='xy')
for i in range(nx):
for j in range(ny):
# treat xv[j,i], yv[j,i]
In the 1-D and 0-D case, the indexing and sparse keywords have no effect.
See Also
--------
mgrid : Construct a multi-dimensional "meshgrid" using indexing notation.
ogrid : Construct an open multi-dimensional "meshgrid" using indexing
notation.
:ref:`how-to-index`
Examples
--------
>>> import numpy as np
>>> nx, ny = (3, 2)
>>> x = np.linspace(0, 1, nx)
>>> y = np.linspace(0, 1, ny)
>>> xv, yv = np.meshgrid(x, y)
>>> xv
array([[0. , 0.5, 1. ],
[0. , 0.5, 1. ]])
>>> yv
array([[0., 0., 0.],
[1., 1., 1.]])
The result of `meshgrid` is a coordinate grid:
>>> import matplotlib.pyplot as plt
>>> plt.plot(xv, yv, marker='o', color='k', linestyle='none')
>>> plt.show()
You can create sparse output arrays to save memory and computation time.
>>> xv, yv = np.meshgrid(x, y, sparse=True)
>>> xv
array([[0. , 0.5, 1. ]])
>>> yv
array([[0.],
[1.]])
`meshgrid` is very useful to evaluate functions on a grid. If the
function depends on all coordinates, both dense and sparse outputs can be
used.
>>> x = np.linspace(-5, 5, 101)
>>> y = np.linspace(-5, 5, 101)
>>> # full coordinate arrays
>>> xx, yy = np.meshgrid(x, y)
>>> zz = np.sqrt(xx**2 + yy**2)
>>> xx.shape, yy.shape, zz.shape
((101, 101), (101, 101), (101, 101))
>>> # sparse coordinate arrays
>>> xs, ys = np.meshgrid(x, y, sparse=True)
>>> zs = np.sqrt(xs**2 + ys**2)
>>> xs.shape, ys.shape, zs.shape
((1, 101), (101, 1), (101, 101))
>>> np.array_equal(zz, zs)
True
>>> h = plt.contourf(x, y, zs)
>>> plt.axis('scaled')
>>> plt.colorbar()
>>> plt.show()
"""
ndim = len(xi)
if indexing not in ['xy', 'ij']:
raise ValueError(
"Valid values for `indexing` are 'xy' and 'ij'.")
s0 = (1,) * ndim
output = [np.asanyarray(x).reshape(s0[:i] + (-1,) + s0[i + 1:])
for i, x in enumerate(xi)]
if indexing == 'xy' and ndim > 1:
# switch first and second axis
output[0].shape = (1, -1) + s0[2:]
output[1].shape = (-1, 1) + s0[2:]
if not sparse:
# Return the full N-D matrix (not only the 1-D vector)
output = np.broadcast_arrays(*output, subok=True)
if copy:
output = tuple(x.copy() for x in output)
return output
def _delete_dispatcher(arr, obj, axis=None):
return (arr, obj)
@array_function_dispatch(_delete_dispatcher)
def delete(arr, obj, axis=None):
"""
Return a new array with sub-arrays along an axis deleted. For a one
dimensional array, this returns those entries not returned by
`arr[obj]`.
Parameters
----------
arr : array_like
Input array.
obj : slice, int or array of ints
Indicate indices of sub-arrays to remove along the specified axis.
.. versionchanged:: 1.19.0
Boolean indices are now treated as a mask of elements to remove,
rather than being cast to the integers 0 and 1.
axis : int, optional
The axis along which to delete the subarray defined by `obj`.
If `axis` is None, `obj` is applied to the flattened array.
Returns
-------
out : ndarray
A copy of `arr` with the elements specified by `obj` removed. Note
that `delete` does not occur in-place. If `axis` is None, `out` is
a flattened array.
See Also
--------
insert : Insert elements into an array.
append : Append elements at the end of an array.
Notes
-----
Often it is preferable to use a boolean mask. For example:
>>> arr = np.arange(12) + 1
>>> mask = np.ones(len(arr), dtype=bool)
>>> mask[[0,2,4]] = False
>>> result = arr[mask,...]
Is equivalent to ``np.delete(arr, [0,2,4], axis=0)``, but allows further
use of `mask`.
Examples
--------
>>> import numpy as np
>>> arr = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])
>>> arr
array([[ 1, 2, 3, 4],
[ 5, 6, 7, 8],
[ 9, 10, 11, 12]])
>>> np.delete(arr, 1, 0)
array([[ 1, 2, 3, 4],
[ 9, 10, 11, 12]])
>>> np.delete(arr, np.s_[::2], 1)
array([[ 2, 4],
[ 6, 8],
[10, 12]])
>>> np.delete(arr, [1,3,5], None)
array([ 1, 3, 5, 7, 8, 9, 10, 11, 12])
"""
conv = _array_converter(arr)
arr, = conv.as_arrays(subok=False)
ndim = arr.ndim
arrorder = 'F' if arr.flags.fnc else 'C'
if axis is None:
if ndim != 1:
arr = arr.ravel()
# needed for np.matrix, which is still not 1d after being ravelled
ndim = arr.ndim
axis = ndim - 1
else:
axis = normalize_axis_index(axis, ndim)
slobj = [slice(None)]*ndim
N = arr.shape[axis]
newshape = list(arr.shape)
if isinstance(obj, slice):
start, stop, step = obj.indices(N)
xr = range(start, stop, step)
numtodel = len(xr)
if numtodel <= 0:
return conv.wrap(arr.copy(order=arrorder), to_scalar=False)
# Invert if step is negative:
if step < 0:
step = -step
start = xr[-1]
stop = xr[0] + 1
newshape[axis] -= numtodel
new = empty(newshape, arr.dtype, arrorder)
# copy initial chunk
if start == 0:
pass
else:
slobj[axis] = slice(None, start)
new[tuple(slobj)] = arr[tuple(slobj)]
# copy end chunk
if stop == N:
pass
else:
slobj[axis] = slice(stop-numtodel, None)
slobj2 = [slice(None)]*ndim
slobj2[axis] = slice(stop, None)
new[tuple(slobj)] = arr[tuple(slobj2)]
# copy middle pieces
if step == 1:
pass
else: # use array indexing.
keep = ones(stop-start, dtype=bool)
keep[:stop-start:step] = False
slobj[axis] = slice(start, stop-numtodel)
slobj2 = [slice(None)]*ndim
slobj2[axis] = slice(start, stop)
arr = arr[tuple(slobj2)]
slobj2[axis] = keep
new[tuple(slobj)] = arr[tuple(slobj2)]
return conv.wrap(new, to_scalar=False)
if isinstance(obj, (int, integer)) and not isinstance(obj, bool):
single_value = True
else:
single_value = False
_obj = obj
obj = np.asarray(obj)
# `size == 0` to allow empty lists similar to indexing, but (as there)
# is really too generic:
if obj.size == 0 and not isinstance(_obj, np.ndarray):
obj = obj.astype(intp)
elif obj.size == 1 and obj.dtype.kind in "ui":
# For a size 1 integer array we can use the single-value path
# (most dtypes, except boolean, should just fail later).
obj = obj.item()
single_value = True
if single_value:
# optimization for a single value
if (obj < -N or obj >= N):
raise IndexError(
"index %i is out of bounds for axis %i with "
"size %i" % (obj, axis, N))
if (obj < 0):
obj += N
newshape[axis] -= 1
new = empty(newshape, arr.dtype, arrorder)
slobj[axis] = slice(None, obj)
new[tuple(slobj)] = arr[tuple(slobj)]
slobj[axis] = slice(obj, None)
slobj2 = [slice(None)]*ndim
slobj2[axis] = slice(obj+1, None)
new[tuple(slobj)] = arr[tuple(slobj2)]
else:
if obj.dtype == bool:
if obj.shape != (N,):
raise ValueError('boolean array argument obj to delete '
'must be one dimensional and match the axis '
'length of {}'.format(N))
# optimization, the other branch is slower
keep = ~obj
else:
keep = ones(N, dtype=bool)
keep[obj,] = False
slobj[axis] = keep
new = arr[tuple(slobj)]
return conv.wrap(new, to_scalar=False)
def _insert_dispatcher(arr, obj, values, axis=None):
return (arr, obj, values)
@array_function_dispatch(_insert_dispatcher)
def insert(arr, obj, values, axis=None):
"""
Insert values along the given axis before the given indices.
Parameters
----------
arr : array_like
Input array.
obj : int, slice or sequence of ints
Object that defines the index or indices before which `values` is
inserted.
.. versionadded:: 1.8.0
Support for multiple insertions when `obj` is a single scalar or a
sequence with one element (similar to calling insert multiple
times).
values : array_like
Values to insert into `arr`. If the type of `values` is different
from that of `arr`, `values` is converted to the type of `arr`.
`values` should be shaped so that ``arr[...,obj,...] = values``
is legal.
axis : int, optional
Axis along which to insert `values`. If `axis` is None then `arr`
is flattened first.
Returns
-------
out : ndarray
A copy of `arr` with `values` inserted. Note that `insert`
does not occur in-place: a new array is returned. If
`axis` is None, `out` is a flattened array.
See Also
--------
append : Append elements at the end of an array.
concatenate : Join a sequence of arrays along an existing axis.
delete : Delete elements from an array.
Notes
-----
Note that for higher dimensional inserts ``obj=0`` behaves very different
from ``obj=[0]`` just like ``arr[:,0,:] = values`` is different from
``arr[:,[0],:] = values``. This is because of the difference between basic
and advanced :ref:`indexing <basics.indexing>`.
Examples
--------
>>> import numpy as np
>>> a = np.arange(6).reshape(3, 2)
>>> a
array([[0, 1],
[2, 3],
[4, 5]])
>>> np.insert(a, 1, 6)
array([0, 6, 1, 2, 3, 4, 5])
>>> np.insert(a, 1, 6, axis=1)
array([[0, 6, 1],
[2, 6, 3],
[4, 6, 5]])
Difference between sequence and scalars,
showing how ``obj=[1]`` behaves different from ``obj=1``:
>>> np.insert(a, [1], [[7],[8],[9]], axis=1)
array([[0, 7, 1],
[2, 8, 3],
[4, 9, 5]])
>>> np.insert(a, 1, [[7],[8],[9]], axis=1)
array([[0, 7, 8, 9, 1],
[2, 7, 8, 9, 3],
[4, 7, 8, 9, 5]])
>>> np.array_equal(np.insert(a, 1, [7, 8, 9], axis=1),
... np.insert(a, [1], [[7],[8],[9]], axis=1))
True
>>> b = a.flatten()
>>> b
array([0, 1, 2, 3, 4, 5])
>>> np.insert(b, [2, 2], [6, 7])
array([0, 1, 6, 7, 2, 3, 4, 5])
>>> np.insert(b, slice(2, 4), [7, 8])
array([0, 1, 7, 2, 8, 3, 4, 5])
>>> np.insert(b, [2, 2], [7.13, False]) # type casting
array([0, 1, 7, 0, 2, 3, 4, 5])
>>> x = np.arange(8).reshape(2, 4)
>>> idx = (1, 3)
>>> np.insert(x, idx, 999, axis=1)
array([[ 0, 999, 1, 2, 999, 3],
[ 4, 999, 5, 6, 999, 7]])
"""
conv = _array_converter(arr)
arr, = conv.as_arrays(subok=False)
ndim = arr.ndim
arrorder = 'F' if arr.flags.fnc else 'C'
if axis is None:
if ndim != 1:
arr = arr.ravel()
# needed for np.matrix, which is still not 1d after being ravelled
ndim = arr.ndim
axis = ndim - 1
else:
axis = normalize_axis_index(axis, ndim)
slobj = [slice(None)]*ndim
N = arr.shape[axis]
newshape = list(arr.shape)
if isinstance(obj, slice):
# turn it into a range object
indices = arange(*obj.indices(N), dtype=intp)
else:
# need to copy obj, because indices will be changed in-place
indices = np.array(obj)
if indices.dtype == bool:
# See also delete
# 2012-10-11, NumPy 1.8
warnings.warn(
"in the future insert will treat boolean arrays and "
"array-likes as a boolean index instead of casting it to "
"integer", FutureWarning, stacklevel=2)
indices = indices.astype(intp)
# Code after warning period:
#if obj.ndim != 1:
# raise ValueError('boolean array argument obj to insert '
# 'must be one dimensional')
#indices = np.flatnonzero(obj)
elif indices.ndim > 1:
raise ValueError(
"index array argument obj to insert must be one dimensional "
"or scalar")
if indices.size == 1:
index = indices.item()
if index < -N or index > N:
raise IndexError(f"index {obj} is out of bounds for axis {axis} "
f"with size {N}")
if (index < 0):
index += N
# There are some object array corner cases here, but we cannot avoid
# that:
values = array(values, copy=None, ndmin=arr.ndim, dtype=arr.dtype)
if indices.ndim == 0:
# broadcasting is very different here, since a[:,0,:] = ... behaves
# very different from a[:,[0],:] = ...! This changes values so that
# it works likes the second case. (here a[:,0:1,:])
values = np.moveaxis(values, 0, axis)
numnew = values.shape[axis]
newshape[axis] += numnew
new = empty(newshape, arr.dtype, arrorder)
slobj[axis] = slice(None, index)
new[tuple(slobj)] = arr[tuple(slobj)]
slobj[axis] = slice(index, index+numnew)
new[tuple(slobj)] = values
slobj[axis] = slice(index+numnew, None)
slobj2 = [slice(None)] * ndim
slobj2[axis] = slice(index, None)
new[tuple(slobj)] = arr[tuple(slobj2)]
return conv.wrap(new, to_scalar=False)
elif indices.size == 0 and not isinstance(obj, np.ndarray):
# Can safely cast the empty list to intp
indices = indices.astype(intp)
indices[indices < 0] += N
numnew = len(indices)
order = indices.argsort(kind='mergesort') # stable sort
indices[order] += np.arange(numnew)
newshape[axis] += numnew
old_mask = ones(newshape[axis], dtype=bool)
old_mask[indices] = False
new = empty(newshape, arr.dtype, arrorder)
slobj2 = [slice(None)]*ndim
slobj[axis] = indices
slobj2[axis] = old_mask
new[tuple(slobj)] = values
new[tuple(slobj2)] = arr
return conv.wrap(new, to_scalar=False)
def _append_dispatcher(arr, values, axis=None):
return (arr, values)
@array_function_dispatch(_append_dispatcher)
def append(arr, values, axis=None):
"""
Append values to the end of an array.
Parameters
----------
arr : array_like
Values are appended to a copy of this array.
values : array_like
These values are appended to a copy of `arr`. It must be of the
correct shape (the same shape as `arr`, excluding `axis`). If
`axis` is not specified, `values` can be any shape and will be
flattened before use.
axis : int, optional
The axis along which `values` are appended. If `axis` is not
given, both `arr` and `values` are flattened before use.
Returns
-------
append : ndarray
A copy of `arr` with `values` appended to `axis`. Note that
`append` does not occur in-place: a new array is allocated and
filled. If `axis` is None, `out` is a flattened array.
See Also
--------
insert : Insert elements into an array.
delete : Delete elements from an array.
Examples
--------
>>> import numpy as np
>>> np.append([1, 2, 3], [[4, 5, 6], [7, 8, 9]])
array([1, 2, 3, ..., 7, 8, 9])
When `axis` is specified, `values` must have the correct shape.
>>> np.append([[1, 2, 3], [4, 5, 6]], [[7, 8, 9]], axis=0)
array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
>>> np.append([[1, 2, 3], [4, 5, 6]], [7, 8, 9], axis=0)
Traceback (most recent call last):
...
ValueError: all the input arrays must have same number of dimensions, but
the array at index 0 has 2 dimension(s) and the array at index 1 has 1
dimension(s)
>>> a = np.array([1, 2], dtype=int)
>>> c = np.append(a, [])
>>> c
array([1., 2.])
>>> c.dtype
float64
Default dtype for empty ndarrays is `float64` thus making the output of dtype
`float64` when appended with dtype `int64`
"""
arr = asanyarray(arr)
if axis is None:
if arr.ndim != 1:
arr = arr.ravel()
values = ravel(values)
axis = arr.ndim-1
return concatenate((arr, values), axis=axis)
def _digitize_dispatcher(x, bins, right=None):
return (x, bins)
@array_function_dispatch(_digitize_dispatcher)
def digitize(x, bins, right=False):
"""
Return the indices of the bins to which each value in input array belongs.
========= ============= ============================
`right` order of bins returned index `i` satisfies
========= ============= ============================
``False`` increasing ``bins[i-1] <= x < bins[i]``
``True`` increasing ``bins[i-1] < x <= bins[i]``
``False`` decreasing ``bins[i-1] > x >= bins[i]``
``True`` decreasing ``bins[i-1] >= x > bins[i]``
========= ============= ============================
If values in `x` are beyond the bounds of `bins`, 0 or ``len(bins)`` is
returned as appropriate.
Parameters
----------
x : array_like
Input array to be binned. Prior to NumPy 1.10.0, this array had to
be 1-dimensional, but can now have any shape.
bins : array_like
Array of bins. It has to be 1-dimensional and monotonic.
right : bool, optional
Indicating whether the intervals include the right or the left bin
edge. Default behavior is (right==False) indicating that the interval
does not include the right edge. The left bin end is open in this
case, i.e., bins[i-1] <= x < bins[i] is the default behavior for
monotonically increasing bins.
Returns
-------
indices : ndarray of ints
Output array of indices, of same shape as `x`.
Raises
------
ValueError
If `bins` is not monotonic.
TypeError
If the type of the input is complex.
See Also
--------
bincount, histogram, unique, searchsorted
Notes
-----
If values in `x` are such that they fall outside the bin range,
attempting to index `bins` with the indices that `digitize` returns
will result in an IndexError.
.. versionadded:: 1.10.0
`numpy.digitize` is implemented in terms of `numpy.searchsorted`.
This means that a binary search is used to bin the values, which scales
much better for larger number of bins than the previous linear search.
It also removes the requirement for the input array to be 1-dimensional.
For monotonically *increasing* `bins`, the following are equivalent::
np.digitize(x, bins, right=True)
np.searchsorted(bins, x, side='left')
Note that as the order of the arguments are reversed, the side must be too.
The `searchsorted` call is marginally faster, as it does not do any
monotonicity checks. Perhaps more importantly, it supports all dtypes.
Examples
--------
>>> import numpy as np
>>> x = np.array([0.2, 6.4, 3.0, 1.6])
>>> bins = np.array([0.0, 1.0, 2.5, 4.0, 10.0])
>>> inds = np.digitize(x, bins)
>>> inds
array([1, 4, 3, 2])
>>> for n in range(x.size):
... print(bins[inds[n]-1], "<=", x[n], "<", bins[inds[n]])
...
0.0 <= 0.2 < 1.0
4.0 <= 6.4 < 10.0
2.5 <= 3.0 < 4.0
1.0 <= 1.6 < 2.5
>>> x = np.array([1.2, 10.0, 12.4, 15.5, 20.])
>>> bins = np.array([0, 5, 10, 15, 20])
>>> np.digitize(x,bins,right=True)
array([1, 2, 3, 4, 4])
>>> np.digitize(x,bins,right=False)
array([1, 3, 3, 4, 5])
"""
x = _nx.asarray(x)
bins = _nx.asarray(bins)
# here for compatibility, searchsorted below is happy to take this
if np.issubdtype(x.dtype, _nx.complexfloating):
raise TypeError("x may not be complex")
mono = _monotonicity(bins)
if mono == 0:
raise ValueError("bins must be monotonically increasing or decreasing")
# this is backwards because the arguments below are swapped
side = 'left' if right else 'right'
if mono == -1:
# reverse the bins, and invert the results
return len(bins) - _nx.searchsorted(bins[::-1], x, side=side)
else:
return _nx.searchsorted(bins, x, side=side)