def power(x, p):
"""Return x**p.
If x contains negative values, it is converted to the complex domain.
If p contains negative values, it is converted to floating point.
Parameters
----------
x : array_like
p : array_like of integers
Returns
-------
array_like
Examples
--------
(We set the printing precision so the example can be auto-tested)
>>> np.set_printoptions(precision=4)
>>> np.lib.scimath.power([2,4],2)
array([ 4, 16])
>>> np.lib.scimath.power([2,4],-2)
array([ 0.25 , 0.0625])
>>> np.lib.scimath.power([-2,4],2)
array([ 4.+0.j, 16.+0.j])
"""
x = _fix_real_lt_zero(x)
p = _fix_int_lt_zero(p)
return nx.power(x, p)
def power(x, p):
"""Return x**p.
If x contains negative values, it is converted to the complex domain.
If p contains negative values, it is converted to floating point.
Parameters
----------
x : array_like
p : array_like of integers
Returns
-------
array_like
Examples
--------
(We set the printing precision so the example can be auto-tested)
>>> import numpy as np; np.set_printoptions(precision=4)
>>> power([2,4],2)
array([ 4, 16])
>>> power([2,4],-2)
array([ 0.25 , 0.0625])
>>> power([-2,4],2)
array([ 4.+0.j, 16.+0.j])
"""
x = _fix_real_lt_zero(x)
p = _fix_int_lt_zero(p)
return nx.power(x, p)
def logspace(start, stop, num=50, endpoint=True, base=10.0):
"""Evenly spaced numbers on a logarithmic scale.
Computes int(num) evenly spaced exponents from base**start to
base**stop. If endpoint=True, then last number is base**stop
"""
y = linspace(start, stop, num=num, endpoint=endpoint)
return _nx.power(base, y)
def logspace(start,stop,num=50,endpoint=True,base=10.0):
"""Evenly spaced numbers on a logarithmic scale.
Computes int(num) evenly spaced exponents from base**start to
base**stop. If endpoint=True, then last number is base**stop
"""
y = linspace(start,stop,num=num,endpoint=endpoint)
return _nx.power(base,y)
def power(x, p):
"""
Return x to the power p, (x**p).
If `x` contains negative values, the output is converted to the
complex domain.
Parameters
----------
x : array_like
The input value(s).
p : array_like of ints
The power(s) to which `x` is raised. If `x` contains multiple values,
`p` has to either be a scalar, or contain the same number of values
as `x`. In the latter case, the result is
``x[0]**p[0], x[1]**p[1], ...``.
Returns
-------
out : ndarray or scalar
The result of ``x**p``. If `x` and `p` are scalars, so is `out`,
otherwise an array is returned.
See Also
--------
numpy.power
Examples
--------
>>> np.set_printoptions(precision=4)
>>> np.lib.scimath.power([2, 4], 2)
array([ 4, 16])
>>> np.lib.scimath.power([2, 4], -2)
array([0.25 , 0.0625])
>>> np.lib.scimath.power([-2, 4], 2)
array([ 4.-0.j, 16.+0.j])
"""
x = _fix_real_lt_zero(x)
p = _fix_int_lt_zero(p)
return nx.power(x, p)
def power(x, p):
"""
Return x to the power p, (x**p).
If `x` contains negative values, the output is converted to the
complex domain.
Parameters
----------
x : array_like
The input value(s).
p : array_like of ints
The power(s) to which `x` is raised. If `x` contains multiple values,
`p` has to either be a scalar, or contain the same number of values
as `x`. In the latter case, the result is
``x[0]**p[0], x[1]**p[1], ...``.
Returns
-------
out : ndarray or scalar
The result of ``x**p``. If `x` and `p` are scalars, so is `out`,
otherwise an array is returned.
See Also
--------
numpy.power
Examples
--------
>>> np.set_printoptions(precision=4)
>>> np.lib.scimath.power([2, 4], 2)
array([ 4, 16])
>>> np.lib.scimath.power([2, 4], -2)
array([ 0.25 , 0.0625])
>>> np.lib.scimath.power([-2, 4], 2)
array([ 4.+0.j, 16.+0.j])
"""
x = _fix_real_lt_zero(x)
p = _fix_int_lt_zero(p)
return nx.power(x, p)
def power(x, p):
x = _fix_real_lt_zero(x)
p = _fix_int_lt_zero(p)
return nx.power(x, p)
def power(x, p):
x = _fix_real_lt_zero(x)
p = _fix_int_lt_zero(p)
return nx.power(x, p)