def logn(n, x):
"""Take log base n of x.
If x contains negative inputs, the answer is computed and returned in the
complex domain.
Parameters
----------
x : array_like
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)
>>> logn(2,[4,8])
array([ 2., 3.])
>>> logn(2,[-4,-8,8])
array([ 2.+4.5324j, 3.+4.5324j, 3.+0.j ])
"""
x = _fix_real_lt_zero(x)
n = _fix_real_lt_zero(n)
return nx.log(x)/nx.log(n)
def log2(x, y=None):
"""
Return the base 2 logarithm of the input array, element-wise.
Parameters
----------
x : array_like
Input array.
y : array_like
Optional output array with the same shape as `x`.
Returns
-------
y : ndarray
The logarithm to the base 2 of `x` element-wise.
NaNs are returned where `x` is negative.
See Also
--------
log, log1p, log10
Examples
--------
>>> np.log2([-1, 2, 4])
array([ NaN, 1., 2.])
"""
x = nx.asanyarray(x)
if y is None:
y = nx.log(x)
else:
nx.log(x, y)
y /= _log2
return y
def logn(n, x):
"""
Take log base n of x.
If `x` contains negative inputs, the answer is computed and returned in the
complex domain.
Parameters
----------
n : array_like
The integer base(s) in which the log is taken.
x : array_like
The value(s) whose log base `n` is (are) required.
Returns
-------
out : ndarray or scalar
The log base `n` of the `x` value(s). If `x` was a scalar, so is
`out`, otherwise an array is returned.
Examples
--------
>>> np.set_printoptions(precision=4)
>>> np.lib.scimath.logn(2, [4, 8])
array([2., 3.])
>>> np.lib.scimath.logn(2, [-4, -8, 8])
array([2.+4.5324j, 3.+4.5324j, 3.+0.j ])
"""
x = _fix_real_lt_zero(x)
n = _fix_real_lt_zero(n)
return nx.log(x)/nx.log(n)
def log2(x, y=None):
"""
Return the base 2 logarithm.
Parameters
----------
x : array_like
Input array.
y : array_like
Optional output array with the same shape as `x`.
Returns
-------
y : ndarray
The logarithm to the base 2 of `x` elementwise.
NaNs are returned where `x` is negative.
See Also
--------
log, log1p, log10
Examples
--------
>>> np.log2([-1,2,4])
array([ NaN, 1., 2.])
"""
x = nx.asanyarray(x)
if y is None:
y = nx.log(x)
else:
nx.log(x, y)
y /= _log2
return y
def logn(n, x):
"""
Take log base n of x.
If `x` contains negative inputs, the answer is computed and returned in the
complex domain.
Parameters
----------
n : int
The base in which the log is taken.
x : array_like
The value(s) whose log base `n` is (are) required.
Returns
-------
out : ndarray or scalar
The log base `n` of the `x` value(s). If `x` was a scalar, so is
`out`, otherwise an array is returned.
Examples
--------
>>> np.set_printoptions(precision=4)
>>> np.lib.scimath.logn(2, [4, 8])
array([ 2., 3.])
>>> np.lib.scimath.logn(2, [-4, -8, 8])
array([ 2.+4.5324j, 3.+4.5324j, 3.+0.j ])
"""
x = _fix_real_lt_zero(x)
n = _fix_real_lt_zero(n)
return nx.log(x)/nx.log(n)
def logn(n, x):
"""Take log base n of x.
If x contains negative inputs, the answer is computed and returned in the
complex domain.
Parameters
----------
x : array_like
Returns
-------
array_like
Examples
--------
(We set the printing precision so the example can be auto-tested)
>>> np.set_printoptions(precision=4)
>>> np.lib.scimath.logn(2,[4,8])
array([ 2., 3.])
>>> np.lib.scimath.logn(2,[-4,-8,8])
array([ 2.+4.5324j, 3.+4.5324j, 3.+0.j ])
"""
x = _fix_real_lt_zero(x)
n = _fix_real_lt_zero(n)
return nx.log(x)/nx.log(n)
def log2(x):
""" Take log base 2 of x.
If x contains negative inputs, the answer is computed and returned in the
complex domain.
Parameters
----------
x : array_like
Returns
-------
array_like
Examples
--------
(We set the printing precision so the example can be auto-tested)
>>> np.set_printoptions(precision=4)
>>> np.lib.scimath.log2([4,8])
array([ 2., 3.])
>>> np.lib.scimath.log2([-4,-8,8])
array([ 2.+4.5324j, 3.+4.5324j, 3.+0.j ])
"""
x = _fix_real_lt_zero(x)
return nx.log(x) / _ln2
def log(x):
"""Return the natural logarithm of x.
If x contains negative inputs, the answer is computed and returned in the
complex domain.
Parameters
----------
x : array_like
Returns
-------
array_like
Examples
--------
>>> import math
>>> np.lib.scimath.log(math.exp(1))
1.0
Negative arguments are correctly handled (recall that for negative
arguments, the identity exp(log(z))==z does not hold anymore):
>>> np.lib.scimath.log(-math.exp(1)) == (1+1j*math.pi)
True
"""
x = _fix_real_lt_zero(x)
return nx.log(x)
def log(x):
"""Return the natural logarithm of x.
If x contains negative inputs, the answer is computed and returned in the
complex domain.
Parameters
----------
x : array_like
Returns
-------
array_like
Examples
--------
>>> import math
>>> log(math.exp(1))
1.0
Negative arguments are correctly handled (recall that for negative
arguments, the identity exp(log(z))==z does not hold anymore):
>>> log(-math.exp(1)) == (1+1j*math.pi)
True
"""
x = _fix_real_lt_zero(x)
return nx.log(x)
def log2(x):
""" Take log base 2 of x.
If x contains negative inputs, the answer is computed and returned in the
complex domain.
Parameters
----------
x : array_like
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)
>>> log2([4,8])
array([ 2., 3.])
>>> log2([-4,-8,8])
array([ 2.+4.5324j, 3.+4.5324j, 3.+0.j ])
"""
x = _fix_real_lt_zero(x)
return nx.log(x)/_ln2
def log2(x, y=None):
"""
Return the base 2 logarithm of the input array, element-wise.
This function is now deprecated, use the np.log2 ufunc instead.
Parameters
----------
x : array_like
Input array.
y : array_like
Optional output array with the same shape as `x`.
Returns
-------
y : ndarray
The logarithm to the base 2 of `x` element-wise.
NaNs are returned where `x` is negative.
See Also
--------
log, log1p, log10
Examples
--------
>>> np.log2([-1, 2, 4])
array([ NaN, 1., 2.])
"""
import warnings
msg = "numpy.lib.log2 is deprecated, use np.log2 instead."
warnings.warn(msg, DeprecationWarning)
x = nx.asanyarray(x)
if y is None:
y = nx.log(x)
else:
nx.log(x, y)
y /= _log2
return y
def log2(x, y=None):
"""
Return the base 2 logarithm of the input array, element-wise.
This function is now deprecated, use the np.log2 ufunc instead.
Parameters
----------
x : array_like
Input array.
y : array_like
Optional output array with the same shape as `x`.
Returns
-------
y : ndarray
The logarithm to the base 2 of `x` element-wise.
NaNs are returned where `x` is negative.
See Also
--------
log, log1p, log10
Examples
--------
>>> np.log2([-1, 2, 4])
array([ NaN, 1., 2.])
"""
import warnings
msg = "numpy.lib.log2 is deprecated, use np.log2 instead."
warnings.warn(msg, DeprecationWarning)
x = nx.asanyarray(x)
if y is None:
y = nx.log(x)
else:
nx.log(x, y)
y /= _log2
return y
def log(x):
"""
Compute the natural logarithm of `x`.
Return the "principal value" (for a description of this, see `numpy.log`)
of :math:`log_e(x)`. For real `x > 0`, this is a real number (``log(0)``
returns ``-inf`` and ``log(np.inf)`` returns ``inf``). Otherwise, the
complex principle value is returned.
Parameters
----------
x : array_like
The value(s) whose log is (are) required.
Returns
-------
out : ndarray or scalar
The log of the `x` value(s). If `x` was a scalar, so is `out`,
otherwise an array is returned.
See Also
--------
numpy.log
Notes
-----
For a log() that returns ``NAN`` when real `x < 0`, use `numpy.log`
(note, however, that otherwise `numpy.log` and this `log` are identical,
i.e., both return ``-inf`` for `x = 0`, ``inf`` for `x = inf`, and,
notably, the complex principle value if ``x.imag != 0``).
Examples
--------
>>> np.emath.log(np.exp(1))
1.0
Negative arguments are handled "correctly" (recall that
``exp(log(x)) == x`` does *not* hold for real ``x < 0``):
>>> np.emath.log(-np.exp(1)) == (1 + np.pi * 1j)
True
"""
x = _fix_real_lt_zero(x)
return nx.log(x)
def log(x):
"""
Compute the natural logarithm of `x`.
Return the "principal value" (for a description of this, see `numpy.log`)
of :math:`log_e(x)`. For real `x > 0`, this is a real number (``log(0)``
returns ``-inf`` and ``log(np.inf)`` returns ``inf``). Otherwise, the
complex principle value is returned.
Parameters
----------
x : array_like
The value(s) whose log is (are) required.
Returns
-------
out : ndarray or scalar
The log of the `x` value(s). If `x` was a scalar, so is `out`,
otherwise an array is returned.
See Also
--------
numpy.log
Notes
-----
For a log() that returns ``NAN`` when real `x < 0`, use `numpy.log`
(note, however, that otherwise `numpy.log` and this `log` are identical,
i.e., both return ``-inf`` for `x = 0`, ``inf`` for `x = inf`, and,
notably, the complex principle value if ``x.imag != 0``).
Examples
--------
>>> np.emath.log(np.exp(1))
1.0
Negative arguments are handled "correctly" (recall that
``exp(log(x)) == x`` does *not* hold for real ``x < 0``):
>>> np.emath.log(-np.exp(1)) == (1 + np.pi * 1j)
True
"""
x = _fix_real_lt_zero(x)
return nx.log(x)
def log9(x):
x = _fix_real_lt_zero(x)
n = _fix_real_lt_zero(9)
return nx.log(x) / nx.log(n)
"""
import numpy.core.numeric as nx
import numpy.core.numerictypes as nt
from numpy.core.numeric import asarray, any
from numpy.core.overrides import array_function_dispatch
from numpy.lib.type_check import isreal
__all__ = [
'sqrt', 'log', 'log2', 'logn', 'log10', 'power', 'arccos', 'arcsin',
'arctanh'
]
_ln2 = nx.log(2.0)
def _tocomplex(arr):
"""Convert its input `arr` to a complex array.
The input is returned as a complex array of the smallest type that will fit
the original data: types like single, byte, short, etc. become csingle,
while others become cdouble.
A copy of the input is always made.
Parameters
----------
arr : array
>>> x = np.array([-np.inf, 0., np.inf])
>>> y = np.array([2, 2, 2])
>>> np.isneginf(x, y)
array([1, 0, 0])
>>> y
array([1, 0, 0])
"""
if y is None:
x = nx.asarray(x)
y = nx.empty(x.shape, dtype=nx.bool_)
nx.logical_and(nx.isinf(x), nx.signbit(x), y)
return y
_log2 = nx.log(2)
def log2(x, y=None):
"""
Return the base 2 logarithm of the input array, element-wise.
This function is now deprecated, use the np.log2 ufunc instead.
Parameters
----------
x : array_like
Input array.
y : array_like
Optional output array with the same shape as `x`.
Returns
def logn(n, x):
""" Take log base n of x.
"""
x = _fix_real_lt_zero(x)
n = _fix_real_lt_zero(n)
return nx.log(x) / nx.log(n)
>>> x = np.array([-np.inf, 0., np.inf])
>>> y = np.array([2, 2, 2])
>>> np.isneginf(x, y)
array([1, 0, 0])
>>> y
array([1, 0, 0])
"""
if y is None:
x = nx.asarray(x)
y = nx.empty(x.shape, dtype=nx.bool_)
nx.logical_and(nx.isinf(x), nx.signbit(x), y)
return y
_log2 = nx.log(2)
def log2(x, y=None):
"""
Return the base 2 logarithm of the input array, element-wise.
This function is now deprecated, use the np.log2 ufunc instead.
Parameters
----------
x : array_like
Input array.
y : array_like
Optional output array with the same shape as `x`.
Returns
-------
y : ndarray
def log2(x):
""" Take log base 2 of x.
"""
x = _fix_real_lt_zero(x)
return nx.log(x) / _ln2
def logn(n, x):
""" Take log base n of x.
"""
x = _fix_real_lt_zero(x)
n = _fix_real_lt_zero(n)
return nx.log(x)/nx.log(n)
def log(x):
x = _fix_real_lt_zero(x)
return nx.log(x)
Similarly, `sqrt`, other base logarithms, `power` and trig functions are
correctly handled. See their respective docstrings for specific examples.
"""
from __future__ import division
__all__ = ['sqrt', 'log', 'log2', 'logn','log10', 'power', 'arccos',
'arcsin', 'arctanh']
import numpy.core.numeric as nx
import numpy.core.numerictypes as nt
from numpy.core.numeric import asarray, any
from numpy.lib.type_check import isreal
_ln2 = nx.log(2.0)
def _tocomplex(arr):
"""Convert its input `arr` to a complex array.
The input is returned as a complex array of the smallest type that will fit
the original data: types like single, byte, short, etc. become csingle,
while others become cdouble.
A copy of the input is always made.
Parameters
----------
arr : array
Returns
def log(x):
x = _fix_real_lt_zero(x)
return nx.log(x)
def log2(x):
""" Take log base 2 of x.
"""
x = _fix_real_lt_zero(x)
return nx.log(x)/_ln2
