Python numpy.core.numeric.log() Examples
The following are 30
code examples of numpy.core.numeric.log().
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Example #1
| Source File: scimath.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
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)
Example #2
| Source File: scimath.py From pySINDy with MIT License | 5 votes |
|
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)
Example #3
| Source File: scimath.py From lambda-packs with MIT License | 5 votes |
|
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)
Example #4
| Source File: scimath.py From twitter-stock-recommendation with MIT License | 5 votes |
|
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)
Example #5
| Source File: scimath.py From lambda-packs with MIT License | 5 votes |
|
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)
Example #6
| Source File: scimath.py From keras-lambda with MIT License | 5 votes |
|
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)
Example #7
| Source File: scimath.py From recruit with Apache License 2.0 | 5 votes |
|
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)
Example #8
| Source File: scimath.py From ImageFusion with MIT License | 5 votes |
|
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)
Example #9
| Source File: scimath.py From auto-alt-text-lambda-api with MIT License | 5 votes |
|
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)
Example #10
| Source File: scimath.py From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License | 5 votes |
|
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)
Example #11
| Source File: scimath.py From vnpy_crypto with MIT License | 5 votes |
|
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)
Example #12
| Source File: scimath.py From Carnets with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
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)
Example #13
| Source File: scimath.py From Splunking-Crime with GNU Affero General Public License v3.0 | 5 votes |
|
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)
Example #14
| Source File: scimath.py From Computable with MIT License | 5 votes |
|
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)
Example #15
| Source File: scimath.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
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)
Example #16
| Source File: scimath.py From coffeegrindsize with MIT License | 5 votes |
|
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)
Example #17
| Source File: scimath.py From Mastering-Elasticsearch-7.0 with MIT License | 5 votes |
|
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)
Example #18
| Source File: scimath.py From Fluid-Designer with GNU General Public License v3.0 | 5 votes |
|
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)
Example #19
| Source File: scimath.py From elasticintel with GNU General Public License v3.0 | 5 votes |
|
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)
Example #20
| Source File: scimath.py From mxnet-lambda with Apache License 2.0 | 5 votes |
|
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)
Example #21
| Source File: scimath.py From mxnet-lambda with Apache License 2.0 | 4 votes |
|
def log2(x):
"""
Compute the logarithm base 2 of `x`.
Return the "principal value" (for a description of this, see
`numpy.log2`) of :math:`log_2(x)`. For real `x > 0`, this is
a real number (``log2(0)`` returns ``-inf`` and ``log2(np.inf)`` returns
``inf``). Otherwise, the complex principle value is returned.
Parameters
----------
x : array_like
The value(s) whose log base 2 is (are) required.
Returns
-------
out : ndarray or scalar
The log base 2 of the `x` value(s). If `x` was a scalar, so is `out`,
otherwise an array is returned.
See Also
--------
numpy.log2
Notes
-----
For a log2() that returns ``NAN`` when real `x < 0`, use `numpy.log2`
(note, however, that otherwise `numpy.log2` and this `log2` 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
--------
We set the printing precision so the example can be auto-tested:
>>> np.set_printoptions(precision=4)
>>> np.emath.log2(8)
3.0
>>> np.emath.log2([-4, -8, 8])
array([ 2.+4.5324j, 3.+4.5324j, 3.+0.j ])
"""
x = _fix_real_lt_zero(x)
return nx.log2(x)
Example #22
| Source File: scimath.py From Splunking-Crime with GNU Affero General Public License v3.0 | 4 votes |
|
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)
Example #23
| Source File: scimath.py From Splunking-Crime with GNU Affero General Public License v3.0 | 4 votes |
|
def log2(x):
"""
Compute the logarithm base 2 of `x`.
Return the "principal value" (for a description of this, see
`numpy.log2`) of :math:`log_2(x)`. For real `x > 0`, this is
a real number (``log2(0)`` returns ``-inf`` and ``log2(np.inf)`` returns
``inf``). Otherwise, the complex principle value is returned.
Parameters
----------
x : array_like
The value(s) whose log base 2 is (are) required.
Returns
-------
out : ndarray or scalar
The log base 2 of the `x` value(s). If `x` was a scalar, so is `out`,
otherwise an array is returned.
See Also
--------
numpy.log2
Notes
-----
For a log2() that returns ``NAN`` when real `x < 0`, use `numpy.log2`
(note, however, that otherwise `numpy.log2` and this `log2` 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
--------
We set the printing precision so the example can be auto-tested:
>>> np.set_printoptions(precision=4)
>>> np.emath.log2(8)
3.0
>>> np.emath.log2([-4, -8, 8])
array([ 2.+4.5324j, 3.+4.5324j, 3.+0.j ])
"""
x = _fix_real_lt_zero(x)
return nx.log2(x)
Example #24
| Source File: scimath.py From ImageFusion with MIT License | 4 votes |
|
def log10(x):
"""
Compute the logarithm base 10 of `x`.
Return the "principal value" (for a description of this, see
`numpy.log10`) of :math:`log_{10}(x)`. For real `x > 0`, this
is a real number (``log10(0)`` returns ``-inf`` and ``log10(np.inf)``
returns ``inf``). Otherwise, the complex principle value is returned.
Parameters
----------
x : array_like or scalar
The value(s) whose log base 10 is (are) required.
Returns
-------
out : ndarray or scalar
The log base 10 of the `x` value(s). If `x` was a scalar, so is `out`,
otherwise an array object is returned.
See Also
--------
numpy.log10
Notes
-----
For a log10() that returns ``NAN`` when real `x < 0`, use `numpy.log10`
(note, however, that otherwise `numpy.log10` and this `log10` 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
--------
(We set the printing precision so the example can be auto-tested)
>>> np.set_printoptions(precision=4)
>>> np.emath.log10(10**1)
1.0
>>> np.emath.log10([-10**1, -10**2, 10**2])
array([ 1.+1.3644j, 2.+1.3644j, 2.+0.j ])
"""
x = _fix_real_lt_zero(x)
return nx.log10(x)
Example #25
| Source File: scimath.py From ImageFusion with MIT License | 4 votes |
|
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)
Example #26
| Source File: scimath.py From elasticintel with GNU General Public License v3.0 | 4 votes |
|
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)
Example #27
| Source File: scimath.py From elasticintel with GNU General Public License v3.0 | 4 votes |
|
def log10(x):
"""
Compute the logarithm base 10 of `x`.
Return the "principal value" (for a description of this, see
`numpy.log10`) of :math:`log_{10}(x)`. For real `x > 0`, this
is a real number (``log10(0)`` returns ``-inf`` and ``log10(np.inf)``
returns ``inf``). Otherwise, the complex principle value is returned.
Parameters
----------
x : array_like or scalar
The value(s) whose log base 10 is (are) required.
Returns
-------
out : ndarray or scalar
The log base 10 of the `x` value(s). If `x` was a scalar, so is `out`,
otherwise an array object is returned.
See Also
--------
numpy.log10
Notes
-----
For a log10() that returns ``NAN`` when real `x < 0`, use `numpy.log10`
(note, however, that otherwise `numpy.log10` and this `log10` 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
--------
(We set the printing precision so the example can be auto-tested)
>>> np.set_printoptions(precision=4)
>>> np.emath.log10(10**1)
1.0
>>> np.emath.log10([-10**1, -10**2, 10**2])
array([ 1.+1.3644j, 2.+1.3644j, 2.+0.j ])
"""
x = _fix_real_lt_zero(x)
return nx.log10(x)
Example #28
| Source File: scimath.py From ImageFusion with MIT License | 4 votes |
|
def log2(x):
"""
Compute the logarithm base 2 of `x`.
Return the "principal value" (for a description of this, see
`numpy.log2`) of :math:`log_2(x)`. For real `x > 0`, this is
a real number (``log2(0)`` returns ``-inf`` and ``log2(np.inf)`` returns
``inf``). Otherwise, the complex principle value is returned.
Parameters
----------
x : array_like
The value(s) whose log base 2 is (are) required.
Returns
-------
out : ndarray or scalar
The log base 2 of the `x` value(s). If `x` was a scalar, so is `out`,
otherwise an array is returned.
See Also
--------
numpy.log2
Notes
-----
For a log2() that returns ``NAN`` when real `x < 0`, use `numpy.log2`
(note, however, that otherwise `numpy.log2` and this `log2` 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
--------
We set the printing precision so the example can be auto-tested:
>>> np.set_printoptions(precision=4)
>>> np.emath.log2(8)
3.0
>>> np.emath.log2([-4, -8, 8])
array([ 2.+4.5324j, 3.+4.5324j, 3.+0.j ])
"""
x = _fix_real_lt_zero(x)
return nx.log2(x)
Example #29
| Source File: scimath.py From Splunking-Crime with GNU Affero General Public License v3.0 | 4 votes |
|
def log10(x):
"""
Compute the logarithm base 10 of `x`.
Return the "principal value" (for a description of this, see
`numpy.log10`) of :math:`log_{10}(x)`. For real `x > 0`, this
is a real number (``log10(0)`` returns ``-inf`` and ``log10(np.inf)``
returns ``inf``). Otherwise, the complex principle value is returned.
Parameters
----------
x : array_like or scalar
The value(s) whose log base 10 is (are) required.
Returns
-------
out : ndarray or scalar
The log base 10 of the `x` value(s). If `x` was a scalar, so is `out`,
otherwise an array object is returned.
See Also
--------
numpy.log10
Notes
-----
For a log10() that returns ``NAN`` when real `x < 0`, use `numpy.log10`
(note, however, that otherwise `numpy.log10` and this `log10` 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
--------
(We set the printing precision so the example can be auto-tested)
>>> np.set_printoptions(precision=4)
>>> np.emath.log10(10**1)
1.0
>>> np.emath.log10([-10**1, -10**2, 10**2])
array([ 1.+1.3644j, 2.+1.3644j, 2.+0.j ])
"""
x = _fix_real_lt_zero(x)
return nx.log10(x)
Example #30
| Source File: scimath.py From coffeegrindsize with MIT License | 4 votes |
|
def log2(x):
"""
Compute the logarithm base 2 of `x`.
Return the "principal value" (for a description of this, see
`numpy.log2`) of :math:`log_2(x)`. For real `x > 0`, this is
a real number (``log2(0)`` returns ``-inf`` and ``log2(np.inf)`` returns
``inf``). Otherwise, the complex principle value is returned.
Parameters
----------
x : array_like
The value(s) whose log base 2 is (are) required.
Returns
-------
out : ndarray or scalar
The log base 2 of the `x` value(s). If `x` was a scalar, so is `out`,
otherwise an array is returned.
See Also
--------
numpy.log2
Notes
-----
For a log2() that returns ``NAN`` when real `x < 0`, use `numpy.log2`
(note, however, that otherwise `numpy.log2` and this `log2` 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
--------
We set the printing precision so the example can be auto-tested:
>>> np.set_printoptions(precision=4)
>>> np.emath.log2(8)
3.0
>>> np.emath.log2([-4, -8, 8])
array([ 2.+4.5324j, 3.+4.5324j, 3.+0.j ])
"""
x = _fix_real_lt_zero(x)
return nx.log2(x)
