/usr/lib64/python2.6/site-packages/numpy/lib
Edit: /usr/lib64/python2.6/site-packages/numpy/lib/scimath.pyo (15867B)
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Wrapper functions to more user-friendly calling of certain math functions
whose output data-type is different than the input data-type in certain
domains of the input.
For example, for functions like log() with branch cuts, the versions in this
module provide the mathematically valid answers in the complex plane:
>>> import math
>>> from numpy.lib import scimath
>>> scimath.log(-math.exp(1)) == (1+1j*math.pi)
True
Similarly, sqrt(), other base logarithms, power() and trig functions are
correctly handled. See their respective docstrings for specific examples.
t sqrtt logt log2t lognt log10t powert arccost arcsint arctanhiÿÿÿÿN( t asarrayt any( t isrealg @c C s_ t | i i t i t i t i t i t i t i f ƒ o | i
t i ƒ S| i
t i ƒ Sd S( sh 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
-------
array
An array with the same input data as the input but in complex form.
Examples
--------
First, consider an input of type short:
>>> a = np.array([1,2,3],np.short)
>>> ac = np.lib.scimath._tocomplex(a); ac
array([ 1.+0.j, 2.+0.j, 3.+0.j], dtype=complex64)
>>> ac.dtype
dtype('complex64')
If the input is of type double, the output is correspondingly of the
complex double type as well:
>>> b = np.array([1,2,3],np.double)
>>> bc = np.lib.scimath._tocomplex(b); bc
array([ 1.+0.j, 2.+0.j, 3.+0.j])
>>> bc.dtype
dtype('complex128')
Note that even if the input was complex to begin with, a copy is still
made, since the astype() method always copies:
>>> c = np.array([1,2,3],np.csingle)
>>> cc = np.lib.scimath._tocomplex(c); cc
array([ 1.+0.j, 2.+0.j, 3.+0.j], dtype=complex64)
>>> c *= 2; c
array([ 2.+0.j, 4.+0.j, 6.+0.j], dtype=complex64)
>>> cc
array([ 1.+0.j, 2.+0.j, 3.+0.j], dtype=complex64)
N( t
issubclasst dtypet typet ntt singlet bytet shortt ubytet ushortt csinglet astypet cdouble( t arr( ( s7 /usr/lib64/python2.6/site-packages/numpy/lib/scimath.pyt
_tocomplex s 8$c C s= t | ƒ } t t | ƒ | d j @ƒ o t | ƒ } n | S( s Convert `x` to complex if it has real, negative components.
Otherwise, output is just the array version of the input (via asarray).
Parameters
----------
x : array_like
Returns
-------
array
Examples
--------
>>> np.lib.scimath._fix_real_lt_zero([1,2])
array([1, 2])
>>> np.lib.scimath._fix_real_lt_zero([-1,2])
array([-1.+0.j, 2.+0.j])
i ( R R
R R ( t x( ( s7 /usr/lib64/python2.6/site-packages/numpy/lib/scimath.pyt _fix_real_lt_zeroZ s c C s; t | ƒ } t t | ƒ | d j @ƒ o | d } n | S( s„ Convert `x` to double if it has real, negative components.
Otherwise, output is just the array version of the input (via asarray).
Parameters
----------
x : array_like
Returns
-------
array
Examples
--------
>>> np.lib.scimath._fix_int_lt_zero([1,2])
array([1, 2])
>>> np.lib.scimath._fix_int_lt_zero([-1,2])
array([-1., 2.])
i g ð?( R R
R ( R ( ( s7 /usr/lib64/python2.6/site-packages/numpy/lib/scimath.pyt _fix_int_lt_zerot s c C sC t | ƒ } t t | ƒ t | ƒ d j @ƒ o t | ƒ } n | S( sš Convert `x` to complex if it has real components x_i with abs(x_i)>1.
Otherwise, output is just the array version of the input (via asarray).
Parameters
----------
x : array_like
Returns
-------
array
Examples
--------
>>> np.lib.scimath._fix_real_abs_gt_1([0,1])
array([0, 1])
>>> np.lib.scimath._fix_real_abs_gt_1([0,2])
array([ 0.+0.j, 2.+0.j])
i ( R R
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Compute the square root of x.
For negative input elements, a complex value is returned
(unlike `numpy.sqrt` which returns NaN).
Parameters
----------
x : array_like
The input value(s).
Returns
-------
out : ndarray or scalar
The square root of `x`. If `x` was a scalar, so is `out`,
otherwise an array is returned.
See Also
--------
numpy.sqrt
Examples
--------
For real, non-negative inputs this works just like `numpy.sqrt`:
>>> np.lib.scimath.sqrt(1)
1.0
>>> np.lib.scimath.sqrt([1, 4])
array([ 1., 2.])
But it automatically handles negative inputs:
>>> np.lib.scimath.sqrt(-1)
(0.0+1.0j)
>>> np.lib.scimath.sqrt([-1,4])
array([ 0.+1.j, 2.+0.j])
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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
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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 ])
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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 ])
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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 ])
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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])
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Compute the inverse cosine of x.
Return the "principal value" (for a description of this, see
`numpy.arccos`) of the inverse cosine of `x`. For real `x` such that
`abs(x) <= 1`, this is a real number in the closed interval
:math:`[0, \pi]`. Otherwise, the complex principle value is returned.
Parameters
----------
x : array_like or scalar
The value(s) whose arccos is (are) required.
Returns
-------
out : ndarray or scalar
The inverse cosine(s) of the `x` value(s). If `x` was a scalar, so
is `out`, otherwise an array object is returned.
See Also
--------
numpy.arccos
Notes
-----
For an arccos() that returns ``NAN`` when real `x` is not in the
interval ``[-1,1]``, use `numpy.arccos`.
Examples
--------
>>> np.set_printoptions(precision=4)
>>> np.emath.arccos(1) # a scalar is returned
0.0
>>> np.emath.arccos([1,2])
array([ 0.-0.j , 0.+1.317j])
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Compute the inverse sine of x.
Return the "principal value" (for a description of this, see
`numpy.arcsin`) of the inverse sine of `x`. For real `x` such that
`abs(x) <= 1`, this is a real number in the closed interval
:math:`[-\pi/2, \pi/2]`. Otherwise, the complex principle value is
returned.
Parameters
----------
x : array_like or scalar
The value(s) whose arcsin is (are) required.
Returns
-------
out : ndarray or scalar
The inverse sine(s) of the `x` value(s). If `x` was a scalar, so
is `out`, otherwise an array object is returned.
See Also
--------
numpy.arcsin
Notes
-----
For an arcsin() that returns ``NAN`` when real `x` is not in the
interval ``[-1,1]``, use `numpy.arcsin`.
Examples
--------
>>> np.set_printoptions(precision=4)
>>> np.emath.arcsin(0)
0.0
>>> np.emath.arcsin([0,1])
array([ 0. , 1.5708])
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Compute the inverse hyperbolic tangent of `x`.
Return the "principal value" (for a description of this, see
`numpy.arctanh`) of `arctanh(x)`. For real `x` such that
`abs(x) < 1`, this is a real number. If `abs(x) > 1`, or if `x` is
complex, the result is complex. Finally, `x = 1` returns``inf`` and
`x=-1` returns ``-inf``.
Parameters
----------
x : array_like
The value(s) whose arctanh is (are) required.
Returns
-------
out : ndarray or scalar
The inverse hyperbolic tangent(s) of the `x` value(s). If `x` was
a scalar so is `out`, otherwise an array is returned.
See Also
--------
numpy.arctanh
Notes
-----
For an arctanh() that returns ``NAN`` when real `x` is not in the
interval ``(-1,1)``, use `numpy.arctanh` (this latter, however, does
return +/-inf for `x = +/-1`).
Examples
--------
>>> np.set_printoptions(precision=4)
>>> np.emath.arctanh(np.matrix(np.eye(2)))
array([[ Inf, 0.],
[ 0., Inf]])
>>> np.emath.arctanh([1j])
array([ 0.+0.7854j])
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