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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.
"""
'sqrt', 'log', 'log2', 'logn', 'log10', 'power', 'arccos', 'arcsin', 'arctanh' ]
"""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) """ if issubclass(arr.dtype.type, (nt.single, nt.byte, nt.short, nt.ubyte, nt.ushort, nt.csingle)): return arr.astype(nt.csingle) else: return arr.astype(nt.cdouble)
"""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])
""" x = asarray(x) if any(isreal(x) & (x < 0)): x = _tocomplex(x) return x
"""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.]) """ x = asarray(x) if any(isreal(x) & (x < 0)): x = x * 1.0 return x
"""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]) """ x = asarray(x) if any(isreal(x) & (abs(x) > 1)): x = _tocomplex(x) return x
return (x,)
def sqrt(x): """ 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])
""" x = _fix_real_lt_zero(x) return nx.sqrt(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 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)
return (n, x,)
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): """ 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)
return (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 arccos(x): """ 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])
""" x = _fix_real_abs_gt_1(x) return nx.arccos(x)
def arcsin(x): """ 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])
""" x = _fix_real_abs_gt_1(x) return nx.arcsin(x)
def arctanh(x): """ 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.eye(2)) array([[ Inf, 0.], [ 0., Inf]]) >>> np.emath.arctanh([1j]) array([ 0.+0.7854j])
""" x = _fix_real_abs_gt_1(x) return nx.arctanh(x) |