Is there a scaled complementary error function in python available?

Question

In matlab there is a special function which is not available in any of the collections for the Python I know (numpy, scipy, mpmath, ...).

Probably there are other places where functions like this one may be found?

UPD For all who think that the question is trivial, please try to compute this function for argument ~30 first.

UPD2 Arbitrary precision is a nice workaround, but if possible I would prefer to avoid it. I need a "standard" machine precision (no more no less) and maximum speed possible.

UPD3 It turns out, mpmath gives surprisingly inaccurate result. Even where standard python math works, mpmath results are worse. Which makes it absolutely worthless.

UPD4 The code to compare different ways to compute erfcx.

import numpy as np 

def int_erfcx(x):
    "Integral which gives erfcx" 
    from scipy import integrate
    def f(xi):
        return np.exp(-x*xi)*np.exp(-0.5*xi*xi)
    return 0.79788456080286535595*integrate.quad(f,
                           0.0,min(2.0,50.0/(1.0+x))+100.0,limit=500)[0] 

def my_erfcx(x):
    """M. M. Shepherd and J. G. Laframboise, 
       MATHEMATICS OF COMPUTATION 36, 249 (1981)
       Note that it is reasonable to compute it in long double 
       (or whatever python has)
    """
    ch_coef=[np.float128(0.1177578934567401754080e+01),
             np.float128(  -0.4590054580646477331e-02),
             np.float128( -0.84249133366517915584e-01),
             np.float128(  0.59209939998191890498e-01),
             np.float128( -0.26658668435305752277e-01),
             np.float128(   0.9074997670705265094e-02),
             np.float128(  -0.2413163540417608191e-02),
             np.float128(    0.490775836525808632e-03),
             np.float128(    -0.69169733025012064e-04),
             np.float128(      0.4139027986073010e-05),
             np.float128(       0.774038306619849e-06),
             np.float128(      -0.218864010492344e-06),
             np.float128(        0.10764999465671e-07),
             np.float128(         0.4521959811218e-08),
             np.float128(         -0.775440020883e-09),
             np.float128(          -0.63180883409e-10),
             np.float128(           0.28687950109e-10),
             np.float128(             0.194558685e-12),
             np.float128(            -0.965469675e-12),
             np.float128(              0.32525481e-13),
             np.float128(              0.33478119e-13),
             np.float128(              -0.1864563e-14),
             np.float128(              -0.1250795e-14),
             np.float128(                 0.74182e-16),
             np.float128(                 0.50681e-16),
             np.float128(                 -0.2237e-17),
             np.float128(                 -0.2187e-17),
             np.float128(                    0.27e-19),
             np.float128(                    0.97e-19),
             np.float128(                     0.3e-20),
             np.float128(                    -0.4e-20)]
    K=np.float128(3.75)
    y = (x-K) / (x+K)
    y2 = np.float128(2.0)*y
    (d, dd) = (ch_coef[-1], np.float128(0.0))
    for cj in ch_coef[-2:0:-1]:             
        (d, dd) = (y2 * d - dd + cj, d)
    d = y * d - dd + ch_coef[0]
    return d/(np.float128(1)+np.float128(2)*x)

def math_erfcx(x):
    import scipy.special as spec
    return spec.erfc(x) * np.exp(x*x)

def mpmath_erfcx(x):
    import mpmath
    return mpmath.exp(x**2) * mpmath.erfc(x)

if __name__ == "__main__":
    x=np.linspace(1.0,26.0,200)
    X=np.linspace(1.0,100.0,200)

    intY  = np.array([int_erfcx(xx*np.sqrt(2)) for xx in X])
    myY   = np.array([my_erfcx(xx) for xx in X])
    myy   = np.array([my_erfcx(xx) for xx in x])
    mathy = np.array([math_erfcx(xx) for xx in x])
    mpmathy = np.array([mpmath_erfcx(xx) for xx in x])
    mpmathY = np.array([mpmath_erfcx(xx) for xx in X])

    print ("Integral vs exact: %g"%max(np.abs(intY-myY)/myY))
    print ("math vs exact:     %g"%max(np.abs(mathy-myy)/myy))
    print ("mpmath vs math:    %g"%max(np.abs(mpmathy-mathy)/mathy))
    print ("mpmath vs integral:%g"%max(np.abs(mpmathY-intY)/intY))

exit()

For me, it gives

Integral vs exact: 6.81236e-16
math vs exact:     7.1137e-16
mpmath vs math:    4.90899e-14
mpmath vs integral:8.85422e-13

Obviously, math gives best possible precision where it works while mpmath gives error couple orders of magnitude larger where math works and even more for larger arguments.

Answer 1

Here is a simple and fast implementation giving 12-13 digit accuracy globally:

from scipy.special import exp, erfc

def erfcx(x):
    if x < 25:
        return erfc(x) * exp(x*x)
    else:
        y = 1. / x
        z = y * y
        s = y*(1.+z*(-0.5+z*(0.75+z*(-1.875+z*(6.5625-29.53125*z)))))
        return s * 0.564189583547756287

Answer 2

I don't know that any of the standard sources include that function, but you can implement it in straightforward fashion, at least if you use mpmath and aren't too worried about performance:

import math
import mpmath

def erfcx(x):
    return math.exp(x**2) * math.erfc(x)

def erfcx_mp(x):
    return mpmath.exp(x**2) * mpmath.erfc(x)

where = mpmath.linspace(1, 50, 10) + mpmath.linspace(100, 1000, 5)
for x in where:
    try:
        std = erfcx(x)
    except OverflowError:
        std = None
    new = erfcx_mp(x)
    approx = (1/(x*mpmath.pi**0.5))
    print x, std, new, (new-approx)/approx 

produces

1.0 0.427583576156 0.427583576155807 -0.242127843858688
6.44444444444444 0.0865286153111 0.0865286153111425 -0.0116285899486798
11.8888888888889 0.0472890800456 0.0472890800455829 -0.00350053472385845
17.3333333333333 0.032495498521 0.0324954985209682 -0.00165596082986796
22.7777777777778 0.024745497 0.0247454970000106 -0.000960939188986022
28.2222222222222 None 0.0199784436993529 -0.000626572735073611
33.6666666666667 None 0.0167507236463156 -0.000440550710337029
39.1111111111111 None 0.0144205913280408 -0.000326545959369654
44.5555555555556 None 0.0126594222570918 -0.00025167403795913
50.0 None 0.0112815362653238 -0.000199880119832415
100.0 None 0.00564161378298943 -4.99925018743586e-5
325.0 None 0.00173595973189465 -4.73366058776083e-6
550.0 None 0.00102579754728657 -1.6528843659911e-6
775.0 None 0.000727985953393782 -8.32464102161289e-7
1000.0 None 0.000564189301453388 -4.9999925011689e-7

And it behaves as it should even when the math.* routines overflow. mpmath's interval support isn't quite up to the task (without some hackery I'm too lazy to do), but for this I'm pretty sure mpfs will suffice, as erfcx is simply the product of two things mpmath can compute nicely.

Answer 3

The gmpy2 library provides access to the MPFR multiple-precision library. For normal precision, it is almost 5x faster than mpmath.

$ py27 -m timeit -s "import mpmath" -s "def erfcx(x):return mpmath.exp(x**2) * mpmath.erfc(x)" "erfcx(30)"
10000 loops, best of 3: 47.3 usec per loop
$ py27 -m timeit -s "import gmpy2" -s "def erfcx(x):return gmpy2.exp(x**2) * gmpy2.erfc(x)" "erfcx(30)"
100000 loops, best of 3: 10.8 usec per loop

Both libraries return the same result for 30.

>>> import mpmath
>>> import gmpy2
>>> mpmath.exp(30**2) * mpmath.erfc(30)
mpf('0.018795888861416751')
>>> gmpy2.exp(30**2) * gmpy2.erfc(30)
mpfr('0.018795888861416751')
>>> 

Disclaimer: I maintain gmpy2. I'm actively working towards a new release but there shouldn't be any issues with the current release for this calculation.

Edit: I was curious about the overhead of making two functions calls instead of just one so I implemented a gmpy2.erfcx() entirely in C but still using MPFR to perform the calculations. The improvement was less than I expected. If you think erfcx() would be useful, I can add it to the next release.

$ py27 -m timeit -s "import gmpy2" "gmpy2.erfcx(30)"
100000 loops, best of 3: 9.45 usec per loop

Answer 4

This function is now available in the newest Version of scipy, see http://docs.scipy.org/doc/scipy/reference/special.html.

Answer 5

A highly optimized C++ implementation of erfcx (for both real and complex arguments) was recently merged into SciPy and should be in SciPy version 0.12.