• Compute integral transforms

  

• Inverse transform without analytic inversion

• Integral kernels as derivatives

  

• Transform input array along any axis of numpy.ndarray

• Output the matrix form

• 1-to-n transform for multiple kernels (TODO)

  

• Easily extensible for other kernels

mcfit computes integral transforms of the form

where F(x) is the input function, G(y) is the output function, and K(xy) is the integral kernel. One is free to scale all three functions by a power law

where f(x)=x^{-q} F(x), g(y)=y^q G(y), and k(t)=t^q K(t). And q is a tilt parameter serving to shift power of x between the input function and the kernel.

mcfit implements the FFTLog algorithm. The idea is to take advantage of the convolution theorem in \ln x and \ln y. It approximates the input function with truncated Fourier series over one period of a periodic approximant, and use the exact Fourier transform of the kernel. One can calculate the latter analytically as a Mellin transform. This algorithm is optimal when the input function is smooth in \ln x, and is ideal for oscillatory kernels with input spanning a wide range in \ln x.

pip install mcfit

See docstring of :class:`mcfit.mcfit`, which also applies to other subclasses of transformations. Also see doc/mcfit.tex for more explanations.

One can perform the following pair of Hankel transforms

easily as follows

def F_fun(x): return 1 / (1 + x*x)**1.5
def G_fun(y): return numpy.exp(-y)

from mcfit import Hankel

x = numpy.logspace(-3, 3, num=60, endpoint=False)
F = F_fun(x)
H = Hankel(x, lowring=True)
y, G = H(F, extrap=True)
numpy.allclose(G, G_fun(y), rtol=1e-8, atol=1e-8)

y = numpy.logspace(-4, 2, num=60, endpoint=False)
G = G_fun(y)
H_inv = Hankel(y, lowring=True)
x, F = H_inv(G, extrap=True)
numpy.allclose(F, F_fun(x), rtol=1e-10, atol=1e-10)

Cosmologists often need to transform a power spectrum to its correlation function

from mcfit import P2xi
k, P = numpy.loadtxt('P.txt', unpack=True)
r, xi = P2xi(k)(P)

and the other way around

from mcfit import xi2P
r, xi = numpy.loadtxt('xi.txt', unpack=True)
k, P = xi2P(r)(xi)

Similarly for the quadrupoles

k, P2 = numpy.loadtxt('P2.txt', unpack=True)
r, xi2 = P2xi(k, l=2)(P2)

Also useful to the cosmologists is the tool below that computes the variance of the overdensity field as a function of radius, from which \sigma_8 can be interpolated.

from mcfit import TophatVar
R, var = TophatVar(k, lowring=True)(P, extrap=True)
from scipy.interpolate import CubicSpline
varR = CubicSpline(R, var)
sigma8 = numpy.sqrt(varR(8))