C# Nmath to Python SciPy

Question

Hello everyone!

I need to port some functions from C# to Python, but i can't implement next code right:

[SqlFunction(IsDeterministic = true, DataAccess = DataAccessKind.None)]
public static SqlDouble LogNormDist(double probability, double mean, double stddev)
{
    LognormalDistribution lnd = new LognormalDistribution(mean,stddev);
    return (SqlDouble)lnd.CDF(probability);
}

This code uses CenterSpace Nmath library.

Anyone can help me to write a right function in python, which will be similar to this code?

Sorry for my English.

UPD Actually, i don't understand which scipy.stats.lognorm.cdf attrs are simillar to C# probability, mean, stddev

If just copy existing order to python, like in answer below, i get wrong number.

Answer 1

Maybe you can use Python.NET (this is NOT IronPython), it allows to access .NET components and services:

http://pythonnet.sourceforge.net/

Answer 2

Scipy has a bunch of distributions defined in the scipy.stats package

import scipy.stats

def LogNormDist(prob, mean=0, stddev=1):
    return scipy.stats.lognorm.cdf(prob,stddev,mean)

Update

Okay, it looks like Scipy's stat definitions are a little nonstandard. Here's the end of the docstring for scipy.stats.lognormal

Lognormal distribution

lognorm.pdf(x,s) = 1/(s*x*sqrt(2*pi)) * exp(-1/2*(log(x)/s)**2) for x > 0, s > 0.

If log x is normally distributed with mean mu and variance sigma**2, then x is log-normally distributed with shape paramter sigma and scale parameter exp(mu).

So maybe try

return scipy.stats.lognorm.cdf(prob,stddev,scipy.exp(mean))

If that still doesn't work, try getting a few sample points and I'll see if I can find a working relationship.

Udpate 2

Oops, I didn't realize that the scale param is a keyword. This one should now work:

import scipy.stats

def LogNormDist(prob, mean=0, stddev=1):
    return scipy.stats.lognorm.cdf(prob,stddev,scale=scipy.exp(mean))

Cheers and good luck with your project!

Answer 3

The Python docs describe a method random.lognormvariate(mu, sigma):

http://docs.python.org/library/random.html

Maybe that's what you want.