Evgeni Burovski ev-br

## mc_integrate_trivial.cc
/************** A simple MC integrator **************/
#include<iostream>
#include<boost/random.hpp>

using namespace std ;

double f(double);  // to be integrated
double w(double);  // probab. dens.


## mc_integrate_metropolis.cc
/************** A simple MC integrator **************/
#include<iostream>
#include<boost/random.hpp>

/************************************************
 In order to calculate $\int f(x) dx$ we write
(all integrals are from 0 to $\infty$, but this doesn't matter much)
$$
  \int f(x) dx = \int w(x)dx \frac{ \int f/w w(x)dx }{ \int w(x)dx } \;.
$$

## gist:6429773
import scipy.stats as stats

g = stats.gamma.pdf(8., 3.)

# no consequences for 'regular' distributions
print "\nregular @df=5:", stats.t.pdf(1., df=5)
print "\nregular @df=25:", stats.t.pdf(1., df=25)
print "\nregular @df=5:", stats.t.pdf(1., df=5)

#freeze

## gist:6447802
'''script to test expect and moments in distributions.stats method

not written as a test, prints results, renamed to prevent nose from running it


'''

import numpy as np
from scipy import stats
#from statsmodels.stats.moment_helpers import mnc2mvsk

## asympt.py
from __future__ import division, print_function, absolute_import
import numpy as np

from scipy.interpolate import BPoly, PPoly

class MixinAsympt(object):
    def _evaluate(self, x, nu, extrapolate, out):
        super(MixinAsympt, self)._evaluate(x, nu, False, out)

        below, above = x < self.x[0], x > self.x[-1]

## gist:7474570
import numpy as np
from numpy.polynomial.hermite_e import HermiteE

from scipy.stats import norm, chi2
from scipy.misc import factorial
import matplotlib.pyplot as plt


def gram_chandler_series(cum):
    """Construct the 4th order Gram-Chandler expansion given cumulants.

## time_bsplines.ipynb

      
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                ev-br
                / time_bsplines.ipynb
            
            
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              January 2, 2016 05:59
            
              
                Timings for scipy pr/3174 (https://github.com/ev-br/scipy/commits/bsplines)
              
          
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## gist:9081651
# -*- coding: utf-8 -*-
# <nbformat>3.0</nbformat>

# <codecell>

from scipy.interpolate import pchip, Akima
import numpy as np

# <codecell>

## gist:9673884
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 "metadata": {
  "name": "Untitled1"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {

## gist:d88bb691095894629efd
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 "metadata": {
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 },
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	/************ A simple MC integrator ************/
	#include<iostream>
	#include<boost/random.hpp>

	using namespace std ;

	double f(double); // to be integrated
	double w(double); // probab. dens.
	import scipy.stats as stats

	g = stats.gamma.pdf(8., 3.)

	# no consequences for 'regular' distributions
	print "\nregular @df=5:", stats.t.pdf(1., df=5)
	print "\nregular @df=25:", stats.t.pdf(1., df=25)
	print "\nregular @df=5:", stats.t.pdf(1., df=5)

	#freeze
	'''script to test expect and moments in distributions.stats method

	not written as a test, prints results, renamed to prevent nose from running it


	'''

	import numpy as np
	from scipy import stats
	#from statsmodels.stats.moment_helpers import mnc2mvsk
	from __future__ import division, print_function, absolute_import
	import numpy as np

	from scipy.interpolate import BPoly, PPoly

	class MixinAsympt(object):
	def _evaluate(self, x, nu, extrapolate, out):
	super(MixinAsympt, self)._evaluate(x, nu, False, out)

	below, above = x < self.x[0], x > self.x[-1]
	import numpy as np
	from numpy.polynomial.hermite_e import HermiteE

	from scipy.stats import norm, chi2
	from scipy.misc import factorial
	import matplotlib.pyplot as plt


	def gram_chandler_series(cum):
	"""Construct the 4th order Gram-Chandler expansion given cumulants.
	# -- coding: utf-8 --
	# <nbformat>3.0</nbformat>

	# <codecell>

	from scipy.interpolate import pchip, Akima
	import numpy as np

	# <codecell>
	{
	"metadata": {
	"name": "Untitled1"
	},
	"nbformat": 3,
	"nbformat_minor": 0,
	"worksheets": [
	{
	"cells": [
	{
	{
	"metadata": {
	"name": ""
	},
	"nbformat": 3,
	"nbformat_minor": 0,
	"worksheets": [
	{
	"cells": [
	{