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import sys

def fun():
  print 'fun'

def test():
  print 'test'

if __name__ == "__main__":
  if len(sys.argv) > 1 and sys.argv[1] == 'test':

j-faria

GLS periodogram

j-faria

real(kind=8) function pearsonr(x, y) result(r)
    ! given two arrays x and y, this function computes their Pearson correlation coefficient r
    implicit none
    real(kind=8), dimension(:) :: x, y
    real(kind=8), dimension(size(x)) :: xt, yt
    real(kind=8) :: ax, ay, df, sxx, sxy, syy
    integer :: n
    
    if (size(x) /= size(y)) STOP 'Dimension mismatch in pearsonr'
    

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import fileinput
import os

# create a copy of the template parameter file
os.system('cp template_file new_file')

# for example, in the template you have
# par1 = 10
# par2 = 'some path'
# and you want to change to

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def HelloWorld:
    print 'Hello World, from Python'

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#!/usr/bin/env python
# -*- coding: utf-8 -*- 

# Generate data with a particular correlation matrix
#
# A useful fact is that if you have a random vector x with covariance matrix Σ, 
# then the random vector Ax has mean AE(x) and covariance matrix Ω=AΣA^T. 
# So, if you start with data that has mean zero, multiplying by A will not 
# change that.
#

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#!/usr/bin/env python
# -*- coding: utf-8 -*- 
#
# Shuffle an array in place using the Knuth-Fisher-Yates algorithm.

from random import randrange
 
def shuffle(x):
    """ Shuffle x in place using the Knuth-Fisher-Yates algorithm """
    for i in xrange(len(x)-1, 0, -1):

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from numpy import *
from pylab import *
from glob import glob
from scipy.stats import nanmean


NN = '088'  # change this


fileglob = 'Residuals_NN' + NN + '_Index*'

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from math import floor
from numpy import arange, exp, pi, flipud, append, conj, real
from numpy.fft import ifft
from numpy.random import randn


def powernoise(alpha, N, randpower=False, normalize=False):
	"""
	Generate samples of power law noise. The power spectrum 
	of the signal scales as f^(-alpha).

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# file configfile.py
# create a custom INI file reader and read a file called 'test.ini'
import ConfigParser

class iniReader(ConfigParser.ConfigParser):

    def as_dict(self):
        d = dict(self._sections)
        for k in d:
            d[k] = dict(self._defaults, **d[k])