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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
Last active August 29, 2015 14:17
Fortran implementation of the Generalized Lomb-Scargle (GLS) periodogram
GLS periodogram
j-faria / pearsonr.f90
Created April 2, 2015 13:41
Calculate the Pearson correlation coefficient of two arrays
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'
j-faria /
Last active August 29, 2015 14:21
Change parameter file, in place
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
def HelloWorld:
print 'Hello World, from Python'
j-faria /
Created October 10, 2013 23:33
Generate data with a particular correlation matrix - resultados da primeira geek meeting
#!/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.
j-faria /
Last active December 25, 2015 16:39
Shuffle an array in place using the Knuth-Fisher-Yates algorithm.
#!/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):
j-faria /
Created November 15, 2013 16:02
Calculate and show the covariance matrix for template noise.
from numpy import *
from pylab import *
from glob import glob
from scipy.stats import nanmean
NN = '088' # change this
fileglob = 'Residuals_NN' + NN + '_Index*'
j-faria /
Created December 14, 2013 16:35
Generate samples of power law noise.
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).
j-faria /
Created January 9, 2014 20:17
INI file reader
# file
# 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])