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João Faria j-faria

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def HelloWorld:
print 'Hello World, from Python'
j-faria /
Created Oct 10, 2013
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 Dec 25, 2015
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 Nov 15, 2013
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 Dec 14, 2013
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).
# 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])
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 Aug 29, 2015
Fortran implementation of the Generalized Lomb-Scargle (GLS) periodogram
View GLS
GLS periodogram
j-faria / pearsonr.f90
Created Apr 2, 2015
Calculate the Pearson correlation coefficient of two arrays
View pearsonr.f90
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 Aug 29, 2015
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
You can’t perform that action at this time.