View cl_arguments.py
import sys | |
def fun(): | |
print 'fun' | |
def test(): | |
print 'test' | |
if __name__ == "__main__": | |
if len(sys.argv) > 1 and sys.argv[1] == 'test': |
View GLS
GLS periodogram |
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' | |
View change_par_file.py
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 |
View hello.py
def HelloWorld: | |
print 'Hello World, from Python' |
View generate_from_cov_matrix.py
#!/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. | |
# |
View fisher_yates.py
#!/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): |
View jorge-noise-covmat.py
from numpy import * | |
from pylab import * | |
from glob import glob | |
from scipy.stats import nanmean | |
NN = '088' # change this | |
fileglob = 'Residuals_NN' + NN + '_Index*' |
View powernoise.py
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). |
View configfile.py
# 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]) |
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