rsnemmen

 def randomvariate(pdf,n=1000,xmin=0,xmax=1):  
  """  
 Rejection method for random number generation  
 ===============================================  
 Uses the rejection method for generating random numbers derived from an arbitrary   
 probability distribution. For reference, see Bevington's book, page 84. Based on  
 rejection*.py.  
   
 Usage:  
 >>> randomvariate(P,N,xmin,xmax)  

rsnemmen

 import uncertainties as unc  
 import uncertainties.unumpy as unumpy  
 import numpy  
 import nemmen  
   
 # Defines x and y  
 x=numpy.linspace(0,10,50)  
 y=numpy.linspace(15,20,50)  
   
 # Defines the error arrays, values follow a normal distribution  

rsnemmen

 def random_normal(mean,std,n):  
      """  
 Returns an array of n elements of random variables, following a normal   
 distribution with the supplied mean and standard deviation.  
      """  
      import scipy  
      return std*scipy.random.standard_normal(n)+mean 

rsnemmen

 import fish  
 import time  
   
 steps=input('How many steps? ')  
   
 # Progress bar initialization  
 peixe = fish.ProgressFish(total=steps)       
   
 for i in range(steps):  
      # Progress bar  

rsnemmen

# Count words in a latex file, without taking into account 
# the latex constructs
detex $file | wc

rsnemmen

#!/usr/bin/perl -w
#
# Source: http://handyfloss.net/2008.09/making-a-pdf-grayscale-with-ghostscript/
# handyfloss blog
#
# Usage: pdf2gray.pl input_color.pdf

use strict;
my $infile = $ARGV[0];
my $outfile = $infile;

rsnemmen

Galaxy rotation curves in Mathematica
Author: Rodrigo Nemmen
Visually illustrating the impact of the presence of dark matter in speeding up the 
rotation of test particles in galaxies

Input
nϕ=15;
rmin=1;rmax=50; nr=40;
tmin=0; tmax=15Pi; 

rsnemmen

#!/bin/bash
#
# Captures regular screenshots and saves them to dropbox. 
# Useful when I leave my linux box doing number crunching
# over long periods.
#
# t = number of seconds in-between screenshots

t=5400 # 1.5 h

rsnemmen

def confband(xd,yd,a,b,conf=0.95,x=None):
	"""
Calculates the confidence band of the linear regression model at the desired confidence
level, using analytical methods. The 2sigma confidence interval is 95% sure to contain 
the best-fit regression line. This is not the same as saying it will contain 95% of 
the data points.

Arguments:
- conf: desired confidence level, by default 0.95 (2 sigma)
- xd,yd: data arrays

rsnemmen

 def scatterfit(x,y,a=None,b=None):  
  """  
 Compute the mean deviation of the data about the linear model given if A,B  
 (y=ax+b) provided as arguments. Otherwise, compute the mean deviation about   
 the best-fit line.  
   
 x,y assumed to be Numpy arrays. a,b scalars.  
 Returns the float sd with the mean deviation.  
   
 Author: Rodrigo Nemmen