rsnemmen

rsnemmen

#!/usr/bin/env python
#
# Given a BibTeX file containing bibliographic references named 
# `refs.bib`, this script changes all bibtexkey fields from the 
# format "Yuan14" to "Yuan2014". 
#
# Here, "Yuan" refers to the paper's first author surname and "14" 
# refers to the two last digits of the year when the paper was 
# published (the script follows precisely this pattern matching). 
#

rsnemmen

rsnemmen

rsnemmen

! Generates arrays of random numbers. Saves them as binary
! unformatted files.

program test
implicit none

real, dimension(:,:), allocatable :: y
integer :: n
character(len=50) :: out

rsnemmen

#!/bin/sh
#
# Convert Markdown to LaTeX 
#
# Run this inside your document folder:
#		md2tex.sh <MARKDOWN FILE> <MAIN TEX FILE> 
#
# where:
# - <MAIN TEX FILE>: main latex file of your project
# - <MARKDOWN FILE>: meat of text written in markdown

rsnemmen

! Opens a datafile and reads the columns as arrays.
! Illustrates the use of dynamic allocated arrays.

program test
implicit none

real, dimension(:), allocatable :: x, y
integer :: i,n
character(len=50) :: in

rsnemmen

########################################################################
#                                                
# Configuration file for Mac Os X (Darwin, serial)
#             
########################################################################

CC       = gcc
CFLAGS   = -c -fast -finline-functions -Winline -Wundef
LDFLAGS  = -bind_at_load -lm 

rsnemmen

# See also http://astropython.blogspot.in/2011/12/script-illustrating-how-to-do-linear.html

 import numpy, pylab, scipy  
 import nemmen  
   
 # Data taken from http://orion.math.iastate.edu/burkardt/data/regression/x01.txt.  
 # I removed the header from the file and left only the data in 'testdata.dat'.  
 xdata,ydata = numpy.loadtxt('testdata.dat',unpack=True,usecols=(1,2))  
 xdata=numpy.log10(xdata) # take the logs  
 ydata=numpy.log10(ydata)  

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