How To: Install and Run Seabird and Triaxus scripts

Instructions for Installing and  Running the Triaxus R script on a windows machine

Installation

Steps

1. Install the latest version of R from http://cran.r-project.org/bin/windows/base/ with default settings. The installated 
2. Download the code from https://github.com/stormxuwz/Triaxus_ShipAlgorithm

Instructions for running the Seabird python script on a linux machine

Download the seabird code from https://github.com/stormxuwz/Seabird

Steps

1. Install the latest version of Python
2. Install Python Package Manager
   1. sudo apt-get install python-pip
3. Install all the required packages in the SciPy library stack
   1. sudo apt-get install python-numpy python-scipy python-matplotlib ipython ipython-notebook python-pandas python-sympy python-nose

4. Install the scikit learn library for python

   1. sudo pip install -U scikit-learn

5. The main entry program for this application is the Seabird.py python script
6. In the script Seabird.py in lines 89,101  specify the directory under which the Seabird input data files are present inside the os.chdir command. In line 102, specify the name of the .btl file which contains the seabird bottle data in the variable   bottle_file.
7. Save and run the script.
   1. python Seabird.py
8. The output files would have been generated inside the directory which you previously specified in step 6.
   
   

Instructions for running the Triaxus R script on a linux machine

Steps

1. Install R
   
   1. sudo apt-get install r-base
   2. sudo apt-get install libgeos++-dev
2. Go to the R execution environment by entering  sudo R
3. Install various prerequisite packages through the following commands

   1. install.packages("ggplot2")

      install.packages("gstat")

      install.packages("moments")

      install.packages("fields")

      install.packages("GA")

      install.packages("spdep")

4. Create a Folder called Result inside the applications directory and a sub folder called Variogram inside the newly created Result folder. These folders would be used by the script to store some meta data.
5. Run the main entry R script for the application, by entering source("Main.R")
6. The final output graph should be available as a pop up on the terminal.