Instructions for installing LCO's photometry reduction pipeline remotely.

So far only tested on Linux (CentOS 7 and Ubuntu 16.04).

Make conda¹ environment

mkdir ~/github; cd ~/github
git clone https://github.com/jfrostburke/pipeline_install
cd pipeline_install/
conda env create --file pipeline_environment.yaml
source activate pipeline
conda install -c pkgw/label/superseded iraf pyraf

Add these² to your ~/.bashrc

export PATH={CONDA_PATH}/envs/pipeline/lib/iraf/bin.linux64:$PATH
export iraf={CONDA_PATH}/envs/pipeline/lib/iraf/
export IRAFARCH=linux64
source activate pipeline
umask 0002

Make iraf

source ~/.bashrc
mkdir ~/iraf; cd ~/iraf
tcsh -c "{CONDA_PATH}/envs/pipeline/bin/mkiraf -term xgterm"
cp {CONDA_PATH}/envs/pipeline/bin/cl /usr/local/bin/cl

This next command isn't actually necessary but keeps you from getting a useless error printed to the screen every time you use iraf.

cd {CONDA_PATH}/envs/pipeline/lib/iraf/unix/hlib; touch motd; cd -

To test if everything's working so far, just type pyraf to start pyraf.

Also install ds9 if you don't have it yet

wget http://ds9.si.edu/archive/linux64/ds9.linux64.7.4.tar.gz
tar -xvf ds9.linux64.7.4.tar.gz
rm -f ds9.linux64.7.4.tar.gz
mv ds9 /usr/bin/

Install pipeline from github

cd ~/github; git clone https://github.com/svalenti/lcogtsnpipe
cd lcogtsnpipe/trunk
python setup.py build -f; python setup.py install -f

Recreate data directory structure

mkdir /supernova/
mkdir /supernova/data/
mkdir /supernova/data/lsc #for 1m images
mkdir /supernova/data/fts #for 2m images
mkdir /supernova/data/0m4 #for 0.4m images

At this point the level of detail will drop because I can't get mariadb installed on docker so it's harder to test things.

Install mariadb, replicate necessary database structure

Install mariadb, the version on SN machine is 10.1.22 if that matters. Setup a user with username supernova and password supernova.

cd ~/github/pipeline_install/
mysql supernova < database_skeleton.sql

Move the "configure" file attached to this email to /supernova/configure. (Don't really want to put this file on github since it contains our super secure passwords.)

At this point the pipeline should be all set up, hopefully. 🎉 To test if it's working, type lscloop.py -n asdf. It should run without errors and return a header with names like IMAGE OBJECT etc., and then say ### total number = 0.

Even though the pipeline is set up you don't have any data to run it on. When you download data from SNEx, untar it and move it into an otherwise empty directory then do

cd {data_dir}
cp ~/github/pipeline_install/sort_ingest.py .
python sort_ingest.py

This is a basic script that sorts data into the correct directories and also ingests it into the database.

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Notes:

1. Conda's a way to manage different versions of python and python packages.
   Installation instructions here: https://conda.io/docs/user-guide/install/index.html
   Commands I ran to install it on Linux:

   wget https://repo.continuum.io/miniconda/Miniconda2-latest-Linux-x86_64.sh -O ./miniconda.sh
   bash miniconda.sh -b -p ~/.miniconda
   rm miniconda.sh

   Then add ~/.miniconda/bin to PATH.
2. Note that {CONDA_PATH} is what I'm calling wherever your conda installation is. For me it's /home/{username}/.conda, but probably different for you.