Installation and Setup

Installation

General Installation Instructions

PP is available from github. You can get the source code by typing into your terminal:

git clone https://github.com/mommermi/photometrypipeline

This will create a photometrypipeline/ directory in your current directory.

Software Requirements

PP requires git for the installation, a number of non-standard Python modules (either Python2 or Python3; available from the Python Package Index through pip):

and some freely available software:

Setup

In order to be able to use PP anywhere on your machine, you have to add the full path of the photometrypipeline/ directory to your PYTHONPATH and PATH variables, and you have to create a PHOTPIPEDIR variable on your system that points to the same directory (include these commands in your .bashrc, .cshrc, or .profile file.)

Installation Instructions for Ubuntu 16.10

Clone the PP github repo:

git clone https://github.com/mommermi/photometrypipeline

Install software requirements for SCAMP, Source Extractor and imagemagick:

sudo apt-get install -y \
       build-essential \
       libssl-dev \
       libffi-dev \
       git \
       sextractor \
       wget \
       imagemagick \
       curl \
       libplplot-dev \
       libshp-dev \
       libcurl4-gnutls-dev \
       liblapack3 liblapack-dev liblapacke liblapacke-dev \
       libfftw3-3 libfftw3-dev libfftw3-single3 \
       libatlas-base-dev

Unpack SCAMP and install using:

./configure --enable-threads
make
sudo make install

Install Python modules:

pip install numpy scipy astropy astroquery matplotlib matplotlib callhorizons future pillow

Add these lines to the .bashrc file in your home directory and replace <path> with the actual path to the PP directory:

# photometry pipeline setup
export PHOTPIPEDIR=<path>/photometrypipeline
export PATH=$PATH:~<path>/photometrypipeline/

Kudos to towicode for figuring out the SCAMP requirements.

Installation Instructions for Mac OS (Sierra)

Install Anaconda: download Anaconda from https://www.continuum.io/downloads In your terminal window type one of the below and follow the instructions:

Anaconda3-4.4.0-MacOSX-x86_64.sh
Anaconda2-4.4.0-MacOSX-x86_64.sh

Install Homebrew:

/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"

Install MacPorts by downloading the installer from their website: https://www.macports.org:

sudo port -v selfupdate

Test if gcc will run by typing gcc in a bash terminal. If prompted to install command-line tools, follow the instructions to do so

Update pip:

python -m pip install –-upgrade pip

Install extra python modules in Anaconda:

pip install –-user numpy scipy matplotlib callhorizons future astroquery Pillow==2.6.1
pip install astropy
conda update astropy

Install SExtractor:

brew install brewsci/science/sextractor

Install SCAMP:

brew install brewsci/science/scamp

Install extra software:

sudo port install wget

Install PP:

git clone https://github.com/mommermi/photometrypipeline

Add to \∼/.bash_profile file by replacing <username> with your system user name and <PyVersion> with the Python version you are using:

export PATH="$PATH:/home/<username>/.local/bin"
export PATH="$PATH:/Users/<username>/photometrypipeline"
export PATH="$PATH:/Users/<username>/Library/Python/<PyVersion>/bin"

Kudos to Annika Gustafsson and Colin Chandler for producing this summary and Kathryn Neugent for providing corrections.

Update your Version of PP

In order to update your version of PP, simply change into photometrypipeline/ and type:

git pull

You should do this regularly as PP is still under constant development.

Example Data

The PP github clone comes with some sample data that can be used to test if the pipeline works properly. The data were taken with the VATT4k camera on the VATT and can be found in example_data/vatt4k. In order to run the pipeline on these images, copy them to a new directory, change there, and run pp_run mscience*fits. If everything works out properly, the results (photometry_3552.dat) should resemble those in example_data/vatt4k/LOG.

Telescope Setup

PP critically relies on information provided in the FITS image headers to handle data properly. While the FITS format is standardized, header keywords are not, leading to additional complications in the interpretation of FITS files. In order to be able to work with a multitude of different telescopes and instruments, PP comes with guidelines of how to read FITS files coming from different telescopes/instruments. These guidelines are imprinted in the setup/telescopes.py file. In order to prevent compatibility issues, you should not change this file directly. Instead, please create and use a setup/mytelescopes.py as described below. You can implement as many telescopes as you want in this file. The advantage is that the file will not be changed as a result of git pull requests.

The ‘telescope file’ includes for each telescope/instrument combination a dictionary (*_param) that translates general descriptions for FITS header keywords into specific keywords used by the respective telescope/instrument combination. For example, the telescope pointing RA keyword might be named RA for one telescope, but TELRA for another – PP will refer to either of those as ra. The telescope file catches these degeneracies and allows the pipeline to understand images coming from a variety of telescopes. The meanings of the individual keys in this dictionary are explained in the comments of the respective key. Furthermore, each telescope/instrument combination must have parameter files for Source Extractor and SCAMP (SWARP is currently not supported). Mask files are used by Source Extractor to mask certain regions of the image detector – mask files are only required if field vignetting or image artifacts (e.g., high noise levels in certain areas of the detector) strongly affect the detection of sources in the field.

If you want to include you own telescope into the telescope file, follow these steps:

  1. Download the mytelescopes.py file into your setup/ directory and duplicate the mytelescope_param dictionary. Change the MYTELESCOPE identifier of the duplicate and give it a unique name (e.g., 42INCH_CCD).
  2. Look at the image header of one of your science images and identify the different fields of the *_param file. Replace the dictionary item values accordingly.
  3. In the setup/ directory, copy the Source Extractor (.sex) and SCAMP (.scamp) parameter files from either telescope and name them after your telescope (e.g., 42inch_ccd.scamp).
  4. Add your telescope’s identifier to the implemented_telescopes list in setup/mytelescopes.py, as well as the telescope_parameters dictionary. Finally, add your telescope’s identifier to the instrument_identifiers dictionary: the value is your telescope’s identifier, the key is the INSTRUME header keyword (this is present in most FITS data).
  5. Run pp_prepare() over one of your images. Check with ds9 or some other tool if the image orientation provided by pp_prepare() is correct. If not, play with the flipx, flipy parameters in your telescope file.

If this sounds too confusing, send me one of your images in an email and I will take care of implementing your telescope.