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Port of the HATPI pipeline for CDIPS Project TESS image-subtraction reductions
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README.md

cdips-pipeline

DOI

This is a time-series photometry pipeline that reduces images to light curves. It is a port of a pipeline originally developed for the HATPI project.

This pipeline has been used for the Cluster Difference Imaging Photometric Survey (CDIPS) image-subtraction reductions. The CDIPS project has made and is making light curves for stars in open clusters, moving groups, etc. It has a stand-alone repo.

In theory, if one wished to reproduce the CDIPS reductions, this pipeline would be the place to start. More practically, the code-base should provide readers and users with an entry-point into a particular set of programs that can be used for photometric and time-series analyses.

We're releasing it for general reproducibility and method-sharing and improvement reasons. (Without the expectation of converting you into a user).

0. How is this structured?

The pipeline is a collection of python functions that can be called from a single "driver script" to go through steps described in the paper. For the CDIPS-I reduction, the driver script is scripts/TESS_reduction.py. The idea is that you run this one program (calling the correct options), and you get light curves from images. This can be done from a shell script. State-awareness (i.e., whether previous reduction attempts succeeded or failed) is minimal, and based on the pre-existence of files and if-else logic. A few similar driver scripts are also in /scripts/, for example to reduce the ETE6 images. Most of the intermediate files in the pipe-trex reduction (e.g., fistar, fiphot, ficonv, etc files) are stored on-disk. A few pieces of metadata (e.g., image quality diagnostics) are collected to a PostgreSQL database and used for the reference frame selection steps.

The most important collections of sub-scripts are in aperturephot.py and imagesubphot.py, which wrap the fitsh tools used to do the aperture photometry and image subtraction. autoimagesub.py rolls up many of these functions to enable pipeline-like functionality.

1. Install

1.1 Operating system

This installation is only tested on linux boxes (Debian/Ubuntu). Compiling the fitsh binaries (see below) might be challenging, but if you do it on MacOS, please submit a PR describing your experience.

1.2 Environment basics

First, clone this repo into a working directory, that we will call $TREX.

git clone https://github.com/waqasbhatti/cdips-pipeline

1.2.1 python3 environment

Have a working version of conda on your path (the 64-bit linux version, presumably). Then

cd $PIPE_TREX
conda env create -f environment_trex_37.yml -n trex_37
source activate trex_37

This deals with most of the details listed below, and is the recommended way to use pipe-trex. (A variety of "new" features since the 2018 development effort depend on using a 3.X environment).

An extra step is that to make pipe-trex accessible to the virtual environment, you currently need to add a .pth file to the approriate site-packages directory. See "Making pipe-trex accessible within virtual environment" below.

1.2.2 python2.7 environment

If you opt instead for a 2.7 environment, make a virtual environment in some local directory, for instance ~/local. We will call the environment trex_27:

pip install virtualenv; virtualenv --python=/usr/bin/python2.7 trex_27

Make sure your $PATH and $PYTHON_PATH environmental variables do not have any python-related things that will break your venv.

Active the empty python 2.7 environment:

source trex_27/bin/activate
(trex_27) cd $PIPE_TREX
(trex_27) pip install -r requirements.txt

This latter step will take some time. Then, ensure pyeebls and bleeding-edge astrobase are installed (they are commented out by default because they require a number of dependencies):

(trex_27) pip install pyeebls
(trex_27) cd $ASTROBASE_DIR
(trex_27) python setup.py develop

where I have assumed you may want to make contributions to astrobase as you develop, which you should!

For transit-fitting, you will want batman, corner, and emcee installed:

(trex_27) pip install batman-package
(trex_27) pip install corner
(trex_27) cd $SOME_DIR
(trex_27) git clone https://github.com/dfm/emcee
(trex_27) cd emcee
(trex_27) python setup.py install

1.3 catalog reading dependencies

In order to perform photometry, we project known-star catalogs onto images in order to know where the stars are. (This is more reliable than source extraction.)

The best catalog in town is Gaia DR2. To access it quickly, gaia2read is a useful program. Jason Kim wrote it for his junior thesis, and his source is available here. Sam Yee added an important piece of functionality: cutting on different magnitudes, his fork is available here.

To install gaia2read to the command line, do the following:

(trex_27) cd $SOME_DIRECTORY
(trex_27) git clone https://github.com/samuelyeewl/gaia2read
(trex_27) cd gaia2read/gaialib2
(trex_27) make
(trex_27) mv gaia2read $BINARY_DIRECTORY_ON_YOUR_PATH
(trex_27) echo "/nfs/phn15/ar0/H/CAT/GaiaDR2" > ~/.gaia2readrc

where $BINARY_DIRECTORY_ON_YOUR_PATH$ is for example ~/bin/, or some other directory from which your path can read binaries.

The assumption of the last line is that you are doing this on the phn/phs/phtess NFS filesystem, where the "DataPreparation" steps have already been performed to download and sort the Gaia DR2 catalog.

1.4 anet and astrometry.net dependencies

You must use either anet or astrometry.net. The latter is strongly recommended, since it's free. To install, follow this page. If you're doing wide-field work, be sure to get both the 4100 and 4200 indexes.

1.5 fitsh and HATpipe dependencies

This code inherits from the fitsh project, developed mostly by Andras Pal. Much of fitsh was inherited by HATpipe circa 2010, when it forked. Again, because they are free, we opt for the public fitsh versions, rather than the closed HATpipe fork versions.

The utilities we want working on our path include: ficalib, fistar, fiphot, grmatch, grtrans, ficonv, fitrans, and ficombine, astrometry.net, and gaia2read (deprecated: 2massread).

Most of these are fitsh tasks. The fitsh installation instructions are here, and they are simple:

cd ~/local
wget http://fitsh.net/download/fitsh/fitsh-0.9.2.tar.gz
tar xvzf fitsh-0.9.2.tar.gz
cd fitsh-0.9.2
./configure
make
make install

Check to make sure this gives you ficalib, fistar, fiphot, grmatch, grtrans, ficonv, fitrans, and ficombine.

1.6 PostgreSQL installation

For bookkeeping, you will also need a PostgreSQL database.

To install for macs, see here. For linux boxes, see here.

(If you're installing on Mac OS X, it is a good idea to change your kernel state by modifying your /etc/sysctl.conf file to include things discussed in the READMEs from the above links.)

Once you've done this:

$ psql -U postgres      # login as master user
postgres=# create user hpx with password 'pwgoeshere' createdb;
$ createdb -U hpx hpx

and add the appropriate password and info to your ~/.pgpass file.

To access the database: psql -U hpx hpx launches the PostgreSQL database named hpx run by user hpx. Or psql -U hpx -h xphtess1 hpx does the same, for a database run on xphtess1, rather than localhost.

To create the tables, run the following:

psql$  \i photometry.sql
psql$  \i xtrnsconvsub.sql
psql$  \i imagesub-refino.sql

Beware that these also remove any information you already had in them. The relations include:

                     List of relations
 Schema |              Name              |   Type   | Owner
--------+--------------------------------+----------+-------
 public | ap_photometry                  | table    | hpx
 public | arefshiftedframes              | table    | hpx
 public | arefshiftedframes_framekey_seq | sequence | hpx
 public | astromrefs                     | table    | hpx
 public | calibratedframes               | table    | hpx
 public | calibratedframes_framekey_seq  | sequence | hpx
 public | filters                        | table    | hpx
 public | frameinfo                      | table    | hpx
 public | frameinfo_framekey_seq         | sequence | hpx
 public | iphotfiles                     | table    | hpx
 public | iphotobjects                   | table    | hpx
 public | ism_photometry                 | table    | hpx
 public | lcinfo                         | table    | hpx
 public | objectinfo                     | table    | hpx
 public | photindex_iphots               | table    | hpx
 public | photometryinfo                 | table    | hpx
 public | photrefs                       | table    | hpx
 public | subtractedframes               | table    | hpx
 public | subtractedframes_framekey_seq  | sequence | hpx

1.7 Making pipe-trex accessible within virtual environment

For the moment, go into the venv's usr/lib/python2.7/site-packages directory and create a .pth file, e.g. pipe-trex.pth with the location of the local git cloned repository in it: /path/to/where/you/cloned/pipe-trex.

Then activate the virtualenv, and see if you can import a module:

py> import imagesubphot as ism

For a conda environment, do the same thing, but the site-packages directory will instead be at a path like /home/lbouma/miniconda3/envs/trex_37/lib/python3.7/site-packages.

2 Getting Started

Some usage examples are given in the scripts/ directory.

2.1 Concepts: Directory structure

Everything must be in its right place for your photometry to Just Work. The assumed directory structure is as follows.

.
├── BASE      #
├── LC        # lightcurves go here
├── PROJ      # project directories for specific users
├── RAW       # raw fits images (or fits.fz images) go here
├── RED       # calibrated fits images go here
└── REDTEMP   # temporary space for reduced files

At the second level:

.
├── BASE
│   ├── CAL                 # calibration frames (bias, flat, dark) go here
│   ├── CAT                 # FOV source catalogs for your field go here
│   └── reference-frames    # astrometric reference frames; sqlite3 databases
├── LC
│   ├── projid12-G577       # lightcurves specific to particular projectids
│   ├── stats_files         # for lightcurve statistics
│   └── projid8-9-10-11-12
├── PROJ
│   ├── lbouma
│   └── wbhatti
├── RAW
│   ├── 1-20161109          # ??? each night gets its own directory under RAW
│   ├── 1-20161110
│   ├── 1-20161111
│   ├── 1-20161130
│   └── tmp
├── RED
│   ├── projid12            # each project gets its own subdirectory under RED
│   └── wbhatti-red-frames

Maintaining this structure is essential. Commands can be run from anywhere, provided that this structure is maintained.

3. Authors

Waqas Bhatti, Luke Bouma, Samuel Yee

4. License

MIT

Appendix. Notes on mac installation

Aside: compiling the HATpipe source on a mac is not advised, because many of the libraries are linux-specific. The entire pipeline is untested on macs, and the following are some notes from a failed attempt at getting the pipeline to work on a mac.

To compile 2massread: cd /Users/luke/local/HATpipe_R3186/source/2mass cp /usr/include/malloc/malloc.h . # change 2massread.c's malloc include statement to be `#include "malloc.h"`. ./hatconf.sh make you then need to put the appropriately formatted ~150Gb of 2MASS index files somewhere accessible, and point to them in your ~/.2massreadrc file.

To compile anrd2xy: cd /Users/luke/local/HATpipe_R3186/source/odoncontrib/anet brew install gsl # this is not a default on macs

If on a mac, you then must edit all six makefiles,

  /Users/luke/local/HATpipe_R3186/source/odoncontrib/anet/Makefile
  /Users/luke/local/HATpipe_R3186/source/odoncontrib/anet/libc*/Makefile
  /Users/luke/local/HATpipe_R3186/source/odoncontrib/anet/libc*/*/Makefile

to use GNU gcp, not cp, because mac cp has different options. Even then though, linking on my mac fails because of architecture problems that I don't understand. This is perhaps a waste of time, and you should just develop on linux, if you have a very good internet connection, or do not develop.

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