Activate your virtualenv as needed.

Add the Subaru-PFS datamodel to your PYTHONPATH. See https://github.com/Subaru-PFS/datamodel/tree/master/python

export PYTHONPATH=/path/to/Subaru-PFS/datamodel/python

Install Amazed library for Subaru-PFS project. See https://github.com/Subaru-PFS/drp_1d

To install Subaru PFS 1D data reduction pipeline, simply run :

pip install .

Note : To run the tests, install pytest : pip install pytest

To run the tests simply run from the drp_1dpipe directory:

pytest

To run the 1d DRP pipeline on a local machine, create a new workdir directory including the two subdirectories spectra and calibration.

workdir
	|-- spectra
	|-- calibration

Put all the psfObject files you want to process inside the spectra directory. Put all the calibration files you need inside the calibration directory.

To process spectra simply run :

drp_1dpipe --workdir=/your/working/directory

At the end, the pipeline creates output/B[N] directories containing 1d DRP product.

workdir
 |-- spectra
       |-- pfsObject-xxxxx-y,y-zzz-XXXXXXXXXXXX.fits
       |-- [...]
 |-- calibration
 |-- output
     |-- config.conf
       |-- B[N]
            |-- pfsObject-xxxxx-y,y-zzz-XXXXXXXXXXXX.fits-1
            |-- pfsZcandidates-xxxxx-y,y-zzz-XXXXXXXXXXXX.fits
			      |-- redshift.csv
			      |-- version.json

For the pfsObject-xxxxx-y,y-zzz-XXXXXXXXXXXX.fits input file :

• pfsObject-xxxxx-y,y-zzz-XXXXXXXXXXXX contains all the temporary processing output
• pfsZcandidates-xxxxx-y,y-zzz-XXXXXXXXXXXX.fits is the 1d DRP product
• config.json is summary file with all the input arguments
• redshift.csv is a summary table of best redshift for every input spectrum
• version.json is the version hash of the pipeline used to process data