3. Reduction using API

There may be cases where you would be interested in accessing the DRAGONS Application Program Interface (API) directly instead of using the command line wrappers to reduce your data. Here we show you how to do the same reduction we did in the previous chapter but using the API.

3.1. The dataset

If you have not already, download and unpack the tutorial’s data package. Refer to Downloading the tutorial datasets for the links and simple instructions.

The dataset specific to this example is described in:

About the dataset.

Here is a copy of the table for quick reference.

Science	S20131121S0075-083	Y-band, 120 s
Darks	S20131121S0369-375	2 s, short darks for BPM
	S20131120S0115-120 S20131121S0010 S20131122S0012 S20131122S0438-439	120 s, for science data
Flats	S20131129S0320-323	20 s, Lamp On, Y-band
	S20131126S1111-116	20 s, Lamp Off, Y-band

3.2. Setting Up

3.2.1. Importing Libraries

We first import the necessary modules and classes:

import glob

from gempy.adlibrary import dataselect
from recipe_system import cal_service
from recipe_system.reduction.coreReduce import Reduce

glob is a Python built-in package. It will be used to return a list with the input file names.

dataselect will be used to create file lists for the darks, the flats and the science observations. The cal_service package is our interface with the local calibration database. Finally, the Reduce class is used to set up and run the data reduction.

3.2.2. Setting up the logger

We recommend using the DRAGONS logger. (See also Double messaging issue.)

from gempy.utils import logutils
logutils.config(file_name='f2_data_reduction.log')

3.2.3. Setting up the Calibration Service

Before we continue, let’s be sure we have properly setup our calibration database and the calibration association service.

First, check that you have already a rsys.cfg file inside the ~/.geminidr/. It should contain:

[calibs]
standalone = True
database_dir = ${path_to_my_data}/f2img_tutorial/playground

This tells the system where to put the calibration database. This database will keep track of the processed calibrations as we add them to it.

Note

The tilde (~) in the path above refers to your home directory. Also, mind the dot in .geminidr.

The calibration database is initialized and the calibration service is configured as follow:

caldb = cal_service.CalibrationService()
caldb.config()
caldb.init()

cal_service.set_calservice()

The calibration service is now ready to use. If you need more details, check the section on using the caldb API in the Recipe System Users Manual.

3.3. Create list of files

Next step is to create lists of files that will be used as input to each of the data reduction steps. Let us start by creating a list of all the FITS files in the directory ../playdata/.

all_files = glob.glob('../playdata/*.fits')
all_files.sort()

The sort() method simply re-organize the list with the file names and is an optional step. Before you carry on, you might want to do print(all_files) to check if they were properly read.

Now we can use the all_files list as an input to select_data(). The dataselect.select_data() function signature is:

select_data(inputs, tags=[], xtags=[], expression='True')

3.3.1. Two lists for the darks

We select the files that will be used to create a master dark for the science observations, those with an exposure time of 120 seconds.

dark_files_120s = dataselect.select_data(
    all_files,
    ['F2', 'DARK', 'RAW'],
    [],
    dataselect.expr_parser('exposure_time==120')
)

Above we are requesting data with tags F2, DARK, and RAW, though since we only have F2 raw data in the directory, DARK would be sufficient in this case. We are not excluding any tags, as represented by the empty list []. The expression setting the exposure time criterion needs to be processed through the dataselect expression parser, expr_parser().

We repeat the same syntax for the 2-second darks:

dark_files_2s = dataselect.select_data(
    all_files,
    ['F2', 'DARK', 'RAW'],
    [],
    dataselect.expr_parser('exposure_time==2')
)

3.3.2. A list for the flats

Now you must create a list of FLAT images for each filter. The expression specifying the filter name is needed only if you have data from multiple filters. It is not really needed in this case.

list_of_flats_Y = dataselect.select_data(
     all_files,
     ['FLAT'],
     [],
     dataselect.expr_parser('filter_name=="Y"')
)

3.3.3. A list for the science data

Finally, the science data can be selected using:

list_of_science_images = dataselect.select_data(
    all_files,
    ['F2'],
    [],
    dataselect.expr_parser('(observation_class=="science" and filter_name=="Y")')
)

The filter name is not really needed in this case since there are only Y-band frames, but it shows how you could have two selection criteria in the expression.

3.4. Create a Master Dark

We first create the master dark for the science target, then add it to the calibration database. The name of the output master dark is N20160102S0423_dark.fits. The output is written to disk and its name is stored in the Reduce instance. The calibration service expects the name of a file on disk.

reduce_darks = Reduce()
reduce_darks.files.extend(dark_files_120s)
reduce_darks.runr()

caldb.add_cal(reduce_darks.output_filenames[0])

The Reduce class is our reduction “controller”. This is where we collect all the information necessary for the reduction. In this case, the only information necessary is the list of input files which we add to the files attribute. The runr() method is where the recipe search is triggered and where it is executed.

Note

The file name of the output processed dark is the file name of the first file in the list with _dark appended as a suffix. This is the general naming scheme used by the Recipe System.

3.5. Create a Bad Pixel Mask

By default, for F2 imaging data, an illumination mask will be added to the data quality plane to identify the pixels beyond the circular aperture as “non-illuminated”. The package does not have a default bad pixel mask for F2 but the user can easily create a fresh bad pixel mask from the flats and recent short darks.

The Bad Pixel Mask is created using as follow:

reduce_bpm = Reduce()
reduce_bpm.files.extend(list_of_flats_Y)
reduce_bpm.files.extend(dark_files_2s)
reduce_bpm.recipename = 'makeProcessedBPM'
reduce_bpm.runr()

bpm_filename = reduce_bpm.output_filenames[0]

The flats must be passed first to the input list to ensure that the recipe library associated with F2 flats is selected. We are setting the recipe name to makeProcessedBPM to select that recipe from the recipe library instead of the using the default (which would create a master flat).

The BPM produced is named S20131129S0320_bpm.fits.

The local calibration manager does not yet support BPMs so we cannot add it to the database. It is a future feature. Until then we have to pass it manually to the Reduce instance to use it, as we will show below.

3.6. Create a Master Flat Field

A F2 master flat is created from a series of lamp-on and lamp-off exposures. Each flavor is stacked, then the lamp-off stack is subtracted from the lamp-on stack and the result normalized.

We create the master flat field and add it to the calibration manager as follows:

reduce_flats = Reduce()
reduce_flats.files.extend(list_of_flats_Y)
reduce_flats.uparms = [('addDQ:user_bpm', bpm_filename)]
reduce_flats.runr()

caldb.add_cal(reduce_flats.output_filenames[0])

Note how we pass in the BPM we created in the previous step. The addDQ primitive, one of the primitives in the recipe, has an input parameter named user_bpm. We assign our BPM to that input parameter. The value of uparms needs to be a list of Tuples.

Once runr() is finished, we add the master flat to the calibration manager (line 59).

3.7. Reduce the Science Images

The science observation uses a dither-on-target pattern. The sky frames will be derived automatically for each science frame from the dithered frames.

The master dark and the master flat will be retrieved automatically from the local calibration database. Again, the user BPM needs to be specified as the user_bpm argument to addDQ.

We use similar commands as before to initiate a new reduction to reduce the science data:

reduce_target = Reduce()
reduce_target.files.extend(list_of_science_images)
reduce_target.uparms = [('addDQ:user_bpm', bpm_filename)]
reduce_target.runr()

The output stack units are in electrons (header keyword BUNIT=electrons). The output stack is stored in a multi-extension FITS (MEF) file. The science signal is in the “SCI” extension, the variance is in the “VAR” extension, and the data quality plane (mask) is in the “DQ” extension.