4. Example 1-B: Extended source - Using the “Reduce” class¶
A reduction can be initiated from the command line as shown in Example 1-A: Extended source - Using the “reduce” command and it can also be done programmatically as we will show here. The classes and modules of the RecipeSystem can be accessed directly for those who want to write Python programs to drive their reduction. In this example we replicate the command line reduction from Example 1-A, this time using the Python interface instead of the command line. Of course what is shown here could be packaged in modules for greater automation.
4.1. 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:
Here is a copy of the table for quick reference.
Science | N20160102S0270-274 (on-target)
N20160102S0275-279 (on-sky)
|
Science darks | N20160102S0423-432 (20 sec, like Science)
|
Flats | N20160102S0373-382 (lamps-on)
N20160102S0363-372 (lamps-off)
|
Short darks | N20160103S0463-472
|
Standard star | N20160102S0295-299
|
4.2. Setting up¶
First, navigate to your work directory in the unpacked data package.
The first steps are to import libraries, set up the calibration manager, and set the logger.
4.2.1. Importing libraries¶
1 2 3 4 5 6 7 | import glob import astrodata import gemini_instruments from recipe_system.reduction.coreReduce import Reduce from recipe_system import cal_service from gempy.adlibrary import dataselect |
The dataselect
module will be used to create file lists for the
darks, the flats and the science observations. The cal_service
package
is our interface to the local calibration database. Finally, the
Reduce
class is used to set up and run the data reduction.
4.2.2. Setting up the logger¶
We recommend using the DRAGONS logger. (See also Double messaging issue.)
9 10 | from gempy.utils import logutils logutils.config(file_name='niri_tutorial.log') |
4.2.3. Set up the Local Calibration Manager¶
DRAGONS comes with a local calibration manager and a local, light weight database
that uses the same calibration association rules as the Gemini Observatory
Archive. This allows the Reduce
instance to make requests for matching
processed calibrations when needed to reduce a dataset.
Let’s set up the local calibration manager for this session.
In ~/.geminidr/
, edit the configuration file rsys.cfg
as follow:
[calibs]
standalone = True
database_dir = <where_the_data_package_is>/niriimg_tutorial/playground
This tells the system where to put the calibration database, the database that will keep track of the processed calibration we are going to send 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 like this:
11 12 13 14 15 | 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 “caldb” documentation in the Recipe System User Manual.
4.3. Create file lists¶
The next step is to create input file lists. The module dataselect
helps
with that. It uses Astrodata tags and descriptors to select the files and
store the filenames to a Python list that can then be fed to the Reduce
class. (See the Astrodata User Manual for information about Astrodata and for a list
of descriptors.)
The first list we create is a list of all the files in the playdata
directory.
16 17 | all_files = glob.glob('../playdata/*.fits') all_files.sort() |
We will search that list for files with specific characteristics. We use
the all_files
list
as an input to the function
dataselect.select_data()
. The function’s signature is:
select_data(inputs, tags=[], xtags=[], expression='True')
We show several usage examples below.
4.3.1. Two lists for the darks¶
We have two sets of darks; one set for the science frames, the 20-second darks, and another for making the BPM, the 1-second darks. We will create two lists.
If you did not know the exposure times for the darks, you could use
dataselect
as follows to see the exposure times of all the darks in the
directory. We use the tag DARK
and the descriptor exposure_time
.
18 19 20 21 | all_darks = dataselect.select_data(all_files, ['DARK']) for dark in all_darks: ad = astrodata.open(dark) print(dark, ' ', ad.exposure_time()) |
../playdata/N20160102S0423.fits 20.002
../playdata/N20160102S0424.fits 20.002
../playdata/N20160102S0425.fits 20.002
../playdata/N20160102S0426.fits 20.002
../playdata/N20160102S0427.fits 20.002
../playdata/N20160102S0428.fits 20.002
../playdata/N20160102S0429.fits 20.002
../playdata/N20160102S0430.fits 20.002
../playdata/N20160102S0431.fits 20.002
../playdata/N20160102S0432.fits 20.002
../playdata/N20160103S0463.fits 1.001
../playdata/N20160103S0464.fits 1.001
../playdata/N20160103S0465.fits 1.001
../playdata/N20160103S0466.fits 1.001
../playdata/N20160103S0467.fits 1.001
../playdata/N20160103S0468.fits 1.001
../playdata/N20160103S0469.fits 1.001
../playdata/N20160103S0470.fits 1.001
../playdata/N20160103S0471.fits 1.001
../playdata/N20160103S0472.fits 1.001
As one can see above the exposure times all have a small fractional increment.
This is just a floating point inaccuracy somewhere in the software that
generates the raw NIRI FITS files. As far as we are concerned here in this
tutorial, we are dealing with 20-second and 1-second darks. The function
dataselect
is smart enough to match those exposure times as “close enough”.
So, in our selection expression, we can use “1” and “20” and ignore the
extra digits.
Note
If a perfect match to 1.001 were required, simply set the
argument strict
to True
in dataselect.expr_parser
, eg.
dataselect.expr_parser(expression, strict=True)
.
Let us create our two lists now. The filenames will be stored in the variables
darks1s
and darks20s
.
22 23 24 25 26 27 28 29 30 31 32 33 34 | darks1s = dataselect.select_data( all_files, ['DARK'], [], dataselect.expr_parser('exposure_time==1') ) darks20s = dataselect.select_data( all_files, ['DARK'], [], dataselect.expr_parser('exposure_time==20') ) |
Note
All expression need to be processed with dataselect.expr_parser
.
4.3.2. A list for the flats¶
The flats are a sequence of lamp-on and lamp-off exposures. We just send all of them to one list.
35 | flats = dataselect.select_data(all_files, ['FLAT']) |
4.3.3. A list for the standard star¶
The standard star sequence is a series of datasets identified as “FS 17”. There are no keywords in the NIRI header identifying this target as a special standard star target. We need to use the target name to select only observations from that star and not our science target.
36 37 38 39 40 41 | stdstar = dataselect.select_data( all_files, [], [], dataselect.expr_parser('object=="FS 17"') ) |
4.3.4. A list for the science observations¶
The science frames are all IMAGE
non-FLAT
that are also not the
standard. Since flats are tagged FLAT
and IMAGE
, we need to exclude
the FLAT
tag.
This translate to the following sequence:
42 43 44 45 46 47 | target = dataselect.select_data( all_files, ['IMAGE'], ['FLAT'], dataselect.expr_parser('object!="FS 17"') ) |
One could have used the name of the science target too, like we did for selecting the standard star observation in the previous section. The example above shows how to exclude a tag if needed and was considered more educational.
4.4. 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.
48 49 50 51 52 | reduce_darks = Reduce() reduce_darks.files.extend(darks20s) 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 Reduce.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 the general naming scheme used by the Recipe System
.
4.5. Bad Pixel Mask¶
The DRAGONS Gemini data reduction package, geminidr
, comes with a static
NIRI bad pixel mask (BPM) that gets automatically added to all the NIRI data
as they get processed. The user can also create a supplemental, fresher BPM
from the flats and recent short darks. That new BPM is later fed to
the reduction process as a user BPM to be combined with the static BPM.
Using both the static and a fresh BPM from recent data lead to a better
representation of the bad pixels. It is an optional but recommended step.
The flats and the short darks are the inputs.
The flats must be passed first to the input list to ensure that the recipe
library associated with NIRI flats is selected. We will not use the default
recipe but rather the special recipe from that library called
makeProcessedBPM
.
53 54 55 56 57 58 59 | reduce_bpm = Reduce() reduce_bpm.files.extend(flats) reduce_bpm.files.extend(darks1s) reduce_bpm.recipename = 'makeProcessedBPM' reduce_bpm.runr() bpm = reduce_bpm.output_filenames[0] |
The BPM produced is named N20160102S0373_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.
4.6. Master Flat Field¶
A NIRI 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.
We create the master flat field and add it to the calibration database as follow:
60 61 62 63 64 65 | reduce_flats = Reduce() reduce_flats.files.extend(flats) reduce_flats.uparms = [('addDQ:user_bpm', bpm)] 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
.
To see the list of available input parameters and their defaults, use the
command line tool showpars
from a terminal. It needs the name of a file
on which the primitive will be run because the defaults are adjusted to match
the input data.
showpars ../playdata/N20160102S0363.fits addDQ
4.7. Standard Star¶
The standard star is reduced more or less the same way as the science target (next section) except that dark frames are not obtained for standard star observations. Therefore the dark correction needs to be turned off.
The processed flat field that we added earlier to the local calibration database will be fetched automatically. The user BPM (optional, but recommended) needs to be specified by the user.
66 67 68 69 70 | reduce_std = Reduce() reduce_std.files.extend(stdstar) reduce_std.uparms = [('addDQ:user_bpm', bpm)] reduce_std.uparms.append(('darkCorrect:do_cal', 'skip')) reduce_std.runr() |
4.8. Science Observations¶
The science target is an extended source. We need to turn off the scaling of the sky because the target fills the field of view and does not represent a reasonable sky background. If scaling is not turned off in this particular case, it results in an over-subtraction of the sky frame.
The sky frame comes from off-target sky observations. We feed the pipeline all the on-target and off-target frames. The software will split the on-target and the off-target appropriately.
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
.
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.
71 72 73 74 75 | reduce_target = Reduce() reduce_target.files.extend(target) reduce_target.uparms = [('addDQ:user_bpm', bpm)] reduce_target.uparms.append(('skyCorrect:scale_sky', False)) reduce_target.runr() |
The attentive reader will note that the reduced image is slightly larger than the individual raw image. This is because of the telescope was dithered between each observation leading to a slightly larger final field of view than that of each individual image. The stacked product is not cropped to the common area, rather the image size is adjusted to include the complete area covered by the whole sequence. Of course the areas covered by less than the full stack of images will have a lower signal-to-noise.