reduce_Kelt11.py

# -*- coding: utf-8 -*-
"""
Created on Mon Mar  8 17:46:01 2021

@author: Alexis Brandeker, alexis@astro.su.se

This is an example reduction script. Note that due to some python quirks and
that PIPE uses the multiprocessing module, this script cannot be run in
an interactive python session but has to be executed independently as
"python reduce_Kelt11.py".

Default is to use up to system CPU cores-1 number of threads. Some parts
are not parallelised yet, in particular the smearing correction part
that can be a bottleneck.

PIPE is not optimised for RAM usage. Rule of thumb is that 6 MB per 
subarray frame is used, so ~8 GB for the 1500 frames of the Kelt-11
visit. This likely means that about 16 GB is recommended. I have only
tried on systems with much more RAM than that, so no guarantees.

The photometric extraction for subarrays and imagettes of the Kelt-11
data takes about 25 min on my system. Producing PSF models takes 
additional 90 min. Normally previous PSF models from stars of similar
spectral type and located on the same part of the detector can be
used, so no need to derive new PSF models for every visit.
"""

if __name__ == '__main__': # This line is to break loops when multiprocessing
    # The pipe_param holds input parameters
    # pipe_control contains the high-level function that controls PIPE
    from pipe import PipeParam, PipeControl

    # Initialise parameter object, see pipe_param.py for default parameters.
    # Here we have put the data from DACE in the "DATADIR/Kelt-11/101/"-
    # directory.
    pps = PipeParam('Kelt-11', '101')

    # Set sub-array range to extract a light curve for only a fraction of the
    # full observation. Mostly used for testing with shorter execution time.
    # The format is (e.g.) pps.sa_range = (10, 20) for starting frame 10
    # and end frame 20. Imagettes are limited to the same epoch range
    # (so higher frame number range)
    pps.sa_range = None

    # This is the number of the PSF principal component library "eigenlib"-
    # file that has been previously produced. In this case the filename
    # is "eigenlib_815_281_70_0.pkl" where the last number is the running
    # number.
    pps.psflib = 0
    pps.darksub = False
    pps.sa_range = (10, 20)

    # In particular for faint objects, flux has been shown to correlate well
    # with determined background. The reason is likely a neglected non-linearity
    # of the detector at low exposure levels. This tweak corrects for the low-
    # exposure linearity and strongly reduces the flux-background correlation.
    # When we have the non-linearity better characterised, this will go into
    # the regular non-linearity correction.
    pps.non_lin_tweak = False

    # The pipe_control object contains high-level methods
    pc = PipeControl(pps)

    # Process the data using 10 principal PSF components (how many are available
    # depends on the library). How many components that is optimal to use
    # varies with circumstances. Too few and the varying PSF is not fit. Too
    # many and noise is fitted. Rule of thumb: klip=1 to 5 for faint targets
    # without imagettes, klip=10 for bright targets.
    pps.klip = 10
    pc.process_eigen()

    # Output data is put in the output directory. "residuals_sa.fits" is
    # a fits-cube and contains residuals between fitted PSF and data, and can
    # be used to inspect if there are systematics left. "maskcube_sa.fits"
    # displays what pixels were masked during the fitting process.

    # The next step need only to be taken if a new set of PSF functions is
    # to be produced. It takes about 40 min per iteration, for 3 iterations.

    # Produce a set of PSFs from the observations (default is one PSF per CHEOPS
    # orbit). These PSFs are used for deriving principal components for PSF
    # variability. PSFs from several visits can be combined for the principal
    # component analysis, but they should be centered on the same location on
    # the detector (since the PSF varies with location).

    # pc.make_psf_lib()