# This code is a part of the Democratising Archival X-ray Astronomy (DAXA) module.
# Last modified by David J Turner (turne540@msu.edu) 15/04/2024, 15:16. Copyright (c) The Contributors
from astropy.units import Quantity
from daxa import NUM_CORES
from daxa.archive.base import Archive
from daxa.process.erosita.assemble import cleaned_evt_lists as eros_cleaned_evt_lists
from daxa.process.erosita.clean import flaregti
from daxa.process.xmm._common import ALLOWED_XMM_MISSIONS
from daxa.process.xmm.assemble import (epchain, emchain, cleaned_evt_lists, merge_subexposures, rgs_events,
rgs_angles, cleaned_rgs_event_lists)
from daxa.process.xmm.check import emanom
from daxa.process.xmm.clean import espfilt
from daxa.process.xmm.setup import cif_build, odf_ingest
[docs]
def full_process_xmm(obs_archive: Archive, lo_en: Quantity = None, hi_en: Quantity = None,
process_unscheduled: bool = True, find_mos_anom_state: bool = False,
num_cores: int = NUM_CORES, timeout: Quantity = None):
"""
This is a convenience function that will fully process and prepare XMM data in an archive using the default
configuration settings of all the cleaning steps. If you wish to exercise finer grained control over the
processing of your data then you can copy the steps of this function and alter the various parameter values.
:param Archive obs_archive: An archive object that contains at least one XMM mission to be processed.
:param Quantity lo_en: If an energy filter should be applied to the final cleaned event lists, this is the
lower energy bound. The default is None, in which case NO ENERGY FILTER is applied.
:param Quantity hi_en: If an energy filter should be applied to the final cleaned event lists, this is the
upper energy bound. The default is None, in which case NO ENERGY FILTER is applied.
:param bool process_unscheduled: Should unscheduled sub-exposures be processed and included in the final event
lists. The default is True.
:param bool find_mos_anom_state: Whether the emanom task should be run to search for anomolous states of the MOS
cameras. This is set to False by default, and you should be aware that I have not found it to be particularly
reliable with the default settings, it tends to remove good chips.
:param int num_cores: The number of cores that can be used by the processing functions. The default is set to
the DAXA NUM_CORES parameter, which is configured to be 90% of the system's cores.
:param Quantity timeout: The amount of time each individual process is allowed to run for, the default is None.
Please note that this is not a timeout for the entire processing stack, but a timeout for the individual
processes of each stage, whether they are at the ObsID, ObsID-Inst, or ObsID-Inst-Subexposure level of
granularity.
"""
from daxa.process.xmm.generate import generate_images_expmaps
# Creates calibration files for the XMM observations
cif_build(obs_archive, num_cores=num_cores, timeout=timeout)
# Prepares the summary files for the XMM observations - used by processes to determine what data there are
# for each observation
odf_ingest(obs_archive, num_cores=num_cores, timeout=timeout)
# It is very much not a given that there will be RGS data to process, so we first check to see if any of the
# XMM missions have actually had RGS data selected
with_rgs = any(['R1' in mission.chosen_instruments or 'R2' in mission.chosen_instruments for mission in obs_archive
if mission.name in ALLOWED_XMM_MISSIONS])
# If RGS has been selected then we will try to process it, but in case there are no actual data we do put a
# try-except
if with_rgs:
try:
rgs_events(obs_archive, process_unscheduled, num_cores=num_cores, timeout=timeout)
rgs_angles(obs_archive, num_cores=num_cores, timeout=timeout)
cleaned_rgs_event_lists(obs_archive, num_cores=num_cores, timeout=timeout)
except (ValueError, IndexError):
pass
# We try to process EPIC PN data, but we use a try-except because it is possible that none will have been
# selected when the mission was defined
try:
# This step combines the separate CCD data for any EPIC-PN observations
epchain(obs_archive, process_unscheduled, num_cores=num_cores, timeout=timeout)
except ValueError:
pass
# Same deal but for the MOS data - all the succeeding steps should handle this by themselves, checking for
# the data that are present
try:
# This step does much the same but for EPIC-MOS observations
emchain(obs_archive, process_unscheduled, num_cores=num_cores, timeout=timeout)
em_data = True
except ValueError:
em_data = False
# The user can choose whether this state is run, if it isn't then cleaned_evt_lists should automatically
# turn off its filtering based on anomolous state codes. Also if there are actually MOS data to use
if find_mos_anom_state and em_data:
# This checks for anomalous CCD states in MOS observations - this step isn't obligatory but is
# probably a good idea
emanom(obs_archive, num_cores=num_cores, timeout=timeout)
# Runs through all available data and checks for periods of soft-proton flaring - this information is used by the
# cleaned event lists function to remove those time periods as part of the cleaning/filtering process
espfilt(obs_archive, num_cores=num_cores, timeout=timeout)
# Creates the cleaned event lists, with SP flaring removed and standard event filters (filtering on pattern etc.)
# applied to both EPIC-PN and EPIC-MOS data.
cleaned_evt_lists(obs_archive, lo_en, hi_en, filt_mos_anom_state=find_mos_anom_state, num_cores=num_cores,
timeout=timeout)
# Finally this function checks for cases where an ObsID-instrument combination has sub-exposures that should be
# merged into a single, final, event list.
merge_subexposures(obs_archive, num_cores=num_cores, timeout=timeout)
# Also added the automatic generation of 0.5-2.0 and 2.0-10.0 keV images and exposure maps
generate_images_expmaps(obs_archive, num_cores=num_cores)
[docs]
def full_process_erosita(obs_archive: Archive, lo_en: Quantity = None, hi_en: Quantity = None,
num_cores: int = NUM_CORES, timeout: Quantity = None):
"""
This is a convenience function that will fully process and prepare eROSITA data in an archive using the default
configuration settings of all the cleaning steps. If you wish to exercise finer grained control over the
processing of your data then you can copy the steps of this function and alter the various parameter values.
:param Archive obs_archive: An archive object that contains at least one eROSITA mission to be processed.
:param Quantity lo_en: If an energy filter should be applied to the final cleaned event lists, this is the
lower energy bound. The default is None, in which case NO ENERGY FILTER is applied.
:param Quantity hi_en: If an energy filter should be applied to the final cleaned event lists, this is the
upper energy bound. The default is None, in which case NO ENERGY FILTER is applied.
:param int num_cores: The number of cores that can be used by the processing functions. The default is set to
the DAXA NUM_CORES parameter, which is configured to be 90% of the system's cores.
:param Quantity timeout: The amount of time each individual process is allowed to run for, the default is None.
Please note that this is not a timeout for the entire processing stack, but a timeout for the individual
processes of each stage.
"""
# This tool attempts to automatically remove any time periods that are heavily affected by soft-proton flaring
flaregti(obs_archive, num_cores=num_cores, timeout=timeout)
# Creates final cleaned event lists for eROSITA missions
eros_cleaned_evt_lists(obs_archive, lo_en, hi_en, num_cores=num_cores, timeout=timeout)
# Also added the automatic generation of 0.5-2.0 and 2.0-10.0 keV images and exposure maps
# generate_images_expmaps(obs_archive, num_cores=num_cores)