# This code is a part of the Democratising Archival X-ray Astronomy (DAXA) module.
# Last modified by David J Turner (turne540@msu.edu) 03/02/2025, 13:38. Copyright (c) The Contributors
import gzip
import io
import os
from multiprocessing import Pool
from shutil import copyfileobj
from typing import List, Union, Any
from warnings import warn
import pandas as pd
import requests
from astropy.coordinates import BaseRADecFrame, FK5
from astropy.io import fits
from astropy.table import Table
from astropy.time import Time
from astropy.units import Quantity
from bs4 import BeautifulSoup
from tqdm import tqdm
from daxa import NUM_CORES
from daxa.exceptions import DAXADownloadError
from daxa.mission.base import BaseMission
# As we're only supporting XIS with the Suzaku mission class, I add that top level directory into all - but the
# sub-directories of XIS that we wish to download depend on whether the user wants pre-processed data or not
# I'm using this - https://heasarc.gsfc.nasa.gov/docs/suzaku/analysis/abc/node6.html#SECTION00610000000000000000 -
# guide to determine which directories are needed
REQUIRED_DIRS = {'all': ['auxil/', 'xis/'],
'raw': {'xis': ['event_uf/', 'hk/', 'products/']},
'processed': {'xis': ['event_uf/', 'event_cl/', 'hk/', 'products/']}}
[docs]
class Suzaku(BaseMission):
"""
The mission class for Suzaku observations, specifically those from the XIS instruments, as XRS' cooling system
was damaged soon after launch, and HXD was not an imaging instrument.
The available observation information is fetched from the HEASArc SUZAMASTER table, and data are downloaded from
the HEASArc https access to their FTP server.
:param List[str]/str insts: The instruments that the user is choosing to download/process data from. You can
pass either a single string value or a list of strings. They may include XIS0, XIS1, XIS2, and XIS3 (the
default is all of them).
:param str save_file_path: An optional argument that can use a DAXA mission class save file to recreate the
state of a previously defined mission (the same filters having been applied etc.)
"""
def __init__(self, insts: Union[List[str], str] = None, save_file_path: str = None):
"""
The mission class for Suzaku observations, specifically those from the XIS instruments, as XRS' cooling system
was damaged soon after launch, and HXD was not an imaging instrument.
The available observation information is fetched from the HEASArc SUZAMASTER table, and data are downloaded
from the HEASArc https access to their FTP server.
:param List[str]/str insts: The instruments that the user is choosing to download/process data from. You can
pass either a single string value or a list of strings. They may include XIS0, XIS1, XIS2, and XIS3 (the
default is all of them).
:param str save_file_path: An optional argument that can use a DAXA mission class save file to recreate the
state of a previously defined mission (the same filters having been applied etc.)
"""
super().__init__()
# Sets the default instruments - all the imaging spectrometers on Suzaku, and the only instruments supported
# by DAXA
if insts is None:
insts = ['XIS0', 'XIS1', 'XIS2', 'XIS3']
elif isinstance(insts, str):
# Makes sure that, if a single instrument is passed as a string, the insts variable is a list for the
# rest of the work done using it
insts = [insts]
# Makes sure everything is uppercase
insts = [i.upper() for i in insts]
# These are the allowed instruments for this mission - the XIS instruments all had their own telescopes
self._miss_poss_insts = ['XIS0', 'XIS1', 'XIS2', 'XIS3']
# The chosen_instruments property setter (see below) will use these to convert possible contractions
# to the names that the module expects. I'm not that familiar with Suzaku currently, so
# I've just put in X0, X1, ... without any real expectation that anyone would use them.
self._alt_miss_inst_names = {'X0': 'XIS0', 'X1': 'XIS1', 'X2': 'XIS2', 'X3': 'XIS3'}
# Deliberately using the property setter, because it calls the internal _check_chos_insts function
# to make sure the input instruments are allowed
self.chosen_instruments = insts
# These are the 'translations' required between energy band and filename identifier for Suzaku images/expmaps -
# it is organised so that top level keys are instruments, middle keys are lower energy bounds, and the lower
# level keys are upper energy bounds, then the value is the filename identifier
self._template_en_trans = {Quantity(0.2, 'keV'): {Quantity(12, 'keV'): ""}}
self._template_inst_trans = {'XIS0': 'xi0', 'XIS1': 'xi1', 'XIS2': 'xi2', 'XIS3': 'xi3'}
# We set up the Suzaku file name templates, so that the user (or other parts of DAXA) can retrieve paths
# to the event lists, images, exposure maps, and background maps that can be downloaded
self._template_evt_name = "xis/event_cl/ae{oi}{i}_*_cl.evt"
self._template_img_name = "xis/products/ae{oi}{i}_*_sk.img"
self._template_exp_name = None
self._template_bck_name = None
# Call the name property to set up the name and pretty name attributes
self.name
# This sets up extra columns which are expected to be present in the all_obs_info pandas dataframe
self._required_mission_specific_cols = ['target_category', 'xis0_expo', 'xis0_num_modes', 'xis1_expo',
'xis1_num_modes', 'xis2_expo', 'xis2_num_modes', 'xis3_expo',
'xis3_num_modes']
# Runs the method which fetches information on all available Suzaku observations and stores that
# information in the all_obs_info property
self._fetch_obs_info()
# Slightly cheesy way of setting the _filter_allowed attribute to be an array identical to the usable
# column of all_obs_info, rather than the initial None value
self.reset_filter()
# We now will read in the previous state, if there is one to be read in.
if save_file_path is not None:
self._load_state(save_file_path)
@property
def name(self) -> str:
"""
Property getter for the name of this mission
:return: The mission name.
:rtype: str
"""
# The name is defined here because this is the pattern for this property defined in
# the BaseMission superclass. Suggest keeping this in a format that would be good for a unix
# directory name (i.e. lowercase + underscores), because it will be used as a directory name
self._miss_name = "suzaku"
# This won't be used to name directories, but will be used for things like progress bar descriptions
self._pretty_miss_name = "Suzaku"
return self._miss_name
@property
def coord_frame(self) -> BaseRADecFrame:
"""
Property getter for the coordinate frame of the RA-Decs of the observations of this mission.
:return: The coordinate frame of the RA-Dec.
:rtype: BaseRADecFrame
"""
# The name is defined here because this is the pattern for this property defined in
# the BaseMission superclass
self._miss_coord_frame = FK5
return self._miss_coord_frame
@property
def id_regex(self) -> str:
"""
Property getter for the regular expression (regex) pattern for observation IDs of this mission.
:return: The regex pattern for observation IDs.
:rtype: str
"""
# The ObsID regular expression is defined here because this is the pattern for this property defined in
# the BaseMission superclass - Suzaku observations seem to have a unique 9-digit ObsID, though I can find
# no discussion of whether there is extra information in the ObsID (i.e. target type).
self._id_format = '^[0-9]{9}$'
return self._id_format
@property
def fov(self) -> Union[Quantity, dict]:
"""
Property getter for the approximate field of view set for this mission. This is the radius/half-side-length of
the field of view. In cases where the field of view is not square/circular, it is the half-side-length of
the longest side.
:return: The approximate field of view(s) for the mission's instrument(s). In cases with multiple instruments
then this may be a dictionary, with keys being instrument names.
:rtype: Union[Quantity, dict]
"""
# The approximate field of view is defined here because I want to force implementation for each
# new mission class - found slightly conflicting values for this, but went with the HEASArc info page's
# number of 19'x19' (https://heasarc.gsfc.nasa.gov/docs/suzaku/about/overview.html). Only need one value
# here because we're just supporting the XIS instruments for this mission
self._approx_fov = Quantity(9.5, 'arcmin')
return self._approx_fov
@property
def all_obs_info(self) -> pd.DataFrame:
"""
A property getter that returns the base dataframe containing information about all the observations available
for an instance of a mission class.
:return: A pandas dataframe with (at minimum) the following columns; 'ra', 'dec', 'ObsID', 'science_usable',
'start', 'duration'
:rtype: pd.DataFrame
"""
return self._obs_info
@all_obs_info.setter
def all_obs_info(self, new_info: pd.DataFrame):
"""
Property setter that allows the setting of a new all-observation-information dataframe. This is the dataframe
that contains information on every possible observation for a mission.
:param pd.DataFrame new_info: The new dataframe to update the all observation information.
"""
# Frankly I'm not really sure why I made this an abstract method, but possibly because I thought some
# missions might need extra checks run on their observation information dataframes?
# This _obs_info_checks method is defined in BaseMission, and uses the ObsID regex defined near the top of
# this class to ensure that the dataframe's ObsID column contains legal values.
self._obs_info_checks(new_info)
self._obs_info = new_info
self.reset_filter()
def _fetch_obs_info(self):
"""
This method adapts the 'browse_extract.pl' script (a copy of which can be found in daxa/files for the proper
credit) to acquire the 'suzamaster' table from HEASArc - this method is much simpler, as it doesn't need to be
dynamic and accept different arguments, and we will filter observations locally. This table describes the
available Suzaku observations, with important information such as pointing coordinates, ObsIDs, and exposure.
"""
# This is the web interface for querying NASA HEASArc catalogues
host_url = "https://heasarc.gsfc.nasa.gov/db-perl/W3Browse/w3query.pl?"
# This returns the requested information in a FITS format - the idea being I will stream this into memory
# and then have a fits table that I can convert into a Pandas dataframe (which I much prefer working with).
down_form = "&displaymode=FitsDisplay"
# This should mean unlimited
result_max = "&ResultMax=0"
# This just tells the interface it's a query (I think?)
action = "&Action=Query"
# Tells the interface that I want to retrieve from the suzamaster (Suzaku Master) catalogue
table_head = "tablehead=name=BATCHRETRIEVALCATALOG_2.0%20suzamaster"
# The definition of all of these fields can be found here:
# (https://heasarc.gsfc.nasa.gov/W3Browse/suzaku/suzamaster.html)
# All the proprietary periods for Suzaku data have passed, so we don't need to download them at this point
# like we do with some other missions
which_cols = ['RA', 'DEC', 'OBSID', 'TIME', 'STOP_TIME', 'Category_Code',
'XIS0_Expo', 'XIS0_Num_Modes', 'XIS1_Expo', 'XIS1_Num_Modes', 'XIS2_Expo', 'XIS2_Num_Modes',
'XIS3_Expo', 'XIS3_Num_Modes']
# This is what will be put into the URL to retrieve just those data fields - there are quite a few more
# but I curated it to only those I think might be useful for DAXA
fields = '&Fields=' + '&varon=' + '&varon='.join(which_cols)
# The full URL that we will pull the data from, with all the components we have previously defined
fetch_url = host_url + table_head + action + result_max + down_form + fields
# Opening that URL, we can access the results of our request!
with requests.get(fetch_url, stream=True) as urlo:
# This opens the data as using the astropy fits interface (using io.BytesIO() to stream it into memory
# first so that fits.open can access it as an already opened file handler).
with fits.open(io.BytesIO(urlo.content)) as full_fits:
# Then convert the data in that fits file just into an astropy table object, and from there to a DF
full_suzaku = Table(full_fits[1].data).to_pandas()
# This cycles through any column with the 'object' data type (string in this instance), and
# strips it of white space (I noticed there was extra whitespace on the end of a lot of the
# string data).
for col in full_suzaku.select_dtypes(['object']).columns:
full_suzaku[col] = full_suzaku[col].apply(lambda x: x.strip())
# Important first step, making any global cuts to the dataframe to remove entries that are not going to be
# useful. For Suzaku I have elected to remove any ObsID with zero exposure in all four XIS instruments
rel_suzaku = full_suzaku[(full_suzaku['XIS0_EXPO'] != 0.0) | (full_suzaku['XIS1_EXPO'] != 0.0) |
(full_suzaku['XIS2_EXPO'] != 0.0) | (full_suzaku['XIS3_EXPO'] != 0.0)]
# We throw a warning that some number of the Suzaku observations are dropped because it doesn't seem that they
# will be at all useful
if len(rel_suzaku) != len(full_suzaku):
warn("{ta} of the {tot} observations located for Suzaku have been removed due to all instrument exposures "
"being zero.".format(ta=len(full_suzaku) - len(rel_suzaku), tot=len(full_suzaku)), stacklevel=2)
# Lower-casing all the column names (personal preference largely).
rel_suzaku = rel_suzaku.rename(columns=str.lower)
# Changing a few column names to match what BaseMission expects
rel_suzaku = rel_suzaku.rename(columns={'obsid': 'ObsID', 'time': 'start', 'stop_time': 'end',
'category_code': 'target_category'})
# We convert the Modified Julian Date (MJD) dates into Pandas datetime objects, which is what the
# BaseMission time selection methods expect
rel_suzaku['start'] = pd.to_datetime(Time(rel_suzaku['start'].values.astype(float), format='mjd',
scale='utc').to_datetime())
rel_suzaku['end'] = pd.to_datetime(Time(rel_suzaku['end'].values.astype(float), format='mjd',
scale='utc').to_datetime())
# Then make a duration column by subtracting one from t'other - there are also exposure and ontime columns
# which I've acquired, but I think total duration is what I will go with here.
rel_suzaku['duration'] = rel_suzaku['end'] - rel_suzaku['start']
# Converting the exposure times to Pandas time deltas
for col in rel_suzaku.columns[rel_suzaku.columns.str.contains('expo')]:
rel_suzaku[col] = pd.to_timedelta(rel_suzaku[col], 's')
# No clear way of defining this from the tables, so we're going to assume that they all are
rel_suzaku['science_usable'] = True
# Convert the categories of target that are present in the dataframe to the DAXA taxonomy
# The Suzaku category codes are here:
# https://heasarc.gsfc.nasa.gov/W3Browse/suzaku/suzamaster.html#category_code
# These translations are pretty hand-wavey honestly
conv_dict = {0: 'MISC', 1: 'CAL', 4: 'GS', 5: 'GS', 7: 'EGS', 8: 'GCL', 9: 'TOO'}
# I construct a mask that tells me which entries have a recognised description - any that don't will be set
# to the 'MISC' code
type_recog = rel_suzaku['target_category'].isin(list(conv_dict.keys()))
# The recognized target category descriptions are converted to DAXA taxonomy
rel_suzaku.loc[type_recog, 'target_category'] = rel_suzaku.loc[type_recog, 'target_category'].apply(
lambda x: conv_dict[x])
# Now I set any unrecognized target category descriptions to MISC - there are none at the time of writing,
# but that could well change
rel_suzaku.loc[~type_recog, 'target_category'] = 'MISC'
# Re-ordering the table, and not including certain columns which have served their purpose
rel_suzaku = rel_suzaku[['ra', 'dec', 'ObsID', 'science_usable', 'start', 'end', 'duration', 'target_category',
'xis0_expo', 'xis0_num_modes', 'xis1_expo', 'xis1_num_modes',
'xis2_expo', 'xis2_num_modes', 'xis3_expo', 'xis3_num_modes']]
# Reset the dataframe index, as some rows will have been removed and the index should be consistent with how
# the user would expect from a fresh dataframe
rel_suzaku = rel_suzaku.reset_index(drop=True)
# Use the setter for all_obs_info to actually add this information to the instance
self.all_obs_info = rel_suzaku
@staticmethod
def _download_call(observation_id: str, insts: List[str], raw_dir: str, download_products: bool):
"""
The internal method called (in a couple of different possible ways) by the download method. This will check
the availability of, acquire, and decompress the specified observation.
:param str observation_id: The ObsID of the observation to be downloaded.
:param List[str] insts: The instruments which the user wishes to acquire data for.
:param str raw_dir: The raw data directory in which to create an ObsID directory and store the downloaded data.
:param bool download_products: This controls whether the data downloaded include the pre-processed event lists
and images stored by HEASArc, or whether they are the original raw event lists. Default is to download
raw data.
"""
insts = [inst.lower() for inst in insts]
req_dir = REQUIRED_DIRS['all']
if download_products:
dir_lookup = REQUIRED_DIRS['processed']
else:
dir_lookup = REQUIRED_DIRS['raw']
# The data on HEASArc are stored in subdirectories that have the first digit of the ObsID as their name
cat_id = observation_id[0]
# This is the path to the HEASArc data directory for this ObsID
obs_dir = "/FTP/suzaku/data/obs/{cid}/{oid}/".format(cid=cat_id, oid=observation_id)
top_url = "https://heasarc.gsfc.nasa.gov" + obs_dir
# This opens a session that will persist - then a lot of the next session is for checking that the expected
# directories are present.
session = requests.Session()
# This uses the beautiful soup module to parse the HTML of the top level archive directory - I want to check
# that the directories that I need to download unprocessed Suzaku data are present
top_data = [en['href'] for en in BeautifulSoup(session.get(top_url).text, "html.parser").find_all("a")
if en['href'] in req_dir]
# If the lengths of top_data and REQUIRED_DIRS are different, then one or more of the expected dirs
# is not present
if len(top_data) != len(req_dir):
# This list comprehension figures out what directory is missing and reports it
missing = [rd for rd in req_dir if rd not in top_data]
raise FileNotFoundError("The archive data directory for {o} does not contain the following required "
"directories; {rq}".format(o=observation_id, rq=", ".join(missing)))
for dat_dir in top_data:
# The lower level URL of the directory we're currently looking at
rel_url = top_url + dat_dir
# This is the directory to which we will be saving this archive directories files
local_dir = raw_dir + '/' + dat_dir
# Make sure that the local directory is created
if not os.path.exists(local_dir):
os.makedirs(local_dir)
if dat_dir == 'auxil/':
# All the files in the auxiliary directory are downloaded
to_down = [en['href'] for en in BeautifulSoup(session.get(rel_url).text, "html.parser").find_all("a")
if '?' not in en['href'] and obs_dir not in en['href']]
else:
# The way the Suzaku archives are laid out, XIS has its own directory, and sub-directories that we
# need to decide whether to download or not
rel_req_dir = dir_lookup[dat_dir[:-1]]
to_down = []
# Here we cycle through the directories that we have found at the instrument URL for this ObsID
for en in BeautifulSoup(session.get(rel_url).text, "html.parser").find_all("a"):
# We have to check that the 'en' isn't some HTML guff that we don't need, and that the
# subdirectories actually should be downloaded (i.e. we won't download event_cl and products
# when the user doesn't want pre-processed data).
if '?' not in en['href'] and obs_dir not in en['href'] and en['href'] in rel_req_dir:
low_rel_url = rel_url + en['href']
files = [en['href'] + '/' + fil['href']
for fil in BeautifulSoup(session.get(low_rel_url).text, "html.parser").find_all("a")
if '?' not in fil['href'] and obs_dir not in fil['href']]
if en['href'] != 'hk/':
# All instrument files are in the same directories in this archive, so we need to quickly
# sweep through and check the files are for the instruments the user has chosen. Though
# why they would decide to remove some of the XIS I don't know
# Also add another entry to catch the gif images that they make with a slightly different
# naming scheme
short_inst = ['xi' + inst[-1] for inst in insts if inst] + ['xis']
files = [fil for fil in files for inst in short_inst if inst in fil]
else:
files = []
# If the current subdirectory has got files that we want to download, then we make sure that
# the subdirectory exists locally
if len(files) != 0 and not os.path.exists(local_dir + en['href']):
os.makedirs(local_dir + en['href'])
# And add the current list of files to the overall downloading list for this instrument
to_down += files
# Now we cycle through the files and download them
for down_file in to_down:
down_url = rel_url + down_file
with session.get(down_url, stream=True) as acquiro:
with open(local_dir + down_file, 'wb') as writo:
copyfileobj(acquiro.raw, writo)
# There are a few compressed fits files in each archive, but I think I'm going to decompress the
# event lists, gifs so people can have a quick look if they so desire, and the fits images
if 'evt.gz' in down_file or 'gif.gz' in down_file or 'img.gz' in down_file:
# Open and decompress the events file
with gzip.open(local_dir + down_file, 'rb') as compresso:
# Open a new file handler for the decompressed data, then funnel the decompressed events there
with open(local_dir + down_file.split('.gz')[0], 'wb') as writo:
copyfileobj(compresso, writo)
# Then remove the tarred file to minimise storage usage
os.remove(local_dir + down_file)
return None
[docs]
def download(self, num_cores: int = NUM_CORES, download_products: bool = True):
"""
A method to acquire and download the Suzaku data that have not been filtered out (if a filter
has been applied, otherwise all data will be downloaded). Instruments specified by the chosen_instruments
property will be downloaded, which is set either on declaration of the class instance or by passing
a new value to the chosen_instruments property.
:param int num_cores: The number of cores that can be used to parallelise downloading the data. Default is
the value of NUM_CORES, specified in the configuration file, or if that hasn't been set then 90%
of the cores available on the current machine.
:param bool download_products: This controls whether the data downloaded include the pre-processed event lists
and images stored by HEASArc, or whether they are the original raw event lists. Default is True.
"""
# Ensures that a directory to store the 'raw' Suzaku data in exists - once downloaded and unpacked
# this data will be processed into a DAXA 'archive' and stored elsewhere.
if not os.path.exists(self.top_level_path + self.name + '_raw'):
os.makedirs(self.top_level_path + self.name + '_raw')
# Grabs the raw data storage path
stor_dir = self.raw_data_path
# We store the type of data that was downloaded
if download_products:
self._download_type = "raw+preprocessed"
else:
self._download_type = "raw"
# A very unsophisticated way of checking whether raw data have been downloaded before (see issue #30)
# If not all data have been downloaded there are also secondary checks on an ObsID by ObsID basis in
# the _download_call method
if all([os.path.exists(stor_dir + '{o}'.format(o=o)) for o in self.filtered_obs_ids]):
self._download_done = True
if not self._download_done:
# If only one core is to be used, then it's simply a case of a nested loop through ObsIDs and instruments
if num_cores == 1:
with tqdm(total=len(self), desc="Downloading {} data".format(self._pretty_miss_name)) as download_prog:
for obs_id in self.filtered_obs_ids:
# Use the internal static method I set up which both downloads and unpacks the Suzaku data
self._download_call(obs_id, insts=self.chosen_instruments,
raw_dir=stor_dir + '{o}'.format(o=obs_id),
download_products=download_products)
# Update the progress bar
download_prog.update(1)
elif num_cores > 1:
# List to store any errors raised during download tasks
raised_errors = []
# This time, as we want to use multiple cores, I also set up a Pool to add download tasks too
with tqdm(total=len(self), desc="Downloading {} data".format(self._pretty_miss_name)) \
as download_prog, Pool(num_cores) as pool:
# The callback function is what is called on the successful completion of a _download_call
def callback(download_conf: Any):
"""
Callback function for the apply_async pool method, gets called when a download task finishes
without error.
:param Any download_conf: The Null value confirming the operation is over.
"""
nonlocal download_prog # The progress bar will need updating
download_prog.update(1)
# The error callback function is what happens when an exception is thrown during a _download_call
def err_callback(err):
"""
The callback function for errors that occur inside a download task running in the pool.
:param err: An error that occurred inside a task.
"""
nonlocal raised_errors
nonlocal download_prog
if err is not None:
# Rather than throwing an error straight away I append them all to a list for later.
raised_errors.append(err)
download_prog.update(1)
# Again nested for loop through ObsIDs and instruments
for obs_id in self.filtered_obs_ids:
# Add each download task to the pool
pool.apply_async(self._download_call,
kwds={'observation_id': obs_id, 'insts': self.chosen_instruments,
'raw_dir': stor_dir + '{o}'.format(o=obs_id),
'download_products': download_products},
error_callback=err_callback, callback=callback)
pool.close() # No more tasks can be added to the pool
pool.join() # Joins the pool, the code will only move on once the pool is empty.
# Raise all the download errors at once, if there are any
if len(raised_errors) != 0:
raise DAXADownloadError(str(raised_errors))
else:
raise ValueError("The value of NUM_CORES must be greater than or equal to 1.")
# This is set to True once the download is done, and is used by archives to tell if data have been
# downloaded for a particular mission or not
self._download_done = True
else:
warn("The raw data for this mission have already been downloaded.", stacklevel=2)
[docs]
def assess_process_obs(self, obs_info: dict):
"""
A slightly unusual method which will allow the Suzaku mission to assess the information on a particular
observation that has been put together by an Archive (the archive assembles it because sometimes this
detailed information only becomes available at the first stages of processing), and make a decision on whether
that particular observation-instrument should be processed further for scientific use.
This method should never need to be triggered by the user, as it will be called automatically when detailed
observation information becomes available to the Archive.
:param dict obs_info: The multi-level dictionary containing available observation information for an
observation.
"""
raise NotImplementedError("The check_process_obs method has not yet been implemented for Suzaku, as "
"we need to see what detailed information are available once processing downloaded "
"data has begun.")
[docs]
def ident_to_obsid(self, ident: str):
"""
A slightly unusual abstract method which will allow each mission convert a unique identifier being used
in the processing steps to the ObsID (as these unique identifiers will contain the ObsID). This is necessary
because XMM, for instance, has processing steps that act on whole ObsIDs (e.g. cifbuild), and processing steps
that act on individual sub-exposures of instruments of ObsIDs, so the ID could be '0201903501M1S001'.
Implemented as an abstract method because the unique identifier style may well be different for different
missions - many will just always be the ObsID, but we want to be able to have low level control.
This method should never need to be triggered by the user, as it will be called automatically when detailed
observation information becomes available to the Archive.
:param str ident: The unique identifier used in a particular processing step.
"""
# raise NotImplementedError("The check_process_obs method has not yet been implemented for {n}, as it isn't yet"
# "clear to me what form the unique identifiers will take once we start processing"
# "{n} data ourselves.".format(n=self.pretty_name))
# Suzaku ObsIDs are always 9 digits, so we just retrieve the first 9
return ident[:9]