Identifying and acquiring relevant X-ray observations
This tutorial will explain the basic concepts and components of the X-ray astronomy Python module ‘Democratising Archival X-ray Astronomy’ (DAXA). We will particularly focus on the various ‘mission’ classes (implemented for each of the X-ray telescopes that DAXA supports), and the functionality that allows for large numbers of observations to be selected and downloaded.
DAXA mission classes allow the user to interact with and search various X-ray telescope archives, all through an identical interface, within a single module, and through Python commands. It should be simple for anyone with a passing familiarity with Python to identify and acquire X-ray data relevant to their research.
Import Statements
[1]:
from daxa.mission import MISS_INDEX, XMMPointed, Chandra, eRASS1DE, eROSITACalPV, NuSTARPointed, Swift, \
ROSATAllSky, ROSATPointed, ASCA, INTEGRALPointed, Suzaku
from datetime import date
import numpy as np
from astropy.units import Quantity
What missions are available?
We have implemented support for access and searching the archives of many X-ray telescopes; we would also be willing to provide an interface to the data archives of other X-ray telescopes, if feasible - please feel free to reach out using the support page if you think there is one we should add.
Some telescopes (such as the ROSATPointed/ROSATAllSky and eROSITACalPV/eRASS1DE classes) are not uniquely represented by a single DAXA mission - this is generally the case when the telescope in question has been used in distinctly different ways (e.g. ROSAT had a survey phase and a pointed phase), such that the data for one mode may not be relevant to all applications.
[2]:
MISS_INDEX
[2]:
{'xmm_pointed': daxa.mission.xmm.XMMPointed,
'nustar_pointed': daxa.mission.nustar.NuSTARPointed,
'erosita_calpv': daxa.mission.erosita.eROSITACalPV,
'erosita_all_sky_de_dr1': daxa.mission.erosita.eRASS1DE,
'chandra': daxa.mission.chandra.Chandra,
'rosat_all_sky': daxa.mission.rosat.ROSATAllSky,
'rosat_pointed': daxa.mission.rosat.ROSATPointed,
'swift': daxa.mission.swift.Swift,
'suzaku': daxa.mission.suzaku.Suzaku,
'asca': daxa.mission.asca.ASCA,
'integral_pointed': daxa.mission.integral.INTEGRALPointed}
XMM-Pointed
This class is for acquiring XMM-Newton data, particularly that taken when the telescope is pointing at specific targets; i.e. it cannot be used to filter and download data taken when the telescope is slewing from one target to the next. XMM is still in use, so the number of observations that are available are constantly increasing - this also means that some data are still in a proprietary period, and DAXA will not be able to access them. Note that only raw, unprocessed, data can currently be downloaded for XMM using DAXA.
The three EPIC instruments are supported (PN, MOS1, and MOS2), as well as the two grating spectrometers (RGS1 and RGS2). We also support the acquisition of XMM optical monitor (OM) data, but support for processing it is more limited.
Values that can be passed to the insts argument of XMMPointed on declaration (either as single strings or as part of a list):
PN
MOS1
MOS2
RGS1
RGS2
OM
[3]:
xm = XMMPointed()
/Users/dt237/code/DAXA/daxa/mission/xmm.py:83: UserWarning: 140 of the 17701 observations located for this mission have been removed due to NaN RA or Dec values
self._fetch_obs_info()
Chandra
The class for acquiring Chandra data, specifically taken when pointing at a target - Chandra does not have easily accessible ‘slew’ data, and this class does not consider it at all.
Data from all instruments can be downloaded by DAXA - though unlike XMM there are not data being taken simultaneously, so each ObsID is generally only associated with one instrument. Note though that the gratings (HETG and LETG) are used with another instrument, so cannot be passed by themselves.
Note that the standard format of Chandra data can be downloaded using DAXA, allowing for the use of the standard Chandra re-processing scripts. This standard download includes pre-made images.
Values that can be passed to the insts argument of Chandra (either as single string or as part of a list):
ACIS-I
ACIS-S
HRC-I
HRC-S
LETG [an instrument that this grating was used with must be specified]
HETG [an instrument that this grating was used with must be specified]
[4]:
ch = Chandra()
eROSITA All-Sky DR1 (German Half)
The class for acquiring data from the first data release by the German part of the eROSITA consortium - this covers half the sky to 1/8th the planned final survey depth of eROSITA. All of this data is taken in survey mode, where the telescope is constantly slewing. Note that the data downloads can include pre-generated images and exposure maps.
The eROSITA telescope is made up of 7 telescope modules that observe simultaneously, and which can be individually selected on declaration with the insts argument - it is not recommended to use DAXA to limit the telescope modules being considered, as we crudely modify the event lists to remove events from non-selected telescope modules.
Values that can be passed to the insts argument of eRASS1DE (either as a single string or as part of a list):
TM1
TM2
TM3
TM4
TM5
TM6
TM7
[5]:
ea = eRASS1DE()
eROSITA Calibration and Performance Verification
This class provides DAXA access to the data from the calibration and performance verification stages of the eROSITA mission, including all pointed observations and survey regions (such as the eROSITA Final Equatorial-Depth Survey; eFEDS). Note that only event list data can be downloaded for this class.
The eROSITA telescope is made up of 7 telescope modules that observe simultaneously, and which can be individually selected on declaration with the insts argument - it is not recommended to use DAXA to limit the telescope modules being considered, as we crudely modify the event lists to remove events from non-selected telescope modules.
Values that can be passed to the insts argument of eROSITACalPV (either as a single string or as part of a list):
TM1
TM2
TM3
TM4
TM5
TM6
TM7
[6]:
ecpv = eROSITACalPV()
NuSTAR-Pointed
This class is for acquiring NuSTAR data, particularly that taken when the telescope is pointing at specific targets; i.e. it cannot be used to filter and download data taken when the telescope is slewing from one target to the next. NuSTAR is still in use, so the number of observations that are available are constantly increasing - this also means that some data are still in a proprietary period, and DAXA will not be able to access them. Note that data downloads can optionally include pre-generated images
NuSTAR has two instruments that observe simultaneously, and are essentially identical, Focal Plane Module A & B.
Values that can be passed to the insts argument of NuSTARPointed on declaration (either as single strings or as part of a list):
FPMA
FPMB
[7]:
nu = NuSTARPointed()
Swift
This class is for acquiring Swift data. Swift is still in use, so the number of observations that are available are constantly increasing. Also, due to the Swift’s primary mission of rapid transient follow-up, and how observations are split up, the table of available observations is unusually large (only INTEGRAL is comparable) - as such this class may take several minutes to declare, depending on your internet connection.
Swift has three instruments that generally observe simultaneously; the X-ray Telescope (XRT), the Burst Alert Telescope (BAT) which is has poor spatial resolution but has a large field of view and is sensitive to very high energy photons, and the Ultraviolet and Optical Telescope (UVOT). Note that data downloads can optionally include pre-generated images, but not for BAT observations.
Values that can be passed to the insts argument of Swift on declaration (either as single strings or as part of a list); the XRT and BAT instruments are selected by default:
XRT
BAT
UVOT
[8]:
sw = Swift()
/Users/dt237/code/DAXA/daxa/mission/swift.py:122: UserWarning: 599 of the 354020 observations located for Swift have been removed due to all instrument exposures being zero.
self._fetch_obs_info()
/Users/dt237/code/DAXA/daxa/mission/swift.py:122: UserWarning: 17 of the 354020 observations located for Swift have been removed due to all chosen instrument (XRT, BAT) exposures being zero.
self._fetch_obs_info()
ROSAT All-Sky Survey
This class provides access to data taken by ROSAT during its all-sky survey. Though ROSAT had multiple instruments, this was all taken with the position-sensitive proportional counters (PSPC) - specifically with the ‘PSPC-C’ instrument (ROSAT had two PSPC instruments). The initial all-sky survey was abandoned after an accidental pass over the sun destroyed PSPC-C, but follow-up observations with PSPC-B were taken towards the end of ROSAT’s lifetime to complete the survey (these are not included in this class).
Note: Data acquired through this class will include just event lists by default, but can also include pre-generated images and exposure maps.
[9]:
ra = ROSATAllSky()
ROSAT-Pointed
This class provides access to data taken by ROSAT during the pointed phase of its lifetime (including follow-up observations used to complete the all-sky survey). ROSAT instruments could not observe simultaneously, so each separate observation uses a single instrument. Note: Data acquired through this class will include just event lists by default, but can also include pre-generated images (PSPC & HRI) and exposure maps (just PSPC).
Values that can be passed to the insts argument of ROSATPointed on declaration (either as single strings or as part of a list):
PSPCB
PSPCC
HRI
[10]:
rp = ROSATPointed()
Suzaku
This class provides access to data taken by the Suzaku X-ray telescope during pointed observations (data taken while slewing are not included in the public archive). Note: Data acquired through this class will include just event lists by default, but can also include pre-generated images.
We provide access to XIS data, but not XRS (as the cooling system was damaged soon after launch) or HXD (as it was not an imaging instrument).
Values that can be passed to the insts argument of Suzaku on declaration (either as single strings or as part of a list):
XIS0
XIS1
XIS2
XIS3
[11]:
su = Suzaku()
/Users/dt237/code/DAXA/daxa/mission/suzaku.py:109: UserWarning: 14 of the 3055 observations located for Suzaku have been removed due to all instrument exposures being zero.
self._fetch_obs_info()
ASCA
This class provides access to data taken by the ASCA X-ray telescope during pointed observations (we cannot find anywhere to access the data taken whilst slewing). Note: Data acquired through this class will include just event lists by default, but can also include pre-generated images, spectra, and lightcurves.
Values that can be passed to the insts argument of ASCA on declaration (either as single strings or as part of a list):
SIS0
SIS1
GIS2
GIS3
[12]:
asca = ASCA()
/Users/dt237/code/DAXA/daxa/mission/asca.py:125: UserWarning: 5 of the 3079 observations located for ASCA have been removed due to all instrument exposures being zero.
self._fetch_obs_info()
INTEGRAL-Pointed
This class is for acquiring INTEGRAL data. INTEGRAL is still in use, so the number of observations that are available are still increasing (though operations will see around the end of 2024). The table of available observations is unusually large (only Swift is comparable) - as such this class may take several minutes to declare, depending on your internet connection.
INTEGRAl has a selection of instruments that cover different parts of the X-ray and Gamma-ray energy range - most of them are based on the ‘coded mask’ technology, and so have very limited spatial resolution. Note that only raw data/calibration files can be downloaded through DAXA, there are no pre-processed images available.
Values that can be passed to the insts argument of INTEGRALPointed on declaration (either as single strings or as part of a list):
JEMX1
JEMX2
ISGRI
PICsIT
SPI
[13]:
inte = INTEGRALPointed()
/Users/dt237/code/DAXA/daxa/mission/integral.py:110: UserWarning: 241 of the 205930 observations located for INTEGRAL have been removed due to all instrument exposures being zero.
self._fetch_obs_info()
/Users/dt237/code/DAXA/daxa/mission/integral.py:110: UserWarning: 7403 of the 205930 observations located for INTEGRAL have been removed due to all chosen instrument (JEMX1, JEMX2, ISGRI) exposures being zero.
self._fetch_obs_info()
Mission properties
Here we run through the basic properties that each of the DAXA mission classes share. We also show examples, particularly in cases where differences between telescopes result in us assigning different values for particular properties.
Name
The name assigned to each mission class, so that they can be differentiated both by the user and by DAXA functions. Each mission class has two names, the ‘internal DAXA name’ (used by DAXA to identify missions) and the ‘pretty name’, which is typically in a more aethsetically pleasing format.
For instance, we show the ‘name’ and ‘pretty name’ of the XMMPointed and eROSITA All-Sky Survey 1 classes:
[14]:
print(xm.name)
print(xm.pretty_name, '\n')
print(ea.name)
print(ea.pretty_name)
xmm_pointed
XMM-Newton Pointed
erosita_all_sky_de_dr1
eRASS DE:1
All Instruments & Chosen Instruments
Most telescopes have multiple instruments, though not all are necessarily selected by default. This can be for a number of reasons, but is generally because they either aren’t suited to archival/serindipitious science (which is the primary reason this module exists) or because they aren’t X-ray telescopes (like the optical monitors on XMM and Swift).
The instruments whose data is to be acquired are generally specified when the mission class is declared (using the insts argument), but can also be set through the chosen_instruments property.
Every available instrument for a mission is stored in the all_mission_instruments property:
[15]:
print(rp.all_mission_instruments)
print(ch.all_mission_instruments)
['PSPCB', 'PSPCC', 'HRI']
['ACIS-I', 'ACIS-S', 'HRC-I', 'HRC-S', 'HETG', 'LETG']
The selected instruments (normally specified on declaration) are stored in the chosen_instruments property, which can also be set:
[16]:
print(rp.chosen_instruments)
print(ch.chosen_instruments)
ch.chosen_instruments = ['ACIS-I', 'ACIS-S']
print(ch.chosen_instruments)
['PSPCB', 'PSPCC', 'HRI']
['ACIS-I', 'ACIS-S', 'HRC-I', 'HRC-S']
['ACIS-I', 'ACIS-S']
ObsID Regular Expression
Each of the mission’s observations are uniquely identified by an ‘ObsID’, and each telescope/mission has a different format of ObsID (generally just made up of numeric characters) - there are points where the mission class may have to check the format of a supplied ObsID, and it does that by comparing to the ObsID regular expression:
[17]:
print(xm.id_regex)
print(rp.id_regex)
^[0-9]{10}$
^(RH|rh|RP|rp|RF|rf|WH|wh|WP|wp|WF|wf)\d{6}([A-Z]\d{2}|)$
Field of View
The field of view (FoV) values attached to DAXA mission classes represent the half-width (or radius) of the region of sky that a telescope/instrument observes. Given that each telescope instrument tends to have a unique geometry, this is a simplification, but it is beneficial to have a single number that represents how much of the sky an instrument can see.
In the simplest cases, the FoV property is just a single quantity, meaning that there is either only one instrument, or that every instrument has the same field of view. In other cases there may be multiple instruments associated with a mission, in which case they will all have their own entry in a FoV dictionary.
Finally, some telescopes (such as Chandra) have instruments which have irregular geometries (the ACIS-S and HRC-S chips), or that are frequently used in different observational modes that enable and disable different parts of the sensor. As such the actual coverage of the FoV can vary dramatically, in cases like these we will have gone with the half-width of the longest possible side of the field of view.
[18]:
print(xm.pretty_name, '-', xm.fov, '\n')
print(rp.pretty_name, '-', rp.fov, '\n')
print(sw.pretty_name, '-', sw.fov, '\n')
print(ch.pretty_name, '-', ch.fov)
XMM-Newton Pointed - 15.0 arcmin
ROSAT Pointed - {'PSPCB': <Quantity 60. arcmin>, 'PSPCC': <Quantity 60. arcmin>, 'HRI': <Quantity 19. arcmin>}
Swift - {'XRT': <Quantity 11.8 arcmin>, 'BAT': <Quantity 50. arcmin>, 'UVOT': <Quantity 8.5 arcmin>}
Chandra - {'ACIS-I': <Quantity 27.8 arcmin>, 'ACIS-S': <Quantity 27.8 arcmin>, 'HRC-I': <Quantity 49.5 arcmin>, 'HRC-S': <Quantity 49.5 arcmin>}
/var/folders/td/gw9qkx6d3szb1nkt_cfvcbzm000vzl/T/ipykernel_23287/579883568.py:4: UserWarning: Chandra FoV are difficult to define, as they can be strongly dependant on observation mode; as such take these as very approximate.
print(ch.pretty_name, '-', ch.fov)
Coordinate Frame
This property contains the coordinate frame for the central positions of observations taken by the mission - this is largely irrelevant to the user, and will be used in cases where the mission class needs to compare an input coordinate to an observation coordinate.
Also, practically speaking the difference between the ICRS and FK5 frames (most commonly used) is neglible compared to the typical spatial uncertainty involved in X-ray data:
[19]:
print(ch.coord_frame)
print(asca.coord_frame)
<class 'astropy.coordinates.builtin_frames.icrs.ICRS'>
<class 'astropy.coordinates.builtin_frames.fk5.FK5'>
Pre-processed Energy Bands
This property will contain the upper and lower energy bounds available for pre-processed data products for a particular mission (if their online dataset supplies energy-bound data products) - these energy bounds are provided on an instrument level (as some missions provide different energy-bound products for different instruments). The left hand column indicates the lower energy bound, and the right hand column the upper energy bound.
An energy bound being present here does not guarantee that all products supplied by the mission online dataset are available in that bound - e.g. some missions provide bound images and a single, general, exposure map.
[20]:
rp.preprocessed_energy_bands
[20]:
{'PSPCB': <Quantity [[0.07, 2.4 ],
[0.07, 0.4 ],
[0.4 , 2.4 ]] keV>,
'PSPCC': <Quantity [[0.07, 2.4 ],
[0.07, 0.4 ],
[0.4 , 2.4 ]] keV>,
'HRI': <Quantity [[0.07, 2.4 ]] keV>}
If the mission in question cannot provide pre-processed data products that are energy bound, then an error will be raised:
[21]:
inte.preprocessed_energy_bands
---------------------------------------------------------------------------
PreProcessedNotSupportedError Traceback (most recent call last)
Cell In [21], line 1
----> 1 inte.preprocessed_energy_bands
File ~/code/DAXA/daxa/mission/base.py:730, in BaseMission.preprocessed_energy_bands(self)
727 # If this attribute is not set then we're going to assume that the archive doesn't provide any products
728 # which are energy bound
729 if self._template_en_trans is None:
--> 730 raise PreProcessedNotSupportedError("This mission's archive does not supply pre-processed products within "
731 "specific energy bands.")
733 # The attribute is organized as a nested dictionary - with two possible configurations, one with instrument
734 # names as top level keys, then lower level keys being lower energy bounds, and the
735 # lowest level keys being upper energy bounds - the other configuration is the same, but doesn't have top
736 # level instrument keys (these then apply to all instruments of a mission).
737 if isinstance(list(self._template_en_trans.keys())[0], Quantity):
PreProcessedNotSupportedError: This mission's archive does not supply pre-processed products within specific energy bands.
Observation Information
One of the most important properties of a DAXA mission class - this returns a dataframe of all the observations that are associated with a mission. This can include observations that are not yet publicly available (i.e. they are still in a proprietary period), but will never include observations that are planned but haven’t been taken yet.
In most cases this data is dynamically updated when a mission is declared (i.e. the table is pulled down from a mission server) - this is not the case for eROSITACalPV and eRASS1DE. Please also note that the Swift and INTEGRALPointed missions have very large ``all_obs_info`` tables due to the way their data/observations are organised.
Some information will be constant across telescopes, and some will be mission specific. We present truncated versions of all_obs_info for every DAXA mission as of the current date:
[22]:
date.today().strftime('%d-%B-%Y')
[22]:
'24-April-2024'
[23]:
xm.all_obs_info
[23]:
| ra | dec | ObsID | start | science_usable | duration | proprietary_end_date | revolution | proprietary_usable | end | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 64.925415 | 55.999440 | 0000110101 | 2001-08-19 07:05:23 | True | 0 days 09:08:33 | 2002-09-29 | 310 | True | 2001-08-19 16:13:56 |
| 1 | 263.674950 | -32.581670 | 0001730201 | 2001-03-09 12:44:21 | True | 0 days 04:44:43 | 2002-05-25 | 229 | True | 2001-03-09 17:29:04 |
| 2 | 263.674950 | -32.581670 | 0001730301 | 2001-03-09 17:30:16 | True | 0 days 02:36:02 | 2002-05-25 | 229 | True | 2001-03-09 20:06:18 |
| 3 | 263.674950 | -32.581670 | 0001730401 | 2001-03-09 09:41:25 | True | 0 days 03:00:59 | 2002-05-25 | 229 | True | 2001-03-09 12:42:24 |
| 4 | 99.349995 | 6.135278 | 0001730501 | 2002-09-17 18:35:28 | True | 0 days 06:05:39 | 2004-12-31 | 508 | True | 2002-09-18 00:41:07 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 17556 | 137.211167 | 55.379083 | 0924140101 | 2024-04-11 19:58:54 | True | 0 days 04:41:40 | NaT | 4458 | False | 2024-04-12 00:40:34 |
| 17557 | 116.748000 | -45.490722 | 0922040801 | 2024-04-14 06:14:32 | True | 0 days 06:43:20 | NaT | 4459 | False | 2024-04-14 12:57:52 |
| 17558 | 179.719375 | 58.583583 | 0924141701 | 2024-04-14 02:14:33 | True | 0 days 02:13:20 | NaT | 4459 | False | 2024-04-14 04:27:53 |
| 17559 | 153.948542 | 54.520361 | 0924141801 | 2024-04-13 19:51:03 | True | 0 days 05:48:20 | NaT | 4459 | False | 2024-04-14 01:39:23 |
| 17560 | 287.956500 | 4.982722 | 0934990101 | 2024-04-14 14:56:52 | True | 0 days 18:35:00 | NaT | 4459 | False | 2024-04-15 09:31:52 |
17561 rows × 10 columns
[24]:
ch.all_obs_info
[24]:
| ra | dec | ObsID | science_usable | proprietary_usable | start | end | duration | proprietary_end_date | target_category | instrument | grating | data_mode | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 274.43140 | -33.01883 | 6616 | True | True | 2006-02-24 04:33:41.000003 | 2007-09-21 00:26:11.000003 | 573 days 19:52:30 | 2007-02-28 | GS | ACIS-S | HETG | CC_000A8 |
| 1 | 83.63292 | 22.01447 | 7587 | True | True | 2007-02-03 09:58:57.000000 | 2008-07-21 21:53:57.000000 | 534 days 11:55:00 | 2008-02-06 | SNR | ACIS-S | HETG | TE_0077C |
| 2 | 202.50000 | 47.20000 | 13814 | True | True | 2012-09-20 07:21:41.999999 | 2012-09-22 12:47:41.999999 | 2 days 05:26:00 | 2013-10-11 | NGS | ACIS-S | NONE | TE_00958 |
| 3 | 266.41667 | -29.00781 | 13842 | True | True | 2012-07-21 11:52:41.000002 | 2012-07-23 17:08:41.000002 | 2 days 05:16:00 | 2012-07-25 | NGS | ACIS-S | HETG | TE_008D0 |
| 4 | 316.72458 | 38.74942 | 13651 | True | True | 2012-02-13 20:18:26.999997 | 2012-02-16 00:51:26.999997 | 2 days 04:33:00 | 2013-02-21 | MISC | HRC-S | LETG | DEFAULT |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 23136 | 332.17000 | 45.74231 | 24644 | True | True | 2020-09-29 01:23:47.000003 | 2020-09-29 01:23:47.000003 | 0 days 00:00:00 | 2020-09-30 | CAL | HRC-I | NONE | DEFAULT |
| 23137 | 84.91458 | -69.74361 | 1203 | True | True | 1999-08-31 03:28:53.000003 | 1999-08-31 03:28:53.000003 | 0 days 00:00:00 | 2003-06-16 | GS | HRC-I | NONE | |
| 23138 | 332.17010 | 45.74230 | 1336 | True | True | 1999-10-03 21:48:53.000004 | 1999-10-03 21:48:53.000004 | 0 days 00:00:00 | 1999-12-14 | CAL | HRC-I | NONE | |
| 23139 | 350.85750 | 58.81483 | 1409 | True | True | 1999-10-23 18:29:33.999999 | 1999-10-23 18:29:33.999999 | 0 days 00:00:00 | 1999-12-09 | SNR | HRC-I | NONE | |
| 23140 | 332.17000 | 45.74231 | 24645 | True | True | 2020-10-25 12:05:32.000001 | 2020-10-25 12:05:32.000001 | 0 days 00:00:00 | 2020-10-27 | CAL | HRC-S | NONE | SCENTER |
23141 rows × 13 columns
[25]:
ea.all_obs_info
[25]:
| ra | dec | ObsID | science_usable | start | end | duration | ra_min | ra_max | dec_min | dec_max | neigh_obs | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 116.703297 | 42.008289 | 117048 | True | 2020-04-15 10:13:39 | 2020-04-19 02:13:48 | 3 days 16:00:09 | 114.725275 | 118.681319 | 40.5 | 43.5 | 114045,118045,122045,113048,121048,112051,1160... |
| 1 | 120.659341 | 42.008289 | 121048 | True | 2020-04-17 10:14:32 | 2020-04-21 10:13:37 | 3 days 23:59:05 | 118.681319 | 122.637363 | 40.5 | 43.5 | 118045,122045,117048,125048,116051,119051,1230... |
| 2 | 124.615385 | 42.008289 | 125048 | True | 2020-04-19 14:14:56 | 2020-04-23 14:14:20 | 3 days 23:59:24 | 122.637363 | 126.593407 | 40.5 | 43.5 | 122045,126045,121048,129048,123051,127051,0000... |
| 3 | 128.571429 | 42.008289 | 129048 | True | 2020-04-21 18:15:19 | 2020-04-25 18:14:56 | 3 days 23:59:37 | 126.593407 | 130.549451 | 40.5 | 43.5 | 126045,130045,125048,133048,127051,131051,0000... |
| 4 | 132.527473 | 42.008289 | 133048 | True | 2020-04-23 22:15:47 | 2020-04-28 02:15:10 | 4 days 03:59:23 | 130.549451 | 134.505495 | 40.5 | 43.5 | 130045,134045,129048,136048,131051,135051,0000... |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2442 | 220.000000 | -87.045181 | 220177 | True | 2020-03-19 20:42:36 | 2020-04-03 04:43:25 | 14 days 08:00:49 | 200.000000 | 240.000000 | -88.5 | -85.5 | 001180,191174,214174,236174,259174,180177,2601... |
| 2443 | 260.000000 | -87.045181 | 260177 | True | NaT | 2020-04-05 12:43:45 | NaT | 240.000000 | 280.000000 | -88.5 | -85.5 | 001180,236174,259174,281174,304174,220177,3001... |
| 2444 | 300.000000 | -87.045181 | 300177 | True | 2020-03-28 16:42:43 | 2020-04-09 04:42:26 | 11 days 11:59:43 | 280.000000 | 320.000000 | -88.5 | -85.5 | 001180,259174,281174,304174,326174,260177,3401... |
| 2445 | 340.000000 | -87.045181 | 340177 | True | 2020-03-31 20:42:37 | 2020-04-12 00:44:26 | 11 days 04:01:49 | 320.000000 | 360.000000 | -88.5 | -85.5 | 001180,304174,326174,349174,011174,300177,0201... |
| 2446 | 0.000000 | -90.000000 | 001180 | True | 2020-03-26 00:45:06 | 2020-04-06 12:45:53 | 11 days 12:00:47 | 0.000000 | 360.000000 | -90.0 | -88.5 | 020177,060177,100177,140177,180177,220177,2601... |
2447 rows × 12 columns
[26]:
ecpv.all_obs_info
[26]:
| ra | dec | ObsID | science_usable | start | end | duration | Field_Name | Field_Type | active_insts | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 129.550000 | 1.500000 | 300007 | True | 2019-11-03 02:42:50 | 2019-11-04 03:36:37 | 89627.0 | EFEDS | SURVEY | TM1,TM2,TM3,TM4,TM5,TM6,TM7 |
| 1 | 133.860000 | 1.500000 | 300008 | True | 2019-11-04 03:49:16 | 2019-11-05 05:16:39 | 91643.0 | EFEDS | SURVEY | TM1,TM2,TM3,TM4,TM5,TM6,TM7 |
| 2 | 138.140000 | 1.500000 | 300009 | True | 2019-11-05 05:29:18 | 2019-11-06 06:40:06 | 90648.0 | EFEDS | SURVEY | TM1,TM2,TM3,TM4,TM5,TM6,TM7 |
| 3 | 142.450000 | 1.500000 | 300010 | True | 2019-11-06 07:24:46 | 2019-11-07 08:20:08 | 89722.0 | EFEDS | SURVEY | TM1,TM2,TM3,TM4,TM5,TM6,TM7 |
| 4 | 130.331300 | -78.963400 | 300004 | True | 2019-11-16 23:14:40 | 2019-11-18 18:17:12 | 154952.0 | ETA_CHA | SURVEY | TM1,TM2,TM3,TM4,TM5,TM6,TM7 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 165 | 284.146250 | -37.909167 | 700008 | True | 2019-10-24 11:11:19 | 2019-10-25 08:55:52 | 78273.0 | 1RXS_J185635_375433 | EXTRAGALACTIC_FIELDS | TM1,TM2,TM3,TM4,TM5,TM6,TM7 |
| 166 | 281.540771 | 79.873726 | 900060 | True | 2019-09-24 15:27:06 | 2019-09-24 21:30:32 | 21806.0 | 3C390_3 | EXTRAGALACTIC_FIELDS | TM6 |
| 167 | 281.500275 | 79.885376 | 900068 | True | 2019-09-28 15:49:51 | 2019-09-28 21:30:32 | 20441.0 | 3C390_3 | EXTRAGALACTIC_FIELDS | TM6 |
| 168 | 281.489410 | 79.888214 | 900069 | True | 2019-09-29 15:23:24 | 2019-09-29 21:30:37 | 22033.0 | 3C390_3 | EXTRAGALACTIC_FIELDS | TM6 |
| 169 | 281.478271 | 79.891029 | 900070 | True | 2019-09-30 15:23:24 | 2019-09-30 21:30:32 | 22028.0 | 3C390_3 | EXTRAGALACTIC_FIELDS | TM5,TM6,TM7 |
170 rows × 10 columns
[27]:
nu.all_obs_info
[27]:
| ra | dec | ObsID | science_usable | proprietary_usable | start | end | duration | proprietary_end_date | target_category | exposure_a | exposure_b | ontime_a | ontime_b | nupsdout | issue_flag | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 83.8281 | -69.2465 | 40001014013 | True | True | 2013-06-29 01:16:07.183998 | 2013-07-05 08:51:07.184004 | 6 days 07:35:00.000006 | 2015-09-17 00:00:00 | SNR | 5 days 11:20:42 | 5 days 11:09:58 | 5 days 20:32:48 | 5 days 20:33:46 | 2119 | 1 |
| 1 | 83.7759 | -69.2677 | 40001014016 | True | True | 2014-04-22 21:06:07.183999 | 2014-04-29 09:31:07.183998 | 6 days 12:24:59.999999 | 2015-09-17 00:00:00 | SNR | 4 days 23:56:47 | 4 days 23:35:17 | 5 days 08:14:46 | 5 days 08:14:09 | 0 | 1 |
| 2 | 83.8965 | -69.2477 | 40001014023 | True | True | 2014-08-01 23:46:07.183998 | 2014-08-08 02:41:07.184000 | 6 days 02:55:00.000002 | 2015-09-17 00:00:00 | SNR | 4 days 22:46:56 | 4 days 22:35:32 | 5 days 07:01:07 | 5 days 07:02:55 | 0 | 0 |
| 3 | 340.6530 | 29.6985 | 60401031004 | True | True | 2018-11-28 22:21:09.184000 | 2018-12-08 17:16:09.183997 | 9 days 18:54:59.999997 | 2019-12-25 00:00:00 | AGN | 4 days 17:35:58 | 4 days 17:01:43 | 5 days 02:48:25 | 5 days 02:49:43 | 0 | 0 |
| 4 | 344.4048 | -36.9765 | 60901012002 | True | False | 2023-11-02 06:06:09.184003 | 2023-11-10 11:41:09.184004 | 8 days 05:35:00.000001 | 2024-05-20 00:00:00 | AGN | 4 days 11:03:16 | 4 days 10:06:55 | 4 days 19:02:15 | 4 days 19:02:36 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 5477 | 0.0000 | 0.0000 | 20627025001 | True | True | 2022-06-04 00:08:52.183997 | 2022-06-04 00:26:09.184004 | 0 days 00:17:17.000007 | 2022-06-13 00:00:00 | SOL | 0 days 00:00:00 | 0 days 00:00:00 | 0 days 00:00:00 | 0 days 00:00:00 | 0 | 1 |
| 5478 | 0.0000 | 0.0000 | 20624008001 | True | True | 2022-06-04 00:26:09.184004 | 2022-06-04 02:01:09.184002 | 0 days 01:34:59.999998 | 2022-06-13 00:00:00 | SOL | 0 days 00:00:00 | 0 days 00:00:00 | 0 days 00:00:00 | 0 days 00:00:00 | 0 | 1 |
| 5479 | 0.0000 | 0.0000 | 20624001002 | True | True | 2022-06-04 02:01:09.184002 | 2022-06-04 03:06:09.184003 | 0 days 01:05:00.000001 | 2022-06-13 00:00:00 | SOL | 0 days 00:00:00 | 0 days 00:00:00 | 0 days 00:00:00 | 0 days 00:00:00 | 0 | 1 |
| 5480 | 0.0000 | 0.0000 | 20601030002 | True | True | 2022-06-04 03:06:09.184003 | 2022-06-04 03:26:09.184004 | 0 days 00:20:00.000001 | 2022-06-13 00:00:00 | SOL | 0 days 00:00:00 | 0 days 00:00:00 | 0 days 00:00:00 | 0 days 00:00:00 | 0 | 0 |
| 5481 | 0.0000 | 0.0000 | 80802021002 | True | True | 2022-06-16 21:56:09.184001 | 2022-06-16 22:16:09.184002 | 0 days 00:20:00.000001 | 2023-01-11 00:00:00 | TOO | 0 days 00:00:00 | 0 days 00:00:00 | 0 days 00:00:00 | 0 days 00:00:00 | 0 | 0 |
5482 rows × 16 columns
[28]:
sw.all_obs_info
[28]:
| ra | dec | ObsID | science_usable | start | end | duration | target_category | xrt_exposure | bat_exposure | uvot_exposure | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 328.70739 | 16.89364 | 00173780007 | True | 2005-12-25 00:42:01.999999 | 2005-12-28 00:00:23.000000 | 2 days 23:18:21.000001 | MISC | 1 days 02:44:09.014000 | 1 days 00:04:10 | 1 days 02:40:01.030000 |
| 1 | 39.95079 | -25.21897 | 00131560002 | True | 2005-06-03 23:59:00.999998 | 2005-06-06 22:47:30.000002 | 2 days 22:48:29.000004 | MISC | 0 days 22:36:12.946000 | 0 days 22:52:03 | 0 days 17:46:44.297000 |
| 2 | 228.46115 | 30.87659 | 00164268014 | True | 2005-12-03 01:00:00.999996 | 2005-12-06 00:07:43.000000 | 2 days 23:07:42.000004 | MISC | 0 days 19:38:55.384000 | 0 days 19:59:15 | 0 days 00:00:00 |
| 3 | 350.64958 | 5.74990 | 00148833003 | True | 2005-08-06 00:26:11.999996 | 2005-08-09 00:50:56.000003 | 3 days 00:24:44.000007 | MISC | 0 days 19:04:07.125000 | 0 days 19:23:36 | 0 days 18:21:43.023000 |
| 4 | 75.27823 | 45.28513 | 00321174011 | True | 2008-08-31 02:25:00.999998 | 2008-09-03 00:34:06.999998 | 2 days 22:09:06 | MISC | 0 days 18:49:28.970000 | 0 days 21:08:01.650000 | 0 days 18:24:15.039000 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 353399 | 225.01926 | -35.01827 | 00067092005 | True | 2005-01-23 13:07:01.000004 | 2005-01-23 23:15:55.999996 | 0 days 10:08:54.999992 | MISC | 0 days 00:00:00 | 0 days 02:57:51 | 0 days 00:00:00 |
| 353400 | 77.63318 | -14.96722 | 00074164018 | True | 2011-05-03 12:25:59.000002 | 2011-05-03 17:31:53.999999 | 0 days 05:05:54.999997 | MISC | 0 days 00:00:00 | 0 days 00:05:31 | 0 days 00:00:00 |
| 353401 | 7.30920 | -73.71531 | 00048706113 | True | 2019-03-04 23:56:36.000001 | 2019-03-05 00:51:04.999997 | 0 days 00:54:28.999996 | MISC | 0 days 00:00:00 | 0 days 00:01:01 | 0 days 00:00:53.499000 |
| 353402 | 51.88545 | 26.38554 | 03111918013 | True | 2022-12-25 10:22:35.999999 | 2022-12-25 10:32:25.999999 | 0 days 00:09:50 | MISC | 0 days 00:00:00 | 0 days 00:00:02 | 0 days 00:00:00 |
| 353403 | 20.08971 | -5.01057 | 00076384008 | True | 2023-05-24 11:05:38.000003 | 2023-05-24 12:42:01.999999 | 0 days 01:36:23.999996 | MISC | 0 days 00:00:00 | 0 days 00:07:14 | 0 days 00:00:00 |
353404 rows × 11 columns
[29]:
ra.all_obs_info
[29]:
| ra | dec | ObsID | science_usable | start | end | duration | target_category | |
|---|---|---|---|---|---|---|---|---|
| 0 | 263.57088 | 67.500 | RS930521N00 | True | 1990-07-11 | 1991-08-13 | 0 days 11:19:06 | ASK |
| 1 | 276.42533 | 67.500 | RS930522N00 | True | 1990-07-11 | 1991-08-13 | 0 days 11:18:11 | ASK |
| 2 | 96.42533 | -67.500 | RS932908N00 | True | 1990-07-11 | 1991-08-13 | 0 days 05:47:48 | ASK |
| 3 | 83.57088 | -67.500 | RS932907N00 | True | 1990-07-11 | 1991-08-13 | 0 days 05:47:39 | ASK |
| 4 | 267.27100 | 61.875 | RS930625N00 | True | 1990-07-11 | 1991-08-13 | 0 days 03:44:35 | ASK |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1373 | 289.08783 | -61.875 | RS932827N00 | True | 1990-09-10 | 1990-10-08 | 0 days 00:02:44 | ASK |
| 1374 | 350.17942 | -33.750 | RS932354N00 | True | 1990-11-09 | 1990-12-02 | 0 days 00:02:42 | ASK |
| 1375 | 278.17942 | -61.875 | RS932826N00 | True | 1990-09-04 | 1990-09-30 | 0 days 00:02:42 | ASK |
| 1376 | 273.75000 | -45.000 | RS932537N00 | True | 1990-09-07 | 1990-09-23 | 0 days 00:02:42 | ASK |
| 1377 | 280.46275 | -50.625 | RS932634N00 | True | 1990-09-13 | 1990-10-01 | 0 days 00:02:34 | ASK |
1378 rows × 8 columns
[30]:
rp.all_obs_info
[30]:
| ra | dec | ObsID | science_usable | start | end | duration | instrument | with_filter | target_category | target_name | proc_rev | fits_type | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 163.1800 | 57.4800 | RH701867A01 | True | 1995-04-15 23:24:16.000001 | 1995-05-11 14:24:47.000001 | 2 days 13:29:32 | HRI | N | AGN | LOCKMAN HOLE | 2 | RFITS V4. |
| 1 | 203.6500 | 37.9100 | RH900717N00 | True | 1997-06-04 16:13:00.999998 | 1997-07-13 22:26:45.000004 | 2 days 07:58:33 | HRI | N | MISC | DEEP SURVEY | 2 | RDF 4_0 |
| 2 | 163.1800 | 57.4800 | RH701867A04 | True | 1997-04-15 21:51:16.999998 | 1997-04-28 16:37:45.999998 | 2 days 06:33:41 | HRI | N | AGN | LOCKMAN HOLE | 2 | RFITS V3. |
| 3 | 350.8700 | 58.8100 | RH500444N00 | True | 1995-12-23 22:18:36.999999 | 1996-02-01 10:17:53.999998 | 2 days 02:07:27 | HRI | N | SNR | CAS A | 2 | RDF 3_4 |
| 4 | 163.1800 | 57.4800 | RH701867A02 | True | 1996-05-01 02:09:33.000002 | 1996-05-29 15:31:24.999997 | 2 days 01:05:24 | HRI | N | AGN | LOCKMAN HOLE | 2 | RFITS V4. |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 11426 | 84.2900 | -80.4700 | RP999998A02 | False | 1991-10-20 04:07:51.999998 | 1991-11-01 22:43:43.000003 | 0 days 00:00:00 | PSPCB | N | EGE | Idle Point | 2 | RDF 3_4 |
| 11427 | 93.1800 | -81.8300 | RH150094N00 | False | 1990-07-29 22:13:42.999997 | 1990-07-29 22:56:58.999998 | 0 days 00:00:00 | HRI | N | STR | Calibration Source | 2 | RFITS V4. |
| 11428 | 218.1540 | -44.2031 | RH001034N00 | False | 1997-08-28 00:29:42.000000 | 1997-08-28 01:09:58.999997 | 0 days 00:00:00 | HRI | N | MISC | 2 | RDF 4_2 | |
| 11429 | 258.1383 | -23.3850 | RH800067M01 | False | 1991-03-19 20:33:48.999997 | 1991-03-19 20:35:59.999997 | 0 days 00:00:00 | HRI | N | GCL | OPHIUCHUS CLUSTER | 2 | RFITS V3. |
| 11430 | 205.4270 | -62.3400 | RH001027N00 | False | 1997-08-25 04:54:58.000003 | 1997-08-25 05:18:28.000000 | 0 days 00:00:00 | HRI | N | MISC | 2 | RDF 4_2 |
11431 rows × 13 columns
[31]:
su.all_obs_info
[31]:
| 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 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 91.1523 | -86.6779 | 701018010 | True | 2006-04-13 16:24:07.999998 | 2006-04-14 01:52:19.000001 | 0 days 09:28:11.000003 | EGS | 0 days 05:29:41.700000 | 2 | 0 days 05:29:41.700000 | 2 | 0 days 05:29:25.700000 | 2 | 0 days 05:29:33.700000 | 2 |
| 1 | 48.9864 | -85.5003 | 404019010 | True | 2009-07-16 14:26:31.000001 | 2009-07-18 03:30:15.999998 | 1 days 13:03:44.999997 | GS | 0 days 17:31:24.300000 | 2 | 0 days 17:31:24.300000 | 2 | 0 days 00:00:00 | 0 | 0 days 17:31:24.300000 | 2 |
| 2 | 239.2836 | -79.2302 | 703059010 | True | 2008-10-13 15:35:23.999997 | 2008-10-15 21:37:21.000000 | 2 days 06:01:57.000003 | EGS | 0 days 22:49:57 | 2 | 0 days 22:49:43.500000 | 2 | 0 days 00:00:00 | 0 | 0 days 22:49:35.500000 | 2 |
| 3 | 265.9438 | -76.3446 | 705013010 | True | 2010-04-14 00:16:05.000002 | 2010-04-14 16:45:11.000002 | 0 days 16:29:06 | EGS | 0 days 11:46:23.600000 | 2 | 0 days 11:46:18.300000 | 2 | 0 days 00:00:00 | 0 | 0 days 11:46:29 | 2 |
| 4 | 74.6207 | -75.2810 | 404036010 | True | 2009-06-14 01:51:18.000003 | 2009-06-16 13:27:19.000002 | 2 days 11:36:00.999999 | GS | 1 days 05:42:37.800000 | 2 | 1 days 05:42:37.800000 | 2 | 0 days 00:00:00 | 0 | 1 days 05:42:29.800000 | 2 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 3036 | 247.6537 | 82.9256 | 706004010 | True | 2011-04-16 00:38:47.000003 | 2011-04-16 10:23:15.000000 | 0 days 09:44:27.999997 | EGS | 0 days 05:13:55.700000 | 3 | 0 days 05:13:42 | 2 | 0 days 00:00:00 | 0 | 0 days 05:13:55.600000 | 2 |
| 3037 | 116.7261 | 85.6829 | 804031010 | True | 2010-02-08 09:53:43.999999 | 2010-02-08 18:15:19.000002 | 0 days 08:21:35.000003 | GCL | 0 days 05:19:56 | 1 | 0 days 05:19:56 | 1 | 0 days 00:00:00 | 0 | 0 days 05:19:56 | 1 |
| 3038 | 112.8158 | 85.6965 | 804030010 | True | 2010-02-08 18:18:12.000001 | 2010-02-09 12:00:25.000004 | 0 days 17:42:13.000003 | GCL | 0 days 10:17:39.100000 | 2 | 0 days 10:17:39.100000 | 2 | 0 days 00:00:00 | 0 | 0 days 10:17:39.100000 | 2 |
| 3039 | 112.8340 | 85.6974 | 804030020 | True | 2010-02-10 04:18:27.999997 | 2010-02-11 06:00:24.000002 | 1 days 01:41:56.000005 | GCL | 0 days 14:36:01.100000 | 2 | 0 days 14:36:01.100000 | 2 | 0 days 00:00:00 | 0 | 0 days 14:36:01.100000 | 2 |
| 3040 | 264.6080 | 87.3047 | 705012010 | True | 2010-04-26 23:41:56.000000 | 2010-04-27 10:16:15.000001 | 0 days 10:34:19.000001 | EGS | 0 days 05:37:59.600000 | 2 | 0 days 05:37:59.600000 | 2 | 0 days 00:00:00 | 0 | 0 days 05:37:59.600000 | 2 |
3041 rows × 16 columns
[32]:
asca.all_obs_info
[32]:
| ra | dec | ObsID | science_usable | start | end | duration | target_category | sis_exposure | gis_exposure | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 288.0116 | 5.0542 | 48002000 | True | 2000-03-23 05:52:13.440 | 2000-04-04 00:00:25.920 | 11 days 18:08:12.480000 | XRB | 5 days 07:25:04 | 5 days 14:47:28 |
| 1 | 253.5540 | 39.8158 | 78002000 | True | 2000-03-01 12:08:12.480 | 2000-03-11 00:00:51.840 | 9 days 11:52:39.360000 | AGN | 4 days 00:45:04 | 5 days 04:32:16 |
| 2 | 14.3115 | -22.4431 | 77036000 | True | 1999-12-03 11:50:38.400 | 1999-12-15 19:00:28.800 | 12 days 07:09:50.400000 | AGN | 4 days 08:14:08 | 5 days 00:42:40 |
| 3 | 203.8856 | -34.3378 | 77003000 | True | 1999-07-19 00:40:19.200 | 1999-07-29 15:40:27.840 | 10 days 15:00:08.640000 | AGN | 4 days 02:03:12 | 4 days 16:33:20 |
| 4 | 266.4864 | -28.9429 | 48004000 | True | 2000-03-11 00:08:29.760 | 2000-03-22 00:00:43.200 | 10 days 23:52:13.440000 | XRB | 3 days 22:37:04 | 4 days 07:30:24 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 3069 | 87.7568 | -32.2321 | 77010010 | True | 1999-10-11 13:47:08.160 | 1999-10-11 14:19:32.160 | 0 days 00:32:24 | AGN | 0 days 00:06:40 | 0 days 00:06:40 |
| 3070 | 150.6550 | -58.6295 | 15000090 | True | 1993-08-05 18:31:58.080 | 1993-08-05 19:12:43.200 | 0 days 00:40:45.120000 | GS | 0 days 00:06:56 | 0 days 00:05:52 |
| 3071 | 335.1555 | -24.7349 | 66013000 | True | 1998-11-16 16:24:48.960 | 1998-11-18 17:34:39.360 | 2 days 01:09:50.400000 | NGS | 0 days 14:38:24 | 0 days 00:00:00 |
| 3072 | 333.8738 | -17.8223 | 77002000 | True | 1999-11-17 04:45:41.760 | 1999-11-19 01:35:54.240 | 1 days 20:50:12.480000 | AGN | 0 days 16:30:24 | 0 days 00:00:00 |
| 3073 | 244.9340 | -15.8493 | 40022010 | True | 1993-08-16 08:40:42.240 | 1993-08-16 10:20:21.120 | 0 days 01:39:38.880000 | XRB | 0 days 00:53:04 | 0 days 00:00:00 |
3074 rows × 10 columns
[33]:
inte.all_obs_info
[33]:
| ra | dec | ObsID | science_usable | proprietary_usable | start | end | duration | target_category | jemx1_exposure | jemx2_exposure | isgri_exposure | picsit_exposure | spi_exposure | scw_ver | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 294.97232 | -89.65222 | 218500510010 | True | True | 2020-01-22 20:40:39.751343 | 2020-01-22 22:17:46.754676 | 0 days 01:37:07.003333 | MISC | 0 days 01:36:12 | 0 days 01:36:12 | 0 days 01:36:10 | 0 days 00:28:29 | 0 days 01:36:12 | 001 |
| 1 | 117.47166 | -89.57144 | 218600510010 | True | True | 2020-01-25 11:40:02.881811 | 2020-01-25 12:38:50.883829 | 0 days 00:58:48.002018 | MISC | 0 days 00:57:57 | 0 days 00:57:57 | 0 days 00:57:55 | 0 days 00:57:27 | 0 days 00:57:57 | 001 |
| 2 | 166.19875 | -89.54264 | 229200810010 | True | True | 2020-11-02 08:42:24.928042 | 2020-11-02 09:41:14.930022 | 0 days 00:58:50.001980 | MISC | 0 days 00:58:00 | 0 days 00:58:00 | 0 days 00:57:44 | 0 days 00:57:29 | 0 days 00:58:00 | 001 |
| 3 | 130.24533 | -89.51414 | 209300110010 | True | True | 2019-05-21 07:51:13.039554 | 2019-05-21 08:24:33.041123 | 0 days 00:33:20.001569 | MISC | 0 days 00:32:25 | 0 days 00:32:25 | 0 days 00:32:23 | 0 days 00:32:04 | 0 days 00:32:25 | 001 |
| 4 | 125.61337 | -89.47669 | 209300370010 | True | True | 2019-05-21 22:51:03.082145 | 2019-05-21 23:24:24.083715 | 0 days 00:33:21.001570 | MISC | 0 days 00:32:30 | 0 days 00:32:30 | 0 days 00:32:28 | 0 days 00:32:02 | 0 days 00:32:30 | 001 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 198281 | 341.90903 | 88.90408 | 260500570010 | True | True | 2023-02-12 08:47:47.388930 | 2023-02-12 09:17:47.390084 | 0 days 00:30:00.001154 | MISC | 0 days 00:29:11 | 0 days 00:29:11 | 0 days 00:29:10 | 0 days 00:28:44 | 0 days 00:29:11 | 001 |
| 198282 | 153.50116 | 88.91008 | 260500660010 | True | True | 2023-02-12 13:36:15.400319 | 2023-02-12 14:06:15.401552 | 0 days 00:30:00.001233 | MISC | 0 days 00:28:40 | 0 days 00:28:40 | 0 days 00:29:09 | 0 days 00:00:00 | 0 days 00:29:10 | 001 |
| 198283 | 213.64879 | 89.06850 | 260900540010 | True | True | 2023-02-22 18:05:50.978884 | 2023-02-22 19:29:20.982045 | 0 days 01:23:30.003161 | MISC | 0 days 01:21:28 | 0 days 01:21:00 | 0 days 01:21:09 | 0 days 00:28:33 | 0 days 01:20:57 | 001 |
| 198284 | 268.11816 | 89.32161 | 260700500010 | True | True | 2023-02-17 10:30:57.695328 | 2023-02-17 11:00:58.696476 | 0 days 00:30:01.001148 | MISC | 0 days 00:29:10 | 0 days 00:29:10 | 0 days 00:29:10 | 0 days 00:28:47 | 0 days 00:29:12 | 001 |
| 198285 | 6.62067 | 89.56795 | 261300370010 | True | True | 2023-03-05 00:34:23.640085 | 2023-03-05 01:04:23.641170 | 0 days 00:30:00.001085 | MISC | 0 days 00:29:04 | 0 days 00:29:04 | 0 days 00:29:48 | 0 days 00:29:19 | 0 days 00:29:52 | 001 |
198286 rows × 15 columns
Filter Array
This is unlikely to ever be accessed directly by the user, but is what defines the observations that a mission currently deems to be accepted/selected. It is a boolean numpy array with a length equal to the number of observations in the all_obs_info dataframe, a True value means the observation is accepted and a False value means it is excluded; all observations start off as accepted.
Various filtering methods can be used to adjust the filter array and set the observations which are to be downloaded/included in a DAXA archive, depending on your particular sample and science case.
It is also possible to manually set this filter array, as is demonstrated below:
[34]:
# The filter array defaults to all True, so all observations are accepted
xm.filter_array
[34]:
array([ True, True, True, ..., True, True, True])
[35]:
# Demonstrating manually setting a filter array - it must be boolean and be the same length as the
# 'all_obs_info' table, otherewise it will not be accepted
demo_filt_arr = np.full(len(xm.all_obs_info), True)
demo_filt_arr[0] = False
xm.filter_array = demo_filt_arr
xm.filter_array
[35]:
array([False, True, True, ..., True, True, True])
Selecting relevant observations
Few users will wish to download, process, and maintain complete observation archives, preferring to just locate data that may be relevant to the sources that they are studying. This can be achieved with the use of several filtering methods which are built into all DAXA missions.
Here we introduce the different filtering methods that are currently implemented for DAXA missions, but we do not provide detailed demonstrations of their use; that is left to specific case studies designed to show scientists with different needs how DAXA can be used in ways that are most relevant to them.
Filtering on ObsID
The most basic filtering method available can be used when you already know which observation(s) you are interested in - if you have the ObsID(s) you can just pass them to the filter_on_obs_ids method, and select only that data:
[36]:
help(xm.filter_on_obs_ids)
Help on method filter_on_obs_ids in module daxa.mission.base:
filter_on_obs_ids(allowed_obs_ids: Union[str, List[str]]) method of daxa.mission.xmm.XMMPointed instance
This filtering method will select only observations with IDs specified by the allowed_obs_ids argument.
Please be aware that filtering methods are cumulative, so running another method will not remove the
filtering that has already been applied, you can use the reset_filter method for that.
:param str/List[str] allowed_obs_ids: The ObsID (or list of ObsIDs) that you wish to be let
through the filter.
Filtering on position
Arguably the most useful type of filtering supported by DAXA missions, the filter_on_positions method allows us to search for observations that are relevant to specific positions on the sky (generally these will represent particular objects). A search can be performed either for a single position, or for a whole sample.
The search_distance argument controls how close the central coordinate of an observation must be to a search position for that observation to be accepted. The default search positions are defined by the field-of-view of the telescope (if a mission has multiple instruments with different field-of-views then each instrument will be searched with the correct field-of-view). The user may also specify their own search distance value (or values), and it is also possible to specify a different
search distance for every position.
The return_pos_obs_info argument controls whether a dataframe is returned that links passed positions to particular observations that are relevant to them - this dataframe would not include positions that are determined to have no relevant observations.
[37]:
help(xm.filter_on_positions)
Help on method filter_on_positions in module daxa.mission.base:
filter_on_positions(positions: Union[list, numpy.ndarray, astropy.coordinates.sky_coordinate.SkyCoord], search_distance: Union[astropy.units.quantity.Quantity, float, int, list, numpy.ndarray, dict] = None, return_pos_obs_info: bool = False) -> Optional[pandas.core.frame.DataFrame] method of daxa.mission.xmm.XMMPointed instance
This method allows you to filter the observations available for a mission based on a set of coordinates for
which you wish to locate observations. The method searches for observations by the current mission that have
central coordinates within the distance set by the search_distance argument.
Please be aware that filtering methods are cumulative, so running another method will not remove the
filtering that has already been applied, you can use the reset_filter method for that.
:param list/np.ndarray/SkyCoord positions: The positions for which you wish to search for observations. They
can be passed either as a list or nested list (i.e. [r, d] OR [[r1, d1], [r2, d2]]), a numpy array, or
an already defined SkyCoord. If a list or array is passed then the coordinates are assumed to be in
degrees, and the default mission frame will be used.
:param Quantity/float/int/list/np.ndarray/dict search_distance: The distance within which to search for
observations by this mission. Distance may be specified either as an Astropy Quantity that can be
converted to degrees (a float/integer will be assumed to be in units of degrees), as a dictionary of
quantities/floats/ints where the keys are names of different instruments (possibly with different field
of views), or as a non-scalar Quantity, list, or numpy array with one entry per set of coordinates (for
when you wish to use different search distances for each object). The default is None, in which case a
value of 1.2 times the approximate field of view defined for each instrument will be used; where different
instruments have different FoVs, observation searches will be undertaken on an instrument-by-instrument
basis using the different field of views.
:param bool return_pos_obs_info: Allows this method to return information (in the form of a Pandas dataframe)
which identifies the positions which have been associated with observations, and the observations they have
been associated with. Default is False.
:return: If return_pos_obs_info is True, then a dataframe containing information on which ObsIDs are relevant
to which positions will be returned. If return_pos_obs_info is False, then None will be returned.
:rtype: Union[None,pd.DataFrame]
Filtering on name
If you are interested in data relevant to named objects, you can pass the name(s) to the filter_on_name method. It will use a lookup service (specifically Sesame), to locate coordinates for the object(s), and then pass that to the filter_on_positions method.
Bear in mind that you are reliant on the lookup service having accurate central coordinates for the named object, so it could be worth checking with your own coordinates and using filter_on_positions directly if you can’t find any observations!
[38]:
help(xm.filter_on_name)
Help on method filter_on_name in module daxa.mission.base:
filter_on_name(object_name: Union[str, List[str]], search_distance: Union[astropy.units.quantity.Quantity, float, int, list, numpy.ndarray, dict] = None, parse_name: bool = False) method of daxa.mission.xmm.XMMPointed instance
This method wraps the 'filter_on_positions' method, and allows you to filter the mission's observations so
that it contains data on a single (or a list of) specific objects. The names are passed by the user, and
then parsed into coordinates using the Sesame resolver. Those coordinates and the search distance are
then used to find observations that might be relevant.
:param str/List[str] object_name: The name(s) of objects you would like to search for.
:param Quantity/float/int/list/np.ndarray/dict search_distance: The distance within which to search for
observations by this mission. Distance may be specified either as an Astropy Quantity that can be
converted to degrees (a float/integer will be assumed to be in units of degrees), as a dictionary of
quantities/floats/ints where the keys are names of different instruments (possibly with different field
of views), or as a non-scalar Quantity, list, or numpy array with one entry per set of coordinates (for
when you wish to use different search distances for each object). The default is None, in which case a
value of 1.2 times the approximate field of view defined for each instrument will be used; where different
instruments have different FoVs, observation searches will be undertaken on an instrument-by-instrument
basis using the different field of views.
:param bool parse_name: Whether to attempt extracting the coordinates from the name by parsing with a regex.
For objects catalog names that have J-coordinates embedded in their names, e.g.,
'CRTS SSS100805 J194428-420209', this may be much faster than a Sesame query for the same object name.
Filtering on target type
It is possible to filter observations based on the type of object that was the original target of the observation. Warning: this should be used with significant caution, as our object taxonomy may not be granular enough to represent all different types of astronomical objects, and conversions between the different target types used by different missions and our target types is imperfect!
Here we display the DAXA source type taxonomy, the short form codes on the left are what should be passed to the filter_on_target_type method - the user may pass either a single target type, or a list of them:
[39]:
xm.show_allowed_target_types()
╒═══════════════╤═══════════════════════════════════════════════╕
│ Target Type │ Description │
╞═══════════════╪═══════════════════════════════════════════════╡
│ AGN │ Active Galaxies and Quasars │
├───────────────┼───────────────────────────────────────────────┤
│ BLZ │ Blazars │
├───────────────┼───────────────────────────────────────────────┤
│ CV │ Cataclysmic Variables │
├───────────────┼───────────────────────────────────────────────┤
│ CAL │ Calibration Observation (possibly of objects) │
├───────────────┼───────────────────────────────────────────────┤
│ EGS │ Extragalactic Surveys │
├───────────────┼───────────────────────────────────────────────┤
│ GCL │ Galaxy Clusters │
├───────────────┼───────────────────────────────────────────────┤
│ GS │ Galactic Survey │
├───────────────┼───────────────────────────────────────────────┤
│ ASK │ All Sky Survey │
├───────────────┼───────────────────────────────────────────────┤
│ MAG │ Magnetars and Rotation-Powered Pulsars │
├───────────────┼───────────────────────────────────────────────┤
│ NGS │ Normal and Starburst Galaxies │
├───────────────┼───────────────────────────────────────────────┤
│ NS │ Neutron stars and Black Holes │
├───────────────┼───────────────────────────────────────────────┤
│ STR │ Non-degenerate and White Dwarf Stars │
├───────────────┼───────────────────────────────────────────────┤
│ OAGN │ Obscured Active Galaxies and Quasars │
├───────────────┼───────────────────────────────────────────────┤
│ SNE │ Non-ToO Supernovae │
├───────────────┼───────────────────────────────────────────────┤
│ SNR │ Supernova Remnants and Galactic diffuse │
├───────────────┼───────────────────────────────────────────────┤
│ SOL │ Solar System Observations │
├───────────────┼───────────────────────────────────────────────┤
│ ULX │ Ultra-luminous X-ray Sources │
├───────────────┼───────────────────────────────────────────────┤
│ XRB │ X-ray Binaries │
├───────────────┼───────────────────────────────────────────────┤
│ TOO │ Targets of Opportunity │
├───────────────┼───────────────────────────────────────────────┤
│ EGE │ Extended galactic or extragalactic │
├───────────────┼───────────────────────────────────────────────┤
│ MISC │ Catch-all for other sources │
╘═══════════════╧═══════════════════════════════════════════════╛
[40]:
help(xm.filter_on_target_type)
Help on method filter_on_target_type in module daxa.mission.base:
filter_on_target_type(target_type: Union[str, List[str]]) method of daxa.mission.xmm.XMMPointed instance
This method allows the filtering of observations based on what type of object their target source was. It
is only supported for missions that have that data available, and will raise an exception for those
missions that don't support this filtering.
WARNING: You should not trust these target types without question, they are the result of crude mappings, and
some may be incorrect. They also don't take into account sources that might serendipitously appear in
a particular observation.
:param str/List[str] target_type: The types of target source you would like to find observations of. For
allowed types, please use the 'show_allowed_target_types' method. Can either be a single type, or
a list of types.
Filtering on time
Observations can be filtered on when they were taken, with the user specifying a time frame (defined by a start and end date-time) from which they wish to select observations. By default any observation that coincides with that time window (either starting in it, ending in it, or starting and ending outside but being taken during it) will be selected - it is also possible to require that an observation must have taken place entirely within the time window.
Warning: Observations of survey missions like eRASS1DE and ROSATAllSky may repeatedly visit a particular location, and all that data may be incorporated as one observation, meaning that there may not constant coverage in such an observation
[41]:
help(xm.filter_on_time)
Help on method filter_on_time in module daxa.mission.base:
filter_on_time(start_datetime: datetime.datetime, end_datetime: datetime.datetime, over_run: bool = True) method of daxa.mission.xmm.XMMPointed instance
This method allows you to filter observations for this mission based on when they were taken. A start
and end time are passed by the user, and observations that fall within that window are allowed through
the filter. The exact behaviour of this filtering method is controlled by the over_run argument, if set
to True then observations with a start or end within the search window will be selected, but if False
then only observations with a start AND end within the window are selected.
Please be aware that filtering methods are cumulative, so running another method will not remove the
filtering that has already been applied, you can use the reset_filter method for that.
:param datetime start_datetime: The beginning of the time window in which to search for observations.
:param datetime end_datetime: The end of the time window in which to search for observations.
:param bool over_run: This controls whether selected observations have to be entirely within the passed
time window or whether either a start or end time can be within the search window. If set
to True then observations with a start or end within the search window will be selected, but if False
then only observations with a start AND end within the window are selected. Default is True.
Filtering on time & position
A method of filtering that combines positional and temporal filtering, so that observations of a particular position, within a particular time window, can be located. The user does not have to filter for one position-time combination at a time - they may pass a set of positions, with a corresponding set of time windows, and find all the observations that fulfill those requirements.
The filter_on_positions_at_time method supports the same arguments passed to filter_on_positions and filter_on_time, which set search distances from the passed coordinate (search_distance), whether a dataframe linking particular input coordinates to particular selected observations (return_obs_info), and whether only observations that start and end within the specified time period should be considered (over_run).
[42]:
help(xm.filter_on_positions_at_time)
Help on method filter_on_positions_at_time in module daxa.mission.base:
filter_on_positions_at_time(positions: Union[list, numpy.ndarray, astropy.coordinates.sky_coordinate.SkyCoord], start_datetimes: Union[numpy.ndarray, datetime.datetime], end_datetimes: Union[numpy.ndarray, datetime.datetime], search_distance: Union[astropy.units.quantity.Quantity, float, int, list, numpy.ndarray, dict] = None, return_obs_info: bool = False, over_run: bool = True) method of daxa.mission.xmm.XMMPointed instance
This method allows you to filter the observations available for a mission based on a set of coordinates for
which you wish to locate observations that were taken within a certain time frame. The method spatially
searches for observations that have central coordinates within the distance set by the search_distance
argument, and temporally by start and end times passed by the user; and observations that fall within that
window are allowed through the filter.
The exact behaviour of the temporal filtering method is controlled by the over_run argument, if set
to True then observations with a start or end within the search window will be selected, but if False
then only observations with a start AND end within the window are selected.
Please be aware that filtering methods are cumulative, so running another method will not remove the
filtering that has already been applied, you can use the reset_filter method for that.
:param list/np.ndarray/SkyCoord positions: The positions for which you wish to search for observations. They
can be passed either as a list or nested list (i.e. [r, d] OR [[r1, d1], [r2, d2]]), a numpy array, or
an already defined SkyCoord. If a list or array is passed then the coordinates are assumed to be in
degrees, and the default mission frame will be used.
:param np.array(datetime)/datetime start_datetimes: The beginnings of time windows in which to search for
observations. There should be one entry per position passed.
:param np.array(datetime)/datetime end_datetimes: The endings of time windows in which to search for
observations. There should be one entry per position passed.
:param Quantity/float/int/list/np.ndarray/dict search_distance: The distance within which to search for
observations by this mission. Distance may be specified either as an Astropy Quantity that can be
converted to degrees (a float/integer will be assumed to be in units of degrees), as a dictionary of
quantities/floats/ints where the keys are names of different instruments (possibly with different field
of views), or as a non-scalar Quantity, list, or numpy array with one entry per set of coordinates (for
when you wish to use different search distances for each object). The default is None, in which case a
value of 1.2 times the approximate field of view defined for each instrument will be used; where different
instruments have different FoVs, observation searches will be undertaken on an instrument-by-instrument
basis using the different field of views.
:param bool return_obs_info: Allows this method to return information (in the form of a Pandas dataframe)
which identifies the positions which have been associated with observations, in the specified time
frame, and the observations they have been associated with. Default is False.
:param bool over_run: This controls whether selected observations have to be entirely within the passed
time window or whether either a start or end time can be within the search window. If set
to True then observations with a start or end within the search window will be selected, but if False
then only observations with a start AND end within the window are selected. Default is True.
Downloading data
Once the user has decided upon a set of observations, for a particular mission, that are relevant to their research - the next step is often to download them. This section specifies what can be downloaded, and how to download it.
What can be downloaded?
The exact data that can be downloaded depends on what a particular mission has made available on their online archive - Event lists are available for all missions bar INTEGRAL, and most missions support the acquisition of pre-generated images, but not all (INTEGRAL and eROSITA CalPV) for instance).
We make a distinction between ‘raw’ and ‘pre-processed’ data, which is necessarily fuzzy due to the disparate natures of the various data archives we have to deal with. Broadly speaking ‘raw’ data means either absolutely raw files that need to be processed into initial event lists (the case with XMMPointed) or just event lists (uncleaned and pre-cleaned); ‘pre-processed’ data includes pre-processed data products such as images, exposure maps, and background maps.
This means that the user can either set up an archive using the pre-processed data, or re-process data using DAXA interfaces to the various telescope backend software packages.
Pre-processed products are downloaded by default (in addition to the raw data), but when setting up an Archive you can choose between using pre-processed data or re-processing the raw data; if you do not wish to acquire pre-processed data products, you can pass download_products=False to the download method (see below).
Event lists are considered raw data (i.e. not pre-processed) for most missions, and will always be downloaded
XMM-Newton
Currently we only support the acquisition of raw data.
Chandra
The following data products can be downloaded:
Full FoV image (if an Archive is constructed including pre-processed Chandra data, this is what is included).
High-resolution central image.
eROSITA All-Sky DR1 (German Half)
The following data products can be downloaded:
Images
Exposure maps
Background maps
eROSITA Calibration and Performance Verification
No pre-processed data products are available, only event lists.
Swift
The following data products can be downloaded (no products are available for BAT, only raw data):
Images (XRT and UVOT)
Exposure maps (XRT and UVOT)
ROSAT-Pointed
The following data products can be downloaded:
Images
Exposure maps (only PSPC, no HRI)
ASCA
The following data products can be downloaded:
Images (SIS0 + SIS1, and GIS2 + GIS3)
Exposure maps
Lightcurves (not included in Archive constructed from pre-processed data)
Spectra (not included in Archive constructed from pre-processed data)
INTEGRAL-Pointed
No pre-processed data (nor even event lists) are available to download for INTEGRAL, only raw data and calibration files.
How can it be downloaded?
The selected data can be downloaded using the download method that is built into each mission class.
If multiple observations have been selected, then downloads can be multi-threaded; this can sometimes offer a speed benefit, though only if the archive and user internet connections are not saturated by the download. The number of cores used can be set via the num_cores argument; by defualt this is set to the NUM_CORES DAXA constant, which can either be set by the user in the DAXA configuration file/by setting the value of daxa.NUM_CORES, or will be 90% of the cores of the system.
Note that it is not necessary to manually activate the download method if you will be creating a DAXA Archive from the filtered mission objects, as that will be done on archive initialisation -see the Archive tutorial for more information.
Here we demonstrate a simple XMM download, as well as a Chandra download that includes pre-generated images (standard Chandra reprocessing scripts can be used on this downloaded data):
[43]:
xm.filter_on_obs_ids('0201903501')
xm.download(num_cores=1)
ch.filter_on_obs_ids('3205')
ch.download(num_cores=1, download_products=True)
Downloading XMM-Newton Pointed data: 100%|██████████████████████████████████████| 1/1 [00:32<00:00, 32.13s/it]
Downloading Chandra data: 100%|█████████████████████████████████████████████████| 1/1 [00:31<00:00, 31.23s/it]
Getting paths to downloaded mission data products
If pre-processed data have been downloaded, it is possible to use methods built into the mission class to retrieve the paths to various data products. If you intend on using an XGA archive, using these methods should not be necessary as the data will be moved to the Archive processed data storage structure, but it may still be useful if you just wish to use DAXA to download data.
There are four different get methods:
get_evt_list_path- to retrieve event lists.get_image_path- to retrieve images.get_expmap_path- to retrieve exposure maps.get_background_path- to retrieve background maps.
They are very easy to use - only the ObsID, instrument (in most cases, not necessary if the mission has only one instrument per ObsID), and lower/upper energy bound (for images, exposure maps, and background maps - though if the products only have one energy band it will be completed automatically) need to be provided. They also provide helpful, detailed, error messages if what is requested isn’t possible.
Here we retrive the path to the event list (note that we do not need to pass the instrument name, as Chandra has only one per ObsID):
[44]:
ch.get_evt_list_path('3205')
[44]:
'/Users/dt237/code/DAXA/docs/source/notebooks/tutorials/daxa_output/chandra_raw/3205/primary/acisf03205N006_evt2.fits'
We can also retrieve the path to the full-FoV image (again we don’t need to pass instrument in this specific instance, nor do we need to pass energy bounds because Chandra only supplies one):
[45]:
ch.get_image_path('3205')
[45]:
'/Users/dt237/code/DAXA/docs/source/notebooks/tutorials/daxa_output/chandra_raw/3205/primary/acisf03205N006_full_img2.fits'
A more typical way of using this function would be this:
[46]:
ch.get_image_path('3205', Quantity(0.5, 'keV'), Quantity(7, 'keV'), 'ACIS-I')
[46]:
'/Users/dt237/code/DAXA/docs/source/notebooks/tutorials/daxa_output/chandra_raw/3205/primary/acisf03205N006_full_img2.fits'
The get methods for exposure and background map paths operate identically to the image path method, though will show an error for Chandra:
[47]:
ch.get_expmap_path('3205', Quantity(0.5, 'keV'), Quantity(7, 'keV'), 'ACIS-I')
---------------------------------------------------------------------------
PreProcessedNotSupportedError Traceback (most recent call last)
Cell In [47], line 1
----> 1 ch.get_expmap_path('3205', Quantity(0.5, 'keV'), Quantity(7, 'keV'), 'ACIS-I')
File ~/code/DAXA/daxa/mission/base.py:2034, in BaseMission.get_expmap_path(self, obs_id, lo_en, hi_en, inst)
2022 """
2023 A get method that provides the path to a downloaded pre-generated exposure map for the current mission (if
2024 available). This method will not work if pre-processed data have not been downloaded.
(...)
2031 :rtype: str
2032 """
2033 if self._template_exp_name is None:
-> 2034 raise PreProcessedNotSupportedError("This mission ({m}) does not support the download of pre-processed "
2035 "exposure maps, so a path cannot be "
2036 "provided.".format(m=self.pretty_name))
2038 if lo_en is not None:
2039 # We make sure that the provided energy bounds are in keV
2040 lo_en = lo_en.to('keV')
PreProcessedNotSupportedError: This mission (Chandra) does not support the download of pre-processed exposure maps, so a path cannot be provided.
Saving mission state
It is possible to save the current ‘state’ of a mission object to a file - which in turn can be used to reload a mission class and have it be in that same state. This is primarily used by internal DAXA methods, to allow saved archives to reinstate their constituent mission classes when they are loaded back in.
However, it is also possible to do this manually, in order to reload missions as they were when they were saved. The save file could also be shared with other users, to allow them to create a mission in the same state.
The state of the mission includes the filtering procedures performed upon it, and the current filtered data - this means that the observations selected when the mission was saved will be reloaded exactly, and the same filtering steps can be re-applied on reload to update the mission (if more observations have become available).
Saving a mission state is simple - call the save() method and pass a root storage directory and optionally a file name (if this is not supplied the save file will be the mission name + ‘_state.json’):
[48]:
xm.save("demo_save/", "demo_xmm_save.json")
A mission can then be reinstated from the file on declaration:
[49]:
reinstated_xm = XMMPointed(save_file_path="demo_save/demo_xmm_save.json")
reinstated_xm.filtered_obs_info
/Users/dt237/code/DAXA/daxa/mission/xmm.py:83: UserWarning: 140 of the 17701 observations located for this mission have been removed due to NaN RA or Dec values
self._fetch_obs_info()
[49]:
| ra | dec | ObsID | start | science_usable | duration | proprietary_end_date | revolution | proprietary_usable | end | |
|---|---|---|---|---|---|---|---|---|---|---|
| 3527 | 149.592285 | -11.0597 | 0201903501 | 2004-06-17 10:38:28 | True | 0 days 04:08:33 | 2004-06-17 | 828 | True | 2004-06-17 14:47:01 |
If some time has passed since the mission state was saved, and you wish to re-run the original filtering procedures to check if new data are available, you can call the update_filtering() method.