diff --git a/daxa/config.py b/daxa/config.py
index 89cb7729..03d674ef 100644
--- a/daxa/config.py
+++ b/daxa/config.py
@@ -1,5 +1,5 @@
 #  This code is a part of the Democratising Archival X-ray Astronomy (DAXA) module.
-#  Last modified by David J Turner (turne540@msu.edu) 18/01/2023, 15:25. Copyright (c) The Contributors
+#  Last modified by David J Turner (turne540@msu.edu) 31/01/2024, 11:35. Copyright (c) The Contributors
 
 import os
 from configparser import ConfigParser
@@ -71,5 +71,14 @@
 SASERROR_LIST = errors["ErrName"].values
 SASWARNING_LIST = warnings["WarnName"].values
 
-# Reading in the data available in the Cal-PV release
-CALPV_INFO = pd.read_csv(pkg_resources.resource_filename(__name__, "files/CALPV_INFO.csv"), header="infer",  dtype={'ObsID': str})
\ No newline at end of file
+# Reading in the file with information on the eROSITA observations that were made available in the
+#  eROSITA CalPV release
+EROSITA_CALPV_INFO = pd.read_csv(pkg_resources.resource_filename(__name__, "files/erosita_calpv_info.csv"),
+                                 header="infer", dtype={'ObsID': str})
+# TODO This may end up changing when we get access to the DR1 release - it could be in a format that makes this
+#  a bad way of doing it
+# Then doing the same thing, but for the German eRASS:1 release
+ERASS_DE_DR1_INFO = pd.read_csv(pkg_resources.resource_filename(__name__, "files/erass_de_dr1_info.csv"),
+                                header="infer", dtype={'ObsID': str, 'FIELD1': str, 'FIELD2': str, 'FIELD3': str,
+                                                       'FIELD4': str, 'FIELD5': str, 'FIELD6': str, 'FIELD7': str,
+                                                       'FIELD8': str, 'FIELD9': str})
diff --git a/daxa/files/CALPV_INFO.csv b/daxa/files/CALPV_INFO.csv
deleted file mode 100644
index 259a340e..00000000
--- a/daxa/files/CALPV_INFO.csv
+++ /dev/null
@@ -1,171 +0,0 @@
-,ObsID,start,duration,end,ra,dec,science_usable,Field_Name,Field_Type,download
-0,300007,2019-11-03T02:42:50,89627.0,2019-11-04T03:36:37,129.55,1.5,True,EFEDS,SURVEY,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/eFEDS.tar.gz
-1,300008,2019-11-04T03:49:16,91643.0,2019-11-05T05:16:39,133.86,1.5,True,EFEDS,SURVEY,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/eFEDS.tar.gz
-2,300009,2019-11-05T05:29:18,90648.0,2019-11-06T06:40:06,138.14,1.5,True,EFEDS,SURVEY,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/eFEDS.tar.gz
-3,300010,2019-11-06T07:24:46,89722.0,2019-11-07T08:20:08,142.45,1.5,True,EFEDS,SURVEY,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/eFEDS.tar.gz
-4,300004,2019-11-16T23:14:40,154952.0,2019-11-18T18:17:12,130.3313,-78.9634,True,ETA_CHA,SURVEY,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/eta_Cha.tar.gz
-5,700016,2019-09-15T09:23:10,113825.0,2019-09-16T17:00:15,83.836372375488,-69.311386108398,True,LMC_SN1987A,MAGELLANIC_CLOUDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/LMC_SN1987A.tar.gz
-6,700161,2019-10-18T16:49:59,80313.0,2019-10-19T15:08:32,83.836375,-69.311388888889,True,LMC_SN1987A,MAGELLANIC_CLOUDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/LMC_SN1987A.tar.gz
-7,700203,2019-12-06T19:03:45,31712.0,2019-12-07T03:52:17,83.836375,-69.311388888889,True,LMC_SN1987A,MAGELLANIC_CLOUDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/LMC_SN1987A.tar.gz
-8,700204,2019-12-07T03:58:56,31712.0,2019-12-07T12:47:28,83.836375,-69.311388888889,True,LMC_SN1987A,MAGELLANIC_CLOUDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/LMC_SN1987A.tar.gz
-9,700156,2019-10-10T01:54:47,43559.0,2019-10-10T14:00:46,81.25917,-69.64417,True,LMC_N132D,MAGELLANIC_CLOUDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/LMC_N132D.tar.gz
-10,700179,2019-11-22T15:53:20,59489.0,2019-11-23T08:24:49,81.25917,-69.64417,True,LMC_N132D,MAGELLANIC_CLOUDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/LMC_N132D.tar.gz
-11,700182,2019-11-27T06:07:24,40296.0,2019-11-27T17:19:00,80.510115854516,-69.85367143598,True,LMC_N132D,MAGELLANIC_CLOUDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/LMC_N132D.tar.gz
-12,700183,2019-11-25T18:57:15,41034.0,2019-11-26T06:21:09,81.993496158843,-69.431537504153,True,LMC_N132D,MAGELLANIC_CLOUDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/LMC_N132D.tar.gz
-13,700184,2019-11-23T21:00:37,40292.0,2019-11-24T08:12:09,80.687582578861,-69.377649654414,True,LMC_N132D,MAGELLANIC_CLOUDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/LMC_N132D.tar.gz
-14,700185,2019-11-25T07:58:39,41267.0,2019-11-25T19:26:26,81.900481092646,-69.892924133714,True,LMC_N132D,MAGELLANIC_CLOUDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/LMC_N132D.tar.gz
-15,700201,2019-12-02T08:59:40,43230.0,2019-12-02T21:00:10,81.25917,-69.64417,True,LMC_N132D,MAGELLANIC_CLOUDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/LMC_N132D.tar.gz
-16,700202,2019-12-04T00:59:40,43230.0,2019-12-04T13:00:10,81.25917,-69.64417,True,LMC_N132D,MAGELLANIC_CLOUDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/LMC_N132D.tar.gz
-17,700206,2019-12-07T21:03:24,21801.0,2019-12-08T03:06:45,85.04515838623,-69.331733703613,True,PSR_J0540_6919,MAGELLANIC_CLOUDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/PSR_J0540m6919.tar.gz
-18,700205,2019-12-07T12:43:24,31641.0,2019-12-07T21:30:45,84.444427490234,-69.171409606934,True,PSR_J0537_6910,MAGELLANIC_CLOUDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/PSR_J0537m6910.tar.gz
-19,700207,2019-12-08T02:39:24,37274.0,2019-12-08T13:00:38,84.742172241211,-69.250358581543,True,PSR_J0540_PSR_J0537,MAGELLANIC_CLOUDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/PSR_J0540_PSR_J0537.tar.gz
-20,700001,2019-11-07T17:03:44,61579.0,2019-11-08T10:10:03,16.008333333333,-72.031944444444,True,1ES_0102_72,MAGELLANIC_CLOUDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/1ES_0102m72.tar.gz
-21,700002,2019-11-08T09:40:42,61041.0,2019-11-09T02:38:03,15.036871800215,-71.888384341232,True,1ES_0102_72,MAGELLANIC_CLOUDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/1ES_0102m72.tar.gz
-22,700003,2019-11-09T02:37:45,61238.0,2019-11-09T19:38:23,16.994661672799,-72.170596682228,True,1ES_0102_72,MAGELLANIC_CLOUDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/1ES_0102m72.tar.gz
-23,700004,2019-11-09T19:09:02,57001.0,2019-11-10T10:59:03,16.282350461656,-71.850878735899,True,1ES_0102_72,MAGELLANIC_CLOUDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/1ES_0102m72.tar.gz
-24,700005,2019-11-10T10:29:12,56006.0,2019-11-11T02:02:38,16.419013700816,-71.760440299341,True,1ES_0102_72,MAGELLANIC_CLOUDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/1ES_0102m72.tar.gz
-25,700018,2019-10-05T14:56:50,117459.0,2019-10-06T23:34:29,119.58333333333,-60.870277777778,True,NGC_2516,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/NGC_2516.tar.gz
-26,700019,2019-10-31T17:34:59,80263.0,2019-11-01T15:52:42,119.58333333333,-60.870277777778,True,NGC_2516,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/NGC_2516.tar.gz
-27,300000,2019-10-14T11:36:38,100246.0,2019-10-15T15:27:24,104.9507625,14.239316666667,True,PSR_B0656_14,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/PSR_B0656p14.tar.gz
-28,900035,2019-09-10T15:13:24,22641.0,2019-09-10T21:30:45,228.84419250488,-59.09810256958,True,PSR_1509_58,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/PSR_1509m58.tar.gz
-29,900043,2019-09-14T15:13:24,28039.0,2019-09-14T23:00:43,228.84716796875,-59.106525421143,True,PSR_1509_58,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/PSR_1509m58.tar.gz
-30,900033,2019-09-09T15:13:24,22641.0,2019-09-09T21:30:45,195.9973449707,-63.97021484375,True,IGR_J13020_6359__2RXP_J130159_635806_,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/IGR_J13020m6359.tar.gz
-31,900062,2019-09-25T15:23:24,22041.0,2019-09-25T21:30:45,248.23999023438,-48.741333007812,True,IGR_J16318_4848,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/IGR_J16318m4848.tar.gz
-32,900072,2019-10-01T14:57:43,21783.0,2019-10-01T21:00:46,255.44781494141,-41.563358306885,True,OAO_1657_415,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/OAO_1657m415.tar.gz
-33,700007,2019-10-16T22:21:52,90249.0,2019-10-17T23:26:01,110.104,-31.430613888889,True,1RXS_J072025_312554,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/1RXS_J072025m312554.tar.gz
-34,700039,2019-10-29T08:51:34,59880.0,2019-10-30T01:29:34,132.19041666667,-45.614166666667,True,VELA_SNR,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Vela_SNR.tar.gz
-35,700199,2019-11-29T17:18:35,30790.0,2019-11-30T01:51:45,126.0325,-42.996666666667,True,PUPPIS_A,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Puppis_A.tar.gz
-36,700200,2019-11-30T02:25:15,30217.0,2019-11-30T10:48:52,126.0325,-42.996666666667,True,PUPPIS_A,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Puppis_A.tar.gz
-37,700167,2019-11-22T10:03:20,19631.0,2019-11-22T15:30:31,120.89583333333,-40.003055555556,True,HR_3165__ZET_PUP_,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/HR_3165.tar.gz
-38,900056,2019-09-21T23:14:30,45962.0,2019-09-22T12:00:32,83.469215393066,21.877420425415,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
-39,900075,2019-10-02T21:51:44,3601.0,2019-10-02T22:51:45,82.570663452148,21.874715805054,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
-40,900076,2019-10-02T22:21:24,4221.0,2019-10-02T23:31:45,82.750221252441,21.875782012939,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
-41,900077,2019-10-02T23:01:24,3621.0,2019-10-03T00:01:45,82.929794311523,21.876649856567,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
-42,900078,2019-10-02T23:31:24,3621.0,2019-10-03T00:31:45,83.037544250488,21.87707901001,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
-43,900079,2019-10-03T00:01:24,3530.0,2019-10-03T01:00:14,83.109382629395,21.877325057983,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
-44,900080,2019-10-03T00:31:24,3621.0,2019-10-03T01:31:45,83.145301818848,21.877435684204,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
-45,900081,2019-10-03T01:01:24,3201.0,2019-10-03T01:54:45,83.199180603027,21.877588272095,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
-46,900082,2019-10-03T01:24:24,3201.0,2019-10-03T02:17:45,83.253051757812,21.877721786499,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
-47,900083,2019-10-03T01:47:24,3201.0,2019-10-03T02:40:45,83.306930541992,21.877838134766,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
-48,900084,2019-10-03T02:10:24,3201.0,2019-10-03T03:03:45,83.342849731445,21.877906799316,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
-49,900085,2019-10-03T02:33:25,3199.0,2019-10-03T03:26:44,83.378768920898,21.877967834473,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
-50,900086,2019-10-03T02:56:24,3201.0,2019-10-03T03:49:45,83.414688110352,21.878019332886,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
-51,900087,2019-10-03T03:19:25,3200.0,2019-10-03T04:12:45,83.450607299805,21.878065109253,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
-52,900088,2019-10-03T03:42:24,3201.0,2019-10-03T04:35:45,83.468566894531,21.878084182739,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
-53,900089,2019-10-03T04:05:25,3199.0,2019-10-03T04:58:44,83.486526489258,21.878101348877,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
-54,900090,2019-10-03T04:28:24,3201.0,2019-10-03T05:21:45,83.522445678711,21.878129959106,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
-55,900091,2019-10-03T04:59:47,2698.0,2019-10-03T05:44:45,83.558364868164,21.878150939941,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
-56,900092,2019-10-03T05:14:24,3201.0,2019-10-03T06:07:45,83.594284057617,21.878164291382,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
-57,900093,2019-10-03T05:37:25,3199.0,2019-10-03T06:30:44,83.63020324707,21.878170013428,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
-58,900094,2019-10-03T06:00:24,3201.0,2019-10-03T06:53:45,83.68408203125,21.878164291382,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
-59,900095,2019-10-03T06:23:24,3201.0,2019-10-03T07:16:45,83.73796081543,21.87813949585,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
-60,900096,2019-10-03T06:46:24,3620.0,2019-10-03T07:46:44,83.791839599609,21.87809753418,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
-61,900097,2019-10-03T07:16:25,3620.0,2019-10-03T08:16:45,83.827758789062,21.878059387207,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
-62,900098,2019-10-03T07:46:25,3619.0,2019-10-03T08:46:44,83.899597167969,21.877962112427,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
-63,900099,2019-10-03T08:16:26,3618.0,2019-10-03T09:16:44,84.007347106934,21.877754211426,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
-64,900100,2019-10-03T08:59:47,3417.0,2019-10-03T09:56:44,84.186920166016,21.877252578735,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
-65,900101,2019-10-03T09:26:26,3262.0,2019-10-03T10:20:48,84.366485595703,21.876556396484,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
-66,900102,2019-10-03T10:21:07,3337.0,2019-10-03T11:16:44,82.836494445801,21.287460327148,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
-67,900103,2019-10-03T10:46:26,4219.0,2019-10-03T11:56:45,82.96248626709,21.40576171875,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
-68,900104,2019-10-03T11:26:25,3620.0,2019-10-03T12:26:45,83.088691711426,21.523977279663,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
-69,900105,2019-10-03T11:56:25,3620.0,2019-10-03T12:56:45,83.164512634277,21.594863891602,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
-70,900106,2019-10-03T12:26:24,3621.0,2019-10-03T13:26:45,83.215103149414,21.642105102539,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
-71,900107,2019-10-03T12:59:45,3420.0,2019-10-03T13:56:45,83.240417480469,21.665719985962,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
-72,900108,2019-10-03T13:26:24,3201.0,2019-10-03T14:19:45,83.278396606445,21.701133728027,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
-73,900109,2019-10-03T13:49:24,3201.0,2019-10-03T14:42:45,83.316390991211,21.736541748047,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
-74,900110,2019-10-03T14:12:24,3201.0,2019-10-03T15:05:45,83.35440826416,21.771940231323,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
-75,900111,2019-10-03T14:35:24,3201.0,2019-10-03T15:28:45,83.379760742188,21.795534133911,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
-76,900112,2019-10-03T14:58:25,3199.0,2019-10-03T15:51:44,83.405128479004,21.819124221802,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
-77,900113,2019-10-03T15:21:24,3201.0,2019-10-03T16:14:45,83.43049621582,21.842710494995,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
-78,900114,2019-10-03T15:44:24,3201.0,2019-10-03T16:37:45,83.455879211426,21.86629486084,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
-79,900115,2019-10-03T16:07:24,3170.0,2019-10-03T17:00:14,83.468566894531,21.878084182739,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
-80,900116,2019-10-03T16:30:25,3199.0,2019-10-03T17:23:44,83.481262207031,21.889873504639,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
-81,900117,2019-10-03T16:59:45,2820.0,2019-10-03T17:46:45,83.506660461426,21.91344833374,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
-82,900118,2019-10-03T17:16:24,3201.0,2019-10-03T18:09:45,83.532066345215,21.937019348145,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
-83,900119,2019-10-03T17:39:24,3201.0,2019-10-03T18:32:45,83.557479858398,21.960586547852,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
-84,900120,2019-10-03T18:02:25,3199.0,2019-10-03T18:55:44,83.582901000977,21.984149932861,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
-85,900121,2019-10-03T18:25:24,3201.0,2019-10-03T19:18:45,83.621047973633,22.019487380981,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
-86,900122,2019-10-03T18:48:24,3201.0,2019-10-03T19:41:45,83.659217834473,22.054817199707,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
-87,900123,2019-10-03T19:11:24,3620.0,2019-10-03T20:11:44,83.697402954102,22.090135574341,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
-88,900124,2019-10-03T19:41:25,3620.0,2019-10-03T20:41:45,83.722869873047,22.113677978516,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
-89,900125,2019-10-03T20:11:25,2928.0,2019-10-03T21:00:13,83.773826599121,22.160747528076,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
-90,900126,2019-10-03T20:41:26,3618.0,2019-10-03T21:41:44,83.850326538086,22.231323242188,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
-91,900127,2019-10-03T21:11:24,4220.0,2019-10-03T22:21:44,83.977996826172,22.348863601685,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
-92,900128,2019-10-03T21:51:26,3259.0,2019-10-03T22:45:45,84.10587310791,22.466297149658,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
-93,900129,2019-10-03T22:46:03,3340.0,2019-10-03T23:41:43,83.468925476074,21.045421600342,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
-94,900130,2019-10-03T23:11:25,4220.0,2019-10-04T00:21:45,83.468856811523,21.211936950684,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
-95,900131,2019-10-03T23:51:24,3621.0,2019-10-04T00:51:45,83.468788146973,21.378463745117,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
-96,900132,2019-10-04T00:21:25,3620.0,2019-10-04T01:21:45,83.468742370605,21.478384017944,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
-97,900133,2019-10-04T00:59:44,3121.0,2019-10-04T01:51:45,83.468719482422,21.544998168945,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
-98,900134,2019-10-04T01:21:24,3621.0,2019-10-04T02:21:45,83.468704223633,21.57830619812,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
-99,900135,2019-10-04T01:51:24,3201.0,2019-10-04T02:44:45,83.468681335449,21.628269195557,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
-100,900136,2019-10-04T02:14:24,3201.0,2019-10-04T03:07:45,83.468658447266,21.678232192993,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
-101,900137,2019-10-04T02:37:24,3201.0,2019-10-04T03:30:45,83.468635559082,21.728193283081,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
-102,900138,2019-10-04T03:00:24,3201.0,2019-10-04T03:53:45,83.468620300293,21.761503219604,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
-103,900139,2019-10-04T03:23:25,3199.0,2019-10-04T04:16:44,83.468605041504,21.794811248779,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
-104,900140,2019-10-04T03:46:24,3201.0,2019-10-04T04:39:45,83.468589782715,21.828121185303,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
-105,900141,2019-10-04T04:09:24,3048.0,2019-10-04T05:00:12,83.468574523926,21.861429214478,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
-106,900142,2019-10-04T04:32:24,3201.0,2019-10-04T05:25:45,83.468566894531,21.878084182739,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
-107,900143,2019-10-04T04:59:45,2939.0,2019-10-04T05:48:44,83.468559265137,21.894739151001,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
-108,900144,2019-10-04T05:18:24,3201.0,2019-10-04T06:11:45,83.468544006348,21.928047180176,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
-109,900145,2019-10-04T05:41:24,3201.0,2019-10-04T06:34:45,83.468528747559,21.961357116699,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
-110,900146,2019-10-04T06:04:24,3201.0,2019-10-04T06:57:45,83.46851348877,21.994665145874,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
-111,900147,2019-10-04T06:27:25,3199.0,2019-10-04T07:20:44,83.46849822998,22.027973175049,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
-112,900148,2019-10-04T06:50:24,3201.0,2019-10-04T07:43:45,83.468475341797,22.077936172485,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
-113,900149,2019-10-04T07:13:24,3201.0,2019-10-04T08:06:45,83.468452453613,22.127899169922,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
-114,900150,2019-10-04T07:36:24,3620.0,2019-10-04T08:36:44,83.46842956543,22.177862167358,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
-115,900151,2019-10-04T08:06:25,3226.0,2019-10-04T09:00:11,83.468414306641,22.211170196533,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
-116,900152,2019-10-04T08:36:25,3619.0,2019-10-04T09:36:44,83.468383789062,22.277784347534,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
-117,900153,2019-10-04T09:06:26,3617.0,2019-10-04T10:06:43,83.468338012695,22.37770652771,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
-118,900154,2019-10-04T09:36:24,4220.0,2019-10-04T10:46:44,83.468254089355,22.544233322144,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
-119,900155,2019-10-04T10:16:26,3256.0,2019-10-04T11:10:42,83.46817779541,22.710750579834,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
-120,900156,2019-10-04T11:11:00,3345.0,2019-10-04T12:06:45,84.101165771484,21.288724899292,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
-121,900157,2019-10-04T11:36:25,4220.0,2019-10-04T12:46:45,83.975067138672,21.40678024292,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
-122,900158,2019-10-04T12:16:26,3619.0,2019-10-04T13:16:45,83.848762512207,21.524745941162,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
-123,900159,2019-10-04T12:59:46,2819.0,2019-10-04T13:46:45,83.772872924805,21.59547996521,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
-124,900160,2019-10-04T13:16:24,3621.0,2019-10-04T14:16:45,83.722244262695,21.64262008667,True,CRAB_4,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_IV.tar.gz
-125,900161,2019-10-04T13:46:24,3621.0,2019-10-04T14:46:45,83.696914672852,21.66618347168,True,CRAB_4,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_IV.tar.gz
-126,900162,2019-10-04T14:16:24,3201.0,2019-10-04T15:09:45,83.658905029297,21.7015209198,True,CRAB_4,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_IV.tar.gz
-127,900163,2019-10-04T14:39:24,3201.0,2019-10-04T15:32:45,83.620872497559,21.736850738525,True,CRAB_4,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_IV.tar.gz
-128,900164,2019-10-04T15:02:24,3201.0,2019-10-04T15:55:45,83.582824707031,21.772172927856,True,CRAB_4,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_IV.tar.gz
-129,900165,2019-10-04T15:25:24,3201.0,2019-10-04T16:18:45,83.55744934082,21.795715332031,True,CRAB_4,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_IV.tar.gz
-130,900166,2019-10-04T15:48:25,3199.0,2019-10-04T16:41:44,83.532066345215,21.819253921509,True,CRAB_4,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_IV.tar.gz
-131,900167,2019-10-04T16:11:27,2928.0,2019-10-04T17:00:15,83.506675720215,21.842788696289,True,CRAB_4,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_IV.tar.gz
-132,900168,2019-10-04T16:34:24,3201.0,2019-10-04T17:27:45,83.481269836426,21.866319656372,True,CRAB_4,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_IV.tar.gz
-133,900169,2019-10-04T17:00:05,3040.0,2019-10-04T17:50:45,83.468566894531,21.878084182739,True,CRAB_4,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_IV.tar.gz
-134,900170,2019-10-04T17:20:25,3199.0,2019-10-04T18:13:44,83.455863952637,21.889846801758,True,CRAB_4,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_IV.tar.gz
-135,900171,2019-10-04T17:43:24,3182.0,2019-10-04T18:36:26,83.430442810059,21.913370132446,True,CRAB_4,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_IV.tar.gz
-136,900172,2019-10-04T18:06:24,3182.0,2019-10-04T18:59:26,83.40502166748,21.936889648438,True,CRAB_4,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_IV.tar.gz
-137,900173,2019-10-04T18:29:24,3183.0,2019-10-04T19:22:27,83.379585266113,21.960403442383,True,CRAB_4,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_IV.tar.gz
-138,900174,2019-10-04T18:52:26,3181.0,2019-10-04T19:45:27,83.354141235352,21.983915328979,True,CRAB_4,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_IV.tar.gz
-139,900175,2019-10-04T19:15:27,3181.0,2019-10-04T20:08:28,83.315963745117,22.019172668457,True,CRAB_4,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_IV.tar.gz
-140,900176,2019-10-04T19:38:27,3181.0,2019-10-04T20:31:28,83.277763366699,22.05442237854,True,CRAB_4,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_IV.tar.gz
-141,900177,2019-10-04T20:01:28,3522.0,2019-10-04T21:00:10,83.239547729492,22.08966255188,True,CRAB_4,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_IV.tar.gz
-142,900178,2019-10-04T20:31:28,3601.0,2019-10-04T21:31:29,83.214057922363,22.113151550293,True,CRAB_4,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_IV.tar.gz
-143,900179,2019-10-04T21:01:29,3616.0,2019-10-04T22:01:45,83.163055419922,22.16011428833,True,CRAB_4,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_IV.tar.gz
-144,900180,2019-10-04T21:31:29,3616.0,2019-10-04T22:31:45,83.086486816406,22.230529785156,True,CRAB_4,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_IV.tar.gz
-145,900181,2019-10-04T22:01:24,4221.0,2019-10-04T23:11:45,82.958709716797,22.347799301147,True,CRAB_4,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_IV.tar.gz
-146,700180,2019-11-19T22:17:20,37251.0,2019-11-20T08:38:11,353.509,-47.241,True,RE_J2334_471,EXTRAGALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/RE_J2334m471.tar.gz
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-167,900068,2019-09-28T15:49:51,20441.0,2019-09-28T21:30:32,281.5002746582,79.885375976562,True,3C390_3,EXTRAGALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/3C390.tar.gz
-168,900069,2019-09-29T15:23:24,22033.0,2019-09-29T21:30:37,281.48941040039,79.888214111328,True,3C390_3,EXTRAGALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/3C390.tar.gz
-169,900070,2019-09-30T15:23:24,22028.0,2019-09-30T21:30:32,281.47827148438,79.89102935791,True,3C390_3,EXTRAGALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/3C390.tar.gz
diff --git a/daxa/files/erass_de_dr1_info.csv b/daxa/files/erass_de_dr1_info.csv
new file mode 100644
index 00000000..e3339a19
--- /dev/null
+++ b/daxa/files/erass_de_dr1_info.csv
@@ -0,0 +1,2448 @@
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+180177,2,160.0,200.0,-88.5,-85.5,180.0,-87.04518072076759,15.74947087361224,15.300393639120756,7,001180,146174,169174,191174,214174,140177,220177,000000,000000
+220177,2,200.0,240.0,-88.5,-85.5,220.0,-87.04518072076759,15.74947087361224,15.300393639120756,7,001180,191174,214174,236174,259174,180177,260177,000000,000000
+260177,2,240.0,280.0,-88.5,-85.5,260.0,-87.04518072076759,15.74947087361224,15.300393639120756,7,001180,236174,259174,281174,304174,220177,300177,000000,000000
+300177,2,280.0,320.0,-88.5,-85.5,300.0,-87.04518072076759,15.74947087361224,15.300393639120756,7,001180,259174,281174,304174,326174,260177,340177,000000,000000
+340177,2,320.0,360.0,-88.5,-85.5,340.0,-87.04518072076759,15.74947087361224,15.300393639120756,7,001180,304174,326174,349174,011174,300177,020177,000000,000000
+001180,2,0.0,360.0,-90.0,-88.5,0.0,-90.0,18.0,18.0,9,020177,060177,100177,140177,180177,220177,260177,300177,340177
diff --git a/daxa/files/erosita_calpv_info.csv b/daxa/files/erosita_calpv_info.csv
new file mode 100644
index 00000000..e2c402ae
--- /dev/null
+++ b/daxa/files/erosita_calpv_info.csv
@@ -0,0 +1,171 @@
+ObsID,start,duration,end,ra,dec,science_usable,Field_Name,Field_Type,download
+300007,2019-11-03T02:42:50,89627.0,2019-11-04T03:36:37,129.55,1.5,True,EFEDS,SURVEY,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/eFEDS.tar.gz
+300008,2019-11-04T03:49:16,91643.0,2019-11-05T05:16:39,133.86,1.5,True,EFEDS,SURVEY,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/eFEDS.tar.gz
+300009,2019-11-05T05:29:18,90648.0,2019-11-06T06:40:06,138.14,1.5,True,EFEDS,SURVEY,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/eFEDS.tar.gz
+300010,2019-11-06T07:24:46,89722.0,2019-11-07T08:20:08,142.45,1.5,True,EFEDS,SURVEY,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/eFEDS.tar.gz
+300004,2019-11-16T23:14:40,154952.0,2019-11-18T18:17:12,130.3313,-78.9634,True,ETA_CHA,SURVEY,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/eta_Cha.tar.gz
+700016,2019-09-15T09:23:10,113825.0,2019-09-16T17:00:15,83.836372375488,-69.311386108398,True,LMC_SN1987A,MAGELLANIC_CLOUDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/LMC_SN1987A.tar.gz
+700161,2019-10-18T16:49:59,80313.0,2019-10-19T15:08:32,83.836375,-69.311388888889,True,LMC_SN1987A,MAGELLANIC_CLOUDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/LMC_SN1987A.tar.gz
+700203,2019-12-06T19:03:45,31712.0,2019-12-07T03:52:17,83.836375,-69.311388888889,True,LMC_SN1987A,MAGELLANIC_CLOUDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/LMC_SN1987A.tar.gz
+700204,2019-12-07T03:58:56,31712.0,2019-12-07T12:47:28,83.836375,-69.311388888889,True,LMC_SN1987A,MAGELLANIC_CLOUDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/LMC_SN1987A.tar.gz
+700156,2019-10-10T01:54:47,43559.0,2019-10-10T14:00:46,81.25917,-69.64417,True,LMC_N132D,MAGELLANIC_CLOUDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/LMC_N132D.tar.gz
+700179,2019-11-22T15:53:20,59489.0,2019-11-23T08:24:49,81.25917,-69.64417,True,LMC_N132D,MAGELLANIC_CLOUDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/LMC_N132D.tar.gz
+700182,2019-11-27T06:07:24,40296.0,2019-11-27T17:19:00,80.510115854516,-69.85367143598,True,LMC_N132D,MAGELLANIC_CLOUDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/LMC_N132D.tar.gz
+700183,2019-11-25T18:57:15,41034.0,2019-11-26T06:21:09,81.993496158843,-69.431537504153,True,LMC_N132D,MAGELLANIC_CLOUDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/LMC_N132D.tar.gz
+700184,2019-11-23T21:00:37,40292.0,2019-11-24T08:12:09,80.687582578861,-69.377649654414,True,LMC_N132D,MAGELLANIC_CLOUDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/LMC_N132D.tar.gz
+700185,2019-11-25T07:58:39,41267.0,2019-11-25T19:26:26,81.900481092646,-69.892924133714,True,LMC_N132D,MAGELLANIC_CLOUDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/LMC_N132D.tar.gz
+700201,2019-12-02T08:59:40,43230.0,2019-12-02T21:00:10,81.25917,-69.64417,True,LMC_N132D,MAGELLANIC_CLOUDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/LMC_N132D.tar.gz
+700202,2019-12-04T00:59:40,43230.0,2019-12-04T13:00:10,81.25917,-69.64417,True,LMC_N132D,MAGELLANIC_CLOUDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/LMC_N132D.tar.gz
+700206,2019-12-07T21:03:24,21801.0,2019-12-08T03:06:45,85.04515838623,-69.331733703613,True,PSR_J0540_6919,MAGELLANIC_CLOUDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/PSR_J0540m6919.tar.gz
+700205,2019-12-07T12:43:24,31641.0,2019-12-07T21:30:45,84.444427490234,-69.171409606934,True,PSR_J0537_6910,MAGELLANIC_CLOUDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/PSR_J0537m6910.tar.gz
+700207,2019-12-08T02:39:24,37274.0,2019-12-08T13:00:38,84.742172241211,-69.250358581543,True,PSR_J0540_PSR_J0537,MAGELLANIC_CLOUDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/PSR_J0540_PSR_J0537.tar.gz
+700001,2019-11-07T17:03:44,61579.0,2019-11-08T10:10:03,16.008333333333,-72.031944444444,True,1ES_0102_72,MAGELLANIC_CLOUDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/1ES_0102m72.tar.gz
+700002,2019-11-08T09:40:42,61041.0,2019-11-09T02:38:03,15.036871800215,-71.888384341232,True,1ES_0102_72,MAGELLANIC_CLOUDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/1ES_0102m72.tar.gz
+700003,2019-11-09T02:37:45,61238.0,2019-11-09T19:38:23,16.994661672799,-72.170596682228,True,1ES_0102_72,MAGELLANIC_CLOUDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/1ES_0102m72.tar.gz
+700004,2019-11-09T19:09:02,57001.0,2019-11-10T10:59:03,16.282350461656,-71.850878735899,True,1ES_0102_72,MAGELLANIC_CLOUDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/1ES_0102m72.tar.gz
+700005,2019-11-10T10:29:12,56006.0,2019-11-11T02:02:38,16.419013700816,-71.760440299341,True,1ES_0102_72,MAGELLANIC_CLOUDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/1ES_0102m72.tar.gz
+700018,2019-10-05T14:56:50,117459.0,2019-10-06T23:34:29,119.58333333333,-60.870277777778,True,NGC_2516,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/NGC_2516.tar.gz
+700019,2019-10-31T17:34:59,80263.0,2019-11-01T15:52:42,119.58333333333,-60.870277777778,True,NGC_2516,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/NGC_2516.tar.gz
+300000,2019-10-14T11:36:38,100246.0,2019-10-15T15:27:24,104.9507625,14.239316666667,True,PSR_B0656_14,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/PSR_B0656p14.tar.gz
+900035,2019-09-10T15:13:24,22641.0,2019-09-10T21:30:45,228.84419250488,-59.09810256958,True,PSR_1509_58,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/PSR_1509m58.tar.gz
+900043,2019-09-14T15:13:24,28039.0,2019-09-14T23:00:43,228.84716796875,-59.106525421143,True,PSR_1509_58,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/PSR_1509m58.tar.gz
+900033,2019-09-09T15:13:24,22641.0,2019-09-09T21:30:45,195.9973449707,-63.97021484375,True,IGR_J13020_6359__2RXP_J130159_635806_,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/IGR_J13020m6359.tar.gz
+900062,2019-09-25T15:23:24,22041.0,2019-09-25T21:30:45,248.23999023438,-48.741333007812,True,IGR_J16318_4848,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/IGR_J16318m4848.tar.gz
+900072,2019-10-01T14:57:43,21783.0,2019-10-01T21:00:46,255.44781494141,-41.563358306885,True,OAO_1657_415,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/OAO_1657m415.tar.gz
+700007,2019-10-16T22:21:52,90249.0,2019-10-17T23:26:01,110.104,-31.430613888889,True,1RXS_J072025_312554,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/1RXS_J072025m312554.tar.gz
+700039,2019-10-29T08:51:34,59880.0,2019-10-30T01:29:34,132.19041666667,-45.614166666667,True,VELA_SNR,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Vela_SNR.tar.gz
+700199,2019-11-29T17:18:35,30790.0,2019-11-30T01:51:45,126.0325,-42.996666666667,True,PUPPIS_A,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Puppis_A.tar.gz
+700200,2019-11-30T02:25:15,30217.0,2019-11-30T10:48:52,126.0325,-42.996666666667,True,PUPPIS_A,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Puppis_A.tar.gz
+700167,2019-11-22T10:03:20,19631.0,2019-11-22T15:30:31,120.89583333333,-40.003055555556,True,HR_3165__ZET_PUP_,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/HR_3165.tar.gz
+900056,2019-09-21T23:14:30,45962.0,2019-09-22T12:00:32,83.469215393066,21.877420425415,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
+900075,2019-10-02T21:51:44,3601.0,2019-10-02T22:51:45,82.570663452148,21.874715805054,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
+900076,2019-10-02T22:21:24,4221.0,2019-10-02T23:31:45,82.750221252441,21.875782012939,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
+900077,2019-10-02T23:01:24,3621.0,2019-10-03T00:01:45,82.929794311523,21.876649856567,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
+900078,2019-10-02T23:31:24,3621.0,2019-10-03T00:31:45,83.037544250488,21.87707901001,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
+900079,2019-10-03T00:01:24,3530.0,2019-10-03T01:00:14,83.109382629395,21.877325057983,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
+900080,2019-10-03T00:31:24,3621.0,2019-10-03T01:31:45,83.145301818848,21.877435684204,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
+900081,2019-10-03T01:01:24,3201.0,2019-10-03T01:54:45,83.199180603027,21.877588272095,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
+900082,2019-10-03T01:24:24,3201.0,2019-10-03T02:17:45,83.253051757812,21.877721786499,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
+900083,2019-10-03T01:47:24,3201.0,2019-10-03T02:40:45,83.306930541992,21.877838134766,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
+900084,2019-10-03T02:10:24,3201.0,2019-10-03T03:03:45,83.342849731445,21.877906799316,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
+900085,2019-10-03T02:33:25,3199.0,2019-10-03T03:26:44,83.378768920898,21.877967834473,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
+900086,2019-10-03T02:56:24,3201.0,2019-10-03T03:49:45,83.414688110352,21.878019332886,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
+900087,2019-10-03T03:19:25,3200.0,2019-10-03T04:12:45,83.450607299805,21.878065109253,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
+900088,2019-10-03T03:42:24,3201.0,2019-10-03T04:35:45,83.468566894531,21.878084182739,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
+900089,2019-10-03T04:05:25,3199.0,2019-10-03T04:58:44,83.486526489258,21.878101348877,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
+900090,2019-10-03T04:28:24,3201.0,2019-10-03T05:21:45,83.522445678711,21.878129959106,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
+900091,2019-10-03T04:59:47,2698.0,2019-10-03T05:44:45,83.558364868164,21.878150939941,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
+900092,2019-10-03T05:14:24,3201.0,2019-10-03T06:07:45,83.594284057617,21.878164291382,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
+900093,2019-10-03T05:37:25,3199.0,2019-10-03T06:30:44,83.63020324707,21.878170013428,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
+900094,2019-10-03T06:00:24,3201.0,2019-10-03T06:53:45,83.68408203125,21.878164291382,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
+900095,2019-10-03T06:23:24,3201.0,2019-10-03T07:16:45,83.73796081543,21.87813949585,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
+900096,2019-10-03T06:46:24,3620.0,2019-10-03T07:46:44,83.791839599609,21.87809753418,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
+900097,2019-10-03T07:16:25,3620.0,2019-10-03T08:16:45,83.827758789062,21.878059387207,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
+900098,2019-10-03T07:46:25,3619.0,2019-10-03T08:46:44,83.899597167969,21.877962112427,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
+900099,2019-10-03T08:16:26,3618.0,2019-10-03T09:16:44,84.007347106934,21.877754211426,True,CRAB_1,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_I.tar.gz
+900100,2019-10-03T08:59:47,3417.0,2019-10-03T09:56:44,84.186920166016,21.877252578735,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
+900101,2019-10-03T09:26:26,3262.0,2019-10-03T10:20:48,84.366485595703,21.876556396484,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
+900102,2019-10-03T10:21:07,3337.0,2019-10-03T11:16:44,82.836494445801,21.287460327148,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
+900103,2019-10-03T10:46:26,4219.0,2019-10-03T11:56:45,82.96248626709,21.40576171875,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
+900104,2019-10-03T11:26:25,3620.0,2019-10-03T12:26:45,83.088691711426,21.523977279663,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
+900105,2019-10-03T11:56:25,3620.0,2019-10-03T12:56:45,83.164512634277,21.594863891602,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
+900106,2019-10-03T12:26:24,3621.0,2019-10-03T13:26:45,83.215103149414,21.642105102539,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
+900107,2019-10-03T12:59:45,3420.0,2019-10-03T13:56:45,83.240417480469,21.665719985962,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
+900108,2019-10-03T13:26:24,3201.0,2019-10-03T14:19:45,83.278396606445,21.701133728027,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
+900109,2019-10-03T13:49:24,3201.0,2019-10-03T14:42:45,83.316390991211,21.736541748047,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
+900110,2019-10-03T14:12:24,3201.0,2019-10-03T15:05:45,83.35440826416,21.771940231323,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
+900111,2019-10-03T14:35:24,3201.0,2019-10-03T15:28:45,83.379760742188,21.795534133911,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
+900112,2019-10-03T14:58:25,3199.0,2019-10-03T15:51:44,83.405128479004,21.819124221802,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
+900113,2019-10-03T15:21:24,3201.0,2019-10-03T16:14:45,83.43049621582,21.842710494995,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
+900114,2019-10-03T15:44:24,3201.0,2019-10-03T16:37:45,83.455879211426,21.86629486084,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
+900115,2019-10-03T16:07:24,3170.0,2019-10-03T17:00:14,83.468566894531,21.878084182739,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
+900116,2019-10-03T16:30:25,3199.0,2019-10-03T17:23:44,83.481262207031,21.889873504639,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
+900117,2019-10-03T16:59:45,2820.0,2019-10-03T17:46:45,83.506660461426,21.91344833374,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
+900118,2019-10-03T17:16:24,3201.0,2019-10-03T18:09:45,83.532066345215,21.937019348145,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
+900119,2019-10-03T17:39:24,3201.0,2019-10-03T18:32:45,83.557479858398,21.960586547852,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
+900120,2019-10-03T18:02:25,3199.0,2019-10-03T18:55:44,83.582901000977,21.984149932861,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
+900121,2019-10-03T18:25:24,3201.0,2019-10-03T19:18:45,83.621047973633,22.019487380981,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
+900122,2019-10-03T18:48:24,3201.0,2019-10-03T19:41:45,83.659217834473,22.054817199707,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
+900123,2019-10-03T19:11:24,3620.0,2019-10-03T20:11:44,83.697402954102,22.090135574341,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
+900124,2019-10-03T19:41:25,3620.0,2019-10-03T20:41:45,83.722869873047,22.113677978516,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
+900125,2019-10-03T20:11:25,2928.0,2019-10-03T21:00:13,83.773826599121,22.160747528076,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
+900126,2019-10-03T20:41:26,3618.0,2019-10-03T21:41:44,83.850326538086,22.231323242188,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
+900127,2019-10-03T21:11:24,4220.0,2019-10-03T22:21:44,83.977996826172,22.348863601685,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
+900128,2019-10-03T21:51:26,3259.0,2019-10-03T22:45:45,84.10587310791,22.466297149658,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
+900129,2019-10-03T22:46:03,3340.0,2019-10-03T23:41:43,83.468925476074,21.045421600342,True,CRAB_2,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_II.tar.gz
+900130,2019-10-03T23:11:25,4220.0,2019-10-04T00:21:45,83.468856811523,21.211936950684,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
+900131,2019-10-03T23:51:24,3621.0,2019-10-04T00:51:45,83.468788146973,21.378463745117,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
+900132,2019-10-04T00:21:25,3620.0,2019-10-04T01:21:45,83.468742370605,21.478384017944,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
+900133,2019-10-04T00:59:44,3121.0,2019-10-04T01:51:45,83.468719482422,21.544998168945,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
+900134,2019-10-04T01:21:24,3621.0,2019-10-04T02:21:45,83.468704223633,21.57830619812,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
+900135,2019-10-04T01:51:24,3201.0,2019-10-04T02:44:45,83.468681335449,21.628269195557,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
+900136,2019-10-04T02:14:24,3201.0,2019-10-04T03:07:45,83.468658447266,21.678232192993,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
+900137,2019-10-04T02:37:24,3201.0,2019-10-04T03:30:45,83.468635559082,21.728193283081,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
+900138,2019-10-04T03:00:24,3201.0,2019-10-04T03:53:45,83.468620300293,21.761503219604,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
+900139,2019-10-04T03:23:25,3199.0,2019-10-04T04:16:44,83.468605041504,21.794811248779,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
+900140,2019-10-04T03:46:24,3201.0,2019-10-04T04:39:45,83.468589782715,21.828121185303,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
+900141,2019-10-04T04:09:24,3048.0,2019-10-04T05:00:12,83.468574523926,21.861429214478,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
+900142,2019-10-04T04:32:24,3201.0,2019-10-04T05:25:45,83.468566894531,21.878084182739,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
+900143,2019-10-04T04:59:45,2939.0,2019-10-04T05:48:44,83.468559265137,21.894739151001,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
+900144,2019-10-04T05:18:24,3201.0,2019-10-04T06:11:45,83.468544006348,21.928047180176,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
+900145,2019-10-04T05:41:24,3201.0,2019-10-04T06:34:45,83.468528747559,21.961357116699,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
+900146,2019-10-04T06:04:24,3201.0,2019-10-04T06:57:45,83.46851348877,21.994665145874,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
+900147,2019-10-04T06:27:25,3199.0,2019-10-04T07:20:44,83.46849822998,22.027973175049,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
+900148,2019-10-04T06:50:24,3201.0,2019-10-04T07:43:45,83.468475341797,22.077936172485,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
+900149,2019-10-04T07:13:24,3201.0,2019-10-04T08:06:45,83.468452453613,22.127899169922,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
+900150,2019-10-04T07:36:24,3620.0,2019-10-04T08:36:44,83.46842956543,22.177862167358,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
+900151,2019-10-04T08:06:25,3226.0,2019-10-04T09:00:11,83.468414306641,22.211170196533,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
+900152,2019-10-04T08:36:25,3619.0,2019-10-04T09:36:44,83.468383789062,22.277784347534,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
+900153,2019-10-04T09:06:26,3617.0,2019-10-04T10:06:43,83.468338012695,22.37770652771,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
+900154,2019-10-04T09:36:24,4220.0,2019-10-04T10:46:44,83.468254089355,22.544233322144,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
+900155,2019-10-04T10:16:26,3256.0,2019-10-04T11:10:42,83.46817779541,22.710750579834,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
+900156,2019-10-04T11:11:00,3345.0,2019-10-04T12:06:45,84.101165771484,21.288724899292,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
+900157,2019-10-04T11:36:25,4220.0,2019-10-04T12:46:45,83.975067138672,21.40678024292,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
+900158,2019-10-04T12:16:26,3619.0,2019-10-04T13:16:45,83.848762512207,21.524745941162,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
+900159,2019-10-04T12:59:46,2819.0,2019-10-04T13:46:45,83.772872924805,21.59547996521,True,CRAB_3,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_III.tar.gz
+900160,2019-10-04T13:16:24,3621.0,2019-10-04T14:16:45,83.722244262695,21.64262008667,True,CRAB_4,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_IV.tar.gz
+900161,2019-10-04T13:46:24,3621.0,2019-10-04T14:46:45,83.696914672852,21.66618347168,True,CRAB_4,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_IV.tar.gz
+900162,2019-10-04T14:16:24,3201.0,2019-10-04T15:09:45,83.658905029297,21.7015209198,True,CRAB_4,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_IV.tar.gz
+900163,2019-10-04T14:39:24,3201.0,2019-10-04T15:32:45,83.620872497559,21.736850738525,True,CRAB_4,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_IV.tar.gz
+900164,2019-10-04T15:02:24,3201.0,2019-10-04T15:55:45,83.582824707031,21.772172927856,True,CRAB_4,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_IV.tar.gz
+900165,2019-10-04T15:25:24,3201.0,2019-10-04T16:18:45,83.55744934082,21.795715332031,True,CRAB_4,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_IV.tar.gz
+900166,2019-10-04T15:48:25,3199.0,2019-10-04T16:41:44,83.532066345215,21.819253921509,True,CRAB_4,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_IV.tar.gz
+900167,2019-10-04T16:11:27,2928.0,2019-10-04T17:00:15,83.506675720215,21.842788696289,True,CRAB_4,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_IV.tar.gz
+900168,2019-10-04T16:34:24,3201.0,2019-10-04T17:27:45,83.481269836426,21.866319656372,True,CRAB_4,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_IV.tar.gz
+900169,2019-10-04T17:00:05,3040.0,2019-10-04T17:50:45,83.468566894531,21.878084182739,True,CRAB_4,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_IV.tar.gz
+900170,2019-10-04T17:20:25,3199.0,2019-10-04T18:13:44,83.455863952637,21.889846801758,True,CRAB_4,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_IV.tar.gz
+900171,2019-10-04T17:43:24,3182.0,2019-10-04T18:36:26,83.430442810059,21.913370132446,True,CRAB_4,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_IV.tar.gz
+900172,2019-10-04T18:06:24,3182.0,2019-10-04T18:59:26,83.40502166748,21.936889648438,True,CRAB_4,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_IV.tar.gz
+900173,2019-10-04T18:29:24,3183.0,2019-10-04T19:22:27,83.379585266113,21.960403442383,True,CRAB_4,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_IV.tar.gz
+900174,2019-10-04T18:52:26,3181.0,2019-10-04T19:45:27,83.354141235352,21.983915328979,True,CRAB_4,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_IV.tar.gz
+900175,2019-10-04T19:15:27,3181.0,2019-10-04T20:08:28,83.315963745117,22.019172668457,True,CRAB_4,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_IV.tar.gz
+900176,2019-10-04T19:38:27,3181.0,2019-10-04T20:31:28,83.277763366699,22.05442237854,True,CRAB_4,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_IV.tar.gz
+900177,2019-10-04T20:01:28,3522.0,2019-10-04T21:00:10,83.239547729492,22.08966255188,True,CRAB_4,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_IV.tar.gz
+900178,2019-10-04T20:31:28,3601.0,2019-10-04T21:31:29,83.214057922363,22.113151550293,True,CRAB_4,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_IV.tar.gz
+900179,2019-10-04T21:01:29,3616.0,2019-10-04T22:01:45,83.163055419922,22.16011428833,True,CRAB_4,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_IV.tar.gz
+900180,2019-10-04T21:31:29,3616.0,2019-10-04T22:31:45,83.086486816406,22.230529785156,True,CRAB_4,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_IV.tar.gz
+900181,2019-10-04T22:01:24,4221.0,2019-10-04T23:11:45,82.958709716797,22.347799301147,True,CRAB_4,GALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/Crab_IV.tar.gz
+700180,2019-11-19T22:17:20,37251.0,2019-11-20T08:38:11,353.509,-47.241,True,RE_J2334_471,EXTRAGALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/RE_J2334m471.tar.gz
+700181,2019-11-20T09:35:20,36830.0,2019-11-20T19:49:10,353.763,-47.526,True,RE_J2334_471,EXTRAGALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/RE_J2334m471.tar.gz
+700177,2019-11-21T10:22:18,80033.0,2019-11-22T08:36:11,55.72822,-53.63013,True,A3158,EXTRAGALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/A3158.tar.gz
+700154,2019-11-11T02:09:28,74248.0,2019-11-11T22:46:56,67.84096,-61.44033,True,A3266,EXTRAGALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/A3266.tar.gz
+300003,2019-10-11T09:00:52,50305.0,2019-10-11T22:59:17,107.17041666667,-49.551666666667,True,1H0707,EXTRAGALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/1H0707.tar.gz
+300011,2019-11-18T18:30:36,60274.0,2019-11-19T11:15:10,359.46254166667,-32.624055555556,True,NGC_7793_P13,EXTRAGALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/NGC_7793P13.tar.gz
+700011,2019-11-01T16:38:30,25810.0,2019-11-01T23:48:40,5.5179793176519,-71.984619744048,True,47_TUC__NGC_104_,EXTRAGALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/47_Tuc.tar.gz
+700012,2019-09-28T23:05:29,19529.0,2019-09-29T04:30:58,6.5339422225952,-72.176284790039,True,47_TUC__NGC_104_,EXTRAGALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/47_Tuc.tar.gz
+700013,2019-11-02T07:43:00,25210.0,2019-11-02T14:43:10,6.3337552599947,-71.924866371827,True,47_TUC__NGC_104_,EXTRAGALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/47_Tuc.tar.gz
+700014,2019-11-02T15:10:20,25210.0,2019-11-02T22:10:30,5.7107587761744,-72.237445497513,True,47_TUC__NGC_104_,EXTRAGALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/47_Tuc.tar.gz
+700163,2019-11-02T00:14:50,25270.0,2019-11-02T07:16:00,6.5339424519341,-72.176284022687,True,47_TUC__NGC_104_,EXTRAGALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/47_Tuc.tar.gz
+700173,2019-11-19T13:12:20,8810.0,2019-11-19T15:39:10,5.5179793176519,-71.984619744048,True,47_TUC__NGC_104_,EXTRAGALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/47_Tuc.tar.gz
+700174,2019-11-19T16:10:20,8390.0,2019-11-19T18:30:10,6.5339424519341,-72.176284022687,True,47_TUC__NGC_104_,EXTRAGALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/47_Tuc.tar.gz
+700175,2019-11-19T19:01:20,8331.0,2019-11-19T21:20:11,6.3337552599947,-71.924866371827,True,47_TUC__NGC_104_,EXTRAGALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/47_Tuc.tar.gz
+300017,2019-10-26T08:12:29,56225.0,2019-10-26T23:49:34,285.591,-37.124,True,TGUH2213P1__DARK_CLOUD_,EXTRAGALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/TGUH2213P1.tar.gz
+300014,2019-10-07T14:38:03,40286.0,2019-10-08T01:49:29,96.706,-54.072,True,A3391_A3395,EXTRAGALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/A3391_A3395.tar.gz
+300005,2019-10-08T15:54:34,61333.0,2019-10-09T08:56:47,96.706,-54.072,True,A3391_A3395,EXTRAGALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/A3391_A3395.tar.gz
+300006,2019-10-09T09:03:26,60547.0,2019-10-10T01:52:33,96.706,-54.072,True,A3391_A3395,EXTRAGALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/A3391_A3395.tar.gz
+300016,2019-10-17T23:59:56,60502.0,2019-10-18T16:48:18,96.706,-54.072,True,A3391_A3395,EXTRAGALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/A3391_A3395.tar.gz
+700008,2019-10-24T11:11:19,78273.0,2019-10-25T08:55:52,284.14625,-37.909166666667,True,1RXS_J185635_375433,EXTRAGALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/1RXS_J185635m375433.tar.gz
+900060,2019-09-24T15:27:06,21806.0,2019-09-24T21:30:32,281.54077148438,79.873725891113,True,3C390_3,EXTRAGALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/3C390.tar.gz
+900068,2019-09-28T15:49:51,20441.0,2019-09-28T21:30:32,281.5002746582,79.885375976562,True,3C390_3,EXTRAGALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/3C390.tar.gz
+900069,2019-09-29T15:23:24,22033.0,2019-09-29T21:30:37,281.48941040039,79.888214111328,True,3C390_3,EXTRAGALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/3C390.tar.gz
+900070,2019-09-30T15:23:24,22028.0,2019-09-30T21:30:32,281.47827148438,79.89102935791,True,3C390_3,EXTRAGALACTIC_FIELDS,https://erosita.mpe.mpg.de/edr/eROSITAObservations/CalPvObs/3C390.tar.gz
diff --git a/daxa/mission/__init__.py b/daxa/mission/__init__.py
index ab9f18f3..3517a826 100644
--- a/daxa/mission/__init__.py
+++ b/daxa/mission/__init__.py
@@ -1,10 +1,10 @@
 #  This code is a part of the Democratising Archival X-ray Astronomy (DAXA) module.
-#  Last modified by David J Turner (turne540@msu.edu) 09/10/2023, 23:36. Copyright (c) The Contributors
+#  Last modified by David J Turner (turne540@msu.edu) 30/01/2024, 16:13. Copyright (c) The Contributors
 
 from .asca import ASCA
 from .base import BaseMission
 from .chandra import Chandra
-from .erosita import eROSITACalPV
+from .erosita import eROSITACalPV, eRASS1DE
 from .integral import INTEGRALPointed
 from .nustar import NuSTARPointed
 from .rosat import ROSATAllSky, ROSATPointed
@@ -14,8 +14,9 @@
 
 # This just links the internal DAXA names of missions to their class
 MISS_INDEX = {'xmm_pointed': XMMPointed, 'nustar_pointed': NuSTARPointed, 'erosita_calpv': eROSITACalPV,
-              'chandra': Chandra, 'rosat_all_sky': ROSATAllSky, 'rosat_pointed': ROSATPointed, 'swift': Swift,
-              'suzaku': Suzaku, 'asca': ASCA, 'integral_pointed': INTEGRALPointed}
+              'erosita_all_sky_de_dr1': eRASS1DE, 'chandra': Chandra, 'rosat_all_sky': ROSATAllSky,
+              'rosat_pointed': ROSATPointed, 'swift': Swift, 'suzaku': Suzaku, 'asca': ASCA,
+              'integral_pointed': INTEGRALPointed}
 
 
 
diff --git a/daxa/mission/base.py b/daxa/mission/base.py
index 499b801b..6e34637f 100644
--- a/daxa/mission/base.py
+++ b/daxa/mission/base.py
@@ -1,5 +1,5 @@
 #  This code is a part of the Democratising Archival X-ray Astronomy (DAXA) module.
-#  Last modified by David J Turner (turne540@msu.edu) 29/01/2024, 15:50. Copyright (c) The Contributors
+#  Last modified by David J Turner (turne540@msu.edu) 30/01/2024, 14:18. Copyright (c) The Contributors
 
 import os.path
 import re
@@ -539,7 +539,8 @@ def _check_chos_insts(self, insts: Union[List[str], str]):
         """
         An internal function to perform some checks on the validity of chosen instrument names for a given mission.
 
-        :param List[str]/str insts:
+        :param List[str]/str insts: Instrument names that are to be checked for the current mission, either a single
+            name or a list of names.
         :return: The list of instruments (possibly altered to match formats expected by this module).
         :rtype: List
         """
diff --git a/daxa/mission/erosita.py b/daxa/mission/erosita.py
index 8f026735..2d1c8166 100644
--- a/daxa/mission/erosita.py
+++ b/daxa/mission/erosita.py
@@ -1,6 +1,7 @@
 #  This code is a part of the Democratising Archival X-ray Astronomy (DAXA) module.
-#  Last modified by David J Turner (turne540@msu.edu) 02/08/2023, 21:18. Copyright (c) The Contributors
+#  Last modified by David J Turner (turne540@msu.edu) 01/02/2024, 11:44. Copyright (c) The Contributors
 
+import gzip
 import os
 import re
 import shutil
@@ -16,14 +17,25 @@
 from astropy.coordinates import BaseRADecFrame, FK5
 from astropy.io import fits
 from astropy.units import Quantity
+from bs4 import BeautifulSoup
 from tqdm import tqdm
 
 from .base import BaseMission
 from .base import _lock_check
 from .. import NUM_CORES
-from ..config import CALPV_INFO
+from ..config import EROSITA_CALPV_INFO, ERASS_DE_DR1_INFO
 from ..exceptions import DAXADownloadError
 
+# So these are the style of directory names that we need - as I'm writing this the directory names are actually
+#  EXP_010 and DET_010, but the numbers there refer to the pipeline that they were processed with, so I don't know
+#  if that will stay forever - I am going to try and make it resilient to possible changes and let the user choose
+#  the pipeline version when downloading
+REQUIRED_DIRS = {'erosita_all_sky_de_dr1': {'all': ['EXP'],
+                                            'products': ['EXP', 'DET']}}
+
+# TODO Make sure the properties, internal methods, and user-facing methods are in the 'right' order for this project.
+#  Perhaps replace the 'get_evlist_path_from_obs' method?
+
 
 class eROSITACalPV(BaseMission):
     """
@@ -45,9 +57,9 @@ def __init__(self, insts: Union[List[str], str] = None, fields: Union[List[str],
         super().__init__() 
 
         # All the allowed names of fields 
-        self._miss_poss_fields = CALPV_INFO["Field_Name"].tolist()
-        # All the allowed types of field, ie. survey, magellanic cloud, galactic field, extragalactic field
-        self._miss_poss_field_types = CALPV_INFO["Field_Type"].unique().tolist()
+        self._miss_poss_fields = EROSITA_CALPV_INFO["Field_Name"].tolist()
+        # All the allowed types of field, i.e. survey, magellanic cloud, galactic field, extragalactic field
+        self._miss_poss_field_types = EROSITA_CALPV_INFO["Field_Type"].unique().tolist()
 
         # This sets up extra columns which are expected to be present in the all_obs_info pandas dataframe
         self._required_mission_specific_cols = ['Field_Name', 'Field_Type']
@@ -81,6 +93,13 @@ def __init__(self, insts: Union[List[str], str] = None, fields: Union[List[str],
         # Call the name property to set up the name and pretty name attributes
         self.name
 
+        # Runs the method which fetches information on all available RASS 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()
+
     # Defining properties first
     @property
     def name(self) -> str:
@@ -220,9 +239,9 @@ def filter_on_obs_ids(self, allowed_obs_ids: Union[str, List[str]]):
 
     def _check_chos_insts(self, insts: Union[List[str], str]):
         """
-        An internal function to check and peform event list filtering for instruments for eROSITA. This
+        An internal function to check and perform event list filtering for instruments for eROSITA. This
         overwrites the version of this method declared in BaseMission, though it does call the super method.
-        This sub-class of BaseMission re-implements this method so that setting chosen instruments also 
+        This subclass of BaseMission re-implements this method so that setting chosen instruments also
         filters event lists for user specified instruments, as eROSITA observations contain all instruments.
 
         :param List[str]/str insts:
@@ -237,7 +256,7 @@ def _check_chos_insts(self, insts: Union[List[str], str]):
             if len(insts) != 7:
                 # Getting all the path for each eventlist corresponding to an obs_id for the
                 #  _inst_filtering function later
-                fits_paths = [self._get_evlist_path_from_obs(obs=o) for o in self.filtered_obs_ids]
+                fits_paths = [self.get_evlist_path_from_obs(o) for o in self.filtered_obs_ids]
 
                 # Filtering out any events from the raw data that arent from the selected instruments
                 if NUM_CORES == 1:
@@ -358,7 +377,7 @@ def filter_on_fields(self, fields: Union[str, List[str]]):
         self.chosen_fields = fields
 
         # Selecting all Obs_IDs from each field
-        field_obs_ids = CALPV_INFO.loc[CALPV_INFO["Field_Name"].isin(fields), "ObsID"].tolist()
+        field_obs_ids = EROSITA_CALPV_INFO.loc[EROSITA_CALPV_INFO["Field_Name"].isin(fields), "ObsID"].tolist()
 
         # Uses the Pandas isin functionality to find the rows of the overall observation table that match the input
         #  ObsIDs. This outputs a boolean array.
@@ -375,15 +394,16 @@ def _fetch_obs_info(self):
         This method uses the hard coded csv file to pull information on all eROSITACalPV observations. 
         The data are processed into a Pandas dataframe and stored.
         """
-        # Hard coded this information and saved it to the CALPV_INFO.csv in /files
-        # Making a copy so that CALPV_INFO remains unchanged
-        calpv_copy = CALPV_INFO
+        # Hard coded this information and saved it to the erosita_calpv_info.csv file in /files
+        # Making a copy so that EROSITA_CALPV_INFO remains unchanged
+        calpv_copy = EROSITA_CALPV_INFO.copy()
 
         # Need to split the times since they go to milisecond precision, 
         #  which is a pain to translate to a datetime object, and is superfluous information anyway
         calpv_copy['start'] = [str(time).split('.', 1)[0] for time in calpv_copy['start']]
         calpv_copy['end'] = [str(time).split('.', 1)[0] for time in calpv_copy['end']]
-        calpv_copy['start'] = pd.to_datetime(calpv_copy['start'], utc=False, format="%Y-%m-%dT%H:%M:%S", errors='coerce')
+        calpv_copy['start'] = pd.to_datetime(calpv_copy['start'], utc=False, format="%Y-%m-%dT%H:%M:%S",
+                                             errors='coerce')
         calpv_copy['end'] = pd.to_datetime(calpv_copy['end'], utc=False, format="%Y-%m-%dT%H:%M:%S", errors='coerce')
 
         # Including the relevant information for the final all_obs_info DataFrame
@@ -411,10 +431,10 @@ def _check_chos_fields(self, fields: Union[List[str], str]):
             raise ValueError("The fields input must be entered as a string, or a list of strings.")
 
         # Converting to upper case and replacing special characters and whitespaces
-        #  with underscores to match the entries in CALPV_INFO 
+        #  with underscores to match the entries in EROSITA_CALPV_INFO
         fields = [re.sub("[-()+/. ]", "_", field.upper()) for field in fields]
 
-        # In case people use roman numerals or dont include the brackets in their input
+        # In case people use roman numerals or don't include the brackets in their input
         # Lovely and hard coded but not sure if there is any better way to do this
         poss_alt_field_names = {"IGR_J13020_6359": "IGR_J13020_6359__2RXP_J130159_635806_", 
                                 "HR_3165": "HR_3165__ZET_PUP_", "CRAB_I": "CRAB_1", "CRAB_II": "CRAB_2",
@@ -424,25 +444,27 @@ def _check_chos_fields(self, fields: Union[List[str], str]):
         
         # Finding if any of the fields entries are not valid CalPV field names or types
         bad_fields = [f for f in fields if f not in poss_alt_field_names and f not in self._miss_poss_fields
-                     and f not in self._miss_poss_field_types and f != 'CRAB']
+                      and f not in self._miss_poss_field_types and f != 'CRAB']
         if len(bad_fields) != 0:
-            raise ValueError("Some field names or field types {bf} are not associated with this mission, please"
-                            " choose from the following fields; {gf} or field types; {gft}".format(
-                            bf=",".join(bad_fields), gf=",".join(self._miss_poss_fields), gft=",".join(self._miss_poss_field_types)))
+            raise ValueError("Some field names or field types {bf} are not associated with this mission, please "
+                             "choose from the following fields; {gf} or field types; "
+                             "{gft}".format(bf=",".join(bad_fields),
+                                            gf=",".join(self._miss_poss_fields),
+                                            gft=",".join(self._miss_poss_field_types)))
         
         # Extracting the alt_fields from fields
         alt_fields = [field for field in fields if field in poss_alt_field_names]
-        #Making a list of the alt_fields DAXA compatible name
+        # Making a list of the alt_fields DAXA compatible name
         alt_fields_proper_name = [poss_alt_field_names[field] for field in alt_fields]
         # Seeing if someone just input 'crab' into the fields argument
         if 'CRAB' in fields:
             crab = ['CRAB_1', 'CRAB_2', 'CRAB_3', 'CRAB_4']
         else:
             crab = []
-        #Then the extracting the field_types
+        # Then the extracting the field_types
         field_types = [field for field in fields if field in self._miss_poss_field_types]
         # Turning the field_types into field_names
-        field_types_proper_name = CALPV_INFO.loc[CALPV_INFO["Field_Type"].isin(field_types), "Field_Name"].tolist()
+        field_types_proper_name = EROSITA_CALPV_INFO.loc[EROSITA_CALPV_INFO["Field_Type"].isin(field_types), "Field_Name"].tolist()
         # Then extracting the field names from fields
         field_names = [field for field in fields if field in self._miss_poss_fields]
     
@@ -460,8 +482,8 @@ def _inst_filtering(insts: List[str], evlist_path: str):
         Method to filter event lists for eROSITACalPV data based on instrument choice.
         
         :param List[str] insts: The self.chosen_instruments attribute.
-        :param str evlist_path: This is the file path to the raw eventlist for a certain ObsID
-         that has NOT been filtered for the users instrument choice yet.
+        :param str evlist_path: This is the file path to the raw event list for a certain ObsID
+            that has NOT been filtered for the users instrument choice yet.
         """
 
         # Getting a string of TM numbers to add to the end of the file name
@@ -486,34 +508,33 @@ def _inst_filtering(insts: List[str], evlist_path: str):
                 # Getting the indexes of events with the chosen insts
                 gd_insts_indx = np.where(np.isin(t_col, gd_insts))[0]
                 
-                # Filtering the data on those tscopes
+                # Filtering the data on those telescopes
                 filtered_data = data[gd_insts_indx]
 
-                # Replacing unfiltered eventlist in the fits file with the new ones
+                # Replacing unfiltered event list in the fits file with the new ones
                 fits_file[1].data = filtered_data
 
                 # Writing this to a new file (the if is for instrument filtered)
                 fits_file.writeto(evlist_path[:-5] + '_if_{}.fits'.format(insts_str))
         
     @staticmethod
-    def _download_call(raw_data_path: str, link: str):
+    def _download_call(raw_dir: str, link: str):
         """
         This internal static method is purely to enable parallelised downloads of data, as defining
         an internal function within download causes issues with pickling for multiprocessing.
 
-        :param str raw_data_path: This is the self.raw_data_path attribute.
+        :param str raw_dir: The raw data directory in which to create an ObsID directory and store
+            the downloaded data.
         :param str link: The download_link of the particular field to be downloaded.
-        :return: A None value.
-        :rtype: Any
         """
         # Since you can't download a single observation for a field, you have to download them all in one tar file,
         #  I am making a temporary directories to download the tar file and unpack it in, then move the observations 
         #  to their own directory afterwards in the _directory_formatting function
 
         # Getting the field name associated with the download link for directory naming purposes
-        field_name = CALPV_INFO.loc[CALPV_INFO['download'].isin([link]), 'Field_Name'].tolist()[0]
+        field_name = EROSITA_CALPV_INFO.loc[EROSITA_CALPV_INFO['download'].isin([link]), 'Field_Name'].tolist()[0]
         # The temporary 
-        temp_dir = os.path.join(raw_data_path, "temp_download", field_name)
+        temp_dir = os.path.join(raw_dir, "temp_download", field_name)
         if not os.path.exists(temp_dir):
             os.makedirs(temp_dir)
         # Download the requested data
@@ -523,11 +544,11 @@ def _download_call(raw_data_path: str, link: str):
             with open(field_dir + "{f}.tar.gz".format(f=field_name), "wb") as writo:
                 copyfileobj(r.raw, writo)
 
-        # unzipping the tar file
-        tarname = field_dir + "{f}.tar.gz".format(f=field_name)
-        with tarfile.open(tarname, "r:gz") as tar:
+        # Unzipping the tar file
+        tar_name = field_dir + "{f}.tar.gz".format(f=field_name)
+        with tarfile.open(tar_name, "r:gz") as tar:
             tar.extractall(field_dir)
-            os.remove(tarname)
+            os.remove(tar_name)
 
         return None
     
@@ -541,7 +562,7 @@ def _directory_formatting(self):
         # Moving the eventlist for each obs_id from its downloaded path to the path DAXA expects
         for obs_id in self.filtered_obs_ids:
             # The field the obs_id was downloaded with
-            field_name = CALPV_INFO["Field_Name"].loc[CALPV_INFO["ObsID"] ==  obs_id].values[0]
+            field_name = EROSITA_CALPV_INFO["Field_Name"].loc[EROSITA_CALPV_INFO["ObsID"] == obs_id].values[0]
             # The path to where the obs_id was initially downloaded
             field_dir = os.path.join(self.raw_data_path, "temp_download", field_name)
             # Only executing the method if new data has been downloaded, 
@@ -559,20 +580,23 @@ def _directory_formatting(self):
                     if len(all_files) == 1:
                         second_field_dir = all_files[0]
                         # redefining all_files so it lists the files in the folder
-                        all_files = [f for f in os.listdir(os.path.join(field_dir, second_field_dir)) if not f.startswith('.')]
-                        #redefining field_dir so in the later block, the source is correct
+                        all_files = [f for f in os.listdir(os.path.join(field_dir, second_field_dir))
+                                     if not f.startswith('.')]
+                        # Redefining field_dir so in the later block, the source is correct
                         field_dir = os.path.join(field_dir, second_field_dir)
 
-                        # Some of the fields are in another folder, so need to perform the same check again (pretty sure this only applies to efeds and eta cha)
+                        # Some of the fields are in another folder, so need to perform the same check again (pretty
+                        #  sure this only applies to efeds and eta cha)
                         if len(all_files) == 1:
                             third_field_dir = all_files[0]
-                            # redefining all_files so it lists the files in the folder
+                            # Redefining all_files, so it lists the files in the folder
                             all_files = os.listdir(os.path.join(field_dir, third_field_dir))
-                            #redefining field_dir so in the later block, the source is correct
+                            # Redefining field_dir so in the later block, the source is correct
                             field_dir = os.path.join(field_dir, third_field_dir)
 
-                    # Selecting the eventlist for the obs_id
-                    obs_file_name =  [obs_file for obs_file in all_files if obs_id in obs_file and "eRO" not in obs_file ][0]
+                    # Selecting the event list for the obs_id
+                    obs_file_name = [obs_file for obs_file in all_files
+                                     if obs_id in obs_file and "eRO" not in obs_file ][0]
                     source = os.path.join(field_dir, obs_file_name)
                     dest = os.path.join(obs_dir, obs_file_name)
                     shutil.move(source, dest)
@@ -581,27 +605,26 @@ def _directory_formatting(self):
                 pass
 
         # Deleting temp_download directory containing the field_name directories that contained
-        #  extra files that were not the obs_id eventlists
+        #  extra files that were not the obs_id event lists
         temp_dir = os.path.join(self.raw_data_path, "temp_download")
         if os.path.exists(temp_dir):
             shutil.rmtree(temp_dir)
 
-    def _get_evlist_path_from_obs(self, obs: str):
-        '''
-        Internal method to get the unfiltered, downloaded event list path for a given
-        obs id, for use in the download method. 
+    def get_evlist_path_from_obs(self, obs_id: str):
+        """
+        Internal method to get the unfiltered, downloaded event list path for a given ObsID.
 
-        :param str obs: The obs id for the event list required.
-        :return: The path of the event list.
+        :param str obs_id: The ObsID of the event list required.
+        :return: The path to the event list.
         :rtype: str
-        '''
-        all_files = os.listdir(os.path.join(self.raw_data_path + obs))
+        """
+        all_files = os.listdir(os.path.join(self.raw_data_path + obs_id))
 
-         # This directory could have instrument filtered files in as well as the eventlist
-         #  so selecting the eventlist by chosing the one with 4 hyphens in
+        # This directory could have instrument filtered files in as well as the event list so
+        #  selecting the event list by choosing the one with 4 hyphens in
         file_name = [file for file in all_files if len(re.findall('_', file)) == 4][0]
 
-        ev_list_path = os.path.join(self.raw_data_path + obs, file_name)
+        ev_list_path = os.path.join(self.raw_data_path, obs_id, file_name)
 
         return ev_list_path
     
@@ -611,7 +634,7 @@ def download(self, num_cores: int = NUM_CORES):
         have not been filtered out (if a filter has been applied, otherwise all data will be downloaded). 
         Fields (or field types) specified by the chosen_fields property will be downloaded, which is set 
         either on declaration of the class instance or by passing a new value to the chosen_fields property. 
-        Donwloaded data is then filtered according to Instruments specified by the chosen_instruments property 
+        Downloaded data is then filtered according to Instruments specified by the chosen_instruments property
         (set in the same manner as chosen_fields).
 
         :param int num_cores: The number of cores that can be used to parallelise downloading the data. Default is
@@ -631,18 +654,22 @@ def download(self, num_cores: int = NUM_CORES):
         if all([os.path.exists(stor_dir + o) for o in self.filtered_obs_ids]):
             self._download_done = True
 
-        # Getting all the obs_ids that havent already been downloaded
+        # Getting all the obs_ids that haven't already been downloaded
         obs_to_download = list(set(self.filtered_obs_ids) - set(os.listdir(stor_dir)))
-        # Getting all the unique download links (since the CalPV data is downloaded in whole fields, rather than individual obs_ids)
-        download_links = list(set(CALPV_INFO.loc[CALPV_INFO['ObsID'].isin(obs_to_download), 'download']))
+        # Getting all the unique download links (since the CalPV data is downloaded in whole fields, rather than
+        #  individual obs_ids)
+        download_links = list(set(EROSITA_CALPV_INFO.loc[EROSITA_CALPV_INFO['ObsID'].isin(
+            obs_to_download), 'download']))
     
         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(download_links), desc="Downloading {} data".format(self._pretty_miss_name)) as download_prog:
+                with (tqdm(total=len(download_links), desc="Downloading {} data".format(self._pretty_miss_name))
+                      as download_prog):
                     for link in download_links:
-                        # Use the internal static method I set up which both downloads and unpacks the eROSITACalPV data
-                        self._download_call(raw_data_path=self.raw_data_path, link=link)
+                        # Use the internal static method I set up which both downloads and unpacks the
+                        #  eROSITACalPV data
+                        self._download_call(raw_dir=self.raw_data_path, link=link)
                         # Update the progress bar
                         download_prog.update(1)
 
@@ -684,8 +711,8 @@ def err_callback(err):
                     for link in download_links:
                         # Add each download task to the pool
                         pool.apply_async(self._download_call,
-                                            kwds={'raw_data_path': self.raw_data_path, 'link': link},
-                                            error_callback=err_callback, callback=callback)
+                                         kwds={'raw_dir': self.raw_data_path, 'link': link},
+                                         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.
 
@@ -703,7 +730,7 @@ def err_callback(err):
             if len(self.chosen_instruments) != 7:
                 # Getting all the path for each eventlist corresponding to an obs_id for the
                 #  _inst_filtering function later
-                fits_paths = [self._get_evlist_path_from_obs(obs=o) for o in self.filtered_obs_ids]
+                fits_paths = [self.get_evlist_path_from_obs(o) for o in self.filtered_obs_ids]
 
                 # Filtering out any events from the raw data that arent from the selected instruments
                 if num_cores == 1:
@@ -725,8 +752,8 @@ def err_callback(err):
                         # 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.
+                            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.
                             """
@@ -768,7 +795,7 @@ def err_callback(err):
             self._download_done = True
         
         else:
-            warn("The raw data for this mission have already been downloaded.")
+            warn("The raw data for this mission have already been downloaded.", stacklevel=2)
 
     def assess_process_obs(self, obs_info: dict):
         """
@@ -803,7 +830,651 @@ def assess_process_obs(self, obs_info: dict):
         return to_return
 
 
+class eRASS1DE(BaseMission):
+    """
+    The mission class for the first data release of the German half of the eROSITA All-Sky Survey
+
+    :param List[str]/str insts: The instruments that the user is choosing to download/process data from.
+    """
+    def __init__(self, insts: Union[List[str], str] = None):
+        """
+        The mission class for the first data release of the German half of the eROSITA All-Sky Survey
+
+        :param List[str]/str insts: The instruments that the user is choosing to download/process data from.
+        """
+        # Call the init of parent class with the required information
+        super().__init__()
+
+        # This sets up extra columns which are expected to be present in the all_obs_info pandas dataframe
+        self._required_mission_specific_cols = ['ra_min', 'ra_max', 'dec_min', 'dec_max', 'neigh_obs']
+
+        # Runs the method which fetches information on all available eROSITACalPV 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()
+
+        # Sets the default instruments
+        if insts is None:
+            insts = ['TM1', 'TM2', 'TM3', 'TM4', 'TM5', 'TM6', 'TM7']
+
+        # Setting all the possible instruments that can be associated with eROSITA data
+        self._miss_poss_insts = ['TM1', 'TM2', 'TM3', 'TM4', 'TM5', 'TM6', 'TM7']
+        # Setting the user specified instruments
+        self.chosen_instruments = insts
+        # Call the name property to set up the name and pretty name attributes
+        self.name
+
+        # Runs the method which fetches information on all available RASS 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()
+
+    # Defining properties first
+    @property
+    def name(self) -> str:
+        """
+        Property getter for the name of this mission.
+
+        :return: The mission name
+        :rtype: str
+        """
+        # This is defined here (as well as in the init of BaseMission) because I want people to just copy this
+        #  property if they're making a new subclass, then replace None with the name of the mission.
+        self._miss_name = "erosita_all_sky_de_dr1"
+        # Used for things like progress bar descriptions
+        self._pretty_miss_name = "eRASS DE:1"
+        return self._miss_name
+
+    @property
+    def chosen_instruments(self) -> List[str]:
+        """
+        Property getter for the names of the currently selected instruments associated with this mission which
+        will be processed into an archive by DAXA functions. Overwritten here because I want to use a custom
+        version of _check_chos_insts for eROSITA.
+
+        :return: A list of instrument names
+        :rtype: List[str]
+        """
+        return self._chos_insts
+
+    @chosen_instruments.setter
+    @_lock_check
+    def chosen_instruments(self, new_insts: List[str]):
+        """
+        Property setter for the instruments associated with this mission that should be processed. This property
+        may only be set to a list that is a subset of the existing property value. Overwritten here because I want
+        to use a custom version of _check_chos_insts for eROSITA.
+
+        :param List[str] new_insts: The new list of instruments associated with this mission which should
+            be processed into the archive.
+        """
+        self._chos_insts = self._check_chos_insts(new_insts)
+
+    @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
+        # FK5 is an assumption because I can't find anything to contradict it - doesn't really matter anyway
+        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
+        # TODO THIS WILL NEED TO BE CHECKED WHEN I GET ACCESS TO DR1
+        self._id_format = '^[0-9]{6}$'
+        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.
+        self._approx_fov = Quantity(1.8, 'degree')
+        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.
+        """
+        self._obs_info_checks(new_info)
+        self._obs_info = new_info
+
+    def _check_chos_insts(self, insts: Union[List[str], str]):
+        """
+        An internal function to check and perform event list filtering for instruments for eROSITA. This
+        overwrites the version of this method declared in BaseMission, though it does call the super method.
+        This subclass of BaseMission re-implements this method so that setting chosen instruments also
+        filters event lists for user specified instruments, as eROSITA observations contain all instruments.
+
+        :param List[str]/str insts: Instrument names that are to be checked for the current mission, either a single
+            name or a list of names.
+        :return: The list of instruments (possibly altered to match formats expected by this module).
+        :rtype: List
+        """
+        insts = super()._check_chos_insts(insts)
+
+        # TODO TRY TO UNDERSTAND WHAT JESSICA DID HERE?
+
+        # Checking if the data has already been downloaded:
+        if all([os.path.exists(self.raw_data_path + '{o}'.format(o=obs)) for obs in self.filtered_obs_ids]):
+            # Only doing the instrument filtering if not all the instruments have been chosen
+            if len(insts) != 7:
+                # Getting all the path for each event list corresponding to an obs_id for the
+                #  _inst_filtering function later
+                fits_paths = [self.get_evlist_path_from_obs(o) for o in self.filtered_obs_ids]
+
+                # Filtering out any events from the raw data that aren't from the selected instruments
+                if NUM_CORES == 1:
+                    with tqdm(total=len(self), desc="Selecting EventLists from {}".format(insts)) as inst_filter_prog:
+                        for path in fits_paths:
+                            self._inst_filtering(insts=insts, evlist_path=path)
+                            # Update the progress bar
+                            inst_filter_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="Selecting EventLists from {}".format(insts)) \
+                            as inst_filter_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 inst_filter_prog  # The progress bar will need updating
+                            inst_filter_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 inst_filter_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)
+                            inst_filter_prog.update(1)
+
+                        # Again nested for loop through each Obs_ID
+                        for path in fits_paths:
+                            # Add each download task to the pool
+                            pool.apply_async(self._inst_filtering, kwds={'insts': insts, 'evlist_path': path},
+                                             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.")
+
+        return insts
+
+    # Then define user-facing methods
+    def _fetch_obs_info(self):
+        """
+        This method uses the hard coded csv file to pull information on all German eRASS:1 observations.
+        The data are processed into a Pandas dataframe and stored.
+        """
+        # Hard coded this information and saved it to the erass_de_dr1_info.csv file in /files
+        # Making a copy so that ERASS_DE_DR1_INFO remains unchanged
+        erass_dr1_copy = ERASS_DE_DR1_INFO.copy()
+
+        # I prefer lowercase column names, so I make sure they are
+        erass_dr1_copy = erass_dr1_copy.rename(columns={cn: cn.lower() for cn in erass_dr1_copy.columns})
+        # Apart from ObsID of course, I prefer that camel case, because why be consistent?
+        erass_dr1_copy = erass_dr1_copy.rename(columns={'obsid': 'ObsID'})
+
+        # Converting the start and end time columns to datetimes - the .%f accounts for the presence of milliseconds
+        #  in the times - probably somewhat superfluous
+        # erass_dr1_copy['start'] = pd.to_datetime(erass_dr1_copy['start'], utc=False, format="%Y-%m-%dT%H:%M:%S.%f",
+        #                                          errors='coerce')
+        # erass_dr1_copy['end'] = pd.to_datetime(erass_dr1_copy['end'], utc=False, format="%Y-%m-%dT%H:%M:%S.%f",
+        #                                        errors='coerce')
+
+        # TODO include real start and end values when I have figured out a way to make them available
+        #  I have just used the start and end dates of this survey for now
+        #  "Started on December 12, 2019, and was completed on June 11, 2020"
+        erass_dr1_copy['start'] = pd.to_datetime('2019-12-12T00:00:00.00', utc=False, format="%Y-%m-%dT%H:%M:%S.%f",
+                                                 errors='coerce')
+        erass_dr1_copy['end'] = pd.to_datetime('2020-06-10T00:00:00.00', utc=False, format="%Y-%m-%dT%H:%M:%S.%f",
+                                               errors='coerce')
+
+        # TODO this is clearly nonsense but have to assume it (see above)
+        erass_dr1_copy['duration'] = erass_dr1_copy['end'] - erass_dr1_copy['start']
+
+        # Have to assume this for all of them for now
+        erass_dr1_copy['science_usable'] = True
+
+        # I want to keep the information about which ObsIDs are neighbours to the one in each row, but not in separate
+        #  columns, so I join them all into a string and stick them in one column
+        field_cols = erass_dr1_copy.columns[erass_dr1_copy.columns.str.contains('field')]
+        erass_dr1_copy['neigh_obs'] = erass_dr1_copy[field_cols].agg(','.join, axis=1)
 
+        # Including the relevant information for the final all_obs_info DataFrame
+        obs_info_pd = erass_dr1_copy[['ra', 'dec', 'ObsID', 'science_usable', 'start', 'end', 'duration',
+                                      'ra_min', 'ra_max', 'dec_min', 'dec_max', 'neigh_obs']]
+        # Finally, setting the all_obs_info property with our dataframe
+        self.all_obs_info = obs_info_pd
+
+    @staticmethod
+    def _inst_filtering(insts: List[str], evlist_path: str):
+        """
+        Method to filter event lists for eRASS1DE data based on instrument choice.
+
+        :param List[str] insts: The self.chosen_instruments attribute.
+        :param str evlist_path: This is the file path to the raw event list for a certain ObsID
+            that has NOT been filtered for the users instrument choice yet.
+        """
+
+        # Getting a string of TM numbers to add to the end of the file name
+        insts_str = ''.join(sorted(re.findall(r'\d+', ''.join(insts))))
+
+        # Checking that this combination of instruments has not been filtered for before for this Obsid
+        #  this is done by checking that there is no file with the _if_{}.fits ending where {} is the
+        # number(s) of the TM(s) that the user has specified when declaring the mission.
+        # Indexing the string at [:-5] removes the .fits part of the file path
+        if os.path.exists(evlist_path[:-5] + '_if_{}.fits'.format(insts_str)):
+            pass
+        else:
+            # Reading in the file
+            with fits.open(evlist_path) as fits_file:
+                # Selecting the telescope module number column
+                data = fits_file[1].data
+                t_col = data["TM_NR"]
+
+                # Putting inst names into correct format to search in t_col for
+                gd_insts = [int(re.sub('[^0-9]', '', tscope)) for tscope in insts]
+
+                # Getting the indexes of events with the chosen insts
+                gd_insts_indx = np.where(np.isin(t_col, gd_insts))[0]
+
+                # Filtering the data on those telescopes
+                filtered_data = data[gd_insts_indx]
+
+                # Replacing unfiltered event list in the fits file with the new ones
+                fits_file[1].data = filtered_data
+
+                # Writing this to a new file (the if is for instrument filtered)
+                fits_file.writeto(evlist_path[:-5] + '_if_{}.fits'.format(insts_str))
+
+    @staticmethod
+    def _download_call(obs_id: str, raw_dir: str, download_products: bool, pipeline_version: str = None):
+        """
+        This internal static method is purely to enable parallelised downloads of data, as defining
+        an internal function within download causes issues with pickling for multiprocessing.
+
+        :param str obs_id: The ObsID (RRRDDD, where RRR is an integer representation of the central RA, and DDD is an
+            integer representation of the central Dec) of the eROSITA All-Sky Survey 1 data to be downloaded.
+        :param str raw_dir: The raw data directory in which to create an ObsID directory and store the downloaded data.
+        :param bool download_products: Controls whether pre-generated images and exposure maps are included in the
+            download of this eRASS1DE ObsID data.
+        :param str pipeline_version: The processing pipeline version used to generate the data that is to be
+            downloaded. The default is None, in which case the latest available will be used.
+        """
+
+        # This, once formatted, is the link to the top-level directory of the specified ObsID - that directory
+        #  contains sub-directories with various other processed products that people might want to download.
+        top_url = 'https://erosita.mpe.mpg.de/dr1/erodat/data/download/{D}/{R}/'
+
+        # Splitting the ObsID into the RA and Dec components, we shall need them for the link
+        rrr = obs_id[:3]
+        ddd = obs_id[3:]
+
+        # The populated link, the directory it leads too should contain the following subdirectories;
+        #  DET_010/ EXP_010/ SOU_010/ UPP_010/
+        obs_url = top_url.format(D=ddd, R=rrr)
+
+        # Relevant directories that we check for are defined here, by the choice of whether to download products\
+        if not download_products:
+            req_dir = REQUIRED_DIRS['erosita_all_sky_de_dr1']['all']
+        else:
+            req_dir = REQUIRED_DIRS['erosita_all_sky_de_dr1']['products']
+
+        # 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
+        all_web_data = [en['href'] for en in BeautifulSoup(session.get(obs_url).text, "html.parser").find_all("a")]
+        top_data = [en for en in all_web_data if any([rd in en for rd in req_dir])]
+
+        # The directory names indicate the version of the processing pipeline that was used to generate the data, and
+        #  as the user can specify the version (and as we want to use the latest version if they didn't) we need to
+        #  see what is available
+        vers = list(set([td.split('_')[-1].replace('/', '') for td in top_data]))
+
+        if pipeline_version is not None and pipeline_version not in vers:
+            raise ValueError("The specified pipeline version ({p}) is not available for "
+                             "{oi}".format(p=pipeline_version, oi=obs_id))
+        else:
+            pipeline_version = vers[np.argmax([int(pv) for pv in vers])]
+
+        # Final check that the online archive directory that we're pointing at does actually contain the data
+        #  directories we expect it too. Every mission I've implemented I seem to have done this in a slightly
+        #  different way, but as eROSITA is an active project things are more liable to change and I think this
+        #  should be able to fail fairly informatively if that does happen
+        req_dir = [(rd + '_' + pipeline_version + '/') for rd in req_dir]
+        req_dir_missing = [rd for rd in req_dir if rd not in all_web_data]
+        if len(req_dir_missing) > 0:
+            raise FileNotFoundError("The archive data directory for {o} does not contain the following required "
+                                    "directories; {rq}".format(o=obs_id, rq=", ".join(req_dir_missing)))
+
+        for rd in req_dir:
+            if 'EXP_' in rd and not download_products:
+                down_patt = ['_EventList_']
+            elif 'EXP_' in rd:
+                down_patt = ['_EventList_', '_Image_']
+            elif 'DET_' in rd:
+                down_patt = ['_ExposureMap_']
+
+            # This is the directory to which we will be saving this archive directories files
+            local_dir = raw_dir + '/{}/'.format(obs_id) + rd
+            # Make sure that the local directory is created
+            if not os.path.exists(local_dir):
+                os.makedirs(local_dir)
+
+            # Set up the url for the current required directory
+            cur_url = obs_url + rd + '/'
+
+            # Then use beautiful soup to find out what files are present at that directory, and reduce those entries
+            #  to just things with 'fits' in
+            all_files = [en['href'] for en in BeautifulSoup(session.get(cur_url).text, "html.parser").find_all("a")
+                         if 'fits' in en['href']]
+
+            # Finally we strip anything that doesn't match the file pattern defined by whether the user wants
+            #  pre-generated products or not
+            to_down = [f for patt in down_patt for f in all_files if patt in f]
+
+            # Now we cycle through the files and download them
+            for down_file in to_down:
+                down_url = cur_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
+                if '.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 minimize storage usage
+                    os.remove(local_dir + down_file)
+
+        return None
+
+    def get_evlist_path_from_obs(self, obs_id: str):
+        """
+        Internal method to get the unfiltered, downloaded event list path for a given ObsID.
+
+        :param str obs_id: The ObsID of the event list required.
+        :return: The path to the event list.
+        :rtype: str
+        """
+        all_files = os.listdir(os.path.join(self.raw_data_path + obs_id + '/EXP_010/'))
+
+        # This directory could have instrument filtered files in as well as the event list so
+        #  selecting the event list by choosing the one with 4 hyphens in
+        file_name = [file for file in all_files if len(re.findall('_', file)) == 4 and 'Event' in file][0]
+
+        ev_list_path = os.path.join(self.raw_data_path, obs_id, 'EXP_010', file_name)
+
+        return ev_list_path
+
+    def download(self, num_cores: int = NUM_CORES, download_products: bool = False, pipeline_version: int = None):
+        """
+        A method to acquire and download the German eROSITA All-Sky Survey DR1 data that
+        have not been filtered out (if a filter has been applied, otherwise all data will be downloaded).
+        Downloaded data is then filtered according to Instruments specified by the chosen_instruments property
+        (set in the same manner as chosen_fields).
+
+        :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 images and exposure maps
+            generated by the eROSITA team and included in the first data release. The default is False.
+        :param int pipeline_version: The processing pipeline version used to generate the data that is to be
+            downloaded. The default is None, in which case the latest available will be used.
+        """
+        # Ensures that a directory to store the 'raw' eRASS1DE 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.raw_data_path):
+            os.makedirs(self.raw_data_path)
+        # Grabs the raw data storage path
+        stor_dir = self.raw_data_path
+
+        # 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) for o in self.filtered_obs_ids]):
+            self._download_done = True
+
+        # Getting all the obs_ids that haven't already been downloaded
+        obs_to_download = list(set(self.filtered_obs_ids) - set(os.listdir(stor_dir)))
+
+        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(obs_to_download), desc="Downloading {} data".format(self._pretty_miss_name))
+                      as download_prog):
+                    for obs_id in obs_to_download:
+                        self._download_call(obs_id=obs_id, raw_dir=self.raw_data_path,
+                                            download_products=download_products, pipeline_version=pipeline_version)
+                        # 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(obs_to_download), 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 obs_to_download:
+                        # Add each download task to the pool
+                        pool.apply_async(self._download_call,
+                                         kwds={'raw_dir': self.raw_data_path, 'obs_id': obs_id,
+                                               'download_products': download_products,
+                                               'pipeline_version': pipeline_version},
+                                         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.")
+
+            # Only doing the instrument filtering step if not all the instruments have been chosen
+            if len(self.chosen_instruments) != 7:
+                # Getting all the path for each event list corresponding to an obs_id for the
+                #  _inst_filtering function later
+                fits_paths = [self.get_evlist_path_from_obs(o) for o in self.filtered_obs_ids]
+
+                # Filtering out any events from the raw data that aren't from the selected instruments
+                if num_cores == 1:
+                    with tqdm(total=len(self), desc="Selecting EventLists from "
+                                                    "{}".format(self.chosen_instruments)) as inst_filter_prog:
+                        for path in fits_paths:
+                            self._inst_filtering(insts=self.chosen_instruments, evlist_path=path)
+                            # Update the progress bar
+                            inst_filter_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="Selecting EventLists from {}".format(self.chosen_instruments)) \
+                            as inst_filter_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 inst_filter_prog  # The progress bar will need updating
+                            inst_filter_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 inst_filter_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)
+                            inst_filter_prog.update(1)
+
+                        # Again nested for loop through each Obs_ID
+                        for path in fits_paths:
+                            # Add each download task to the pool
+                            pool.apply_async(self._inst_filtering, kwds={'insts': self.chosen_instruments,
+                                                                         'evlist_path': path},
+                                             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.")
+
+            self._download_done = True
+
+        else:
+            warn("The raw data for this mission have already been downloaded.", stacklevel=2)
+
+    def assess_process_obs(self, obs_info: dict):
+        """
+        A slightly unusual method which will allow the eRASS1DE 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.
+        """
+
+        insts = list(obs_info.keys())
+
+        # The dictionary which will be set back will have top level instrument dictionaries with the
+        # following keys lower level sub keys:
+        #   usable --> this dependent on the filter_wheel setting
+        #   included --> this is to indicate whether this instrument is included in the chosen instruments
+        # We start off by assuming all the filters are set to OPEN and all the instruments are included
+        to_return = {inst: {'usable': True} for inst in insts}
+
+        for inst in to_return:
+            rel_info = obs_info[inst]
+
+            # Want to check that the observation was taken when the filter wheel was on OPEN or FILTER
+            if rel_info['filter'] not in ['OPEN', 'FILTER']:
+                to_return[inst]['usable'] = False
+
+        return to_return
 
 
 
diff --git a/daxa/mission/suzaku.py b/daxa/mission/suzaku.py
index e19214b8..45c99aa1 100644
--- a/daxa/mission/suzaku.py
+++ b/daxa/mission/suzaku.py
@@ -1,5 +1,5 @@
 #  This code is a part of the Democratising Archival X-ray Astronomy (DAXA) module.
-#  Last modified by David J Turner (turne540@msu.edu) 09/10/2023, 20:48. Copyright (c) The Contributors
+#  Last modified by David J Turner (turne540@msu.edu) 31/01/2024, 12:26. Copyright (c) The Contributors
 
 import gzip
 import io
@@ -501,7 +501,7 @@ def err_callback(err):
             self._download_done = True
 
         else:
-            warn("The raw data for this mission have already been downloaded.")
+            warn("The raw data for this mission have already been downloaded.", stacklevel=2)
 
     def assess_process_obs(self, obs_info: dict):
         """
diff --git a/daxa/process/erosita/__init__.py b/daxa/process/erosita/__init__.py
index c6594b14..1ace7625 100644
--- a/daxa/process/erosita/__init__.py
+++ b/daxa/process/erosita/__init__.py
@@ -1,5 +1,5 @@
 #  This code is a part of the Democratising Archival X-ray Astronomy (DAXA) module.
-#  Last modified by Jessica Pilling (jp735@sussex.ac.uk) 21/07/2023, 15:38. Copyright (c) The Contributors
+#  Last modified by David J Turner (turne540@msu.edu) 01/02/2024, 11:15. Copyright (c) The Contributors
 
 from .clean import flaregti
-from .setup import _prepare_erositacalpv_info
+from .setup import _prepare_erosita_info
diff --git a/daxa/process/erosita/_common.py b/daxa/process/erosita/_common.py
index 2bad0a91..292d4ba8 100644
--- a/daxa/process/erosita/_common.py
+++ b/daxa/process/erosita/_common.py
@@ -1,27 +1,25 @@
-# This code is a part of the Democratising Archival X-ray Astronomy (DAXA) module.
-# Last modified by David J Turner (turne540@msu.edu) Thu Apr 13 2023, 15:16. Copyright (c) The Contributors
+#  This code is a part of the Democratising Archival X-ray Astronomy (DAXA) module.
+#  Last modified by David J Turner (turne540@msu.edu) 01/02/2024, 11:15. Copyright (c) The Contributors
 import glob
-from typing import Tuple, List
-from warnings import warn
 import os.path
-from subprocess import Popen, PIPE
+from enum import Flag
 from functools import wraps
 from multiprocessing.dummy import Pool
-from enum import Flag
+from subprocess import Popen, PIPE
+from typing import Tuple, List
+from warnings import warn
 
-import itertools
 from astropy.units import UnitConversionError
-from tqdm import tqdm
 from exceptiongroup import ExceptionGroup
+from tqdm import tqdm
 
 from daxa.archive.base import Archive
 from daxa.exceptions import NoEROSITAMissionsError
 from daxa.process._backend_check import find_esass
 
-from daxa.process.erosita.setup import _prepare_erositacalpv_info
-
-ALLOWED_EROSITA_MISSIONS = ['erosita_calpv']
+ALLOWED_EROSITA_MISSIONS = ['erosita_calpv', 'erosita_all_sky_de_dr1']
 
+# TODO Make this compliant with how I normally do docstrings
 class _eSASS_Flag(Flag):
     """
     This class was written by Toby Wallage found on Github @TobyWallage.
@@ -218,7 +216,7 @@ def esass_call(esass_func):
     @wraps(esass_func)
     def wrapper(*args, **kwargs):
         # This is here to avoid a circular import issue
-        from daxa.process.erosita.setup import _prepare_erositacalpv_info
+        from daxa.process.erosita.setup import _prepare_erosita_info
 
         # The first argument of all the eSASS processing functions will be an archive instance, and pulling
         #  that out of the arguments will be useful later
@@ -236,7 +234,7 @@ def wrapper(*args, **kwargs):
                 #   processing functions. It will also populate the _process_extra_info dictionary for the archive
                 #   with top level keys of the erositacalpv mission and lower level keys of obs_ids with lower level keys
                 #   of 'path', which will store the raw data path for that obs id.
-                _prepare_erositacalpv_info(obs_archive, miss)
+                _prepare_erosita_info(obs_archive, miss)
 
         # This is the output from whatever function this is a decorator for
         miss_cmds, miss_final_paths, miss_extras, process_message, cores, disable, timeout, esass_in_docker = esass_func(*args, **kwargs)
diff --git a/daxa/process/erosita/assemble.py b/daxa/process/erosita/assemble.py
index a40efa71..b0370d79 100644
--- a/daxa/process/erosita/assemble.py
+++ b/daxa/process/erosita/assemble.py
@@ -1,21 +1,24 @@
-# This code is a part of the Democratising Archival X-ray Astronomy (DAXA) module.
-# Last modified by Jessica Pilling (jp735@sussex.ac.uk) Wed Jul 19 2023, 13:52. Copyright (c) The Contributors
-from random import randint
+#  This code is a part of the Democratising Archival X-ray Astronomy (DAXA) module.
+#  Last modified by David J Turner (turne540@msu.edu) 01/02/2024, 09:04. Copyright (c) The Contributors
+
 import os.path
+from random import randint
 
-from astropy.units import Quantity
+from astropy.units import Quantity, UnitConversionError
 
 from daxa import NUM_CORES
 from daxa.archive.base import Archive
+from daxa.exceptions import NoDependencyProcessError
 from daxa.process._cleanup import _last_process
 from daxa.process.erosita._common import _esass_process_setup, ALLOWED_EROSITA_MISSIONS, esass_call, _is_valid_flag
-from daxa.exceptions import NoDependencyProcessError
+
 
 @_last_process(ALLOWED_EROSITA_MISSIONS, 1)
 @esass_call
-def cleaned_evt_lists(obs_archive: Archive, lo_en: Quantity = Quantity(0.2, 'keV'), hi_en: Quantity = Quantity(10, 'keV'),
-                      flag: int = 0xc0000000, flag_invert: bool = True, pattern: int = 15, num_cores: int = NUM_CORES,
-                      disable_progress: bool = False, timeout: Quantity = None):
+def cleaned_evt_lists(obs_archive: Archive, lo_en: Quantity = Quantity(0.2, 'keV'),
+                      hi_en: Quantity = Quantity(10, 'keV'), flag: int = 0xc0000000, flag_invert: bool = True,
+                      pattern: int = 15, num_cores: int = NUM_CORES, disable_progress: bool = False,
+                      timeout: Quantity = None):
 
     """
     The function wraps the eROSITA eSASS task evtool, which is used for selecting events.
@@ -26,20 +29,21 @@ def cleaned_evt_lists(obs_archive: Archive, lo_en: Quantity = Quantity(0.2, 'keV
     which are ready to be analysed.
 
     :param Archive obs_archive: An Archive instance containing eROSITA mission instances with observations for
-        which cleaned event lists should be created. This function will fail if no eROSITA missions are present in the archive.
+        which cleaned event lists should be created. This function will fail if no eROSITA missions are present in
+        the archive.
     :param Quantity lo_en: The lower bound of an energy filter to be applied to the cleaned, filtered, event lists. If
         'lo_en' is set to an Astropy Quantity, then 'hi_en' must be as well. Default is None, in which case no
         energy filter is applied.
     :param Quantity hi_en: The upper bound of an energy filter to be applied to the cleaned, filtered, event lists. If
         'hi_en' is set to an Astropy Quantity, then 'lo_en' must be as well. Default is None, in which case no
         energy filter is applied.
-    :param int flag: FLAG parameter to select events based on owner, information, rejection, quality, and corrupted data. The eROSITA
-        website contains the full description of event flags in section 1.1.2 of the following link:
-        https://erosita.mpe.mpg.de/edr/DataAnalysis/prod_descript/EventFiles_edr.html. The default parameter will remove all events 
-        flagged as either singly corrupt or as part of a corrupt frame.
+    :param int flag: FLAG parameter to select events based on owner, information, rejection, quality, and corrupted
+        data. The eROSITA website contains the full description of event flags in section 1.1.2 of the following link:
+        https://erosita.mpe.mpg.de/edr/DataAnalysis/prod_descript/EventFiles_edr.html. The default parameter will
+        remove all events flagged as either singly corrupt or as part of a corrupt frame.
     :param bool flag_invert: If set to True, this function will discard all events selected by the flag parameter.
-    :param int pattern: Selects events of a certain pattern chosen by the integer key. The default of 15 selects all four of the
-        recognized legal patterns.
+    :param int pattern: Selects events of a certain pattern chosen by the integer key. The default of 15 selects
+        all four of the recognized legal patterns.
     :param int num_cores: The number of cores to use, default is set to 90% of available.
     :param bool disable_progress: Setting this to true will turn off the eSASS generation progress bar.
     :param Quantity timeout: The amount of time each individual process is allowed to run for, the default is None.
@@ -84,16 +88,16 @@ def cleaned_evt_lists(obs_archive: Archive, lo_en: Quantity = Quantity(0.2, 'keV
             raise ValueError("{} is not a valid eSASS flag, see the eROSITA website"
                              " for valid flags.".format(flag))
     
-    # Checking user has input flag_invert as a boolean
+    # Checking user has input flag_invert as a boolean
     if not isinstance(flag_invert, bool):
         raise TypeError("The flag_invert parameter must be a boolean.")
     
-    # Checking user has input pattern as an integer
+    # Checking user has input pattern as an integer
     if not isinstance(pattern, int):
         raise TypeError("The pattern parameter must be an integer between 1 and 15 inclusive.")
     
-    #  Checking user has input a valid pattern
-    if (pattern <= 0 or pattern >= 16):
+    # Checking user has input a valid pattern
+    if pattern <= 0 or pattern >= 16:
         raise ValueError("Valid eROSITA patterns are between 1 and 15 inclusive")
     
     # Converting the parameters to the correct format for the esass command
@@ -114,8 +118,8 @@ def cleaned_evt_lists(obs_archive: Archive, lo_en: Quantity = Quantity(0.2, 'keV
     miss_final_paths = {}
     miss_extras = {}
 
-    # Just grabs the eROSITA missions, we already know there will be at least one because otherwise _esass_process_setup
-    #  would have thrown an error
+    # Just grabs the eROSITA missions, we already know there will be at least one because otherwise
+    #  _esass_process_setup would have thrown an error
     erosita_miss = [mission for mission in obs_archive if mission.name in ALLOWED_EROSITA_MISSIONS]
     # We are iterating through erosita missions (options could include erosita_cal_pv for instance).
     for miss in erosita_miss:
@@ -129,12 +133,13 @@ def cleaned_evt_lists(obs_archive: Archive, lo_en: Quantity = Quantity(0.2, 'keV
             
         # Checking that any valid observations are left after the get_obs_to_process function is run
         if len(all_obs_info) == 0:
-            raise FileNotFoundError("No valid observations have been found, so cleanded_evt_lists may not be run.")
+            raise FileNotFoundError("No valid observations have been found, so cleaned_evt_lists may not be run.")
 
         # all_obs_info is a list of lists, where each list is of the format: [ObsID, Inst, 'usable'].
-        #   There is a new list for each instrument, but I just want to loop over the ObsID in the following bit of code, 
-        #   I also want to know all the instruments that the ObsID contains events for
-        #   So here I am just making a dictionary of the format: {ObsID: insts}
+        # There is a new list for each instrument, but I just want to loop over the ObsID in the following
+        #  bit of code,
+        # I also want to know all the instruments that the ObsID contains events for
+        # So here I am just making a dictionary of the format: {ObsID: insts}
         # Getting unique obs_ids in all_obs_info
         obs_ids = list(set([all_obs_info_list[0] for all_obs_info_list in all_obs_info]))
         obs_info_dict = {}
@@ -145,13 +150,13 @@ def cleaned_evt_lists(obs_archive: Archive, lo_en: Quantity = Quantity(0.2, 'keV
             #   and append it to the dict
             obs_info_dict[obs] = ''.join(ch for ch in insts if ch.isdigit())
 
-        # Counter for number of ObsIDs that flaregti has not been run succesfully on
+        # Counter for number of ObsIDs that flaregti has not been run successfully on
         bad_obs_counter = 0
         # We iterate through the valid identifying information
         for obs_id in obs_info_dict:
             try:
                 # Checking that flaregti has been run successfully on this observation so that it can be cleaned
-                # then only writing a command for ObsIDs that have had flaregti sucessfully run on them
+                # Then only writing a command for ObsIDs that have had flaregti successfully run on them
                 obs_archive.check_dependence_success(miss.name, obs_id, 'flaregti')
                 
                 # Getting the insts associated with this obs for file naming purposes 
@@ -182,8 +187,8 @@ def cleaned_evt_lists(obs_archive: Archive, lo_en: Quantity = Quantity(0.2, 'keV
                 if not os.path.exists(temp_dir):
                     os.makedirs(temp_dir)
                 
-                cmd = evtool_cmd.format(d=temp_dir, ef=evt_list_file, of=filt_evt_name, f=flag, fi=flag_invert, p=pattern,
-                    emin=lo_en, emax=hi_en, fep=filt_evt_path)
+                cmd = evtool_cmd.format(d=temp_dir, ef=evt_list_file, of=filt_evt_name, f=flag, fi=flag_invert,
+                                        p=pattern, emin=lo_en, emax=hi_en, fep=filt_evt_path)
 
                 # Now store the bash command, the path, and extra info in the dictionaries
                 miss_cmds[miss.name][obs_id] = cmd
@@ -193,26 +198,17 @@ def cleaned_evt_lists(obs_archive: Archive, lo_en: Quantity = Quantity(0.2, 'keV
                 
             except NoDependencyProcessError:
                 # If archive.check_dependence_success raises this error, it means flaregti was not run
-                # sucessfully, and so a warning will be raised saying this observation has not been cleaned
+                # successfully, and so a warning will be raised saying this observation has not been cleaned
                 bad_obs_counter += 1
                 pass
     
         # If no observations have had flaregti run successfully, then no events can be cleaned
         if bad_obs_counter == len(obs_info_dict):
             raise NoDependencyProcessError("The required process flaregti has not been run successfully"
-                                            "for any data in {mn}".format(mn=miss.name))
+                                           "for any data in {mn}".format(mn=miss.name))
 
     # This is just used for populating a progress bar during the process run
     process_message = 'Generating final event lists'
 
-    return miss_cmds, miss_final_paths, miss_extras, process_message, num_cores, disable_progress, timeout, esass_in_docker
-
-            
-
-    
-
-    
-
-    
-
-        
\ No newline at end of file
+    return (miss_cmds, miss_final_paths, miss_extras, process_message, num_cores, disable_progress, timeout,
+            esass_in_docker)
diff --git a/daxa/process/erosita/clean.py b/daxa/process/erosita/clean.py
index fe8ea5e8..9b01a0ac 100644
--- a/daxa/process/erosita/clean.py
+++ b/daxa/process/erosita/clean.py
@@ -1,5 +1,5 @@
-# This code is a part of the Democratising Archival X-ray Astronomy (DAXA) module.
-# Last modified by David J Turner (turne540@msu.edu) Thu Apr 20 2023, 10:52. Copyright (c) The Contributors
+#  This code is a part of the Democratising Archival X-ray Astronomy (DAXA) module.
+#  Last modified by David J Turner (turne540@msu.edu) 01/02/2024, 09:04. Copyright (c) The Contributors
 import os
 from random import randint
 from typing import Union
@@ -8,13 +8,13 @@
 
 from daxa import NUM_CORES
 from daxa.archive.base import Archive
-from daxa.exceptions import NoDependencyProcessError
-from daxa.process.erosita.setup import sb_rate
 from daxa.process.erosita._common import _esass_process_setup, ALLOWED_EROSITA_MISSIONS, esass_call
+from daxa.process.erosita.setup import sb_rate
 
 # Adding this to the enabled astropy units so that it can be used in flaregti to define thresholds
 add_enabled_units([sb_rate])
 
+
 @esass_call
 def flaregti(obs_archive: Archive, pimin: Quantity = Quantity(200, 'eV'), pimax: Quantity = Quantity(10000, 'eV'), mask_pimin: Quantity = Quantity(200, 'eV'), 
             mask_pimax: Quantity = Quantity(10000, 'eV'), binsize: int = 1200, detml: Union[float, int] = 10, timebin: Quantity = Quantity(20, 's'), 
diff --git a/daxa/process/erosita/setup.py b/daxa/process/erosita/setup.py
index 6eb1f871..a07ad93c 100644
--- a/daxa/process/erosita/setup.py
+++ b/daxa/process/erosita/setup.py
@@ -1,24 +1,26 @@
-# This code is a part of the Democratising Archival X-ray Astronomy (DAXA) module.
-# Last modified by Jessica E Pilling (jp735@sussex.ac.uk) Wed May 10 2023, 11:22. Copyright (c) The Contributors
-import numpy as np
-from typing import List
+#  This code is a part of the Democratising Archival X-ray Astronomy (DAXA) module.
+#  Last modified by David J Turner (turne540@msu.edu) 01/02/2024, 11:44. Copyright (c) The Contributors
+
 import re
+from typing import Union
 
+import numpy as np
 from astropy.io import fits
 from astropy.units import def_unit, ct, deg, s
 
-from daxa import NUM_CORES
 from daxa import BaseMission
 from daxa.archive.base import Archive
+from daxa.mission import eRASS1DE, eROSITACalPV
 
 # Defining surface brightness rate astropy unit for use in flaregti to measure thresholds in 
 sb_rate = def_unit('sb_rate', ct / (deg**2 *s)) 
 
-def _prepare_erositacalpv_info(archive: Archive, mission: BaseMission):
+
+def _prepare_erosita_info(archive: Archive, mission: BaseMission):
     """
     A function to be used with in the esass_call wrapper. This is called only
     if no erosita processing has taken place yet. It populates two dictionaries
-    with necessary information for esass tasks to be excecuted. The first is the 
+    with necessary information for esass tasks to be executed. The first is the
     _process_extra_info attribute for the given Archive, and it stores paths to raw data
     for each observation. The second dictionary is the observation summaries of the archive,
     which for erosita just parses the filter wheel status. 
@@ -27,10 +29,10 @@ def _prepare_erositacalpv_info(archive: Archive, mission: BaseMission):
         which esass_func wraps. 
     :param BaseMission mission: The eROSITACalPV mission for which this information must be prepared.
     """
-    def get_obs_path(mission: BaseMission, obs_id: str):
+    def get_obs_path(mission: Union[eRASS1DE, eROSITACalPV], obs_id: str):
         """
         A function that returns the absolute raw data path for eROSITA Calibration 
-        and Performance validtion data, for a given mission and obs_id. Since the names of 
+        and Performance validation data, for a given mission and obs_id. Since the names of
         the data files change depending on the user's instrument choice for each mission, 
         this function is necessary for the esass functions to point to the correct raw data path.
         This method is used to populate an Archive._process_extra_info['erositacalpv']['obs']['path']
@@ -42,13 +44,12 @@ def get_obs_path(mission: BaseMission, obs_id: str):
         :return: The raw data path of the obs_id, with the approriate instrument filtered suffix.
         :rtype: str
         """
-        # In the case that no instrument filtering has taken place, the 
-        #   name of the raw fits file is unchanged from the output of 
-        #   the __get_evlist_path_from_obs method
-        obs_path = mission._get_evlist_path_from_obs(obs=obs_id)
+        # In the case that no instrument filtering has taken place, the name of the raw fits file is unchanged
+        #  from the output of the get_evlist_path_from_obs method
+        obs_path = mission.get_evlist_path_from_obs(obs_id)
 
         if len(mission.chosen_instruments) != 7:
-            # Other wise, need to format the name of the fits file according to the instruments
+            # Otherwise, need to format the name of the fits file according to the instruments
             insts = mission.chosen_instruments
             # Getting an ordered string of the telescope module numbers, which is how the fits
             #   file is named
@@ -60,10 +61,10 @@ def get_obs_path(mission: BaseMission, obs_id: str):
     
     def parse_erositacalpv_sum(raw_obs_path: str):
         """
-        A function that takes a path to raw eROSITA Calibration and Performance validtion data
+        A function that takes a path to raw eROSITA Calibration and Performance validation data
         that has been filtered for user's instrument choice. The header of the data will be read in 
         and parsed so that information relevant to DAXA processing valid scientific observations can
-        be extracted. This includes information such aswhether the instrument was active, is the instrument 
+        be extracted. This includes information such as to whether the instrument was active, is the instrument
         included in this observation, and whether the fitler wheel was closed or on the calibration source.
 
         :param str sum_path: The path to the raw data file whose header is to be parsed into a dictionary
@@ -114,4 +115,4 @@ def parse_erositacalpv_sum(raw_obs_path: str):
         # Then adding to the parsed_obs_info dictionary
         parsed_obs_info[mission.name][obs] = parse_erositacalpv_sum(path_to_obs)
      
-    archive.observation_summaries = parsed_obs_info 
\ No newline at end of file
+    archive.observation_summaries = parsed_obs_info
diff --git a/docs/requirements.txt b/docs/requirements.txt
index 54c9c167..1a5b91b3 100644
--- a/docs/requirements.txt
+++ b/docs/requirements.txt
@@ -1,6 +1,6 @@
 versioneer~=0.18
 setuptools~=65.5.0
-Sphinx~=3.1.2
+Sphinx~=5.0.0
 nbsphinx
 sphinx_rtd_theme>=0.3.1
 getdist>=1.1.3
diff --git a/docs/source/daxa.process.erosita.rst b/docs/source/daxa.process.erosita.rst
new file mode 100644
index 00000000..f4eef59e
--- /dev/null
+++ b/docs/source/daxa.process.erosita.rst
@@ -0,0 +1,26 @@
+process.xmm
+===========
+
+process.erosita.assemble
+--------------------
+
+.. automodule:: daxa.process.erosita.assemble
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+process.erosita.clean
+--------------------
+
+.. automodule:: daxa.process.erosita.clean
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+process.erosita.setup
+--------------------
+
+.. automodule:: daxa.process.erosita.setup
+   :members:
+   :undoc-members:
+   :show-inheritance:
\ No newline at end of file
diff --git a/docs/source/daxa.process.rst b/docs/source/daxa.process.rst
index 6d3d7555..4d7b22d2 100644
--- a/docs/source/daxa.process.rst
+++ b/docs/source/daxa.process.rst
@@ -15,9 +15,15 @@ process.simple
    :undoc-members:
    :show-inheritance:
 
+.. automodule:: daxa.process.erosita
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
 .. toctree::
    :maxdepth: 2
    :caption: Instrument Specific:
 
    daxa.process.xmm <daxa.process.xmm.rst>
+   daxa.process.erosita <daxa.process.erosita.rst>
 
diff --git a/docs/source/future.rst b/docs/source/future.rst
index 0194f444..9567ad7f 100644
--- a/docs/source/future.rst
+++ b/docs/source/future.rst
@@ -1,10 +1,6 @@
 Planned DAXA Features
 ========================
 
-* **eROSITA Commissioning Data** - eROSITA commissioning data can be downloaded using DAXA, and processing is being implemented.
-
-* **eROSITA All-Sky Survey Data** - On the release of eRASS:1DE, another mission class will be implemented to allow for the acquisition of eROSITA All-Sky Survey data.
-
 * **NuSTAR Data** - Data can be acquired and downloaded, but not processed yet.
 
 * **Chandra Data** - Data can be acquired and downloaded, but not processed yet. Data can also be downloaded in a form that will work with the standard Chandra reprocessing scripts.
diff --git a/docs/source/intro.rst b/docs/source/intro.rst
index fb32709f..c34c3640 100644
--- a/docs/source/intro.rst
+++ b/docs/source/intro.rst
@@ -20,7 +20,8 @@ Which missions are supported?
 limited. Support for more telescopes is either currently under development or being actively planned.*
 
     * XMM-Newton Pointed
-    * [Under Development - data acquisition implemented] eROSITA Commissioning
+    * eROSITA Commissioning
+    * eROSITA All-Sky Survey DR1 (German Half)
     * [Under Development - data acquisition implemented] NuSTAR Pointed
     * [Under Development - data acquisition implemented] Chandra
     * [Under Development - RASS/pointed data acquisition implemented] ROSAT