DL3 Event List with EventPreprocessor

[kosack]

This adds a FeatureSet to the EventPreprocessor for transforming DL2 to DL3 observed events. This is a work-in-progress to see what is needed to replace some of what is in #2979

To support coordinate transforms, I added a altaz_to_icrs helper function, and allow arbitrary objects to be passed into the FeatureGenerator (in this case the observatory location).

TODO:

• use correct GADF column names
• add metadata to table

Example:

preprocess = EventPreprocessor(feature_set="dl2_to_dl3")

with TableLoader(test_dl2, dl2=True, pointing=True, observation_info=True) as loader:
      processed = []
      for chunk in tqdm(
          loader.read_subarray_events_chunked(chunk_size=10_000, stop=100_000)
      ):
          processed.append(
              preprocess(chunk.data, LOCATION=loader.subarray.reference_location)
          )
processed = QTable(vstack(processed))

EVENT_ID	TIME	RA	DEC	ENERGY	ALT	AZ	FOV_LON	FOV_LAT	GAMMANESS
		deg	deg	TeV	deg	deg	deg	deg
3611	0.0	36.39662350967956	13.181637086091095	0.7886559320362269	73.47979669912594	191.68312922045783	3.308260464769828	3.7976173873254258	0.635594432581715
7815	0.0	41.497736016190096	11.13345829907106	0.1201553049782346	71.70613976698166	174.58745551052763	-1.6974919647423072	1.7815269359464097	0.5352792594560092
10605	0.0	nan	nan	nan	nan	nan	nan	nan	nan
10607	0.0	44.119883677161845	9.231156218646664	4.5139644334844435	69.48510550212504	167.61302288026295	-4.311362381591428	-0.07564972460559523	0.855976625014405
22312	0.0	nan	nan	nan	nan	nan	nan	nan	nan
36609	0.0	nan	nan	nan	nan	nan	nan	nan	nan
36611	0.0	39.32701254373573	6.380309010565509	0.1071564794967893	67.0024948143038	181.01639543139208	0.3976198153673294	-2.994190390275193	0.844323214214656
42217	0.0	39.758252574150056	13.182609322182753	0.9676237982439976	73.8115075459884	180.0999841805689	0.02793579401849974	3.8115311089855477	0.8351008610837526
101604	0.0	35.49254214507106	7.759876584987491	0.8787181468947538	67.98702098851416	191.28771591764462	4.208977246630804	-1.622427043094159	0.8820927737812743
...	...	...	...	...	...	...	...	...	...
752600	0.0	42.10742472015945	11.599367854919294	0.41113608299100585	72.1158017345081	172.53367176818304	-2.288765887411892	2.2560905763764167	0.8814234287985462
752601	0.0	41.98723511795694	11.637984282652196	0.35472578628359946	72.16646055132047	172.9023774705431	-2.1703681338113268	2.2929095016497767	0.7525534351333694
799915	0.0	37.809847621855184	7.47057923538318	0.7347241179828652	68.00797207094908	185.10600332313112	1.9109700122261497	-1.9119729025655396	0.5581226046512989
814810	0.0	39.68103797197321	15.548286472017384	0.11767882880254765	76.17601379325806	180.5141171245