Working with Instrumental Descriptions¶
the instrumental description is loaded by the event source, and consists of a hierarchy of classes in the ctapipe.instrument module, the base of which is the SubarrayDescription
[1]:
from ctapipe.utils.datasets import get_dataset_path
from ctapipe.io import EventSource
import numpy as np
filename = get_dataset_path("gamma_prod5.simtel.zst")
with EventSource(filename, max_events=1) as source:
subarray = source.subarray
the SubarrayDescription:¶
[2]:
subarray.info()
Subarray : MonteCarloArray
Num Tels : 180
Footprint: 4.92 km2
Type Count Tel IDs
----------------- ----- ---------------
SST_ASTRI_CHEC 120 30-99,131-180
LST_LST_LSTCam 4 1-4
MST_MST_FlashCam 28 5-29,125-127
MST_MST_NectarCam 28 100-124,128-130
[3]:
subarray.to_table()
[3]:
Table length=180
| tel_id | name | type | pos_x | pos_y | pos_z | camera_name | optics_name | camera_index | optics_index | tel_description |
|---|---|---|---|---|---|---|---|---|---|---|
| m | m | m | ||||||||
| int16 | str5 | str3 | float32 | float32 | float32 | str9 | str5 | int64 | int64 | str17 |
| 1 | LST | LST | -20.643 | -64.817 | 34.3 | LSTCam | LST | 2 | 1 | LST_LST_LSTCam |
| 2 | LST | LST | 79.993996 | -0.768 | 29.4 | LSTCam | LST | 2 | 1 | LST_LST_LSTCam |
| 3 | LST | LST | -19.396 | 65.2 | 31.0 | LSTCam | LST | 2 | 1 | LST_LST_LSTCam |
| 4 | LST | LST | -120.033 | 1.151 | 33.1 | LSTCam | LST | 2 | 1 | LST_LST_LSTCam |
| 5 | MST | MST | -0.017 | -0.001 | 24.35 | FlashCam | MST | 1 | 2 | MST_MST_FlashCam |
| 6 | MST | MST | -1.468 | -151.221 | 31.0 | FlashCam | MST | 1 | 2 | MST_MST_FlashCam |
| 7 | MST | MST | -3.1379998 | -325.245 | 39.0 | FlashCam | MST | 1 | 2 | MST_MST_FlashCam |
| 8 | MST | MST | 1.4339999 | 151.22 | 25.0 | FlashCam | MST | 1 | 2 | MST_MST_FlashCam |
| 9 | MST | MST | 3.1039999 | 325.243 | 23.5 | FlashCam | MST | 1 | 2 | MST_MST_FlashCam |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 171 | ASTRI | SST | 260.0 | 920.0 | 45.0 | CHEC | ASTRI | 0 | 0 | SST_ASTRI_CHEC |
| 172 | ASTRI | SST | 260.0 | -920.0 | 65.0 | CHEC | ASTRI | 0 | 0 | SST_ASTRI_CHEC |
| 173 | ASTRI | SST | -500.0 | 815.0 | 15.0 | CHEC | ASTRI | 0 | 0 | SST_ASTRI_CHEC |
| 174 | ASTRI | SST | -500.0 | -815.0 | 75.0 | CHEC | ASTRI | 0 | 0 | SST_ASTRI_CHEC |
| 175 | ASTRI | SST | 500.0 | 815.0 | 45.0 | CHEC | ASTRI | 0 | 0 | SST_ASTRI_CHEC |
| 176 | ASTRI | SST | 500.0 | -815.0 | 53.0 | CHEC | ASTRI | 0 | 0 | SST_ASTRI_CHEC |
| 177 | ASTRI | SST | -810.0 | 655.0 | 12.0 | CHEC | ASTRI | 0 | 0 | SST_ASTRI_CHEC |
| 178 | ASTRI | SST | -810.0 | -655.0 | 68.0 | CHEC | ASTRI | 0 | 0 | SST_ASTRI_CHEC |
| 179 | ASTRI | SST | 810.0 | 655.0 | 20.0 | CHEC | ASTRI | 0 | 0 | SST_ASTRI_CHEC |
| 180 | ASTRI | SST | 810.0 | -655.0 | 41.0 | CHEC | ASTRI | 0 | 0 | SST_ASTRI_CHEC |
[ ]:
You can also get a table of just the OpticsDescriptions (CameraGeometry is more complex and can’t be stored on a single table row, so each one can be converted to a table separately)
[4]:
subarray.to_table(kind="optics")
[4]:
Table length=3
| optics_name | size_type | reflector_shape | mirror_area | n_mirrors | n_mirror_tiles | equivalent_focal_length | effective_focal_length |
|---|---|---|---|---|---|---|---|
| m2 | m | m | |||||
| str5 | str3 | str20 | float64 | int64 | int64 | float64 | float64 |
| ASTRI | SST | SCHWARZSCHILD_COUDER | 14.126235008239746 | 2 | 2 | 2.1500000953674316 | 2.1519100666046143 |
| LST | LST | PARABOLIC | 386.7332458496094 | 1 | 198 | 28.0 | 29.30565071105957 |
| MST | MST | HYBRID | 106.2413558959961 | 1 | 86 | 16.0 | 16.445049285888672 |
Make a sub-array with only SC-type telescopes:
[5]:
sc_tels = [tel_id for tel_id, tel in subarray.tel.items() if tel.optics.n_mirrors == 2]
newsub = subarray.select_subarray(sc_tels, name="SCTels")
newsub.info()
Subarray : SCTels
Num Tels : 120
Footprint: 4.92 km2
Type Count Tel IDs
-------------- ----- -------------
SST_ASTRI_CHEC 120 30-99,131-180
can also do this by using Table.group_by
Explore some of the details of the telescopes¶
[6]:
tel = subarray.tel[1]
tel
[6]:
TelescopeDescription(type='LST', optics_name='LST', camera_name='LSTCam')
[7]:
tel.optics.mirror_area
[7]:
$386.73325 \; \mathrm{m^{2}}$
[8]:
tel.optics.n_mirror_tiles
[8]:
198
[9]:
tel.optics.equivalent_focal_length
[9]:
$28 \; \mathrm{m}$
[10]:
tel.camera
[10]:
CameraDescription(name=LSTCam, geometry=LSTCam, readout=LSTCam)
[11]:
tel.camera.geometry.pix_x
[11]:
$[0,~-0.037796701,~-0.047245475,~\dots,~0.67088033,~-0.45356484,~-0.50081024] \; \mathrm{m}$
[12]:
%matplotlib inline
from ctapipe.visualization import CameraDisplay
CameraDisplay(tel.camera.geometry)
[12]:
<ctapipe.visualization.mpl_camera.CameraDisplay at 0x7f4eca383580>
[13]:
CameraDisplay(subarray.tel[98].camera.geometry)
[13]:
<ctapipe.visualization.mpl_camera.CameraDisplay at 0x7f4ec8394f70>
Plot the subarray¶
We’ll make a subarray by telescope type and plot each separately, so they appear in different colors. We also calculate the radius using the mirror area (and exagerate it a bit).
This is just for debugging and info, for any “real” use, a visualization.ArrayDisplay should be used
[14]:
subarray.peek()
[15]:
subarray.footprint
[15]:
$4.9234632 \; \mathrm{km^{2}}$
Get info about the subarray in general¶
[16]:
subarray.telescope_types
[16]:
(TelescopeDescription(type='SST', optics_name='ASTRI', camera_name='CHEC'),
TelescopeDescription(type='LST', optics_name='LST', camera_name='LSTCam'),
TelescopeDescription(type='MST', optics_name='MST', camera_name='FlashCam'),
TelescopeDescription(type='MST', optics_name='MST', camera_name='NectarCam'))
[17]:
subarray.camera_types
[17]:
(CameraDescription(name=CHEC, geometry=CHEC, readout=CHEC),
CameraDescription(name=FlashCam, geometry=FlashCam, readout=FlashCam),
CameraDescription(name=LSTCam, geometry=LSTCam, readout=LSTCam),
CameraDescription(name=NectarCam, geometry=NectarCam, readout=NectarCam))
[18]:
subarray.optics_types
[18]:
(OpticsDescription(name=ASTRI, size_type=SST, reflector_shape=SCHWARZSCHILD_COUDER, equivalent_focal_length=2.15 m, effective_focal_length=2.15 m, n_mirrors=2, mirror_area=14.13 m2),
OpticsDescription(name=LST, size_type=LST, reflector_shape=PARABOLIC, equivalent_focal_length=28.00 m, effective_focal_length=29.31 m, n_mirrors=1, mirror_area=386.73 m2),
OpticsDescription(name=MST, size_type=MST, reflector_shape=HYBRID, equivalent_focal_length=16.00 m, effective_focal_length=16.45 m, n_mirrors=1, mirror_area=106.24 m2))
[19]:
from astropy.coordinates import SkyCoord
from ctapipe.coordinates import GroundFrame
center = SkyCoord("10.0 m", "2.0 m", "0.0 m", frame="groundframe")
coords = subarray.tel_coords # a flat list of coordinates by tel_index
coords.separation(center)
[19]:
[$115^\circ37{}^\prime48.53728809{}^{\prime\prime}$
$23^\circ16{}^\prime48.71368376{}^{\prime\prime}$
$94^\circ46{}^\prime57.06512996{}^{\prime\prime}$
$160^\circ38{}^\prime17.99179741{}^{\prime\prime}$
$90^\circ02{}^\prime22.86896225{}^{\prime\prime}$
$101^\circ37{}^\prime15.59471603{}^{\prime\prime}$
$101^\circ46{}^\prime37.67470941{}^{\prime\prime}$
$78^\circ18{}^\prime27.84261148{}^{\prime\prime}$
$78^\circ10{}^\prime28.36657617{}^{\prime\prime}$
$45^\circ27{}^\prime35.97543537{}^{\prime\prime}$
$16^\circ59{}^\prime24.86749269{}^{\prime\prime}$
$39^\circ58{}^\prime36.5305221{}^{\prime\prime}$
$69^\circ06{}^\prime00.41200774{}^{\prime\prime}$
$110^\circ55{}^\prime43.84261342{}^{\prime\prime}$
$139^\circ53{}^\prime52.10746639{}^{\prime\prime}$
$161^\circ53{}^\prime57.44575812{}^{\prime\prime}$
$134^\circ14{}^\prime04.61794311{}^{\prime\prime}$
$45^\circ19{}^\prime31.8677554{}^{\prime\prime}$
$15^\circ41{}^\prime09.62278432{}^{\prime\prime}$
$12^\circ23{}^\prime30.02446134{}^{\prime\prime}$
$39^\circ16{}^\prime01.93418447{}^{\prime\prime}$
$69^\circ02{}^\prime23.91555142{}^{\prime\prime}$
$111^\circ01{}^\prime02.98397763{}^{\prime\prime}$
$140^\circ35{}^\prime18.81775813{}^{\prime\prime}$
$166^\circ57{}^\prime33.12823197{}^{\prime\prime}$
$163^\circ35{}^\prime33.54325283{}^{\prime\prime}$
$134^\circ29{}^\prime00.15614806{}^{\prime\prime}$
$11^\circ55{}^\prime58.302677{}^{\prime\prime}$
$167^\circ37{}^\prime52.38371474{}^{\prime\prime}$
$26^\circ01{}^\prime54.63781737{}^{\prime\prime}$
$49^\circ42{}^\prime53.3573094{}^{\prime\prime}$
$130^\circ15{}^\prime08.98906098{}^{\prime\prime}$
$153^\circ27{}^\prime53.98923833{}^{\prime\prime}$
$56^\circ58{}^\prime06.54760364{}^{\prime\prime}$
$80^\circ40{}^\prime58.98009562{}^{\prime\prime}$
$99^\circ20{}^\prime36.68467668{}^{\prime\prime}$
$122^\circ54{}^\prime22.08156543{}^{\prime\prime}$
$78^\circ08{}^\prime42.65380699{}^{\prime\prime}$
$101^\circ49{}^\prime26.16909516{}^{\prime\prime}$
$34^\circ01{}^\prime48.4871336{}^{\prime\prime}$
$57^\circ46{}^\prime41.25947597{}^{\prime\prime}$
$122^\circ15{}^\prime35.5320416{}^{\prime\prime}$
$145^\circ39{}^\prime14.42191405{}^{\prime\prime}$
$1^\circ14{}^\prime54.5162229{}^{\prime\prime}$
$24^\circ26{}^\prime06.70955766{}^{\prime\prime}$
$155^\circ24{}^\prime16.53048746{}^{\prime\prime}$
$176^\circ30{}^\prime58.85678108{}^{\prime\prime}$
$78^\circ08{}^\prime35.43447641{}^{\prime\prime}$
$101^\circ49{}^\prime13.04461153{}^{\prime\prime}$
$15^\circ25{}^\prime15.9893754{}^{\prime\prime}$
$39^\circ07{}^\prime10.57205245{}^{\prime\prime}$
$140^\circ50{}^\prime37.19773001{}^{\prime\prime}$
$164^\circ04{}^\prime49.92718766{}^{\prime\prime}$
$45^\circ17{}^\prime39.84001018{}^{\prime\prime}$
$69^\circ00{}^\prime41.87664707{}^{\prime\prime}$
$111^\circ01{}^\prime39.52843975{}^{\prime\prime}$
$134^\circ31{}^\prime54.15211982{}^{\prime\prime}$
$11^\circ51{}^\prime48.04299327{}^{\prime\prime}$
$167^\circ57{}^\prime00.7607586{}^{\prime\prime}$
$62^\circ40{}^\prime06.90027025{}^{\prime\prime}$
$86^\circ22{}^\prime57.30949883{}^{\prime\prime}$
$93^\circ37{}^\prime39.95895171{}^{\prime\prime}$
$117^\circ14{}^\prime49.7522144{}^{\prime\prime}$
$78^\circ08{}^\prime46.65029204{}^{\prime\prime}$
$101^\circ49{}^\prime21.94647892{}^{\prime\prime}$
$28^\circ49{}^\prime16.21053425{}^{\prime\prime}$
$52^\circ34{}^\prime56.22025098{}^{\prime\prime}$
$127^\circ28{}^\prime14.89982613{}^{\prime\prime}$
$150^\circ53{}^\prime43.36851833{}^{\prime\prime}$
$2^\circ37{}^\prime25.28096743{}^{\prime\prime}$
$26^\circ19{}^\prime10.19296874{}^{\prime\prime}$
$153^\circ38{}^\prime45.55173649{}^{\prime\prime}$
$176^\circ17{}^\prime30.67883187{}^{\prime\prime}$
$15^\circ25{}^\prime49.43804438{}^{\prime\prime}$
$39^\circ09{}^\prime24.17330046{}^{\prime\prime}$
$140^\circ51{}^\prime45.78771005{}^{\prime\prime}$
$164^\circ14{}^\prime51.16830131{}^{\prime\prime}$
$52^\circ19{}^\prime45.41515578{}^{\prime\prime}$
$76^\circ00{}^\prime54.35432629{}^{\prime\prime}$
$104^\circ00{}^\prime40.3191603{}^{\prime\prime}$
$127^\circ35{}^\prime18.49132396{}^{\prime\prime}$
$38^\circ28{}^\prime24.20830686{}^{\prime\prime}$
$62^\circ09{}^\prime24.90488478{}^{\prime\prime}$
$117^\circ52{}^\prime38.73247866{}^{\prime\prime}$
$141^\circ23{}^\prime18.86339205{}^{\prime\prime}$
$11^\circ51{}^\prime40.55787668{}^{\prime\prime}$
$168^\circ03{}^\prime13.67455894{}^{\prime\prime}$
$66^\circ00{}^\prime25.50011034{}^{\prime\prime}$
$89^\circ41{}^\prime49.40711649{}^{\prime\prime}$
$90^\circ14{}^\prime45.66080857{}^{\prime\prime}$
$113^\circ56{}^\prime46.48499003{}^{\prime\prime}$
$25^\circ52{}^\prime55.46889846{}^{\prime\prime}$
$49^\circ36{}^\prime25.53139002{}^{\prime\prime}$
$130^\circ25{}^\prime28.16246205{}^{\prime\prime}$
$153^\circ52{}^\prime16.80179427{}^{\prime\prime}$
$5^\circ18{}^\prime28.02210585{}^{\prime\prime}$
$29^\circ01{}^\prime06.88086914{}^{\prime\prime}$
$150^\circ59{}^\prime18.82490611{}^{\prime\prime}$
$174^\circ17{}^\prime23.56686487{}^{\prime\prime}$
$90^\circ02{}^\prime22.86896225{}^{\prime\prime}$
$101^\circ37{}^\prime15.59471603{}^{\prime\prime}$
$101^\circ46{}^\prime37.67470941{}^{\prime\prime}$
$78^\circ18{}^\prime27.84261148{}^{\prime\prime}$
$78^\circ10{}^\prime28.36657617{}^{\prime\prime}$
$45^\circ27{}^\prime35.97543537{}^{\prime\prime}$
$16^\circ59{}^\prime24.86749269{}^{\prime\prime}$
$39^\circ58{}^\prime36.5305221{}^{\prime\prime}$
$69^\circ06{}^\prime00.41200774{}^{\prime\prime}$
$110^\circ55{}^\prime43.84261342{}^{\prime\prime}$
$139^\circ53{}^\prime52.10746639{}^{\prime\prime}$
$161^\circ53{}^\prime57.44575812{}^{\prime\prime}$
$134^\circ14{}^\prime04.61794311{}^{\prime\prime}$
$45^\circ19{}^\prime31.8677554{}^{\prime\prime}$
$15^\circ41{}^\prime09.62278432{}^{\prime\prime}$
$12^\circ23{}^\prime30.02446134{}^{\prime\prime}$
$39^\circ16{}^\prime01.93418447{}^{\prime\prime}$
$69^\circ02{}^\prime23.91555142{}^{\prime\prime}$
$111^\circ01{}^\prime02.98397763{}^{\prime\prime}$
$140^\circ35{}^\prime18.81775813{}^{\prime\prime}$
$166^\circ57{}^\prime33.12823197{}^{\prime\prime}$
$163^\circ35{}^\prime33.54325283{}^{\prime\prime}$
$134^\circ29{}^\prime00.15614806{}^{\prime\prime}$
$11^\circ55{}^\prime58.302677{}^{\prime\prime}$
$167^\circ37{}^\prime52.38371474{}^{\prime\prime}$
$136^\circ57{}^\prime45.95755748{}^{\prime\prime}$
$135^\circ07{}^\prime08.42537213{}^{\prime\prime}$
$144^\circ03{}^\prime07.34583759{}^{\prime\prime}$
$136^\circ57{}^\prime45.95755748{}^{\prime\prime}$
$135^\circ07{}^\prime08.42537213{}^{\prime\prime}$
$144^\circ03{}^\prime07.34583759{}^{\prime\prime}$
$12^\circ21{}^\prime10.63672941{}^{\prime\prime}$
$11^\circ49{}^\prime31.42733953{}^{\prime\prime}$
$14^\circ26{}^\prime28.56626411{}^{\prime\prime}$
$117^\circ32{}^\prime31.4678604{}^{\prime\prime}$
$16^\circ04{}^\prime14.73718876{}^{\prime\prime}$
$62^\circ57{}^\prime07.77000849{}^{\prime\prime}$
$78^\circ42{}^\prime38.29587693{}^{\prime\prime}$
$101^\circ14{}^\prime22.04343375{}^{\prime\prime}$
$118^\circ48{}^\prime09.35939992{}^{\prime\prime}$
$141^\circ08{}^\prime27.59692398{}^{\prime\prime}$
$38^\circ36{}^\prime57.36221721{}^{\prime\prime}$
$61^\circ14{}^\prime04.57040488{}^{\prime\prime}$
$104^\circ39{}^\prime08.29781247{}^{\prime\prime}$
$127^\circ08{}^\prime29.11683192{}^{\prime\prime}$
$52^\circ44{}^\prime50.28926073{}^{\prime\prime}$
$75^\circ22{}^\prime30.28675274{}^{\prime\prime}$
$154^\circ17{}^\prime04.33890125{}^{\prime\prime}$
$175^\circ51{}^\prime30.60324792{}^{\prime\prime}$
$3^\circ07{}^\prime40.20738999{}^{\prime\prime}$
$25^\circ40{}^\prime46.72611271{}^{\prime\prime}$
$155^\circ31{}^\prime05.76852749{}^{\prime\prime}$
$177^\circ07{}^\prime48.37033358{}^{\prime\prime}$
$1^\circ51{}^\prime33.76315166{}^{\prime\prime}$
$24^\circ28{}^\prime05.09034799{}^{\prime\prime}$
$168^\circ33{}^\prime14.25941047{}^{\prime\prime}$
$11^\circ18{}^\prime49.45565605{}^{\prime\prime}$
$89^\circ48{}^\prime50.06947064{}^{\prime\prime}$
$112^\circ43{}^\prime37.64931806{}^{\prime\prime}$
$67^\circ11{}^\prime56.82656885{}^{\prime\prime}$
$90^\circ11{}^\prime11.75785172{}^{\prime\prime}$
$125^\circ45{}^\prime46.14049369{}^{\prime\prime}$
$148^\circ07{}^\prime09.66819251{}^{\prime\prime}$
$31^\circ38{}^\prime07.22086606{}^{\prime\prime}$
$54^\circ16{}^\prime21.09751607{}^{\prime\prime}$
$143^\circ36{}^\prime39.3424254{}^{\prime\prime}$
$165^\circ48{}^\prime19.81864969{}^{\prime\prime}$
$13^\circ45{}^\prime49.96366223{}^{\prime\prime}$
$36^\circ22{}^\prime59.16585{}^{\prime\prime}$
$94^\circ28{}^\prime09.44371894{}^{\prime\prime}$
$117^\circ00{}^\prime03.42456501{}^{\prime\prime}$
$62^\circ56{}^\prime30.19964872{}^{\prime\prime}$
$85^\circ32{}^\prime22.19145376{}^{\prime\prime}$
$110^\circ12{}^\prime59.25351694{}^{\prime\prime}$
$132^\circ40{}^\prime35.18429669{}^{\prime\prime}$
$47^\circ13{}^\prime11.25823017{}^{\prime\prime}$
$69^\circ48{}^\prime47.82814446{}^{\prime\prime}$
$129^\circ43{}^\prime35.22132347{}^{\prime\prime}$
$152^\circ07{}^\prime05.15361244{}^{\prime\prime}$
$27^\circ40{}^\prime14.4110196{}^{\prime\prime}$
$50^\circ18{}^\prime25.99569671{}^{\prime\prime}$]
Telescope IDs vs Indices¶
Note that subarray.tel is a dict mapped by tel_id (the indentifying number of a telescope). It is possible to have telescope IDs that do not start at 0, are not contiguouous (e.g. if a subarray is selected). Some functions and properties like tel_coords are numpy arrays (not dicts) so they are not mapped to the telescope ID, but rather the index within this SubarrayDescription. To convert between the two concepts you can do:
[20]:
subarray.tel_ids_to_indices([1, 5, 23])
[20]:
array([ 0, 4, 22])
or you can get the indexing array directly in numpy or dict form:
[21]:
subarray.tel_index_array
[21]:
array([ -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24,
25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37,
38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,
51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76,
77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,
90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102,
103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115,
116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128,
129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141,
142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154,
155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,
168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179])
[22]:
subarray.tel_index_array[[1, 5, 23]]
[22]:
array([ 0, 4, 22])
[23]:
subarray.tel_indices[
1
] # this is a dict of tel_id -> tel_index, so we can only do one at once
[23]:
0
[24]:
ids = subarray.get_tel_ids_for_type(subarray.telescope_types[0])
ids
[24]:
(30,
31,
32,
33,
34,
35,
36,
37,
38,
39,
40,
41,
42,
43,
44,
45,
46,
47,
48,
49,
50,
51,
52,
53,
54,
55,
56,
57,
58,
59,
60,
61,
62,
63,
64,
65,
66,
67,
68,
69,
70,
71,
72,
73,
74,
75,
76,
77,
78,
79,
80,
81,
82,
83,
84,
85,
86,
87,
88,
89,
90,
91,
92,
93,
94,
95,
96,
97,
98,
99,
131,
132,
133,
134,
135,
136,
137,
138,
139,
140,
141,
142,
143,
144,
145,
146,
147,
148,
149,
150,
151,
152,
153,
154,
155,
156,
157,
158,
159,
160,
161,
162,
163,
164,
165,
166,
167,
168,
169,
170,
171,
172,
173,
174,
175,
176,
177,
178,
179,
180)
[25]:
idx = subarray.tel_ids_to_indices(ids)
idx
[25]:
array([ 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41,
42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,
68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80,
81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93,
94, 95, 96, 97, 98, 130, 131, 132, 133, 134, 135, 136, 137,
138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150,
151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163,
164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176,
177, 178, 179])
[26]:
subarray.tel_coords[idx]
[26]:
<SkyCoord (GroundFrame: reference_location=None): (x, y, z) in m
[( 207.036 , 156.949 , 14.25),
( 203.986 , -160.894 , 19.75),
( -204.02 , 160.893 , 22.25),
( -207.07 , -156.95 , 28.25),
( 168.88199, 423.275 , 18.25),
( 160.729 , -426.44 , 33.25),
( -160.76201, 426.43802 , 10.25),
( -168.916 , -423.27698 , 42.25),
( 4.972 , 519.86896 , 11.75),
( -5.006 , -519.87103 , 41.25),
( 395.51 , 399.996 , 11.25),
( 387.762 , -407.513 , 28.25),
( -387.795 , 407.512 , 13.25),
( -395.545 , -399.99698 , 50.25),
( 495.605 , 105.269005 , 9.25),
( 493.494 , -114.760994 , 11.75),
( -493.528 , 114.76 , 28.25),
( -495.64 , -105.269005 , 30.25),
( 6.927 , 723.596 , 11.75),
( -6.961 , -723.599 , 59.75),
( 621.223 , 312.711 , 7.25),
( 615.114 , -324.075 , 19.25),
( -615.14197, 324.575 , 19.75),
( -621.259 , -312.711 , 49.25),
( 441.802 , 669.11206 , 30.25),
( 428.882 , -677.47 , 47.25),
( -428.91397, 677.46704 , 7.25),
( -441.838 , -669.114 , 70.25),
( 820.094 , -7.8690004, 4.25),
( -820.128 , 7.87 , 30.75),
( 228.46 , 794.887 , 25.25),
( 213.165 , -799.128 , 56.25),
( -213.19801, 799.125 , 10.25),
( -228.495 , -794.89 , 68.25),
( 9.046 , 944.425 , 27.25),
( -9.08 , -944.43 , 75.25),
( 668.034 , 562.918 , 13.25),
( 657.111 , -575.635 , 43.25),
( -657.142 , 575.635 , 8.25),
( -668.07 , -562.919 , 66.25),
( 885.687 , 219.253 , 6.25),
( 881.318 , -236.20801 , 9.25),
( -881.35 , 236.21 , 24.25),
( -885.72205, -219.252 , 42.25),
( 920.53705, 463.474 , 12.75),
( 911.476 , -481.054 , 26.25),
( -911.507 , 481.054 , 11.25),
( -920.574 , -463.47302 , 59.25),
( 480.36603, 966.80896 , 56.25),
( 461.727 , -975.85297 , 65.75),
( -461.758 , 975.849 , 16.75),
( -480.403 , -966.813 , 87.25),
( 714.73206, 843.133 , 49.25),
( 698.425 , -856.69604 , 53.75),
( -698.455 , 856.694 , 14.75),
( -714.77 , -843.135 , 84.75),
( 1100.131 , -10.556 , 3.75),
(-1100.165 , 10.558001 , 27.75),
( 249.865 , 1107.552 , 55.25),
( 228.566 , -1112.148 , 77.75),
( -227.39801, 1112.131 , 25.25),
( -249.9 , -1107.557 , 89.25),
( 964.01 , 730.71704 , 27.25),
( 949.81396, -749.08295 , 46.25),
( -949.844 , 749.08203 , 17.25),
( -964.048 , -730.71704 , 83.25),
( 1199.684 , 357.573 , 10.25),
( 1192.605 , -380.52698 , 19.25),
(-1192.635 , 380.53 , 12.75),
(-1199.7211 , -357.57 , 47.25),
( 1100.131 , -20. , 3.75),
( 880.094 , -7.8690004, 4.25),
( 785. , -42.9 , 4.25),
( -228.495 , -779.49896 , 68.25),
( 910. , 471. , 12.75),
( 228.46 , 810. , 25.25),
( -0. , 350. , 21. ),
( 0. , -350. , 39. ),
( -270. , 320. , 22. ),
( -270. , -320. , 40. ),
( 270. , 320. , 20. ),
( 270. , -320. , 30. ),
( -280. , 575. , 10. ),
( -280. , -575.00104 , 50. ),
( 280. , 575. , 20. ),
( 280. , -575. , 40. ),
( -685. , 175. , 25. ),
( -685. , -175. , 35. ),
( 685. , 175. , 10. ),
( 685. , -175. , 18. ),
(-1070. , 250. , 20. ),
(-1070. , -250. , 42.25),
( 1070. , 250. , 5. ),
( 1070. , -250. , 11.75),
( -970. , -0. , 30. ),
( 970. , -0. , 5. ),
( 120. , -590.00104 , 45. ),
( -120. , -590. , 49. ),
( 120. , 590. , 13. ),
( -120. , 590. , 11. ),
( -500. , 465. , 10. ),
( -500. , -465. , 52. ),
( 500. , 465. , 15. ),
( 500. , -465. , 30. ),
( -770. , 360. , 20. ),
( -770. , -360. , 53. ),
( 770. , 360. , 10. ),
( 770. , -360. , 17. ),
( -260. , 920. , 25. ),
( -260. , -920. , 75. ),
( 260. , 920. , 45. ),
( 260. , -920. , 65. ),
( -500. , 815. , 15. ),
( -500. , -815. , 75. ),
( 500. , 815. , 45. ),
( 500. , -815. , 53. ),
( -810. , 655. , 12. ),
( -810. , -655. , 68. ),
( 810. , 655. , 20. ),
( 810. , -655. , 41. )]>
so, with that method you can quickly get many telescope positions at once (the alternative is to use the dict positions which maps tel_id to a position on the ground
[27]:
subarray.positions[1]
[27]:
$[-20.643,~-64.817001,~34.299999] \; \mathrm{m}$