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_test_large.simtel.gz") # try this one as well
filename = get_dataset_path("gamma_test_large.simtel.gz")

with EventSource(filename, max_events=1) as source:
    subarray = source.subarray

the SubarrayDescription:¶

[2]:

subarray.info()

Subarray : MonteCarloArray
Num Tels : 98
Footprint: 4.92 km2

       Type        Count Tel IDs
------------------ ----- -------
SST_ASTRI_ASTRICam    69 30-98
    LST_LST_LSTCam     4 1-4
  MST_MST_FlashCam    25 5-29

[3]:

subarray.to_table()

[3]:

Table length=98

tel_id	pos_x	pos_y	pos_z	name	type	camera_type	camera_index	optics_index	tel_description
	m	m	m
int16	float64	float64	float64	str5	str3	str8	int64	int64	str18
1	-20.0	65.0	16.0	LST	LST	LSTCam	2	1	LST_LST_LSTCam
2	-20.0	-65.0	16.0	LST	LST	LSTCam	2	1	LST_LST_LSTCam
3	80.0	0.0	16.0	LST	LST	LSTCam	2	1	LST_LST_LSTCam
4	-120.0	0.0	16.0	LST	LST	LSTCam	2	1	LST_LST_LSTCam
5	0.0	0.0	10.0	MST	MST	FlashCam	0	2	MST_MST_FlashCam
6	0.0	151.1999969482422	10.0	MST	MST	FlashCam	0	2	MST_MST_FlashCam
7	0.0	-151.1999969482422	10.0	MST	MST	FlashCam	0	2	MST_MST_FlashCam
8	146.65599060058594	75.5999984741211	10.0	MST	MST	FlashCam	0	2	MST_MST_FlashCam
9	146.65599060058594	-75.5999984741211	10.0	MST	MST	FlashCam	0	2	MST_MST_FlashCam
...	...	...	...	...	...	...	...	...	...
89	956.7870483398438	739.822998046875	5.0	ASTRI	SST	ASTRICam	1	0	SST_ASTRI_ASTRICam
90	956.7870483398438	-739.822998046875	5.0	ASTRI	SST	ASTRICam	1	0	SST_ASTRI_ASTRICam
91	-239.19699096679688	1109.7349853515625	5.0	ASTRI	SST	ASTRICam	1	0	SST_ASTRI_ASTRICam
92	-239.19699096679688	-1109.7349853515625	5.0	ASTRI	SST	ASTRICam	1	0	SST_ASTRI_ASTRICam
93	-956.7870483398438	739.822998046875	5.0	ASTRI	SST	ASTRICam	1	0	SST_ASTRI_ASTRICam
94	-956.7870483398438	-739.822998046875	5.0	ASTRI	SST	ASTRICam	1	0	SST_ASTRI_ASTRICam
95	1195.9840087890625	369.9119873046875	5.0	ASTRI	SST	ASTRICam	1	0	SST_ASTRI_ASTRICam
96	1195.9840087890625	-369.9119873046875	5.0	ASTRI	SST	ASTRICam	1	0	SST_ASTRI_ASTRICam
97	-1195.9840087890625	369.9119873046875	5.0	ASTRI	SST	ASTRICam	1	0	SST_ASTRI_ASTRICam
98	-1195.9840087890625	-369.9119873046875	5.0	ASTRI	SST	ASTRICam	1	0	SST_ASTRI_ASTRICam

[ ]:

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

description	name	type	mirror_area	num_mirrors	num_mirror_tiles	equivalent_focal_length
			m2			m
str18	str5	str3	float64	int64	int64	float64
SST_ASTRI_ASTRICam	ASTRI	SST	14.562566757202148	2	2	2.1500000953674316
LST_LST_LSTCam	LST	LST	386.7332458496094	1	198	28.0
MST_MST_FlashCam	MST	MST	103.83055877685547	1	84	16.0

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.num_mirrors ==  2]
newsub = subarray.select_subarray(sc_tels, name="SCTels")
newsub.info()

Subarray : SCTels
Num Tels : 69
Footprint: 4.92 km2

       Type        Count Tel IDs
------------------ ----- -------
SST_ASTRI_ASTRICam    69 30-98

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, name=LST, optics=LST, camera=LSTCam)

[7]:

tel.optics.mirror_area

[7]:

$386.73325 \; \mathrm{m^{2}}$

[8]:

tel.optics.num_mirror_tiles

[8]:

[9]:

tel.optics.equivalent_focal_length

[9]:

$28 \; \mathrm{m}$

[10]:

tel.camera

[10]:

CameraDescription(camera_name=LSTCam, geometry=LSTCam, readout=LSTCam)

[11]:

tel.camera.geometry.pix_x

[11]:

$[0,~-0.0094487737,~-0.047244198,~\dots,~-0.6519913,~-0.6141959,~-0.62364468] \; \mathrm{m}$

[12]:

%matplotlib inline
from ctapipe.visualization import CameraDisplay
CameraDisplay(tel.camera.geometry)

[12]:

<ctapipe.visualization.mpl_camera.CameraDisplay at 0x7f6531f765b0>

../_images/examples_InstrumentDescription_18_1.png

[13]:

CameraDisplay(subarray.tel[98].camera.geometry)

[13]:

<ctapipe.visualization.mpl_camera.CameraDisplay at 0x7f6527350b20>

../_images/examples_InstrumentDescription_19_1.png

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()

../_images/examples_InstrumentDescription_21_0.png

[15]:

subarray.footprint

[15]:

$4.9235437 \; \mathrm{km^{2}}$

Get info about the subarray in general¶

[16]:

subarray.telescope_types

[16]:

[TelescopeDescription(type=SST, name=ASTRI, optics=ASTRI, camera=ASTRICam),
 TelescopeDescription(type=LST, name=LST, optics=LST, camera=LSTCam),
 TelescopeDescription(type=MST, name=MST, optics=MST, camera=FlashCam)]

[17]:

subarray.camera_types

[17]:

[CameraDescription(camera_name=FlashCam, geometry=FlashCam, readout=FlashCam),
 CameraDescription(camera_name=ASTRICam, geometry=ASTRICam, readout=ASTRICam),
 CameraDescription(camera_name=LSTCam, geometry=LSTCam, readout=LSTCam)]

[18]:

subarray.optics_types

[18]:

[OpticsDescription(name=LST, equivalent_focal_length=28.00 m, num_mirrors=1, mirror_area=386.73 m2),
 OpticsDescription(name=MST, equivalent_focal_length=16.00 m, num_mirrors=1, mirror_area=103.83 m2),
 OpticsDescription(name=ASTRI, equivalent_focal_length=2.15 m, num_mirrors=2, mirror_area=14.56 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]:

[$95^\circ38{}^\prime18.00148086{}^{\prime\prime}$ $117^\circ35{}^\prime31.27302433{}^{\prime\prime}$ $15^\circ56{}^\prime32.52698026{}^{\prime\prime}$ $166^\circ24{}^\prime15.7253012{}^{\prime\prime}$ $90^\circ00{}^\prime00{}^{\prime\prime}$ $78^\circ42{}^\prime54.16815351{}^{\prime\prime}$ $101^\circ17{}^\prime05.83184649{}^{\prime\prime}$ $16^\circ19{}^\prime25.41596944{}^{\prime\prime}$ $38^\circ42{}^\prime43.34925204{}^{\prime\prime}$ $138^\circ18{}^\prime26.2816408{}^{\prime\prime}$ $160^\circ45{}^\prime08.60655268{}^{\prime\prime}$ $45^\circ50{}^\prime11.58180769{}^{\prime\prime}$ $68^\circ26{}^\prime09.42492861{}^{\prime\prime}$ $11^\circ27{}^\prime33.95564116{}^{\prime\prime}$ $111^\circ33{}^\prime50.57507139{}^{\prime\prime}$ $134^\circ09{}^\prime48.41819231{}^{\prime\prime}$ $168^\circ32{}^\prime26.04435884{}^{\prime\prime}$ $78^\circ41{}^\prime43.72807471{}^{\prime\prime}$ $101^\circ18{}^\prime16.27192529{}^{\prime\prime}$ $16^\circ02{}^\prime24.20158042{}^{\prime\prime}$ $38^\circ36{}^\prime32.84913188{}^{\prime\prime}$ $141^\circ23{}^\prime27.15086812{}^{\prime\prime}$ $163^\circ57{}^\prime35.79841958{}^{\prime\prime}$ $45^\circ48{}^\prime42.20493911{}^{\prime\prime}$ $68^\circ25{}^\prime33.02273995{}^{\prime\prime}$ $11^\circ21{}^\prime07.59158637{}^{\prime\prime}$ $111^\circ34{}^\prime26.97726005{}^{\prime\prime}$ $134^\circ11{}^\prime17.79506089{}^{\prime\prime}$ $168^\circ38{}^\prime52.40841363{}^{\prime\prime}$ $49^\circ01{}^\prime53.88209151{}^{\prime\prime}$ $130^\circ58{}^\prime06.11790849{}^{\prime\prime}$ $153^\circ34{}^\prime33.89607713{}^{\prime\prime}$ $57^\circ29{}^\prime13.31292386{}^{\prime\prime}$ $80^\circ06{}^\prime19.07824957{}^{\prime\prime}$ $99^\circ53{}^\prime40.92175043{}^{\prime\prime}$ $122^\circ30{}^\prime46.68707614{}^{\prime\prime}$ $1^\circ21{}^\prime19.767395{}^{\prime\prime}$ $23^\circ51{}^\prime29.09114511{}^{\prime\prime}$ $156^\circ08{}^\prime30.90885489{}^{\prime\prime}$ $178^\circ38{}^\prime40.232605{}^{\prime\prime}$ $78^\circ41{}^\prime26.15149805{}^{\prime\prime}$ $101^\circ18{}^\prime33.84850195{}^{\prime\prime}$ $34^\circ34{}^\prime02.39137677{}^{\prime\prime}$ $57^\circ11{}^\prime07.33168189{}^{\prime\prime}$ $122^\circ48{}^\prime52.66831811{}^{\prime\prime}$ $145^\circ25{}^\prime57.60862323{}^{\prime\prime}$ $15^\circ57{}^\prime57.37624672{}^{\prime\prime}$ $38^\circ34{}^\prime56.93351141{}^{\prime\prime}$ $141^\circ25{}^\prime03.06648859{}^{\prime\prime}$ $164^\circ02{}^\prime02.62375328{}^{\prime\prime}$ $78^\circ41{}^\prime25.22810173{}^{\prime\prime}$ $101^\circ18{}^\prime34.77189827{}^{\prime\prime}$ $11^\circ18{}^\prime54.92436156{}^{\prime\prime}$ $168^\circ41{}^\prime05.07563844{}^{\prime\prime}$ $45^\circ48{}^\prime11.26179707{}^{\prime\prime}$ $68^\circ25{}^\prime20.46124877{}^{\prime\prime}$ $111^\circ34{}^\prime39.53875123{}^{\prime\prime}$ $134^\circ11{}^\prime48.73820293{}^{\prime\prime}$ $63^\circ12{}^\prime03.82140047{}^{\prime\prime}$ $85^\circ49{}^\prime13.70626482{}^{\prime\prime}$ $29^\circ21{}^\prime33.48064028{}^{\prime\prime}$ $51^\circ58{}^\prime41.62927485{}^{\prime\prime}$ $3^\circ09{}^\prime32.00022804{}^{\prime\prime}$ $25^\circ45{}^\prime53.65643251{}^{\prime\prime}$ $94^\circ10{}^\prime46.29373518{}^{\prime\prime}$ $116^\circ47{}^\prime56.17859953{}^{\prime\prime}$ $128^\circ01{}^\prime18.37072515{}^{\prime\prime}$ $150^\circ38{}^\prime26.51935972{}^{\prime\prime}$ $154^\circ14{}^\prime06.34356749{}^{\prime\prime}$ $176^\circ50{}^\prime27.99977196{}^{\prime\prime}$ $78^\circ41{}^\prime24.82136926{}^{\prime\prime}$ $101^\circ18{}^\prime35.17863074{}^{\prime\prime}$ $15^\circ57{}^\prime47.41407165{}^{\prime\prime}$ $38^\circ34{}^\prime53.48248332{}^{\prime\prime}$ $141^\circ25{}^\prime06.51751668{}^{\prime\prime}$ $164^\circ02{}^\prime12.58592835{}^{\prime\prime}$ $11^\circ18{}^\prime46.40953986{}^{\prime\prime}$ $168^\circ41{}^\prime13.59046014{}^{\prime\prime}$ $52^\circ49{}^\prime06.0104884{}^{\prime\prime}$ $75^\circ26{}^\prime16.42056571{}^{\prime\prime}$ $38^\circ57{}^\prime04.26530019{}^{\prime\prime}$ $61^\circ34{}^\prime14.31046822{}^{\prime\prime}$ $104^\circ33{}^\prime43.57943429{}^{\prime\prime}$ $127^\circ10{}^\prime53.9895116{}^{\prime\prime}$ $118^\circ25{}^\prime45.68953178{}^{\prime\prime}$ $141^\circ02{}^\prime55.73469981{}^{\prime\prime}$ $66^\circ31{}^\prime35.8563378{}^{\prime\prime}$ $89^\circ08{}^\prime46.531159{}^{\prime\prime}$ $26^\circ24{}^\prime12.85346442{}^{\prime\prime}$ $49^\circ01{}^\prime22.34800835{}^{\prime\prime}$ $90^\circ51{}^\prime13.468841{}^{\prime\prime}$ $113^\circ28{}^\prime24.1436622{}^{\prime\prime}$ $130^\circ58{}^\prime37.65199165{}^{\prime\prime}$ $153^\circ35{}^\prime47.14653558{}^{\prime\prime}$ $5^\circ52{}^\prime51.91477485{}^{\prime\prime}$ $28^\circ29{}^\prime50.48159858{}^{\prime\prime}$ $151^\circ30{}^\prime09.51840142{}^{\prime\prime}$ $174^\circ07{}^\prime08.08522515{}^{\prime\prime}$]

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])

[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]:

[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]

[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])

[26]:

subarray.tel_coords[idx]

[26]:

<SkyCoord (GroundFrame): (x, y, z) in m
    [(  205.5       ,  -158.8999939 , 5.),
     ( -205.5       ,   158.8999939 , 5.),
     ( -205.5       ,  -158.8999939 , 5.),
     (  164.82301331,   424.82397461, 5.),
     (  164.82301331,  -424.82397461, 5.),
     ( -164.82301331,   424.82397461, 5.),
     ( -164.82301331,  -424.82397461, 5.),
     (  494.46899414,   110.        , 5.),
     (  494.46899414,  -110.        , 5.),
     ( -494.46899414,   110.        , 5.),
     ( -494.46899414,  -110.        , 5.),
     (    0.        ,   519.79498291, 5.),
     (    0.        ,  -519.79498291, 5.),
     (  391.60601807,   403.73898315, 5.),
     (  391.60601807,  -403.73898315, 5.),
     ( -391.60601807,   403.73898315, 5.),
     ( -391.60601807,  -403.73898315, 5.),
     (  618.07299805,   318.61099243, 5.),
     (  618.07299805,  -318.61099243, 5.),
     ( -618.07299805,   318.61099243, 5.),
     ( -618.07299805,  -318.61099243, 5.),
     (    0.        ,   723.52703857, 5.),
     (    0.        ,  -723.52703857, 5.),
     (  820.        ,     0.        , 5.),
     ( -820.        ,     0.        , 5.),
     (  435.3039856 ,   673.18603516, 5.),
     (  435.3039856 ,  -673.18603516, 5.),
     ( -435.3039856 ,   673.18603516, 5.),
     ( -435.3039856 ,  -673.18603516, 5.),
     (  220.8440094 ,   796.9029541 , 5.),
     (  220.8440094 ,  -796.9029541 , 5.),
     (  662.53295898,   569.21600342, 5.),
     (  662.53295898,  -569.21600342, 5.),
     (  883.37701416,   227.68699646, 5.),
     (  883.37701416,  -227.68699646, 5.),
     ( -220.8440094 ,   796.9029541 , 5.),
     ( -220.8440094 ,  -796.9029541 , 5.),
     ( -662.53295898,   569.21600342, 5.),
     ( -662.53295898,  -569.21600342, 5.),
     ( -883.37701416,   227.68699646, 5.),
     ( -883.37701416,  -227.68699646, 5.),
     (    0.        ,   944.30102539, 5.),
     (    0.        ,  -944.30102539, 5.),
     (  915.92297363,   472.15100098, 5.),
     (  915.92297363,  -472.15100098, 5.),
     ( -915.92297363,   472.15100098, 5.),
     ( -915.92297363,  -472.15100098, 5.),
     ( 1100.        ,     0.        , 5.),
     (-1100.        ,     0.        , 5.),
     (  471.01199341,   971.21002197, 5.),
     (  471.01199341,  -971.21002197, 5.),
     (  706.51794434,   849.80895996, 5.),
     (  706.51794434,  -849.80895996, 5.),
     ( -471.01199341,   971.21002197, 5.),
     ( -471.01199341,  -971.21002197, 5.),
     ( -706.51794434,   849.80895996, 5.),
     ( -706.51794434,  -849.80895996, 5.),
     (  239.19699097,  1109.73498535, 5.),
     (  239.19699097, -1109.73498535, 5.),
     (  956.78704834,   739.82299805, 5.),
     (  956.78704834,  -739.82299805, 5.),
     ( -239.19699097,  1109.73498535, 5.),
     ( -239.19699097, -1109.73498535, 5.),
     ( -956.78704834,   739.82299805, 5.),
     ( -956.78704834,  -739.82299805, 5.),
     ( 1195.98400879,   369.9119873 , 5.),
     ( 1195.98400879,  -369.9119873 , 5.),
     (-1195.98400879,   369.9119873 , 5.),
     (-1195.98400879,  -369.9119873 , 5.)]>

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]:

$[-20,~65,~16] \; \mathrm{m}$