@testset "adstring" begin

@test @inferred(adstring((30.4, -1.23), truncate=true)) ==

@inferred(adstring([30.4, -1.23], truncate=true)) ==

" 02 01 35.9 -01 13 48"

@test @inferred(adstring(15, -.5)) == " 01 00 00.0 -00 30 00"

@test @inferred(adstring(19.19321, truncate=true)) == "+19 11 35.5"

@test @inferred(adstring(ten(36,24,15.015), -ten(8,24,36.0428), precision=3)) ==

" 02 25 37.0010 -08 24 36.043"

@test adstring.([30.4, -15.63], [-1.23, 48.41], precision=2) ==

[" 02 01 36.000 -01 13 48.00", " 22 57 28.800 +48 24 36.00"]

@test adstring.([(-58, 724)]) == [" 20 08 00.0 +724 00 00"]

@testset "airtovac" begin

@test airtovac.([1234 6056.125]) ≈ [1234.0 6057.801930991426]

@test @inferred(airtovac(2100)) ≈ 2100.666421596007

@testset "aitoff" begin

@test @inferred(aitoff([227.23, 130], [-8.890, -35])) ==

([-137.92196683723276, 115.17541338020645],

[-11.772527357473054, -44.491889962090085])

@test @inferred(aitoff([375], [2.437])) ==

([16.63760711611838],[2.712427279646118])

@test @inferred(aitoff((227.23, -8.890))) ==

(-137.92196683723276,-11.772527357473054)

@testset "altaz2hadec" begin

@test @inferred(altaz2hadec(59.086111, 133.30806, 43.07833)) ==

@inferred(altaz2hadec((59.086111, 133.30806), 43.07833)) ==

(336.68286017949157, 19.182449588316555)

@test @inferred(altaz2hadec([15, 25, 35], [25.12, 45.32, -20.3],

[-23.44, 45.0, 52.5])) ==

([324.9881067314537, 256.7468302330436, 132.4919217875949] ,

[44.38225395397647, 48.542947077386664, 67.33061196497327])

@testset "baryvel" begin

dvelh_o, dvelb_o = @inferred(baryvel(2000))

@test dvelh_o ≈ [1.582939967296732e-7, -1.0743272343303577e-7, -4.852410351888098e-8]

@test dvelb_o ≈ [1.583299045307303e-7, -1.0736465601278539e-7, -4.849238001189245e-8]

dvelh_o, dvelb_o = @inferred(baryvel(AstroLib.J2000, 1950))

@test dvelh_o ≈ [-29.85888093436655, -4.684571288755146, -2.0305672776315777]

@test dvelb_o ≈ [-29.849737241231153, -4.696440255370953, -2.035884790519881]

dvelh_o, dvelb_o = @inferred(baryvel(jdcnv(1987, 4, 10, 0)))

@test dvelh_o ≈ [9.514730366039178, -25.83862824722249, -11.202290688985778]

@test dvelb_o ≈ [9.51331763088569, -25.84985540772467, -11.207031996297271]

@testset "bprecess" begin

ra, dec = @inferred(bprecess([ten(13, 42, 12.74)*15], [ten(8, 23, 17.69)],

reshape(100*[-15*0.0257, -0.090], 2, 1)))

@test ra ≈ [204.93552515632123]

@test dec ≈ [8.641287183886163]

ra, dec = @inferred(bprecess(82, 19))

@test ra ≈ 81.26467916346334

@test dec ≈ 18.959495700195394

ra, dec = @inferred(bprecess([57], [23], 2024))

@test ra ≈ [56.26105898810067]

@test dec ≈ [22.84693298145991]

ra, dec = @inferred(bprecess([57], [23], reshape([9, 86], 2, 1), parallax=[1],

radvel=[4]))

@test ra ≈ [56.25988479854577]

@test dec ≈ [22.83493370392355]

ra, dec = @inferred(bprecess((-57, -23), 2024))

@test ra ≈ 302.2593299643789

@test dec ≈ -23.150089972802036

ra, dec = bprecess((-57, -23), [9, 86], parallax=1, radvel=4) # Inferred Type Error

@test ra ≈ 302.2580376402947

@test dec ≈ -23.16208183899836

@testset "calz_unred" begin

@test calz_unred.(collect(900:1000:9900), ones(Float64, 10), -0.1) ≈

[1.0, 0.43189326452379095, 0.5203675483533704, 0.594996469192435,

0.6569506252451913, 0.7080829505773865, 0.7502392743978797, 0.7861262388745882,

0.8151258710444882,0.8390325371659836]

@testset "co_aberration" begin

d_ra, d_dec = @inferred(co_aberration(jdcnv(1987, 4, 10, 0), ten(2,46,11.331)*15,

ten(49,20,54.54), 1))

@test d_ra ≈ -18.692441865574867

@test d_dec ≈ -9.070782150537646

ao, bo = @inferred(co_aberration([57555.0, -6.44311e5], [302.282, 69.5667],

[37.1519, 20.6847]))

@test ao[1] ≈ 21.673056337579048

@test ao[2] ≈ 18.496516329468466

@test bo[1] ≈ -6.773070772568567

@test bo[2] ≈ 2.9205843718089457

@testset "co_nutate" begin

an,bn,cn,dn,en = @inferred(co_nutate([jdcnv(2028,11,13,4,56), jdcnv(2013, 4, 16)],

[10, 160],[80,30]))

@test an ≈ [0.001209279097382776, 0.002465026741191423 ]

@test bn ≈ [0.0017471459185713911, -0.0018134211486149354]

@test cn ≈ [0.40904016038217567, 0.4090340058477726 ]

@test dn ≈ [14.8593894278967, 12.102640377483143 ]

@test en ≈ [2.7038090372351267, -5.86229256359996 ]

ra_out, dec_out, eps_out, d_psi_out, d_eps_out = @inferred(co_nutate(2.451545e6,

325, 0))

@test ra_out ≈ -0.0035484441576727477

@test dec_out ≈ -0.00034017946720967174

@test eps_out ≈ 0.4090646078966446

@test d_psi_out ≈ -13.923152677481191

@test d_eps_out ≈ -5.773909654153591

@testset "co_refract" begin

@test @inferred(co_refract(0.8)) ≈ 0.3714184384944585

@test co_refract.([5.86,20], 50, 568.967, 273, 0.15, to_observe=true) ≈

[5.94252628176525 , 20.02627520026167]

@test @inferred(co_refract(14, 15000)) ≈ 13.990329255193124

@testset "ct2lst" begin

@test ct2lst.(-76.72, -4, [DateTime(2008, 7, 30, 15, 53)]) ≈ [11.356505172312609]

@test ct2lst.(9, [jdcnv(2015, 11, 24, 12, 21)]) ≈ [17.159574059885927]

@testset "daycnv" begin

@test @inferred(daycnv(2440000.0)) == DateTime(1968, 05, 23, 12)

# Test daycnv with Julian Calendar in force (same result as IDL AstroLib's

# daycnv).

@test @inferred(daycnv(2000000.0)) == DateTime(763, 09, 18, 12)

@test @inferred(daycnv(0.0)) == DateTime(-4713, 11, 24, 12)

@testset "deredd" begin

by0, m0, c0, ub0 = @inferred(deredd([0.5, -0.5], [0.2, 0.5], [1, 1], [1, 1],

[0.1, 0.3]))

@test by0 ≈ [-0.3,0.5]

@test m0 ≈ [1.165,1.0]

@test c0 ≈ [0.905,1.0]

@test ub0 ≈ [-0.665,0.3]

@testset "eci2geo" begin

lat, long, alt = @inferred(eci2geo([0], [0], [0], [2452343]))

@test lat ≈ [0]

@test long ≈ [12.992783145436988]

@test alt ≈ [-6378.137]

lat, long, alt = @inferred(eci2geo((6978.137, 0, 0), jdcnv("2015-06-30T14:03:12.857")))

@test lat ≈ 0

@test long ≈ 230.87301833205856

@test alt ≈ 600

# Test `eci2geo' is the inverse of `geo2eci'

jd = @inferred(get_juldate())

lat, long, alt = @inferred(eci2geo(geo2eci(10, 10, 10, jd), jd))

@test lat ≈ 10

@test long ≈ 10

@test alt ≈ 10

@testset "eq2hor" begin

alt_o, az_o, ha_o = eq2hor(259.20238631600944, 49.674907472176095,

AstroLib.J2000, "pbo", B1950=true)

@test alt_o ≈ 43.804935603297004

@test az_o ≈ 56.74141416977815

@test ha_o ≈ 291.2750910119525

alt_o, az_o, ha_o = eq2hor(142.2933457820434, -34.218006262991786, 2e6, 54.435, -34.78,

1000.34, ws=true, B1950=true, precession = false,

nutate=false, aberration=false,

refract=false, pressure = 500.345, temperature = 293.343)

@test alt_o ≈ 1.3449999999999924

@test az_o ≈ 359.43

@test ha_o ≈ 359.3108663499664

alt_o, az_o, ha_o = eq2hor(3.3222617779538037, 15.190516725395284, 2466879.7083333,

"kpno", pressure = 711, temperature = 273)

@test alt_o ≈ 37.91138916818937

@test az_o ≈ 264.918333213257

@test ha_o ≈ 54.61193155973385

alt_o, az_o, ha_o = @inferred(AstroLib._eq2hor(259.52076321839485, 49.62352289872951,

Float64(AstroLib.J2000), 43.0783,

-89.865, 0.0, NaN, NaN, false, false,

true, true, true, true))

@test alt_o ≈ 43.687900264047116

@test az_o ≈ 56.68399934960606

@test ha_o ≈ 291.0817909922114

alt_o, az_o = eq2hor(hor2eq(25, 55, 2.05e6, "pbo")[1:2]..., 2.05e6, "pbo")

@test alt_o ≈ 24.99993224731665

@test az_o ≈ 54.99993893556545

@testset "eqpole" begin

x, y = @inferred(eqpole([100], [35], southpole=true))

@test x ≈ [-111.18287262822456]

@test y ≈ [ -19.604540237028665]

x, y = @inferred(eqpole([80], [19]))

@test x ≈ [72.78853915267848]

@test y ≈ [12.83458333897169]

@testset "euler" begin

glong, glat = @inferred(euler(299.590315, 35.201604, 1))

@test glong ≈ 71.33498957116959

@test glat ≈ 3.0668335310640984

ra, dec = @inferred(euler((71.33498957116959, 3.0668335310640984), 2))

@test ra ≈ 299.590315

@test dec ≈ 35.201604

elong, elat = @inferred(euler(3.141592653589793, 0.6143838917832061, 3,

FK4 = true, radians=true))

@test elong ≈ 2.8679433080257506

@test elat ≈ 0.557258307291505

ra, dec = @inferred(euler((2.8679433080257506, 0.557258307291505), 4,

FK4 = true, radians=true))

@test ra ≈ 3.141592653589793

@test dec ≈ 0.6143838917832061

ecl, gal = euler.(30.45, 76.54, [5,6])

@test ecl[1] ≈ 103.50477919192522

@test ecl[2] ≈ 18.01965967759107

@test gal[1] ≈ 194.96100731553986

@test gal[2] ≈ 34.46136801388695

@test @inferred(euler(183/pi, pi/180, 2, FK4=false, radians=true)) ==

(5.682517110086799, 0.947078051715398)

glong, glat = @inferred(euler([0.45, 130], [16.28, 53.65], 5))

@test glong ≈ [96.9525940157568, 138.09922696730337]

@test glat ≈ [-43.90672396295434, 46.95527026543361]

@test_throws DomainError @inferred(euler((45,45), 7))

@testset "flux2mag" begin

@test flux2mag.([1.5e-12, 8.7e-15, 4.4e-10]) ≈

[8.459771852360795, 14.051201868453454, 2.291368308784527]

@test @inferred(flux2mag(1)) ≈ -21.1

@test @inferred(flux2mag(5.2e-15)) ≈ 14.609991640913002

@test @inferred(flux2mag(5.2e-15, 15)) ≈ 20.709991640913003

@test flux2mag(5.2e-15, ABwave=15) ≈ 27.423535345634598 # Inferred Type Error

@testset "gal_uvw" begin

u, v, w = @inferred(gal_uvw([ten(1,9,42.3)*15], [ten(61,32,49.5)], [627.89], [77.84],

[-321.4], [1e3/129], lsr=true))

@test u ≈ [118.2110474553902]

@test v ≈ [-466.4828898385057]

@test w ≈ [88.16573278565097]

u, v, w = @inferred(gal_uvw(1, 2, 3, 4, 5, 6))

@test u ≈ 4.0228405867158745

@test v ≈ 3.7912174342038227

@test w ≈ -3.1700191400725464

@testset "geo2eci" begin

x, y, z = @inferred(geo2eci([0], [0], [0], [2452343]))

@test x ≈ [6214.846433007192]

@test y ≈ [-1433.9858454345972]

@test z ≈ [0.0]

x, y, z = @inferred(geo2eci((0,0,0), jdcnv("2015-06-30T14:03:12.857")))

@test x ≈ -4024.8671780315185

@test y ≈ 4947.835465127513

@test z ≈ 0.0

@testset "geo2geodetic" begin

lat, long, alt = @inferred(geo2geodetic([90], [0], [0], "Jupiter"))

@test lat ≈ [90]

@test long ≈ [0]

@test alt ≈ [4638]

lat, long, alt = @inferred(geo2geodetic((90, 0, 0)))

@test lat ≈ 90

@test long ≈ 0

@test alt ≈ 21.38499999999931

lat, long, alt = @inferred(geo2geodetic((43.16, -24.32, 3.87), 8724.32, 8619.19))

@test lat ≈ 43.849399515234516

@test long ≈ -24.32

@test alt ≈ 53.53354478670836

lat, long, alt = @inferred(geo2geodetic([43.16], [-24.32], [3.87], 8724.32, 8619.19))

@test lat ≈ [ 43.849399515234516]

@test long ≈ [-24.32]

@test alt ≈ [ 53.53354478670836]

@testset "geo2mag" begin

lat, long = @inferred(geo2mag(ten(35,0,42), ten(135,46,6), 2016))

@test lat ≈ 36.86579228937769

@test long ≈ -60.184060536651614

lat, long = @inferred(geo2mag([15], [24], 2016))

@test lat ≈ [ 11.452100529696096]

@test long ≈ [-169.86030510727102]

@testset "geodetic2geo" begin

lat, long, alt = @inferred(geodetic2geo([90], [0], [0], "Jupiter"))

@test lat ≈ [90]

@test long == [0]

@test alt ≈ [-4638]

lat, long, alt = @inferred(geodetic2geo((90, 0, 0)))

@test lat ≈ 90

@test long == 0

@test alt ≈ -21.38499999999931

lat, long, alt = @inferred(geodetic2geo((43.16, -24.32, 3.87), 8724.32, 8619.19))

@test lat ≈ 42.46772711708433

@test long == -24.32

@test alt ≈ -44.52902080669082

lat, long, alt = @inferred(geodetic2geo([43.16], [-24.32], [3.87], 8724.32, 8619.19))

@test lat ≈ [42.46772711708433]

@test long == [-24.32]

@test alt ≈ [-44.52902080669082]

# Test geodetic2geo is the inverse of geo2geodetic, within a certain

# tolerance.

lat, long, alt = @inferred(geodetic2geo(geo2geodetic(67.2,13.4,1.2)))

@test lat ≈ 67.2 atol = 1e-8

@test long == 13.4

@test alt ≈ 1.2 atol = 1e-9

@testset "get_date" begin

@test @inferred(get_date(DateTime(2001,09,25,14,56,14), old=true,timetag=true)) ==

@inferred(get_date(2001,09,25,14,56,14, old=true,timetag=true)) ==

@inferred(get_date("2001-09-25T14:56:14", old=true,timetag=true)) ==

"25/09/2001T14:56:14"

@test @inferred(get_date(DateTime(2001,09,25,14,56,14))) ==

@inferred(get_date(2001,09,25,14,56,14)) ==

@inferred(get_date("2001-09-25T14:56:14")) ==

"2001-09-25"

@test get_date.([DateTime(2024), Date(2016, 3, 14)]) ==

get_date.([Date(2024), "2016-03-14"]) ==

get_date.(["2024-01", DateTime(2016, 3, 14)]) == ["2024-01-01", "2016-03-14"]

@testset "gcirc" begin

@test gcirc.(0, [0,1,2], [1,2,3], [2,3,4], [3,4,5]) ≈

[1.222450611061632, 2.500353926443337, 1.5892569925227757]

@test @inferred(gcirc(0, 120, -43, 175, +22)) ≈ 1.590442261600714

@test @inferred(gcirc(1, (120, -43), 175, +22)) ≈ 415908.56615322345

@test @inferred(gcirc(2, 120, -43, (175, +22))) ≈ 296389.3666794745

@test @inferred(gcirc(0, (120, -43), (175, +22)) ) ≈ 1.590442261600714

@test gcirc.(1, [120], [-43], 175, +22) ≈ [415908.56615322345]

@test gcirc.(2, 120, -43, [175], [+22]) ≈ [296389.3666794745]

@test_throws DomainError @inferred(gcirc(3, 0, 0, 0, 0))

@testset "hadec2altaz" begin

alt1, az1 = @inferred(hadec2altaz([0], [11.978165], [ten(43,4,42)]))

@test alt1 ≈ [58.89983166666667]

@test az1 ≈ [180.0]

@test @inferred(hadec2altaz((0, 11.978165), ten(43,4,42), ws=true)[2]) ≈ 0.0

alt1, az1 = 50, 20

alt2, az2 = @inferred(hadec2altaz(altaz2hadec(alt1, az1, 40), 40))

@test alt1 ≈ alt2

@test az1 ≈ az2

@testset "helio_jd" begin

@test helio_jd(juldate(2016, 6, 15, 11, 40), ten(20, 9, 7.8)*15, ten(37, 9, 7)) ≈

57554.98808289718

@test @inferred(helio_jd(1000, 23, 67, B1950=true)) ≈ 999.9997659545342

@test @inferred(helio_jd(2000, 12, 88, diff=true)) ≈ -167.24845957792076

@testset "helio_rv" begin

@test @inferred(helio_rv(helio_jd(juldate(94, 10, 25, 17, 30), ten(04, 38, 16)*15,

ten(20, 41, 05)), 46487.5303, 2.0563056, -6, 59.3)) ≈

-62.965570109145034

@test helio_rv.([0.1, 0.9], 0, 1, 0, 100, 0.6, 45) ≈

[-45.64994926111004, 89.7820347358485]

@testset "helio" begin

@test_throws DomainError @inferred(helio(jdcnv(2005,07,17,2,6,9), 10))

hrad_out, hlong_out, hlat_out = @inferred(helio(jdcnv(2000,08,23,0), 2, true))

@test hrad_out ≈ 0.7213758288364316

@test hlong_out ≈ 3.462574978561256

@test hlat_out ≈ 0.050393862449261535

hrad_out, hlong_out, hlat_out = @inferred(helio([AstroLib.J2000], [7]))

@test hrad_out[1] ≈ 19.921687573575788

@test hlong_out[1] ≈ 316.4011812518626

@test hlat_out[1] ≈ -0.6846115653974465

@testset "hor2eq" begin

ra_o, dec_o, ha_o = hor2eq(43.6879, 56.684, AstroLib.J2000, "pbo", B1950=true)

@test ra_o ≈ 259.3943636338339

@test dec_o ≈ 49.67396411401468

@test ha_o ≈ 291.0833116221485

ra_o, dec_o, ha_o = hor2eq(1.345, 359.43, 2e6, 54.435, -34.78, 1000.34, ws=true,

B1950=true, precession = false, nutate=false,

aberration=false, refract=false, pressure = 500.345,

temperature = 293.343)

@test ra_o ≈ 142.2933457820434

@test dec_o ≈ -34.218006262991786

@test ha_o ≈ 359.3108663499664

ra_o, dec_o, ha_o = hor2eq(ten(37,54,41), ten(264,55,06), 2466879.7083333,

"kpno", pressure = 711, temperature = 273)

@test ra_o ≈ 3.3222617779538037

@test dec_o ≈ 15.190516725395284

@test ha_o ≈ 54.61193186104758

ra_o, dec_o, ha_o = @inferred(AstroLib._hor2eq(43.6879, 56.684, Float64(AstroLib.J2000),

43.0783, -89.865, 0.0, NaN, NaN, false,

false, true, true, true, true))

@test ra_o ≈ 259.52076321839485

@test dec_o ≈ 49.62352289872951

@test ha_o ≈ 291.0817908419628

ra_o, dec_o = hor2eq(eq2hor(45, 60, 2e6, "pbo")[1:2]..., 2e6, "pbo")

@test ra_o ≈ 45.0001735428487

@test dec_o ≈ 59.999995143349956

@testset "imf" begin

@test_throws DimensionMismatch @inferred(imf([5], [-6.75], [0.9]))

@test @inferred(imf([0.1, 0.01], [-0.6, -1], [ 0.007, 1.8, 110])) ≈

[0.49627714725007616, 1.9757149090208912]

@test imf.([[3],[5]], [[-1.35], [-0.6, -1.7]], [[0.1, 100], [0.007, 1.8, 110]]) ≈

[[0.038948937298846235], [0.027349915327755464]]

@testset "ismeuv" begin

@test @inferred(ismeuv(304, 1e20)) ≈ 58.30508020244554

@test ismeuv.([50, 75, 343], 1e18) ≈

[0.004486567212077276, 0.01601219540569213, 0.7811526665834057]

@test ismeuv.([96, 41, 233], 1e18, 1e17, 1e17) ≈

[0.04657033979688484, 0.0035293162863089807, 0.30662192095758833]

@test ismeuv.([96.6, 41.056, 233.19], 1e18, 1e17, 1e17, true) ≈

[0.04733922036264192, 0.003543553203857853, 0.3103333860793942]

@test ismeuv.([480, 910], 1e19, 5e17, 5e17) ≈

[14.273937721143753, 62.68379602891728]

@test @inferred(ismeuv(4500, 1e18)) ≈ 0

@testset "jdcnv" begin

@test @inferred(jdcnv(-4713, 11, 24, 12)) ≈ 0.0

@test @inferred(jdcnv(763, 09, 18, 12)) == @inferred(jdcnv("763-09-18T12")) == 2000000

@test (jd=1234567.89; @inferred(jdcnv(daycnv(jd))) == jd)

@test jdcnv.([DateTime(2016, 07, 31), "1969-07-20"]) ==

jdcnv.([Date(2016, 07, 31), DateTime(1969, 07, 20)]) ==

jdcnv.(["2016-07-31", Date(1969, 07, 20)])

@testset "jprecess" begin

ra, dec = @inferred(jprecess([ten(13, 39, 44.526)*15], [ten(8, 38, 28.63)],

reshape(100*[-15*0.0259, -0.093], 2, 1)))

@test ra ≈ [205.5530845731372]

@test dec ≈ [8.388247441904628]

ra, dec = @inferred(jprecess(82, 19))

@test ra ≈ 82.73568745151148

@test dec ≈ 19.036972917272056

ra, dec = @inferred(jprecess([57], [23], 2024))

@test ra ≈ [57.74049975335702]

@test dec ≈ [23.150053754297726]

ra, dec = @inferred(jprecess([57], [23], reshape([9, 86], 2, 1), parallax=[1],

radvel=[4]))

@test ra ≈ [57.74180294549785]

@test dec ≈ [23.16200582079095]

ra, dec = @inferred(jprecess((-57, -23), 2024))

@test ra ≈ 303.73910971499015

@test dec ≈ -22.846895476784482

ra, dec = jprecess((-57, -23), [9, 86], parallax=1, radvel=4) # Inferred Type Error

@test ra ≈ 303.7402950607101

@test dec ≈ -22.834931625610313

@testset "juldate" begin

@test @inferred(juldate(DateTime(2016, 1, 1, 8))) ≈ (57388.5 + 1//3)

# Test juldate with Julian Calendar in force, for different centuries. This

# also makes sure precision of the result is high enough.

@test @inferred(juldate(1582, 10, 1, 20)) ≈ (-100843 + 1//3)

@test @inferred(juldate("1000-01-01T20")) ≈ (-313692 + 1//3)

@test @inferred(juldate("100-10-25T20")) ≈ (-642119 + 1//3)

@test @inferred(juldate(-4713, 1, 1, 12)) ≈ -2.4e6

@test @inferred(juldate(2016, 06, 30, 00, 05, 53, 120)) ≈

jdcnv(2016, 06, 30, 00, 05, 53, 120) - 2.4e6

# Test daycnv and juldate together, with Gregorian Calendar in force. Note that

# they are not expected to be one the inverse of the other during Julian

# Calendar.

@test (dt=DateTime(2016, 1, 1, 20, 45, 33, 457);

@inferred(daycnv(juldate(dt) + 2.4e6)) == dt)

@testset "kepler_solver" begin

for e in 0:0.1:1

M = collect(0:1e-3:2pi)

E = mod2pi.(kepler_solver.(M, e))

# Make sure E is the solution of the Kepler's Equation with high precision.

@test M ≈ E .- e .* sin.(E) rtol = 1e-15

end

@test kepler_solver.([pi/4, pi/6, 8pi/3], 0) ≈ [pi/4, pi/6, 2pi/3]

@test @inferred(kepler_solver(0, 1)) == 0.0

@test_throws DomainError @inferred(kepler_solver(pi, -0.5))

@test_throws DomainError @inferred(kepler_solver(pi, 1.5))

@testset "lsf_rotate" begin

vel, lsf = @inferred(lsf_rotate(3, 90))

@test @inferred(length(vel)) == @inferred(length(lsf)) == 61

vel, lsf = @inferred(lsf_rotate(5, 10))

@test vel ≈ collect(-10.0:5.0:10.0)

@test lsf ≈ [0.0, 0.556914447710896, 0.6933098861837907, 0.556914447710896, 0.0]

@testset "mag2flux" begin

@test @inferred(mag2flux(4.83, 21.12)) ≈ 4.1686938347033296e-11

@test flux2mag(mag2flux(15, ABwave=12.), ABwave=12) ≈ 15.0 # Inferred Type Error

@test @inferred(mag2flux(8.3)) ≈ 1.7378008287493692e-12

@test @inferred(mag2flux(8.3, 12)) ≈ 7.58577575029182e-9

@test mag2flux(8.3, ABwave=12) ≈ 3.6244115683017193e-7 # Inferred Type Error

@testset "mag2geo" begin

lat, long = @inferred(mag2geo(90, 0, 2016))

@test lat ≈ 86.395

@test long ≈ -166.29000000000002

lat, long = @inferred(mag2geo([15], [24], 2016))

@test lat ≈ [11.702066965890157]

@test long ≈ [-142.6357492442842]

# Test geo2mag is approximately the inverse of mag2geo

lat, long = @inferred(geo2mag(mag2geo(12.34, 56.78, 2016)..., 2016))

@test lat ≈ 12.34

@test long ≈ 56.78

@testset "mean_obliquity" begin

@test @inferred(mean_obliquity(AstroLib.J2000)) ≈ 0.4090926006005829

@test mean_obliquity.(jdcnv.([DateTime(1916, 09, 22, 03, 39),

DateTime(2063, 10, 13, 09)])) ≈

[0.4092816887615259, 0.40894777540460037]

@testset "month_cnv" begin

@test month_cnv.([" januavv ", "SEPPES ", " aUgUsT", "la"]) == [1, 9, 8, -1]

@test month_cnv.([2, 12, 6], short=true, low=true) == ["feb", "dec", "jun"]

@test @inferred(month_cnv(5, up=true)) == "MAY"

@test (list=[1, 2, 3]; month_cnv.(month_cnv.(list)) == list)

@test (list=["July", "March", "November"]; month_cnv.(month_cnv.(list)) == list)

@testset "moonpos" begin

ra, dec, dis, lng, lat = @inferred(moonpos(jdcnv(1992, 4, 12)))

@test ra ≈ 134.68846854844108

@test dec ≈ 13.768366630560255

@test dis ≈ 368409.68481612665

@test lng ≈ 133.16726428105378

@test lat ≈ -3.2291264192144356

ra, dec, dis, lng, lat = @inferred(moonpos([2457521], radians=true))

@test ra ≈ [2.2587950290926178]

@test dec ≈ [0.26183388011392217]

@test dis ≈ [385634.68772395694]

@test lng ≈ [2.232459255739553]

@test lat ≈ [-0.059294466326164315]

@testset "mphase" begin

@test mphase.([2457520, 2457530, 2457650]) ≈

[0.2781695910737857, 0.9969808583803166, 0.9580708477591693]

@testset "nutate" begin

long, obl = @inferred(nutate(jdcnv(1987, 4, 10)))

@test long ≈ -3.787931077110755

@test obl ≈ 9.442520698644401

long, obl = @inferred(nutate(2457521))

@test long ≈ -4.401443629818089

@test obl ≈ -9.26823431959121

long, obl = @inferred(nutate([2457000, 2458000]))

@test long ≈ [ 4.327189321653877, -9.686089990639474]

@test obl ≈ [-9.507794266102866, -6.970768250588256]

@testset "paczynski" begin

@test @inferred(paczynski(-1e-10)) ≈ -1e10

@test @inferred(paczynski(1e-1)) ≈ 10.037461005722337

@test @inferred(paczynski(-1)) ≈ -1.3416407864998738

@test @inferred(paczynski(10)) ≈ 1.0001922892047386

@test @inferred(paczynski(-1e10)) ≈ -1

@testset "planck_freq" begin

@test @inferred(planck_freq(2000, 5000)) ≈ 6.1447146126144004e-30

@testset "planck_wave" begin

@test @inferred(planck_wave(2000, 5000)) ≈ 8.127064833530511e-24

@testset "planet_coords" begin

@test_throws DomainError @inferred(planet_coords(DateTime(2013, 07, 22,

03, 19, 06),0))

ra_out, dec_out = @inferred(planet_coords([AstroLib.J2000, 2.45e6], [2,8]))

@test ra_out[1] ≈ 239.8965221579066

@test ra_out[2] ≈ 294.55483837772476

@test dec_out[1] ≈ -18.450868549676304

@test dec_out[2] ≈ -20.992319312642262

ra_out, dec_out = @inferred(planet_coords(2.45e6, 9))

@test ra_out ≈ 238.3131013864547

@test dec_out ≈ -6.964788781133789

@test @inferred(planet_coords(juldate(1), 3) == (0, 0))

@testset "polrec" begin

x, y = @inferred(polrec([1, 2, 3], [pi, pi/2.0, pi/4.0]))

@test x ≈ [-1.0, 0.0, 1.5*sqrt(2.0)]

@test y ≈ [ 0.0, 2.0, 1.5*sqrt(2.0)]

x, y = @inferred(polrec((2, 135), degrees=true))

@test x ≈ -sqrt(2)

@test y ≈ sqrt(2)

@testset "posang" begin

@test @inferred(posang(1, ten(13, 25, 13.5), ten(54, 59, 17),

ten(13, 23, 55.5), ten(54, 55, 31))) ≈ -108.46011246802047

@test posang.(0, [0,1,2], [1,2,3], [2,3,4], [3,4,5]) ≈

[1.27896824717634, 1.6840484573313608, 0.2609280020139511]

@test @inferred(posang(0, 120, -43, 175, +22)) ≈ -1.5842896165356724

@test @inferred(posang(1, (120, -43), 175, +22)) ≈ 82.97831348792039

@test @inferred(posang(2, 120, -43, (175, +22))) ≈ 50.02816530382374

@test @inferred(posang(0, (120, -43), (175, +22))) ≈ -1.5842896165356724

@test posang.(1, [120], [-43], 175, +22) ≈ [82.97831348792039]

@test posang.(2, 120, -43, [175], [+22]) ≈ [50.02816530382374]

@test_throws DomainError @inferred(posang(3, 0, 0, 0, 0))

@testset "precess" begin

ra1, dec1 = @inferred(precess((ten(2,31,46.3)*15, ten(89,15,50.6)), 2000, 1985))

@test ra1 ≈ 34.09470328718033

@test dec1 ≈ 89.19647174928589

ra2, dec2 = @inferred(precess([ten(21, 59, 33.053)*15], [ten(-56, 59, 33.053)],

1950, 1975, FK4=true))

@test ra2 ≈ [330.3144305418865]

@test dec2 ≈ [-56.87186126487889]

@testset "precess_cd" begin

@test @inferred(precess_cd([30 60; 60 90], 1950, 2000, [13, 8], [43, 23])) ≈

[30.919029003435927 62.343060521017435;

61.93905850970097 93.56509103294071 ]

@test @inferred(precess_cd([30 60; 60 90], 2000, 1950, [13, 8], [43, 23])) ≈

[30.919029003435927 62.343060521017435;

61.93905850970097 93.56509103294071 ]

@test @inferred(precess_cd([12.45 56.7; 66 89], 2000, 1985, [67.4589455, 0.345345],

[37.94291666666666, 89.26405555555556])) ≈

[963.4252080520984 4387.890452343343 ;

5107.504395433958 6887.55936949333 ]

@test @inferred(precess_cd([30.0 28.967; 60.45 90.65], 2000, 1975, [13, 10.658],

[35.54, 67], true)) ≈

[64.78429186351575 62.637156996728194 ;

130.49379143419722 195.9699513801844 ]

@testset "precess_xyz" begin

x1, y1, z1 = @inferred(precess_xyz((1.2, 2.3, 1.7), 2000, 2050))

@test x1 ≈ 1.165933061423247

@test y1 ≈ 2.313228746401996

@test z1 ≈ 1.7057470102860104

x2, y2, z2 = @inferred(precess_xyz([0.7, -2.4], [3.3, 6.6], [0, 4], 2000, 2016))

@test x2 ≈ [0.688187142071843, -2.429815562246262]

@test y2 ≈ [3.3024835038223532, 6.591359330834213]

@test z2 ≈ [0.001079105285993004, 3.9962455511755794]

@testset "premat" begin

@test @inferred(premat(1967, 1982, FK4=true)) ≈

[0.9999933170034135 -0.0033529069683496567 -0.0014573823699636742;

0.00335290696825777 0.9999943789886484 -2.443304965138481e-6 ;

0.0014573823701750721 -2.4431788671274868e-6 0.9999989380147651 ]

@test @inferred(premat(1995, 2003)) ≈

[ 0.9999980977132219 -0.0017889257711428855 -0.0007773766929507687;

0.0017889257711354528 0.9999983998707707 -6.953448226403318e-7 ;

0.0007773766929678732 -6.953257000046125e-7 0.9999996978424512 ]

@testset "radec" begin

@test @inferred(radec(15.90, -0.85)) == (1.0, 3.0, 36.0, -0.0, 51.0, 0.0)

@test @inferred(radec(-0.85,15.9)) == (23.0,56.0,36.0,15.0,54.0,0.0)

@test @inferred(radec(-20,4,hours=true)) == (4.0,0.0,0.0,4.0,0.0,0.0)

@test @inferred(radec([15.90, -0.85], [-0.85,15.9])) ==

([1.0, 23.0], [3.0, 56.0], [36.0, 36.0],

[-0.0, 15.0], [51.0, 54.0], [0.0, 0.0])

@testset "recpol" begin

r = a = zeros(Float64, 3)

r, a = @inferred(recpol([0, sqrt(2.0), 2.0*sqrt(3.0)], [0, sqrt(2.0), 2.0]))

@test r ≈ [0.0, 2.0, 4.0]

@test a ≈ [0.0, pi/4.0, pi/6.0]

r, a = @inferred(recpol(1, 1))

@test r ≈ sqrt(2.0)

@test a ≈ pi/4.0

# Test polrec is the inverse of recpol

xi, yi, x, y, = 6.3, -2.7, 0.0, 0.0

x, y = @inferred(polrec(recpol((xi, yi), degrees=true), degrees=true))

@test x ≈ xi

@test y ≈ yi

@testset "rhotheta" begin

ρ, θ = @inferred(rhotheta(41.623, 1934.008, 0.2763, 0.907, 59.025, 23.717, 219.907,

1980))

@test ρ ≈ 0.41101776646245836

@test θ ≈ 318.4242564860495

@testset "sixty" begin

@test @inferred(sixty(-51.36)) ≈ [-51.0, 21.0, 36.0]

@test @inferred(ten(sixty(-0.10934835545824395))) ≈ -0.10934835545824395

@test @inferred(sixty(1)) ≈ [1.0, 0.0, 0.0]

@testset "sphdist" begin

@test sphdist.([0,1,2], [1,2,3], [2,3,4], [3,4,5]) ≈

[1.222450611061632, 2.500353926443337, 1.5892569925227762]

@test @inferred(sphdist(120, -43, 175, +22)) ≈ 1.5904422616007134

@test sphdist.([120], [-43], 175, +22) ≈ [1.5904422616007134]

@test sphdist.(120, -43, [175], [+22]) ≈ [1.5904422616007134]

@testset "sunpos" begin

ra, dec, lon, obl = @inferred(sunpos(jdcnv(1982, 5, 1)))

@test ra ≈ 37.88589057369026

@test dec ≈ 14.909699471099517

@test lon ≈ 40.31067053890748

@test obl ≈ 23.440840980112657

ra, dec, lon, obl = @inferred(sunpos(jdcnv.([DateTime(2016, 5, 10)]), radians=true))

@test ra ≈ [0.8259691339090751]

@test dec ≈ [0.3085047454107549]

@test lon ≈ [0.8687853454154388]

@test obl ≈ [0.40901175207670365]

ra, dec, lon, obl = @inferred(sunpos([2457531]))

@test ra ≈ [59.71655864208797]

@test dec ≈ [20.52127006818727]

@test lon ≈ [61.824436793991545]

@test obl ≈ [23.434648653514724]

@testset "ten" begin

@test @inferred(ten(0, -23, 34)) == @inferred(ten((0, -23, 34))) ==

@inferred(ten([0, -23, 34])) == ten(" : 0 -23 :: 34") == -0.37388888888888894

@test @inferred(ten(-0.0, 60)) == @inferred(ten((-0.0, 60))) ==

@inferred(ten([-0.0, 60])) == @inferred(ten("-0.0 60")) == -1.0

@test @inferred(ten(-5, -60, -3600)) == @inferred(ten((-5, -60, -3600))) ==

@inferred(ten([-5, -60, -3600])) == @inferred(ten(" -5: :-60: -3600")) == -3.0

@test ten("") == 0.0

@test ten.([0, -0.0, -5], [-23, 60, -60], [34, 0, -3600]) ==

ten.([(0, -23,34), ":-0.0:60", (-5, -60, -3600)]) ==

ten.(["0 -23 :: 34", (-0.0, 60), " -5:-60: -3600"]) ==

[-0.37388888888888894, -1.0, -3.0]

@test ten.([12.0, -0.0], [24, 30]) == ten.([" 12::24", " -0:30: "]) == [12.4, -0.5]

@testset "tic_one" begin

min2, tic1 = @inferred(tic_one(30.2345, 12.74, 10))

@test min2 ≈ 30.333333333333332

@test tic1 ≈ 7.554820000000081

min2, tic1 = @inferred(tic_one(45, 50, 4, true))

@test min2 ≈ 46.0

@test tic1 ≈ 50.0

min2, tic1 = @inferred(tic_one(pi\8, tics(90, 45, 1000, 10)...))

@test min2 ≈ 2.5

@test tic1 ≈ 1.0318357862412286

@testset "ticpos" begin

@test ticpos.([16,8,4],[1024,512,256], [150,75,37.5]) ==

[(256.0, 4, "Degrees"), (128.0, 2, "Degrees"), (64.0, 1, "Degrees")]

@test @inferred(ticpos(2, 512, 75)) == (128.0, 30, "Arc Minutes")

@test @inferred(ticpos(1.5, 512, 75)) == (85.33333333333333, 15, "Arc Minutes")

@test @inferred(ticpos(1.5, 512, 50)) == (56.888888888888886, 10, "Arc Minutes")

@test @inferred(ticpos(1.6, 1024, 50)) == (53.333333333333336, 5, "Arc Minutes")

@test @inferred(ticpos(0.2, 512, 75)) == (85.33333333333333, 2, "Arc Minutes")

@test @inferred(ticpos(0.5, 512, 10)) == (17.066666666666666, 1, "Arc Minutes")

@test @inferred(ticpos(0.1, 1024, 50)) == (85.33333333333333, 30, "Arc Seconds")

@test @inferred(ticpos(0.08, 1024, 40)) == (53.333333333333336, 15, "Arc Seconds")

@test @inferred(ticpos(0.025, 512, 50)) == (56.888888888888886, 10, "Arc Seconds")

@test @inferred(ticpos(pi/100, 1024, 40)) == (45.27073936836133, 5, "Arc Seconds")

@test @inferred(ticpos(0.06, 2048, 20)) == (18.962962962962965, 2, "Arc Seconds")

@test @inferred(ticpos(0.016, 1024, 20)) == (17.77777777777778, 1, "Arc Seconds")

@testset "tics" begin

@test @inferred(tics(30, 90, 30, 1)) == (3.8666666666666667, 480)

@test @inferred(tics(30, 90, 3, 3, true)) == (4.0, 240)

@test @inferred(tics(30, 70, 3, 1, true)) == (0.75, 60)

@test tics.([30,50],[70,60], [6,12], [3,0.5], [true, false]) ==

[(3.75,120), (0.55,30)]

@test @inferred(tics(45, 55, 30, 0.5)) == (0.725, 15)

@test @inferred(tics(45, 60, 10, 0.1)) == (0.1, 10)

@test @inferred(tics(55, 60, 100.0, 1/2)) == (0.66, 2)

@test @inferred(tics(25, 30, 50, 2, true)) == (2.45, 1)

@test @inferred(tics(20, 80, 600, 0.03)) == (0.04159722222222222, 0.25)

@test @inferred(tics(25, 75, 500, 0.02)) == (0.02772222222222222, 0.16666666666666666)

@test @inferred(tics(10, 12, 25, 0.01)) == (0.016666666666666666, 0.08333333333333333)

@test @inferred(tics(20, 80, 6000, 0.03)) ==

(0.055546296296296295, 0.03333333333333333)

@test @inferred(tics(30, 60, 200, 0.02, true)) ==

(0.02763888888888889, 0.016666666666666666)

@test @inferred(tics(60, 70, 125, 0.001)) ==