SkyCoords.jl

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SkyCoords.jl provides a type system for astronomical coordinate systems with appropriate conversions between them.

Installation

From the Julia REPL

(v1.2) pkg> add SkyCoords

julia> using SkyCoords

Usage

There are currently three supported coordinate systems. The following immutable types are used to represent coordinates in each system:

Each type holds a longitude and latitude, and each is a subtype of AbstractSkyCoords.

julia> c1 = ICRSCoords(0.0, 0.0)  # inputs are ra, dec in radians
ICRSCoords{Float64}(0.0, 0.0)

julia> c1.ra # access ra, dec individually
0.0

julia> c2 = convert(GalCoords, c1) # convert to a different system
GalCoords{Float64}(1.6814027872278692, -1.0504884034813007)

julia> c2.l # Note that galactic coordinate fields are l, b
1.6814027872278692

julia> c1 |> FK5Coords{2000} # Can use piping syntax for conversion
FK5Coords{2000, Float64}(1.1102233723050001e-7, 4.411803426976326e-8)

Parsing from strings

The AstroAngles.jl package provides convenient string parsing utilities

julia> using AstroAngles

julia> c3 = ICRSCoords(hms"05:34:31.94", dms"+22:00:52.2")
ICRSCoords{Float64}(1.4596726677614607, 0.3842255081802917)

for example, to load coordinates from a target list

julia> using CSV, DataFrames

julia> table = CSV.File("target_list.csv") |> DataFrame;

julia> [table.ra table.dec]
203×2 Matrix{String}:
 "00 05 01.42"  "40 03 35.82"
 "00 05 07.52"  "73 13 11.34"
 "00 36 01.40"  "-11 12 13.00"
[...]

julia> coords = @. ICRSCoords(hms2rad(table.ra), dms2rad(table.dec))
203-element Vector{ICRSCoords{Float64}}:
 ICRSCoords{Float64}(0.021919880964005448, 0.6991780256843024)
 ICRSCoords{Float64}(0.022363485482220672, 1.277926878539953)
 ICRSCoords{Float64}(0.15718144355252264, -0.19553990200190915)
[...]

Angular Separation between Coordinates

The separation function allows you to compute the angular (great-circle) distance between two coordinates, in radians, using the Vincenty formula. The coordinates can be also given in different systems. For example, according to SIMBAD the FK5Coords{2000} coordinates of Mizar are

julia> mizar = FK5Coords{2000}(3.507787, 0.958628)
FK5Coords{2000, Float64}(3.507787, 0.958628)

while the GalCoords coordinates of Alcor are

julia> alcor = GalCoords(1.968189, 1.072829)
GalCoords{Float64}(1.968189, 1.072829)

Their angular separation is given by

julia> separation(mizar, alcor) # Radians
0.003435309169452965

julia> rad2deg(separation(mizar, alcor)) * 60 # Arcminutes
11.809723003934822

with an angle

julia> position_angle(mizar, alcor) # radians
1.2446024012417884

julia> position_angle(mizar, alcor) |> rad2deg # degrees
71.31046476300233

Accuracy

All the supported conversions have been compared to the results of astropy.coordinates (to better than 0.0001 arcsec agreement for Float64). In turn, astropy.coordinates has been tested against many other tools.

Performance

For small and moderate numbers of coordinates, conversions are much faster than astropy.coordinates in Python. The following plot shows the performance for converting ICRS coordinates to various other systems (Galactic, FK5J2000 and FK5J1975), using astropy.coordinates (py_* labels) and SkyCoords.jl (jl_* labels). The x axis denotes the number of coordinates being simultaneously converted, with 1 corresponding to scalar coordinates.

times

Specs
CPUIntel core i5-8259U @ 2.3GHz (4 cores)
RAM16GB
Julia Version1.2
Python Version3.7
Astropy Version3.1.2

For scalar coordinates, SkyCoords.jl is up to 100,000 times faster. For very large vectors of one million coordinates or more, SkyCoords.jl is 2-4 times faster. The source code for these benchmarks can be found in bench/.

Contributing

If you would like to contribute to SkyCoords please head over to the GitHub page and file an issue or open a pull request!