VLBIData.jl

VLBIData reads and writes a range of data formats typically used in very long baseline interferometry (VLBI).

For convenience, it exports itself as VLBI: non-exported functions are accessed as VLBI.load.

See the table of contents and sections below for the list of supported formats. The proper file format is guessed automatically, and typically one just needs the VLBI.load(file) method.

Some formats, such as "image FITS", may actually contain multiple datasets of different kinds (eg image + model). In such cases, VLBI.load(file) reads the main dataset (image) by default. Use VLBI.load(type, file) to read the dataset of type from the file: see examples below.

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md"""

!!! info "VLBIData.jl"

`VLBIData` reads and writes a range of data formats typically used in very long baseline interferometry (VLBI).

For convenience, it exports itself as `VLBI`: non-exported functions are accessed as `VLBI.load`.

See the table of contents and sections below for the list of supported formats.

The proper file format is guessed automatically, and typically one just needs the `VLBI.load(file)` method.

Some formats, such as "image FITS", may actually contain multiple datasets of different kinds (eg image + model). In such cases, `VLBI.load(file)` reads the main dataset (image) by default. Use `VLBI.load(type, file)` to read the dataset of `type` from the `file`: see examples below.

"""

5.1 ms

VLBIData is well-tested. It is regularly confirmed to read all files in the Astrogeo database successfully.

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md"""

`VLBIData` is well-tested. It is regularly confirmed to read all files in the [Astrogeo](http://astrogeo.org/vlbi_images/) database successfully.

"""

402 μs

Image FITS

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md"""

# Image FITS

"""

8.0 ms

Read image itself

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7.7 ms

Load the image data from a FITS file:

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265 μs

fimg = VLBI.load("./data/map.fits");

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3.1 s

The returned object contains both the header information, and the image data as an array:

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254 μs

"J0000+0248"

fimg.header["OBJECT"]

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16.0 μs

KeyedArray(fimg) |> display

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2-dimensional KeyedArray(NamedDimsArray(...)) with keys:
↓   ra ∈ 512-element StepRangeLen{Unitful.Quantity{Float64,...}
→   dec ∈ 512-element StepRangeLen{Unitful.Quantity{Float64,...}
And data, 512×512 Matrix{Float32}:
              (-51.2 mas)            …  (51.0 mas)
  (51.0 mas)           -4.92649f-6               0.000202965
  (50.8 mas)            2.129f-5                 0.000178501
  (50.6 mas)            7.48033f-5               0.000125342
           ⋮                         ⋱  
 (-50.6 mas)            0.000115528  …          -0.0001272
 (-50.8 mas)            8.01195f-5              -7.8384f-5
 (-51.0 mas)            9.25147f-6               5.1111f-6
 (-51.2 mas)           -7.57209f-5               9.37739f-5

2.1 s

As you see, the image data is a KeyedArray. It contains information about axes and their units for convenient look up, indexing, plotting, ...:

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269 μs

KeyedArray(fimg)(dec=Near(5u"mas")) |> display

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1-dimensional KeyedArray(NamedDimsArray(...)) with keys:
↓   ra ∈ 512-element StepRangeLen{Unitful.Quantity{Float64,...}
And data, 512-element view(::Matrix{Float32}, :, 282) with eltype Float32:
  (51.00000085048702 mas)  -9.41605f-5
  (50.80000084715178 mas)  -0.00010086887
  (50.60000084381653 mas)  -0.000102033504
  (50.40000084048129 mas)  -9.566722f-5
  (50.20000083714605 mas)  -8.480035f-5
                        ⋮  
 (-50.60000084381653 mas)  -4.763418f-5
 (-50.80000084715178 mas)  -5.425684f-6
 (-51.00000085048702 mas)   1.2369155f-5
 (-51.20000085382226 mas)   3.7464752f-6

470 ms

let

fig, ax, plt = image(KeyedArray(fimg), colormap=:inferno, colorscale=SymLog(5e-4, vmin=0))

Colorbar(fig[1,2], plt, label="Jy/beam")

fig

end

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9.2 s

Access the so-called "image beam" - the effective point spread function - and add it to the image:

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736 μs

EllipticGaussian{Quantity{Float64, NoDims, Unitful.FreeUnits{(mas^2,), NoDims, nothing}}, Quantity{Float64, NoDims, Unitful.FreeUnits{(mas,), NoDims, nothing}}, Quantity{Float64, NoDims, Unitful.FreeUnits{(mas,), NoDims, nothing}}, Float64}EllipticGaussian

flux

6.551945156399848 mas^2

σ_major

1.7109050766198861 mas

ratio_minor_major

0.35623661482068336

pa_major

-0.04649986326694402

coords

StaticArraysCore.SVector{2, Unitful.Quantity{Float64, NoDims, Unitful.FreeUnits{(mas,), NoDims, nothing}}}: StaticArraysCore.SVector{2, Unitful.Quantity{Float64, NoDims, Unitful.FreeUnits{(mas,), NoDims, nothing}}}:

0.0 mas

0.0 mas

beam(fimg)

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145 ms

let

fig, ax, plt = image(KeyedArray(fimg)((-30..20)u"mas", (-25..25)u"mas"), colormap=:inferno, colorscale=SymLog(5e-4, vmin=0))

beampoly!(ax, ustrip(beam(fimg)), position=(0.1, 0.1), color=:lightgreen)

# plt.gca().add_artist(ScalebarArtist([(1, "mas")], loc="lower right", color=:w))

fig

end

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2.8 s

Read source model

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226 μs

FITS image files commonly contain the source model itself, often as a set of delta functions - "CLEAN components". Components are stored in the AIPS CC FITS table.

To load this model instead of the image array, pass the corresponding type to VLBI.load:

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296 μs

MultiComponentModel{Vector{InterferometricModels.Point{Quantity{Float32, 𝐌 𝐓^-2, Unitful.FreeUnits{(Jy,), 𝐌 𝐓^-2, nothing}}, Quantity{Float64, NoDims, Unitful.FreeUnits{(mas,), NoDims, nothing}}}}}MultiComponentModel

components

InterferometricModels.Point{Unitful.Quantity{Float32, 𝐌 𝐓^-2, Unitful.FreeUnits{(Jy,), 𝐌 𝐓^-2, nothing}}, Unitful.Quantity{Float64, NoDims, Unitful.FreeUnits{(mas,), NoDims, nothing}}}