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A Julia package for reading and writing Flexible Image Transport System (FITS) files, based on the cfitsio library.

The interface is inspired by Erin Sheldon's fitsio Python package.


The Libcfitsio submodule has been moved to CFITSIO.jl and will be deprecated in a future release.


FITSIO.jl can be installed using the built-in package manager

pkg> add FITSIO


To open an existing file for reading:

julia> using FITSIO

julia> f = FITS("file.fits")
File: file.fits
Mode: "w" (read-write)
HDUs: Num  Name  Type
      1          Image
      2          Table

(At the REPL, information about the file contents is shown.)

A FITS file consists of one or more header-data units (HDUs), concatenated one after the other. The FITS object therefore is represented as a collection of these HDUs.

Get information about the first HDU:

julia> f[1]
File: file.fits
HDU: 1
Type: Image
Datatype: Float64
Datasize: (800, 800)

Iterate over HDUs in the file:

julia> for hdu in f; println(typeof(hdu)); end

Each HDU can contain image data, or table data (either binary or ASCII-formatted). For image extensions, get the size of the image without reading it:

julia> ndims(f[1])

julia> size(f[1])

julia> size(f[1], 2)

Read an image from disk:

julia> data = read(f[1]);  # read an image from disk

julia> data = read(f[1], :, 790:end);  # read just a subset of image

Show info about a binary table:

julia> f[2]
File: file.fits
HDU: 2
Type: Table
Rows: 20
Columns: Name  Size  Type    TFORM
         col2        String  5A
         col1        Int64   1K

Read a column from the table:

julia> data = read(f[2], "col1")

Table HDUs implement the Tables.jl interface, so you can load them into other table types, like DataFrames.

julia> df = DataFrame(f[2])

Variable length columns are not supported by the Tables.jl interface, and Tables methods will ignore them.

Read the entire header into memory and get values from it:

julia> header = read_header(f[1]);  # read the entire header from disk

julia> length(header)  # total number of records in header

julia> haskey(header, "NAXIS1")  # check if a key exists

julia> header["NAXIS1"]  # get value by keyword

julia> header[4]  # get value by position

julia> get_comment(header, "NAXIS")  # get comment for a given keyword
"length of data axis 1"

Read just a single header record without reading the entire header:

julia> read_key(f[1], 4)  # by position
("NAXIS1",800,"length of data axis 1")

julia> read_key(f[1], "NAXIS1")  # read by keyword
(800,"length of data axis 1")

Manipulate a header in memory:

julia> header["NEWKEY"] = 10  # change or add a keyword

julia> set_comment!(header, "NEWKEY", "this is a comment")

Close the file:

julia> close(f)

(FITS objects are also closed automatically when garbage collected.)

Open a new file for writing:

julia> f = FITS("newfile.fits", "w");

The second argument can be "r" (read-only; default), "r+" (read-write) or "w" (write). In "write" mode, any existing file of the same name is overwritten.

Write an image to the file:

julia> data = reshape([1:100;], 5, 20)

julia> write(f, data)  # Write a new image extension with the data
julia> close(f)

To write some header keywords in the new extension, pass a FITSHeader instance as a keyword: write(f, data; header=header)

Overwrite image data in an existing file:

julia> f = FITS("newfile.fits", "r+")  # Reopen the file in read-write mode
julia> data = reshape([101:200;], 5, 20)  # Prepare new image data
julia> image_hdu = f[1]
julia> write(image_hdu, data)  # Overwrite the image

Write a table to the file:

julia> data = Dict("col1"=>[1., 2., 3.], "col2"=>[1, 2, 3]);

julia> write(f, data)  # write a new binary table to a new extension
Compressed storage

Setting the file extension to .gz will automatically use GZIP compression and save on storage space.

julia> FITS("abc.fits", "w") do f # save the image uncompressed
           write(f, ones(200,200))

julia> filesize("abc.fits")

julia> FITS("abc.fits.gz", "w") do f # save the image compressed
            write(f, ones(200,200))

julia> filesize("abc.fits.gz")

Alternately the compression algorithm might be specified in square brackets after the filename. Check the CFITSIO website for the details of this usage.

julia> FITS("abc.fits[compress R 100,100]", "w") do f # Rice algorithm with a 100 x 100 pixel tile size
           write(f, ones(200,200))

julia> filesize("abc.fits")

Compression is "loss-less" for images with integer pixel values, and might be lossy for floating-point images.