juliaastro / cfitsio.jl Goto Github PK

When doing pkg> add CFITSIO in Julia 1.0 to 1.2 (inclusive), I get the following error:

ERROR: failed to clone from CFITSIO,, error: GitError(Code:ERROR, Class:Net, unsupported URL protocol)

is it on purpose or is because no backward compatibility is possible?

The docs of fits_insert_img states

One can force a new primary array to be inserted at the beginning of the FITS file by setting status = PREPEND_PRIMARY

except PREPEND_PRIMARY is defined in a header file as

#define PREPEND_PRIMARY    -9  /* used in ffiimg to insert new primary array */

and I don't think that this is exported (I can't find a corresponding symbol exported when I run nm on the library). Perhaps we may just hardcode the symbol here, but is there any other way to access the symbol from the header file?

Hi,

I need to create FITS files where the first HDU is empty (only containing a header). Given the structure of fits_create_img I did not find a way to do this (please let me know if I overlooked something!). So I extended the code with the following:

function fits_create_empty_hdu(f::FITSFile)

    status = Ref{Cint}(0)
    naxesr = C_NULL
    N = 0
    bitpix = bitpix_from_type(Int16)

    ccall(
         (:ffcrimll, libcfitsio),
         Cint,
         (Ptr{Cvoid}, Cint, Cint, Ptr{Int64}, Ref{Cint}),
         f.ptr,
         bitpix,
         N,
         naxesr,
         status,
     )

    fits_assert_ok(status[])
end

and a small fits_create_img wrapper

function fits_create_img(f::FITSFile, a::Nothing)
    status = fits_create_empty_hdu(f)
end

This appears to work ok but I am not sure whether this is superfluous. If it is useful, I'm happy to create a PR with this, but I must confess I am a tad uncertain about the logic of the runtests.jl so if you have any hints on how to write a test conforming to the code preferences, I'd be happy to hear them!

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What if we just permuted data on read? I'd like to see the arguments against, but I would really prefer to see data in its row-major orientation by default.

In terms of implementation, I have no clue what the most efficient and transparent solution is besides calling permutedims. I'm interested how PyCall.jl facilitates this when it hands off arrays to numpy.

cc @giordano