DSII TESS cube memory profiling with FITS memmap

measure_memory.py

"""
Modify the amount of sub-cube you wish to extract in the
``fits_mmap()`` function. Then run this::

    python measure_memory.py

My results below. This shows that memory mapping should only
consume the memory used in accessed sub-cube, not the entire cube.
Trying to calculate the mean of the entire cube did not work,
as expected.

Line #    Mem usage    Increment   Line Contents
================================================
     6     41.7 MiB     41.7 MiB   @profile
     7                             def fits_mmap():
     8                                 # ImageHDU  (2, 1282, 2136, 2078)  float32
     9     41.7 MiB      0.0 MiB       with fits.open('tess-s0001-1-1-cube.fits', memmap=True) as pf:
    10     47.5 MiB      5.7 MiB           data = pf[1].data[0, 0, :10, :10]
    11     47.6 MiB      0.2 MiB           print(data.mean())
    12     47.6 MiB      0.0 MiB       return data

Line #    Mem usage    Increment   Line Contents
================================================
     6     41.7 MiB     41.7 MiB   @profile
     7                             def fits_mmap():
     8                                 # ImageHDU  (2, 1282, 2136, 2078)  float32
     9     41.7 MiB      0.0 MiB       with fits.open('tess-s0001-1-1-cube.fits', memmap=True) as pf:
    10     47.5 MiB      5.7 MiB           data = pf[1].data[0, :, :100, :100]
    11     57.5 MiB     10.1 MiB           print(data.mean())
    12     57.5 MiB      0.0 MiB       return data

"""
from astropy.io import fits

from memory_profiler import profile


@profile
def fits_mmap():
    # ImageHDU  (2, 1282, 2136, 2078)  float32
    with fits.open('tess-s0001-1-1-cube.fits', memmap=True) as pf:
        data = pf[1].data[0, 0, :100, :100]
        print(data.mean())
    return data


if __name__ == '__main__':
    fits_mmap()