MappedTensor disk-mapped variables and lazy loading

A class that represents data remaining on disk with a very low memory foot-print, but behaves as a normal numeric array.

This is a class that creates a "numeric variable" representing some data remaining on disk. This way you can transparently treat data larger than the available memory, aka lazy loading and memory-mapped disk data.

You can:

• create an array of ANY size
• populate objects with huge data (stored on disk)
• use many math operators (131 methods)
• import from: EDF, HDF5, Analyze, ADSC Detector, MAR CCD, MAT 7.3, MRC CCP4, NifTI, NumPy NPY NRRD, Uncompressed TIFF and tomography VOL/PAR.
• export to: HDF5, Analyze, NumPy NPY, NRRD and NifTI.

Example:

m1 = load(MappedTensor, 'hugefile1.nrrd');
m2 = MappedTensor('hugefile2', [1000 1000 1000],'Format','double');
m3 = m1+m2; % sizes must match
save(m3, 'hugefile_sum.hdf5:/Data'); % creates a 'Data' entry

This work was initiated by D. Muir at http://dylan-muir.com/articles/mapped_tensor/ and is now highly improved (operators, load, save). It benefited from the HDF5 mem mapped object, and more generally is inspired by the memmapfile Matlab object, but in a much more advanced way.

Warning : of course, operators will be slower than when done in memory. Expect an order of magnitude slower.