try to figure out formulas for multidimensional indices given the 1d index and the strides in each array direction
i learned julia. it seems to be quite a nice language (more free than matlab/dipimage and faster than python)
it seems to come with bindings to most of the things i need (fft, svd)
i find the "end" that indicates the end of the ics header
i load the complex data from the file but it seems to be wrong (containing NaN's)
i verified that raw data is read from the correct starting position in the file
i can scale image data now and output pgm files (but the data is broken)
the data was stored as complex single-float in the ics file
loading all the data seems to be rather fast in julia
svd with 6720x24311 took 632s
the svd on my big dataset actually indicates slightly below 1000 modes (as theoretically predicted) i'm still not sure how to cope with the other polarization
store averages over illumination angles and averages over first order
create masks for angle and fiber end plane
cutting away the pixels with low light intensity in the angular range and on the fiber plane reduces the transfer matrix to 3360x9724 and the singular value decomposition takes 65s (10x faster than before)
reconstruct the fields on the fiber end from u (part of the decomposition of asmall, the matrix that only contains significant camera pixels and illumination angles)
also store the reconstruction of v
pinv calculates the pseudoinverse directly, time consumption is similar to svdfact
multiply pseudo inverse with the second measurement
store the the backpropagated second measurement
configuration files for openh264 to create movie