Added a function that takes up dictionaries and provides a sparse representation of the prior.
Temporal smoother is now created using sparse mtxs which should reduce memory footprint. *Not tested*
Use sparse matrix
The prior hessian is now efficient in terms of memory
Fixed some problems with intermediate sparse matrices in the spatial smoother, as it created pretty large matrices that are in effect sparse
Hessian inversion can be done row by row if the matrix is too large, resulting in some sort of sparse matrix I hope. For some reason, calculating the standard deviation takes forever, which is probably a consequence of using a lil matrix, but right now, it works for larger problems, or for smaller memory"