Code optimization (hos)

[hubert-leterme]

I worked on the hos branch, but there are still some problems to fix and code to optimize.

Module pycs.astro.wl.mass_mapping

• Remove for loops wherever possible
• Vectorize mass mapping methods: operate on stacks of images rather than single images.
• Class massmap2d: it would be better to create a separate class for each mass mapping method, inheriting from a base class implementing a forward or predict method. This way, it would be easier to write code which is shared among all mass mapping methods. E.g., BaseMassmap2d, ProxWiener, ProxMSE, SparseWiener.
• Improve intermediate methods such as massmap2d._prepare_data, massmap2d._get_Wfc and massmap2d._noise_realizations: instead of returning many variables, register them as instance attributes or properties for the classes massmap2d or shear_data?
• Class and variable names: should respect the Python standards.
• Classes shear_data, massmap2d and starlet2d: put attribute declaration within the __init__ methods. Moreover, merge init_massmap and init_starlet into the respective __init__ methods.
• Are the attributes nx and ny really necessary for massmap2d? What about starlet2d?
• Remove argument ind from _prepare_data (remove np.where wherever possible; useless most of the time)
• Improve mask = (Ncv < 1e2).astype(int) (could be placed before)
• Bug correction in gamma_to_cf_kappa (raise NotImplementedError, to be done later if necessary)
• Optimize kappa_to_gamma, gamma_to_cf_kappa, gamma_to_kappa, H_operator_eb2g and H_adjoint_g2eb: work on complex arrays
• Remove redundancy with the above methods

Module pycs.sparsity.sparse2d.starlet

• Class MRStarlet: modify methods transform and recons in order them to operate on stacks of images (n-D tensors). Modify the C implementation, or implement them in PyTorch to take advantage of GPU acceleration. As a workaround, I used np.vectorize to achieve the same results, but computational efficiency could be widely improved.

Package pycs

• Work with dtype = np.float32 and np.complex64, or dtype = np.float64 and np.complex128? Harmonize code.
• Remove np.copy() wherever possible (useless memory usage)