mlprocessors: invoke processor classes directly -- including validation

[magland]

for example

import ml_ephys ml_ephys.compute_templates(timeseries=...,etc)

That does not require mountainlab.

[paquiteau]

Hi, I have the same problem. I also want to disable FFT setup for the computation of density compensation. I am using CUDA 11.0 and cupy on my end, but I don't think it matters.

I have try to go around the problem by accessing the functions CUFINUFFT_SPREAD2D or CUFINUFFT_SPREAD3D , and their Interp counterpart, without success for two main reasons:

1. The setup of the plan has to be done manually, following what the test/spread2d_test.cu does.
2. I am not that comfortable with using ctype for (mangled) functions with custom type, like the cufinufft_plan

Ideally, end-user should be able to call flawlessy make_plan, set_pts, exec_plan , with or without the fft running (this being setup by opts.gpu_spreadinterponly =1

An alternative is to use tensorflow-nufft and tensorflow-mri: https://mrphys.github.io/tensorflow-mri/api_docs/tfmri/sampling/estimate_density

This requires tensorflow-2.8 and hence has a strong dependency on CUDA 11.2