Scalib

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* SASCA: support modular ADD and MUL operations (18)
* TTest: Performance improvement by using a mix of 2 passes and 1 pass algorithms
* MTTest: First implementation of multivariate T-test.
* Improved documentation and README.rst
* SNR: use pooled formulas for better correctness then there are few traces,
saves RAM (up to 75% reduction) and improves perf (about 2x single-threaded).
* Bump python minimum version to 3.7
* Revamp multi-threading handling thanks to new `scalib.threading` module.
* AVX2: Wheels on PyPi are built with AVX2 feature.

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* Release GC in SASCA's `run_bp` .
* Release GC in `rank_accurary` and `rank_nbin`.
* `LDA.predict_proba` is marked thread-safe.
* Hide by default the progress bar of `SASCAGraph.run_bp` (can be re-enable
with the `progress` parameter).

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* Solving minor issues in `MultiLDA` and `LDAClassifier`. Allowing multiple
threads in `predict_proba()` and add a `done` flag to `solve()`.

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* Chunk `SNR.fit_u` to maintain similar performances with long traces and
adding a progress bar

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* Add `max_nb_bin` parameter to `postprocessing.rank_accuracy` (that was
previously hard-coded).

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* Rename `num_threads` parameter of `modeling.MultiLDA` to `num_cpus`.
* Fix rank estimation when there is only one key chunk.
* Improve performance of `SNR.get_snr`.