Pysindy

Should fix the compatibility errors with scikit-learn >= 1.0

What's Changed
* Add common virtual environment directories to .gitignore by Jacob-Stevens-Haas in https://github.com/dynamicslab/pysindy/pull/177
* ENH: Raise explicit error when FROLS has linearly dependent columns. by Jacob-Stevens-Haas in https://github.com/dynamicslab/pysindy/pull/193
* BUG: SINDyDerivative set_params() missed sklearn interface by Jacob-Stevens-Haas in https://github.com/dynamicslab/pysindy/pull/207

We have implemented new techniques for computing the weak formulation of SINDy, reducing the runtime by at least an order of magnitude. The "num_pts_per_domain" option in the weak formulation is now deprecated, and see the revamped Example 12 notebook for the new performance. This release also addresses Issues 155 158 159 164. We have some minor fixes planned for the future, and hope to have a version of model.predict and model.simulate working for the weak formulation in the coming few months.

The GeneralizedLibrary class had a bug if libraries were tensored together while using the ensembling functionality. This small patch fixes that issue.

This is PySINDy's second major release, representing new source code, documentation-generating scripts, examples, unit tests, and a markdown version of the new PySINDy JOSS submission [openjournals/joss-reviews3994](https://github.com/openjournals/joss-reviews/issues/3994). There is already a DOI via Zenodo, [](https://doi.org/10.5281/zenodo.5842612).

This minor release adds a verbose option to all the optimizers so users can optionally track the error terms in the various optimization problems. See example Jupyter notebook 1 for a simple use case. It also makes some minor fixes, including:

1. Fixed a minor test error (see Issue 149)
2. Fixed an issue when using multiple_trajectories or ensemble flags with PDEs and Weak PDEs, see Issue 148
3. Added Matplotlib to the dependencies so that (hopefully) the binder notebooks are now working properly online.
4. Fixed some math notation in the optimizer documentations.

This release allows for differentiation along specific axes, revamps the PDE-FIND and weak SINDy functionalities to work in arbitrary spatial dimensions, and moves all the array-reshaping to the internal code so the user-interface is easier. Finite difference coefficients are now computed on the fly depending on the derivative order "d" and accuracy order "order", i.e. finite differences now work for arbitrarily high derivative and accuracy order.

References
- See [this Youtube playlist](https://www.youtube.com/playlist?list=PLN90bHJU-JLoOfEk0KyBs2qLTV7OkMZ25) for tips on using pysindy in practice and examples of new functionality.

Other updates
- Added a SpectralDerivative() class for spectral differentiation at faster speed than is available with the "derivative" python package.
- Finite Differences now have periodic boundary condition support.
- Cleaned up the example notebooks a bit.