Pysindy

- New method for SINDyPI?
- Derivative methods now also return smoothed X values.

This is a pre-release of major version 2 of SINDY

This pre-release preserves a significant amount of backwards compatibility that
will be removed in 2.0.0.

Internal array structure is made explicit via the
`AxesArray` class. `AxesArray` objects carry axis label attributes, such as
`arr.ax_time`, as well as shape attributes, such as `arr.n_spatial`.
Currently, these attributes are incorrect when slicing, but are preserved in
nearly all other operations.

This release also adds an `EnsemblingOptimizer` class to handle data and library
bagging. While passing ensembling parameters via feature libraries and `SINDy`
objects is still supported, they simply dispatch to an `EnsemblingOptimizer`.
Stable versions of 2.x will remove this backwards compatibility, forcing the
use of the `EnsemblingOptimizer`. In addition, ensembling both data and
library terms creates each ensemble member from one data bag and one library
bag. Previously, each ensemble member came from one library bag and another
ensemble of data bags, which required nested loops and $O(n_{bags}^2)$ run
time.

What's Changed
* Think we have now actually fixed the example notebook documentation building so that they should all render properly on the readthedocs documentation site.
* Fixed the SCS version requirements to avoid errors in TrappingSR3 with certain versions of Python.
* Switched CI to test newer Python version 3.8, 3.9, 3.10.

What's Changed
* Made fixes to the example notebook documentation building so that they should all render properly on the readthedocs documentation site.

What's Changed
* Added ParameterizedLibrary by znicolaou in https://github.com/dynamicslab/pysindy/pull/273 from this recent paper https://arxiv.org/abs/2301.02673. SINDy with control parameters (SINDyCP) is described further in the [examples](https://github.com/dynamicslab/pysindy/blob/master/examples/17_parameterized_pattern_formation/parameterized_pattern_formation.ipynb).
* Added big benchmark functionality by OliviaZ0826, akaptano and znicolaou from https://github.com/dynamicslab/pysindy/pull/266 from this recent paper https://arxiv.org/abs/2302.10787. Big benchmarks are shown using the [dysts](https://github.com/williamgilpin/dysts) database and the results/functionality can also be found in the [examples](https://github.com/dynamicslab/pysindy/tree/master/examples/16_noise_robustness).
* Added StableLinearSR3 optimizer by akaptano from https://github.com/dynamicslab/pysindy/pull/269 that allows users to build arbitrarily large linear models that are guaranteed to be stable for any initial condition. Also allows for any number of linear equality and inequality constraints on the coefficients.
* Fixed a few minor bugs with the Gurobipy version, the sphinx setup with the development tools in requirements-dev.txt, and flake8 website move from gitlab to GitHub.

What's Changed

Added new mixed-integer optimization algorithm called MIOSR.
Added new external unit tests of all the jupyter notebook examples.
Made Gurobipy and cvxpy optional dependencies. Gurobipy is required for using MIOSR and cvxpy is needed for using inequality-constrained optimizers, the trapping SINDy algorithm, or the SINDy-PI algorithm.
Ensembling functionality moved to the "EnsembleOptimizer", see updated Example 1 notebook for this.