Symbolica

**Notable changes**
- Integration of polynomials and rational polynomials
- Level range restrictions for pattern matching
- `Together` and `apart` for expressions
- Simple algebraic number support
- Add dense univariate polynomial
- Add resultant function for fields and resultant_prs for rings
- Code coverage testing suite
- New Python API documentation

**Upgrade note**
- Rust users will have to replace the module `representations` by `atom`

Notable changes:
- Remove explicit workspace and state construction by the user. This greatly improves Rust ergonomics (see [Zulip](https://reform.zulipchat.com/#narrow/stream/364659-announcements/topic/rust.20ergonomics)) and is one of the final milestones for a Symbolica 1.0 release
- Introduce linear algebra and matrix manipulation
- Performance improvements for rational polynomial arithmetic
- Computational graph generation with C++ output for dependent expressions
- Add numerical integration state importing and exporting for easy use on HPC clusters
- Add convenience methods in Rust that allocate and ones that recycle memory, i.e. `expand()` vs `expand_into(&mut atom)`

Notable changes:
- Conditions on patterns are now logical expressions and use overloaded operators, `&`,`|`,`!` (`~` in Python)
- Replacement now accepts settings, currently the only setting is `non_greedy_wildcards`
- `Function.NUM` has been renamed to `Function.COEFF` and `to_num` to `to_coeff`

Migration guide:

- In Rust, you can now use `into()` to convert a tuple of the wildcard identifier and the pattern restriction into a condition
- In Python `req_ntype(..)` and `req_ngreedy` are deprecated. Use `~x__.req_type(..)` and option `non_greedy_wildcards=[x__]` instead

A very short and incomplete overview of its features are given here:
- Pattern matching
- Common operations such as expansion and derivative taking
- State-of-the-art polynomial arithmetic
- Parallel execution over terms
- Fully functional and easy to use Python API
- Expression simplification for numerical evaluation
- Highly customizable numerical integration tool

Click [here](https://symbolica.io/docs/get_started.html) to start using Symbolica!