Topic

Introducing a new library which can compile symbolic expressions into fast NumPy functions - I hope it can help widen the bridge between mathematicians and programmers

A SymPy expression or string is accepted along with sample data, then directly used or converted into a loop over the first dimension of those arrays, and finally compiled with Numba's ahead-of-time mode using the types from the data and exposed through the object's `__call__()`

The given sample data can also be used to internally verify the results match in Python, SymPy (substitution), and the compiled function

Extra features (such as support for summations) and arbitrary helper functions can become embedded and made available too

Expressive can also parse indexed names like x[i-1] to refer to arbitrary offsets of the input arrays and also members of the result array (which can be provided filled or with seed values or generated dynamically)

Proposed Agenda
* Expressive overview
* How fast is this thing?
* Use cases
* Other cool stuff this can do
* Configuration system
* What's next?