`tensor`{.:python} is a tensor manipulation library.
It provides the following features:
- Patterns on the cell relationships are parametrized by a `Topology`{.:python}.
Consider checkerboard pattern. Note how this is a minimal syntax to describe the crux of a pattern, which is what "sets are open".
Consider a convolutional layer. (Run trhough).
- References at the cell-level for network graph construction.
Features to add: Framework for network architecture, framed as a network topology.
type annotation for arrays
[TODO} try and integrate in networkx complex graph structs
modularization of array operators. Every operator is a class. This enables symbolic expression computation at the type-level (what else?)
Tensor strengthens the type system the operator of numpy arrays. * embeds tensors with a dependent type for their geometry. This can be later extended so a static checker can check that geometry of tensor operators is well-formed. * provides type classes for all the
Helpful because the file system structure helps infer the structure of the operators.
Geometry[11111]
Simple example on dualities.