Release notes

Release notes#

pydantic is a data validation and settings management library for Python, based on type annotations. It allows you to define data models with type hints, and it automatically validates and parses input data according to those types. Instances of a parametrised model must use a parameterised type, e.g. Resource[MyType], where MyType is a concrete type that satisfies the constraints of the model. (#10)
Ensure that lcm returns 0 when input is empty and fix stubs for lcm and gcd to return an integer. (#12)
Fix Lattice.__ge__ method to correctly compare two Lattice objects. The previous implementation did not account for all cases, leading to incorrect comparisons. The new implementation ensures that the comparison is based on the underlying data structure and its properties, providing accurate results when using the greater than or equal to operator. (#13)

Add an IntegerSet class able to efficiently represent subset of integers using either:
- a compact form if array.array can be used;
- a list of integers.
The FiniteSubSet uses now IntegerSet.

(#2)
Create one named tuple for clarifying code:
- Neighbourhood for representing element neighbourhoods;
(#3)
Make changes to the function _reduced_context_completion(). In-place enlarge the lattice with an iterable of elements. Implementation of a naive version of this algorithm. Should be optimized.

Also makes changes to testing files and add test_extend_idempotence(). Test of the idempotency property of the RᴇᴅᴜᴄᴇᴅCᴏɴᴛᴇxᴛCᴏᴍᴘʟᴇᴛɪᴏɴ algorithm. (#4)
Give the possibility to create a finite subset using a sorted array of intervals of int.

Implement FiniteUniverse methods:
- parts()
- singletons()
Implement FiniteUniverse attributes:
- empty
- whole.
Implement FiniteSubSet methods:
- from_intervals()
- intervals()
- subsets()
- supersets()
(#8)

Major overhaul of the GALACTIC core, separating it into several parts, including the kernel that makes up this package. (#7)