# Causality and K-best

This directory is a self-contained example based on the current Butadiene code.
Nothing in `ILP/butadien` or `mod/butadien` is changed.

## 1. Enumerate K best supports with Gurobi

Set `K` near the top of `butadiensynthesis_k_best.py` and run:

```bash
python3 butadiensynthesis_k_best.py
```

The script is the existing Butadiene ILP with one small generalisation: instead of
computing only the best and second-best support, it repeatedly excludes every
previously found support. It writes all solutions to `gurobi_flows.json`.

Thus "K best" here means K best distinct reaction supports, matching the meaning
of the existing second-best code.

## 2. Check the Gurobi solutions for causality

Activate the MØD environment and run this directory with MØD:

```bash
mod -f check_causality.py
```

The checker reads `gurobi_flows.json`, maps each reaction to the current MØD
derivation graph by educt/product SMILES, fixes exactly that hyperflow, and runs
`causality.RealisabilityQuery`.

Typical output is:

```text
solution 1: causally realisable: YES
solution 2: causally realisable: NO
...
```

`butadienNetwork.py` and `graph.py` are local helper copies for constructing
the same Butadiene MØD network.