Contextualiy

A library for handling sheaf-theoretic empirical models. The library currently supports the following computation:

1. Contextual fraction
2. Signalling fraction
3. CbD measure for binary cyclic measurement scenarios

Install

Method 1

pip install --upgrade git+https://github.com/kinianlo/contextuality.git

Method 2

1. Clone the repository:

git clone https://github.com/kinianlo/contextuality.git

2. Change directory into contextuality:

cd contextuality

3. Install the package using pip:

pip install . --upgrade

Note: The --upgrade flag here is to ensure that the newest version gets installed even if an older version is previously installed.

Examples

from contextuality.model import pr_model
pr_box = pr_model()
print(pr)
cf = pr_box.contextual_fraction()
print(f"The contetual fraction of the PR box is {cf:.4f}."}

One can also construct an empirical model from a table of distributions.

from contextuality.model import Model, chsh_scenario
scneario = chsh_scenario()
table = [[4/8, 0/8, 0/8, 4/8],
         [3/8, 1/8, 1/8, 3/8],
         [3/8, 1/8, 1/8, 3/8],
         [1/8, 3/8, 3/8, 1/8]]
bell_model = Model(scneario, table)
cf = bell_model.contextual_fraction()
print(f"The contetual fraction of the Bell model is {cf:.4f}.")