fheteroticyukawas is a package designed to calculate the Yukawa couplings/quark masses of general line bundles on the tetraquadric. Necessary inputs are the complex and K"ahler moduli, line bundle indices, and closed representatives of the 1-form bundle cohomology.
Given that, it calculates, using NN models in TensorFlow, Calabi-Yau metrics, bundle metrics and harmonic representatives of the cohomology. It is based off cymetric, a Python package for learning of moduli-dependent Calabi-Yau metrics using neural networks implemented in TensorFlow.
A calculation can be replicated by navigating to heteroticyukawas/heteroticyukawas setting the moduli in generate_and_train_all_nnsHOLO.py, setting the settings in Model_13_Do.py, and then simply running:
source ~/cymetric/bin/activate
python Model_13_Do.py 0.0000 'phi'This calculates the matter fields Yukawa couplings for the second model in the paper, for the stabilised value of the K"ahler moduli, and for complex structure modulus
This guide assumes that you have a working Python 3 (preferably python 3.7 or above) installation (and Sage and Mathematica, if you want to use these features as well). So running python3 should work on your system. Moreover, it assumes that you have installed git. Note that both are standard on Mac and most Linux distributions. For Windows, you will typically have to install them and make sure that for example Python works correctly with Mathematica if you are planing on using the Mathematica interface.
If you want to use any existing python installation (note that we recommend using a virtual environment, see below), just run in a terminal
pip install git+https://github.com/kitft/heteroticyukawas.gitTo run the example notebooks, you need jupyter. You can install it with
pip install jupyter notebookCreate a new virtual environment in a terminal with
python3 -m venv ~/cymetricThen install with pip directly from github
source ~/cymetric/bin/activate
pip install --upgrade pip
pip install git+https://github.com/kitft/heteroticyukawas.git
pip install jupyter notebook
python -m ipykernel install --user --name=cymetricCreate a new environment with
conda create -n cymetric python=3.9Then install with pip directly from github
conda activate cymetric
pip install git+https://github.com/pythoncymetric/cymetric.gitWe summarize the mathematical conventions we use in this .pdf file.
We welcome contributions to the project. Those can be bug reports or new features, that you have or want to be implemented. Please read more here.