{ "cells": [ { "cell_type": "markdown", "id": "dcddb3be-940b-4416-aec5-7159354c7cc0", "metadata": {}, "source": [ "

Markov Chain Monte Carlo for fun and profit

\n", "

๐ŸŽฒ โ›“๏ธ ๐Ÿ‘‰ ๐Ÿงช

" ] }, { "cell_type": "code", "execution_count": 2, "id": "6e51fe6c-a8b8-48ed-9e7f-70e18945e597", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "# This loads some custom styles for matplotlib\n", "import json, matplotlib\n", "with open(\"assets/matplotlibrc.json\") as f: matplotlib.rcParams.update(json.load(f))" ] }, { "cell_type": "markdown", "id": "6327bffe-5a27-4643-929e-a9d41672cd2c", "metadata": {}, "source": [ "## Packaging\n", "\n", "Now that we have some code, we're going to factor it out into a little python package, this has a few benefits:\n", " - foo\n", " - bah" ] }, { "cell_type": "code", "execution_count": null, "id": "58bef986-9d69-4ef6-8a64-9eaf29c3424e", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:jupyter3.9] *", "language": "python", "name": "conda-env-jupyter3.9-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.7" } }, "nbformat": 4, "nbformat_minor": 5 }