{
        "cells": [
                {
                        "cell_type": "markdown",
                        "id": "2d1d275c",
                        "metadata": {},
                        "source": [
                                "# Feynman\n",
                                "\n",
                                "https://github.com/GkAntonius/feynman\n",
                                "\n",
                                "http://gkantonius.github.io/feynman/index.html"
                        ]
                },
                {
                        "cell_type": "code",
                        "execution_count": 1,
                        "id": "3ee3cc39",
                        "metadata": {},
                        "outputs": [],
                        "source": [
                                "from pyfeyn2.feynmandiagram import FeynmanDiagram, Leg, Propagator, Vertex\n",
                                "from pyfeyn2.render.mpl.feynmanrender import FeynmanRender\n",
                                "from pyfeyn2.render.latex.dot import feynman_adjust_points\n",
                                "import pyfeyn2\n",
                                "print(pyfeyn2.__version__)"
                        ]
                },
                {
                        "cell_type": "code",
                        "execution_count": 2,
                        "id": "82d2f15d",
                        "metadata": {},
                        "outputs": [
                                {
                                        "data": {
                                                "image/png": 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\n",
                                                "text/plain": [
                                                        "<Figure size 1000x1000 with 1 Axes>"
                                                ]
                                        },
                                        "metadata": {},
                                        "output_type": "display_data"
                                }
                        ],
                        "source": [
                                "\n",
                                "v1 = Vertex(x=0.3,y=0.4)\n",
                                "v2 = Vertex(x=0.5,y=0.4)\n",
                                "\n",
                                "fd = FeynmanDiagram().add(\n",
                                "    v1,v2,\n",
                                "     Propagator(type=\"photon\",label=r\"$\\gamma$\").connect(v1, v2),\n",
                                "    Leg(type=\"fermion\",label=r\"$\\chi$\").with_target(v1).with_xy(0.2, 0.5).with_incoming(),\n",
                                "    Leg(type=\"fermion\").with_target(v1).with_xy(-0, 0.3).with_incoming(),\n",
                                "    Leg(type=\"fermion\").with_target(v2).with_xy(0.6, 0.5).with_outgoing(),\n",
                                "    Leg(type=\"fermion\").with_target(v2).with_xy(1.0, 0.3).with_outgoing()\n",
                                ")\n",
                                "FeynmanRender(fd).render(show=True)"
                        ]
                },
                {
                        "cell_type": "code",
                        "execution_count": null,
                        "id": "11c187c0",
                        "metadata": {},
                        "outputs": [],
                        "source": []
                },
                {
                        "cell_type": "code",
                        "execution_count": null,
                        "id": "4561118e",
                        "metadata": {},
                        "outputs": [],
                        "source": []
                }
        ],
        "metadata": {
                "kernelspec": {
                        "display_name": "Python 3 (ipykernel)",
                        "language": "python",
                        "name": "python3"
                },
                "language_info": {
                        "codemirror_mode": {
                                "name": "ipython",
                                "version": 3
                        },
                        "file_extension": ".py",
                        "mimetype": "text/x-python",
                        "name": "python",
                        "nbconvert_exporter": "python",
                        "pygments_lexer": "ipython3",
                        "version": "3.9.16"
                },
                "vscode": {
                        "interpreter": {
                                "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
                        }
                }
        },
        "nbformat": 4,
        "nbformat_minor": 5
}