{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Activity Driven\n"
      ],
      "id": "d6db4934"
    },
    {
      "cell_type": "raw",
      "metadata": {
        "raw_mimetype": "text/restructuredtext"
      },
      "source": [
        ".. admonition:: Definition\n",
        "\n",
        "   Activity-driven temporal hypergraphs activate nodes with rates and create hyperedges at each time step.\n"
      ]
    },
    {
      "cell_type": "raw",
      "metadata": {
        "raw_mimetype": "text/restructuredtext"
      },
      "source": [
        ".. admonition:: You will learn\n",
        "\n",
        "   Generate temporal activity-driven hypergraphs and inspect their dynamics.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Overview\n",
        "\n",
        "- Generate a temporal activity-driven hypergraph from model parameters.\n",
        "- Inspect the resulting structure and basic statistics.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Setup\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {},
      "execution_count": null,
      "outputs": [],
      "source": [
        "import matplotlib as mpl\n",
        "\n",
        "mpl.rcParams.update({\n",
        "    \"figure.figsize\": (6, 4),\n",
        "    \"figure.dpi\": 120,\n",
        "    \"savefig.dpi\": 150,\n",
        "})\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [],
      "source": [
        "import sys\n",
        "from collections import Counter\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "sys.path.append(\"..\")\n",
        "from hypergraphx.communities.hy_mmsbm.model import HyMMSBM\n",
        "from hypergraphx.core.temporal_hypergraph import TemporalHypergraph\n",
        "from hypergraphx.generation.hy_mmsbm_sampling import HyMMSBMSampler\n",
        "from hypergraphx.linalg.linalg import *\n",
        "from hypergraphx.dynamics.randwalk import *\n",
        "from hypergraphx.generation.activity_driven import *\n",
        "import random"
      ],
      "id": "d8e66971"
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [],
      "source": [
        "def rnd_pwl(xmin, xmax, g, size=1):\n",
        "    r = np.random.random(size=size)\n",
        "    return (r*( xmax**(1.-g) - xmin**(1.-g) ) + xmin**(1.-g) )**(1./(1.-g))\n",
        "\n",
        "orders = [1,2,5]\n",
        "beta_act = 2.25\n",
        "eps = 0.001\n",
        "N = 100\n",
        "activities = rnd_pwl(eps, 1.0, beta_act, N) \n",
        "activities_per_order = {order: activities for order in orders}"
      ],
      "id": "113cd096"
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "HG = HOADmodel(N, activities_per_order, time=100)"
      ],
      "id": "df585bba"
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0.00122684, 0.0030016 , 0.00115932, 0.00232638, 0.00123835,\n",
              "       0.00146535, 0.01942779, 0.00155625, 0.00158992, 0.00117872,\n",
              "       0.00175645, 0.00120718, 0.00166537, 0.0011417 , 0.00111237,\n",
              "       0.00224994, 0.00124012, 0.00108481, 0.00169755, 0.00108913,\n",
              "       0.00258711, 0.0018887 , 0.00139368, 0.00207586, 0.00112409,\n",
              "       0.00202954, 0.00135815, 0.00280731, 0.00119374, 0.00239654,\n",
              "       0.00145397, 0.00196627, 0.00517187, 0.0019846 , 0.00245724,\n",
              "       0.00273485, 0.00279709, 0.00210364, 0.00404598, 0.00395033,\n",
              "       0.00138353, 0.00288704, 0.0037446 , 0.0023175 , 0.00162702,\n",
              "       0.00100739, 0.00183573, 0.00129032, 0.00234852, 0.00165624,\n",
              "       0.00150109, 0.00169891, 0.00111026, 0.0017071 , 0.0079329 ,\n",
              "       0.00201479, 0.00122535, 0.00246889, 0.00207982, 0.00255218,\n",
              "       0.00500995, 0.00154157, 0.00337264, 0.00143736, 0.00138248,\n",
              "       0.00147987, 0.00121604, 0.00161813, 0.00101783, 0.00245326,\n",
              "       0.00224526, 0.0012563 , 0.00116481, 0.00209183, 0.00128031,\n",
              "       0.00226608, 0.00180236, 0.00105422, 0.00232507, 0.00604522,\n",
              "       0.00198912, 0.00190691, 0.0033083 , 0.00222755, 0.00118875,\n",
              "       0.00102142, 0.00129231, 0.00102529, 0.01145748, 0.00485772,\n",
              "       0.00252576, 0.00178571, 0.00263117, 0.00146426, 0.00157329,\n",
              "       0.00484214, 0.00192983, 0.00132683, 0.0013145 , 0.00407684])"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "activities_per_order[1]"
      ],
      "id": "29e5254f"
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "hgx-installation",
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