test_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from hadml import utils, datamodules\n",
    "from hadml.datamodules.components.herwig import HerwigEventMultiHadronDataset\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading existing pids map:  data/Herwig/pids_to_ix.pkl\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing...\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "data = HerwigEventMultiHadronDataset(\n",
    "    \"data/Herwig\",\n",
    "    raw_file_list=[\"AllClusters_paper4.dat\"],\n",
    "    processed_file_name=\"herwig_multihadron_graph_obs_data_variation.pt\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Data(x=[13, 8], had_kin_rest_frame=[30, 4], had_kin=[30, 4], had_type_indices=[30, 1], cluster_labels=[30])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_clusters = []\n",
    "n_hadrons = []\n",
    "# n_hadrons_per_cluster = []\n",
    "had_kin = []\n",
    "for d in data:\n",
    "    n_clusters.append(len(d.x))\n",
    "    n_hadrons.append(len(d.had_kin))\n",
    "    had_kin.append(d.had_kin)\n",
    "had_kin = torch.cat(had_kin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(n_clusters)\n",
    "plt.xlabel(\"Number of clusters\")\n",
    "plt.ylabel(\"Number of events\")\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "plt.hist(n_hadrons)\n",
    "plt.xlabel(\"Number of hadrons\")\n",
    "plt.ylabel(\"Number of events\")\n",
    "plt.show()\n",
    "plt.clf()\n",
    "\n",
    "plt.hist(had_kin[:, 0])\n",
    "plt.xlabel(\"E [GeV]\")\n",
    "plt.ylabel(\"Number of hadrons\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}