loading_and_sorting_the_data.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EkRGbgLvACBj"
      },
      "source": [
        "# Loading and Sorting the Pythia 8 Dataset\n",
        "\n",
        "Classifying Pythia8 Quark and Gluon Jets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lwBhoVa0ACBn"
      },
      "source": [
        "$\\bf\\text{Database Information: Pythia8 Quark and Gluon Jets for Energy Flow}$\n",
        "\n",
        "Two datasets of quark and gluon jets generated with Pythia 8, one with all kinematically realizable quark jets and one that excludes charm and bottom quark jets (at the level of the hard process). The one without c and b jets was originally used in Energy Flow Networks: Deep Sets for Particle Jets. Generation parameters are listed below:\n",
        "\n",
        "Pythia 8.226 (without bc jets), Pythia 8.235 (with bc jets), s√=14TeV\n",
        "Quarks from WeakBosonAndParton:qg2gmZq, gluons from WeakBosonAndParton:qqbar2gmZg with the Z decaying to neutrinos\n",
        "FastJet 3.3.0, anti-ki jets with R=0.4\n",
        "pjetT∈[500,550]GeV,|yjet|<1.7\n",
        "There are 20 files in each dataset, each in compressed NumPy format. Files including charm and bottom jets have 'withbc' in their filename. There are two arrays in each file\n",
        "\n",
        "X: (100000,M,4), exactly 50k quark and 50k gluon jets, randomly sorted, where M is the max multiplicity of the jets in that file (other jets have been padded with zero-particles), and the features of each particle are its pt, rapidity, azimuthal angle, and pdgid.\n",
        "\n",
        "\n",
        "y: (100000,), an array of labels for the jets where gluon is 0 and quark is 1.\n",
        "\n",
        "[1] P. T. Komiske, E. M. Metodiev, J. Thaler, Energy Flow Networks: Deep Sets for Particle Jets, JHEP 01 (2019) 121, arXiv:1810.05165."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rlgz-Hf0ACBp"
      },
      "source": [
        "## Preprocess Data\n",
        "\n",
        "1. We will utlize a package called particle in order to find the particle masses based on their pdgid's.\n",
        "2. We will take the given features $p_{T,\\alpha}^{(i)},y_{\\alpha}^{(i)},\\phi_{\\alpha}^{(i)}$ to create new physically realizable features including the\n",
        "\n",
        "$\\bf\\text{Transverse Mass Per Multiplicity}$ ($m_{\\alpha,T}^{(i)}$)\n",
        "$$m_{\\alpha,T}^{(i)} = \\sqrt{m_{\\alpha}^{(i)^2} + p_{\\alpha,T}^{(i)^2} }$$\n",
        "$\\bf\\text{Energy Per Multiplicity}$ ($E_{\\alpha}^{(i)}$)\n",
        "$$E_{\\alpha}^{(i)} = m_{\\alpha,T}^{(i)} \\text{cosh} y_{\\alpha}^{(i)}$$\n",
        "$\\bf\\text{Kinematic Momenta Components Per Multiplicity}$ ($\\vec{p}_\\alpha^{(i)} = (p_{x,\\alpha}^{(i)},p_{y,\\alpha}^{(i)},p_{z,\\alpha}^{(i)})$ )\n",
        "$$p_{x,\\alpha}^{(i)} = p_{T,\\alpha}^{(i)} \\text{cos} \\phi_{\\alpha}^{(i)}$$\n",
        "$$p_{y,\\alpha}^{(i)} = p_{T,\\alpha}^{(i)} \\text{sin} \\phi_{\\alpha}^{(i)}$$\n",
        "$$p_{z,\\alpha}^{(i)} = m_{T,\\alpha}^{(i)} \\text{sinh} y_{\\alpha}^{(i)}$$\n",
        "Only add the above masses, energy, and three momentum components to the node features.\n",
        "\n",
        "For additional checks, we also save\n",
        "\n",
        "$\\bf\\text{Kinematic Momenta Components Per Jet}$ ($\\vec{p}_\\alpha = (p_{x,\\alpha},p_{y,\\alpha},p_{z,\\alpha})$)\n",
        "$$\\vec{p}_\\alpha = \\sum_i \\vec{p}_\\alpha^{(i)}$$\n",
        "$\\bf\\text{Transverse Momenta Per Jet}$ ($p_{T,\\alpha} =\\sqrt{ |( \\sum_i p_{x,\\alpha}^{(i)},  \\sum_i p_{y,\\alpha}^{(i)})|^2}$)\n",
        "$$\\vec{p}_{T,\\alpha} = \\sqrt{\\left[  (\\sum_i \\vec{p}_{x,\\alpha}^{(i)})^2 +   (\\sum_i \\vec{p}_{y,\\alpha}^{(i)})^2 \\right] }$$\n",
        "$\\bf\\text{Mass Per Jet}$ ($m_{\\alpha}$)\n",
        "$$m_{\\alpha} = \\sqrt{ (E_\\alpha - \\vec{p}_{\\alpha}^{(i)} \\cdot \\vec{p}_{\\alpha}^{(i)} )^2}$$\n",
        "$\\bf\\text{Rapidity Per Jet}$ ($y_{\\alpha}$)\n",
        "$$y_{\\alpha} =\\frac{1}{2}\\ln \\left( \\frac{E_\\alpha + p_{z,\\alpha}}{E_\\alpha - p_{z,\\alpha}} \\right)$$\n",
        "2. Make Edges: Connect three nearest neighbors via $R = \\sqrt{\\Delta \\phi^2 + \\Delta y^2}$\n",
        "$$a_{ij} = \\sqrt{ \\left( \\phi_{\\alpha}^{(i)} - \\phi_{\\alpha}^{(j)} \\right)^2 + \\left( y_{\\alpha}^{(i)} - y_{\\alpha}^{(j)} \\right)^2 }$$\n",
        "Note: One can also add an edge connection based on the invariant mass between particles\n",
        "$$m_{ij} = \\left( E_{\\alpha}^{(i)} - E_{\\alpha}^{(j)} \\right)^2 - \\left( \\vec{p}_{\\alpha}^{(i)} - \\vec{p}_{\\alpha}^{(j)} \\right)^2$$\n",
        "although this was not used in our analysis."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "R6bEkdWHACBp"
      },
      "outputs": [],
      "source": [
        "# !pip install particle\n",
        "# !pip install torch_geometric\n",
        "# !pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-1.13.0+cpu.html\n",
        "# pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-1.13.0+cpu.html"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "tags": [],
        "id": "Q1GR-XS5ACBq",
        "outputId": "70e687ae-7361-4900-91e2-ce03567ba67d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Device:  cpu\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<torch._C.Generator at 0x11d02cc10>"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import numpy as np\n",
        "from numpy.random import default_rng\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from tqdm import tqdm\n",
        "from time import time\n",
        "import os\n",
        "import copy\n",
        "from particle import Particle\n",
        "from sklearn.model_selection import train_test_split\n",
        "from gnn_egnn.preprocess import *\n",
        "\n",
        "import torch\n",
        "from torch import nn as nn\n",
        "#from torch.utils.data import TensorDataset, DataLoader, Dataset\n",
        "from torchvision import datasets\n",
        "from torchvision.transforms import ToTensor\n",
        "from torch.nn import Linear\n",
        "import torch.nn.functional as F\n",
        "\n",
        "from torch_geometric.data import Data\n",
        "from torch_geometric.loader import DataLoader\n",
        "from torch_geometric.data import Dataset, InMemoryDataset, download_url\n",
        "from torch_geometric.nn import GCNConv\n",
        "from torch_geometric.nn import GATConv\n",
        "from torch_geometric.nn import Sequential\n",
        "from torch_geometric import nn\n",
        "from torch_geometric.nn import global_mean_pool\n",
        "\n",
        "\n",
        "torch.set_default_dtype(torch.float64)\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "print('Device: ',device)\n",
        "\n",
        "np.random.seed(0)\n",
        "torch.manual_seed(0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "cylqjfIxACBr"
      },
      "outputs": [],
      "source": [
        "path = 'Data_wo_bc/pythia8/'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HJRe372OACBr"
      },
      "source": [
        "Run the code below to create two numpy arrays for the features X and labels Y out of the 20 data files without bottom and charm quarks in the Pythia 8 dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "-W1OD4_kACBs"
      },
      "outputs": [],
      "source": [
        "# files = []\n",
        "# for i in range(20):\n",
        "#     if i<10:\n",
        "#         files.append(np.load(path+f'QG_jets_0{i}.npz', allow_pickle=True))\n",
        "#     else:\n",
        "#         files.append(np.load(path+f'QG_jets_{str(i)[0]+str(i)[1]}.npz', allow_pickle=True))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "oZTnzkmDACBs"
      },
      "outputs": [],
      "source": [
        "# len(files)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "iBOeQ90DACBs"
      },
      "outputs": [],
      "source": [
        "# M=np.zeros(len(files),)\n",
        "# for i,file in enumerate(files):\n",
        "#     lst = file.files\n",
        "#     for j,item in enumerate(lst):\n",
        "#         if j==0:\n",
        "#             M[i] = file[item][0,:,0].shape[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "KXOatAhTACBs"
      },
      "outputs": [],
      "source": [
        "# max_multiplicity = int(M.max())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "81mJ6AluACBt"
      },
      "outputs": [],
      "source": [
        "# #names = []\n",
        "# #data = []\n",
        "# for i,file in enumerate(files):\n",
        "#     lst = file.files\n",
        "#     print(f'File: {i}')\n",
        "#     for j,item in enumerate(lst):\n",
        "#         #names.append(item)\n",
        "#         #data.append(file[item])\n",
        "#         if j==0:\n",
        "#             if i==0:\n",
        "#                 x_data = np.pad(array=file[item],pad_width=((0,0),(0,int(max_multiplicity-file[item].shape[1])),(0,0)), mode='constant', constant_values=((None,None),(None,0.),(None,None)))\n",
        "#                 print('X Data Shape: ', x_data.shape)\n",
        "#             else:\n",
        "#                 x_new = np.pad(array=file[item],pad_width=((0,0),(0,int(max_multiplicity-file[item].shape[1])),(0,0)), mode='constant', constant_values=((None,None),(None,0.),(None,None)))\n",
        "#                 print('X Data Shape: ', x_new.shape)\n",
        "#                 x_data = np.vstack([x_data,x_new])\n",
        "#         elif j==1:\n",
        "#             if i==0:\n",
        "#                 y_data = file[item]\n",
        "#                 print('Y Data Shape: ', y_data.shape)\n",
        "#             else:\n",
        "#                 y_data = np.vstack([y_data,file[item]])\n",
        "#                 print('Y Data Shape: ', y_data.shape)\n",
        "#             #names.append(item)\n",
        "#             #print(files[item].shape)#.item()['0'].shape)\n",
        "#             #data.append(files[item])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "L_kE8gx5ACBt"
      },
      "outputs": [],
      "source": [
        "# np.save(path+'appended_data.npy',x_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "kMEm77XgACBt"
      },
      "outputs": [],
      "source": [
        "# np.save(path+'appended_labels.npy',y_data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vqgc1lhRACBt"
      },
      "source": [
        "Of course, I have already done this, therefore, they can be loaded in as below."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "kfQqOPXeACBu"
      },
      "outputs": [],
      "source": [
        "x_data = np.load(path+'appended_data.npy')\n",
        "y_data = np.load(path+'appended_labels.npy').reshape(x_data.shape[0],)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "KgTxqz5MACBu",
        "outputId": "fb9cd897-88bf-4703-a50d-bc66272b12bf"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(2000000, 148, 4)"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x_data[:,:,:].shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "o442H67lACBu",
        "outputId": "f82a3af1-5e52-4dbf-e740-6a6fd811b622"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(2000000,)"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_data.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zULautU_ACBv"
      },
      "source": [
        "## Data Checks"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "6A3EyxhmACBw",
        "outputId": "b5a61590-2ba9-4507-d411-42eb3f2a238c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([  22.,   22., -211., -321., -211.,   22.,   22.,  211.,  321.,\n",
              "       -211.,   22.,   22.,   22.,   22.,   22.,   22.,   22.,   22.,\n",
              "          0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,\n",
              "          0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,\n",
              "          0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,\n",
              "          0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,\n",
              "          0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,\n",
              "          0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,\n",
              "          0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,\n",
              "          0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,\n",
              "          0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,\n",
              "          0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,\n",
              "          0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,\n",
              "          0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,\n",
              "          0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,\n",
              "          0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,\n",
              "          0.,    0.,    0.,    0.])"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x_data[0,:,3]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4zAENvDZACBw"
      },
      "source": [
        "Look at a Particular Jet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "y-QewAChACBx"
      },
      "outputs": [],
      "source": [
        "i=13"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "97ENiGcfACBx",
        "outputId": "5746f66a-8e97-47c9-bd47-ec61ad392fdd"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "49"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "(x_data[i,:,0]>0).sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "GsPqorrlACBx",
        "outputId": "b4bf17bf-ca35-4ec9-aa14-9fd486a3aece"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'particle number')"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "i=13\n",
        "id2 = 50\n",
        "plt.scatter(np.linspace(0,id2,id2),x_data[i,:id2,0])\n",
        "plt.ylabel('$p_T$')\n",
        "plt.title(f'Jet {i}')\n",
        "plt.xlabel('particle number')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "5I0lLuHnACBx",
        "outputId": "dbc5c8e1-877e-474c-b857-1dd7f21469e7"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "id2-=1\n",
        "plt.scatter(x_data[13,:id2,1],x_data[13,:id2,2],c=np.log10(x_data[13,:id2,0]),s=50,marker='^',cmap='rainbow')\n",
        "plt.title(f'Jet',fontsize=25)\n",
        "plt.ylabel('$\\phi$',fontsize=25)\n",
        "plt.xlabel('$y$',fontsize=25)\n",
        "plt.xticks(fontsize=16)\n",
        "plt.yticks(fontsize=16)\n",
        "cbar = plt.colorbar()\n",
        "cbar.set_label(label='$p_T$',size=25)\n",
        "cbar.ax.tick_params(labelsize=12)\n",
        "plt.savefig('jet_example.png',bbox_inches='tight')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "3_s770RTACBx",
        "outputId": "3f3abde5-fea2-4ae4-b6cf-a1e2489f153f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "49"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "np.sum(x_data[13,:,0]>1e-10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tTfjIr9yACBy"
      },
      "source": [
        "Out of all jets, find the minimum number of particles measured amongst them"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "8LN9Tu9IACBy",
        "outputId": "17996fd0-711b-47f5-e256-8f58d3b0cb5d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "np.min(np.sum(x_data[:,:,0]>1e-10,axis=1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "lZP9rTFIACBy",
        "outputId": "48c498bd-2d4c-4aa5-a695-f4ef757e73f0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "70617"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "np.argmin(np.sum(x_data[:,:,0]>1e-10,axis=1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "xa6UxY6zACBy"
      },
      "outputs": [],
      "source": [
        "id3 = 70617"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "PfKxdMvdACBy",
        "outputId": "6582fa82-6ece-4adb-9174-14e00e414f60"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([22.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
              "        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
              "        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
              "        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
              "        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
              "        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
              "        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
              "        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
              "        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
              "        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
              "        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
              "        0.,  0.,  0.,  0.,  0.])"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x_data[id3,:,3]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "oBsHTdhzACBz",
        "outputId": "59d31ad5-f246-483a-b544-744a58b31fba"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.hist(np.sum(x_data[:,:,0]>1e-10,axis=1),bins=50)\n",
        "plt.yscale('log')\n",
        "plt.ylabel('Count')\n",
        "plt.xlabel('Number of particles in a jet')\n",
        "plt.title('Distribution of the number of particles contained in each jet')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YUaL24yRACBz"
      },
      "source": [
        "How many jets contain exactly 25 particles"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "EXVJJb-LACBz",
        "outputId": "6bb98d1d-be77-4946-df10-1ceb893a42dd"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "40166"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "(np.sum(x_data[:,:,0]>1e-10,axis=1)==25).sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "GRCA4mjhACBz"
      },
      "outputs": [],
      "source": [
        "pt = x_data[:,:,0]\n",
        "phi= x_data[:,:,2]\n",
        "px = pt*np.cos(phi)\n",
        "py = pt*np.sin(phi)\n",
        "px_tot = np.sum(px,axis=1)\n",
        "py_tot = np.sum(py,axis=1)\n",
        "sum_sq = px_tot**2 + py_tot**2\n",
        "pt_per_jet = np.sqrt(sum_sq)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "ZkbG_HpOACBz",
        "outputId": "7a417e32-6667-4245-dc54-c15385dd1e3a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig = plt.figure(figsize=(8,6))\n",
        "plt.hist(pt_per_jet, color='dodgerblue', alpha=0.8, linewidth=1.2)\n",
        "# plt.title('Distribution of total transverse momentum per jet',fontsize=25)\n",
        "# plt.ylabel('Count',fontsize=25)\n",
        "plt.xlabel(r'$p_{T,\\alpha}$ (GeV)', fontsize=25)\n",
        "plt.xticks(fontsize=16)\n",
        "plt.yticks(fontsize=16)\n",
        "plt.savefig('pt_distribution.png',bbox_inches='tight')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "ASH42UbDACB0"
      },
      "outputs": [],
      "source": [
        "inds25 = (np.sum(x_data[:,:,0]>1e-10,axis=1)==25)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "PhcdGidQACB0"
      },
      "outputs": [],
      "source": [
        "x25 = x_data[inds25,:25,:]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "M4zFUY94ACB0"
      },
      "outputs": [],
      "source": [
        "x25_sorted = np.zeros((x25.shape[0],x25.shape[1]))\n",
        "for i in range(40166):\n",
        "    x25_sorted[i,:] = np.sort(x25[i,:,0])[::-1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "6xYUWEX2ACB7",
        "outputId": "b57b5aa6-81c9-4855-ae34-c93b9b0e08c5"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.hist(np.sum(x25_sorted[:,:15],axis=1))\n",
        "plt.title('Distribution of Total momentum amongst the 15 largest\\n transverse momentum particles for Jets with exactly 25 particles')\n",
        "plt.ylabel('Count')\n",
        "plt.xlabel('Total $p_T$ amongst 15 particles with the largest $p_T$')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zGZ40AjsACB7"
      },
      "source": [
        "Because this is not representative of the entire dataset moemntum distribution, reduce to 10 particles and take all of them"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "UzpOiG7TACB7"
      },
      "outputs": [],
      "source": [
        "inds10 = (np.sum(x_data[:,:,0]>1e-10,axis=1)==10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "h6a1vXt0ACB7"
      },
      "outputs": [],
      "source": [
        "x10 = x_data[inds10,:10,:]\n",
        "y10 = y_data[inds10]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "Egwhumt4ACB7",
        "outputId": "d4ebbda7-60ea-4646-c8a8-1adb09150866"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1691"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y10.shape[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "ofGmZyqaACB8",
        "outputId": "d9adc568-6373-432a-d7a2-dbd106543b04"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1639"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "(y10==1).sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "Tq3E7NhaACB8"
      },
      "outputs": [],
      "source": [
        "i=0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "ovR5AYPyACB8",
        "outputId": "ee750b18-7453-4889-a85c-f95844aaaf0a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([2.45459550e-01, 4.96069327e+00, 3.48907409e+00, 1.11288630e+00,\n",
              "       1.94903155e-01, 8.23072425e-01, 2.68559874e+00, 2.50188856e-01,\n",
              "       4.92518385e+02, 2.26165834e-02])"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x10[i,:,0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "tyG15o3EACB8",
        "outputId": "2e0c91e9-c255-49cc-ea7e-d19c4250dc2b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([4.92518385e+02, 4.96069327e+00, 3.48907409e+00, 2.68559874e+00,\n",
              "       1.11288630e+00, 8.23072425e-01, 2.50188856e-01, 2.45459550e-01,\n",
              "       1.94903155e-01, 2.26165834e-02])"
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "np.sort(x10[i,:,0])[::-1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "r2ZC1G8qACB9"
      },
      "outputs": [],
      "source": [
        "sort_nodes = np.argsort(x10[i,:,0])[::-1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "5amNTqRnACB9"
      },
      "outputs": [],
      "source": [
        "x10i_sorted = x10[i,sort_nodes,:]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "VLfQdFOPACB9",
        "outputId": "1395837a-d96a-4060-d3fb-396054ae870c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([4.92518385e+02, 4.96069327e+00, 3.48907409e+00, 2.68559874e+00,\n",
              "       1.11288630e+00, 8.23072425e-01, 2.50188856e-01, 2.45459550e-01,\n",
              "       1.94903155e-01, 2.26165834e-02])"
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x10i_sorted[:,0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "6j_A5PorACB9"
      },
      "outputs": [],
      "source": [
        "x10_sorted = np.zeros((x10.shape[0],x10.shape[1]))\n",
        "for i in range(x10.shape[0]):\n",
        "    x10_sorted[i,:] = np.sort(x10[i,:,0])[::-1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "gxkv-6sTACB9",
        "outputId": "f74acae8-fb2e-499a-e727-114f02493c11"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.hist(np.sum(x10_sorted[:,:10],axis=1))\n",
        "plt.title('Distribution of $p_T$ for the Sorted Jets with Only 10 Particles')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "8thmZna9ACB-",
        "outputId": "437d93bf-d5cd-402b-9a98-225d02753e34"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "id4=10\n",
        "plt.scatter(x_data[0,:id4,1],x_data[0,:id4,2],c=np.log10(x_data[0,:id4,0]),cmap='rainbow')\n",
        "plt.colorbar()\n",
        "plt.title(f'Graph Example for Jet {i}')\n",
        "plt.ylabel('$\\phi$')\n",
        "plt.xlabel('$\\eta$')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "-QSxXFNGACB-",
        "outputId": "084d650b-be2d-4fa0-dfe8-b37393ea3ed8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.9692489651094027"
            ]
          },
          "execution_count": 45,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "(y10==1).sum()/y10.shape[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tU1Uaz_BACB-"
      },
      "source": [
        "As shown above, although the reduction to 10 particle jets seems nice, they are predominantly (97%) classified as quarks (y=1)\n",
        "\n",
        "For this reason, take largest 10 momentum particles for all jets with $\\geq 10$ particles. This will be representative of the entire dataset with $\\geq 10$ particles."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "xoz4tfe8ACB-"
      },
      "outputs": [],
      "source": [
        "inds10 = np.logical_and(np.sum(x_data[:,:,0]>1e-10,axis=1)>=10,np.sum(x_data[:,:,0]>1e-10,axis=1)<=120)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "fWJ69q7TACB_"
      },
      "outputs": [],
      "source": [
        "x10 = x_data[inds10,:,:]\n",
        "y10 = y_data[inds10]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "_E4N6Ze6ACB_",
        "outputId": "4f49847b-b778-4e08-922f-1c638ef2a809"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1997445"
            ]
          },
          "execution_count": 48,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y10.shape[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "ovsnTqvrACCB",
        "outputId": "c3d3a921-c319-4182-ed12-71a4cc9ee7b2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "997805"
            ]
          },
          "execution_count": 49,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "(y10==1).sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pWHBdaJZACCB"
      },
      "source": [
        "Below is the array we will use containing jets with at least 10 partciles sorted by their transverse momentum (largest to smallest)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "bSQ7PSb9ACCB"
      },
      "outputs": [],
      "source": [
        "x10_all_features_correctly_sorted = np.zeros((x10.shape[0],10,x10.shape[2]))\n",
        "for i in range(x10.shape[0]):\n",
        "    sort_nodes = np.argsort(x10[i,:,0])[::-1]\n",
        "    x10_all_features_correctly_sorted[i,:,:] = x10[i,sort_nodes[:10],:]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UlXgU9UsACCC"
      },
      "source": [
        "Choose a small fraction of jets to check the preprocessing steps"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "Btu2hCMWACCC"
      },
      "outputs": [],
      "source": [
        "Ntry = 1000"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "Nh9y4BT0ACCC",
        "outputId": "46b31b8c-6d18-4e47-81f6-6ab29d859f46"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.colorbar.Colorbar at 0x12ff12090>"
            ]
          },
          "execution_count": 52,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 500x300 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(5,3))\n",
        "plt.scatter(x_data[:Ntry,0,0],x_data[:Ntry,0,1],s=1,c=y_data[:Ntry],cmap='RdBu')#,vmin=0.,vmax=1.)\n",
        "# plt.scatter(-x_data[:Ntry,0,0],x_data[:Ntry,0,1],s=1,c=y_data[:Ntry],cmap='RdBu')#,vmin=0.,vmax=1.)\n",
        "plt.xlabel('$p_T$')\n",
        "plt.ylabel('$\\eta$')\n",
        "plt.xlim(-5,5)\n",
        "plt.title('')\n",
        "plt.colorbar(label='Class')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "a4XJ1SjkACCC"
      },
      "outputs": [],
      "source": [
        "unique_particles = np.unique(x_data[:Ntry,:,3])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "oBOHB3cZACCD",
        "outputId": "acf4aa79-e26a-4883-deb0-bff38e066392"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([-2212., -2112.,  -321.,  -211.,   -13.,   -11.,     0.,    11.,\n",
              "          13.,    22.,   130.,   211.,   321.,  2112.,  2212.])"
            ]
          },
          "execution_count": 54,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "unique_particles"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "_3aepFkKACCD",
        "outputId": "84a99a7a-52af-4a92-c75e-01ed7bee9502"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0 -2212.0 p~ 0.93827208816\n",
            "1 -2112.0 n~ 0.9395654204999999\n",
            "2 -321.0 K- 0.49367700000000003\n",
            "3 -211.0 pi- 0.13957039000000002\n",
            "4 -13.0 mu+ 0.1056583755\n",
            "5 -11.0 e+ 0.0005109989499999999\n",
            "7 11.0 e- 0.0005109989499999999\n",
            "8 13.0 mu- 0.1056583755\n",
            "9 22.0 gamma 0.0\n",
            "10 130.0 K(L)0 0.49761099999999997\n",
            "11 211.0 pi+ 0.13957039000000002\n",
            "12 321.0 K+ 0.49367700000000003\n",
            "13 2112.0 n 0.9395654204999999\n",
            "14 2212.0 p 0.93827208816\n"
          ]
        }
      ],
      "source": [
        "for i,particle in enumerate(unique_particles):\n",
        "    if particle!=0:\n",
        "        symbol =Particle.from_pdgid(particle).name\n",
        "        mass = Particle.from_pdgid(particle).mass/1000\n",
        "        print(i,particle,symbol,mass)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "h6PAu8k3ACCD"
      },
      "outputs": [],
      "source": [
        "#np.save('masses.npy',masses)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "biKxE5sNACCD"
      },
      "outputs": [],
      "source": [
        "# masses = torch.tensor(np.load('masses.npy'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "trh3uciOACCE",
        "outputId": "428a30ea-208a-48e1-abf6-093c7a3f47db"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--- Finding All Unique Particles ---\n",
            "\n",
            "--- Inserting Masses ---\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "15it [00:00, 730.10it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "--- Calculating Momenta and Energies ---\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "print('--- Finding All Unique Particles ---')\n",
        "unique_particles = np.unique(x_data[:Ntry,:,3])\n",
        "print()\n",
        "print('--- Inserting Masses ---')\n",
        "masses = np.zeros((x_data[:Ntry].shape[0],x_data[:Ntry].shape[1]))\n",
        "for i,particle in tqdm(enumerate(unique_particles)):\n",
        "    if particle!=0:\n",
        "        mass = Particle.from_pdgid(particle).mass/1000\n",
        "        inds = np.where(particle==x_data[:Ntry,:,3])\n",
        "        masses[inds]=mass # GeV\n",
        "print()\n",
        "print('--- Calculating Momenta and Energies ---')\n",
        "#theta = torch.arctan(torch.exp(-X[:,:,1]))*2 # polar angle\n",
        "pt        = x_data[:Ntry,:,0]     # transverse momentum\n",
        "rapidity  = x_data[:Ntry,:,1]     # rapidity\n",
        "phi       = x_data[:Ntry,:,2]     # azimuthal angle\n",
        "\n",
        "mt        = np.sqrt(pt**2+masses**2) # Transverse mass\n",
        "energy    = mt*np.cosh(rapidity) # Energy per multiplicity bin\n",
        "e_per_jet = energy.sum(axis=1)  # total energy per jet summed across multiplicity bins\n",
        "\n",
        "px = pt*np.cos(phi)  # momentum in x\n",
        "py = pt*np.sin(phi)  # momentum in y\n",
        "pz = mt*np.sinh(rapidity)  # momentum in z\n",
        "\n",
        "# three momentum\n",
        "p = np.stack(( px[:,:,None],\n",
        "               py[:,:,None],\n",
        "               pz[:,:,None]), axis=2 )\n",
        "\n",
        "p_per_jet        = np.sum(p,axis=1)  # total componet momentum per jet\n",
        "#pt_per_Mbin      = np.sqrt(np.sum(p_per_jet[:,:2]**2, axis=1))  # transverse momentum per jet\n",
        "#mass_per_jet     = np.sqrt(e_per_jet**2-np.sum(p_per_jet**2,axis=1)) # mass per jet\n",
        "#rapidity_per_jet = np.log( (e_per_jet+p_per_jet[:,2])/(e_per_jet-p_per_jet[:,2]) )/2  # rapidity per jet from analytical formula\n",
        "#end_multiplicity_indx_per_jet = np.sum(pt!=0,axis=1).int() # see where the jet (graph) ends"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v87VssijACCE"
      },
      "source": [
        "NOTE: The plots below are only a test for 1000 of the two million total jets."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "236OiMm6ACCE",
        "outputId": "ef20d5e2-1bcf-47d1-9fcd-0d55384dc08a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(8,6))\n",
        "plt.hist(e_per_jet, color='b', alpha=0.3, bins=80, label='Energy (GeV)')\n",
        "plt.hist(np.sqrt(np.sum((p_per_jet)**2,axis=1)), color='r', alpha=0.3, bins=100,label='Momentum (GeV)')\n",
        "# plt.title('Distributions of Total Energy and Momenta Per Jet')\n",
        "plt.xlabel('$E$ and $p$ (GeV)',fontsize=25)\n",
        "# plt.ylabel('Count')\n",
        "plt.legend(fontsize=20)\n",
        "plt.xticks(fontsize=12)\n",
        "plt.yticks(fontsize=12)\n",
        "plt.savefig('Ep_distributions.png',bbox_inches='tight')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "HyIu8mJ8ACCE",
        "outputId": "ee32d5f7-06c5-4714-dd1f-b478998dcf59"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(8,6))\n",
        "plt.hist(np.sqrt(np.sum((p_per_jet)**2,axis=1)),color='r', alpha=0.8, bins=40)#,label='Momentum (GeV)')\n",
        "plt.xlabel(r'$p_{\\alpha}$ (GeV)',fontsize=25)\n",
        "# plt.legend(fontsize=20)\n",
        "plt.xticks(fontsize=16)\n",
        "plt.yticks(fontsize=16)\n",
        "plt.savefig('p_distribution.png',bbox_inches='tight')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "OuOhbhupACCF",
        "outputId": "95701f42-1d22-4f96-9441-7158e6bc01c3"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(8,6))\n",
        "plt.hist(e_per_jet, color='b', alpha=0.8, bins=40)#, label='Energy (GeV)')\n",
        "plt.xlabel(r'$E_\\alpha$ (GeV)',fontsize=25)\n",
        "# plt.legend(fontsize=20)\n",
        "plt.xticks(fontsize=16)\n",
        "plt.yticks(fontsize=16)\n",
        "plt.savefig('e_distribution.png',bbox_inches='tight')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J59HNHETACCF"
      },
      "source": [
        "## Preprocess data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "uC7LxrQoACCF"
      },
      "outputs": [],
      "source": [
        "# x_data_proc, y_data_proc, edge_tensor, edge_indx_tensor, edge_attr_matrix, graph_help = preprocess_general(x_data[indices],y_data[indices]) #,masses[:N])\n",
        "# x_data_proc, y_data_proc, edge_tensor, edge_indx_tensor, edge_attr_matrix, graph_help = preprocess_fixed_nodes(x10_all_features_correctly_sorted[indices],y10[indices],nodes_per_graph = nodes_per_graph) #,masses[:N])\n",
        "# N = x_data_proc.shape[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "PlNrxIhAACCF"
      },
      "outputs": [],
      "source": [
        "N=12500\n",
        "rng = default_rng(seed=0)\n",
        "indices = rng.choice(x10.shape[0], size=N, replace=False)\n",
        "nodes_per_graph = 10\n",
        "x10_sorted_12500 = x10_all_features_correctly_sorted[indices]\n",
        "y10_sorted_12500 = y10[indices]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "q_nmn1_CACCF"
      },
      "outputs": [],
      "source": [
        "# np.save('x10_sorted_12500.npy',x10_all_features_correctly_sorted[indices])\n",
        "# np.save('y10_sorted_12500.npy',y10[indices])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "bxxsDssIACCG"
      },
      "outputs": [],
      "source": [
        "# N=12500\n",
        "# x10_sorted_12500 = np.load('x10_sorted_12500.npy')\n",
        "# y10_sorted_12500 = np.load('y10_sorted_12500.npy')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "nhePpHzSACCG",
        "outputId": "5624dec6-fb83-4e46-83b6-440ddd4d0356"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--- Finding All Unique Particles ---\n",
            "\n",
            "--- Inserting Masses ---\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "14it [00:00, 378.55it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "--- Calculating Momenta and Energies ---\n",
            "\n",
            "--- Calculating Edge Tensors ---\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████████████████████████████████████████████████████████████████| 12500/12500 [00:13<00:00, 939.49it/s]\n"
          ]
        }
      ],
      "source": [
        "nodes_per_graph = 10\n",
        "x_data_proc, y_data_proc, edge_tensor, edge_indx_tensor, edge_attr_matrix, graph_help = preprocess_fixed_nodes(x10_sorted_12500,y10_sorted_12500,nodes_per_graph = nodes_per_graph) #,masses[:N])\n",
        "N = x_data_proc.shape[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "c_bXFmFQACCG"
      },
      "outputs": [],
      "source": [
        "# N=100000\n",
        "# rng = default_rng(seed=0)\n",
        "# indices = rng.choice(x_data.shape[0], size=N, replace=False)\n",
        "# nodes_per_graph = 10\n",
        "# # x_data_proc, y_data_proc, edge_tensor, edge_indx_tensor, edge_attr_matrix, graph_help = preprocess_general(x_data[indices],y_data[indices]) #,masses[:N])\n",
        "# x_data_proc, y_data_proc, edge_tensor, edge_indx_tensor, edge_attr_matrix, graph_help = preprocess_fixed_nodes(x_data[indices],y_data[indices],nodes_per_graph = nodes_per_graph) #,masses[:N])\n",
        "# N = x_data_proc.shape[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "90IAKQQVACCG",
        "outputId": "001812fa-7593-4645-ef02-834f7d6d9cb9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "----- Check for the correctly sized tensors -----\n",
            "\n",
            "Jet Data: torch.Size([12500, 10, 8])\n",
            "Classification: torch.Size([12500])\n",
            "Full Edge Information: torch.Size([12500, 10, 10])\n",
            "Edge Index Information: torch.Size([12500, 2, 90])\n",
            "Edge Attribute Information: torch.Size([12500, 90, 1])\n",
            "Graph Structure Information: torch.Size([12500, 4])\n",
            "\n",
            "\n",
            "----- Check that each tensor is normalized -----\n",
            "\n",
            "Jet Data max: 1.0\n",
            "Classification Max: 1\n",
            "Edge Length Max: 1.0\n"
          ]
        }
      ],
      "source": [
        "print('----- Check for the correctly sized tensors -----')\n",
        "print()\n",
        "print(f'Jet Data: {x_data_proc.shape}')\n",
        "print(f'Classification: {y_data_proc.shape}')\n",
        "print(f'Full Edge Information: {edge_tensor.shape}')\n",
        "print(f'Edge Index Information: {edge_indx_tensor.shape}')\n",
        "print(f'Edge Attribute Information: {edge_attr_matrix.shape}')\n",
        "print(f'Graph Structure Information: {graph_help.shape}')\n",
        "print('\\n')\n",
        "print('----- Check that each tensor is normalized -----')\n",
        "print()\n",
        "print(f'Jet Data max: {torch.max(x_data_proc)}')\n",
        "print(f'Classification Max: {torch.max(y_data_proc)}')\n",
        "print(f'Edge Length Max: {torch.max(edge_tensor)}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "daArD0D3ACCG",
        "outputId": "f98eb4cb-c299-4841-e341-b6f11dbc4fc8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "----- Check the structure of the dataset -----\n",
            "\n",
            "Number of graphs (jets): 12500\n",
            "Number of features per node (p_T(M),y,phi,m,E,px,py,pz): 8\n",
            "Number of classes (2: Quarks or Gluons): 2\n",
            "\n",
            "----- Check the structure of one graph -----\n",
            "\n",
            "Number of nodes (particle IDs): 10\n",
            "Number of edges: 90.0\n",
            "Average node degree: 9.00\n",
            "Is undirected: True\n"
          ]
        }
      ],
      "source": [
        "print('----- Check the structure of the dataset -----')\n",
        "print()\n",
        "print(f'Number of graphs (jets): {x_data_proc.shape[0]}')\n",
        "print(r'Number of features per node (p_T(M),y,phi,m,E,px,py,pz): '+f'{x_data_proc.shape[2]}')\n",
        "print(f'Number of classes (2: Quarks or Gluons): {np.unique(y_data_proc).shape[0]}')\n",
        "print()\n",
        "print('----- Check the structure of one graph -----')\n",
        "print()\n",
        "i=0\n",
        "number_of_edges = graph_help[i,3]\n",
        "# Gather some statistics about the first graph.\n",
        "print(f'Number of nodes (particle IDs): {x_data_proc[i,:,0].shape[0]}')\n",
        "print(f'Number of edges: {number_of_edges}')\n",
        "print(f'Average node degree: {number_of_edges / x_data_proc[i,:,0].shape[0]:.2f}')\n",
        "print(f'Is undirected: {True}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "d075W9VsACCH"
      },
      "outputs": [],
      "source": [
        "# Create a dataset class\n",
        "class Create_Dataset(Dataset):\n",
        "    def __init__(self, edge_index, x, edge_attr, y, graph_info, transform=None):\n",
        "        self.edge_index = edge_index\n",
        "        self.x          = x\n",
        "        self.edge_attr  = edge_attr\n",
        "        self.y          = y\n",
        "        self.graph_info = graph_info\n",
        "        self.transform  = transform\n",
        "\n",
        "    def __len__(self):\n",
        "        return self.y.shape[0]\n",
        "\n",
        "    # Could also just define an x,y,z then pass these variables\n",
        "    # in the training functions over the loop for batch (actually think that way is easier to read)\n",
        "    def __getitem__(self, index):\n",
        "        sample = {'edge_index': self.edge_index[index,:,:self.graph_info[index,3]],\n",
        "                'x': self.x[index,:int(self.graph_info[index,2]),:],\n",
        "                'edge_attr': self.edge_attr[index,:self.graph_info[index,3]],\n",
        "                'y': self.y[index] }\n",
        "\n",
        "        if self.transform is not None:\n",
        "            sample = self.transform(sample)\n",
        "        return sample"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "4XqlYPzhACCI",
        "outputId": "d76a73c7-91c4-4ba9-f6f2-e27df27ae95b"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|█████████████████████████████████████████████████████████████████████| 12500/12500 [00:02<00:00, 4227.50it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of training graphs: 10000\n",
            "Number of validation graphs: 1250\n",
            "Number of test graphs: 1250\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "train_index   = int(N*0.8)#int(N*0.7)\n",
        "val_index    = int(N*0.9)\n",
        "batch_size = 64\n",
        "# Create Datasets for Torch for the training and testing pahses\n",
        "dataset = []\n",
        "for i in tqdm(range(N)):\n",
        "    dataset.append(Data(edge_index=edge_indx_tensor[i,:,:],\n",
        "                        x=x_data_proc[i,:,:],\n",
        "                        edge_attr=edge_attr_matrix[i,:],\n",
        "                        y=y_data_proc[i]))\n",
        "\n",
        "training_data = dataset[:train_index]\n",
        "val_data      = dataset[train_index:val_index]\n",
        "test_data     = dataset[val_index:]\n",
        "\n",
        "# Create data loaders\n",
        "train_dataloader = DataLoader(training_data,\n",
        "                              batch_size = batch_size,\n",
        "                              shuffle = True,\n",
        "                              num_workers = 0)\n",
        "\n",
        "val_dataloader  = DataLoader(val_data,\n",
        "                              batch_size = batch_size,\n",
        "                              shuffle = False,\n",
        "                              num_workers = 0)\n",
        "\n",
        "test_dataloader  = DataLoader(test_data,\n",
        "                              batch_size = batch_size,\n",
        "                              shuffle = False,\n",
        "                              num_workers = 0)\n",
        "\n",
        "print(f'Number of training graphs: {train_index}')\n",
        "print(f'Number of validation graphs: {val_index-train_index}')\n",
        "print(f'Number of test graphs: {N-val_index}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "dYXBglksACCJ",
        "outputId": "e3cf3339-2b49-425e-d9f2-a5ec94f4ba8e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "4982\n"
          ]
        }
      ],
      "source": [
        "train_y = 0\n",
        "for idx,graph in enumerate(training_data):\n",
        "    if graph.y==1:\n",
        "        train_y+=1\n",
        "print(train_y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "GBy1qYpmACCJ",
        "outputId": "dd10cb9d-0983-4ad0-de95-5f5bc6ffc0be"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "658\n"
          ]
        }
      ],
      "source": [
        "val_y = 0\n",
        "for idx,graph in enumerate(val_data):\n",
        "    if graph.y==1:\n",
        "        val_y+=1\n",
        "print(val_y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [],
        "id": "pLOEsTdhACCK",
        "outputId": "d9ad62b6-6e03-491a-8878-d8b4b55a4ba3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "583\n"
          ]
        }
      ],
      "source": [
        "test_y = 0\n",
        "for idx,graph in enumerate(test_data):\n",
        "    if graph.y==1:\n",
        "        test_y+=1\n",
        "print(test_y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7rgheW7KACCK"
      },
      "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": "3.11.3"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}