Upload COLLIDE2V_example_notebook.ipynb

COLLIDE2V_example_notebook.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "432e48eb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/afs/cern.ch/user/p/phploner/.local/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "import awkward as ak\n",
+    "import pyarrow as pa\n",
+    "import pyarrow.parquet as pq\n",
+    "import os\n",
+    "# SPECIFY HERE A VALID CACHE DIRECTORY WITH ENOUGH STORAGE FOR THE DATASETS\n",
+    "os.environ[\"HF_HOME\"] = \"/eos/project/f/foundational-model-dataset/samples/philip_production/full_production_1percent/.cache/huggingface\"\n",
+    "from datasets import load_dataset\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e31fb7f",
+   "metadata": {},
+   "source": [
+    "<small>One can download the whole dataset or parts of it using the `load_dataset` function. \n",
+    "Specify the process folder to be inspected using `data_dir` or specify single parquet files using the `data_files` argument.\n",
+    "Without these arguments the whole dataset will be accessible. \n",
+    "Since the dataset is huge, streaming will be necessary to access it.</small>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a938312a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load dataset of one process folder\n",
+    "# if one wants to load the entire dataset, skip the data_dir or data_files argument\n",
+    "dataset = load_dataset(\"fastmachinelearning/collide-1m\", \n",
+    "                       data_dir=\"WJetsToLNu_13TeV-madgraphMLM-pythia8\",\n",
+    "                       streaming=True)\n",
+    "dataset = dataset[\"train\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "743e1cbb",
+   "metadata": {},
+   "source": [
+    "<small> Here are the different columns that are available in the dataset: </small>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "14412cb7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "FullReco_PFCand_PT\n",
+      "FullReco_PFCand_Eta\n",
+      "FullReco_PFCand_Phi\n",
+      "FullReco_PFCand_PID\n",
+      "FullReco_PFCand_Charge\n",
+      "FullReco_PFCand_Mass\n",
+      "FullReco_PFCand_D0\n",
+      "FullReco_PFCand_DZ\n",
+      "FullReco_PFCand_ErrorD0\n",
+      "FullReco_PFCand_ErrorDZ\n",
+      "FullReco_PFCand_fUniqueID\n",
+      "FullReco_PFCand_PuppiW\n",
+      "FullReco_PUPPIPart_PT\n",
+      "FullReco_PUPPIPart_Eta\n",
+      "FullReco_PUPPIPart_Phi\n",
+      "FullReco_PUPPIPart_Charge\n",
+      "FullReco_PUPPIPart_Mass\n",
+      "FullReco_PUPPIPart_PID\n",
+      "FullReco_PUPPIPart_D0\n",
+      "FullReco_PUPPIPart_DZ\n",
+      "FullReco_PUPPIPart_ErrorD0\n",
+      "FullReco_PUPPIPart_ErrorDZ\n",
+      "FullReco_PUPPIPart_fUniqueID\n",
+      "FullReco_PUPPIPart_PuppiW\n",
+      "FullReco_Electron_PT\n",
+      "FullReco_Electron_Eta\n",
+      "FullReco_Electron_Phi\n",
+      "FullReco_Electron_EhadOverEem\n",
+      "FullReco_Electron_IsolationVarRhoCorr\n",
+      "FullReco_MuonTight_PT\n",
+      "FullReco_MuonTight_Eta\n",
+      "FullReco_MuonTight_Phi\n",
+      "FullReco_MuonTight_IsolationVarRhoCorr\n",
+      "FullReco_PhotonTight_PT\n",
+      "FullReco_PhotonTight_Eta\n",
+      "FullReco_PhotonTight_Phi\n",
+      "FullReco_JetAK4_PT\n",
+      "FullReco_JetAK4_Eta\n",
+      "FullReco_JetAK4_Phi\n",
+      "FullReco_JetAK4_Mass\n",
+      "FullReco_JetAK4_BTag\n",
+      "FullReco_JetAK4_BTagPhys\n",
+      "FullReco_JetAK4_Charge\n",
+      "FullReco_JetAK4_Constituents\n",
+      "FullReco_JetAK8_PT\n",
+      "FullReco_JetAK8_Eta\n",
+      "FullReco_JetAK8_Phi\n",
+      "FullReco_JetAK8_Mass\n",
+      "FullReco_JetAK8_BTag\n",
+      "FullReco_JetAK8_BTagPhys\n",
+      "FullReco_JetAK8_Charge\n",
+      "FullReco_JetPuppiAK4_PT\n",
+      "FullReco_JetPuppiAK4_Eta\n",
+      "FullReco_JetPuppiAK4_Phi\n",
+      "FullReco_JetPuppiAK4_Mass\n",
+      "FullReco_JetPuppiAK4_BTag\n",
+      "FullReco_JetPuppiAK4_BTagPhys\n",
+      "FullReco_JetPuppiAK4_Charge\n",
+      "FullReco_JetPuppiAK4_Constituents\n",
+      "FullReco_JetPuppiAK8_PT\n",
+      "FullReco_JetPuppiAK8_Eta\n",
+      "FullReco_JetPuppiAK8_Phi\n",
+      "FullReco_JetPuppiAK8_Mass\n",
+      "FullReco_JetPuppiAK8_BTag\n",
+      "FullReco_JetPuppiAK8_BTagPhys\n",
+      "FullReco_JetPuppiAK8_Charge\n",
+      "FullReco_MET_MET\n",
+      "FullReco_MET_Phi\n",
+      "FullReco_MET_Eta\n",
+      "FullReco_PUPPIMET_MET\n",
+      "FullReco_PUPPIMET_Phi\n",
+      "FullReco_PUPPIMET_Eta\n",
+      "FullReco_GenMissingET_MET\n",
+      "FullReco_GenMissingET_Eta\n",
+      "FullReco_GenMissingET_Phi\n",
+      "FullReco_GenPart_PT\n",
+      "FullReco_GenPart_Eta\n",
+      "FullReco_GenPart_Phi\n",
+      "FullReco_GenPart_PID\n",
+      "FullReco_GenPart_M1\n",
+      "FullReco_GenPart_M2\n",
+      "FullReco_GenPart_D1\n",
+      "FullReco_GenPart_D2\n",
+      "FullReco_GenPart_Status\n",
+      "FullReco_GenPart_IsPU\n",
+      "FullReco_GenJetAK4_PT\n",
+      "FullReco_GenJetAK4_Eta\n",
+      "FullReco_GenJetAK4_Phi\n",
+      "FullReco_GenJetAK4_Mass\n",
+      "FullReco_GenJetAK8_PT\n",
+      "FullReco_GenJetAK8_Eta\n",
+      "FullReco_GenJetAK8_Phi\n",
+      "FullReco_GenJetAK8_Mass\n",
+      "FullReco_PrimaryVertex_X\n",
+      "FullReco_PrimaryVertex_Y\n",
+      "FullReco_PrimaryVertex_Z\n",
+      "FullReco_PrimaryVertex_T\n",
+      "FullReco_PrimaryVertex_SumPT2\n",
+      "L1T_PFCand_PT\n",
+      "L1T_PFCand_Eta\n",
+      "L1T_PFCand_Phi\n",
+      "L1T_PFCand_PID\n",
+      "L1T_PFCand_Charge\n",
+      "L1T_PFCand_Mass\n",
+      "L1T_PFCand_D0\n",
+      "L1T_PFCand_DZ\n",
+      "L1T_PFCand_ErrorD0\n",
+      "L1T_PFCand_ErrorDZ\n",
+      "L1T_PFCand_fUniqueID\n",
+      "L1T_PFCand_PuppiW\n",
+      "L1T_PUPPIPart_PT\n",
+      "L1T_PUPPIPart_Eta\n",
+      "L1T_PUPPIPart_Phi\n",
+      "L1T_PUPPIPart_Charge\n",
+      "L1T_PUPPIPart_Mass\n",
+      "L1T_PUPPIPart_PID\n",
+      "L1T_PUPPIPart_D0\n",
+      "L1T_PUPPIPart_DZ\n",
+      "L1T_PUPPIPart_ErrorD0\n",
+      "L1T_PUPPIPart_ErrorDZ\n",
+      "L1T_PUPPIPart_fUniqueID\n",
+      "L1T_PUPPIPart_PuppiW\n",
+      "L1T_Electron_PT\n",
+      "L1T_Electron_Eta\n",
+      "L1T_Electron_Phi\n",
+      "L1T_Electron_EhadOverEem\n",
+      "L1T_Electron_IsolationVarRhoCorr\n",
+      "L1T_MuonTight_PT\n",
+      "L1T_MuonTight_Eta\n",
+      "L1T_MuonTight_Phi\n",
+      "L1T_MuonTight_IsolationVarRhoCorr\n",
+      "L1T_PhotonTight_PT\n",
+      "L1T_PhotonTight_Eta\n",
+      "L1T_PhotonTight_Phi\n",
+      "L1T_JetAK4_PT\n",
+      "L1T_JetAK4_Eta\n",
+      "L1T_JetAK4_Phi\n",
+      "L1T_JetAK4_Mass\n",
+      "L1T_JetAK4_BTag\n",
+      "L1T_JetAK4_BTagPhys\n",
+      "L1T_JetAK4_Charge\n",
+      "L1T_JetAK4_Constituents\n",
+      "L1T_JetAK8_PT\n",
+      "L1T_JetAK8_Eta\n",
+      "L1T_JetAK8_Phi\n",
+      "L1T_JetAK8_Mass\n",
+      "L1T_JetAK8_BTag\n",
+      "L1T_JetAK8_BTagPhys\n",
+      "L1T_JetAK8_Charge\n",
+      "L1T_JetPuppiAK4_PT\n",
+      "L1T_JetPuppiAK4_Eta\n",
+      "L1T_JetPuppiAK4_Phi\n",
+      "L1T_JetPuppiAK4_Mass\n",
+      "L1T_JetPuppiAK4_BTag\n",
+      "L1T_JetPuppiAK4_BTagPhys\n",
+      "L1T_JetPuppiAK4_Charge\n",
+      "L1T_JetPuppiAK4_Constituents\n",
+      "L1T_JetPuppiAK8_PT\n",
+      "L1T_JetPuppiAK8_Eta\n",
+      "L1T_JetPuppiAK8_Phi\n",
+      "L1T_JetPuppiAK8_Mass\n",
+      "L1T_JetPuppiAK8_BTag\n",
+      "L1T_JetPuppiAK8_BTagPhys\n",
+      "L1T_JetPuppiAK8_Charge\n",
+      "L1T_MET_MET\n",
+      "L1T_MET_Phi\n",
+      "L1T_MET_Eta\n",
+      "L1T_PUPPIMET_MET\n",
+      "L1T_PUPPIMET_Phi\n",
+      "L1T_PUPPIMET_Eta\n"
+     ]
+    }
+   ],
+   "source": [
+    "cols = list(dataset.features.keys())\n",
+    "for c in cols:\n",
+    "    print(c)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f48be1e",
+   "metadata": {},
+   "source": [
+    "<small> The dataset will have one row for each event, one can access the respective next row by calling `row=next(iter(dataset))`. Once a row is loaded, one can easily access the respective features using `row['feature']`, where 'feature' is any of the above listed columns. Loading multiple rows at once might lead to memory issues. </small>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "adb5ae4d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "row1 = next(iter(dataset))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "869dccac",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre>[0.291,\n",
+       " 0.226,\n",
+       " 0.454,\n",
+       " 0.468,\n",
+       " 0.412,\n",
+       " 0.22,\n",
+       " 0.755,\n",
+       " 0.963,\n",
+       " 0.484,\n",
+       " 0.814,\n",
+       " ...,\n",
+       " 5.42,\n",
+       " 0.735,\n",
+       " 2.6,\n",
+       " 1.99,\n",
+       " 2.98,\n",
+       " 2.21,\n",
+       " 3.04,\n",
+       " 1.14,\n",
+       " 3.48]\n",
+       "--------------------\n",
+       "backend: cpu\n",
+       "nbytes: 34.5 kB\n",
+       "type: 4318 * float64</pre>"
+      ],
+      "text/plain": [
+       "<Array [0.291, 0.226, 0.454, ..., 3.04, 1.14, 3.48] type='4318 * float64'>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "example_arr = ak.Array(row1['FullReco_PFCand_PT'])\n",
+    "example_arr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "9efa96de",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ArrayType(NumpyType('float64'), 4318, None)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "example_arr.type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7e6369c9",
+   "metadata": {},
+   "source": [
+    "<small> Once the data is saved in an array in this way, all the usual awkward array operations can be applied. </small>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "29f6f3d6",
+   "metadata": {},
+   "source": [
+    "<small> To access a specific row, one can use the following `get_row_by_index` function. </small>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "64a97872",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_row_by_index(dataset, target_index):\n",
+    "    for i, row in enumerate(dataset):\n",
+    "        if i == target_index:\n",
+    "            return row\n",
+    "    return None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "0b5da1cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "row100 = get_row_by_index(dataset, 100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "30afc6bc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre>[1.47,\n",
+       " 1.01,\n",
+       " 1.14,\n",
+       " 0.894,\n",
+       " 0.905,\n",
+       " 0.647,\n",
+       " 1.15,\n",
+       " 0.616,\n",
+       " 0.736,\n",
+       " 0.521,\n",
+       " ...,\n",
+       " 1.26,\n",
+       " 2.95,\n",
+       " 6.43,\n",
+       " 1.82,\n",
+       " 1.76,\n",
+       " 2.72,\n",
+       " 2.86,\n",
+       " 1.29,\n",
+       " 0.773]\n",
+       "--------------------\n",
+       "backend: cpu\n",
+       "nbytes: 29.7 kB\n",
+       "type: 3709 * float64</pre>"
+      ],
+      "text/plain": [
+       "<Array [1.47, 1.01, 1.14, 0.894, ..., 2.86, 1.29, 0.773] type='3709 * float64'>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "example_arr100 = ak.Array(row100['FullReco_PFCand_PT'])\n",
+    "example_arr100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "8ffdc8ed",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ArrayType(NumpyType('float64'), 3709, None)"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "example_arr100.type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7002061",
+   "metadata": {},
+   "source": [
+    "<small> We hope you enjoy looking through this preliminary dataset and aim to have the full dataset uploaded soon. If you have any questions or suggestions please let us know at [email protected] or [email protected] .</small>"
+   ]
+  }
+ ],
+ "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": "3.9.21"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}