{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c9b77d49-e51e-4ce0-9291-ad5caa0a53dc",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "168c6241-63b1-415d-a4ce-ea92b161fbcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from datasets import ClassLabel, Dataset, DatasetDict, Features, Image, load_dataset\n",
    "from energyflow.utils import (\n",
    "    center_ptyphims,\n",
    "    phi_fix,\n",
    "    pixelate,\n",
    "    ptyphims_from_p4s,\n",
    "    reflect_ptyphims,\n",
    "    rotate_ptyphims,\n",
    ")\n",
    "from tqdm.auto import tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d39bd4a-569c-43a1-a34d-978505182aa6",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "da622147-c81b-457a-8444-5781a4d02001",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using custom data configuration dl4phys--top_landscape-961457c8730ac19c\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading and preparing dataset parquet/dl4phys--top_landscape to /Users/lewtun/.cache/huggingface/datasets/parquet/dl4phys--top_landscape-961457c8730ac19c/0.0.0/0b6d5799bb726b24ad7fc7be720c170d8e497f575d02d47537de9a5bac074901...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8002bb9640d74c5a81fd1fc5fefebbe3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data files:   0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e90f3005ccac49c99211384679840244",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data:   0%|          | 0.00/1.28G [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a49088067d3f44b582eed1b82948f091",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data:   0%|          | 0.00/509M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b6e236a13c544dd498ca936b34c03dac",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data:   0%|          | 0.00/508M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "25c6b13a82834c15bc02b7669cff9c8b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Extracting data files:   0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset parquet downloaded and prepared to /Users/lewtun/.cache/huggingface/datasets/parquet/dl4phys--top_landscape-961457c8730ac19c/0.0.0/0b6d5799bb726b24ad7fc7be720c170d8e497f575d02d47537de9a5bac074901. Subsequent calls will reuse this data.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8a82090c8c074af6833fbdee83558a15",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds = load_dataset(\"dl4phys/top_landscape\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "c4c6d156-7545-46d8-862e-353f8ea4995b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_to_numpy(split):\n",
    "    df = ds[split].to_pandas()\n",
    "    return (df.to_numpy()[:, :800]).reshape(-1, 200, 4), df[\"is_signal_new\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "9b975524-3aa7-44ca-90a1-feef78d848b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "events, labels = convert_to_numpy(\"test\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d32e789-897f-4285-afa5-18e83eb66bf5",
   "metadata": {},
   "source": [
    "## Rotate basis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "6a317845-4672-4947-b8ed-2fb26d37da45",
   "metadata": {},
   "outputs": [],
   "source": [
    "def rotate_basis(events):\n",
    "    ptyphims = []\n",
    "\n",
    "    for event in tqdm(events, desc=\"Rotating to (pT,eta,phi,m)\"):\n",
    "        ptyphims.append(ptyphims_from_p4s(event, phi_ref=\"hardest\", mass=True))\n",
    "\n",
    "    return np.array(ptyphims)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bc97994-3805-4e77-bb8f-30126dc05ae8",
   "metadata": {},
   "source": [
    "## Centre image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "6c9a636d-cc35-4ce9-bf28-34909e1be3aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "def centre_images(ptyphims):\n",
    "\n",
    "    ptyphims_center = []\n",
    "\n",
    "    for event in tqdm(ptyphims, desc=\"Centring images\"):\n",
    "        ptyphims_center.append(center_ptyphims(event))\n",
    "    return np.array(ptyphims_center)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b778e7d2-590a-4e0c-a595-d84c15618762",
   "metadata": {},
   "source": [
    "## Reflect and rotate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "1e8fc265-d10c-4393-9c98-3778a967b98d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def reflect_and_rotate(ptyphims_center):\n",
    "\n",
    "    ptyphims_center_rr = []\n",
    "    for event in tqdm(ptyphims_center, desc=\"Reflect and rotate\"):\n",
    "        ptyphims_center_rr.append(reflect_ptyphims(rotate_ptyphims(event)))\n",
    "    return np.array(ptyphims_center_rr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b021d17-3b7f-4e0b-8e14-478f802b3f04",
   "metadata": {},
   "source": [
    "## Pixelate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "15d8cd5a-8596-4c3f-a445-d3688b59f677",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_images(events):\n",
    "    ptyphims = rotate_basis(events)\n",
    "    ptyphims_center = centre_images(ptyphims)\n",
    "    ptyphims_center_rr = reflect_and_rotate(ptyphims_center)\n",
    "\n",
    "    images = []\n",
    "    for event in tqdm(ptyphims_center_rr, \"Pixelate\"):\n",
    "        images.append(\n",
    "            pixelate(\n",
    "                event,\n",
    "                npix=40,\n",
    "                img_width=0.8,\n",
    "                nb_chan=1,\n",
    "                norm=False,\n",
    "                charged_counts_only=False,\n",
    "            )\n",
    "        )\n",
    "    return np.array(images).reshape(len(images), 40, 40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "26037400-a45a-4a89-9e06-1e8b30a32818",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_dataset():\n",
    "    dsets = DatasetDict()\n",
    "    features = Features({\"image\": Image(), \"label\": ClassLabel(names=[\"qcd\", \"top\"])})\n",
    "\n",
    "    for split in [\"train\", \"validation\", \"test\"]:\n",
    "        events, labels = convert_to_numpy(split)\n",
    "        images = create_images(events)\n",
    "        dsets[split] = Dataset.from_dict(\n",
    "            {\"image\": images, \"label\": labels}, features=features\n",
    "        )\n",
    "\n",
    "    return dsets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "ff5ebefa-ec76-449a-ad96-939046675681",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e39fb375772b46f89673ff2739acdb91",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Rotating to (pT,eta,phi,m):   0%|          | 0/1211000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9cea61a8a18540ad88df11c98d79daef",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Centring images:   0%|          | 0/1211000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9af8031b371740818f10b76494784687",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Reflect and rotate:   0%|          | 0/1211000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ff9d6606093f47aeb3569d106edf19f6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Pixelate:   0%|          | 0/1211000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "772b3e63eb974c4c804cb501c554033a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Rotating to (pT,eta,phi,m):   0%|          | 0/403000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9134b103a01642fdae44a25a04528d16",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Centring images:   0%|          | 0/403000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d54864822909467cb6061e2e160fd272",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Reflect and rotate:   0%|          | 0/403000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "98588c31e21c4b289a8614293d9bff73",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Pixelate:   0%|          | 0/403000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "45cf7dc169344653a86de6cedfce5796",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Rotating to (pT,eta,phi,m):   0%|          | 0/404000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6eb133792e4543129423a155e537daed",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Centring images:   0%|          | 0/404000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e5e1f07da3b44bf19422cf09d975d635",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Reflect and rotate:   0%|          | 0/404000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2c11a5af58b3497e950e146ee6021aab",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Pixelate:   0%|          | 0/404000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dsets = create_dataset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "3ab0fa0c-f8b1-45b2-a6ab-847e135ee0ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['image', 'label'],\n",
       "        num_rows: 1211000\n",
       "    })\n",
       "    validation: Dataset({\n",
       "        features: ['image', 'label'],\n",
       "        num_rows: 403000\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['image', 'label'],\n",
       "        num_rows: 404000\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dsets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "d8a7a571-e339-42b3-b3c0-b924399fef77",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Pushing split train to the Hub.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "868b7073ee0248adb5727faefceb8456",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Pushing dataset shards to the dataset hub:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ac0b59780a774e90a01d3440bb75fd64",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1211 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Pushing split validation to the Hub.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "aa4d2782f68c4b7fa7f1e2d89363413f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Pushing dataset shards to the dataset hub:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "81216189abd340aeb9ae6678c2a3c25b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/403 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Pushing split test to the Hub.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9b94c38a09e24a29ade3032dcd004317",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Pushing dataset shards to the dataset hub:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "445154bf625a495b8f4751ce63553605",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/404 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dsets.push_to_hub(\"dl4phys/top_landscape_images\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "a3897885-c44a-4fbb-b344-6e99671356b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<PIL.PngImagePlugin.PngImageFile image mode=L size=40x40>,\n",
       " <PIL.PngImagePlugin.PngImageFile image mode=L size=40x40>,\n",
       " <PIL.PngImagePlugin.PngImageFile image mode=L size=40x40>]"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dsets[\"train\"][\"image\"][:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "b49d6a92-cac5-4f4c-993a-6ca2fb0bbdf6",
   "metadata": {},
   "outputs": [],
   "source": [
    "dsets.set_format(\"numpy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "15674b90-2059-46f1-84a6-8a5fe1bb7f8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "sample = dsets[\"train\"].shuffle(seed=42).select(range(10_000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "a350535c-3bad-4f5e-a1ea-d25a5deca99c",
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_labels = np.array(sample[\"label\"][:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "8982fc85-ec72-4a54-ad80-c7edaa4e3630",
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_images = np.asarray([np.array(img) for img in sample[:][\"image\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "1435a954-9dfd-427d-957b-0b952cfe5128",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 40, 40)"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "16c60c6d-557c-4c3f-a484-664e8be094e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10,)"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "4b674182-6d10-4ac4-8ec9-758f11dfa942",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]], dtype=uint8)"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(dsets[\"train\"][0][\"image\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "20ce1145-09b7-4c30-b6fc-c0b0093cbb81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b1d2e843521c40a1b8a45af085a6fc62",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Rotating to (pT,eta,phi,m):   0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "501071866f634923abfad021c4763b4b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Centring images:   0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "45b7513400604428946e434dc9fae8d2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Reflect and rotate:   0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f4381efba8c84b1b87a56f4c473bc996",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Pixelate:   0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "images = create_images(events)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "2a16e0c9-7918-49bb-ac99-9a53ad884543",
   "metadata": {},
   "outputs": [],
   "source": [
    "top_images = sample_images[sample_labels.flatten() == 1]\n",
    "qcd_images = sample_images[sample_labels.flatten() == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "6976667b-022a-4fe1-ac6b-6a7df597de83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(5):\n",
    "    plt.figure(figsize=(8, 4))\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.title(\"QCD\")\n",
    "    plt.imshow(qcd_images[i])\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.title(\"top\")\n",
    "    plt.imshow(top_images[i])\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "76f95308-03f5-4322-ad06-4ada5a32e018",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 4))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.imshow(np.sum(qcd_images, axis=0))\n",
    "plt.title(\"Average QCD\")\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.imshow(np.sum(top_images, axis=0))\n",
    "plt.title(\"Average Top\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "8defd2e1-ea2e-4e0f-8e41-a7e03375e2b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "features = Features({\"image\": Image(), \"label\": ClassLabel(names=[\"qcd\", \"top\"])})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "eb612755-bf67-4cb5-93b4-54abc0bccc24",
   "metadata": {},
   "outputs": [],
   "source": [
    "image_ds = Dataset.from_dict(\n",
    "    {\"image\": images[:10], \"label\": labels[:10]}, features=features\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "f80849ed-51f2-449c-b28e-5668c7ad8f8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'top'"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_ds.features[\"label\"].int2str(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "e27cfaf2-ec35-4f7f-acb5-290b53bab405",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAACgAAAAoCAAAAACpleexAAAAMUlEQVR4nGNgGFaAcaAdMAqIB2cG0G6WgTPMA1OICYt5Zpu/4TGE9gmdmeY2jIKRAwBzSQMHEvdnSQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=L size=40x40>"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_ds[0][\"image\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "cdb8d9d7-1651-4253-8e71-204057fcca52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['image', 'label'],\n",
       "    num_rows: 10\n",
       "})"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "9d878580-8ef7-428c-89e8-93049f9ed0d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4ec63edd3a57415e9fc578aa099fdc3f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Pushing dataset shards to the dataset hub:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b5174ae5bbc94ff6824b2ab272f95a4e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_ds.push_to_hub(\"dl4phys/top_landscape_images\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f2e222f-9d9c-4ebf-9afb-34ff678c14de",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "dl4phys",
   "language": "python",
   "name": "dl4phys"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}