feature_pipeline.ipynb

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   "source": [
    "import uproot\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from pathlib import Path\n",
    "from tqdm.auto import tqdm\n",
    "from helper_functions.feature_engineering import create_features\n",
    "\n",
    "plt.style.use(\"default\")\n",
    "plt.rc(\"text\", usetex=True)\n",
    "plt.rc(\"font\", family=\"cm\")\n",
    "plt.rcParams[\"grid.color\"] = (0.5, 0.5, 0.5, 0.2)"
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   "source": [
    "# load utils\n",
    "df_pmt_id_conversion = pd.read_csv(\"/home/ferracci/new_dataset/utils/PMT_ID_conversion.csv\")\n",
    "df_pmt_position = pd.read_csv(\"/home/ferracci/new_dataset/utils/PMTPos_CD_LPMT.csv\")\n",
    "df_spmt_id_conversion = pd.read_csv(\"/home/ferracci/new_dataset/utils/SPMT_ID_conversion.csv\")\n",
    "df_spmt_position = pd.read_csv(\"/home/ferracci/new_dataset/utils/PMTPos_CD_SPMT.csv\")\n",
    "\n",
    "# computes PMTs positions \n",
    "x = np.array(df_pmt_position['x']/1000).reshape((-1, 1))\n",
    "y = np.array(df_pmt_position['y']/1000).reshape((-1, 1))\n",
    "z = np.array(df_pmt_position['z']/1000).reshape((-1, 1))\n",
    "pos = np.hstack((x, y, z))\n",
    "\n",
    "x_s = np.array(df_spmt_position['x']/1000).reshape((-1, 1))\n",
    "y_s = np.array(df_spmt_position['y']/1000).reshape((-1, 1))\n",
    "z_s = np.array(df_spmt_position['z']/1000).reshape((-1, 1))\n",
    "pos_s = np.hstack((x_s, y_s, z_s))\n",
    "\n",
    "pmt_id_raw_to_id_map = dict(zip(df_pmt_id_conversion['CdID'], df_pmt_id_conversion['PMTID']))\n",
    "pmt_id_to_pos_map = dict(zip(df_pmt_position['PMTID'], pos))\n",
    "\n",
    "spmt_id_raw_to_id_map = dict(zip(df_spmt_id_conversion['CdID'], df_spmt_id_conversion['SPMTID']))\n",
    "spmt_id_to_pos_map = dict(zip(df_spmt_position['SPMTID'], pos_s))"
   ]
  },
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   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training Data"
   ]
  },
  {
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   "execution_count": null,
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   "source": [
    "files = list(Path(\"/mnt/data/train_flat_dataset_processed/\").glob(\"*\"))\n",
    "features, features_dataframe, targets_dataframe = [], [], []\n",
    "\n",
    "for filename in tqdm(files):\n",
    "    hits = np.load(filename, allow_pickle=True)[\"hits\"]\n",
    "    targets = np.load(filename, allow_pickle=True)[\"primaries\"]\n",
    "\n",
    "    pmt_id_raw, charge, fht = hits[0, :], hits[1, :], hits[2, :]\n",
    "    spmt_id_raw, charge_s, fht_s = hits[3, :], hits[4, :], hits[5, :]\n",
    "\n",
    "    # maps raw ids (CdID) to standard ids (PMTID)\n",
    "    pmt_id = np.array([np.array([pmt_id_raw_to_id_map[n] for n in event]) for event in pmt_id_raw], dtype=object)\n",
    "    spmt_id = np.array([np.array([spmt_id_raw_to_id_map[n] for n in event]) for event in spmt_id_raw], dtype=object)\n",
    "\n",
    "    # maps standard ids (PMTID) to PMTs positions\n",
    "    pmt_pos = np.array([np.array([pmt_id_to_pos_map[n] for n in event]) for event in pmt_id], dtype=object)\n",
    "    spmt_pos = np.array([np.array([spmt_id_to_pos_map[n] for n in event]) for event in spmt_id], dtype=object)\n",
    "\n",
    "    # compute features on preprocessed data\n",
    "    _, f, f_df = create_features(pmt_pos, charge, fht, spmt_pos, charge_s, fht_s, return_dataframe=True)\n",
    "\n",
    "    t_df = pd.DataFrame({\"Qedep\": targets[:, 1], \"Redep\": targets[:, 5]})\n",
    "\n",
    "    # implement fiducial volume cut at 17.2 m\n",
    "    f = f[t_df[\"Redep\"] < 17.2]\n",
    "    f_df = f_df[t_df[\"Redep\"] < 17.2]\n",
    "    t_df = t_df[t_df[\"Redep\"] < 17.2]\n",
    "\n",
    "    features.append(f)\n",
    "    features_dataframe.append(f_df)\n",
    "    targets_dataframe.append(t_df)\n",
    "\n",
    "features = np.vstack(features)\n",
    "features_dataframe = pd.concat(features_dataframe, axis=0, ignore_index=True)\n",
    "targets_dataframe = pd.concat(targets_dataframe, axis=0, ignore_index=True)\n",
    "\n",
    "# save compressed features and targets\n",
    "np.savez_compressed(\"/mnt/ferracci/features_new\", a=features)\n",
    "features_dataframe.to_csv(\"/mnt/ferracci/features_dataframe_new.csv.gz\", index=False, compression='gzip')\n",
    "targets_dataframe.to_csv(\"/mnt/ferracci/targets_dataframe_new.csv.gz\", compression=\"gzip\")"
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   "source": [
    "### Testing Data"
   ]
  },
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   "source": [
    "directories = list(Path(\"/mnt/data/test_dataset_processed/\").glob(\"*\"))\n",
    "\n",
    "for directory in tqdm(directories):\n",
    "    files = list(Path(f\"{directory}/\").glob(\"*\"))\n",
    "    features, features_dataframe, targets_dataframe = [], [], []\n",
    "    if directory == Path(\"/mnt/data/test_dataset_processed/e+_0_3\") or directory == Path(\"/mnt/data/test_dataset_processed/e+_0_6\"):\n",
    "        for filename in files:\n",
    "            hits = np.load(filename, allow_pickle=True)[\"hits\"]\n",
    "            targets = np.load(filename, allow_pickle=True)[\"primaries\"]\n",
    "\n",
    "            pmt_id_raw, charge, fht = hits[0, :], hits[1, :], hits[2, :]\n",
    "            spmt_id_raw, charge_s, fht_s = hits[3, :], hits[4, :], hits[5, :]\n",
    "\n",
    "            # maps raw ids (CdID) to standard ids (PMTID)\n",
    "            pmt_id = np.array([np.array([pmt_id_raw_to_id_map[n] for n in event]) for event in pmt_id_raw], dtype=object)\n",
    "            spmt_id = np.array([np.array([spmt_id_raw_to_id_map[n] for n in event]) for event in spmt_id_raw], dtype=object)\n",
    "\n",
    "            # maps standard ids (PMTID) to PMTs positions\n",
    "            pmt_pos = np.array([np.array([pmt_id_to_pos_map[n] for n in event]) for event in pmt_id], dtype=object)\n",
    "            spmt_pos = np.array([np.array([spmt_id_to_pos_map[n] for n in event]) for event in spmt_id], dtype=object)\n",
    "\n",
    "            # compute features on preprocessed data\n",
    "            _, f, f_df = create_features(pmt_pos, charge, fht, spmt_pos, charge_s, fht_s, return_dataframe=True)\n",
    "\n",
    "            t_df = pd.DataFrame({\"Qedep\": targets[:, 1], \"Redep\": targets[:, 5]})\n",
    "\n",
    "            # implement fiducial volume cut at 17.2 m\n",
    "            f = f[t_df[\"Redep\"] < 17.2]\n",
    "            f_df = f_df[t_df[\"Redep\"] < 17.2]\n",
    "            t_df = t_df[t_df[\"Redep\"] < 17.2]\n",
    "\n",
    "            features.append(f)\n",
    "            features_dataframe.append(f_df)\n",
    "            targets_dataframe.append(t_df)\n",
    "\n",
    "        features = np.vstack(features)\n",
    "        features_dataframe = pd.concat(features_dataframe, axis=0, ignore_index=True)\n",
    "        targets_dataframe = pd.concat(targets_dataframe, axis=0, ignore_index=True)\n",
    "\n",
    "        # save compressed features and targets\n",
    "        np.savez_compressed(f\"/mnt/ferracci/features_test_{directory.name}\", a=features)\n",
    "        features_dataframe.to_csv(f\"/mnt/ferracci/features_dataframe_test_{directory.name}.csv.gz\", index=False, compression='gzip')\n",
    "        targets_dataframe.to_csv(f\"/mnt/ferracci/targets_dataframe_test_{directory.name}.csv.gz\", compression=\"gzip\")"
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