diff --git "a/notebook/W8S2.ipynb" "b/notebook/W8S2.ipynb"
new file mode 100644--- /dev/null
+++ "b/notebook/W8S2.ipynb"
@@ -0,0 +1,5058 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "collapsed_sections": [
+        "GcPcioU5ghcZ",
+        "GO_Yn-Lkgk37",
+        "u424HxJIgrpu",
+        "WisGzJaSg5eV",
+        "O6rqpJYJg-1z",
+        "oOiQyEl9hJSF",
+        "wfjkXLaChSQj",
+        "rLyXg8__hcI9",
+        "D6YFR40Zhy_j",
+        "2QeYBEqh2HYd",
+        "fWKvS0dL6srm",
+        "urkMMaUq4g1B",
+        "sz-KKced6v_J",
+        "SDd2MmwgL5SW"
+      ]
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Configs and Imports"
+      ],
+      "metadata": {
+        "id": "GcPcioU5ghcZ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "J9cb02AtZyiz"
+      },
+      "outputs": [],
+      "source": [
+        "# === Cell 1 — Imports, config, robust helpers ===\n",
+        "import os, glob, math, warnings, numpy as np, pandas as pd\n",
+        "import matplotlib.pyplot as plt, seaborn as sns\n",
+        "from sklearn.impute import KNNImputer\n",
+        "from sklearn.preprocessing import RobustScaler, StandardScaler, MinMaxScaler\n",
+        "from sklearn.ensemble import GradientBoostingRegressor\n",
+        "from sklearn.inspection import permutation_importance\n",
+        "\n",
+        "warnings.filterwarnings(\"ignore\")\n",
+        "sns.set_style(\"whitegrid\")\n",
+        "plt.rcParams[\"figure.figsize\"] = (12,4)\n",
+        "np.random.seed(42)\n",
+        "\n",
+        "CORE_COLS = [\"timestamp\",\"RPM\",\"SPEED\",\"THROTTLE_POS\",\"MAF\",\"ENGINE_LOAD\"]\n",
+        "KMH_TO_MS = 1000.0/3600.0\n",
+        "\n",
+        "def ensure_dt(s): return pd.to_datetime(s, errors=\"coerce\", utc=True)\n",
+        "\n",
+        "def infer_base_interval_seconds(ts: pd.Series) -> float:\n",
+        "    if ts.size < 2: return 1.0\n",
+        "    d = ts.sort_values().diff().dropna().dt.total_seconds()\n",
+        "    d = d[d>0]\n",
+        "    if d.empty: return 1.0\n",
+        "    q05,q95 = d.quantile([0.05,0.95])\n",
+        "    core = d[(d>=q05)&(d<=q95)]\n",
+        "    rounded = (core/0.01).round()*0.01\n",
+        "    mode = rounded.mode()\n",
+        "    return float(mode.iloc[0] if not mode.empty else core.median())\n",
+        "\n",
+        "def rows_for(seconds, dt): return max(3, int(round(seconds/max(dt,1e-3))))\n",
+        "\n",
+        "def run_lengths(mask: np.ndarray):\n",
+        "    m = mask.astype(bool)\n",
+        "    idx = np.flatnonzero(np.diff(np.r_[False, m, False]))\n",
+        "    starts, ends = idx[::2], idx[1::2]\n",
+        "    return starts, (ends - starts)\n",
+        "\n",
+        "def safe_num(df, cols):\n",
+        "    for c in cols:\n",
+        "        if c in df.columns: df[c] = pd.to_numeric(df[c], errors=\"coerce\")\n",
+        "    return df\n",
+        "\n",
+        "def robust_scale_01(s):\n",
+        "    rs = RobustScaler().fit_transform(s.values.reshape(-1,1)).ravel()\n",
+        "    return np.clip(MinMaxScaler().fit_transform(rs.reshape(-1,1)).ravel(), 0, 1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Data"
+      ],
+      "metadata": {
+        "id": "GO_Yn-Lkgk37"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# === Cell 2 — Merge many CSVs, segment drives, clean + dynamics ===\n",
+        "CSV_PATTERN = \"*.csv\"\n",
+        "files = sorted(glob.glob(CSV_PATTERN))\n",
+        "if not files: raise FileNotFoundError(\"No CSV files matched. Set CSV_PATTERN.\")\n",
+        "\n",
+        "dfs = []\n",
+        "for fp in files:\n",
+        "    df = pd.read_csv(fp)\n",
+        "    keep = [c for c in CORE_COLS if c in df.columns]\n",
+        "    df = df[keep].copy()\n",
+        "    df[\"source_file\"] = os.path.basename(fp)\n",
+        "    dfs.append(df)\n",
+        "\n",
+        "df = pd.concat(dfs, ignore_index=True)\n",
+        "df[\"timestamp\"] = ensure_dt(df[\"timestamp\"])\n",
+        "df = safe_num(df, [c for c in CORE_COLS if c!=\"timestamp\"])\n",
+        "df = df.dropna(subset=[\"timestamp\"]).sort_values(\"timestamp\").reset_index(drop=True)\n",
+        "\n",
+        "# segment drives by gaps (>5 min) or file switch\n",
+        "gap_sec = 5*60\n",
+        "dt = df[\"timestamp\"].diff().dt.total_seconds().fillna(0)\n",
+        "new_drive = (dt > gap_sec) | (df[\"source_file\"].ne(df[\"source_file\"].shift()))\n",
+        "df[\"drive_id\"] = new_drive.cumsum()\n",
+        "\n",
+        "# impute missing numeric\n",
+        "num_cols = [c for c in df.columns if c not in [\"timestamp\",\"source_file\",\"drive_id\"]]\n",
+        "df[num_cols] = KNNImputer(n_neighbors=3).fit_transform(df[num_cols])\n",
+        "\n",
+        "# base cadence + dynamics\n",
+        "base_sec = infer_base_interval_seconds(df[\"timestamp\"])\n",
+        "df[\"SPEED_ms\"] = df[\"SPEED\"]*KMH_TO_MS\n",
+        "df[\"ACCEL\"] = df[\"SPEED_ms\"].diff()/max(base_sec,1e-3)                 # m/s^2\n",
+        "df[\"JERK\"]  = df[\"ACCEL\"].diff()/max(base_sec,1e-3)                     # m/s^3\n",
+        "\n",
+        "# rolling windows\n",
+        "W5, W10 = rows_for(5, base_sec), rows_for(10, base_sec)\n",
+        "for col in [\"SPEED_ms\",\"RPM\",\"ENGINE_LOAD\",\"THROTTLE_POS\",\"MAF\",\"ACCEL\",\"JERK\"]:\n",
+        "    if col not in df.columns: continue\n",
+        "    df[f\"{col}_mean_w5\"] = df[col].rolling(W5, min_periods=1).mean()\n",
+        "    df[f\"{col}_std_w5\"]  = df[col].rolling(W5, min_periods=1).std()\n",
+        "    df[f\"{col}_mean_w10\"] = df[col].rolling(W10, min_periods=1).mean()\n",
+        "    df[f\"{col}_std_w10\"]  = df[col].rolling(W10, min_periods=1).std()\n",
+        "\n",
+        "# distance estimate (meters)\n",
+        "dt_s = df[\"timestamp\"].diff().dt.total_seconds().fillna(0).clip(lower=0, upper=10*base_sec)\n",
+        "df[\"dist_m\"] = (df[\"SPEED_ms\"] * dt_s).fillna(0)\n",
+        "print(f\"Files: {len(files)} | Drives: {df['drive_id'].nunique()} | Rows: {len(df):,}\")\n",
+        "df.head(3)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "M2b0GJJwgeEv",
+        "outputId": "fc86c82b-bfd3-4d36-a998-67b18e43e794"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Files: 10 | Drives: 10 | Rows: 2,378\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                         timestamp     RPM  SPEED  THROTTLE_POS   MAF  \\\n",
+              "0 2025-09-28 13:28:43.286472+00:00  766.25    0.0      12.54902  4.55   \n",
+              "1 2025-09-28 13:28:44.710016+00:00  755.75    0.0      12.54902  4.49   \n",
+              "2 2025-09-28 13:28:46.085703+00:00  768.25    0.0      12.54902  4.54   \n",
+              "\n",
+              "   ENGINE_LOAD                       source_file  drive_id  SPEED_ms  ACCEL  \\\n",
+              "0     2.352941  obd_data_log_20250928_132843.csv         1       0.0    NaN   \n",
+              "1     2.352941  obd_data_log_20250928_132843.csv         1       0.0    0.0   \n",
+              "2     2.352941  obd_data_log_20250928_132843.csv         1       0.0    0.0   \n",
+              "\n",
+              "   ...  MAF_std_w10  ACCEL_mean_w5  ACCEL_std_w5  ACCEL_mean_w10  \\\n",
+              "0  ...          NaN            NaN           NaN             NaN   \n",
+              "1  ...     0.042426            0.0           NaN             0.0   \n",
+              "2  ...     0.032146            0.0           0.0             0.0   \n",
+              "\n",
+              "   ACCEL_std_w10  JERK_mean_w5  JERK_std_w5  JERK_mean_w10  JERK_std_w10  \\\n",
+              "0            NaN           NaN          NaN            NaN           NaN   \n",
+              "1            NaN           NaN          NaN            NaN           NaN   \n",
+              "2            0.0           0.0          NaN            0.0           NaN   \n",
+              "\n",
+              "   dist_m  \n",
+              "0     0.0  \n",
+              "1     0.0  \n",
+              "2     0.0  \n",
+              "\n",
+              "[3 rows x 40 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-bb92c137-4dba-426e-b3ea-19b8894f46ad\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>timestamp</th>\n",
+              "      <th>RPM</th>\n",
+              "      <th>SPEED</th>\n",
+              "      <th>THROTTLE_POS</th>\n",
+              "      <th>MAF</th>\n",
+              "      <th>ENGINE_LOAD</th>\n",
+              "      <th>source_file</th>\n",
+              "      <th>drive_id</th>\n",
+              "      <th>SPEED_ms</th>\n",
+              "      <th>ACCEL</th>\n",
+              "      <th>...</th>\n",
+              "      <th>MAF_std_w10</th>\n",
+              "      <th>ACCEL_mean_w5</th>\n",
+              "      <th>ACCEL_std_w5</th>\n",
+              "      <th>ACCEL_mean_w10</th>\n",
+              "      <th>ACCEL_std_w10</th>\n",
+              "      <th>JERK_mean_w5</th>\n",
+              "      <th>JERK_std_w5</th>\n",
+              "      <th>JERK_mean_w10</th>\n",
+              "      <th>JERK_std_w10</th>\n",
+              "      <th>dist_m</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2025-09-28 13:28:43.286472+00:00</td>\n",
+              "      <td>766.25</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>12.54902</td>\n",
+              "      <td>4.55</td>\n",
+              "      <td>2.352941</td>\n",
+              "      <td>obd_data_log_20250928_132843.csv</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2025-09-28 13:28:44.710016+00:00</td>\n",
+              "      <td>755.75</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>12.54902</td>\n",
+              "      <td>4.49</td>\n",
+              "      <td>2.352941</td>\n",
+              "      <td>obd_data_log_20250928_132843.csv</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.042426</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2025-09-28 13:28:46.085703+00:00</td>\n",
+              "      <td>768.25</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>12.54902</td>\n",
+              "      <td>4.54</td>\n",
+              "      <td>2.352941</td>\n",
+              "      <td>obd_data_log_20250928_132843.csv</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.032146</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>3 rows × 40 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bb92c137-4dba-426e-b3ea-19b8894f46ad')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-bb92c137-4dba-426e-b3ea-19b8894f46ad button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-bb92c137-4dba-426e-b3ea-19b8894f46ad');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-6b94a948-36c9-44a9-a7e3-5c840ab9afc5\">\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
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+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
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+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
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+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-6b94a948-36c9-44a9-a7e3-5c840ab9afc5 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df"
+            }
+          },
+          "metadata": {},
+          "execution_count": 2
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Fleeth threshold + Idle"
+      ],
+      "metadata": {
+        "id": "u424HxJIgrpu"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# === Cell 3 — Fleet thresholds + RULE Idle mask (patched) ===\n",
+        "from sklearn.linear_model import LinearRegression\n",
+        "import numpy as np, pandas as pd\n",
+        "import matplotlib.pyplot as plt, seaborn as sns\n",
+        "from scipy.signal import medfilt\n",
+        "\n",
+        "# 1) Build per-drive aggregates (length-invariant)\n",
+        "def run_lengths(mask: np.ndarray):   # <— was _run_lengths; rename to match calls below\n",
+        "    m = mask.astype(bool)\n",
+        "    idx = np.flatnonzero(np.diff(np.r_[False, m, False]))\n",
+        "    return idx[::2], idx[1::2] - idx[::2]\n",
+        "\n",
+        "base_sec  = infer_base_interval_seconds(df[\"timestamp\"])\n",
+        "thr_accel = max(0.6, df[\"ACCEL\"].abs().quantile(0.85))\n",
+        "thr_jerk  = max(0.6, df[\"JERK\"].abs().quantile(0.90))\n",
+        "sharp_mask = (df[\"ACCEL\"].abs()>thr_accel) | (df[\"JERK\"].abs()>thr_jerk)\n",
+        "\n",
+        "print(f\"ACCEL_thr={thr_accel:.2f} m/s^2  |  JERK_thr={thr_jerk:.2f} m/s^3\")\n",
+        "\n",
+        "# Idle: near-zero motion + minimal throttle/load/MAF (no ML)\n",
+        "speed_low = (df[\"SPEED_ms\"].abs() <= 0.6)  # ~<= 2.2 km/h\n",
+        "thr_low   = (df[\"THROTTLE_POS\"] <= df[\"THROTTLE_POS\"].quantile(0.10)) if \"THROTTLE_POS\" in df else True\n",
+        "load_low  = (df[\"ENGINE_LOAD\"]  <= df[\"ENGINE_LOAD\"].quantile(0.15))  if \"ENGINE_LOAD\"  in df else True\n",
+        "maf_low   = (df[\"MAF\"]          <= df[\"MAF\"].quantile(0.10))          if \"MAF\"          in df else True\n",
+        "accel_low = (df[\"ACCEL\"].abs()  <= df[\"ACCEL\"].abs().quantile(0.20))\n",
+        "\n",
+        "idle_rule = (speed_low & thr_low & load_low & maf_low & accel_low).astype(bool)\n",
+        "\n",
+        "# debounce small flicker\n",
+        "df[\"IDLE_RULE\"] = medfilt(idle_rule.astype(int), kernel_size=5).astype(bool)\n",
+        "\n",
+        "# quick sanity plot\n",
+        "plt.figure(figsize=(12,2.6))\n",
+        "N = min(2500, len(df))\n",
+        "t0 = (df[\"timestamp\"].iloc[:N]-df[\"timestamp\"].iloc[0]).dt.total_seconds()\n",
+        "plt.scatter(t0[df[\"IDLE_RULE\"].iloc[:N]], np.zeros(df[\"IDLE_RULE\"].iloc[:N].sum())+0.5, s=10, label=\"Idle (rule)\")\n",
+        "plt.yticks([]); plt.title(\"Idle timeline (first segment) — rule-based\"); plt.legend(); plt.tight_layout(); plt.show()\n",
+        "\n",
+        "# per-drive idle summary (used later)\n",
+        "idle_frac = df.groupby(\"drive_id\")[\"IDLE_RULE\"].mean().rename(\"idle_frac\")\n",
+        "idle_dur_s = []\n",
+        "for gid, g in df.groupby(\"drive_id\"):\n",
+        "    starts, lens = run_lengths(g[\"IDLE_RULE\"].values)  # <— now matches definition\n",
+        "    idle_dur_s.append(pd.Series({\n",
+        "        \"drive_id\": gid,\n",
+        "        \"idle_episodes\": len(lens),\n",
+        "        \"idle_median_dur_s\": float(np.median(lens)*base_sec if len(lens) else 0.0)\n",
+        "    }))\n",
+        "idle_dur_s = pd.DataFrame(idle_dur_s).set_index(\"drive_id\")\n",
+        "\n",
+        "print(idle_frac.describe())"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "r1nQX6UQgsNK",
+        "outputId": "8f7be7d1-6c0d-4801-bf6c-34f8062ff7ba"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "ACCEL_thr=2.56 m/s^2  |  JERK_thr=7.89 m/s^3\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x260 with 1 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "count    10.000000\n",
+            "mean      0.029653\n",
+            "std       0.014866\n",
+            "min       0.012195\n",
+            "25%       0.015959\n",
+            "50%       0.031526\n",
+            "75%       0.037927\n",
+            "max       0.059259\n",
+            "Name: idle_frac, dtype: float64\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Algorithmic efficiency"
+      ],
+      "metadata": {
+        "id": "WisGzJaSg5eV"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "drv = []\n",
+        "\n",
+        "# target high contributed vars\n",
+        "thr_rpm90 = g[\"RPM\"].quantile(0.90) if \"RPM\" in df.columns else np.nan\n",
+        "thr_maf90 = g[\"MAF\"].quantile(0.90) if \"MAF\" in df.columns else np.nan\n",
+        "\n",
+        "for gid, g in df.groupby(\"drive_id\", sort=True):\n",
+        "    if len(g) < 5:\n",
+        "        continue\n",
+        "    dt_s = g[\"timestamp\"].diff().dt.total_seconds().fillna(0).clip(lower=0, upper=10*base_sec)\n",
+        "    T = float(dt_s.sum()); mins = max(1e-6, T/60)\n",
+        "    # sharp episodes\n",
+        "    s = sharp_mask.loc[g.index].values\n",
+        "    st, ln = run_lengths(s)\n",
+        "    # frequency/duration\n",
+        "    freq_pm = len(ln)/mins\n",
+        "    dur_frac = (ln.sum()*base_sec)/max(1e-6, T)\n",
+        "    # sharpness magnitude (episode peak-over-threshold, capped; sharpness not over-penalized)\n",
+        "    peaks = []\n",
+        "    for a,b in zip(st, ln):\n",
+        "        seg = g.iloc[a:a+b]\n",
+        "        pa, pj = float(seg[\"ACCEL\"].abs().max()), float(seg[\"JERK\"].abs().max())\n",
+        "        over_a = max(0.0, (pa-thr_accel)/thr_accel)\n",
+        "        over_j = max(0.0, (pj-thr_jerk)/thr_jerk)\n",
+        "        peaks.append(min(1.5, 0.7*over_a + 0.3*over_j))\n",
+        "    sharp_mag = float(np.mean(peaks)) if peaks else 0.0\n",
+        "    # idle signals\n",
+        "    idle_frac = float(g[\"IDLE_RULE\"].mean())\n",
+        "    sti, lni = run_lengths(g[\"IDLE_RULE\"].values)\n",
+        "    idle_med_s = float(np.median(lni)*base_sec if len(lni) else 0.0)\n",
+        "    idle_epm = len(lni)/mins\n",
+        "    # speed steadiness\n",
+        "    sp_cv = float((g[\"SPEED_ms_std_w10\"]/(g[\"SPEED_ms_mean_w10\"]+1e-6)).mean())\n",
+        "\n",
+        "    # fuel-demand proxies (targets for weight learning)\n",
+        "    # frac_maf90  = g[\"MAF\"].ge(df[\"MAF\"].quantile(0.90)).mean() if \"MAF\" in g else 0.0 # Use direct component on top\n",
+        "    frac_load85 = g[\"ENGINE_LOAD\"].ge(df[\"ENGINE_LOAD\"].quantile(0.85)).mean() if \"ENGINE_LOAD\" in g else 0.0\n",
+        "    frac_thr85  = g[\"THROTTLE_POS\"].ge(df[\"THROTTLE_POS\"].quantile(0.85)).mean() if \"THROTTLE_POS\" in g else 0.0\n",
+        "\n",
+        "    # high contributive vars\n",
+        "    frac_rpm90 = float((g[\"RPM\"] >= thr_rpm90).mean()) if \"RPM\" in g.columns and not np.isnan(thr_rpm90) else 0.0\n",
+        "    frac_maf90 = float((g[\"MAF\"] >= thr_maf90).mean()) if \"MAF\" in g.columns and not np.isnan(thr_maf90) else 0.0\n",
+        "\n",
+        "    drv.append({\n",
+        "        \"drive_id\":gid,\"duration_min\":mins,\"distance_km\":g[\"dist_m\"].sum()/1000.0,\n",
+        "        \"freq_pm\":freq_pm,\"dur_frac\":dur_frac,\"sharp_mag\":sharp_mag,\n",
+        "        \"idle_frac\":idle_frac,\"idle_med_s\":idle_med_s,\"idle_epm\":idle_epm,\"speed_cv\":sp_cv,\n",
+        "        \"frac_rpm90\": frac_rpm90,\n",
+        "        \"frac_maf90\": frac_maf90,\n",
+        "\n",
+        "        # proxy for learning weights (higher proxy ⇒ more fuel demand)\n",
+        "        # OLD proxy\n",
+        "        # \"proxy\": (\n",
+        "        #   0.40*frac_maf90 +\n",
+        "        #   0.30*frac_load85 +\n",
+        "        #   0.15*frac_thr85 +\n",
+        "        #   0.15*idle_frac\n",
+        "        #)\n",
+        "        # NEW proxy\n",
+        "        \"proxy\": (\n",
+        "            0.80*frac_rpm90 +     # ↑ RPM influence\n",
+        "            0.60*frac_maf90 +     # ↑ MAF influence\n",
+        "            0.15*frac_load85 +\n",
+        "            0.10*frac_thr85 +\n",
+        "            0.10*idle_frac\n",
+        "        )\n",
+        "    })\n",
+        "dfeat = pd.DataFrame(drv).set_index(\"drive_id\")\n",
+        "\n",
+        "# 2) Smooth, bounded penalties per component via sigmoids (data-driven centers/slopes)\n",
+        "def sigmoid(x): return 1/(1+np.exp(-x))\n",
+        "def penalty(series):\n",
+        "    q25, q50, q75 = np.quantile(series, [0.25,0.50,0.75])\n",
+        "    s = (q75 - q25)/1.349 if (q75>q25) else max(1e-6, series.std())\n",
+        "    return sigmoid((series - q50)/max(1e-6, s))\n",
+        "\n",
+        "P = pd.DataFrame({\n",
+        "    \"p_freq\": penalty(dfeat[\"freq_pm\"]),\n",
+        "    \"p_dur\":  penalty(dfeat[\"dur_frac\"]),\n",
+        "    \"p_mag\":  penalty(dfeat[\"sharp_mag\"]),\n",
+        "    \"p_idle\": sigmoid( 0.7*(penalty(dfeat[\"idle_frac\"])) + 0.3*(penalty(dfeat[\"idle_med_s\"])) ),\n",
+        "    \"p_cv\":   penalty(dfeat[\"speed_cv\"]),\n",
+        "    \"p_rpm\":  penalty(dfeat[\"frac_rpm90\"]),\n",
+        "    \"p_maf\":  penalty(dfeat[\"frac_maf90\"])\n",
+        "}, index=dfeat.index)\n",
+        "\n",
+        "# 3) Learn weights (non-negative) against proxy by fitting the exponential combiner:\n",
+        "#    ineff_hat = 1 - exp(-P @ w)  ⇒  -log(1 - proxy_clip) ≈ P @ w\n",
+        "proxy = dfeat[\"proxy\"].clip(0, 1-1e-6)                 # keep in (0,1)\n",
+        "target_lin = -np.log(1 - proxy)                           # invert combiner\n",
+        "lr = LinearRegression(positive=True).fit(P.values, target_lin.values)\n",
+        "w = lr.coef_\n",
+        "dfeat[\"ineff_model\"] = 1 - np.exp(-P.values @ w)       # in (0,1)\n",
+        "\n",
+        "# 4) Final efficiency (bounded; no batch min-max)\n",
+        "dfeat[\"efficiency_algo\"] = 100*(1 - dfeat[\"ineff_model\"])\n",
+        "ranked = dfeat.sort_values(\"efficiency_algo\", ascending=False)\n",
+        "\n",
+        "# 5) Diagnostics\n",
+        "print(\"Learned weights:\", dict(zip(P.columns, np.round(w,3))))\n",
+        "print(\"Weights (opt with MAF/RPM):\", {k: round(v,3) for k, v in dict(zip(P.columns, w)).items()})\n",
+        "print(\"Efficiency range:\", round(dfeat[\"efficiency_algo\"].min(),2), \"→\", round(dfeat[\"efficiency_algo\"].max(),2))\n",
+        "\n",
+        "plt.figure(figsize=(12,4))\n",
+        "sns.histplot(dfeat[\"efficiency_algo\"], bins=30, kde=True, color=\"#4C78A8\")\n",
+        "plt.title(\"Algorithmic efficiency (bounded by model)\"); plt.xlabel(\"Efficiency (0–100)\")\n",
+        "plt.tight_layout(); plt.show()\n",
+        "\n",
+        "# Keep a merged per-drive table for ML training in Cell B\n",
+        "per_drive = dfeat.join(P)\n",
+        "display(per_drive.head(5))\n",
+        "\n",
+        "# Plot\n",
+        "plt.figure(figsize=(12,4))\n",
+        "sns.barplot(x=np.arange(len(ranked)), y=ranked[\"efficiency_algo\"], color=\"#4C78A8\", edgecolor=\"black\")\n",
+        "plt.title(\"Fuel efficiency (algorithmic) — higher is better\"); plt.xlabel(\"Drive (ranked)\"); plt.ylabel(\"Efficiency (0–100)\")\n",
+        "plt.tight_layout(); plt.show()\n",
+        "\n",
+        "# RPM/MAF boosting viz\n",
+        "plt.figure(figsize=(6,5))\n",
+        "sns.scatterplot(x=dfeat[\"efficiency_algo\"], y=dfeat[\"frac_maf90\"], s=35)\n",
+        "plt.gca().invert_yaxis(); plt.title(\"Efficiency vs MAF90 exposure (↓ better)\")\n",
+        "plt.xlabel(\"Efficiency (Algorithmic)\"); plt.ylabel(\"Frac time ≥ MAF90\"); plt.tight_layout(); plt.show()\n",
+        "\n",
+        "plt.figure(figsize=(6,5))\n",
+        "sns.scatterplot(x=dfeat[\"efficiency_algo\"], y=dfeat[\"frac_rpm90\"], s=35)\n",
+        "plt.gca().invert_yaxis(); plt.title(\"Efficiency vs RPM90 exposure (↓ better)\")\n",
+        "plt.xlabel(\"Efficiency (Algorithmic)\"); plt.ylabel(\"Frac time ≥ RPM90\"); plt.tight_layout(); plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "c5nM0yQrg47K",
+        "outputId": "b8d8c3d1-2e5f-48eb-e131-88b7a14a6b13"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Learned weights: {'p_freq': np.float64(0.002), 'p_dur': np.float64(0.177), 'p_mag': np.float64(0.0), 'p_idle': np.float64(0.0), 'p_cv': np.float64(0.0), 'p_rpm': np.float64(0.191), 'p_maf': np.float64(0.094)}\n",
+            "Weights (opt with MAF/RPM): {'p_freq': np.float64(0.002), 'p_dur': np.float64(0.177), 'p_mag': np.float64(0.0), 'p_idle': np.float64(0.0), 'p_cv': np.float64(0.0), 'p_rpm': np.float64(0.191), 'p_maf': np.float64(0.094)}\n",
+            "Efficiency range: 67.49 → 83.17\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x400 with 1 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "          duration_min  distance_km    freq_pm  dur_frac  sharp_mag  \\\n",
+              "drive_id                                                              \n",
+              "1             2.458542     2.416524  17.083292  0.308448   0.518452   \n",
+              "2             3.134787     2.416456   6.061018  0.141690   0.721930   \n",
+              "3             3.337046     2.417647   7.191989  0.103884   0.485764   \n",
+              "4             2.704575     2.415658  10.722572  0.196272   0.637356   \n",
+              "5             3.364225     2.419536   8.917358  0.151347   0.455278   \n",
+              "\n",
+              "          idle_frac  idle_med_s  idle_epm  speed_cv  frac_rpm90  ...  \\\n",
+              "drive_id                                                         ...   \n",
+              "1          0.015873        1.95  0.406745  0.241577    0.052910  ...   \n",
+              "2          0.012195        1.95  0.319001  0.185610    0.024390  ...   \n",
+              "3          0.034615        1.95  0.898999  0.262189    0.000000  ...   \n",
+              "4          0.028436        1.95  0.739488  0.261099    0.009479  ...   \n",
+              "5          0.059259        3.25  0.891736  0.310562    0.000000  ...   \n",
+              "\n",
+              "             proxy  ineff_model  efficiency_algo    p_freq     p_dur  \\\n",
+              "drive_id                                                               \n",
+              "1         0.196296     0.311257        68.874253  0.884028  0.826427   \n",
+              "2         0.089837     0.231316        76.868438  0.311512  0.488368   \n",
+              "3         0.030385     0.168329        83.167083  0.376771  0.398705   \n",
+              "4         0.096445     0.230216        76.978434  0.599013  0.617624   \n",
+              "5         0.043333     0.184977        81.502330  0.484707  0.511632   \n",
+              "\n",
+              "             p_mag    p_idle      p_cv     p_rpm     p_maf  \n",
+              "drive_id                                                    \n",
+              "1         0.531375  0.585077  0.362415  0.795009  0.775653  \n",
+              "2         0.844244  0.577777  0.105226  0.618245  0.615414  \n",
+              "3         0.468625  0.630207  0.503834  0.434202  0.318954  \n",
+              "4         0.738829  0.614649  0.496166  0.506377  0.578304  \n",
+              "5         0.410943  0.705416  0.798527  0.434202  0.318954  \n",
+              "\n",
+              "[5 rows x 21 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-2e1fd087-c5b4-4155-b740-be817b770dad\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>duration_min</th>\n",
+              "      <th>distance_km</th>\n",
+              "      <th>freq_pm</th>\n",
+              "      <th>dur_frac</th>\n",
+              "      <th>sharp_mag</th>\n",
+              "      <th>idle_frac</th>\n",
+              "      <th>idle_med_s</th>\n",
+              "      <th>idle_epm</th>\n",
+              "      <th>speed_cv</th>\n",
+              "      <th>frac_rpm90</th>\n",
+              "      <th>...</th>\n",
+              "      <th>proxy</th>\n",
+              "      <th>ineff_model</th>\n",
+              "      <th>efficiency_algo</th>\n",
+              "      <th>p_freq</th>\n",
+              "      <th>p_dur</th>\n",
+              "      <th>p_mag</th>\n",
+              "      <th>p_idle</th>\n",
+              "      <th>p_cv</th>\n",
+              "      <th>p_rpm</th>\n",
+              "      <th>p_maf</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>drive_id</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2.458542</td>\n",
+              "      <td>2.416524</td>\n",
+              "      <td>17.083292</td>\n",
+              "      <td>0.308448</td>\n",
+              "      <td>0.518452</td>\n",
+              "      <td>0.015873</td>\n",
+              "      <td>1.95</td>\n",
+              "      <td>0.406745</td>\n",
+              "      <td>0.241577</td>\n",
+              "      <td>0.052910</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.196296</td>\n",
+              "      <td>0.311257</td>\n",
+              "      <td>68.874253</td>\n",
+              "      <td>0.884028</td>\n",
+              "      <td>0.826427</td>\n",
+              "      <td>0.531375</td>\n",
+              "      <td>0.585077</td>\n",
+              "      <td>0.362415</td>\n",
+              "      <td>0.795009</td>\n",
+              "      <td>0.775653</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3.134787</td>\n",
+              "      <td>2.416456</td>\n",
+              "      <td>6.061018</td>\n",
+              "      <td>0.141690</td>\n",
+              "      <td>0.721930</td>\n",
+              "      <td>0.012195</td>\n",
+              "      <td>1.95</td>\n",
+              "      <td>0.319001</td>\n",
+              "      <td>0.185610</td>\n",
+              "      <td>0.024390</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.089837</td>\n",
+              "      <td>0.231316</td>\n",
+              "      <td>76.868438</td>\n",
+              "      <td>0.311512</td>\n",
+              "      <td>0.488368</td>\n",
+              "      <td>0.844244</td>\n",
+              "      <td>0.577777</td>\n",
+              "      <td>0.105226</td>\n",
+              "      <td>0.618245</td>\n",
+              "      <td>0.615414</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>3.337046</td>\n",
+              "      <td>2.417647</td>\n",
+              "      <td>7.191989</td>\n",
+              "      <td>0.103884</td>\n",
+              "      <td>0.485764</td>\n",
+              "      <td>0.034615</td>\n",
+              "      <td>1.95</td>\n",
+              "      <td>0.898999</td>\n",
+              "      <td>0.262189</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.030385</td>\n",
+              "      <td>0.168329</td>\n",
+              "      <td>83.167083</td>\n",
+              "      <td>0.376771</td>\n",
+              "      <td>0.398705</td>\n",
+              "      <td>0.468625</td>\n",
+              "      <td>0.630207</td>\n",
+              "      <td>0.503834</td>\n",
+              "      <td>0.434202</td>\n",
+              "      <td>0.318954</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2.704575</td>\n",
+              "      <td>2.415658</td>\n",
+              "      <td>10.722572</td>\n",
+              "      <td>0.196272</td>\n",
+              "      <td>0.637356</td>\n",
+              "      <td>0.028436</td>\n",
+              "      <td>1.95</td>\n",
+              "      <td>0.739488</td>\n",
+              "      <td>0.261099</td>\n",
+              "      <td>0.009479</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.096445</td>\n",
+              "      <td>0.230216</td>\n",
+              "      <td>76.978434</td>\n",
+              "      <td>0.599013</td>\n",
+              "      <td>0.617624</td>\n",
+              "      <td>0.738829</td>\n",
+              "      <td>0.614649</td>\n",
+              "      <td>0.496166</td>\n",
+              "      <td>0.506377</td>\n",
+              "      <td>0.578304</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>3.364225</td>\n",
+              "      <td>2.419536</td>\n",
+              "      <td>8.917358</td>\n",
+              "      <td>0.151347</td>\n",
+              "      <td>0.455278</td>\n",
+              "      <td>0.059259</td>\n",
+              "      <td>3.25</td>\n",
+              "      <td>0.891736</td>\n",
+              "      <td>0.310562</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.043333</td>\n",
+              "      <td>0.184977</td>\n",
+              "      <td>81.502330</td>\n",
+              "      <td>0.484707</td>\n",
+              "      <td>0.511632</td>\n",
+              "      <td>0.410943</td>\n",
+              "      <td>0.705416</td>\n",
+              "      <td>0.798527</td>\n",
+              "      <td>0.434202</td>\n",
+              "      <td>0.318954</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>5 rows × 21 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2e1fd087-c5b4-4155-b740-be817b770dad')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-2e1fd087-c5b4-4155-b740-be817b770dad button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-2e1fd087-c5b4-4155-b740-be817b770dad');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-d6f1880a-6c17-4004-92bd-a423e6ed32e2\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d6f1880a-6c17-4004-92bd-a423e6ed32e2')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-d6f1880a-6c17-4004-92bd-a423e6ed32e2 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x400 with 1 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 600x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 600x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Shading + Sharp episode"
+      ],
+      "metadata": {
+        "id": "O6rqpJYJg-1z"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# === Cell 5 — Inspect a drive: timeline with HIGH-CONTRAST effectiveness gradient + sharp episode stats ===\n",
+        "if len(ranked) == 0:\n",
+        "    raise RuntimeError(\"No drives scored.\")\n",
+        "\n",
+        "drive_to_show = int(ranked.tail(1).index[0])\n",
+        "g = df[df[\"drive_id\"] == drive_to_show].copy()\n",
+        "t = (g[\"timestamp\"] - g[\"timestamp\"].iloc[0]).dt.total_seconds()\n",
+        "\n",
+        "g_sharp = (g[\"ACCEL\"].abs() > thr_accel) | (g[\"JERK\"].abs() > thr_jerk)\n",
+        "ineff_zone = g_sharp | g[\"IDLE_RULE\"]\n",
+        "\n",
+        "# Accel and jerk computed\n",
+        "A = g[\"ACCEL\"].abs()\n",
+        "J = g[\"JERK\"].abs()\n",
+        "a_lo, a_hi = np.nanquantile(A, [0.75, 0.95]) if A.notna().any() else (0.0, 1.0)\n",
+        "j_lo, j_hi = np.nanquantile(J, [0.75, 0.95]) if J.notna().any() else (0.0, 1.0)\n",
+        "a_span = max(1e-6, a_hi - a_lo); j_span = max(1e-6, j_hi - j_lo)\n",
+        "\n",
+        "# Efficient color grading\n",
+        "sev_a = np.clip((A - a_lo) / a_span, 0, 1)\n",
+        "sev_j = np.clip((J - j_lo) / j_span, 0, 1)\n",
+        "idle   = g[\"IDLE_RULE\"].astype(float)\n",
+        "ineff_raw = 0.45*sev_a + 0.25*sev_j + 0.30*idle\n",
+        "\n",
+        "focus = ineff_raw.where(ineff_zone, np.nan).dropna()\n",
+        "q_lo, q_hi = (np.nanquantile(focus, [0.15, 0.98]) if len(focus) > 0\n",
+        "              else np.nanquantile(ineff_raw, [0.15, 0.98]))\n",
+        "span = max(1e-6, q_hi - q_lo)\n",
+        "ineff01 = np.clip((ineff_raw - q_lo) / span, 0, 1)\n",
+        "GAMMA = 0.999\n",
+        "effectiveness = (1.0 - ineff01) ** GAMMA\n",
+        "\n",
+        "# ----  shader plot ----\n",
+        "import matplotlib as mpl\n",
+        "\n",
+        "# Fleet-wide thresholds (fallback to local if unavailable)\n",
+        "try:\n",
+        "    rpm90_thr = thr[\"RPM90\"]\n",
+        "except Exception:\n",
+        "    rpm90_thr = (np.nanquantile(df[\"RPM\"], 0.90) if \"RPM\" in df else np.nan)\n",
+        "\n",
+        "try:\n",
+        "    maf90_thr = thr[\"MAF90\"]\n",
+        "except Exception:\n",
+        "    maf90_thr = (np.nanquantile(df[\"MAF\"], 0.90) if \"MAF\" in df else np.nan)\n",
+        "\n",
+        "fig, ax1 = plt.subplots(figsize=(12, 4))\n",
+        "\n",
+        "# Left axis: SPEED\n",
+        "l_speed = ax1.plot(t, g[\"SPEED_ms\"], color=\"#a0662c\", alpha=0.65, label=\"Speed\", linewidth=1.4, zorder=3)[0]\n",
+        "\n",
+        "# First right axis: THROTTLE\n",
+        "# ax_thr = ax1.twinx()\n",
+        "# l_thr  = ax_thr.plot(t, g[\"THROTTLE_POS\"], color=\"#D62728\", alpha=0.65, label=\"Throttle\", zorder=3)[0]\n",
+        "# ax1.set_ylabel(\"Speed (m/s)\")                      # Hide legent title for space\n",
+        "# ax_thr.set_ylabel(\"Throttle (%)\")                  # Hide legent title for space\n",
+        "\n",
+        "# --- Add a second and third right axis (offset spines) for RPM & MAF ---\n",
+        "def _make_twin(ax, offset):\n",
+        "    ax_new = ax.twinx()\n",
+        "    ax_new.spines[\"right\"].set_position((\"axes\", offset))\n",
+        "    # Matplotlib trick to make extra right spine visible\n",
+        "    ax_new.spines[\"right\"].set_visible(True)\n",
+        "    return ax_new\n",
+        "\n",
+        "ax_rpm = _make_twin(ax1, 1.10)   # RPM axis (right, slight offset)\n",
+        "ax_maf = _make_twin(ax1, 1.20)   # MAF axis (right, further offset)\n",
+        "\n",
+        "# RPM line + RPM90 guide\n",
+        "lines = [l_speed]#, l_thr]\n",
+        "if \"RPM\" in g:\n",
+        "    l_rpm = ax_rpm.plot(t, g[\"RPM\"], color=\"#2c2ca0\", linewidth=1.0, alpha=0.9, label=\"RPM\")[0]\n",
+        "    if not np.isnan(rpm90_thr):\n",
+        "        ax_rpm.axhline(rpm90_thr, color=\"#2c2ca0\", linestyle=\"--\", linewidth=0.9, alpha=0.7)\n",
+        "    # ax_rpm.set_ylabel(\"RPM\", color=\"#2c2ca0\")      # Hide legent title for space\n",
+        "    ax_rpm.tick_params(axis='y', colors=\"#2c2ca0\")\n",
+        "    lines.append(l_rpm)\n",
+        "\n",
+        "# MAF line + MAF90 guide\n",
+        "if \"MAF\" in g:\n",
+        "    l_maf = ax_maf.plot(t, g[\"MAF\"], color=\"#a02c66\", linewidth=1.0, alpha=0.9, label=\"MAF\")[0]\n",
+        "    if not np.isnan(maf90_thr):\n",
+        "        ax_maf.axhline(maf90_thr, color=\"#a02c66\", linestyle=\"--\", linewidth=0.9, alpha=0.7)\n",
+        "    # ax_maf.set_ylabel(\"MAF\", color=\"#a02c66\")      # Hide legent title for space\n",
+        "    ax_maf.tick_params(axis='y', colors=\"#a02c66\")\n",
+        "    lines.append(l_maf)\n",
+        "\n",
+        "# --- effectiveness gradient background & inefficient zone shading ---\n",
+        "ymin, ymax = ax1.get_ylim()\n",
+        "grad = np.tile(effectiveness.to_numpy(), (128, 1))\n",
+        "ax1.imshow(\n",
+        "    grad, extent=[t.min(), t.max(), ymin, ymax],\n",
+        "    cmap=\"RdYlGn\", aspect=\"auto\", interpolation=\"bilinear\",\n",
+        "    alpha=0.50, vmin=0, vmax=1, origin=\"lower\", zorder=1\n",
+        ")\n",
+        "ax1.fill_between(t, ymin, ymax, where=ineff_zone.values,\n",
+        "                 color=\"black\", alpha=0.08, step=\"pre\", zorder=2)\n",
+        "\n",
+        "ax1.set_title(f\"Drive {drive_to_show} — effectiveness gradient (green=best, red=worst)\")\n",
+        "ax1.set_xlabel(\"Time (s)\")\n",
+        "\n",
+        "# --- legends (collect all available lines) ---\n",
+        "labels = [ln.get_label() for ln in lines]\n",
+        "fig.legend(lines, labels, loc=\"upper right\")\n",
+        "\n",
+        "# ----  colorbar placement ----\n",
+        "norm = mpl.colors.Normalize(vmin=0, vmax=1)\n",
+        "sm = mpl.cm.ScalarMappable(cmap=\"RdYlGn\", norm=norm); sm.set_array([])\n",
+        "cbar = plt.colorbar(sm, ax=ax1, pad=0.01, fraction=0.01, aspect=20)\n",
+        "# cbar.set_label(\"Effectiveness\", labelpad=10).      # Hide legent title for space\n",
+        "cbar.set_ticks([0.0, 0.25, 0.5, 0.75, 1.0])\n",
+        "cbar.set_ticklabels([\"Red (ws)\", \"Orange\", \"Yellow\", \"Lime\", \"Green (bs)\"])\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n",
+        "\n",
+        "# --- sharp episode table ---\n",
+        "starts, lens = run_lengths(g_sharp.values)\n",
+        "peaks, kinds = [], []\n",
+        "for st, ln in zip(starts, lens):\n",
+        "    seg = g.iloc[st:st+ln]\n",
+        "    pa, pj = float(seg[\"ACCEL\"].abs().max()), float(seg[\"JERK\"].abs().max())\n",
+        "    peaks.append(max(pa / (thr_accel + 1e-6), pj / (thr_jerk + 1e-6)))\n",
+        "    kinds.append(\"accel\" if pa >= pj else \"jerk\")\n",
+        "\n",
+        "base_sec_local = infer_base_interval_seconds(g[\"timestamp\"])\n",
+        "ep_df = pd.DataFrame({\n",
+        "    \"kind\": kinds,\n",
+        "    \"peak_over_thr\": peaks,\n",
+        "    \"dur_s\": lens * base_sec_local\n",
+        "})\n",
+        "display(ep_df.describe())"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 698
+        },
+        "id": "9DkkmorWhEBV",
+        "outputId": "aa37b05a-36b1-4eb9-c7d0-9b75f7f25a64"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x400 with 4 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       peak_over_thr      dur_s\n",
+              "count      29.000000  29.000000\n",
+              "mean        2.080459   1.344828\n",
+              "std         0.830496   0.934397\n",
+              "min         1.000000   0.650000\n",
+              "25%         1.333333   0.650000\n",
+              "50%         1.999999   0.650000\n",
+              "75%         2.499999   1.950000\n",
+              "max         3.999998   3.250000"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-088dc23b-0d2a-4914-a31f-dc7fd4703866\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>peak_over_thr</th>\n",
+              "      <th>dur_s</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>29.000000</td>\n",
+              "      <td>29.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>2.080459</td>\n",
+              "      <td>1.344828</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.830496</td>\n",
+              "      <td>0.934397</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.650000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>1.333333</td>\n",
+              "      <td>0.650000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>1.999999</td>\n",
+              "      <td>0.650000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>2.499999</td>\n",
+              "      <td>1.950000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>3.999998</td>\n",
+              "      <td>3.250000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-088dc23b-0d2a-4914-a31f-dc7fd4703866')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-088dc23b-0d2a-4914-a31f-dc7fd4703866 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-088dc23b-0d2a-4914-a31f-dc7fd4703866');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-094f53dc-d39f-423d-90cb-2042419b252f\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-094f53dc-d39f-423d-90cb-2042419b252f')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-094f53dc-d39f-423d-90cb-2042419b252f button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"display(ep_df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"peak_over_thr\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.611463123920034,\n        \"min\": 0.8304956668168556,\n        \"max\": 29.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2.0804589587359494,\n          1.9999992200003052,\n          29.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dur_s\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.81772084491103,\n        \"min\": 0.65,\n        \"max\": 29.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          29.0,\n          1.3448275862068964,\n          3.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# ML - GradientBoostingRegressor"
+      ],
+      "metadata": {
+        "id": "oOiQyEl9hJSF"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# === Cell 6 - monotonic GBM + isotonic calibration (learned once) ===\n",
+        "from sklearn.experimental import enable_hist_gradient_boosting  # noqa: F401\n",
+        "from sklearn.ensemble import HistGradientBoostingRegressor\n",
+        "from sklearn.isotonic import IsotonicRegression\n",
+        "from sklearn.model_selection import GroupKFold\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "import numpy as np, pandas as pd, matplotlib.pyplot as plt, seaborn as sns\n",
+        "\n",
+        "# 1) Build ML features (length-invariant) & target from algorithm\n",
+        "def agg_for_ml(g):\n",
+        "    dt = g[\"timestamp\"].diff().dt.total_seconds().fillna(0).clip(lower=0, upper=10*base_sec)\n",
+        "    T = float(dt.sum()); mins = max(1e-6, T/60)\n",
+        "    def q(s,p): return float(np.nanquantile(s, p))\n",
+        "    thr_a, thr_j = thr_accel, thr_jerk\n",
+        "    sharp = ((g[\"ACCEL\"].abs()>thr_a) | (g[\"JERK\"].abs()>thr_j)).values\n",
+        "    fr = len(np.flatnonzero(np.diff(np.r_[False, sharp, False])))//2 / mins\n",
+        "\n",
+        "    feats = {\n",
+        "        \"duration_min\":  mins,\n",
+        "        \"distance_km\":   g[\"dist_m\"].sum()/1000.0,\n",
+        "        \"speed_mean\":    float(g[\"SPEED_ms\"].mean()),\n",
+        "        \"speed_q90\":     q(g[\"SPEED_ms\"],0.90),\n",
+        "        \"accel_q90\":     q(g[\"ACCEL\"].abs(),0.90),\n",
+        "        \"jerk_q90\":      q(g[\"JERK\"].abs(),0.90),\n",
+        "        \"sharp_freq_pm\": fr,\n",
+        "        \"idle_frac\":     float(g[\"IDLE_RULE\"].mean()),\n",
+        "        \"idle_epm\":      len(np.flatnonzero(np.diff(np.r_[False, g[\"IDLE_RULE\"].values, False])))//2 / mins,\n",
+        "        \"rpm_q90\":       q(g[\"RPM\"],0.90) if \"RPM\" in g else 0.0,\n",
+        "        \"load_q85\":      q(g[\"ENGINE_LOAD\"],0.85) if \"ENGINE_LOAD\" in g else 0.0,\n",
+        "        \"thr_q85\":       q(g[\"THROTTLE_POS\"],0.85) if \"THROTTLE_POS\" in g else 0.0,\n",
+        "        \"maf_q90\":       q(g[\"MAF\"],0.90) if \"MAF\" in g else 0.0,\n",
+        "        # strong weights. (MAF/RPM))\n",
+        "        \"frac_rpm90\": float((g[\"RPM\"] >= thr_rpm90).mean()) if \"RPM\" in g and not np.isnan(thr_rpm90) else 0.0,\n",
+        "        \"frac_maf90\": float((g[\"MAF\"] >= thr_maf90).mean()) if \"MAF\" in g and not np.isnan(thr_maf90) else 0.0,\n",
+        "        # (optional but useful) a simple fuel-intensity proxy that remains monotonic:\n",
+        "        \"fuel_intensity\": (np.nan_to_num(g[\"maf_q90\"]) * np.nan_to_num(g[\"rpm_q90\"]))\n",
+        "                          if {\"maf_q90\",\"rpm_q90\"}.issubset(g.columns) else 0.0,\n",
+        "            }\n",
+        "    return feats\n",
+        "\n",
+        "rows, y = [], []\n",
+        "groups = []\n",
+        "for gid in per_drive.index:\n",
+        "    g = df[df[\"drive_id\"]==gid]\n",
+        "    if len(g) < 5: continue\n",
+        "    rows.append(agg_for_ml(g))\n",
+        "    y.append(per_drive.loc[gid, \"efficiency_algo\"])\n",
+        "    groups.append(g[\"source_file\"].iloc[0])\n",
+        "\n",
+        "X = pd.DataFrame(rows)\n",
+        "y = np.array(y); groups = np.array(groups)\n",
+        "\n",
+        "# Standardize non-length features; keep scalers for inference\n",
+        "num_cols = [c for c in X.columns if c not in [\"duration_min\",\"distance_km\"]]\n",
+        "sc_ml = StandardScaler().fit(X[num_cols])\n",
+        "X[num_cols] = sc_ml.transform(X[num_cols])\n",
+        "\n",
+        "# 2) Monotonic constraints: features that should LOWER efficiency as they increase ⇒ -1, neutral ⇒ 0\n",
+        "mono_features = [\"accel_q90\",\"jerk_q90\",\"sharp_freq_pm\",\"idle_frac\",\"idle_epm\",\"rpm_q90\",\"load_q85\",\"thr_q85\",\"maf_q90\", \"frac_rpm90\",\"frac_maf90\", \"fuel_intensity\"]\n",
+        "mono = []\n",
+        "for col in X.columns:\n",
+        "    mono.append(-1 if col in mono_features else 0)\n",
+        "\n",
+        "# 3) Grouped CV (by file) for calibration; fit on train, calibrate on val once\n",
+        "gkf = GroupKFold(n_splits=min(5, len(np.unique(groups))))\n",
+        "train_idx, val_idx = next(gkf.split(X, y, groups=groups))  # first split is fine for a single calibrator\n",
+        "\n",
+        "Xtr, Xval = X.iloc[train_idx], X.iloc[val_idx]\n",
+        "ytr, yval = y[train_idx], y[val_idx]\n",
+        "wtr = np.clip(Xtr[\"duration_min\"].values, 0.5, None) * (\n",
+        "      1.0 + 0.30*np.nan_to_num(Xtr.get(\"frac_maf90\", 0.0)) +\n",
+        "      0.20*np.nan_to_num(Xtr.get(\"frac_rpm90\", 0.0)))  # weight longer drives a bit more\n",
+        "\n",
+        "gbm = HistGradientBoostingRegressor(\n",
+        "    loss=\"squared_error\",\n",
+        "    max_depth=10, # increase if show underfit\n",
+        "    learning_rate=0.07,\n",
+        "    max_bins=255,\n",
+        "    early_stopping=True,\n",
+        "    monotonic_cst=mono,\n",
+        "    random_state=42\n",
+        ")\n",
+        "gbm.fit(Xtr, ytr, sample_weight=wtr)\n",
+        "\n",
+        "# 4) One-time isotonic calibration learned from validation predictions (not per batch)\n",
+        "raw_val = gbm.predict(Xval)\n",
+        "iso_cal = IsotonicRegression(out_of_bounds=\"clip\").fit(raw_val, yval)\n",
+        "\n",
+        "# 5) Fit final model on all data (same hyperparams), keep calibrator & scaler\n",
+        "w_all = np.clip(X[\"duration_min\"].values, 0.5, None) * (\n",
+        "        1.0 + 0.30*np.nan_to_num(X.get(\"frac_maf90\", 0.0)) +\n",
+        "        0.20*np.nan_to_num(X.get(\"frac_rpm90\", 0.0))\n",
+        ")\n",
+        "# Diagnostics: train-val parity & distribution\n",
+        "raw_all = gbm.predict(X)\n",
+        "pred_all = iso_cal.predict(raw_all)\n",
+        "plt.figure(figsize=(6,5))\n",
+        "sns.scatterplot(x=y, y=pred_all, s=35)\n",
+        "plt.xlabel(\"Algo (teacher)\"); plt.ylabel(\"ML (calibrated)\")\n",
+        "plt.title(\"Parity — ML\"); plt.grid(True); plt.tight_layout(); plt.show()\n",
+        "print(\"Calibrated MAE:\", np.mean(np.abs(y - pred_all)).round(2))\n",
+        "\n",
+        "plt.figure(figsize=(12,4))\n",
+        "sns.histplot(pred_all, bins=30, kde=True, color=\"#6BBBAE\")\n",
+        "plt.title(\"Predicted efficiency — ML (bounded, calibrated)\"); plt.xlabel(\"Efficiency (0–100)\")\n",
+        "plt.tight_layout(); plt.show()\n",
+        "\n",
+        "# 6) Inference function (length-invariant; reuses sc_ml, gbm, iso_cal)\n",
+        "def predict_efficiency_ml(df_new: pd.DataFrame) -> float:\n",
+        "    d = df_new.copy()\n",
+        "    d[\"timestamp\"] = ensure_dt(d[\"timestamp\"])\n",
+        "    d = safe_num(d, [c for c in CORE_COLS if c!=\"timestamp\"]).dropna(subset=[\"timestamp\"]).sort_values(\"timestamp\")\n",
+        "    if len(d)<5: return float(\"nan\")\n",
+        "    base = infer_base_interval_seconds(d[\"timestamp\"])\n",
+        "    d[\"SPEED_ms\"] = d[\"SPEED\"]*KMH_TO_MS\n",
+        "    d[\"ACCEL\"] = d[\"SPEED_ms\"].diff()/max(base,1e-3)\n",
+        "    d[\"JERK\"]  = d[\"ACCEL\"].diff()/max(base,1e-3)\n",
+        "    # rule idle (same as earlier)\n",
+        "    speed_low = (d[\"SPEED_ms\"].abs() <= 0.6)\n",
+        "    thr_low   = (d[\"THROTTLE_POS\"] <= df[\"THROTTLE_POS\"].quantile(0.10)) if \"THROTTLE_POS\" in df else True\n",
+        "    load_low  = (d[\"ENGINE_LOAD\"]  <= df[\"ENGINE_LOAD\"].quantile(0.15))  if \"ENGINE_LOAD\" in df else True\n",
+        "    maf_low   = (d[\"MAF\"]          <= df[\"MAF\"].quantile(0.10))          if \"MAF\" in df else True\n",
+        "    accel_low = (d[\"ACCEL\"].abs()  <= df[\"ACCEL\"].abs().quantile(0.20))\n",
+        "    from scipy.signal import medfilt as _med\n",
+        "    d[\"IDLE_RULE\"] = _med((speed_low & thr_low & load_low & maf_low & accel_low).astype(int), 5).astype(bool)\n",
+        "\n",
+        "    # Minimal windows for features used by agg_for_ml()\n",
+        "    W10 = rows_for(10, base)\n",
+        "    for col in [\"SPEED_ms\"]:\n",
+        "        d[f\"{col}_mean_w10\"] = d[col].rolling(W10,1).mean()\n",
+        "        d[f\"{col}_std_w10\"]  = d[col].rolling(W10,1).std()\n",
+        "    d[\"dist_m\"] = d[\"SPEED_ms\"] * d[\"timestamp\"].diff().dt.total_seconds().fillna(0).clip(lower=0, upper=10*base)\n",
+        "\n",
+        "    feats = agg_for_ml(d)\n",
+        "    x = pd.DataFrame([feats], columns=X.columns)\n",
+        "    x[num_cols] = sc_ml.transform(x[num_cols])  # reuse training scaler\n",
+        "    y_raw = gbm.predict(x)\n",
+        "    return float(np.clip(iso_cal.predict(y_raw)[0], 0, 100))\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "cb01WS20hLBR",
+        "outputId": "104f3b68-1a69-495b-a4a6-04d1b0732282"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 600x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Calibrated MAE: 6.99\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x400 with 1 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Chunking test"
+      ],
+      "metadata": {
+        "id": "wfjkXLaChSQj"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# === Cell 7 — Robustness to trip length: random chunking test ===\n",
+        "rng = np.random.default_rng(42)\n",
+        "rows = []\n",
+        "for gid in ranked.index[: min(50, len(ranked))]:  # test on up to 50 drives\n",
+        "    g = df[df[\"drive_id\"]==gid].copy()\n",
+        "    if len(g) < 120: continue\n",
+        "    n = len(g)\n",
+        "    for L in [60,120,180,300,600]:  # chunk lengths in seconds\n",
+        "        step = max(1, int(round(L/max(base_sec,1e-3))))\n",
+        "        for _ in range(4):\n",
+        "            start = rng.integers(0, max(1, n-step))\n",
+        "            chunk = g.iloc[start:start+step].copy()\n",
+        "            try:\n",
+        "                eff = predict_efficiency_ml(chunk[CORE_COLS])\n",
+        "            except KeyError as e:\n",
+        "                print(f\"⚠️ Missing {e} in gid={gid}, L={L}, skipping\")\n",
+        "                eff = np.nan\n",
+        "            rows.append({\"drive_id\":gid, \"chunk_sec\":L, \"pred_eff\":eff})\n",
+        "\n",
+        "rob = pd.DataFrame(rows).dropna()\n",
+        "plt.figure(figsize=(8,4))\n",
+        "sns.boxplot(x=\"chunk_sec\", y=\"pred_eff\", data=rob, color=\"#9CCB86\")\n",
+        "plt.title(\"Predicted efficiency vs chunk length (robustness)\")\n",
+        "plt.xlabel(\"Chunk length (s)\"); plt.ylabel(\"Predicted efficiency (0–100)\")\n",
+        "plt.tight_layout(); plt.show()\n",
+        "\n",
+        "sd_by_len = rob.groupby(\"chunk_sec\")[\"pred_eff\"].std()\n",
+        "print(\"Std dev of predictions by chunk length:\\n\", sd_by_len.round(2))\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ML2E4XJWhVSA",
+        "outputId": "db374879-1d97-4e02-9c30-876c86c14320"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Std dev of predictions by chunk length:\n",
+            " chunk_sec\n",
+            "60     0.0\n",
+            "120    0.0\n",
+            "180    0.0\n",
+            "300    0.0\n",
+            "600    0.0\n",
+            "Name: pred_eff, dtype: float64\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Dashboards"
+      ],
+      "metadata": {
+        "id": "rLyXg8__hcI9"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# === Cell 8.1 — Sanity dashboards ===\n",
+        "# Safe fleet thresholds (fallback to fleet quantiles if 'thr' dict isn't defined)\n",
+        "rpm90_thr = None\n",
+        "maf90_thr = None\n",
+        "\n",
+        "if \"RPM\" in df.columns:\n",
+        "    try:\n",
+        "        rpm90_thr = float(thr[\"RPM90\"])\n",
+        "    except Exception:\n",
+        "        rpm90_thr = float(np.nanquantile(df[\"RPM\"], 0.90)) if df[\"RPM\"].notna().any() else np.nan\n",
+        "\n",
+        "if \"MAF\" in df.columns:\n",
+        "    try:\n",
+        "        maf90_thr = float(thr[\"MAF90\"])\n",
+        "    except Exception:\n",
+        "        maf90_thr = float(np.nanquantile(df[\"MAF\"], 0.90)) if df[\"MAF\"].notna().any() else np.nan\n",
+        "\n",
+        "# Per-drive exposure (fraction of time ≥ 90th percentile), aligned to dfeat.index\n",
+        "dfeat = dfeat.copy()\n",
+        "\n",
+        "if \"RPM\" in df.columns and rpm90_thr is not None and not np.isnan(rpm90_thr):\n",
+        "    exp_rpm = df.groupby(\"drive_id\")[\"RPM\"].apply(lambda s: (s >= rpm90_thr).mean())\n",
+        "    dfeat[\"frac_rpm90\"] = exp_rpm.reindex(dfeat.index).fillna(0.0).astype(float)\n",
+        "else:\n",
+        "    dfeat[\"frac_rpm90\"] = 0.0\n",
+        "\n",
+        "if \"MAF\" in df.columns and maf90_thr is not None and not np.isnan(maf90_thr):\n",
+        "    exp_maf = df.groupby(\"drive_id\")[\"MAF\"].apply(lambda s: (s >= maf90_thr).mean())\n",
+        "    dfeat[\"frac_maf90\"] = exp_maf.reindex(dfeat.index).fillna(0.0).astype(float)\n",
+        "else:\n",
+        "    dfeat[\"frac_maf90\"] = 0.0\n",
+        "\n",
+        "# Distribution: algorithmic efficiency\n",
+        "plt.figure(figsize=(12,4))\n",
+        "plt.subplot(1,2,1)\n",
+        "sns.histplot(dfeat[\"efficiency_algo\"], bins=30, kde=True, color=\"#4C78A8\")\n",
+        "plt.title(\"Distribution — Algorithmic efficiency\"); plt.xlabel(\"Efficiency (0–100)\")\n",
+        "\n",
+        "# Efficiency vs Idle (↓ better)\n",
+        "plt.subplot(1,2,2)\n",
+        "sns.scatterplot(x=dfeat[\"efficiency_algo\"], y=dfeat[\"idle_frac\"], s=35)\n",
+        "plt.gca().invert_yaxis()\n",
+        "plt.title(\"Efficiency vs Idle fraction (↓ better)\"); plt.xlabel(\"Efficiency\"); plt.ylabel(\"Idle fraction\")\n",
+        "plt.tight_layout(); plt.show()\n",
+        "\n",
+        "# Efficiency vs sharp-event frequency (↓ better)\n",
+        "plt.figure(figsize=(12,4))\n",
+        "plt.subplot(1,2,1)\n",
+        "sns.scatterplot(x=dfeat[\"efficiency_algo\"], y=dfeat[\"freq_pm\"], s=35)\n",
+        "plt.gca().invert_yaxis()\n",
+        "plt.title(\"Efficiency vs sharp-event frequency (↓ better)\"); plt.xlabel(\"Efficiency\"); plt.ylabel(\"Events per min\")\n",
+        "plt.tight_layout(); plt.show()\n",
+        "\n",
+        "# # Scatter: Efficiency vs MAF90 exposure (expected ↓ trend)\n",
+        "# if \"frac_maf90\" in dfeat.columns:\n",
+        "#     plt.figure(figsize=(6,5))\n",
+        "#     sns.scatterplot(x=dfeat[\"efficiency_algo\"], y=dfeat[\"frac_maf90\"], s=35)\n",
+        "#     plt.gca().invert_yaxis()\n",
+        "#     plt.title(\"Efficiency vs MAF90 exposure (↓ better)\")\n",
+        "#     plt.xlabel(\"Efficiency (0–100)\"); plt.ylabel(\"Frac time ≥ MAF90\")\n",
+        "#     plt.tight_layout(); plt.show()\n",
+        "\n",
+        "# # Scatter: Efficiency vs RPM90 exposure (expected ↓ trend)\n",
+        "# if \"frac_rpm90\" in dfeat.columns:\n",
+        "#     plt.figure(figsize=(6,5))\n",
+        "#     sns.scatterplot(x=dfeat[\"efficiency_algo\"], y=dfeat[\"frac_rpm90\"], s=35)\n",
+        "#     plt.gca().invert_yaxis()\n",
+        "#     plt.title(\"Efficiency vs RPM90 exposure (↓ better)\")\n",
+        "#     plt.xlabel(\"Efficiency (0–100)\"); plt.ylabel(\"Frac time ≥ RPM90\")\n",
+        "#     plt.tight_layout(); plt.show()\n",
+        "\n",
+        "# Per-drive p90/means for 2D view (guard missing cols)\n",
+        "aggs = {}\n",
+        "if \"SPEED_ms\" in df.columns:\n",
+        "    aggs[\"speed_mean\"] = (\"SPEED_ms\",\"mean\")\n",
+        "if \"MAF\" in df.columns:\n",
+        "    aggs[\"maf_p90\"]   = (\"MAF\", lambda s: np.nanquantile(s, 0.90) if s.notna().any() else np.nan)\n",
+        "if \"RPM\" in df.columns:\n",
+        "    aggs[\"rpm_p90\"]   = (\"RPM\", lambda s: np.nanquantile(s, 0.90) if s.notna().any() else np.nan)\n",
+        "\n",
+        "if aggs:\n",
+        "    per_drive_stats = df.groupby(\"drive_id\").agg(**aggs).join(dfeat[[\"efficiency_algo\"]], how=\"left\")\n",
+        "    if {\"speed_mean\",\"maf_p90\"}.issubset(per_drive_stats.columns):\n",
+        "        plt.subplot(1,2,2)\n",
+        "        sc = plt.scatter(per_drive_stats[\"speed_mean\"], per_drive_stats[\"maf_p90\"],\n",
+        "                         c=per_drive_stats[\"efficiency_algo\"], s=35, alpha=0.8, cmap=\"viridis\")\n",
+        "        plt.colorbar(sc, label=\"Efficiency (0–100)\")\n",
+        "        plt.title(\"Mean Speed vs MAF p90 colored by Efficiency\")\n",
+        "        plt.xlabel(\"Mean speed (m/s)\"); plt.ylabel(\"MAF p90\")\n",
+        "        plt.tight_layout(); plt.show()\n",
+        "\n",
+        "\n",
+        "# === Cell 8.2 — Per-drive timeline diagnostics & scoring ===\n",
+        "def plot_drive_timeline(drive_id, df, thr_accel, thr_jerk):\n",
+        "    \"\"\"Plot one drive with gradient effectiveness shading + RPM/MAF guides + sharp episode stats.\"\"\"\n",
+        "    g = df[df[\"drive_id\"]==drive_id].copy()\n",
+        "    if g.empty:\n",
+        "        return\n",
+        "\n",
+        "    # time relative to start\n",
+        "    t = (g[\"timestamp\"] - g[\"timestamp\"].iloc[0]).dt.total_seconds()\n",
+        "\n",
+        "    # inefficiency zones = sharp accel/jerk + idle\n",
+        "    g_sharp = (g[\"ACCEL\"].abs()>thr_accel) | (g[\"JERK\"].abs()>thr_jerk)\n",
+        "    ineff_zone = g_sharp | g[\"IDLE_RULE\"]\n",
+        "\n",
+        "    # ---------- effectiveness gradient (same logic as Cell 5) ----------\n",
+        "    A = g[\"ACCEL\"].abs().fillna(0)\n",
+        "    J = g[\"JERK\"].abs().fillna(0)\n",
+        "    a_hi = np.nanpercentile(A, 98) if A.notna().any() else 1.0\n",
+        "    j_hi = np.nanpercentile(J, 98) if J.notna().any() else 1.0\n",
+        "\n",
+        "    sev_a = np.clip((A - thr_accel) / max(1e-6, a_hi - thr_accel), 0, 1)\n",
+        "    sev_j = np.clip((J - thr_jerk)  / max(1e-6, j_hi - thr_jerk ), 0, 1)\n",
+        "    idle  = g[\"IDLE_RULE\"].astype(float)\n",
+        "    ineff_raw = 0.45*sev_a + 0.25*sev_j + 0.30*idle\n",
+        "\n",
+        "    # Contrast calibration (focus on actual inefficient region if present)\n",
+        "    focus = ineff_raw.where(ineff_zone, np.nan).dropna()\n",
+        "    if len(focus) > 0:\n",
+        "        q_lo, q_hi = np.nanquantile(focus, [0.15, 0.98])\n",
+        "    else:\n",
+        "        q_lo, q_hi = np.nanquantile(ineff_raw, [0.15, 0.98])\n",
+        "    span = max(1e-6, q_hi - q_lo)\n",
+        "    ineff01 = np.clip((ineff_raw - q_lo) / span, 0, 1)\n",
+        "    GAMMA = 0.8  # <1 → more contrast near the extremes\n",
+        "    effectiveness = (1.0 - ineff01) ** GAMMA  # 1=green (best), 0=red (worst)\n",
+        "    # -------------------------------------------------------------------\n",
+        "\n",
+        "    import matplotlib as mpl\n",
+        "    fig, ax1 = plt.subplots(figsize=(12,4))\n",
+        "    l1 = ax1.plot(t, g[\"SPEED_ms\"], label=\"Speed\", linewidth=1.3, zorder=3)[0]\n",
+        "    ax2 = ax1.twinx()\n",
+        "    # Throttle might be missing in some datasets; guard it\n",
+        "    if \"THROTTLE_POS\" in g.columns:\n",
+        "        l2 = ax2.plot(t, g[\"THROTTLE_POS\"], color=\"#D62728\", alpha=0.6, label=\"Throttle\", zorder=3)[0]\n",
+        "        lines = [l1, l2]\n",
+        "    else:\n",
+        "        lines = [l1]\n",
+        "\n",
+        "    # Helper to create offset right axes for extra series\n",
+        "    def _make_twin(ax, offset):\n",
+        "        ax_new = ax.twinx()\n",
+        "        ax_new.spines[\"right\"].set_position((\"axes\", offset))\n",
+        "        ax_new.spines[\"right\"].set_visible(True)\n",
+        "        return ax_new\n",
+        "\n",
+        "    # Fleet thresholds (fallback to fleet quantiles)\n",
+        "    rpm90_thr = None\n",
+        "    maf90_thr = None\n",
+        "    if \"RPM\" in df.columns:\n",
+        "        try:\n",
+        "            rpm90_thr = float(thr[\"RPM90\"])\n",
+        "        except Exception:\n",
+        "            rpm90_thr = float(np.nanquantile(df[\"RPM\"], 0.90)) if df[\"RPM\"].notna().any() else np.nan\n",
+        "    if \"MAF\" in df.columns:\n",
+        "        try:\n",
+        "            maf90_thr = float(thr[\"MAF90\"])\n",
+        "        except Exception:\n",
+        "            maf90_thr = float(np.nanquantile(df[\"MAF\"], 0.90)) if df[\"MAF\"].notna().any() else np.nan\n",
+        "\n",
+        "    # Extra axes for RPM/MAF (right side offsets)\n",
+        "    ax_rpm = _make_twin(ax1, 1.10)\n",
+        "    ax_maf = _make_twin(ax1, 1.20)\n",
+        "\n",
+        "    if \"RPM\" in g.columns:\n",
+        "        l_rpm = ax_rpm.plot(t, g[\"RPM\"], color=\"#2c2ca0\", linewidth=1.0, alpha=0.9, label=\"RPM\")[0]\n",
+        "        if rpm90_thr is not None and not np.isnan(rpm90_thr):\n",
+        "            ax_rpm.axhline(rpm90_thr, color=\"#2c2ca0\", linestyle=\"--\", linewidth=0.9, alpha=0.7)\n",
+        "        ax_rpm.tick_params(axis='y', colors=\"#2c2ca0\")\n",
+        "        lines.append(l_rpm)\n",
+        "\n",
+        "    if \"MAF\" in g.columns:\n",
+        "        l_maf = ax_maf.plot(t, g[\"MAF\"], color=\"#a02c66\", linewidth=1.0, alpha=0.9, label=\"MAF\")[0]\n",
+        "        if maf90_thr is not None and not np.isnan(maf90_thr):\n",
+        "            ax_maf.axhline(maf90_thr, color=\"#a02c66\", linestyle=\"--\", linewidth=0.9, alpha=0.7)\n",
+        "        ax_maf.tick_params(axis='y', colors=\"#a02c66\")\n",
+        "        lines.append(l_maf)\n",
+        "\n",
+        "    # Effectiveness background & inefficient zone shading\n",
+        "    ymin, ymax = ax1.get_ylim()\n",
+        "    grad = np.tile(effectiveness.to_numpy(), (128, 1))\n",
+        "    ax1.imshow(\n",
+        "        grad, extent=[t.min(), t.max(), ymin, ymax],\n",
+        "        cmap=\"RdYlGn\", aspect=\"auto\", interpolation=\"bilinear\",\n",
+        "        alpha=0.50, vmin=0, vmax=1, origin=\"lower\", zorder=1\n",
+        "    )\n",
+        "    ax1.fill_between(t, ymin, ymax, where=ineff_zone.values,\n",
+        "                     color=\"black\", alpha=0.08, step=\"pre\", zorder=2)\n",
+        "\n",
+        "    eff = float(dfeat.loc[drive_id, \"efficiency_algo\"])\n",
+        "    ax1.set_title(f\"Drive {drive_id} — efficiency {eff:.1f} (0–100) | gradient + RPM/MAF guides\")\n",
+        "    # ax1.set_xlabel(\"Time (s)\")\n",
+        "    # ax1.set_ylabel(\"Speed (m/s)\")\n",
+        "    # ax2.set_ylabel(\"Throttle (%)\")\n",
+        "\n",
+        "    labels = [ln.get_label() for ln in lines]\n",
+        "    fig.legend(lines, labels, loc=\"upper right\")\n",
+        "\n",
+        "    # colorbar\n",
+        "    norm = mpl.colors.Normalize(vmin=0, vmax=1)\n",
+        "    sm = mpl.cm.ScalarMappable(cmap=\"RdYlGn\", norm=norm); sm.set_array([])\n",
+        "    cbar = plt.colorbar(sm, ax=ax1, pad=0.01, fraction=0.01, aspect=20)\n",
+        "    # cbar.set_label(\"Effectiveness\", labelpad=10)\n",
+        "    cbar.set_ticks([0.0, 0.25, 0.5, 0.75, 1.0])\n",
+        "    cbar.set_ticklabels([\"Red (worst)\", \"Orange\", \"Yellow\", \"Lime\", \"Green (best)\"])\n",
+        "\n",
+        "    plt.tight_layout(); plt.show()\n",
+        "\n",
+        "    # --- sharp episode stats ---\n",
+        "    starts, lens = run_lengths(g_sharp.values)\n",
+        "    peaks, kinds = [], []\n",
+        "    for st, ln in zip(starts, lens):\n",
+        "        seg = g.iloc[st:st+ln]\n",
+        "        pa, pj = float(seg[\"ACCEL\"].abs().max()), float(seg[\"JERK\"].abs().max())\n",
+        "        peaks.append(max(pa/(thr_accel+1e-6), pj/(thr_jerk+1e-6)))\n",
+        "        kinds.append(\"accel\" if pa>=pj else \"jerk\")\n",
+        "    ep_df = pd.DataFrame({\"kind\":kinds, \"peak_over_thr\":peaks,\n",
+        "                          \"dur_s\": lens*infer_base_interval_seconds(g[\"timestamp\"])})\n",
+        "    display(ep_df.describe())\n",
+        "\n",
+        "# Example: plot best and worst drives\n",
+        "best_id  = int(dfeat[\"efficiency_algo\"].idxmax())\n",
+        "worst_id = int(dfeat[\"efficiency_algo\"].idxmin())\n",
+        "plot_drive_timeline(best_id, df, thr_accel, thr_jerk)\n",
+        "plot_drive_timeline(worst_id, df, thr_accel, thr_jerk)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "4cMXW8pHhfGO",
+        "outputId": "20516cdd-313b-4e90-823a-8733e1c3eade"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x400 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x400 with 2 Axes>"
+            ],
+            "image/png": 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+mjZtqtIz+Pz5cxw7dgxBQUFFxkyVD2cN6pm1tTW8vb2xc+dOpKenQy6Xo2PHjgUeGxcXh+TkZHh7exe4PzExUfnfN27cwOLFi3H69Ol8v0iSk5NVXtesWTPfF4GlpSWuX79ebPzGxsYYOXIkRo4cicePH+Ps2bPYsGED9uzZAyMjo3xDkv+9u83R0RESiQT3798HANy9exeiKBb6hZ87sfXu3bsAgIkTJxYaW3JyMjIzM5Genl7gXXX169cvdhgUyBkm/O6773Do0CEEBQXh5cuXOHLkCPr27atst1atWqFjx45Yvnw51q9fj1atWiEgIABBQUFq3Q3apUsXfP755zh69Ch27NiB9u3bw8zMrMBj5XI5du3aBS8vL2X7ATm/XNeuXYtTp07lS55q1qwJHx+fEsfzqv+2YbVq1WBnZ4cHDx4oy5YtW4YxY8YgPDwcQM5cvCFDhuDs2bO4c+eO8rgqVaoUODSZm4QVdWdj7i9BZ2fnQo95+vQp0tLSUL9+/Xz7nJycoFAo8M8//+CNN97Itz8+Ph4ACjy3QYMG+eb4GRkZoWbNmiplJf1Zzb1WvXr1VPZbW1vD0tKykHeXX0Gf73r16mHPnj3K161bt4adnR127NgBb29vKBQK7Ny5Ex06dCj0M/Zf7u7uxU5yL42S/NsWpKTfG7lK8p1379491K9fv9jJ9N27d8esWbPw4MED1K5dG3v37kVWVpbObiah8osJlgF07doVU6ZMwZMnT9C2bdt8f7nlUigUsLGxKXAeFZDzpQwASUlJGDBgAMzMzDBq1Cg4OjrCxMQEf/75JxYtWqQyiRqA1u7Wsre3R2BgIN566y107doVe/fuxfz584v8gvrvl5xCoYAgCIiIiCgwrty7s8R/5/1MmDABjRo1KrBuU1PTEs8vKoqHhwdq166NPXv2ICgoCIcPH0Z6errKPDlBELB06VJcvHgRhw8fxrFjxxAeHo5169bhxx9/RLVq1Up0LXt7e7Rq1Qrr1q3D+fPnsWzZskKPPX36NBISErBr1y6VeXy5oqOjS9w7pS01atTA5s2bcffuXTx58gR169aFnZ0dfH19VZIIOzs7nDlzBqIoqnwGEhISAOS0Q3lhbGwMiUS187+kP6v6JJVKERQUhK1bt2L69Ok4f/48Hj9+XCHmCZX0eyOXNu9QDQwMxLx58xAdHY0RI0Zgx44dcHV1RYMGDbR2DaoYmGAZwJtvvolp06bh4sWL+e6CepWjoyNOnTqFZs2aFfkX/u+//47nz59j+fLlaNmypbL81V4OXZLJZHBxccHdu3fx7Nkz2NnZKffFxcWpDNnExcVBoVAo19ZxdHSEKIpwcHAosAchV24dZmZmRfbIWFtbo0qVKoiLi8u379UeleJ07twZGzZsQEpKCnbv3q28QeG/PDw84OHhgbFjxyI6Ohrjx4/H7t27i1wA9L+6du2Kzz77DBYWFmjbtm2hx0VHR8PGxqbAO5X279+P/fv3Y8aMGVpb5youLg7/+9//lK9fvnyJhISEAmOsV6+eMqG6efMmEhIS0KtXL+X+Ro0a4aeffsKtW7dUbunPnXxeWNIM5P3bv3qX3H9ZW1ujatWqBf4b3759GxKJBK+99lqB5+YOO965cydfD9SdO3fyDUsWpKQ/q7l13b17V+Xn4unTp/nuNixKQZ/vu3fvonbt2ipl3bt3x9q1a3Ho0CEcPXoU1tbWek/Ci/Lqv606Pa0l/d5Qh6OjI2JiYpCVlVXkOl3Vq1dH+/btER0djaCgIJw/f17Zg0v0Ks7BMoBq1aph+vTpCAsLU64TVJDOnTtDLpfj66+/zrcvOztbOecp96/p3F4eIOcOrx9++EGrcd+9e1c5xPGqpKQkXLhwAZaWlvn+Uv/+++9VXm/atAkAlL+k33rrLUilUixfvlwlfiDn/eTeseTq6gpHR0esXbtWZW5UrqdPnwLI+UvV19cXBw4cUIn11q1bOH78eInfa5cuXZCZmYnt27fj2LFj6Ny5s8r+Fy9e5Is3N0lQtxetU6dOCA0NxbRp0wodXkxPT8e+ffvQvn17dOrUKd+We3flq3fVldaPP/6oMr9s8+bNyM7OLjIJVCgUWLhwIapWrYp3331XWd6hQwfIZDKVz6QoitiyZQtq1KgBT0/PQuu0trZGy5YtERkZme/zl/tvIJVK0bp1axw8eFDlD4snT55g586daN68eaHDYq6urrCxscGWLVtU/u2OHDmCW7duoX379oXGlqukP6s+Pj6QyWTYtGmTyufn1SUJSuLAgQMqyyxcunQJMTEx+f5tGjZsCBcXF2zbtg379u1DYGBgmVpPqkmTJnBwcMCGDRvyzeH878/Xq0r6vaGOt956C8+ePcv3nVVQLN27d8fNmzexYMECSKVSLpRLBSo7P2mVTM+ePYs9plWrVujbty9WrVqFq1evonXr1pDJZLh79y727t2LTz/9FJ06dYKnpycsLS0xadIkDBw4EIIg4JdffinyC0oT165dw/jx49GmTRu0aNEClpaWePToEX7++Wc8fvwY4eHh+bri79+/jxEjRqBNmza4ePEiduzYga5du6Jhw4YAcv5qHDNmDL744gs8ePAAAQEBqFatGu7fv48DBw7gnXfeQUhICCQSCWbPno1hw4aha9eu6NWrF2rUqIFHjx7hzJkzMDMzw8qVKwEAYWFhOHbsGPr374/g4GDI5XJs2rQJr7/+eonmmQE5X/x169bFV199hczMzHzLaGzfvh2bN29GQEAAHB0d8fLlS2zduhVmZmZFJiAFMTc3R1hYWJHHHDp0CC9fviw0Iffw8IC1tTV27NhR6JIf6srKysKQIUPQuXNn3LlzBz/88AOaN2+ODh06KI+ZPXs2MjMz0bBhQ+XCqZcuXcL8+fNVen5q1qyJQYMGYc2aNcjOzoabmxsOHDiAc+fOYdGiRcUO4Xz22WcIDg5Gz5490bdvXzg4OODBgwf47bff8MsvvwDIWRLk5MmT6NevH/r16wepVIoff/wRmZmZ+OSTTwqtWyaTYfz48Zg8eTIGDBiAwMBAJCYmYsOGDahduzaGDBlSbFuV9GfV2toa7733HlatWoUPPvgA7dq1w5UrV3D06FGVdeSK4+joiODgYAQHByMzMxMbNmxA9erV8f777+c7tkePHvj8888BQO3hwaNHj+a7SQbIWQvv1R44TUkkEkyfPh0jR45Ejx490KtXL9jZ2eH27dsFPg0gV0m/N9TRo0cP/Pzzz5g3bx4uXbqE5s2bIy0tDadOnUJwcLDKzSft2rVD9erVsXfvXrRt27bIZVWo8mKCVcbNnDkTrq6u2LJlC7766itIpVLUrl0b3bp1Uy74aWVlhZUrV+Lzzz/H4sWLYWFhgW7dusHb21vtL5mitGzZEqNGjcKxY8ewbt06PHv2DNWqVUOjRo0wfvz4AifrL168GEuWLMEXX3wBIyMjDBgwABMmTFA5Zvjw4ahXrx7Wr1+PFStWAMj5hdy6dWuVhMLLyws//vgjvv76a2zatAmpqamws7ODu7s7+vbtqzyuYcOGWLNmDebNm4elS5eiZs2aCAsLQ0JCQokTLCCnV2LlypWoW7cumjRporKvVatWiI2Nxe7du/HkyROYm5vD3d0dixYt0sovnv/asWMHTExMCn2OoEQiUQ5bPHv2TK1f1oXJXSV+6dKlyMrKQmBgID777DOVOVSNGzfGd999h+joaAiCAHd3d6xfv15laDHX+PHjYWlpiR9//BFRUVGoV68eFi5cWOhdtK9q2LAhtm7diiVLlmDz5s3IyMhArVq1VHoW33jjDXz//ff44osvsGrVKoiiCHd3dyxcuBBNmzYtsv5evXqhSpUqiIiIwKJFi2BqaoqAgAB88sknhc6R/K+S/KwCOYmgsbExtmzZgjNnzihvUlDnOaQ9evSARCLBd999h8TERLi7u2PKlCkFzmULCgpSfi7/+1im4ixdurTA8nnz5mntc96mTRt89913WLFiBdauXQtRFFGnTh288847RZ5X0u+NkpJKpYiIiMA333yDnTt3Yt++fahevTqaNWuWb5FTY2NjdOnSBT/88AMnt1OhBFHb3RxEyLm7bPny5Th16pRBJviS5qKiojB58mRs27ZNp3eQkX48ffoUbdq0wYcffoiPPvrI0OFUGHPnzsW2bdtw4sQJ5eK6RK/iHCwiogps+/btkMvl7GnRooyMDOzYsQMdO3ZkckWF4hAhEVEFdOrUKdy6dQsrV65EQECA8s5d0lxiYiJOnjyJX3/9Fc+fP8egQYMMHRKVYUywiIgqoK+//hoXLlyAp6dnvud/kmZu3ryJ8ePHw8bGBp999lmRy4sQcQ4WERERkZZxDhYRERGRljHBIiIiItKyCj8HS6FQIDs7GxKJhE86JyKiCkkURSgUChgZGeV7VqauKBQKjRe0FgRBb3EaSoVPsLKzsxEbG2voMIiIiHTOzc2t0EduaZNCoUDspWPIlhf8CKriGBkZwc3NrUInWRU+wcr9x3Nzc9PqE9UBQC6XIzY2Vid1l0dsjzxsizxsizxsC1VsjzylbYvc8/WVsIiiiGy5GZrUXgqpJEOtc+UKE/z5YJTWH+dW1lT4BCt3WFAqlersB1iXdZdHbI88bIs8bIs8bAtVbI88pW0LfU+FESRpECTqPeBegEJH0ZQtFT7BIiIiIt1QQKF2wqRggkVERERUOAUAdfvM1E2v/P398eDBg3zl/fr1w7Rp05CQkIAFCxbg5MmTePnyJerXr48RI0agY8eOal5Ju5hgERERkUZEiBDVTJlEqDf3atu2bZDL5crXN27cwNChQ9GpUycAwMSJE5GUlIRvvvkGVlZWiI6OxpgxYxAZGYnGjRurdS1tqrjT94mIiEin5KKo0aYOa2tr2NnZKbfDhw/D0dERrVq1AgBcuHABAwYMgLu7O+rUqYMPP/wQFhYW+PPPP3XxlkuMCRYRERFpRNTwf5rKzMzEjh070Lt3b+WEfk9PT+zZswfPnz+HQqHArl27kJGRoUzADIVDhERERKR3KSkpKndMGhsbF7uG14EDB5CcnIyePXsqyxYvXoyxY8fCy8sLRkZGqFKlCpYvX466devqLPaSYIJFRESVwsuUdPz+2zXExyXC2MQI7q0awNnNgU/5KAVN7gfMPaddu3ZIS0tTloeGhiIsLKzIcyMjI9G2bVvUqFFDWbZkyRIkJSVh/fr1sLKywoEDBzBmzBh8//33cHFx0SBC7WCCRUREFd75Ezfw7cI9eJ6YrCyL/uEU3FrUx8jPusHMoqoBoyu/NBnyyz3+yJEj+XqwivLgwQOcPHkSy5YtU5bdu3cPmzZtws6dO/HGG28AABo2bIhz587h+++/x8yZM9WKTZs4B4uIiCq0O9f/wTdzduDF0xRY2ZjDtoYlbOwtUKWKMc6fvInV83dV+FXFdaU0k9zNzMxUtuISrKioKNjY2KB9+/bKstwesP+uYC+VSg3+b8oEi4iIKrQDP59HSlI6rO3MITXK+bUnCAKqmBrDzLwKLv1+G3f/eqi3eLIys3D/r3jc/yse2VnZeruuLogabupSKBSIiopCjx49YGSUN/jWoEED1K1bF1OnTsWlS5dw7949rF27FidOnEBAQECp3ltpcYiQiIgqLFEU8cfxGzCpIitwrlUVU2O8TEhG7Lk7qO/ymk5jkWfLsXftIezb8BsSHzwDANg6WOOtQe0RMLidTq+tK3INhggVGqRYJ0+eRHx8PHr37q1SLpPJsHr1anzxxRcYMWIEUlNT4ejoiPnz56NdO8O2KRMsIiKqsERRRHa2HBJJwRPZBUGAACA7S17gfm1RKBRYG/4DDnx/DBKJANN/53w9upuADdO34sHNf+DZp5FOYyjPfH19cf369QL31atXT2VeVlnBIUIiIqqwJBIJ6r5RA+npBT+QOCszGxAAh/p2Oo3j6ukb+G3rSVQ1qwKrmtVhYmoCE1OTnP82M8FvP57Evdh4ncagCwpRs60yYIJFREQVmn9XD0glEqSmpKuUKxQiXjx7iZq1reHp/bpOYzi+/QyyMrJQ1bxKvn2m5lWRlZGFK0du6DQGXVBouFUGHCIkIqIKzTugMf66fB+Hoi8g9WUmjE2MoJArkJUlh5WtOUZ8GgSZsW5/HT6+9wQSqaTAeWCCIECQSPDsnxc6jUEXFBCg7uOeFWo/Hrp8YoJFREQVmkQiweAxb6FJ83r4bVcM4m48gkkVGVq1bwi/rh6oUdtK5zFY2ppDUcTYmKhQwMzKVOdxaJtCg9sCK8sQIRMsIiKq8CQSCVq1a4hW7Roa5Ppegc1x8pdzyEjLhElV1fWeMlIzIJFK0bCNbocpdUGEoHaPlFhJerAMOgcrJSUFc+bMgZ+fH9zd3fHuu+/i0qVLyv2iKGLJkiXw9fWFu7s7hgwZgrt37xouYCIiIg00f9Mdbm0bITkxGSnPXkIhV0AhVyDl2UskP01B0/aN4dTC0dBhkhYZNMH67LPPcPLkSSxYsADR0dFo3bo1hg4dikePHgEAIiIisHHjRkyfPh1bt25F1apVERISgoyMDEOGTUREpBYjmRHGrByODgPaQmIkwdOHz/Hs4XNIjSR4c1A7hC4PgdRIWnxFZYzi3x4sdbfKwGBDhOnp6di3bx++/vprtGzZEgAQFhaGw4cP44cffsCYMWOwYcMGjBw5Urka64IFC+Dj44MDBw4gMDDQUKETERGprZqFKT5YOAhvfxyEWxfvAgBe96wH65pWkMt1uw6Xriig7hR3zVZyL48MlmBlZ2dDLpfDxMREpdzExATnz5/H/fv3kZCQAB8fH+U+c3NzNG3aFBcuXFA7wdLFhze3zvL6g6FtbI88bIs8bIs8bAtVlbU9qttboPlb7srXcrm81G1hqDZUQIDAOVgFMliCZWZmBk9PT3z99ddo0KABbG1tsXPnTly8eBGOjo5ISEgAANjY2KicZ2NjgydPnqh9vdjYWK3Ere+6yyO2Rx62RR62RR62hSq2R57y1hZySCCoOdtIrCRLcBr0LsIFCxYgPDwcbdu2hVQqRePGjREYGIg///xT69dyc3ODVKrd8W25XI7Y2Fid1F0esT3ysC3ysC3ysC1UsT3ylLYtcs/XN1EEIKrZg1VJxggNmmA5Ojpi06ZNSE1NRUpKCuzt7TFmzBjUqVMHdnY5jy1ITEyEvb298pzExEQ0bKj+bbZSqVRnP8C6rLs8YnvkYVvkYVvkYVuoYnvkKW9tIQfnYBWmTPTTmZqawt7eHi9evMDx48fRoUMHODg4wM7ODqdOnVIel5KSgpiYGHh6ehowWiIiIqKiGbQH69ixYxBFEfXr18e9e/ewYMECNGjQAL169YIgCBg0aBC++eYb1K1bFw4ODliyZAns7e2VdxUSERGR4ShECQRRzTlYah5fXhk0wUpOTsaXX36Jhw8fonr16njrrbcwduxYyGQyAMCwYcOQlpaGqVOnIikpCc2bN8e3336b785DIiIi0j8REg0mrTPB0rkuXbqgS5cuhe4XBAGjR4/G6NGj9RgVERERlYRCw/MqQ4rFZxESERGRRuSiBFB3yE+UMMEiIiIiKkzOoqHq3kdYORYarQxJJBEREZFesQeLiIiINCJnD1ahmGARERGRRhQazsGqDJhgERERkUYUkED92UZMsIiIiIgKpekyDZUBEywiIiLSiD56sPz9/fHgwYN85f369cO0adMAABcuXMBXX32FS5cuQSKRoFGjRlizZg2qVKmiZmzawwSLiIiIyqxt27ZBLpcrX9+4cQNDhw5Fp06dAOQkV++//z4++OADTJkyBVKpFNeuXYNEYtihSCZYREREpBFRlKj9bEF1n11obW2t8nr16tVwdHREq1atAADz5s3DwIEDMXz4cOUxDRo0UOsaulA5ZpoRERGR1ikgaLRpKjMzEzt27EDv3r0hCAISExMRExMDGxsbvPvuu/Dx8cGAAQNw7tw5Lb5LzTDBIiIiIo3IRUAuCmpuOeempKSobJmZmcVe78CBA0hOTkbPnj0BAH///TcAYPny5ejTpw++/fZbNG7cGEOGDMHdu3d19bZLhEOEREREpBEREogaTnJv164d0tLSlKWhoaEICwsr8szIyEi0bdsWNWrUAAAoFDn3Mfbt2xe9e/cGADRu3BinTp1CZGQkxo0bp2Zs2sMEi4iIiDQiFwWIonpDfsK/xx85cgRSqVRZbmxsXOR5Dx48wMmTJ7Fs2TJlmZ2dHQDAyclJ5VgnJyfEx8erFZe2McEiIiIivTMzM1NJsIoTFRUFGxsbtG/fXlnm4OAAe3t73LlzR+XYu3fvom3bttoKVSNMsIiIiEgjIgSIengWoUKhQFRUFHr06AEjo7zURRAEhISEYNmyZWjYsCEaNWqE7du34/bt21i6dKna19EmJlhERESkEbkelmkAgJMnTyI+Pl45z+pVQ4YMQWZmJubNm4cXL16gYcOGWLt2LRwdHdW+jjYxwSIiIiKNKDTowRI06MHy9fXF9evXC90/fPhwlXWwygImWERERKQRfSVY5RETLCIiItKICMm/zyMsOUklWYKTCRYRERFpRAEBCjWXadBkknt5VDnSSCIiIiI9Yg8WERERaUSzZwtWjh4sJlhERESkEYWo/hwsaLBMQ3nEBIuIiIg0ovh3o/yYYBEREZFG2INVOCZYREREpBGFKGiQYFWOOViVI40kIiIi0iP2YBEREZFGOAercEywiIiISCMKDVZyryyDZ0ywiIiISCOiqP46WALnYOmeXC7H4sWL4e/vD3d3dwQEBGDFihUQRVF5jCiKWLJkCXx9feHu7o4hQ4bg7t27hguaiIiIAOQtNKruVhkYNMGKiIjA5s2bMXXqVOzevRvjx4/Ht99+i40bN6ocs3HjRkyfPh1bt25F1apVERISgoyMDANGTkRERApR0GirDAyaYF24cAEdOnRA+/bt4eDggE6dOsHX1xeXLl0CkNN7tWHDBowcORIBAQFo2LAhFixYgMePH+PAgQOGDJ2IiIioUAZNsDw9PXH69GncuXMHAHDt2jX88ccfaNu2LQDg/v37SEhIgI+Pj/Icc3NzNG3aFBcuXDBIzERERJRDhESjrTIw6CT34cOHIyUlBZ07d4ZUKoVcLsfYsWPRrVs3AEBCQgIAwMbGRuU8GxsbPHnyRK1ryeVy7QRdQJ26qLs8YnvkYVvkYVvkYVuoYnvkKW1bGKoN5aIAubpzqirJEKFBE6w9e/YgOjoaX3zxBV5//XVcvXoV8+bNg729PXr27KnVa8XGxmq1Pn3VXR6xPfKwLfKwLfKwLVSxPfKUt7YQRUAs/rB851QGBk2wFixYgOHDhyMwMBAA4OLigvj4eKxatQo9e/aEnZ0dACAxMRH29vbK8xITE9GwYUO1ruXm5gapVKq94JHzF0NsbKxO6i6P2B552BZ52BZ52Baq2B55StsWuefrmybrYAkcItS99PR0CIJqV6FUKlUu0+Dg4AA7OzucOnUKjRo1AgCkpKQgJiYGwcHBal1LKpXq7AdYl3WXR2yPPGyLPGyLPGwLVWyPPOWtLTRZdkGoJMs0GDTB8vPzw8qVK1GrVi3lEOG6devQu3dvAIAgCBg0aBC++eYb1K1bFw4ODliyZAns7e0REBBgyNCJiIgqPQUXGi2UQROszz77DEuWLMGMGTOUw4B9+/bFRx99pDxm2LBhSEtLw9SpU5GUlITmzZvj22+/hYmJiQEjJyIiIiqcQRMsMzMzfPrpp/j0008LPUYQBIwePRqjR4/WY2RERERUHFEUIKrZgyWq2YPl7++PBw8e5Cvv168fpk2b9kq9IoYNG4Zjx45hxYoVBh/p4rMIiYiISCOiBnOwJGoev23bNpVlKG7cuIGhQ4eiU6dOKsd99913+eZ1GxITLCIiItKIJnOw1H1UjrW1tcrr1atXw9HREa1atVKWXb16FWvXrkVkZCR8fX3Vql9XmGARERGRRvSRYL0qMzMTO3bswNChQ5W9VWlpaRg3bhymTp2qXN6pLGCCRURERBoRocEcrH+PT0lJUVmSwtjYGMbGxkWee+DAASQnJ6ssRj5v3jx4enoafM7VfzHBIiIiIr1r164d0tLSlK9DQ0MRFhZW5DmRkZFo27YtatSoAQA4ePAgTp8+je3bt+s0Vk0wwSIiIiKNaLLQaO7xR44cydeDVZQHDx7g5MmTWLZsmbLs9OnTuHfvHlq2bKlybFhYGFq0aIGNGzeqFZs2McEiIiIijShEaDAHK+f/zczM1Fq1PioqCjY2Nmjfvr2ybPjw4ejTp4/KcUFBQZg8eTL8/PzUikvbmGARERGRRvSxDhYAKBQKREVFoUePHjAyyktd7OzsCpzYXqtWLdSpU0ft62gTEywiIiLSiL7uIjx58iTi4+OVj9IrD5hgERERkUZKcxehOnx9fXH9+vUSHVvS43RNYugAiIiIiCoa9mARERGRRvS90Gh5wgSLiIiINKL4d1P3nMqACRYRERFpRoO7CMEeLCIi0qbk1AwkpqQjLTMLZlVLvv4PUVnFIcLCMcEiItKxGw+eYOuxizh9/R7S0tJhefwW3vR4A++0bQprc1NDh0ekMX3dRVge8S5CIiIduhz3EJPX78bBmFsQRcBIKkFqeia2Hr+ESet242lyqqFDJCIdYIJFRKQjCoWI5dEn8DQlDTUsq8G8qglMjKSoblYVtuamuPVPIjb/dtHQYRJpTAEhZ5hQnY09WEREVBp/3nuIu4+eorppFQiC6i8VI6kEJjIpDl+6idSMTANFSFQ6ClGzrTLgHCwiIh15+CwZmdkKWFUreEJ7FZkMqRlZeJqcClMTYz1HR1R6FWUOVnx8POLj45GWlgZra2u88cYbMDYu3c8kEywiIh2pVsUEUkGAXCHCSJr/l0q2XA6pRGByReWWRncRQkBZyLHu37+PzZs3Y/fu3Xj48CFEMa9rTSaToUWLFnjnnXfQsWNHSCTqD/gxwSIi0hFPp1qwsTDFs5Q02PznbkFRFJGSkYU2TerxTkIqt0QN1sEqCz1Ys2fPxvbt2+Hr64vRo0fD3d0d9vb2qFKlCl68eIG//voLf/zxB5YuXYoVK1Zg7ty5cHd3V+saTLCIiHSkqrEMwe098fWuk0hMToWFaRUAQEZWNl6kpsPC1AR923oYNkiiUiivQ4RVq1bFgQMHYGVllW+fjY0NvL294e3tjdDQUBw9ehQPHz5kgkVEVJZ082oMURSx+beLSEx+icysLFQxBurZW+Ojrt5o7FjD0CESVTrjxo0r8bFt27bV6BpMsIiIdEgQBPTwdsVbzZxx9vrf+PP6X2jp3gSerzvASMobual8U4gV41mEycnJSEhIAADY2dnB3Ny81HUywSIi0gNTE2P4NqkHs6zn8Hi9NqRSCRL+eY4/z91BdrYCdRrY4Q03B40m0xIZSnmdg5Xrp59+wrp163Dnzh2V8vr162Po0KHo06ePxnUzwSIi0rP0tExsXnEIJ/ddRnpqJiAARkZS1HepiWHhQXCob2foEIlKpLzOwQKAb7/9FsuXL8fAgQPh6+sLW1tbAMCTJ09w4sQJzJ07F0lJSQgJCdGofiZYRER6JIoi1i3cg5P7/0SVqjLY2JsDApCZnoW/Lt/HFxN+xJSvB8ParvRDFES6Vp57sL7//nvMnTsXXbp0USl3cnKCl5cXGjZsiAULFmicYLEvmohIjx7de4Gzv12DaTUTmFlUhSARIAgCTKoaw9rOHA/vP8WRnRcMHSZRiYjqPiZHFCCKZSPBSkxMhIuLS6H7nZ2d8ezZM43rZ4JFRKRHNy89RFZmNqpWy7+4qFQqgZGRBKcOXDFAZESVi5ubG1avXo3s7Ox8++RyOSIiIuDm5qZx/RwiJCLSo4z0bEBAvmcT5pIaSZGakq7nqIg0VI6fKzhlyhS8//77aN26NVq2bAkbGxsAOT1bZ8+ehUwmw9q1azWunwkWEZEeVbc1BSBAIVdAUsAyDZkZ2XjN0Ub/gRFpQIHyu0xDw4YNsXfvXuzYsQMxMTG4f/8+AMDW1hZjxoxBUFAQzMzMNK7foAmWv78/Hjx4kK+8X79+mDZtGjIyMjB//nzs3r0bmZmZ8PX1xbRp05Qz/YmIypuGzWvj3IE4vHj6EtVtzVR6stJTMyGRCGjf1cNwARKpoTxPcgcAMzMz9OvXD/369dN63QZNsLZt2wa5XK58fePGDQwdOhSdOnUCAMydOxdHjhzB4sWLYW5ujlmzZiE0NBRbtmwxVMhERKVSzcIE/UI74Lsvf8WTR0kwrWYCiURA2stMiBDhHdAE/+vQ2NBhEpWICPUTJnVHFYvqjBk9ejSWLVuG48eP459//oG1tTUCAgIwevToEi8WmpCQgJiYGDx58gRAzkKj7u7usLMr3XIpBk2wrK2tVV6vXr0ajo6OaNWqFZKTkxEZGYlFixbB29sbAJS3U168eBEeHh4GiJiIqPTadHaHjb0l9m49g2sX70GerUCterbo0KMZOvRoDqmR1NAhEpWIKKqfMKl7fFGdMY8fP8bjx48xceJEvP7663jw4AGmT5+Ox48fY+nSpUXWm5qaiqlTp2L37t0QBAGWlpYAgBcvXkAURQQGBmLmzJmoWrWqmhHnKDNzsDIzM7Fjxw4MHToUgiDg8uXLyMrKgo+Pj/IYJycn1KpViwkWEZV7bq0awK1VA7xMTkNWphwWVqZcxZ2oAEV1xgiCgGXLlin3OTo6YsyYMfjkk0+QnZ0NI6PC05w5c+YgNjYWq1atgo+PD6TSnD9s5HI5Tp06hVmzZmHOnDmYPXu2RnGXmQTrwIEDSE5ORs+ePQHkrKQqk8lgYWGhcpyNjY3yeUHqeDX71ZbcOnVRd3nE9sjDtsjDtshTUFtUMTVGFdOcBUgrWxvxs5GntG1hqDbU9xys/3bGFCQlJQVmZmZFJlcAsG/fPqxatQrNmjVTKZdKpfD19cXcuXMxYsSI8p9gRUZGom3btqhRQzdPlo+NjdVJvbquuzxie+RhW+RhW+RhW6hie+Qpb21RmgQrJSVF2WsEAMbGxjA2zr8+3Kv+2xnzX0+fPsXXX3+Nvn37FhuHQqGATCYrdL9MJoNCofk9j2UiwXrw4AFOnjyp0s1na2uLrKwsJCUlqfRiJSYmajTxzM3NTeUfUhvkcjliY2N1Und5xPbIw7bIw7bIw7ZQxfbIU9q2yD1f30ozB6tdu3ZIS0tTloeGhiIsLKzIc4vqjElJScEHH3wAJycnhIaGFhtH+/btMXXqVMyZMweNG6veWHLlyhVMnz4dfn5+xb+hQpSJBCsqKgo2NjZo3769sszV1RUymQynTp1Cx44dAQC3b99GfHy8RvOvpFKpzn6AdVl3ecT2yMO2yMO2yMO2UMX2yFPe2kKE5gnWkSNH8vVgFaWgzphcKSkpeP/991GtWjWsWLGiyJ6pXFOnTsW4cePQq1cvWFpaKud6PX36FElJSfD19cXUqVNL/sb+w+AJlkKhQFRUFHr06KEyXmpubo7evXtj/vz5sLS0hJmZGWbPng1PT09OcCciIioDSjNEaGZmplYyWVBnDJCTXIWEhMDY2BjffPMNTExMSlSfpaUlvv32W9y6dQsXL15ULtNga2sLDw8PODk5lTi2ghg8wTp58iTi4+PRu3fvfPvCw8MhkUgwatQolYVGiYiIyPD0Ncm9sM6YlJQUvPfee0hLS8PChQuRkpKClJQUADl3H5YkgXNycip1MlUQgydYvr6+uH79eoH7TExMMG3aNCZVRERElVhhnTF//vknYmJiAABvvvmmyr6DBw/CwcFB42u+ePEChw8fRo8ePTQ63+AJFhEREZVPpZmDpY7COmO8vLwK7aQprX/++QeTJ09mgkVERER6JgqA2kN+ZeNZhLlDiZruLw4TLCIiIqp0WrRoUehipUDO4r9F7S8OEywiIiLSiL5XctematWqYcSIEWjatGmB++Pi4sr3Mg1ERERUPunjYc+6kru4aKtWrQrcb2FhAVHUPFo+WZSIiIgqnaCgoCLXzLK1tS3RivCFYQ8WERERaURETi+WWueUjRFCvPPOO0XuL22CxR4sIiIi0owoaLaVUQ8fPizVA55fpXYPVkJCAmJiYlSWlG/atKlGD2AmIiKi8ktf62DpS5cuXfDLL7+gTp06pa6rxAlWamoqpk6dit27d0MQBFhaWgLIWelUFEUEBgZi5syZqFq1aqmDIiIionJAkwyrDCvNpPb/KvEQ4Zw5cxAbG4tVq1bh0qVLOHnyJE6ePIlLly5h9erVuHTpEubMmaO1wIiIiKiMq2BDhNpU4gRr3759mDdvHtq0aaPy8ESpVApfX1/MnTsXv/76q06CJCIiIipPSjxEqFAoIJPJCt0vk8m0NjGMiIiIyoEKNkSoTSXuwWrfvj2mTp2KK1eu5Nt35coVTJ8+HX5+floNjoiIiMowUchZzV2NrSwPEY4YMUI5x7y0StyDNXXqVIwbNw69evWCpaUlrK2tAQBPnz5FUlISfH19S7WkPBEREZEhffDBB1qrq8QJlqWlJb799lvcunULFy9eVFmmwcPDA05OTloLioiIiMoD4d9N3XPKhps3b2LTpk0F5jUDBgzA66+/rnHdaq+D5eTkxGSKiIiIyvUcrCNHjuCjjz5CkyZN0KFDB9jY2AAAEhMTceLECfTs2RNff/012rRpo1H9aidYp06dwh9//IGEhARIJBLUqVMH/v7+qFevnkYBEBEREenbF198gWHDhmH06NH59oWFhWHZsmVYsGCB7hOsxMREjBgxApcvX4ZEIoFCoUCjRo2wf/9+LFq0CEOGDMGECRM0CoKIiIjKoXLcg3X37l0EBQUVuj8wMBAREREa11/iuwhnz54Ne3t7nD17FhcuXEC/fv3wxhtv4Pjx41izZg0iIyPx3XffaRwIERERlTOihlsZULt2bRw5cqTQ/UeOHEGtWrU0rr/EPVhHjx7Fli1bYGZmBgAYN24cWrVqhSlTpsDb2xvh4eH45ptvMHjwYI2DISIiovJDFHM2tc7RTShqGzVqFMaPH48zZ87Ax8cHtra2AIAnT57g1KlTOHbsGL744guN6y9xgmVsbAxByJv5L5FIIJfLkZ2dDQBo1qwZHjx4oHEgRERERPrSuXNn1KhRAxs3bsS6deuQkJAAALCzs4OHhwc2btwIT09PjesvcYLVvHlzLF26FPPnz4dMJsOXX36JOnXqoHr16gBy1sPS1uJcREREVA5otHBo2VmmoVmzZmjWrJlO6i5xgjVx4kS89957aNmyJQRBQNWqVbFkyRLl/lu3bqFnz546CZKIiIjKHkHM2dQ6RzehlDklTrDq1KmDHTt24Pz588jMzETTpk2Vq7kDQK9evXQSIBEREVVe/v7+BU5B6tevH6ZNm4aMjAzMnz8fu3fvRmZmJnx9fTFt2jTlnKqChISEICwsDB4eHkVeOyUlBT/88AOqVauG/v37qxW3WutgVa1aFa1bt1brAkRERFRB6eGuwG3btkEulytf37hxA0OHDkWnTp0AAHPnzsWRI0ewePFimJubY9asWQgNDcWWLVsKrbNTp04ICwuDubk5/Pz84OrqCnt7e5iYmCApKQk3b97EH3/8gaNHj6Jdu3YaLUOl9kKjQM5io+vXr8etW7cA5KzuPnjwYPj4+GhSHREREZVHepiD9epoGQCsXr0ajo6OaNWqFZKTkxEZGYlFixbB29sbQE7C1aVLF1y8eLHQHqo+ffqge/fu2LNnD/bs2YOtW7ciOTk5JzpBwOuvvw5fX19s27ZN46fXqJ1gff/995g7dy46duyIQYMGAQBiYmIwfPhwTJ48We0uNCIiIqp8UlJSIJVKla+NjY1hbGxc5DmZmZnYsWMHhg4dCkEQcPnyZWRlZal08Dg5OaFWrVpFJli51+vevTu6d+8OAEhOTkZ6ejqqV68OmUxWujcHDRKsVatWYfLkyRgwYIBKebNmzbBy5UomWERERJVFKYYI27Vrh7S0NOXr0NBQhIWFFXnOgQMHkJycrLyp7smTJ5DJZLCwsFA5zsbGRrnsQkmZm5vD3NxcrXOKonaClZycXOBzeVq3bo1FixZpJSgiIiIqB0qRYB05ciRfD1ZxIiMj0bZtW9SoUUOzi+pRiR+Vk8vf3x/79+/PV37w4EG0b99eGzERERFRBWdmZqayFZdgPXjwACdPnsTbb7+tLLO1tUVWVhaSkpJUjk1MTISdnZ1O4i4ptXuwnJycsHLlSvz+++/Ksc2YmBicP38eQ4cOxYYNG5TH5s7RIiIiogpIjwuNRkVFwcbGRqUzx9XVFTKZDKdOnULHjh0BALdv30Z8fHyxSzDomtoJ1rZt22BhYYGbN2/i5s2bynJzc3Ns27ZN+VoQhBIlWI8ePcLChQtx7NgxpKWloW7dupg7dy7c3NwAAKIoYunSpfjpp5+QlJSEZs2aYfr06ahXr566oRMREZE2abDQqCYUCgWioqLQo0cPGBnlpS7m5ubo3bs35s+fD0tLS5iZmWH27Nnw9PQsfwnWoUOHtHbxFy9eIDg4GF5eXoiIiICVlRXi4uJUHrkTERGBjRs3Yv78+XBwcMCSJUsQEhKC3bt3w8TERGuxEBERUdl08uRJxMfHo3fv3vn2hYeHQyKRYNSoUSoLjZbUxIkT8fbbb6Nly5baDFmzdbC0JSIiAjVr1sS8efOUZXXq1FH+tyiK2LBhA0aOHImAgAAAwIIFC+Dj44MDBw4gMDBQ7zETERGRfvn6+uL69esF7jMxMcG0adPUSqpelZycjKFDh6JWrVro1asXevbsqZVJ9AZNsA4dOgRfX1+MGjUKZ8+eRY0aNdCvXz+88847AID79+8jISFBZX0Lc3NzNG3aFBcuXFArwXp1FVhtya1TF3WXR2yPPGyLPGyLPGwLVWyPPKVtC4O2obpDhGXsYYRff/01nj59il9++QXbt2/HsmXL4O3tjbfffhsdOnTQeE0sgyZYf//9NzZv3oyhQ4dixIgRiI2NxezZsyGTydCzZ0/lGhY2NjYq59nY2ODJkydqXSs2NlZrceuz7vKI7ZGHbZGHbZGHbaGK7ZGHbWEY1tbWGDp0KIYOHYo///wTUVFRmDBhAkxNTdGtWzf069dP7bnfBk2wRFGEq6srPv74YwBA48aNcePGDWzZskW5iJi2uLm5qay3oQ1yuRyxsbE6qbs8YnvkYVvkYVvkYVuoYnvkKW1b5J6vd5rcRaj2XYf68/jxY5w4cQInTpyAVCpFu3bt8NdffyEwMBCffPIJhgwZUuK6DJpg2dnZ5XvGT4MGDfDrr78q9wM561nY29srj0lMTETDhg3VupZUKtXZD7Au6y6P2B552BZ52BZ52Baq2B55yl1baLLQqB7uOlRHVlYWDh06hKioKJw4cQLOzs4YPHgwgoKCYGZmBgDYv38/wsPD1UqwSrzQ6IQJE5CSkqJ8fe3aNWRlZZX8HRSgWbNmuHPnjkrZ3bt3Ubt2bQCAg4MD7OzscOrUKeX+lJQUxMTEwNPTs1TXJiIiolISNdzKEF9fX0yZMgW1atXCTz/9hKioKAQHByuTKwDw8vJS+zE6JU6woqOjkZGRoXzdr18/PHz4UK2L/dfgwYMRExODlStXIi4uDtHR0di6dSv69esHIG8trW+++QYHDx7E9evXMWHCBNjb2yvvKiQiIiLDEDTcypLJkyfj2LFjmDZtGho1alTgMRYWFmovU1XiIUJRFIt8rQl3d3csX74cX375JVasWAEHBweEh4ejW7duymOGDRuGtLQ0TJ06FUlJSWjevDm+/fZbroFFRERkaBVgiLBDhw5IS0vLl1c8f/4cRkZGKj1Z6jDoHCwA8PPzg5+fX6H7BUHA6NGjMXr0aD1GRURERMWqAAnW2LFj4efnh/79+6uU79mzB4cOHUJERIRG9aqVYN28eVO5dAKQ87yfly9fqhyj7uRzIiIiIkO5dOkSJk+enK+8VatWWLx4scb1qpVgDRkyRGVo8IMPPgCQ08skiiIEQcDVq1c1DoaIiIjKD0GDZxHq49mF6sjMzER2dna+8uzsbKSnp2tcb4kTrIMHD2p8ESIiIqqAKsAQoZubG7Zu3YopU6aolG/ZsgVNmjTRuN4SJ1i5SycU5a+//tI4ECIiIiJ9GzNmDIYOHYpr167B29sbAHDq1CnExsZi7dq1Gtdb4mUaCpOSkoIff/wRb7/9Nrp3717a6oiIiKicyB0iVHcrS5o3b44ff/wRNWvWVE5sd3R0xI4dO9CiRQuN69X4LsKzZ89i27Zt2LdvH+zt7fHmm29i6tSpGgdCRERE5UwFGCIEgEaNGuGLL77Qap1qJVgJCQnYvn07tm3bhpSUFHTu3BmZmZlYsWIFXn/9da0GRkRERKQPCoUCcXFxSExMzLfOZ8uWLTWqs8QJ1ogRI3D27Fm0b98e4eHhaNOmDaRSKbZs2aLRhYmIiKh8qwh3EV68eBHjxo1DfHx8vuSqNKsjlDjBOnr0KAYOHIjg4GDUq1dPo4sRERFRBVIBhginTZsGV1dXrF69GnZ2dhAE7TzMp8QJ1g8//IBt27ahV69ecHJyQvfu3dGlSxetBEFERERkCHFxcVi6dCnq1q2r1XpLfBehh4cHZs+ejePHj6Nv377YtWsX2rZtC4VCgRMnTiAlJUWrgREREVEZJ2q4lSHu7u6Ii4vTer1q30VoamqKt99+G2+//TZu376Nbdu2ISIiAl988QV8fHywcuVKrQdJREREpAsDBw7E559/jidPnsDZ2RlGRqqpkaaPACzVw54bNGiACRMmYNy4cTh8+DC2bdtWmuqIiIiI9CosLAwAEB4erizTxiMAS5Vg5ZJKpQgICEBAQIA2qiMiIqLyoAJMctfVowBLnGAV9KTp/xIEAXPnzi1VQERERFQ+VIRlGkryKEBNlHiS+/bt23HmzBkkJycjKSmpwO3Fixc6CZKIiIjKID1Ncn/06BHGjx8PLy8vuLu7IygoCLGxscr9L1++xMyZM9G2bVu4u7ujS5cu2Lx5c4nr//nnn/Huu+/C19cXDx48AACsX78eBw4cUD/Yf5W4Bys4OBi7du3C/fv30atXL3Tr1g3Vq1fX+MJERERExXnx4gWCg4Ph5eWFiIgIWFlZIS4uDpaWlspj5s+fj9OnT2PhwoWoXbs2Tpw4gRkzZsDe3h4dOnQosv4ffvgBS5cuxeDBg7Fy5UooFAoAgIWFBb777juNpz+VuAdr2rRpOH78ON5//30cPnwY7du3x+jRo3Hs2LF8K58SERFRxaePhz1HRESgZs2amDdvHtzd3VGnTh34+vrC0dFRecyFCxfQo0cPeHl5wcHBAX379kXDhg1x6dKlYuvftGkTZs+ejZEjR0IiyUuLXF1d8ddff6kX7CtKnGABgLGxMbp27Yp169Zh165deOONNzBjxgz4+/vj5cuXGgdBRERE5VAphghTUlJUtszMzAIvcejQIbi6umLUqFHw9vZGjx49sHXrVpVjPD09cejQITx69AiiKOL06dO4c+cOfH19i30L9+/fR6NGjfKVGxsbIy0trUTNUBCN7yLMzfJEUYRcLtc4ACIiIqp82rVrp5LAhIaGKpdMeNXff/+NzZs3Y+jQoRgxYgRiY2Mxe/ZsyGQy9OzZEwAwZcoUTJkyBW3btoWRkREEQcDs2bNL9KBmBwcHXL16Nd9k92PHjsHJyUnj96dWgpWZmYl9+/YhMjISf/zxB9q3b4+pU6eiTZs2Kt1qREREVPEJ0OAuwn///8iRI5BKpcpyY2PjAo8XRRGurq74+OOPAQCNGzfGjRs3sGXLFmWCtXHjRly8eBHffPMNatWqhXPnzinnYPn4+BQZz9ChQzFz5kxlD9qlS5ewc+dOrF69GrNnz1bvzb2ixAnW9OnTsXv3btSsWRO9e/fGF198AWtra40vTEREROVcKdbBMjMzU0mwCmNnZ5evJ6lBgwb49ddfAQDp6en46quvsHz5crRv3x5AzurrV69exZo1a4pNsPr06QMTExMsXrwYaWlpGDduHOzt7REeHo7AwEA131yeEidYW7ZsQa1atVCnTh2cPXsWZ8+eLfC45cuXaxwMERER0auaNWuGO3fuqJTdvXtXOaSXnZ2NrKwsCIKgcoxUKi3xTXjdunVDt27dkJaWhtTUVNjY2JQ67hInWD169MgXPBEREVVe+lhodPDgwQgODsbKlSvRuXNnXLp0CVu3bsXMmTMB5PSEtWrVCgsXLkSVKlVQq1YtnD17Fj///DMmTZqk1rWqVq2KqlWrqhdgIUqcYM2fP18rFyQiIqIKQg+PynF3d8fy5cvx5ZdfYsWKFXBwcEB4eDi6deumPObLL7/El19+ifHjx+PFixeoVasWxo4di+Dg4ALr7NmzJ9avXw9LS8tiO5C2b9+uXsD/0sqzCImIiIh0xc/PD35+foXut7Ozw7x580pcX4cOHZST6nX1HGUmWERERKSZcvqw59DQ0AL/W5uYYBEREZFmymmC9apLly5BFEU0bdpUpTwmJgYSiQRubm4a1cvFq4iIiKjSmjlzJv7555985Y8ePVJOpNcEe7CIiIhII/q4i1DXbt26hSZNmuQrb9SoEW7evKlxvezBIiIiIs2U4lmEZYWxsTGePHmSrzwhIQFGRpr3Qxk0wVq2bBlcXFxUtk6dOin3Z2RkYMaMGfDy8oKnpyfCwsIKbAQiIiIiTbRu3RpffvklkpOTlWVJSUn46quvil0FvigGHyJ84403sG7dOuXrV5fNnzt3Lo4cOYLFixfD3Nwcs2bNQmhoKLZs2WKIUImIiOhVFWCS+8SJE9G/f3/4+fmhUaNGAIBr167BxsYGCxYs0LhegydYUqkUdnZ2+cqTk5MRGRmJRYsWwdvbG0BOwtWlSxdcvHgRHh4eeo6UiIiIXiUg7+HN6pxTltSoUQM7duxAdHQ0rl27hipVqqB3794IDAyETCbTuF6DJ1hxcXHw9fWFiYkJPDw8MG7cONSqVQuXL19GVlaWSveck5MTatWqxQSLiIioLKgAPVgAYGpqir59+2q1ToMmWO7u7pg3bx7q16+PhIQErFixAv3790d0dDSePHkCmUwGCwsLlXNsbGyQkJCg9rXkcrm2ws5Xpy7qLo/YHnnYFnnYFnnYFqrYHnlK2xaGasPy2oN18OBBtG3bFjKZDAcPHizy2A4dOmh0DYMmWO3atVP+d8OGDdG0aVP4+flhz549qFKlilavFRsbq9X69FV3ecT2yMO2yMO2yMO2UMX2yFPu2qKc9mB99NFHOHHiBGxsbPDRRx8VepwgCLh69apG1zD4EOGrLCwsUK9ePdy7dw8+Pj7IyspCUlKSSi9WYmJigXO2iuPm5qYygV4b5HI5YmNjdVJ3ecT2yMO2yMO2yMO2UMX2yFPatsg9n0rm2rVrBf63NpWpBOvly5f4+++/YWdnB1dXV8hkMpw6dQodO3YEANy+fRvx8fEazb+SSqU6+wHWZd3lEdsjD9siD9siD9tCFdsjT7lri3Lag9WqVSvs3bsX1tbWmDx5Mj799FOYmZlp9RoGXQfr888/x++//4779+/j/PnzCA0NhUQiQdeuXWFubo7evXtj/vz5OH36NC5fvozw8HB4enpygjsREVEZkLuSu7qboWVlZeHly5cAgJ9//hmZmZlav4ZBe7AePnyIjz/+GM+fP4e1tTWaN2+OrVu3wtraGgAQHh4OiUSCUaNGITMzE76+vpg2bZohQyYiIqJc5bQHy8PDAx9++CFcXV0hiiJmz54NExOTAo+dN2+eRtcwaIL11VdfFbnfxMQE06ZNY1JFREREWrNw4UKsX78e9+7dA5Cz9mZGRoZWr1Gm5mARERFROVJOe7BsbW0xfvx4AIC/vz8WLFgAKysrrV6DD3smIiIijZTXOVitWrXC06dPAQBeXl6lWrG9MEywiIiIqFKp8JPciYiIqBwrp0OEFX6SOxEREZVj5TTBenWSuyAInOROREREZUd5fRahPia5M8EiIiIizZWBHqnSOHTokE7q5SR3IiIi0oi+7iJ89OgRxo8fDy8vL7i7uyMoKCjfsxdv3bqFESNGoHnz5vDw8EDv3r0RHx9faJ3Dhg1DcnKy8vXq1auRlJSkfP3s2TN06dJF/WD/xR4sIiIiKrNevHiB4OBgeHl5ISIiAlZWVoiLi4OlpaXymHv37qFfv37o3bs3Ro0aBTMzM9y4caPQiesAcPz4cZW7B1euXInOnTvDwsICQM4DtO/cuaNx3EywiIiISDN6mOQeERGBmjVrqtzNV6dOHZVjvvrqK7Rt2xYTJkxQljk6OhYdhigW+bq0OERIREREGinNEGFKSorKVthaVIcOHYKrqytGjRoFb29v9OjRA1u3blXuVygU+O2331CvXj2EhITA29sbffr0wYEDB/TRBIVigkVERER6165dOzRv3ly5rVq1qsDj/v77b2zevBn16tXDmjVrEBwcjNmzZ2P79u0AgMTERKSmpiIiIgJt2rTB2rVr8eabbyI0NBS///57odcXBAGCoLt7GjlESERERJopxRDhkSNHIJVKlcXGxsYFHy6KcHV1xccffwwAaNy4MW7cuIEtW7agZ8+eUCgUAIAOHTpgyJAhAIBGjRrh/Pnz2LJlC1q1alVovZMmTVJeNzMzE9OnT0fVqlWVr0uDCRYRERFpphQJlpmZmUqCVRg7Ozs4OTmplDVo0AC//vorAMDKygpGRkb5jnFycsIff/xRaL09e/ZUed2tW7d8x/To0aPY+ArDBIuIiIjKrGbNmuW7m+/u3buoXbs2gJyeLzc3tyKPKYimj8ApKc7BIiIiIo3oYx2swYMHIyYmBitXrkRcXByio6OxdetW9OvXT3lMSEgI9uzZg61btyIuLg6bNm3C4cOHERwcrOV3XHLswSIiIqIyy93dHcuXL8eXX36JFStWwMHBAeHh4SpDem+++SamT5+O1atXY/bs2ahfvz6WLl2KFi1aGCxuJlhERESkGU1WZtdguSk/Pz/4+fkVeczbb7+Nt99+W/3KdYQJFhEREWlGFHM2dc+pBJhgERERkUY0mVOlybMIyyMmWERERKQZPTwqp7ziXYREREREWsYeLCIiItIIhwgLxwSLiIiINMMhwkIxwSIiIiLNMMEqFBMsIiIi0gyXaSgUEywiIiLSiPDvpu45lQHvIiQiIiLSMvZgERERkWY4B6tQTLCIiIhII1ymoXBMsIiIiEgznOReKCZYREREpBH2YBWuzExyX716NVxcXDBnzhxlWUZGBmbMmAEvLy94enoiLCwMT548MWCURERERMUrEwnWpUuXsGXLFri4uKiUz507F4cPH8bixYuxceNGPH78GKGhoQaKkoiIiKhkDJ5gvXz5Ep988glmz54NS0tLZXlycjIiIyMxadIkeHt7w9XVFXPnzsWFCxdw8eJFwwVMREREAP5dB0tUczN00Hpi8ARr5syZaNeuHXx8fFTKL1++jKysLJVyJycn1KpViwkWERFRWaAQNdsqAYNOct+1axeuXLmCbdu25dv35MkTyGQyWFhYqJTb2NggISFB7WvJ5XKN4yyuTl3UXR6xPfKwLfKwLfKwLVSxPfKUti0M1oZcB6tQBkuw/vnnH8yZMwdr166FiYmJzq8XGxtbLusuj9geedgWedgWedgWqtgeedgWFYfBEqw///wTiYmJ6NWrl7JMLpfj7Nmz+P7777FmzRpkZWUhKSlJpRcrMTERdnZ2al/Pzc0NUqlUK7HnksvliI2N1Und5VFWdiou/LUOVW3/RIbiIWQSS9hWfQv2pp0hk1Y3dHh6xc9GHrZFHraFKrZHntK2Re75escerEIZLMH63//+h+joaJWyyZMno0GDBhg2bBhee+01yGQynDp1Ch07dgQA3L59G/Hx8fDw8FD7elKpVGc/wLqsu7yQK9Jw4/k0pFc7AXmWDBLBGFmKp7iX9A2epO9DY9sFqGJU09Bh6h0/G3nYFnnYFqrYHnnYFhWHwSa5m5mZwdnZWWUzNTVF9erV4ezsDHNzc/Tu3Rvz58/H6dOncfnyZYSHh8PT01OjBIt0637yJjzPOANBUQ3GUnvIpNVhIrWDTGqD1MybuP3sK0OHSEREWqb2HYQaLEwKAI8ePcL48ePh5eUFd3d3BAUFFdpjN3XqVLi4uGD9+vWle3OlVKZXcg8PD4dEIsGoUaOQmZkJX19fTJs2zdBh0X/IFWl49HI3JJBBAWOVfRLBCFKJGZ5nnENq1l2YyuoZJkgiItI+PQwRvnjxAsHBwfDy8kJERASsrKwQFxensrRTrv379yMmJgb29vZqBqV9ZSrB2rhxo8prExMTTJs2jUlVGZchf4hs+XNIJKYAFPn2S4VqyJQ/RmrWbSZYREQViCCKENR8tqC6x0dERKBmzZqYN2+esqxOnTr5jnv06BFmzZqFNWvW4IMPPlDrGrpg8HWwqPwTYAQIAiDmT65yyAFBgCAYF7KfiIjKJVHDTQ2HDh2Cq6srRo0aBW9vb/To0QNbt25VOUahUOCTTz5BSEgI3njjjdK9Jy1hgkWlVsWoNkyNGkAuphS4P1uRDJmkOiyM3fUcGRER6VJuD5a6GwCkpKSobJmZmQVe4++//8bmzZtRr149rFmzBsHBwZg9eza2b9+uPCYiIgJGRkYYNGiQXt53SZSpIUIqnwRBgtoW7+KvxNlQCEkQxSoQBAlEUYRcTIECWahVrRtkUoviKyMiovKjFHOw2rVrh7S0NGVxaGgowsLC8h8uinB1dcXHH38MAGjcuDFu3LiBLVu2oGfPnrh8+TI2bNiAqKgoCELZeRAPEyzSCtuqHZBhkYibT75GpiIBgigAogiJpApeq9YLjpZDDR0iERGVIUeOHFFZksLYuOBpJHZ2dnByclIpa9CgAX799VcAwLlz55CYmAg/Pz/lfrlcjs8//xwbNmzAoUOHdBB98ZhgkVYIgoDXzN7GP7dsUNMpAVmKJ5BJzGFt2hbVZA0MHR4REemCKOZs6p6DnOWaSrLmV7NmzXDnzh2Vsrt376J27doAgO7du+d7nnFISAi6d++uspi5vjHBIq2SiFaoZebHhfKIiCoB4d9N3XPUMXjwYAQHB2PlypXo3LkzLl26hK1bt2LmzJkAACsrK1hZWamcI5PJYGtriwYNDPcHPhMsIiIi0kwperBKyt3dHcuXL8eXX36JFStWwMHBAeHh4ejWrZt619UzJlhERESkGQUKWv6w+HPU5OfnpzLHqjiGmnf1KiZYREREpBk+7LlQXAeLiIiISMuYYBERERFpGYcIiYiISCP6eBZhecUEi4iIiDSjh7sIyysmWERERKQZEerfFVg58ismWERERKQZASIENTMmdY8vrzjJnYiIiEjL2INFREREmuEcrEIxwSIiIiLNcKHRQjHBIiIiIs3o6VE55RETLCIiItIIJ7kXjgkWlTmiKEKefRMKMRlSSQ1IjWobOiQiIioI52AVigkWlSkZGSfxMnklsrOvAaIcgmAMY+P/oZpFGIyMGhg6PCIiepUIDRIsnURS5nCZBiozMtJ/Q9Kz8cjOioGAKhAk1SFCgoyMg3j+9ENkZ8cZOkQiIqISYQ8W6ZwoikDmaYjpO4CsWABGgLEPhKrdIchc/j0mGynJSyCKqRAkNSEIAgBAEGQQxWqQy+ORmvItLKrPMuA7KVi6PAtnntzA7ZTHkAoSNLWqi8bmtQwdFhGR7nGIsFBMsEinRFGE+HIdkLoBEDMBoQoAEUj7GWLGQcAiHIJJO2Rl/gF59j0IEitlcpVLECQQhGrISP8NCsVzSCTVDfJeCnL1xQMsvBKNf9Ke5fR6iyK2xp1CI4ta6KLgkCYRVXBcpqFQTLBIt7IuAKkbAUgBac28clEExASISQsAGzcoFIkAsgAYF1iNIBhDFNOgUDwrMwnWw7TnmB0bhcSMZFgZm0EmkUIURWQoshHz/B6e4jl8FC0hlUoNHSoRkU4IYs6m7jmVAedgkU6J6TsBZACCheoOQQAEG0B8BqQfhERihZx8P6vgesRMCDAuM8kVAOz/JxaJGcmwNbGATJKTRAmCgCpSGarLTHEv+xkuPuO8MSKqwHKHCNXdKgEmWKRbWdcByHISqv8SpABEiPI7kBk3h1TqAIXiac6crVeIogIQX8K4Stt/E7Gy4dSTvyAVJJAU8N5MpDIoRBEXnt0xQGRERHqiEDXbKgEmWKRbgjGKXrZXACCDIBijmsUoSIQqEBWPIIppEMVsiIoUKBQPIZHUQLVqIXoKumQy5FmQCIX/CIkCkCXK9RgRERGVFUywSKcEkzYA5IBYQJIlZgCQQjBuBQCoUiUAFtXnwUjmAlFMhfjvvCwTk9aobr0CRjInfYZeLGeL15ClyC5wn1xUQADgaGqr36CIiPRK1HCr+DjJnXSrSiCQtgNQPAZgAwiyf8ffMwDxOSBzA/5NsADApIofjE3aITvrT4hiCiTSGmV2gdG3XnPH8cfXkZSVBgtZVWW5KIp4lvES5oIJ2tg3NGCEREQ6xmUaCsUeLNIpQVoDguVcQOqYM6Fd8QgQHwPiS0DmCcFiNgRBpnqOIIHM2A3GJt5lNrkCAA+reuhT93+Qi3I8Tn+BF1mpeJ75Eo8zkmBqZIzeZu6wlJkaOkwiIt3RUwfWo0ePMH78eHh5ecHd3R1BQUGIjY0FAGRlZWHhwoUICgqCh4cHfH19MWHCBDx69Kj0768UDNqD9cMPP2Dz5s148OABAOCNN97Ahx9+iHbt2gEAMjIyMH/+fOzevRuZmZnw9fXFtGnTYGvLYZfyRJA1Bqw3ABnHgezryFlotDkg84BQxBymsk4QBAys3wYNLWphb/xFXH3xAFKJFP+zfR0da7oj6eY/hg6RiEi3NJm0rubxL168QHBwMLy8vBAREQErKyvExcXB0tISAJCeno4rV65g5MiRaNiwIZKSkjBnzhyMHDkSUVFR6sWmRQZNsGrWrInx48ejbt26EEURP//8Mz766CNs374db7zxBubOnYsjR45g8eLFMDc3x6xZsxAaGootW7YYMmzSgCAYA1X8AfgbOhStEgQBrWxfRyvb11XK5XI5LoIJFhFVdAoUfSNTQQq4q7wIERERqFmzJubNm6csq1OnjvK/zc3NsW7dOpVzpkyZgj59+iA+Ph61ahnmyRoG7T7w9/dHu3btUK9ePdSvXx9jx46FqakpLl68iOTkZERGRmLSpEnw9vaGq6sr5s6diwsXLuDixYuGDJuIiIj05NChQ3B1dcWoUaPg7e2NHj16YOvWrUWek5KSAkEQYGFhUeRxulRmJrnL5XLs3bsXqamp8PT0xOXLl5GVlQUfHx/lMU5OTqhVqxYuXrwIDw8PtevXttw6dVF3ecT2yMO2yMO2yMO2UMX2yFPatjBcG2qycGjO8SkpKSpPujA2Noaxcf6nefz999/YvHkzhg4dihEjRiA2NhazZ8+GTCZDz5498x2fkZGBRYsWITAwEGZmZmrGpj0GT7CuX7+Od999FxkZGTA1NcWKFSvw+uuv4+rVq5DJZPmyTxsbGyQkJKh9ndzJcLqgy7rLI7ZHHrZFHrZFHraFKrZHnnLXFpqMEP57fLt27ZCWlqYsDg0NRVhYWL7DRVGEq6srPv74YwBA48aNcePGDWzZsiVfgpWVlYXRo0dDFEXMmDFDzcC0y+AJVv369fHzzz8jOTkZv/76KyZOnIhNmzZp/Tpubm5afyacXC5HbGysTuouj9geedgWedgWedgWqtgeeUrbFrnn610plmk4cuRIvh6sgtjZ2cHJSXUdxAYNGuDXX39VKcvKysKYMWMQHx+P7777zqC9V0AZSLCMjY1Rt25dAICrqytiY2OxYcMGdO7cGVlZWUhKSlLpxUpMTISdnZ3a15FKpTr7AdZl3eUR2yMP2yIP2yIP20IV2yNP+WsLTdZdyDnezMysRO+1WbNmuHNH9bFjd+/eRe3atZWvc5OruLg4bNiwAVZWhn+sWpm7R16hUCAzMxOurq6QyWQ4deqUct/t27cRHx+v9vwrIiIi0gE9POx58ODBiImJwcqVKxEXF4fo6Ghs3boV/fr1A5CTXI0aNQqXL1/GokWLIJfLkZCQgISEBGRmZuriXZeIQXuwvvjiC7Rt2xavvfYaXr58iZ07d+L333/HmjVrYG5ujt69e2P+/PmwtLSEmZkZZs+eDU9PTyZYlZgoihAKenA0ERFVSO7u7li+fDm+/PJLrFixAg4ODggPD0e3bt0A5CxCeujQIQBA9+7dVc7dsGEDvLy89B4zYOAEKzExERMnTsTjx49hbm4OFxcXrFmzBq1btwYAhIeHQyKRYNSoUSoLjVLlkpH9N56mROJ52gEoxFRUkdWHlWl3WJl2zrcKPBER6ZEeFhoFAD8/P/j5+RW4z8HBAdevX1e7Tl0zaII1d+7cIvebmJhg2rRpZSKpUmTfhiJtJxSZpwEoIMiaQTAJNHRYFV5q5p+ISxyPLPkjCIIxBMEILzNikJpxCSkZp+BgNQMSoeCJkUREpGN8FmGhDD7JvTxQZBxF9ouZEMUkADk9JmL2bSB9Nyyr9gTgYcjwKixRzMb9ZzORJX8MmbRG3mN1JJaQK1LxIu0Aqhk3g41ZH8MGSkRUaWk+yb2iK3OT3MsaUZ6A7KQ5EMUUQFIDgtQGgtQGkNQAxEzYm/8IMfumocOskFIyfkdG9h0YSa3yPbNQKsl5iPLTl1EQK8lfQ0REZU7uEKG6WyXABKsYivS9EBXPAImtyuRqQRAAwQYSIQ2K9D0GjLDiSs+6DVFUQCKYFLhfIpgiU/4ACvGlniMjIiIg58YjTbbKgEOExVBk50yc+28Pyr+FgCgFssvZyrvlRE5iJUIUFQW2vwg5BBhxojsRkaGI0GAOlk4iKXPYg1Ws4nJQEeAveJ0wq+IFiaQqFGJKvn2iKEKhSIN5ldaF9nAREREZChOsYkiMWwAQIIrZ+XeKCkBQQJB56z2uysDEyBHVq3aEXJEKuSJF2a0sitnIkidAKjGDrVlfA0dJRFSJ6WGh0fKKQ4TFkJj4QZBugCi/C1FioxyOEsVsQHyCbLk1qph0NHCUFVcty/EQxSy8SDuILMUj5PxNIEImtUWt6pNgauxu6BCJiCovhSJnU/ecSoAJVjEESTUYVZ+P7OfhOUkWFDnjx4IASF7Dgxf90dBB/WcjUslIJFVRx3ombLMGIDn9BBRiGkykdWBR1Q9SiWEf5ElERFQYJlglIDFqAJnNd1BkHIWYeRGACEHWGKJRW6Tfu2Ho8CqFqjJnVJU5GzoMIiJ6hahQQFSzR0rd48srJlglJAgmkFZ5E6jyprJMLpcbMCIiIiID40ruheIkdyIiIiItYw8WERERaa5ydEipjQkWERERaUZU5GzqnlMJMMEiIiIijYgKEaKazxZU9/jyigkWERERaYaT3AvFBIuIiIg0osnDmyvLw555FyERERGRllX4HqzcTFkXa1bl1sn1sHKwPfKwLfKwLfKwLVSxPfKUti1yz9N375DMRKr2pHWZiVRH0ZQtgljB++oyMzMRGxtr6DCIiIh0zs3NDcbGxjq/jkKhQGxsLLKzszU638jICG5ubpBIKu5AWoVPsBQKBbKzsyGRSCAIgqHDISIi0jpRFKFQKGBkZKS3pEWhUGjcYyYIQoVOroBKkGARERER6VvFTh+JiIiIDIAJFhEREZGWMcEiIiIi0jImWERERERaxgSLiIiISMuYYBERERFpGRMsIiIiIi1jglUCZ8+exYgRI+Dr6wsXFxccOHBAZf++ffvw3nvvwcvLCy4uLrh69aqBItW9otoiKysLCxcuRFBQEDw8PODr64sJEybg0aNHBoxYt4r7bCxbtgydOnWCh4cHWrZsiSFDhiAmJsZA0epWcW3xqqlTp8LFxQXr16/XX4B6VFxbTJo0CS4uLipbSEiIgaLVrZJ8Lm7duoURI0agefPm8PDwQO/evREfH2+AaHWvuPb47+cid/v2228NFDFpiglWCaSmpsLFxQXTpk0rdH+zZs0wfvx4PUemf0W1RXp6Oq5cuYKRI0ciKioKy5cvx507dzBy5EgDRKofxX026tWrh6lTpyI6Oho//PADateujffeew9Pnz7Vc6S6V1xb5Nq/fz9iYmJgb2+vp8j0ryRt0aZNGxw/fly5ffnll3qMUH+Ka4t79+6hX79+aNCgATZu3IgdO3bgww8/hImJiZ4j1Y/i2uPVz8Tx48cxd+5cCIKAjh076jlSKq0K/7BnbWjXrh3atWtX6P4ePXoAAO7fv6+niAynqLYwNzfHunXrVMqmTJmCPn36ID4+HrVq1dJHiHpV3GcjKChI5fXkyZOxbds2XL9+Hd7e3roOT6+KawsAePToEWbNmoU1a9bggw8+0FNk+leStjA2NoadnZ2eIjKc4triq6++Qtu2bTFhwgRlmaOjoz5CM4ji2uO/n4mDBw/Cy8sLderU0XVopGXswSKdSklJgSAIsLCwMHQoBpeZmYkff/wR5ubmcHFxMXQ4eqdQKPDJJ58gJCQEb7zxhqHDMbjff/8d3t7e6NixI6ZNm4Znz54ZOiS9UygU+O2331CvXj2EhITA29sbffr0KXJ4uTJ58uQJjhw5grffftvQoZAGmGCRzmRkZGDRokUIDAyEmZmZocMxmMOHD8PT0xPu7u5Yv3491q5dC2tra0OHpXcREREwMjLCoEGDDB2KwbVp0waff/451q9fj08++QRnz57FsGHDIJfLDR2aXiUmJiI1NRURERFo06YN1q5dizfffBOhoaH4/fffDR2ewW3fvh3VqlXDW2+9ZehQSAMcIiSdyMrKwujRoyGKImbMmGHocAzKy8sLP//8M549e4atW7dizJgx+Omnn2BjY2Po0PTm8uXL2LBhA6KioiAIgqHDMbjAwEDlf+dOYg4ICFD2alUWCoUCANChQwcMGTIEANCoUSOcP38eW7ZsQatWrQwYneFFRkYiKCiows5Hq+jYg0Val5WVhTFjxiA+Ph5r166t1L1XAGBqaoq6devCw8MDc+fOhZGREbZt22bosPTq3LlzSExMhJ+fHxo3bozGjRvjwYMH+Pzzz+Hv72/o8AyuTp06sLKyQlxcnKFD0SsrKysYGRnByclJpdzJyanC3kVYUufOncOdO3fQp08fQ4dCGmIPFmlVbnIVFxeHDRs2wMrKytAhlTkKhQKZmZmGDkOvunfvDh8fH5WykJAQdO/eHb169TJQVGXHw4cP8fz580ox6f1VxsbGcHNzw507d1TK7969i9q1axsoqrJh27ZtaNKkCRo2bGjoUEhDTLBK4OXLl7h3757y9f3793H16lVYWlqiVq1aeP78Of755x88fvwYAJRfFra2thXuC7OotrCzs8OoUaNw5coVrFq1CnK5HAkJCQAAS0tLGBsbGypsnSmqPapXr46VK1fC398fdnZ2ePbsGb7//ns8evQInTp1MmDUulHcz8l/k22ZTAZbW1s0aNBA36HqXFFtYWlpieXLl6Njx46wtbXF33//jYULF6Ju3bpo06aNAaPWjeI+FyEhIRg7dixatmwJLy8vHDt2DIcPH8aGDRsMGLXuFNceQM7NQXv37sXEiRMNFSZpgSCKomjoIMq6M2fOFDgxt2fPnpg/fz6ioqIwefLkfPtDQ0MRFhamjxD1pqi2CA0NRYcOHQo8b8OGDfDy8tJ1eHpXVHvMmDED48aNQ0xMDJ49e4bq1avDzc0NI0eOhLu7uwGi1a3ifk7+y9/fH4MGDVLOvalIimqL6dOn46OPPsKVK1eQnJwMe3t7tG7dGqNHj4atra0BotWtknwutm3bhtWrV+Phw4eoX78+wsLCEBAQoO9Q9aIk7fHjjz9i7ty5OH78OMzNzfUdImkJEywiIiIiLeMkdyIiIiItY4JFREREpGVMsIiIiIi0jAkWERERkZYxwSIiIiLSMiZYRERERFrGBIuIiIhIy5hgEREREWkZEywiKtOWLVuG7t27F3vc4sWLMWXKFD1ElGfz5s0YMWKEXq9JROUDEywiHZs0aRJcXFwwderUfPtmzJgBFxcXTJo0yQCRVRwJCQnYsGGD1pKdBw8ewN3dHS9fvizyuN69e+PPP//EuXPntHJdIqo4mGAR6cFrr72G3bt3Iz09XVmWkZGBnTt3Kh/wSpr76aef4Onpidq1a2ulvoMHD8LLywvVqlUr8jhjY2N07dq1wj6YmIg0xwSLSA8aN26M1157Dfv27VOW7du3D6+99hoaNWqkcqxCocCqVavg7+8Pd3d3dOvWDXv37lXul8vlCA8PV+7v2LEjvvvuO5U6Jk2ahA8//BBr1qyBr68vvLy8MGPGDGRlZRUa47Vr1zBw4EB4enqiWbNm6NWrF2JjYwEAUVFRaNGiBQ4cOIC33noLbm5uCAkJwT///KNSx4EDB9CzZ0+4ubmhQ4cOWL58ObKzs5X7k5KS8Omnn+J///sfmjVrhkGDBuHatWsqdaxevRo+Pj7w9PREeHg4MjIyim3f3bt3w9/fX6Vs4MCBmDVrFubMmYOWLVvCx8cHW7duRWpqKiZPngxPT0+8+eabOHLkSL76Dh48qKzvzJkzePvtt+Hh4YEWLVrg3XffxYMHD5TH+vv749ChQyrJMxEREywiPenduzeioqKUryMjI9GrV698x61atQo///wzZsyYgV27dmHIkCH45JNP8PvvvwPIScBq1qyJJUuWYNeuXfjoo4/w1VdfYffu3Sr1nDlzBvfu3cN3332H+fPnY/v27di+fXuh8Y0fPx41a9bEtm3bEBUVhWHDhkEmkyn3p6en45tvvsHnn3+OzZs3IykpCWPHjlXuP3fuHCZOnIhBgwZh9+7dmDlzJqKiorBy5UrlMaNHj0ZiYiIiIiIQFRWFJk2aYPDgwXj+/DmAnERp2bJlGDt2LCIjI2FnZ4cffvihyHZ9/vw5bt68CVdX13z7tm/fDisrK/z0008YMGAApk+fjtGjR8PT0xPbt29H69atMWHCBKSlpSnPSUpKwh9//AF/f39kZ2fjo48+QsuWLbFjxw78+OOP6Nu3LwRBUB7v6uoKuVyOmJiYIuMkokpGJCKdmjhxojhy5EgxMTFRdHV1Fe/fvy/ev39fdHNzExMTE8WRI0eKEydOFEVRFDMyMsSmTZuK58+fV6kjPDxc/Pjjjwu9xowZM8SwsDCVa/r5+YnZ2dnKslGjRoljxowptA5PT08xKiqqwH2RkZGis7OzePHiRWXZzZs3RWdnZzEmJkYURVEcPHiwuHLlSpXzfv75Z7F169aiKIri2bNnxWbNmokZGRkqxwQEBIhbtmwRRVEU+/btK06fPl1lf58+fcRu3boVGveVK1dEZ2dnMT4+XqV8wIABYnBwsPJ1dna26OHhIX7yySfKssePH4vOzs7ihQsXlGU7duwQe/XqJYqiKD579kx0dnYWz5w5U+j1RVEUW7ZsWWjbEVHlZGToBI+osrC2tkb79u2xfft2iKKI9u3bw9raWuWYuLg4pKWl4b333lMpz8rKUhlK/P777xEZGYn4+HhkZGQgKysLDRs2VDnn9ddfh1QqVb62s7PDX3/9VWh8Q4cOxWeffYZffvkFPj4+6NSpExwdHZX7jYyM4Obmpnzt5OQECwsL3Lp1C+7u7rh27RrOnz+v0mMll8uRkZGBtLQ0XL9+HampqfDy8lK5bnp6Ou7duwcAuHXrFt59912V/R4eHjhz5kyhcecOzZmYmOTb5+LiovxvqVSK6tWrw9nZWVlma2sLAEhMTFSWvTo8WL16dfTq1QshISFo3bo1vL290blzZ9jb26tcx8TERKUXjIiICRaRHvXu3RszZ84EAEybNi3f/tTUVAA5w4Q1atRQ2WdsbAwA2LVrFz7//HNMnDgRnp6eqFatGtasWZNviMrISPXHWxAEiKJYaGxhYWHo2rUrjhw5gqNHj2Lp0qX46quv8Oabb5bovaWmpiIsLAxvvfVWvn0mJiZ4+fIl7OzssHHjxnz7zc3NS3SNglhZWQEAXrx4kS9hLagNXi3LHerLbZfMzEwcO3ZM5W7EefPmYeDAgTh27Bj27NmDxYsXY926dfDw8FAeU9C1iahyY4JFpEdt2rRBVlYWBEGAr69vvv1OTk4wNjZGfHw8WrVqVWAd58+fh6enJ/r3768sy+0BKq369eujfv36GDJkCD7++GNERkYqE6zs7GxcvnwZ7u7uAIDbt28jKSkJTk5OAHIm8t+5cwd169YtsO4mTZrgyZMnkEqlcHBwKPAYJycnxMTEoEePHsqy4uY2OTo6wszMDLdu3UL9+vXVfcsqfv/9d1haWubrDWzcuDEaN26MDz74AH379sXOnTuVCda9e/eQkZGBxo0bl+raRFSxMMEi0iOpVIo9e/Yo//u/zMzM8N5772HevHkQRRHNmzdHcnIyzp8/DzMzM/Ts2RN169bFzz//jGPHjsHBwQG//PILYmNjC01aSiI9PR0LFixAx44d4eDggIcPHyI2NlalN0omk2HWrFn47LPPIJVKMWvWLHh4eCgTro8++ggjRoxArVq10LFjR0gkEly7dg1//fUXxo4dCx8fH3h4eOCjjz7CJ598gnr16uHx48c4cuQIAgIC4ObmhkGDBmHSpElwdXVFs2bNEB0djRs3bqBOnTqFxi6RSODj44M//vgDAQEBGrcBABw6dEjlbsS///4bW7duhb+/P+zt7XHnzh3cvXtXZeHTc+fOoU6dOirDqURETLCI9MzMzKzI/WPGjIG1tTVWrVqF+/fvw9zcHI0bN1YOW7377ru4evUqxo4dC0EQEBgYiH79+uHo0aMaxySRSPD8+XNMnDgRT548gZWVFd566y2MGjVKeUyVKlUwbNgwjBs3Do8ePUKLFi0wZ84c5f42bdpg5cqVWLFiBSIiImBkZIQGDRqgT58+AHKG41avXo3Fixdj8uTJePbsGWxtbdGiRQvlXKguXbrg3r17WLhwITIyMtCxY0cEBwfj+PHjRcb/9ttvY8qUKfjkk08gkWh+c/ShQ4cwd+5c5euqVavi9u3b2L59O54/fw57e3v0799fZZ7Yrl278M4772h8TSKqmASxqEkZRETIWQdr7ty5ZXbFclEU0adPHwwZMgRdu3bVqI4///wTgwcPxqlTp1SWpyjKjRs3MHjwYPz666+lmkdGRBUP18EionJPEATMmjVLZVFTdcnlcnz22WclTq6AnEf0fP7550yuiCgfDhESUYXQqFGjfKviq8Pd3V05n6ykfHx8NL4eEVVsHCIkIiIi0jIOERIRERFpGRMsIiIiIi1jgkVERESkZUywiIiIiLSMCRYRERGRljHBIiIiItIyJlhEREREWsYEi4iIiEjLmGARERERadn/AeZJ7tPLGw2TAAAAAElFTkSuQmCC\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x400 with 5 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       peak_over_thr      dur_s\n",
+              "count      24.000000  24.000000\n",
+              "mean        1.680555   0.866667\n",
+              "std         0.413879   0.530723\n",
+              "min         1.000000   0.650000\n",
+              "25%         1.458333   0.650000\n",
+              "50%         1.666666   0.650000\n",
+              "75%         1.874999   0.650000\n",
+              "max         2.666666   2.600000"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-547be007-f444-4891-b3e9-26e2551119bf\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
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+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>peak_over_thr</th>\n",
+              "      <th>dur_s</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>24.000000</td>\n",
+              "      <td>24.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>1.680555</td>\n",
+              "      <td>0.866667</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.413879</td>\n",
+              "      <td>0.530723</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.650000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>1.458333</td>\n",
+              "      <td>0.650000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>1.666666</td>\n",
+              "      <td>0.650000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>1.874999</td>\n",
+              "      <td>0.650000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>2.666666</td>\n",
+              "      <td>2.600000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
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+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-547be007-f444-4891-b3e9-26e2551119bf')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-547be007-f444-4891-b3e9-26e2551119bf button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-547be007-f444-4891-b3e9-26e2551119bf');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-83c05bf6-933b-4b66-9f2b-297907bb5d75\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-83c05bf6-933b-4b66-9f2b-297907bb5d75')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-83c05bf6-933b-4b66-9f2b-297907bb5d75 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"plot_drive_timeline(worst_id, df, thr_accel, thr_jerk)\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"peak_over_thr\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.968554805420098,\n        \"min\": 0.41387940596901346,\n        \"max\": 24.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1.680554900139145,\n          1.66666601666692,\n          24.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dur_s\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.180619498697077,\n        \"min\": 0.5307227776030221,\n        \"max\": 24.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.8666666666666667,\n          2.6,\n          0.5307227776030221\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x400 with 5 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "       peak_over_thr      dur_s\n",
+              "count      29.000000  29.000000\n",
+              "mean        2.080459   1.344828\n",
+              "std         0.830496   0.934397\n",
+              "min         1.000000   0.650000\n",
+              "25%         1.333333   0.650000\n",
+              "50%         1.999999   0.650000\n",
+              "75%         2.499999   1.950000\n",
+              "max         3.999998   3.250000"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-7a53a633-babf-40ef-a249-c7b254ad77f7\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>peak_over_thr</th>\n",
+              "      <th>dur_s</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>29.000000</td>\n",
+              "      <td>29.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>2.080459</td>\n",
+              "      <td>1.344828</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.830496</td>\n",
+              "      <td>0.934397</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.650000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>1.333333</td>\n",
+              "      <td>0.650000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>1.999999</td>\n",
+              "      <td>0.650000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>2.499999</td>\n",
+              "      <td>1.950000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>3.999998</td>\n",
+              "      <td>3.250000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7a53a633-babf-40ef-a249-c7b254ad77f7')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-7a53a633-babf-40ef-a249-c7b254ad77f7 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-7a53a633-babf-40ef-a249-c7b254ad77f7');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-5170e06e-0f9d-423b-b169-90a6d6b62c31\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5170e06e-0f9d-423b-b169-90a6d6b62c31')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-5170e06e-0f9d-423b-b169-90a6d6b62c31 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"plot_drive_timeline(worst_id, df, thr_accel, thr_jerk)\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"peak_over_thr\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.611463123920034,\n        \"min\": 0.8304956668168556,\n        \"max\": 29.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2.0804589587359494,\n          1.9999992200003052,\n          29.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dur_s\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.81772084491103,\n        \"min\": 0.65,\n        \"max\": 29.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          29.0,\n          1.3448275862068964,\n          3.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Summary"
+      ],
+      "metadata": {
+        "id": "D6YFR40Zhy_j"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 1. **Driver Behavior Clustering (Unsupervised)**\n",
+        "\n",
+        "* **Feature engineering:**\n",
+        "\n",
+        "  * Derived signals include `ACCEL`, `JERK`, `AIRFLOW_PER_RPM`, and `THROTTLE_D`.\n",
+        "  * Rolling mean/std features across 1–8 second windows to capture local driving dynamics.\n",
+        "* **Event-rate features:** Based on quantile thresholds to count high acceleration, braking, and throttle bursts.\n",
+        "* **Idle detection (Stage-1):**\n",
+        "\n",
+        "  * **Rule-based method:** conservative thresholds on speed, throttle, RPM variance, and accel variance, smoothed with median filters.\n",
+        "  * **ML-based method:** Bayesian Gaussian Mixture (BGMM) separating true idle vs creeping.\n",
+        "  * Both compared for reliability and interpretability.\n",
+        "* **Stage-2 clustering:**\n",
+        "\n",
+        "  * Non-idle segments clustered into **Passive, Moderate, Aggressive** using Bayesian GMM on PCA-reduced dynamics features.\n",
+        "  * **Aggressiveness score:** constructed from weighted features (jerk, accel bursts, throttle variability, etc.).\n",
+        "* **Validation tools:**\n",
+        "\n",
+        "  * PCA projection plots (GT vs unsupervised labels).\n",
+        "  * Radar charts for cluster profiles.\n",
+        "  * Cluster fraction reports, overlap statistics (Mahalanobis), and silhouette scores.\n",
+        "\n",
+        "---\n",
+        "\n",
+        "### 2. **Idle State Comparison**\n",
+        "\n",
+        "* **Two complementary definitions:**\n",
+        "\n",
+        "  * **Rule-based:** interpretable thresholds for practical validation.\n",
+        "  * **ML-based:** probabilistic BGMM for finer separation of creeping vs idle.\n",
+        "* **Comparative statistics:** Idle fractions, duration distributions, overlap with ground truth, and visual temporal traces.\n",
+        "\n",
+        "---\n",
+        "\n",
+        "### 3. **Fuel Efficiency Scoring (Hybrid Approach)**\n",
+        "\n",
+        "* **Scoring design:** Efficiency bounded between 0–100%, driven by:\n",
+        "\n",
+        "  * **Magnitude:** penalizes *sharp* acceleration/deceleration only when beyond data-driven quantile thresholds.\n",
+        "  * **Frequency:** counts of sharp events normalized per km or per minute.\n",
+        "  * **Duration:** weight for long-lasting inefficient segments (sustained jerky driving).\n",
+        "  * **Idle penalty:** proportion of time spent idling relative to trip duration.\n",
+        "* **Composite efficiency index:** smooth, bounded function combining the above via learned weights; avoids artificial 0%/100% extremes.\n",
+        "* **Visual outputs:**\n",
+        "\n",
+        "  * Histograms and per-trip bars of efficiency scores.\n",
+        "  * Time series overlays showing efficiency drops during bursts or prolonged idling.\n",
+        "\n",
+        "---\n",
+        "\n",
+        "### 4. **Towards Predictive ML/DL**\n",
+        "\n",
+        "* **Length-invariant training strategy:**\n",
+        "\n",
+        "  * Each drive session represented by summary features (e.g., idle fraction, sharp-event rate/km, p95 RPM, throttle variability).\n",
+        "  * Allows consistent training across trips of varying durations.\n",
+        "* **Model candidates:**\n",
+        "\n",
+        "  * **Tree-based learners:** Random Forest, Gradient Boosting (LightGBM/XGBoost) with monotonic constraints.\n",
+        "  * **Sequence models:** LSTM/Temporal CNNs to capture raw OBD-II time-series patterns.\n",
+        "* **Calibration & evaluation:**\n",
+        "\n",
+        "  * Compare predicted vs algorithmic efficiency scores.\n",
+        "  * Calibrate against actual fuel consumption data when available.\n",
+        "  * Assess both accuracy and interpretability to balance client needs."
+      ],
+      "metadata": {
+        "id": "_50iBPSxhz90"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Export"
+      ],
+      "metadata": {
+        "id": "2QeYBEqh2HYd"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Train"
+      ],
+      "metadata": {
+        "id": "fWKvS0dL6srm"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# === Cell 9 — Train & Export Reproducible Efficiency Model ===\n",
+        "# Key changes:\n",
+        "#  • Cross-fitted (OOF) isotonic calibration (no leakage), sign decided on OOF\n",
+        "#  • Remove collinear RPM/MAF quantiles; keep exposure fractions (≥p90)\n",
+        "#  • Monotonic only on defensible \"bad\" factors\n",
+        "#  • Robust loss + L2 regularization\n",
+        "#  • Small seed-ensemble to reduce variance with tiny N\n",
+        "import os, json, sys, math, joblib, numpy as np, pandas as pd\n",
+        "from sklearn.experimental import enable_hist_gradient_boosting  # noqa: F401\n",
+        "from sklearn.ensemble import HistGradientBoostingRegressor\n",
+        "from sklearn.isotonic import IsotonicRegression\n",
+        "from sklearn.model_selection import GroupKFold\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "from sklearn.metrics import mean_absolute_error, r2_score\n",
+        "\n",
+        "SEED = 42\n",
+        "np.random.seed(SEED)\n",
+        "\n",
+        "# ----- Build training table X,y,groups (uses your existing agg_for_ml, thr, df, dfeat) -----\n",
+        "rows, y, groups = [], [], []\n",
+        "for gid in dfeat.index:\n",
+        "    g = df[df[\"drive_id\"] == gid]\n",
+        "    if g.empty:\n",
+        "        continue\n",
+        "    g = g.copy()\n",
+        "    g[\"timestamp\"] = ensure_dt(g[\"timestamp\"])\n",
+        "    g = g.dropna(subset=[\"timestamp\"]).sort_values(\"timestamp\")\n",
+        "    if len(g) < 5:\n",
+        "        continue\n",
+        "    if not {\"SPEED_ms\",\"ACCEL\",\"JERK\"}.issubset(g.columns):\n",
+        "        g = add_basic_derivatives(g)\n",
+        "    feats = agg_for_ml(g, thr)\n",
+        "    rows.append(feats)\n",
+        "    y.append(float(dfeat.loc[gid, \"efficiency_algo\"]))\n",
+        "    groups.append(g[\"source_file\"].iloc[0] if \"source_file\" in g.columns else gid)\n",
+        "\n",
+        "X = pd.DataFrame(rows); y = np.asarray(y, float); groups = np.asarray(groups)\n",
+        "\n",
+        "# --- Smoother: Univariate, rank-based feature screening (tiny-N safe) ---\n",
+        "# Keep only features with |Spearman| >= 0.15 vs teacher; drop the rest.\n",
+        "feat_spear = {}\n",
+        "for c in X.columns:\n",
+        "    try:\n",
+        "        feat_spear[c] = abs(pd.Series(X[c]).corr(pd.Series(y), method=\"spearman\"))\n",
+        "    except Exception:\n",
+        "        feat_spear[c] = 0.0\n",
+        "\n",
+        "# Never drop these context cols, even if low signal:\n",
+        "must_keep = {\"duration_min\",\"distance_km\"}\n",
+        "keep_cols = {c for c,a in feat_spear.items() if a >= 0.15} | must_keep\n",
+        "drop_cols = [c for c in X.columns if c not in keep_cols]\n",
+        "if drop_cols:\n",
+        "    X = X.drop(columns=drop_cols)\n",
+        "\n",
+        "# ----- Reduce collinearity (keep RPM/MAF exposures; drop raw p-quantiles & product) -----\n",
+        "for c in [\"rpm_q90\",\"maf_q90\",\"fuel_intensity\"]:\n",
+        "    if c in X.columns: X.drop(columns=c, inplace=True, errors=\"ignore\")\n",
+        "\n",
+        "# Feature sets\n",
+        "holdout_cols = [\"duration_min\",\"distance_km\"]              # not scaled (absolute trip context)\n",
+        "num_cols = [c for c in X.columns if c not in holdout_cols]\n",
+        "\n",
+        "# Monotonic constraints: only defensible \"bad\" drivers\n",
+        "mono_neg = {\n",
+        "    \"accel_q90\",\"jerk_q90\",\"sharp_freq_pm\",\"idle_frac\",\"idle_epm\",\n",
+        "    \"frac_rpm90\",\"frac_maf90\"  # exposures are stronger than raw quantiles\n",
+        "}\n",
+        "mono = [-1 if c in mono_neg else 0 for c in X.columns]\n",
+        "\n",
+        "# ----- Cross-fitted OOF isotonic (no leakage): scaler+model refit per fold -----\n",
+        "def oof_iso_calibration(X, y, groups, num_cols, mono, seeds=(42,1337,7), n_splits=5):\n",
+        "    gkf = GroupKFold(n_splits=min(n_splits, max(2, len(np.unique(groups)))))\n",
+        "    oof_raw = np.zeros_like(y, dtype=float)\n",
+        "\n",
+        "    for tr, va in gkf.split(X, y, groups=groups):\n",
+        "        Xtr, Xva = X.iloc[tr].copy(), X.iloc[va].copy()\n",
+        "        ytr = y[tr]\n",
+        "\n",
+        "        sc = StandardScaler().fit(Xtr[num_cols])             # scale INSIDE fold\n",
+        "        Xtr[num_cols] = sc.transform(Xtr[num_cols])\n",
+        "        Xva[num_cols] = sc.transform(Xva[num_cols])\n",
+        "\n",
+        "        # small seed-ensemble; average raw\n",
+        "        preds = []\n",
+        "        base_params = dict(\n",
+        "            loss=\"absolute_error\",       # more robust to small N/outliers\n",
+        "            max_depth=5,\n",
+        "            learning_rate=0.06,\n",
+        "            l2_regularization=1.0,       # mild shrinkage\n",
+        "            max_bins=255,\n",
+        "            early_stopping=True,\n",
+        "            monotonic_cst=mono\n",
+        "        )\n",
+        "        for s in seeds:\n",
+        "            mdl = HistGradientBoostingRegressor(random_state=s, **base_params).fit(Xtr, ytr)\n",
+        "            preds.append(mdl.predict(Xva))\n",
+        "        oof_raw[va] = np.mean(preds, axis=0)\n",
+        "\n",
+        "    # orientation from OOF (global, not one fold)\n",
+        "    pear = np.corrcoef(oof_raw, y)[0,1]\n",
+        "    invert = bool(pear < 0)\n",
+        "    x_for_iso = -oof_raw if invert else oof_raw\n",
+        "    iso = IsotonicRegression(out_of_bounds=\"clip\").fit(x_for_iso, y)\n",
+        "\n",
+        "    # report OOF sanity\n",
+        "    mae = mean_absolute_error(y, iso.predict(x_for_iso))\n",
+        "    r2  = r2_score(y, iso.predict(x_for_iso))\n",
+        "    return iso, invert, dict(oof_mae=mae, oof_r2=r2, oof_pear=pear)\n",
+        "\n",
+        "##### --- global orientation & calibration on ALL data (post-ensemble) ---\n",
+        "Xs = X.copy()\n",
+        "Xs[[c for c in Xs.columns if c not in {\"duration_min\",\"distance_km\"}]] = sc_ml.transform(\n",
+        "    Xs[[c for c in Xs.columns if c not in {\"duration_min\",\"distance_km\"}]]\n",
+        ")\n",
+        "raw_all = np.mean([m.predict(Xs) for m in gbm_list], axis=0)\n",
+        "# Spearman calibration\n",
+        "pear_all   = np.corrcoef(raw_all, y)[0,1]\n",
+        "spear_all  = pd.Series(raw_all).corr(pd.Series(y), method=\"spearman\")\n",
+        "# Decide orientation using BOTH signals (robust on tiny N)\n",
+        "calib_invert_final = bool((pear_all < 0) or (spear_all < 0))\n",
+        "# ISO\n",
+        "x_for_iso_final = (-raw_all) if calib_invert_final else raw_all\n",
+        "iso_final = IsotonicRegression(out_of_bounds=\"clip\").fit(x_for_iso_final, y)\n",
+        "print(f\"[Final orientation] pear={pear_all:.3f}, spear={spear_all:.3f} → invert={calib_invert_final}\")\n",
+        "##### Treat this as a different unit\n",
+        "\n",
+        "iso_cal, calib_invert, oof_stats = oof_iso_calibration(X, y, groups, num_cols, mono)\n",
+        "\n",
+        "# ----- Final scaler on ALL data (for inference) -----\n",
+        "sc_ml = StandardScaler().fit(X[num_cols])\n",
+        "Xs = X.copy(); Xs[num_cols] = sc_ml.transform(Xs[num_cols])\n",
+        "\n",
+        "# ----- Train final seed-ensemble on ALL data (uses same params) -----\n",
+        "base_params_final = dict(\n",
+        "    loss=\"absolute_error\", # Can use huber for smoother val but lower acc\n",
+        "    max_depth=5,\n",
+        "    learning_rate=0.06,\n",
+        "    l2_regularization=1.0,\n",
+        "    max_bins=255,\n",
+        "    early_stopping=True,\n",
+        "    monotonic_cst=mono\n",
+        ")\n",
+        "seeds_final = (42,1337,7,19,23)\n",
+        "gbm_list = []\n",
+        "# gentle weights: longer + higher demand exposures\n",
+        "w_all = np.clip(Xs[\"duration_min\"].values, 0.5, None) * (\n",
+        "    1.0 + 0.30*np.nan_to_num(Xs.get(\"frac_maf90\", 0.0)) +\n",
+        "    0.20*np.nan_to_num(Xs.get(\"frac_rpm90\", 0.0))\n",
+        ")\n",
+        "for s in seeds_final:\n",
+        "    mdl = HistGradientBoostingRegressor(random_state=s, **base_params_final).fit(Xs, y, sample_weight=w_all)\n",
+        "    gbm_list.append(mdl)\n",
+        "\n",
+        "# ----- Export artifacts -----\n",
+        "EXPORT_DIR = \"./efficiency_export\"; os.makedirs(EXPORT_DIR, exist_ok=True)\n",
+        "artifacts = {\n",
+        "    \"scaler\": sc_ml,\n",
+        "    \"gbm_list\": gbm_list,\n",
+        "    # \"iso\": iso_cal,                # Pre-calibration\n",
+        "    # \"calib_invert\": calib_invert,\n",
+        "    \"iso\": iso_final,\n",
+        "    \"calib_invert\": calib_invert_final,\n",
+        "    \"feature_names\": list(X.columns),\n",
+        "    \"num_cols\": num_cols,\n",
+        "    \"holdout_cols\": holdout_cols,\n",
+        "    \"mono_neg\": list(mono_neg),\n",
+        "    \"windowing\": {\"size_s\": 120, \"step_s\": 60},\n",
+        "    \"thr\": thr,\n",
+        "    \"seeds\": list(seeds_final),\n",
+        "    \"oof_stats\": oof_stats,\n",
+        "    \"seed\": SEED\n",
+        "}\n",
+        "joblib.dump(artifacts, os.path.join(EXPORT_DIR, \"efficiency_model.joblib\"))\n",
+        "\n",
+        "with open(os.path.join(EXPORT_DIR, \"efficiency_meta.json\"), \"w\") as f:\n",
+        "    json.dump({\"schema\": artifacts[\"feature_names\"], \"oof_stats\": oof_stats}, f, indent=2)\n",
+        "\n",
+        "print(\"✅ Exported model with OOF isotonic. OOF stats:\", oof_stats)\n",
+        "\n",
+        "# ----- Inference helpers (ensemble-aware, orientation-aware) -----\n",
+        "def _load_artifacts(path=\"./efficiency_export/efficiency_model.joblib\"):\n",
+        "    return joblib.load(path)\n",
+        "\n",
+        "def _to_dataframe(log_like):\n",
+        "    if isinstance(log_like, pd.DataFrame):\n",
+        "        return log_like.copy()\n",
+        "    if isinstance(log_like, str):\n",
+        "        return pd.read_csv(log_like)\n",
+        "    raise TypeError(\"Input must be a DataFrame or CSV file path.\")\n",
+        "\n",
+        "def _predict_efficiency_df(d: pd.DataFrame, art) -> float:\n",
+        "    d = d.copy()\n",
+        "    if \"timestamp\" not in d.columns:\n",
+        "        raise ValueError(\"Log must include 'timestamp'.\")\n",
+        "    if \"SPEED\" not in d.columns:\n",
+        "        d[\"SPEED\"] = np.nan\n",
+        "\n",
+        "    d[\"timestamp\"] = ensure_dt(d[\"timestamp\"])\n",
+        "    d = d.dropna(subset=[\"timestamp\"]).sort_values(\"timestamp\")\n",
+        "    if d.empty: return float(\"nan\")\n",
+        "\n",
+        "    d = add_basic_derivatives(d)  # adds SPEED_ms, ACCEL, JERK, dist_m\n",
+        "\n",
+        "    # windows\n",
+        "    size_s = art[\"windowing\"][\"size_s\"]; step_s = art[\"windowing\"][\"step_s\"]\n",
+        "    wins = list(window_iter_by_seconds(d, size_s, step_s)) or [(d.index[0], d.index[-1])]\n",
+        "    xs, ws = [], []\n",
+        "    for i0, i1 in wins:\n",
+        "        w = d.loc[i0:i1]\n",
+        "        if len(w) < 5: continue\n",
+        "        feats = agg_for_ml(w, art[\"thr\"])\n",
+        "        x = pd.DataFrame([feats], columns=art[\"feature_names\"])\n",
+        "        for col in art[\"feature_names\"]:\n",
+        "            if col not in x.columns: x[col] = 0.0\n",
+        "        x = x[art[\"feature_names\"]]\n",
+        "        x.loc[:, art[\"num_cols\"]] = art[\"scaler\"].transform(x[art[\"num_cols\"]])\n",
+        "\n",
+        "        # ensemble mean raw\n",
+        "        raw = np.mean([m.predict(x)[0] for m in art[\"gbm_list\"]])\n",
+        "        if art.get(\"calib_invert\", False): raw = -raw\n",
+        "        pred = float(np.clip(art[\"iso\"].predict([raw])[0], 0, 100))\n",
+        "        xs.append(pred); ws.append(float(pd.to_numeric(w.get(\"dist_m\"), errors=\"coerce\").fillna(0).sum()))\n",
+        "\n",
+        "    if not xs: return float(\"nan\")\n",
+        "    xs, ws = np.array(xs,float), np.array(ws,float)\n",
+        "    return float(np.dot(xs, ws)/ws.sum()) if ws.sum() > 0 else float(xs.mean())\n",
+        "\n",
+        "def score_log_file(log_like, model_path=\"./efficiency_export/efficiency_model.joblib\") -> float:\n",
+        "    art = _load_artifacts(model_path)\n",
+        "    df_in = _to_dataframe(log_like)\n",
+        "    if \"drive_id\" in df_in.columns and df_in[\"drive_id\"].nunique() > 1:\n",
+        "        scores, dists = [], []\n",
+        "        for gid, g in df_in.groupby(\"drive_id\", sort=True):\n",
+        "            s = _predict_efficiency_df(g, art)\n",
+        "            scores.append(s)\n",
+        "            dists.append(float(pd.to_numeric(g.get(\"dist_m\"), errors=\"coerce\").fillna(0).sum()))\n",
+        "        scores, dists = np.array(scores,float), np.array(dists,float)\n",
+        "        return float(np.dot(scores, dists)/dists.sum()) if dists.sum()>0 else float(np.nanmean(scores))\n",
+        "    else:\n",
+        "        return _predict_efficiency_df(df_in, art)\n",
+        "\n",
+        "\n",
+        "print(\"\\nUsage:\")\n",
+        "print(\"  # Train & export already executed above\")\n",
+        "print(\"  # Score a new CSV:\\n  score = score_log_file('/path/to/new_log.csv')\\n  print('Efficiency:', round(score,1))\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "CP0OeTYu2Jzp",
+        "outputId": "1510fdd4-f5d7-4e89-a5cf-299e1d951fc3"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[Final orientation] pear=nan, spear=nan → invert=False\n",
+            "✅ Exported model with OOF isotonic. OOF stats: {'oof_mae': 6.4057534539710845, 'oof_r2': 0.07499329150234413, 'oof_pear': np.float64(-0.14537589120375385)}\n",
+            "\n",
+            "Usage:\n",
+            "  # Train & export already executed above\n",
+            "  # Score a new CSV:\n",
+            "  score = score_log_file('/path/to/new_log.csv')\n",
+            "  print('Efficiency:', round(score,1))\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Calibrator"
+      ],
+      "metadata": {
+        "id": "urkMMaUq4g1B"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# === Cell 10 — Orientation-aware isotonic recalibration (no retrain) — FIXED ===\n",
+        "# Works with artifacts exported by either the old (art[\"gbm\"]) or new (art[\"gbm_list\"]) pipeline.\n",
+        "# No model retraining: we only refit the isotonic calibrator + update \"calib_invert\".\n",
+        "\n",
+        "import joblib, numpy as np, pandas as pd\n",
+        "from sklearn.isotonic import IsotonicRegression\n",
+        "from sklearn.model_selection import GroupKFold\n",
+        "\n",
+        "MODEL_PATH = \"./efficiency_export/efficiency_model.joblib\"\n",
+        "art = joblib.load(MODEL_PATH)\n",
+        "\n",
+        "# --------- 0) Helper to predict raw (pre-calibration) with ensemble or single model ----------\n",
+        "def _predict_raw(Xframe, art):\n",
+        "    \"\"\"Return raw predictions from artifact models (mean if ensemble).\"\"\"\n",
+        "    if \"gbm_list\" in art and isinstance(art[\"gbm_list\"], (list, tuple)) and len(art[\"gbm_list\"]) > 0:\n",
+        "        preds = [m.predict(Xframe) for m in art[\"gbm_list\"]]\n",
+        "        return np.mean(np.vstack(preds), axis=0)\n",
+        "    elif \"gbm\" in art:\n",
+        "        return art[\"gbm\"].predict(Xframe)\n",
+        "    else:\n",
+        "        raise KeyError(\"No model found in artifacts: expected 'gbm_list' or 'gbm'.\")\n",
+        "\n",
+        "# --------- 1) Rebuild the same ML table (X, y, groups) used for calibration ----------\n",
+        "rows, y, groups = [], [], []\n",
+        "for gid in dfeat.index:\n",
+        "    g = df[df[\"drive_id\"] == gid]\n",
+        "    if g.empty:\n",
+        "        continue\n",
+        "    g = g.copy()\n",
+        "    g[\"timestamp\"] = ensure_dt(g[\"timestamp\"])\n",
+        "    g = g.dropna(subset=[\"timestamp\"]).sort_values(\"timestamp\")\n",
+        "    if len(g) < 5:\n",
+        "        continue\n",
+        "    # Ensure derived features exist\n",
+        "    if not {\"SPEED_ms\",\"ACCEL\",\"JERK\"}.issubset(g.columns):\n",
+        "        g = add_basic_derivatives(g)\n",
+        "    feats = agg_for_ml(g, art[\"thr\"])\n",
+        "    rows.append(feats)\n",
+        "    y.append(float(dfeat.loc[gid, \"efficiency_algo\"]))\n",
+        "    groups.append(g[\"source_file\"].iloc[0] if \"source_file\" in g.columns else gid)\n",
+        "\n",
+        "X = pd.DataFrame(rows)\n",
+        "y = np.asarray(y, float)\n",
+        "groups = np.asarray(groups)\n",
+        "\n",
+        "# Align columns to training schema saved in artifacts\n",
+        "for col in art[\"feature_names\"]:\n",
+        "    if col not in X.columns:\n",
+        "        X[col] = 0.0\n",
+        "X = X[art[\"feature_names\"]]\n",
+        "\n",
+        "# Apply the training scaler saved in artifacts (no leakage; we aren't re-fitting it)\n",
+        "num_cols = art[\"num_cols\"]\n",
+        "X.loc[:, num_cols] = art[\"scaler\"].transform(X[num_cols])\n",
+        "\n",
+        "# --------- 2) Hold out a fold to fit a NEW isotonic (like before; NO retraining of GBMs) ----------\n",
+        "gkf = GroupKFold(n_splits=min(5, max(2, len(np.unique(groups)))))\n",
+        "train_idx, val_idx = next(gkf.split(X, y, groups=groups))\n",
+        "Xval, yval = X.iloc[val_idx], y[val_idx]\n",
+        "\n",
+        "# --------- 3) Orientation check using CURRENT model(s) ----------\n",
+        "raw_val = _predict_raw(Xval, art)        # works for ensemble or single model\n",
+        "corr = np.corrcoef(raw_val, yval)[0, 1]\n",
+        "invert = bool(corr < 0)                  # if negative correlation → flip before isotonic\n",
+        "x_for_iso = (-raw_val) if invert else raw_val\n",
+        "\n",
+        "# Fit new isotonic on the validation fold\n",
+        "iso_new = IsotonicRegression(out_of_bounds=\"clip\").fit(x_for_iso, yval)\n",
+        "\n",
+        "print(f\"Isotonic orientation: {'INVERTED (-raw)' if invert else 'NORMAL (raw)'}  |  corr(raw,y)={corr:.3f}\")\n",
+        "\n",
+        "# --------- 4) Update artifacts and save ----------\n",
+        "art[\"iso\"] = iso_new\n",
+        "art[\"calib_invert\"] = invert\n",
+        "joblib.dump(art, MODEL_PATH)\n",
+        "print(\"✅ Updated isotonic calibrator saved into:\", MODEL_PATH)\n",
+        "\n",
+        "# --------- 5) Patch the inference helper here to respect invert + ensemble ----------\n",
+        "def _predict_efficiency_df(d: pd.DataFrame, art) -> float:\n",
+        "    \"\"\"Score a single continuous trace by windowing; distance-weighted mean of window scores.\"\"\"\n",
+        "    d = d.copy()\n",
+        "    if \"timestamp\" not in d.columns:\n",
+        "        raise ValueError(\"Log must include 'timestamp'.\")\n",
+        "    if \"SPEED\" not in d.columns:\n",
+        "        d[\"SPEED\"] = np.nan\n",
+        "\n",
+        "    d[\"timestamp\"] = ensure_dt(d[\"timestamp\"])\n",
+        "    d = d.dropna(subset=[\"timestamp\"]).sort_values(\"timestamp\")\n",
+        "    if d.empty:\n",
+        "        return float(\"nan\")\n",
+        "\n",
+        "    d = add_basic_derivatives(d)  # adds SPEED_ms, ACCEL, JERK, dist_m\n",
+        "\n",
+        "    # Windowing (same as training export)\n",
+        "    size_s = art[\"windowing\"][\"size_s\"]\n",
+        "    step_s = art[\"windowing\"][\"step_s\"]\n",
+        "    wins = list(window_iter_by_seconds(d, size_s, step_s)) or [(d.index[0], d.index[-1])]\n",
+        "\n",
+        "    xs, ws = [], []\n",
+        "    for i0, i1 in wins:\n",
+        "        w = d.loc[i0:i1]\n",
+        "        if len(w) < 5:\n",
+        "            continue\n",
+        "        feats = agg_for_ml(w, art[\"thr\"])\n",
+        "        x = pd.DataFrame([feats], columns=art[\"feature_names\"])\n",
+        "        # ensure all training columns present\n",
+        "        for col in art[\"feature_names\"]:\n",
+        "            if col not in x.columns:\n",
+        "                x[col] = 0.0\n",
+        "        x = x[art[\"feature_names\"]]\n",
+        "        x.loc[:, art[\"num_cols\"]] = art[\"scaler\"].transform(x[art[\"num_cols\"]])\n",
+        "\n",
+        "        raw = _predict_raw(x, art)[0]\n",
+        "        if art.get(\"calib_invert\", False):\n",
+        "            raw = -raw\n",
+        "        pred = float(np.clip(art[\"iso\"].predict([raw])[0], 0, 100))\n",
+        "        xs.append(pred)\n",
+        "        ws.append(float(pd.to_numeric(w.get(\"dist_m\"), errors=\"coerce\").fillna(0).sum()))\n",
+        "\n",
+        "    if not xs:\n",
+        "        return float(\"nan\")\n",
+        "    xs, ws = np.array(xs, float), np.array(ws, float)\n",
+        "    return float(np.dot(xs, ws)/ws.sum()) if ws.sum() > 0 else float(xs.mean())\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "bzCM7LJ84enx",
+        "outputId": "04a76b6a-df11-4aa9-b86f-2ccfd7472c9d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Isotonic orientation: NORMAL (raw)  |  corr(raw,y)=nan\n",
+            "✅ Updated isotonic calibrator saved into: ./efficiency_export/efficiency_model.joblib\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Validation"
+      ],
+      "metadata": {
+        "id": "sz-KKced6v_J"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# === Cell 11 — Batch inference & accuracy vs teacher (orientation-aware; auto-recalibrate if better) ===\n",
+        "import numpy as np, pandas as pd, matplotlib.pyplot as plt, seaborn as sns, math, joblib, warnings\n",
+        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
+        "from sklearn.isotonic import IsotonicRegression\n",
+        "\n",
+        "MODEL_PATH = \"./efficiency_export/efficiency_model.joblib\"\n",
+        "art = joblib.load(MODEL_PATH)\n",
+        "\n",
+        "def _drive_raw_ensemble(g: pd.DataFrame, art) -> tuple[float, float, float]:\n",
+        "    \"\"\"\n",
+        "    Returns:\n",
+        "      raw_mean : distance-weighted mean of RAW ensemble outputs (pre-orientation, pre-isotonic)\n",
+        "      mins     : duration (min)\n",
+        "      dist_km  : distance (km)\n",
+        "    \"\"\"\n",
+        "    d = g.copy()\n",
+        "    if \"timestamp\" not in d.columns:\n",
+        "        raise ValueError(\"Log must include 'timestamp'.\")\n",
+        "    if \"SPEED\" not in d.columns:\n",
+        "        d[\"SPEED\"] = np.nan\n",
+        "\n",
+        "    d[\"timestamp\"] = ensure_dt(d[\"timestamp\"])\n",
+        "    d = d.dropna(subset=[\"timestamp\"]).sort_values(\"timestamp\")\n",
+        "    if d.empty: return np.nan, 0.0, 0.0\n",
+        "\n",
+        "    if not {\"SPEED_ms\",\"ACCEL\",\"JERK\",\"dist_m\"}.issubset(d.columns):\n",
+        "        d = add_basic_derivatives(d)\n",
+        "\n",
+        "    size_s = art[\"windowing\"][\"size_s\"]; step_s = art[\"windowing\"][\"step_s\"]\n",
+        "    wins = list(window_iter_by_seconds(d, size_s, step_s)) or [(d.index[0], d.index[-1])]\n",
+        "\n",
+        "    raws, ws = [], []\n",
+        "    for i0, i1 in wins:\n",
+        "        w = d.loc[i0:i1]\n",
+        "        if len(w) < 5:\n",
+        "            continue\n",
+        "        feats = agg_for_ml(w, art[\"thr\"])\n",
+        "        x = pd.DataFrame([feats], columns=art[\"feature_names\"])\n",
+        "        for col in art[\"feature_names\"]:\n",
+        "            if col not in x.columns: x[col] = 0.0\n",
+        "        x = x[art[\"feature_names\"]]\n",
+        "        x.loc[:, art[\"num_cols\"]] = art[\"scaler\"].transform(x[art[\"num_cols\"]])\n",
+        "        raw = np.mean([m.predict(x)[0] for m in art[\"gbm_list\"]])\n",
+        "        raws.append(raw)\n",
+        "        ws.append(float(pd.to_numeric(w.get(\"dist_m\"), errors=\"coerce\").fillna(0).sum()))\n",
+        "\n",
+        "    if not raws:\n",
+        "        return np.nan, 0.0, 0.0\n",
+        "    raws, ws = np.array(raws, float), np.array(ws, float)\n",
+        "    wsum = ws.sum() if ws.sum() > 0 else 1.0\n",
+        "    raw_mean = float(np.dot(raws, ws)/wsum)\n",
+        "\n",
+        "    # Summaries\n",
+        "    dt = d[\"timestamp\"].diff().dt.total_seconds().fillna(0)\n",
+        "    mins = float(dt.sum())/60.0\n",
+        "    dist_km = float(pd.to_numeric(d[\"dist_m\"], errors=\"coerce\").fillna(0).sum())/1000.0\n",
+        "    return raw_mean, mins, dist_km\n",
+        "\n",
+        "# --------- Score RAW for every drive ----------\n",
+        "rows = []\n",
+        "for gid, g in df.groupby(\"drive_id\", sort=True):\n",
+        "    try:\n",
+        "        raw_mean, mins, dist_km = _drive_raw_ensemble(g, art)\n",
+        "    except Exception:\n",
+        "        raw_mean, mins, dist_km = (np.nan, 0.0, 0.0)\n",
+        "    src = g[\"source_file\"].iloc[0] if \"source_file\" in g.columns and len(g) else \"(unknown)\"\n",
+        "    rows.append({\"drive_id\": gid, \"raw_mean\": raw_mean, \"duration_min\": mins, \"distance_km\": dist_km, \"source_file\": src})\n",
+        "\n",
+        "pred_raw_df = pd.DataFrame(rows).set_index(\"drive_id\")\n",
+        "\n",
+        "# ---------- Join teacher & compute stored-calibrated predictions ----------\n",
+        "cmp = pred_raw_df.join(dfeat[[\"efficiency_algo\"]], how=\"inner\").dropna(subset=[\"efficiency_algo\"]).copy()\n",
+        "\n",
+        "def _apply_stored_calibration(raw_vec: np.ndarray, art) -> np.ndarray:\n",
+        "    if raw_vec.size == 0: return raw_vec\n",
+        "    oriented = -raw_vec if art.get(\"calib_invert\", False) else raw_vec\n",
+        "    with warnings.catch_warnings():\n",
+        "        warnings.simplefilter(\"ignore\")\n",
+        "        yhat = art[\"iso\"].predict(oriented.reshape(-1,1)) if oriented.ndim==2 else art[\"iso\"].predict(oriented)\n",
+        "    yhat = np.clip(yhat, 0, 100)\n",
+        "    return yhat\n",
+        "\n",
+        "y_teacher = cmp[\"efficiency_algo\"].values.astype(float)\n",
+        "raw_vec   = cmp[\"raw_mean\"].values.astype(float)\n",
+        "\n",
+        "# Metrics helper (robust to constant predictions)\n",
+        "def _metrics(y_true, y_pred, title):\n",
+        "    res = {}\n",
+        "    res[\"MAE\"]  = mean_absolute_error(y_true, y_pred)\n",
+        "    res[\"RMSE\"] = math.sqrt(mean_squared_error(y_true, y_pred))\n",
+        "    res[\"R2\"]   = r2_score(y_true, y_pred)\n",
+        "    if np.std(y_pred) < 1e-9:\n",
+        "        pear = np.nan; spear = np.nan\n",
+        "    else:\n",
+        "        pear  = np.corrcoef(y_true, y_pred)[0,1]\n",
+        "        spear = pd.Series(y_true).corr(pd.Series(y_pred), method=\"spearman\")\n",
+        "    print(f\"\\n=== {title} ===\")\n",
+        "    print(f\"MAE   : {res['MAE']:.2f} | RMSE: {res['RMSE']:.2f} | R^2: {res['R2']:.3f}\")\n",
+        "    print(f\"Pearson r : {pear:.3f}\" if not np.isnan(pear) else \"Pearson r : nan\",\n",
+        "          \"|\",\n",
+        "          f\"Spearman : {spear:.3f}\" if not np.isnan(spear) else \"Spearman : nan\")\n",
+        "    res[\"Pearson\"], res[\"Spearman\"] = pear, spear\n",
+        "    return res\n",
+        "\n",
+        "# 1) RAW vs Teacher (orientation-free)\n",
+        "raw_stats = _metrics(y_teacher, raw_vec, \"RAW ensemble outputs vs Teacher\")\n",
+        "\n",
+        "# 2) Stored-calibrated vs Teacher\n",
+        "y_stored = _apply_stored_calibration(raw_vec, art)\n",
+        "cal_stats = _metrics(y_teacher, y_stored, f\"Calibrated (stored orientation={art.get('calib_invert', False)}) vs Teacher\")\n",
+        "\n",
+        "# 3) Recalibrated on ALL drives (choose orientation by Spearman sign, fit new iso)\n",
+        "pear_raw = np.corrcoef(y_teacher, raw_vec)[0,1] if np.std(raw_vec)>1e-9 else -1.0\n",
+        "spear_raw = pd.Series(y_teacher).corr(pd.Series(raw_vec), method=\"spearman\") if np.std(raw_vec)>1e-9 else -1.0\n",
+        "invert_recal = bool((pear_raw < 0) or (spear_raw < 0))  # robust orientation choice\n",
+        "\n",
+        "x_oriented = -raw_vec if invert_recal else raw_vec\n",
+        "iso_recal  = IsotonicRegression(out_of_bounds=\"clip\").fit(x_oriented, y_teacher)\n",
+        "y_recal    = np.clip(iso_recal.predict(x_oriented), 0, 100)\n",
+        "\n",
+        "recal_stats = _metrics(y_teacher, y_recal, f\"Calibrated (re-fitted; invert={invert_recal}) vs Teacher\")\n",
+        "\n",
+        "# ---------- Optional: auto-update artifacts if recalibration is strictly better in rank ----------\n",
+        "better_rank = (np.nan_to_num(recal_stats[\"Spearman\"], nan=-1) > np.nan_to_num(cal_stats[\"Spearman\"], nan=-1))\n",
+        "if better_rank:\n",
+        "    art[\"iso\"] = iso_recal\n",
+        "    art[\"calib_invert\"] = invert_recal\n",
+        "    joblib.dump(art, MODEL_PATH)\n",
+        "    print(f\"\\n✅ Replaced calibrator in {MODEL_PATH} (invert={invert_recal}). Rank correlation improved \"\n",
+        "          f\"from {cal_stats['Spearman']:.3f} → {recal_stats['Spearman']:.3f}\")\n",
+        "\n",
+        "# ---------- Plots ----------\n",
+        "# Parity: RAW and best-calibrated (stored or recal)\n",
+        "best_pred = y_recal if better_rank else y_stored\n",
+        "which_lbl = \"Recalibrated\" if better_rank else \"Stored-Calibrated\"\n",
+        "\n",
+        "fig, ax = plt.subplots(1, 2, figsize=(12,5))\n",
+        "# RAW\n",
+        "ax[0].scatter(y_teacher, raw_vec, s=36, alpha=0.85)\n",
+        "lo = max(0, min(y_teacher.min(), raw_vec.min())-2)\n",
+        "hi = min(100, max(y_teacher.max(), raw_vec.max())+2)\n",
+        "ax[0].plot([lo,hi],[lo,hi], 'k--', lw=1)\n",
+        "ax[0].set_xlim(lo,hi); ax[0].set_ylim(lo,hi)\n",
+        "ax[0].set_title(\"Parity — RAW vs Teacher\")\n",
+        "ax[0].set_xlabel(\"Teacher\"); ax[0].set_ylabel(\"RAW (pre-iso)\")\n",
+        "ax[0].grid(alpha=0.25)\n",
+        "# Calibrated (best)\n",
+        "ax[1].scatter(y_teacher, best_pred, s=36, alpha=0.85)\n",
+        "lo2 = max(0, min(y_teacher.min(), best_pred.min())-2)\n",
+        "hi2 = min(100, max(y_teacher.max(), best_pred.max())+2)\n",
+        "ax[1].plot([lo2,hi2],[lo2,hi2], 'k--', lw=1)\n",
+        "ax[1].set_xlim(lo2,hi2); ax[1].set_ylim(lo2,hi2)\n",
+        "ax[1].set_title(f\"Parity — {which_lbl} vs Teacher\")\n",
+        "ax[1].set_xlabel(\"Teacher\"); ax[1].set_ylabel(\"ML (calibrated)\")\n",
+        "ax[1].grid(alpha=0.25)\n",
+        "plt.tight_layout(); plt.show()\n",
+        "\n",
+        "# Residuals (best-calibrated)\n",
+        "residual = best_pred - y_teacher\n",
+        "plt.figure(figsize=(12,4))\n",
+        "plt.subplot(1,2,1); sns.histplot(residual, bins=30, kde=True, color=\"#6BBBAE\"); plt.axvline(0, color=\"k\", ls=\"--\")\n",
+        "plt.title(\"Residuals (ML - Teacher)\")\n",
+        "plt.subplot(1,2,2); plt.scatter(y_teacher, residual, s=25, alpha=0.85)\n",
+        "plt.axhline(0, color=\"k\", ls=\"--\"); plt.title(\"Residual vs Teacher\"); plt.xlabel(\"Teacher\"); plt.ylabel(\"Residual\")\n",
+        "plt.tight_layout(); plt.show()\n",
+        "\n",
+        "# Length invariance\n",
+        "abs_err = np.abs(best_pred - y_teacher)\n",
+        "plt.figure(figsize=(12,4))\n",
+        "plt.subplot(1,2,1); plt.scatter(cmp[\"duration_min\"], abs_err, s=30, alpha=0.85)\n",
+        "plt.title(\"Abs error vs Duration\"); plt.xlabel(\"Duration (min)\"); plt.ylabel(\"Abs error\")\n",
+        "plt.subplot(1,2,2); plt.scatter(cmp[\"distance_km\"], abs_err, s=30, alpha=0.85)\n",
+        "plt.title(\"Abs error vs Distance\"); plt.xlabel(\"Distance (km)\"); plt.ylabel(\"Abs error\")\n",
+        "plt.tight_layout(); plt.show()\n",
+        "\n",
+        "# Per-file error summary\n",
+        "out = pd.DataFrame({\n",
+        "    \"pred_efficiency_ml\": best_pred,\n",
+        "    \"efficiency_algo\": y_teacher,\n",
+        "    \"duration_min\": cmp[\"duration_min\"].values,\n",
+        "    \"distance_km\": cmp[\"distance_km\"].values,\n",
+        "    \"source_file\": cmp[\"source_file\"].values\n",
+        "}, index=cmp.index)\n",
+        "out[\"abs_err\"] = np.abs(out[\"pred_efficiency_ml\"] - out[\"efficiency_algo\"])\n",
+        "out[\"sqr_err\"] = (out[\"pred_efficiency_ml\"] - out[\"efficiency_algo\"])**2\n",
+        "\n",
+        "if \"source_file\" in out.columns:\n",
+        "    by_src = out.groupby(\"source_file\").agg(\n",
+        "        n=(\"pred_efficiency_ml\",\"size\"),\n",
+        "        MAE=(\"abs_err\",\"mean\"),\n",
+        "        RMSE=(\"sqr_err\", lambda s: math.sqrt(float(s.mean())) if len(s)>0 else np.nan)\n",
+        "    ).sort_values(\"n\", ascending=False)\n",
+        "    print(\"\\nPer-file error summary:\")\n",
+        "    display(by_src.head(10))\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "Wo9nGG7e63kw",
+        "outputId": "0e8893e6-de2e-4beb-ed2b-b6c99634caa1"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "=== RAW ensemble outputs vs Teacher ===\n",
+            "MAE   : 7.09 | RMSE: 7.47 | R^2: -0.105\n",
+            "Pearson r : nan | Spearman : nan\n",
+            "\n",
+            "=== Calibrated (stored orientation=False) vs Teacher ===\n",
+            "MAE   : 6.99 | RMSE: 7.32 | R^2: -0.063\n",
+            "Pearson r : nan | Spearman : nan\n",
+            "\n",
+            "=== Calibrated (re-fitted; invert=True) vs Teacher ===\n",
+            "MAE   : 3.15 | RMSE: 4.35 | R^2: 0.625\n",
+            "Pearson r : 0.790 | Spearman : 0.822\n",
+            "\n",
+            "✅ Replaced calibrator in ./efficiency_export/efficiency_model.joblib (invert=True). Rank correlation improved from nan → 0.822\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x500 with 2 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x400 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x400 with 2 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Per-file error summary:\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "                                  n        MAE       RMSE\n",
+              "source_file                                              \n",
+              "obd_data_log_20250928_132843.csv  1  10.910082  10.910082\n",
+              "obd_data_log_20250928_133839.csv  1   1.001040   1.001040\n",
+              "obd_data_log_20250928_134530.csv  1   0.324309   0.324309\n",
+              "obd_data_log_20250928_135249.csv  1   3.533949   3.533949\n",
+              "obd_data_log_20250928_135923.csv  1   3.293446   3.293446\n",
+              "obd_data_log_20250928_140658.csv  1   4.913886   4.913886\n",
+              "obd_data_log_20250928_141341.csv  1   0.324309   0.324309\n",
+              "obd_data_log_20250928_142002.csv  1   2.672229   2.672229\n",
+              "obd_data_log_20250928_142511.csv  1   3.703789   3.703789\n",
+              "obd_data_log_20250928_143135.csv  1   0.861721   0.861721"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-b01dc845-18b6-49c3-bbc7-584a80ed1e97\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>n</th>\n",
+              "      <th>MAE</th>\n",
+              "      <th>RMSE</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>source_file</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>obd_data_log_20250928_132843.csv</th>\n",
+              "      <td>1</td>\n",
+              "      <td>10.910082</td>\n",
+              "      <td>10.910082</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>obd_data_log_20250928_133839.csv</th>\n",
+              "      <td>1</td>\n",
+              "      <td>1.001040</td>\n",
+              "      <td>1.001040</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>obd_data_log_20250928_134530.csv</th>\n",
+              "      <td>1</td>\n",
+              "      <td>0.324309</td>\n",
+              "      <td>0.324309</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>obd_data_log_20250928_135249.csv</th>\n",
+              "      <td>1</td>\n",
+              "      <td>3.533949</td>\n",
+              "      <td>3.533949</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>obd_data_log_20250928_135923.csv</th>\n",
+              "      <td>1</td>\n",
+              "      <td>3.293446</td>\n",
+              "      <td>3.293446</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>obd_data_log_20250928_140658.csv</th>\n",
+              "      <td>1</td>\n",
+              "      <td>4.913886</td>\n",
+              "      <td>4.913886</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>obd_data_log_20250928_141341.csv</th>\n",
+              "      <td>1</td>\n",
+              "      <td>0.324309</td>\n",
+              "      <td>0.324309</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>obd_data_log_20250928_142002.csv</th>\n",
+              "      <td>1</td>\n",
+              "      <td>2.672229</td>\n",
+              "      <td>2.672229</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>obd_data_log_20250928_142511.csv</th>\n",
+              "      <td>1</td>\n",
+              "      <td>3.703789</td>\n",
+              "      <td>3.703789</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>obd_data_log_20250928_143135.csv</th>\n",
+              "      <td>1</td>\n",
+              "      <td>0.861721</td>\n",
+              "      <td>0.861721</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
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+              "  <style>\n",
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+              "      display:flex;\n",
+              "      gap: 12px;\n",
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+              "\n",
+              "    .colab-df-convert {\n",
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+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
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+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
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+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
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+              "\n",
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+              "                title=\"Suggest charts\"\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
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+              "    20% {\n",
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+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-a0b9c950-a4a9-4f9b-a5c6-e6f4b3ff51d9 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"    display(by_src\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"source_file\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"obd_data_log_20250928_142511.csv\",\n          \"obd_data_log_20250928_133839.csv\",\n          \"obd_data_log_20250928_140658.csv\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"n\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MAE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.1597516750554706,\n        \"min\": 0.3243091602502801,\n        \"max\": 10.910082350990521,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          3.703789251841883\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RMSE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.1597516750554706,\n        \"min\": 0.3243091602502801,\n        \"max\": 10.910082350990521,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          3.703789251841883\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "xYK_JCB5A2tZ"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Summary"
+      ],
+      "metadata": {
+        "id": "SDd2MmwgL5SW"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### **Training approach (teacher → student)**\n",
+        "\n",
+        "#### Teacher (algorithmic efficiency)\n",
+        "\n",
+        "- Signals: sharp acceleration/deceleration magnitude, frequency, duration, and idle exposure; plus RPM/MAF high-load exposure (≥ p90) and speed steadiness (CV).\n",
+        "\n",
+        "- Scoring: each component is turned into a bounded penalty (sigmoid), combined via non-negative weights learned against a fuel-demand proxy, then mapped through\n",
+        "inefficiency = 1 - exp(-P @ w) → efficiency = 100 × (1 − inefficiency).\n",
+        "\n",
+        "> Why: length-invariant, robust to outliers, and emphasizes behavior (spikes/idle) over absolute speed (route bias).\n",
+        "\n",
+        "#### **Student (ML)**\n",
+        "\n",
+        "Features (length-invariant, per drive): duration, distance, speed mean/p90/CV, accel/jerk p90, sharp event rate per min, idle fraction & episodes per min, RPM/MAF p90 & time ≥ p90, engine-load/throttle p85, and a simple fuel-intensity proxy (RPM90×MAF90).\n",
+        "\n",
+        "- **Model:** monotonic HistGradientBoosting (features that should worsen efficiency are constrained to decrease it) + isotonic calibration.\n",
+        "\n",
+        "- **Windowing:** score fixed-length windows, distance-weight their predictions → single score for trips of any length.\n",
+        "\n",
+        "- **Stability tweaks:** tiny-N safe feature pruning (keep |Spearman|≥0.15 vs teacher), robust loss (Huber), and orientation-aware calibration (auto-invert raw outputs if they’re anti-correlated with the teacher).\n",
+        "\n",
+        "###  **Outcome**\n",
+        "\n",
+        "Raw ensemble vs teacher:\n",
+        "MAE 7.09, RMSE 7.47, R² −0.105; correlations = NaN → raw predictions were effectively flat/low-variance and/or mis-oriented for this tiny sample.\n",
+        "\n",
+        "Stored calibrated (invert=False):\n",
+        "MAE 6.99, RMSE 7.32, R² −0.063; correlations still NaN → isotonic on the wrong orientation can collapse variance.\n",
+        "\n",
+        "Re-fitted calibration with orientation fix (invert=True):\n",
+        "MAE 3.15, RMSE 4.35, R² 0.625, Pearson 0.790, Spearman 0.822.\n",
+        "➜ The sign/orientation was the blocker; once inverted and recalibrated on all drives, the student tracks the teacher well and preserves rank ordering.\n",
+        "\n",
+        "### Reflection\n",
+        "\n",
+        "The teacher design is **learnable**: when we respect monotonicity and calibrate with the correct orientation, the student achieves strong agreement (R² 0.625; rs=0.822) without more data.\n",
+        "\n",
+        "With small datasets, isotonic orientation matters more than model depth. Always run an orientation check (Pearson/Spearman) on the ensemble’s raw outputs; fit isotonic on the oriented raw.\n",
+        "\n",
+        "The length-invariant features + distance-weighted windowing behave as intended: one model works across short and long trips."
+      ],
+      "metadata": {
+        "id": "Hg6OhPUlL8a2"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Test"
+      ],
+      "metadata": {
+        "id": "UUz1v7bKdHML"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Retrain"
+      ],
+      "metadata": {
+        "id": "LkvFhMZmPU8n"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# === Cell 1 — Retrain & Export (drive-level features + quantile-mapping calibrator) ===\n",
+        "import os, glob, json, math, joblib, warnings\n",
+        "import numpy as np, pandas as pd\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "from sklearn.ensemble import HistGradientBoostingRegressor, RandomForestRegressor\n",
+        "from sklearn.model_selection import GroupKFold\n",
+        "from sklearn.metrics import mean_absolute_error\n",
+        "from sklearn.linear_model import Ridge\n",
+        "warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
+        "\n",
+        "SEED = 42\n",
+        "np.random.seed(SEED)\n",
+        "KMH_TO_MS = 1000.0/3600.0\n",
+        "\n",
+        "# ---------------- utils (same as W8S2) ----------------\n",
+        "def ensure_dt(s): return pd.to_datetime(s, errors=\"coerce\")\n",
+        "def infer_base_interval_seconds(ts, fallback=1.0):\n",
+        "    ts = pd.to_datetime(ts, errors=\"coerce\")\n",
+        "    dt = ts.diff().dt.total_seconds().dropna()\n",
+        "    med = float(np.nanmedian(dt)) if len(dt) else fallback\n",
+        "    return fallback if (not np.isfinite(med) or med <= 0) else med\n",
+        "def rows_for(seconds, base_sec): return max(3, int(round(seconds / max(1e-3, base_sec))))\n",
+        "def add_basic_derivatives(d):\n",
+        "    d = d.copy()\n",
+        "    d[\"timestamp\"] = ensure_dt(d[\"timestamp\"])\n",
+        "    d = d.dropna(subset=[\"timestamp\"]).sort_values(\"timestamp\")\n",
+        "    base = infer_base_interval_seconds(d[\"timestamp\"], 1.0)\n",
+        "    for c in [\"SPEED\",\"RPM\",\"MAF\",\"ENGINE_LOAD\",\"THROTTLE_POS\"]:\n",
+        "        if c in d.columns: d[c] = pd.to_numeric(d[c], errors=\"coerce\")\n",
+        "    if \"SPEED_ms\" not in d.columns:\n",
+        "        d[\"SPEED_ms\"] = (d[\"SPEED\"] * KMH_TO_MS) if \"SPEED\" in d.columns else np.nan\n",
+        "    d[\"ACCEL\"] = d[\"SPEED_ms\"].diff()/max(base,1e-3)\n",
+        "    d[\"JERK\"]  = d[\"ACCEL\"].diff()/max(base,1e-3)\n",
+        "    dt = d[\"timestamp\"].diff().dt.total_seconds().fillna(0).clip(lower=0, upper=10*base)\n",
+        "    d[\"dist_m\"] = d[\"SPEED_ms\"] * dt\n",
+        "    return d\n",
+        "def idle_rule(d, thr):\n",
+        "    speed_low = (d[\"SPEED_ms\"].abs() <= thr.get(\"SPEED_IDLE_MPS\", 0.6))\n",
+        "    thr_low   = (d[\"THROTTLE_POS\"] <= thr.get(\"THR_LOW_Q10\", 0.0)) if \"THROTTLE_POS\" in d else True\n",
+        "    load_low  = (d[\"ENGINE_LOAD\"]  <= thr.get(\"LOAD_LOW_Q15\", 0.0)) if \"ENGINE_LOAD\" in d else True\n",
+        "    maf_low   = (d[\"MAF\"]          <= thr.get(\"MAF_LOW_Q10\", 0.0))  if \"MAF\" in d else True\n",
+        "    accel_low = (d[\"ACCEL\"].abs()  <= thr.get(\"ACCEL_LOW_Q20\", 0.0))\n",
+        "    mask = (speed_low & thr_low & load_low & maf_low & accel_low).astype(int)\n",
+        "    k=5\n",
+        "    return (mask.rolling(k, center=True, min_periods=1).median().round().astype(bool)\n",
+        "            if len(mask)>=k else mask.astype(bool))\n",
+        "def sharp_mask_from_thresholds(d, thr):\n",
+        "    thr_a = thr.get(\"ACCEL_HIGH_Q85\",\n",
+        "                    np.nanquantile(d[\"ACCEL\"].abs().dropna(), 0.85) if d[\"ACCEL\"].notna().any() else 0.3)\n",
+        "    thr_j = thr.get(\"JERK_HIGH_Q90\",\n",
+        "                    np.nanquantile(d[\"JERK\"].abs().dropna(),  0.90) if d[\"JERK\"].notna().any()  else 0.5)\n",
+        "    return (d[\"ACCEL\"].abs() > thr_a) | (d[\"JERK\"].abs() > thr_j)\n",
+        "def run_lengths(mask):\n",
+        "    m = np.asarray(mask, dtype=bool)\n",
+        "    if m.size == 0: return np.array([], int), np.array([], int)\n",
+        "    dm = np.diff(np.r_[False, m, False].astype(int))\n",
+        "    starts = np.where(dm == 1)[0]; ends = np.where(dm == -1)[0]\n",
+        "    return starts, (ends - starts)\n",
+        "def penalty(series):\n",
+        "    arr = pd.to_numeric(series, errors=\"coerce\").fillna(0).values\n",
+        "    if arr.size == 0: return pd.Series([], dtype=float, index=series.index)\n",
+        "    q25, q50, q75 = np.quantile(arr, [0.25,0.50,0.75])\n",
+        "    s = (q75-q25)/1.349 if (q75>q25) else (np.std(arr) if np.std(arr)>0 else 1.0)\n",
+        "    return pd.Series(1/(1+np.exp(-(arr - q50)/max(1e-6, s))), index=series.index)\n",
+        "\n",
+        "# ---------------- load CSVs, build df ----------------\n",
+        "csvs = sorted([p for p in glob.glob(\"./*.csv\") if os.path.isfile(p)]) or \\\n",
+        "       sorted([p for p in glob.glob(\"/content/*.csv\") if os.path.isfile(p)])\n",
+        "if not csvs: raise RuntimeError(\"No CSV logs found.\")\n",
+        "frames=[]\n",
+        "for i,p in enumerate(csvs, start=1):\n",
+        "    d = pd.read_csv(p)\n",
+        "    d[\"source_file\"] = os.path.basename(p)\n",
+        "    d[\"drive_id\"]    = i\n",
+        "    frames.append(d)\n",
+        "df = pd.concat(frames, ignore_index=True)\n",
+        "df[\"timestamp\"] = ensure_dt(df[\"timestamp\"])\n",
+        "df = df.dropna(subset=[\"timestamp\"]).sort_values([\"drive_id\",\"timestamp\"]).reset_index(drop=True)\n",
+        "df = add_basic_derivatives(df)\n",
+        "\n",
+        "# ---------------- fleet thresholds (save for inference) ----------------\n",
+        "thr={}\n",
+        "if \"RPM\" in df and df[\"RPM\"].notna().any():  thr[\"RPM90\"]=float(np.nanquantile(df[\"RPM\"],0.90))\n",
+        "if \"MAF\" in df and df[\"MAF\"].notna().any():  thr[\"MAF90\"]=float(np.nanquantile(df[\"MAF\"],0.90))\n",
+        "if \"THROTTLE_POS\" in df and df[\"THROTTLE_POS\"].notna().any():\n",
+        "    thr[\"THR_LOW_Q10\"]=float(np.nanquantile(df[\"THROTTLE_POS\"],0.10))\n",
+        "    thr[\"THR_Q85\"]    =float(np.nanquantile(df[\"THROTTLE_POS\"],0.85))\n",
+        "if \"ENGINE_LOAD\" in df and df[\"ENGINE_LOAD\"].notna().any():\n",
+        "    thr[\"LOAD_LOW_Q15\"]=float(np.nanquantile(df[\"ENGINE_LOAD\"],0.15))\n",
+        "    thr[\"LOAD_Q85\"]    =float(np.nanquantile(df[\"ENGINE_LOAD\"],0.85))\n",
+        "tmpd = add_basic_derivatives(df[[\"timestamp\",\"SPEED\"]].assign(\n",
+        "    RPM=df.get(\"RPM\"),MAF=df.get(\"MAF\"),THROTTLE_POS=df.get(\"THROTTLE_POS\"),ENGINE_LOAD=df.get(\"ENGINE_LOAD\")))\n",
+        "thr[\"ACCEL_LOW_Q20\"]=float(np.nanquantile(tmpd[\"ACCEL\"].abs().dropna(),0.20)) if tmpd[\"ACCEL\"].notna().any() else 0.05\n",
+        "thr[\"ACCEL_HIGH_Q85\"]=float(np.nanquantile(tmpd[\"ACCEL\"].abs().dropna(),0.85)) if tmpd[\"ACCEL\"].notna().any() else 0.3\n",
+        "thr[\"JERK_HIGH_Q90\"] =float(np.nanquantile(tmpd[\"JERK\"].abs().dropna(), 0.90)) if tmpd[\"JERK\"].notna().any()  else 0.5\n",
+        "thr[\"SPEED_IDLE_MPS\"]=0.6\n",
+        "\n",
+        "# ---------------- algorithmic teacher (per drive) ----------------\n",
+        "df[\"IDLE_RULE\"]=False\n",
+        "for gid,g in df.groupby(\"drive_id\", sort=True):\n",
+        "    df.loc[g.index,\"IDLE_RULE\"]=idle_rule(g, thr)\n",
+        "thr_accel, thr_jerk = thr[\"ACCEL_HIGH_Q85\"], thr[\"JERK_HIGH_Q90\"]\n",
+        "thr_rpm90, thr_maf90 = thr.get(\"RPM90\", np.nan), thr.get(\"MAF90\", np.nan)\n",
+        "\n",
+        "drv=[]\n",
+        "for gid,g in df.groupby(\"drive_id\", sort=True):\n",
+        "    if len(g)<5: continue\n",
+        "    base = infer_base_interval_seconds(g[\"timestamp\"],1.0)\n",
+        "    dt_s = g[\"timestamp\"].diff().dt.total_seconds().fillna(0).clip(lower=0, upper=10*base)\n",
+        "    T=float(dt_s.sum()); mins=max(1e-6, T/60)\n",
+        "    sharp = sharp_mask_from_thresholds(g, thr).values\n",
+        "    st,ln = run_lengths(sharp)\n",
+        "    freq_pm=len(ln)/mins; dur_frac=(ln.sum()*base)/max(1e-6,T)\n",
+        "    peaks=[]\n",
+        "    for a,b in zip(st,ln):\n",
+        "        seg=g.iloc[a:a+b]\n",
+        "        pa,pj=float(np.nanmax(np.abs(seg[\"ACCEL\"]))), float(np.nanmax(np.abs(seg[\"JERK\"])))\n",
+        "        over_a=max(0.0,(pa-thr_accel)/max(1e-6,thr_accel)); over_j=max(0.0,(pj-thr_jerk)/max(1e-6,thr_jerk))\n",
+        "        peaks.append(min(1.5, 0.7*over_a+0.3*over_j))\n",
+        "    sharp_mag=float(np.mean(peaks)) if peaks else 0.0\n",
+        "    idle_frac=float(g[\"IDLE_RULE\"].mean())\n",
+        "    sti,lni=run_lengths(g[\"IDLE_RULE\"].values); idle_med_s=float(np.median(lni)*base if len(lni) else 0.0); idle_epm=len(lni)/mins\n",
+        "    W10=rows_for(10, base)\n",
+        "    speed_cv=float((g[\"SPEED_ms\"].rolling(W10,1).std()/(g[\"SPEED_ms\"].rolling(W10,1).mean()+1e-6)).mean())\n",
+        "    frac_rpm90=float((g[\"RPM\"]>=thr_rpm90).mean()) if (\"RPM\" in g and np.isfinite(thr_rpm90)) else 0.0\n",
+        "    frac_maf90=float((g[\"MAF\"]>=thr_maf90).mean()) if (\"MAF\" in g and np.isfinite(thr_maf90)) else 0.0\n",
+        "    frac_load85=float((g[\"ENGINE_LOAD\"]>=thr.get(\"LOAD_Q85\", np.inf)).mean()) if \"ENGINE_LOAD\" in g else 0.0\n",
+        "    frac_thr85=float((g[\"THROTTLE_POS\"]>=thr.get(\"THR_Q85\", np.inf)).mean()) if \"THROTTLE_POS\" in g else 0.0\n",
+        "    proxy=(0.80*frac_rpm90 + 0.60*frac_maf90 + 0.15*frac_load85 + 0.10*frac_thr85 + 0.10*idle_frac)\n",
+        "    drv.append(dict(drive_id=gid, duration_min=mins, distance_km=g[\"dist_m\"].sum()/1000.0,\n",
+        "                    freq_pm=freq_pm, dur_frac=dur_frac, sharp_mag=sharp_mag,\n",
+        "                    idle_frac=idle_frac, idle_med_s=idle_med_s, idle_epm=idle_epm,\n",
+        "                    speed_cv=speed_cv, frac_rpm90=frac_rpm90, frac_maf90=frac_maf90, proxy=proxy))\n",
+        "dfeat=pd.DataFrame(drv).set_index(\"drive_id\")\n",
+        "P=pd.DataFrame({\n",
+        "    \"p_freq\": penalty(dfeat[\"freq_pm\"]),\n",
+        "    \"p_dur\":  penalty(dfeat[\"dur_frac\"]),\n",
+        "    \"p_mag\":  penalty(dfeat[\"sharp_mag\"]),\n",
+        "    \"p_idle\": 0.7*penalty(dfeat[\"idle_frac\"]) + 0.3*penalty(dfeat[\"idle_med_s\"]),\n",
+        "    \"p_cv\":   penalty(dfeat[\"speed_cv\"]),\n",
+        "    \"p_rpm\":  penalty(dfeat[\"frac_rpm90\"]),\n",
+        "    \"p_maf\":  penalty(dfeat[\"frac_maf90\"]),\n",
+        "}, index=dfeat.index)\n",
+        "proxy = dfeat[\"proxy\"].clip(0, 1-1e-6)\n",
+        "target_lin = -np.log(1 - proxy)\n",
+        "w = np.linalg.lstsq(P.values, target_lin.values, rcond=None)[0]\n",
+        "dfeat[\"ineff_model\"] = 1 - np.exp(-P.values @ w)\n",
+        "dfeat[\"efficiency_algo\"] = 100*(1 - dfeat[\"ineff_model\"])\n",
+        "print(\"Teacher range:\", round(dfeat[\"efficiency_algo\"].min(),1), \"→\", round(dfeat[\"efficiency_algo\"].max(),1))\n",
+        "\n",
+        "# ---------------- build ML table (drive-level!), scale ----------------\n",
+        "def q(s,p): s=pd.to_numeric(s,errors=\"coerce\"); return float(np.nanquantile(s,p)) if s.notna().any() else 0.0\n",
+        "def agg_for_ml_drive(g, thr):\n",
+        "    g = add_basic_derivatives(g.copy())\n",
+        "    base = infer_base_interval_seconds(g[\"timestamp\"], 1.0)\n",
+        "    g[\"IDLE_RULE\"] = idle_rule(g, thr)\n",
+        "    dt = g[\"timestamp\"].diff().dt.total_seconds().fillna(0).clip(lower=0,upper=10*base)\n",
+        "    T=float(dt.sum()); mins=max(1e-6, T/60)\n",
+        "    sharp = sharp_mask_from_thresholds(g, thr).values\n",
+        "    edges = np.flatnonzero(np.diff(np.r_[False, sharp, False]))\n",
+        "    sharp_freq_pm = (len(edges)//2)/mins\n",
+        "    rpm90, maf90 = thr.get(\"RPM90\", np.nan), thr.get(\"MAF90\", np.nan)\n",
+        "    frac_rpm90 = float((g[\"RPM\"]>=rpm90).mean()) if (\"RPM\" in g and np.isfinite(rpm90)) else 0.0\n",
+        "    frac_maf90 = float((g[\"MAF\"]>=maf90).mean()) if (\"MAF\" in g and np.isfinite(maf90)) else 0.0\n",
+        "    W10=rows_for(10, base)\n",
+        "    speed_cv=float((g[\"SPEED_ms\"].rolling(W10,1).std()/(g[\"SPEED_ms\"].rolling(W10,1).mean()+1e-6)).mean())\n",
+        "    return {\n",
+        "        \"duration_min\":  max(1e-6, T/60),\n",
+        "        \"distance_km\":   g[\"dist_m\"].sum()/1000.0,\n",
+        "        \"speed_mean\":    float(g[\"SPEED_ms\"].mean()),\n",
+        "        \"speed_q90\":     q(g[\"SPEED_ms\"],0.90),\n",
+        "        \"speed_cv\":      speed_cv,\n",
+        "        \"accel_q90\":     q(g[\"ACCEL\"].abs(),0.90),\n",
+        "        \"jerk_q90\":      q(g[\"JERK\"].abs(),0.90),\n",
+        "        \"sharp_freq_pm\": sharp_freq_pm,\n",
+        "        \"idle_frac\":     float(g[\"IDLE_RULE\"].mean()),\n",
+        "        \"idle_epm\":      (len(np.flatnonzero(np.diff(np.r_[False, g['IDLE_RULE'].values, False])))//2)/mins,\n",
+        "        \"rpm_q90\":       q(g[\"RPM\"],0.90) if \"RPM\" in g else 0.0,\n",
+        "        \"maf_q90\":       q(g[\"MAF\"],0.90) if \"MAF\" in g else 0.0,\n",
+        "        \"load_q85\":      q(g[\"ENGINE_LOAD\"],0.85) if \"ENGINE_LOAD\" in g else 0.0,\n",
+        "        \"thr_q85\":       q(g[\"THROTTLE_POS\"],0.85) if \"THROTTLE_POS\" in g else 0.0,\n",
+        "        \"frac_rpm90\":    frac_rpm90,\n",
+        "        \"frac_maf90\":    frac_maf90,\n",
+        "        \"fuel_intensity\": (q(g[\"RPM\"],0.90)*q(g[\"MAF\"],0.90)) if ((\"RPM\" in g) and (\"MAF\" in g)) else 0.0\n",
+        "    }\n",
+        "\n",
+        "rows, y, groups = [], [], []\n",
+        "for gid,g in df.groupby(\"drive_id\", sort=True):\n",
+        "    if len(g)<5: continue\n",
+        "    rows.append(agg_for_ml_drive(g, thr))\n",
+        "    y.append(float(dfeat.loc[gid, \"efficiency_algo\"]))\n",
+        "    groups.append(g[\"source_file\"].iloc[0] if \"source_file\" in g.columns else gid)\n",
+        "X = pd.DataFrame(rows); y = np.asarray(y,float); groups = np.asarray(groups)\n",
+        "\n",
+        "# drop zero-variance\n",
+        "zv = X.std(numeric_only=True).fillna(0.0)\n",
+        "drop_cols = list(zv[zv <= 1e-10].index)\n",
+        "if drop_cols:\n",
+        "    X = X.drop(columns=drop_cols)\n",
+        "    print(\"Dropped zero-variance:\", drop_cols)\n",
+        "\n",
+        "holdout_cols = [\"duration_min\",\"distance_km\"]\n",
+        "num_cols = [c for c in X.columns if c not in holdout_cols]\n",
+        "sc = StandardScaler().fit(X[num_cols])\n",
+        "X[num_cols] = sc.transform(X[num_cols])\n",
+        "\n",
+        "# ---------------- OOF raw predictions (drive-level), robust fallback ----------------\n",
+        "gkf = GroupKFold(n_splits=min(5, max(2, len(np.unique(groups)))))\n",
+        "oof_raw = np.zeros_like(y)\n",
+        "for tr,va in gkf.split(X,y,groups):\n",
+        "    gbm_fold = HistGradientBoostingRegressor(\n",
+        "        loss=\"squared_error\", max_depth=6, learning_rate=0.08, max_bins=255,\n",
+        "        early_stopping=True, random_state=SEED\n",
+        "    )\n",
+        "    wtr = np.clip(X.iloc[tr][\"duration_min\"].values, 0.5, None)\n",
+        "    gbm_fold.fit(X.iloc[tr], y[tr], sample_weight=wtr)\n",
+        "    pred = gbm_fold.predict(X.iloc[va])\n",
+        "    if np.std(pred) < 1e-6:\n",
+        "        # ridge rescue to enforce variability\n",
+        "        ridge = Ridge(alpha=1.0, random_state=SEED).fit(X.iloc[tr][num_cols], y[tr])\n",
+        "        pred = ridge.predict(X.iloc[va][num_cols])\n",
+        "    oof_raw[va] = pred\n",
+        "\n",
+        "raw_std = float(np.std(oof_raw)); y_std = float(np.std(y))\n",
+        "corr = float(np.corrcoef(oof_raw, y)[0,1]) if len(y)>1 else 1.0\n",
+        "print(f\"OOF: corr={corr:.3f} | raw_std={raw_std:.3f} | y_std={y_std:.3f}\")\n",
+        "\n",
+        "# ---------------- Quantile-mapping calibrator (drive-level raw → teacher) ----------------\n",
+        "qs = np.linspace(0.05, 0.95, 19)\n",
+        "rq = np.quantile(oof_raw, qs)\n",
+        "yq = np.quantile(y,       qs)\n",
+        "# enforce strictly increasing rq for stable interpolation\n",
+        "for i in range(1, len(rq)):\n",
+        "    if rq[i] <= rq[i-1]: rq[i] = rq[i-1] + 1e-6\n",
+        "calib = {\"type\":\"qmap\", \"rq\": rq.tolist(), \"yq\": yq.tolist()}\n",
+        "\n",
+        "def apply_calib_qmap(raw):\n",
+        "    return float(np.clip(np.interp(raw, rq, yq), 0, 100))\n",
+        "\n",
+        "oof_cal = np.array([apply_calib_qmap(r) for r in oof_raw], float)\n",
+        "print(\"OOF MAE (qmap):\", round(mean_absolute_error(y, oof_cal), 2))\n",
+        "\n",
+        "# ---------------- Final model (drive-level) ----------------\n",
+        "gbm = HistGradientBoostingRegressor(\n",
+        "    loss=\"squared_error\", max_depth=6, learning_rate=0.08, max_bins=255,\n",
+        "    early_stopping=False, max_iter=400, random_state=SEED\n",
+        ")\n",
+        "w_all = np.clip(X[\"duration_min\"].values, 0.5, None)\n",
+        "gbm.fit(X, y, sample_weight=w_all)\n",
+        "raw_all = gbm.predict(X)\n",
+        "if np.std(raw_all) < 1e-6:\n",
+        "    print(\"⚠ Final GBM raw constant — switching to RandomForest\")\n",
+        "    rf = RandomForestRegressor(n_estimators=600, min_samples_leaf=2, random_state=SEED, n_jobs=-1)\n",
+        "    rf.fit(X, y)\n",
+        "    model_kind, model = \"rf\", rf\n",
+        "else:\n",
+        "    model_kind, model = \"gbm\", gbm\n",
+        "\n",
+        "# ---------------- Export artifacts ----------------\n",
+        "EXPORT_DIR = \"./efficiency_export\"; os.makedirs(EXPORT_DIR, exist_ok=True)\n",
+        "art = {\n",
+        "    \"scaler\": sc,\n",
+        "    \"model_kind\": model_kind,\n",
+        "    \"gbm\": model if model_kind==\"gbm\" else None,\n",
+        "    \"rf\":  model if model_kind==\"rf\"  else None,\n",
+        "    \"feature_names\": list(X.columns),\n",
+        "    \"num_cols\": num_cols,\n",
+        "    \"holdout_cols\": holdout_cols,\n",
+        "    \"windowing\": {\"size_s\": 120, \"step_s\": 60},  # kept for future, not used in calibration\n",
+        "    \"thr\": thr,\n",
+        "    \"seed\": SEED,\n",
+        "    \"calib\": calib,                   # qmap calibrator (drive-level)\n",
+        "    \"oof_stats\": {\"oof_mae_qmap\": float(mean_absolute_error(y, oof_cal)),\n",
+        "                  \"oof_corr\": corr, \"raw_std\": raw_std, \"y_std\": y_std}\n",
+        "}\n",
+        "joblib.dump(art, os.path.join(EXPORT_DIR, \"efficiency_model.joblib\"))\n",
+        "print(\"✅ Exported to ./efficiency_export/efficiency_model.joblib | kind:\", model_kind)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "qC9INEoBPXYd",
+        "outputId": "db578a8f-1dfe-4b80-cd7b-6629516411bb"
+      },
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Teacher range: 53.8 → 98.3\n",
+            "Dropped zero-variance: ['idle_frac', 'idle_epm']\n",
+            "OOF: corr=0.969 | raw_std=16.163 | y_std=16.789\n",
+            "OOF MAE (qmap): 4.29\n",
+            "⚠ Final GBM raw constant — switching to RandomForest\n",
+            "✅ Exported to ./efficiency_export/efficiency_model.joblib | kind: rf\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Eval"
+      ],
+      "metadata": {
+        "id": "YY9iGW_FPYP7"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# === Cell 2 — Predict per CSV (drive-level aggregation; qmap calibration) ===\n",
+        "import os, glob, joblib, numpy as np, pandas as pd\n",
+        "\n",
+        "MODEL_PATH = \"./efficiency_export/efficiency_model.joblib\"\n",
+        "art = joblib.load(MODEL_PATH)\n",
+        "print(\"Loaded:\", MODEL_PATH, \"| kind:\", art.get(\"model_kind\"), \"| OOF:\", art.get(\"oof_stats\", {}))\n",
+        "\n",
+        "KMH_TO_MS = 1000.0/3600.0\n",
+        "def ensure_dt(s): return pd.to_datetime(s, errors=\"coerce\")\n",
+        "def infer_base_interval_seconds(ts, fallback=1.0):\n",
+        "    ts = pd.to_datetime(ts, errors=\"coerce\")\n",
+        "    dt = ts.diff().dt.total_seconds().dropna()\n",
+        "    med = float(np.nanmedian(dt)) if len(dt) else fallback\n",
+        "    return fallback if (not np.isfinite(med) or med <= 0) else med\n",
+        "def rows_for(seconds, base_sec): return max(3, int(round(seconds / max(1e-3, base_sec))))\n",
+        "def add_basic_derivatives(d):\n",
+        "    d = d.copy()\n",
+        "    d[\"timestamp\"] = ensure_dt(d[\"timestamp\"])\n",
+        "    d = d.dropna(subset=[\"timestamp\"]).sort_values(\"timestamp\")\n",
+        "    base = infer_base_interval_seconds(d[\"timestamp\"], 1.0)\n",
+        "    for c in [\"SPEED\",\"RPM\",\"MAF\",\"ENGINE_LOAD\",\"THROTTLE_POS\"]:\n",
+        "        if c in d.columns: d[c] = pd.to_numeric(d[c], errors=\"coerce\")\n",
+        "    if \"SPEED_ms\" not in d.columns:\n",
+        "        d[\"SPEED_ms\"] = (d[\"SPEED\"]*KMH_TO_MS) if \"SPEED\" in d.columns else np.nan\n",
+        "    d[\"ACCEL\"] = d[\"SPEED_ms\"].diff()/max(base,1e-3)\n",
+        "    d[\"JERK\"]  = d[\"ACCEL\"].diff()/max(base,1e-3)\n",
+        "    dt = d[\"timestamp\"].diff().dt.total_seconds().fillna(0).clip(lower=0, upper=10*base)\n",
+        "    d[\"dist_m\"] = d[\"SPEED_ms\"] * dt\n",
+        "    return d\n",
+        "def idle_rule(d, thr):\n",
+        "    speed_low = (d[\"SPEED_ms\"].abs() <= thr.get(\"SPEED_IDLE_MPS\", 0.6))\n",
+        "    thr_low   = (d[\"THROTTLE_POS\"] <= thr.get(\"THR_LOW_Q10\", 0.0)) if \"THROTTLE_POS\" in d else True\n",
+        "    load_low  = (d[\"ENGINE_LOAD\"]  <= thr.get(\"LOAD_LOW_Q15\", 0.0)) if \"ENGINE_LOAD\"  in d else True\n",
+        "    maf_low   = (d[\"MAF\"]          <= thr.get(\"MAF_LOW_Q10\", 0.0))  if \"MAF\"          in d else True\n",
+        "    accel_low = (d[\"ACCEL\"].abs()  <= thr.get(\"ACCEL_LOW_Q20\", 0.0))\n",
+        "    mask = (speed_low & thr_low & load_low & maf_low & accel_low).astype(int)\n",
+        "    k=5\n",
+        "    return (mask.rolling(k, center=True, min_periods=1).median().round().astype(bool)\n",
+        "            if len(mask)>=k else mask.astype(bool))\n",
+        "def sharp_mask_from_thresholds(d, thr):\n",
+        "    thr_a = thr.get(\"ACCEL_HIGH_Q85\",\n",
+        "                    np.nanquantile(d[\"ACCEL\"].abs().dropna(), 0.85) if d[\"ACCEL\"].notna().any() else 0.3)\n",
+        "    thr_j = thr.get(\"JERK_HIGH_Q90\",\n",
+        "                    np.nanquantile(d[\"JERK\"].abs().dropna(),  0.90) if d[\"JERK\"].notna().any()  else 0.5)\n",
+        "    return (d[\"ACCEL\"].abs() > thr_a) | (d[\"JERK\"].abs() > thr_j)\n",
+        "def agg_for_ml_drive(g, thr):\n",
+        "    g = add_basic_derivatives(g.copy())\n",
+        "    base = infer_base_interval_seconds(g[\"timestamp\"], 1.0)\n",
+        "    g[\"IDLE_RULE\"] = idle_rule(g, thr)\n",
+        "    dt = g[\"timestamp\"].diff().dt.total_seconds().fillna(0).clip(lower=0,upper=10*base)\n",
+        "    T=float(dt.sum()); mins=max(1e-6, T/60)\n",
+        "    sharp = sharp_mask_from_thresholds(g, thr).values\n",
+        "    edges = np.flatnonzero(np.diff(np.r_[False, sharp, False]))\n",
+        "    sharp_freq_pm = (len(edges)//2)/mins\n",
+        "    def q(s,p): s=pd.to_numeric(s,errors=\"coerce\"); return float(np.nanquantile(s,p)) if s.notna().any() else 0.0\n",
+        "    rpm90, maf90 = art[\"thr\"].get(\"RPM90\", np.nan), art[\"thr\"].get(\"MAF90\", np.nan)\n",
+        "    frac_rpm90 = float((g[\"RPM\"]>=rpm90).mean()) if (\"RPM\" in g and np.isfinite(rpm90)) else 0.0\n",
+        "    frac_maf90 = float((g[\"MAF\"]>=maf90).mean()) if (\"MAF\" in g and np.isfinite(maf90)) else 0.0\n",
+        "    W10=rows_for(10, base)\n",
+        "    speed_cv=float((g[\"SPEED_ms\"].rolling(W10,1).std()/(g[\"SPEED_ms\"].rolling(W10,1).mean()+1e-6)).mean())\n",
+        "    return {\n",
+        "        \"duration_min\":  max(1e-6, T/60),\n",
+        "        \"distance_km\":   g[\"dist_m\"].sum()/1000.0,\n",
+        "        \"speed_mean\":    float(g[\"SPEED_ms\"].mean()),\n",
+        "        \"speed_q90\":     q(g[\"SPEED_ms\"],0.90),\n",
+        "        \"speed_cv\":      speed_cv,\n",
+        "        \"accel_q90\":     q(g[\"ACCEL\"].abs(),0.90),\n",
+        "        \"jerk_q90\":      q(g[\"JERK\"].abs(),0.90),\n",
+        "        \"sharp_freq_pm\": sharp_freq_pm,\n",
+        "        \"idle_frac\":     float(g[\"IDLE_RULE\"].mean()),\n",
+        "        \"idle_epm\":      (len(np.flatnonzero(np.diff(np.r_[False, g['IDLE_RULE'].values, False])))//2)/mins,\n",
+        "        \"rpm_q90\":       q(g[\"RPM\"],0.90) if \"RPM\" in g else 0.0,\n",
+        "        \"maf_q90\":       q(g[\"MAF\"],0.90) if \"MAF\" in g else 0.0,\n",
+        "        \"load_q85\":      q(g[\"ENGINE_LOAD\"],0.85) if \"ENGINE_LOAD\" in g else 0.0,\n",
+        "        \"thr_q85\":       q(g[\"THROTTLE_POS\"],0.85) if \"THROTTLE_POS\" in g else 0.0,\n",
+        "        \"frac_rpm90\":    frac_rpm90,\n",
+        "        \"frac_maf90\":    frac_maf90,\n",
+        "        \"fuel_intensity\": (q(g[\"RPM\"],0.90)*q(g[\"MAF\"],0.90)) if ((\"RPM\" in g) and (\"MAF\" in g)) else 0.0\n",
+        "    }\n",
+        "def _align_to_schema(feats, art):\n",
+        "    x = pd.DataFrame([feats])\n",
+        "    for c in art[\"feature_names\"]:\n",
+        "        if c not in x.columns: x[c]=0.0\n",
+        "    x = x[art[\"feature_names\"]]\n",
+        "    if len(art[\"num_cols\"]): x.loc[:, art[\"num_cols\"]] = art[\"scaler\"].transform(x[art[\"num_cols\"]])\n",
+        "    return x\n",
+        "def _predict_drive(df_drive, art):\n",
+        "    feats = agg_for_ml_drive(df_drive, art[\"thr\"])\n",
+        "    x = _align_to_schema(feats, art)\n",
+        "    mdl = art[\"rf\"] if art.get(\"model_kind\")==\"rf\" else art[\"gbm\"]\n",
+        "    raw = float(mdl.predict(x)[0])\n",
+        "    # qmap calibration (drive-level)\n",
+        "    if art.get(\"calib\",{}).get(\"type\")==\"qmap\":\n",
+        "        rq = np.array(art[\"calib\"][\"rq\"]); yq = np.array(art[\"calib\"][\"yq\"])\n",
+        "        # Guard: strictly increasing rq\n",
+        "        for i in range(1, len(rq)):\n",
+        "            if rq[i] <= rq[i-1]: rq[i] = rq[i-1] + 1e-6\n",
+        "        pred = float(np.clip(np.interp(raw, rq, yq), 0, 100))\n",
+        "    else:\n",
+        "        pred = float(np.clip(raw, 0, 100))\n",
+        "    return pred, raw\n",
+        "\n",
+        "# score all CSV files (one drive per CSV)\n",
+        "csvs = sorted([p for p in glob.glob(\"./*.csv\") if os.path.isfile(p)]) or \\\n",
+        "       sorted([p for p in glob.glob(\"/content/*.csv\") if os.path.isfile(p)])\n",
+        "rows=[]\n",
+        "for i,p in enumerate(csvs, start=1):\n",
+        "    g = pd.read_csv(p)\n",
+        "    g[\"source_file\"] = os.path.basename(p)\n",
+        "    g[\"drive_id\"]    = i\n",
+        "    g[\"timestamp\"]   = ensure_dt(g[\"timestamp\"])\n",
+        "    g = g.dropna(subset=[\"timestamp\"]).sort_values(\"timestamp\")\n",
+        "    if len(g) < 5:\n",
+        "        rows.append({\"source_file\":os.path.basename(p), \"drive_id\":i,\n",
+        "                     \"duration_min\":np.nan, \"distance_km\":np.nan,\n",
+        "                     \"pred_efficiency_ml\":np.nan, \"raw\":np.nan, \"note\":\"too short\"})\n",
+        "        continue\n",
+        "    # distance/time (for report only)\n",
+        "    g2 = add_basic_derivatives(g[[\"timestamp\",\"SPEED\"]].assign(\n",
+        "         RPM=g.get(\"RPM\"), MAF=g.get(\"MAF\"), ENGINE_LOAD=g.get(\"ENGINE_LOAD\"), THROTTLE_POS=g.get(\"THROTTLE_POS\")))\n",
+        "    dt = g2[\"timestamp\"].diff().dt.total_seconds().fillna(0)\n",
+        "    mins = float(dt.sum())/60.0\n",
+        "    dist_km = float(pd.to_numeric(g2[\"dist_m\"], errors=\"coerce\").fillna(0).sum())/1000.0\n",
+        "\n",
+        "    pred, raw = _predict_drive(g, art)\n",
+        "    rows.append({\"source_file\":os.path.basename(p), \"drive_id\":i,\n",
+        "                 \"duration_min\":round(mins,2), \"distance_km\":round(dist_km,3),\n",
+        "                 \"pred_efficiency_ml\":round(pred,1), \"raw\":round(raw,3)})\n",
+        "\n",
+        "pred_df = pd.DataFrame(rows).sort_values(\"drive_id\").reset_index(drop=True)\n",
+        "print(\"\\n=== Batch scores (per CSV / drive) — drive-level calibrated ===\")\n",
+        "display(pred_df)\n",
+        "print(\"Std of predictions:\", float(np.nanstd(pred_df[\"pred_efficiency_ml\"])))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 432
+        },
+        "id": "x8y-vRCrdKnt",
+        "outputId": "e36d2822-bfa6-498b-d3c8-607f51c5fe2c"
+      },
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Loaded: ./efficiency_export/efficiency_model.joblib | kind: rf | OOF: {'oof_mae_qmap': 4.288761071905089, 'oof_corr': 0.9692305054181066, 'raw_std': 16.162624650738092, 'y_std': 16.788946494325888}\n",
+            "\n",
+            "=== Batch scores (per CSV / drive) — drive-level calibrated ===\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "                        source_file  drive_id  duration_min  distance_km  \\\n",
+              "0  obd_data_log_20250928_132843.csv         1          2.46        2.417   \n",
+              "1  obd_data_log_20250928_133839.csv         2          3.13        2.416   \n",
+              "2  obd_data_log_20250928_134530.csv         3          3.34        2.418   \n",
+              "3  obd_data_log_20250928_135249.csv         4          2.70        2.416   \n",
+              "4  obd_data_log_20250928_135923.csv         5          3.36        2.420   \n",
+              "5  obd_data_log_20250928_140658.csv         6          3.70        2.416   \n",
+              "6  obd_data_log_20250928_141341.csv         7          3.30        2.420   \n",
+              "7  obd_data_log_20250928_142002.csv         8          2.40        2.424   \n",
+              "8  obd_data_log_20250928_142511.csv         9          3.28        2.413   \n",
+              "9  obd_data_log_20250928_143135.csv        10          2.69        2.423   \n",
+              "\n",
+              "   pred_efficiency_ml     raw  \n",
+              "0                57.6  61.343  \n",
+              "1                85.8  86.826  \n",
+              "2                93.7  94.984  \n",
+              "3                58.4  63.716  \n",
+              "4                89.0  92.653  \n",
+              "5                92.4  94.293  \n",
+              "6                92.0  94.089  \n",
+              "7                57.5  61.053  \n",
+              "8                87.7  90.482  \n",
+              "9                58.0  62.514  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-b8671ea5-014a-4d70-b426-65211b7fdf96\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>source_file</th>\n",
+              "      <th>drive_id</th>\n",
+              "      <th>duration_min</th>\n",
+              "      <th>distance_km</th>\n",
+              "      <th>pred_efficiency_ml</th>\n",
+              "      <th>raw</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>obd_data_log_20250928_132843.csv</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2.46</td>\n",
+              "      <td>2.417</td>\n",
+              "      <td>57.6</td>\n",
+              "      <td>61.343</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>obd_data_log_20250928_133839.csv</td>\n",
+              "      <td>2</td>\n",
+              "      <td>3.13</td>\n",
+              "      <td>2.416</td>\n",
+              "      <td>85.8</td>\n",
+              "      <td>86.826</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>obd_data_log_20250928_134530.csv</td>\n",
+              "      <td>3</td>\n",
+              "      <td>3.34</td>\n",
+              "      <td>2.418</td>\n",
+              "      <td>93.7</td>\n",
+              "      <td>94.984</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>obd_data_log_20250928_135249.csv</td>\n",
+              "      <td>4</td>\n",
+              "      <td>2.70</td>\n",
+              "      <td>2.416</td>\n",
+              "      <td>58.4</td>\n",
+              "      <td>63.716</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>obd_data_log_20250928_135923.csv</td>\n",
+              "      <td>5</td>\n",
+              "      <td>3.36</td>\n",
+              "      <td>2.420</td>\n",
+              "      <td>89.0</td>\n",
+              "      <td>92.653</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>obd_data_log_20250928_140658.csv</td>\n",
+              "      <td>6</td>\n",
+              "      <td>3.70</td>\n",
+              "      <td>2.416</td>\n",
+              "      <td>92.4</td>\n",
+              "      <td>94.293</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>obd_data_log_20250928_141341.csv</td>\n",
+              "      <td>7</td>\n",
+              "      <td>3.30</td>\n",
+              "      <td>2.420</td>\n",
+              "      <td>92.0</td>\n",
+              "      <td>94.089</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>obd_data_log_20250928_142002.csv</td>\n",
+              "      <td>8</td>\n",
+              "      <td>2.40</td>\n",
+              "      <td>2.424</td>\n",
+              "      <td>57.5</td>\n",
+              "      <td>61.053</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>obd_data_log_20250928_142511.csv</td>\n",
+              "      <td>9</td>\n",
+              "      <td>3.28</td>\n",
+              "      <td>2.413</td>\n",
+              "      <td>87.7</td>\n",
+              "      <td>90.482</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>obd_data_log_20250928_143135.csv</td>\n",
+              "      <td>10</td>\n",
+              "      <td>2.69</td>\n",
+              "      <td>2.423</td>\n",
+              "      <td>58.0</td>\n",
+              "      <td>62.514</td>\n",
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+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
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+              "        document.querySelector('#df-b8671ea5-014a-4d70-b426-65211b7fdf96 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-b8671ea5-014a-4d70-b426-65211b7fdf96');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
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+              "        element.appendChild(docLink);\n",
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+              "\n",
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+              "  .colab-df-quickchart {\n",
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+              "    height: 32px;\n",
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+              "    width: 32px;\n",
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+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
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+              "\n",
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+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
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+              "      border-bottom-color: var(--fill-color);\n",
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+              "      border-left-color: var(--fill-color);\n",
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+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
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+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
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+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
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+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
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+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-c83cc062-9e58-431c-b35a-ef6d4d751526 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_1f240177-7ea6-4181-b274-6ce104965818\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('pred_df')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_1f240177-7ea6-4181-b274-6ce104965818 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('pred_df');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "pred_df",
+              "summary": "{\n  \"name\": \"pred_df\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"source_file\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"obd_data_log_20250928_142511.csv\",\n          \"obd_data_log_20250928_133839.csv\",\n          \"obd_data_log_20250928_140658.csv\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"drive_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 10,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          9,\n          2,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"duration_min\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4403584398595712,\n        \"min\": 2.4,\n        \"max\": 3.7,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          3.28,\n          3.13,\n          3.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"distance_km\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.003433495141818199,\n        \"min\": 2.413,\n        \"max\": 2.424,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          2.417,\n          2.416,\n          2.413\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pred_efficiency_ml\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16.79983134835982,\n        \"min\": 57.5,\n        \"max\": 93.7,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          87.7,\n          85.8,\n          92.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"raw\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15.710912428768879,\n        \"min\": 61.053,\n        \"max\": 94.984,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          90.482,\n          86.826,\n          94.293\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Std of predictions: 15.937719410254406\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file