openrun/examples/notebooks/exploration.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Garmin data exploration\n",
    "\n",
    "Run `uv run auth.py` and `uv run sync.py --full` once before using this notebook.\n",
    "\n",
    "In VSCode, pick the `.venv` Python interpreter at the top right of this notebook as the kernel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "activities                        378\n",
      "activity_fit_files                349\n",
      "activity_splits                  2285\n",
      "activity_time_in_zone             345\n",
      "daily_body_battery                365\n",
      "daily_hrv                         363\n",
      "daily_intensity_minutes           365\n",
      "daily_resting_hr                  363\n",
      "daily_sleep                      1462\n",
      "daily_steps                      1497\n",
      "daily_stress                      365\n",
      "manual_activities                   0\n",
      "race_plan                           0\n",
      "sqlite_sequence                     0\n",
      "sync_state                          3\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from openrun import open_conn\n",
    "\n",
    "conn = open_conn()\n",
    "\n",
    "# What tables do we have, and how many rows in each?\n",
    "tables = pd.read_sql(\"SELECT name FROM sqlite_master WHERE type='table' ORDER BY name\", conn)\n",
    "for t in tables['name']:\n",
    "    n = conn.execute(f'SELECT COUNT(*) FROM {t}').fetchone()[0]\n",
    "    print(f'{t:30s} {n:>6}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Activities — load into pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>activity_id</th>\n",
       "      <th>start_time_local</th>\n",
       "      <th>activity_type</th>\n",
       "      <th>activity_name</th>\n",
       "      <th>distance_m</th>\n",
       "      <th>duration_s</th>\n",
       "      <th>avg_hr</th>\n",
       "      <th>max_hr</th>\n",
       "      <th>calories</th>\n",
       "      <th>elevation_gain_m</th>\n",
       "      <th>training_load</th>\n",
       "      <th>aerobic_te</th>\n",
       "      <th>anaerobic_te</th>\n",
       "      <th>distance_km</th>\n",
       "      <th>duration_min</th>\n",
       "      <th>pace_min_per_km</th>\n",
       "      <th>week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22836022213</td>\n",
       "      <td>2026-05-10 10:43:58</td>\n",
       "      <td>running</td>\n",
       "      <td>Milford Running</td>\n",
       "      <td>17198.130859</td>\n",
       "      <td>8742.279297</td>\n",
       "      <td>156.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>1482.0</td>\n",
       "      <td>231.0</td>\n",
       "      <td>126.186203</td>\n",
       "      <td>3.7</td>\n",
       "      <td>0.9</td>\n",
       "      <td>17.198131</td>\n",
       "      <td>145.704655</td>\n",
       "      <td>8.472122</td>\n",
       "      <td>2026-05-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22803352843</td>\n",
       "      <td>2026-05-07 20:17:15</td>\n",
       "      <td>running</td>\n",
       "      <td>Milford Running</td>\n",
       "      <td>3647.889893</td>\n",
       "      <td>1754.900024</td>\n",
       "      <td>170.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>367.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>63.818512</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.647890</td>\n",
       "      <td>29.248334</td>\n",
       "      <td>8.017877</td>\n",
       "      <td>2026-05-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22755455510</td>\n",
       "      <td>2026-05-03 17:03:51</td>\n",
       "      <td>running</td>\n",
       "      <td>Milford Running</td>\n",
       "      <td>9317.000000</td>\n",
       "      <td>4746.172852</td>\n",
       "      <td>155.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>825.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>81.749390</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>9.317000</td>\n",
       "      <td>79.102881</td>\n",
       "      <td>8.490166</td>\n",
       "      <td>2026-04-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22755454604</td>\n",
       "      <td>2026-05-02 14:10:35</td>\n",
       "      <td>running</td>\n",
       "      <td>Milford Running</td>\n",
       "      <td>12422.639648</td>\n",
       "      <td>5750.320801</td>\n",
       "      <td>148.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>1047.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>102.831665</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.422640</td>\n",
       "      <td>95.838680</td>\n",
       "      <td>7.714840</td>\n",
       "      <td>2026-04-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22755453569</td>\n",
       "      <td>2026-04-30 19:41:35</td>\n",
       "      <td>running</td>\n",
       "      <td>Milford Running</td>\n",
       "      <td>4834.930176</td>\n",
       "      <td>2435.958984</td>\n",
       "      <td>158.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>479.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>58.665405</td>\n",
       "      <td>2.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.834930</td>\n",
       "      <td>40.599316</td>\n",
       "      <td>8.397084</td>\n",
       "      <td>2026-04-27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   activity_id    start_time_local activity_type    activity_name  \\\n",
       "0  22836022213 2026-05-10 10:43:58       running  Milford Running   \n",
       "1  22803352843 2026-05-07 20:17:15       running  Milford Running   \n",
       "2  22755455510 2026-05-03 17:03:51       running  Milford Running   \n",
       "3  22755454604 2026-05-02 14:10:35       running  Milford Running   \n",
       "4  22755453569 2026-04-30 19:41:35       running  Milford Running   \n",
       "\n",
       "     distance_m   duration_s  avg_hr  max_hr  calories  elevation_gain_m  \\\n",
       "0  17198.130859  8742.279297   156.0   178.0    1482.0             231.0   \n",
       "1   3647.889893  1754.900024   170.0   190.0     367.0              36.0   \n",
       "2   9317.000000  4746.172852   155.0   191.0     825.0             139.0   \n",
       "3  12422.639648  5750.320801   148.0   177.0    1047.0             151.0   \n",
       "4   4834.930176  2435.958984   158.0   190.0     479.0              82.0   \n",
       "\n",
       "   training_load  aerobic_te  anaerobic_te  distance_km  duration_min  \\\n",
       "0     126.186203         3.7           0.9    17.198131    145.704655   \n",
       "1      63.818512         3.0           0.0     3.647890     29.248334   \n",
       "2      81.749390         3.1           0.7     9.317000     79.102881   \n",
       "3     102.831665         3.5           0.0    12.422640     95.838680   \n",
       "4      58.665405         2.9           0.0     4.834930     40.599316   \n",
       "\n",
       "   pace_min_per_km       week  \n",
       "0         8.472122 2026-05-04  \n",
       "1         8.017877 2026-05-04  \n",
       "2         8.490166 2026-04-27  \n",
       "3         7.714840 2026-04-27  \n",
       "4         8.397084 2026-04-27  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "activities = pd.read_sql(\n",
    "    \"SELECT activity_id, start_time_local, activity_type, activity_name, \"\n",
    "    \"distance_m, duration_s, avg_hr, max_hr, calories, elevation_gain_m, \"\n",
    "    \"training_load, aerobic_te, anaerobic_te \"\n",
    "    \"FROM activities ORDER BY start_time_local DESC\",\n",
    "    conn,\n",
    "    parse_dates=['start_time_local'],\n",
    ")\n",
    "activities['distance_km'] = activities['distance_m'] / 1000\n",
    "activities['duration_min'] = activities['duration_s'] / 60\n",
    "activities['pace_min_per_km'] = activities['duration_min'] / activities['distance_km']\n",
    "activities['week'] = activities['start_time_local'].dt.to_period('W').dt.start_time\n",
    "activities.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "activity_type\n",
       "running                335\n",
       "trail_running           15\n",
       "cycling                  9\n",
       "transition_v2            7\n",
       "open_water_swimming      6\n",
       "multi_sport              4\n",
       "mountain_biking          1\n",
       "assistance               1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "activities['activity_type'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Weekly running mileage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "runs = activities[activities['activity_type'].str.contains('running', case=False, na=False)]\n",
    "weekly = runs.groupby('week')['distance_km'].sum()\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 4))\n",
    "weekly.plot(kind='bar', ax=ax)\n",
    "ax.set_ylabel('km')\n",
    "ax.set_title('Weekly running mileage')\n",
    "ax.set_xlabel('')\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sleep, stress, HRV — daily timeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>total_steps</th>\n",
       "      <th>sleep_score</th>\n",
       "      <th>avg_stress</th>\n",
       "      <th>hrv</th>\n",
       "      <th>resting_hr</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>calendar_date</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>2026-05-04</th>\n",
       "      <td>4134</td>\n",
       "      <td>None</td>\n",
       "      <td>30.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026-05-05</th>\n",
       "      <td>5588</td>\n",
       "      <td>None</td>\n",
       "      <td>32.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026-05-06</th>\n",
       "      <td>6791</td>\n",
       "      <td>None</td>\n",
       "      <td>23.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026-05-07</th>\n",
       "      <td>8062</td>\n",
       "      <td>None</td>\n",
       "      <td>34.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026-05-08</th>\n",
       "      <td>2501</td>\n",
       "      <td>None</td>\n",
       "      <td>27.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026-05-09</th>\n",
       "      <td>7450</td>\n",
       "      <td>None</td>\n",
       "      <td>25.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026-05-10</th>\n",
       "      <td>30430</td>\n",
       "      <td>None</td>\n",
       "      <td>37.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026-05-11</th>\n",
       "      <td>5712</td>\n",
       "      <td>None</td>\n",
       "      <td>31.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026-05-12</th>\n",
       "      <td>3709</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026-05-13</th>\n",
       "      <td>4219</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026-05-14</th>\n",
       "      <td>5936</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026-05-15</th>\n",
       "      <td>4634</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026-05-16</th>\n",
       "      <td>7592</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2026-05-17</th>\n",
       "      <td>20264</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               total_steps sleep_score  avg_stress   hrv  resting_hr\n",
       "calendar_date                                                       \n",
       "2026-05-04            4134        None        30.0  71.0        52.0\n",
       "2026-05-05            5588        None        32.0  70.0        50.0\n",
       "2026-05-06            6791        None        23.0  65.0        52.0\n",
       "2026-05-07            8062        None        34.0  75.0        52.0\n",
       "2026-05-08            2501        None        27.0  47.0        51.0\n",
       "2026-05-09            7450        None        25.0  73.0        49.0\n",
       "2026-05-10           30430        None        37.0  70.0        51.0\n",
       "2026-05-11            5712        None        31.0  62.0        53.0\n",
       "2026-05-12            3709        None         NaN   NaN         NaN\n",
       "2026-05-13            4219        None         NaN   NaN         NaN\n",
       "2026-05-14            5936        None         NaN   NaN         NaN\n",
       "2026-05-15            4634        None         NaN   NaN         NaN\n",
       "2026-05-16            7592        None         NaN   NaN         NaN\n",
       "2026-05-17           20264        None         NaN   NaN         NaN"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wellness = pd.read_sql(\n",
    "    \"\"\"\n",
    "    SELECT s.calendar_date,\n",
    "           s.total_steps,\n",
    "           sl.sleep_score,\n",
    "           st.avg_stress,\n",
    "           h.last_night_avg AS hrv,\n",
    "           rh.resting_hr\n",
    "    FROM daily_steps s\n",
    "    LEFT JOIN daily_sleep sl    ON sl.calendar_date = s.calendar_date\n",
    "    LEFT JOIN daily_stress st   ON st.calendar_date = s.calendar_date\n",
    "    LEFT JOIN daily_hrv h       ON h.calendar_date  = s.calendar_date\n",
    "    LEFT JOIN daily_resting_hr rh ON rh.calendar_date = s.calendar_date\n",
    "    ORDER BY s.calendar_date\n",
    "    \"\"\",\n",
    "    conn,\n",
    "    parse_dates=['calendar_date'],\n",
    ").set_index('calendar_date')\n",
    "wellness.tail(14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "no numeric data to plot",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mTypeError\u001b[39m                                 Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[6]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m      1\u001b[39m fig, axes = plt.subplots(\u001b[32m4\u001b[39m, \u001b[32m1\u001b[39m, figsize=(\u001b[32m12\u001b[39m, \u001b[32m8\u001b[39m), sharex=\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m wellness[\u001b[33m'sleep_score'\u001b[39m].plot(ax=axes[\u001b[32m0\u001b[39m], title=\u001b[33m'Sleep score'\u001b[39m)\n\u001b[32m      3\u001b[39m wellness[\u001b[33m'resting_hr'\u001b[39m].plot(ax=axes[\u001b[32m1\u001b[39m], title=\u001b[33m'Resting HR'\u001b[39m)\n\u001b[32m      4\u001b[39m wellness[\u001b[33m'hrv'\u001b[39m].plot(ax=axes[\u001b[32m2\u001b[39m], title=\u001b[33m'HRV (last night avg)'\u001b[39m)\n\u001b[32m      5\u001b[39m wellness[\u001b[33m'avg_stress'\u001b[39m].plot(ax=axes[\u001b[32m3\u001b[39m], title=\u001b[33m'Avg stress'\u001b[39m)\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/obsidian/RunningLog/garmin/.venv/lib/python3.13/site-packages/pandas/plotting/_core.py:1185\u001b[39m, in \u001b[36mPlotAccessor.__call__\u001b[39m\u001b[34m(self, *args, **kwargs)\u001b[39m\n\u001b[32m   1182\u001b[39m             label_name = label_kw \u001b[38;5;129;01mor\u001b[39;00m data.columns\n\u001b[32m   1183\u001b[39m             data.columns = label_name\n\u001b[32m-> \u001b[39m\u001b[32m1185\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43mplot_backend\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mplot\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mdata\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mkind\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mkind\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mkwargs\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/obsidian/RunningLog/garmin/.venv/lib/python3.13/site-packages/pandas/plotting/_matplotlib/__init__.py:71\u001b[39m, in \u001b[36mplot\u001b[39m\u001b[34m(data, kind, **kwargs)\u001b[39m\n\u001b[32m     69\u001b[39m         kwargs[\u001b[33m\"\u001b[39m\u001b[33max\u001b[39m\u001b[33m\"\u001b[39m] = \u001b[38;5;28mgetattr\u001b[39m(ax, \u001b[33m\"\u001b[39m\u001b[33mleft_ax\u001b[39m\u001b[33m\"\u001b[39m, ax)\n\u001b[32m     70\u001b[39m plot_obj = PLOT_CLASSES[kind](data, **kwargs)\n\u001b[32m---> \u001b[39m\u001b[32m71\u001b[39m \u001b[30;43mplot_obj\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mgenerate\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m     72\u001b[39m plt.draw_if_interactive()\n\u001b[32m     73\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m plot_obj.result\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/obsidian/RunningLog/garmin/.venv/lib/python3.13/site-packages/pandas/plotting/_matplotlib/core.py:516\u001b[39m, in \u001b[36mMPLPlot.generate\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m    514\u001b[39m \u001b[38;5;129m@final\u001b[39m\n\u001b[32m    515\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mgenerate\u001b[39m(\u001b[38;5;28mself\u001b[39m) -> \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m516\u001b[39m     \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m_compute_plot_data\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    517\u001b[39m     fig = \u001b[38;5;28mself\u001b[39m.fig\n\u001b[32m    518\u001b[39m     \u001b[38;5;28mself\u001b[39m._make_plot(fig)\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/Documents/obsidian/RunningLog/garmin/.venv/lib/python3.13/site-packages/pandas/plotting/_matplotlib/core.py:716\u001b[39m, in \u001b[36mMPLPlot._compute_plot_data\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m    714\u001b[39m \u001b[38;5;66;03m# no non-numeric frames or series allowed\u001b[39;00m\n\u001b[32m    715\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m is_empty:\n\u001b[32m--> \u001b[39m\u001b[32m716\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[33m\"\u001b[39m\u001b[33mno numeric data to plot\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m    718\u001b[39m \u001b[38;5;28mself\u001b[39m.data = numeric_data.apply(\u001b[38;5;28mtype\u001b[39m(\u001b[38;5;28mself\u001b[39m)._convert_to_ndarray)\n",
      "\u001b[31mTypeError\u001b[39m: no numeric data to plot"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(4, 1, figsize=(12, 8), sharex=True)\n",
    "wellness['sleep_score'].plot(ax=axes[0], title='Sleep score')\n",
    "wellness['resting_hr'].plot(ax=axes[1], title='Resting HR')\n",
    "wellness['hrv'].plot(ax=axes[2], title='HRV (last night avg)')\n",
    "wellness['avg_stress'].plot(ax=axes[3], title='Avg stress')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Querying the raw JSON\n",
    "\n",
    "Every table has a `raw` column with the full Garmin response. Use SQLite's JSON1 functions, or load and `pd.json_normalize`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "raw_rows = pd.read_sql('SELECT activity_id, raw FROM activities LIMIT 5', conn)\n",
    "expanded = pd.json_normalize([json.loads(r) for r in raw_rows['raw']])\n",
    "print(f'{len(expanded.columns)} columns in the raw activity payload')\n",
    "expanded.columns.tolist()[:30]"
   ]
  }
 ],
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