openrun/examples/notebooks/01_overview.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 01 — Overview\n",
    "\n",
    "Sanity-check the synced data: what's there, how complete it is, and what you've been up to recently.\n",
    "\n",
    "**Kernel:** pick the `.venv` from `garmin/` (top-right of this notebook)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.insert(0, '..')  # so we can import analysis.py from the project root\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from analysis import open_conn, load_activities, load_wellness\n",
    "\n",
    "pd.options.display.float_format = '{:.2f}'.format\n",
    "conn = open_conn()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data coverage"
   ]
  },
  {
   "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>table</th>\n",
       "      <th>rows</th>\n",
       "      <th>from</th>\n",
       "      <th>to</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>activities</td>\n",
       "      <td>360</td>\n",
       "      <td>2022-04-09 08:30:18</td>\n",
       "      <td>2026-05-10 10:43:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>activity_fit_files</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>activity_splits</td>\n",
       "      <td>2285</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>daily_body_battery</td>\n",
       "      <td>365</td>\n",
       "      <td>2025-05-13</td>\n",
       "      <td>2026-05-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>daily_hrv</td>\n",
       "      <td>363</td>\n",
       "      <td>2025-05-13</td>\n",
       "      <td>2026-05-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>daily_intensity_minutes</td>\n",
       "      <td>365</td>\n",
       "      <td>2025-05-13</td>\n",
       "      <td>2026-05-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>daily_resting_hr</td>\n",
       "      <td>363</td>\n",
       "      <td>2025-05-13</td>\n",
       "      <td>2026-05-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>daily_sleep</td>\n",
       "      <td>365</td>\n",
       "      <td>2025-05-13</td>\n",
       "      <td>2026-05-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>daily_steps</td>\n",
       "      <td>364</td>\n",
       "      <td>2025-05-13</td>\n",
       "      <td>2026-05-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>daily_stress</td>\n",
       "      <td>365</td>\n",
       "      <td>2025-05-13</td>\n",
       "      <td>2026-05-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>sync_state</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      table  rows                 from                   to\n",
       "0                activities   360  2022-04-09 08:30:18  2026-05-10 10:43:58\n",
       "1        activity_fit_files     0                                          \n",
       "2           activity_splits  2285                                          \n",
       "3        daily_body_battery   365           2025-05-13           2026-05-12\n",
       "4                 daily_hrv   363           2025-05-13           2026-05-12\n",
       "5   daily_intensity_minutes   365           2025-05-13           2026-05-12\n",
       "6          daily_resting_hr   363           2025-05-13           2026-05-11\n",
       "7               daily_sleep   365           2025-05-13           2026-05-12\n",
       "8               daily_steps   364           2025-05-13           2026-05-11\n",
       "9              daily_stress   365           2025-05-13           2026-05-12\n",
       "10               sync_state     1                                          "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary = []\n",
    "for (table,) in conn.execute(\"SELECT name FROM sqlite_master WHERE type='table' ORDER BY name\"):\n",
    "    n = conn.execute(f'SELECT COUNT(*) FROM \"{table}\"').fetchone()[0]\n",
    "    date_col = 'start_time_local' if table == 'activities' else ('calendar_date' if table.startswith('daily_') else None)\n",
    "    if date_col and n:\n",
    "        mn, mx = conn.execute(f'SELECT MIN({date_col}), MAX({date_col}) FROM {table}').fetchone()\n",
    "    else:\n",
    "        mn = mx = ''\n",
    "    summary.append({'table': table, 'rows': n, 'from': mn, 'to': mx})\n",
    "pd.DataFrame(summary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Activity types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>activity_type</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>running</th>\n",
       "      <td>331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trail_running</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cycling</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>multi_sport</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>open_water_swimming</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>assistance</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mountain_biking</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count\n",
       "activity_type             \n",
       "running                331\n",
       "trail_running           15\n",
       "cycling                  6\n",
       "multi_sport              4\n",
       "open_water_swimming      2\n",
       "assistance               1\n",
       "mountain_biking          1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "activities = load_activities(conn)\n",
    "activities['activity_type'].value_counts().to_frame('count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Last 10 activities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>start_time_local</th>\n",
       "      <th>activity_type</th>\n",
       "      <th>activity_name</th>\n",
       "      <th>distance_km</th>\n",
       "      <th>duration_min</th>\n",
       "      <th>pace_min_per_km</th>\n",
       "      <th>avg_hr</th>\n",
       "      <th>training_load</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2026-05-10 10:43:58</td>\n",
       "      <td>running</td>\n",
       "      <td>Milford Running</td>\n",
       "      <td>17.20</td>\n",
       "      <td>145.70</td>\n",
       "      <td>8.41</td>\n",
       "      <td>156.00</td>\n",
       "      <td>126.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2026-05-07 20:17:15</td>\n",
       "      <td>running</td>\n",
       "      <td>Milford Running</td>\n",
       "      <td>3.65</td>\n",
       "      <td>29.25</td>\n",
       "      <td>7.71</td>\n",
       "      <td>170.00</td>\n",
       "      <td>63.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2026-05-03 17:03:51</td>\n",
       "      <td>running</td>\n",
       "      <td>Milford Running</td>\n",
       "      <td>9.32</td>\n",
       "      <td>79.10</td>\n",
       "      <td>8.33</td>\n",
       "      <td>155.00</td>\n",
       "      <td>81.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2026-05-02 14:10:35</td>\n",
       "      <td>running</td>\n",
       "      <td>Milford Running</td>\n",
       "      <td>12.42</td>\n",
       "      <td>95.84</td>\n",
       "      <td>7.67</td>\n",
       "      <td>148.00</td>\n",
       "      <td>102.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2026-04-30 19:41:35</td>\n",
       "      <td>running</td>\n",
       "      <td>Milford Running</td>\n",
       "      <td>4.83</td>\n",
       "      <td>40.60</td>\n",
       "      <td>8.31</td>\n",
       "      <td>158.00</td>\n",
       "      <td>58.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2026-04-25 08:21:12</td>\n",
       "      <td>running</td>\n",
       "      <td>Milford Running</td>\n",
       "      <td>15.94</td>\n",
       "      <td>130.58</td>\n",
       "      <td>8.02</td>\n",
       "      <td>149.00</td>\n",
       "      <td>102.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2026-04-23 18:34:03</td>\n",
       "      <td>running</td>\n",
       "      <td>Milford Running</td>\n",
       "      <td>4.49</td>\n",
       "      <td>36.66</td>\n",
       "      <td>7.96</td>\n",
       "      <td>148.00</td>\n",
       "      <td>51.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2026-04-22 17:25:33</td>\n",
       "      <td>running</td>\n",
       "      <td>Milford Running</td>\n",
       "      <td>5.55</td>\n",
       "      <td>45.77</td>\n",
       "      <td>8.23</td>\n",
       "      <td>167.00</td>\n",
       "      <td>75.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2026-04-22 16:48:43</td>\n",
       "      <td>running</td>\n",
       "      <td>Milford Running</td>\n",
       "      <td>3.75</td>\n",
       "      <td>29.48</td>\n",
       "      <td>7.69</td>\n",
       "      <td>156.00</td>\n",
       "      <td>52.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2026-04-12 13:25:00</td>\n",
       "      <td>running</td>\n",
       "      <td>Milford Running</td>\n",
       "      <td>7.19</td>\n",
       "      <td>57.90</td>\n",
       "      <td>7.91</td>\n",
       "      <td>163.00</td>\n",
       "      <td>81.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     start_time_local activity_type    activity_name  distance_km  \\\n",
       "0 2026-05-10 10:43:58       running  Milford Running        17.20   \n",
       "1 2026-05-07 20:17:15       running  Milford Running         3.65   \n",
       "2 2026-05-03 17:03:51       running  Milford Running         9.32   \n",
       "3 2026-05-02 14:10:35       running  Milford Running        12.42   \n",
       "4 2026-04-30 19:41:35       running  Milford Running         4.83   \n",
       "5 2026-04-25 08:21:12       running  Milford Running        15.94   \n",
       "6 2026-04-23 18:34:03       running  Milford Running         4.49   \n",
       "7 2026-04-22 17:25:33       running  Milford Running         5.55   \n",
       "8 2026-04-22 16:48:43       running  Milford Running         3.75   \n",
       "9 2026-04-12 13:25:00       running  Milford Running         7.19   \n",
       "\n",
       "   duration_min  pace_min_per_km  avg_hr  training_load  \n",
       "0        145.70             8.41  156.00         126.19  \n",
       "1         29.25             7.71  170.00          63.82  \n",
       "2         79.10             8.33  155.00          81.75  \n",
       "3         95.84             7.67  148.00         102.83  \n",
       "4         40.60             8.31  158.00          58.67  \n",
       "5        130.58             8.02  149.00         102.70  \n",
       "6         36.66             7.96  148.00          51.16  \n",
       "7         45.77             8.23  167.00          75.78  \n",
       "8         29.48             7.69  156.00          52.06  \n",
       "9         57.90             7.91  163.00          81.90  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = ['start_time_local', 'activity_type', 'activity_name', 'distance_km', 'duration_min', 'pace_min_per_km', 'avg_hr', 'training_load']\n",
    "activities[cols].tail(10).iloc[::-1].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20e72ec0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Monthly running mileage, year-over-year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1100x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "runs = activities[activities['activity_type'].str.contains('running', case=False, na=False)].copy()\n",
    "monthly = (runs.assign(month_no=runs['start_time_local'].dt.month)\n",
    "                .groupby(['year', 'month_no'])['distance_km'].sum()\n",
    "                .unstack('year'))\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(11, 4))\n",
    "monthly.plot(ax=ax, marker='o')\n",
    "ax.set_xticks(range(1, 13))\n",
    "ax.set_xticklabels(['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec'])\n",
    "ax.set_ylabel('km')\n",
    "ax.set_xlabel('')\n",
    "ax.set_title('Monthly running km — year over year')\n",
    "ax.legend(title='Year')\n",
    "ax.grid(alpha=0.3)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wellness completeness (last 90 days)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>% missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>total_steps</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sleep_score</th>\n",
       "      <td>7.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg_stress</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hrv_last_night</th>\n",
       "      <td>7.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>resting_hr</th>\n",
       "      <td>1.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bb_charged</th>\n",
       "      <td>1.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                % missing\n",
       "total_steps          0.00\n",
       "sleep_score          7.78\n",
       "avg_stress           0.00\n",
       "hrv_last_night       7.78\n",
       "resting_hr           1.11\n",
       "bb_charged           1.11"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w = load_wellness(conn).tail(90)\n",
    "missing = w[['total_steps','sleep_score','avg_stress','hrv_last_night','resting_hr','bb_charged']].isna().mean().to_frame('% missing') * 100\n",
    "missing"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "garmin (.venv)",
   "language": "python",
   "name": "garmin"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}