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{
"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",
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"execution_count": 1,
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"metadata": {},
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"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"
]
}
],
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"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
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"from openrun import open_conn\n",
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"\n",
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"conn = open_conn()\n",
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"\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": [
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"## Activities — load into pandas"
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]
},
{
"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"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"
}
],
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"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",
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"execution_count": 3,
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"metadata": {},
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"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"
}
],
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"source": [
"activities['activity_type'].value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Weekly running mileage"
]
},
{
"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1200x400 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
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"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": [
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"## Sleep, stress, HRV — daily timeline"
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]
},
{
"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"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"
}
],
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"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",
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"execution_count": 6,
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"metadata": {},
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"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"
}
],
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"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]"
]
}
],
"metadata": {
"kernelspec": {
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"display_name": "garmin (3.13.13.final.0)",
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"language": "python",
"name": "python3"
},
"language_info": {
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"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"
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}
},
"nbformat": 4,
"nbformat_minor": 5
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}
