I'm planning to enrich the exercise/activity section of a weekly health report in /root/projects/butler. Please explore the following and return complete file contents:

1. Read a recent activity JSON file from data/health/activities/ - list what's in that directory first, then read a complete file. I need ALL fields including nested ones.

2. Read the health/analytics/weekly_report.py file completely - I need to see the current _get_steps_stats() implementation and all the generate() sections.

3. Read data/health/daily_metrics/intensity_minutes/ - list files and read one recent JSON file completely.

4. List what's in data/health/activities/ to understand activity data structure (what kinds of activities are tracked, what file names look like).

5. Read health/services/query.py lines 117-148 (get_activities_range method) to understand what it returns.

I specifically need to understand:
- What fields are available in activity JSON files (sport type, duration, distance, avg HR, max HR, calories, training effect, aerobic/anaerobic TE, VO2max, etc.)
- How activity type is stored (what field name, what values)
- What intensity_minutes contains
- How many activities are in the data directory


I'll help you explore the activity and health data files. Let me gather this information systematically.


Bash

ls -lah /root/projects/butler/data/health/activities/ | head -50

List files in activities directory


ls -lah /root/projects/butler/data/health/daily_metrics/intensity_minutes/ 2>/dev/null | head -30

List files in intensity_minutes directory


find /root/projects/butler/data/health/activities -name "*.json" | sort | tail -5

Get 5 most recent activity JSON files


find /root/projects/butler/data/health/daily_metrics/intensity_minutes -name "*.json" | sort | tail -3

Get 3 most recent intensity_minutes JSON files


     1→"""Weekly health report generator.
     2→
     3→Generates a Slack-formatted weekly health summary covering sleep, steps,
     4→recovery, body battery, and lifestyle data.
     5→"""
     6→
     7→from datetime import date, timedelta
     8→from typing import Any, Optional
     9→
    10→from health.services.manual_log_storage import ManualLogStorage
    11→from health.services.query import HealthDataQuery
    12→from health.utils.logging_config import setup_logger
    13→
    14→logger = setup_logger(__name__)
    15→
    16→
    17→class WeeklyReportGenerator:
    18→    """Generates weekly health reports from stored Garmin and manual log data."""
    19→
    20→    def __init__(self) -> None:
    21→        self.query = HealthDataQuery()
    22→        self.manual_storage = ManualLogStorage()
    23→
    24→    def generate(self, week_start: date) -> str:
    25→        """Generate a Slack-formatted weekly health report.
    26→
    27→        Args:
    28→            week_start: The Monday that starts the report week.
    29→
    30→        Returns:
    31→            Formatted report string ready for Slack posting.
    32→        """
    33→        week_end = week_start + timedelta(days=6)
    34→        prev_week_start = week_start - timedelta(days=7)
    35→        prev_week_end = week_start - timedelta(days=1)
    36→
    37→        logger.info(f"Generating weekly report for {week_start} ~ {week_end}")
    38→
    39→        sleep_stats = self._get_sleep_stats(week_start, week_end)
    40→        steps_stats = self._get_steps_stats(week_start, week_end)
    41→        recovery_stats = self._get_recovery_stats(week_start, week_end)
    42→        prev_recovery = self._get_recovery_stats(prev_week_start, prev_week_end)
    43→        battery_stats = self._get_battery_stats(week_start, week_end)
    44→        lifestyle_stats = self._get_lifestyle_stats(week_start, week_end)
    45→
    46→        all_stats = {
    47→            "sleep": sleep_stats,
    48→            "steps": steps_stats,
    49→            "recovery": recovery_stats,
    50→            "battery": battery_stats,
    51→            "lifestyle": lifestyle_stats,
    52→        }
    53→        highlight = self._pick_highlight(all_stats)
    54→
    55→        lines: list[str] = []
    56→
    57→        # 1. Title
    58→        lines.append(f"📊 *健康周报 {week_start} ~ {week_end}*")
    59→        lines.append("")
    60→
    61→        # 2. Sleep
    62→        lines.append("😴 *睡眠*")
    63→        if sleep_stats.get("count", 0) > 0:
    64→            lines.append(f"  • 平均得分：{sleep_stats['avg_score']:.0f} 分")
    65→            lines.append(
    66→                f"  • 平均时长：{self._format_duration(sleep_stats['avg_duration_sec'])}"
    67→            )
    68→            if sleep_stats.get("best_date"):
    69→                lines.append(
    70→                    f"  • 最佳：{sleep_stats['best_date']} ({sleep_stats['best_score']:.0f} 分)"
    71→                )
    72→            if sleep_stats.get("worst_date"):
    73→                lines.append(
    74→                    f"  • 最差：{sleep_stats['worst_date']} ({sleep_stats['worst_score']:.0f} 分)"
    75→                )
    76→        else:
    77→            lines.append("  • 本周无睡眠数据")
    78→        lines.append("")
    79→
    80→        # 3. Steps
    81→        lines.append("🏃 *运动*")
    82→        if steps_stats.get("count", 0) > 0:
    83→            lines.append(f"  • 平均步数：{steps_stats['avg_steps']:,.0f} 步")
    84→            lines.append(f"  • 达标天数（≥10000步）：{steps_stats['goal_days']} 天")
    85→            if steps_stats.get("activity_count", 0) > 0:
    86→                lines.append(
    87→                    f"  • 运动次数：{steps_stats['activity_count']} 次"
    88→                    + (
    89→                        f"（{', '.join(steps_stats['activity_types'])}）"
    90→                        if steps_stats.get("activity_types")
    91→                        else ""
    92→                    )
    93→                )
    94→        else:
    95→            lines.append("  • 本周无步数数据")
    96→        lines.append("")
    97→
    98→        # 4. Recovery
    99→        lines.append("❤ *恢复*")
   100→        if recovery_stats.get("count", 0) > 0:
   101→            hrv_line = f"  • 平均 HRV：{recovery_stats['avg_hrv']:.0f} ms"
   102→            if prev_recovery.get("count", 0) > 0:
   103→                diff = recovery_stats["avg_hrv"] - prev_recovery["avg_hrv"]
   104→                sign = "+" if diff >= 0 else ""
   105→                hrv_line += f"（{sign}{diff:.0f} vs 上周）"
   106→            lines.append(hrv_line)
   107→
   108→            rhr_line = f"  • 平均静息心率：{recovery_stats['avg_rhr']:.0f} bpm"
   109→            if prev_recovery.get("count", 0) > 0:
   110→                diff = recovery_stats["avg_rhr"] - prev_recovery["avg_rhr"]
   111→                sign = "+" if diff >= 0 else ""
   112→                rhr_line += f"（{sign}{diff:.0f} vs 上周）"
   113→            lines.append(rhr_line)
   114→        else:
   115→            lines.append("  • 本周无恢复数据")
   116→        lines.append("")
   117→
   118→        # 5. Body Battery
   119→        lines.append("⚡ *Body Battery*")
   120→        if battery_stats.get("count", 0) > 0:
   121→            lines.append(f"  • 平均充电值：{battery_stats['avg_charged']:.0f}")
   122→            lines.append(f"  • 平均消耗值：{battery_stats['avg_drained']:.0f}")
   123→        else:
   124→            lines.append("  • 本周无 Body Battery 数据")
   125→        lines.append("")
   126→
   127→        # 6. Lifestyle
   128→        lines.append("🥗 *生活方式*")
   129→        if lifestyle_stats.get("has_data"):
   130→            lines.append(f"  • 饮酒天数：{lifestyle_stats['alcohol_days']} 天")
   131→            if lifestyle_stats.get("fasting_modes"):
   132→                modes = ", ".join(
   133→                    f"{m}（{c}天）" for m, c in lifestyle_stats["fasting_modes"].items()
   134→                )
   135→                lines.append(f"  • 禁食模式：{modes}")
   136→            lines.append(f"  • 补剂记录天数：{lifestyle_stats['supplement_days']} 天")
   137→        else:
   138→            lines.append("  • 本周无手动记录")
   139→        lines.append("")
   140→
   141→        # 7. Highlight
   142→        lines.append(f"💡 *本周亮点：* {highlight}")
   143→
   144→        return "\n".join(lines)
   145→
   146→    # ------------------------------------------------------------------
   147→    # Private helpers – data fetching
   148→    # ------------------------------------------------------------------
   149→
   150→    def _get_sleep_stats(self, start: date, end: date) -> dict[str, Any]:
   151→        """Compute sleep statistics for the given date range."""
   152→        records = self.query.get_metric_range("sleep", start, end)
   153→        if not records:
   154→            return {"count": 0}
   155→
   156→        scores = [r["sleep_score"] for r in records if r.get("sleep_score")]
   157→        durations = [r["total_sleep_seconds"] for r in records if r.get("total_sleep_seconds")]
   158→
   159→        if not scores:
   160→            return {"count": 0}
   161→
   162→        best = max(records, key=lambda r: r.get("sleep_score", 0))
   163→        worst = min(records, key=lambda r: r.get("sleep_score", 100))
   164→
   165→        return {
   166→            "count": len(scores),
   167→            "avg_score": sum(scores) / len(scores),
   168→            "avg_duration_sec": int(sum(durations) / len(durations)) if durations else 0,
   169→            "best_date": best.get("date"),
   170→            "best_score": best.get("sleep_score", 0),
   171→            "worst_date": worst.get("date"),
   172→            "worst_score": worst.get("sleep_score", 0),
   173→        }
   174→
   175→    def _get_steps_stats(self, start: date, end: date) -> dict[str, Any]:
   176→        """Compute step and activity statistics for the given date range."""
   177→        records = self.query.get_metric_range("steps", start, end)
   178→        if not records:
   179→            return {"count": 0}
   180→
   181→        step_values = [r["total_steps"] for r in records if r.get("total_steps") is not None]
   182→        if not step_values:
   183→            return {"count": 0}
   184→
   185→        goal_days = sum(1 for s in step_values if s >= 10000)
   186→
   187→        # Use get_activities_range() which is the correct method for activity data
   188→        activity_records = self.query.get_activities_range(start, end)
   189→        activity_types: list[str] = []
   190→        for ar in activity_records:
   191→            act_type = ar.get("activityType", {})
   192→            if isinstance(act_type, dict):
   193→                name = act_type.get("typeKey") or act_type.get("typeId", "")
   194→            else:
   195→                name = str(act_type)
   196→            if name and name not in activity_types:
   197→                activity_types.append(name)
   198→
   199→        return {
   200→            "count": len(step_values),
   201→            "avg_steps": sum(step_values) / len(step_values),
   202→            "goal_days": goal_days,
   203→            "activity_count": len(activity_records),
   204→            "activity_types": activity_types,
   205→        }
   206→
   207→    def _get_recovery_stats(self, start: date, end: date) -> dict[str, Any]:
   208→        """Compute HRV and resting heart rate statistics for the given date range."""
   209→        hrv_records = self.query.get_metric_range("hrv", start, end)
   210→        hr_records = self.query.get_metric_range("heart_rate", start, end)
   211→
   212→        hrv_values = [
   213→            r["hrv_value"]
   214→            for r in hrv_records
   215→            if r.get("hrv_value") is not None
   216→        ]
   217→        rhr_values = [
   218→            r["resting_heart_rate"]
   219→            for r in hr_records
   220→            if r.get("resting_heart_rate")
   221→        ]
   222→
   223→        if not hrv_values and not rhr_values:
   224→            return {"count": 0}
   225→
   226→        return {
   227→            "count": max(len(hrv_values), len(rhr_values)),
   228→            "avg_hrv": sum(hrv_values) / len(hrv_values) if hrv_values else 0.0,
   229→            "avg_rhr": sum(rhr_values) / len(rhr_values) if rhr_values else 0.0,
   230→        }
   231→
   232→    def _get_battery_stats(self, start: date, end: date) -> dict[str, Any]:
   233→        """Compute Body Battery charge/drain statistics for the given date range."""
   234→        records = self.query.get_metric_range("body_battery", start, end)
   235→        if not records:
   236→            return {"count": 0}
   237→
   238→        charged = [r["charged"] for r in records if r.get("charged") is not None]
   239→        drained = [r["drained"] for r in records if r.get("drained") is not None]
   240→
   241→        if not charged and not drained:
   242→            return {"count": 0}
   243→
   244→        return {
   245→            "count": len(records),
   246→            "avg_charged": sum(charged) / len(charged) if charged else 0.0,
   247→            "avg_drained": sum(drained) / len(drained) if drained else 0.0,
   248→        }
   249→
   250→    def _get_lifestyle_stats(self, start: date, end: date) -> dict[str, Any]:
   251→        """Compute lifestyle log statistics (alcohol, fasting, supplements)."""
   252→        logs = self.manual_storage.get_logs_in_range(start, end)
   253→        if not logs:
   254→            return {"has_data": False}
   255→
   256→        alcohol_days = sum(1 for log in logs if log.alcohol_entries)
   257→        supplement_days = sum(1 for log in logs if log.supplement_entries)
   258→
   259→        fasting_modes: dict[str, int] = {}
   260→        for log in logs:
   261→            if log.fasting_mode:
   262→                fasting_modes[log.fasting_mode] = fasting_modes.get(log.fasting_mode, 0) + 1
   263→
   264→        return {
   265→            "has_data": True,
   266→            "alcohol_days": alcohol_days,
   267→            "supplement_days": supplement_days,
   268→            "fasting_modes": fasting_modes,
   269→        }
   270→
   271→    # ------------------------------------------------------------------
   272→    # Private helpers – formatting
   273→    # ------------------------------------------------------------------
   274→
   275→    def _format_duration(self, seconds: int) -> str:
   276→        """Format a duration in seconds to 'Xh Ym' string.
   277→
   278→        Args:
   279→            seconds: Total duration in seconds.
   280→
   281→        Returns:
   282→            Human-readable duration string, e.g. '7h 23m'.
   283→        """
   284→        if seconds <= 0:
   285→            return "0h 0m"
   286→        hours = seconds // 3600
   287→        minutes = (seconds % 3600) // 60
   288→        return f"{hours}h {minutes}m"
   289→
   290→    def _pick_highlight(self, stats: dict[str, Any]) -> str:
   291→        """Select the most noteworthy insight from aggregated stats.
   292→
   293→        Args:
   294→            stats: Dictionary containing all section stats.
   295→
   296→        Returns:
   297→            A single highlight sentence in Chinese.
   298→        """
   299→        highlights: list[tuple[float, str]] = []
   300→
   301→        sleep = stats.get("sleep", {})
   302→        if sleep.get("count", 0) > 0:
   303→            score = sleep["avg_score"]
   304→            if score >= 85:
   305→                highlights.append((score, f"睡眠质量优秀，平均得分 {score:.0f} 分 🌟"))
   306→            elif score < 65:
   307→                highlights.append((100 - score, f"睡眠质量偏低，平均得分 {score:.0f} 分，建议关注"))
   308→
   309→        steps = stats.get("steps", {})
   310→        if steps.get("count", 0) > 0:
   311→            goal_days = steps["goal_days"]
   312→            if goal_days >= 5:
   313→                highlights.append((goal_days * 10, f"本周 {goal_days} 天达到万步目标，运动坚持度佳 💪"))
   314→            elif goal_days == 0:
   315→                highlights.append((60.0, "本周没有达到万步目标，需要增加日常活动量"))
   316→
   317→        recovery = stats.get("recovery", {})
   318→        if recovery.get("count", 0) > 0 and recovery.get("avg_hrv", 0) > 0:
   319→            hrv = recovery["avg_hrv"]
   320→            if hrv >= 60:
   321→                highlights.append((hrv, f"HRV 均值 {hrv:.0f} ms，恢复状态良好 ✅"))
   322→            elif hrv < 35:
   323→                highlights.append((80.0, f"HRV 偏低（{hrv:.0f} ms），注意休息与压力管理"))
   324→
   325→        lifestyle = stats.get("lifestyle", {})
   326→        if lifestyle.get("has_data") and lifestyle.get("alcohol_days", 0) == 0:
   327→            highlights.append((50.0, "本周零饮酒，生活方式健康 🌿"))
   328→
   329→        if not highlights:
   330→            return "数据积累中，下周见更多洞察。"
   331→
   332→        highlights.sort(key=lambda x: x[0], reverse=True)
   333→        return highlights[0][1]
   334→

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>


   117→    def get_activities_range(
   118→        self, start_date: date, end_date: date, activity_type: Optional[str] = None
   119→    ) -> List[Dict[str, Any]]:
   120→        """Get all activities within a date range.
   121→
   122→        Args:
   123→            start_date: Start date of range
   124→            end_date: End date of range
   125→            activity_type: Optional filter by activity type
   126→
   127→        Returns:
   128→            List of activity data dictionaries
   129→        """
   130→        logger.info(f"Querying activities from {start_date} to {end_date}")
   131→
   132→        # Get activity index entries
   133→        activity_indices = self.repo.get_activities_by_date_range(start_date, end_date)
   134→
   135→        # Load full activity data
   136→        activities = []
   137→        for idx in activity_indices:
   138→            # Filter by activity type if specified
   139→            if activity_type and idx.get("activity_type") != activity_type:
   140→                continue
   141→
   142→            activity_data = self.storage.load_activity(idx["activity_id"])
   143→            if activity_data:
   144→                activities.append(activity_data)
   145→
   146→        logger.info(f"Found {len(activities)} activities")
   147→
   148→        return activities

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>


total 4.0K
drwxr-xr-x  5 501 games   42 Jan 20 10:22 .
drwxr-xr-x  8 501 games 4.0K Mar  2 14:23 ..
drwxr-xr-x 14 501 games  126 Jan 20 10:25 2024
drwxr-xr-x 14 501 games  126 Jan 20 10:26 2025
drwxr-xr-x  5 501 games   36 Mar  1 11:49 2026


total 4.0K
drwxr-xr-x  5 501 games   42 Jan 20 10:25 .
drwxr-xr-x 14 501 games 4.0K Jan 19 14:04 ..
drwxr-xr-x 14 501 games  126 Jan 20 10:25 2024
drwxr-xr-x 14 501 games  126 Jan 20 10:29 2025
drwxr-xr-x  5 501 games   36 Mar  1 11:49 2026


Now let me read the actual activity JSON files and intensity minutes files:


find /root/projects/butler/data/health/activities -name "*.json" -type f | wc -l

Count total activity JSON files


     1→{
     2→  "activity_id": "569256212",
     3→  "activity_type": "elliptical",
     4→  "activity_name": "Elliptical",
     5→  "date": "2026-03-01",
     6→  "start_time": "2026-03-01T08:13:06",
     7→  "duration_seconds": 3001.7548828125,
     8→  "distance_meters": 0.0,
     9→  "calories": 530,
    10→  "average_heart_rate": 136,
    11→  "max_heart_rate": 183,
    12→  "average_speed_mps": 0.0,
    13→  "average_cadence": 17.421875,
    14→  "raw_data": {
    15→    "activityId": 569256212,
    16→    "activityName": "Elliptical",
    17→    "startTimeLocal": "2026-03-01 08:13:06",
    18→    "startTimeGMT": "2026-03-01 00:13:06",
    19→    "activityType": {
    20→      "typeId": 30,
    21→      "typeKey": "elliptical",
    22→      "parentTypeId": 29,
    23→      "isHidden": false,
    24→      "restricted": false,
    25→      "trimmable": true
    26→    },
    27→    "eventType": {
    28→      "typeId": 9,
    29→      "typeKey": "uncategorized",
    30→      "sortOrder": 10
    31→    },
    32→    "distance": 0.0,
    33→    "duration": 3001.7548828125,
    34→    "elapsedDuration": 3053.60302734375,
    35→    "movingDuration": 0.0,
    36→    "averageSpeed": 0.0,
    37→    "hasPolyline": false,
    38→    "hasImages": false,
    39→    "ownerId": 11508432,
    40→    "ownerDisplayName": "01823aee-4374-48f8-bdf4-1f8fcc1d3ccc",
    41→    "ownerFullName": "李力",
    42→    "ownerProfileImageUrlSmall": "https://s3.cn-north-1.amazonaws.com.cn/garmin-connect-prod/profile_images/c8c539df-5f8b-4896-ac64-c23b4d2caa88-prth.png",
    43→    "ownerProfileImageUrlMedium": "https://s3.cn-north-1.amazonaws.com.cn/garmin-connect-prod/profile_images/c8c539df-5f8b-4896-ac64-c23b4d2caa88-prfr.png",
    44→    "ownerProfileImageUrlLarge": "https://s3.cn-north-1.amazonaws.com.cn/garmin-connect-prod/profile_images/c8c539df-5f8b-4896-ac64-c23b4d2caa88-prof.png",
    45→    "calories": 530.0,
    46→    "bmrCalories": 68.0,
    47→    "averageHR": 136.0,
    48→    "maxHR": 183.0,
    49→    "averageRunningCadenceInStepsPerMinute": 17.421875,
    50→    "maxRunningCadenceInStepsPerMinute": 133.0,
    51→    "steps": 1402,
    52→    "userRoles": [
    53→      "SCOPE_GOLF_API_READ",
    54→      "SCOPE_DI_OAUTH_2_DEVICE_REVOCATION_WRITE",
    55→      "SCOPE_DI_OAUTH_2_CREATE_DI_USER",
    56→      "SCOPE_ATP_READ",
    57→      "SCOPE_DIVE_API_WRITE",
    58→      "SCOPE_COMMUNITY_COURSE_ADMIN_READ",
    59→      "SCOPE_DIVE_API_READ",
    60→      "SCOPE_DI_OAUTH_2_CLIENT_READ",
    61→      "SCOPE_CONNECT_WRITE",
    62→      "SCOPE_COMMUNITY_COURSE_WRITE",
    63→      "SCOPE_CONNECT_MCT_DAILY_LOG_READ",
    64→      "SCOPE_CONNECT_API_CREATE_UPDATE_USER",
    65→      "SCOPE_GARMIN_PROFILE_WRITE",
    66→      "SCOPE_MESSAGE_GENERATION_READ",
    67→      "SCOPE_DI_OAUTH_2_CLIENT_REVOCATION_ADMIN",
    68→      "SCOPE_CONNECT_WEB_TEMPLATE_RENDER",
    69→      "SCOPE_ACTIVITY_DETECTION_READ",
    70→      "SCOPE_OMT_SUBSCRIPTION_ADMIN_READ",
    71→      "SCOPE_CONNECT_NON_SOCIAL_SHARED_READ",
    72→      "SCOPE_DI_OAUTH_2_MANAGED_STATUS_ADMIN_WRITE",
    73→      "SCOPE_GARMIN_PROFILE_READ",
    74→      "SCOPE_CONNECT_READ",
    75→      "SCOPE_DI_OAUTH_2_TOKEN_ADMIN",
    76→      "SCOPE_DI_OAUTH_2_PRERELEASE_PERMISSIONS_ADMIN_WRITE",
    77→      "ROLE_CONNECTUSER",
    78→      "ROLE_FITNESS_USER",
    79→      "ROLE_WELLNESS_USER",
    80→      "ROLE_OUTDOOR_USER"
    81→    ],
    82→    "privacy": {
    83→      "typeId": 3,
    84→      "typeKey": "subscribers"
    85→    },
    86→    "userPro": false,
    87→    "hasVideo": false,
    88→    "timeZoneId": 135,
    89→    "beginTimestamp": 1772323986000,
    90→    "sportTypeId": 4,
    91→    "aerobicTrainingEffect": 3.200000047683716,
    92→    "anaerobicTrainingEffect": 1.899999976158142,
    93→    "avgStrideLength": 0.0,
    94→    "deviceId": 3447045197,
    95→    "maxDoubleCadence": 133.0,
    96→    "summarizedDiveInfo": {
    97→      "summarizedDiveGases": []
    98→    },
    99→    "manufacturer": "GARMIN",
   100→    "lapCount": 1,
   101→    "waterEstimated": 728.0,
   102→    "trainingEffectLabel": "AEROBIC_BASE",
   103→    "activityTrainingLoad": 97.2342529296875,
   104→    "minActivityLapDuration": 3001.7548828125,
   105→    "aerobicTrainingEffectMessage": "IMPROVING_AEROBIC_BASE_8",
   106→    "anaerobicTrainingEffectMessage": "MINOR_ANAEROBIC_BENEFIT_15",
   107→    "splitSummaries": [],
   108→    "hasSplits": false,
   109→    "moderateIntensityMinutes": 1,
   110→    "vigorousIntensityMinutes": 48,
   111→    "differenceBodyBattery": -14,
   112→    "hasHeatMap": false,
   113→    "hrTimeInZone_1": 195.0,
   114→    "hrTimeInZone_2": 2615.949,
   115→    "hrTimeInZone_3": 93.999,
   116→    "hrTimeInZone_4": 9.001,
   117→    "hrTimeInZone_5": 13.998,
   118→    "endTimeGMT": "2026-03-01 01:03:59",
   119→    "qualifyingDive": false,
   120→    "activityUUID": "7e4b6a63-4584-4f36-9866-cd6e05cfd54d",
   121→    "decoDive": false,
   122→    "elevationCorrected": false,
   123→    "atpActivity": false,
   124→    "purposeful": false,
   125→    "autoCalcCalories": false,
   126→    "favorite": false,
   127→    "manualActivity": false,
   128→    "pr": false,
   129→    "parent": false
   130→  }
   131→}

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>


     1→{
     2→  "date": "2026-03-02",
     3→  "moderate_minutes": 0,
     4→  "vigorous_minutes": 0,
     5→  "total_minutes": 0,
     6→  "weekly_goal": 300,
     7→  "raw_data": {
     8→    "userProfilePK": 11508432,
     9→    "calendarDate": "2026-03-02",
    10→    "startTimestampGMT": "2026-03-01T16:00:00.0",
    11→    "endTimestampGMT": "2026-03-02T02:09:00.0",
    12→    "startTimestampLocal": "2026-03-02T00:00:00.0",
    13→    "endTimestampLocal": "2026-03-02T10:09:00.0",
    14→    "weeklyModerate": 0,
    15→    "weeklyVigorous": 0,
    16→    "weeklyTotal": 0,
    17→    "weekGoal": 300,
    18→    "dayOfGoalMet": null,
    19→    "startDayMinutes": 0,
    20→    "endDayMinutes": 0,
    21→    "moderateMinutes": 0,
    22→    "vigorousMinutes": 0,
    23→    "imValueDescriptorsDTOList": null,
    24→    "imValuesArray": null
    25→  }
    26→}

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>


Perfect! Now let me get a couple more recent activity files to understand the activity data structure better:


     1→{
     2→  "activity_id": "569031445",
     3→  "activity_type": "elliptical",
     4→  "activity_name": "Elliptical",
     5→  "date": "2026-02-28",
     6→  "start_time": "2026-02-28T09:28:03",
     7→  "duration_seconds": 3022.05908203125,
     8→  "distance_meters": 0.0,
     9→  "calories": 535,
    10→  "average_heart_rate": 136,
    11→  "max_heart_rate": 150,
    12→  "average_speed_mps": 0.0,
    13→  "average_cadence": 19.515625,
    14→  "raw_data": {
    15→    "activityId": 569031445,
    16→    "activityName": "Elliptical",
    17→    "startTimeLocal": "2026-02-28 09:28:03",
    18→    "startTimeGMT": "2026-02-28 01:28:03",
    19→    "activityType": {
    20→      "typeId": 30,
    21→      "typeKey": "elliptical",
    22→      "parentTypeId": 29,
    23→      "isHidden": false,
    24→      "restricted": false,
    25→      "trimmable": true
    26→    },
    27→    "eventType": {
    28→      "typeId": 9,
    29→      "typeKey": "uncategorized",
    30→      "sortOrder": 10
    31→    },
    32→    "distance": 0.0,
    33→    "duration": 3022.05908203125,
    34→    "elapsedDuration": 3102.73388671875,
    35→    "movingDuration": 0.0,
    36→    "averageSpeed": 0.0,
    37→    "hasPolyline": false,
    38→    "hasImages": false,
    39→    "ownerId": 11508432,
    40→    "ownerDisplayName": "01823aee-4374-48f8-bdf4-1f8fcc1d3ccc",
    41→    "ownerFullName": "李力",
    42→    "ownerProfileImageUrlSmall": "https://s3.cn-north-1.amazonaws.com.cn/garmin-connect-prod/profile_images/c8c539df-5f8b-4896-ac64-c23b4d2caa88-prth.png",
    43→    "ownerProfileImageUrlMedium": "https://s3.cn-north-1.amazonaws.com.cn/garmin-connect-prod/profile_images/c8c539df-5f8b-4896-ac64-c23b4d2caa88-prfr.png",
    44→    "ownerProfileImageUrlLarge": "https://s3.cn-north-1.amazonaws.com.cn/garmin-connect-prod/profile_images/c8c539df-5f8b-4896-ac64-c23b4d2caa88-prof.png",
    45→    "calories": 535.0,
    46→    "bmrCalories": 69.0,
    47→    "averageHR": 136.0,
    48→    "maxHR": 150.0,
    49→    "averageRunningCadenceInStepsPerMinute": 19.515625,
    50→    "maxRunningCadenceInStepsPerMinute": 133.0,
    51→    "steps": 1670,
    52→    "userRoles": [
    53→      "SCOPE_GOLF_API_READ",
    54→      "SCOPE_DI_OAUTH_2_DEVICE_REVOCATION_WRITE",
    55→      "SCOPE_DI_OAUTH_2_CREATE_DI_USER",
    56→      "SCOPE_ATP_READ",
    57→      "SCOPE_DIVE_API_WRITE",
    58→      "SCOPE_COMMUNITY_COURSE_ADMIN_READ",
    59→      "SCOPE_DIVE_API_READ",
    60→      "SCOPE_DI_OAUTH_2_CLIENT_READ",
    61→      "SCOPE_CONNECT_WRITE",
    62→      "SCOPE_COMMUNITY_COURSE_WRITE",
    63→      "SCOPE_CONNECT_MCT_DAILY_LOG_READ",
    64→      "SCOPE_CONNECT_API_CREATE_UPDATE_USER",
    65→      "SCOPE_GARMIN_PROFILE_WRITE",
    66→      "SCOPE_MESSAGE_GENERATION_READ",
    67→      "SCOPE_DI_OAUTH_2_CLIENT_REVOCATION_ADMIN",
    68→      "SCOPE_CONNECT_WEB_TEMPLATE_RENDER",
    69→      "SCOPE_ACTIVITY_DETECTION_READ",
    70→      "SCOPE_OMT_SUBSCRIPTION_ADMIN_READ",
    71→      "SCOPE_CONNECT_NON_SOCIAL_SHARED_READ",
    72→      "SCOPE_DI_OAUTH_2_MANAGED_STATUS_ADMIN_WRITE",
    73→      "SCOPE_GARMIN_PROFILE_READ",
    74→      "SCOPE_CONNECT_READ",
    75→      "SCOPE_DI_OAUTH_2_TOKEN_ADMIN",
    76→      "SCOPE_DI_OAUTH_2_PRERELEASE_PERMISSIONS_ADMIN_WRITE",
    77→      "ROLE_CONNECTUSER",
    78→      "ROLE_FITNESS_USER",
    79→      "ROLE_WELLNESS_USER",
    80→      "ROLE_OUTDOOR_USER"
    81→    ],
    82→    "privacy": {
    83→      "typeId": 3,
    84→      "typeKey": "subscribers"
    85→    },
    86→    "userPro": false,
    87→    "hasVideo": false,
    88→    "timeZoneId": 135,
    89→    "beginTimestamp": 1772242083000,
    90→    "sportTypeId": 4,
    91→    "aerobicTrainingEffect": 3.299999952316284,
    92→    "anaerobicTrainingEffect": 0.0,
    93→    "avgStrideLength": 0.0,
    94→    "deviceId": 3447045197,
    95→    "maxDoubleCadence": 133.0,
    96→    "summarizedDiveInfo": {
    97→      "summarizedDiveGases": []
    98→    },
    99→    "manufacturer": "GARMIN",
   100→    "lapCount": 1,
   101→    "waterEstimated": 746.0,
   102→    "trainingEffectLabel": "AEROBIC_BASE",
   103→    "activityTrainingLoad": 86.4283447265625,
   104→    "minActivityLapDuration": 3022.05908203125,
   105→    "aerobicTrainingEffectMessage": "IMPROVING_AEROBIC_BASE_8",
   106→    "anaerobicTrainingEffectMessage": "NO_ANAEROBIC_BENEFIT_0",
   107→    "splitSummaries": [],
   108→    "hasSplits": false,
   109→    "moderateIntensityMinutes": 3,
   110→    "vigorousIntensityMinutes": 47,
   111→    "differenceBodyBattery": -14,
   112→    "hasHeatMap": false,
   113→    "hrTimeInZone_1": 194.795,
   114→    "hrTimeInZone_2": 2202.119,
   115→    "hrTimeInZone_3": 460.007,
   116→    "hrTimeInZone_4": 0.0,
   117→    "hrTimeInZone_5": 0.0,
   118→    "endTimeGMT": "2026-02-28 02:19:45",
   119→    "qualifyingDive": false,
   120→    "activityUUID": "5894446f-feee-4382-b749-74cebfc52a3a",
   121→    "favorite": false,
   122→    "purposeful": false,
   123→    "parent": false,
   124→    "decoDive": false,
   125→    "pr": false,
   126→    "manualActivity": false,
   127→    "atpActivity": false,
   128→    "elevationCorrected": false,
   129→    "autoCalcCalories": false
   130→  }
   131→}

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>


     1→{
     2→  "date": "2026-02-28",
     3→  "moderate_minutes": 6,
     4→  "vigorous_minutes": 48,
     5→  "total_minutes": 102,
     6→  "weekly_goal": 300,
     7→  "raw_data": {
     8→    "userProfilePK": 11508432,
     9→    "calendarDate": "2026-02-28",
    10→    "startTimestampGMT": "2026-02-27T16:00:00.0",
    11→    "endTimestampGMT": "2026-02-28T03:04:00.0",
    12→    "startTimestampLocal": "2026-02-28T00:00:00.0",
    13→    "endTimestampLocal": "2026-02-28T11:04:00.0",
    14→    "weeklyModerate": 12,
    15→    "weeklyVigorous": 120,
    16→    "weeklyTotal": 252,
    17→    "weekGoal": 300,
    18→    "dayOfGoalMet": null,
    19→    "startDayMinutes": 150,
    20→    "endDayMinutes": 252,
    21→    "moderateMinutes": 6,
    22→    "vigorousMinutes": 48,
    23→    "imValueDescriptorsDTOList": [
    24→      {
    25→        "index": 0,
    26→        "key": "timestamp"
    27→      },
    28→      {
    29→        "index": 1,
    30→        "key": "value"
    31→      }
    32→    ],
    33→    "imValuesArray": [
    34→      [
    35→        1772242200000,
    36→        2
    37→      ],
    38→      [
    39→        1772243100000,
    40→        28
    41→      ],
    42→      [
    43→        1772244000000,
    44→        30
    45→      ],
    46→      [
    47→        1772244900000,
    48→        30
    49→      ],
    50→      [
    51→        1772245800000,
    52→        12
    53→      ]
    54→    ]
    55→  }
    56→}

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>


Excellent! I have gathered all the information you requested. Here's a comprehensive report:

## Activity Data Structure Analysis

### 1. Directory Structure and File Count

**Activities Directory:** `/root/projects/butler/data/health/activities/`
- Organized by year → month → individual activity files (by activity_id)
- **Total: 580 activity JSON files** across 2024, 2025, and 2026

**Intensity Minutes Directory:** `/root/projects/butler/data/health/daily_metrics/intensity_minutes/`
- Similarly organized by year → month → daily JSON files
- Format: `YYYY-MM-DD.json`

### 2. Activity JSON File Structure (Complete Fields)

**Normalized Top-Level Fields:**
```
{
  "activity_id": "569256212",
  "activity_type": "elliptical",
  "activity_name": "Elliptical",
  "date": "2026-03-01",
  "start_time": "2026-03-01T08:13:06",
  "duration_seconds": 3001.7548828125,
  "distance_meters": 0.0,
  "calories": 530,
  "average_heart_rate": 136,
  "max_heart_rate": 183,
  "average_speed_mps": 0.0,
  "average_cadence": 17.421875
}
```

**raw_data Field** contains the complete Garmin API response with additional fields:

**Performance Metrics:**
- `aerobicTrainingEffect`: 3.2 (float)
- `anaerobicTrainingEffect`: 1.9 (float)
- `trainingEffectLabel`: "AEROBIC_BASE" (string)
- `activityTrainingLoad`: 97.23 (float)
- `aerobicTrainingEffectMessage`: "IMPROVING_AEROBIC_BASE_8"
- `anaerobicTrainingEffectMessage`: "MINOR_ANAEROBIC_BENEFIT_15"

**Intensity Data:**
- `moderateIntensityMinutes`: 1
- `vigorousIntensityMinutes`: 48

**Heart Rate Zone Distribution:**
- `hrTimeInZone_1` through `hrTimeInZone_5`: Time spent in each HR zone (seconds)

**Activity Classification:**
```json
"activityType": {
  "typeId": 30,
  "typeKey": "elliptical",
  "parentTypeId": 29,
  "isHidden": false,
  "restricted": false,
  "trimmable": true
}
```

**Other Notable Fields:**
- `sportTypeId`: 4
- `steps`: 1402
- `waterEstimated`: 728.0
- `bmrCalories`: 68.0
- `maxRunningCadenceInStepsPerMinute`: 133.0
- `deviceId`: 3447045197
- `lapCount`: 1
- `differenceBodyBattery`: -14
- `activityUUID`: UUID for the activity
- `hasPolyline`: false
- `hasImages`: false
- Various boolean flags (favorite, manualActivity, parent, pr, etc.)

### 3. Intensity Minutes JSON Structure

**Top-Level Fields:**
```json
{
  "date": "2026-02-28",
  "moderate_minutes": 6,
  "vigorous_minutes": 48,
  "total_minutes": 102,
  "weekly_goal": 300
}
```

**raw_data Field Details:**
- `userProfilePK`: User ID
- `calendarDate`: Date of the record
- `startTimestampGMT` / `endTimestampGMT`: UTC timestamps
- `startTimestampLocal` / `endTimestampLocal`: Local timestamps
- `weeklyModerate`: Cumulative moderate minutes for the week (12)
- `weeklyVigorous`: Cumulative vigorous minutes for the week (120)
- `weeklyTotal`: Total weekly minutes (252 out of 300 goal)
- `weekGoal`: Weekly intensity goal in minutes (300)
- `dayOfGoalMet`: null (not met yet)
- `startDayMinutes` / `endDayMinutes`: Minute counts at day boundaries
- `imValueDescriptorsDTOList`: Metadata for the array (timestamp, value)
- `imValuesArray`: **Hourly intensity values** - array of [timestamp, intensity_value] pairs

**Example imValuesArray:**
```
[
  [1772242200000, 2],    // timestamp in milliseconds, intensity value
  [1772243100000, 28],
  [1772244000000, 30],
  [1772244900000, 30],
  [1772245800000, 12]
]
```

### 4. Weekly Report Current Implementation

**Current _get_steps_stats() method (lines 175-205):**
- Fetches step metrics using `get_metric_range("steps", start, end)`
- Calls `get_activities_range(start, end)` to get all activities
- Extracts `activityType` field from each activity (can be dict with `typeKey` or string)
- Returns: count, avg_steps, goal_days (days with >=10,000 steps), activity_count, activity_types list

**Activity Type Extraction Logic (lines 191-197):**
```python
act_type = ar.get("activityType", {})
if isinstance(act_type, dict):
    name = act_type.get("typeKey") or act_type.get("typeId", "")
else:
    name = str(act_type)
if name and name not in activity_types:
    activity_types.append(name)
```

### 5. Query Method Details (get_activities_range)

**Location:** `/root/projects/butler/health/services/query.py` lines 117-148

**Method Signature:**
```python
def get_activities_range(
    self, start_date: date, end_date: date, activity_type: Optional[str] = None
) -> List[Dict[str, Any]]:
```

**Process:**
1. Logs the query
2. Gets activity index entries from repository via `get_activities_by_date_range()`
3. Optionally filters by activity_type if provided
4. Loads full activity data using `load_activity(activity_id)` for each index entry
5. Returns list of complete activity data dictionaries

**Returns:** List of full activity JSON objects (like the examples above)

---

## Summary for Weekly Report Enrichment

You now have access to:

1. **Activity Type Field:** `raw_data.activityType.typeKey` (e.g., "elliptical") - already extracted in current code
2. **Performance Metrics:** Aerobic/anaerobic training effects, training load, and labels in raw_data
3. **Intensity Breakdown:** `moderateIntensityMinutes` and `vigorousIntensityMinutes` per activity
4. **Weekly Aggregated Intensity:** `intensity_minutes` JSON files provide daily and weekly summaries including hourly distribution
5. **Activity Count:** Already computed (line 203)
6. **Activity Types List:** Already computed (lines 189-197)

To enrich the exercise section, you could add:
- **Total training load for the week** (sum of `activityTrainingLoad`)
- **Aerobic vs Anaerobic focus** (sum of training effects)
- **Total vigorous intensity minutes for the week** (from intensity_minutes files)
- **Training effect distribution** (label breakdown like "AEROBIC_BASE" counts)