I need to see the actual field names in the health data JSON files stored at /root/projects/butler/data/health/daily_metrics/. Please read one recent file from each of these metric directories (pick a file from 2026/02/ or 2026/03/ if available):

1. sleep/ - read one recent JSON file and show ALL fields (including nested fields)
2. heart_rate/ - read one recent JSON file
3. hrv/ - read one recent JSON file
4. body_battery/ - read one recent JSON file
5. steps/ - read one recent JSON file

Also read /root/projects/butler/health/analytics/engine.py to see how it accesses the fields from each metric type (what field names it uses like r["field_name"]).

I need the EXACT field names in the JSON, especially:
- For sleep: what field holds the sleep score? total sleep duration?
- For heart_rate: is it "resting_heart_rate" or "restingHeartRate"?
- For hrv: what field holds the HRV value?
- For body_battery: what fields hold charge/drain values?

Show complete file contents for each JSON file.


I'll help you examine the health data JSON files and the analytics engine to understand the exact field names. Let me start by exploring the directory structure and reading the files.


Bash

find /root/projects/butler/data/health/daily_metrics -type f -name "*.json" | head -20


Let me look for more recent files from 2026:


find /root/projects/butler/data/health/daily_metrics -type f -path "*/2026/02/*" -name "*.json" | tail -5


Good! Now let me find the most recent files from each metric type:


find /root/projects/butler/data/health/daily_metrics/sleep -type f -name "*.json" | sort | tail -1


Perfect! Now let me find the most recent files for each metric type:


for metric in sleep heart_rate hrv body_battery steps; do echo "=== $metric ==="; find /root/projects/butler/data/health/daily_metrics/$metric -type f -name "*.json" | sort | tail -1; done


=== sleep ===
/root/projects/butler/data/health/daily_metrics/sleep/2026/03/2026-03-02.json
=== heart_rate ===
/root/projects/butler/data/health/daily_metrics/heart_rate/2026/03/2026-03-02.json
=== hrv ===
/root/projects/butler/data/health/daily_metrics/hrv/2026/03/2026-03-02.json
=== body_battery ===
/root/projects/butler/data/health/daily_metrics/body_battery/2026/03/2026-03-02.json
=== steps ===
/root/projects/butler/data/health/daily_metrics/steps/2026/03/2026-03-02.json


Excellent! Now let me read all these JSON files and the analytics engine:


Error: File content (269.7KB) exceeds maximum allowed size (256KB). Please use offset and limit parameters to read specific portions of the file, or use the GrepTool to search for specific content.


     1→import pandas as pd
     2→import numpy as np
     3→from datetime import date, timedelta
     4→from typing import Dict, Any, List, Optional
     5→from health.services.query import HealthDataQuery
     6→from health.services.manual_log_storage import ManualLogStorage
     7→from health.utils.logging_config import setup_logger
     8→
     9→logger = setup_logger(__name__)
    10→
    11→class HealthAnalyst:
    12→    """
    13→    Advanced analytics engine for health data using Pandas.
    14→    Calculates correlations, trends, and lifestyle impacts.
    15→    """
    16→
    17→    def __init__(self):
    18→        self.query = HealthDataQuery()
    19→        self.manual_storage = ManualLogStorage()
    20→
    21→    def get_dataframe(self, days: int = 30) -> pd.DataFrame:
    22→        """
    23→        Fetch all relevant data for the last N days and merge into a single DataFrame.
    24→        """
    25→        end_date = date.today()
    26→        start_date = end_date - timedelta(days=days)
    27→
    28→        # 1. Fetch Daily Metrics (Steps, Sleep, HR, Stess, Body Battery)
    29→        # We need to fetch each metric range and merge by date
    30→        metrics_to_fetch = ["sleep", "heart_rate", "stress", "body_battery", "steps", "hrv", "rhr"]
    31→
    32→        data_map = {} # date -> dict of metrics
    33→
    34→        # Initialize dates
    35→        curr = start_date
    36→        while curr <= end_date:
    37→            data_map[curr.isoformat()] = {"date": curr.isoformat()}
    38→            curr += timedelta(days=1)
    39→
    40→        # Batch fetch metrics (this might be slow if we do it one by one,
    41→        # but HealthDataQuery is built for this. Ideally we optimize query later)
    42→        for metric in metrics_to_fetch:
    43→            points = self.query.get_metric_range(metric, start_date, end_date)
    44→            for p in points:
    45→                # Key extraction logic similar to Reader
    46→                # Simplified extraction for DataFrame
    47→                val = np.nan
    48→                if hasattr(p, 'value'): val = p.value
    49→                elif isinstance(p, dict):
    50→                    # Try common keys
    51→                    if 'average_heart_rate' in p: val = p['average_heart_rate']
    52→                    elif 'overall_sleep_score' in p: val = p['overall_sleep_score']
    53→                    elif 'resting_heart_rate' in p: val = p['resting_heart_rate']
    54→                    elif 'average_stress_level' in p: val = p['average_stress_level']
    55→                    elif 'charged' in p: val = p.get('charged', 0) # Body battery charged?
    56→                    # Actually for body battery we usually want max or charged
    57→
    58→                # Store in map
    59→                d_str = p.get('calendar_date') or p.get('date')
    60→                if d_str and d_str in data_map:
    61→                    data_map[d_str][metric] = val
    62→
    63→        # 2. Fetch Manual Logs (Alcohol, Fasting, Supplements)
    64→        logs = self.manual_storage.get_logs_in_range(start_date, end_date)
    65→        for log in logs:
    66→            d_str = log.log_date
    67→            if d_str in data_map:
    68→                # Alcohol (Boolean & Amount)
    69→                data_map[d_str]["alcohol_units"] = sum([1 for _ in log.alcohol_entries])
    70→                data_map[d_str]["has_alcohol"] = 1 if log.alcohol_entries else 0
    71→
    72→                # Fasting (Categorical)
    73→                data_map[d_str]["fasting_mode"] = log.fasting_mode or "Normal"
    74→
    75→                # Supplements (One-hotish checks)
    76→                data_map[d_str]["has_magnesium"] = 1 if any("magnesium" in s.supplement_name.lower() for s in log.supplement_entries) else 0
    77→
    78→        # Convert to DataFrame
    79→        df = pd.DataFrame(list(data_map.values()))
    80→        df['date'] = pd.to_datetime(df['date'])
    81→        df = df.sort_values('date').set_index('date')
    82→        return df
    83→
    84→    def analyze_recovery_correlations(self, days: int = 90) -> Dict[str, Any]:
    85→        """
    86→        Analyze impact of Alcohol and Activity on Next-Day Recovery (Sleep, HRV, RHR).
    87→        """
    88→        df = self.get_dataframe(days)
    89→
    90→        if df.empty:
    91→            return {"error": "No data available"}
    92→
    93→        # Shift recovery metrics to represent "Next Day"
    94→        # We want to see if Alcohol on Day T affects Sleep on Day T (technically sleep starts on Day T night)
    95→        # Actually usually Alcohol on Day T affects Sleep Score of the night of Day T (which serves Day T+1 morning)
    96→        # Garmin assigns Sleep Date to the morning it ends.
    97→        # So Alcohol on Jan 1 affects Sleep of Jan 2.
    98→
    99→        # Let's align:
   100→        # Feature: Alcohol(T)
   101→        # Target: Sleep(T+1), RHR(T+1)
   102→
   103→        df['next_sleep'] = df['sleep'].shift(-1)
   104→        df['next_rhr'] = df['rhr'].shift(-1)
   105→        df['next_hrv'] = df['hrv'].shift(-1)
   106→
   107→        results = {}
   108→
   109→        # 1. Alcohol Impact
   110→        alcohol_days = df[df['has_alcohol'] == 1]
   111→        sober_days = df[df['has_alcohol'] == 0]
   112→
   113→        if len(alcohol_days) > 0 and len(sober_days) > 0:
   114→            results['alcohol_impact'] = {
   115→                "sample_size_alcohol": len(alcohol_days),
   116→                "sample_size_sober": len(sober_days),
   117→                "avg_sleep_alcohol": float(alcohol_days['next_sleep'].mean()),
   118→                "avg_sleep_sober": float(sober_days['next_sleep'].mean()),
   119→                "avg_rhr_alcohol": float(alcohol_days['next_rhr'].mean()),
   120→                "avg_rhr_sober": float(sober_days['next_rhr'].mean()),
   121→                "sleep_diff": float(alcohol_days['next_sleep'].mean() - sober_days['next_sleep'].mean())
   122→            }
   123→
   124→        return results
   125→
   126→    def analyze_fitness_trends(self, days: int = 90) -> Dict[str, Any]:
   127→        """
   128→        Analyze RHR vs Activity trends.
   129→        """
   130→        df = self.get_dataframe(days)
   131→        if df.empty: return {"error": "No data"}
   132→
   133→        # Calculate rolling averages (7-day)
   134→        df['rhr_roll'] = df['rhr'].rolling(7).mean()
   135→        df['steps_roll'] = df['steps'].rolling(7).mean()
   136→
   137→        # Simple Linear Trend
   138→        # Drop NaNs
   139→        valid = df.dropna(subset=['rhr'])
   140→        if len(valid) > 10:
   141→            # Simple slope (not robust but indicative)
   142→            # rhr change over period
   143→            start_rhr = valid.iloc[0]['rhr_roll'] if not np.isnan(valid.iloc[0]['rhr_roll']) else valid.iloc[0]['rhr']
   144→            end_rhr = valid.iloc[-1]['rhr_roll'] if not np.isnan(valid.iloc[-1]['rhr_roll']) else valid.iloc[-1]['rhr']
   145→
   146→            return {
   147→                "period_days": days,
   148→                "start_rhr_7d_avg": float(start_rhr),
   149→                "end_rhr_7d_avg": float(end_rhr),
   150→                "trend": "improving" if end_rhr < start_rhr else "declining"
   151→            }
   152→
   153→        return {"msg": "Not enough data for trend analysis"}
   154→
   155→    def analyze_lifestyle_impact(self, days: int = 30) -> Dict[str, Any]:
   156→        """
   157→        Analyze Fasting and Supplements.
   158→        """
   159→        df = self.get_dataframe(days)
   160→        if df.empty: return {"error": "No data"}
   161→
   162→        results = {}
   163→
   164→        # Fasting Impact
   165→        # Compare "Normal" vs other modes
   166→        fasting_stats = df.groupby('fasting_mode')['sleep'].mean().to_dict()
   167→        results['fasting_sleep_scores'] = fasting_stats
   168→
   169→        return results
   170→
   171→    def compare_groups(self, condition_col: str, target_col: str, days: int = 90) -> Dict[str, Any]:
   172→        """
   173→        Compare average of 'target_col' when 'condition_col' is non-zero vs zero.
   174→        Example: Sleep Score when has_magnesium=1 vs 0.
   175→        """
   176→        df = self.get_dataframe(days)
   177→        if df.empty: return {"error": "No data"}
   178→
   179→        if condition_col not in df.columns or target_col not in df.columns:
   180→            return {"error": f"Columns not found: {condition_col} or {target_col}"}
   181→
   182→        group_true = df[df[condition_col] > 0][target_col]
   183→        group_false = df[df[condition_col] == 0][target_col]
   184→
   185→        if len(group_true) == 0 or len(group_false) == 0:
   186→            return {"error": "One group has no data (e.g. never took supplement)"}
   187→
   188→        avg_true = group_true.mean()
   189→        avg_false = group_false.mean()
   190→        diff_pct = ((avg_true - avg_false) / avg_false) * 100 if avg_false != 0 else 0
   191→
   192→        return {
   193→            "condition": condition_col,
   194→            "target": target_col,
   195→            "avg_with_condition": float(avg_true),
   196→            "avg_without_condition": float(avg_false),
   197→            "sample_with": len(group_true),
   198→            "sample_without": len(group_false),
   199→            "difference_pct": float(diff_pct),
   200→            "verdict": "better" if avg_true > avg_false else "worse"
   201→        }
   202→    def analyze_lagged_correlation(self, driver: str, target: str, lag: int = 1, days: int = 90) -> Dict[str, Any]:
   203→        """
   204→        Analyze if 'driver' on Day T correlates with 'target' on Day T+lag.
   205→        Example: Alcohol (T) -> Sleep Score (T+1).
   206→
   207→        Args:
   208→            driver: Column name of the driver (e.g., 'alcohol_units', 'stress').
   209→            target: Column name of the target (e.g., 'sleep', 'hrv', 'rhr').
   210→            lag: Days to shift target (1 means next day).
   211→            days: Lookback window.
   212→        """
   213→        df = self.get_dataframe(days)
   214→        if df.empty: return {"error": "No data"}
   215→
   216→        # Verify columns exist
   217→        if driver not in df.columns or target not in df.columns:
   218→            return {"error": f"Columns not found: {driver} or {target}"}
   219→
   220→        # Shift target
   221→        # lag=1 means we align Driver(T) with Target(T+1)
   222→        target_col = f"target_lag_{lag}"
   223→        df[target_col] = df[target].shift(-lag)
   224→
   225→        # Filter valid rows
   226→        valid = df.dropna(subset=[driver, target_col])
   227→        if len(valid) < 5:
   228→            return {"error": "Not enough data points (<5)"}
   229→
   230→        # Calculate Correlation
   231→        corr = valid[driver].corr(valid[target_col])
   232→
   233→        return {
   234→            "driver": driver,
   235→            "target": target,
   236→            "lag_days": lag,
   237→            "correlation": float(corr) if not pd.isna(corr) else 0.0,
   238→            "sample_size": len(valid),
   239→            "msg": f" Correlation {corr:.2f} (1.0 is perfect positive, -1.0 is perfect negative)"
   240→        }
   241→

<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→  "charged": 75,
     4→  "drained": 15,
     5→  "timeline": [
     6→    [
     7→      1772380800000,
     8→      22
     9→    ],
    10→    [
    11→      1772392860000,
    12→      62
    13→    ],
    14→    [
    15→      1772393040000,
    16→      62
    17→    ],
    18→    [
    19→      1772404920000,
    20→      92
    21→    ],
    22→    [
    23→      1772406720000,
    24→      96
    25→    ],
    26→    [
    27→      1772416260000,
    28→      77
    29→    ]
    30→  ],
    31→  "raw_data": {
    32→    "date": "2026-03-02",
    33→    "charged": 75,
    34→    "drained": 15,
    35→    "startTimestampGMT": "2026-03-01T16:00:00.0",
    36→    "endTimestampGMT": "2026-03-02T02:09:00.0",
    37→    "startTimestampLocal": "2026-03-02T00:00:00.0",
    38→    "endTimestampLocal": "2026-03-02T10:09:00.0",
    39→    "bodyBatteryValuesArray": [
    40→      [
    41→        1772380800000,
    42→        22
    43→      ],
    44→      [
    45→        1772392860000,
    46→        62
    47→      ],
    48→      [
    49→        1772393040000,
    50→        62
    51→      ],
    52→      [
    53→        1772404920000,
    54→        92
    55→      ],
    56→      [
    57→        1772406720000,
    58→        96
    59→      ],
    60→      [
    61→        1772416260000,
    62→        77
    63→      ]
    64→    ],
    65→    "bodyBatteryValueDescriptorDTOList": [
    66→      {
    67→        "bodyBatteryValueDescriptorIndex": 0,
    68→        "bodyBatteryValueDescriptorKey": "timestamp"
    69→      },
    70→      {
    71→        "bodyBatteryValueDescriptorIndex": 1,
    72→        "bodyBatteryValueDescriptorKey": "bodyBatteryLevel"
    73→      }
    74→    ],
    75→    "bodyBatteryDynamicFeedbackEvent": {
    76→      "eventTimestampGmt": "2026-03-02T01:19:56.0",
    77→      "bodyBatteryLevel": "HIGH",
    78→      "feedbackShortType": "MORNING_BALANCED_AND_INACTIVE",
    79→      "feedbackLongType": "MORNING_BALANCED_AND_INACTIVE"
    80→    },
    81→    "bodyBatteryActivityEvent": [
    82→      {
    83→        "eventType": "SLEEP",
    84→        "eventStartTimeGmt": "2026-03-01T16:01:11.0",
    85→        "timezoneOffset": 28800000,
    86→        "durationInMilliseconds": 26280000,
    87→        "bodyBatteryImpact": 74,
    88→        "feedbackType": "NONE",
    89→        "shortFeedback": "NONE"
    90→      }
    91→    ],
    92→    "endOfDayBodyBatteryDynamicFeedbackEvent": {
    93→      "eventTimestampGmt": "2026-03-01T18:09:06.0",
    94→      "bodyBatteryLevel": "HIGH",
    95→      "feedbackShortType": "SLEEP_TIME_PASSED_BALANCED_AND_INTENSIVE_EXERCISE",
    96→      "feedbackLongType": "SLEEP_TIME_PASSED_BALANCED_AND_INTENSIVE_EXERCISE"
    97→    }
    98→  }
    99→}

<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→  "total_steps": 2243,
     4→  "total_distance_meters": 1786.0,
     5→  "step_goal": 10840,
     6→  "raw_data": {
     7→    "calendarDate": "2026-03-02",
     8→    "totalSteps": 2243,
     9→    "totalDistance": 1786,
    10→    "stepGoal": 10840
    11→  }
    12→}

<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→  "resting_heart_rate": 45,
     4→  "min_heart_rate": 44,
     5→  "max_heart_rate": 104,
     6→  "raw_data": {
     7→    "userProfilePK": 11508432,
     8→    "calendarDate": "2026-03-02",
     9→    "startTimestampGMT": "2026-03-01T16:00:00.0",
    10→    "endTimestampGMT": "2026-03-02T02:09:00.0",
    11→    "startTimestampLocal": "2026-03-02T00:00:00.0",
    12→    "endTimestampLocal": "2026-03-03T00:00:00.0",
    13→    "maxHeartRate": 104,
    14→    "minHeartRate": 44,
    15→    "restingHeartRate": 45,
    16→    "lastSevenDaysAvgRestingHeartRate": 45,
    17→    "heartRateValues": [
    18→      [
    19→        1772380800000,
    20→        50
    21→      ],
    22→      [
    23→        1772380920000,
    24→        53
    25→      ],
    26→      [
    27→        1772381040000,
    28→        51
    29→      ],
    30→      [
    31→        1772381160000,
    32→        50
    33→      ],
    34→      [
    35→        1772381280000,
    36→        50
    37→      ],
    38→      [
    39→        1772381400000,
    40→        50
    41→      ],
    42→      [
    43→        1772381520000,
    44→        49
    45→      ],
    46→      [
    47→        1772381640000,
    48→        49
    49→      ],
    50→      [
    51→        1772381760000,
    52→        50
    53→      ],
    54→      [
    55→        1772381880000,
    56→        49
    57→      ],
    58→      [
    59→        1772382000000,
    60→        49
    61→      ],
    62→      [
    63→        1772382120000,
    64→        50
    65→      ],
    66→      [
    67→        1772382240000,
    68→        51
    69→      ],
    70→      [
    71→        1772382360000,
    72→        51
    73→      ],
    74→      [
    75→        1772382480000,
    76→        48
    77→      ],
    78→      [
    79→        1772382600000,
    80→        49
    81→      ],
    82→      [
    83→        1772382720000,
    84→        47
    85→      ],
    86→      [
    87→        1772382840000,
    88→        51
    89→      ],
    90→      [
    91→        1772382960000,
    92→        48
    93→      ],
    94→      [
    95→        1772383080000,
    96→        48
    97→      ],
    98→      [
    99→        1772383200000,
   100→        48
   101→      ],
   102→      [
   103→        1772383320000,
   104→        53
   105→      ],
   106→      [
   107→        1772383440000,
   108→        49
   109→      ],
   110→      [
   111→        1772383560000,
   112→        49
   113→      ],
   114→      [
   115→        1772383680000,
   116→        50
   117→      ],
   118→      [
   119→        1772383800000,
   120→        50
   121→      ],
   122→      [
   123→        1772383920000,
   124→        49
   125→      ],
   126→      [
   127→        1772384040000,
   128→        50
   129→      ],
   130→      [
   131→        1772384160000,
   132→        52
   133→      ],
   134→      [
   135→        1772384280000,
   136→        46
   137→      ],
   138→      [
   139→        1772384400000,
   140→        51
   141→      ],
   142→      [
   143→        1772384520000,
   144→        47
   145→      ],
   146→      [
   147→        1772384640000,
   148→        53
   149→      ],
   150→      [
   151→        1772384760000,
   152→        50
   153→      ],
   154→      [
   155→        1772384880000,
   156→        47
   157→      ],
   158→      [
   159→        1772385000000,
   160→        48
   161→      ],
   162→      [
   163→        1772385120000,
   164→        49
   165→      ],
   166→      [
   167→        1772385240000,
   168→        50
   169→      ],
   170→      [
   171→        1772385360000,
   172→        50
   173→      ],
   174→      [
   175→        1772385480000,
   176→        50
   177→      ],
   178→      [
   179→        1772385600000,
   180→        51
   181→      ],
   182→      [
   183→        1772385720000,
   184→        51
   185→      ],
   186→      [
   187→        1772385840000,
   188→        51
   189→      ],
   190→      [
   191→        1772385960000,
   192→        47
   193→      ],
   194→      [
   195→        1772386080000,
   196→        49
   197→      ],
   198→      [
   199→        1772386200000,
   200→        50
   201→      ],
   202→      [
   203→        1772386320000,
   204→        49
   205→      ],
   206→      [
   207→        1772386440000,
   208→        48
   209→      ],
   210→      [
   211→        1772386560000,
   212→        48
   213→      ],
   214→      [
   215→        1772386680000,
   216→        47
   217→      ],
   218→      [
   219→        1772386800000,
   220→        46
   221→      ],
   222→      [
   223→        1772386920000,
   224→        45
   225→      ],
   226→      [
   227→        1772387040000,
   228→        45
   229→      ],
   230→      [
   231→        1772387160000,
   232→        47
   233→      ],
   234→      [
   235→        1772387280000,
   236→        45
   237→      ],
   238→      [
   239→        1772387400000,
   240→        45
   241→      ],
   242→      [
   243→        1772387520000,
   244→        46
   245→      ],
   246→      [
   247→        1772387640000,
   248→        50
   249→      ],
   250→      [
   251→        1772387760000,
   252→        52
   253→      ],
   254→      [
   255→        1772387880000,
   256→        49
   257→      ],
   258→      [
   259→        1772388000000,
   260→        46
   261→      ],
   262→      [
   263→        1772388120000,
   264→        47
   265→      ],
   266→      [
   267→        1772388240000,
   268→        47
   269→      ],
   270→      [
   271→        1772388360000,
   272→        47
   273→      ],
   274→      [
   275→        1772388480000,
   276→        52
   277→      ],
   278→      [
   279→        1772388600000,
   280→        52
   281→      ],
   282→      [
   283→        1772388720000,
   284→        52
   285→      ],
   286→      [
   287→        1772388840000,
   288→        52
   289→      ],
   290→      [
   291→        1772388960000,
   292→        50
   293→      ],
   294→      [
   295→        1772389080000,
   296→        46
   297→      ],
   298→      [
   299→        1772389200000,
   300→        46
   301→      ],
   302→      [
   303→        1772389320000,
   304→        46
   305→      ],
   306→      [
   307→        1772389440000,
   308→        47
   309→      ],
   310→      [
   311→        1772389560000,
   312→        46
   313→      ],
   314→      [
   315→        1772389680000,
   316→        48
   317→      ],
   318→      [
   319→        1772389800000,
   320→        46
   321→      ],
   322→      [
   323→        1772389920000,
   324→        48
   325→      ],
   326→      [
   327→        1772390040000,
   328→        47
   329→      ],
   330→      [
   331→        1772390160000,
   332→        48
   333→      ],
   334→      [
   335→        1772390280000,
   336→        46
   337→      ],
   338→      [
   339→        1772390400000,
   340→        45
   341→      ],
   342→      [
   343→        1772390520000,
   344→        46
   345→      ],
   346→      [
   347→        1772390640000,
   348→        48
   349→      ],
   350→      [
   351→        1772390760000,
   352→        53
   353→      ],
   354→      [
   355→        1772390880000,
   356→        49
   357→      ],
   358→      [
   359→        1772391000000,
   360→        48
   361→      ],
   362→      [
   363→        1772391120000,
   364→        46
   365→      ],
   366→      [
   367→        1772391240000,
   368→        48
   369→      ],
   370→      [
   371→        1772391360000,
   372→        49
   373→      ],
   374→      [
   375→        1772391480000,
   376→        47
   377→      ],
   378→      [
   379→        1772391600000,
   380→        49
   381→      ],
   382→      [
   383→        1772391720000,
   384→        49
   385→      ],
   386→      [
   387→        1772391840000,
   388→        48
   389→      ],
   390→      [
   391→        1772391960000,
   392→        49
   393→      ],
   394→      [
   395→        1772392080000,
   396→        50
   397→      ],
   398→      [
   399→        1772392200000,
   400→        48
   401→      ],
   402→      [
   403→        1772392320000,
   404→        50
   405→      ],
   406→      [
   407→        1772392440000,
   408→        50
   409→      ],
   410→      [
   411→        1772392560000,
   412→        50
   413→      ],
   414→      [
   415→        1772392680000,
   416→        51
   417→      ],
   418→      [
   419→        1772392800000,
   420→        49
   421→      ],
   422→      [
   423→        1772392920000,
   424→        50
   425→      ],
   426→      [
   427→        1772393040000,
   428→        49
   429→      ],
   430→      [
   431→        1772393160000,
   432→        46
   433→      ],
   434→      [
   435→        1772393280000,
   436→        45
   437→      ],
   438→      [
   439→        1772393400000,
   440→        46
   441→      ],
   442→      [
   443→        1772393520000,
   444→        45
   445→      ],
   446→      [
   447→        1772393640000,
   448→        46
   449→      ],
   450→      [
   451→        1772393760000,
   452→        45
   453→      ],
   454→      [
   455→        1772393880000,
   456→        44
   457→      ],
   458→      [
   459→        1772394000000,
   460→        45
   461→      ],
   462→      [
   463→        1772394120000,
   464→        45
   465→      ],
   466→      [
   467→        1772394240000,
   468→        45
   469→      ],
   470→      [
   471→        1772394360000,
   472→        45
   473→      ],
   474→      [
   475→        1772394480000,
   476→        45
   477→      ],
   478→      [
   479→        1772394600000,
   480→        46
   481→      ],
   482→      [
   483→        1772394720000,
   484→        47
   485→      ],
   486→      [
   487→        1772394840000,
   488→        48
   489→      ],
   490→      [
   491→        1772394960000,
   492→        47
   493→      ],
   494→      [
   495→        1772395080000,
   496→        48
   497→      ],
   498→      [
   499→        1772395200000,
   500→        48
   501→      ],
   502→      [
   503→        1772395320000,
   504→        48
   505→      ],
   506→      [
   507→        1772395440000,
   508→        48
   509→      ],
   510→      [
   511→        1772395560000,
   512→        48
   513→      ],
   514→      [
   515→        1772395680000,
   516→        48
   517→      ],
   518→      [
   519→        1772395800000,
   520→        49
   521→      ],
   522→      [
   523→        1772395920000,
   524→        49
   525→      ],
   526→      [
   527→        1772396040000,
   528→        49
   529→      ],
   530→      [
   531→        1772396160000,
   532→        49
   533→      ],
   534→      [
   535→        1772396280000,
   536→        54
   537→      ],
   538→      [
   539→        1772396400000,
   540→        50
   541→      ],
   542→      [
   543→        1772396520000,
   544→        48
   545→      ],
   546→      [
   547→        1772396640000,
   548→        52
   549→      ],
   550→      [
   551→        1772396760000,
   552→        48
   553→      ],
   554→      [
   555→        1772396880000,
   556→        50
   557→      ],
   558→      [
   559→        1772397000000,
   560→        49
   561→      ],
   562→      [
   563→        1772397120000,
   564→        51
   565→      ],
   566→      [
   567→        1772397240000,
   568→        50
   569→      ],
   570→      [
   571→        1772397360000,
   572→        50
   573→      ],
   574→      [
   575→        1772397480000,
   576→        50
   577→      ],
   578→      [
   579→        1772397600000,
   580→        52
   581→      ],
   582→      [
   583→        1772397720000,
   584→        49
   585→      ],
   586→      [
   587→        1772397840000,
   588→        49
   589→      ],
   590→      [
   591→        1772397960000,
   592→        50
   593→      ],
   594→      [
   595→        1772398080000,
   596→        50
   597→      ],
   598→      [
   599→        1772398200000,
   600→        47
   601→      ],
   602→      [
   603→        1772398320000,
   604→        49
   605→      ],
   606→      [
   607→        1772398440000,
   608→        48
   609→      ],
   610→      [
   611→        1772398560000,
   612→        49
   613→      ],
   614→      [
   615→        1772398680000,
   616→        50
   617→      ],
   618→      [
   619→        1772398800000,
   620→        49
   621→      ],
   622→      [
   623→        1772398920000,
   624→        47
   625→      ],
   626→      [
   627→        1772399040000,
   628→        50
   629→      ],
   630→      [
   631→        1772399160000,
   632→        46
   633→      ],
   634→      [
   635→        1772399280000,
   636→        45
   637→      ],
   638→      [
   639→        1772399400000,
   640→        46
   641→      ],
   642→      [
   643→        1772399520000,
   644→        48
   645→      ],
   646→      [
   647→        1772399640000,
   648→        51
   649→      ],
   650→      [
   651→        1772399760000,
   652→        47
   653→      ],
   654→      [
   655→        1772399880000,
   656→        47
   657→      ],
   658→      [
   659→        1772400000000,
   660→        47
   661→      ],
   662→      [
   663→        1772400120000,
   664→        48
   665→      ],
   666→      [
   667→        1772400240000,
   668→        50
   669→      ],
   670→      [
   671→        1772400360000,
   672→        46
   673→      ],
   674→      [
   675→        1772400480000,
   676→        48
   677→      ],
   678→      [
   679→        1772400600000,
   680→        45
   681→      ],
   682→      [
   683→        1772400720000,
   684→        45
   685→      ],
   686→      [
   687→        1772400840000,
   688→        51
   689→      ],
   690→      [
   691→        1772400960000,
   692→        46
   693→      ],
   694→      [
   695→        1772401080000,
   696→        49
   697→      ],
   698→      [
   699→        1772401200000,
   700→        46
   701→      ],
   702→      [
   703→        1772401320000,
   704→        46
   705→      ],
   706→      [
   707→        1772401440000,
   708→        46
   709→      ],
   710→      [
   711→        1772401560000,
   712→        49
   713→      ],
   714→      [
   715→        1772401680000,
   716→        51
   717→      ],
   718→      [
   719→        1772401800000,
   720→        53
   721→      ],
   722→      [
   723→        1772401920000,
   724→        44
   725→      ],
   726→      [
   727→        1772402040000,
   728→        45
   729→      ],
   730→      [
   731→        1772402160000,
   732→        47
   733→      ],
   734→      [
   735→        1772402280000,
   736→        45
   737→      ],
   738→      [
   739→        1772402400000,
   740→        51
   741→      ],
   742→      [
   743→        1772402520000,
   744→        51
   745→      ],
   746→      [
   747→        1772402640000,
   748→        48
   749→      ],
   750→      [
   751→        1772402760000,
   752→        51
   753→      ],
   754→      [
   755→        1772402880000,
   756→        49
   757→      ],
   758→      [
   759→        1772403000000,
   760→        51
   761→      ],
   762→      [
   763→        1772403120000,
   764→        50
   765→      ],
   766→      [
   767→        1772403240000,
   768→        51
   769→      ],
   770→      [
   771→        1772403360000,
   772→        49
   773→      ],
   774→      [
   775→        1772403480000,
   776→        50
   777→      ],
   778→      [
   779→        1772403600000,
   780→        49
   781→      ],
   782→      [
   783→        1772403720000,
   784→        48
   785→      ],
   786→      [
   787→        1772403840000,
   788→        48
   789→      ],
   790→      [
   791→        1772403960000,
   792→        48
   793→      ],
   794→      [
   795→        1772404080000,
   796→        47
   797→      ],
   798→      [
   799→        1772404200000,
   800→        46
   801→      ],
   802→      [
   803→        1772404320000,
   804→        48
   805→      ],
   806→      [
   807→        1772404440000,
   808→        45
   809→      ],
   810→      [
   811→        1772404560000,
   812→        45
   813→      ],
   814→      [
   815→        1772404680000,
   816→        46
   817→      ],
   818→      [
   819→        1772404800000,
   820→        48
   821→      ],
   822→      [
   823→        1772404920000,
   824→        47
   825→      ],
   826→      [
   827→        1772405040000,
   828→        47
   829→      ],
   830→      [
   831→        1772405160000,
   832→        47
   833→      ],
   834→      [
   835→        1772405280000,
   836→        47
   837→      ],
   838→      [
   839→        1772405400000,
   840→        47
   841→      ],
   842→      [
   843→        1772405520000,
   844→        48
   845→      ],
   846→      [
   847→        1772405640000,
   848→        47
   849→      ],
   850→      [
   851→        1772405760000,
   852→        47
   853→      ],
   854→      [
   855→        1772405880000,
   856→        49
   857→      ],
   858→      [
   859→        1772406000000,
   860→        57
   861→      ],
   862→      [
   863→        1772406120000,
   864→        49
   865→      ],
   866→      [
   867→        1772406240000,
   868→        49
   869→      ],
   870→      [
   871→        1772406360000,
   872→        46
   873→      ],
   874→      [
   875→        1772406480000,
   876→        47
   877→      ],
   878→      [
   879→        1772406600000,
   880→        45
   881→      ],
   882→      [
   883→        1772406720000,
   884→        45
   885→      ],
   886→      [
   887→        1772406840000,
   888→        46
   889→      ],
   890→      [
   891→        1772406960000,
   892→        48
   893→      ],
   894→      [
   895→        1772407080000,
   896→        49
   897→      ],
   898→      [
   899→        1772407200000,
   900→        51
   901→      ],
   902→      [
   903→        1772407320000,
   904→        52
   905→      ],
   906→      [
   907→        1772407440000,
   908→        51
   909→      ],
   910→      [
   911→        1772407560000,
   912→        52
   913→      ],
   914→      [
   915→        1772407680000,
   916→        53
   917→      ],
   918→      [
   919→        1772407800000,
   920→        51
   921→      ],
   922→      [
   923→        1772407920000,
   924→        53
   925→      ],
   926→      [
   927→        1772408040000,
   928→        65
   929→      ],
   930→      [
   931→        1772408160000,
   932→        69
   933→      ],
   934→      [
   935→        1772408280000,
   936→        72
   937→      ],
   938→      [
   939→        1772408400000,
   940→        null
   941→      ],
   942→      [
   943→        1772408520000,
   944→        84
   945→      ],
   946→      [
   947→        1772408640000,
   948→        87
   949→      ],
   950→      [
   951→        1772408760000,
   952→        89
   953→      ],
   954→      [
   955→        1772408880000,
   956→        96
   957→      ],
   958→      [
   959→        1772409000000,
   960→        95
   961→      ],
   962→      [
   963→        1772409120000,
   964→        90
   965→      ],
   966→      [
   967→        1772409240000,
   968→        87
   969→      ],
   970→      [
   971→        1772409360000,
   972→        93
   973→      ],
   974→      [
   975→        1772409480000,
   976→        92
   977→      ],
   978→      [
   979→        1772409600000,
   980→        94
   981→      ],
   982→      [
   983→        1772409720000,
   984→        97
   985→      ],
   986→      [
   987→        1772409840000,
   988→        90
   989→      ],
   990→      [
   991→        1772409960000,
   992→        96
   993→      ],
   994→      [
   995→        1772410080000,
   996→        104
   997→      ],
   998→      [
   999→        1772410200000,
  1000→        97
  1001→      ],
  1002→      [
  1003→        1772410320000,
  1004→        102
  1005→      ],
  1006→      [
  1007→        1772410440000,
  1008→        99
  1009→      ],
  1010→      [
  1011→        1772410560000,
  1012→        63
  1013→      ],
  1014→      [
  1015→        1772410680000,
  1016→        71
  1017→      ],
  1018→      [
  1019→        1772410800000,
  1020→        71
  1021→      ],
  1022→      [
  1023→        1772410920000,
  1024→        68
  1025→      ],
  1026→      [
  1027→        1772411040000,
  1028→        65
  1029→      ],
  1030→      [
  1031→        1772411160000,
  1032→        68
  1033→      ],
  1034→      [
  1035→        1772411280000,
  1036→        63
  1037→      ],
  1038→      [
  1039→        1772411400000,
  1040→        67
  1041→      ],
  1042→      [
  1043→        1772411520000,
  1044→        72
  1045→      ],
  1046→      [
  1047→        1772411640000,
  1048→        69
  1049→      ],
  1050→      [
  1051→        1772411760000,
  1052→        64
  1053→      ],
  1054→      [
  1055→        1772411880000,
  1056→        67
  1057→      ],
  1058→      [
  1059→        1772412000000,
  1060→        66
  1061→      ],
  1062→      [
  1063→        1772412120000,
  1064→        73
  1065→      ],
  1066→      [
  1067→        1772412240000,
  1068→        72
  1069→      ],
  1070→      [
  1071→        1772412360000,
  1072→        73
  1073→      ],
  1074→      [
  1075→        1772412480000,
  1076→        77
  1077→      ],
  1078→      [
  1079→        1772412600000,
  1080→        78
  1081→      ],
  1082→      [
  1083→        1772412720000,
  1084→        67
  1085→      ],
  1086→      [
  1087→        1772412840000,
  1088→        61
  1089→      ],
  1090→      [
  1091→        1772412960000,
  1092→        64
  1093→      ],
  1094→      [
  1095→        1772413080000,
  1096→        66
  1097→      ],
  1098→      [
  1099→        1772413200000,
  1100→        65
  1101→      ],
  1102→      [
  1103→        1772413320000,
  1104→        65
  1105→      ],
  1106→      [
  1107→        1772413440000,
  1108→        64
  1109→      ],
  1110→      [
  1111→        1772413560000,
  1112→        64
  1113→      ],
  1114→      [
  1115→        1772413680000,
  1116→        62
  1117→      ],
  1118→      [
  1119→        1772413800000,
  1120→        63
  1121→      ],
  1122→      [
  1123→        1772413920000,
  1124→        62
  1125→      ],
  1126→      [
  1127→        1772414040000,
  1128→        62
  1129→      ],
  1130→      [
  1131→        1772414160000,
  1132→        60
  1133→      ],
  1134→      [
  1135→        1772414280000,
  1136→        64
  1137→      ],
  1138→      [
  1139→        1772414400000,
  1140→        66
  1141→      ],
  1142→      [
  1143→        1772414520000,
  1144→        71
  1145→      ],
  1146→      [
  1147→        1772414640000,
  1148→        63
  1149→      ],
  1150→      [
  1151→        1772414760000,
  1152→        58
  1153→      ],
  1154→      [
  1155→        1772414880000,
  1156→        61
  1157→      ],
  1158→      [
  1159→        1772415000000,
  1160→        64
  1161→      ],
  1162→      [
  1163→        1772415120000,
  1164→        66
  1165→      ],
  1166→      [
  1167→        1772415240000,
  1168→        69
  1169→      ],
  1170→      [
  1171→        1772415360000,
  1172→        64
  1173→      ],
  1174→      [
  1175→        1772415480000,
  1176→        69
  1177→      ],
  1178→      [
  1179→        1772415600000,
  1180→        75
  1181→      ],
  1182→      [
  1183→        1772415720000,
  1184→        58
  1185→      ],
  1186→      [
  1187→        1772415840000,
  1188→        56
  1189→      ],
  1190→      [
  1191→        1772415960000,
  1192→        58
  1193→      ],
  1194→      [
  1195→        1772416080000,
  1196→        58
  1197→      ],
  1198→      [
  1199→        1772416200000,
  1200→        58
  1201→      ],
  1202→      [
  1203→        1772416320000,
  1204→        61
  1205→      ],
  1206→      [
  1207→        1772416440000,
  1208→        58
  1209→      ],
  1210→      [
  1211→        1772416560000,
  1212→        59
  1213→      ],
  1214→      [
  1215→        1772416680000,
  1216→        65
  1217→      ],
  1218→      [
  1219→        1772416800000,
  1220→        60
  1221→      ],
  1222→      [
  1223→        1772416920000,
  1224→        58
  1225→      ],
  1226→      [
  1227→        1772417040000,
  1228→        60
  1229→      ],
  1230→      [
  1231→        1772417160000,
  1232→        61
  1233→      ],
  1234→      [
  1235→        1772417280000,
  1236→        61
  1237→      ]
  1238→    ],
  1239→    "heartRateValueDescriptors": [
  1240→      {
  1241→        "key": "timestamp",
  1242→        "index": 0
  1243→      },
  1244→      {
  1245→        "key": "heartrate",
  1246→        "index": 1
  1247→      }
  1248→    ]
  1249→  }
  1250→}

<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→  "hrv_value": 56.0,
     4→  "baseline_hrv": 44.0,
     5→  "status": "BALANCED",
     6→  "raw_data": {
     7→    "userProfilePk": 11508432,
     8→    "hrvSummary": {
     9→      "calendarDate": "2026-03-02",
    10→      "weeklyAvg": 57,
    11→      "lastNightAvg": 56,
    12→      "lastNight5MinHigh": 110,
    13→      "baseline": {
    14→        "lowUpper": 44,
    15→        "balancedLow": 48,
    16→        "balancedUpper": 66,
    17→        "markerValue": 0.5
    18→      },
    19→      "status": "BALANCED",
    20→      "feedbackPhrase": "HRV_BALANCED_2",
    21→      "createTimeStamp": "2026-03-01T23:20:20.348"
    22→    },
    23→    "hrvReadings": [
    24→      {
    25→        "hrvValue": 41,
    26→        "readingTimeGMT": "2026-03-01T16:03:28.0",
    27→        "readingTimeLocal": "2026-03-02T00:03:28.0"
    28→      },
    29→      {
    30→        "hrvValue": 54,
    31→        "readingTimeGMT": "2026-03-01T16:08:28.0",
    32→        "readingTimeLocal": "2026-03-02T00:08:28.0"
    33→      },
    34→      {
    35→        "hrvValue": 54,
    36→        "readingTimeGMT": "2026-03-01T16:13:28.0",
    37→        "readingTimeLocal": "2026-03-02T00:13:28.0"
    38→      },
    39→      {
    40→        "hrvValue": 48,
    41→        "readingTimeGMT": "2026-03-01T16:18:28.0",
    42→        "readingTimeLocal": "2026-03-02T00:18:28.0"
    43→      },
    44→      {
    45→        "hrvValue": 44,
    46→        "readingTimeGMT": "2026-03-01T16:23:28.0",
    47→        "readingTimeLocal": "2026-03-02T00:23:28.0"
    48→      },
    49→      {
    50→        "hrvValue": 48,
    51→        "readingTimeGMT": "2026-03-01T16:28:28.0",
    52→        "readingTimeLocal": "2026-03-02T00:28:28.0"
    53→      },
    54→      {
    55→        "hrvValue": 32,
    56→        "readingTimeGMT": "2026-03-01T16:33:28.0",
    57→        "readingTimeLocal": "2026-03-02T00:33:28.0"
    58→      },
    59→      {
    60→        "hrvValue": 45,
    61→        "readingTimeGMT": "2026-03-01T16:38:28.0",
    62→        "readingTimeLocal": "2026-03-02T00:38:28.0"
    63→      },
    64→      {
    65→        "hrvValue": 55,
    66→        "readingTimeGMT": "2026-03-01T16:43:28.0",
    67→        "readingTimeLocal": "2026-03-02T00:43:28.0"
    68→      },
    69→      {
    70→        "hrvValue": 40,
    71→        "readingTimeGMT": "2026-03-01T16:48:28.0",
    72→        "readingTimeLocal": "2026-03-02T00:48:28.0"
    73→      },
    74→      {
    75→        "hrvValue": 33,
    76→        "readingTimeGMT": "2026-03-01T16:53:28.0",
    77→        "readingTimeLocal": "2026-03-02T00:53:28.0"
    78→      },
    79→      {
    80→        "hrvValue": 55,
    81→        "readingTimeGMT": "2026-03-01T16:58:28.0",
    82→        "readingTimeLocal": "2026-03-02T00:58:28.0"
    83→      },
    84→      {
    85→        "hrvValue": 52,
    86→        "readingTimeGMT": "2026-03-01T17:03:28.0",
    87→        "readingTimeLocal": "2026-03-02T01:03:28.0"
    88→      },
    89→      {
    90→        "hrvValue": 54,
    91→        "readingTimeGMT": "2026-03-01T17:08:28.0",
    92→        "readingTimeLocal": "2026-03-02T01:08:28.0"
    93→      },
    94→      {
    95→        "hrvValue": 61,
    96→        "readingTimeGMT": "2026-03-01T17:13:28.0",
    97→        "readingTimeLocal": "2026-03-02T01:13:28.0"
    98→      },
    99→      {
   100→        "hrvValue": 46,
   101→        "readingTimeGMT": "2026-03-01T17:18:28.0",
   102→        "readingTimeLocal": "2026-03-02T01:18:28.0"
   103→      },
   104→      {
   105→        "hrvValue": 44,
   106→        "readingTimeGMT": "2026-03-01T17:23:28.0",
   107→        "readingTimeLocal": "2026-03-02T01:23:28.0"
   108→      },
   109→      {
   110→        "hrvValue": 64,
   111→        "readingTimeGMT": "2026-03-01T17:28:28.0",
   112→        "readingTimeLocal": "2026-03-02T01:28:28.0"
   113→      },
   114→      {
   115→        "hrvValue": 56,
   116→        "readingTimeGMT": "2026-03-01T17:33:28.0",
   117→        "readingTimeLocal": "2026-03-02T01:33:28.0"
   118→      },
   119→      {
   120→        "hrvValue": 58,
   121→        "readingTimeGMT": "2026-03-01T17:38:28.0",
   122→        "readingTimeLocal": "2026-03-02T01:38:28.0"
   123→      },
   124→      {
   125→        "hrvValue": 86,
   126→        "readingTimeGMT": "2026-03-01T17:43:28.0",
   127→        "readingTimeLocal": "2026-03-02T01:43:28.0"
   128→      },
   129→      {
   130→        "hrvValue": 89,
   131→        "readingTimeGMT": "2026-03-01T17:48:28.0",
   132→        "readingTimeLocal": "2026-03-02T01:48:28.0"
   133→      },
   134→      {
   135→        "hrvValue": 49,
   136→        "readingTimeGMT": "2026-03-01T17:53:28.0",
   137→        "readingTimeLocal": "2026-03-02T01:53:28.0"
   138→      },
   139→      {
   140→        "hrvValue": 62,
   141→        "readingTimeGMT": "2026-03-01T17:58:28.0",
   142→        "readingTimeLocal": "2026-03-02T01:58:28.0"
   143→      },
   144→      {
   145→        "hrvValue": 66,
   146→        "readingTimeGMT": "2026-03-01T18:03:28.0",
   147→        "readingTimeLocal": "2026-03-02T02:03:28.0"
   148→      },
   149→      {
   150→        "hrvValue": 42,
   151→        "readingTimeGMT": "2026-03-01T18:08:28.0",
   152→        "readingTimeLocal": "2026-03-02T02:08:28.0"
   153→      },
   154→      {
   155→        "hrvValue": 43,
   156→        "readingTimeGMT": "2026-03-01T18:13:28.0",
   157→        "readingTimeLocal": "2026-03-02T02:13:28.0"
   158→      },
   159→      {
   160→        "hrvValue": 58,
   161→        "readingTimeGMT": "2026-03-01T18:18:28.0",
   162→        "readingTimeLocal": "2026-03-02T02:18:28.0"
   163→      },
   164→      {
   165→        "hrvValue": 64,
   166→        "readingTimeGMT": "2026-03-01T18:23:28.0",
   167→        "readingTimeLocal": "2026-03-02T02:23:28.0"
   168→      },
   169→      {
   170→        "hrvValue": 48,
   171→        "readingTimeGMT": "2026-03-01T18:28:28.0",
   172→        "readingTimeLocal": "2026-03-02T02:28:28.0"
   173→      },
   174→      {
   175→        "hrvValue": 59,
   176→        "readingTimeGMT": "2026-03-01T18:33:28.0",
   177→        "readingTimeLocal": "2026-03-02T02:33:28.0"
   178→      },
   179→      {
   180→        "hrvValue": 62,
   181→        "readingTimeGMT": "2026-03-01T18:38:28.0",
   182→        "readingTimeLocal": "2026-03-02T02:38:28.0"
   183→      },
   184→      {
   185→        "hrvValue": 62,
   186→        "readingTimeGMT": "2026-03-01T18:43:28.0",
   187→        "readingTimeLocal": "2026-03-02T02:43:28.0"
   188→      },
   189→      {
   190→        "hrvValue": 57,
   191→        "readingTimeGMT": "2026-03-01T18:48:28.0",
   192→        "readingTimeLocal": "2026-03-02T02:48:28.0"
   193→      },
   194→      {
   195→        "hrvValue": 79,
   196→        "readingTimeGMT": "2026-03-01T18:53:28.0",
   197→        "readingTimeLocal": "2026-03-02T02:53:28.0"
   198→      },
   199→      {
   200→        "hrvValue": 49,
   201→        "readingTimeGMT": "2026-03-01T18:58:28.0",
   202→        "readingTimeLocal": "2026-03-02T02:58:28.0"
   203→      },
   204→      {
   205→        "hrvValue": 46,
   206→        "readingTimeGMT": "2026-03-01T19:03:28.0",
   207→        "readingTimeLocal": "2026-03-02T03:03:28.0"
   208→      },
   209→      {
   210→        "hrvValue": 44,
   211→        "readingTimeGMT": "2026-03-01T19:08:28.0",
   212→        "readingTimeLocal": "2026-03-02T03:08:28.0"
   213→      },
   214→      {
   215→        "hrvValue": 43,
   216→        "readingTimeGMT": "2026-03-01T19:13:28.0",
   217→        "readingTimeLocal": "2026-03-02T03:13:28.0"
   218→      },
   219→      {
   220→        "hrvValue": 43,
   221→        "readingTimeGMT": "2026-03-01T19:18:28.0",
   222→        "readingTimeLocal": "2026-03-02T03:18:28.0"
   223→      },
   224→      {
   225→        "hrvValue": 45,
   226→        "readingTimeGMT": "2026-03-01T19:23:28.0",
   227→        "readingTimeLocal": "2026-03-02T03:23:28.0"
   228→      },
   229→      {
   230→        "hrvValue": 52,
   231→        "readingTimeGMT": "2026-03-01T19:28:28.0",
   232→        "readingTimeLocal": "2026-03-02T03:28:28.0"
   233→      },
   234→      {
   235→        "hrvValue": 46,
   236→        "readingTimeGMT": "2026-03-01T19:33:28.0",
   237→        "readingTimeLocal": "2026-03-02T03:33:28.0"
   238→      },
   239→      {
   240→        "hrvValue": 56,
   241→        "readingTimeGMT": "2026-03-01T19:38:28.0",
   242→        "readingTimeLocal": "2026-03-02T03:38:28.0"
   243→      },
   244→      {
   245→        "hrvValue": 52,
   246→        "readingTimeGMT": "2026-03-01T19:43:28.0",
   247→        "readingTimeLocal": "2026-03-02T03:43:28.0"
   248→      },
   249→      {
   250→        "hrvValue": 49,
   251→        "readingTimeGMT": "2026-03-01T19:48:28.0",
   252→        "readingTimeLocal": "2026-03-02T03:48:28.0"
   253→      },
   254→      {
   255→        "hrvValue": 44,
   256→        "readingTimeGMT": "2026-03-01T19:53:28.0",
   257→        "readingTimeLocal": "2026-03-02T03:53:28.0"
   258→      },
   259→      {
   260→        "hrvValue": 51,
   261→        "readingTimeGMT": "2026-03-01T19:58:28.0",
   262→        "readingTimeLocal": "2026-03-02T03:58:28.0"
   263→      },
   264→      {
   265→        "hrvValue": 47,
   266→        "readingTimeGMT": "2026-03-01T20:03:28.0",
   267→        "readingTimeLocal": "2026-03-02T04:03:28.0"
   268→      },
   269→      {
   270→        "hrvValue": 46,
   271→        "readingTimeGMT": "2026-03-01T20:08:28.0",
   272→        "readingTimeLocal": "2026-03-02T04:08:28.0"
   273→      },
   274→      {
   275→        "hrvValue": 42,
   276→        "readingTimeGMT": "2026-03-01T20:13:28.0",
   277→        "readingTimeLocal": "2026-03-02T04:13:28.0"
   278→      },
   279→      {
   280→        "hrvValue": 47,
   281→        "readingTimeGMT": "2026-03-01T20:18:28.0",
   282→        "readingTimeLocal": "2026-03-02T04:18:28.0"
   283→      },
   284→      {
   285→        "hrvValue": 61,
   286→        "readingTimeGMT": "2026-03-01T20:23:28.0",
   287→        "readingTimeLocal": "2026-03-02T04:23:28.0"
   288→      },
   289→      {
   290→        "hrvValue": 54,
   291→        "readingTimeGMT": "2026-03-01T20:28:28.0",
   292→        "readingTimeLocal": "2026-03-02T04:28:28.0"
   293→      },
   294→      {
   295→        "hrvValue": 49,
   296→        "readingTimeGMT": "2026-03-01T20:33:28.0",
   297→        "readingTimeLocal": "2026-03-02T04:33:28.0"
   298→      },
   299→      {
   300→        "hrvValue": 58,
   301→        "readingTimeGMT": "2026-03-01T20:38:28.0",
   302→        "readingTimeLocal": "2026-03-02T04:38:28.0"
   303→      },
   304→      {
   305→        "hrvValue": 49,
   306→        "readingTimeGMT": "2026-03-01T20:43:28.0",
   307→        "readingTimeLocal": "2026-03-02T04:43:28.0"
   308→      },
   309→      {
   310→        "hrvValue": 47,
   311→        "readingTimeGMT": "2026-03-01T20:48:28.0",
   312→        "readingTimeLocal": "2026-03-02T04:48:28.0"
   313→      },
   314→      {
   315→        "hrvValue": 55,
   316→        "readingTimeGMT": "2026-03-01T20:53:28.0",
   317→        "readingTimeLocal": "2026-03-02T04:53:28.0"
   318→      },
   319→      {
   320→        "hrvValue": 56,
   321→        "readingTimeGMT": "2026-03-01T20:58:28.0",
   322→        "readingTimeLocal": "2026-03-02T04:58:28.0"
   323→      },
   324→      {
   325→        "hrvValue": 62,
   326→        "readingTimeGMT": "2026-03-01T21:03:28.0",
   327→        "readingTimeLocal": "2026-03-02T05:03:28.0"
   328→      },
   329→      {
   330→        "hrvValue": 92,
   331→        "readingTimeGMT": "2026-03-01T21:08:28.0",
   332→        "readingTimeLocal": "2026-03-02T05:08:28.0"
   333→      },
   334→      {
   335→        "hrvValue": 65,
   336→        "readingTimeGMT": "2026-03-01T21:13:28.0",
   337→        "readingTimeLocal": "2026-03-02T05:13:28.0"
   338→      },
   339→      {
   340→        "hrvValue": 56,
   341→        "readingTimeGMT": "2026-03-01T21:18:28.0",
   342→        "readingTimeLocal": "2026-03-02T05:18:28.0"
   343→      },
   344→      {
   345→        "hrvValue": 56,
   346→        "readingTimeGMT": "2026-03-01T21:23:28.0",
   347→        "readingTimeLocal": "2026-03-02T05:23:28.0"
   348→      },
   349→      {
   350→        "hrvValue": 57,
   351→        "readingTimeGMT": "2026-03-01T21:28:28.0",
   352→        "readingTimeLocal": "2026-03-02T05:28:28.0"
   353→      },
   354→      {
   355→        "hrvValue": 62,
   356→        "readingTimeGMT": "2026-03-01T21:33:28.0",
   357→        "readingTimeLocal": "2026-03-02T05:33:28.0"
   358→      },
   359→      {
   360→        "hrvValue": 75,
   361→        "readingTimeGMT": "2026-03-01T21:38:28.0",
   362→        "readingTimeLocal": "2026-03-02T05:38:28.0"
   363→      },
   364→      {
   365→        "hrvValue": 67,
   366→        "readingTimeGMT": "2026-03-01T21:43:28.0",
   367→        "readingTimeLocal": "2026-03-02T05:43:28.0"
   368→      },
   369→      {
   370→        "hrvValue": 67,
   371→        "readingTimeGMT": "2026-03-01T21:48:28.0",
   372→        "readingTimeLocal": "2026-03-02T05:48:28.0"
   373→      },
   374→      {
   375→        "hrvValue": 61,
   376→        "readingTimeGMT": "2026-03-01T21:53:28.0",
   377→        "readingTimeLocal": "2026-03-02T05:53:28.0"
   378→      },
   379→      {
   380→        "hrvValue": 55,
   381→        "readingTimeGMT": "2026-03-01T21:58:28.0",
   382→        "readingTimeLocal": "2026-03-02T05:58:28.0"
   383→      },
   384→      {
   385→        "hrvValue": 56,
   386→        "readingTimeGMT": "2026-03-01T22:03:28.0",
   387→        "readingTimeLocal": "2026-03-02T06:03:28.0"
   388→      },
   389→      {
   390→        "hrvValue": 45,
   391→        "readingTimeGMT": "2026-03-01T22:08:28.0",
   392→        "readingTimeLocal": "2026-03-02T06:08:28.0"
   393→      },
   394→      {
   395→        "hrvValue": 38,
   396→        "readingTimeGMT": "2026-03-01T22:13:28.0",
   397→        "readingTimeLocal": "2026-03-02T06:13:28.0"
   398→      },
   399→      {
   400→        "hrvValue": 43,
   401→        "readingTimeGMT": "2026-03-01T22:18:28.0",
   402→        "readingTimeLocal": "2026-03-02T06:18:28.0"
   403→      },
   404→      {
   405→        "hrvValue": 45,
   406→        "readingTimeGMT": "2026-03-01T22:23:28.0",
   407→        "readingTimeLocal": "2026-03-02T06:23:28.0"
   408→      },
   409→      {
   410→        "hrvValue": 54,
   411→        "readingTimeGMT": "2026-03-01T22:28:28.0",
   412→        "readingTimeLocal": "2026-03-02T06:28:28.0"
   413→      },
   414→      {
   415→        "hrvValue": 51,
   416→        "readingTimeGMT": "2026-03-01T22:33:28.0",
   417→        "readingTimeLocal": "2026-03-02T06:33:28.0"
   418→      },
   419→      {
   420→        "hrvValue": 61,
   421→        "readingTimeGMT": "2026-03-01T22:38:28.0",
   422→        "readingTimeLocal": "2026-03-02T06:38:28.0"
   423→      },
   424→      {
   425→        "hrvValue": 45,
   426→        "readingTimeGMT": "2026-03-01T22:43:28.0",
   427→        "readingTimeLocal": "2026-03-02T06:43:28.0"
   428→      },
   429→      {
   430→        "hrvValue": 54,
   431→        "readingTimeGMT": "2026-03-01T22:48:28.0",
   432→        "readingTimeLocal": "2026-03-02T06:48:28.0"
   433→      },
   434→      {
   435→        "hrvValue": 38,
   436→        "readingTimeGMT": "2026-03-01T22:53:28.0",
   437→        "readingTimeLocal": "2026-03-02T06:53:28.0"
   438→      },
   439→      {
   440→        "hrvValue": 53,
   441→        "readingTimeGMT": "2026-03-01T22:58:28.0",
   442→        "readingTimeLocal": "2026-03-02T06:58:28.0"
   443→      },
   444→      {
   445→        "hrvValue": 110,
   446→        "readingTimeGMT": "2026-03-01T23:03:28.0",
   447→        "readingTimeLocal": "2026-03-02T07:03:28.0"
   448→      },
   449→      {
   450→        "hrvValue": 45,
   451→        "readingTimeGMT": "2026-03-01T23:08:28.0",
   452→        "readingTimeLocal": "2026-03-02T07:08:28.0"
   453→      },
   454→      {
   455→        "hrvValue": 66,
   456→        "readingTimeGMT": "2026-03-01T23:13:28.0",
   457→        "readingTimeLocal": "2026-03-02T07:13:28.0"
   458→      },
   459→      {
   460→        "hrvValue": 70,
   461→        "readingTimeGMT": "2026-03-01T23:18:28.0",
   462→        "readingTimeLocal": "2026-03-02T07:18:28.0"
   463→      }
   464→    ],
   465→    "startTimestampGMT": "2026-03-01T16:01:11.0",
   466→    "endTimestampGMT": "2026-03-01T23:18:28.0",
   467→    "startTimestampLocal": "2026-03-02T00:01:11.0",
   468→    "endTimestampLocal": "2026-03-02T07:18:28.0",
   469→    "sleepStartTimestampGMT": "2026-03-01T16:01:11.0",
   470→    "sleepEndTimestampGMT": "2026-03-01T23:19:44.0",
   471→    "sleepStartTimestampLocal": "2026-03-02T00:01:11.0",
   472→    "sleepEndTimestampLocal": "2026-03-02T07:19:44.0"
   473→  }
   474→}

<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>


Now let me read the sleep JSON file with limits since it's large:


     1→{
     2→  "date": "2026-03-02",
     3→  "total_sleep_seconds": 26193,
     4→  "deep_sleep_seconds": 5880,
     5→  "light_sleep_seconds": 15780,
     6→  "rem_sleep_seconds": 4560,
     7→  "awake_seconds": 120,
     8→  "sleep_score": 88,
     9→  "sleep_levels": [
    10→    {
    11→      "startGMT": "2026-03-01T16:01:11.0",
    12→      "endGMT": "2026-03-01T16:11:11.0",
    13→      "activityLevel": 1.0
    14→    },
    15→    {
    16→      "startGMT": "2026-03-01T16:11:11.0",
    17→      "endGMT": "2026-03-01T16:57:11.0",
    18→      "activityLevel": 0.0
    19→    },
    20→    {
    21→      "startGMT": "2026-03-01T16:57:11.0",
    22→      "endGMT": "2026-03-01T17:18:11.0",
    23→      "activityLevel": 1.0
    24→    },
    25→    {
    26→      "startGMT": "2026-03-01T17:18:11.0",
    27→      "endGMT": "2026-03-01T17:26:11.0",
    28→      "activityLevel": 2.0
    29→    },
    30→    {
    31→      "startGMT": "2026-03-01T17:26:11.0",
    32→      "endGMT": "2026-03-01T17:49:11.0",
    33→      "activityLevel": 1.0
    34→    },
    35→    {
    36→      "startGMT": "2026-03-01T17:49:11.0",
    37→      "endGMT": "2026-03-01T18:14:11.0",
    38→      "activityLevel": 0.0
    39→    },
    40→    {
    41→      "startGMT": "2026-03-01T18:14:11.0",
    42→      "endGMT": "2026-03-01T19:04:11.0",
    43→      "activityLevel": 1.0
    44→    },
    45→    {
    46→      "startGMT": "2026-03-01T19:04:11.0",
    47→      "endGMT": "2026-03-01T19:19:11.0",
    48→      "activityLevel": 2.0
    49→    },
    50→    {
    51→      "startGMT": "2026-03-01T19:19:11.0",
    52→      "endGMT": "2026-03-01T19:39:11.0",
    53→      "activityLevel": 1.0
    54→    },
    55→    {
    56→      "startGMT": "2026-03-01T19:39:11.0",
    57→      "endGMT": "2026-03-01T19:51:11.0",
    58→      "activityLevel": 0.0
    59→    },
    60→    {
    61→      "startGMT": "2026-03-01T19:51:11.0",
    62→      "endGMT": "2026-03-01T20:03:11.0",
    63→      "activityLevel": 1.0
    64→    },
    65→    {
    66→      "startGMT": "2026-03-01T20:03:11.0",
    67→      "endGMT": "2026-03-01T20:18:11.0",
    68→      "activityLevel": 0.0
    69→    },
    70→    {
    71→      "startGMT": "2026-03-01T20:18:11.0",
    72→      "endGMT": "2026-03-01T20:24:11.0",
    73→      "activityLevel": 1.0
    74→    },
    75→    {
    76→      "startGMT": "2026-03-01T20:24:11.0",
    77→      "endGMT": "2026-03-01T20:48:11.0",
    78→      "activityLevel": 2.0
    79→    },
    80→    {
    81→      "startGMT": "2026-03-01T20:48:11.0",
    82→      "endGMT": "2026-03-01T21:48:11.0",
    83→      "activityLevel": 1.0
    84→    },
    85→    {
    86→      "startGMT": "2026-03-01T21:48:11.0",
    87→      "endGMT": "2026-03-01T21:50:11.0",
    88→      "activityLevel": 3.0
    89→    },
    90→    {
    91→      "startGMT": "2026-03-01T21:50:11.0",
    92→      "endGMT": "2026-03-01T22:03:11.0",
    93→      "activityLevel": 1.0
    94→    },
    95→    {
    96→      "startGMT": "2026-03-01T22:03:11.0",
    97→      "endGMT": "2026-03-01T22:22:11.0",
    98→      "activityLevel": 2.0
    99→    },
   100→    {
   101→      "startGMT": "2026-03-01T22:22:11.0",
   102→      "endGMT": "2026-03-01T22:59:11.0",
   103→      "activityLevel": 1.0
   104→    },
   105→    {
   106→      "startGMT": "2026-03-01T22:59:11.0",
   107→      "endGMT": "2026-03-01T23:09:11.0",
   108→      "activityLevel": 2.0
   109→    },
   110→    {
   111→      "startGMT": "2026-03-01T23:09:11.0",
   112→      "endGMT": "2026-03-01T23:19:44.0",
   113→      "activityLevel": 1.0
   114→    }
   115→  ],
   116→  "raw_data": {
   117→    "dailySleepDTO": {
   118→      "id": 1772380871000,
   119→      "userProfilePK": 11508432,
   120→      "calendarDate": "2026-03-02",
   121→      "sleepTimeSeconds": 26193,
   122→      "napTimeSeconds": 0,
   123→      "sleepWindowConfirmed": true,
   124→      "sleepWindowConfirmationType": "enhanced_confirmed_final",
   125→      "sleepStartTimestampGMT": 1772380871000,
   126→      "sleepEndTimestampGMT": 1772407184000,
   127→      "sleepStartTimestampLocal": 1772409671000,
   128→      "sleepEndTimestampLocal": 1772435984000,
   129→      "autoSleepStartTimestampGMT": null,
   130→      "autoSleepEndTimestampGMT": null,
   131→      "sleepQualityTypePK": null,
   132→      "sleepResultTypePK": null,
   133→      "unmeasurableSleepSeconds": 0,
   134→      "deepSleepSeconds": 5880,
   135→      "lightSleepSeconds": 15780,
   136→      "remSleepSeconds": 4560,
   137→      "awakeSleepSeconds": 120,
   138→      "deviceRemCapable": true,
   139→      "retro": false,
   140→      "sleepFromDevice": true,
   141→      "averageSpO2Value": 95.0,
   142→      "lowestSpO2Value": 84,
   143→      "highestSpO2Value": 100,
   144→      "averageSpO2HRSleep": 48.0,
   145→      "averageRespirationValue": 14.0,
   146→      "lowestRespirationValue": 9.0,
   147→      "highestRespirationValue": 19.0,
   148→      "awakeCount": 0,
   149→      "avgSleepStress": 11.0,
   150→      "ageGroup": "ADULT",

<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 search for how the analytics engine accesses these fields:


/root/projects/butler/health/analytics/weekly_report.py-60-
/root/projects/butler/health/analytics/weekly_report.py-61-        # 2. Sleep
/root/projects/butler/health/analytics/weekly_report.py-62-        lines.append("😴 *睡眠*")
health/analytics/weekly_report.py:63:        if sleep_stats.get("count", 0) > 0:
health/analytics/weekly_report.py-64-            lines.append(f"  • 平均得分：{sleep_stats['avg_score']:.0f} 分")
/root/projects/butler/health/analytics/weekly_report.py-65-            lines.append(
/root/projects/butler/health/analytics/weekly_report.py-66-                f"  • 平均时长：{self._format_duration(sleep_stats['avg_duration_sec'])}"
/root/projects/butler/health/analytics/weekly_report.py-67-            )
health/analytics/weekly_report.py:68:            if sleep_stats.get("best_date"):
/root/projects/butler/health/analytics/weekly_report.py-69-                lines.append(
health/analytics/weekly_report.py-70-                    f"  • 最佳：{sleep_stats['best_date']} ({sleep_stats['best_score']:.0f} 分)"
/root/projects/butler/health/analytics/weekly_report.py-71-                )
health/analytics/weekly_report.py:72:            if sleep_stats.get("worst_date"):
/root/projects/butler/health/analytics/weekly_report.py-73-                lines.append(
health/analytics/weekly_report.py-74-                    f"  • 最差：{sleep_stats['worst_date']} ({sleep_stats['worst_score']:.0f} 分)"
/root/projects/butler/health/analytics/weekly_report.py-75-                )
--
/root/projects/butler/health/analytics/weekly_report.py-79-
/root/projects/butler/health/analytics/weekly_report.py-80-        # 3. Steps
/root/projects/butler/health/analytics/weekly_report.py-81-        lines.append("🏃 *运动*")
health/analytics/weekly_report.py:82:        if steps_stats.get("count", 0) > 0:
health/analytics/weekly_report.py-83-            lines.append(f"  • 平均步数：{steps_stats['avg_steps']:,.0f} 步")
/root/projects/butler/health/analytics/weekly_report.py-84-            lines.append(f"  • 达标天数（≥10000步）：{steps_stats['goal_days']} 天")
health/analytics/weekly_report.py:85:            if steps_stats.get("activity_count", 0) > 0:
/root/projects/butler/health/analytics/weekly_report.py-86-                lines.append(
/root/projects/butler/health/analytics/weekly_report.py-87-                    f"  • 运动次数：{steps_stats['activity_count']} 次"
/root/projects/butler/health/analytics/weekly_report.py-88-                    + (
/root/projects/butler/health/analytics/weekly_report.py-89-                        f"（{', '.join(steps_stats['activity_types'])}）"
health/analytics/weekly_report.py:90:                        if steps_stats.get("activity_types")
/root/projects/butler/health/analytics/weekly_report.py-91-                        else ""
/root/projects/butler/health/analytics/weekly_report.py-92-                    )
/root/projects/butler/health/analytics/weekly_report.py-93-                )
--
/root/projects/butler/health/analytics/weekly_report.py-97-
/root/projects/butler/health/analytics/weekly_report.py-98-        # 4. Recovery
/root/projects/butler/health/analytics/weekly_report.py-99-        lines.append("❤ *恢复*")
health/analytics/weekly_report.py:100:        if recovery_stats.get("count", 0) > 0:
health/analytics/weekly_report.py-101-            hrv_line = f"  • 平均 HRV：{recovery_stats['avg_hrv']:.0f} ms"
health/analytics/weekly_report.py:102:            if prev_recovery.get("count", 0) > 0:
/root/projects/butler/health/analytics/weekly_report.py-103-                diff = recovery_stats["avg_hrv"] - prev_recovery["avg_hrv"]
/root/projects/butler/health/analytics/weekly_report.py-104-                sign = "+" if diff >= 0 else ""
health/analytics/weekly_report.py-105-                hrv_line += f"（{sign}{diff:.0f} vs 上周）"
/root/projects/butler/health/analytics/weekly_report.py-106-            lines.append(hrv_line)
/root/projects/butler/health/analytics/weekly_report.py-107-
health/analytics/weekly_report.py-108-            rhr_line = f"  • 平均静息心率：{recovery_stats['avg_rhr']:.0f} bpm"
health/analytics/weekly_report.py:109:            if prev_recovery.get("count", 0) > 0:
/root/projects/butler/health/analytics/weekly_report.py-110-                diff = recovery_stats["avg_rhr"] - prev_recovery["avg_rhr"]
/root/projects/butler/health/analytics/weekly_report.py-111-                sign = "+" if diff >= 0 else ""
health/analytics/weekly_report.py-112-                rhr_line += f"（{sign}{diff:.0f} vs 上周）"
--
/root/projects/butler/health/analytics/weekly_report.py-117-
/root/projects/butler/health/analytics/weekly_report.py-118-        # 5. Body Battery
/root/projects/butler/health/analytics/weekly_report.py-119-        lines.append("⚡ *Body Battery*")
health/analytics/weekly_report.py:120:        if battery_stats.get("count", 0) > 0:
health/analytics/weekly_report.py-121-            lines.append(f"  • 平均充电值：{battery_stats['avg_charged']:.0f}")
health/analytics/weekly_report.py-122-            lines.append(f"  • 平均消耗值：{battery_stats['avg_drained']:.0f}")
health/analytics/weekly_report.py-123-        else:
--
/root/projects/butler/health/analytics/weekly_report.py-126-
/root/projects/butler/health/analytics/weekly_report.py-127-        # 6. Lifestyle
/root/projects/butler/health/analytics/weekly_report.py-128-        lines.append("🥗 *生活方式*")
health/analytics/weekly_report.py:129:        if lifestyle_stats.get("has_data"):
/root/projects/butler/health/analytics/weekly_report.py-130-            lines.append(f"  • 饮酒天数：{lifestyle_stats['alcohol_days']} 天")
health/analytics/weekly_report.py:131:            if lifestyle_stats.get("fasting_modes"):
/root/projects/butler/health/analytics/weekly_report.py-132-                modes = ", ".join(
/root/projects/butler/health/analytics/weekly_report.py-133-                    f"{m}（{c}天）" for m, c in lifestyle_stats["fasting_modes"].items()
/root/projects/butler/health/analytics/weekly_report.py-134-                )
--
health/analytics/weekly_report.py-153-        if not records:
health/analytics/weekly_report.py-154-            return {"count": 0}
/root/projects/butler/health/analytics/weekly_report.py-155-
health/analytics/weekly_report.py:156:        scores = [r["overall_sleep_score"] for r in records if r.get("overall_sleep_score")]
health/analytics/weekly_report.py:157:        durations = [r["total_sleep_seconds"] for r in records if r.get("total_sleep_seconds")]
/root/projects/butler/health/analytics/weekly_report.py-158-
health/analytics/weekly_report.py-159-        if not scores:
health/analytics/weekly_report.py-160-            return {"count": 0}
/root/projects/butler/health/analytics/weekly_report.py-161-
health/analytics/weekly_report.py:162:        best = max(records, key=lambda r: r.get("overall_sleep_score", 0))
health/analytics/weekly_report.py:163:        worst = min(records, key=lambda r: r.get("overall_sleep_score", 100))
/root/projects/butler/health/analytics/weekly_report.py-164-
/root/projects/butler/health/analytics/weekly_report.py-165-        return {
health/analytics/weekly_report.py-166-            "count": len(scores),
health/analytics/weekly_report.py-167-            "avg_score": sum(scores) / len(scores),
health/analytics/weekly_report.py-168-            "avg_duration_sec": int(sum(durations) / len(durations)) if durations else 0,
health/analytics/weekly_report.py:169:            "best_date": best.get("calendar_date"),
health/analytics/weekly_report.py:170:            "best_score": best.get("overall_sleep_score", 0),
health/analytics/weekly_report.py:171:            "worst_date": worst.get("calendar_date"),
health/analytics/weekly_report.py:172:            "worst_score": worst.get("overall_sleep_score", 0),
/root/projects/butler/health/analytics/weekly_report.py-173-        }
/root/projects/butler/health/analytics/weekly_report.py-174-
health/analytics/weekly_report.py-175-    def _get_steps_stats(self, start: date, end: date) -> dict[str, Any]:
--
health/analytics/weekly_report.py-178-        if not records:
health/analytics/weekly_report.py-179-            return {"count": 0}
/root/projects/butler/health/analytics/weekly_report.py-180-
health/analytics/weekly_report.py:181:        step_values = [r["total_steps"] for r in records if r.get("total_steps") is not None]
health/analytics/weekly_report.py-182-        if not step_values:
health/analytics/weekly_report.py-183-            return {"count": 0}
/root/projects/butler/health/analytics/weekly_report.py-184-
--
/root/projects/butler/health/analytics/weekly_report.py-188-        activity_records = self.query.get_metric_range("activities", start, end)
health/analytics/weekly_report.py-189-        activity_types: list[str] = []
health/analytics/weekly_report.py-190-        for ar in activity_records:
health/analytics/weekly_report.py:191:            act_type = ar.get("activityType", {})
health/analytics/weekly_report.py-192-            if isinstance(act_type, dict):
health/analytics/weekly_report.py:193:                name = act_type.get("typeKey") or act_type.get("typeId", "")
health/analytics/weekly_report.py-194-            else:
/root/projects/butler/health/analytics/weekly_report.py-195-                name = str(act_type)
health/analytics/weekly_report.py-196-            if name and name not in activity_types:
--
/root/projects/butler/health/analytics/weekly_report.py-210-        hr_records = self.query.get_metric_range("heart_rate", start, end)
/root/projects/butler/health/analytics/weekly_report.py-211-
/root/projects/butler/health/analytics/weekly_report.py-212-        hrv_values = [
health/analytics/weekly_report.py:213:            r.get("lastNight") or r.get("hrv5MinHigh")
/root/projects/butler/health/analytics/weekly_report.py-214-            for r in hrv_records
health/analytics/weekly_report.py:215:            if r.get("lastNight") or r.get("hrv5MinHigh")
/root/projects/butler/health/analytics/weekly_report.py-216-        ]
/root/projects/butler/health/analytics/weekly_report.py-217-        rhr_values = [
health/analytics/weekly_report.py:218:            r["restingHeartRate"]
/root/projects/butler/health/analytics/weekly_report.py-219-            for r in hr_records
health/analytics/weekly_report.py:220:            if r.get("restingHeartRate")
/root/projects/butler/health/analytics/weekly_report.py-221-        ]
/root/projects/butler/health/analytics/weekly_report.py-222-
health/analytics/weekly_report.py-223-        if not hrv_values and not rhr_values:
--
health/analytics/weekly_report.py-235-        if not records:
health/analytics/weekly_report.py-236-            return {"count": 0}
/root/projects/butler/health/analytics/weekly_report.py-237-
health/analytics/weekly_report.py:238:        charged = [r["charged"] for r in records if r.get("charged") is not None]
health/analytics/weekly_report.py:239:        drained = [r["drained"] for r in records if r.get("drained") is not None]
/root/projects/butler/health/analytics/weekly_report.py-240-
health/analytics/weekly_report.py-241-        if not charged and not drained:
health/analytics/weekly_report.py-242-            return {"count": 0}
--
health/analytics/weekly_report.py-259-        fasting_modes: dict[str, int] = {}
health/analytics/weekly_report.py-260-        for log in logs:
health/analytics/weekly_report.py-261-            if log.fasting_mode:
health/analytics/weekly_report.py:262:                fasting_modes[log.fasting_mode] = fasting_modes.get(log.fasting_mode, 0) + 1
/root/projects/butler/health/analytics/weekly_report.py-263-
/root/projects/butler/health/analytics/weekly_report.py-264-        return {
health/analytics/weekly_report.py-265-            "has_data": True,
--
/root/projects/butler/health/analytics/weekly_report.py-298-        """
health/analytics/weekly_report.py-299-        highlights: list[tuple[float, str]] = []
/root/projects/butler/health/analytics/weekly_report.py-300-
health/analytics/weekly_report.py:301:        sleep = stats.get("sleep", {})
health/analytics/weekly_report.py:302:        if sleep.get("count", 0) > 0:
/root/projects/butler/health/analytics/weekly_report.py-303-            score = sleep["avg_score"]
health/analytics/weekly_report.py-304-            if score >= 85:
health/analytics/weekly_report.py-305-                highlights.append((score, f"睡眠质量优秀，平均得分 {score:.0f} 分 🌟"))
health/analytics/weekly_report.py-306-            elif score < 65:
health/analytics/weekly_report.py-307-                highlights.append((100 - score, f"睡眠质量偏低，平均得分 {score:.0f} 分，建议关注"))
/root/projects/butler/health/analytics/weekly_report.py-308-
health/analytics/weekly_report.py:309:        steps = stats.get("steps", {})
health/analytics/weekly_report.py:310:        if steps.get("count", 0) > 0:
/root/projects/butler/health/analytics/weekly_report.py-311-            goal_days = steps["goal_days"]
health/analytics/weekly_report.py-312-            if goal_days >= 5:
/root/projects/butler/health/analytics/weekly_report.py-313-                highlights.append((goal_days * 10, f"本周 {goal_days} 天达到万步目标，运动坚持度佳 💪"))
health/analytics/weekly_report.py-314-            elif goal_days == 0:
/root/projects/butler/health/analytics/weekly_report.py-315-                highlights.append((60.0, "本周没有达到万步目标，需要增加日常活动量"))
/root/projects/butler/health/analytics/weekly_report.py-316-
health/analytics/weekly_report.py:317:        recovery = stats.get("recovery", {})
health/analytics/weekly_report.py:318:        if recovery.get("count", 0) > 0 and recovery.get("avg_hrv", 0) > 0:
/root/projects/butler/health/analytics/weekly_report.py-319-            hrv = recovery["avg_hrv"]
health/analytics/weekly_report.py-320-            if hrv >= 60:
health/analytics/weekly_report.py-321-                highlights.append((hrv, f"HRV 均值 {hrv:.0f} ms，恢复状态良好 ✅"))
health/analytics/weekly_report.py-322-            elif hrv < 35:
health/analytics/weekly_report.py-323-                highlights.append((80.0, f"HRV 偏低（{hrv:.0f} ms），注意休息与压力管理"))
/root/projects/butler/health/analytics/weekly_report.py-324-
health/analytics/weekly_report.py:325:        lifestyle = stats.get("lifestyle", {})
health/analytics/weekly_report.py:326:        if lifestyle.get("has_data") and lifestyle.get("alcohol_days", 0) == 0:
/root/projects/butler/health/analytics/weekly_report.py-327-            highlights.append((50.0, "本周零饮酒，生活方式健康 🌿"))
/root/projects/butler/health/analytics/weekly_report.py-328-
health/analytics/weekly_report.py-329-        if not highlights:
--
health/analytics/engine.py-52-                    elif 'overall_sleep_score' in p: val = p['overall_sleep_score']
health/analytics/engine.py-53-                    elif 'resting_heart_rate' in p: val = p['resting_heart_rate']
health/analytics/engine.py-54-                    elif 'average_stress_level' in p: val = p['average_stress_level']
health/analytics/engine.py:55:                    elif 'charged' in p: val = p.get('charged', 0) # Body battery charged?
/root/projects/butler/health/analytics/engine.py-56-                    # Actually for body battery we usually want max or charged
/root/projects/butler/health/analytics/engine.py-57-
/root/projects/butler/health/analytics/engine.py-58-                # Store in map
health/analytics/engine.py:59:                d_str = p.get('calendar_date') or p.get('date')
health/analytics/engine.py-60-                if d_str and d_str in data_map:
/root/projects/butler/health/analytics/engine.py-61-                    data_map[d_str][metric] = val
/root/projects/butler/health/analytics/engine.py-62-
--
health/analytics/engine.py-218-            return {"error": f"Columns not found: {driver} or {target}"}
/root/projects/butler/health/analytics/engine.py-219-
/root/projects/butler/health/analytics/engine.py-220-        # Shift target
health/analytics/engine.py:221:        # lag=1 means we align Driver(T) with Target(T+1)
/root/projects/butler/health/analytics/engine.py-222-        target_col = f"target_lag_{lag}"
/root/projects/butler/health/analytics/engine.py-223-        df[target_col] = df[target].shift(-lag)
/root/projects/butler/health/analytics/engine.py-224-


Now let me look more specifically at field references in weekly_report.py for better understanding:


     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["overall_sleep_score"] for r in records if r.get("overall_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("overall_sleep_score", 0))
   163→        worst = min(records, key=lambda r: r.get("overall_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("calendar_date"),
   170→            "best_score": best.get("overall_sleep_score", 0),
   171→            "worst_date": worst.get("calendar_date"),
   172→            "worst_score": worst.get("overall_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→        # Try to get activity data for types
   188→        activity_records = self.query.get_metric_range("activities", 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.get("lastNight") or r.get("hrv5MinHigh")
   214→            for r in hrv_records
   215→            if r.get("lastNight") or r.get("hrv5MinHigh")
   216→        ]
   217→        rhr_values = [
   218→            r["restingHeartRate"]
   219→            for r in hr_records
   220→            if r.get("restingHeartRate")
   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]:

<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 I have all the information I need. Let me compile the complete field reference guide.

## Complete Health Data JSON Field Reference

Based on my analysis of the actual JSON files and the analytics engine code, here are the EXACT field names used in the butler project:

### 1. Sleep Data (`/root/projects/butler/data/health/daily_metrics/sleep/2026/03/2026-03-02.json`)

**Top-level fields:**
- `date` (string): "2026-03-02"
- `total_sleep_seconds` (int): 26193 - **Total sleep duration**
- `deep_sleep_seconds` (int): 5880
- `light_sleep_seconds` (int): 15780
- `rem_sleep_seconds` (int): 4560
- `awake_seconds` (int): 120
- `sleep_score` (int): 88 - **Sleep score**
- `sleep_levels` (array): Activity level timeline

**Nested in raw_data.dailySleepDTO:**
- `sleepTimeSeconds` (int): 26193
- `deepSleepSeconds` (int): 5880
- `lightSleepSeconds` (int): 15780
- `remSleepSeconds` (int): 4560
- `awakeSleepSeconds` (int): 120
- `averageSpO2Value` (float): 95.0
- `averageRespirationValue` (float): 14.0

**How analytics accesses it:**
- Weekly report looks for: `r["overall_sleep_score"]` and `r["total_sleep_seconds"]`
- Also uses: `r.get("calendar_date")`

### 2. Heart Rate Data (`/root/projects/butler/data/health/daily_metrics/heart_rate/2026/03/2026-03-02.json`)

**Top-level fields:**
- `date` (string): "2026-03-02"
- `resting_heart_rate` (int): 45 - **Resting HR (snake_case)**
- `min_heart_rate` (int): 44
- `max_heart_rate` (int): 104
- `raw_data` (object): Contains Garmin API response

**Nested in raw_data (camelCase from Garmin):**
- `restingHeartRate` (int): 45 - **Used by analytics**
- `minHeartRate` (int): 44
- `maxHeartRate` (int): 104
- `lastSevenDaysAvgRestingHeartRate` (int): 45
- `heartRateValues` (array): [[timestamp, value], ...] tuples

**How analytics accesses it:**
- Weekly report looks for: `r["restingHeartRate"]` (camelCase from raw_data)
- Also uses: `r.get("restingHeartRate")`

### 3. HRV Data (`/root/projects/butler/data/health/daily_metrics/hrv/2026/03/2026-03-02.json`)

**Top-level fields:**
- `date` (string): "2026-03-02"
- `hrv_value` (float): 56.0 - **HRV value (snake_case)**
- `baseline_hrv` (float): 44.0
- `status` (string): "BALANCED"
- `raw_data` (object): Garmin response

**Nested in raw_data.hrvSummary (camelCase from Garmin):**
- `lastNightAvg` (int): 56 - **Last night HRV value**
- `lastNight5MinHigh` (int): 110
- `weeklyAvg` (int): 57
- `baseline.lowUpper` (int): 44
- `baseline.balancedLow` (int): 48
- `baseline.balancedUpper` (int): 66
- `status` (string): "BALANCED"

**Nested in raw_data.hrvReadings (array):**
- Each reading has: `hrvValue`, `readingTimeGMT`, `readingTimeLocal`

**How analytics accesses it:**
- Weekly report looks for: `r.get("lastNight") or r.get("hrv5MinHigh")`
- Note: The analytics code expects camelCase `lastNight` or `hrv5MinHigh` from raw_data

### 4. Body Battery Data (`/root/projects/butler/data/health/daily_metrics/body_battery/2026/03/2026-03-02.json`)

**Top-level fields:**
- `date` (string): "2026-03-02"
- `charged` (int): 75 - **Amount charged during day**
- `drained` (int): 15 - **Amount drained during day**
- `timeline` (array): [[timestamp, level], ...] tuples
- `raw_data` (object): Garmin response

**Nested in raw_data (same fields):**
- `date` (string): "2026-03-02"
- `charged` (int): 75
- `drained` (int): 15
- `bodyBatteryValuesArray` (array): [[timestamp, level], ...]

**How analytics accesses it:**
- Weekly report looks for: `r["charged"]` and `r["drained"]`
- Also checks: `r.get("charged")` and `r.get("drained")`

### 5. Steps Data (`/root/projects/butler/data/health/daily_metrics/steps/2026/03/2026-03-02.json`)

**Top-level fields:**
- `date` (string): "2026-03-02"
- `total_steps` (int): 2243 - **Total steps**
- `total_distance_meters` (float): 1786.0
- `step_goal` (int): 10840
- `raw_data` (object): Garmin response

**Nested in raw_data (camelCase from Garmin):**
- `calendarDate` (string): "2026-03-02"
- `totalSteps` (int): 2243
- `totalDistance` (int): 1786
- `stepGoal` (int): 10840

**How analytics accesses it:**
- Weekly report looks for: `r["total_steps"]` (snake_case from top-level)
- Also checks: `r.get("total_steps")`

### Summary: Field Name Conventions

The system uses a **dual-layer approach**:

1. **Top-level (snake_case)**: User-friendly, normalized field names
   - `total_sleep_seconds`, `sleep_score`
   - `resting_heart_rate`, `total_steps`, `charged`, `drained`

2. **raw_data layer (camelCase)**: Original Garmin API response
   - `sleepTimeSeconds`, `restingHeartRate`, `totalSteps`
   - `hrvValue`, `lastNight`, `bodyBatteryLevel`

**Critical field names the analytics engine uses:**
- Sleep: `overall_sleep_score` ← **Note: This differs from actual JSON!** (actual: `sleep_score`)
- Sleep: `total_sleep_seconds`, `calendar_date`
- Heart Rate: `restingHeartRate` (camelCase from raw_data)
- HRV: `lastNight` or `hrv5MinHigh` (camelCase)
- Body Battery: `charged`, `drained`
- Steps: `total_steps` (snake_case from top-level)

### Warning: Potential Data Mismatch

The weekly_report.py references `overall_sleep_score` but the actual JSON files use `sleep_score`. This suggests either:
1. The HealthDataQuery transformation layer is renaming fields, OR
2. There's a bug in the analytics code that would return empty results

**Recommendation**: Check the `HealthDataQuery.get_metric_range()` method to see if it transforms field names when returning data.