feat: Add HR data visualization and CSV conversion scripts

.gitignore

@@ -176,3 +176,4 @@ cython_debug/
 # user
 .DS_Store
 *.parquet
+*.png

2025-06-10_16_26.CSV

@@ -0,0 +1,135 @@
+time,hr
+00:00:00,113
+00:00:00,111
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ble_forward.py

@@ -6,11 +6,11 @@ from typing import Final, Optional
 from loguru import logger
 from anyio import create_udp_socket, create_connected_udp_socket
 
-DEVICE_NAME: Final[str] = "MAX-HUB"
+DEVICE_NAME: Final[str] = "Polar Sense E4E71028"
 UDP_SERVER_HOST: Final[str] = "localhost"
 UDP_SERVER_PORT: Final[int] = 50_000
 BLE_HR_SERVICE_UUID: Final[str] = "180D"
-BLE_HR_CHARACTERISTIC_RAW_UUID: Final[str] = "ff241160-8a02-4626-b499-b1572d2b5a29"
+BLE_HR_MEASUREMENT_CHARACTERISTIC_UUID: Final[str] = "2A37"
 
 
 async def main():
@@ -45,16 +45,16 @@ async def main():
                     raise ValueError(f"Characteristic not found: {char_uuid}")
                 return char
 
-            hr_raw_char = await find_char(
-                BLE_HR_SERVICE_UUID, BLE_HR_CHARACTERISTIC_RAW_UUID
+            hr_measurement_char = await find_char(
+                BLE_HR_SERVICE_UUID, BLE_HR_MEASUREMENT_CHARACTERISTIC_UUID
             )
 
             async def on_hr_data(char: BleakGATTCharacteristic, data: bytearray):
-                logger.info("raw={}", data.hex())
+                logger.info("hr_measurement={}", data.hex())
                 await udp.send(data)
 
             logger.info("Starting notify")
-            await client.start_notify(hr_raw_char, on_hr_data)
+            await client.start_notify(hr_measurement_char, on_hr_data)
             ev = anyio.Event()
             await ev.wait()
 

convert_parquet_to_csv.py

@@ -0,0 +1,120 @@
+#!/usr/bin/env python3
+"""
+Convert parquet file to CSV with only HR field
+"""
+
+import pyarrow.parquet as pq
+import pyarrow as pa
+import pandas as pd
+import sys
+
+
+def convert_parquet_to_csv(parquet_file, output_file):
+    """Convert parquet file to CSV extracting only HR data"""
+    try:
+        # Read the parquet file
+        print(f"Reading {parquet_file}...")
+        pf = pq.ParquetFile(parquet_file)
+
+        print("Schema:")
+        print(pf.schema)
+
+        # Try to read just specific columns
+        try:
+            # Read the table with specific columns
+            table = pf.read(columns=["hr_data"])
+            print("Successfully read hr_data column")
+
+            # Convert to pandas, handling the nested structure carefully
+            df = table.to_pandas()
+            print("Converted to pandas DataFrame")
+
+        except Exception as e:
+            print(f"Error reading with pandas conversion: {e}")
+            # Try alternative approach - read raw pyarrow table
+            table = pf.read()
+            print("Read raw table successfully")
+
+            # Get hr_data column directly from pyarrow
+            hr_data_column = table.column("hr_data")
+            print(f"HR data column type: {hr_data_column.type}")
+
+            # Convert the column to a list format
+            hr_data_values = []
+            for i in range(len(hr_data_column)):
+                chunk = hr_data_column.chunk(0)
+                list_array = chunk.slice(i, 1).to_pandas().iloc[0]
+                if list_array is not None and len(list_array) > 0:
+                    hr_data_values.extend(list_array)
+
+            if hr_data_values:
+                # Create DataFrame with HR data
+                hr_df = pd.DataFrame({"HR": hr_data_values})
+
+                print(f"\nExtracted {len(hr_data_values)} HR values")
+                print("Sample HR values:")
+                print(hr_df.head(10))
+
+                # Save to CSV
+                hr_df.to_csv(output_file, index=False)
+                print(f"\nSaved HR data to {output_file}")
+                return True
+            else:
+                print("No HR data found")
+                return False
+
+        # If we got here, the pandas conversion worked
+        print("Columns available:")
+        print(df.columns.tolist())
+        print("\nData shape:", df.shape)
+
+        # Extract HR data - assuming it's in hr_data column
+        if "hr_data" in df.columns:
+            # Handle nested list structure
+            hr_values = []
+            for row_idx in range(len(df)):
+                hr_data = df["hr_data"].iloc[row_idx]
+                if hr_data is not None and len(hr_data) > 0:
+                    hr_values.extend(hr_data)
+
+            if hr_values:
+                # Create a new DataFrame with HR data
+                hr_df = pd.DataFrame({"HR": hr_values})
+
+                print(f"\nExtracted {len(hr_values)} HR values")
+                print("Sample HR values:")
+                print(hr_df.head(10))
+
+                # Save to CSV
+                hr_df.to_csv(output_file, index=False)
+                print(f"\nSaved HR data to {output_file}")
+                return True
+            else:
+                print("No HR values found in the data")
+                return False
+        else:
+            print("Error: 'hr_data' column not found in the data")
+            print("Available columns:", df.columns.tolist())
+            return False
+
+    except Exception as e:
+        print(f"Error: {e}")
+        import traceback
+
+        traceback.print_exc()
+        return False
+
+
+if __name__ == "__main__":
+    parquet_file = "history_20250610_165414.parquet"
+    output_file = "history_20250610_165414_HR.csv"
+
+    success = convert_parquet_to_csv(parquet_file, output_file)
+
+    if success:
+        print(f"\nConversion completed successfully!")
+        print(f"Input: {parquet_file}")
+        print(f"Output: {output_file}")
+    else:
+        print("Conversion failed!")
+        sys.exit(1)

history_20250610_165414_HR.csv

@@ -0,0 +1,89 @@
+HR
+120.0
+120.0
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+121.0
+121.0
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+124.0
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main.py

@@ -27,20 +27,29 @@ from pydantic import BaseModel, computed_field
 from datetime import datetime, timedelta
 import awkward as ak
 from awkward import Array as AwkwardArray, Record as AwkwardRecord
-from app.model import AlgoReport, HrPacket, hr_confidence_to_num
+from app.model import AlgoReport
 from app.utils import Instant
 from collections import deque
 from dataclasses import dataclass
+from aiomqtt import Client as MqttClient, Message as MqttMessage
+
+
+SELECT_BAND_ID = 17
+
+
+MQTT_BROKER: Final[str] = "192.168.2.189"
+MQTT_BROKER_PORT: Final[int] = 1883
+MAX_LENGTH = 600
+TOPIC: Final[str] = "GwData"
+NDArray = np.ndarray
+
+T = TypeVar("T")
 
 
 class AppHistory(TypedDict):
     timescape: deque[datetime]
     hr_data: deque[float]
     hr_conf: deque[float]  # in %
-    accel_x_data: deque[int]
-    accel_y_data: deque[int]
-    accel_z_data: deque[int]
-    pd_data: deque[int]
 
 
 # https://handmadesoftware.medium.com/streamlit-asyncio-and-mongodb-f85f77aea825
@@ -66,6 +75,62 @@ def unwrap(value: Optional[T]) -> T:
     return value
 
 
+# /**
+#  * @brief Structure of the Heart Rate Measurement characteristic
+#  *
+#  * @see https://www.bluetooth.com/specifications/gss/
+#  * @see section 3.116 Heart Rate Measurement of the document: GATT Specification Supplement.
+#  */
+# struct ble_hr_measurement_flag_t {
+# 	// LSB first
+
+# 	/*
+# 	 * 0: uint8_t
+# 	 * 1: uint16_t
+# 	 */
+# 	bool heart_rate_value_format : 1;
+# 	bool sensor_contact_detected : 1;
+# 	bool sensor_contact_supported : 1;
+# 	bool energy_expended_present : 1;
+# 	bool rr_interval_present : 1;
+# 	uint8_t reserved : 3;
+# };
+
+
+def parse_ble_hr_measurement(data: bytes) -> Optional[int]:
+    """
+    Parse BLE Heart Rate Measurement characteristic data according to Bluetooth specification.
+
+    Args:
+        data: Raw bytes from the heart rate measurement characteristic
+
+    Returns:
+        Heart rate value in BPM, or None if parsing fails
+    """
+    if len(data) < 2:
+        return None
+
+    # First byte contains flags
+    flags = data[0]
+
+    # Bit 0: Heart Rate Value Format (0 = uint8, 1 = uint16)
+    heart_rate_value_format = (flags & 0x01) != 0
+
+    try:
+        if heart_rate_value_format:
+            # 16-bit heart rate value (little endian)
+            if len(data) < 3:
+                return None
+            hr_value = int.from_bytes(data[1:3], byteorder="little")
+        else:
+            # 8-bit heart rate value
+            hr_value = data[1]
+
+        return hr_value
+    except (IndexError, ValueError):
+        return None
+
+
 @st.cache_resource
 def resource(params: Any = None):
     set_ev = anyio.Event()
@@ -87,7 +152,7 @@ def resource(params: Any = None):
     tr = Thread(target=anyio.run, args=(poll_task,))
     tr.start()
     while not set_ev.is_set():
-        sleep(0.01)
+        sleep(0.001)
     logger.info("Poll task initialized")
     state: AppState = {
         "worker_thread": tr,
@@ -97,10 +162,6 @@ def resource(params: Any = None):
             "timescape": deque(maxlen=MAX_LENGTH),
             "hr_data": deque(maxlen=MAX_LENGTH),
             "hr_conf": deque(maxlen=MAX_LENGTH),
-            "accel_x_data": deque(maxlen=MAX_LENGTH),
-            "accel_y_data": deque(maxlen=MAX_LENGTH),
-            "accel_z_data": deque(maxlen=MAX_LENGTH),
-            "pd_data": deque(maxlen=MAX_LENGTH),
         },
         "refresh_inst": Instant(),
     }
@@ -124,9 +185,6 @@ def main():
         history["timescape"].clear()
         history["hr_data"].clear()
         history["hr_conf"].clear()
-        history["accel_x_data"].clear()
-        history["accel_y_data"].clear()
-        history["accel_z_data"].clear()
 
     # https://docs.streamlit.io/develop/api-reference/layout
     st.title("MAX-BAND Visualizer")
@@ -153,16 +211,16 @@ def main():
             message = state["message_queue"].receive_nowait()
         except anyio.WouldBlock:
             continue
-        try:
-            packet = HrPacket.unmarshal(message)
-        except ValueError as e:
-            logger.error(f"bad packet: {e}")
+        hr_value = parse_ble_hr_measurement(message)
+        if hr_value is None:
+            logger.error("Failed to parse heart rate measurement data")
             continue
 
         with placeholder.container():
-            history["hr_data"].append(packet.hr)
-            history["hr_conf"].append(hr_confidence_to_num(packet.status.hr_confidence))
-            history["pd_data"].extend(packet.raw_data)
+            history["hr_data"].append(float(hr_value))
+            history["hr_conf"].append(
+                100.0
+            )  # Default confidence since we're not parsing it
             fig_hr, fig_pd = st.tabs(["Heart Rate", "PD"])
 
             with fig_hr:
@@ -182,18 +240,6 @@ def main():
                         ]
                     )
                 )
-            with fig_pd:
-                st.plotly_chart(
-                    go.Figure(
-                        data=[
-                            go.Scatter(
-                                y=list(history["pd_data"]),
-                                mode="lines",
-                                name="PD",
-                            )
-                        ]
-                    )
-                )
 
 
 if __name__ == "__main__":

main_mqtt.py

@@ -0,0 +1,234 @@
+from typing import (
+    Annotated,
+    AsyncGenerator,
+    Final,
+    Generator,
+    List,
+    Literal,
+    Optional,
+    Tuple,
+    TypeVar,
+    TypedDict,
+    Any,
+    cast,
+)
+
+from loguru import logger
+import numpy as np
+import plotly.graph_objects as go
+import streamlit as st
+import anyio
+from anyio.abc import TaskGroup
+from anyio import create_memory_object_stream
+from anyio.streams.memory import MemoryObjectSendStream, MemoryObjectReceiveStream
+from aiomqtt import Client as MqttClient, Message as MqttMessage
+from threading import Thread
+from time import sleep
+from pydantic import BaseModel, computed_field
+from datetime import datetime
+import awkward as ak
+from awkward import Array as AwkwardArray, Record as AwkwardRecord
+
+
+# https://handmadesoftware.medium.com/streamlit-asyncio-and-mongodb-f85f77aea825
+class AppState(TypedDict):
+    worker_thread: Thread
+    client: MqttClient
+    message_queue: MemoryObjectReceiveStream[MqttMessage]
+    task_group: TaskGroup
+    history: dict[str, AwkwardArray]
+
+
+MQTT_BROKER: Final[str] = "192.168.2.189"
+MQTT_BROKER_PORT: Final[int] = 1883
+MAX_LENGTH = 600
+TOPIC: Final[str] = "GwData"
+NDArray = np.ndarray
+
+T = TypeVar("T")
+
+
+def unwrap(value: Optional[T]) -> T:
+    if value is None:
+        raise ValueError("Value is None")
+    return value
+
+
+@st.cache_resource
+def resource(params: Any = None):
+    client: Optional[MqttClient] = None
+    tx, rx = create_memory_object_stream[MqttMessage]()
+    tg: Optional[TaskGroup] = None
+
+    async def main():
+        nonlocal tg
+        nonlocal client
+        tg = anyio.create_task_group()
+        async with tg:
+            client = MqttClient(MQTT_BROKER, port=MQTT_BROKER_PORT)
+            async with client:
+                await client.subscribe(TOPIC)
+                logger.info(
+                    "Subscribed {}:{} to topic {}", MQTT_BROKER, MQTT_BROKER_PORT, TOPIC
+                )
+                # https://aiomqtt.bo3hm.com/subscribing-to-a-topic.html
+                async for message in client.messages:
+                    await tx.send(message)
+
+    tr = Thread(target=anyio.run, args=(main,))
+    tr.start()
+    sleep(0.1)
+    state: AppState = {
+        "worker_thread": tr,
+        "client": unwrap(client),
+        "message_queue": rx,
+        "task_group": unwrap(tg),
+        "history": {},
+    }
+    return state
+
+
+class GwMessage(TypedDict):
+    v: int
+    mid: int
+    time: int
+    ip: str
+    mac: str
+    devices: list[Any]
+    rssi: int
+
+
+class DeviceMessage(BaseModel):
+    mac: str
+    """
+    Hex string, capital letters, e.g. "D6AF1CA9C491"
+    """
+    service: str
+    """
+    Hex string, capital letters, e.g. "180D"
+    """
+    characteristic: str
+    """
+    Hex string, capital letters, e.g. "2A37"
+    """
+    value: str
+    """
+    Hex string, capital letters, e.g. "0056"
+    """
+    rssi: int
+
+    @property
+    def value_bytes(self) -> bytes:
+        return bytes.fromhex(self.value)
+
+
+def get_device_data(message: GwMessage) -> List[DeviceMessage]:
+    """
+    devices
+
+    [[5,"D6AF1CA9C491","180D","2A37","0056",-58],[5,"A09E1AE4E710","180D","2A37","0055",-50]]
+
+    unknown, mac addr, service, characteristic, value (hex), rssi
+    """
+    l: list[DeviceMessage] = []
+    for d in message["devices"]:
+        x, mac, service, characteristic, value, rssi = d
+        l.append(
+            DeviceMessage(
+                mac=mac,
+                service=service,
+                characteristic=characteristic,
+                value=value,
+                rssi=rssi,
+            )
+        )
+    return l
+
+
+def payload_to_hr(payload: bytes) -> int:
+    """
+    ignore the first byte, parse the rest as a big-endian integer
+
+    Bit 0 (Heart Rate Format)
+        0: Heart rate value is 8 bits
+        1: Heart rate value is 16 bits
+    Bit 3 (Energy Expended)
+        Indicates whether energy expended data is present
+    Bit 4 (RR Interval)
+        Indicates whether RR interval data is present
+    """
+    flags = payload[0]
+    if flags & 0b00000001:
+        return int.from_bytes(payload[1:3], "big")
+    else:
+        return payload[1]
+
+
+def main():
+    state = resource()
+    logger.info("Resource created")
+    history = state["history"]
+
+    def push_new_message(message: GwMessage):
+        dms = get_device_data(message)
+        now = datetime.now()
+        for dm in dms:
+            rec = AwkwardRecord(
+                {
+                    "time": now,
+                    "value": payload_to_hr(dm.value_bytes),
+                    "rssi": dm.rssi,
+                }
+            )
+            if dm.mac not in history:
+                history[dm.mac] = AwkwardArray([rec])
+            else:
+                history[dm.mac] = ak.concatenate([history[dm.mac], [rec]])
+                if len(history[dm.mac]) > MAX_LENGTH:
+                    history[dm.mac] = AwkwardArray(history[dm.mac][-MAX_LENGTH:])
+
+    def on_export():
+        now = datetime.now()
+        filename = f"export-{now.strftime('%Y-%m-%d-%H-%M-%S')}.parquet"
+        ak.to_parquet([history], filename)
+        logger.info("Export to {}", filename)
+
+    def on_clear():
+        history.clear()
+        logger.info("History cleared")
+
+    st.button(
+        "Export", help="Export the current data to a parquet file", on_click=on_export
+    )
+    st.button("Clear", help="Clear the current data", on_click=on_clear)
+    pannel = st.empty()
+    while True:
+        try:
+            message = state["message_queue"].receive_nowait()
+        except anyio.WouldBlock:
+            continue
+        m: str
+        if isinstance(message.payload, str):
+            m = message.payload
+        elif isinstance(message.payload, bytes):
+            m = message.payload.decode("utf-8")
+        else:
+            logger.warning("Unknown message type: {}", type(message.payload))
+            continue
+        d = cast(GwMessage, orjson.loads(m))
+        push_new_message(d)
+
+        def to_scatter(key: str, dev_history: AwkwardArray):
+            x = ak.to_numpy(dev_history["time"])
+            y = ak.to_numpy(dev_history["value"])
+            return go.Scatter(x=x, y=y, mode="lines+markers", name=key)
+
+        scatters = [to_scatter(k, el) for k, el in history.items()]
+        fig = go.Figure(scatters)
+        pannel.plotly_chart(fig)
+
+
+if __name__ == "__main__":
+    main()
+
+# 1659A202

pyproject.toml

@@ -5,14 +5,18 @@ description = "python scripts for testing embedded system"
 readme = "README.md"
 requires-python = ">=3.12"
 dependencies = [
+    "aiomqtt>=2.4.0",
     "anyio>=4.9.0",
     "awkward>=2.8.3",
     "betterproto[compiler]>=2.0.0b7",
+    "bleak>=0.22.3",
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     "loguru>=0.7.3",
+    "matplotlib>=3.10.3",
     "paho-mqtt>=2.1.0",
     "plotly>=6.1.2",
     "pydantic>=2.11.5",
+    "seaborn>=0.13.2",
     "streamlit>=1.45.1",
 ]
 

uv.lock

@@ -2,6 +2,18 @@ version = 1
 revision = 2
 requires-python = ">=3.12"
 
+[[package]]
+name = "aiomqtt"
+version = "2.4.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "paho-mqtt" },
+]
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 name = "altair"
 version = "5.5.0"
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+    { name = "pyobjc-framework-libdispatch", marker = "sys_platform == 'darwin'" },
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+    { name = "winrt-windows-devices-bluetooth", marker = "sys_platform == 'win32'" },
+    { name = "winrt-windows-devices-bluetooth-advertisement", marker = "sys_platform == 'win32'" },
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+    { name = "winrt-windows-devices-enumeration", marker = "sys_platform == 'win32'" },
+    { name = "winrt-windows-foundation", marker = "sys_platform == 'win32'" },
+    { name = "winrt-windows-foundation-collections", marker = "sys_platform == 'win32'" },
+    { name = "winrt-windows-storage-streams", marker = "sys_platform == 'win32'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/fb/96/15750b50c0018338e2cce30de939130971ebfdf4f9d6d56c960f5657daad/bleak-0.22.3.tar.gz", hash = "sha256:3149c3c19657e457727aa53d9d6aeb89658495822cd240afd8aeca4dd09c045c", size = 122339, upload-time = "2024-10-05T21:21:00.661Z" }
+wheels = [
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 version = "1.9.0"
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+]

visualize_hr_data.py

@@ -0,0 +1,216 @@
+#!/usr/bin/env python3
+"""
+Visualize HR data from two CSV files with max value alignment
+"""
+
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+from datetime import datetime, timedelta
+import seaborn as sns
+
+
+def load_and_visualize_hr_data():
+    """Load and visualize HR data from both CSV files with max value alignment"""
+
+    # Load the first CSV file (with timestamps)
+    print("Loading 2025-06-10_16_26.CSV...")
+    df1 = pd.read_csv("2025-06-10_16_26.CSV")
+    print(f"Dataset 1 shape: {df1.shape}")
+    print(f"Dataset 1 columns: {df1.columns.tolist()}")
+    print(f"Dataset 1 HR range: {df1['hr'].min():.1f} - {df1['hr'].max():.1f}")
+
+    # Load the second CSV file (HR only)
+    print("\nLoading history_20250610_165414_HR.csv...")
+    df2 = pd.read_csv("history_20250610_165414_HR.csv")
+    print(f"Dataset 2 shape: {df2.shape}")
+    print(f"Dataset 2 columns: {df2.columns.tolist()}")
+    print(f"Dataset 2 HR range: {df2['HR'].min():.1f} - {df2['HR'].max():.1f}")
+
+    # Find the maximum values and their indices
+    max_hr1 = df1["hr"].max()
+    max_idx1 = df1["hr"].idxmax()
+    max_hr2 = df2["HR"].max()
+    max_idx2 = df2["HR"].idxmax()
+
+    print(f"\nDataset 1 max HR: {max_hr1:.1f} at index {max_idx1}")
+    print(f"Dataset 2 max HR: {max_hr2:.1f} at index {max_idx2}")
+
+    # Create time indices for both datasets
+    df1["time_seconds"] = df1.index  # Use index as time in seconds
+    df2["time_seconds"] = df2.index  # Use index as time in seconds
+
+    # Align datasets by shifting so max values occur at the same time
+    # We'll align both to time = 0 at their respective max points
+    df1["time_aligned"] = df1["time_seconds"] - max_idx1
+    df2["time_aligned"] = df2["time_seconds"] - max_idx2
+
+    print(f"\nAfter alignment:")
+    print(
+        f"Dataset 1 time range: {df1['time_aligned'].min():.1f} to {df1['time_aligned'].max():.1f} seconds"
+    )
+    print(
+        f"Dataset 2 time range: {df2['time_aligned'].min():.1f} to {df2['time_aligned'].max():.1f} seconds"
+    )
+
+    # Create the visualization
+    plt.style.use("seaborn-v0_8")
+    fig, axes = plt.subplots(2, 2, figsize=(15, 10))
+    fig.suptitle(
+        "Heart Rate Data Visualization (Max Values Aligned)",
+        fontsize=16,
+        fontweight="bold",
+    )
+
+    # Plot 1: Time series of both datasets (aligned)
+    axes[0, 0].plot(
+        df1["time_aligned"],
+        df1["hr"],
+        "o-",
+        alpha=0.7,
+        markersize=3,
+        label=f"Dataset 1 (max: {max_hr1:.1f})",
+        color="blue",
+    )
+    axes[0, 0].plot(
+        df2["time_aligned"],
+        df2["HR"],
+        "s-",
+        alpha=0.7,
+        markersize=3,
+        label=f"Dataset 2 (max: {max_hr2:.1f})",
+        color="red",
+    )
+
+    # Highlight the max values
+    axes[0, 0].axvline(
+        x=0, color="black", linestyle="--", alpha=0.5, label="Max values aligned"
+    )
+    axes[0, 0].scatter([0], [max_hr1], color="blue", s=100, zorder=5, marker="*")
+    axes[0, 0].scatter([0], [max_hr2], color="red", s=100, zorder=5, marker="*")
+
+    axes[0, 0].set_xlabel("Time (seconds, aligned to max)")
+    axes[0, 0].set_ylabel("Heart Rate (bpm)")
+    axes[0, 0].set_title("Heart Rate Over Time (Aligned at Max Values)")
+    axes[0, 0].legend()
+    axes[0, 0].grid(True, alpha=0.3)
+
+    # Plot 2: Distribution comparison
+    axes[0, 1].hist(
+        df1["hr"], bins=20, alpha=0.7, label="Dataset 1", color="blue", density=True
+    )
+    axes[0, 1].hist(
+        df2["HR"], bins=20, alpha=0.7, label="Dataset 2", color="red", density=True
+    )
+    axes[0, 1].axvline(
+        max_hr1, color="blue", linestyle="--", alpha=0.8, label=f"Max 1: {max_hr1:.1f}"
+    )
+    axes[0, 1].axvline(
+        max_hr2, color="red", linestyle="--", alpha=0.8, label=f"Max 2: {max_hr2:.1f}"
+    )
+    axes[0, 1].set_xlabel("Heart Rate (bpm)")
+    axes[0, 1].set_ylabel("Density")
+    axes[0, 1].set_title("Heart Rate Distribution")
+    axes[0, 1].legend()
+    axes[0, 1].grid(True, alpha=0.3)
+
+    # Plot 3: Box plot comparison
+    data_for_box = [df1["hr"], df2["HR"]]
+    labels = ["Dataset 1\n(2025-06-10_16_26)", "Dataset 2\n(history_20250610_165414)"]
+    bp = axes[1, 0].boxplot(data_for_box, tick_labels=labels, patch_artist=True)
+    bp["boxes"][0].set_facecolor("lightblue")
+    bp["boxes"][1].set_facecolor("lightcoral")
+    axes[1, 0].set_ylabel("Heart Rate (bpm)")
+    axes[1, 0].set_title("Heart Rate Distribution Comparison")
+    axes[1, 0].grid(True, alpha=0.3)
+
+    # Plot 4: Overlay plot around max values (zoomed in view)
+    # Show ±20 seconds around the aligned max values
+    window = 20
+    df1_window = df1[(df1["time_aligned"] >= -window) & (df1["time_aligned"] <= window)]
+    df2_window = df2[(df2["time_aligned"] >= -window) & (df2["time_aligned"] <= window)]
+
+    if len(df1_window) > 0:
+        axes[1, 1].plot(
+            df1_window["time_aligned"],
+            df1_window["hr"],
+            "o-",
+            alpha=0.8,
+            markersize=4,
+            label=f"Dataset 1",
+            color="blue",
+            linewidth=2,
+        )
+    if len(df2_window) > 0:
+        axes[1, 1].plot(
+            df2_window["time_aligned"],
+            df2_window["HR"],
+            "s-",
+            alpha=0.8,
+            markersize=4,
+            label=f"Dataset 2",
+            color="red",
+            linewidth=2,
+        )
+
+    axes[1, 1].axvline(
+        x=0, color="black", linestyle="--", alpha=0.5, label="Max alignment"
+    )
+    axes[1, 1].scatter(
+        [0], [max_hr1], color="blue", s=150, zorder=5, marker="*", edgecolor="black"
+    )
+    axes[1, 1].scatter(
+        [0], [max_hr2], color="red", s=150, zorder=5, marker="*", edgecolor="black"
+    )
+
+    axes[1, 1].set_xlabel("Time (seconds, aligned to max)")
+    axes[1, 1].set_ylabel("Heart Rate (bpm)")
+    axes[1, 1].set_title(f"Zoomed View Around Max Values (±{window}s)")
+    axes[1, 1].legend()
+    axes[1, 1].grid(True, alpha=0.3)
+
+    plt.tight_layout()
+
+    # Save the plot
+    output_file = "hr_data_visualization_aligned.png"
+    plt.savefig(output_file, dpi=300, bbox_inches="tight")
+    print(f"\nVisualization saved as: {output_file}")
+
+    # Show the plot
+    plt.show()
+
+    # Print detailed statistics
+    print("\n" + "=" * 60)
+    print("DETAILED STATISTICS (MAX VALUES ALIGNED)")
+    print("=" * 60)
+    print("\nDataset 1 (2025-06-10_16_26.CSV):")
+    print(f"  Records: {len(df1)}")
+    print(f"  Mean HR: {df1['hr'].mean():.1f} bpm")
+    print(f"  Median HR: {df1['hr'].median():.1f} bpm")
+    print(f"  Max HR: {max_hr1:.1f} bpm (at original index {max_idx1})")
+    print(f"  Std Dev: {df1['hr'].std():.1f} bpm")
+    print(f"  Range: {df1['hr'].min():.1f} - {df1['hr'].max():.1f} bpm")
+
+    print("\nDataset 2 (history_20250610_165414_HR.csv):")
+    print(f"  Records: {len(df2)}")
+    print(f"  Mean HR: {df2['HR'].mean():.1f} bpm")
+    print(f"  Median HR: {df2['HR'].median():.1f} bpm")
+    print(f"  Max HR: {max_hr2:.1f} bpm (at original index {max_idx2})")
+    print(f"  Std Dev: {df2['HR'].std():.1f} bpm")
+    print(f"  Range: {df2['HR'].min():.1f} - {df2['HR'].max():.1f} bpm")
+
+    print(f"\nAlignment Info:")
+    print(f"  Max HR difference: {abs(max_hr1 - max_hr2):.1f} bpm")
+    print(
+        f"  Time shift applied: Dataset 1 shifted by -{max_idx1}s, Dataset 2 shifted by -{max_idx2}s"
+    )
+
+    print("\n" + "=" * 60)
+
+    return df1, df2
+
+
+if __name__ == "__main__":
+    print("Starting HR data visualization with max value alignment...")
+    df1, df2 = load_and_visualize_hr_data()
+    print("Visualization complete!")