### Install python-aqi and ffmpeg

Source: https://github.com/switchdoclabs/sdl_pi_weathersense/blob/main/README.md

Install necessary Python libraries and ffmpeg before proceeding with the WeatherSense Monitor setup. These are required for specific functionalities.

```bash
sudo pip3 install python-aqi
sudo pip3 install ffmpeg
```

--------------------------------

### Install mysqlclient

Source: https://github.com/switchdoclabs/sdl_pi_weathersense/blob/main/README.md

Install the mysqlclient Python package, which is required for the WeatherSense Monitor to interact with the MySQL database. Ensure python3-dev and libmysqlclient-dev are installed first.

```bash
sudo apt-get install python3-dev libmysqlclient-dev
sudo pip3 install mysqlclient
```

--------------------------------

### Initialize MariaDB database

Source: https://context7.com/switchdoclabs/sdl_pi_weathersense/llms.txt

Install required dependencies and import the SQL schema to prepare the database for sensor data.

```bash
# Install MariaDB (Raspberry Pi)
# Follow: https://pimylifeup.com/raspberry-pi-mysql/

# Install Python MySQL client
sudo apt-get install python3-dev libmysqlclient-dev
sudo pip3 install mysqlclient

# Create database and tables
sudo mysql -u root -p < WeatherSenseWireless.sql
```

--------------------------------

### Install Required Python Packages for WeatherSense

Source: https://context7.com/switchdoclabs/sdl_pi_weathersense/llms.txt

Installs essential Python libraries for the WeatherSense project, including data processing, scheduling, and dashboarding tools. Run these commands before starting the monitor.

```bash
# Install required Python packages
sudo pip3 install python-aqi
sudo pip3 install ffmpeg
sudo pip3 install SafecastPy
sudo pip3 install paho-mqtt
sudo pip3 install apscheduler
sudo pip3 install dash dash-bootstrap-components plotly
sudo pip3 install gpiozero
sudo pip3 install Pillow
```

--------------------------------

### Start WeatherSense Web Dashboard

Source: https://context7.com/switchdoclabs/sdl_pi_weathersense/llms.txt

Starts the web dashboard application in the second terminal. This provides a user interface to view weather data.

```bash
# Start the web dashboard (terminal 2)
cd ~/SDL_Pi_WeatherSenseWireless/dash_app
sudo python3 index.py
```

--------------------------------

### WeatherSense Monitor Expected Output

Source: https://context7.com/switchdoclabs/sdl_pi_weathersense/llms.txt

Example output shown when the WeatherSense monitor starts successfully, indicating the software version, scheduled jobs, and the initiation of sensor scanning.

```text
# Expected output:
# -----------------
# WeatherSense Monitoring Software
# Software Version  V018
# -----------------
# Scheduled Jobs
# -----------------
# readSensors
# cleanPictures (daily at 3:04 AM)
# cleanTimeLapses (daily at 3:10 AM)
# buildTimeLapse (daily at 5:30 AM)
# ######
# Read Wireless Sensors
# ######
# starting 433MHz scanning
```

--------------------------------

### Start WeatherSense Monitor Daemon

Source: https://context7.com/switchdoclabs/sdl_pi_weathersense/llms.txt

Starts the main WeatherSense monitoring daemon in the first terminal. This script reads sensor data and manages scheduled tasks.

```bash
# Start the sensor monitor (terminal 1)
cd ~/SDL_Pi_WeatherSenseWireless
sudo python3 WeatherSenseMonitor.py
```

--------------------------------

### Install rtl_433 SDR Software

Source: https://context7.com/switchdoclabs/sdl_pi_weathersense/llms.txt

Installs the rtl_433 software, which is necessary for decoding signals from 433 MHz sensors. This involves cloning the repository, navigating into it, and following its build instructions.

```bash
# Install rtl_433 SDR software
cd ~
git clone https://github.com/switchdoclabs/rtl_433
cd rtl_433
# Follow build instructions in repository
```

--------------------------------

### Clone rtl_433 Repository

Source: https://github.com/switchdoclabs/sdl_pi_weathersense/blob/main/README.md

Clone the rtl_433 repository from GitHub to install the necessary SDL libraries for the WeatherSense Monitor. Follow the provided installation instructions within the repository.

```bash
cd
git clone https://github.com/switchdoclabs/rtl_433
```

--------------------------------

### Run WeatherSense Dashboard App

Source: https://github.com/switchdoclabs/sdl_pi_weathersense/blob/main/README.md

Start the dashboard application for WeatherSense in a separate terminal window. This provides a web interface to view the collected data.

```bash
cd SDL_Pi_WeatherSenseWireless
cd dash_app
sudo python3 index.py
```

--------------------------------

### Access WeatherSense Dashboard

Source: https://context7.com/switchdoclabs/sdl_pi_weathersense/llms.txt

Provides the URLs to access the WeatherSense web dashboard after starting the application. It can be accessed via localhost or the Raspberry Pi's IP address.

```text
# Access dashboard at:
# http://localhost:8050/ 
# http://<raspberry-pi-ip>:8050/
```

--------------------------------

### Initialize WeatherSenseWireless Database

Source: https://github.com/switchdoclabs/sdl_pi_weathersense/blob/main/README.md

Run the WeatherSenseWireless.sql script to initialize or set up the database for the WeatherSense Monitor. This command requires root privileges and prompts for a password.

```bash
sudo mysql -u root -p < WeatherSenseWireless.sql
```

--------------------------------

### Configure WeatherSense Monitor settings

Source: https://context7.com/switchdoclabs/sdl_pi_weathersense/llms.txt

Modify the config.py file to set database credentials, MQTT broker details, and sensor recording frequencies.

```python
# config.py - Main configuration file

# Debug mode
SWDEBUG = True

# Altitude for barometric pressure calculations (meters)
altitude_m = 620

# SkyCam camera rotation settings per device ID
DefaultCameraRotation = 90
SkyCamRotationArray = {}
SkyCamRotationArray["DE45"] = 90
SkyCamRotationArray["3BAD"] = 270

# MySQL Database Configuration
enable_MySQL_Logging = True
MySQL_Host = "localhost"
MySQL_User = "root"
MySQL_Password = "your_password"
MySQL_Schema = "WeatherSenseWireless"

# Unit system: 0 = English (Fahrenheit, mph), 1 = Metric (Celsius, m/s)
English_Metric = 0

# MQTT Configuration
enable_MQTT = True
MQTThost = "localhost"
MQTTport = 1883
MQTTqos = 0

# WeatherRack2 recording frequency
# Default 20 = record every ~7.5 minutes (sensors transmit every ~45 seconds)
RecordEveryXReadings = 20

# Indoor T/H sensor recording frequency
# Default 10 = record every ~10 minutes per sensor
IndoorRecordEveryXReadings = 10
```

--------------------------------

### Run WeatherSense Monitor

Source: https://github.com/switchdoclabs/sdl_pi_weathersense/blob/main/README.md

Execute the main Python script for the WeatherSense Monitor. This command should be run from the SDL_Pi_WeatherSenseWireless directory.

```bash
cd
cd SDL_Pi_WeatherSenseWireless
sudo python3 WeatherSenseMonitor.py
```

--------------------------------

### Build Daily Timelapse Video

Source: https://context7.com/switchdoclabs/sdl_pi_weathersense/llms.txt

Creates MP4 videos from the previous day's camera pictures. It queries a MySQL database for picture timestamps and names, then copies the relevant images to a temporary directory before using ffmpeg to generate the video. Ensure the database connection details and paths are correctly configured.

```python
import datetime
import shutil
import os

DELETE_FILES_OLDER_THAN_DAYS = 14
DELETE_TIME_LAPSES_OLDER_THAN_DAYS = 14
TIME_LAPSE_START_HOUR = 5

def buildTimeLapse(source):
    """
    Build daily timelapse video from SkyCam pictures.
    Scheduled to run daily at 5:30 AM via APScheduler.

    Creates MP4 videos at 20fps from previous day's pictures (5am to 5am).
    Output: static/TimeLapses/{cameraID}/{cameraID}_(YYYY-MM-DD).mp4
    """
    # Time range: 5am yesterday to 5am today
    yesterday = datetime.date.today() - datetime.timedelta(days=1)
    today = datetime.date.today()
    starttime = datetime.datetime.combine(yesterday, datetime.time(hour=5))
    endtime = datetime.datetime.combine(today, datetime.time(hour=5))

    my_dir_path = 'static/SkyCam/'
    devices = os.listdir(my_dir_path)
    os.makedirs("static/TimeLapses", exist_ok=True)

    for device in devices:
        if config.enable_MySQL_Logging:
            con = mdb.connect(config.MySQL_Host, config.MySQL_User,
                             config.MySQL_Password, config.MySQL_Schema)
            cur = con.cursor()

            # Query pictures for the day
            query = "SELECT timestamp, cameraID, picturename FROM SkyCamPictures " \
                    "WHERE cameraID = '%s' AND timestamp >= '%s' AND timestamp < '%s' " \
                    "ORDER BY timestamp" % (device, starttime, endtime)
            cur.execute(query)
            filerecords = cur.fetchall()
            cur.close()
            con.close()

        if len(filerecords) == 0:
            print(f"device {device} has no current pictures")
            continue

        # Prepare temporary directory with numbered images
        dirpath = 'static/BuildTimeLapse'
        shutil.rmtree(dirpath, ignore_errors=True)
        os.makedirs(dirpath, exist_ok=True)

        count = 0
        for record in filerecords:
            recordname = record[2]
            # Extract date from filename for directory path
            splitName = recordname.split("_")[2].split("-")
            dayname = f"{splitName[0]}-{splitName[1]}-{splitName[2]}"

            fromPath = f"{my_dir_path}{device}/{dayname}/{recordname}"
            toPath = f"{dirpath}/pic_{count:04d}.jpg"

            try:
                shutil.copyfile(fromPath, toPath)
                count += 1
            except:
                pass

        # Generate video with ffmpeg
        cwdir = os.getcwd()
        inputFiles = f"{cwdir}/{dirpath}/pic_%04d.jpg"
        outDir = f"{cwdir}/static/TimeLapses/{device}"
        os.makedirs(outDir, exist_ok=True)
        outputFile = f"{outDir}/{device}_{dayname}.mp4"

        # Remove existing file if present
        try:
            os.remove(outputFile)
        except:
            pass

        # Create timelapse at 20fps
        command = f"/usr/bin/ffmpeg -r 20 -i {inputFiles} -c:v libx264 {outputFile}"
        os.system(f"{command} > /dev/null 2>&1")
```

--------------------------------

### Publish Sensor Data to MQTT

Source: https://context7.com/switchdoclabs/sdl_pi_weathersense/llms.txt

Publishes sensor data as a JSON string to a specified MQTT topic. Requires configuration of MQTT broker details.

```python
from paho.mqtt import publish

def mqtt_publish_single(message, topic):
    """
    Publish sensor data to MQTT broker.

    Args:
        message: JSON string of sensor data
        topic: Sub-topic name (e.g., "WeatherRack3", "WSAQI", "WSLightning")

    MQTT Topics published:
        weathersense/WeatherRack3   - Weather station data
        weathersense/WR3Power       - WeatherRack3 solar power data
        weathersense/FT020T         - WeatherRack2 weather data
        weathersense/F016TH         - Indoor T/H sensor data
        weathersense/WSAQI          - Air quality sensor data
        weathersense/WSLightning    - Lightning detector data
        weathersense/WSAfterShock   - Earthquake detector data
        weathersense/WSRadiation    - Radiation detector data
        weathersense/WSSolarMAX     - SolarMAX power monitor data
    """
    full_topic = 'weathersense/{0}'.format(topic)

    try:
        publish.single(
            topic=full_topic,
            payload=message,
            hostname=config.MQTThost,
            port=config.MQTTport,
            qos=config.MQTTqos
        )
    except Exception:
        print('MQTT broker not available')
```

--------------------------------

### Process Indoor Temperature and Humidity Sensors

Source: https://context7.com/switchdoclabs/sdl_pi_weathersense/llms.txt

Handles F016TH sensor data, including Fahrenheit to Celsius conversion and database logging with throttling.

```python
def processF016TH(sLine, ReadingCountArray):
    """
    Process indoor temperature/humidity sensor data.
    Supports up to 8 sensors on channels 1-8.

    Args:
        sLine: Raw JSON string from rtl_433
        ReadingCountArray: Array of counters per channel [ch1, ch2, ..., ch8]
    """
    var = json.loads(sLine)

    # Publish to MQTT
    if config.enable_MQTT:
        mqtt_publish_single(sLine, "F016TH")

    # Throttle database writes per channel
    chan_array_pos = var['channel'] - 1
    if (ReadingCountArray[chan_array_pos] % config.IndoorRecordEveryXReadings) != 0:
        ReadingCountArray[chan_array_pos] += 1
        return ""
    ReadingCountArray[chan_array_pos] += 1

    # Convert Fahrenheit to Celsius
    IndoorTemperature = round(((var["temperature_F"] - 32.0) / (9.0 / 5.0)), 2)

    # Store in database
    if config.enable_MySQL_Logging:
        con = mdb.connect(config.MySQL_Host, config.MySQL_User,
                         config.MySQL_Password, config.MySQL_Schema)
        cur = con.cursor()

        fields = "DeviceID, ChannelID, Temperature, Humidity, BatteryOK, TimeRead"
        values = '%d, %d, %6.2f, %6.2f, "%s", "%s"' % (
            var["device"], var["channel"], IndoorTemperature,
            var["humidity"], var["battery"], var["time"])

        query = "INSERT INTO IndoorTHSensors (%s) VALUES(%s)" % (fields, values)
        cur.execute(query)
        con.commit()
        cur.close()
        con.close()
```

--------------------------------

### Create IndoorTHSensors Table Schema

Source: https://context7.com/switchdoclabs/sdl_pi_weathersense/llms.txt

Defines the schema for the IndoorTHSensors table, which logs indoor temperature and humidity readings from specific devices and channels, including battery status.

```sql
-- Indoor temperature/humidity sensors
CREATE TABLE `IndoorTHSensors` (
  `id` int(11) NOT NULL AUTO_INCREMENT,
  `TimeStamp` timestamp NOT NULL DEFAULT current_timestamp(),
  `DeviceID` int(11) NOT NULL,
  `ChannelID` int(11) NOT NULL,
  `Temperature` float NOT NULL,
  `Humidity` int(11) NOT NULL,
  `BatteryOK` text NOT NULL,
  PRIMARY KEY (`id`)
);
```

--------------------------------

### Process WeatherSense Lightning Data

Source: https://context7.com/switchdoclabs/sdl_pi_weathersense/llms.txt

Handles data from WeatherSense ThunderBoard devices. Logs to MQTT and MySQL if enabled. Requires WeatherSense Protocol 16.

```python
def processWeatherSenseTB(sLine):
    """
    Process WeatherSense Lightning Detector (ThunderBoard) data.
    WeatherSense Protocol 16.

    Records lightning strike count, distance, and interrupt events.
    """
    state = json.loads(sLine)

    if config.enable_MQTT:
        mqtt_publish_single(sLine, "WSLightning")

    if config.enable_MySQL_Logging:
        con = mdb.connect(config.MySQL_Host, config.MySQL_User,
                         config.MySQL_Password, config.MySQL_Schema)
        cur = con.cursor()

        fields = "deviceid, irqsource, previousinterruptresult, " \
                 "lightninglastdistance, lightningcount, interruptcount, " \
                 "batteryvoltage, solarvoltage"
        values = "%d, %d, %d, %d, %d, %d, %6.2f, %6.2f" % (
            state["deviceid"], state['irqsource'],
            state['previousinterruptresult'], state['lightninglastdistance'],
            state['lightningcount'], state['interruptcount'],
            state["batteryvoltage"], state["solarpanelvoltage"])

        query = "INSERT INTO TB433MHZ (%s) VALUES(%s)" % (fields, values)
        cur.execute(query)
        con.commit()
        cur.close()
        con.close()
```

--------------------------------

### Create AQI433MHZ Table Schema

Source: https://context7.com/switchdoclabs/sdl_pi_weathersense/llms.txt

Defines the schema for the AQI433MHZ table, storing air quality index data, including PM1.0, PM2.5, PM10 concentrations, and 24-hour AQI.

```sql
-- Air Quality Index sensor data
CREATE TABLE `AQI433MHZ` (
  `ID` int(11) NOT NULL AUTO_INCREMENT,
  `TimeStamp` timestamp NOT NULL DEFAULT current_timestamp(),
  `PM1_0S` int(11) NOT NULL,
  `PM2_5S` int(11) NOT NULL,
  `PM10S` int(11) NOT NULL,
  `AQI` int(11) NOT NULL,
  `AQI24Hour` float NOT NULL,
  PRIMARY KEY (`ID`)
);
```

--------------------------------

### Retrieve and Format Current Weather Data

Source: https://context7.com/switchdoclabs/sdl_pi_weathersense/llms.txt

Fetches the latest record from the WeatherData table and calculates rain totals for the current day and the last 24 hours. It also applies unit conversions if English units are configured.

```python
import MySQLdb as mdb
import datetime

def generateCurrentWeatherJSON():
    """
    Generate JSON object with current weather readings and calculated values.

    Returns dictionary with:
        - OutdoorTemperature, OutdoorHumidity
        - IndoorTemperature, IndoorHumidity
        - WindSpeed, WindGust, WindDirection
        - BarometricPressure, BarometricPressureSeaLevel
        - SunlightVisible, SunlightUVIndex
        - AQI, AQI24Average
        - TotalRain, CalendarDayRain, CalendarMonthRain
        - 24HourRain, 7DaysRain, 30DayRain
        - All with corresponding Units suffixes
    """
    con = mdb.connect(config.MySQL_Host, config.MySQL_User,
                     config.MySQL_Password, config.MySQL_Schema)
    cur = con.cursor()

    # Get latest weather reading
    query = "SELECT * FROM `WeatherData` ORDER BY id DESC LIMIT 1"
    cur.execute(query)
    records = cur.fetchall()

    # Build JSON from column names and values
    query = "SHOW COLUMNS FROM WeatherData"
    cur.execute(query)
    names = cur.fetchall()

    CWJSON = {}
    for i, name in enumerate(names):
        if name[0] == "TimeStamp":
            CWJSON[name[0]] = records[0][i] if records else 0
        elif name[0] == "BatteryOK":
            CWJSON[name[0]] = records[0][i] if records else "LOW"
        else:
            CWJSON[name[0]] = float(records[0][i]) if records else 0

    # Calculate rain totals
    # Calendar day rain
    query = "SELECT id, TotalRain FROM WeatherData WHERE DATE(TimeStamp) = CURDATE()"
    cur.execute(query)
    rainrecords = cur.fetchall()
    if rainrecords:
        CWJSON["CalendarDayRain"] = round(rainrecords[-1][1] - rainrecords[0][1], 2)

    # Last 24 hours rain
    timeDelta = datetime.timedelta(days=1)
    before = (datetime.datetime.now() - timeDelta).strftime('%Y-%m-%d %H:%M:%S')
    query = f"SELECT id, TotalRain FROM WeatherData WHERE TimeStamp > '{before}'"
    cur.execute(query)
    rainrecords = cur.fetchall()
    if rainrecords:
        CWJSON["24HourRain"] = round(rainrecords[-1][1] - rainrecords[0][1], 2)

    # Apply unit conversions based on config
    if not config.English_Metric:  # English units
        CWJSON["OutdoorTemperature"] = round((9.0/5.0 * CWJSON["OutdoorTemperature"]) + 32, 1)
        CWJSON["WindSpeed"] = round(CWJSON["WindSpeed"] * 2.23694, 1)  # m/s to mph
        CWJSON["TotalRain"] = round(CWJSON["TotalRain"] / 25.4, 1)     # mm to inches
        CWJSON["BarometricPressureSeaLevel"] = round(
            CWJSON["BarometricPressureSeaLevel"] * 0.2953, 2)          # hPa to inHg

    cur.close()
    con.close()
    return CWJSON
```

--------------------------------

### Update WeatherSenseWireless Database

Source: https://github.com/switchdoclabs/sdl_pi_weathersense/blob/main/README.md

Execute SQL scripts to update the WeatherSenseWireless database to the latest schema version. This is crucial when updating from older database versions.

```bash
sudo mysql -u root -p WeatherSenseWireless < updateWeatherSenseWireless.sql
sudo mysql -u root -p WeatherSenseWireless < update018WeatherSenseWireless.sql
```

--------------------------------

### Create TB433MHZ Table Schema

Source: https://context7.com/switchdoclabs/sdl_pi_weathersense/llms.txt

Defines the schema for the TB433MHZ table, used to store data from a lightning detector, including last detected distance, lightning count, and interrupt count.

```sql
-- Lightning detector data
CREATE TABLE `TB433MHZ` (
  `ID` int(11) NOT NULL AUTO_INCREMENT,
  `TimeStamp` timestamp NOT NULL DEFAULT current_timestamp(),
  `lightninglastdistance` int(11) NOT NULL,
  `lightningcount` int(11) NOT NULL,
  `interruptcount` int(11) NOT NULL,
  PRIMARY KEY (`ID`)
);
```

--------------------------------

### Process WeatherRack3 Sensor Data

Source: https://context7.com/switchdoclabs/sdl_pi_weathersense/llms.txt

Parses JSON data from WeatherRack3 sensors, calculates various environmental metrics, logs to MySQL, and publishes to MQTT. Skips duplicate readings based on timestamp.

```python
import json
import aqi
import MySQLdb as mdb
from paho.mqtt import publish

def processWeatherRack3(sLine, lastWeatherRack3TimeStamp, ReadingCount):
    """
    Process WeatherRack3 weather station data from JSON SDR output.

    Args:
        sLine: Raw JSON string from rtl_433
        lastWeatherRack3TimeStamp: Previous timestamp for duplicate detection
        ReadingCount: Counter for recording frequency

    Returns:
        Updated timestamp string or empty string if duplicate
    """
    var = json.loads(sLine)

    # Publish to MQTT topic weathersense/WeatherRack3
    if config.enable_MQTT:
        mqtt_publish_single(sLine, "WeatherRack3")

    # Skip duplicate readings
    if lastWeatherRack3TimeStamp == var["time"]:
        return ""

    # Parse sensor values
    OutdoorTemperature = var["temperature"] / 10.0  # Celsius
    OutdoorHumidity = var["humidity"] / 10.0        # Percent
    WindSpeed = var["windspeed"] / 10.0             # m/s
    WindDirection = var["winddirectiondegrees"]     # Degrees
    TotalRain = var["rain"] / 10.0                  # mm
    PM2_5 = var["PM2_5"]
    PM10 = var["PM10"]
    Noise = var["noise"] / 10.0                     # dB
    BarometricPressure = var["pressure"]            # hPa

    # Calculate AQI from PM values
    myaqi = aqi.to_aqi([
        (aqi.POLLUTANT_PM25, PM2_5),
        (aqi.POLLUTANT_PM10, PM10)
    ])
    AQI = min(myaqi, 500)  # Cap at 500

    # Calculate sea level pressure using altitude
    BarometricPressureSeaLevel = (BarometricPressure * 100000) / \
        pow(1.0 - config.altitude_m / 44330.0, 5.255) / 100000

    # Store in database
    if config.enable_MySQL_Logging:
        con = mdb.connect(config.MySQL_Host, config.MySQL_User,
                         config.MySQL_Password, config.MySQL_Schema)
        cur = con.cursor()

        fields = "MessageID, SerialNumber, OutdoorTemperature, OutdoorHumidity, " \
                 "TotalRain, WindSpeed, WindDirection, Noise, BarometricPressure, " \
                 "BarometricPressureSeaLevel, AQI, PM2_5, PM10"
        values = "%d, %d, %6.2f, %6.2f, %6.2f, %6.2f, %6.2f, %6.2f, %6.2f, %6.2f, " \
                 "%6.2f, %d, %d" % (var["messageid"], var["deviceid"],
                 OutdoorTemperature, OutdoorHumidity, TotalRain, WindSpeed,
                 WindDirection, Noise, BarometricPressure, BarometricPressureSeaLevel,
                 AQI, PM2_5, PM10)

        query = "INSERT INTO WeatherRack3 (%s) VALUES(%s)" % (fields, values)
        cur.execute(query)
        con.commit()
        cur.close()
        con.close()

    return var["time"]
```

--------------------------------

### Process WeatherRack3 Data Function

Source: https://context7.com/switchdoclabs/sdl_pi_weathersense/llms.txt

Placeholder comment indicating the location of the function responsible for processing incoming data from WeatherRack3 weather stations.

```python
# wirelessSensors.py - processWeatherRack3()

```

--------------------------------

### Create RAD433MHZ Table Schema

Source: https://context7.com/switchdoclabs/sdl_pi_weathersense/llms.txt

Defines the schema for the RAD433MHZ table, storing radiation detector data, including counts per minute (CPM) and dose rates (nSVh, uSVh) over different periods.

```sql
-- Radiation detector data
CREATE TABLE `RAD433MHZ` (
  `ID` int(11) NOT NULL AUTO_INCREMENT,
  `TimeStamp` timestamp NOT NULL DEFAULT current_timestamp(),
  `CPM` int(11) NOT NULL,
  `nSVh` int(11) NOT NULL,
  `uSVh` float NOT NULL,
  `uSVh24Hour` float NOT NULL,
  PRIMARY KEY (`ID`)
);
```

--------------------------------

### Process Air Quality Index Sensors

Source: https://context7.com/switchdoclabs/sdl_pi_weathersense/llms.txt

Calculates AQI from PM2.5/PM10 values and maintains a 24-hour rolling average for database storage.

```python
import aqi

def processWeatherSenseAQI(sLine):
    """
    Process WeatherSense Air Quality Index sensor data.
    WeatherSense Protocol 15.

    Calculates AQI from PM2.5 and PM10 values and maintains
    24-hour rolling average.
    """
    state = json.loads(sLine)

    if config.enable_MQTT:
        mqtt_publish_single(sLine, "WSAQI")

    if config.enable_MySQL_Logging:
        con = mdb.connect(config.MySQL_Host, config.MySQL_User,
                         config.MySQL_Password, config.MySQL_Schema)
        cur = con.cursor()

        # Calculate 24-hour AQI average
        timeDelta = datetime.timedelta(days=1)
        before = (datetime.datetime.now() - timeDelta).strftime('%Y-%m-%d %H:%M:%S')
        query = "SELECT AQI, TimeStamp FROM AQI433MHZ WHERE " \
                "(TimeStamp > '%s') ORDER BY TimeStamp" % before
        cur.execute(query)
        myAQIRecords = cur.fetchall()

        if len(myAQIRecords) > 0:
            myAQITotal = sum(record[0] for record in myAQIRecords)
            AQI24Hour = (myAQITotal + float(state['AQI'])) / (len(myAQIRecords) + 1)
        else:
            AQI24Hour = 0.0

        # Recalculate AQI from raw PM values for accuracy
        myaqi = aqi.to_aqi([
            (aqi.POLLUTANT_PM25, state['PM2.5A']),
            (aqi.POLLUTANT_PM10, state['PM10A'])
        ])
        state['AQI'] = min(myaqi, 500)

        # Calculate power values
        batteryPower = float(state["batterycurrent"]) * float(state["batteryvoltage"])
        loadPower = float(state["loadcurrent"]) * float(state["loadvoltage"])
        solarPower = float(state["solarpanelcurrent"]) * float(state["solarpanelvoltage"])

        fields = "PM1_0S, PM2_5S, PM10S, PM1_0A, PM2_5A, PM10A, AQI, AQI24Hour, " \
                 "batteryvoltage, solarvoltage, batterypower, solarpower"
        values = "%d, %d, %d, %d, %d, %d, %d, %6.2f, %6.2f, %6.2f, %6.2f, %6.2f" % (
            state['PM1.0S'], state['PM2.5S'], state['PM10S'],
            state['PM1.0A'], state['PM2.5A'], state['PM10A'],
            state['AQI'], AQI24Hour, state["batteryvoltage"],
            state["solarpanelvoltage"], batteryPower, solarPower)

        query = "INSERT INTO AQI433MHZ (%s) VALUES(%s)" % (fields, values)
        cur.execute(query)
        con.commit()
        cur.close()
        con.close()
```

--------------------------------

### Create WeatherData Table Schema

Source: https://context7.com/switchdoclabs/sdl_pi_weathersense/llms.txt

Defines the schema for the WeatherData table, used to store readings from WeatherRack2/FT020T stations, including outdoor temperature, humidity, rain, sunlight, wind, and AQI.

```sql
-- Key database tables created by WeatherSenseWireless.sql

-- Weather station data (WeatherRack2/FT020T)
CREATE TABLE `WeatherData` (
  `ID` int(11) NOT NULL AUTO_INCREMENT,
  `TimeStamp` timestamp NOT NULL DEFAULT current_timestamp(),
  `OutdoorTemperature` float NOT NULL,
  `OutdoorHumidity` float NOT NULL,
  `TotalRain` float NOT NULL,
  `SunlightVisible` float NOT NULL,
  `SunlightUVIndex` float NOT NULL,
  `WindSpeed` float NOT NULL,
  `WindGust` float NOT NULL,
  `WindDirection` float NOT NULL,
  `AQI` float NOT NULL,
  `BatteryOK` text NOT NULL,
  PRIMARY KEY (`ID`)
);
```

--------------------------------

### Create WeatherRack3 Table Schema

Source: https://context7.com/switchdoclabs/sdl_pi_weathersense/llms.txt

Defines the schema for the WeatherRack3 table, storing advanced weather data such as temperature, humidity, rain, wind, noise, barometric pressure, and air quality (PM2.5, PM10).

```sql
-- WeatherRack3 advanced station data
CREATE TABLE `WeatherRack3` (
  `ID` int(11) NOT NULL AUTO_INCREMENT,
  `TimeStamp` timestamp NOT NULL DEFAULT current_timestamp(),
  `OutdoorTemperature` float NOT NULL,
  `OutdoorHumidity` float NOT NULL,
  `TotalRain` float NOT NULL,
  `WindSpeed` float NOT NULL,
  `WindDirection` float NOT NULL,
  `Noise` float NOT NULL,
  `BarometricPressure` float NOT NULL,
  `AQI` float NOT NULL,
  `PM2_5` int(11) NOT NULL,
  `PM10` int(11) NOT NULL,
  PRIMARY KEY (`ID`)
);
```

--------------------------------

### Calculate Wind Rose Data

Source: https://context7.com/switchdoclabs/sdl_pi_weathersense/llms.txt

Aggregates wind speed and direction into a 6x8 matrix for visualization. Requires a datetime.timedelta object to define the query window.

```python
def fetchWindData(timeDelta):
    """
    Fetch wind data for wind rose visualization.

    Args:
        timeDelta: datetime.timedelta for time range

    Returns:
        6x8 array: [speed_bucket][direction_bucket] with percentages
        Speed buckets: <1, 1-2.3, 2.3-4.4, 4.4-8.5, 8.5-11, >11 m/s
        Direction buckets: N, NE, E, SE, S, SW, W, NW
    """
    con = mdb.connect(config.MySQL_Host, config.MySQL_User,
                     config.MySQL_Password, config.MySQL_Schema)
    cur = con.cursor()

    before = (datetime.datetime.now() - timeDelta).strftime('%Y-%m-%d %H:%M:%S')
    query = f"SELECT WindSpeed, WindDirection FROM WeatherData WHERE TimeStamp > '{before}'"
    cur.execute(query)
    records = cur.fetchall()

    # Initialize 6 speed buckets x 8 direction buckets
    df = [[0]*8 for _ in range(6)]

    for windSpeed, windDirection in records:
        # Determine cardinal direction bucket (0-7)
        if windDirection >= 337.5 or windDirection < 22.5:
            CB = 0  # N
        elif windDirection < 67.5:
            CB = 1  # NE
        elif windDirection < 112.5:
            CB = 2  # E
        elif windDirection < 157.5:
            CB = 3  # SE
        elif windDirection < 202.5:
            CB = 4  # S
        elif windDirection < 247.5:
            CB = 5  # SW
        elif windDirection < 292.5:
            CB = 6  # W
        else:
            CB = 7  # NW

        # Determine speed bucket (0-5)
        if windSpeed < 1.0:
            SB = 0
        elif windSpeed < 2.3:
            SB = 1
        elif windSpeed < 4.4:
            SB = 2
        elif windSpeed < 8.5:
            SB = 3
        elif windSpeed < 11.0:
            SB = 4
        else:
            SB = 5

        df[SB][CB] += 1

    # Normalize to percentages
    totalRecords = len(records)
    if totalRecords > 0:
        for bucket in df:
            for i in range(8):
                bucket[i] = round(100.0 * bucket[i] / totalRecords, 2)

    cur.close()
    con.close()
    return df
```

--------------------------------

### Calculate Battery Percentage

Source: https://context7.com/switchdoclabs/sdl_pi_weathersense/llms.txt

Calculates remaining battery percentage from voltage, supporting both 12V lead acid and LiPo battery types.

```python
def returnPercentLeftInBattery(currentVoltage, maxVolt):
    """
    Calculate battery percentage from voltage reading.

    Args:
        currentVoltage: Current battery voltage
        maxVolt: Maximum voltage (4.2V for LiPo, 13.2V for 12V lead acid)

    Returns:
        Battery percentage (0-100)
    """
    if maxVolt > 11:
        # 12V lead acid battery (11.0V - 12.9V range)
        returnPercent = ((currentVoltage - 11.0) / 1.9) * 100.0
        return max(0.0, min(100.0, returnPercent))
    else:
        # LiPo battery with non-linear discharge curve
        scaledVolts = currentVoltage / maxVolt
        scaledVolts = min(1.0, scaledVolts)

        # Lookup table for LiPo discharge curve
        if scaledVolts > 0.9686:
            return 10 * (1 - (1.0 - scaledVolts) / (1.0 - 0.9686)) + 90
        if scaledVolts > 0.9374:
            return 10 * (1 - (0.9686 - scaledVolts) / (0.9686 - 0.9374)) + 80
        if scaledVolts > 0.9063:
            return 30 * (1 - (0.9374 - scaledVolts) / (0.9374 - 0.9063)) + 50
        if scaledVolts > 0.8749:
            return 20 * (1 - (0.8749 - scaledVolts) / (0.9063 - 0.8749)) + 11
        if scaledVolts > 0.8437:
            return 15 * (1 - (0.8437 - scaledVolts) / (0.8749 - 0.8437)) + 1
        if scaledVolts > 0.8126:
            return 7 * (1 - (0.8126 - scaledVolts) / (0.8437 - 0.8126)) + 2
        if scaledVolts > 0.7812:
            return 4 * (1 - (0.7812 - scaledVolts) / (0.8126 - 0.7812)) + 1

        return 0
```

--------------------------------

### Process SkyCam Picture Chunks

Source: https://context7.com/switchdoclabs/sdl_pi_weathersense/llms.txt

Reassembles base64-encoded image chunks into a complete file and applies a timestamp overlay using the PIL library.

```python
from PIL import ImageFont, ImageDraw, Image
import base64
import datetime
import os

CameraChunkData = {}

def saveChunk(jsonmsg):
    """
    Store incoming picture chunk from SkyCam.
    Pictures are transmitted in chunks over MQTT and reassembled.

    Args:
        jsonmsg: Dictionary with chunk data:
            - id: Camera ID (e.g., "DE45")
            - messageid: Picture message ID
            - chunknumber: Current chunk (0-indexed)
            - totalchunknumbers: Total chunks in picture
            - chunk: Base64-encoded chunk data
            - picturesize: Total file size
            - resolution: Camera resolution setting
    """
    cameraID = jsonmsg["id"]
    chunkNumber = jsonmsg["chunknumber"]
    totalChunks = jsonmsg["totalchunknumbers"]
    chunk = jsonmsg["chunk"]

    # Clear data for new picture (chunk 0)
    if chunkNumber == 0:
        try:
            del CameraChunkData[cameraID]
        except:
            pass

    # Store chunk
    try:
        currentIDList = CameraChunkData[cameraID]
    except:
        currentIDList = []

    currentIDList.append({
        'chunknumber': chunkNumber,
        'totalchunks': totalChunks,
        'picturesize': jsonmsg["picturesize"],
        'chunk': chunk,
        'resends': jsonmsg["totalchunkresends"],
        'resolution': jsonmsg["resolution"]
    })
    CameraChunkData[cameraID] = currentIDList

    # Process when all chunks received
    if int(chunkNumber) + 1 == int(totalChunks):
        processPicture(cameraID, jsonmsg["messageid"], CameraChunkData[cameraID])
        del CameraChunkData[cameraID]


def processPicture(cameraID, messageID, CameraChunkList):
    """
    Reassemble picture chunks and save with timestamp overlay.

    Directory structure:
        static/SkyCam/{cameraID}/{YYYY-MM-DD}/{cameraID}_{messageID}_{timestamp}.jpg
        static/CurrentPicture/{cameraID}.jpg  (latest picture)
    """
    fileDate = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
    fileDay = datetime.datetime.now().strftime("%Y-%m-%d")

    singlefilename = f"{cameraID}_{messageID}_{fileDate}.jpg"
    dirpathname = f"static/SkyCam/{cameraID}/{fileDay}"
    os.makedirs(dirpathname, exist_ok=True)
    filename = f"{dirpathname}/{singlefilename}"

    # Reassemble chunks into complete image
    mutable_bytes = bytearray()
    for myChunk in CameraChunkList:
        theChunk = myChunk["chunk"].encode()
        mutable_bytes += base64.b64decode(theChunk)

    # Write raw image file
    with open(filename, "wb") as f:
        f.write(mutable_bytes)

    # Process image with PIL
    pil_im = Image.open(filename)

    # Apply camera rotation from config
    try:
        myCameraRotation = config.SkyCamRotationArray[cameraID]
    except:
        myCameraRotation = config.DefaultCameraRotation
    pil_im = pil_im.rotate(myCameraRotation)

    # Add timestamp overlay
    draw = ImageDraw.Draw(pil_im)
    font = ImageFont.truetype("/usr/share/fonts/truetype/freefont/FreeSans.ttf", 25)
    myText = f"WeatherSense SkyCam-{cameraID} {datetime.datetime.now().strftime('%d-%b-%Y %H:%M:%S')}"

    # Create text background
    text_size = font.getsize(myText)
    button_size = (text_size[0] + 20, text_size[1] + 10)
    button_img = Image.new('RGBA', button_size, "black")
    button_draw = ImageDraw.Draw(button_img)
    button_draw.text((10, 5), myText, fill='rgb(255,255,255)', font=font)
    pil_im.paste(button_img, (0, 0))

    # Save processed image
    pil_im.save(filename, format='JPEG')
    pil_im.save(f"static/CurrentPicture/{cameraID}.jpg", format='JPEG')
```

--------------------------------

### Convert Temperature Units

Source: https://context7.com/switchdoclabs/sdl_pi_weathersense/llms.txt

Converts temperature between Celsius and Fahrenheit based on the global state.EnglishMetric setting.

```python
def returnTemperatureCF(temperature):
    """Convert Celsius to Fahrenheit based on state.EnglishMetric setting."""
    if state.EnglishMetric:
        return temperature  # Metric - return Celsius
    else:
        return (9.0/5.0) * temperature + 32.0  # English - return Fahrenheit
```

--------------------------------

### Convert Wind Direction Degrees

Source: https://context7.com/switchdoclabs/sdl_pi_weathersense/llms.txt

Maps wind direction in degrees to a cardinal or intercardinal direction string.

```python
def returnWindDirection(windDirection):
    """
    Convert wind direction degrees to cardinal/intercardinal direction string.

    Args:
        windDirection: Degrees (0-360, where 0/360 = North)

    Returns:
        String like "N", "NE", "E", "SE", "S", "SW", "W", "NW",
        or intercardinals like "NNW", "ESE", etc.
    """
    directions = [
        (337.5, "N"), (315.0, "NNW"), (292.5, "NW"), (270.0, "WNW"),
        (247.5, "W"), (225.0, "WSW"), (202.5, "SW"), (180.0, "SSW"),
        (157.5, "S"), (135.0, "SSE"), (112.5, "SE"), (90.0, "ESE"),
        (67.5, "E"), (45.0, "ENE"), (22.5, "NE"), (0.0, "NNE")
    ]

    for threshold, direction in directions:
        if windDirection > threshold + 1.0:
            return direction
    return "N"
```

--------------------------------

### Process WeatherSense Radiation Data

Source: https://context7.com/switchdoclabs/sdl_pi_weathersense/llms.txt

Processes data from WeatherSense Radiation Detectors using Protocol 19. Calculates 24-hour radiation averages and uploads data to external services. Requires datetime and updateWeb modules.

```python
def processWeatherSenseRadiation(sLine):
    """
    Process WeatherSense Radiation Detector data.
    WeatherSense Protocol 19.

    Records CPM (counts per minute), µSv/h radiation levels,
    calculates 24-hour average, and uploads to external services.
    """
    state = json.loads(sLine)

    if config.enable_MQTT:
        mqtt_publish_single(sLine, "WSRadiation")

    if config.enable_MySQL_Logging:
        con = mdb.connect(config.MySQL_Host, config.MySQL_User,
                         config.MySQL_Password, config.MySQL_Schema)
        cur = con.cursor()

        # Calculate 24-hour radiation average
        timeDelta = datetime.timedelta(days=1)
        before = (datetime.datetime.now() - timeDelta).strftime('%Y-%m-%d %H:%M:%S')
        query = "SELECT uSVh FROM RAD433MHZ WHERE (TimeStamp > '%s')" % before
        cur.execute(query)
        myRADRecords = cur.fetchall()

        if len(myRADRecords) > 0:
            myRADTotal = sum(record[0] for record in myRADRecords)
            RAD24Hour = (myRADTotal + float(state['uSVh'])) / (len(myRADRecords) + 1)
        else:
            RAD24Hour = 0.0

        fields = "CPM, nSVh, uSVh, uSVh24Hour, deviceid"
        values = "%d, %d, %6.2f, %6.2f, %d" % (
            state['CPM'], state['nSVh'], state['uSVh'],
            RAD24Hour, state["deviceid"])

        query = "INSERT INTO RAD433MHZ (%s) VALUES(%s)" % (fields, values)
        cur.execute(query)
        con.commit()
        cur.close()
        con.close()

        # Upload to external radiation monitoring services
        CPM = state['CPM']
        uSVh = state['uSVh']
        updateWeb.update_SafeCast(CPM, uSVh)
        updateWeb.update_RadMon(CPM)
        updateWeb.update_GMCMap(CPM, uSVh)
```

--------------------------------

### Clean Old Image Files

Source: https://context7.com/switchdoclabs/sdl_pi_weathersense/llms.txt

Removes image files from the 'static/SkyCam/' directory that are older than a specified number of days (default 14). This function helps manage disk space by deleting outdated pictures.

```python
def cleanPictures(source):
    """
    Remove pictures older than 14 days.
    Scheduled to run daily at 3:04 AM.
    """
    dir_path = 'static/SkyCam/'
    threshold = time.time() - DELETE_FILES_OLDER_THAN_DAYS * 86400

    for device in os.listdir(dir_path):
        device_dir_path = f"{dir_path}{device}/"
        for day in os.listdir(device_dir_path):
            day_dir_path = f"{device_dir_path}{day}/"
            for myFile in os.listdir(day_dir_path):
                myFilePath = f"{day_dir_path}{myFile}"
                if os.stat(myFilePath).st_ctime < threshold:
                    os.remove(myFilePath)
```

--------------------------------

### Clean Old Timelapse Videos

Source: https://context7.com/switchdoclabs/sdl_pi_weathersense/llms.txt

Removes timelapse video files from the 'static/TimeLapses/' directory that are older than a specified number of days (default 14). This function is essential for managing storage space occupied by generated videos.

```python
def cleanTimeLapses(source):
    """
    Remove timelapse videos older than 14 days.
    Scheduled to run daily at 3:10 AM.
    """
    dir_path = 'static/TimeLapses/'
    threshold = time.time() - DELETE_TIME_LAPSES_OLDER_THAN_DAYS * 86400

    for device in os.listdir(dir_path):
        device_dir_path = f"{dir_path}{device}/"
        for myFile in os.listdir(device_dir_path):
            myFilePath = f"{device_dir_path}{myFile}"
            if os.stat(myFilePath).st_ctime < threshold:
                os.remove(myFilePath)
```

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