Unit Conversions

def fahrenheit(value):
    """
    Convert Celsius to Fahrenheit.
    
    Parameters:
    - value: float, temperature in Celsius
    
    Returns:
    float, temperature in Fahrenheit rounded to 1 decimal place
    
    Formula: °F = (°C × 9/5) + 32
    """

def kelvin(value):
    """
    Convert Celsius to Kelvin.
    
    Parameters:
    - value: float, temperature in Celsius
    
    Returns:
    float, temperature in Kelvin rounded to 1 decimal place
    
    Formula: K = °C + 273.15
    """

def inches(value):
    """
    Convert millimeters to inches.
    
    Parameters:
    - value: float, precipitation in millimeters
    
    Returns:
    float, precipitation in inches rounded to 3 decimal places
    
    Formula: inches = mm / 25.4
    """

def feet(value):
    """
    Convert meters to feet.
    
    Parameters:
    - value: float, distance/elevation in meters
    
    Returns:
    float, distance/elevation in feet rounded to 1 decimal place
    
    Formula: feet = meters / 0.3048
    """

def ms(value):
    """
    Convert kilometers per hour to meters per second.
    
    Parameters:
    - value: float, wind speed in km/h
    
    Returns:
    float, wind speed in m/s rounded to 1 decimal place
    
    Formula: m/s = km/h / 3.6
    """

def mph(value):
    """
    Convert kilometers per hour to miles per hour.
    
    Parameters:
    - value: float, wind speed in km/h
    
    Returns:
    float, wind speed in mph rounded to 1 decimal place
    
    Formula: mph = km/h × 0.6214
    """

def direction(value):
    """
    Convert wind direction in degrees to cardinal direction string.
    
    Parameters:
    - value: float, wind direction in degrees (0-360)
    
    Returns:
    str, cardinal direction (N, NE, E, SE, S, SW, W, NW)
    """

def condition(value):
    """
    Convert Meteostat weather condition code to descriptive string.
    
    Parameters:
    - value: float, weather condition code (1-27)
    
    Returns:
    str, weather condition description
    
    Condition codes:
    1-4: Clear to overcast
    5-6: Fog conditions
    7-13: Rain and freezing rain
    14-16: Snow conditions  
    17-22: Shower conditions
    23-27: Thunderstorm and severe weather
    """

# Imperial unit system
imperial: dict = {
    "temp": fahrenheit,      # °C to °F
    "tavg": fahrenheit,      # Average temperature
    "tmin": fahrenheit,      # Minimum temperature
    "tmax": fahrenheit,      # Maximum temperature  
    "dwpt": fahrenheit,      # Dew point temperature
    "prcp": inches,          # mm to inches
    "snow": inches,          # Snow depth mm to inches
    "wspd": mph,             # km/h to mph
    "wpgt": mph,             # Wind gust km/h to mph
    "distance": feet         # m to feet (for station metadata)
}

# Scientific unit system  
scientific: dict = {
    "temp": kelvin,          # °C to K
    "tavg": kelvin,          # Average temperature
    "tmin": kelvin,          # Minimum temperature
    "tmax": kelvin,          # Maximum temperature
    "dwpt": kelvin,          # Dew point temperature
    "wspd": ms,              # km/h to m/s
    "wpgt": ms               # Wind gust km/h to m/s
}

from meteostat import units

# Temperature conversions
temp_c = 25.0
temp_f = units.fahrenheit(temp_c)  # 77.0°F
temp_k = units.kelvin(temp_c)      # 298.2 K

print(f"{temp_c}°C = {temp_f}°F = {temp_k} K")

# Precipitation conversion
precip_mm = 12.7
precip_in = units.inches(precip_mm)  # 0.500 inches
print(f"{precip_mm} mm = {precip_in} inches")

# Wind speed conversions
wind_kmh = 36.0
wind_ms = units.ms(wind_kmh)   # 10.0 m/s  
wind_mph = units.mph(wind_kmh) # 22.4 mph
print(f"{wind_kmh} km/h = {wind_ms} m/s = {wind_mph} mph")

from meteostat import units

# Wind direction conversion
wind_degrees = [0, 45, 90, 135, 180, 225, 270, 315, 360]
directions = [units.direction(deg) for deg in wind_degrees]
print("Wind directions:", dict(zip(wind_degrees, directions)))

# Weather condition conversion
condition_codes = [1, 5, 8, 15, 18, 25]
conditions = [units.condition(code) for code in condition_codes]
print("Weather conditions:", dict(zip(condition_codes, conditions)))

from datetime import datetime
from meteostat import Point, Daily, units

# Get daily weather data
location = Point(40.7128, -74.0060)  # New York
start = datetime(2020, 1, 1)
end = datetime(2020, 3, 31)

data = Daily(location, start, end)

# Convert to Imperial units using dictionary
imperial_data = data.convert(units.imperial)
imperial_df = imperial_data.fetch()

print("Weather data in Imperial units:")
print(imperial_df[['tavg', 'tmin', 'tmax', 'prcp', 'wspd']].head())

# Convert to Scientific units
scientific_data = data.convert(units.scientific)  
scientific_df = scientific_data.fetch()

print("Weather data in Scientific units:")
print(scientific_df[['tavg', 'tmin', 'tmax', 'wspd']].head())

from meteostat import Point, Hourly, units

# Create custom unit conversion dictionary
custom_units = {
    'temp': units.fahrenheit,    # Temperature in Fahrenheit
    'wspd': units.ms,           # Wind speed in m/s  
    'prcp': units.inches,       # Precipitation in inches
    'pres': lambda x: x * 0.02953,  # hPa to inHg (custom function)
}

# Apply custom conversions
location = Point(51.5074, -0.1278)  # London
data = Hourly(location, datetime(2020, 6, 1), datetime(2020, 6, 7))

custom_data = data.convert(custom_units)
custom_df = custom_data.fetch()

print("Custom unit combination:")
print(custom_df[['temp', 'wspd', 'prcp', 'pres']].head())

from datetime import datetime
from meteostat import Point, Hourly, units
import pandas as pd

# Get hourly data with weather conditions
location = Point(52.5200, 13.4050)  # Berlin
start = datetime(2020, 7, 1)
end = datetime(2020, 7, 31)

data = Hourly(location, start, end)
hourly_df = data.fetch()

# Convert wind direction to cardinal directions
hourly_df['wind_direction'] = hourly_df['wdir'].apply(units.direction)

# Convert condition codes to descriptions
hourly_df['weather_condition'] = hourly_df['coco'].apply(units.condition)

# Analyze weather patterns
weather_summary = hourly_df['weather_condition'].value_counts()
print("July 2020 weather conditions in Berlin:")
print(weather_summary)

wind_summary = hourly_df['wind_direction'].value_counts()
print("\nPrevailing wind directions:")
print(wind_summary)

from meteostat import Stations, units

# Get nearby stations
stations = Stations().nearby(37.7749, -122.4194, 50000)  # San Francisco

# Convert station metadata to Imperial units
imperial_stations = stations.convert({
    'elevation': units.feet,
    'distance': units.feet
})

station_data = imperial_stations.fetch()
print("Station elevations and distances in feet:")
print(station_data[['name', 'elevation', 'distance']].head())

# Precision specifications
temperature_precision = 1    # 1 decimal place (0.1°C/°F/K)
precipitation_precision = 3  # 3 decimal places (0.001 inches)
speed_precision = 1         # 1 decimal place (0.1 mph/m/s)
distance_precision = 1      # 1 decimal place (0.1 feet)

condition_codes = {
    1: "Clear",           2: "Fair",            3: "Cloudy",
    4: "Overcast",        5: "Fog",             6: "Freezing Fog", 
    7: "Light Rain",      8: "Rain",            9: "Heavy Rain",
    10: "Freezing Rain",  11: "Heavy Freezing Rain", 12: "Sleet",
    13: "Heavy Sleet",    14: "Light Snowfall", 15: "Snowfall",
    16: "Heavy Snowfall", 17: "Rain Shower",    18: "Heavy Rain Shower",
    19: "Sleet Shower",   20: "Heavy Sleet Shower", 21: "Snow Shower",
    22: "Heavy Snow Shower", 23: "Lightning",   24: "Hail",
    25: "Thunderstorm",   26: "Heavy Thunderstorm", 27: "Storm"
}

# Chain operations: aggregate then convert
data = Hourly(location, start, end)
daily_imperial = data.aggregate('D').convert(units.imperial)

# Or convert then aggregate
imperial_daily = data.convert(units.imperial).aggregate('D')

# Both approaches yield equivalent results