frequenz-client-weather 0.3.1

Weather API Client for Python

Frequenz Weather API Client

Introduction

Weather API Client for Python providing access to historical and live weather forecast data.

Supported Platforms

The following platforms are officially supported (tested):

• Python: 3.11
• Operating System: Ubuntu Linux 20.04
• Architectures: amd64, arm64

Contributing

If you want to know how to build this project and contribute to it, please check out the Contributing Guide.

Usage

Installation

pip install frequenz-client-weather

Available Features

The available features are listed here.

Initialize the client

The Client can optionally be initialized with keep alive.

from frequenz.client.weather import Client
from frequenz.client.base.channel import ChannelOptions, KeepAliveOptions, SslOptions
from datetime import timedelta

client = Client(
    service_address,
    channel_defaults=ChannelOptions(
        ssl=SslOptions(
            enabled=False,
        ),
        keep_alive=KeepAliveOptions(
            enabled=True,
            timeout=timedelta(minutes=5),
            interval=timedelta(seconds=20),
        ),
    ),
)

Get historical weather forecast

from datetime import datetime
import pandas as pd
from frequenz.client.weather._types import ForecastFeature, Location

# Define a list of locations, features and a time range to request historical forecasts for
locations = [Location(latitude=46.2276, longitude=15.2137, country_code="DE")]
features = [ForecastFeature.TEMPERATURE_2_METRE, ForecastFeature.V_WIND_COMPONENT_10_METRE]
start = datetime(2024, 1, 1)
end = datetime(2024, 1, 31)

forecast_iterator = await client.stream_historical_forecast(
    features=features, locations=locations, start=start, end=end
)

# Collect and flatten forecasts
flat_forecasts = [f.flatten() async for f in forecast_iterator]
forecast_records = [record for batch in flat_forecasts for record in batch]

# E.g. convert to DataFrame and sort
forecast_df = pd.DataFrame(forecast_records).sort_values(["create_time", "valid_time", "latitude", "longitude"])
print(forecast_df)

Get live weather forecast

import pandas as pd
from frequenz.client.weather._types import ForecastFeature, Location

# Define a list of locations and features to request live forecasts for
locations = [Location(latitude=46.2276, longitude=15.2137, country_code="DE")]
features = [ForecastFeature.TEMPERATURE_2_METRE, ForecastFeature.V_WIND_COMPONENT_10_METRE]

# Returns a Receiver object that can be iterated over asynchronously
stream = await client.stream_live_forecast(
    locations=locations,
    features=features,
)

# Process incoming forecasts as they arrive
async for forecast in stream:
    # The to_ndarray_vlf method converts the forecast data to a 3D numpy array,
    # where the dimensions correspond to validity_ts, location, feature
    # The method can also take filters for validity_ts, locations and features
    # E.g. filter the forecast for wind features
    wind_forecast = forecast.to_ndarray_vlf(features=[ForecastFeature.V_WIND_COMPONENT_10_METRE])
    print(wind_forecast)

Command Line Interface

The package also provides a command line interface to get weather forecast data. Use -h to see the available options.

Get historical weather forecast

weather-cli \
    --url <service-address> \
    --location "40,15" \
    --feature U_WIND_COMPONENT_100_METRE \
    --start 2024-03-14 \
    --end 2024-03-15 \
    --mode historical

Get live weather forecast

weather-cli \
    --url <service-address> \
    --location "40, 15" \
    --feature TEMPERATURE_2_METRE \
    --mode live