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¤
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 = client.hist_forecast_iterator(
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(["creation_ts", "validity_ts", "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