manager.exo.weather.get_weather_features

manager.exo.weather.get_weather_features(
    data,
    start,
    cov_end,
    forecast_horizon,
    latitude=51.5136,
    longitude=7.4653,
    timezone='UTC',
    freq='h',
    window_periods=None,
    window_functions=None,
    fallback_on_failure=True,
    cache_home=None,
    verbose=False,
)

Fetch weather data and compute rolling-window features.

Downloads weather observations/forecasts for the requested period, aligns them to a regular freq grid, and applies :class:~feature_engine.timeseries.forecasting.WindowFeatures to produce rolling-mean, -max, and -min features over configurable windows.

Parameters

Name	Type	Description	Default
data	pd.DataFrame	Reference time series DataFrame used only for validation (shape / temporal coverage checks via :func:~spotforecast2_safe.preprocessing.curate_data.curate_weather).	required
start	Union[str, pd.Timestamp]	Start of the feature window. String values are parsed with utc=True.	required
cov_end	Union[str, pd.Timestamp]	Inclusive end of the feature window (must cover the full forecast horizon beyond end). String values are parsed with utc=True.	required
forecast_horizon	int	Number of forecast steps; passed to :func:~spotforecast2_safe.preprocessing.curate_data.curate_weather for validation.	required
latitude	float	Latitude of the target location in decimal degrees. Defaults to 51.5136 (Dortmund, Germany).	51.5136
longitude	float	Longitude of the target location in decimal degrees. Defaults to 7.4653 (Dortmund, Germany).	7.4653
timezone	str	Timezone label applied to the generated index. Defaults to "UTC".	'UTC'
freq	str	Pandas-compatible frequency string for the output index. Defaults to "h" (hourly).	'h'
window_periods	Optional[List[str]]	Rolling window sizes passed to :class:~feature_engine.timeseries.forecasting.WindowFeatures. Defaults to ["1D", "7D"].	None
window_functions	Optional[List[str]]	Aggregation functions applied over each window. Defaults to ["mean", "max", "min"].	None
fallback_on_failure	bool	If True, use locally cached fallback data when the weather API is unavailable. Defaults to True.	True
cache_home	Optional[Union[str, Path]]	Optional path to cache directory. When provided, fetched weather data is cached in <cache_home>/weather_cache.parquet. When None (default), no caching is performed.	None
verbose	bool	If True, print progress messages to stdout. Defaults to False.	False

Returns

Name	Type	Description
	pd.DataFrame	tuple[pd.DataFrame, pd.DataFrame]: A two-element tuple:
	pd.DataFrame	- weather_features – DataFrame with rolling-window weather features aligned to the [start, cov_end] index.
	Tuple[pd.DataFrame, pd.DataFrame]	- weather_aligned – Raw weather DataFrame reindexed to the same [start, cov_end] hourly grid (forward-filled).

Raises

Name	Type	Description
	ValueError	If no numeric weather columns are found, or if missing values cannot be filled after fetching.

Examples:

::: {#9ef8d1e8 .cell execution_count=1}
``` {.python .cell-code}
import pandas as pd
from spotforecast2_safe.data.fetch_data import fetch_data, get_package_data_home
from spotforecast2_safe.preprocessing.curate_data import (
    agg_and_resample_data,
    get_start_end,
)
from spotforecast2_safe.manager.exo.weather import get_weather_features

# Load and resample demo data
data = fetch_data(filename=str(get_package_data_home() / "demo10.csv"))
data = agg_and_resample_data(data, verbose=False)
start, end, _, cov_end = get_start_end(data=data, forecast_horizon=24, verbose=False)

# Fetch weather features for one week of data
week_start = pd.Timestamp("2020-06-01", tz="UTC")
week_end = pd.Timestamp("2020-06-08", tz="UTC")
weather_features, weather_aligned = get_weather_features(
    data=data,
    start=week_start,
    cov_end=week_end,
    forecast_horizon=24,
    verbose=False,
)
print("weather_features shape:", weather_features.shape)
print("weather_aligned shape:", weather_aligned.shape)
```

::: {.cell-output .cell-output-stdout}
```
Weather dataframe has wrong shape.
weather_features shape: (169, 105)
weather_aligned shape: (169, 15)
```
:::
:::