def load_dataset(case="fmi", frames=14):
    """
    Load a sequence of radar composites from the pysteps example data.

    To print the available datasets run

    >>> from pysteps import datasets
    >>> datasets.info()

    This function load by default 14 composites, corresponding to a 1h and 10min
    time window.
    For example, the first two composites can be used to obtain the motion field of
    the precipitation pattern, while the remaining twelve composites can be used to
    evaluate the quality of our forecast.

    Calling this function requires the pysteps-data installed, otherwise an exception
    is raised. To install the pysteps example data check the `example_data` section.

    Parameters
    ----------
    case: str
        Case to load.
    frames: int
        Number composites (radar images).
        Max allowed value: 24 (35 for MRMS product)
        Default: 14

    Returns
    -------
    rainrate: array-like
        Precipitation data in mm/h. Dimensions: [time, lat, lon]
    metadata: dict
        The metadata observations attributes.
    timestep: number
        Time interval between composites in minutes.
    """

    case = case.lower()

    if case == "mrms":
        max_frames = 36
    else:
        max_frames = 24
    if frames > max_frames:
        raise ValueError(
            f"The number of frames should be smaller than {max_frames + 1}"
        )

    case_date = datetime.strptime(_precip_events[case], "%Y%m%d%H%M")

    source = "".join([i for i in case if not i.isdigit()])
    data_source = pysteps.rcparams.data_sources[source]

    # Find the input files from the archive
    file_names = io.archive.find_by_date(
        case_date,
        data_source["root_path"],
        data_source["path_fmt"],
        data_source["fn_pattern"],
        data_source["fn_ext"],
        data_source["timestep"],
        num_prev_files=0,
        num_next_files=frames - 1,
    )

    if None in file_names[0]:
        raise FileNotFoundError(f"Error loading {case} case. Some files are missing.")

    # Read the radar composites
    importer = io.get_method(data_source["importer"], "importer")
    importer_kwargs = data_source["importer_kwargs"]
    reflectivity, _, metadata = io.read_timeseries(
        file_names, importer, **importer_kwargs
    )

    # Convert to rain rate
    precip, metadata = conversion.to_rainrate(reflectivity, metadata)

    return precip, metadata, data_source["timestep"]
Load a sequence of radar composites from the pysteps example data.

To print the available datasets run

>>> from pysteps import datasets
>>> datasets.info()

This function load by default 14 composites, corresponding to a 1h and 10min
time window.
For example, the first two composites can be used to obtain the motion field of
the precipitation pattern, while the remaining twelve composites can be used to
evaluate the quality of our forecast.

Calling this function requires the pysteps-data installed, otherwise an exception
is raised. To install the pysteps example data check the `example_data` section.

Parameters
----------
case: str
Case to load.
frames: int
Number composites (radar images).
Max allowed value: 24 (35 for MRMS product)
Default: 14

Returns
-------
rainrate: array-like
Precipitation data in mm/h. Dimensions: [time, lat, lon]
metadata: dict
The metadata observations attributes.
timestep: number
Time interval between composites in minutes.
pysteps/datasets.py