How to use the eemeter.features.merge_features function in eemeter

def occupancy_precursor_only_nan(il_electricity_cdd_hdd_hourly):
    meter_data = il_electricity_cdd_hdd_hourly["meter_data"]
    meter_data = meter_data[datetime(2017, 1, 4) : datetime(2017, 6, 1)]
    meter_data.iloc[-1] = np.nan
    # Simulates a segment where there is only a single nan value
    temperature_data = il_electricity_cdd_hdd_hourly["temperature_data"]
    time_features = compute_time_features(meter_data.index)
    temperature_features = compute_temperature_features(
        meter_data.index,
        temperature_data,
        heating_balance_points=[50],
        cooling_balance_points=[65],
        degree_day_method="hourly",
    )
    return merge_features(
        [meter_data.value.to_frame("meter_value"), temperature_features, time_features]
    )

def test_merge_features():
    index = pd.date_range("2017-01-01", periods=100, freq="H", tz="UTC")
    features = merge_features(
        [
            pd.Series(1, index=index, name="a"),
            pd.DataFrame({"b": 2}, index=index),
            pd.DataFrame({"c": 3, "d": 4}, index=index),
        ]
    )
    assert list(features.columns) == ["a", "b", "c", "d"]
    assert features.shape == (100, 4)
    assert features.sum().sum() == 1000
    assert features.a.sum() == 100
    assert features.b.sum() == 200
    assert features.c.sum() == 300
    assert features.d.sum() == 400
    assert features.index[0] == index[0]
    assert features.index[-1] == index[-1]

def occupancy_precursor(il_electricity_cdd_hdd_hourly):
    meter_data = il_electricity_cdd_hdd_hourly["meter_data"]
    temperature_data = il_electricity_cdd_hdd_hourly["temperature_data"]
    time_features = compute_time_features(meter_data.index)
    temperature_features = compute_temperature_features(
        meter_data.index,
        temperature_data,
        heating_balance_points=[50],
        cooling_balance_points=[65],
        degree_day_method="hourly",
    )
    return merge_features(
        [meter_data.value.to_frame("meter_value"), temperature_features, time_features]
    )

def test_merge_features_empty_raises():
    with pytest.raises(ValueError):
        features = merge_features([])

occupancy = occupancy_lookup[segment_name]
    occupancy_feature = compute_occupancy_feature(
        hour_of_week_feature.hour_of_week, occupancy
    )

    # get temperature bin features
    temperatures = segmented_data
    bin_endpoints_list = (
        temperature_bins[segment_name].index[temperature_bins[segment_name]].tolist()
    )
    temperature_bin_features = compute_temperature_bin_features(
        segmented_data.temperature_mean, bin_endpoints_list
    )

    # combine features
    return merge_features(
        [
            hour_of_week_feature,
            occupancy_feature,
            temperature_bin_features,
            segmented_data.weight,
        ]

if temperature_file is not None:
        gzipped = temperature_file.name.endswith(".gz")
        temperature_data = temperature_data_from_csv(
            temperature_file, gzipped=gzipped, freq="hourly"
        )
    else:
        raise click.ClickException("Temperature data not specified.")

    usage_per_day = compute_usage_per_day_feature(meter_data)
    temperature_features = compute_temperature_features(
        meter_data.index,
        temperature_data,
        heating_balance_points=heating_balance_points,
        cooling_balance_points=cooling_balance_points,
    )
    return merge_features([usage_per_day, temperature_features])

# get occupied feature
    hour_of_week = segmented_data.hour_of_week
    occupancy = occupancy_lookup[segment_name]
    occupancy_feature = compute_occupancy_feature(hour_of_week, occupancy)

    # get temperature bin features
    temperatures = segmented_data.temperature_mean
    bin_endpoints_list = (
        temperature_bins[segment_name].index[temperature_bins[segment_name]].tolist()
    )
    temperature_bin_features = compute_temperature_bin_features(
        segmented_data.temperature_mean, bin_endpoints_list
    )

    # combine features
    return merge_features(
        [
            segmented_data[["meter_value", "hour_of_week"]],
            occupancy_feature,
            temperature_bin_features,
            segmented_data.weight,
        ]

Returns
    -------
    design_matrix : :any:`pandas.DataFrame`
        A design matrix with meter_value, hour_of_week, hdd_50, and cdd_65 features.
    """
    time_features = compute_time_features(
        meter_data.index, hour_of_week=True, hour_of_day=False, day_of_week=False
    )
    temperature_features = compute_temperature_features(
        meter_data.index,
        temperature_data,
        heating_balance_points=[50],
        cooling_balance_points=[65],
        degree_day_method="hourly",
    )
    design_matrix = merge_features(
        [meter_data.value.to_frame("meter_value"), temperature_features, time_features]
    )
    return design_matrix

hour_of_week = segmented_data.hour_of_week
    occupancy = occupancy_lookup[segment_name]
    occupancy_feature = compute_occupancy_feature(hour_of_week, occupancy)

    # get temperature bin features
    temperatures = segmented_data.temperature_mean
    bin_endpoints_list = (
        temperature_bins[segment_name].index[temperature_bins[segment_name]].tolist()
    )
    # TODO(philngo): combine with compute_temperature_features
    temperature_bin_features = compute_temperature_bin_features(
        segmented_data.temperature_mean, bin_endpoints_list
    )

    # combine features
    return merge_features(
        [
            segmented_data[["meter_value", "hour_of_week"]],
            occupancy_feature,
            temperature_bin_features,
            segmented_data.weight,
        ]