How to use the eemeter.features.compute_time_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 segmented_data():
    index = pd.date_range(start="2017-01-01", periods=24, freq="H", tz="UTC")
    time_features = compute_time_features(index)
    segmented_data = pd.DataFrame(
        {
            "hour_of_week": time_features.hour_of_week,
            "temperature_mean": np.linspace(0, 100, 24),
            "meter_value": np.linspace(10, 70, 24),
            "weight": np.ones((24,)),
        },
        index=index,
    )
    return segmented_data

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_compute_time_features_none():
    index = pd.date_range("2017-01-01", periods=168, freq="H", tz="UTC")
    with pytest.raises(ValueError):
        compute_time_features(
            index, hour_of_week=False, day_of_week=False, hour_of_day=False
        )

def segmented_data_nans():
    num_periods = 200
    index = pd.date_range(start="2017-01-01", periods=num_periods, freq="H", tz="UTC")
    time_features = compute_time_features(index)
    segmented_data = pd.DataFrame(
        {
            "hour_of_week": time_features.hour_of_week,
            "temperature_mean": np.linspace(0, 100, num_periods),
            "meter_value": np.linspace(10, 70, num_periods),
            "weight": np.ones((num_periods,)),
        },
        index=index,
    )
    return segmented_data

def test_compute_time_features_all():
    index = pd.date_range("2017-01-01", periods=168, freq="H", tz="UTC")
    features = compute_time_features(index)
    assert list(features.columns) == ["day_of_week", "hour_of_day", "hour_of_week"]
    assert features.shape == (168, 3)
    assert features.sum().sum() == 16464.0
    with pytest.raises(TypeError):  # categoricals
        features.day_of_week.sum()
    with pytest.raises(TypeError):
        features.hour_of_day.sum()
    with pytest.raises(TypeError):
        features.hour_of_week.sum()
    assert features.day_of_week.astype("float").sum() == sum(range(7)) * 24
    assert features.hour_of_day.astype("float").sum() == sum(range(24)) * 7
    assert features.hour_of_week.astype("float").sum() == sum(range(168))
    assert features.index[0] == index[0]
    assert features.index[-1] == index[-1]

def test_compute_occupancy_feature_hour_of_week_has_nan(even_occupancy):
    index = pd.date_range("2017-01-01", periods=72, freq="H", tz="UTC")
    time_features = compute_time_features(index, hour_of_week=True)
    hour_of_week = time_features.hour_of_week
    hour_of_week.iloc[-1] = np.nan
    occupancy = compute_occupancy_feature(hour_of_week, even_occupancy)
    assert occupancy.name == "occupancy"
    assert occupancy.shape == (72,)
    assert occupancy.sum() == 36

def segmented_data_nans_less_than_week():
    num_periods = 4
    index = pd.date_range(start="2017-01-01", periods=num_periods, freq="H", tz="UTC")
    time_features = compute_time_features(index)
    segmented_data = pd.DataFrame(
        {
            "hour_of_week": time_features.hour_of_week,
            "temperature_mean": np.linspace(0, 100, num_periods),
            "meter_value": np.linspace(10, 70, num_periods),
            "weight": np.ones((num_periods,)),
        },
        index=index,
    )
    return segmented_data

def caltrack_hourly_prediction_feature_processor(
    segment_name, segmented_data, occupancy_lookup, temperature_bins
):
    # hour of week feature
    hour_of_week_feature = compute_time_features(
        segmented_data.index, hour_of_week=True, day_of_week=False, hour_of_day=False
    )

    # occupancy feature
    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

def caltrack_hourly_prediction_feature_processor(
    segment_name, segmented_data, occupancy_lookup, temperature_bins
):
    # hour of week feature
    hour_of_week_feature = compute_time_features(segmented_data.index)

    # occupancy feature
    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()
    )
    # TODO(philngo): combine with compute_temperature_features
    temperature_bin_features = compute_temperature_bin_features(
        segmented_data.temperature_mean, bin_endpoints_list
    )