How to use the eemeter.structures.EnergyTrace function in eemeter

def test_data_and_valid_unit(
        interpretation, unnormalized_unit_with_target_unit, unit_timeseries):

    unnormalized_unit, normalized_unit, mult = \
        unnormalized_unit_with_target_unit

    et = EnergyTrace(interpretation=interpretation, data=unit_timeseries,
                     unit=unnormalized_unit)
    assert et.interpretation == interpretation
    assert et.unit == normalized_unit
    np.testing.assert_allclose(
        et.data.value.iloc[0], (unit_timeseries.value * mult).iloc[0],
        rtol=1e-3, atol=1e-3)
    assert not et.data.estimated.iloc[0]
    assert not et.placeholder

def test_data_but_no_unit(interpretation):
    with pytest.raises(ValueError):
        EnergyTrace(interpretation=interpretation, data=pd.DataFrame())

def test_no_data_no_placeholder(interpretation):
    with pytest.raises(ValueError):
        EnergyTrace(interpretation=interpretation)

def test_data_and_placeholder(interpretation):
    with pytest.raises(ValueError):
        EnergyTrace(interpretation=interpretation, data=pd.DataFrame(),
                    placeholder=True)

def test_bad_column_name_data(interpretation, unit):

    data = {"energy": [1, np.nan], "estimated": [False, False]}
    columns = ["energy", "estimated"]
    index = pd.date_range('2000-01-01', periods=2, freq='D')

    df = pd.DataFrame(data, index=index, columns=columns)

    with pytest.raises(ValueError):
        EnergyTrace(interpretation=interpretation, data=df, unit=unit)

def test_has_trace_id_and_interval():
    et = EnergyTrace(
        interpretation='ELECTRICITY_CONSUMPTION_SUPPLIED',
        placeholder=True
    )
    assert et.trace_id is None
    assert et.interval is None

    et = EnergyTrace(
        interpretation='ELECTRICITY_CONSUMPTION_SUPPLIED',
        placeholder=True,
        trace_id='ABC',
        interval='daily'
    )
    assert et.trace_id == 'ABC'
    assert et.interval == 'daily'

    assert 'ABC' in str(et)

"value": [1, 1, 1, 1, np.nan] + [1,]*13,
        "estimated": [False, False, True, False, False] + [False,]*13
    }
    columns = ["value", "estimated"]
    index = [
        datetime(2011, 1, 1, tzinfo=pytz.UTC),
        datetime(2011, 2, 1, tzinfo=pytz.UTC),
        datetime(2011, 3, 2, tzinfo=pytz.UTC),
        datetime(2011, 4, 3, tzinfo=pytz.UTC),
        datetime(2011, 4, 29, tzinfo=pytz.UTC),
    ] + [
        datetime(2011, 6, 1, tzinfo=pytz.UTC) + timedelta(days=30*i)
        for i in range(13)
    ]
    df = pd.DataFrame(data, index=index, columns=columns)
    return EnergyTrace("ELECTRICITY_CONSUMPTION_SUPPLIED", df, unit="KWH")

def test_non_timeseries_data(interpretation, unit):

    data = {"value": [1, np.nan], "estimated": [False, False]}
    columns = ["value", "estimated"]

    df = pd.DataFrame(data, columns=columns)

    with pytest.raises(ValueError):
        EnergyTrace(interpretation=interpretation, data=df, unit=unit)

def daily_trace():
    data = {
        "value": np.tile(1, (365,)),
        "estimated": np.tile(False, (365,)),
    }
    columns = ["value", "estimated"]
    index = pd.date_range('2000-01-01', periods=365, freq='D', tz=pytz.UTC)
    df = pd.DataFrame(data, index=index, columns=columns)
    return EnergyTrace("ELECTRICITY_CONSUMPTION_SUPPLIED", df, unit="KWH")

# Get all consumption records, group by fuel type.
        for flow_direction, records in self._get_consumption_record_groups():

            if len(records) > 0:
                fuel_type_records = defaultdict(list)
                for record in records:
                    fuel_type_records[record["fuel_type"]].append(record)

                # Wrap records in EnergyTrace objects, by fuel type.
                for fuel_type, records in fuel_type_records.items():
                    if fuel_type is None:
                        fuel_type = service_kind_default
                    selector = (fuel_type, flow_direction)
                    interpretation = INTERPRETATION_MAPPING[selector]
                    yield EnergyTrace(
                        interpretation,
                        records=records,
                        unit=records[0]["unit_name"],
                        serializer=ArbitrarySerializer())