How to use the eemeter.evaluation.Period function in eemeter

period = Period(None, datetime(2015,1,3, tzinfo=pytz.UTC))
    filtered_data = consumption_data_kWh_interval.filter_by_period(period).data
    assert_allclose(filtered_data, np.array([1,1,1]),
            rtol=RTOL, atol=ATOL)
    assert filtered_data.index[0] == datetime(2015,1,1, tzinfo=pytz.UTC)
    assert filtered_data.index[2] == datetime(2015,1,3, tzinfo=pytz.UTC)

    period = Period(datetime(2015,1,9, tzinfo=pytz.UTC),None)
    filtered_data = consumption_data_kWh_interval.filter_by_period(period).data
    assert_allclose(filtered_data, np.array([1,1]),
            rtol=RTOL, atol=ATOL)
    assert filtered_data.index[0] == datetime(2015,1,9, tzinfo=pytz.UTC)
    assert filtered_data.index[1] == datetime(2015,1,10, tzinfo=pytz.UTC)

    period = Period(None,None)
    filtered_data = consumption_data_kWh_interval.filter_by_period(period).data
    assert_allclose(filtered_data, np.array([1,1,1,1,1,1,1,1,1,1]),
            rtol=RTOL, atol=ATOL)
    assert filtered_data.index[0] == datetime(2015,1,1, tzinfo=pytz.UTC)
    assert filtered_data.index[9] == datetime(2015,1,10, tzinfo=pytz.UTC)

def test_consumption_data_filter_by_period_arbitrary(
        consumption_data_kWh_arbitrary):
    period = Period(datetime(2015,1,1), datetime(2015,1,3))
    filtered_data = consumption_data_kWh_arbitrary.filter_by_period(period).data
    assert_allclose(filtered_data, np.array([1,1,np.nan]),
            rtol=RTOL, atol=ATOL)
    assert filtered_data.index[0] == datetime(2015,1,1)
    assert filtered_data.index[2] == datetime(2015,1,3)

    period = Period(datetime(2014,1,1), datetime(2014,1,3,1))
    filtered_data = consumption_data_kWh_arbitrary.filter_by_period(period).data
    assert_allclose(filtered_data, [],
            rtol=RTOL, atol=ATOL)

    period = Period(None, datetime(2015,1,3))
    filtered_data = consumption_data_kWh_arbitrary.filter_by_period(period).data
    assert_allclose(filtered_data, np.array([1,1,np.nan]),
            rtol=RTOL, atol=ATOL)
    assert filtered_data.index[0] == datetime(2015,1,1)
    assert filtered_data.index[2] == datetime(2015,1,3)

    period = Period(datetime(2015,1,9),None)
    filtered_data = consumption_data_kWh_arbitrary.filter_by_period(period).data
    assert_allclose(filtered_data, np.array([1,1,np.nan]),
            rtol=RTOL, atol=ATOL)
    assert filtered_data.index[0] == datetime(2015,1,9)
    assert filtered_data.index[2] == datetime(2015,1,11)

    period = Period(None,None)
    filtered_data = consumption_data_kWh_arbitrary.filter_by_period(period).data
    assert_allclose(filtered_data, np.array([1,1,1,1,1,1,1,1,1,1,np.nan]),

def test_project_attributes(generated_consumption_data_1):
    cd,params = generated_consumption_data_1
    baseline_period = Period(datetime(2014,1,1),datetime(2014,1,1))
    location = Location(zipcode="91104")
    project = Project(location,[cd],baseline_period,None)
    meter = ProjectAttributes(project)
    result = meter.evaluate_raw(project=project)
    assert result["location"].zipcode == location.zipcode
    assert result["consumption"][0] is not None
    assert result["baseline_period"] is not None
    assert result["reporting_period"] is None
    assert result["other_periods"] == []
    assert result["weather_source"].station_id == "722880"
    assert result["weather_normal_source"].station_id == "722880"

@pytest.mark.slow
def test_annualized_gross_savings_metric(
        generated_consumption_data_pre_post_with_annualized_gross_savings_1,
        gsod_722880_2012_2014_weather_source, tmy3_722880_weather_source):

    model = AverageDailyTemperatureSensitivityModel(heating=True, cooling=True)
    meter = AnnualizedGrossSavingsMeter(temperature_unit_str="degF", model=model)

    cd, params_pre, params_post, retrofit_date, savings = \
            generated_consumption_data_pre_post_with_annualized_gross_savings_1

    reporting_period = Period(retrofit_date, datetime(2015,1,1))

    result = meter.evaluate_raw(
            model_params_baseline=params_pre,
            model_params_reporting=params_post,
            consumption_data_reporting=cd.filter_by_period(reporting_period),
            weather_normal_source=tmy3_722880_weather_source,
            energy_unit_str="kWh")

    assert_allclose(result["annualized_gross_savings"], savings,
            rtol=RTOL, atol=ATOL)

def test_generate_monthly_billing_datetimes():
    period = Period(datetime(2012,1,1, tzinfo=pytz.UTC),datetime(2013,1,1, tzinfo=pytz.UTC))
    datetimes_30d = generate_monthly_billing_datetimes(period,
            randint(30,31))
    assert datetimes_30d[0] == datetime(2012,1,1, tzinfo=pytz.UTC)
    assert datetimes_30d[1] == datetime(2012,1,31, tzinfo=pytz.UTC)
    assert datetimes_30d[11] == datetime(2012,11,26, tzinfo=pytz.UTC)
    assert datetimes_30d[12] == datetime(2012,12,26, tzinfo=pytz.UTC)

    datetimes_1d = generate_monthly_billing_datetimes(period, randint(1,2))
    assert datetimes_1d[0] == datetime(2012,1,1, tzinfo=pytz.UTC)
    assert datetimes_1d[1] == datetime(2012,1,2, tzinfo=pytz.UTC)
    assert datetimes_1d[330] == datetime(2012,11,26, tzinfo=pytz.UTC)
    assert datetimes_1d[331] == datetime(2012,11,27, tzinfo=pytz.UTC)

def test_period_name():
    p = Period(name="baseline")
    assert p.name == "baseline"

assert filtered_data.index[0] == datetime(2015,1,1, tzinfo=pytz.UTC)
    assert filtered_data.index[2] == datetime(2015,1,3, tzinfo=pytz.UTC)

    period = Period(datetime(2014,1,1, tzinfo=pytz.UTC), datetime(2014,1,3,1, tzinfo=pytz.UTC))
    filtered_data = consumption_data_kWh_arbitrary.filter_by_period(period).data
    assert_allclose(filtered_data, [],
            rtol=RTOL, atol=ATOL)

    period = Period(None, datetime(2015,1,3, tzinfo=pytz.UTC))
    filtered_data = consumption_data_kWh_arbitrary.filter_by_period(period).data
    assert_allclose(filtered_data, np.array([1,1,np.nan]),
            rtol=RTOL, atol=ATOL)
    assert filtered_data.index[0] == datetime(2015,1,1, tzinfo=pytz.UTC)
    assert filtered_data.index[2] == datetime(2015,1,3, tzinfo=pytz.UTC)

    period = Period(datetime(2015,1,9, tzinfo=pytz.UTC),None)
    filtered_data = consumption_data_kWh_arbitrary.filter_by_period(period).data
    assert_allclose(filtered_data, np.array([1,1,np.nan]),
            rtol=RTOL, atol=ATOL)
    assert filtered_data.index[0] == datetime(2015,1,9, tzinfo=pytz.UTC)
    assert filtered_data.index[2] == datetime(2015,1,11, tzinfo=pytz.UTC)

    period = Period(None,None)
    filtered_data = consumption_data_kWh_arbitrary.filter_by_period(period).data
    assert_allclose(filtered_data, np.array([1,1,1,1,1,1,1,1,1,1,np.nan]),
            rtol=RTOL, atol=ATOL)
    assert filtered_data.index[0] == datetime(2015,1,1, tzinfo=pytz.UTC)
    assert filtered_data.index[10] == datetime(2015,1,11, tzinfo=pytz.UTC)

def test_project_attributes(generated_consumption_data_1):
    cd,params = generated_consumption_data_1
    baseline_period = Period(datetime(2014,1,1),datetime(2014,1,1))
    location = Location(zipcode="91104")
    project = Project(location,[cd],baseline_period,None)
    meter = ProjectAttributes(project)
    result = meter.evaluate_raw(project=project)
    assert result["location"].zipcode == location.zipcode
    assert result["consumption"][0] is not None
    assert result["baseline_period"] is not None
    assert result["reporting_period"] is None
    assert result["other_periods"] == []
    assert result["weather_source"].station_id == "722880"
    assert result["weather_normal_source"].station_id == "722880"

def _normalize_period(self, period):
        start = self._normalize_datetime(period.start)
        year_offset = period.end.year - period.start.year
        end = self._normalize_datetime(period.end, year_offset)
        return Period(start, end)

"""Sums the total observed HDD in a normal year

        Parameters
        ----------
        weather_normal_source : eemeter.weather.WeatherSourceBase and eemeter.weather.WeatherNormalMixin
            A weather data source from a location as geographically and
            climatically close as possible to the target project.

        Returns
        -------
        out : dict
            - "normal_annual_hdd": the total heating degree days observed
              during a typical meteorological year
        """
        # year of this annual period will be ignored
        annual_period = Period(datetime(2013,1,1), datetime(2014,1,1))
        hdd = weather_normal_source.hdd(annual_period,
                self.temperature_unit_str, self.base)
        return { "normal_annual_hdd": hdd }