From 4320c7e60549e987800f09f88dc37add38bbfab2 Mon Sep 17 00:00:00 2001 From: Perry Date: Fri, 14 Oct 2022 15:31:59 -0600 Subject: [PATCH 01/31] added new private method for filtering on daily aggregated data --- rdtools/analysis_chains.py | 46 ++++++++++++++++++++++++++++++++++++-- 1 file changed, 44 insertions(+), 2 deletions(-) diff --git a/rdtools/analysis_chains.py b/rdtools/analysis_chains.py index 54590d01..6e22c7bd 100644 --- a/rdtools/analysis_chains.py +++ b/rdtools/analysis_chains.py @@ -474,6 +474,45 @@ def _filter(self, energy_normalized, case): elif case == 'clearsky': self.clearsky_filter = bool_filter self.clearsky_filter_components = filter_components + + def _daily_filter(self, aggregated, case): + """ + Mirrors the _filter private function, but with daily filters applied. + These daily filters based on those in rdtools.filtering. Uses + self.filter_params, which is a dict, the keys of which are names of + functions in rdtools.filtering, and the values of which are dicts + containing the associated parameters with which to run the filtering + functions. See examples for details on how to modify filter parameters. + + Parameters + ---------- + aggregated : pandas.Series + Time series of daily-aggregated normalized AC energy + case : str + 'sensor' or 'clearsky' which filtering protocol to apply. Affects + whether result is stored in self.sensor_filter_daily or + self.clearsky_filter_daily) + + Returns + ------- + None + """ + filter_components_daily = {'default': + pd.Series(True, index=aggregated.index)} + # Add daily filters as they come online--this logic is omitted until + # we add the hampel clearsky filter and other daily filters. + # Convert the dictionary into a dataframe (after running filters) + filter_components_daily = pd.DataFrame( + filter_components_daily).fillna(False) + bool_filter_daily = filter_components_daily.all(axis=1) + filter_components_daily = filter_components_daily.drop( + columns=['default']) + if case == 'sensor': + self.sensor_filter_daily = bool_filter_daily + self.sensor_filter_components_daily = filter_components_daily + elif case == 'clearsky': + self.clearsky_filter_daily = bool_filter_daily + self.clearsky_filter_components_daily = filter_components_daily def _filter_check(self, post_filter): ''' @@ -621,8 +660,11 @@ def _sensor_preprocess(self): self._filter(energy_normalized, 'sensor') aggregated, aggregated_insolation = self._aggregate( energy_normalized[self.sensor_filter], insolation[self.sensor_filter]) - self.sensor_aggregated_performance = aggregated - self.sensor_aggregated_insolation = aggregated_insolation + # Run daily filters on aggregated data + self._daily_filter(aggregated, 'sensor') + # Apply daily filter to aggregated data and store + self.sensor_aggregated_performance = aggregated[self.sensor_filter_daily] + self.sensor_aggregated_insolation = aggregated_insolation[self.sensor_filter_daily] def _clearsky_preprocess(self): ''' From d625ea8c99588ae9d9fdf93714f173b7182bd1c5 Mon Sep 17 00:00:00 2001 From: Perry Date: Fri, 14 Oct 2022 16:06:20 -0600 Subject: [PATCH 02/31] remove trailing whitespaces --- rdtools/analysis_chains.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/rdtools/analysis_chains.py b/rdtools/analysis_chains.py index 6e22c7bd..90e3334a 100644 --- a/rdtools/analysis_chains.py +++ b/rdtools/analysis_chains.py @@ -474,10 +474,10 @@ def _filter(self, energy_normalized, case): elif case == 'clearsky': self.clearsky_filter = bool_filter self.clearsky_filter_components = filter_components - + def _daily_filter(self, aggregated, case): """ - Mirrors the _filter private function, but with daily filters applied. + Mirrors the _filter private function, but with daily filters applied. These daily filters based on those in rdtools.filtering. Uses self.filter_params, which is a dict, the keys of which are names of functions in rdtools.filtering, and the values of which are dicts From b41cccccca9b6a52f71c1ab0f2e59ceb46f136e5 Mon Sep 17 00:00:00 2001 From: Perry Date: Mon, 17 Oct 2022 11:14:32 -0600 Subject: [PATCH 03/31] updated the clearsky routine + added ad hoc filter + added filter_params_daily --- rdtools/analysis_chains.py | 29 +++++++++++++++++++++++++---- 1 file changed, 25 insertions(+), 4 deletions(-) diff --git a/rdtools/analysis_chains.py b/rdtools/analysis_chains.py index 90e3334a..4259bb8a 100644 --- a/rdtools/analysis_chains.py +++ b/rdtools/analysis_chains.py @@ -133,6 +133,9 @@ def __init__(self, pv, poa_global=None, temperature_cell=None, temperature_ambie 'csi_filter': {}, 'ad_hoc_filter': None # use this to include an explict filter } + self.filter_params_daily = { + 'ad_hoc_filter': None + } # remove tcell_filter from list if power_expected is passed in if power_expected is not None and temperature_cell is None: del self.filter_params['tcell_filter'] @@ -499,11 +502,26 @@ def _daily_filter(self, aggregated, case): """ filter_components_daily = {'default': pd.Series(True, index=aggregated.index)} - # Add daily filters as they come online--this logic is omitted until - # we add the hampel clearsky filter and other daily filters. + # Add daily filters as they come online here. # Convert the dictionary into a dataframe (after running filters) filter_components_daily = pd.DataFrame( filter_components_daily).fillna(False) + # Run the ad-hoc filter from filter_params_daily, if available + if self.filter_params_daily.get('ad_hoc_filter', None) is not None: + ad_hoc_filter_daily = self.filter_params_daily['ad_hoc_filter'] + + if ad_hoc_filter_daily.isnull().any(): + warnings.warn('ad_hoc_filter contains NaN values; setting to False (excluding)') + ad_hoc_filter_daily = ad_hoc_filter_daily.fillna(False) + + if not filter_components_daily.index.equals(ad_hoc_filter_daily.index): + warnings.warn('Daily ad_hoc_filter index does not match index of other filters; missing ' + 'values will be set to True (kept). Align the index with the index ' + 'of the filter_components_daily attribute to prevent this warning') + ad_hoc_filter_daily = ad_hoc_filter_daily.reindex(filter_components_daily.index).fillna(True) + + filter_components_daily['ad_hoc_filter'] = ad_hoc_filter_daily + bool_filter_daily = filter_components_daily.all(axis=1) filter_components_daily = filter_components_daily.drop( columns=['default']) @@ -693,8 +711,11 @@ def _clearsky_preprocess(self): self._filter(cs_normalized, 'clearsky') cs_aggregated, cs_aggregated_insolation = self._aggregate( cs_normalized[self.clearsky_filter], cs_insolation[self.clearsky_filter]) - self.clearsky_aggregated_performance = cs_aggregated - self.clearsky_aggregated_insolation = cs_aggregated_insolation + # Run daily filters on aggregated data + self._daily_filter(cs_aggregated, 'clearsky') + # Apply daily filter to aggregated data and store + self.clearsky_aggregated_performance = cs_aggregated[self.clearsky_filter_daily] + self.clearsky_aggregated_insolation = cs_aggregated_insolation[self.clearsky_filter_daily] def sensor_analysis(self, analyses=['yoy_degradation'], yoy_kwargs={}, srr_kwargs={}): ''' From 4f32df0181246c31f61802ea555224feeda4cd54 Mon Sep 17 00:00:00 2001 From: Perry Date: Mon, 17 Oct 2022 11:16:56 -0600 Subject: [PATCH 04/31] added pep8 styling --- rdtools/analysis_chains.py | 22 ++++++++++++++-------- 1 file changed, 14 insertions(+), 8 deletions(-) diff --git a/rdtools/analysis_chains.py b/rdtools/analysis_chains.py index 4259bb8a..275567e1 100644 --- a/rdtools/analysis_chains.py +++ b/rdtools/analysis_chains.py @@ -135,7 +135,7 @@ def __init__(self, pv, poa_global=None, temperature_cell=None, temperature_ambie } self.filter_params_daily = { 'ad_hoc_filter': None - } + } # remove tcell_filter from list if power_expected is passed in if power_expected is not None and temperature_cell is None: del self.filter_params['tcell_filter'] @@ -255,7 +255,8 @@ def _calc_clearsky_poa(self, times=None, rescale=True, **kwargs): clearsky_poa = clearsky_poa['poa_global'] if aggregate: - interval_id = pd.Series(range(len(self.poa_global)), index=self.poa_global.index) + interval_id = pd.Series( + range(len(self.poa_global)), index=self.poa_global.index) interval_id = interval_id.reindex(times, method='backfill') clearsky_poa = clearsky_poa.groupby(interval_id).mean() clearsky_poa.index = self.poa_global.index @@ -408,7 +409,8 @@ def _filter(self, energy_normalized, case): # at once. However, we add a default value of True, with the same index as # energy_normalized, so that the output is still correct even when all # filters have been disabled. - filter_components = {'default': pd.Series(True, index=energy_normalized.index)} + filter_components = {'default': pd.Series( + True, index=energy_normalized.index)} if case == 'sensor': poa = self.poa_global @@ -458,14 +460,16 @@ def _filter(self, energy_normalized, case): ad_hoc_filter = self.filter_params['ad_hoc_filter'] if ad_hoc_filter.isnull().any(): - warnings.warn('ad_hoc_filter contains NaN values; setting to False (excluding)') + warnings.warn( + 'ad_hoc_filter contains NaN values; setting to False (excluding)') ad_hoc_filter = ad_hoc_filter.fillna(False) if not filter_components.index.equals(ad_hoc_filter.index): warnings.warn('ad_hoc_filter index does not match index of other filters; missing ' 'values will be set to True (kept). Align the index with the index ' 'of the filter_components attribute to prevent this warning') - ad_hoc_filter = ad_hoc_filter.reindex(filter_components.index).fillna(True) + ad_hoc_filter = ad_hoc_filter.reindex( + filter_components.index).fillna(True) filter_components['ad_hoc_filter'] = ad_hoc_filter @@ -511,17 +515,19 @@ def _daily_filter(self, aggregated, case): ad_hoc_filter_daily = self.filter_params_daily['ad_hoc_filter'] if ad_hoc_filter_daily.isnull().any(): - warnings.warn('ad_hoc_filter contains NaN values; setting to False (excluding)') + warnings.warn( + 'ad_hoc_filter contains NaN values; setting to False (excluding)') ad_hoc_filter_daily = ad_hoc_filter_daily.fillna(False) if not filter_components_daily.index.equals(ad_hoc_filter_daily.index): warnings.warn('Daily ad_hoc_filter index does not match index of other filters; missing ' 'values will be set to True (kept). Align the index with the index ' 'of the filter_components_daily attribute to prevent this warning') - ad_hoc_filter_daily = ad_hoc_filter_daily.reindex(filter_components_daily.index).fillna(True) + ad_hoc_filter_daily = ad_hoc_filter_daily.reindex( + filter_components_daily.index).fillna(True) filter_components_daily['ad_hoc_filter'] = ad_hoc_filter_daily - + bool_filter_daily = filter_components_daily.all(axis=1) filter_components_daily = filter_components_daily.drop( columns=['default']) From 668db1e6a05e659691a884e519504b8f799e26ed Mon Sep 17 00:00:00 2001 From: Perry Date: Mon, 17 Oct 2022 11:30:41 -0600 Subject: [PATCH 05/31] update the line length on warning --- rdtools/analysis_chains.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/rdtools/analysis_chains.py b/rdtools/analysis_chains.py index 275567e1..9cee66a5 100644 --- a/rdtools/analysis_chains.py +++ b/rdtools/analysis_chains.py @@ -520,9 +520,10 @@ def _daily_filter(self, aggregated, case): ad_hoc_filter_daily = ad_hoc_filter_daily.fillna(False) if not filter_components_daily.index.equals(ad_hoc_filter_daily.index): - warnings.warn('Daily ad_hoc_filter index does not match index of other filters; missing ' - 'values will be set to True (kept). Align the index with the index ' - 'of the filter_components_daily attribute to prevent this warning') + warnings.warn('Daily ad_hoc_filter index does not match index of other ' + 'filters; missing values will be set to True (kept). ' + 'Align the index with the index of the ' + 'filter_components_daily attribute to prevent this warning') ad_hoc_filter_daily = ad_hoc_filter_daily.reindex( filter_components_daily.index).fillna(True) From b590b92fd8386cfc16a281a4024b059321821711 Mon Sep 17 00:00:00 2001 From: Kirsten Perry <70228568+kperrynrel@users.noreply.github.com> Date: Tue, 25 Oct 2022 14:05:52 -0600 Subject: [PATCH 06/31] Update rdtools/analysis_chains.py Co-authored-by: Kevin Anderson --- rdtools/analysis_chains.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/rdtools/analysis_chains.py b/rdtools/analysis_chains.py index 9cee66a5..6e993a89 100644 --- a/rdtools/analysis_chains.py +++ b/rdtools/analysis_chains.py @@ -516,7 +516,7 @@ def _daily_filter(self, aggregated, case): if ad_hoc_filter_daily.isnull().any(): warnings.warn( - 'ad_hoc_filter contains NaN values; setting to False (excluding)') + 'daily ad_hoc_filter contains NaN values; setting to False (excluding)') ad_hoc_filter_daily = ad_hoc_filter_daily.fillna(False) if not filter_components_daily.index.equals(ad_hoc_filter_daily.index): From 28d67a53573b7417b05a0b5ba36cce245f95d089 Mon Sep 17 00:00:00 2001 From: Perry Date: Thu, 27 Oct 2022 10:12:26 -0600 Subject: [PATCH 07/31] added unit test checking daily adhoc filter --- rdtools/test/analysis_chains_test.py | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/rdtools/test/analysis_chains_test.py b/rdtools/test/analysis_chains_test.py index ad5b5b2c..c0d3624c 100644 --- a/rdtools/test/analysis_chains_test.py +++ b/rdtools/test/analysis_chains_test.py @@ -178,6 +178,17 @@ def test_sensor_analysis_ad_hoc_filter(sensor_parameters): with pytest.raises(ValueError, match="Less than two years of data left after filtering"): rd_analysis.sensor_analysis(analyses=['yoy_degradation']) +def test_sensor_analysis_daily_ad_hoc_filter(sensor_parameters): + # by excluding all but a few points, we should trigger the <2yr error + filt = pd.Series(False, + index=sensor_parameters['pv'].index) + filt =filt.resample('1D').first().dropna(how='all') + filt = df3 = filt[~filt.index.duplicated(keep='first')] + filt.iloc[-500:] = True + rd_analysis = TrendAnalysis(**sensor_parameters, power_dc_rated=1.0) + rd_analysis.filter_params_daily['ad_hoc_filter'] = filt + with pytest.raises(ValueError, match="Less than two years of data left after filtering"): + rd_analysis.sensor_analysis(analyses=['yoy_degradation']) def test_filter_components(sensor_parameters): poa = sensor_parameters['poa_global'] From 6deb1ac279975e16a979fbecdb8083768af9503c Mon Sep 17 00:00:00 2001 From: Perry Date: Thu, 27 Oct 2022 10:15:01 -0600 Subject: [PATCH 08/31] fix pep8 error --- rdtools/test/analysis_chains_test.py | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) diff --git a/rdtools/test/analysis_chains_test.py b/rdtools/test/analysis_chains_test.py index c0d3624c..77716de7 100644 --- a/rdtools/test/analysis_chains_test.py +++ b/rdtools/test/analysis_chains_test.py @@ -178,11 +178,12 @@ def test_sensor_analysis_ad_hoc_filter(sensor_parameters): with pytest.raises(ValueError, match="Less than two years of data left after filtering"): rd_analysis.sensor_analysis(analyses=['yoy_degradation']) + def test_sensor_analysis_daily_ad_hoc_filter(sensor_parameters): # by excluding all but a few points, we should trigger the <2yr error filt = pd.Series(False, index=sensor_parameters['pv'].index) - filt =filt.resample('1D').first().dropna(how='all') + filt = filt.resample('1D').first().dropna(how='all') filt = df3 = filt[~filt.index.duplicated(keep='first')] filt.iloc[-500:] = True rd_analysis = TrendAnalysis(**sensor_parameters, power_dc_rated=1.0) @@ -190,12 +191,14 @@ def test_sensor_analysis_daily_ad_hoc_filter(sensor_parameters): with pytest.raises(ValueError, match="Less than two years of data left after filtering"): rd_analysis.sensor_analysis(analyses=['yoy_degradation']) + def test_filter_components(sensor_parameters): poa = sensor_parameters['poa_global'] poa_filter = (poa > 200) & (poa < 1200) rd_analysis = TrendAnalysis(**sensor_parameters, power_dc_rated=1.0) rd_analysis.sensor_analysis(analyses=['yoy_degradation']) - assert (poa_filter == rd_analysis.sensor_filter_components['poa_filter']).all() + assert (poa_filter == + rd_analysis.sensor_filter_components['poa_filter']).all() def test_filter_components_no_filters(sensor_parameters): @@ -362,8 +365,10 @@ def test_index_mismatch(): # GH #277 times = pd.date_range('2019-01-01', '2022-01-01', freq='15min') pv = pd.Series(1.0, index=times) - dummy_series = pd.Series(1.0, index=times[::4]) # low-frequency weather inputs - keys = ['poa_global', 'temperature_cell', 'temperature_ambient', 'power_expected', 'windspeed'] + # low-frequency weather inputs + dummy_series = pd.Series(1.0, index=times[::4]) + keys = ['poa_global', 'temperature_cell', + 'temperature_ambient', 'power_expected', 'windspeed'] kwargs = {key: dummy_series.copy() for key in keys} rd_analysis = TrendAnalysis(pv, **kwargs) for key in keys: From 9e0506a27dad331a87974d958a4ea00ff5e9baf5 Mon Sep 17 00:00:00 2001 From: Perry Date: Thu, 27 Oct 2022 10:49:22 -0600 Subject: [PATCH 09/31] fix test case error --- rdtools/test/analysis_chains_test.py | 1 - 1 file changed, 1 deletion(-) diff --git a/rdtools/test/analysis_chains_test.py b/rdtools/test/analysis_chains_test.py index 77716de7..0c34a98f 100644 --- a/rdtools/test/analysis_chains_test.py +++ b/rdtools/test/analysis_chains_test.py @@ -184,7 +184,6 @@ def test_sensor_analysis_daily_ad_hoc_filter(sensor_parameters): filt = pd.Series(False, index=sensor_parameters['pv'].index) filt = filt.resample('1D').first().dropna(how='all') - filt = df3 = filt[~filt.index.duplicated(keep='first')] filt.iloc[-500:] = True rd_analysis = TrendAnalysis(**sensor_parameters, power_dc_rated=1.0) rd_analysis.filter_params_daily['ad_hoc_filter'] = filt From 039d1abd391d2ac517349792f454587ef41ed025 Mon Sep 17 00:00:00 2001 From: "Perry, Kirsten" Date: Fri, 28 Oct 2022 13:26:05 -0600 Subject: [PATCH 10/31] added second unit test for daily ad hoc filter --- rdtools/analysis_chains.py | 2 +- rdtools/test/analysis_chains_test.py | 40 ++++++++++++++++++++++++++++ 2 files changed, 41 insertions(+), 1 deletion(-) diff --git a/rdtools/analysis_chains.py b/rdtools/analysis_chains.py index 6e993a89..61f3d501 100644 --- a/rdtools/analysis_chains.py +++ b/rdtools/analysis_chains.py @@ -486,7 +486,7 @@ def _daily_filter(self, aggregated, case): """ Mirrors the _filter private function, but with daily filters applied. These daily filters based on those in rdtools.filtering. Uses - self.filter_params, which is a dict, the keys of which are names of + self.filter_params_daily, which is a dict, the keys of which are names of functions in rdtools.filtering, and the values of which are dicts containing the associated parameters with which to run the filtering functions. See examples for details on how to modify filter parameters. diff --git a/rdtools/test/analysis_chains_test.py b/rdtools/test/analysis_chains_test.py index 0c34a98f..c9b1f706 100644 --- a/rdtools/test/analysis_chains_test.py +++ b/rdtools/test/analysis_chains_test.py @@ -244,6 +244,46 @@ def test_filter_ad_hoc_warnings(workflow, sensor_parameters): # NaN values set to False assert not components['ad_hoc_filter'].iloc[10] assert components.drop(components.index[10])['ad_hoc_filter'].all() + + +@pytest.mark.parametrize('workflow', ['sensor', 'clearsky']) +def test_daily_filter_ad_hoc_warnings(workflow, sensor_parameters): + rd_analysis = TrendAnalysis(**sensor_parameters, power_dc_rated=1.0) + rd_analysis.set_clearsky(pvlib_location=pvlib.location.Location(40, -80), + poa_global_clearsky=rd_analysis.poa_global) + + # warning for incomplete index + daily_ad_hoc_filter = pd.Series(True, + index=sensor_parameters['pv'].index[:-5]) + daily_ad_hoc_filter = daily_ad_hoc_filter.resample('1D').first().dropna(how='all') + rd_analysis.filter_params_daily['ad_hoc_filter'] = daily_ad_hoc_filter + with pytest.warns(UserWarning, match='ad_hoc_filter index does not match index'): + if workflow == 'sensor': + rd_analysis.sensor_analysis(analyses=['yoy_degradation']) + components = rd_analysis.sensor_filter_components_daily + else: + rd_analysis.clearsky_analysis(analyses=['yoy_degradation']) + components = rd_analysis.clearsky_filter_components_daily + + # missing values set to True + assert components['ad_hoc_filter'].all() + + # warning about NaNs + daily_ad_hoc_filter = pd.Series(True, index=sensor_parameters['pv'].index) + daily_ad_hoc_filter = daily_ad_hoc_filter.resample('1D').first().dropna(how='all') + daily_ad_hoc_filter.iloc[10] = np.nan + rd_analysis.filter_params_daily['ad_hoc_filter'] = daily_ad_hoc_filter + with pytest.warns(UserWarning, match='ad_hoc_filter contains NaN values; setting to False'): + if workflow == 'sensor': + rd_analysis.sensor_analysis(analyses=['yoy_degradation']) + components = rd_analysis.sensor_filter_components_daily + else: + rd_analysis.clearsky_analysis(analyses=['yoy_degradation']) + components = rd_analysis.clearsky_filter_components_daily + + # NaN values set to False + assert not components['ad_hoc_filter'].iloc[10] + assert components.drop(components.index[10])['ad_hoc_filter'].all() def test_cell_temperature_model_invalid(sensor_parameters): From 24033f2b6a2526b647bf5f42c5a2b192ab06e8c7 Mon Sep 17 00:00:00 2001 From: "Perry, Kirsten" Date: Fri, 28 Oct 2022 13:38:28 -0600 Subject: [PATCH 11/31] removed whitespace pep8 error --- rdtools/test/analysis_chains_test.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/rdtools/test/analysis_chains_test.py b/rdtools/test/analysis_chains_test.py index c9b1f706..30e35952 100644 --- a/rdtools/test/analysis_chains_test.py +++ b/rdtools/test/analysis_chains_test.py @@ -244,8 +244,8 @@ def test_filter_ad_hoc_warnings(workflow, sensor_parameters): # NaN values set to False assert not components['ad_hoc_filter'].iloc[10] assert components.drop(components.index[10])['ad_hoc_filter'].all() - - + + @pytest.mark.parametrize('workflow', ['sensor', 'clearsky']) def test_daily_filter_ad_hoc_warnings(workflow, sensor_parameters): rd_analysis = TrendAnalysis(**sensor_parameters, power_dc_rated=1.0) @@ -255,7 +255,8 @@ def test_daily_filter_ad_hoc_warnings(workflow, sensor_parameters): # warning for incomplete index daily_ad_hoc_filter = pd.Series(True, index=sensor_parameters['pv'].index[:-5]) - daily_ad_hoc_filter = daily_ad_hoc_filter.resample('1D').first().dropna(how='all') + daily_ad_hoc_filter = daily_ad_hoc_filter.resample( + '1D').first().dropna(how='all') rd_analysis.filter_params_daily['ad_hoc_filter'] = daily_ad_hoc_filter with pytest.warns(UserWarning, match='ad_hoc_filter index does not match index'): if workflow == 'sensor': @@ -270,7 +271,8 @@ def test_daily_filter_ad_hoc_warnings(workflow, sensor_parameters): # warning about NaNs daily_ad_hoc_filter = pd.Series(True, index=sensor_parameters['pv'].index) - daily_ad_hoc_filter = daily_ad_hoc_filter.resample('1D').first().dropna(how='all') + daily_ad_hoc_filter = daily_ad_hoc_filter.resample( + '1D').first().dropna(how='all') daily_ad_hoc_filter.iloc[10] = np.nan rd_analysis.filter_params_daily['ad_hoc_filter'] = daily_ad_hoc_filter with pytest.warns(UserWarning, match='ad_hoc_filter contains NaN values; setting to False'): From 0a4b6c09892e6bc9f48e7948d063dab9d428c614 Mon Sep 17 00:00:00 2001 From: "Perry, Kirsten" Date: Fri, 28 Oct 2022 14:40:14 -0600 Subject: [PATCH 12/31] debug unit test --- rdtools/test/analysis_chains_test.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/rdtools/test/analysis_chains_test.py b/rdtools/test/analysis_chains_test.py index 30e35952..d8f7cfe1 100644 --- a/rdtools/test/analysis_chains_test.py +++ b/rdtools/test/analysis_chains_test.py @@ -270,6 +270,9 @@ def test_daily_filter_ad_hoc_warnings(workflow, sensor_parameters): assert components['ad_hoc_filter'].all() # warning about NaNs + rd_analysis = TrendAnalysis(**sensor_parameters, power_dc_rated=1.0) + rd_analysis.set_clearsky(pvlib_location=pvlib.location.Location(40, -80), + poa_global_clearsky=rd_analysis.poa_global) daily_ad_hoc_filter = pd.Series(True, index=sensor_parameters['pv'].index) daily_ad_hoc_filter = daily_ad_hoc_filter.resample( '1D').first().dropna(how='all') From ef33a4ea0a5bc3d00779b6675a64acb7e6db0c80 Mon Sep 17 00:00:00 2001 From: "Perry, Kirsten" Date: Mon, 31 Oct 2022 13:35:06 -0600 Subject: [PATCH 13/31] updated unit tests to remove index based filter when running daily filter tests --- rdtools/test/analysis_chains_test.py | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/rdtools/test/analysis_chains_test.py b/rdtools/test/analysis_chains_test.py index d8f7cfe1..ca757d20 100644 --- a/rdtools/test/analysis_chains_test.py +++ b/rdtools/test/analysis_chains_test.py @@ -214,7 +214,6 @@ def test_filter_ad_hoc_warnings(workflow, sensor_parameters): rd_analysis = TrendAnalysis(**sensor_parameters, power_dc_rated=1.0) rd_analysis.set_clearsky(pvlib_location=pvlib.location.Location(40, -80), poa_global_clearsky=rd_analysis.poa_global) - # warning for incomplete index ad_hoc_filter = pd.Series(True, index=sensor_parameters['pv'].index[:-5]) rd_analysis.filter_params['ad_hoc_filter'] = ad_hoc_filter @@ -251,7 +250,7 @@ def test_daily_filter_ad_hoc_warnings(workflow, sensor_parameters): rd_analysis = TrendAnalysis(**sensor_parameters, power_dc_rated=1.0) rd_analysis.set_clearsky(pvlib_location=pvlib.location.Location(40, -80), poa_global_clearsky=rd_analysis.poa_global) - + rd_analysis.filter_params = {} # disable all index-based filters # warning for incomplete index daily_ad_hoc_filter = pd.Series(True, index=sensor_parameters['pv'].index[:-5]) @@ -270,21 +269,22 @@ def test_daily_filter_ad_hoc_warnings(workflow, sensor_parameters): assert components['ad_hoc_filter'].all() # warning about NaNs - rd_analysis = TrendAnalysis(**sensor_parameters, power_dc_rated=1.0) - rd_analysis.set_clearsky(pvlib_location=pvlib.location.Location(40, -80), - poa_global_clearsky=rd_analysis.poa_global) + rd_analysis_2 = TrendAnalysis(**sensor_parameters, power_dc_rated=1.0) + rd_analysis_2.set_clearsky(pvlib_location=pvlib.location.Location(40, -80), + poa_global_clearsky=rd_analysis_2.poa_global) + rd_analysis_2.filter_params = {} # disable all index-based filters daily_ad_hoc_filter = pd.Series(True, index=sensor_parameters['pv'].index) daily_ad_hoc_filter = daily_ad_hoc_filter.resample( '1D').first().dropna(how='all') daily_ad_hoc_filter.iloc[10] = np.nan - rd_analysis.filter_params_daily['ad_hoc_filter'] = daily_ad_hoc_filter + rd_analysis_2.filter_params_daily['ad_hoc_filter'] = daily_ad_hoc_filter with pytest.warns(UserWarning, match='ad_hoc_filter contains NaN values; setting to False'): if workflow == 'sensor': - rd_analysis.sensor_analysis(analyses=['yoy_degradation']) - components = rd_analysis.sensor_filter_components_daily + rd_analysis_2.sensor_analysis(analyses=['yoy_degradation']) + components = rd_analysis_2.sensor_filter_components_daily else: - rd_analysis.clearsky_analysis(analyses=['yoy_degradation']) - components = rd_analysis.clearsky_filter_components_daily + rd_analysis_2.clearsky_analysis(analyses=['yoy_degradation']) + components = rd_analysis_2.clearsky_filter_components_daily # NaN values set to False assert not components['ad_hoc_filter'].iloc[10] From 6f13492b046b79250d2e7342ec008ce99392d118 Mon Sep 17 00:00:00 2001 From: "Perry, Kirsten" Date: Mon, 31 Oct 2022 16:08:23 -0600 Subject: [PATCH 14/31] added additional unit tests per @kanderson-nrel's recommendation --- rdtools/test/analysis_chains_test.py | 27 +++++++++++++++++++++++++-- 1 file changed, 25 insertions(+), 2 deletions(-) diff --git a/rdtools/test/analysis_chains_test.py b/rdtools/test/analysis_chains_test.py index ca757d20..39dd6aa8 100644 --- a/rdtools/test/analysis_chains_test.py +++ b/rdtools/test/analysis_chains_test.py @@ -198,6 +198,19 @@ def test_filter_components(sensor_parameters): rd_analysis.sensor_analysis(analyses=['yoy_degradation']) assert (poa_filter == rd_analysis.sensor_filter_components['poa_filter']).all() + +def test_daily_filter_components(sensor_parameters): + daily_ad_hoc_filter = pd.Series(True, + index=sensor_parameters['pv'].index) + daily_ad_hoc_filter[:600] = False + daily_ad_hoc_filter = daily_ad_hoc_filter.resample( + '1D').first().dropna(how='all') + rd_analysis = TrendAnalysis(**sensor_parameters, power_dc_rated=1.0) + rd_analysis.filter_params = {} # disable all index-based filters + rd_analysis.filter_params_daily['ad_hoc_filter'] = daily_ad_hoc_filter + rd_analysis.sensor_analysis(analyses=['yoy_degradation']) + assert (daily_ad_hoc_filter == + rd_analysis.sensor_filter_components_daily['ad_hoc_filter']).all() def test_filter_components_no_filters(sensor_parameters): @@ -207,6 +220,16 @@ def test_filter_components_no_filters(sensor_parameters): expected = pd.Series(True, index=rd_analysis.pv_energy.index) pd.testing.assert_series_equal(rd_analysis.sensor_filter, expected) assert rd_analysis.sensor_filter_components.empty + + +def test_daily_filter_components_no_filters(sensor_parameters): + rd_analysis = TrendAnalysis(**sensor_parameters, power_dc_rated=1.0) + rd_analysis.filter_params = {} # disable all index-based filters + rd_analysis.daily_filter_params = {} # disable all daily filters + rd_analysis.sensor_analysis(analyses=['yoy_degradation']) + expected = pd.Series(True, index=rd_analysis.pv_energy.index) + pd.testing.assert_series_equal(rd_analysis.sensor_filter, expected) + assert rd_analysis.sensor_filter_components.empty @pytest.mark.parametrize('workflow', ['sensor', 'clearsky']) @@ -250,7 +273,7 @@ def test_daily_filter_ad_hoc_warnings(workflow, sensor_parameters): rd_analysis = TrendAnalysis(**sensor_parameters, power_dc_rated=1.0) rd_analysis.set_clearsky(pvlib_location=pvlib.location.Location(40, -80), poa_global_clearsky=rd_analysis.poa_global) - rd_analysis.filter_params = {} # disable all index-based filters + rd_analysis.filter_params = {'csi_filter': {}} # disable all filters outside of CSI # warning for incomplete index daily_ad_hoc_filter = pd.Series(True, index=sensor_parameters['pv'].index[:-5]) @@ -272,7 +295,7 @@ def test_daily_filter_ad_hoc_warnings(workflow, sensor_parameters): rd_analysis_2 = TrendAnalysis(**sensor_parameters, power_dc_rated=1.0) rd_analysis_2.set_clearsky(pvlib_location=pvlib.location.Location(40, -80), poa_global_clearsky=rd_analysis_2.poa_global) - rd_analysis_2.filter_params = {} # disable all index-based filters + rd_analysis_2.filter_params = {'csi_filter': {}} # disable all filters outside of CSI daily_ad_hoc_filter = pd.Series(True, index=sensor_parameters['pv'].index) daily_ad_hoc_filter = daily_ad_hoc_filter.resample( '1D').first().dropna(how='all') From 68bf7d88720526d2298eb02c101ff803770f4c91 Mon Sep 17 00:00:00 2001 From: "Perry, Kirsten" Date: Mon, 31 Oct 2022 16:12:31 -0600 Subject: [PATCH 15/31] fix pep8 errors --- rdtools/test/analysis_chains_test.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/rdtools/test/analysis_chains_test.py b/rdtools/test/analysis_chains_test.py index 39dd6aa8..a8d1c9be 100644 --- a/rdtools/test/analysis_chains_test.py +++ b/rdtools/test/analysis_chains_test.py @@ -273,7 +273,7 @@ def test_daily_filter_ad_hoc_warnings(workflow, sensor_parameters): rd_analysis = TrendAnalysis(**sensor_parameters, power_dc_rated=1.0) rd_analysis.set_clearsky(pvlib_location=pvlib.location.Location(40, -80), poa_global_clearsky=rd_analysis.poa_global) - rd_analysis.filter_params = {'csi_filter': {}} # disable all filters outside of CSI + rd_analysis.filter_params = {'csi_filter': {}} # disable all filters outside of CSI # warning for incomplete index daily_ad_hoc_filter = pd.Series(True, index=sensor_parameters['pv'].index[:-5]) From fde7cae818b3c5bbe015e5659a35b44d7df67de9 Mon Sep 17 00:00:00 2001 From: "Perry, Kirsten" Date: Tue, 1 Nov 2022 09:46:15 -0600 Subject: [PATCH 16/31] remove flake8 errors --- rdtools/test/analysis_chains_test.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/rdtools/test/analysis_chains_test.py b/rdtools/test/analysis_chains_test.py index a8d1c9be..e9274bfb 100644 --- a/rdtools/test/analysis_chains_test.py +++ b/rdtools/test/analysis_chains_test.py @@ -198,7 +198,8 @@ def test_filter_components(sensor_parameters): rd_analysis.sensor_analysis(analyses=['yoy_degradation']) assert (poa_filter == rd_analysis.sensor_filter_components['poa_filter']).all() - + + def test_daily_filter_components(sensor_parameters): daily_ad_hoc_filter = pd.Series(True, index=sensor_parameters['pv'].index) @@ -220,7 +221,7 @@ def test_filter_components_no_filters(sensor_parameters): expected = pd.Series(True, index=rd_analysis.pv_energy.index) pd.testing.assert_series_equal(rd_analysis.sensor_filter, expected) assert rd_analysis.sensor_filter_components.empty - + def test_daily_filter_components_no_filters(sensor_parameters): rd_analysis = TrendAnalysis(**sensor_parameters, power_dc_rated=1.0) @@ -273,7 +274,8 @@ def test_daily_filter_ad_hoc_warnings(workflow, sensor_parameters): rd_analysis = TrendAnalysis(**sensor_parameters, power_dc_rated=1.0) rd_analysis.set_clearsky(pvlib_location=pvlib.location.Location(40, -80), poa_global_clearsky=rd_analysis.poa_global) - rd_analysis.filter_params = {'csi_filter': {}} # disable all filters outside of CSI + # disable all filters outside of CSI + rd_analysis.filter_params = {'csi_filter': {}} # warning for incomplete index daily_ad_hoc_filter = pd.Series(True, index=sensor_parameters['pv'].index[:-5]) @@ -295,7 +297,8 @@ def test_daily_filter_ad_hoc_warnings(workflow, sensor_parameters): rd_analysis_2 = TrendAnalysis(**sensor_parameters, power_dc_rated=1.0) rd_analysis_2.set_clearsky(pvlib_location=pvlib.location.Location(40, -80), poa_global_clearsky=rd_analysis_2.poa_global) - rd_analysis_2.filter_params = {'csi_filter': {}} # disable all filters outside of CSI + # disable all filters outside of CSI + rd_analysis_2.filter_params = {'csi_filter': {}} daily_ad_hoc_filter = pd.Series(True, index=sensor_parameters['pv'].index) daily_ad_hoc_filter = daily_ad_hoc_filter.resample( '1D').first().dropna(how='all') From c731809862a7ce112fa9831eade9911fe153c36c Mon Sep 17 00:00:00 2001 From: "Perry, Kirsten" Date: Tue, 1 Nov 2022 16:54:58 -0600 Subject: [PATCH 17/31] updated the pending changelog with pr --- docs/sphinx/source/changelog/pending.rst | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/docs/sphinx/source/changelog/pending.rst b/docs/sphinx/source/changelog/pending.rst index 4451d6e9..6ce369b5 100644 --- a/docs/sphinx/source/changelog/pending.rst +++ b/docs/sphinx/source/changelog/pending.rst @@ -17,3 +17,7 @@ Requirements * Bump ``sphinx`` version from 3.2 to 4.5 and ``nbsphinx`` version from 0.8.5 to 0.8.8 in the optional ``[doc]`` requirements (:pull:`317`, :pull:`325`) * A number of other requirements updates (:pull:`337`) + +Enhancements +------------ +* Added framework for running daily filters in ``analysis_chains.py`` (:pull:`348`) From bd6084faad5b67a8c0302f055450a4d47468dc77 Mon Sep 17 00:00:00 2001 From: "Perry, Kirsten" Date: Tue, 8 Nov 2022 11:25:49 -0700 Subject: [PATCH 18/31] pushed updates recommended by @mdeceglie --- rdtools/analysis_chains.py | 74 ++++++++++++++-------------- rdtools/test/analysis_chains_test.py | 32 ++++++------ 2 files changed, 54 insertions(+), 52 deletions(-) diff --git a/rdtools/analysis_chains.py b/rdtools/analysis_chains.py index 886bc88c..d073a551 100644 --- a/rdtools/analysis_chains.py +++ b/rdtools/analysis_chains.py @@ -133,7 +133,7 @@ def __init__(self, pv, poa_global=None, temperature_cell=None, temperature_ambie 'csi_filter': {}, 'ad_hoc_filter': None # use this to include an explict filter } - self.filter_params_daily = { + self.filter_params_aggregated = { 'ad_hoc_filter': None } # remove tcell_filter from list if power_expected is passed in @@ -482,11 +482,11 @@ def _filter(self, energy_normalized, case): self.clearsky_filter = bool_filter self.clearsky_filter_components = filter_components - def _daily_filter(self, aggregated, case): + def _aggregated_filter(self, aggregated, case): """ - Mirrors the _filter private function, but with daily filters applied. - These daily filters based on those in rdtools.filtering. Uses - self.filter_params_daily, which is a dict, the keys of which are names of + Mirrors the _filter private function, but with aggregated filters applied. + These aggregated filters based on those in rdtools.filtering. Uses + self.filter_params_aggregated, which is a dict, the keys of which are names of functions in rdtools.filtering, and the values of which are dicts containing the associated parameters with which to run the filtering functions. See examples for details on how to modify filter parameters. @@ -494,50 +494,50 @@ def _daily_filter(self, aggregated, case): Parameters ---------- aggregated : pandas.Series - Time series of daily-aggregated normalized AC energy + Time series of aggregated normalized AC energy case : str 'sensor' or 'clearsky' which filtering protocol to apply. Affects - whether result is stored in self.sensor_filter_daily or - self.clearsky_filter_daily) + whether result is stored in self.sensor_filter_aggregated or + self.clearsky_filter_aggregated) Returns ------- None """ - filter_components_daily = {'default': + filter_components_aggregated = {'default': pd.Series(True, index=aggregated.index)} - # Add daily filters as they come online here. + # Add daily aggregate filters as they come online here. # Convert the dictionary into a dataframe (after running filters) - filter_components_daily = pd.DataFrame( - filter_components_daily).fillna(False) - # Run the ad-hoc filter from filter_params_daily, if available - if self.filter_params_daily.get('ad_hoc_filter', None) is not None: - ad_hoc_filter_daily = self.filter_params_daily['ad_hoc_filter'] + filter_components_aggregated = pd.DataFrame( + filter_components_aggregated).fillna(False) + # Run the ad-hoc filter from filter_params_aggregated, if available + if self.filter_params_aggregated.get('ad_hoc_filter', None) is not None: + ad_hoc_filter_aggregated = self.filter_params_aggregated['ad_hoc_filter'] - if ad_hoc_filter_daily.isnull().any(): + if ad_hoc_filter_aggregated.isnull().any(): warnings.warn( - 'daily ad_hoc_filter contains NaN values; setting to False (excluding)') - ad_hoc_filter_daily = ad_hoc_filter_daily.fillna(False) + 'aggregated ad_hoc_filter contains NaN values; setting to False (excluding)') + ad_hoc_filter_aggregated = ad_hoc_filter_aggregated.fillna(False) - if not filter_components_daily.index.equals(ad_hoc_filter_daily.index): - warnings.warn('Daily ad_hoc_filter index does not match index of other ' + if not filter_components_aggregated.index.equals(ad_hoc_filter_aggregated.index): + warnings.warn('Aggregated ad_hoc_filter index does not match index of other ' 'filters; missing values will be set to True (kept). ' 'Align the index with the index of the ' - 'filter_components_daily attribute to prevent this warning') - ad_hoc_filter_daily = ad_hoc_filter_daily.reindex( - filter_components_daily.index).fillna(True) + 'filter_components_aggregated attribute to prevent this warning') + ad_hoc_filter_aggregated = ad_hoc_filter_aggregated.reindex( + filter_components_aggregated.index).fillna(True) - filter_components_daily['ad_hoc_filter'] = ad_hoc_filter_daily + filter_components_aggregated['ad_hoc_filter'] = ad_hoc_filter_aggregated - bool_filter_daily = filter_components_daily.all(axis=1) - filter_components_daily = filter_components_daily.drop( + bool_filter_aggregated = filter_components_aggregated.all(axis=1) + filter_components_aggregated = filter_components_aggregated.drop( columns=['default']) if case == 'sensor': - self.sensor_filter_daily = bool_filter_daily - self.sensor_filter_components_daily = filter_components_daily + self.sensor_filter_aggregated = bool_filter_aggregated + self.sensor_filter_components_aggregated = filter_components_aggregated elif case == 'clearsky': - self.clearsky_filter_daily = bool_filter_daily - self.clearsky_filter_components_daily = filter_components_daily + self.clearsky_filter_aggregated = bool_filter_aggregated + self.clearsky_filter_components_aggregated = filter_components_aggregated def _filter_check(self, post_filter): ''' @@ -686,10 +686,10 @@ def _sensor_preprocess(self): aggregated, aggregated_insolation = self._aggregate( energy_normalized[self.sensor_filter], insolation[self.sensor_filter]) # Run daily filters on aggregated data - self._daily_filter(aggregated, 'sensor') - # Apply daily filter to aggregated data and store - self.sensor_aggregated_performance = aggregated[self.sensor_filter_daily] - self.sensor_aggregated_insolation = aggregated_insolation[self.sensor_filter_daily] + self._aggregated_filter(aggregated, 'sensor') + # Apply filter to aggregated data and store + self.sensor_aggregated_performance = aggregated[self.sensor_filter_aggregated] + self.sensor_aggregated_insolation = aggregated_insolation[self.sensor_filter_aggregated] def _clearsky_preprocess(self): ''' @@ -719,10 +719,10 @@ def _clearsky_preprocess(self): cs_aggregated, cs_aggregated_insolation = self._aggregate( cs_normalized[self.clearsky_filter], cs_insolation[self.clearsky_filter]) # Run daily filters on aggregated data - self._daily_filter(cs_aggregated, 'clearsky') + self._aggregated_filter(cs_aggregated, 'clearsky') # Apply daily filter to aggregated data and store - self.clearsky_aggregated_performance = cs_aggregated[self.clearsky_filter_daily] - self.clearsky_aggregated_insolation = cs_aggregated_insolation[self.clearsky_filter_daily] + self.clearsky_aggregated_performance = cs_aggregated[self.clearsky_filter_aggregated] + self.clearsky_aggregated_insolation = cs_aggregated_insolation[self.clearsky_filter_aggregated] def sensor_analysis(self, analyses=['yoy_degradation'], yoy_kwargs={}, srr_kwargs={}): ''' diff --git a/rdtools/test/analysis_chains_test.py b/rdtools/test/analysis_chains_test.py index c9528f6d..3004db67 100644 --- a/rdtools/test/analysis_chains_test.py +++ b/rdtools/test/analysis_chains_test.py @@ -179,14 +179,14 @@ def test_sensor_analysis_ad_hoc_filter(sensor_parameters): rd_analysis.sensor_analysis(analyses=['yoy_degradation']) -def test_sensor_analysis_daily_ad_hoc_filter(sensor_parameters): +def test_sensor_analysis_aggregated_ad_hoc_filter(sensor_parameters): # by excluding all but a few points, we should trigger the <2yr error filt = pd.Series(False, index=sensor_parameters['pv'].index) filt = filt.resample('1D').first().dropna(how='all') filt.iloc[-500:] = True rd_analysis = TrendAnalysis(**sensor_parameters, power_dc_rated=1.0) - rd_analysis.filter_params_daily['ad_hoc_filter'] = filt + rd_analysis.filter_params_aggregated['ad_hoc_filter'] = filt with pytest.raises(ValueError, match="Less than two years of data left after filtering"): rd_analysis.sensor_analysis(analyses=['yoy_degradation']) @@ -200,7 +200,7 @@ def test_filter_components(sensor_parameters): rd_analysis.sensor_filter_components['poa_filter']).all() -def test_daily_filter_components(sensor_parameters): +def test_aggregated_filter_components(sensor_parameters): daily_ad_hoc_filter = pd.Series(True, index=sensor_parameters['pv'].index) daily_ad_hoc_filter[:600] = False @@ -208,10 +208,10 @@ def test_daily_filter_components(sensor_parameters): '1D').first().dropna(how='all') rd_analysis = TrendAnalysis(**sensor_parameters, power_dc_rated=1.0) rd_analysis.filter_params = {} # disable all index-based filters - rd_analysis.filter_params_daily['ad_hoc_filter'] = daily_ad_hoc_filter + rd_analysis.filter_params_aggregated['ad_hoc_filter'] = daily_ad_hoc_filter rd_analysis.sensor_analysis(analyses=['yoy_degradation']) assert (daily_ad_hoc_filter == - rd_analysis.sensor_filter_components_daily['ad_hoc_filter']).all() + rd_analysis.sensor_filter_components_aggregated['ad_hoc_filter']).all() def test_filter_components_no_filters(sensor_parameters): @@ -223,13 +223,15 @@ def test_filter_components_no_filters(sensor_parameters): assert rd_analysis.sensor_filter_components.empty -def test_daily_filter_components_no_filters(sensor_parameters): +def test_aggregated_filter_components_no_filters(sensor_parameters): rd_analysis = TrendAnalysis(**sensor_parameters, power_dc_rated=1.0) rd_analysis.filter_params = {} # disable all index-based filters - rd_analysis.daily_filter_params = {} # disable all daily filters + rd_analysis.aggregated_filter_params = {} # disable all daily filters rd_analysis.sensor_analysis(analyses=['yoy_degradation']) expected = pd.Series(True, index=rd_analysis.pv_energy.index) - pd.testing.assert_series_equal(rd_analysis.sensor_filter, expected) + daily_expected = expected.resample('1D').first().dropna(how='all') + pd.testing.assert_series_equal(rd_analysis.sensor_filter_aggregated, + expected) assert rd_analysis.sensor_filter_components.empty @@ -270,7 +272,7 @@ def test_filter_ad_hoc_warnings(workflow, sensor_parameters): @pytest.mark.parametrize('workflow', ['sensor', 'clearsky']) -def test_daily_filter_ad_hoc_warnings(workflow, sensor_parameters): +def test_aggregated_filter_ad_hoc_warnings(workflow, sensor_parameters): rd_analysis = TrendAnalysis(**sensor_parameters, power_dc_rated=1.0) rd_analysis.set_clearsky(pvlib_location=pvlib.location.Location(40, -80), poa_global_clearsky=rd_analysis.poa_global) @@ -281,14 +283,14 @@ def test_daily_filter_ad_hoc_warnings(workflow, sensor_parameters): index=sensor_parameters['pv'].index[:-5]) daily_ad_hoc_filter = daily_ad_hoc_filter.resample( '1D').first().dropna(how='all') - rd_analysis.filter_params_daily['ad_hoc_filter'] = daily_ad_hoc_filter + rd_analysis.filter_params_aggregated['ad_hoc_filter'] = daily_ad_hoc_filter with pytest.warns(UserWarning, match='ad_hoc_filter index does not match index'): if workflow == 'sensor': rd_analysis.sensor_analysis(analyses=['yoy_degradation']) - components = rd_analysis.sensor_filter_components_daily + components = rd_analysis.sensor_filter_components_aggregated else: rd_analysis.clearsky_analysis(analyses=['yoy_degradation']) - components = rd_analysis.clearsky_filter_components_daily + components = rd_analysis.clearsky_filter_components_aggregated # missing values set to True assert components['ad_hoc_filter'].all() @@ -303,14 +305,14 @@ def test_daily_filter_ad_hoc_warnings(workflow, sensor_parameters): daily_ad_hoc_filter = daily_ad_hoc_filter.resample( '1D').first().dropna(how='all') daily_ad_hoc_filter.iloc[10] = np.nan - rd_analysis_2.filter_params_daily['ad_hoc_filter'] = daily_ad_hoc_filter + rd_analysis_2.filter_params_aggregated['ad_hoc_filter'] = daily_ad_hoc_filter with pytest.warns(UserWarning, match='ad_hoc_filter contains NaN values; setting to False'): if workflow == 'sensor': rd_analysis_2.sensor_analysis(analyses=['yoy_degradation']) - components = rd_analysis_2.sensor_filter_components_daily + components = rd_analysis_2.sensor_filter_components_aggregated else: rd_analysis_2.clearsky_analysis(analyses=['yoy_degradation']) - components = rd_analysis_2.clearsky_filter_components_daily + components = rd_analysis_2.clearsky_filter_components_aggregated # NaN values set to False assert not components['ad_hoc_filter'].iloc[10] From 25b1a9adcbffe55517349f344026491492c0a222 Mon Sep 17 00:00:00 2001 From: "Perry, Kirsten" Date: Tue, 8 Nov 2022 11:28:37 -0700 Subject: [PATCH 19/31] fixed unit test bug --- rdtools/test/analysis_chains_test.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/rdtools/test/analysis_chains_test.py b/rdtools/test/analysis_chains_test.py index 3004db67..2fd269cb 100644 --- a/rdtools/test/analysis_chains_test.py +++ b/rdtools/test/analysis_chains_test.py @@ -231,7 +231,7 @@ def test_aggregated_filter_components_no_filters(sensor_parameters): expected = pd.Series(True, index=rd_analysis.pv_energy.index) daily_expected = expected.resample('1D').first().dropna(how='all') pd.testing.assert_series_equal(rd_analysis.sensor_filter_aggregated, - expected) + daily_expected) assert rd_analysis.sensor_filter_components.empty From 54e57edf171a1f8468476f6361607687edec385d Mon Sep 17 00:00:00 2001 From: "Perry, Kirsten" Date: Tue, 8 Nov 2022 11:40:08 -0700 Subject: [PATCH 20/31] updated the docstrings per @mdeceglie's recommendations --- rdtools/analysis_chains.py | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/rdtools/analysis_chains.py b/rdtools/analysis_chains.py index d073a551..b06ac846 100644 --- a/rdtools/analysis_chains.py +++ b/rdtools/analysis_chains.py @@ -75,6 +75,14 @@ class TrendAnalysis(): filter_params defaults to empty dicts for each function in rdtools.filtering, in which case those functions use default parameter values, `ad_hoc_filter` defaults to None. See examples for more information. + filter_params_aggregated: dict + parameters to be passed to rdtools.filtering functions that specifically handle + aggregated data (dily filters, etc). Keys are the names of the rdtools.filtering functions. + Values are dicts of parameters to be passed to those functions. Also has a special key + `ad_hoc_filter`; this filter is a boolean mask joined with the rest of the filters. + filter_params_aggregated defaults to empty dicts for each function in rdtools.filtering, + in which case those functions use default parameter values, `ad_hoc_filter` + defaults to None. See examples for more information. results : dict Nested dict used to store the results of methods ending with `_analysis` ''' @@ -387,7 +395,8 @@ def _filter(self, energy_normalized, case): self.filter_params, which is a dict, the keys of which are names of functions in rdtools.filtering, and the values of which are dicts containing the associated parameters with which to run the filtering - functions. See examples for details on how to modify filter parameters. + functions. This private method is specifically for the original indexed + data. See examples for details on how to modify filter parameters. Parameters ---------- @@ -485,7 +494,7 @@ def _filter(self, energy_normalized, case): def _aggregated_filter(self, aggregated, case): """ Mirrors the _filter private function, but with aggregated filters applied. - These aggregated filters based on those in rdtools.filtering. Uses + These aggregated filters are based on those in rdtools.filtering. Uses self.filter_params_aggregated, which is a dict, the keys of which are names of functions in rdtools.filtering, and the values of which are dicts containing the associated parameters with which to run the filtering From 630cff908158abbec139c1a5c17ff7882a4aea99 Mon Sep 17 00:00:00 2001 From: "Perry, Kirsten" Date: Tue, 8 Nov 2022 11:49:55 -0700 Subject: [PATCH 21/31] fixed pep8 errors --- rdtools/analysis_chains.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/rdtools/analysis_chains.py b/rdtools/analysis_chains.py index b06ac846..44cbc6c0 100644 --- a/rdtools/analysis_chains.py +++ b/rdtools/analysis_chains.py @@ -514,7 +514,7 @@ def _aggregated_filter(self, aggregated, case): None """ filter_components_aggregated = {'default': - pd.Series(True, index=aggregated.index)} + pd.Series(True, index=aggregated.index)} # Add daily aggregate filters as they come online here. # Convert the dictionary into a dataframe (after running filters) filter_components_aggregated = pd.DataFrame( @@ -731,7 +731,8 @@ def _clearsky_preprocess(self): self._aggregated_filter(cs_aggregated, 'clearsky') # Apply daily filter to aggregated data and store self.clearsky_aggregated_performance = cs_aggregated[self.clearsky_filter_aggregated] - self.clearsky_aggregated_insolation = cs_aggregated_insolation[self.clearsky_filter_aggregated] + self.clearsky_aggregated_insolation = \ + cs_aggregated_insolation[self.clearsky_filter_aggregated] def sensor_analysis(self, analyses=['yoy_degradation'], yoy_kwargs={}, srr_kwargs={}): ''' From ea7dbfa02de4b2a73764d23a0856851cd6eeed90 Mon Sep 17 00:00:00 2001 From: "Perry, Kirsten" Date: Tue, 8 Nov 2022 15:46:25 -0700 Subject: [PATCH 22/31] added reindexing to stop soiling error --- docs/TrendAnalysis_example_pvdaq4.ipynb | 34 ++++++++++++++++++++----- rdtools/analysis_chains.py | 5 ++++ 2 files changed, 32 insertions(+), 7 deletions(-) diff --git a/docs/TrendAnalysis_example_pvdaq4.ipynb b/docs/TrendAnalysis_example_pvdaq4.ipynb index 31e81321..ef2553d9 100644 --- a/docs/TrendAnalysis_example_pvdaq4.ipynb +++ b/docs/TrendAnalysis_example_pvdaq4.ipynb @@ -140,12 +140,14 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -227,16 +229,34 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\KANDERSO\\Software\\Anaconda3\\envs\\rdtools310\\lib\\site-packages\\rdtools\\soiling.py:14: UserWarning: The soiling module is currently experimental. The API, results, and default behaviors may change in future releases (including MINOR and PATCH releases) as the code matures.\n", - " warnings.warn(\n", - "C:\\Users\\KANDERSO\\Software\\Anaconda3\\envs\\rdtools310\\lib\\site-packages\\rdtools\\soiling.py:366: UserWarning: 20% or more of the daily data is assigned to invalid soiling intervals. This can be problematic with the \"half_norm_clean\" and \"random_clean\" cleaning assumptions. Consider more permissive validity criteria such as increasing \"max_relative_slope_error\" and/or \"max_negative_step\" and/or decreasing \"min_interval_length\". Alternatively, consider using method=\"perfect_clean\". For more info see https://github.com/NREL/rdtools/issues/272\n", + "C:\\Users\\kperry\\AppData\\Roaming\\Python\\Python37\\site-packages\\rdtools\\soiling.py:28: UserWarning: The soiling module is currently experimental. The API, results, and default behaviors may change in future releases (including MINOR and PATCH releases) as the code matures.\n", + " 'The soiling module is currently experimental. The API, results, '\n", + "C:\\Users\\kperry\\AppData\\Roaming\\Python\\Python37\\site-packages\\rdtools\\soiling.py:379: UserWarning: 20% or more of the daily data is assigned to invalid soiling intervals. This can be problematic with the \"half_norm_clean\" and \"random_clean\" cleaning assumptions. Consider more permissive validity criteria such as increasing \"max_relative_slope_error\" and/or \"max_negative_step\" and/or decreasing \"min_interval_length\". Alternatively, consider using method=\"perfect_clean\". For more info see https://github.com/NREL/rdtools/issues/272\n", " warnings.warn('20% or more of the daily data is assigned to invalid soiling '\n" ] + }, + { + "ename": "ValueError", + "evalue": "Soiling SRR analysis requires daily aggregation.", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mdaily_filter\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mta\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msensor_aggregated_performance\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0.8\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[0mta\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfilter_params_aggregated\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'ad_hoc_filter'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdaily_filter\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mta\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msensor_analysis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0manalyses\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'yoy_degradation'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'srr_soiling'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\rdtools\\analysis_chains.py\u001b[0m in \u001b[0;36msensor_analysis\u001b[1;34m(self, analyses, yoy_kwargs, srr_kwargs)\u001b[0m\n\u001b[0;32m 766\u001b[0m srr_results = self._srr_soiling(self.sensor_aggregated_performance,\n\u001b[0;32m 767\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msensor_aggregated_insolation\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 768\u001b[1;33m **srr_kwargs)\n\u001b[0m\u001b[0;32m 769\u001b[0m \u001b[0msensor_results\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'srr_soiling'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msrr_results\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 770\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\rdtools\\analysis_chains.py\u001b[0m in \u001b[0;36m_srr_soiling\u001b[1;34m(self, energy_normalized_daily, insolation_daily, **kwargs)\u001b[0m\n\u001b[0;32m 656\u001b[0m insolation_daily.index.freq != daily_freq):\n\u001b[0;32m 657\u001b[0m raise ValueError(\n\u001b[1;32m--> 658\u001b[1;33m 'Soiling SRR analysis requires daily aggregation.')\n\u001b[0m\u001b[0;32m 659\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 660\u001b[0m sr, sr_ci, soiling_info = soiling.soiling_srr(\n", + "\u001b[1;31mValueError\u001b[0m: Soiling SRR analysis requires daily aggregation." + ] } ], "source": [ "ta.sensor_analysis(analyses=['yoy_degradation','srr_soiling'])\n", - "ta.clearsky_analysis()" + "#ta.clearsky_analysis()\n", + "\n", + "ta._sensor_preprocess()\n", + "daily_filter = (ta.sensor_aggregated_performance > 0.8)\n", + "ta.filter_params_aggregated['ad_hoc_filter'] = daily_filter\n", + "ta.sensor_analysis(analyses=['yoy_degradation', 'srr_soiling'])" ] }, { @@ -62456,7 +62476,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.4" + "version": "3.7.10" } }, "nbformat": 4, diff --git a/rdtools/analysis_chains.py b/rdtools/analysis_chains.py index 44cbc6c0..d02344fa 100644 --- a/rdtools/analysis_chains.py +++ b/rdtools/analysis_chains.py @@ -699,6 +699,11 @@ def _sensor_preprocess(self): # Apply filter to aggregated data and store self.sensor_aggregated_performance = aggregated[self.sensor_filter_aggregated] self.sensor_aggregated_insolation = aggregated_insolation[self.sensor_filter_aggregated] + # Reindex the data after the fact, so it's on the aggregated interval + self.sensor_aggregated_performance = self.sensor_aggregated_performance.reindex( + aggregated.index) + self.sensor_aggregated_insolation = self.sensor_aggregated_insolation.reindex( + aggregated.index) def _clearsky_preprocess(self): ''' From 8e1c21243f6bc45952f0b289089f0764937f3960 Mon Sep 17 00:00:00 2001 From: "Perry, Kirsten" Date: Tue, 8 Nov 2022 16:12:06 -0700 Subject: [PATCH 23/31] reworked index issue --- rdtools/analysis_chains.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/rdtools/analysis_chains.py b/rdtools/analysis_chains.py index d02344fa..b2714608 100644 --- a/rdtools/analysis_chains.py +++ b/rdtools/analysis_chains.py @@ -700,10 +700,10 @@ def _sensor_preprocess(self): self.sensor_aggregated_performance = aggregated[self.sensor_filter_aggregated] self.sensor_aggregated_insolation = aggregated_insolation[self.sensor_filter_aggregated] # Reindex the data after the fact, so it's on the aggregated interval - self.sensor_aggregated_performance = self.sensor_aggregated_performance.reindex( - aggregated.index) - self.sensor_aggregated_insolation = self.sensor_aggregated_insolation.reindex( - aggregated.index) + self.sensor_aggregated_performance = self.sensor_aggregated_performance.asfreq( + self.aggregation_freq) + self.sensor_aggregated_insolation = self.sensor_aggregated_insolation.asfreq( + self.aggregation_freq) def _clearsky_preprocess(self): ''' From 85886d11d5007bd686575f27e14981ea996a1b5f Mon Sep 17 00:00:00 2001 From: "Perry, Kirsten" Date: Tue, 15 Nov 2022 10:40:49 -0700 Subject: [PATCH 24/31] Fixed unit test filter_params_aggregated misname --- docs/TrendAnalysis_example_pvdaq4.ipynb | 8252 ++++++++++++----------- rdtools/test/analysis_chains_test.py | 2 +- 2 files changed, 4138 insertions(+), 4116 deletions(-) diff --git a/docs/TrendAnalysis_example_pvdaq4.ipynb b/docs/TrendAnalysis_example_pvdaq4.ipynb index ef2553d9..f4b020ca 100644 --- a/docs/TrendAnalysis_example_pvdaq4.ipynb +++ b/docs/TrendAnalysis_example_pvdaq4.ipynb @@ -232,26 +232,15 @@ "C:\\Users\\kperry\\AppData\\Roaming\\Python\\Python37\\site-packages\\rdtools\\soiling.py:28: UserWarning: The soiling module is currently experimental. The API, results, and default behaviors may change in future releases (including MINOR and PATCH releases) as the code matures.\n", " 'The soiling module is currently experimental. The API, results, '\n", "C:\\Users\\kperry\\AppData\\Roaming\\Python\\Python37\\site-packages\\rdtools\\soiling.py:379: UserWarning: 20% or more of the daily data is assigned to invalid soiling intervals. This can be problematic with the \"half_norm_clean\" and \"random_clean\" cleaning assumptions. Consider more permissive validity criteria such as increasing \"max_relative_slope_error\" and/or \"max_negative_step\" and/or decreasing \"min_interval_length\". Alternatively, consider using method=\"perfect_clean\". For more info see https://github.com/NREL/rdtools/issues/272\n", + " warnings.warn('20% or more of the daily data is assigned to invalid soiling '\n", + "C:\\Users\\kperry\\AppData\\Roaming\\Python\\Python37\\site-packages\\rdtools\\soiling.py:379: UserWarning: 20% or more of the daily data is assigned to invalid soiling intervals. This can be problematic with the \"half_norm_clean\" and \"random_clean\" cleaning assumptions. Consider more permissive validity criteria such as increasing \"max_relative_slope_error\" and/or \"max_negative_step\" and/or decreasing \"min_interval_length\". Alternatively, consider using method=\"perfect_clean\". For more info see https://github.com/NREL/rdtools/issues/272\n", " warnings.warn('20% or more of the daily data is assigned to invalid soiling '\n" ] - }, - { - "ename": "ValueError", - "evalue": "Soiling SRR analysis requires daily aggregation.", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mdaily_filter\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mta\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msensor_aggregated_performance\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0.8\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m 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\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msensor_aggregated_insolation\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 768\u001b[1;33m **srr_kwargs)\n\u001b[0m\u001b[0;32m 769\u001b[0m \u001b[0msensor_results\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'srr_soiling'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msrr_results\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 770\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\rdtools\\analysis_chains.py\u001b[0m in \u001b[0;36m_srr_soiling\u001b[1;34m(self, energy_normalized_daily, insolation_daily, **kwargs)\u001b[0m\n\u001b[0;32m 656\u001b[0m insolation_daily.index.freq != daily_freq):\n\u001b[0;32m 657\u001b[0m raise ValueError(\n\u001b[1;32m--> 658\u001b[1;33m 'Soiling SRR analysis requires daily aggregation.')\n\u001b[0m\u001b[0;32m 659\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 660\u001b[0m sr, sr_ci, soiling_info = soiling.soiling_srr(\n", - "\u001b[1;31mValueError\u001b[0m: Soiling SRR analysis requires daily aggregation." - ] } ], "source": [ "ta.sensor_analysis(analyses=['yoy_degradation','srr_soiling'])\n", - "#ta.clearsky_analysis()\n", + "ta.clearsky_analysis()\n", "\n", "ta._sensor_preprocess()\n", "daily_filter = (ta.sensor_aggregated_performance > 0.8)\n", @@ -285,8 +274,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "-0.454\n", - "[-0.605 -0.253]\n" + "-0.282\n", + "[-0.428 -0.185]\n" ] } ], @@ -326,8 +315,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.949\n", - "[0.944 0.954]\n" + "0.974\n", + "[0.97 0.977]\n" ] } ], @@ -352,12 +341,14 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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" ] @@ -473,25 +470,27 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\KANDERSO\\Software\\Anaconda3\\envs\\rdtools310\\lib\\site-packages\\rdtools\\plotting.py:165: UserWarning: The soiling module is currently experimental. The API, results, and default behaviors may change in future releases (including MINOR and PATCH releases) as the code matures.\n", - " warnings.warn(\n" + "C:\\Users\\kperry\\AppData\\Roaming\\Python\\Python37\\site-packages\\rdtools\\plotting.py:173: UserWarning: The soiling module is currently experimental. The API, results, and default behaviors may change in future releases (including MINOR and PATCH releases) as the code matures.\n", + " 'The soiling module is currently experimental. The API, results, '\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -501,25 +500,27 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\KANDERSO\\Software\\Anaconda3\\envs\\rdtools310\\lib\\site-packages\\rdtools\\plotting.py:225: UserWarning: The soiling module is currently experimental. The API, results, and default behaviors may change in future releases (including MINOR and PATCH releases) as the code matures.\n", - " warnings.warn(\n" + "C:\\Users\\kperry\\AppData\\Roaming\\Python\\Python37\\site-packages\\rdtools\\plotting.py:233: UserWarning: The soiling module is currently experimental. The API, results, and default behaviors may change in future releases (including MINOR and PATCH releases) as the code matures.\n", + " 'The soiling module is currently experimental. The API, results, '\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -529,25 +530,27 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\KANDERSO\\Software\\Anaconda3\\envs\\rdtools310\\lib\\site-packages\\rdtools\\plotting.py:265: UserWarning: The soiling module is currently experimental. The API, results, and default behaviors may change in future releases (including MINOR and PATCH releases) as the code matures.\n", - " warnings.warn(\n" + "C:\\Users\\kperry\\AppData\\Roaming\\Python\\Python37\\site-packages\\rdtools\\plotting.py:273: UserWarning: The soiling module is currently experimental. The API, results, and default behaviors may change in future releases (including MINOR and PATCH releases) as the code matures.\n", + " 'The soiling module is currently experimental. The API, results, '\n" ] }, { "data": { - "image/png": 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -564,7 +567,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -601,63 +604,63 @@ " \n", " \n", " \n", - " 6\n", - " 2010-03-11 00:00:00-07:00\n", - " 2010-04-08 00:00:00-07:00\n", - " -0.001467\n", - " -0.004335\n", + " 0\n", + " 2010-03-06 00:00:00-07:00\n", + " 2010-04-11 00:00:00-07:00\n", + " -0.000377\n", + " -0.001731\n", " 0.000000\n", - " 1.037802\n", - " 0.996718\n", - " 28\n", + " 1.030865\n", + " 1.017286\n", + " 36\n", " True\n", " \n", " \n", - " 8\n", + " 1\n", " 2010-04-12 00:00:00-07:00\n", " 2010-06-13 00:00:00-07:00\n", - " -0.000701\n", - " -0.001058\n", - " -0.000336\n", - " 1.012347\n", - " 0.968893\n", + " -0.000539\n", + " -0.000893\n", + " -0.000131\n", + " 0.991487\n", + " 0.958089\n", " 62\n", " True\n", " \n", " \n", - " 10\n", - " 2010-06-15 00:00:00-07:00\n", + " 4\n", + " 2010-06-16 00:00:00-07:00\n", " 2010-07-13 00:00:00-07:00\n", - " -0.000639\n", - " -0.001763\n", - " 0.000000\n", - " 1.082671\n", - " 1.064776\n", - " 28\n", + " -0.000743\n", + " -0.002043\n", + " -0.000014\n", + " 1.059114\n", + " 1.039045\n", + " 27\n", " True\n", " \n", " \n", - " 13\n", - " 2010-10-11 00:00:00-07:00\n", - " 2011-02-04 00:00:00-07:00\n", - " -0.001216\n", - " -0.001407\n", - " -0.001022\n", - " 1.064267\n", - " 0.923230\n", - " 116\n", + " 8\n", + " 2010-10-25 00:00:00-07:00\n", + " 2010-11-18 00:00:00-07:00\n", + " -0.002513\n", + " -0.004121\n", + " -0.000682\n", + " 1.051962\n", + " 0.991655\n", + " 24\n", " True\n", " \n", " \n", - " 16\n", - " 2011-02-13 00:00:00-07:00\n", - " 2011-03-03 00:00:00-07:00\n", - " -0.003159\n", - " -0.005266\n", - " -0.001054\n", - " 1.028467\n", - " 0.971602\n", - " 18\n", + " 9\n", + " 2010-11-19 00:00:00-07:00\n", + " 2011-01-11 00:00:00-07:00\n", + " -0.001204\n", + " -0.001641\n", + " -0.000681\n", + " 1.007855\n", + " 0.944064\n", + " 53\n", " True\n", " \n", " \n", @@ -665,29 +668,29 @@ "" ], "text/plain": [ - " start end soiling_rate \\\n", - "6 2010-03-11 00:00:00-07:00 2010-04-08 00:00:00-07:00 -0.001467 \n", - "8 2010-04-12 00:00:00-07:00 2010-06-13 00:00:00-07:00 -0.000701 \n", - "10 2010-06-15 00:00:00-07:00 2010-07-13 00:00:00-07:00 -0.000639 \n", - "13 2010-10-11 00:00:00-07:00 2011-02-04 00:00:00-07:00 -0.001216 \n", - "16 2011-02-13 00:00:00-07:00 2011-03-03 00:00:00-07:00 -0.003159 \n", + " start end soiling_rate \\\n", + "0 2010-03-06 00:00:00-07:00 2010-04-11 00:00:00-07:00 -0.000377 \n", + "1 2010-04-12 00:00:00-07:00 2010-06-13 00:00:00-07:00 -0.000539 \n", + "4 2010-06-16 00:00:00-07:00 2010-07-13 00:00:00-07:00 -0.000743 \n", + "8 2010-10-25 00:00:00-07:00 2010-11-18 00:00:00-07:00 -0.002513 \n", + "9 2010-11-19 00:00:00-07:00 2011-01-11 00:00:00-07:00 -0.001204 \n", "\n", - " soiling_rate_low soiling_rate_high inferred_start_loss \\\n", - "6 -0.004335 0.000000 1.037802 \n", - "8 -0.001058 -0.000336 1.012347 \n", - "10 -0.001763 0.000000 1.082671 \n", - "13 -0.001407 -0.001022 1.064267 \n", - "16 -0.005266 -0.001054 1.028467 \n", + " soiling_rate_low soiling_rate_high inferred_start_loss \\\n", + "0 -0.001731 0.000000 1.030865 \n", + "1 -0.000893 -0.000131 0.991487 \n", + "4 -0.002043 -0.000014 1.059114 \n", + "8 -0.004121 -0.000682 1.051962 \n", + "9 -0.001641 -0.000681 1.007855 \n", "\n", - " inferred_end_loss length valid \n", - "6 0.996718 28 True \n", - "8 0.968893 62 True \n", - "10 1.064776 28 True \n", - "13 0.923230 116 True \n", - "16 0.971602 18 True " + " inferred_end_loss length valid \n", + "0 1.017286 36 True \n", + "1 0.958089 62 True \n", + "4 1.039045 27 True \n", + "8 0.991655 24 True \n", + "9 0.944064 53 True " ] }, - "execution_count": 21, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -707,14 +710,14 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\KANDERSO\\Software\\Anaconda3\\envs\\rdtools310\\lib\\site-packages\\rdtools\\filtering.py:641: UserWarning: The XGBoost filter is an experimental clipping filter that is still under development. The API, results, and default behaviors may change in future releases (including MINOR and PATCH). Use at your own risk!\n", + "C:\\Users\\kperry\\AppData\\Roaming\\Python\\Python37\\site-packages\\rdtools\\filtering.py:641: UserWarning: The XGBoost filter is an experimental clipping filter that is still under development. The API, results, and default behaviors may change in future releases (including MINOR and PATCH). Use at your own risk!\n", " warnings.warn(\"The XGBoost filter is an experimental clipping filter \"\n" ] } @@ -738,7 +741,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -824,7 +827,7 @@ "2010-02-25 14:20:00-07:00 True " ] }, - "execution_count": 23, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -836,7 +839,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -850,7 +853,7 @@ "Freq: T, dtype: bool" ] }, - "execution_count": 24, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -862,7 +865,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -870,71 +873,75 @@ "text/html": [ "