diff --git a/ITR/data/base_providers.py b/ITR/data/base_providers.py index 37c721b9..395f1543 100644 --- a/ITR/data/base_providers.py +++ b/ITR/data/base_providers.py @@ -226,7 +226,7 @@ def _validate_projected_trajectories(self, companies: List[ICompanyData]) -> Lis else: return companies - # Because this presently defaults to S1S2 always, targets spec'd for S1 only ro S1+S2+S3 are not well-handled. + # Because this presently defaults to S1S2 always, targets spec'd for S1 only, S2 only, or S1+S2+S3 are not well-handled. def _convert_projections_to_series(self, company: ICompanyData, feature: str, scope: EScope = EScope.S1S2) -> pd.Series: """ @@ -248,22 +248,20 @@ def _convert_projections_to_series(self, company: ICompanyData, feature: str, for s in scopes: projection_series[s] = pd.Series( {p['year']: p['value'] for p in company_dict[feature][s]['projections']}, - name=company.company_id, dtype=f'pint[{emissions_units}/{production_units}]') + name=company.company_id, dtype=f'pint[{emissions_units}/({production_units})]') series_adder = partial(pd.Series.add, fill_value=0) res = reduce(series_adder, projection_series.values()) return res elif len(projection_scopes) == 0: return pd.Series( {year: np.nan for year in range(self.historic_years[-1] + 1, self.projection_controls.TARGET_YEAR + 1)}, - name=company.company_id, dtype=f'pint[{emissions_units}/{production_units}]' + name=company.company_id, dtype=f'pint[{emissions_units}/({production_units})]' ) else: - # This clause is only accessed if the scope is S1S2 or S1S2S3 of which only one scope is provided. - projections = company_dict[feature][scopes[0]]['projections'] - # projections = [] + projections = company_dict[feature][list(projection_scopes.keys())[0]]['projections'] return pd.Series( {p['year']: p['value'] for p in projections}, - name=company.company_id, dtype=f'pint[{emissions_units}/{production_units}]') + name=company.company_id, dtype=f'pint[{emissions_units}/({production_units})]') def _calculate_target_projections(self, production_bm: BaseProviderProductionBenchmark): """ @@ -723,7 +721,6 @@ def project_ei_targets(self, company: ICompanyData, production_bm: pd.Series) -> warnings.warn(f"Emission intensity at base year for scope {scope} target for company " f"{company.company_name} is estimated with trajectory projection.") - # Removed condition base year > first_year. Do we care as long as base_year_qty is known? last_year, value_last_year = last_year_data.year, last_year_data.value target_year = target.target_end_year # Attribute target_reduction_pct of ITargetData is currently a fraction, not a percentage. diff --git a/ITR/data/data_warehouse.py b/ITR/data/data_warehouse.py index b3d232d9..326fd7b6 100644 --- a/ITR/data/data_warehouse.py +++ b/ITR/data/data_warehouse.py @@ -116,4 +116,7 @@ def _get_cumulative_emissions(self, projected_ei: pd.DataFrame, projected_produc :return: cumulative emissions based on weighted sum of emissions intensity * production """ projected_emissions = projected_ei.multiply(projected_production) - return projected_emissions.sum(axis=1).astype('pint[Mt CO2]') + projected_emissions = projected_emissions.applymap(lambda x: x if isinstance(x,float) else x if np.isfinite(x.m) else np.nan) + null_idx = projected_emissions.index[projected_emissions.isnull().all(axis=1)] + return pd.concat([projected_emissions.loc[null_idx, projected_emissions.columns[0]], + projected_emissions.loc[projected_emissions.index.difference(null_idx)].sum(axis=1)]).astype('pint[Mt CO2]') diff --git a/ITR/data/excel.py b/ITR/data/excel.py index 048b4991..87995154 100644 --- a/ITR/data/excel.py +++ b/ITR/data/excel.py @@ -22,8 +22,8 @@ # Excel spreadsheets don't have units elaborated, so we translate sectors to units -sector_to_production_metric = {'Electricity Utilities': 'GJ', 'Steel': 'Fe_ton', 'Oil & Gas': 'boe', 'Autos': 'passenger_km'} -sector_to_intensity_metric = {'Electricity Utilities': 't CO2/MWh', 'Steel': 't CO2/Fe_ton', 'Oil & Gas': 'kg CO2/boe', 'Autos': 'g CO2/passenger_km'} +sector_to_production_metric = {'Electricity Utilities': 'GJ', 'Steel': 'Fe_ton', 'Oil & Gas': 'boe', 'Autos': 'passenger km'} +sector_to_intensity_metric = {'Electricity Utilities': 't CO2/MWh', 'Steel': 't CO2/Fe_ton', 'Oil & Gas': 'kg CO2/boe', 'Autos': 'g CO2/(passenger km)'} # TODO: Force validation for excel benchmarks diff --git a/ITR/data/osc_units.py b/ITR/data/osc_units.py index b31caacf..5919a4f2 100644 --- a/ITR/data/osc_units.py +++ b/ITR/data/osc_units.py @@ -8,8 +8,7 @@ # openscm_units doesn't make it easy to set preprocessors. This is one way to do it. unit_registry.preprocessors=[ - lambda s1: s1.replace('passenger km', 'passenger_km'), - lambda s2: s2.replace('BoE', 'boe'), + lambda s1: s1.replace('BoE', 'boe'), ] PintType.ureg = unit_registry @@ -20,7 +19,7 @@ ureg.define("CO2e = CO2 = CO2eq = CO2_eq") ureg.define("Fe_ton = [produced_ton]") -ureg.define("passenger_km = nan km") +ureg.define("passenger = [passenger_unit]") # These are for later ureg.define('fraction = [] = frac') @@ -33,6 +32,8 @@ ureg.define("btu = Btu") ureg.define("boe = 5.712 GJ") +ureg.define("mboe = 1e3 boe") +ureg.define("mmboe = 1e6 boe") # These are for later still # ureg.define("HFC = [ HFC_emissions ]") diff --git a/ITR/data/template.py b/ITR/data/template.py index cf841dfe..d358a7f2 100644 --- a/ITR/data/template.py +++ b/ITR/data/template.py @@ -119,6 +119,15 @@ def _fixup_name(x): logger.error(error_message) raise ValueError(error_message) + # ignore company data that does not come with emissions and/or production metrics + missing_esg_metrics_df = df_fundamentals[ColumnsConfig.COMPANY_ID][ + df_fundamentals[ColumnsConfig.EMISSIONS_METRIC].isnull() | df_fundamentals[ColumnsConfig.PRODUCTION_METRIC].isnull()] + if len(missing_esg_metrics_df)>0: + logger.warning(f"Missing ESG metrics for companies with ID (will be ignored): " + f"{missing_esg_metrics_df.to_list()}.") + df_fundamentals = df_fundamentals[~df_fundamentals.index.isin(missing_esg_metrics_df.index)] + + # The nightmare of naming columns 20xx_metric instead of metric_20xx...and potentially dealing with data from 1990s... historic_columns = [col for col in df_fundamentals.columns if col[:1].isdigit()] historic_scopes = ['S1', 'S2', 'S3', 'S1S2', 'S1S2S3', 'production'] diff --git a/ITR/interfaces.py b/ITR/interfaces.py index 064b77cd..ad95c5ce 100644 --- a/ITR/interfaces.py +++ b/ITR/interfaces.py @@ -24,7 +24,7 @@ def unit_must_be_production(cls, v): return v if qty.is_compatible_with("Fe_ton"): return v - if qty.is_compatible_with("passenger_km"): + if qty.is_compatible_with("passenger km"): return v if qty.is_compatible_with("boe"): return v @@ -51,7 +51,7 @@ def units_must_be_EI(cls, v): return v if qty.is_compatible_with("t CO2/Fe_ton"): return v - if qty.is_compatible_with("g CO2/passenger_km"): + if qty.is_compatible_with("g CO2/(passenger km)"): return v if qty.is_compatible_with("kg CO2/boe"): return v @@ -464,11 +464,11 @@ def _fixup_ei_projections(self, projections, production_metric, emissions_metric inferred_production_metric = 'MWh' else: inferred_production_metric = 'Fe_ton' - inferred_ei_metric = f"{inferred_emissions_metric}/{inferred_production_metric}" + inferred_ei_metric = f"{inferred_emissions_metric}/({inferred_production_metric})" else: inferred_emissions_metric = emissions_metric['units'] inferred_production_metric = production_metric['units'] - inferred_ei_metric = f"{inferred_emissions_metric}/{inferred_production_metric}" + inferred_ei_metric = f"{inferred_emissions_metric}/({inferred_production_metric})" for scope in projections: if projections[scope] is None: continue @@ -509,7 +509,7 @@ def _fixup_historic_data(self, historic_data, production_metric, emissions_metri emissions_intensities = None else: emissions_intensities = {} - inferred_ei_metric = f"{inferred_emissions_metric}/{inferred_production_metric}" + inferred_ei_metric = f"{inferred_emissions_metric}/({inferred_production_metric})" for scope in historic_data['emissions_intensities']: emissions_intensities[scope] = self._fixup_year_value_list(IEIRealization, historic_data['emissions_intensities'][scope], None, inferred_ei_metric) model_historic_data = IHistoricData(productions=productions, emissions=emissions, emissions_intensities=emissions_intensities) diff --git a/ITR/portfolio_aggregation.py b/ITR/portfolio_aggregation.py index c657e6bc..d01b349d 100644 --- a/ITR/portfolio_aggregation.py +++ b/ITR/portfolio_aggregation.py @@ -117,9 +117,11 @@ def _calculate_aggregate_score(self, data: pd.DataFrame, input_column: str, # Calculate the total emissions of all companies emissions = data.loc[use_S1S2, self.c.COLS.GHG_SCOPE12].sum() + data.loc[use_S3, self.c.COLS.GHG_SCOPE3].sum() try: - weights_series = pd.Series((data[self.c.COLS.GHG_SCOPE12].where(use_S1S2,0) + data[self.c.COLS.GHG_SCOPE3].where(use_S3, 0)) \ - / emissions * data[input_column]) - return weights_series + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + weights_series = pd.Series((data[self.c.COLS.GHG_SCOPE12].where(use_S1S2,0) + data[self.c.COLS.GHG_SCOPE3].where(use_S3, 0)) \ + / emissions * data[input_column]) + return weights_series except ZeroDivisionError: raise ValueError("The total emissions should be higher than zero") diff --git a/docs/DataTemplateRequirements.rst b/docs/DataTemplateRequirements.rst index d9855320..27b446d6 100644 --- a/docs/DataTemplateRequirements.rst +++ b/docs/DataTemplateRequirements.rst @@ -231,7 +231,7 @@ The brackets listed near the top left corner of each executable cell will change Filing Issues and Updating the ITR Repository --------------------------------------------- -Once you are able to run the `quick_template_score_calc.ipynb` sample notebook with the provided sample data (:code:`examples/data/20220306 ITR Tool Sample Data.xlsx`), you are ready to start trying things with your own data. The notebook explains how to do this at the heading labeled :code:`Download/load the sample template data` before Cell 6. As you try loading your own data, you will inevitably find errors--sometimes with the data you receive, sometimes with the data you present to the tool, sometimes with the way the tool loads or does not load your data, sometimes with the way the tool interprets or presents your data. It is the goal of the Data Commons to streamline and simplify access to data so as to reduce the first to cases of errors, and it is the goal of the ITR project team to continuously improve the ITR tool to reduce the other cases of errors. In all cases, the correction of errors begins with an error reporting process and ends with an effective update process. +Once you are able to run the `quick_template_score_calc.ipynb` sample notebook with the provided sample data (:code:`examples/data/20220720 ITR Tool Sample Data.xlsx`), you are ready to start trying things with your own data. The notebook explains how to do this at the heading labeled :code:`Download/load the sample template data` before Cell 6. As you try loading your own data, you will inevitably find errors--sometimes with the data you receive, sometimes with the data you present to the tool, sometimes with the way the tool loads or does not load your data, sometimes with the way the tool interprets or presents your data. It is the goal of the Data Commons to streamline and simplify access to data so as to reduce the first to cases of errors, and it is the goal of the ITR project team to continuously improve the ITR tool to reduce the other cases of errors. In all cases, the correction of errors begins with an error reporting process and ends with an effective update process. To report errors, please use the GitHub Issues interface for the ITR tool: https://github.com/os-climate/ITR/issues @@ -245,8 +245,8 @@ At some point you will receive notice that your issue has been addressed with a 2. Change your directory to the top of your ITR tree: :code:`cd ~/os-climate/ITR` (or some such) 3. Pull changes from upstream: git pull 4. If git complains that you have modified some files (such as your notebook, which is "modified" every time you run it), you can - 1. remove the notebook file: :code:`rm examples/data/20220306\ ITR\ Tool\ Sample\ Data.xlsx` - 2. restore it from the updated repository: :code:`git restore examples/data/20220306\ ITR\ Tool\ Sample\ Data.xlsx` + 1. remove the notebook file: :code:`rm examples/data/20220720\ ITR\ Tool\ Sample\ Data.xlsx` + 2. restore it from the updated repository: :code:`git restore examples/data/20220720\ ITR\ Tool\ Sample\ Data.xlsx` 5. Restart your jupyter-lab server Over time you may do other things to your local repository that makes it difficult to sync with git. You can file an issue for help, you can do your own research (many of us find answers on github community forums or StackOverflow), or you can go with Option #1: run the installation process from top to bottom in a new directory. diff --git a/examples/ITR_UI.py b/examples/ITR_UI.py index a928ae8f..13295e23 100644 --- a/examples/ITR_UI.py +++ b/examples/ITR_UI.py @@ -2,6 +2,9 @@ # visit http://127.0.0.1:8050/ in your web browser +import argparse +import sys + import pandas as pd import numpy as np import json @@ -54,7 +57,16 @@ root = os.path.abspath('') # load company data -company_data="20220415 ITR Tool Sample Data.xlsx" # this file is provided initially +parser = argparse.ArgumentParser() +parser.add_argument('template', nargs='+', help='enter filename of XLSX data template') +parser.set_defaults(template="20220415 ITR Tool Sample Data.xlsx") +if len(sys.argv)>1: + print(sys.argv) + company_data=parser.parse_args(sys.argv).template[-1] + print(company_data) +else: + company_data="20220720 ITR Tool Sample Data.xlsx" # this file is provided initially + template_company_data = TemplateProviderCompany(excel_path=os.path.join(root, examples_dir, data_dir, company_data)) # load production benchmarks diff --git a/examples/data/20220720 ITR Tool Sample Data.xlsx b/examples/data/20220720 ITR Tool Sample Data.xlsx index d7570ba4..5f8529c7 100644 Binary files a/examples/data/20220720 ITR Tool Sample Data.xlsx and b/examples/data/20220720 ITR Tool Sample Data.xlsx differ diff --git a/examples/data/json-units/benchmark_EI_OECM.json b/examples/data/json-units/benchmark_EI_OECM.json index f7f50822..d313dbc6 100644 --- a/examples/data/json-units/benchmark_EI_OECM.json +++ b/examples/data/json-units/benchmark_EI_OECM.json @@ -1249,7 +1249,7 @@ { "sector": "Autos", "region": "Global", - "benchmark_metric": { "units": "g CO2/passenger_km" }, + "benchmark_metric": { "units": "g CO2/(passenger km)" }, "projections": [ { "year": 2019, @@ -1382,12 +1382,12 @@ ], "scenario name": "OECM 1.5 Degrees", "release date": "2022", - "unit": "t CO2/GJ" + "unit": "g CO2/(passenger km)" }, { "sector": "Autos", "region": "Europe", - "benchmark_metric": { "units": "g CO2/passenger_km" }, + "benchmark_metric": { "units": "g CO2/(passenger km)" }, "projections": [ { "year": 2019, @@ -1520,12 +1520,12 @@ ], "scenario name": "OECM 1.5 Degrees", "release date": "2022", - "unit": "t CO2/GJ" + "unit": "g CO2/(passenger km)" }, { "sector": "Autos", "region": "North America", - "benchmark_metric": { "units": "g CO2/passenger_km" }, + "benchmark_metric": { "units": "g CO2/(passenger km)" }, "projections": [ { "year": 2019, @@ -1658,7 +1658,7 @@ ], "scenario name": "OECM 1.5 Degrees", "release date": "2022", - "unit": "t CO2/GJ" + "unit": "g CO2/(passenger km)" } ] }, diff --git a/examples/data/json-units/benchmark_EI_TPI_1_5_degrees.json b/examples/data/json-units/benchmark_EI_TPI_1_5_degrees.json index 9c821962..0468f82e 100644 --- a/examples/data/json-units/benchmark_EI_TPI_1_5_degrees.json +++ b/examples/data/json-units/benchmark_EI_TPI_1_5_degrees.json @@ -969,9 +969,149 @@ "scenario name": "1.5 Degrees", "release date": "1-4-2022", "unit": "Carbon intensity (tonnes of CO2 per tonne of steel)" + }, + { + "sector": "Autos", + "region": "Global", + "benchmark_metric": { "units": "g CO2/(passenger km)" }, + "projections": [ + + + { + "year": 2019, + "value": 123.0 + }, + { + "year": 2020, + "value": 117.0 + }, + { + "year": 2021, + "value": 116.0 + }, + { + "year": 2022, + "value": 114.0 + }, + { + "year": 2023, + "value": 112.0 + }, + { + "year": 2024, + "value": 111.0 + }, + { + "year": 2025, + "value": 109.0 + }, + { + "year": 2026, + "value": 108.0 + }, + { + "year": 2027, + "value": 107.0 + }, + { + "year": 2028, + "value": 106.0 + }, + { + "year": 2029, + "value": 105.0 + }, + { + "year": 2030, + "value": 104.0 + }, + { + "year": 2031, + "value": 103.0 + }, + { + "year": 2032, + "value": 102.0 + }, + { + "year": 2033, + "value": 101.0 + }, + { + "year": 2034, + "value": 100.0 + }, + { + "year": 2035, + "value": 100.0 + }, + { + "year": 2036, + "value": 99.0 + }, + { + "year": 2037, + "value": 98.0 + }, + { + "year": 2038, + "value": 97.0 + }, + { + "year": 2039, + "value": 96.0 + }, + { + "year": 2040, + "value": 95.0 + }, + { + "year": 2041, + "value": 94.0 + }, + { + "year": 2042, + "value": 93.0 + }, + { + "year": 2043, + "value": 92.0 + }, + { + "year": 2044, + "value": 91.0 + }, + { + "year": 2045, + "value": 91.0 + }, + { + "year": 2046, + "value": 90.0 + }, + { + "year": 2047, + "value": 89.0 + }, + { + "year": 2048, + "value": 88.0 + }, + { + "year": 2049, + "value": 87.0 + }, + { + "year": 2050, + "value": 86.0 + } + ], + "scenario name": "1.5 Degrees", + "release date": "1-4-2022", + "unit": "g CO2/passenger kilometer" } ] }, "S3": null, "S1S2S3": null -} \ No newline at end of file +} diff --git a/examples/data/json-units/benchmark_EI_TPI_2_degrees.json b/examples/data/json-units/benchmark_EI_TPI_2_degrees.json index 2511dc6d..98ee8729 100644 --- a/examples/data/json-units/benchmark_EI_TPI_2_degrees.json +++ b/examples/data/json-units/benchmark_EI_TPI_2_degrees.json @@ -421,145 +421,7 @@ { "sector": "Autos", "region": "Global", - "benchmark_metric": { "units": "g CO2/km" }, - "projections": [ - { - "year": 2019, - "value": 119.0 - }, - { - "year": 2020, - "value": 113.0 - }, - { - "year": 2021, - "value": 104.0 - }, - { - "year": 2022, - "value": 95.0 - }, - { - "year": 2023, - "value": 86.0 - }, - { - "year": 2024, - "value": 77.0 - }, - { - "year": 2025, - "value": 68.0 - }, - { - "year": 2026, - "value": 62.0 - }, - { - "year": 2027, - "value": 57.0 - }, - { - "year": 2028, - "value": 51.0 - }, - { - "year": 2029, - "value": 46.0 - }, - { - "year": 2030, - "value": 40.0 - }, - { - "year": 2031, - "value": 37.0 - }, - { - "year": 2032, - "value": 34.0 - }, - { - "year": 2033, - "value": 31.0 - }, - { - "year": 2034, - "value": 27.0 - }, - { - "year": 2035, - "value": 24.0 - }, - { - "year": 2036, - "value": 22.0 - }, - { - "year": 2037, - "value": 21.0 - }, - { - "year": 2038, - "value": 19.0 - }, - { - "year": 2039, - "value": 17.0 - }, - { - "year": 2040, - "value": 15.0 - }, - { - "year": 2041, - "value": 14.0 - }, - { - "year": 2042, - "value": 13.0 - }, - { - "year": 2043, - "value": 12.0 - }, - { - "year": 2044, - "value": 11.0 - }, - { - "year": 2045, - "value": 10.0 - }, - { - "year": 2046, - "value": 9.0 - }, - { - "year": 2047, - "value": 8.0 - }, - { - "year": 2048, - "value": 8.0 - }, - { - "year": 2049, - "value": 7.0 - }, - { - "year": 2050, - "value": 6.0 - } - ], - "scenario name": "2 Degrees (High Efficiency)", - "release date": "1-12-2020", - "unit": "Average new vehicle emissions (grams of CO2 per kilometre [NEDC])" - }, - { - "sector": "Autos", - "region": "Global", - "benchmark_metric": { "units": "g CO2/km" }, + "benchmark_metric": { "units": "g CO2/(passenger km)" }, "projections": [ { "year": 2019, @@ -698,4 +560,4 @@ }, "S3": null, "S1S2S3": null -} \ No newline at end of file +} diff --git a/examples/data/json-units/benchmark_EI_TPI_below_2_degrees.json b/examples/data/json-units/benchmark_EI_TPI_below_2_degrees.json index d7631fbe..47fa2ac4 100644 --- a/examples/data/json-units/benchmark_EI_TPI_below_2_degrees.json +++ b/examples/data/json-units/benchmark_EI_TPI_below_2_degrees.json @@ -969,9 +969,147 @@ "scenario name": "Below 2 Degrees", "release date": "1-4-2022", "unit": "Carbon intensity (tonnes of CO2 per tonne of steel)" + }, + { + "sector": "Autos", + "region": "Global", + "benchmark_metric": { "units": "g CO2/(passenger km)" }, + "projections": [ + { + "year": 2019, + "value": 119.0 + }, + { + "year": 2020, + "value": 113.0 + }, + { + "year": 2021, + "value": 104.0 + }, + { + "year": 2022, + "value": 95.0 + }, + { + "year": 2023, + "value": 86.0 + }, + { + "year": 2024, + "value": 77.0 + }, + { + "year": 2025, + "value": 68.0 + }, + { + "year": 2026, + "value": 62.0 + }, + { + "year": 2027, + "value": 57.0 + }, + { + "year": 2028, + "value": 51.0 + }, + { + "year": 2029, + "value": 46.0 + }, + { + "year": 2030, + "value": 40.0 + }, + { + "year": 2031, + "value": 37.0 + }, + { + "year": 2032, + "value": 34.0 + }, + { + "year": 2033, + "value": 31.0 + }, + { + "year": 2034, + "value": 27.0 + }, + { + "year": 2035, + "value": 24.0 + }, + { + "year": 2036, + "value": 22.0 + }, + { + "year": 2037, + "value": 21.0 + }, + { + "year": 2038, + "value": 19.0 + }, + { + "year": 2039, + "value": 17.0 + }, + { + "year": 2040, + "value": 15.0 + }, + { + "year": 2041, + "value": 14.0 + }, + { + "year": 2042, + "value": 13.0 + }, + { + "year": 2043, + "value": 12.0 + }, + { + "year": 2044, + "value": 11.0 + }, + { + "year": 2045, + "value": 10.0 + }, + { + "year": 2046, + "value": 9.0 + }, + { + "year": 2047, + "value": 8.0 + }, + { + "year": 2048, + "value": 8.0 + }, + { + "year": 2049, + "value": 7.0 + }, + { + "year": 2050, + "value": 6.0 + } + ], + "scenario name": "Below 2 Degrees", + "release date": "1-4-2022", + "unit": "Average new vehicle emissions (grams of CO2 per kilometre [NEDC])" } ] }, "S3": null, "S1S2S3": null -} \ No newline at end of file +} diff --git a/examples/quick_template_score_calc.ipynb b/examples/quick_template_score_calc.ipynb index 8480af17..812259b7 100644 --- a/examples/quick_template_score_calc.ipynb +++ b/examples/quick_template_score_calc.ipynb @@ -71,11 +71,12 @@ "['/Users/michael/Documents/GitHub/ITR/examples',\n", " '/Users/michael/Documents/GitHub/ITR/examples',\n", " '/Library/Application Support/Blackmagic Design/DaVinci Resolve/Developer/Scripting/Modules',\n", - " '/Users/michael/opt/miniconda3/envs/ITR/lib/python310.zip',\n", - " '/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10',\n", - " '/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/lib-dynload',\n", + " '/Users/michael/opt/miniconda3/envs/itr_env/lib/python39.zip',\n", + " '/Users/michael/opt/miniconda3/envs/itr_env/lib/python3.9',\n", + " '/Users/michael/opt/miniconda3/envs/itr_env/lib/python3.9/lib-dynload',\n", " '',\n", - " '/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages',\n", + " '/Users/michael/.local/lib/python3.9/site-packages',\n", + " '/Users/michael/opt/miniconda3/envs/itr_env/lib/python3.9/site-packages',\n", " '/Users/michael/Documents/GitHub/ITR']" ] }, @@ -275,8 +276,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "2022-07-20 07:45:17,487 - ITR.data.template - WARNING - Missing market capitalisation values are estimated for companies with ID: ['US18551QAA58', 'CA2908761018'].\n", - "2022-07-20 07:45:17,830 - ITR.data.template - WARNING - Missing target start year set to 2021 for companies with ID: ['US001000AUTO', 'US0185223007', 'US0188021085', 'US0236081024', 'US0236081024', 'US0255371017', 'US05351W1036', 'US0921131092', 'US0921131092', 'US1442851036', 'US18551QAA58', 'US25746U1097', 'US26441C2044', 'US30034W1062', 'US5526901096']\n" + "2022-09-11 12:10:51,977 - ITR.data.template - WARNING - Missing ESG metrics for companies with ID (will be ignored): ['US3704421052'].\n", + "2022-09-11 12:10:51,987 - ITR.data.template - WARNING - Missing market capitalisation values are estimated for companies with ID: ['US18551QAA58', 'CA2908761018'].\n", + "2022-09-11 12:10:52,474 - ITR.data.template - WARNING - Missing target start year set to 2021 for companies with ID: ['US0185223007', 'US0188021085', 'US0236081024', 'US0236081024', 'US0255371017', 'US05351W1036', 'US0921131092', 'US0921131092', 'US1442851036', 'US18551QAA58', 'US25746U1097', 'US26441C2044', 'US30034W1062', 'US5526901096']\n" ] } ], @@ -319,7 +321,7 @@ { "data": { "text/plain": [ - "ProductionMetric(units='boe')" + "ProductionMetric(units='GWh')" ] }, "execution_count": 9, @@ -336,115 +338,6 @@ "execution_count": 10, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "2022-07-20 07:45:27,789 - ITR.data.base_providers - WARNING - No target data for Exelon Corp.\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n" - ] - }, { "name": "stdout", "output_type": "stream", @@ -453,18 +346,6 @@ "Benchmark Global Budget = 521.0526315789474 CO2 * gigametric_ton\n", "AFOLU included = False\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n", - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n" - ] } ], "source": [ @@ -485,13 +366,13 @@ { "data": { "text/html": [ - "1.0 CO2 kilogram/passenger_km" + "1.0 CO2 kilogram/(kilometer passenger)" ], "text/latex": [ - "$1.0\\ \\frac{\\mathrm{CO2} \\cdot \\mathrm{kilogram}}{\\mathrm{passenger\\_km}}$" + "$1.0\\ \\frac{\\mathrm{CO2} \\cdot \\mathrm{kilogram}}{\\left(\\mathrm{kilometer} \\cdot \\mathrm{passenger}\\right)}$" ], "text/plain": [ - "1.0 " + "1.0 " ] }, "execution_count": 11, @@ -500,7 +381,7 @@ } ], "source": [ - "ureg(\"kg CO2/passenger km\")" + "ureg(\"kg CO2/(passenger km)\")" ] }, { @@ -516,7 +397,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -549,44 +430,44 @@ " \n", " \n", " \n", - " 50\n", + " 59\n", " Versant Power\n", " NQZVQT2P5IUF2PGA1Q48\n", " CA2908761018\n", " CA2908761018\n", - " 245914\n", + " 128664\n", " \n", " \n", - " 51\n", + " 60\n", " Vistra Corp.\n", " 549300KP43CPCUJOOG15\n", " US92840M1027\n", " US92840M1027\n", - " 200210\n", + " 36711\n", " \n", " \n", - " 52\n", + " 61\n", " WEC Energy Group\n", " 549300IGLYTZUK3PVP70\n", " US92939U1060\n", " US92939U1060\n", - " 130869\n", + " 118509\n", " \n", " \n", - " 53\n", + " 62\n", " WORTHINGTON INDUSTRIES INC\n", " 1WRCIANKYOIK6KYE5E82\n", " US9818111026\n", " US9818111026\n", - " 38704\n", + " 98818\n", " \n", " \n", - " 54\n", + " 63\n", " Xcel Energy, Inc.\n", " LGJNMI9GH8XIDG5RCM61\n", " US98389B1008\n", " US98389B1008\n", - " 140754\n", + " 142775\n", " \n", " \n", "\n", @@ -594,18 +475,18 @@ ], "text/plain": [ " company_name company_lei company_id \\\n", - "50 Versant Power NQZVQT2P5IUF2PGA1Q48 CA2908761018 \n", - "51 Vistra Corp. 549300KP43CPCUJOOG15 US92840M1027 \n", - "52 WEC Energy Group 549300IGLYTZUK3PVP70 US92939U1060 \n", - "53 WORTHINGTON INDUSTRIES INC 1WRCIANKYOIK6KYE5E82 US9818111026 \n", - "54 Xcel Energy, Inc. LGJNMI9GH8XIDG5RCM61 US98389B1008 \n", + "59 Versant Power NQZVQT2P5IUF2PGA1Q48 CA2908761018 \n", + "60 Vistra Corp. 549300KP43CPCUJOOG15 US92840M1027 \n", + "61 WEC Energy Group 549300IGLYTZUK3PVP70 US92939U1060 \n", + "62 WORTHINGTON INDUSTRIES INC 1WRCIANKYOIK6KYE5E82 US9818111026 \n", + "63 Xcel Energy, Inc. LGJNMI9GH8XIDG5RCM61 US98389B1008 \n", "\n", " company_isin investment_value \n", - "50 CA2908761018 245914 \n", - "51 US92840M1027 200210 \n", - "52 US92939U1060 130869 \n", - "53 US9818111026 38704 \n", - "54 US98389B1008 140754 " + "59 CA2908761018 128664 \n", + "60 US92840M1027 36711 \n", + "61 US92939U1060 118509 \n", + "62 US9818111026 98818 \n", + "63 US98389B1008 142775 " ] }, "metadata": {}, @@ -626,7 +507,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -643,18 +524,9 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n" - ] - } - ], + "outputs": [], "source": [ "temperature_score = TemperatureScore(\n", " time_frames = [ETimeFrames.LONG],\n", @@ -673,7 +545,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -706,450 +578,101 @@ " \n", " \n", " 0\n", - " Oil and Gas A\n", - " LONG\n", - " S1S2\n", - " 1.47\n", - " \n", - " \n", - " 1\n", - " Oil and Gas B\n", - " LONG\n", - " S1S2\n", - " 1.41\n", - " \n", - " \n", - " 2\n", " AES Corp.\n", " LONG\n", " S1S2\n", - " 1.89\n", + " 1.93\n", " \n", " \n", - " 3\n", + " 1\n", " ALLETE, Inc.\n", " LONG\n", " S1S2\n", - " 1.77\n", + " 1.98\n", " \n", " \n", - " 4\n", + " 2\n", " Alliant Energy\n", " LONG\n", " S1S2\n", - " 1.75\n", + " 1.85\n", " \n", " \n", - " 5\n", + " 3\n", " Ameren Corp.\n", " LONG\n", " S1S2\n", - " 2.42\n", + " 2.57\n", " \n", " \n", - " 6\n", + " 4\n", " American Electric Power Co., Inc.\n", " LONG\n", " S1S2\n", - " 2.06\n", - " \n", - " \n", - " 7\n", - " Avangrid, Inc.\n", - " LONG\n", - " S1S2\n", - " 2.22\n", - " \n", - " \n", - " 8\n", - " Black Hills Corp.\n", - " LONG\n", - " S1S2\n", - " 2.13\n", - " \n", - " \n", - " 9\n", - " CARPENTER TECHNOLOGY CORP\n", - " LONG\n", - " S1S2\n", - " 1.92\n", - " \n", - " \n", - " 10\n", - " CLEVELAND-CLIFFS INC\n", - " LONG\n", - " S1S2\n", - " 1.56\n", - " \n", - " \n", - " 11\n", - " CMS Energy Corp.\n", - " LONG\n", - " S1S2\n", - " 2.16\n", - " \n", - " \n", - " 12\n", - " COMMERCIAL METALS CO\n", - " LONG\n", - " S1S2\n", - " 1.6\n", - " \n", - " \n", - " 13\n", - " Cleco Partners LP\n", - " LONG\n", - " S1S2\n", - " 2.55\n", - " \n", - " \n", - " 14\n", - " Consolidated Edison, Inc.\n", - " LONG\n", - " S1S2\n", - " 2.2\n", - " \n", - " \n", - " 15\n", - " DTE Energy\n", - " LONG\n", - " S1S2\n", - " 3.02\n", - " \n", - " \n", - " 16\n", - " Dominion Energy\n", - " LONG\n", - " S1S2\n", - " 1.85\n", - " \n", - " \n", - " 17\n", - " Duke Energy Corp.\n", - " LONG\n", - " S1S2\n", - " 1.93\n", - " \n", - " \n", - " 18\n", - " Edison International\n", - " LONG\n", - " S1S2\n", - " 3.16\n", - " \n", - " \n", - " 19\n", - " Entergy Corp.\n", - " LONG\n", - " S1S2\n", - " 1.93\n", - " \n", - " \n", - " 20\n", - " Evergy, Inc.\n", - " LONG\n", - " S1S2\n", - " 1.89\n", - " \n", - " \n", - " 21\n", - " Eversource Energy\n", - " LONG\n", - " S1S2\n", - " 1.23\n", - " \n", - " \n", - " 22\n", - " Exelon Corp.\n", - " LONG\n", - " S1S2\n", - " 4.37\n", - " \n", - " \n", - " 23\n", - " FirstEnergy Corp.\n", - " LONG\n", - " S1S2\n", - " 1.79\n", - " \n", - " \n", - " 24\n", - " Fortis, Inc.\n", - " LONG\n", - " S1S2\n", - " 1.7\n", - " \n", - " \n", - " 25\n", - " GERDAU S.A.\n", - " LONG\n", - " S1S2\n", - " 1.63\n", - " \n", - " \n", - " 26\n", - " Hawaiian Electric Industries, Inc.\n", - " LONG\n", - " S1S2\n", - " 2.61\n", - " \n", - " \n", - " 27\n", - " MDU Resources Group\n", - " LONG\n", - " S1S2\n", - " 2.48\n", - " \n", - " \n", - " 28\n", - " NUCOR CORP\n", - " LONG\n", - " S1S2\n", - " 1.73\n", - " \n", - " \n", - " 29\n", - " National Grid PLC\n", - " LONG\n", - " S1S2\n", - " 2.02\n", - " \n", - " \n", - " 30\n", - " NextEra Energy, Inc.\n", - " LONG\n", - " S1S2\n", - " 1.86\n", - " \n", - " \n", - " 31\n", - " NIPPON STEEL CORP\n", - " LONG\n", - " S1S2\n", - " 1.92\n", - " \n", - " \n", - " 32\n", - " Nisource Inc.\n", - " LONG\n", - " S1S2\n", - " 2.02\n", - " \n", - " \n", - " 33\n", - " Northwestern Corp.\n", - " LONG\n", - " S1S2\n", - " 1.85\n", - " \n", - " \n", - " 34\n", - " OG&E Energy Corp.\n", - " LONG\n", - " S1S2\n", - " 2.45\n", - " \n", - " \n", - " 35\n", - " PG&E Corp.\n", - " LONG\n", - " S1S2\n", - " 2.71\n", - " \n", - " \n", - " 36\n", - " PNM Resources, Inc.\n", - " LONG\n", - " S1S2\n", - " 2.05\n", - " \n", - " \n", - " 37\n", - " POSCO\n", - " LONG\n", - " S1S2\n", - " 1.94\n", - " \n", - " \n", - " 38\n", - " PPL Corp.\n", - " LONG\n", - " S1S2\n", - " 2.39\n", - " \n", - " \n", - " 39\n", - " Pinnacle West Capital Corp.\n", - " LONG\n", - " S1S2\n", - " 2.31\n", - " \n", - " \n", - " 40\n", - " Portland General Electric Co.\n", - " LONG\n", - " S1S2\n", - " 1.87\n", - " \n", - " \n", - " 41\n", - " Public Service Enterprise Group\n", - " LONG\n", - " S1S2\n", - " 1.53\n", - " \n", - " \n", - " 42\n", - " Sempra\n", - " LONG\n", - " S1S2\n", - " 2.54\n", - " \n", - " \n", - " 43\n", - " Southern Co.\n", - " LONG\n", - " S1S2\n", - " 2.01\n", - " \n", - " \n", - " 44\n", - " STEEL DYNAMICS INC\n", - " LONG\n", - " S1S2\n", - " 1.81\n", - " \n", - " \n", - " 45\n", - " TC Energy Corp.\n", - " LONG\n", - " S1S2\n", - " 2.83\n", - " \n", - " \n", - " 46\n", - " TENARIS SA\n", - " LONG\n", - " S1S2\n", - " 1.62\n", - " \n", - " \n", - " 47\n", - " TERNIUM S.A.\n", - " LONG\n", - " S1S2\n", - " 1.73\n", + " 2.19\n", " \n", " \n", - " 48\n", - " TIMKENSTEEL CORP\n", - " LONG\n", - " S1S2\n", - " 1.59\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", " \n", " \n", - " 49\n", - " UNITED STATES STEEL CORP\n", - " LONG\n", - " S1S2\n", - " 1.76\n", - " \n", - " \n", - " 50\n", + " 59\n", " Versant Power\n", " LONG\n", " S1S2\n", - " 1.58\n", + " 1.81\n", " \n", " \n", - " 51\n", + " 60\n", " Vistra Corp.\n", " LONG\n", " S1S2\n", - " 2.36\n", + " 2.44\n", " \n", " \n", - " 52\n", + " 61\n", " WEC Energy Group\n", " LONG\n", " S1S2\n", - " 1.95\n", + " 2.15\n", " \n", " \n", - " 53\n", + " 62\n", " WORTHINGTON INDUSTRIES INC\n", " LONG\n", " S1S2\n", - " 1.32\n", + " 1.34\n", " \n", " \n", - " 54\n", + " 63\n", " Xcel Energy, Inc.\n", " LONG\n", " S1S2\n", - " 1.8\n", + " 1.82\n", " \n", " \n", "\n", + "

64 rows × 4 columns

\n", "" ], "text/plain": [ - " company_name time_frame scope temperature_score\n", - "0 Oil and Gas A LONG S1S2 1.47\n", - "1 Oil and Gas B LONG S1S2 1.41\n", - "2 AES Corp. LONG S1S2 1.89\n", - "3 ALLETE, Inc. LONG S1S2 1.77\n", - "4 Alliant Energy LONG S1S2 1.75\n", - "5 Ameren Corp. LONG S1S2 2.42\n", - "6 American Electric Power Co., Inc. LONG S1S2 2.06\n", - "7 Avangrid, Inc. LONG S1S2 2.22\n", - "8 Black Hills Corp. LONG S1S2 2.13\n", - "9 CARPENTER TECHNOLOGY CORP LONG S1S2 1.92\n", - "10 CLEVELAND-CLIFFS INC LONG S1S2 1.56\n", - "11 CMS Energy Corp. LONG S1S2 2.16\n", - "12 COMMERCIAL METALS CO LONG S1S2 1.6\n", - "13 Cleco Partners LP LONG S1S2 2.55\n", - "14 Consolidated Edison, Inc. LONG S1S2 2.2\n", - "15 DTE Energy LONG S1S2 3.02\n", - "16 Dominion Energy LONG S1S2 1.85\n", - "17 Duke Energy Corp. LONG S1S2 1.93\n", - "18 Edison International LONG S1S2 3.16\n", - "19 Entergy Corp. LONG S1S2 1.93\n", - "20 Evergy, Inc. LONG S1S2 1.89\n", - "21 Eversource Energy LONG S1S2 1.23\n", - "22 Exelon Corp. LONG S1S2 4.37\n", - "23 FirstEnergy Corp. LONG S1S2 1.79\n", - "24 Fortis, Inc. LONG S1S2 1.7\n", - "25 GERDAU S.A. LONG S1S2 1.63\n", - "26 Hawaiian Electric Industries, Inc. LONG S1S2 2.61\n", - "27 MDU Resources Group LONG S1S2 2.48\n", - "28 NUCOR CORP LONG S1S2 1.73\n", - "29 National Grid PLC LONG S1S2 2.02\n", - "30 NextEra Energy, Inc. LONG S1S2 1.86\n", - "31 NIPPON STEEL CORP LONG S1S2 1.92\n", - "32 Nisource Inc. LONG S1S2 2.02\n", - "33 Northwestern Corp. LONG S1S2 1.85\n", - "34 OG&E Energy Corp. LONG S1S2 2.45\n", - "35 PG&E Corp. LONG S1S2 2.71\n", - "36 PNM Resources, Inc. LONG S1S2 2.05\n", - "37 POSCO LONG S1S2 1.94\n", - "38 PPL Corp. LONG S1S2 2.39\n", - "39 Pinnacle West Capital Corp. LONG S1S2 2.31\n", - "40 Portland General Electric Co. LONG S1S2 1.87\n", - "41 Public Service Enterprise Group LONG S1S2 1.53\n", - "42 Sempra LONG S1S2 2.54\n", - "43 Southern Co. LONG S1S2 2.01\n", - "44 STEEL DYNAMICS INC LONG S1S2 1.81\n", - "45 TC Energy Corp. LONG S1S2 2.83\n", - "46 TENARIS SA LONG S1S2 1.62\n", - "47 TERNIUM S.A. LONG S1S2 1.73\n", - "48 TIMKENSTEEL CORP LONG S1S2 1.59\n", - "49 UNITED STATES STEEL CORP LONG S1S2 1.76\n", - "50 Versant Power LONG S1S2 1.58\n", - "51 Vistra Corp. LONG S1S2 2.36\n", - "52 WEC Energy Group LONG S1S2 1.95\n", - "53 WORTHINGTON INDUSTRIES INC LONG S1S2 1.32\n", - "54 Xcel Energy, Inc. LONG S1S2 1.8" + " company_name time_frame scope temperature_score\n", + "0 AES Corp. LONG S1S2 1.93\n", + "1 ALLETE, Inc. LONG S1S2 1.98\n", + "2 Alliant Energy LONG S1S2 1.85\n", + "3 Ameren Corp. LONG S1S2 2.57\n", + "4 American Electric Power Co., Inc. LONG S1S2 2.19\n", + ".. ... ... ... ...\n", + "59 Versant Power LONG S1S2 1.81\n", + "60 Vistra Corp. LONG S1S2 2.44\n", + "61 WEC Energy Group LONG S1S2 2.15\n", + "62 WORTHINGTON INDUSTRIES INC LONG S1S2 1.34\n", + "63 Xcel Energy, Inc. LONG S1S2 1.82\n", + "\n", + "[64 rows x 4 columns]" ] }, "metadata": {}, @@ -1172,7 +695,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -1190,22 +713,22 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "2.028177727328954 delta_degree_Celsius" + "1.9663907886496512 delta_degree_Celsius" ], "text/latex": [ - "$2.028177727328954\\ \\mathrm{delta\\_degree\\_Celsius}$" + "$1.9663907886496512\\ \\mathrm{delta\\_degree\\_Celsius}$" ], "text/plain": [ - "2.028177727328954 " + "1.9663907886496512 " ] }, - "execution_count": 18, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -1237,20 +760,11 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n" - ] - } - ], + "outputs": [], "source": [ "grouping = ['sector', 'region']\n", "temperature_score.grouping = grouping\n", @@ -1274,14 +788,14 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ - "
" + "
" ] }, "metadata": { @@ -1297,7 +811,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -1334,16 +848,16 @@ " Steel-Asia\n", " NIPPON STEEL CORP\n", " JP3381000003\n", - " 1.92 delta_degree_Celsius\n", - " 64.47775290245785 percent\n", + " 1.9 delta_degree_Celsius\n", + " 63.17325793838325 percent\n", " \n", " \n", " 1\n", " Steel-Asia\n", " POSCO\n", " KR7005490008\n", - " 1.94 delta_degree_Celsius\n", - " 35.52224709754215 percent\n", + " 1.91 delta_degree_Celsius\n", + " 36.82674206161675 percent\n", " \n", " \n", "\n", @@ -1351,15 +865,15 @@ ], "text/plain": [ " group company_name company_id temperature_score \\\n", - "0 Steel-Asia NIPPON STEEL CORP JP3381000003 1.92 delta_degree_Celsius \n", - "1 Steel-Asia POSCO KR7005490008 1.94 delta_degree_Celsius \n", + "0 Steel-Asia NIPPON STEEL CORP JP3381000003 1.9 delta_degree_Celsius \n", + "1 Steel-Asia POSCO KR7005490008 1.91 delta_degree_Celsius \n", "\n", " contribution_relative \n", - "0 64.47775290245785 percent \n", - "1 35.52224709754215 percent " + "0 63.17325793838325 percent \n", + "1 36.82674206161675 percent " ] }, - "execution_count": 21, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -1393,18 +907,9 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 30, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/michael/opt/miniconda3/envs/ITR/lib/python3.10/site-packages/pint_pandas/pint_array.py:648: UnitStrippedWarning: The unit of the quantity is stripped when downcasting to ndarray.\n", - " return np.array(qtys, dtype=\"object\", copy=copy)\n" - ] - } - ], + "outputs": [], "source": [ "time_frames = [ETimeFrames.LONG]\n", "scopes = [EScope.S1S2]\n", @@ -1413,7 +918,7 @@ "\n", "temperature_score = TemperatureScore(time_frames=time_frames,\n", " scopes=scopes,\n", - " grouping=grouping)\n", + " grouping=grouping, aggregation_method=PortfolioAggregationMethod.WATS)\n", "enhanced_portfolio = temperature_score.calculate(data_warehouse=template_provider, portfolio=companies)\n", "aggregated_portfolio = temperature_score.aggregate_scores(enhanced_portfolio)\n", "with warnings.catch_warnings():\n", @@ -1423,12 +928,12 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 29, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -1454,7 +959,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 23, "metadata": {}, "outputs": [ { @@ -1489,104 +994,104 @@ " \n", " \n", " \n", - " 7\n", - " STEEL DYNAMICS INC\n", - " US8581191009\n", + " 0\n", + " POSCO\n", + " KR7005490008\n", " Steel\n", - " 2.7558113586151483 percent\n", - " 1.81 delta_degree_Celsius\n", + " 18.0654098665291 percent\n", + " 1.91 delta_degree_Celsius\n", " 0.00\n", - " 3.09\n", + " 1.01\n", " \n", " \n", - " 11\n", - " CLEVELAND-CLIFFS INC\n", - " US1858991011\n", + " 1\n", + " NIPPON STEEL CORP\n", + " JP3381000003\n", " Steel\n", - " 2.5575109683489123 percent\n", - " 1.56 delta_degree_Celsius\n", - " 0.01\n", - " 3.33\n", + " 17.50905233506998 percent\n", + " 1.9 delta_degree_Celsius\n", + " 0.00\n", + " 1.75\n", " \n", " \n", - " 22\n", + " 6\n", " UNITED STATES STEEL CORP\n", " US9129091081\n", " Steel\n", - " 1.9636631996367924 percent\n", - " 1.76 delta_degree_Celsius\n", - " 0.01\n", - " 2.26\n", + " 5.7650266504767655 percent\n", + " 1.74 delta_degree_Celsius\n", + " 0.00\n", + " 1.35\n", " \n", " \n", - " 23\n", + " 7\n", + " CLEVELAND-CLIFFS INC\n", + " US1858991011\n", + " Steel\n", + " 5.747295879148133 percent\n", + " 1.57 delta_degree_Celsius\n", + " 0.00\n", + " 1.02\n", + " \n", + " \n", + " 8\n", + " TERNIUM S.A.\n", + " US8808901081\n", + " Steel\n", + " 3.5293688797543474 percent\n", + " 1.72 delta_degree_Celsius\n", + " 0.03\n", + " 1.25\n", + " \n", + " \n", + " 10\n", " GERDAU S.A.\n", " US3737371050\n", " Steel\n", - " 1.9450148926165305 percent\n", - " 1.63 delta_degree_Celsius\n", + " 2.44003972308551 percent\n", + " 1.73 delta_degree_Celsius\n", " 0.01\n", - " 2.42\n", + " 1.26\n", " \n", " \n", - " 25\n", + " 11\n", " NUCOR CORP\n", " US6703461052\n", " Steel\n", - " 1.851255548080782 percent\n", - " 1.73 delta_degree_Celsius\n", + " 2.15935318326576 percent\n", + " 1.71 delta_degree_Celsius\n", " 0.00\n", - " 2.17\n", + " 0.78\n", " \n", " \n", - " 27\n", - " NIPPON STEEL CORP\n", - " JP3381000003\n", + " 13\n", + " STEEL DYNAMICS INC\n", + " US8581191009\n", " Steel\n", - " 1.8125153154159122 percent\n", - " 1.92 delta_degree_Celsius\n", + " 1.0886541859141898 percent\n", + " 1.83 delta_degree_Celsius\n", " 0.00\n", - " 1.91\n", + " 1.41\n", " \n", " \n", - " 34\n", + " 14\n", " COMMERCIAL METALS CO\n", " US2017231034\n", " Steel\n", - " 1.5263437189477258 percent\n", - " 1.6 delta_degree_Celsius\n", - " 0.01\n", - " 1.93\n", - " \n", - " \n", - " 36\n", - " TIMKENSTEEL CORP\n", - " US8873991033\n", - " Steel\n", - " 1.182843558893695 percent\n", - " 1.59 delta_degree_Celsius\n", - " 0.03\n", - " 1.51\n", - " \n", - " \n", - " 41\n", - " POSCO\n", - " KR7005490008\n", - " Steel\n", - " 0.9985555327848478 percent\n", - " 1.94 delta_degree_Celsius\n", + " 0.5340128507864669 percent\n", + " 1.66 delta_degree_Celsius\n", " 0.00\n", - " 1.04\n", + " 0.82\n", " \n", " \n", - " 44\n", + " 16\n", " TENARIS SA\n", " US88031M1099\n", " Steel\n", - " 0.7751761399822685 percent\n", - " 1.62 delta_degree_Celsius\n", - " 0.01\n", - " 0.97\n", + " 0.3206960173166971 percent\n", + " 1.71 delta_degree_Celsius\n", + " 0.03\n", + " 1.62\n", " \n", " \n", "\n", @@ -1594,31 +1099,31 @@ ], "text/plain": [ " company_name company_id sector contribution \\\n", - "7 STEEL DYNAMICS INC US8581191009 Steel 2.7558113586151483 percent \n", - "11 CLEVELAND-CLIFFS INC US1858991011 Steel 2.5575109683489123 percent \n", - "22 UNITED STATES STEEL CORP US9129091081 Steel 1.9636631996367924 percent \n", - "23 GERDAU S.A. US3737371050 Steel 1.9450148926165305 percent \n", - "25 NUCOR CORP US6703461052 Steel 1.851255548080782 percent \n", - "27 NIPPON STEEL CORP JP3381000003 Steel 1.8125153154159122 percent \n", - "34 COMMERCIAL METALS CO US2017231034 Steel 1.5263437189477258 percent \n", - "36 TIMKENSTEEL CORP US8873991033 Steel 1.182843558893695 percent \n", - "41 POSCO KR7005490008 Steel 0.9985555327848478 percent \n", - "44 TENARIS SA US88031M1099 Steel 0.7751761399822685 percent \n", + "0 POSCO KR7005490008 Steel 18.0654098665291 percent \n", + "1 NIPPON STEEL CORP JP3381000003 Steel 17.50905233506998 percent \n", + "6 UNITED STATES STEEL CORP US9129091081 Steel 5.7650266504767655 percent \n", + "7 CLEVELAND-CLIFFS INC US1858991011 Steel 5.747295879148133 percent \n", + "8 TERNIUM S.A. US8808901081 Steel 3.5293688797543474 percent \n", + "10 GERDAU S.A. US3737371050 Steel 2.44003972308551 percent \n", + "11 NUCOR CORP US6703461052 Steel 2.15935318326576 percent \n", + "13 STEEL DYNAMICS INC US8581191009 Steel 1.0886541859141898 percent \n", + "14 COMMERCIAL METALS CO US2017231034 Steel 0.5340128507864669 percent \n", + "16 TENARIS SA US88031M1099 Steel 0.3206960173166971 percent \n", "\n", " temperature_score ownership_percentage portfolio_percentage \n", - "7 1.81 delta_degree_Celsius 0.00 3.09 \n", - "11 1.56 delta_degree_Celsius 0.01 3.33 \n", - "22 1.76 delta_degree_Celsius 0.01 2.26 \n", - "23 1.63 delta_degree_Celsius 0.01 2.42 \n", - "25 1.73 delta_degree_Celsius 0.00 2.17 \n", - "27 1.92 delta_degree_Celsius 0.00 1.91 \n", - "34 1.6 delta_degree_Celsius 0.01 1.93 \n", - "36 1.59 delta_degree_Celsius 0.03 1.51 \n", - "41 1.94 delta_degree_Celsius 0.00 1.04 \n", - "44 1.62 delta_degree_Celsius 0.01 0.97 " + "0 1.91 delta_degree_Celsius 0.00 1.01 \n", + "1 1.9 delta_degree_Celsius 0.00 1.75 \n", + "6 1.74 delta_degree_Celsius 0.00 1.35 \n", + "7 1.57 delta_degree_Celsius 0.00 1.02 \n", + "8 1.72 delta_degree_Celsius 0.03 1.25 \n", + "10 1.73 delta_degree_Celsius 0.01 1.26 \n", + "11 1.71 delta_degree_Celsius 0.00 0.78 \n", + "13 1.83 delta_degree_Celsius 0.00 1.41 \n", + "14 1.66 delta_degree_Celsius 0.00 0.82 \n", + "16 1.71 delta_degree_Celsius 0.03 1.62 " ] }, - "execution_count": 24, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -1642,7 +1147,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 24, "metadata": { "pycharm": { "name": "#%%\n" @@ -1665,7 +1170,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -1689,14 +1194,14 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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