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finish-feature_scoring_heatmap-gas-vensim-NO-policy.py

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+# from ema_workbench.analysis import feature_scoring
+# import matplotlib.pyplot as plt
+# import seaborn as sns
+#
+# from ema_workbench import ema_logging, load_results
+#
+# ema_logging.log_to_stderr(level=ema_logging.INFO)
+#
+# # load data
+# file_name = r'./data/5000 new gas cases no policy.tar.gz'
+# experiments, outcomes = load_results(file_name)
+#
+# x = experiments
+# y = outcomes
+#
+# fs = feature_scoring.get_feature_scores_all(x, y)
+# sns.heatmap(fs, cmap='viridis', annot=True)
+# plt.show()
+
+################################ OR THE FOLLOWING CODE ##################################
+import matplotlib.pyplot as plt
+import numpy as np
+import seaborn as sns
+
+from ema_workbench import ema_logging, load_results
+from ema_workbench.analysis import feature_scoring
+
+ema_logging.log_to_stderr(level=ema_logging.INFO)
+
+
+# load data
+fn = r'./data/10000 gass cases no policy.tar.gz'
+x, outcomes = load_results(fn)
+
+
+# we have timeseries so we need scalars
+y = {'Profit': outcomes['Cumulative profit'][:, -1],
+    'NG production': np.max(outcomes['NG Production capacity'], axis=1),
+    'H2 production': np.max(outcomes['H2 Production capacity'], axis=1)}
+
+
+scores = feature_scoring.get_feature_scores_all(x, y)
+sns.heatmap(scores, annot=True, cmap='viridis')
+plt.show()
+
+# from ema_workbench import (RealParameter, TimeSeriesOutcome, ema_logging,
+#                            perform_experiments, MultiprocessingEvaluator, save_results)
+#
+# from ema_workbench.connectors.vensim import VensimModel
+#
+# if __name__ == "__main__":
+#     ema_logging.log_to_stderr(ema_logging.INFO)
+#
+#     model = VensimModel("InvestmentCase", wd='./models/gas',
+#                         model_file='GasInvestmentDynamicsVDSS2.vpmx')
+#
+#     # outcomes
+#     model.outcomes = [TimeSeriesOutcome('Cumulative profit'),
+#                       TimeSeriesOutcome('NG Production capacity'),
+#                       TimeSeriesOutcome('H2 Production capacity')]
+#                       # ,TimeSeriesOutcome('Hydrogen platforms')]
+#
+#     # Plain Parametric Uncertainties
+#     model.uncertainties = [
+#         RealParameter('Methane intensity', 0, 120),
+#         RealParameter('CO2 emissions per m3 NG produced', 10, 90),
+#         RealParameter('Carbon price multiplier', 1, 1.2),
+#         RealParameter('CH4 emissions per completed well', 0, 4),
+#         RealParameter('CO2 emissions per completed well', 3, 18),
+#         RealParameter('Exergy efficiency', 0.6, 1),
+#         RealParameter('Initial well productivity per year', 22000000, 30000000),
+#         RealParameter('Capacity decay multiplier', 0.145, 0.15),
+#         RealParameter('Average well lifetime', 20, 30),
+#         RealParameter('Number spot well distribution', 3, 5),
+#         RealParameter('NG RD delay', 0.5, 3),
+#         RealParameter('Initial costs for well development', 22000000, 27000000),
+#         RealParameter('NG learning rate factor', 0.01, 0.1),
+#         RealParameter('Property costs per development well', 500000, 2000000),
+#         # RealParameter('Success fraction', 0.5, 0.6),#correlates with transitioning factor weird
+#         RealParameter('Discovery delay', 2, 5),
+#         RealParameter('Initial investable capital', 25000000, 40000000),
+#         RealParameter('Operating costs per m3 NG', 0.03, 0.07),
+#         RealParameter('Development acceptance fraction', 0.8, 0.95),
+#         RealParameter('Construction cost exploration well', 10000000, 15000000),
+#         RealParameter('Property desire factor development', 10, 18),
+#         RealParameter('Property costs per exploration well', 500000, 1000000),
+#         RealParameter('Exploration license delay', 0.75, 2),
+#         RealParameter('Property desire factor exploration', 10, 18),
+#         RealParameter('Exploration acceptance fraction', 0.8, 0.95),
+#
+#         RealParameter('Societal urge to become carbon neutral', 0.002, 0.01),
+#         RealParameter('Subsidy provision factor', 0.5, 1),
+#         RealParameter('Budgeted green investment', 0.02, 0.16),
+#         RealParameter('Transport and compression efficiency', 0.005, 0.035),
+#         RealParameter('Operating cost reduction fraction', 0.75, 1.25),
+#         RealParameter('Effect of weather conditions on stack lifetime', 0.5, 1),
+#         RealParameter('Hydrogen system lifetime', 20, 27),
+#         RealParameter('Factor onshore to offshore BoP costs', 3, 5),
+#         RealParameter('Frequency of new market entrants', 6, 16),
+#         RealParameter('Hydrogen construction license delay', 2, 5),
+#         RealParameter('Offshore wind acceptance fraction', 0.9, 1),
+#         RealParameter('Construction license delay wind', 0.5, 2),
+#         RealParameter('Competition constant offshore hydrogen production', 1, 5),
+#         RealParameter('Platform distance to shore', 100, 300),
+#         RealParameter('Platform modification costs', 3000000, 8000000),
+#         RealParameter('Transitioning factor', 1.5, 1.9),
+#
+#         RealParameter('Price per kg', 1, 6),
+#         RealParameter('Volatility parameter', 0.8, 1),
+#         RealParameter('Price volatility', 33, 34)]
+#
+#     nr_experiments = 1000
+#     with MultiprocessingEvaluator(model) as evaluator:
+#         results = perform_experiments(model, nr_experiments,
+#                                       evaluator=evaluator)
+#
+#     save_results(results, './data/1000 newn gas cases no policy.tar.gz')