class ResultsExtractor:
def __init__(self):
self.analysis = AnalyseResults()
def extract_bau_results(self, results_dir, keys, output_dir=None):
"""Extract BAU model results"""
# All REP files
filenames = [f for f in os.listdir(results_dir) if 'bau_case' in f]
# Container for results
results = {}
for f in filenames:
# Extract information for each key
for k in keys:
if k == 'YEAR_AVERAGE_PRICE':
r = self.analysis.get_average_prices(
results_dir, f, None, 'PRICES', -1)
r = r.loc[:, 'average_price_real']
else:
r = self.analysis.extract_results(results_dir,
f,
k,
stage=None,
iteration='max',
model=None)
# Append results to main container
results[k] = r.to_dict()
# Save results
if output_dir is not None:
filename = 'bau_results.pickle'
self.save_results(results, filename, output_dir)
return results
def extract_rep_results(self, results_dir, keys, output_dir=None):
"""Extract REP model results"""
# All REP files
filenames = [f for f in os.listdir(results_dir) if 'rep' in f]
# Container for results
results = {}
for f in filenames:
print(f'Processing: {f}')
# Get carbon price from filename
carbon_price = int(f.split('-')[1].replace('.pickle', ''))
if carbon_price not in results.keys():
results[carbon_price] = {}
# Extract information for each key
for k in keys:
if k == 'YEAR_AVERAGE_PRICE':
r = self.analysis.get_average_prices(
results_dir, f, 'stage_2_rep', 'PRICES', -1)
r = r.loc[:, 'average_price_real']
else:
r = self.analysis.extract_results(results_dir,
f,
k,
stage='stage_2_rep',
iteration='max',
model=None)
# Append results to main container
results[carbon_price][k] = r.to_dict()
# Save results
if output_dir is not None:
filename = 'rep_results.pickle'
self.save_results(results, filename, output_dir)
return results
def extract_carbon_tax_results(self, results_dir, keys, output_dir=None):
"""Extract carbon tax results"""
# All REP files
filenames = [f for f in os.listdir(results_dir) if 'rep' in f]
# Container for results
results = {}
for f in filenames:
print(f'Processing: {f}')
# Get carbon price from filename
carbon_price = int(f.split('-')[1].replace('.pickle', ''))
if carbon_price not in results.keys():
results[carbon_price] = {}
# Extract information for each key
for k in keys:
if k == 'YEAR_AVERAGE_PRICE':
r = self.analysis.get_average_prices(
results_dir, f, 'stage_1_carbon_tax', 'PRICES', -1)
r = r.loc[:, 'average_price_real']
else:
r = self.analysis.extract_results(
results_dir,
f,
k,
stage='stage_1_carbon_tax',
iteration='max',
model=None)
# Append results to main container
results[carbon_price][k] = r.to_dict()
# Save results
if output_dir is not None:
filename = 'carbon_tax_results.pickle'
self.save_results(results, filename, output_dir)
return results
def extract_price_targeting_results(self,
filename_filter,
results_dir,
keys,
output_dir=None):
"""Extract price targeting scenario model results"""
# All REP files
filenames = [
f for f in os.listdir(results_dir) if filename_filter in f
]
# Container for results
results = {}
for f in filenames:
print(f'Processing: {f}')
# Get carbon price and transition year from filename
transition_year = int(f.split('-')[1].replace('_cp', ''))
carbon_price = int(f.split('-')[2].replace('.pickle', ''))
if transition_year not in results.keys():
results[transition_year] = {}
if carbon_price not in results[transition_year].keys():
results[transition_year][carbon_price] = {}
# Extract information for each key
for k in keys:
if k == 'YEAR_AVERAGE_PRICE':
r = self.analysis.get_average_prices(
results_dir, f, 'stage_3_price_targeting', 'PRICES',
-1)
r = r.loc[:, 'average_price_real']
else:
r = self.analysis.extract_results(
results_dir,
f,
k,
stage='stage_3_price_targeting',
iteration='max',
model='primal')
# Append results to main container
results[transition_year][carbon_price][k] = r.to_dict()
# Save results
if output_dir is not None:
filename = f'{filename_filter}_results.pickle'
self.save_results(results, filename, output_dir)
return results
@staticmethod
def save_results(results, filename, output_dir):
"""Save model results"""
print(f'Saving results: {filename}')
with open(os.path.join(output_dir, filename), 'wb') as f:
pickle.dump(results, f)
def extract_all_results(self, results_dir, output_dir):
"""Extract results for different scenarios"""
print('Extracting BAU results')
bau_results_keys = [
'YEAR_EMISSIONS', 'baseline', 'YEAR_AVERAGE_PRICE', 'x_c'
]
self.extract_bau_results(results_dir, bau_results_keys, output_dir)
# Extract these keys for all other scenarios
result_keys = [
'YEAR_EMISSIONS', 'baseline', 'YEAR_AVERAGE_PRICE',
'YEAR_CUMULATIVE_SCHEME_REVENUE', 'x_c',
'YEAR_SCHEME_EMISSIONS_INTENSITY', 'YEAR_EMISSIONS_INTENSITY'
]
print('Extracting tax results')
self.extract_carbon_tax_results(results_dir, result_keys, output_dir)
print('Extracting REP results')
self.extract_rep_results(results_dir, result_keys, output_dir)
print('Extracting heuristic results')
for i in ['heuristic_baudev', 'heuristic_ptar', 'heuristic_pdev']:
self.extract_price_targeting_results(i, results_dir, result_keys,
output_dir)
@staticmethod
def load_results(directory, filename):
"""Load results"""
with open(os.path.join(directory, filename), 'rb') as f:
results = pickle.load(f)
return results
def combine_results(self, output_dir):
"""Combine extracted results"""
# Load BAU, tax, and REP results
bau = self.load_results(output_dir, 'bau_results.pickle')
tax = self.load_results(output_dir, 'carbon_tax_results.pickle')
rep = self.load_results(output_dir, 'rep_results.pickle')
# Load price targeting results
price_deviation = self.load_results(output_dir,
'heuristic_pdev_results.pickle')
bau_deviation = self.load_results(output_dir,
'heuristic_baudev_results.pickle')
trajectory_deviation = self.load_results(
output_dir, 'heuristic_ptar_results.pickle')
# Combine into single dictionary
combined = {
'bau': bau,
'tax': tax,
'rep': rep,
'pdev': price_deviation,
'baudev': bau_deviation,
'ptar': trajectory_deviation
}
# Save combine results
with open(os.path.join(output_dir, 'model_results.pickle'), 'wb') as f:
pickle.dump(combined, f)
return combined
# plot_gep_prices(results_directory, output_directory)
# # Plot generation expansion planning model baselines
# plot_gep_baselines(results_directory, output_directory)
#
# # Plot cumulative installed capacity
# plot_cumulative_installed_capacity(results_directory, output_directory, 'rep_case.pickle')
# Plot average prices under different schemes
# plot_average_prices(results_directory, output_directory)
results_dir, output_dir = results_directory, output_directory
transition_year = 2022
# Prices from different models
p_bau = analysis.get_average_prices(results_dir, 'bau_case.pickle', None,
'PRICES', -1)
p_rep = analysis.get_average_prices(results_dir, 'rep_case.pickle',
'stage_2_rep', 'PRICES', -1)
p_tax = analysis.get_average_prices(results_dir, 'rep_case.pickle',
'stage_1_carbon_tax', 'PRICES', -1)
p_mppdc = analysis.get_average_prices(
results_dir,
f'mppdc_price_change_deviation_case_transition_year_{transition_year}.pickle',
'stage_3_price_targeting', 'lamb', 1)
p_heuristic = analysis.get_average_prices(
results_dir,
f'heuristic_price_change_deviation_case_transition_year_{transition_year}.pickle',
'stage_3_price_targeting', 'PRICES', -1)
# Baselines
b_rep = analysis.extract_results(results_dir,