def calc_conifer_credits(project, current_site_scale, current_facres):
'''
calculates credits resulting from local-scale uplift only (excluding
any site-scale uplift) to reflect uplift from conifer and anthro
removal.
:param project: an instance of the CreditData class.
:param current_site_scale: the scored current site scale dataframe
:param current_facres: the current_facres report to compare conifer for the
purpose of calculating credits.
'''
desktop_results = project.desktop_results
projected_ls = project.projected_ls
# Create current functional acre report
conifer_facres = calc.calc_facres(desktop_results, current_site_scale,
projected_ls)
# Caculate credits for uplift from conifer removal
conifer_credits = calc.calc_credits(project, desktop_results,
current_facres, conifer_facres)
return conifer_credits
def calc_scenario_credits(scenarios, project, current_facres):
'''
calculate functional acres report for each scenario and compares to current
functional acres report to calculate credits.
:param scenarios: nested dict of scenarios and levels of effort, generated by
run_base_scenarios()
:param project: an instance of the CreditData class.
:param current_facres: the current_facres report to compare scenarios for the
purpose of calculating credits.
'''
desktop_results = project.desktop_results
site_scale_values = project.site_scale_values
projected_ls = project.projected_ls
credit_reports = {}
for scenario_name, scenario_results in scenarios.items():
for effort, projected_values in scenario_results.items():
# Project site-scale values
projected_site_scale = calc.project_site_scale(
projected_values, site_scale_values)
# Score projected site-scale values
projected_scores = calc.score_site_scale(project,
projected_site_scale)
# Create projected functional acre report
projected_facres = calc.calc_facres(desktop_results,
projected_scores, projected_ls)
# Calculate projected credits
projected_credits = calc.calc_credits(project, desktop_results,
current_facres,
projected_facres)
# Append scenario name and dataframe to credit_reports dictionary
credit_reports[scenario_name + '_' + effort] = projected_credits
return credit_reports
def run_scenario_report(project, save_interims=False):
'''
returns dataframe with saleable credits per scenario.
:param project: an instance of the CreditData class.
:param save_interims: if True, the full credit report for each scenario
and for the conifer scenario is saved as a csv.'''
# read from database
site_scale_values = project.site_scale_values
current_ls = project.current_ls
desktop_results = project.desktop_results
# set index of site_scale values for use in run_scenario
site_scale_values_indexed = site_scale_values.set_index('map_unit_id')
# Get current facres report to streamline comparison with scenarios
# Score current site-scale values
current_site_scale = calc.score_site_scale(project, site_scale_values)
# Create current functional acre report
current_facres = calc.calc_facres(desktop_results, current_site_scale,
current_ls)
# Run scenarios
scenarios = run_base_scenarios(site_scale_values_indexed)
# Calculate credits for each scenario
credit_reports = calc_scenario_credits(scenarios, project, current_facres)
# Compile saleable_credits for each scenario into a single dataframe
list_of_credit_reports = list(
credit_reports.items()) # make list to maintain order
first_scenario, first_scenario_report = list_of_credit_reports[0]
scenario_report = first_scenario_report[[
'map_unit_id', 'map_unit_name', 'meadow', 'map_unit_area',
'saleable_credits'
]].copy()
scenario_report.rename(columns={'saleable_credits': first_scenario},
inplace=True)
for name, data in list_of_credit_reports[1:]:
data.rename(columns={'saleable_credits': name}, inplace=True)
scenario_report = pd.merge(scenario_report,
data[['map_unit_id', name]],
how='outer',
on='map_unit_id',
suffixes=(False, False))
# Calculate credits from conifer alone
conifer_credits = calc_conifer_credits(project, current_site_scale,
current_facres)
# Append to scenario report
conifer_report = conifer_credits[['map_unit_id',
'saleable_credits']].copy()
conifer_report.rename(columns={'saleable_credits': 'conifer'},
inplace=True)
scenario_report = pd.merge(scenario_report,
conifer_report,
how='outer',
on='map_unit_id',
suffixes=(False, False))
if save_interims:
# Save outputs
for name, data in credit_reports.items():
save_name = name + '.csv'
calc.save_output(workspace, data, save_name)
# Save outputs
calc.save_output(workspace, conifer_credits, 'conifer_credits.csv')
return scenario_report