def calculate(self, vintages, years):
self.vintages = vintages
self.years = years
if self.data and self.raw_values is not None:
self.remap(time_index_name='vintage')
self.values.replace(0,1,inplace=True)
util.convert_age(self, vintages=self.vintages, years=self.years, attr_from='values', attr_to='values', reverse=True)
def levelize_costs(self):
if hasattr(self, 'is_levelized'):
inflation = float(cfg.cfgfile.get('case', 'inflation_rate'))
rate = self.cost_of_capital - inflation
if self.is_levelized == 0:
self.values_level = -np.pmt(rate, self.book_life, 1, 0,
'end') * self.values
util.convert_age(self,
vintages=self.vintages,
years=self.years,
attr_from='values_level',
attr_to='values_level',
reverse=False)
else:
self.values_level = self.values.copy()
util.convert_age(self,
vintages=self.vintages,
years=self.years,
attr_from='values_level',
attr_to='values_level',
reverse=False)
self.values = np.pv(rate, self.book_life, -1, 0,
'end') * self.values
else:
raise ValueError(
'Supply Technology id %s needs to indicate whether costs are levelized '
% self.id)
def calculate(self, vintages, years):
self.vintages = vintages
self.years = years
if self.data and self.raw_values is not None:
self.remap(time_index_name='vintage')
util.convert_age(self, vintages=self.vintages, years=self.years, attr_from='values', attr_to='values', reverse=True)
elif self.data is False:
index = pd.MultiIndex.from_product([cfg.geo.geographies[cfg.cfgfile.get('case','primary_geography')],self.vintages], names=[cfg.cfgfile.get('case', 'primary_geography'),'vintage'])
self.values = util.empty_df(index,columns=years,fill_value=0.0)
self.data = True
def calculate(self, vintages, years):
self.vintages = vintages
self.years = years
if self._has_data and self.raw_values is not None:
self.remap(time_index_name='vintage', converted_geography=GeoMapper.supply_primary_geography)
util.convert_age(self, vintages=self.vintages, years=self.years, attr_from='values', attr_to='values', reverse=True)
elif not self._has_data:
index = pd.MultiIndex.from_product([GeoMapper.geography_to_gau[GeoMapper.supply_primary_geography],self.vintages], names=[GeoMapper.supply_primary_geography,'vintage'])
self.values = util.empty_df(index,columns=years,fill_value=0.0)
self._has_data = True
def calculate(self, vintages, years):
self.vintages = vintages
self.years = years
if self.data and self.raw_values is not None:
self.remap(map_from='raw_values', map_to='values', time_index_name='vintage')
util.convert_age(self, reverse=True, vintages=self.vintages, years=self.years)
if self.data is False:
self.absolute = False
if self.raw_values is None:
# if the class is empty, then there is no data for conversion, so the class is considered converted
self.absolute = True
def calculate(self, vintages, years):
self.vintages = vintages
self.years = years
if self.data and self.raw_values is not None:
self.remap(map_from='raw_values', map_to='values', time_index_name='vintage')
util.convert_age(self, reverse=True, vintages=self.vintages, years=self.years)
if self.data is False:
self.absolute = False
if self.raw_values is None:
# if the class is empty, then there is no data for conversion, so the class is considered converted
self.absolute = True
def calculate(self, vintages, years):
self.vintages = vintages
self.years = years
if self.data and self.raw_values is not None:
self.remap(time_index_name='vintage')
self.values.replace(0, 1, inplace=True)
util.convert_age(self,
vintages=self.vintages,
years=self.years,
attr_from='values',
attr_to='values',
reverse=True)
def calculate(self, vintages, years):
self.vintages = vintages
self.years = years
if self._has_data and self.raw_values is not None and (cfg.rio_supply_run is not True or 'vintage' in self.raw_values.index.names):
self.remap(time_index_name='vintage', converted_geography=GeoMapper.supply_primary_geography,fill_value=np.nan)
self.values.replace(0,1,inplace=True)
util.convert_age(self, vintages=self.vintages, years=self.years, attr_from='values', attr_to='values', reverse=True)
elif self._has_data and self.raw_values is not None and cfg.rio_supply_run==True:
self.remap(time_index_name='year', converted_geography=GeoMapper.supply_primary_geography, fill_value=np.nan)
self.values.replace(0, 1, inplace=True)
self.values = util.add_and_set_index(self.values,'vintage',self.vintages,index_location=-1)
self.values = self.values.squeeze().unstack(level='year')
def calculate(self, vintages, years):
self.vintages = vintages
self.years = years
if self._has_data and self.raw_values is not None:
self.convert()
self.remap(map_from='values', map_to='values', converted_geography=GeoMapper.supply_primary_geography, time_index_name='vintage')
util.convert_age(self, vintages=self.vintages, years=self.years, attr_from='values', attr_to='values_level', reverse=False)
if not self._has_data:
self.absolute = False
if self.raw_values is None:
# if the class is empty, then there is no data for conversion, so the class is considered converted
self.absolute = True
def levelize_costs(self):
if hasattr(self, 'is_levelized'):
inflation = float(cfg.cfgfile.get('case', 'inflation_rate'))
rate = self.cost_of_capital - inflation
if self.is_levelized == 0:
self.values_level = - np.pmt(rate, self.book_life, 1, 0, 'end') * self.values
util.convert_age(self, vintages=self.vintages, years=self.years, attr_from='values_level', attr_to='values_level', reverse=False)
else:
self.values_level = self.values.copy()
util.convert_age(self, vintages=self.vintages, years=self.years, attr_from='values_level', attr_to='values_level', reverse=False)
self.values = np.pv(rate, self.book_life, -1, 0, 'end') * self.values
else:
raise ValueError('Supply Technology id %s needs to indicate whether costs are levelized ' %self.id)
def calculate(self, vintages, years):
self.vintages = vintages
self.years = years
if self.data:
self.remap(map_from='raw_values', map_to='values', time_index_name='vintage')
util.convert_age(self, reverse=True, vintages=self.vintages, years=self.years)
elif self.data is False:
setattr(self, 'converted', False)
if self.empty is True:
# if the class is empty, then there is no data for conversion, so the class is considered converted
self.converted = True
else:
self.empty = False
def calculate(self, vintages, years):
self.vintages = vintages
self.years = years
if self.data and self.empty is False:
self.convert()
self.remap(map_from='values', map_to='values', time_index_name='vintage')
util.convert_age(self, vintages=self.vintages, years=self.years, attr_from='values', attr_to='values', reverse=True)
elif self.data is False:
setattr(self, 'converted', False)
else:
self.converted = False
# adds fixed output shapes for technologies which are not dispatchable
if self.empty is True:
# if the class is empty, then there is no data for conversion, so the class is considered converted
self.converted = True
else:
self.empty = False
def calculate(self, vintages, years):
self.vintages = vintages
self.years = years
if self.data and self.raw_values is not None:
self.convert()
self.remap(map_from='values',
map_to='values',
time_index_name='vintage')
util.convert_age(self,
reverse=True,
vintages=self.vintages,
years=self.years)
self.values = util.remove_df_levels(self.values,
cfg.removed_demand_levels,
agg_function='mean')
if self.data is False:
self.absolute = False
if self.raw_values is None:
# if the class is empty, then there is no data for conversion, so the class is considered converted
self.absolute = True
def calculate(self, vintages, years):
self.vintages = vintages
self.years = years
if self.data and self.empty is False:
self.remap()
util.convert_age(self, vintages=self.vintages, years=self.years, attr_from='values', attr_to='values', reverse=True)
elif self.data is False:
index = pd.MultiIndex.from_product([cfg.geo.geographies[cfg.cfgfile.get('case','primary_geography')],self.vintages], names=[cfg.cfgfile.get('case', 'primary_geography'),'vintage'])
self.values = util.empty_df(index,columns=years,fill_value=1.0)
self.data = True
self.empty = False
setattr(self, 'converted', False)
else:
self.converted = False
# adds fixed output shapes for technologies which are not dispatchable
if self.empty is True:
# if the class is empty, then there is no data for conversion, so the class is considered converted
self.converted = True
else:
self.empty = False
def calculate(self, vintages, years):
self.vintages = vintages
self.years = years
if self._has_data and self.raw_values is not None and cfg.rio_supply_run and 'year' in self.raw_values.index.names:
self.remap(map_from='raw_values', map_to='values', current_geography=cfg.rio_geography,converted_geography=GeoMapper.supply_primary_geography,
time_index_name='year', lower=None, missing_intensity_geos=False)
self.values = self.values.unstack('year')
self.values.columns = self.values.columns.droplevel()
self.values = pd.concat([self.values] *len(self.vintages),keys=self.vintages,names=['vintage'])
self.values['efficiency_type'] = 'consumed'
self.values = self.values.set_index('efficiency_type',append=True)
elif self._has_data and self.raw_values is not None:
self.convert()
self.remap(map_from='values', map_to='values', converted_geography=GeoMapper.supply_primary_geography, time_index_name='vintage', lower=None,missing_intensity_geos=False)
util.convert_age(self, vintages=self.vintages, years=self.years, attr_from='values', attr_to='values', reverse=True)
if not self._has_data:
self.absolute = False
if self.raw_values is None:
# if the class is empty, then there is no data for conversion, so the class is considered converted
self.absolute = True
def levelize_costs(self):
if self.is_levelized == 1:
inflation = float(cfg.cfgfile.get('case', 'inflation_rate'))
rate = self.cost_of_capital - inflation
if self.is_levelized == 0:
self.values_level = -np.pmt(rate, self.book_life, 1, 0,
'end') * self.values
util.convert_age(self,
attr_from='values_level',
attr_to='values_level',
reverse=False,
vintages=self.vintages,
years=self.years)
else:
self.values_level = self.values.copy()
util.convert_age(self,
attr_from='values_level',
attr_to='values_level',
reverse=False,
vintages=self.vintages,
years=self.years)
self.values = np.pv(rate, self.book_life, -1, 0,
'end') * self.values
else:
util.convert_age(self,
reverse=False,
vintages=self.vintages,
years=self.years)
def levelize_costs(self):
if hasattr(self, 'is_levelized') and (
self.definition == 'absolute' or
(self.definition == 'relative'
and self.reference_tech_operation == 'add')):
inflation = cfg.getParamAsFloat('inflation_rate')
rate = self.cost_of_capital - inflation
if self.is_levelized == 0:
self.values_level = -np.pmt(rate, self.book_life, 1, 0,
'end') * self.values
util.convert_age(self,
attr_from='values_level',
attr_to='values_level',
reverse=False,
vintages=self.vintages,
years=self.years)
else:
self.values_level = self.values.copy()
util.convert_age(self,
attr_from='values_level',
attr_to='value_level',
reverse=False,
vintages=self.vintages,
years=self.years)
self.values = np.pv(rate, self.book_life, -1, 0,
'end') * self.values
else:
util.convert_age(self,
attr_from='values',
attr_to='values_level',
reverse=False,
vintages=self.vintages,
years=self.years)
def calculate(self, vintages, years):
self.vintages = vintages
self.years = years
if self._has_data and self.raw_values is not None:
self.remap(map_from='raw_values',
map_to='values',
time_index_name='vintage',
converted_geography=GeoMapper.demand_primary_geography)
util.convert_age(self,
attr_from='values',
attr_to='values',
reverse=False,
vintages=self.vintages,
years=self.years)
self.values = util.remove_df_levels(self.values,
cfg.removed_demand_levels,
agg_function='mean')
if not self._has_data:
self.absolute = False
if self.raw_values is None:
# if the class is empty, then there is no data for conversion, so the class is considered converted
self.absolute = True
def levelize_costs(self):
if hasattr(self, 'is_levelized'):
inflation = float(cfg.cfgfile.get('case', 'inflation_rate'))
rate = self.cost_of_capital - inflation
if self.is_levelized == 0:
self.values_level = - np.pmt(rate, self.book_life, 1, 0, 'end') * self.values
util.convert_age(self, vintages=self.vintages, years=self.years, attr_from='values_level', attr_to='values_level', reverse=False)
else:
self.values_level = self.values.copy()
util.convert_age(self, vintages=self.vintages, years=self.years, attr_from='values_level', attr_to='values_level', reverse=False)
self.values = np.pv(rate, self.book_life, -1, 0, 'end') * self.values
else:
util.convert_age(self, vintages=self.vintages, years=self.years, reverse=False)
def levelize_costs(self):
if hasattr(self, 'is_levelized'):
inflation = cfg.getParamAsFloat('inflation_rate')
try:
rate = self.cost_of_capital - inflation
except:
pdb.set_trace()
if self.is_levelized == 0:
self.values_level = - np.pmt(rate, self.book_life, 1, 0, 'end') * self.values
util.convert_age(self, vintages=self.vintages, years=self.years, attr_from='values_level', attr_to='values_level', reverse=False)
elif self.is_levelized==1:
self.values_level = self.values.copy()
util.convert_age(self, vintages=self.vintages, years=self.years, attr_from='values_level', attr_to='values_level', reverse=False)
self.values = np.pv(rate, self.book_life, -1, 0, 'end') * self.values
elif self.definition == 'relative':
self.values_level = self.values.copy()
util.convert_age(self, vintages=self.vintages, years=self.years, attr_from='values_level', attr_to='values_level', reverse=False)
else:
raise ValueError("no specification of whether the technology cost is levelized")
else:
raise ValueError('Supply Technology id %s needs to indicate whether costs are levelized ' %self.name)
