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(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 make_flat_load_shape(index, column='value'):
assert 'weather_datetime' in index.names
flat_shape = util.empty_df(fill_value=1.,
index=index,
columns=[column])
group_to_normalize = [
n for n in flat_shape.index.names if n != 'weather_datetime'
]
flat_shape = flat_shape.groupby(level=group_to_normalize).transform(
lambda x: x / x.sum()) * Shapes.get_instance().num_active_years
return flat_shape
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 make_flat_load_shape(self, index, column='value'):
assert 'weather_datetime' in index.names
flat_shape = util.empty_df(fill_value=1., index=index, columns=[column])
group_to_normalize = [n for n in flat_shape.index.names if n!='weather_datetime']
flat_shape = flat_shape.groupby(level=group_to_normalize).transform(lambda x: x / x.sum())*self.num_active_years
return flat_shape