Python datamapfunctions.Abstract类代码示例

 def __init__(self, id, service_demand_unit, **kwargs):
     self.id = id
     self.service_demand_unit = service_demand_unit
     self.sql_id_table = 'DemandServiceEfficiency'
     self.sql_data_table = 'DemandServiceEfficiencyData'
     self.input_type = 'intensity'
     Abstract.__init__(self, self.id, 'subsector_id')

 def __init__(self, tech, sql_id_table, sql_data_table, **kwargs):
     self.id = tech.id
     self.service_demand_unit = tech.service_demand_unit
     self.input_type = 'intensity'
     self.sql_id_table = sql_id_table
     self.sql_data_table = sql_data_table
     Abstract.__init__(self, self.id, 'demand_tech_id')

 def __init__(self, tech, sql_id_table, sql_data_table, scenario=None, **kwargs):
     self.id = tech.id
     self.scenario = scenario
     self.input_type = 'intensity'
     self.sql_id_table = sql_id_table
     self.sql_data_table = sql_data_table
     Abstract.__init__(self, self.id, 'demand_technology_id')

 def __init__(self, id, sql_id_table, sql_data_table):
     self.id = id
     self.sql_id_table = sql_id_table
     self.sql_data_table = sql_data_table
     self.mapped = False
     Abstract.__init__(self, self.id, data_id_key='parent_id')
     self.input_type='total'

 def __init__(self, id,**kwargs):
     self.id = id
     self.sql_id_table = 'DispatchFeedersAllocation'
     self.sql_data_table = 'DispatchFeedersAllocationData'
     Abstract.__init__(self, self.id, primary_key='id', data_id_key='parent_id')
     self.remap()
     self.values.sort_index(inplace=True)

 def __init__(self, id, scenario, **kwargs):
     self.id = id
     self.input_type = 'intensity'
     self.sql_id_table = 'SupplyTechsCO2Capture'
     self.sql_data_table = 'SupplyTechsCO2CaptureData'
     self.scenario = scenario
     Abstract.__init__(self, id, 'supply_tech_id')

 def __init__(self, service_link_id, id, sql_id_table, sql_data_table, **kwargs):
     self.service_link_id = service_link_id
     self.id = id
     self.input_type = 'intensity'
     self.sql_id_table = sql_id_table
     self.sql_data_table = sql_data_table
     Abstract.__init__(self, self.id)

 def __init__(self, id, subsector_id, sql_id_table, sql_data_table):
     self.id = id
     self.subsector_id = subsector_id
     self.sql_id_table = sql_id_table
     self.sql_data_table = sql_data_table
     self.mapped = False
     Abstract.__init__(self, self.id)

 def __init__(self, id, sql_id_table, sql_data_table, book_life=None, **kwargs):
     self.id = id
     self.name = sql_data_table
     self.input_type = 'intensity'
     self.sql_id_table = sql_id_table
     self.sql_data_table = sql_data_table
     self.book_life = book_life
     Abstract.__init__(self, id, primary_key='supply_tech_id', data_id_key='supply_tech_id')

 def __init__(self, id):
     self.id = id
     self.sql_id_table = 'DemandFlexibleLoadMeasures'
     self.sql_data_table = 'DemandFlexibleLoadMeasuresData'
     Abstract.__init__(self, self.id, primary_key='id', data_id_key='parent_id')
     self.input_type = 'intensity'
     self.remap()
     self.values.sort_index(inplace=True)

 def __init__(self, id, cost_of_capital, **kwargs):
     self.id = id
     self.sql_id_table = 'DemandEnergyEfficiencyMeasures'
     self.sql_data_table = 'DemandEnergyEfficiencyMeasuresData'
     Abstract.__init__(self, self.id, primary_key='id', data_id_key='parent_id')
     DemandMeasure.__init__(self)
     self.calculate_book_life()
     self.cost_of_capital = cost_of_capital
     self.cost = DemandMeasureCost(id, self.cost_of_capital, self.book_life, 'DemandEnergyEfficiencyMeasuresCost', 'DemandEnergyEfficiencyMeasuresCostData')

 def __init__(self, id, cost_of_capital, service_demand_unit, **kwargs):
     self.id = id
     self.service_demand_unit = service_demand_unit
     self.sql_id_table = 'DemandServiceDemandMeasures'
     self.sql_data_table = 'DemandServiceDemandMeasuresData'
     Abstract.__init__(self, self.id)
     DemandMeasure.__init__(self)
     self.cost_of_capital = cost_of_capital
     self.calculate_book_life()
     self.cost = DemandMeasureCost(id, self.cost_of_capital, self.book_life, 'DemandServiceDemandMeasuresCost',
                                   'DemandServiceDemandMeasuresCostData')

    def _setup_and_validate(self):
        Abstract.__init__(self, self.id, primary_key='id', data_id_key='parent_id')
        if self.raw_values is None:
            self._setup_zero_constraints()
            return

        self._validate_gaus()
        self.values = self.clean_timeseries(attr='raw_values', inplace=False, time_index=cfg.supply_years, time_index_name='year', interpolation_method=self.interpolation_method, extrapolation_method=self.extrapolation_method)

        # fill in any missing combinations of geographies
        self.values = util.reindex_df_level_with_new_elements(self.values, 'geography_from', cfg.dispatch_geographies)
        self.values = util.reindex_df_level_with_new_elements(self.values, 'geography_to', cfg.dispatch_geographies)
        self.values = self.values.fillna(0)
        self.values = self.values.sort()

 def __init__(self, id, supply_node_id, sql_id_table, sql_data_table, primary_key, data_id_key, reference=False):
     self.id = id
     self.input_type = 'total'
     self.supply_node_id = supply_node_id
     self.sql_id_table = sql_id_table
     self.sql_data_table = sql_data_table
     self.mapped = False
     if reference:
         for col, att in util.object_att_from_table(self.sql_id_table, self.supply_node_id, primary_key):
             setattr(self, col, att)
         DataMapFunctions.__init__(self, data_id_key)
         self.read_timeseries_data(supply_node_id=self.supply_node_id)
         self.raw_values = util.remove_df_levels(self.raw_values, 'supply_technology')
     else:
         # measure specific sales does not require technology filtering
         Abstract.__init__(self, self.id, primary_key=primary_key, data_id_key=data_id_key)

 def __init__(self, id, supply_node_id, sql_id_table, sql_data_table, reference=False):
     self.id = id
     self.supply_node_id = supply_node_id
     self.sql_id_table = sql_id_table
     self.sql_data_table = sql_data_table
     self.mapped = False
     self.input_type = 'intensity'
     if reference:
             for col, att in util.object_att_from_table(self.sql_id_table, self.supply_node_id, 'supply_node_id'):
                 if att is not None:
                     setattr(self, col, att)
             DataMapFunctions.__init__(self, 'supply_technology')
             self.read_timeseries_data()
             self.raw_values = util.remove_df_levels(self.raw_values, ['supply_node','supply_technology'])
     else:
         # measure specific sales share does not require technology filtering
         Abstract.__init__(self, self.id)