411        end_date,
 412        granularity,
 413        customer=None,
 414        group_by=[], 415        proration=None,
 416    ) -> dict[Customer.customer_name, dict[datetime.datetime, float]]:
 417        from metering_billing.billable_metrics import METRIC_HANDLER_MAP

 444        return usage
 445
 446    def get_earned_usage_per_day(
 447        self, start, end, customer, group_by=[], proration=None 448    ):
 449        from metering_billing.billable_metrics import METRIC_HANDLER_MAP
 450

  75    def get_current_usage(
  76        self,
  77        subscription: Subscription,
  78        group_by: list[str] = [],  79    ) -> float:
  80        """This method will be used to calculate how much usage a customer currently has on a subscription. THough there are cases where get_usage and get_current_usage will be the same, there are cases where they will not. For example, if your billable metric is Stateful with a Max aggregation, then your usage over some period will be the max over past readings, but your current usage will be the latest reading."""
  81        pass

  86        start: datetime.date,
  87        end: datetime.date,
  88        customer: Customer,
  89        group_by: list[str] = [],  90        proration: Optional[METRIC_GRANULARITY] = None,
  91    ) -> dict[datetime.datetime, float]:
  92        """This method will be used when calculating a concept known as "earned revenue" which is very important in accounting. It essentially states that revenue is "earned" not when someone pays, but when you deliver the goods/services at a previously agreed upon price. To accurately calculate accounting metrics, we will need to be able to tell for a given susbcription, where each cent of revenue came from, and the first step for that is to calculate how much billable usage was delivered each day. This method will be used to calculate that.

  95        pass
  96
  97    @abc.abstractmethod
  98    def _build_filter_kwargs(self, start, end, customer, group_by=[]):  99        """This method will be used to build the filter args for the get_usage and get_earned_usage_per_day methods. You should build the filter args for the Event model, and return them as a dictionary. You should also handle the case where customer is None, which means that you should return the usage for all customers."""
 100        now = now_utc()
 101        filter_kwargs = {

 131        return filter_args, filter_kwargs
 132
 133    @abc.abstractmethod
 134    def _build_pre_groupby_annotation_kwargs(self, group_by=[]): 135        pre_groupby_annotation_kwargs = {
 136            "customer_name": F("customer__customer_name"),
 137        }

 147
 148    @abc.abstractmethod
 149    def _build_groupby_kwargs(
 150        self, customer, results_granularity, start, group_by=[], proration=None 151    ):
 152        groupby_kwargs = {}
 153        for group_by_property in group_by:

 255            METRIC_AGGREGATION.MAX,
 256        ]
 257
 258    def _build_filter_kwargs(self, start, end, customer, group_by=[]): 259        return super()._build_filter_kwargs(start, end, customer, group_by)
 260
 261    def _build_pre_groupby_annotation_kwargs(self, group_by=[]):

 258    def _build_filter_kwargs(self, start, end, customer, group_by=[]):
 259        return super()._build_filter_kwargs(start, end, customer, group_by)
 260
 261    def _build_pre_groupby_annotation_kwargs(self, group_by=[]): 262        return super()._build_pre_groupby_annotation_kwargs(group_by)
 263
 264    def _build_groupby_kwargs(

 262        return super()._build_pre_groupby_annotation_kwargs(group_by)
 263
 264    def _build_groupby_kwargs(
 265        self, customer, results_granularity, start, group_by=[], proration=None 266    ):
 267        return super()._build_groupby_kwargs(
 268            customer, results_granularity, start, group_by, proration

 274        start,
 275        end,
 276        customer=None,
 277        group_by=[], 278        proration=None,
 279    ):
 280        filter_args, filter_kwargs = self._build_filter_kwargs(

 325            return_dict[cust_name][unique_tup][tc_trunc] = usage_qty
 326        return return_dict
 327
 328    def get_current_usage(self, subscription, group_by=[]): 329        per_customer = self.get_usage(
 330            start=subscription.start_date,
 331            end=subscription.end_date,

 340        return per_customer
 341
 342    def get_earned_usage_per_day(
 343        self, start, end, customer, group_by=[], proration=None 344    ):
 345        filter_args, filter_kwargs = self._build_filter_kwargs(
 346            start, end, customer, group_by

 551        assert property_name, "[METRIC TYPE: STATEFUL] Must specify property name."
 552        return data
 553
 554    def _build_filter_kwargs(self, start, end, customer, group_by=[]): 555        return super()._build_filter_kwargs(start, end, customer, group_by)
 556
 557    def _build_pre_groupby_annotation_kwargs(self, group_by=[]):

 554    def _build_filter_kwargs(self, start, end, customer, group_by=[]):
 555        return super()._build_filter_kwargs(start, end, customer, group_by)
 556
 557    def _build_pre_groupby_annotation_kwargs(self, group_by=[]): 558        return super()._build_pre_groupby_annotation_kwargs(group_by)
 559
 560    def _build_groupby_kwargs(

 558        return super()._build_pre_groupby_annotation_kwargs(group_by)
 559
 560    def _build_groupby_kwargs(
 561        self, customer, results_granularity, start, group_by=[], proration=None 562    ):
 563        return super()._build_groupby_kwargs(
 564            customer, results_granularity, start, group_by, proration

 570        start,
 571        end,
 572        customer=None,
 573        group_by=[], 574        proration=None,
 575    ):
 576        filter_args, filter_kwargs = self._build_filter_kwargs(

 819            usage_dict = new_usage_dict
 820        return usage_dict
 821
 822    def get_current_usage(self, subscription, group_by=[]): 823        cur_usg_agg = self.usage_aggregation_type
 824        cur_granularity = self.granularity
 825        self.usage_aggregation_type = METRIC_AGGREGATION.LATEST

 837        return usg
 838
 839    def get_earned_usage_per_day(
 840        self, start, end, customer, group_by=[], proration=None 841    ):
 842        per_customer = self.get_usage(
 843            start=start,

1021            start = None
1022        return start, end
1023
1024    def _build_filter_kwargs(self, start, end, customer, group_by=[]):1025        return super()._build_filter_kwargs(start, end, customer, group_by)
1026
1027    def _build_pre_groupby_annotation_kwargs(self, group_by=[]):

1024    def _build_filter_kwargs(self, start, end, customer, group_by=[]):
1025        return super()._build_filter_kwargs(start, end, customer, group_by)
1026
1027    def _build_pre_groupby_annotation_kwargs(self, group_by=[]):1028        return super()._build_pre_groupby_annotation_kwargs(group_by)
1029
1030    def _build_groupby_kwargs(

1028        return super()._build_pre_groupby_annotation_kwargs(group_by)
1029
1030    def _build_groupby_kwargs(
1031        self, customer, results_granularity, start, group_by=[], proration=None1032    ):
1033        return super()._build_groupby_kwargs(
1034            customer, results_granularity, start, group_by, proration

1034            customer, results_granularity, start, group_by, proration
1035        )
1036
1037    def get_current_usage(self, subscription, group_by=[]):1038        start, end = self._get_current_query_start_end()
1039        start = start if start else subscription.start_date
1040        filter_args, filter_kwargs = self._build_filter_kwargs(

1096        start,
1097        end,
1098        customer=None,
1099        group_by=[],1100        proration=None,
1101    ):
1102        filter_args, filter_kwargs = self._build_filter_kwargs(

1164        return return_dict
1165
1166    def get_earned_usage_per_day(
1167        self, start, end, customer, group_by=[], proration=None1168    ):
1169        per_customer = self.get_usage(
1170            start=start,