remove unused code

Author: lsabor

questions/utils.py

@@ -276,117 +276,3 @@ def multiple_choice_add_options(
     build_question_forecasts(question)
 
     return question
-
-
-# def multiple_choice_interpret_forecasts(
-#     forecasts: list[Forecast | AggregateForecast],
-#     options_history: OptionsHistoryType | None,
-# ) -> list[Forecast | AggregateForecast]:
-#     """Interprets multiple choice forecasts with respect to the history of options.
-#     Returns an altered list of forecasts with the PMFs reflecting the total list of
-#     values ever available for the question.
-
-#     `forecasts` param must be sorted by `start_time`
-
-#     Example:
-#         options_history = [
-#             (0, ["a", "other"]),
-#             (100, ["a", "b", "other"]),
-#         ]
-#         pmf = [0.6, 0.15, 0.25] at timestamp 50 (a pre-registered forecast)
-#     option "b" is added at timestamp 100
-#     interpreted_pmf at time 50 = [0.6, 0.4, 0.4]
-#     interpreted_pmf at time 100 = [0.6, 0.15, 0.25]
-
-#     This allows the resolution to be an index used universally across all forecasts
-#     interpreted this way. A resolution of "b" would correspond to index `1`, meaning
-#     that the forecast would be `0.4` at time 50 and `0.15` at time 100.
-#     Similarly, a resolution of "other" would correspond to a forecast of `0.4` at time
-#     50 and `0.25` at time 100.
-
-#     NOTE: intepreted "PMF"s are no longer valid forecasts, and the resulting forecasts
-#     should NOT be saved.
-#     """
-#     if not options_history or len(options_history) == 1 or not forecasts:
-#         # we have no change in options, no intepretation is required
-#         return forecasts
-
-#     list_of_all_options = get_all_options_from_history(options_history)
-
-#     def interpret_pmf(
-#         pmf: list[float],
-#         current_options: list[str],
-#         next_options: list[str],
-#     ) -> list[float]:
-#         if len(pmf) != len(current_options):
-#             if len(pmf) < len(current_options) or len(pmf) != len(next_options):
-#                 raise ValueError(
-#                     f"pmf {pmf} not interpretable as "
-#                     f"{current_options} or {next_options}"
-#                 )
-#             # translate it into equivalent current options
-#             current_pmf = [0.0] * len(current_options)
-#             for value, opt in zip(pmf, next_options):
-#                 index = current_options.index(opt) if opt in current_options else -1
-#                 current_pmf[index] += value
-#             pmf = current_pmf
-#         interpreted_pmf = [
-#             pmf[current_options.index(opt) if opt in current_options else -1]
-#             for opt in list_of_all_options
-#         ]
-#         return interpreted_pmf
-
-#     interpreted_forecasts: list[Forecast | AggregateForecast] = []
-#     options_index = -1
-#     next_step = datetime.min.replace(tzinfo=dt_timezone.utc)
-#     for forecast in forecasts:
-#         while (
-#             forecast.start_time >= next_step
-#         ):  # important for forecasts to be in order
-#             options_index += 1
-#             _, current_options = options_history[options_index]
-#             if options_index + 1 < len(options_history):
-#                 next_ts, next_options = options_history[options_index + 1]
-#                 next_step = datetime.fromtimestamp(next_ts).replace(
-#                     tzinfo=dt_timezone.utc
-#                 )
-#             else:  # there is no next step
-#                 next_step = datetime.max.replace(tzinfo=dt_timezone.utc)
-#                 next_options = current_options
-
-#         # annotate question type for efficient get_pmf() call
-#         forecast.question_type = Question.QuestionType.MULTIPLE_CHOICE
-#         pmf = forecast.get_pmf()
-#         current_pmf = interpret_pmf(pmf, current_options, next_options)
-#         if isinstance(forecast, Forecast):
-#             forecast.probability_yes_per_category = current_pmf
-#         else:
-#             forecast.forecast_values = current_pmf
-#         if not forecast.end_time or forecast.end_time > next_step:
-#             # we need to split the forecast
-#             next_pmf = interpret_pmf(pmf, next_options, next_options)
-#             if isinstance(forecast, Forecast):
-#                 extra_forecast = Forecast(
-#                     probability_yes_per_category=next_pmf,
-#                     start_time=next_step,
-#                     end_time=forecast.end_time,
-#                     author_id=forecast.author_id,
-#                     question_id=forecast.question_id,
-#                     post_id=forecast.post_id,
-#                     source=Forecast.SourceChoices.AUTOMATIC,
-#                 )
-#             else:
-#                 extra_forecast = AggregateForecast(
-#                     forecast_values=next_pmf,
-#                     start_time=next_step,
-#                     end_time=forecast.end_time,
-#                     method=forecast.method,
-#                     question_id=forecast.question_id,
-#                     forecaster_count=forecast.forecaster_count,
-#                 )
-#             forecast.end_time = next_step
-#             extra_forecast.question_type = Question.QuestionType.MULTIPLE_CHOICE
-#             interpreted_forecasts.append(extra_forecast)
-#         interpreted_forecasts.append(forecast)
-#     interpreted_forecasts.sort(key=lambda x: x.start_time)
-#     return interpreted_forecasts