scenario_annual

bounded_source_variables(target_variable: str, anomaly_scheme: str) -> list[str]

Source variables of target_variable that anomaly_scheme cannot bias-correct.

Value-bounded variables are floored and clipped to cdc.VALUE_BOUNDS instead of being stabilised, so they run under cdc.BOUNDED_VARIABLE_SCHEMES only; see check_bounded_variable_scheme for why the other schemes are refused. The bounds are keyed on the daily variable, so the annual target has to be resolved through its sources the same way the de-bias check is.

Source code in src/climate_data/generate/scenario_annual.py

def bounded_source_variables(target_variable: str, anomaly_scheme: str) -> list[str]:
    """Source variables of `target_variable` that `anomaly_scheme` cannot bias-correct.

    Value-bounded variables are floored and clipped to `cdc.VALUE_BOUNDS` instead of being
    stabilised, so they run under `cdc.BOUNDED_VARIABLE_SCHEMES` only; see
    `check_bounded_variable_scheme` for why the other schemes are refused. The bounds are
    keyed on the daily variable, so the annual target has to be resolved through its
    sources the same way the de-bias check is.
    """
    if anomaly_scheme in cdc.BOUNDED_VARIABLE_SCHEMES:
        return []
    bounded = []
    for source_variable in TRANSFORM_MAP[target_variable].source_variables:
        if source_variable in cdc.VALUE_BOUNDS:
            bounded.append(source_variable)
    return bounded

forecast_jobs_for_anomaly_scheme(to_run: list[tuple[str, str, str, str]], anomaly_scheme: str) -> list[tuple[str, str, str, str]]

Drop forecast jobs whose variable the anomaly scheme cannot be applied to.

Filtered per job rather than per variable because the historical scenario never reaches generate_scenario_daily_main -- it reads the daily results off disk -- so the scheme does not constrain it, and an additive variable is perfectly runnable there. Only the forecast scenarios pass through compute_anomaly.

Two classes are dropped, and they are not mirror images. Additive variables run under the stabilised monthly scheme and no other. Value-bounded variables are the reverse: they run under cdc.BOUNDED_VARIABLE_SCHEMES only, which means they have to be dropped under the default scheme too -- this stage calls generate_scenario_daily_main in memory, so a bounded forecast job reaches check_bounded_variable_scheme and raises in the worker after being scheduled. That is the failure the daily launcher's filter already prevents.

A filter that removed every job is a usage error rather than an empty run, because the caller asked for work that cannot be done and an empty fan-out looks like success.

Source code in src/climate_data/generate/scenario_annual.py

def forecast_jobs_for_anomaly_scheme(
    to_run: list[tuple[str, str, str, str]],
    anomaly_scheme: str,
) -> list[tuple[str, str, str, str]]:
    """Drop forecast jobs whose variable the anomaly scheme cannot be applied to.

    Filtered per job rather than per variable because the `historical` scenario never
    reaches `generate_scenario_daily_main` -- it reads the daily results off disk -- so
    the scheme does not constrain it, and an additive variable is perfectly runnable
    there. Only the forecast scenarios pass through `compute_anomaly`.

    Two classes are dropped, and they are not mirror images. Additive variables run under
    the stabilised `monthly` scheme and no other. Value-bounded variables are the reverse:
    they run under `cdc.BOUNDED_VARIABLE_SCHEMES` only, which means they have to be dropped
    under the default scheme too -- this stage calls `generate_scenario_daily_main` in
    memory, so a bounded forecast job reaches `check_bounded_variable_scheme` and raises in
    the worker after being scheduled. That is the failure the daily launcher's filter
    already prevents.

    A filter that removed every job is a usage error rather than an empty run, because the
    caller asked for work that cannot be done and an empty fan-out looks like success.
    """
    keep = []
    skipped_additive = set()
    skipped_bounded = set()
    dropped_additive = 0
    dropped_bounded = 0
    for job in to_run:
        variable, scenario = job[0], job[1]
        forecast = scenario != "historical"
        if forecast and bounded_source_variables(variable, anomaly_scheme):
            skipped_bounded.add(variable)
            dropped_bounded += 1
        elif (
            forecast
            and anomaly_scheme != cdc.ANOMALY_SCHEME_MONTHLY
            and ANOMALY_TYPES[variable] != "multiplicative"
        ):
            skipped_additive.add(variable)
            dropped_additive += 1
        else:
            keep.append(job)

    if skipped_additive:
        print(
            f"Anomaly scheme '{anomaly_scheme}' applies to multiplicative variables only;"
            f" skipping {dropped_additive} forecast tasks for:"
            f" {', '.join(sorted(skipped_additive))}."
        )
    if skipped_bounded:
        print(
            f"Anomaly scheme '{anomaly_scheme}' does not apply to value-bounded variables,"
            f" which run under {', '.join(cdc.BOUNDED_VARIABLE_SCHEMES)} only;"
            f" skipping {dropped_bounded} forecast tasks for:"
            f" {', '.join(sorted(skipped_bounded))}."
        )
    if to_run and not keep:
        msg = (
            f"Anomaly scheme '{anomaly_scheme}' can run none of the selected variables, so"
            f" there is nothing to submit. Skipped:"
            f" {', '.join(sorted(skipped_additive | skipped_bounded))}."
        )
        if skipped_bounded:
            msg += (
                f" Value-bounded variables need"
                f" --anomaly-scheme {cdc.BOUNDED_VARIABLE_SCHEMES[0]}."
            )
        raise click.UsageError(msg)
    return keep