Allow passing of iterable arguments downstream.

[jakobwes]

Option:

• add iterable_args option in apply-function. Unpacking function from ISIMIP used if is not None
• as proof of concept implement glm-based debiasing
• *iterable_args or **iterable_args?

[jakobwes]

Need to discuss whether this is really necessary: do we need locationwise arguments also for glm based debiasing?

    # Helpers
    @staticmethod
    def _unpack_locationwise_args_and_get_locationwise_info(locationwise_args, i, j):
        return {
            key: value[
                :,
                i,
                j,
            ]
            for key, value in locationwise_args.items()
        }

    @staticmethod
    def map_over_locations(func, output_size, obs, cm_hist, cm_future, locationwise_args=None, **kwargs):
        output = np.empty(output_size, dtype=cm_future.dtype)
        if locationwise_args is None:
            for i, j in tqdm(np.ndindex(obs.shape[1:]), total=np.prod(obs.shape[1:])):
                output[:, i, j] = func(obs[:, i, j], cm_hist[:, i, j], cm_future[:, i, j], **kwargs)
        else:
            for i, j in tqdm(np.ndindex(obs.shape[1:]), total=np.prod(obs.shape[1:])):
                output[:, i, j] = func(
                    obs[:, i, j],
                    cm_hist[:, i, j],
                    cm_future[:, i, j],
                    Debiaser._unpack_locationwise_args_and_get_locationwise_info(locationwise_args, i, j),
                    **kwargs,
                )

        return output

    # Apply functions:
    def apply_location(self, obs, cm_hist, cm_future, locationwise_args, **kwargs):
        raise NotImplementedError(
            "apply_location is an abstract method which needs to be overriden in derived classes."
        )

    def apply(self, obs, cm_hist, cm_future, locationwise_args=None, **kwargs):
        print("----- Running debiasing -----")
        Debiaser.check_inputs(obs, cm_hist, cm_future)

        output = Debiaser.map_over_locations(
            self.apply_location,
            output_size=cm_future.shape,
            obs=obs,
            cm_hist=cm_hist,
            cm_future=cm_future,
            locationwise_args=locationwise_args,
            **kwargs,
        )
        return output