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# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
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# software distributed under the License is distributed on an
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# KIND, either express or implied.  See the License for the
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from typing import Dict

from flask_babel import gettext as _
from pandas import DataFrame

from superset.exceptions import InvalidPostProcessingError
from superset.utils.pandas_postprocessing.utils import (
    _append_columns,
    ALLOWLIST_CUMULATIVE_FUNCTIONS,
    validate_column_args,
)


@validate_column_args("columns")
def cum(
    df: DataFrame,
    operator: str,
    columns: Dict[str, str],
) -> DataFrame:
    """
    Calculate cumulative sum/product/min/max for select columns.

    :param df: DataFrame on which the cumulative operation will be based.
    :param columns: columns on which to perform a cumulative operation, mapping source
           column to target column. For instance, `{'y': 'y'}` will replace the column
           `y` with the cumulative value in `y`, while `{'y': 'y2'}` will add a column
           `y2` based on cumulative values calculated from `y`, leaving the original
           column `y` unchanged.
    :param operator: cumulative operator, e.g. `sum`, `prod`, `min`, `max`
    :return: DataFrame with cumulated columns
    """
    columns = columns or {}
    df_cum = df.loc[:, columns.keys()]
    operation = "cum" + operator
    if operation not in ALLOWLIST_CUMULATIVE_FUNCTIONS or not hasattr(
        df_cum, operation
    ):
        raise InvalidPostProcessingError(
            _("Invalid cumulative operator: %(operator)s", operator=operator)
        )
    df_cum = _append_columns(df, getattr(df_cum, operation)(), columns)
    return df_cum