core.py 文件源码

def adjustments_from_deltas_no_sids(dense_dates,
                                    sparse_dates,
                                    column_idx,
                                    column_name,
                                    asset_idx,
                                    deltas):
    """Collect all the adjustments that occur in a dataset that does not
    have a sid column.

    Parameters
    ----------
    dense_dates : pd.DatetimeIndex
        The dates requested by the loader.
    sparse_dates : pd.DatetimeIndex
        The dates that were in the raw data.
    column_idx : int
        The index of the column in the dataset.
    column_name : str
        The name of the column to compute deltas for.
    asset_idx : pd.Series[int -> int]
        The mapping of sids to their index in the output.
    deltas : pd.DataFrame
        The overwrites that should be applied to the dataset.

    Returns
    -------
    adjustments : dict[idx -> Float64Overwrite]
        The adjustments dictionary to feed to the adjusted array.
    """
    ad_series = deltas[AD_FIELD_NAME]
    idx = 0, len(asset_idx) - 1
    return {
        dense_dates.get_loc(kd): overwrite_from_dates(
            ad_series.loc[kd],
            dense_dates,
            sparse_dates,
            idx,
            v,
        ) for kd, v in deltas[column_name].iteritems()
    }