xarray.DataArray.pr.add_aggregates_coordinates

xarray.DataArray.pr.add_aggregates_coordinates#

DataArray.pr.add_aggregates_coordinates(agg_info: dict[str, dict[str, list[str] | dict[str, float | str | list[str] | dict[str, str | list[str]]]]], tolerance: float | None = 0.01, skipna: bool | None = True, min_count: int | None = 1) → DataArray#

Manually aggregate data for coordinates

There are no sanity checks regarding what you aggregate, so you could combine IPCC2006 categories 2 and 3 to 1.

If an aggregated time-series is present the aggregate data are merged to check if aggregated and existing data agree within the given tolerance.

Parameters:
agg_info:

dict of the following form:

agg_info = {
    <coord1>: {
        <new_value>: {
            'sources': [source_values],
            <add_coord_name>: <value for additional coordinate> (optional),
            'tolerance': <non-default tolerance> (optional),
            'filter': <filter in pr.loc style> (optional),
        },
    },
    <coord2>: { # simplified format for coord2
        <new_value>: [source_values]
        ...
    },
    ...
}

All values in filter must be lists to keep the dimensions in the data returned by da.pr.loc The normal format and the simplified list format can be mixed also within a coordinate

tolerance:

non-default tolerance for merging (default = 0.01 (1%))

skipna: bool, optional

If True (default), skip missing values (as marked by NaN). By default, only skips missing values for float dtypes; other dtypes either do not have a sentinel missing value (int) or skipna=True has not been implemented (object, datetime64 or timedelta64).

min_count: int (default None, but set to 1 if skipna=True)

The minimal number of non-NA values in a sum that is necessary for a non-NA result. This only has an effect if skipna=True. As an example: you sum data for a region for a certain sector, gas and year. If skipna=False, all countries in the region need to have non-NA data for that sector, gas, year combination. If skipna=True and min_count=1 then one country with non-NA data is enough for a non-NA result. All NA values will be treated as zero. If min_count=0 all NA values will be treated as zero also if there is no single non-NA value in the data that is to be summed.

Returns:
xr.DataArray

Input array, but with aggregated values for coordinates / dimensions as specified in the agg_info dict

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