atoti.scope.siblings_scope module#

class atoti.SiblingsScope#

Scope to perform a “siblings” aggregation.

With a siblings scope, the value for the member of a given level in the hierarchy is computed by taking the contribution of all of the members on the same level (its siblings).

A siblings aggregation is an appropriate tool for operations such as marginal aggregations (marginal VaR, marginal mean) for non-linear aggregation functions.

Example

>>> df = pd.DataFrame(
...     columns=["Year", "Month", "Day", "Quantity"],
...     data=[
...         (2019, 7, 1, 15),
...         (2019, 7, 2, 20),
...         (2019, 7, 3, 30),
...         (2019, 6, 1, 25),
...         (2019, 6, 2, 15),
...         (2018, 7, 1, 5),
...         (2018, 7, 2, 10),
...         (2018, 6, 1, 15),
...         (2018, 6, 2, 5),
...     ],
... )
>>> table = session.read_pandas(
...     df,
...     table_name="Siblings",
...     default_values={"Year": 0, "Month": 0, "Day": 0},
... )
>>> cube = session.create_cube(table, mode="manual")
>>> h, l, m = cube.hierarchies, cube.levels, cube.measures
>>> h["Date"] = [table["Year"], table["Month"], table["Day"]]
>>> m["Quantity.SUM"] = tt.agg.sum(table["Quantity"])
>>> m["Siblings quantity"] = tt.agg.sum(
...     m["Quantity.SUM"], scope=tt.SiblingsScope(hierarchy=h["Date"])
... )
>>> m["Siblings quantity excluding self"] = tt.agg.sum(
...     m["Quantity.SUM"],
...     scope=tt.SiblingsScope(hierarchy=h["Date"], exclude_self=True),
... )
>>> cube.query(
...     m["Quantity.SUM"],
...     m["Siblings quantity"],
...     m["Siblings quantity excluding self"],
...     levels=[l["Day"]],
...     include_totals=True,
... )
                Quantity.SUM Siblings quantity Siblings quantity excluding self
Year  Month Day
Total                    140               140                                0
2018                      35               140                              105
      6                   20                35                               15
            1             15                20                                5
            2              5                20                               15
      7                   15                35                               20
            1              5                15                               10
            2             10                15                                5
2019                     105               140                               35
      6                   40               105                               65
            1             25                40                               15
            2             15                40                               25
      7                   65               105                               40
            1             15                65                               50
            2             20                65                               45
            3             30                65                               35

__init__(*, hierarchy, exclude_self=False)#

Parameters

hierarchy (Hierarchy) – The hierarchy containing the levels along which the aggregation is performed.
exclude_self (bool) – Whether to include the current member’s contribution in its cumulative value.