> ## Documentation Index
> Fetch the complete documentation index at: https://docs.activeviam.com/llms.txt
> Use this file to discover all available pages before exploring further.

# atoti.OriginScope

### *final class* atoti.OriginScope

Scope performing an aggregation at the given origin.

The input of the aggregation function will be evaluated at the given [`levels`](#atoti.OriginScope.levels) and the aggregation function will be applied “above” these intermediate aggregates.

### Example

Using this scope with [`atoti.agg.mean()`](./atoti.agg.mean#atoti.agg.mean) to average quantities summed by month:

```pycon theme={"languages":{"custom":["/engine/python-sdk/0.9/languages/pycon.tmLanguage.json"]}}
>>> 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,
...     default_values={"Year": 0, "Month": 0, "Day": 0},
...     table_name="Origin",
... )
>>> 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["Average of monthly quantities"] = tt.agg.mean(
...     m["Quantity.SUM"], scope=tt.OriginScope({l["Month"]})
... )
```

Average of monthly quantities will evaluate Quantity.SUM for each Month and average these values “above” this level:

```pycon theme={"languages":{"custom":["/engine/python-sdk/0.9/languages/pycon.tmLanguage.json"]}}
>>> cube.query(
...     m["Quantity.SUM"],
...     m["Average of monthly quantities"],
...     levels=[l["Day"]],
...     include_totals=True,
... )
                Quantity.SUM Average of monthly quantities
Year  Month Day
Total                    140                         35.00
2018                      35                         17.50
      6                   20                         20.00
            1             15                         15.00
            2              5                          5.00
      7                   15                         15.00
            1              5                          5.00
            2             10                         10.00
2019                     105                         52.50
      6                   40                         40.00
            1             25                         25.00
            2             15                         15.00
      7                   65                         65.00
            1             15                         15.00
            2             20                         20.00
            3             30                         30.00
```

The aggregation function can be changed again to compute the max of these averages:

```pycon theme={"languages":{"custom":["/engine/python-sdk/0.9/languages/pycon.tmLanguage.json"]}}
>>> m["Max average of monthly quantities"] = tt.agg.max(
...     m["Average of monthly quantities"],
...     scope=tt.OriginScope({l["Year"]}),
... )
>>> cube.query(
...     m["Average of monthly quantities"],
...     m["Max average of monthly quantities"],
...     levels=[l["Year"]],
...     include_totals=True,
... )
      Average of monthly quantities Max average of monthly quantities
Year
Total                         35.00                             52.50
2018                          17.50                             17.50
2019                          52.50                             52.50
```

#### levels *: [Set](https://docs.python.org/3/library/collections.abc.html#collections.abc.Set)\[HasIdentifier\[LevelIdentifier] | LevelIdentifier]*

The levels constituting the origin of the aggregation.