Measures in joined table do not aggregate

[Wi1iku]

Issue

The problem I'm facing is that when I try to aggregate the measure from the Sensis table (not the base store)
I can´t aggregate the measure sensitivity to every hierarchy from the base store (not even by the fields used to join both tables).
However, the measure called PL.SUM has the functionality to be aggregable by every hierarchy.
What we would like to know is if there is any method or attribute for measures from other stores (that are not the base store) to
have the functionality to be aggregable by any base store hierarchy, just like PL.SUM.
I know it is possible to create a new measure that aggregates the original measure to a certain hierarchy, but it is not efficient to
be creatinga measure every time I want to aggregate the measure to a different hierarchy, as it happens with measures from the
base store, they can be aggregable without creating new measures.

Thank you very much!

Steps to reproduce

Code

Whole notebook code

import atoti as tt
import pandas as pd
import numpy as np
session = tt.Session(port=9080)
df_pl = pd.read_csv('Base_Store_PL_ex.csv', sep = ';')
df_pl.head()
df_sens = pd.read_csv('sensis_ex.csv', sep = ';')
df_sens.head()
pl_store = session.read_pandas(\n,
    df_pl,\n,
    keys=['asOfDate', 'country', 'portfolio', 'deal'],\n,
    table_name=\PL\)
sensis_store = session.read_pandas(\n,
    df_sens,\n,
    keys=['asOfDate', 'country', 'portfolio', 'deal', 'underlying'],\n,
    table_name=\Sensis\)
cube = session.create_cube(pl_store)
pl_store.join(sensis_store, mapping={\asOfDate\: \asOfDate\, 'country': 'country', 'portfolio': 'portfolio', 'deal': 'deal'})
m, h, l = cube.measures, cube.hierarchies, cube.levels
cube
m['sensitivity'] = tt.value(sensis_store[\sensitivity\])
session.link()

Actual Result

Does not aggregate measures automaticly

Expected Result

When not aggregating from sensis tables, measures are created

Environment

• atoti: 0.6.6
• Python: 3.10
• Operating System: win32

Logs (if relevant)

Aditional Data

[Skyscimitar]

Hi,

The tt.value function creates a measure representing the value of the column, it doesn't provide any information on how to aggregate it. The value function propagates the value of the column through levels as long as it stays the same (see the documentation for a more detailed example. In the screenshot you shared, since all the sensitivity values are different, without information on how to aggregate it there is no value to display using only the as_of_date and country levels, therefore the value is None and the column in the pivot table is not displayed since it contains no data.

You can use any of the functions in the tt.agg module to create aggregated measures.
For example, if you want the sum of your sensitivities you can use tt.agg.sum(sensis_store["sensitivity"])