foundryts.functions.cumulative_aggregate

foundryts.functions.cumulative_aggregate(aggregate)

Returns a function that computes the cumulative aggregate of all values in a single time series.

The cumulative aggregate is calculated progressively for each point in the input time series, considering all preceding points up to and including the current point.

Aggregation functions supported:

Aggregation function	Description
min	Smallest value up to the current point in the time series.
max	Largest value up to the current point in the time series.
count	Count of all points up to the current point in the time series.
sum	Sum of all point values up to the current point.
product	Product of point values up to the current point.
mean	Average of all point values up to the current point.
standard_deviation	Standard deviation of all point values up to the current point.
difference	Difference between the current point’s value and the first point’s value in the time series, providing the relative change within the series.
percent_change	Percent change between the current point’s value and the first point’s value in the time series, providing the relative rate of change within the series.
first	Value of the first point in the time series.
last	Value of the current (last) point in the time series.

Parameters: aggregate (str) – Aggregation function to apply on each point, use one of the values from Aggregation Function above.
Returns: A function that takes a single time series as input and computes the specified aggregate for each point, considering all preceding points up to the point being evaluated.
Return type: (FunctionNode) -> FunctionNode

Dataframe schema

Column name	Type	Description
timestamp	pandas.Timestamp	Timestamp of the point
value	Union[float, str]	Value of the point

Examples

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>>> series = F.points(
...     (2, 10.0), (5, 20.0), (6, 30.0), (7, 40.0), (8, 50.0), (12, 60.0), name="series-1"
... )
>>> series.to_pandas()
                      timestamp  value
0 1970-01-01 00:00:00.000000002   10.0
1 1970-01-01 00:00:00.000000005   20.0
2 1970-01-01 00:00:00.000000006   30.0
3 1970-01-01 00:00:00.000000007   40.0
4 1970-01-01 00:00:00.000000008   50.0
5 1970-01-01 00:00:00.000000012   60.0

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>>> cumulative_agg = F.cumulative_aggregate("mean")(series)
>>> cumulative_agg.to_pandas()
                      timestamp  value
0 1970-01-01 00:00:00.000000002   10.0
1 1970-01-01 00:00:00.000000005   15.0
2 1970-01-01 00:00:00.000000006   20.0
3 1970-01-01 00:00:00.000000007   25.0
4 1970-01-01 00:00:00.000000008   30.0
5 1970-01-01 00:00:00.000000012   35.0