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Version: Python

rolling_wavg_time

rolling_wavg_time creates a time-based windowed weighted average operator to be used in an update_by table operation. Data is windowed by reverse and forward time intervals relative to the current row, and the rolling weighted average of values within the window is calculated.

Syntax​

rolling_wavg_time(
    ts_col: str,
    wcol: str,
    cols: Union[str, list[str]],
    rev_time: Union[int, str],
    fwd_time: Union[int, str],
) -> UpdateByOperation

Parameters​

ParameterTypeDescription
ts_colstr

The name of the column containing timestamps.

colsUnion[str, list[str]]

The column(s) to be operated on. These can include expressions to rename the output (e.g., NewCol = Col). If None, the rolling sample standard deviation is calculated for all applicable columns.

wcolstr

The column containing the weight values.

rev_timeUnion[int,str]

The look-behind window size. This can be expressed as an integer in nanoseconds or a string duration, e.g., "PT00:00:00.001" or "PTnHnMnS", where H is hour, M is minute, and S is second.

fwd_timeUnion[int,str]

The look-forward window size. This can be expressed as an integer in nanoseconds or a string duration, e.g., "PT00:00:00.001" or "PTnHnMnS", where H is hour, M is minute, and S is second.

Returns​

An UpdateByOperation to be used in an update_by table operation.

Examples​

The following example performs an update_by on the source table using three rolling_wavg_time operations. Each operation gives varying rev_time and fwd_time values to show how they affect the output. The windows for each operation are as follows:

  • op_before: The window starts five seconds before the current row, and ends one second before the current row.
  • op_after: The window starts one second after the current row, and ends five seconds after of the current row.
  • op_middle: The window starts three seconds before the current row, and ends three seconds after the current row.
from deephaven.updateby import rolling_wavg_time
from deephaven.time import dh_now
from deephaven import empty_table

base_time = dh_now()

source = empty_table(10).update(
    [
        "Timestamp = base_time + i * SECOND",
        "Letter = (i % 2 == 0) ? `A` : `B`",
        "X = i",
        "X = ii",
        "Weight = i * 2",
    ]
)

op_before = rolling_wavg_time(
    ts_col="Timestamp",
    wcol="Weight",
    cols=["Wavg1 = X"],
    rev_time=int(5e9),
    fwd_time=int(-1e9),
)
op_after = rolling_wavg_time(
    ts_col="Timestamp",
    wcol="Weight",
    cols=["Wavg2 = X"],
    rev_time="PT-1S",
    fwd_time="PT5S",
)
op_middle = rolling_wavg_time(
    ts_col="Timestamp",
    wcol="Weight",
    cols=["Wavg3 = X"],
    rev_time="PT3S",
    fwd_time="PT3S",
)

result = source.update_by(ops=[op_before, op_after, op_middle], by=["Letter"])