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
| Parameter | Type | Description |
|---|---|---|
| ts_col | str | The name of the column containing timestamps. |
| cols | Union[str, list[str]] | The column(s) to be operated on. These can include expressions to rename the output (e.g., |
| wcol | str | The column containing the weight values. |
| rev_time | Union[int,str] | The look-behind window size. This can be expressed as an integer in nanoseconds or a string duration, e.g., |
| fwd_time | Union[int,str] | The look-forward window size. This can be expressed as an integer in nanoseconds or a string duration, e.g., |
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"])