Version: Python

# ems_tick

ems_tick creates a tick-based (row-based) EMS (exponential moving sum) for an update_by table operation. The formula for the tick-based EMS of a column $X$ is:

$a = e^{\frac{-1}{\tau}}$

$\mathcal{S}_0 = x_0$

$\mathcal{S}_i = a*\mathcal{S}_{i-1} + x_i$

Where:

• $\tau$ is decay_ticks, an input parameter to the method.
• $\mathcal{S}_i$ is the exponential moving sum of $X$ at step $i$.
• $x_i$ is the current value.
• $i$ denotes the time step, ranging from $i=1$ to $i = n-1$, where $n$ is the number of elements in $X$.

## Syntax​

ems_tick(    decay_ticks: int,    cols: list[str],    op_control: OperationControl = None,) -> UpdateByOperation

## Parameters​

ParameterTypeDescription
decay_ticksint

The decay rate in ticks.

colslist[str]

The column(s) to be operated on. These can include expressions to rename the output (e.g., NewCol = Col). When left empty, the rolling count is calculated for all applicable columns.

op_controlOperationControl

Defines how special cases should behave. When None, default OperationControl settings will be used. See OperationControl for information.

## Returns​

An UpdateByOperation to be used in an update_by table operation.

## Examples​

### One column, no groups​

The following example calculates the tick-based (row-based) EMS (Exponential Moving Sum) of the X column, renaming the resulting column to EmsX. The decay rate, decay_ticks is set to 2. No grouping columns are specified, so the EMS is calculated over all rows.

from deephaven.updateby import ems_tickfrom deephaven import empty_tablesource = empty_table(10).update(    ["Letter = (i % 2 == 0) ? A : B", "X = randomInt(0,25)"])result = source.update_by(ops=ems_tick(decay_ticks=2, cols=["EmsX = X"]))

### One EMS column, one grouping column​

The following example builds on the previous by specifying Letter as the key column. Thus, the EMS is calculated on a per-letter basis.

from deephaven.updateby import ems_tickfrom deephaven import empty_tablesource = empty_table(10).update(    ["Letter = (i % 2 == 0) ? A : B", "X = randomInt(0,25)"])result = source.update_by(ops=ems_tick(decay_ticks=2, cols=["EmsX = X"]), by=["Letter"])

### Multiple EMS columns, multiple grouping columns​

The following example builds on the previous by calculating the EMS of multiple columns in the same UpdateByOperation. Also, the groups are defined by unique combinations of letter and boolean in the Letter and Truth columns, respectively.

from deephaven.updateby import ems_tickfrom deephaven import empty_tablesource = empty_table(20).update(    [        "Letter = (i % 2 == 0) ? A : B",        "Truth = randomBool()",        "X = randomInt(0,25)",        "Y = randomInt(0,25)",    ])result = source.update_by(    ops=ems_tick(decay_ticks=2, cols=["EmsX = X", "EmsY = Y"]), by=["Letter", "Truth"])

### Multiple UpdateByOperations, multiple grouping columns​

The following example builds on the previous by calculating the EMS of multiple columns, each with its own UpdateByOperation. This allows each EMS to have its own decay rate. The different decay rates are reflected in the renamed resultant column names.

from deephaven.updateby import ems_tickfrom deephaven import empty_tablesource = empty_table(20).update(    [        "Letter = (i % 2 == 0) ? A : B",        "Truth = randomBool()",        "X = randomInt(0,25)",        "Y = randomInt(0,25)",    ])EmsX = ems_tick(decay_ticks=2, cols=["EmsX2rows = X"])ems_y = ems_tick(decay_ticks=4, cols=["EmsY5rows = Y"])result = source.update_by(ops=[EmsX, ems_y], by=["Letter", "Truth"])