deephaven.updateby¶

This module supports building various operations for use with the update-by Table operation.

class BadDataBehavior(value)[source]¶

Bases: enum.Enum

An Enum defining ways to handle invalid data during update-by operations.

POISON = io.deephaven.api.updateby.BadDataBehavior(objectRef=0x3cfd788)¶: Allow the bad data to poison the result. This is only valid for use with NaN

RESET = io.deephaven.api.updateby.BadDataBehavior(objectRef=0x3cfd778)¶: Reset the state for the bucket to None when invalid data is encountered

SKIP = io.deephaven.api.updateby.BadDataBehavior(objectRef=0x3cfd780)¶: Skip and do not process the invalid data without changing state

THROW = io.deephaven.api.updateby.BadDataBehavior(objectRef=0x3cfd770)¶: Throw an exception and abort processing when bad data is encountered

class MathContext(value)[source]¶

Bases: enum.Enum

An Enum for predefined precision and rounding settings in numeric calculation.

DECIMAL128 = java.math.MathContext(objectRef=0x3cfd7c8)¶: a precision setting matching the IEEE 754R Decimal128 format, 34 digits, rounding is half-even

DECIMAL32 = java.math.MathContext(objectRef=0x3cfd7b8)¶: a precision setting matching the IEEE 754R Decimal32 format, 7 digits, rounding is half-even

DECIMAL64 = java.math.MathContext(objectRef=0x3cfd7c0)¶: a precision setting matching the IEEE 754R Decimal64 format, 16 digits, rounding is half-even

UNLIMITED = java.math.MathContext(objectRef=0x3cfd7b0)¶: unlimited precision arithmetic, rounding is half-up

class OperationControl(on_null=BadDataBehavior.SKIP, on_nan=BadDataBehavior.SKIP, on_null_time=BadDataBehavior.SKIP, on_negative_deltatime=BadDataBehavior.THROW, on_zero_deltatime=BadDataBehavior.SKIP, big_value_context=MathContext.DECIMAL128)[source]¶

Bases: deephaven._wrapper.JObjectWrapper

A OperationControl represents control parameters for performing operations with the table UpdateByOperation.

j_object_type¶: alias of io.deephaven.api.updateby.OperationControl

class UpdateByOperation(j_updateby_op)[source]¶

Bases: deephaven._wrapper.JObjectWrapper

A UpdateByOperation represents an operator for the Table update-by operation.

j_object_type¶: alias of io.deephaven.api.updateby.UpdateByOperation

cum_max(cols)[source]¶

Creates a cumulative maximum UpdateByOperation for the supplied column names.

Parameters: cols (Union[str, List[str]]) – the column(s) to be operated on, can include expressions to rename the output, i.e. “new_col = col”; when empty, update_by performs the cumulative maximum operation on all the applicable columns.
Return type: UpdateByOperation
Returns: an UpdateByOperation
Raises: DHError –

cum_min(cols)[source]¶

Creates a cumulative minimum UpdateByOperation for the supplied column names.

Parameters: cols (Union[str, List[str]]) – the column(s) to be operated on, can include expressions to rename the output, i.e. “new_col = col”; when empty, update_by perform the cumulative minimum operation on all the applicable columns.
Return type: UpdateByOperation
Returns: an UpdateByOperation
Raises: DHError –

cum_prod(cols)[source]¶

Creates a cumulative product UpdateByOperation for the supplied column names.

Parameters: cols (Union[str, List[str]]) – the column(s) to be operated on, can include expressions to rename the output, i.e. “new_col = col”; when empty, update_by performing the cumulative product operation on all the applicable columns.
Return type: UpdateByOperation
Returns: an UpdateByOperation
Raises: DHError –

cum_sum(cols)[source]¶

Creates a cumulative sum UpdateByOperation for the supplied column names.

Parameters: cols (Union[str, List[str]]) – the column(s) to be operated on, can include expressions to rename the output, i.e. “new_col = col”; when empty, update_by performs the cumulative sum operation on all the applicable columns.
Return type: UpdateByOperation
Returns: an UpdateByOperation
Raises: DHError –

ema_tick_decay(time_scale_ticks, cols, op_control=None)[source]¶

Creates an EMA (exponential moving average) UpdateByOperation for the supplied column names, using ticks as the decay unit.

The formula used is: a = e^(-1 / time_scale_ticks) ema_next = a * ema_last + (1 - a) * value

Parameters

time_scale_ticks (int) – the decay rate in ticks
cols (Union[str, List[str]]) – the column(s) to be operated on, can be renaming expressions, i.e. “new_col = col”
op_control (OperationControl) – defines how special cases should behave, when None, the default OperationControl settings as specified in __init__() will be used

Return type

UpdateByOperation

Returns

an UpdateByOperation

Raises

DHError –

ema_time_decay(ts_col, time_scale, cols, op_control=None)[source]¶

Creates an EMA(exponential moving average) UpdateByOperation for the supplied column names, using time as the decay unit.

The formula used is

a = e^(-dt / time_scale_nanos) ema_next = a * ema_last + (1 - a) * value

Args:

ts_col (str): the column in the source table to use for timestamps

time_scale (Union[int, str]): the decay rate, can be expressed as an integer in nanoseconds or a time: interval string, e.g. “00:00:00.001”

cols (Union[str, List[str]]): the column(s) to be operated on, can be renaming expressions, i.e. “new_col = col” op_control (OperationControl): defines how special cases should behave, when None, the default OperationControl

settings as specified in __init__() will be used

Return type: UpdateByOperation
Returns: an UpdateByOperation
Raises: DHError –

forward_fill(cols)[source]¶

Creates a forward fill UpdateByOperation for the supplied column names. Null values in the columns are replaced by the last known non-null values. This operation is forward only.

Parameters: cols (Union[str, List[str]]) – the column(s) to be operated on, can include expressions to rename the output, i.e. “new_col = col”; when empty, update_by perform the forward fill operation on all columns.
Return type: UpdateByOperation
Returns: an UpdateByOperation
Raises: DHError –