pm4py.algo.filtering.log.timestamp.timestamp_filter module#

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class pm4py.algo.filtering.log.timestamp.timestamp_filter.Parameters(*values)[source]#

Bases: Enum

TIMESTAMP_KEY = 'pm4py:param:timestamp_key'#
pm4py.algo.filtering.log.timestamp.timestamp_filter.trace_attr_is_contained(trace: Trace, dt1: str | datetime, dt2: str | datetime, trace_attr: str) → bool[source]#

Checks if the given attribute at the trace level is contained in the provided range

Parameters:

  • trace – Trace object

  • dt1 – Left extreme of the time interval

  • dt2 – Right extreme of the time interval

  • trace_attr – Attribute at the trace level that is considered for the filtering

Returns:

Boolean value

Return type:

boolean

pm4py.algo.filtering.log.timestamp.timestamp_filter.filter_on_trace_attribute(log: EventLog, dt1: str | datetime, dt2: str | datetime, parameters: Dict[str | Parameters, Any] | None = None) → EventLog[source]#

Filters the traces of the event log that have a given trace attribute falling in the provided range

Parameters:

  • log – Event log

  • dt1 – Left extreme of the time interval

  • dt2 – Right extreme of the time interval

  • parameters – Parameters of the filtering, including: - Parameters.TIMESTAMP_KEY => trace attribute to use for the filtering

Returns:

Filtered event log

Return type:

filtered_log

pm4py.algo.filtering.log.timestamp.timestamp_filter.is_contained(trace, dt1, dt2, timestamp_key)[source]#

Check if a trace is contained in the given interval

Parameters:

  • trace – Trace to check

  • dt1 – Lower bound to the interval

  • dt2 – Upper bound to the interval

  • timestamp_key – Timestamp attribute

Returns:

Is true if the trace is contained

Return type:

boolean

pm4py.algo.filtering.log.timestamp.timestamp_filter.filter_traces_contained(log: EventLog, dt1: str | datetime, dt2: str | datetime, parameters: Dict[str | Parameters, Any] | None = None) → EventLog[source]#

Get traces that are contained in the given interval

Parameters:

  • log – Trace log

  • dt1 – Lower bound to the interval

  • dt2 – Upper bound to the interval

  • parameters –

    Possible parameters of the algorithm, including:

    Parameters.TIMESTAMP_KEY -> Attribute to use as timestamp

Returns:

Filtered log

Return type:

filtered_log

pm4py.algo.filtering.log.timestamp.timestamp_filter.is_intersecting(trace, dt1, dt2, timestamp_key)[source]#

Check if a trace is intersecting in the given interval

Parameters:

  • trace – Trace to check

  • dt1 – Lower bound to the interval

  • dt2 – Upper bound to the interval

  • timestamp_key – Timestamp attribute

Returns:

Is true if the trace is contained

Return type:

boolean

pm4py.algo.filtering.log.timestamp.timestamp_filter.filter_traces_intersecting(log: EventLog, dt1: str | datetime, dt2: str | datetime, parameters: Dict[str | Parameters, Any] | None = None) → EventLog[source]#

Filter traces intersecting the given interval

Parameters:

  • log – Trace log

  • dt1 – Lower bound to the interval

  • dt2 – Upper bound to the interval

  • parameters –

    Possible parameters of the algorithm, including:

    Parameters.TIMESTAMP_KEY -> Attribute to use as timestamp

Returns:

Filtered log

Return type:

filtered_log

pm4py.algo.filtering.log.timestamp.timestamp_filter.apply_events(log: EventLog, dt1: str | datetime, dt2: str | datetime, parameters: Dict[str | Parameters, Any] | None = None) → EventLog[source]#

Get a new log containing all the events contained in the given interval

Parameters:

  • log – Log

  • dt1 – Lower bound to the interval

  • dt2 – Upper bound to the interval

  • parameters –

    Possible parameters of the algorithm, including:

    Parameters.TIMESTAMP_KEY -> Attribute to use as timestamp

Returns:

Filtered log

Return type:

filtered_log

pm4py.algo.filtering.log.timestamp.timestamp_filter.has_attribute_in_timeframe(trace, attribute, attribute_value, dt1, dt2, timestamp_key)[source]#
pm4py.algo.filtering.log.timestamp.timestamp_filter.filter_traces_attribute_in_timeframe(log: EventLog, attribute: str, attribute_value: Any, dt1: str | datetime, dt2: str | datetime, parameters: Dict[str | Parameters, Any] | None = None) → EventLog[source]#

Get a new log containing all the traces that have an event in the given interval with the specified attribute value

Parameters:

  • log – Log

  • attribute – The attribute to filter on

  • attribute_value – The attribute value to filter on

  • dt1 – Lower bound to the interval

  • dt2 – Upper bound to the interval

  • parameters –

    Possible parameters of the algorithm, including:

    Parameters.TIMESTAMP_KEY -> Attribute to use as timestamp

Returns:

Filtered log

Return type:

filtered_log

pm4py.algo.filtering.log.timestamp.timestamp_filter.filter_traces_starting_in_timeframe(log: EventLog, dt1: str | datetime, dt2: str | datetime, parameters: Dict[str | Parameters, Any] | None = None) → EventLog[source]#

Keep/Exclude traces whose first event timestamp falls in [dt1, dt2].

Parameters:

  • log – Event log

  • dt1 – Lower bound to the interval

  • dt2 – Upper bound to the interval

  • parameters –

    Possible parameters of the algorithm, including:

    Parameters.TIMESTAMP_KEY -> attribute to use as timestamp “positive” (bool, default True) -> keep (True) or exclude (False) the matching cases

Returns:

Filtered event log

Return type:

filtered_log

pm4py.algo.filtering.log.timestamp.timestamp_filter.filter_traces_completing_in_timeframe(log: EventLog, dt1: str | datetime, dt2: str | datetime, parameters: Dict[str | Parameters, Any] | None = None) → EventLog[source]#

Keep/Exclude traces whose last event timestamp falls in [dt1, dt2].

Parameters:

  • log – Event log

  • dt1 – Lower bound to the interval

  • dt2 – Upper bound to the interval

  • parameters –

    Possible parameters of the algorithm, including:

    Parameters.TIMESTAMP_KEY -> attribute to use as timestamp “positive” (bool, default True) -> keep (True) or exclude (False) the matching cases

Returns:

Filtered event log

Return type:

filtered_log

pm4py.algo.filtering.log.timestamp.timestamp_filter.apply(df, parameters=None)[source]#
pm4py.algo.filtering.log.timestamp.timestamp_filter.apply_auto_filter(df, parameters=None)[source]#