pm4py.statistics.overlap.interval_events.log package#

PM4Py – A Process Mining Library for Python

This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details.

You should have received a copy of the GNU Affero General Public License along with this program. If not, see this software project’s root or visit <https://www.gnu.org/licenses/>.

Website: https://processintelligence.solutions Contact: info@processintelligence.solutions

Submodules#

pm4py.statistics.overlap.interval_events.log.get module#

PM4Py – A Process Mining Library for Python

You should have received a copy of the GNU Affero General Public License along with this program. If not, see this software project’s root or visit <https://www.gnu.org/licenses/>.

Website: https://processintelligence.solutions Contact: info@processintelligence.solutions

class pm4py.statistics.overlap.interval_events.log.get.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#

Bases: Enum

START_TIMESTAMP_KEY = 'pm4py:param:start_timestamp_key'#

TIMESTAMP_KEY = 'pm4py:param:timestamp_key'#

pm4py.statistics.overlap.interval_events.log.get.apply(log: EventLog | EventStream, parameters: Dict[str | Parameters, Any] | None = None) → List[int][source]#

Counts the intersections of each interval event with the other interval events of the log (all the events are considered, not looking at the activity)

Parameters#

log: Event log
parameters: Parameters of the algorithm, including: - Parameters.START_TIMESTAMP_KEY => the attribute to consider as start timestamp - Parameters.TIMESTAMP_KEY => the attribute to consider as timestamp

Returns#

overlap: For each interval event, ordered by the order of appearance in the log, associates the number of intersecting events.