pm4py.statistics.passed_time.log.variants package#

This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).

PM4Py is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

PM4Py 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 General Public License for more details.

You should have received a copy of the GNU General Public License along with PM4Py. If not, see <https://www.gnu.org/licenses/>.

Submodules#

pm4py.statistics.passed_time.log.variants.post module#

This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).

PM4Py is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

PM4Py 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 General Public License for more details.

You should have received a copy of the GNU General Public License along with PM4Py. If not, see <https://www.gnu.org/licenses/>.

pm4py.statistics.passed_time.log.variants.post.apply(log: EventLog, activity: str, parameters: Optional[Dict[Any, Any]] = None) Dict[str, Any][source]#

Gets the time passed to each succeeding activity

Parameters#

log

Log

activity

Activity that we are considering

parameters

Possible parameters of the algorithm

Returns#

dictio

Dictionary containing a ‘post’ key with the list of aggregates times from the given activity to each succeeding activity

pm4py.statistics.passed_time.log.variants.pre module#

This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).

PM4Py is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

PM4Py 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 General Public License for more details.

You should have received a copy of the GNU General Public License along with PM4Py. If not, see <https://www.gnu.org/licenses/>.

pm4py.statistics.passed_time.log.variants.pre.apply(log: EventLog, activity: str, parameters: Optional[Dict[Any, Any]] = None) Dict[str, Any][source]#

Gets the time passed from each preceding activity

Parameters#

log

Log

activity

Activity that we are considering

parameters

Possible parameters of the algorithm

Returns#

dictio

Dictionary containing a ‘pre’ key with the list of aggregates times from each preceding activity to the given activity

pm4py.statistics.passed_time.log.variants.prepost module#

This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).

PM4Py is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

PM4Py 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 General Public License for more details.

You should have received a copy of the GNU General Public License along with PM4Py. If not, see <https://www.gnu.org/licenses/>.

pm4py.statistics.passed_time.log.variants.prepost.apply(log: EventLog, activity: str, parameters: Optional[Dict[Any, Any]] = None) Dict[str, Any][source]#

Gets the time passed from each preceding activity and to each succeeding activity

Parameters#

log

Log

activity

Activity that we are considering

parameters

Possible parameters of the algorithm

Returns#

dictio

Dictionary containing a ‘pre’ key with the list of aggregated times from each preceding activity to the given activity and a ‘post’ key with the list of aggregates times from the given activity to each succeeding activity