'''
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/>.
'''
from enum import Enum
from typing import Dict, Optional, Any, Union
import pandas as pd
from pm4py.statistics.traces.cycle_time.util import compute
from pm4py.util import exec_utils, constants, xes_constants
[docs]class Parameters(Enum):
TIMESTAMP_KEY = constants.PARAMETER_CONSTANT_TIMESTAMP_KEY
START_TIMESTAMP_KEY = constants.PARAMETER_CONSTANT_START_TIMESTAMP_KEY
CASE_ID_KEY = constants.PARAMETER_CONSTANT_CASEID_KEY
[docs]def apply(df: pd.DataFrame, parameters: Optional[Dict[Union[str, Parameters], Any]] = None) -> float:
"""
Computes the cycle time starting from a Pandas dataframe
The definition that has been followed is the one proposed in:
https://www.presentationeze.com/presentations/lean-manufacturing-just-in-time/lean-manufacturing-just-in-time-full-details/process-cycle-time-analysis/calculate-cycle-time/#:~:text=Cycle%20time%20%3D%20Average%20time%20between,is%2024%20minutes%20on%20average.
So:
Cycle time = Average time between completion of units.
Example taken from the website:
Consider a manufacturing facility, which is producing 100 units of product per 40 hour week.
The average throughput rate is 1 unit per 0.4 hours, which is one unit every 24 minutes.
Therefore the cycle time is 24 minutes on average.
Parameters
------------------
df
Dataframe
parameters
Parameters of the algorithm, including:
- Parameters.START_TIMESTAMP_KEY => the attribute acting as start timestamp
- Parameters.TIMESTAMP_KEY => the attribute acting as timestamp
- Parameters.CASE_ID_KEY => the attribute acting as case identifier
Returns
------------------
cycle_time
Cycle time
"""
if parameters is None:
parameters = {}
timestamp_key = exec_utils.get_param_value(Parameters.TIMESTAMP_KEY, parameters,
xes_constants.DEFAULT_TIMESTAMP_KEY)
start_timestamp_key = exec_utils.get_param_value(Parameters.START_TIMESTAMP_KEY, parameters,
timestamp_key)
case_id_key = exec_utils.get_param_value(Parameters.CASE_ID_KEY, parameters, constants.CASE_CONCEPT_NAME)
events = [(x[start_timestamp_key].timestamp(), x[timestamp_key].timestamp()) for x in
df[{start_timestamp_key, timestamp_key}].to_dict("records")]
return compute.cycle_time(events, df[case_id_key].nunique())