'''
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/>.
'''
import pandas as pd
from enum import Enum
from pm4py.util import exec_utils, constants, xes_constants
from pm4py.util.business_hours import soj_time_business_hours_diff
from typing import Optional, Dict, Any, Union
[docs]class Parameters(Enum):
ACTIVITY_KEY = constants.PARAMETER_CONSTANT_ACTIVITY_KEY
START_TIMESTAMP_KEY = constants.PARAMETER_CONSTANT_START_TIMESTAMP_KEY
TIMESTAMP_KEY = constants.PARAMETER_CONSTANT_TIMESTAMP_KEY
AGGREGATION_MEASURE = "aggregationMeasure"
BUSINESS_HOURS = "business_hours"
WORKTIMING = "worktiming"
WEEKENDS = "weekends"
WORKCALENDAR = "workcalendar"
DIFF_KEY = "@@diff"
[docs]def apply(dataframe: pd.DataFrame, parameters: Optional[Dict[Union[str, Parameters], Any]] = None) -> Dict[str, float]:
"""
Gets the sojourn time per activity on a Pandas dataframe
Parameters
--------------
dataframe
Pandas dataframe
parameters
Parameters of the algorithm, including:
- Parameters.ACTIVITY_KEY => activity key
- Parameters.START_TIMESTAMP_KEY => start timestamp key
- Parameters.TIMESTAMP_KEY => timestamp key
- Parameters.BUSINESS_HOURS => calculates the difference of time based on the business hours, not the total time.
Default: False
- Parameters.WORKTIMING => work schedule of the company (provided as a list where the first number is the start
of the work time, and the second number is the end of the work time), if business hours are enabled
Default: [7, 17] (work shift from 07:00 to 17:00)
- Parameters.WEEKENDS => indexes of the days of the week that are weekend
Default: [6, 7] (weekends are Saturday and Sunday)
- Parameters.AGGREGATION_MEASURE => performance aggregation measure (sum, min, max, mean, median)
Returns
--------------
soj_time_dict
Sojourn time dictionary
"""
if parameters is None:
parameters = {}
business_hours = exec_utils.get_param_value(Parameters.BUSINESS_HOURS, parameters, False)
worktiming = exec_utils.get_param_value(Parameters.WORKTIMING, parameters, [7, 17])
weekends = exec_utils.get_param_value(Parameters.WEEKENDS, parameters, [6, 7])
workcalendar = exec_utils.get_param_value(Parameters.WORKCALENDAR, parameters, constants.DEFAULT_BUSINESS_HOURS_WORKCALENDAR)
activity_key = exec_utils.get_param_value(Parameters.ACTIVITY_KEY, parameters, xes_constants.DEFAULT_NAME_KEY)
start_timestamp_key = exec_utils.get_param_value(Parameters.START_TIMESTAMP_KEY, parameters,
xes_constants.DEFAULT_TIMESTAMP_KEY)
timestamp_key = exec_utils.get_param_value(Parameters.TIMESTAMP_KEY, parameters,
xes_constants.DEFAULT_TIMESTAMP_KEY)
aggregation_measure = exec_utils.get_param_value(Parameters.AGGREGATION_MEASURE,
parameters, "mean")
if business_hours:
dataframe[DIFF_KEY] = dataframe.apply(
lambda x: soj_time_business_hours_diff(x[start_timestamp_key], x[timestamp_key], worktiming,
weekends, workcalendar), axis=1)
else:
dataframe[DIFF_KEY] = (
dataframe[timestamp_key] - dataframe[start_timestamp_key]
).astype('timedelta64[s]')
dataframe = dataframe.reset_index()
column = dataframe.groupby(activity_key)[DIFF_KEY]
if aggregation_measure == "median":
ret_dict = column.median().to_dict()
elif aggregation_measure == "min":
ret_dict = column.min().to_dict()
elif aggregation_measure == "max":
ret_dict = column.max().to_dict()
elif aggregation_measure == "sum":
ret_dict = column.sum().to_dict()
else:
ret_dict = column.mean().to_dict()
# assure to avoid problems with np.float64, by using the Python float type
for el in ret_dict:
ret_dict[el] = float(ret_dict[el])
return ret_dict