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How to use list_activities method in localstack

Best Python code snippet using localstack_python

log_disconnected.py

Source:log_disconnected.py

1'''2    This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).3    PM4Py is free software: you can redistribute it and/or modify4    it under the terms of the GNU General Public License as published by5    the Free Software Foundation, either version 3 of the License, or6    (at your option) any later version.7    PM4Py is distributed in the hope that it will be useful,8    but WITHOUT ANY WARRANTY; without even the implied warranty of9    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the10    GNU General Public License for more details.11    You should have received a copy of the GNU General Public License12    along with PM4Py.  If not, see <https://www.gnu.org/licenses/>.13'''14from enum import Enum15from pm4py.objects.log.util import sorting16from pm4py.util import constants, exec_utils17from pm4py.util import points_subset18from pm4py.util import xes_constants as xes19from pm4py.objects.log.util import basic_filter20from typing import Optional, Dict, Any, Union, Tuple, List21from pm4py.objects.log.obj import EventLog, EventStream22class Parameters(Enum):23    ACTIVITY_KEY = constants.PARAMETER_CONSTANT_ACTIVITY_KEY24    TIMESTAMP_KEY = constants.PARAMETER_CONSTANT_TIMESTAMP_KEY25    CASE_ID_KEY = constants.PARAMETER_CONSTANT_CASEID_KEY26    ATTRIBUTE_KEY = constants.PARAMETER_CONSTANT_ATTRIBUTE_KEY27    PARAMETER_SAMPLE_SIZE = "sample_size"28    SORT_LOG_REQUIRED = "sort_log_required"29def apply(log: EventLog, list_activities: List[str], sample_size: int, parameters: Optional[Dict[Union[str, Parameters], Any]] = None) -> Dict[str, Any]:30    """31    Finds the disconnected performance spectrum provided a log32    and a list of activities33    Parameters34    -------------35    log36        Log37    list_activities38        List of activities interesting for the performance spectrum (at least two)39    sample_size40        Size of the sample41    parameters42        Parameters of the algorithm,  including:43            - Parameters.ACTIVITY_KEY44            - Parameters.TIMESTAMP_KEY45    Returns46    -------------47    points48        Points of the performance spectrum49    """50    if parameters is None:51        parameters = {}52    sort_log_required = exec_utils.get_param_value(Parameters.SORT_LOG_REQUIRED, parameters, True)53    all_acti_combs = set(tuple(list_activities[j:j + i]) for i in range(2, len(list_activities) + 1) for j in54                         range(0, len(list_activities) - i + 1))55    two_acti_combs = set((list_activities[i], list_activities[i + 1]) for i in range(len(list_activities) - 1))56    activity_key = exec_utils.get_param_value(Parameters.ACTIVITY_KEY, parameters, xes.DEFAULT_NAME_KEY)57    timestamp_key = exec_utils.get_param_value(Parameters.TIMESTAMP_KEY, parameters, xes.DEFAULT_TIMESTAMP_KEY)58    case_id_key = exec_utils.get_param_value(Parameters.CASE_ID_KEY, parameters, xes.DEFAULT_TRACEID_KEY)59    parameters[Parameters.ATTRIBUTE_KEY] = activity_key60    log = basic_filter.filter_log_events_attr(log, list_activities, parameters=parameters)61    if sort_log_required:62        log = sorting.sort_timestamp_log(log, timestamp_key=timestamp_key)63    points = []64    for trace in log:65        matches = [(i, i + 1) for i in range(len(trace) - 1) if66                   (trace[i][activity_key], trace[i + 1][activity_key]) in two_acti_combs]67        i = 068        while i < len(matches) - 1:69            matchAct = (trace[mi][activity_key] for mi in (matches[i] + matches[i + 1][1:]))70            if matches[i][-1] == matches[i + 1][0] and matchAct in all_acti_combs:71                matches[i] = matches[i] + matches[i + 1][1:]72                del matches[i + 1]73                i = 074            else:75                i += 176        if matches:77            matches = set(matches)78            timest_comb = [{'points': [(trace[i][activity_key], trace[i][timestamp_key].timestamp()) for i in match]}79                           for match in matches]80            for p in timest_comb:81                p['case_id'] = trace.attributes[case_id_key]82            points += timest_comb83    points = sorted(points, key=lambda x: min(x['points'], key=lambda x: x[1])[1])84    if len(points) > sample_size:85        points = points_subset.pick_chosen_points_list(sample_size, points)...

algorithm.py

Source:algorithm.py

1'''2    This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).3    PM4Py is free software: you can redistribute it and/or modify4    it under the terms of the GNU General Public License as published by5    the Free Software Foundation, either version 3 of the License, or6    (at your option) any later version.7    PM4Py is distributed in the hope that it will be useful,8    but WITHOUT ANY WARRANTY; without even the implied warranty of9    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the10    GNU General Public License for more details.11    You should have received a copy of the GNU General Public License12    along with PM4Py.  If not, see <https://www.gnu.org/licenses/>.13'''14from pm4py.algo.discovery.performance_spectrum.variants import dataframe, log, dataframe_disconnected, log_disconnected15from pm4py.util import exec_utils16import pkgutil17from enum import Enum18from pm4py.util import constants19from typing import Optional, Dict, Any, Union, Tuple, List20from pm4py.objects.log.obj import EventLog, EventStream21import pandas as pd22class Parameters(Enum):23    ACTIVITY_KEY = constants.PARAMETER_CONSTANT_ACTIVITY_KEY24    TIMESTAMP_KEY = constants.PARAMETER_CONSTANT_TIMESTAMP_KEY25    CASE_ID_KEY = constants.PARAMETER_CONSTANT_CASEID_KEY26    ATTRIBUTE_KEY = constants.PARAMETER_CONSTANT_ATTRIBUTE_KEY27    PARAMETER_SAMPLE_SIZE = "sample_size"28class Outputs(Enum):29    LIST_ACTIVITIES = "list_activities"30    POINTS = "points"31class Variants(Enum):32    DATAFRAME = dataframe33    LOG = log34    DATAFRAME_DISCONNECTED = dataframe_disconnected35    LOG_DISCONNECTED = log_disconnected36def apply(log: Union[EventLog, EventStream, pd.DataFrame], list_activities: List[str], variant=None, parameters: Optional[Dict[Any, Any]] = None) -> Dict[str, Any]:37    """38    Finds the performance spectrum provided a log/dataframe39    and a list of activities40    Parameters41    -------------42    log43        Event log/Dataframe44    list_activities45        List of activities interesting for the performance spectrum (at least two)46    variant47        Variant to be used (see Variants Enum)48    parameters49        Parameters of the algorithm, including:50            - Parameters.ACTIVITY_KEY51            - Parameters.TIMESTAMP_KEY52    Returns53    -------------54    ps55        Performance spectrum object (dictionary)56    """57    from pm4py.objects.conversion.log import converter as log_conversion58    if parameters is None:59        parameters = {}60    sample_size = exec_utils.get_param_value(Parameters.PARAMETER_SAMPLE_SIZE, parameters, 10000)61    if len(list_activities) < 2:62        raise Exception("performance spectrum can be applied providing at least two activities!")63    points = None64    if pkgutil.find_loader("pandas"):65        import pandas as pd66        if type(log) is pd.DataFrame:67            if variant is None:68                variant = Variants.DATAFRAME69            points = exec_utils.get_variant(variant).apply(log, list_activities, sample_size, parameters)70    if points is None:71        if variant is None:72            variant = Variants.LOG73        points = exec_utils.get_variant(variant).apply(log_conversion.apply(log), list_activities, sample_size,74                                                            parameters)75    ps = {Outputs.LIST_ACTIVITIES.value: list_activities, Outputs.POINTS.value: points}...

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