'''
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 pm4py.visualization.sna.variants import networkx, pyvis
from enum import Enum
from pm4py.util import exec_utils
[docs]class Variants(Enum):
NETWORKX = networkx
PYVIS = pyvis
DEFAULT_VARIANT = Variants.NETWORKX
[docs]def apply(metric_values, parameters=None, variant=DEFAULT_VARIANT):
"""
Perform SNA visualization starting from the Matrix Container object
and the Resource-Resource matrix
Parameters
-------------
metric_values
Value of the metrics
parameters
Possible parameters of the algorithm
variant
Variant of the algorithm to use, possible values:
- Variants.NETWORKX
- Variants.PYVIS
Returns
-------------
temp_file_name
Name of a temporary file where the visualization is placed
"""
return exec_utils.get_variant(variant).apply(metric_values, parameters=parameters)
[docs]def view(temp_file_name, parameters=None, variant=DEFAULT_VARIANT):
"""
View the SNA visualization on the screen
Parameters
-------------
temp_file_name
Temporary file name
parameters
Possible parameters of the algorithm
"""
return exec_utils.get_variant(variant).view(temp_file_name, parameters=parameters)
[docs]def save(temp_file_name, dest_file, parameters=None, variant=DEFAULT_VARIANT):
"""
Save the SNA visualization from a temporary file to a well-defined destination file
Parameters
-------------
temp_file_name
Temporary file name
dest_file
Destination file
parameters
Possible parameters of the algorithm
"""
return exec_utils.get_variant(variant).save(temp_file_name, dest_file, parameters=parameters)