'''
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 shutil
import tempfile
from copy import copy
from enum import Enum
import matplotlib
import numpy as np
from pm4py.util import exec_utils, vis_utils
[docs]class Parameters(Enum):
WEIGHT_THRESHOLD = "weight_threshold"
FORMAT = "format"
[docs]def get_temp_file_name(format):
"""
Gets a temporary file name for the image
Parameters
------------
format
Format of the target image
"""
filename = tempfile.NamedTemporaryFile(suffix='.' + format)
return filename.name
[docs]def apply(metric_values, parameters=None):
"""
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, including:
- Parameters.WEIGHT_THRESHOLD -> the weight threshold to use in displaying the graph
- Parameters.FORMAT -> format of the output image (png, svg ...)
Returns
-------------
temp_file_name
Name of a temporary file where the visualization is placed
"""
import networkx as nx
if parameters is None:
parameters = {}
weight_threshold = exec_utils.get_param_value(Parameters.WEIGHT_THRESHOLD, parameters, 0)
format = exec_utils.get_param_value(Parameters.FORMAT, parameters, "png")
directed = metric_values[2]
temp_file_name = get_temp_file_name(format)
rows, cols = np.where(metric_values[0] > weight_threshold)
edges = zip(rows.tolist(), cols.tolist())
if directed:
graph = nx.DiGraph()
else:
graph = nx.Graph()
labels = {}
nodes = []
for index, item in enumerate(metric_values[1]):
labels[index] = item
nodes.append(index)
graph.add_nodes_from(nodes)
graph.add_edges_from(edges)
current_backend = copy(matplotlib.get_backend())
matplotlib.use('Agg')
from matplotlib import pyplot
pyplot.clf()
nx.draw(graph, with_labels=True, labels=labels, node_size=500, pos=nx.circular_layout(graph))
pyplot.savefig(temp_file_name, bbox_inches="tight")
pyplot.clf()
matplotlib.use(current_backend)
return temp_file_name
[docs]def view(temp_file_name, parameters=None):
"""
View the SNA visualization on the screen
Parameters
-------------
temp_file_name
Temporary file name
parameters
Possible parameters of the algorithm
"""
if parameters is None:
parameters = {}
if vis_utils.check_visualization_inside_jupyter():
vis_utils.view_image_in_jupyter(temp_file_name)
else:
vis_utils.open_opsystem_image_viewer(temp_file_name)
[docs]def save(temp_file_name, dest_file, parameters=None):
"""
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
"""
if parameters is None:
parameters = {}
shutil.copyfile(temp_file_name, dest_file)