Source code for pm4py.visualization.sna.variants.networkx

import shutil
import tempfile
from copy import copy
from enum import Enum

import matplotlib

from pm4py.util import exec_utils, vis_utils
from pm4py.objects.org.sna.obj import SNA
from pm4py.util import constants
from pm4py.util import nx_utils
import networkx as nx




class Parameters(Enum):
    WEIGHT_THRESHOLD = "weight_threshold"
    FORMAT = "format"





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)
    filename.close()

    return filename.name





def apply(sna: SNA, parameters=None):
    """
    Perform SNA visualization starting from the Matrix Container object
    and the Resource-Resource matrix

    Parameters
    -------------
    sna
        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
    """
    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 = sna.is_directed

    temp_file_name = get_temp_file_name(format)

    if directed:
        graph = nx_utils.DiGraph()
    else:
        graph = nx_utils.Graph()

    connections = {
        x for x, y in sna.connections.items() if y >= weight_threshold
    }

    graph.add_edges_from(connections)

    current_backend = copy(matplotlib.get_backend())
    matplotlib.use("Agg")
    from matplotlib import pyplot

    pyplot.clf()
    nx.draw(
        graph, node_size=500, with_labels=True, pos=nx.circular_layout(graph)
    )
    pyplot.savefig(temp_file_name, bbox_inches="tight")
    pyplot.clf()

    matplotlib.use(current_backend)

    return temp_file_name





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 constants.DEFAULT_ENABLE_VISUALIZATIONS_VIEW:
        if constants.DEFAULT_GVIZ_VIEW == "matplotlib_view":
            import matplotlib.pyplot as plt
            import matplotlib.image as mpimg

            img = mpimg.imread(temp_file_name)
            plt.axis("off")
            plt.tight_layout(pad=0, w_pad=0, h_pad=0)
            plt.imshow(img)
            plt.show()
            return

        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)





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)