'''
PM4Py – A Process Mining Library for Python
Copyright (C) 2026 Process Intelligence Solutions GmbH
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as
published by the Free Software Foundation, either version 3 of the
License, or any later version.
This program 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 Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see this software project's root or
visit <https://www.gnu.org/licenses/>.
Website: https://processintelligence.solutions
Contact: info@processintelligence.solutions
'''
import math
import tempfile
from uuid import uuid4
from enum import Enum
from typing import Optional, Dict, Any, Union
from graphviz import Digraph
from pm4py.objects.heuristics_net.obj import HeuristicsNet
from pm4py.util import constants, exec_utils
from pm4py.util.vis_utils import human_readable_stat
[docs]
class Parameters(Enum):
FORMAT = "format"
BGCOLOR = "bgcolor"
ENABLE_GRAPH_TITLE = "enable_graph_title"
GRAPH_TITLE = "graph_title"
[docs]
def get_corr_hex(num):
"""
Gets correspondence between a number
and an hexadecimal string
Parameters
-------------
num
Number
Returns
-------------
hex_string
Hexadecimal string
"""
if num < 10:
return str(int(num))
elif num < 11:
return "A"
elif num < 12:
return "B"
elif num < 13:
return "C"
elif num < 14:
return "D"
elif num < 15:
return "E"
elif num < 16:
return "F"
[docs]
def get_graph(
heu_net: HeuristicsNet,
parameters: Optional[Dict[Union[str, Parameters], Any]] = None,
) -> Digraph:
"""
Gets a representation of an Heuristics Net
Parameters
-------------
heu_net
Heuristics net
parameters
Possible parameters of the algorithm, including:
- Parameters.FORMAT
Returns
------------
graph
Graphviz graph
"""
if parameters is None:
parameters = {}
enable_graph_title = exec_utils.get_param_value(
Parameters.ENABLE_GRAPH_TITLE,
parameters,
constants.DEFAULT_ENABLE_GRAPH_TITLES,
)
graph_title = exec_utils.get_param_value(
Parameters.GRAPH_TITLE, parameters, "Heuristics Net"
)
bgcolor = exec_utils.get_param_value(
Parameters.BGCOLOR, parameters, constants.DEFAULT_BGCOLOR
)
image_format = exec_utils.get_param_value(
Parameters.FORMAT, parameters, "png"
)
filename = tempfile.NamedTemporaryFile(suffix=".gv")
filename.close()
graph = Digraph(filename=filename.name, strict=True, engine="dot")
graph.attr(bgcolor=bgcolor)
if enable_graph_title:
graph.attr(
label=graph_title,
labelloc="top",
labeljust="center",
fontsize="20",
)
corr_nodes = {}
corr_nodes_names = {}
is_frequency = False
start_end_nodes_set = set()
for index, sa_list in enumerate(heu_net.start_activities):
start_end_nodes_set = start_end_nodes_set.union(
{n for n in sa_list if n in corr_nodes_names}
)
for index, ea_list in enumerate(heu_net.end_activities):
start_end_nodes_set = start_end_nodes_set.union(
{n for n in ea_list if n in corr_nodes_names}
)
for node_name in heu_net.nodes:
node = heu_net.nodes[node_name]
if (
node_name in start_end_nodes_set
or node.input_connections
or node.output_connections
):
node_occ = node.node_occ
graycolor = transform_to_hex_2(
max(255 - math.log(node_occ) * 9, 0)
)
node_id = str(uuid4())
if node.node_type == "frequency":
is_frequency = True
node_label = f"{node_name} ({node_occ})"
else:
sojourn = (
human_readable_stat(heu_net.sojourn_times[node_name])
if node_name in heu_net.sojourn_times
else "0s"
)
node_label = f"{node_name} ({sojourn})"
graph.node(
node_id,
label=node_label,
shape="box",
style="filled",
fillcolor=node.get_fill_color(graycolor),
fontcolor=node.get_font_color(),
)
corr_nodes[node] = node_id
corr_nodes_names[node_name] = node_id
# gets max arc value
max_arc_value = -1
for node_name in heu_net.nodes:
node = heu_net.nodes[node_name]
for other_node in node.output_connections:
if other_node in corr_nodes:
for edge in node.output_connections[other_node]:
max_arc_value = max(max_arc_value, edge.repr_value)
for node_name in heu_net.nodes:
node = heu_net.nodes[node_name]
for other_node in node.output_connections:
if other_node in corr_nodes:
for edge in node.output_connections[other_node]:
this_pen_width = 1.0 + math.log(1 + edge.repr_value) / 11.0
repr_value = str(edge.repr_value)
edge_kwargs = {
"color": edge.get_color(),
"fontcolor": edge.get_font_color(),
"penwidth": str(edge.get_penwidth(this_pen_width)),
}
if edge.net_name:
if node.node_type == "frequency":
label = f"{edge.net_name} ({repr_value})"
else:
label = (
f"{edge.net_name} "
f"({human_readable_stat(repr_value)})"
)
else:
if node.node_type == "frequency":
label = repr_value
else:
label = human_readable_stat(repr_value)
graph.edge(
corr_nodes[node], corr_nodes[other_node], label=label, **edge_kwargs
)
for index, sa_list in enumerate(heu_net.start_activities):
effective_sa_list = [n for n in sa_list if n in corr_nodes_names]
if effective_sa_list:
start_id = f"start_{index}"
graph.node(
start_id,
label="@@S",
color=heu_net.default_edges_color[index],
fontsize="8",
fontcolor="#32CD32",
fillcolor="#32CD32",
style="filled",
)
for node_name in effective_sa_list:
sa = corr_nodes_names[node_name]
if type(heu_net.start_activities[index]) is dict:
occ = heu_net.start_activities[index][node_name]
if occ >= heu_net.min_dfg_occurrences:
if is_frequency:
this_pen_width = 1.0 + math.log(1 + occ) / 11.0
penwidth = str(this_pen_width)
if heu_net.net_name[index]:
label = f"{heu_net.net_name[index]} ({occ})"
else:
label = str(occ)
graph.edge(
start_id,
sa,
label=label,
color=heu_net.default_edges_color[index],
fontcolor=heu_net.default_edges_color[index],
penwidth=penwidth,
)
else:
graph.edge(
start_id,
sa,
label=heu_net.net_name[index],
color=heu_net.default_edges_color[index],
fontcolor=heu_net.default_edges_color[index],
)
else:
graph.edge(
start_id,
sa,
label=heu_net.net_name[index],
color=heu_net.default_edges_color[index],
fontcolor=heu_net.default_edges_color[index],
)
for index, ea_list in enumerate(heu_net.end_activities):
effective_ea_list = [n for n in ea_list if n in corr_nodes_names]
if effective_ea_list:
end_id = f"end_{index}"
graph.node(
end_id,
label="@@E",
color="#FFA500",
fillcolor="#FFA500",
fontcolor="#FFA500",
fontsize="8",
style="filled",
)
for node_name in effective_ea_list:
ea = corr_nodes_names[node_name]
if type(heu_net.end_activities[index]) is dict:
occ = heu_net.end_activities[index][node_name]
if occ >= heu_net.min_dfg_occurrences:
if is_frequency:
this_pen_width = 1.0 + math.log(1 + occ) / 11.0
penwidth = str(this_pen_width)
if heu_net.net_name[index]:
label = f"{heu_net.net_name[index]} ({occ})"
else:
label = str(occ)
graph.edge(
ea,
end_id,
label=label,
color=heu_net.default_edges_color[index],
fontcolor=heu_net.default_edges_color[index],
penwidth=penwidth,
)
else:
graph.edge(
ea,
end_id,
label=heu_net.net_name[index],
color=heu_net.default_edges_color[index],
fontcolor=heu_net.default_edges_color[index],
)
else:
graph.edge(
ea,
end_id,
label=heu_net.net_name[index],
color=heu_net.default_edges_color[index],
fontcolor=heu_net.default_edges_color[index],
)
graph.format = image_format.replace("html", "plain-ext")
return graph
[docs]
def apply(
heu_net: HeuristicsNet,
parameters: Optional[Dict[Union[str, Parameters], Any]] = None,
) -> str:
"""
Gets a representation of an Heuristics Net
Parameters
-------------
heu_net
Heuristics net
parameters
Possible parameters of the algorithm, including:
- Parameters.FORMAT
Returns
------------
gviz
Representation of the Heuristics Net
"""
if parameters is None:
parameters = {}
graph = get_graph(heu_net, parameters=parameters)
return graph
