#38: generate a WfInstance from a .dot file

requirements.txt

@@ -1,6 +1,6 @@
 jsonschema~=3.2.0
 matplotlib>=3.3.0
-networkx>=2.4
+networkx>=3.0
 numpy>=1.19.1
 python-dateutil>=2.8.1
 requests>=2.24.0

wfcommons/common/workflow.py

@@ -192,7 +192,7 @@ def read_dot(self, dot_file_path: Optional[pathlib.Path] = None) -> None:
 
         tasks_map = {}
         for node in graph.nodes(data=True):
-            task_name = f"{node[1]['label']}_{node[0]}"
+            task_name = f"{node[1]['label']}_ID{node[0]}"
             task = Task(name=task_name, task_type=TaskType.COMPUTE, runtime=0, task_id=node[0])
             self.add_task(task)
             tasks_map[node[0]] = task_name

wfcommons/wfchef/duplicate.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 #
-# Copyright (c) 2021 The WfCommons Team.
+# Copyright (c) 2021-2024 The WfCommons Team.
 #
 # This program is free software: you can redistribute it and/or modify
 # it under the terms of the GNU General Public License as published by
@@ -92,25 +92,38 @@ def duplicate(path: pathlib.Path,
             f"Cannot create synthentic graph with {num_nodes} nodes from base graph with {graph.order()} nodes")
 
     all_microstructures = json.loads(base_path.joinpath("microstructures.json").read_text())
-    microstructures, freqs = map(list, zip(*[(ms, ms["frequency"]) for ms_hash, ms in all_microstructures.items()]))
-
-    p: List[float] = (np.array(freqs) / np.sum(freqs)).tolist()
-    while graph.order() < num_nodes and microstructures:
-        i = random.choice(range(len(microstructures)))
-        ms = microstructures[i]
-        while ms["nodes"]:
-            j = random.choice(range(len(ms["nodes"])))
-            structure = ms["nodes"][j]
-            if graph.order() + len(structure) > num_nodes:
-                del ms["nodes"][j]
-            else:
+    try:
+        microstructures, freqs = map(list, zip(*[(ms, ms["frequency"]) for ms_hash, ms in all_microstructures.items()]))
+
+        p: List[float] = (np.array(freqs) / np.sum(freqs)).tolist()
+        while graph.order() < num_nodes and microstructures:
+            i = random.choice(range(len(microstructures)))
+            ms = microstructures[i]
+            while ms["nodes"]:
+                j = random.choice(range(len(ms["nodes"])))
+                structure = ms["nodes"][j]
+                if graph.order() + len(structure) > num_nodes:
+                    del ms["nodes"][j]
+                else:
+                    break
+
+            if not ms["nodes"]:  # delete microstructure
+                del microstructures[i]
+                del p[i]
+                continue
+
+            duplicate_nodes(graph, structure)
+
+    except ValueError as e:
+        nodes = graph.copy().nodes
+        while graph.order() < num_nodes:
+            structure = set[str]()
+            if graph.order() + len(nodes) - 2 > num_nodes:
                 break
+            for node in nodes:
+                if str(node) not in ["SRC", "DST"]:
+                    structure.add(node)
 
-        if not ms["nodes"]:  # delete microstructure
-            del microstructures[i]
-            del p[i]
-            continue
-
-        duplicate_nodes(graph, structure)
+            duplicate_nodes(graph, structure)
 
     return graph

wfcommons/wfchef/find_microstructures.py

@@ -1,26 +1,26 @@
 #!/usr/bin/env python
 # -*- coding: utf-8 -*-
 #
-# Copyright (c) 2021 The WfCommons Team.
+# Copyright (c) 2021-2024 The WfCommons Team.
 #
 # This program 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.
 
-import networkx as nx
-from hashlib import sha256
-from typing import Union, Set, Optional, Dict, Hashable, List
-from uuid import uuid4
-import pathlib
 import json
-from itertools import product
-from networkx.readwrite import read_gpickle, write_gpickle
+import math
+import networkx as nx
 import numpy as np
+import pathlib
+import pickle
+
+from hashlib import sha256
 from itertools import chain, combinations
-import argparse
-from .utils import create_graph, string_hash, type_hash, combine_hashes, annotate, draw
-import math
+from typing import Union, Set, Optional, Dict, List
+from uuid import uuid4
+
+from .utils import create_graph, combine_hashes, annotate, draw
 
 this_dir = pathlib.Path(__file__).resolve().parent
 
@@ -233,7 +233,8 @@ def save_microstructures(workflow_path: Union[pathlib.Path],
         g_savedir.mkdir(exist_ok=True, parents=True)
 
         base_graph_path = g_savedir.joinpath("base_graph.pickle")
-        write_gpickle(graph, str(base_graph_path))
+        with open(base_graph_path, 'wb') as pf:
+            pickle.dump(graph, pf, pickle.HIGHEST_PROTOCOL)
         summary["base_graphs"][graph.name] = {
             "size": graph.size(),
             "order": graph.order()