0
# Visualization Functions
1

2
Extensive visualization capabilities for process models, statistics, and analysis results. PM4PY provides both viewing and saving options for various visualizations including process models, performance charts, and statistical graphs.
3

4
## Capabilities
5

6
### Petri Net Visualization
7

8
Visualize Petri net models with markings, annotations, and custom formatting options.
9

10
```python { .api }
11
def view_petri_net(petri_net, initial_marking=None, final_marking=None, format='png', **kwargs):
12
    """
13
    View Petri net visualization in interactive window or notebook.
14
    
15
    Parameters:
16
    - petri_net (PetriNet): Petri net model to visualize
17
    - initial_marking (Optional[Marking]): Initial marking for visualization
18
    - final_marking (Optional[Marking]): Final marking for visualization
19
    - format (str): Output format ('png', 'svg', 'pdf')
20
    - **kwargs: Additional visualization parameters
21
    
22
    Returns:
23
    None: Displays visualization
24
    """
25

26
def save_vis_petri_net(petri_net, initial_marking, final_marking, file_path, **kwargs):
27
    """
28
    Save Petri net visualization to file.
29
    
30
    Parameters:
31
    - petri_net (PetriNet): Petri net model
32
    - initial_marking (Marking): Initial marking
33
    - final_marking (Marking): Final marking
34
    - file_path (str): Output file path
35
    - **kwargs: Additional visualization parameters
36
    
37
    Returns:
38
    None: Saves visualization to file
39
    """
40
```
41

42
### DFG Visualization
43

44
Visualize Directly-Follows Graphs with frequency and performance annotations.
45

46
```python { .api }
47
def view_dfg(dfg, start_activities=None, end_activities=None, format='png', **kwargs):
48
    """
49
    View Directly-Follows Graph visualization.
50
    
51
    Parameters:
52
    - dfg (dict): Directly-follows graph with frequencies
53
    - start_activities (Optional[dict]): Start activities and frequencies
54
    - end_activities (Optional[dict]): End activities and frequencies
55
    - format (str): Output format ('png', 'svg', 'pdf')
56
    - **kwargs: Additional visualization parameters
57
    
58
    Returns:
59
    None: Displays visualization
60
    """
61

62
def save_vis_dfg(dfg, start_activities, end_activities, file_path, **kwargs):
63
    """
64
    Save DFG visualization to file.
65
    
66
    Parameters:
67
    - dfg (dict): Directly-follows graph
68
    - start_activities (dict): Start activities and frequencies
69
    - end_activities (dict): End activities and frequencies
70
    - file_path (str): Output file path
71
    - **kwargs: Additional visualization parameters
72
    
73
    Returns:
74
    None: Saves visualization to file
75
    """
76

77
def view_performance_dfg(dfg, start_activities=None, end_activities=None, format='png', **kwargs):
78
    """
79
    View performance DFG with timing information.
80
    
81
    Parameters:
82
    - dfg (dict): Performance directly-follows graph
83
    - start_activities (Optional[dict]): Start activities
84
    - end_activities (Optional[dict]): End activities
85
    - format (str): Output format
86
    - **kwargs: Additional visualization parameters
87
    
88
    Returns:
89
    None: Displays visualization
90
    """
91

92
def save_vis_performance_dfg(dfg, start_activities, end_activities, file_path, **kwargs):
93
    """
94
    Save performance DFG visualization to file.
95
    
96
    Parameters:
97
    - dfg (dict): Performance directly-follows graph
98
    - start_activities (dict): Start activities
99
    - end_activities (dict): End activities
100
    - file_path (str): Output file path
101
    - **kwargs: Additional visualization parameters
102
    
103
    Returns:
104
    None: Saves visualization to file
105
    """
106
```
107

108
### Process Tree Visualization
109

110
Visualize hierarchical process tree structures with block compositions.
111

112
```python { .api }
113
def view_process_tree(tree, format='png', **kwargs):
114
    """
115
    View process tree visualization showing hierarchical structure.
116
    
117
    Parameters:
118
    - tree (ProcessTree): Process tree model
119
    - format (str): Output format ('png', 'svg', 'pdf')
120
    - **kwargs: Additional visualization parameters
121
    
122
    Returns:
123
    None: Displays visualization
124
    """
125

126
def save_vis_process_tree(tree, file_path, **kwargs):
127
    """
128
    Save process tree visualization to file.
129
    
130
    Parameters:
131
    - tree (ProcessTree): Process tree model
132
    - file_path (str): Output file path
133
    - **kwargs: Additional visualization parameters
134
    
135
    Returns:
136
    None: Saves visualization to file
137
    """
138
```
139

140
### BPMN Visualization
141

142
Visualize Business Process Model and Notation (BPMN) diagrams.
143

144
```python { .api }
145
def view_bpmn(bpmn_graph, format='png', **kwargs):
146
    """
147
    View BPMN model visualization.
148
    
149
    Parameters:
150
    - bpmn_graph (BPMN): BPMN model object
151
    - format (str): Output format ('png', 'svg', 'pdf')
152
    - **kwargs: Additional visualization parameters
153
    
154
    Returns:
155
    None: Displays visualization
156
    """
157

158
def save_vis_bpmn(bpmn_graph, file_path, **kwargs):
159
    """
160
    Save BPMN visualization to file.
161
    
162
    Parameters:
163
    - bpmn_graph (BPMN): BPMN model object
164
    - file_path (str): Output file path
165
    - **kwargs: Additional visualization parameters
166
    
167
    Returns:
168
    None: Saves visualization to file
169
    """
170
```
171

172
### Heuristics Net Visualization
173

174
Visualize heuristics nets with dependency information and thresholds.
175

176
```python { .api }
177
def view_heuristics_net(heu_net, format='png', **kwargs):
178
    """
179
    View heuristics net visualization with dependency metrics.
180
    
181
    Parameters:
182
    - heu_net (HeuristicsNet): Heuristics net object
183
    - format (str): Output format ('png', 'svg', 'pdf')
184
    - **kwargs: Additional visualization parameters
185
    
186
    Returns:
187
    None: Displays visualization
188
    """
189

190
def save_vis_heuristics_net(heu_net, file_path, **kwargs):
191
    """
192
    Save heuristics net visualization to file.
193
    
194
    Parameters:
195
    - heu_net (HeuristicsNet): Heuristics net object
196
    - file_path (str): Output file path
197
    - **kwargs: Additional visualization parameters
198
    
199
    Returns:
200
    None: Saves visualization to file
201
    """
202
```
203

204
### Object-Centric Visualizations
205

206
Visualize Object-Centric process models and graphs.
207

208
```python { .api }
209
def view_ocdfg(ocdfg, format='png', **kwargs):
210
    """
211
    View Object-Centric Directly-Follows Graph visualization.
212
    
213
    Parameters:
214
    - ocdfg (dict): Object-centric DFG structure
215
    - format (str): Output format ('png', 'svg', 'pdf')
216
    - **kwargs: Additional visualization parameters
217
    
218
    Returns:
219
    None: Displays visualization
220
    """
221

222
def save_vis_ocdfg(ocdfg, file_path, **kwargs):
223
    """
224
    Save OC-DFG visualization to file.
225
    
226
    Parameters:
227
    - ocdfg (dict): Object-centric DFG structure
228
    - file_path (str): Output file path
229
    - **kwargs: Additional visualization parameters
230
    
231
    Returns:
232
    None: Saves visualization to file
233
    """
234

235
def view_ocpn(ocpn, format='png', **kwargs):
236
    """
237
    View Object-Centric Petri Net visualization.
238
    
239
    Parameters:
240
    - ocpn (OCPN): Object-centric Petri net
241
    - format (str): Output format ('png', 'svg', 'pdf')
242
    - **kwargs: Additional visualization parameters
243
    
244
    Returns:
245
    None: Displays visualization
246
    """
247

248
def save_vis_ocpn(ocpn, file_path, **kwargs):
249
    """
250
    Save OC-PN visualization to file.
251
    
252
    Parameters:
253
    - ocpn (OCPN): Object-centric Petri net
254
    - file_path (str): Output file path
255
    - **kwargs: Additional visualization parameters
256
    
257
    Returns:
258
    None: Saves visualization to file
259
    """
260

261
def view_object_graph(ocel, format='png', **kwargs):
262
    """
263
    View object interaction graph from OCEL data.
264
    
265
    Parameters:
266
    - ocel (OCEL): Object-centric event log
267
    - format (str): Output format ('png', 'svg', 'pdf')
268
    - **kwargs: Additional visualization parameters
269
    
270
    Returns:
271
    None: Displays visualization
272
    """
273

274
def save_vis_object_graph(ocel, file_path, **kwargs):
275
    """
276
    Save object graph visualization to file.
277
    
278
    Parameters:
279
    - ocel (OCEL): Object-centric event log
280
    - file_path (str): Output file path
281
    - **kwargs: Additional visualization parameters
282
    
283
    Returns:
284
    None: Saves visualization to file
285
    """
286
```
287

288
### Log-Based Visualizations
289

290
Visualize event log characteristics and patterns.
291

292
```python { .api }
293
def view_sna(sna, format='png', **kwargs):
294
    """
295
    View Social Network Analysis visualization.
296
    
297
    Parameters:
298
    - sna (SNA): Social network analysis object
299
    - format (str): Output format ('png', 'svg', 'pdf')
300
    - **kwargs: Additional visualization parameters
301
    
302
    Returns:
303
    None: Displays visualization
304
    """
305

306
def save_vis_sna(sna, file_path, **kwargs):
307
    """
308
    Save SNA visualization to file.
309
    
310
    Parameters:
311
    - sna (SNA): Social network analysis object
312
    - file_path (str): Output file path
313
    - **kwargs: Additional visualization parameters
314
    
315
    Returns:
316
    None: Saves visualization to file
317
    """
318

319
def view_dotted_chart(log, **kwargs):
320
    """
321
    View dotted chart showing case timeline patterns.
322
    
323
    Parameters:
324
    - log (Union[EventLog, pd.DataFrame]): Event log data
325
    - **kwargs: Additional visualization parameters
326
    
327
    Returns:
328
    None: Displays visualization
329
    """
330

331
def save_vis_dotted_chart(log, file_path, **kwargs):
332
    """
333
    Save dotted chart to file.
334
    
335
    Parameters:
336
    - log (Union[EventLog, pd.DataFrame]): Event log data
337
    - file_path (str): Output file path
338
    - **kwargs: Additional visualization parameters
339
    
340
    Returns:
341
    None: Saves visualization to file
342
    """
343

344
def view_performance_spectrum(log, **kwargs):
345
    """
346
    View performance spectrum visualization showing case durations.
347
    
348
    Parameters:
349
    - log (Union[EventLog, pd.DataFrame]): Event log data
350
    - **kwargs: Additional visualization parameters
351
    
352
    Returns:
353
    None: Displays visualization
354
    """
355

356
def save_vis_performance_spectrum(log, file_path, **kwargs):
357
    """
358
    Save performance spectrum to file.
359
    
360
    Parameters:
361
    - log (Union[EventLog, pd.DataFrame]): Event log data
362
    - file_path (str): Output file path
363
    - **kwargs: Additional visualization parameters
364
    
365
    Returns:
366
    None: Saves visualization to file
367
    """
368
```
369

370
### Statistical Charts
371

372
Generate statistical visualizations for process analysis.
373

374
```python { .api }
375
def view_case_duration_graph(log, **kwargs):
376
    """
377
    View case duration distribution graph.
378
    
379
    Parameters:
380
    - log (Union[EventLog, pd.DataFrame]): Event log data
381
    - **kwargs: Additional visualization parameters
382
    
383
    Returns:
384
    None: Displays visualization
385
    """
386

387
def save_vis_case_duration_graph(log, file_path, **kwargs):
388
    """
389
    Save case duration graph to file.
390
    
391
    Parameters:
392
    - log (Union[EventLog, pd.DataFrame]): Event log data
393
    - file_path (str): Output file path
394
    - **kwargs: Additional visualization parameters
395
    
396
    Returns:
397
    None: Saves visualization to file
398
    """
399

400
def view_events_per_time_graph(log, **kwargs):
401
    """
402
    View events per time period graph showing activity patterns.
403
    
404
    Parameters:
405
    - log (Union[EventLog, pd.DataFrame]): Event log data
406
    - **kwargs: Additional visualization parameters
407
    
408
    Returns:
409
    None: Displays visualization
410
    """
411

412
def save_vis_events_per_time_graph(log, file_path, **kwargs):
413
    """
414
    Save events per time graph to file.
415
    
416
    Parameters:
417
    - log (Union[EventLog, pd.DataFrame]): Event log data
418
    - file_path (str): Output file path
419
    - **kwargs: Additional visualization parameters
420
    
421
    Returns:
422
    None: Saves visualization to file
423
    """
424

425
def view_events_distribution_graph(log, **kwargs):
426
    """
427
    View events distribution graph showing frequency patterns.
428
    
429
    Parameters:
430
    - log (Union[EventLog, pd.DataFrame]): Event log data
431
    - **kwargs: Additional visualization parameters
432
    
433
    Returns:
434
    None: Displays visualization
435
    """
436

437
def save_vis_events_distribution_graph(log, file_path, **kwargs):
438
    """
439
    Save events distribution graph to file.
440
    
441
    Parameters:
442
    - log (Union[EventLog, pd.DataFrame]): Event log data
443
    - file_path (str): Output file path
444
    - **kwargs: Additional visualization parameters
445
    
446
    Returns:
447
    None: Saves visualization to file
448
    """
449
```
450

451
### Advanced Visualizations
452

453
Specialized visualizations for complex analysis tasks.
454

455
```python { .api }
456
def view_network_analysis(log, **kwargs):
457
    """
458
    View network analysis visualization for organizational mining.
459
    
460
    Parameters:
461
    - log (Union[EventLog, pd.DataFrame]): Event log data
462
    - **kwargs: Additional visualization parameters
463
    
464
    Returns:
465
    None: Displays visualization
466
    """
467

468
def save_vis_network_analysis(log, file_path, **kwargs):
469
    """
470
    Save network analysis to file.
471
    
472
    Parameters:
473
    - log (Union[EventLog, pd.DataFrame]): Event log data
474
    - file_path (str): Output file path
475
    - **kwargs: Additional visualization parameters
476
    
477
    Returns:
478
    None: Saves visualization to file
479
    """
480

481
def view_transition_system(transition_system, **kwargs):
482
    """
483
    View transition system visualization showing state space.
484
    
485
    Parameters:
486
    - transition_system (TransitionSystem): Transition system model
487
    - **kwargs: Additional visualization parameters
488
    
489
    Returns:
490
    None: Displays visualization
491
    """
492

493
def save_vis_transition_system(transition_system, file_path, **kwargs):
494
    """
495
    Save transition system visualization to file.
496
    
497
    Parameters:
498
    - transition_system (TransitionSystem): Transition system model
499
    - file_path (str): Output file path
500
    - **kwargs: Additional visualization parameters
501
    
502
    Returns:
503
    None: Saves visualization to file
504
    """
505

506
def view_prefix_tree(prefix_tree, **kwargs):
507
    """
508
    View prefix tree visualization showing trace structure.
509
    
510
    Parameters:
511
    - prefix_tree (Trie): Prefix tree structure
512
    - **kwargs: Additional visualization parameters
513
    
514
    Returns:
515
    None: Displays visualization
516
    """
517

518
def save_vis_prefix_tree(prefix_tree, file_path, **kwargs):
519
    """
520
    Save prefix tree visualization to file.
521
    
522
    Parameters:
523
    - prefix_tree (Trie): Prefix tree structure
524
    - file_path (str): Output file path
525
    - **kwargs: Additional visualization parameters
526
    
527
    Returns:
528
    None: Saves visualization to file
529
    """
530

531
def view_alignments(alignments, **kwargs):
532
    """
533
    View alignment visualization showing trace-model mapping.
534
    
535
    Parameters:
536
    - alignments (List): Alignment results
537
    - **kwargs: Additional visualization parameters
538
    
539
    Returns:
540
    None: Displays visualization
541
    """
542

543
def save_vis_alignments(alignments, file_path, **kwargs):
544
    """
545
    Save alignment visualization to file.
546
    
547
    Parameters:
548
    - alignments (List): Alignment results
549
    - file_path (str): Output file path
550
    - **kwargs: Additional visualization parameters
551
    
552
    Returns:
553
    None: Saves visualization to file
554
    """
555

556
def view_footprints(footprints, **kwargs):
557
    """
558
    View footprints visualization for model comparison.
559
    
560
    Parameters:
561
    - footprints (Dict): Footprints structure
562
    - **kwargs: Additional visualization parameters
563
    
564
    Returns:
565
    None: Displays visualization
566
    """
567

568
def save_vis_footprints(footprints, file_path, **kwargs):
569
    """
570
    Save footprints visualization to file.
571
    
572
    Parameters:
573
    - footprints (Dict): Footprints structure
574
    - file_path (str): Output file path
575
    - **kwargs: Additional visualization parameters
576
    
577
    Returns:
578
    None: Saves visualization to file
579
    """
580

581
def view_powl(powl_model, **kwargs):
582
    """
583
    View POWL model visualization.
584
    
585
    Parameters:
586
    - powl_model (POWL): POWL model object
587
    - **kwargs: Additional visualization parameters
588
    
589
    Returns:
590
    None: Displays visualization
591
    """
592

593
def save_vis_powl(powl_model, file_path, **kwargs):
594
    """
595
    Save POWL visualization to file.
596
    
597
    Parameters:
598
    - powl_model (POWL): POWL model object
599
    - file_path (str): Output file path
600
    - **kwargs: Additional visualization parameters
601
    
602
    Returns:
603
    None: Saves visualization to file
604
    """
605
```
606

607
## Usage Examples
608

609
### Basic Process Model Visualization
610

611
```python
612
import pm4py
613

614
# Load log and discover model
615
log = pm4py.read_xes('event_log.xes')
616
net, initial_marking, final_marking = pm4py.discover_petri_net_inductive(log)
617

618
# View Petri net in interactive window/notebook
619
pm4py.view_petri_net(net, initial_marking, final_marking)
620

621
# Save Petri net to file
622
pm4py.save_vis_petri_net(net, initial_marking, final_marking, 'petri_net.png')
623

624
# View process tree
625
tree = pm4py.discover_process_tree_inductive(log)
626
pm4py.view_process_tree(tree)
627
pm4py.save_vis_process_tree(tree, 'process_tree.svg')
628
```
629

630
### DFG and Performance Visualization
631

632
```python
633
import pm4py
634

635
# Discover and visualize DFG
636
dfg, start_activities, end_activities = pm4py.discover_dfg(log)
637
pm4py.view_dfg(dfg, start_activities, end_activities)
638

639
# Discover and visualize performance DFG
640
perf_dfg, start_acts, end_acts = pm4py.discover_performance_dfg(log)
641
pm4py.view_performance_dfg(perf_dfg, start_acts, end_acts)
642

643
# Save DFG visualizations
644
pm4py.save_vis_dfg(dfg, start_activities, end_activities, 'dfg_frequency.png')
645
pm4py.save_vis_performance_dfg(perf_dfg, start_acts, end_acts, 'dfg_performance.png')
646
```
647

648
### Statistical Visualizations
649

650
```python
651
import pm4py
652

653
# View case duration distribution
654
pm4py.view_case_duration_graph(log)
655

656
# View events per time period
657
pm4py.view_events_per_time_graph(log)
658

659
# View events distribution
660
pm4py.view_events_distribution_graph(log)
661

662
# View dotted chart for timeline analysis
663
pm4py.view_dotted_chart(log)
664

665
# View performance spectrum
666
pm4py.view_performance_spectrum(log)
667

668
# Save all statistical charts
669
pm4py.save_vis_case_duration_graph(log, 'case_durations.png')
670
pm4py.save_vis_events_per_time_graph(log, 'events_timeline.png')
671
pm4py.save_vis_events_distribution_graph(log, 'events_distribution.png')
672
pm4py.save_vis_dotted_chart(log, 'dotted_chart.png')
673
pm4py.save_vis_performance_spectrum(log, 'performance_spectrum.png')
674
```
675

676
### Object-Centric Visualizations
677

678
```python
679
import pm4py
680

681
# Load OCEL and create visualizations
682
ocel = pm4py.read_ocel('ocel_data.csv')
683

684
# Discover and visualize OC-DFG
685
ocdfg = pm4py.discover_ocdfg(ocel)
686
pm4py.view_ocdfg(ocdfg)
687
pm4py.save_vis_ocdfg(ocdfg, 'ocdfg.png')
688

689
# Visualize object interaction graph
690
pm4py.view_object_graph(ocel)
691
pm4py.save_vis_object_graph(ocel, 'object_graph.png')
692

693
# Discover and visualize OC Petri net
694
ocpn = pm4py.discover_oc_petri_net(ocel)
695
pm4py.view_ocpn(ocpn)
696
pm4py.save_vis_ocpn(ocpn, 'ocpn.png')
697
```
698

699
### Organizational and Social Network Visualization
700

701
```python
702
import pm4py
703

704
# Discover handover network
705
sna = pm4py.discover_handover_of_work_network(log)
706
pm4py.view_sna(sna)
707
pm4py.save_vis_sna(sna, 'handover_network.png')
708

709
# Network analysis visualization
710
pm4py.view_network_analysis(log)
711
pm4py.save_vis_network_analysis(log, 'network_analysis.png')
712
```
713

714
### Advanced Model Visualizations
715

716
```python
717
import pm4py
718

719
# Discover and visualize transition system
720
ts = pm4py.discover_transition_system(log)
721
pm4py.view_transition_system(ts)
722
pm4py.save_vis_transition_system(ts, 'transition_system.png')
723

724
# Discover and visualize prefix tree
725
prefix_tree = pm4py.discover_prefix_tree(log)
726
pm4py.view_prefix_tree(prefix_tree)
727
pm4py.save_vis_prefix_tree(prefix_tree, 'prefix_tree.png')
728

729
# Discover and visualize POWL model
730
powl_model = pm4py.discover_powl(log)
731
pm4py.view_powl(powl_model)
732
pm4py.save_vis_powl(powl_model, 'powl_model.png')
733

734
# Visualize BPMN model
735
bpmn = pm4py.discover_bpmn_inductive(log)
736
pm4py.view_bpmn(bpmn)
737
pm4py.save_vis_bpmn(bpmn, 'bpmn_model.png')
738
```
739

740
### Conformance Visualization
741

742
```python
743
import pm4py
744

745
# Get alignments and visualize
746
alignments = pm4py.conformance_diagnostics_alignments(log, net, initial_marking, final_marking)
747
pm4py.view_alignments(alignments)
748
pm4py.save_vis_alignments(alignments, 'alignments.png')
749

750
# Discover and visualize footprints
751
footprints = pm4py.discover_footprints(log)
752
pm4py.view_footprints(footprints)
753
pm4py.save_vis_footprints(footprints, 'footprints.png')
754
```
755

756
### Visualization with Custom Parameters
757

758
```python
759
import pm4py
760

761
# Custom DFG visualization with parameters
762
pm4py.view_dfg(
763
    dfg, start_activities, end_activities,
764
    format='svg',
765
    bgcolor='white',
766
    rankdir='TB',  # Top to bottom layout
767
    fontsize=12,
768
    edge_color='blue'
769
)
770

771
# Custom Petri net with annotations
772
pm4py.view_petri_net(
773
    net, initial_marking, final_marking,
774
    format='pdf',
775
    show_labels=True,
776
    show_initial_marking=True,
777
    show_final_marking=True,
778
    decorations={'frequency': True}
779
)
780

781
# High-resolution export
782
pm4py.save_vis_process_tree(
783
    tree, 'high_res_tree.png',
784
    dpi=300,
785
    bgcolor='transparent',
786
    fontsize=14
787
)
788
```
789

790
### Batch Visualization Creation
791

792
```python
793
import pm4py
794
import os
795

796
def create_full_analysis_report(log, output_dir='analysis_output'):
797
    """Create comprehensive visualization report."""
798
    
799
    os.makedirs(output_dir, exist_ok=True)
800
    
801
    # Discover models
802
    net, im, fm = pm4py.discover_petri_net_inductive(log)
803
    tree = pm4py.discover_process_tree_inductive(log)
804
    dfg, start_acts, end_acts = pm4py.discover_dfg(log)
805
    perf_dfg, perf_start, perf_end = pm4py.discover_performance_dfg(log)
806
    
807
    # Save process models
808
    pm4py.save_vis_petri_net(net, im, fm, f'{output_dir}/petri_net.png')
809
    pm4py.save_vis_process_tree(tree, f'{output_dir}/process_tree.png')
810
    pm4py.save_vis_dfg(dfg, start_acts, end_acts, f'{output_dir}/dfg.png')
811
    pm4py.save_vis_performance_dfg(perf_dfg, perf_start, perf_end, f'{output_dir}/perf_dfg.png')
812
    
813
    # Save statistical charts
814
    pm4py.save_vis_case_duration_graph(log, f'{output_dir}/case_durations.png')
815
    pm4py.save_vis_events_per_time_graph(log, f'{output_dir}/events_timeline.png')
816
    pm4py.save_vis_dotted_chart(log, f'{output_dir}/dotted_chart.png')
817
    pm4py.save_vis_performance_spectrum(log, f'{output_dir}/performance_spectrum.png')
818
    
819
    # Organizational analysis
820
    sna = pm4py.discover_handover_of_work_network(log)
821
    pm4py.save_vis_sna(sna, f'{output_dir}/handover_network.png')
822
    
823
    print(f"Analysis report saved to {output_dir}/")
824

825
# Create full analysis
826
create_full_analysis_report(log)
827
```