Visualization¶

synamine supports two kinds of visualization: model rendering (Graphviz-based) and statistics charts (Matplotlib-based).

Prerequisites

Install the visualization extras: pip install "synamine[viz]"

For DFG, Petri Net, and Process Tree rendering, you also need the Graphviz system binary.

Saving model visualizations¶

Use save_visualization to save any model to file. The format is inferred from the extension (.png, .svg, .pdf):

import synamine

log = synamine.read_xes("events.xes")

dfg = synamine.discover_dfg(log)
synamine.save_visualization(dfg, "dfg.png")

pn = synamine.discover_petri_net(log, algorithm="alpha")
synamine.save_visualization(pn, "petri_net.svg")

pt = synamine.discover_process_tree(log)
synamine.save_visualization(pt, "process_tree.pdf")

trie = synamine.discover_variant_trie(log)
synamine.save_visualization(trie, "trie.png")

Interactive viewing¶

Open a model in your system's default viewer:

synamine.view_dfg(dfg)
synamine.view_petri_net(pn)
synamine.view_process_tree(pt)
synamine.view_variant_trie(trie)

Variant Trie options¶

Label strategy¶

Controls how node labels are rendered when the tree is dense:

Value	Behavior
`"auto"` (default)	Boxes sized to fit the full activity label. Font size scales with tree density.
`"truncate"`	Long labels are trimmed with an ellipsis (`...`) to fit a maximum box width.
`"hide"`	Nodes with < 5% frequency show only their count (e.g. `(12)`) instead of the activity name.

Percentage reference¶

Controls what edge percentages are relative to:

Value	Behavior
`"absolute"` (default)	Percentage of all traces (root count).
`"relative"`	Percentage of the parent node's count (branching probability).

Examples¶

trie = synamine.discover_variant_trie(log)

# Default: full labels, absolute percentages
synamine.save_visualization(trie, "trie.png")

# Truncate long labels, show branching probabilities
synamine.save_visualization(
    trie, "trie.png",
    label_strategy="truncate",
    pct_reference="relative",
)

# Hide rare nodes, keep absolute percentages
synamine.save_visualization(trie, "trie.svg", label_strategy="hide")

# Interactive viewer with options
synamine.view_variant_trie(
    trie,
    label_strategy="hide",
    pct_reference="relative",
)

Pruning¶

Remove low-frequency branches before visualization:

trie = synamine.discover_variant_trie(log)
pruned = trie.prune(min_count=10)  # keep only variants with >= 10 traces
synamine.save_visualization(pruned, "trie_pruned.png")

Statistics charts¶

All chart functions follow the pattern plot_*(log, *, path=None) -> Figure | None:

With path: saves the chart to file and returns None
Without path: returns a matplotlib Figure for customization

Available charts¶

Function	Chart Type
`plot_activity_frequencies(log)`	Horizontal bar chart
`plot_case_durations(log)`	Duration histogram (hours)
`plot_case_lengths(log)`	Case length histogram
`plot_start_end_activities(log)`	Grouped bar chart (start vs end)
`plot_variant_frequencies(log, top_k=10)`	Top-k variant bar chart
`plot_cases_over_time(log, freq="W")`	Case arrivals line chart

Save to file¶

synamine.plot_activity_frequencies(log, path="activities.png")
synamine.plot_case_durations(log, path="durations.png")
synamine.plot_case_lengths(log, path="lengths.png")
synamine.plot_start_end_activities(log, path="start_end.png")
synamine.plot_variant_frequencies(log, path="variants.png", top_k=10)
synamine.plot_cases_over_time(log, path="cases_over_time.png", freq="W")

Get Figure for customization¶

fig = synamine.plot_case_durations(log)
fig.axes[0].set_title("My Custom Title")
fig.savefig("custom.png")

This is especially useful in Jupyter notebooks where you can display figures inline.