Opp Repl Chart Tests

Chart tests

Chart tests render each analysis chart and compare the resulting image against a baseline stored in the project's media_folder. A visual diff catches regressions in analysis pipelines, not in the simulation itself — e.g. a buggy pandas aggregation or a changed axis label.

Upstream reference: https://github.com/omnetpp/opp_repl/blob/main/doc/chart_tests.md

Requirements

• chart extra installed: pip install "opp_repl[chart]".
• media_folder set on the SimulationProject (INET convention: doc/media).

Python API

# Seed baselines (first time, or after intentional chart changes)
update_chart_test_results(simulation_project=inet_project)

# Run tests
run_chart_tests(simulation_project=inet_project)

# Scoped
run_chart_tests(simulation_project=inet_project,
                working_directory_filter="showcases")

Result codes: PASS / FAIL (INSERT/UPDATE/KEEP on updates).

Command line

opp_update_chart_test_results --load inet.opp -p inet
opp_run_chart_tests           --load inet.opp -p inet

When chart tests catch what fingerprint tests miss

• The numerical results are correct but the plotting code regressed.
• A new matplotlib/seaborn release changed default styles.
• An analysis notebook's filter criteria drifted.

Pitfalls

• Matplotlib backends, fonts, and DPI all affect pixel output. Pin the stack (matplotlib==X.Y, optionally MPLBACKEND=Agg) across CI to avoid spurious diffs.
• Anti-aliasing quirks between Linux distros can cause flapping. For reproducible CI, run chart tests inside a fixed container.
• Tolerance is pixel-wise, not structural; a one-pixel shift may show as FAIL. Consult the live docstring for tolerance knobs.
• When a chart test reports FAIL, inspect the pixel diff with compare_charts() in the REPL or the opp_diff_charts GUI (requires the diffcharts extra: pip install "opp_repl[diffcharts]").

See also

• opp-repl-running-simulations — produces the scalars charts read.
• opp-repl-tasks-and-results — inspect FAILed chart tests.
• opp-repl-module-image-tests — sibling suite for module/network rendering regressions (visual regression of how modules and networks are drawn), as opposed to the analysis-plot regressions caught here.