Mass spectrometry visualization backend comparison

mass-spectrometry-visualization-backend-comparison

Summary

Compare execution time and performance characteristics of mass spectrometry visualizations across multiple plotting backends (matplotlib, Bokeh, Plotly) to assess backend suitability for different deployment contexts. This skill validates whether a visualization library's cross-backend integration functions correctly and identifies performance trade-offs between static and interactive rendering.

When to use

You have a mass spectrometry visualization library that claims to support multiple plotting backends and need to verify that: (1) all backends produce functionally equivalent outputs, (2) execution times are consistent with reported benchmarks, and (3) the library scales appropriately across backend choices. Apply this skill when validating a new release, evaluating backend-specific memory or latency constraints, or benchmarking visualization performance for production deployment.

When NOT to use

• Input is a single visualization or small batch (< 5 scripts); backend comparison requires statistically meaningful sample size across diverse visualization types.
• You do not have all three backends installed or cannot isolate backend switching; comparison requires identical environment state except for the active backend.
• The library does not claim cross-backend API consistency; benchmark differences may reflect intentional design trade-offs rather than bugs or performance regressions.

Inputs

• Python 3.12 environment with pyOpenMS-Viz installed
• Gallery script collection (19 representative mass spectrometry visualization scripts)
• Three installed plotting backends: matplotlib, Bokeh, Plotly
• Reported benchmark table (execution times and memory consumption)
• System with stable resource allocation for timing measurements

Outputs

• Structured benchmark table (CSV or DataFrame) with columns: Script name, matplotlib execution time, Bokeh execution time, Plotly execution time, total gallery time
• Comparison report: measured times vs. reported benchmark values
• Per-backend performance profile (median, min, max execution times across all scripts)
• Outlier analysis identifying scripts with atypical backend behavior

How to apply

Set up a Python 3.12 environment with the visualization library and all three backends (matplotlib, Bokeh, Plotly) installed. Execute a representative suite of gallery scripts (e.g., 19 scripts covering chromatogram, spectrum, mobilogram, and peakmap visualization types) sequentially on each backend, recording wall-clock execution time for each script using Python's time module or Unix time command. Aggregate per-script execution times and compute total gallery execution time across all scripts and all backends. Tabulate results in a structured format (columns: Example name, Time per backend, Memory usage) and compare measured values against reported benchmarks. Evaluate consistency of execution profiles across backends; investigate backend-specific outliers (scripts that deviate significantly from the median backend behavior) as potential compatibility or optimization issues.

Related tools

• pyOpenMS-Viz (mass spectrometry visualization library providing Pandas DataFrame API with three plotting backend support (matplotlib, Bokeh, Plotly); subject of the benchmark comparison) — https://github.com/OpenMS/pyopenms_viz
• matplotlib (static plotting backend for rendering mass spectrometry visualizations (chromatograms, spectra, peakmap 2D/3D))
• Bokeh (interactive plotting backend for rendering mass spectrometry visualizations with web-based interactivity)
• Plotly (interactive plotting backend for rendering mass spectrometry visualizations including 3D peakmap support)
• Pandas (data manipulation and DataFrame interface for mass spectrometry data input; timing measurements recorded in DataFrame output)
• Python time module (wall-clock timing instrumentation for measuring per-script execution time)

Examples

import time; import pandas as pd; backends = ['matplotlib', 'bokeh', 'plotly']; times = {b: [] for b in backends}; exec(open('gallery_script_1.py').read()); t0 = time.time(); exec(open('gallery_script_2.py').read()); times['matplotlib'].append(time.time() - t0); df = pd.DataFrame({'Example': script_names, **{b: times[b] for b in backends}}); df.to_csv('benchmark_results.csv', index=False)

Evaluation signals

• All 19 gallery scripts execute without error on each of the three backends, producing functionally equivalent visualizations (no missing plots, rendering errors, or API mismatches).
• Total gallery execution time measured across all backends matches or is within 10% of the reported benchmark (e.g., reported 00:06.221; measured < 6.84 seconds).
• Per-script execution times show consistent ordering and magnitude across backends, with no backend showing anomalous outliers (> 2 standard deviations from the backend median).
• Comparison table structure matches the article's reported schema (Example, Time, Mem columns); CSV export is valid and complete with no missing entries.
• Memory consumption profiles (if measured) remain stable and proportional to visualization complexity across all three backends, with no backend leaking resources between script executions.

Limitations

• Execution times depend on system load, CPU thermal state, and available memory; repeated runs on identical hardware are needed to establish statistical confidence. Measurements should be taken in a controlled environment with minimal competing processes.
• Interactive backends (Bokeh, Plotly) may show higher latency on headless systems or remote rendering contexts compared to static matplotlib, limiting applicability of direct time comparisons across deployment scenarios.
• Gallery script suite may not represent user-facing workloads (e.g., real-time streaming, large dataset visualization); benchmark reflects artificial representativeness and may not predict production performance.
• Backend interchangeability is API-level only; visual output may differ subtly (font rendering, axis styling, color palette defaults), requiring manual inspection to confirm semantic equivalence.

Evidence

• [other] What are the per-script execution times for the 19 gallery scripts across the three plotting backends (matplotlib, Bokeh, Plotly), and what is the total gallery execution time?: "What are the per-script execution times for the 19 gallery scripts across the three plotting backends (matplotlib, Bokeh, Plotly), and what is the total gallery execution time?"
• [other] Sequentially execute each of the 19 gallery scripts listed in the computation times table, recording wall-clock execution time for each script using a timer (e.g. Python's time module or Unix time command).: "Sequentially execute each of the 19 gallery scripts listed in the computation times table, recording wall-clock execution time for each script using a timer (e.g. Python's time module or Unix time"
• [other] Aggregate per-script execution times and compute the total execution time across all 19 scripts. Compare reported execution time for each script against the measured value; compare reported total (00:06.221) against the measured aggregate.: "Aggregate per-script execution times and compute the total execution time across all 19 scripts. Compare reported execution time for each script against the measured value; compare reported total"
• [other] The library integrates seamlessly with three plotting library backends: matplotlib, Bokeh, and Plotly, which are the backends used for the 19 gallery scripts whose execution times must be measured.: "The library integrates seamlessly with three plotting library backends: matplotlib, Bokeh, and Plotly, which are the backends used for the 19 gallery scripts whose execution times must be measured."
• [readme] It integrates seamlessly with various plotting library backends (matpotlib, bokeh and plotly) and leverages the power of Pandas for data manipulation and representation.: "It integrates seamlessly with various plotting library backends (matpotlib, bokeh and plotly) and leverages the power of Pandas for data manipulation and representation."
• [other] Tabulate results in a dataframe matching the structure of the reported table (Example, Time, Mem columns) and export as CSV.: "Tabulate results in a dataframe matching the structure of the reported table (Example, Time, Mem columns) and export as CSV."
• [other] Set up a Python 3.12 environment with pyOpenMS-Viz installed via pip and all three plotting backends (matplotlib, Bokeh, Plotly) available.: "Set up a Python 3.12 environment with pyOpenMS-Viz installed via pip and all three plotting backends (matplotlib, Bokeh, Plotly) available."