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Module 8: Visualization and Reporting

  • Objectives
    • Create dashboards and pipeline plots
    • Read stage summaries and annotate findings
  • Time: 45–60 minutes

8.1 Dashboard

from mneme.analysis.visualization import FieldVisualizer
from mneme.types import AnalysisResult

# Suppose you already have `result: AnalysisResult`
viz = FieldVisualizer()
fig = viz.create_analysis_dashboard(result)
fig.savefig('dashboard.png', dpi=300)

8.2 Pipeline stage plots

fig2 = viz.plot_pipeline_results(result.metadata.get('stage_results', {}) if isinstance(result.metadata, dict) else {})
fig2.savefig('pipeline.png', dpi=300)

Note: `mneme.utils.io.load_results` now returns an `AnalysisResult` directly for HDF5 paths (both `.h5` and `.hdf5`), so you can pass it straight into `create_analysis_dashboard`.

Exercises

  1. Add titles and annotations to highlight key topology features
  2. Save a persistence image for H1 using compute_persistence_image and place it in your report

Run log (MVP)

  • The HDF5 loader now returns an AnalysisResult; dashboards render without conversion.

Solutions (outline)

  • Use Matplotlib annotations; summarize feature vector stats on the figure
  • Derived images help visualize the distribution of persistence across birth/persistence space