TriadicFrameworks
Overview

📂 tops Overlays

The overlays directory contains dashboards, symbolic overlays, and warp chamber designs.
Overlays visualize resonance predictions, lineage, and orchestration flows.

✨ New in v1.3: Resonance Clarity
All overlays now inherit the --basetype lens, ensuring every glyph, fold, and warp chamber is lineage‑tagged.

Structure#

  • Dashboards → runtime visualizations
  • Symbolic overlays → glyph and fold resonance maps
  • Warp chamber designs → experimental visualization scaffolds

Purpose#

Overlays are the eyes of tops. They make resonance visible, traceable, and remixable.
With Resonance Clarity, overlays declare their base lens in both visual output and metadata.

🔧 Overlay Code Scaffolding#

1. Update overlay entry functions to accept basetype:

def render_overlay(data, glyph="🧬", basetype="decimal"):
    """
    Render overlay glyphs with resonance clarity.
    """
    # Apply base lens transformation
    x, y = data["coords"]
    x, y = apply_base_lens(x, y, basetype)
 
    # Render glyph overlay
    plt.scatter(x, y, marker=glyph, alpha=0.7)
    plt.title(f"Overlay Glyphs (Base: {basetype})")
    plt.show()

2. Ensure dashboards and warp chambers pass basetype:

def overlay_dashboard(session_data, basetype="decimal"):
    for glyph, coords in session_data.items():
        render_overlay({"coords": coords}, glyph=glyph, basetype=basetype)

🔧 Metadata Logging#

Every overlay run should log its base lens:

log = {
    "overlay": glyph,
    "corridor": corridor_name,
    "basetype": basetype,
    "timestamp": datetime.utcnow().isoformat()
}
write_to_resonance_log(log)

🔧 Example CLI Usage#

# Overlay dashboard with golden ratio base
python overlay_dashboard.py --basetype=phi
 
# Warp chamber overlay with negabinary base
python warp_overlay.py --basetype=negabinary
 
# Symbolic glyph overlay with corridor6.9 base
python glyph_overlay.py --basetype=corridor6.9

✨ Why this matters#

  • Consistency: Overlays now align with direct, reflective, inversion, and grid ops.
  • Lineage clarity: Every overlay declares its base lens in both visuals and logs.
  • Remixability: Future remixers can trace exactly which base lens shaped each overlay.
  • ai_pipeline → provides predictive data
  • folds → overlays fold resonance
  • agents → feed overlays with live data
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