Mermaid diagram renderers for gds-framework specifications
Project description
gds-viz
Mermaid diagram renderers for gds-framework specifications.
uv add gds-viz
# or: pip install gds-viz
Views
gds-viz provides six views — each a different projection of the GDS specification {h, X}:
| View | Function | Input | Answers |
|---|---|---|---|
| 1. Structural | system_to_mermaid() |
SystemIR |
What blocks exist and how are they wired? |
| 2. Canonical GDS | canonical_to_mermaid() |
CanonicalGDS |
What is the formal decomposition h = f ∘ g? |
| 3. Architecture (role) | spec_to_mermaid() |
GDSSpec |
How do blocks group by GDS role? |
| 4. Architecture (domain) | spec_to_mermaid(group_by=...) |
GDSSpec |
How do blocks group by domain/agent? |
| 5. Parameter influence | params_to_mermaid() |
GDSSpec |
What does each parameter control? |
| 6. Traceability | trace_to_mermaid() |
GDSSpec |
What can affect a specific state variable? |
View 1: Structural
The compiled block graph from SystemIR. Shows composition topology — sequential, parallel, feedback, temporal — with role-based shapes (stadium for boundary, double-bracket for mechanism) and wiring types (solid, dashed, thick).
from gds_viz import system_to_mermaid
mermaid = system_to_mermaid(system)
View 2: Canonical GDS
The mathematical decomposition: X_t → U → g → f → X_{t+1} with parameter space Θ. Derives from CanonicalGDS (via project_canonical(spec)). Shows state variables in X nodes, role subgraphs, labeled update edges, and parameter dependencies.
from gds.canonical import project_canonical
from gds_viz import canonical_to_mermaid
mermaid = canonical_to_mermaid(project_canonical(spec))
Views 3 & 4: Architecture
Domain-level diagrams from GDSSpec. Show entity state cylinders, typed wire labels (from Wire.space), and mechanism-to-entity update edges. View 3 groups by GDS role; View 4 groups by any tag key.
from gds_viz import spec_to_mermaid
by_role = spec_to_mermaid(spec) # View 3
by_agent = spec_to_mermaid(spec, group_by="domain") # View 4
Tags are set on blocks at definition time:
sensor = BoundaryAction(name="Sensor", ..., tags={"domain": "Observation"})
View 5: Parameter Influence
Shows Θ → block → entity causal map. Hexagon nodes for parameters, dashed edges to blocks that use them, then forward through the dependency graph to entities. Answers: "if I change parameter X, what state is affected?"
from gds_viz import params_to_mermaid
mermaid = params_to_mermaid(spec)
View 6: Traceability
For a single entity variable, traces every block that can transitively affect it and every parameter feeding those blocks. Right-to-left layout with thick edges for direct updates. Answers: "what controls this variable?"
from gds_viz import trace_to_mermaid
mermaid = trace_to_mermaid(spec, "Susceptible", "count")
What gds-viz does NOT cover
The six views above exhaust what is derivable from the GDS specification {h, X}. Two commonly requested views are deliberately excluded:
State Machine View — requires discrete states and transition guards. GDS defines a continuous state space X, not a finite set of named states. Discretizing X is domain-specific interpretation, not derivable from {h, X}.
Simulation / Execution Order View — requires operational semantics (when blocks execute, in what order, with what timing). GDS specifies only structural relationships. The composition algebra defines topology, not a runtime.
| Concern | In GDS? | Where it belongs |
|---|---|---|
| State space, block topology, dependencies, parameters | Yes | GDSSpec, SystemIR, SpecQuery |
| Discrete state machine | No | Domain-specific layer or gds-sim |
| Execution schedule, time semantics | No | Simulator / runtime (gds-sim) |
A future gds-sim package could add execution semantics, making these views derivable from (GDSSpec, SimConfig).
License
Apache-2.0
Built with Claude Code. All code is test-driven and human-reviewed.
Credits & Attribution
Author: Rohan Mehta — BlockScience
Theoretical foundation: Dr. Michael Zargham and Dr. Jamsheed Shorish — Generalized Dynamical Systems, Part I: Foundations (2021).
Architectural inspiration: Sean McOwen — MSML and bdp-lib.
Contributors:
- Michael Zargham — Project direction, GDS theory guidance, and technical review (BlockScience).
- Peter Hacker — Code auditing and review (BlockScience).
Lineage: Part of the cadCAD ecosystem for Complex Adaptive Dynamics.
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