feat: integrate SimpleCADAPI 2.0.2 CAD workflows
This commit is contained in:
Jerry
@@ -10,14 +10,19 @@ The long-form schema reference remains [`../operation_graph_json_spec.md`](../op
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import json
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import simplecadapi as scad
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with scad.GraphSession() as session:
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body = scad.make_box_rsolid(10, 6, 2)
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hole = scad.make_cylinder_rsolid(1, 4, bottom_face_center=(0, 0, -1))
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@scad.model(graph_id="drilled_block")
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def build_model():
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body = scad.make_box_rsolid(width=10, height=6, depth=2)
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hole = scad.make_cylinder_rsolid(
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radius=1, height=4, bottom_face_center=(0, 0, -1)
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)
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result = scad.cut_rsolid(body, hole)
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scad.capture_result(value=result)
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return result
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model_json = scad.export_model_json(session)
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payload = json.loads(model_json)
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rebuilt = scad.replay_model_json(model_json)
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model = build_model()
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payload = json.loads(model.model_json)
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rebuilt = model.replay()
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```
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Inspect these fields:
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@@ -29,6 +34,17 @@ Inspect these fields:
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- `node["inputs"]`: upstream node ids used by replay.
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- `payload["leaf_ids"]`: explicit final result node ids.
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- `payload["expression_graph"]`: expression DAG used by expression-backed parameters.
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- `payload["tolerance_graph"]`: dimension-chain requirements and validation evidence.
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For new top-level models, `ModelResult.model_json` is the preferred artifact
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accessor. Use `@scad.requires_session` for reusable builders and
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`scad.capture_result(...)` when the final output should not be inferred from
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all graph leaves. If a model invocation also needs durable CAD/viewer files,
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pass `export_dir=...` to `@scad.model`; its captured geometry/product values
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then produce one self-contained `<graph_id>.scene.zip`. It embeds
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`model/model.json`, mapped project-relative Python sources, and the evaluated
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render/selection assets. It does not create adjacent model/session JSON, STEP,
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STL, or FCStd files. No files are written when `export_dir` is omitted.
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## Important rule: source API is not always graph API
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@@ -52,11 +68,13 @@ Many user-facing functions are convenience APIs. During an active `GraphSession`
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- [Primitive and profile operations](primitives-and-profiles.md)
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- [Features, booleans, transforms, patterns, and selectors](features-booleans-transforms.md)
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- [Expressions and replay behavior](expressions-and-replay.md)
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- [Physical units and dimension inference](../physical-units.md)
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- [Dimension tolerance chains](../dimension-tolerance-chains.md)
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## Example
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## Examples
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See [`../../../examples/07_serialization_operation_tree.py`](../../../examples/07_serialization_operation_tree.py). It intentionally exercises every canonical core operation and writes:
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- `examples/out/serialization_operation_tree.model.json`
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- `examples/out/serialization_operation_tree.summary.md`
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- `examples/out/serialization_operation_tree.step`
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The retained examples use the same model/session contract. See
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[`../../../examples/08_constrained_sketch.py`](../../../examples/08_constrained_sketch.py)
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for sketch promotion and replay, and
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[`../../../examples/10_part_assembly.py`](../../../examples/10_part_assembly.py)
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for product hierarchy and automatic artifact export.
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@@ -15,14 +15,15 @@ This lets consumers choose between:
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```python
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import simplecadapi as scad
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width = scad.var("width", 24.0, comment="plate width")
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height = scad.var("height", 12.0, comment="plate height")
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thickness = scad.var("thickness", 4.0, comment="plate thickness")
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width = scad.var("width", 24.0, unit="mm", comment="plate width", tolerance=0.1)
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height = scad.var("height", 12.0, unit="mm", comment="plate height", tolerance=0.1)
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thickness = scad.var("thickness", 4.0, unit="mm", comment="plate thickness", tolerance=(-0.05, 0.1))
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with scad.GraphSession() as session:
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plate = scad.make_box_rsolid(width, height, thickness)
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rib = scad.make_box_rsolid(width / 4.0, height, thickness * 2.0)
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part = scad.union_rsolid(plate, rib)
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session.require_tolerance(width + height, 0.2, tolerance_unit="mm", name="plate_envelope")
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model_json = scad.export_model_json(session)
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```
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@@ -48,7 +49,9 @@ A node with expression-backed params may look like:
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}
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```
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`params.distance` is the evaluated snapshot. `param_exprs.distance` says the value came from expression node `var_thickness`.
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`params.distance` is the evaluated canonical snapshot. Unit-aware lengths are
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stored in millimeters and angles in degrees. `param_exprs.distance` says the value
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came from expression node `var_thickness` and preserves its declaration metadata.
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For tuple/list params, `param_exprs` mirrors the shape of the parameter and uses `null` where no expression is present:
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@@ -78,12 +81,24 @@ Consumers that want parameterization should:
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Consumers that only want geometry can ignore `param_exprs` and `expression_graph`.
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Variable nodes may contain `unit`, `tolerance`, and `tolerance_unit`. Registered
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units use string symbols; custom units use `{symbol, dimension,
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scale_to_canonical}` objects. Import reconstructs the expression graph and reruns
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dimension inference rather than trusting external dimension claims.
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Session/model payloads store derived-dimension requirements in `tolerance_graph`.
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See [Physical Units](../physical-units.md) and [Dimension Tolerance
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Chains](../dimension-tolerance-chains.md) for inference, propagation, and
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validation semantics.
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## Replay policy in current implementation
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`replay_model_json(model_json)` currently uses the canonical low-level `graph` and the numeric values in `node.params`.
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That means replay is deterministic with respect to the exported snapshot. It does not currently re-solve expressions with changed variable values.
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Replay does validate stored tolerance requirements before rebuilding the nominal geometry. A failing tolerance chain blocks replay, but passing bounds do not cause replay to sample or regenerate limit geometry.
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In practical terms:
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```python
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@@ -122,6 +122,40 @@ Replay effect:
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2. Replay path wire from input 1.
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3. Call `sweep_rsolid(profile, path, is_frenet=...)`.
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## Twisted Sweep
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Source:
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```python
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profile = scad.make_rectangle_rface(width=2.0, height=1.0)
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solid = scad.twisted_sweep_rsolid(
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profile=profile,
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distance=8.0,
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twist_angle=30.0,
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)
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```
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Serialized node:
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```json
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{
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"op": "make_twisted_sweep_rsolid",
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"params": {
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"axis": [0.0, 0.0, 1.0],
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"origin": [0.0, 0.0, 0.0],
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"distance": 8.0,
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"twist_angle": 30.0,
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"guide_radius": 1.0
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},
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"inputs": ["node_for_profile_face"],
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"output_count": 1
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}
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```
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Replay reconstructs the continuous auxiliary-spine rotation law from the
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recorded parameters and invokes `twisted_sweep_rsolid(...)`. No sampled loft
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sections are stored or inferred.
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## Helical sweep macro lowering
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Source:
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