87 pedantic

README.md

@@ -17,7 +17,7 @@ We compute the smallest enclosing circle/ball for a set of points.
 >>> points = np.array([[2.0, 4.0], [0, 0], [2.5, 2.0]])
 
 # compute the circle, pick any solver supporting conic constraints
->>> radius, centre = min_circle_cvx(points, solver="CLARABEL")
+>>> result = min_circle_cvx(points, solver="CLARABEL")
 
 ```
 

pyproject.toml

@@ -10,6 +10,7 @@ dependencies = [
     "numpy>=2",
     "clarabel>=0.10.0",
     "numpy-flight>=0.0.7",
+    "pydantic>=2.10.6",
 ]
 
 [project.urls]

src/cvx/ball/server.py

@@ -17,10 +17,10 @@ def f(self, matrices: dict[str, np.ndarray]) -> dict[str, np.ndarray]:
 
         # Compute the smallest enclosing ball
         self.logger.info("Computing smallest enclosing ball...")
-        radius, midpoint = min_circle_cvx(matrix, solver="CLARABEL")
+        result = min_circle_cvx(matrix, solver="CLARABEL")
 
         # return a dictionary of np.ndarrays
-        return {"radius": radius, "midpoint": midpoint, "points": matrix}
+        return result.model_dump()
 
 
 # entry point for Docker

src/cvx/ball/solver.py

@@ -1,5 +1,17 @@
 import cvxpy as cp
 import numpy as np
+from pydantic import BaseModel, Field
+
+
+class ResultData(BaseModel):
+    radius: float = Field(..., description="Radius of the ball", ge=0)
+    midpoint: np.ndarray = Field(..., description="Midpoint of the ball")
+    points: np.ndarray = Field(..., description="Points of the ball")
+
+    class Config:
+        arbitrary_types_allowed = True  # Allow numpy arrays in Pydantic
+        validate_assignment = True  # Validate on attribute assignment
+        frozen = True
 
 
 def min_circle_cvx(points, **kwargs):
@@ -19,4 +31,5 @@ def min_circle_cvx(points, **kwargs):
     problem = cp.Problem(objective=objective, constraints=constraints)
     problem.solve(**kwargs)
 
-    return r.value[0], x.value
+    # return r.value[0], x.value
+    return ResultData(radius=float(r.value[0]), midpoint=x.value, points=points)

src/tests/test_solver.py

@@ -6,7 +6,7 @@
 
 def test_random():
     p = np.array([[2.0, 4.0], [0.0, 0.0], [2.5, 2.0]])
-    radius, center = min_circle_cvx(p, solver="CLARABEL")
+    result = min_circle_cvx(p, solver="CLARABEL")
 
-    assert radius == pytest.approx(2.2360679626271796, 1e-6)
-    assert center == pytest.approx([1.0, 2.0], 1e-4)
+    assert result.radius == pytest.approx(2.2360679626271796, 1e-6)
+    assert result.midpoint == pytest.approx([1.0, 2.0], 1e-4)