Fix/application ready polish

.github/copilot-instructions.md

@@ -78,4 +78,5 @@ Before considering a change complete, the project should pass:
 ruff format --check .
 ruff check .
 mypy src
-python -m pytest
+python -m pytest
+```

.github/workflows/ci.yml

@@ -35,11 +35,11 @@ jobs:
       - name: Upgrade pip
         run: python -m pip install --upgrade pip
 
-              - name: Install PyTorch
+      - name: Install PyTorch
         run: >
           python -m pip install torch
           --index-url https://download.pytorch.org/whl/cpu
-          
+
       - name: Install CalculiX
         run: |
           sudo apt-get update

docs/ai_assisted_development.md

@@ -103,4 +103,5 @@ The accepted implementation was verified using:
 ruff format --check .
 ruff check .
 mypy src
-python -m pytest
+python -m pytest
+```

src/bodysimpy/analysis/stochastic.py

@@ -29,6 +29,15 @@ class DistributionSummary:
     percentile_5: float
     percentile_95: float
 
+    @property
+    def coefficient_of_variation_percent(self) -> float:
+        """Return the sample coefficient of variation as a percentage."""
+
+        if self.mean == 0.0:
+            raise ValueError("Coefficient of variation is undefined for a zero mean.")
+
+        return abs(self.standard_deviation / self.mean) * 100.0
+
 
 @dataclass(frozen=True, slots=True)
 class SensitivityCoefficient:

src/bodysimpy/domain/materials.py

@@ -15,8 +15,8 @@ def __post_init__(self) -> None:
         if self.youngs_modulus_pa <= 0.0:
             raise ValueError("Young's modulus must be positive.")
 
-        if not 0.0 < self.poisson_ratio < 0.5:
-            raise ValueError("Poisson ratio must be between 0 and 0.5.")
+        if not -1.0 < self.poisson_ratio < 0.5:
+            raise ValueError("Poisson ratio must be between -1.0 and 0.5.")
 
         if self.density_kg_m3 <= 0.0:
             raise ValueError("Density must be positive.")

tests/unit/test_crossmember_model.py

@@ -47,3 +47,33 @@ def test_build_crossmember_model() -> None:
     assert model.material.youngs_modulus_pa == pytest.approx(210e9)
     assert model.tip_force_n == pytest.approx(1000.0)
     assert model.mesh_elements == 20
+
+
+def test_build_crossmember_model_accepts_valid_negative_poisson_ratio() -> None:
+    config = SimulationConfig.model_validate(
+        {
+            "project": {"name": "auxetic_crossmember"},
+            "geometry": {
+                "type": "rectangular_hollow_section",
+                "length_m": 1.0,
+                "width_m": 0.080,
+                "height_m": 0.040,
+                "thickness_m": 0.0015,
+            },
+            "material": {
+                "youngs_modulus_pa": 210e9,
+                "poisson_ratio": -0.20,
+                "density_kg_m3": 7850.0,
+            },
+            "loading": {"tip_force_n": 1000.0},
+            "mesh": {"elements": 20},
+            "analysis": {
+                "static": True,
+                "modal": {"modes": 10},
+            },
+        }
+    )
+
+    model = build_crossmember_model(config)
+
+    assert model.material.poisson_ratio == pytest.approx(-0.20)

tests/unit/test_stochastic.py

@@ -53,6 +53,25 @@ def test_distribution_summary() -> None:
 
     assert summary.percentile_95 > summary.mean
 
+    assert summary.coefficient_of_variation_percent == pytest.approx(
+        summary.standard_deviation / summary.mean * 100.0
+    )
+
+
+def test_distribution_summary_rejects_zero_mean_cov() -> None:
+    values = np.array(
+        [
+            -1.0,
+            1.0,
+        ],
+        dtype=float,
+    )
+
+    summary = summarize_distribution(values)
+
+    with pytest.raises(ValueError, match="zero mean"):
+        _ = summary.coefficient_of_variation_percent
+
 
 def test_stochastic_inputs_are_reproducible() -> None:
     config = build_stochastic_config()