chore(deps): lock file maintenance

exp/2026/01/28/smas/data/31-inverse-smas46-muscle46-activation-stable-neo-hookean-easy-metrics.json

@@ -26,4 +26,4 @@
     "forward_best_objective": 1481.1424852869957,
     "finite_outputs": true
   }
-]
+]

exp/2026/01/28/smas/data/31-inverse-smas46-muscle46-activation-stable-neo-hookean-easy.json

@@ -38,4 +38,4 @@
     "adjoint-relative-residual": "/home/liblaf/github/liblaf/apple/exp/2026/01/28/smas/data/31-inverse-smas46-muscle46-activation-stable-neo-hookean-easy-metrics-adjoint-relative-residual.png",
     "inverse-grad-absolute": "/home/liblaf/github/liblaf/apple/exp/2026/01/28/smas/data/31-inverse-smas46-muscle46-activation-stable-neo-hookean-easy-metrics-inverse-grad-absolute.png"
   }
-}
+}

exp/2026/01/28/smas/data/31-inverse-smas46-muscle46-activation-stable-neo-hookean-zero-metrics.json

@@ -404,4 +404,4 @@
     "forward_best_objective": 1485.5675728610533,
     "finite_outputs": true
   }
-]
+]

exp/2026/01/28/smas/data/31-inverse-smas46-muscle46-activation-stable-neo-hookean-zero.json

@@ -43,4 +43,4 @@
     "adjoint-relative-residual": "/home/liblaf/github/liblaf/apple/exp/2026/01/28/smas/data/31-inverse-smas46-muscle46-activation-stable-neo-hookean-zero-metrics-adjoint-relative-residual.png",
     "inverse-grad-absolute": "/home/liblaf/github/liblaf/apple/exp/2026/01/28/smas/data/31-inverse-smas46-muscle46-activation-stable-neo-hookean-zero-metrics-inverse-grad-absolute.png"
   }
-}
+}

exp/2026/01/28/smas/data/31-inverse-smas46-muscle46-activation-stable-neo-hookean.json

@@ -88,4 +88,4 @@
       }
     }
   ]
-}
+}

exp/2026/05/20/inverse-face/data/20-inverse-face-3152k-summary.json

@@ -136,4 +136,4 @@
   "repeated/forward_failure": false,
   "repeated/adjoint_failure": false,
   "passed": true
-}
+}

exp/2026/05/20/inverse-face/data/20-inverse-face-fresh-nu049-smas1-noclamp-super-loose-reg-summary.json

@@ -16721,4 +16721,4 @@
   "repeated/forward_failure": false,
   "repeated/adjoint_failure": false,
   "passed": true
-}
+}

exp/2026/05/20/inverse-face/data/20-inverse-face-summary.json

@@ -125,4 +125,4 @@
   "repeated/forward_failure": false,
   "repeated/adjoint_failure": false,
   "passed": true
-}
+}

exp/2026/05/20/inverse-face/data/30-transfer-activation-3152k-summary.json

@@ -57,4 +57,4 @@
   "forward/line_search_steps": 0,
   "forward/stagnation_count": 0,
   "time/forward_s": 16.038381466001738
-}
+}

exp/2026/05/20/inverse-face/data/31-transfer-activation-3152k-smas1-summary.json

@@ -57,4 +57,4 @@
   "forward/line_search_steps": 0,
   "forward/stagnation_count": 0,
   "time/forward_s": 6.737611743999878
-}
+}

exp/2026/05/20/inverse-face/data/40-transfer-activation-515k-smas100-summary.json

@@ -57,4 +57,4 @@
   "forward/line_search_steps": 0,
   "forward/stagnation_count": 0,
   "time/forward_s": 3.82475233200239
-}
+}

exp/2026/05/20/inverse-face/data/41-transfer-activation-515k-smas1-summary.json

@@ -57,4 +57,4 @@
   "forward/line_search_steps": 0,
   "forward/stagnation_count": 0,
   "time/forward_s": 3.6330396700068377
-}
+}

exp/2026/05/20/inverse-face/data/42-transfer-activation-3152k-smas100-summary.json

@@ -57,4 +57,4 @@
   "forward/line_search_steps": 0,
   "forward/stagnation_count": 0,
   "time/forward_s": 4.520490328024607
-}
+}

exp/2026/05/20/inverse-face/data/43-transfer-activation-3152k-smas1-summary.json

@@ -57,4 +57,4 @@
   "forward/line_search_steps": 0,
   "forward/stagnation_count": 0,
   "time/forward_s": 6.646797652996611
-}
+}

exp/2026/05/20/inverse-face/docs/40-transfer-activation-matrix.md

@@ -29,7 +29,7 @@ The four concrete cases were:
 ## Results
 
 | case | cells | active tets | steps | target mean cm | target RMS cm | target max cm | all RMS cm | all max cm | forward s |
-|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|
+| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
 | 515k, SMAS 100 | 253876 | 58494 | 421 | 0.422151 | 0.475055 | 1.297573 | 0.368954 | 9.127733 | 3.825 |
 | 515k, SMAS 1 | 253876 | 58494 | 244 | 0.274502 | 0.318913 | 0.942866 | 0.243835 | 1.195187 | 3.633 |
 | 3152k, SMAS 100 | 1127541 | 283391 | 82 | 0.242941 | 0.309361 | 1.033624 | 0.200444 | 3.558038 | 4.520 |

exp/2026/05/20/inverse-face/src/10-prepare-inverse-face.py

@@ -123,7 +123,9 @@ def add_material_fields(mesh: pv.UnstructuredGrid, cfg: Config) -> None:
     zero_activation_fields(mesh)
 
 
-def fixed_point_mask(mesh: pv.UnstructuredGrid, cfg: Config) -> tuple[np.ndarray, dict[str, int]]:
+def fixed_point_mask(
+    mesh: pv.UnstructuredGrid, cfg: Config
+) -> tuple[np.ndarray, dict[str, int]]:
     fixed = np.zeros(mesh.n_points, dtype=bool)
     counts: dict[str, int] = {}
     for name in cfg.fixed_point_masks:
@@ -222,8 +224,12 @@ def metric_summary(
         "n_active_tets": int(np.asarray(mesh.cell_data["ActivationMask"]).sum()),
         "n_target_points": int(target_mask.sum()),
         "n_fixed_points": int(np.asarray(mesh.point_data["FixedBoundary"]).sum()),
-        "n_fixed_cranium_points": int(np.asarray(mesh.point_data["FixedCranium"]).sum()),
-        "n_fixed_mandible_points": int(np.asarray(mesh.point_data["FixedMandible"]).sum()),
+        "n_fixed_cranium_points": int(
+            np.asarray(mesh.point_data["FixedCranium"]).sum()
+        ),
+        "n_fixed_mandible_points": int(
+            np.asarray(mesh.point_data["FixedMandible"]).sum()
+        ),
         "muscle_fraction_volume": float(np.sum(muscle * volume)),
         "smas_fraction_volume": float(np.sum(smas * volume)),
         "target_displacement_mean": float(target_norm.mean()),

exp/2026/05/27/forward-face/data/20-forward-face-3152k-a087-summary.json

@@ -56,4 +56,4 @@
   "IsLipBottom/target_rms": 0.3993310920970844,
   "IsLipBottom/target_max": 1.0284059764202034,
   "IsLipBottom/rms_ratio_to_target": 0.30975080437456287
-}
+}

exp/2026/05/27/forward-face/data/20-forward-face-3152k-summary.json

@@ -56,4 +56,4 @@
   "IsLipBottom/target_rms": 0.3993310920970844,
   "IsLipBottom/target_max": 1.0284059764202034,
   "IsLipBottom/rms_ratio_to_target": 0.3094006908630162
-}
+}

exp/2026/05/27/forward-face/data/20-forward-face-515k-nosmas-summary.json

@@ -57,4 +57,4 @@
   "IsLipBottom/target_rms": 0.42244197929946425,
   "IsLipBottom/target_max": 1.016519397545366,
   "IsLipBottom/rms_ratio_to_target": 0.12364134545496203
-}
+}

exp/2026/05/27/forward-face/data/30-inverse-face-515k-nosmas-summary.json

@@ -4974,4 +4974,4 @@
   "activation_inv/error_rms": 0.2491769718442089,
   "activation_inv/error_max_norm": 0.6141888156955176,
   "passed": true
-}
+}

exp/2026/05/27/forward-face/src/10-prepare-forward-face.py

@@ -236,8 +236,12 @@ def metric_summary(
         "activation/n_tets": int(active.sum()),
         "target/n_points": int(target_mask.sum()),
         "fixed/n_points": int(fixed.sum()),
-        "fixed/n_cranium_points": int(np.asarray(mesh.point_data["FixedCranium"]).sum()),
-        "fixed/n_mandible_points": int(np.asarray(mesh.point_data["FixedMandible"]).sum()),
+        "fixed/n_cranium_points": int(
+            np.asarray(mesh.point_data["FixedCranium"]).sum()
+        ),
+        "fixed/n_mandible_points": int(
+            np.asarray(mesh.point_data["FixedMandible"]).sum()
+        ),
         "volume/muscle_fraction": float(np.sum(muscle * volume)),
         "volume/smas_fraction": float(np.sum(smas * volume)),
         "volume/smas_stiffness_fraction": float(np.sum(smas_stiffness * volume)),

exp/2026/05/27/forward-face/src/20-forward-face.py

@@ -153,7 +153,9 @@ def set_material(
     mesh.cell_data[FRACTION.vtk] = np.asarray(fraction, dtype=np.float64)
 
 
-def unpack_symmetric(values: tuple[float, float, float, float, float, float]) -> np.ndarray:
+def unpack_symmetric(
+    values: tuple[float, float, float, float, float, float],
+) -> np.ndarray:
     xx, yy, zz, xy, xz, yz = values
     return np.asarray(
         [[xx, xy, xz], [xy, yy, yz], [xz, yz, zz]],
@@ -304,7 +306,9 @@ def summarize(
     elapsed_s: float,
     cfg: Config,
 ) -> dict[str, Any]:
-    target_displacement = np.asarray(target.point_data["Displacement"], dtype=np.float64)
+    target_displacement = np.asarray(
+        target.point_data["Displacement"], dtype=np.float64
+    )
     active = np.asarray(mesh.cell_data["ActivationMask"], dtype=bool)
     volume = np.asarray(mesh.cell_data["Volume"], dtype=np.float64)
     muscle = np.asarray(mesh.cell_data["MuscleFraction"], dtype=np.float64)
@@ -356,7 +360,9 @@ def make_result_mesh(
     metrics: dict[str, float | int | bool | str],
 ) -> pv.UnstructuredGrid:
     result = mesh.copy(deep=True)
-    target_displacement = np.asarray(target.point_data["Displacement"], dtype=np.float64)
+    target_displacement = np.asarray(
+        target.point_data["Displacement"], dtype=np.float64
+    )
     error = displacement - target_displacement
     result.point_data["Displacement"] = displacement
     result.point_data["DisplacementNorm"] = np.linalg.norm(displacement, axis=1)

exp/2026/05/27/forward-face/src/30-inverse-face-515k.py

@@ -297,7 +297,9 @@ def local_deltas_from_activation_inv(
     local_activation_inv = eye + matrix_from_symmetric(local_activation_inv_delta)
     local_activation = np.linalg.inv(local_activation_inv)
     local_activation_delta = pack_symmetric_np(local_activation[None, ...] - eye)[0]
-    return local_activation_delta, np.asarray(local_activation_inv_delta, dtype=np.float64)
+    return local_activation_delta, np.asarray(
+        local_activation_inv_delta, dtype=np.float64
+    )
 
 
 def activation_inv_from_local_delta(
@@ -553,7 +555,9 @@ def make_result_mesh(
 
     result = mesh.copy(deep=True)
     add_masks(result, target_ids, active_ids)
-    target_displacement = np.asarray(target.point_data["Displacement"], dtype=np.float64)
+    target_displacement = np.asarray(
+        target.point_data["Displacement"], dtype=np.float64
+    )
     error = displacement - target_displacement
     result.point_data["Displacement"] = displacement
     result.point_data["DisplacementNorm"] = np.linalg.norm(displacement, axis=1)
@@ -698,7 +702,9 @@ def adjoint_solve(self, u_grad: torch.Tensor) -> Any:
         device=torch.get_default_device(),
     )
     local_activation_inv_delta = torch.nn.Parameter(
-        torch.zeros(6, dtype=torch.get_default_dtype(), device=torch.get_default_device())
+        torch.zeros(
+            6, dtype=torch.get_default_dtype(), device=torch.get_default_device()
+        )
     )
     optimizer = torch.optim.Adam(
         [local_activation_inv_delta],
@@ -936,7 +942,9 @@ def summarize(
 ) -> dict[str, Any]:
     from liblaf.apple.common import ACTIVATION_INV
 
-    target_displacement = np.asarray(target.point_data["Displacement"], dtype=np.float64)
+    target_displacement = np.asarray(
+        target.point_data["Displacement"], dtype=np.float64
+    )
     error = displacement - target_displacement
     target_error = error[target_ids]
     target_error_norm = np.linalg.norm(target_error, axis=1)
@@ -1039,10 +1047,12 @@ def main(cfg: Config) -> None:
     _, local_activation_inv_delta = local_deltas_from_activation_inv(
         recovered_local_activation_inv_delta
     )
-    activation, activation_inv, local_activation_delta = full_activation_fields_from_local(
-        mesh,
-        active_ids,
-        local_activation_inv_delta,
+    activation, activation_inv, local_activation_delta = (
+        full_activation_fields_from_local(
+            mesh,
+            active_ids,
+            local_activation_inv_delta,
+        )
     )
     total_elapsed_s = time.perf_counter() - total_start
     summary = summarize(
@@ -1075,7 +1085,9 @@ def main(cfg: Config) -> None:
     try:
         save_snapshot(cfg.output_snapshot, result)
     except (OSError, RuntimeError, ValueError):
-        logger.warning("failed to save snapshot: %s", cfg.output_snapshot, exc_info=True)
+        logger.warning(
+            "failed to save snapshot: %s", cfg.output_snapshot, exc_info=True
+        )
     save_json(cfg.output_summary, summary)
     cherries.log_metrics(numeric_metrics(summary, exclude=frozenset({"trace"})))
     print(