black

Author: valentin-gauthier-geosiris

energyml-utils/src/energyml/utils/data/crs.py

@@ -889,7 +889,7 @@ def extract_crs_info(
     """
     if crs_obj is None:
         return CrsInfo()
-    
+
     if isinstance(crs_obj, CrsInfo):
         return crs_obj
 

energyml-utils/src/energyml/utils/data/helper.py

@@ -514,9 +514,9 @@ def hermite_interpolation(md_target, md_start, md_end, p_start, p_end, v_start,
 
 def _interp1d_vectorized(
     ctrl_params: np.ndarray,  # (K,)
-    ctrl_pts: np.ndarray,     # (K, d)
-    query: np.ndarray,        # (Q,)
-) -> np.ndarray:              # (Q, d)
+    ctrl_pts: np.ndarray,  # (K, d)
+    query: np.ndarray,  # (Q,)
+) -> np.ndarray:  # (Q, d)
     """Vectorised piecewise-linear interpolation of a 1-D parametric curve."""
     result = np.empty((len(query), ctrl_pts.shape[1]), dtype=np.float64)
     for dim in range(ctrl_pts.shape[1]):
@@ -526,9 +526,9 @@ def _interp1d_vectorized(
 
 def _natural_cubic_spline_eval(
     ctrl_params: np.ndarray,  # (K,)
-    ctrl_pts: np.ndarray,     # (K, d)
-    query: np.ndarray,        # (Q,)
-) -> np.ndarray:              # (Q, d)
+    ctrl_pts: np.ndarray,  # (K, d)
+    query: np.ndarray,  # (Q,)
+) -> np.ndarray:  # (Q, d)
     """
     Evaluate a natural cubic spline (second derivatives = 0 at endpoints) via
     ``scipy.interpolate.CubicSpline``.
@@ -552,10 +552,10 @@ def _natural_cubic_spline_eval(
 
 def _minimum_curvature_eval(
     ctrl_params: np.ndarray,  # (K,)  P-values (e.g. depth) at knots
-    ctrl_pts: np.ndarray,     # (K, 3)  XYZ at knots
-    tangents: np.ndarray,     # (K, 3)  unit tangent vectors at knots
-    query: np.ndarray,        # (Q,)  query P-values
-) -> np.ndarray:              # (Q, 3)
+    ctrl_pts: np.ndarray,  # (K, 3)  XYZ at knots
+    tangents: np.ndarray,  # (K, 3)  unit tangent vectors at knots
+    query: np.ndarray,  # (Q,)  query P-values
+) -> np.ndarray:  # (Q, 3)
     """
     Minimum-curvature interpolation (RESQML line kind 5).
 
@@ -612,10 +612,10 @@ def _minimum_curvature_eval(
 def _evaluate_one_pillar(
     kind: int,
     ctrl_params: Optional[np.ndarray],  # (K,) — may be None for kind=0
-    ctrl_pts: np.ndarray,               # (K, d) — d=2 for kind=0, d=3 otherwise
-    tangents: Optional[np.ndarray],     # (K, 3) — only for kinds 3, 5
-    query_params: np.ndarray,           # (Q,) query P-values for this pillar
-) -> np.ndarray:                        # (Q, 3)
+    ctrl_pts: np.ndarray,  # (K, d) — d=2 for kind=0, d=3 otherwise
+    tangents: Optional[np.ndarray],  # (K, 3) — only for kinds 3, 5
+    query_params: np.ndarray,  # (Q,) query P-values for this pillar
+) -> np.ndarray:  # (Q, 3)
     """
     Evaluate a single parametric pillar at *query_params*, returning `(Q, 3)` XYZ.
 
@@ -728,14 +728,10 @@ def resolve_parametric_line_array(
     # ParametricLineFromRepresentationLatticeArray path (RESQML v2.0.1).
     supporting_rep_dor = getattr(parametric_lines_obj, "supporting_representation", None)
     if supporting_rep_dor is None:
-        raise ValueError(
-            "ParametricLineFromRepresentationLatticeArray has no supporting_representation reference."
-        )
+        raise ValueError("ParametricLineFromRepresentationLatticeArray has no supporting_representation reference.")
 
     if workspace is None:
-        raise ValueError(
-            "A workspace is required to resolve ParametricLineFromRepresentationLatticeArray."
-        )
+        raise ValueError("A workspace is required to resolve ParametricLineFromRepresentationLatticeArray.")
 
     sup_uri = get_obj_uri(supporting_rep_dor)
     sup_obj = workspace.get_object(sup_uri)
@@ -747,9 +743,7 @@ def resolve_parametric_line_array(
     if not pla_results:
         pla_results = search_attribute_matching_name_with_path(sup_obj, "parametric_lines")
     if not pla_results:
-        raise ValueError(
-            f"Cannot find a ParametricLineArray in supporting representation {sup_uri}."
-        )
+        raise ValueError(f"Cannot find a ParametricLineArray in supporting representation {sup_uri}.")
     _, sup_pla = pla_results[0]
 
     # Read the pillar index selection (IntegerLatticeArray).
@@ -760,9 +754,7 @@ def resolve_parametric_line_array(
 
     from energyml.utils.data.helper import read_array as _read_array_helper  # for local use
 
-    raw_indices = _read_array_helper(
-        energyml_array=idx_obj, root_obj=root_obj, workspace=workspace
-    )
+    raw_indices = _read_array_helper(energyml_array=idx_obj, root_obj=root_obj, workspace=workspace)
     if not isinstance(raw_indices, np.ndarray):
         raw_indices = np.array(raw_indices, dtype=np.int64)
     raw_indices = raw_indices.flatten().astype(np.int64)
@@ -791,7 +783,7 @@ def evaluate_parametric_line_array(
     query_parameters: np.ndarray,  # shape (NKL, n_pillars)
     ni: int,
     nj: int,
-) -> np.ndarray:                   # shape (NKL, n_pillars, 3) float64
+) -> np.ndarray:  # shape (NKL, n_pillars, 3) float64
     """
     Evaluate a ``ParametricLineArray`` at the given query P-values and return
     3-D Cartesian coordinates for every grid node.
@@ -906,7 +898,7 @@ def evaluate_parametric_line_array(
     for p_idx in range(n_pillars):
         kind = int(kinds[p_idx])
         q_p = query_parameters[:, p_idx]  # (NKL,) P-values for this pillar
-        cp_p = ctrl_pts[:, p_idx, :]      # (K, d)
+        cp_p = ctrl_pts[:, p_idx, :]  # (K, d)
 
         # ctrl_params_p: (K,) — derived from global or pillar-specific params.
         # For kind=0, ctrl_params is None (vertical) and we pass None.
@@ -1970,7 +1962,7 @@ def from_hsv(hsv_obj: Any) -> "RgbaColor":
         a = hsv_obj.alpha if hsv_obj.alpha is not None else 1.0
         r, g, b = colorsys.hsv_to_rgb(h, s, v)
         return RgbaColor(r, g, b, a)
-    
+
     @staticmethod
     def random() -> "RgbaColor":
         """Generate a random RGBA color (for testing)."""
@@ -1982,7 +1974,7 @@ def random() -> "RgbaColor":
             b=random.random(),
             a=1.0,
         )
-    
+
     @staticmethod
     def random_from_uuid(uuid_str: str) -> "RgbaColor":
         """Generate a random RGBA color based on a UUID string (for consistent testing)."""
@@ -1991,7 +1983,7 @@ def random_from_uuid(uuid_str: str) -> "RgbaColor":
 
         # Create a hash of the UUID string to seed the random generator
         hash_bytes = hashlib.sha256(uuid_str.encode()).digest()
-        seed = int.from_bytes(hash_bytes, 'big')
+        seed = int.from_bytes(hash_bytes, "big")
         random.seed(seed)
 
         return RgbaColor(
@@ -2051,9 +2043,7 @@ def to_vtk_lut(self, n_colors: int = 256) -> np.ndarray:
 
         if not self.is_continuous:
             # One exact row per integer entry - no interpolation needed.
-            return np.array(
-                [e.color.to_uint8() for e in sorted_entries], dtype=np.uint8
-            )
+            return np.array([e.color.to_uint8() for e in sorted_entries], dtype=np.uint8)
 
         # Continuous: sample n_colors levels with linear interpolation in RGBA.
         indices = np.array([e.index for e in sorted_entries], dtype=np.float64)
@@ -2064,9 +2054,7 @@ def to_vtk_lut(self, n_colors: int = 256) -> np.ndarray:
         t = np.linspace(indices[0], indices[-1], n_colors)
         result = np.zeros((n_colors, 4), dtype=np.uint8)
         for ch in range(4):
-            result[:, ch] = np.clip(
-                np.interp(t, indices, float_colors[:, ch]) * 255, 0, 255
-            ).round().astype(np.uint8)
+            result[:, ch] = np.clip(np.interp(t, indices, float_colors[:, ch]) * 255, 0, 255).round().astype(np.uint8)
         return result
 
     def scalar_range(self) -> Tuple[float, float]:
@@ -2271,7 +2259,9 @@ def read_graphical_rendering_info(
     result = ScalarRenderingInfo(target_obj_uuid=target_uuid)
     found = False
 
-    gis_infos: List[Any] = getattr(graphical_information_set, "graphical_information", []) or ([graphical_information_set] if not isinstance(graphical_information_set, list) else graphical_information_set)
+    gis_infos: List[Any] = getattr(graphical_information_set, "graphical_information", []) or (
+        [graphical_information_set] if not isinstance(graphical_information_set, list) else graphical_information_set
+    )
 
     for info in gis_infos:
         # Each AbstractGraphicalInformation targets ≥1 objects via target_object[].
@@ -2308,10 +2298,7 @@ def read_graphical_rendering_info(
             raw_alphas = getattr(info, "alpha", []) or []
             if raw_indices and raw_alphas:
                 try:
-                    result.alpha_control_points = [
-                        (float(idx), float(a))
-                        for idx, a in zip(raw_indices, raw_alphas)
-                    ]
+                    result.alpha_control_points = [(float(idx), float(a)) for idx, a in zip(raw_indices, raw_alphas)]
                 except (TypeError, ValueError) as exc:
                     logging.warning(f"read_graphical_rendering_info: cannot parse AlphaInformation indices: {exc}")
 
@@ -2323,7 +2310,7 @@ def read_graphical_rendering_info(
                 result.size_min_max = (mm.minimum, mm.maximum)
 
         elif "DefaultGraphicalInformation" in type_name:
-            for elem_info in (getattr(info, "indexable_element_info", []) or []):
+            for elem_info in getattr(info, "indexable_element_info", []) or []:
                 if (getattr(elem_info, "is_visible", None)) is False:
                     result.is_visible = False
                 const_col = getattr(elem_info, "constant_color", None)
@@ -2340,4 +2327,4 @@ def read_graphical_rendering_info(
         # AnnotationInformation is intentionally not mapped to ScalarRenderingInfo
         # because it drives label text, not colour/size - handle separately if needed.
 
-    return result if found else None
+    return result if found else None

energyml-utils/src/energyml/utils/data/mesh.py

@@ -1133,10 +1133,7 @@ def read_property_interpreted_with_cbt(
                 # empty while another is not).  Pad all columns with None up to
                 # the maximum column length so that np.array() can build a
                 # rectangular (n_columns, max_rows) matrix before transposing.
-                col_values = [
-                    list(v) if not isinstance(v, list) else v
-                    for v in category_lookup_data.values()
-                ]
+                col_values = [list(v) if not isinstance(v, list) else v for v in category_lookup_data.values()]
                 max_len = max((len(c) for c in col_values), default=0)
                 if max_len == 0:
                     # All columns empty — nothing to look up.

energyml-utils/src/energyml/utils/data/mesh_numpy.py

@@ -242,7 +242,7 @@ def flat_patches(self) -> List["NumpyMesh"]:
         for child in self.children:
             result.extend(child.flat_patches())
         return result
-    
+
     def flat_children(self) -> List["NumpyMultiMesh"]:
         """Return all child containers in depth-first order."""
         result: List[NumpyMultiMesh] = list(self.children)
@@ -639,10 +639,7 @@ def read_numpy_polyline_representation(
                 pt_offsets = np.concatenate([[0], np.cumsum(nc_arr)])
 
                 # Gather contiguous point ranges for the selected polylines.
-                keep_ranges = [
-                    np.arange(int(pt_offsets[i]), int(pt_offsets[i + 1]), dtype=np.int64)
-                    for i in _valid
-                ]
+                keep_ranges = [np.arange(int(pt_offsets[i]), int(pt_offsets[i + 1]), dtype=np.int64) for i in _valid]
                 keep_pts = np.concatenate(keep_ranges) if keep_ranges else np.empty(0, dtype=np.int64)
 
                 # Build a full remapping: old_pt_idx → new_pt_idx (-1 = not kept).
@@ -1363,9 +1360,7 @@ def read_numpy_seismic_wellbore_frame_representation(
         for patch in result.flat_patches():
             patch.extra_arrays["node_time_values"] = node_time_values
     except (IndexError, Exception) as exc:
-        logging.warning(
-            f"SeismicWellboreFrameRepresentation: could not read NodeTimeValues: {exc}"
-        )
+        logging.warning(f"SeismicWellboreFrameRepresentation: could not read NodeTimeValues: {exc}")
     result.source_type = type(energyml_object).__name__
     return result
 
@@ -1464,9 +1459,9 @@ def _build_split_pillar_map(
     pillar_map = np.zeros((nj, ni, 4), dtype=np.int64)
     for j in range(nj):
         for i in range(ni):
-            pillar_map[j, i, 0] = j * (ni + 1) + i           # TL
-            pillar_map[j, i, 1] = j * (ni + 1) + (i + 1)     # TR
-            pillar_map[j, i, 2] = (j + 1) * (ni + 1) + i     # BL
+            pillar_map[j, i, 0] = j * (ni + 1) + i  # TL
+            pillar_map[j, i, 1] = j * (ni + 1) + (i + 1)  # TR
+            pillar_map[j, i, 2] = (j + 1) * (ni + 1) + i  # BL
             pillar_map[j, i, 3] = (j + 1) * (ni + 1) + (i + 1)  # BR
 
     for split_idx in range(n_splits):
@@ -1483,13 +1478,13 @@ def _build_split_pillar_map(
                 continue
             # Identify which corner of this column corresponds to (orig_j, orig_i)
             if orig_j == col_j and orig_i == col_i:
-                pillar_map[col_j, col_i, 0] = new_pillar_idx   # TL
+                pillar_map[col_j, col_i, 0] = new_pillar_idx  # TL
             elif orig_j == col_j and orig_i == col_i + 1:
-                pillar_map[col_j, col_i, 1] = new_pillar_idx   # TR
+                pillar_map[col_j, col_i, 1] = new_pillar_idx  # TR
             elif orig_j == col_j + 1 and orig_i == col_i:
-                pillar_map[col_j, col_i, 2] = new_pillar_idx   # BL
+                pillar_map[col_j, col_i, 2] = new_pillar_idx  # BL
             elif orig_j == col_j + 1 and orig_i == col_i + 1:
-                pillar_map[col_j, col_i, 3] = new_pillar_idx   # BR
+                pillar_map[col_j, col_i, 3] = new_pillar_idx  # BR
 
     return pillar_map
 
@@ -1642,9 +1637,7 @@ def _read_point3d_parametric_array(
     # --- 4. Resolve ParametricLineArray ---
     pla_raw = getattr(pts_obj, "parametric_lines", None)
     if pla_raw is None:
-        raise ValueError(
-            "Point3dParametricArray.parametric_lines is required but absent."
-        )
+        raise ValueError("Point3dParametricArray.parametric_lines is required but absent.")
     pla = resolve_parametric_line_array(pla_raw, energyml_object, ws, n_pillars_total)
 
     # --- 5. Evaluate pillar splines ---
@@ -1755,13 +1748,16 @@ def read_numpy_ijk_grid_representation(
             if pi_list:
                 pi_path, pi_obj = pi_list[0]
                 pillar_indices_arr = _read_array_np(
-                    pi_obj, energyml_object,
-                    f"geometry.column_layer_split_coordinate_lines.{pi_path}", ws,
+                    pi_obj,
+                    energyml_object,
+                    f"geometry.column_layer_split_coordinate_lines.{pi_path}",
+                    ws,
                 )
             cps_obj = getattr(split_cl, "columns_per_split_coordinate_line", None)
             if cps_obj is not None:
                 columns_per_split = _decode_jagged_array(
-                    cps_obj, energyml_object,
+                    cps_obj,
+                    energyml_object,
                     "geometry.column_layer_split_coordinate_lines.columns_per_split_coordinate_line",
                     ws,
                 )
@@ -1835,9 +1831,9 @@ def read_numpy_ijk_grid_representation(
 
         kl_b = kl_bottom[ik_arr]  # (ni, nj, nk)
         kl_t = kl_top[ik_arr]
-        p_tl = ij_arr * (ni + 1) + ii_arr            # pillar TL
-        p_tr = ij_arr * (ni + 1) + (ii_arr + 1)     # pillar TR
-        p_bl = (ij_arr + 1) * (ni + 1) + ii_arr     # pillar BL
+        p_tl = ij_arr * (ni + 1) + ii_arr  # pillar TL
+        p_tr = ij_arr * (ni + 1) + (ii_arr + 1)  # pillar TR
+        p_bl = (ij_arr + 1) * (ni + 1) + ii_arr  # pillar BL
         p_br = (ij_arr + 1) * (ni + 1) + (ii_arr + 1)  # pillar BR
 
         def _nidx(kl, pl):
@@ -1979,8 +1975,7 @@ def read_numpy_unstructured_grid_representation(
     fpc_obj = getattr(geom, "faces_per_cell", None)
     if npf_obj is None or fpc_obj is None:
         logging.warning(
-            "UnstructuredGridRepresentation: missing nodes_per_face or faces_per_cell "
-            "— returning point-set mesh"
+            "UnstructuredGridRepresentation: missing nodes_per_face or faces_per_cell " "— returning point-set mesh"
         )
         label = f"{src_type}_patch_0"
         multi = NumpyMultiMesh(
@@ -2180,9 +2175,7 @@ def _import_pyvista() -> Any:
     try:
         import pyvista as pv  # type: ignore[import]
     except ImportError as exc:
-        raise ImportError(
-            "pyvista is not installed.  Install it with: pip install pyvista"
-        ) from exc
+        raise ImportError("pyvista is not installed.  Install it with: pip install pyvista") from exc
     # Enable strict n_faces mode: makes n_faces return n_faces_strict (polygon
     # count) instead of raising AttributeError in PyVista >= 0.46.
     if hasattr(pv.PolyData, "use_strict_n_faces"):

energyml-utils/src/energyml/utils/data/representation_context.py

@@ -20,7 +20,10 @@ def collect_graphical_info(obj: Any, workspace: EnergymlStorageInterface) -> dic
     obj_uuid = get_obj_uuid(obj)
     return collect_graphical_info_from_rels(rels, obj_uuid, workspace)
 
-def collect_graphical_info_from_rels(rels: List[Relationship], obj_uuid: str, workspace: EnergymlStorageInterface) -> dict:
+
+def collect_graphical_info_from_rels(
+    rels: List[Relationship], obj_uuid: str, workspace: EnergymlStorageInterface
+) -> dict:
     graphical_info_result = {}
     # Collect graphical information entries whose target matches this representation
     for r in rels:
@@ -44,7 +47,7 @@ def collect_graphical_info_from_rels(rels: List[Relationship], obj_uuid: str, wo
                             graphical_info_result[graphical_info_set_uri].append(graphical_info)
                             break
     return graphical_info_result
-                        
+
 
 class RepresentationContext(BaseModel):
 
@@ -116,7 +119,7 @@ def _collect_crs(self):
     def _collect_graphical_info(self, rels: List[Relationship]):
         # Collect graphical information entries whose target matches this representation
         self.graphical_info = collect_graphical_info_from_rels(rels, self.uri.uuid, self.workspace)
-                            
+
     def get_default_color(self) -> ScalarRenderingInfo:
         """Search for a default color (first found) for the representation, and return it as an RGBA tuple.  Returns a random color (generated from uuid) if no color information is found."""
         for gis_uri, entries in self.graphical_info.items():
@@ -128,7 +131,9 @@ def get_default_color(self) -> ScalarRenderingInfo:
                 except Exception as exc:
                     logging.debug(f"Error reading graphical rendering info for entry {entry}: {exc}")
         # No color information found, generate a random color from uuid
-        return ScalarRenderingInfo(target_obj_uuid=self.uri.uuid, constant_color=RgbaColor.random_from_uuid(self.uri.uuid))
+        return ScalarRenderingInfo(
+            target_obj_uuid=self.uri.uuid, constant_color=RgbaColor.random_from_uuid(self.uri.uuid)
+        )
 
     def get_property(self, property_uuid: str) -> Optional[Any]:
         """Return the property object with the given uuid, or None."""