Update material/grid and results handling

Author: YousefRezek

python/damask/_configmaterial.py

@@ -156,10 +156,10 @@ def load_DREAM3D(fname: str,
 
 
     @staticmethod
-    def load_kanapy(src: Union[str, Path, Mapping[str, Any]], outputs: list) -> 'ConfigMaterial':
+    def load_MiMedat(src: Union[str, Path, Mapping[str, Any]], outputs: list) -> 'ConfigMaterial':
 
         """
-        Build a DAMASK ConfigMaterial from a Kanapy-style JSON/dict.
+        Build a DAMASK ConfigMaterial from a JSON file path following the MiMedat schema.
 
         This function reads phase-level information from a Kanapy JSON file
         (or a pre-loaded dict) that follows the microstructure-sensitive
@@ -281,7 +281,7 @@ def load_kanapy(src: Union[str, Path, Mapping[str, Any]], outputs: list) -> 'Con
         all_phase_labels = []
         for phase in phases:
             phase_id = phase.get("phase_id")
-            phase_name = phase.get("phase_identifier")
+            phase_name = phase.get("phase_name")
             phase_key = str(phase_name if phase_name is not None else phase_id)
 
             lattice_str = phase.get("lattice_structure")

python/damask/_geomgrid.py

@@ -399,9 +399,9 @@ def load_DREAM3D(fname: Union[str, Path],
                        )
 
     @staticmethod
-    def load_kanapy(src: Union[str, Path, Mapping[str, Any]],keys: Dict[str, str] = None) -> 'GeomGrid':
+    def load_MiMedat(src: Union[str, Path, Mapping[str, Any]],keys: Dict[str, str] = None) -> 'GeomGrid':
         """
-        Read geometry from a Kanapy JSON **file path**, a **JSON string**, or a **dict**,
+        Read geometry from a JSON file path according to MiMedat schema, a JSON string, or a dict,
         normalize lengths to meters (m), and return a GeomGrid with:
           - material: int64 (Nx, Ny, Nz), Fortran-order (grain labels, 0..G-1; zeros if absent)
           - size: axis lengths in m (float[3])
@@ -465,7 +465,12 @@ def _length_unit_to_m_factor(u: str) -> float:
 
         size_m = np.asarray(data[keys["size"]], float) * f_m  # (3,)
         spacing_m = np.asarray(data[keys["spacing"]], float) * f_m  # (3,)
-        origin_m = np.asarray(data.get(keys["origin"], [0.0, 0.0, 0.0]), float) * f_m
+        origin_raw = data.get(keys["origin"], [0.0, 0.0, 0.0])
+        # If origin is not a 3-vector (e.g., your provenance dict), fall back.
+        if not (isinstance(origin_raw, (list, tuple, np.ndarray)) and len(origin_raw) == 3):
+            origin_raw = [0.0, 0.0, 0.0]
+
+        origin_m = np.asarray(origin_raw, dtype=float) * f_m
         N_json = int(data[keys["count"]])
 
         if size_m.shape != (3,) or spacing_m.shape != (3,) or origin_m.shape != (3,):

python/damask/_result.py

@@ -2168,7 +2168,7 @@ def create_and_open(obj,name):
                     for name,value in zip(names,values):
                         add_attribute(geom,name,value)
 
-    def export_kanapy(
+    def export_MiMedat(
         self,
         json_path: Union[str, Path],
         quantities: Sequence[str] = ("O", "F"),
@@ -2177,7 +2177,7 @@ def export_kanapy(
     ) -> Path:
         """
         Export selected voxel-resolved DAMASK results from this Result (HDF5) into a
-        Kanapy-style JSON microstructure time series.
+        MiMedat-style JSON microstructure time series.
 
         The JSON file must contain exactly one microstructure snapshot (the initial,
         undeformed state). This function appends one new snapshot per DAMASK increment
@@ -2289,7 +2289,7 @@ def _msg(s: str) -> None:
 
         grid_status = str(grid_meta.get("status", "unknown")).lower()
         _msg(f"[INFO] Initial grid status: '{grid_status}'")
-        if grid_status != "regular":
+        if grid_status != "undeformed":
             raise ValueError(
                 f"Expected initial grid status 'regular' (undeformed), got '{grid_status}'.")