Merge branch '471-typehints-for-pandas-and-scipy' into 'development'

Resolve "Typehints for pandas and scipy"

Closes #471

See merge request damask/DAMASK!1030

Author: MarDiehl

.gitlab-ci.yml

@@ -71,7 +71,7 @@ create_testroot:
 
 .python_default:
   extends: .python_base
-  variables: {PYTHON_IMAGE: 2025.01.05}
+  variables: {PYTHON_IMAGE: 2025.02.25}
 
 .python_min:
   extends: .python_base

PRIVATE

@@ -301,7 +301,7 @@ def at(self,
         return interp.interp1d(np.linspace(0,1,self.N),
                                self.colors,
                                axis=0,
-                               assume_sorted=True)(fraction)
+                               assume_sorted=True)(np.asarray(fraction))
 
 
     def shade(self,

python/damask/_colormap.py

@@ -564,8 +564,7 @@ def from_Voronoi_tessellation(cells: IntSequence,
 
         """
         coords = grid_filters.coordinates0_point(cells,size).reshape(-1,3)
-        tree = spatial.KDTree(seeds,boxsize=size) if periodic else \
-               spatial.KDTree(seeds)
+        tree = spatial.KDTree(seeds,boxsize=np.asarray(size) if periodic else None)
         material_ = tree.query(coords, workers = int(os.environ.get('OMP_NUM_THREADS',4)))[1]
 
         return GeomGrid(material = (material_ if material is None else np.array(material)[material_]).reshape(cells),

python/damask/_geomgrid.py

@@ -97,6 +97,9 @@ def __getitem__(self,
         1     3     4
         2     6     7
         3     9    10
+        >>> tbl[3]
+           colA  colB  colC
+        3     9    10    11
         >>> tbl[::2,['colB','colA']]
            colB  colA
         0     1     0
@@ -109,8 +112,9 @@ def __getitem__(self,
         """
         item_ = (item,slice(None,None,None)) if isinstance(item,(slice,np.ndarray)) else \
                 (np.array(item),slice(None,None,None)) if isinstance(item,list) and np.array(item).dtype == np.bool_ else \
+                (slice(item,item),slice(None,None,None)) if isinstance(item,int) else \
                 (np.array(item[0]),item[1]) if isinstance(item[0],list) else \
-                item if isinstance(item[0],(slice,np.ndarray)) else \
+                (item[0], item[1]) if isinstance(item[0],(slice,np.ndarray)) else \
                 (slice(None,None,None),item)
         sliced = self.data.loc[item_]
         cols = np.array(sliced.columns if isinstance(sliced,pd.core.frame.DataFrame) else [item_[1]])
@@ -423,7 +427,7 @@ def add_comment(label: str, shape: Tuple[int, ...],info: str) -> List[str]:
                 idx = np.ravel_multi_index(tuple(map(int,m.group(2).split(","))),
                                            self.shapes[key])
                 iloc = dup.data.columns.get_loc(key).tolist().index(True) + idx                     # type: ignore
-                dup.data.iloc[:,iloc] = data
+                dup.data.iloc[:,iloc] = data                                                        # type: ignore
             else:
                 dup.data[label]       = data.reshape(dup.data[label].shape)
 

python/damask/_table.py

@@ -12,7 +12,7 @@
 
 """
 
-from typing import Tuple as _Tuple
+from typing import Tuple as _Tuple, Union as _Union
 
 from scipy import spatial as _spatial
 import numpy as _np
@@ -665,7 +665,7 @@ def unravel(d_raveled: _np.ndarray,
 
 def regrid(size: _FloatSequence,
            F: _np.ndarray,
-           cells: _IntSequence) -> _np.ndarray:
+           cells: _IntSequence) -> _Union[_np.intp, _np.ndarray]:
     """
     Map a deformed grid A back to a rectilinear grid B.
 

python/damask/grid_filters.py

@@ -37,13 +37,13 @@ def from_random(size: _FloatSequence,
         Seed coordinates in 3D space.
 
     """
-    size_ = _np.array(size,float)
+    size_ = _np.asarray(size,float)
     rng = _np.random.default_rng(rng_seed)
     if cells is None:
         coords = rng.random((N_seeds,3)) * size_
     else:
         grid_coords = _grid_filters.coordinates0_point(cells,size).reshape(-1,3,order='F')
-        coords = grid_coords[rng.choice(_np.prod(cells),N_seeds, replace=False)] \
+        coords = grid_coords[rng.choice(_np.prod(cells),N_seeds,replace=False)] \
                + _np.broadcast_to(size_/_np.array(cells,_np.int64),(N_seeds,3))*(rng.random((N_seeds,3))*.5-.25) # wobble w/o leaving grid
 
     return coords
@@ -81,24 +81,23 @@ def from_Poisson_disc(size: _FloatSequence,
         Seed coordinates in 3D space.
 
     """
+    size_ = _np.asarray(size,float)
     rng = _np.random.default_rng(rng_seed)
     coords = _np.empty((N_seeds,3))
-    coords[0] = rng.random(3) * _np.array(size,float)
 
-    s = 1
+    s = 0
     i = 0
-    progress = _util.ProgressBar(N_seeds+1,'',50)
+    progress = _util.ProgressBar(N_seeds,'',50)
     while s < N_seeds:
         i += 1
-        candidates = rng.random((N_candidates,3))*_np.broadcast_to(size,(N_candidates,3))
-        tree = _spatial.cKDTree(coords[:s],boxsize=size) if periodic else \
-               _spatial.cKDTree(coords[:s])
-        distances = tree.query(candidates)[0]
+        candidates = rng.random((N_candidates,3))*_np.broadcast_to(size_,(N_candidates,3))
+        tree = _spatial.cKDTree(coords[:s],boxsize=size_ if periodic else None)
+        distances = _np.asarray(tree.query(candidates)[0])
         if distances.max() > distance:                                                              # require minimum separation
-            i = 0
             coords[s] = candidates[distances.argmax()]                                              # maximum separation to existing point cloud
-            s += 1
             progress.update(s)
+            s += 1
+            i = 0
 
         if i >= 100:
             raise ValueError('seeding not possible')

python/damask/seeds.py

@@ -2,8 +2,6 @@
 warn_redundant_casts = True
 plugins = numpy.typing.mypy_plugin
 
-[mypy-scipy.*]
-ignore_missing_imports = True
 [mypy-h5py.*]
 ignore_missing_imports = True
 [mypy-vtkmodules.*]
@@ -12,7 +10,5 @@ ignore_missing_imports = True
 ignore_missing_imports = True
 [mypy-matplotlib.*]
 ignore_missing_imports = True
-[mypy-pandas.*]
-ignore_missing_imports = True
 [mypy-wx.*]
 ignore_missing_imports = True

python/mypy.ini

@@ -84,6 +84,8 @@ def test_allclose(self,default):
     @pytest.mark.parametrize('N',[1,3,4])
     def test_slice(self,np_rng,default,N):
         mask = np_rng.choice([True,False],len(default))
+        assert len(default[N]) == 1
+        assert len(default[1:N]) == N
         assert len(default[:N]) == 1+N
         assert len(default[:N,['F','s']]) == 1+N
         assert len(default[mask,['F','s']]) == np.count_nonzero(mask)