Merge branch 'housekeeping_2025-01-14' into 'development'

housekeeping_ 2025-01-14

See merge request damask/DAMASK!1027

Author: eisenlohr

.gitlab-ci.yml

@@ -81,11 +81,7 @@ pytest:
     - coverage report --fail-under=90 --show-missing
 
 doctest:
-  stage: python
-  tags:
-    - matesting2-container-runner
-  image: git.damask-multiphysics.org:5050/damask/damask:python310_2407
-  before_script: []
+  extends: .python
   script:
     - export DAMASK_ROOT=$(pwd)
     - PYTHONPATH=${DAMASK_ROOT}/python:$PYTHONPATH
@@ -313,7 +309,7 @@ grid_performance:
   tags:
     - bare-metal
   before_script:
-    - ${LOCAL_HOME}/bin/queue2 ${CI_JOB_ID} ${TOKEN} --blocking
+    - ${LOCAL_HOME}/bin/queue ${CI_JOB_ID} ${TOKEN} --blocking
     - source env/DAMASK.sh
     - echo Job start:" $(date)"
   script:
@@ -355,7 +351,7 @@ update_revision:
   tags:
     - bare-metal
   before_script:
-    - ${LOCAL_HOME}/bin/queue2 ${CI_JOB_ID} ${TOKEN}
+    - ${LOCAL_HOME}/bin/queue ${CI_JOB_ID} ${TOKEN}
     - echo Job start:" $(date)"
   script:
     - cd $(mktemp -d)

PRIVATE

@@ -208,7 +208,6 @@ def from_range(low: FloatSequence,
             raise ValueError(f'invalid color model "{model}"')
 
         low_high = np.vstack((low,high)).astype(float)
-        out_of_bounds = np.bool_(False)
 
         if   model.lower() == 'rgb':
             out_of_bounds = np.any(low_high<0) or np.any(low_high>1)
@@ -218,6 +217,8 @@ def from_range(low: FloatSequence,
             out_of_bounds = np.any(low_high<0) or np.any(low_high>[360,1,1])
         elif model.lower() == 'lab':
             out_of_bounds = np.any(low_high[:,0]<0)
+        else:
+            out_of_bounds = np.bool_(False)
 
         if out_of_bounds:
             raise ValueError(f'{model.upper()} colors {low_high[0]} | {low_high[1]} are out of bounds')

python/damask/_colormap.py

@@ -1,7 +1,9 @@
 import copy
+import warnings
 from typing import Optional, Union, TypeVar, Literal, Sequence, NamedTuple # mypy 1.11, overload
 
 import numpy as np
+import numpy.typing as npt
 
 from ._typehints import FloatSequence, IntSequence, CrystalFamily, BravaisLattice, NumpyRngSeed
 from . import Rotation
@@ -156,9 +158,8 @@ def __copy__(self: MyType,
     copy = __copy__
 
 
-
-    def __eq__(self,
-               other: object) -> bool:
+    def __eq__(self,                                                                                # type: ignore[override]
+               other: object) -> npt.NDArray[np.bool_]:
         """
         Return self==other.
 
@@ -177,8 +178,8 @@ def __eq__(self,
                         self.parameters == other.parameters
         return np.logical_and(matching_type,super(self.__class__,self.reduced).__eq__(other.reduced))
 
-    def __ne__(self,
-               other: object) -> bool:
+    def __ne__(self,                                                                                # type: ignore[override]
+               other: object) -> npt.NDArray[np.bool_]:
         """
         Return self!=other.
 
@@ -190,14 +191,14 @@ def __ne__(self,
             Orientation to check for equality.
 
         """
-        return np.logical_not(self==other) if isinstance(other, Orientation) else NotImplemented
+        return np.logical_not(self==other)
 
 
     def isclose(self: MyType,
                 other: MyType,
                 rtol: float = 1e-5,
                 atol: float = 1e-8,
-                equal_nan: bool = True) -> bool:
+                equal_nan: bool = True) -> np.ndarray:
         """
         Report where values are approximately equal to corresponding ones of other Orientation.
 
@@ -223,13 +224,11 @@ def isclose(self: MyType,
                         self.parameters == other.parameters
         return np.logical_and(matching_type,super(self.__class__,self.reduced).isclose(other.reduced))
 
-
-
     def allclose(self: MyType,
                  other: MyType,
                  rtol: float = 1e-5,
                  atol: float = 1e-8,
-                 equal_nan: bool = True) -> bool:
+                 equal_nan: bool = True) -> np.bool_:
         """
         Test whether all values are approximately equal to corresponding ones of other Orientation.
 
@@ -250,7 +249,7 @@ def allclose(self: MyType,
             Whether all values are close between both orientations.
 
         """
-        return bool(np.all(self.isclose(other,rtol,atol,equal_nan)))
+        return np.all(self.isclose(other,rtol,atol,equal_nan))
 
 
     def __mul__(self: MyType,
@@ -1359,15 +1358,16 @@ def average(self: MyType,
     # mypy 1.11
     #@overload
     #def to_SST(self, vector: FloatSequence, proper: bool = False,                                   # noqa
-    #           return_operators: Literal[False] = False) -> np.ndarray: ...
+    #           return_operator: Literal[False] = False) -> np.ndarray: ...
     #@overload
     #def to_SST(self, vector: FloatSequence, proper: bool = False,                                   # noqa
-    #           return_operators: Literal[True] = True) -> Tuple[np.ndarray,np.ndarray]: ...
+    #           return_operator: Literal[True] = True) -> Tuple[np.ndarray,np.ndarray]: ...
     def to_SST(self,
                vector: FloatSequence,
                proper: bool = False,
+               return_operator: bool = False,
     #           return_operators: bool = False) -> Union[np.ndarray,Tuple[np.ndarray,np.ndarray]]:
-               return_operator: bool = False) -> Union[np.ndarray, ToSSTTuple]:
+               return_operators: bool = False) -> Union[np.ndarray, ToSSTTuple]:
         """
         Rotate lab frame vector to ensure it falls into (improper or proper) standard stereographic triangle of crystal symmetry.
 
@@ -1392,6 +1392,8 @@ def to_SST(self,
             Index of the symmetrically equivalent orientation that rotated vector to SST.
 
         """
+        if return_operators:
+            warnings.warn('"return_operators" is deprecated, use "return_operator".',DeprecationWarning,stacklevel=2)
         vector_ = np.array(vector,float)
         if vector_.shape[-1] != 3:
             raise ValueError('input is not a field of three-dimensional vectors')
@@ -1520,13 +1522,13 @@ def IPF_color(self,
             components_improper = np.around(np.einsum('...ji,...i',
                                                       np.broadcast_to(self.standard_triangle['improper'], vector_.shape+(3,)),
                                                       vector_), 12)
-            in_SST_ = np.all(components_proper   >= 0.0,axis=-1) \
-                    | np.all(components_improper >= 0.0,axis=-1)
-            components = np.where((in_SST_ & np.all(components_proper   >= 0.0,axis=-1))[...,np.newaxis],
+            in_SST_ = np.logical_or(np.all(components_proper   >= 0.0,axis=-1),
+                                    np.all(components_improper >= 0.0,axis=-1))
+            components = np.where((np.logical_and(in_SST_,np.all(components_proper >= 0.0,axis=-1)))[...,np.newaxis],
                                   components_proper,components_improper)
         else:
             components = np.around(np.einsum('...ji,...i',
-                                             np.broadcast_to(self .standard_triangle['improper'], vector_.shape+(3,)),
+                                             np.broadcast_to(self.standard_triangle['improper'], vector_.shape+(3,)),
                                              np.block([vector_[...,:2],np.abs(vector_[...,2:3])])), 12)
 
             in_SST_ = np.all(components >= 0.0,axis=-1)

python/damask/_orientation.py

@@ -5,6 +5,7 @@
 from typing import Optional, Union, Sequence, Tuple, Literal, List, TypeVar, NamedTuple
 
 import numpy as np
+import numpy.typing as npt
 
 from ._typehints import FloatSequence, IntSequence, NumpyRngSeed
 from . import tensor
@@ -144,8 +145,8 @@ def __getitem__(self: MyType,
         return self.copy(np.stack([c[i][item] for i in range(4)],axis=-1))
 
 
-    def __eq__(self,
-               other: object) -> bool:
+    def __eq__(self,                                                                                # type: ignore[override]
+               other: object) -> npt.NDArray[np.bool_]:
         """
         Return self==other.
 
@@ -161,9 +162,8 @@ def __eq__(self,
                np.logical_or(np.all(self.quaternion ==     other.quaternion,axis=-1),
                              np.all(self.quaternion == -1.*other.quaternion,axis=-1))
 
-
-    def __ne__(self,
-               other: object) -> bool:
+    def __ne__(self,                                                                                # type: ignore[override]
+               other: object) -> npt.NDArray[np.bool_]:
         """
         Return self!=other.
 
@@ -175,13 +175,14 @@ def __ne__(self,
             Rotation to check for inequality.
 
         """
-        return np.logical_not(self==other) if isinstance(other, Rotation) else NotImplemented
+        return np.logical_not(self==other)
+
 
     def isclose(self: MyType,
                 other: MyType,
                 rtol: float = 1.e-5,
                 atol: float = 1.e-8,
-                equal_nan: bool = True) -> bool:
+                equal_nan: bool = True) -> npt.NDArray[np.bool_]:
         """
         Report where values are approximately equal to corresponding ones of other Rotation.
 
@@ -207,12 +208,11 @@ def isclose(self: MyType,
         return np.logical_or(np.all(np.isclose(s,    o,rtol,atol,equal_nan),axis=-1),
                              np.all(np.isclose(s,-1.*o,rtol,atol,equal_nan),axis=-1))
 
-
     def allclose(self: MyType,
                  other: MyType,
                  rtol: float = 1.e-5,
                  atol: float = 1.e-8,
-                 equal_nan: bool = True) -> Union[np.bool_, bool]:
+                 equal_nan: bool = True) -> np.bool_:
         """
         Test whether all values are approximately equal to corresponding ones of other Rotation.
 

python/damask/_rotation.py

@@ -13,7 +13,7 @@ class Table:
 
     def __init__(self,
                  shapes: Mapping[str,Union[Union[int,np.integer],Tuple[Union[int,np.integer],...]]] = {},
-                 data: Optional[np.ndarray] = None,
+                 data: Optional[Union[np.ndarray,pd.DataFrame]] = None,
                  comments: Union[None, str, Iterable[str]] = None):
         """
         New spreadsheet.
@@ -421,7 +421,7 @@ def add_comment(label: str, shape: Tuple[int, ...],info: str) -> List[str]:
             if m:
                 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
+                iloc = dup.data.columns.get_loc(key).tolist().index(True) + idx                     # type: ignore
                 dup.data.iloc[:,iloc] = data
             else:
                 dup.data[label]       = data.reshape(dup.data[label].shape)
@@ -598,15 +598,15 @@ def save(self,
                 if self.shapes[l] == (1,):
                     labels.append(f'{l}')
                 elif len(self.shapes[l]) == 1:
-                    labels += [f'{i+1}_{l}' \
-                              for i in range(self.shapes[l][0])]
+                    labels += [f'{i+1}_{l}'
+                               for i in range(self.shapes[l][0])]
                 else:
-                    labels += [f'{util.srepr(self.shapes[l],"x")}:{i+1}_{l}' \
-                            for i in range(np.prod(self.shapes[l],dtype=np.int64))]                 # type: ignore
+                    labels += [f'{util.srepr(self.shapes[l],"x")}:{i+1}_{l}'
+                               for i in range(np.prod(self.shapes[l],dtype=np.int64))]                 # type: ignore
 
         with util.open_text(fname,'w') as f:
             f.write('\n'.join([f'# {c}' for c in self.comments] + [' '.join(labels)])+('\n' if labels else ''))
-            try:                                                                                    # backward compatibility
+            try:                                                                                       # backward compatibility (pandas<1.5)
                 self.data.to_csv(f,sep=' ',na_rep='nan',index=False,header=False,lineterminator='\n')
             except TypeError:
-                self.data.to_csv(f,sep=' ',na_rep='nan',index=False,header=False,line_terminator='\n')
+                self.data.to_csv(f,sep=' ',na_rep='nan',index=False,header=False,line_terminator='\n') # type: ignore

python/damask/_table.py

@@ -459,7 +459,7 @@ def hybrid_IA(dist: _FloatSequence,
 
     """
     N_opt_samples = max(_np.count_nonzero(dist),N)                                                  # random subsampling if too little samples requested
-    N_inv_samples = 0
+    N_inv_samples = _np.int_(0)
 
     scale_,scale,inc_factor = (0.0,float(N_opt_samples),1.0)
     while (not _np.isclose(scale, scale_)) and (N_inv_samples != N_opt_samples):

python/damask/util.py

@@ -36,7 +36,7 @@ dependencies = [
     'pyyaml>=5.3',
 ]
 
-readme = 'Readme.md'
+readme = 'README.md'
 dynamic = ['version']
 
 [project.urls]

python/pyproject.toml

@@ -187,7 +187,7 @@ def test_clean_reference(self,default,update,res_path,distance,selection,periodi
     @pytest.mark.parametrize('data_type',['array','list'])
     @pytest.mark.parametrize('invert',[True,False])
     def test_clean_invert(self,np_rng,default,data_type,invert):
-        selection = np_rng.integers(1,20,6) if data_type is 'array' else list(np_rng.integers(1,20,6))
+        selection = np_rng.integers(1,20,6) if data_type == 'array' else list(np_rng.integers(1,20,6))
         selection_inverse = np.setdiff1d(default.material,selection)
         assert default.clean(selection=selection,invert_selection=invert,rng_seed=0) == \
                default.clean(selection=selection_inverse,invert_selection=not invert,rng_seed=0)
@@ -362,7 +362,7 @@ def test_vicinity_offset(self,np_rng,selection):
     @pytest.mark.parametrize('data_type',['array','list'])
     @pytest.mark.parametrize('invert',[True,False])
     def test_vicinity_offset_invert(self,np_rng,random,data_type,invert):
-        selection = np_rng.integers(1,20,6) if data_type is 'array' else list(np_rng.integers(1,20,6))
+        selection = np_rng.integers(1,20,6) if data_type == 'array' else list(np_rng.integers(1,20,6))
         selection_inverse = np.setdiff1d(random.material,selection)
         assert random.vicinity_offset(selection=selection        ,invert_selection=not invert) == \
                random.vicinity_offset(selection=selection_inverse,invert_selection=    invert)

python/tests/test_GeomGrid.py

@@ -427,7 +427,8 @@ def test_Schmid_twin_direction(self,c_a,mode):
     @pytest.mark.parametrize('lattice',['hP','cI','cF','tI'])
     def test_Schmid_vectorization(self,np_rng,assert_allclose,lattice):
         shape = np_rng.integers(1,4,np_rng.integers(1,4))
-        O = Orientation.from_random(shape=shape,lattice=lattice,c=(1.2 if lattice == 'tI' else None),rng_seed=np_rng)  # noqa
+        O = Orientation.from_random(shape=shape,lattice=lattice,
+                                    c=(1.2 if lattice == 'tI' else None),rng_seed=np_rng)           # noqa
         for mode in ['slip']+([] if lattice == 'tI' else ['twin']):
             Ps = O.Schmid(N_slip='*') if mode == 'slip' else O.Schmid(N_twin='*')
             for i in itertools.product(*map(range,tuple(shape))):
@@ -437,11 +438,11 @@ def test_Schmid_vectorization(self,np_rng,assert_allclose,lattice):
                 #assert_allclose(P,Ps[:,*i,:,:])                                                    # ok for Python >= 3.13
 
     @pytest.mark.parametrize('family',crystal_families)
-    @pytest.mark.parametrize('shape',[(1),(2,3),(4,3,2)])
+    @pytest.mark.parametrize('shape',[(1,),(2,3),(4,3,2)])
     def test_reduced_vectorization(self,np_rng,family,shape):
         o = Orientation.from_random(family=family,shape=shape,rng_seed=np_rng)
-        for r, theO in zip(o.reduced.flatten(),o.flatten()):
-            assert r == theO.reduced
+        for i in itertools.product(*map(range,tuple(shape))):
+            assert o[i].reduced == o[i]
 
 
     @pytest.mark.parametrize('family',crystal_families)

python/tests/test_Orientation.py

@@ -49,21 +49,32 @@ def test_empty_init(self):
             t = t.set(label,data)
         assert np.allclose(t.get('scal').flatten()*3,t.get('vctr')[:,0])
 
-    def test_set(self,default):
+    def test_set_tensor(self,default):
         d = default.set('F',np.zeros((5,3,3)),'set to zero').get('F')
         assert np.allclose(d,0.0) and d.shape[1:] == (3,3)
 
+    def test_set_scalar(self,np_rng,default):
+        d = np_rng.random((5,9))
+        assert np.allclose(d,
+                           default
+                           .set('nine',d,'random data')
+                           .get('nine'))
+
+    def test_set_overwrite(self,np_rng,default):
+        d = np_rng.random((5,9))
+        assert np.allclose(d,
+                           default
+                           .set('nine',np.ones((5,9)),'zeros')
+                           .set('nine',d,'random data')
+                           .get('nine'))
+
     def test_set_component(self,default):
         d = default.set('F[0,0]',np.zeros((5)),'set to zero').get('F')
         assert np.allclose(d[...,0,0],0.0) and d.shape[1:] == (3,3)
 
     def test_labels(self,default):
         assert default.labels == ['F','v','s']
 
-    def test_add(self,np_rng,default):
-        d = np_rng.random((5,9))
-        assert np.allclose(d,default.set('nine',d,'random data').get('nine'))
-
     def test_isclose(self,default):
         assert default.isclose(default).all()