Merge remote-tracking branch 'origin/development' into spring-cleaning

VERSION

@@ -1 +1 @@
-v3.0.0-alpha2-670-ge6143f6ee
+v3.0.0-alpha2-673-g0c08c9753

python/damask/_table.py

@@ -27,20 +27,69 @@ class Table:
         self.comments = [] if comments_ is None else [c for c in comments_]
         self.data = pd.DataFrame(data=data)
         self.shapes = { k:(v,) if isinstance(v,(np.int64,np.int32,int)) else v for k,v in shapes.items() }
-        self._label_uniform()
+        self._relabel('uniform')
+
 
     def __repr__(self):
         """Brief overview."""
-        return '\n'.join(['# '+c for c in self.comments])+'\n'+self.data.__repr__()
+        self._relabel('shapes')
+        data_repr = self.data.__repr__()
+        self._relabel('uniform')
+        return '\n'.join(['# '+c for c in self.comments])+'\n'+data_repr
+
 
     def __getitem__(self,item):
-        """Return slice according to item."""
+        """
-        return self.__class__(data=self.data[item],shapes=self.shapes,comments=self.comments)
+        Slice the Table according to item.
+
+        Parameters
+        ----------
+        item : row and/or column indexer
+            Slice to select from Table.
+
+        Returns
+        -------
+        slice : Table
+            Sliced part of the Table.
+
+        Examples
+        --------
+        >>> import damask
+        >>> import numpy as np
+        >>> tbl = damask.Table(data=np.arange(12).reshape((4,3)),
+        ...                    shapes=dict(colA=(1,),colB=(1,),colC=(1,)))
+        >>> tbl['colA','colB']
+           colA  colB
+        0     0     1
+        1     3     4
+        2     6     7
+        3     9    10
+        >>> tbl[::2,['colB','colA']]
+           colB  colA
+        0     1     0
+        2     7     6
+        >>> tbl[1:2,'colB']
+           colB
+        1     4
+        2     7
+
+        """
+        item = (item,slice(None,None,None)) if isinstance(item,slice) else \
+               item if isinstance(item[0],slice) 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]])
+        _,idx = np.unique(cols,return_index=True)
+        return self.__class__(data=sliced,
+                              shapes = {k:self.shapes[k] for k in cols[np.sort(idx)]},
+                              comments=self.comments)
+
 
     def __len__(self):
         """Number of rows."""
         return len(self.data)
 
+
     def __copy__(self):
         """Create deep copy."""
         return copy.deepcopy(self)
@@ -48,21 +97,51 @@ class Table:
     copy = __copy__
 
 
-    def _label_discrete(self):
+    def _label(self,what,how):
-        """Label data individually, e.g. v v v ==> 1_v 2_v 3_v."""
+        """
+        Expand labels according to data shape.
+
+        Parameters
+        ----------
+        what : str or list
+            Labels to expand.
+        how : str
+            Mode of labeling.
+            'uniform' ==> v v v
+            'shapes'  ==> 3:v v v
+            'linear'  ==> 1_v 2_v 3_v
+
+        """
+        what = [what] if isinstance(what,str) else what
         labels = []
-        for label,shape in self.shapes.items():
+        for label in what:
-            size = int(np.prod(shape))
+            shape = self.shapes[label]
+            size = np.prod(shape,dtype=int)
+            if   how == 'uniform':
+                labels += [label] * size
+            elif how == 'shapes':
+                labels += [('' if size == 1 or i>0 else f'{util.srepr(shape,"x")}:')+label for i in range(size)]
+            elif how == 'linear':
                 labels += [('' if size == 1 else f'{i+1}_')+label for i in range(size)]
-        self.data.columns = labels
+            else:
+                raise KeyError
+        return labels
 
 
-    def _label_uniform(self):
+    def _relabel(self,how):
-        """Label data uniformly, e.g. 1_v 2_v 3_v ==> v v v."""
+        """
-        labels = []
+        Modify labeling of data in-place.
-        for label,shape in self.shapes.items():
+
-            labels += [label] * int(np.prod(shape))
+        Parameters
-        self.data.columns = labels
+        ----------
+        how : str
+            Mode of labeling.
+            'uniform' ==> v v v
+            'shapes'  ==> 3:v v v
+            'linear'  ==> 1_v 2_v 3_v
+
+        """
+        self.data.columns = self._label(self.shapes,how)
 
 
     def _add_comment(self,label,shape,info):
@@ -72,6 +151,62 @@ class Table:
             self.comments.append(f'{specific} / {general}')
 
 
+    def isclose(self,other,rtol=1e-5,atol=1e-8,equal_nan=True):
+        """
+        Report where values are approximately equal to corresponding ones of other Table.
+
+        Parameters
+        ----------
+        other : Table
+            Table to compare against.
+        rtol : float, optional
+            Relative tolerance of equality.
+        atol : float, optional
+            Absolute tolerance of equality.
+        equal_nan : bool, optional
+            Consider matching NaN values as equal. Defaults to True.
+
+        Returns
+        -------
+        mask : numpy.ndarray bool
+            Mask indicating where corresponding table values are close.
+
+        """
+        return np.isclose( self.data.to_numpy(),
+                          other.data.to_numpy(),
+                          rtol=rtol,
+                          atol=atol,
+                          equal_nan=equal_nan)
+
+
+    def allclose(self,other,rtol=1e-5,atol=1e-8,equal_nan=True):
+        """
+        Test whether all values are approximately equal to corresponding ones of other Table.
+
+        Parameters
+        ----------
+        other : Table
+            Table to compare against.
+        rtol : float, optional
+            Relative tolerance of equality.
+        atol : float, optional
+            Absolute tolerance of equality.
+        equal_nan : bool, optional
+            Consider matching NaN values as equal. Defaults to True.
+
+        Returns
+        -------
+        answer : bool
+            Whether corresponding values are close between both tables.
+
+        """
+        return np.allclose( self.data.to_numpy(),
+                           other.data.to_numpy(),
+                           rtol=rtol,
+                           atol=atol,
+                           equal_nan=equal_nan)
+
+
     @staticmethod
     def load(fname):
         """
@@ -120,12 +255,13 @@ class Table:
 
         return Table(data,shapes,comments)
 
+
     @staticmethod
     def load_ang(fname):
         """
         Load from ang file.
 
-        A valid TSL ang file needs to contains the following columns:
+        A valid TSL ang file has to have the following columns:
 
         - Euler angles (Bunge notation) in radians, 3 floats, label 'eu'.
         - Spatial position in meters, 2 floats, label 'pos'.
@@ -216,10 +352,12 @@ class Table:
         """
         dup = self.copy()
         dup._add_comment(label,data.shape[1:],info)
-
+        m = re.match(r'(.*)\[((\d+,)*(\d+))\]',label)
-        if re.match(r'[0-9]*?_',label):
+        if m:
-            idx,key = label.split('_',1)
+            key = m.group(1)
-            iloc = dup.data.columns.get_loc(key).tolist().index(True) + int(idx) -1
+            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
             dup.data.iloc[:,iloc] = data
         else:
             dup.data[label]       = data.reshape(dup.data[label].shape)
@@ -322,10 +460,18 @@ class Table:
             Updated table.
 
         """
+        labels_ = [labels] if isinstance(labels,str) else labels.copy()
+        for i,l in enumerate(labels_):
+            m = re.match(r'(.*)\[((\d+,)*(\d+))\]',l)
+            if m:
+                idx = np.ravel_multi_index(tuple(map(int,m.group(2).split(','))),
+                                           self.shapes[m.group(1)])
+                labels_[i] = f'{1+idx}_{m.group(1)}'
+
         dup = self.copy()
-        dup._label_discrete()
+        dup._relabel('linear')
-        dup.data.sort_values(labels,axis=0,inplace=True,ascending=ascending)
+        dup.data.sort_values(labels_,axis=0,inplace=True,ascending=ascending)
-        dup._label_uniform()
+        dup._relabel('uniform')
         dup.comments.append(f'sorted {"ascending" if ascending else "descending"} by {labels}')
         return dup
 

python/tests/test_ConfigMaterial.py

@@ -86,9 +86,12 @@ class TestConfigMaterial:
 
     def test_from_table(self):
         N = np.random.randint(3,10)
-        a = np.vstack((np.hstack((np.arange(N),np.arange(N)[::-1])),np.ones(N*2),np.zeros(N*2),np.ones(N*2),np.ones(N*2))).T
+        a = np.vstack((np.hstack((np.arange(N),np.arange(N)[::-1])),
-        t = Table(a,{'varying':1,'constant':4})
+                       np.ones(N*2),np.zeros(N*2),np.ones(N*2),np.ones(N*2),
-        c = ConfigMaterial.from_table(t,**{'phase':'varying','O':'constant','homogenization':'4_constant'})
+                       np.ones(N*2),
+                      )).T
+        t = Table(a,{'varying':1,'constant':4,'ones':1})
+        c = ConfigMaterial.from_table(t,**{'phase':'varying','O':'constant','homogenization':'ones'})
         assert len(c['material']) == N
         for i,m in enumerate(c['material']):
             assert m['homogenization'] == 1 and (m['constituents'][0]['O'] == [1,0,1,1]).all()

python/tests/test_Grid.py

@@ -407,7 +407,8 @@ class TestGrid:
         z=np.ones(cells.prod())
         z[cells[:2].prod()*int(cells[2]/2):]=0
         t = Table(np.column_stack((coords,z)),{'coords':3,'z':1})
-        g = Grid.from_table(t,'coords',['1_coords','z'])
+        t = t.add('indicator',t.get('coords')[:,0])
+        g = Grid.from_table(t,'coords',['indicator','z'])
         assert g.N_materials == g.cells[0]*2 and (g.material[:,:,-1]-g.material[:,:,0] == cells[0]).all()
 
 

python/tests/test_Table.py

@@ -36,13 +36,33 @@ class TestTable:
         d = default.get('F')
         assert np.allclose(d,1.0) and d.shape[1:] == (3,3)
 
-    def test_get_component(self,default):
+    def test_set(self,default):
-        d = default.get('5_F')
+        d = default.set('F',np.zeros((5,3,3)),'set to zero').get('F')
-        assert np.allclose(d,1.0) and d.shape[1:] == (1,)
+        assert np.allclose(d,0.0) and d.shape[1:] == (3,3)
 
-    @pytest.mark.parametrize('N',[10,40])
+    def test_set_component(self,default):
-    def test_getitem(self,N):
+        d = default.set('F[0,0]',np.zeros((5)),'set to zero').get('F')
-        assert len(Table(np.random.rand(N,1),{'X':1})[:N//2]) == N//2
+        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,default):
+        d = np.random.random((5,9))
+        assert np.allclose(d,default.add('nine',d,'random data').get('nine'))
+
+    def test_isclose(self,default):
+        assert default.isclose(default).all()
+
+    def test_allclose(self,default):
+        assert default.allclose(default)
+
+    @pytest.mark.parametrize('N',[1,3,4])
+    def test_slice(self,default,N):
+        assert len(default[:N]) == 1+N
+        assert len(default[:N,['F','s']]) == 1+N
+        assert default[N:].get('F').shape == (len(default)-N,3,3)
+        assert (default[:N,['v','s']].data == default['v','s'][:N].data).all().all()
 
     @pytest.mark.parametrize('mode',['str','path'])
     def test_write_read(self,default,tmp_path,mode):
@@ -84,21 +104,6 @@ class TestTable:
         with open(ref_path/fname) as f:
             Table.load(f)
 
-    def test_set(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_component(self,default):
-        d = default.set('1_F',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,default):
-        d = np.random.random((5,9))
-        assert np.allclose(d,default.add('nine',d,'random data').get('nine'))
-
     def test_rename_equivalent(self):
         x = np.random.random((5,13))
         t = Table(x,{'F':(3,3),'v':(3,),'s':(1,)},['random test data'])
@@ -169,15 +174,15 @@ class TestTable:
     def test_sort_component(self):
         x = np.random.random((5,12))
         t = Table(x,{'F':(3,3),'v':(3,)},['random test data'])
-        unsort = t.get('4_F')
+        unsort = t.get('F')[:,1,0]
-        sort   = t.sort_by('4_F').get('4_F')
+        sort   = t.sort_by('F[1,0]').get('F')[:,1,0]
         assert np.all(np.sort(unsort,0)==sort)
 
     def test_sort_revert(self):
         x = np.random.random((5,12))
         t = Table(x,{'F':(3,3),'v':(3,)},['random test data'])
-        sort = t.sort_by('4_F',ascending=False).get('4_F')
+        sort = t.sort_by('F[1,0]',ascending=False).get('F')[:,1,0]
-        assert np.all(np.sort(sort,0)==sort[::-1,:])
+        assert np.all(np.sort(sort,0)==sort[::-1])
 
     def test_sort(self):
         t = Table(np.array([[0,1,],[2,1,]]),
@@ -185,4 +190,4 @@ class TestTable:
                   ['test data'])\
             .add('s',np.array(['b','a']))\
             .sort_by('s')
-        assert np.all(t.get('1_v') == np.array([2,0]).reshape(2,1))
+        assert np.all(t.get('v')[:,0] == np.array([2,0]))