table.__init__ now has common order of arguments (label, data)

PRIVATE

@@ -1 +1 @@
-Subproject commit 9c1f83babb7894bfaa16255d6c15a4a438c7f168
+Subproject commit 317345ab8fffbb120630846a47ab25922d466e14

python/damask/_colormap.py

@@ -373,7 +373,7 @@ class Colormap(mpl.colors.ListedColormap):
 
         """
         labels = {'RGBA':4} if self.colors.shape[1] == 4 else {'RGB': 3}
-        t = Table(self.colors,labels,f'Creator: {util.execution_stamp("Colormap")}')
+        t = Table(labels,self.colors,f'Creator: {util.execution_stamp("Colormap")}')
         t.save(self._get_file_handle(fname,'.txt'))
 
 

python/damask/_table.py

@@ -13,26 +13,27 @@ class Table:
     """Manipulate multi-dimensional spreadsheet-like data."""
 
     def __init__(self,
-                 data: np.ndarray,
                  shapes: dict,
+                 data: np.ndarray,
                  comments: Union[str, list] = None):
         """
         New spreadsheet.
 
         Parameters
         ----------
-        data : numpy.ndarray or pandas.DataFrame
-            Data. Column labels from a pandas.DataFrame will be replaced.
         shapes : dict with str:tuple pairs
-            Shapes of the columns. Example 'F':(3,3) for a deformation gradient.
+            Shapes of the data columns.
+            For instance, 'F':(3,3) for a deformation gradient, or 'r':(1,) for a scalar.
+        data : numpy.ndarray or pandas.DataFrame
+            Data. Existing column labels of a pandas.DataFrame will be replaced.
         comments : str or iterable of str, optional
             Additional, human-readable information.
 
         """
         comments_ = [comments] if isinstance(comments,str) else comments
         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.data = pd.DataFrame(data=data)
         self._relabel('uniform')
 
 
@@ -70,8 +71,8 @@ class Table:
         --------
         >>> import damask
         >>> import numpy as np
-        >>> tbl = damask.Table(data=np.arange(12).reshape((4,3)),
+        >>> tbl = damask.Table(shapes=dict(colA=(1,),colB=(1,),colC=(1,)),
-        ...                    shapes=dict(colA=(1,),colB=(1,),colC=(1,)))
+        ...                    data=np.arange(12).reshape((4,3)))
         >>> tbl['colA','colB']
            colA  colB
         0     0     1
@@ -282,7 +283,7 @@ class Table:
 
         data = pd.read_csv(f,names=list(range(len(labels))),sep=r'\s+')
 
-        return Table(data,shapes,comments)
+        return Table(shapes,data,comments)
 
 
     @staticmethod
@@ -329,7 +330,7 @@ class Table:
         if (remainder := data.shape[1]-sum(shapes.values())) > 0:
             shapes['unknown'] = remainder
 
-        return Table(data,shapes,comments)
+        return Table(shapes,data,comments)
 
 
     @property

python/tests/test_ConfigMaterial.py

@@ -90,7 +90,7 @@ class TestConfigMaterial:
                        np.ones(N*2),np.zeros(N*2),np.ones(N*2),np.ones(N*2),
                        np.ones(N*2),
                       )).T
-        t = Table(a,{'varying':1,'constant':4,'ones':1})
+        t = Table({'varying':1,'constant':4,'ones':1},a)
         c = ConfigMaterial.from_table(t,**{'phase':'varying','O':'constant','homogenization':'ones'})
         assert len(c['material']) == N
         for i,m in enumerate(c['material']):
@@ -102,7 +102,7 @@ class TestConfigMaterial:
                        np.ones(N*2),np.zeros(N*2),np.ones(N*2),np.ones(N*2),
                        np.ones(N*2),
                       )).T
-        t = Table(a,{'varying':1,'constant':4,'ones':1})
+        t = Table({'varying':1,'constant':4,'ones':1},a)
         c = ConfigMaterial.from_table(t,**{'phase':'varying','O':'constant','homogenization':1})
         assert len(c['material']) == N
         for i,m in enumerate(c['material']):

python/tests/test_Grid.py

@@ -435,9 +435,9 @@ class TestGrid:
         cells = np.random.randint(60,100,3)
         size = np.ones(3)+np.random.rand(3)
         coords = grid_filters.coordinates0_point(cells,size).reshape(-1,3,order='F')
-        z=np.ones(cells.prod())
+        z = np.ones(cells.prod())
-        z[cells[:2].prod()*int(cells[2]/2):]=0
+        z[cells[:2].prod()*int(cells[2]/2):] = 0
-        t = Table(np.column_stack((coords,z)),{'coords':3,'z':1})
+        t = Table({'coords':3,'z':1},np.column_stack((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()
@@ -449,7 +449,7 @@ class TestGrid:
         s = seeds.from_random(size,np.random.randint(60,100))
         grid = Grid.from_Voronoi_tessellation(cells,size,s)
         coords = grid_filters.coordinates0_point(cells,size)
-        t = Table(np.column_stack((coords.reshape(-1,3,order='F'),grid.material.flatten(order='F'))),{'c':3,'m':1})
+        t = Table({'c':3,'m':1},np.column_stack((coords.reshape(-1,3,order='F'),grid.material.flatten(order='F'))))
         assert grid.sort().renumber() == Grid.from_table(t,'c',['m'])
 
 

python/tests/test_Table.py

@@ -8,7 +8,9 @@ from damask import Table
 def default():
     """Simple Table."""
     x = np.ones((5,13),dtype=float)
-    return Table(x,{'F':(3,3),'v':(3,),'s':(1,)},['test data','contains five rows of only ones'])
+    return Table({'F':(3,3),'v':(3,),'s':(1,)},
+                 x,
+                 ['test data','contains five rows of only ones'])
 
 @pytest.fixture
 def ref_path(ref_path_base):
@@ -22,7 +24,7 @@ class TestTable:
 
     @pytest.mark.parametrize('N',[10,40])
     def test_len(self,N):
-        assert len(Table(np.random.rand(N,3),{'X':3})) == N
+        assert len(Table({'X':3},np.random.rand(N,3))) == N
 
     def test_get_scalar(self,default):
         d = default.get('s')
@@ -110,7 +112,7 @@ class TestTable:
 
     def test_rename_equivalent(self):
         x = np.random.random((5,13))
-        t = Table(x,{'F':(3,3),'v':(3,),'s':(1,)},['random test data'])
+        t = Table({'F':(3,3),'v':(3,),'s':(1,)},x,['random test data'])
         s = t.get('s')
         u = t.rename('s','u').get('u')
         assert np.all(s == u)
@@ -129,35 +131,35 @@ class TestTable:
 
     def test_join(self):
         x = np.random.random((5,13))
-        a = Table(x,{'F':(3,3),'v':(3,),'s':(1,)},['random test data'])
+        a = Table({'F':(3,3),'v':(3,),'s':(1,)},x,['random test data'])
         y = np.random.random((5,3))
-        b = Table(y,{'u':(3,)},['random test data'])
+        b = Table({'u':(3,)},y,['random test data'])
         c = a.join(b)
         assert np.array_equal(c.get('u'), b.get('u'))
 
     def test_join_invalid(self):
         x = np.random.random((5,13))
-        a = Table(x,{'F':(3,3),'v':(3,),'s':(1,)},['random test data'])
+        a = Table({'F':(3,3),'v':(3,),'s':(1,)},x,['random test data'])
         with pytest.raises(KeyError):
             a.join(a)
 
     def test_append(self):
         x = np.random.random((5,13))
-        a = Table(x,{'F':(3,3),'v':(3,),'s':(1,)},['random test data'])
+        a = Table({'F':(3,3),'v':(3,),'s':(1,)},x,['random test data'])
         b = a.append(a)
         assert np.array_equal(b.data[:5].to_numpy(),b.data[5:].to_numpy())
 
     def test_append_invalid(self):
         x = np.random.random((5,13))
-        a = Table(x,{'F':(3,3),'v':(3,),'s':(1,)},['random test data'])
+        a = Table({'F':(3,3),'v':(3,),'s':(1,)},x,['random test data'])
-        b = Table(x,{'F':(3,3),'u':(3,),'s':(1,)},['random test data'])
+        b = Table({'F':(3,3),'u':(3,),'s':(1,)},x,['random test data'])
         with pytest.raises(KeyError):
             a.append(b)
 
     def test_invalid_initialization(self):
         x = np.random.random((5,10))
         with pytest.raises(ValueError):
-            Table(x,{'F':(3,3)})
+            Table({'F':(3,3)},x)
 
     def test_invalid_set(self,default):
         x = default.get('v')
@@ -170,27 +172,27 @@ class TestTable:
 
     def test_sort_scalar(self):
         x = np.random.random((5,13))
-        t = Table(x,{'F':(3,3),'v':(3,),'s':(1,)},['random test data'])
+        t = Table({'F':(3,3),'v':(3,),'s':(1,)},x,['random test data'])
         unsort = t.get('s')
         sort   = t.sort_by('s').get('s')
         assert np.all(np.sort(unsort,0)==sort)
 
     def test_sort_component(self):
         x = np.random.random((5,12))
-        t = Table(x,{'F':(3,3),'v':(3,)},['random test data'])
+        t = Table({'F':(3,3),'v':(3,)},x,['random test data'])
         unsort = t.get('F')[:,1,0]
         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'])
+        t = Table({'F':(3,3),'v':(3,)},x,['random test data'])
         sort = t.sort_by('F[1,0]',ascending=False).get('F')[:,1,0]
         assert np.all(np.sort(sort,0)==sort[::-1])
 
     def test_sort(self):
-        t = Table(np.array([[0,1,],[2,1,]]),
+        t = Table({'v':(2,)},
-                  {'v':(2,)},
+                  np.array([[0,1,],[2,1,]]),
                   ['test data'])\
             .add('s',np.array(['b','a']))\
             .sort_by('s')

python/tests/test_VTK.py

@@ -179,7 +179,7 @@ class TestVTK:
         for k,s in shapes.items():
             d[k] = dict(shape = s,
                         data = np.random.random(N*np.prod(s)).reshape((N,-1)))
-        new = default.add(Table(np.column_stack([d[k]['data'] for k in shapes.keys()]),shapes))
+        new = default.add(Table(shapes,np.column_stack([d[k]['data'] for k in shapes.keys()])))
         for k,s in shapes.items():
             assert np.allclose(np.squeeze(d[k]['data']),new.get(k),rtol=1e-7)