Python 3.8 magic: The walrus operator ":="

python/damask/_colormap.py

@@ -266,9 +266,7 @@ class Colormap(mpl.colors.ListedColormap):
         l,r = (field[mask].min(),field[mask].max()) if bounds is None else \
               (bounds[0],bounds[1])
 
-        delta,avg = r-l,0.5*abs(r+l)
+        if abs(delta := r-l) * 1e8 <= (avg := 0.5*abs(r+l)):                                        # delta is similar to numerical noise
-
-        if abs(delta) * 1e8 <= avg:                                                                 # delta is similar to numerical noise
             l,r = l-0.5*avg*np.sign(delta),r+0.5*avg*np.sign(delta),                                # extend range to have actual data centered within
 
         return Image.fromarray(

python/damask/_grid.py

@@ -234,8 +234,7 @@ class Grid:
         content = f.readlines()
         for i,line in enumerate(content[:header_length]):
             items = line.split('#')[0].lower().strip().split()
-            key = items[0] if items else ''
+            if (key := items[0] if items else '') ==  'grid':
-            if   key == 'grid':
                 cells  = np.array([  int(dict(zip(items[1::2],items[2::2]))[i]) for i in ['a','b','c']])
             elif key == 'size':
                 size   = np.array([float(dict(zip(items[1::2],items[2::2]))[i]) for i in ['x','y','z']])
@@ -247,8 +246,7 @@ class Grid:
         material = np.empty(int(cells.prod()))                                                      # initialize as flat array
         i = 0
         for line in content[header_length:]:
-            items = line.split('#')[0].split()
+            if len(items := line.split('#')[0].split()) == 3:
-            if len(items) == 3:
                 if items[1].lower() == 'of':
                     material_entry = np.ones(int(items[0]))*float(items[2])
                 elif items[1].lower() == 'to':
@@ -813,8 +811,7 @@ class Grid:
         # materials: 1
 
         """
-        valid = ['x','y','z']
+        if not set(directions).issubset(valid := ['x', 'y', 'z']):
-        if not set(directions).issubset(valid):
             raise ValueError(f'invalid direction {set(directions).difference(valid)} specified')
 
         limits: Sequence[Optional[int]] = [None,None] if reflect else [-2,0]
@@ -852,8 +849,7 @@ class Grid:
             Updated grid-based geometry.
 
         """
-        valid = ['x','y','z']
+        if not set(directions).issubset(valid := ['x', 'y', 'z']):
-        if not set(directions).issubset(valid):
             raise ValueError(f'invalid direction {set(directions).difference(valid)} specified')
 
 
@@ -1190,8 +1186,7 @@ class Grid:
             VTK-based geometry of grain boundary network.
 
         """
-        valid = ['x','y','z']
+        if not set(directions).issubset(valid := ['x', 'y', 'z']):
-        if not set(directions).issubset(valid):
             raise ValueError(f'invalid direction {set(directions).difference(valid)} specified')
 
         o = [[0, self.cells[0]+1,           np.prod(self.cells[:2]+1)+self.cells[0]+1, np.prod(self.cells[:2]+1)],

python/damask/_table.py

@@ -319,8 +319,7 @@ class Table:
         data = np.loadtxt(content)
 
         shapes = {'eu':3, 'pos':2, 'IQ':1, 'CI':1, 'ID':1, 'intensity':1, 'fit':1}
-        remainder = data.shape[1]-sum(shapes.values())
+        if (remainder := data.shape[1]-sum(shapes.values())) > 0:
-        if remainder > 0:                                                       # 3.8 can do: if (remainder := data.shape[1]-sum(shapes.values())) > 0
             shapes['unknown'] = remainder
 
         return Table(data,shapes,comments)
@@ -376,8 +375,7 @@ class Table:
         """
         dup = self.copy()
         dup._add_comment(label, data.shape[1:], info)
-        m = re.match(r'(.*)\[((\d+,)*(\d+))\]',label)
+        if m := re.match(r'(.*)\[((\d+,)*(\d+))\]',label):
-        if m:
             key = m.group(1)
             idx = np.ravel_multi_index(tuple(map(int,m.group(2).split(","))),
                                        self.shapes[key])
@@ -495,8 +493,7 @@ class 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 := 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)}'

python/damask/_vtk.py

@@ -22,7 +22,7 @@ class VTK:
     High-level interface to VTK.
     """
 
-    def __init__(self, 
+    def __init__(self,
                  vtk_data: vtk.vtkDataSet):
         """
         New spatial visualization.
@@ -202,8 +202,7 @@ class VTK:
         """
         if not os.path.isfile(fname):                                                               # vtk has a strange error handling
             raise FileNotFoundError(f'No such file: {fname}')
-        ext = Path(fname).suffix
+        if (ext := Path(fname).suffix) == '.vtk' or dataset_type is not None:
-        if ext == '.vtk' or dataset_type is not None:
             reader = vtk.vtkGenericDataObjectReader()
             reader.SetFileName(str(fname))
             if dataset_type is None:

python/damask/seeds.py

@@ -92,10 +92,9 @@ def from_Poisson_disc(size: _FloatSequence,
         tree = _spatial.cKDTree(coords[:s],boxsize=size) if periodic else \
                _spatial.cKDTree(coords[:s])
         distances = tree.query(candidates)[0]
-        best = distances.argmax()
+        if distances.max() > distance:                                                              # require minimum separation
-        if distances[best] > distance:                                                              # require minimum separation
             i = 0
-            coords[s] = candidates[best]                                                            # maximum separation to existing point cloud
+            coords[s] = candidates[distances.argmax()]                                              # maximum separation to existing point cloud
             s += 1
             progress.update(s)
 

python/damask/tensor.py

@@ -66,8 +66,7 @@ def eigenvectors(T_sym: _np.ndarray,
     """
     (u,v) = _np.linalg.eigh(symmetric(T_sym))
 
-    if RHS:
+    if RHS: v[_np.linalg.det(v) < 0.0,:,2] *= -1.0
-        v[_np.linalg.det(v) < 0.0,:,2] *= -1.0
     return v
 
 

python/damask/util.py

@@ -753,9 +753,8 @@ class ProgressBar:
                iteration: int) -> None:
 
         fraction = (iteration+1) / self.total
-        filled_length = int(self.bar_length * fraction)
 
-        if filled_length > int(self.bar_length * self.fraction_last) or \
+        if filled_length := int(self.bar_length * fraction) > int(self.bar_length * self.fraction_last) or \
             datetime.datetime.now() - self.time_last_update > datetime.timedelta(seconds=10):
             self.time_last_update = datetime.datetime.now()
             bar = '█' * filled_length + '░' * (self.bar_length - filled_length)