Merge branch 'numpy_dtype_int_to_int64' into 'development'

Prevent windows overflow error when using numpy dtype=int instead of int64

Closes #175

See merge request damask/DAMASK!598

python/damask/_colormap.py

@@ -385,7 +385,7 @@ class Colormap(mpl.colors.ListedColormap):
         GOM_str = '1 1 {name} 9 {name} '.format(name=self.name.replace(" ","_")) \
                 +  '0 1 0 3 0 0 -1 9 \\ 0 0 0 255 255 255 0 0 255 ' \
                 + f'30 NO_UNIT 1 1 64 64 64 255 1 0 0 0 0 0 0 3 0 {self.N}' \
-                + ' '.join([f' 0 {c[0]} {c[1]} {c[2]} 255 1' for c in reversed((self.colors*255).astype(int))]) \
+                + ' '.join([f' 0 {c[0]} {c[1]} {c[2]} 255 1' for c in reversed((self.colors*255).astype(np.int64))]) \
                 + '\n'
 
         self._get_file_handle(fname,'.legend').write(GOM_str)

python/damask/_grid.py

@@ -117,8 +117,8 @@ class Grid:
         self._material = np.copy(material)
 
         if self.material.dtype in np.sctypes['float'] and \
-           np.all(self.material == self.material.astype(int).astype(float)):
+           np.all(self.material == self.material.astype(np.int64).astype(float)):
-            self._material = self.material.astype(int)
+            self._material = self.material.astype(np.int64)
 
 
     @property
@@ -285,7 +285,7 @@ class Grid:
             raise TypeError(f'mismatch between {cells.prod()} expected entries and {i} found')
 
         if not np.any(np.mod(material,1) != 0.0):                                                   # no float present
-            material = material.astype(int) - (1 if material.min() > 0 else 0)
+            material = material.astype(np.int64) - (1 if material.min() > 0 else 0)
 
         return Grid(material = material.reshape(cells,order='F'),
                     size     = size,
@@ -916,7 +916,7 @@ class Grid:
                                            cval=np.nanmax(self.material) + 1 if fill is None else fill)
             # avoid scipy interpolation errors for rotations close to multiples of 90°
             material = material_temp if np.prod(material_temp.shape) != np.prod(material.shape) else \
-                       np.rot90(material,k=np.rint(angle/90.).astype(int),axes=axes)
+                       np.rot90(material,k=np.rint(angle/90.).astype(np.int64),axes=axes)
 
         origin = self.origin-(np.asarray(material.shape)-self.cells)*.5 * self.size/self.cells
 
@@ -1094,7 +1094,7 @@ class Grid:
 
         rng = np.random.default_rng(rng_seed)
 
-        d = np.floor(distance).astype(int)
+        d = np.floor(distance).astype(np.int64)
         ext = np.linspace(-d,d,1+2*d,dtype=float),
         xx,yy,zz = np.meshgrid(ext,ext,ext)
         footprint = xx**2+yy**2+zz**2 <= distance**2+distance*1e-8
@@ -1197,7 +1197,7 @@ class Grid:
             mask = np.sum(np.power(coords_rot/r,2.0**np.array(exponent)),axis=-1) > 1.0
 
         if periodic:                                                                                # translate back to center
-            mask = np.roll(mask,((c/self.size-0.5)*self.cells).round().astype(int),(0,1,2))
+            mask = np.roll(mask,((c/self.size-0.5)*self.cells).round().astype(np.int64),(0,1,2))
 
         return Grid(material = np.where(np.logical_not(mask) if inverse else mask,
                                         self.material,
@@ -1249,7 +1249,7 @@ class Grid:
             return np.any(stencil != me if selection is None else
                           np.in1d(stencil,np.array(list(selection - {me}))))
 
-        d = np.floor(distance).astype(int)
+        d = np.floor(distance).astype(np.int64)
         ext = np.linspace(-d,d,1+2*d,dtype=float),
         xx,yy,zz = np.meshgrid(ext,ext,ext)
         footprint = xx**2+yy**2+zz**2 <= distance**2+distance*1e-8

python/damask/_table.py

@@ -134,7 +134,7 @@ class Table:
         labels = []
         for label in what:
             shape = self.shapes[label]
-            size = np.prod(shape,dtype=int)
+            size = np.prod(shape,dtype=np.int64)
             if   how == 'uniform':
                 labels += [label] * size
             elif how == 'shapes':
@@ -168,7 +168,7 @@ class Table:
                      shape: Tuple[int, ...],
                      info: str = None):
         if info is not None:
-            specific = f'{label}{" "+str(shape) if np.prod(shape,dtype=int) > 1 else ""}: {info}'
+            specific = f'{label}{" "+str(shape) if np.prod(shape,dtype=np.int64) > 1 else ""}: {info}'
             general  = util.execution_stamp('Table')
             self.comments.append(f'{specific} / {general}')
 
@@ -401,7 +401,7 @@ class Table:
         else:
 
             dup.shapes[label] = data.shape[1:] if len(data.shape) > 1 else (1,)
-            size = np.prod(data.shape[1:],dtype=int)
+            size = np.prod(data.shape[1:],dtype=np.int64)
             new = pd.DataFrame(data=data.reshape(-1,size),
                                columns=[label]*size,
                               )

python/damask/_vtk.py

@@ -187,7 +187,7 @@ class VTK:
         ----------
         nodes : numpy.ndarray, shape (:,3)
             Spatial position of the nodes.
-        connectivity : numpy.ndarray of np.dtype = int
+        connectivity : numpy.ndarray of np.dtype = np.int64
             Cell connectivity (0-based), first dimension determines #Cells,
             second dimension determines #Nodes/Cell.
         cell_type : str

python/damask/seeds.py

@@ -45,7 +45,7 @@ def from_random(size: _FloatSequence,
     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)] \
-               + _np.broadcast_to(size_/_np.array(cells,int),(N_seeds,3))*(rng.random((N_seeds,3))*.5-.25) # wobble w/o leaving grid
+               + _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
 

python/damask/util.py

@@ -314,7 +314,7 @@ def scale_to_coprime(v: FloatSequence) -> np.ndarray:
             return a * b // np.gcd(a, b)
 
     v_ = np.array(v)
-    m = (v_ * reduce(lcm, map(lambda x: int(get_square_denominator(x)),v_))**0.5).astype(int)
+    m = (v_ * reduce(lcm, map(lambda x: int(get_square_denominator(x)),v_))**0.5).astype(np.int64)
     m = m//reduce(np.gcd,m)
 
     with np.errstate(invalid='ignore'):