Prevent windows overflow error when using numpy dtype=int instead of int64
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d.mentock
parent
5155a3f958
commit
b9214fcc6c
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@ -385,7 +385,7 @@ class Colormap(mpl.colors.ListedColormap):
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GOM_str = '1 1 {name} 9 {name} '.format(name=self.name.replace(" ","_")) \
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+ '0 1 0 3 0 0 -1 9 \\ 0 0 0 255 255 255 0 0 255 ' \
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+ f'30 NO_UNIT 1 1 64 64 64 255 1 0 0 0 0 0 0 3 0 {self.N}' \
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+ ' '.join([f' 0 {c[0]} {c[1]} {c[2]} 255 1' for c in reversed((self.colors*255).astype(int))]) \
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+ ' '.join([f' 0 {c[0]} {c[1]} {c[2]} 255 1' for c in reversed((self.colors*255).astype(np.int64))]) \
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+ '\n'
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self._get_file_handle(fname,'.legend').write(GOM_str)
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@ -117,8 +117,8 @@ class Grid:
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self._material = np.copy(material)
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if self.material.dtype in np.sctypes['float'] and \
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np.all(self.material == self.material.astype(int).astype(float)):
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self._material = self.material.astype(int)
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np.all(self.material == self.material.astype(np.int64).astype(float)):
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self._material = self.material.astype(np.int64)
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@property
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@ -285,7 +285,7 @@ class Grid:
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raise TypeError(f'mismatch between {cells.prod()} expected entries and {i} found')
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if not np.any(np.mod(material,1) != 0.0): # no float present
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material = material.astype(int) - (1 if material.min() > 0 else 0)
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material = material.astype(np.int64) - (1 if material.min() > 0 else 0)
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return Grid(material = material.reshape(cells,order='F'),
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size = size,
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@ -916,7 +916,7 @@ class Grid:
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cval=np.nanmax(self.material) + 1 if fill is None else fill)
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# avoid scipy interpolation errors for rotations close to multiples of 90°
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material = material_temp if np.prod(material_temp.shape) != np.prod(material.shape) else \
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np.rot90(material,k=np.rint(angle/90.).astype(int),axes=axes)
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np.rot90(material,k=np.rint(angle/90.).astype(np.int64),axes=axes)
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origin = self.origin-(np.asarray(material.shape)-self.cells)*.5 * self.size/self.cells
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@ -1094,7 +1094,7 @@ class Grid:
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rng = np.random.default_rng(rng_seed)
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d = np.floor(distance).astype(int)
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d = np.floor(distance).astype(np.int64)
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ext = np.linspace(-d,d,1+2*d,dtype=float),
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xx,yy,zz = np.meshgrid(ext,ext,ext)
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footprint = xx**2+yy**2+zz**2 <= distance**2+distance*1e-8
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@ -1197,7 +1197,7 @@ class Grid:
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mask = np.sum(np.power(coords_rot/r,2.0**np.array(exponent)),axis=-1) > 1.0
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if periodic: # translate back to center
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mask = np.roll(mask,((c/self.size-0.5)*self.cells).round().astype(int),(0,1,2))
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mask = np.roll(mask,((c/self.size-0.5)*self.cells).round().astype(np.int64),(0,1,2))
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return Grid(material = np.where(np.logical_not(mask) if inverse else mask,
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self.material,
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@ -1249,7 +1249,7 @@ class Grid:
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return np.any(stencil != me if selection is None else
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np.in1d(stencil,np.array(list(selection - {me}))))
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d = np.floor(distance).astype(int)
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d = np.floor(distance).astype(np.int64)
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ext = np.linspace(-d,d,1+2*d,dtype=float),
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xx,yy,zz = np.meshgrid(ext,ext,ext)
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footprint = xx**2+yy**2+zz**2 <= distance**2+distance*1e-8
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@ -134,7 +134,7 @@ class Table:
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labels = []
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for label in what:
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shape = self.shapes[label]
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size = np.prod(shape,dtype=int)
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size = np.prod(shape,dtype=np.int64)
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if how == 'uniform':
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labels += [label] * size
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elif how == 'shapes':
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@ -168,7 +168,7 @@ class Table:
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shape: Tuple[int, ...],
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info: str = None):
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if info is not None:
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specific = f'{label}{" "+str(shape) if np.prod(shape,dtype=int) > 1 else ""}: {info}'
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specific = f'{label}{" "+str(shape) if np.prod(shape,dtype=np.int64) > 1 else ""}: {info}'
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general = util.execution_stamp('Table')
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self.comments.append(f'{specific} / {general}')
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@ -401,7 +401,7 @@ class Table:
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else:
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dup.shapes[label] = data.shape[1:] if len(data.shape) > 1 else (1,)
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size = np.prod(data.shape[1:],dtype=int)
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size = np.prod(data.shape[1:],dtype=np.int64)
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new = pd.DataFrame(data=data.reshape(-1,size),
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columns=[label]*size,
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)
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@ -187,7 +187,7 @@ class VTK:
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----------
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nodes : numpy.ndarray, shape (:,3)
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Spatial position of the nodes.
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connectivity : numpy.ndarray of np.dtype = int
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connectivity : numpy.ndarray of np.dtype = np.int64
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Cell connectivity (0-based), first dimension determines #Cells,
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second dimension determines #Nodes/Cell.
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cell_type : str
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@ -45,7 +45,7 @@ def from_random(size: _FloatSequence,
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else:
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grid_coords = _grid_filters.coordinates0_point(cells,size).reshape(-1,3,order='F')
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coords = grid_coords[rng.choice(_np.prod(cells),N_seeds, replace=False)] \
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+ _np.broadcast_to(size_/_np.array(cells,int),(N_seeds,3))*(rng.random((N_seeds,3))*.5-.25) # wobble w/o leaving grid
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+ _np.broadcast_to(size_/_np.array(cells,_np.int64),(N_seeds,3))*(rng.random((N_seeds,3))*.5-.25) # wobble w/o leaving grid
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return coords
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@ -314,7 +314,7 @@ def scale_to_coprime(v: FloatSequence) -> np.ndarray:
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return a * b // np.gcd(a, b)
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v_ = np.array(v)
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m = (v_ * reduce(lcm, map(lambda x: int(get_square_denominator(x)),v_))**0.5).astype(int)
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m = (v_ * reduce(lcm, map(lambda x: int(get_square_denominator(x)),v_))**0.5).astype(np.int64)
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m = m//reduce(np.gcd,m)
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with np.errstate(invalid='ignore'):
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