functionality to sort material id
convenient for layered materials
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@ -800,6 +800,20 @@ class Geom:
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)
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def sort(self):
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"""Sort material indices such that min(material) is located at (0,0,0)."""
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a = self.material.flatten(order='F')
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from_ma = pd.unique(a)
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sort_idx = np.argsort(from_ma)
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ma = np.unique(a)[sort_idx][np.searchsorted(from_ma,a,sorter = sort_idx)]
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return Geom(material = ma.reshape(self.grid,order='F'),
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size = self.size,
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origin = self.origin,
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comments = self.comments+[util.execution_stamp('Geom','sort')],
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)
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def vicinity_offset(self,vicinity=1,offset=None,trigger=[],periodic=True):
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"""
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Offset material index of points in the vicinity of xxx.
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@ -6,6 +6,7 @@ from damask import Geom
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from damask import Table
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from damask import Rotation
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from damask import util
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from damask import seeds
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from damask import grid_filters
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@ -204,6 +205,12 @@ class TestGeom:
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assert np.array_equiv(t,f) or (not geom_equal(modified,default))
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assert geom_equal(default, modified.substitute(t,f))
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def test_sort(self):
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grid = np.random.randint(5,20,3)
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m = Geom(np.random.randint(1,20,grid)*3,np.ones(3)).sort().material.flatten(order='F')
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assert m[0] == m.min()
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for i,v in enumerate(m[1:]):
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assert v > m[:i+1].max() or v in m[:i+1]
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@pytest.mark.parametrize('axis_angle',[np.array([1,0,0,86.7]), np.array([0,1,0,90.4]), np.array([0,0,1,90]),
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np.array([1,0,0,175]),np.array([0,-1,0,178]),np.array([0,0,1,180])])
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@ -384,3 +391,13 @@ class TestGeom:
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t = Table(np.column_stack((coords,z)),{'coords':3,'z':1})
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g = Geom.from_table(t,'coords',['1_coords','z'])
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assert g.N_materials == g.grid[0]*2 and (g.material[:,:,-1]-g.material[:,:,0] == grid[0]).all()
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def test_from_table_recover(self,tmp_path):
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grid = np.random.randint(60,100,3)
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size = np.ones(3)+np.random.rand(3)
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s = seeds.from_random(size,np.random.randint(60,100))
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geom = Geom.from_Voronoi_tessellation(grid,size,s)
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coords = grid_filters.cell_coord0(grid,size)
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t = Table(np.column_stack((coords.reshape(-1,3,order='F'),geom.material.flatten(order='F'))),{'c':3,'m':1})
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assert geom_equal(geom.sort().renumber(),Geom.from_table(t,'c',['m']))
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