365 lines
18 KiB
Python
365 lines
18 KiB
Python
import pytest
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import numpy as np
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from damask import grid_filters
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from damask import Grid
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from damask import seeds
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class TestGridFilters:
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def test_coordinates0_point(self):
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size = np.random.random(3) # noqa
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cells = np.random.randint(8,32,(3))
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coord = grid_filters.coordinates0_point(cells,size)
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assert np.allclose(coord[0,0,0],size/cells*.5) and coord.shape == tuple(cells) + (3,)
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def test_coordinates0_node(self):
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size = np.random.random(3) # noqa
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cells = np.random.randint(8,32,(3))
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coord = grid_filters.coordinates0_node(cells,size)
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assert np.allclose(coord[-1,-1,-1],size) and coord.shape == tuple(cells+1) + (3,)
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def test_coord0(self):
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size = np.random.random(3) # noqa
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cells = np.random.randint(8,32,(3))
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c = grid_filters.coordinates0_point(cells+1,size+size/cells)
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n = grid_filters.coordinates0_node(cells,size) + size/cells*.5
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assert np.allclose(c,n)
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@pytest.mark.parametrize('mode',['point','node'])
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def test_grid_DNA(self,mode):
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"""Ensure that cellsSizeOrigin_coordinates0_xx is the inverse of coordinates0_xx.""" # noqa
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cells = np.random.randint(8,32,(3))
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size = np.random.random(3)
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origin = np.random.random(3)
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coord0 = eval(f'grid_filters.coordinates0_{mode}(cells,size,origin)') # noqa
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_cells,_size,_origin = eval(f'grid_filters.cellsSizeOrigin_coordinates0_{mode}(coord0.reshape(-1,3,order="F"))')
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assert np.allclose(cells,_cells) and np.allclose(size,_size) and np.allclose(origin,_origin)
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def test_displacement_fluct_equivalence(self):
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"""Ensure that fluctuations are periodic.""" # noqa
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size = np.random.random(3)
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cells = np.random.randint(8,32,(3))
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F = np.random.random(tuple(cells)+(3,3))
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assert np.allclose(grid_filters.displacement_fluct_node(size,F),
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grid_filters.point_to_node(grid_filters.displacement_fluct_point(size,F)))
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def test_interpolation_to_node(self):
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size = np.random.random(3) # noqa
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cells = np.random.randint(8,32,(3))
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F = np.random.random(tuple(cells)+(3,3))
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assert np.allclose(grid_filters.coordinates_node(size,F) [1:-1,1:-1,1:-1],
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grid_filters.point_to_node(grid_filters.coordinates_point(size,F))[1:-1,1:-1,1:-1])
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def test_interpolation_to_cell(self):
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cells = np.random.randint(1,30,(3)) # noqa
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coordinates_node_x = np.linspace(0,np.pi*2,num=cells[0]+1)
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node_field_x = np.cos(coordinates_node_x)
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node_field = np.broadcast_to(node_field_x.reshape(-1,1,1),cells+1)
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coordinates0_point_x = coordinates_node_x[:-1]+coordinates_node_x[1]*.5
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cell_field_x = np.interp(coordinates0_point_x,coordinates_node_x,node_field_x,period=np.pi*2.)
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cell_field = np.broadcast_to(cell_field_x.reshape(-1,1,1),cells)
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assert np.allclose(cell_field,grid_filters.node_to_point(node_field))
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@pytest.mark.parametrize('mode',['point','node'])
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def test_coordinates0_origin(self,mode):
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origin= np.random.random(3) # noqa
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size = np.random.random(3) # noqa
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cells = np.random.randint(8,32,(3))
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shifted = eval(f'grid_filters.coordinates0_{mode}(cells,size,origin)')
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unshifted = eval(f'grid_filters.coordinates0_{mode}(cells,size)')
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if mode == 'cell':
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assert np.allclose(shifted,unshifted+np.broadcast_to(origin,tuple(cells) +(3,)))
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elif mode == 'node':
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assert np.allclose(shifted,unshifted+np.broadcast_to(origin,tuple(cells+1)+(3,)))
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@pytest.mark.parametrize('function',[grid_filters.displacement_avg_point,
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grid_filters.displacement_avg_node])
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def test_displacement_avg_vanishes(self,function):
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"""Ensure that random fluctuations in F do not result in average displacement.""" # noqa
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size = np.random.random(3)
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cells = np.random.randint(8,32,(3))
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F = np.random.random(tuple(cells)+(3,3))
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F += np.eye(3) - np.average(F,axis=(0,1,2))
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assert np.allclose(function(size,F),0.0)
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@pytest.mark.parametrize('function',[grid_filters.displacement_fluct_point,
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grid_filters.displacement_fluct_node])
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def test_displacement_fluct_vanishes(self,function):
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"""Ensure that constant F does not result in fluctuating displacement.""" # noqa
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size = np.random.random(3)
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cells = np.random.randint(8,32,(3))
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F = np.broadcast_to(np.random.random((3,3)), tuple(cells)+(3,3))
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assert np.allclose(function(size,F),0.0)
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@pytest.mark.parametrize('function',[grid_filters.cellsSizeOrigin_coordinates0_point,
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grid_filters.cellsSizeOrigin_coordinates0_node])
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def test_invalid_coordinates(self,function):
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invalid_coordinates = np.random.random((np.random.randint(12,52),3))
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with pytest.raises(ValueError):
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function(invalid_coordinates)
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@pytest.mark.parametrize('function',[grid_filters.coordinates0_point,
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grid_filters.coordinates0_node])
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def test_valid_coordinates_check(self,function):
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valid_coordinates = function(np.random.randint(4,10,(3)),np.random.rand(3))
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assert grid_filters.coordinates0_valid(valid_coordinates.reshape(-1,3,order='F'))
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def test_invalid_coordinates_check(self):
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invalid_coordinates = np.random.random((np.random.randint(12,52),3))
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assert not grid_filters.coordinates0_valid(invalid_coordinates)
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@pytest.mark.parametrize('function',[grid_filters.cellsSizeOrigin_coordinates0_node,
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grid_filters.cellsSizeOrigin_coordinates0_point])
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def test_uneven_spaced_coordinates(self,function):
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start = np.random.random(3)
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end = np.random.random(3)*10. + start
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cells = np.random.randint(8,32,(3))
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uneven = np.stack(np.meshgrid(np.logspace(start[0],end[0],cells[0]),
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np.logspace(start[1],end[1],cells[1]),
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np.logspace(start[2],end[2],cells[2]),indexing = 'ij'),
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axis = -1).reshape((cells.prod(),3),order='F')
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with pytest.raises(ValueError):
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function(uneven)
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@pytest.mark.parametrize('mode',[True,False])
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@pytest.mark.parametrize('function',[grid_filters.cellsSizeOrigin_coordinates0_node,
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grid_filters.cellsSizeOrigin_coordinates0_point])
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def test_unordered_coordinates(self,function,mode):
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origin = np.random.random(3)
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size = np.random.random(3)*10.+origin
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cells = np.random.randint(8,32,(3))
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unordered = grid_filters.coordinates0_node(cells,size,origin).reshape(-1,3)
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if mode:
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with pytest.raises(ValueError):
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function(unordered,mode)
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else:
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function(unordered,mode)
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def test_regrid_identity(self):
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size = np.random.random(3) # noqa
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cells = np.random.randint(8,32,(3))
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F = np.broadcast_to(np.eye(3), (*cells,3,3))
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assert (grid_filters.regrid(size,F,cells).flatten() == np.arange(cells.prod())).all
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def test_regrid_double_cells(self):
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size = np.random.random(3) # noqa
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cells = np.random.randint(8,32,(3))
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g = Grid.from_Voronoi_tessellation(cells,size,seeds.from_random(size,10))
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F = np.broadcast_to(np.eye(3), (*cells,3,3))
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assert g.scale(cells*2) == g.assemble(grid_filters.regrid(size,F,cells*2))
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@pytest.mark.parametrize('differential_operator',[grid_filters.curl,
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grid_filters.divergence,
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grid_filters.gradient])
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def test_differential_operator_constant(self,differential_operator):
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size = np.random.random(3)+1.0
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cells = np.random.randint(8,32,(3))
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shapes = {
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grid_filters.curl: [(3,),(3,3)],
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grid_filters.divergence:[(3,),(3,3)],
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grid_filters.gradient: [(1,),(3,)]
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}
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for shape in shapes[differential_operator]:
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field = np.ones(tuple(cells)+shape)*np.random.random()*1.0e5
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assert np.allclose(differential_operator(size,field),0.0)
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grad_test_data = [
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(['np.sin(np.pi*2*nodes[...,0]/size[0])', '0.0', '0.0'],
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['np.cos(np.pi*2*nodes[...,0]/size[0])*np.pi*2/size[0]', '0.0', '0.0',
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'0.0', '0.0', '0.0',
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'0.0', '0.0', '0.0']),
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(['0.0', 'np.cos(np.pi*2*nodes[...,1]/size[1])', '0.0' ],
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['0.0', '0.0', '0.0',
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'0.0', '-np.pi*2/size[1]*np.sin(np.pi*2*nodes[...,1]/size[1])', '0.0',
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'0.0', '0.0', '0.0' ]),
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(['1.0', '0.0', '2.0*np.cos(np.pi*2*nodes[...,2]/size[2])'],
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['0.0', '0.0', '0.0',
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'0.0', '0.0', '0.0',
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'0.0', '0.0', '-2.0*np.pi*2/size[2]*np.sin(np.pi*2*nodes[...,2]/size[2])']),
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(['np.cos(np.pi*2*nodes[...,2]/size[2])', '3.0', 'np.sin(np.pi*2*nodes[...,2]/size[2])'],
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['0.0', '0.0', '-np.sin(np.pi*2*nodes[...,2]/size[2])*np.pi*2/size[2]',
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'0.0', '0.0', '0.0',
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'0.0', '0.0', ' np.cos(np.pi*2*nodes[...,2]/size[2])*np.pi*2/size[2]']),
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(['np.sin(np.pi*2*nodes[...,0]/size[0])',
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'np.sin(np.pi*2*nodes[...,1]/size[1])',
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'np.sin(np.pi*2*nodes[...,2]/size[2])'],
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['np.cos(np.pi*2*nodes[...,0]/size[0])*np.pi*2/size[0]', '0.0', '0.0',
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'0.0', 'np.cos(np.pi*2*nodes[...,1]/size[1])*np.pi*2/size[1]', '0.0',
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'0.0', '0.0', 'np.cos(np.pi*2*nodes[...,2]/size[2])*np.pi*2/size[2]']),
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(['np.sin(np.pi*2*nodes[...,0]/size[0])'],
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['np.cos(np.pi*2*nodes[...,0]/size[0])*np.pi*2/size[0]', '0.0', '0.0']),
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(['8.0'],
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['0.0', '0.0', '0.0' ])
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]
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@pytest.mark.parametrize('field_def,grad_def',grad_test_data)
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def test_grad(self,field_def,grad_def):
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size = np.random.random(3)+1.0
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cells = np.random.randint(8,32,(3))
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nodes = grid_filters.coordinates0_point(cells,size)
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my_locals = locals() # needed for list comprehension
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field = np.stack([np.broadcast_to(eval(f,globals(),my_locals),cells) for f in field_def],axis=-1)
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field = field.reshape(tuple(cells) + ((3,) if len(field_def)==3 else (1,)))
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grad = np.stack([np.broadcast_to(eval(c,globals(),my_locals),cells) for c in grad_def], axis=-1)
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grad = grad.reshape(tuple(cells) + ((3,3) if len(grad_def)==9 else (3,)))
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assert np.allclose(grad,grid_filters.gradient(size,field))
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curl_test_data = [
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(['np.sin(np.pi*2*nodes[...,2]/size[2])', '0.0', '0.0',
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'0.0', '0.0', '0.0',
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'0.0', '0.0', '0.0'],
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['0.0' , '0.0', '0.0',
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'np.cos(np.pi*2*nodes[...,2]/size[2])*np.pi*2/size[2]', '0.0', '0.0',
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'0.0', '0.0', '0.0']),
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(['np.cos(np.pi*2*nodes[...,1]/size[1])', '0.0', '0.0',
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'0.0', '0.0', '0.0',
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'np.cos(np.pi*2*nodes[...,0]/size[0])', '0.0', '0.0'],
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['0.0', '0.0', '0.0',
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'0.0', '0.0', '0.0',
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'np.sin(np.pi*2*nodes[...,1]/size[1])*np.pi*2/size[1]', '0.0', '0.0']),
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(['np.sin(np.pi*2*nodes[...,0]/size[0])','np.cos(np.pi*2*nodes[...,1]/size[1])','np.sin(np.pi*2*nodes[...,2]/size[2])',
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'np.sin(np.pi*2*nodes[...,0]/size[0])','np.cos(np.pi*2*nodes[...,1]/size[1])','np.sin(np.pi*2*nodes[...,2]/size[2])',
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'np.sin(np.pi*2*nodes[...,0]/size[0])','np.cos(np.pi*2*nodes[...,1]/size[1])','np.sin(np.pi*2*nodes[...,2]/size[2])'],
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['0.0', '0.0', '0.0',
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'0.0', '0.0', '0.0',
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'0.0', '0.0', '0.0']),
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(['5.0', '0.0', '0.0',
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'0.0', '0.0', '0.0',
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'0.0', '0.0', '2*np.cos(np.pi*2*nodes[...,1]/size[1])'],
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['0.0', '0.0', '-2*np.pi*2/size[1]*np.sin(np.pi*2*nodes[...,1]/size[1])',
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'0.0', '0.0', '0.0',
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'0.0', '0.0', '0.0']),
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([ '4*np.sin(np.pi*2*nodes[...,2]/size[2])',
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'8*np.sin(np.pi*2*nodes[...,0]/size[0])',
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'16*np.sin(np.pi*2*nodes[...,1]/size[1])'],
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['16*np.pi*2/size[1]*np.cos(np.pi*2*nodes[...,1]/size[1])',
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'4*np.pi*2/size[2]*np.cos(np.pi*2*nodes[...,2]/size[2])',
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'8*np.pi*2/size[0]*np.cos(np.pi*2*nodes[...,0]/size[0])']),
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(['0.0',
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'np.cos(np.pi*2*nodes[...,0]/size[0])+5*np.cos(np.pi*2*nodes[...,2]/size[2])',
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'0.0'],
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['5*np.sin(np.pi*2*nodes[...,2]/size[2])*np.pi*2/size[2]',
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'0.0',
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'-np.sin(np.pi*2*nodes[...,0]/size[0])*np.pi*2/size[0]'])
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]
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@pytest.mark.parametrize('field_def,curl_def',curl_test_data)
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def test_curl(self,field_def,curl_def):
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size = np.random.random(3)+1.0
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cells = np.random.randint(8,32,(3))
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nodes = grid_filters.coordinates0_point(cells,size)
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my_locals = locals() # needed for list comprehension
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field = np.stack([np.broadcast_to(eval(f,globals(),my_locals),cells) for f in field_def],axis=-1)
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field = field.reshape(tuple(cells) + ((3,3) if len(field_def)==9 else (3,)))
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curl = np.stack([np.broadcast_to(eval(c,globals(),my_locals),cells) for c in curl_def], axis=-1)
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curl = curl.reshape(tuple(cells) + ((3,3) if len(curl_def)==9 else (3,)))
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assert np.allclose(curl,grid_filters.curl(size,field))
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div_test_data =[
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(['np.sin(np.pi*2*nodes[...,0]/size[0])', '0.0', '0.0',
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'0.0' , '0.0', '0.0',
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'0.0' , '0.0', '0.0'],
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['np.cos(np.pi*2*nodes[...,0]/size[0])*np.pi*2/size[0]','0.0', '0.0']),
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(['0.0', '0.0', '0.0',
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'0.0', 'np.cos(np.pi*2*nodes[...,1]/size[1])', '0.0',
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'0.0', '0.0', '0.0'],
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['0.0', '-np.sin(np.pi*2*nodes[...,1]/size[1])*np.pi*2/size[1]', '0.0']),
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(['1.0', '0.0', '0.0',
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'0.0', '0.0', '0.0',
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'0.0', '0.0', '2*np.cos(np.pi*2*nodes[...,2]/size[2])' ],
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['0.0', '0.0', '-2.0*np.pi*2/size[2]*np.sin(np.pi*2*nodes[...,2]/size[2])']
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),
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([ '23.0', '0.0', 'np.sin(np.pi*2*nodes[...,2]/size[2])',
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'0.0', '100.0', 'np.sin(np.pi*2*nodes[...,2]/size[2])',
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'0.0', '0.0', 'np.sin(np.pi*2*nodes[...,2]/size[2])'],
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['np.cos(np.pi*2*nodes[...,2]/size[2])*np.pi*2/size[2]',\
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'np.cos(np.pi*2*nodes[...,2]/size[2])*np.pi*2/size[2]', \
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'np.cos(np.pi*2*nodes[...,2]/size[2])*np.pi*2/size[2]']),
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(['400.0', '0.0', '0.0',
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'np.sin(np.pi*2*nodes[...,0]/size[0])', 'np.sin(np.pi*2*nodes[...,1]/size[1])', 'np.sin(np.pi*2*nodes[...,2]/size[2])',
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'0.0', '10.0', '6.0'],
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['0.0','np.sum(np.cos(np.pi*2*nodes/size)*np.pi*2/size,axis=-1)', '0.0' ]),
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(['np.sin(np.pi*2*nodes[...,0]/size[0])', '0.0', '0.0'],
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['np.cos(np.pi*2*nodes[...,0]/size[0])*np.pi*2/size[0]',]),
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(['0.0', 'np.cos(np.pi*2*nodes[...,1]/size[1])', '0.0' ],
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['-np.sin(np.pi*2*nodes[...,1]/size[1])*np.pi*2/size[1]'])
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]
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@pytest.mark.parametrize('field_def,div_def',div_test_data)
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def test_div(self,field_def,div_def):
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size = np.random.random(3)+1.0
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cells = np.random.randint(8,32,(3))
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nodes = grid_filters.coordinates0_point(cells,size)
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my_locals = locals() # needed for list comprehension
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field = np.stack([np.broadcast_to(eval(f,globals(),my_locals),cells) for f in field_def],axis=-1)
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field = field.reshape(tuple(cells) + ((3,3) if len(field_def)==9 else (3,)))
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div = np.stack([np.broadcast_to(eval(c,globals(),my_locals),cells) for c in div_def], axis=-1)
|
|
if len(div_def)==3:
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|
div = div.reshape(tuple(cells) + ((3,)))
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|
else:
|
|
div=div.reshape(tuple(cells))
|
|
|
|
assert np.allclose(div,grid_filters.divergence(size,field))
|
|
|
|
|
|
def test_ravel_index(self):
|
|
cells = np.random.randint(8,32,(3))
|
|
|
|
indices = np.block(np.meshgrid(np.arange(cells[0]),
|
|
np.arange(cells[1]),
|
|
np.arange(cells[2]),indexing='ij')).reshape(tuple(cells)+(3,),order='F')
|
|
x,y,z = map(np.random.randint,cells)
|
|
assert grid_filters.ravel_index(indices)[x,y,z] == np.arange(0,np.product(cells)).reshape(cells,order='F')[x,y,z]
|
|
|
|
def test_unravel_index(self):
|
|
cells = np.random.randint(8,32,(3))
|
|
indices = np.arange(np.prod(cells)).reshape(cells,order='F')
|
|
x,y,z = map(np.random.randint,cells)
|
|
assert np.all(grid_filters.unravel_index(indices)[x,y,z] == [x,y,z])
|
|
|
|
def test_ravel_unravel_index(self):
|
|
cells = np.random.randint(8,32,(3))
|
|
indices = np.random.randint(0,np.prod(cells),cells).reshape(cells)
|
|
assert np.all(indices==grid_filters.ravel_index(grid_filters.unravel_index(indices)))
|
|
|
|
def test_unravel_ravel_index(self):
|
|
cells = np.hstack([np.random.randint(8,32,(3)),1])
|
|
indices = np.block([np.random.randint(0,cells[0],cells),
|
|
np.random.randint(0,cells[1],cells),
|
|
np.random.randint(0,cells[2],cells)])
|
|
assert np.all(indices==grid_filters.unravel_index(grid_filters.ravel_index(indices)))
|