merge test into gridFilters
This commit is contained in:
f.basile
parent
a122a307b9
commit
26f55781ae
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@ -1,63 +0,0 @@
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import pytest
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import numpy as np
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from damask import grid_filters
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class TestGridFilters:
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div_test_data =[(['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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),
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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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),
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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]','np.cos(np.pi*2*nodes[...,2]/size[2])*np.pi*2/size[2]', 'np.cos(np.pi*2*nodes[...,2]/size[2])*np.pi*2/size[2]']
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),
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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.cos(np.pi*2*nodes[...,0]/size[0])*np.pi*2/size[0]+np.cos(np.pi*2*nodes[...,1]/size[1])*np.pi*2/size[1]+np.cos(np.pi*2*nodes[...,2]/size[2])*np.pi*2/size[2]','0.0' ]
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),
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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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),
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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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]
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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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grid = np.random.randint(8,32,(3))
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nodes = grid_filters.cell_coord0(grid,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),grid) for f in field_def],axis=-1)
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field = field.reshape(tuple(grid) + ((3,3) if len(field_def)==9 else (3,)))
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div = np.stack([np.broadcast_to(eval(c,globals(),my_locals),grid) for c in div_def], axis=-1)
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if len(div_def)==3:
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div = div.reshape(tuple(grid) + ((3,)))
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else:
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div=div.reshape(tuple(grid))
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assert np.allclose(div,grid_filters.divergence(size,field))
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@ -1,49 +0,0 @@
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import pytest
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import numpy as np
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from damask import grid_filters
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class TestGridFilters:
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grad_test_data = [(['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])','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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@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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grid = np.random.randint(8,32,(3))
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nodes = grid_filters.cell_coord0(grid,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),grid) for f in field_def],axis=-1)
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field = field.reshape(tuple(grid) + ((3,) if len(field_def)==3 else (1,)))
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grad = np.stack([np.broadcast_to(eval(c,globals(),my_locals),grid) for c in grad_def], axis=-1)
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grad = grad.reshape(tuple(grid) + ((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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@ -139,68 +139,32 @@ class TestGridFilters:
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assert np.allclose(differential_operator(size,field),0.0)
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@pytest.mark.parametrize('field_def,curl_def',
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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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),
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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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)
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])
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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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grid = np.random.randint(8,32,(3))
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nodes = grid_filters.cell_coord0(grid,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),grid) for f in field_def],axis=-1)
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field = field.reshape(tuple(grid) + ((3,3) if len(field_def)==9 else (3,)))
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curl = np.stack([np.broadcast_to(eval(c,globals(),my_locals),grid) for c in curl_def], axis=-1)
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curl = curl.reshape(tuple(grid) + ((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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grad_test_data = [(['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])','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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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])','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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@pytest.mark.parametrize('field_def,grad_def',grad_test_data)
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@ -218,3 +182,103 @@ class TestGridFilters:
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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])', '8*np.sin(np.pi*2*nodes[...,0]/size[0])', '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', 'np.cos(np.pi*2*nodes[...,0]/size[0])+5*np.cos(np.pi*2*nodes[...,2]/size[2])', '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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@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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grid = np.random.randint(8,32,(3))
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nodes = grid_filters.cell_coord0(grid,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),grid) for f in field_def],axis=-1)
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field = field.reshape(tuple(grid) + ((3,3) if len(field_def)==9 else (3,)))
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curl = np.stack([np.broadcast_to(eval(c,globals(),my_locals),grid) for c in curl_def], axis=-1)
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curl = curl.reshape(tuple(grid) + ((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',
|
||||
'0.0', '0.0', '0.0',
|
||||
'0.0', '0.0', '2*np.cos(np.pi*2*nodes[...,2]/size[2])' ],
|
||||
['0.0', '0.0', '-2.0*np.pi*2/size[2]*np.sin(np.pi*2*nodes[...,2]/size[2])']
|
||||
),
|
||||
([ '23.0', '0.0', 'np.sin(np.pi*2*nodes[...,2]/size[2])',
|
||||
'0.0', '100.0', 'np.sin(np.pi*2*nodes[...,2]/size[2])',
|
||||
'0.0', '0.0', 'np.sin(np.pi*2*nodes[...,2]/size[2])'],
|
||||
['np.cos(np.pi*2*nodes[...,2]/size[2])*np.pi*2/size[2]','np.cos(np.pi*2*nodes[...,2]/size[2])*np.pi*2/size[2]', 'np.cos(np.pi*2*nodes[...,2]/size[2])*np.pi*2/size[2]']),
|
||||
(['400.0', '0.0', '0.0',
|
||||
'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])',
|
||||
'0.0', '10.0', '6.0' ],
|
||||
['0.0','np.cos(np.pi*2*nodes[...,0]/size[0])*np.pi*2/size[0]+np.cos(np.pi*2*nodes[...,1]/size[1])*np.pi*2/size[1]+np.cos(np.pi*2*nodes[...,2]/size[2])*np.pi*2/size[2]','0.0' ]),
|
||||
(['np.sin(np.pi*2*nodes[...,0]/size[0])', '0.0', '0.0'],
|
||||
['np.cos(np.pi*2*nodes[...,0]/size[0])*np.pi*2/size[0]',]),
|
||||
(['0.0', 'np.cos(np.pi*2*nodes[...,1]/size[1])', '0.0' ],
|
||||
['-np.sin(np.pi*2*nodes[...,1]/size[1])*np.pi*2/size[1]'])
|
||||
]
|
||||
|
||||
@pytest.mark.parametrize('field_def,div_def',div_test_data)
|
||||
|
||||
def test_div(self,field_def,div_def):
|
||||
size = np.random.random(3)+1.0
|
||||
grid = np.random.randint(8,32,(3))
|
||||
|
||||
nodes = grid_filters.cell_coord0(grid,size)
|
||||
my_locals = locals() # needed for list comprehension
|
||||
|
||||
field = np.stack([np.broadcast_to(eval(f,globals(),my_locals),grid) for f in field_def],axis=-1)
|
||||
field = field.reshape(tuple(grid) + ((3,3) if len(field_def)==9 else (3,)))
|
||||
div = np.stack([np.broadcast_to(eval(c,globals(),my_locals),grid) for c in div_def], axis=-1)
|
||||
if len(div_def)==3:
|
||||
div = div.reshape(tuple(grid) + ((3,)))
|
||||
else:
|
||||
div=div.reshape(tuple(grid))
|
||||
|
||||
assert np.allclose(div,grid_filters.divergence(size,field))
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue