Tests for gradient
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@ -168,3 +168,63 @@ class TestGridFilters:
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curl = curl.reshape(tuple(grid) + ((3,3) if len(curl_def)==9 else (3,)))
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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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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])','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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]
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# @pytest.mark.parametrize('field_def,grad_def',
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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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# ])
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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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size = np.array([1.0,1.0,1.0])
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grid = np.array([2,5,3])
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nodes = grid_filters.cell_coord0(grid,size)
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# print('y nodes are',nodes[...,1])
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# print('inner bracket is',np.pi*2*nodes[...,1]/size[1])
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my_locals = locals() # needed for list comprehension
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print('field length is',len(field_def))
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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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print('field initial shape is',field.shape)
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field = field.reshape(tuple(grid) + ((3,3) if len(field_def)==9 else (3,)))
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print('field is',field.shape)
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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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print('gradient is',grad.shape)
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print('code gradient is',grid_filters.gradient(size,field))
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assert np.allclose(grad,grid_filters.gradient(size,field))
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