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less whitespace, 

alingment only within one vector/tensor
This commit is contained in:
Martin Diehl 2020-05-10 13:02:26 +02:00
parent c8701a4665
commit 6928a8290f
1 changed files with 74 additions and 80 deletions
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154
python/tests/test_grid_filters.py
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@ -140,44 +140,45 @@ class TestGridFilters:
grad_test_data = [
(['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', '0.0',
'0.0', '0.0', '0.0',
'0.0', '0.0', '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', '0.0',
'0.0', '0.0', '0.0',
'0.0', '0.0', '0.0']),
(['0.0', 'np.cos(np.pi*2*nodes[...,1]/size[1])', '0.0' ],
['0.0', '0.0', '0.0',
'0.0', '-np.pi*2/size[1]*np.sin(np.pi*2*nodes[...,1]/size[1])', '0.0',
'0.0', '0.0', '0.0' ]),
(['0.0', 'np.cos(np.pi*2*nodes[...,1]/size[1])', '0.0' ],
['0.0', '0.0', '0.0',
'0.0', '-np.pi*2/size[1]*np.sin(np.pi*2*nodes[...,1]/size[1])', '0.0',
'0.0', '0.0', '0.0' ]),
(['1.0', '0.0', '2.0*np.cos(np.pi*2*nodes[...,2]/size[2])'],
['0.0', '0.0', '0.0',
'0.0', '0.0', '0.0',
'0.0', '0.0', '-2.0*np.pi*2/size[2]*np.sin(np.pi*2*nodes[...,2]/size[2])']),
(['1.0', '0.0', '2.0*np.cos(np.pi*2*nodes[...,2]/size[2])'],
['0.0', '0.0', '0.0',
'0.0', '0.0', '0.0',
'0.0', '0.0', '-2.0*np.pi*2/size[2]*np.sin(np.pi*2*nodes[...,2]/size[2])']),
(['np.cos(np.pi*2*nodes[...,2]/size[2])', '3.0', 'np.sin(np.pi*2*nodes[...,2]/size[2])'],
['0.0', '0.0', '-np.sin(np.pi*2*nodes[...,2]/size[2])*np.pi*2/size[2]',
'0.0', '0.0', '0.0',
'0.0', '0.0', 'np.cos(np.pi*2*nodes[...,2]/size[2])*np.pi*2/size[2]']),
(['np.cos(np.pi*2*nodes[...,2]/size[2])', '3.0', 'np.sin(np.pi*2*nodes[...,2]/size[2])'],
['0.0', '0.0', '-np.sin(np.pi*2*nodes[...,2]/size[2])*np.pi*2/size[2]',
'0.0', '0.0', '0.0',
'0.0', '0.0', ' np.cos(np.pi*2*nodes[...,2]/size[2])*np.pi*2/size[2]']),
(['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])'],
['np.cos(np.pi*2*nodes[...,0]/size[0])*np.pi*2/size[0]', '0.0', '0.0',
'0.0', 'np.cos(np.pi*2*nodes[...,1]/size[1])*np.pi*2/size[1]', '0.0',
'0.0', '0.0', 'np.cos(np.pi*2*nodes[...,2]/size[2])*np.pi*2/size[2]']),
(['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])'],
['np.cos(np.pi*2*nodes[...,0]/size[0])*np.pi*2/size[0]', '0.0', '0.0',
'0.0', 'np.cos(np.pi*2*nodes[...,1]/size[1])*np.pi*2/size[1]', '0.0',
'0.0', '0.0', 'np.cos(np.pi*2*nodes[...,2]/size[2])*np.pi*2/size[2]']),
(['np.sin(np.pi*2*nodes[...,0]/size[0])' ],
['np.cos(np.pi*2*nodes[...,0]/size[0])*np.pi*2/size[0]', '0.0', '0.0' ]),
(['np.sin(np.pi*2*nodes[...,0]/size[0])'],
['np.cos(np.pi*2*nodes[...,0]/size[0])*np.pi*2/size[0]', '0.0', '0.0']),
(['8.0' ],
['0.0', '0.0', '0.0' ])]
(['8.0'],
['0.0', '0.0', '0.0' ])
]
@pytest.mark.parametrize('field_def,grad_def',grad_test_data)
def test_grad(self,field_def,grad_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
@ -187,22 +188,19 @@ class TestGridFilters:
grad = grad.reshape(tuple(grid) + ((3,3) if len(grad_def)==9 else (3,)))
assert np.allclose(grad,grid_filters.gradient(size,field))
curl_test_data =[
(['np.sin(np.pi*2*nodes[...,2]/size[2])', '0.0', '0.0',
'0.0', '0.0', '0.0',
'0.0', '0.0', '0.0'],
curl_test_data = [
(['np.sin(np.pi*2*nodes[...,2]/size[2])', '0.0', '0.0',
'0.0', '0.0', '0.0',
'0.0', '0.0', '0.0'],
['0.0' , '0.0', '0.0',
'np.cos(np.pi*2*nodes[...,2]/size[2])*np.pi*2/size[2]', '0.0', '0.0',
'0.0', '0.0', '0.0']),
(['np.cos(np.pi*2*nodes[...,1]/size[1])', '0.0', '0.0',
'0.0', '0.0', '0.0',
'np.cos(np.pi*2*nodes[...,0]/size[0])', '0.0', '0.0'],
(['np.cos(np.pi*2*nodes[...,1]/size[1])', '0.0', '0.0',
'0.0', '0.0', '0.0',
'np.cos(np.pi*2*nodes[...,0]/size[0])', '0.0', '0.0'],
['0.0', '0.0', '0.0',
'0.0', '0.0', '0.0',
'np.sin(np.pi*2*nodes[...,1]/size[1])*np.pi*2/size[1]', '0.0', '0.0']),
@ -221,20 +219,22 @@ class TestGridFilters:
'0.0', '0.0', '0.0',
'0.0', '0.0', '0.0']),
(['4*np.sin(np.pi*2*nodes[...,2]/size[2])', \
'8*np.sin(np.pi*2*nodes[...,0]/size[0])', \
([ '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])'],
['16*np.pi*2/size[1]*np.cos(np.pi*2*nodes[...,1]/size[1])', \
'4*np.pi*2/size[2]*np.cos(np.pi*2*nodes[...,2]/size[2])', \
['16*np.pi*2/size[1]*np.cos(np.pi*2*nodes[...,1]/size[1])',
'4*np.pi*2/size[2]*np.cos(np.pi*2*nodes[...,2]/size[2])',
'8*np.pi*2/size[0]*np.cos(np.pi*2*nodes[...,0]/size[0])']),
(['0.0', 'np.cos(np.pi*2*nodes[...,0]/size[0])+5*np.cos(np.pi*2*nodes[...,2]/size[2])', '0.0'],
['5*np.sin(np.pi*2*nodes[...,2]/size[2])*np.pi*2/size[2]',\
'0.0',\
'-np.sin(np.pi*2*nodes[...,0]/size[0])*np.pi*2/size[0]'])]
(['0.0',
'np.cos(np.pi*2*nodes[...,0]/size[0])+5*np.cos(np.pi*2*nodes[...,2]/size[2])',
'0.0'],
['5*np.sin(np.pi*2*nodes[...,2]/size[2])*np.pi*2/size[2]',
'0.0',
'-np.sin(np.pi*2*nodes[...,0]/size[0])*np.pi*2/size[0]'])
]
@pytest.mark.parametrize('field_def,curl_def',curl_test_data)
@pytest.mark.parametrize('field_def,curl_def',curl_test_data)
def test_curl(self,field_def,curl_def):
size = np.random.random(3)+1.0
grid = np.random.randint(8,32,(3))
@ -251,47 +251,43 @@ class TestGridFilters:
div_test_data =[
(['np.sin(np.pi*2*nodes[...,0]/size[0])', '0.0', '0.0',
'0.0' , '0.0', '0.0',
'0.0' , '0.0', '0.0'],
['np.cos(np.pi*2*nodes[...,0]/size[0])*np.pi*2/size[0]' ,'0.0', '0.0']),
(['np.sin(np.pi*2*nodes[...,0]/size[0])', '0.0', '0.0',
'0.0' , '0.0', '0.0',
'0.0' , '0.0', '0.0'],
['np.cos(np.pi*2*nodes[...,0]/size[0])*np.pi*2/size[0]','0.0', '0.0']),
(['0.0', '0.0', '0.0',
'0.0', 'np.cos(np.pi*2*nodes[...,1]/size[1])', '0.0',
'0.0', '0.0', '0.0'],
(['0.0', '0.0', '0.0',
'0.0', 'np.cos(np.pi*2*nodes[...,1]/size[1])', '0.0',
'0.0', '0.0', '0.0'],
['0.0', '-np.sin(np.pi*2*nodes[...,1]/size[1])*np.pi*2/size[1]', '0.0']),
(['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])']
(['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])'],
([ '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]']),
'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' ]),
(['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.sum(np.cos(np.pi*2*nodes/size)*np.pi*2/size,axis=-1)', '0.0' ]),
(['np.sin(np.pi*2*nodes[...,0]/size[0])', '0.0', '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' ],
(['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)
@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))
@ -308,5 +304,3 @@ class TestGridFilters:
div=div.reshape(tuple(grid))
assert np.allclose(div,grid_filters.divergence(size,field))