223031012
/
DAMASK_EICMD
mirror of https://github.com/achalhp/DAMASK_EICMD.git
1
0
Fork 0
Code Issues Projects Releases Wiki Activity

merge test into gridFilters

This commit is contained in:
f.basile 2020-05-06 14:26:15 +02:00
parent a122a307b9
commit 26f55781ae
3 changed files with 126 additions and 174 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

63
python/tests/test_div.py
View File

@ -1,63 +0,0 @@
import pytest
import numpy as np
from damask import grid_filters
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']
),
(['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])']
),
([ '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))

49
python/tests/test_grad.py
View File

@ -1,49 +0,0 @@
import pytest
import numpy as np
from damask import grid_filters
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']),
(['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])']),
(['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.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' ])]
@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
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,) if len(field_def)==3 else (1,)))
grad = np.stack([np.broadcast_to(eval(c,globals(),my_locals),grid) for c in grad_def], axis=-1)
grad = grad.reshape(tuple(grid) + ((3,3) if len(grad_def)==9 else (3,)))
assert np.allclose(grad,grid_filters.gradient(size,field))

142
python/tests/test_grid_filters.py
View File

@ -139,68 +139,32 @@ class TestGridFilters:
assert np.allclose(differential_operator(size,field),0.0) assert np.allclose(differential_operator(size,field),0.0)
@pytest.mark.parametrize('field_def,curl_def', grad_test_data = [
[(['np.sin(np.pi*2*nodes[...,2]/size[2])', '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' ],
['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'],
['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']
)
])
def test_curl(self,field_def,curl_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,)))
curl = np.stack([np.broadcast_to(eval(c,globals(),my_locals),grid) for c in curl_def], axis=-1)
curl = curl.reshape(tuple(grid) + ((3,3) if len(curl_def)==9 else (3,)))
assert np.allclose(curl,grid_filters.curl(size,field))
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', ['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', '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', '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.pi*2/size[1]*np.sin(np.pi*2*nodes[...,1]/size[1])', '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', '0.0', '0.0' ]),
(['1.0', '0.0', '2.0*np.cos(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', '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])']), '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])'], (['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', '-np.sin(np.pi*2*nodes[...,2]/size[2])*np.pi*2/size[2]',
'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', '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[...,0]/size[0])','np.sin(np.pi*2*nodes[...,1]/size[1])',\
'np.sin(np.pi*2*nodes[...,2]/size[2])'], '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', ['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', '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]']), '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[...,0]/size[0])' ],
['np.cos(np.pi*2*nodes[...,0]/size[0])*np.pi*2/size[0]', '0.0', '0.0' ]), ['np.cos(np.pi*2*nodes[...,0]/size[0])*np.pi*2/size[0]', '0.0', '0.0' ]),
(['8.0' ], (['8.0' ],
['0.0', '0.0', '0.0' ]) ['0.0', '0.0', '0.0' ])]
]
@pytest.mark.parametrize('field_def,grad_def',grad_test_data) @pytest.mark.parametrize('field_def,grad_def',grad_test_data)
@ -218,3 +182,103 @@ class TestGridFilters:
assert np.allclose(grad,grid_filters.gradient(size,field)) 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' ],
['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'],
['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']),
(['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])',
'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])',
'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])'],
['0.0', '0.0', '0.0',
'0.0', '0.0', '0.0',
'0.0', '0.0', '0.0']),
(['5.0', '0.0', '0.0',
'0.0', '0.0', '0.0',
'0.0', '0.0', '2*np.cos(np.pi*2*nodes[...,1]/size[1])'],
['0.0', '0.0', '-2*np.pi*2/size[1]*np.sin(np.pi*2*nodes[...,1]/size[1])',
'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])', '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])', \
'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]'])]
@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))
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,)))
curl = np.stack([np.broadcast_to(eval(c,globals(),my_locals),grid) for c in curl_def], axis=-1)
curl = curl.reshape(tuple(grid) + ((3,3) if len(curl_def)==9 else (3,)))
assert np.allclose(curl,grid_filters.curl(size,field))
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']),
(['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])']
),
([ '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))