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

grad test done

This commit is contained in:
f.basile 2020-05-06 13:05:53 +02:00
parent 8b4baecdee
commit d4091a21a8
1 changed files with 49 additions and 0 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

49
python/tests/test_grad.py Normal file
View File

@ -0,0 +1,49 @@
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))