191 lines
8.8 KiB
Python
191 lines
8.8 KiB
Python
import random
|
|
import os
|
|
|
|
import pytest
|
|
import numpy as np
|
|
from scipy import stats
|
|
import h5py
|
|
|
|
from damask import util
|
|
|
|
|
|
class TestUtil:
|
|
|
|
def test_execute_direct(self):
|
|
out,err = util.execute('echo test')
|
|
assert out=='test\n' and err==''
|
|
|
|
def test_execute_env(self):
|
|
out,err = util.execute('sh -c "echo $test_for_execute"',env={'test_for_execute':'test'})
|
|
assert out=='test\n' and err==''
|
|
|
|
def test_execute_runtime_error(self):
|
|
with pytest.raises(RuntimeError):
|
|
util.execute('false')
|
|
|
|
@pytest.mark.parametrize('input,output',
|
|
[
|
|
([0,-2],[0,-1]),
|
|
([-0.5,0.5],[-1,1]),
|
|
([1./2.,1./3.],[3,2]),
|
|
([2./3.,1./2.,1./3.],[4,3,2]),
|
|
])
|
|
|
|
def test_scale2coprime(self,input,output):
|
|
assert np.allclose(util.scale_to_coprime(np.array(input)),
|
|
np.array(output).astype(int))
|
|
|
|
def test_lackofprecision(self):
|
|
with pytest.raises(ValueError):
|
|
util.scale_to_coprime(np.array([1/333.333,1,1]))
|
|
|
|
|
|
@pytest.mark.parametrize('rv',[stats.rayleigh(),stats.weibull_min(1.2),stats.halfnorm(),stats.pareto(2.62)])
|
|
def test_hybridIA(self,rv):
|
|
bins = np.linspace(0,10,100000)
|
|
centers = (bins[1:]+bins[:-1])/2
|
|
N_samples = bins.shape[0]-1000
|
|
dist = rv.pdf(centers)
|
|
selected = util.hybrid_IA(dist,N_samples)
|
|
dist_sampled = np.histogram(centers[selected],bins)[0]/N_samples*np.sum(dist)
|
|
assert np.sqrt(((dist - dist_sampled) ** 2).mean()) < .025 and selected.shape[0]==N_samples
|
|
|
|
@pytest.mark.parametrize('point,direction,normalize,keepdims,answer',
|
|
[
|
|
([1,0,0],'z',False,True, [1,0,0]),
|
|
([1,0,0],'z',True, False,[1,0]),
|
|
([0,1,1],'z',False,True, [0,0.5,0]),
|
|
([0,1,1],'y',True, False,[0.41421356,0]),
|
|
([1,1,0],'x',False,False,[0.5,0]),
|
|
([1,1,1],'y',True, True, [0.3660254, 0,0.3660254]),
|
|
])
|
|
def test_project_stereographic(self,point,direction,normalize,keepdims,answer):
|
|
assert np.allclose(util.project_stereographic(np.array(point),direction=direction,
|
|
normalize=normalize,keepdims=keepdims),answer)
|
|
|
|
@pytest.mark.parametrize('fro,to,mode,answer',
|
|
[
|
|
((),(1,),'left',(1,)),
|
|
((1,),(7,),'right',(1,)),
|
|
((1,2),(1,1,2,2),'right',(1,1,2,1)),
|
|
((1,2),(1,1,2,2),'left',(1,1,1,2)),
|
|
((1,2,3),(1,1,2,3,4),'right',(1,1,2,3,1)),
|
|
((10,2),(10,3,2,2,),'right',(10,1,2,1)),
|
|
((10,2),(10,3,2,2,),'left',(10,1,1,2)),
|
|
((2,2,3),(2,2,2,3,4),'left',(1,2,2,3,1)),
|
|
((2,2,3),(2,2,2,3,4),'right',(2,2,1,3,1)),
|
|
])
|
|
def test_shapeshifter(self,fro,to,mode,answer):
|
|
assert util.shapeshifter(fro,to,mode) == answer
|
|
|
|
@pytest.mark.parametrize('fro,to,mode',
|
|
[
|
|
((10,3,4),(10,3,2,2),'left'),
|
|
((2,3),(10,3,2,2),'right'),
|
|
])
|
|
def test_invalid_shapeshifter(self,fro,to,mode):
|
|
with pytest.raises(ValueError):
|
|
util.shapeshifter(fro,to,mode)
|
|
|
|
@pytest.mark.parametrize('a,b,answer',
|
|
[
|
|
((),(1,),(1,)),
|
|
((1,),(),(1,)),
|
|
((1,),(7,),(1,7)),
|
|
((2,),(2,2),(2,2)),
|
|
((1,2),(2,2),(1,2,2)),
|
|
((1,2,3),(2,3,4),(1,2,3,4)),
|
|
((1,2,3),(1,2,3),(1,2,3)),
|
|
])
|
|
def test_shapeblender(self,a,b,answer):
|
|
assert util.shapeblender(a,b) == answer
|
|
|
|
@pytest.mark.parametrize('style',[util.emph,util.deemph,util.warn,util.strikeout])
|
|
def test_decorate(self,style):
|
|
assert 'DAMASK' in style('DAMASK')
|
|
|
|
@pytest.mark.parametrize('complete',[True,False])
|
|
def test_D3D_base_group(self,tmp_path,complete):
|
|
base_group = ''.join(random.choices('DAMASK', k=10))
|
|
with h5py.File(tmp_path/'base_group.dream3d','w') as f:
|
|
f.create_group(os.path.join(base_group,'_SIMPL_GEOMETRY'))
|
|
if complete:
|
|
f[os.path.join(base_group,'_SIMPL_GEOMETRY')].create_dataset('SPACING',data=np.ones(3))
|
|
|
|
if complete:
|
|
assert base_group == util.DREAM3D_base_group(tmp_path/'base_group.dream3d')
|
|
else:
|
|
with pytest.raises(ValueError):
|
|
util.DREAM3D_base_group(tmp_path/'base_group.dream3d')
|
|
|
|
@pytest.mark.parametrize('complete',[True,False])
|
|
def test_D3D_cell_data_group(self,tmp_path,complete):
|
|
base_group = ''.join(random.choices('DAMASK', k=10))
|
|
cell_data_group = ''.join(random.choices('KULeuven', k=10))
|
|
cells = np.random.randint(1,50,3)
|
|
with h5py.File(tmp_path/'cell_data_group.dream3d','w') as f:
|
|
f.create_group(os.path.join(base_group,'_SIMPL_GEOMETRY'))
|
|
f[os.path.join(base_group,'_SIMPL_GEOMETRY')].create_dataset('SPACING',data=np.ones(3))
|
|
f[os.path.join(base_group,'_SIMPL_GEOMETRY')].create_dataset('DIMENSIONS',data=cells[::-1])
|
|
f[base_group].create_group(cell_data_group)
|
|
if complete:
|
|
f[os.path.join(base_group,cell_data_group)].create_dataset('data',shape=np.append(cells,1))
|
|
|
|
if complete:
|
|
assert cell_data_group == util.DREAM3D_cell_data_group(tmp_path/'cell_data_group.dream3d')
|
|
else:
|
|
with pytest.raises(ValueError):
|
|
util.DREAM3D_cell_data_group(tmp_path/'cell_data_group.dream3d')
|
|
|
|
|
|
@pytest.mark.parametrize('full,reduced',[({}, {}),
|
|
({'A':{}}, {}),
|
|
({'A':{'B':{}}}, {}),
|
|
({'A':{'B':'C'}},)*2,
|
|
({'A':{'B':{},'C':'D'}}, {'A':{'C':'D'}})])
|
|
def test_prune(self,full,reduced):
|
|
assert util.dict_prune(full) == reduced
|
|
|
|
|
|
@pytest.mark.parametrize('full,reduced',[({}, {}),
|
|
({'A':{}}, {}),
|
|
({'A':'F'}, 'F'),
|
|
({'A':{'B':{}}}, {}),
|
|
({'A':{'B':'C'}}, 'C'),
|
|
({'A':1,'B':2},)*2,
|
|
({'A':{'B':'C','D':'E'}}, {'B':'C','D':'E'}),
|
|
({'B':'C','D':'E'},)*2,
|
|
({'A':{'B':{},'C':'D'}}, {'B':{},'C':'D'})])
|
|
def test_flatten(self,full,reduced):
|
|
assert util.dict_flatten(full) == reduced
|
|
|
|
|
|
def test_double_Bravais_to_Miller(self):
|
|
with pytest.raises(KeyError):
|
|
util.Bravais_to_Miller(uvtw=np.ones(4),hkil=np.ones(4))
|
|
|
|
def test_double_Miller_to_Bravais(self):
|
|
with pytest.raises(KeyError):
|
|
util.Miller_to_Bravais(uvw=np.ones(4),hkl=np.ones(4))
|
|
|
|
|
|
@pytest.mark.parametrize('vector',np.array([
|
|
[1,0,0],
|
|
[1,1,0],
|
|
[1,1,1],
|
|
[1,0,-2],
|
|
]))
|
|
@pytest.mark.parametrize('kw_Miller,kw_Bravais',[('uvw','uvtw'),('hkl','hkil')])
|
|
def test_Miller_Bravais_Miller(self,vector,kw_Miller,kw_Bravais):
|
|
assert np.all(vector == util.Bravais_to_Miller(**{kw_Bravais:util.Miller_to_Bravais(**{kw_Miller:vector})}))
|
|
|
|
@pytest.mark.parametrize('vector',np.array([
|
|
[1,0,-1,2],
|
|
[1,-1,0,3],
|
|
[1,1,-2,-3],
|
|
[0,0,0,1],
|
|
]))
|
|
@pytest.mark.parametrize('kw_Miller,kw_Bravais',[('uvw','uvtw'),('hkl','hkil')])
|
|
def test_Bravais_Miller_Bravais(self,vector,kw_Miller,kw_Bravais):
|
|
assert np.all(vector == util.Miller_to_Bravais(**{kw_Miller:util.Bravais_to_Miller(**{kw_Bravais:vector})}))
|