533 lines
19 KiB
Python
533 lines
19 KiB
Python
import sys
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import random
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import pydoc
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import pytest
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import numpy as np
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from scipy import stats
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import h5py
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from damask import util
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class TestUtil:
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@pytest.mark.xfail(sys.platform == 'win32', reason='echo is not a Windows command')
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def test_run_direct(self):
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out,err = util.run('echo test')
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assert out=='test\n' and err==''
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@pytest.mark.xfail(sys.platform == 'win32', reason='echo is not a Windows command')
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def test_run_env(self):
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out,err = util.run('sh -c "echo $test_for_execute"',env={'test_for_execute':'test'})
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assert out=='test\n' and err==''
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@pytest.mark.xfail(sys.platform == 'win32', reason='false is not a Windows command')
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def test_run_runtime_error(self):
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with pytest.raises(RuntimeError):
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util.run('false')
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@pytest.mark.parametrize('input,glue,quote,output',
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[
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(None,'',False,'None'),
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([None,None],'\n',False,'None\nNone'),
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([-0.5,0.5],'=',False,'-0.5=0.5'),
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([1,2,3],'_',False,'1_2_3'),
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([1,2,3],'/',True,'"1"/"2"/"3"'),
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])
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def test_srepr(self,input,glue,quote,output):
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assert output == util.srepr(input,glue,quote)
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@pytest.mark.parametrize('input,output',
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[
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([0,-2],[0,-1]),
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([-0.5,0.5],[-1,1]),
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([1./2.,1./3.],[3,2]),
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([2./3.,1./2.,1./3.],[4,3,2]),
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])
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def test_scale2coprime(self,input,output):
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assert np.allclose(util.scale_to_coprime(np.array(input)),
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np.array(output).astype(int))
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def test_lackofprecision(self):
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with pytest.raises(ValueError):
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util.scale_to_coprime(np.array([1/333.333,1,1]))
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@pytest.mark.parametrize('rv',[stats.rayleigh(),stats.weibull_min(1.2),stats.halfnorm(),stats.pareto(2.62)])
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def test_hybridIA_distribution(self,rv):
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bins = np.linspace(0,10,100000)
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centers = (bins[1:]+bins[:-1])/2
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N_samples = bins.shape[0]-1000
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dist = rv.pdf(centers)
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selected = util.hybrid_IA(dist,N_samples)
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dist_sampled = np.histogram(centers[selected],bins)[0]/N_samples*np.sum(dist)
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assert np.sqrt(((dist - dist_sampled) ** 2).mean()) < .025 and selected.shape[0]==N_samples
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def test_hybridIA_constant(self):
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N_bins = np.random.randint(20,400)
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m = np.random.randint(1,20)
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N_samples = m * N_bins
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dist = np.ones(N_bins)*np.random.rand()
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assert np.all(np.sort(util.hybrid_IA(dist,N_samples))==np.arange(N_samples).astype(int)//m)
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def test_hybridIA_linear(self):
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N_points = np.random.randint(10,200)
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m = np.random.randint(1,20)
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dist = np.arange(N_points)
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N_samples = m * np.sum(dist)
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assert np.all(np.bincount(util.hybrid_IA(dist*np.random.rand(),N_samples)) == dist*m)
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@pytest.mark.parametrize('point,direction,normalize,keepdims,answer',
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[
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([1,0,0],'z',False,True, [1,0,0]),
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([1,0,0],'z',True, False,[1,0]),
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([0,1,1],'z',False,True, [0,0.5,0]),
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([0,1,1],'y',True, False,[0.41421356,0]),
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([1,1,0],'x',False,False,[0.5,0]),
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([1,1,1],'y',True, True, [0.3660254, 0,0.3660254]),
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])
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def test_project_equal_angle(self,point,direction,normalize,keepdims,answer):
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assert np.allclose(util.project_equal_angle(np.array(point),direction=direction,
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normalize=normalize,keepdims=keepdims),answer)
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@pytest.mark.parametrize('point,direction,normalize,keepdims,answer',
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[
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([1,0,0],'z',False,True, [1,0,0]),
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([1,0,0],'z',True, False,[1,0]),
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([0,1,1],'z',False,True, [0,0.70710678,0]),
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([0,1,1],'y',True, False,[0.5411961,0]),
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([1,1,0],'x',False,False,[0.70710678,0]),
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([1,1,1],'y',True, True, [0.45970084,0,0.45970084]),
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])
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def test_project_equal_area(self,point,direction,normalize,keepdims,answer):
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assert np.allclose(util.project_equal_area(np.array(point),direction=direction,
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normalize=normalize,keepdims=keepdims),answer)
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@pytest.mark.parametrize('fro,to,mode,answer',
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[
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((),(1,),'left',(1,)),
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((1,),(7,),'right',(1,)),
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((1,2),(1,1,2,2),'right',(1,1,2,1)),
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((1,2),(1,1,2,2),'left',(1,1,1,2)),
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((1,2,3),(1,1,2,3,4),'right',(1,1,2,3,1)),
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((10,2),(10,3,2,2,),'right',(10,1,2,1)),
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((10,2),(10,3,2,2,),'left',(10,1,1,2)),
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((2,2,3),(2,2,2,3,4),'left',(1,2,2,3,1)),
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((2,2,3),(2,2,2,3,4),'right',(2,2,1,3,1)),
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])
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def test_shapeshifter(self,fro,to,mode,answer):
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assert util.shapeshifter(fro,to,mode) == answer
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@pytest.mark.parametrize('fro,to,mode',
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[
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((10,3,4),(10,3,2,2),'left'),
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((2,3),(10,3,2,2),'right'),
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])
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def test_invalid_shapeshifter(self,fro,to,mode):
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with pytest.raises(ValueError):
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util.shapeshifter(fro,to,mode)
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@pytest.mark.parametrize('a,b,answer',
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[
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((),(1,),(1,)),
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((1,),(),(1,)),
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((1,),(7,),(1,7)),
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((2,),(2,2),(2,2)),
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((1,2),(2,2),(1,2,2)),
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((1,2,3),(2,3,4),(1,2,3,4)),
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((1,2,3),(1,2,3),(1,2,3)),
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])
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def test_shapeblender(self,a,b,answer):
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assert util.shapeblender(a,b) == answer
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@pytest.mark.parametrize('style',[util.emph,util.deemph,util.warn,util.strikeout])
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def test_decorate(self,style):
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assert 'DAMASK' in style('DAMASK')
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@pytest.mark.parametrize('complete',[True,False])
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def test_D3D_base_group(self,tmp_path,complete):
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base_group = ''.join(random.choices('DAMASK', k=10))
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with h5py.File(tmp_path/'base_group.dream3d','w') as f:
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f.create_group('/'.join((base_group,'_SIMPL_GEOMETRY')))
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if complete:
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f['/'.join((base_group,'_SIMPL_GEOMETRY'))].create_dataset('SPACING',data=np.ones(3))
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if complete:
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assert base_group == util.DREAM3D_base_group(tmp_path/'base_group.dream3d')
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else:
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with pytest.raises(ValueError):
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util.DREAM3D_base_group(tmp_path/'base_group.dream3d')
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@pytest.mark.parametrize('complete',[True,False])
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def test_D3D_cell_data_group(self,tmp_path,complete):
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base_group = ''.join(random.choices('DAMASK', k=10))
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cell_data_group = ''.join(random.choices('KULeuven', k=10))
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cells = np.random.randint(1,50,3)
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with h5py.File(tmp_path/'cell_data_group.dream3d','w') as f:
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f.create_group('/'.join((base_group,'_SIMPL_GEOMETRY')))
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f['/'.join((base_group,'_SIMPL_GEOMETRY'))].create_dataset('SPACING',data=np.ones(3))
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f['/'.join((base_group,'_SIMPL_GEOMETRY'))].create_dataset('DIMENSIONS',data=cells[::-1])
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f[base_group].create_group(cell_data_group)
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if complete:
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f['/'.join((base_group,cell_data_group))].create_dataset('data',shape=np.append(cells,1))
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if complete:
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assert cell_data_group == util.DREAM3D_cell_data_group(tmp_path/'cell_data_group.dream3d')
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else:
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with pytest.raises(ValueError):
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util.DREAM3D_cell_data_group(tmp_path/'cell_data_group.dream3d')
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@pytest.mark.parametrize('full,reduced',[({}, {}),
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({'A':{}}, {}),
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({'A':{'B':{}}}, {}),
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({'A':{'B':'C'}},)*2,
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({'A':{'B':{},'C':'D'}}, {'A':{'C':'D'}})])
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def test_prune(self,full,reduced):
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assert util.dict_prune(full) == reduced
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@pytest.mark.parametrize('full,reduced',[({}, {}),
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({'A':{}}, {}),
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({'A':'F'}, 'F'),
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({'A':{'B':{}}}, {}),
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({'A':{'B':'C'}}, 'C'),
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({'A':1,'B':2},)*2,
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({'A':{'B':'C','D':'E'}}, {'B':'C','D':'E'}),
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({'B':'C','D':'E'},)*2,
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({'A':{'B':{},'C':'D'}}, {'B':{},'C':'D'})])
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def test_flatten(self,full,reduced):
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assert util.dict_flatten(full) == reduced
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def test_double_Bravais_to_Miller(self):
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with pytest.raises(KeyError):
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util.Bravais_to_Miller(uvtw=np.ones(4),hkil=np.ones(4))
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def test_double_Miller_to_Bravais(self):
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with pytest.raises(KeyError):
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util.Miller_to_Bravais(uvw=np.ones(4),hkl=np.ones(4))
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@pytest.mark.parametrize('vector',np.array([
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[1,0,0],
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[1,1,0],
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[1,1,1],
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[1,0,-2],
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]))
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@pytest.mark.parametrize('kw_Miller,kw_Bravais',[('uvw','uvtw'),('hkl','hkil')])
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def test_Miller_Bravais_Miller(self,vector,kw_Miller,kw_Bravais):
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assert np.all(vector == util.Bravais_to_Miller(**{kw_Bravais:util.Miller_to_Bravais(**{kw_Miller:vector})}))
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@pytest.mark.parametrize('vector',np.array([
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[1,0,-1,2],
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[1,-1,0,3],
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[1,1,-2,-3],
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[0,0,0,1],
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]))
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@pytest.mark.parametrize('kw_Miller,kw_Bravais',[('uvw','uvtw'),('hkl','hkil')])
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def test_Bravais_Miller_Bravais(self,vector,kw_Miller,kw_Bravais):
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assert np.all(vector == util.Miller_to_Bravais(**{kw_Miller:util.Bravais_to_Miller(**{kw_Bravais:vector})}))
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@pytest.mark.parametrize('adopted_parameters',[
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pytest.param("""
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p2 : str, optional
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p2 description 1
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p2 description 2
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""",
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id = 'standard'),
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pytest.param("""
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p2 : str, optional
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p2 description 1
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p2 description 2
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""",
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id = 'indented'),
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pytest.param("""
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p2 : str, optional
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p2 description 1
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p2 description 2
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""",
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id = 'no_indent')])
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def test_extend_docstring_parameters_string(self,adopted_parameters):
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test_docstring = """
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Function description.
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Parameters
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----------
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p0 : numpy.ndarray, shape (...,4)
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p0 description 1
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p0 description 2
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p1 : int, optional
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p1 description
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Remaining description\n"""
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assert util._docstringer(test_docstring,adopted_parameters) ==\
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"""
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Function description.
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Parameters
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----------
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p0 : numpy.ndarray, shape (...,4)
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p0 description 1
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p0 description 2
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p1 : int, optional
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p1 description
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p2 : str, optional
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p2 description 1
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p2 description 2
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Remaining description\n"""
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def test_extend_docstring_parameters_function(self):
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test_docstring = """
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Function description.
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Parameters
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----------
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p0 : numpy.ndarray, shape (...,4)
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p0 description 1
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"""
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def testfunction_1():
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"""
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Function description.
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Parameters
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----------
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p1 : int, optional
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p1 description
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Notes
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-----
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Function Notes 1
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Function Notes 2
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References
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----------
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Reference 1
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<reference link>
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Reference 2
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<reference link>
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"""
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pass
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assert (test_docstring:=util._docstringer(test_docstring,adopted_references = testfunction_1)) == \
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"""
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Function description.
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Parameters
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----------
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p0 : numpy.ndarray, shape (...,4)
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p0 description 1
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References
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----------
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Reference 1
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<reference link>
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Reference 2
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<reference link>\n"""
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assert (test_docstring:=util._docstringer(test_docstring,adopted_notes = testfunction_1)) == \
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"""
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Function description.
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Parameters
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----------
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p0 : numpy.ndarray, shape (...,4)
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p0 description 1
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Notes
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-----
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Function Notes 1
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Function Notes 2
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References
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----------
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Reference 1
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<reference link>
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Reference 2
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<reference link>\n"""
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def testfunction_2():
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"""
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Function description.
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References
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----------
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Reference 3
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<reference link>
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"""
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assert (test_docstring:=util._docstringer(test_docstring,adopted_references = testfunction_2)) == \
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"""
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Function description.
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Parameters
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----------
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p0 : numpy.ndarray, shape (...,4)
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p0 description 1
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Notes
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-----
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Function Notes 1
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Function Notes 2
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References
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----------
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Reference 1
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<reference link>
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Reference 2
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<reference link>
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Reference 3
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<reference link>\n"""
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def return_bound_method():
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class TestClassDecorated:
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def decorated_func_bound(self) -> 'TestClassDecorated':
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pass
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return TestClassDecorated.decorated_func_bound
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def return_simple_function():
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class TestClassDecorated:
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pass
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def decorated_func() -> TestClassDecorated:
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pass
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return decorated_func
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@pytest.mark.parametrize('adopted_return',[
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pytest.param(return_simple_function(),
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id = 'decorated_func'),
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pytest.param(return_bound_method(),
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id = 'decorated_func_bound'),
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pytest.param('test_util.TestClassDecorated',
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id = 'decorated_func_bound')])
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def test_replace_docstring_return(self,adopted_return):
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class TestClassOriginal:
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pass
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def original_func() -> TestClassOriginal:
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pass
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original_func.__doc__ = """
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Function description.
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Returns
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-------
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Return value : test_util.TestClassOriginal
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Remaining description
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"""
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assert util._docstringer(original_func,adopted_return=adopted_return) == """
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Function description.
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Returns
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-------
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Return value : test_util.TestClassDecorated
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Remaining description\n"""
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@pytest.mark.parametrize('adopted_func_doc',[
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pytest.param("""
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Function description.
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Parameters
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----------
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b : float 4
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b description
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c : float 5
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c description differing
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d : float 6
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d description
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Remaining description\n
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""",
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id = 'append'),
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pytest.param("""
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Function description.
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Parameters
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----------
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d : float 6
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d description
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a : float 7
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a description\n""",
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id = 'insert')])
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def test_extend_docstring_overlapping_section_content(self,adopted_func_doc):
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original_func_doc = """
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Function description.
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Parameters
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----------
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a : float 1
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a description
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b : float 2
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b description
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c : float 3
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c description
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Remaining description\n"""
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expected = """
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Function description.
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Parameters
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----------
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a : float 1
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a description
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b : float 2
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b description
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c : float 3
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c description
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d : float 6
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d description
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Remaining description\n"""
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assert util._docstringer(original_func_doc,adopted_parameters=adopted_func_doc) == expected
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def test_passon_result(self):
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def testfunction_inner(a=None,b=None):
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return a+b
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@util.pass_on('inner_result',testfunction_inner)
|
|
def testfunction_outer(**kwargs):
|
|
return kwargs['inner_result']+";"+kwargs['c']+kwargs['d']
|
|
assert testfunction_outer(a='1',b='2',c='3',d='4',e='5') == '12;34'
|
|
|
|
def test_passon_signature(self):
|
|
def testfunction_inner(a='1',b='2'):
|
|
return a+b
|
|
|
|
def testfunction_extra(e='5',f='6'):
|
|
return e+f
|
|
|
|
@util.pass_on('inner_result', testfunction_inner, wrapped=testfunction_extra)
|
|
def testfunction_outer(**kwargs):
|
|
return kwargs['inner_result']+";"+kwargs['c']+kwargs['d']
|
|
assert [(param.name, param.default) for param in testfunction_outer.__signature__.parameters.values()] == \
|
|
[('a', '1'), ('b', '2'), ('e', '5'), ('f', '6')]
|
|
|
|
def test_passon_help(self):
|
|
def testfunction_inner(a=None,b=None):
|
|
return a+b
|
|
|
|
def testfunction_extra(*,c=None,d=None):
|
|
return c+d
|
|
|
|
@util.pass_on('inner_result', testfunction_inner, wrapped=testfunction_extra)
|
|
def testfunction_outer(**kwargs) -> int:
|
|
return kwargs['inner_result']+kwargs['c']+kwargs['d']
|
|
|
|
assert pydoc.render_doc(testfunction_outer, renderer=pydoc.plaintext).split("\n")[-2] ==\
|
|
'testfunction_outer(*, a=None, b=None, c=None, d=None) -> int'
|