import sys
import random
import pydoc
import pytest
import numpy as np
from scipy import stats
import h5py
from damask import util
class TestUtil:
@pytest.mark.xfail(sys.platform == 'win32', reason='echo is not a Windows command')
def test_run_direct(self):
out,err = util.run('echo test')
assert out=='test\n' and err==''
@pytest.mark.xfail(sys.platform == 'win32', reason='echo is not a Windows command')
def test_run_env(self):
out,err = util.run('sh -c "echo $test_for_execute"',env={'test_for_execute':'test'})
assert out=='test\n' and err==''
@pytest.mark.xfail(sys.platform == 'win32', reason='false is not a Windows command')
def test_run_runtime_error(self):
with pytest.raises(RuntimeError):
util.run('false')
@pytest.mark.parametrize('input,glue,quote,output',
[
(None,'',False,'None'),
([None,None],'\n',False,'None\nNone'),
([-0.5,0.5],'=',False,'-0.5=0.5'),
([1,2,3],'_',False,'1_2_3'),
([1,2,3],'/',True,'"1"/"2"/"3"'),
])
def test_srepr(self,input,glue,quote,output):
assert output == util.srepr(input,glue,quote)
@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_distribution(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
def test_hybridIA_constant(self):
N_bins = np.random.randint(20,400)
m = np.random.randint(1,20)
N_samples = m * N_bins
dist = np.ones(N_bins)*np.random.rand()
assert np.all(np.sort(util.hybrid_IA(dist,N_samples))==np.arange(N_samples).astype(int)//m)
def test_hybridIA_linear(self):
N_points = np.random.randint(10,200)
m = np.random.randint(1,20)
dist = np.arange(N_points)
N_samples = m * np.sum(dist)
assert np.all(np.bincount(util.hybrid_IA(dist*np.random.rand(),N_samples)) == dist*m)
@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_equal_angle(self,point,direction,normalize,keepdims,answer):
assert np.allclose(util.project_equal_angle(np.array(point),direction=direction,
normalize=normalize,keepdims=keepdims),answer)
@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, [0,0.70710678,0]),
([0,1,1],'y',True, False,[0.5411961,0]),
([1,1,0],'x',False,False,[0.70710678,0]),
([1,1,1],'y',True, True, [0.45970084,0,0.45970084]),
])
def test_project_equal_area(self,point,direction,normalize,keepdims,answer):
assert np.allclose(util.project_equal_area(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('/'.join((base_group,'_SIMPL_GEOMETRY')))
if complete:
f['/'.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('/'.join((base_group,'_SIMPL_GEOMETRY')))
f['/'.join((base_group,'_SIMPL_GEOMETRY'))].create_dataset('SPACING',data=np.ones(3))
f['/'.join((base_group,'_SIMPL_GEOMETRY'))].create_dataset('DIMENSIONS',data=cells[::-1])
f[base_group].create_group(cell_data_group)
if complete:
f['/'.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})}))
@pytest.mark.parametrize('adopted_parameters',[
pytest.param("""
p2 : str, optional
p2 description 1
p2 description 2
""",
id = 'standard'),
pytest.param("""
p2 : str, optional
p2 description 1
p2 description 2
""",
id = 'indented'),
pytest.param("""
p2 : str, optional
p2 description 1
p2 description 2
""",
id = 'no_indent')])
def test_extend_docstring_parameters_string(self,adopted_parameters):
test_docstring = """
Function description.
Parameters
----------
p0 : numpy.ndarray, shape (...,4)
p0 description 1
p0 description 2
p1 : int, optional
p1 description
Remaining description\n"""
assert util._docstringer(test_docstring,adopted_parameters) ==\
"""
Function description.
Parameters
----------
p0 : numpy.ndarray, shape (...,4)
p0 description 1
p0 description 2
p1 : int, optional
p1 description
p2 : str, optional
p2 description 1
p2 description 2
Remaining description\n"""
def test_extend_docstring_parameters_function(self):
test_docstring = """
Function description.
Parameters
----------
p0 : numpy.ndarray, shape (...,4)
p0 description 1
"""
def testfunction_1():
"""
Function description.
Parameters
----------
p1 : int, optional
p1 description
Notes
-----
Function Notes 1
Function Notes 2
References
----------
Reference 1
<reference link>
Reference 2
<reference link>
"""
pass
assert (test_docstring:=util._docstringer(test_docstring,adopted_references = testfunction_1)) == \
"""
Function description.
Parameters
----------
p0 : numpy.ndarray, shape (...,4)
p0 description 1
References
----------
Reference 1
<reference link>
Reference 2
<reference link>\n"""
assert (test_docstring:=util._docstringer(test_docstring,adopted_notes = testfunction_1)) == \
"""
Function description.
Parameters
----------
p0 : numpy.ndarray, shape (...,4)
p0 description 1
Notes
-----
Function Notes 1
Function Notes 2
References
----------
Reference 1
<reference link>
Reference 2
<reference link>\n"""
def testfunction_2():
"""
Function description.
References
----------
Reference 3
<reference link>
"""
assert (test_docstring:=util._docstringer(test_docstring,adopted_references = testfunction_2)) == \
"""
Function description.
Parameters
----------
p0 : numpy.ndarray, shape (...,4)
p0 description 1
Notes
-----
Function Notes 1
Function Notes 2
References
----------
Reference 1
<reference link>
Reference 2
<reference link>
Reference 3
<reference link>\n"""
def return_bound_method():
class TestClassDecorated:
def decorated_func_bound(self) -> 'TestClassDecorated':
pass
return TestClassDecorated.decorated_func_bound
def return_simple_function():
class TestClassDecorated:
pass
def decorated_func() -> TestClassDecorated:
pass
return decorated_func
@pytest.mark.parametrize('adopted_return',[
pytest.param(return_simple_function(),
id = 'decorated_func'),
pytest.param(return_bound_method(),
id = 'decorated_func_bound'),
pytest.param('test_util.TestClassDecorated',
id = 'decorated_func_bound')])
def test_replace_docstring_return(self,adopted_return):
class TestClassOriginal:
pass
def original_func() -> TestClassOriginal:
pass
original_func.__doc__ = """
Function description.
Returns
-------
Return value : test_util.TestClassOriginal
Remaining description
"""
assert util._docstringer(original_func,adopted_return=adopted_return) == """
Function description.
Returns
-------
Return value : test_util.TestClassDecorated
Remaining description\n"""
@pytest.mark.parametrize('adopted_func_doc',[
pytest.param("""
Function description.
Parameters
----------
b : float 4
b description
c : float 5
c description differing
d : float 6
d description
Remaining description\n
""",
id = 'append'),
pytest.param("""
Function description.
Parameters
----------
d : float 6
d description
a : float 7
a description\n""",
id = 'insert')])
def test_extend_docstring_overlapping_section_content(self,adopted_func_doc):
original_func_doc = """
Function description.
Parameters
----------
a : float 1
a description
b : float 2
b description
c : float 3
c description
Remaining description\n"""
expected = """
Function description.
Parameters
----------
a : float 1
a description
b : float 2
b description
c : float 3
c description
d : float 6
d description
Remaining description\n"""
assert util._docstringer(original_func_doc,adopted_parameters=adopted_func_doc) == expected
def test_passon_result(self):
def testfunction_inner(a=None,b=None):
return a+b
@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'