157 lines
5.5 KiB
Python
157 lines
5.5 KiB
Python
from pathlib import Path
|
|
import datetime
|
|
import os
|
|
|
|
import numpy as np
|
|
import pytest
|
|
import matplotlib as mpl
|
|
if os.name == 'posix' and 'DISPLAY' not in os.environ:
|
|
mpl.use('Agg')
|
|
import matplotlib.pyplot as plt
|
|
|
|
import damask
|
|
|
|
|
|
patched_version = '99.99.99-9999-pytest'
|
|
@pytest.fixture
|
|
def patch_damask_version(monkeypatch):
|
|
"""Set damask.version for reproducible tests results."""
|
|
monkeypatch.setattr(damask, 'version', patched_version)
|
|
|
|
|
|
patched_date = datetime.datetime(2019, 11, 2, 11, 58, 0)
|
|
@pytest.fixture
|
|
def patch_datetime_now(monkeypatch):
|
|
"""Set datetime.datetime.now for reproducible tests results."""
|
|
class mydatetime:
|
|
@classmethod
|
|
def now(cls):
|
|
return patched_date
|
|
|
|
monkeypatch.setattr(datetime, 'datetime', mydatetime)
|
|
|
|
|
|
@pytest.fixture
|
|
def patch_execution_stamp(monkeypatch):
|
|
"""Set damask.util.execution_stamp for reproducible tests results."""
|
|
def execution_stamp(class_name,function_name=None):
|
|
_function_name = '' if function_name is None else f'.{function_name}'
|
|
return f'damask.{class_name}{_function_name} v{patched_version} ({patched_date})'
|
|
|
|
monkeypatch.setattr(damask.util, 'execution_stamp', execution_stamp)
|
|
|
|
|
|
@pytest.fixture
|
|
def patch_plt_show(monkeypatch):
|
|
def _None(block=None):
|
|
pass
|
|
monkeypatch.setattr(plt, 'show', _None, raising=True)
|
|
|
|
|
|
def pytest_addoption(parser):
|
|
parser.addoption("--update",
|
|
action="store_true",
|
|
default=False)
|
|
|
|
|
|
@pytest.fixture
|
|
def update(request):
|
|
"""Store current results as new reference results."""
|
|
return request.config.getoption("--update")
|
|
|
|
|
|
@pytest.fixture
|
|
def reference_dir_base():
|
|
"""Directory containing reference results."""
|
|
return Path(__file__).parent/'reference'
|
|
|
|
|
|
@pytest.fixture
|
|
def set_of_quaternions():
|
|
"""A set of n random rotations."""
|
|
def random_quaternions(N):
|
|
r = np.random.rand(N,3)
|
|
|
|
A = np.sqrt(r[:,2])
|
|
B = np.sqrt(1.0-r[:,2])
|
|
qu = np.column_stack([np.cos(2.0*np.pi*r[:,0])*A,
|
|
np.sin(2.0*np.pi*r[:,1])*B,
|
|
np.cos(2.0*np.pi*r[:,1])*B,
|
|
np.sin(2.0*np.pi*r[:,0])*A])
|
|
qu[:,0]*=np.sign(qu[:,0])
|
|
|
|
return qu
|
|
|
|
n = 600
|
|
scatter=1.e-2
|
|
specials = np.array([
|
|
[1.0, 0.0, 0.0, 0.0],
|
|
#----------------------
|
|
[0.0, 1.0, 0.0, 0.0],
|
|
[0.0, 0.0, 1.0, 0.0],
|
|
[0.0, 0.0, 0.0, 1.0],
|
|
[0.0,-1.0, 0.0, 0.0],
|
|
[0.0, 0.0,-1.0, 0.0],
|
|
[0.0, 0.0, 0.0,-1.0],
|
|
#----------------------
|
|
[1.0, 1.0, 0.0, 0.0],
|
|
[1.0, 0.0, 1.0, 0.0],
|
|
[1.0, 0.0, 0.0, 1.0],
|
|
[0.0, 1.0, 1.0, 0.0],
|
|
[0.0, 1.0, 0.0, 1.0],
|
|
[0.0, 0.0, 1.0, 1.0],
|
|
#----------------------
|
|
[1.0,-1.0, 0.0, 0.0],
|
|
[1.0, 0.0,-1.0, 0.0],
|
|
[1.0, 0.0, 0.0,-1.0],
|
|
[0.0, 1.0,-1.0, 0.0],
|
|
[0.0, 1.0, 0.0,-1.0],
|
|
[0.0, 0.0, 1.0,-1.0],
|
|
#----------------------
|
|
[0.0, 1.0,-1.0, 0.0],
|
|
[0.0, 1.0, 0.0,-1.0],
|
|
[0.0, 0.0, 1.0,-1.0],
|
|
#----------------------
|
|
[0.0,-1.0,-1.0, 0.0],
|
|
[0.0,-1.0, 0.0,-1.0],
|
|
[0.0, 0.0,-1.0,-1.0],
|
|
#----------------------
|
|
[1.0, 1.0, 1.0, 0.0],
|
|
[1.0, 1.0, 0.0, 1.0],
|
|
[1.0, 0.0, 1.0, 1.0],
|
|
[1.0,-1.0, 1.0, 0.0],
|
|
[1.0,-1.0, 0.0, 1.0],
|
|
[1.0, 0.0,-1.0, 1.0],
|
|
[1.0, 1.0,-1.0, 0.0],
|
|
[1.0, 1.0, 0.0,-1.0],
|
|
[1.0, 0.0, 1.0,-1.0],
|
|
[1.0,-1.0,-1.0, 0.0],
|
|
[1.0,-1.0, 0.0,-1.0],
|
|
[1.0, 0.0,-1.0,-1.0],
|
|
#----------------------
|
|
[0.0, 1.0, 1.0, 1.0],
|
|
[0.0, 1.0,-1.0, 1.0],
|
|
[0.0, 1.0, 1.0,-1.0],
|
|
[0.0,-1.0, 1.0, 1.0],
|
|
[0.0,-1.0,-1.0, 1.0],
|
|
[0.0,-1.0, 1.0,-1.0],
|
|
[0.0,-1.0,-1.0,-1.0],
|
|
#----------------------
|
|
[1.0, 1.0, 1.0, 1.0],
|
|
[1.0,-1.0, 1.0, 1.0],
|
|
[1.0, 1.0,-1.0, 1.0],
|
|
[1.0, 1.0, 1.0,-1.0],
|
|
[1.0,-1.0,-1.0, 1.0],
|
|
[1.0,-1.0, 1.0,-1.0],
|
|
[1.0, 1.0,-1.0,-1.0],
|
|
[1.0,-1.0,-1.0,-1.0],
|
|
])
|
|
specials /= np.linalg.norm(specials,axis=1).reshape(-1,1)
|
|
specials_scatter = specials + np.broadcast_to((np.random.rand(4)*2.-1.)*scatter,specials.shape)
|
|
specials_scatter /= np.linalg.norm(specials_scatter,axis=1).reshape(-1,1)
|
|
specials_scatter[specials_scatter[:,0]<0]*=-1
|
|
|
|
return np.array([s for s in specials] + \
|
|
[s for s in specials_scatter] + \
|
|
[s for s in random_quaternions(n-2*len(specials))])
|