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