def test_init_cases():
"""Tests initialization cases
"""
try:
Data()
except:
pytest.fail('Data initialization failed')
def test_rebin():
"""Tests data rebinning
"""
data = Data(h=np.linspace(0, 1, 101), k=0, l=0, e=0, temp=0,
detector=functions.gaussian([0, 0, 10, 0.5, 0.5], np.linspace(0, 1, 101)),
monitor=np.ones(101), time=np.ones(101))
data_bin = data.bin(dict(h=[0, 1., 51], k=[-0.1, 0.1, 1], l=[-0.1, 0.1, 1], e=[-0.5, 0.5, 1]))
assert (data_bin.Q.shape[0] == 51)
assert (data_bin.monitor.shape[0] == 51)
assert (data_bin.detector.shape[0] == 51)
assert (np.average(data_bin.monitor) == np.average(data.monitor))
assert (abs(simps(data_bin.detector, data_bin.Q[:, 0]) - simps(data.detector, data.Q[:, 0])) <= 0.1)
assert (np.abs(data_bin.integrate() - data.integrate()) < 1e-1)
assert (np.abs(data_bin.position()[0] - data.position()[0]) < 1e-1)
assert (np.abs(data_bin.width()[0] - data.width()[0]) < 1e-1)
def _test():
data_bin = data.bin(dict(blah=[1, 2, 4]))
with pytest.raises(KeyError):
_test()
def build_3d_data():
"""Builds 3D data object
"""
p = np.array([0, 0, 1, 0, 0, 0.1])
x, y = np.linspace(-1, 1, 81), np.linspace(-1, 1, 81)
X, Y = np.meshgrid(x, y)
z = functions.gaussian2d(p, (X, Y))
mon = 1e5
tim = 15
output = Data(Q=np.vstack((item.ravel() for item in np.meshgrid(x, y, 0., 0., 300.))).T,
detector=z.ravel(), monitor=np.full(X.ravel().shape, mon, dtype=float),
time=np.full(X.ravel().shape, tim, dtype=float))
return output
def test_background_subtraction():
"""Test background subtraction
"""
data = build_data(clean=True)
background_data1 = build_data(clean=False)
background_data2 = Data(detector=np.random.rand(101), monitor=np.full(101, 1, dtype=float),
time=np.full(101, 1, dtype=float), h=np.linspace(-1, 1, 101))
try:
data.subtract_background(background_data1, ret=False)
data.subtract_background(background_data1, x='h', ret=False)
except:
pytest.fail('background subtraction failed')
with pytest.raises(ValueError):
data.subtract_background(background_data2, ret=False)
def build_data(clean=True):
"""Builds data object
"""
p = np.array([20., 0., 3., -0.15, 0.08, 0.2, 3., 0.15, 0.08, 0.2])
x = np.linspace(-1, 1, 81)
if clean:
y = functions.voigt(p, x)
mon = 1e5
tim = 15
else:
y = functions.voigt(p, x) + np.random.normal(loc=0., scale=5, size=len(x))
mon = 1e3
tim = 5
output = Data(Q=np.vstack((item.ravel() for item in np.meshgrid(x, 0., 0., 0., 300.))).T,
detector=y, monitor=np.full(x.shape, mon, dtype=float), time=np.full(x.shape, tim, dtype=float))
return output
def test_rebin():
"""Tests data rebinning
"""
data = Data(h=np.linspace(0, 1, 101), k=0, l=0, e=0, temp=0,
detector=functions.gaussian([0, 0, 10, 0.5, 0.5], np.linspace(0, 1, 101)),
monitor=np.ones(101), time=np.ones(101))
data_bin = data.bin(dict(h=[0, 1., 51], k=[-0.1, 0.1, 1], l=[-0.1, 0.1, 1], e=[-0.5, 0.5, 1]))
assert (data_bin.Q.shape[0] == 51)
assert (data_bin.monitor.shape[0] == 51)
assert (data_bin.detector.shape[0] == 51)
assert (np.average(data_bin.monitor) == np.average(data.monitor))
assert (abs(simps(data_bin.detector, data_bin.Q[:, 0]) - simps(data.detector, data.Q[:, 0])) <= 0.1)
assert (np.abs(data_bin.integrate() - data.integrate()) < 1e-1)
assert (np.abs(data_bin.position()[0] - data.position()[0]) < 1e-1)
assert (np.abs(data_bin.width()[0] - data.width()[0]) < 1e-1)
def _test():
data_bin = data.bin(dict(blah=[1, 2, 4]))
with pytest.raises(KeyError):
_test()
def test_analysis():
"""Tests analysis methods
"""
x = np.linspace(-2, 2, 100)
y = functions.gaussian([0, 0, 1, 0, 0.5], x)
data = Data(Q=np.vstack((item.ravel() for item in np.meshgrid(x, 0., 0., 4., 300.))).T,
detector=y, monitor=np.full(x.shape, 1, dtype=float), time=np.full(x.shape, 1, dtype=float))
assert (np.abs(data.integrate() - 1) < 1e-5)
assert (np.abs(data.position()[0]) < 1e-5)
assert (abs(data.width(fwhm=True)[0] - 0.5) < 1e-1)
assert isinstance(data.position(hkle=False), dict)
assert isinstance(data.width(hkle=False), dict)
assert (np.abs(data.integrate(hkle=False) - 1) < 1e-5)
assert (np.abs(data.position(hkle=False)['h']) < 1e-5)
assert (abs(data.width(fwhm=True, hkle=False)['h'] - 0.5) < 1e-1)
bounds = (data.h >= -1) & (data.h <= 1)
assert (np.abs(data.integrate(bounds=bounds) - 1) < 1e-5)
assert (np.abs(data.position(bounds=bounds)[0]) < 1e-5)
assert (abs(data.width(bounds=bounds, fwhm=True)[0] - 0.5) < 1e-1)
background = dict(type='constant', value=0.)
assert (np.abs(data.integrate(background=background) - 1) < 1e-5)
assert (np.abs(data.position(background=background)[0]) < 1e-5)
assert (abs(data.width(background=background, fwhm=True)[0] - 0.5) < 1e-1)
background = dict(type='percent', value=2)
assert (np.abs(data.integrate(background=background) - 1) < 1e-5)
background = dict(type='minimum')
assert (np.abs(data.integrate(background=background) - 1) < 1e-5)
background = dict(type='blah')
assert (np.abs(data.integrate(background=background) - 1) < 1e-5)
def test_polarization_correction():
data = Data()
scattering.polarization.polarization_correction(data, data, data, data)
def test_build_data():
data = Data(blah='test')
data = Data(error=1)
def test_analysis():
"""Tests analysis methods
"""
x = np.linspace(-2, 2, 100)
y = functions.gaussian([0, 0, 1, 0, 0.5], x)
data = Data(Q=np.vstack((item.ravel() for item in np.meshgrid(x, 0., 0., 4., 300.))).T,
detector=y, monitor=np.full(x.shape, 1, dtype=float), time=np.full(x.shape, 1, dtype=float))
assert (np.abs(data.integrate() - 1) < 1e-5)
assert (np.abs(data.position()[0]) < 1e-5)
assert (abs(data.width(fwhm=True)[0] - 0.5) < 1e-1)
assert isinstance(data.position(hkle=False), dict)
assert isinstance(data.width(hkle=False), dict)
assert (np.abs(data.integrate(hkle=False) - 1) < 1e-5)
assert (np.abs(data.position(hkle=False)['h']) < 1e-5)
assert (abs(data.width(fwhm=True, hkle=False)['h'] - 0.5) < 1e-1)
bounds = (data.h >= -1) & (data.h <= 1)
assert (np.abs(data.integrate(bounds=bounds) - 1) < 1e-5)
assert (np.abs(data.position(bounds=bounds)[0]) < 1e-5)
assert (abs(data.width(bounds=bounds, fwhm=True)[0] - 0.5) < 1e-1)
background = dict(type='constant', value=0.)
assert (np.abs(data.integrate(background=background) - 1) < 1e-5)
assert (np.abs(data.position(background=background)[0]) < 1e-5)
assert (abs(data.width(background=background, fwhm=True)[0] - 0.5) < 1e-1)
background = dict(type='percent', value=2)
assert (np.abs(data.integrate(background=background) - 1) < 1e-5)
background = dict(type='minimum')
assert (np.abs(data.integrate(background=background) - 1) < 1e-5)
background = dict(type='blah')
assert (np.abs(data.integrate(background=background) - 1) < 1e-5)