def test_gauss_norm():
"""Test 1d gaussian
"""
p = np.array([0., 0., 1., -30., 3., 1., 30., 3.])
x = np.linspace(-1e6, 1e6, 8e6 + 1)
y = functions.gaussian(p, x)
integ = simps(y, x)
assert (abs(integ - 2.) < 1e-5)
def test_voigt_fit_of_gauss_rand():
"""Test voigt fit of gaussian
"""
p = np.array([0., 0., 3., 0., 0.3])
x = np.linspace(-1, 1, 201)
y = functions.gaussian(p, x) + np.random.normal(loc=0., scale=0.1, size=201)
err = np.sqrt(np.abs(y))
fitobj = Fitter(residuals=residuals, data=(functions.voigt, x, y, err))
fitobj.parinfo = [{'fixed': fix} for fix in np.asarray([0, 0, 0, 0, 0, 0]).astype(bool)]
fitobj.fit(params0=np.concatenate((p, np.array([0.2]))))
assert ((fitobj.chi2_min < 5.))
def test_gauss_fit_of_gauss_rand():
"""Test gaussian fit
"""
p = np.array([0., 0., 3., 0., 0.3])
x = np.linspace(-1, 1, 201)
np.random.seed(0)
y = functions.gaussian(p, x) + np.random.normal(loc=0., scale=0.05, size=201)
err = np.sqrt(np.abs(y))
fitobj = Fitter(residuals=residuals, data=(functions.gaussian, x, y, err))
fitobj.parinfo = [{'fixed': fix} for fix in np.asarray([0, 0, 0, 0, 0]).astype(bool)]
fitobj.fit(params0=p)
assert ((fitobj.chi2_min < 5.))
def test_voigt_fit_of_gauss_rand():
"""Test voigt fit of gaussian
"""
p = np.array([0., 0., 3., 0., 0.3])
x = np.linspace(-1, 1, 201)
np.random.seed(0)
y = functions.gaussian(p, x) + np.random.normal(
loc=0., scale=0.1, size=201)
err = np.sqrt(np.abs(y))
fitobj = Fitter(residuals=residuals, data=(functions.voigt, x, y, err))
fitobj.parinfo = [{
'fixed': fix
} for fix in np.asarray([0, 0, 0, 0, 0, 0]).astype(bool)]
fitobj.fit(params0=np.concatenate((p, np.array([0.2]))))
assert ((fitobj.chi2_min < 5.))
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_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)