def test_XAS_full_spec_attributes():
x = np.random.uniform(size=100)
y = np.random.uniform(size=100)
structure = Structure.from_file(
os.path.join(TEST_FILE_DIR, 'Cu_structure.cif'))
absorbing_site = 0
full_spectrum = np.random.uniform(size=(100, 6))
spec = XAS_Spectrum(x,
y,
structure,
absorbing_site,
full_spectrum=full_spectrum)
assert isinstance(spec, XAS_Spectrum)
assert np.array_equal(spec.E, full_spectrum[:, 0])
assert np.array_equal(spec.Enorm, full_spectrum[:, 1])
assert np.array_equal(spec.k, full_spectrum[:, 2])
assert np.array_equal(spec.mu, full_spectrum[:, 3])
assert np.array_equal(spec.mu0, full_spectrum[:, 4])
assert np.array_equal(spec.chi, full_spectrum[:, 5])
assert spec.abs_idx == 0
assert isinstance(spec.as_dict(), dict)
def test_load_from_doc_and_object():
with open(os.path.join(TEST_FILE_DIR, 'sample_spectrum_e.txt'), 'r') as f:
data = loads(f.readline())
spec1 = XAS_Spectrum.from_atomate_document(data)
spec2 = XAS_Spectrum.load_from_object(data)
line = dumps(data)
spec3 = XAS_Spectrum.load_from_object(line)
for spec in [spec1, spec2, spec3]:
assert isinstance(spec, XAS_Spectrum)
assert spec.has_full_spectrum()
assert spec.E[0] == 8334.08
assert spec.Enorm[0] == -9.293
assert spec.k[0] == -0.8
assert spec.mu[0] == 0.0519168
assert spec.mu0[0] == 0.0795718
assert spec.chi[0] == -0.027655
assert len(spec.E) == 100
assert len(spec.Enorm) == 100
assert len(spec.mu) == 100
assert len(spec.mu0) == 100
assert len(spec.k) == 100
assert len(spec.chi) == 100
enorm = spec1.Enorm
sub_enorm = np.add(enorm, 1)
assert np.isclose(sub_enorm, spec.shifted_Enorm(1)).all()
def test_sanity_check():
X = np.linspace(0, 1, 100)
Y = np.linspace(.5, -.5, 100)
bad_spec = XAS_Spectrum(x=X, y=Y)
assert bad_spec.sanity_check() is False
good_spec = XAS_Spectrum(x=X, y=X)
assert good_spec.sanity_check() is True
Y = np.linspace(-0.001, 0.001, 100)
bad_spec = XAS_Spectrum(x=X, y=Y)
assert bad_spec.sanity_check() is False
def test_instantiate_XAS_spectra(fake_structure):
x = np.random.uniform(size=100)
y = np.random.uniform(size=100)
absorbing_site = 0
spec = XAS_Spectrum(x, y, fake_structure, absorbing_site)
assert isinstance(spec, XAS_Spectrum)
def test_XAS_gradient():
X = np.linspace(0, 1, 500)
Y = np.sin(X)
Yprime = np.cos(X)
spec = XAS_Spectrum(X, Y)
dy = spec.dy
assert np.isclose(dy, Yprime, atol=.001).all()
def test_projection():
x = np.linspace(0, np.pi / 2, 100)
y = np.sin(x)
spec = XAS_Spectrum(x, y)
# Test that interpolation method works
new_xvals = np.linspace(0, np.pi / 2, 300)
new_yvals = spec.project_to_x_range(new_xvals)
assert (max(np.abs(y - new_yvals[::3])) < 1e-2)
new_xvals = np.linspace(-np.pi / 2, np.pi, 300)
new_yvals = spec.project_to_x_range(new_xvals)
true_yvals = np.sin(new_xvals)
# Test that it pads to 0 on the left edge
assert np.isclose(0.0, np.abs(new_yvals[:33])).all()
# Test that it extrapolates correctly for a few points
assert (np.isclose(new_yvals[200:205], true_yvals[200:205])).all()
def parse_spectrum(output: str,
feff_inp: str = '',
kind='2dcsv',
*args,
**kwargs):
"""
Open a spectrum file with x as the first column
and y as the second
:param feff_inp:
:param output:
:param kind: default, 2-dimensional comma separated values
:return:
"""
if kind == '2dcsv':
values = loadtxt(fname=output, *args, **kwargs)
return XAS_Spectrum(x=values[:, 1], y=values[:, 0])
if kind == '2dcsv_spec':
values = loadtxt(fname=output, *args, **kwargs)
return XAS_Spectrum(x=values[:, 0], y=values[:, 1])
if kind == 'feff_mu':
xmu = Xmu.from_file(output, feff_inp)
return XAS_Spectrum(x=xmu.energies, y=xmu.mu)
if kind == 'json':
line = open(output, 'r').readline().strip()
xas = loads(line)
return XAS_Spectrum.from_dict(xas)
else:
raise NotImplementedError("Type of data file not found")
def test_broaden_spectrum():
x = np.linspace(0, 1, 100)
y = np.sin(x)
spec = XAS_Spectrum(x, y)
assert x[-1] - x[0] == 1.0
spec.broaden_spectrum_mult(0)
assert spec.x[-1] - spec.x[0] == 1.0
spec.broaden_spectrum_mult(.05)
assert np.isclose(spec.x[-1] - spec.x[0], 1.05)
assert spec.x[-1] == 1.025
assert spec.x[0] == -.025
x = np.linspace(0, 1, 100)
y = np.sin(x)
spec = XAS_Spectrum(x, y)
spec.broaden_spectrum_mult(-.05)
assert np.isclose(spec.x[-1] - spec.x[0], .95)
assert spec.x[-1] == .975
assert spec.x[0] == .025
def fake_spectrum(fake_structure):
x = np.random.uniform(size=100)
y = np.random.uniform(size=100)
return XAS_Spectrum(x, y, structure=fake_structure, absorbing_site=0)
def test_XAS_Spectrum_methods(fake_spectrum):
assert isinstance(str(fake_spectrum), str)
assert 'H' in str(fake_spectrum)
assert isinstance(fake_spectrum.as_dict(), dict)
assert isinstance(fake_spectrum.as_str(), str)
assert fake_spectrum.has_full_spectrum() is False
X = np.linspace(0, 1, 100)
Y = np.sin(X)
assert np.isclose(np.add(fake_spectrum.x, 10),
fake_spectrum.shifted_x(10)).all()
fake_spectrum_2 = XAS_Spectrum(X, Y)
assert fake_spectrum_2.get_peak_idx() == 99
fake_spectrum_2.normalize('sum')
assert np.isclose(np.sum(fake_spectrum_2.y), 1.0)
fake_spectrum_2.normalize('max')
assert np.isclose(np.max(fake_spectrum_2.y), 1.0)
# Reset and shift up
fake_spectrum_2 = XAS_Spectrum(X, Y + .2)
fake_spectrum_2.normalize('minmax')
assert np.isclose(np.min(fake_spectrum_2.y), 0)
assert np.isclose(np.max(fake_spectrum_2.y), 1.0)
fake_spectrum_2.normalize('l2')
assert np.isclose(np.linalg.norm(fake_spectrum_2.y, ord=2), 1.0)