def test_getDValues(self):
generator = ReflectionGenerator(self.crystalStructure)
hkls = [V3D(1, 0, 0), V3D(1, 1, 1)]
dValues = generator.getDValues(hkls)
self.assertEqual(len(hkls), len(dValues))
self.assertAlmostEqual(dValues[0], 5.431, places=10)
self.assertAlmostEqual(dValues[1], 5.431 / np.sqrt(3.), places=10)
def test_getDValues(self):
generator = ReflectionGenerator(self.crystalStructure)
hkls = [V3D(1, 0, 0), V3D(1, 1, 1)]
dValues = generator.getDValues(hkls)
self.assertEqual(len(hkls), len(dValues))
self.assertAlmostEqual(dValues[0], 5.431, places=10)
self.assertAlmostEqual(dValues[1], 5.431 / np.sqrt(3.), places=10)
def _update_distributions_and_weights(self):
crystal_structure = CrystalStructure(str(self._unit_cell), str(self._space_group), str(self._atoms))
reflection_generator = ReflectionGenerator(crystal_structure)
# Calculate all unique reflections within the specified resolution limits, including structure factors
unique_hkls = reflection_generator.getUniqueHKLs(self.d_min, self.d_max)
structure_factors = reflection_generator.getFsSquared(unique_hkls)
# Calculate multiplicities of the reflections
point_group = crystal_structure.getSpaceGroup().getPointGroup()
multiplicities = [len(point_group.getEquivalents(hkl)) for hkl in unique_hkls]
# Calculate weights as F^2 * multiplicity and normalize so that Sum(weights) = 1
weights = np.array([x * y for x, y in zip(structure_factors, multiplicities)])
self._parameter_lock.acquire()
self._peak_weights = weights / sum(weights)
d_values = reflection_generator.getDValues(unique_hkls)
self._peak_distributions = [partial(np.random.normal, loc=d, scale=self._relative_sigma * d) for d in d_values]
self._parameter_lock.release()
class StructFactor():
def __init__(self, cif_file, qrange=[1, 6], unique=False):
self.cif = cif_file
self.qrange = qrange
self.unique = unique
self.structf()
def structf(self):
sample_ws = CreateSampleWorkspace()
LoadCIF(sample_ws, self.cif)
self.sample = sample_ws.sample().getCrystalStructure()
self.generator = ReflectionGenerator(self.sample)
if self.unique == True:
hkls = self.generator.getUniqueHKLsUsingFilter(
self.qrange[0], self.qrange[1],
ReflectionConditionFilter.StructureFactor)
else:
hkls = self.generator.getHKLsUsingFilter(
self.qrange[0], self.qrange[1],
ReflectionConditionFilter.StructureFactor)
pg = self.sample.getSpaceGroup().getPointGroup()
df = pd.DataFrame(data=np.array(hkls), columns=list('hkl'))
df['d(A)'] = self.generator.getDValues(hkls)
df['F^2'] = self.generator.getFsSquared(hkls)
df['hkl'] = hkls
df['M'] = df['hkl'].map(lambda x: len(pg.getEquivalents(x)))
df['II_powder'] = df['F^2'] * df['M']
df['q'] = 2 * np.pi / df['d(A)']
df['qh'] = df['h'].map(lambda x: np.sign(x) * 2 * np.pi / self.
generator.getDValues([V3D(x, 0, 0)])[0])
df['qk'] = df['k'].map(lambda x: np.sign(x) * 2 * np.pi / self.
generator.getDValues([V3D(0, x, 0)])[0])
df['ql'] = df['l'].map(lambda x: np.sign(x) * 2 * np.pi / self.
generator.getDValues([V3D(0, 0, x)])[0])
self.data = df
def compute_dvalues(d_min, d_max, structure):
generator = ReflectionGenerator(structure)
hkls = generator.getUniqueHKLsUsingFilter(d_min, d_max, ReflectionConditionFilter.StructureFactor)
dvalues = np.sort(np.array(generator.getDValues(hkls)))[::-1]
return dvalues
def compute_dvalues(d_min, d_max, structure):
generator = ReflectionGenerator(structure)
hkls = generator.getUniqueHKLsUsingFilter(
d_min, d_max, ReflectionConditionFilter.StructureFactor)
dvalues = np.sort(np.array(generator.getDValues(hkls)))[::-1]
return dvalues
