def test_q_points_data(self):
import yaml
trajectory = io.initialize_from_hdf5_file('test_gan.h5',
self.structure,
read_trajectory=True,
initial_cut=1,
final_cut=3000,
memmap=True)
self.calculation = dynaphopy.Quasiparticle(trajectory)
self.calculation.select_power_spectra_algorithm(2)
self.calculation.write_atomic_displacements([0, 0, 1],
'atomic_displacements.dat')
self.calculation.write_quasiparticles_data(
filename='quasiparticles_data.yaml')
self.calculation.write_renormalized_phonon_dispersion_bands(
filename='bands_data.yaml')
reference = np.loadtxt('GaN_data/atomic_displacements.dat')
data = np.loadtxt('atomic_displacements.dat')
test_range = np.arange(-5, 5, 0.1)
for i in range(1, data.shape[1]):
diff_square = np.square(
np.interp(test_range, data[:, 0], data[:,
i], right=0, left=0) -
np.interp(test_range,
reference[:, 0],
reference[:, i],
right=0,
left=0))
rms = np.sqrt(np.average(diff_square))
self.assertLess(rms, 0.05)
def assertDictAlmostEqual(dict, reference, decimal=6):
for key, value in dict.items():
np.testing.assert_array_almost_equal(np.array(value),
np.array(reference[key]),
decimal=decimal)
files = ['quasiparticles_data.yaml']
for file in files:
print('file: {}'.format(file))
with open(file) as stream:
data = yaml.load(stream)
with open('GaN_data/' + file) as stream:
reference = yaml.load(stream)
for dict_data, dict_reference in zip(data, reference):
assertDictAlmostEqual(dict_data, dict_reference, decimal=1)
def test_force_constants_self_consistency(self):
trajectory = io.initialize_from_hdf5_file('test_gan.h5',
self.structure,
read_trajectory=False,
initial_cut=1,
final_cut=3000,
memmap=True)
self.calculation = dynaphopy.Quasiparticle(trajectory)
self.calculation.select_power_spectra_algorithm(2)
renormalized_force_constants = self.calculation.get_renormalized_force_constants().get_array()
harmonic_force_constants = self.calculation.dynamic.structure.get_force_constants().get_array()
self.assertEqual(np.allclose(renormalized_force_constants, harmonic_force_constants, rtol=1, atol=1.e-2), True)
def test_thermal_properties(self):
trajectory = io.initialize_from_hdf5_file('test_gan.h5',
self.structure,
read_trajectory=False,
initial_cut=1000,
final_cut=4000,
memmap=False)
self.calculation = dynaphopy.Quasiparticle(trajectory)
self.calculation.select_power_spectra_algorithm(2)
harmonic = np.array(self.calculation.get_thermal_properties())
anharmonic = np.array(self.calculation.get_thermal_properties(
force_constants=self.calculation.get_renormalized_force_constants()))
print(harmonic)
print(anharmonic)
maximum = np.max((harmonic-anharmonic)**2/harmonic)
print('maximum: {}'.format(maximum))
self.assertLess(maximum, 0.1)
def test_q_points_data(self):
import yaml
trajectory = io.initialize_from_hdf5_file('test_gan.h5',
self.structure,
read_trajectory=True,
initial_cut=1,
final_cut=3000,
memmap=True)
self.calculation = dynaphopy.Quasiparticle(trajectory)
self.calculation.select_power_spectra_algorithm(2)
self.calculation.write_atomic_displacements([0, 0, 1], 'atomic_displacements.dat')
self.calculation.write_quasiparticles_data(filename='quasiparticles_data.yaml')
self.calculation.write_renormalized_phonon_dispersion_bands(filename='bands_data.yaml')
reference = np.loadtxt('GaN_data/atomic_displacements.dat')
data = np.loadtxt('atomic_displacements.dat')
test_range = np.arange(-5, 5, 0.1)
for i in range(1, data.shape[1]):
diff_square = np.square(np.interp(test_range, data[:,0], data[:,i], right=0, left=0) -
np.interp(test_range, reference[:,0], reference[:,i], right=0, left=0))
rms = np.sqrt(np.average(diff_square))
self.assertLess(rms, 0.05)
def assertDictAlmostEqual(dict, reference, decimal=6):
for key, value in dict.items():
np.testing.assert_array_almost_equal(np.array(value),
np.array(reference[key]),
decimal=decimal)
files = ['quasiparticles_data.yaml']
for file in files:
print ('file: {}'.format(file))
with open(file) as stream:
data = yaml.load(stream)
with open('GaN_data/' + file) as stream:
reference = yaml.load(stream)
for dict_data, dict_reference in zip(data, reference):
assertDictAlmostEqual(dict_data, dict_reference, decimal=1)
def test_force_constants_self_consistency(self):
trajectory = io.initialize_from_hdf5_file('test_gan.h5',
self.structure,
read_trajectory=False,
initial_cut=1,
final_cut=3000,
memmap=True)
self.calculation = dynaphopy.Quasiparticle(trajectory)
self.calculation.select_power_spectra_algorithm(2)
renormalized_force_constants = self.calculation.get_renormalized_force_constants(
).get_array()
harmonic_force_constants = self.calculation.dynamic.structure.get_force_constants(
).get_array()
self.assertEqual(
np.allclose(renormalized_force_constants,
harmonic_force_constants,
rtol=1,
atol=1.e-2), True)
def test_thermal_properties(self):
trajectory = io.initialize_from_hdf5_file('test_gan.h5',
self.structure,
read_trajectory=False,
initial_cut=1000,
final_cut=4000,
memmap=False)
self.calculation = dynaphopy.Quasiparticle(trajectory)
self.calculation.select_power_spectra_algorithm(2)
harmonic = np.array(self.calculation.get_thermal_properties())
anharmonic = np.array(
self.calculation.get_thermal_properties(
force_constants=self.calculation.
get_renormalized_force_constants()))
print(harmonic)
print(anharmonic)
maximum = np.max((harmonic - anharmonic)**2 / harmonic)
print('maximum: {}'.format(maximum))
self.assertLess(maximum, 0.4)
def test_adp(self):
trajectory = io.initialize_from_hdf5_file('test_gan.h5',
self.structure,
read_trajectory=True,
initial_cut=1,
final_cut=4000,
memmap=False)
self.calculation = dynaphopy.Quasiparticle(trajectory)
self.calculation.get_anisotropic_displacement_parameters()
positions_average = self.calculation.dynamic.average_positions(to_unit_cell=True).real
positions = self.structure.get_positions()
difference = positions - positions_average
norm = np.linalg.norm(self.structure.get_cell(), axis=1)
difference = np.mod(difference, norm)
multiples = np.divide(difference, norm)
self.assertLess(np.max(np.abs(multiples - np.round(multiples))), 1e-4)
def test_adp(self):
trajectory = io.initialize_from_hdf5_file('test_gan.h5',
self.structure,
read_trajectory=True,
initial_cut=1,
final_cut=4000,
memmap=False)
self.calculation = dynaphopy.Quasiparticle(trajectory)
self.calculation.get_anisotropic_displacement_parameters()
positions_average = self.calculation.dynamic.average_positions(
to_unit_cell=True).real
positions = self.structure.get_positions()
difference = positions - positions_average
norm = np.linalg.norm(self.structure.get_cell(), axis=1)
difference = np.mod(difference, norm)
multiples = np.divide(difference, norm)
self.assertLess(np.max(np.abs(multiples - np.round(multiples))), 1e-4)
