def get_frequencies(self):
if self._frequencies is None:
print("Getting frequencies & eigenvectors from Phonopy")
self._eigenvectors, self._frequencies = (
pho_interface.obtain_eigenvectors_from_phonopy(self.dynamic.structure,
self.parameters.reduced_q_vector,
NAC=self.parameters.use_NAC))
return self._frequencies
def generate_test_trajectory(structure, reduced_q_vector, super_cell=(4,4,4)):
print('Generating ideal harmonic data for testing')
kb_boltzmann = 0.831446 # u * A^2 / ( ps^2 * K )
#Getting data from file instead of calculating (has to be the same object type generated by this function)
if False:
dump_file = open( "trajectory.save", "r" )
trajectory = pickle.load(dump_file)
return trajectory
number_of_atoms = structure.get_number_of_cell_atoms()
number_of_primitive_atoms = structure.get_number_of_primitive_atoms()
positions = structure.get_positions(super_cell=super_cell)
masses = structure.get_masses(super_cell=super_cell)
#Parameters used to generate harmonic trajectory
total_time = 2
time_step = 0.002
amplitude = 7.0
temperature = 1200
# print('Freq Num',number_of_frequencies)
for i in range(structure.get_number_of_dimensions()):
number_of_atoms *= super_cell[i]
# print('At Num',number_of_atoms)
number_of_primitive_cells = number_of_atoms/number_of_primitive_atoms
atom_type = structure.get_atom_type_index(super_cell=super_cell)
# print('At type',atom_type)
#print(structure.get_atomic_types(super_cell=super_cell))
#Generate an xyz file for checking
xyz_file = open('test.xyz','w')
#Generate additional random wave vectors sample for further testing
number_of_additional_wave_vectors = 0
q_vector_list=np.random.random([number_of_additional_wave_vectors, 3])
q_vector_list=np.concatenate((q_vector_list, [reduced_q_vector]),axis=0)
print('test wave vectors')
print(q_vector_list)
#Generate frequencies and eigenvectors for the testing wave vector samples
eigenvectors_r = []
frequencies_r = []
for i in range(len(q_vector_list)):
print(q_vector_list[i])
eigenvectors, frequencies = pho_interface.obtain_eigenvectors_from_phonopy(structure, q_vector_list[i])
eigenvectors_r.append(eigenvectors)
frequencies_r.append(frequencies)
number_of_frequencies = len(frequencies_r[0])
print('obtained frequencies')
print(frequencies_r)
print(np.pi*2.0*np.linalg.inv(structure.get_primitive_cell()).T)
#Generating trajectory
trajectory = []
for time in np.arange(total_time,step=time_step):
print(time)
xyz_file.write(str(number_of_atoms) + '\n\n')
coordinates = []
for i_atom in range(number_of_atoms):
# coordinate = map(complex,positions[i_atom])
coordinate = np.array(positions[i_atom,:], dtype=complex)
for i_freq in range(number_of_frequencies):
for i_long in range(q_vector_list.shape[0]):
q_vector = np.dot(q_vector_list[i_long,:], 2*np.pi*np.linalg.inv(structure.get_primitive_cell()))
# Beware in the testing amplitude!! Normalized for all phonons to have the same height!!
if abs(frequencies_r[i_long][i_freq]) > 0.01: #Prevent dividing by 0
amplitude = 2 * np.sqrt(kb_boltzmann * temperature / (pow(frequencies_r[i_long][i_freq] * 2 * np.pi,2)) / number_of_primitive_cells) + random.uniform(-1,1)*0.05
# normal_mode_coordinate = 1/(2*np.pi*frequencies_r[i_long][i_freq]) *amplitude * np.exp(np.complex(0, -1) * frequencies_r[i_long][i_freq] * 2.0 * np.pi * time)
normal_mode_coordinate = amplitude * np.exp(np.complex(0, -1) * frequencies_r[i_long][i_freq] * 2.0 * np.pi * time)
phase = np.exp(np.complex(0, 1) * np.dot(q_vector, positions[i_atom, :]))
coordinate += (1.0 / np.sqrt(masses[i_atom]) *
eigenvectors_r[i_long][i_freq, atom_type[i_atom]] *
phase *
normal_mode_coordinate)
coordinate = coordinate.real
xyz_file.write(structure.get_atomic_types(super_cell=super_cell)[i_atom]+'\t' +
'\t'.join([str(item) for item in coordinate.real]) + '\n')
coordinates.append(coordinate)
trajectory.append(coordinates)
xyz_file.close()
trajectory = np.array(trajectory)
print(trajectory.shape[0])
time = np.array([ i*time_step for i in range(trajectory.shape[0])],dtype=float)
energy = np.array([ 0*i for i in range(trajectory.shape[0])],dtype=float)
#Save a trajectory object to file for later recovery
dump_file = open("trajectory.save", "w")
pickle.dump(dyn.Dynamics(structure=structure,
trajectory=np.array(trajectory, dtype=complex),
energy=np.array(energy),
time=time,
super_cell=np.dot(np.diagflat(super_cell),structure.get_cell())),
dump_file)
dump_file.close()
print(np.dot(np.diagflat(super_cell),structure.get_cell()))
return dyn.Dynamics(structure=structure,
trajectory=np.array(trajectory,dtype=complex),
energy=np.array(energy),
time=time,
super_cell=np.dot(np.diagflat(super_cell),structure.get_cell()))
