def load_datagrid_sim(folder, arr, x):
'''
Load cif files from folder into ase crystal objects, simulate crystal
diffraction and represent data in timeseries
Args:
folder (string): Where we load the data from.
arr (list): List of cif filenames in folder.
x (nparray): representation of 1/angström for powder simulation.
Returns:
fd (datagrid): representation of intensity values and 1/angström
values in datagrid object.
'''
filename_arr = []
crys_arr = []
for file in arr:
filename = os.sep.join([folder, file])
filename_arr.append(file)
crys = read(filename)
crys = Crystal.from_ase(crys)
diff = powdersim(crys, x)
diff_norm = diff / diff.max()
crys_arr.append(diff_norm)
crys_arr = np.array(crys_arr)
print(crys_arr)
return crys_arr
def pow_sim(path1):
filename1 = Path(path1).name
#filename2 = Path(path2).name
# load cif file to an ase object
cry1 = read(path1)
#cry2 = read(path2)
# crystal object for powdersim in skued lib
crys1 = Crystal.from_ase(cry1)
#crys2 = Crystal.from_ase(cry2)
# powder simulation with range of q
q = np.linspace(1, 7, 512)
diff1 = powdersim(crys1, q, fwhm_g = 0.01, fwhm_l=0.03)
#diff2 = powdersim(crys2, q)
# matplotlib config
plt.figure()
plt.plot(q, diff1/diff1.max(), '-b', label=filename1, alpha= 0.3)
#plt.plot(q, diff2/diff2.max(), '-r', label=filename2, alpha= 0.3)
plt.legend()
plt.xlim([q.min(), q.max()])
plt.xlabel('$q (1/\AA)$')
plt.ylabel('Diffracted intensity (A.u.)')
plt.title('Crystal diffraction ')
plt.show()
def load_datagrid_sim(folder, arr, x):
'''
Load cif files from folder into ase crystal objects, simulate crystal
diffraction and represent data in timeseries
Args:
folder (string): Where we load the data from.
arr (list): List of cif filenames in folder.
x (nparray): representation of 1/angström for powder simulation.
Returns:
fd (datagrid): representation of intensity values and 1/angström
values in datagrid object.
'''
filename_arr = []
crys_arr = []
for file in arr:
filename = os.sep.join([folder, file])
filename_arr.append(file)
crys = read(filename)
crys = Crystal.from_ase(crys)
diff = powdersim(crys, x, fwhm_l=50)
diff_norm = diff / diff.max()
crys_arr.append(diff_norm)
crys_arr = np.array(crys_arr)
fd = FDataGrid(crys_arr,
x,
dataset_name='Diffraction Curves',
argument_names=[r'$q (1/\AA)$'],
coordinate_names=['Diffracted intensity (A.u.)'])
print(fd)
return fd
def test_back_and_forth(self):
""" Test conversion to and from ase Atoms """
to_ase = self.crystal.to_ase()
crystal2 = Crystal.from_ase(to_ase)
# ase has different handling of coordinates which can lead to
# rounding beyond 1e-3. Therefore, we cannot compare directly sets
# self.assertSetEqual(set(self.crystal), set(crystal2))
self.assertEqual(len(self.crystal), len(crystal2))
def test_ase_atoms_back_and_forth(name):
""" Test conversion to and from ase Atoms """
crystal = Crystal.from_database(name)
to_ase = crystal.to_ase()
crystal2 = Crystal.from_ase(to_ase)
# ase has different handling of coordinates which can lead to
# rounding beyond 1e-3. Therefore, we cannot compare directly sets
# assertSetEqual(set(crystal), set(crystal2))
assert len(crystal) == len(crystal2)
import numpy as np from skued import powdersim #from skued import Crystal filename1 = r"C:\Python\Projects\crystal-phase-prediction\crystal_data\CIFs\Ag_HfO2_cat_3.125_222_m.cif" filename2 = r"C:\Python\Projects\crystal-phase-prediction\crystal_data\CIFs\Ag_HfO2_cat_3.125_222_o.cif" # load cif file to an ase object s = read(filename1) s2 = read(filename2) s1 = crystal(s) # crystal object for powdersim in skued lib crys = Crystal.from_ase(s1) pure_cry = Crystal.from_ase(s2) # visualize crystal view(s) #print(s.get_positions()) #print(s.get_atomic_numbers()) #print(s.get_cell()[:]) # powder simulation with range of q q = np.linspace(1, 5, 100) diff1 = powdersim(crys, q) diff2 = powdersim(pure_cry, q) # diff1 = powdersim(cry1, q, fwhm_g=0.01, fwhm_l=1) # diff2 = powdersim(cry2, q, fwhm_g=0.01, fwhm_l=1)
