def load_data_from_URLs(self, URLs): # -> create data[BE,kx,ky]
# Last element in URLs are the parameters. All other elements
# are individual orbitals to load. Each is a list of length 2
# with first entry being the URL, the second the meta_data
# dictionary
*orbitals, options = URLs
name, *parameters = options
self.data = SlicedData.init_from_orbitals(name, orbitals, parameters)
self.change_slice(0, 0)
def load_data_from_URL(self, URL): # -> create data[photon_energy,kx,ky]
# Last element in URL are the parameters. The first element
# is the orbital to load as a list of length 2
# with first entry being the URL, the second the meta_data
# dictionary
*orbital, options = URL
name, *parameters = options
self.data = SlicedData.init_from_orbital_photonenergy(
name, orbital, parameters)
self.change_slice(0, 0)
def load_data_from_cube(self, URL): # -> create either psi[x,y,z]
# or psik[kx,ky,kz]
# Last element in URL are the parameters. The first element
# is the orbital to load as a list of length 2
# with first entry being the URL, the second the meta_data
# dictionary
*orbital, options = URL
name, *parameters = options
self.data = SlicedData.init_from_orbital_cube(
name, orbital, parameters)
self.change_slice(0, 0)
def load_data_from_URL(self, URL, ID=None): # -> create data[photon_energy,kx,ky]
# Last element in URL are the parameters. The first element
# is the orbital to load as a list of length 2
# with first entry being the URL, the second the meta_data
# dictionary
*orbital, options = URL
name, *parameters = options
s_share = float(config.get_key('orbital', 's_share_sliced'))
self.data = SlicedData.init_from_orbital_photonenergy(
name, orbital, parameters, s_share=s_share, ID=ID)
self.load_data = ['load_from_URL', URL]
self.change_slice(0, 0)
def load_data_from_URLs(self, URLs, ID=None): # -> create data[BE,kx,ky]
# Last element in URLs are the parameters. All other elements
# are individual orbitals to load. Each is a list of length 2
# with first entry being the URL, the second the meta_data
# dictionary
*orbitals, options = URLs
name, *parameters = options
s_share = float(config.get_key('orbital', 's_share_sliced'))
self.data = SlicedData.init_from_orbitals(name, orbitals,
parameters, s_share=s_share,
ID=ID)
self.load_data = ['load_from_URLs', URLs]
self.change_slice(0, 0)
def setUpClass(cls):
file_path = os.path.dirname(os.path.realpath(__file__))
input_path = file_path + '/../../example/data/'
sliced_path = input_path + 'example5_6584.hdf5'
cls.sliced_data = SlicedData.init_from_hdf5(sliced_path)
orbital_paths = [
'PTCDA_C.cube', 'PTCDA_D.cube', 'PTCDA_E.cube', 'PTCDA_F.cube'
]
cls.orbitals = [
OrbitalData.init_from_file(input_path + path, ID)
for ID, path in enumerate(orbital_paths)
]
cls.expected = np.loadtxt(file_path + '/output/weights_PTCDA')
cls.background_expected = np.loadtxt(file_path +
'/output/background_expected')
def test_initialization_from_hdf5(self):
sliced_data = SlicedData.init_from_hdf5(
__directory__ / '../example/data/example5_6584.hdf5')
npt.assert_almost_equal(sliced_data.slice_from_index(2).data[145, 235],
194.848388671875,
decimal=14)
(sliced_data.name, '6584estep0.0170213final.txt')
npt.assert_equal(
sliced_data.meta_data, {
'alias': 'M3 PTCDA/Ag(110)',
'arcwidth': '0.7800000000000011',
'fermiLevel': '28.2',
'filenumber': '6584estep0.0170213final.txt',
'kStepSize': '0.02',
'negPolar_avgs': 'False',
'polarshift': '0.0',
'rotation': '-24.0',
'sym_anglemax': '180.0',
'sym_anglemin': '0.0',
'symmode': '2-fold'
})
def load_data_from_path(self, path, ID=None):
log = logging.getLogger('kmap')
possible_paths = [path]
file_name = Path(path).name
possible_paths.append(Path(config.get_key('paths', 'hdf5_start')) /
file_name)
for path in config.get_key('paths', 'path').split(','):
possible_paths.append(Path(path) / file_name)
for path in possible_paths:
log.info(f'Looking for {file_name} in {path}.')
if os.path.isfile(path):
log.info(f'Found.')
self.data = SlicedData.init_from_hdf5(path, ID=ID)
self.load_data = ['load_from_path', path]
self.change_slice(0, 0)
return
else:
continue
# No path worked
print(f'ERROR: File {file_name} wasn\'t found. Please add its location to the search path (general_settings.paths.path')
# Third Party Imports
import matplotlib.pyplot as plt
import numpy as np
# kMap.py Imports
from kmap.library.sliceddata import SlicedData
from kmap.library.orbitaldata import OrbitalData
from kmap.model.lmfit_model import LMFitModel
# Path to data folder; replace with your own; use '/' instead of '+'
# when concatenating with strings
data_path = Path(__file__).parent / Path('../data/')
# Load experimental data as SlicedData object
exp_data = SlicedData.init_from_hdf5(data_path / 'example5_6584.hdf5')
# Load orbitals for fitting as OrbitalData objects
orbital_paths = [
'PTCDA_C.cube', 'PTCDA_D.cube', 'PTCDA_E.cube', 'PTCDA_F.cube'
]
orbitals = [
OrbitalData.init_from_file(data_path / path, ID)
for ID, path in enumerate(orbital_paths)
]
# Initialize fit as LMFitModel object
lmfit = LMFitModel(exp_data, orbitals)
# Set common range and delta-k-grid for exp. and sim. kmaps
range_, dk = [-3.0, 3.0], 0.04
0.0, # fermi_energy (float): Fermi energy in eV
0.4, # energy_broadening (float): FWHM of Gaussian energy broadenening in eV
0.03, # dk (float): Desired k-resolution in kmap in Angstroem^-1.
0, # phi (float): Euler orientation angle phi in degree.
0, # theta (float): Euler orientation angle phi in degree.
0, # psi (float): Euler orientation angle phi in degree.
'no', # Ak_type (string): Treatment of |A.k|^2: either 'no', 'toroid' or 'NanoESCA'.
'p', # polarization (string): Either 'p', 's', 'C+', 'C-' or 'CDAD'.
0, # alpha (float): Angle of incidence plane in degree.
0, # beta (float): Azimuth of incidence plane in degree.
'auto', # gamma (float/str): Damping factor for final state in Angstroem^-1. str = 'auto' sets gamma automatically
'no'
] # symmetrization (str): either 'no', '2-fold', '2-fold+mirror',
# '3-fold', '3-fold+mirror','4-fold', '4-fold+mirror'
# initialize SlicedData object
kmap_stack = SlicedData.init_from_orbitals(name, orbitals, parameters)
# Plot some slices
fig, _ax = plt.subplots(3, 3)
ax = _ax.flatten()
nplots = len(ax)
nslice = kmap_stack.data.shape[0]
count = 0
for i in range(0, nslice, 1 + nslice // nplots):
plot_data = kmap_stack.slice_from_index(i)
ax[count].imshow(plot_data.data)
count += 1
plt.show()
# ... or choose URL pointing to cubefile
#cube_file = 'http://143.50.77.12/OrganicMolecule/B3LYP/5A/charge0mult1/5A_MO_73'
# Create SlicedData object
orbital = [[cube_file, {}]]
name = '5A H**O'
parameters = [
'k-space', # either 'real-space' or 'k-space'
0.15, # desired resolution for 3D-Fourier-Transform.
150, # maximum kinetic energy in eV
'real'
] # choose between 'real', 'imag', 'abs' or 'abs2'
# for Re(), Im(), |..| or |..|^2
orbital_slices = SlicedData.init_from_orbital_cube(name, orbital, parameters)
# Plot some slices
fig, _ax = plt.subplots(3, 3)
ax = _ax.flatten()
nplots = len(ax)
nslice = orbital_slices.data.shape[0]
count = 0
for i in range(0, nslice, 1 + nslice // nplots):
plot_data = orbital_slices.slice_from_index(i)
ax[count].imshow(plot_data.data)
count += 1
plt.show()
def load_data_from_path(self, path):
self.data = SlicedData.init_from_hdf5(path)
self.load_data = ['load_from_path', path]
self.change_slice(0, 0)
# Third Party Imports
import matplotlib.pyplot as plt
import numpy as np
# kMap.py Imports
from kmap.library.sliceddata import SlicedData
from kmap.library.orbitaldata import OrbitalData
from kmap.model.lmfit_model import LMFitModel
# Path to data folder; replace with your own; use '/' instead of '+'
# when concatenating with strings
data_path = Path(__file__).parent / Path('../data/')
# Load experimental data as SlicedData object
exp_data = SlicedData.init_from_hdf5(data_path / 'example4_3271.hdf5')
# Load orbital for fitting as OrbitalData objects
orbital_paths = ['pentacene_HOMO.cube']
orbitals = [OrbitalData.init_from_file(
data_path / path, ID) for ID, path in enumerate(orbital_paths)]
# Initialize fit as LMFitModel object
lmfit = LMFitModel(exp_data, orbitals)
# Set common range and delta-k-grid for exp. and sim. kmaps
range_, dk = [-3.0, 3.0], 0.04
lmfit.set_axis_by_step_size(range_, dk)
lmfit.set_polarization('toroid', 'p')
lmfit.set_symmetrization('2-fold')
0.0, # fermi_energy (float): Fermi energy in eV
0.02, # dk (float): Desired k-resolution in kmap in Angstroem^-1.
0, # phi (float): Euler orientation angle phi in degree.
0, # theta (float): Euler orientation angle phi in degree.
0, # psi (float): Euler orientation angle phi in degree.
'no', # Ak_type (string): Treatment of |A.k|^2: either 'no', 'toroid' or 'NanoESCA'.
'p', # polarization (string): Either 'p', 's', 'C+', 'C-' or 'CDAD'.
0, # alpha (float): Angle of incidence plane in degree.
0, # beta (float): Azimuth of incidence plane in degree.
'auto', # gamma (float/str): Damping factor for final state in Angstroem^-1. str = 'auto' sets gamma automatically
'no'
] # symmetrization (str): either 'no', '2-fold', '2-fold+mirror',
# '3-fold', '3-fold+mirror','4-fold', '4-fold+mirror'
# initialize SlicedData object
kmap_stack = SlicedData.init_from_orbital_photonenergy(name, orbital,
parameters)
# Plot some slices
fig, _ax = plt.subplots(3, 3)
ax = _ax.flatten()
nplots = len(ax)
nslice = kmap_stack.data.shape[0]
count = 0
for i in range(0, nslice, 1 + nslice // nplots):
plot_data = kmap_stack.slice_from_index(i)
ax[count].imshow(plot_data.data)
count += 1
plt.show()
# Python Imports
from pathlib import Path
# Third Party Imports
import matplotlib.pyplot as plt
# kMap.py Imports
from kmap.library.sliceddata import SlicedData
# Path to data folder; replace with your own; use '/' instead of '+'
# when concatenating with strings
data_path = Path(__file__).parent / Path('../data/')
# Import the sliced data class from the model folder
# Get path to the directory this file is in
# ATTENTION FILE DOES NOT EXIT ANYMORE
file_path = str(data_path / 'example5_6584.hdf5')
# Loading from file (Initialisation methods are usually class members)
sliced_data = SlicedData.init_from_hdf5(file_path)
# Get a PlotData object consisting of the 10th slice and its axes
plot_data = sliced_data.slice_from_index(10)
# Plot
__, axes = plt.subplots()
axes.imshow(plot_data.data)
plt.show()