def test_stoichiometry_found_from_theoretical_EELS(self):
if True:
return
from atomic_eels import atomic_diff_cross_section
# Make BN eels data using FEFF.
atomic_numbers=[5,7]
amps =[1.0,1.0]
edge_label = 'K'
beam_energy_keV = 200.0
convergence_angle_mrad = 1.5
collection_angle_mrad = 1.5
egrid_eV = numpy.arange(0.0, 1000.0, 0.5) # Define energy grid
energyDiffSigma_total = numpy.zeros_like(egrid_eV) # Initialize total cross section.
edge_onsets = [0.0, 0.0]
edge_deltas = [0.0, 0.0]
background_ranges = [numpy.zeros(2),numpy.zeros(2)]
iEdge = 0
for atomic_number in atomic_numbers:
#print(atomic_number,edge_label,beam_energy_keV,convergence_angle_mrad,collection_angle_mrad)
energyDiffSigma,edge_onsets[iEdge] = atomic_diff_cross_section(atomic_number, edge_label, beam_energy_keV,
convergence_angle_mrad, collection_angle_mrad, egrid_eV)
energyDiffSigma_total = numpy.add(energyDiffSigma_total,energyDiffSigma*amps[iEdge])
# set background ranges and offsets, etc.
background_ranges[iEdge][0] = max(edge_onsets[iEdge]-30.0,0.0)
background_ranges[iEdge][1] = max(edge_onsets[iEdge]-5.0,0.0)
edge_deltas[iEdge] = 30.0
iEdge += 1
#print('bgr',background_ranges,edge_onsets)
bgfunc = lambda x: 1.0e-3/(x+10.0)**3
background = numpy.vectorize(bgfunc)
energyDiffSigma_total = numpy.add(energyDiffSigma_total,background(egrid_eV))*10.0e12
#plt.plot(egrid_eV,energyDiffSigma_total)
#plt.show()
#noise = numpy.random.normal(0.0, max(energyDiffSigma_total)/100.0,energyDiffSigma_total.size)
#energyDiffSigma_total = numpy.add(noise,energyDiffSigma_total)
erange = numpy.zeros(2)
estep = (egrid_eV[-1] - egrid_eV[0])/(egrid_eV.size - 1)
print(estep)
erange[0] = egrid_eV[0]
erange[1] = egrid_eV[-1] + estep
stoich_data = EELS_DataAnalysis.stoichiometry_from_eels(energyDiffSigma_total,erange,background_ranges,atomic_numbers,edge_onsets,edge_deltas,
beam_energy_keV*1000.0, convergence_angle_mrad/1000.0, collection_angle_mrad/1000.0)
stoichiometry = stoich_data[0]
error_in_stoichiometry = stoich_data[1]
iAtom = 0
print("Stoichiometry from theoretical EELS signal of BN:")
for atomic_number in atomic_numbers:
print(atomic_number, stoichiometry[iAtom][0], '+/-', error_in_stoichiometry[iAtom][0])
assert abs(stoichiometry[iAtom][0]/amps[iAtom]*amps[0]-0.5) < 0.01 # Test stoichiometry found is better than 1%
iAtom += 1
def test_stoichiometry_found_from_experimental_eels(self):
# Read EELS data from file (BN). This is data from Tracy, taken from a thin part of the sample,
# and represents to some extent a best case scenario.
test_data_dir = Path(__file__).parent / 'Test_Data'
data_files = [test_data_dir / 'BN_0-0910eV_.msa', test_data_dir / 'CaCO3.msa', test_data_dir / 'CuO.msa']
labels = ['BN', 'CaCO_3','CuO']
#data_file = Path('./Test_Data/EELS_Thick.csv')
#data_file = Path('./Test_Data/EELS_Thin.csv')
atomic_number_arrays = [[7,5],[8,20,6],[29,8]]
beam_energies = [200.0,200.0,200.0]
convergence_angles = [0.0, 0.0, 0.0]
collection_angles = [100.0, 100.0, 100.0]
edge_onset_arrays = [[401.0, 188.0],[532.0, 346.0, 284.0], [931.0,532.0]]
edge_delta_arrays = [[25.0,25.0],[40.0,25.0,25.0],[40.0,40.0]]
background_arrays = [ [numpy.array([358.0,393.0]),numpy.array([167.0,183.0])], [numpy.array([474.0, 521.0]),numpy.array([308.0,339.0]),numpy.array([253.0,278.0])],
[numpy.array([831.0,912.0]),numpy.array([474.0,521.0])]]
DM_Stoichiometries = [[1.0, 0.83], [1.0, 0.76, 0.27],[1.0,0.09]]
True_Stoichiometries = [[1.0,1.0],[1.0, 0.3333, 0.3333],[1.0,1.0]]
iData = 0
for data_file in data_files:
energy_grid,spectrum = numpy.loadtxt(data_file, delimiter=',',unpack=True)
# Set up input to stoichiometry quantification. All settings are hard coded to
# BN to match DM 2.32.888.
beam_energy_keV = beam_energies[iData]
atomic_numbers = atomic_number_arrays[iData]
convergence_angle_mrad = convergence_angles[iData]
collection_angle_mrad = collection_angles[iData]
edge_onsets = edge_onset_arrays[iData]
edge_deltas = edge_delta_arrays[iData]
background_ranges = background_arrays[iData]
erange = numpy.zeros(2)
erange[0] = energy_grid[0]
erange[1] = energy_grid[-1]
stoich_data = EELS_DataAnalysis.stoichiometry_from_eels(spectrum,erange,background_ranges,atomic_numbers,edge_onsets,edge_deltas,
beam_energy_keV*1000.0, convergence_angle_mrad/1000.0, collection_angle_mrad/1000.0)
stoich = stoich_data[0]
error_in_stoich = stoich_data[1]
iAtom = 0
DM_Stoichometry = DM_Stoichiometries[iData]
True_Stoichiometry = True_Stoichiometries[iData]
print("----------------------------------------------------------------------------\n\n\n")
print('Stoichiometry from experimental EELS data from the EELS Atlas:' + labels[iData])
print('atomic#, N, N from DM, True N')
for atomic_number in atomic_numbers:
print(atomic_number, stoich[iAtom][0],'+/-',error_in_stoich[iAtom][0],DM_Stoichometry[iAtom], True_Stoichiometry[iAtom])
iAtom += 1
print("----------------------------------------------------------------------------")
iData += 1
def test_stoichiometry_from_multidimensional_EELS(self):
# Make BN eels data using FEFF.
atomic_numbers=[5,7]
nSpectra = 100
amps =[1.0,1.0]
edge_label = 'K'
beam_energy_keV = 200.0
convergence_angle_mrad = 1.5
collection_angle_mrad = 1.5
egrid_eV = numpy.arange(0.0, 1000.0, 0.5) # Define energy grid
energyDiffSigma_total = numpy.array([numpy.zeros_like(egrid_eV)]*nSpectra) # Initialize total cross section.
energyDiffSigma = numpy.array([numpy.zeros_like(egrid_eV)]*2) # Initialize total cross section.
edge_onsets = [0.0, 0.0]
edge_deltas = [0.0, 0.0]
background_ranges = [numpy.zeros(2),numpy.zeros(2)]
iEdge = 0
for atomic_number in atomic_numbers:
energyDiffSigma[iEdge],edge_onsets[iEdge] = atomic_diff_cross_section(atomic_number, edge_label, beam_energy_keV,
convergence_angle_mrad, collection_angle_mrad, egrid_eV)
iEdge += 1
iEdge = 0
for atomic_number in atomic_numbers:
iSpectrum = 0
while iSpectrum < nSpectra:
if iEdge == 0:
amps[iEdge] = numpy.sin(float(iSpectrum)/float(nSpectra)*numpy.pi/2.0)**2
else:
amps[iEdge] = numpy.cos(float(iSpectrum)/float(nSpectra)*numpy.pi/2.0)**2
energyDiffSigma_total[iSpectrum] = numpy.add(energyDiffSigma_total[iSpectrum],energyDiffSigma[iEdge]*amps[iEdge])
iSpectrum += 1
# set background ranges and offsets, etc.
background_ranges[iEdge][0] = max(edge_onsets[iEdge]-30.0,0.0)
background_ranges[iEdge][1] = max(edge_onsets[iEdge]-5.0,0.0)
edge_deltas[iEdge] = 30.0
iEdge += 1
iSpectrum = 0
# Add background.
bgfunc = lambda x: 1.0e-3/(x+10.0)**3
background = numpy.vectorize(bgfunc)
while iSpectrum < nSpectra:
energyDiffSigma_total[iSpectrum] = numpy.add(energyDiffSigma_total[iSpectrum],background(egrid_eV))*10.0e12
iSpectrum += 1
#print('bgr',background_ranges,edge_onsets)
#plt.plot(egrid_eV,energyDiffSigma_total)
#plt.show()
#noise = numpy.random.normal(0.0, max(energyDiffSigma_total)/100.0,energyDiffSigma_total.size)
#energyDiffSigma_total = numpy.add(noise,energyDiffSigma_total)
erange = numpy.zeros(2)
erange[0] = egrid_eV[0]
erange[1] = egrid_eV[-1]
stoich_data = EELS_DataAnalysis.stoichiometry_from_eels(energyDiffSigma_total,erange,background_ranges,atomic_numbers,edge_onsets,edge_deltas,
beam_energy_keV*1000.0, convergence_angle_mrad/1000.0, collection_angle_mrad/1000.0)
stoichiometry = stoich_data[0]
error_in_stoich = stoich_data[1]
iAtom = 0
print("Stoichiometry from multidimensional spectrum array.")
for atomic_number in atomic_numbers:
print(atomic_number)
print(stoichiometry[iAtom])
iAtom += 1
def test_eels_quantification(self):
if True: # For now turn this test off. I will keep the directory of all EELS Atlas data on hand for more testing.
return
import glob
import pandas
df = pandas.read_csv("EELS_Atlas_Major/files_HE.dat",
delim_whitespace=True,
header=None)
file_names, tmp1, efin, tmp2, estart = df.to_numpy().T
i_search = 0
total_extra_found = 0
total_edges_edb = 0
total_edges_matched = 0
mintol = 0.1
nfail = 0
ntot = 0
ptable = PeriodicTable.PeriodicTable()
for file_name in file_names:
# Load data from text file.
#data_file = glob.glob('./EELS_Atlas_Major/**/' + file_name,recursive = True)[0]
print('\n\n\n')
data_file = glob.glob('./EELS_Atlas_Major/**/' + file_name,
recursive=True)[0]
#edge_file = glob.glob('./EELS_Atlas_Major/**/' + 'edges_' + os.path.splitext(file_name)[0]+'.dat', recursive=True)[0]
edge_file = glob.glob('./EELS_Atlas_Major/**/' + 'edges_' +
os.path.splitext(file_name)[0] + '.dat',
recursive=True)[0]
energies, eels_spectrum = numpy.loadtxt(data_file,
delimiter=',',
unpack=True)
energy_step = (energies[-1] - energies[0]) / energies.size
energy_range_ev = numpy.array(
[energies[0], energies[-1] + energy_step])
if estart[i_search] < 0:
estart[i_search] = tmp1[i_search]
search_range = [estart[i_search], efin[i_search]]
chem_formula = file_name.split('_')[0]
elements_exp = [
elem.strip(string.digits)
for elem in re.findall('[A-Z][^A-Z]*', chem_formula)
if str(ptable.atomic_number(elem.strip(string.digits))) in
ptable.find_elements_in_energy_interval(search_range)
]
print(file_name, ':')
experimental_edge_data = EELS_DataAnalysis.find_experimental_edge_energies(
eels_spectrum,
energy_range_ev,
search_range,
debug_plotting=False)
df = pandas.read_csv(edge_file, delim_whitespace=True, header=None)
edge_data = EELS_DataAnalysis.find_species_from_experimental_edge_data(
eels_spectrum,
energy_range_ev,
experimental_edge_data,
search_range_ev=search_range,
only_major_edges=True)
elements_found = [ed[0] for ed in edge_data]
edge_data = EELS_DataAnalysis.find_species_from_experimental_edge_data(
eels_spectrum,
energy_range_ev,
experimental_edge_data,
search_range_ev=search_range,
only_major_edges=False,
element_list=elements_found)
edge_energies = experimental_edge_data[0]
# Print edges found, and edges found by visual inspection for comparison.
# The edge finder will not find all edges necessarily, and might find extra edges.
elements_found = [ptable.element_symbol(ed[0]) for ed in edge_data]
ntot += 1
if (all(x in elements_found for x in elements_exp)):
missing = False
else:
print(chem_formula, ": Failed to find all elements")
i_search += 1
continue
edge_data = [
ed for ed in edge_data
if ptable.element_symbol(ed[0]) in elements_exp
]
#print('edge_data', edge_data)
i_search += 1
# We now have the elements and the edges we want to analyze, lets do the quantification
# Set microscope parameters
beam_energy_keV = 200.0
convergence_angle_mrad = 0.0
collection_angle_mrad = 100.0
# Set up the atomic numbers, edge onsets, and background ranges
atomic_numbers = []
background_ranges = []
edge_onsets = []
edge_deltas = []
iElement = 0
for ed in edge_data:
iEdge = 0
edge_onset = []
while iEdge < len(ed[1]):
edge_onsets = edge_onsets + [ed[1][iEdge][2]]
atomic_numbers = atomic_numbers + [ed[0]]
iEdge += 1
#print('Atoms in system:', atomic_numbers)
deltas = 30.0
for i, onset in enumerate(edge_onsets):
if i + 1 < len(edge_onsets) and atomic_numbers[
i] == atomic_numbers[i + 1]:
if edge_onsets[i + 1] - onset > deltas:
edge_deltas = edge_deltas + [deltas]
else:
edge_deltas = edge_deltas + [
deltas
] #[edge_onsets[i+1] - onset]
#print(edge_deltas[-1])
else:
edge_deltas = edge_deltas + [
min(deltas, energy_range_ev[1] - onset)
]
if i > 0:
if atomic_numbers[i] == atomic_numbers[i - 1]:
#background_ranges = background_ranges + [numpy.array([max(onset - 30.0,edge_onsets[i-1]), onset - 10.0])]
background_ranges = background_ranges + [
numpy.array([
max(onset - 30.0, energy_range_ev[0]),
onset - 10.0
])
]
else:
background_ranges = background_ranges + [
numpy.array([
max(onset - 30.0, energy_range_ev[0]),
onset - 10.0
])
]
else:
background_ranges = background_ranges + [
numpy.array([
max(onset - 30.0, energy_range_ev[0]), onset - 10.0
])
]
#print(edge_onsets)
#print(edge_deltas)
#print(background_ranges)
stoich, error_in_stoich, quant_data, diff_cross, egrid_ev = EELS_DataAnalysis.stoichiometry_from_eels(
eels_spectrum, energy_range_ev, background_ranges,
atomic_numbers, edge_onsets, edge_deltas,
beam_energy_keV * 1000.0, convergence_angle_mrad / 1000.0,
collection_angle_mrad / 1000.0)
for iat, atm in enumerate(atomic_numbers):
erange = (edge_onsets[iat] - 50.0,
edge_onsets[iat] + edge_deltas[iat] + 50)
edges = ptable.find_all_edges_in_energy_interval(erange, atm)
print(ptable.element_symbol(atm), ':')
print('Energy Range: ', edge_onsets[iat],
edge_onsets[iat] + edge_deltas[iat])
for edg in edges:
edgestr = edg.get_shell_str_in_eels_notation(
include_subshell=True)
print('\t', edgestr)
print('\t', stoich[iat], error_in_stoich[iat])
print('\n\n###############################################')
if False:
import matplotlib.pyplot as plt
e_step = (quant_data[iat][4][1] - quant_data[iat][4][0]
) / quant_data[iat][1][0].size
profile_grid = numpy.arange(quant_data[iat][4][0],
quant_data[iat][4][1], e_step)
plt.plot(profile_grid, quant_data[iat][1][0])
plt.plot(profile_grid, quant_data[iat][3][0])
plt.plot(energies, eels_spectrum)
plt.xlim(quant_data[iat][4][0] - 50,
quant_data[iat][4][1] + 50)
plt.plot(egrid_ev[iat], diff_cross[iat])
plt.show()