def calculate_results(x, y, upperx, lowerx, power):
"""Taking the x and y coordinates and the upper and lower x energies to fit"""
arr_lower_lim = find_array_equivalent(x, lowerx)
arr_upper_lim = find_array_equivalent(x, upperx)
if arr_upper_lim <= arr_lower_lim:
x_cut = x[arr_upper_lim:arr_lower_lim]
y_cut = y[arr_upper_lim:arr_lower_lim]
elif arr_lower_lim < arr_upper_lim:
x_cut = x[arr_lower_lim:arr_upper_lim]
y_cut = y[arr_lower_lim:arr_upper_lim]
a_poly = np.polyfit(x_cut, y_cut, power)
a_polygen = np.poly1d(a_poly)
ia_bg = [a_polygen(e) for e in x]
a_bg = np.array(ia_bg)
a_p_norm = y - a_bg
x_return = x
return x_return, a_p_norm, a_bg
def calculate_results1(x, y, lowerx, upperx, power, offset):
"""Taking the x and y coordinates and the upper and lower x energies to fit"""
if y.ndim == 1:
arr_lower_lim = int(find_array_equivalent(x, lowerx))
arr_upper_lim = int(find_array_equivalent(x, upperx))
if offset == "off":
if arr_lower_lim <= arr_upper_lim:
x_cut = np.concatenate((x[:arr_lower_lim], x[arr_upper_lim:]),
axis=0)
y_cut = np.concatenate((y[:arr_lower_lim], y[arr_upper_lim:]),
axis=0)
elif arr_lower_lim > arr_upper_lim:
x_cut = np.concatenate((x[:arr_upper_lim], x[arr_lower_lim:]),
axis=0)
y_cut = np.concatenate((y[:arr_upper_lim], y[arr_lower_lim:]),
axis=0)
else:
offset = y[arr_lower_lim] - y[arr_upper_lim]
if arr_lower_lim <= arr_upper_lim:
x_cut = np.concatenate((x[:arr_lower_lim], x[arr_upper_lim:]),
axis=0)
y_cut = np.concatenate(
(y[:arr_lower_lim], y[arr_upper_lim:] + offset), axis=0)
elif arr_lower_lim > arr_upper_lim:
x_cut = np.concatenate((x[:arr_upper_lim], x[arr_lower_lim:]),
axis=0)
y_cut = np.concatenate(
(y[:arr_upper_lim], y[arr_lower_lim:] + offset), axis=0)
a_poly = np.polyfit(x_cut, y_cut, power)
a_polygen = np.poly1d(a_poly)
ia_bg = [a_polygen(e) for e in x]
a_bg = np.array(ia_bg)
a_p_norm = y - a_bg
elif absorption.ndim == 2:
print("Deal with at a higher level to avoid clutter")
return x, a_p_norm, a_bg
def Normalise_spectra(x, y, p1, p2):
"""Normalises to 1"""
if p1 and p2:
normalised_spectra = y / (y[find_array_equivalent(x, p2)] -
y[find_array_equivalent(x, p1)])
else:
normalised_spectra = y
return x, normalised_spectra
def Transpose_spectra(energy, absorption, args):
"""Transpose_spectra(energy, absorption, args) requires args.x_value_for_transpose and args.action"""
loc_energy = np.array(energy)
loc_absorption = np.array(absorption)
if len(np.array(loc_absorption).shape) > 1:
n_files = len(np.array(loc_absorption))
else:
n_files = 1
transposed_spectra = []
for i in range(n_files):
if args.action == "on":
transposed_spectra_i = (
loc_absorption[i] - loc_absorption[i][find_array_equivalent(
loc_energy[i], args.x_value_for_transpose)])
else:
transposed_spectra_i = loc_absorption[i]
transposed_spectra.append(transposed_spectra_i)
return transposed_spectra
def calculate_pre_edge_fit(x, y, pre_feature_min, pre_feature_max,
post_feature_min, post_feature_max):
if x.ndim == 1:
# find array point
pre_feature_min_arr = int(find_array_equivalent(x, pre_feature_min))
pre_feature_max_arr = int(find_array_equivalent(x, pre_feature_max))
post_feature_min_arr = int(find_array_equivalent(x, post_feature_min))
post_feature_max_arr = int(find_array_equivalent(x, post_feature_max))
# concatenate data for fitting
# fit_resolution = len(energy[:edge])
x_cut = np.concatenate((
x[pre_feature_min_arr:pre_feature_max_arr],
x[post_feature_min_arr:post_feature_max_arr],
))
y_cut = np.concatenate((
y[pre_feature_min_arr:pre_feature_max_arr],
y[post_feature_min_arr:post_feature_max_arr],
))
# cut data to edge
y_out = y[pre_feature_min_arr:post_feature_max_arr]
x_out = x[pre_feature_min_arr:post_feature_max_arr]
initial_guess = [1.4, 6003, 2, 1]
popt, pcov = scipy.optimize.curve_fit(gaussian_func,
x_cut,
y_cut,
p0=initial_guess)
# append guassian model to fit
fit = []
for item in range(len(x_out)):
fit.append(gaussian_func(x_out, *popt)[item])
fit = np.array(fit)
y_out = y_out - fit
return x_out, y_out, fit
elif x.ndim == 2:
print("2d")
def evaluate(self):
x = np.array(self.inputarray.read()["data"][0])
y = np.array(self.inputarray.read()["data"][1])
if self.lookup.default:
if x.ndim and y.ndim == 1:
value = [y[find_array_equivalent(x, self.lookup.default)]]
elif x.ndim and y.ndim == 2:
value = []
for i in range(x.shape[0]):
value_i = y[i][find_array_equivalent(
x[i], self.lookup.default)]
value.append(value_i)
else:
value = None
self.value = value
self.lines = [self.lookup.default]
def evaluate(self):
x = np.array(self.input.read()["data"][0])
y = np.array(self.input.read()["data"][1])
if x.ndim and y.ndim == 1:
start = find_array_equivalent(x, self.start.default)
mid = find_array_equivalent(x, self.mid.default)
end = find_array_equivalent(x, self.end.default)
a_l3 = integrate.cumtrapz(
y[start:mid],
x[start:mid],
initial=0,
)
a_l2 = integrate.cumtrapz(
y[mid:end],
x[mid:end],
initial=0,
)
b_ratio = [a_l3[-1] / (a_l2[-1] + a_l3[-1])]
elif x.ndim and y.ndim == 2:
bratio_list = []
for i in range(x.shape[0]):
start = find_array_equivalent(x[i], self.start.default)
mid = find_array_equivalent(x[i], self.mid.default)
end = find_array_equivalent(x[i], self.end.default)
a_l3 = integrate.cumtrapz(
y[i][start:mid],
x[i][start:mid],
initial=0,
)
a_l2 = integrate.cumtrapz(
y[i][mid:end],
x[i][mid:end],
initial=0,
)
b_ratio = a_l3[-1] / (a_l2[-1] + a_l3[-1])
bratio_list.append(b_ratio)
b_ratio = list(bratio_list)
self.value_calc = b_ratio
self.lines = [self.start.default, self.mid.default, self.end.default]
def background1(energy, absorption, args):
loc_energy = np.array(energy)
loc_absorption = np.array(absorption)
if len(np.array(loc_absorption).shape) > 1:
n_files = len(np.array(loc_absorption))
else:
n_files = 1
a_bg = []
a_p_norm = []
for i in range(n_files):
# if we have inital values
if args.p_end and args.p_start:
# Calc absorption level difference between end of pre-edge and start of post-edge
if args.apply_offset == "off":
a_offset = 0
a_offset1 = 0
else:
# move post edge to preedge for fitting if called.
post_edge_intensity = loc_absorption[i][find_array_equivalent(
loc_energy[i], args.p_end)]
pre_edge_intensity = loc_absorption[i][find_array_equivalent(
loc_energy[i], args.p_start)]
a_offset = post_edge_intensity - pre_edge_intensity
# polynomial
if (args.fit ==
"linear (fits for all e < 'Background_start' and e > ' Background_end') "
):
# Polyfit and generate background series from it
e_cut = np.concatenate((
loc_energy[i]
[:find_array_equivalent(loc_energy[i], args.p_start)],
loc_energy[i]
[find_array_equivalent(loc_energy[i], args.p_end):],
))
a_cut = np.concatenate((
loc_absorption[i]
[:find_array_equivalent(loc_energy[i], args.p_start)],
(loc_absorption[i]
[find_array_equivalent(loc_energy[i], args.p_end):] -
a_offset),
))
a_poly = np.polyfit(e_cut, a_cut, 1)
a_polygen = np.poly1d(a_poly)
ia_bg = [a_polygen(e) for e in loc_energy[i]]
if args.fit == "polynomial (fits for energies inside limits) ":
# Polyfit and generate background series from it
# e_cut = loc_energy[i] [ find_array_equivalent(loc_energy[i], args.p_start) : find_array_equivalent(loc_energy[i], args.p_end) ]
# a_cut = loc_absorption[i] [ find_array_equivalent(loc_energy[i], args.p_start) : find_array_equivalent(loc_energy[i], args.p_end) ]
if find_array_equivalent(loc_energy[i],
args.p_end) <= find_array_equivalent(
loc_energy[i], args.p_start):
e_cut = loc_energy[i][find_array_equivalent(
loc_energy[i], args.p_end
):find_array_equivalent(loc_energy[i], args.p_start)]
a_cut = loc_absorption[i][find_array_equivalent(
loc_energy[i], args.p_end
):find_array_equivalent(loc_energy[i], args.p_start)]
elif find_array_equivalent(
loc_energy[i], args.p_start) < find_array_equivalent(
loc_energy[i], args.p_end):
e_cut = loc_energy[i][find_array_equivalent(
loc_energy[i], args.p_start
):find_array_equivalent(loc_energy[i], args.p_end)]
a_cut = loc_absorption[i][find_array_equivalent(
loc_energy[i], args.p_start
):find_array_equivalent(loc_energy[i], args.p_end)]
a_poly = np.polyfit(e_cut, a_cut, args.power)
a_polygen = np.poly1d(a_poly)
ia_bg = [a_polygen(e) for e in loc_energy[i]]
if (args.fit ==
"polynomial (fits for all e < 'Background_start' and e > ' Background_end')"
):
# Polyfit and generate background series from it
e_cut = np.concatenate((
loc_energy[i]
[:find_array_equivalent(loc_energy[i], args.p_start)],
loc_energy[i]
[find_array_equivalent(loc_energy[i], args.p_end):],
))
a_cut = np.concatenate((
loc_absorption[i]
[:find_array_equivalent(loc_energy[i], args.p_start)],
(loc_absorption[i]
[find_array_equivalent(loc_energy[i], args.p_end):] -
a_offset),
))
a_poly = np.polyfit(e_cut, a_cut, args.power)
a_polygen = np.poly1d(a_poly)
ia_bg = [a_polygen(e) for e in loc_energy[i]]
# exponential decay
elif (args.fit ==
"exp decay (fits for all e < 'Background_start' and e > ' Background_end')"
):
a_offset1 = 0 # ppp1-ooo1
e_cut = (np.concatenate((
loc_energy[i]
[:find_array_equivalent(loc_energy[i], args.p_start)],
loc_energy[i]
[find_array_equivalent(loc_energy[i], args.p_end):],
)) - 750)
a_cut = np.concatenate((
loc_absorption[i]
[:find_array_equivalent(loc_energy[i], args.p_start)],
loc_absorption[i]
[find_array_equivalent(loc_energy[i], args.p_end):] -
a_offset1,
))
transposed_energy = np.asarray(loc_energy[i]) - 750
# fit values, and mean
def func(x, a, b, c):
return a * np.exp(-b * x) + c
y = func(transposed_energy, 2.3, 1.3, 1)
popt, pcov = curve_fit(func, e_cut, a_cut)
ia_bg = func(transposed_energy, *popt)
# fit only considered 2 points at edge_start_index and edge_stop_index
elif args.fit == "2 point linear (straight line between 2 points)":
e_cut = [
loc_energy[i][find_array_equivalent(
loc_energy[i], args.p_start)],
loc_energy[i][find_array_equivalent(
loc_energy[i], args.p_end)],
]
a_cut = [
loc_absorption[i][find_array_equivalent(
loc_energy[i], args.p_start)],
loc_absorption[i][find_array_equivalent(
loc_energy[i], args.p_end)],
]
a_poly = np.polyfit(e_cut, a_cut, 1)
a_polygen = np.poly1d(a_poly)
ia_bg = [a_polygen(e) for e in loc_energy[i]]
elif args.fit == "No fit":
ia_bg = np.zeros(len(loc_absorption[i]))
else:
ia_bg = np.zeros(len(loc_absorption[i]))
ia_bg = np.array(ia_bg)
ia_p_norm = np.array(loc_absorption[i]) - np.array(ia_bg)
a_p_norm.append(ia_p_norm)
a_bg.append(ia_bg)
return a_bg, a_p_norm
def step2(energy, absorption, other_spectra, args):
loc_energy = energy
loc_absorption = absorption
loc_other_spectra = other_spectra
if len(np.array(loc_absorption).shape) > 1:
n_files = len(np.array(loc_absorption))
else:
n_files = 1
step = []
subtracted_step = []
for i in range(n_files):
if args.step_stop and args.step_start:
if args.apply_step == "on":
# L3_peak, L2_peak = Identify_peak_position(args, loc_energy, loc_absorption)
peaks = find_peaks_scipy1(loc_energy[i], loc_absorption[i])
L3_peak_pos = peaks[0]
L2_peak_pos = peaks[1]
# peak_fwhm_calculation(args, L3_peak, L2_peak)
# find array point
step_stop_energy = find_array_equivalent(
loc_energy[i], args.step_stop)
step_start_energy = find_array_equivalent(
loc_energy[i], args.step_start)
step_intermediate_energy = find_array_equivalent(
loc_energy[i], args.step_intermediate)
# dummy variables
fit_type = 1
l2_peak = L2_peak_pos
l3_peak = L3_peak_pos
# el3_cut = len(energy[int(l3_peak-l3_fwhm):int(l3_peak+l3_fwhm)])
# el2_cut = len(energy[int(l2_peak-l2_fwhm):int(l2_peak+l2_fwhm)])
el3_cut = len(loc_energy[i][int(l3_peak - 20):int(l3_peak +
20)])
el2_cut = len(loc_energy[i][int(l2_peak - 20):int(l2_peak +
20)])
# create a linspace of equal length.
xl3 = np.linspace(0, 1, el3_cut)
xl2 = np.linspace(0, 1, el2_cut)
xl3_arctan = np.linspace(-10, 10, el3_cut)
xl2_arctan = np.linspace(-10, 10, el2_cut)
# make a step function using the linspace created.
# voight or a tangent function
if args.fit_function == "Voight":
y = smoothstep(xl3, N=4)
u = smoothstep(xl2, N=4)
elif args.fit_function == "Arctan":
y = np.arctan(xl3_arctan)
u = np.arctan(xl2_arctan)
y = y - min(y)
u = u - min(u)
y = y / max(y)
u = u / max(u)
if args.fit_type == "Alpha":
"""
1-- Identifies the average intensity post step_stop and the intensity at step_start.
2-- Average the parallel and antiparallel spectra finding difference between average post step_stop and min post (L2 peak, pre step_stop)
-- Using this we take difference in intensitys of 1 and subtract 2 to get step height.
-- The step start is then shifted to intensity at step_start.
"""
# absorption_difference = ((min(loc_absorption[i][l2_peak:find_array_equivalent(loc_energy[i], args.step_stop)])) - (loc_absorption[i][find_array_equivalent(loc_energy[i], args.step_start)])) / 3
# average both north and south spectra together.
average_spectra = (loc_absorption[i] +
loc_other_spectra[i]) / 2
# find minima post L2_peak to step_stop
minima_post_l2_step_stop = min(
average_spectra[l2_peak:find_array_equivalent(
loc_energy[i], args.step_stop)])
# (mean intensity post step stop - step_start intensity) - (average spectra post step stop - minima post l2 peak)
absorption_difference = (
np.mean(loc_absorption[i][step_stop_energy:-1]) -
(loc_absorption[i][step_start_energy]) -
(np.mean(average_spectra[step_stop_energy:-1]) -
minima_post_l2_step_stop)) / 3
# transpose the step to intensity at step start energy.
l3_transpose = loc_absorption[i][find_array_equivalent(
loc_energy[i], args.step_start)]
elif args.fit_type == "Beta":
"""
--Simply identifies the intensity at step_stop and step_start and takes difference to find step height
--step_start is then shifted so that it starts at step_start.
--The step is always centered at the peak position with height 2/3 at L3 peak and 1/3 at L2 peak.
"""
# calculate difference in step stop and step start intensity
absorption_difference = (
loc_absorption[i][step_stop_energy] -
loc_absorption[i][step_start_energy]) / 3
# transpose the step to intensity at step start energy.
l3_transpose = loc_absorption[i][find_array_equivalent(
loc_energy[i], args.step_start)]
else:
absorption_difference = (
(min(average[l2_peak:edge_stop_index]) +
(np.mean(loc_absorption[i][height:] -
np.mean(average[height:])))) -
(((average[edge_start_index])))) / 3
l3_transpose = loc_absorption[i][edge_start_index]
y = y * absorption_difference * 2
u = u * absorption_difference
# find max gradient in steps
l3_p = np.diff(y, n=1)
oo = find_peaks_scipy(l3_p, l3_p)
l2_p = np.diff(u, n=1)
oooo = find_peaks_scipy(l2_p, l2_p)
# create zeros up to L3 step
pre = zerolistmaker(int(l3_peak - oo[0]) - 1)
# concatenate
pre_plus_l3 = np.concatenate([pre, y])
# zeros of length than transpose to step.
mid = (zerolistmaker(l2_peak - oooo[0] - len(pre_plus_l3)) +
pre_plus_l3[-1])
# concatenate
pre_l3_mid = np.concatenate([pre_plus_l3, mid, (u + mid[-1])])
# post region
post = (zerolistmaker(len(loc_energy[i]) - len(pre_l3_mid)) +
pre_l3_mid[-1])
# concatenate
total = np.concatenate([pre_l3_mid, post])
total = total + l3_transpose
elif args.apply_step == "off":
# subtracted_step = loc_absorption[i]
# stepfunction = np.zeros(len(loc_energy[i]))
total = np.zeros(len(loc_energy[i]))
# loc_energy = energy.read()[0]
# loc_absorption = absorption.read()[0]
else:
total = np.zeros(len(loc_energy[i]))
post_step = loc_absorption[i] - total
step.append(total)
subtracted_step.append(post_step)
return step, subtracted_step
def step3(x, y, start, mid, end, function, fittype):
if np.array(x).ndim == 1 and np.array(y).ndim == 1:
# L3_peak, L2_peak = Identify_peak_position(args, loc_energy, loc_absorption)
peaks = find_peaks_scipy1(x, y)
L3_peak_pos = peaks[0]
L2_peak_pos = peaks[1]
# peak_fwhm_calculation(args, L3_peak, L2_peak)
# find array point
step_stop_arr = find_array_equivalent(x, end)
step_start_arr = find_array_equivalent(x, start)
step_intermediate_arr = find_array_equivalent(x, mid)
# el3_cut = len(energy[int(l3_peak-l3_fwhm):int(l3_peak+l3_fwhm)])
# el2_cut = len(energy[int(l2_peak-l2_fwhm):int(l2_peak+l2_fwhm)])
el3_cut = len(x[int(L3_peak_pos - 20):int(L3_peak_pos + 20)])
el2_cut = len(x[int(L2_peak_pos - 20):int(L2_peak_pos + 20)])
# create a linspace of equal length.
xl3 = np.linspace(0, 1, el3_cut)
xl2 = np.linspace(0, 1, el2_cut)
xl3_arctan = np.linspace(-10, 10, el3_cut)
xl2_arctan = np.linspace(-10, 10, el2_cut)
# make a step function using the linspace created.
# voight or a tangent function
if function == "Voight":
yp = smoothstep(xl3, N=4)
u = smoothstep(xl2, N=4)
elif function == "Arctan":
yp = np.arctan(xl3_arctan)
u = np.arctan(xl2_arctan)
yp = yp - min(yp)
u = u - min(u)
yp = yp / max(yp)
u = u / max(u)
if fittype == "Beta" or "Alpha":
"""
--Simply identifies the intensity at step_stop and step_start and takes difference to find step height
--step_start is then shifted so that it starts at step_start.
--The step is always centered at the peak position with height 2/3 at L3 peak and 1/3 at L2 peak.
"""
# calculate difference in step stop and step start intensity
absorption_difference = (y[step_stop_arr] - y[step_start_arr]) / 3
# transpose the step to intensity at step start energy.
l3_transpose = y[step_start_arr]
yp = yp * absorption_difference * 2
u = u * absorption_difference
# find max gradient in steps
l3_p = np.diff(yp, n=1)
oo = find_peaks_scipy(l3_p, l3_p)
l2_p = np.diff(u, n=1)
oooo = find_peaks_scipy(l2_p, l2_p)
# create zeros up to L3 step
pre = zerolistmaker(int(L3_peak_pos - oo[0]) - 1)
# concatenate
pre_plus_l3 = np.concatenate([pre, yp])
# zeros of length than transpose to step.
mid = zerolistmaker(L2_peak_pos - oooo[0] -
len(pre_plus_l3)) + pre_plus_l3[-1]
# concatenate
pre_l3_mid = np.concatenate([pre_plus_l3, mid, (u + mid[-1])])
# post region
post = zerolistmaker(len(x) - len(pre_l3_mid)) + pre_l3_mid[-1]
# concatenate
total = np.concatenate([pre_l3_mid, post])
total = total + l3_transpose
post_step = y - total
return x, post_step, total
def single_step_xanes(energy: np.ndarray, absorption: np.ndarray, args):
if energy.ndim == 1:
# find array point
step_stop_energy = find_array_equivalent(energy, args.step_stop)
step_start_energy = find_array_equivalent(energy, args.step_start)
edge = find_array_equivalent(energy, args.edge)
# concatenate data for fitting
fit_resolution = len(energy[:edge])
x = np.concatenate([
energy[:step_start_energy],
energy[step_stop_energy:edge],
])
y = np.concatenate([
energy[:step_start_energy],
energy[step_stop_energy:edge],
])
# cut data to edge
output_ydata = absorption[:edge]
x_all = energy[:edge]
initial_guess = [1.4, 6003, 2, 6000]
popt, pcov = scipy.optimize.curve_fit(gaussian_func,
x,
y,
p0=initial_guess)
output_xdata = x_all
# append guassian model to fit
fit = []
for item in range(len(output_xdata)):
fit.append(gaussian_func(output_xdata, *popt)[item])
elif energy.ndim == 2:
print("2d")
if len(np.array(loc_absorption).shape) > 1:
n_files = len(np.array(loc_absorption))
else:
n_files = 1
fit_calc = []
subtracted_fit_calc = []
xdata_calc = []
for i in range(n_files):
if args.step_stop and args.step_start and args.edge:
if args.apply_step == "on":
# find array point
step_stop_energy = find_array_equivalent(
loc_energy[i], args.step_stop)
step_start_energy = find_array_equivalent(
loc_energy[i], args.step_start)
edge = find_array_equivalent(loc_energy[i], args.edge)
# concatenate data for fitting
fit_resolution = len(loc_energy[i][:edge])
x = np.concatenate([
loc_energy[i][:step_start_energy],
loc_energy[i][step_stop_energy:edge],
])
y = np.concatenate([
loc_absorption[i][:step_start_energy],
loc_absorption[i][step_stop_energy:edge],
])
# cut data to edge
output_ydata = loc_absorption[i][:edge]
x_all = loc_energy[i][:edge]
initial_guess = [1.4, 6003, 2, 6000]
popt, pcov = scipy.optimize.curve_fit(gaussian_func,
x,
y,
p0=initial_guess)
output_xdata = x_all
# append guassian model to fit
fit = []
for item in range(len(output_xdata)):
fit.append(gaussian_func(output_xdata, *popt)[item])
elif args.apply_step == "off":
# subtracted_step = loc_absorption[i]
# stepfunction = np.zeros(len(loc_energy[i]))
output_xdata = loc_energy[i]
output_ydata = loc_absorption[i]
fit = np.zeros(len(loc_energy[i]))
else:
output_xdata = loc_energy[i]
output_ydata = loc_absorption[i]
fit = np.zeros(len(loc_energy[i]))
subtracted_fit = output_ydata - fit
fit_calc.append(fit)
subtracted_fit_calc.append(subtracted_fit)
xdata_calc.append(output_xdata)
return xdata_calc, fit_calc, subtracted_fit_calc
def evaluate(self):
local_arguments = args_xas(self)
ax = np.array(self.a_p_norm.read()["data"][0])
bx = np.array(self.a_a_norm.read()["data"][0])
ay = np.array(self.a_p_norm.read()["data"][1])
by = np.array(self.a_a_norm.read()["data"][1])
if ax.ndim and bx.ndim == 1:
add = by + ay
integral_start = find_array_equivalent(
ax, local_arguments.background_start)
integral_end = find_array_equivalent(
ax, local_arguments.background_stop)
xas_bg_i, xas_i = background_xmcd(
energy=np.array(ax),
xmcd=add,
args=local_arguments,
)
xas_integral_i = integrate.cumtrapz(
xas_i[integral_start:integral_end],
ax[integral_start:integral_end],
)
# self.xas_integral = np.append(xas_integral[0], xas_integral[0][-1])
xas_integral1 = np.concatenate([
zerolistmaker(len(ax[0:integral_start] + 1)),
xas_integral_i,
(zerolistmaker(len(ax[integral_end:]) + 1) +
xas_integral_i[-1]),
])
xas_integral = np.array(xas_integral1)
xas_bg = np.array(xas_bg_i)
xas = np.array(xas_i)
elif ax.ndim and bx.ndim == 2:
xas_bg = []
xas = []
xas_integral = []
for i in range(ax.shape[0]):
add = (np.array(self.a_p_norm.read()["data"][1])[i] +
np.array(self.a_a_norm.read()["data"][1])[i])
integral_start = find_array_equivalent(
self.a_a_norm.read()["data"][0][i],
local_arguments.background_start)
integral_end = find_array_equivalent(
self.a_a_norm.read()["data"][0][i],
local_arguments.background_stop)
xas_bg_i, xas_i = background_xmcd(
energy=np.array(self.a_a_norm.read()["data"][0])[i],
xmcd=add,
args=local_arguments,
)
xas_integral_i = integrate.cumtrapz(
xas_i[integral_start:integral_end],
self.a_a_norm.read()["data"][0][i]
[integral_start:integral_end],
)
# self.xas_integral = np.append(xas_integral[0], xas_integral[0][-1])
xas_integral1 = np.concatenate([
zerolistmaker(
len(self.a_a_norm.read()["data"][0][0]
[0:integral_start] + 1)),
xas_integral_i,
(zerolistmaker(
len(self.a_a_norm.read()["data"][0][0][integral_end:])
+ 1) + xas_integral_i[-1]),
])
xas_integral_i = list(xas_integral1)
xas_integral.append(xas_integral_i)
xas.append(xas_i)
xas_bg.append(xas_bg_i)
xas_bg = np.array(xas_bg)
xas = np.array(xas)
xas_integral = np.array(xas_integral)
self.xas_calc = xas
self.xas_bg_calc = xas_bg
self.xas_integral_calc = xas_integral
self.lines = [
self.background_start.default, self.background_stop.default
]
def background_xmcd(energy, xmcd, args):
loc_energy = np.array(energy)
loc_absorption = np.array(xmcd)
if len(np.array(loc_absorption).shape) > 1:
n_files = len(np.array(loc_absorption))
else:
n_files = 1
a_bg = []
a_p_norm = []
if args.fit == "linear":
# Polyfit and generate background series from it
e_cut = np.concatenate((
loc_energy[:find_array_equivalent(loc_energy, args.background_start
)],
loc_energy[find_array_equivalent(loc_energy, args.background_stop
):],
))
a_cut = np.concatenate((
loc_absorption[:find_array_equivalent(loc_energy, args.
background_start)],
(loc_absorption[find_array_equivalent(loc_energy, args.
background_stop):]),
))
a_poly = np.polyfit(e_cut, a_cut, 1)
a_polygen = np.poly1d(a_poly)
ia_bg = [a_polygen(e) for e in loc_energy]
if args.fit == "polynomial":
# Polyfit and generate background series from it
e_cut = np.concatenate((
loc_energy[:find_array_equivalent(loc_energy, args.background_start
)],
loc_energy[find_array_equivalent(loc_energy, args.background_stop
):],
))
a_cut = np.concatenate((
loc_absorption[:find_array_equivalent(loc_energy, args.
background_start)],
(loc_absorption[find_array_equivalent(loc_energy, args.
background_stop):]),
))
a_poly = np.polyfit(e_cut, a_cut, args.power)
a_polygen = np.poly1d(a_poly)
ia_bg = [a_polygen(e) for e in loc_energy]
# exponential decay
elif args.fit == "exp decay":
a_offset1 = 0 # ppp1-ooo1
e_cut = (np.concatenate((
loc_energy[:find_array_equivalent(loc_energy, args.background_start
)],
loc_energy[find_array_equivalent(loc_energy, args.background_stop
):],
)) - 750)
a_cut = np.concatenate((
loc_absorption[:find_array_equivalent(loc_energy, args.
background_start)],
loc_absorption[find_array_equivalent(loc_energy, args.
background_stop):],
))
transposed_energy = np.asarray(loc_energy) - 750
# fit values, and mean
def func(x, a, b, c):
return a * np.exp(-b * x) + c
y = func(transposed_energy, 2.3, 1.3, 1)
popt, pcov = curve_fit(func, e_cut, a_cut)
ia_bg = func(transposed_energy, *popt)
# fit only considered 2 points at edge_start_index and edge_stop_index
elif args.fit == "2 point linear":
e_cut = [
loc_energy[find_array_equivalent(loc_energy,
args.background_start)],
loc_energy[find_array_equivalent(loc_energy,
args.background_stop)],
]
a_cut = [
loc_absorption[find_array_equivalent(loc_energy,
args.background_start)],
loc_absorption[find_array_equivalent(loc_energy,
args.background_stop)],
]
a_poly = np.polyfit(e_cut, a_cut, 1)
a_polygen = np.poly1d(a_poly)
ia_bg = [a_polygen(e) for e in loc_energy]
elif args.fit == "Do Nothing!!":
ia_bg = np.zeros(len(loc_energy))
# for i in range(n_files):
# fit only considered 2 points at edge_start_index and edge_stop_index
# e_cut=[loc_energy[find_array_equivalent(loc_energy, args.background_start)],loc_energy[find_array_equivalent(loc_energy, args.background_stop)]]
# a_cut=[loc_xmcd[find_array_equivalent(loc_energy, args.background_start)],loc_xmcd[find_array_equivalent(loc_energy, args.background_stop)]]
# a_poly=np.polyfit(e_cut, a_cut, 1)
# a_polygen = np.poly1d(a_poly)
# ia_bg = [a_polygen(e) for e in loc_energy]
background = np.array(ia_bg)
subtracted_background = np.array(loc_absorption) - np.array(ia_bg)
# a_p_norm.append(subtracted_background)
# a_bg.append(background)
return background, subtracted_background