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Commit 34265f8e authored by Harouna-Mayer, Sani's avatar Harouna-Mayer, Sani
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import os
from glob import glob
import numpy as np
import subprocess
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit, minimize_scalar
def find_idx(arr, a):
return (np.abs(np.array(arr) - a)).argmin()
def save_xy(fname, x, y, header=''):
np.savetxt(fname, np.vstack((x, y)).T, header=header, comments='#')
return
def load_xy(fname, skiprows=0):
data = np.loadtxt(fname, skiprows=skiprows).T
x = data[0]
y = data[1]
return x, y
def q2tt(q, energy):
""" q: array or float, [A-1]; energy: [keV] """
wl = 12.39842 / energy
tt = 2 * 180 / np.pi * np.arcsin(q * wl / 4 / np.pi)
return tt
def tt2q(tt, energy):
""" q: array or float, [A-1]; energy: [keV] """
wl = 12.39842 / energy
q = 4 * np.pi / wl * np.sin(tt / 2 * np.pi / 180)
return q
def convertpattern_q2tt(fname, energy, skiprows=0, dtype_out="xy"):
x, y = load_xy(fname, skiprows=skiprows)
x = q2tt(x, energy=energy)
dtype = fname.split(".")[-1]
fname_out = fname[:-(len(dtype) + 1)] + "_in_tt." + dtype_out
save_xy(fname_out, x, y, header="converted from q to two theta, energy = {} keV\ntwo theta intensity".format(energy))
return
def convertpattern_tt2q(fname, energy, skiprows=0, dtype_out="xy"):
x, y = load_xy(fname, skiprows=skiprows)
x = tt2q(x, energy=energy)
dtype = fname.split(".")[-1]
fname_out = fname[:-(len(dtype) + 1)] + "_in_q." + dtype_out
save_xy(fname_out, x, y, header="converted from two theta to q, energy = {} keV\ntwo theta intensity".format(energy))
return
def avg(path2data, dtype=".dat", fname_timetemp="", temp0=30, avg_num=60, overwrite=False):
fnames = glob(os.path.join(path2data, '*' + dtype))
fnames.sort()
path2avg = os.path.join(os.path.dirname(os.path.dirname(path2data)), "avg{}_iqs".format(avg_num), os.path.basename(path2data))
if not os.path.isdir(path2avg):
os.makedirs(path2avg)
timetempdir = "time_vs_temp"
if not fname_timetemp:
fname_timetemp = os.path.join(os.path.dirname(path2data), timetempdir, "timetemps_" + os.path.basename(path2data) + ".txt")
times, temps = load_xy(fname_timetemp)
idx0 = find_idx(temps, temp0)
idxend = -((len(fnames)-idx0) % avg_num)
print("average {} from file idx {} to {}".format(os.path.basename(path2avg), idx0, idxend))
temps_crop = temps[idx0:idxend:avg_num]
fnames_crop = fnames[idx0:idxend] # cut before idx0 and every pattern which doesnt have enough for time resolution
for temp, fnames_to_avg in zip(temps_crop, np.reshape(fnames_crop, (int(len(fnames_crop) / avg_num), avg_num))):
fname_avg = os.path.join(path2avg,
os.path.basename(fnames_to_avg[0][:-len(dtype)]) + "_avg_" + str(avg_num) + dtype)
if os.path.isfile(fname_avg) and not overwrite:
print("skippidiskippy")
continue
else:
y_avgs = []
for fname in fnames_to_avg:
x, y = load_xy(fname)
y_avgs.append(y)
y_avg = np.mean(y_avgs, axis=0)
header = 'averaged {} files\n'.format(avg_num)
header += 'from path: {}\nto {}\n'.format(path2data, path2avg)
header += 'temperature (°C): {}\n'.format(temp)
header += 'q (A^-1) I (a.u.)'
save_xy(fname_avg, x, y_avg, header=header)
# print(fname_avg)
return
def avg_all_in_dir(parentdir, overwrite=False):
subdirs = [os.path.join(parentdir, subdir) for subdir in os.listdir(parentdir)\
if os.path.isdir(os.path.join(parentdir, subdir))]
timetempdir = "time_vs_temp"
skipsubdirs = [os.path.join(parentdir, timetempdir)]
for skipsubdir in skipsubdirs:
subdirs.remove(skipsubdir)
for dir in subdirs:
avg(dir, overwrite=overwrite)
return
def bkg_sub(path2data, path2bkg, path2sub="", x_range=[1.05, 1.35], dtype=".dat", fancyapproach=False, bkgscale=1.0):
if not path2sub:
path2sub = os.path.join(os.path.dirname(os.path.dirname(path2data)), "bkgsub_iqs", os.path.basename(path2data))
if not os.path.isdir(path2sub):
os.makedirs(path2sub)
fnames = glob(os.path.join(path2data, '*' + dtype))
fnames.sort()
fnames_bkg = glob(os.path.join(path2bkg, '*' + dtype))
fnames_bkg.sort()
for i in range(len(fnames) - len(fnames_bkg)):
fnames_bkg.append(fnames_bkg[-1])
x0, y0 = load_xy(fnames[0])
idx_range = [find_idx(x0, xi) for xi in x_range]
scale_approach_value = 0.00001
from scipy.optimize import minimize_scalar
def initial_scale(y1, y2):
def func(s):
return np.mean(abs(y1 - s * y2))
res = minimize_scalar(func)
return res['x']
for fname, fname_bkg in zip(fnames, fnames_bkg):
x, y = load_xy(fname)
x_bkg, y_bkg = load_xy(fname_bkg)
with open(fname, 'r') as f:
temp = float(f.readlines()[3].split()[-1])
if fancyapproach:
# here fancy subtracton by finding scale so in the givin area background goes as close to zero as possible without beeing negative
s = initial_scale(y[idx_range[0]:idx_range[1]], y_bkg[idx_range[0]:idx_range[1]])
# s = round(s, len(str(scale_approach_value).split('.')[1]))
# s = round(s, abs(int(np.log10(scale_approach_value))))
y_sub = y - s * y_bkg
if np.any(y_sub[idx_range[0]:idx_range[1]] <= 0.0):
while np.any(y_sub[idx_range[0]:idx_range[1]] <= 0.0):
s -= scale_approach_value
y_sub = y - s * y_bkg
else:
while np.all(y_sub[idx_range[0]:idx_range[1]] > 0.0):
s += scale_approach_value
y_sub = y - s * y_bkg
s -= scale_approach_value
header = 'background subtraction by minimizing background subtracted file but not going below 0 between\n'
header += 'q > {} and q < {} A^-1\n'.format(*x_range)
header += 'scale: {} ±{}\n'.format(s, scale_approach_value)
header += 'temperature (°C): {}\n'.format(temp)
header += 'unsubtracted file: {}\n'.format(fname)
header += 'background file: {}\n'.format(fname_bkg)
header += 'q (A^-1) I (a.u.)'
else:
# for fixed bkg scale
s = bkgscale
y_sub = y - s * y_bkg
header = 'normal background subtraction\n'
header += '\n'.format(*x_range)
header += 'scale: {}\n'.format(s)
header += 'temperature (°C): {}\n'.format(temp)
header += 'unsubtracted file: {}\n'.format(fname)
header += 'background file: {}\n'.format(fname_bkg)
header += 'q (A^-1) I (a.u.)'
fname_sub = os.path.join(path2sub, os.path.basename(fname[:-len(dtype)]) + "_bkgsub" + dtype)
save_xy(fname_sub, x, y_sub, header=header)
return
def make_pdf(fname_iq, fname_gr, composition, qmin, qmax, qmax_inst, rpoly, rmax=30.0):
command = [
'pdfgetx3',
str(fname_iq),
'--force=True',
'--format=QA',
'--mode=xray',
'--composition=' + str(composition),
'--qmin=' + str(qmin),
'--qmax=' + str(qmax),
'--qmaxinst=' + str(qmax_inst),
'--rpoly=' + str(rpoly),
'--rmax=' + str(rmax),
'-t=gr',
'-o=' + str(fname_gr),
]
subprocess.run(command)
return
def make_pdf_all_in_dir(path2data, path2pdf="", dtype=".dat",
composition="Cu3PdN", qmin=0.7, qmax=10.3, qmax_inst=24.0, rpoly=0.9, rmax=30.0):
print(path2data)
if not path2pdf:
path2pdf = os.path.join(os.path.dirname(os.path.dirname(path2data)), "pdfs", os.path.basename(path2data))
if not os.path.isdir(path2pdf):
os.makedirs(path2pdf)
fnames = glob(os.path.join(path2data, '*' + dtype))
fnames.sort()
for fname_iq in fnames:
fname_gr = os.path.join(path2pdf, os.path.basename(fname_iq)[:-len(dtype)] + ".gr")
make_pdf(fname_iq, fname_gr, composition=composition, qmin=qmin, qmax=qmax, qmax_inst=qmax_inst, rpoly=rpoly, rmax=rmax)
# print(os.path.basename(fname_gr))
print(path2pdf)
return
def bkg_sub_n_makepdf_fitglasspeak(fname_data, fname_bkg, fname_bkgsub, fname_gr):
# fname_bkg = "/Users/admin/data/23_bt_2304_accell/avg60_iqs/bkg_bnh2_140_sdd600/bkg_bnh2_140_sdd600-01848_avg_60.dat"
# fname_data = "/Users/admin/data/23_bt_2304_accell/avg60_iqs/cupdn_140_3_sdd600/cupdn_140_3_sdd600-03734_avg_60.dat"
# fname_bkgsub = "/Users/admin/data/23_bt_2304_accell/bkgsub_iqs/cupdn_140_3_sdd600_glassfit/cupdn_140_3_sdd600-06914_avg_60.dat"
# fname_gr = "/Users/admin/data/23_bt_2304_accell/pdfs/cupdn_140_3_sdd600_glassfit/cupdn_140_3_sdd600-06914_avg_60.gr"
if not os.path.isdir(os.path.dirname(fname_bkgsub)):
os.makedirs(os.path.dirname(fname_bkgsub))
if not os.path.isdir(os.path.dirname(fname_gr)):
os.makedirs(os.path.dirname(fname_gr))
q, i_bkg = load_xy(fname_bkg)
q, i_data = load_xy(fname_data)
r_min = 1.2
r_max = 1.8
s_arr = []
a_arr = []
x0_arr = []
fwhm_arr = []
m_arr = []
y0_arr = []
def scale():
def gaussian(x, x0, a, fwhm, m, y0):
return a * np.exp(- (x - x0) ** 2 / 2 / (fwhm / 2.35482) ** 2) + m * x + y0
def func(s):
save_xy(fname_bkgsub, q, i_data - i_bkg * s)
# make_pdf(fname_bkgsub, fname_gr, "Cu3PdN", qmin=0.7, qmax=10.3, qmax_inst=24.0, rpoly=0.9)
make_pdf(fname_bkgsub, fname_gr, "Cu3PdN", qmin=0.4, qmax=10.3, qmax_inst=17.5, rpoly=0.9)
r, g = load_xy(fname_gr, skiprows=23)
idx_min = find_idx(r, r_min)
idx_max = find_idx(r, r_max)
initial_guess = [1.6, 0.1, 0.5, 0.1, -3.0]
nobound = (-np.inf, np.inf)
restraints = np.array([(1.5, 1.7), (0.0, np.inf), (0.3, 0.8), (-1, 1), nobound]).T
popt, pcov = curve_fit(gaussian, r[idx_min:idx_max + 1], g[idx_min:idx_max + 1], p0=initial_guess, bounds=restraints)
a = popt[1]
a_err = np.sqrt(pcov[1, 1])
s_arr.append(s)
x0_arr.append(popt[0])
a_arr.append(popt[1])
fwhm_arr.append(popt[2])
m_arr.append(popt[3])
y0_arr.append(popt[4])
return a
res = minimize_scalar(func)
return res['x']
s = scale()
# print(s)
with open(fname_data, 'r') as f:
temp = float(f.readlines()[3].split()[-1])
header = 'glassfit background subtraction\n'
header += 'scale: {}\n'.format(s)
header += 'temperature (°C): {}\n'.format(temp)
header += 'unsubtracted file: {}\n'.format(fname_data)
header += 'background file: {}\n'.format(fname_bkg)
header += 'q (A^-1) I (a.u.)'
save_xy(fname_bkgsub, q, i_data - i_bkg * s)
# make_pdf(fname_bkgsub, fname_gr, "Cu3PdN", qmin=0.7, qmax=10.3, qmax_inst=24.0, rpoly=0.9)
make_pdf(fname_bkgsub, fname_gr, "Cu3PdN", qmin=0.4, qmax=10.3, qmax_inst=17.5, rpoly=0.9)
# r, g = load_xy(fname_gr, skiprows=23)
#
# fit_dict = {
# "s": {"values": s_arr},
# "a": {"values": a_arr},
# "x0": {"values": x0_arr},
# "fwhm": {"values": fwhm_arr},
# "m": {"values": m_arr},
# "y0": {"values": y0_arr},
# }
#
# def key_ax(ax, key):
# ax.plot(fit_dict[key]["values"], label=key)
# ax.set_xlabel("iteration")
# ax.set_ylabel(key)
# return ax
#
# keys = ["s", "a", "x0", "fwhm", "m", "y0"]
# figsize = (6, 2 * len(keys))
# fig, axs = plt.subplots(len(keys), 1, sharex=True, sharey=False, gridspec_kw={'hspace': 0}, figsize=figsize)
# lines = []
# labels = []
# for i, keys in enumerate(keys):
# axs[i] = key_ax(axs[i], keys)
# axs[i].legend(frameon=False)
# # lines_i, labels_i = axs[i].get_legend_handles_labels()
# # [lines.append(line) for line in lines_i if line not in lines]
# # [labels.append(label) for label in labels_i if label not in labels]
# # axs[0].legend(lines, labels, loc='best', frameon=False)
# fig.align_ylabels()
# plt.show()
#
# plt.plot(r, g)
# plt.xlim(0, 7.5)
# plt.title(s)
# plt.show()
return s
def bkg_sub_n_makepdf_all_in_dir_glassfit(path2data, path2bkg, path2sub="", path2pdf="", dtype=".dat"):
if not path2sub:
path2sub = os.path.join(os.path.dirname(os.path.dirname(path2data)), "bkgsub_iqs", os.path.basename(path2data) + "_glassfit")
if not os.path.isdir(path2sub):
os.makedirs(path2sub)
if not path2pdf:
path2pdf = os.path.join(os.path.dirname(os.path.dirname(path2data)), "pdfs", os.path.basename(path2data) + "_glassfit")
if not os.path.isdir(path2pdf):
os.makedirs(path2pdf)
fnames = glob(os.path.join(path2data, '*' + dtype))
fnames.sort()
fnames_bkg = glob(os.path.join(path2bkg, '*' + dtype))
fnames_bkg.sort()
for i in range(len(fnames) - len(fnames_bkg)):
fnames_bkg.append(fnames_bkg[-1])
s_arr = []
for fname, fname_bkg in zip(fnames, fnames_bkg):
print(fname)
fname_bkgsub = os.path.join(path2sub, os.path.basename(fname[:-len(dtype)]) + "_bkgsub" + dtype)
fname_gr = os.path.join(path2pdf, os.path.basename(fname[:-len(dtype)]) + ".gr")
s = bkg_sub_n_makepdf_fitglasspeak(fname, fname_bkg, fname_bkgsub, fname_gr)
s_arr.append(s)
plt.plot(s_arr)
plt.ylabel("scale")
plt.xlabel("time (min)")
plt.show()
def bkg_sub_n_makepdf_bymaxint(fname_data, fname_bkg, fname_bkgsub, fname_gr):
if not os.path.isdir(os.path.dirname(fname_bkgsub)):
os.makedirs(os.path.dirname(fname_bkgsub))
if not os.path.isdir(os.path.dirname(fname_gr)):
os.makedirs(os.path.dirname(fname_gr))
q, i_bkg = load_xy(fname_bkg)
q, i_data = load_xy(fname_data)
r_min = 1.9
r_max = 4.0
def scale():
def func(s):
save_xy(fname_bkgsub, q, i_data - i_bkg * s)
# make_pdf(fname_bkgsub, fname_gr, "Cu3PdN", qmin=0.7, qmax=10.3, qmax_inst=24.0, rpoly=0.9)
make_pdf(fname_bkgsub, fname_gr, "Cu3PdN", qmin=0.4, qmax=10.3, qmax_inst=17.5, rpoly=0.9)
r, g = load_xy(fname_gr, skiprows=23)
idx_min = find_idx(r, r_min)
idx_max = find_idx(r, r_max)
return -np.max(g[idx_min:idx_max])
res = minimize_scalar(func)
return res['x']
s = scale()
print(s)
with open(fname_data, 'r') as f:
temp = float(f.readlines()[3].split()[-1])
header = 'max pdf intensity (between {} adn {} A^-1) background subtraction\n'.format(r_min, r_max)
header += 'scale: {}\n'.format(s)
header += 'temperature (°C): {}\n'.format(temp)
header += 'unsubtracted file: {}\n'.format(fname_data)
header += 'background file: {}\n'.format(fname_bkg)
header += 'q (A^-1) I (a.u.)'
save_xy(fname_bkgsub, q, i_data - i_bkg * s)
make_pdf(fname_bkgsub, fname_gr, "Cu3PdN", qmin=0.4, qmax=10.3, qmax_inst=17.5, rpoly=0.9)
return s
def bkg_sub_n_makepdf_all_in_dir_baymaxint(path2data, path2bkg, path2sub="", path2pdf="", dtype=".dat"):
if not path2sub:
path2sub = os.path.join(os.path.dirname(os.path.dirname(path2data)), "bkgsub_iqs", os.path.basename(path2data) + "_bymaxint")
if not os.path.isdir(path2sub):
os.makedirs(path2sub)
if not path2pdf:
path2pdf = os.path.join(os.path.dirname(os.path.dirname(path2data)), "pdfs", os.path.basename(path2data) + "_bymaxint")
if not os.path.isdir(path2pdf):
os.makedirs(path2pdf)
fnames = glob(os.path.join(path2data, '*' + dtype))
fnames.sort()
fnames_bkg = glob(os.path.join(path2bkg, '*' + dtype))
fnames_bkg.sort()
for i in range(len(fnames) - len(fnames_bkg)):
fnames_bkg.append(fnames_bkg[-1])
s_arr = []
for fname, fname_bkg in zip(fnames, fnames_bkg):
print(fname)
fname_bkgsub = os.path.join(path2sub, os.path.basename(fname[:-len(dtype)]) + "_bkgsub" + dtype)
fname_gr = os.path.join(path2pdf, os.path.basename(fname[:-len(dtype)]) + ".gr")
s = bkg_sub_n_makepdf_bymaxint(fname, fname_bkg, fname_bkgsub, fname_gr)
s_arr.append(s)
plt.plot(s_arr)
plt.ylabel("scale")
plt.xlabel("time (min)")
plt.show()
path2data = "/Users/admin/Wolke/data/23_bt_2304_accell/avg60_iqs/cupdn_140_3_sdd600"
path2bkg = "/Users/admin/Wolke/data/23_bt_2304_accell/avg60_iqs/bkg_bnh2_140_sdd600"
path2sub = "/Users/admin/Wolke/data/23_bt_2304_accell/bkgsub_iqs/cupdn_140_3_sdd600_bymaxint"
path2pdf = "/Users/admin/Wolke/data/23_bt_2304_accell/pdfs/cupdn_140_3_sdd600_bymaxint"
bkg_sub_n_makepdf_all_in_dir_baymaxint(path2data, path2bkg)
def avg_specific(path2data, dtype=".dat", avg_startnum=0, avg_endnum=120):
fnames = glob(os.path.join(path2data, '*' + dtype))
fnames.sort()
path2avg = "/Users/admin/data/23_bt_2304_accell/abcheckauis/starting_point_wobkgsub/iqs"
avg_num = avg_endnum - avg_startnum
fnames_to_avg = fnames[avg_startnum:avg_endnum]
fname_avg = os.path.join(path2avg,
os.path.basename(fnames_to_avg[0][:-len(dtype)]) + "_avg_" + str(avg_num) + dtype)
y_avgs = []
for fname in fnames_to_avg:
x, y = load_xy(fname)
y_avgs.append(y)
y_avg = np.mean(y_avgs, axis=0)
header = 'averaged {} files\n'.format(avg_num)
header += 'from path: {}\nto {}\n'.format(path2data, path2avg)
header += 'q (A^-1) I (a.u.)'
save_xy(fname_avg, x, y_avg, header=header)
# datadirdir = "/Users/admin/data/23_bt_2304_accell/raw_iqs/"
# dir = "sh_pdn_180_1"
# path2data = os.path.join(datadirdir, dir)
# avg_specific(path2data)
# path2data = "/Users/admin/data/23_bt_2304_accell/raw_iqs/bkg_bnh2_160_1"
# avg(path2data, overwrite=True)
# path2bkg = "/Users/admin/data/23_bt_2304_accell/raw_iqs/cufen_160_1"
# avg(path2bkg, overwrite=True)
# bkg_sub_n_makepdf_all_in_dir_glassfit("/Users/admin/data/23_bt_2304_accell/avg60_iqs/cufen_160_1", "/Users/admin/data/23_bt_2304_accell/avg60_iqs/bkg_bnh2_160_1")
print('kukuk')
# path2bkg = "/Users/admin/data/23_bt_2304_accell/raw_iqs/lk_bg_fe3s4_180_1"
# avg(path2bkg, overwrite=True)
# path2data = "/Users/admin/data/23_bt_2304_accell/raw_iqs/lk_fe3s4_180_1"
# avg(path2data, overwrite=True)
# bkg_sub_n_makepdf_all_in_dir_glassfit("/Users/admin/data/23_bt_2304_accell/avg60_iqs/lk_fe3s4_180_1", "/Users/admin/data/23_bt_2304_accell/avg60_iqs/lk_bg_fe3s4_180_1")
# path2bkg = "/Users/admin/data/23_bt_2304_accell/raw_iqs/lk_bg_fe3s4_100_1"
# avg(path2bkg, overwrite=True)
# path2data = "/Users/admin/data/23_bt_2304_accell/raw_iqs/lk_fe3s4_100_1"
# avg(path2data, overwrite=True)
# bkg_sub_n_makepdf_all_in_dir_glassfit("/Users/admin/data/23_bt_2304_accell/avg60_iqs/lk_fe3s4_100_1", "/Users/admin/data/23_bt_2304_accell/avg60_iqs/lk_bg_fe3s4_100_1")
# path2bkg = "/Users/admin/data/23_bt_2304_accell/avg60_iqs/bkg_bnh2_140_sdd600"
# path2data = "/Users/admin/data/23_bt_2304_accell/avg60_iqs/cupdn_140_3_sdd600"
# bkg_sub(path2data, path2bkg, fancyapproach=False, bkgscale=1.0)
# path2data = "/Users/admin/data/23_bt_2304_accell/bkgsub_iqs/cupdn_140_3_sdd600"
# make_pdf_all_in_dir(path2data, composition="Cu3PdN", qmin=0.4, qmax=10.3, qmax_inst=17.5, rpoly=0.9)
# path2data = "/Users/admin/data/23_bt_2304_accell/bkgsub_iqs/cu3n_140_1"
# make_pdf_all_in_dir(path2data, composition="Cu3N", qmin=0.7, qmax=10.3, qmax_inst=24.0, rpoly=0.9)
# path2data = "/Users/admin/data/23_bt_2304_accell/bkgsub_iqs/cu3n_140_2"
# make_pdf_all_in_dir(path2data, composition="Cu3N", qmin=0.7, qmax=10.3, qmax_inst=24.0, rpoly=0.9)
# path2data = "/Users/admin/data/23_bt_2304_accell/bkgsub_iqs/cupdn_140_1"
# make_pdf_all_in_dir(path2data, composition="Cu3PdN", qmin=0.7, qmax=10.3, qmax_inst=24.0, rpoly=0.9)
# path2data = "/Users/admin/data/23_bt_2304_accell/bkgsub_iqs/cupdn_140_2"
# make_pdf_all_in_dir(path2data, composition="Cu3PdN", qmin=0.7, qmax=10.3, qmax_inst=24.0, rpoly=0.9)
# path2data = "/Users/admin/data/23_bt_2304_accell/bkgsub_iqs/sh_pdn_140_2"
# make_pdf_all_in_dir(path2data, composition="Pd", qmin=0.7, qmax=10.3, qmax_inst=24.0, rpoly=0.9)
# make_pdf("/Users/admin/data/23_bt_2304_accell/calibauis/lab6_ac_cell_385-00067.dat",
# "/Users/admin/data/23_bt_2304_accell/calibauis/lab6_ac_cell_385-00067.dat",
# composition="LaB6", qmin=0.7, qmax=10.3, qmax_inst=24.0, rpoly=0.9)
# path2data = "/Users/admin/data/23_bt_2304_accell/bkgsub_iqs/cupdn_140_3_sdd600"
# path2pdf = "/Users/admin/data/23_bt_2304_accell/pdfs/cupdn_140_3_sdd600_qmax16p3"
# make_pdf_all_in_dir(path2data, composition="Cu3PdN", qmin=0.7, qmax=16.3, qmax_inst=18.3, rpoly=0.9)
#
# make_pdf("/Users/admin/data/23_bt_2304_accell/calibauis/lab6_ac_cell_385-00067.dat",
# "/Users/admin/data/23_bt_2304_accell/calibauis/lab6_ac_cell_385-00067_qmax16p3.gr",
# composition="LaB6", qmin=0.7, qmax=16.3, qmax_inst=18.3, rpoly=0.9)
print('hi')
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