tof_vcorr_fac = voltage_and_bowl.mod_full_voltage_correction(
p_volt, np.ones_like(epos['tof']), epos['v_dc'])
tof_bcorr_fac = voltage_and_bowl.mod_geometric_bowl_correction(
p_bowl, np.ones_like(epos['tof']), epos['x_det'], epos['y_det'])
# find the voltage and bowl coefficients for the doubles data
tof_vcorr_fac_d = tof_vcorr_fac[doub_idxs]
tof_bcorr_fac_d = tof_bcorr_fac[doub_idxs]
# Find transform to m/z space
m2q_corr, p_m2q = m2q_calib.align_m2q_to_ref_m2q(epos_s['m2q'], tof_sing_corr)
epos['m2q'] = m2q_calib.mod_physics_m2q_calibration(
p_m2q, mod_full_vb_correction(epos, p_volt, p_bowl))
plotting_stuff.plot_histo(epos['m2q'],
fig_idx=1,
user_xlim=[0, 250],
user_bin_width=0.03)
import GaN_fun
pk_data = GaN_type_peak_assignments.GaN_with_H()
bg_rois = [[0.4, 0.9]]
pk_params, glob_bg_param, Ga1p_idxs, Ga2p_idxs = GaN_fun.fit_spectrum(
epos=epos, pk_data=pk_data, peak_height_fraction=0.05, bg_rois=bg_rois)
cts, compositions, is_peak = GaN_fun.count_and_get_compositions(
epos=epos,
pk_data=pk_data,
pk_params=pk_params,
glob_bg_param=glob_bg_param,
def __main__():
plt.close('all')
#
# Load data
fn = r"Q:\NIST_Projects\EUV_APT_IMS\BWC\R45_data\R45_00504-v56.epos"
fn = r"\\cfs2w.campus.nist.gov\647\NIST_Projects\EUV_APT_IMS\BWC\R45_data\R45_04472-v02_allVfromAnn.epos"
#fn = r"Q:\NIST_Projects\EUV_APT_IMS\BWC\GaN epos files\R20_07148-v01_vbm_corr.epos"
#fn = r"D:\Users\clifford\Documents\Python Scripts\NIST_DATA\R20_07094-v03.epos"
#fn = r"D:\Users\clifford\Documents\Python Scripts\NIST_DATA\R45_04472-v03.epos"
#fn = r"Q:\NIST_Projects\EUV_APT_IMS\BWC\Final EPOS for APL Mat Paper\R20_07263-v02.epos"
#fn = r"Q:\NIST_Projects\EUV_APT_IMS\BWC\Final EPOS for APL Mat Paper\R20_07080-v01.epos"
#fn = r"Q:\NIST_Projects\EUV_APT_IMS\BWC\Final EPOS for APL Mat Paper\R20_07086-v01.epos"
#fn = r"Q:\NIST_Projects\EUV_APT_IMS\BWC\Final EPOS for APL Mat Paper\R20_07276-v03.epos"
# fn = r"Q:\NIST_Projects\EUV_APT_IMS\BWC\R45_data\R45_04472-v03.epos"
#fn = r"Q:\NIST_Projects\EUV_APT_IMS\BWC\R45_data\R45_04472-v02.epos"
# fn = r"Q:\NIST_Projects\EUV_APT_IMS\BWC\GaN epos files\R20_07148-v01.epos" # Mg doped
# fn = fn[:-5]+'_vbm_corr.epos'
epos = apt_fileio.read_epos_numpy(fn)
# plotting_stuff.plot_TOF_vs_time(epos['m2q'],epos,1,clearFigure=True,user_ylim=[0,150])
# epos = epos[0:2**20]
cts_per_slice=2**10
m2q_roi = [0.8,75]
import time
t_start = time.time()
pointwise_scales,piecewise_scales = get_all_scale_coeffs(epos['m2q'],
m2q_roi=m2q_roi,
cts_per_slice=cts_per_slice,
max_scale=1.15)
t_end = time.time()
print('Total Time = ',t_end-t_start)
# Plot histogram in log space
lys_corr = np.log(epos['m2q'])-np.log(pointwise_scales)
N,x_edges,ly_edges = create_histogram(lys_corr,y_roi=m2q_roi,cts_per_slice=cts_per_slice)
# fig = plt.figure(figsize=(8,8))
# plt.imshow(np.log1p(np.transpose(N)), aspect='auto', interpolation='none',
# extent=extents(x_edges) + extents(ly_edges), origin='lower')
#
# Compute corrected data
m2q_corr = epos['m2q']/pointwise_scales
# Plot data uncorrected and corrected
TEST_PEAK = 32
ax = plotting_stuff.plot_TOF_vs_time(epos['m2q'],epos,111,clearFigure=True,user_ylim=[0,75])
ax.plot(pointwise_scales*TEST_PEAK)
plotting_stuff.plot_TOF_vs_time(m2q_corr,epos,222,clearFigure=True,user_ylim=[0,75])
# Plot histograms uncorrected and corrected
plotting_stuff.plot_histo(m2q_corr,333,user_xlim=[0, 75],user_bin_width=0.01)
plotting_stuff.plot_histo(epos['m2q'],333,user_xlim=[0, 75],clearFigure=False,user_bin_width=0.01)
# epos['m2q'] = m2q_corr
# apt_fileio.write_epos_numpy(epos,'Q:\\NIST_Projects\\EUV_APT_IMS\\BWC\\GaN epos files\\R20_07148-v01_vbmq_corr.epos')
_, ys = bin_dat(m2q_corr,isBinAligned=True,bin_width=0.01,user_roi=[0,75])
print(np.sum(np.square(ys)))
return 0
# custom imports
import apt_fileio
import plotting_stuff
import initElements_P3
import peak_param_determination as ppd
from histogram_functions import bin_dat
# Read in data
fn = r'C:\Users\capli\Downloads\JAVASS_R44_03187-v01.epos'
epos = apt_fileio.read_epos_numpy(fn)
plotting_stuff.plot_histo(epos['m2q'],1)
isSingle = np.nonzero(epos['ipp'] == 1)
ed = initElements_P3.initElements()
pk_data = np.array( [ (1, ed['Si'].isotopes[28][0]/2),
(1, ed['Si'].isotopes[29][0]/2),
(1, ed['Si'].isotopes[30][0]/2),
(1, ed['Si'].isotopes[28][0]),
(1, ed['Si'].isotopes[29][0]),
(1, ed['Si'].isotopes[30][0])],
dtype=[('Si','i4'),('m2q','f4')] )
pk_params = ppd.get_peak_ranges(epos,pk_data['m2q'],peak_height_fraction=0.01)
p_volt = np.array([])
p_bowl = np.array([])
# only use singles for V+B
# subsample down to 1 million ions for speedier computation
vb_idxs = np.random.choice(epos_s.size, int(np.min([epos_s.size, 1000*1000])), replace=False)
vb_epos = epos_s[vb_idxs]
tof_sing_corr, p_volt, p_bowl = do_voltage_and_bowl(vb_epos,p_volt,p_bowl)
# Find transform to m/z space
m2q_corr, p_m2q = m2q_calib.align_m2q_to_ref_m2q(epos_s['m2q'],tof_sing_corr)
epos['m2q'] = m2q_calib.mod_physics_m2q_calibration(p_m2q,mod_full_vb_correction(epos,p_volt,p_bowl))
plotting_stuff.plot_histo(epos['m2q'],fig_idx=1)
import GaN_fun
pk_data = GaN_type_peak_assignments.GaN_with_H()
bg_rois=[[0.4,0.9]]
pk_params, glob_bg_param, Ga1p_idxs, Ga2p_idxs = GaN_fun.fit_spectrum(
epos=epos,
pk_data=pk_data,
peak_height_fraction=0.1,
bg_rois=bg_rois)
cts, compositions, is_peak = GaN_fun.count_and_get_compositions(
epos=epos,
# Voltage and bowl correct ToF data
p_volt = np.array([])
p_bowl = np.array([])
t_i = time.time()
tof_corr, p_volt, p_bowl = do_voltage_and_bowl(epos, p_volt, p_bowl)
print("time to voltage and bowl correct: " + str(time.time() - t_i) +
" seconds")
# Find c and t0 for ToF data based on aligning to reference spectrum
m2q_corr, p_m2q = m2q_calib.align_m2q_to_ref_m2q(ref_epos['m2q'],
tof_corr,
nom_voltage=np.mean(
epos['v_dc']))
print(p_m2q)
plotting_stuff.plot_histo(ref_epos['m2q'], 4, user_label='ref')
plotting_stuff.plot_histo(m2q_corr[m2q_corr.size // 2:-1],
4,
clearFigure=False,
user_label='[c,t0] corr')
xs, ys = bin_dat(tof_corr, isDensity=True, bin_width=0.25)
bg_lvl = np.mean(ys[(xs > 1000) & (xs < 3500)])
idxs = np.nonzero((xs > 200) & (xs < 1000))
xs = xs[idxs]
ys = ys[idxs]
fig = plt.figure(num=113)
ed = initElements_P3.initElements()
ref_pk_m2qs = np.array([
ed['H'].isotopes[1][0], ed['Si'].isotopes[28][0] / 3,
ed['Si'].isotopes[28][0] / 2, ed['O'].isotopes[16][0] / 1,
(ed['Si'].isotopes[28][0] + ed['O'].isotopes[16][0]) / 2,
ed['O'].isotopes[16][0] * 2,
(ed['Si'].isotopes[28][0] + ed['O'].isotopes[16][0]) / 1,
(ed['Si'].isotopes[28][0] + 2 * ed['O'].isotopes[16][0]) / 1
])
# Perform 'linearization' m2q calibration
m2q_corr2 = m2q_calib.calibrate_m2q_by_peak_location(m2q_corr_q, ref_pk_m2qs)
# Plot the reference spectrum, (c, t0) corr spectrum and linearized spectrum
# to confirm that mass calibration went ok
plotting_stuff.plot_histo(ref_epos['m2q'], fig_idx=4, user_label='ref')
plotting_stuff.plot_histo(m2q_corr_q,
fig_idx=4,
clearFigure=False,
user_label='[c,t0] corr')
ax = plotting_stuff.plot_histo(m2q_corr2,
4,
clearFigure=False,
user_label='linearized')
for ref_pk_m2q in ref_pk_m2qs:
ax.plot(ref_pk_m2q * np.ones(2), np.array([1, 1e4]), 'k--')
# Save the data as a new epos file
epos['m2q'] = m2q_corr2
new_fn = fn[:-5] + '_vbmq_corr.epos'
apt_fileio.write_epos_numpy(epos, new_fn)
def __main__():
plt.close('all')
#
# Load data
fn = r"Q:\NIST_Projects\EUV_APT_IMS\BWC\R45_data\R45_00504-v56.epos"
#fn = r"Q:\NIST_Projects\EUV_APT_IMS\BWC\GaN epos files\R20_07148-v01_vbm_corr.epos"
#fn = r"D:\Users\clifford\Documents\Python Scripts\NIST_DATA\R20_07094-v03.epos"
#fn = r"D:\Users\clifford\Documents\Python Scripts\NIST_DATA\R45_04472-v03.epos"
#fn = r"Q:\NIST_Projects\EUV_APT_IMS\BWC\Final EPOS for APL Mat Paper\R20_07263-v02.epos"
#fn = r"Q:\NIST_Projects\EUV_APT_IMS\BWC\Final EPOS for APL Mat Paper\R20_07080-v01.epos"
#fn = r"Q:\NIST_Projects\EUV_APT_IMS\BWC\Final EPOS for APL Mat Paper\R20_07086-v01.epos"
#fn = r"Q:\NIST_Projects\EUV_APT_IMS\BWC\Final EPOS for APL Mat Paper\R20_07276-v03.epos"
# fn = r"Q:\NIST_Projects\EUV_APT_IMS\BWC\R45_data\R45_04472-v03.epos"
#fn = r"Q:\NIST_Projects\EUV_APT_IMS\BWC\R45_data\R45_04472-v02.epos"
# fn = r"Q:\NIST_Projects\EUV_APT_IMS\BWC\GaN epos files\R20_07148-v01.epos" # Mg doped
# fn = fn[:-5]+'_vbm_corr.epos'
epos = apt_fileio.read_epos_numpy(fn)
# plotting_stuff.plot_TOF_vs_time(epos['m2q'],epos,1,clearFigure=True,user_ylim=[0,150])
epos = epos[0:2**20]
cts_per_slice = 2**10
#m2q_roi = [0.9,190]
m2q_roi = [0.8, 80]
# m2q_roi = [60,100]
import time
t_start = time.time()
pointwise_scales, piecewise_scales = get_all_scale_coeffs(
epos['m2q'],
m2q_roi=m2q_roi,
cts_per_slice=cts_per_slice,
max_scale=1.15)
t_end = time.time()
print('Total Time = ', t_end - t_start)
# Plot histogram in log space
lys_corr = np.log(epos['m2q']) - np.log(pointwise_scales)
N, x_edges, ly_edges = create_histogram(lys_corr,
y_roi=m2q_roi,
cts_per_slice=cts_per_slice)
fig = plt.figure(figsize=(8, 8))
plt.imshow(np.log1p(np.transpose(N)),
aspect='auto',
interpolation='none',
extent=extents(x_edges) + extents(ly_edges),
origin='lower')
# Compute corrected data
m2q_corr = epos['m2q'] / pointwise_scales
# Plot data uncorrected and corrected
TEST_PEAK = 32
ax = plotting_stuff.plot_TOF_vs_time(epos['m2q'],
epos,
111,
clearFigure=True,
user_ylim=[0, 150])
ax.plot(pointwise_scales * TEST_PEAK)
plotting_stuff.plot_TOF_vs_time(m2q_corr,
epos,
222,
clearFigure=True,
user_ylim=[0, 150])
# Plot histograms uncorrected and corrected
plotting_stuff.plot_histo(m2q_corr, 333, user_xlim=[0, 150])
plotting_stuff.plot_histo(epos['m2q'],
333,
user_xlim=[0, 150],
clearFigure=False)
#import pandas as pd
#
#
#fig = plt.figure(figsize=(8,8))
#ax = fig.gca()
#
#df = pd.read_csv(r"Q:\NIST_Projects\EUV_APT_IMS\BWC\R45_data\R45_04472_LaserPositionHist.csv")
#ax.plot(df['Ion Sequence Number'],df['Laser X Position (mic)'])
#ax.plot(df['Ion Sequence Number'],df['Laser Y Position (mic)'])
#ax.plot(df['Ion Sequence Number'],df['Laser Z Position (mic)'])
#
#ax.clear()
#ax.plot(pointwise_scales)
#ax.grid()
#ax.plot(np.convolve(np.diff(epos['v_dc']),np.ones(101)/101,mode='same')+1)
##ax.plot(np.convolve(np.diff(epos['v_dc']),np.ones(1)/1,mode='same')+1)
#
#
#wall_time = np.cumsum(epos['pslep'])/500000.0
#
#dt = ppd.moving_average(np.diff(wall_time),n=512)
#ra = 1/dt
#
#fig = plt.figure(figsize=(8,8))
#ax = fig.gca()
#ax.plot(ra)
#
#dsc_pointwise = np.r_[np.diff(pointwise_scales),0]
#
##ax.plot(dsc_pointwise*10000000)
#
#ax.plot((pointwise_scales-1)*100000/3)
#
#ax.plot((pointwise_scales-1)*100/3)
#
#
#fig = plt.gcf()
#ax = fig.gca()
#
dsc = np.r_[np.diff(piecewise_scales), 0]
#fig = plt.figure(figsize=(8,8))
#ax = fig.gca()
#ax.plot(dsc)
#fig = plt.figure(figsize=(8,8))
#ax = fig.gca()
ax.hist(dsc, bins=64, range=[-0.01, 0.01])
dsc_cut = scipy.stats.trimboth(dsc, 0.025)
outlier_lims = [
np.mean(dsc_cut) - 7 * np.std(dsc_cut),
np.mean(dsc_cut) + 7 * np.std(dsc_cut)
]
epos['m2q'] = m2q_corr
#apt_fileio.write_epos_numpy(epos,'Q:\\NIST_Projects\\EUV_APT_IMS\\BWC\\GaN epos files\\R20_07148-v01_vbmq_corr.epos')
return 0
markersize=1,
marker='.',
markeredgecolor='tab:blue')
ax.set(xlabel='ion index (thousands)', ylabel='ToF (ns)', ylim=user_ylim)
ax_twin = ax.twinx()
ax_twin.plot(np.arange(0,epos.size,STEP )/1000,epos['v_dc'][0::STEP ], color='tab:orange', lw=2)
ax_twin.set(ylabel='V_dc')
fig.savefig('APT_Soft_volt.png')
# Plot raw
ax = plotting_stuff.plot_histo(epos['tof'],fig_idx=3,user_label='raw tof', user_bin_width=0.25, user_xlim=[0,1000])
# Plot raw
ax = plotting_stuff.plot_histo(tof_vbcorr,fig_idx=3,user_label='V+B corrected tof', user_bin_width=0.25, user_xlim=[0,1000], clearFigure=False)
ax.set(ylim=[5e1,1e6])
plt.gcf().savefig('APT_Soft_corr_histos.png')
fig = plt.figure(num=3)
ax = fig.gca()
delta = 3
# Plot x cut
STEP = 10
ax.plot(np.arange(0,epos.size,STEP )/1000,tof_bcorr[0::STEP ],'.',
csr = np.full(len(idxs_list), -1.0)
Ga_comp = np.full(len(idxs_list), -1.0)
Ga_comp_std = np.full(len(idxs_list), -1.0)
Ga_comp_glob = np.full(len(idxs_list), -1.0)
Ga_comp_std_glob = np.full(len(idxs_list), -1.0)
keys = list(pk_data.dtype.fields.keys())
keys.remove('m2q')
Ga_idx = keys.index('Ga')
for loop_idx, idxs in enumerate(idxs_list):
sub_epos = epos[idxs[0]:idxs[1]]
plotting_stuff.plot_histo(sub_epos['m2q'],
321,
user_xlim=[0, 275],
user_bin_width=0.1,
user_label=loop_idx)
plt.waitforbuttonpress()
# r = np.sqrt(sub_epos['x_det']**2+sub_epos['y_det']**2)
# sub_idxs = np.nonzero(r>=0)
# cts = ppd.do_counting(sub_epos[sub_idxs],pk_params,glob_bg_param)
#
# tot_bg_ct = epos['m2q'][(epos['m2q']>=80) & (epos['m2q']<=120)].size
# sub_bg_ct = sub_epos['m2q'][(sub_epos['m2q']>=80) & (sub_epos['m2q']<=120)].size
#
# loc_bg_param = glob_bg_param*sub_bg_ct/tot_bg_ct
glob_bg_param_chunk = ppd.get_glob_bg(sub_epos['m2q'], rois=bg_rois)
cts = ppd.do_counting(sub_epos, pk_params, glob_bg_param_chunk)
Ga_comp[loop_idx] = ppd.do_composition(pk_data, cts)[0][0][Ga_idx]
label='Y')
#ax.set_xlim(start_idx,epos.size)
ax.set_ylim(85,90)
ax.set_ylim(0,1000)
fig.tight_layout()
plotting_stuff.plot_histo(epos['m2q'],131131,user_label='raw')
plotting_stuff.plot_histo(epos['m2q']/pointwise_scales,131131,user_label='corr',clearFigure=False)
cts_per_slice=2**12
import time
t_start = time.time()
pointwise_scales,piecewise_scales = sel_align_m2q_log_xcorr_v2.get_all_scale_coeffs(epos['m2q'],
m2q_roi=[0.8,80],
cts_per_slice=cts_per_slice,
max_scale=1.15)
t_end = time.time()
print('Total Time = ',t_end-t_start)
ref_pk_m2qs = np.array([
ed['H'].isotopes[1][0], 2 * ed['H'].isotopes[1][0],
(1 / 2) * ed['N'].isotopes[14][0], ed['N'].isotopes[14][0],
2 * ed['N'].isotopes[14][0], (1 / 2) * ed['Ga'].isotopes[69][0],
(1 / 2) * ed['Ga'].isotopes[71][0], ed['Ga'].isotopes[69][0],
ed['Ga'].isotopes[71][0]
])
# Perform 'linearization' m2q calibration
m2q_corr2 = m2q_calib.calibrate_m2q_by_peak_location(m2q_corr, ref_pk_m2qs)
# Plot the reference spectrum, (c, t0) corr spectrum and linearized spectrum
# to confirm that mass calibration went ok
user_xlim = [0, 150]
plotting_stuff.plot_histo(ref_epos['m2q'],
4,
user_label='ref',
user_xlim=user_xlim)
plotting_stuff.plot_histo(m2q_corr,
4,
clearFigure=False,
user_label='[c,t0] corr',
user_xlim=user_xlim)
ax = plotting_stuff.plot_histo(m2q_corr2,
4,
clearFigure=False,
user_label='linearized',
user_xlim=user_xlim)
for ref_pk_m2q in ref_pk_m2qs:
ax.plot(ref_pk_m2q * np.ones(2), np.array([1, 1e4]), 'k--')
# Save the data as a new epos file
scales_short = 10.0**((shifts0 + shifts1) * (y_edges[1] - y_edges[0]))
fig = plt.figure(figsize=(8, 8))
ax = fig.gca()
ax.plot(scales_short, 'o-')
m2q_corr = epos['m2q'] / scales
plotting_stuff.plot_TOF_vs_time(m2q_corr,
epos,
111,
clearFigure=True,
user_ylim=[0, 150])
plotting_stuff.plot_histo(m2q_corr, 222, user_xlim=[0, 200])
plotting_stuff.plot_histo(epos['m2q'],
222,
user_xlim=[0, 200],
clearFigure=False)
#
#dsc = np.r_[np.diff(scales_short),0]
#fig = plt.figure(figsize=(8,8))
#ax = fig.gca()
#ax.plot(dsc)
#
#fig = plt.figure(figsize=(8,8))
#ax = fig.gca()
#
#ax.hist(dsc,bins=512,range=[-0.01,0.01])
ax.axis('equal')
ax.axis('square')
ax.set_xlabel('x')
ax.set_ylabel('y')
vc = np.sqrt(5000) / np.sqrt(epos['v_dc'])
plotting_stuff.plot_TOF_vs_time(epos['tof'] / vc, epos, 1, user_ylim=[0, 5000])
# Find transform to m/z space
epos_R44['m2q'], p_m2q_R44 = m2q_calib.align_m2q_to_ref_m2q(
ref_epos['m2q'], mod_full_vb_correction(epos_R44, p_volt_R44, p_bowl_R44))
epos_R20['m2q'], p_m2q_R20 = m2q_calib.align_m2q_to_ref_m2q(
ref_epos['m2q'], mod_full_vb_correction(epos_R20, p_volt_R20, p_bowl_R20))
plotting_stuff.plot_histo(epos_R44['m2q'],
fig_idx=1,
user_label='R44',
scale_factor=1 / epos_R44.size)
plotting_stuff.plot_histo(epos_R20['m2q'],
fig_idx=1,
user_label='R20',
clearFigure=False,
scale_factor=1 / epos_R20.size)
# Make a correlation histogram of doubles
epos_vb_R44 = epos_R44.copy()
epos_vb_R44['tof'] = np.sqrt(1e4 * epos_R44['m2q'])
edges, ch = corrhist(epos_vb_R44, roi=[0, 1000], delta=1)
centers = (edges[1:] + edges[:-1]) / 2.0
fig2 = plt.figure(num=3)
bin_dat(q_tof[1::2], isBinAligned=True, bin_width=0.1,
user_roi=tof_roi)[1])
plt.plot(x, y, label='ref2')
plt.figure(32132313)
pointwise_scales, piecewise_scales = scaling_correction.get_all_scale_coeffs(
varying_tof,
m2q_roi=tof_roi,
cts_per_slice=2**6,
max_scale=1.075,
delta_ly=1e-4)
ax = plotting_stuff.plot_histo(q_tof,
4,
user_xlim=[0, 800],
user_bin_width=0.1,
user_label='orig')
ax = plotting_stuff.plot_histo(varying_tof,
4,
clearFigure=False,
user_xlim=[0, 800],
user_bin_width=0.1,
user_label='messed')
ax = plotting_stuff.plot_histo(varying_tof / pointwise_scales,
4,
clearFigure=False,
user_xlim=[0, 800],
user_bin_width=0.1,
user_label='messed')
'R20_18160-v01.epos', 'R20_18161-v01.epos', 'R20_18162-v01.epos'
]
fold = "GaN epos files"
fig = plt.figure(1)
fig.clf()
ax = fig.gca()
for i in range(len(fns)):
epos = apt_fileio.read_epos_numpy(fold + "\\" + fns[i])
plotting_stuff.plot_histo(
epos['m2q'],
1,
user_label=fns[i],
clearFigure=False,
user_xlim=[0, 100],
user_bin_width=0.03,
scale_factor=10**i,
user_color=None,
)
CSR = [0.038, 0.005, 0.1, 0.09, 0.24, 0.01]
N = [34.1, 17, 41.5, 40.5, 49.7, 23.2]
Ga = [65.9, 83, 58.5, 59.5, 53.3, 76.8]
fig = plt.figure(5)
plt.plot(CSR, Ga, 'ko', label="Ga")
plt.plot(CSR, N, 'rs', label="N")
plt.xscale('log')
plt.legend()
#fn = fold+"\\"+'R20_18161-v01.epos'
