def do_chi2v3(tof, tof_roi, N_lower, N_upper):
tof1 = tof[0::2]
tof1_idxs = np.array(range(0, tof1.size)) * 2
tof2 = tof[1::2]
tof2_idxs = np.array(range(0, tof2.size)) * 2 + 1
N = N_upper - N_lower + 1
slicings = np.logspace(N_lower, N_upper, N, base=2)
opt_res = np.zeros(N)
time_res = np.zeros(N)
for idx, cts_per_slice in enumerate(slicings):
t_start = time.time()
pointwise_scales, piecewise_scales = scaling_correction.get_all_scale_coeffs(
tof1,
m2q_roi=tof_roi,
cts_per_slice=cts_per_slice,
max_scale=1.075,
delta_ly=1e-4)
t_end = time.time()
time_res[idx] = t_end - t_start
print('Total Time = ', time_res[idx])
f = scipy.interpolate.interp1d(tof1_idxs,
pointwise_scales,
fill_value='extrapolate')
# Compute corrected data
tof_corr = tof1 / f(tof1_idxs)
_, ys = bin_dat(tof_corr,
isBinAligned=True,
bin_width=0.1,
user_roi=tof_roi)
opt_res[idx] = chi2(ys)
print(opt_res[idx])
print(slicings)
print(opt_res / np.max(opt_res))
print(time_res)
return (slicings, opt_res)
def do_wid(tof, tof_roi, N_lower, N_upper, t_guess, t_width):
tof1 = tof[0::2]
tof2 = tof[1::2]
N = N_upper - N_lower + 1
slicings = np.logspace(N_lower, N_upper, N, base=2)
opt_res = np.zeros(N)
time_res = np.zeros(N)
curr_res = None
for idx, cts_per_slice in enumerate(slicings):
prev_res = curr_res
t_start = time.time()
pointwise_scales, piecewise_scales = scaling_correction.get_all_scale_coeffs(
tof1,
m2q_roi=tof_roi,
cts_per_slice=cts_per_slice,
max_scale=1.075,
delta_ly=2e-4)
t_end = time.time()
time_res[idx] = t_end - t_start
print('Total Time = ', time_res[idx])
# Compute corrected data
tof_corr = tof2 / pointwise_scales
# curr_res = fit_to_g_off(tof_corr,t_guess,t_width)
# opt_res[idx] = curr_res[1]/curr_res[2]
curr_res = get_wid(tof_corr, t_guess, t_width)
print(curr_res)
opt_res[idx] = 1 / curr_res
# print(opt_res[idx])
print(slicings)
print(opt_res)
print(time_res)
return (slicings, opt_res)
def do_chi2(tof, tof_roi, N_lower, N_upper):
tof1 = tof[0::2]
tof2 = tof[1::2]
N = N_upper - N_lower + 1
slicings = np.logspace(N_lower, N_upper, N, base=2)
opt_res = np.zeros(N)
time_res = np.zeros(N)
for idx, cts_per_slice in enumerate(slicings):
t_start = time.time()
pointwise_scales, piecewise_scales = scaling_correction.get_all_scale_coeffs(
tof1,
m2q_roi=tof_roi,
cts_per_slice=cts_per_slice,
max_scale=1.075,
delta_ly=2e-4)
t_end = time.time()
time_res[idx] = t_end - t_start
print('Total Time = ', time_res[idx])
# Compute corrected data
tof_corr = tof2 / pointwise_scales
_, ys = bin_dat(tof_corr,
isBinAligned=True,
bin_width=0.1,
user_roi=tof_roi)
opt_res[idx] = chi2(ys)
print(opt_res[idx])
print(slicings)
print(opt_res / np.max(opt_res))
print(time_res)
return (slicings, opt_res / np.max(opt_res))
def ceria_histo():
def shaded_plot(ax, x, y, idx, col_idx=None, min_val=None):
if col_idx is None:
col_idx = idx
if min_val is None:
min_val = np.min(y)
cols = [
'#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b',
'#e377c2', '#7f7f7f', '#bcbd22', '#17becf'
]
xlim = ax.get_xlim()
idxs = np.nonzero((x >= xlim[0]) & (x <= xlim[1]))
ax.fill_between(x[idxs],
y[idxs],
min_val,
color=cols[col_idx],
linestyle='None',
lw=0)
# ax.plot(x,y+idx*sc, color='k')
return
fn = r"Q:\NIST_Projects\EUV_APT_IMS\BWC\R45_data\R45_00504-v56.epos"
epos = apt_fileio.read_epos_numpy(fn)
red_epos = epos[100000::10]
# Voltage and bowl correct ToF data
p_volt = np.array([])
p_bowl = np.array([])
t_i = time.time()
_, p_volt, p_bowl = do_voltage_and_bowl(red_epos, p_volt, p_bowl)
print("time to voltage and bowl correct: " + str(time.time() - t_i) +
" seconds")
tof_vcorr = voltage_and_bowl.mod_full_voltage_correction(
p_volt, epos['tof'], epos['v_dc'])
tof_corr = voltage_and_bowl.mod_geometric_bowl_correction(
p_bowl, tof_vcorr, epos['x_det'], epos['y_det'])
m2q_roi = [10, 250]
m2q_to_tof = 1025 / np.sqrt(172)
tof_roi = [m2q_roi[0] * m2q_to_tof, m2q_roi[1] * m2q_to_tof]
tof_roi = [200, 1200]
cts_per_slice = 2**9
#m2q_roi = [0.9,190]
# tof_roi = [0, 1000]
t_start = time.time()
pointwise_scales, piecewise_scales = scaling_correction.get_all_scale_coeffs(
tof_corr,
m2q_roi=tof_roi,
cts_per_slice=cts_per_slice,
max_scale=1.075)
t_end = time.time()
print('Total Time = ', t_end - t_start)
# fake_tof_corr = fake_tof/np.sqrt(pointwise_scales)
q_tof_corr = tof_corr / pointwise_scales
OVERALL_CALIB_FACTOR = 0.9956265249773827
m2q_corr = OVERALL_CALIB_FACTOR * m2q_to_tof**-2 * q_tof_corr**2
m2q_vbcorr = OVERALL_CALIB_FACTOR * m2q_to_tof**-2 * tof_corr**2
fig = plt.figure(constrained_layout=True,
figsize=(FIGURE_SCALE_FACTOR * 3.14961,
FIGURE_SCALE_FACTOR * 3.14961),
num=8,
dpi=100)
plt.clf()
gs = plt.GridSpec(2, 4, figure=fig)
ax0 = fig.add_subplot(gs[0, :])
# identical to ax1 = plt.subplot(gs.new_subplotspec((0, 0), colspan=3))
ax1 = fig.add_subplot(gs[1, 0:2])
#ax2 = fig.add_subplot(gs[1,1])
ax3 = fig.add_subplot(gs[1, 2:4])
dat = m2q_vbcorr
user_bin_width = 0.02
user_xlim = [0, 200]
ax0.set(xlim=user_xlim)
dat = m2q_corr
xs, ys = bin_dat(dat,
isBinAligned=True,
bin_width=user_bin_width,
user_roi=user_xlim)
shaded_plot(ax0, xs, 10 * (1 + ys), 1, min_val=10)
dat = m2q_vbcorr
xs, ys = bin_dat(dat,
isBinAligned=True,
bin_width=user_bin_width,
user_roi=user_xlim)
shaded_plot(ax0, xs, 1 + ys, 0, min_val=1)
ax0.set(xlabel='m/z (Da)', ylabel='counts', xlim=user_xlim)
ax0.set_yscale('log')
ax0.set(ylim=[10, None])
# user_bin_width = 0.02
user_xlim = [75, 85]
ax1.set(xlim=user_xlim)
dat = m2q_corr
xs, ys = bin_dat(dat,
isBinAligned=True,
bin_width=user_bin_width,
user_roi=user_xlim)
shaded_plot(ax1, xs, 10 * (1 + ys), 1, min_val=10)
dat = m2q_vbcorr
xs, ys = bin_dat(dat,
isBinAligned=True,
bin_width=user_bin_width,
user_roi=user_xlim)
shaded_plot(ax1, xs, 1 + ys, 0, min_val=1)
ax1.set(xlabel='m/z (Da)', ylabel='counts', xlim=user_xlim)
ax1.set_yscale('log')
ax1.set(ylim=[10, None])
#
#
##user_bin_width = 0.01
#user_xlim = [30,34]
#ax2.set(xlim=user_xlim)
#
#
#dat = m2q_corr
#xs, ys = bin_dat(dat,isBinAligned=True,bin_width=user_bin_width,user_roi=user_xlim)
#shaded_plot(ax2,xs,100*(1+ys),1,min_val=100)
#
#
#dat = epos['m2q']
#xs, ys = bin_dat(dat,isBinAligned=True,bin_width=user_bin_width,user_roi=user_xlim)
#shaded_plot(ax2,xs,1+ys,0,min_val=1)
#
#
#ax2.set(xlabel='m/z (Da)', ylabel='counts', xlim=user_xlim)
#ax2.set_yscale('log')
# user_bin_width = 0.03
user_xlim = [154, 170]
ax3.set(xlim=user_xlim)
dat = m2q_corr
xs, ys = bin_dat(dat,
isBinAligned=True,
bin_width=user_bin_width,
user_roi=user_xlim)
shaded_plot(ax3, xs, 10 * (1 + ys), 1, min_val=10)
dat = m2q_vbcorr
xs, ys = bin_dat(dat,
isBinAligned=True,
bin_width=user_bin_width,
user_roi=user_xlim)
shaded_plot(ax3, xs, 1 + ys, 0, min_val=1)
ax3.set(xlabel='m/z (Da)', ylabel='counts', xlim=user_xlim)
ax3.set_yscale('log')
ax3.set(ylim=[10, 1e4])
ax3.set_xticks(np.arange(154, 170 + 1, 4))
fig.tight_layout()
fig.savefig(
r'Q:\users\bwc\APT\scale_corr_paper\Ceria_NUV_corrected_hist.pdf',
format='pdf',
dpi=600)
return 0
def sio2_R45_histo():
def shaded_plot(ax, x, y, idx, col_idx=None, min_val=None):
if col_idx is None:
col_idx = idx
if min_val is None:
min_val = np.min(y)
sc = 150
cols = [
'#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b',
'#e377c2', '#7f7f7f', '#bcbd22', '#17becf'
]
xlim = ax.get_xlim()
idxs = np.nonzero((x >= xlim[0]) & (x <= xlim[1]))
ax.fill_between(x[idxs],
y[idxs],
min_val,
color=cols[col_idx],
linestyle='None',
lw=0)
# ax.plot(x,y+idx*sc, color='k')
return
fn = r"Q:\NIST_Projects\EUV_APT_IMS\BWC\R45_data\R45_04472-v03.epos"
epos = apt_fileio.read_epos_numpy(fn)
epos = epos[25000:]
epos = epos[:400000]
# 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")
# fake_tof = np.sqrt((296/312)*epos['m2q']/1.393e-4)
m2q_roi = [0.8, 80]
m2q_to_tof = 613 / np.sqrt(59)
tof_roi = [m2q_roi[0] * m2q_to_tof, m2q_roi[1] * m2q_to_tof]
cts_per_slice = 2**7
#m2q_roi = [0.9,190]
# tof_roi = [0, 1000]
t_start = time.time()
pointwise_scales, piecewise_scales = scaling_correction.get_all_scale_coeffs(
tof_corr,
m2q_roi=tof_roi,
cts_per_slice=cts_per_slice,
max_scale=1.075)
t_end = time.time()
print('Total Time = ', t_end - t_start)
# fake_tof_corr = fake_tof/np.sqrt(pointwise_scales)
q_tof_corr = tof_corr / pointwise_scales
OVERALL_CALIB_FACTOR = 1.0047693561704287
m2q_corr = OVERALL_CALIB_FACTOR * m2q_to_tof**-2 * q_tof_corr**2
m2q_vbcorr = OVERALL_CALIB_FACTOR * m2q_to_tof**-2 * tof_corr**2
fig = plt.figure(constrained_layout=True,
figsize=(FIGURE_SCALE_FACTOR * 3.14961,
FIGURE_SCALE_FACTOR * 3.14961),
num=7,
dpi=100)
plt.clf()
gs = plt.GridSpec(2, 3, figure=fig)
ax0 = fig.add_subplot(gs[0, :])
# identical to ax1 = plt.subplot(gs.new_subplotspec((0, 0), colspan=3))
ax1 = fig.add_subplot(gs[1, 0:2])
#ax2 = fig.add_subplot(gs[1,1])
ax3 = fig.add_subplot(gs[1, 2])
dat = m2q_vbcorr
user_bin_width = 0.02
user_xlim = [0, 65]
ax0.set(xlim=user_xlim)
dat = m2q_corr
xs, ys = bin_dat(dat,
isBinAligned=True,
bin_width=user_bin_width,
user_roi=user_xlim)
shaded_plot(ax0, xs, 100 * (1 + ys), 1, min_val=100)
dat = m2q_vbcorr
xs, ys = bin_dat(dat,
isBinAligned=True,
bin_width=user_bin_width,
user_roi=user_xlim)
shaded_plot(ax0, xs, 1 + ys, 0, min_val=1)
ax0.set(xlabel='m/z (Da)', ylabel='counts', xlim=user_xlim)
ax0.set_yscale('log')
# user_bin_width = 0.02
user_xlim = [13, 19]
ax1.set(xlim=user_xlim)
dat = m2q_corr
xs, ys = bin_dat(dat,
isBinAligned=True,
bin_width=user_bin_width,
user_roi=user_xlim)
shaded_plot(ax1, xs, 100 * (1 + ys), 1, min_val=100)
dat = m2q_vbcorr
xs, ys = bin_dat(dat,
isBinAligned=True,
bin_width=user_bin_width,
user_roi=user_xlim)
shaded_plot(ax1, xs, 1 + ys, 0, min_val=1)
ax1.set(xlabel='m/z (Da)', ylabel='counts', xlim=user_xlim)
ax1.set_yscale('log')
#
#
##user_bin_width = 0.01
#user_xlim = [30,34]
#ax2.set(xlim=user_xlim)
#
#
#dat = m2q_corr
#xs, ys = bin_dat(dat,isBinAligned=True,bin_width=user_bin_width,user_roi=user_xlim)
#shaded_plot(ax2,xs,100*(1+ys),1,min_val=100)
#
#
#dat = epos['m2q']
#xs, ys = bin_dat(dat,isBinAligned=True,bin_width=user_bin_width,user_roi=user_xlim)
#shaded_plot(ax2,xs,1+ys,0,min_val=1)
#
#
#ax2.set(xlabel='m/z (Da)', ylabel='counts', xlim=user_xlim)
#ax2.set_yscale('log')
#user_bin_width = 0.01
user_xlim = [58, 64]
ax3.set(xlim=user_xlim)
dat = m2q_corr
xs, ys = bin_dat(dat,
isBinAligned=True,
bin_width=user_bin_width,
user_roi=user_xlim)
shaded_plot(ax3, xs, 100 * (1 + ys), 1, min_val=100)
dat = m2q_vbcorr
xs, ys = bin_dat(dat,
isBinAligned=True,
bin_width=user_bin_width,
user_roi=user_xlim)
shaded_plot(ax3, xs, 1 + ys, 0, min_val=1)
ax3.set(xlabel='m/z (Da)', ylabel='counts', xlim=user_xlim)
ax3.set_yscale('log')
ax0.set(ylim=[1, None])
ax1.set(ylim=[1, None])
# ax2.set(ylim=[1,None])
ax3.set(ylim=[1, None])
fig.tight_layout()
fig.savefig(
r'Q:\users\bwc\APT\scale_corr_paper\SiO2_NUV_corrected_hist.pdf',
format='pdf',
dpi=600)
return 0
def sio2_R45_corr():
fn = r"Q:\NIST_Projects\EUV_APT_IMS\BWC\R45_data\R45_04472-v03.epos"
epos = apt_fileio.read_epos_numpy(fn)
epos = epos[25000:]
epos = epos[:400000]
# 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")
# fake_tof = np.sqrt((296/312)*epos['m2q']/1.393e-4)
m2q_roi = [0.8, 80]
m2q_to_tof = 613 / np.sqrt(59)
tof_roi = [m2q_roi[0] * m2q_to_tof, m2q_roi[1] * m2q_to_tof]
cts_per_slice = 2**7
#m2q_roi = [0.9,190]
# tof_roi = [0, 1000]
t_start = time.time()
pointwise_scales, piecewise_scales = scaling_correction.get_all_scale_coeffs(
tof_corr,
m2q_roi=tof_roi,
cts_per_slice=cts_per_slice,
max_scale=1.075)
t_end = time.time()
print('Total Time = ', t_end - t_start)
# fake_tof_corr = fake_tof/np.sqrt(pointwise_scales)
q_tof_corr = tof_corr / pointwise_scales
# m2q_corr = epos['m2q']/pointwise_scales
# Plot histogram for sio2
fig = plt.figure(figsize=(FIGURE_SCALE_FACTOR * 3.14961,
FIGURE_SCALE_FACTOR * 3.14961),
num=6,
dpi=100)
plt.clf()
ax1, ax2 = fig.subplots(2, 1, sharex=True)
N, x_edges, y_edges = create_histogram(tof_corr,
y_roi=[280, 360],
cts_per_slice=2**9,
delta_y=.25)
im = plot_2d_histo(ax1, N, x_edges, y_edges)
plt.colorbar(im)
ax1.set(ylabel='flight time (ns)')
ax1twin = ax1.twinx()
ax1twin.plot(pointwise_scales,
'-',
linewidth=1,
color=mcd.XKCD_COLORS['xkcd:white'])
ax1twin.set(ylabel='correction factor, c',
ylim=[0.98, 1.2],
xlim=[0, 400000])
N, x_edges, y_edges = create_histogram(q_tof_corr,
y_roi=[280, 360],
cts_per_slice=2**9,
delta_y=.25)
im = plot_2d_histo(ax2, N, x_edges, y_edges)
plt.colorbar(im)
ax2.set(xlabel='ion sequence', ylabel='corrected flight time (ns)')
fig.tight_layout()
fig.savefig(r'Q:\users\bwc\APT\scale_corr_paper\SiO2_NUV_corrected.pdf',
format='pdf',
dpi=600)
return 0
m2q_corr2 = f(m2q_corr)
epos['m2q'] = m2q_corr2[:]
#
#
#
#
#apt_fileio.write_epos_numpy(epos,r'C:\Users\capli\Google Drive\NIST\pos_and_epos_files\SiO2_Nov092020\R44_03376-v01_vb.epos')
import sys
sys.exit(0)
import scaling_correction
pointwise_scales, piecewise_scales = scaling_correction.get_all_scale_coeffs(
m2q_corr, m2q_roi=[0.5, 75], cts_per_slice=2**10, max_scale=1.15)
# Compute corrected data
m2q_corr_q = m2q_corr / pointwise_scales
# Convert back to tof
tof_corr_q = np.sqrt(m2q_corr_q / (p_m2q[0] * 1e-4)) + p_m2q[1]
# Plot TOF vs event index with voltage overlaid to show if voltage corr went ok
ax = plotting_stuff.plot_TOF_vs_time(tof_corr_q, epos, 2)
ax.set_title('voltage and bowl corrected')
# Plot slices from the detector to show if bowl corr went ok
plotting_stuff.plot_bowl_slices(tof_corr_q,
epos,
3,
clearFigure=True,
y = np.array([1, 1]) * chi2(
bin_dat(q_tof[0::2], isBinAligned=True, bin_width=0.1,
user_roi=tof_roi)[1])
plt.plot(x, y, label='ref1')
x = [2**3, 2**10]
y = np.array([1, 1]) * chi2(
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,
