def steel():
# Load and subsample data (for easier processing)
fn = r"Q:\NIST_Projects\EUV_APT_IMS\BWC\R44_02203-v01.epos"
epos = apt_fileio.read_epos_numpy(fn)
epos = epos[100000::10]
# 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")
# Only apply bowl correction
tof_bcorr = voltage_and_bowl.mod_geometric_bowl_correction(
p_bowl, epos['tof'], epos['x_det'], epos['y_det'])
# Plot histogram for steel
fig = plt.figure(figsize=(FIGURE_SCALE_FACTOR * 3.14961,
FIGURE_SCALE_FACTOR * 3.14961),
num=1,
dpi=100)
plt.clf()
ax1, ax2 = fig.subplots(2, 1, sharex=True)
N, x_edges, y_edges = create_histogram(tof_bcorr,
y_roi=[400, 600],
cts_per_slice=2**9,
delta_y=0.25)
im = plot_2d_histo(ax1, N, x_edges, y_edges)
# plt.colorbar(im)
ax1.set(ylabel='flight time (ns)')
ax1twin = ax1.twinx()
ax1twin.plot(epos['v_dc'],
'-',
linewidth=2,
color=mcd.XKCD_COLORS['xkcd:white'])
ax1twin.set(ylabel='applied voltage (volts)',
ylim=[0, 6000],
xlim=[0, 400000])
N, x_edges, y_edges = create_histogram(tof_corr,
y_roi=[425, 475],
cts_per_slice=2**9,
delta_y=0.25)
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\metal_not_wandering.pdf',
format='pdf',
dpi=600)
return 0
def sio2_R20():
fn = r"Q:\NIST_Projects\EUV_APT_IMS\BWC\R20_07080-v01.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")
# Only apply bowl correction
tof_bcorr = voltage_and_bowl.mod_geometric_bowl_correction(
p_bowl, epos['tof'], epos['x_det'], epos['y_det'])
# Plot histogram for sio2
fig = plt.figure(figsize=(FIGURE_SCALE_FACTOR * 3.14961,
FIGURE_SCALE_FACTOR * 3.14961),
num=4,
dpi=100)
plt.clf()
ax1, ax2 = fig.subplots(2, 1, sharex=True)
N, x_edges, y_edges = create_histogram(tof_bcorr,
y_roi=[320, 380],
cts_per_slice=2**9,
delta_y=.25)
plot_2d_histo(ax1, N, x_edges, y_edges)
ax1.set(ylabel='flight time (ns)')
ax1twin = ax1.twinx()
ax1twin.plot(epos['v_dc'],
'-',
linewidth=2,
color=mcd.XKCD_COLORS['xkcd:white'])
ax1twin.set(ylabel='applied voltage (volts)',
ylim=[0000, 5000],
xlim=[0, 400000])
N, x_edges, y_edges = create_histogram(tof_corr,
y_roi=[320, 380],
cts_per_slice=2**9,
delta_y=.25)
plot_2d_histo(ax2, N, x_edges, y_edges)
ax2.set(xlabel='ion sequence', ylabel='corrected flight time (ns)')
fig.tight_layout()
fig.savefig(r'SiO2_EUV_wandering.pdf', format='pdf', dpi=600)
return 0
def get_vb_corr_sio2_tof():
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)
return tof_corr
def get_vb_corr_ceria_tof():
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'])
return tof_corr[0:1000000]
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
def corr_idea():
fig = plt.figure(num=5)
plt.close(fig)
fig = plt.figure(constrained_layout=True,
figsize=(FIGURE_SCALE_FACTOR * 3.14961,
FIGURE_SCALE_FACTOR * 1.5 * 3.14961),
num=5,
dpi=100)
gs = fig.add_gridspec(3, 1)
ax2 = fig.add_subplot(gs[:2, :])
ax1 = fig.add_subplot(gs[2, :])
def shaded_plot(ax, x, y, idx, col_idx=None):
if col_idx is None:
col_idx = idx
sc = 50
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] + idx * sc, (idx - 0.005) * sc,
color=cols[col_idx])
# ax.plot(x,y+idx*sc, color='k')
return
fn = r"Q:\NIST_Projects\EUV_APT_IMS\BWC\R45_data\R45_04472-v02_allVfromAnn.epos"
epos = apt_fileio.read_epos_numpy(fn)
epos = epos[25000:]
# 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")
# Plot histogram for sio2
# fig = plt.figure(figsize=(2*3.14961,2*3.14961),num=654321,dpi=100)
# plt.clf()
# ax2 = fig.subplots(1,1)
N, x_edges, y_edges = create_histogram(tof_corr,
y_roi=[80, 400],
cts_per_slice=2**10,
delta_y=0.0625)
#ax1.imshow(np.log10(1+1*np.transpose(N)), aspect='auto',
# extent=extents(x_edges) + extents(y_edges), origin='lower', cmap=cc.cm.CET_L8,
# interpolation='bilinear')
event_idx_range_ref = [0, 0 + 1024]
event_idx_range_mov = [124000, 124000 + 1024]
x_centers = edges_to_centers(x_edges)
idxs_ref = (x_centers >= event_idx_range_ref[0]) & (x_centers <=
event_idx_range_ref[1])
idxs_mov = (x_centers >= event_idx_range_mov[0]) & (x_centers <=
event_idx_range_mov[1])
ref_hist = np.sum(N[idxs_ref, :], axis=0)
mov_hist = np.sum(N[idxs_mov, :], axis=0)
y_centers = edges_to_centers(y_edges)
ax2.set(xlim=[290, 320])
N, x_edges, y_edges = create_histogram(0.98 * tof_corr,
y_roi=[80, 400],
cts_per_slice=2**10,
delta_y=0.0625)
mov_hist = np.sum(N[idxs_mov, :], axis=0)
shaded_plot(ax2, y_centers, mov_hist, 2, 2)
N, x_edges, y_edges = create_histogram(0.99 * tof_corr,
y_roi=[80, 400],
cts_per_slice=2**10,
delta_y=0.0625)
mov_hist = np.sum(N[idxs_mov, :], axis=0)
shaded_plot(ax2, y_centers, ref_hist, 3, 3)
shaded_plot(ax2, y_centers, mov_hist, 1, 1)
N, x_edges, y_edges = create_histogram(1.0 * tof_corr,
y_roi=[80, 400],
cts_per_slice=2**10,
delta_y=0.0625)
mov_hist = np.sum(N[idxs_mov, :], axis=0)
shaded_plot(ax2, y_centers, mov_hist, 0, col_idx=0)
cs = np.linspace(0.975, 1.005, 256)
dp = np.zeros_like(cs)
for idx, c in enumerate(cs):
N, x_edges, y_edges = create_histogram(c * tof_corr,
y_roi=[80, 400],
cts_per_slice=2**10,
delta_y=0.0625)
mov_hist = np.sum(N[idxs_mov, :], axis=0)
dp[idx] = np.sum(
(mov_hist / np.sum(mov_hist)) * (ref_hist / np.sum(ref_hist)))
ax1.set(xlim=[0.975, 1.005], ylim=[-0.1, 1.1])
f = scipy.interpolate.interp1d(cs, dp / np.max(dp))
cols = [
'#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b',
'#e377c2', '#7f7f7f', '#bcbd22', '#17becf'
]
xq = [0.98, 0.99017, 1.0]
for idx in [0, 1, 2]:
ax1.plot(xq[idx], f(xq[idx]), 'o', markersize=14, color=cols[2 - idx])
ax1.plot(cs, dp / np.max(dp), 'k')
ax1.set_xlabel('correction factor, c')
ax1.set_ylabel('dot product (norm)')
ax2.set_xlabel('corrected time of flight (ns)')
ax2.set_ylabel('counts')
plt.pause(0.1)
fig.tight_layout()
fig.savefig(r'Q:\users\bwc\APT\scale_corr_paper\correction_idea.pdf',
format='pdf',
dpi=600)
return 0
def sio2_R44():
fn = r"C:\Users\bwc\Documents\NetBeansProjects\R44_03200\recons\recon-v02\default\R44_03200-v02.epos"
epos = apt_fileio.read_epos_numpy(fn)
# 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")
# Only apply bowl correction
tof_bcorr = voltage_and_bowl.mod_geometric_bowl_correction(
p_bowl, epos['tof'], epos['x_det'], epos['y_det'])
# Plot histogram for sio2
fig = plt.figure(figsize=(FIGURE_SCALE_FACTOR * 3.14961,
FIGURE_SCALE_FACTOR * 3.14961),
num=3,
dpi=100)
plt.clf()
ax1, ax2 = fig.subplots(2, 1, sharex=True)
roi = [1400000, 1800000]
N, x_edges, y_edges = create_histogram(tof_bcorr[roi[0]:roi[1]],
y_roi=[300, 310],
cts_per_slice=2**7,
delta_y=.2)
im = plot_2d_histo(ax1, N, x_edges, y_edges)
plt.colorbar(im)
ax1.set(ylabel='flight time (ns)')
ax1twin = ax1.twinx()
ax1twin.plot(epos['v_dc'][roi[0]:roi[1]],
'-',
linewidth=2,
color=mcd.XKCD_COLORS['xkcd:white'])
ax1twin.set(ylabel='applied voltage (volts)',
ylim=[0000, 7000],
xlim=[0, None])
N, x_edges, y_edges = create_histogram(tof_corr[roi[0]:roi[1]],
y_roi=[300, 310],
cts_per_slice=2**7,
delta_y=0.2)
im = plot_2d_histo(ax2, N, x_edges, y_edges)
plt.colorbar(im)
ax2.set(xlabel='ion sequence', ylabel='corrected flight time (ns)')
ax2.set_xlim(0, roi[1] - roi[0])
fig.tight_layout()
fig.savefig(r'Q:\users\bwc\APT\scale_corr_paper\Figure_R44NUV.pdf',
format='pdf',
dpi=600)
return 0
epos_d = epos[doub_idxs]
# voltage and bowl correct ToF data.
p_volt = np.array([1, 0])
p_volt = np.array([])
p_bowl = np.array([0.89964083, -0.43114144, -0.27484715, -0.25883824])
# 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)
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))
ax = plotting_stuff.plot_TOF_vs_time(epos['tof'], epos, 1)
ax.plot(roi_event_idxs[0] * np.ones(2), [0, 1200], '--k')
ax.plot(roi_event_idxs[-1] * np.ones(2), [0, 1200], '--k')
ax.set_title('roi selected to start analysis')
epos = epos[roi_event_idxs]
# Compute some extra information from epos information
wall_time = np.cumsum(epos['pslep']) / 10000.0
pulse_idx = np.arange(0, epos.size)
isSingle = np.nonzero(epos['ipp'] == 1)
# 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)
#
import sel_align_m2q_log_xcorr
pointwise_scales, piecewise_scales = sel_align_m2q_log_xcorr.get_all_scale_coeffs(
m2q_corr, m2q_roi=[0.5, 75], cts_per_slice=2**8, 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]
def dummy():
# Voltage and bowl correct ToF data
from voltage_and_bowl import do_voltage_and_bowl
p_volt = np.array([])
p_bowl = np.array([])
tof_corr, p_volt, p_bowl = do_voltage_and_bowl(epos, p_volt, p_bowl)
epos_vb = epos.copy()
epos_vb['tof'] = tof_corr.copy()
import voltage_and_bowl
tof_vcorr = voltage_and_bowl.mod_full_voltage_correction(
p_volt, epos['tof'], epos['v_dc'])
epos_v = epos.copy()
epos_v['tof'] = tof_vcorr.copy()
tof_bcorr = voltage_and_bowl.mod_geometric_bowl_correction(
p_bowl, epos['tof'], epos['x_det'], epos['y_det'])
epos_b = epos.copy()
epos_b['tof'] = tof_bcorr.copy()
ROI = [0, None]
ch = histogram_functions.corrhist(epos)
fig1 = plt.figure(num=1)
plt.clf()
plt.imshow(np.log2(1 + ch))
plt.title('raw')
fig1.gca().set_xlim(ROI[0], ROI[1])
fig1.gca().set_ylim(ROI[0], ROI[1])
ch = histogram_functions.corrhist(epos_v)
fig2 = plt.figure(num=2)
plt.clf()
plt.imshow(np.log2(1 + ch))
plt.title('volt')
fig2.gca().set_xlim(ROI[0], ROI[1])
fig2.gca().set_ylim(ROI[0], ROI[1])
ch = histogram_functions.corrhist(epos_b)
fig3 = plt.figure(num=3)
plt.clf()
plt.imshow(np.log2(1 + ch))
plt.title('bowl')
fig3.gca().set_xlim(ROI[0], ROI[1])
fig3.gca().set_ylim(ROI[0], ROI[1])
ch = histogram_functions.corrhist(epos_vb)
fig4 = plt.figure(num=4)
plt.clf()
plt.imshow(np.log10(1 + ch))
plt.title('v+b')
# fig4.gca().set_xlim(ROI[0],ROI[1])
# fig4.gca().set_ylim(ROI[0],ROI[1])
idxs = np.where(epos['ipp'] == 2)[0]
fig5 = plt.figure(num=5)
plt.clf()
dts = np.abs(tof_corr[idxs] - tof_corr[idxs + 1])
plt.hist(dts, bins=np.arange(0, 2000, .5), label='deltaT')
plt.hist(tof_corr[np.r_[idxs, idxs + 1]],
bins=np.arange(0, 2000, .5),
label='since t0')
fig66 = plt.figure(num=66)
plt.clf()
dts = np.abs(tof_corr[idxs] - tof_corr[idxs + 1])
# sus = np.sqrt(tof_corr[idxs]**2+tof_corr[idxs+1]**2)
# sus = np.fmax(tof_corr[idxs],tof_corr[idxs+1])
sus = (tof_corr[idxs] + tof_corr[idxs + 1]) / np.sqrt(2)
plt.plot(sus, dts, '.', ms=1, alpha=1)
# fig66.gca().axis('equal')
fig66.gca().set_xlim(0, 7000)
fig66.gca().set_ylim(-100, 800)
return
fn_R44 = r'C:\Users\capli\Google Drive\NIST\pos_and_epos_files\MgO\R44_03636-v01.epos' fn_R20 = r'C:\Users\capli\Google Drive\NIST\pos_and_epos_files\MgO\R20_08084-v01.epos' ref_epos = apt_fileio.read_epos_numpy(fn_R44) epos_R20 = apt_fileio.read_epos_numpy(fn_R20) epos_R20 = epos_R20[20000:] epos_R44 = apt_fileio.read_epos_numpy(fn_R44) # voltage and bowl correct ToF data. p_volt = np.array([]) p_bowl = np.array([ 0.89964083, -0.43114144, -0.27484715, -0.25883824]) _, p_volt_R44, p_bowl_R44 = do_voltage_and_bowl(epos_R44[epos_R44['ipp']==1],p_volt,p_bowl) _, p_volt_R20, p_bowl_R20 = do_voltage_and_bowl(epos_R20[epos_R20['ipp']==1],p_volt,p_bowl) # 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)
