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
# 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))
plotting_stuff.plot_histo(epos['m2q'],
fig_idx=1,
user_xlim=[0, 250],
from voltage_and_bowl import do_voltage_and_bowl
import voltage_and_bowl
plt.close('all')
fn = r'C:\Users\capli\Google Drive\NIST\pos_and_epos_files\GaN_manuscript\R20_07148-v01_vbm_corr.epos'
epos = apt_fileio.read_epos_numpy(fn)
# Voltage and bowl correct ToF data
p_volt = np.array([])
p_bowl = np.array([])
tof_vbcorr, p_volt, p_bowl = do_voltage_and_bowl(epos,p_volt,p_bowl)
tof_vcorr = voltage_and_bowl.mod_full_voltage_correction(p_volt,epos['tof'],epos['v_dc'])
tof_bcorr = voltage_and_bowl.mod_geometric_bowl_correction(p_bowl,epos['tof'],epos['x_det'],epos['y_det'])
epos_vb = epos.copy()
epos_vb['tof'] = tof_vbcorr.copy()
user_ylim=[670,740]
# Plot raw
fig = plt.figure(num=1)
ax = fig.gca()
delta = 3
fn = r"C:\Users\bwc\Documents\NetBeansProjects\R44_03200\recons\recon-v02\default\R44_03200-v02.epos"
epos = apt_fileio.read_epos_numpy(fn)
epos_red = epos[0::4]
# 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_red,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'])
tof_corr = voltage_and_bowl.mod_full_voltage_correction(p_volt,tof_bcorr,epos['v_dc'])
laser_df = pd.read_csv(r'C:\Users\bwc\Documents\NetBeansProjects\R44_03200\R44_03200_LaserPositionHist.csv')
power_df = pd.read_csv(r'C:\Users\bwc\Documents\NetBeansProjects\R44_03200\R44_03200_LaserPowerHist.csv')
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
