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 load_epos(run_number, epos_trim, fig_idx=-1):
import GaN_data_paths
import apt_fileio
# fn = GaN_data_paths.get_epos_path(run_number=run_number)
# fn = fn.rsplit(sep='.', maxsplit=1)[0]+'_vbm_corr.epos'
# fn = r"GaN epos files\R20_18162-v01_vbm_corr.epos"
fn = "GaN epos files\\" + run_number + "-v01_vbm_corr.epos"
epos = apt_fileio.read_epos_numpy(fn)
# Plot m2q vs event index and show the current ROI selection
roi_event_idxs = np.arange(epos_trim[0], epos.size - epos_trim[1])
if fig_idx > 0:
ax = plotting_stuff.plot_m2q_vs_time(epos['m2q'],
epos,
fig_idx=fig_idx)
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')
sub_epos = epos[roi_event_idxs]
print('ROI includes the following % of events: ',
sub_epos.size / epos.size)
return sub_epos
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 __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
from histogram_functions import bin_dat import scipy.interpolate import image_registration.register_images #import sel_align_m2q_log_xcorr from voltage_and_bowl import do_voltage_and_bowl import voltage_and_bowl import colorcet as cc import matplotlib._color_data as mcd fn = r'Q:\NIST_Projects\EUV_APT_IMS\BWC\GaN epos files\R20_07148-v01.epos' #fn = r'Q:\NIST_Projects\EUV_APT_IMS\BWC\Final EPOS for APL Mat Paper\R20_07080-v01_vbmq_corr.epos' epos = apt_fileio.read_epos_numpy(fn) sing_tof = epos['tof'][epos['ipp'] == 1] sing_m2q = epos['m2q'][epos['ipp'] == 1] sing_x = epos['x_det'][epos['ipp'] == 1] sing_y = epos['y_det'][epos['ipp'] == 1] sing_ipp = epos['ipp'][epos['ipp'] == 1] fig = plt.figure(num=10) plt.clf() ax = fig.gca() #ax.plot(sing_tof[0:-1],sing_tof[1:],',') ax.plot(sing_m2q[0:-1], sing_m2q[1:], ',') from matplotlib.colors import LogNorm
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
from histogram_functions import bin_dat
fns = [
'R20_18155-v01.epos', 'R20_18156-v01.epos', 'R20_18157-v01.epos',
'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)
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
import m2q_calib
import plotting_stuff
import initElements_P3
import peak_param_determination as ppd
from histogram_functions import bin_dat
from voltage_and_bowl import do_voltage_and_bowl
plt.close('all')
# Read in template spectrum
#ref_fn = r"Q:\NIST_Projects\EUV_APT_IMS\BWC\Final EPOS for APL Mat Paper\R20_07263-v02.epos"
ref_fn = r'C:\Users\capli\Google Drive\NIST\pos_and_epos_files\SiO2_Nov092020\R20_07276-v03.epos'
ref_epos = apt_fileio.read_epos_numpy(ref_fn)
#ref_epos = ref_epos[130000:]
# Read in data
#fn = r"Q:\NIST_Projects\EUV_APT_IMS\BWC\Final EPOS for APL Mat Paper\R20_07263-v02.epos" # 25 K
#fn = r"Q:\NIST_Projects\EUV_APT_IMS\BWC\Final EPOS for APL Mat Paper\R20_07080-v01.epos" # 50 K
#fn = r"Q:\NIST_Projects\EUV_APT_IMS\BWC\Final EPOS for APL Mat Paper\R20_07086-v01.epos" # 125 K
#fn = r"Q:\NIST_Projects\EUV_APT_IMS\BWC\Final EPOS for APL Mat Paper\R20_07276-v03.epos" # 150 K
fn = r'C:\Users\capli\Google Drive\NIST\pos_and_epos_files\SiO2_Nov092020\R44_03376-v01.epos'
epos = apt_fileio.read_epos_numpy(fn)
#epos = epos[epos.size//2:-1]
# Plot TOF vs event index and show the current ROI selection
#roi_event_idxs = np.arange(1000,epos.size-1000)
roi_event_idxs = np.arange(epos.size)
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
import scipy.interpolate
plt.close('all')
nuv_fn = r"Q:\NIST_Projects\EUV_APT_IMS\BWC\R45_data\R45_04472-v03.epos"
#euv_fn = r"Q:\NIST_Projects\EUV_APT_IMS\BWC\Final EPOS for APL Mat Paper\R20_07263-v02.epos"
euv_fn = r"Q:\NIST_Projects\EUV_APT_IMS\BWC\Final EPOS for APL Mat Paper\R20_07080-v01.epos"
#euv_fn = r"Q:\NIST_Projects\EUV_APT_IMS\BWC\Final EPOS for APL Mat Paper\R20_07086-v01.epos"
#euv_fn = r"Q:\NIST_Projects\EUV_APT_IMS\BWC\Final EPOS for APL Mat Paper\R20_07276-v03.epos"
#epos = apt_fileio.read_epos_numpy(nuv_fn)
#plotting_stuff.plot_TOF_vs_time(epos['m2q'],epos,1,clearFigure=True,user_ylim=[0,150])
epos = apt_fileio.read_epos_numpy(euv_fn)
#epos = apt_fileio.read_epos_numpy(nuv_fn)
plotting_stuff.plot_TOF_vs_time(epos['m2q'],
epos,
1,
clearFigure=True,
user_ylim=[0, 80])
cts_per_slice = 2**10
#m2q_roi = [0.9,190]
m2q_roi = [0.8, 80]
import time
t_start = time.time()
pointwise_scales, piecewise_scales = sel_align_m2q_log_xcorr.get_all_scale_coeffs(
epos['m2q'], m2q_roi=m2q_roi, cts_per_slice=cts_per_slice, max_scale=1.15)
t_end = time.time()
def testing():
# 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"C:\Users\bwc\Documents\NetBeansProjects\R44_03115\recons\recon-v02\default\R44_03115-v02.epos"
epos = apt_fileio.read_epos_numpy(fn)
# epos = epos[0:1000000]
epos1 = epos[0:-1:2]
epos2 = epos[1::2]
N_lower = 8
N_upper = 16
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):
m2q_roi = [0.9,180]
import time
t_start = time.time()
pointwise_scales,piecewise_scales = get_all_scale_coeffs(epos1['m2q'],
m2q_roi=m2q_roi,
cts_per_slice=cts_per_slice,
max_scale=1.15)
t_end = time.time()
time_res[idx] = t_end-t_start
print('Total Time = ',time_res[idx])
# Compute corrected data
m2q_corr = epos2['m2q']/pointwise_scales
_, ys = bin_dat(m2q_corr,isBinAligned=True,bin_width=0.01,user_roi=[0,250])
opt_res[idx] = chi2(ys)
# ys = ys/np.sum(ys)
# opt_res[idx] = np.sum(np.square(ys))
# opt_res[idx] = test_1d_vec(m2q_corr[(m2q_corr>0.8) & (m2q_corr<80)])
print(opt_res[idx])
print(slicings)
print(opt_res/np.max(opt_res))
print(time_res)
fig = plt.figure(num=666)
fig.clear()
ax = fig.gca()
ax.plot(tmpx,tmpy,'s-',
markersize=8,label='SiO2')
ax.plot(slicings,opt_res/np.max(opt_res),'o-',
markersize=8,label='ceria')
ax.set(xlabel='N (events per chunk)', ylabel='compactness metric (normalized)')
ax.set_xscale('log')
ax.legend()
ax.set_xlim(5,1e5)
ax.set_ylim(0.15, 1.05)
fig.tight_layout()
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
# return index.reshape(-1, k)
def cartesian_product(arrays):
ndim = len(arrays)
return np.stack(np.meshgrid(*arrays), axis=-1).reshape(-1, ndim)
plt.close('all')
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
