#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)
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)
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
from voltage_and_bowl import do_voltage_and_bowl
# Read in template spectrum
fn = r"Q:\NIST_Projects\EUV_APT_IMS\BWC\180821_GaN_A71\R20_07094-v03.epos"
ref_epos = apt_fileio.read_epos_numpy(fn)
ref_epos = ref_epos[0:ref_epos.size // 2]
# Read in data
fn = r"Q:\NIST_Projects\EUV_APT_IMS\BWC\180821_GaN_A71\R20_07094-v03.epos"
epos = apt_fileio.read_epos_numpy(fn)
epos = epos[epos.size // 2:-1]
# Plot TOF vs wall_time and show the current ROI selection
roi_event_idxs = np.arange(1000, epos.size - 1000)
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)
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
#N,x_edges,ly_edges = create_histogram(lys_corr,y_roi=m2q_roi,cts_per_slice=cts_per_slice)
## Plot histogram
#fig = plt.figure(figsize=(7,4.5))
#plt.imshow(np.log1p(np.transpose(N)), aspect='auto', interpolation='none',
# extent=extents(x_edges) + extents(ly_edges), origin='lower')
fig = plt.figure(figsize=(7,4.5))
ax = fig.gca()
#ax.plot(piecewise_scales,'-')
m2q_corr = epos['m2q']/pointwise_scales
ax = plotting_stuff.plot_TOF_vs_time(epos['m2q'],epos,111,clearFigure=True,user_ylim=[0,75])
ax.set_ylabel('m/z (Da)')
import pandas as pd
fig = plt.figure(figsize=(7,4.5))
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)'])
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 = 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")
# Only apply bowl correction
tof_bcorr = voltage_and_bowl.mod_geometric_bowl_correction(p_bowl,epos['tof'],epos['x_det'],epos['y_det'])
ax = plotting_stuff.plot_TOF_vs_time(tof_bcorr,epos,2)
# Plot histogram for sio2
fig = plt.figure(figsize=(2*3.14961,2*3.14961),num=45321,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)
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')
ax1.set(ylabel='flight time (ns)')
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"Q:\NIST_Projects\EUV_APT_IMS\BWC\R45_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"
epos = apt_fileio.read_epos_numpy(fn)
plotting_stuff.plot_TOF_vs_time(epos['m2q'],
epos,
1,
clearFigure=True,
user_ylim=[0, 150])
# Create histogram
cts_per_histo = 2**10
nx = int(epos.size / cts_per_histo)
xs = np.arange(epos.size)
y_roi = np.log10(np.array([0.5, 200]))
ny = 2**12
ys = np.log10(epos['m2q'])
N, x_edges, y_edges = np.histogram2d(xs,
ys,
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'])
ax = plotting_stuff.plot_TOF_vs_time(tof_bcorr, epos, 2)
# Plot histogram for steel
fig = plt.figure(figsize=(2 * 3.14961, 2 * 3.14961), num=321, 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**10,
delta_y=0.5)
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,
idxs = np.where((epos['tof'] > roi[0]) & (epos['tof'] < roi[1]))[0]
fig = plt.figure(num=10)
fig.clf()
ax = fig.gca()
ax.scatter(epos['x_det'][idxs],
epos['y_det'][idxs],
alpha=0.1,
s=0.1,
c=epos['v_dc'][idxs] / epos['v_dc'].max())
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,