def plot_spectrum_gory_detail(epos, user_roi, pk_params, bg_rois,
glob_bg_param, is_peak, fig_idx):
from histogram_functions import bin_dat
import peak_param_determination as ppd
xs, ys = bin_dat(epos['m2q'], user_roi=user_roi, isBinAligned=True)
#ys_sm = ppd.do_smooth_with_gaussian(ys,30)
ys_sm = ppd.moving_average(ys, 30)
glob_bg = ppd.physics_bg(xs, glob_bg_param)
fig = plt.figure(num=fig_idx)
fig.clear()
ax = fig.gca()
ax.plot(xs, ys_sm, label='hist')
ax.plot(xs, glob_bg, label='global bg')
ax.set(xlabel='m/z (Da)', ylabel='counts')
ax.grid()
fig.tight_layout()
fig.canvas.manager.window.raise_()
ax.set_yscale('log')
ax.legend()
for idx, pk_param in enumerate(pk_params):
if is_peak[idx]:
ax.plot(
np.array([1, 1]) * pk_param['pre_rng'],
np.array([0.5, (pk_param['amp'] + pk_param['off'])]), 'k--')
ax.plot(
np.array([1, 1]) * pk_param['post_rng'],
np.array([0.5, (pk_param['amp'] + pk_param['off'])]), 'k--')
ax.plot(
np.array([1, 1]) * pk_param['pre_bg_rng'],
np.array([0.5, (pk_param['amp'] + pk_param['off'])]), 'm--')
ax.plot(
np.array([1, 1]) * pk_param['post_bg_rng'],
np.array([0.5, (pk_param['amp'] + pk_param['off'])]), 'm--')
ax.plot(
np.array([pk_param['pre_bg_rng'], pk_param['post_bg_rng']]),
np.ones(2) * pk_param['loc_bg'], 'g--')
else:
ax.plot(
np.array([1, 1]) * pk_param['x0_mean_shift'],
np.array([0.5, (pk_param['amp'] + pk_param['off'])]), 'r--')
for roi in bg_rois:
xbox = np.array([roi[0], roi[0], roi[1], roi[1]])
ybox = np.array([0.1, np.max(ys_sm) / 10, np.max(ys_sm) / 10, 0.1])
ax.fill(xbox, ybox, 'b', alpha=0.2)
plt.pause(0.1)
return None
def bin_and_smooth_spectrum(epos, user_roi, bin_wid_mDa=30, smooth_wid_mDa=-1):
from histogram_functions import bin_dat
import peak_param_determination as ppd
xs, ys = bin_dat(epos['m2q'],
user_roi=user_roi,
isBinAligned=True,
bin_width=bin_wid_mDa / 1000.0)
if smooth_wid_mDa > 0:
ys_sm = ppd.moving_average(ys, smooth_wid) * smooth_wid
else:
ys_sm = ys
return (xs, ys_sm)
print('Total Ranged Global Background Ions: ' + str(np.sum(cts['global_bg'])))
print('Total Ions: ' + str(epos.size))
print('Overall CSR (no bg) : ' +
str(np.sum(cts['total'][Ga2p_idxs]) / np.sum(cts['total'][Ga1p_idxs])))
print('Overall CSR (local bg) : ' + str(
(np.sum(cts['total'][Ga2p_idxs]) - np.sum(cts['local_bg'][Ga2p_idxs])) /
(np.sum(cts['total'][Ga1p_idxs]) - np.sum(cts['local_bg'][Ga1p_idxs]))))
print('Overall CSR (global bg): ' + str(
(np.sum(cts['total'][Ga2p_idxs]) - np.sum(cts['global_bg'][Ga2p_idxs])) /
(np.sum(cts['total'][Ga1p_idxs]) - np.sum(cts['global_bg'][Ga1p_idxs]))))
# Plot all the things
xs, ys = bin_dat(epos['m2q'], user_roi=[0.5, 100], isBinAligned=True)
#ys_sm = ppd.do_smooth_with_gaussian(ys,10)
ys_sm = ppd.moving_average(ys, 10)
glob_bg = ppd.physics_bg(xs, glob_bg_param)
fig = plt.figure(num=100)
fig.clear()
ax = fig.gca()
ax.plot(xs, ys_sm, label='hist')
ax.plot(xs, glob_bg, label='global bg')
ax.set(xlabel='m/z (Da)', ylabel='counts')
ax.grid()
fig.tight_layout()
fig.canvas.manager.window.raise_()
ax.set_yscale('log')
#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()
print('Total Time = ', t_end - t_start)
# Compute corrected data
m2q_corr = epos['m2q'] / pointwise_scales
wall_time = np.cumsum(epos['pslep']) / 10000.0
wall_time = np.cumsum(epos['pslep']) / 500000.0
dt = ppd.moving_average(np.diff(wall_time), n=512)
ra = 1 / dt
pks = [14, 16, 32.2, 44.3, 60.3]
#pks = [14,16,32,44,60]
wid = 1
mask = None
for pk in pks:
if mask is None:
mask = np.abs(m2q_corr - pk) <= wid / 2
else:
mask = mask | (np.abs(m2q_corr - pk) <= wid / 2)
fig = plt.figure(num=111)
ax = fig.gca()
