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
ppd.pretty_print_compositions(compositions,pk_data)
# Plot the full spectrum
xs, ys_sm = GaN_fun.bin_and_smooth_spectrum(epos=epos,
user_roi=[0,150],
bin_wid_mDa=30,
smooth_wid_mDa=-1)
fig = plt.figure(num=1)
fig.set_size_inches(w=6.69, h=3)
fig.clear()
ax = fig.gca()
ax.plot(xs, ys_sm, lw=1, label='full spec',color='k')
glob_bg = ppd.physics_bg(xs,glob_bg_param)
ax.plot(xs, glob_bg, lw=1, label='bg', alpha=1,color='r')
ax.set_xlim(0,100)
ax.set_ylim(1e1,5e4)
ax.grid(b=True)
ax.set(xlabel='m/z', ylabel='counts')
ax.set_yscale('log')
ax.legend()
fig.tight_layout()
fig.savefig('GaN_full_spectrum.pdf')
fig.savefig('GaN_full_spectrum.jpg', dpi=300)
ppd.pretty_print_compositions(compositions, pk_data)
# Plot the full spectrum
xs, ys_sm = GaN_fun.bin_and_smooth_spectrum(epos=epos,
user_roi=[0, 150],
bin_wid_mDa=100,
smooth_wid_mDa=-1)
fig = plt.figure(num=1)
fig.set_size_inches(w=3.5, h=2)
fig.clear()
ax = fig.gca()
ax.plot(xs, ys_sm, lw=1, label='full spec', color='k')
glob_bg = ppd.physics_bg(xs, glob_bg_param)
ax.plot(xs, glob_bg, lw=1, label='bg', alpha=1, color='r')
ax.set_xlim(0, 80)
ax.set_ylim(1e1, 5e4)
ax.grid(b=True)
ax.set(xlabel='m/z', ylabel='counts')
ax.set_yscale('log')
ax.legend()
fig.tight_layout()
fig.savefig('AlGaN_full_spectrum.pdf')
fig.savefig('AlGaN_full_spectrum.jpg', dpi=300)
#### END BASIC ANALYSIS ####
def CSR_plot(run_number, comp_csr_fig_idx, spec_fig_num, multi_fig_num):
# Read in data
epos = GaN_fun.load_epos(run_number=run_number,
epos_trim=[5000, 5000],
fig_idx=plt.figure().number)
pk_data = GaN_type_peak_assignments.GaN()
bg_rois = [[0.4, 0.9]]
pk_params, glob_bg_param, Ga1p_idxs, Ga2p_idxs = GaN_fun.fit_spectrum(
epos=epos, pk_data=pk_data, peak_height_fraction=0.1, bg_rois=bg_rois)
cts, compositions, is_peak = GaN_fun.count_and_get_compositions(
epos=epos,
pk_data=pk_data,
pk_params=pk_params,
glob_bg_param=glob_bg_param,
bg_frac=1,
noise_threshhold=2)
ppd.pretty_print_compositions(compositions, pk_data)
# Plot the full spectrum
xs, ys_sm = GaN_fun.bin_and_smooth_spectrum(epos=epos,
user_roi=[0, 150],
bin_wid_mDa=30,
smooth_wid_mDa=-1)
fig = plt.figure(num=spec_fig_num)
ax = fig.gca()
scale_factor = 1E4**next(counter)
ax.plot(xs, scale_factor * (ys_sm + 1), lw=1, label=run_number)
glob_bg = ppd.physics_bg(xs, glob_bg_param)
ax.plot(xs,
scale_factor * (glob_bg + 1),
lw=1,
label=run_number + ' (bg)',
alpha=1)
# Plot some detector hitmaps
GaN_fun.create_det_hit_plots(epos,
pk_data,
pk_params,
fig_idx=plt.figure().number)
# Find the pole center and show it
ax = plt.gcf().get_axes()[0]
m2q_roi = [3, 100]
sel_idxs = np.where((epos['m2q'] > m2q_roi[0])
& (epos['m2q'] < m2q_roi[1]))
xc, yc = GaN_fun.mean_shift(epos['x_det'][sel_idxs],
epos['y_det'][sel_idxs])
a_circle = plt.Circle((xc[-1], yc[-1]),
10,
facecolor='none',
edgecolor='k',
lw=2,
ls='-')
ax.add_artist(a_circle)
# Chop data up by Radius AND time
time_chunk_centers, r_centers, idxs_list = GaN_fun.chop_data_rad_and_time(
epos, [xc[-1], yc[-1]], time_chunk_size=2**16, N_ann_chunks=3)
N_time_chunks = time_chunk_centers.size
N_ann_chunks = r_centers.size
# Charge state ratios
csr = np.full([N_time_chunks, N_ann_chunks], -1.0)
# Gallium atomic % and standard deviation (no bg)
Ga_comp = np.full([N_time_chunks, N_ann_chunks], -1.0)
Ga_comp_std = np.full([N_time_chunks, N_ann_chunks], -1.0)
# Gallium atomic % and standard deviation (global bg)
Ga_comp_glob = np.full([N_time_chunks, N_ann_chunks], -1.0)
Ga_comp_std_glob = np.full([N_time_chunks, N_ann_chunks], -1.0)
# Multiplicy
hit_multiplicity = np.full([N_time_chunks, N_ann_chunks], -1.0)
tot_cts = np.full([N_time_chunks, N_ann_chunks], -1.0)
keys = list(pk_data.dtype.fields.keys())
keys.remove('m2q')
Ga_idx = keys.index('Ga')
for t_idx in np.arange(N_time_chunks):
for a_idx in np.arange(N_ann_chunks):
idxs = idxs_list[t_idx][a_idx]
sub_epos = epos[idxs]
bg_frac_roi = [120, 150]
bg_frac = np.sum((sub_epos['m2q']>bg_frac_roi[0]) & (sub_epos['m2q']<bg_frac_roi[1])) \
/ np.sum((epos['m2q']>bg_frac_roi[0]) & (epos['m2q']<bg_frac_roi[1]))
cts, compositions, is_peak = GaN_fun.count_and_get_compositions(
epos=sub_epos,
pk_data=pk_data,
pk_params=pk_params,
glob_bg_param=glob_bg_param,
bg_frac=bg_frac,
noise_threshhold=2)
csr[t_idx, a_idx] = np.sum(cts['total'][Ga2p_idxs] -
cts['global_bg'][Ga2p_idxs]) / np.sum(
cts['total'][Ga1p_idxs] -
cts['global_bg'][Ga1p_idxs])
Ga_comp[t_idx, a_idx] = ppd.do_composition(pk_data,
cts)[0][0][Ga_idx]
Ga_comp_std[t_idx, a_idx] = ppd.do_composition(pk_data,
cts)[0][1][Ga_idx]
Ga_comp_glob[t_idx, a_idx] = ppd.do_composition(pk_data,
cts)[2][0][Ga_idx]
Ga_comp_std_glob[t_idx,
a_idx] = ppd.do_composition(pk_data,
cts)[2][1][Ga_idx]
low_mz_idxs = np.where(sub_epos['m2q'] < 100)[0]
hit_multiplicity[t_idx, a_idx] = np.sum(
sub_epos[low_mz_idxs]['ipp'] != 1) / sub_epos[low_mz_idxs].size
compositions = ppd.do_composition(pk_data, cts)
# ppd.pretty_print_compositions(compositions,pk_data)
# print('COUNTS IN CHUNK: ',np.sum(cts['total']))
tot_cts[t_idx, a_idx] = np.sum(cts['total'])
fig = plt.figure(num=comp_csr_fig_idx)
#fig.clear()
ax = fig.gca()
#ax.errorbar(csr.flatten(),Ga_comp.flatten(),yerr=Ga_comp_std.flatten(),fmt='.',capsize=4,label='det based (radial)')
ax.errorbar(csr.flatten(),
Ga_comp_glob.flatten(),
yerr=Ga_comp_std_glob.flatten(),
fmt='.',
capsize=4,
label=run_number)
fig = plt.figure(num=multi_fig_num)
#fig.clear()
ax = fig.gca()
ax.plot(csr.flatten(), hit_multiplicity.flatten(), '.', label=run_number)
# Write data to console for copy/pasting
print('CSR', '\t', 'Ga comp (glob bg)', '\t', 'Ga comp std (glob bg)')
for i in np.arange(csr.size):
print(csr.flatten()[i], '\t',
Ga_comp_glob.flatten()[i], '\t',
Ga_comp_std_glob.flatten()[i])
return fig
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'],
np.array([0.5, (pk_param['amp'] + pk_param['off'])]), 'r--')
ax.plot(
np.array([1, 1]) * pk_param['post_bg'],
np.array([0.5, (pk_param['amp'] + pk_param['off'])]), 'r--')
ax.set_yscale('log')
ax.set(ylim=[0.1, 1000])
glob_bg_param = ppd.fit_uncorr_bg(m2q_corr)
glob_bg = ppd.physics_bg(xs_full_1mDa, glob_bg_param)
#ax.clear()
#ax.plot(xs_full_1mDa,ys_full_5mDa_sm,label='5 mDa smooth')
ax.plot(xs_full_1mDa, ys_full_fwd_sm, label='fwd')
ax.plot(xs_full_1mDa, ys_full_bwd_sm, label='bwd')
ax.plot(xs_full_1mDa, glob_bg, label='glob_bg')
ax.legend()
stoich = np.c_[pk_data['N_at_ct'][pk_data['is_anal_pk'], None],
pk_data['Ga_at_ct'][pk_data['is_anal_pk'], None]]
cts = np.zeros((np.sum(pk_data['is_anal_pk']), 3)) - 1
for idx, pk_param in enumerate(pk_params):
pre_pk_rng = [pk_param['pre_bg'] - 0.15, pk_param['pre_bg'] - 0.05]