def count_and_get_compositions(epos,
pk_data,
pk_params,
glob_bg_param,
bg_frac=1,
noise_threshhold=2):
import peak_param_determination as ppd
# Count the peaks, local bg, and global bg
cts = ppd.do_counting(epos, pk_params, glob_bg_param)
cts['local_bg'] = cts['local_bg'] * bg_frac
cts['global_bg'] = cts['global_bg'] * bg_frac
# Test for peak S/N and throw out craptastic peaks
B = np.max(np.c_[cts['local_bg'][:, None], cts['global_bg'][:, None]],
1)[:, None]
T = cts['total'][:, None]
S = T - B
std_S = np.sqrt(T + B)
# Make up a threshold for peak detection... for the most part this won't matter
# since weak peaks don't contribute to stoichiometry much... except for Mg!
is_peak = S > (noise_threshhold * np.sqrt(2 * B))
for idx, ct in enumerate(cts):
if not is_peak[idx]:
for i in np.arange(len(ct)):
ct[i] = 0
# Calculate compositions
compositions = ppd.do_composition(pk_data, cts)
return (cts, compositions, is_peak)
# Test for peak S/N and throw out craptastic peaks
B = np.max(np.c_[cts['local_bg'][:, None], cts['global_bg'][:, None]], 1)[:,
None]
T = cts['total'][:, None]
S = T - B
std_S = np.sqrt(T + B)
# Make up a threshold for peak detection... for the most part this won't matter
# since weak peaks don't contribute to stoichiometry much... except for Mg!
is_peak = S > 2 * np.sqrt(2 * B)
for idx, ct in enumerate(cts):
if not is_peak[idx]:
for i in np.arange(len(ct)):
ct[i] = 0
# Calculate compositions
compositions = ppd.do_composition(pk_data, cts)
ppd.pretty_print_compositions(compositions, pk_data)
print('Total Ranged Ions: ' + str(np.sum(cts['total'])))
print('Total Ranged Local Background Ions: ' + str(np.sum(cts['local_bg'])))
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]))))
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
# tot_cts_roi[t_idx,a_idx] = np.sum(ys_sm)
tot_cts_roi[t_idx, a_idx] = sub_epos.size
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]
N_comp_glob[t_idx, a_idx] = ppd.do_composition(pk_data,
cts)[2][0][N_idx]
N_comp_std_glob[t_idx, a_idx] = ppd.do_composition(pk_data,
cts)[2][1][N_idx]
Al_comp_glob[t_idx, a_idx] = ppd.do_composition(pk_data,
cts)[2][0][Al_idx]
# Test for peak S/N and throw out craptastic peaks
B = np.max(np.c_[cts['local_bg'][:, None], cts['global_bg'][:, None]], 1)[:,
None]
T = cts['total'][:, None]
S = T - B
std_S = np.sqrt(T + B)
# Make up a threshold for peak detection... for the most part this won't matter
# since weak peaks don't contribute to stoichiometry much... except for Mg!
is_peak = S > 2 * np.sqrt(2 * B)
for idx, ct in enumerate(cts):
if not is_peak[idx]:
for i in np.arange(len(ct)):
ct[i] = 0
# Calculate compositions
compositions = ppd.do_composition(pk_data, cts)
ppd.pretty_print_compositions(compositions, pk_data)
print('Total Ranged Ions: ' + str(np.sum(cts['total'])))
print('Total Ranged Local Background Ions: ' + str(np.sum(cts['local_bg'])))
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]))))
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[z_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[z_idx] = ppd.do_composition(pk_data, cts)[0][0][Ga_idx]
Ga_comp_std[z_idx] = ppd.do_composition(pk_data, cts)[0][1][Ga_idx]
N_comp[z_idx] = ppd.do_composition(pk_data, cts)[0][0][N_idx]
N_comp_std[z_idx] = ppd.do_composition(pk_data, cts)[0][1][N_idx]
Ga_comp_glob[z_idx] = ppd.do_composition(pk_data, cts)[2][0][Ga_idx]
Ga_comp_std_glob[z_idx] = ppd.do_composition(pk_data, cts)[2][1][Ga_idx]
N_comp_glob[z_idx] = ppd.do_composition(pk_data, cts)[2][0][N_idx]
N_comp_std_glob[z_idx] = ppd.do_composition(pk_data, cts)[2][1][N_idx]
low_mz_idxs = np.where(sub_epos['m2q'] < 100)[0]
hit_multiplicity[z_idx] = np.sum(
sub_epos[low_mz_idxs]['ipp'] != 1) / sub_epos[low_mz_idxs].size