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)
# Define which peaks to use for CSR calcs
Ga1p_m2qs = [ed['Ga'].isotopes[69][0], ed['Ga'].isotopes[71][0]]
Ga2p_m2qs = [ed['Ga'].isotopes[69][0] / 2, ed['Ga'].isotopes[71][0] / 2]
Ga1p_idxs = [np.argmin(np.abs(m2q - pk_data['m2q'])) for m2q in Ga1p_m2qs]
Ga2p_idxs = [np.argmin(np.abs(m2q - pk_data['m2q'])) for m2q in Ga2p_m2qs]
# Range the peaks
pk_params = ppd.get_peak_ranges(epos, pk_data['m2q'], peak_height_fraction=0.1)
# Determine the global background
glob_bg_param = ppd.get_glob_bg(epos['m2q'])
# Count the peaks, local bg, and global bg
cts = ppd.do_counting(epos, pk_params, glob_bg_param)
# 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
Ga1p_m2qs = [ed['Ga'].isotopes[69][0], ed['Ga'].isotopes[71][0]]
Ga2p_m2qs = [ed['Ga'].isotopes[69][0] / 2, ed['Ga'].isotopes[71][0] / 2]
Ga1p_idxs = [np.argmin(np.abs(m2q - pk_data['m2q'])) for m2q in Ga1p_m2qs]
Ga2p_idxs = [np.argmin(np.abs(m2q - pk_data['m2q'])) for m2q in Ga2p_m2qs]
# Range the peaks
pk_params = ppd.get_peak_ranges(epos, pk_data['m2q'], peak_height_fraction=0.1)
# Determine the global background
#glob_bg_param = ppd.fit_uncorr_bg(epos['m2q'],fit_roi=[3.5,6.5])
bg_rois = [[3.5, 6.5], [90, 110]]
glob_bg_param = ppd.get_glob_bg(epos['m2q'], rois=bg_rois)
# Count the peaks, local bg, and global bg
cts = ppd.do_counting(epos, pk_params, glob_bg_param)
# 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
isSingle = np.nonzero(epos['ipp'] == 1)
ed = initElements_P3.initElements()
pk_data = np.array( [ (1, ed['Si'].isotopes[28][0]/2),
(1, ed['Si'].isotopes[29][0]/2),
(1, ed['Si'].isotopes[30][0]/2),
(1, ed['Si'].isotopes[28][0]),
(1, ed['Si'].isotopes[29][0]),
(1, ed['Si'].isotopes[30][0])],
dtype=[('Si','i4'),('m2q','f4')] )
pk_params = ppd.get_peak_ranges(epos,pk_data['m2q'],peak_height_fraction=0.01)
glob_bg_param = ppd.get_glob_bg(epos['m2q'], rois=[[24,27]])
cts = ppd.do_counting(epos,pk_params,glob_bg_param)
# 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>1*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
# Define which peaks to use for CSR calcs
Ga1p_m2qs = [ed['Ga'].isotopes[69][0], ed['Ga'].isotopes[71][0]]
Ga2p_m2qs = [ed['Ga'].isotopes[69][0] / 2, ed['Ga'].isotopes[71][0] / 2]
Ga1p_idxs = [np.argmin(np.abs(m2q - pk_data['m2q'])) for m2q in Ga1p_m2qs]
Ga2p_idxs = [np.argmin(np.abs(m2q - pk_data['m2q'])) for m2q in Ga2p_m2qs]
# Range the peaks
pk_params = ppd.get_peak_ranges(epos, pk_data['m2q'], peak_height_fraction=0.1)
# Determine the global background
glob_bg_param = ppd.get_glob_bg(epos['m2q'])
# Count the peaks, local bg, and global bg
cts = ppd.do_counting(epos, pk_params, glob_bg_param)
# 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
