def _add_peak_labels(self, line):
opt = self.options
xs = line.index.get_data()
ys = line.value.get_data()
if xs.shape[0]:
xp, yp = fast_find_peaks(ys, xs)
self.peaks = xp
if opt.label_all_peaks:
border = opt.peak_label_border
border_color = opt.peak_label_border_color
bgcolor = opt.peak_label_bgcolor if opt.peak_label_bgcolor_enabled else 'transparent'
for xi, yi in zip(xp, yp):
label = PeakLabel(line,
data_point=(xi, yi),
label_text=floatfmt(
xi, n=opt.peak_label_sigfigs),
border_visible=bool(border),
border_width=border,
border_color=border_color,
bgcolor=bgcolor)
line.overlays.append(label)
def _add_peak_labels(self, line, ages, errors):
xs = line.index.get_data()
ys = line.value.get_data()
xp, yp = fast_find_peaks(ys, xs)
for xi, yi in zip(xp, yp):
label = PeakLabel(line,
data_point=(xi, yi),
label_text=floatfmt(xi, n=3),
border_visible=False,
marker_visible=False,
show_label_coords=False)
line.overlays.append(label)
def _add_peak_labels(self, line, ages, errors):
xs = line.index.get_data()
ys = line.value.get_data()
xp, yp = fast_find_peaks(ys, xs)
for xi, yi in zip(xp, yp):
label = PeakLabel(line,
data_point=(xi, yi),
label_text=floatfmt(xi, n=3),
border_visible=False,
marker_visible=False,
show_label_coords=False)
line.overlays.append(label)
def _calculate_stats(self, xs, ys):
ag = self.analysis_group
ag.attribute = self.options.index_attr
ag.weighted_age_error_kind = self.options.error_calc_method
ag.include_j_error_in_mean = self.options.include_j_error_in_mean
ag.include_j_error_in_individual_analyses = self.options.include_j_error
mswd, valid_mswd, n = self.analysis_group.get_mswd_tuple()
if self.options.mean_calculation_kind == 'kernel':
wm, we = 0, 0
peak_xs, peak_ys = fast_find_peaks(ys, xs)
wm = peak_xs[0]
# wm = np_max(maxs, axis=1)[0]
else:
wage = self.analysis_group.weighted_age
wm, we = wage.nominal_value, wage.std_dev
return wm, we, mswd, valid_mswd
def _calculate_stats(self, xs, ys):
ag = self.analysis_group
ag.attribute = self.options.index_attr
ag.weighted_age_error_kind = self.options.error_calc_method
ag.include_j_error_in_mean = self.options.include_j_error_in_mean
ag.include_j_error_in_individual_analyses = self.options.include_j_error
mswd, valid_mswd, n = self.analysis_group.get_mswd_tuple()
if self.options.mean_calculation_kind == 'kernel':
wm, we = 0, 0
peak_xs, peak_ys = fast_find_peaks(ys, xs)
wm = peak_xs[0]
# wm = np_max(maxs, axis=1)[0]
else:
wage = self.analysis_group.weighted_age
wm, we = wage.nominal_value, wage.std_dev
return wm, we, mswd, valid_mswd
def _calculate_stats(self, xs, ys):
ag = self.analysis_group
options = self.options
ag.attribute = options.index_attr
ag.weighted_age_error_kind = options.error_calc_method
ag.set_j_error(options.include_j_position_error,
options.include_j_error_in_mean,
dirty=True)
mswd, valid_mswd, n = self.analysis_group.get_mswd_tuple()
if options.mean_calculation_kind == 'kernel':
wm, we = 0, 0
peak_xs, peak_ys = fast_find_peaks(ys, xs)
wm = peak_xs[0]
# wm = np_max(maxs, axis=1)[0]
else:
wage = self.analysis_group.weighted_age
wm, we = nominal_value(wage), std_dev(wage)
return wm, we, mswd, valid_mswd, n
