def _find_peaks(self):
if self.graph.plots:
#clear peaks
self.graph.remove_rulers()
data = self.graph.plots[0].data
xs = data.get_data('x0')
ys = data.get_data('y0')
if len(xs) and len(ys):
lookahead = max(1, int(self.min_peak_separation / self.fstep_dac))
mxp, mip = find_peaks(ys, xs,
lookahead=lookahead,
delta=self.delta)
pks = []
isos = self.spectrometer.molecular_weights.keys()
isos = sort_isotopes(isos)
for dac, v in mxp:
if v > self.min_peak_height:
l = self.graph.add_vertical_rule(dac)
pks.append(CalibrationPeak(dac=dac,
isotopes=isos,
ruler=l))
self.calibration_peaks = pks
self._redraw()
def _find_peaks(self):
if self.graph.plots:
# clear peaks
self.graph.remove_rulers()
data = self.graph.plots[0].data
xs = data.get_data('x0')
ys = data.get_data('y0')
if len(xs) and len(ys):
lookahead = max(1,
int(self.min_peak_separation / self.fstep_dac))
mxp, mip = find_peaks(ys,
xs,
lookahead=lookahead,
delta=self.delta)
pks = []
isos = list(self.spectrometer.molecular_weights.keys())
isos = sort_isotopes(isos)
for dac, v in mxp:
if v > self.min_peak_height:
l = self.graph.add_vertical_rule(dac)
pks.append(
CalibrationPeak(dac=dac, isotopes=isos, ruler=l))
self.calibration_peaks = pks
self._redraw()
def _calculate_stats(self, ages, errors, xs, ys):
mswd, valid_mswd, n =self.analysis_group.get_mswd_tuple()
if self.options.mean_calculation_kind == 'kernel':
wm, we = 0, 0
delta = 1
maxs, _mins = find_peaks(ys, xs, delta=delta, lookahead=1)
wm = max(maxs, axis=1)[0]
else:
wm, we = calculate_weighted_mean(ages, errors)
we = self._calc_error(we, mswd)
return wm, we, mswd, valid_mswd
def _calculate_stats(self, ages, errors, xs, ys):
mswd, valid_mswd, n = self.analysis_group.get_mswd_tuple()
if self.options.mean_calculation_kind == 'kernel':
wm, we = 0, 0
delta = 1
maxs, _mins = find_peaks(ys, xs, delta=delta, lookahead=1)
wm = max(maxs, axis=1)[0]
else:
wm, we = calculate_weighted_mean(ages, errors)
we = self._calc_error(we, mswd)
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
delta = 1
maxs, _mins = find_peaks(ys, xs, delta=delta, lookahead=1)
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
delta = 1
maxs, _mins = find_peaks(ys, xs, delta=delta, lookahead=1)
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 _calc_error(self, we, mswd):
# ec = self.options.error_calc_method
# n = self.options.nsigma
# if ec == 'SEM':
# a = 1
# elif ec == 'SEM, but if MSWD>1 use SEM * sqrt(MSWD)':
# a = 1
# if mswd > 1:
# a = mswd ** 0.5
# return we * a * n
# ============= EOF =============================================
# def _add_mean_indicator2(self, g, scatter, bins, probs, pid):
# offset = 0
# percentH = 1 - 0.954 # 2sigma
#
# maxp = max(probs)
# wm, we, mswd, valid_mswd = self._calculate_stats(self.xs, self.xes,
# bins, probs)
# #ym = maxp * percentH + offset
# #set ym in screen space
# #convert to data space
# ogid = self.group_id
# gid = ogid + 1
# sgid = ogid * 3
#
# ym = maxp * 0.1 * gid
#
# s, p = g.new_series(
# [wm], [ym],
# type='scatter',
# marker='circle',
# #selection_marker_size=3,
# marker_size=3,
# #selection_marker='circle',
# #selection_color=scatter.color,
# #selection_outline_color=scatter.color,
# color=scatter.color,
# plotid=0
# )
#
# g.set_series_label('Mean-{}'.format(gid), series=sgid + 2, plotid=pid)
#
# self._add_error_bars(s, [we], 'x', self.options.nsigma)
# # display_mean_indicator = self._get_plot_option(self.options,
# 'display_mean_indicator', default=True)
# if not self.options.display_mean_indicator:
# s.visible = False
#
# label = None
# # display_mean = self._get_plot_option(self.options, 'display_mean_text', default=True)
# if self.options.display_mean:
# text = self._build_label_text(wm, we, mswd, valid_mswd, len(self.xs))
# # font = self._get_plot_option(self.options, 'data_label_font', default='modern 12')
# self._add_data_label(s, text, (wm, ym),
# # font=font
# )
# # add a tool to move the mean age point
# s.tools.append(PointMoveTool(component=s,
# label=label,
# constrain='y'))
def test_max_peak_x2(self):
maxp, minp = find_peaks(self.ys, self.xs, lookahead=1)
self.assertAlmostEqual(maxp[1][0], 20.0, 0)
def test_find_max_peaks(self):
maxp, minp = find_peaks(self.ys, self.xs, lookahead=1)
self.assertEqual(len(maxp), 2)
