def _calculate_probability_curve(self, ages, errors, calculate_limits=False, limits=None):
xmi, xma = None, None
if limits:
xmi, xma = limits
if not xmi and not xma:
xmi, xma = self.graph.get_x_limits()
if xmi == -Inf or xma == Inf:
xmi, xma = self.xmi, self.xma
opt = self.options
if opt.probability_curve_kind == 'kernel':
return kernel_density(ages, errors, xmi, xma, n=N)
else:
if opt.use_asymptotic_limits and calculate_limits:
cfunc = lambda x1, x2: cumulative_probability(ages, errors, x1, x2, n=N)
# bins,probs=cfunc(xmi,xma)
# wa = self.analysis_group.weighted_age
# m, e = nominal_value(wa), std_dev(wa)
# xmi, xma = m - e, m + e
bins, probs, x1, x2 = self._calculate_asymptotic_limits(cfunc,
tol=(opt.asymptotic_height_percent or 10))
self.trait_setq(xmi=x1, xma=x2)
return bins, probs
else:
return cumulative_probability(ages, errors, xmi, xma, n=N)
def _calculate_probability_curve(self,
ages,
errors,
calculate_limits=False,
limits=None):
xmi, xma = None, None
if limits:
xmi, xma = limits
if not xmi and not xma:
xmi, xma = self.graph.get_x_limits()
if xmi == -Inf or xma == Inf:
xmi, xma = self.xmi, self.xma
opt = self.options
if opt.probability_curve_kind == 'kernel':
return kernel_density(ages, errors, xmi, xma, n=N)
else:
if opt.use_asymptotic_limits and calculate_limits:
cfunc = lambda x1, x2: cumulative_probability(
ages, errors, x1, x2, n=N)
# bins,probs=cfunc(xmi,xma)
# wa = self.analysis_group.weighted_age
# m, e = nominal_value(wa), std_dev(wa)
# xmi, xma = m - e, m + e
bins, probs, x1, x2 = self._calculate_asymptotic_limits(
cfunc, tol=(opt.asymptotic_height_percent or 10))
self.trait_setq(xmi=x1, xma=x2)
return bins, probs
else:
return cumulative_probability(ages, errors, xmi, xma, n=N)
def _calculate_probability_curve(self,
ages,
errors,
calculate_limits=False,
limits=None):
xmi, xma = None, None
if limits:
xmi, xma = limits
if not xmi and not xma:
xmi, xma = self.graph.get_x_limits()
if xmi == -Inf or xma == Inf:
xmi, xma = self.xmi, self.xma
opt = self.options
if opt.probability_curve_kind == 'kernel':
return kernel_density(ages, errors, xmi, xma, n=N)
else:
if opt.use_asymptotic_limits and calculate_limits:
def cfunc(x1, x2):
return cumulative_probability(ages, errors, x1, x2, n=N)
bins, probs, x1, x2 = self._calculate_asymptotic_limits(
cfunc, tol=(opt.asymptotic_height_percent or 10))
self.trait_setq(xmi=x1, xma=x2)
return bins, probs
else:
return cumulative_probability(ages, errors, xmi, xma, n=N)
def _plot_relative_probability(self, po, plot, pid):
graph = self.graph
bins, probs = self._calculate_probability_curve(self.xs, self.xes, calculate_limits=True)
ogid = self.group_id
gid = ogid + 1
sgid = ogid * 2
plotkw = self.options.get_plot_dict(ogid)
line, _ = graph.new_series(x=bins, y=probs, plotid=pid, **plotkw)
if self.options.label_all_peaks:
self._add_peak_labels(line, self.xs, self.xes)
graph.set_series_label('Current-{}'.format(gid), series=sgid, plotid=pid)
# add the dashed original line
graph.new_series(x=bins, y=probs,
plotid=pid,
visible=False,
color=line.color,
line_style='dash')
graph.set_series_label('Original-{}'.format(gid), series=sgid + 1, plotid=pid)
self._add_info(graph, plot)
self._add_mean_indicator(graph, line, po, bins, probs, pid)
mi, ma = min(probs), max(probs)
# if not po.has_ylimits():
self._set_y_limits(mi, ma, min_=0, pad='0.025')
d = lambda a, b, c, d: self.update_index_mapper(a, b, c, d)
plot.index_mapper.on_trait_change(d, 'updated')
if self.options.display_inset:
xs = self.xs
n = xs.shape[0]
startidx = 1
if self.group_id > 0:
for ov in plot.overlays:
if isinstance(ov, IdeogramPointsInset):
print 'ideogram point inset', self.group_id, startidx, ov.value.get_bounds()[1] + 1
startidx = max(startidx, ov.value.get_bounds()[1] + 1)
else:
startidx = 1
if self.options.analysis_number_sorting == 'Oldest @Top':
ys = arange(startidx, startidx + n)
else:
ys = arange(startidx + n - 1, startidx - 1, -1)
yma2 = max(ys) + 1
h = self.options.inset_height / 2.0
if self.group_id == 0:
# bgcolor = self.options.get_formatting_value('plot_bgcolor')
bgcolor = self.options.plot_bgcolor
else:
bgcolor = 'transparent'
d = self.options.get_plot_dict(ogid)
o = IdeogramPointsInset(self.xs, ys,
color=d['color'],
outline_color=d['color'],
bgcolor=bgcolor,
width=self.options.inset_width,
height=h,
visible_axes=False,
xerror=ArrayDataSource(self.xes),
location=self.options.inset_location)
# o.x_axis.visible = False
plot.overlays.append(o)
cfunc = lambda x1, x2: cumulative_probability(self.xs, self.xes, x1, x2, n=N)
xs, ys, xmi, xma = self._calculate_asymptotic_limits(cfunc,
# asymptotic_width=10,
tol=self.options.asymptotic_height_percent)
oo = IdeogramInset(xs, ys,
color=d['color'],
bgcolor=bgcolor,
yoffset=h,
visible_axes=self.group_id == 0,
width=self.options.inset_width,
height=self.options.inset_height,
location=self.options.inset_location)
yma = max(ys)
if self.group_id > 0:
for ov in plot.overlays:
if isinstance(ov, IdeogramInset):
mi, ma = ov.get_x_limits()
xmi = min(mi, xmi)
xma = max(ma, xma)
_, ma = ov.get_y_limits()
yma = max(ma, yma)
plot.overlays.append(oo)
for ov in plot.overlays:
if isinstance(ov, IdeogramInset):
ov.set_x_limits(xmi, xma)
ov.set_y_limits(0, yma * 1.1)
elif isinstance(ov, IdeogramPointsInset):
ov.set_x_limits(xmi, xma)
ov.set_y_limits(0, yma2)
def setUp(self):
xs = [10, 10, 10, 20, 20, 20]
es = [0.1, 0.1, 0.1, 0.1, 0.1, 0.1]
self.xs, self.ys = cumulative_probability(xs, es, 5, 25)
def _plot_relative_probability(self, po, plot, pid):
graph = self.graph
bins, probs = self._calculate_probability_curve(self.xs,
self.xes,
calculate_limits=True)
ogid = self.group_id
gid = ogid + 1
sgid = ogid * 2
plotkw = self.options.get_plot_dict(ogid)
line, _ = graph.new_series(x=bins, y=probs, plotid=pid, **plotkw)
if self.options.label_all_peaks:
self._add_peak_labels(line, self.xs, self.xes)
graph.set_series_label('Current-{}'.format(gid),
series=sgid,
plotid=pid)
# add the dashed original line
graph.new_series(x=bins,
y=probs,
plotid=pid,
visible=False,
color=line.color,
line_style='dash')
graph.set_series_label('Original-{}'.format(gid),
series=sgid + 1,
plotid=pid)
self._add_info(graph, plot)
self._add_mean_indicator(graph, line, po, bins, probs, pid)
mi, ma = min(probs), max(probs)
# if not po.has_ylimits():
self._set_y_limits(mi, ma, min_=0, pad='0.025')
d = lambda a, b, c, d: self.update_index_mapper(a, b, c, d)
plot.index_mapper.on_trait_change(d, 'updated')
if self.options.display_inset:
xs = self.xs
n = xs.shape[0]
startidx = 1
if self.group_id > 0:
for ov in plot.overlays:
if isinstance(ov, IdeogramPointsInset):
print 'ideogram point inset', self.group_id, startidx, ov.value.get_bounds(
)[1] + 1
startidx = max(startidx, ov.value.get_bounds()[1] + 1)
else:
startidx = 1
if self.options.analysis_number_sorting == 'Oldest @Top':
ys = arange(startidx, startidx + n)
else:
ys = arange(startidx + n - 1, startidx - 1, -1)
yma2 = max(ys) + 1
h = self.options.inset_height / 2.0
if self.group_id == 0:
# bgcolor = self.options.get_formatting_value('plot_bgcolor')
bgcolor = self.options.plot_bgcolor
else:
bgcolor = 'transparent'
d = self.options.get_plot_dict(ogid)
o = IdeogramPointsInset(self.xs,
ys,
color=d['color'],
outline_color=d['color'],
bgcolor=bgcolor,
width=self.options.inset_width,
height=h,
visible_axes=False,
xerror=ArrayDataSource(self.xes),
location=self.options.inset_location)
# o.x_axis.visible = False
plot.overlays.append(o)
cfunc = lambda x1, x2: cumulative_probability(
self.xs, self.xes, x1, x2, n=N)
xs, ys, xmi, xma = self._calculate_asymptotic_limits(
cfunc,
# asymptotic_width=10,
tol=self.options.asymptotic_height_percent)
oo = IdeogramInset(xs,
ys,
color=d['color'],
bgcolor=bgcolor,
yoffset=h,
visible_axes=self.group_id == 0,
width=self.options.inset_width,
height=self.options.inset_height,
location=self.options.inset_location)
yma = max(ys)
if self.group_id > 0:
for ov in plot.overlays:
if isinstance(ov, IdeogramInset):
mi, ma = ov.get_x_limits()
xmi = min(mi, xmi)
xma = max(ma, xma)
_, ma = ov.get_y_limits()
yma = max(ma, yma)
plot.overlays.append(oo)
for ov in plot.overlays:
if isinstance(ov, IdeogramInset):
ov.set_x_limits(xmi, xma)
ov.set_y_limits(0, yma * 1.1)
elif isinstance(ov, IdeogramPointsInset):
ov.set_x_limits(xmi, xma)
ov.set_y_limits(0, yma2)
def cfunc(x1, x2):
return cumulative_probability(ages, errors, x1, x2, n=N)
def cfunc(x1, x2):
return cumulative_probability(self.xs, self.xes, x1, x2, n=N)
