def _make_graph(self, lumens, duration):
g = StackedGraph(container_dict=dict(stack_order="top_to_bottom"))
g.new_plot(ytitle="Output (W)")
g.new_series()
g.new_plot(ytitle="Residual")
g.new_series(plotid=1)
g.new_plot(ytitle="Lumens", xtitle="time (s)")
g.new_series(plotid=2)
g.add_horizontal_rule(lumens, plotid=2)
g.set_x_limits(0, duration * 1.1)
return g
def _make_graph(self, lumens, duration):
g = StackedGraph(container_dict=dict(stack_order='top_to_bottom'))
g.new_plot(ytitle='Output (W)')
g.new_series()
g.new_plot(ytitle='Residual')
g.new_series(plotid=1)
g.new_plot(ytitle='Lumens', xtitle='time (s)')
g.new_series(plotid=2)
g.add_horizontal_rule(lumens, plotid=2)
g.set_x_limits(0, duration * 1.1)
return g
def setup_graph(self, an):
container = HPlotContainer()
container_dict = {'spacing': 5, 'stack_order': 'top_to_bottom'}
sg = StackedGraph(container_dict=container_dict)
sg.plotcontainer.spacing = 5
sg.plotcontainer.stack_order = 'top_to_bottom'
isos = an.sorted_values(reverse=False)
add_sniff = True
sisos = [iso for iso in isos if iso.sniff.offset_xs.shape[0]]
for i, iso in enumerate(sisos):
sniff = iso.sniff
sg.new_plot(ytitle=iso.name,
xtitle='Time (s)',
title='Equilibration')
sg.new_series(sniff.offset_xs,
sniff.ys,
marker='circle',
type='scatter')
sg.set_y_limits(pad='0.1', plotid=i)
sg.set_x_limits(min_=0, max_=max(sniff.offset_xs) * 1.05, plotid=i)
bg = StackedRegressionGraph(container_dict=container_dict)
add_baseline = True
ig = StackedRegressionGraph(container_dict=container_dict)
iisos = [iso for iso in isos if iso.offset_xs.shape[0]]
baselines = []
for i, iso in enumerate(iisos):
if iso.baseline.offset_xs.shape[0]:
baselines.append(iso.baseline)
ig.new_plot(ytitle='{}({})'.format(iso.name, iso.detector),
xtitle='Time (s)',
title='Isotope')
ig.new_series(iso.offset_xs,
iso.ys,
display_filter_bounds=True,
filter_outliers_dict=iso.filter_outliers_dict,
color='blue',
type='scatter',
fit=iso.efit)
ig.set_regressor(iso.regressor, i)
ig.set_y_limits(pad='0.1', plotid=i)
ig.set_x_limits(min_=0, max_=max(iso.offset_xs) * 1.05, plotid=i)
ig.refresh()
# bisos = [iso for iso in isos if iso.baseline.offset_xs.shape[0]]
# plotted_baselines = []
# for i, iso in enumerate(bisos):
# baseline = iso.baseline
# if baseline.detector in plotted_baselines:
# continue
# plotted_baselines.append(baseline.detector)
# for iso in bisos:
for i, baseline in enumerate(baselines):
bg.new_plot(ytitle=baseline.detector,
xtitle='Time (s)',
title='Baseline')
bg.new_series(baseline.offset_xs,
baseline.ys,
filter_outliers_dict=baseline.filter_outliers_dict,
display_filter_bounds=True,
color='red',
type='scatter',
fit=baseline.efit)
bg.set_regressor(baseline.regressor, i)
bg.set_y_limits(pad='0.1', plotid=i)
bg.set_x_limits(pad='0.025', plotid=i)
bg.refresh()
container.add(sg.plotcontainer)
container.add(ig.plotcontainer)
container.add(bg.plotcontainer)
self.container = container
