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