def setup_graph(self, an):
container = HPlotContainer()
sg = StackedGraph()
sg.plotcontainer.spacing = 5
sg.plotcontainer.stack_order = 'top_to_bottom'
isos = an.sorted_values(reverse=False)
for i, iso in enumerate(isos):
sniff = iso.sniff
sg.new_plot(ytitle=iso.name, xtitle='Time (s)', title='Equilibration')
if sniff.xs.shape[0]:
sg.new_series(sniff.offset_xs, sniff.ys, marker='circle', type='scatter')
sg.set_y_limits(pad='0.1', plotid=i)
bg = StackedRegressionGraph()
bg.plotcontainer.spacing = 5
bg.plotcontainer.stack_order = 'top_to_bottom'
for i, iso in enumerate(isos):
baseline = iso.baseline
bg.new_plot(ytitle=baseline.detector, xtitle='Time (s)', title='Baseline')
if baseline.xs.shape[0]:
bg.new_series(baseline.offset_xs, baseline.ys,
color='red', type='scatter', fit=baseline.fit)
bg.set_y_limits(pad='0.1', plotid=i)
ig = StackedRegressionGraph()
ig.plotcontainer.spacing = 5
ig.plotcontainer.stack_order = 'top_to_bottom'
for i, iso in enumerate(isos):
ig.new_plot(ytitle=iso.name, xtitle='Time (s)', title='Isotope')
if iso.xs.shape[0]:
ig.new_series(iso.offset_xs, iso.ys,
color='blue', type='scatter', fit=iso.fit)
ig.set_y_limits(pad='0.1', plotid=i)
container.add(sg.plotcontainer)
container.add(ig.plotcontainer)
container.add(bg.plotcontainer)
self.container = container
def show_evolutions_factory(record_id, isotopes, show_evo=True, show_equilibration=False, show_baseline=False):
if WINDOW_CNT > 20:
information(None, 'You have too many Isotope Evolution windows open. Close some before proceeding')
return
from pychron.graph.stacked_regression_graph import StackedRegressionGraph
if not show_evo:
xmi = Inf
xma = -Inf
else:
xmi, xma = 0, -Inf
g = StackedRegressionGraph(resizable=True)
g.plotcontainer.spacing = 10
g.window_height = min(275 * len(isotopes), 800)
g.window_x = OX + XOFFSET * WINDOW_CNT
g.window_y = OY + YOFFSET * WINDOW_CNT
isotopes = sort_isotopes(isotopes, reverse=False, key=lambda x: x.name)
for i, iso in enumerate(isotopes):
ymi, yma = Inf, -Inf
p = g.new_plot(padding=[80, 10, 10, 40])
g.add_limit_tool(p, 'x')
g.add_limit_tool(p, 'y')
g.add_axis_tool(p, p.x_axis)
g.add_axis_tool(p, p.y_axis)
p.y_axis.title_spacing = 50
if show_equilibration:
sniff = iso.sniff
g.new_series(sniff.xs, sniff.ys,
type='scatter',
fit=None,
color='red')
ymi, yma = min_max(ymi, yma, sniff.ys)
xmi, xma = min_max(xmi, xma, sniff.xs)
if show_evo:
g.new_series(iso.xs, iso.ys,
fit=iso.fit,
filter_outliers_dict=iso.filter_outliers_dict,
color='black')
ymi, yma = min_max(ymi, yma, iso.ys)
xmi, xma = min_max(xmi, xma, iso.xs)
if show_baseline:
baseline = iso.baseline
g.new_series(baseline.xs, baseline.ys,
type='scatter', fit=baseline.fit,
filter_outliers_dict=baseline.filter_outliers_dict,
color='blue')
ymi, yma = min_max(ymi, yma, baseline.ys)
xmi, xma = min_max(xmi, xma, baseline.xs)
g.set_x_limits(min_=xmi, max_=xma, pad='0.025,0.05')
g.set_y_limits(min_=ymi, max_=yma, pad='0.05', plotid=i)
g.set_x_title('Time (s)', plotid=i)
g.set_y_title('{} (fA)'.format(iso.name), plotid=i)
g.refresh()
g.window_title = '{} {}'.format(record_id, ','.join([i.name for i in reversed(isotopes)]))
return g
def show_evolutions_factory(record_id,
isotopes,
show_evo=True,
show_equilibration=False,
show_baseline=False):
if WINDOW_CNT > 20:
information(
None,
'You have too many Isotope Evolution windows open. Close some before proceeding'
)
return
from pychron.graph.stacked_regression_graph import StackedRegressionGraph
if not show_evo:
xmi = Inf
xma = -Inf
else:
xmi, xma = 0, -Inf
g = StackedRegressionGraph(resizable=True)
g.plotcontainer.spacing = 10
g.window_height = min(275 * len(isotopes), 800)
g.window_x = OX + XOFFSET * WINDOW_CNT
g.window_y = OY + YOFFSET * WINDOW_CNT
isotopes = sort_isotopes(isotopes, reverse=False, key=lambda x: x.name)
for i, iso in enumerate(isotopes):
ymi, yma = Inf, -Inf
p = g.new_plot(padding=[80, 10, 10, 40])
g.add_limit_tool(p, 'x')
g.add_limit_tool(p, 'y')
g.add_axis_tool(p, p.x_axis)
g.add_axis_tool(p, p.y_axis)
p.y_axis.title_spacing = 50
if show_equilibration:
sniff = iso.sniff
g.new_series(sniff.xs,
sniff.ys,
type='scatter',
fit=None,
color='red')
ymi, yma = min_max(ymi, yma, sniff.ys)
xmi, xma = min_max(xmi, xma, sniff.xs)
if show_evo:
g.new_series(iso.xs,
iso.ys,
fit=iso.fit,
filter_outliers_dict=iso.filter_outliers_dict,
color='black')
ymi, yma = min_max(ymi, yma, iso.ys)
xmi, xma = min_max(xmi, xma, iso.xs)
if show_baseline:
baseline = iso.baseline
g.new_series(baseline.xs,
baseline.ys,
type='scatter',
fit=baseline.fit,
filter_outliers_dict=baseline.filter_outliers_dict,
color='blue')
ymi, yma = min_max(ymi, yma, baseline.ys)
xmi, xma = min_max(xmi, xma, baseline.xs)
g.set_x_limits(min_=xmi, max_=xma, pad='0.025,0.05')
g.set_y_limits(min_=ymi, max_=yma, pad='0.05', plotid=i)
g.set_x_title('Time (s)', plotid=i)
g.set_y_title('{} (fA)'.format(iso.name), plotid=i)
g.refresh()
g.window_title = '{} {}'.format(
record_id, ','.join([i.name for i in reversed(isotopes)]))
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
