def setup_graph(self, iso):
self.is_good = None
g = StackedRegressionGraph()
g.new_plot(padding=[60, 10, 10, 40])
if iso:
g.new_series(iso.xs, iso.ys,
fit=iso.fit,
filter_outliers_dict=iso.filter_outliers_dict)
g.set_x_limits(min_=0, max_=iso.xs[-1] * 1.1)
g.set_y_title(iso.name)
g.set_x_title('Time (s)')
g.refresh()
self.graph = g
def setup_graph(self, iso):
self.klass = None
g = StackedRegressionGraph()
g.new_plot(padding=[60, 10, 10, 40])
if iso:
g.new_series(iso.xs,
iso.ys,
fit=iso.fit,
filter_outliers_dict=iso.filter_outliers_dict)
g.set_x_limits(min_=0, max_=iso.xs[-1] * 1.1)
g.set_y_title(iso.name)
g.set_x_title('Time (s)')
g.refresh()
self.graph = g
def handle_show_iso_evo(self, obj):
from pychron.graph.stacked_regression_graph import StackedRegressionGraph
self.manager.load_raw_data(self.model)
g = StackedRegressionGraph()
for ni in obj[::-1]:
iso = next((i for i in self.model.isotopes.itervalues() if i.name == ni.name), None)
g.new_plot(padding=[60, 10, 10, 40])
g.new_series(iso.xs, iso.ys,
fit=iso.fit,
filter_outliers_dict=iso.filter_outliers_dict)
g.set_x_limits(min_=0, max_=iso.xs[-1] * 1.1)
g.set_x_title('Time (s)')
g.set_y_title(iso.name)
g.refresh()
g.window_title = '{} {}'.format(self.name, ','.join([i.name for i in obj]))
self.manager.application.open_view(g)
def handle_show_iso_evo(self, obj):
from pychron.graph.stacked_regression_graph import StackedRegressionGraph
self.manager.load_raw_data(self.model)
g = StackedRegressionGraph()
for ni in obj[::-1]:
iso = next((i for i in self.model.isotopes.itervalues()
if i.name == ni.name), None)
g.new_plot(padding=[60, 10, 10, 40])
g.new_series(iso.xs,
iso.ys,
fit=iso.fit,
filter_outliers_dict=iso.filter_outliers_dict)
g.set_x_limits(min_=0, max_=iso.xs[-1] * 1.1)
g.set_x_title('Time (s)')
g.set_y_title(iso.name)
g.refresh()
g.window_title = '{} {}'.format(self.name,
','.join([i.name for i in obj]))
self.manager.application.open_view(g)
def show_blank_time_series(self):
g = StackedRegressionGraph(window_height=0.75)
isotopes = self.blank_selected[0].isotopes
keys = sort_isotopes([iso.isotope for iso in isotopes], reverse=False)
_mi, _ma = None, None
for k in keys:
iso = next((i for i in isotopes if i.isotope == k))
# print iso.analyses
g.new_plot(padding_right=10)
g.set_time_xaxis()
g.set_y_title(iso.isotope)
g.new_series([self.timestamp], [iso.value],
marker_size=3,
fit=False,
type='scatter',
marker_color='black')
vs = iso.values
if vs:
ts = [vi.timestamp for vi in vs]
_mi = min(ts)
_ma = max(ts)
g.new_series(ts, [vi.value for vi in vs],
yerror=ArrayDataSource([vi.error for vi in vs]),
marker_size=3,
fit=(iso.fit, 'SD'),
type='scatter',
marker_color='red')
if not _mi:
_mi, _ma = self.timestamp - 86400, self.timestamp + 86400
g.set_x_limits(_mi, _ma, pad='0.1')
g.refresh()
g.set_x_title('Time', plotid=0)
g.edit_traits()
def show_blank_time_series(self):
g = StackedRegressionGraph(window_height=0.75)
isotopes = self.blank_selected[0].isotopes
keys = sort_isotopes([iso.isotope for iso in isotopes], reverse=False)
_mi, _ma = None, None
for k in keys:
iso = next((i for i in isotopes if i.isotope == k))
# print iso.analyses
g.new_plot(padding_right=10)
g.set_time_xaxis()
g.set_y_title(iso.isotope)
g.new_series([self.timestamp], [iso.value],
marker_size=3,
fit=False,
type='scatter', marker_color='black')
vs = iso.values
if vs:
ts = [vi.timestamp for vi in vs]
_mi = min(ts)
_ma = max(ts)
g.new_series(ts,
[vi.value for vi in vs],
yerror=ArrayDataSource([vi.error for vi in vs]),
marker_size=3,
fit=(iso.fit, 'SD'),
type='scatter', marker_color='red')
if not _mi:
_mi, _ma = self.timestamp - 86400, self.timestamp + 86400
g.set_x_limits(_mi, _ma, pad='0.1')
g.refresh()
g.set_x_title('Time', plotid=0)
g.edit_traits()
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