def _graph_factory(self, **kw):
from pychron.graph.stacked_regression_graph import StackedRegressionGraph
g = StackedRegressionGraph(container_dict=dict(
stack_order='top_to_bottom', use_backbuffer=True, spacing=5),
**kw)
return g
def _graph_factory(self):
return StackedRegressionGraph(container_dict=dict(padding=5, bgcolor='gray',
stack_order=self.stack_order,
spacing=5),
bind_index=False,
use_data_tool=False,
padding_bottom=35)
def setup_graph(self):
cd = dict(padding=20, spacing=5, stack_order='top_to_bottom')
g = StackedRegressionGraph(container_dict=cd)
self.graph = g
self.refresh_plot()
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 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_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_plot(self, data):
cd = dict(padding=5, stack_order='top_to_bottom')
csnames = self.column_names
xmin = np.Inf
xmax = -np.Inf
if self.as_series:
g = RegressionGraph(container_dict=cd)
p = g.new_plot(padding=[50, 5, 5, 50],
xtitle=''
)
p.value_range.tight_bounds = False
p.value_range.margin = 0.1
else:
g = StackedRegressionGraph(container_dict=cd)
regressable = False
# metadata = None
for i, csi in enumerate(self.data_selectors):
if not self.as_series:
p = g.new_plot(padding=[50, 5, 5, 50])
p.value_range.tight_bounds = False
p.value_range.margin = 0.1
plotid = i
else:
plotid = 0
try:
x = data[csnames.index(csi.index)]
y = data[csnames.index(csi.value)]
xmin = min(xmin, min(x))
xmax = max(xmax, max(x))
fit = csi.fit if csi.fit != NULL_STR else None
g.new_series(x, y, fit=fit,
filter_outliers=csi.use_filter,
type=csi.plot_type,
plotid=plotid)
g.set_x_title(csi.index, plotid=plotid)
g.set_y_title(csi.value, plotid=plotid)
if fit:
regressable = True
except IndexError:
pass
g.set_x_limits(xmin, xmax, pad='0.1')
self._graph_count += 1
if regressable:
gg = StatsGraph(graph=g)
gii = gg
else:
gii = g
g._update_graph()
def show(gi):
gi.window_title = '{} Graph {}'.format(self.short_name, self._graph_count)
gi.window_x = self._graph_count * 20 + 400
gi.window_y = self._graph_count * 20 + 20
gi.edit_traits()
show(gii)
def show_evolutions_factory(record_id,
isotopes,
show_evo=True,
show_equilibration=False,
show_baseline=False,
show_statistics=False,
ncols=1,
scale_to_equilibration=False):
if WINDOW_CNT > 20:
information(
None,
'You have too many Isotope Evolution windows open. Close some before proceeding'
)
return
if not show_evo:
xmi = Inf
xma = -Inf
else:
xmi, xma = 0, -Inf
if ncols > 1:
isotopes = sort_isotopes(isotopes,
reverse=True,
key=attrgetter('name'))
def reorder(l, n):
l = [l[i:i + n] for i in range(0, len(l), n)]
nl = []
for ri in range(len(l[0])):
for col in l:
try:
nl.append(col[ri])
except IndexError:
pass
return nl
nrows = ceil(len(isotopes) / ncols)
isotopes = reorder(isotopes, nrows)
g = ColumnStackedRegressionGraph(resizable=True,
ncols=ncols,
nrows=nrows,
container_dict={
'padding_top': 15 * nrows,
'spacing': (0, 15),
'padding_bottom': 40
})
resizable = 'hv'
else:
resizable = 'h'
isotopes = sort_isotopes(isotopes,
reverse=False,
key=attrgetter('name'))
g = StackedRegressionGraph(resizable=True,
container_dict={'spacing': 15})
# 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
for i, iso in enumerate(isotopes):
ymi, yma = Inf, -Inf
p = g.new_plot(padding=[80, 10, 10, 40], resizable=resizable)
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)
if show_statistics:
g.add_statistics(i)
p.y_axis.title_spacing = 50
if show_equilibration:
sniff = iso.sniff
if sniff.xs.shape[0]:
g.new_series(sniff.offset_xs,
sniff.ys,
type='scatter',
fit=None,
color='red')
ymi, yma = min_max(ymi, yma, sniff.ys)
xmi, xma = min_max(xmi, xma, sniff.offset_xs)
if show_evo:
if iso.fit is None:
iso.fit = 'linear'
g.new_series(iso.offset_xs,
iso.ys,
fit=iso.efit,
truncate=iso.truncate,
filter_outliers_dict=iso.filter_outliers_dict,
color='black')
g.set_regressor(iso.regressor, i)
xmi, xma = min_max(xmi, xma, iso.offset_xs)
if not scale_to_equilibration:
ymi, yma = min_max(ymi, yma, iso.ys)
if show_baseline:
baseline = iso.baseline
g.new_series(baseline.offset_xs,
baseline.ys,
type='scatter',
fit=baseline.efit,
filter_outliers_dict=baseline.filter_outliers_dict,
color='blue')
xmi, xma = min_max(xmi, xma, baseline.offset_xs)
if not scale_to_equilibration:
ymi, yma = min_max(ymi, yma, baseline.ys)
xpad = '0.025,0.05'
ypad = '0.05'
if scale_to_equilibration:
ypad = None
r = (yma - ymi) * 0.02
# ymi = yma - r
fit = iso.fit
if fit != 'average':
fit, _ = convert_fit(iso.fit)
fy = polyval(polyfit(iso.offset_xs, iso.ys, fit), 0)
if ymi > fy:
ymi = fy - r
fy = polyval(polyfit(iso.offset_xs, iso.ys, fit), xma)
if fy > yma:
yma = fy
elif fy < ymi:
ymi = fy - r
# yma += r
g.set_x_limits(min_=xmi, max_=xma, pad=xpad)
g.set_y_limits(min_=ymi, max_=yma, pad=ypad, plotid=i)
g.set_x_title('Time (s)', plotid=i)
g.set_y_title('{} ({})'.format(iso.name, iso.units), plotid=i)
g.refresh()
g.window_title = '{} {}'.format(
record_id, ','.join([i.name for i in reversed(isotopes)]))
return g
def setup_graph(self, iso):
self.klass = None
self.pklass = str(self.clf.predict_isotope(iso))
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 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, 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 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 _graph_default(self):
return StackedRegressionGraph(container_dict=dict(
stack_order='top_to_bottom'))
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 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 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
