def _graph_grid(self, x, y, z, ze, r, reg, refresh):
self.min_j = min(z)
self.max_j = max(z)
g = self.graph
layout = FigureLayout(fixed='filled_grid')
nrows, ncols = layout.calculate(len(x))
if not isinstance(g, Graph):
g = RegressionGraph(container_dict={
'bgcolor': 'gray',
'kind': 'g',
'shape': (nrows, ncols)
})
self.graph = g
def get_ip(xi, yi):
return next((ip for ip in self.monitor_positions
if ((ip.x - xi)**2 + (ip.y - yi)**2)**0.5 < 0.01),
None)
opt = self.plotter_options
monage = opt.monitor_age * 1e6
lk = opt.lambda_k
ans = self._analyses[0]
scale = opt.flux_scalar
for r in range(nrows):
for c in range(ncols):
idx = c + ncols * r
if refresh:
try:
yy = z[idx] * scale
ye = ze[idx] * scale
except IndexError:
continue
# if hasattr(g, 'rules'):
# if idx in g.rules:
# l1, l2, l3 = g.rules[idx]
# l1.value = yy
# l2.value = yy + ye
# l3.value = yy - ye
else:
plot = g.new_plot(padding_left=65,
padding_right=5,
padding_top=30,
padding_bottom=5)
try:
ip = get_ip(x[idx], y[idx])
except IndexError:
continue
add_axes_tools(g, plot)
yy = z[idx] * scale
ye = ze[idx] * scale
plot.title = 'Identifier={} Position={}'.format(
ip.identifier, ip.hole_id)
plot.x_axis.visible = False
if c == 0 and r == nrows // 2:
plot.y_axis.title = 'J x{}'.format(scale)
if not ip.use:
continue
# get ip via x,y
ais = [
a for a in ans if a.irradiation_position == ip.hole_id
]
n = len(ais)
# plot mean value
# l1 = g.add_horizontal_rule(yy, color='black', line_style='solid', plotid=idx)
# l2 = g.add_horizontal_rule(yy + ye, plotid=idx)
# l3 = g.add_horizontal_rule(yy - ye, plotid=idx)
# rs = (l1, l2, l3)
# d = {idx: rs}
# if hasattr(g, 'rules'):
# g.rules.update(d)
# else:
# g.rules = d
# plot individual analyses
fs = [a.model_j(monage, lk) * scale for a in ais]
fs = sorted(fs)
iys = array([nominal_value(fi) for fi in fs])
ies = array([std_dev(fi) for fi in fs])
if self.plotter_options.use_weighted_fit:
fit = 'weighted mean'
else:
fit = 'average'
ek = self.plotter_options.error_kind
if ek == MSEM:
ek = 'msem'
fit = '{}_{}'.format(fit, ek)
p_, s, l_ = g.new_series(linspace(0, n - 1, n),
iys,
yerror=ies,
type='scatter',
fit=fit,
add_point_inspector=False,
add_inspector=False,
marker='circle',
marker_size=3)
g.set_x_limits(0, n - 1, pad='0.1', plotid=idx)
g.set_y_limits(min(iys - ies),
max(iys + ies),
pad='0.1',
plotid=idx)
g.add_statistics(plotid=idx)
ebo = ErrorBarOverlay(component=s, orientation='y')
s.underlays.append(ebo)
s.error_bars = ebo
add_analysis_inspector(s, ais)
s.index.on_trait_change(
self._grid_update_graph_metadata(ais),
'metadata_changed')
self.suppress_metadata_change = True
sel = [i for i, a in enumerate(ais) if a.is_omitted()]
s.index.metadata['selections'] = sel
self.suppress_metadata_change = False
g.refresh()
def _graph_linear_j(self, x, y, r, reg, refresh):
g = self.graph
if not isinstance(g, RegressionGraph):
g = RegressionGraph(
container_dict={'bgcolor': self.plotter_options.bgcolor})
self.graph = g
po = self.plotter_options
g.clear()
plot = g.new_plot(padding=po.get_paddings())
if po.model_kind == WEIGHTED_MEAN_1D:
fit = 'weighted mean'
else:
fit = po.least_squares_fit
_, scatter, line = g.new_series(x=reg.xs,
y=reg.ys,
yerror=reg.yserr,
fit=fit)
ebo = ErrorBarOverlay(component=scatter, orientation='y')
scatter.underlays.append(ebo)
scatter.error_bars = ebo
add_inspector(scatter, self._additional_info)
add_axes_tools(g, plot)
g.set_x_title(po.one_d_axis)
g.set_y_title('J')
g.add_statistics()
miy = 100
may = -1
if self._individual_analyses_enabled:
sel = [
i for i, (a, x, y, e) in enumerate(zip(*self._analyses))
if a.is_omitted()
]
# plot the individual analyses
iscatter, iys = self._graph_individual_analyses(fit=None,
add_tools=False)
miy = min(iys)
may = max(iys)
# set metadata last because it will trigger a refresh
self.suppress_metadata_change = True
iscatter.index.metadata['selections'] = sel
self.suppress_metadata_change = False
g.set_y_limits(min_=miy, max_=may, pad='0.1')
g.set_x_limits(pad='0.1')
g.refresh()
fys = line.value.get_data()
self.max_j = fys.max()
self.min_j = fys.min()
