def update(self, fit_minimizers, fit_minimizer_errors):
try:
x = fit_minimizers[self.fit_param_name]
delta_x = fit_minimizer_errors[self.fit_param_name]
except KeyError as e:
if not self.has_warned:
logger.warn(
"Unknown reference to fit parameter '%s' in point of interest",
str(e))
self.has_warned = True
# TODO: Remove POI.
return
if np.isnan(delta_x) or delta_x == 0.0:
# If the covariance extraction failed, just don't display the
# confidence interval at all.
delta_x = 0.0
label = str(x)
else:
label = uncertainty_to_string(
x * self.x_data_to_display_scale,
delta_x * self.x_data_to_display_scale)
self.center_line.label.setFormat(label + self.x_unit_suffix)
self.left_line.setPos(x - delta_x)
self.center_line.setPos(x)
self.right_line.setPos(x + delta_x)
def _redraw(self):
x = self._position_source.get()
if x is None:
return
if not self._added_to_plot:
self._view_box.addItem(self._left_line, ignoreBounds=True)
self._view_box.addItem(self._center_line, ignoreBounds=True)
self._view_box.addItem(self._right_line, ignoreBounds=True)
self._added_to_plot = True
delta_x = None
if self._uncertainty_source:
delta_x = self._uncertainty_source.get()
if delta_x is None or numpy.isnan(delta_x) or delta_x == 0.0:
# If the covariance extraction failed, just don't display the
# confidence interval at all.
delta_x = 0.0
label = str(x * self._x_data_to_display_scale)
else:
label = uncertainty_to_string(
x * self._x_data_to_display_scale,
delta_x * self._x_data_to_display_scale)
self._center_line.label.setFormat(label + self._x_unit_suffix)
self._left_line.setPos(x - delta_x)
self._center_line.setPos(x)
self._right_line.setPos(x + delta_x)