def update_label(self):
plot_data = self.plot_item.get_plot_data()
if plot_data == None:
intensity = 0
elif np.isnan(plot_data.data).all():
intensity = np.nan
else:
cut = self.model.cut_from_data(plot_data, region='center')
# Normalize by dividing by the number of non nan elements
if config.get_key('crosshair', 'normalized_intensity') == 'True':
intensity = normalize(cut.data)
else:
intensity = np.nansum(cut.data)
if abs(intensity) > 1000:
self.point_value_label.setText('%.2fk' % (intensity / 1000))
else:
self.point_value_label.setText('%.2f' % intensity)
x = self.model.x
y = self.model.y
self.distance_value_label.setText('%.2f' % np.sqrt(x**2 + y**2))
def plot(self,
data,
title,
crosshair,
region,
phi_sample=720,
line_sample=500,
normalized=False):
index = len(self.plot_item.listDataItems())
colors = config.get_key('profile_plot', 'colors')
color = colors.split(',')[index % len(colors)]
line_width = int(config.get_key('profile_plot', 'line_width'))
symbols = config.get_key('profile_plot', 'symbols')
symbol = symbols.split(',')[index % len(symbols)]
symbol_size = int(config.get_key('profile_plot', 'symbol_size'))
if isinstance(data, PlotData):
x, y = self.model.get_plot_data(data, crosshair, region,
phi_sample, line_sample)
else:
data, axis = data
x = np.array(data.axes[axis].axis)
y = []
for i in range(len(x)):
slice_ = data.slice_from_index(i, axis=axis)
plot_data = crosshair.cut_from_data(slice_, region=region).data
if np.isnan(plot_data).all():
y.append(np.nan)
else:
if config.get_key('crosshair',
'normalized_intensity') == 'True':
y.append(normalize(plot_data))
else:
y.append(np.nansum(plot_data))
y = np.array(y)
x = x[~np.isnan(y)]
y = y[~np.isnan(y)]
if normalized:
y = y / max(y)
self.plot_item.plot(x,
y,
name=title + self.title_suffixes[region],
pen=mkPen(color, width=line_width),
symbol=symbol,
symbolPen=mkPen(color, width=symbol_size),
symbolBrush=mkBrush(color))
def update_label(self):
plot_data = self.plot_item.get_plot_data()
super().update_label()
if plot_data == None:
intensity = 0
else:
cut = self.model.cut_from_data(plot_data, region='ring')
# Normalize by dividing by the number of non nan elements
if config.get_key('crosshair', 'normalized_intensity') == 'True':
intensity = normalize(cut.data)
else:
intensity = np.nansum(cut.data)
if abs(intensity) > 1000:
self.ring_value_label.setText('%.2fk' % (intensity / 1000))
else:
self.ring_value_label.setText('%.2f' % intensity)