def createWidget(self, parent):
from silx.gui.plot.ComplexImageView import ComplexImageView
widget = ComplexImageView(parent=parent)
widget.getPlot().setKeepDataAspectRatio(True)
widget.getXAxis().setLabel('X')
widget.getYAxis().setLabel('Y')
return widget
def createWidget(self, parent):
from silx.gui.plot.ComplexImageView import ComplexImageView
widget = ComplexImageView(parent=parent)
widget.setColormap(self.defaultColormap(), mode=ComplexImageView.Mode.ABSOLUTE)
widget.setColormap(self.defaultColormap(), mode=ComplexImageView.Mode.SQUARE_AMPLITUDE)
widget.setColormap(self.defaultColormap(), mode=ComplexImageView.Mode.REAL)
widget.setColormap(self.defaultColormap(), mode=ComplexImageView.Mode.IMAGINARY)
widget.getPlot().getColormapAction().setColorDialog(self.defaultColorDialog())
widget.getPlot().getIntensityHistogramAction().setVisible(True)
widget.getPlot().setKeepDataAspectRatio(True)
widget.getXAxis().setLabel('X')
widget.getYAxis().setLabel('Y')
return widget
def createWidget(self, parent):
from silx.gui.plot.ComplexImageView import ComplexImageView
widget = ComplexImageView(parent=parent)
widget.setColormap(self.defaultColormap(), mode=ComplexImageView.Mode.ABSOLUTE)
widget.setColormap(self.defaultColormap(), mode=ComplexImageView.Mode.SQUARE_AMPLITUDE)
widget.setColormap(self.defaultColormap(), mode=ComplexImageView.Mode.REAL)
widget.setColormap(self.defaultColormap(), mode=ComplexImageView.Mode.IMAGINARY)
widget.getPlot().getColormapAction().setColorDialog(self.defaultColorDialog())
widget.getPlot().getIntensityHistogramAction().setVisible(True)
widget.getPlot().setKeepDataAspectRatio(True)
widget.getXAxis().setLabel('X')
widget.getYAxis().setLabel('Y')
return widget
class ArrayComplexImagePlot(qt.QWidget):
"""
Widget for plotting an image of complex from a multi-dimensional signal array
and two 1D axes array.
The signal array can have an arbitrary number of dimensions, the only
limitation being that the last two dimensions must have the same length as
the axes arrays.
Sliders are provided to select indices on the first (n - 2) dimensions of
the signal array, and the plot is updated to show the image corresponding
to the selection.
If one or both of the axes does not have regularly spaced values, the
the image is plotted as a coloured scatter plot.
"""
def __init__(self, parent=None, colormap=None):
"""
:param parent: Parent QWidget
"""
super(ArrayComplexImagePlot, self).__init__(parent)
self.__signals = None
self.__signals_names = None
self.__x_axis = None
self.__x_axis_name = None
self.__y_axis = None
self.__y_axis_name = None
self._plot = ComplexImageView(self)
if colormap is not None:
for mode in (ComplexImageView.ComplexMode.ABSOLUTE,
ComplexImageView.ComplexMode.SQUARE_AMPLITUDE,
ComplexImageView.ComplexMode.REAL,
ComplexImageView.ComplexMode.IMAGINARY):
self._plot.setColormap(colormap, mode)
self._plot.getPlot().getIntensityHistogramAction().setVisible(True)
self._plot.setKeepDataAspectRatio(True)
# not closable
self._selector = NumpyAxesSelector(self)
self._selector.setNamedAxesSelectorVisibility(False)
self._selector.selectionChanged.connect(self._updateImage)
self._auxSigSlider = HorizontalSliderWithBrowser(parent=self)
self._auxSigSlider.setMinimum(0)
self._auxSigSlider.setValue(0)
self._auxSigSlider.valueChanged[int].connect(self._sliderIdxChanged)
self._auxSigSlider.setToolTip("Select auxiliary signals")
layout = qt.QVBoxLayout()
layout.addWidget(self._plot)
layout.addWidget(self._selector)
layout.addWidget(self._auxSigSlider)
self.setLayout(layout)
def _sliderIdxChanged(self, value):
self._updateImage()
def getPlot(self):
"""Returns the plot used for the display
:rtype: PlotWidget
"""
return self._plot.getPlot()
def setImageData(self,
signals,
x_axis=None,
y_axis=None,
signals_names=None,
xlabel=None,
ylabel=None,
title=None):
"""
:param signals: list of n-D datasets, whose last 2 dimensions are used as the
image's values, or list of 3D datasets interpreted as RGBA image.
:param x_axis: 1-D dataset used as the image's x coordinates. If
provided, its lengths must be equal to the length of the last
dimension of ``signal``.
:param y_axis: 1-D dataset used as the image's y. If provided,
its lengths must be equal to the length of the 2nd to last
dimension of ``signal``.
:param signals_names: Names for each image, used as subtitle and legend.
:param xlabel: Label for X axis
:param ylabel: Label for Y axis
:param title: Graph title
"""
self._selector.selectionChanged.disconnect(self._updateImage)
self._auxSigSlider.valueChanged.disconnect(self._sliderIdxChanged)
self.__signals = signals
self.__signals_names = signals_names
self.__x_axis = x_axis
self.__x_axis_name = xlabel
self.__y_axis = y_axis
self.__y_axis_name = ylabel
self.__title = title
self._selector.clear()
self._selector.setAxisNames(["Y", "X"])
self._selector.setData(signals[0])
if len(signals[0].shape) <= 2:
self._selector.hide()
else:
self._selector.show()
self._auxSigSlider.setMaximum(len(signals) - 1)
if len(signals) > 1:
self._auxSigSlider.show()
else:
self._auxSigSlider.hide()
self._auxSigSlider.setValue(0)
self._updateImage()
self._plot.getPlot().resetZoom()
self._selector.selectionChanged.connect(self._updateImage)
self._auxSigSlider.valueChanged.connect(self._sliderIdxChanged)
def _updateImage(self):
selection = self._selector.selection()
auxSigIdx = self._auxSigSlider.value()
images = [img[selection] for img in self.__signals]
image = images[auxSigIdx]
x_axis = self.__x_axis
y_axis = self.__y_axis
if x_axis is None and y_axis is None:
xcalib = NoCalibration()
ycalib = NoCalibration()
else:
if x_axis is None:
# no calibration
x_axis = numpy.arange(image.shape[1])
elif numpy.isscalar(x_axis) or len(x_axis) == 1:
# constant axis
x_axis = x_axis * numpy.ones((image.shape[1], ))
elif len(x_axis) == 2:
# linear calibration
x_axis = x_axis[0] * numpy.arange(image.shape[1]) + x_axis[1]
if y_axis is None:
y_axis = numpy.arange(image.shape[0])
elif numpy.isscalar(y_axis) or len(y_axis) == 1:
y_axis = y_axis * numpy.ones((image.shape[0], ))
elif len(y_axis) == 2:
y_axis = y_axis[0] * numpy.arange(image.shape[0]) + y_axis[1]
xcalib = ArrayCalibration(x_axis)
ycalib = ArrayCalibration(y_axis)
self._plot.setData(image)
if xcalib.is_affine():
xorigin, xscale = xcalib(0), xcalib.get_slope()
else:
_logger.warning("Unsupported complex image X axis calibration")
xorigin, xscale = 0., 1.
if ycalib.is_affine():
yorigin, yscale = ycalib(0), ycalib.get_slope()
else:
_logger.warning("Unsupported complex image Y axis calibration")
yorigin, yscale = 0., 1.
self._plot.setOrigin((xorigin, yorigin))
self._plot.setScale((xscale, yscale))
if self.__title:
title = self.__title
if len(self.__signals_names) > 1:
# Append dataset name only when there is many datasets
title += '\n' + self.__signals_names[auxSigIdx]
else:
title = self.__signals_names[auxSigIdx]
self._plot.setGraphTitle(title)
self._plot.getXAxis().setLabel(self.__x_axis_name)
self._plot.getYAxis().setLabel(self.__y_axis_name)
def clear(self):
old = self._selector.blockSignals(True)
self._selector.clear()
self._selector.blockSignals(old)
self._plot.setData(None)