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
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)