def setArrayData(self, data, labels=None, copy=True, editable=False): """Set the data array. Update frame browsers and labels. :param data: Numpy array or similar object (e.g. nested sequence, h5py dataset...) :param labels: list of labels for each dimension of the array, or boolean ``True`` to use default labels ("dimension 0", "dimension 1", ...). `None` to disable labels (default). :param bool copy: If *True*, store a copy of *data* in the model. If *False*, store a reference to *data* if possible (only possible if *data* is a proper numpy array or an object that implements the same methods). :param bool editable: Flag to enable editing data. Default is *False* """ self._data_shape = _get_shape(data) n_widgets = len(self._browserWidgets) n_dimensions = len(self._data_shape) # Reset text of labels self._dimensionLabelsText = [] for i in range(n_dimensions): if labels in [True, 1]: label_text = "Dimension %d" % i elif labels is None or i >= len(labels): label_text = "" else: label_text = labels[i] self._dimensionLabelsText.append(label_text) # not enough widgets, create new ones (we need n_dim - 2) for i in range(n_widgets, n_dimensions - 2): browser = HorizontalSliderWithBrowser(self.browserContainer) self.browserLayout.addWidget(browser, i, 1) self._browserWidgets.append(browser) browser.valueChanged.connect(self._browserSlot) browser.setEnabled(False) browser.hide() label = qt.QLabel(self.browserContainer) self._browserLabels.append(label) self.browserLayout.addWidget(label, i, 0) label.hide() n_widgets = len(self._browserWidgets) for i in range(n_widgets): label = self._browserLabels[i] browser = self._browserWidgets[i] if (i + 2) < n_dimensions: label.setText(self._dimensionLabelsText[i]) browser.setRange(0, self._data_shape[i] - 1) browser.setEnabled(True) browser.show() if labels is not None: label.show() else: label.hide() else: browser.setEnabled(False) browser.hide() label.hide() # set model self.model.setArrayData(data, copy=copy, editable=editable) # some linux distributions need this call self.view.setModel(self.model) if editable: self.view.enableCut() self.view.enablePaste() # initialize & connect axesSelector self.axesSelector.setNDimensions(n_dimensions) self.axesSelector.sigDimensionsChanged.connect(self.setFrameAxes)
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)
class ArrayImagePlot(qt.QWidget): """ Widget for plotting an image 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): """ :param parent: Parent QWidget """ super(ArrayImagePlot, 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 = Plot2D(self) self._plot.setDefaultColormap( Colormap(name="viridis", vmin=None, vmax=None, normalization=Colormap.LINEAR)) self._plot.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: Plot2D """ return self._plot def setImageData(self, signals, x_axis=None, y_axis=None, signals_names=None, xlabel=None, ylabel=None, title=None, isRgba=False, xscale=None, yscale=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 :param isRgba: True if data is a 3D RGBA image :param str xscale: Scale of X axis in (None, 'linear', 'log') :param str yscale: Scale of Y axis in (None, 'linear', 'log') """ 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() if not isRgba: self._selector.setAxisNames(["Y", "X"]) img_ndim = 2 else: self._selector.setAxisNames(["Y", "X", "RGB(A) channel"]) img_ndim = 3 self._selector.setData(signals[0]) if len(signals[0].shape) <= img_ndim: 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._axis_scales = xscale, yscale self._updateImage() self._plot.resetZoom() self._selector.selectionChanged.connect(self._updateImage) self._auxSigSlider.valueChanged.connect(self._sliderIdxChanged) def _updateImage(self): selection = self._selector.selection() auxSigIdx = self._auxSigSlider.value() legend = self.__signals_names[auxSigIdx] 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.remove(kind=( "scatter", "image", )) if xcalib.is_affine() and ycalib.is_affine(): # regular image xorigin, xscale = xcalib(0), xcalib.get_slope() yorigin, yscale = ycalib(0), ycalib.get_slope() origin = (xorigin, yorigin) scale = (xscale, yscale) self._plot.getXAxis().setScale('linear') self._plot.getYAxis().setScale('linear') self._plot.addImage(image, legend=legend, origin=origin, scale=scale, replace=True, resetzoom=False) else: xaxisscale, yaxisscale = self._axis_scales if xaxisscale is not None: self._plot.getXAxis().setScale('log' if xaxisscale == 'log' else 'linear') if yaxisscale is not None: self._plot.getYAxis().setScale('log' if yaxisscale == 'log' else 'linear') scatterx, scattery = numpy.meshgrid(x_axis, y_axis) # fixme: i don't think this can handle "irregular" RGBA images self._plot.addScatter(numpy.ravel(scatterx), numpy.ravel(scattery), numpy.ravel(image), legend=legend) 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.clear()
class XYVScatterPlot(qt.QWidget): """ Widget for plotting one or more scatters (with identical x, y coordinates). """ def __init__(self, parent=None): """ :param parent: Parent QWidget """ super(XYVScatterPlot, self).__init__(parent) self.__y_axis = None """1D array""" self.__y_axis_name = None self.__values = None """List of 1D arrays (for multiple scatters with identical x, y coordinates)""" self.__x_axis = None self.__x_axis_name = None self.__x_axis_errors = None self.__y_axis = None self.__y_axis_name = None self.__y_axis_errors = None self._plot = ScatterView(self) self._plot.setColormap( Colormap(name="viridis", vmin=None, vmax=None, normalization=Colormap.LINEAR)) self._slider = HorizontalSliderWithBrowser(parent=self) self._slider.setMinimum(0) self._slider.setValue(0) self._slider.valueChanged[int].connect(self._sliderIdxChanged) self._slider.setToolTip("Select auxiliary signals") layout = qt.QGridLayout() layout.setContentsMargins(0, 0, 0, 0) layout.addWidget(self._plot, 0, 0) layout.addWidget(self._slider, 1, 0) self.setLayout(layout) def _sliderIdxChanged(self, value): self._updateScatter() def getScatterView(self): """Returns the :class:`ScatterView` used for the display :rtype: ScatterView """ return self._plot def getPlot(self): """Returns the plot used for the display :rtype: PlotWidget """ return self._plot.getPlotWidget() def setScattersData(self, y, x, values, yerror=None, xerror=None, ylabel=None, xlabel=None, title="", scatter_titles=None, xscale=None, yscale=None): """ :param ndarray y: 1D array for y (vertical) coordinates. :param ndarray x: 1D array for x coordinates. :param List[ndarray] values: List of 1D arrays of values. This will be used to compute the color map and assign colors to the points. There should be as many arrays in the list as scatters to be represented. :param ndarray yerror: 1D array of errors for y (same shape), or None. :param ndarray xerror: 1D array of errors for x, or None :param str ylabel: Label for Y axis :param str xlabel: Label for X axis :param str title: Main graph title :param List[str] scatter_titles: Subtitles (one per scatter) :param str xscale: Scale of X axis in (None, 'linear', 'log') :param str yscale: Scale of Y axis in (None, 'linear', 'log') """ self.__y_axis = y self.__x_axis = x self.__x_axis_name = xlabel or "X" self.__y_axis_name = ylabel or "Y" self.__x_axis_errors = xerror self.__y_axis_errors = yerror self.__values = values self.__graph_title = title or "" self.__scatter_titles = scatter_titles self._slider.valueChanged[int].disconnect(self._sliderIdxChanged) self._slider.setMaximum(len(values) - 1) if len(values) > 1: self._slider.show() else: self._slider.hide() self._slider.setValue(0) self._slider.valueChanged[int].connect(self._sliderIdxChanged) if xscale is not None: self._plot.getXAxis().setScale('log' if xscale == 'log' else 'linear') if yscale is not None: self._plot.getYAxis().setScale('log' if yscale == 'log' else 'linear') self._updateScatter() def _updateScatter(self): x = self.__x_axis y = self.__y_axis idx = self._slider.value() if self.__graph_title: title = self.__graph_title # main NXdata @title if len(self.__scatter_titles) > 1: # Append dataset name only when there is many datasets title += '\n' + self.__scatter_titles[idx] else: title = self.__scatter_titles[idx] # scatter dataset name self._plot.setGraphTitle(title) self._plot.setData(x, y, self.__values[idx], xerror=self.__x_axis_errors, yerror=self.__y_axis_errors) self._plot.resetZoom() self._plot.getXAxis().setLabel(self.__x_axis_name) self._plot.getYAxis().setLabel(self.__y_axis_name) def clear(self): self._plot.getPlotWidget().clear()
class ArrayImagePlot(qt.QWidget): """ Widget for plotting an image 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): """ :param parent: Parent QWidget """ super(ArrayImagePlot, 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 = Plot2D(self) self._plot.setDefaultColormap(Colormap(name="viridis", vmin=None, vmax=None, normalization=Colormap.LINEAR)) self._plot.getIntensityHistogramAction().setVisible(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: Plot2D """ return self._plot def setImageData(self, signals, x_axis=None, y_axis=None, signals_names=None, xlabel=None, ylabel=None, title=None, isRgba=False): """ :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 :param isRgba: True if data is a 3D RGBA image """ 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() if not isRgba: self._selector.setAxisNames(["Y", "X"]) img_ndim = 2 else: self._selector.setAxisNames(["Y", "X", "RGB(A) channel"]) img_ndim = 3 self._selector.setData(signals[0]) if len(signals[0].shape) <= img_ndim: 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._selector.selectionChanged.connect(self._updateImage) self._auxSigSlider.valueChanged.connect(self._sliderIdxChanged) def _updateImage(self): selection = self._selector.selection() auxSigIdx = self._auxSigSlider.value() legend = self.__signals_names[auxSigIdx] 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.remove(kind=("scatter", "image",)) if xcalib.is_affine() and ycalib.is_affine(): # regular image xorigin, xscale = xcalib(0), xcalib.get_slope() yorigin, yscale = ycalib(0), ycalib.get_slope() origin = (xorigin, yorigin) scale = (xscale, yscale) self._plot.addImage(image, legend=legend, origin=origin, scale=scale, replace=True) else: scatterx, scattery = numpy.meshgrid(x_axis, y_axis) # fixme: i don't think this can handle "irregular" RGBA images self._plot.addScatter(numpy.ravel(scatterx), numpy.ravel(scattery), numpy.ravel(image), legend=legend) title = "" if self.__title: title += self.__title if not title.strip().endswith(self.__signals_names[auxSigIdx]): title += "\n" + self.__signals_names[auxSigIdx] self._plot.setGraphTitle(title) self._plot.getXAxis().setLabel(self.__x_axis_name) self._plot.getYAxis().setLabel(self.__y_axis_name) self._plot.resetZoom() def clear(self): old = self._selector.blockSignals(True) self._selector.clear() self._selector.blockSignals(old) self._plot.clear()
class XYVScatterPlot(qt.QWidget): """ Widget for plotting one or more scatters (with identical x, y coordinates). """ def __init__(self, parent=None): """ :param parent: Parent QWidget """ super(XYVScatterPlot, self).__init__(parent) self.__y_axis = None """1D array""" self.__y_axis_name = None self.__values = None """List of 1D arrays (for multiple scatters with identical x, y coordinates)""" self.__x_axis = None self.__x_axis_name = None self.__x_axis_errors = None self.__y_axis = None self.__y_axis_name = None self.__y_axis_errors = None self._plot = ScatterView(self) self._plot.setColormap(Colormap(name="viridis", vmin=None, vmax=None, normalization=Colormap.LINEAR)) self._slider = HorizontalSliderWithBrowser(parent=self) self._slider.setMinimum(0) self._slider.setValue(0) self._slider.valueChanged[int].connect(self._sliderIdxChanged) self._slider.setToolTip("Select auxiliary signals") layout = qt.QGridLayout() layout.setContentsMargins(0, 0, 0, 0) layout.addWidget(self._plot, 0, 0) layout.addWidget(self._slider, 1, 0) self.setLayout(layout) def _sliderIdxChanged(self, value): self._updateScatter() def getPlot(self): """Returns the plot used for the display :rtype: PlotWidget """ return self._plot.getPlotWidget() def setScattersData(self, y, x, values, yerror=None, xerror=None, ylabel=None, xlabel=None, title="", scatter_titles=None): """ :param ndarray y: 1D array for y (vertical) coordinates. :param ndarray x: 1D array for x coordinates. :param List[ndarray] values: List of 1D arrays of values. This will be used to compute the color map and assign colors to the points. There should be as many arrays in the list as scatters to be represented. :param ndarray yerror: 1D array of errors for y (same shape), or None. :param ndarray xerror: 1D array of errors for x, or None :param str ylabel: Label for Y axis :param str xlabel: Label for X axis :param str title: Main graph title :param List[str] scatter_titles: Subtitles (one per scatter) """ self.__y_axis = y self.__x_axis = x self.__x_axis_name = xlabel or "X" self.__y_axis_name = ylabel or "Y" self.__x_axis_errors = xerror self.__y_axis_errors = yerror self.__values = values self.__graph_title = title or "" self.__scatter_titles = scatter_titles self._slider.valueChanged[int].disconnect(self._sliderIdxChanged) self._slider.setMaximum(len(values) - 1) if len(values) > 1: self._slider.show() else: self._slider.hide() self._slider.setValue(0) self._slider.valueChanged[int].connect(self._sliderIdxChanged) self._updateScatter() def _updateScatter(self): x = self.__x_axis y = self.__y_axis idx = self._slider.value() title = "" if self.__graph_title: title += self.__graph_title + "\n" # main NXdata @title title += self.__scatter_titles[idx] # scatter dataset name self._plot.setGraphTitle(title) self._plot.setData(x, y, self.__values[idx], xerror=self.__x_axis_errors, yerror=self.__y_axis_errors) self._plot.resetZoom() self._plot.getXAxis().setLabel(self.__x_axis_name) self._plot.getYAxis().setLabel(self.__y_axis_name) def clear(self): self._plot.getPlotWidget().clear()