def __init__(self, parent=None):
"""Constructor
:param QWidget parent: The parent of the widget
"""
super(DataViewer, self).__init__(parent)
self.__stack = qt.QStackedWidget(self)
self.__numpySelection = NumpyAxesSelector(self)
self.__numpySelection.selectedAxisChanged.connect(self.__numpyAxisChanged)
self.__numpySelection.selectionChanged.connect(self.__numpySelectionChanged)
self.__numpySelection.customAxisChanged.connect(self.__numpyCustomAxisChanged)
self.setLayout(qt.QVBoxLayout(self))
self.layout().addWidget(self.__stack, 1)
group = qt.QGroupBox(self)
group.setLayout(qt.QVBoxLayout())
group.layout().addWidget(self.__numpySelection)
group.setTitle("Axis selection")
self.__axisSelection = group
self.layout().addWidget(self.__axisSelection)
self.__currentAvailableViews = []
self.__currentView = None
self.__data = None
self.__useAxisSelection = False
self.__userSelectedView = None
self.__views = []
self.__index = {}
"""store stack index for each views"""
self._initializeViews()
def test_none(self):
data = numpy.arange(3 * 3 * 3)
widget = NumpyAxesSelector()
widget.setData(data)
widget.setData(None)
result = widget.selectedData()
self.assertIsNone(result)
def __init__(self, parent=None):
"""
:param parent: Parent QWidget
"""
super(ArrayCurvePlot, self).__init__(parent)
self.__signals = None
self.__signals_names = None
self.__signal_errors = None
self.__axis = None
self.__axis_name = None
self.__x_axis_errors = None
self.__values = None
self._plot = Plot1D(self)
self.selectorDock = qt.QDockWidget("Data selector", self._plot)
# not closable
self.selectorDock.setFeatures(qt.QDockWidget.DockWidgetMovable
| qt.QDockWidget.DockWidgetFloatable)
self._selector = NumpyAxesSelector(self.selectorDock)
self._selector.setNamedAxesSelectorVisibility(False)
self.__selector_is_connected = False
self.selectorDock.setWidget(self._selector)
self._plot.addTabbedDockWidget(self.selectorDock)
self._plot.sigActiveCurveChanged.connect(
self._setYLabelFromActiveLegend)
layout = qt.QGridLayout()
layout.setContentsMargins(0, 0, 0, 0)
layout.addWidget(self._plot, 0, 0)
self.setLayout(layout)
def __init__(self, parent=None):
"""
:param parent: Parent QWidget
"""
super(ArrayCurvePlot, self).__init__(parent)
self.__signals = None
self.__signals_names = None
self.__signal_errors = None
self.__axis = None
self.__axis_name = None
self.__x_axis_errors = None
self.__values = None
self._plot = Plot1D(self)
self._selector = NumpyAxesSelector(self)
self._selector.setNamedAxesSelectorVisibility(False)
self.__selector_is_connected = False
self._plot.sigActiveCurveChanged.connect(
self._setYLabelFromActiveLegend)
layout = qt.QVBoxLayout()
layout.setContentsMargins(0, 0, 0, 0)
layout.addWidget(self._plot)
layout.addWidget(self._selector)
self.setLayout(layout)
def __init__(self, parent=None):
"""
:param parent: Parent QWidget
"""
super(ArrayStackPlot, self).__init__(parent)
self.__signal = None
self.__signal_name = None
# the Z, Y, X axes apply to the last three dimensions of the signal
# (in that order)
self.__z_axis = None
self.__z_axis_name = None
self.__y_axis = None
self.__y_axis_name = None
self.__x_axis = None
self.__x_axis_name = None
self._stack_view = StackView(self)
self._hline = qt.QFrame(self)
self._hline.setFrameStyle(qt.QFrame.HLine)
self._hline.setFrameShadow(qt.QFrame.Sunken)
self._legend = qt.QLabel(self)
self._selector = NumpyAxesSelector(self)
self._selector.setNamedAxesSelectorVisibility(False)
self.__selector_is_connected = False
layout = qt.QVBoxLayout()
layout.addWidget(self._stack_view)
layout.addWidget(self._hline)
layout.addWidget(self._legend)
layout.addWidget(self._selector)
self.setLayout(layout)
def test_none(self):
data = numpy.arange(3 * 3 * 3)
widget = NumpyAxesSelector()
widget.setData(data)
widget.setData(None)
result = widget.selectedData()
self.assertIsNone(result)
def test_data_event(self):
data = numpy.arange(3 * 3 * 3)
widget = NumpyAxesSelector()
listener = SignalListener()
widget.dataChanged.connect(listener)
widget.setData(data)
widget.setData(None)
self.assertEqual(listener.callCount(), 2)
def test_data_event(self):
data = numpy.arange(3 * 3 * 3)
widget = NumpyAxesSelector()
listener = SignalListener()
widget.dataChanged.connect(listener)
widget.setData(data)
widget.setData(None)
self.assertEqual(listener.callCount(), 2)
def test_output_1dim(self):
data = numpy.arange(3 * 3 * 3)
data.shape = 3, 3, 3
expectedResult = data[0, 0, 0]
widget = NumpyAxesSelector()
widget.setData(data)
result = widget.selectedData()
self.assertTrue(numpy.array_equal(result, expectedResult))
def test_output_1dim(self):
data = numpy.arange(3 * 3 * 3)
data.shape = 3, 3, 3
expectedResult = data[0, 0, 0]
widget = NumpyAxesSelector()
widget.setData(data)
result = widget.selectedData()
self.assertTrue(numpy.array_equal(result, expectedResult))
def __init__(self, parent=None):
"""
:param parent: Parent QWidget
"""
super(ArrayVolumePlot, self).__init__(parent)
self.__signal = None
self.__signal_name = None
# the Z, Y, X axes apply to the last three dimensions of the signal
# (in that order)
self.__z_axis = None
self.__z_axis_name = None
self.__y_axis = None
self.__y_axis_name = None
self.__x_axis = None
self.__x_axis_name = None
from silx.gui.plot3d.ScalarFieldView import ScalarFieldView
from silx.gui.plot3d import SFViewParamTree
self._view = ScalarFieldView(self)
def computeIsolevel(data):
data = data[numpy.isfinite(data)]
if len(data) == 0:
return 0
else:
return numpy.mean(data) + numpy.std(data)
self._view.addIsosurface(computeIsolevel, '#FF0000FF')
# Create a parameter tree for the scalar field view
options = SFViewParamTree.TreeView(self._view)
options.setSfView(self._view)
# Add the parameter tree to the main window in a dock widget
dock = qt.QDockWidget()
dock.setWidget(options)
self._view.addDockWidget(qt.Qt.RightDockWidgetArea, dock)
self._hline = qt.QFrame(self)
self._hline.setFrameStyle(qt.QFrame.HLine)
self._hline.setFrameShadow(qt.QFrame.Sunken)
self._legend = qt.QLabel(self)
self._selector = NumpyAxesSelector(self)
self._selector.setNamedAxesSelectorVisibility(False)
self.__selector_is_connected = False
layout = qt.QVBoxLayout()
layout.addWidget(self._view)
layout.addWidget(self._hline)
layout.addWidget(self._legend)
layout.addWidget(self._selector)
self.setLayout(layout)
def test_output_moredim(self):
data = numpy.arange(3 * 3 * 3 * 3)
data.shape = 3, 3, 3, 3
expectedResult = data
widget = NumpyAxesSelector()
widget.setAxisNames(["x", "y", "z", "boum"])
widget.setData(data[0])
result = widget.selectedData()
self.assertEqual(result, None)
widget.setData(data)
result = widget.selectedData()
self.assertTrue(numpy.array_equal(result, expectedResult))
def __init__(self, parent=None):
"""
:param parent: Parent QWidget
"""
super(ArrayCurvePlot, self).__init__(parent)
self.__signals = None
self.__signals_names = None
self.__signal_errors = None
self.__axis = None
self.__axis_name = None
self.__x_axis_errors = None
self.__values = None
self._plot = Plot1D(self)
self._selector = NumpyAxesSelector(self)
self._selector.setNamedAxesSelectorVisibility(False)
self.__selector_is_connected = False
self._plot.sigActiveCurveChanged.connect(self._setYLabelFromActiveLegend)
layout = qt.QVBoxLayout()
layout.setContentsMargins(0, 0, 0, 0)
layout.addWidget(self._plot)
layout.addWidget(self._selector)
self.setLayout(layout)
def __init__(self, parent=None):
"""
:param parent: Parent QWidget
"""
super(ArrayStackPlot, self).__init__(parent)
self.__signal = None
self.__signal_name = None
# the Z, Y, X axes apply to the last three dimensions of the signal
# (in that order)
self.__z_axis = None
self.__z_axis_name = None
self.__y_axis = None
self.__y_axis_name = None
self.__x_axis = None
self.__x_axis_name = None
self._stack_view = StackView(self)
self._hline = qt.QFrame(self)
self._hline.setFrameStyle(qt.QFrame.HLine)
self._hline.setFrameShadow(qt.QFrame.Sunken)
self._legend = qt.QLabel(self)
self._selector = NumpyAxesSelector(self)
self._selector.setNamedAxesSelectorVisibility(False)
self.__selector_is_connected = False
layout = qt.QVBoxLayout()
layout.addWidget(self._stack_view)
layout.addWidget(self._hline)
layout.addWidget(self._legend)
layout.addWidget(self._selector)
self.setLayout(layout)
def test_output_moredim(self):
data = numpy.arange(3 * 3 * 3 * 3)
data.shape = 3, 3, 3, 3
expectedResult = data
widget = NumpyAxesSelector()
widget.setAxisNames(["x", "y", "z", "boum"])
widget.setData(data[0])
result = widget.selectedData()
self.assertEqual(result, None)
widget.setData(data)
result = widget.selectedData()
self.assertTrue(numpy.array_equal(result, expectedResult))
def __init__(self):
super(QWidget, self).__init__()
self.layout = QVBoxLayout(self)
self.raw_data = None
self.flatfield_image = None
self.path = None
self.diagram_data_array = []
self.angles = []
# Initialize tab screen
self.tabs = QTabWidget()
self.raw_data_tab = QWidget()
# Create an unfolding data tab
self.unfolded_data_tab = UnfoldingDataTab(self)
self.diagram_tab = QWidget()
self.fitting_data_tab = QWidget()
# Create raw data display tab
self.raw_data_tab.layout = QVBoxLayout(self.raw_data_tab)
self.raw_data_viewer = RawDataViewer(self.raw_data_tab)
# Create diagram plot data tab
self.diagram_tab.layout = QVBoxLayout(self.diagram_tab)
self.diagram_data_plot = Plot1D(self.diagram_tab)
# Create fitting curve tab
self.fitting_data_tab.layout = QVBoxLayout(self.fitting_data_tab)
self.fitting_data_selector = NumpyAxesSelector(self.fitting_data_tab)
self.fitting_data_plot = Plot1D(self.fitting_data_tab)
self.fitting_widget = self.fitting_data_plot.getFitAction()
self.fit_action = FitAction(plot=self.fitting_data_plot,
parent=self.fitting_data_plot)
self.toolbar = QToolBar("New")
# Create automatic fitting tab
self.automatic_fit_tab = FittingDataTab(self)
self.unfolded_data_tab.viewer.get_unfold_with_flatfield_action(
).unfoldWithFlatfieldClicked.connect(self.get_calibration)
self.unfolded_data_tab.viewer.get_unfold_action(
).unfoldClicked.connect(self.get_calibration)
self.unfolded_data_tab.unfoldingFinished.connect(
self.create_diagram_array)
self.init_UI()
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)
maskToolWidget = self._plot.getPlot().getMaskToolsDockWidget().widget()
maskToolWidget.setItemMaskUpdated(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 test_selected_data_event(self):
data = numpy.arange(3 * 3 * 3)
data.shape = 3, 3, 3
widget = NumpyAxesSelector()
listener = SignalListener()
widget.selectionChanged.connect(listener)
widget.setData(data)
widget.setAxisNames(["x"])
widget.setData(None)
self.assertEqual(listener.callCount(), 3)
listener.clear()
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.selectorDock = qt.QDockWidget("Data selector", self._plot)
# not closable
self.selectorDock.setFeatures(qt.QDockWidget.DockWidgetMovable
| qt.QDockWidget.DockWidgetFloatable)
self._selector = NumpyAxesSelector(self.selectorDock)
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._auxSigSlider)
self.selectorDock.setWidget(self._selector)
self._plot.addTabbedDockWidget(self.selectorDock)
self.setLayout(layout)
def __init__(self, parent):
super().__init__(parent=parent)
self.scatter_view = ScatterView(self)
colormap = Colormap('viridis', normalization='log')
self.scatter_view.setGraphTitle("Stack of unfolded data")
self.scatter_view.setColormap(colormap)
self.plot = self.scatter_view.getPlotWidget()
self.plot.setGraphXLabel("two-theta (°)")
self.plot.setGraphYLabel("psi (°)")
self.plot.setKeepDataAspectRatio(False)
self.plot.setYAxisInverted(True)
self.scatter_selector = NumpyAxesSelector(self)
# Prevent user from changing dimensions for the plot
self.scatter_selector.setNamedAxesSelectorVisibility(False)
self.scatter_selector.setVisible(True)
self.scatter_selector.setAxisNames("12")
self.layout = QVBoxLayout(self)
self.layout.addWidget(self.scatter_view)
self.layout.addWidget(self.scatter_selector)
self.stack = []
self.initial_data_flag = True
self.toolbar = QToolBar("Custom toolbar 1")
self.scatter_view.addToolBar(self.toolbar)
self.action_unfold = Unfold(self.plot, parent=self)
self.action_unfold_with_flatfield = UnfoldWithFlatfield(self.plot,
parent=self)
self.action_save = SaveAction(self.plot, parent=self)
self.toolbar.addAction(self.action_unfold)
self.toolbar.addAction(self.action_unfold_with_flatfield)
self.toolbar.addAction(self.action_save)
self.scatter_selector.selectionChanged.connect(
self.change_displayed_data)
def test_h5py_dataset(self):
with self.h5_temporary_file() as h5file:
dataset = h5file["data"]
expectedResult = dataset[0]
widget = NumpyAxesSelector()
widget.setData(dataset)
widget.setAxisNames(["y", "x"])
result = widget.selectedData()
self.assertTrue(numpy.array_equal(result, expectedResult))
def test_selected_data_event(self):
data = numpy.arange(3 * 3 * 3)
data.shape = 3, 3, 3
widget = NumpyAxesSelector()
listener = SignalListener()
widget.selectionChanged.connect(listener)
widget.setData(data)
widget.setAxisNames(["x"])
widget.setData(None)
self.assertEqual(listener.callCount(), 3)
listener.clear()
def __init__(self, parent=None):
"""
:param parent: Parent QWidget
"""
super(ArrayImagePlot, self).__init__(parent)
self.__signal = None
self.__signal_name = 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({
"name": "viridis",
"vmin": 0.,
"vmax": 1., # ignored (autoscale) but mandatory
"normalization": "linear",
"autoscale": True
})
self.selectorDock = qt.QDockWidget("Data selector", self._plot)
# not closable
self.selectorDock.setFeatures(qt.QDockWidget.DockWidgetMovable
| qt.QDockWidget.DockWidgetFloatable)
self._legend = qt.QLabel(self)
self._selector = NumpyAxesSelector(self.selectorDock)
self._selector.setNamedAxesSelectorVisibility(False)
self.__selector_is_connected = False
layout = qt.QVBoxLayout()
layout.addWidget(self._plot)
layout.addWidget(self._legend)
self.selectorDock.setWidget(self._selector)
self._plot.addTabbedDockWidget(self.selectorDock)
self.setLayout(layout)
def __init__(self, parent=None):
"""
:param parent: Parent QWidget
"""
super(ArrayCurvePlot, self).__init__(parent)
self.__signal = None
self.__signal_name = None
self.__signal_errors = None
self.__axis = None
self.__axis_name = None
self.__axis_errors = None
self.__values = None
self.__first_curve_added = False
self._plot = Plot1D(self)
self._plot.setDefaultColormap( # for scatters
{"name": "viridis",
"vmin": 0., "vmax": 1., # ignored (autoscale) but mandatory
"normalization": "linear",
"autoscale": True})
self.selectorDock = qt.QDockWidget("Data selector", self._plot)
# not closable
self.selectorDock.setFeatures(qt.QDockWidget.DockWidgetMovable
| qt.QDockWidget.DockWidgetFloatable)
self._selector = NumpyAxesSelector(self.selectorDock)
self._selector.setNamedAxesSelectorVisibility(False)
self.__selector_is_connected = False
self.selectorDock.setWidget(self._selector)
self._plot.addTabbedDockWidget(self.selectorDock)
layout = qt.QGridLayout()
layout.setContentsMargins(0, 0, 0, 0)
layout.addWidget(self._plot, 0, 0)
self.setLayout(layout)
def __init__(self, parent, data_to_fit=None):
super().__init__(parent)
self._data_to_fit = data_to_fit
self._fitted_data = []
self.layout = QVBoxLayout(self)
self.automatic_plot = Plot1D(self)
self.fitting_data_selector = NumpyAxesSelector(self)
self.fit = FitManager()
self.fit.addtheory("pearson7",
function=pearson7bg,
parameters=[
'backgr', 'slopeLin', 'amplitude', 'center',
'fwhm', 'exposant'
],
estimate=estimate_pearson7)
"""
self.fitting_widget = self.fitting_data_plot.getFitAction()
self.fit_action = FitAction(plot=self.fitting_data_plot, parent=self.fitting_data_plot)
self.toolbar = QToolBar("New")
"""
self.init_ui()
def test_h5py_dataset(self):
with self.h5_temporary_file() as h5file:
dataset = h5file["data"]
expectedResult = dataset[0]
widget = NumpyAxesSelector()
widget.setData(dataset)
widget.setAxisNames(["y", "x"])
result = widget.selectedData()
self.assertTrue(numpy.array_equal(result, expectedResult))
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 __init__(self, parent=None):
"""Constructor
:param QWidget parent: The parent of the widget
"""
super(DataViewer, self).__init__(parent)
self.__stack = qt.QStackedWidget(self)
self.__numpySelection = NumpyAxesSelector(self)
self.__numpySelection.selectedAxisChanged.connect(self.__numpyAxisChanged)
self.__numpySelection.selectionChanged.connect(self.__numpySelectionChanged)
self.__numpySelection.customAxisChanged.connect(self.__numpyCustomAxisChanged)
self.setLayout(qt.QVBoxLayout(self))
self.layout().addWidget(self.__stack, 1)
group = qt.QGroupBox(self)
group.setLayout(qt.QVBoxLayout())
group.layout().addWidget(self.__numpySelection)
group.setTitle("Axis selection")
self.__axisSelection = group
self.layout().addWidget(self.__axisSelection)
self.__currentAvailableViews = []
self.__currentView = None
self.__data = None
self.__info = None
self.__useAxisSelection = False
self.__userSelectedView = None
self.__hooks = None
self.__views = []
self.__index = {}
"""store stack index for each views"""
self._initializeViews()
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 DataViewer(qt.QFrame):
"""Widget to display any kind of data
.. image:: img/DataViewer.png
The method :meth:`setData` allows to set any data to the widget. Mostly
`numpy.array` and `h5py.Dataset` are supported with adapted views. Other
data types are displayed using a text viewer.
A default view is automatically selected when a data is set. The method
:meth:`setDisplayMode` allows to change the view. To have a graphical tool
to select the view, prefer using the widget :class:`DataViewerFrame`.
The dimension of the input data and the expected dimension of the selected
view can differ. For example you can display an image (2D) from 4D
data. In this case a :class:`NumpyAxesSelector` is displayed to allow the
user to select the axis mapping and the slicing of other axes.
.. code-block:: python
import numpy
data = numpy.random.rand(500,500)
viewer = DataViewer()
viewer.setData(data)
viewer.setVisible(True)
"""
# TODO: Can be removed for silx 0.8
@classproperty
@deprecation.deprecated(replacement="DataViews.EMPTY_MODE", since_version="0.7", skip_backtrace_count=2)
def EMPTY_MODE(self):
return DataViews.EMPTY_MODE
# TODO: Can be removed for silx 0.8
@classproperty
@deprecation.deprecated(replacement="DataViews.PLOT1D_MODE", since_version="0.7", skip_backtrace_count=2)
def PLOT1D_MODE(self):
return DataViews.PLOT1D_MODE
# TODO: Can be removed for silx 0.8
@classproperty
@deprecation.deprecated(replacement="DataViews.PLOT2D_MODE", since_version="0.7", skip_backtrace_count=2)
def PLOT2D_MODE(self):
return DataViews.PLOT2D_MODE
# TODO: Can be removed for silx 0.8
@classproperty
@deprecation.deprecated(replacement="DataViews.PLOT3D_MODE", since_version="0.7", skip_backtrace_count=2)
def PLOT3D_MODE(self):
return DataViews.PLOT3D_MODE
# TODO: Can be removed for silx 0.8
@classproperty
@deprecation.deprecated(replacement="DataViews.RAW_MODE", since_version="0.7", skip_backtrace_count=2)
def RAW_MODE(self):
return DataViews.RAW_MODE
# TODO: Can be removed for silx 0.8
@classproperty
@deprecation.deprecated(replacement="DataViews.RAW_ARRAY_MODE", since_version="0.7", skip_backtrace_count=2)
def RAW_ARRAY_MODE(self):
return DataViews.RAW_ARRAY_MODE
# TODO: Can be removed for silx 0.8
@classproperty
@deprecation.deprecated(replacement="DataViews.RAW_RECORD_MODE", since_version="0.7", skip_backtrace_count=2)
def RAW_RECORD_MODE(self):
return DataViews.RAW_RECORD_MODE
# TODO: Can be removed for silx 0.8
@classproperty
@deprecation.deprecated(replacement="DataViews.RAW_SCALAR_MODE", since_version="0.7", skip_backtrace_count=2)
def RAW_SCALAR_MODE(self):
return DataViews.RAW_SCALAR_MODE
# TODO: Can be removed for silx 0.8
@classproperty
@deprecation.deprecated(replacement="DataViews.STACK_MODE", since_version="0.7", skip_backtrace_count=2)
def STACK_MODE(self):
return DataViews.STACK_MODE
# TODO: Can be removed for silx 0.8
@classproperty
@deprecation.deprecated(replacement="DataViews.HDF5_MODE", since_version="0.7", skip_backtrace_count=2)
def HDF5_MODE(self):
return DataViews.HDF5_MODE
displayedViewChanged = qt.Signal(object)
"""Emitted when the displayed view changes"""
dataChanged = qt.Signal()
"""Emitted when the data changes"""
currentAvailableViewsChanged = qt.Signal()
"""Emitted when the current available views (which support the current
data) change"""
def __init__(self, parent=None):
"""Constructor
:param QWidget parent: The parent of the widget
"""
super(DataViewer, self).__init__(parent)
self.__stack = qt.QStackedWidget(self)
self.__numpySelection = NumpyAxesSelector(self)
self.__numpySelection.selectedAxisChanged.connect(self.__numpyAxisChanged)
self.__numpySelection.selectionChanged.connect(self.__numpySelectionChanged)
self.__numpySelection.customAxisChanged.connect(self.__numpyCustomAxisChanged)
self.setLayout(qt.QVBoxLayout(self))
self.layout().addWidget(self.__stack, 1)
group = qt.QGroupBox(self)
group.setLayout(qt.QVBoxLayout())
group.layout().addWidget(self.__numpySelection)
group.setTitle("Axis selection")
self.__axisSelection = group
self.layout().addWidget(self.__axisSelection)
self.__currentAvailableViews = []
self.__currentView = None
self.__data = None
self.__info = None
self.__useAxisSelection = False
self.__userSelectedView = None
self.__hooks = None
self.__views = []
self.__index = {}
"""store stack index for each views"""
self._initializeViews()
def _initializeViews(self):
"""Inisialize the available views"""
views = self.createDefaultViews(self.__stack)
self.__views = list(views)
self.setDisplayMode(DataViews.EMPTY_MODE)
def setGlobalHooks(self, hooks):
"""Set a data view hooks for all the views
:param DataViewHooks context: The hooks to use
"""
self.__hooks = hooks
for v in self.__views:
v.setHooks(hooks)
def createDefaultViews(self, parent=None):
"""Create and returns available views which can be displayed by default
by the data viewer. It is called internally by the widget. It can be
overwriten to provide a different set of viewers.
:param QWidget parent: QWidget parent of the views
:rtype: List[silx.gui.data.DataViews.DataView]
"""
viewClasses = [
DataViews._EmptyView,
DataViews._Hdf5View,
DataViews._NXdataView,
DataViews._Plot1dView,
DataViews._ImageView,
DataViews._Plot3dView,
DataViews._RawView,
DataViews._StackView,
]
views = []
for viewClass in viewClasses:
try:
view = viewClass(parent)
views.append(view)
except Exception:
_logger.warning("%s instantiation failed. View is ignored" % viewClass.__name__)
_logger.debug("Backtrace", exc_info=True)
return views
def clear(self):
"""Clear the widget"""
self.setData(None)
def normalizeData(self, data):
"""Returns a normalized data if the embed a numpy or a dataset.
Else returns the data."""
return _normalizeData(data)
def __getStackIndex(self, view):
"""Get the stack index containing the view.
:param silx.gui.data.DataViews.DataView view: The view
"""
if view not in self.__index:
widget = view.getWidget()
index = self.__stack.addWidget(widget)
self.__index[view] = index
else:
index = self.__index[view]
return index
def __clearCurrentView(self):
"""Clear the current selected view"""
view = self.__currentView
if view is not None:
view.clear()
def __numpyCustomAxisChanged(self, name, value):
view = self.__currentView
if view is not None:
view.setCustomAxisValue(name, value)
def __updateNumpySelectionAxis(self):
"""
Update the numpy-selector according to the needed axis names
"""
previous = self.__numpySelection.blockSignals(True)
self.__numpySelection.clear()
info = self._getInfo()
axisNames = self.__currentView.axesNames(self.__data, info)
if info.isArray and info.size != 0 and self.__data is not None and axisNames is not None:
self.__useAxisSelection = True
self.__numpySelection.setAxisNames(axisNames)
self.__numpySelection.setCustomAxis(self.__currentView.customAxisNames())
data = self.normalizeData(self.__data)
self.__numpySelection.setData(data)
if hasattr(data, "shape"):
isVisible = not (len(axisNames) == 1 and len(data.shape) == 1)
else:
isVisible = True
self.__axisSelection.setVisible(isVisible)
else:
self.__useAxisSelection = False
self.__axisSelection.setVisible(False)
self.__numpySelection.blockSignals(previous)
def __updateDataInView(self):
"""
Update the views using the current data
"""
if self.__useAxisSelection:
self.__displayedData = self.__numpySelection.selectedData()
else:
self.__displayedData = self.__data
# TODO: would be good to avoid that, it should be synchonous
qt.QTimer.singleShot(10, self.__setDataInView)
def __setDataInView(self):
self.__currentView.setData(self.__displayedData)
def setDisplayedView(self, view):
"""Set the displayed view.
Change the displayed view according to the view itself.
:param silx.gui.data.DataViews.DataView view: The DataView to use to display the data
"""
self.__userSelectedView = view
self._setDisplayedView(view)
def _setDisplayedView(self, view):
"""Internal set of the displayed view.
Change the displayed view according to the view itself.
:param silx.gui.data.DataViews.DataView view: The DataView to use to display the data
"""
if self.__currentView is view:
return
self.__clearCurrentView()
self.__currentView = view
self.__updateNumpySelectionAxis()
self.__updateDataInView()
stackIndex = self.__getStackIndex(self.__currentView)
if self.__currentView is not None:
self.__currentView.select()
self.__stack.setCurrentIndex(stackIndex)
self.displayedViewChanged.emit(view)
def getViewFromModeId(self, modeId):
"""Returns the first available view which have the requested modeId.
Return None if modeId does not correspond to an existing view.
:param int modeId: Requested mode id
:rtype: silx.gui.data.DataViews.DataView
"""
for view in self.__views:
if view.modeId() == modeId:
return view
return None
def setDisplayMode(self, modeId):
"""Set the displayed view using display mode.
Change the displayed view according to the requested mode.
:param int modeId: Display mode, one of
- `DataViews.EMPTY_MODE`: display nothing
- `DataViews.PLOT1D_MODE`: display the data as a curve
- `DataViews.IMAGE_MODE`: display the data as an image
- `DataViews.PLOT3D_MODE`: display the data as an isosurface
- `DataViews.RAW_MODE`: display the data as a table
- `DataViews.STACK_MODE`: display the data as a stack of images
- `DataViews.HDF5_MODE`: display the data as a table of HDF5 info
- `DataViews.NXDATA_MODE`: display the data as NXdata
"""
try:
view = self.getViewFromModeId(modeId)
except KeyError:
raise ValueError("Display mode %s is unknown" % modeId)
self._setDisplayedView(view)
def displayedView(self):
"""Returns the current displayed view.
:rtype: silx.gui.data.DataViews.DataView
"""
return self.__currentView
def addView(self, view):
"""Allow to add a view to the dataview.
If the current data support this view, it will be displayed.
:param DataView view: A dataview
"""
if self.__hooks is not None:
view.setHooks(self.__hooks)
self.__views.append(view)
# TODO It can be skipped if the view do not support the data
self.__updateAvailableViews()
def removeView(self, view):
"""Allow to remove a view which was available from the dataview.
If the view was displayed, the widget will be updated.
:param DataView view: A dataview
"""
self.__views.remove(view)
self.__stack.removeWidget(view.getWidget())
# invalidate the full index. It will be updated as expected
self.__index = {}
if self.__userSelectedView is view:
self.__userSelectedView = None
if view is self.__currentView:
self.__updateView()
else:
# TODO It can be skipped if the view is not part of the
# available views
self.__updateAvailableViews()
def __updateAvailableViews(self):
"""
Update available views from the current data.
"""
data = self.__data
info = self._getInfo()
# sort available views according to priority
priorities = [v.getDataPriority(data, info) for v in self.__views]
views = zip(priorities, self.__views)
views = filter(lambda t: t[0] > DataViews.DataView.UNSUPPORTED, views)
views = sorted(views, reverse=True)
# store available views
if len(views) == 0:
self.__setCurrentAvailableViews([])
available = []
else:
available = [v[1] for v in views]
self.__setCurrentAvailableViews(available)
def __updateView(self):
"""Display the data using the widget which fit the best"""
data = self.__data
# update available views for this data
self.__updateAvailableViews()
available = self.__currentAvailableViews
# display the view with the most priority (the default view)
view = self.getDefaultViewFromAvailableViews(data, available)
self.__clearCurrentView()
try:
self._setDisplayedView(view)
except Exception as e:
# in case there is a problem to read the data, try to use a safe
# view
view = self.getSafeViewFromAvailableViews(data, available)
self._setDisplayedView(view)
raise e
def getSafeViewFromAvailableViews(self, data, available):
"""Returns a view which is sure to display something without failing
on rendering.
:param object data: data which will be displayed
:param List[view] available: List of available views, from highest
priority to lowest.
:rtype: DataView
"""
hdf5View = self.getViewFromModeId(DataViewer.HDF5_MODE)
if hdf5View in available:
return hdf5View
return self.getViewFromModeId(DataViews.EMPTY_MODE)
def getDefaultViewFromAvailableViews(self, data, available):
"""Returns the default view which will be used according to available
views.
:param object data: data which will be displayed
:param List[view] available: List of available views, from highest
priority to lowest.
:rtype: DataView
"""
if len(available) > 0:
# returns the view with the highest priority
if self.__userSelectedView in available:
return self.__userSelectedView
self.__userSelectedView = None
view = available[0]
else:
# else returns the empty view
view = self.getViewFromModeId(DataViews.EMPTY_MODE)
return view
def __setCurrentAvailableViews(self, availableViews):
"""Set the current available viewa
:param List[DataView] availableViews: Current available viewa
"""
self.__currentAvailableViews = availableViews
self.currentAvailableViewsChanged.emit()
def currentAvailableViews(self):
"""Returns the list of available views for the current data
:rtype: List[DataView]
"""
return self.__currentAvailableViews
def availableViews(self):
"""Returns the list of registered views
:rtype: List[DataView]
"""
return self.__views
def setData(self, data):
"""Set the data to view.
It mostly can be a h5py.Dataset or a numpy.ndarray. Other kind of
objects will be displayed as text rendering.
:param numpy.ndarray data: The data.
"""
self.__data = data
self._invalidateInfo()
self.__displayedData = None
self.__updateView()
self.__updateNumpySelectionAxis()
self.__updateDataInView()
self.dataChanged.emit()
def __numpyAxisChanged(self):
"""
Called when axis selection of the numpy-selector changed
"""
self.__clearCurrentView()
def __numpySelectionChanged(self):
"""
Called when data selection of the numpy-selector changed
"""
self.__updateDataInView()
def data(self):
"""Returns the data"""
return self.__data
def _invalidateInfo(self):
"""Invalidate DataInfo cache."""
self.__info = None
def _getInfo(self):
"""Returns the DataInfo of the current selected data.
This value is cached.
:rtype: DataInfo
"""
if self.__info is None:
self.__info = DataViews.DataInfo(self.__data)
return self.__info
def displayMode(self):
"""Returns the current display mode"""
return self.__currentView.modeId()
def replaceView(self, modeId, newView):
"""Replace one of the builtin data views with a custom view.
Return True in case of success, False in case of failure.
.. note::
This method must be called just after instantiation, before
the viewer is used.
:param int modeId: Unique mode ID identifying the DataView to
be replaced. One of:
- `DataViews.EMPTY_MODE`
- `DataViews.PLOT1D_MODE`
- `DataViews.IMAGE_MODE`
- `DataViews.PLOT2D_MODE`
- `DataViews.COMPLEX_IMAGE_MODE`
- `DataViews.PLOT3D_MODE`
- `DataViews.RAW_MODE`
- `DataViews.STACK_MODE`
- `DataViews.HDF5_MODE`
- `DataViews.NXDATA_MODE`
- `DataViews.NXDATA_INVALID_MODE`
- `DataViews.NXDATA_SCALAR_MODE`
- `DataViews.NXDATA_CURVE_MODE`
- `DataViews.NXDATA_XYVSCATTER_MODE`
- `DataViews.NXDATA_IMAGE_MODE`
- `DataViews.NXDATA_STACK_MODE`
:param DataViews.DataView newView: New data view
:return: True if replacement was successful, else False
"""
assert isinstance(newView, DataViews.DataView)
isReplaced = False
for idx, view in enumerate(self.__views):
if view.modeId() == modeId:
if self.__hooks is not None:
newView.setHooks(self.__hooks)
self.__views[idx] = newView
isReplaced = True
break
elif isinstance(view, DataViews.CompositeDataView):
isReplaced = view.replaceView(modeId, newView)
if isReplaced:
break
if isReplaced:
self.__updateAvailableViews()
return isReplaced
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 ArrayCurvePlot(qt.QWidget):
"""
Widget for plotting a curve from a multi-dimensional signal array
and a 1D axis array.
The signal array can have an arbitrary number of dimensions, the only
limitation being that the last dimension must have the same length as
the axis array.
The widget provides sliders to select indices on the first (n - 1)
dimensions of the signal array, and buttons to add/replace selected
curves to the plot.
This widget also handles simple 2D or 3D scatter plots (third dimension
displayed as colour of points).
"""
def __init__(self, parent=None):
"""
:param parent: Parent QWidget
"""
super(ArrayCurvePlot, self).__init__(parent)
self.__signals = None
self.__signals_names = None
self.__signal_errors = None
self.__axis = None
self.__axis_name = None
self.__x_axis_errors = None
self.__values = None
self._plot = Plot1D(self)
self._selector = NumpyAxesSelector(self)
self._selector.setNamedAxesSelectorVisibility(False)
self.__selector_is_connected = False
self._plot.sigActiveCurveChanged.connect(
self._setYLabelFromActiveLegend)
layout = qt.QVBoxLayout()
layout.setContentsMargins(0, 0, 0, 0)
layout.addWidget(self._plot)
layout.addWidget(self._selector)
self.setLayout(layout)
def getPlot(self):
"""Returns the plot used for the display
:rtype: Plot1D
"""
return self._plot
def setCurvesData(self,
ys,
x=None,
yerror=None,
xerror=None,
ylabels=None,
xlabel=None,
title=None,
xscale=None,
yscale=None):
"""
:param List[ndarray] ys: List of arrays to be represented by the y (vertical) axis.
It can be multiple n-D array whose last dimension must
have the same length as x (and values must be None)
:param ndarray x: 1-D dataset used as the curve's x values. If provided,
its lengths must be equal to the length of the last dimension of
``y`` (and equal to the length of ``value``, for a scatter plot).
:param ndarray yerror: Single array of errors for y (same shape), or None.
There can only be one array, and it applies to the first/main y
(no y errors for auxiliary_signals curves).
:param ndarray xerror: 1-D dataset of errors for x, or None
:param str ylabels: Labels for each curve's Y axis
:param str xlabel: Label for X axis
:param str title: Graph title
:param str xscale: Scale of X axis in (None, 'linear', 'log')
:param str yscale: Scale of Y axis in (None, 'linear', 'log')
"""
self.__signals = ys
self.__signals_names = ylabels or (["Y"] * len(ys))
self.__signal_errors = yerror
self.__axis = x
self.__axis_name = xlabel
self.__x_axis_errors = xerror
if self.__selector_is_connected:
self._selector.selectionChanged.disconnect(self._updateCurve)
self.__selector_is_connected = False
self._selector.setData(ys[0])
self._selector.setAxisNames(["Y"])
if len(ys[0].shape) < 2:
self._selector.hide()
else:
self._selector.show()
self._plot.setGraphTitle(title or "")
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._updateCurve()
if not self.__selector_is_connected:
self._selector.selectionChanged.connect(self._updateCurve)
self.__selector_is_connected = True
def _updateCurve(self):
selection = self._selector.selection()
ys = [sig[selection] for sig in self.__signals]
y0 = ys[0]
len_y = len(y0)
x = self.__axis
if x is None:
x = numpy.arange(len_y)
elif numpy.isscalar(x) or len(x) == 1:
# constant axis
x = x * numpy.ones_like(y0)
elif len(x) == 2 and len_y != 2:
# linear calibration a + b * x
x = x[0] + x[1] * numpy.arange(len_y)
self._plot.remove(kind=("curve", ))
for i in range(len(self.__signals)):
legend = self.__signals_names[i]
# errors only supported for primary signal in NXdata
y_errors = None
if i == 0 and self.__signal_errors is not None:
y_errors = self.__signal_errors[self._selector.selection()]
self._plot.addCurve(x,
ys[i],
legend=legend,
xerror=self.__x_axis_errors,
yerror=y_errors)
if i == 0:
self._plot.setActiveCurve(legend)
self._plot.resetZoom()
self._plot.getXAxis().setLabel(self.__axis_name)
self._plot.getYAxis().setLabel(self.__signals_names[0])
def _setYLabelFromActiveLegend(self, previous_legend, new_legend):
for ylabel in self.__signals_names:
if new_legend is not None and new_legend == ylabel:
self._plot.getYAxis().setLabel(ylabel)
break
def clear(self):
old = self._selector.blockSignals(True)
self._selector.clear()
self._selector.blockSignals(old)
self._plot.clear()
class UnfoldedDataViewer(QWidget):
def __init__(self, parent):
super().__init__(parent=parent)
self.scatter_view = ScatterView(self)
colormap = Colormap('viridis', normalization='log')
self.scatter_view.setGraphTitle("Stack of unfolded data")
self.scatter_view.setColormap(colormap)
self.plot = self.scatter_view.getPlotWidget()
self.plot.setGraphXLabel("two-theta (°)")
self.plot.setGraphYLabel("psi (°)")
self.plot.setKeepDataAspectRatio(False)
self.plot.setYAxisInverted(True)
self.scatter_selector = NumpyAxesSelector(self)
# Prevent user from changing dimensions for the plot
self.scatter_selector.setNamedAxesSelectorVisibility(False)
self.scatter_selector.setVisible(True)
self.scatter_selector.setAxisNames("12")
self.layout = QVBoxLayout(self)
self.layout.addWidget(self.scatter_view)
self.layout.addWidget(self.scatter_selector)
self.stack = []
self.initial_data_flag = True
self.toolbar = QToolBar("Custom toolbar 1")
self.scatter_view.addToolBar(self.toolbar)
self.action_unfold = Unfold(self.plot, parent=self)
self.action_unfold_with_flatfield = UnfoldWithFlatfield(self.plot,
parent=self)
self.action_save = SaveAction(self.plot, parent=self)
self.toolbar.addAction(self.action_unfold)
self.toolbar.addAction(self.action_unfold_with_flatfield)
self.toolbar.addAction(self.action_save)
self.scatter_selector.selectionChanged.connect(
self.change_displayed_data)
def add_scatter(self, scatter_image: tuple, scatter_factor: int):
# Add an image to the stack. If it is the first, emit the selectionChanged signal to plot the first image
self.stack.append((scatter_image[0][::scatter_factor],
scatter_image[1][::scatter_factor],
scatter_image[2][::scatter_factor]))
if self.initial_data_flag:
self.scatter_selector.selectionChanged.emit()
self.initial_data_flag = False
def set_stack_slider(self, nb_images: int):
# Set the size of the sliderbar that will let the user navigate the images
self.clear_scatter_view()
self.scatter_selector.setData(numpy.zeros((nb_images, 1, 1)))
def change_displayed_data(self):
# If there is at least one unfolded image, clear the view, unpack the data and plot a scatter view of the image
if len(self.stack) > 0:
self.clear_scatter_view()
tth_array, psi_array, intensity = self.stack[
self.scatter_selector.selection()[0]]
self.plot.setGraphXLimits(
min(tth_array) - 0.0,
max(tth_array) + 0.0)
self.plot.setGraphYLimits(
min(psi_array) - 5.0,
max(psi_array) + 5.0)
start = time.time()
self.scatter_view.setData(tth_array,
psi_array,
intensity,
copy=False)
end = time.time()
print("Setting the data took :", (end - start) * 1000.0, " ms")
def clear_scatter_view(self):
self.scatter_view.setData(None, None, None)
def reset_scatter_view(self):
self.clear_scatter_view()
self.stack = []
self.initial_data_flag = True
def get_scatter_item(self, index: int) -> tuple:
return self.stack[index]
def get_scatter_items(self) -> list:
return self.stack
def get_unfold_action(self):
return self.action_unfold
def get_unfold_with_flatfield_action(self):
return self.action_unfold_with_flatfield
def test_creation(self):
data = numpy.arange(3 * 3 * 3)
data.shape = 3, 3, 3
widget = NumpyAxesSelector()
widget.setVisible(True)
class ArrayVolumePlot(qt.QWidget):
"""
Widget for plotting a n-D array (n >= 3) as a 3D scalar field.
Three axis arrays can be provided to calibrate the axes.
The signal array can have an arbitrary number of dimensions, the only
limitation being that the last 3 dimensions must have the same length as
the axes arrays.
Sliders are provided to select indices on the first (n - 3) dimensions of
the signal array, and the plot is updated to load the stack corresponding
to the selection.
"""
def __init__(self, parent=None):
"""
:param parent: Parent QWidget
"""
super(ArrayVolumePlot, self).__init__(parent)
self.__signal = None
self.__signal_name = None
# the Z, Y, X axes apply to the last three dimensions of the signal
# (in that order)
self.__z_axis = None
self.__z_axis_name = None
self.__y_axis = None
self.__y_axis_name = None
self.__x_axis = None
self.__x_axis_name = None
from ._VolumeWindow import VolumeWindow
self._view = VolumeWindow(self)
self._hline = qt.QFrame(self)
self._hline.setFrameStyle(qt.QFrame.HLine)
self._hline.setFrameShadow(qt.QFrame.Sunken)
self._legend = qt.QLabel(self)
self._selector = NumpyAxesSelector(self)
self._selector.setNamedAxesSelectorVisibility(False)
self.__selector_is_connected = False
layout = qt.QVBoxLayout()
layout.addWidget(self._view)
layout.addWidget(self._hline)
layout.addWidget(self._legend)
layout.addWidget(self._selector)
self.setLayout(layout)
def getVolumeView(self):
"""Returns the plot used for the display
:rtype: SceneWindow
"""
return self._view
def setData(self,
signal,
x_axis=None,
y_axis=None,
z_axis=None,
signal_name=None,
xlabel=None,
ylabel=None,
zlabel=None,
title=None):
"""
:param signal: n-D dataset, whose last 3 dimensions are used as the
3D stack values.
: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 z_axis: 1-D dataset used as the image's z. If provided,
its lengths must be equal to the length of the 3rd to last
dimension of ``signal``.
:param signal_name: Label used in the legend
:param xlabel: Label for X axis
:param ylabel: Label for Y axis
:param zlabel: Label for Z axis
:param title: Graph title
"""
if self.__selector_is_connected:
self._selector.selectionChanged.disconnect(self._updateVolume)
self.__selector_is_connected = False
self.__signal = signal
self.__signal_name = signal_name or ""
self.__x_axis = x_axis
self.__x_axis_name = xlabel
self.__y_axis = y_axis
self.__y_axis_name = ylabel
self.__z_axis = z_axis
self.__z_axis_name = zlabel
self._selector.setData(signal)
self._selector.setAxisNames(["Y", "X", "Z"])
self._updateVolume()
# the legend label shows the selection slice producing the volume
# (only interesting for ndim > 3)
if signal.ndim > 3:
self._selector.setVisible(True)
self._legend.setVisible(True)
self._hline.setVisible(True)
else:
self._selector.setVisible(False)
self._legend.setVisible(False)
self._hline.setVisible(False)
if not self.__selector_is_connected:
self._selector.selectionChanged.connect(self._updateVolume)
self.__selector_is_connected = True
def _updateVolume(self):
"""Update displayed stack according to the current axes selector
data."""
x_axis = self.__x_axis
y_axis = self.__y_axis
z_axis = self.__z_axis
offset = []
scale = []
for axis in [x_axis, y_axis, z_axis]:
if axis is None:
calibration = NoCalibration()
elif len(axis) == 2:
calibration = LinearCalibration(y_intercept=axis[0],
slope=axis[1])
else:
calibration = ArrayCalibration(axis)
if not calibration.is_affine():
_logger.warning("Axis has not linear values, ignored")
offset.append(0.)
scale.append(1.)
else:
offset.append(calibration(0))
scale.append(calibration.get_slope())
legend = self.__signal_name + "["
for sl in self._selector.selection():
if sl == slice(None):
legend += ":, "
else:
legend += str(sl) + ", "
legend = legend[:-2] + "]"
self._legend.setText("Displayed data: " + legend)
# Update SceneWidget
data = self._selector.selectedData()
volumeView = self.getVolumeView()
volumeView.setData(data, offset=offset, scale=scale)
volumeView.setAxesLabels(self.__x_axis_name, self.__y_axis_name,
self.__z_axis_name)
def clear(self):
old = self._selector.blockSignals(True)
self._selector.clear()
self._selector.blockSignals(old)
self.getVolumeView().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.__signal = None
self.__signal_name = 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({
"name": "viridis",
"vmin": 0.,
"vmax": 1., # ignored (autoscale) but mandatory
"normalization": "linear",
"autoscale": True
})
self.selectorDock = qt.QDockWidget("Data selector", self._plot)
# not closable
self.selectorDock.setFeatures(qt.QDockWidget.DockWidgetMovable
| qt.QDockWidget.DockWidgetFloatable)
self._legend = qt.QLabel(self)
self._selector = NumpyAxesSelector(self.selectorDock)
self._selector.setNamedAxesSelectorVisibility(False)
self.__selector_is_connected = False
layout = qt.QVBoxLayout()
layout.addWidget(self._plot)
layout.addWidget(self._legend)
self.selectorDock.setWidget(self._selector)
self._plot.addTabbedDockWidget(self.selectorDock)
self.setLayout(layout)
def setImageData(self,
signal,
x_axis=None,
y_axis=None,
signal_name=None,
xlabel=None,
ylabel=None,
title=None):
"""
:param signal: n-D dataset, whose last 2 dimensions are used as the
image's values.
: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 signal_name: Label used in the legend
:param xlabel: Label for X axis
:param ylabel: Label for Y axis
:param title: Graph title
"""
if self.__selector_is_connected:
self._selector.selectionChanged.disconnect(self._updateImage)
self.__selector_is_connected = False
self.__signal = signal
self.__signal_name = signal_name or ""
self.__x_axis = x_axis
self.__x_axis_name = xlabel
self.__y_axis = y_axis
self.__y_axis_name = ylabel
self._selector.setData(signal)
self._selector.setAxisNames([ylabel or "Y", xlabel or "X"])
if len(signal.shape) < 3:
self.selectorDock.hide()
else:
self.selectorDock.show()
self._plot.setGraphTitle(title or "")
self._plot.setGraphXLabel(self.__x_axis_name or "X")
self._plot.setGraphYLabel(self.__y_axis_name or "Y")
self._updateImage()
if not self.__selector_is_connected:
self._selector.selectionChanged.connect(self._updateImage)
self.__selector_is_connected = True
def _updateImage(self):
legend = self.__signal_name + "["
for sl in self._selector.selection():
if sl == slice(None):
legend += ":, "
else:
legend += str(sl) + ", "
legend = legend[:-2] + "]"
self._legend.setText("Displayed data: " + legend)
img = self._selector.selectedData()
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(img.shape[-1])
elif numpy.isscalar(x_axis) or len(x_axis) == 1:
# constant axis
x_axis = x_axis * numpy.ones((img.shape[-1], ))
elif len(x_axis) == 2:
# linear calibration
x_axis = x_axis[0] * numpy.arange(img.shape[-1]) + x_axis[1]
if y_axis is None:
y_axis = numpy.arange(img.shape[-2])
elif numpy.isscalar(y_axis) or len(y_axis) == 1:
y_axis = y_axis * numpy.ones((img.shape[-2], ))
elif len(y_axis) == 2:
y_axis = y_axis[0] * numpy.arange(img.shape[-2]) + 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(img, legend=legend, origin=origin, scale=scale)
else:
scatterx, scattery = numpy.meshgrid(x_axis, y_axis)
self._plot.addScatter(numpy.ravel(scatterx),
numpy.ravel(scattery),
numpy.ravel(img),
legend=legend)
self._plot.setGraphXLabel(self.__x_axis_name)
self._plot.setGraphYLabel(self.__y_axis_name)
self._plot.resetZoom()
def clear(self):
self._plot.clear()
class ArrayStackPlot(qt.QWidget):
"""
Widget for plotting a n-D array (n >= 3) as a stack of images.
Three axis arrays can be provided to calibrate the axes.
The signal array can have an arbitrary number of dimensions, the only
limitation being that the last 3 dimensions must have the same length as
the axes arrays.
Sliders are provided to select indices on the first (n - 3) dimensions of
the signal array, and the plot is updated to load the stack corresponding
to the selection.
"""
def __init__(self, parent=None):
"""
:param parent: Parent QWidget
"""
super(ArrayStackPlot, self).__init__(parent)
self.__signal = None
self.__signal_name = None
# the Z, Y, X axes apply to the last three dimensions of the signal
# (in that order)
self.__z_axis = None
self.__z_axis_name = None
self.__y_axis = None
self.__y_axis_name = None
self.__x_axis = None
self.__x_axis_name = None
self._stack_view = StackView(self)
self._hline = qt.QFrame(self)
self._hline.setFrameStyle(qt.QFrame.HLine)
self._hline.setFrameShadow(qt.QFrame.Sunken)
self._legend = qt.QLabel(self)
self._selector = NumpyAxesSelector(self)
self._selector.setNamedAxesSelectorVisibility(False)
self.__selector_is_connected = False
layout = qt.QVBoxLayout()
layout.addWidget(self._stack_view)
layout.addWidget(self._hline)
layout.addWidget(self._legend)
layout.addWidget(self._selector)
self.setLayout(layout)
def getStackView(self):
"""Returns the plot used for the display
:rtype: StackView
"""
return self._stack_view
def setStackData(self,
signal,
x_axis=None,
y_axis=None,
z_axis=None,
signal_name=None,
xlabel=None,
ylabel=None,
zlabel=None,
title=None):
"""
:param signal: n-D dataset, whose last 3 dimensions are used as the
3D stack values.
: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 z_axis: 1-D dataset used as the image's z. If provided,
its lengths must be equal to the length of the 3rd to last
dimension of ``signal``.
:param signal_name: Label used in the legend
:param xlabel: Label for X axis
:param ylabel: Label for Y axis
:param zlabel: Label for Z axis
:param title: Graph title
"""
if self.__selector_is_connected:
self._selector.selectionChanged.disconnect(self._updateStack)
self.__selector_is_connected = False
self.__signal = signal
self.__signal_name = signal_name or ""
self.__x_axis = x_axis
self.__x_axis_name = xlabel
self.__y_axis = y_axis
self.__y_axis_name = ylabel
self.__z_axis = z_axis
self.__z_axis_name = zlabel
self._selector.setData(signal)
self._selector.setAxisNames(["Y", "X", "Z"])
self._stack_view.setGraphTitle(title or "")
# by default, the z axis is the image position (dimension not plotted)
self._stack_view.getPlotWidget().getXAxis().setLabel(self.__x_axis_name
or "X")
self._stack_view.getPlotWidget().getYAxis().setLabel(self.__y_axis_name
or "Y")
self._updateStack()
ndims = len(signal.shape)
self._stack_view.setFirstStackDimension(ndims - 3)
# the legend label shows the selection slice producing the volume
# (only interesting for ndim > 3)
if ndims > 3:
self._selector.setVisible(True)
self._legend.setVisible(True)
self._hline.setVisible(True)
else:
self._selector.setVisible(False)
self._legend.setVisible(False)
self._hline.setVisible(False)
if not self.__selector_is_connected:
self._selector.selectionChanged.connect(self._updateStack)
self.__selector_is_connected = True
@staticmethod
def _get_origin_scale(axis):
"""Assuming axis is a regularly spaced 1D array,
return a tuple (origin, scale) where:
- origin = axis[0]
- scale = (axis[n-1] - axis[0]) / (n -1)
:param axis: 1D numpy array
:return: Tuple (axis[0], (axis[-1] - axis[0]) / (len(axis) - 1))
"""
return axis[0], (axis[-1] - axis[0]) / (len(axis) - 1)
def _updateStack(self):
"""Update displayed stack according to the current axes selector
data."""
stk = self._selector.selectedData()
x_axis = self.__x_axis
y_axis = self.__y_axis
z_axis = self.__z_axis
calibrations = []
for axis in [z_axis, y_axis, x_axis]:
if axis is None:
calibrations.append(NoCalibration())
elif len(axis) == 2:
calibrations.append(
LinearCalibration(y_intercept=axis[0], slope=axis[1]))
else:
calibrations.append(ArrayCalibration(axis))
legend = self.__signal_name + "["
for sl in self._selector.selection():
if sl == slice(None):
legend += ":, "
else:
legend += str(sl) + ", "
legend = legend[:-2] + "]"
self._legend.setText("Displayed data: " + legend)
self._stack_view.setStack(stk, calibrations=calibrations)
self._stack_view.setLabels(labels=[
self.__z_axis_name, self.__y_axis_name, self.__x_axis_name
])
def clear(self):
old = self._selector.blockSignals(True)
self._selector.clear()
self._selector.blockSignals(old)
self._stack_view.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 FittingDataTab(QWidget):
def __init__(self, parent, data_to_fit=None):
super().__init__(parent)
self._data_to_fit = data_to_fit
self._fitted_data = []
self.layout = QVBoxLayout(self)
self.automatic_plot = Plot1D(self)
self.fitting_data_selector = NumpyAxesSelector(self)
self.fit = FitManager()
self.fit.addtheory("pearson7",
function=pearson7bg,
parameters=[
'backgr', 'slopeLin', 'amplitude', 'center',
'fwhm', 'exposant'
],
estimate=estimate_pearson7)
"""
self.fitting_widget = self.fitting_data_plot.getFitAction()
self.fit_action = FitAction(plot=self.fitting_data_plot, parent=self.fitting_data_plot)
self.toolbar = QToolBar("New")
"""
self.init_ui()
def init_ui(self):
self.setLayout(self.layout)
self.layout.addWidget(self.automatic_plot)
self.layout.addWidget(self.fitting_data_selector)
self.fitting_data_selector.setNamedAxesSelectorVisibility(False)
self.fitting_data_selector.setVisible(True)
self.fitting_data_selector.setAxisNames("12")
# self.fitting_data_selector.selectionChanged.connect(self.automatic_plot_fit)
def set_data_to_fit(self, data_to_fit):
self._data_to_fit = data_to_fit
self.fitting_data_selector.setData(
numpy.zeros((len(data_to_fit), 1, 1)))
self.start_automatic_fit()
def automatic_fit(self):
if self._data_to_fit is not None:
prominence = max(
self._data_to_fit[0][1][~numpy.isnan(self._data_to_fit[0][1])])
print(prominence)
peaks, _ = find_peaks(self._data_to_fit[0][1],
prominence=prominence / 3.0)
plt.plot(peaks, self._data_to_fit[0][1][peaks], "xr")
plt.plot(self._data_to_fit[0][1])
plt.legend(['Test detection with prominence'])
plt.show()
def plot_fit(self):
if len(self._fitted_data) > 0 and len(self._data_to_fit) > 0:
self.automatic_plot.addCurve(
self._data_to_fit[self.fitting_data_selector.selection()[0]]
[0], self._data_to_fit[self.fitting_data_selector.selection()
[0]][1], 'Data to fit')
self.automatic_plot.addCurve(
self._fitted_data[self.fitting_data_selector.selection()[0]]
[0], self._fitted_data[self.fitting_data_selector.selection()
[0]][1], 'Fitted data')
def start_automatic_fit(self):
self.fit.settheory("pearson7")
print("Start fitting...")
for data in self._data_to_fit:
# copy the data arrays, with all the values even the nan ones to keep the size
# the copy is used to not erase data on arrays ploted in the last panel
x = data[0].copy()
y = data[1].copy()
# get the max of the y array without any nan value (it would be the maximum)
maximum = max(y[~numpy.isnan(y)])
# current maximum / peak we are looking for (for the first iteration it will be the max)
current_maximum = max(y[~numpy.isnan(y)])
try:
cpt_peak = 0
print("Searching peak and fitting it...")
# plot the original curve, were we are going to plot each fitted peak.
self.automatic_plot.addCurve(x, y, "Data to fit")
print("Max : ", maximum, " Current max : ", current_maximum)
# this threshold means that we only want peaks that are at least at 1/4 distance in y axis of the max.
while current_maximum > (maximum / 4.0):
peak = numpy.where(y == current_maximum)[0][0]
left = peak - 35 if peak - 35 > 0 else 0
right = peak + 35 if peak + 35 < len(x) else len(x) - 1
x_peak = x[left:right]
y_peak = y[left:right]
# set the data to fit, i.e only the peak without all the curve
self.fit.setdata(x=x_peak, y=y_peak)
# use the estimate function we made to make a first guess of the parameters of the function that will fit our peak.
self.fit.estimate()
# fit the function.
self.fit.runfit()
# draw the resulted function of the fit.
self.automatic_plot.addCurve(
x_peak,
pearson7bg(
x_peak,
*(param['fitresult']
for param in self.fit.fit_results)),
f"Peak number {cpt_peak}")
self._fitted_data.append(
self.automatic_plot.getActiveCurve())
backgr = self.fit.fit_results[0]['fitresult']
fwhm = self.fit.fit_results[4]['fitresult']
# erase the peak to make it easier to found other peaks.
y[left:right] = statistics.mean(y[~numpy.isnan(y)])
current_maximum = max(y[~numpy.isnan(y)])
cpt_peak += 1
print("new current_max : ", current_maximum)
except (numpy.linalg.LinAlgError, TypeError):
print(
"Singular matrix error: fit is impossible with the given parameters"
)
class ArrayVolumePlot(qt.QWidget):
"""
Widget for plotting a n-D array (n >= 3) as a 3D scalar field.
Three axis arrays can be provided to calibrate the axes.
The signal array can have an arbitrary number of dimensions, the only
limitation being that the last 3 dimensions must have the same length as
the axes arrays.
Sliders are provided to select indices on the first (n - 3) dimensions of
the signal array, and the plot is updated to load the stack corresponding
to the selection.
"""
def __init__(self, parent=None):
"""
:param parent: Parent QWidget
"""
super(ArrayVolumePlot, self).__init__(parent)
self.__signal = None
self.__signal_name = None
# the Z, Y, X axes apply to the last three dimensions of the signal
# (in that order)
self.__z_axis = None
self.__z_axis_name = None
self.__y_axis = None
self.__y_axis_name = None
self.__x_axis = None
self.__x_axis_name = None
from silx.gui.plot3d.ScalarFieldView import ScalarFieldView
from silx.gui.plot3d import SFViewParamTree
self._view = ScalarFieldView(self)
def computeIsolevel(data):
data = data[numpy.isfinite(data)]
if len(data) == 0:
return 0
else:
return numpy.mean(data) + numpy.std(data)
self._view.addIsosurface(computeIsolevel, '#FF0000FF')
# Create a parameter tree for the scalar field view
options = SFViewParamTree.TreeView(self._view)
options.setSfView(self._view)
# Add the parameter tree to the main window in a dock widget
dock = qt.QDockWidget()
dock.setWidget(options)
self._view.addDockWidget(qt.Qt.RightDockWidgetArea, dock)
self._hline = qt.QFrame(self)
self._hline.setFrameStyle(qt.QFrame.HLine)
self._hline.setFrameShadow(qt.QFrame.Sunken)
self._legend = qt.QLabel(self)
self._selector = NumpyAxesSelector(self)
self._selector.setNamedAxesSelectorVisibility(False)
self.__selector_is_connected = False
layout = qt.QVBoxLayout()
layout.addWidget(self._view)
layout.addWidget(self._hline)
layout.addWidget(self._legend)
layout.addWidget(self._selector)
self.setLayout(layout)
def getVolumeView(self):
"""Returns the plot used for the display
:rtype: ScalarFieldView
"""
return self._view
def normalizeComplexData(self, data):
"""
Converts a complex data array to its amplitude, if necessary.
:param data: the data to normalize
:return:
"""
if hasattr(data, "dtype"):
isComplex = numpy.issubdtype(data.dtype, numpy.complexfloating)
else:
isComplex = isinstance(data, numbers.Complex)
if isComplex:
data = numpy.absolute(data)
return data
def setData(self,
signal,
x_axis=None,
y_axis=None,
z_axis=None,
signal_name=None,
xlabel=None,
ylabel=None,
zlabel=None,
title=None):
"""
:param signal: n-D dataset, whose last 3 dimensions are used as the
3D stack values.
: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 z_axis: 1-D dataset used as the image's z. If provided,
its lengths must be equal to the length of the 3rd to last
dimension of ``signal``.
:param signal_name: Label used in the legend
:param xlabel: Label for X axis
:param ylabel: Label for Y axis
:param zlabel: Label for Z axis
:param title: Graph title
"""
signal = self.normalizeComplexData(signal)
if self.__selector_is_connected:
self._selector.selectionChanged.disconnect(self._updateVolume)
self.__selector_is_connected = False
self.__signal = signal
self.__signal_name = signal_name or ""
self.__x_axis = x_axis
self.__x_axis_name = xlabel
self.__y_axis = y_axis
self.__y_axis_name = ylabel
self.__z_axis = z_axis
self.__z_axis_name = zlabel
self._selector.setData(signal)
self._selector.setAxisNames(["Y", "X", "Z"])
self._view.setAxesLabels(self.__x_axis_name or 'X', self.__y_axis_name
or 'Y', self.__z_axis_name or 'Z')
self._updateVolume()
# the legend label shows the selection slice producing the volume
# (only interesting for ndim > 3)
if signal.ndim > 3:
self._selector.setVisible(True)
self._legend.setVisible(True)
self._hline.setVisible(True)
else:
self._selector.setVisible(False)
self._legend.setVisible(False)
self._hline.setVisible(False)
if not self.__selector_is_connected:
self._selector.selectionChanged.connect(self._updateVolume)
self.__selector_is_connected = True
def _updateVolume(self):
"""Update displayed stack according to the current axes selector
data."""
data = self._selector.selectedData()
x_axis = self.__x_axis
y_axis = self.__y_axis
z_axis = self.__z_axis
offset = []
scale = []
for axis in [x_axis, y_axis, z_axis]:
if axis is None:
calibration = NoCalibration()
elif len(axis) == 2:
calibration = LinearCalibration(y_intercept=axis[0],
slope=axis[1])
else:
calibration = ArrayCalibration(axis)
if not calibration.is_affine():
_logger.warning("Axis has not linear values, ignored")
offset.append(0.)
scale.append(1.)
else:
offset.append(calibration(0))
scale.append(calibration.get_slope())
legend = self.__signal_name + "["
for sl in self._selector.selection():
if sl == slice(None):
legend += ":, "
else:
legend += str(sl) + ", "
legend = legend[:-2] + "]"
self._legend.setText("Displayed data: " + legend)
self._view.setData(data, copy=False)
self._view.setScale(*scale)
self._view.setTranslation(*offset)
self._view.setAxesLabels(self.__x_axis_name, self.__y_axis_name,
self.__z_axis_name)
def clear(self):
old = self._selector.blockSignals(True)
self._selector.clear()
self._selector.blockSignals(old)
self._view.setData(None)
class XpadVisualisation(QWidget):
unfoldButtonClicked = pyqtSignal()
def __init__(self):
super(QWidget, self).__init__()
self.layout = QVBoxLayout(self)
self.raw_data = None
self.flatfield_image = None
self.path = None
self.diagram_data_array = []
self.angles = []
# Initialize tab screen
self.tabs = QTabWidget()
self.raw_data_tab = QWidget()
# Create an unfolding data tab
self.unfolded_data_tab = UnfoldingDataTab(self)
self.diagram_tab = QWidget()
self.fitting_data_tab = QWidget()
# Create raw data display tab
self.raw_data_tab.layout = QVBoxLayout(self.raw_data_tab)
self.raw_data_viewer = RawDataViewer(self.raw_data_tab)
# Create diagram plot data tab
self.diagram_tab.layout = QVBoxLayout(self.diagram_tab)
self.diagram_data_plot = Plot1D(self.diagram_tab)
# Create fitting curve tab
self.fitting_data_tab.layout = QVBoxLayout(self.fitting_data_tab)
self.fitting_data_selector = NumpyAxesSelector(self.fitting_data_tab)
self.fitting_data_plot = Plot1D(self.fitting_data_tab)
self.fitting_widget = self.fitting_data_plot.getFitAction()
self.fit_action = FitAction(plot=self.fitting_data_plot,
parent=self.fitting_data_plot)
self.toolbar = QToolBar("New")
# Create automatic fitting tab
self.automatic_fit_tab = FittingDataTab(self)
self.unfolded_data_tab.viewer.get_unfold_with_flatfield_action(
).unfoldWithFlatfieldClicked.connect(self.get_calibration)
self.unfolded_data_tab.viewer.get_unfold_action(
).unfoldClicked.connect(self.get_calibration)
self.unfolded_data_tab.unfoldingFinished.connect(
self.create_diagram_array)
self.init_UI()
def init_UI(self):
self.tabs.resize(400, 300)
# Add tabs
self.tabs.addTab(self.raw_data_tab, "Raw data")
self.tabs.addTab(self.unfolded_data_tab, "Unfolded data")
self.tabs.addTab(self.diagram_tab, "Diffraction diagram")
self.tabs.addTab(self.fitting_data_tab, "Fitted data")
self.tabs.addTab(self.automatic_fit_tab, "Automatic fit")
self.raw_data_tab.layout.addWidget(self.raw_data_viewer)
self.diagram_tab.layout.addWidget(self.diagram_data_plot)
self.diagram_data_plot.setGraphTitle(f"Diagram diffraction")
self.diagram_data_plot.setGraphXLabel("two-theta (°)")
self.diagram_data_plot.setGraphYLabel("intensity")
self.diagram_data_plot.setYAxisLogarithmic(True)
self.fitting_data_selector.setNamedAxesSelectorVisibility(False)
self.fitting_data_selector.setVisible(True)
self.fitting_data_selector.setAxisNames("12")
self.fitting_data_plot.setYAxisLogarithmic(True)
self.fitting_data_plot.setGraphXLabel("two-theta (°)")
self.fitting_data_plot.setGraphYLabel("intensity")
self.fitting_data_plot.getRoiAction().trigger()
self.fitting_widget.setXRangeUpdatedOnZoom(False)
self.toolbar.addAction(self.fit_action)
self.fit_action.setVisible(True)
self.fitting_data_plot.addToolBar(self.toolbar)
self.fitting_data_tab.layout.addWidget(self.fitting_data_plot)
self.fitting_data_tab.layout.addWidget(self.fitting_data_selector)
# Add tabs to widget
self.layout.addWidget(self.tabs)
# self.unfold_timer.timeout.connect(self.unfold_data)
self.fitting_data_selector.selectionChanged.connect(self.fitting_curve)
self.fitting_data_plot.getCurvesRoiWidget().sigROIWidgetSignal.connect(
self.get_roi_list)
self.unfolded_data_tab.viewer.scatter_selector.selectionChanged.connect(
self.synchronize_visualisation)
@pyqtSlot()
def on_click(self):
print("\n")
for currentQTableWidgetItem in self.tableWidget.selectedItems():
print(currentQTableWidgetItem.row(),
currentQTableWidgetItem.column(),
currentQTableWidgetItem.text())
def set_data(self, path: str) -> None:
self.path = path
with File(os.path.join(path), mode='r') as h5file:
self.raw_data = get_dataset(h5file,
DataPath.IMAGE_INTERPRETATION.value)[:]
# We put the raw data in the dataviewer
self.raw_data_viewer.set_movie(self.raw_data, self.flatfield_image)
self.unfolded_data_tab.images = self.raw_data
self.unfolded_data_tab.path = self.path
# We allocate a number of view in the stack of unfolded data and fitting data
self.unfolded_data_tab.viewer.set_stack_slider(self.raw_data.shape[0])
self.fitting_data_selector.setData(
numpy.zeros((self.raw_data.shape[0], 1, 1)))
def set_calibration(self, calibration):
# Check if there is a empty list of coordinate in the direct beam calibration
if not [] in [value for value in calibration.values()]:
self.unfolded_data_tab.set_calibration(calibration)
if self.unfolded_data_tab.images is not None:
self.unfolded_data_tab.start_unfolding()
else:
print("Direct beam not calibrated yet.")
def get_calibration(self):
self.unfoldButtonClicked.emit()
def create_diagram_array(self):
self.diagram_data_array = []
numpy.seterr(divide='ignore', invalid='ignore')
for image in self.unfolded_data_tab.viewer.get_scatter_items():
self.diagram_data_array.append(
extract_diffraction_diagram(
image[0],
image[1],
image[2],
1.0 / self.unfolded_data_tab.geometry["calib"],
-100,
100,
patch_data_flag=True))
self.plot_diagram()
self.automatic_fit_tab.set_data_to_fit(self.diagram_data_array)
self.fitting_data_selector.selectionChanged.emit()
set_plot_limits(self.diagram_data_plot,
self.diagram_data_plot.getActiveCurve())
def plot_diagram(self, images_to_remove=[-1]):
self.diagram_data_plot.setGraphTitle(
f"Diagram diffraction of {self.path.split('/')[-1]}")
for index, curve in enumerate(self.diagram_data_array):
if index not in images_to_remove:
self.diagram_data_plot.addCurve(curve[0],
curve[1],
f'Data of image {index}',
color="#0000FF",
replace=False,
symbol='o')
def get_flatfield(self, flat_img: numpy.ndarray):
self.flatfield_image = flat_img
self.raw_data_viewer.get_action_flatfield().set_flatfield(
self.flatfield_image)
self.unfolded_data_tab.flatfield = flat_img
def synchronize_visualisation(self):
# When user change the unfolded view, it set the raw image to the same frame
self.raw_data_viewer.setFrameNumber(
self.unfolded_data_tab.viewer.scatter_selector.selection()[0])
def fitting_curve(self):
if len(self.diagram_data_array) > 0:
self.clear_plot_fitting_widget()
curve = self.diagram_data_array[
self.fitting_data_selector.selection()[0]]
self.fitting_data_plot.addCurve(curve[0], curve[1], symbol='o')
set_plot_limits(self.fitting_data_plot, curve)
def get_roi_list(self, events: dict):
self.rois_list = list(events["roilist"])
def clear_plot_fitting_widget(self):
self.fitting_data_plot.clear()
self.fitting_data_plot.clearMarkers()
class ArrayCurvePlot(qt.QWidget):
"""
Widget for plotting a curve from a multi-dimensional signal array
and a 1D axis array.
The signal array can have an arbitrary number of dimensions, the only
limitation being that the last dimension must have the same length as
the axis array.
The widget provides sliders to select indices on the first (n - 1)
dimensions of the signal array, and buttons to add/replace selected
curves to the plot.
This widget also handles simple 2D or 3D scatter plots (third dimension
displayed as colour of points).
"""
def __init__(self, parent=None):
"""
:param parent: Parent QWidget
"""
super(ArrayCurvePlot, self).__init__(parent)
self.__signals = None
self.__signals_names = None
self.__signal_errors = None
self.__axis = None
self.__axis_name = None
self.__x_axis_errors = None
self.__values = None
self._plot = Plot1D(self)
self._selector = NumpyAxesSelector(self)
self._selector.setNamedAxesSelectorVisibility(False)
self.__selector_is_connected = False
self._plot.sigActiveCurveChanged.connect(self._setYLabelFromActiveLegend)
layout = qt.QVBoxLayout()
layout.setContentsMargins(0, 0, 0, 0)
layout.addWidget(self._plot)
layout.addWidget(self._selector)
self.setLayout(layout)
def getPlot(self):
"""Returns the plot used for the display
:rtype: Plot1D
"""
return self._plot
def setCurvesData(self, ys, x=None,
yerror=None, xerror=None,
ylabels=None, xlabel=None, title=None):
"""
:param List[ndarray] ys: List of arrays to be represented by the y (vertical) axis.
It can be multiple n-D array whose last dimension must
have the same length as x (and values must be None)
:param ndarray x: 1-D dataset used as the curve's x values. If provided,
its lengths must be equal to the length of the last dimension of
``y`` (and equal to the length of ``value``, for a scatter plot).
:param ndarray yerror: Single array of errors for y (same shape), or None.
There can only be one array, and it applies to the first/main y
(no y errors for auxiliary_signals curves).
:param ndarray xerror: 1-D dataset of errors for x, or None
:param str ylabels: Labels for each curve's Y axis
:param str xlabel: Label for X axis
:param str title: Graph title
"""
self.__signals = ys
self.__signals_names = ylabels or (["Y"] * len(ys))
self.__signal_errors = yerror
self.__axis = x
self.__axis_name = xlabel
self.__x_axis_errors = xerror
if self.__selector_is_connected:
self._selector.selectionChanged.disconnect(self._updateCurve)
self.__selector_is_connected = False
self._selector.setData(ys[0])
self._selector.setAxisNames(["Y"])
if len(ys[0].shape) < 2:
self._selector.hide()
else:
self._selector.show()
self._plot.setGraphTitle(title or "")
self._updateCurve()
if not self.__selector_is_connected:
self._selector.selectionChanged.connect(self._updateCurve)
self.__selector_is_connected = True
def _updateCurve(self):
selection = self._selector.selection()
ys = [sig[selection] for sig in self.__signals]
y0 = ys[0]
len_y = len(y0)
x = self.__axis
if x is None:
x = numpy.arange(len_y)
elif numpy.isscalar(x) or len(x) == 1:
# constant axis
x = x * numpy.ones_like(y0)
elif len(x) == 2 and len_y != 2:
# linear calibration a + b * x
x = x[0] + x[1] * numpy.arange(len_y)
self._plot.remove(kind=("curve",))
for i in range(len(self.__signals)):
legend = self.__signals_names[i]
# errors only supported for primary signal in NXdata
y_errors = None
if i == 0 and self.__signal_errors is not None:
y_errors = self.__signal_errors[self._selector.selection()]
self._plot.addCurve(x, ys[i], legend=legend,
xerror=self.__x_axis_errors,
yerror=y_errors)
if i == 0:
self._plot.setActiveCurve(legend)
self._plot.resetZoom()
self._plot.getXAxis().setLabel(self.__axis_name)
self._plot.getYAxis().setLabel(self.__signals_names[0])
def _setYLabelFromActiveLegend(self, previous_legend, new_legend):
for ylabel in self.__signals_names:
if new_legend is not None and new_legend == ylabel:
self._plot.getYAxis().setLabel(ylabel)
break
def clear(self):
old = self._selector.blockSignals(True)
self._selector.clear()
self._selector.blockSignals(old)
self._plot.clear()
class ArrayStackPlot(qt.QWidget):
"""
Widget for plotting a n-D array (n >= 3) as a stack of images.
Three axis arrays can be provided to calibrate the axes.
The signal array can have an arbitrary number of dimensions, the only
limitation being that the last 3 dimensions must have the same length as
the axes arrays.
Sliders are provided to select indices on the first (n - 3) dimensions of
the signal array, and the plot is updated to load the stack corresponding
to the selection.
"""
def __init__(self, parent=None):
"""
:param parent: Parent QWidget
"""
super(ArrayStackPlot, self).__init__(parent)
self.__signal = None
self.__signal_name = None
# the Z, Y, X axes apply to the last three dimensions of the signal
# (in that order)
self.__z_axis = None
self.__z_axis_name = None
self.__y_axis = None
self.__y_axis_name = None
self.__x_axis = None
self.__x_axis_name = None
self._stack_view = StackView(self)
self._hline = qt.QFrame(self)
self._hline.setFrameStyle(qt.QFrame.HLine)
self._hline.setFrameShadow(qt.QFrame.Sunken)
self._legend = qt.QLabel(self)
self._selector = NumpyAxesSelector(self)
self._selector.setNamedAxesSelectorVisibility(False)
self.__selector_is_connected = False
layout = qt.QVBoxLayout()
layout.addWidget(self._stack_view)
layout.addWidget(self._hline)
layout.addWidget(self._legend)
layout.addWidget(self._selector)
self.setLayout(layout)
def getStackView(self):
"""Returns the plot used for the display
:rtype: StackView
"""
return self._stack_view
def setStackData(self, signal,
x_axis=None, y_axis=None, z_axis=None,
signal_name=None,
xlabel=None, ylabel=None, zlabel=None,
title=None):
"""
:param signal: n-D dataset, whose last 3 dimensions are used as the
3D stack values.
: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 z_axis: 1-D dataset used as the image's z. If provided,
its lengths must be equal to the length of the 3rd to last
dimension of ``signal``.
:param signal_name: Label used in the legend
:param xlabel: Label for X axis
:param ylabel: Label for Y axis
:param zlabel: Label for Z axis
:param title: Graph title
"""
if self.__selector_is_connected:
self._selector.selectionChanged.disconnect(self._updateStack)
self.__selector_is_connected = False
self.__signal = signal
self.__signal_name = signal_name or ""
self.__x_axis = x_axis
self.__x_axis_name = xlabel
self.__y_axis = y_axis
self.__y_axis_name = ylabel
self.__z_axis = z_axis
self.__z_axis_name = zlabel
self._selector.setData(signal)
self._selector.setAxisNames(["Y", "X", "Z"])
self._stack_view.setGraphTitle(title or "")
# by default, the z axis is the image position (dimension not plotted)
self._stack_view.getPlot().getXAxis().setLabel(self.__x_axis_name or "X")
self._stack_view.getPlot().getYAxis().setLabel(self.__y_axis_name or "Y")
self._updateStack()
ndims = len(signal.shape)
self._stack_view.setFirstStackDimension(ndims - 3)
# the legend label shows the selection slice producing the volume
# (only interesting for ndim > 3)
if ndims > 3:
self._selector.setVisible(True)
self._legend.setVisible(True)
self._hline.setVisible(True)
else:
self._selector.setVisible(False)
self._legend.setVisible(False)
self._hline.setVisible(False)
if not self.__selector_is_connected:
self._selector.selectionChanged.connect(self._updateStack)
self.__selector_is_connected = True
@staticmethod
def _get_origin_scale(axis):
"""Assuming axis is a regularly spaced 1D array,
return a tuple (origin, scale) where:
- origin = axis[0]
- scale = (axis[n-1] - axis[0]) / (n -1)
:param axis: 1D numpy array
:return: Tuple (axis[0], (axis[-1] - axis[0]) / (len(axis) - 1))
"""
return axis[0], (axis[-1] - axis[0]) / (len(axis) - 1)
def _updateStack(self):
"""Update displayed stack according to the current axes selector
data."""
stk = self._selector.selectedData()
x_axis = self.__x_axis
y_axis = self.__y_axis
z_axis = self.__z_axis
calibrations = []
for axis in [z_axis, y_axis, x_axis]:
if axis is None:
calibrations.append(NoCalibration())
elif len(axis) == 2:
calibrations.append(
LinearCalibration(y_intercept=axis[0],
slope=axis[1]))
else:
calibrations.append(ArrayCalibration(axis))
legend = self.__signal_name + "["
for sl in self._selector.selection():
if sl == slice(None):
legend += ":, "
else:
legend += str(sl) + ", "
legend = legend[:-2] + "]"
self._legend.setText("Displayed data: " + legend)
self._stack_view.setStack(stk, calibrations=calibrations)
self._stack_view.setLabels(
labels=[self.__z_axis_name,
self.__y_axis_name,
self.__x_axis_name])
def clear(self):
old = self._selector.blockSignals(True)
self._selector.clear()
self._selector.blockSignals(old)
self._stack_view.clear()
class DataViewer(qt.QFrame):
"""Widget to display any kind of data
.. image:: img/DataViewer.png
The method :meth:`setData` allows to set any data to the widget. Mostly
`numpy.array` and `h5py.Dataset` are supported with adapted views. Other
data types are displayed using a text viewer.
A default view is automatically selected when a data is set. The method
:meth:`setDisplayMode` allows to change the view. To have a graphical tool
to select the view, prefer using the widget :class:`DataViewerFrame`.
The dimension of the input data and the expected dimension of the selected
view can differ. For example you can display an image (2D) from 4D
data. In this case a :class:`NumpyAxesSelector` is displayed to allow the
user to select the axis mapping and the slicing of other axes.
.. code-block:: python
import numpy
data = numpy.random.rand(500,500)
viewer = DataViewer()
viewer.setData(data)
viewer.setVisible(True)
"""
EMPTY_MODE = 0
PLOT1D_MODE = 10
PLOT2D_MODE = 20
PLOT3D_MODE = 30
RAW_MODE = 40
RAW_ARRAY_MODE = 41
RAW_RECORD_MODE = 42
RAW_SCALAR_MODE = 43
STACK_MODE = 50
HDF5_MODE = 60
displayedViewChanged = qt.Signal(object)
"""Emitted when the displayed view changes"""
dataChanged = qt.Signal()
"""Emitted when the data changes"""
currentAvailableViewsChanged = qt.Signal()
"""Emitted when the current available views (which support the current
data) change"""
def __init__(self, parent=None):
"""Constructor
:param QWidget parent: The parent of the widget
"""
super(DataViewer, self).__init__(parent)
self.__stack = qt.QStackedWidget(self)
self.__numpySelection = NumpyAxesSelector(self)
self.__numpySelection.selectedAxisChanged.connect(
self.__numpyAxisChanged)
self.__numpySelection.selectionChanged.connect(
self.__numpySelectionChanged)
self.__numpySelection.customAxisChanged.connect(
self.__numpyCustomAxisChanged)
self.setLayout(qt.QVBoxLayout(self))
self.layout().addWidget(self.__stack, 1)
group = qt.QGroupBox(self)
group.setLayout(qt.QVBoxLayout())
group.layout().addWidget(self.__numpySelection)
group.setTitle("Axis selection")
self.__axisSelection = group
self.layout().addWidget(self.__axisSelection)
self.__currentAvailableViews = []
self.__currentView = None
self.__data = None
self.__useAxisSelection = False
self.__userSelectedView = None
self.__views = []
self.__index = {}
"""store stack index for each views"""
self._initializeViews()
def _initializeViews(self):
"""Inisialize the available views"""
views = self.createDefaultViews(self.__stack)
self.__views = list(views)
self.setDisplayMode(self.EMPTY_MODE)
def createDefaultViews(self, parent=None):
"""Create and returns available views which can be displayed by default
by the data viewer. It is called internally by the widget. It can be
overwriten to provide a different set of viewers.
:param QWidget parent: QWidget parent of the views
:rtype: list[silx.gui.data.DataViews.DataView]
"""
viewClasses = [
DataViews._EmptyView,
DataViews._Hdf5View,
DataViews._NXdataView,
DataViews._Plot1dView,
DataViews._ImageView,
DataViews._Plot3dView,
DataViews._RawView,
DataViews._StackView,
]
views = []
for viewClass in viewClasses:
try:
view = viewClass(parent)
views.append(view)
except Exception:
_logger.warning("%s instantiation failed. View is ignored" %
viewClass.__name__)
_logger.debug("Backtrace", exc_info=True)
return views
def clear(self):
"""Clear the widget"""
self.setData(None)
def normalizeData(self, data):
"""Returns a normalized data if the embed a numpy or a dataset.
Else returns the data."""
return _normalizeData(data)
def __getStackIndex(self, view):
"""Get the stack index containing the view.
:param silx.gui.data.DataViews.DataView view: The view
"""
if view not in self.__index:
widget = view.getWidget()
index = self.__stack.addWidget(widget)
self.__index[view] = index
else:
index = self.__index[view]
return index
def __clearCurrentView(self):
"""Clear the current selected view"""
view = self.__currentView
if view is not None:
view.clear()
def __numpyCustomAxisChanged(self, name, value):
view = self.__currentView
if view is not None:
view.setCustomAxisValue(name, value)
def __updateNumpySelectionAxis(self):
"""
Update the numpy-selector according to the needed axis names
"""
previous = self.__numpySelection.blockSignals(True)
self.__numpySelection.clear()
info = DataViews.DataInfo(self.__data)
axisNames = self.__currentView.axesNames(self.__data, info)
if info.isArray and self.__data is not None and axisNames is not None:
self.__useAxisSelection = True
self.__numpySelection.setAxisNames(axisNames)
self.__numpySelection.setCustomAxis(
self.__currentView.customAxisNames())
data = self.normalizeData(self.__data)
self.__numpySelection.setData(data)
if hasattr(data, "shape"):
isVisible = not (len(axisNames) == 1 and len(data.shape) == 1)
else:
isVisible = True
self.__axisSelection.setVisible(isVisible)
else:
self.__useAxisSelection = False
self.__axisSelection.setVisible(False)
self.__numpySelection.blockSignals(previous)
def __updateDataInView(self):
"""
Update the views using the current data
"""
if self.__useAxisSelection:
self.__displayedData = self.__numpySelection.selectedData()
else:
self.__displayedData = self.__data
qt.QTimer.singleShot(10, self.__setDataInView)
def __setDataInView(self):
self.__currentView.setData(self.__displayedData)
def setDisplayedView(self, view):
"""Set the displayed view.
Change the displayed view according to the view itself.
:param silx.gui.data.DataViews.DataView view: The DataView to use to display the data
"""
self.__userSelectedView = view
self._setDisplayedView(view)
def _setDisplayedView(self, view):
"""Internal set of the displayed view.
Change the displayed view according to the view itself.
:param silx.gui.data.DataViews.DataView view: The DataView to use to display the data
"""
if self.__currentView is view:
return
self.__clearCurrentView()
self.__currentView = view
self.__updateNumpySelectionAxis()
self.__updateDataInView()
stackIndex = self.__getStackIndex(self.__currentView)
if self.__currentView is not None:
self.__currentView.select()
self.__stack.setCurrentIndex(stackIndex)
self.displayedViewChanged.emit(view)
def getViewFromModeId(self, modeId):
"""Returns the first available view which have the requested modeId.
:param int modeId: Requested mode id
:rtype: silx.gui.data.DataViews.DataView
"""
for view in self.__views:
if view.modeId() == modeId:
return view
return view
def setDisplayMode(self, modeId):
"""Set the displayed view using display mode.
Change the displayed view according to the requested mode.
:param int modeId: Display mode, one of
- `EMPTY_MODE`: display nothing
- `PLOT1D_MODE`: display the data as a curve
- `PLOT2D_MODE`: display the data as an image
- `PLOT3D_MODE`: display the data as an isosurface
- `RAW_MODE`: display the data as a table
- `STACK_MODE`: display the data as a stack of images
- `HDF5_MODE`: display the data as a table
"""
try:
view = self.getViewFromModeId(modeId)
except KeyError:
raise ValueError("Display mode %s is unknown" % modeId)
self._setDisplayedView(view)
def displayedView(self):
"""Returns the current displayed view.
:rtype: silx.gui.data.DataViews.DataView
"""
return self.__currentView
def addView(self, view):
"""Allow to add a view to the dataview.
If the current data support this view, it will be displayed.
:param DataView view: A dataview
"""
self.__views.append(view)
# TODO It can be skipped if the view do not support the data
self.__updateAvailableViews()
def removeView(self, view):
"""Allow to remove a view which was available from the dataview.
If the view was displayed, the widget will be updated.
:param DataView view: A dataview
"""
self.__views.remove(view)
self.__stack.removeWidget(view.getWidget())
# invalidate the full index. It will be updated as expected
self.__index = {}
if self.__userSelectedView is view:
self.__userSelectedView = None
if view is self.__currentView:
self.__updateView()
else:
# TODO It can be skipped if the view is not part of the
# available views
self.__updateAvailableViews()
def __updateAvailableViews(self):
"""
Update available views from the current data.
"""
data = self.__data
# sort available views according to priority
info = DataViews.DataInfo(data)
priorities = [v.getDataPriority(data, info) for v in self.__views]
views = zip(priorities, self.__views)
views = filter(lambda t: t[0] > DataViews.DataView.UNSUPPORTED, views)
views = sorted(views, reverse=True)
# store available views
if len(views) == 0:
self.__setCurrentAvailableViews([])
available = []
else:
available = [v[1] for v in views]
self.__setCurrentAvailableViews(available)
def __updateView(self):
"""Display the data using the widget which fit the best"""
data = self.__data
# update available views for this data
self.__updateAvailableViews()
available = self.__currentAvailableViews
# display the view with the most priority (the default view)
view = self.getDefaultViewFromAvailableViews(data, available)
self.__clearCurrentView()
try:
self._setDisplayedView(view)
except Exception as e:
# in case there is a problem to read the data, try to use a safe
# view
view = self.getSafeViewFromAvailableViews(data, available)
self._setDisplayedView(view)
raise e
def getSafeViewFromAvailableViews(self, data, available):
"""Returns a view which is sure to display something without failing
on rendering.
:param object data: data which will be displayed
:param list[view] available: List of available views, from highest
priority to lowest.
:rtype: DataView
"""
hdf5View = self.getViewFromModeId(DataViewer.HDF5_MODE)
if hdf5View in available:
return hdf5View
return self.getViewFromModeId(DataViewer.EMPTY_MODE)
def getDefaultViewFromAvailableViews(self, data, available):
"""Returns the default view which will be used according to available
views.
:param object data: data which will be displayed
:param list[view] available: List of available views, from highest
priority to lowest.
:rtype: DataView
"""
if len(available) > 0:
# returns the view with the highest priority
if self.__userSelectedView in available:
return self.__userSelectedView
self.__userSelectedView = None
view = available[0]
else:
# else returns the empty view
view = self.getViewFromModeId(DataViewer.EMPTY_MODE)
return view
def __setCurrentAvailableViews(self, availableViews):
"""Set the current available viewa
:param List[DataView] availableViews: Current available viewa
"""
self.__currentAvailableViews = availableViews
self.currentAvailableViewsChanged.emit()
def currentAvailableViews(self):
"""Returns the list of available views for the current data
:rtype: List[DataView]
"""
return self.__currentAvailableViews
def availableViews(self):
"""Returns the list of registered views
:rtype: List[DataView]
"""
return self.__views
def setData(self, data):
"""Set the data to view.
It mostly can be a h5py.Dataset or a numpy.ndarray. Other kind of
objects will be displayed as text rendering.
:param numpy.ndarray data: The data.
"""
self.__data = data
self.__displayedData = None
self.__updateView()
self.__updateNumpySelectionAxis()
self.__updateDataInView()
self.dataChanged.emit()
def __numpyAxisChanged(self):
"""
Called when axis selection of the numpy-selector changed
"""
self.__clearCurrentView()
def __numpySelectionChanged(self):
"""
Called when data selection of the numpy-selector changed
"""
self.__updateDataInView()
def data(self):
"""Returns the data"""
return self.__data
def displayMode(self):
"""Returns the current display mode"""
return self.__currentView.modeId()
class ArrayCurvePlot(qt.QWidget):
"""
Widget for plotting a curve from a multi-dimensional signal array
and a 1D axis array.
The signal array can have an arbitrary number of dimensions, the only
limitation being that the last dimension must have the same length as
the axis array.
The widget provides sliders to select indices on the first (n - 1)
dimensions of the signal array, and buttons to add/replace selected
curves to the plot.
This widget also handles simple 2D or 3D scatter plots (third dimension
displayed as colour of points).
"""
def __init__(self, parent=None):
"""
:param parent: Parent QWidget
"""
super(ArrayCurvePlot, self).__init__(parent)
self.__signal = None
self.__signal_name = None
self.__signal_errors = None
self.__axis = None
self.__axis_name = None
self.__axis_errors = None
self.__values = None
self.__first_curve_added = False
self._plot = Plot1D(self)
self._plot.setDefaultColormap( # for scatters
{"name": "viridis",
"vmin": 0., "vmax": 1., # ignored (autoscale) but mandatory
"normalization": "linear",
"autoscale": True})
self.selectorDock = qt.QDockWidget("Data selector", self._plot)
# not closable
self.selectorDock.setFeatures(qt.QDockWidget.DockWidgetMovable
| qt.QDockWidget.DockWidgetFloatable)
self._selector = NumpyAxesSelector(self.selectorDock)
self._selector.setNamedAxesSelectorVisibility(False)
self.__selector_is_connected = False
self.selectorDock.setWidget(self._selector)
self._plot.addTabbedDockWidget(self.selectorDock)
layout = qt.QGridLayout()
layout.setContentsMargins(0, 0, 0, 0)
layout.addWidget(self._plot, 0, 0)
self.setLayout(layout)
def setCurveData(self,
y,
x=None,
values=None,
yerror=None,
xerror=None,
ylabel=None,
xlabel=None,
title=None):
"""
:param y: dataset to be represented by the y (vertical) axis.
For a scatter, this must be a 1D array and x and values must be
1-D arrays of the same size.
In other cases, it can be a n-D array whose last dimension must
have the same length as x (and values must be None)
:param x: 1-D dataset used as the curve's x values. If provided,
its lengths must be equal to the length of the last dimension of
``y`` (and equal to the length of ``value``, for a scatter plot).
:param values: Values, to be provided for a x-y-value scatter plot.
This will be used to compute the color map and assign colors
to the points.
:param yerror: 1-D dataset of errors for y, or None
:param xerror: 1-D dataset of errors for x, or None
:param ylabel: Label for Y axis
:param xlabel: Label for X axis
:param title: Graph title
"""
self.__signal = y
self.__signal_name = ylabel
self.__signal_errors = yerror
self.__axis = x
self.__axis_name = xlabel
self.__axis_errors = xerror
self.__values = values
if self.__selector_is_connected:
self._selector.selectionChanged.disconnect(self._updateCurve)
self.__selector_is_connected = False
self._selector.setData(y)
self._selector.setAxisNames([ylabel or "Y"])
if len(y.shape) < 2:
self.selectorDock.hide()
else:
self.selectorDock.show()
self._plot.setGraphTitle(title or "")
self._plot.setGraphXLabel(self.__axis_name or "X")
self._plot.setGraphYLabel(self.__signal_name or "Y")
self._updateCurve()
if not self.__selector_is_connected:
self._selector.selectionChanged.connect(self._updateCurve)
self.__selector_is_connected = True
def _updateCurve(self):
y = self._selector.selectedData()
x = self.__axis
if x is None:
x = numpy.arange(len(y))
elif numpy.isscalar(x) or len(x) == 1:
# constant axis
x = x * numpy.ones_like(y)
elif len(x) == 2 and len(y) != 2:
# linear calibration a + b * x
x = x[0] + x[1] * numpy.arange(len(y))
legend = self.__signal_name + "["
for sl in self._selector.selection():
if sl == slice(None):
legend += ":, "
else:
legend += str(sl) + ", "
legend = legend[:-2] + "]"
if self.__signal_errors is not None:
y_errors = self.__signal_errors[self._selector.selection()]
else:
y_errors = None
self._plot.remove(kind=("curve", "scatter"))
# values: x-y-v scatter
if self.__values is not None:
self._plot.addScatter(x,
y,
self.__values,
legend=legend,
xerror=self.__axis_errors,
yerror=y_errors)
# x monotonically increasing: curve
elif numpy.all(numpy.diff(x) > 0):
self._plot.addCurve(x,
y,
legend=legend,
xerror=self.__axis_errors,
yerror=y_errors)
# scatter
else:
self._plot.addScatter(x,
y,
value=numpy.ones_like(y),
legend=legend,
xerror=self.__axis_errors,
yerror=y_errors)
self._plot.resetZoom()
self._plot.setGraphXLabel(self.__axis_name)
self._plot.setGraphYLabel(self.__signal_name)
def clear(self):
self._plot.clear()
