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 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 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 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 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()
