def populate_spectrogram_widget(self, widget: GraphicsLayoutWidget,
game_name: str, player_name: PlayerName,
electrode_name: str):
# https://stackoverflow.com/questions/51312923/plotting-the-spectrum-of-a-wavfile-in-pyqtgraph-using-scipy-signal-spectrogram
f, t, Sxx = self._acquire_spectrogram_signal(game_name, player_name,
electrode_name)
plot = widget.addPlot()
plot.setTitle('Frequency over time for electrode %s' %
ELECTRODES[electrode_name])
img = ImageItem()
plot.addItem(img)
hist = HistogramLUTItem()
hist.setImageItem(img)
widget.addItem(hist)
hist.setLevels(np.min(Sxx), np.max(Sxx))
hist.gradient.restoreState({
'mode':
'rgb',
'ticks': [(0.5, (0, 182, 188, 255)), (1.0, (246, 111, 0, 255)),
(0.0, (75, 0, 113, 255))]
})
img.setImage(Sxx)
img.scale(t[-1] / np.size(Sxx, axis=1), f[-1] / np.size(Sxx, axis=0))
plot.setLimits(xMin=0, xMax=t[-1], yMin=0, yMax=f[-1])
plot.setLabel('bottom', "Time", units='s')
plot.setLabel('left', "Frequency", units='Hz')
class Bp2DWidget(PlotWidget):
def __init__(self):
super(Bp2DWidget, self).__init__()
# M.B. plot add to params
self.setXRange(-60, 60)
self.setYRange(-60, 60)
self.img = ImageItem()
self.addItem(self.img)
_translate = QCoreApplication.translate
self.setLabels(title=_translate("Bp2DWidget", "Beam pattern"),
left=_translate("Bp2DWidget", "Elevation, °"),
bottom=_translate("Bp2DWidget", "Azimuth, °"))
self.setLogMode()
colormap = ColorMap(*zip(*Gradients["bipolar"]["ticks"]))
self.img.setLookupTable(colormap.getLookupTable())
#gradient_legend = GradientLegend(10, 10)
#self.addItem(gradient_legend)
@pyqtSlot()
def on_data_changed(self):
sender = self.sender()
self.img.setImage(np.rot90(sender.data, -1))
self.img.setRect(self.__ensure_rect(np.shape(sender.data)))
def __ensure_rect(self, shape):
x_offset = 120. / (shape[0]-1) / 2
y_offset = 120. / (shape[1]-1) / 2
return QRectF(-60 - x_offset, 60 + y_offset, 120 + x_offset * 2, -120 - y_offset * 2)
def update_img(self, i: int, j: int, img: pg.ImageItem):
"""Template function for creating image update callback functions.
i is the row axis, j is the col axis corresponding to the image. xy is 0, 1 and zy is 2, 1"""
data_slice = [self.cursor.index[i].val for i in range(self.data.ndim)]
data_slice[i] = slice(None)
data_slice[j] = slice(None)
if j > i:
img.setImage(self.data_slice2d(i, j))
else:
img.setImage(self.data_slice2d(i, j).T)
def _update_preview_image(image_data: Optional[np.ndarray],
image: ImageItem,
redraw_histogram: Optional[Callable[[Any],
None]]):
image.clear()
image.setImage(image_data)
if redraw_histogram:
# Update histogram
redraw_histogram(image.getHistogram())
class PreviewWidget(GraphicsLayoutWidget):
def __init__(self):
super(PreviewWidget, self).__init__()
self.setMinimumHeight(250)
self.setMinimumWidth(250)
self.view = self.addViewBox(lockAspect=True, enableMenu=False)
self.imageitem = ImageItem()
self.textitem = TextItem(anchor=(0.5, 0))
self.textitem.setFont(QFont("Zero Threes"))
self.imgdata = None
self.imageitem.setOpts(axisOrder="row-major")
self.view.addItem(self.imageitem)
self.view.addItem(self.textitem)
self.textitem.hide()
self.setSizePolicy(QSizePolicy.Minimum, QSizePolicy.Minimum)
# def textItemBounds(axis, frac=1.0, orthoRange=None):
# b = self.textitem.boundingRect()
# sx, sy = self.view.viewPixelSize()
# x, y = sx*b.width(), sy*b.height()
# if axis == 0: return (-x/2, x/2)
# if axis == 1: return (0, y)
#
# self.textitem.dataBounds = textItemBounds
def sizeHint(self):
return QSize(250, 250)
def preview_header(self, header: NonDBHeader):
try:
data = header.meta_array()[0]
self.setImage(data)
except IndexError:
self.imageitem.clear()
self.setText("UNKNOWN DATA FORMAT")
def setImage(self, imgdata):
self.imageitem.clear()
self.textitem.hide()
self.imgdata = imgdata
self.imageitem.setImage(np.log(self.imgdata * (self.imgdata > 0) +
(self.imgdata < 1)),
autoLevels=True)
self.imageitem.setTransform(
QTransform(1, 0, 0, -1, 0, self.imgdata.shape[-2]))
self.view.autoRange()
def setText(self, text):
self.textitem.setText(text)
self.imageitem.clear()
self.textitem.setVisible(True)
self.view.autoRange()
class CamView(PlotWidget):
def __init__(self, cam: Cam, parent=None) -> None:
super().__init__(parent=parent)
self.cam = cam
self.main_plot_item = self.plotItem
self.last_centers: List[Tuple[float]] = []
self.image_item = ImageItem(cam.read_cam())
#cmap = colormap.get('CET-C2')
#self.image_item.setLookupTable(cmap.getLookupTable())
self.target = TargetItem(movable=False)
self.main_plot_item.addItem(self.image_item)
self.main_plot_item.addItem(self.target)
self.iso_curve = IsocurveItem()
self.iso_curve.setParentItem(self.image_item)
self.iso_curve2 = IsocurveItem()
self.iso_curve2.setParentItem(self.image_item)
self.main_plot_item.addItem(self.iso_curve)
self.center_hist = self.main_plot_item.plot(pen='r')
self.update_cam()
@Slot()
def update_cam(self):
#print('update')
image = self.cam.last_image
self.image_item.setImage(image)
if not self.cam.last_fit is None:
lf = self.cam.last_fit
center = (lf.params['x0'], lf.params['y0'])
self.target.setPos(*center)
self.last_centers = [center] + self.last_centers[:30]
self.iso_curve.setData(lf.best_fit, lf.best_fit.max() * 0.5)
self.iso_curve2.setData(lf.best_fit,
lf.best_fit.max() * np.exp(-2))
arr = np.array(self.last_centers)
self.center_hist.setData(x=arr[:, 0], y=arr[:, 1])
@Slot()
def reset_hist(self):
self.last_centers = []
class SiriusSpectrogramView(GraphicsLayoutWidget, PyDMWidget, PyDMColorMap,
ReadingOrder):
"""
A SpectrogramView with support for Channels and more from PyDM.
If there is no :attr:`channelWidth` it is possible to define the width of
the image with the :attr:`width` property.
The :attr:`normalizeData` property defines if the colors of the images are
relative to the :attr:`colorMapMin` and :attr:`colorMapMax` property or to
the minimum and maximum values of the image.
Use the :attr:`newImageSignal` to hook up to a signal that is emitted when
a new image is rendered in the widget.
Parameters
----------
parent : QWidget
The parent widget for the Label
image_channel : str, optional
The channel to be used by the widget for the image data.
xaxis_channel : str, optional
The channel to be used by the widget to receive the image width
(if ReadingOrder == Clike), and to set the xaxis values
yaxis_channel : str, optional
The channel to be used by the widget to receive the image width
(if ReadingOrder == Fortranlike), and to set the yaxis values
background : QColor, optional
QColor to set the background color of the GraphicsView
"""
Q_ENUMS(PyDMColorMap)
Q_ENUMS(ReadingOrder)
color_maps = cmaps
def __init__(self,
parent=None,
image_channel=None,
xaxis_channel=None,
yaxis_channel=None,
roioffsetx_channel=None,
roioffsety_channel=None,
roiwidth_channel=None,
roiheight_channel=None,
title='',
background='w',
image_width=0,
image_height=0):
"""Initialize widget."""
GraphicsLayoutWidget.__init__(self, parent)
PyDMWidget.__init__(self)
self.thread = None
self._imagechannel = None
self._xaxischannel = None
self._yaxischannel = None
self._roioffsetxchannel = None
self._roioffsetychannel = None
self._roiwidthchannel = None
self._roiheightchannel = None
self._channels = 7 * [
None,
]
self.image_waveform = np.zeros(0)
self._image_width = image_width if not xaxis_channel else 0
self._image_height = image_height if not yaxis_channel else 0
self._roi_offsetx = 0
self._roi_offsety = 0
self._roi_width = 0
self._roi_height = 0
self._normalize_data = False
self._auto_downsample = True
self._last_yaxis_data = None
self._last_xaxis_data = None
self._auto_colorbar_lims = True
self.format_tooltip = '{0:.4g}, {1:.4g}'
# ViewBox and imageItem.
self._view = ViewBox()
self._image_item = ImageItem()
self._view.addItem(self._image_item)
# ROI
self.ROICurve = PlotCurveItem([0, 0, 0, 0, 0], [0, 0, 0, 0, 0])
self.ROIColor = QColor('red')
pen = mkPen()
pen.setColor(QColor('transparent'))
pen.setWidth(1)
self.ROICurve.setPen(pen)
self._view.addItem(self.ROICurve)
# Axis.
self.xaxis = AxisItem('bottom')
self.xaxis.setPen(QColor(0, 0, 0))
if not xaxis_channel:
self.xaxis.setVisible(False)
self.yaxis = AxisItem('left')
self.yaxis.setPen(QColor(0, 0, 0))
if not yaxis_channel:
self.yaxis.setVisible(False)
# Colorbar legend.
self.colorbar = _GradientLegend()
# Title.
start_row = 0
if title:
self.title = LabelItem(text=title, color='#000000')
self.addItem(self.title, 0, 0, 1, 3)
start_row = 1
# Set layout.
self.addItem(self._view, start_row, 1)
self.addItem(self.yaxis, start_row, 0)
self.addItem(self.colorbar, start_row, 2)
self.addItem(self.xaxis, start_row + 1, 1)
self.setBackground(background)
self.ci.layout.setColumnSpacing(0, 0)
self.ci.layout.setRowSpacing(start_row, 0)
# Set color map limits.
self.cm_min = 0.0
self.cm_max = 255.0
# Set default reading order of numpy array data to Clike.
self._reading_order = ReadingOrder.Clike
# Make a right-click menu for changing the color map.
self.cm_group = QActionGroup(self)
self.cmap_for_action = {}
for cm in self.color_maps:
action = self.cm_group.addAction(cmap_names[cm])
action.setCheckable(True)
self.cmap_for_action[action] = cm
# Set the default colormap.
self._cm_colors = None
self.colorMap = PyDMColorMap.Inferno
# Setup the redraw timer.
self.needs_redraw = False
self.redraw_timer = QTimer(self)
self.redraw_timer.timeout.connect(self.redrawImage)
self._redraw_rate = 30
self.maxRedrawRate = self._redraw_rate
self.newImageSignal = self._image_item.sigImageChanged
# Set Channels.
self.imageChannel = image_channel
self.xAxisChannel = xaxis_channel
self.yAxisChannel = yaxis_channel
self.ROIOffsetXChannel = roioffsetx_channel
self.ROIOffsetYChannel = roioffsety_channel
self.ROIWidthChannel = roiwidth_channel
self.ROIHeightChannel = roiheight_channel
# --- Context menu ---
def widget_ctx_menu(self):
"""
Fetch the Widget specific context menu.
It will be populated with additional tools by `assemble_tools_menu`.
Returns
-------
QMenu or None
If the return of this method is None a new QMenu will be created by
`assemble_tools_menu`.
"""
self.menu = ViewBoxMenu(self._view)
cm_menu = self.menu.addMenu("Color Map")
for act in self.cmap_for_action.keys():
cm_menu.addAction(act)
cm_menu.triggered.connect(self._changeColorMap)
return self.menu
# --- Colormap methods ---
def _changeColorMap(self, action):
"""
Method invoked by the colormap Action Menu.
Changes the current colormap used to render the image.
Parameters
----------
action : QAction
"""
self.colorMap = self.cmap_for_action[action]
@Property(float)
def colorMapMin(self):
"""
Minimum value for the colormap.
Returns
-------
float
"""
return self.cm_min
@colorMapMin.setter
@Slot(float)
def colorMapMin(self, new_min):
"""
Set the minimum value for the colormap.
Parameters
----------
new_min : float
"""
if self.cm_min != new_min:
self.cm_min = new_min
if self.cm_min > self.cm_max:
self.cm_max = self.cm_min
@Property(float)
def colorMapMax(self):
"""
Maximum value for the colormap.
Returns
-------
float
"""
return self.cm_max
@colorMapMax.setter
@Slot(float)
def colorMapMax(self, new_max):
"""
Set the maximum value for the colormap.
Parameters
----------
new_max : float
"""
if self.cm_max != new_max:
self.cm_max = new_max
if self.cm_max < self.cm_min:
self.cm_min = self.cm_max
def setColorMapLimits(self, mn, mx):
"""
Set the limit values for the colormap.
Parameters
----------
mn : int
The lower limit
mx : int
The upper limit
"""
if mn >= mx:
return
self.cm_max = mx
self.cm_min = mn
@Property(PyDMColorMap)
def colorMap(self):
"""
Return the color map used by the SpectrogramView.
Returns
-------
PyDMColorMap
"""
return self._colormap
@colorMap.setter
def colorMap(self, new_cmap):
"""
Set the color map used by the SpectrogramView.
Parameters
-------
new_cmap : PyDMColorMap
"""
self._colormap = new_cmap
self._cm_colors = self.color_maps[new_cmap]
self.setColorMap()
for action in self.cm_group.actions():
if self.cmap_for_action[action] == self._colormap:
action.setChecked(True)
else:
action.setChecked(False)
def setColorMap(self, cmap=None):
"""
Update the image colormap.
Parameters
----------
cmap : ColorMap
"""
if not cmap:
if not self._cm_colors.any():
return
# Take default values
pos = np.linspace(0.0, 1.0, num=len(self._cm_colors))
cmap = ColorMap(pos, self._cm_colors)
self._view.setBackgroundColor(cmap.map(0))
lut = cmap.getLookupTable(0.0, 1.0, alpha=False)
self.colorbar.setIntColorScale(colors=lut)
self._image_item.setLookupTable(lut)
# --- Connection Slots ---
@Slot(bool)
def image_connection_state_changed(self, conn):
"""
Callback invoked when the Image Channel connection state is changed.
Parameters
----------
conn : bool
The new connection state.
"""
if conn:
self.redraw_timer.start()
else:
self.redraw_timer.stop()
@Slot(bool)
def yaxis_connection_state_changed(self, connected):
"""
Callback invoked when the TimeAxis Channel connection state is changed.
Parameters
----------
conn : bool
The new connection state.
"""
self._timeaxis_connected = connected
@Slot(bool)
def roioffsetx_connection_state_changed(self, conn):
"""
Run when the ROIOffsetX Channel connection state changes.
Parameters
----------
conn : bool
The new connection state.
"""
if not conn:
self._roi_offsetx = 0
@Slot(bool)
def roioffsety_connection_state_changed(self, conn):
"""
Run when the ROIOffsetY Channel connection state changes.
Parameters
----------
conn : bool
The new connection state.
"""
if not conn:
self._roi_offsety = 0
@Slot(bool)
def roiwidth_connection_state_changed(self, conn):
"""
Run when the ROIWidth Channel connection state changes.
Parameters
----------
conn : bool
The new connection state.
"""
if not conn:
self._roi_width = 0
@Slot(bool)
def roiheight_connection_state_changed(self, conn):
"""
Run when the ROIHeight Channel connection state changes.
Parameters
----------
conn : bool
The new connection state.
"""
if not conn:
self._roi_height = 0
# --- Value Slots ---
@Slot(np.ndarray)
def image_value_changed(self, new_image):
"""
Callback invoked when the Image Channel value is changed.
We try to do as little as possible in this method, because it
gets called every time the image channel updates, which might
be extremely often. Basically just store the data, and set
a flag requesting that the image be redrawn.
Parameters
----------
new_image : np.ndarray
The new image data. This can be a flat 1D array, or a 2D array.
"""
if new_image is None or new_image.size == 0:
return
logging.debug("SpectrogramView Received New Image: Needs Redraw->True")
self.image_waveform = new_image
self.needs_redraw = True
if not self._image_height and self._image_width:
self._image_height = new_image.size / self._image_width
elif not self._image_width and self._image_height:
self._image_width = new_image.size / self._image_height
@Slot(np.ndarray)
@Slot(float)
def xaxis_value_changed(self, new_array):
"""
Callback invoked when the Image Width Channel value is changed.
Parameters
----------
new_array : np.ndarray
The new x axis array
"""
if new_array is None:
return
if isinstance(new_array, float):
new_array = np.array([
new_array,
])
self._last_xaxis_data = new_array
if self._reading_order == self.Clike:
self._image_width = new_array.size
else:
self._image_height = new_array.size
self.needs_redraw = True
@Slot(np.ndarray)
@Slot(float)
def yaxis_value_changed(self, new_array):
"""
Callback invoked when the TimeAxis Channel value is changed.
Parameters
----------
new_array : np.array
The new y axis array
"""
if new_array is None:
return
if isinstance(new_array, float):
new_array = np.array([
new_array,
])
self._last_yaxis_data = new_array
if self._reading_order == self.Fortranlike:
self._image_width = new_array.size
else:
self._image_height = new_array.size
self.needs_redraw = True
@Slot(int)
def roioffsetx_value_changed(self, new_offset):
"""
Run when the ROIOffsetX Channel value changes.
Parameters
----------
new_offsetx : int
The new image ROI horizontal offset
"""
if new_offset is None:
return
self._roi_offsetx = new_offset
self.redrawROI()
@Slot(int)
def roioffsety_value_changed(self, new_offset):
"""
Run when the ROIOffsetY Channel value changes.
Parameters
----------
new_offsety : int
The new image ROI vertical offset
"""
if new_offset is None:
return
self._roi_offsety = new_offset
self.redrawROI()
@Slot(int)
def roiwidth_value_changed(self, new_width):
"""
Run when the ROIWidth Channel value changes.
Parameters
----------
new_width : int
The new image ROI width
"""
if new_width is None:
return
self._roi_width = int(new_width)
self.redrawROI()
@Slot(int)
def roiheight_value_changed(self, new_height):
"""
Run when the ROIHeight Channel value changes.
Parameters
----------
new_height : int
The new image ROI height
"""
if new_height is None:
return
self._roi_height = int(new_height)
self.redrawROI()
# --- Image update methods ---
def process_image(self, image):
"""
Boilerplate method.
To be used by applications in order to add calculations and also modify
the image before it is displayed at the widget.
.. warning::
This code runs in a separated QThread so it **MUST** not try to
write to QWidgets.
Parameters
----------
image : np.ndarray
The Image Data as a 2D numpy array
Returns
-------
np.ndarray
The Image Data as a 2D numpy array after processing.
"""
return image
def redrawImage(self):
"""
Set the image data into the ImageItem, if needed.
If necessary, reshape the image to 2D first.
"""
if self.thread is not None and not self.thread.isFinished():
logger.warning(
"Image processing has taken longer than the refresh rate.")
return
self.thread = SpectrogramUpdateThread(self)
self.thread.updateSignal.connect(self._updateDisplay)
logging.debug("SpectrogramView RedrawImage Thread Launched")
self.thread.start()
@Slot(list)
def _updateDisplay(self, data):
logging.debug("SpectrogramView Update Display with new image")
# Update axis
if self._last_xaxis_data is not None:
szx = self._last_xaxis_data.size
xMin = self._last_xaxis_data.min()
xMax = self._last_xaxis_data.max()
else:
szx = self.imageWidth if self.readingOrder == self.Clike \
else self.imageHeight
xMin = 0
xMax = szx
if self._last_yaxis_data is not None:
szy = self._last_yaxis_data.size
yMin = self._last_yaxis_data.min()
yMax = self._last_yaxis_data.max()
else:
szy = self.imageHeight if self.readingOrder == self.Clike \
else self.imageWidth
yMin = 0
yMax = szy
self.xaxis.setRange(xMin, xMax)
self.yaxis.setRange(yMin, yMax)
self._view.setLimits(xMin=0,
xMax=szx,
yMin=0,
yMax=szy,
minXRange=szx,
maxXRange=szx,
minYRange=szy,
maxYRange=szy)
# Update image
if self.autoSetColorbarLims:
self.colorbar.setLimits(data)
mini, maxi = data[0], data[1]
img = data[2]
self._image_item.setLevels([mini, maxi])
self._image_item.setImage(img,
autoLevels=False,
autoDownsample=self.autoDownsample)
# ROI update methods
def redrawROI(self):
startx = self._roi_offsetx
endx = self._roi_offsetx + self._roi_width
starty = self._roi_offsety
endy = self._roi_offsety + self._roi_height
self.ROICurve.setData([startx, startx, endx, endx, startx],
[starty, endy, endy, starty, starty])
def showROI(self, show):
"""Set ROI visibility."""
pen = mkPen()
if show:
pen.setColor(self.ROIColor)
else:
pen.setColor(QColor('transparent'))
self.ROICurve.setPen(pen)
# --- Properties ---
@Property(bool)
def autoDownsample(self):
"""
Return if we should or not apply the autoDownsample option.
Return
------
bool
"""
return self._auto_downsample
@autoDownsample.setter
def autoDownsample(self, new_value):
"""
Whether we should or not apply the autoDownsample option.
Parameters
----------
new_value: bool
"""
if new_value != self._auto_downsample:
self._auto_downsample = new_value
@Property(bool)
def autoSetColorbarLims(self):
"""
Return if we should or not auto set colorbar limits.
Return
------
bool
"""
return self._auto_colorbar_lims
@autoSetColorbarLims.setter
def autoSetColorbarLims(self, new_value):
"""
Whether we should or not auto set colorbar limits.
Parameters
----------
new_value: bool
"""
if new_value != self._auto_colorbar_lims:
self._auto_colorbar_lims = new_value
@Property(int)
def imageWidth(self):
"""
Return the width of the image.
Return
------
int
"""
return self._image_width
@imageWidth.setter
def imageWidth(self, new_width):
"""
Set the width of the image.
Can be overridden by :attr:`xAxisChannel` and :attr:`yAxisChannel`.
Parameters
----------
new_width: int
"""
boo = self._image_width != int(new_width)
boo &= not self._xaxischannel
boo &= not self._yaxischannel
if boo:
self._image_width = int(new_width)
@Property(int)
def imageHeight(self):
"""
Return the height of the image.
Return
------
int
"""
return self._image_height
@Property(int)
def ROIOffsetX(self):
"""
Return the ROI offset in X axis in pixels.
Return
------
int
"""
return self._roi_offsetx
@ROIOffsetX.setter
def ROIOffsetX(self, new_offset):
"""
Set the ROI offset in X axis in pixels.
Can be overridden by :attr:`ROIOffsetXChannel`.
Parameters
----------
new_offset: int
"""
if new_offset is None:
return
boo = self._roi_offsetx != int(new_offset)
boo &= not self._roioffsetxchannel
if boo:
self._roi_offsetx = int(new_offset)
self.redrawROI()
@Property(int)
def ROIOffsetY(self):
"""
Return the ROI offset in Y axis in pixels.
Return
------
int
"""
return self._roi_offsety
@ROIOffsetY.setter
def ROIOffsetY(self, new_offset):
"""
Set the ROI offset in Y axis in pixels.
Can be overridden by :attr:`ROIOffsetYChannel`.
Parameters
----------
new_offset: int
"""
if new_offset is None:
return
boo = self._roi_offsety != int(new_offset)
boo &= not self._roioffsetychannel
if boo:
self._roi_offsety = int(new_offset)
self.redrawROI()
@Property(int)
def ROIWidth(self):
"""
Return the ROI width in pixels.
Return
------
int
"""
return self._roi_width
@ROIWidth.setter
def ROIWidth(self, new_width):
"""
Set the ROI width in pixels.
Can be overridden by :attr:`ROIWidthChannel`.
Parameters
----------
new_width: int
"""
if new_width is None:
return
boo = self._roi_width != int(new_width)
boo &= not self._roiwidthchannel
if boo:
self._roi_width = int(new_width)
self.redrawROI()
@Property(int)
def ROIHeight(self):
"""
Return the ROI height in pixels.
Return
------
int
"""
return self._roi_height
@ROIHeight.setter
def ROIHeight(self, new_height):
"""
Set the ROI height in pixels.
Can be overridden by :attr:`ROIHeightChannel`.
Parameters
----------
new_height: int
"""
if new_height is None:
return
boo = self._roi_height != int(new_height)
boo &= not self._roiheightchannel
if boo:
self._roi_height = int(new_height)
self.redrawROI()
@Property(bool)
def normalizeData(self):
"""
Return True if the colors are relative to data maximum and minimum.
Returns
-------
bool
"""
return self._normalize_data
@normalizeData.setter
@Slot(bool)
def normalizeData(self, new_norm):
"""
Define if the colors are relative to minimum and maximum of the data.
Parameters
----------
new_norm: bool
"""
if self._normalize_data != new_norm:
self._normalize_data = new_norm
@Property(ReadingOrder)
def readingOrder(self):
"""
Return the reading order of the :attr:`imageChannel` array.
Returns
-------
ReadingOrder
"""
return self._reading_order
@readingOrder.setter
def readingOrder(self, order):
"""
Set reading order of the :attr:`imageChannel` array.
Parameters
----------
order: ReadingOrder
"""
if self._reading_order != order:
self._reading_order = order
if order == self.Clike:
if self._last_xaxis_data is not None:
self._image_width = self._last_xaxis_data.size
if self._last_yaxis_data is not None:
self._image_height = self._last_yaxis_data.size
elif order == self.Fortranlike:
if self._last_yaxis_data is not None:
self._image_width = self._last_yaxis_data.size
if self._last_xaxis_data is not None:
self._image_height = self._last_xaxis_data.size
@Property(int)
def maxRedrawRate(self):
"""
The maximum rate (in Hz) at which the plot will be redrawn.
The plot will not be redrawn if there is not new data to draw.
Returns
-------
int
"""
return self._redraw_rate
@maxRedrawRate.setter
def maxRedrawRate(self, redraw_rate):
"""
The maximum rate (in Hz) at which the plot will be redrawn.
The plot will not be redrawn if there is not new data to draw.
Parameters
-------
redraw_rate : int
"""
self._redraw_rate = redraw_rate
self.redraw_timer.setInterval(int((1.0 / self._redraw_rate) * 1000))
# --- Events rederivations ---
def keyPressEvent(self, ev):
"""Handle keypress events."""
return
def mouseMoveEvent(self, ev):
if not self._image_item.width() or not self._image_item.height():
super().mouseMoveEvent(ev)
return
pos = ev.pos()
posaux = self._image_item.mapFromDevice(ev.pos())
if posaux.x() < 0 or posaux.x() >= self._image_item.width() or \
posaux.y() < 0 or posaux.y() >= self._image_item.height():
super().mouseMoveEvent(ev)
return
pos_scene = self._view.mapSceneToView(pos)
x = round(pos_scene.x())
y = round(pos_scene.y())
if self.xAxisChannel and self._last_xaxis_data is not None:
maxx = len(self._last_xaxis_data) - 1
x = x if x < maxx else maxx
valx = self._last_xaxis_data[x]
else:
valx = x
if self.yAxisChannel and self._last_yaxis_data is not None:
maxy = len(self._last_yaxis_data) - 1
y = y if y < maxy else maxy
valy = self._last_yaxis_data[y]
else:
valy = y
txt = self.format_tooltip.format(valx, valy)
QToolTip.showText(self.mapToGlobal(pos), txt, self, self.geometry(),
5000)
super().mouseMoveEvent(ev)
# --- Channels ---
@Property(str)
def imageChannel(self):
"""
The channel address in use for the image data .
Returns
-------
str
Channel address
"""
if self._imagechannel:
return str(self._imagechannel.address)
else:
return ''
@imageChannel.setter
def imageChannel(self, value):
"""
The channel address in use for the image data .
Parameters
----------
value : str
Channel address
"""
if self._imagechannel != value:
# Disconnect old channel
if self._imagechannel:
self._imagechannel.disconnect()
# Create and connect new channel
self._imagechannel = PyDMChannel(
address=value,
connection_slot=self.image_connection_state_changed,
value_slot=self.image_value_changed,
severity_slot=self.alarmSeverityChanged)
self._channels[0] = self._imagechannel
self._imagechannel.connect()
@Property(str)
def xAxisChannel(self):
"""
The channel address in use for the x-axis of image.
Returns
-------
str
Channel address
"""
if self._xaxischannel:
return str(self._xaxischannel.address)
else:
return ''
@xAxisChannel.setter
def xAxisChannel(self, value):
"""
The channel address in use for the x-axis of image.
Parameters
----------
value : str
Channel address
"""
if self._xaxischannel != value:
# Disconnect old channel
if self._xaxischannel:
self._xaxischannel.disconnect()
# Create and connect new channel
self._xaxischannel = PyDMChannel(
address=value,
connection_slot=self.connectionStateChanged,
value_slot=self.xaxis_value_changed,
severity_slot=self.alarmSeverityChanged)
self._channels[1] = self._xaxischannel
self._xaxischannel.connect()
@Property(str)
def yAxisChannel(self):
"""
The channel address in use for the time axis.
Returns
-------
str
Channel address
"""
if self._yaxischannel:
return str(self._yaxischannel.address)
else:
return ''
@yAxisChannel.setter
def yAxisChannel(self, value):
"""
The channel address in use for the time axis.
Parameters
----------
value : str
Channel address
"""
if self._yaxischannel != value:
# Disconnect old channel
if self._yaxischannel:
self._yaxischannel.disconnect()
# Create and connect new channel
self._yaxischannel = PyDMChannel(
address=value,
connection_slot=self.yaxis_connection_state_changed,
value_slot=self.yaxis_value_changed,
severity_slot=self.alarmSeverityChanged)
self._channels[2] = self._yaxischannel
self._yaxischannel.connect()
@Property(str)
def ROIOffsetXChannel(self):
"""
Return the channel address in use for the image ROI horizontal offset.
Returns
-------
str
Channel address
"""
if self._roioffsetxchannel:
return str(self._roioffsetxchannel.address)
else:
return ''
@ROIOffsetXChannel.setter
def ROIOffsetXChannel(self, value):
"""
Return the channel address in use for the image ROI horizontal offset.
Parameters
----------
value : str
Channel address
"""
if self._roioffsetxchannel != value:
# Disconnect old channel
if self._roioffsetxchannel:
self._roioffsetxchannel.disconnect()
# Create and connect new channel
self._roioffsetxchannel = PyDMChannel(
address=value,
connection_slot=self.roioffsetx_connection_state_changed,
value_slot=self.roioffsetx_value_changed,
severity_slot=self.alarmSeverityChanged)
self._channels[3] = self._roioffsetxchannel
self._roioffsetxchannel.connect()
@Property(str)
def ROIOffsetYChannel(self):
"""
Return the channel address in use for the image ROI vertical offset.
Returns
-------
str
Channel address
"""
if self._roioffsetychannel:
return str(self._roioffsetychannel.address)
else:
return ''
@ROIOffsetYChannel.setter
def ROIOffsetYChannel(self, value):
"""
Return the channel address in use for the image ROI vertical offset.
Parameters
----------
value : str
Channel address
"""
if self._roioffsetychannel != value:
# Disconnect old channel
if self._roioffsetychannel:
self._roioffsetychannel.disconnect()
# Create and connect new channel
self._roioffsetychannel = PyDMChannel(
address=value,
connection_slot=self.roioffsety_connection_state_changed,
value_slot=self.roioffsety_value_changed,
severity_slot=self.alarmSeverityChanged)
self._channels[4] = self._roioffsetychannel
self._roioffsetychannel.connect()
@Property(str)
def ROIWidthChannel(self):
"""
Return the channel address in use for the image ROI width.
Returns
-------
str
Channel address
"""
if self._roiwidthchannel:
return str(self._roiwidthchannel.address)
else:
return ''
@ROIWidthChannel.setter
def ROIWidthChannel(self, value):
"""
Return the channel address in use for the image ROI width.
Parameters
----------
value : str
Channel address
"""
if self._roiwidthchannel != value:
# Disconnect old channel
if self._roiwidthchannel:
self._roiwidthchannel.disconnect()
# Create and connect new channel
self._roiwidthchannel = PyDMChannel(
address=value,
connection_slot=self.roiwidth_connection_state_changed,
value_slot=self.roiwidth_value_changed,
severity_slot=self.alarmSeverityChanged)
self._channels[5] = self._roiwidthchannel
self._roiwidthchannel.connect()
@Property(str)
def ROIHeightChannel(self):
"""
Return the channel address in use for the image ROI height.
Returns
-------
str
Channel address
"""
if self._roiheightchannel:
return str(self._roiheightchannel.address)
else:
return ''
@ROIHeightChannel.setter
def ROIHeightChannel(self, value):
"""
Return the channel address in use for the image ROI height.
Parameters
----------
value : str
Channel address
"""
if self._roiheightchannel != value:
# Disconnect old channel
if self._roiheightchannel:
self._roiheightchannel.disconnect()
# Create and connect new channel
self._roiheightchannel = PyDMChannel(
address=value,
connection_slot=self.roiheight_connection_state_changed,
value_slot=self.roiheight_value_changed,
severity_slot=self.alarmSeverityChanged)
self._channels[6] = self._roiheightchannel
self._roiheightchannel.connect()
def channels(self):
"""
Return the channels being used for this Widget.
Returns
-------
channels : list
List of PyDMChannel objects
"""
return self._channels
def channels_for_tools(self):
"""Return channels for tools."""
return [self._imagechannel]
class SliceableGraphicsView(GraphicsView):
sigToggleHorizontalSlice = Signal(bool)
sigToggleVerticalSlice = Signal(bool)
sigMakePrimary = Signal(object, object)
def __init__(self):
super(SliceableGraphicsView, self).__init__()
self.setContentsMargins(0, 0, 0, 0)
# Add axes
self.view = SliceableAxes()
self.view.axes["left"]["item"].setZValue(10)
self.view.axes["top"]["item"].setZValue(10)
self.setCentralItem(self.view)
self.view.sigToggleVerticalSlice.connect(self.sigToggleVerticalSlice)
self.view.sigToggleHorizontalSlice.connect(self.sigToggleHorizontalSlice)
self.view.sigMakePrimary.connect(self.sigMakePrimary)
# Add imageitem
self.image_item = ImageItem()
self.view.addItem(self.image_item)
# add crosshair
self.crosshair = BetterCrosshairROI((0, 0), parent=self.view, resizable=False)
self.view.getViewBox().addItem(self.crosshair)
def setData(self, data):
xvals = data.coords[data.dims[-1]]
yvals = data.coords[data.dims[-2]]
xmin = float(xvals.min())
xmax = float(xvals.max())
ymin = float(yvals.min())
ymax = float(yvals.max())
# Position the image according to coords
shape = data.shape
a = [(0, shape[-1]), (shape[-2] - 1, shape[-1]), (shape[-2] - 1, 1), (0, 1)]
# b = [(ymin, xmax), (ymax, xmax), (ymax, xmin), (ymin, xmin)]
b = [(xmax, ymin), (xmax, ymax), (xmin, ymax), (xmin, ymin)]
quad1 = QPolygonF()
quad2 = QPolygonF()
for p, q in zip(a, b):
quad1.append(QPointF(*p))
quad2.append(QPointF(*q))
transform = QTransform()
QTransform.quadToQuad(quad1, quad2, transform)
# Bind coords from the xarray to the timeline axis
# super(SliceableGraphicsView, self).setImage(img, autoRange, autoLevels, levels, axes, np.asarray(img.coords[img.dims[0]]), pos, scale, transform, autoHistogramRange, levelMode)
self.image_item.setImage(np.asarray(data), autoLevels=False)
self.image_item.setTransform(transform)
# Label the image axes
self.view.setLabel('left', data.dims[-2])
self.view.setLabel('bottom', data.dims[-1])
def resetCrosshair(self):
transform = self.image_item.viewTransform()
new_pos = transform.map(self.image_item.boundingRect().center())
self.crosshair.setPos(new_pos)
self.crosshair.sigMoved.emit(new_pos)
def updateImage(self, autoHistogramRange=True):
## Redraw image on screen
if self.image is None:
return
image = self.getProcessedImage()
if autoHistogramRange:
self.ui.histogram.setHistogramRange(self.levelMin, self.levelMax)
# Transpose image into order expected by ImageItem
if self.imageItem.axisOrder == 'col-major':
axorder = ['t', 'x', 'y', 'c']
else:
axorder = ['t', 'y', 'x', 'c']
axorder = [self.axes[ax] for ax in axorder if self.axes[ax] is not None]
ax_swap = [image.dims[ax_index] for ax_index in axorder]
image = image.transpose(*ax_swap)
# Select time index
if self.axes['t'] is not None:
self.ui.roiPlot.show()
image = image[self.currentIndex]
self.imageItem.updateImage(np.asarray(image))
def quickMinMax(self, data):
"""
Estimate the min/max values of *data* by subsampling. MODIFIED TO USE THE 99TH PERCENTILE instead of max.
"""
if data is None:
return 0, 0
sl = slice(None, None, max(1, int(data.size // 1e6)))
data = np.asarray(data[sl])
levels = (np.nanmin(data), np.nanpercentile(np.where(data < np.nanmax(data), data, np.nanmin(data)), 99))
return [levels]
class PsdWaterfallPlotWidget(GraphicsLayoutWidget):
"""This class manages and displays the power spectrum distribution (PSD)
data in a waterfall plot.
Attributes
----------
arraySize : int
The size of the data array to display.
boundingRect : QtCore.QRectF
The actual coordinate space base on frequency and time of acquisition.
data : numpy.ndarray
The 2D array for the PSD data.
image : pyqtgraph.ImageItem
The instance of the image item for display.
timeScale : float
The total time for the buffer to accumulate at the ROI FPS.
"""
def __init__(self, parent=None):
"""Initialize the class.
Parameters
----------
parent : None, optional
Top-level widget.
"""
super().__init__(parent)
self.plot = self.addPlot()
self.plot.invertY()
self.image = ImageItem()
self.image.setOpts(axisOrder='row-major')
self.plot.addItem(self.image)
self.data = None
self.arraySize = None
self.boundingRect = None
self.timeScale = None
self.colorMap = 'viridis'
self.image.setLookupTable(getLutFromColorMap(self.colorMap))
def clearPlot(self):
"""Reset all data and clear the plot.
"""
self.data = None
self.boundingRect = None
self.image.clear()
def getConfiguration(self):
"""Get the current plot configuration.
Returns
-------
int, str
The set of current configuration parameters.
"""
return self.arraySize, self.colorMap
def setConfiguration(self, config):
"""Set the new parameters into the widget.
Parameters
----------
config : `config.PsdPlotConfig`
The new parameters to apply.
"""
numBins = config.numWaterfallBins
if self.arraySize != numBins:
self.arraySize = numBins
# Invalidate data
self.data = None
self.boundingRect = None
colorMap = config.waterfallColorMap
if self.colorMap != colorMap:
self.colorMap = colorMap
self.image.setLookupTable(getLutFromColorMap(self.colorMap))
def setTimeScale(self, timeScale):
"""Update the stored timescale and invalidate data and bounding rect.
Parameters
----------
timeScale : float
The new timescale.
"""
self.timeScale = timeScale
self.data = None
self.boundingRect = None
def setup(self, arraySize, timeScale, axisLabel):
"""Setup the widget with the array size.
Parameters
----------
arraySize : int
The size fo the data array to display in terms of history.
timeScale : float
The total time for the buffer to accumulate at the ROI FPS.
axisLabel : str
Label for particular centroid coordinate.
"""
self.arraySize = arraySize
self.timeScale = timeScale
self.plot.setLabel('bottom', '{} {}'.format(axisLabel, HTML_NU), units='Hz')
self.plot.setLabel('left', 'Time', units='s')
def updatePlot(self, psd, freqs):
"""Update the current plot with the given data.
Parameters
----------
psd : numpy.array
The PSD data of a given centroid coordinate.
freqs : numpy.array
The frequency array associated with the PSD data.
"""
if self.data is None:
self.data = np.zeros((self.arraySize, psd.size))
else:
self.data[1:, ...] = self.data[:-1, ...]
self.data[0, ...] = np.log(psd)
self.image.setImage(self.data)
if self.boundingRect is None:
self.boundingRect = QtCore.QRectF(0, 0, freqs[-1], self.arraySize * self.timeScale)
self.image.setRect(self.boundingRect)
def setImage(self, image=None, **kwds):
if image is not None and self.__transpose is True:
image = np.swapaxes(image, 0, 1)
return ImageItem.setImage(self, image, **kwds)
class MIMiniImageView(GraphicsLayout, BadDataOverlay):
def __init__(self, name: str = "MIMiniImageView"):
super().__init__()
self.name = name.title()
self.im = ImageItem()
self.vb = ViewBox(invertY=True, lockAspect=True, name=name)
self.vb.addItem(self.im)
self.hist = HistogramLUTItem(self.im)
graveyard.append(self.vb)
# Sub-layout prevents resizing issues when details text changes
image_layout = self.addLayout(colspan=2)
image_layout.addItem(self.vb)
image_layout.addItem(self.hist)
self.hist.setFixedWidth(100) # HistogramLUTItem used pixel sizes
self.nextRow()
self.details = self.addLabel("", colspan=2)
self.im.hoverEvent = lambda ev: self.mouse_over(ev)
self.axis_siblings: "WeakSet[MIMiniImageView]" = WeakSet()
self.histogram_siblings: "WeakSet[MIMiniImageView]" = WeakSet()
@property
def image_item(self) -> ImageItem:
return self.im
@property
def viewbox(self) -> ViewBox:
return self.vb
def clear(self):
self.im.clear()
def setImage(self, *args, **kwargs):
self.im.setImage(*args, **kwargs)
self.check_for_bad_data()
@staticmethod
def set_siblings(sibling_views: List["MIMiniImageView"],
axis=False,
hist=False):
for view1 in sibling_views:
for view2 in sibling_views:
if view2 is not view1:
if axis:
view1.add_axis_sibling(view2)
if hist:
view1.add_hist_sibling(view2)
def add_axis_sibling(self, sibling: "MIMiniImageView"):
self.axis_siblings.add(sibling)
def add_hist_sibling(self, sibling: "MIMiniImageView"):
self.histogram_siblings.add(sibling)
def get_parts(self) -> Tuple[ImageItem, ViewBox, HistogramLUTItem]:
return self.im, self.vb, self.hist
def mouse_over(self, ev):
# Ignore events triggered by leaving window or right clicking
if ev.exit:
return
pos = CloseEnoughPoint(ev.pos())
self.show_value(pos)
for img_view in self.axis_siblings:
img_view.show_value(pos)
def show_value(self, pos):
image = self.im.image
if image is not None and pos.y < image.shape[
0] and pos.x < image.shape[1]:
pixel_value = image[pos.y, pos.x]
value_string = ("%.6f" % pixel_value)[:8]
self.details.setText(f"{self.name}: {value_string}")
def link_sibling_axis(self):
# Linking multiple viewboxes with locked aspect ratios causes
# odd resizing behaviour. Use workaround from
# https://github.com/pyqtgraph/pyqtgraph/issues/1348
self.vb.setAspectLocked(True)
for view1, view2 in pairwise(chain([self], self.axis_siblings)):
view2.vb.linkView(ViewBox.XAxis, view1.vb)
view2.vb.linkView(ViewBox.YAxis, view1.vb)
view2.vb.setAspectLocked(False)
def unlink_sibling_axis(self):
for img_view in chain([self], self.axis_siblings):
img_view.vb.linkView(ViewBox.XAxis, None)
img_view.vb.linkView(ViewBox.YAxis, None)
img_view.vb.setAspectLocked(True)
def link_sibling_histogram(self):
for view1, view2 in pairwise(chain([self], self.histogram_siblings)):
view1.hist.vb.linkView(ViewBox.YAxis, view2.hist.vb)
for img_view in chain([self], self.histogram_siblings):
img_view.hist.sigLevelChangeFinished.connect(
img_view.update_sibling_histograms)
def unlink_sibling_histogram(self):
for img_view in chain([self], self.histogram_siblings):
img_view.hist.vb.linkView(ViewBox.YAxis, None)
try:
img_view.hist.sigLevelChangeFinished.disconnect()
except TypeError:
# This is expected if there are slots currently connected
pass
def update_sibling_histograms(self):
hist_range = self.hist.getLevels()
for img_view in self.histogram_siblings:
with BlockQtSignals(img_view.hist):
img_view.hist.setLevels(*hist_range)
class SliceableGraphicsView(GraphicsView, SlicingView):
sigToggleHorizontalSlice = Signal(bool)
sigToggleVerticalSlice = Signal(bool)
sigToggleDepthSlice = Signal(bool)
sigMakePrimary = Signal(object, object)
sigCrosshairMoved = Signal()
SUPPORTED_NDIM = 2
def __init__(self, slice_direction, parent=None, xlink=None, ylink=None):
super(SliceableGraphicsView, self).__init__(parent=parent)
self.slice_direction = slice_direction
self.setContentsMargins(0, 0, 0, 0)
# Add axes
self.view = SliceableAxes(slice_direction)
self.view.axes["left"]["item"].setZValue(10)
self.view.axes["top"]["item"].setZValue(10)
self.setCentralItem(self.view)
for sig in [
'sigToggleVerticalSlice', 'sigToggleHorizontalSlice',
'sigToggleDepthSlice', 'sigMakePrimary'
]:
if hasattr(self.view, sig):
getattr(self.view, sig).connect(getattr(self, sig))
# Add imageitem
self.image_item = ImageItem(axisOrder='row-major')
self.image_item.setOpts()
self.view.addItem(self.image_item)
# add crosshair
self.crosshair = BetterCrosshairROI((0, 0),
parent=self.view,
resizable=False)
self.crosshair.sigMoved.connect(self.sigCrosshairMoved)
self.view.getViewBox().addItem(self.crosshair)
# find top-level parent NDImageView
while not isinstance(parent, NDImageView):
parent = parent.parent()
# Initialize lut, levels
self.image_item.setLevels(parent.levels, update=True)
self.image_item.setLookupTable(parent.lut, update=True)
# Link axes
if ylink:
self.view.vb.setYLink(ylink)
if xlink:
self.view.vb.setXLink(xlink)
def setData(self, data):
# Constrain squareness when units match
is_square = data.dims[-2].split('(')[-1] == data.dims[-1].split(
'(')[-1]
self.view.vb.setAspectLocked(is_square)
xvals = data.coords[data.dims[-1]]
yvals = data.coords[data.dims[-2]]
xmin = float(xvals.min())
xmax = float(xvals.max())
ymin = float(yvals.min())
ymax = float(yvals.max())
# Position the image according to coords
shape = data.shape
a = [(0, shape[-2]), (shape[-1], shape[-2]), (shape[-1], 0), (0, 0)]
# b = [(ymin, xmax), (ymax, xmax), (ymax, xmin), (ymin, xmin)]
if self.slice_direction in ['horizontal', 'depth']:
b = [(xmin, ymin), (xmax, ymin), (xmax, ymax), (xmin, ymax)]
elif self.slice_direction == 'vertical':
b = [(xmax, ymax), (xmin, ymax), (xmin, ymin), (xmax, ymin)]
quad1 = QPolygonF()
quad2 = QPolygonF()
for p, q in zip(a, b):
quad1.append(QPointF(*p))
quad2.append(QPointF(*q))
transform = QTransform()
QTransform.quadToQuad(quad1, quad2, transform)
# Bind coords from the xarray to the timeline axis
# super(SliceableGraphicsView, self).setImage(img, autoRange, autoLevels, levels, axes, np.asarray(img.coords[img.dims[0]]), pos, scale, transform, autoHistogramRange, levelMode)
self.image_item.setImage(np.asarray(data), autoLevels=False)
self.image_item.setTransform(transform)
# Label the image axes
self.view.setLabel('left', data.dims[-2])
self.view.setLabel('bottom', data.dims[-1])
def resetCrosshair(self):
transform = self.image_item.viewTransform()
new_pos = transform.map(self.image_item.boundingRect().center())
self.crosshair.setPos(new_pos)
# self.crosshair.sigMoved.emit(new_pos)
def updateImage(self, autoHistogramRange=True):
## Redraw image on screen
if self.image is None:
return
image = self.getProcessedImage()
if autoHistogramRange:
self.ui.histogram.setHistogramRange(self.levelMin, self.levelMax)
# Transpose image into order expected by ImageItem
if self.imageItem.axisOrder == 'col-major':
axorder = ['t', 'x', 'y', 'c']
else:
axorder = ['t', 'y', 'x', 'c']
axorder = [
self.axes[ax] for ax in axorder if self.axes[ax] is not None
]
ax_swap = [image.dims[ax_index] for ax_index in axorder]
image = image.transpose(*ax_swap)
# Select time index
if self.axes['t'] is not None:
self.ui.roiPlot.show()
image = image[self.currentIndex]
self.imageItem.updateImage(np.asarray(image))
def updateCrosshair(self, x, y):
self.crosshair.setPos(x, y)
def quickMinMax(self, data):
"""
Estimate the min/max values of *data* by subsampling. MODIFIED TO USE THE 99TH PERCENTILE instead of max.
"""
if data is None:
return 0, 0
sl = slice(None, None, max(1, int(data.size // 1e6)))
data = np.asarray(data[sl])
levels = (np.nanmin(data),
np.nanpercentile(
np.where(data < np.nanmax(data), data, np.nanmin(data)),
99))
return [levels]
class BlendView(GraphicsView):
def __init__(self, parent: QWidget):
GraphicsView.__init__(self, parent)
layout = GraphicsLayout()
self.setCentralItem(layout)
self._image_item = ImageItem()
self.viewbox = ViewBox(layout, lockAspect=True, invertY=True)
self.viewbox.addItem(self._image_item)
layout.addItem(self.viewbox)
self.scale = ScaleBar(size=10, suffix='μm')
self.scale.setParentItem(self.viewbox)
self.scale.anchor((1, 1), (1, 1), offset=(-20, -20))
self.scale.hide()
self._show_mask = False
self.blend_mode = QPainter.CompositionMode_Screen
self.items: List[ChannelImageItem] = None
def clear(self):
self.viewbox.clear()
self.items = None
def set_images(self, items: List[ChannelImageItem]):
self.items = items
if items is None or len(items) == 0:
return
images = []
for item in items:
image = scale_image(item.image, item.channel.settings.max, item.channel.settings.levels)
images.append(array2qimage(image, normalize=False))
self._draw_images(images)
def _draw_images(self, images: List[QImage]):
result = QImage(images[0].size(), QImage.Format_ARGB32_Premultiplied)
painter = QPainter(result)
painter.setCompositionMode(QPainter.CompositionMode_Source)
painter.fillRect(images[0].rect(), Qt.transparent)
painter.setCompositionMode(self.blend_mode)
for i in images:
painter.drawImage(0, 0, i)
painter.end()
self._image_item.setImage(rgb_view(result))
def refresh_images(self):
self.set_images(self.items)
def set_blend_mode(self, modename: str):
self.blend_mode = getattr(QPainter, 'CompositionMode_' + modename)
self.set_images(self.items)
def show_scale_bar(self, state: bool):
if state:
self.scale.show()
else:
self.scale.hide()
def progress_fn(self, n):
print("%d%% done" % n)
def process_result(self, result):
print("PROCESS RESULTS!")
if self._show_mask:
self._image_item.setImage(result)
def thread_complete(self):
print("THREAD COMPLETE!")
def show_mask(self, state: bool):
self._show_mask = state
if not state:
self.refresh_images()
return
if Manager.data.selected_mask is None or Manager.data.view_mode is ViewMode.GREYSCALE:
return
mask = Manager.data.selected_mask.image
blend_image = self._image_item.image
# Pass the function to execute
worker = Worker(apply_mask, image=blend_image, mask=mask) # Any other args, kwargs are passed to the run function
worker.signals.result.connect(self.process_result)
worker.signals.finished.connect(self.thread_complete)
worker.signals.progress.connect(self.progress_fn)
# Execute
Manager.threadpool.start(worker)
class ESR_plot(QtGui.QWidget):
def __init__(self, parent=None):
QtGui.QWidget.__init__(self, parent)
self.initGUI()
def initGUI(self):
self.plot = PlotWidget()
self.sensorplot = PlotWidget()
self.schemeplot = PlotWidget()
self.scheme_plot = UTILS_QT.myplot(self.schemeplot,xlabel = ['time', 's'], ylabel =['',''],logmode=False)
self.psp = UTILS_QT.pulses_scheme_plot(self.scheme_plot)
date_axis = TimeAxisItem(orientation='bottom')
#date_axis = pg.graphicsItems.DateAxisItem.DateAxisItem(orientation = 'bottom')
self.sensorplot = PlotWidget(axisItems = {'bottom': date_axis})
win = GraphicsLayoutWidget()
win2 = PlotWidget()
self.view = win.addViewBox(border = 'w', invertY = True)
self.view.setAspectLocked(True)
self.img = ImageItem()
self.plotaxes = win2.getPlotItem()
#self.view.addItem(self.img)
#self.view.addItem(self.plotaxes)
#self.view.
data = np.random.normal(size=(1, 600, 600), loc=1024, scale=64).astype(np.uint16)
self.img.setImage(data[0])
self.plotaxes.getViewBox().addItem(self.img)
# colormap
pos = np.array([0., 1., 0.5, 0.25, 0.75])
#pos2 = np.array([1.0,0.75,0.5,0.25,0.])
pos2 = np.array([0.,0.25,0.5,0.75,1.0])
color2 = np.array([[255,242,15,255], [245,124,15,255],[170,69,16,255],[91,50,0,255],[0,0,0,255]],dtype=np.ubyte)
color = np.array([[0,255,255,255], [255,255,0,255], [0,0,0,255], (0, 0, 255, 255), (255, 0, 0, 255)], dtype=np.ubyte)
cmap = pg.ColorMap(pos2, color2)
lut = cmap.getLookupTable(0.0, 1.0, 256)
self.img.setLookupTable(lut)
#self.img.setLevels([-50,1])
self.tw = QtGui.QTabWidget()
self.tw.addTab(win2,'ESR data')
self.tw.addTab(self.sensorplot,'B field')
self.tw.addTab(self.schemeplot,'Scheme')
layout = QtGui.QVBoxLayout()
layout.addWidget(self.plot)
#layout.addWidget(win2)
layout.addWidget(self.tw)
self.setLayout(layout)
self.p1 = self.plot.getPlotItem()
self.p2 = self.plot.getPlotItem()
self.ps = self.sensorplot.getPlotItem()
#self.p1.addLegend()
self.p1data = self.p1.plot([0],pen = 'r')
self.p2data = self.p1.plot([0],pen = 'g')
self.psdata = self.ps.plot([],pen = 'w')
self.ps.setLabel('left','Magnetic field', 'uT')
self.vLine5 = pg.InfiniteLine(angle=90, movable=True)
self.vLine6 = pg.InfiniteLine(angle=90, movable=True)
self.plotaxes.addItem(self.vLine5, ignoreBounds=True)
self.plotaxes.addItem(self.vLine6, ignoreBounds=True)
class PreviewWidget(GraphicsLayoutWidget):
def __init__(self):
super(PreviewWidget, self).__init__()
self.setMinimumHeight(250)
self.setMinimumWidth(250)
self.view = self.addViewBox(lockAspect=True, enableMenu=False)
self.imageitem = ImageItem()
self.textitem = TextItem(anchor=(0.5, 0))
self.textitem.setFont(QFont("Zero Threes"))
self.imgdata = None
self.imageitem.setOpts(axisOrder="row-major")
self.view.addItem(self.imageitem)
self.view.addItem(self.textitem)
self.textitem.hide()
self.setSizePolicy(QSizePolicy.Minimum, QSizePolicy.Minimum)
# def textItemBounds(axis, frac=1.0, orthoRange=None):
# b = self.textitem.boundingRect()
# sx, sy = self.view.viewPixelSize()
# x, y = sx*b.width(), sy*b.height()
# if axis == 0: return (-x/2, x/2)
# if axis == 1: return (0, y)
#
# self.textitem.dataBounds = textItemBounds
def sizeHint(self):
return QSize(250, 250)
@threads.method(threadkey="preview", showBusy=False)
def preview(self, data):
if isinstance(data, NonDBHeader):
self.preview_header(data)
else:
self.preview_catalog(data)
@staticmethod
def guess_stream_field(catalog: BlueskyRun):
# TODO: use some metadata (techniques?) for guidance about how to get a preview
streams = bluesky_utils.streams_from_run(catalog)
if "primary" in streams:
streams.remove("primary")
streams.insert(0, "primary")
for stream in streams:
descriptor = bluesky_utils.descriptors_from_stream(
catalog, stream)[0]
fields = bluesky_utils.fields_from_descriptor(descriptor)
for field in fields:
field_ndims = bluesky_utils.ndims_from_descriptor(
descriptor, field)
if field_ndims > 1:
return stream, field
def preview_catalog(self, catalog: BlueskyRun):
threads.invoke_in_main_thread(self.setText, "LOADING...")
try:
stream, field = self.guess_stream_field(catalog)
data = getattr(catalog, stream).to_dask()[field].squeeze()
for i in range(len(data.shape) - 2):
data = data[0]
threads.invoke_in_main_thread(self.setImage,
np.asarray(data.compute()))
except Exception as ex:
msg.logError(ex)
threads.invoke_in_main_thread(self.imageitem.clear)
threads.invoke_in_main_thread(self.setText, "UNKNOWN DATA FORMAT")
def preview_header(self, header: NonDBHeader):
try:
data = header.meta_array()[0]
threads.invoke_in_main_thread(self.setImage, data)
except IndexError:
threads.invoke_in_main_thread(self.imageitem.clear)
threads.invoke_in_main_thread(self.setText, "UNKNOWN DATA FORMAT")
def setImage(self, imgdata):
self.imageitem.clear()
self.textitem.hide()
self.imgdata = imgdata
self.imageitem.setImage(np.log(self.imgdata * (self.imgdata > 0) +
(self.imgdata < 1)),
autoLevels=True)
self.imageitem.setTransform(
QTransform(1, 0, 0, -1, 0, self.imgdata.shape[-2]))
self.view.autoRange()
def setText(self, text):
self.textitem.setText(text)
self.imageitem.clear()
self.textitem.setVisible(True)
self.view.autoRange()
class Plot2DWidget(PlotWidget):
def __init__(self, model_wrapper, statusbar, parent=None):
"""
The Plot2DWidget is responsible for rendering the 2D chromatogram data.
:param model_wrapper: the wrapper of the model.
:param parent: the parent of this Widget.
"""
super().__init__(parent=parent)
self.listener = Plot2DListener(self, model_wrapper, statusbar)
""" The listener for the 2D plot """
self.img = ImageItem()
""" The image of the chromatogram"""
self.wrapper_temp = model_wrapper # TEMPORARY TODO What is this for?
"""A temporary reference to the wrapper?"""
# Add the image to the plot.
self.addItem(self.img)
# Disable right click context menu.
self.getPlotItem().setMenuEnabled(False)
self.getPlotItem().getAxis('bottom').enableAutoSIPrefix(False)
self.getPlotItem().getAxis('left').enableAutoSIPrefix(False)
model_wrapper.add_observer(self, self.notify)
# call notify to draw the model. NOTE: The if statement isn't nesessary, it checks in notify if there is
# a model or not.
self.notify('model', model_wrapper.model)
def refresh_x_period(self, x_period):
if x_period == 0:
self.getPlotItem().getAxis('bottom').setScale(1)
else:
self.getPlotItem().getAxis('bottom').setScale(
x_period / self.wrapper_temp.model.get_width())
def refresh_y_period(self, y_period):
if y_period == 0:
self.getPlotItem().getAxis('left').setScale(1)
else:
self.getPlotItem().getAxis('left').setScale(
y_period / self.wrapper_temp.model.get_height())
def refresh_x_unit(self, x_unit):
if x_unit is TimeUnit.NONE:
self.getPlotItem().getAxis('bottom').setLabel(units="")
else:
self.getPlotItem().getAxis('bottom').setLabel(
units=x_unit.name.lower())
def refresh_y_unit(self, y_unit):
if y_unit is TimeUnit.NONE:
self.getPlotItem().getAxis('left').setLabel(units="")
else:
self.getPlotItem().getAxis('left').setLabel(
units=y_unit.name.lower())
def notify(self, name, value):
"""
Updates the image rendered to match the model.
:return: None
"""
if name == 'newIntegration':
self.addItem(value.selector.roi)
value.selector.set_viewport(self.img)
elif name == 'removeIntegration':
self.removeItem(value.selector.roi)
elif name in {'model', 'model.viewTransformed'}:
if value is None or value.get_2d_chromatogram_data() is None:
self.img.clear()
else:
self.img.setImage(value.get_2d_chromatogram_data().clip(
value.lower_bound, value.upper_bound),
lut=value.palette)
self.refresh_x_period(
self.wrapper_temp.get_preference(ScaleEnum.X_PERIOD))
self.refresh_y_period(
self.wrapper_temp.get_preference(ScaleEnum.Y_PERIOD))
self.refresh_x_unit(
self.wrapper_temp.get_preference(ScaleEnum.X_UNIT))
self.refresh_y_unit(
self.wrapper_temp.get_preference(ScaleEnum.Y_UNIT))
elif name == 'model.palette':
self.img.setLookupTable(value.palette)
elif name == 'model.lower_bound' or name == 'model.upper_bound':
self.img.setImage(value.get_2d_chromatogram_data().clip(
value.lower_bound, value.upper_bound),
lut=value.palette)
elif name == ScaleEnum.X_UNIT.name:
self.refresh_x_unit(value)
elif name == ScaleEnum.Y_UNIT.name:
self.refresh_y_unit(value)
elif name == ScaleEnum.X_PERIOD.name:
self.refresh_x_period(value)
elif name == ScaleEnum.Y_PERIOD.name:
self.refresh_y_period(value)
class ImageViewer(GraphicsView):
"""ImageViewer class for Paint4Brains.
This class contains the implementation of a series of functions required for the GUI of the Paint4Brains project.
Args:
brain (class): BrainData class for Paint4Brains
parent (class): Base or parent class
"""
def __init__(self, brain, parent=None):
super(ImageViewer, self).__init__(parent=parent)
# Inputting data
self.brain = brain
# Creating viewing box to see data
self.view = ModViewBox()
self.setCentralItem(self.view)
# Making Images out of data
self.over_img = ImageItem(
self.brain.current_label_data_slice,
autoDownSmaple=False,
opacity=1,
compositionMode=QtGui.QPainter.CompositionMode_Plus)
self.mid_img = ImageItem(
np.zeros(self.brain.current_other_labels_data_slice.shape),
autoDownSmaple=False,
opacity=0.7,
compositionMode=QtGui.QPainter.CompositionMode_Plus)
self.img = ImageItem(
self.brain.current_data_slice,
autoDownsample=False,
compositionMode=QtGui.QPainter.CompositionMode_SourceOver,
levels=(0., 1.))
# Colouring the labelled data
lut = np.array([[0, 0, 0, 0], [250, 0, 0, 255]])
self.over_img.setLookupTable(lut)
self.over_img.setLevels([0, 1])
# Maybe the visualization lecture was not that useless...
self.colours = [[166, 206, 27], [31, 120, 180], [178, 223, 138],
[51, 160, 44], [251, 154, 153], [227, 26, 28],
[253, 191, 111], [255, 127, 0], [202, 178, 214],
[106, 61, 154], [255, 255, 153], [177, 89, 40]]
self.update_colormap()
# Adding the images to the viewing box and setting it to drawing mode (if there is labeled data)
self.view.addItem(self.img)
self.view.addItem(self.mid_img)
self.view.addItem(self.over_img)
self.select_mode = False
self.see_all_labels = False
if self.brain.label_filename is not None:
self.enable_drawing()
self.update_colormap()
self.refresh_image()
self.dropbox = SelectLabel(self)
self.bonus = BonusBrush()
self.bonus.buttn.clicked.connect(self.new_brush)
def refresh_image(self):
"""Image Refresher
Sets the images displayed by the Image viewer to the current data slices.
It will only show all the labels if the self.see_all_labels parameters is True.
"""
#Creating slice copy
slice = self.brain.current_data_slice
new_slice = np.clip(
np.log2(1 + slice.astype(float)) * self.brain.intensity, 0,
self.brain.scale)
self.img.setImage(new_slice, levels=(0., 1.))
self.over_img.setImage(self.brain.current_label_data_slice,
autoLevels=False)
if self.see_all_labels:
self.mid_img.setImage(self.brain.current_other_labels_data_slice,
autoLevels=False)
else:
self.mid_img.setImage(np.zeros(
self.brain.current_other_labels_data_slice.shape),
autoLevels=False)
def recenter(self):
"""Brain Recenter
Recenter the brain into the middle of the image viewer.
The implementation may seem weird, but this is a predefined action by PyQt5.
"""
self.view.menu.actions()[0].trigger()
def update_colormap(self):
"""Label Colormap Update
Updates the colormap to account for the current number of distinct labels.
There are only 12 distinct colours (not including the "invisible" colour)
"""
num = len(self.brain.different_labels) + 1
self.mid_img.setLookupTable(
np.array([[0, 0, 0]] +
int(num / len(self.colours) + 1) * self.colours)[:num])
self.mid_img.setLevels([0, np.max(self.brain.different_labels)])
def enable_drawing(self):
"""Activates drawing mode
The default pen is a voxel in size.
"""
self.over_img.setDrawKernel(dot, mask=dot, center=(0, 0), mode='add')
self.view.drawing = True
def disable_drawing(self):
"""Deactivates drawing mode
It does this by deactivating the drawing kernel and setting the value of the drawing parameter in the modified view box to False.
"""
if self.view.drawing:
self.over_img.drawKernel = None
self.view.drawing = False
self.view.state["mouseMode"] = 3
else:
self.enable_drawing()
def edit_button1(self):
"""Sets the drawing mode to DOT
This is basically a square of one voxel in size with value one.
For all the editing buttons the matrix used to edit is defined at the top of the file
"""
self.view.drawing = True
self.over_img.setDrawKernel(dot, mask=dot, center=(0, 0), mode='add')
def edit_button2(self):
"""Sets the drawing mode to RUBBER
This is basically a square of one voxel in size with value one.
For all the editing buttons the matrix used to edit is defined at the top of the file
Removes the label from voxels.
"""
self.view.drawing = True
self.over_img.setDrawKernel(rubber,
mask=rubber,
center=(0, 0),
mode='add')
def edit_button3(self):
"""Sets the drawing mode to BRUSH (or cross)
This sets the paintbrush to a cross of 3x3 voxels in size.
For all the editing buttons the matrix used to edit is defined at the top of the file
"""
self.view.drawing = True
self.over_img.setDrawKernel(cross,
mask=cross,
center=(1, 1),
mode='add')
def bonus_brush(self):
"""Allows the user to design their own brush
This opens a window in which the user can design a brush by using the pen and rubber.
"""
self.enable_drawing()
self.bonus.setVisible(True)
def new_brush(self):
"""Sets the drawing mode to the user designed BRUSH (or bonus_brush)
This sets the paintbrush to a user defined brush.
"""
self.enable_drawing()
cent = len(self.bonus.pen) // 2
self.over_img.setDrawKernel(self.bonus.pen,
mask=self.bonus.pen,
center=(cent, cent),
mode='add')
def select_label(self):
"""Select label of interest
Allows the user to select the location of the label to be edited next.
Will only have effect if there are multiple labels from which to select
The bulk of the implementation for this method is in the modified mouseReleasEevent method
"""
if self.brain.multiple_labels:
self.over_img.drawKernel = None
self.select_mode = True
def view_back_labels(self):
"""Toggle all/single label.
Switch that determines whether all segmented areas are visible or just one.
If see_all_labels was False, it makes all labels visible.
If it was True it makes all labels except the one the user is currently editing invisible.
"""
self.see_all_labels = not self.see_all_labels
self.refresh_image()
def next_label(self):
"""Label forward scroll
Brings the next label in the list to be edited
Lets you iterate through all existing labels
"""
if self.brain.multiple_labels:
new_index = np.where(self.brain.different_labels ==
self.brain.current_label)[0][0] + 1
if new_index < len(self.brain.different_labels):
self.brain.current_label = self.brain.different_labels[
new_index]
else:
self.brain.current_label = self.brain.different_labels[1]
self.refresh_image()
self.dropbox.update_box()
def previous_label(self):
"""Label backward scroll
Brings the previous label in the list to be edited
Lets you iterate through all existing labels
"""
if self.brain.multiple_labels:
old_index = np.where(
self.brain.different_labels == self.brain.current_label)[0][0]
if old_index != 1:
self.brain.current_label = self.brain.different_labels[
old_index - 1]
else:
self.brain.current_label = self.brain.different_labels[-1]
self.refresh_image()
self.dropbox.update_box()
def undo_previous_edit(self):
"""Undo function
This function reverts the previous user action and refreshes the image.
"""
current = self.brain.current_edit
if current > 1:
self.brain.label_data = self.brain.edit_history[current - 2][0]
self.brain.other_labels_data = self.brain.edit_history[current -
2][1]
self.brain.current_edit = self.brain.current_edit - 1
self.refresh_image()
def redo_previous_edit(self):
"""Redo function
This function re-does a previously reverted actions.
"""
current = self.brain.current_edit
if current < len(self.brain.edit_history):
self.brain.label_data = self.brain.edit_history[current][0]
self.brain.other_labels_data = self.brain.edit_history[current][1]
self.brain.current_edit = self.brain.current_edit + 1
self.refresh_image()
def mouseReleaseEvent(self, ev):
"""Mouse event tracker
This function keeps track of the actions performed by the mouse, while taking the selcted mode into account.
If when select_mode is activated, the left button is released on a previously labeled area, then the pen is set to that label. Otherwise, everything should work as normal (the default)
Now when you release the left button it assumes an edit has been made and stores it into the BrainData.
Args:
ev: signal emitted when user releases a mouse button.
"""
if self.select_mode:
if ev.button() == Qt.LeftButton:
pos = ev.pos()
mouse_x = int(self.img.mapFromScene(pos).x())
mouse_y = int(self.img.mapFromScene(pos).y())
location = self.brain.position_as_voxel(mouse_x, mouse_y)
within = 0 < location[0] < self.brain.shape[0] and 0 < location[1] < self.brain.shape[1] and 0 < \
location[2] < self.brain.shape[2]
if within:
label = self.brain.other_labels_data[location]
if label > 0:
self.brain.current_label = self.brain.other_labels_data[
location]
self.select_mode = False
self.refresh_image()
self.enable_drawing()
self.dropbox.update_box()
super(ImageViewer, self).mouseReleaseEvent(ev)
if self.view.drawing and ev.button() == Qt.LeftButton:
self.brain.store_edit()
def wheelEvent(self, ev):
""" Overwriting the wheel functionality.
If you scroll it will move along slices.
If you scroll while holding the Ctrl button, it will zoom in and out
Args:
ev: signal emitted when user releases scrolls the wheel.
"""
if ev.modifiers() == Qt.ControlModifier:
super(ImageViewer, self).wheelEvent(ev)
else:
if ev.angleDelta().y(
) > 0 and self.brain.i < self.brain.shape[self.brain.section] - 1:
self.brain.i = self.brain.i + 1
self.refresh_image()
elif ev.angleDelta().y() < 0 < self.brain.i:
self.brain.i = self.brain.i - 1
self.refresh_image()
class astraPlotWidget(QWidget):
twissplotLayout = [
{
'name': 'sigma_x',
'range': [0, 1],
'scale': 1e3
},
{
'name': 'sigma_y',
'range': [0, 1],
'scale': 1e3
},
{
'name': 'kinetic_energy',
'range': [0, 250],
'scale': 1e-6
},
'next_row',
{
'name': 'sigma_p',
'range': [0, 0.015],
'scale': 1e6
},
{
'name': 'sigma_z',
'range': [0, 0.6],
'scale': 1e3
},
{
'name': 'enx',
'range': [0.5, 1.5],
'scale': 1e6
},
'next_row',
{
'name': 'eny',
'range': [0.5, 1.5],
'scale': 1e6
},
{
'name': 'beta_x',
'range': [0, 150],
'scale': 1
},
{
'name': 'beta_y',
'range': [0, 150],
'scale': 1
},
]
def __init__(self, directory='.', **kwargs):
super(astraPlotWidget, self).__init__(**kwargs)
self.beam = raf.beam()
self.twiss = rtf.twiss()
self.directory = directory
''' twissPlotWidget '''
self.twissPlotView = GraphicsView(useOpenGL=True)
self.twissPlotWidget = GraphicsLayout()
self.twissPlotView.setCentralItem(self.twissPlotWidget)
self.latticePlotData = imageio.imread('lattice_plot.png')
self.latticePlots = {}
self.twissPlots = {}
i = -1
for entry in self.twissplotLayout:
if entry == 'next_row':
self.twissPlotWidget.nextRow()
else:
i += 1
p = self.twissPlotWidget.addPlot(title=entry['name'])
p.showGrid(x=True, y=True)
vb = p.vb
vb.setYRange(*entry['range'])
latticePlot = ImageItem(self.latticePlotData)
latticePlot.setOpts(axisOrder='row-major')
vb.addItem(latticePlot)
latticePlot.setZValue(-1) # make sure this image is on top
# latticePlot.setOpacity(0.5)
self.twissPlots[entry['name']] = p.plot(
pen=mkPen('b', width=3))
self.latticePlots[p.vb] = latticePlot
p.vb.sigRangeChanged.connect(self.scaleLattice)
''' beamPlotWidget '''
self.beamPlotWidget = QWidget()
self.beamPlotLayout = QVBoxLayout()
self.item = ImageItem()
self.beamPlotWidget.setLayout(self.beamPlotLayout)
self.beamPlotView = ImageView(imageItem=self.item)
self.rainbow = rainbow()
self.item.setLookupTable(self.rainbow)
self.item.setLevels([0, 1])
# self.beamPlotWidgetGraphicsLayout = GraphicsLayout()
# p = self.beamPlotWidgetGraphicsLayout.addPlot(title='beam')
# p.showGrid(x=True, y=True)
# self.beamPlot = p.plot(pen=None, symbol='+')
# self.beamPlotView.setCentralItem(self.beamPlotWidgetGraphicsLayout)
self.beamPlotXAxisCombo = QComboBox()
self.beamPlotXAxisCombo.addItems(
['x', 'y', 'zn', 'cpx', 'cpy', 'BetaGamma'])
self.beamPlotYAxisCombo = QComboBox()
self.beamPlotYAxisCombo.addItems(
['x', 'y', 'zn', 'cpx', 'cpy', 'BetaGamma'])
self.beamPlotNumberBins = QSpinBox()
self.beamPlotNumberBins.setRange(10, 500)
self.beamPlotNumberBins.setSingleStep(10)
self.histogramBins = 100
self.beamPlotNumberBins.setValue(self.histogramBins)
self.beamPlotAxisWidget = QWidget()
self.beamPlotAxisLayout = QHBoxLayout()
self.beamPlotAxisWidget.setLayout(self.beamPlotAxisLayout)
self.beamPlotAxisLayout.addWidget(self.beamPlotXAxisCombo)
self.beamPlotAxisLayout.addWidget(self.beamPlotYAxisCombo)
self.beamPlotAxisLayout.addWidget(self.beamPlotNumberBins)
self.beamPlotXAxisCombo.currentIndexChanged.connect(self.plotDataBeam)
self.beamPlotYAxisCombo.currentIndexChanged.connect(self.plotDataBeam)
self.beamPlotNumberBins.valueChanged.connect(self.plotDataBeam)
# self.beamPlotXAxisCombo.setCurrentIndex(2)
# self.beamPlotYAxisCombo.setCurrentIndex(5)
self.beamPlotLayout.addWidget(self.beamPlotAxisWidget)
self.beamPlotLayout.addWidget(self.beamPlotView)
''' slicePlotWidget '''
self.sliceParams = [
{
'name': 'slice_normalized_horizontal_emittance',
'units': 'm-rad',
'text': 'enx'
},
{
'name': 'slice_normalized_vertical_emittance',
'units': 'm-rad',
'text': 'eny'
},
{
'name': 'slice_peak_current',
'units': 'A',
'text': 'PeakI'
},
{
'name': 'slice_relative_momentum_spread',
'units': '%',
'text': 'sigma-p'
},
]
self.slicePlotWidget = QWidget()
self.slicePlotLayout = QVBoxLayout()
self.slicePlotWidget.setLayout(self.slicePlotLayout)
# self.slicePlotView = GraphicsView(useOpenGL=True)
self.slicePlotWidgetGraphicsLayout = GraphicsLayoutWidget()
# self.slicePlots = {}
self.slicePlotCheckbox = {}
self.curve = {}
self.sliceaxis = {}
self.slicePlotCheckboxWidget = QWidget()
self.slicePlotCheckboxLayout = QVBoxLayout()
self.slicePlotCheckboxWidget.setLayout(self.slicePlotCheckboxLayout)
self.slicePlot = self.slicePlotWidgetGraphicsLayout.addPlot(
title='Slice', row=0, col=50)
self.slicePlot.showAxis('left', False)
self.slicePlot.showGrid(x=True, y=True)
i = -1
colors = ['b', 'r', 'g', 'k']
for param in self.sliceParams:
i += 1
axis = AxisItem("left")
labelStyle = {'color': '#' + colorStr(mkColor(colors[i]))[0:-2]}
axis.setLabel(text=param['text'],
units=param['units'],
**labelStyle)
viewbox = ViewBox()
axis.linkToView(viewbox)
viewbox.setXLink(self.slicePlot.vb)
self.sliceaxis[param['name']] = [axis, viewbox]
self.curve[param['name']] = PlotDataItem(pen=colors[i], symbol='+')
viewbox.addItem(self.curve[param['name']])
col = self.findFirstEmptyColumnInGraphicsLayout()
self.slicePlotWidgetGraphicsLayout.ci.addItem(axis,
row=0,
col=col,
rowspan=1,
colspan=1)
self.slicePlotWidgetGraphicsLayout.ci.addItem(viewbox,
row=0,
col=50)
p.showGrid(x=True, y=True)
# self.slicePlots[param] = self.slicePlot.plot(pen=colors[i], symbol='+')
self.slicePlotCheckbox[param['name']] = QCheckBox(param['text'])
self.slicePlotCheckboxLayout.addWidget(
self.slicePlotCheckbox[param['name']])
self.slicePlotCheckbox[param['name']].stateChanged.connect(
self.plotDataSlice)
# self.slicePlotView.setCentralItem(self.slicePlotWidgetGraphicsLayout)
self.slicePlotSliceWidthWidget = QSpinBox()
self.slicePlotSliceWidthWidget.setMaximum(1000)
self.slicePlotSliceWidthWidget.setValue(100)
self.slicePlotSliceWidthWidget.setSingleStep(10)
self.slicePlotSliceWidthWidget.setSuffix("fs")
self.slicePlotSliceWidthWidget.setSpecialValueText('Automatic')
self.slicePlotAxisWidget = QWidget()
self.slicePlotAxisLayout = QHBoxLayout()
self.slicePlotAxisWidget.setLayout(self.slicePlotAxisLayout)
self.slicePlotAxisLayout.addWidget(self.slicePlotCheckboxWidget)
self.slicePlotAxisLayout.addWidget(self.slicePlotSliceWidthWidget)
# self.slicePlotXAxisCombo.currentIndexChanged.connect(self.plotDataSlice)
self.slicePlotSliceWidthWidget.valueChanged.connect(
self.changeSliceLength)
# self.beamPlotXAxisCombo.setCurrentIndex(2)
# self.beamPlotYAxisCombo.setCurrentIndex(5)
self.slicePlotLayout.addWidget(self.slicePlotAxisWidget)
self.slicePlotLayout.addWidget(self.slicePlotWidgetGraphicsLayout)
self.layout = QVBoxLayout()
self.setLayout(self.layout)
self.tabWidget = QTabWidget()
self.folderButton = QPushButton('Select Directory')
self.folderLineEdit = QLineEdit()
self.folderLineEdit.setReadOnly(True)
self.folderLineEdit.setText(self.directory)
self.reloadButton = QPushButton()
self.reloadButton.setIcon(qApp.style().standardIcon(
QStyle.SP_BrowserReload))
self.folderWidget = QGroupBox()
self.folderLayout = QHBoxLayout()
self.folderLayout.addWidget(self.folderButton)
self.folderLayout.addWidget(self.folderLineEdit)
self.folderLayout.addWidget(self.reloadButton)
self.folderWidget.setLayout(self.folderLayout)
self.folderWidget.setMaximumWidth(800)
self.reloadButton.clicked.connect(
lambda: self.changeDirectory(self.directory))
self.folderButton.clicked.connect(self.changeDirectory)
self.fileSelector = QComboBox()
self.fileSelector.currentIndexChanged.connect(self.updateScreenCombo)
self.screenSelector = QComboBox()
self.screenSelector.currentIndexChanged.connect(self.changeScreen)
self.beamWidget = QGroupBox()
self.beamLayout = QHBoxLayout()
self.beamLayout.addWidget(self.fileSelector)
self.beamLayout.addWidget(self.screenSelector)
self.beamWidget.setLayout(self.beamLayout)
self.beamWidget.setMaximumWidth(800)
self.beamWidget.setVisible(False)
self.folderBeamWidget = QWidget()
self.folderBeamLayout = QHBoxLayout()
self.folderBeamLayout.setAlignment(Qt.AlignLeft)
self.folderBeamWidget.setLayout(self.folderBeamLayout)
self.folderBeamLayout.addWidget(self.folderWidget)
self.folderBeamLayout.addWidget(self.beamWidget)
self.tabWidget.addTab(self.twissPlotView, 'Twiss Plots')
self.tabWidget.addTab(self.beamPlotWidget, 'Beam Plots')
self.tabWidget.addTab(self.slicePlotWidget, 'Slice Beam Plots')
self.tabWidget.currentChanged.connect(self.changeTab)
self.layout.addWidget(self.folderBeamWidget)
self.layout.addWidget(self.tabWidget)
self.plotType = 'Twiss'
self.changeDirectory(self.directory)
def findFirstEmptyColumnInGraphicsLayout(self):
rowsfilled = self.slicePlotWidgetGraphicsLayout.ci.rows.get(0,
{}).keys()
for i in range(49):
if not i in rowsfilled:
return i
def changeTab(self, i):
if self.tabWidget.tabText(i) == 'Beam Plots':
self.plotType = 'Beam'
self.beamWidget.setVisible(True)
elif self.tabWidget.tabText(i) == 'Slice Beam Plots':
self.plotType = 'Slice'
self.beamWidget.setVisible(True)
else:
self.plotType = 'Twiss'
self.beamWidget.setVisible(False)
self.loadDataFile()
def changeDirectory(self, directory=None):
if directory == None or directory == False:
self.directory = str(
QFileDialog.getExistingDirectory(self, "Select Directory",
self.directory,
QFileDialog.ShowDirsOnly))
else:
self.directory = directory
self.folderLineEdit.setText(self.directory)
self.currentFileText = self.fileSelector.currentText()
self.currentScreenText = self.screenSelector.currentText()
self.getScreenFiles()
self.updateFileCombo()
self.updateScreenCombo()
self.loadDataFile()
def getScreenFiles(self):
self.screenpositions = {}
files = glob.glob(self.directory + '/*.????.???')
filenames = [
'.'.join(os.path.basename(f).split('.')[:-2]) for f in files
]
print 'filenames = ', filenames
runnumber = [os.path.basename(f).split('.')[-1] for f in files]
for f, r in list(set(zip(filenames, runnumber))):
files = glob.glob(self.directory + '/' + f + '.????.???')
screenpositions = [
re.search(f + '\.(\d\d\d\d)\.\d\d\d', s).group(1)
for s in files
]
print 'screenpositions = ', screenpositions
self.screenpositions[f] = {
'screenpositions': sorted(screenpositions),
'run': r
}
def updateFileCombo(self):
self.fileSelector.clear()
i = -1
screenfirstpos = []
for f in self.screenpositions:
screenfirstpos.append(
[f, min(self.screenpositions[f]['screenpositions'])])
screenfirstpos = np.array(screenfirstpos)
sortedscreennames = screenfirstpos[np.argsort(
np.array(screenfirstpos)[:, 1])]
print 'sortedscreennames = ', sortedscreennames
for f in sortedscreennames:
self.fileSelector.addItem(f[0])
i += 1
if f[0] == self.currentFileText:
self.fileSelector.setCurrentIndex(i)
def changeScreen(self, i):
run = self.screenpositions[str(self.fileSelector.currentText())]['run']
self.beamFileName = str(self.fileSelector.currentText()) + '.' + str(
self.screenSelector.currentText()) + '.' + str(run)
# print 'beamFileName = ', self.beamFileName
self.loadDataFile()
def updateScreenCombo(self):
self.screenSelector.clear()
i = -1
for s in self.screenpositions[str(
self.fileSelector.currentText())]['screenpositions']:
self.screenSelector.addItem(s)
i += 1
if s == self.currentScreenText:
self.screenSelector.setCurrentIndex(i)
def loadDataFile(self):
if self.plotType == 'Twiss':
files = sorted(glob.glob(self.directory + "/*Xemit*"))
self.twiss.read_astra_emit_files(files)
self.plotDataTwiss()
elif self.plotType == 'Beam' or self.plotType == 'Slice':
if hasattr(
self,
'beamFileName') and os.path.isfile(self.directory + '/' +
self.beamFileName):
# starttime = time.time()
self.beam.read_astra_beam_file(self.directory + '/' +
self.beamFileName)
# print 'reading file took ', time.time()-starttime, 's'
# print 'Read file: ', self.beamFileName
if self.plotType == 'Beam':
self.plotDataBeam()
else:
self.beam.bin_time()
self.plotDataSlice()
def plotDataTwiss(self):
for entry in self.twissplotLayout:
if entry == 'next_row':
pass
else:
x = self.twiss['z']
y = self.twiss[entry['name']] * entry['scale']
xy = np.transpose(np.array([x, y]))
x, y = np.transpose(xy[np.argsort(xy[:, 0])])
self.twissPlots[entry['name']].setData(x=x,
y=y,
pen=mkPen('b', width=3))
def plotDataBeam(self):
self.histogramBins = self.beamPlotNumberBins.value()
x = getattr(self.beam, str(self.beamPlotXAxisCombo.currentText()))
y = getattr(self.beam, str(self.beamPlotYAxisCombo.currentText()))
h, xedges, yedges = np.histogram2d(x,
y,
self.histogramBins,
normed=True)
x0 = xedges[0]
y0 = yedges[0]
xscale = (xedges[-1] - xedges[0]) / len(xedges)
yscale = (yedges[-1] - yedges[0]) / len(yedges)
self.item.setImage(h)
self.item.setLookupTable(self.rainbow)
# self.item.setLevels([0,1])
def changeSliceLength(self):
self.beam.slice_length = self.slicePlotSliceWidthWidget.value() * 1e-15
self.beam.bin_time()
self.plotDataSlice()
def plotDataSlice(self):
for param in self.sliceParams:
if self.slicePlotCheckbox[param['name']].isChecked():
x = self.beam.slice_bins
self.slicePlot.setRange(xRange=[min(x), max(x)])
# self.plot.setRange(xRange=[-0.5,1.5])
y = getattr(self.beam, param['name'])
self.curve[param['name']].setData(x=x, y=y)
self.sliceaxis[param['name']][0].setVisible(True)
# currentrange = self.sliceaxis[param['name']][0].range
# print 'currentrange = ', currentrange
# self.sliceaxis[param['name']][0].setRange(0, currentrange[1])
else:
# pass
self.curve[param['name']].setData(x=[], y=[])
self.sliceaxis[param['name']][0].setVisible(False)
self.sliceaxis[param['name']][1].autoRange()
currentrange = self.sliceaxis[param['name']][1].viewRange()
self.sliceaxis[param['name']][1].setYRange(0, currentrange[1][1])
def scaleLattice(self, vb, range):
yrange = range[1]
scaleY = 0.05 * abs(yrange[1] - yrange[0])
rect = QRectF(0, yrange[0] + 2 * scaleY, 49.2778, 4 * scaleY)
self.latticePlots[vb].setRect(rect)
class CustomImageViewer(GraphicsLayoutWidget):
@property
def view_box(self):
return self.image_plot.vb
def __init__(self, parent=None, **kwargs):
setConfigOptions(imageAxisOrder='row-major')
super(CustomImageViewer, self).__init__(parent)
self._scale = (1., 1.)
self._center = (0, 0)
self.__init_ui__()
def __init_ui__(self):
self.setWindowTitle('Image Viewer')
self.image_plot = self.addPlot()
self.image_plot.vb.setAspectLocked()
self.image_plot.vb.invertY()
self.image_item = ImageItem()
self.image_plot.addItem(self.image_item)
self.hist = HistogramLUTItem()
self.hist.setImageItem(self.image_item)
self.addItem(self.hist)
def set_data(self, data, change_limits: bool = True, reset_axes: bool = False):
if data is None:
return
self.image_item.setImage(data, change_limits)
if change_limits:
self.hist.setLevels(data.min(), data.max())
if reset_axes:
self.image_item.resetTransform()
self.set_default_range()
def set_default_range(self):
axes = self.get_axes()
self.image_plot.setRange(xRange=axes[0], yRange=axes[1])
def set_auto_range(self):
self.image_plot.autoRange()
def set_levels(self, levels=None):
if levels:
self.hist.setLevels(levels[0], levels[1])
else:
self.hist.setLevels(self.image_item.image.min(),
self.image_item.image.max())
def get_levels(self):
return self.hist.getLevels()
def set_center(self, center: tuple, pixel_units: bool = True):
if not pixel_units:
scale = self.get_scale()
center = (center[0] / scale[0], center[1] / scale[1])
if self._center != (0, 0) or self._scale != (1., 1.):
self.image_item.resetTransform()
self.image_item.scale(*self._scale)
self.image_item.translate(- center[0], - center[1])
self._center = center
self.set_default_range()
def set_scale(self, scale: float or tuple):
if isinstance(scale, float) or isinstance(scale, int):
scale = (scale, scale)
if self._center != (0, 0) or self._scale != (1., 1.):
self.image_item.resetTransform()
self.image_item.scale(*scale)
if self._center != (0, 0):
self.image_item.translate(- self._center[0], - self._center[1])
self._scale = scale
self.set_default_range()
def get_scale(self):
# scale property is occupied by Qt superclass.
return self._scale
def get_center(self):
return self._center
def set_x_axis(self, x_min, x_max):
self._set_axis(x_min, x_max, 0)
self.set_default_range()
def set_y_axis(self, y_min, y_max):
self._set_axis(y_min, y_max, 1)
self.set_default_range()
def _set_axis(self, min_: float, max_: float, axis_ind: int):
shape = self.image_item.image.shape
scale = np.array(self._scale)
scale[axis_ind] = (max_ - min_) / shape[axis_ind]
center = np.array(self._center)
center[axis_ind] = - min_ / scale[axis_ind]
if self._center != (0, 0) or self._scale != (1., 1.):
self.image_item.resetTransform()
self.image_item.scale(scale[0], scale[1])
self.image_item.translate(- center[0], - center[1])
self._scale = tuple(scale)
self._center = tuple(center)
def get_axes(self):
shape = np.array(self.image_item.image.shape)
scale = np.array(self._scale)
min_ = - np.array(self._center) * scale
max_ = min_ + shape * scale
return (min_[0], max_[0]), (min_[1], max_[1])
class FilterPreviews(GraphicsLayoutWidget):
image_before: ImageItem
image_after: ImageItem
image_diff: ImageItem
histogram: Optional[PlotItem]
def __init__(self, parent=None, **kwargs):
super().__init__(parent, **kwargs)
widget_location = self.mapToGlobal(QPoint(self.width() // 2, 0))
# allow the widget to take up to 80% of the desktop's height
if QGuiApplication.screenAt(widget_location) is not None:
screen_height = QGuiApplication.screenAt(
widget_location).availableGeometry().height()
else:
screen_height = max(
QGuiApplication.primaryScreen().availableGeometry().height(),
600)
LOG.info(
"Unable to detect current screen. Setting screen height to %s"
% screen_height)
self.ALLOWED_HEIGHT: QRect = screen_height * 0.8
self.histogram = None
self.addLabel("Image before")
self.addLabel("Image after")
self.addLabel("Image difference")
self.nextRow()
self.imageview_before = MIMiniImageView(name="before")
self.imageview_after = MIMiniImageView(name="after")
self.imageview_difference = MIMiniImageView(name="difference")
self.all_imageviews = [
self.imageview_before, self.imageview_after,
self.imageview_difference
]
MIMiniImageView.set_siblings(self.all_imageviews, axis=True)
MIMiniImageView.set_siblings(
[self.imageview_before, self.imageview_after], hist=True)
self.image_before, self.image_before_vb, self.image_before_hist = self.imageview_before.get_parts(
)
self.image_after, self.image_after_vb, self.image_after_hist = self.imageview_after.get_parts(
)
self.image_difference, self.image_difference_vb, self.image_difference_hist = \
self.imageview_difference.get_parts()
self.all_histograms = [
self.image_before_hist, self.image_after_hist,
self.image_difference_hist
]
self.image_diff_overlay = ImageItem()
self.image_diff_overlay.setZValue(10)
self.image_after_vb.addItem(self.image_diff_overlay)
# Ensure images resize equally
self.image_layout: GraphicsLayout = self.addLayout(colspan=3)
self.image_layout.addItem(self.imageview_before)
self.image_layout.addItem(self.imageview_after)
self.image_layout.addItem(self.imageview_difference)
self.nextRow()
self.init_histogram()
# Work around for https://github.com/mantidproject/mantidimaging/issues/565
self.scene().contextMenu = [
item for item in self.scene().contextMenu
if "export" not in item.text().lower()
]
self.auto_colour_actions = []
self._add_auto_colour_action(self.image_before_hist, self.image_before)
self._add_auto_colour_action(self.image_after_hist, self.image_after)
self._add_auto_colour_action(self.image_difference_hist,
self.image_difference)
self.imageview_before.link_sibling_axis()
self.imageview_before.enable_nan_check()
self.imageview_after.enable_nan_check()
def resizeEvent(self, ev: QResizeEvent):
if ev is not None and isinstance(self.histogram, PlotItem):
size = ev.size()
self.histogram.setFixedHeight(
min(size.height() * 0.7, self.ALLOWED_HEIGHT) * 0.25)
super().resizeEvent(ev)
def clear_items(self, clear_before: bool = True):
if clear_before:
self.imageview_before.clear()
self.imageview_after.clear()
self.imageview_difference.clear()
self.image_diff_overlay.clear()
def init_histogram(self):
self.histogram = self.addPlot(row=histogram_coords.row,
col=histogram_coords.col,
labels=histogram_axes_labels,
lockAspect=True,
colspan=3)
self.addLabel("Pixel values",
row=label_coords.row,
col=label_coords.col)
self.legend = self.histogram.addLegend()
self.legend.setOffset((0, 1))
def update_histogram_data(self):
# Plot any histogram that has data, and add a legend if both exist
before_data = self.imageview_before.image_item.getHistogram()
after_data = self.imageview_after.image_item.getHistogram()
if _data_valid_for_histogram(before_data):
before_plot = self.histogram.plot(*before_data,
pen=before_pen,
clear=True)
self.legend.addItem(before_plot, "Before")
if _data_valid_for_histogram(after_data):
after_plot = self.histogram.plot(*after_data, pen=after_pen)
self.legend.addItem(after_plot, "After")
@property
def histogram_legend(self) -> Optional[LegendItem]:
if self.histogram and self.histogram.legend:
return self.histogram.legend
return None
def link_all_views(self):
self.imageview_before.link_sibling_axis()
def unlink_all_views(self):
self.imageview_before.unlink_sibling_axis()
def add_difference_overlay(self, diff, nan_change):
diff = np.absolute(diff)
diff[diff > OVERLAY_THRESHOLD] = 1.0
diff[nan_change] = 1.0
pos = np.array([0, 1])
color = np.array([[0, 0, 0, 0], OVERLAY_COLOUR_DIFFERENCE],
dtype=np.ubyte)
map = ColorMap(pos, color)
self.image_diff_overlay.setVisible(True)
self.image_diff_overlay.setImage(diff)
lut = map.getLookupTable(0, 1, 2)
self.image_diff_overlay.setLookupTable(lut)
def add_negative_overlay(self):
self.imageview_after.enable_nonpositive_check()
def hide_difference_overlay(self):
self.image_diff_overlay.setVisible(False)
def hide_negative_overlay(self):
self.imageview_after.enable_nonpositive_check(False)
def auto_range(self):
# This will cause the previews to all show by just causing autorange on self.image_before_vb
self.image_before_vb.autoRange()
def record_histogram_regions(self):
self.before_region = self.image_before_hist.region.getRegion()
self.diff_region = self.image_difference_hist.region.getRegion()
self.after_region = self.image_after_hist.region.getRegion()
def restore_histogram_regions(self):
self.image_before_hist.region.setRegion(self.before_region)
self.image_difference_hist.region.setRegion(self.diff_region)
self.image_after_hist.region.setRegion(self.after_region)
def link_before_after_histogram_scales(self, create_link: bool):
"""
Connects or disconnects the scales of the before/after histograms.
:param create_link: Whether the link should be created or removed.
"""
if create_link:
self.imageview_after.link_sibling_histogram()
else:
self.imageview_after.unlink_sibling_histogram()
def set_histogram_log_scale(self):
"""
Sets the y-values of the before and after histogram plots to a log scale.
"""
set_histogram_log_scale(self.image_before_hist)
set_histogram_log_scale(self.image_after_hist)
def _add_auto_colour_action(self, histogram: HistogramLUTItem,
image: ImageItem):
"""
Adds an "Auto" action to the histogram right-click menu.
:param histogram: The HistogramLUTItem
:param image: The ImageItem to have the Jenks/Otsu algorithm performed on it.
"""
self.auto_colour_actions.append(QAction("Auto"))
self.auto_colour_actions[-1].triggered.connect(
lambda: self._on_change_colour_palette(histogram, image))
action = histogram.gradient.menu.actions()[12]
histogram.gradient.menu.insertAction(action,
self.auto_colour_actions[-1])
histogram.gradient.menu.insertSeparator(self.auto_colour_actions[-1])
def _on_change_colour_palette(self, main_histogram: HistogramLUTItem,
image: ImageItem):
"""
Creates a Palette Changer window when the "Auto" option has been selected.
:param main_histogram: The HistogramLUTItem.
:param image: The ImageItem.
"""
other_histograms = self.all_histograms[:]
other_histograms.remove(main_histogram)
change_colour_palette = PaletteChangerView(self, main_histogram,
image.image,
other_histograms)
change_colour_palette.show()
class FilterPreviews(GraphicsLayoutWidget):
image_before: ImageItem
image_after: ImageItem
image_diff: ImageItem
histogram_before: Optional[PlotItem]
histogram_after: Optional[PlotItem]
histogram: Optional[PlotItem]
def __init__(self, parent=None, **kwargs):
super(FilterPreviews, self).__init__(parent, **kwargs)
widget_location = self.mapToGlobal(QPoint(self.width() / 2, 0))
# allow the widget to take up to 80% of the desktop's height
self.ALLOWED_HEIGHT: QRect = QGuiApplication.screenAt(widget_location).availableGeometry().height() * 0.8
self.before_histogram_data = None
self.after_histogram_data = None
self.histogram = None
self.before_histogram = None
self.after_histogram = None
self.combined_histograms = True
self.histogram_legend_visible = True
self.addLabel("Image before")
self.addLabel("Image after")
self.addLabel("Image difference")
self.nextRow()
self.image_before, self.image_before_vb, self.image_before_hist = self.image_in_vb(name="before")
self.image_after, self.image_after_vb, self.image_after_hist = self.image_in_vb(name="after")
self.image_difference, self.image_difference_vb, self.image_difference_hist = self.image_in_vb(
name="difference")
self.image_after_overlay = ImageItem()
self.image_after_overlay.setZValue(10)
self.image_after_vb.addItem(self.image_after_overlay)
# Ensure images resize equally
self.image_layout: GraphicsLayout = self.addLayout(colspan=6)
self.image_layout.addItem(self.image_before_vb, 0, 0)
self.image_layout.addItem(self.image_before_hist, 0, 1)
self.image_layout.addItem(self.image_after_vb, 0, 2)
self.image_layout.addItem(self.image_after_hist, 0, 3)
self.image_layout.addItem(self.image_difference_vb, 0, 4)
self.image_layout.addItem(self.image_difference_hist, 0, 5)
self.nextRow()
before_details = self.addLabel("")
after_details = self.addLabel("")
difference_details = self.addLabel("")
self.display_formatted_detail = {
self.image_before: lambda val: before_details.setText(f"Before: {val:.6f}"),
self.image_after: lambda val: after_details.setText(f"After: {val:.6f}"),
self.image_difference: lambda val: difference_details.setText(f"Difference: {val:.6f}"),
}
for img in self.image_before, self.image_after, self.image_difference:
img.hoverEvent = lambda ev: self.mouse_over(ev)
self.init_histogram()
def resizeEvent(self, ev: QResizeEvent):
if ev is not None:
size = ev.size()
self.image_layout.setFixedHeight(min(size.height() * 0.7, self.ALLOWED_HEIGHT))
super().resizeEvent(ev)
def image_in_vb(self, name=None):
im = ImageItem()
vb = ViewBox(invertY=True, lockAspect=True, name=name)
vb.addItem(im)
hist = HistogramLUTItem(im)
return im, vb, hist
def clear_items(self):
self.image_before.clear()
self.image_after.clear()
self.image_difference.clear()
self.image_after_overlay.clear()
def init_histogram(self):
self.histogram = self.addPlot(row=histogram_coords["combined"].row,
col=histogram_coords["combined"].col,
labels=histogram_axes_labels,
lockAspect=True,
colspan=3)
self.addLabel("Pixel values", row=label_coords["combined"].row, col=label_coords["combined"].col)
self.legend = self.histogram.addLegend()
def update_histogram_data(self):
# Plot any histogram that has data, and add a legend if both exist
before_data = self.image_before.getHistogram()
after_data = self.image_after.getHistogram()
if _data_valid_for_histogram(before_data):
if self.combined_histograms:
before_plot = self.histogram.plot(*before_data, pen=before_pen, clear=True)
self.legend.addItem(before_plot, "Before")
else:
self.before_histogram.plot(*before_data, pen=before_pen, clear=True)
if _data_valid_for_histogram(after_data):
if self.combined_histograms:
after_plot = self.histogram.plot(*after_data, pen=after_pen)
self.legend.addItem(after_plot, "After")
else:
self.after_histogram.plot(*after_data, pen=after_pen, clear=True)
def init_separate_histograms(self):
hc = histogram_coords
self.before_histogram = self.addPlot(row=hc["before"].row,
col=hc["before"].col,
labels=histogram_axes_labels,
lockAspect=True)
self.after_histogram = self.addPlot(row=hc["after"].row,
col=hc["after"].col,
labels=histogram_axes_labels,
lockAspect=True)
lc = label_coords
self.addLabel("Pixel values before", row=lc["before"].row, col=lc["before"].col)
self.addLabel("Pixel values after", row=lc["after"].row, col=lc["after"].col)
if _data_valid_for_histogram(self.before_histogram_data):
self.before_histogram.plot(*self.before_histogram_data, pen=before_pen)
if _data_valid_for_histogram(self.after_histogram_data):
self.after_histogram.plot(*self.after_histogram_data, pen=after_pen)
def delete_histograms(self):
coords = set(c for c in histogram_coords.values())
histograms = (self.getItem(*coord) for coord in coords)
for histogram in filter(lambda h: h is not None, histograms):
self.removeItem(histogram)
self.histogram = None
self.before_histogram = None
self.after_histogram = None
def delete_histogram_labels(self):
coords = set(c for c in label_coords.values())
labels = (self.getItem(*coord) for coord in coords)
for label in filter(lambda h: h is not None, labels):
self.removeItem(label)
@property
def histogram_legend(self) -> Optional[LegendItem]:
if self.histogram and self.histogram.legend:
return self.histogram.legend
return None
def mouse_over(self, ev):
# Ignore events triggered by leaving window or right clicking
if ev.exit:
return
pos = CloseEnoughPoint(ev.pos())
for img in self.image_before, self.image_after, self.image_difference:
if img.image is not None and pos.x < img.image.shape[0] and pos.y < img.image.shape[1]:
pixel_value = img.image[pos.y, pos.x]
self.display_formatted_detail[img](pixel_value)
def link_all_views(self):
for view1, view2 in [[self.image_before_vb, self.image_after_vb],
[self.image_after_vb, self.image_difference_vb],
[self.image_after_hist.vb, self.image_before_hist.vb]]:
view1.linkView(ViewBox.XAxis, view2)
view1.linkView(ViewBox.YAxis, view2)
def unlink_all_views(self):
for view in self.image_before_vb, self.image_after_vb, self.image_after_hist.vb:
view.linkView(ViewBox.XAxis, None)
view.linkView(ViewBox.YAxis, None)
def add_difference_overlay(self, diff):
diff = -diff
diff[diff > 0.0] = 1.0
pos = np.array([0, 1])
color = np.array([[0, 0, 0, 0], [255, 0, 0, 255]], dtype=np.ubyte)
map = ColorMap(pos, color)
self.image_after_overlay.setOpacity(1)
self.image_after_overlay.setImage(diff)
lut = map.getLookupTable(0, 1, 2)
self.image_after_overlay.setLookupTable(lut)
def hide_difference_overlay(self):
self.image_after_overlay.setOpacity(0)
def auto_range(self):
# This will cause the previews to all show by just causing autorange on self.image_before_vb
self.image_before_vb.autoRange()
class PreviewWidget(GraphicsLayoutWidget):
def __init__(self):
super(PreviewWidget, self).__init__()
self.setMinimumHeight(250)
self.setMinimumWidth(250)
self.view = self.addViewBox(lockAspect=True, enableMenu=False)
self.imageitem = ImageItem()
self.textitem = TextItem(anchor=(0.5, 0))
self.textitem.setFont(QFont("Zero Threes"))
self.imgdata = None
self.imageitem.setOpts(axisOrder="row-major")
self.view.addItem(self.imageitem)
self.view.addItem(self.textitem)
self.textitem.hide()
self.setSizePolicy(QSizePolicy.Minimum, QSizePolicy.Minimum)
# def textItemBounds(axis, frac=1.0, orthoRange=None):
# b = self.textitem.boundingRect()
# sx, sy = self.view.viewPixelSize()
# x, y = sx*b.width(), sy*b.height()
# if axis == 0: return (-x/2, x/2)
# if axis == 1: return (0, y)
#
# self.textitem.dataBounds = textItemBounds
def sizeHint(self):
return QSize(250, 250)
def preview(self, data):
if isinstance(data, NonDBHeader):
self.preview_header(data)
else:
self.preview_catalog(data)
def preview_catalog(self, catalog: BlueskyRun):
try:
dask_array = catalog.primary.to_dask()
fields = dask_array.keys()
# Filter out seq num and uid
field = next(field for field in fields if not field in ["seq_num", "uid"])
data = dask_array[field]
for i in range(len(data.shape) - 2):
data = data[0]
self.setImage(np.asarray(data.compute()))
except IndexError:
self.imageitem.clear()
self.setText("UNKNOWN DATA FORMAT")
def preview_header(self, header: NonDBHeader):
try:
data = header.meta_array()[0]
self.setImage(data)
except IndexError:
self.imageitem.clear()
self.setText("UNKNOWN DATA FORMAT")
def setImage(self, imgdata):
self.imageitem.clear()
self.textitem.hide()
self.imgdata = imgdata
self.imageitem.setImage(np.log(self.imgdata * (self.imgdata > 0) + (self.imgdata < 1)), autoLevels=True)
self.imageitem.setTransform(QTransform(1, 0, 0, -1, 0, self.imgdata.shape[-2]))
self.view.autoRange()
def setText(self, text):
self.textitem.setText(text)
self.imageitem.clear()
self.textitem.setVisible(True)
self.view.autoRange()
class ClusteringWidget(QSplitter):
def __init__(self, headermodel, selectionmodel):
super(ClusteringWidget, self).__init__()
self.headermodel = headermodel
self.selectionmodel = selectionmodel
# init some values
self.selectMapidx = 0
self.embedding = None
self.labels = None
self.mean_spectra = None
# split between cluster image and scatter plot
self.image_and_scatter = QSplitter()
# split between image&scatter and spec plot, vertical split
self.leftsplitter = QSplitter()
self.leftsplitter.setOrientation(Qt.Vertical)
# split between params, buttons and map list, vertical split
self.rightsplitter = QSplitter()
self.rightsplitter.setOrientation(Qt.Vertical)
self.clusterImage = MapViewWidget()
self.clusterScatterPlot = ScatterPlotWidget()
self.rawSpecPlot = SpectraPlotWidget()
self.clusterMeanPlot = ClusterSpectraWidget()
# ParameterTree
self.parametertree = ClusteringParameters()
self.parameter = self.parametertree.parameter
# buttons layout
self.buttons = QWidget()
self.buttonlayout = QGridLayout()
self.buttons.setLayout(self.buttonlayout)
# set up buttons
self.fontSize = 12
font = QFont("Helvetica [Cronyx]", self.fontSize)
self.computeBtn = QPushButton()
self.computeBtn.setText('Compute clusters')
self.computeBtn.setFont(font)
self.saveBtn = QPushButton()
self.saveBtn.setText('Save clusters')
self.saveBtn.setFont(font)
# add all buttons
self.buttonlayout.addWidget(self.computeBtn)
self.buttonlayout.addWidget(self.saveBtn)
# Headers listview
self.headerlistview = QListView()
self.headerlistview.setModel(headermodel)
self.headerlistview.setSelectionModel(
selectionmodel) # This might do weird things in the map view?
self.headerlistview.setSelectionMode(QListView.SingleSelection)
# add title to list view
self.mapListWidget = QWidget()
self.listLayout = QVBoxLayout()
self.mapListWidget.setLayout(self.listLayout)
mapListTitle = QLabel('Maps list')
mapListTitle.setFont(font)
self.listLayout.addWidget(mapListTitle)
self.listLayout.addWidget(self.headerlistview)
# assemble widgets
self.image_and_scatter.addWidget(self.clusterImage)
self.image_and_scatter.addWidget(self.clusterScatterPlot)
self.leftsplitter.addWidget(self.image_and_scatter)
self.leftsplitter.addWidget(self.rawSpecPlot)
self.leftsplitter.addWidget(self.clusterMeanPlot)
self.leftsplitter.setSizes([200, 50, 50])
self.rightsplitter.addWidget(self.parametertree)
self.rightsplitter.addWidget(self.buttons)
self.rightsplitter.addWidget(self.mapListWidget)
self.rightsplitter.setSizes([300, 50, 50])
self.addWidget(self.leftsplitter)
self.addWidget(self.rightsplitter)
self.setSizes([500, 100])
# setup ROI item
sideLen = 10
self.roi = PolyLineROI(positions=[[0, 0], [sideLen, 0],
[sideLen, sideLen], [0, sideLen]],
closed=True)
self.roi.hide()
self.roiInitState = self.roi.getState()
# set up mask item
self.maskItem = ImageItem(np.ones((1, 1)),
axisOrder="row-major",
autoLevels=True,
opacity=0.3)
self.maskItem.hide()
# set up select mask item
self.selectMaskItem = ImageItem(np.ones((1, 1)),
axisOrder="row-major",
autoLevels=True,
opacity=0.3,
lut=np.array([[0, 0, 0], [255, 0, 0]]))
self.selectMaskItem.hide()
self.clusterImage.view.addItem(self.roi)
self.clusterImage.view.addItem(self.maskItem)
self.clusterImage.view.addItem(self.selectMaskItem)
# Connect signals
self.computeBtn.clicked.connect(self.computeEmbedding)
self.saveBtn.clicked.connect(self.saveCluster)
self.clusterImage.sigShowSpectra.connect(self.rawSpecPlot.showSpectra)
self.clusterImage.sigShowSpectra.connect(
self.clusterScatterPlot.clickFromImage)
self.clusterScatterPlot.sigScatterRawInd.connect(
self.rawSpecPlot.showSpectra)
self.clusterScatterPlot.sigScatterClicked.connect(self.showClusterMean)
self.clusterScatterPlot.sigScatterRawInd.connect(self.setImageCross)
self.parametertree.sigParamChanged.connect(self.updateClusterParams)
self.selectionmodel.selectionChanged.connect(self.updateMap)
self.selectionmodel.selectionChanged.connect(self.updateRoiMask)
def computeEmbedding(self):
# get current map idx
if not self.isMapOpen():
return
msg.showMessage('Compute embedding.')
# Select wavenumber region
wavROIList = []
for entry in self.parameter['Wavenumber Range'].split(','):
try:
wavROIList.append(val2ind(int(entry), self.wavenumbers))
except:
continue
if len(wavROIList) % 2 == 0:
wavROIList = sorted(wavROIList)
wavROIidx = []
for i in range(len(wavROIList) // 2):
wavROIidx += list(
range(wavROIList[2 * i], wavROIList[2 * i + 1] + 1))
else:
msg.logMessage('"Wavenumber Range" values must be in pairs',
msg.ERROR)
MsgBox('Clustering computation aborted.', 'error')
return
self.wavenumbers_select = self.wavenumbers[wavROIidx]
self.N_w = len(self.wavenumbers_select)
# get current dataset
if self.selectedPixels is None:
n_spectra = len(self.data)
self.dataset = np.zeros((n_spectra, self.N_w))
for i in range(n_spectra):
self.dataset[i, :] = self.data[i][wavROIidx]
else:
n_spectra = len(self.selectedPixels)
self.dataset = np.zeros((n_spectra, self.N_w))
for i in range(n_spectra): # i: ith selected pixel
row_col = tuple(self.selectedPixels[i])
self.dataset[i, :] = self.data[self.rc2ind[row_col]][wavROIidx]
# get parameters and compute embedding
n_components = self.parameter['Components']
if self.parameter['Embedding'] == 'UMAP':
n_neighbors = self.parameter['Neighbors']
metric = self.parameter['Metric']
min_dist = np.clip(self.parameter['Min Dist'], 0, 1)
self.umap = UMAP(n_neighbors=n_neighbors,
min_dist=min_dist,
n_components=n_components,
metric=metric,
random_state=0)
self.embedding = self.umap.fit_transform(self.dataset)
elif self.parameter['Embedding'] == 'PCA':
# normalize and mean center
if self.parameter['Normalization'] == 'L1': # normalize
data_norm = Normalizer(norm='l1').fit_transform(self.dataset)
elif self.parameter['Normalization'] == 'L2':
data_norm = Normalizer(norm='l2').fit_transform(self.dataset)
else:
data_norm = self.dataset
# subtract mean
data_centered = StandardScaler(
with_std=False).fit_transform(data_norm)
# Do PCA
self.PCA = PCA(n_components=n_components)
self.PCA.fit(data_centered)
self.embedding = self.PCA.transform(data_centered)
# save embedding to standardModelItem
self.item.embedding = self.embedding
# update cluster map
self.computeCluster()
def computeCluster(self):
# check if embeddings exist
if self.embedding is None:
return
msg.showMessage('Compute clusters.')
# get num of clusters
n_clusters = self.parameter['Clusters']
# set colorLUT
self.colorLUT = cm.get_cmap('viridis',
n_clusters + 1).colors[:, :3] * 255
# compute cluster
cluster_object = KMeans(n_clusters=n_clusters,
random_state=0).fit(self.embedding)
self.labels = cluster_object.labels_ + 1
# update cluster image
if self.selectedPixels is None: # full map
self.cluster_map = self.labels.reshape(self.imgShape[0],
self.imgShape[1])
elif self.selectedPixels.size == 0:
self.cluster_map = np.zeros((self.imgShape[0], self.imgShape[1]),
dtype=int)
else:
self.cluster_map = np.zeros((self.imgShape[0], self.imgShape[1]),
dtype=int)
self.cluster_map[self.selectedPixels[:, 0],
self.selectedPixels[:, 1]] = self.labels
self.cluster_map = np.flipud(self.cluster_map)
self.clusterImage.setImage(self.cluster_map, levels=[0, n_clusters])
# self.clusterImage.setImage(self.cluster_map)
self.clusterImage._image = self.cluster_map
self.clusterImage.rc2ind = self.rc2ind
self.clusterImage.row, self.clusterImage.col = self.imgShape[
0], self.imgShape[1]
self.clusterImage.txt.setPos(self.clusterImage.col, 0)
self.clusterImage.cross.show()
# update cluster mean
mean_spectra = []
self.dfGroups = []
if self.selectedPixels is None:
n_spectra = len(self.data)
self.dataList = np.zeros((n_spectra, len(self.wavenumbers)))
dataIdx = np.arange(n_spectra)
for i in range(n_spectra):
self.dataList[i] = self.data[i]
else:
n_spectra = len(self.selectedPixels)
self.dataList = np.zeros((n_spectra, len(self.wavenumbers)))
dataIdx = np.zeros(n_spectra, dtype=int)
for i in range(n_spectra): # i: ith selected pixel
row_col = tuple(self.selectedPixels[i])
dataIdx[i] = self.rc2ind[row_col]
self.dataList[i] = self.data[dataIdx[i]]
for ii in range(1, n_clusters + 1):
sel = (self.labels == ii)
# save each group spectra to a dataFrame
self.dfGroups.append(
pd.DataFrame(self.dataList[sel],
columns=self.wavenumbers.tolist(),
index=dataIdx[sel]))
this_mean = np.mean(self.dataset[sel, :], axis=0)
mean_spectra.append(this_mean)
self.mean_spectra = np.vstack(mean_spectra)
self.clusterMeanPlot.setColors(self.colorLUT)
self.clusterMeanPlot._data = self.mean_spectra
self.clusterMeanPlot.wavenumbers = self.wavenumbers_select
self.clusterMeanPlot.plotClusterSpectra()
# update scatter plot
self.updateScatterPlot()
def saveCluster(self):
if hasattr(self, 'cluster_map') and hasattr(self, 'mean_spectra'):
filePath = self.pathList[self.selectMapidx]
# get dirname and old filename
dirName = os.path.dirname(filePath)
oldFileName = os.path.basename(filePath)
n_clusters = self.parameter['Clusters']
for i in range(n_clusters):
# save dataFrames to csv file
csvName = oldFileName[:-3] + f'_cluster{i+1}.csv'
newFilePath = os.path.join(dirName, csvName)
self.dfGroups[i].to_csv(newFilePath)
MsgBox(
f'Cluster spectra groups were successfully saved at: {newFilePath}!'
)
def updateScatterPlot(self):
if (self.embedding is None) or (self.labels is None):
return
# get scatter x, y values
self.clusterScatterPlot.scatterData = self.embedding[:, [
self.parameter['X Component'] - 1, self.parameter['Y Component'] -
1
]]
# get colormapings
brushes = [mkBrush(self.colorLUT[x, :]) for x in self.labels]
# make plots
if hasattr(self, 'scatterPlot'):
self.clusterScatterPlot.plotItem.clearPlots()
self.scatterPlot = self.clusterScatterPlot.plotItem.plot(
self.clusterScatterPlot.scatterData,
pen=None,
symbol='o',
symbolBrush=brushes)
self.clusterScatterPlot.getViewBox().autoRange(padding=0.1)
self.clusterScatterPlot.getNN()
def updateClusterParams(self, name):
if name == 'Components':
self.computeEmbedding()
elif name == 'Clusters':
self.computeCluster()
elif name in ['X Component', 'Y Component']:
self.updateScatterPlot()
def updateMap(self):
# get current map idx
if not self.selectionmodel.selectedIndexes(): # no map is open
return
else:
self.selectMapidx = self.selectionmodel.selectedIndexes()[0].row()
# get current item
self.item = self.headermodel.item(self.selectMapidx)
if hasattr(self.item, 'embedding'):
# compute embedding
self.computeEmbedding()
else:
# reset custer image and plots
self.cleanUp()
def showClusterMean(self, i):
if self.mean_spectra is None:
return
self.clusterMeanPlot.curveHighLight(self.labels[i] - 1)
def setImageCross(self, ind):
row, col = self.ind2rc[ind]
# update cross
self.clusterImage.cross.setData([col + 0.5],
[self.imgShape[0] - row - 0.5])
# update text
self.clusterImage.txt.setHtml(
toHtml(f'Point: #{ind}', size=8) + toHtml(f'X: {col}', size=8) +
toHtml(f'Y: {row}', size=8) + toHtml(
f'Val: {self.clusterImage._image[self.imgShape[0] - row - 1, col] :d}',
size=8))
def cleanUp(self):
if self.selectionmodel.hasSelection():
self.selectMapIdx = self.selectionmodel.selectedIndexes()[0].row()
elif self.headermodel.rowCount() > 0:
self.selectMapIdx = 0
else:
return
if hasattr(self,
'imgShapes') and (self.selectMapIdx < len(self.imgShapes)):
# self.clusterImage.clear()
img = np.zeros((self.imgShapes[self.selectMapIdx][0],
self.imgShapes[self.selectMapIdx][1]))
self.clusterImage.setImage(img=img)
if hasattr(self, 'scatterPlot'):
self.clusterScatterPlot.plotItem.clearPlots()
self.clusterScatterPlot.scatterData = None
self.rawSpecPlot.clearAll()
self.rawSpecPlot._data = None
self.clusterMeanPlot.clearAll()
self.clusterMeanPlot._data = None
def updateRoiMask(self):
if self.selectionmodel.hasSelection():
self.selectMapIdx = self.selectionmodel.selectedIndexes()[0].row()
elif self.headermodel.rowCount() > 0:
self.selectMapIdx = 0
else:
return
# update roi
try:
roiState = self.headermodel.item(self.selectMapIdx).roiState
if roiState[0]: # roi on
self.roi.show()
else:
self.roi.hide()
# update roi state
self.roi.blockSignals(True)
self.roi.setState(roiState[1])
self.roi.blockSignals(False)
except Exception:
self.roi.hide()
# update automask
try:
maskState = self.headermodel.item(self.selectMapIdx).maskState
self.maskItem.setImage(maskState[1])
if maskState[0]: # automask on
self.maskItem.show()
else:
self.maskItem.hide()
except Exception:
pass
# update selectMask
try:
selectMaskState = self.headermodel.item(
self.selectMapIdx).selectState
self.selectMaskItem.setImage(selectMaskState[1])
if selectMaskState[0]: # selectmask on
self.selectMaskItem.show()
else:
self.selectMaskItem.hide()
except Exception:
pass
def setHeader(self, field: str):
self.headers = [
self.headermodel.item(i).header
for i in range(self.headermodel.rowCount())
]
self.field = field
self.wavenumberList = []
self.imgShapes = []
self.rc2indList = []
self.ind2rcList = []
self.pathList = []
self.dataSets = []
# get wavenumbers, imgShapes, rc2ind
for header in self.headers:
dataEvent = next(header.events(fields=[field]))
self.wavenumberList.append(dataEvent['wavenumbers'])
self.imgShapes.append(dataEvent['imgShape'])
self.rc2indList.append(dataEvent['rc_index'])
self.ind2rcList.append(dataEvent['index_rc'])
self.pathList.append(dataEvent['path'])
# get raw spectra
data = None
try: # spectra datasets
data = header.meta_array('spectra')
except IndexError:
msg.logMessage(
'Header object contained no frames with field '
'{field}'
'.', msg.ERROR)
if data is not None:
self.dataSets.append(data)
self.cleanUp()
def isMapOpen(self):
if not self.selectionmodel.selectedIndexes(): # no map is open
return False
else:
self.selectMapidx = self.selectionmodel.selectedIndexes()[0].row()
# get current data
self.item = self.headermodel.item(self.selectMapidx)
self.selectedPixels = self.item.selectedPixels
self.clusterScatterPlot.selectedPixels = self.selectedPixels
self.currentHeader = self.headers[self.selectMapidx]
self.wavenumbers = self.wavenumberList[self.selectMapidx]
self.rc2ind = self.rc2indList[self.selectMapidx]
self.ind2rc = self.ind2rcList[self.selectMapidx]
self.clusterScatterPlot.ind2rc = self.ind2rc
self.clusterScatterPlot.rc2ind = self.rc2ind
self.imgShape = self.imgShapes[self.selectMapidx]
self.data = self.dataSets[self.selectMapidx]
self.rawSpecPlot.setHeader(self.currentHeader, 'spectra')
if self.selectedPixels is not None:
self.clusterScatterPlot.selPx_rc2ind = {
tuple(self.selectedPixels[i]): i
for i in range(len(self.selectedPixels))
}
self.clusterScatterPlot.selPx_ind2rc = {
i: tuple(self.selectedPixels[i])
for i in range(len(self.selectedPixels))
}
return True
class PreviewWidget(GraphicsLayoutWidget):
def __init__(self):
super(PreviewWidget, self).__init__()
self.setMinimumHeight(250)
self.setMinimumWidth(250)
self.view = self.addViewBox(lockAspect=True, enableMenu=False)
self.imageitem = ImageItem()
self.textitem = TextItem(anchor=(0.5, 0))
self.textitem.setFont(QFont("Zero Threes"))
self.imageitem.setOpts(axisOrder="row-major")
self.view.addItem(self.imageitem)
self.view.addItem(self.textitem)
self.textitem.hide()
self.setSizePolicy(QSizePolicy.Minimum, QSizePolicy.Minimum)
# def textItemBounds(axis, frac=1.0, orthoRange=None):
# b = self.textitem.boundingRect()
# sx, sy = self.view.viewPixelSize()
# x, y = sx*b.width(), sy*b.height()
# if axis == 0: return (-x/2, x/2)
# if axis == 1: return (0, y)
#
# self.textitem.dataBounds = textItemBounds
def sizeHint(self):
return QSize(250, 250)
@threads.method(threadkey="preview", showBusy=False)
def preview(self, data):
if isinstance(data, NonDBHeader):
self.preview_header(data)
else:
self.preview_catalog(data)
def preview_catalog(self, catalog: BlueskyRun):
threads.invoke_in_main_thread(self.setText, "LOADING...")
try:
stream, field = bluesky_utils.guess_stream_field(catalog)
data = bluesky_utils.preview(catalog, stream, field)
threads.invoke_in_main_thread(self.setImage, data)
except Exception as ex:
msg.logError(ex)
threads.invoke_in_main_thread(self.imageitem.clear)
threads.invoke_in_main_thread(self.setText, "UNKNOWN DATA FORMAT")
def preview_header(self, header: NonDBHeader):
try:
data = header.meta_array()[0]
threads.invoke_in_main_thread(self.setImage, data)
except IndexError:
threads.invoke_in_main_thread(self.imageitem.clear)
threads.invoke_in_main_thread(self.setText, "UNKNOWN DATA FORMAT")
def setImage(self, imgdata):
self.imageitem.clear()
self.textitem.hide()
self.imageitem.setImage(np.log(imgdata * (imgdata > 0) +
(imgdata < 1)),
autoLevels=True)
self.imageitem.setTransform(
QTransform(1, 0, 0, -1, 0, imgdata.shape[-2]))
self.view.autoRange()
def setText(self, text):
self.textitem.setText(text)
self.imageitem.clear()
self.textitem.setVisible(True)
self.view.autoRange()
def _update_preview_image(image_data: Optional[np.ndarray],
image: ImageItem):
image.clear()
image.setImage(image_data)
class FilterPreviews(GraphicsLayoutWidget):
image_before: ImageItem
image_after: ImageItem
image_diff: ImageItem
histogram_before: Optional[PlotItem]
histogram_after: Optional[PlotItem]
histogram: Optional[PlotItem]
def __init__(self, parent=None, **kwargs):
super(FilterPreviews, self).__init__(parent, **kwargs)
self.before_histogram_data = None
self.after_histogram_data = None
self.histogram = None
self.before_histogram = None
self.after_histogram = None
self.combined_histograms = True
self.histogram_legend_visible = True
self.addLabel("Image before")
self.addLabel("Image after")
self.addLabel("Image difference")
self.nextRow()
self.image_before, self.image_before_vb = self.image_in_vb(
name="before")
self.image_after, self.image_after_vb = self.image_in_vb(name="after")
self.image_difference, self.image_difference_vb = self.image_in_vb(
name="difference")
self.image_after_overlay = ImageItem()
self.image_after_overlay.setZValue(10)
self.image_after_vb.addItem(self.image_after_overlay)
# Ensure images resize equally
image_layout = self.addLayout(colspan=3)
image_layout.addItem(self.image_before_vb, 0, 0)
image_layout.addItem(self.image_after_vb, 0, 1)
image_layout.addItem(self.image_difference_vb, 0, 2)
self.nextRow()
before_details = self.addLabel("")
after_details = self.addLabel("")
difference_details = self.addLabel("")
self.display_formatted_detail = {
self.image_before:
lambda val: before_details.setText(f"Before: {val:.6f}"),
self.image_after:
lambda val: after_details.setText(f"After: {val:.6f}"),
self.image_difference:
lambda val: difference_details.setText(f"Difference: {val:.6f}"),
}
for img in self.image_before, self.image_after, self.image_difference:
img.hoverEvent = lambda ev: self.mouse_over(ev)
def image_in_vb(self, name=None):
im = ImageItem()
im.setAutoDownsample(False)
vb = ViewBox(invertY=True, lockAspect=True, name=name)
vb.addItem(im)
return im, vb
def clear_items(self):
self.image_before.clear()
self.image_after.clear()
self.image_difference.clear()
self.image_after_overlay.clear()
self.delete_histograms()
# There seems to be a bug with pyqtgraph.PlotDataItem.setData not forcing a redraw.
# We work around this by redrawing everything completely every time, which is unreasonably fast anyway.
def redraw_histograms(self):
self.delete_histograms()
self.delete_histogram_labels()
if self.combined_histograms:
self.draw_combined_histogram()
else:
self.draw_separate_histograms()
def delete_histograms(self):
coords = set(c for c in histogram_coords.values())
histograms = (self.getItem(*coord) for coord in coords)
for histogram in filter(lambda h: h is not None, histograms):
self.removeItem(histogram)
self.histogram = None
self.before_histogram = None
self.after_histogram = None
self.diff_histogram = None
def delete_histogram_labels(self):
coords = set(c for c in label_coords.values())
labels = (self.getItem(*coord) for coord in coords)
for label in filter(lambda h: h is not None, labels):
self.removeItem(label)
def draw_combined_histogram(self):
self.histogram = self.addPlot(row=histogram_coords["combined"].row,
col=histogram_coords["combined"].col,
labels=histogram_axes_labels,
lockAspect=True,
colspan=3)
self.addLabel("Pixel values",
row=label_coords["combined"].row,
col=label_coords["combined"].col)
legend = self.histogram.addLegend()
# Plot any histogram that has data, and add a legend if both exist
if _data_valid_for_histogram(self.before_histogram_data):
before_plot = self.histogram.plot(*self.before_histogram_data,
pen=before_pen)
legend.addItem(before_plot, "Before")
if _data_valid_for_histogram(self.after_histogram_data):
after_plot = self.histogram.plot(*self.after_histogram_data,
pen=after_pen)
legend.addItem(after_plot, "After")
def draw_separate_histograms(self):
hc = histogram_coords
self.before_histogram = self.addPlot(row=hc["before"].row,
col=hc["before"].col,
labels=histogram_axes_labels,
lockAspect=True)
self.after_histogram = self.addPlot(row=hc["after"].row,
col=hc["after"].col,
labels=histogram_axes_labels,
lockAspect=True)
lc = label_coords
self.addLabel("Pixel values before",
row=lc["before"].row,
col=lc["before"].col)
self.addLabel("Pixel values after",
row=lc["after"].row,
col=lc["after"].col)
if _data_valid_for_histogram(self.before_histogram_data):
self.before_histogram.plot(*self.before_histogram_data,
pen=before_pen)
if _data_valid_for_histogram(self.after_histogram_data):
self.after_histogram.plot(*self.after_histogram_data,
pen=after_pen)
def set_before_histogram(self, data: Tuple[ndarray]):
self.before_histogram_data = data
self.redraw_histograms()
def set_after_histogram(self, data: Tuple[ndarray]):
self.after_histogram_data = data
self.redraw_histograms()
@property
def histogram_legend(self) -> Optional[LegendItem]:
if self.histogram and self.histogram.legend:
return self.histogram.legend
return None
def mouse_over(self, ev):
# Ignore events triggered by leaving window or right clicking
if ev.exit:
return
pos = CloseEnoughPoint(ev.pos())
for img in self.image_before, self.image_after, self.image_difference:
if img.image is not None and pos.x < img.image.shape[
0] and pos.y < img.image.shape[1]:
pixel_value = img.image[pos.y, pos.x]
self.display_formatted_detail[img](pixel_value)
def link_all_views(self):
for view1, view2 in zip(
[self.image_before_vb, self.image_after_vb],
[self.image_after_vb, self.image_difference_vb]):
view1.linkView(ViewBox.XAxis, view2)
view1.linkView(ViewBox.YAxis, view2)
def unlink_all_views(self):
for view in self.image_before_vb, self.image_after_vb, self.image_difference_vb:
view.linkView(ViewBox.XAxis, None)
view.linkView(ViewBox.YAxis, None)
def add_difference_overlay(self, diff):
diff = -diff
diff[diff > 0.0] = 1.0
pos = np.array([0, 1])
color = np.array([[0, 0, 0, 0], [255, 0, 0, 255]], dtype=np.ubyte)
map = ColorMap(pos, color)
self.image_after_overlay.setOpacity(1)
self.image_after_overlay.setImage(diff)
lut = map.getLookupTable(0, 1, 2)
self.image_after_overlay.setLookupTable(lut)
def hide_difference_overlay(self):
self.image_after_overlay.setOpacity(0)
class SideView(GraphicsView):
"""SideView class
This class contains a series of functions allowing the user to simultaneously view the 3D volume from all orientations.
Args:
diff (int): Flag indicating the different brain view orientations.
parent (class): Base or parent class
"""
def __init__(self, diff, parent=None):
super(SideView, self).__init__(parent=parent)
self.parent = parent
self.brain = parent.brain
self.diff = diff
self.view1 = ViewBox()
self.setCentralItem(self.view1)
# Making Images out of data
self.brain.section = (self.brain.section + self.diff) % 3
self.i = int(self.brain.shape[self.brain.section] / 2)
data_slice = self.brain.get_data_slice(self.i)
self.brain_img1 = ImageItem(
data_slice,
autoDownsample=False,
compositionMode=QtGui.QPainter.CompositionMode_SourceOver)
self.brain.section = (self.brain.section - self.diff) % 3
self.view1.addItem(self.brain_img1)
self.view1.setAspectLocked(True)
self.view1.setFixedHeight(250)
self.view1.setFixedWidth(250)
self.setMinimumHeight(250)
self.vLine = InfiniteLine(angle=90, movable=False)
self.hLine = InfiniteLine(angle=0, movable=False)
self.vLine.setVisible(False)
self.hLine.setVisible(False)
self.view1.addItem(self.vLine, ignoreBounds=True)
self.view1.addItem(self.hLine, ignoreBounds=True)
def refresh_image(self):
"""Refresh Image
This function refreshes the displayed volume orientation image.
"""
self.brain.section = (self.brain.section + self.diff) % 3
data_slice = self.brain.get_data_slice(self.i)
self.brain_img1.setImage(data_slice)
self.brain.section = (self.brain.section - self.diff) % 3
def refresh_all_images(self):
"""Refresh all images
This function refreshes both the side view images when triggered.
"""
self.parent.main_widget._update_section_helper()
self.parent.win1.refresh_image()
self.parent.win1.view1.menu.actions()[0].trigger()
self.parent.win2.refresh_image()
self.parent.win2.view1.menu.actions()[0].trigger()
def mouseDoubleClickEvent(self, event):
"""Click trigger
Tracks a mouse double click event which triggers the refreshing of all the views
Args
event (event): Mouse double click events for the widget.
"""
super(SideView, self).mouseDoubleClickEvent(event)
self.brain.section = (self.brain.section + self.diff) % 3
self.refresh_all_images()
def set_i(self, position, out_of_box=False):
"""Set position
This function sets the cursor position for each window.
Args:
position (tuple): Tuple containing the required coordinates.
out_of_box (bool): Flag indicating if event possition is outisde of the considered volume.
"""
section = (self.brain.section + self.diff) % 3
if out_of_box:
self.i = int(self.brain.shape[section] / 2)
self.vLine.setVisible(False)
self.hLine.setVisible(False)
else:
i = position[section]
self.i = np.clip(i, 0, self.brain.shape[section] - 1)
self.vLine.setVisible(True)
self.hLine.setVisible(True)
self.brain.section = (self.brain.section + self.diff) % 3
x, y = self.brain.voxel_as_position(position[0], position[1],
position[2])
self.brain.section = (self.brain.section - self.diff) % 3
self.vLine.setPos(x)
self.hLine.setPos(y)
class CustomImageViewer(GraphicsLayoutWidget):
@property
def view_box(self):
return self.image_plot.vb
def __init__(self,
parent=None,
*,
hist_range: tuple = None,
sigma_factor: float = 3,
**kwargs):
setConfigOptions(imageAxisOrder='row-major')
super(CustomImageViewer, self).__init__(parent)
self._raw_data = None
self._use_clahe: bool = True
self._scale = (1., 1.)
self._center = (0, 0)
self._hist_range = hist_range
self._sigma_factor = sigma_factor
self._init_ui(**kwargs)
def _init_ui(self, **kwargs):
self.setWindowTitle('Image Viewer')
self.image_plot = self.addPlot(**kwargs)
self.image_plot.vb.setAspectLocked()
self.image_plot.vb.invertY()
self.image_item = ImageItem()
self.image_plot.addItem(self.image_item)
self.image_plot.setMenuEnabled(False)
self.hist = HistogramLUTItem()
self.hist.setImageItem(self.image_item)
self.addItem(self.hist)
self.hist.vb.menu = CustomViewBoxMenu(self.hist.vb)
self.hist.vb.menu.sigSigmaChanged.connect(self.set_sigma_factor)
self.hist.vb.menu.sigRangeAsDefault.connect(self.set_limit_as_default)
self.hist.vb.menu.sigUseClahe.connect(self.enable_clahe)
def set_data(self, data, *, reset_axes: bool = False):
self._raw_data = data
if data is None:
return
if self._use_clahe:
data = standard_contrast_correction(data)
self.image_item.setImage(data)
self.set_levels()
if reset_axes:
self.image_item.resetTransform()
self.set_default_range()
def hist_params(self) -> dict:
return dict(sigma_factor=self._sigma_factor,
hist_range=self._hist_range)
def clear_image(self):
self.set_data(np.zeros((1, 1)))
def set_default_range(self):
if self.image_item.image is None:
return
# self.set_auto_range()
axes = self.get_axes()
self.image_plot.setRange(xRange=axes[1], yRange=axes[0])
@pyqtSlot(bool)
def enable_clahe(self, enable: bool):
self._use_clahe = enable
self.set_data(self._raw_data)
def set_auto_range(self):
self.image_plot.autoRange()
def set_levels(self):
img = self.image_item.image
if img is None:
return
if self._sigma_factor and self._sigma_factor > 0:
m, s = img.flatten().mean(
), img.flatten().std() * self._sigma_factor
self.hist.setLevels(max(m - s, img.min()), min(m + s, img.max()))
elif self._hist_range:
self.hist.setLevels(*self._hist_range)
else:
self.hist.setLevels(self.image_item.image.min(),
self.image_item.image.max())
@pyqtSlot(float)
def set_sigma_factor(self, sigma_factor: float):
self._sigma_factor = sigma_factor
self.set_levels()
@pyqtSlot()
def set_limit_as_default(self):
self._hist_range = self.hist.getLevels()
self._sigma_factor = None
self.set_levels()
def get_levels(self):
return self.hist.getLevels()
def set_center(self, center: tuple, pixel_units: bool = True):
if not pixel_units:
scale = self.get_scale()
center = (center[0] / scale[0], center[1] / scale[1])
if self._center != (0, 0) or self._scale != (1., 1.):
self.image_item.resetTransform()
self.image_item.scale(*self._scale)
self.image_item.translate(-center[0], -center[1])
self._center = center
self.set_default_range()
def set_scale(self, scale: float or tuple):
if isinstance(scale, float) or isinstance(scale, int):
scale = (scale, scale)
if self._center != (0, 0) or self._scale != (1., 1.):
self.image_item.resetTransform()
self.image_item.scale(*scale)
if self._center != (0, 0):
self.image_item.translate(-self._center[0], -self._center[1])
self._scale = scale
self.set_default_range()
def get_scale(self) -> tuple:
# scale property is occupied by Qt superclass.
return self._scale
def get_center(self) -> tuple:
return self._center
def set_x_axis(self, x_min, x_max):
self._set_axis(x_min, x_max, 0)
self.set_default_range()
def set_y_axis(self, y_min, y_max):
self._set_axis(y_min, y_max, 1)
self.set_default_range()
def _set_axis(self, min_: float, max_: float, axis_ind: int):
shape = self.image_item.image.shape
scale = np.array(self._scale)
scale[axis_ind] = (max_ - min_) / shape[axis_ind]
center = np.array(self._center)
center[axis_ind] = -min_ / scale[axis_ind]
if self._center != (0, 0) or self._scale != (1., 1.):
self.image_item.resetTransform()
self.image_item.scale(scale[0], scale[1])
self.image_item.translate(-center[0], -center[1])
self._scale = tuple(scale)
self._center = tuple(center)
def get_axes(self):
shape = np.array(self.image_item.image.shape)
scale = np.array(self._scale)
min_ = -np.array((self._center[1], self._center[0])) * scale
max_ = min_ + shape * scale
return (min_[0], max_[0]), (min_[1], max_[1])
