220 lines
8.3 KiB
Python
220 lines
8.3 KiB
Python
from ..Qt import QtGui, QtCore
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from .PlotWidget import PlotWidget
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from .DataFilterWidget import DataFilterParameter
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from .ColorMapWidget import ColorMapParameter
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from .. import parametertree as ptree
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from .. import functions as fn
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from .. import getConfigOption
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from ..graphicsItems.TextItem import TextItem
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import numpy as np
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from ..pgcollections import OrderedDict
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__all__ = ['ScatterPlotWidget']
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class ScatterPlotWidget(QtGui.QSplitter):
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"""
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This is a high-level widget for exploring relationships in tabular data.
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Given a multi-column record array, the widget displays a scatter plot of a
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specific subset of the data. Includes controls for selecting the columns to
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plot, filtering data, and determining symbol color and shape.
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The widget consists of four components:
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1) A list of column names from which the user may select 1 or 2 columns
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to plot. If one column is selected, the data for that column will be
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plotted in a histogram-like manner by using :func:`pseudoScatter()
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<pyqtgraph.pseudoScatter>`. If two columns are selected, then the
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scatter plot will be generated with x determined by the first column
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that was selected and y by the second.
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2) A DataFilter that allows the user to select a subset of the data by
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specifying multiple selection criteria.
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3) A ColorMap that allows the user to determine how points are colored by
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specifying multiple criteria.
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4) A PlotWidget for displaying the data.
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"""
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sigScatterPlotClicked = QtCore.Signal(object, object)
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def __init__(self, parent=None):
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QtGui.QSplitter.__init__(self, QtCore.Qt.Horizontal)
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self.ctrlPanel = QtGui.QSplitter(QtCore.Qt.Vertical)
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self.addWidget(self.ctrlPanel)
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self.fieldList = QtGui.QListWidget()
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self.fieldList.setSelectionMode(self.fieldList.ExtendedSelection)
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self.ptree = ptree.ParameterTree(showHeader=False)
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self.filter = DataFilterParameter()
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self.colorMap = ColorMapParameter()
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self.params = ptree.Parameter.create(name='params', type='group', children=[self.filter, self.colorMap])
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self.ptree.setParameters(self.params, showTop=False)
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self.plot = PlotWidget()
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self.ctrlPanel.addWidget(self.fieldList)
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self.ctrlPanel.addWidget(self.ptree)
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self.addWidget(self.plot)
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bg = fn.mkColor(getConfigOption('background'))
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bg.setAlpha(150)
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self.filterText = TextItem(border=getConfigOption('foreground'), color=bg)
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self.filterText.setPos(60,20)
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self.filterText.setParentItem(self.plot.plotItem)
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self.data = None
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self.mouseOverField = None
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self.scatterPlot = None
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self.style = dict(pen=None, symbol='o')
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self.fieldList.itemSelectionChanged.connect(self.fieldSelectionChanged)
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self.filter.sigFilterChanged.connect(self.filterChanged)
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self.colorMap.sigColorMapChanged.connect(self.updatePlot)
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def setFields(self, fields, mouseOverField=None):
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"""
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Set the list of field names/units to be processed.
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The format of *fields* is the same as used by
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:func:`ColorMapWidget.setFields <pyqtgraph.widgets.ColorMapWidget.ColorMapParameter.setFields>`
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"""
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self.fields = OrderedDict(fields)
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self.mouseOverField = mouseOverField
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self.fieldList.clear()
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for f,opts in fields:
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item = QtGui.QListWidgetItem(f)
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item.opts = opts
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item = self.fieldList.addItem(item)
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self.filter.setFields(fields)
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self.colorMap.setFields(fields)
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def setData(self, data):
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"""
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Set the data to be processed and displayed.
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Argument must be a numpy record array.
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"""
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self.data = data
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self.filtered = None
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self.updatePlot()
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def fieldSelectionChanged(self):
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sel = self.fieldList.selectedItems()
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if len(sel) > 2:
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self.fieldList.blockSignals(True)
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try:
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for item in sel[1:-1]:
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item.setSelected(False)
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finally:
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self.fieldList.blockSignals(False)
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self.updatePlot()
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def filterChanged(self, f):
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self.filtered = None
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self.updatePlot()
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desc = self.filter.describe()
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if len(desc) == 0:
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self.filterText.setVisible(False)
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else:
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self.filterText.setText('\n'.join(desc))
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self.filterText.setVisible(True)
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def updatePlot(self):
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self.plot.clear()
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if self.data is None:
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return
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if self.filtered is None:
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self.filtered = self.filter.filterData(self.data)
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data = self.filtered
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if len(data) == 0:
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return
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colors = np.array([fn.mkBrush(*x) for x in self.colorMap.map(data)])
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style = self.style.copy()
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## Look up selected columns and units
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sel = list([str(item.text()) for item in self.fieldList.selectedItems()])
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units = list([item.opts.get('units', '') for item in self.fieldList.selectedItems()])
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if len(sel) == 0:
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self.plot.setTitle('')
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return
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if len(sel) == 1:
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self.plot.setLabels(left=('N', ''), bottom=(sel[0], units[0]), title='')
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if len(data) == 0:
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return
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#x = data[sel[0]]
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#y = None
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xy = [data[sel[0]], None]
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elif len(sel) == 2:
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self.plot.setLabels(left=(sel[1],units[1]), bottom=(sel[0],units[0]))
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if len(data) == 0:
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return
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xy = [data[sel[0]], data[sel[1]]]
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#xydata = []
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#for ax in [0,1]:
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#d = data[sel[ax]]
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### scatter catecorical values just a bit so they show up better in the scatter plot.
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##if sel[ax] in ['MorphologyBSMean', 'MorphologyTDMean', 'FIType']:
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##d += np.random.normal(size=len(cells), scale=0.1)
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#xydata.append(d)
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#x,y = xydata
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## convert enum-type fields to float, set axis labels
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enum = [False, False]
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for i in [0,1]:
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axis = self.plot.getAxis(['bottom', 'left'][i])
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if xy[i] is not None and (self.fields[sel[i]].get('mode', None) == 'enum' or xy[i].dtype.kind in ('S', 'O')):
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vals = self.fields[sel[i]].get('values', list(set(xy[i])))
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xy[i] = np.array([vals.index(x) if x in vals else len(vals) for x in xy[i]], dtype=float)
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axis.setTicks([list(enumerate(vals))])
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enum[i] = True
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else:
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axis.setTicks(None) # reset to automatic ticking
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## mask out any nan values
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mask = np.ones(len(xy[0]), dtype=bool)
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if xy[0].dtype.kind == 'f':
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mask &= ~np.isnan(xy[0])
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if xy[1] is not None and xy[1].dtype.kind == 'f':
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mask &= ~np.isnan(xy[1])
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xy[0] = xy[0][mask]
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style['symbolBrush'] = colors[mask]
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## Scatter y-values for a histogram-like appearance
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if xy[1] is None:
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## column scatter plot
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xy[1] = fn.pseudoScatter(xy[0])
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else:
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## beeswarm plots
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xy[1] = xy[1][mask]
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for ax in [0,1]:
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if not enum[ax]:
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continue
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imax = int(xy[ax].max()) if len(xy[ax]) > 0 else 0
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for i in range(imax+1):
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keymask = xy[ax] == i
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scatter = fn.pseudoScatter(xy[1-ax][keymask], bidir=True)
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if len(scatter) == 0:
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continue
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smax = np.abs(scatter).max()
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if smax != 0:
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scatter *= 0.2 / smax
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xy[ax][keymask] += scatter
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if self.scatterPlot is not None:
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try:
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self.scatterPlot.sigPointsClicked.disconnect(self.plotClicked)
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except:
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pass
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self.scatterPlot = self.plot.plot(xy[0], xy[1], data=data[mask], **style)
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self.scatterPlot.sigPointsClicked.connect(self.plotClicked)
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def plotClicked(self, plot, points):
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self.sigScatterPlotClicked.emit(self, points)
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