Source code for pyqtgraph.widgets.ScatterPlotWidget

from collections import OrderedDict

import numpy as np

from .. import functions as fn
from .. import getConfigOption
from .. import parametertree as ptree
from ..graphicsItems.TextItem import TextItem
from ..Qt import QtCore, QtWidgets
from .ColorMapWidget import ColorMapParameter
from .DataFilterWidget import DataFilterParameter
from .PlotWidget import PlotWidget

__all__ = ['ScatterPlotWidget']

[docs] class ScatterPlotWidget(QtWidgets.QSplitter): """ This is a high-level widget for exploring relationships in tabular data. Given a multi-column record array, the widget displays a scatter plot of a specific subset of the data. Includes controls for selecting the columns to plot, filtering data, and determining symbol color and shape. The widget consists of four components: 1) A list of column names from which the user may select 1 or 2 columns to plot. If one column is selected, the data for that column will be plotted in a histogram-like manner by using :func:`pseudoScatter() <pyqtgraph.pseudoScatter>`. If two columns are selected, then the scatter plot will be generated with x determined by the first column that was selected and y by the second. 2) A DataFilter that allows the user to select a subset of the data by specifying multiple selection criteria. 3) A ColorMap that allows the user to determine how points are colored by specifying multiple criteria. 4) A PlotWidget for displaying the data. """ sigScatterPlotClicked = QtCore.Signal(object, object, object) sigScatterPlotHovered = QtCore.Signal(object, object, object)
[docs] def __init__(self, parent=None): QtWidgets.QSplitter.__init__(self, QtCore.Qt.Orientation.Horizontal) self.ctrlPanel = QtWidgets.QSplitter(QtCore.Qt.Orientation.Vertical) self.addWidget(self.ctrlPanel) self.fieldList = QtWidgets.QListWidget() self.fieldList.setSelectionMode(self.fieldList.SelectionMode.ExtendedSelection) self.ptree = ptree.ParameterTree(showHeader=False) self.filter = DataFilterParameter() self.colorMap = ColorMapParameter() self.params = ptree.Parameter.create(name='params', type='group', children=[self.filter, self.colorMap]) self.ptree.setParameters(self.params, showTop=False) self.plot = PlotWidget() self.ctrlPanel.addWidget(self.fieldList) self.ctrlPanel.addWidget(self.ptree) self.addWidget(self.plot) fg = fn.mkColor(getConfigOption('foreground')) fg.setAlpha(150) self.filterText = TextItem(border=getConfigOption('foreground'), color=fg) self.filterText.setPos(60,20) self.filterText.setParentItem(self.plot.plotItem) = None self.indices = None self.mouseOverField = None self.scatterPlot = None self.selectionScatter = None self.selectedIndices = [] = dict(pen=None, symbol='o') self._visibleXY = None # currently plotted points self._visibleData = None # currently plotted records self._visibleIndices = None self._indexMap = None self.fieldList.itemSelectionChanged.connect(self.fieldSelectionChanged) self.filter.sigFilterChanged.connect(self.filterChanged) self.colorMap.sigColorMapChanged.connect(self.updatePlot)
[docs] def setFields(self, fields, mouseOverField=None): """ Set the list of field names/units to be processed. The format of *fields* is the same as used by :meth:`~pyqtgraph.widgets.ColorMapWidget.ColorMapParameter.setFields` """ self.fields = OrderedDict(fields) self.mouseOverField = mouseOverField self.fieldList.clear() for f,opts in fields: item = QtWidgets.QListWidgetItem(f) item.opts = opts item = self.fieldList.addItem(item) self.filter.setFields(fields) self.colorMap.setFields(fields)
def setSelectedFields(self, *fields): self.fieldList.itemSelectionChanged.disconnect(self.fieldSelectionChanged) try: self.fieldList.clearSelection() for f in fields: i = list(self.fields.keys()).index(f) item = self.fieldList.item(i) item.setSelected(True) finally: self.fieldList.itemSelectionChanged.connect(self.fieldSelectionChanged) self.fieldSelectionChanged()
[docs] def setData(self, data): """ Set the data to be processed and displayed. Argument must be a numpy record array. """ = data self.indices = np.arange(len(data)) self.filtered = None self.filteredIndices = None self.updatePlot()
[docs] def setSelectedIndices(self, inds): """Mark the specified indices as selected. Must be a sequence of integers that index into the array given in setData(). """ self.selectedIndices = inds self.updateSelected()
[docs] def setSelectedPoints(self, points): """Mark the specified points as selected. Must be a list of points as generated by the sigScatterPlotClicked signal. """ self.setSelectedIndices([pt.originalIndex for pt in points])
def fieldSelectionChanged(self): sel = self.fieldList.selectedItems() if len(sel) > 2: self.fieldList.blockSignals(True) try: for item in sel[1:-1]: item.setSelected(False) finally: self.fieldList.blockSignals(False) self.updatePlot() def filterChanged(self, f): self.filtered = None self.updatePlot() desc = self.filter.describe() if len(desc) == 0: self.filterText.setVisible(False) else: self.filterText.setText('\n'.join(desc)) self.filterText.setVisible(True) def updatePlot(self): self.plot.clear() if is None or len( == 0: return if self.filtered is None: mask = self.filter.generateMask( self.filtered =[mask] self.filteredIndices = self.indices[mask] data = self.filtered if len(data) == 0: return colors = np.array([fn.mkBrush(*x) for x in]) style = ## Look up selected columns and units sel = list([str(item.text()) for item in self.fieldList.selectedItems()]) units = list([item.opts.get('units', '') for item in self.fieldList.selectedItems()]) if len(sel) == 0: self.plot.setTitle('') return if len(sel) == 1: self.plot.setLabels(left=('N', ''), bottom=(sel[0], units[0]), title='') if len(data) == 0: return #x = data[sel[0]] #y = None xy = [data[sel[0]], None] elif len(sel) == 2: self.plot.setLabels(left=(sel[1],units[1]), bottom=(sel[0],units[0])) if len(data) == 0: return xy = [data[sel[0]], data[sel[1]]] #xydata = [] #for ax in [0,1]: #d = data[sel[ax]] ### scatter catecorical values just a bit so they show up better in the scatter plot. ##if sel[ax] in ['MorphologyBSMean', 'MorphologyTDMean', 'FIType']: ##d += np.random.normal(size=len(cells), scale=0.1) #xydata.append(d) #x,y = xydata ## convert enum-type fields to float, set axis labels enum = [False, False] for i in [0,1]: axis = self.plot.getAxis(['bottom', 'left'][i]) if xy[i] is not None and (self.fields[sel[i]].get('mode', None) == 'enum' or xy[i].dtype.kind in ('S', 'O')): vals = self.fields[sel[i]].get('values', list(set(xy[i]))) xy[i] = np.array([vals.index(x) if x in vals else len(vals) for x in xy[i]], dtype=float) axis.setTicks([list(enumerate(vals))]) enum[i] = True else: axis.setTicks(None) # reset to automatic ticking ## mask out any nan values mask = np.ones(len(xy[0]), dtype=bool) if xy[0].dtype.kind == 'f': mask &= np.isfinite(xy[0]) if xy[1] is not None and xy[1].dtype.kind == 'f': mask &= np.isfinite(xy[1]) xy[0] = xy[0][mask] style['symbolBrush'] = colors[mask] data = data[mask] indices = self.filteredIndices[mask] ## Scatter y-values for a histogram-like appearance if xy[1] is None: ## column scatter plot xy[1] = fn.pseudoScatter(xy[0]) else: ## beeswarm plots xy[1] = xy[1][mask] for ax in [0,1]: if not enum[ax]: continue imax = int(xy[ax].max()) if len(xy[ax]) > 0 else 0 for i in range(imax+1): keymask = xy[ax] == i scatter = fn.pseudoScatter(xy[1-ax][keymask], bidir=True) if len(scatter) == 0: continue smax = np.abs(scatter).max() if smax != 0: scatter *= 0.2 / smax xy[ax][keymask] += scatter if self.scatterPlot is not None: try: self.scatterPlot.sigPointsClicked.disconnect(self.plotClicked) except: pass self._visibleXY = xy self._visibleData = data self._visibleIndices = indices self._indexMap = None self.scatterPlot = self.plot.plot(xy[0], xy[1], data=data, **style) self.scatterPlot.sigPointsClicked.connect(self.plotClicked) self.scatterPlot.sigPointsHovered.connect(self.plotHovered) self.updateSelected() def updateSelected(self): if self._visibleXY is None: return # map from global index to visible index indMap = self._getIndexMap() inds = [indMap[i] for i in self.selectedIndices if i in indMap] x,y = self._visibleXY[0][inds], self._visibleXY[1][inds] if self.selectionScatter is not None: self.plot.plotItem.removeItem(self.selectionScatter) if len(x) == 0: return self.selectionScatter = self.plot.plot(x, y, pen=None, symbol='s', symbolSize=12, symbolBrush=None, symbolPen='y') def _getIndexMap(self): # mapping from original data index to visible point index if self._indexMap is None: self._indexMap = {j:i for i,j in enumerate(self._visibleIndices)} return self._indexMap def plotClicked(self, plot, points, ev): # Tag each point with its index into the original dataset for pt in points: pt.originalIndex = self._visibleIndices[pt.index()] self.sigScatterPlotClicked.emit(self, points, ev) def plotHovered(self, plot, points, ev): self.sigScatterPlotHovered.emit(self, points, ev)