Source code for pyqtgraph.widgets.ScatterPlotWidget

from ..Qt import QtGui, QtCore
from .PlotWidget import PlotWidget
from .DataFilterWidget import DataFilterParameter
from .ColorMapWidget import ColorMapParameter
from .. import parametertree as ptree
from .. import functions as fn
from .. import getConfigOption
from ..graphicsItems.TextItem import TextItem
import numpy as np
from ..pgcollections import OrderedDict

__all__ = ['ScatterPlotWidget']

[docs]class ScatterPlotWidget(QtGui.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. """
[docs] def __init__(self, parent=None): QtGui.QSplitter.__init__(self, QtCore.Qt.Horizontal) self.ctrlPanel = QtGui.QSplitter(QtCore.Qt.Vertical) self.addWidget(self.ctrlPanel) self.fieldList = QtGui.QListWidget() self.fieldList.setSelectionMode(self.fieldList.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) bg = fn.mkColor(getConfigOption('background')) bg.setAlpha(150) self.filterText = TextItem(border=getConfigOption('foreground'), color=bg) self.filterText.setPos(60,20) self.filterText.setParentItem(self.plot.plotItem) self.data = None self.mouseOverField = None self.scatterPlot = None self.style = dict(pen=None, symbol='o') 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 :func:`ColorMapWidget.setFields <pyqtgraph.widgets.ColorMapWidget.ColorMapParameter.setFields>` """ self.fields = OrderedDict(fields) self.mouseOverField = mouseOverField self.fieldList.clear() for f,opts in fields: item = QtGui.QListWidgetItem(f) item.opts = opts item = self.fieldList.addItem(item) self.filter.setFields(fields) self.colorMap.setFields(fields)
[docs] def setData(self, data): """ Set the data to be processed and displayed. Argument must be a numpy record array. """ self.data = data self.filtered = None self.updatePlot()
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 self.data is None: return if self.filtered is None: self.filtered = self.filter.filterData(self.data) data = self.filtered if len(data) == 0: return colors = np.array([fn.mkBrush(*x) for x in self.colorMap.map(data)]) style = self.style.copy() ## 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.isnan(xy[0]) if xy[1] is not None and xy[1].dtype.kind == 'f': mask &= ~np.isnan(xy[1]) xy[0] = xy[0][mask] style['symbolBrush'] = colors[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.scatterPlot = self.plot.plot(xy[0], xy[1], data=data[mask], **style) self.scatterPlot.sigPointsClicked.connect(self.plotClicked) def plotClicked(self, plot, points): pass