# 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']

    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.
        """
        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)

        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)

        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