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']
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)
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)
self.data = None
self.indices = None
self.mouseOverField = None
self.scatterPlot = None
self.selectionScatter = None
self.selectedIndices = []
self.style = 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)
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()
def setData(self, data):
"""
Set the data to be processed and displayed.
Argument must be a numpy record array.
"""
self.data = data
self.indices = np.arange(len(data))
self.filtered = None
self.filteredIndices = None
self.updatePlot()
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()
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 self.data is None or len(self.data) == 0:
return
if self.filtered is None:
mask = self.filter.generateMask(self.data)
self.filtered = self.data[mask]
self.filteredIndices = self.indices[mask]
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.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)