219 lines
8.2 KiB
Python
219 lines
8.2 KiB
Python
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
|
|
|
|
|