pyqtgraph/widgets/ScatterPlotWidget.py

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from ..Qt import QtGui, QtCore
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from .PlotWidget import PlotWidget
from .DataFilterWidget import DataFilterParameter
from .ColorMapWidget import ColorMapParameter
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from .. import parametertree as ptree
from .. import functions as fn
from .. import getConfigOption
from ..graphicsItems.TextItem import TextItem
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import numpy as np
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from ..pgcollections import OrderedDict
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__all__ = ['ScatterPlotWidget']
class ScatterPlotWidget(QtGui.QSplitter):
"""
Given a record array, display a scatter plot of a specific set of data.
This widget includes controls for selecting the columns to plot,
filtering data, and determining symbol color and shape. This widget allows
the user to explore relationships between columns in a record array.
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)
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bg = fn.mkColor(getConfigOption('background'))
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bg.setAlpha(150)
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self.filterText = TextItem(border=getConfigOption('foreground'), color=bg)
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self.filterText.setPos(60,20)
self.filterText.setParentItem(self.plot.plotItem)
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self.data = None
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self.mouseOverField = None
self.scatterPlot = None
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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)
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def setFields(self, fields, mouseOverField=None):
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"""
Set the list of field names/units to be processed.
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The format of *fields* is the same as used by
:func:`ColorMapWidget.setFields <pyqtgraph.widgets.ColorMapWidget.ColorMapParameter.setFields>`
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"""
self.fields = OrderedDict(fields)
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self.mouseOverField = mouseOverField
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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()
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desc = self.filter.describe()
if len(desc) == 0:
self.filterText.setVisible(False)
else:
self.filterText.setText('\n'.join(desc))
self.filterText.setVisible(True)
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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
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#x = data[sel[0]]
#y = None
xy = [data[sel[0]], None]
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elif len(sel) == 2:
self.plot.setLabels(left=(sel[1],units[1]), bottom=(sel[0],units[0]))
if len(data) == 0:
return
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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
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## convert enum-type fields to float, set axis labels
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enum = [False, False]
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for i in [0,1]:
axis = self.plot.getAxis(['bottom', 'left'][i])
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if xy[i] is not None and (self.fields[sel[i]].get('mode', None) == 'enum' or xy[i].dtype.kind in ('S', 'O')):
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vals = self.fields[sel[i]].get('values', list(set(xy[i])))
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xy[i] = np.array([vals.index(x) if x in vals else len(vals) for x in xy[i]], dtype=float)
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axis.setTicks([list(enumerate(vals))])
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enum[i] = True
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else:
axis.setTicks(None) # reset to automatic ticking
## mask out any nan values
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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]
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style['symbolBrush'] = colors[mask]
## Scatter y-values for a histogram-like appearance
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if xy[1] is None:
## column scatter plot
xy[1] = fn.pseudoScatter(xy[0])
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else:
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## beeswarm plots
xy[1] = xy[1][mask]
for ax in [0,1]:
if not enum[ax]:
continue
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imax = int(xy[ax].max()) if len(xy[ax]) > 0 else 0
for i in range(imax+1):
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keymask = xy[ax] == i
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scatter = fn.pseudoScatter(xy[1-ax][keymask], bidir=True)
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if len(scatter) == 0:
continue
smax = np.abs(scatter).max()
if smax != 0:
scatter *= 0.2 / smax
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xy[ax][keymask] += scatter
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if self.scatterPlot is not None:
try:
self.scatterPlot.sigPointsClicked.disconnect(self.plotClicked)
except:
pass
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self.scatterPlot = self.plot.plot(xy[0], xy[1], data=data[mask], **style)
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self.scatterPlot.sigPointsClicked.connect(self.plotClicked)
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def plotClicked(self, plot, points):
pass