def scatterplot(data, name="", xlabel="", ylabel="", size= 3):
"""
Creates a scatter plot from x,y data.
*data* is a list of (x,y) tuples.
"""
xAxis = NumberAxis(xlabel)
xAxis.setAutoRangeIncludesZero(False)
yAxis = NumberAxis(ylabel)
yAxis.setAutoRangeIncludesZero(False)
series = XYSeries("Values");
for (i,j) in data:
series.add(i, j)
dataset = XYSeriesCollection()
dataset.addSeries(series);
chart = ChartFactory.createScatterPlot(name, xlabel, ylabel, dataset,
PlotOrientation.VERTICAL, True, True, False)
plot = chart.getPlot()
plot.getRenderer().setSeriesShape(0,
ShapeUtilities.createRegularCross(size,size));
return Chart(chart)
def scatterplot(data, name="", xlabel="", ylabel="", size= 3):
"""
Creates a scatter plot from x,y data.
*data* is a list of (x,y) tuples.
"""
xAxis = NumberAxis(xlabel)
xAxis.setAutoRangeIncludesZero(False)
yAxis = NumberAxis(ylabel)
yAxis.setAutoRangeIncludesZero(False)
series = XYSeries("Values");
for (i,j) in data:
series.add(i, j)
dataset = XYSeriesCollection()
dataset.addSeries(series);
chart = ChartFactory.createScatterPlot(name, xlabel, ylabel, dataset,
PlotOrientation.VERTICAL, True, True, False)
plot = chart.getPlot()
plot.getRenderer().setSeriesShape(0,
ShapeUtilities.createRegularCross(size,size));
return Chart(chart)
def createChart(self):
dataset = XYSeriesCollection()
for s in self.allSeries:
dataset.addSeries(s)
# title is None
chart = ChartFactory.createScatterPlot( \
None, xlabel(), ylabel(), \
dataset, PlotOrientation.VERTICAL, \
True, True, False)
return chart
def createChart(self):
dataset = XYSeriesCollection()
for s in self.allSeries:
dataset.addSeries(s)
# title is None
chart = ChartFactory.createScatterPlot( \
None, xlabel(), ylabel(), \
dataset, PlotOrientation.VERTICAL, \
True, True, False)
return chart
def plot2D(points, Ca, Cb): maxIntensity = 255.0 dataset = XYSeriesCollection() seriesNN = XYSeries(channels[Ca+1]+" -ve "+channels[Cb+1]+" -ve") seriesPP = XYSeries(channels[Ca+1]+" +ve "+channels[Cb+1]+" +ve") seriesNP = XYSeries(channels[Ca+1]+" -ve "+channels[Cb+1]+" +ve") seriesPN = XYSeries(channels[Ca+1]+" +ve "+channels[Cb+1]+" -ve") for p in points: posA = channels[Ca+1] in thresholds and p[Ca]>thresholds[ channels[Ca+1] ] posB = channels[Cb+1] in thresholds and p[Cb]>thresholds[ channels[Cb+1] ] if posA and posB: seriesPP.add(p[Cb], p[Ca]) elif posA: seriesPN.add(p[Cb], p[Ca]) elif posB: seriesNP.add(p[Cb], p[Ca]) else: seriesNN.add(p[Cb], p[Ca]) dataset.addSeries(seriesNN) dataset.addSeries(seriesPN) dataset.addSeries(seriesNP) dataset.addSeries(seriesPP) chart = ChartFactory.createScatterPlot( title+" - "+channels[Cb+1]+" vs "+channels[Ca+1], channels[Cb+1], channels[Ca+1], dataset, PlotOrientation.VERTICAL, False,True,False ) plot = chart.getPlot() plot.getDomainAxis().setRange(Range(0.00, maxIntensity), True, False) plot.getRangeAxis().setRange(Range(0.00, maxIntensity), True, False) renderer = chart.getPlot().getRenderer() renderer.setSeriesPaint(0, Color(64,64,64)) #NN renderer.setSeriesPaint(1, Color(0,255,0)) #PN renderer.setSeriesPaint(2, Color(0,0,255)) #NP renderer.setSeriesPaint(3, Color(0,255,255)) #PP shape = Ellipse2D.Float(-1,-1,3,3) renderer.setSeriesShape(0, shape ) renderer.setSeriesShape(1, shape ) renderer.setSeriesShape(2, shape ) renderer.setSeriesShape(3, shape ) frame = ChartFrame(title+" - "+channels[Cb+1]+" vs "+channels[Ca+1], chart) frame.setSize(800, 800) frame.setLocationRelativeTo(None) frame.setVisible(True)
def measure(stack, cells, nuclei):
time = [ (t-1)*cal.frameInterval for t in range(T+1) ]
cellValues0 = [ 0.0 for t in range(T+1) ]
cellValues1 = [ 0.0 for t in range(T+1) ]
cellAreas0 = [ 0.0 for t in range(T+1) ]
cellAreas1 = [ 0.0 for t in range(T+1) ]
nucleusValues0 = [ 0.0 for t in range(T+1) ]
nucleusValues1 = [ 0.0 for t in range(T+1) ]
nucleusAreas0 = [ 0.0 for t in range(T+1) ]
nucleusAreas1 = [ 0.0 for t in range(T+1) ]
nonNucleusValues0 = [ 0.0 for t in range(T+1) ]
nonNucleusValues1 = [ 0.0 for t in range(T+1) ]
for t in range(1,T+1):
ip = stack.getProcessor(t)
if cells[t] is None:
continue
#subtract background Z from all intensity Z measurements
if cells [t] is None:
print("Nocellsfound" + str(t))
bothCells = ShapeRoi(cells[t][0]).or(ShapeRoi(cells[t][1]))
backRoi = ShapeRoi(Rectangle(0,0,imp.getWidth(),imp.getHeight())).not( bothCells )
ip.setRoi(backRoi)
backMean = ip.getStatistics().mean
ip.setRoi( cells[t][0] )
stats0 = ip.getStatistics()
cellValues0[t] = stats0.mean - backMean
cellAreas0[t] = stats0.area * cal.pixelWidth * cal.pixelHeight
nuc0 = None
for nuc in nuclei[t]:
rect = nuc.getBounds()
nx = int(rect.x+(rect.width/2.0))
ny = int(rect.y+(rect.height/2.0))
if cells[t][0].contains(nx,ny):
nuc0 = nuc
break
if nuc0 is not None:
ip.setRoi( nuc0 )
nucStats0 = ip.getStatistics()
nucleusValues0[t] = nucStats0.mean - backMean
nucleusAreas0[t] = nucStats0.area * cal.pixelWidth * cal.pixelHeight
nuc0.setPosition(0,0,t)
nuc0.setStrokeColor(Color.CYAN)
ol.add(nuc0)
nonnucRoi0 = ShapeRoi(cells[t][0]).not( ShapeRoi(nuc0) )
ip.setRoi( nonnucRoi0 )
nonNucleusValues0[t] = ip.getStatistics().mean - backMean
ip.setRoi( cells[t][1] )
stats1 = ip.getStatistics()
cellValues1[t] = stats1.mean - backMean
cellAreas1[t] = stats1.area * cal.pixelWidth * cal.pixelHeight
nuc1 = None
for nuc in nuclei[t]:
rect = nuc.getBounds()
nx = int(rect.x+(rect.width/2.0))
ny = int(rect.y+(rect.height/2.0))
if cells[t][1].contains(nx,ny):
nuc1 = nuc
break
if nuc1 is not None:
ip.setRoi( nuc1 )
nucStats1 = ip.getStatistics()
nucleusValues1[t] = nucStats1.mean - backMean
nucleusAreas1[t] = nucStats1.area * cal.pixelWidth * cal.pixelHeight
nuc1.setPosition(0,0,t)
nuc1.setStrokeColor(Color.CYAN)
ol.add(nuc1)
nonnucRoi1 = ShapeRoi(cells[t][1]).not( ShapeRoi(nuc1) )
ip.setRoi( nonnucRoi1 )
nonNucleusValues1[t] = ip.getStatistics().mean - backMean
rt = ResultsTable()
rt.showRowNumbers(False)
for t in range(1,T+1):
rt.setValue("Time ("+cal.getTimeUnit()+")", t-1, IJ.d2s(time[t],1))
areaRatio = cellAreas0[t] / cellAreas1[t] if cellAreas0[t]>0 and cellAreas1[t]>0 else 0.0
rt.setValue("Cell 0:Cell 1 Area Ratio", t-1, areaRatio)
nucleusRatio = nucleusValues0[t] / nucleusValues1[t] if nucleusValues0[t]>0 and nucleusValues1[t]>0 else 0.0
rt.setValue("Cell 0:Cell 1 Nucleus Ratio", t-1, nucleusRatio)
nonNucleusRatio = nonNucleusValues0[t] / nonNucleusValues1[t] if nonNucleusValues0[t]>0 and nonNucleusValues1[t]>0 else 0.0
rt.setValue("Cell 0:Cell 1 Non-Nucleus Ratio", t-1, nonNucleusRatio)
nnnRatio0 = nucleusValues0[t] / nonNucleusValues0[t] if nucleusValues0[t]>0 and nonNucleusValues0[t]>0 else 0.0
rt.setValue("Cell 0 Nucleus:Non-Nucleus Ratio", t-1, nnnRatio0)
nnnRatio1 = nucleusValues1[t] / nonNucleusValues1[t] if nucleusValues1[t]>0 and nonNucleusValues1[t]>0 else 0.0
rt.setValue("Cell 1 Nucleus:Non-Nucleus Ratio", t-1, nnnRatio1)
rt.setValue("Cell 0 (red) Area ("+cal.getUnit()+u"\u00b2"+")", t-1, cellAreas0[t])
rt.setValue("Cell 0 Nucleus Area ("+cal.getUnit()+u"\u00b2"+")", t-1, nucleusAreas0[t])
rt.setValue("Cell 0 All", t-1, cellValues0[t])
rt.setValue("Cell 0 Nucleus", t-1, nucleusValues0[t])
rt.setValue("Cell 0 Non-Nucleus", t-1, nonNucleusValues0[t])
rt.setValue("Cell 1 (green) Area ("+cal.getUnit()+u"\u00b2"+")", t-1, cellAreas1[t])
rt.setValue("Cell 1 Nucleus Area ("+cal.getUnit()+u"\u00b2"+")", t-1, nucleusAreas1[t])
rt.setValue("Cell 1 All", t-1, cellValues1[t])
rt.setValue("Cell 1 Nucleus", t-1, nucleusValues1[t])
rt.setValue("Cell 1 Non-Nucleus", t-1, nonNucleusValues1[t])
rt.show(imp.getTitle()+"-Results")
dataset = DefaultXYDataset()
dataset.addSeries( "Cell 0", [time[1:], cellValues0[1:]] )
dataset.addSeries( "Cell 1", [time[1:], cellValues1[1:]] )
dataset.addSeries( "Nucleus 0", [time[1:], nucleusValues0[1:]] )
dataset.addSeries( "Nucleus 1", [time[1:], nucleusValues1[1:]] )
dataset.addSeries( "Non-Nucleus 0", [time[1:], nonNucleusValues0[1:]] )
dataset.addSeries( "Non-Nucleus 1", [time[1:], nonNucleusValues1[1:]] )
chart = ChartFactory.createScatterPlot( imp.getTitle(), "Time ("+cal.getTimeUnit()+")", "Intensity Z", dataset, PlotOrientation.VERTICAL, True,True,False )
plot = chart.getPlot()
plot.setBackgroundPaint(Color(64, 128, 255))
plot.setDomainGridlinePaint(Color.BLACK)
plot.setRangeGridlinePaint(Color.BLACK)
renderer = plot.getRenderer()
legend = LegendItemCollection()
shapeR = 2.0
nucShape = Ellipse2D.Float(-shapeR,-shapeR,shapeR*2,shapeR*2)
nonNucShape = Path2D.Float()
nonNucShape.moveTo(-shapeR,-shapeR)
nonNucShape.lineTo(shapeR,shapeR)
nonNucShape.moveTo(shapeR,-shapeR)
nonNucShape.lineTo(-shapeR,shapeR)
for s in range(dataset.getSeriesCount()):
if s == 0:
renderer.setSeriesLinesVisible(s, True)
renderer.setSeriesShapesVisible(s, False)
renderer.setSeriesStroke(s, BasicStroke(1))
renderer.setSeriesPaint(s, Color.RED)
legend.add( LegendItem("Cell 0", Color.RED) )
elif s == 1:
renderer.setSeriesLinesVisible(s, True)
renderer.setSeriesShapesVisible(s, False)
renderer.setSeriesStroke(s, BasicStroke(1))
renderer.setSeriesPaint(s, Color.GREEN)
legend.add( LegendItem("Cell 1", Color.GREEN) )
elif s == 2:
renderer.setSeriesLinesVisible(s, False)
renderer.setSeriesShapesVisible(s, True)
renderer.setSeriesShape(s, nucShape)
renderer.setSeriesPaint(s, Color.RED)
elif s == 3:
renderer.setSeriesLinesVisible(s, False)
renderer.setSeriesShapesVisible(s, True)
renderer.setSeriesShape(s, nucShape)
renderer.setSeriesPaint(s, Color.GREEN)
elif s == 4:
renderer.setSeriesLinesVisible(s, False)
renderer.setSeriesShapesVisible(s, True)
renderer.setSeriesShape(s, nonNucShape)
renderer.setSeriesPaint(s, Color.RED)
elif s == 5:
renderer.setSeriesLinesVisible(s, False)
renderer.setSeriesShapesVisible(s, True)
renderer.setSeriesShape(s, nonNucShape)
renderer.setSeriesPaint(s, Color.GREEN)
plot.setFixedLegendItems(legend)
frame = ChartFrame(imp.getTitle()+" Z-Normalised Intensity", chart)
frame.pack()
frame.setSize( Dimension(800, 800) )
frame.setLocationRelativeTo(None)
frame.setVisible(True)
def scatter(data,x=None,y=None,**kwargs):
''' Creates a scatter plot comparing two elements. At minimum, takes a collection of data.
The second and third arguments, if they exist, are treated as the two elements to
compare. If these arguments do not exist, the first two elements in the list are
compared. If an optional regress=True argument is present, superimposes a linear
regression for each series and prints some related info (R-value etc).
Note that scatter plots can be zoomed with the mouse. Returns the plot object in
case you want to customize the graph in some way. Takes an optional showMissing argument
which determines whether missing values (-9999.0) should be displayed.
Examples: scatter(data), scatter(data,"tmin","tmax",regress=True)
'''
from org.jfree.data.xy import XYSeriesCollection,XYSeries
from org.jfree.data import UnknownKeyException
from org.jfree.chart import ChartFactory,ChartFrame
from org.jfree.chart.plot import PlotOrientation,DatasetRenderingOrder
from org.jfree.chart.renderer.xy import XYLineAndShapeRenderer
from java.awt import Color
regress=kwargs.get('regress',False)
showMissing=kwargs.get('showMissing',False)
# Try to be flexible about element parameters
if x is not None: x = findElement(x).name
if y is not None: y = findElement(y).name
# Create a dataset from the data
collection = XYSeriesCollection()
for ob in data.groupedByObservation().items():
key,values = ob
name = str(key[0])
if x==None:
x = values[0].element.name
try:
xFact = (i for i in values if i.element.name == x).next()
except StopIteration: # missing value
continue
xval = xFact.value
if xval in missingValues and not showMissing: continue
if y==None:
try:
y = values[1].element.name
except IndexError:
raise Exception("Error! Your data request returned only 1 value per observation. "
"Must have 2 values to generate a scatter plot.")
try:
yFact = (i for i in values if i.element.name == y).next()
except StopIteration: # missing value
continue
yval = yFact.value
if yval in missingValues and not showMissing: continue
try:
series = collection.getSeries(name)
except UnknownKeyException:
collection.addSeries(XYSeries(name))
series = collection.getSeries(name)
series.add(float(xval),float(yval))
# Create chart from dataset
chart = ChartFactory.createScatterPlot( "", x, y, collection, PlotOrientation.VERTICAL,
True, True, False );
plot = chart.getPlot()
frame = ChartFrame("Scatter Plot", chart);
frame.pack();
frame.setVisible(True);
# Superimpose regression if desired
if regress:
regressioncollection = XYSeriesCollection()
for series in collection.getSeries():
regression = _getregression(series)
x1 = series.getMinX()
y1 = regression.predict(x1)
x2 = series.getMaxX()
y2 = regression.predict(x2)
regressionseries = XYSeries(series.getKey())
regressionseries.add(float(x1),float(y1))
regressionseries.add(float(x2),float(y2))
regressioncollection.addSeries(regressionseries)
print series.getKey(),":"
print " R: %8.4f" % regression.getR()
print " R-squared: %8.4f" % regression.getRSquare()
print " Significance: %8.4f" % regression.getSignificance()
print
plot.setDataset(1,regressioncollection)
regressionRenderer = XYLineAndShapeRenderer(True,False)
plot.setRenderer(1,regressionRenderer)
plot.setDatasetRenderingOrder(DatasetRenderingOrder.FORWARD);
colors = [0xec0000,0x58b911,0x6886ea,0xedd612,0xa93bb9,0xffb71b,0xe200df,0x1de2b6,0xdc91db,0x383838,0xb09344,0x4ea958,0xd78c9e,0x64008d,0xb0c95b]
mainRenderer = plot.getRenderer(0)
for i in range(collection.getSeriesCount()):
try:
mainRenderer.setSeriesPaint(i,Color(colors[i]))
regressionRenderer.setSeriesPaint(i,Color(colors[i]))
except IndexError: # Finite # of colors in the color array; beyond that let jfreechart pick
break
'''
# Jump through some hoops to ensure regressions are same color as scatters for each series.
# Initially: doesn't work because series are not indexed the same. And I don't see a way
# to get the actual series from the renderer in order to compare names or something.
mainRenderer = plot.getRenderer(0)
print "Renderer is",type(mainRenderer)
index = 0
paint = mainRenderer.lookupSeriesPaint(index)
print "Paint is",type(paint)
while (paint is not None):
print "Setting paint."
regressionRenderer.setSeriesPaint(index,paint)
index += 1
paint = mainRenderer.getSeriesPaint(index)
'''
return plot
#imgB = WindowManager.getImage("26.93/25.90:test_file")
imgA = WindowManager.getImage("12.12:test_file")
imgB = WindowManager.getImage("13.01/12.12:test_file")
ui = UI.getInstance()
rois = ui.getRoiManager().getAllROIs()
print "Create empty dataset"
dataset = DefaultXYDataset()
print "Create ScatterPlot"
chart = ChartFactory.createScatterPlot(
"Scatter Plot", imgA.getTitle(), imgB.getTitle(),
dataset, PlotOrientation.VERTICAL,
True, False, False);
# random test data
#
#print "Add series to dataset"
#npoints = 1000
#data = [[],[]]
#for i in range(npoints):
# data[0].append(i)
# data[1].append(2*i+random.gauss(0,50))
#
# this trick is needed to go from a 2D python array
# to a 2D java array of doubles, see:
# http://fiji.sc/wiki/index.php/Jython_Scripting#Creating_multi-dimensional_native_java_arrays
#twoDimArr = array(data, Class.forName('[D'))
