def generatePlotPanel(self, plotTitle, listData, showLegend, showTooltip):
"""
1) Create a PieDataset
2) Create a PieChart (or Create a PiePlot and put it in a JFreeChart)
3) Put the PieChart in a ChartPanel
"""
# Get a dictionary of value occurence in the list {value1:count, value2:count...}
dataDico = Counter(listData)
#print dataDico # value: counts OK
# Create a Pie dataset from the dicoData
pieDataset = DefaultPieDataset()
for key, value in dataDico.items():
#print key, value
pieDataset.setValue(key, value)
# Create an instance of JFreeChart
urls = False
chart = ChartFactory.createPieChart(plotTitle, pieDataset, showLegend, showTooltip, urls)
# Alternative way
#piePlot = PiePlot(pieDataset)
#chart = JFreeChart(plotTitle, piePlot)
return ChartPanel(chart)
def graph(*data,**kwargs):
''' Creates a simple time series graph. Takes the output from getData (a list of Facts).
Alternately, you can pass the same set of parameters that are passed to getData().
Takes an optional elements argument which expresses which elements to show. Also
takes an optional showMissing argument which determines whether missing values (-9999.0)
should be displayed. Also takes an optional lineWidth argument which lets you set lines
heavier or lighter. Example: graph(data,elements=("temp","precip"),showMissing=False).
Returns the plot object in case you want to customize the graph in some way.
'''
from org.jfree.chart import ChartFactory,ChartFrame
from org.jfree.data.time import TimeSeries,TimeSeriesCollection, Minute
from java.awt import BasicStroke
# Were we passed a dataset or parameters for obtaining a dataset?
if len(data) == 3:
station,date,element = data
graph(getData(station,date,element))
return
else:
data = data[0] # unwrap from tuple
showMissing = kwargs.get('showMissing',True)
lineWidth = kwargs.get('lineWidth',2.0)
title = kwargs.get('title',"Time series graph")
elementsToShow = [e.name for e in findElements(kwargs.get('elements',""))]
datasets = {}
for fact in sorted(data):
if fact.value in missingValues and not showMissing: continue
station = str(fact.station.name)
element = fact.element.name
if elementsToShow and element not in elementsToShow: continue
series = station+":"+element
date = fact.datetime
hour = date.getHour()
if fact.subhourlyTime is not None:
subhourlyTime = int(fact.subhourlyTime)
else:
subhourlyTime = 0
if subhourlyTime == 60: # JFreeChart can't handle the 1-60 representation of minute in CRN
subhourlyTime = 0
minute = Minute(subhourlyTime,hour,date.getDay(),date.getMonth()+1,date.getYear()) # JFreeChart's representation of time
if series not in datasets:
datasets[series] = TimeSeries(series)
datasets[series].add(minute,float(fact.value))
timeSeriesCollection = TimeSeriesCollection()
for dataset in datasets.values():
timeSeriesCollection.addSeries(dataset)
chart = ChartFactory.createTimeSeriesChart("", "UTC Date", "Value", timeSeriesCollection, True, True, False)
plot = chart.getPlot()
r = plot.getRenderer()
r.setStroke(BasicStroke(lineWidth))
frame = ChartFrame(title, chart)
frame.pack()
frame.setVisible(True)
return plot
def __init__(self, automations):
# Create the frame
frame = JFrame("Automation Viewer")
frame.setSize(500, 300)
frame.setLayout(BorderLayout())
series = AutomationSeries
# Finalize the window
frame.setDefaultCloseOperation(WindowConstants.DISPOSE_ON_CLOSE)
frame.setVisible(True)
# Create an XY dataset
dataset = XYSeriesCollection()
for autoname in automations:
automation = ModbusPal.getAutomation(autoname)
series = AutomationSeries(automation)
dataset.addSeries(series)
frame.addWindowListener(series)
# Create chart
chart = ChartFactory.createXYLineChart("Automation Viewer",
"Time (seconds)", "Value",
dataset,
PlotOrientation.VERTICAL,
Boolean.TRUE, Boolean.TRUE,
Boolean.FALSE)
panel = ChartPanel(chart)
# Add chart to panel
frame.add(panel, BorderLayout.CENTER)
def curve(data, name="", smooth=True, trid=True):
"""
Creates a curve based on a list of (x,y) tuples.
Setting *smooth* to ``True`` results in a spline renderer renderer is used.
Setting *trid* to ``True`` results in a 3D plot. In this case the ``smooth``
argument is ignored.
"""
dataset = XYSeriesCollection()
xy = XYSeries(name)
for d in data:
xy.add(d[0], d[1])
dataset.addSeries(xy)
chart = ChartFactory.createXYLineChart(None, None, None, dataset,
PlotOrientation.VERTICAL, True,
True, False)
if smooth:
chart.getXYPlot().setRenderer(XYSplineRenderer())
if trid:
chart.getXYPlot().setRenderer(XYLine3DRenderer())
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 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 pie(data, name='', trid=False):
"""
Creates a pie chart.
*data* is a ``dict`` whose keys are category names and values are numeric
values.
Setting *trid* to ``True`` results in a 3D char.
"""
dataset = DefaultPieDataset();
for k,v in data.iteritems():
dataset.setValue(k, v)
if trid:
chart = ChartFactory.createPieChart3D(name, dataset, True, True, False)
else:
chart = ChartFactory.createPieChart(name, dataset, True, True, False)
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 __init__(self):
""" generated source for method __init__ """
super(ConfigurableDetailPanel, self).__init__(GridBagLayout())
model = DefaultTableModel()
model.addColumn("Step")
model.addColumn("My Move")
model.addColumn("Time spent")
model.addColumn("Out of time?")
self.moveTable = JZebraTable(model)
self.moveTable.setShowHorizontalLines(True)
self.moveTable.setShowVerticalLines(True)
sidePanel = JPanel()
self.memUsage = TimeSeries("Used Memory")
self.memTotal = TimeSeries("Total Memory")
self.memUsage.setMaximumItemCount(36000)
self.memTotal.setMaximumItemCount(36000)
memory = TimeSeriesCollection()
memory.addSeries(self.memUsage)
memory.addSeries(self.memTotal)
memChart = ChartFactory.createTimeSeriesChart(None, None, "Megabytes", memory, True, True, False)
memChart.setBackgroundPaint(getBackground())
memChartPanel = ChartPanel(memChart)
memChartPanel.setPreferredSize(Dimension(500, 175))
sidePanel.add(memChartPanel)
self.counters = HashSet()
self.countersCollection = TimeSeriesCollection()
counterChart = ChartFactory.createTimeSeriesChart(None, None, None, self.countersCollection, True, True, False)
counterChart.getXYPlot().setRangeAxis(LogarithmicAxis("Count per 100ms"))
counterChart.getXYPlot().getRangeAxis().setAutoRangeMinimumSize(1.0)
counterChart.setBackgroundPaint(getBackground())
counterChartPanel = ChartPanel(counterChart)
counterChartPanel.setPreferredSize(Dimension(500, 175))
sidePanel.add(counterChartPanel)
self.scoreCountersCollection = TimeSeriesCollection()
scoreCounterChart = ChartFactory.createTimeSeriesChart(None, None, "Score", self.scoreCountersCollection, True, True, False)
scoreCounterChart.getXYPlot().getRangeAxis().setRange(0, 100)
scoreCounterChart.setBackgroundPaint(getBackground())
scoreCounterChartPanel = ChartPanel(scoreCounterChart)
scoreCounterChartPanel.setPreferredSize(Dimension(500, 175))
sidePanel.add(scoreCounterChartPanel)
self.add(JScrollPane(self.moveTable, ScrollPaneConstants.VERTICAL_SCROLLBAR_AS_NEEDED, ScrollPaneConstants.HORIZONTAL_SCROLLBAR_AS_NEEDED), GridBagConstraints(0, 0, 1, 2, 1.0, 1.0, GridBagConstraints.CENTER, GridBagConstraints.BOTH, Insets(0, 0, 0, 0), 0, 0))
self.add(sidePanel, GridBagConstraints(1, 0, 1, 1, 1.0, 1.0, GridBagConstraints.CENTER, GridBagConstraints.BOTH, Insets(0, 0, 0, 0), 0, 0))
self.add(JButton(resetButtonMethod()), GridBagConstraints(1, 1, 1, 1, 0.0, 0.0, GridBagConstraints.SOUTHEAST, GridBagConstraints.NONE, Insets(0, 0, 0, 0), 0, 0))
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):
self.chart = ChartFactory.createXYLineChart(
self.getTitle(), # chart title
self.getXAxisLabel(), # x axis label
self.getYAxisLabel(), # y axis label
self.dataSets[0], # data (primary)
PlotOrientation.VERTICAL,
True, # include legend
False, # tooltips
False # urls
)
def category(data, name='', xlabel='', ylabel='', stacked=False, trid=False):
"""
Creates a category bar chart.
*data* is a ``dict`` whose keys are category names and whose values are
numerical (the height of the bar).
To plot multiple series *data* is specified as a ``dict`` whose keys are
series names and values are a ``dict`` of category names to numerical values.
Setting *stacked* to ``True`` results in a stacked bar char. Setting *trid*
to ``True`` results in a 3D bar chart.
"""
dataset = DefaultCategoryDataset()
for k, v in data.iteritems():
if isinstance(v, dict):
for k2, v2 in v.iteritems():
dataset.addValue(v2, k2, k)
else:
dataset.addValue(v, "", k)
if trid:
if stacked:
chart = ChartFactory.createStackedBarChart3D(
name, xlabel, ylabel, dataset, PlotOrientation.VERTICAL, True,
True, True)
else:
chart = ChartFactory.createBarChart3D(name, xlabel, ylabel,
dataset,
PlotOrientation.VERTICAL,
True, True, True)
else:
if stacked:
chart = ChartFactory.createStackedBarChart(
name, xlabel, ylabel, dataset, PlotOrientation.VERTICAL, True,
True, True)
else:
chart = ChartFactory.createBarChart(name, xlabel, ylabel, dataset,
PlotOrientation.VERTICAL, True,
True, True)
return Chart(chart)
def box(data):
"""
Creates a box and whiskers plot.
*data* is a ``dict`` whose keys are category names and values are list of
numeric values.
"""
dataset = DefaultBoxAndWhiskerCategoryDataset()
for name, values in data.iteritems():
dataset.add(values, "", name);
chart = ChartFactory.createBoxAndWhiskerChart("", "", "", dataset, True)
return Chart(chart)
def category(data, name="", xlabel="", ylabel="", stacked=False, trid=False):
"""
Creates a category bar chart.
*data* is a ``dict`` whose keys are category names and whose values are
numerical (the height of the bar).
To plot multiple series *data* is specified as a ``dict`` whose keys are
series names and values are a ``dict`` of category names to numerical values.
Setting *stacked* to ``True`` results in a stacked bar char. Setting *trid*
to ``True`` results in a 3D bar chart.
"""
dataset = DefaultCategoryDataset()
for k, v in data.iteritems():
if isinstance(v, dict):
for k2, v2 in v.iteritems():
dataset.addValue(v2, k2, k)
else:
dataset.addValue(v, "", k)
if trid:
if stacked:
chart = ChartFactory.createStackedBarChart3D(
name, xlabel, ylabel, dataset, PlotOrientation.VERTICAL, True, True, True
)
else:
chart = ChartFactory.createBarChart3D(
name, xlabel, ylabel, dataset, PlotOrientation.VERTICAL, True, True, True
)
else:
if stacked:
chart = ChartFactory.createStackedBarChart(
name, xlabel, ylabel, dataset, PlotOrientation.VERTICAL, True, True, True
)
else:
chart = ChartFactory.createBarChart(
name, xlabel, ylabel, dataset, PlotOrientation.VERTICAL, True, True, True
)
return Chart(chart)
def plot(title, x_label, y_label, *curves):
dataset = XYSeriesCollection()
for legend, curve in curves:
series = XYSeries(legend)
for x, y in curve:
series.add(x, y)
dataset.addSeries(series)
chart = ChartFactory.createXYLineChart(title, x_label, y_label, dataset,
PlotOrientation.VERTICAL, True,
True, False)
frame = ChartFrame(title, chart)
frame.setVisible(True)
frame.setSize(400, 300)
def xy(data, name='', xlabel='', ylabel=''):
"""
Creates a xy bar chart.
*data* is a list of (x,y) tuples
"""
series = XYSeries(name);
for x,y in data:
series.add(x,y)
dataset = XYSeriesCollection(series)
chart = ChartFactory.createXYBarChart(None, xlabel, False, ylabel, dataset,
PlotOrientation.VERTICAL, True, True, False)
return Chart(chart)
def createHistogram(data):
''' Creates a histogram from a list of arbitrary numerical data in range from 0..1'''
from org.jfree.data.category import DefaultCategoryDataset
from org.jfree.chart import ChartFactory,ChartFrame
from org.jfree.chart.plot import PlotOrientation
from java.lang import Float
numBins=20
datamin = min(data)
datamax = max(data)
#binsize = 1.01 / numBins
bins = {}
for d in data:
binkey = round(d,1)
bin = bins.setdefault(binkey,0)
bins[binkey] = bin + 1
# Create dataset from bins
dataset = DefaultCategoryDataset()
# Ensure that bins exist even if they're empty
i = datamin
while i <= 1.0:
bins.setdefault(round(float(i),1),0)
i += .1
#print "Number of bins:",len(bins)
for bin in sorted(bins):
#print "bin:",bin,type(bin)
dataset.addValue(bins[bin],"","%05.2f"%(bin))
# Create chart from dataset
chart = ChartFactory.createBarChart(
"", # chart title
"Bin", # domain axis label
"Number of occurrences", # range axis label
dataset, # data
PlotOrientation.VERTICAL, # orientation
True, # include legend
True, # tooltips?
False # URLs?
)
plot = chart.getPlot()
plot.getRenderer().setShadowVisible(False)
frame = ChartFrame("Histogram", chart);
frame.pack();
frame.setVisible(True);
return plot
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 _createEmptyChart(self, dataset = None):
chart = ChartFactory.createXYLineChart(
self.title, # chart title
"He", # x axis label
"Fst", # y axis label
dataset, # data
PlotOrientation.VERTICAL,
True, # include legend
True, # tooltips
False # urls
)
chart.setBackgroundPaint(Color.white)
# get a reference to the plot for further customisation...
# change the auto tick unit selection to integer units only...
#rangeAxis = plot.getRangeAxis()
#rangeAxis.setStandardTickUnits(NumberAxis.createIntegerTickUnits())
self.confArea = None
return chart
def graphArbitraryData(data,title=""):
''' Creates a line graph of arbitrary data (as opposed to the rather specific format used by graph()). Pass in a
list of datapoints, each consisting of a 3-part tuple: (x,y,category). If you're only graphing one sort of
data (ie you want a graph with one line), you can pass in any constant for category.
Example (one category):
data = []
for i in range(10):
data.append((i,i*i,0))
graphArbitraryData(data)
Example (multiple categories):
data = []
for i in range(30):
data.append((i,i*i,"squared"))
data.append((i,i*i*i,"cubed"))
graphArbitraryData(data)
'''
from org.jfree.data.category import DefaultCategoryDataset
from org.jfree.chart import ChartFactory, ChartFrame, ChartPanel
from org.jfree.chart.plot import PlotOrientation
from org.jfree.data.xy import XYSeriesCollection, XYSeries
datasets = {} # dict of all series
# First, create the individual series from the data
for item in data:
seriesname = str(item[2])
if seriesname not in datasets:
datasets[seriesname] = XYSeries(seriesname)
datasets[seriesname].add(float(item[0]), float(item[1]));
# Second, add those series to a collection
datasetcollection = XYSeriesCollection()
for key in datasets:
datasetcollection.addSeries(datasets[key])
chart = ChartFactory.createXYLineChart("","","",datasetcollection,PlotOrientation.VERTICAL,True,True,False)
frame = ChartFrame(title, chart);
frame.pack();
frame.setVisible(True);
panel = ChartPanel(chart)
return chart.getPlot()
def xy(data, name='', xlabel='', ylabel=''):
"""
Creates a xy bar chart.
*data* is a list of (x,y) tuples
"""
series = XYSeries(name)
for x, y in data:
series.add(x, y)
dataset = XYSeriesCollection(series)
if len(data) > 1:
# hack to set interval width
x0, x1 = data[0][0], data[1][0]
dataset.setIntervalWidth(x1 - x0)
chart = ChartFactory.createXYBarChart(None, xlabel, False, ylabel, dataset,
PlotOrientation.VERTICAL, True, True,
False)
return Chart(chart)
def xy(data, name="", xlabel="", ylabel=""):
"""
Creates a xy bar chart.
*data* is a list of (x,y) tuples
"""
series = XYSeries(name)
for x, y in data:
series.add(x, y)
dataset = XYSeriesCollection(series)
if len(data) > 1:
# hack to set interval width
x0, x1 = data[0][0], data[1][0]
dataset.setIntervalWidth(x1 - x0)
chart = ChartFactory.createXYBarChart(
None, xlabel, False, ylabel, dataset, PlotOrientation.VERTICAL, True, True, False
)
return Chart(chart)
def renderHistogram(values,
n_bins,
min_max=None,
title="Histogram",
color=Color.red,
show=True,
setThemeFn=setTheme):
""" values: a list or array of numeric values.
n_bins: the number of bins to use.
min_max: defaults to None, a tuple with the minimum and maximum value.
title: defaults to "Histogram", must be not None.
show: defaults to True, showing the histogram in a new window.
setThemeFn: defaults to setTheme, can be None or another function that takes a chart
as argument and sets rendering colors etc.
Returns a tuple of the JFreeChart instance and the window JFrame, if shown.
"""
hd = HistogramDataset()
hd.setType(HistogramType.RELATIVE_FREQUENCY)
print min_max
if min_max:
hd.addSeries(title, values, n_bins, min_max[0], min_max[1])
else:
hd.addSeries(title, values, n_bins)
chart = ChartFactory.createHistogram(title, "", "", hd,
PlotOrientation.VERTICAL, False,
False, False)
# Adjust series color
chart.getXYPlot().getRendererForDataset(hd).setSeriesPaint(0, color)
#
if setThemeFn:
setThemeFn(chart)
frame = None
if show:
frame = JFrame(title)
frame.getContentPane().add(ChartPanel(chart))
frame.pack()
frame.setVisible(True)
return chart, frame
def generatePlotPanel(self, plotTitle, listData):
"""
1) Create a CategoryDataset
2) Create a BarChart (or Create a BarPlot and put it in a JFreeChart)
3) Put the BarChart in a ChartPanel
"""
# Get a dictionary of value occurence in the list {value1:count, value2:count...}
dataDico = Counter(listData)
#print dataDico # value: counts OK
# Create a Pie dataset from the dicoData
dataset = DefaultCategoryDataset()
for key, value in dataDico.items():
#print key, value
dataset.setValue(value, key, "")
# Create an instance of JFreeChart
chart = ChartFactory.createBarChart(plotTitle, "Categories", "Count", dataset)
# Alternative way
#piePlot = PiePlot(pieDataset)
#chart = JFreeChart(plotTitle, piePlot)
return ChartPanel(chart)
def histogram(title, values):
dataset = HistogramDataset()
dataset.setType(HistogramType.RELATIVE_FREQUENCY)
#NBINS = int(maths.sqrt(len(values)))
#NBINS = int( (max(values)-min(values))/binW )
NBINS = 64
dataset.addSeries(title, values, NBINS)
chart = ChartFactory.createHistogram(title, "Distance (nm)",
"Relative Frequency", dataset,
PlotOrientation.VERTICAL, False, True,
False)
plot = chart.getXYPlot()
renderer = plot.getRenderer()
renderer.setSeriesPaint(0, Colour.BLUE)
painter = StandardXYBarPainter()
renderer.setBarPainter(painter)
frame = ChartFrame(title, chart)
frame.setSize(1200, 800)
frame.setLocationRelativeTo(None)
frame.setVisible(True)
def _createEmptyChart(self, dataset=None):
if self.isTemporal:
hText = "Hs"
fText = "Fgt"
else:
hText = "He"
fText = "Fst"
chart = ChartFactory.createXYLineChart(
self.title, # chart title
hText, # x axis label
fText, # y axis label
dataset, # data
PlotOrientation.VERTICAL,
True, # include legend
True, # tooltips
False # urls
)
chart.setBackgroundPaint(Color.white)
# get a reference to the plot for further customisation...
# change the auto tick unit selection to integer units only...
#rangeAxis = plot.getRangeAxis()
#rangeAxis.setStandardTickUnits(NumberAxis.createIntegerTickUnits())
self.confArea = None
return chart
def curve(data, name="", smooth=True, trid=True):
"""
Creates a curve based on a list of (x,y) tuples.
Setting *smooth* to ``True`` results in a spline renderer renderer is used.
Setting *trid* to ``True`` results in a 3D plot. In this case the ``smooth``
argument is ignored.
"""
dataset = XYSeriesCollection()
xy = XYSeries(name);
for d in data:
xy.add(d[0], d[1])
dataset.addSeries(xy);
chart = ChartFactory.createXYLineChart(None, None, None, dataset,
PlotOrientation.VERTICAL, True, True, False)
if smooth:
chart.getXYPlot().setRenderer(XYSplineRenderer())
if trid:
chart.getXYPlot().setRenderer(XYLine3DRenderer())
return Chart(chart)
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
for j in range(0, i):#Generate 20 values for each serie
d2 = d + Math.random() * (d1 - d)
arraylist.add(Double(d2))
return arraylist
#Create the default dataset for the BoxAndWhisker
def createDataSet():
dataset = DefaultBoxAndWhiskerCategoryDataset()
for i in range(0, 3):#Generate 3 series for each category
for j in range(0,5):#Generate 5 Categories
list = createValueList(0.0, 20.0, 20)
dataset.add(list, "Series " + str(i), "Category " + str(j))#Add a list of value for 1 rowKey (Serie), 1 columnKey (Category)
return dataset
boxandwhiskercategorydataset=createDataSet()
jfreechart = ChartFactory.createBoxAndWhiskerChart('Box and Whisker Chart Demo 1', 'Category', 'Value', boxandwhiskercategorydataset, True)
categoryplot = jfreechart.getPlot()
jfreechart.setBackgroundPaint(Color.white)
categoryplot.setBackgroundPaint(Color.lightGray)
categoryplot.setDomainGridlinePaint(Color.white)
categoryplot.setDomainGridlinesVisible(True)
categoryplot.setRangeGridlinePaint(Color.white)
numberaxis = categoryplot.getRangeAxis()
numberaxis.setStandardTickUnits(NumberAxis.createIntegerTickUnits())
#Plot the Chart into an ImagePlus
bi = jfreechart.createBufferedImage(600, 400)
imp = ImagePlus('Chart Test', bi)
imp.show()
from org.apache.batik.dom import GenericDOMImplementation
from org.apache.batik.svggen import SVGGraphics2D
#Define the dataset
dataset = DefaultCategoryDataset()
dataset.addValue(25, 'S1', 'Slide XXX')#Value, serie name, X label
dataset.addValue(40, 'S1', 'Slide YYY')
dataset.addValue(60, 'S1', 'Slide ZZZ')
##############
# Plot Chart #
##############
#chart = ChartFactory.createLineChart(None,'Slide','% Brown Area',dataset,PlotOrientation.VERTICAL,False,True,False)
chart = ChartFactory.createBarChart("% Brown Area per Slide", 'Slide', '% Brown Area', dataset, PlotOrientation.VERTICAL, False,True,False)
# set the background color for the chart...
chart.setBackgroundPaint(Color.WHITE)
plot = chart.getPlot()
plot.setBackgroundPaint(Color.WHITE)
plot.setRangeGridlinesVisible(False)
plot.setAxisOffset(RectangleInsets.ZERO_INSETS)
#customise the range axis...
rangeAxis = plot.getRangeAxis()
rangeAxis.setStandardTickUnits(NumberAxis.createIntegerTickUnits())
rangeAxis.setAutoRangeIncludesZero(True)
#Set the Min Max value of the y axis
rangeAxis.setRange(0, 100)
#Create a custom BarRenderer
from ij import IJ
from org.jfree.chart import ChartFactory, ChartPanel
from org.jfree.data.statistics import HistogramDataset, HistogramType
from javax.swing import JFrame
from java.awt import Color
imp = IJ.getImage()
pixels = imp.getProcessor().convertToFloat().getPixels()
# Data and parameter of the histogram
values = list(pixels)
n_bins = 256 # number of histogram bins
# Construct the histogram from the pixel data
hist = HistogramDataset()
hist.setType(HistogramType.RELATIVE_FREQUENCY)
hist.addSeries("my data", values, n_bins)
# Create a JFreeChart histogram
chart = ChartFactory.createHistogram("My histogram", "the bins", "counts", hist)
# Adjust series color
chart.getXYPlot().getRendererForDataset(hist).setSeriesPaint(0, Color.blue)
# Show the histogram in an interactive window
# where the right-click menu enables saving to PNG or SVG, and adjusting properties
frame = JFrame("Histogram window")
frame.getContentPane().add(ChartPanel(chart))
frame.pack()
frame.setVisible(True)
#--------------------Plot with jfreeChart environment-----------
values = xhistovector
bins = 20
dataset = HistogramDataset()
dataset.setType( HistogramType.FREQUENCY ) #other options: RELATIVE_FREQUENCY, SCALE_AREA_TO_1
dataset.addSeries( "Node count", values, bins)
chart = ChartFactory.createHistogram(
"Node Count Histogram",
"Bins",
"Node count",
dataset,
PlotOrientation.VERTICAL,
True, # showLegend
True, # toolTips
True,) # urls
# Save it as a PNG:
ChartUtilities.saveChartAsPNG(
File("/Users/berthola/Desktop/Histotest/foo.png"),
chart,
800,
600)
from org.jfree.chart import ChartPanel
from javax.swing import JFrame
from java.io import File
from java.awt import Dimension
values = [ random.randint(0,50) for x in xrange(1,100) ]
bins = 20
dataset = HistogramDataset()
dataset.setType( HistogramType.RELATIVE_FREQUENCY )
dataset.addSeries( "Random Stuff", values, bins)
chart = ChartFactory.createHistogram(
"Example JFreeChart histogram",
"This is the x axis",
"This is the y axis",
dataset,
PlotOrientation.VERTICAL,
True, # showLegend
True, # toolTips
True,) # urls
# Save it as a PNG:
ChartUtilities.saveChartAsPNG(
File("/tmp/foo.png"),
chart,
800,
600)
from org.jfree.chart import ChartPanel
from javax.swing import JFrame
def histogram(*data,**kwargs):
''' Creates a histogram. Takes the output from getData (a list of Facts), or alternately the
same parameters that getData() takes. Takes an optional 'numBins=k' argument, where k
specifies the number of bins. Returns the plot object in case you want to customize the
graph in some way. Takes an optional 'title' argument.
'''
# TODO: offset labels.
from org.jfree.data.category import DefaultCategoryDataset
from org.jfree.chart import ChartFactory,ChartFrame
from org.jfree.chart.plot import PlotOrientation
from java.lang import Float
# Were we passed a dataset or parameters for obtaining a dataset?
if len(data) == 3:
station,date,element = data
histogram(getData(station,date,element),**kwargs)
return
else:
data = data[0] # unwrap from tuple
# Find min and max; decide on number of bins
numBins=kwargs.get('numBins',16)
datamin,datamax = _getminmax(data)
binsize = abs((datamax - datamin) / (Decimal(numBins)*Decimal("0.999"))) # divide by .999; otherwise there's always a final bin with one member, the max
if binsize == 0:
raise Exception("Cannot create histogram; all values are equal to "+str(datamin))
title = kwargs.get('title',"Histogram")
# Create bins based on value.
stations = {}
for d in data:
if d.value in missingValues: continue
binkey = round(float(datamin + binsize * int((d.value - datamin) / binsize)),2)
name = d.station.getNameString()+", "+d.element.name
bin = stations.setdefault(name,{}).setdefault(binkey,0)
stations[name][binkey] = bin + 1
# Create dataset from bins
dataset = DefaultCategoryDataset()
for station in stations:
# Ensure that bins exist even if they're empty
i = datamin
while i < datamax:
stations[station].setdefault(round(float(i),2),0)
i += binsize
#print "Number of bins:",len(stations[station])
for bin in sorted(stations[station]):
#print "bin:",bin,type(bin)
dataset.addValue(stations[station][bin],station,Float(bin))
# Create chart from dataset
chart = ChartFactory.createBarChart(
"", # chart title
"Bin", # domain axis label
"Number of occurrences", # range axis label
dataset, # data
PlotOrientation.VERTICAL, # orientation
True, # include legend
True, # tooltips?
False # URLs?
)
plot = chart.getPlot()
plot.getRenderer().setShadowVisible(False)
frame = ChartFrame(title, chart);
frame.pack();
frame.setVisible(True);
return plot
print "Create empty dataset"
dataset = DefaultStatisticalCategoryDataset()
# dataset.add(Mean, StdDev, "Series", "Condition")
print "Add elements to dataset"
dataset.add(15.0, 2.4, "Row 1", "Column 1")
dataset.add(15.0, 4.4, "Row 1", "Column 2")
dataset.add(13.0, 2.1, "Row 1", "Column 3")
dataset.add(7.0, 1.3, "Row 1", "Column 4")
dataset.add(2.0, 2.4, "Row 2", "Column 1")
dataset.add(18.0, 4.4, "Row 2", "Column 2")
dataset.add(28.0, 2.1, "Row 2", "Column 3")
dataset.add(17.0, 1.3, "Row 2", "Column 4")
print "Create LineChart"
chart = ChartFactory.createLineChart(None, "Treatment", "Measurement", dataset,
PlotOrientation.VERTICAL, False, True,
False)
# set the background color for the chart...
chart.setBackgroundPaint(Color.white)
plot = chart.getPlot()
plot.setBackgroundPaint(Color.white)
plot.setRangeGridlinesVisible(False)
plot.setAxisOffset(RectangleInsets.ZERO_INSETS)
# make a buffered image, create imageplu and show
bi = chart.createBufferedImage(600, 400)
imp = ImagePlus("Chart Test", bi)
imp.show()
print "Create empty dataset"
dataset = DefaultStatisticalCategoryDataset()
# dataset.add(Mean, StdDev, "Series", "Condition")
print "Add elements to dataset"
dataset.add(15.0, 2.4, "Row 1", "Column 1")
dataset.add(15.0, 4.4, "Row 1", "Column 2")
dataset.add(13.0, 2.1, "Row 1", "Column 3")
dataset.add(7.0, 1.3, "Row 1", "Column 4")
dataset.add(2.0, 2.4, "Row 2", "Column 1")
dataset.add(18.0, 4.4, "Row 2", "Column 2")
dataset.add(28.0, 2.1, "Row 2", "Column 3")
dataset.add(17.0, 1.3, "Row 2", "Column 4")
print "Create LineChart"
chart = ChartFactory.createLineChart(None, "Treatment", "Measurement", dataset, PlotOrientation.VERTICAL, False, True, False)
# set the background color for the chart...
chart.setBackgroundPaint(Color.white);
plot = chart.getPlot()
plot.setBackgroundPaint(Color.white)
plot.setRangeGridlinesVisible(False)
plot.setAxisOffset(RectangleInsets.ZERO_INSETS)
# make a buffered image, create imageplu and show
bi = chart.createBufferedImage(600, 400)
imp = ImagePlus("Chart Test", bi)
imp.show()
#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'))
