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 __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 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 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 getDataSets(self, txName):
"""
For a given txNum name, return a set of JFreeChart datasets
with data on tx/sec, response times, and bandwidth.
"""
logger.debug("getting data sets for " + txName)
if self._txNameDatasets == None:
logger.warn("Building data sets.")
txSecDataset = None
# build 'em
self._txNameDatasets = {}
txNums = self._summaryData.getTxNumNameMap().keys()
for txNum in txNums:
logger.debug("DEBUG: building DS for " + txNum)
dataSetGroup = {}
txSecDataset = XYSeriesCollection() # not returning a new object
bandwidthDataSet = XYSeriesCollection()
simpleResponseTimeDataset = XYSeriesCollection()
responseTimeDataset = DefaultTableXYDataset()
txSecPassSeries = XYSeries("passed")
txSecFailSeries = XYSeries("failed")
responseTimeSeries = XYSeries("seconds")
finishTimeSeries = XYSeries("complete", True, False)
resolveHostSeries = XYSeries("resolveHost", True, False)
connectSeries = XYSeries("connect", True, False)
firstByteSeries = XYSeries("firstByte", True, False)
bandwidthSeries = XYSeries("KB/sec")
for bucket in self.bucketList:
txSecPass = bucket.getTxSecPass(txNum)
txSecPassSeries.add(bucket.getStartTime() / 1000.0, txSecPass)
txSecFail = bucket.getTxSecFail(txNum)
txSecFailSeries.add(bucket.getStartTime() / 1000.0, txSecFail)
responseTimeSeries.add(bucket.getStartTime() / 1000.0, bucket.getMeanResponseTime(txNum))
if ga.constants.VORPAL.getPlugin("analyzer").isHTTP():
bandwidthSeries.add(bucket.getStartTime() / 1000.0, bucket.getMeanThroughputKBSec(txNum))
finishTimeSeries.add(bucket.getStartTime() / 1000.0, bucket.getMeanFinishTime(txNum))
resolveHostSeries.add(bucket.getStartTime() / 1000.0, bucket.getMeanResolveHostTime(txNum))
connectSeries.add(bucket.getStartTime() / 1000.0, bucket.getMeanConnectTime(txNum))
firstByteSeries.add(bucket.getStartTime() / 1000.0, bucket.getMeanFirstByteTime(txNum))
txSecDataset.addSeries(txSecPassSeries)
txSecDataset.addSeries(txSecFailSeries)
responseTimeDataset.addSeries(resolveHostSeries)
responseTimeDataset.addSeries(connectSeries)
responseTimeDataset.addSeries(firstByteSeries)
responseTimeDataset.addSeries(finishTimeSeries)
simpleResponseTimeDataset.addSeries(responseTimeSeries)
bandwidthDataSet.addSeries(bandwidthSeries)
dataSetGroup[TX_SEC_KEY] = txSecDataset
dataSetGroup[FULL_RESPONSE_TIME_KEY] = responseTimeDataset
dataSetGroup[THROUGHPUT_KEY] = bandwidthDataSet
dataSetGroup[SIMPLE_RESPONSE_TIME_KEY] = simpleResponseTimeDataset
self._txNameDatasets[txNum] = dataSetGroup
logger.debug("DEBUG: done building data sets.")
return self._txNameDatasets[txName]
def regression(data, regtype=0):
xAxis = NumberAxis("x")
xAxis.setAutoRangeIncludesZero(False)
yAxis = NumberAxis("y")
yAxis.setAutoRangeIncludesZero(False)
series = XYSeries("values")
xmax = xmin = None
for (x, y) in data:
series.add(x, y)
if xmax is None:
xmax = xmin = x
else:
xmax = max(xmax, x)
xmin = min(xmin, x)
dataset = XYSeriesCollection()
dataset.addSeries(series)
renderer1 = XYDotRenderer()
plot = XYPlot(dataset, xAxis, yAxis, renderer1)
if regtype == 1:
coefficients = Regression.getPowerRegression(dataset, 0)
curve = PowerFunction2D(coefficients[0], coefficients[1])
regdesc = "Power Regression"
else:
coefficients = Regression.getOLSRegression(dataset, 0)
curve = LineFunction2D(coefficients[0], coefficients[1])
regdesc = "Linear Regression"
regressionData = DatasetUtilities.sampleFunction2D(
curve, xmin, xmax, 100, "Fitted Regression Line")
plot.setDataset(1, regressionData)
renderer2 = XYLineAndShapeRenderer(True, False)
renderer2.setSeriesPaint(0, Color.blue)
plot.setRenderer(1, renderer2)
jfchart = JFreeChart(regdesc, JFreeChart.DEFAULT_TITLE_FONT, plot, True)
chart = Chart(jfchart)
chart.coeffs = coefficients
return chart
def regression(data, regtype=0):
xAxis = NumberAxis("x")
xAxis.setAutoRangeIncludesZero(False)
yAxis = NumberAxis("y")
yAxis.setAutoRangeIncludesZero(False)
series = XYSeries("values");
xmax = xmin = None
for (x,y) in data:
series.add(x, y);
if xmax is None:
xmax = xmin = x
else:
xmax = max(xmax, x)
xmin = min(xmin, x)
dataset = XYSeriesCollection()
dataset.addSeries(series);
renderer1 = XYDotRenderer()
plot = XYPlot(dataset, xAxis, yAxis, renderer1)
if regtype == 1:
coefficients = Regression.getPowerRegression(dataset, 0)
curve = PowerFunction2D(coefficients[0], coefficients[1])
regdesc = "Power Regression"
else:
coefficients = Regression.getOLSRegression(dataset, 0)
curve = LineFunction2D(coefficients[0], coefficients[1])
regdesc = "Linear Regression"
regressionData = DatasetUtilities.sampleFunction2D(curve, xmin, xmax, 100,
"Fitted Regression Line")
plot.setDataset(1, regressionData)
renderer2 = XYLineAndShapeRenderer(True, False)
renderer2.setSeriesPaint(0, Color.blue)
plot.setRenderer(1, renderer2)
jfchart = JFreeChart(regdesc, JFreeChart.DEFAULT_TITLE_FONT, plot, True);
chart = Chart(jfchart)
chart.coeffs = coefficients
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 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 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 updateChartDataset(self, drawLabels=True):
dataset = XYSeriesCollection()
#self._calculateConfArea()
#dataset.addSeries(self.confArea)
dataset.addSeries(self.markers)
plot = self.chart.getPlot()
rangeAxis = plot.getRangeAxis()
domainAxis = plot.getDomainAxis()
rangeAxis.setRange(self.minY, self.maxY) #change
domainAxis.setRange(self.minX, self.maxX)
#plot.setBackgroundPaint(Color.lightGray);
#plot.setAxisOffset(new RectangleInsets(5.0, 5.0, 5.0, 5.0));
#plot.setDomainGridlinePaint(Color.white);
#plot.setRangeGridlinePaint(Color.white);
markerRenderer = XYLineAndShapeRenderer(False, True)
#print self.markerColor
markerRenderer.setSeriesPaint(0, self.markerColor)
markerRenderer.setSeriesShape(0, Ellipse2D.Double(-3, -3, 6, 6))
#markerRenderer.setToolTipGenerator(FDistToolTipGenerator(self.pointNames))
plot.setRenderer(0, markerRenderer)
plot.setDataset(0, dataset)
dataset = XYSeriesCollection()
if self.drawCI:
dataset = YIntervalSeriesCollection()
CIRenderer = DeviationRenderer(True, False)
# CIRenderer.setOutline(True)
# CIRenderer.setRoundXCoordinates(True)
dataset.addSeries(self.bottom)
dataset.addSeries(self.top)
dataset.addSeries(self.limit)
CIRenderer.setSeriesFillPaint(0, self.balColor)
CIRenderer.setSeriesFillPaint(1, self.neuColor)
CIRenderer.setSeriesFillPaint(2, self.posColor)
CIRenderer.setSeriesPaint(0, self.balColor)
CIRenderer.setSeriesPaint(1, self.neuColor)
CIRenderer.setSeriesPaint(2, self.posColor)
plot.setDataset(1, dataset)
plot.setRenderer(1, CIRenderer)
plot.setDataset(1, dataset)
if drawLabels:
self.drawLabels()
def updateChartDataset(self, drawLabels=True):
dataset = XYSeriesCollection()
#self._calculateConfArea()
#dataset.addSeries(self.confArea)
dataset.addSeries(self.markers)
plot = self.chart.getPlot()
rangeAxis = plot.getRangeAxis()
domainAxis = plot.getDomainAxis()
rangeAxis.setRange(self.minY, self.maxY) #change
domainAxis.setRange(0.0, self.maxX)
#plot.setBackgroundPaint(Color.lightGray);
#plot.setAxisOffset(new RectangleInsets(5.0, 5.0, 5.0, 5.0));
#plot.setDomainGridlinePaint(Color.white);
#plot.setRangeGridlinePaint(Color.white);
markerRenderer = XYLineAndShapeRenderer(False, True)
#print self.markerColor
markerRenderer.setSeriesPaint(0, self.markerColor)
markerRenderer.setSeriesShape(0, Ellipse2D.Double(-3, -3, 6, 6))
#markerRenderer.setToolTipGenerator(FDistToolTipGenerator(self.pointNames))
plot.setRenderer(0, markerRenderer)
plot.setDataset(0, dataset)
dataset = XYSeriesCollection()
if self.drawCI:
dataset = YIntervalSeriesCollection()
CIRenderer = DeviationRenderer(True, False)
#CIRenderer.setOutline(True)
#CIRenderer.setRoundXCoordinates(True)
dataset.addSeries(self.bottom)
dataset.addSeries(self.top)
dataset.addSeries(self.limit)
CIRenderer.setSeriesFillPaint(0, self.balColor)
CIRenderer.setSeriesFillPaint(1, self.neuColor)
CIRenderer.setSeriesFillPaint(2, self.posColor)
CIRenderer.setSeriesPaint(0, self.balColor)
CIRenderer.setSeriesPaint(1, self.neuColor)
CIRenderer.setSeriesPaint(2, self.posColor)
plot.setDataset(1, dataset)
plot.setRenderer(1, CIRenderer)
plot.setDataset(1, dataset)
if drawLabels: self.drawLabels()
def getDataSets(self, txName):
'''
For a given txNum name, return a set of JFreeChart datasets
with data on tx/sec, response times, and bandwidth.
'''
logger.debug("getting data sets for " + txName)
if self._txNameDatasets == None:
logger.warn("Building data sets.")
txSecDataset = None
# build 'em
self._txNameDatasets = {}
txNums = self._summaryData.getTxNumNameMap().keys()
for txNum in txNums:
logger.debug("DEBUG: building DS for " + txNum)
dataSetGroup = {}
txSecDataset = XYSeriesCollection(
) # not returning a new object
bandwidthDataSet = XYSeriesCollection()
simpleResponseTimeDataset = XYSeriesCollection()
responseTimeDataset = DefaultTableXYDataset()
txSecPassSeries = XYSeries("passed")
txSecFailSeries = XYSeries("failed")
responseTimeSeries = XYSeries("seconds")
finishTimeSeries = XYSeries("complete", True, False)
resolveHostSeries = XYSeries("resolveHost", True, False)
connectSeries = XYSeries("connect", True, False)
firstByteSeries = XYSeries("firstByte", True, False)
bandwidthSeries = XYSeries("KB/sec")
for bucket in self.bucketList:
txSecPass = bucket.getTxSecPass(txNum)
txSecPassSeries.add(bucket.getStartTime() / 1000.0,
txSecPass)
txSecFail = bucket.getTxSecFail(txNum)
txSecFailSeries.add(bucket.getStartTime() / 1000.0,
txSecFail)
responseTimeSeries.add(bucket.getStartTime() / 1000.0,
bucket.getMeanResponseTime(txNum))
if ga.constants.VORPAL.getPlugin("analyzer").isHTTP():
bandwidthSeries.add(
bucket.getStartTime() / 1000.0,
bucket.getMeanThroughputKBSec(txNum))
finishTimeSeries.add(bucket.getStartTime() / 1000.0,
bucket.getMeanFinishTime(txNum))
resolveHostSeries.add(
bucket.getStartTime() / 1000.0,
bucket.getMeanResolveHostTime(txNum))
connectSeries.add(bucket.getStartTime() / 1000.0,
bucket.getMeanConnectTime(txNum))
firstByteSeries.add(bucket.getStartTime() / 1000.0,
bucket.getMeanFirstByteTime(txNum))
txSecDataset.addSeries(txSecPassSeries)
txSecDataset.addSeries(txSecFailSeries)
responseTimeDataset.addSeries(resolveHostSeries)
responseTimeDataset.addSeries(connectSeries)
responseTimeDataset.addSeries(firstByteSeries)
responseTimeDataset.addSeries(finishTimeSeries)
simpleResponseTimeDataset.addSeries(responseTimeSeries)
bandwidthDataSet.addSeries(bandwidthSeries)
dataSetGroup[TX_SEC_KEY] = txSecDataset
dataSetGroup[FULL_RESPONSE_TIME_KEY] = responseTimeDataset
dataSetGroup[THROUGHPUT_KEY] = bandwidthDataSet
dataSetGroup[
SIMPLE_RESPONSE_TIME_KEY] = simpleResponseTimeDataset
self._txNameDatasets[txNum] = dataSetGroup
logger.debug("DEBUG: done building data sets.")
return self._txNameDatasets[txName]
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
