cf = CurveFitter(exposures, list(levels))
cf.doFit(CurveFitter.STRAIGHT_LINE)
fitParams = cf.getParams()
slope = fitParams[1]
intercept = fitParams[0]
rSqr = cf.getRSquared()
print("slope=", slope, " ; intercept=", intercept, " ; rSquared=", rSqr)
# Plot the data and the regression line
newPlotFlags = Plot.TRIANGLE + Plot.X_GRID + Plot.X_NUMBERS + Plot.Y_GRID + Plot.Y_NUMBERS
newPlot = Plot("DARK NOISE", "EXPOSURE, ms", "MEAN GRAY LEVEL", newPlotFlags)
newPlot.setLineWidth(2)
newPlot.setColor("red")
newPlot.add("triangle", exposures, list(levels))
newPlot.setLineWidth(1)
newPlot.setColor("black")
newPlot.drawLine(exposures[0], cf.f(exposures[0]), exposures[-1],
cf.f(exposures[-1]))
newPlot.setColor("blue")
newPlot.setFontSize(20)
newPlot.addText("y = a+bx", 100.0, 13000.0)
newPlot.addText("a = " + str(round(intercept, 2)), 100.0, 12250.0)
newPlot.addText("b = " + str(round(slope, 2)), 100.0, 11500.0)
newPlot.addText("R squared = " + str(round(rSqr, 3)), 100.0, 10750.0)
newPlot.show()
# Place the plot data into a ResultsTable
rt = newPlot.getResultsTable()
rt.show("Dark Noise Results")
