def fit_gauss(lroi, imp, p, peak_id, id_, type_, rm):
lroi.setName("{}_{}_{}".format(str(id_), peak_id, type_))
imp.setRoi(lroi)
rm.addRoi(lroi)
prof = ProfilePlot(imp)
y = prof.getProfile()
x = xrange(len(y))
fitter = CurveFitter(x, y)
fitter.doFit(CurveFitter.GAUSSIAN)
param_values = fitter.getParams()
std = param_values[3]
fwhm = 2.3548 * std
r2 = fitter.getFitGoodness()
y_ = [fitter.f(x_) for x_ in x]
area_profile = sum(y) - len(y) *min(y)
area_gauss = sum(y_) - len(y_)*min(y_)
output = {}
output["x_pos"] = p.x
output["y_pos"] = p.y
output["fwhm"] = fwhm
output["fwhm_nm"] = pixel_size_nm * fwhm
output["r2_GoF"] = r2
output["id"] = id_
output["peak_id"] = peak_id
output["type"] = type_
# yai, excel maagic :-)
output["avg_fwhm"] = '=AVERAGEIFS(F:F,B:B,B{},F:F,"<>"&"")'.format(id_+2)
output["area_profile"] = area_profile
output["area_gauss"] = area_gauss
if peak_id == DEBUG:
plot = Plot("ROI peak {} type {}".format(peak_id, type_), "X (gray)", "Y (fit window)")
plot.setLineWidth(2)
plot.setColor(Color.RED)
plot.addPoints(x, y, Plot.LINE)
plot.setColor(Color.BLUE)
plot.addPoints(x, y_, Plot.LINE)
plot.show()
return output
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")
# http://hyperphysics.phy-astr.gsu.edu/hbase/math/gaufcn2.html
import ij.measure.CurveFitter
imp = IJ.getImage()
prof = ProfilePlot(imp)
curprofile = prof.getProfile()
print(len(curprofile));
#for i in curprofile:
# print(i)
pixx = range(len(curprofile))
fitA = range(len(curprofile))
cf = CurveFitter(pixx, curprofile)
cf.doFit(cf.GAUSSIAN)
#print(cf.getFormula())
print(cf.getResultString())
cfp = cf.getParams()
EstimatedDiameter = "Diameter = " + str(2.355*cfp[3])
IJ.log(EstimatedDiameter)
for i in range(len(curprofile)):
fitA[i] = cf.f(cfp, i)
fitplot = Plot("fitted", "pixels", "intensity", pixx, fitA)
fitplot.addPoints(pixx, curprofile, fitplot.CIRCLE)
fitplot.addLabel(0.1, 0.1, EstimatedDiameter)
fitplot.show()
xtofit = [ i * frame_interval for i in range( n_slices - bleach_frame ) ]
ytofit = normalized_curve[ bleach_frame : n_slices ]
# Fitter
fitter = CurveFitter(xtofit, ytofit)
fitter.doFit(CurveFitter.EXP_RECOVERY_NOOFFSET)
IJ.log("Fit FRAP curve by " + fitter.getFormula() )
param_values = fitter.getParams()
IJ.log( fitter.getResultString() )
# Overlay fit curve, with oversampling (for plot)
xfit = [ (t / 10.0 + bleach_frame) * frame_interval for t in range(10 * len(xtofit) ) ]
yfit = []
for xt in xfit:
yfit.append( fitter.f( fitter.getParams(), xt - xfit[0]) )
plot = Plot("Normalized FRAP curve for " + current_imp.getTitle(), "Time ("+time_units+')', "NU", [], [])
plot.setLimits(0, max(x), 0, 1.5 );
plot.setLineWidth(2)
plot.setColor(Color.BLACK)
plot.addPoints(x, y, Plot.LINE)
plot.addPoints(x,y,PlotWindow.X);
plot.setColor(Color.RED)
plot.addPoints(xfit, yfit, Plot.LINE)
