# convert to java array
jxa = jarray.array(xa, 'd')
jya = jarray.array(ya, 'd')
# construct a CurveFitter instance
cf = CurveFitter(jxa, jya)
# actual fitting
# fit models:
# see http://rsb.info.nih.gov/ij/developer/api/constant-values.html#ij.measure.CurveFitter.STRAIGHT_LINE
cf.doFit(CurveFitter.STRAIGHT_LINE)
#print out fitted parameters.
print(cf.getParams()[0], cf.getParams()[1])
print(cf.getFormula())
plt = cf.getPlot()
plt.draw()
left = 0
right = 5
bottom = 0
top = 5
nPoints = 4
rng = right - left
# Create filled plot
plt = Plot("Line plot", "X", "Y")
plt.setLimits(left, right, bottom, top)
plt.setFrameSize(600, 300)
# Calculate normalized curve
normalized_curve = []
for i in range(n_slices):
normalized_curve.append( (If[i] - min_intensity) / (mean_If - min_intensity) * mean_In / In[i] )
x = [i * frame_interval for i in range( n_slices ) ]
y = normalized_curve
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)
cv = CurveFitter([a[0] for a in spshifts],
[8 - a[1] for a in spshifts])
# fit the distortion measurements
cv.doFit(CurveFitter.EXP_WITH_OFFSET)
# cv.doFit(CurveFitter.EXPONENTIAL)
# cv.doFit(CurveFitter.POLY2)
# cv.doFit(CurveFitter.POLY3)
plot = cv.getPlot()
plot.show()
8 / 0
plot_images.append(plot.getImagePlus())
IJ.log('sFOV ' + str(sFOV))
IJ.log('fitGoodness ' + str(cv.getFitGoodness()))
IJ.log('formula ' + str(cv.getFormula()))
fit_params = cv.getParams()
IJ.log('fit_params' + str(fit_params))
# write the fit results to file
g.write('sFOV\t' + str(sFOV) + '\t' + 'a\t' + str(fit_params[0]) +
'\t' + 'b\t' + str(fit_params[1]) + '\t' + 'c\t' +
str(fit_params[2]) + '\t' + 'formula\t' +
str(cv.getFormula()) + '\t' + 'fitGoodness\t' +
str(cv.getFitGoodness()) + '\n')
# # # # # The following commented code applies a scan correction using bigwarp.
# # # # # A grid of landmarks is generated, saved into bigwarp format, then applied.
# # # # # It is slow and needs to be optimized.
# # # # create mesh
