diff --git a/script/fit.py b/script/fit.py
new file mode 100644
index 0000000..e957c64
--- /dev/null
+++ b/script/fit.py
@@ -0,0 +1,40 @@
+from mathutils import estimate_peak_indexes, fit_gaussians, create_fit_point_list, Gaussian
+import java.awt.Color as Color
+
+def fit(ydata, xdata = None):
+    if xdata is None:
+        xdata = frange(0, len(ydata), 1)
+    max_y= max(ydata)    
+    index_max = ydata.index(max_y)    
+    max_x= xdata[index_max]
+    print "Max index:" + str(index_max),
+    print " x:" + str(max_x),
+    print " y:" + str(max_y)
+    
+    gaussians = fit_gaussians(ydata, xdata, [index_max,])
+    (norm, mean, sigma) = gaussians[0]
+
+    p = plot([ydata],["data"],[xdata], context="Fit" )[0] 
+    fitted_gaussian_function = Gaussian(norm, mean, sigma)    
+    scale_x = [float(min(xdata)), float(max(xdata)) ]
+    points = max((len(xdata)+1), 100)
+    resolution = (scale_x[1]-scale_x[0]) / points    
+    fit_y = []
+    fit_x = frange(scale_x[0],scale_x[1],resolution, True)
+    for x in fit_x:
+        fit_y.append(fitted_gaussian_function.value(x))    
+    p.addSeries(LinePlotSeries("fit"))
+    p.getSeries(1).setData(fit_x, fit_y)    
+
+    print "---"
+    print mean, xdata[index_max], abs(mean - xdata[index_max])
+    print scale_x, (scale_x[0] + scale_x[1])/2, abs((scale_x[0] + scale_x[1])/2)
+    print abs(mean - xdata[index_max]) < abs((scale_x[0] + scale_x[1])/2)
+    if abs(mean - xdata[index_max]) < abs((scale_x[0] + scale_x[1])/2):
+        print "Mean -> " +  str(mean)
+        p.addMarker(mean, None, "Mean="+str(round(mean,4)), Color.MAGENTA.darker())
+        return (norm, mean, sigma)
+    else:
+        p.addMarker(max_x, None, "Max="+str(round(max_x,4)), Color.GRAY)
+        print "Invalid gaussian fit: " +  str(mean)
+        return (None, None, None)