script/tutorial/61_MultipleGaussianFit.js

@@ -0,0 +1,43 @@
+///////////////////////////////////////////////////////////////////////////////////////////////////
+// Multiple Gaussians peak search with mathutils.py
+/////////////////////////////////////////////////////////////////////////////////////////////////// 
+
+
+run("mathutils")
+
+start = 0
+end = 50
+step_size = 0.2
+
+result= lscan(ao1,ai1,start,end,[step_size,])
+
+readable = result.getReadable(0)
+positions = result.getPositions(0)
+
+min = Math.min.apply(null, readable) 
+max = Math.max.apply(null, readable) 
+
+threshold = (min + max)/2
+min_peak_distance = 5.0
+
+peaks = estimate_peak_indexes(readable, positions, threshold, min_peak_distance)
+print ("Peak indexes: " + peaks)
+print ("Peak x: " + peaks.map(function(x) {return positions[x]}))
+print ("Peak y: " + peaks.map(function(x) {return readable[x]}))
+
+gaussians = fit_gaussians(readable, positions, peaks)
+
+plots = plot([readable],["sin"],[positions], undefined, title="Data" )
+for (var i=0; i< peaks.length; i++){
+    peak = peaks[i]    
+    pars_gaussian = gaussians[i]
+    normalization = pars_gaussian[0]
+    mean_val = pars_gaussian[1]
+    sigma = pars_gaussian [2]    
+    if (Math.abs(mean_val - positions[peak]) < min_peak_distance){
+        print ("Peak -> " + mean_val)
+        plots[0].addMarker(mean_val, null, "N="+ Math.round(normalization,2), new Color(210,0,0))
+    }else {
+        print ("Invalid gaussian fit: " + mean_val)
+    }
+}