Startup

script/GaussianFit.py

@@ -0,0 +1,40 @@
+"""
+Multi-peak search and gaussian fitting
+""" 
+
+from mathutils import estimate_peak_indexes, fit_gaussians, create_fit_point_list
+
+start = 0 
+end = 30
+step_size = 0.2
+
+result= lscan(sout,sinp,start,end,[step_size,],0.05)
+
+readable = result.getReadable(0)
+positions = result.getPositions(0)
+
+threshold = (min(readable) + max(readable))/2
+min_peak_distance = 5.0
+
+peaks = estimate_peak_indexes(readable, positions, threshold, min_peak_distance)
+print "Peak indexes: " + str(peaks)
+print "Peak x: " + str(map(lambda x:positions[x], peaks))
+print "Peak y: " + str(map(lambda x:readable[x], peaks))
+
+
+
+gaussians = fit_gaussians(readable, positions, peaks)
+
+
+plots = plot([readable],["Multi-peak search"],[positions] )
+for i in range(len(peaks)):
+    peak = peaks[i]
+    (norm, mean, sigma) = gaussians[i]
+    if abs(mean - positions[peak]) < min_peak_distance:
+        print "Peak -> " +  str(mean)
+        plots[0].addMarker(mean, None, "N="+str(round(norm,2)), None)
+    else:
+        print "Invalid gaussian fit: " +  str(mean)
+
+
+

script/local.py

@@ -0,0 +1,26 @@
+import random
+
+
+class SimulatedOutput(Writable):
+    def getName(self):
+        return "SimulatedOutput"
+
+    def write(self, value):
+        pass
+
+
+class SimulatedInput(Readable):
+    def __init__(self):
+        self.x = 0.0
+
+    def getName(self):
+        return "SimulatedInput"
+
+    def read(self):
+        self.x = self.x + 0.2
+        noise = (random.random() - 0.5) / 20.0
+        return math.sin(self.x)  + noise
+
+
+sout = SimulatedOutput()
+sinp = SimulatedInput()