gac-x04sa
34 lines
1.2 KiB
Python
34 lines
1.2 KiB
Python
import mathutils
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import plotutils
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#A area scan (2d) on x & y
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ascan([scx, scy], [scd,], [scx.minValue,scy.minValue], [scx.maxValue,scy.maxValue], [0.1, 0.1], latency = 0.0, relative = False, zigzag = True)
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# A line scn (1d) on Y
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scy.move(0.0)
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#r = lscan(scx, scd, scx.minValue, scx.maxValue, 0.1, latency = 0.0)
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xdata = r.getPositions(0)
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ydata = r.getReadable(0)
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maxy=max(ydata)
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maxx=ydata.indexOf(maxy)
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print "maxx =" , maxx
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print "maxy =" , maxy
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#Fitting a gaussian on the 1d scan
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(normalization, mean_val, sigma) = mathutils.fit_gaussian(ydata, xdata)
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print "fit mean = ", mean_val
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#Plotting the gaussian
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gaussian = mathutils.Gaussian(normalization, mean_val, sigma)
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plotutils.plot_function(get_plots(None)[0], gaussian, "Fit", frange(scx.minValue, scx.maxValue, 0.01))
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#Binary search for maximum
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r = bsearch([scx, scy], scd, [scx.minValue,scy.minValue], [scx.maxValue,scy.maxValue], [0.01, 0.01], maximum=True, strategy = "FullNeighborhood", latency = 0.01, title = "Binary Search")
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#Hill-climbing search for maximum
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r = hsearch([scx, scy], scd, [scx.minValue,scy.minValue], [scx.maxValue,scy.maxValue], [0.2, 0.2], [0.01, 0.01], noise_filtering_steps = 3, relative = False, maximum=True, latency = 0.01, title = "Hill Climbing")
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