| 1 | import numpy as np |
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| 2 | |
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| 3 | def noise(n): |
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| 4 | return ( np.random.random(n) - 0.5 ) / 10 |
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| 5 | |
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| 6 | ws = CreateWorkspace('-10,10,-10,10,-10,10','0,0,0,0,0,0',NSpec=3) |
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| 7 | ws = Rebin('ws','-10,0.01,10') |
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| 8 | n = ws.blocksize() |
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| 9 | x = ws.readX(0) |
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| 10 | ws.setY(0, np.exp( -(x+1.0)**2/0.99 ) * 1.1 + noise(n) ) |
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| 11 | ws.setY(1, np.exp( -(x)**2 ) + noise(n) ) |
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| 12 | ws.setY(2, np.exp( -(x-1.0)**2/1.01 ) * 0.9 + noise(n) ) |
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| 13 | |
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| 14 | fun = 'name=Gaussian,$domains=i,Height=1,Sigma=1,PeakCentre=0' |
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| 15 | muli_domain = 'composite=MultiDomainFunction;(%s);(%s);(%s);ties=(f2.Sigma=f1.Sigma=f0.Sigma)' % (fun,fun,fun) |
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| 16 | |
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| 17 | kwargs = {\ |
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| 18 | 'WorkspaceIndex': 0, |
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| 19 | 'InputWorkspace_1':'ws', |
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| 20 | 'WorkspaceIndex_1': 1, |
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| 21 | 'InputWorkspace_2':'ws', |
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| 22 | 'WorkspaceIndex_2': 2, |
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| 23 | } |
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| 24 | |
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| 25 | Fit(muli_domain,'ws',Output='out',**kwargs) |
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