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- from lmfit import minimize, Parameters
- import numpy
- station1 = numpy.array((779.000,818.390,-100,148.977))
- station2 = numpy.array((876.010,921.300,-110,99.610))
- station3 = numpy.array((978.910,824.290,-120,62.104))
- station4 = numpy.array((881.910,721.380,-130,130.883))
- #station5 = numpy.array((881.910,721.380,-130,130.883))
- stationarray = numpy.array((station1,station2,station3,station4))
- # position = 926.349 , 837.748 , 89.794
- #ranges = numpy.array((148.977,99.610,62.104,130.883))
- def residualfunct(params, stationarray):
- X = params['solutionX'].value
- Y = params['solutionY'].value
- Z = params['solutionZ'].value
- result = numpy.array([s[3] - linalg.norm(array((X,Y,Z))-array((s[0],s[1],s[2]))) for s in stationarray])
- print result
- return result
- params = Parameters()
- params.add('solutionX',value=0)
- params.add('solutionY',value=0)
- params.add('solutionZ',value=0)
- position = minimize(residualfunct, params, args=(stationarray))
- print position
- print report
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