def Gs(i, j): return lambda: random.gauss(Qs[0][i, j], math.sqrt(Qs[1][i, j])) import Pymatr.synthesis as Syn from Pymatr.utils import numerical L = red.dEigen nsyn = 100 Gen = Syn.MatrixRngOpt(numerical(A), numerical(E / L), Gs, nsyn) def average(): s = sum(Gen()) av = s / nsyn # print(" \n sum {}, average: {}\n".format(r, av) ) return av lln = red.lln() import Pymatr.byPieces as Bp Bp.plot(lln) import Pymatr.histogram as H nsample = 1000 H.plot(nsample, average) import matplotlib.pyplot as plt #plt.xticks( [ Q1r[0].evalf() ], ["µ"] ) plt.show()
return lambda : random.gauss( Qs[0][i,j], math.sqrt(Qs[1][i,j]) ) import Pymatr.synthesis as Syn from Pymatr.utils import numerical L= red.dEigen nsyn=100 Gen = Syn.MatrixRngOpt(numerical(A),numerical(E/L), Gs, nsyn) def average(): s= sum(Gen()) av= s /nsyn # print(" \n sum {}, average: {}\n".format(r, av) ) return av lln=red.lln() import Pymatr.byPieces as Bp Bp.plot(lln) import Pymatr.histogram as H nsample=1000 H.plot(nsample, average) import matplotlib.pyplot as plt #plt.xticks( [ Q1r[0].evalf() ], ["µ"] ) plt.show()