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()