def testMonteCarlo1(self):
print("====== MonteCarlo 1 ===================")
N = 100
ran = numpy.random
ran.seed(12345)
noise = ran.standard_normal(N)
x = numpy.arange(N, dtype=float) - 3
nn = 0.1
for k in range(5):
y = noise * nn
m = PolynomialModel(0)
ftr = Fitter(x, m)
par = ftr.fit(y)
std = ftr.getStandardDeviations()
chisq = ftr.chisq
mc = MonteCarlo(x, m, ftr.covariance)
mc.mcycles = 1000
lmce = ftr.monteCarloError(monteCarlo=mc)
print("noise : ", fmt(nn),
"===========================================")
print("params : ", fmt(par, format="%8.5f"))
print("stdevs : ", fmt(std, format="%8.5f"))
print("scale : ", fmt(ftr.scale, format="%8.5f"), fmt(nn))
print("chisq : ", fmt(chisq, format="%8.5f"),
fmt(mc._eigenvalues, format="%8.5f"),
fmt(mc._eigenvectors, format="%8.5f"))
print("covar : ", fmt(ftr.covariance, format="%8.5f"))
print("mcerr : ", fmt(lmce[0], format="%8.5f"))
self.assertTrue(abs(std[0] - lmce[0]) < 0.1 * std[0])
self.assertTrue(par[0] < 0.05 * nn)
nn *= 10
def testMonteCarlo2(self, doplot=False):
print("====== MonteCarlo 2 ===================")
x = numpy.arange(7, dtype=float) - 3
y = numpy.asarray([-1, -1, -1, 0, 1, 1, 1], dtype=float)
m = PolynomialModel(1)
ftr = Fitter(x, m)
par = ftr.fit(y)
std = ftr.getStandardDeviations()
yfit = m.result(x)
chisq = ftr.chisq
hes = ftr.hessian
# mc = MonteCarlo( x, m, chisq, hessian=hes )
mc = MonteCarlo(x, m, ftr.covariance)
mc.mcycles = 1000
lmce = ftr.monteCarloError(monteCarlo=mc)
print("params : ", par)
print("stdevs : ", std)
print("scale : ", ftr.getScale())
print("chisq : ", chisq)
print("evals : ", mc._eigenvalues)
print(mc._eigenvectors)
print("hessi :\n", ftr.hessian)
print("covar :\n", ftr.covariance)
print("mcerr : ", lmce)
numpy.testing.assert_array_almost_equal(
par, numpy.asarray([0.0, 0.42857142857142855]))
# numpy.testing.assert_array_almost_equal( std, numpy.asarray([0.1564921592871903,0.07824607964359515]) )
self.assertAlmostEqual(chisq, 0.857142857143)
if doplot:
pyplot.plot(x, y, 'k*')
pyplot.plot(x, yfit, 'g-')
pyplot.plot(x, yfit + lmce, 'r-')
pyplot.plot(x, yfit - lmce, 'r-')
pyplot.show()
