def test_lnpost(self):
data = (self.xvals, self.yvals, self.evals)
f = _parallel_likelihood_calculator(gauss,
data_tuple=data)
lnprob = f(self.params)
def lnpost(pars, generative, y, e):
resid = y - generative
resid /= e
resid *= resid
resid += np.log(2 * np.pi * e**2)
return -0.5 * np.sum(resid)
g = _parallel_likelihood_calculator(gauss,
data_tuple=data,
lnpost=lnpost)
lnprob2 = g(self.params)
assert_equal(lnprob2, lnprob)
pars_copy = deepcopy(self.params)
f = CurveFitter(gauss,
data,
self.params)
res = f.emcee(steps=10, burn=0, thin=1, seed=1)
g = CurveFitter(gauss,
data,
pars_copy,
lnpost=lnpost)
res2 = g.emcee(steps=10, burn=0, thin=1, seed=1)
assert_almost_equal(values(res.params), values(res2.params))
def test_emcee_vs_lm(self):
# test mcmc output vs lm
if not HAS_EMCEE:
return True
f = CurveFitter(gauss, self.xvals, self.yvals, self.params,
edata=self.evals)
np.random.seed(123456)
# start = time.time()
out = f.emcee(nwalkers=100, steps=300, burn=100, thin=10)
# finish = time.time()
# print(finish - start)
within_sigma(self.best_weighted, out.params)
# test mcmc output vs lm, some parameters not bounded
self.params['p1'].max = None
f = CurveFitter(gauss, self.xvals, self.yvals, self.params,
edata=self.evals)
np.random.seed(123456)
f.emcee(nwalkers=100, steps=300, burn=100, thin=5)
within_sigma(self.best_weighted, out.params)
# test mcmc output vs lm, some parameters not bounded
self.params['p1'].min = None
f = CurveFitter(gauss, self.xvals, self.yvals, self.params,
edata=self.evals)
f.emcee(nwalkers=100, steps=300, burn=100, thin=5)
within_sigma(self.best_weighted, out.params)
def test_reflectivity_emcee(self):
transform = reflect.Transform('logY')
yt, et = transform.transform(self.qvals361,
self.rvals361,
self.evals361)
kws = {'transform': transform.transform}
fitfunc = RFF(transform=transform.transform, dq=5.)
fitter = CurveFitter(fitfunc,
(self.qvals361, yt, et),
self.params361,
fcn_kws=kws)
res = fitter.fit()
res_em = fitter.emcee(steps=10)
def test_emcee_vs_lm(self):
# test mcmc output vs lm
f = CurveFitter(gauss,
(self.xvals, self.yvals, self.evals),
self.params)
np.random.seed(123456)
out = f.emcee(nwalkers=100, steps=500, burn=250, thin=20)
within_sigma(self.best_weighted, out.params)
# test if the sigmas are similar as well (within 20 %)
errs = np.array([out.params[par].stderr for par in out.params])
assert_allclose(errs, self.best_weighted_errors, rtol=0.2)
# now try with resampling MC
out = f._resample_mc(500, params=self.params, method='leastsq')
within_sigma(self.best_weighted, out.params)
# test if the sigmas are similar as well (within 20 %)
errs = np.array([out.params[par].stderr for par in out.params])
assert_allclose(errs, self.best_weighted_errors, rtol=0.2)
# test mcmc output vs lm, some parameters not bounded
self.params['p1'].max = np.inf
f = CurveFitter(gauss,
(self.xvals, self.yvals, self.evals),
self.params)
np.random.seed(123456)
f.emcee(nwalkers=100, steps=300, burn=100, thin=5)
within_sigma(self.best_weighted, out.params)
# test mcmc output vs lm, some parameters not bounded
self.params['p1'].min = -np.inf
f = CurveFitter(gauss,
(self.xvals, self.yvals, self.evals),
self.params)
f.emcee(nwalkers=100, steps=300, burn=100, thin=5)
within_sigma(self.best_weighted, out.params)
def test_reflectivity_emcee(self):
transform = reflect.Transform('logY')
yt, et = transform.transform(self.qvals361,
self.rvals361,
self.evals361)
kws = {'transform': transform.transform}
fitfunc = RFF(transform=transform.transform, dq=5.)
fitter = CurveFitter(fitfunc,
(self.qvals361, yt, et),
self.params361,
fcn_kws=kws)
res = fitter.fit()
res_em = fitter.emcee(steps=10, seed=1)
assert_allclose(values(res.params), values(res_em.params), rtol=1e-2)
