def test_globfit_modelvals_same_as_individual(self): # make sure that the global fit would return the same model values as # the individual fitobject values = self.f.model(self.params) a = GlobalFitter([self.f]) values2 = a.model(a.params) assert_almost_equal(values2, values)
def test_multipledataset_corefinement(self): # test corefinement of three datasets e361 = np.loadtxt(os.path.join(CURDIR, 'e361r.txt')) e365 = np.loadtxt(os.path.join(CURDIR, 'e365r.txt')) e366 = np.loadtxt(os.path.join(CURDIR, 'e366r.txt')) coefs361 = np.zeros(16) coefs361[0] = 2 coefs361[1] = 1. coefs361[2] = 2.07 coefs361[4] = 6.36 coefs361[6] = 2e-5 coefs361[7] = 3 coefs361[8] = 10 coefs361[9] = 3.47 coefs361[11] = 4 coefs361[12] = 200 coefs361[13] = 1 coefs361[15] = 3 coefs365 = np.copy(coefs361) coefs366 = np.copy(coefs361) coefs365[4] = 3.47 coefs366[4] = -0.56 qvals361, rvals361, evals361 = np.hsplit(e361, 3) qvals365, rvals365, evals365 = np.hsplit(e365, 3) qvals366, rvals366, evals366 = np.hsplit(e366, 3) lowlim = np.zeros(16) lowlim[4] = -0.8 hilim = 2 * coefs361 bounds = list(zip(lowlim, hilim)) params361 = curvefitter.to_parameters(coefs361, bounds=bounds, varies=[False] * 16) params365 = curvefitter.to_parameters(coefs365, bounds=bounds, varies=[False] * 16) params366 = curvefitter.to_parameters(coefs366, bounds=bounds, varies=[False] * 16) assert_(len(params361), 16) assert_(len(params365), 16) assert_(len(params366), 16) fit = [1, 6, 8, 12, 13] for p in fit: params361['p%d' % p].vary = True params365['p%d' % p].vary = True params366['p%d' % p].vary = True a = CurveFitter(reflect_fitfunc, (qvals361.flatten(), np.log10(rvals361.flatten())), params361) b = CurveFitter(reflect_fitfunc, (qvals365.flatten(), np.log10(rvals365.flatten())), params365) c = CurveFitter(reflect_fitfunc, (qvals366.flatten(), np.log10(rvals366.flatten())), params366) g = GlobalFitter([a, b, c], constraints=['d1:p8=d0:p8', 'd2:p8=d0:p8', 'd1:p12=d0:p12', 'd2:p12 = d0:p12'], kws={'seed': 1}) indiv_chisqr = (a.residuals(a.params) ** 2 + b.residuals(b.params) ** 2 + c.residuals(c.params) ** 2) global_chisqr = g.residuals(g.params) ** 2 assert_almost_equal(indiv_chisqr.sum(), global_chisqr.sum()) # import time res = g.fit('differential_evolution') # start = time.time() # g.emcee(params=res.params, nwalkers=300, steps=500, workers=1) # finish = time.time() # print(finish - start) assert_almost_equal(res.chisqr, 0.774590447535, 4) # updating of constraints should happen during the fit assert_almost_equal(a.params['p12'].value, res.params['p12_d0'].value) assert_almost_equal(b.params['p12'].value, a.params['p12'].value) assert_almost_equal(c.params['p12'].value, a.params['p12'].value) g.params['p8_d0'].value = 10.123456 # shouldn't need to call update constraints within the gfitter, that # happens when you retrieve a specific value assert_almost_equal(g.params['p8_d1'].value, g.params['p8_d0'].value) # However, you have to call model or residuals to redistribute the # parameters to the original fitters g.model() assert_almost_equal(a.params['p8'].value, 10.123456) assert_almost_equal(b.params['p8'].value, 10.123456)