def test_chained_lsqfit_p0(self):
" MultiFitter.chained_lsqfit(...) "
# sequential fit
fitter = MultiFitter(models=self.make_models(ncg=1))
p0 = gv.BufferDict({'a': 0.9991638707908023, 'b': 0.4995927960301173})
p0list = [
p0,
gv.BufferDict(a=0.9991638707908023),
gv.BufferDict(a=0.9991638707908023)
]
fit1 = fitter.chained_lsqfit(data=self.data, prior=self.prior, p0=p0)
self.assertTrue(self.agree_ref(fit1.p))
self.assertEqual(list(fit1.chained_fits.keys()), ['l', 'c1', 'c2'])
self.assertEqual(fit1.p0, p0list)
fit1 = fitter.chained_lsqfit(data=self.data,
prior=self.prior,
p0=3 * [p0])
self.assertTrue(self.agree_ref(fit1.p))
self.assertEqual(list(fit1.chained_fits.keys()), ['l', 'c1', 'c2'])
self.assertEqual(fit1.p0, p0list)
fn = 'test_multifitter.p'
fit1 = fitter.chained_lsqfit(data=self.data, prior=self.prior, p0=fn)
fit2 = fitter.chained_lsqfit(data=self.data, prior=self.prior, p0=fn)
self.assertTrue(self.agree_ref(fit1.p))
self.assertEqual(list(fit1.chained_fits.keys()), ['l', 'c1', 'c2'])
self.assertEqual([f.pmean for f in fit1.chained_fits.values()],
fit2.p0)
os.unlink(fn)
def test_chained_lsqfit(self):
" MultiFitter.chained_lsqfit(models=[m1, m2, m3], ...) "
# sequential fit
fitter = MultiFitter(models=self.make_models(ncg=1))
fit1 = fitter.chained_lsqfit(data=self.data, prior=self.prior)
self.assertEqual(str(fit1.p), "{'a': 0.99929(48),'b': 0.50004(81)}")
self.assertEqual(list(fit1.chained_fits.keys()), ['l', 'c1', 'c2'])
# with coarse grain, marginalization and extend, and with fast=False
prior = gv.BufferDict([
('log(a)', gv.log(self.prior['a'])),
('b', self.prior['b']),
])
prior['log(aa)'] = prior['log(a)'] + gv.gvar('0(1)') * 1e-6
fitter = MultiFitter(models=self.make_models(ncg=2), fast=False)
fit2 = fitter.chained_lsqfit(data=self.data,
prior=prior,
mopt=True,
extend=True)
self.assertEqual(
str(fit2.p),
"{'log(a)': -0.00073(48),'b': 0.50015(82),"
"'log(aa)': -0.00073(48),'a': 0.99927(48),"
"'aa': 0.99927(48)}",
)
def test_chained_fit_kargs(self):
" MultiFitter(models=[m1, dict(...), m2, ...]) "
models = self.make_models(ncg=1)
models = [models[2], dict(mopt=True), models[1], models[0]]
fitter = MultiFitter(models=models, mopt=None)
fit = fitter.chained_lsqfit(data=self.data,
prior=self.prior,
fast=True,
wavg_all=False)
self.assertTrue(fit.p['b'] is self.prior['b'])
self.assertEqual(fitter.mopt, None)
fitter = MultiFitter(models=fitter.flatten_models(models), mopt=None)
fit = fitter.chained_lsqfit(data=self.data,
prior=self.prior,
fast=True)
self.assertTrue(fit.p['b'] is not self.prior['b'])
def test_chained_fit_simul(self):
" MultiFitter(models=[m1, (m2,m3)], ...) "
models = self.make_models(ncg=1)
models = [models[0], tuple(models[1:])]
fitter = MultiFitter(models=models)
fit3 = fitter.chained_lsqfit(data=self.data, prior=self.prior)
self.assertTrue(self.agree_ref(fit3.p))
self.assertEqual(list(fit3.chained_fits.keys()), ['l', '(c1,c2)'])
# with coarse grain, marginalization and extend
models = self.make_models(ncg=2)
models = [models[0], tuple(models[1:])]
prior = gv.BufferDict([('log(a)', gv.log(self.prior['a'])),
('b', self.prior['b'])])
fitter = MultiFitter(models=self.make_models(ncg=2))
fit4 = fitter.chained_lsqfit(data=self.data, prior=prior, mopt=True)
self.assertTrue(self.agree_ref(fit4.p))
def test_dump_chained_lsqfit(self):
" MultiFitter.chained_lsqfit(...) "
# sequential fit
fitter = MultiFitter(models=self.make_models(ncg=1))
fit1 = gv.loads(
gv.dumps(fitter.chained_lsqfit(data=self.data, prior=self.prior)))
self.assertTrue(self.agree_ref(fit1.p))
self.assertEqual(list(fit1.chained_fits.keys()), ['l', 'c1', 'c2'])
def test_chained_lsqfit(self):
" MultiFitter.chained_lsqfit(...) "
# sequential fit
fitter = MultiFitter(models=self.make_models(ncg=1))
fit1 = fitter.chained_lsqfit(data=self.data, prior=self.prior)
self.assertTrue(self.agree_ref(fit1.p))
self.assertEqual(list(fit1.chained_fits.keys()), ['l', 'c1', 'c2'])
# with coarse grain, marginalization and extend, and with fast=False
prior = gv.BufferDict([
('log(a)', gv.log(self.prior['a'])),
('b', self.prior['b']),
])
prior['log(aa)'] = prior['log(a)'] + gv.gvar('0(1)') * 1e-6
fitter = MultiFitter(models=self.make_models(ncg=2), fast=False)
fit2 = fitter.chained_lsqfit(data=self.data, prior=prior, mopt=True)
self.assertTrue(self.agree_ref(fit2.p))
def test_bootstrap_chained_lsqfit(self):
fitter = MultiFitter(models=self.make_models(ncg=1))
fit = fitter.chained_lsqfit(data=self.data, prior=self.prior)
datalist = gv.bootstrap_iter(self.data, n=10)
ds = gv.dataset.Dataset()
for bf in fit.bootstrapped_fit_iter(datalist=datalist):
ds.append(bf.pmean)
p = gv.dataset.avg_data(ds, bstrap=True)
self.assertTrue(abs(p['a'].mean - 1.) < 5 * p['a'].sdev)
self.assertTrue(abs(p['b'].mean - 0.5) < 5 * p['b'].sdev)
def test_chained_fit_seq_simul(self):
" MultiFitter.chained_lsqfit(models=[m1, (m2,m3)], ...) "
models = self.make_models(ncg=1)
models = [models[0], tuple(models[1:])]
fitter = MultiFitter(models=models)
fit3 = fitter.chained_lsqfit(data=self.data, prior=self.prior)
self.assertEqual(str(fit3.p), "{'a': 0.99931(48),'b': 0.50000(81)}")
self.assertEqual(list(fit3.chained_fits.keys()), ['l', '(c1,c2)'])
# with coarse grain, marginalization and extend
models = self.make_models(ncg=2)
models = [models[0], tuple(models[1:])]
prior = gv.BufferDict([('log(a)', gv.log(self.prior['a'])),
('b', self.prior['b'])])
fitter = MultiFitter(models=self.make_models(ncg=2))
fit4 = fitter.chained_lsqfit(data=self.data,
prior=prior,
mopt=True,
extend=True)
self.assertEqual(str(fit4.p),
"{'log(a)': -0.00073(48),'a': 0.99927(48)}")
def test_chained_fit_seq_parallel(self):
" MultiFitter.chained_lsqfit(models=[m1, [m2,m3]], ...) "
models = self.make_models(ncg=1)
models = [models[0], models[1:]]
fitter = MultiFitter(models=models)
fit5 = fitter.chained_lsqfit(data=self.data, prior=self.prior)
self.assertEqual(str(fit5.p), "{'a': 0.99932(48),'b': 0.49998(81)}")
self.assertEqual(list(fit5.chained_fits.keys()), ['l', '[c1,c2]'])
self.assertEqual(list(fit5.chained_fits['[c1,c2]'].sub_fits.keys()),
['c1', 'c2'])
# with coarse grain, marginalization and extend
models = self.make_models(ncg=2)
models = [models[0], models[1:]]
prior = gv.BufferDict([('log(a)', gv.log(self.prior['a'])),
('b', self.prior['b'])])
fitter = MultiFitter(models=self.make_models(ncg=2))
fit6 = fitter.chained_lsqfit(data=self.data,
prior=prior,
mopt=True,
extend=True)
self.assertEqual(str(fit6.p),
"{'log(a)': -0.00073(48),'a': 0.99927(48)}")
def test_chained_fit_parallel(self):
" MultiFitter(models=[m1, [m2,m3]], ...) "
models = self.make_models(ncg=1)
models = [models[0], models[1:]]
fitter = MultiFitter(models=models)
fit5 = fitter.chained_lsqfit(data=self.data, prior=self.prior)
self.assertTrue(self.agree_ref(fit5.p))
self.assertEqual(list(fit5.chained_fits.keys()),
['l', 'c1', 'c2', 'wavg(c1,c2)'])
# degenerate parallel fit (nfit=1)
models = self.make_models(ncg=1)
models = [[models[0]], models[1:]]
fitter = MultiFitter(models=models)
fit5 = fitter.chained_lsqfit(data=self.data, prior=self.prior)
self.assertTrue(self.agree_ref(fit5.p))
self.assertEqual(list(fit5.chained_fits.keys()),
['l', 'c1', 'c2', 'wavg(c1,c2)'])
# dictionaries in parallel fits
models = self.make_models(ncg=1)
models = [[dict(svdcut=1e-12), models[0]],
[dict(svdcut=1e-12)] + models[1:]]
fitter = MultiFitter(models=models)
fit5 = fitter.chained_lsqfit(data=self.data, prior=self.prior)
self.assertTrue(self.agree_ref(fit5.p))
self.assertEqual(list(fit5.chained_fits.keys()),
['l', 'c1', 'c2', 'wavg(c1,c2)'])
# with coarse grain, marginalization
models = self.make_models(ncg=2)
models = [models[0], models[1:]]
prior = gv.BufferDict([('log(a)', gv.log(self.prior['a'])),
('b', self.prior['b'])])
fitter = MultiFitter(models=self.make_models(ncg=2))
fit6 = fitter.chained_lsqfit(data=self.data, prior=prior, mopt=True)
self.assertTrue(self.agree_ref(fit6.p))