def fit_interpolation(self, simultaneous=None):
if simultaneous is None:
simultaneous = self._simultaneous
if self._fit_interpolation is None or simultaneous != self._simultaneous:
self._simultaneous = simultaneous
#make_gvar = lambda g : gv.gvar(gv.mean(g), gv.sdev(g))
#y_data = make_gvar(1 / self.fit_data['a/w'])
make_gvar = lambda g: gv.gvar(gv.mean(g), gv.sdev(g))
if self.observable == 'w0':
data = {
self.model_info['name'] + '_interpolation':
1 / make_gvar(self.fit_data['a/w'])
}
elif self.observable == 't0':
data = {
self.model_info['name'] + '_interpolation':
make_gvar(self.fit_data['t/a^2'])
}
if simultaneous:
data[self.model_info['name']] = self.y
models = self._make_models(interpolation=True,
simultaneous=simultaneous)
prior = self._make_prior(interpolation=True,
simultaneous=simultaneous)
fitter = lsqfit.MultiFitter(models=models)
fit = fitter.lsqfit(data=data, prior=prior, fast=False, mopt=False)
self._fit_interpolation = fit
return self._fit_interpolation
def main():
# initial data
data = gv.gvar(
dict(
d1=['1.154(10)', '2.107(16)', '3.042(22)', '3.978(29)'],
d2=['0.692(10)', '1.196(16)', '1.657(22)', '2.189(29)'],
d3=['0.107(10)', '0.030(16)', '-0.027(22)', '-0.149(29)'],
d4=['0.002(10)', '-0.197(16)', '-0.382(22)', '-0.627(29)'],
))
models = [
Linear('d1', x=[1, 2, 3, 4], intercept='a', slope='s1', ncg=2),
Linear('d2', x=[1, 2, 3, 4], intercept='a', slope='s2'),
Linear('d3', x=[1, 2, 3, 4], intercept='a', slope='s3'),
Linear('d4', x=[1, 2, 3, 4], intercept='a', slope='s4'),
]
# N.B., use log-normal prior for s1
prior = gv.gvar(
collections.OrderedDict([
('a', '0(1)'),
('log(s1)', '0(1)'),
('s2', '0(1)'),
('s3', '0(1)'),
('s4', '0(1)'),
]))
# reconfigure models and make fitter
models = [[tuple(models[0:2]),
dict(tol=1e-5), models[2]],
dict(tol=1e-8, mopt=True), models[3]]
fitter = lsqfit.MultiFitter(models=models)
# simultaneous fit
print(30 * '-', 'lsqfit')
fit = fitter.lsqfit(data=data, prior=prior, eps=1e-10)
print(fit.formatall())
if SHOW_PLOTS:
fit.show_plots(view='diff')
# chained fit
print('\n' + 30 * '-' + ' chained_lsqfit')
fit = fitter.chained_lsqfit(data=data, prior=prior)
print(fit.format())
print(fit.formatall())
# if SHOW_PLOTS:
# fit.show_plots()
if SHOW_PLOTS:
fit.qqplot_residuals().show()
# bootstrap last fit
for bfit in fit.bootstrapped_fit_iter(n=2):
print(20 * '*', 'bootstrap')
print(gv.fmt(bfit.p, ndecimal=3))
def fit(self):
if self._fit is None:
models = self._make_models()
y_data = {self.model_info['name']: self.y}
prior = self._make_prior()
fitter = lsqfit.MultiFitter(models=models)
fit = fitter.lsqfit(data=y_data,
prior=prior,
fast=False,
mopt=False)
self._fit = fit
return self._fit
def builddata(self, data):
" Extract the model's fit data from data. "
return data[self.datatag]
models = [
Linear('d1', x=[1, 2, 3, 4], intercept='a', slope='s1'),
Linear('d2', x=[1, 2, 3, 4], intercept='a', slope='s2'),
Linear('d3', x=[1, 2, 3, 4], intercept='a', slope='s3'),
Linear('d4', x=[1, 2, 3, 4], intercept='a', slope='s4'),
]
prior = gv.gvar(dict(a='0(1)', s1='0(1)', s2='0(1)', s3='0(1)', s4='0(1)'))
banner("SIMULTANEOUS FIT")
fitter = lsf.MultiFitter(models=models)
fit = fitter.lsqfit(data=data, prior=prior)
print(fit)
print(f"intercept = {fit.p['a']}")
fit.show_plots(view='std')
banner("Marginalizing over different slopes")
marginalized = fitter.lsqfit(data=data, prior=prior, mopt=True)
print(marginalized)
print(f"intercept = {marginalized.p['a']}")
banner("CHAINED FIT")
chained = fitter.chained_lsqfit(data=data, prior=prior)
def test_svd_diagnosis(self):
" svd_diagnosis "
# random correlated data (10x10 correlation matrix)
chebval = np.polynomial.chebyshev.chebval
gv.ranseed(1)
x = np.linspace(-.9, .9, 10)
c = gv.raniter(gv.gvar(len(x) * ['0(1)']))
# small dataset (big svdcut)
dset = []
for n in range(15):
dset.append(chebval(x, next(c)))
gv.ranseed(2)
s = gv.dataset.svd_diagnosis(dset)
self.assertGreater(s.svdcut, 0.01)
# print(s.svdcut)
# s.plot_ratio(show=True)
# test with dictionary
gv.ranseed(2)
sd = gv.dataset.svd_diagnosis(dict(a=dset))
self.assertEqual(s.svdcut, sd.svdcut)
# large dataset (small or no svdcut)
dset = []
for n in range(100):
dset.append(chebval(x, next(c)))
gv.ranseed(3)
s = svd_diagnosis(dset)
self.assertGreater(0.01, s.svdcut)
# print(s.svdcut)
# s.plot_ratio(show=True)
# with models (only if lsqfit installed)
if lsqfit is None:
return
class Linear(lsqfit.MultiFitterModel):
def __init__(self, datatag, x, intercept, slope):
super(Linear, self).__init__(datatag)
self.x = np.array(x)
self.intercept = intercept
self.slope = slope
def fitfcn(self, p):
return p[self.intercept] + p[self.slope] * self.x
def buildprior(self, prior, mopt=None):
" Extract the model's parameters from prior. "
newprior = {}
newprior[self.intercept] = prior[self.intercept]
newprior[self.slope] = prior[self.slope]
return newprior
def builddata(self, data):
" Extract the model's fit data from data. "
return data[self.datatag]
def builddataset(self, dset):
" Extract the model's fit data from a dataset. "
return dset[self.datatag]
x = np.array([1., 2., 3., 4., 5., 6., 7., 8., 9., 10.])
y_samples = [
[
2.8409, 4.8393, 6.8403, 8.8377, 10.8356, 12.8389, 14.8356,
16.8362, 18.8351, 20.8341
],
[
2.8639, 4.8612, 6.8597, 8.8559, 10.8537, 12.8525, 14.8498,
16.8487, 18.8460, 20.8447
],
[
3.1048, 5.1072, 7.1071, 9.1076, 11.1090, 13.1107, 15.1113,
17.1134, 19.1145, 21.1163
],
[
3.0710, 5.0696, 7.0708, 9.0705, 11.0694, 13.0681, 15.0693,
17.0695, 19.0667, 21.0678
],
[
3.0241, 5.0223, 7.0198, 9.0204, 11.0191, 13.0193, 15.0198,
17.0163, 19.0154, 21.0155
],
[
2.9719, 4.9700, 6.9709, 8.9706, 10.9707, 12.9705, 14.9699,
16.9686, 18.9676, 20.9686
],
[
3.0688, 5.0709, 7.0724, 9.0730, 11.0749, 13.0776, 15.0790,
17.0800, 19.0794, 21.0795
],
[
3.1471, 5.1468, 7.1452, 9.1451, 11.1429, 13.1445, 15.1450,
17.1435, 19.1425, 21.1432
],
[
3.0233, 5.0233, 7.0225, 9.0224, 11.0225, 13.0216, 15.0224,
17.0217, 19.0208, 21.0222
],
[
2.8797, 4.8792, 6.8803, 8.8794, 10.8800, 12.8797, 14.8801,
16.8797, 18.8803, 20.8812
],
[
3.0388, 5.0407, 7.0409, 9.0439, 11.0443, 13.0459, 15.0455,
17.0479, 19.0493, 21.0505
],
[
3.1353, 5.1368, 7.1376, 9.1367, 11.1360, 13.1377, 15.1369,
17.1400, 19.1384, 21.1396
],
[
3.0051, 5.0063, 7.0022, 9.0052, 11.0040, 13.0033, 15.0007,
16.9989, 18.9994, 20.9995
],
[
3.0221, 5.0197, 7.0193, 9.0183, 11.0179, 13.0184, 15.0164,
17.0177, 19.0159, 21.0155
],
[
3.0188, 5.0200, 7.0184, 9.0183, 11.0189, 13.0188, 15.0191,
17.0183, 19.0177, 21.0186
],
]
dset = dict(y=y_samples)
model = Linear('y', x, intercept='y0', slope='s')
prior = gv.gvar(dict(y0='1(1)', s='2(2)'))
gv.ranseed(4)
s = svd_diagnosis(dset, models=[model])
self.assertGreater(s.nmod, 0)
self.assertGreater(s.svdcut, s.val[s.nmod - 1] / s.val[-1])
self.assertGreater(s.val[s.nmod] / s.val[-1], s.svdcut)
return
# skip rest
fitter = lsqfit.MultiFitter(models=[model])
fit = fitter.lsqfit(prior=prior, svdcut=s.svdcut, data=s.avgdata)
print(fit)
# s.avgdata = gv.gvar(gv.mean(s.avgdata), gv.sdev(s.avgdata))
fit = fitter.lsqfit(prior=prior, data=s.avgdata)
print(fit)
s.plot_ratio(show=True)