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fix: fits and root now work when the value of the zeroth input is

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exactly zero. Tests extended.
This commit is contained in:
Fabian Joswig 2021-12-07 18:40:36 +00:00
parent 968cdf3181
commit 9ddadaf6b3
3 changed files with 33 additions and 4 deletions
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6
pyerrors/fits.py
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@ -301,7 +301,7 @@ def total_least_squares(x, y, func, silent=False, **kwargs):
result = [] result = []
for i in range(n_parms): for i in range(n_parms):
result.append(derived_observable(lambda my_var, **kwargs: my_var[0] / x.ravel()[0].value * out.beta[i], list(x.ravel()) + list(y), man_grad=list(deriv_x[i]) + list(deriv_y[i]))) result.append(derived_observable(lambda my_var, **kwargs: (my_var[0] + np.finfo(np.float64).eps) / (x.ravel()[0].value + np.finfo(np.float64).eps) * out.beta[i], list(x.ravel()) + list(y), man_grad=list(deriv_x[i]) + list(deriv_y[i])))
output.fit_parameters = result + const_par output.fit_parameters = result + const_par
@ -418,7 +418,7 @@ def _prior_fit(x, y, func, priors, silent=False, **kwargs):
result = [] result = []
for i in range(n_parms): for i in range(n_parms):
result.append(derived_observable(lambda x, **kwargs: x[0] / y[0].value * params[i], list(y) + list(loc_priors), man_grad=list(deriv[i]))) result.append(derived_observable(lambda x, **kwargs: (x[0] + np.finfo(np.float64).eps) / (y[0].value + np.finfo(np.float64).eps) * params[i], list(y) + list(loc_priors), man_grad=list(deriv[i])))
output.fit_parameters = result output.fit_parameters = result
output.chisquare = chisqfunc(np.asarray(params)) output.chisquare = chisqfunc(np.asarray(params))
@ -612,7 +612,7 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
result = [] result = []
for i in range(n_parms): for i in range(n_parms):
result.append(derived_observable(lambda x, **kwargs: x[0] / y[0].value * fit_result.x[i], list(y), man_grad=list(deriv[i]))) result.append(derived_observable(lambda x, **kwargs: (x[0] + np.finfo(np.float64).eps) / (y[0].value + np.finfo(np.float64).eps) * fit_result.x[i], list(y), man_grad=list(deriv[i])))
output.fit_parameters = result + const_par output.fit_parameters = result + const_par

3
pyerrors/roots.py
View file

@ -1,3 +1,4 @@
import numpy as np
import scipy.optimize import scipy.optimize
from autograd import jacobian from autograd import jacobian
from .obs import derived_observable from .obs import derived_observable
@ -33,5 +34,5 @@ def find_root(d, func, guess=1.0, **kwargs):
da = jacobian(lambda u, v: func(v, u))(d.value, root[0]) da = jacobian(lambda u, v: func(v, u))(d.value, root[0])
deriv = - da / dx deriv = - da / dx
res = derived_observable(lambda x, **kwargs: x[0] / d.value * root[0], [d], man_grad=[deriv]) res = derived_observable(lambda x, **kwargs: (x[0] + np.finfo(np.float64).eps) / (d.value + np.finfo(np.float64).eps) * root[0], [d], man_grad=[deriv])
return res return res

28
tests/fits_test.py
View file

@ -10,6 +10,26 @@ import pytest
np.random.seed(0) np.random.seed(0)
def test_fit_lin():
x = [0, 2]
y = [pe.pseudo_Obs(0, 0.1, 'ensemble'),
pe.pseudo_Obs(2, 0.1, 'ensemble')]
res = pe.fits.fit_lin(x, y)
assert res[0] == y[0]
assert res[1] == (y[1] - y[0]) / (x[1] - x[0])
x = y = [pe.pseudo_Obs(0, 0.1, 'ensemble'),
pe.pseudo_Obs(2, 0.1, 'ensemble')]
res = pe.fits.fit_lin(x, y)
m = (y[1] - y[0]) / (x[1] - x[0])
assert res[0] == y[1] - x[1] * m
assert res[1] == m
def test_least_squares(): def test_least_squares():
dim = 10 + int(30 * np.random.rand()) dim = 10 + int(30 * np.random.rand())
x = np.arange(dim) x = np.arange(dim)
@ -32,6 +52,10 @@ def test_least_squares():
out = pe.least_squares(x, oy, func, expected_chisquare=True, resplot=True, qqplot=True) out = pe.least_squares(x, oy, func, expected_chisquare=True, resplot=True, qqplot=True)
beta = out.fit_parameters beta = out.fit_parameters
str(out)
repr(out)
len(out)
for i in range(2): for i in range(2):
beta[i].gamma_method(S=1.0) beta[i].gamma_method(S=1.0)
assert math.isclose(beta[i].value, popt[i], abs_tol=1e-5) assert math.isclose(beta[i].value, popt[i], abs_tol=1e-5)
@ -136,6 +160,10 @@ def test_total_least_squares():
out = pe.total_least_squares(ox, oy, func, expected_chisquare=True) out = pe.total_least_squares(ox, oy, func, expected_chisquare=True)
beta = out.fit_parameters beta = out.fit_parameters
str(out)
repr(out)
len(out)
for i in range(2): for i in range(2):
beta[i].gamma_method(S=1.0) beta[i].gamma_method(S=1.0)
assert math.isclose(beta[i].value, output.beta[i], rel_tol=1e-5) assert math.isclose(beta[i].value, output.beta[i], rel_tol=1e-5)