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[Fix] Migrate to odrpack because of scipy.odr deprecation (#279)

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* [Fix] Migrate to odrpack because of scipy.odr deprecation in recent
release

* [Fix] Fix behaviour for rank deficient fits. Add test

* [Fix] Relax test_merge_obs tolerance to machine epsilon and update ODR docstring to reference odrpack

* [ci] Re-add -Werror to pytest workflow

* [Fix] Handle platform-dependent rank-deficient warning in ODR tests

* [Fix] Improve rank-deficient detection and bump odrpack to >=0.5

Fix incorrect ODRPACK95 info code parsing: rank deficiency is encoded
in the tens digit (info // 10 % 10), not the hundreds digit. Add
irank and inv_condnum to the warning message for diagnostics.
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Fabian Joswig 2026-04-19 19:50:36 +02:00 • committed by GitHub
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6 changed files with 119 additions and 44 deletions
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89
tests/fits_test.py
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@ -1,9 +1,10 @@
import warnings
import numpy as np
import autograd.numpy as anp
import matplotlib.pyplot as plt
import math
import scipy.optimize
from scipy.odr import ODR, Model, RealData
from odrpack import odr_fit
from scipy.linalg import cholesky
from scipy.stats import norm
import iminuit
@ -397,11 +398,21 @@ def test_total_least_squares():
y = a[0] * anp.exp(-a[1] * x)
return y
data = RealData([o.value for o in ox], [o.value for o in oy], sx=[o.dvalue for o in ox], sy=[o.dvalue for o in oy])
model = Model(func)
odr = ODR(data, model, [0, 0], partol=np.finfo(np.float64).eps)
odr.set_job(fit_type=0, deriv=1)
output = odr.run()
# odrpack expects f(x, beta), but pyerrors convention is f(beta, x)
def wrapped_func(x, beta):
return func(beta, x)
output = odr_fit(
wrapped_func,
np.array([o.value for o in ox]),
np.array([o.value for o in oy]),
beta0=np.array([0.0, 0.0]),
weight_x=1.0 / np.array([o.dvalue for o in ox]) ** 2,
weight_y=1.0 / np.array([o.dvalue for o in oy]) ** 2,
partol=np.finfo(np.float64).eps,
task='explicit-ODR',
diff_scheme='central'
)
out = pe.total_least_squares(ox, oy, func, expected_chisquare=True)
beta = out.fit_parameters
@ -458,6 +469,21 @@ def test_total_least_squares():
assert((outc.fit_parameters[1] - betac[1]).is_zero())
def test_total_least_squares_vanishing_chisquare():
"""Test that a saturated fit (n_obs == n_parms) works without exception."""
def func(a, x):
return a[0] + a[1] * x
x = [pe.pseudo_Obs(1.0, 0.1, 'x0'), pe.pseudo_Obs(2.0, 0.1, 'x1')]
y = [pe.pseudo_Obs(1.0, 0.1, 'y0'), pe.pseudo_Obs(2.0, 0.1, 'y1')]
# Rank-deficient warning may or may not fire depending on platform/solver numerics.
with warnings.catch_warnings():
warnings.filterwarnings("ignore", message="ODR fit is rank deficient", category=RuntimeWarning)
out = pe.total_least_squares(x, y, func, silent=True)
assert len(out.fit_parameters) == 2
def test_odr_derivatives():
x = []
y = []
@ -502,7 +528,10 @@ def test_r_value_persistence():
assert np.isclose(fitp[1].value, fitp[1].r_values['a'])
assert np.isclose(fitp[1].value, fitp[1].r_values['b'])
fitp = pe.fits.total_least_squares(y, y, f)
# Rank-deficient warning may or may not fire depending on platform/solver numerics.
with warnings.catch_warnings():
warnings.filterwarnings("ignore", message="ODR fit is rank deficient", category=RuntimeWarning)
fitp = pe.fits.total_least_squares(y, y, f)
assert np.isclose(fitp[0].value, fitp[0].r_values['a'])
assert np.isclose(fitp[0].value, fitp[0].r_values['b'])
@ -1431,11 +1460,11 @@ def fit_general(x, y, func, silent=False, **kwargs):
global print_output, beta0
print_output = 1
if 'initial_guess' in kwargs:
beta0 = kwargs.get('initial_guess')
beta0 = np.asarray(kwargs.get('initial_guess'), dtype=np.float64)
if len(beta0) != n_parms:
raise Exception('Initial guess does not have the correct length.')
else:
beta0 = np.arange(n_parms)
beta0 = np.arange(n_parms, dtype=np.float64)
if len(x) != len(y):
raise Exception('x and y have to have the same length')
@ -1463,23 +1492,45 @@ def fit_general(x, y, func, silent=False, **kwargs):
xerr = kwargs.get('xerr')
# odrpack expects f(x, beta), but pyerrors convention is f(beta, x)
def wrapped_func(x, beta):
return func(beta, x)
if length == len(obs):
assert 'x_constants' in kwargs
data = RealData(kwargs.get('x_constants'), obs, sy=yerr)
fit_type = 2
x_data = np.asarray(kwargs.get('x_constants'))
y_data = np.asarray(obs)
# Ordinary least squares (no x errors)
output = odr_fit(
wrapped_func,
x_data,
y_data,
beta0=beta0,
weight_y=1.0 / np.asarray(yerr) ** 2,
partol=np.finfo(np.float64).eps,
task='explicit-OLS',
diff_scheme='central'
)
elif length == len(obs) // 2:
data = RealData(obs[:length], obs[length:], sx=xerr, sy=yerr)
fit_type = 0
x_data = np.asarray(obs[:length])
y_data = np.asarray(obs[length:])
# ODR with x errors
output = odr_fit(
wrapped_func,
x_data,
y_data,
beta0=beta0,
weight_x=1.0 / np.asarray(xerr) ** 2,
weight_y=1.0 / np.asarray(yerr) ** 2,
partol=np.finfo(np.float64).eps,
task='explicit-ODR',
diff_scheme='central'
)
else:
raise Exception('x and y do not fit together.')
model = Model(func)
odr = ODR(data, model, beta0, partol=np.finfo(np.float64).eps)
odr.set_job(fit_type=fit_type, deriv=1)
output = odr.run()
if print_output and not silent:
print(*output.stopreason)
print(output.stopreason)
print('chisquare/d.o.f.:', output.res_var)
print_output = 0
beta0 = output.beta