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fix(covmodel): ensure positive percentile_scale using bounded brentq #419

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fix(covmodel): ensure positive percentile_scale using bounded brentq #419

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10 changes: 7 additions & 3 deletions src/gstools/covmodel/tools.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,7 +27,7 @@
import numpy as np
from hankel import SymmetricFourierTransform as SFT
from scipy import special as sps
from scipy.optimize import root
from scipy.optimize import root_scalar

from gstools.tools.geometric import no_of_angles, set_angles, set_anis
from gstools.tools.misc import list_format
Expand Down Expand Up @@ -439,8 +439,12 @@ def percentile_scale(model, per=0.9):
def curve(x):
return 1.0 - model.correlation(x) - per

# take 'per * len_rescaled' as initial guess
return root(curve, per * model.len_rescaled)["x"][0]

@MuellerSeb MuellerSeb Aug 4, 2026

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Just a return abs(root(curve, per * model.len_rescaled)["x"][0]) would be sufficient as the correlation function is symmetric around 0.

# upper bound for bracket where curve(b) > 0
b = float(max(model.len_rescaled, 1e-5))
while curve(b) <= 0:
b *= 2.0

return root_scalar(curve, bracket=[0.0, b], method="brentq").root


def set_arg_bounds(model, check_args=True, **kwargs):
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6 changes: 6 additions & 0 deletions tests/test_covmodel.py
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Expand Up @@ -351,6 +351,12 @@ def test_covmodel_class(self):
self.assertRaises(ValueError, Gau_fix, latlon=True)
# check inputs
self.assertRaises(ValueError, model_std.percentile_scale, per=-1.0)
# check positive percentile scale for SuperSpherical across nu values
for nu in (0.5, 1.0, 2.0, 5.0, 10.0):
m = SuperSpherical(dim=2, nu=nu, len_scale=10.0)
ps = m.percentile_scale(0.9)
self.assertGreater(ps, 0.0)
self.assertAlmostEqual(m.correlation(ps), 0.1, places=5)
self.assertRaises(ValueError, Gaussian, anis=-1.0)
self.assertRaises(ValueError, Gaussian, len_scale=[1, -1])
self.assertRaises(ValueError, check_arg_in_bounds, model_std, "wrong")
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