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fix: Error handling for fits and root finding with numpy instead of autograd.numpy

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improved. Tests added.
This commit is contained in:
Fabian Joswig 2022-03-02 15:10:07 +00:00
parent 7f5989dfb9
commit b14314b424
4 changed files with 53 additions and 10 deletions
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36
tests/fits_test.py
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@ -1,4 +1,5 @@
import autograd.numpy as np
import numpy as np
import autograd.numpy as anp
import math
import scipy.optimize
from scipy.odr import ODR, Model, RealData
@ -41,12 +42,12 @@ def test_least_squares():
oy.append(pe.pseudo_Obs(y[i], yerr[i], str(i)))
def f(x, a, b):
return a * np.exp(-b * x)
return a * anp.exp(-b * x)
popt, pcov = scipy.optimize.curve_fit(f, x, y, sigma=[o.dvalue for o in oy], absolute_sigma=True)
def func(a, x):
y = a[0] * np.exp(-a[1] * x)
y = a[0] * anp.exp(-a[1] * x)
return y
out = pe.least_squares(x, oy, func, expected_chisquare=True, resplot=True, qqplot=True)
@ -95,7 +96,7 @@ def test_alternative_solvers():
oy.append(pe.pseudo_Obs(y[i], yerr[i], 'test'))
def func(a, x):
y = a[0] * np.exp(-a[1] * x)
y = a[0] * anp.exp(-a[1] * x)
return y
chisquare_values = []
@ -145,7 +146,7 @@ def test_correlated_fit():
return p[1] + p[0] * x
else:
def fitf(p, x):
return p[1] * np.exp(-p[0] * x)
return p[1] * anp.exp(-p[0] * x)
fitp = pe.least_squares(x, data, fitf, expected_chisquare=True)
@ -172,10 +173,10 @@ def test_total_least_squares():
oy.append(pe.pseudo_Obs(y[i], yerr[i], str(i)))
def f(x, a, b):
return a * np.exp(-b * x)
return a * anp.exp(-b * x)
def func(a, x):
y = a[0] * np.exp(-a[1] * x)
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])
@ -336,6 +337,27 @@ def test_error_band():
pe.fits.error_band(x, f, fitp.fit_parameters)
def test_fit_no_autograd():
dim = 10
x = np.arange(dim)
y = 2 * np.exp(-0.08 * x) + np.random.normal(0.0, 0.15, dim)
yerr = 0.1 + 0.1 * np.random.rand(dim)
oy = []
for i, item in enumerate(x):
oy.append(pe.pseudo_Obs(y[i], yerr[i], str(i)))
def func(a, x):
y = a[0] * np.exp(-a[1] * x)
return y
with pytest.raises(Exception):
pe.least_squares(x, oy, func)
with pytest.raises(Exception):
pe.total_least_squares(oy, oy, func)
def test_ks_test():
def f(a, x):
y = a[0] + a[1] * x