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Merge branch 'develop' into documentation

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fjosw 2022-10-07 17:11:57 +00:00
commit 4eea17d1fb
4 changed files with 125 additions and 12 deletions
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65
tests/fits_test.py
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@ -83,8 +83,65 @@ def test_least_squares():
assert math.isclose(pcov[i, i], betac[i].dvalue ** 2, abs_tol=1e-3)
def test_least_squares_num_grad():
x = []
y = []
for i in range(2, 5):
x.append(i * 0.01)
y.append(pe.pseudo_Obs(i * 0.01, 0.0001, "ens"))
num = pe.fits.least_squares(x, y, lambda a, x: np.exp(a[0] * x) + a[1], num_grad=True)
auto = pe.fits.least_squares(x, y, lambda a, x: anp.exp(a[0] * x) + a[1], num_grad=False)
assert(num[0] == auto[0])
assert(num[1] == auto[1])
def test_prior_fit_num_grad():
x = []
y = []
for i in range(2, 5):
x.append(i * 0.01)
y.append(pe.pseudo_Obs(i * 0.01, 0.0001, "ens"))
num = pe.fits.least_squares(x, y, lambda a, x: np.exp(a[0] * x) + a[1], num_grad=True, priors=y[:2])
auto = pe.fits.least_squares(x, y, lambda a, x: anp.exp(a[0] * x) + a[1], num_grad=False, piors=y[:2])
def test_least_squares_num_grad():
x = []
y = []
for i in range(2, 5):
x.append(i * 0.01)
y.append(pe.pseudo_Obs(i * 0.01, 0.0001, "ens"))
num = pe.fits.least_squares(x, y, lambda a, x: np.exp(a[0] * x) + a[1], num_grad=True)
auto = pe.fits.least_squares(x, y, lambda a, x: anp.exp(a[0] * x) + a[1], num_grad=False)
assert(num[0] == auto[0])
assert(num[1] == auto[1])
assert(num[0] == auto[0])
assert(num[1] == auto[1])
def test_total_least_squares_num_grad():
x = []
y = []
for i in range(2, 5):
x.append(pe.pseudo_Obs(i * 0.01, 0.0001, "ens"))
y.append(pe.pseudo_Obs(i * 0.01, 0.0001, "ens"))
num = pe.fits.total_least_squares(x, y, lambda a, x: np.exp(a[0] * x) + a[1], num_grad=True)
auto = pe.fits.total_least_squares(x, y, lambda a, x: anp.exp(a[0] * x) + a[1], num_grad=False)
assert(num[0] == auto[0])
assert(num[1] == auto[1])
def test_alternative_solvers():
dim = 192
dim = 92
x = np.arange(dim)
y = 2 * np.exp(-0.06 * x) + np.random.normal(0.0, 0.15, dim)
yerr = 0.1 + 0.1 * np.random.rand(dim)
@ -158,7 +215,7 @@ def test_correlated_fit():
def test_fit_corr_independent():
dim = 50
dim = 30
x = np.arange(dim)
y = 0.84 * np.exp(-0.12 * x) + np.random.normal(0.0, 0.1, dim)
yerr = [0.1] * dim
@ -470,7 +527,7 @@ def test_correlated_fit_vs_jackknife():
def test_fit_no_autograd():
dim = 10
dim = 3
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)
@ -486,6 +543,8 @@ def test_fit_no_autograd():
with pytest.raises(Exception):
pe.least_squares(x, oy, func)
pe.least_squares(x, oy, func, num_grad=True)
with pytest.raises(Exception):
pe.total_least_squares(oy, oy, func)