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Merge branch 'develop' into documentation

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fjosw 2022-06-10 09:22:25 +00:00
commit 75cc221421
5 changed files with 49 additions and 8 deletions
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26
tests/fits_test.py
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@ -184,6 +184,26 @@ def test_fit_corr_independent():
assert (out[1] - out_corr[1]).is_zero(atol=1e-5)
def test_linear_fit_guesses():
for err in [10, 0.1, 0.001]:
xvals = []
yvals = []
for x in range(1, 8, 2):
xvals.append(x)
yvals.append(pe.pseudo_Obs(x + np.random.normal(0.0, err), err, 'test1') + pe.pseudo_Obs(0, err / 100, 'test2', samples=87))
lin_func = lambda a, x: a[0] + a[1] * x
with pytest.raises(Exception):
pe.least_squares(xvals, yvals, lin_func)
[o.gamma_method() for o in yvals];
with pytest.raises(Exception):
pe.least_squares(xvals, yvals, lin_func, initial_guess=[5])
bad_guess = pe.least_squares(xvals, yvals, lin_func, initial_guess=[999, 999])
good_guess = pe.least_squares(xvals, yvals, lin_func, initial_guess=[0, 1])
assert np.isclose(bad_guess.chisquare, good_guess.chisquare, atol=1e-8)
assert np.all([(go - ba).is_zero(atol=1e-6) for (go, ba) in zip(good_guess, bad_guess)])
def test_total_least_squares():
dim = 10 + int(30 * np.random.rand())
x = np.arange(dim) + np.random.normal(0.0, 0.15, dim)
@ -223,7 +243,7 @@ def test_total_least_squares():
beta[i].gamma_method(S=1.0)
assert math.isclose(beta[i].value, output.beta[i], rel_tol=1e-5)
assert math.isclose(output.cov_beta[i, i], beta[i].dvalue ** 2, rel_tol=2.5e-1), str(output.cov_beta[i, i]) + ' ' + str(beta[i].dvalue ** 2)
assert math.isclose(pe.covariance([beta[0], beta[1]])[0, 1], output.cov_beta[0, 1], rel_tol=2.5e-1)
assert math.isclose(pe.covariance([beta[0], beta[1]])[0, 1], output.cov_beta[0, 1], rel_tol=3.5e-1)
out = pe.total_least_squares(ox, oy, func, const_par=[beta[1]])
@ -246,7 +266,7 @@ def test_total_least_squares():
betac[i].gamma_method(S=1.0)
assert math.isclose(betac[i].value, output.beta[i], rel_tol=1e-3)
assert math.isclose(output.cov_beta[i, i], betac[i].dvalue ** 2, rel_tol=2.5e-1), str(output.cov_beta[i, i]) + ' ' + str(betac[i].dvalue ** 2)
assert math.isclose(pe.covariance([betac[0], betac[1]])[0, 1], output.cov_beta[0, 1], rel_tol=2.5e-1)
assert math.isclose(pe.covariance([betac[0], betac[1]])[0, 1], output.cov_beta[0, 1], rel_tol=3.5e-1)
outc = pe.total_least_squares(oxc, oyc, func, const_par=[betac[1]])
@ -261,7 +281,7 @@ def test_total_least_squares():
betac[i].gamma_method(S=1.0)
assert math.isclose(betac[i].value, output.beta[i], rel_tol=1e-3)
assert math.isclose(output.cov_beta[i, i], betac[i].dvalue ** 2, rel_tol=2.5e-1), str(output.cov_beta[i, i]) + ' ' + str(betac[i].dvalue ** 2)
assert math.isclose(pe.covariance([betac[0], betac[1]])[0, 1], output.cov_beta[0, 1], rel_tol=2.5e-1)
assert math.isclose(pe.covariance([betac[0], betac[1]])[0, 1], output.cov_beta[0, 1], rel_tol=3.5e-1)
outc = pe.total_least_squares(oxc, oy, func, const_par=[betac[1]])

17
tests/obs_test.py
View file

@ -736,6 +736,23 @@ def test_covariance_factorizing():
assert np.isclose(pe.covariance([mt0, tt[1]])[0, 1], -pe.covariance(tt)[0, 1])
def test_covariance_smooth_eigenvalues():
for c_coeff in range(0, 14, 2):
length = 14
sm = 5
t_fac = 1.5
tt = pe.misc.gen_correlated_data(np.zeros(length), 1 - 0.1 ** c_coeff * np.ones((length, length)) + 0.1 ** c_coeff * np.diag(np.ones(length)), 'test', tau=0.5 + t_fac * np.random.rand(length), samples=200)
[o.gamma_method() for o in tt]
full_corr = pe.covariance(tt, correlation=True)
cov = pe.covariance(tt, smooth=sm, correlation=True)
full_evals = np.linalg.eigh(full_corr)[0]
sm_length = np.where(full_evals < np.mean(full_evals[:-sm]))[0][-1]
evals = np.linalg.eigh(cov)[0]
assert np.all(np.isclose(evals[:sm_length], evals[0], atol=1e-8))
def test_covariance_alternation():
length = 12
t_fac = 2.5