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first version of new bookeeping system

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Fabian Joswig 2021-11-01 17:14:30 +00:00
parent b86dda2394
commit e4d7d56180
4 changed files with 38 additions and 54 deletions
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3
tests/fits_test.py
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@ -30,9 +30,8 @@ def test_least_squares():
out = pe.least_squares(x, oy, func)
beta = out.fit_parameters
pe.Obs.e_tag_global = 5
for i in range(2):
beta[i].gamma_method(e_tag=5, S=1.0)
beta[i].gamma_method(S=1.0)
assert math.isclose(beta[i].value, popt[i], abs_tol=1e-5)
assert math.isclose(pcov[i, i], beta[i].dvalue ** 2, abs_tol=1e-3)
assert math.isclose(pe.covariance(beta[0], beta[1]), pcov[0, 1], abs_tol=1e-3)

29
tests/pyerrors_test.py
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@ -135,7 +135,7 @@ def test_fft():
test_obs2 = copy.deepcopy(test_obs1)
test_obs1.gamma_method()
test_obs2.gamma_method(fft=False)
assert max(np.abs(test_obs1.e_rho[''] - test_obs2.e_rho[''])) <= 10 * np.finfo(np.float64).eps
assert max(np.abs(test_obs1.e_rho['t'] - test_obs2.e_rho['t'])) <= 10 * np.finfo(np.float64).eps
assert np.abs(test_obs1.dvalue - test_obs2.dvalue) <= 10 * max(test_obs1.dvalue, test_obs2.dvalue) * np.finfo(np.float64).eps
@ -190,22 +190,19 @@ def test_derived_observables():
assert i_am_one.e_ddvalue['t'] <= 2 * np.finfo(np.float64).eps
def test_multi_ens_system():
names = []
for i in range(100 + int(np.random.rand() * 50)):
tmp_string = ''
for _ in range(int(2 + np.random.rand() * 4)):
tmp_string += random.choice(string.ascii_uppercase + string.ascii_lowercase + string.digits)
names.append(tmp_string)
names = list(set(names))
samples = [np.random.rand(5)] * len(names)
new_obs = pe.Obs(samples, names)
def test_multi_ens():
names = ['A0', 'A1|r001', 'A1|r002']
test_obs = pe.Obs([np.random.rand(50), np.random.rand(50), np.random.rand(50)], names)
assert test_obs.e_names == ['A0', 'A1']
assert test_obs.e_content['A0'] == ['A0']
assert test_obs.e_content['A1'] == ['A1|r001', 'A1|r002']
for e_tag_length in range(1, 6):
new_obs.gamma_method(e_tag=e_tag_length)
e_names = sorted(set([n[:e_tag_length] for n in names]))
assert e_names == new_obs.e_names
assert sorted(x for y in sorted(new_obs.e_content.values()) for x in y) == sorted(new_obs.names)
my_sum = 0
ensembles = []
for i in range(100):
my_sum += pe.Obs([np.random.rand(50)], [str(i)])
ensembles.append(str(i))
assert my_sum.e_names == sorted(ensembles)
def test_overloaded_functions():