pyerrors/tests/correlators_test.py
2023-07-17 11:12:21 +01:00

694 lines
21 KiB
Python

import os
import numpy as np
import scipy
import matplotlib.pyplot as plt
import pyerrors as pe
import pytest
np.random.seed(0)
def test_function_overloading():
corr_content_a = []
corr_content_b = []
for t in range(24):
corr_content_a.append(pe.pseudo_Obs(np.random.normal(1e-10, 1e-8), 1e-4, 't'))
corr_content_b.append(pe.pseudo_Obs(np.random.normal(1e8, 1e10), 1e7, 't'))
corr_a = pe.correlators.Corr(corr_content_a)
corr_b = pe.correlators.Corr(corr_content_b)
fs = [lambda x: x[0] + x[1], lambda x: x[1] + x[0], lambda x: x[0] - x[1], lambda x: x[1] - x[0],
lambda x: x[0] * x[1], lambda x: x[1] * x[0], lambda x: x[0] / x[1], lambda x: x[1] / x[0],
lambda x: np.exp(x[0]), lambda x: np.sin(x[0]), lambda x: np.cos(x[0]), lambda x: np.tan(x[0]),
lambda x: np.log(x[0] + 0.1), lambda x: np.sqrt(np.abs(x[0])),
lambda x: np.sinh(x[0]), lambda x: np.cosh(x[0]), lambda x: np.tanh(x[0])]
for i, f in enumerate(fs):
t1 = f([corr_a, corr_b])
t1.gamma_method()
for o_a, o_b, con in zip(corr_content_a, corr_content_b, t1.content):
t2 = f([o_a, o_b])
t2.gamma_method()
assert np.isclose(con[0].value, t2.value)
assert np.isclose(con[0].dvalue, t2.dvalue)
assert np.allclose(con[0].deltas['t'], t2.deltas['t'])
np.arcsin(corr_a)
np.arccos(corr_a)
np.arctan(corr_a)
np.arcsinh(corr_a)
np.arccosh(corr_a + 1.1)
np.arctanh(corr_a)
def test_modify_correlator():
corr_content = []
for t in range(24):
exponent = np.random.normal(3, 5)
corr_content.append(pe.pseudo_Obs(2 + 10 ** exponent, 10 ** (exponent - 1), 't'))
corr = pe.Corr(corr_content)
with pytest.warns(RuntimeWarning):
corr.symmetric()
with pytest.warns(RuntimeWarning):
corr.anti_symmetric()
for pad in [0, 2]:
corr = pe.Corr(corr_content, padding=[pad, pad])
corr.roll(np.random.randint(100))
corr.deriv(variant="symmetric")
corr.deriv(variant="forward")
corr.deriv(variant="backward")
corr.deriv(variant="improved")
corr.deriv(variant="log")
corr.deriv().deriv()
corr.second_deriv(variant="symmetric")
corr.second_deriv(variant="big_symmetric")
corr.second_deriv(variant="improved")
corr.second_deriv(variant="log")
corr.second_deriv().second_deriv()
for i, e in enumerate(corr.content):
corr.content[i] = None
for func in [pe.Corr.deriv, pe.Corr.second_deriv]:
for variant in ["symmetric", "improved", "forward", "gibberish", None]:
with pytest.raises(Exception):
func(corr, variant=variant)
def test_deriv():
corr_content = []
for t in range(24):
exponent = 1.2
corr_content.append(pe.pseudo_Obs(2 + t ** exponent, 0.2, 't'))
corr = pe.Corr(corr_content)
forward = corr.deriv(variant="forward")
backward = corr.deriv(variant="backward")
sym = corr.deriv(variant="symmetric")
assert np.all([o == 0 for o in (0.5 * (forward + backward) - sym)[1:-1]])
assert np.all([o == 0 for o in (corr.deriv('forward').deriv('backward') - corr.second_deriv())[1:-1]])
assert np.all([o == 0 for o in (corr.deriv('backward').deriv('forward') - corr.second_deriv())[1:-1]])
corr_content = []
exponent = -0.05
for t in range(24):
corr_content.append(pe.pseudo_Obs(np.exp(t * exponent), np.exp(t * exponent) * 0.02, 't'))
corr = pe.Corr(corr_content)
for o in [(corr.deriv('log') / corr / exponent - 1)[10], (corr.second_deriv('log') / corr / exponent**2 - 1)[12]]:
o.gamma_method()
assert (o.is_zero_within_error() and np.isclose(0.0, o.value, 1e-12, 1e-12))
def test_m_eff():
for padding in [0, 4]:
my_corr = pe.correlators.Corr([pe.pseudo_Obs(10, 0.1, 't'), pe.pseudo_Obs(9, 0.05, 't'), pe.pseudo_Obs(9, 0.1, 't'), pe.pseudo_Obs(10, 0.05, 't')], padding=[padding, padding])
my_corr.m_eff('log')
my_corr.m_eff('logsym')
my_corr.m_eff('cosh')
my_corr.m_eff('arccosh')
corr_content = []
exponent = -2.2
for t in range(24):
corr_content.append(pe.pseudo_Obs(np.exp(t * exponent), np.exp(t * exponent) * 0.02, 't'))
corr = pe.Corr(corr_content)
for variant in ['log', 'logsym']:
o = (corr.m_eff(variant) / exponent + 1)[7]
o.gamma_method()
assert (o.is_zero_within_error() and np.isclose(0.0, o.value, 1e-12, 1e-12))
with pytest.warns(RuntimeWarning):
my_corr.m_eff('sinh')
with pytest.raises(Exception):
my_corr.m_eff('unkown_variant')
def test_m_eff_negative_values():
for padding in [0, 4]:
my_corr = pe.correlators.Corr([1.0 * pe.pseudo_Obs(10, 0.1, 't'), 1.0 * pe.pseudo_Obs(9, 0.05, 't'), -pe.pseudo_Obs(9, 0.1, 't')], padding=[padding, padding])
m_eff_log = my_corr.m_eff('log')
assert m_eff_log[padding + 1] is None
m_eff_cosh = my_corr.m_eff('cosh')
assert m_eff_cosh[padding + 1] is None
with pytest.raises(Exception):
my_corr.m_eff('logsym')
def test_reweighting():
my_corr = pe.correlators.Corr([pe.pseudo_Obs(10, 0.1, 't'), pe.pseudo_Obs(0, 0.05, 't')])
assert my_corr.reweighted is False
r_my_corr = my_corr.reweight(pe.pseudo_Obs(1, 0.1, 't'))
assert r_my_corr.reweighted is True
def test_correlate():
my_corr = pe.correlators.Corr([pe.pseudo_Obs(10, 0.1, 't'), pe.pseudo_Obs(0, 0.05, 't')])
corr1 = my_corr.correlate(my_corr)
corr2 = my_corr.correlate(my_corr[0])
with pytest.raises(Exception):
corr3 = my_corr.correlate(7.3)
def test_T_symmetry():
my_corr = pe.correlators.Corr([pe.pseudo_Obs(10, 0.1, 't'), pe.pseudo_Obs(0, 0.05, 't')])
with pytest.warns(RuntimeWarning):
T_symmetric = my_corr.T_symmetry(my_corr)
def test_fit_correlator():
my_corr = pe.correlators.Corr([pe.pseudo_Obs(1.01324, 0.05, 't'), pe.pseudo_Obs(2.042345, 0.0004, 't')])
def f(a, x):
y = a[0] + a[1] * x
return y
fit_res = my_corr.fit(f)
assert fit_res[0] == my_corr[0]
assert fit_res[1] == my_corr[1] - my_corr[0]
with pytest.raises(Exception):
my_corr.fit(f, "from 0 to 3")
with pytest.raises(Exception):
my_corr.fit(f, [0, 2, 3])
def test_plateau():
my_corr = pe.correlators.Corr([pe.pseudo_Obs(1.01324, 0.05, 't'), pe.pseudo_Obs(1.042345, 0.008, 't')])
my_corr.plateau([0, 1], method="fit")
my_corr.plateau([0, 1], method="mean")
with pytest.raises(Exception):
my_corr.plateau()
def test_padded_correlator():
my_list = [pe.Obs([np.random.normal(1.0, 0.1, 100)], ['ens1']) for o in range(8)]
my_corr = pe.Corr(my_list, padding=[7, 3])
my_corr.reweighted
[o for o in my_corr]
def test_corr_exceptions():
obs_a = pe.Obs([np.random.normal(0.1, 0.1, 100)], ['test'])
obs_b= pe.Obs([np.random.normal(0.1, 0.1, 99)], ['test'])
with pytest.raises(Exception):
pe.Corr([obs_a, obs_b])
obs_a = pe.Obs([np.random.normal(0.1, 0.1, 100)], ['test'])
obs_b= pe.Obs([np.random.normal(0.1, 0.1, 100)], ['test'], idl=[range(1, 200, 2)])
with pytest.raises(Exception):
pe.Corr([obs_a, obs_b])
obs_a = pe.Obs([np.random.normal(0.1, 0.1, 100)], ['test'])
obs_b= pe.Obs([np.random.normal(0.1, 0.1, 100)], ['test2'])
with pytest.raises(Exception):
pe.Corr([obs_a, obs_b])
def test_utility():
corr_content = []
for t in range(8):
exponent = np.random.normal(3, 5)
corr_content.append(pe.pseudo_Obs(2 + 10 ** exponent, 10 ** (exponent - 1), 't'))
corr = pe.correlators.Corr(corr_content)
corr.gamma_method()
corr.print()
corr.print([2, 4])
corr.show()
corr.show(comp=corr)
corr.dump('test_dump', datatype="pickle", path='.')
corr.dump('test_dump', datatype="pickle")
new_corr = pe.load_object('test_dump.p')
new_corr.gamma_method()
os.remove('test_dump.p')
for o_a, o_b in zip(corr.content, new_corr.content):
assert np.isclose(o_a[0].value, o_b[0].value)
assert np.isclose(o_a[0].dvalue, o_b[0].dvalue)
assert np.allclose(o_a[0].deltas['t'], o_b[0].deltas['t'])
corr.dump('test_dump', datatype="json.gz", path='.')
corr.dump('test_dump', datatype="json.gz")
new_corr = pe.input.json.load_json('test_dump')
new_corr.gamma_method()
os.remove('test_dump.json.gz')
for o_a, o_b in zip(corr.content, new_corr.content):
assert np.isclose(o_a[0].value, o_b[0].value)
assert np.isclose(o_a[0].dvalue, o_b[0].dvalue)
assert np.allclose(o_a[0].deltas['t'], o_b[0].deltas['t'])
def test_prange():
corr_content = []
for t in range(8):
corr_content.append(pe.pseudo_Obs(2 + 10 ** (1.1 * t), 0.2, 't'))
corr = pe.correlators.Corr(corr_content)
corr.set_prange([2, 4])
with pytest.raises(Exception):
corr.set_prange([2])
with pytest.raises(Exception):
corr.set_prange([2, 2.3])
with pytest.raises(Exception):
corr.set_prange([4, 1])
def test_matrix_corr():
corr_aa = _gen_corr(1)
corr_ab = 0.5 * corr_aa
corr_mat = pe.Corr(np.array([[corr_aa, corr_ab], [corr_ab, corr_aa]]))
corr_mat.gamma_method()
corr_mat.item(0, 0)
for (ts, sort) in zip([None, 1, 1], ["Eigenvalue", "Eigenvector", None]):
vecs = corr_mat.GEVP(0, ts=ts, sort=sort)
corr_0 = corr_mat.projected(vecs[0])
corr_1 = corr_mat.projected(vecs[1])
assert np.all([o == 0 for o in corr_0 - corr_aa])
assert np.all([o == 0 for o in corr_1 - corr_aa])
corr_mat.matrix_symmetric()
corr_mat.GEVP(0, state=0)
corr_mat.Eigenvalue(2, state=0)
def test_corr_none_entries():
a = pe.pseudo_Obs(1.0, 0.1, 'a')
la = np.asarray([[a, a], [a, a]])
n = np.asarray([[None, None], [None, None]])
x = [la, n]
matr = pe.Corr(x)
matr.projected(np.asarray([1.0, 0.0]))
matr * 2 - 2 * matr
matr * matr + matr ** 2 / matr
for func in [np.sqrt, np.log, np.exp, np.sin, np.cos, np.tan, np.sinh, np.cosh, np.tanh]:
func(matr)
def test_GEVP_warnings():
corr_aa = _gen_corr(1)
corr_ab = 0.5 * corr_aa
corr_mat = pe.Corr(np.array([[corr_aa, corr_ab], [corr_ab, corr_aa]]))
corr_mat.item(0, 0)
with pytest.warns(RuntimeWarning):
corr_mat.GEVP(0, 1, sort="Eigenvalue")
with pytest.warns(DeprecationWarning):
corr_mat.GEVP(0, sorted_list="Eigenvalue")
def test_GEVP_exceptions():
corr_aa = _gen_corr(1)
corr_ab = 0.5 * corr_aa
corr_mat = pe.Corr(np.array([[corr_aa, corr_ab], [corr_ab, corr_aa]]))
corr_mat.item(0, 0)
with pytest.raises(Exception):
corr_mat.item(0, 0).projected()
with pytest.raises(Exception):
corr_mat.item(0, 0).GEVP(2)
with pytest.raises(Exception):
corr_mat.item(0, 0).matrix_symmetric()
with pytest.raises(Exception):
corr_mat.GEVP(0, 0, sort=None)
with pytest.raises(Exception):
corr_mat.GEVP(0, sort=None)
with pytest.raises(Exception):
corr_mat.GEVP(1, 0, sort="Eigenvector")
with pytest.raises(Exception):
corr_mat.GEVP(0, 1, sort="This sorting method does not exist.")
with pytest.raises(Exception):
corr_mat.plottable()
with pytest.raises(Exception):
corr_mat.spaghetti_plot()
with pytest.raises(Exception):
corr_mat.show()
with pytest.raises(Exception):
corr_mat.m_eff()
with pytest.raises(Exception):
corr_mat.Hankel(2)
with pytest.raises(Exception):
corr_mat.plateau()
with pytest.raises(Exception):
corr_mat.plateau([2, 4])
with pytest.raises(Exception):
corr_mat.hankel(3)
with pytest.raises(Exception):
corr_mat.fit(lambda x: x[0])
with pytest.raises(Exception):
corr_0.item(0, 0)
with pytest.raises(Exception):
corr_0.matrix_symmetric()
n_corr_mat = -corr_mat
with pytest.raises(np.linalg.LinAlgError):
n_corr_mat.GEVP(2)
def test_matrix_symmetric():
corr_aa = _gen_corr(1)
corr_ab = _gen_corr(0.3)
corr_ba = _gen_corr(0.2)
corr_bb = _gen_corr(0.8)
corr_mat = pe.Corr(np.array([[corr_aa, corr_ab], [corr_ba, corr_bb]]))
sym_corr_mat = corr_mat.matrix_symmetric()
assert np.all([np.all(o == o.T) for o in sym_corr_mat])
t_obs = pe.pseudo_Obs(1.0, 0.1, 'test')
o_mat = np.array([[t_obs, t_obs], [t_obs, t_obs]])
corr1 = pe.Corr([o_mat, None, o_mat])
corr2 = pe.Corr([o_mat, np.array([[None, None], [None, None]]), o_mat])
corr3 = pe.Corr([o_mat, np.array([[t_obs, None], [None, t_obs]], dtype=object), o_mat])
corr1.matrix_symmetric()
corr2.matrix_symmetric()
corr3.matrix_symmetric()
def test_is_matrix_symmetric():
corr_data = []
for t in range(4):
mat = np.zeros((4, 4), dtype=object)
for i in range(4):
for j in range(i, 4):
obs = pe.pseudo_Obs(0.1, 0.047, "rgetrasrewe53455b153v13v5/*/*sdfgb")
mat[i, j] = obs
if i != j:
mat[j, i] = obs
corr_data.append(mat)
corr = pe.Corr(corr_data, padding=[0, 2])
assert corr.is_matrix_symmetric()
corr[0][0, 1] = 1.0 * corr[0][0, 1]
assert corr.is_matrix_symmetric()
corr[3][2, 1] = (1 + 1e-14) * corr[3][2, 1]
assert corr.is_matrix_symmetric()
corr[0][0, 1] = 1.1 * corr[0][0, 1]
assert not corr.is_matrix_symmetric()
def test_GEVP_solver():
mat1 = np.random.rand(15, 15)
mat2 = np.random.rand(15, 15)
mat1 = mat1 @ mat1.T
mat2 = mat2 @ mat2.T
sp_val, sp_vecs = scipy.linalg.eigh(mat1, mat2)
sp_vecs = [sp_vecs[:, np.argsort(sp_val)[-i]] for i in range(1, sp_vecs.shape[0] + 1)]
sp_vecs = [v / np.sqrt((v.T @ mat2 @ v)) for v in sp_vecs]
assert np.allclose(sp_vecs, pe.correlators._GEVP_solver(mat1, mat2), atol=1e-14)
def test_GEVP_none_entries():
t_obs = pe.pseudo_Obs(1.0, 0.1, 'test')
t_obs2 = pe.pseudo_Obs(0.1, 0.1, 'test')
o_mat = np.array([[t_obs, t_obs2], [t_obs2, t_obs2]])
n_arr = np.array([[None, None], [None, None]])
corr = pe.Corr([o_mat, o_mat, o_mat, o_mat, o_mat, o_mat, None, o_mat, n_arr, None, o_mat])
corr.GEVP(t0=2)
def test_hankel():
corr_content = []
for t in range(8):
exponent = 1.2
corr_content.append(pe.pseudo_Obs(2 + t ** exponent, 0.2, 't'))
corr = pe.Corr(corr_content)
corr.Hankel(2)
corr.Hankel(6, periodic=True)
def test_thin():
c = pe.Corr([pe.pseudo_Obs(i, .1, 'test') for i in range(10)])
c *= pe.cov_Obs(1., .1, '#ren')
thin = c.thin()
thin.gamma_method()
thin.fit(lambda a, x: a[0] * x)
c.thin(offset=1)
c.thin(3, offset=1)
def test_corr_matrix_none_entries():
dim = 8
x = np.arange(dim)
y = 2 * np.exp(-0.06 * x) + np.random.normal(0.0, 0.15, dim)
yerr = [0.1] * dim
oy = []
for i, item in enumerate(x):
oy.append(pe.pseudo_Obs(y[i], yerr[i], 'test'))
corr = pe.Corr(oy)
corr = corr.deriv()
pe.Corr(np.array([[corr, corr], [corr, corr]]))
def test_corr_vector_operations():
my_corr = _gen_corr(1.0)
my_vec = np.arange(1, 17)
my_corr + my_vec
my_corr - my_vec
my_corr * my_vec
my_corr / my_vec
assert np.all([o == 0 for o in ((my_corr + my_vec) - my_vec) - my_corr])
assert np.all([o == 0 for o in ((my_corr - my_vec) + my_vec) - my_corr])
assert np.all([o == 0 for o in ((my_corr * my_vec) / my_vec) - my_corr])
assert np.all([o == 0 for o in ((my_corr / my_vec) * my_vec) - my_corr])
def test_spaghetti_plot():
corr = _gen_corr(12, 50)
corr += pe.pseudo_Obs(0.0, 0.1, 'another_ensemble|r0')
corr += pe.pseudo_Obs(0.0, 0.1, 'another_ensemble|r1')
corr += pe.cov_Obs(0.0, 0.01 ** 2, 'covobs')
corr.spaghetti_plot(True)
corr.spaghetti_plot(False)
plt.close('all')
def _gen_corr(val, samples=2000):
corr_content = []
for t in range(16):
corr_content.append(pe.pseudo_Obs(val, 0.1, 't', samples))
return pe.correlators.Corr(corr_content)
def test_prune():
corr_aa = _gen_corr(1)
corr_ab = 0.5 * corr_aa
corr_ac = 0.25 * corr_aa
corr_mat = pe.Corr(np.array([[corr_aa, corr_ab, corr_ac], [corr_ab, corr_aa, corr_ab], [corr_ac, corr_ab, corr_aa]]))
p = corr_mat.prune(2)
assert(all([o.is_zero() for o in p.item(0, 1)]))
a = [(o - 1) for o in p.item(1, 1)]
[o.gamma_method() for o in a]
assert(all([o.is_zero_within_error() for o in a]))
with pytest.raises(Exception):
corr_mat.prune(3)
corr_mat.prune(4)
def test_complex_Corr():
o1 = pe.pseudo_Obs(1.0, 0.1, "test")
cobs = pe.CObs(o1, -o1)
ccorr = pe.Corr([cobs, cobs, cobs])
assert np.all([ccorr.imag[i] == -ccorr.real[i] for i in range(ccorr.T)])
print(ccorr)
mcorr = pe.Corr(np.array([[ccorr, ccorr], [ccorr, ccorr]]))
assert np.all([mcorr.imag[i] == -mcorr.real[i] for i in range(mcorr.T)])
def test_corr_no_filtering():
li = [-pe.pseudo_Obs(.2, .1, 'a', samples=10) for i in range(96)]
for i in range(len(li)):
li[i].idl['a'] = range(1, 21, 2)
c= pe.Corr(li)
b = pe.pseudo_Obs(1, 1e-11, 'a', samples=30)
c *= b
assert np.all([c[0].idl == o.idl for o in c])
def test_corr_symmetric():
obs = []
for _ in range(4):
obs.append(pe.pseudo_Obs(np.random.rand(), 0.1, "test"))
for corr in [pe.Corr([obs[0] + 8, obs[1], obs[2], obs[3]]),
pe.Corr([obs[0] + 8, obs[1], obs[2], None]),
pe.Corr([None, obs[1], obs[2], obs[3]])]:
scorr = corr.symmetric()
assert scorr[1] == scorr[3]
assert scorr[2] == corr[2]
assert scorr[0] == corr[0]
def test_two_matrix_corr_inits():
T = 4
rn = lambda : np.random.normal(0.5, 0.1)
# Generate T random CObs in a list
list_of_timeslices =[]
for i in range(T):
re = pe.pseudo_Obs(rn(), rn(), "test")
im = pe.pseudo_Obs(rn(), rn(), "test")
list_of_timeslices.append(pe.CObs(re, im))
# First option: Correlator of matrix of correlators
corr = pe.Corr(list_of_timeslices)
mat_corr1 = pe.Corr(np.array([[corr, corr], [corr, corr]]))
# Second option: Correlator of list of arrays per timeslice
list_of_arrays = [np.array([[elem, elem], [elem, elem]]) for elem in list_of_timeslices]
mat_corr2 = pe.Corr(list_of_arrays)
for el in mat_corr1 - mat_corr2:
assert np.all(el == 0)
def test_matmul_overloading():
N = 4
rn = lambda : np.random.normal(0.5, 0.1)
# Generate N^2 random CObs and assemble them in an array
ll =[]
for i in range(N ** 2):
re = pe.pseudo_Obs(rn(), rn(), "test")
im = pe.pseudo_Obs(rn(), rn(), "test")
ll.append(pe.CObs(re, im))
mat = np.array(ll).reshape(N, N)
# Multiply with gamma matrix
corr = pe.Corr([mat] * 4, padding=[0, 1])
# __matmul__
mcorr = corr @ pe.dirac.gammaX
comp = mat @ pe.dirac.gammaX
for i in range(4):
assert np.all(mcorr[i] == comp)
# __rmatmul__
mcorr = pe.dirac.gammaX @ corr
comp = pe.dirac.gammaX @ mat
for i in range(4):
assert np.all(mcorr[i] == comp)
test_mat = pe.dirac.gamma5 + pe.dirac.gammaX
icorr = corr @ test_mat @ np.linalg.inv(test_mat)
tt = corr - icorr
for i in range(4):
assert np.all(tt[i] == 0)
# associative property
tt = (corr.real @ pe.dirac.gammaX + corr.imag @ (pe.dirac.gammaX * 1j)) - corr @ pe.dirac.gammaX
for el in tt:
if el is not None:
assert np.all(el == 0)
corr2 = corr @ corr
for i in range(4):
np.all(corr2[i] == corr[i] @ corr[i])
def test_matrix_trace():
N = 4
rn = lambda : np.random.normal(0.5, 0.1)
# Generate N^2 random CObs and assemble them in an array
ll =[]
for i in range(N ** 2):
re = pe.pseudo_Obs(rn(), rn(), "test")
im = pe.pseudo_Obs(rn(), rn(), "test")
ll.append(pe.CObs(re, im))
mat = np.array(ll).reshape(N, N)
corr = pe.Corr([mat] * 4)
# Explicitly check trace
for el in corr.trace():
el == np.sum(np.diag(mat))
# Trace is cyclic
for one, two in zip((pe.dirac.gammaX @ corr).trace(), (corr @ pe.dirac.gammaX).trace()):
assert np.all(one == two)
# Antisymmetric matrices are traceless.
mat = (mat - mat.T) / 2
corr = pe.Corr([mat] * 4)
for el in corr.trace():
assert el == 0
with pytest.raises(ValueError):
corr.item(0, 0).trace()
def test_corr_roll():
T = 4
rn = lambda : np.random.normal(0.5, 0.1)
ll = []
for i in range(T):
re = pe.pseudo_Obs(rn(), rn(), "test")
im = pe.pseudo_Obs(rn(), rn(), "test")
ll.append(pe.CObs(re, im))
# Rolling by T should produce the same correlator
corr = pe.Corr(ll)
tt = corr - corr.roll(T)
for el in tt:
assert np.all(el == 0)
mcorr = pe.Corr(np.array([[corr, corr + 0.1], [corr - 0.1, 2 * corr]]))
tt = mcorr.roll(T) - mcorr
for el in tt:
assert np.all(el == 0)