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Fix/gaps (#169)

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* fix: Fixed range detection in gamma_method

* Corrected test in dobsio

* Changed expansion paradigm in gamma_method

* Extended tests

* Updated docstrings

* Removed unnecessary intermediate variable

* Removed unnecessary code

* Fixed previously introduced bug in output in obs.details()

* Fixed previously introduced bug in window determination

* New criterion for matching gapped replica, fixed determination of w_max and fixed tau_exp analysis with gaps

* tests: split up consistency test for gamma method.

---------

Co-authored-by: Fabian Joswig <fabian.joswig@ed.ac.uk>
This commit is contained in:
s-kuberski 2023-04-20 18:30:57 +02:00 • committed by GitHub
parent edcb4de8aa
commit a214465413
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2 changed files with 125 additions and 60 deletions
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87
tests/obs_test.py
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@ -539,6 +539,12 @@ def test_merge_idx():
assert pe.obs._merge_idx([range(10, 1010, 10), range(10, 1010, 50)]) == range(10, 1010, 10)
assert pe.obs._merge_idx([range(500, 6050, 50), range(500, 6250, 250)]) == range(500, 6250, 50)
idl = [list(np.arange(1, 14)) + list(range(16, 100, 4)), range(4, 604, 4), [2, 4, 5, 6, 8, 9, 12, 24], range(1, 20, 1), range(50, 789, 7)]
new_idx = pe.obs._merge_idx(idl)
assert(new_idx[-1] > new_idx[0])
for i in range(1, len(new_idx)):
assert(new_idx[i - 1] < new_idx[i])
def test_intersection_idx():
assert pe.obs._intersection_idx([range(1, 100), range(1, 100), range(1, 100)]) == range(1, 100)
@ -549,6 +555,7 @@ def test_intersection_idx():
for ids in [[list(range(1, 80, 3)), list(range(1, 100, 2))], [range(1, 80, 3), range(1, 100, 2), range(1, 100, 7)]]:
assert list(pe.obs._intersection_idx(ids)) == pe.obs._intersection_idx([list(o) for o in ids])
def test_merge_intersection():
for idl_list in [[range(1, 100), range(1, 100), range(1, 100)],
[range(4, 80, 6), range(4, 80, 6)],
@ -585,6 +592,18 @@ def test_irregular_error_propagation():
assert obs1 == obs1 + (obs2 - obs2)
def test_gamma_method_consistent():
dat = np.sin(np.arange(100) / 100)
for idl in [np.arange(100), np.arange(0, 1000, 10)]:
my_obs = pe.Obs([dat], ["test_ens"], idl=[idl])
assert np.isclose(my_obs.value, 0.4554865083873183)
my_obs.gm(S=0)
assert np.isclose(my_obs.dvalue, 0.02495954189079061)
my_obs.gm()
assert np.isclose(my_obs.dvalue, 0.11817931680985193)
def test_gamma_method_irregular():
N = 20000
arr = np.random.normal(1, .2, size=N)
@ -690,6 +709,30 @@ def test_gamma_method_irregular():
assert np.isclose(tau_a, tau_b)
dat = [np.random.normal(loc=1., size=10) for i in range(2)]
idl = [[0, 2, 4, 8, 10, 12, 14, 16, 18, 20], np.arange(0, 20, 2)]
o = pe.Obs(dat, ['A|r1', 'A|r2'], idl=idl)
o.gm()
assert(pe.obs._determine_gap(o, o.e_content, 'A') == 2)
dat = [np.random.normal(loc=1., size=10) for i in range(3)]
idl = [[0, 2, 4, 8, 10, 12, 14, 16, 18, 20], np.arange(0, 20, 2), range(10)]
o = pe.Obs(dat, ['A|r1', 'A|r2', 'A|r5'], idl=idl)
o.gm()
assert(pe.obs._determine_gap(o, o.e_content, 'A') == 1)
dat = np.sin(np.arange(100) / 100)
idl = [np.arange(100), np.arange(0, 1000, 10), list(np.arange(0, 100, 10)) + list(np.arange(180, 1080, 10)), range(1, 500, 5)]
my_obs = pe.Obs([dat for i in range(len(idl))], ['%s|%d' % ('A', i) for i in range(len(idl))], idl=idl)
my_obs.gm()
idl = idl[1:]
my_obs = pe.Obs([dat for i in range(len(idl))], ['%s|%d' % ('A', i) for i in range(len(idl))], idl=idl)
my_obs.gm()
idl += [range(1, 400, 4)]
my_obs = pe.Obs([dat for i in range(len(idl))], ['%s|%d' % ('A', i) for i in range(len(idl))], idl=idl)
with pytest.raises(Exception):
my_obs.gm()
def test_irregular_gapped_dtauint():
my_idl = list(range(0, 5010, 10))
@ -697,15 +740,36 @@ def test_irregular_gapped_dtauint():
my_idl2 = list(range(0, 501, 1))
my_idl2.remove(40)
my_data = np.random.normal(1.1, 0.2, 500)
obs = pe.Obs([my_data], ["B1"], idl=[my_idl])
obs.gamma_method()
for i in range(42):
my_data = np.random.normal(1.1, 0.2, 500)
obs = pe.Obs([my_data], ["B1"], idl=[my_idl])
obs.gamma_method()
obs2 = pe.Obs([my_data], ["B2"], idl=[my_idl2])
obs2.gamma_method()
obs2 = pe.Obs([my_data], ["B2"], idl=[my_idl2])
obs2.gamma_method()
assert np.isclose(obs.e_tauint["B1"], obs2.e_tauint["B2"])
assert np.isclose(obs.e_dtauint["B1"], obs2.e_dtauint["B2"])
assert np.isclose(obs.e_tauint["B1"], obs2.e_tauint["B2"])
assert np.isclose(obs.e_dtauint["B1"], obs2.e_dtauint["B2"])
assert np.isclose(obs.e_dvalue["B1"], obs2.e_dvalue["B2"])
assert np.isclose(obs.e_ddvalue["B1"], obs2.e_ddvalue["B2"])
assert len(obs.e_rho["B1"]) == len(obs2.e_rho["B2"])
obs.gamma_method(tau_exp=1)
obs2.gamma_method(tau_exp=1)
assert np.isclose(obs.e_tauint["B1"], obs2.e_tauint["B2"])
assert np.isclose(obs.e_dtauint["B1"], obs2.e_dtauint["B2"])
assert np.isclose(obs.e_dvalue["B1"], obs2.e_dvalue["B2"])
assert np.isclose(obs.e_ddvalue["B1"], obs2.e_ddvalue["B2"])
assert len(obs.e_rho["B1"]) == len(obs2.e_rho["B2"])
obs.gamma_method(S=0)
obs2.gamma_method(S=0)
assert np.isclose(obs.e_tauint["B1"], obs2.e_tauint["B2"])
assert np.isclose(obs.e_dtauint["B1"], obs2.e_dtauint["B2"])
assert np.isclose(obs.e_dvalue["B1"], obs2.e_dvalue["B2"])
assert np.isclose(obs.e_ddvalue["B1"], obs2.e_ddvalue["B2"])
def test_covariance_is_variance():
@ -854,6 +918,7 @@ def test_covariance_rank_deficient():
with pytest.warns(RuntimeWarning):
pe.covariance(obs)
def test_covariance_idl():
range1 = range(10, 1010, 10)
range2 = range(10, 1010, 50)
@ -1000,14 +1065,6 @@ def test_reduce_deltas():
assert(np.alltrue([float(i) for i in idx_new] == new))
def test_merge_idx():
idl = [list(np.arange(1, 14)) + list(range(16, 100, 4)), range(4, 604, 4), [2, 4, 5, 6, 8, 9, 12, 24], range(1, 20, 1), range(50, 789, 7)]
new_idx = pe.obs._merge_idx(idl)
assert(new_idx[-1] > new_idx[0])
for i in range(1, len(new_idx)):
assert(new_idx[i - 1] < new_idx[i])
def test_cobs_array():
cobs = pe.Obs([np.random.normal(1.0, 0.1, 100)], ['t']) * (1 + 2j)
np.identity(4) + cobs