mirror of
https://github.com/fjosw/pyerrors.git
synced 2025-05-14 19:43:41 +02:00
Merge branch 'develop' into documentation
This commit is contained in:
fjosw
commit
752b82f48b
7 changed files with 114 additions and 62 deletions
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@ -12,7 +12,7 @@ def test_jsonio():
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o = pe.pseudo_Obs(1.0, .2, 'one')
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o2 = pe.pseudo_Obs(0.5, .1, 'two|r1')
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o3 = pe.pseudo_Obs(0.5, .1, 'two|r2')
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o4 = pe.merge_obs([o2, o3])
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o4 = pe.merge_obs([o2, o3, pe.pseudo_Obs(0.5, .1, 'two|r3', samples=3221)])
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otag = 'This has been merged!'
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o4.tag = otag
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do = o - .2 * o4
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@ -101,8 +101,8 @@ def test_json_string_reconstruction():
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def test_json_corr_io():
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my_list = [pe.Obs([np.random.normal(1.0, 0.1, 100)], ['ens1']) for o in range(8)]
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rw_list = pe.reweight(pe.Obs([np.random.normal(1.0, 0.1, 100)], ['ens1']), my_list)
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my_list = [pe.Obs([np.random.normal(1.0, 0.1, 100), np.random.normal(1.0, 0.1, 321)], ['ens1|r1', 'ens1|r2'], idl=[range(1, 201, 2), range(321)]) for o in range(8)]
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rw_list = pe.reweight(pe.Obs([np.random.normal(1.0, 0.1, 100), np.random.normal(1.0, 0.1, 321)], ['ens1|r1', 'ens1|r2'], idl=[range(1, 201, 2), range(321)]), my_list)
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for obs_list in [my_list, rw_list]:
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for tag in [None, "test"]:
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@ -111,40 +111,51 @@ def test_json_corr_io():
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for corr_tag in [None, 'my_Corr_tag']:
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for prange in [None, [3, 6]]:
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for gap in [False, True]:
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my_corr = pe.Corr(obs_list, padding=[pad, pad], prange=prange)
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my_corr.tag = corr_tag
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if gap:
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my_corr.content[4] = None
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pe.input.json.dump_to_json(my_corr, 'corr')
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recover = pe.input.json.load_json('corr')
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os.remove('corr.json.gz')
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assert np.all([o.is_zero() for o in [x for x in (my_corr - recover) if x is not None]])
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for index, entry in enumerate(my_corr):
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if entry is None:
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assert recover[index] is None
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assert my_corr.tag == recover.tag
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assert my_corr.prange == recover.prange
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assert my_corr.reweighted == recover.reweighted
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for mult in [1., pe.cov_Obs([12.22, 1.21], [.212**2, .11**2], 'renorm')[0]]:
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my_corr = mult * pe.Corr(obs_list, padding=[pad, pad], prange=prange)
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my_corr.tag = corr_tag
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if gap:
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my_corr.content[4] = None
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pe.input.json.dump_to_json(my_corr, 'corr')
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recover = pe.input.json.load_json('corr')
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os.remove('corr.json.gz')
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assert np.all([o.is_zero() for o in [x for x in (my_corr - recover) if x is not None]])
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for index, entry in enumerate(my_corr):
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if entry is None:
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assert recover[index] is None
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assert my_corr.tag == recover.tag
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assert my_corr.prange == recover.prange
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assert my_corr.reweighted == recover.reweighted
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def test_json_corr_2d_io():
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obs_list = [np.array([[pe.pseudo_Obs(1.0 + i, 0.1 * i, 'test'), pe.pseudo_Obs(0.0, 0.1 * i, 'test')], [pe.pseudo_Obs(0.0, 0.1 * i, 'test'), pe.pseudo_Obs(1.0 + i, 0.1 * i, 'test')]]) for i in range(4)]
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obs_list = [np.array([
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[
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pe.merge_obs([pe.pseudo_Obs(1.0 + i, 0.1 * i, 'test|r2'), pe.pseudo_Obs(1.0 + i, 0.1 * i, 'test|r1', samples=321)]),
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pe.merge_obs([pe.pseudo_Obs(0.0, 0.1 * i, 'test|r2'), pe.pseudo_Obs(0.0, 0.1 * i, 'test|r1', samples=321)]),
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],
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[
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pe.merge_obs([pe.pseudo_Obs(0.0, 0.1 * i, 'test|r2'), pe.pseudo_Obs(0.0, 0.1 * i, 'test|r1', samples=321),]),
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pe.merge_obs([pe.pseudo_Obs(1.0 + i, 0.1 * i, 'test|r2'), pe.pseudo_Obs(1.0 + i, 0.1 * i, 'test|r1', samples=321)]),
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],
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]) for i in range(4)]
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for tag in [None, "test"]:
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obs_list[3][0, 1].tag = tag
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for padding in [0, 1]:
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for prange in [None, [3, 6]]:
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my_corr = pe.Corr(obs_list, padding=[padding, padding], prange=prange)
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my_corr.tag = tag
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pe.input.json.dump_to_json(my_corr, 'corr')
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recover = pe.input.json.load_json('corr')
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os.remove('corr.json.gz')
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assert np.all([np.all([o.is_zero() for o in q]) for q in [x.ravel() for x in (my_corr - recover) if x is not None]])
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for index, entry in enumerate(my_corr):
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if entry is None:
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assert recover[index] is None
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assert my_corr.tag == recover.tag
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assert my_corr.prange == recover.prange
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for mult in [1., pe.cov_Obs([12.22, 1.21], [.212**2, .11**2], 'renorm')[0]]:
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my_corr = mult * pe.Corr(obs_list, padding=[padding, padding], prange=prange)
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my_corr.tag = tag
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pe.input.json.dump_to_json(my_corr, 'corr')
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recover = pe.input.json.load_json('corr')
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os.remove('corr.json.gz')
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assert np.all([np.all([o.is_zero() for o in q]) for q in [x.ravel() for x in (my_corr - recover) if x is not None]])
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for index, entry in enumerate(my_corr):
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if entry is None:
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assert recover[index] is None
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assert my_corr.tag == recover.tag
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assert my_corr.prange == recover.prange
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def test_json_dict_io():
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@ -211,6 +222,7 @@ def test_json_dict_io():
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'd': pe.pseudo_Obs(.01, .001, 'testd', samples=10) * pe.cov_Obs(1, .01, 'cov1'),
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'se': None,
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'sf': 1.2,
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'k': pe.cov_Obs(.1, .001**2, 'cov') * pe.merge_obs([pe.pseudo_Obs(1.0, 0.1, 'test|r2'), pe.pseudo_Obs(1.0, 0.1, 'test|r1', samples=321)]),
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}
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}
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@ -314,7 +326,7 @@ def test_dobsio():
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o2 = pe.pseudo_Obs(0.5, .1, 'two|r1')
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o3 = pe.pseudo_Obs(0.5, .1, 'two|r2')
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o4 = pe.merge_obs([o2, o3])
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o4 = pe.merge_obs([o2, o3, pe.pseudo_Obs(0.5, .1, 'two|r3', samples=3221)])
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otag = 'This has been merged!'
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o4.tag = otag
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do = o - .2 * o4
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@ -328,7 +340,7 @@ def test_dobsio():
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o5 /= co2[0]
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o5.tag = 2 * otag
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tt1 = pe.Obs([np.random.rand(100), np.random.rand(100)], ['t|r1', 't|r2'], idl=[range(2, 202, 2), range(22, 222, 2)])
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tt1 = pe.Obs([np.random.rand(100), np.random.rand(102)], ['t|r1', 't|r2'], idl=[range(2, 202, 2), range(22, 226, 2)])
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tt3 = pe.Obs([np.random.rand(102)], ['qe|r1'])
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tt = tt1 + tt3
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@ -337,7 +349,7 @@ def test_dobsio():
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tt4 = pe.Obs([np.random.rand(100), np.random.rand(100)], ['t|r1', 't|r2'], idl=[range(1, 101, 1), range(2, 202, 2)])
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ol = [o2, o3, o4, do, o5, tt, tt4, np.log(tt4 / o5**2), np.exp(o5 + np.log(co3 / tt3 + o4) / tt)]
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ol = [o2, o3, o4, do, o5, tt, tt4, np.log(tt4 / o5**2), np.exp(o5 + np.log(co3 / tt3 + o4) / tt), o4.reweight(o4)]
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print(ol)
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fname = 'test_rw'
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@ -362,9 +374,12 @@ def test_dobsio():
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def test_reconstruct_non_linear_r_obs(tmp_path):
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to = pe.Obs([np.random.rand(500), np.random.rand(500), np.random.rand(111)],
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["e|r1", "e|r2", "my_new_ensemble_54^£$|8'[@124435%6^7&()~#"],
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idl=[range(1, 501), range(0, 500), range(1, 999, 9)])
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to = (
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pe.Obs([np.random.rand(500), np.random.rand(1200)],
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["e|r1", "e|r2", ],
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idl=[range(1, 501), range(0, 1200)])
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+ pe.Obs([np.random.rand(111)], ["my_new_ensemble_54^£$|8'[@124435%6^7&()~#"], idl=[range(1, 999, 9)])
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)
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to = np.log(to ** 2) / to
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to.dump((tmp_path / "test_equality").as_posix())
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ro = pe.input.json.load_json((tmp_path / "test_equality").as_posix())
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@ -372,9 +387,12 @@ def test_reconstruct_non_linear_r_obs(tmp_path):
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def test_reconstruct_non_linear_r_obs_list(tmp_path):
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to = pe.Obs([np.random.rand(500), np.random.rand(500), np.random.rand(111)],
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["e|r1", "e|r2", "my_new_ensemble_54^£$|8'[@124435%6^7&()~#"],
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idl=[range(1, 501), range(0, 500), range(1, 999, 9)])
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to = (
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pe.Obs([np.random.rand(500), np.random.rand(1200)],
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["e|r1", "e|r2", ],
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idl=[range(1, 501), range(0, 1200)])
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+ pe.Obs([np.random.rand(111)], ["my_new_ensemble_54^£$|8'[@124435%6^7&()~#"], idl=[range(1, 999, 9)])
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)
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to = np.log(to ** 2) / to
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for to_list in [[to, to, to], np.array([to, to, to])]:
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pe.input.json.dump_to_json(to_list, (tmp_path / "test_equality_list").as_posix())
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@ -34,7 +34,7 @@ def test_matmul():
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my_list = []
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length = 100 + np.random.randint(200)
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for i in range(dim ** 2):
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my_list.append(pe.Obs([np.random.rand(length), np.random.rand(length + 1)], ['t1', 't2']))
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my_list.append(pe.Obs([np.random.rand(length)], ['t1']) + pe.Obs([np.random.rand(length + 1)], ['t2']))
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my_array = const * np.array(my_list).reshape((dim, dim))
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tt = pe.linalg.matmul(my_array, my_array) - my_array @ my_array
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for t, e in np.ndenumerate(tt):
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@ -43,8 +43,8 @@ def test_matmul():
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my_list = []
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length = 100 + np.random.randint(200)
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for i in range(dim ** 2):
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my_list.append(pe.CObs(pe.Obs([np.random.rand(length), np.random.rand(length + 1)], ['t1', 't2']),
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pe.Obs([np.random.rand(length), np.random.rand(length + 1)], ['t1', 't2'])))
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my_list.append(pe.CObs(pe.Obs([np.random.rand(length)], ['t1']) + pe.Obs([np.random.rand(length + 1)], ['t2']),
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pe.Obs([np.random.rand(length)], ['t1']) + pe.Obs([np.random.rand(length + 1)], ['t2'])))
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my_array = np.array(my_list).reshape((dim, dim)) * const
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tt = pe.linalg.matmul(my_array, my_array) - my_array @ my_array
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for t, e in np.ndenumerate(tt):
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@ -151,7 +151,7 @@ def test_multi_dot():
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my_list = []
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length = 1000 + np.random.randint(200)
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for i in range(dim ** 2):
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my_list.append(pe.Obs([np.random.rand(length), np.random.rand(length + 1)], ['t1', 't2']))
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my_list.append(pe.Obs([np.random.rand(length)], ['t1']) + pe.Obs([np.random.rand(length + 1)], ['t2']))
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my_array = pe.cov_Obs(1.0, 0.002, 'cov') * np.array(my_list).reshape((dim, dim))
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tt = pe.linalg.matmul(my_array, my_array, my_array, my_array) - my_array @ my_array @ my_array @ my_array
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for t, e in np.ndenumerate(tt):
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@ -160,8 +160,8 @@ def test_multi_dot():
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my_list = []
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length = 1000 + np.random.randint(200)
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for i in range(dim ** 2):
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my_list.append(pe.CObs(pe.Obs([np.random.rand(length), np.random.rand(length + 1)], ['t1', 't2']),
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pe.Obs([np.random.rand(length), np.random.rand(length + 1)], ['t1', 't2'])))
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my_list.append(pe.CObs(pe.Obs([np.random.rand(length)], ['t1']) + pe.Obs([np.random.rand(length + 1)], ['t2']),
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pe.Obs([np.random.rand(length)], ['t1']) + pe.Obs([np.random.rand(length + 1)], ['t2'])))
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my_array = np.array(my_list).reshape((dim, dim)) * pe.cov_Obs(1.0, 0.002, 'cov')
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tt = pe.linalg.matmul(my_array, my_array, my_array, my_array) - my_array @ my_array @ my_array @ my_array
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for t, e in np.ndenumerate(tt):
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@ -209,7 +209,7 @@ def test_irregular_matrix_inverse():
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for idl in [range(8, 508, 10), range(250, 273), [2, 8, 19, 20, 78, 99, 828, 10548979]]:
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irregular_array = []
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for i in range(dim ** 2):
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irregular_array.append(pe.Obs([np.random.normal(1.1, 0.2, len(idl)), np.random.normal(0.25, 0.1, 10)], ['ens1', 'ens2'], idl=[idl, range(1, 11)]))
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irregular_array.append(pe.Obs([np.random.normal(1.1, 0.2, len(idl))], ['ens1'], idl=[idl]) + pe.Obs([np.random.normal(0.25, 0.1, 10)], ['ens2'], idl=[range(1, 11)]))
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irregular_matrix = np.array(irregular_array).reshape((dim, dim)) * pe.cov_Obs(1.0, 0.002, 'cov') * pe.pseudo_Obs(1.0, 0.002, 'ens2|r23')
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invertible_irregular_matrix = np.identity(dim) + irregular_matrix @ irregular_matrix.T
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@ -333,7 +333,7 @@ def test_derived_observables():
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def test_multi_ens():
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names = ['A0', 'A1|r001', 'A1|r002']
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test_obs = pe.Obs([np.random.rand(50), np.random.rand(50), np.random.rand(50)], names)
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test_obs = pe.Obs([np.random.rand(50)], names[:1]) + pe.Obs([np.random.rand(50), np.random.rand(50)], names[1:])
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assert test_obs.e_names == ['A0', 'A1']
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assert test_obs.e_content['A0'] == ['A0']
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assert test_obs.e_content['A1'] == ['A1|r001', 'A1|r002']
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@ -345,6 +345,9 @@ def test_multi_ens():
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ensembles.append(str(i))
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assert my_sum.e_names == sorted(ensembles)
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with pytest.raises(ValueError):
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test_obs = pe.Obs([np.random.rand(50), np.random.rand(50), np.random.rand(50)], names)
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def test_multi_ens2():
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names = ['ens', 'e', 'en', 'e|r010', 'E|er', 'ens|', 'Ens|34', 'ens|r548984654ez4e3t34terh']
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@ -498,18 +501,25 @@ def test_reweighting():
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with pytest.raises(ValueError):
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pe.reweight(my_irregular_obs, [my_obs])
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my_merged_obs = my_obs + pe.Obs([np.random.rand(1000)], ['q'])
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with pytest.raises(ValueError):
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pe.reweight(my_merged_obs, [my_merged_obs])
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def test_merge_obs():
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my_obs1 = pe.Obs([np.random.rand(100)], ['t'])
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my_obs2 = pe.Obs([np.random.rand(100)], ['q'], idl=[range(1, 200, 2)])
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my_obs1 = pe.Obs([np.random.normal(1, .1, 100)], ['t|1'])
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my_obs2 = pe.Obs([np.random.normal(1, .1, 100)], ['t|2'], idl=[range(1, 200, 2)])
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merged = pe.merge_obs([my_obs1, my_obs2])
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diff = merged - my_obs2 - my_obs1
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assert diff == -(my_obs1.value + my_obs2.value) / 2
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diff = merged - (my_obs2 + my_obs1) / 2
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assert np.isclose(0, diff.value, atol=1e-16)
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with pytest.raises(ValueError):
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pe.merge_obs([my_obs1, my_obs1])
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my_covobs = pe.cov_Obs(1.0, 0.003, 'cov')
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with pytest.raises(ValueError):
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pe.merge_obs([my_obs1, my_covobs])
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my_obs3 = pe.Obs([np.random.rand(100)], ['q|2'], idl=[range(1, 200, 2)])
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with pytest.raises(ValueError):
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pe.merge_obs([my_obs1, my_obs3])
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@ -542,6 +552,9 @@ def test_correlate():
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my_obs6 = pe.Obs([np.random.rand(100)], ['t'], idl=[range(5, 505, 5)])
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corr3 = pe.correlate(my_obs5, my_obs6)
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assert my_obs5.idl == corr3.idl
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my_obs7 = pe.Obs([np.random.rand(99)], ['q'])
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with pytest.raises(ValueError):
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pe.correlate(my_obs1, my_obs7)
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my_new_obs = pe.Obs([np.random.rand(100)], ['q3'])
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with pytest.raises(ValueError):
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@ -681,14 +694,14 @@ def test_gamma_method_irregular():
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assert (a.dvalue - 5 * a.ddvalue < expe and expe < a.dvalue + 5 * a.ddvalue)
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arr2 = np.random.normal(1, .2, size=N)
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afull = pe.Obs([arr, arr2], ['a1', 'a2'])
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afull = pe.Obs([arr], ['a1']) + pe.Obs([arr2], ['a2'])
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configs = np.ones_like(arr2)
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for i in np.random.uniform(0, len(arr2), size=int(.8*N)):
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configs[int(i)] = 0
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zero_arr2 = [arr2[i] for i in range(len(arr2)) if not configs[i] == 0]
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idx2 = [i + 1 for i in range(len(configs)) if configs[i] == 1]
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a = pe.Obs([zero_arr, zero_arr2], ['a1', 'a2'], idl=[idx, idx2])
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a = pe.Obs([zero_arr], ['a1'], idl=[idx]) + pe.Obs([zero_arr2], ['a2'], idl=[idx2])
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afull.gamma_method()
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a.gamma_method()
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@ -1022,7 +1035,7 @@ def test_correlation_intersection_of_idls():
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def test_covariance_non_identical_objects():
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obs1 = pe.Obs([np.random.normal(1.0, 0.1, 1000), np.random.normal(1.0, 0.1, 1000), np.random.normal(1.0, 0.1, 732)], ["ens|r1", "ens|r2", "ens2"])
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obs1 = pe.Obs([np.random.normal(1.0, 0.1, 1000), np.random.normal(1.0, 0.1, 1000)], ["ens|r1", "ens|r2"]) + pe.Obs([np.random.normal(1.0, 0.1, 732)], ['ens2'])
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obs1.gamma_method()
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obs2 = obs1 + 1e-18
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obs2.gamma_method()
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@ -1106,6 +1119,9 @@ def test_reweight_method():
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obs1 = pe.pseudo_Obs(0.2, 0.01, 'test')
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rw = pe.pseudo_Obs(0.999, 0.001, 'test')
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assert obs1.reweight(rw) == pe.reweight(rw, [obs1])[0]
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rw2 = pe.pseudo_Obs(0.999, 0.001, 'test2')
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with pytest.raises(ValueError):
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obs1.reweight(rw2)
|
||||
|
||||
|
||||
def test_jackknife():
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue