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Merge pull request #147 from fjosw/fix/non_overlapping_cnfgs
Fix non overlapping configurations
This commit is contained in:
Fabian Joswig
GitHub
commit
2e66f0323a
4 changed files with 127 additions and 30 deletions
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@ -397,7 +397,7 @@ def read_pobs(fname, full_output=False, gz=True, separator_insertion=None):
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# this is based on Mattia Bruno's implementation at https://github.com/mbruno46/pyobs/blob/master/pyobs/IO/xml.py
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def import_dobs_string(content, noempty=False, full_output=False, separator_insertion=True):
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def import_dobs_string(content, full_output=False, separator_insertion=True):
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"""Import a list of Obs from a string in the Zeuthen dobs format.
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Tags are not written or recovered automatically.
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@ -406,9 +406,6 @@ def import_dobs_string(content, noempty=False, full_output=False, separator_inse
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----------
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content : str
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XML string containing the data
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noemtpy : bool
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If True, ensembles with no contribution to the Obs are not included.
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If False, ensembles are included as written in the file, possibly with vanishing entries.
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full_output : bool
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If True, a dict containing auxiliary information and the data is returned.
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If False, only the data is returned as list.
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@ -457,7 +454,6 @@ def import_dobs_string(content, noempty=False, full_output=False, separator_inse
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_check(dobs[4].tag == "ne")
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ne = int(dobs[4].text.strip())
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_check(dobs[5].tag == "nc")
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nc = int(dobs[5].text.strip())
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idld = {}
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deltad = {}
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@ -507,7 +503,11 @@ def import_dobs_string(content, noempty=False, full_output=False, separator_inse
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for name in names:
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for i in range(len(deltad[name])):
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deltad[name][i] = np.array(deltad[name][i]) + mean[i]
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tmp = np.zeros_like(deltad[name][i])
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for j in range(len(deltad[name][i])):
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if deltad[name][i][j] != 0.:
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tmp[j] = deltad[name][i][j] + mean[i]
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deltad[name][i] = tmp
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res = []
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for i in range(len(mean)):
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@ -516,11 +516,19 @@ def import_dobs_string(content, noempty=False, full_output=False, separator_inse
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obs_names = []
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for name in names:
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h = np.unique(deltad[name][i])
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if len(h) == 1 and np.all(h == mean[i]) and noempty:
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if len(h) == 1 and np.all(h == mean[i]):
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continue
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deltas.append(deltad[name][i])
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obs_names.append(name)
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idl.append(idld[name])
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repdeltas = []
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repidl = []
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for j in range(len(deltad[name][i])):
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if deltad[name][i][j] != 0.:
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repdeltas.append(deltad[name][i][j])
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repidl.append(idld[name][j])
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if len(repdeltas) > 0:
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obs_names.append(name)
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deltas.append(repdeltas)
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idl.append(repidl)
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res.append(Obs(deltas, obs_names, idl=idl))
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res[-1]._value = mean[i]
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_check(len(e_names) == ne)
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@ -528,13 +536,10 @@ def import_dobs_string(content, noempty=False, full_output=False, separator_inse
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cnames = list(covd.keys())
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for i in range(len(res)):
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new_covobs = {name: Covobs(0, covd[name], name, grad=gradd[name][i]) for name in cnames}
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if noempty:
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for name in cnames:
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if np.all(new_covobs[name].grad == 0):
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del new_covobs[name]
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cnames_loc = list(new_covobs.keys())
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else:
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cnames_loc = cnames
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for name in cnames:
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if np.all(new_covobs[name].grad == 0):
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del new_covobs[name]
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cnames_loc = list(new_covobs.keys())
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for name in cnames_loc:
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res[i].names.append(name)
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res[i].shape[name] = 1
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@ -546,8 +551,6 @@ def import_dobs_string(content, noempty=False, full_output=False, separator_inse
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res[i].tag = symbol[i]
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if res[i].tag == 'None':
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res[i].tag = None
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if not noempty:
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_check(len(res[0].covobs.keys()) == nc)
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if full_output:
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retd = {}
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tool = file_origin.get('tool', None)
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@ -568,7 +571,7 @@ def import_dobs_string(content, noempty=False, full_output=False, separator_inse
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return res
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def read_dobs(fname, noempty=False, full_output=False, gz=True, separator_insertion=True):
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def read_dobs(fname, full_output=False, gz=True, separator_insertion=True):
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"""Import a list of Obs from an xml.gz file in the Zeuthen dobs format.
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Tags are not written or recovered automatically.
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@ -577,9 +580,6 @@ def read_dobs(fname, noempty=False, full_output=False, gz=True, separator_insert
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----------
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fname : str
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Filename of the input file.
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noemtpy : bool
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If True, ensembles with no contribution to the Obs are not included.
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If False, ensembles are included as written in the file.
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full_output : bool
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If True, a dict containing auxiliary information and the data is returned.
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If False, only the data is returned as list.
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@ -615,7 +615,7 @@ def read_dobs(fname, noempty=False, full_output=False, gz=True, separator_insert
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with open(fname, 'r') as fin:
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content = fin.read()
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return import_dobs_string(content, noempty, full_output, separator_insertion=separator_insertion)
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return import_dobs_string(content, full_output, separator_insertion=separator_insertion)
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def _dobsdict_to_xmlstring(d):
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@ -782,7 +782,7 @@ def create_dobs_string(obsl, name, spec='dobs v1.0', origin='', symbol=[], who=N
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o = obsl[oi]
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if repname in o.idl:
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if counters[oi] < 0:
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num = offsets[oi]
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num = 0
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if num == 0:
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data += '0 '
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else:
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@ -798,7 +798,7 @@ def create_dobs_string(obsl, name, spec='dobs v1.0', origin='', symbol=[], who=N
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if counters[oi] >= len(o.idl[repname]):
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counters[oi] = -1
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else:
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num = offsets[oi]
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num = 0
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if num == 0:
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data += '0 '
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else:
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@ -1097,7 +1097,7 @@ def _expand_deltas_for_merge(deltas, idx, shape, new_idx):
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ret = np.zeros(new_idx[-1] - new_idx[0] + 1)
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for i in range(shape):
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ret[idx[i] - new_idx[0]] = deltas[i]
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return np.array([ret[new_idx[i] - new_idx[0]] for i in range(len(new_idx))])
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return np.array([ret[new_idx[i] - new_idx[0]] for i in range(len(new_idx))]) * len(new_idx) / len(idx)
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def derived_observable(func, data, array_mode=False, **kwargs):
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@ -339,7 +339,7 @@ def test_dobsio():
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dobsio.write_dobs(ol, fname, 'TEST')
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rl = dobsio.read_dobs(fname, noempty=True)
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rl = dobsio.read_dobs(fname)
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os.remove(fname + '.xml.gz')
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[o.gamma_method() for o in rl]
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@ -566,7 +566,7 @@ def test_intersection_reduce():
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intersection = pe.obs._intersection_idx([o.idl["ens"] for o in [obs1, obs_merge]])
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coll = pe.obs._reduce_deltas(obs_merge.deltas["ens"], obs_merge.idl["ens"], range1)
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assert np.all(coll == obs1.deltas["ens"])
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assert np.allclose(coll, obs1.deltas["ens"] * (len(obs_merge.idl["ens"]) / len(range1)))
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def test_irregular_error_propagation():
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@ -878,7 +878,7 @@ def test_correlation_intersection_of_idls():
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cov1 = pe.covariance([obs1, obs2_a])
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corr1 = pe.covariance([obs1, obs2_a], correlation=True)
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obs2_b = obs2_a + pe.Obs([np.random.normal(1.0, 0.1, len(range2))], ["ens"], idl=[range2])
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obs2_b = (obs2_a + pe.Obs([np.random.normal(1.0, 0.1, len(range2))], ["ens"], idl=[range2])) / 2
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obs2_b.gamma_method()
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cov2 = pe.covariance([obs1, obs2_b])
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@ -1038,6 +1038,7 @@ def test_hash():
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assert hash(obs) != hash(o1)
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assert hash(o1) != hash(o2)
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def test_gm_alias():
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samples = np.random.rand(500)
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@ -1049,3 +1050,99 @@ def test_gm_alias():
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assert np.isclose(tt1.dvalue, tt2.dvalue)
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def test_overlapping_missing_cnfgs():
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length = 200000
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l_samp = np.random.normal(2.87, 0.5, length)
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s_samp = np.random.normal(7.87, 0.7, length // 2)
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o1 = pe.Obs([l_samp], ["test"])
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o2 = pe.Obs([s_samp], ["test"], idl=[range(1, length, 2)])
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a2 = pe.Obs([s_samp], ["alt"])
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t1 = o1 + o2
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t1.gm(S=0)
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t2 = o1 + a2
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t2.gm(S=0)
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assert np.isclose(t1.value, t2.value)
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assert np.isclose(t1.dvalue, t2.dvalue, rtol=0.01)
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def test_non_overlapping_missing_cnfgs():
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length = 100000
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xsamp = np.random.normal(1.0, 1.0, length)
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full = pe.Obs([xsamp], ["ensemble"], idl=[range(0, length)])
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full.gm()
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even = pe.Obs([xsamp[0:length:2]], ["ensemble"], idl=[range(0, length, 2)])
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odd = pe.Obs([xsamp[1:length:2]], ["ensemble"], idl=[range(1, length, 2)])
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average = (even + odd) / 2
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average.gm(S=0)
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assert np.isclose(full.value, average.value)
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assert np.isclose(full.dvalue, average.dvalue, rtol=0.01)
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def test_non_overlapping_operations():
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length = 100000
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samples = np.random.normal(0.93, 0.5, length)
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e = pe.Obs([samples[0:length:2]], ["ensemble"], idl=[range(0, length, 2)])
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o = pe.Obs([samples[1:length:2]], ["ensemble"], idl=[range(1, length, 2)])
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e2 = pe.Obs([samples[0:length:2]], ["even"])
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o2 = pe.Obs([samples[1:length:2]], ["odd"])
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for func in [lambda a, b: a + b,
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lambda a, b: a - b,
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lambda a, b: a * b,
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lambda a, b: a / b,
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lambda a, b: a ** b]:
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res1 = func(e, o)
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res1.gm(S=0)
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res2 = func(e2, o2)
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res2.gm(S=0)
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print(res1, res2)
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print((res1.dvalue - res2.dvalue) / res1.dvalue)
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assert np.isclose(res1.value, res2.value)
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assert np.isclose(res1.dvalue, res2.dvalue, rtol=0.01)
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def test_non_overlapping_operations_different_lengths():
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length = 100000
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samples = np.random.normal(0.93, 0.5, length)
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first = samples[:length // 5]
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second = samples[length // 5:]
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f1 = pe.Obs([first], ["ensemble"], idl=[range(1, length // 5 + 1)])
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s1 = pe.Obs([second], ["ensemble"], idl=[range(length // 5, length)])
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f2 = pe.Obs([first], ["first"])
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s2 = pe.Obs([second], ["second"])
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for func in [lambda a, b: a + b,
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lambda a, b: a - b,
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lambda a, b: a * b,
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lambda a, b: a / b,
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lambda a, b: a ** b,
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lambda a, b: a ** 2 + b ** 2 / a]:
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res1 = func(f1, f1)
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res1.gm(S=0)
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res2 = func(f2, f2)
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res2.gm(S=0)
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assert np.isclose(res1.value, res2.value)
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assert np.isclose(res1.dvalue, res2.dvalue, rtol=0.01)
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