restruct for easier tests, test drop of sfcf params
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2 changed files with 103 additions and 113 deletions
149
corrlib/find.py
149
corrlib/find.py
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@ -162,6 +162,78 @@ def _db_lookup(db: Path, ensemble: str, correlator_name: str, code: str, project
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return results
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def _sfcf_drop(param, **kwargs):
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if 'offset' in kwargs:
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if kwargs.get('offset') != param['offset']:
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return True
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if 'quark_kappas' in kwargs:
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kappas = kwargs['quark_kappas']
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if (not np.isclose(kappas[0], param['quarks'][0]['mass']) or not np.isclose(kappas[1], param['quarks'][1]['mass'])):
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return True
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if 'quark_masses' in kwargs:
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masses = kwargs['quark_masses']
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if (not np.isclose(masses[0], k2m(param['quarks'][0]['mass'])) or not np.isclose(masses[1], k2m(param['quarks'][1]['mass']))):
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return True
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if 'qk1' in kwargs:
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quark_kappa1 = kwargs['qk1']
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if not isinstance(quark_kappa1, list):
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if (not np.isclose(quark_kappa1, param['quarks'][0]['mass'])):
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return True
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else:
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if len(quark_kappa1) == 2:
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if (quark_kappa1[0] > param['quarks'][0]['mass']) or (quark_kappa1[1] < param['quarks'][0]['mass']):
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return True
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else:
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raise ValueError("quark_kappa1 has to have length 2")
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if 'qk2' in kwargs:
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quark_kappa2 = kwargs['qk2']
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if not isinstance(quark_kappa2, list):
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if (not np.isclose(quark_kappa2, param['quarks'][1]['mass'])):
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return True
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else:
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if len(quark_kappa2) == 2:
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if (quark_kappa2[0] > param['quarks'][1]['mass']) or (quark_kappa2[1] < param['quarks'][1]['mass']):
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return True
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else:
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raise ValueError("quark_kappa2 has to have length 2")
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if 'qm1' in kwargs:
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quark_mass1 = kwargs['qm1']
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if not isinstance(quark_mass1, list):
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if (not np.isclose(quark_mass1, k2m(param['quarks'][0]['mass']))):
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return True
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else:
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if len(quark_mass1) == 2:
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if (quark_mass1[0] > k2m(param['quarks'][0]['mass'])) or (quark_mass1[1] < k2m(param['quarks'][0]['mass'])):
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return True
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else:
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raise ValueError("quark_mass1 has to have length 2")
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if 'qm2' in kwargs:
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quark_mass2 = kwargs['qm2']
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if not isinstance(quark_mass2, list):
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if (not np.isclose(quark_mass2, k2m(param['quarks'][1]['mass']))):
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return True
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else:
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if len(quark_mass2) == 2:
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if (quark_mass2[0] > k2m(param['quarks'][1]['mass'])) or (quark_mass2[1] < k2m(param['quarks'][1]['mass'])):
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return True
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else:
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raise ValueError("quark_mass2 has to have length 2")
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if 'quark_thetas' in kwargs:
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quark_thetas = kwargs['quark_thetas']
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if (quark_thetas[0] != param['quarks'][0]['thetas'] and quark_thetas[1] != param['quarks'][1]['thetas']) or (quark_thetas[0] != param['quarks'][1]['thetas'] and quark_thetas[1] != param['quarks'][0]['thetas']):
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return True
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# careful, this is not save, when multiple contributions are present!
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if 'wf1' in kwargs:
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wf1 = kwargs['wf1']
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if not (np.isclose(wf1[0][0], param['wf1'][0][0], 1e-8) and np.isclose(wf1[0][1][0], param['wf1'][0][1][0], 1e-8) and np.isclose(wf1[0][1][1], param['wf1'][0][1][1], 1e-8)):
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return True
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if 'wf2' in kwargs:
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wf2 = kwargs['wf2']
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if not (np.isclose(wf2[0][0], param['wf2'][0][0], 1e-8) and np.isclose(wf2[0][1][0], param['wf2'][0][1][0], 1e-8) and np.isclose(wf2[0][1][1], param['wf2'][0][1][1], 1e-8)):
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return True
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return False
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def sfcf_filter(results: pd.DataFrame, **kwargs: Any) -> pd.DataFrame:
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r"""
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Filter method for the Database entries holding SFCF calculations.
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@ -191,84 +263,13 @@ def sfcf_filter(results: pd.DataFrame, **kwargs: Any) -> pd.DataFrame:
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results: pd.DataFrame
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The filtered DataFrame, only holding the records that fit to the parameters given.
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"""
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drops = []
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for ind in range(len(results)):
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result = results.iloc[ind]
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param = json.loads(result['parameters'])
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if 'offset' in kwargs:
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if kwargs.get('offset') != param['offset']:
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drops.append(ind)
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continue
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if 'quark_kappas' in kwargs:
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kappas = kwargs['quark_kappas']
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if (not np.isclose(kappas[0], param['quarks'][0]['mass']) or not np.isclose(kappas[1], param['quarks'][1]['mass'])):
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drops.append(ind)
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continue
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if 'quark_masses' in kwargs:
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masses = kwargs['quark_masses']
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if (not np.isclose(masses[0], k2m(param['quarks'][0]['mass'])) or not np.isclose(masses[1], k2m(param['quarks'][1]['mass']))):
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drops.append(ind)
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continue
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if 'qk1' in kwargs:
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quark_kappa1 = kwargs['qk1']
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if not isinstance(quark_kappa1, list):
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if (not np.isclose(quark_kappa1, param['quarks'][0]['mass'])):
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drops.append(ind)
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continue
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else:
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if len(quark_kappa1) == 2:
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if (quark_kappa1[0] > param['quarks'][0]['mass']) or (quark_kappa1[1] < param['quarks'][0]['mass']):
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drops.append(ind)
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continue
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if 'qk2' in kwargs:
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quark_kappa2 = kwargs['qk2']
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if not isinstance(quark_kappa2, list):
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if (not np.isclose(quark_kappa2, param['quarks'][1]['mass'])):
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drops.append(ind)
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continue
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else:
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if len(quark_kappa2) == 2:
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if (quark_kappa2[0] > param['quarks'][1]['mass']) or (quark_kappa2[1] < param['quarks'][1]['mass']):
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drops.append(ind)
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continue
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if 'qm1' in kwargs:
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quark_mass1 = kwargs['qm1']
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if not isinstance(quark_mass1, list):
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if (not np.isclose(quark_mass1, k2m(param['quarks'][0]['mass']))):
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drops.append(ind)
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continue
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else:
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if len(quark_mass1) == 2:
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if (quark_mass1[0] > k2m(param['quarks'][0]['mass'])) or (quark_mass1[1] < k2m(param['quarks'][0]['mass'])):
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drops.append(ind)
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continue
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if 'qm2' in kwargs:
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quark_mass2 = kwargs['qm2']
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if not isinstance(quark_mass2, list):
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if (not np.isclose(quark_mass2, k2m(param['quarks'][1]['mass']))):
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drops.append(ind)
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continue
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else:
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if len(quark_mass2) == 2:
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if (quark_mass2[0] > k2m(param['quarks'][1]['mass'])) or (quark_mass2[1] < k2m(param['quarks'][1]['mass'])):
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drops.append(ind)
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continue
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if 'quark_thetas' in kwargs:
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quark_thetas = kwargs['quark_thetas']
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if (quark_thetas[0] != param['quarks'][0]['thetas'] and quark_thetas[1] != param['quarks'][1]['thetas']) or (quark_thetas[0] != param['quarks'][1]['thetas'] and quark_thetas[1] != param['quarks'][0]['thetas']):
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drops.append(ind)
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continue
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# careful, this is not save, when multiple contributions are present!
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if 'wf1' in kwargs:
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wf1 = kwargs['wf1']
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if not (np.isclose(wf1[0][0], param['wf1'][0][0], 1e-8) and np.isclose(wf1[0][1][0], param['wf1'][0][1][0], 1e-8) and np.isclose(wf1[0][1][1], param['wf1'][0][1][1], 1e-8)):
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drops.append(ind)
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continue
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if 'wf2' in kwargs:
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wf2 = kwargs['wf2']
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if not (np.isclose(wf2[0][0], param['wf2'][0][0], 1e-8) and np.isclose(wf2[0][1][0], param['wf2'][0][1][0], 1e-8) and np.isclose(wf2[0][1][1], param['wf2'][0][1][1], 1e-8)):
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drops.append(ind)
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continue
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if _sfcf_drop(param, **kwargs):
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drops.append(ind)
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return results.drop(drops)
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@ -211,62 +211,51 @@ def test_db_lookup(tmp_path: Path) -> None:
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conn.close()
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def test_sfcf_filter() -> None:
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record_0 = ["f_A", "ensA", "sfcf", "archive/SF_A/f_A/Project_A.json.gz::asdfasdfasdf", "SF_A", '{"par_A": 5.0, "par_B": 5.0}', "projects/SF_A/input.in",
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'2025-03-26 12:55:18.229966', '2025-03-26 12:55:18.229966']
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record_1 = ["f_A", "ensA", "sfcf", "archive/SF_A/f_A/Project_A.json.gz::asdfasdfasdf", "SF_A", '{"par_A": 5.0, "par_B": 5.0}', "projects/SF_A/input.in",
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'2025-03-26 12:55:18.229966', '2025-03-26 12:55:18.229966']
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record_2 = ["f_P", "ensA", "sfcf", "archive/SF_A/f_A/Project_A.json.gz::asdfasdfasdf", "SF_A", '{"par_A": 5.0, "par_B": 5.0}', "projects/SF_A/input.in",
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'2025-03-26 12:55:18.229966', '2025-03-26 12:55:18.229966']
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record_3 = ["f_P", "ensA", "sfcf", "archive/SF_A/f_A/Project_A.json.gz::asdfasdfasdf", "SF_A", '{"par_A": 5.0, "par_B": 5.0}', "projects/SF_A/input.in",
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'2025-03-26 12:55:18.229966', '2025-03-26 12:55:18.229966']
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data = [
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record_0,
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record_1,
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record_2,
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record_3,
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]
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cols = ["name",
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"ensemble",
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"code",
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"path",
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"project",
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"parameters",
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"parameter_file",
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"created_at",
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"updated_at"]
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df = pd.DataFrame(data,columns=cols)
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def test_sfcf_drop() -> None:
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parameters0 = {
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'offset': [0,0,0],
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'quarks': [{'mass': 1, 'thetas': [0,0,0]}, {'mass': 2, 'thetas': [0,0,1]}], # m0s = -3.5, -3.75
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'wf1': [[1, [0, 0]], [0.5, [1, 0]], [.75, [.5, .5]]],
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'wf2': [[1, [2, 1]], [2, [0.5, -0.5]], [.5, [.75, .72]]],
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}
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assert True
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assert not find._sfcf_drop(parameters0, offset=[0,0,0])
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assert find._sfcf_drop(parameters0, offset=[1,0,0])
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assert not find._sfcf_drop(parameters0, quark_kappas = [1, 2])
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assert find._sfcf_drop(parameters0, quark_kappas = [-3.1, -3.72])
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assert not find._sfcf_drop(parameters0, quark_masses = [-3.5, -3.75])
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assert find._sfcf_drop(parameters0, quark_masses = [-3.1, -3.72])
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assert not find._sfcf_drop(parameters0, qk1 = 1)
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assert not find._sfcf_drop(parameters0, qk2 = 2)
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assert find._sfcf_drop(parameters0, qk1 = 2)
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assert find._sfcf_drop(parameters0, qk2 = 1)
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assert not find._sfcf_drop(parameters0, qk1 = [0.5,1.5])
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assert not find._sfcf_drop(parameters0, qk2 = [1.5,2.5])
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assert find._sfcf_drop(parameters0, qk1 = 2)
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assert find._sfcf_drop(parameters0, qk2 = 1)
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with pytest.raises(ValueError):
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assert not find._sfcf_drop(parameters0, qk1 = [0.5,1,5])
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with pytest.raises(ValueError):
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assert not find._sfcf_drop(parameters0, qk2 = [1,5,2.5])
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assert find._sfcf_drop(parameters0, qm1 = 1.2)
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assert find._sfcf_drop(parameters0, qm2 = 2.2)
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assert not find._sfcf_drop(parameters0, qm1 = -3.5)
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assert not find._sfcf_drop(parameters0, qm2 = -3.75)
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assert find._sfcf_drop(parameters0, qm2 = 1.2)
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assert find._sfcf_drop(parameters0, qm1 = 2.2)
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with pytest.raises(ValueError):
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assert not find._sfcf_drop(parameters0, qm1 = [0.5,1,5])
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with pytest.raises(ValueError):
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assert not find._sfcf_drop(parameters0, qm2 = [1,5,2.5])
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def test_openQCD_filter() -> None:
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record_0 = ["f_A", "ensA", "sfcf", "archive/SF_A/f_A/Project_A.json.gz::asdfasdfasdf", "SF_A", '{"par_A": 5.0, "par_B": 5.0}', "projects/SF_A/input.in",
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'2025-03-26 12:55:18.229966', '2025-03-26 12:55:18.229966']
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record_1 = ["f_A", "ensA", "sfcf", "archive/SF_A/f_A/Project_A.json.gz::asdfasdfasdf", "SF_A", '{"par_A": 5.0, "par_B": 5.0}', "projects/SF_A/input.in",
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'2025-03-26 12:55:18.229966', '2025-03-26 12:55:18.229966']
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record_2 = ["f_P", "ensA", "sfcf", "archive/SF_A/f_A/Project_A.json.gz::asdfasdfasdf", "SF_A", '{"par_A": 5.0, "par_B": 5.0}', "projects/SF_A/input.in",
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'2025-03-26 12:55:18.229966', '2025-03-26 12:55:18.229966']
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record_3 = ["f_P", "ensA", "sfcf", "archive/SF_A/f_A/Project_A.json.gz::asdfasdfasdf", "SF_A", '{"par_A": 5.0, "par_B": 5.0}', "projects/SF_A/input.in",
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'2025-03-26 12:55:18.229966', '2025-03-26 12:55:18.229966']
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data = [
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record_0,
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record_1,
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record_2,
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record_3,
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]
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cols = ["name",
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"ensemble",
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"code",
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"path",
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"project",
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"parameters",
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"parameter_file",
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"created_at",
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"updated_at"]
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df = pd.DataFrame(data,columns=cols)
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find.openQCD_filter(df)
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assert True
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