diff --git a/corrlib/find.py b/corrlib/find.py index 4c51e05..7b07321 100644 --- a/corrlib/find.py +++ b/corrlib/find.py @@ -6,8 +6,12 @@ import numpy as np from .input.implementations import codes from .tools import k2m, get_db_file from .tracker import get +from .integrity import has_valid_times from typing import Any, Optional from pathlib import Path +import datetime as dt +from collections.abc import Callable +import warnings def _project_lookup_by_alias(db: Path, alias: str) -> str: @@ -38,7 +42,7 @@ def _project_lookup_by_alias(db: Path, alias: str) -> str: return str(results[0][0]) -def _project_lookup_by_id(db: Path, uuid: str) -> list[tuple[str, str]]: +def _project_lookup_by_id(db: Path, uuid: str) -> list[tuple[str, ...]]: """ Return the project information available in the database by UUID. @@ -62,8 +66,56 @@ def _project_lookup_by_id(db: Path, uuid: str) -> list[tuple[str, str]]: return results -def _db_lookup(db: Path, ensemble: str, correlator_name: str, code: str, project: Optional[str]=None, parameters: Optional[str]=None, - created_before: Optional[str]=None, created_after: Optional[Any]=None, updated_before: Optional[Any]=None, updated_after: Optional[Any]=None) -> pd.DataFrame: +def _time_filter(results: pd.DataFrame, created_before: Optional[str]=None, created_after: Optional[Any]=None, updated_before: Optional[Any]=None, updated_after: Optional[Any]=None) -> pd.DataFrame: + """ + Filter the results from the database in terms of the creation and update times. + + Parameters + ---------- + results: pd.DataFrame + The dataframe holding the unfilteres results from the database. + created_before: str + Contraint on the creation date in datetime.datetime.isoformat. Note that this is exclusive. The creation date has to be truly before the date and time given. + created_after: str + Contraint on the creation date in datetime.datetime.isoformat. Note that this is exclusive. The creation date has to be truly after the date and time given. + updated_before: str + Contraint on the creation date in datetime.datetime.isoformat. Note that this is exclusive. The date of the last update has to be truly before the date and time given. + updated_after: str + Contraint on the creation date in datetime.datetime.isoformat. Note that this is exclusive. The date of the last update has to be truly after the date and time given. + """ + drops = [] + for ind in range(len(results)): + result = results.iloc[ind] + created_at = dt.datetime.fromisoformat(result['created_at']) + updated_at = dt.datetime.fromisoformat(result['updated_at']) + db_times_valid = has_valid_times(result) + if not db_times_valid: + raise ValueError('Time stamps not valid for result with path', result["path"]) + + if created_before is not None: + date_created_before = dt.datetime.fromisoformat(created_before) + if date_created_before < created_at: + drops.append(ind) + continue + if created_after is not None: + date_created_after = dt.datetime.fromisoformat(created_after) + if date_created_after > created_at: + drops.append(ind) + continue + if updated_before is not None: + date_updated_before = dt.datetime.fromisoformat(updated_before) + if date_updated_before < updated_at: + drops.append(ind) + continue + if updated_after is not None: + date_updated_after = dt.datetime.fromisoformat(updated_after) + if date_updated_after > updated_at: + drops.append(ind) + continue + return results.drop(drops) + + +def _db_lookup(db: Path, ensemble: str, correlator_name: str, code: str, project: Optional[str]=None, parameters: Optional[str]=None) -> pd.DataFrame: """ Look up a correlator record in the database by the data given to the method. @@ -105,20 +157,84 @@ def _db_lookup(db: Path, ensemble: str, correlator_name: str, code: str, project search_expr += f" AND code = '{code}'" if parameters: search_expr += f" AND parameters = '{parameters}'" - if created_before: - search_expr += f" AND created_at < '{created_before}'" - if created_after: - search_expr += f" AND created_at > '{created_after}'" - if updated_before: - search_expr += f" AND updated_at < '{updated_before}'" - if updated_after: - search_expr += f" AND updated_at > '{updated_after}'" conn = sqlite3.connect(db) results = pd.read_sql(search_expr, conn) conn.close() return results +def _sfcf_drop(param: dict[str, Any], **kwargs: Any) -> bool: + if 'offset' in kwargs: + if kwargs.get('offset') != param['offset']: + return True + if 'quark_kappas' in kwargs: + kappas = kwargs['quark_kappas'] + if (not np.isclose(kappas[0], param['quarks'][0]['mass']) or not np.isclose(kappas[1], param['quarks'][1]['mass'])): + return True + if 'quark_masses' in kwargs: + masses = kwargs['quark_masses'] + if (not np.isclose(masses[0], k2m(param['quarks'][0]['mass'])) or not np.isclose(masses[1], k2m(param['quarks'][1]['mass']))): + return True + if 'qk1' in kwargs: + quark_kappa1 = kwargs['qk1'] + if not isinstance(quark_kappa1, list): + if (not np.isclose(quark_kappa1, param['quarks'][0]['mass'])): + return True + else: + if len(quark_kappa1) == 2: + if (quark_kappa1[0] > param['quarks'][0]['mass']) or (quark_kappa1[1] < param['quarks'][0]['mass']): + return True + else: + raise ValueError("quark_kappa1 has to have length 2") + if 'qk2' in kwargs: + quark_kappa2 = kwargs['qk2'] + if not isinstance(quark_kappa2, list): + if (not np.isclose(quark_kappa2, param['quarks'][1]['mass'])): + return True + else: + if len(quark_kappa2) == 2: + if (quark_kappa2[0] > param['quarks'][1]['mass']) or (quark_kappa2[1] < param['quarks'][1]['mass']): + return True + else: + raise ValueError("quark_kappa2 has to have length 2") + if 'qm1' in kwargs: + quark_mass1 = kwargs['qm1'] + if not isinstance(quark_mass1, list): + if (not np.isclose(quark_mass1, k2m(param['quarks'][0]['mass']))): + return True + else: + if len(quark_mass1) == 2: + if (quark_mass1[0] > k2m(param['quarks'][0]['mass'])) or (quark_mass1[1] < k2m(param['quarks'][0]['mass'])): + return True + else: + raise ValueError("quark_mass1 has to have length 2") + if 'qm2' in kwargs: + quark_mass2 = kwargs['qm2'] + if not isinstance(quark_mass2, list): + if (not np.isclose(quark_mass2, k2m(param['quarks'][1]['mass']))): + return True + else: + if len(quark_mass2) == 2: + if (quark_mass2[0] > k2m(param['quarks'][1]['mass'])) or (quark_mass2[1] < k2m(param['quarks'][1]['mass'])): + return True + else: + raise ValueError("quark_mass2 has to have length 2") + if 'quark_thetas' in kwargs: + quark_thetas = kwargs['quark_thetas'] + 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']): + return True + # careful, this is not save, when multiple contributions are present! + if 'wf1' in kwargs: + wf1 = kwargs['wf1'] + 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)): + return True + if 'wf2' in kwargs: + wf2 = kwargs['wf2'] + 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)): + return True + return False + + def sfcf_filter(results: pd.DataFrame, **kwargs: Any) -> pd.DataFrame: r""" Filter method for the Database entries holding SFCF calculations. @@ -136,9 +252,9 @@ def sfcf_filter(results: pd.DataFrame, **kwargs: Any) -> pd.DataFrame: qk2: float, optional Mass parameter $\kappa_2$ of the first quark. qm1: float, optional - Bare quak mass $m_1$ of the first quark. + Bare quark mass $m_1$ of the first quark. qm2: float, optional - Bare quak mass $m_1$ of the first quark. + Bare quark mass $m_2$ of the first quark. quarks_thetas: list[list[float]], optional wf1: optional wf2: optional @@ -148,101 +264,80 @@ def sfcf_filter(results: pd.DataFrame, **kwargs: Any) -> pd.DataFrame: results: pd.DataFrame The filtered DataFrame, only holding the records that fit to the parameters given. """ + drops = [] for ind in range(len(results)): result = results.iloc[ind] param = json.loads(result['parameters']) - if 'offset' in kwargs: - if kwargs.get('offset') != param['offset']: - drops.append(ind) - continue - if 'quark_kappas' in kwargs: - kappas = kwargs['quark_kappas'] - if (not np.isclose(kappas[0], param['quarks'][0]['mass']) or not np.isclose(kappas[1], param['quarks'][1]['mass'])): - drops.append(ind) - continue - if 'quark_masses' in kwargs: - masses = kwargs['quark_masses'] - if (not np.isclose(masses[0], k2m(param['quarks'][0]['mass'])) or not np.isclose(masses[1], k2m(param['quarks'][1]['mass']))): - drops.append(ind) - continue - if 'qk1' in kwargs: - quark_kappa1 = kwargs['qk1'] - if not isinstance(quark_kappa1, list): - if (not np.isclose(quark_kappa1, param['quarks'][0]['mass'])): - drops.append(ind) - continue - else: - if len(quark_kappa1) == 2: - if (quark_kappa1[0] > param['quarks'][0]['mass']) or (quark_kappa1[1] < param['quarks'][0]['mass']): - drops.append(ind) - continue - if 'qk2' in kwargs: - quark_kappa2 = kwargs['qk2'] - if not isinstance(quark_kappa2, list): - if (not np.isclose(quark_kappa2, param['quarks'][1]['mass'])): - drops.append(ind) - continue - else: - if len(quark_kappa2) == 2: - if (quark_kappa2[0] > param['quarks'][1]['mass']) or (quark_kappa2[1] < param['quarks'][1]['mass']): - drops.append(ind) - continue - if 'qm1' in kwargs: - quark_mass1 = kwargs['qm1'] - if not isinstance(quark_mass1, list): - if (not np.isclose(quark_mass1, k2m(param['quarks'][0]['mass']))): - drops.append(ind) - continue - else: - if len(quark_mass1) == 2: - if (quark_mass1[0] > k2m(param['quarks'][0]['mass'])) or (quark_mass1[1] < k2m(param['quarks'][0]['mass'])): - drops.append(ind) - continue - if 'qm2' in kwargs: - quark_mass2 = kwargs['qm2'] - if not isinstance(quark_mass2, list): - if (not np.isclose(quark_mass2, k2m(param['quarks'][1]['mass']))): - drops.append(ind) - continue - else: - if len(quark_mass2) == 2: - if (quark_mass2[0] > k2m(param['quarks'][1]['mass'])) or (quark_mass2[1] < k2m(param['quarks'][1]['mass'])): - drops.append(ind) - continue - if 'quark_thetas' in kwargs: - quark_thetas = kwargs['quark_thetas'] - 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']): - drops.append(ind) - continue - # careful, this is not save, when multiple contributions are present! - if 'wf1' in kwargs: - wf1 = kwargs['wf1'] - 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)): - drops.append(ind) - continue - if 'wf2' in kwargs: - wf2 = kwargs['wf2'] - 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)): - drops.append(ind) - continue + if _sfcf_drop(param, **kwargs): + drops.append(ind) return results.drop(drops) +def openQCD_filter(results:pd.DataFrame, **kwargs: Any) -> pd.DataFrame: + """ + Filter for parameters of openQCD. + + Parameters + ---------- + results: pd.DataFrame + The unfiltered list of results from the database. + + Returns + ------- + results: pd.DataFrame + The filtered results. + + """ + warnings.warn("A filter for openQCD parameters is no implemented yet.", Warning) + + return results + + +def _code_filter(results: pd.DataFrame, code: str, **kwargs: Any) -> pd.DataFrame: + """ + Abstraction of the filters for the different codes that are available. + At the moment, only openQCD and SFCF are known. + The possible key words for the parameters can be seen in the descriptionso f the code-specific filters. + + Parameters + ---------- + results: pd.DataFrame + The unfiltered list of results from the database. + code: str + The name of the code that produced the record at hand. + kwargs: + The keyworkd args that are handed over to the code-specific filters. + + Returns + ------- + results: pd.DataFrame + The filtered results. + """ + if code == "sfcf": + return sfcf_filter(results, **kwargs) + elif code == "openQCD": + return openQCD_filter(results, **kwargs) + else: + raise ValueError(f"Code {code} is not known.") + + def find_record(path: Path, ensemble: str, correlator_name: str, code: str, project: Optional[str]=None, parameters: Optional[str]=None, - created_before: Optional[str]=None, created_after: Optional[str]=None, updated_before: Optional[str]=None, updated_after: Optional[str]=None, revision: Optional[str]=None, **kwargs: Any) -> pd.DataFrame: + created_before: Optional[str]=None, created_after: Optional[str]=None, updated_before: Optional[str]=None, updated_after: Optional[str]=None, + revision: Optional[str]=None, + customFilter: Optional[Callable[[pd.DataFrame], pd.DataFrame]] = None, + **kwargs: Any) -> pd.DataFrame: db_file = get_db_file(path) db = path / db_file if code not in codes: raise ValueError("Code " + code + "unknown, take one of the following:" + ", ".join(codes)) get(path, db_file) - results = _db_lookup(db, ensemble, correlator_name,code, project, parameters=parameters, created_before=created_before, created_after=created_after, updated_before=updated_before, updated_after=updated_after) - if code == "sfcf": - results = sfcf_filter(results, **kwargs) - elif code == "openQCD": - pass - else: - raise Exception + results = _db_lookup(db, ensemble, correlator_name,code, project, parameters=parameters) + if any([arg is not None for arg in [created_before, created_after, updated_before, updated_after]]): + results = _time_filter(results, created_before, created_after, updated_before, updated_after) + results = _code_filter(results, code, **kwargs) + if customFilter is not None: + results = customFilter(results) print("Found " + str(len(results)) + " result" + ("s" if len(results)>1 else "")) return results.reset_index() diff --git a/corrlib/integrity.py b/corrlib/integrity.py new file mode 100644 index 0000000..d865944 --- /dev/null +++ b/corrlib/integrity.py @@ -0,0 +1,45 @@ +import datetime as dt +from pathlib import Path +from .tools import get_db_file +import pandas as pd +import sqlite3 + + +def has_valid_times(result: pd.Series) -> bool: + # we expect created_at <= updated_at <= now + created_at = dt.datetime.fromisoformat(result['created_at']) + updated_at = dt.datetime.fromisoformat(result['updated_at']) + if created_at > updated_at: + return False + if updated_at > dt.datetime.now(): + return False + return True + +def are_keys_unique(db: Path, table: str, col: str) -> bool: + conn = sqlite3.connect(db) + c = conn.cursor() + c.execute(f"SELECT COUNT( DISTINCT CAST(path AS nvarchar(4000))), COUNT({col}) FROM {table};") + results = c.fetchall()[0] + conn.close() + return bool(results[0] == results[1]) + + +def check_db_integrity(path: Path) -> None: + db = get_db_file(path) + + if not are_keys_unique(db, 'backlogs', 'path'): + raise Exception("The paths the backlog table of the database links are not unique.") + + search_expr = "SELECT * FROM 'backlogs'" + conn = sqlite3.connect(db) + results = pd.read_sql(search_expr, conn) + + for _, result in results.iterrows(): + if not has_valid_times(result): + raise ValueError(f"Result with id {result[id]} has wrong time signatures.") + + + +def full_integrity_check(path: Path) -> None: + check_db_integrity(path) + diff --git a/tests/find_test.py b/tests/find_test.py index b63b246..cc455f9 100644 --- a/tests/find_test.py +++ b/tests/find_test.py @@ -3,6 +3,9 @@ import sqlite3 from pathlib import Path import corrlib.initialization as cinit import pytest +import pandas as pd +import datalad.api as dl +import datetime as dt def make_sql(path: Path) -> Path: @@ -10,6 +13,7 @@ def make_sql(path: Path) -> Path: cinit._create_db(db) return db + def test_find_lookup_by_one_alias(tmp_path: Path) -> None: db = make_sql(tmp_path) conn = sqlite3.connect(db) @@ -31,3 +35,398 @@ def test_find_lookup_by_one_alias(tmp_path: Path) -> None: with pytest.raises(Exception): assert uuid == find._project_lookup_by_alias(db, "fun_project") conn.close() + +def test_find_lookup_by_id(tmp_path: Path) -> None: + db = make_sql(tmp_path) + conn = sqlite3.connect(db) + c = conn.cursor() + uuid = "test_uuid" + alias_str = "fun_project" + tag_str = "tt" + owner = "tester" + code = "test_code" + c.execute("INSERT INTO projects (id, aliases, customTags, owner, code, created_at, updated_at) VALUES (?, ?, ?, ?, ?, datetime('now'), datetime('now'))", + (uuid, alias_str, tag_str, owner, code)) + conn.commit() + conn.close() + result = find._project_lookup_by_id(db, uuid)[0] + assert uuid == result[0] + assert alias_str == result[1] + assert tag_str == result[2] + assert owner == result[3] + assert code == result[4] + + +def test_time_filter() -> None: + record_A = ["f_A", "ensA", "sfcf", "archive/SF_A/f_A/Project_A.json.gz::asdfasdfasdf0", "SF_A", '{"par_A": 5.0, "par_B": 5.0}', "projects/SF_A/input.in", + '2025-03-26 12:55:18.229966', '2025-03-26 12:55:18.229966'] # only created + record_B = ["f_A", "ensA", "sfcf", "archive/SF_A/f_A/Project_A.json.gz::asdfasdfasdf1", "SF_A", '{"par_A": 5.0, "par_B": 5.0}', "projects/SF_A/input.in", + '2025-03-26 12:55:18.229966', '2025-04-26 12:55:18.229966'] # created and updated + record_C = ["f_A", "ensA", "sfcf", "archive/SF_A/f_A/Project_A.json.gz::asdfasdfasdf2", "SF_A", '{"par_A": 5.0, "par_B": 5.0}', "projects/SF_A/input.in", + '2026-03-26 12:55:18.229966', '2026-04-14 12:55:18.229966'] # created and updated later + record_D = ["f_A", "ensA", "sfcf", "archive/SF_A/f_A/Project_A.json.gz::asdfasdfasdf3", "SF_A", '{"par_A": 5.0, "par_B": 5.0}', "projects/SF_A/input.in", + '2026-03-26 12:55:18.229966', '2026-03-27 12:55:18.229966'] + record_E = ["f_A", "ensA", "sfcf", "archive/SF_A/f_A/Project_A.json.gz::asdfasdfasdf4", "SF_A", '{"par_A": 5.0, "par_B": 5.0}', "projects/SF_A/input.in", + '2024-03-26 12:55:18.229966', '2024-03-26 12:55:18.229966'] # only created, earlier + record_F = ["f_A", "ensA", "sfcf", "archive/SF_A/f_A/Project_A.json.gz::asdfasdfasdf5", "SF_A", '{"par_A": 5.0, "par_B": 5.0}', "projects/SF_A/input.in", + '2026-03-26 12:55:18.229966', '2024-03-26 12:55:18.229966'] # this is invalid... + record_G = ["f_A", "ensA", "sfcf", "archive/SF_A/f_A/Project_A.json.gz::asdfasdfasdf2", "SF_A", '{"par_A": 5.0, "par_B": 5.0}', "projects/SF_A/input.in", + '2026-03-26 12:55:18.229966', str(dt.datetime.now() + dt.timedelta(days=2, hours=3, minutes=5, seconds=30))] # created and updated later + + data = [record_A, record_B, record_C, record_D, record_E] + cols = ["name", + "ensemble", + "code", + "path", + "project", + "parameters", + "parameter_file", + "created_at", + "updated_at"] + df = pd.DataFrame(data,columns=cols) + + results = find._time_filter(df, created_before='2023-03-26 12:55:18.229966') + assert results.empty + results = find._time_filter(df, created_before='2027-03-26 12:55:18.229966') + assert len(results) == 5 + results = find._time_filter(df, created_before='2026-03-25 12:55:18.229966') + assert len(results) == 3 + results = find._time_filter(df, created_before='2026-03-26 12:55:18.229965') + assert len(results) == 3 + results = find._time_filter(df, created_before='2025-03-04 12:55:18.229965') + assert len(results) == 1 + + results = find._time_filter(df, created_after='2023-03-26 12:55:18.229966') + assert len(results) == 5 + results = find._time_filter(df, created_after='2027-03-26 12:55:18.229966') + assert results.empty + results = find._time_filter(df, created_after='2026-03-25 12:55:18.229966') + assert len(results) == 2 + results = find._time_filter(df, created_after='2026-03-26 12:55:18.229965') + assert len(results) == 2 + results = find._time_filter(df, created_after='2025-03-04 12:55:18.229965') + assert len(results) == 4 + + results = find._time_filter(df, updated_before='2023-03-26 12:55:18.229966') + assert results.empty + results = find._time_filter(df, updated_before='2027-03-26 12:55:18.229966') + assert len(results) == 5 + results = find._time_filter(df, updated_before='2026-03-25 12:55:18.229966') + assert len(results) == 3 + results = find._time_filter(df, updated_before='2026-03-26 12:55:18.229965') + assert len(results) == 3 + results = find._time_filter(df, updated_before='2025-03-04 12:55:18.229965') + assert len(results) == 1 + + results = find._time_filter(df, updated_after='2023-03-26 12:55:18.229966') + assert len(results) == 5 + results = find._time_filter(df, updated_after='2027-03-26 12:55:18.229966') + assert results.empty + results = find._time_filter(df, updated_after='2026-03-25 12:55:18.229966') + assert len(results) == 2 + results = find._time_filter(df, updated_after='2026-03-26 12:55:18.229965') + assert len(results) == 2 + results = find._time_filter(df, updated_after='2025-03-04 12:55:18.229965') + assert len(results) == 4 + + data = [record_A, record_B, record_C, record_D, record_F] + cols = ["name", + "ensemble", + "code", + "path", + "project", + "parameters", + "parameter_file", + "created_at", + "updated_at"] + df = pd.DataFrame(data,columns=cols) + + with pytest.raises(ValueError): + results = find._time_filter(df, created_before='2023-03-26 12:55:18.229966') + + data = [record_A, record_B, record_C, record_D, record_G] + cols = ["name", + "ensemble", + "code", + "path", + "project", + "parameters", + "parameter_file", + "created_at", + "updated_at"] + df = pd.DataFrame(data,columns=cols) + + with pytest.raises(ValueError): + results = find._time_filter(df, created_before='2023-03-26 12:55:18.229966') + + +def test_db_lookup(tmp_path: Path) -> None: + db = make_sql(tmp_path) + conn = sqlite3.connect(db) + c = conn.cursor() + + corr = "f_A" + ensemble = "SF_A" + code = "openQCD" + meas_path = "archive/SF_A/f_A/Project_A.json.gz::asdfasdfasdf" + uuid = "Project_A" + pars = "{par_A: 3.0, par_B: 5.0}" + parameter_file = "projects/Project_A/myinput.in" + c.execute("INSERT INTO backlogs (name, ensemble, code, path, project, parameters, parameter_file, created_at, updated_at) VALUES (?, ?, ?, ?, ?, ?, ?, datetime('now'), datetime('now'))", + (corr, ensemble, code, meas_path, uuid, pars, parameter_file)) + conn.commit() + + results = find._db_lookup(db, ensemble, corr, code) + assert len(results) == 1 + results = find._db_lookup(db, "SF_B", corr, code) + assert results.empty + results = find._db_lookup(db, ensemble, "g_A", code) + assert results.empty + results = find._db_lookup(db, ensemble, corr, "sfcf") + assert results.empty + results = find._db_lookup(db, ensemble, corr, code, project = "Project_A") + assert len(results) == 1 + results = find._db_lookup(db, ensemble, corr, code, project = "Project_B") + assert results.empty + results = find._db_lookup(db, ensemble, corr, code, parameters = pars) + assert len(results) == 1 + results = find._db_lookup(db, ensemble, corr, code, parameters = '{"par_A": 3.0, "par_B": 4.0}') + assert results.empty + + corr = "g_A" + ensemble = "SF_A" + code = "openQCD" + meas_path = "archive/SF_A/f_A/Project_A.json.gz::asdfasdfasdf" + uuid = "Project_A" + pars = '{"par_A": 3.0, "par_B": 4.0}' + parameter_file = "projects/Project_A/myinput.in" + c.execute("INSERT INTO backlogs (name, ensemble, code, path, project, parameters, parameter_file, created_at, updated_at) VALUES (?, ?, ?, ?, ?, ?, ?, datetime('now'), datetime('now'))", + (corr, ensemble, code, meas_path, uuid, pars, parameter_file)) + conn.commit() + + corr = "f_A" + results = find._db_lookup(db, ensemble, corr, code) + assert len(results) == 1 + results = find._db_lookup(db, "SF_B", corr, code) + assert results.empty + results = find._db_lookup(db, ensemble, "g_A", code) + assert len(results) == 1 + results = find._db_lookup(db, ensemble, corr, "sfcf") + assert results.empty + results = find._db_lookup(db, ensemble, corr, code, project = "Project_A") + assert len(results) == 1 + results = find._db_lookup(db, ensemble, "g_A", code, project = "Project_A") + assert len(results) == 1 + results = find._db_lookup(db, ensemble, corr, code, project = "Project_B") + assert results.empty + results = find._db_lookup(db, ensemble, "g_A", code, project = "Project_B") + assert results.empty + results = find._db_lookup(db, ensemble, corr, code, parameters = pars) + assert results.empty + results = find._db_lookup(db, ensemble, "g_A", code, parameters = '{"par_A": 3.0, "par_B": 4.0}') + assert len(results) == 1 + + conn.close() + + +def test_sfcf_drop() -> None: + parameters0 = { + 'offset': [0,0,0], + 'quarks': [{'mass': 1, 'thetas': [0,0,0]}, {'mass': 2, 'thetas': [0,0,1]}], # m0s = -3.5, -3.75 + 'wf1': [[1, [0, 0]], [0.5, [1, 0]], [.75, [.5, .5]]], + 'wf2': [[1, [2, 1]], [2, [0.5, -0.5]], [.5, [.75, .72]]], + } + + assert not find._sfcf_drop(parameters0, offset=[0,0,0]) + assert find._sfcf_drop(parameters0, offset=[1,0,0]) + + assert not find._sfcf_drop(parameters0, quark_kappas = [1, 2]) + assert find._sfcf_drop(parameters0, quark_kappas = [-3.1, -3.72]) + + assert not find._sfcf_drop(parameters0, quark_masses = [-3.5, -3.75]) + assert find._sfcf_drop(parameters0, quark_masses = [-3.1, -3.72]) + + assert not find._sfcf_drop(parameters0, qk1 = 1) + assert not find._sfcf_drop(parameters0, qk2 = 2) + assert find._sfcf_drop(parameters0, qk1 = 2) + assert find._sfcf_drop(parameters0, qk2 = 1) + + assert not find._sfcf_drop(parameters0, qk1 = [0.5,1.5]) + assert not find._sfcf_drop(parameters0, qk2 = [1.5,2.5]) + assert find._sfcf_drop(parameters0, qk1 = 2) + assert find._sfcf_drop(parameters0, qk2 = 1) + with pytest.raises(ValueError): + assert not find._sfcf_drop(parameters0, qk1 = [0.5,1,5]) + with pytest.raises(ValueError): + assert not find._sfcf_drop(parameters0, qk2 = [1,5,2.5]) + + assert find._sfcf_drop(parameters0, qm1 = 1.2) + assert find._sfcf_drop(parameters0, qm2 = 2.2) + assert not find._sfcf_drop(parameters0, qm1 = -3.5) + assert not find._sfcf_drop(parameters0, qm2 = -3.75) + + assert find._sfcf_drop(parameters0, qm2 = 1.2) + assert find._sfcf_drop(parameters0, qm1 = 2.2) + with pytest.raises(ValueError): + assert not find._sfcf_drop(parameters0, qm1 = [0.5,1,5]) + with pytest.raises(ValueError): + assert not find._sfcf_drop(parameters0, qm2 = [1,5,2.5]) + + +def test_openQCD_filter() -> None: + 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", + '2025-03-26 12:55:18.229966', '2025-03-26 12:55:18.229966'] + 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", + '2025-03-26 12:55:18.229966', '2025-03-26 12:55:18.229966'] + 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", + '2025-03-26 12:55:18.229966', '2025-03-26 12:55:18.229966'] + 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", + '2025-03-26 12:55:18.229966', '2025-03-26 12:55:18.229966'] + data = [ + record_0, + record_1, + record_2, + record_3, + ] + cols = ["name", + "ensemble", + "code", + "path", + "project", + "parameters", + "parameter_file", + "created_at", + "updated_at"] + df = pd.DataFrame(data,columns=cols) + + with pytest.warns(Warning): + find.openQCD_filter(df, a = "asdf") + + +def test_code_filter() -> None: + 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", + '2025-03-26 12:55:18.229966', '2025-03-26 12:55:18.229966'] + 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", + '2025-03-26 12:55:18.229966', '2025-03-26 12:55:18.229966'] + 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", + '2025-03-26 12:55:18.229966', '2025-03-26 12:55:18.229966'] + 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", + '2025-03-26 12:55:18.229966', '2025-03-26 12:55:18.229966'] + record_4 = ["f_A", "ensA", "openQCD", "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", + '2025-03-26 12:55:18.229966', '2025-03-26 12:55:18.229966'] + record_5 = ["f_A", "ensA", "openQCD", "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", + '2025-03-26 12:55:18.229966', '2025-03-26 12:55:18.229966'] + record_6 = ["f_P", "ensA", "openQCD", "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", + '2025-03-26 12:55:18.229966', '2025-03-26 12:55:18.229966'] + record_7 = ["f_P", "ensA", "openQCD", "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", + '2025-03-26 12:55:18.229966', '2025-03-26 12:55:18.229966'] + record_8 = ["f_P", "ensA", "openQCD", "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", + '2025-03-26 12:55:18.229966', '2025-03-26 12:55:18.229966'] + data = [ + record_0, + record_1, + record_2, + record_3, + ] + cols = ["name", + "ensemble", + "code", + "path", + "project", + "parameters", + "parameter_file", + "created_at", + "updated_at"] + df = pd.DataFrame(data,columns=cols) + + res = find._code_filter(df, "sfcf") + assert len(res) == 4 + + data = [ + record_4, + record_5, + record_6, + record_7, + record_8, + ] + cols = ["name", + "ensemble", + "code", + "path", + "project", + "parameters", + "parameter_file", + "created_at", + "updated_at"] + df = pd.DataFrame(data,columns=cols) + + res = find._code_filter(df, "openQCD") + assert len(res) == 5 + with pytest.raises(ValueError): + res = find._code_filter(df, "asdf") + + +def test_find_record() -> None: + assert True + + +def test_find_project(tmp_path: Path) -> None: + cinit.create(tmp_path) + db = tmp_path / "backlogger.db" + dl.unlock(str(db), dataset=str(tmp_path)) + conn = sqlite3.connect(db) + c = conn.cursor() + uuid = "test_uuid" + alias_str = "fun_project" + tag_str = "tt" + owner = "tester" + code = "test_code" + c.execute("INSERT INTO projects (id, aliases, customTags, owner, code, created_at, updated_at) VALUES (?, ?, ?, ?, ?, datetime('now'), datetime('now'))", + (uuid, alias_str, tag_str, owner, code)) + conn.commit() + + assert uuid == find.find_project(tmp_path, "fun_project") + + uuid = "test_uuid2" + alias_str = "fun_project" + c.execute("INSERT INTO projects (id, aliases, customTags, owner, code, created_at, updated_at) VALUES (?, ?, ?, ?, ?, datetime('now'), datetime('now'))", + (uuid, alias_str, tag_str, owner, code)) + conn.commit() + + with pytest.raises(Exception): + assert uuid == find._project_lookup_by_alias(tmp_path, "fun_project") + conn.close() + + +def test_list_projects(tmp_path: Path) -> None: + cinit.create(tmp_path) + db = tmp_path / "backlogger.db" + dl.unlock(str(db), dataset=str(tmp_path)) + conn = sqlite3.connect(db) + c = conn.cursor() + uuid = "test_uuid" + alias_str = "fun_project" + tag_str = "tt" + owner = "tester" + code = "test_code" + + c.execute("INSERT INTO projects (id, aliases, customTags, owner, code, created_at, updated_at) VALUES (?, ?, ?, ?, ?, datetime('now'), datetime('now'))", + (uuid, alias_str, tag_str, owner, code)) + uuid = "test_uuid2" + alias_str = "fun_project2" + c.execute("INSERT INTO projects (id, aliases, customTags, owner, code, created_at, updated_at) VALUES (?, ?, ?, ?, ?, datetime('now'), datetime('now'))", + (uuid, alias_str, tag_str, owner, code)) + uuid = "test_uuid3" + alias_str = "fun_project3" + c.execute("INSERT INTO projects (id, aliases, customTags, owner, code, created_at, updated_at) VALUES (?, ?, ?, ?, ?, datetime('now'), datetime('now'))", + (uuid, alias_str, tag_str, owner, code)) + uuid = "test_uuid4" + alias_str = "fun_project4" + c.execute("INSERT INTO projects (id, aliases, customTags, owner, code, created_at, updated_at) VALUES (?, ?, ?, ?, ?, datetime('now'), datetime('now'))", + (uuid, alias_str, tag_str, owner, code)) + conn.commit() + conn.close() + results = find.list_projects(tmp_path) + assert len(results) == 4 + for i in range(4): + assert len(results[i]) == 2