Justus Kuhlmann
4631769e81
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292 lines
11 KiB
Python
292 lines
11 KiB
Python
import sqlite3
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import os
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import json
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import pandas as pd
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import numpy as np
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from .input.implementations import codes
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from .tools import k2m, get_db_file
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from .tracker import get
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from typing import Any, Optional
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def _project_lookup_by_alias(db: str, alias: str) -> str:
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"""
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Lookup a projects UUID by its (human-readable) alias.
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Parameters
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----------
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db: str
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The database to look up the project.
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alias: str
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The alias to look up.
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Returns
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-------
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uuid: str
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The UUID of the project with the given alias.
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"""
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conn = sqlite3.connect(db)
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c = conn.cursor()
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c.execute(f"SELECT * FROM 'projects' WHERE alias = '{alias}'")
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results = c.fetchall()
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conn.close()
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if len(results)>1:
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print("Error: multiple projects found with alias " + alias)
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elif len(results) == 0:
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raise Exception("Error: no project found with alias " + alias)
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return str(results[0][0])
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def _project_lookup_by_id(db: str, uuid: str) -> list[tuple[str, str]]:
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"""
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Return the project information available in the database by UUID.
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Parameters
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----------
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db: str
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The database to look up the project.
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uuid: str
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The uuid of the project in question.
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Returns
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-------
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results: list
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The row of the project in the database.
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"""
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conn = sqlite3.connect(db)
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c = conn.cursor()
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c.execute(f"SELECT * FROM 'projects' WHERE id = '{uuid}'")
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results = c.fetchall()
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conn.close()
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return results
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def _db_lookup(db: str, ensemble: str, correlator_name: str, code: str, project: Optional[str]=None, parameters: Optional[str]=None,
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created_before: Optional[str]=None, created_after: Optional[Any]=None, updated_before: Optional[Any]=None, updated_after: Optional[Any]=None) -> pd.DataFrame:
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"""
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Look up a correlator record in the database by the data given to the method.
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Parameters
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----------
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db: str
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The database to look up the record.
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ensemble: str
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The ensemble the record is associated with.
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correlator_name: str
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The name of the correlator in question.
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code: str
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The name of the code which was used to calculate the correlator.
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project: str, optional
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The UUID of the project the correlator was calculated in.
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parameters: str, optional
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A dictionary holding the exact parameters for the measurement that are held in the database.
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created_before: str, optional
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Timestamp string before which the meaurement has been created.
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created_after: str, optional
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Timestamp string after which the meaurement has been created.
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updated_before: str, optional
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Timestamp string before which the meaurement has been updated.
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updated_after: str, optional
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Timestamp string after which the meaurement has been updated.
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Returns
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-------
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results: pd.DataFrame
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A pandas DataFrame holding the information received form the DB query.
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"""
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project_str = project
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search_expr = f"SELECT * FROM 'backlogs' WHERE name = '{correlator_name}' AND ensemble = '{ensemble}'"
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if project:
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search_expr += f" AND project = '{project_str}'"
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if code:
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search_expr += f" AND code = '{code}'"
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if parameters:
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search_expr += f" AND parameters = '{parameters}'"
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if created_before:
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search_expr += f" AND created_at < '{created_before}'"
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if created_after:
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search_expr += f" AND created_at > '{created_after}'"
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if updated_before:
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search_expr += f" AND updated_at < '{updated_before}'"
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if updated_after:
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search_expr += f" AND updated_at > '{updated_after}'"
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conn = sqlite3.connect(db)
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results = pd.read_sql(search_expr, conn)
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conn.close()
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return results
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def sfcf_filter(results: pd.DataFrame, **kwargs: Any) -> pd.DataFrame:
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"""
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Filter method for the Database entries holding SFCF calculations.
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Parameters
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----------
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results: pd.DataFrame
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The unfiltered pandas DataFrame holding the entries from the database.
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offset: list[float], optional
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quark_kappas: list[float]
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quarks_masses: list[float]
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qk1: float, optional
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Mass parameter $\kappa_1$ of the first quark.
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qk2: float, optional
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Mass parameter $\kappa_2$ of the first quark.
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qm1: float, optional
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Bare quak mass $m_1$ of the first quark.
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qm2: float, optional
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Bare quak mass $m_1$ of the first quark.
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quarks_thetas: list[list[float]], optional
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wf1: optional
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wf2: optional
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Results
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-------
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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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return results.drop(drops)
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def find_record(path: str, ensemble: str, correlator_name: str, code: str, project: Optional[str]=None, parameters: Optional[str]=None,
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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:
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db_file = get_db_file(path)
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db = os.path.join(path, db_file)
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if code not in codes:
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raise ValueError("Code " + code + "unknown, take one of the following:" + ", ".join(codes))
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get(path, db_file)
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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)
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if code == "sfcf":
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results = sfcf_filter(results, **kwargs)
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elif code == "openQCD":
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pass
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else:
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raise Exception
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print("Found " + str(len(results)) + " result" + ("s" if len(results)>1 else ""))
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return results.reset_index()
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def find_project(path: str, name: str) -> str:
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"""
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Find a project by it's human readable name.
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Parameters
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----------
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path: str
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The path of the library.
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name: str
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The name of the project to look for in the library.
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Returns
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-------
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uuid: str
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The uuid of the project in question.
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"""
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db_file = get_db_file(path)
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get(path, db_file)
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return _project_lookup_by_alias(os.path.join(path, db_file), name)
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def list_projects(path: str) -> list[tuple[str, str]]:
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"""
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List all projects known to the library.
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Parameters
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----------
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path: str
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The path of the library.
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Returns
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-------
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results: list[Any]
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The projects known to the library.
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"""
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db_file = get_db_file(path)
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get(path, db_file)
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conn = sqlite3.connect(os.path.join(path, db_file))
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c = conn.cursor()
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c.execute("SELECT id,aliases FROM projects")
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results = c.fetchall()
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conn.close()
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return results
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