# coding: utf-8
# Copyright (c) Max-Planck-Institut für Eisenforschung GmbH - Computational Materials Design (CM) Department
# Distributed under the terms of "New BSD License", see the LICENSE file.
"""
DatabaseAccess class deals with accessing the database
"""
import fnmatch
import re
import warnings
from abc import ABC, abstractmethod
from collections.abc import Iterable
from typing import List, Literal, Optional, Union
import numpy as np
import pandas
from pyiron_snippets.deprecate import deprecate
from pyiron_snippets.logger import logger
__author__ = "Murat Han Celik"
__copyright__ = (
"Copyright 2020, Max-Planck-Institut für Eisenforschung GmbH"
" - Computational Materials Design (CM) Department"
)
__version__ = "1.0"
__maintainer__ = "Jan Janssen"
__email__ = "janssen@mpie.de"
__status__ = "production"
__date__ = "Sep 1, 2017"
[docs]
class IsDatabase(ABC):
"""
Captures common interface for all database types in pyiron, e.g. SQL/SQLite/FileTable.
"""
@property
def view_mode(self) -> bool:
"""
Get view_mode - if view_moded is enable pyiron has read only access to the database.
Some implementations do not allow to set this value.
Returns:
bool: True when view_mode is enabled
"""
return False
@abstractmethod
def _get_job_table(
self,
sql_query: str,
user: str,
project_path: str,
recursive: bool = True,
columns: Optional[List[str]] = None,
element_lst: Optional[List[str]] = None,
) -> pandas.DataFrame:
pass
@staticmethod
def _get_filtered_job_table(
df: pandas.DataFrame,
mode: Literal["regex", "glob"] = "glob",
**kwargs: dict,
) -> pandas.DataFrame:
"""
Get a job table in a project based on matching values from any column in the project database
The values in `kwargs` can be wildcards. The matches can be given
either via "glob" or "regex".
Args:
df (pandas.DataFrame): DataFrame to be filtered
**kwargs (dict): Optional arguments for filtering with keys matching the project database column name
(eg. status="finished")
Returns:
list: DataFrame containing filtered jobs
"""
if len(kwargs) == 0 or df.empty:
return df
mask = np.ones_like(df.index, dtype=bool)
for key in kwargs.keys():
if key not in list(df.columns):
raise ValueError(
f"Column name {key} does not exist in the project database!"
)
for key, val in kwargs.items():
if mode == "regex":
pattern = re.compile(str(val))
update = df[key].apply(pattern.search).astype(bool)
elif mode == "glob":
if str(val).startswith("!"):
logger.warn(
"It looks like you are using an old pyiron convention."
" If you meant to exclude the term following '!', use"
" `mode='regex' and use a regex convention (such as"
" `^(?!term$)`)"
)
arr = np.asarray(df[key]).astype(str)
matches = fnmatch.filter(arr, str(val))
update = np.array([k in matches for k in arr])
mask &= update
return df[mask]
[docs]
def job_table(
self,
sql_query: str,
user: str,
project_path: str,
recursive: bool = True,
columns: Optional[List[str]] = None,
all_columns: bool = False,
sort_by: str = "id",
max_colwidth: int = 200,
full_table: bool = False,
element_lst: Optional[List[str]] = None,
job_name_contains: str = "",
mode: Literal["regex", "glob"] = "glob",
**kwargs,
):
"""
Access the job_table.
Args:
sql_query (str): SQL query to enter a more specific request
user (str): username of the user whoes user space should be searched
project_path (str): root_path - this is in contrast to the project_path in GenericPath
recursive (bool): search subprojects [True/False]
columns (list): by default only the columns ['job', 'project', 'chemicalformula'] are selected, but the
user can select a subset of ['id', 'status', 'chemicalformula', 'job', 'subjob', 'project',
'projectpath', 'timestart', 'timestop', 'totalcputime', 'computer', 'hamilton', 'hamversion',
'parentid', 'masterid']
all_columns (bool): Select all columns - this overwrites the columns option.
sort_by (str): Sort by a specific column
max_colwidth (int): set the column width
full_table (bool): Whether to show the entire pandas table
element_lst (list): list of elements required in the chemical formular - by default None
job_name_contains (str): (deprecated) A string which should be contained in every job_name
mode (str): search mode when kwargs are given.
**kwargs (dict): Optional arguments for filtering with keys matching the project database column name
(eg. status="finished"). Asterisk can be used to denote a wildcard, for zero or more
instances of any character
Returns:
pandas.Dataframe: Return the result as a pandas.Dataframe object
"""
if columns is None:
columns = ["job", "project", "chemicalformula"]
if all_columns:
columns = [
"id",
"status",
"chemicalformula",
"job",
"subjob",
"projectpath",
"project",
"timestart",
"timestop",
"totalcputime",
"computer",
"hamilton",
"hamversion",
"parentid",
"masterid",
]
if sort_by not in columns:
columns = list(columns) + [sort_by]
if full_table:
pandas.set_option("display.max_rows", None)
pandas.set_option("display.max_columns", None)
else:
pandas.reset_option("display.max_rows")
pandas.reset_option("display.max_columns")
pandas.set_option("display.max_colwidth", max_colwidth)
df = self._get_job_table(
user=user,
sql_query=sql_query,
project_path=project_path,
recursive=recursive,
columns=columns,
)
if job_name_contains != "":
warnings.warn(
"`job_name_contains` is deprecated - use `job='*term*'` instead"
)
kwargs["job"] = "*{}*".format(job_name_contains)
df = self._get_filtered_job_table(df, mode=mode, **kwargs)
if sort_by is not None:
return df.sort_values(by=sort_by)
return df
@abstractmethod
def _get_table_headings(self, table_name: Optional[str] = None):
pass
def item_update(self, par_dict: dict, item_id: int) -> None:
if isinstance(item_id, Iterable):
return self._items_update(par_dict=par_dict, item_ids=item_id)
return self._item_update(par_dict=par_dict, item_id=item_id)
@abstractmethod
def _item_update(self, par_dict: dict, item_id: int) -> None:
pass
def _items_update(self, par_dict: dict, item_ids: List) -> None:
"""
For now simply loops over all item_ids to call item_update,
but can be made more efficient.
Should be made an asbtract method when defined in inheriting classes
Args:
par_dict (_type_): _description_
item_ids (_type_): _description_
"""
for i_id in item_ids:
self._item_update(par_dict=par_dict, item_id=i_id)
[docs]
def set_job_status(self, status: str, job_id: Union[int, List[int]]) -> None:
"""
Set status of a job or multiple jobs if job_id is iterable.
Args:
status (str): status
job_id (int, Iterable): job id
"""
if isinstance(job_id, Iterable):
return self._items_update(
par_dict={"status": status},
item_ids=job_id,
)
return self._item_update(
par_dict={"status": status},
item_id=job_id,
)
[docs]
def get_table_headings(self, table_name: Optional[str] = None) -> List[str]:
"""
Get column names; if given table_name can select one of multiple tables defined in the database, but subclasses
may ignore it
Args:
table_name (str): simple string of a table_name like: 'jobs_username'
Returns:
list: list of column names like:
['id',
'parentid',
'masterid',
'projectpath',
'project',
'job',
'subjob',
'chemicalformula',
'status',
'hamilton',
'hamversion',
'username',
'computer',
'timestart',
'timestop',
'totalcputime']
"""
return self._get_table_headings(table_name=table_name)
[docs]
@deprecate("use get_table_headings()")
def get_db_columns(self) -> List[str]:
"""
Get column names
Returns:
list: list of column names like:
['id',
'parentid',
'masterid',
'projectpath',
'project',
'job',
'subjob',
'chemicalformula',
'status',
'hamilton',
'hamversion',
'username',
'computer',
'timestart',
'timestop',
'totalcputime']
"""
return self.get_table_headings()
@abstractmethod
def _get_jobs(
self,
sql_query: str,
user: str,
project_path: str,
recursive: bool = True,
columns: Optional[List[str]] = None,
) -> List[dict]:
pass
[docs]
def get_jobs(
self,
sql_query: str,
user: str,
project_path: str,
recursive: bool = True,
columns: Optional[List[str]] = None,
) -> List[dict]:
"""
Internal function to return the jobs as dictionary rather than a pandas.Dataframe
Args:
sql_query (str): SQL query to enter a more specific request
user (str): username of the user whoes user space should be searched
project_path (str): root_path - this is in contrast to the project_path in GenericPath
recursive (bool): search subprojects [True/False]
columns (list): by default only the columns ['id', 'project'] are selected, but the user can select a subset
of ['id', 'status', 'chemicalformula', 'job', 'subjob', 'project', 'projectpath', 'timestart',
'timestop', 'totalcputime', 'computer', 'hamilton', 'hamversion', 'parentid', 'masterid']
Returns:
dict: columns are used as keys and point to a list of the corresponding values
"""
if columns is None:
columns = ["id", "project"]
return self._get_jobs(sql_query, user, project_path, recursive, columns)
[docs]
def get_job_ids(
self, sql_query: str, user: str, project_path: str, recursive: bool = True
) -> List[int]:
"""
Return the job IDs matching a specific query
Args:
database (DatabaseAccess): Database object
sql_query (str): SQL query to enter a more specific request
user (str): username of the user whoes user space should be searched
project_path (str): root_path - this is in contrast to the project_path in GenericPath
recursive (bool): search subprojects [True/False]
Returns:
list: a list of job IDs
"""
return self.get_jobs(
sql_query=sql_query,
user=user,
project_path=project_path,
recursive=recursive,
)["id"]
