pyiron_base.database.generic.DatabaseAccess

pyiron_base.database.generic.DatabaseAccess#

class pyiron_base.database.generic.DatabaseAccess(connection_string: str, table_name: str, timeout: int = 60)[source]#

Bases: IsDatabase

A core element of PyIron, which generally deals with accessing the database: getting, sending, changing some data to the db.

Parameters:

  • connection_string (str) – SQLalchemy connection string which specifies the database to connect to typical form: dialect+driver://username:password@host:port/database example: ‘postgresql://scott:tiger@cmcent56.mpie.de/mdb’

  • table_name (str) – database table name, a simple string like: ‘simulation’

Murat Han Celik

__init__(connection_string: str, table_name: str, timeout: int = 60)[source]#

Initialize the Database connection

Parameters:

  • connection_string (str) – SQLalchemy connection string which specifies the database to connect to typical form: dialect+driver://username:password@host:port/database example: ‘postgresql://scott:tiger@cmcent56.mpie.de/mdb’

  • table_name (str) – database table name, a simple string like: ‘simulation’

  • timeout (int) – time in seconds before unused database connection are closed

Methods

__init__(connection_string, table_name[, ...])

Initialize the Database connection

add_column(col_name, col_type)

Add an additional column - required for modification on the database

add_item_dict(par_dict[, check_duplicates])

Create a new database item

change_column_type(col_name, col_type)

Modify data type of an existing column - required for modification on the database

delete_item(item_id)

Delete Item from database

get_db_columns()

Get column names

get_item_by_id(item_id)

Get item from database by searching for a specific item Id.

get_items_dict(item_dict[, return_all_columns])

param item_dict:

a dict type, which has a certain syntax for this function:

get_job_ids(sql_query, user, project_path[, ...])

Return the job IDs matching a specific query

get_job_status(job_id)

get_job_working_directory(job_id)

get_jobs(sql_query, user, project_path[, ...])

Internal function to return the jobs as dictionary rather than a pandas.Dataframe

get_table_headings([table_name])

Get column names; if given table_name can select one of multiple tables defined in the database, but subclasses may ignore it

item_update(par_dict, item_id)

job_table(sql_query, user, project_path[, ...])

Access the job_table.

query_for_element(element)

regexp(expr, item)

Regex function for SQLite :param expr: str, regex expression :param item: str, item which needs to be checked

set_job_status(status, job_id)

Set status of a job or multiple jobs if job_id is iterable.

Attributes

view_mode

Get view_mode - if view_moded is enable pyiron has read only access to the database.
add_column(col_name: str | List[str], col_type: str | List[str]) → None[source]#

Add an additional column - required for modification on the database

Parameters:

  • col_name (str, list) – name of the new column, normal string like: ‘myColumn’

  • (str (col_type) – SQL type of the new column, SQL type like: ‘varchar(50)’

  • list – SQL type of the new column, SQL type like: ‘varchar(50)’

Returns:

add_item_dict(par_dict: dict, check_duplicates: bool = False) → int[source]#

Create a new database item

Parameters:

  • par_dict (dict) –

    Dictionary with the item values and column names as keys, like:

    {‘chemicalformula’: ‘BO’,

    ’computer’: ‘localhost’, ‘hamilton’: ‘VAMPS’, ‘hamversion’: ‘1.1’, ‘job’: ‘testing’, ‘subjob’ : ‘SubJob’, ‘parentid’: 0L, ‘myCol’: ‘Blubbablub’, ‘project’: ‘database.testing’, ‘projectpath’: ‘/root/directory/tmp’, ‘status’: ‘KAAAA’, ‘timestart’: datetime(2016, 5, 2, 11, 31, 4, 253377), ‘timestop’: datetime(2016, 5, 2, 11, 31, 4, 371165), ‘totalcputime’: 0.117788, ‘username’: ‘Test’}

  • check_duplicates (bool) – Check for duplicate entries in the database

Returns:

Database ID of the item created as an int, like: 3

Return type:

int

change_column_type(col_name: str | List[str], col_type: str | List[str]) → None[source]#

Modify data type of an existing column - required for modification on the database

Parameters:

  • col_name (str, list) – name of the new column, normal string like: ‘myColumn’

  • (str (col_type) – SQL type of the new column, SQL type like: ‘varchar(50)’

  • list – SQL type of the new column, SQL type like: ‘varchar(50)’

Returns:

delete_item(item_id: int) → None[source]#

Delete Item from database

Parameters:

item_id (int) – Databse Item ID (Integer), like: 38

Returns:

get_db_columns() → List[str]#

Get column names

Returns:

list of column names like:

[‘id’, ‘parentid’, ‘masterid’, ‘projectpath’, ‘project’, ‘job’, ‘subjob’, ‘chemicalformula’, ‘status’, ‘hamilton’, ‘hamversion’, ‘username’, ‘computer’, ‘timestart’, ‘timestop’, ‘totalcputime’]

Return type:

list

get_item_by_id(item_id: int) → dict[source]#

Get item from database by searching for a specific item Id.

Parameters:

item_id (int) – Databse Item ID (Integer), like: 38

Returns:

Dictionary where the key is the column name, like:
{‘chemicalformula’: u’BO’,

’computer’: u’localhost’, ‘hamilton’: u’VAMPS’, ‘hamversion’: u’1.1’, ‘id’: 1, ‘job’: u’testing’, ‘masterid’: None, ‘parentid’: 0, ‘project’: u’database.testing’, ‘projectpath’: u’/root/directory/tmp’, ‘status’: u’KAAAA’, ‘subjob’: u’SubJob’, ‘timestart’: datetime.datetime(2016, 5, 2, 11, 31, 4, 253377), ‘timestop’: datetime.datetime(2016, 5, 2, 11, 31, 4, 371165), ‘totalcputime’: 0.117788, ‘username’: u’Test’}

Return type:

dict

get_items_dict(item_dict: dict, return_all_columns: bool = True) → List[dict][source]#
Parameters:

  • item_dict (dict) –

    a dict type, which has a certain syntax for this function: a normal dict like {‘hamilton’: ‘VAMPE’, ‘hamversion’: ‘1.1’} has similarities with a simple query like

    select * from table_name where hamilton = ‘VAMPE AND hamversion = ‘1.1’

    as seen it puts an AND for every key, value combination in the dict and searches for it.

    another syntax is for an OR statement, simply: {‘hamilton’: [‘VAMPE’, ‘LAMMPS’]}, the query would be:

    select * from table_name where hamilton = ‘VAMPE’ OR hamilton = ‘LAMMPS’

    and lastly for a LIKE statement, simply: {‘project’: ‘database.%’}, the query would be

    select * from table_name where project LIKE ‘database.%’

    that means you can simply add the syntax for a like statement like ‘%’ and it will automatically operate a like-search

    of course you can also use a more complex select method, with everything in use:
    {‘hamilton’: [‘VAMPE’, ‘LAMMPS’],

    ’project’: ‘databse%’, ‘hamversion’: ‘1.1’}

    select * from table_name where (hamilton = ‘VAMPE’ Or hamilton = ‘LAMMPS’) AND

    (project LIKE ‘database%’) AND hamversion = ‘1.1’

  • return_all_columns (bool) – return all columns or only the ‘id’ - still the format stays the same.

Returns:

the function returns a list of dicts, but it does not format datetime:
[{‘chemicalformula’: u’Ni108’,

’computer’: u’mapc157’, ‘hamilton’: u’LAMMPS’, ‘hamversion’: u’1.1’, ‘id’: 24, ‘job’: u’DOF_1_0’, ‘parentid’: 21L, ‘project’: u’lammps.phonons.Ni_fcc’, ‘projectpath’: u’D:/PyIron/PyIron_data/projects’, ‘status’: u’finished’, ‘timestart’: datetime.datetime(2016, 6, 24, 10, 17, 3, 140000), ‘timestop’: datetime.datetime(2016, 6, 24, 10, 17, 3, 173000), ‘totalcputime’: 0.033, ‘username’: u’test’},

{‘chemicalformula’: u’Ni108’,

’computer’: u’mapc157’, ‘hamilton’: u’LAMMPS’, ‘hamversion’: u’1.1’, ‘id’: 21, ‘job’: u’ref’, ‘parentid’: 20L, ‘project’: u’lammps.phonons.Ni_fcc’, ‘projectpath’: u’D:/PyIron/PyIron_data/projects’, ‘status’: u’finished’, ‘timestart’: datetime.datetime(2016, 6, 24, 10, 17, 2, 429000), ‘timestop’: datetime.datetime(2016, 6, 24, 10, 17, 2, 463000), ‘totalcputime’: 0.034, ‘username’: u’test’},…….]

Return type:

list

get_job_ids(sql_query: str, user: str, project_path: str, recursive: bool = True) → List[int]#

Return the job IDs matching a specific query

Parameters:

  • 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:

a list of job IDs

Return type:

list

get_jobs(sql_query: str, user: str, project_path: str, recursive: bool = True, columns: List[str] | None = None) → List[dict]#

Internal function to return the jobs as dictionary rather than a pandas.Dataframe

Parameters:

  • 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:

columns are used as keys and point to a list of the corresponding values

Return type:

dict

get_table_headings(table_name: str | None = 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

Parameters:

table_name (str) – simple string of a table_name like: ‘jobs_username’

Returns:

list of column names like:

[‘id’, ‘parentid’, ‘masterid’, ‘projectpath’, ‘project’, ‘job’, ‘subjob’, ‘chemicalformula’, ‘status’, ‘hamilton’, ‘hamversion’, ‘username’, ‘computer’, ‘timestart’, ‘timestop’, ‘totalcputime’]

Return type:

list

job_table(sql_query: str, user: str, project_path: str, recursive: bool = True, columns: List[str] | None = None, all_columns: bool = False, sort_by: str = 'id', max_colwidth: int = 200, full_table: bool = False, element_lst: List[str] | None = None, job_name_contains: str = '', mode: Literal['regex', 'glob'] = 'glob', **kwargs)#

Access the job_table.

Parameters:

  • 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:

Return the result as a pandas.Dataframe object

Return type:

pandas.Dataframe

static regexp(expr: str, item: str) → str | None[source]#

Regex function for SQLite :param expr: str, regex expression :param item: str, item which needs to be checked

Returns:

set_job_status(status: str, job_id: int | List[int]) → None#

Set status of a job or multiple jobs if job_id is iterable.

Parameters:

  • status (str) – status

  • job_id (int, Iterable) – job id

property view_mode: 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:

True when view_mode is enabled

Return type:

bool

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