# 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.
"""
File based database interface
"""
import datetime
import os
from abc import ABCMeta
from collections.abc import Iterable
from typing import List, Optional, Union
import numpy as np
import pandas
from h5io_browser.base import _read_hdf, _write_hdf
from pyfileindex import PyFileIndex
from pyiron_base.database.interface import IsDatabase
__author__ = "Jan Janssen"
__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 8, 2020"
table_columns = {
"id": None,
"status": None,
"chemicalformula": None,
"job": None,
"subjob": None,
"projectpath": None,
"project": None,
"hamilton": None,
"hamversion": None,
"timestart": None,
"computer": None,
"parentid": None,
"username": None,
"timestop": None,
"totalcputime": None,
"masterid": None,
}
[docs]
class FileTableSingleton(ABCMeta):
"""
Indexing the file system for each `FileTable` can be expensive, so we use a
singleton system that does this once for each path instead.
"""
_instances = {}
def __call__(cls, index_from: str):
path = os.path.abspath(os.path.expanduser(index_from))
if path not in cls._instances:
cls._instances[path] = super().__call__(
index_from=path,
fileindex=cls._get_fileindex_if_theres_a_common_path(path),
)
return cls._instances[path]
def _get_fileindex_if_theres_a_common_path(
cls, path: str
) -> Union[PyFileIndex, None]:
common_path = _get_most_common_path(
path=path, reference_paths=cls._instances.keys()
)
if common_path is not None:
return cls._instances[common_path]._fileindex.open(path)
else:
return None
[docs]
class FileTable(IsDatabase, metaclass=FileTableSingleton):
"""
File table should behave to the user like a database, but it infers project
hierarchy directly from the file system hierarchy.
Because indexing the file system can be expensive, and projects sometimes get
re-initialized, it is important to keep the (re)instantiation cost for this class
as minimal as possible.
Args:
index_from (str): The file path to start indexing at, i.e. the project path.
fileindex (PyFileIndex): In case the file path in index_from is already indexed,
then the index can be provided as additional input parameter.
"""
[docs]
def __init__(self, index_from: str, fileindex: PyFileIndex = None):
self._fileindex = None
self._job_table = None
self._path = os.path.abspath(index_from)
self._columns = list(table_columns.keys())
self.force_reset(fileindex=fileindex)
[docs]
def add_item_dict(self, par_dict: dict) -> int:
"""
Create a new database item
Args:
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'}
Returns:
int: Database ID of the item created as an int, like: 3
"""
par_dict = dict((key.lower(), value) for key, value in par_dict.items())
if len(self._job_table) != 0:
job_id = np.max(self._job_table.id.values) + 1
else:
job_id = 1
default_values = {
"id": job_id,
"status": "initialized",
"chemicalformula": None,
"timestart": datetime.datetime.now(),
}
par_dict_merged = table_columns.copy()
par_dict_merged.update(default_values)
par_dict_merged.update(par_dict)
self._job_table = pandas.concat(
[self._job_table, pandas.DataFrame([par_dict_merged])[self._columns]]
).reset_index(drop=True)
return int(par_dict_merged["id"])
[docs]
def delete_item(self, item_id: int) -> None:
"""
Delete Item from database
Args:
item_id (int): Databse Item ID (Integer), like: 38
"""
item_id = int(item_id)
if item_id in [int(v) for v in self._job_table.id.values]:
self._job_table = self._job_table[
self._job_table.id != item_id
].reset_index(drop=True)
else:
raise ValueError
[docs]
def force_reset(self, fileindex: Optional[PyFileIndex] = None) -> None:
"""
Reset cache of the FileTable object
Args:
fileindex (PyFileIndex): File index for the current directory
"""
if fileindex is not None:
self._fileindex = fileindex
else:
self._fileindex = PyFileIndex(
path=self._path, filter_function=filter_function
)
df = pandas.DataFrame(self.init_table(fileindex=self._fileindex.dataframe))
if len(df) != 0:
df.id = df.id.astype(int)
self._job_table = df[np.array(self._columns)]
else:
self._job_table = pandas.DataFrame({k: [] for k in self._columns})
[docs]
def get_child_ids(
self,
job_specifier: Union[str, int],
project: Optional[str] = None,
status: Optional[str] = None,
) -> List[int]:
"""
Get the childs for a specific job
Args:
job_specifier (str): name of the master job or the master jobs job ID
project (str): project_path - this is in contrast to the project_path in GenericPath
status (str): filter childs which match a specific status - None by default
Returns:
list: list of child IDs
"""
if project is None:
project = self._path
self.update()
id_master = self.get_job_id(project=project, job_specifier=job_specifier)
if id_master is None:
return []
else:
df_tmp = self._job_table[self._job_table.id == id_master]
working_directory = (df_tmp["project"] + df_tmp["job"] + "_hdf5/").values[0]
if status is not None:
id_lst = self._job_table[
(self._job_table.project == working_directory)
& (self._job_table.status == status)
].id.values
else:
id_lst = self._job_table[
(self._job_table.project == working_directory)
].id.values
return sorted(id_lst)
[docs]
def get_item_by_id(self, item_id: int) -> dict:
"""
Get item from database by searching for a specific item Id.
Args:
item_id (int): Databse Item ID (Integer), like: 38
Returns:
dict: 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'}
"""
item_id = int(item_id)
return {
k: list(v.values())[0]
for k, v in self._job_table[self._job_table.id == item_id].to_dict().items()
}
[docs]
def get_items_dict(
self, item_dict: dict, return_all_columns: bool = True
) -> List[dict]:
"""
Get list of jobs which fulfills the query in the dictionary
Args:
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:
list: 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'},.......]
"""
df = self._job_table
if not isinstance(item_dict, dict):
raise TypeError
for k, v in item_dict.items():
if k in ["id", "parentid", "masterid"]:
df = df[df[k] == int(v)]
elif "%" not in str(v):
df = df[df[k] == v]
else:
df = df[df[k].str.contains(v.replace("%", ""))]
df_dict = df.to_dict()
if return_all_columns:
return [{k: v[i] for k, v in df_dict.items()} for i in df_dict["id"].keys()]
else:
return [{"id": i} for i in df_dict["id"].values()]
def _get_jobs(
self,
user: str,
sql_query: str,
project: Optional[str] = None,
recursive: bool = True,
columns: Optional[List[str]] = None,
) -> dict:
"""
Get jobs as dictionary from filetable
Args:
user/sql_query: ignored for compat with IsDatabase
project (str/ None): path to the project
recursive (boolean): recursively iterate over all sub projects
columns (list/ None): list of columns to return
Returns:
dict: job entries as dictionary
"""
if project is None:
project = self._path
if columns is None:
columns = ["id", "project"]
df = self.job_table(
sql_query=None,
user=None,
project_path=project,
recursive=recursive,
columns=columns,
)
if len(df) == 0:
dictionary = {}
for key in columns:
dictionary[key] = list()
return dictionary
dictionary = {}
for key in df.keys():
dictionary[key] = df[
key
].tolist() # ToDo: Check difference of tolist and to_list
return dictionary
[docs]
def get_job_id(
self, job_specifier: Union[str, int], project: Optional[str] = None
) -> int:
"""
Get job ID from filetable
Args:
job_specifier (str): Job ID or job name
project (str/ None): project_path as string
Returns:
int/ None: job ID
"""
if project is None:
project = self._path
if isinstance(job_specifier, (int, np.integer)):
return job_specifier # is id
if len(self._job_table) == 0:
return None
job_specifier.replace(".", "_")
job_id_lst = self._job_table[
(self._job_table.project == project)
& (self._job_table.job == job_specifier)
].id.values
if len(job_id_lst) == 0:
job_id_lst = self._job_table[
self._job_table.project.str.contains(project)
& (self._job_table.job == job_specifier)
].id.values
if len(job_id_lst) == 0:
return None
elif len(job_id_lst) == 1:
return int(job_id_lst[0])
else:
raise ValueError(
"job name '{0}' in this project is not unique".format(job_specifier)
)
[docs]
def get_job_status(self, job_id: int) -> str:
"""
Get status of a given job selected by its job ID
Args:
job_id (int): job ID as integer
Returns:
str: status of the job
"""
return self._job_table[self._job_table.id == job_id].status.values[0]
[docs]
def get_job_working_directory(self, job_id: int) -> Union[str, None]:
"""
Get the working directory of a particular job
Args:
job_id (int): job ID as integer
Returns:
str: working directory as absolute path
"""
try:
db_entry = self.get_item_by_id(job_id)
if db_entry and len(db_entry) > 0:
job_name = db_entry["subjob"][1:]
return os.path.join(
db_entry["project"],
job_name + "_hdf5",
job_name,
)
else:
return None
except KeyError:
return None
[docs]
def init_table(
self, fileindex: PyFileIndex, working_dir_lst: Optional[List[str]] = None
) -> List[dict]:
"""
Initialize the filetable class
Args:
fileindex (pandas.DataFrame): file system index for the current project path
working_dir_lst (list/ None): list of working directories
Returns:
list: list of dictionaries
"""
if working_dir_lst is None:
working_dir_lst = []
fileindex = fileindex[~fileindex.is_directory]
fileindex = fileindex.iloc[fileindex.path.values.argsort()]
job_lst = []
for path, mtime in zip(fileindex.path, fileindex.mtime):
try: # Ignore HDF5 files which are not created by pyiron
job_dict = self.get_extract(path, mtime)
except (ValueError, OSError):
pass
else:
job_dict["id"] = len(working_dir_lst) + 1
working_dir_lst.append(
job_dict["project"][:-1] + job_dict["subjob"] + "_hdf5/"
)
if job_dict["project"] in working_dir_lst:
job_dict["masterid"] = (
working_dir_lst.index(job_dict["project"]) + 1
)
else:
job_dict["masterid"] = None
job_lst.append(job_dict)
return job_lst
def _item_update(self, par_dict: dict, item_id: int) -> None:
"""
Modify Item in database
Args:
par_dict (dict): Dictionary of the parameters to be modified,, where the key is the column name.
{'job' : 'maximize',
'subjob' : 'testing',
........}
item_id (int, list): Database Item ID (Integer) - '38' can also be [38]
"""
if isinstance(item_id, str):
item_id = float(item_id)
for k, v in par_dict.items():
self._job_table.loc[self._job_table.id == int(item_id), k] = v
[docs]
def set_job_status(self, job_id: int, status: str) -> None:
"""
Set job status
Args:
job_id (int): job ID as integer
status (str): job status
"""
super().set_job_status(job_id=job_id, status=status)
self._update_hdf5_status(job_id=job_id, status=status)
def _update_hdf5_status(self, job_id: int, status: str) -> None:
"""
Update the status of a job in its HDF5 file.
Args:
job_id (int): The ID of the job.
status (str): The new status of the job.
"""
if isinstance(job_id, Iterable):
for j_id in job_id:
db_entry = self.get_item_by_id(item_id=j_id)
_write_hdf(
hdf_filehandle=db_entry["project"] + db_entry["subjob"] + ".h5",
data=status,
h5_path=db_entry["subjob"][1:] + "/status",
overwrite="update",
)
else:
db_entry = self.get_item_by_id(item_id=job_id)
_write_hdf(
hdf_filehandle=db_entry["project"] + db_entry["subjob"] + ".h5",
data=status,
h5_path=db_entry["subjob"][1:] + "/status",
overwrite="update",
)
[docs]
def update(self) -> None:
"""
Update the filetable cache
"""
self._job_table.status = [
self._get_job_status_from_hdf5(job_id)
for job_id in self._job_table.id.values
]
self._fileindex.update()
if len(self._job_table) != 0:
files_lst, working_dir_lst = zip(
*[
[project + subjob[1:] + ".h5", project + subjob[1:] + "_hdf5"]
for project, subjob in zip(
self._job_table.project.values, self._job_table.subjob.values
)
]
)
sanitized_paths = self._fileindex.dataframe.path.str.replace("\\", "/")
# The files_list is generated using project path values
# In pyiron, these are all forced to be posix-like with /
# But _fileindex is of type PyFileIndex, which does _not_ modify paths
# so to get the two compatible for an isin check, we need to sanitize the
# _fileindex.dataframe.path results
df_new = self._fileindex.dataframe[
~self._fileindex.dataframe.is_directory
& ~sanitized_paths.isin(files_lst)
]
else:
files_lst, working_dir_lst = [], []
df_new = self._fileindex.dataframe[~self._fileindex.dataframe.is_directory]
if len(df_new) > 0:
job_lst = self.init_table(
fileindex=df_new, working_dir_lst=list(working_dir_lst)
)
if len(job_lst) > 0:
df = pandas.DataFrame(job_lst)[self._columns]
if len(files_lst) != 0 and len(working_dir_lst) != 0:
self._job_table = pandas.concat([self._job_table, df]).reset_index(
drop=True
)
else:
self._job_table = df
def _get_job_status_from_hdf5(self, job_id: int) -> str:
"""
Get the status of a job from its HDF5 file.
Args:
job_id (int): The ID of the job.
Returns:
str: The status of the job.
"""
db_entry = self.get_item_by_id(job_id)
job_name = db_entry["subjob"][1:]
return get_job_status_from_file(
hdf5_file=os.path.join(db_entry["project"], job_name + ".h5"),
job_name=job_name,
)
def _get_job_table(
self,
sql_query: str,
user: str,
project_path: Optional[str] = None,
recursive: bool = True,
columns: Optional[List[str]] = None,
element_lst: Optional[List[str]] = None,
) -> pandas.DataFrame:
"""
Get the job table based on the specified parameters.
Args:
sql_query (str): SQL query string.
user (str): User name.
project_path (str, optional): Project path. Defaults to None.
recursive (bool, optional): Recursive flag. Defaults to True.
columns (List[str], optional): List of columns to return. Defaults to None.
element_lst (List[str], optional): List of elements. Defaults to None.
Returns:
pandas.DataFrame: The job table.
"""
self.update()
if project_path is None:
project_path = self._path
if len(self._job_table) != 0:
if recursive:
return self._job_table[
self._job_table.project.str.contains(project_path)
]
else:
return self._job_table[
self._job_table.project.str.endswith(project_path)
]
else:
return self._job_table
def _get_table_headings(self, table_name: Optional[str] = None) -> List[str]:
"""
Get column names
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._job_table.columns.values
def _get_view_mode(self) -> bool:
"""
Get the view mode of the file table.
Returns:
bool: The view mode of the file table - Always False.
"""
return False
[docs]
def filter_function(file_name: str) -> bool:
"""
Filter function to check if a file name contains ".h5".
Args:
file_name (str): The name of the file.
Returns:
bool: True if the file name contains ".h5", False otherwise.
"""
return ".h5" in file_name
[docs]
def get_hamilton_from_file(hdf5_file: str, job_name: str) -> str:
"""
Get the Hamilton type from an HDF5 file.
Args:
hdf5_file (str): The path to the HDF5 file.
job_name (str): The name of the job.
Returns:
str: The Hamilton type.
"""
return (
_read_hdf(hdf_filehandle=hdf5_file, h5_path=job_name + "/TYPE")
.split(".")[-1]
.split("'")[0]
)
[docs]
def get_hamilton_version_from_file(hdf5_file: str, job_name: str) -> str:
"""
Get the Hamilton version from an HDF5 file.
Args:
hdf5_file (str): The path to the HDF5 file.
job_name (str): The name of the job.
Returns:
str: The Hamilton version.
"""
return _read_hdf(hdf_filehandle=hdf5_file, h5_path=job_name + "/VERSION")
[docs]
def get_job_status_from_file(hdf5_file: str, job_name: str) -> Union[str, None]:
"""
Get the status of a job from an HDF5 file.
Args:
hdf5_file (str): The path to the HDF5 file.
job_name (str): The name of the job.
Returns:
Union[str, None]: The status of the job, or None if the file does not exist.
"""
if os.path.exists(hdf5_file):
return _read_hdf(hdf_filehandle=hdf5_file, h5_path=job_name + "/status")
else:
return None
def _get_most_common_path(path: str, reference_paths: List[str]) -> Union[str, None]:
"""
Get the most common path between the given path and a list of reference paths.
Args:
path (str): The path to compare.
reference_paths (List[str]): The list of reference paths.
Returns:
Union[str, None]: The most common path or None if no common path is found.
"""
path_match_lst = [p for p in reference_paths if os.path.commonpath([path, p]) == p]
if len(path_match_lst) > 0:
return max(path_match_lst, key=len)
else:
return None
