pyiron.base.job package

Submodules

pyiron.base.job.core module

class pyiron.base.job.core.JobCore(project, job_name)[source]

Bases: pyiron.base.generic.template.PyironObject

The JobCore the most fundamental pyiron job class. From this class the GenericJob as well as the reduced JobPath class are derived. While JobPath only provides access to the HDF5 file it is about one order faster.

Parameters:
  • project (ProjectHDFio) – ProjectHDFio instance which points to the HDF5 file the job is stored in
  • job_name (str) – name of the job, which has to be unique within the project
.. attribute:: job_name

name of the job, which has to be unique within the project

.. attribute:: status
execution status of the job, can be one of the following [initialized, appended, created, submitted, running,
aborted, collect, suspended, refresh, busy, finished]
.. attribute:: job_id

unique id to identify the job in the pyiron database

.. attribute:: parent_id

job id of the predecessor job - the job which was executed before the current one in the current job series

.. attribute:: master_id

job id of the master job - a meta job which groups a series of jobs, which are executed either in parallel or in serial.

.. attribute:: child_ids

list of child job ids - only meta jobs have child jobs - jobs which list the meta job as their master

.. attribute:: project

Project instance the jobs is located in

.. attribute:: project_hdf5

ProjectHDFio instance which points to the HDF5 file the job is stored in

.. attribute:: job_info_str

short string to describe the job by it is job_name and job ID - mainly used for logging

.. attribute:: working_directory

working directory of the job is executed in - outside the HDF5 file

.. attribute:: path

path to the job as a combination of absolute file system path and path within the HDF5 file.

check_if_job_exists(job_name=None, project=None)[source]

Check if a job already exists in an specific project.

Parameters:
  • job_name (str) – Job name (optional)
  • project (ProjectHDFio, Project) – Project path (optional)
Returns:

True / False

Return type:

(bool)

child_ids

list of child job ids - only meta jobs have child jobs - jobs which list the meta job as their master

Returns:list of child job ids
Return type:list
compress(files_to_compress=None)[source]
copy()[source]

Copy the JobCore object which links to the HDF5 file

Returns:New FileHDFio object pointing to the same HDF5 file
Return type:JobCore
copy_to(project, new_database_entry=True, copy_files=True)[source]

Copy the content of the job including the HDF5 file to a new location

Parameters:
  • project (ProjectHDFio) – project to copy the job to
  • new_database_entry (bool) – [True/False] to create a new database entry - default True
  • copy_files (bool) – [True/False] copy the files inside the working directory - default True
Returns:

JobCore object pointing to the new location.

Return type:

JobCore

decompress()[source]
from_hdf(hdf, group_name='group')[source]

Restore object from hdf5 format - The function has to be implemented by the derived classes - usually the GenericJob class

Parameters:
  • hdf (ProjectHDFio) – Optional hdf5 file, otherwise self is used.
  • group_name (str) – Optional hdf5 group in the hdf5 file.
get(name)[source]

Internal wrapper function for __getitem__() - self[name]

Parameters:key (str, slice) – path to the data or key of the data object
Returns:data or data object
Return type:dict, list, float, int
get_from_table(path, name)[source]

Get a specific value from a pandas.Dataframe

Parameters:
  • path (str) – relative path to the data object
  • name (str) – parameter key
Returns:

the value associated to the specific parameter key

Return type:

dict, list, float, int

get_job_id(job_specifier=None)[source]

get the job_id for job named job_name in the local project path from database

Parameters:job_specifier (str, int) – name of the job or job ID
Returns:job ID of the job
Return type:int
get_pandas(name)[source]

Load a dictionary from the HDF5 file and display the dictionary as pandas Dataframe

Parameters:name (str) – HDF5 node name
Returns:The dictionary is returned as pandas.Dataframe object
Return type:pandas.Dataframe
id

Unique id to identify the job in the pyiron database - use self.job_id instead

Returns:job id
Return type:int
inspect(job_specifier)[source]

Inspect an existing pyiron object - most commonly a job - from the database

Parameters:job_specifier (str, int) – name of the job or job ID
Returns:Access to the HDF5 object - not a GenericJob object - use load() instead.
Return type:JobCore
is_compressed()[source]
is_master_id(job_id)[source]

Check if the job ID job_id is the master ID for any child job

Parameters:job_id (int) – job ID of the master job
Returns:[True/False]
Return type:bool
is_self_archived()[source]
job_id

Unique id to identify the job in the pyiron database

Returns:job id
Return type:int
job_info_str

Short string to describe the job by it is job_name and job ID - mainly used for logging

Returns:job info string
Return type:str
job_name

Get name of the job, which has to be unique within the project

Returns:job name
Return type:str
list_all()[source]

List all groups and nodes of the HDF5 file - where groups are equivalent to directories and nodes to files.

Returns:{‘groups’: [list of groups], ‘nodes’: [list of nodes]}
Return type:dict
list_childs()[source]

List child jobs as JobPath objects - not loading the full GenericJob objects for each child

Returns:list of child jobs
Return type:list
list_files()[source]

List files inside the working directory

Parameters:extension (str) – filter by a specific extension
Returns:list of file names
Return type:list
list_groups()[source]

equivalent to os.listdirs (consider groups as equivalent to dirs)

Returns:list of groups in pytables for the path self.h5_path
Return type:(list)
list_nodes()[source]

List all groups and nodes of the HDF5 file

Returns:list of nodes
Return type:list
load(job_specifier, convert_to_object=True)[source]

Load an existing pyiron object - most commonly a job - from the database

Parameters:
  • job_specifier (str, int) – name of the job or job ID
  • convert_to_object (bool) – convert the object to an pyiron object or only access the HDF5 file - default=True accessing only the HDF5 file is about an order of magnitude faster, but only provides limited functionality. Compare the GenericJob object to JobCore object.
Returns:

Either the full GenericJob object or just a reduced JobCore object

Return type:

GenericJob, JobCore

load_object(convert_to_object=True, project=None)[source]

Load object to convert a JobPath to an GenericJob object.

Parameters:
  • convert_to_object (bool) – convert the object to an pyiron object or only access the HDF5 file - default=True accessing only the HDF5 file is about an order of magnitude faster, but only provides limited functionality. Compare the GenericJob object to JobCore object.
  • project (ProjectHDFio) – ProjectHDFio to load the object with - optional
Returns:

depending on convert_to_object

Return type:

GenericJob, JobPath

master_id

Get job id of the master job - a meta job which groups a series of jobs, which are executed either in parallel or in serial.

Returns:master id
Return type:int
move_to(project)[source]

Move the content of the job including the HDF5 file to a new location

Parameters:project (ProjectHDFio) – project to move the job to
Returns:JobCore object pointing to the new location.
Return type:JobCore
name

Get name of the job, which has to be unique within the project

Returns:job name
Return type:str
parent_id

Get job id of the predecessor job - the job which was executed before the current one in the current job series

Returns:parent id
Return type:int
path

Absolute path of the HDF5 group starting from the system root - combination of the absolute system path plus the absolute path inside the HDF5 file starting from the root group.

Returns:absolute path
Return type:str
project

Project instance the jobs is located in

Returns:project the job is located in
Return type:Project
project_hdf5

Get the ProjectHDFio instance which points to the HDF5 file the job is stored in

Returns:HDF5 project
Return type:ProjectHDFio
remove(_protect_childs=True)[source]

Remove the job - this removes the HDF5 file, all data stored in the HDF5 file an the corresponding database entry.

Parameters:_protect_childs (bool) – [True/False] by default child jobs can not be deleted, to maintain the consistency - default=True
remove_child()[source]

internal function to remove command that removes also child jobs. Do never use this command, since it will destroy the integrity of your project.

rename(new_job_name)[source]

Rename the job - by changing the job name

Parameters:new_job_name (str) – new job name
save()[source]

The save function has to be implemented by the derived classes - usually the GenericJob class

self_archive()[source]
self_unarchive()[source]
show_hdf()[source]

Iterating over the HDF5 datastructure and generating a human readable graph.

status
Execution status of the job, can be one of the following [initialized, appended, created, submitted, running,
aborted, collect, suspended, refresh, busy, finished]
Returns:status
Return type:(str)
to_hdf(hdf, group_name='group')[source]

Store object in hdf5 format - The function has to be implemented by the derived classes - usually the GenericJob class

Parameters:
  • hdf (ProjectHDFio) – Optional hdf5 file, otherwise self is used.
  • group_name (str) – Optional hdf5 group in the hdf5 file.
to_object(object_type=None, **qwargs)[source]

Load the full pyiron object from an HDF5 file

Parameters:
  • object_type – if the ‘TYPE’ node is not available in the HDF5 file a manual object type can be set - optional
  • **qwargs – optional parameters [‘job_name’, ‘project’] - to specify the location of the HDF5 path
Returns:

pyiron object

Return type:

GenericJob

working_directory

working directory of the job is executed in - outside the HDF5 file

Returns:working directory
Return type:str

pyiron.base.job.executable module

class pyiron.base.job.executable.Executable(path_binary_codes, codename=None, module=None, code=None, overwrite_nt_flag=False)[source]

Bases: object

available_versions

List all available exectuables in the path_binary_codes for the specified codename.

Returns:list of the available version
Return type:list
executable_path

Get the executable path

Returns:absolute path
Return type:str
mpi

Check if the message processing interface is activated.

Returns:[True/False]
Return type:bool
version

Version of the Executable

Returns:version
Return type:str

pyiron.base.job.generic module

class pyiron.base.job.generic.GenericJob(project, job_name)[source]

Bases: pyiron.base.job.core.JobCore

Generic Job class extends the JobCore class with all the functionality to run the job object. From this class all specific Hamiltonians are derived. Therefore it should contain the properties/routines common to all jobs. The functions in this module should be as generic as possible.

Parameters:
  • project (ProjectHDFio) – ProjectHDFio instance which points to the HDF5 file the job is stored in
  • job_name (str) – name of the job, which has to be unique within the project
.. attribute:: job_name

name of the job, which has to be unique within the project

.. attribute:: status
execution status of the job, can be one of the following [initialized, appended, created, submitted, running,
aborted, collect, suspended, refresh, busy, finished]
.. attribute:: job_id

unique id to identify the job in the pyiron database

.. attribute:: parent_id

job id of the predecessor job - the job which was executed before the current one in the current job series

.. attribute:: master_id

job id of the master job - a meta job which groups a series of jobs, which are executed either in parallel or in serial.

.. attribute:: child_ids

list of child job ids - only meta jobs have child jobs - jobs which list the meta job as their master

.. attribute:: project

Project instance the jobs is located in

.. attribute:: project_hdf5

ProjectHDFio instance which points to the HDF5 file the job is stored in

.. attribute:: job_info_str

short string to describe the job by it is job_name and job ID - mainly used for logging

.. attribute:: working_directory

working directory of the job is executed in - outside the HDF5 file

.. attribute:: path

path to the job as a combination of absolute file system path and path within the HDF5 file.

.. attribute:: version

Version of the hamiltonian, which is also the version of the executable unless a custom executable is used.

.. attribute:: executable

Executable used to run the job - usually the path to an external executable.

.. attribute:: library_activated

For job types which offer a Python library pyiron can use the python library instead of an external executable.

.. attribute:: server

Server object to handle the execution environment for the job.

.. attribute:: queue_id

the ID returned from the queuing system - it is most likely not the same as the job ID.

.. attribute:: logger

logger object to monitor the external execution and internal pyiron warnings.

.. attribute:: restart_file_list

list of files which are used to restart the calculation from these files.

.. attribute:: job_type
Job type object with all the available job types: [‘ExampleJob’, ‘SerialMaster’, ‘ParallelMaster’, ‘ScriptJob’,
‘ListMaster’]
append(job)[source]

Metajobs like GenericMaster, ParallelMaster, SerialMaser or ListMaster allow other jobs to be appended. In the GenericJob definition this is only a template function.

clear_job()[source]

Convenience function to clear job info after suspend. Mimics deletion of all the job info after suspend in a local test environment.

collect_logfiles()[source]

Collect the log files of the external executable and store the information in the HDF5 file. This method has to be implemented in the individual hamiltonians.

collect_output()[source]

Collect the output files of the external executable and store the information in the HDF5 file. This method has to be implemented in the individual hamiltonians.

convergence_check()[source]

Validate the convergence of the calculation.

Returns:If the calculation is converged
Return type:(bool)
copy()[source]

Copy the GenericJob object which links to the job and its HDF5 file

Returns:New GenericJob object pointing to the same job
Return type:GenericJob
copy_file_to_working_directory(file)[source]
copy_template(project, new_job_name=None)[source]

Copy the content of the job including the HDF5 file but without the output data to a new location

Parameters:
  • project (ProjectHDFio) – project to copy the job to
  • new_job_name (str) – to duplicate the job within the same porject it is necessary to modify the job name - optional
Returns:

GenericJob object pointing to the new location.

Return type:

GenericJob

copy_to(project=None, new_job_name=None, input_only=False, new_database_entry=True)[source]

Copy the content of the job including the HDF5 file to a new location

Parameters:
  • project (ProjectHDFio) – project to copy the job to
  • new_job_name (str) – to duplicate the job within the same porject it is necessary to modify the job name - optional
  • input_only (bool) – [True/False] to copy only the input - default False
  • new_database_entry (bool) – [True/False] to create a new database entry - default True
Returns:

GenericJob object pointing to the new location.

Return type:

GenericJob

create_job(job_type, job_name)[source]

Create one of the following jobs: - ‘ExampleJob’: example job just generating random number - ‘SerialMaster’: series of jobs run in serial - ‘ParallelMaster’: series of jobs run in parallel - ‘ScriptJob’: Python script or jupyter notebook job container - ‘ListMaster’: list of jobs

Parameters:
  • job_type (str) – job type can be [‘ExampleJob’, ‘SerialMaster’, ‘ParallelMaster’, ‘ScriptJob’, ‘ListMaster’]
  • job_name (str) – name of the job
Returns:

job object depending on the job_type selected

Return type:

GenericJob

db_entry()[source]

Generate the initial database entry for the current GenericJob

Returns:
database dictionary {“username”, “projectpath”, “project”, “job”, “subjob”, “hamversion”,
”hamilton”, “status”, “computer”, “timestart”, “masterid”, “parentid”}
Return type:(dict)
drop_status_to_aborted()[source]
executable

Get the executable used to run the job - usually the path to an external executable.

Returns:exectuable path
Return type:(str)
from_hdf(hdf=None, group_name=None)[source]

Restore the GenericJob from an HDF5 file

Parameters:
  • hdf (ProjectHDFio) – HDF5 group object - optional
  • group_name (str) – HDF5 subgroup name - optional
job_file_name(file_name, cwd=None)[source]

combine the file name file_name with the path of the current working directory

Parameters:
  • file_name (str) – name of the file
  • cwd (str) – current working directory - this overwrites self.project_hdf5.working_directory - optional
Returns:

absolute path to the file in the current working directory

Return type:

str

job_type
[‘ExampleJob’, ‘SerialMaster’, ‘ParallelMaster’, ‘ScriptJob’,
‘ListMaster’]
Returns:Job type object
Return type:JobTypeChoice
Type:Job type object with all the available job types
kill()[source]
logger

Get the logger object to monitor the external execution and internal pyiron warnings.

Returns:logger object
Return type:logging.getLogger()
queue_id

Get the queue ID, the ID returned from the queuing system - it is most likely not the same as the job ID.

Returns:queue ID
Return type:int
refresh_job_status()[source]

Refresh job status by updating the job status with the status from the database if a job ID is available.

remove_child()[source]

internal function to remove command that removes also child jobs. Do never use this command, since it will destroy the integrity of your project.

reset_job_id(job_id=None)[source]

Reset the job id sets the job_id to None in the GenericJob as well as all connected modules like JobStatus.

restart(snapshot=-1, job_name=None, job_type=None)[source]

Create an restart calculation from the current calculation - in the GenericJob this is the same as create_job(). A restart is only possible after the current job has finished. If you want to run the same job again with different input parameters use job.run(run_again=True) instead.

Parameters:
  • snapshot (int) – time step from which to restart the calculation - default=-1 - the last time step
  • job_name (str) – job name of the new calculation - default=<job_name>_restart
  • job_type (str) – job type of the new calculation - default is the same type as the exeisting calculation

Returns:

restart_file_dict

A dictionary of the new name of the copied restart files

restart_file_list

Get the list of files which are used to restart the calculation from these files.

Returns:list of files
Return type:list
run(run_again=False, repair=False, debug=False, run_mode=None, que_wait_for=None)[source]

This is the main run function, depending on the job status [‘initialized’, ‘created’, ‘submitted’, ‘running’, ‘collect’,’finished’, ‘refresh’, ‘suspended’] the corresponding run mode is chosen.

Parameters:
  • run_again (bool) – Delete the existing job and run the simulation again.
  • repair (bool) – Set the job status to created and run the simulation again.
  • debug (bool) – Debug Mode - defines the log level of the subprocess the job is executed in.
  • run_mode (str) – [‘modal’, ‘non_modal’, ‘queue’, ‘manual’] overwrites self.server.run_mode
  • que_wait_for (int) – Que ID to wait for before this job is executed.
run_if_interactive()[source]

For jobs which executables are available as Python library, those can also be executed with a library call instead of calling an external executable. This is usually faster than a single core python job.

run_if_interactive_non_modal()[source]

For jobs which executables are available as Python library, those can also be executed with a library call instead of calling an external executable. This is usually faster than a single core python job.

run_if_manually(_manually_print=True)[source]

The run if manually function is called by run if the user decides to execute the simulation manually - this might be helpful to debug a new job type or test updated executables.

Parameters:_manually_print (bool) – Print explanation how to run the simulation manually - default=True.
run_if_modal()[source]

The run if modal function is called by run to execute the simulation, while waiting for the output. For this we use subprocess.check_output()

run_if_non_modal()[source]

The run if non modal function is called by run to execute the simulation in the background. For this we use subprocess.Popen()

run_if_scheduler(que_wait_for=None)[source]

The run if queue function is called by run if the user decides to submit the job to and queing system. The job is submitted to the queuing system using subprocess.Popen()

Parameters:que_wait_for (int) – Job ID the current job should be waiting for before being submitted.
Returns:Returns the queue ID for the job.
Return type:int
run_static()[source]

The run static function is called by run to execute the simulation.

save()[source]

Save the object, by writing the content to the HDF5 file and storing an entry in the database.

Returns:Job ID stored in the database
Return type:(int)
send_to_database()[source]

if the jobs should be store in the external/public database this could be implemented here, but currently it is just a placeholder.

server

Get the server object to handle the execution environment for the job.

Returns:server object
Return type:Server
signal_intercept(sig, frame)[source]
suspend()[source]

Suspend the job by storing the object and its state persistently in HDF5 file and exit it.

to_hdf(hdf=None, group_name=None)[source]

Store the GenericJob in an HDF5 file

Parameters:
  • hdf (ProjectHDFio) – HDF5 group object - optional
  • group_name (str) – HDF5 subgroup name - optional
update_master()[source]

After a job is finished it checks whether it is linked to any metajob - meaning the master ID is pointing to this jobs job ID. If this is the case and the master job is in status suspended - the child wakes up the master job, sets the status to refresh and execute run on the master job. During the execution the master job is set to status refresh. If another child calls update_master, while the master is in refresh the status of the master is set to busy and if the master is in status busy at the end of the update_master process another update is triggered.

validate_ready_to_run()[source]

Validate that the calculation is ready to be executed. By default no generic checks are performed, but one could check that the input information is complete or validate the consistency of the input at this point.

version

Get the version of the hamiltonian, which is also the version of the executable unless a custom executable is used.

Returns:version number
Return type:str
working_directory

Get the working directory of the job is executed in - outside the HDF5 file. The working directory equals the path but it is represented by the filesystem:

/absolute/path/to/the/file.h5/path/inside/the/hdf5/file
becomes:
/absolute/path/to/the/file_hdf5/path/inside/the/hdf5/file
Returns:absolute path to the working directory
Return type:str
write_input()[source]

Write the input files for the external executable. This method has to be implemented in the individual hamiltonians.

pyiron.base.job.generic.multiprocess_wrapper(job_id, working_dir, debug=False)[source]

pyiron.base.job.interactive module

class pyiron.base.job.interactive.InteractiveBase(project, job_name)[source]

Bases: pyiron.base.job.generic.GenericJob

from_hdf(hdf=None, group_name=None)[source]

Restore the InteractiveBase object in the HDF5 File

Parameters:
  • hdf (ProjectHDFio) – HDF5 group object - optional
  • group_name (str) – HDF5 subgroup name - optional
interactive_close()[source]
interactive_flush(path='interactive', include_last_step=False)[source]
interactive_flush_frequency
interactive_is_activated()[source]
interactive_store_in_cache(key, value)[source]
interactive_write_frequency
run_if_interactive()[source]

For jobs which executables are available as Python library, those can also be executed with a library call instead of calling an external executable. This is usually faster than a single core python job.

run_if_interactive_non_modal()[source]

For jobs which executables are available as Python library, those can also be executed with a library call instead of calling an external executable. This is usually faster than a single core python job.

to_hdf(hdf=None, group_name=None)[source]

Store the InteractiveBase object in the HDF5 File

Parameters:
  • hdf (ProjectHDFio) – HDF5 group object - optional
  • group_name (str) – HDF5 subgroup name - optional

pyiron.base.job.jobstatus module

class pyiron.base.job.jobstatus.JobStatus(initial_status='initialized', db=None, job_id=None)[source]

Bases: object

The JobStatus object handles the different states a job could have. The available states are:
initialized: The object for the corresponding job was just created. appended: The job was appended to an master job. created: The files required for the simulation were written to the harddisk. submitted: The job was submitted to the jobscheduler and is waiting to be executed. running: The job is currently executed. aborted: The job failed to execute. collect: The job finished successfully and the written files are being collected. suspended: The job was set to sleep, waiting until other related jobs are finished, before it continous. refresh: The job was suspended before and it is currently checking if there are new tasks it can execute. busy: The job is refreshing, but during the refresh more related jobs finished so another refresh is necessary. finished: The job and all connected sub jobs are finished.
Parameters:
  • initial_status (str) – If no initial status is provided the status is set to ‘initialized’
  • db (DatabaseAccess) – The database which is responsible for this job.
  • job_id (int) – job ID
.. attribute:: database

the database which is responsible for this job.

.. attribute:: job_id

Job ID

.. attribute:: string

job status as string

database

Get the database which is responsible for this job. If no database is linked it returns None. :returns: The database which is responsible for this job. :rtype: DatabaseAccess

job_id

Get the job id of the job this jobstatus is associated to. :returns: job id :rtype: int

refresh_status()[source]

Refresh the job status - check if the database and job_id are set and if this is the case load the job status from the database.

string
The object for the corresponding job was just created.

appended: The job was appended to an master job. created: The files required for the simulation were written to the harddisk. submitted: The job was submitted to the jobscheduler and is waiting to be executed. running: The job is currently executed. aborted: The job failed to execute. collect: The job finished successfully and the written files are being collected. suspended: The job was set to sleep, waiting until other related jobs are finished, before it continous. refresh: The job was suspended before and it is currently checking if there are new tasks it can execute. busy: The job is refreshing, but during the refresh more related jobs finished so another refresh is

necessary.

finished: The job and all connected sub jobs are finished.

Returns:
status [initialized, appended, created, submitted, running, aborted, collect, suspended, refresh,
busy, finished]
Return type:(str)
Type:Get the current status as string, it can be
Type:initialized

pyiron.base.job.jobtype module

class pyiron.base.job.jobtype.JobType[source]

Bases: object

The JobTypeBase class creates a new object of a given class type.

static convert_str_to_class(job_class_dict, class_name)[source]
class pyiron.base.job.jobtype.JobTypeChoice[source]

Bases: object

Helper class to choose the job type directly from the project, autocompletion is enabled by overwriting the __dir__() function.

Parameters:job_class_dict – dictionary with the jobtypes to choose from.
job_class_dict
class pyiron.base.job.jobtype.Singleton[source]

Bases: type

Implemented with suggestions from

http://stackoverflow.com/questions/6760685/creating-a-singleton-in-python

pyiron.base.job.path module

class pyiron.base.job.path.JobPath(db, job_id=None, db_entry=None, user=None)[source]

Bases: pyiron.base.job.core.JobCore

The JobPath class is derived from the JobCore and is used as a lean version of the GenericJob class. Instead of loading the full pyiron object the JobPath class only provides access to the HDF5 file, which should be enough for most analysis.

Parameters:
  • db (DatabaseAccess) – database object
  • job_id (int) – Job ID - optional, but either a job ID or a database entry db_entry has to be provided.
  • db_entry (dict) – database entry {“job”:, “subjob”:, “projectpath”:, “project”:, “hamilton”:, “hamversion”:, “status”:} and optional entries are {“id”:, “masterid”:, “parentid”:}
  • user (str) – current unix/linux/windows user who is running pyiron
.. attribute:: job_name

name of the job, which has to be unique within the project

.. attribute:: status
execution status of the job, can be one of the following [initialized, appended, created, submitted, running,
aborted, collect, suspended, refresh, busy, finished]
.. attribute:: job_id

unique id to identify the job in the pyiron database

.. attribute:: parent_id

job id of the predecessor job - the job which was executed before the current one in the current job series

.. attribute:: master_id

job id of the master job - a meta job which groups a series of jobs, which are executed either in parallel or in serial.

.. attribute:: child_ids

list of child job ids - only meta jobs have child jobs - jobs which list the meta job as their master

.. attribute:: project

Project instance the jobs is located in

.. attribute:: project_hdf5

ProjectHDFio instance which points to the HDF5 file the job is stored in

.. attribute:: job_info_str

short string to describe the job by it is job_name and job ID - mainly used for logging

.. attribute:: working_directory

working directory of the job is executed in - outside the HDF5 file

.. attribute:: path

path to the job as a combination of absolute file system path and path within the HDF5 file.

.. attribute:: is_root

boolean if the HDF5 object is located at the root level of the HDF5 file

.. attribute:: is_open

boolean if the HDF5 file is currently opened - if an active file handler exists

.. attribute:: is_empty

boolean if the HDF5 file is empty

.. attribute:: base_name

name of the HDF5 file but without any file extension

.. attribute:: file_path

directory where the HDF5 file is located

.. attribute:: h5_path

path inside the HDF5 file - also stored as absolute path

base_name

Name of the HDF5 file - but without the file extension .h5

Returns:file name without the file extension
Return type:str
close()[source]

Close the current HDF5 path and return to the path before the last open

create_group(name)[source]

Create an HDF5 group - similar to a folder in the filesystem - the HDF5 groups allow the users to structure their data.

Parameters:name (str) – name of the HDF5 group
Returns:FileHDFio object pointing to the new group
Return type:FileHDFio
file_path

Path where the HDF5 file is located - posixpath.dirname()

Returns:HDF5 file location
Return type:str
groups()[source]

Filter HDF5 file by groups

Returns:an HDF5 file which is filtered by groups
Return type:FileHDFio
h5_path

Get the path in the HDF5 file starting from the root group - meaning this path starts with ‘/’

Returns:HDF5 path
Return type:str
is_empty

Check if the HDF5 file is empty

Returns:[True/False]
Return type:bool
is_root

Check if the current h5_path is pointing to the HDF5 root group.

Returns:[True/False]
Return type:bool
items()[source]

List all keys and values as items of all groups and nodes of the HDF5 file

Returns:list of sets (key, value)
Return type:list
keys()[source]

List all groups and nodes of the HDF5 file - where groups are equivalent to directories and nodes to files.

Returns:all groups and nodes
Return type:list
list_dirs()[source]

equivalent to os.listdirs (consider groups as equivalent to dirs)

Returns:list of groups in pytables for the path self.h5_path
Return type:(list)
listdirs()[source]

equivalent to os.listdirs (consider groups as equivalent to dirs)

Returns:list of groups in pytables for the path self.h5_path
Return type:(list)
nodes()[source]

Filter HDF5 file by nodes

Returns:an HDF5 file which is filtered by nodes
Return type:FileHDFio
open(h5_rel_path)[source]

Create an HDF5 group and enter this specific group. If the group exists in the HDF5 path only the h5_path is set correspondingly otherwise the group is created first.

Parameters:h5_rel_path (str) – relative path from the current HDF5 path - h5_path - to the new group
Returns:FileHDFio object pointing to the new group
Return type:FileHDFio
put(key, value)[source]

Store data inside the HDF5 file

Parameters:
  • key (str) – key to store the data
  • value (pandas.DataFrame, pandas.Series, dict, list, float, int) – basically any kind of data is supported
remove_file()[source]

Remove the HDF5 file with all the related content

values()[source]

List all values for all groups and nodes of the HDF5 file

Returns:list of all values
Return type:list

pyiron.base.job.script module

class pyiron.base.job.script.Notebook[source]

Bases: object

class for pyiron notebook objects

static get_custom_dict()[source]
static store_custom_output_dict(output_dict)[source]
class pyiron.base.job.script.ScriptJob(project, job_name)[source]

Bases: pyiron.base.job.generic.GenericJob

The ScriptJob class allows to submit Python scripts and Jupyter notebooks to the pyiron job management system.

Parameters:
  • project (ProjectHDFio) – ProjectHDFio instance which points to the HDF5 file the job is stored in
  • job_name (str) – name of the job, which has to be unique within the project
attribute

job_name

name of the job, which has to be unique within the project

.. attribute:: status
execution status of the job, can be one of the following [initialized, appended, created, submitted, running,
aborted, collect, suspended, refresh, busy, finished]
.. attribute:: job_id

unique id to identify the job in the pyiron database

.. attribute:: parent_id

job id of the predecessor job - the job which was executed before the current one in the current job series

.. attribute:: master_id

job id of the master job - a meta job which groups a series of jobs, which are executed either in parallel or in serial.

.. attribute:: child_ids

list of child job ids - only meta jobs have child jobs - jobs which list the meta job as their master

.. attribute:: project

Project instance the jobs is located in

.. attribute:: project_hdf5

ProjectHDFio instance which points to the HDF5 file the job is stored in

.. attribute:: job_info_str

short string to describe the job by it is job_name and job ID - mainly used for logging

.. attribute:: working_directory

working directory of the job is executed in - outside the HDF5 file

.. attribute:: path

path to the job as a combination of absolute file system path and path within the HDF5 file.

.. attribute:: version

Version of the hamiltonian, which is also the version of the executable unless a custom executable is used.

.. attribute:: executable

Executable used to run the job - usually the path to an external executable.

.. attribute:: library_activated

For job types which offer a Python library pyiron can use the python library instead of an external executable.

.. attribute:: server

Server object to handle the execution environment for the job.

.. attribute:: queue_id

the ID returned from the queuing system - it is most likely not the same as the job ID.

.. attribute:: logger

logger object to monitor the external execution and internal pyiron warnings.

.. attribute:: restart_file_list

list of files which are used to restart the calculation from these files.

.. attribute:: job_type
Job type object with all the available job types: [‘ExampleJob’, ‘SerialMaster’, ‘ParallelMaster’, ‘ScriptJob’,
‘ListMaster’]
.. attribute:: script_path

the absolute path to the python script

collect_logfiles()[source]

Compatibility function - but no log files are being collected

collect_output()[source]

Collect output function updates the master ID entries for all the child jobs created by this script job, if the child job is already assigned to an master job nothing happens - master IDs are not overwritten.

from_hdf(hdf=None, group_name=None)[source]

Restore the ScriptJob from an HDF5 file

Parameters:
  • hdf (ProjectHDFio) – HDF5 group object - optional
  • group_name (str) – HDF5 subgroup name - optional
run_if_lib()[source]

Compatibility function - but library run mode is not available

script_path

Python script path

Returns:absolute path to the python script
Return type:str
to_hdf(hdf=None, group_name=None)[source]

Store the ScriptJob in an HDF5 file

Parameters:
  • hdf (ProjectHDFio) – HDF5 group object - optional
  • group_name (str) – HDF5 subgroup name - optional
write_input()[source]

Copy the script to the working directory - only python scripts and jupyter notebooks are supported

pyiron.base.job.wrapper module

class pyiron.base.job.wrapper.JobWrapper(working_directory, job_id, debug=False)[source]

Bases: object

The job wrapper is called from the run_job.py script, it restores the job from hdf5 and executes it.

Parameters:
  • working_directory (str) – working directory of the job
  • job_id (int) – job ID
  • debug (bool) – enable debug mode [True/False] (optional)
run()[source]

The job wrapper run command, sets the job status to ‘running’ and executes run_if_modal().

static setup_logger(debug=False)[source]

Setup the error logger

Parameters:debug (bool) – the level of logging, enable debug mode [True/False] (optional)
Returns:logger object instance
Return type:logger

Module contents