CCPEM-Pipeliner API

The pipeliner api provides access to all of the main functions of the pipeliner

PipelinerProject

To interact with a pipeliner project it must be created as a PipelinerProject object

class pipeliner.api.manage_project.PipelinerProject(pipeline_name: str = 'default', project_name: str | None = None, description: str | None = None, make_new_project: bool = False)

Bases: object

This class forms the basis for a project.

pipeline_name

The name of the pipeline. Defaults to default if not set. There is really no good reason to give the pipeline any other name.

Type:: str

abort_job(job_name: str) → None

Abort a running job.

This function signals to the job that it should abort, but does not wait for the job to respond.

Parameters:

job_name – The job name. This must be exact, use parse_procname to find the job if you need to find it from a partial name, number or alias.

Raises:

ValueError – If there is no job with the given name
RuntimeError – If the job is in any state except Running

add_missing_nodes_from_all_jobs() → List[str]

Add any missing nodes from all jobs in the pipeline.

One reason why this might be necessary is that the Pipeliner makes more nodes than Relion, so if a pipeline is converted the missing nodes must be added.

cleanup_all(harsh: bool = False)

Runs cleanup on all jobs in a project

Parameters:: harsh (bool) – Should harsh cleaning be performed?

compare_job_parameters(jobs_list: List[str]) → dict

Compare the running parameters of multiple jobs

Parameters:

jobs_list (list) – The jobs to compare

Returns:

{parameter: [value, value, value]}

Return type:

dict

Raises:

ValueError – If any of the jobs is not found
ValueError – If the jobs being compared are not of the same type

continue_job(job_to_continue: str, comment: str | None = None, run_in_foreground=False) → PipelinerJob

Continue a job that has already been run

To change the parameters in a continuation the user needs to edit the continue_job.star file in the job’s directory

Parameters:

job_to_continue (str) – The name of the job to continue
comment (str) – Comments for the job’s jobinfo file

Returns:

The: PipelinerJob object for the created job

Return type:

PipelinerJob

Raises:

ValueError – If the continue_job.star file is not found and there is no job.star file in the job’s directory to use as a backup
ValueError – If the job is of a type that needs a optimizer file to continue and this file is not found
ValueError – The job has iterations but the parameters specified would result in no additional iterations being run

create_archive(job: str, full: bool = False, tar: bool = True) → str

Creates an archive

Archives can be full or simple. Simple archives contain the directory structure of the project, the parameter files for each job and a script to rerun the project through the terminal job. The full archive contains the full job dirs for the terminal job and all of its children

Parameters:

job (str) – The name of the terminal job in the workflow
full (bool) – If True a full archive is written else a simple archive is written
tar (bool) – Should the newly written archive be compressed?

Returns:

A message telling the type of archive and its name

Return type:

str

create_reference_report(terminal_job: str) → Tuple[str, int]

Create a report on all the references used in the project

Parameters:: terminal_job (str) – use this job and all its parents
Returns:: (The name of the report created, number of jobs in report)
Return type:: tuple

delete_job(job: str) → bool

Delete a job

Removes the job from the main project and moves it and its children it to the Trash

Parameters:: job (str) – The name of the job to be deleted
Returns:: True If a job was deleted, False if no jobs were deleted
Return type:: bool

edit_comment(job_name: str, comment: str | None = None, overwrite: bool = False)

Edit the comment of a job

Parameters:

job_name (str) – The name of the job to eddit the comment for
comment (str) – The comment to add/append
overwrite (bool) – if True overwrites otiginal comment, otherwise appends it to the current comment

Raises:

ValueError – If the new rank is not None or an integer

static empty_trash()

Deletes all the files and dirs in the Trash directory

Returns:: True if any files were deleted, False If no files were deleted
Return type:: bool

find_job_by_comment(contains: List[str] | None = None, not_contains: List[str] | None = None, job_type: str | None = None, command: bool = False) → List[str]

Find Jobs by their comments or command

Parameters:

contains (list) – Find jobs that contain all of the strings in this list
not_contains (list) – Find jobs that do not contain any of these strings
job_type (str) – Only consider jobs who’s type contain this string
command (bool) – If True searches the job’s command history rather than its comments

Returns:

Names of all the jobs found

Return type:

list

Raises:

ValueError – If nothing is specified for contains and not_contains

get_continuation_job(job_name: str) → PipelinerJob

Returns a PipelinerJob ready for continuation

All JobOption values are the ones from the parent job unless they have been updated by the job’s prepare_for_continuation() method

Returns:: The new continuation job ready to use for generating a new job in Doppio
Return type:: PipelinerJob

get_job(job_name: str) → PipelinerJob

Get an existing job from the project.

Parameters:: job_name (str) – The name of the job to get
Raises:: ValueError – if the named job cannot be found

parse_proclist(list_o_procs: list, search_trash: bool = False) → list

Finds full process names for multiple processes

Returns full process names IE: Import/job001/ from job001 or 1

Parameters:

list_o_procs (list) – A list of string process names
search_trash (bool) – Should the trash also be search?

Returns:

All of the full process names

Return type:

list

parse_procname(in_proc: str, search_trash: bool = False) → str

Find process name with the ability for parse ambiguous input.

Returns full process names IE: Import/job001/ from job001 or 1 Can look in both active processes and the Trash Can, accepts inputs containing only job number and process type and alias IE Import/my_alias

Parameters:

in_proc (str) – The text that is being checked against the list of processes
search_trash (bool) – Should it return the process name if the process is in the trash?

Returns:

the process name

Return type:

str

Raises:

ValueError – if the process was in the trash but search_trash is false
ValueError – if the process name is not in the pipeliner format, jobxxx, or a number. IE: An unrelated string
ValueError – if the process name is not found

static prepare_deposition(terminal_job: str, depo_type: Literal['emdb', 'empiar'], depo_id: str | None = None, jobstar_file: str | None = None, empiar_do_mov: bool = True, empiar_do_mics: bool = True, empiar_do_parts: bool = True, empiar_do_rparts: bool = True) → str

Prepare a deposition for EMPIAR, EMDB, or PDB databases

Parameters:

terminal_job (str) – This job and all its parents will be included in the deposition
depo_type (Literal["emdb", "empiar"]) – The type of deposition
depo_id (Optional[str]) – A name for the deposition
jobstar_file – (Optional[str]): For EMPIAR; A job.star file that contains additional required information that cannot be gathered from the jobs themselves see ‘:class:~pipeliner.jobs.other.empiar_deposition_job.EmpiarDepositionJob’ for the specifics
empiar_do_mov (bool) – For EMPIAR; should raw movies be included?
empiar_do_mics (bool) – For EMPIAR; Should corrected micrographs be included?
empiar_do_parts (bool) – For EMPIAR; Should particles be included?
empiar_do_parts – For EMPIAR; Should polished particles be included?

Returns:

The name path of the created archive

Return type:

str

prepare_metadata_report(jobname: str) → Tuple[str, int]

Returns a full metadata trace for a job and all upstream jobs

Parameters:

jobname – The name of the job to run on

Returns:

(dict: str: file written, int:: number of jobs in the report)

Return type:

tuple

restore_all_cleaned_up_files() → None: Restore all cleaned up files back to their original jobs

restore_from_cleanup(job: str) → None

Restore files from a job that were removed by the cleanup function

Parameters:: job (str) – The name of the job to restore files from

run_cleanup(jobs: list, harsh: bool = False) → bool

Run the cleanup function for multiple jobs

Each job defines its own method for cleanup and harsh cleanup

Parameters:

jobs (list) – List of string job names to operate on
harsh (bool) – Should harsh cleaning be performed

Returns:

True if cleanup is successful, otherwise False

Return type:

bool

run_job(jobinput: str | dict | PipelinerJob, overwrite: str | None = None, alias: str | None = None, ignore_invalid_joboptions=False, run_in_foreground=False) → PipelinerJob

Run a new job in the project

If a file is specified the job will be created from the parameters in that file If a dict is input the job will be created with defaults for all options except those specified in the dict.

If a dict is used for input it MUST contain at minimum {“_rlnJobTypeLabel”: <the jobtype>}

Parameters:

jobinput (str, dict, PipelinerJob) – The path to a run.job or job.star file that defines the parameters for the job or a dict specifying job parameters or a PipelinerJob object
overwrite (str) – The name of a job to overwrite, if None a new job will be created. A job can only be overwritten by a job of the same type
alias (str) – Alias to assign to the new job
ignore_invalid_joboptions (bool) – Run the job anyway even if the job options appear to be invalid
run_in_foreground (bool) – Run job in the main process blocking anything else from happening until it completes

Returns:

The job that was run. Note that unless run_in_foreground: was set, the job has been started and is probably still running when this function returns.

Return type:

PipelinerJob

Raises:

ValueError – If this method is used to continue a job
ValueError – If the job options are invalid and ignore_invalid_joboptions is not set

run_schedule(fn_sched: str, job_ids: List[str], nr_repeat: int = 1, minutes_wait: int = 0, minutes_wait_before: int = 0, seconds_wait_after: int = 5) → str

Runs a list of scheduled jobs

Parameters:

fn_sched (str) – A name to assign to the schedule
job_ids (list) – A list of string job names to run
nr_repeat (int) – Number of times to repeat the entire schedule
minutes_wait (int) – Minimum number of minutes to wait between running each subsequent job
minutes_wait_before (int) – Initial number of minutes to wait before starting to run the schedules.
seconds_wait_after (int) – Time to wait after running each job

Returns:

The name of the schedule that is run

Return type:

str

Raises:

ValueError – If the schedule name is already in use

run_scheduled_job(job: PipelinerJob, run_in_foreground: bool = False)

Run a job that has been scheduled

Parameters:

job (PipelinerJob) – The job to run
run_in_foreground (bool) – Should the job be run in the foreground instead of being spun off as a separate process, for testing

schedule_continue_job(job_to_continue: str, params_dict: dict | None = None, comments: str | None = None) → PipelinerJob

Schedule a job to run

Adds the job to the pipeline with scheduled status, does not run it

Parameters:

job_to_continue (str) – the name of the job to continue
params_dict (dict) – Parameters to change in the continuation job.star file. {param name: value}
comments (str) – comments to add to the job’s jobinfo file

Returns:

The: PipelinerJob object for the newly scheduled job

Return type:

PipelinerJob

Schedule a job to run

Adds the job to the pipeline with scheduled status, does not run it

Parameters:

job_input (str) – The path to a run.job or job.star file that defines the parameters for the job or a dictionary containing job parameters
comment (str) – Comments to put in the job’s jobinfo file
alias (str) – Alias to give to the job. Note that the alias is checked and might be modified when the job is added to the pipeline. The final alias used can be retrieved from the returned job’s alias attribute.

Returns:

The PipelinerJob object for the scheduled job

set_alias(job: str, new_alias: str | None)

Set the alias for a job

Parameters:

job (str) – The name of the job to set the alias for
new_alias (str) – The new alias

Raises:

ValueError – If the alias could not be set for any reason.

stop_schedule(schedule_name: str) → bool

Stops a currently running schedule

Kills the process running the schedule and marks the currently running job as aborted. Works to stop schedules that were started using the RELION GUI or pipeliner.

Parameters:: schedule_name (str) – The name of the schedule to stop
Returns:: True If the schedule was stopped, False if the schedule could not be found to stop
Return type:: bool

undelete_all_trashed_jobs() → None

undelete_job(job: str) → bool

Restores a job from the Trash back into the project

Also restores the job’s alias if one existed

Parameters:: job (str) – The job to undelete
Returns:: True If a job was restored, otherwise False
Return type:: bool

update_job_status(job: str, new_status: str)

Mark a job as finished, failed or aborted

If is_failed and is_aborted are both False the job is marked as finished.

Parameters:

job (str) – The name of the job to update
new_status (str) – The new status for the job; Choose from “Running”, , “Scheduled”, “Succeeded”, “Failed or “Aborted”. Status names are not case sensitive

Raises:

ValueError – If the new status is not one of the options

wait_for_job_to_finish(job: str | PipelinerJob, ping: float = 1.0, timeout: float = 86400.0, error_on_fail: bool = False, error_on_abort: bool = False) → str

Wait for the job to finish, with any status

Parameters:

job (PipelinerJob) – The job to wait for
ping (float) – How long to wait before checking for the file again (in seconds)
timeout (float) – Raise an error after this much time has elapsed, even if the job hasn’t finished (in seconds). The default is 24 hours.
error_on_fail (bool) – Raise an error if the job fails
error_on_abort (bool) – Raise an error if the job is aborted

Returns:

The final status of the job, Failed, Succeeded, or Aborted

Return type:

str

Raises:

RuntimeError – upon the watched job failing, being aborted, or either if error_on_fail or error_on_abort are True
RuntimeError – If the job still hasn’t finished by the timeout time

pipeliner.api.manage_project.convert_pipeline(pipeline_name: str = 'default') → bool

Converts a pipeline file from the RELION 2.0-3.1 format

This format has integer node, process, and status IDs. The pipeliner format uses string IDs

Parameters:

pipeline_name (str) – The name of the pipeline to be converted

Returns:

The result of the conversion

True if the pipeline was converted, False if the pipeline was already in pipeliner format

Return type:

bool

pipeliner.api.manage_project.delete_all_cleaned_job_files() → None: Delete all files the jobs that have been moved to the trash

pipeliner.api.manage_project.delete_all_trashed_job_files() → None: Delete all files the jobs that have been moved to the trash

pipeliner.api.manage_project.delete_cleaned_job_files(job) → None

Delete files in the trash from a cleaned up job

If the job is not found, a message is printed and nothing else happens.

Parameters:: job (str) – The job name; WITHOUT the cleanup dir prepended

pipeliner.api.manage_project.delete_summary_data_archive(filename: str)

Remove an archive from the summary data

Args: filename (str): The name of the archive zip file or dir

pipeliner.api.manage_project.delete_summary_data_metadata_report(filename: str)

Remove a metadata report from the summary data

Args: filename (str): The name of the report file

pipeliner.api.manage_project.delete_summary_data_reference_report(filename: str)

Remove a reference report from the summary data

Args: filename (str): The name of the report file

pipeliner.api.manage_project.delete_trashed_job_files(job) → None

Delete files in the trash for a specific job

If the job is not found, a message is printed and nothing else happens.

Parameters:: job (str) – The job name; WITHOUT the trash dir prepended

pipeliner.api.manage_project.get_archives_list_from_summary_file() → Tuple[List[str], List[List[str]]]

Get a list of the reference reports in the summary file

Returns:: ([column, headers], [[line, 1, data], [line, 2, data]]
Return type:: tuple

pipeliner.api.manage_project.get_cleaned_up_jobs_and_sizes(show_deleted: bool = False) → Dict[str, str]

Get names and sizes of all files that have been removed by cleanup

Parameters:: show_deleted (bool) – Show cleaned files from deleted jobs. Should be false if it is for display as these files cannot be restored if the job is in trash
Returns:: {filename: size} for each file
Return type:: Dict[str, str]

pipeliner.api.manage_project.get_commands_and_nodes(job_file: str) → tuple

Tell what commands a job file would return and nodes that would be created

Parameters:

job_file (str) – The path to a run.job or job.star file

Returns:

A list of commands. Each item in the commands list is a list of commands arguments. IE: [[com1-arg1, com1-arg2],[com2-arg1]]
A list of input nodes that would be created. Each item in the list is a tuple: [(name, type), (name, type)]
A list of output nodes that would be created. Each item in the list is a tuple: [(name, type), (name, type)]
A list of any PipelinerWarning raised by joboption validation
A list of the ExternalProgram objects used by the job

Return type:

tuple

pipeliner.api.manage_project.get_deleted_jobs_and_sizes() → Dict[str, str]

Get all the jobs that have been moved to the trash and their sizes

Returns:: The names of the trashed jobs and the sizes of their deleted directories.
Return type:: Dict[str, str]

pipeliner.api.manage_project.get_image_metadata_from_node(node_name: str, image_name: str = '') → Mapping[str, float | int | str | dict | list]

Return the metadata for an image using the method from its node.

Parameters:

node_name (str) – Name of the node the image belongs to
image_name (str) – The name of the image. Usually this will be a path to an image file, relative to the project directory, but in some cases it might be a label that this node type should understand.

Returns:

A dictionary of metadata about the image.

pipeliner.api.manage_project.get_metadata_reports_from_summary_file() → Tuple[List[str], List[List[str]]]

Get a list of the metadata reports in the summary file

Returns:: ([column, headers], [[line, 1, data], [line, 2, data]]
Return type:: tuple

pipeliner.api.manage_project.get_ref_reports_from_summary_file() → Tuple[List[str], List[List[str]]]

Get a list of the reference reports in the summary file

Returns:: ([column, headers], [[line, 1, data], [line, 2, data]]
Return type:: tuple

pipeliner.api.manage_project.look_for_project(pipeline_name: str = 'default') → dict | None

See if a pipeliner project exists in the current directory

Parameters:: pipeline_name (str) – The name of the pipeline to look for. This is the same as the pipeline file name with “_pipeline.star” removed.
Returns:: Info about the project, as a dict, or None if there is no existing project.

api_utilities

Utility functions do not require an existing project

pipeliner.api.api_utils.edit_jobstar(fn_template: str, params_to_change: dict, out_fn: str) → str

Modify one or more parameters in a job.star file

Parameters:

fn_template (str) – The name of the job.star file to use as a template
params_to_change (dict) – The parameters to change in the format {param_name: new_value}
out_fn (str) – Name for the new file to be written

Returns:

The name of the output file written

Return type:

str

pipeliner.api.api_utils.get_job_info(job_type: str) → JobInfo | None

Get information about a job

Parameters:: job_type (str) – The type of job to return info on
Returns:: JobInfo object with info about the job and its references
Return type:: JobInfo
Raises:: ValueError – If the job type is not found

pipeliner.api.api_utils.job_default_parameters_dict(jobtype: str) → dict

Get dictionary of a job’s parameters

Parameters:

jobtype (str) – The type of job to get the dict for

Returns:

The parameters dict. Suitable for running a job from: run_job()

Return type:

dict

pipeliner.api.api_utils.validate_starfile(fn_in: str)

Checks for inappropriate use of reserved words in STAR files

Writes a corrected version with proper quotation if possible. The original file is saved with a ‘.orig’ suffix added.

Parameters:: fn_in (str) – The name of the file to check

pipeliner.api.api_utils.write_default_jobstar(job_type: str, out_fn: str | None = None, relionstyle: bool = False)

Write a job.star file for the specified type of job

The default job.star contains all the job options with their values set as the defaults

Parameters:

job_type (str) – The type of job
out_fn (str) – Name of the file to write the output to. If left blank defaults to <job_type>_job.star
relionstyle (bool) – Should the job.star files be written in the relion format? Relion files are compatible with the pipeliner, but the pipeliner versions are not back compatible with Relion. If this option is selected a Relion job type should be used for job_type

Returns:

The name of the output file written

Return type:

str

pipeliner.api.api_utils.write_default_runjob(job_type: str, out_fn: str | None = None) → str

Write a run.job file for the specified type of job

The default runjob contains all the job option labels with their values set as the defaults

Parameters:

job_type (str) – The type of job
out_fn (str) – Name of the file to write the output to. If left blank defaults to <job_type>_run.job

Returns:

The name of the output file written

Return type:

str

user_settings

User settings which can control the pipeliner’s behaviour, environment and default job option values.

user_settings.py

Settings for the CCP-EM pipeliner.

Settings can be provided by the user in a JSON-formatted settings file or in environment variables. When the pipeliner is first run, a new settings file is created in a platform-specific directory (typically ~/.config/ccpem/pipeliner/ on Linux or ~/Library/Application Support/ccpem/pipeliner/ on Mac). This contains default settings values. These settings can be edited in that file, or overridden by setting environment variables before running the pipeliner. Both RELION- and Pipeliner-type environment variables are supported. For example, the setting for the default qsub script template can be set by editing the value for "qsub_template" in the settings file or by setting either the PIPELINER_QSUB_TEMPLATE or RELION_QSUB_TEMPLATE environment variable. If more than one of these is set, PIPELINER_QSUB_TEMPLATE will be used by preference, followed by RELION_QSUB_TEMPLATE, followed by the value from the settings file.

For programmatic access to settings values, use the helper functions which are provided at the module level (e.g. user_settings.get_qsub_template(). These allow easy access to the correctly-typed value of each individual setting.

This module also contains a Settings class and some internal functions, but there should be no need to access any of these directly from outside this module. To add a new setting, create a new get_<setting name>() function at the top of this module and add a new setting definition in Settings.add_all_settings(), making sure to use the correct types for the function calls and default value.

class pipeliner.user_settings.BoolSettingDefinition(name: str, env_vars: List[str], default: bool)

Bases: SettingDefinition

Class for defining a setting with a bool value.

default: bool

static get_value_from_string(value_str: str) → bool

class pipeliner.user_settings.IntSettingDefinition(name: str, env_vars: List[str], default: int)

Bases: SettingDefinition

Class for defining a setting with an int value.

default: int

static get_value_from_string(value_str: str) → int

class pipeliner.user_settings.OptionalIntSettingDefinition(name: str, env_vars: List[str], default: int | None)

Bases: SettingDefinition

Class for defining a setting with an optional int value (i.e. int or None).

default: int | None

static get_value_from_string(value_str: str) → int | None

class pipeliner.user_settings.OptionalStringSettingDefinition(name: str, env_vars: List[str], default: str | None)

Bases: SettingDefinition

Class for defining a setting with a string value.

default: str | None

static get_value_from_string(value_str: str | None) → str

class pipeliner.user_settings.PathListSettingDefinition(name: str, env_vars: List[str], default: List[str])

Bases: SettingDefinition

Class for defining a setting with a value which is a list of file paths.

In JSON format, the value should appear as a list of strings. When stored in an environment variable, the value should take the form of a single string with the individual paths separated by os.pathsep (typically :).

default: List[str]

static get_value_from_string(value_str: str) → List[str]

class pipeliner.user_settings.SettingDefinition(name: str, env_vars: List[str], default: Any)

Bases: object

Base class for setting definitions. Not intended to be used directly.

default: Any

env_vars: List[str]

static get_value_from_string(value_str: str) → Any

name: str

class pipeliner.user_settings.Settings(settings_file_override=None)

Bases: object

Container class to hold settings definitions and the get_<type>() functions that fetch their values.

add_all_settings()

add_bool_setting(*, name: str, env_vars: List[str], default: bool)

add_int_setting(*, name: str, env_vars: List[str], default: int)

add_optional_int_setting(*, name: str, env_vars: List[str], default: int | None)

add_optional_string_setting(*, name: str, env_vars: List[str], default: str | None)

add_path_list_setting(*, name: str, env_vars: List[str], default: List[str])

add_qsub_extra_settings()

add_string_setting(*, name: str, env_vars: List[str], default: str)

check_for_extra_keys()

check_qsub_extras()

get_bool(name: str) → bool

get_int(name: str) → int

get_list(name: str) → List[str]

get_optional_int(name: str) → int | None

get_optional_string(name: str) → str

get_qsub_extras(number: int) → Dict[str, str]: Add definitions for the requested qsub extra settings and get their values

get_setting_value(name: str)

get_string(name: str) → str

class pipeliner.user_settings.StringSettingDefinition(name: str, env_vars: List[str], default: str)

Bases: SettingDefinition

Class for defining a setting with a string value.

default: str

static get_value_from_string(value_str: str) → str

pipeliner.user_settings.get_additional_program_paths() → List[str]

Paths to prepend to the PATH environment variable before searching for program executables. Use this setting to make third-party software available to the pipeliner. Note that the paths should be to the directory containing the executable, not to the executable itself.

In JSON format, this setting is a list of strings. It can also be set using the PIPELINER_ADDITIONAL_PROGRAM_PATHS environment variable, which should be a PATH-style string containing individual paths separated by path separators (typically ‘:’ on Linux and Mac).

pipeliner.user_settings.get_aretomo_executable() → str: The default AreTomo executable.

pipeliner.user_settings.get_batchtomo_executable() → str: The default batchruntomo executable.

pipeliner.user_settings.get_ccpem_share_dir() → str: Path to the share dir in an installation of ccpem for rvapi to use

pipeliner.user_settings.get_cryocare_executables_dir() → str: The default cryocare executables directory.

pipeliner.user_settings.get_ctffind_executable() → str: The default CTFFIND-4.1+ executable.

pipeliner.user_settings.get_default_nrmpi() → int: The default for ‘Number of MPI procs’.

pipeliner.user_settings.get_default_nrthreads() → int: The default for ‘Number of threads’.

pipeliner.user_settings.get_dynamight_executable() → str: The default DynaMight executable.

pipeliner.user_settings.get_gctf_executable() → str: The default Gctf executable.

pipeliner.user_settings.get_minimum_dedicated() → int | None: The default for ‘Minimum dedicated cores per node’.

pipeliner.user_settings.get_modelangelo_executable() → str: The default ModelAngelo executable.

pipeliner.user_settings.get_modelcraft_executable() → str: The default Modelcraft executable.

pipeliner.user_settings.get_motioncor2_executable() → str: The default MotionCor2 executable.

pipeliner.user_settings.get_mpi_max() → int | None: The maximum number of MPI processes available from the GUI.

pipeliner.user_settings.get_mpirun_command() → str: The default command prepended to MPI jobs, including ‘XXXmpinodesXXX’ which will be substituted with the number of MPI nodes to use.

pipeliner.user_settings.get_path_to_source_files() → List[str]

Script files to set up the environment variables for the pipeliner. These files will be sourced when the pipeliner starts up and their environment read in and used to update the pipeliner’s environment variables. This is a clunky mechanism intended mainly for setting up CCP4 and the old version of CCP-EM. Avoid using it for new packages.

In JSON format, this setting is a list of strings. It can also be set using the PIPELINER_PATH_TO_SOURCE_FILES environment variable, which should be a PATH-style string containing individual paths separated by path separators (typically ‘:’ on Linux and Mac).

pipeliner.user_settings.get_qsub_command() → str: The default for ‘Queue submit command’.

pipeliner.user_settings.get_qsub_extra_count() → int: The number of extra qsub template substitution variables to use.

pipeliner.user_settings.get_qsub_extras(number: int) → Dict[str, str]

Return a dictionary of values for a single numbered set of extra qsub settings.

For example:

get_qsub_extras(1) -> {
    "name": "Label for qsub_extra1",
    "default": "Default value for qsub_extra1",
    "help": "Help text for qsub_extra1",
}

pipeliner.user_settings.get_qsub_template() → str: The default queue submission script template.

pipeliner.user_settings.get_queue_name() → str: The default for ‘Queue Name”.

pipeliner.user_settings.get_queue_use() → bool: The default for ‘Submit to queue?’.

pipeliner.user_settings.get_resmap_executable() → str: The default ResMap executable.

pipeliner.user_settings.get_scratch_dir() → str: The default scratch directory.

pipeliner.user_settings.get_thread_max() → int | None: The maximum number of threads per MPI process available from the GUI.

pipeliner.user_settings.get_topaz_executable() → str: The default Topaz executable.

pipeliner.user_settings.get_warning_local_mpi() → int | None: Warn if users try to submit local jobs with more than this many MPI nodes.