BioBlend

About

BioBlend is a Python library for interacting with the Galaxy API.

BioBlend is supported and tested on:

Python 3.7 - 3.11
Galaxy release 19.05 and later.

BioBlend's goal is to make it easier to script and automate the running of Galaxy analyses and administering of a Galaxy server. In practice, it makes it possible to do things like this:

Interact with Galaxy via a straightforward API:

from bioblend.galaxy import GalaxyInstance
gi = GalaxyInstance('<Galaxy IP>', key='your API key')
libs = gi.libraries.get_libraries()
gi.workflows.show_workflow('workflow ID')
wf_invocation = gi.workflows.invoke_workflow('workflow ID', inputs)

Interact with Galaxy via an object-oriented API:

from bioblend.galaxy.objects import GalaxyInstance
gi = GalaxyInstance("URL", "API_KEY")
wf = gi.workflows.list()[0]
hist = gi.histories.list()[0]
inputs = hist.get_datasets()[:2]
input_map = dict(zip(wf.input_labels, inputs))
params = {"Paste1": {"delimiter": "U"}}
wf_invocation = wf.invoke(input_map, params=params)

About the library name

The library was originally called just Blend but we renamed it to reflect more of its domain and a make it bit more unique so it can be easier to find. The name was intended to be short and easily pronounceable. In its original implementation, the goal was to provide a lot more support for CloudMan and other integration capabilities, allowing them to be blended together via code. BioBlend fitted the bill.

Installation

Stable releases of BioBlend are best installed via pip from PyPI:

$ python3 -m pip install bioblend

Alternatively, the most current source code from our Git repository can be installed with:

$ python3 -m pip install git+https://github.com/galaxyproject/bioblend

After installing the library, you will be able to simply import it into your Python environment with import bioblend. For details on the available functionality, see the API documentation.

BioBlend requires a number of Python libraries. These libraries are installed automatically when BioBlend itself is installed, regardless whether it is installed via PyPi or by running python3 setup.py install command. The current list of required libraries is always available from setup.py in the source code repository.

If you also want to run tests locally, some extra libraries are required. To install them, run:

$ python3 setup.py test

Usage

To get started using BioBlend, install the library as described above. Once the library becomes available on the given system, it can be developed against. The developed scripts do not need to reside in any particular location on the system.

It is probably best to take a look at the example scripts in docs/examples source directory and browse the API documentation. Beyond that, it's up to your creativity :).

Development

Anyone interested in contributing or tweaking the library is more then welcome to do so. To start, simply fork the Git repository on Github and start playing with it. Then, issue pull requests.

API Documentation

BioBlend's API focuses around and matches the services it wraps. Thus, there are two top-level sets of APIs, each corresponding to a separate service and a corresponding step in the automation process. Note that each of the service APIs can be used completely independently of one another.

Effort has been made to keep the structure and naming of those API's consistent across the library but because they do bridge different services, some discrepancies may exist. Feel free to point those out and/or provide fixes.

For Galaxy, an alternative object-oriented API is also available. This API provides an explicit modeling of server-side Galaxy instances and their relationships, providing higher-level methods to perform operations such as retrieving all datasets for a given history, etc. Note that, at the moment, the oo API is still incomplete, providing access to a more restricted set of Galaxy modules with respect to the standard one.

Galaxy API

API used to manipulate genomic analyses within Galaxy, including data management and workflow execution.

API documentation for interacting with Galaxy

GalaxyInstance

Config

Contains possible interaction dealing with Galaxy configuration.

class bioblend.galaxy.config.ConfigClient(galaxy_instance: GalaxyInstance)

A generic Client interface defining the common fields.

All clients must define the following field (which will be used as part of the URL composition (e.g., http://<galaxy_instance>/api/libraries): self.module = 'workflows' | 'libraries' | 'histories' | ...

get_config() → dict

Get a list of attributes about the Galaxy instance. More attributes will be present if the user is an admin.

Return type:

list

Returns:

A list of attributes. For example:

{'allow_library_path_paste': False,
 'allow_user_creation': True,
 'allow_user_dataset_purge': True,
 'allow_user_deletion': False,
 'enable_unique_workflow_defaults': False,
 'ftp_upload_dir': '/SOMEWHERE/galaxy/ftp_dir',
 'ftp_upload_site': 'galaxy.com',
 'library_import_dir': 'None',
 'logo_url': None,
 'support_url': 'https://galaxyproject.org/support',
 'terms_url': None,
 'user_library_import_dir': None,
 'wiki_url': 'https://galaxyproject.org/'}

get_version() → dict

Get the current version of the Galaxy instance.

Return type:

dict

Returns:

Version of the Galaxy instance For example:

{'extra': {}, 'version_major': '17.01'}

module: str = 'configuration'

reload_toolbox() → None

Reload the Galaxy toolbox (but not individual tools)

Return type:: None
Returns:: None

whoami() → dict

Return information about the current authenticated user.

Return type:

dict

Returns:

Information about current authenticated user For example:

{'active': True,
 'deleted': False,
 'email': 'user@example.org',
 'id': '4aaaaa85aacc9caa',
 'last_password_change': '2021-07-29T05:34:54.632345',
 'model_class': 'User',
 'username': 'julia'}

Datasets

Contains possible interactions with the Galaxy Datasets

class bioblend.galaxy.datasets.DatasetClient(galaxy_instance: GalaxyInstance)

A generic Client interface defining the common fields.

All clients must define the following field (which will be used as part of the URL composition (e.g., http://<galaxy_instance>/api/libraries): self.module = 'workflows' | 'libraries' | 'histories' | ...

download_dataset(dataset_id: str, file_path: None = None, use_default_filename: bool = True, require_ok_state: bool = True, maxwait: float = 12000) → bytes

download_dataset(dataset_id: str, file_path: str, use_default_filename: bool = True, require_ok_state: bool = True, maxwait: float = 12000) → str

Download a dataset to file or in memory. If the dataset state is not ‘ok’, a DatasetStateException will be thrown, unless require_ok_state=False.

Parameters:

dataset_id (str) – Encoded dataset ID
file_path (str) – If this argument is provided, the dataset will be streamed to disk at that path (should be a directory if use_default_filename=True). If the file_path argument is not provided, the dataset content is loaded into memory and returned by the method (Memory consumption may be heavy as the entire file will be in memory).
use_default_filename (bool) – If True, the exported file will be saved as file_path/%s, where %s is the dataset name. If False, file_path is assumed to contain the full file path including the filename.
require_ok_state (bool) – If False, datasets will be downloaded even if not in an ‘ok’ state, issuing a DatasetStateWarning rather than raising a DatasetStateException.
maxwait (float) – Total time (in seconds) to wait for the dataset state to become terminal. If the dataset state is not terminal within this time, a DatasetTimeoutException will be thrown.

Return type:

bytes or str

Returns:

If a file_path argument is not provided, returns the file content. Otherwise returns the local path of the downloaded file.

Get the latest datasets, or select another subset by specifying optional arguments for filtering (e.g. a history ID).

Since the number of datasets may be very large, limit and offset parameters are required to specify the desired range.

If the user is an admin, this will return datasets for all the users, otherwise only for the current user.

Parameters:

limit (int) – Maximum number of datasets to return.
offset (int) – Return datasets starting from this specified position. For example, if limit is set to 100 and offset to 200, datasets 200-299 will be returned.
name (str) – Dataset name to filter on.
extension (str or list of str) – Dataset extension (or list of extensions) to filter on.
state (str or list of str) – Dataset state (or list of states) to filter on.
visible (bool) – Optionally filter datasets by their visible attribute.
deleted (bool) – Optionally filter datasets by their deleted attribute.
purged (bool) – Optionally filter datasets by their purged attribute.
tool_id (str) – Tool ID to filter on.
tag (str) – Dataset tag to filter on.
history_id (str) – Encoded history ID to filter on.
create_time_min (str) – Show only datasets created after the provided time and date, which should be formatted as YYYY-MM-DDTHH-MM-SS.
create_time_max (str) – Show only datasets created before the provided time and date, which should be formatted as YYYY-MM-DDTHH-MM-SS.
update_time_min (str) – Show only datasets last updated after the provided time and date, which should be formatted as YYYY-MM-DDTHH-MM-SS.
update_time_max (str) – Show only datasets last updated before the provided time and date, which should be formatted as YYYY-MM-DDTHH-MM-SS.
order (str) – One or more of the following attributes for ordering datasets: create_time (default), extension, hid, history_id, name, update_time. Optionally, -asc or -dsc (default) can be appended for ascending and descending order respectively. Multiple attributes can be stacked as a comma-separated list of values, e.g. create_time-asc,hid-dsc.

Return type:

list

Param:

A list of datasets

gi: GalaxyInstance

module: str = 'datasets'

publish_dataset(dataset_id: str, published: bool = False) → Dict[str, Any]

Make a dataset publicly available or private. For more fine-grained control (assigning different permissions to specific roles), use the update_permissions() method.

Parameters:

dataset_id (str) – dataset ID
published (bool) – Whether to make the dataset published (True) or private (False).

Return type:

dict

Returns:

Details of the updated dataset

Note

This method works only on Galaxy 19.05 or later.

show_dataset(dataset_id: str, deleted: bool = False, hda_ldda: Literal['hda', 'ldda'] = 'hda') → Dict[str, Any]

Get details about a given dataset. This can be a history or a library dataset.

Parameters:

dataset_id (str) – Encoded dataset ID
deleted (bool) – Whether to return results for a deleted dataset
hda_ldda (str) – Whether to show a history dataset (‘hda’ - the default) or library dataset (‘ldda’).

Return type:

dict

Returns:

Information about the HDA or LDDA

update_permissions(dataset_id: str, access_ids: list | None = None, manage_ids: list | None = None, modify_ids: list | None = None) → dict

Set access, manage or modify permissions for a dataset to a list of roles.

Parameters:

dataset_id (str) – dataset ID
access_ids (list) – role IDs which should have access permissions for the dataset.
manage_ids (list) – role IDs which should have manage permissions for the dataset.
modify_ids (list) – role IDs which should have modify permissions for the dataset.

Return type:

dict

Returns:

Current roles for all available permission types.

Note

This method works only on Galaxy 19.05 or later.

wait_for_dataset(dataset_id: str, maxwait: float = 12000, interval: float = 3, check: bool = True) → Dict[str, Any]

Wait until a dataset is in a terminal state.

Parameters:

dataset_id (str) – dataset ID
maxwait (float) – Total time (in seconds) to wait for the dataset state to become terminal. If the dataset state is not terminal within this time, a DatasetTimeoutException will be raised.
interval (float) – Time (in seconds) to wait between 2 consecutive checks.
check (bool) – Whether to check if the dataset terminal state is ‘ok’.

Return type:

dict

Returns:

Details of the given dataset.

exception bioblend.galaxy.datasets.DatasetStateException

exception bioblend.galaxy.datasets.DatasetStateWarning

exception bioblend.galaxy.datasets.DatasetTimeoutException

Dataset collections

class bioblend.galaxy.dataset_collections.CollectionDescription(name: str, type: str = 'list', elements: List[CollectionElement | SimpleElement] | Dict[str, Any] | None = None)

to_dict() → Dict[str, str | List]

class bioblend.galaxy.dataset_collections.CollectionElement(name: str, type: str = 'list', elements: List[CollectionElement | SimpleElement] | Dict[str, Any] | None = None)

to_dict() → Dict[str, str | List]

class bioblend.galaxy.dataset_collections.DatasetCollectionClient(galaxy_instance: GalaxyInstance)

A generic Client interface defining the common fields.

All clients must define the following field (which will be used as part of the URL composition (e.g., http://<galaxy_instance>/api/libraries): self.module = 'workflows' | 'libraries' | 'histories' | ...

download_dataset_collection(dataset_collection_id: str, file_path: str) → Dict[str, Any]

Download a history dataset collection as an archive.

Parameters:

dataset_collection_id (str) – Encoded dataset collection ID
file_path (str) – The path to which the archive will be downloaded

Return type:

dict

Returns:

Information about the downloaded archive.

Note

This method downloads a zip archive for Galaxy 21.01 and later. For earlier versions of Galaxy this method downloads a tgz archive.

gi: GalaxyInstance

module: str = 'dataset_collections'

show_dataset_collection(dataset_collection_id: str, instance_type: str = 'history') → Dict[str, Any]

Get details of a given dataset collection of the current user

Parameters:

dataset_collection_id (str) – dataset collection ID
instance_type (str) – instance type of the collection - ‘history’ or ‘library’

Return type:

dict

Returns:

element view of the dataset collection

wait_for_dataset_collection(dataset_collection_id: str, maxwait: float = 12000, interval: float = 3, proportion_complete: float = 1.0, check: bool = True) → Dict[str, Any]

Wait until all or a specified proportion of elements of a dataset collection are in a terminal state.

Parameters:

dataset_collection_id (str) – dataset collection ID
maxwait (float) – Total time (in seconds) to wait for the dataset states in the dataset collection to become terminal. If not all datasets are in a terminal state within this time, a DatasetCollectionTimeoutException will be raised.
interval (float) – Time (in seconds) to wait between two consecutive checks.
proportion_complete (float) – Proportion of elements in this collection that have to be in a terminal state for this method to return. Must be a number between 0 and 1. For example: if the dataset collection contains 2 elements, and proportion_complete=0.5 is specified, then wait_for_dataset_collection will return as soon as 1 of the 2 datasets is in a terminal state. Default is 1, i.e. all elements must complete.
check (bool) – Whether to check if all the terminal states of datasets in the dataset collection are ‘ok’. This will raise an Exception if a dataset is in a terminal state other than ‘ok’.

Return type:

dict

Returns:

Details of the given dataset collection.

class bioblend.galaxy.dataset_collections.HistoryDatasetCollectionElement(name: str, id: str)

class bioblend.galaxy.dataset_collections.HistoryDatasetElement(name: str, id: str)

class bioblend.galaxy.dataset_collections.LibraryDatasetElement(name: str, id: str)

Datatypes

Contains possible interactions with the Galaxy Datatype

class bioblend.galaxy.datatypes.DatatypesClient(galaxy_instance: GalaxyInstance)

A generic Client interface defining the common fields.

All clients must define the following field (which will be used as part of the URL composition (e.g., http://<galaxy_instance>/api/libraries): self.module = 'workflows' | 'libraries' | 'histories' | ...

get_datatypes(extension_only: bool = False, upload_only: bool = False) → List[str]

Get the list of all installed datatypes.

Parameters:

extension_only (bool) – Return only the extension rather than the datatype name
upload_only (bool) – Whether to return only datatypes which can be uploaded

Return type:

list

Returns:

A list of datatype names. For example:

['snpmatrix',
 'snptest',
 'tabular',
 'taxonomy',
 'twobit',
 'txt',
 'vcf',
 'wig',
 'xgmml',
 'xml']

get_sniffers() → List[str]

Get the list of all installed sniffers.

Return type:

list

Returns:

A list of sniffer names. For example:

['galaxy.datatypes.tabular:Vcf',
 'galaxy.datatypes.binary:TwoBit',
 'galaxy.datatypes.binary:Bam',
 'galaxy.datatypes.binary:Sff',
 'galaxy.datatypes.xml:Phyloxml',
 'galaxy.datatypes.xml:GenericXml',
 'galaxy.datatypes.sequence:Maf',
 'galaxy.datatypes.sequence:Lav',
 'galaxy.datatypes.sequence:csFasta']

module: str = 'datatypes'

Folders

Contains possible interactions with the Galaxy library folders

class bioblend.galaxy.folders.FoldersClient(galaxy_instance: GalaxyInstance)

A generic Client interface defining the common fields.

All clients must define the following field (which will be used as part of the URL composition (e.g., http://<galaxy_instance>/api/libraries): self.module = 'workflows' | 'libraries' | 'histories' | ...

create_folder(parent_folder_id: str, name: str, description: str | None = None) → Dict[str, Any]

Create a folder.

Parameters:

parent_folder_id (str) – Folder’s description
name (str) – name of the new folder
description (str) – folder’s description

Return type:

dict

Returns:

details of the updated folder

delete_folder(folder_id: str, undelete: bool = False) → Dict[str, Any]

Marks the folder with the given id as deleted (or removes the deleted mark if the undelete param is True).

Parameters:

folder_id (str) – the folder’s encoded id, prefixed by ‘F’
undelete (bool) – If set to True, the folder will be undeleted (i.e. the deleted mark will be removed)

Returns:

detailed folder information

Return type:

dict

get_permissions(folder_id: str, scope: Literal['current', 'available'] = 'current') → Dict[str, Any]

Get the permissions of a folder.

Parameters:

folder_id (str) – the folder’s encoded id, prefixed by ‘F’
scope (str) – scope of permissions, either ‘current’ or ‘available’

Return type:

dict

Returns:

dictionary including details of the folder permissions

module: str = 'folders'

set_permissions(folder_id: str, action: Literal['set_permissions'] = 'set_permissions', add_ids: List[str] | None = None, manage_ids: List[str] | None = None, modify_ids: List[str] | None = None) → Dict[str, Any]

Set the permissions of a folder.

Parameters:

folder_id (str) – the folder’s encoded id, prefixed by ‘F’
action (str) – action to execute, only “set_permissions” is supported.
add_ids (list of str) – list of role IDs which can add datasets to the folder
manage_ids (list of str) – list of role IDs which can manage datasets in the folder
modify_ids (list of str) – list of role IDs which can modify datasets in the folder

Return type:

dict

Returns:

dictionary including details of the folder

show_folder(folder_id: str, contents: bool = False) → Dict[str, Any]

Display information about a folder.

Parameters:

folder_id (str) – the folder’s encoded id, prefixed by ‘F’
contents (bool) – True to get the contents of the folder, rather than just the folder details.

Return type:

dict

Returns:

dictionary including details of the folder

update_folder(folder_id: str, name: str, description: str | None = None) → Dict[str, Any]

Update folder information.

Parameters:

folder_id (str) – the folder’s encoded id, prefixed by ‘F’
name (str) – name of the new folder
description (str) – folder’s description

Return type:

dict

Returns:

details of the updated folder

Forms

Contains possible interactions with the Galaxy Forms

class bioblend.galaxy.forms.FormsClient(galaxy_instance: GalaxyInstance)

A generic Client interface defining the common fields.

All clients must define the following field (which will be used as part of the URL composition (e.g., http://<galaxy_instance>/api/libraries): self.module = 'workflows' | 'libraries' | 'histories' | ...

create_form(form_xml_text: str) → List[Dict[str, Any]]

Create a new form.

Parameters:: form_xml_text (str) – Form xml to create a form on galaxy instance
Return type:: list of dicts
Returns:: List with a single dictionary describing the created form

get_forms() → List[Dict[str, Any]]

Get the list of all forms.

Return type:

list

Returns:

Displays a collection (list) of forms. For example:

[{'id': 'f2db41e1fa331b3e',
  'model_class': 'FormDefinition',
  'name': 'First form',
  'url': '/api/forms/f2db41e1fa331b3e'},
 {'id': 'ebfb8f50c6abde6d',
  'model_class': 'FormDefinition',
  'name': 'second form',
  'url': '/api/forms/ebfb8f50c6abde6d'}]

module: str = 'forms'

show_form(form_id: str) → Dict[str, Any]

Get details of a given form.

Parameters:

form_id (str) – Encoded form ID

Return type:

dict

Returns:

A description of the given form. For example:

{'desc': 'here it is ',
 'fields': [],
 'form_definition_current_id': 'f2db41e1fa331b3e',
 'id': 'f2db41e1fa331b3e',
 'layout': [],
 'model_class': 'FormDefinition',
 'name': 'First form',
 'url': '/api/forms/f2db41e1fa331b3e'}

FTP files

Contains possible interactions with the Galaxy FTP Files

class bioblend.galaxy.ftpfiles.FTPFilesClient(galaxy_instance: GalaxyInstance)

A generic Client interface defining the common fields.

All clients must define the following field (which will be used as part of the URL composition (e.g., http://<galaxy_instance>/api/libraries): self.module = 'workflows' | 'libraries' | 'histories' | ...

get_ftp_files(deleted: bool = False) → List[dict]

Get a list of local files.

Parameters:: deleted (bool) – Whether to include deleted files
Return type:: list
Returns:: A list of dicts with details on individual files on FTP

module: str = 'ftp_files'

Genomes

Contains possible interactions with the Galaxy Histories

class bioblend.galaxy.genomes.GenomeClient(galaxy_instance: GalaxyInstance)

A generic Client interface defining the common fields.

All clients must define the following field (which will be used as part of the URL composition (e.g., http://<galaxy_instance>/api/libraries): self.module = 'workflows' | 'libraries' | 'histories' | ...

get_genomes() → list

Returns a list of installed genomes

Return type:: list
Returns:: List of installed genomes

Download and/or index a genome.

Parameters:

func (str) – Allowed values: ‘download’, Download and index; ‘index’, Index only
source (str) – Data source for this build. Can be: UCSC, Ensembl, NCBI, URL
dbkey (str) – DB key of the build to download, ignored unless ‘UCSC’ is specified as the source
ncbi_name (str) – NCBI’s genome identifier, ignored unless NCBI is specified as the source
ensembl_dbkey (str) – Ensembl’s genome identifier, ignored unless Ensembl is specified as the source
url_dbkey (str) – DB key to use for this build, ignored unless URL is specified as the source
indexers (list) – POST array of indexers to run after downloading (indexers[] = first, indexers[] = second, …)

Return type:

dict

Returns:

dict( status: ‘ok’, job: <job ID> ) If error: dict( status: ‘error’, error: <error message> )

module: str = 'genomes'

show_genome(id: str, num: str | None = None, chrom: str | None = None, low: str | None = None, high: str | None = None) → Dict[str, Any]

Returns information about build <id>

Parameters:

id (str) – Genome build ID to use
num (str) – num
chrom (str) – chrom
low (str) – low
high (str) – high

Return type:

dict

Returns:

Information about the genome build

Groups

Contains possible interactions with the Galaxy Groups

class bioblend.galaxy.groups.GroupsClient(galaxy_instance: GalaxyInstance)

A generic Client interface defining the common fields.

All clients must define the following field (which will be used as part of the URL composition (e.g., http://<galaxy_instance>/api/libraries): self.module = 'workflows' | 'libraries' | 'histories' | ...

add_group_role(group_id: str, role_id: str) → Dict[str, Any]

Add a role to the given group.

Parameters:

group_id (str) – Encoded group ID
role_id (str) – Encoded role ID to add to the group

Return type:

dict

Returns:

Added group role’s info

add_group_user(group_id: str, user_id: str) → Dict[str, Any]

Add a user to the given group.

Parameters:

group_id (str) – Encoded group ID
user_id (str) – Encoded user ID to add to the group

Return type:

dict

Returns:

Added group user’s info

create_group(group_name: str, user_ids: List[str] | None = None, role_ids: List[str] | None = None) → List[Dict[str, Any]]

Create a new group.

Parameters:

group_name (str) – A name for the new group
user_ids (list) – A list of encoded user IDs to add to the new group
role_ids (list) – A list of encoded role IDs to add to the new group

Return type:

list

Returns:

A (size 1) list with newly created group details, like:

[{'id': '7c9636938c3e83bf',
  'model_class': 'Group',
  'name': 'My Group Name',
  'url': '/api/groups/7c9636938c3e83bf'}]

delete_group_role(group_id: str, role_id: str) → Dict[str, Any]

Remove a role from the given group.

Parameters:

group_id (str) – Encoded group ID
role_id (str) – Encoded role ID to remove from the group

Return type:

dict

Returns:

The role which was removed

delete_group_user(group_id: str, user_id: str) → Dict[str, Any]

Remove a user from the given group.

Parameters:

group_id (str) – Encoded group ID
user_id (str) – Encoded user ID to remove from the group

Return type:

dict

Returns:

The user which was removed

get_group_roles(group_id: str) → List[Dict[str, Any]]

Get the list of roles associated to the given group.

Parameters:: group_id (str) – Encoded group ID
Return type:: list of dicts
Returns:: List of group roles’ info

get_group_users(group_id: str) → List[Dict[str, Any]]

Get the list of users associated to the given group.

Parameters:: group_id (str) – Encoded group ID
Return type:: list of dicts
Returns:: List of group users’ info

get_groups() → List[Dict[str, Any]]

Get all (not deleted) groups.

Return type:

list

Returns:

A list of dicts with details on individual groups. For example:

[{'id': '33abac023ff186c2',
  'model_class': 'Group',
  'name': 'Listeria',
  'url': '/api/groups/33abac023ff186c2'},
 {'id': '73187219cd372cf8',
  'model_class': 'Group',
  'name': 'LPN',
  'url': '/api/groups/73187219cd372cf8'}]

module: str = 'groups'

show_group(group_id: str) → Dict[str, Any]

Get details of a given group.

Parameters:

group_id (str) – Encoded group ID

Return type:

dict

Returns:

A description of group For example:

{'id': '33abac023ff186c2',
 'model_class': 'Group',
 'name': 'Listeria',
 'roles_url': '/api/groups/33abac023ff186c2/roles',
 'url': '/api/groups/33abac023ff186c2',
 'users_url': '/api/groups/33abac023ff186c2/users'}

update_group(group_id: str, group_name: str | None = None, user_ids: List[str] | None = None, role_ids: List[str] | None = None) → None

Update a group.

Parameters:

group_id (str) – Encoded group ID
group_name (str) – A new name for the group. If None, the group name is not changed.
user_ids (list) – New list of encoded user IDs for the group. It will substitute the previous list of users (with [] if not specified)
role_ids (list) – New list of encoded role IDs for the group. It will substitute the previous list of roles (with [] if not specified)

Return type:

None

Returns:

None

Histories

Contains possible interactions with the Galaxy Histories

class bioblend.galaxy.histories.HistoryClient(galaxy_instance: GalaxyInstance)

A generic Client interface defining the common fields.

All clients must define the following field (which will be used as part of the URL composition (e.g., http://<galaxy_instance>/api/libraries): self.module = 'workflows' | 'libraries' | 'histories' | ...

copy_content(history_id: str, content_id: str, source: Literal['hda', 'hdca', 'library', 'library_folder'] = 'hda') → Dict[str, Any]

Copy existing content (e.g. a dataset) to a history.

Parameters:

history_id (str) – ID of the history to which the content should be copied
content_id (str) – ID of the content to copy
source (str) – Source of the content to be copied: ‘hda’ (for a history dataset, the default), ‘hdca’ (for a dataset collection), ‘library’ (for a library dataset) or ‘library_folder’ (for all datasets in a library folder).

Return type:

dict

Returns:

Information about the copied content

copy_dataset(history_id: str, dataset_id: str, source: Literal['hda', 'library', 'library_folder'] = 'hda') → Dict[str, Any]

Copy a dataset to a history.

Parameters:

history_id (str) – history ID to which the dataset should be copied
dataset_id (str) – dataset ID
source (str) – Source of the dataset to be copied: ‘hda’ (the default), ‘library’ or ‘library_folder’

Return type:

dict

Returns:

Information about the copied dataset

create_dataset_collection(history_id: str, collection_description: CollectionDescription | Dict[str, Any]) → Dict[str, Any]

Create a new dataset collection

Parameters:

history_id (str) – Encoded history ID

collection_description (bioblend.galaxy.dataset_collections.CollectionDescription) –

a description of the dataset collection For example:

{'collection_type': 'list',
 'element_identifiers': [{'id': 'f792763bee8d277a',
                          'name': 'element 1',
                          'src': 'hda'},
                         {'id': 'f792763bee8d277a',
                          'name': 'element 2',
                          'src': 'hda'}],
 'name': 'My collection list'}

Return type:

dict

Returns:

Information about the new HDCA

create_history(name: str | None = None) → Dict[str, Any]

Create a new history, optionally setting the name.

Parameters:: name (str) – Optional name for new history
Return type:: dict
Returns:: Dictionary containing information about newly created history

create_history_tag(history_id: str, tag: str) → Dict[str, Any]

Create history tag

Parameters:

history_id (str) – Encoded history ID
tag (str) – Add tag to history

Return type:

dict

Returns:

A dictionary with information regarding the tag. For example:

{'id': 'f792763bee8d277a',
 'model_class': 'HistoryTagAssociation',
 'user_tname': 'NGS_PE_RUN',
 'user_value': None}

delete_dataset(history_id: str, dataset_id: str, purge: bool = False) → None

Mark corresponding dataset as deleted.

Parameters:

history_id (str) – Encoded history ID
dataset_id (str) – Encoded dataset ID
purge (bool) – if True, also purge (permanently delete) the dataset

Return type:

None

Returns:

None

Note

The purge option works only if the Galaxy instance has the allow_user_dataset_purge option set to true in the config/galaxy.yml configuration file.

delete_dataset_collection(history_id: str, dataset_collection_id: str) → None

Mark corresponding dataset collection as deleted.

Parameters:

history_id (str) – Encoded history ID
dataset_collection_id (str) – Encoded dataset collection ID

Return type:

None

Returns:

None

delete_history(history_id: str, purge: bool = False) → Dict[str, Any]

Delete a history.

Parameters:

history_id (str) – Encoded history ID
purge (bool) – if True, also purge (permanently delete) the history

Return type:

dict

Returns:

An error object if an error occurred or a dictionary containing: id (the encoded id of the history), deleted (if the history was marked as deleted), purged (if the history was purged).

Note

The purge option works only if the Galaxy instance has the allow_user_dataset_purge option set to true in the config/galaxy.yml configuration file.

download_history(history_id: str, jeha_id: str, outf: IO[bytes], chunk_size: int = 4096) → None

Download a history export archive. Use export_history() to create an export.

Parameters:

history_id (str) – history ID
jeha_id (str) – jeha ID (this should be obtained via export_history())
outf (file) – output file object, open for writing in binary mode
chunk_size (int) – how many bytes at a time should be read into memory

Return type:

None

Returns:

None

export_history(history_id: str, gzip: bool = True, include_hidden: bool = False, include_deleted: bool = False, wait: bool = False, maxwait: float | None = None) → str

Start a job to create an export archive for the given history.

Parameters:

history_id (str) – history ID
gzip (bool) – create .tar.gz archive if True, else .tar
include_hidden (bool) – whether to include hidden datasets in the export
include_deleted (bool) – whether to include deleted datasets in the export
wait (bool) – if True, block until the export is ready; else, return immediately
maxwait (float) – Total time (in seconds) to wait for the export to become ready. When set, implies that wait is True.

Return type:

str

Returns:

jeha_id of the export, or empty if wait is False and the export is not ready.

get_extra_files(history_id: str, dataset_id: str) → List[str]

Get extra files associated with a composite dataset, or an empty list if there are none.

Parameters:

history_id (str) – history ID
dataset_id (str) – dataset ID

Return type:

list

Returns:

List of extra files

Get all histories, or select a subset by specifying optional arguments for filtering (e.g. a history name).

Parameters:

name (str) – History name to filter on.
deleted (bool) – whether to filter for the deleted histories (True) or for the non-deleted ones (False)
published (bool or None) – whether to filter for the published histories (True) or for the non-published ones (False). If not set, no filtering is applied. Note the filtering is only applied to the user’s own histories; to access all histories published by any user, use the get_published_histories method.
slug (str) – History slug to filter on
all (bool) – Whether to include histories from other users. This parameter works only on Galaxy 20.01 or later and can be specified only if the user is a Galaxy admin.

Return type:

list

Returns:

List of history dicts.

Changed in version 0.17.0: Using the deprecated history_id parameter now raises a ValueError exception.

get_most_recently_used_history() → Dict[str, Any]

Returns the current user’s most recently used history (not deleted).

Return type:: dict
Returns:: History representation

get_published_histories(name: str | None = None, deleted: bool = False, slug: str | None = None) → List[Dict[str, Any]]

Get all published histories (by any user), or select a subset by specifying optional arguments for filtering (e.g. a history name).

Parameters:

name (str) – History name to filter on.
deleted (bool) – whether to filter for the deleted histories (True) or for the non-deleted ones (False)
slug (str) – History slug to filter on

Return type:

list

Returns:

List of history dicts.

get_status(history_id: str) → Dict[str, Any]

Returns the state of this history

Parameters:: history_id (str) – Encoded history ID
Return type:: dict
Returns:: A dict documenting the current state of the history. Has the following keys: ‘state’ = This is the current state of the history, such as ok, error, new etc. ‘state_details’ = Contains individual statistics for various dataset states. ‘percent_complete’ = The overall number of datasets processed to completion.

gi: GalaxyInstance

import_history(file_path: str | None = None, url: str | None = None) → Dict[str, Any]

Import a history from an archive on disk or a URL.

Parameters:

file_path (str) – Path to exported history archive on disk.
url (str) – URL for an exported history archive

Return type:

dict

Returns:

Dictionary containing information about the imported history

module: str = 'histories'

open_history(history_id: str) → None

Open Galaxy in a new tab of the default web browser and switch to the specified history.

Parameters:: history_id (str) – ID of the history to switch to
Return type:: NoneType
Returns:: None

Warning

After opening the specified history, all previously opened Galaxy tabs in the browser session will have the current history changed to this one, even if the interface still shows another history. Refreshing any such tab is recommended.

show_dataset(history_id: str, dataset_id: str) → Dict[str, Any]

Get details about a given history dataset.

Parameters:

history_id (str) – Encoded history ID
dataset_id (str) – Encoded dataset ID

Return type:

dict

Returns:

Information about the dataset

show_dataset_collection(history_id: str, dataset_collection_id: str) → Dict[str, Any]

Get details about a given history dataset collection.

Parameters:

history_id (str) – Encoded history ID
dataset_collection_id (str) – Encoded dataset collection ID

Return type:

dict

Returns:

Information about the dataset collection

show_dataset_provenance(history_id: str, dataset_id: str, follow: bool = False) → Dict[str, Any]

Get details related to how dataset was created (id, job_id, tool_id, stdout, stderr, parameters, inputs, etc…).

Parameters:

history_id (str) – Encoded history ID
dataset_id (str) – Encoded dataset ID
follow (bool) – If True, recursively fetch dataset provenance information for all inputs and their inputs, etc.

Return type:

dict

Returns:

Dataset provenance information For example:

{'id': '6fbd9b2274c62ebe',
 'job_id': '5471ba76f274f929',
 'parameters': {'chromInfo': '"/usr/local/galaxy/galaxy-dist/tool-data/shared/ucsc/chrom/mm9.len"',
                'dbkey': '"mm9"',
                'experiment_name': '"H3K4me3_TAC_MACS2"',
                'input_chipseq_file1': {'id': '6f0a311a444290f2',
                                        'uuid': 'null'},
                'input_control_file1': {'id': 'c21816a91f5dc24e',
                                        'uuid': '16f8ee5e-228f-41e2-921e-a07866edce06'},
                'major_command': '{"gsize": "2716965481.0", "bdg": "False", "__current_case__": 0, "advanced_options": {"advanced_options_selector": "off", "__current_case__": 1}, "input_chipseq_file1": 104715, "xls_to_interval": "False", "major_command_selector": "callpeak", "input_control_file1": 104721, "pq_options": {"pq_options_selector": "qvalue", "qvalue": "0.05", "__current_case__": 1}, "bw": "300", "nomodel_type": {"nomodel_type_selector": "create_model", "__current_case__": 1}}'},
 'stderr': '',
 'stdout': '',
 'tool_id': 'toolshed.g2.bx.psu.edu/repos/ziru-zhou/macs2/modencode_peakcalling_macs2/2.0.10.2',
 'uuid': '5c0c43f5-8d93-44bd-939d-305e82f213c6'}

show_history(history_id: str, contents: Literal[False] = False) → Dict[str, Any]

show_history(history_id: str, contents: Literal[True], deleted: bool | None = None, visible: bool | None = None, details: str | None = None, types: List[str] | None = None) → List[Dict[str, Any]]

Get details of a given history. By default, just get the history meta information.

Parameters:

history_id (str) – Encoded history ID to filter on
contents (bool) – When True, instead of the history details, return a list with info for all datasets in the given history. Note that inside each dataset info dict, the id which should be used for further requests about this history dataset is given by the value of the id (not dataset_id) key.
deleted (bool or None) – When contents=True, whether to filter for the deleted datasets (True) or for the non-deleted ones (False). If not set, no filtering is applied.
visible (bool or None) – When contents=True, whether to filter for the visible datasets (True) or for the hidden ones (False). If not set, no filtering is applied.
details (str) – When contents=True, include dataset details. Set to ‘all’ for the most information.
types (list) – When contents=True, filter for history content types. If set to ['dataset'], return only datasets. If set to ['dataset_collection'], return only dataset collections. If not set, no filtering is applied.

Return type:

dict or list of dicts

Returns:

details of the given history or list of dataset info

Note

As an alternative to using the contents=True parameter, consider using gi.datasets.get_datasets(history_id=history_id) which offers more extensive functionality for filtering and ordering the results.

show_matching_datasets(history_id: str, name_filter: str | Pattern[str] | None = None) → List[Dict[str, Any]]

Get dataset details for matching datasets within a history.

Parameters:

history_id (str) – Encoded history ID
name_filter (str) – Only datasets whose name matches the name_filter regular expression will be returned; use plain strings for exact matches and None to match all datasets in the history

Return type:

list

Returns:

List of dictionaries

undelete_history(history_id: str) → str

Undelete a history

Parameters:: history_id (str) – Encoded history ID
Return type:: str
Returns:: ‘OK’ if it was deleted

update_dataset(history_id: str, dataset_id: str, **kwargs: Any) → Dict[str, Any]

Update history dataset metadata. Some of the attributes that can be modified are documented below.

Parameters:

history_id (str) – Encoded history ID
dataset_id (str) – ID of the dataset
name (str) – Replace history dataset name with the given string
datatype (str) – Replace the datatype of the history dataset with the given string. The string must be a valid Galaxy datatype, both the current and the target datatypes must allow datatype changes, and the dataset must not be in use as input or output of a running job (including uploads), otherwise an error will be raised.
genome_build (str) – Replace history dataset genome build (dbkey)
annotation (str) – Replace history dataset annotation with given string
deleted (bool) – Mark or unmark history dataset as deleted
visible (bool) – Mark or unmark history dataset as visible

Return type:

dict

Returns:

details of the updated dataset

Changed in version 0.8.0: Changed the return value from the status code (type int) to a dict.

update_dataset_collection(history_id: str, dataset_collection_id: str, **kwargs: Any) → Dict[str, Any]

Update history dataset collection metadata. Some of the attributes that can be modified are documented below.

Parameters:

history_id (str) – Encoded history ID
dataset_collection_id (str) – Encoded dataset_collection ID
name (str) – Replace history dataset collection name with the given string
deleted (bool) – Mark or unmark history dataset collection as deleted
visible (bool) – Mark or unmark history dataset collection as visible

Return type:

dict

Returns:

the updated dataset collection attributes

Changed in version 0.8.0: Changed the return value from the status code (type int) to a dict.

update_history(history_id: str, **kwargs: Any) → Dict[str, Any]

Update history metadata information. Some of the attributes that can be modified are documented below.

Parameters:

history_id (str) – Encoded history ID
name (str) – Replace history name with the given string
annotation (str) – Replace history annotation with given string
deleted (bool) – Mark or unmark history as deleted
purged (bool) – If True, mark history as purged (permanently deleted).
published (bool) – Mark or unmark history as published
importable (bool) – Mark or unmark history as importable
tags (list) – Replace history tags with the given list

Return type:

dict

Returns:

details of the updated history

Changed in version 0.8.0: Changed the return value from the status code (type int) to a dict.

upload_dataset_from_library(history_id: str, lib_dataset_id: str) → Dict[str, Any]

Upload a dataset into the history from a library. Requires the library dataset ID, which can be obtained from the library contents.

Parameters:

history_id (str) – Encoded history ID
lib_dataset_id (str) – Encoded library dataset ID

Return type:

dict

Returns:

Information about the newly created HDA

Invocations

Contains possible interactions with the Galaxy workflow invocations

class bioblend.galaxy.invocations.InvocationClient(galaxy_instance: GalaxyInstance)

A generic Client interface defining the common fields.

All clients must define the following field (which will be used as part of the URL composition (e.g., http://<galaxy_instance>/api/libraries): self.module = 'workflows' | 'libraries' | 'histories' | ...

cancel_invocation(invocation_id: str) → Dict[str, Any]

Cancel the scheduling of a workflow.

Parameters:: invocation_id (str) – Encoded workflow invocation ID
Return type:: dict
Returns:: The workflow invocation being cancelled

get_invocation_biocompute_object(invocation_id: str) → Dict[str, Any]

Get a BioCompute object for an invocation.

Parameters:: invocation_id (str) – Encoded workflow invocation ID
Return type:: dict
Returns:: The BioCompute object

get_invocation_report(invocation_id: str) → Dict[str, Any]

Get a Markdown report for an invocation.

Parameters:

invocation_id (str) – Encoded workflow invocation ID

Return type:

dict

Returns:

The invocation report. For example:

{'markdown': '\n# Workflow Execution Summary of Example workflow\n\n
 ## Workflow Inputs\n\n\n## Workflow Outputs\n\n\n
 ## Workflow\n```galaxy\n
 workflow_display(workflow_id=f2db41e1fa331b3e)\n```\n',
 'render_format': 'markdown',
 'workflows': {'f2db41e1fa331b3e': {'name': 'Example workflow'}}}

get_invocation_report_pdf(invocation_id: str, file_path: str, chunk_size: int = 4096) → None

Get a PDF report for an invocation.

Parameters:

invocation_id (str) – Encoded workflow invocation ID
file_path (str) – Path to save the report

get_invocation_step_jobs_summary(invocation_id: str) → List[Dict[str, Any]]

Get a detailed summary of an invocation, listing all jobs with their job IDs and current states.

Parameters:

invocation_id (str) – Encoded workflow invocation ID

Return type:

list of dicts

Returns:

The invocation step jobs summary. For example:

[{'id': 'e85a3be143d5905b',
  'model': 'Job',
  'populated_state': 'ok',
  'states': {'ok': 1}},
 {'id': 'c9468fdb6dc5c5f1',
  'model': 'Job',
  'populated_state': 'ok',
  'states': {'running': 1}},
 {'id': '2a56795cad3c7db3',
  'model': 'Job',
  'populated_state': 'ok',
  'states': {'new': 1}}]

get_invocation_summary(invocation_id: str) → Dict[str, Any]

Get a summary of an invocation, stating the number of jobs which succeed, which are paused and which have errored.

Parameters:

invocation_id (str) – Encoded workflow invocation ID

Return type:

dict

Returns:

The invocation summary. For example:

{'states': {'paused': 4, 'error': 2, 'ok': 2},
 'model': 'WorkflowInvocation',
 'id': 'a799d38679e985db',
 'populated_state': 'ok'}

get_invocations(workflow_id: str | None = None, history_id: str | None = None, user_id: str | None = None, include_terminal: bool = True, limit: int | None = None, view: str = 'collection', step_details: bool = False) → List[Dict[str, Any]]

Get all workflow invocations, or select a subset by specifying optional arguments for filtering (e.g. a workflow ID).

Parameters:

workflow_id (str) – Encoded workflow ID to filter on
history_id (str) – Encoded history ID to filter on
user_id (str) – Encoded user ID to filter on. This must be your own user ID if your are not an admin user.
include_terminal (bool) – Whether to include terminal states.
limit (int) – Maximum number of invocations to return - if specified, the most recent invocations will be returned.
view (str) – Level of detail to return per invocation, either ‘element’ or ‘collection’.
step_details (bool) – If ‘view’ is ‘element’, also include details on individual steps.

Return type:

list

Returns:

A list of workflow invocations. For example:

[{'history_id': '2f94e8ae9edff68a',
  'id': 'df7a1f0c02a5b08e',
  'model_class': 'WorkflowInvocation',
  'state': 'new',
  'update_time': '2015-10-31T22:00:22',
  'uuid': 'c8aa2b1c-801a-11e5-a9e5-8ca98228593c',
  'workflow_id': '03501d7626bd192f'}]

gi: GalaxyInstance

module: str = 'invocations'

rerun_invocation(invocation_id: str, inputs_update: dict | None = None, params_update: dict | None = None, history_id: str | None = None, history_name: str | None = None, import_inputs_to_history: bool = False, replacement_params: dict | None = None, allow_tool_state_corrections: bool = False, inputs_by: Literal['step_index|step_uuid', 'step_index', 'step_id', 'step_uuid', 'name'] | None = None, parameters_normalized: bool = False) → Dict[str, Any]

Rerun a workflow invocation. For more extensive documentation of all parameters, see the gi.workflows.invoke_workflow() method.

Parameters:

invocation_id (str) – Encoded workflow invocation ID to be rerun
inputs_update (dict) – If different datasets should be used to the original invocation, this should contain a mapping of workflow inputs to the new datasets and dataset collections.
params_update (dict) – If different non-dataset tool parameters should be used to the original invocation, this should contain a mapping of the new parameter values.
history_id (str) – The encoded history ID where to store the workflow outputs. Alternatively, history_name may be specified to create a new history.
history_name (str) – Create a new history with the given name to store the workflow outputs. If both history_id and history_name are provided, history_name is ignored. If neither is specified, a new ‘Unnamed history’ is created.
import_inputs_to_history (bool) – If True, used workflow inputs will be imported into the history. If False, only workflow outputs will be visible in the given history.
allow_tool_state_corrections (bool) – If True, allow Galaxy to fill in missing tool state when running workflows. This may be useful for workflows using tools that have changed over time or for workflows built outside of Galaxy with only a subset of inputs defined.
replacement_params (dict) – pattern-based replacements for post-job actions
inputs_by (str) – Determines how inputs are referenced. Can be “step_index|step_uuid” (default), “step_index”, “step_id”, “step_uuid”, or “name”.
parameters_normalized (bool) – Whether Galaxy should normalize the input parameters to ensure everything is referenced by a numeric step ID. Default is False, but when setting parameters for a subworkflow, True is required.

Return type:

dict

Returns:

A dict describing the new workflow invocation.

Note

This method works only on Galaxy 21.01 or later.

run_invocation_step_action(invocation_id: str, step_id: str, action: Any) → Dict[str, Any]

Execute an action for an active workflow invocation step. The nature of this action and what is expected will vary based on the the type of workflow step (the only currently valid action is True/False for pause steps).

Parameters:

invocation_id (str) – Encoded workflow invocation ID
step_id (str) – Encoded workflow invocation step ID
action (object) – Action to use when updating state, semantics depends on step type.

Return type:

dict

Returns:

Representation of the workflow invocation step

show_invocation(invocation_id: str) → Dict[str, Any]

Get a workflow invocation dictionary representing the scheduling of a workflow. This dictionary may be sparse at first (missing inputs and invocation steps) and will become more populated as the workflow is actually scheduled.

Parameters:

invocation_id (str) – Encoded workflow invocation ID

Return type:

dict

Returns:

The workflow invocation. For example:

{'history_id': '2f94e8ae9edff68a',
 'id': 'df7a1f0c02a5b08e',
 'inputs': {'0': {'id': 'a7db2fac67043c7e',
   'src': 'hda',
   'uuid': '7932ffe0-2340-4952-8857-dbaa50f1f46a'}},
 'model_class': 'WorkflowInvocation',
 'state': 'ready',
 'steps': [{'action': None,
   'id': 'd413a19dec13d11e',
   'job_id': None,
   'model_class': 'WorkflowInvocationStep',
   'order_index': 0,
   'state': None,
   'update_time': '2015-10-31T22:00:26',
   'workflow_step_id': 'cbbbf59e8f08c98c',
   'workflow_step_label': None,
   'workflow_step_uuid': 'b81250fd-3278-4e6a-b269-56a1f01ef485'},
  {'action': None,
   'id': '2f94e8ae9edff68a',
   'job_id': 'e89067bb68bee7a0',
   'model_class': 'WorkflowInvocationStep',
   'order_index': 1,
   'state': 'new',
   'update_time': '2015-10-31T22:00:26',
   'workflow_step_id': '964b37715ec9bd22',
   'workflow_step_label': None,
   'workflow_step_uuid': 'e62440b8-e911-408b-b124-e05435d3125e'}],
 'update_time': '2015-10-31T22:00:26',
 'uuid': 'c8aa2b1c-801a-11e5-a9e5-8ca98228593c',
 'workflow_id': '03501d7626bd192f'}

show_invocation_step(invocation_id: str, step_id: str) → Dict[str, Any]

See the details of a particular workflow invocation step.

Parameters:

invocation_id (str) – Encoded workflow invocation ID
step_id (str) – Encoded workflow invocation step ID

Return type:

dict

Returns:

The workflow invocation step. For example:

{'action': None,
 'id': '63cd3858d057a6d1',
 'job_id': None,
 'model_class': 'WorkflowInvocationStep',
 'order_index': 2,
 'state': None,
 'update_time': '2015-10-31T22:11:14',
 'workflow_step_id': '52e496b945151ee8',
 'workflow_step_label': None,
 'workflow_step_uuid': '4060554c-1dd5-4287-9040-8b4f281cf9dc'}

wait_for_invocation(invocation_id: str, maxwait: float = 12000, interval: float = 3, check: bool = True) → Dict[str, Any]

Wait until an invocation is in a terminal state.

Parameters:

invocation_id (str) – Invocation ID to wait for.
maxwait (float) – Total time (in seconds) to wait for the invocation state to become terminal. If the invocation state is not terminal within this time, a TimeoutException will be raised.
interval (float) – Time (in seconds) to wait between 2 consecutive checks.
check (bool) – Whether to check if the invocation terminal state is ‘scheduled’.

Return type:

dict

Returns:

Details of the workflow invocation.

Jobs

Contains possible interactions with the Galaxy Jobs

class bioblend.galaxy.jobs.JobsClient(galaxy_instance: GalaxyInstance)

A generic Client interface defining the common fields.

All clients must define the following field (which will be used as part of the URL composition (e.g., http://<galaxy_instance>/api/libraries): self.module = 'workflows' | 'libraries' | 'histories' | ...

cancel_job(job_id: str) → bool

Cancel a job, deleting output datasets.

Parameters:: job_id (str) – job ID
Return type:: bool
Returns:: True if the job was successfully cancelled, False if it was already in a terminal state before the cancellation.

get_common_problems(job_id: str) → Dict[str, Any]

Query inputs and jobs for common potential problems that might have resulted in job failure.

Parameters:: job_id (str) – job ID
Return type:: dict
Returns:: dict containing potential problems

Note

This method works only on Galaxy 19.05 or later.

get_destination_params(job_id: str) → Dict[str, Any]

Get destination parameters for a job, describing the environment and location where the job is run.

Parameters:: job_id (str) – job ID
Return type:: dict
Returns:: Destination parameters for the given job

Note

This method works only on Galaxy 20.05 or later and if the user is a Galaxy admin.

get_inputs(job_id: str) → List[Dict[str, Any]]

Get dataset inputs used by a job.

Parameters:: job_id (str) – job ID
Return type:: list of dicts
Returns:: Inputs for the given job

get_jobs(state: str | None = None, history_id: str | None = None, invocation_id: str | None = None, tool_id: str | None = None, workflow_id: str | None = None, user_id: str | None = None, date_range_min: str | None = None, date_range_max: str | None = None, limit: int = 500, offset: int = 0, user_details: bool = False, order_by: Literal['create_time', 'update_time'] = 'update_time') → List[Dict[str, Any]]

Get all jobs, or select a subset by specifying optional arguments for filtering (e.g. a state).

If the user is an admin, this will return jobs for all the users, otherwise only for the current user.

Parameters:

state (str or list of str) – Job states to filter on.
history_id (str) – Encoded history ID to filter on.
invocation_id (string) – Encoded workflow invocation ID to filter on.
tool_id (str or list of str) – Tool IDs to filter on.
workflow_id (string) – Encoded workflow ID to filter on.
user_id (str) – Encoded user ID to filter on. Only admin users can access the jobs of other users.
date_range_min (str) – Mininum job update date (in YYYY-MM-DD format) to filter on.
date_range_max (str) – Maximum job update date (in YYYY-MM-DD format) to filter on.
limit (int) – Maximum number of jobs to return.
offset (int) – Return jobs starting from this specified position. For example, if limit is set to 100 and offset to 200, jobs 200-299 will be returned.
user_details (bool) – If True and the user is an admin, add the user email to each returned job dictionary.
order_by (str) – Whether to order jobs by create_time or update_time (the default).

Return type:

list of dict

Returns:

Summary information for each selected job. For example:

[{'create_time': '2014-03-01T16:16:48.640550',
  'exit_code': 0,
  'id': 'ebfb8f50c6abde6d',
  'model_class': 'Job',
  'state': 'ok',
  'tool_id': 'fasta2tab',
  'update_time': '2014-03-01T16:16:50.657399'},
 {'create_time': '2014-03-01T16:05:34.851246',
  'exit_code': 0,
  'id': '1cd8e2f6b131e891',
  'model_class': 'Job',
  'state': 'ok',
  'tool_id': 'upload1',
  'update_time': '2014-03-01T16:05:39.558458'}]

Note

The following parameters work only on Galaxy 21.05 or later: user_id, limit, offset, workflow_id, invocation_id.

get_metrics(job_id: str) → List[Dict[str, Any]]

Return job metrics for a given job.

Parameters:: job_id (str) – job ID
Return type:: list
Returns:: list containing job metrics

Note

Calling show_job() with full_details=True also returns the metrics for a job if the user is an admin. This method allows to fetch metrics even as a normal user as long as the Galaxy instance has the expose_potentially_sensitive_job_metrics option set to true in the config/galaxy.yml configuration file.

get_outputs(job_id: str) → List[Dict[str, Any]]

Get dataset outputs produced by a job.

Parameters:: job_id (str) – job ID
Return type:: list of dicts
Returns:: Outputs of the given job

get_state(job_id: str) → str

Display the current state for a given job of the current user.

Parameters:: job_id (str) – job ID
Return type:: str
Returns:: state of the given job among the following values: new, queued, running, waiting, ok. If the state cannot be retrieved, an empty string is returned.

New in version 0.5.3.

module: str = 'jobs'

report_error(job_id: str, dataset_id: str, message: str, email: str | None = None) → Dict[str, Any]

Report an error for a given job and dataset to the server administrators.

Parameters:

job_id (str) – job ID
dataset_id (str) – Dataset ID
message (str) – Error message
email (str) – Email for error report submission. If not specified, the email associated with the Galaxy user account is used by default.

Return type:

dict

Returns:

dict containing job error reply

Note

This method works only on Galaxy 20.01 or later.

rerun_job(job_id: str, remap: bool = False, tool_inputs_update: Dict[str, Any] | None = None, history_id: str | None = None) → Dict[str, Any]

Rerun a job.

Parameters:

job_id (str) – job ID
remap (bool) – when True, the job output(s) will be remapped onto the dataset(s) created by the original job; if other jobs were waiting for this job to finish successfully, they will be resumed using the new outputs of this tool run. When False, new job output(s) will be created. Note that if Galaxy does not permit remapping for the job in question, specifying True will result in an error.
tool_inputs_update (dict) – dictionary specifying any changes which should be made to tool parameters for the rerun job. This dictionary should have the same structure as is required when submitting the tool_inputs dictionary to gi.tools.run_tool(), but only needs to include the inputs or parameters to be updated for the rerun job.
history_id (str) – ID of the history in which the job should be executed; if not specified, the same history will be used as the original job run.

Return type:

dict

Returns:

Information about outputs and the rerun job

Note

This method works only on Galaxy 21.01 or later.

resume_job(job_id: str) → List[Dict[str, Any]]

Resume a job if it is paused.

Parameters:: job_id (str) – job ID
Return type:: list of dicts
Returns:: list of dictionaries containing output dataset associations

search_jobs(tool_id: str, inputs: Dict[str, Any], state: str | None = None) → List[Dict[str, Any]]

Return jobs matching input parameters.

Parameters:

tool_id (str) – only return jobs associated with this tool ID
inputs (dict) – return only jobs that have matching inputs
state (str) – only return jobs in this state

Return type:

list of dicts

Returns:

Summary information for each matching job

This method is designed to scan the list of previously run jobs and find records of jobs with identical input parameters and datasets. This can be used to minimize the amount of repeated work by simply recycling the old results.

Changed in version 0.16.0: Replaced the job_info parameter with separate tool_id, inputs and state.

show_job(job_id: str, full_details: bool = False) → Dict[str, Any]

Get details of a given job of the current user.

Parameters:

job_id (str) – job ID
full_details (bool) – when True, the complete list of details for the given job.

Return type:

dict

Returns:

A description of the given job. For example:

{'create_time': '2014-03-01T16:17:29.828624',
 'exit_code': 0,
 'id': 'a799d38679e985db',
 'inputs': {'input': {'id': 'ebfb8f50c6abde6d', 'src': 'hda'}},
 'model_class': 'Job',
 'outputs': {'output': {'id': 'a799d38679e985db', 'src': 'hda'}},
 'params': {'chromInfo': '"/opt/galaxy-central/tool-data/shared/ucsc/chrom/?.len"',
            'dbkey': '"?"',
            'seq_col': '"2"',
            'title_col': '["1"]'},
 'state': 'ok',
 'tool_id': 'tab2fasta',
 'update_time': '2014-03-01T16:17:31.930728'}

show_job_lock() → bool

Show whether the job lock is active or not. If it is active, no jobs will dispatch on the Galaxy server.

Return type:: bool
Returns:: Status of the job lock

Note

This method works only on Galaxy 20.05 or later and if the user is a Galaxy admin.

update_job_lock(active: bool = False) → bool

Update the job lock status by setting active to either True or False. If True, all job dispatching will be blocked.

Return type:: bool
Returns:: Updated status of the job lock

Note

This method works only on Galaxy 20.05 or later and if the user is a Galaxy admin.

wait_for_job(job_id: str, maxwait: float = 12000, interval: float = 3, check: bool = True) → Dict[str, Any]

Wait until a job is in a terminal state.

Parameters:

job_id (str) – job ID
maxwait (float) – Total time (in seconds) to wait for the job state to become terminal. If the job state is not terminal within this time, a TimeoutException will be raised.
interval (float) – Time (in seconds) to wait between 2 consecutive checks.
check (bool) – Whether to check if the job terminal state is ‘ok’.

Return type:

dict

Returns:

Details of the given job.

Libraries

Contains possible interactions with the Galaxy Data Libraries

class bioblend.galaxy.libraries.LibraryClient(galaxy_instance: GalaxyInstance)

A generic Client interface defining the common fields.

All clients must define the following field (which will be used as part of the URL composition (e.g., http://<galaxy_instance>/api/libraries): self.module = 'workflows' | 'libraries' | 'histories' | ...

copy_from_dataset(library_id: str, dataset_id: str, folder_id: str | None = None, message: str = '') → Dict[str, Any]

Copy a Galaxy dataset into a library.

Parameters:

library_id (str) – id of the library where to place the uploaded file
dataset_id (str) – id of the dataset to copy from
folder_id (str) – id of the folder where to place the uploaded files. If not provided, the root folder will be used
message (str) – message for copying action

Return type:

dict

Returns:

LDDA information

create_folder(library_id: str, folder_name: str, description: str | None = None, base_folder_id: str | None = None) → List[Dict[str, Any]]

Create a folder in a library.

Parameters:

library_id (str) – library id to use
folder_name (str) – name of the new folder in the data library
description (str) – description of the new folder in the data library
base_folder_id (str) – id of the folder where to create the new folder. If not provided, the root folder will be used

Return type:

list

Returns:

List with a single dictionary containing information about the new folder

create_library(name: str, description: str | None = None, synopsis: str | None = None) → Dict[str, Any]

Create a data library with the properties defined in the arguments.

Parameters:

name (str) – Name of the new data library
description (str) – Optional data library description
synopsis (str) – Optional data library synopsis

Return type:

dict

Returns:

Details of the created library. For example:

{'id': 'f740ab636b360a70',
 'name': 'Library from bioblend',
 'url': '/api/libraries/f740ab636b360a70'}

delete_library(library_id: str) → Dict[str, Any]

Delete a data library.

Parameters:: library_id (str) – Encoded data library ID identifying the library to be deleted
Return type:: dict
Returns:: Information about the deleted library

Warning

Deleting a data library is irreversible - all of the data from the library will be permanently deleted.

delete_library_dataset(library_id: str, dataset_id: str, purged: bool = False) → Dict[str, Any]

Delete a library dataset in a data library.

Parameters:

library_id (str) – library id where dataset is found in
dataset_id (str) – id of the dataset to be deleted
purged (bool) – Indicate that the dataset should be purged (permanently deleted)

Return type:

dict

Returns:

A dictionary containing the dataset id and whether the dataset has been deleted. For example:

{'deleted': True,
 'id': '60e680a037f41974'}

get_dataset_permissions(dataset_id: str) → Dict[str, Any]

Get the permissions for a dataset.

Parameters:: dataset_id (str) – id of the dataset
Return type:: dict
Returns:: dictionary with all applicable permissions’ values

get_folders(library_id: str, folder_id: str | None = None, name: str | None = None) → List[Dict[str, Any]]

Get all the folders in a library, or select a subset by specifying a folder name for filtering.

Parameters:

library_id (str) – library id to use
name (str) – Folder name to filter on. For name specify the full path of the folder starting from the library’s root folder, e.g. /subfolder/subsubfolder.

Return type:

list

Returns:

list of dicts each containing basic information about a folder

Changed in version 1.1.1: Using the deprecated folder_id parameter now raises a ValueError exception.

get_libraries(library_id: str | None = None, name: str | None = None, deleted: bool | None = False) → List[Dict[str, Any]]

Get all libraries, or select a subset by specifying optional arguments for filtering (e.g. a library name).

Parameters:

name (str) – Library name to filter on.
deleted (bool) – If False (the default), return only non-deleted libraries. If True, return only deleted libraries. If None, return both deleted and non-deleted libraries.

Return type:

list

Returns:

list of dicts each containing basic information about a library

Changed in version 1.1.1: Using the deprecated library_id parameter now raises a ValueError exception.

get_library_permissions(library_id: str) → Dict[str, Any]

Get the permissions for a library.

Parameters:: library_id (str) – id of the library
Return type:: dict
Returns:: dictionary with all applicable permissions’ values

module: str = 'libraries'

set_dataset_permissions(dataset_id: str, access_in: List[str] | None = None, modify_in: List[str] | None = None, manage_in: List[str] | None = None) → Dict[str, Any]

Set the permissions for a dataset. Note: it will override all security for this dataset even if you leave out a permission type.

Parameters:

dataset_id (str) – id of the dataset
access_in (list) – list of role ids
modify_in (list) – list of role ids
manage_in (list) – list of role ids

Return type:

dict

Returns:

dictionary with all applicable permissions’ values

set_library_permissions(library_id: str, access_in: List[str] | None = None, modify_in: List[str] | None = None, add_in: List[str] | None = None, manage_in: List[str] | None = None) → Dict[str, Any]

Set the permissions for a library. Note: it will override all security for this library even if you leave out a permission type.

Parameters:

library_id (str) – id of the library
access_in (list) – list of role ids
modify_in (list) – list of role ids
add_in (list) – list of role ids
manage_in (list) – list of role ids

Return type:

dict

Returns:

General information about the library

show_dataset(library_id: str, dataset_id: str) → Dict[str, Any]

Get details about a given library dataset. The required library_id can be obtained from the datasets’s library content details.

Parameters:

library_id (str) – library id where dataset is found in
dataset_id (str) – id of the dataset to be inspected

Return type:

dict

Returns:

A dictionary containing information about the dataset in the library

show_folder(library_id: str, folder_id: str) → Dict[str, Any]

Get details about a given folder. The required folder_id can be obtained from the folder’s library content details.

Parameters:

library_id (str) – library id to inspect folders in
folder_id (str) – id of the folder to be inspected

Return type:

dict

Returns:

Information about the folder

show_library(library_id: str, contents: bool = False) → Dict[str, Any]

Get information about a library.

Parameters:

library_id (str) – filter for library by library id
contents (bool) – whether to get contents of the library (rather than just the library details)

Return type:

dict

Returns:

details of the given library

update_library_dataset(dataset_id: str, **kwargs: Any) → Dict[str, Any]

Update library dataset metadata. Some of the attributes that can be modified are documented below.

Parameters:

dataset_id (str) – id of the dataset to be updated
name (str) – Replace library dataset name with the given string
misc_info (str) – Replace library dataset misc_info with given string
file_ext (str) – Replace library dataset extension (must exist in the Galaxy registry)
genome_build (str) – Replace library dataset genome build (dbkey)
tags (list) – Replace library dataset tags with the given list

Return type:

dict

Returns:

details of the updated dataset

upload_file_contents(library_id: str, pasted_content: str, folder_id: str | None = None, file_type: str = 'auto', dbkey: str = '?', tags: List[str] | None = None) → List[Dict[str, Any]]

Upload pasted_content to a data library as a new file.

Parameters:

library_id (str) – id of the library where to place the uploaded file
pasted_content (str) – Content to upload into the library
folder_id (str) – id of the folder where to place the uploaded file. If not provided, the root folder will be used
file_type (str) – Galaxy file format name
dbkey (str) – Dbkey
tags (list) – A list of tags to add to the datasets

Return type:

list

Returns:

List with a single dictionary containing information about the LDDA

upload_file_from_local_path(library_id: str, file_local_path: str, folder_id: str | None = None, file_type: str = 'auto', dbkey: str = '?', tags: List[str] | None = None) → List[Dict[str, Any]]

Read local file contents from file_local_path and upload data to a library.

Parameters:

library_id (str) – id of the library where to place the uploaded file
file_local_path (str) – path of local file to upload
folder_id (str) – id of the folder where to place the uploaded file. If not provided, the root folder will be used
file_type (str) – Galaxy file format name
dbkey (str) – Dbkey
tags (list) – A list of tags to add to the datasets

Return type:

list

Returns:

List with a single dictionary containing information about the LDDA

upload_file_from_server(library_id: str, server_dir: str, folder_id: str | None = None, file_type: str = 'auto', dbkey: str = '?', link_data_only: Literal['copy_files', 'link_to_files'] | None = None, roles: str = '', preserve_dirs: bool = False, tag_using_filenames: bool = False, tags: List[str] | None = None) → List[Dict[str, Any]]

Upload all files in the specified subdirectory of the Galaxy library import directory to a library.

Parameters:

library_id (str) – id of the library where to place the uploaded file
server_dir (str) – relative path of the subdirectory of library_import_dir to upload. All and only the files (i.e. no subdirectories) contained in the specified directory will be uploaded
folder_id (str) – id of the folder where to place the uploaded files. If not provided, the root folder will be used
file_type (str) – Galaxy file format name
dbkey (str) – Dbkey
link_data_only (str) – either ‘copy_files’ (default) or ‘link_to_files’. Setting to ‘link_to_files’ symlinks instead of copying the files
roles (str) –
???
preserve_dirs (bool) – Indicate whether to preserve the directory structure when importing dir
tag_using_filenames (bool) –
Indicate whether to generate dataset tags from filenames.

Changed in version 0.14.0: Changed the default from True to False.
tags (list) – A list of tags to add to the datasets

Return type:

list

Returns:

List with a single dictionary containing information about the LDDA

Note

This method works only if the Galaxy instance has the library_import_dir option configured in the config/galaxy.yml configuration file.

upload_file_from_url(library_id: str, file_url: str, folder_id: str | None = None, file_type: str = 'auto', dbkey: str = '?', tags: List[str] | None = None) → List[Dict[str, Any]]

Upload a file to a library from a URL.

Parameters:

library_id (str) – id of the library where to place the uploaded file
file_url (str) – URL of the file to upload
folder_id (str) – id of the folder where to place the uploaded file. If not provided, the root folder will be used
file_type (str) – Galaxy file format name
dbkey (str) – Dbkey
tags (list) – A list of tags to add to the datasets

Return type:

list

Returns:

List with a single dictionary containing information about the LDDA

upload_from_galaxy_filesystem(library_id: str, filesystem_paths: str, folder_id: str | None = None, file_type: str = 'auto', dbkey: str = '?', link_data_only: Literal['copy_files', 'link_to_files'] | None = None, roles: str = '', preserve_dirs: bool = False, tag_using_filenames: bool = False, tags: List[str] | None = None) → List[Dict[str, Any]]

Upload a set of files already present on the filesystem of the Galaxy server to a library.

Parameters:

library_id (str) – id of the library where to place the uploaded file
filesystem_paths (str) – file paths on the Galaxy server to upload to the library, one file per line
folder_id (str) – id of the folder where to place the uploaded files. If not provided, the root folder will be used
file_type (str) – Galaxy file format name
dbkey (str) – Dbkey
link_data_only (str) – either ‘copy_files’ (default) or ‘link_to_files’. Setting to ‘link_to_files’ symlinks instead of copying the files
roles (str) –
???
preserve_dirs (bool) – Indicate whether to preserve the directory structure when importing dir
tag_using_filenames (bool) –
Indicate whether to generate dataset tags from filenames.

Changed in version 0.14.0: Changed the default from True to False.
tags (list) – A list of tags to add to the datasets

Return type:

list

Returns:

List of dictionaries containing information about each uploaded LDDA.

Note

This method works only if the Galaxy instance has the allow_path_paste option set to true in the config/galaxy.yml configuration file.

wait_for_dataset(library_id: str, dataset_id: str, maxwait: float = 12000, interval: float = 3) → Dict[str, Any]

Wait until the library dataset state is terminal (‘ok’, ‘empty’, ‘error’, ‘discarded’ or ‘failed_metadata’).

Parameters:

library_id (str) – library id where dataset is found in
dataset_id (str) – id of the dataset to wait for
maxwait (float) – Total time (in seconds) to wait for the dataset state to become terminal. If the dataset state is not terminal within this time, a DatasetTimeoutException will be thrown.
interval (float) – Time (in seconds) to wait between 2 consecutive checks.

Return type:

dict

Returns:

A dictionary containing information about the dataset in the library

Quotas

Contains possible interactions with the Galaxy Quota

class bioblend.galaxy.quotas.QuotaClient(galaxy_instance: GalaxyInstance)

A generic Client interface defining the common fields.

All clients must define the following field (which will be used as part of the URL composition (e.g., http://<galaxy_instance>/api/libraries): self.module = 'workflows' | 'libraries' | 'histories' | ...

create_quota(name: str, description: str, amount: str, operation: Literal['+', '-', '='], default: Literal['no', 'registered', 'unregistered'] | None = 'no', in_users: List[str] | None = None, in_groups: List[str] | None = None) → Dict[str, Any]

Create a new quota

Parameters:

name (str) – Name for the new quota. This must be unique within a Galaxy instance.
description (str) – Quota description
amount (str) – Quota size (E.g. 10000MB, 99 gb, 0.2T, unlimited)
operation (str) – One of (+, -, =)
default (str) – Whether or not this is a default quota. Valid values are no, unregistered, registered. None is equivalent to no.
in_users (list of str) – A list of user IDs or user emails.
in_groups (list of str) – A list of group IDs or names.

Return type:

dict

Returns:

A description of quota. For example:

{'url': '/galaxy/api/quotas/386f14984287a0f7',
 'model_class': 'Quota',
 'message': "Quota 'Testing' has been created with 1 associated users and 0 associated groups.",
 'id': '386f14984287a0f7',
 'name': 'Testing'}

delete_quota(quota_id: str) → str

Delete a quota

Before a quota can be deleted, the quota must not be a default quota.

Parameters:

quota_id (str) – Encoded quota ID.

Return type:

str

Returns:

A description of the changes, mentioning the deleted quota. For example:

"Deleted 1 quotas: Testing-B"

get_quotas(deleted: bool = False) → List[Dict[str, Any]]

Get a list of quotas

Parameters:

deleted (bool) – Only return quota(s) that have been deleted

Return type:

list

Returns:

A list of dicts with details on individual quotas. For example:

[{'id': '0604c8a56abe9a50',
  'model_class': 'Quota',
  'name': 'test ',
  'url': '/api/quotas/0604c8a56abe9a50'},
 {'id': '1ee267091d0190af',
  'model_class': 'Quota',
  'name': 'workshop',
  'url': '/api/quotas/1ee267091d0190af'}]

module: str = 'quotas'

show_quota(quota_id: str, deleted: bool = False) → Dict[str, Any]

Display information on a quota

Parameters:

quota_id (str) – Encoded quota ID
deleted (bool) – Search for quota in list of ones already marked as deleted

Return type:

dict

Returns:

A description of quota. For example:

{'bytes': 107374182400,
 'default': [],
 'description': 'just testing',
 'display_amount': '100.0 GB',
 'groups': [],
 'id': '0604c8a56abe9a50',
 'model_class': 'Quota',
 'name': 'test ',
 'operation': '=',
 'users': []}

undelete_quota(quota_id: str) → str

Undelete a quota

Parameters:

quota_id (str) – Encoded quota ID.

Return type:

str

Returns:

A description of the changes, mentioning the undeleted quota. For example:

"Undeleted 1 quotas: Testing-B"

Update an existing quota

Parameters:

quota_id (str) – Encoded quota ID
name (str) – Name for the new quota. This must be unique within a Galaxy instance.
description (str) – Quota description. If you supply this parameter, but not the name, an error will be thrown.
amount (str) – Quota size (E.g. 10000MB, 99 gb, 0.2T, unlimited)
operation (str) – One of (+, -, =). If you wish to change this value, you must also provide the amount, otherwise it will not take effect.
default (str) – Whether or not this is a default quota. Valid values are no, unregistered, registered. Calling this method with default="no" on a non-default quota will throw an error. Not passing this parameter is equivalent to passing no.
in_users (list of str) – A list of user IDs or user emails.
in_groups (list of str) – A list of group IDs or names.

Return type:

str

Returns:

A semicolon separated list of changes to the quota. For example:

"Quota 'Testing-A' has been renamed to 'Testing-B'; Quota 'Testing-e' is now '-100.0 GB'; Quota 'Testing-B' is now the default for unregistered users"

Roles

Contains possible interactions with the Galaxy Roles

class bioblend.galaxy.roles.RolesClient(galaxy_instance: GalaxyInstance)

A generic Client interface defining the common fields.

All clients must define the following field (which will be used as part of the URL composition (e.g., http://<galaxy_instance>/api/libraries): self.module = 'workflows' | 'libraries' | 'histories' | ...

create_role(role_name: str, description: str, user_ids: List[str] | None = None, group_ids: List[str] | None = None) → Dict[str, Any]

Create a new role.

Parameters:

role_name (str) – A name for the new role
description (str) – Description for the new role
user_ids (list) – A list of encoded user IDs to add to the new role
group_ids (list) – A list of encoded group IDs to add to the new role

Return type:

dict

Returns:

Details of the newly created role. For example:

{'description': 'desc',
 'url': '/api/roles/ebfb8f50c6abde6d',
 'model_class': 'Role',
 'type': 'admin',
 'id': 'ebfb8f50c6abde6d',
 'name': 'Foo'}

Changed in version 0.15.0: Changed the return value from a 1-element list to a dict.

get_roles() → List[Dict[str, Any]]

Displays a collection (list) of roles.

Return type:

list

Returns:

A list of dicts with details on individual roles. For example:

[{"id": "f2db41e1fa331b3e",
  "model_class": "Role",
  "name": "Foo",
  "url": "/api/roles/f2db41e1fa331b3e"},
 {"id": "f597429621d6eb2b",
  "model_class": "Role",
  "name": "Bar",
  "url": "/api/roles/f597429621d6eb2b"}]

module: str = 'roles'

show_role(role_id: str) → Dict[str, Any]

Display information on a single role

Parameters:

role_id (str) – Encoded role ID

Return type:

dict

Returns:

Details of the given role. For example:

{"description": "Private Role for Foo",
 "id": "f2db41e1fa331b3e",
 "model_class": "Role",
 "name": "Foo",
 "type": "private",
 "url": "/api/roles/f2db41e1fa331b3e"}

Tools

Tool data tables

Contains possible interactions with the Galaxy Tool data tables

class bioblend.galaxy.tool_data.ToolDataClient(galaxy_instance: GalaxyInstance)

A generic Client interface defining the common fields.

All clients must define the following field (which will be used as part of the URL composition (e.g., http://<galaxy_instance>/api/libraries): self.module = 'workflows' | 'libraries' | 'histories' | ...

delete_data_table(data_table_id: str, values: str) → Dict[str, Any]

Delete an item from a data table.

Parameters:

data_table_id (str) – ID of the data table
values (str) – a “|” separated list of column contents, there must be a value for all the columns of the data table

Return type:

dict

Returns:

Remaining contents of the given data table

get_data_tables() → List[Dict[str, Any]]

Get the list of all data tables.

Return type:

list

Returns:

A list of dicts with details on individual data tables. For example:

[{"model_class": "TabularToolDataTable", "name": "fasta_indexes"},
 {"model_class": "TabularToolDataTable", "name": "bwa_indexes"}]

module: str = 'tool_data'

reload_data_table(data_table_id: str) → Dict[str, Any]

Reload a data table.

Parameters:

data_table_id (str) – ID of the data table

Return type:

dict

Returns:

A description of the given data table and its content. For example:

{'columns': ['value', 'dbkey', 'name', 'path'],
 'fields': [['test id',
             'test',
             'test name',
             '/opt/galaxy-dist/tool-data/test/seq/test id.fa']],
 'model_class': 'TabularToolDataTable',
 'name': 'all_fasta'}

show_data_table(data_table_id: str) → Dict[str, Any]

Get details of a given data table.

Parameters:

data_table_id (str) – ID of the data table

Return type:

dict

Returns:

A description of the given data table and its content. For example:

{'columns': ['value', 'dbkey', 'name', 'path'],
 'fields': [['test id',
             'test',
             'test name',
             '/opt/galaxy-dist/tool-data/test/seq/test id.fa']],
 'model_class': 'TabularToolDataTable',
 'name': 'all_fasta'}

Tool dependencies

Contains interactions dealing with Galaxy dependency resolvers.

class bioblend.galaxy.tool_dependencies.ToolDependenciesClient(galaxy_instance: GalaxyInstance)

A generic Client interface defining the common fields.

All clients must define the following field (which will be used as part of the URL composition (e.g., http://<galaxy_instance>/api/libraries): self.module = 'workflows' | 'libraries' | 'histories' | ...

delete_unused_dependency_paths(paths: List[str]) → None

Delete unused paths

Parameters:: paths (list) – paths to delete

module: str = 'dependency_resolvers'

summarize_toolbox(index: int | None = None, tool_ids: List[str] | None = None, resolver_type: str | None = None, include_containers: bool = False, container_type: str | None = None, index_by: Literal['requirements', 'tools'] = 'requirements') → list

Summarize requirements across toolbox (for Tool Management grid).

Parameters:

index (int) – index of the dependency resolver with respect to the dependency resolvers config file
tool_ids (list) – tool_ids to return when index_by=tools
resolver_type (str) – restrict to specified resolver type
include_containers (bool) – include container resolvers in resolution
container_type (str) – restrict to specified container type
index_by (str) – By default results are grouped by requirements. Set to ‘tools’ to return one entry per tool.

Return type:

list of dicts

Returns:

dictified descriptions of the dependencies, with attribute dependency_type: None if no match was found. For example:

[{'requirements': [{'name': 'galaxy_sequence_utils',
                    'specs': [],
                    'type': 'package',
                    'version': '1.1.4'},
                   {'name': 'bx-python',
                    'specs': [],
                    'type': 'package',
                    'version': '0.8.6'}],
  'status': [{'cacheable': False,
              'dependency_type': None,
              'exact': True,
              'model_class': 'NullDependency',
              'name': 'galaxy_sequence_utils',
              'version': '1.1.4'},
              {'cacheable': False,
              'dependency_type': None,
              'exact': True,
              'model_class': 'NullDependency',
              'name': 'bx-python',
              'version': '0.8.6'}],
  'tool_ids': ['vcf_to_maf_customtrack1']}]

Note

This method works only on Galaxy 20.01 or later and if the user is a Galaxy admin. It relies on an experimental API particularly tied to the GUI and therefore is subject to breaking changes.

unused_dependency_paths() → List[str]: List unused dependencies

ToolShed

Users

Visual

Workflows

Contains possible interactions with the Galaxy Workflows

class bioblend.galaxy.workflows.WorkflowClient(galaxy_instance: GalaxyInstance)

A generic Client interface defining the common fields.

All clients must define the following field (which will be used as part of the URL composition (e.g., http://<galaxy_instance>/api/libraries): self.module = 'workflows' | 'libraries' | 'histories' | ...

cancel_invocation(workflow_id: str, invocation_id: str) → Dict[str, Any]

Cancel the scheduling of a workflow.

Parameters:

workflow_id (str) – Encoded workflow ID
invocation_id (str) – Encoded workflow invocation ID

Return type:

dict

Returns:

The workflow invocation being cancelled

delete_workflow(workflow_id: str) → None

Delete a workflow identified by workflow_id.

Parameters:: workflow_id (str) – Encoded workflow ID

Warning

Deleting a workflow is irreversible in Galaxy versions < 23.01 - all workflow data will be permanently deleted.

export_workflow_dict(workflow_id: str, version: int | None = None) → Dict[str, Any]

Exports a workflow.

Parameters:

workflow_id (str) – Encoded workflow ID
version (int) – Workflow version to export

Return type:

dict

Returns:

Dictionary representing the requested workflow

export_workflow_to_local_path(workflow_id: str, file_local_path: str, use_default_filename: bool = True) → None

Exports a workflow in JSON format to a given local path.

Parameters:

workflow_id (str) – Encoded workflow ID
file_local_path (str) – Local path to which the exported file will be saved. (Should not contain filename if use_default_name=True)
use_default_filename (bool) – If the use_default_name parameter is True, the exported file will be saved as file_local_path/Galaxy-Workflow-%s.ga, where %s is the workflow name. If use_default_name is False, file_local_path is assumed to contain the full file path including filename.

Return type:

None

Returns:

None

extract_workflow_from_history(history_id: str, workflow_name: str, job_ids: List[str] | None = None, dataset_hids: List[str] | None = None, dataset_collection_hids: List[str] | None = None) → Dict[str, Any]

Extract a workflow from a history.

Parameters:

history_id (str) – Encoded history ID
workflow_name (str) – Name of the workflow to create
job_ids (list) – Optional list of job IDs to filter the jobs to extract from the history
dataset_hids (list) – Optional list of dataset hids corresponding to workflow inputs when extracting a workflow from history
dataset_collection_hids (list) – Optional list of dataset collection hids corresponding to workflow inputs when extracting a workflow from history

Return type:

dict

Returns:

A description of the created workflow

get_invocations(workflow_id: str) → List[Dict[str, Any]]

Get a list containing all the workflow invocations corresponding to the specified workflow.

For more advanced filtering use InvocationClient.get_invocations().

Parameters:

workflow_id (str) – Encoded workflow ID

Return type:

list

Returns:

A list of workflow invocations. For example:

[{'history_id': '2f94e8ae9edff68a',
  'id': 'df7a1f0c02a5b08e',
  'model_class': 'WorkflowInvocation',
  'state': 'new',
  'update_time': '2015-10-31T22:00:22',
  'uuid': 'c8aa2b1c-801a-11e5-a9e5-8ca98228593c',
  'workflow_id': '03501d7626bd192f'}]

get_workflow_inputs(workflow_id: str, label: str) → List[str]

Get a list of workflow input IDs that match the given label. If no input matches the given label, an empty list is returned.

Parameters:

workflow_id (str) – Encoded workflow ID
label (str) – label to filter workflow inputs on

Return type:

list

Returns:

list of workflow inputs matching the label query

get_workflows(workflow_id: str | None = None, name: str | None = None, published: bool = False) → List[Dict[str, Any]]

Get all workflows, or select a subset by specifying optional arguments for filtering (e.g. a workflow name).

Parameters:

name (str) – Workflow name to filter on.
published (bool) – if True, return also published workflows

Return type:

list

Returns:

A list of workflow dicts. For example:

[{'id': '92c56938c2f9b315',
  'name': 'Simple',
  'url': '/api/workflows/92c56938c2f9b315'}]

Changed in version 1.1.1: Using the deprecated workflow_id parameter now raises a ValueError exception.

import_shared_workflow(workflow_id: str) → Dict[str, Any]

Imports a new workflow from the shared published workflows.

Parameters:

workflow_id (str) – Encoded workflow ID

Return type:

dict

Returns:

A description of the workflow. For example:

{'id': 'ee0e2b4b696d9092',
 'model_class': 'StoredWorkflow',
 'name': 'Super workflow that solves everything!',
 'published': False,
 'tags': [],
 'url': '/api/workflows/ee0e2b4b696d9092'}

import_workflow_dict(workflow_dict: Dict[str, Any], publish: bool = False) → Dict[str, Any]

Imports a new workflow given a dictionary representing a previously exported workflow.

Parameters:

workflow_dict (dict) – dictionary representing the workflow to be imported
publish (bool) – if True the uploaded workflow will be published; otherwise it will be visible only by the user which uploads it (default)

Return type:

dict

Returns:

Information about the imported workflow. For example:

{'name': 'Training: 16S rRNA sequencing with mothur: main tutorial',
 'tags': [],
 'deleted': false,
 'latest_workflow_uuid': '368c6165-ccbe-4945-8a3c-d27982206d66',
 'url': '/api/workflows/94bac0a90086bdcf',
 'number_of_steps': 44,
 'published': false,
 'owner': 'jane-doe',
 'model_class': 'StoredWorkflow',
 'id': '94bac0a90086bdcf'}

import_workflow_from_local_path(file_local_path: str, publish: bool = False) → Dict[str, Any]

Imports a new workflow given the path to a file containing a previously exported workflow.

Parameters:

file_local_path (str) – File to upload to the server for new workflow
publish (bool) – if True the uploaded workflow will be published; otherwise it will be visible only by the user which uploads it (default)

Return type:

dict

Returns:

Information about the imported workflow. For example:

{'name': 'Training: 16S rRNA sequencing with mothur: main tutorial',
 'tags': [],
 'deleted': false,
 'latest_workflow_uuid': '368c6165-ccbe-4945-8a3c-d27982206d66',
 'url': '/api/workflows/94bac0a90086bdcf',
 'number_of_steps': 44,
 'published': false,
 'owner': 'jane-doe',
 'model_class': 'StoredWorkflow',
 'id': '94bac0a90086bdcf'}

invoke_workflow(workflow_id: str, inputs: dict | None = None, params: dict | None = None, history_id: str | None = None, history_name: str | None = None, import_inputs_to_history: bool = False, replacement_params: dict | None = None, allow_tool_state_corrections: bool = False, inputs_by: Literal['step_index|step_uuid', 'step_index', 'step_id', 'step_uuid', 'name'] | None = None, parameters_normalized: bool = False, require_exact_tool_versions: bool = True) → Dict[str, Any]

Invoke the workflow identified by workflow_id. This will cause a workflow to be scheduled and return an object describing the workflow invocation.

Parameters:

workflow_id (str) – Encoded workflow ID
inputs (dict) –
A mapping of workflow inputs to datasets and dataset collections. The datasets source can be a LibraryDatasetDatasetAssociation (ldda), LibraryDataset (ld), HistoryDatasetAssociation (hda), or HistoryDatasetCollectionAssociation (hdca).

The map must be in the following format: {'<input_index>': {'id': <encoded dataset ID>, 'src': '[ldda, ld, hda, hdca]'}} (e.g. {'2': {'id': '29beef4fadeed09f', 'src': 'hda'}})

This map may also be indexed by the UUIDs of the workflow steps, as indicated by the uuid property of steps returned from the Galaxy API. Alternatively workflow steps may be addressed by the label that can be set in the workflow editor. If using uuid or label you need to also set the inputs_by parameter to step_uuid or name.
params (dict) – A mapping of non-datasets tool parameters (see below)
history_id (str) – The encoded history ID where to store the workflow output. Alternatively, history_name may be specified to create a new history.
history_name (str) – Create a new history with the given name to store the workflow output. If both history_id and history_name are provided, history_name is ignored. If neither is specified, a new ‘Unnamed history’ is created.
import_inputs_to_history (bool) – If True, used workflow inputs will be imported into the history. If False, only workflow outputs will be visible in the given history.
allow_tool_state_corrections (bool) – If True, allow Galaxy to fill in missing tool state when running workflows. This may be useful for workflows using tools that have changed over time or for workflows built outside of Galaxy with only a subset of inputs defined.
replacement_params (dict) – pattern-based replacements for post-job actions (see below)
inputs_by (str) – Determines how inputs are referenced. Can be “step_index|step_uuid” (default), “step_index”, “step_id”, “step_uuid”, or “name”.
parameters_normalized (bool) – Whether Galaxy should normalize params to ensure everything is referenced by a numeric step ID. Default is False, but when setting params for a subworkflow, True is required.
require_exact_tool_versions (bool) – Whether invocation should fail if Galaxy does not have the exact tool versions. Default is True. Parameter does not any effect for Galaxy versions < 22.05.

Return type:

dict

Returns:

A dict containing the workflow invocation describing the scheduling of the workflow. For example:

{'history_id': '2f94e8ae9edff68a',
 'id': 'df7a1f0c02a5b08e',
 'inputs': {'0': {'id': 'a7db2fac67043c7e',
                  'src': 'hda',
                  'uuid': '7932ffe0-2340-4952-8857-dbaa50f1f46a'}},
 'model_class': 'WorkflowInvocation',
 'state': 'ready',
 'steps': [{'action': None,
            'id': 'd413a19dec13d11e',
            'job_id': None,
            'model_class': 'WorkflowInvocationStep',
            'order_index': 0,
            'state': None,
            'update_time': '2015-10-31T22:00:26',
            'workflow_step_id': 'cbbbf59e8f08c98c',
            'workflow_step_label': None,
            'workflow_step_uuid': 'b81250fd-3278-4e6a-b269-56a1f01ef485'},
           {'action': None,
            'id': '2f94e8ae9edff68a',
            'job_id': 'e89067bb68bee7a0',
            'model_class': 'WorkflowInvocationStep',
            'order_index': 1,
            'state': 'new',
            'update_time': '2015-10-31T22:00:26',
            'workflow_step_id': '964b37715ec9bd22',
            'workflow_step_label': None,
            'workflow_step_uuid': 'e62440b8-e911-408b-b124-e05435d3125e'}],
 'update_time': '2015-10-31T22:00:26',
 'uuid': 'c8aa2b1c-801a-11e5-a9e5-8ca98228593c',
 'workflow_id': '03501d7626bd192f'}

The params dict should be specified as follows:

{STEP_ID: PARAM_DICT, ...}

where PARAM_DICT is:

{PARAM_NAME: VALUE, ...}

For backwards compatibility, the following (deprecated) format is also supported for params:

{TOOL_ID: PARAM_DICT, ...}

in which case PARAM_DICT affects all steps with the given tool id. If both by-tool-id and by-step-id specifications are used, the latter takes precedence.

Finally (again, for backwards compatibility), PARAM_DICT can also be specified as:

{'param': PARAM_NAME, 'value': VALUE}

Note that this format allows only one parameter to be set per step.

For a repeat parameter, the names of the contained parameters needs to be specified as <repeat name>_<repeat index>|<param name>, with the repeat index starting at 0. For example, if the tool XML contains:

<repeat name="cutoff" title="Parameters used to filter cells" min="1">
    <param name="name" type="text" value="n_genes" label="Name of param...">
        <option value="n_genes">n_genes</option>
        <option value="n_counts">n_counts</option>
    </param>
    <param name="min" type="float" min="0" value="0" label="Min value"/>
</repeat>

then the PARAM_DICT should be something like:

{...
 "cutoff_0|name": "n_genes",
 "cutoff_0|min": "2",
 "cutoff_1|name": "n_counts",
 "cutoff_1|min": "4",
 ...}

At the time of this writing, it is not possible to change the number of times the contained parameters are repeated. Therefore, the parameter indexes can go from 0 to n-1, where n is the number of times the repeated element was added when the workflow was saved in the Galaxy UI.

The replacement_params dict should map parameter names in post-job actions (PJAs) to their runtime values. For instance, if the final step has a PJA like the following:

{'RenameDatasetActionout_file1': {'action_arguments': {'newname': '${output}'},
                                  'action_type': 'RenameDatasetAction',
                                  'output_name': 'out_file1'}}

then the following renames the output dataset to ‘foo’:

replacement_params = {'output': 'foo'}

Object-oriented Galaxy API

Client

Wrappers

Usage documentation

This page describes some sample use cases for the Galaxy API and provides examples for these API calls. In addition to this page, there are functional examples of complete scripts in the docs/examples directory of the BioBlend source code repository.

Connect to a Galaxy server

To connect to a running Galaxy server, you will need an account on that Galaxy instance and an API key for the account. Instructions on getting an API key can be found at https://galaxyproject.org/develop/api/ .

To open a connection call:

from bioblend.galaxy import GalaxyInstance

gi = GalaxyInstance(url='http://example.galaxy.url', key='your-API-key')

We now have a GalaxyInstance object which allows us to interact with the Galaxy server under our account, and access our data. If the account is a Galaxy admin account we also will be able to use this connection to carry out admin actions.

View Histories and Datasets

Methods for accessing histories and datasets are grouped under GalaxyInstance.histories.* and GalaxyInstance.datasets.* respectively.

To get information on the Histories currently in your account, call:

>>> gi.histories.get_histories()
[{'id': 'f3c2b0f3ecac9f02',
  'name': 'RNAseq_DGE_BASIC_Prep',
  'url': '/api/histories/f3c2b0f3ecac9f02'},
 {'id': '8a91dcf1866a80c2',
  'name': 'June demo',
  'url': '/api/histories/8a91dcf1866a80c2'}]

This returns a list of dictionaries containing basic metadata, including the id and name of each History. In this case, we have two existing Histories in our account, 'RNAseq_DGE_BASIC_Prep' and 'June demo'. To get more detailed information about a History we can pass its id to the show_history method:

>>> gi.histories.show_history('f3c2b0f3ecac9f02', contents=False)
{'annotation': '',
 'contents_url': '/api/histories/f3c2b0f3ecac9f02/contents',
 'id': 'f3c2b0f3ecac9f02',
 'name': 'RNAseq_DGE_BASIC_Prep',
 'nice_size': '93.5 MB',
 'state': 'ok',
 'state_details': {'discarded': 0,
                   'empty': 0,
                   'error': 0,
                   'failed_metadata': 0,
                   'new': 0,
                   'ok': 7,
                   'paused': 0,
                   'queued': 0,
                   'running': 0,
                   'setting_metadata': 0,
                   'upload': 0},
 'state_ids': {'discarded': [],
               'empty': [],
               'error': [],
               'failed_metadata': [],
               'new': [],
               'ok': ['d6842fb08a76e351',
                      '10a4b652da44e82a',
                      '81c601a2549966a0',
                      'a154f05e3bcee26b',
                      '1352fe19ddce0400',
                      '06d549c52d753e53',
                      '9ec54455d6279cc7'],
               'paused': [],
               'queued': [],
               'running': [],
               'setting_metadata': [],
               'upload': []}}

This gives us a dictionary containing the History's metadata. With contents=False (the default), we only get a list of ids of the datasets contained within the History; with contents=True we would get metadata on each dataset. We can also directly access more detailed information on a particular dataset by passing its id to the show_dataset method:

>>> gi.datasets.show_dataset('10a4b652da44e82a')
{'data_type': 'fastqsanger',
 'deleted': False,
 'file_size': 16527060,
 'genome_build': 'dm3',
 'id': 17499,
 'metadata_data_lines': None,
 'metadata_dbkey': 'dm3',
 'metadata_sequences': None,
 'misc_blurb': '15.8 MB',
 'misc_info': 'Noneuploaded fastqsanger file',
 'model_class': 'HistoryDatasetAssociation',
 'name': 'C1_R2_1.chr4.fq',
 'purged': False,
 'state': 'ok',
 'visible': True}

Uploading Datasets to a History

To upload a local file to a Galaxy server, you can run the upload_file method, supplying the path to a local file:

>>> gi.tools.upload_file('test.txt', 'f3c2b0f3ecac9f02')
{'implicit_collections': [],
 'jobs': [{'create_time': '2015-07-28T17:52:39.756488',
           'exit_code': None,
           'id': '9752b387803d3e1e',
           'model_class': 'Job',
           'state': 'new',
           'tool_id': 'upload1',
           'update_time': '2015-07-28T17:52:39.987509'}],
 'output_collections': [],
 'outputs': [{'create_time': '2015-07-28T17:52:39.331176',
              'data_type': 'galaxy.datatypes.data.Text',
              'deleted': False,
              'file_ext': 'auto',
              'file_size': 0,
              'genome_build': '?',
              'hda_ldda': 'hda',
              'hid': 16,
              'history_content_type': 'dataset',
              'history_id': 'f3c2b0f3ecac9f02',
              'id': '59c76a119581e190',
              'metadata_data_lines': None,
              'metadata_dbkey': '?',
              'misc_blurb': None,
              'misc_info': None,
              'model_class': 'HistoryDatasetAssociation',
              'name': 'test.txt',
              'output_name': 'output0',
              'peek': '<table cellspacing="0" cellpadding="3"></table>',
              'purged': False,
              'state': 'queued',
              'tags': [],
              'update_time': '2015-07-28T17:52:39.611887',
              'uuid': 'ff0ee99b-7542-4125-802d-7a193f388e7e',
              'visible': True}]}

If files are greater than 2GB in size, they will need to be uploaded via FTP. Importing files from the user's FTP folder can be done via running the upload tool again:

>>> gi.tools.upload_from_ftp('test.txt', 'f3c2b0f3ecac9f02')
{'implicit_collections': [],
 'jobs': [{'create_time': '2015-07-28T17:57:43.704394',
           'exit_code': None,
           'id': '82b264d8c3d11790',
           'model_class': 'Job',
           'state': 'new',
           'tool_id': 'upload1',
           'update_time': '2015-07-28T17:57:43.910958'}],
 'output_collections': [],
 'outputs': [{'create_time': '2015-07-28T17:57:43.209041',
              'data_type': 'galaxy.datatypes.data.Text',
              'deleted': False,
              'file_ext': 'auto',
              'file_size': 0,
              'genome_build': '?',
              'hda_ldda': 'hda',
              'hid': 17,
              'history_content_type': 'dataset',
              'history_id': 'f3c2b0f3ecac9f02',
              'id': 'a676e8f07209a3be',
              'metadata_data_lines': None,
              'metadata_dbkey': '?',
              'misc_blurb': None,
              'misc_info': None,
              'model_class': 'HistoryDatasetAssociation',
              'name': 'test.txt',
              'output_name': 'output0',
              'peek': '<table cellspacing="0" cellpadding="3"></table>',
              'purged': False,
              'state': 'queued',
              'tags': [],
              'update_time': '2015-07-28T17:57:43.544407',
              'uuid': '2cbe8f0a-4019-47c4-87e2-005ce35b8449',
              'visible': True}]}

View Data Libraries

Methods for accessing Data Libraries are grouped under GalaxyInstance.libraries.*. Most Data Library methods are available to all users, but as only administrators can create new Data Libraries within Galaxy, the create_folder and create_library methods can only be called using an API key belonging to an admin account.

We can view the Data Libraries available to our account using:

>>> gi.libraries.get_libraries()
[{'id': '8e6f930d00d123ea',
  'name': 'RNA-seq workshop data',
  'url': '/api/libraries/8e6f930d00d123ea'},
 {'id': 'f740ab636b360a70',
  'name': '1000 genomes',
  'url': '/api/libraries/f740ab636b360a70'}]

This gives a list of metadata dictionaries with basic information on each library. We can get more information on a particular Data Library by passing its id to the show_library method:

>>> gi.libraries.show_library('8e6f930d00d123ea')
{'contents_url': '/api/libraries/8e6f930d00d123ea/contents',
 'description': 'RNA-Seq workshop data',
 'name': 'RNA-Seq',
 'synopsis': 'Data for the RNA-Seq tutorial'}

Upload files to a Data Library

We can get files into Data Libraries in several ways: by uploading from our local machine, by retrieving from a URL, by passing the new file content directly into the method, or by importing a file from the filesystem on the Galaxy server.

For instance, to upload a file from our machine we might call:

>>> gi.libraries.upload_file_from_local_path('8e6f930d00d123ea', '/local/path/to/mydata.fastq', file_type='fastqsanger')

Note that we have provided the id of the destination Data Library, and in this case we have specified the type that Galaxy should assign to the new dataset. The default value for file_type is 'auto', in which case Galaxy will attempt to guess the dataset type.

View Workflows

Methods for accessing workflows are grouped under GalaxyInstance.workflows.*.

To get information on the Workflows currently in your account, use:

>>> gi.workflows.get_workflows()
[{'id': 'e8b85ad72aefca86',
  'name': 'TopHat + cufflinks part 1',
  'url': '/api/workflows/e8b85ad72aefca86'},
 {'id': 'b0631c44aa74526d',
  'name': 'CuffDiff',
  'url': '/api/workflows/b0631c44aa74526d'}]

This returns a list of metadata dictionaries. We can get the details of a particular Workflow, including its steps, by passing its id to the show_workflow method:

>>> gi.workflows.show_workflow('e8b85ad72aefca86')
{'id': 'e8b85ad72aefca86',
 'inputs': {'252': {'label': 'Input RNA-seq fastq', 'value': ''}},
 'name': 'TopHat + cufflinks part 1',
 'steps': {'250': {'id': 250,
                   'input_steps': {'input1': {'source_step': 252,
                                              'step_output': 'output'}},
                   'tool_id': 'tophat',
                   'type': 'tool'},
           '251': {'id': 251,
                   'input_steps': {'input': {'source_step': 250,
                                             'step_output': 'accepted_hits'}},
                   'tool_id': 'cufflinks',
                   'type': 'tool'},
           '252': {'id': 252,
                   'input_steps': {},
                   'tool_id': None,
                   'type': 'data_input'}},
 'url': '/api/workflows/e8b85ad72aefca86'}

Export or import a workflow

Workflows can be exported from or imported into Galaxy. This makes it possible to archive workflows, or to move them between Galaxy instances.

To export a workflow, we can call:

>>> workflow_dict = gi.workflows.export_workflow_dict('e8b85ad72aefca86')

This gives us a complex dictionary representing the workflow. We can import this dictionary as a new workflow with:

>>> gi.workflows.import_workflow_dict(workflow_dict)
{'id': 'c0bacafdfe211f9a',
 'name': 'TopHat + cufflinks part 1 (imported from API)',
 'url': '/api/workflows/c0bacafdfe211f9a'}

This call returns a dictionary containing basic metadata on the new workflow. Since in this case we have imported the dictionary into the original Galaxy instance, we now have a duplicate of the original workflow in our account:

>>> gi.workflows.get_workflows()
[{'id': 'c0bacafdfe211f9a',
  'name': 'TopHat + cufflinks part 1 (imported from API)',
  'url': '/api/workflows/c0bacafdfe211f9a'},
 {'id': 'e8b85ad72aefca86',
  'name': 'TopHat + cufflinks part 1',
  'url': '/api/workflows/e8b85ad72aefca86'},
 {'id': 'b0631c44aa74526d',
  'name': 'CuffDiff',
  'url': '/api/workflows/b0631c44aa74526d'}]

Instead of using dictionaries directly, workflows can be exported to or imported from files on the local disk using the export_workflow_to_local_path and import_workflow_from_local_path methods. See the API reference for details.

Note

If we export a workflow from one Galaxy instance and import it into another, Galaxy will only run it without modification if it has the same versions of the tool wrappers installed. This is to ensure reproducibility. Otherwise, we will need to manually update the workflow to use the new tool versions.

Invoke a workflow

To invoke a workflow, we need to tell Galaxy which datasets to use for which workflow inputs. We can use datasets from histories or data libraries.

Examine the workflow above. We can see that it takes only one input file. That is:

>>> wf = gi.workflows.show_workflow('e8b85ad72aefca86')
>>> wf['inputs']
{'252': {'label': 'Input RNA-seq fastq', 'value': ''}}

There is one input, labelled 'Input RNA-seq fastq'. This input is passed to the Tophat tool and should be a fastq file. We will use the dataset we examined above, under View Histories and Datasets, which had name 'C1_R2_1.chr4.fq' and id '10a4b652da44e82a'.

To specify the inputs, we build a data map and pass this to the invoke_workflow method. This data map is a nested dictionary object which maps inputs to datasets. We call:

>>> datamap = {'252': {'src':'hda', 'id':'10a4b652da44e82a'}}
>>> gi.workflows.invoke_workflow('e8b85ad72aefca86', inputs=datamap, history_name='New output history')
{'history': '0a7b7992a7cabaec',
 'outputs': ['33be8ad9917d9207',
             'fbee1c2dc793c114',
             '85866441984f9e28',
             '1c51aa78d3742386',
             'a68e8770e52d03b4',
             'c54baf809e3036ac',
             'ba0db8ce6cd1fe8f',
             'c019e4cf08b2ac94']}

In this case the only input id is '252' and the corresponding dataset id is '10a4b652da44e82a'. We have specified the dataset source to be 'hda' (HistoryDatasetAssociation) since the dataset is stored in a History. See the API reference for allowed dataset specifications. We have also requested that a new History be created and used to store the results of the run, by setting history_name='New output history'.

The invoke_workflow call submits all the jobs which need to be run to the Galaxy workflow engine, with the appropriate dependencies so that they will run in order. The call returns immediately, so we can continue to submit new jobs while waiting for this workflow to execute. invoke_workflow returns the a dictionary describing the workflow invocation.

If we view the output History immediately after calling invoke_workflow, we will see something like:

>>> gi.histories.show_history('0a7b7992a7cabaec')
{'annotation': '',
 'contents_url': '/api/histories/0a7b7992a7cabaec/contents',
 'id': '0a7b7992a7cabaec',
 'name': 'New output history',
 'nice_size': '0 bytes',
 'state': 'queued',
 'state_details': {'discarded': 0,
                   'empty': 0,
                   'error': 0,
                   'failed_metadata': 0,
                   'new': 0,
                   'ok': 0,
                   'paused': 0,
                   'queued': 8,
                   'running': 0,
                   'setting_metadata': 0,
                   'upload': 0},
 'state_ids': {'discarded': [],
               'empty': [],
               'error': [],
               'failed_metadata': [],
               'new': [],
               'ok': [],
               'paused': [],
               'queued': ['33be8ad9917d9207',
                          'fbee1c2dc793c114',
                          '85866441984f9e28',
                          '1c51aa78d3742386',
                          'a68e8770e52d03b4',
                          'c54baf809e3036ac',
                          'ba0db8ce6cd1fe8f',
                          'c019e4cf08b2ac94'],
               'running': [],
               'setting_metadata': [],
               'upload': []}}

In this case, because the submitted jobs have not had time to run, the output History contains 8 datasets in the 'queued' state and has a total size of 0 bytes. If we make this call again later we should instead see completed output files.

View Users

Methods for managing users are grouped under GalaxyInstance.users.*. User management is only available to Galaxy administrators, that is, the API key used to connect to Galaxy must be that of an admin account.

To get a list of users, call:

>>> gi.users.get_users()
[{'email': 'userA@example.org',
  'id': '975a9ce09b49502a',
  'quota_percent': None,
  'url': '/api/users/975a9ce09b49502a'},
 {'email': 'userB@example.org',
  'id': '0193a95acf427d2c',
  'quota_percent': None,
  'url': '/api/users/0193a95acf427d2c'}]

Using BioBlend for raw API calls

BioBlend can be used to make HTTP requests to the Galaxy API in a more convenient way than using e.g. the requests Python library. There are 5 available methods corresponding to the most common HTTP methods: make_get_request, make_post_request, make_put_request, make_delete_request and make_patch_request. One advantage of using these methods is that the API keys stored in the GalaxyInstance object is automatically added to the request.

To make a GET request to the Galaxy API with BioBlend, call:

>>> gi.make_get_request(gi.base_url + "/api/version").json()
{'version_major': '19.05',
 'extra': {}}

To make a POST request to the Galaxy API with BioBlend, call:

>>> gi.make_post_request(gi.base_url + "/api/histories", payload={"name": "test history"})
{'importable': False,
 'create_time': '2019-07-05T20:10:04.823716',
 'contents_url': '/api/histories/a77b3f95070d689a/contents',
 'id': 'a77b3f95070d689a',
 'size': 0, 'user_id': '5b732999121d4593',
 'username_and_slug': None,
 'annotation': None,
 'state_details': {'discarded': 0,
                   'ok': 0,
                   'failed_metadata': 0,
                   'upload': 0,
                   'paused': 0,
                   'running': 0,
                   'setting_metadata': 0,
                   'error': 0,
                   'new': 0,
                   'queued': 0,
                   'empty': 0},
 'state': 'new',
 'empty': True,
 'update_time': '2019-07-05T20:10:04.823742',
 'tags': [],
 'deleted': False,
 'genome_build': None,
 'slug': None,
 'name': 'test history',
 'url': '/api/histories/a77b3f95070d689a',
 'state_ids': {'discarded': [],
               'ok': [],
               'failed_metadata': [],
               'upload': [],
               'paused': [],
               'running': [],
               'setting_metadata': [],
               'error': [],
               'new': [],
               'queued': [],
               'empty': []},
 'published': False,
 'model_class': 'History',
 'purged': False}

Toolshed API

API used to interact with the Galaxy Toolshed, including repository management.

API documentation for interacting with the Galaxy Toolshed

ToolShedInstance

Categories

Repositories

Tools

Configuration

BioBlend allows library-wide configuration to be set in external files. These configuration files can be used to specify access keys, for example.

Configuration documents for BioBlend

BioBlend

exception bioblend.ConnectionError(message: str, body: bytes | str | None = None, status_code: int | None = None)

An exception class that is raised when unexpected HTTP responses come back.

Should make it easier to debug when strange HTTP things happen such as a proxy server getting in the way of the request etc. @see: body attribute to see the content of the http response

class bioblend.NullHandler(level=0)

Initializes the instance - basically setting the formatter to None and the filter list to empty.

emit(record: LogRecord) → None

Do whatever it takes to actually log the specified logging record.

This version is intended to be implemented by subclasses and so raises a NotImplementedError.

exception bioblend.TimeoutException

bioblend.get_version() → str: Returns a string with the current version of the library (e.g., "0.2.0")

bioblend.init_logging() → None: Initialize BioBlend's logging from a configuration file.

bioblend.set_file_logger(name: str, filepath: str, level: int | str = 20, format_string: str | None = None) → None

bioblend.set_stream_logger(name: str, level: int | str = 10, format_string: str | None = None) → None

Config

class bioblend.config.Config(path: str | None = None, fp: IO[str] | None = None, do_load: bool = True)

BioBlend allows library-wide configuration to be set in external files. These configuration files can be used to specify access keys, for example. By default we use two locations for the BioBlend configurations:

System wide: /etc/bioblend.cfg
Individual user: ~/.bioblend (which works on both Windows and Unix)

Testing

If you would like to do more than just a mock test, you need to point BioBlend to an instance of Galaxy. Do so by exporting the following two variables:

$ export BIOBLEND_GALAXY_URL=http://127.0.0.1:8080
$ export BIOBLEND_GALAXY_API_KEY=<API key>

The unit tests, stored in the tests folder, can be run using pytest. From the project root:

$ pytest

Getting help

If you have run into issues, found a bug, or can't seem to find an answer to your question regarding the use and functionality of BioBlend, please use the Github Issues page to ask your question.

BioBlend

About

About the library name

Installation

Usage

Development

API Documentation

Galaxy API

API documentation for interacting with Galaxy

GalaxyInstance

Config

Datasets

Dataset collections

Datatypes

Folders

Forms

FTP files

Genomes

Groups

Histories

Invocations

Jobs

Libraries

Quotas

Roles

Tools

Tool data tables

Tool dependencies

ToolShed

Users

Visual

Workflows

Object-oriented Galaxy API

Client

Wrappers

Usage documentation

Connect to a Galaxy server

View Histories and Datasets

Uploading Datasets to a History

View Data Libraries

Upload files to a Data Library

View Workflows

Export or import a workflow

Invoke a workflow

View Users

Using BioBlend for raw API calls

Toolshed API

API documentation for interacting with the Galaxy Toolshed

ToolShedInstance

Categories

Repositories

Tools

Configuration

Configuration documents for BioBlend

BioBlend

Config

Testing

Getting help

Related documentation

Indices and tables