API documentation for interacting with Galaxy

GalaxyInstance

class bioblend.galaxy.GalaxyInstance(url: str, key: str | None = None, email: str | None = None, password: str | None = None, *, verify: bool = True, user_agent: str | None = None)[source]

A base representation of a connection to a Galaxy instance, identified by the server URL and user credentials.

After you have created a GalaxyInstance object, access various modules via the class fields. For example, to work with histories and get a list of all the user’s histories, the following should be done:

from bioblend import galaxy

gi = galaxy.GalaxyInstance(url='http://127.0.0.1:8000', key='your_api_key')

hl = gi.histories.get_histories()

Parameters:

url (str) – A FQDN or IP for a given instance of Galaxy. For example: http://127.0.0.1:8080 . If a Galaxy instance is served under a prefix (e.g., http://127.0.0.1:8080/galaxy/), supply the entire URL including the prefix (note that the prefix must end with a slash). If a Galaxy instance has HTTP Basic authentication with username and password, then the credentials should be included in the URL, e.g. http://user:pass@host:port/galaxy/
key (str) – User’s API key for the given instance of Galaxy, obtained from the user preferences. If a key is not supplied, an email address and password must be and the key will automatically be created for the user.
email (str) – Galaxy e-mail address corresponding to the user. Ignored if key is supplied directly.
password (str) – Password of Galaxy account corresponding to the above e-mail address. Ignored if key is supplied directly.
verify (bool) – Whether to verify the server’s TLS certificate

__init__(url: str, key: str | None = None, email: str | None = None, password: str | None = None, *, verify: bool = True, user_agent: str | None = None) → None[source]

A base representation of a connection to a Galaxy instance, identified by the server URL and user credentials.

After you have created a GalaxyInstance object, access various modules via the class fields. For example, to work with histories and get a list of all the user’s histories, the following should be done:

from bioblend import galaxy

gi = galaxy.GalaxyInstance(url='http://127.0.0.1:8000', key='your_api_key')

hl = gi.histories.get_histories()

Parameters:

url (str) – A FQDN or IP for a given instance of Galaxy. For example: http://127.0.0.1:8080 . If a Galaxy instance is served under a prefix (e.g., http://127.0.0.1:8080/galaxy/), supply the entire URL including the prefix (note that the prefix must end with a slash). If a Galaxy instance has HTTP Basic authentication with username and password, then the credentials should be included in the URL, e.g. http://user:pass@host:port/galaxy/
key (str) – User’s API key for the given instance of Galaxy, obtained from the user preferences. If a key is not supplied, an email address and password must be and the key will automatically be created for the user.
email (str) – Galaxy e-mail address corresponding to the user. Ignored if key is supplied directly.
password (str) – Password of Galaxy account corresponding to the above e-mail address. Ignored if key is supplied directly.
verify (bool) – Whether to verify the server’s TLS certificate

Config

Contains possible interaction dealing with Galaxy configuration.

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

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[source]

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[source]

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[source]

Reload the Galaxy toolbox (but not individual tools)

Return type:: None
Returns:: None

whoami() → dict[source]

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)[source]

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[source]

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. After this time, a TimeoutException will be raised.

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][source]

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, hda_ldda: Literal['hda', 'ldda'] = 'hda') → Dict[str, Any][source]

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

Parameters:

dataset_id (str) – Encoded dataset ID
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[source]

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][source]

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. After 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 dataset terminal state is ‘ok’.

Return type:

dict

Returns:

Details of the given dataset.

exception bioblend.galaxy.datasets.DatasetStateException[source]

exception bioblend.galaxy.datasets.DatasetStateWarning[source]

Dataset collections

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

to_dict() → Dict[str, str | List][source]

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

to_dict() → Dict[str, str | List][source]

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

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][source]

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][source]

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][source]

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. After this time, a TimeoutException 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)[source]

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

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

Datatypes

Contains possible interactions with the Galaxy Datatype

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

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][source]

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][source]

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)[source]

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][source]

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][source]

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][source]

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][source]

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][source]

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][source]

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)[source]

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]][source]

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]][source]

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][source]

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)[source]

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][source]

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)[source]

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[source]

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][source]

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)[source]

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][source]

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][source]

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]][source]

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][source]

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][source]

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]][source]

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]][source]

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]][source]

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][source]

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[source]

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)[source]

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][source]

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][source]

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], copy_elements: bool = True) → Dict[str, Any][source]

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'}

copy_elements (bool) – Whether to make a copy of the elements of the collection being created

Return type:

dict

Returns:

Information about the new HDCA

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

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][source]

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, wait: bool = False) → None[source]

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
wait – Whether to wait for the dataset to be purged.

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[source]

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][source]

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[source]

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[source]

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][source]

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.
create_time_min (str) – Return histories created after the provided time and date, which should be formatted as YYYY-MM-DDTHH-MM-SS.
create_time_max (str) – Return histories created before the provided time and date, which should be formatted as YYYY-MM-DDTHH-MM-SS.
update_time_min (str) – Return histories last updated after the provided time and date, which should be formatted as YYYY-MM-DDTHH-MM-SS.
update_time_max (str) – Return histories last updated before the provided time and date, which should be formatted as YYYY-MM-DDTHH-MM-SS.
view (str) – Options are ‘summary’ or ‘detailed’. This defaults to ‘summary’. Setting view to ‘detailed’ results in a larger number of fields returned.
keys (List[str]) – List of fields to return
limit (int) – How many items to return (upper bound).
offset (int) – skip the first (offset) items and begin returning items at index offset (i.e. start with the element offset+1).

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][source]

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

Return type:: dict
Returns:: History representation

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
create_time_min (str) – Return histories created after the provided time and date, which should be formatted as YYYY-MM-DDTHH-MM-SS.
create_time_max (str) – Return histories created before the provided time and date, which should be formatted as YYYY-MM-DDTHH-MM-SS.
update_time_min (str) – Return histories last updated after the provided time and date, which should be formatted as YYYY-MM-DDTHH-MM-SS.
update_time_max (str) – Return histories last updated before the provided time and date, which should be formatted as YYYY-MM-DDTHH-MM-SS.

Return type:

list

Returns:

List of history dicts.

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

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][source]

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[source]

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][source]

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][source]

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][source]

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][source]

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.
keys (List[str]) – List of fields to return

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]][source]

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[source]

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][source]

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][source]

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][source]

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][source]

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)[source]

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][source]

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, maxwait: float = 1200) → Dict[str, Any][source]

Get a BioCompute object for an invocation.

Parameters:

invocation_id (str) – Encoded workflow invocation ID
maxwait (float) – Total time (in seconds) to wait for the BioCompute object to become ready. After this time, a TimeoutException will be raised.

Return type:

dict

Returns:

The BioCompute object

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

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[source]

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]][source]

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][source]

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]][source]

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][source]

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][source]

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][source]

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][source]

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][source]

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. After 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)[source]

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[source]

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][source]

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][source]

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]][source]

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]][source]

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]][source]

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]][source]

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[source]

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.

Added 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][source]

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][source]

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]][source]

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]][source]

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][source]

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[source]

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[source]

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][source]

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. After 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)[source]

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][source]

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]][source]

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][source]

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][source]

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][source]

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][source]

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]][source]

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]][source]

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][source]

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][source]

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][source]

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][source]

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][source]

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][source]

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][source]

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]][source]

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]][source]

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]][source]

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]][source]

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]][source]

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][source]

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. After this time, a TimeoutException will be raised.
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)[source]

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][source]

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” and None. 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[source]

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]][source]

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][source]

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[source]

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” and None. Calling this method with default="no" on a non-default quota will throw an error. Passing None is equivalent to not changing the current status.
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)[source]

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][source]

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]][source]

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][source]

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

Contains possible interaction dealing with Galaxy tools.

class bioblend.galaxy.tools.ToolClient(galaxy_instance: GalaxyInstance)[source]

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' | ...

build(tool_id: str, inputs: Dict[str, Any] | None = None, tool_version: str | None = None, history_id: str | None = None) → Dict[str, Any][source]

This method returns the tool model, which includes an updated input parameter array for the given tool, based on user-defined “inputs”.

Parameters:

inputs (dict) –

(optional) inputs for the payload. For example:

{
    "num_lines": "1",
    "input": {
        "values": [
            {
                "src": "hda",
                "id": "4d366c1196c36d18"
            }
        ]
    },
    "seed_source|seed_source_selector": "no_seed",
}

tool_id (str) – id of the requested tool
history_id (str) – id of the requested history
tool_version (str) – version of the requested tool

Return type:

dict

Returns:

Returns a tool model including dynamic parameters and updated values, repeats block etc. For example:

{
    "model_class": "Tool",
    "id": "random_lines1",
    "name": "Select random lines",
    "version": "2.0.2",
    "description": "from a file",
    "labels": [],
    "edam_operations": [],
    "edam_topics": [],
    "hidden": "",
    "is_workflow_compatible": True,
    "xrefs": [],
    "config_file": "/Users/joshij/galaxy/tools/filters/randomlines.xml",
    "panel_section_id": "textutil",
    "panel_section_name": "Text Manipulation",
    "form_style": "regular",
    "inputs": [
        {
            "model_class": "IntegerToolParameter",
            "name": "num_lines",
            "argument": None,
            "type": "integer",
            "label": "Randomly select",
            "help": "lines",
            "refresh_on_change": False,
            "min": None,
            "max": None,
            "optional": False,
            "hidden": False,
            "is_dynamic": False,
            "value": "1",
            "area": False,
            "datalist": [],
            "default_value": "1",
            "text_value": "1",
        },
    ],
    "help": 'This tool selects N random lines from a file, with no repeats, and preserving ordering.',
    "citations": False,
    "sharable_url": None,
    "message": "",
    "warnings": "",
    "versions": ["2.0.2"],
    "requirements": [],
    "errors": {},
    "tool_errors": None,
    "state_inputs": {
        "num_lines": "1",
        "input": {"values": [{"id": "4d366c1196c36d18", "src": "hda"}]},
        "seed_source": {"seed_source_selector": "no_seed", "__current_case__": 0},
    },
    "job_id": None,
    "job_remap": None,
    "history_id": "c9468fdb6dc5c5f1",
    "display": True,
    "action": "/tool_runner/index",
    "license": None,
    "creator": None,
    "method": "post",
    "enctype": "application/x-www-form-urlencoded",
}

get_citations(tool_id: str) → List[dict][source]

Get BibTeX citations for a given tool ID.

Parameters:: tool_id (str) – id of the requested tool
Return type:: list of dicts
Param:: list containing the citations

get_tool_panel() → List[Dict[str, Any]][source]

Get a list of available tool elements in Galaxy’s configured toolbox.

Return type:: list
Returns:: List containing tools (if not in sections) or tool sections with nested tool descriptions.

Tool data tables

Contains possible interactions with the Galaxy Tool data tables

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

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][source]

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]][source]

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][source]

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][source]

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)[source]

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[source]

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[source]

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][source]: List unused dependencies

ToolShed

Interaction with a Galaxy Tool Shed.

class bioblend.galaxy.toolshed.ToolShedClient(galaxy_instance: GalaxyInstance)[source]

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_repositories() → List[Dict[str, Any]][source]

Get the list of all installed Tool Shed repositories on this Galaxy instance.

Return type:

list

Returns:

a list of dictionaries containing information about repositories present in the Tool Shed. For example:

[{'changeset_revision': '4afe13ac23b6',
  'deleted': False,
  'dist_to_shed': False,
  'error_message': '',
  'name': 'velvet_toolsuite',
  'owner': 'edward-kirton',
  'status': 'Installed'}]

Changed in version 0.4.1: Changed method name from get_tools to get_repositories to better align with the Tool Shed concepts

Users

Contains possible interaction dealing with Galaxy users.

Most of these methods must be executed by a registered Galaxy admin user.

class bioblend.galaxy.users.UserClient(galaxy_instance: GalaxyInstance)[source]

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_local_user(username: str, user_email: str, password: str) → Dict[str, Any][source]

Create a new Galaxy local user.

Note

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

Parameters:

username (str) – username of the user to be created
user_email (str) – email of the user to be created
password (str) – password of the user to be created

Return type:

dict

Returns:

a dictionary containing information about the created user

create_remote_user(user_email: str) → Dict[str, Any][source]

Create a new Galaxy remote user.

Note

This method works only if the Galaxy instance has the allow_user_creation and use_remote_user options set to true in the config/galaxy.yml configuration file. Also note that setting use_remote_user will require an upstream authentication proxy server; however, if you do not have one, access to Galaxy via a browser will not be possible.

Parameters:: user_email (str) – email of the user to be created
Return type:: dict
Returns:: a dictionary containing information about the created user

create_user_apikey(user_id: str) → str[source]

Create a new API key for a given user.

Parameters:: user_id (str) – encoded user ID
Return type:: str
Returns:: the API key for the user

delete_user(user_id: str, purge: bool = False) → Dict[str, Any][source]

Delete a user.

Note

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

Parameters:

user_id (str) – encoded user ID
purge (bool) – if True, also purge (permanently delete) the history

Return type:

dict

Returns:

a dictionary containing information about the deleted user

get_current_user() → Dict[str, Any][source]

Display information about the user associated with this Galaxy connection.

Return type:: dict
Returns:: a dictionary containing information about the current user

get_or_create_user_apikey(user_id: str) → str[source]

Get the current API key for a given user, creating one if it doesn’t exist yet.

Parameters:: user_id (str) – encoded user ID
Return type:: str
Returns:: the API key for the user

Note

This method works only on Galaxy 21.01 or later.

get_user_apikey(user_id: str) → str[source]

Get the current API key for a given user.

Parameters:: user_id (str) – encoded user ID
Return type:: str
Returns:: the API key for the user, or ‘Not available.’ if it doesn’t exist yet.

get_users(deleted: bool = False, f_email: str | None = None, f_name: str | None = None, f_any: str | None = None) → List[Dict[str, Any]][source]

Get a list of all registered users. If deleted is set to True, get a list of deleted users.

Parameters:

deleted (bool) – Whether to include deleted users
f_email (str) – filter for user emails. The filter will be active for non-admin users only if the Galaxy instance has the expose_user_email option set to true in the config/galaxy.yml configuration file.
f_name (str) – filter for user names. The filter will be active for non-admin users only if the Galaxy instance has the expose_user_name option set to true in the config/galaxy.yml configuration file.
f_any (str) – filter for user email or name. Each filter will be active for non-admin users only if the Galaxy instance has the corresponding expose_user_* option set to true in the config/galaxy.yml configuration file.

Return type:

list

Returns:

a list of dicts with user details. For example:

[{'email': 'a_user@example.org',
  'id': 'dda47097d9189f15',
  'url': '/api/users/dda47097d9189f15'}]

module: str = 'users'

show_user(user_id: str, deleted: bool = False) → Dict[str, Any][source]

Display information about a user.

Parameters:

user_id (str) – encoded user ID
deleted (bool) – whether to return results for a deleted user

Return type:

dict

Returns:

a dictionary containing information about the user

update_user(user_id: str, user_data: Dict | None = None, **kwargs: Any) → Dict[str, Any][source]

Update user information. You can either pass the attributes you want to change in the user_data dictionary, or provide them separately as keyword arguments. For attributes that cannot be expressed as keywords (e.g. extra_user_preferences use a | sign), pass them in user_data.

Parameters:

user_id (str) – encoded user ID
user_data (dict) – a dict containing the values to be updated, eg. { “username” : “newUsername”, “email”: “new@email” }
username (str) – Replace user name with the given string
email (str) – Replace user email with the given string

Return type:

dict

Returns:

details of the updated user

Visual

Contains possible interactions with the Galaxy visualization

class bioblend.galaxy.visual.VisualClient(galaxy_instance: GalaxyInstance)[source]

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_visualizations() → List[Dict[str, Any]][source]

Get the list of all visualizations.

Return type:

list

Returns:

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

[{'dbkey': 'eschColi_K12',
  'id': 'df1c7c96fc427c2d',
  'title': 'AVTest1',
  'type': 'trackster',
  'url': '/api/visualizations/df1c7c96fc427c2d'},
 {'dbkey': 'mm9',
  'id': 'a669f50f8bf55b02',
  'title': 'Bam to Bigwig',
  'type': 'trackster',
  'url': '/api/visualizations/a669f50f8bf55b02'}]

module: str = 'visualizations'

show_visualization(visual_id: str) → Dict[str, Any][source]

Get details of a given visualization.

Parameters:

visual_id (str) – Encoded visualization ID

Return type:

dict

Returns:

A description of the given visualization. For example:

{'annotation': None,
 'dbkey': 'mm9',
 'id': '18df9134ea75e49c',
 'latest_revision': {  ... },
 'model_class': 'Visualization',
 'revisions': ['aa90649bb3ec7dcb', '20622bc6249c0c71'],
 'slug': 'visualization-for-grant-1',
 'title': 'Visualization For Grant',
 'type': 'trackster',
 'url': '/u/azaron/v/visualization-for-grant-1',
 'user_id': '21e4aed91386ca8b'}

Workflows

Contains possible interactions with the Galaxy Workflows

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

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][source]

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[source]

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][source]

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[source]

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][source]

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]][source]

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][source]

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]][source]

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][source]

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][source]

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][source]

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][source]

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

class bioblend.galaxy.objects.galaxy_instance.GalaxyInstance(url: str, api_key: str | None = None, email: str | None = None, password: str | None = None, *, verify: bool = True, user_agent: str | None = None)[source]

A representation of an instance of Galaxy, identified by a URL and a user’s API key.

Parameters:

url (str) – a FQDN or IP for a given instance of Galaxy. For example: http://127.0.0.1:8080
api_key (str) – user’s API key for the given instance of Galaxy, obtained from the Galaxy web UI.

This is actually a factory class which instantiates the entity-specific clients.

Example: get a list of all histories for a user with API key ‘foo’:

from bioblend.galaxy.objects import GalaxyInstance
gi = GalaxyInstance('http://127.0.0.1:8080', api_key='foo')
histories = gi.histories.list()

Client

Clients for interacting with specific Galaxy entity types.

Classes in this module should not be instantiated directly, but used via their handles in GalaxyInstance.

class bioblend.galaxy.objects.client.ObjClient(obj_gi: GalaxyInstance)[source]

abstract get(id_: str) → Wrapper[source]: Retrieve the object corresponding to the given id.

abstract get_previews(**kwargs: Any) → list[source]

Get a list of object previews.

Previews entity summaries provided by REST collection URIs, e.g. http://host:port/api/libraries. Being the most lightweight objects associated to the various entities, these are the ones that should be used to retrieve their basic info.

Return type:: list
Returns:: a list of object previews

abstract list() → list[source]

Get a list of objects.

This method first gets the entity summaries, then gets the complete description for each entity with an additional GET call, so may be slow.

Return type:: list
Returns:: a list of objects

class bioblend.galaxy.objects.client.ObjDatasetClient(obj_gi: GalaxyInstance)[source]

Interacts with Galaxy datasets.

get(id_: str, hda_ldda: Literal['hda'] = 'hda') → HistoryDatasetAssociation[source]

get(id_: str, hda_ldda: Literal['ldda']) → LibraryDatasetDatasetAssociation

Retrieve the dataset corresponding to the given id.

Parameters:: hda_ldda (str) – Whether to show a history dataset (‘hda’ - the default) or library dataset (‘ldda’)
Return type:: HistoryDatasetAssociation or LibraryDatasetDatasetAssociation
Returns:: the history or library dataset corresponding to id_

get_previews(**kwargs: Any) → list[source]

Get a list of object previews.

Previews entity summaries provided by REST collection URIs, e.g. http://host:port/api/libraries. Being the most lightweight objects associated to the various entities, these are the ones that should be used to retrieve their basic info.

Return type:: list
Returns:: a list of object previews

list() → list[source]

Get a list of objects.

This method first gets the entity summaries, then gets the complete description for each entity with an additional GET call, so may be slow.

Return type:: list
Returns:: a list of objects

class bioblend.galaxy.objects.client.ObjDatasetCollectionClient(obj_gi: GalaxyInstance)[source]

Interacts with Galaxy dataset collections.

get(id_: str) → HistoryDatasetCollectionAssociation[source]

Retrieve the dataset collection corresponding to the given id.

Return type:: HistoryDatasetCollectionAssociation
Returns:: the history dataset collection corresponding to id_

get_previews(**kwargs: Any) → list[source]

Get a list of object previews.

Previews entity summaries provided by REST collection URIs, e.g. http://host:port/api/libraries. Being the most lightweight objects associated to the various entities, these are the ones that should be used to retrieve their basic info.

Return type:: list
Returns:: a list of object previews

list() → list[source]

Get a list of objects.

This method first gets the entity summaries, then gets the complete description for each entity with an additional GET call, so may be slow.

Return type:: list
Returns:: a list of objects

class bioblend.galaxy.objects.client.ObjDatasetContainerClient(obj_gi: GalaxyInstance)[source]

CONTAINER_PREVIEW_TYPE: Type[DatasetContainerPreview]

CONTAINER_TYPE: Type[DatasetContainer]

get(id_: str) → DatasetContainerSubtype[source]: Retrieve the dataset container corresponding to the given id.

get_previews(name: str | None = None, deleted: bool = False, **kwargs: Any) → List[DatasetContainerPreviewSubtype][source]

Get a list of object previews.

Previews entity summaries provided by REST collection URIs, e.g. http://host:port/api/libraries. Being the most lightweight objects associated to the various entities, these are the ones that should be used to retrieve their basic info.

Return type:: list
Returns:: a list of object previews

class bioblend.galaxy.objects.client.ObjHistoryClient(obj_gi: GalaxyInstance)[source]

Interacts with Galaxy histories.

CONTAINER_PREVIEW_TYPE: alias of HistoryPreview

CONTAINER_TYPE: alias of History

create(name: str | None = None) → History[source]

Create a new Galaxy history, optionally setting its name.

Return type:: History
Returns:: the history just created

delete(id_: str | None = None, name: str | None = None, purge: bool = False) → None[source]

Delete the history with the given id or name.

Fails if multiple histories have the same name.

Parameters:: purge (bool) – if True, also purge (permanently delete) the history

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.

list(name: str | None = None, deleted: bool = False) → List[History][source]

Get histories owned by the user of this Galaxy instance.

Parameters:

name (str) – return only histories with this name
deleted (bool) – if True, return histories that have been deleted

Return type:

list of History

class bioblend.galaxy.objects.client.ObjInvocationClient(obj_gi: GalaxyInstance)[source]

Interacts with Galaxy Invocations.

get(id_: str) → Invocation[source]

Get an invocation by ID.

Return type:: Invocation
Param:: invocation object

get_previews(**kwargs: Any) → List[InvocationPreview][source]

Get previews of all invocations.

Return type:: list of InvocationPreview
Param:: previews of invocations

list(workflow: Workflow | None = None, history: History | None = None, include_terminal: bool = True, limit: int | None = None) → List[Invocation][source]

Get full listing of workflow invocations, or select a subset by specifying optional arguments for filtering (e.g. a workflow).

Parameters:

workflow (wrappers.Workflow) – Include only invocations associated with this workflow
history (wrappers.History) – Include only invocations associated with this history
include_terminal – bool
limit (int) – Maximum number of invocations to return - if specified, the most recent invocations will be returned.

Param:

Whether to include invocations in terminal states

Return type:

list of Invocation

Param:

invocation objects

class bioblend.galaxy.objects.client.ObjJobClient(obj_gi: GalaxyInstance)[source]

Interacts with Galaxy jobs.

get(id_: str, full_details: bool = False) → Job[source]

Retrieve the job corresponding to the given id.

Parameters:: full_details (bool) – if True, return the complete list of details for the given job.
Return type:: Job
Returns:: the job corresponding to id_

get_previews(**kwargs: Any) → List[JobPreview][source]

Get a list of object previews.

Previews entity summaries provided by REST collection URIs, e.g. http://host:port/api/libraries. Being the most lightweight objects associated to the various entities, these are the ones that should be used to retrieve their basic info.

Return type:: list
Returns:: a list of object previews

list() → List[Job][source]

Get the list of jobs of the current user.

Return type:: list of Job

class bioblend.galaxy.objects.client.ObjLibraryClient(obj_gi: GalaxyInstance)[source]

Interacts with Galaxy libraries.

CONTAINER_PREVIEW_TYPE: alias of LibraryPreview

CONTAINER_TYPE: alias of Library

create(name: str, description: str | None = None, synopsis: str | None = None) → Library[source]

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

Return type:: Library
Returns:: the library just created

delete(id_: str | None = None, name: str | None = None) → None[source]

Delete the library with the given id or name.

Fails if multiple libraries have the specified name.

Warning

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

list(name: str | None = None, deleted: bool = False) → List[Library][source]

Get libraries owned by the user of this Galaxy instance.

Parameters:

name (str) – return only libraries with this name
deleted (bool) – if True, return libraries that have been deleted

Return type:

list of Library

class bioblend.galaxy.objects.client.ObjToolClient(obj_gi: GalaxyInstance)[source]

Interacts with Galaxy tools.

get(id_: str, io_details: bool = False, link_details: bool = False) → Tool[source]

Retrieve the tool corresponding to the given id.

Parameters:

io_details (bool) – if True, get also input and output details
link_details (bool) – if True, get also link details

Return type:

Tool

Returns:

the tool corresponding to id_

get_previews(name: str | None = None, trackster: bool = False, **kwargs: Any) → List[Tool][source]

Get the list of tools installed on the Galaxy instance.

Parameters:

name (str) – return only tools with this name
trackster (bool) – if True, only tools that are compatible with Trackster are returned

Return type:

list of Tool

list(name: str | None = None, trackster: bool = False) → List[Tool][source]

Get the list of tools installed on the Galaxy instance.

Parameters:

name (str) – return only tools with this name
trackster (bool) – if True, only tools that are compatible with Trackster are returned

Return type:

list of Tool

class bioblend.galaxy.objects.client.ObjWorkflowClient(obj_gi: GalaxyInstance)[source]

Interacts with Galaxy workflows.

delete(id_: str | None = None, name: str | None = None) → None[source]

Delete the workflow with the given id or name.

Fails if multiple workflows have the specified name.

Warning

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

get(id_: str) → Workflow[source]

Retrieve the workflow corresponding to the given id.

Return type:: Workflow
Returns:: the workflow corresponding to id_

get_previews(name: str | None = None, published: bool = False, **kwargs: Any) → List[WorkflowPreview][source]

Get a list of object previews.

Previews entity summaries provided by REST collection URIs, e.g. http://host:port/api/libraries. Being the most lightweight objects associated to the various entities, these are the ones that should be used to retrieve their basic info.

Return type:: list
Returns:: a list of object previews

import_new(src: str | Dict[str, Any], publish: bool = False) → Workflow[source]

Imports a new workflow into Galaxy.

Parameters:

src (dict or str) – deserialized (dictionary) or serialized (str) JSON dump of the workflow (this is normally obtained by exporting a workflow from Galaxy).
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:

Workflow

Returns:

the workflow just imported

import_shared(id_: str) → Workflow[source]

Imports a shared workflow to the user’s space.

Parameters:: id (str) – workflow id
Return type:: Workflow
Returns:: the workflow just imported

list(name: str | None = None, published: bool = False) → List[Workflow][source]

Get workflows owned by the user of this Galaxy instance.

Parameters:

name (str) – return only workflows with this name
published (bool) – if True, return also published workflows

Return type:

list of Workflow

Wrappers

A basic object-oriented interface for Galaxy entities.

class bioblend.galaxy.objects.wrappers.Dataset(*args: Any, **kwargs: Any)[source]

Abstract base class for Galaxy datasets.

Parameters:

wrapped (dict) – JSON-serializable dictionary
parent (Wrapper) – the parent of this wrapper
gi (GalaxyInstance) – the GalaxyInstance through which we can access this wrapper

BASE_ATTRS: Tuple[str, ...] = ('id', 'data_type', 'file_ext', 'file_name', 'file_size', 'genome_build', 'misc_info', 'name', 'state')

POLLING_INTERVAL = 1

container: DatasetContainer

download(file_object: IO[bytes], chunk_size: int = 4096) → None[source]

Open dataset for reading and save its contents to file_object.

Parameters:: file_object (file) – output file object

See get_stream() for info on other params.

genome_build: str

get_contents(chunk_size: int = 4096) → bytes[source]

Open dataset for reading and return its full contents.

See get_stream() for param info.

get_stream(chunk_size: int = 4096) → Iterator[bytes][source]

Open dataset for reading and return an iterator over its contents.

Parameters:: chunk_size (int) – read this amount of bytes at a time

gi: GalaxyInstance

misc_info: str

name: str

peek(chunk_size: int = 4096) → bytes[source]

Open dataset for reading and return the first chunk.

See get_stream() for param info.

refresh() → DatasetSubtype[source]

Re-fetch the attributes pertaining to this object.

Returns:: self

state: str

wait(polling_interval: float = 1, break_on_error: bool = True) → None[source]

Wait for this dataset to come out of the pending states.

Parameters:

polling_interval (float) – polling interval in seconds
break_on_error (bool) – if True, raise a RuntimeError exception if the dataset ends in the ‘error’ state.

Warning

This is a blocking operation that can take a very long time. Also, note that this method does not return anything; however, this dataset is refreshed (possibly multiple times) during the execution.

class bioblend.galaxy.objects.wrappers.DatasetCollection(*args: Any, **kwargs: Any)[source]

Abstract base class for Galaxy dataset collections.

Parameters:

wrapped (dict) – JSON-serializable dictionary
parent (Wrapper) – the parent of this wrapper
gi (GalaxyInstance) – the GalaxyInstance through which we can access this wrapper

API_MODULE = 'dataset_collections'

BASE_ATTRS: Tuple[str, ...] = ('id', 'collection_type', 'deleted', 'name', 'state')

collection_type: str

container: DatasetCollection | History

abstract delete() → None[source]: Delete this dataset collection.

deleted: bool

gi: GalaxyInstance

refresh() → DatasetCollectionSubtype[source]

Re-fetch the attributes pertaining to this object.

Returns:: self

class bioblend.galaxy.objects.wrappers.DatasetContainer(*args: Any, **kwargs: Any)[source]

Abstract base class for dataset containers (histories and libraries).

Parameters:: content_infos (list of ContentInfo) – info objects for the container’s contents

API_MODULE: str

BASE_ATTRS: Tuple[str, ...] = ('id', 'deleted', 'name')

CONTENT_INFO_TYPE: Type[ContentInfo]

DS_TYPE: ClassVar[Callable]

content_infos: List[ContentInfo]

property dataset_ids: List[str]: Return the ids of the contained datasets.

abstract delete() → None[source]: Delete this dataset container.

deleted: bool

get_dataset(ds_id: str) → DatasetSubtype[source]

Retrieve the dataset corresponding to the given id.

Parameters:: ds_id (str) – dataset id
Return type:: HistoryDatasetAssociation or LibraryDataset
Returns:: the dataset corresponding to ds_id

get_datasets(name: str | None = None) → List[DatasetSubtype][source]

Get all datasets contained inside this dataset container.

Parameters:: name (str) – return only datasets with this name
Return type:: list of HistoryDatasetAssociation or list of LibraryDataset
Returns:: datasets with the given name contained inside this container

Note

when filtering library datasets by name, specify their full paths starting from the library’s root folder, e.g., /seqdata/reads.fastq. Full paths are available through the content_infos attribute of Library objects.

gi: GalaxyInstance

name: str

obj_gi_client: client.ObjDatasetContainerClient

preview() → DatasetContainerPreview[source]

refresh() → DatasetContainerSubtype[source]

Re-fetch the attributes pertaining to this object.

Returns:: self

class bioblend.galaxy.objects.wrappers.Folder(*args: Any, **kwargs: Any)[source]

Maps to a folder in a Galaxy library.

Parameters:

wrapped (dict) – JSON-serializable dictionary
parent (Wrapper) – the parent of this wrapper
gi (GalaxyInstance) – the GalaxyInstance through which we can access this wrapper

BASE_ATTRS: Tuple[str, ...] = ('id', 'deleted', 'description', 'item_count', 'name')

container: Library

description: str

gi: GalaxyInstance

name: str

property parent: Folder | None

The parent folder of this folder. The parent of the root folder is None.

Return type:: Folder
Returns:: the parent of this folder

refresh() → Folder[source]

Re-fetch the attributes pertaining to this object.

Returns:: self

class bioblend.galaxy.objects.wrappers.History(*args: Any, **kwargs: Any)[source]

Maps to a Galaxy history.

Parameters:: content_infos (list of ContentInfo) – info objects for the container’s contents

API_MODULE: str = 'histories'

BASE_ATTRS: Tuple[str, ...] = ('id', 'deleted', 'name', 'annotation', 'published', 'state', 'state_ids', 'state_details', 'tags')

CONTENT_INFO_TYPE: alias of HistoryContentInfo

DSC_TYPE: alias of HistoryDatasetCollectionAssociation

DS_TYPE: alias of HistoryDatasetAssociation

annotation: str

create_dataset_collection(collection_description: CollectionDescription, copy_elements: bool = True) → HistoryDatasetCollectionAssociation[source]

Create a new dataset collection in the history by providing a collection description.

Parameters:

collection_description (bioblend.galaxy.dataset_collections.CollectionDescription) – a description of the dataset collection
copy_elements (bool) – Whether to make a copy of the elements of the collection being created

Return type:

HistoryDatasetCollectionAssociation

Returns:

the new dataset collection

delete(purge: bool = False) → None[source]

Delete this history.

Parameters:: purge (bool) – if True, also purge (permanently delete) the history

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(jeha_id: str, outf: IO[bytes], chunk_size: int = 4096) → None[source]: Download an export archive for this history. Use export() to create an export and get the required jeha_id. See download_history() for parameter and return value info.

export(gzip: bool = True, include_hidden: bool = False, include_deleted: bool = False, wait: bool = False, maxwait: int | None = None) → str[source]: Start a job to create an export archive for this history. See export_history() for parameter and return value info.

get_dataset_collection(dsc_id: str) → HistoryDatasetCollectionAssociation[source]

Retrieve the dataset collection corresponding to the given id.

Parameters:: dsc_id (str) – dataset collection id
Return type:: HistoryDatasetCollectionAssociation
Returns:: the dataset collection corresponding to dsc_id

import_dataset(lds: LibraryDataset) → HistoryDatasetAssociation[source]

Import a dataset into the history from a library.

Parameters:: lds (LibraryDataset) – the library dataset to import
Return type:: HistoryDatasetAssociation
Returns:: the imported history dataset

paste_content(content: str, **kwargs: Any) → HistoryDatasetAssociation[source]

Upload a string to a new dataset in this history.

Parameters:: content (str) – content of the new dataset to upload

See upload_file() for the optional parameters (except file_name).

Return type:: HistoryDatasetAssociation
Returns:: the uploaded dataset

published: bool

tags: List[str]

update(**kwargs: Any) → History[source]

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

Parameters:

name (str) – Replace history name with the given string
annotation (str) – Replace history annotation with the 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

upload_dataset(path: str, **kwargs: Any) → HistoryDatasetAssociation

Upload the file specified by path to this history.

Parameters:: path (str) – path of the file to upload

See upload_file() for the optional parameters.

Return type:: HistoryDatasetAssociation
Returns:: the uploaded dataset

upload_file(path: str, **kwargs: Any) → HistoryDatasetAssociation[source]

Upload the file specified by path to this history.

Parameters:: path (str) – path of the file to upload

See upload_file() for the optional parameters.

Return type:: HistoryDatasetAssociation
Returns:: the uploaded dataset

upload_from_ftp(path: str, **kwargs: Any) → HistoryDatasetAssociation[source]

Upload the file specified by path from the user’s FTP directory to this history.

Parameters:: path (str) – path of the file in the user’s FTP directory

See upload_file() for the optional parameters.

Return type:: HistoryDatasetAssociation
Returns:: the uploaded dataset

class bioblend.galaxy.objects.wrappers.HistoryContentInfo(*args: Any, **kwargs: Any)[source]

Instances of this class wrap dictionaries obtained by getting /api/histories/<ID>/contents from Galaxy.

Parameters:

wrapped (dict) – JSON-serializable dictionary
parent (Wrapper) – the parent of this wrapper
gi (GalaxyInstance) – the GalaxyInstance through which we can access this wrapper

BASE_ATTRS: Tuple[str, ...] = ('id', 'name', 'type', 'deleted', 'state', 'visible')

class bioblend.galaxy.objects.wrappers.HistoryDatasetAssociation(*args: Any, **kwargs: Any)[source]

Maps to a Galaxy HistoryDatasetAssociation.

Parameters:

wrapped (dict) – JSON-serializable dictionary
parent (Wrapper) – the parent of this wrapper
gi (GalaxyInstance) – the GalaxyInstance through which we can access this wrapper

BASE_ATTRS: Tuple[str, ...] = ('id', 'data_type', 'file_ext', 'file_name', 'file_size', 'genome_build', 'misc_info', 'name', 'state', 'annotation', 'deleted', 'purged', 'tags', 'visible')

SRC = 'hda'

annotation: str

delete(purge: bool = False, wait: bool = False) → None[source]

Delete this history dataset.

Parameters:

purge (bool) – if True, also purge (permanently delete) the dataset
wait – Whether to wait for the dataset to be 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.

deleted: bool

get_stream(chunk_size: int = 4096) → Iterator[bytes][source]

Open dataset for reading and return an iterator over its contents.

Parameters:: chunk_size (int) – read this amount of bytes at a time

purged: bool

update(**kwargs: Any) → HistoryDatasetAssociation[source]

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

Parameters:

name (str) – Replace history dataset name with the given string
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

class bioblend.galaxy.objects.wrappers.HistoryDatasetCollectionAssociation(*args: Any, **kwargs: Any)[source]

Maps to a Galaxy HistoryDatasetCollectionAssociation.

Parameters:

wrapped (dict) – JSON-serializable dictionary
parent (Wrapper) – the parent of this wrapper
gi (GalaxyInstance) – the GalaxyInstance through which we can access this wrapper

BASE_ATTRS: Tuple[str, ...] = ('id', 'collection_type', 'deleted', 'name', 'state', 'tags', 'visible', 'elements')

SRC = 'hdca'

delete() → None[source]: Delete this dataset collection.

elements: List[Dict]

class bioblend.galaxy.objects.wrappers.HistoryPreview(*args: Any, **kwargs: Any)[source]

Models Galaxy history ‘previews’.

Instances of this class wrap dictionaries obtained by getting /api/histories from Galaxy.

Parameters:

wrapped (dict) – JSON-serializable dictionary
parent (Wrapper) – the parent of this wrapper
gi (GalaxyInstance) – the GalaxyInstance through which we can access this wrapper

BASE_ATTRS: Tuple[str, ...] = ('id', 'deleted', 'name', 'annotation', 'published', 'purged', 'tags')

class bioblend.galaxy.objects.wrappers.Job(*args: Any, **kwargs: Any)[source]

Maps to a Galaxy job.

Parameters:

wrapped (dict) – JSON-serializable dictionary
parent (Wrapper) – the parent of this wrapper
gi (GalaxyInstance) – the GalaxyInstance through which we can access this wrapper

BASE_ATTRS: Tuple[str, ...] = ('id', 'state')

class bioblend.galaxy.objects.wrappers.Library(*args: Any, **kwargs: Any)[source]

Maps to a Galaxy library.

Parameters:: content_infos (list of ContentInfo) – info objects for the container’s contents

API_MODULE: str = 'libraries'

BASE_ATTRS: Tuple[str, ...] = ('id', 'deleted', 'name', 'description', 'synopsis')

CONTENT_INFO_TYPE: alias of LibraryContentInfo

DS_TYPE: alias of LibraryDataset

copy_from_dataset(hda: HistoryDatasetAssociation, folder: Folder | None = None, message: str = '') → LibraryDataset[source]

Copy a history dataset into this library.

Parameters:: hda (HistoryDatasetAssociation) – history dataset to copy into the library

See upload_data() for info on other params.

create_folder(name: str, description: str | None = None, base_folder: Folder | None = None) → Folder[source]