Provided by: python3-bioblend_1.2.0-2_all 
NAME
bioblend - BioBlend Documentation
ABOUT
BioBlend is a Python library for interacting with the Galaxy API.
BioBlend is supported and tested on:
• Python 3.7 - 3.11
• Galaxy release 19.05 and later.
BioBlend's goal is to make it easier to script and automate the running of Galaxy analyses and
administering of a Galaxy server. In practice, it makes it possible to do things like this:
• Interact with Galaxy via a straightforward API:
from bioblend.galaxy import GalaxyInstance
gi = GalaxyInstance('<Galaxy IP>', key='your API key')
libs = gi.libraries.get_libraries()
gi.workflows.show_workflow('workflow ID')
wf_invocation = gi.workflows.invoke_workflow('workflow ID', inputs)
• Interact with Galaxy via an object-oriented API:
from bioblend.galaxy.objects import GalaxyInstance
gi = GalaxyInstance("URL", "API_KEY")
wf = gi.workflows.list()[0]
hist = gi.histories.list()[0]
inputs = hist.get_datasets()[:2]
input_map = dict(zip(wf.input_labels, inputs))
params = {"Paste1": {"delimiter": "U"}}
wf_invocation = wf.invoke(input_map, params=params)
About the library name
The library was originally called just Blend but we renamed it to reflect more of its domain and a make
it bit more unique so it can be easier to find. The name was intended to be short and easily
pronounceable. In its original implementation, the goal was to provide a lot more support for CloudMan
and other integration capabilities, allowing them to be blended together via code. BioBlend fitted the
bill.
INSTALLATION
Stable releases of BioBlend are best installed via pip from PyPI:
$ python3 -m pip install bioblend
Alternatively, the most current source code from our Git repository can be installed with:
$ python3 -m pip install git+https://github.com/galaxyproject/bioblend
After installing the library, you will be able to simply import it into your Python environment with
import bioblend. For details on the available functionality, see the API documentation.
BioBlend requires a number of Python libraries. These libraries are installed automatically when BioBlend
itself is installed, regardless whether it is installed via PyPi or by running python3 setup.py install
command. The current list of required libraries is always available from setup.py in the source code
repository.
If you also want to run tests locally, some extra libraries are required. To install them, run:
$ python3 setup.py test
USAGE
To get started using BioBlend, install the library as described above. Once the library becomes available
on the given system, it can be developed against. The developed scripts do not need to reside in any
particular location on the system.
It is probably best to take a look at the example scripts in docs/examples source directory and browse
the API documentation. Beyond that, it's up to your creativity :).
DEVELOPMENT
Anyone interested in contributing or tweaking the library is more then welcome to do so. To start, simply
fork the Git repository on Github and start playing with it. Then, issue pull requests.
API DOCUMENTATION
BioBlend's API focuses around and matches the services it wraps. Thus, there are two top-level sets of
APIs, each corresponding to a separate service and a corresponding step in the automation process. Note
that each of the service APIs can be used completely independently of one another.
Effort has been made to keep the structure and naming of those API's consistent across the library but
because they do bridge different services, some discrepancies may exist. Feel free to point those out
and/or provide fixes.
For Galaxy, an alternative object-oriented API is also available. This API provides an explicit modeling
of server-side Galaxy instances and their relationships, providing higher-level methods to perform
operations such as retrieving all datasets for a given history, etc. Note that, at the moment, the oo
API is still incomplete, providing access to a more restricted set of Galaxy modules with respect to the
standard one.
Galaxy API
API used to manipulate genomic analyses within Galaxy, including data management and workflow execution.
API documentation for interacting with Galaxy
GalaxyInstance
----
Config
Contains possible interaction dealing with Galaxy configuration.
class bioblend.galaxy.config.ConfigClient(galaxy_instance: GalaxyInstance)
A generic Client interface defining the common fields.
All clients must define the following field (which will be used as part of the URL composition
(e.g., http://<galaxy_instance>/api/libraries): self.module = 'workflows' | 'libraries' |
'histories' | ...
get_config() -> dict
Get a list of attributes about the Galaxy instance. More attributes will be present if the
user is an admin.
Return type
list
Returns
A list of attributes. For example:
{'allow_library_path_paste': False,
'allow_user_creation': True,
'allow_user_dataset_purge': True,
'allow_user_deletion': False,
'enable_unique_workflow_defaults': False,
'ftp_upload_dir': '/SOMEWHERE/galaxy/ftp_dir',
'ftp_upload_site': 'galaxy.com',
'library_import_dir': 'None',
'logo_url': None,
'support_url': 'https://galaxyproject.org/support',
'terms_url': None,
'user_library_import_dir': None,
'wiki_url': 'https://galaxyproject.org/'}
get_version() -> dict
Get the current version of the Galaxy instance.
Return type
dict
Returns
Version of the Galaxy instance For example:
{'extra': {}, 'version_major': '17.01'}
module: str = 'configuration'
reload_toolbox() -> None
Reload the Galaxy toolbox (but not individual tools)
Return type
None
Returns
None
whoami() -> dict
Return information about the current authenticated user.
Return type
dict
Returns
Information about current authenticated user For example:
{'active': True,
'deleted': False,
'email': 'user@example.org',
'id': '4aaaaa85aacc9caa',
'last_password_change': '2021-07-29T05:34:54.632345',
'model_class': 'User',
'username': 'julia'}
----
Datasets
Contains possible interactions with the Galaxy Datasets
class bioblend.galaxy.datasets.DatasetClient(galaxy_instance: GalaxyInstance)
A generic Client interface defining the common fields.
All clients must define the following field (which will be used as part of the URL composition
(e.g., http://<galaxy_instance>/api/libraries): self.module = 'workflows' | 'libraries' |
'histories' | ...
download_dataset(dataset_id: str, file_path: None = None, use_default_filename: bool = True,
require_ok_state: bool = True, maxwait: float = 12000) -> bytes
download_dataset(dataset_id: str, file_path: str, use_default_filename: bool = True,
require_ok_state: bool = True, maxwait: float = 12000) -> str
Download a dataset to file or in memory. If the dataset state is not 'ok', a
DatasetStateException will be thrown, unless require_ok_state=False.
Parameters
• dataset_id (str) -- Encoded dataset ID
• file_path (str) -- If this argument is provided, the dataset will be streamed to
disk at that path (should be a directory if use_default_filename=True). If the
file_path argument is not provided, the dataset content is loaded into memory and
returned by the method (Memory consumption may be heavy as the entire file will be
in memory).
• use_default_filename (bool) -- If True, the exported file will be saved as
file_path/%s, where %s is the dataset name. If False, file_path is assumed to
contain the full file path including the filename.
• require_ok_state (bool) -- If False, datasets will be downloaded even if not in an
'ok' state, issuing a DatasetStateWarning rather than raising a
DatasetStateException.
• maxwait (float) -- Total time (in seconds) to wait for the dataset state to become
terminal. If the dataset state is not terminal within this time, a
DatasetTimeoutException will be thrown.
Return type
bytes or str
Returns
If a file_path argument is not provided, returns the file content. Otherwise returns
the local path of the downloaded file.
get_datasets(limit: int = 500, offset: int = 0, name: str | None = None, extension: str |
List[str] | None = None, state: str | List[str] | None = None, visible: bool | None = None,
deleted: bool | None = None, purged: bool | None = None, tool_id: str | None = None, tag: str |
None = None, history_id: str | None = None, create_time_min: str | None = None, create_time_max:
str | None = None, update_time_min: str | None = None, update_time_max: str | None = None, order:
str = 'create_time-dsc') -> List[Dict[str, Any]]
Get the latest datasets, or select another subset by specifying optional arguments for
filtering (e.g. a history ID).
Since the number of datasets may be very large, limit and offset parameters are required to
specify the desired range.
If the user is an admin, this will return datasets for all the users, otherwise only for
the current user.
Parameters
• limit (int) -- Maximum number of datasets to return.
• offset (int) -- Return datasets starting from this specified position. For
example, if limit is set to 100 and offset to 200, datasets 200-299 will be
returned.
• name (str) -- Dataset name to filter on.
• extension (str or list of str) -- Dataset extension (or list of extensions) to
filter on.
• state (str or list of str) -- Dataset state (or list of states) to filter on.
• visible (bool) -- Optionally filter datasets by their visible attribute.
• deleted (bool) -- Optionally filter datasets by their deleted attribute.
• purged (bool) -- Optionally filter datasets by their purged attribute.
• tool_id (str) -- Tool ID to filter on.
• tag (str) -- Dataset tag to filter on.
• history_id (str) -- Encoded history ID to filter on.
• create_time_min (str) -- Show only datasets created after the provided time and
date, which should be formatted as YYYY-MM-DDTHH-MM-SS.
• create_time_max (str) -- Show only datasets created before the provided time and
date, which should be formatted as YYYY-MM-DDTHH-MM-SS.
• update_time_min (str) -- Show only datasets last updated after the provided time
and date, which should be formatted as YYYY-MM-DDTHH-MM-SS.
• update_time_max (str) -- Show only datasets last updated before the provided time
and date, which should be formatted as YYYY-MM-DDTHH-MM-SS.
• order (str) -- One or more of the following attributes for ordering datasets:
create_time (default), extension, hid, history_id, name, update_time. Optionally,
-asc or -dsc (default) can be appended for ascending and descending order
respectively. Multiple attributes can be stacked as a comma-separated list of
values, e.g. create_time-asc,hid-dsc.
Return type
list
Param A list of datasets
gi: GalaxyInstance
module: str = 'datasets'
publish_dataset(dataset_id: str, published: bool = False) -> Dict[str, Any]
Make a dataset publicly available or private. For more fine-grained control (assigning
different permissions to specific roles), use the update_permissions() method.
Parameters
• dataset_id (str) -- dataset ID
• published (bool) -- Whether to make the dataset published (True) or private
(False).
Return type
dict
Returns
Details of the updated dataset
NOTE:
This method works only on Galaxy 19.05 or later.
show_dataset(dataset_id: str, deleted: bool = False, hda_ldda: Literal['hda', 'ldda'] = 'hda') ->
Dict[str, Any]
Get details about a given dataset. This can be a history or a library dataset.
Parameters
• dataset_id (str) -- Encoded dataset ID
• deleted (bool) -- Whether to return results for a deleted dataset
• hda_ldda (str) -- Whether to show a history dataset ('hda' - the default) or
library dataset ('ldda').
Return type
dict
Returns
Information about the HDA or LDDA
update_permissions(dataset_id: str, access_ids: list | None = None, manage_ids: list | None =
None, modify_ids: list | None = None) -> dict
Set access, manage or modify permissions for a dataset to a list of roles.
Parameters
• dataset_id (str) -- dataset ID
• access_ids (list) -- role IDs which should have access permissions for the
dataset.
• manage_ids (list) -- role IDs which should have manage permissions for the
dataset.
• modify_ids (list) -- role IDs which should have modify permissions for the
dataset.
Return type
dict
Returns
Current roles for all available permission types.
NOTE:
This method works only on Galaxy 19.05 or later.
wait_for_dataset(dataset_id: str, maxwait: float = 12000, interval: float = 3, check: bool = True)
-> Dict[str, Any]
Wait until a dataset is in a terminal state.
Parameters
• dataset_id (str) -- dataset ID
• maxwait (float) -- Total time (in seconds) to wait for the dataset state to become
terminal. If the dataset state is not terminal within this time, a
DatasetTimeoutException will be raised.
• interval (float) -- Time (in seconds) to wait between 2 consecutive checks.
• check (bool) -- Whether to check if the dataset terminal state is 'ok'.
Return type
dict
Returns
Details of the given dataset.
exception bioblend.galaxy.datasets.DatasetStateException
exception bioblend.galaxy.datasets.DatasetStateWarning
exception bioblend.galaxy.datasets.DatasetTimeoutException
----
Dataset collections
class bioblend.galaxy.dataset_collections.CollectionDescription(name: str, type: str = 'list', elements:
List[CollectionElement | SimpleElement] | Dict[str, Any] | None = None)
to_dict() -> Dict[str, str | List]
class bioblend.galaxy.dataset_collections.CollectionElement(name: str, type: str = 'list', elements:
List[CollectionElement | SimpleElement] | Dict[str, Any] | None = None)
to_dict() -> Dict[str, str | List]
class bioblend.galaxy.dataset_collections.DatasetCollectionClient(galaxy_instance: GalaxyInstance)
A generic Client interface defining the common fields.
All clients must define the following field (which will be used as part of the URL composition
(e.g., http://<galaxy_instance>/api/libraries): self.module = 'workflows' | 'libraries' |
'histories' | ...
download_dataset_collection(dataset_collection_id: str, file_path: str) -> Dict[str, Any]
Download a history dataset collection as an archive.
Parameters
• dataset_collection_id (str) -- Encoded dataset collection ID
• file_path (str) -- The path to which the archive will be downloaded
Return type
dict
Returns
Information about the downloaded archive.
NOTE:
This method downloads a zip archive for Galaxy 21.01 and later. For earlier versions of
Galaxy this method downloads a tgz archive.
gi: GalaxyInstance
module: str = 'dataset_collections'
show_dataset_collection(dataset_collection_id: str, instance_type: str = 'history') -> Dict[str,
Any]
Get details of a given dataset collection of the current user
Parameters
• dataset_collection_id (str) -- dataset collection ID
• instance_type (str) -- instance type of the collection - 'history' or 'library'
Return type
dict
Returns
element view of the dataset collection
wait_for_dataset_collection(dataset_collection_id: str, maxwait: float = 12000, interval: float =
3, proportion_complete: float = 1.0, check: bool = True) -> Dict[str, Any]
Wait until all or a specified proportion of elements of a dataset collection are in a
terminal state.
Parameters
• dataset_collection_id (str) -- dataset collection ID
• maxwait (float) -- Total time (in seconds) to wait for the dataset states in the
dataset collection to become terminal. If not all datasets are in a terminal state
within this time, a DatasetCollectionTimeoutException will be raised.
• interval (float) -- Time (in seconds) to wait between two consecutive checks.
• proportion_complete (float) -- Proportion of elements in this collection that have
to be in a terminal state for this method to return. Must be a number between 0
and 1. For example: if the dataset collection contains 2 elements, and
proportion_complete=0.5 is specified, then wait_for_dataset_collection will return
as soon as 1 of the 2 datasets is in a terminal state. Default is 1, i.e. all
elements must complete.
• check (bool) -- Whether to check if all the terminal states of datasets in the
dataset collection are 'ok'. This will raise an Exception if a dataset is in a
terminal state other than 'ok'.
Return type
dict
Returns
Details of the given dataset collection.
class bioblend.galaxy.dataset_collections.HistoryDatasetCollectionElement(name: str, id: str)
class bioblend.galaxy.dataset_collections.HistoryDatasetElement(name: str, id: str)
class bioblend.galaxy.dataset_collections.LibraryDatasetElement(name: str, id: str)
----
Datatypes
Contains possible interactions with the Galaxy Datatype
class bioblend.galaxy.datatypes.DatatypesClient(galaxy_instance: GalaxyInstance)
A generic Client interface defining the common fields.
All clients must define the following field (which will be used as part of the URL composition
(e.g., http://<galaxy_instance>/api/libraries): self.module = 'workflows' | 'libraries' |
'histories' | ...
get_datatypes(extension_only: bool = False, upload_only: bool = False) -> List[str]
Get the list of all installed datatypes.
Parameters
• extension_only (bool) -- Return only the extension rather than the datatype name
• upload_only (bool) -- Whether to return only datatypes which can be uploaded
Return type
list
Returns
A list of datatype names. For example:
['snpmatrix',
'snptest',
'tabular',
'taxonomy',
'twobit',
'txt',
'vcf',
'wig',
'xgmml',
'xml']
get_sniffers() -> List[str]
Get the list of all installed sniffers.
Return type
list
Returns
A list of sniffer names. For example:
['galaxy.datatypes.tabular:Vcf',
'galaxy.datatypes.binary:TwoBit',
'galaxy.datatypes.binary:Bam',
'galaxy.datatypes.binary:Sff',
'galaxy.datatypes.xml:Phyloxml',
'galaxy.datatypes.xml:GenericXml',
'galaxy.datatypes.sequence:Maf',
'galaxy.datatypes.sequence:Lav',
'galaxy.datatypes.sequence:csFasta']
module: str = 'datatypes'
----
Folders
Contains possible interactions with the Galaxy library folders
class bioblend.galaxy.folders.FoldersClient(galaxy_instance: GalaxyInstance)
A generic Client interface defining the common fields.
All clients must define the following field (which will be used as part of the URL composition
(e.g., http://<galaxy_instance>/api/libraries): self.module = 'workflows' | 'libraries' |
'histories' | ...
create_folder(parent_folder_id: str, name: str, description: str | None = None) -> Dict[str, Any]
Create a folder.
Parameters
• parent_folder_id (str) -- Folder's description
• name (str) -- name of the new folder
• description (str) -- folder's description
Return type
dict
Returns
details of the updated folder
delete_folder(folder_id: str, undelete: bool = False) -> Dict[str, Any]
Marks the folder with the given id as deleted (or removes the deleted mark if the undelete
param is True).
Parameters
• folder_id (str) -- the folder's encoded id, prefixed by 'F'
• undelete (bool) -- If set to True, the folder will be undeleted (i.e. the deleted
mark will be removed)
Returns
detailed folder information
Return type
dict
get_permissions(folder_id: str, scope: Literal['current', 'available'] = 'current') -> Dict[str,
Any]
Get the permissions of a folder.
Parameters
• folder_id (str) -- the folder's encoded id, prefixed by 'F'
• scope (str) -- scope of permissions, either 'current' or 'available'
Return type
dict
Returns
dictionary including details of the folder permissions
module: str = 'folders'
set_permissions(folder_id: str, action: Literal['set_permissions'] = 'set_permissions', add_ids:
List[str] | None = None, manage_ids: List[str] | None = None, modify_ids: List[str] | None = None)
-> Dict[str, Any]
Set the permissions of a folder.
Parameters
• folder_id (str) -- the folder's encoded id, prefixed by 'F'
• action (str) -- action to execute, only "set_permissions" is supported.
• add_ids (list of str) -- list of role IDs which can add datasets to the folder
• manage_ids (list of str) -- list of role IDs which can manage datasets in the
folder
• modify_ids (list of str) -- list of role IDs which can modify datasets in the
folder
Return type
dict
Returns
dictionary including details of the folder
show_folder(folder_id: str, contents: bool = False) -> Dict[str, Any]
Display information about a folder.
Parameters
• folder_id (str) -- the folder's encoded id, prefixed by 'F'
• contents (bool) -- True to get the contents of the folder, rather than just the
folder details.
Return type
dict
Returns
dictionary including details of the folder
update_folder(folder_id: str, name: str, description: str | None = None) -> Dict[str, Any]
Update folder information.
Parameters
• folder_id (str) -- the folder's encoded id, prefixed by 'F'
• name (str) -- name of the new folder
• description (str) -- folder's description
Return type
dict
Returns
details of the updated folder
----
Forms
Contains possible interactions with the Galaxy Forms
class bioblend.galaxy.forms.FormsClient(galaxy_instance: GalaxyInstance)
A generic Client interface defining the common fields.
All clients must define the following field (which will be used as part of the URL composition
(e.g., http://<galaxy_instance>/api/libraries): self.module = 'workflows' | 'libraries' |
'histories' | ...
create_form(form_xml_text: str) -> List[Dict[str, Any]]
Create a new form.
Parameters
form_xml_text (str) -- Form xml to create a form on galaxy instance
Return type
list of dicts
Returns
List with a single dictionary describing the created form
get_forms() -> List[Dict[str, Any]]
Get the list of all forms.
Return type
list
Returns
Displays a collection (list) of forms. For example:
[{'id': 'f2db41e1fa331b3e',
'model_class': 'FormDefinition',
'name': 'First form',
'url': '/api/forms/f2db41e1fa331b3e'},
{'id': 'ebfb8f50c6abde6d',
'model_class': 'FormDefinition',
'name': 'second form',
'url': '/api/forms/ebfb8f50c6abde6d'}]
module: str = 'forms'
show_form(form_id: str) -> Dict[str, Any]
Get details of a given form.
Parameters
form_id (str) -- Encoded form ID
Return type
dict
Returns
A description of the given form. For example:
{'desc': 'here it is ',
'fields': [],
'form_definition_current_id': 'f2db41e1fa331b3e',
'id': 'f2db41e1fa331b3e',
'layout': [],
'model_class': 'FormDefinition',
'name': 'First form',
'url': '/api/forms/f2db41e1fa331b3e'}
----
FTP files
Contains possible interactions with the Galaxy FTP Files
class bioblend.galaxy.ftpfiles.FTPFilesClient(galaxy_instance: GalaxyInstance)
A generic Client interface defining the common fields.
All clients must define the following field (which will be used as part of the URL composition
(e.g., http://<galaxy_instance>/api/libraries): self.module = 'workflows' | 'libraries' |
'histories' | ...
get_ftp_files(deleted: bool = False) -> List[dict]
Get a list of local files.
Parameters
deleted (bool) -- Whether to include deleted files
Return type
list
Returns
A list of dicts with details on individual files on FTP
module: str = 'ftp_files'
----
Genomes
Contains possible interactions with the Galaxy Histories
class bioblend.galaxy.genomes.GenomeClient(galaxy_instance: GalaxyInstance)
A generic Client interface defining the common fields.
All clients must define the following field (which will be used as part of the URL composition
(e.g., http://<galaxy_instance>/api/libraries): self.module = 'workflows' | 'libraries' |
'histories' | ...
get_genomes() -> list
Returns a list of installed genomes
Return type
list
Returns
List of installed genomes
install_genome(func: Literal['download', 'index'] = 'download', source: str | None = None, dbkey:
str | None = None, ncbi_name: str | None = None, ensembl_dbkey: str | None = None, url_dbkey: str
| None = None, indexers: list | None = None) -> Dict[str, Any]
Download and/or index a genome.
Parameters
• func (str) -- Allowed values: 'download', Download and index; 'index', Index only
• source (str) -- Data source for this build. Can be: UCSC, Ensembl, NCBI, URL
• dbkey (str) -- DB key of the build to download, ignored unless 'UCSC' is specified
as the source
• ncbi_name (str) -- NCBI's genome identifier, ignored unless NCBI is specified as
the source
• ensembl_dbkey (str) -- Ensembl's genome identifier, ignored unless Ensembl is
specified as the source
• url_dbkey (str) -- DB key to use for this build, ignored unless URL is specified
as the source
• indexers (list) -- POST array of indexers to run after downloading (indexers[] =
first, indexers[] = second, ...)
Return type
dict
Returns
dict( status: 'ok', job: <job ID> ) If error: dict( status: 'error', error: <error
message> )
module: str = 'genomes'
show_genome(id: str, num: str | None = None, chrom: str | None = None, low: str | None = None,
high: str | None = None) -> Dict[str, Any]
Returns information about build <id>
Parameters
• id (str) -- Genome build ID to use
• num (str) -- num
• chrom (str) -- chrom
• low (str) -- low
• high (str) -- high
Return type
dict
Returns
Information about the genome build
Groups
Contains possible interactions with the Galaxy Groups
class bioblend.galaxy.groups.GroupsClient(galaxy_instance: GalaxyInstance)
A generic Client interface defining the common fields.
All clients must define the following field (which will be used as part of the URL composition
(e.g., http://<galaxy_instance>/api/libraries): self.module = 'workflows' | 'libraries' |
'histories' | ...
add_group_role(group_id: str, role_id: str) -> Dict[str, Any]
Add a role to the given group.
Parameters
• group_id (str) -- Encoded group ID
• role_id (str) -- Encoded role ID to add to the group
Return type
dict
Returns
Added group role's info
add_group_user(group_id: str, user_id: str) -> Dict[str, Any]
Add a user to the given group.
Parameters
• group_id (str) -- Encoded group ID
• user_id (str) -- Encoded user ID to add to the group
Return type
dict
Returns
Added group user's info
create_group(group_name: str, user_ids: List[str] | None = None, role_ids: List[str] | None =
None) -> List[Dict[str, Any]]
Create a new group.
Parameters
• group_name (str) -- A name for the new group
• user_ids (list) -- A list of encoded user IDs to add to the new group
• role_ids (list) -- A list of encoded role IDs to add to the new group
Return type
list
Returns
A (size 1) list with newly created group details, like:
[{'id': '7c9636938c3e83bf',
'model_class': 'Group',
'name': 'My Group Name',
'url': '/api/groups/7c9636938c3e83bf'}]
delete_group_role(group_id: str, role_id: str) -> Dict[str, Any]
Remove a role from the given group.
Parameters
• group_id (str) -- Encoded group ID
• role_id (str) -- Encoded role ID to remove from the group
Return type
dict
Returns
The role which was removed
delete_group_user(group_id: str, user_id: str) -> Dict[str, Any]
Remove a user from the given group.
Parameters
• group_id (str) -- Encoded group ID
• user_id (str) -- Encoded user ID to remove from the group
Return type
dict
Returns
The user which was removed
get_group_roles(group_id: str) -> List[Dict[str, Any]]
Get the list of roles associated to the given group.
Parameters
group_id (str) -- Encoded group ID
Return type
list of dicts
Returns
List of group roles' info
get_group_users(group_id: str) -> List[Dict[str, Any]]
Get the list of users associated to the given group.
Parameters
group_id (str) -- Encoded group ID
Return type
list of dicts
Returns
List of group users' info
get_groups() -> List[Dict[str, Any]]
Get all (not deleted) groups.
Return type
list
Returns
A list of dicts with details on individual groups. For example:
[{'id': '33abac023ff186c2',
'model_class': 'Group',
'name': 'Listeria',
'url': '/api/groups/33abac023ff186c2'},
{'id': '73187219cd372cf8',
'model_class': 'Group',
'name': 'LPN',
'url': '/api/groups/73187219cd372cf8'}]
module: str = 'groups'
show_group(group_id: str) -> Dict[str, Any]
Get details of a given group.
Parameters
group_id (str) -- Encoded group ID
Return type
dict
Returns
A description of group For example:
{'id': '33abac023ff186c2',
'model_class': 'Group',
'name': 'Listeria',
'roles_url': '/api/groups/33abac023ff186c2/roles',
'url': '/api/groups/33abac023ff186c2',
'users_url': '/api/groups/33abac023ff186c2/users'}
update_group(group_id: str, group_name: str | None = None, user_ids: List[str] | None = None,
role_ids: List[str] | None = None) -> None
Update a group.
Parameters
• group_id (str) -- Encoded group ID
• group_name (str) -- A new name for the group. If None, the group name is not
changed.
• user_ids (list) -- New list of encoded user IDs for the group. It will substitute
the previous list of users (with [] if not specified)
• role_ids (list) -- New list of encoded role IDs for the group. It will substitute
the previous list of roles (with [] if not specified)
Return type
None
Returns
None
----
Histories
Contains possible interactions with the Galaxy Histories
class bioblend.galaxy.histories.HistoryClient(galaxy_instance: GalaxyInstance)
A generic Client interface defining the common fields.
All clients must define the following field (which will be used as part of the URL composition
(e.g., http://<galaxy_instance>/api/libraries): self.module = 'workflows' | 'libraries' |
'histories' | ...
copy_content(history_id: str, content_id: str, source: Literal['hda', 'hdca', 'library',
'library_folder'] = 'hda') -> Dict[str, Any]
Copy existing content (e.g. a dataset) to a history.
Parameters
• history_id (str) -- ID of the history to which the content should be copied
• content_id (str) -- ID of the content to copy
• source (str) -- Source of the content to be copied: 'hda' (for a history dataset,
the default), 'hdca' (for a dataset collection), 'library' (for a library dataset)
or 'library_folder' (for all datasets in a library folder).
Return type
dict
Returns
Information about the copied content
copy_dataset(history_id: str, dataset_id: str, source: Literal['hda', 'library', 'library_folder']
= 'hda') -> Dict[str, Any]
Copy a dataset to a history.
Parameters
• history_id (str) -- history ID to which the dataset should be copied
• dataset_id (str) -- dataset ID
• source (str) -- Source of the dataset to be copied: 'hda' (the default), 'library'
or 'library_folder'
Return type
dict
Returns
Information about the copied dataset
create_dataset_collection(history_id: str, collection_description: CollectionDescription |
Dict[str, Any]) -> Dict[str, Any]
Create a new dataset collection
Parameters
• history_id (str) -- Encoded history ID
• collection_description (bioblend.galaxy.dataset_collections.CollectionDescription)
--
a description of the dataset collection For example:
{'collection_type': 'list',
'element_identifiers': [{'id': 'f792763bee8d277a',
'name': 'element 1',
'src': 'hda'},
{'id': 'f792763bee8d277a',
'name': 'element 2',
'src': 'hda'}],
'name': 'My collection list'}
Return type
dict
Returns
Information about the new HDCA
create_history(name: str | None = None) -> Dict[str, Any]
Create a new history, optionally setting the name.
Parameters
name (str) -- Optional name for new history
Return type
dict
Returns
Dictionary containing information about newly created history
create_history_tag(history_id: str, tag: str) -> Dict[str, Any]
Create history tag
Parameters
• history_id (str) -- Encoded history ID
• tag (str) -- Add tag to history
Return type
dict
Returns
A dictionary with information regarding the tag. For example:
{'id': 'f792763bee8d277a',
'model_class': 'HistoryTagAssociation',
'user_tname': 'NGS_PE_RUN',
'user_value': None}
delete_dataset(history_id: str, dataset_id: str, purge: bool = False) -> None
Mark corresponding dataset as deleted.
Parameters
• history_id (str) -- Encoded history ID
• dataset_id (str) -- Encoded dataset ID
• purge (bool) -- if True, also purge (permanently delete) the dataset
Return type
None
Returns
None
NOTE:
The purge option works only if the Galaxy instance has the allow_user_dataset_purge
option set to true in the config/galaxy.yml configuration file.
delete_dataset_collection(history_id: str, dataset_collection_id: str) -> None
Mark corresponding dataset collection as deleted.
Parameters
• history_id (str) -- Encoded history ID
• dataset_collection_id (str) -- Encoded dataset collection ID
Return type
None
Returns
None
delete_history(history_id: str, purge: bool = False) -> Dict[str, Any]
Delete a history.
Parameters
• history_id (str) -- Encoded history ID
• purge (bool) -- if True, also purge (permanently delete) the history
Return type
dict
Returns
An error object if an error occurred or a dictionary containing: id (the encoded id
of the history), deleted (if the history was marked as deleted), purged (if the
history was purged).
NOTE:
The purge option works only if the Galaxy instance has the allow_user_dataset_purge
option set to true in the config/galaxy.yml configuration file.
download_history(history_id: str, jeha_id: str, outf: IO[bytes], chunk_size: int = 4096) -> None
Download a history export archive. Use export_history() to create an export.
Parameters
• history_id (str) -- history ID
• jeha_id (str) -- jeha ID (this should be obtained via export_history())
• outf (file) -- output file object, open for writing in binary mode
• chunk_size (int) -- how many bytes at a time should be read into memory
Return type
None
Returns
None
export_history(history_id: str, gzip: bool = True, include_hidden: bool = False, include_deleted:
bool = False, wait: bool = False, maxwait: float | None = None) -> str
Start a job to create an export archive for the given history.
Parameters
• history_id (str) -- history ID
• gzip (bool) -- create .tar.gz archive if True, else .tar
• include_hidden (bool) -- whether to include hidden datasets in the export
• include_deleted (bool) -- whether to include deleted datasets in the export
• wait (bool) -- if True, block until the export is ready; else, return immediately
• maxwait (float) -- Total time (in seconds) to wait for the export to become ready.
When set, implies that wait is True.
Return type
str
Returns
jeha_id of the export, or empty if wait is False and the export is not ready.
get_extra_files(history_id: str, dataset_id: str) -> List[str]
Get extra files associated with a composite dataset, or an empty list if there are none.
Parameters
• history_id (str) -- history ID
• dataset_id (str) -- dataset ID
Return type
list
Returns
List of extra files
get_histories(history_id: str | None = None, name: str | None = None, deleted: bool = False,
published: bool | None = None, slug: str | None = None, all: bool | None = False) ->
List[Dict[str, Any]]
Get all histories, or select a subset by specifying optional arguments for filtering (e.g.
a history name).
Parameters
• name (str) -- History name to filter on.
• deleted (bool) -- whether to filter for the deleted histories (True) or for the
non-deleted ones (False)
• published (bool or None) -- whether to filter for the published histories (True)
or for the non-published ones (False). If not set, no filtering is applied. Note
the filtering is only applied to the user's own histories; to access all histories
published by any user, use the get_published_histories method.
• slug (str) -- History slug to filter on
• all (bool) -- Whether to include histories from other users. This parameter works
only on Galaxy 20.01 or later and can be specified only if the user is a Galaxy
admin.
Return type
list
Returns
List of history dicts.
Changed in version 0.17.0: Using the deprecated history_id parameter now raises a
ValueError exception.
get_most_recently_used_history() -> Dict[str, Any]
Returns the current user's most recently used history (not deleted).
Return type
dict
Returns
History representation
get_published_histories(name: str | None = None, deleted: bool = False, slug: str | None = None)
-> List[Dict[str, Any]]
Get all published histories (by any user), or select a subset by specifying optional
arguments for filtering (e.g. a history name).
Parameters
• name (str) -- History name to filter on.
• deleted (bool) -- whether to filter for the deleted histories (True) or for the
non-deleted ones (False)
• slug (str) -- History slug to filter on
Return type
list
Returns
List of history dicts.
get_status(history_id: str) -> Dict[str, Any]
Returns the state of this history
Parameters
history_id (str) -- Encoded history ID
Return type
dict
Returns
A dict documenting the current state of the history. Has the following keys: 'state'
= This is the current state of the history, such as ok, error, new etc.
'state_details' = Contains individual statistics for various dataset states.
'percent_complete' = The overall number of datasets processed to completion.
gi: GalaxyInstance
import_history(file_path: str | None = None, url: str | None = None) -> Dict[str, Any]
Import a history from an archive on disk or a URL.
Parameters
• file_path (str) -- Path to exported history archive on disk.
• url (str) -- URL for an exported history archive
Return type
dict
Returns
Dictionary containing information about the imported history
module: str = 'histories'
open_history(history_id: str) -> None
Open Galaxy in a new tab of the default web browser and switch to the specified history.
Parameters
history_id (str) -- ID of the history to switch to
Return type
NoneType
Returns
None
WARNING:
After opening the specified history, all previously opened Galaxy tabs in the browser
session will have the current history changed to this one, even if the interface still
shows another history. Refreshing any such tab is recommended.
show_dataset(history_id: str, dataset_id: str) -> Dict[str, Any]
Get details about a given history dataset.
Parameters
• history_id (str) -- Encoded history ID
• dataset_id (str) -- Encoded dataset ID
Return type
dict
Returns
Information about the dataset
show_dataset_collection(history_id: str, dataset_collection_id: str) -> Dict[str, Any]
Get details about a given history dataset collection.
Parameters
• history_id (str) -- Encoded history ID
• dataset_collection_id (str) -- Encoded dataset collection ID
Return type
dict
Returns
Information about the dataset collection
show_dataset_provenance(history_id: str, dataset_id: str, follow: bool = False) -> Dict[str, Any]
Get details related to how dataset was created (id, job_id, tool_id, stdout, stderr,
parameters, inputs, etc...).
Parameters
• history_id (str) -- Encoded history ID
• dataset_id (str) -- Encoded dataset ID
• follow (bool) -- If True, recursively fetch dataset provenance information for all
inputs and their inputs, etc.
Return type
dict
Returns
Dataset provenance information For example:
{'id': '6fbd9b2274c62ebe',
'job_id': '5471ba76f274f929',
'parameters': {'chromInfo': '"/usr/local/galaxy/galaxy-dist/tool-data/shared/ucsc/chrom/mm9.len"',
'dbkey': '"mm9"',
'experiment_name': '"H3K4me3_TAC_MACS2"',
'input_chipseq_file1': {'id': '6f0a311a444290f2',
'uuid': 'null'},
'input_control_file1': {'id': 'c21816a91f5dc24e',
'uuid': '16f8ee5e-228f-41e2-921e-a07866edce06'},
'major_command': '{"gsize": "2716965481.0", "bdg": "False", "__current_case__": 0, "advanced_options": {"advanced_options_selector": "off", "__current_case__": 1}, "input_chipseq_file1": 104715, "xls_to_interval": "False", "major_command_selector": "callpeak", "input_control_file1": 104721, "pq_options": {"pq_options_selector": "qvalue", "qvalue": "0.05", "__current_case__": 1}, "bw": "300", "nomodel_type": {"nomodel_type_selector": "create_model", "__current_case__": 1}}'},
'stderr': '',
'stdout': '',
'tool_id': 'toolshed.g2.bx.psu.edu/repos/ziru-zhou/macs2/modencode_peakcalling_macs2/2.0.10.2',
'uuid': '5c0c43f5-8d93-44bd-939d-305e82f213c6'}
show_history(history_id: str, contents: Literal[False] = False) -> Dict[str, Any]
show_history(history_id: str, contents: Literal[True], deleted: bool | None = None, visible: bool
| None = None, details: str | None = None, types: List[str] | None = None) -> List[Dict[str, Any]]
show_history(history_id: str, contents: bool = False, deleted: bool | None = None, visible: bool |
None = None, details: str | None = None, types: List[str] | None = None) -> Dict[str, Any] |
List[Dict[str, Any]]
Get details of a given history. By default, just get the history meta information.
Parameters
• history_id (str) -- Encoded history ID to filter on
• contents (bool) -- When True, instead of the history details, return a list with
info for all datasets in the given history. Note that inside each dataset info
dict, the id which should be used for further requests about this history dataset
is given by the value of the id (not dataset_id) key.
• deleted (bool or None) -- When contents=True, whether to filter for the deleted
datasets (True) or for the non-deleted ones (False). If not set, no filtering is
applied.
• visible (bool or None) -- When contents=True, whether to filter for the visible
datasets (True) or for the hidden ones (False). If not set, no filtering is
applied.
• details (str) -- When contents=True, include dataset details. Set to 'all' for the
most information.
• types (list) -- When contents=True, filter for history content types. If set to
['dataset'], return only datasets. If set to ['dataset_collection'], return only
dataset collections. If not set, no filtering is applied.
Return type
dict or list of dicts
Returns
details of the given history or list of dataset info
NOTE:
As an alternative to using the contents=True parameter, consider using
gi.datasets.get_datasets(history_id=history_id) which offers more extensive
functionality for filtering and ordering the results.
show_matching_datasets(history_id: str, name_filter: str | Pattern[str] | None = None) ->
List[Dict[str, Any]]
Get dataset details for matching datasets within a history.
Parameters
• history_id (str) -- Encoded history ID
• name_filter (str) -- Only datasets whose name matches the name_filter regular
expression will be returned; use plain strings for exact matches and None to match
all datasets in the history
Return type
list
Returns
List of dictionaries
undelete_history(history_id: str) -> str
Undelete a history
Parameters
history_id (str) -- Encoded history ID
Return type
str
Returns
'OK' if it was deleted
update_dataset(history_id: str, dataset_id: str, **kwargs: Any) -> Dict[str, Any]
Update history dataset metadata. Some of the attributes that can be modified are documented
below.
Parameters
• history_id (str) -- Encoded history ID
• dataset_id (str) -- ID of the dataset
• name (str) -- Replace history dataset name with the given string
• datatype (str) -- Replace the datatype of the history dataset with the given
string. The string must be a valid Galaxy datatype, both the current and the
target datatypes must allow datatype changes, and the dataset must not be in use
as input or output of a running job (including uploads), otherwise an error will
be raised.
• genome_build (str) -- Replace history dataset genome build (dbkey)
• annotation (str) -- Replace history dataset annotation with given string
• deleted (bool) -- Mark or unmark history dataset as deleted
• visible (bool) -- Mark or unmark history dataset as visible
Return type
dict
Returns
details of the updated dataset
Changed in version 0.8.0: Changed the return value from the status code (type int) to a
dict.
update_dataset_collection(history_id: str, dataset_collection_id: str, **kwargs: Any) -> Dict[str,
Any]
Update history dataset collection metadata. Some of the attributes that can be modified are
documented below.
Parameters
• history_id (str) -- Encoded history ID
• dataset_collection_id (str) -- Encoded dataset_collection ID
• name (str) -- Replace history dataset collection name with the given string
• deleted (bool) -- Mark or unmark history dataset collection as deleted
• visible (bool) -- Mark or unmark history dataset collection as visible
Return type
dict
Returns
the updated dataset collection attributes
Changed in version 0.8.0: Changed the return value from the status code (type int) to a
dict.
update_history(history_id: str, **kwargs: Any) -> Dict[str, Any]
Update history metadata information. Some of the attributes that can be modified are
documented below.
Parameters
• history_id (str) -- Encoded history ID
• name (str) -- Replace history name with the given string
• annotation (str) -- Replace history annotation with given string
• deleted (bool) -- Mark or unmark history as deleted
• purged (bool) -- If True, mark history as purged (permanently deleted).
• published (bool) -- Mark or unmark history as published
• importable (bool) -- Mark or unmark history as importable
• tags (list) -- Replace history tags with the given list
Return type
dict
Returns
details of the updated history
Changed in version 0.8.0: Changed the return value from the status code (type int) to a
dict.
upload_dataset_from_library(history_id: str, lib_dataset_id: str) -> Dict[str, Any]
Upload a dataset into the history from a library. Requires the library dataset ID, which
can be obtained from the library contents.
Parameters
• history_id (str) -- Encoded history ID
• lib_dataset_id (str) -- Encoded library dataset ID
Return type
dict
Returns
Information about the newly created HDA
----
Invocations
Contains possible interactions with the Galaxy workflow invocations
class bioblend.galaxy.invocations.InvocationClient(galaxy_instance: GalaxyInstance)
A generic Client interface defining the common fields.
All clients must define the following field (which will be used as part of the URL composition
(e.g., http://<galaxy_instance>/api/libraries): self.module = 'workflows' | 'libraries' |
'histories' | ...
cancel_invocation(invocation_id: str) -> Dict[str, Any]
Cancel the scheduling of a workflow.
Parameters
invocation_id (str) -- Encoded workflow invocation ID
Return type
dict
Returns
The workflow invocation being cancelled
get_invocation_biocompute_object(invocation_id: str) -> Dict[str, Any]
Get a BioCompute object for an invocation.
Parameters
invocation_id (str) -- Encoded workflow invocation ID
Return type
dict
Returns
The BioCompute object
get_invocation_report(invocation_id: str) -> Dict[str, Any]
Get a Markdown report for an invocation.
Parameters
invocation_id (str) -- Encoded workflow invocation ID
Return type
dict
Returns
The invocation report. For example:
{'markdown': '\n# Workflow Execution Summary of Example workflow\n\n
## Workflow Inputs\n\n\n## Workflow Outputs\n\n\n
## Workflow\n```galaxy\n
workflow_display(workflow_id=f2db41e1fa331b3e)\n```\n',
'render_format': 'markdown',
'workflows': {'f2db41e1fa331b3e': {'name': 'Example workflow'}}}
get_invocation_report_pdf(invocation_id: str, file_path: str, chunk_size: int = 4096) -> None
Get a PDF report for an invocation.
Parameters
• invocation_id (str) -- Encoded workflow invocation ID
• file_path (str) -- Path to save the report
get_invocation_step_jobs_summary(invocation_id: str) -> List[Dict[str, Any]]
Get a detailed summary of an invocation, listing all jobs with their job IDs and current
states.
Parameters
invocation_id (str) -- Encoded workflow invocation ID
Return type
list of dicts
Returns
The invocation step jobs summary. For example:
[{'id': 'e85a3be143d5905b',
'model': 'Job',
'populated_state': 'ok',
'states': {'ok': 1}},
{'id': 'c9468fdb6dc5c5f1',
'model': 'Job',
'populated_state': 'ok',
'states': {'running': 1}},
{'id': '2a56795cad3c7db3',
'model': 'Job',
'populated_state': 'ok',
'states': {'new': 1}}]
get_invocation_summary(invocation_id: str) -> Dict[str, Any]
Get a summary of an invocation, stating the number of jobs which succeed, which are paused
and which have errored.
Parameters
invocation_id (str) -- Encoded workflow invocation ID
Return type
dict
Returns
The invocation summary. For example:
{'states': {'paused': 4, 'error': 2, 'ok': 2},
'model': 'WorkflowInvocation',
'id': 'a799d38679e985db',
'populated_state': 'ok'}
get_invocations(workflow_id: str | None = None, history_id: str | None = None, user_id: str | None
= None, include_terminal: bool = True, limit: int | None = None, view: str = 'collection',
step_details: bool = False) -> List[Dict[str, Any]]
Get all workflow invocations, or select a subset by specifying optional arguments for
filtering (e.g. a workflow ID).
Parameters
• workflow_id (str) -- Encoded workflow ID to filter on
• history_id (str) -- Encoded history ID to filter on
• user_id (str) -- Encoded user ID to filter on. This must be your own user ID if
your are not an admin user.
• include_terminal (bool) -- Whether to include terminal states.
• limit (int) -- Maximum number of invocations to return - if specified, the most
recent invocations will be returned.
• view (str) -- Level of detail to return per invocation, either 'element' or
'collection'.
• step_details (bool) -- If 'view' is 'element', also include details on individual
steps.
Return type
list
Returns
A list of workflow invocations. For example:
[{'history_id': '2f94e8ae9edff68a',
'id': 'df7a1f0c02a5b08e',
'model_class': 'WorkflowInvocation',
'state': 'new',
'update_time': '2015-10-31T22:00:22',
'uuid': 'c8aa2b1c-801a-11e5-a9e5-8ca98228593c',
'workflow_id': '03501d7626bd192f'}]
gi: GalaxyInstance
module: str = 'invocations'
rerun_invocation(invocation_id: str, inputs_update: dict | None = None, params_update: dict | None
= None, history_id: str | None = None, history_name: str | None = None, import_inputs_to_history:
bool = False, replacement_params: dict | None = None, allow_tool_state_corrections: bool = False,
inputs_by: Literal['step_index|step_uuid', 'step_index', 'step_id', 'step_uuid', 'name'] | None =
None, parameters_normalized: bool = False) -> Dict[str, Any]
Rerun a workflow invocation. For more extensive documentation of all parameters, see the
gi.workflows.invoke_workflow() method.
Parameters
• invocation_id (str) -- Encoded workflow invocation ID to be rerun
• inputs_update (dict) -- If different datasets should be used to the original
invocation, this should contain a mapping of workflow inputs to the new datasets
and dataset collections.
• params_update (dict) -- If different non-dataset tool parameters should be used to
the original invocation, this should contain a mapping of the new parameter
values.
• history_id (str) -- The encoded history ID where to store the workflow outputs.
Alternatively, history_name may be specified to create a new history.
• history_name (str) -- Create a new history with the given name to store the
workflow outputs. If both history_id and history_name are provided, history_name
is ignored. If neither is specified, a new 'Unnamed history' is created.
• import_inputs_to_history (bool) -- If True, used workflow inputs will be imported
into the history. If False, only workflow outputs will be visible in the given
history.
• allow_tool_state_corrections (bool) -- If True, allow Galaxy to fill in missing
tool state when running workflows. This may be useful for workflows using tools
that have changed over time or for workflows built outside of Galaxy with only a
subset of inputs defined.
• replacement_params (dict) -- pattern-based replacements for post-job actions
• inputs_by (str) -- Determines how inputs are referenced. Can be
"step_index|step_uuid" (default), "step_index", "step_id", "step_uuid", or "name".
• parameters_normalized (bool) -- Whether Galaxy should normalize the input
parameters to ensure everything is referenced by a numeric step ID. Default is
False, but when setting parameters for a subworkflow, True is required.
Return type
dict
Returns
A dict describing the new workflow invocation.
NOTE:
This method works only on Galaxy 21.01 or later.
run_invocation_step_action(invocation_id: str, step_id: str, action: Any) -> Dict[str, Any]
Execute an action for an active workflow invocation step. The nature of this action and
what is expected will vary based on the the type of workflow step (the only currently valid
action is True/False for pause steps).
Parameters
• invocation_id (str) -- Encoded workflow invocation ID
• step_id (str) -- Encoded workflow invocation step ID
• action (object) -- Action to use when updating state, semantics depends on step
type.
Return type
dict
Returns
Representation of the workflow invocation step
show_invocation(invocation_id: str) -> Dict[str, Any]
Get a workflow invocation dictionary representing the scheduling of a workflow. This
dictionary may be sparse at first (missing inputs and invocation steps) and will become
more populated as the workflow is actually scheduled.
Parameters
invocation_id (str) -- Encoded workflow invocation ID
Return type
dict
Returns
The workflow invocation. For example:
{'history_id': '2f94e8ae9edff68a',
'id': 'df7a1f0c02a5b08e',
'inputs': {'0': {'id': 'a7db2fac67043c7e',
'src': 'hda',
'uuid': '7932ffe0-2340-4952-8857-dbaa50f1f46a'}},
'model_class': 'WorkflowInvocation',
'state': 'ready',
'steps': [{'action': None,
'id': 'd413a19dec13d11e',
'job_id': None,
'model_class': 'WorkflowInvocationStep',
'order_index': 0,
'state': None,
'update_time': '2015-10-31T22:00:26',
'workflow_step_id': 'cbbbf59e8f08c98c',
'workflow_step_label': None,
'workflow_step_uuid': 'b81250fd-3278-4e6a-b269-56a1f01ef485'},
{'action': None,
'id': '2f94e8ae9edff68a',
'job_id': 'e89067bb68bee7a0',
'model_class': 'WorkflowInvocationStep',
'order_index': 1,
'state': 'new',
'update_time': '2015-10-31T22:00:26',
'workflow_step_id': '964b37715ec9bd22',
'workflow_step_label': None,
'workflow_step_uuid': 'e62440b8-e911-408b-b124-e05435d3125e'}],
'update_time': '2015-10-31T22:00:26',
'uuid': 'c8aa2b1c-801a-11e5-a9e5-8ca98228593c',
'workflow_id': '03501d7626bd192f'}
show_invocation_step(invocation_id: str, step_id: str) -> Dict[str, Any]
See the details of a particular workflow invocation step.
Parameters
• invocation_id (str) -- Encoded workflow invocation ID
• step_id (str) -- Encoded workflow invocation step ID
Return type
dict
Returns
The workflow invocation step. For example:
{'action': None,
'id': '63cd3858d057a6d1',
'job_id': None,
'model_class': 'WorkflowInvocationStep',
'order_index': 2,
'state': None,
'update_time': '2015-10-31T22:11:14',
'workflow_step_id': '52e496b945151ee8',
'workflow_step_label': None,
'workflow_step_uuid': '4060554c-1dd5-4287-9040-8b4f281cf9dc'}
wait_for_invocation(invocation_id: str, maxwait: float = 12000, interval: float = 3, check: bool =
True) -> Dict[str, Any]
Wait until an invocation is in a terminal state.
Parameters
• invocation_id (str) -- Invocation ID to wait for.
• maxwait (float) -- Total time (in seconds) to wait for the invocation state to
become terminal. If the invocation state is not terminal within this time, a
TimeoutException will be raised.
• interval (float) -- Time (in seconds) to wait between 2 consecutive checks.
• check (bool) -- Whether to check if the invocation terminal state is 'scheduled'.
Return type
dict
Returns
Details of the workflow invocation.
----
Jobs
Contains possible interactions with the Galaxy Jobs
class bioblend.galaxy.jobs.JobsClient(galaxy_instance: GalaxyInstance)
A generic Client interface defining the common fields.
All clients must define the following field (which will be used as part of the URL composition
(e.g., http://<galaxy_instance>/api/libraries): self.module = 'workflows' | 'libraries' |
'histories' | ...
cancel_job(job_id: str) -> bool
Cancel a job, deleting output datasets.
Parameters
job_id (str) -- job ID
Return type
bool
Returns
True if the job was successfully cancelled, False if it was already in a terminal
state before the cancellation.
get_common_problems(job_id: str) -> Dict[str, Any]
Query inputs and jobs for common potential problems that might have resulted in job
failure.
Parameters
job_id (str) -- job ID
Return type
dict
Returns
dict containing potential problems
NOTE:
This method works only on Galaxy 19.05 or later.
get_destination_params(job_id: str) -> Dict[str, Any]
Get destination parameters for a job, describing the environment and location where the job
is run.
Parameters
job_id (str) -- job ID
Return type
dict
Returns
Destination parameters for the given job
NOTE:
This method works only on Galaxy 20.05 or later and if the user is a Galaxy admin.
get_inputs(job_id: str) -> List[Dict[str, Any]]
Get dataset inputs used by a job.
Parameters
job_id (str) -- job ID
Return type
list of dicts
Returns
Inputs for the given job
get_jobs(state: str | None = None, history_id: str | None = None, invocation_id: str | None =
None, tool_id: str | None = None, workflow_id: str | None = None, user_id: str | None = None,
date_range_min: str | None = None, date_range_max: str | None = None, limit: int = 500, offset:
int = 0, user_details: bool = False, order_by: Literal['create_time', 'update_time'] =
'update_time') -> List[Dict[str, Any]]
Get all jobs, or select a subset by specifying optional arguments for filtering (e.g. a
state).
If the user is an admin, this will return jobs for all the users, otherwise only for the
current user.
Parameters
• state (str or list of str) -- Job states to filter on.
• history_id (str) -- Encoded history ID to filter on.
• invocation_id (string) -- Encoded workflow invocation ID to filter on.
• tool_id (str or list of str) -- Tool IDs to filter on.
• workflow_id (string) -- Encoded workflow ID to filter on.
• user_id (str) -- Encoded user ID to filter on. Only admin users can access the
jobs of other users.
• date_range_min (str) -- Mininum job update date (in YYYY-MM-DD format) to filter
on.
• date_range_max (str) -- Maximum job update date (in YYYY-MM-DD format) to filter
on.
• limit (int) -- Maximum number of jobs to return.
• offset (int) -- Return jobs starting from this specified position. For example,
if limit is set to 100 and offset to 200, jobs 200-299 will be returned.
• user_details (bool) -- If True and the user is an admin, add the user email to
each returned job dictionary.
• order_by (str) -- Whether to order jobs by create_time or update_time (the
default).
Return type
list of dict
Returns
Summary information for each selected job. For example:
[{'create_time': '2014-03-01T16:16:48.640550',
'exit_code': 0,
'id': 'ebfb8f50c6abde6d',
'model_class': 'Job',
'state': 'ok',
'tool_id': 'fasta2tab',
'update_time': '2014-03-01T16:16:50.657399'},
{'create_time': '2014-03-01T16:05:34.851246',
'exit_code': 0,
'id': '1cd8e2f6b131e891',
'model_class': 'Job',
'state': 'ok',
'tool_id': 'upload1',
'update_time': '2014-03-01T16:05:39.558458'}]
NOTE:
The following parameters work only on Galaxy 21.05 or later: user_id, limit, offset,
workflow_id, invocation_id.
get_metrics(job_id: str) -> List[Dict[str, Any]]
Return job metrics for a given job.
Parameters
job_id (str) -- job ID
Return type
list
Returns
list containing job metrics
NOTE:
Calling show_job() with full_details=True also returns the metrics for a job if the user
is an admin. This method allows to fetch metrics even as a normal user as long as the
Galaxy instance has the expose_potentially_sensitive_job_metrics option set to true in
the config/galaxy.yml configuration file.
get_outputs(job_id: str) -> List[Dict[str, Any]]
Get dataset outputs produced by a job.
Parameters
job_id (str) -- job ID
Return type
list of dicts
Returns
Outputs of the given job
get_state(job_id: str) -> str
Display the current state for a given job of the current user.
Parameters
job_id (str) -- job ID
Return type
str
Returns
state of the given job among the following values: new, queued, running, waiting,
ok. If the state cannot be retrieved, an empty string is returned.
New in version 0.5.3.
module: str = 'jobs'
report_error(job_id: str, dataset_id: str, message: str, email: str | None = None) -> Dict[str,
Any]
Report an error for a given job and dataset to the server administrators.
Parameters
• job_id (str) -- job ID
• dataset_id (str) -- Dataset ID
• message (str) -- Error message
• email (str) -- Email for error report submission. If not specified, the email
associated with the Galaxy user account is used by default.
Return type
dict
Returns
dict containing job error reply
NOTE:
This method works only on Galaxy 20.01 or later.
rerun_job(job_id: str, remap: bool = False, tool_inputs_update: Dict[str, Any] | None = None,
history_id: str | None = None) -> Dict[str, Any]
Rerun a job.
Parameters
• job_id (str) -- job ID
• remap (bool) -- when True, the job output(s) will be remapped onto the dataset(s)
created by the original job; if other jobs were waiting for this job to finish
successfully, they will be resumed using the new outputs of this tool run. When
False, new job output(s) will be created. Note that if Galaxy does not permit
remapping for the job in question, specifying True will result in an error.
• tool_inputs_update (dict) -- dictionary specifying any changes which should be
made to tool parameters for the rerun job. This dictionary should have the same
structure as is required when submitting the tool_inputs dictionary to
gi.tools.run_tool(), but only needs to include the inputs or parameters to be
updated for the rerun job.
• history_id (str) -- ID of the history in which the job should be executed; if not
specified, the same history will be used as the original job run.
Return type
dict
Returns
Information about outputs and the rerun job
NOTE:
This method works only on Galaxy 21.01 or later.
resume_job(job_id: str) -> List[Dict[str, Any]]
Resume a job if it is paused.
Parameters
job_id (str) -- job ID
Return type
list of dicts
Returns
list of dictionaries containing output dataset associations
search_jobs(tool_id: str, inputs: Dict[str, Any], state: str | None = None) -> List[Dict[str,
Any]]
Return jobs matching input parameters.
Parameters
• tool_id (str) -- only return jobs associated with this tool ID
• inputs (dict) -- return only jobs that have matching inputs
• state (str) -- only return jobs in this state
Return type
list of dicts
Returns
Summary information for each matching job
This method is designed to scan the list of previously run jobs and find records of jobs
with identical input parameters and datasets. This can be used to minimize the amount of
repeated work by simply recycling the old results.
Changed in version 0.16.0: Replaced the job_info parameter with separate tool_id, inputs
and state.
show_job(job_id: str, full_details: bool = False) -> Dict[str, Any]
Get details of a given job of the current user.
Parameters
• job_id (str) -- job ID
• full_details (bool) -- when True, the complete list of details for the given job.
Return type
dict
Returns
A description of the given job. For example:
{'create_time': '2014-03-01T16:17:29.828624',
'exit_code': 0,
'id': 'a799d38679e985db',
'inputs': {'input': {'id': 'ebfb8f50c6abde6d', 'src': 'hda'}},
'model_class': 'Job',
'outputs': {'output': {'id': 'a799d38679e985db', 'src': 'hda'}},
'params': {'chromInfo': '"/opt/galaxy-central/tool-data/shared/ucsc/chrom/?.len"',
'dbkey': '"?"',
'seq_col': '"2"',
'title_col': '["1"]'},
'state': 'ok',
'tool_id': 'tab2fasta',
'update_time': '2014-03-01T16:17:31.930728'}
show_job_lock() -> bool
Show whether the job lock is active or not. If it is active, no jobs will dispatch on the
Galaxy server.
Return type
bool
Returns
Status of the job lock
NOTE:
This method works only on Galaxy 20.05 or later and if the user is a Galaxy admin.
update_job_lock(active: bool = False) -> bool
Update the job lock status by setting active to either True or False. If True, all job
dispatching will be blocked.
Return type
bool
Returns
Updated status of the job lock
NOTE:
This method works only on Galaxy 20.05 or later and if the user is a Galaxy admin.
wait_for_job(job_id: str, maxwait: float = 12000, interval: float = 3, check: bool = True) ->
Dict[str, Any]
Wait until a job is in a terminal state.
Parameters
• job_id (str) -- job ID
• maxwait (float) -- Total time (in seconds) to wait for the job state to become
terminal. If the job state is not terminal within this time, a TimeoutException
will be raised.
• interval (float) -- Time (in seconds) to wait between 2 consecutive checks.
• check (bool) -- Whether to check if the job terminal state is 'ok'.
Return type
dict
Returns
Details of the given job.
----
Libraries
Contains possible interactions with the Galaxy Data Libraries
class bioblend.galaxy.libraries.LibraryClient(galaxy_instance: GalaxyInstance)
A generic Client interface defining the common fields.
All clients must define the following field (which will be used as part of the URL composition
(e.g., http://<galaxy_instance>/api/libraries): self.module = 'workflows' | 'libraries' |
'histories' | ...
copy_from_dataset(library_id: str, dataset_id: str, folder_id: str | None = None, message: str =
'') -> Dict[str, Any]
Copy a Galaxy dataset into a library.
Parameters
• library_id (str) -- id of the library where to place the uploaded file
• dataset_id (str) -- id of the dataset to copy from
• folder_id (str) -- id of the folder where to place the uploaded files. If not
provided, the root folder will be used
• message (str) -- message for copying action
Return type
dict
Returns
LDDA information
create_folder(library_id: str, folder_name: str, description: str | None = None, base_folder_id:
str | None = None) -> List[Dict[str, Any]]
Create a folder in a library.
Parameters
• library_id (str) -- library id to use
• folder_name (str) -- name of the new folder in the data library
• description (str) -- description of the new folder in the data library
• base_folder_id (str) -- id of the folder where to create the new folder. If not
provided, the root folder will be used
Return type
list
Returns
List with a single dictionary containing information about the new folder
create_library(name: str, description: str | None = None, synopsis: str | None = None) ->
Dict[str, Any]
Create a data library with the properties defined in the arguments.
Parameters
• name (str) -- Name of the new data library
• description (str) -- Optional data library description
• synopsis (str) -- Optional data library synopsis
Return type
dict
Returns
Details of the created library. For example:
{'id': 'f740ab636b360a70',
'name': 'Library from bioblend',
'url': '/api/libraries/f740ab636b360a70'}
delete_library(library_id: str) -> Dict[str, Any]
Delete a data library.
Parameters
library_id (str) -- Encoded data library ID identifying the library to be deleted
Return type
dict
Returns
Information about the deleted library
WARNING:
Deleting a data library is irreversible - all of the data from the library will be
permanently deleted.
delete_library_dataset(library_id: str, dataset_id: str, purged: bool = False) -> Dict[str, Any]
Delete a library dataset in a data library.
Parameters
• library_id (str) -- library id where dataset is found in
• dataset_id (str) -- id of the dataset to be deleted
• purged (bool) -- Indicate that the dataset should be purged (permanently deleted)
Return type
dict
Returns
A dictionary containing the dataset id and whether the dataset has been deleted.
For example:
{'deleted': True,
'id': '60e680a037f41974'}
get_dataset_permissions(dataset_id: str) -> Dict[str, Any]
Get the permissions for a dataset.
Parameters
dataset_id (str) -- id of the dataset
Return type
dict
Returns
dictionary with all applicable permissions' values
get_folders(library_id: str, folder_id: str | None = None, name: str | None = None) ->
List[Dict[str, Any]]
Get all the folders in a library, or select a subset by specifying a folder name for
filtering.
Parameters
• library_id (str) -- library id to use
• name (str) -- Folder name to filter on. For name specify the full path of the
folder starting from the library's root folder, e.g. /subfolder/subsubfolder.
Return type
list
Returns
list of dicts each containing basic information about a folder
Changed in version 1.1.1: Using the deprecated folder_id parameter now raises a ValueError
exception.
get_libraries(library_id: str | None = None, name: str | None = None, deleted: bool | None =
False) -> List[Dict[str, Any]]
Get all libraries, or select a subset by specifying optional arguments for filtering (e.g.
a library name).
Parameters
• name (str) -- Library name to filter on.
• deleted (bool) -- If False (the default), return only non-deleted libraries. If
True, return only deleted libraries. If None, return both deleted and non-deleted
libraries.
Return type
list
Returns
list of dicts each containing basic information about a library
Changed in version 1.1.1: Using the deprecated library_id parameter now raises a ValueError
exception.
get_library_permissions(library_id: str) -> Dict[str, Any]
Get the permissions for a library.
Parameters
library_id (str) -- id of the library
Return type
dict
Returns
dictionary with all applicable permissions' values
module: str = 'libraries'
set_dataset_permissions(dataset_id: str, access_in: List[str] | None = None, modify_in: List[str]
| None = None, manage_in: List[str] | None = None) -> Dict[str, Any]
Set the permissions for a dataset. Note: it will override all security for this dataset
even if you leave out a permission type.
Parameters
• dataset_id (str) -- id of the dataset
• access_in (list) -- list of role ids
• modify_in (list) -- list of role ids
• manage_in (list) -- list of role ids
Return type
dict
Returns
dictionary with all applicable permissions' values
set_library_permissions(library_id: str, access_in: List[str] | None = None, modify_in: List[str]
| None = None, add_in: List[str] | None = None, manage_in: List[str] | None = None) -> Dict[str,
Any]
Set the permissions for a library. Note: it will override all security for this library
even if you leave out a permission type.
Parameters
• library_id (str) -- id of the library
• access_in (list) -- list of role ids
• modify_in (list) -- list of role ids
• add_in (list) -- list of role ids
• manage_in (list) -- list of role ids
Return type
dict
Returns
General information about the library
show_dataset(library_id: str, dataset_id: str) -> Dict[str, Any]
Get details about a given library dataset. The required library_id can be obtained from the
datasets's library content details.
Parameters
• library_id (str) -- library id where dataset is found in
• dataset_id (str) -- id of the dataset to be inspected
Return type
dict
Returns
A dictionary containing information about the dataset in the library
show_folder(library_id: str, folder_id: str) -> Dict[str, Any]
Get details about a given folder. The required folder_id can be obtained from the folder's
library content details.
Parameters
• library_id (str) -- library id to inspect folders in
• folder_id (str) -- id of the folder to be inspected
Return type
dict
Returns
Information about the folder
show_library(library_id: str, contents: bool = False) -> Dict[str, Any]
Get information about a library.
Parameters
• library_id (str) -- filter for library by library id
• contents (bool) -- whether to get contents of the library (rather than just the
library details)
Return type
dict
Returns
details of the given library
update_library_dataset(dataset_id: str, **kwargs: Any) -> Dict[str, Any]
Update library dataset metadata. Some of the attributes that can be modified are documented
below.
Parameters
• dataset_id (str) -- id of the dataset to be updated
• name (str) -- Replace library dataset name with the given string
• misc_info (str) -- Replace library dataset misc_info with given string
• file_ext (str) -- Replace library dataset extension (must exist in the Galaxy
registry)
• genome_build (str) -- Replace library dataset genome build (dbkey)
• tags (list) -- Replace library dataset tags with the given list
Return type
dict
Returns
details of the updated dataset
upload_file_contents(library_id: str, pasted_content: str, folder_id: str | None = None,
file_type: str = 'auto', dbkey: str = '?', tags: List[str] | None = None) -> List[Dict[str, Any]]
Upload pasted_content to a data library as a new file.
Parameters
• library_id (str) -- id of the library where to place the uploaded file
• pasted_content (str) -- Content to upload into the library
• folder_id (str) -- id of the folder where to place the uploaded file. If not
provided, the root folder will be used
• file_type (str) -- Galaxy file format name
• dbkey (str) -- Dbkey
• tags (list) -- A list of tags to add to the datasets
Return type
list
Returns
List with a single dictionary containing information about the LDDA
upload_file_from_local_path(library_id: str, file_local_path: str, folder_id: str | None = None,
file_type: str = 'auto', dbkey: str = '?', tags: List[str] | None = None) -> List[Dict[str, Any]]
Read local file contents from file_local_path and upload data to a library.
Parameters
• library_id (str) -- id of the library where to place the uploaded file
• file_local_path (str) -- path of local file to upload
• folder_id (str) -- id of the folder where to place the uploaded file. If not
provided, the root folder will be used
• file_type (str) -- Galaxy file format name
• dbkey (str) -- Dbkey
• tags (list) -- A list of tags to add to the datasets
Return type
list
Returns
List with a single dictionary containing information about the LDDA
upload_file_from_server(library_id: str, server_dir: str, folder_id: str | None = None, file_type:
str = 'auto', dbkey: str = '?', link_data_only: Literal['copy_files', 'link_to_files'] | None =
None, roles: str = '', preserve_dirs: bool = False, tag_using_filenames: bool = False, tags:
List[str] | None = None) -> List[Dict[str, Any]]
Upload all files in the specified subdirectory of the Galaxy library import directory to a
library.
Parameters
• library_id (str) -- id of the library where to place the uploaded file
• server_dir (str) -- relative path of the subdirectory of library_import_dir to
upload. All and only the files (i.e. no subdirectories) contained in the specified
directory will be uploaded
• folder_id (str) -- id of the folder where to place the uploaded files. If not
provided, the root folder will be used
• file_type (str) -- Galaxy file format name
• dbkey (str) -- Dbkey
• link_data_only (str) -- either 'copy_files' (default) or 'link_to_files'. Setting
to 'link_to_files' symlinks instead of copying the files
• roles (str) --
???
• preserve_dirs (bool) -- Indicate whether to preserve the directory structure when
importing dir
• tag_using_filenames (bool) --
Indicate whether to generate dataset tags from filenames.
Changed in version 0.14.0: Changed the default from True to False.
• tags (list) -- A list of tags to add to the datasets
Return type
list
Returns
List with a single dictionary containing information about the LDDA
NOTE:
This method works only if the Galaxy instance has the library_import_dir option
configured in the config/galaxy.yml configuration file.
upload_file_from_url(library_id: str, file_url: str, folder_id: str | None = None, file_type: str
= 'auto', dbkey: str = '?', tags: List[str] | None = None) -> List[Dict[str, Any]]
Upload a file to a library from a URL.
Parameters
• library_id (str) -- id of the library where to place the uploaded file
• file_url (str) -- URL of the file to upload
• folder_id (str) -- id of the folder where to place the uploaded file. If not
provided, the root folder will be used
• file_type (str) -- Galaxy file format name
• dbkey (str) -- Dbkey
• tags (list) -- A list of tags to add to the datasets
Return type
list
Returns
List with a single dictionary containing information about the LDDA
upload_from_galaxy_filesystem(library_id: str, filesystem_paths: str, folder_id: str | None =
None, file_type: str = 'auto', dbkey: str = '?', link_data_only: Literal['copy_files',
'link_to_files'] | None = None, roles: str = '', preserve_dirs: bool = False, tag_using_filenames:
bool = False, tags: List[str] | None = None) -> List[Dict[str, Any]]
Upload a set of files already present on the filesystem of the Galaxy server to a library.
Parameters
• library_id (str) -- id of the library where to place the uploaded file
• filesystem_paths (str) -- file paths on the Galaxy server to upload to the
library, one file per line
• folder_id (str) -- id of the folder where to place the uploaded files. If not
provided, the root folder will be used
• file_type (str) -- Galaxy file format name
• dbkey (str) -- Dbkey
• link_data_only (str) -- either 'copy_files' (default) or 'link_to_files'. Setting
to 'link_to_files' symlinks instead of copying the files
• roles (str) --
???
• preserve_dirs (bool) -- Indicate whether to preserve the directory structure when
importing dir
• tag_using_filenames (bool) --
Indicate whether to generate dataset tags from filenames.
Changed in version 0.14.0: Changed the default from True to False.
• tags (list) -- A list of tags to add to the datasets
Return type
list
Returns
List of dictionaries containing information about each uploaded LDDA.
NOTE:
This method works only if the Galaxy instance has the allow_path_paste option set to
true in the config/galaxy.yml configuration file.
wait_for_dataset(library_id: str, dataset_id: str, maxwait: float = 12000, interval: float = 3) ->
Dict[str, Any]
Wait until the library dataset state is terminal ('ok', 'empty', 'error', 'discarded' or
'failed_metadata').
Parameters
• library_id (str) -- library id where dataset is found in
• dataset_id (str) -- id of the dataset to wait for
• maxwait (float) -- Total time (in seconds) to wait for the dataset state to become
terminal. If the dataset state is not terminal within this time, a
DatasetTimeoutException will be thrown.
• interval (float) -- Time (in seconds) to wait between 2 consecutive checks.
Return type
dict
Returns
A dictionary containing information about the dataset in the library
----
Quotas
Contains possible interactions with the Galaxy Quota
class bioblend.galaxy.quotas.QuotaClient(galaxy_instance: GalaxyInstance)
A generic Client interface defining the common fields.
All clients must define the following field (which will be used as part of the URL composition
(e.g., http://<galaxy_instance>/api/libraries): self.module = 'workflows' | 'libraries' |
'histories' | ...
create_quota(name: str, description: str, amount: str, operation: Literal['+', '-', '='], default:
Literal['no', 'registered', 'unregistered'] | None = 'no', in_users: List[str] | None = None,
in_groups: List[str] | None = None) -> Dict[str, Any]
Create a new quota
Parameters
• name (str) -- Name for the new quota. This must be unique within a Galaxy
instance.
• description (str) -- Quota description
• amount (str) -- Quota size (E.g. 10000MB, 99 gb, 0.2T, unlimited)
• operation (str) -- One of (+, -, =)
• default (str) -- Whether or not this is a default quota. Valid values are no,
unregistered, registered. None is equivalent to no.
• in_users (list of str) -- A list of user IDs or user emails.
• in_groups (list of str) -- A list of group IDs or names.
Return type
dict
Returns
A description of quota. For example:
{'url': '/galaxy/api/quotas/386f14984287a0f7',
'model_class': 'Quota',
'message': "Quota 'Testing' has been created with 1 associated users and 0 associated groups.",
'id': '386f14984287a0f7',
'name': 'Testing'}
delete_quota(quota_id: str) -> str
Delete a quota
Before a quota can be deleted, the quota must not be a default quota.
Parameters
quota_id (str) -- Encoded quota ID.
Return type
str
Returns
A description of the changes, mentioning the deleted quota. For example:
"Deleted 1 quotas: Testing-B"
get_quotas(deleted: bool = False) -> List[Dict[str, Any]]
Get a list of quotas
Parameters
deleted (bool) -- Only return quota(s) that have been deleted
Return type
list
Returns
A list of dicts with details on individual quotas. For example:
[{'id': '0604c8a56abe9a50',
'model_class': 'Quota',
'name': 'test ',
'url': '/api/quotas/0604c8a56abe9a50'},
{'id': '1ee267091d0190af',
'model_class': 'Quota',
'name': 'workshop',
'url': '/api/quotas/1ee267091d0190af'}]
module: str = 'quotas'
show_quota(quota_id: str, deleted: bool = False) -> Dict[str, Any]
Display information on a quota
Parameters
• quota_id (str) -- Encoded quota ID
• deleted (bool) -- Search for quota in list of ones already marked as deleted
Return type
dict
Returns
A description of quota. For example:
{'bytes': 107374182400,
'default': [],
'description': 'just testing',
'display_amount': '100.0 GB',
'groups': [],
'id': '0604c8a56abe9a50',
'model_class': 'Quota',
'name': 'test ',
'operation': '=',
'users': []}
undelete_quota(quota_id: str) -> str
Undelete a quota
Parameters
quota_id (str) -- Encoded quota ID.
Return type
str
Returns
A description of the changes, mentioning the undeleted quota. For example:
"Undeleted 1 quotas: Testing-B"
update_quota(quota_id: str, name: str | None = None, description: str | None = None, amount: str |
None = None, operation: Literal['+', '-', '='] | None = None, default: str = 'no', in_users:
List[str] | None = None, in_groups: List[str] | None = None) -> str
Update an existing quota
Parameters
• quota_id (str) -- Encoded quota ID
• name (str) -- Name for the new quota. This must be unique within a Galaxy
instance.
• description (str) -- Quota description. If you supply this parameter, but not the
name, an error will be thrown.
• amount (str) -- Quota size (E.g. 10000MB, 99 gb, 0.2T, unlimited)
• operation (str) -- One of (+, -, =). If you wish to change this value, you must
also provide the amount, otherwise it will not take effect.
• default (str) -- Whether or not this is a default quota. Valid values are no,
unregistered, registered. Calling this method with default="no" on a non-default
quota will throw an error. Not passing this parameter is equivalent to passing no.
• in_users (list of str) -- A list of user IDs or user emails.
• in_groups (list of str) -- A list of group IDs or names.
Return type
str
Returns
A semicolon separated list of changes to the quota. For example:
"Quota 'Testing-A' has been renamed to 'Testing-B'; Quota 'Testing-e' is now '-100.0 GB'; Quota 'Testing-B' is now the default for unregistered users"
----
Roles
Contains possible interactions with the Galaxy Roles
class bioblend.galaxy.roles.RolesClient(galaxy_instance: GalaxyInstance)
A generic Client interface defining the common fields.
All clients must define the following field (which will be used as part of the URL composition
(e.g., http://<galaxy_instance>/api/libraries): self.module = 'workflows' | 'libraries' |
'histories' | ...
create_role(role_name: str, description: str, user_ids: List[str] | None = None, group_ids:
List[str] | None = None) -> Dict[str, Any]
Create a new role.
Parameters
• role_name (str) -- A name for the new role
• description (str) -- Description for the new role
• user_ids (list) -- A list of encoded user IDs to add to the new role
• group_ids (list) -- A list of encoded group IDs to add to the new role
Return type
dict
Returns
Details of the newly created role. For example:
{'description': 'desc',
'url': '/api/roles/ebfb8f50c6abde6d',
'model_class': 'Role',
'type': 'admin',
'id': 'ebfb8f50c6abde6d',
'name': 'Foo'}
Changed in version 0.15.0: Changed the return value from a 1-element list to a dict.
get_roles() -> List[Dict[str, Any]]
Displays a collection (list) of roles.
Return type
list
Returns
A list of dicts with details on individual roles. For example:
[{"id": "f2db41e1fa331b3e",
"model_class": "Role",
"name": "Foo",
"url": "/api/roles/f2db41e1fa331b3e"},
{"id": "f597429621d6eb2b",
"model_class": "Role",
"name": "Bar",
"url": "/api/roles/f597429621d6eb2b"}]
module: str = 'roles'
show_role(role_id: str) -> Dict[str, Any]
Display information on a single role
Parameters
role_id (str) -- Encoded role ID
Return type
dict
Returns
Details of the given role. For example:
{"description": "Private Role for Foo",
"id": "f2db41e1fa331b3e",
"model_class": "Role",
"name": "Foo",
"type": "private",
"url": "/api/roles/f2db41e1fa331b3e"}
----
Tools
----
Tool data tables
Contains possible interactions with the Galaxy Tool data tables
class bioblend.galaxy.tool_data.ToolDataClient(galaxy_instance: GalaxyInstance)
A generic Client interface defining the common fields.
All clients must define the following field (which will be used as part of the URL composition
(e.g., http://<galaxy_instance>/api/libraries): self.module = 'workflows' | 'libraries' |
'histories' | ...
delete_data_table(data_table_id: str, values: str) -> Dict[str, Any]
Delete an item from a data table.
Parameters
• data_table_id (str) -- ID of the data table
• values (str) -- a "|" separated list of column contents, there must be a value for
all the columns of the data table
Return type
dict
Returns
Remaining contents of the given data table
get_data_tables() -> List[Dict[str, Any]]
Get the list of all data tables.
Return type
list
Returns
A list of dicts with details on individual data tables. For example:
[{"model_class": "TabularToolDataTable", "name": "fasta_indexes"},
{"model_class": "TabularToolDataTable", "name": "bwa_indexes"}]
module: str = 'tool_data'
reload_data_table(data_table_id: str) -> Dict[str, Any]
Reload a data table.
Parameters
data_table_id (str) -- ID of the data table
Return type
dict
Returns
A description of the given data table and its content. For example:
{'columns': ['value', 'dbkey', 'name', 'path'],
'fields': [['test id',
'test',
'test name',
'/opt/galaxy-dist/tool-data/test/seq/test id.fa']],
'model_class': 'TabularToolDataTable',
'name': 'all_fasta'}
show_data_table(data_table_id: str) -> Dict[str, Any]
Get details of a given data table.
Parameters
data_table_id (str) -- ID of the data table
Return type
dict
Returns
A description of the given data table and its content. For example:
{'columns': ['value', 'dbkey', 'name', 'path'],
'fields': [['test id',
'test',
'test name',
'/opt/galaxy-dist/tool-data/test/seq/test id.fa']],
'model_class': 'TabularToolDataTable',
'name': 'all_fasta'}
----
Tool dependencies
Contains interactions dealing with Galaxy dependency resolvers.
class bioblend.galaxy.tool_dependencies.ToolDependenciesClient(galaxy_instance: GalaxyInstance)
A generic Client interface defining the common fields.
All clients must define the following field (which will be used as part of the URL composition
(e.g., http://<galaxy_instance>/api/libraries): self.module = 'workflows' | 'libraries' |
'histories' | ...
delete_unused_dependency_paths(paths: List[str]) -> None
Delete unused paths
Parameters
paths (list) -- paths to delete
module: str = 'dependency_resolvers'
summarize_toolbox(index: int | None = None, tool_ids: List[str] | None = None, resolver_type: str
| None = None, include_containers: bool = False, container_type: str | None = None, index_by:
Literal['requirements', 'tools'] = 'requirements') -> list
Summarize requirements across toolbox (for Tool Management grid).
Parameters
• index (int) -- index of the dependency resolver with respect to the dependency
resolvers config file
• tool_ids (list) -- tool_ids to return when index_by=tools
• resolver_type (str) -- restrict to specified resolver type
• include_containers (bool) -- include container resolvers in resolution
• container_type (str) -- restrict to specified container type
• index_by (str) -- By default results are grouped by requirements. Set to 'tools'
to return one entry per tool.
Return type
list of dicts
Returns
dictified descriptions of the dependencies, with attribute dependency_type: None if
no match was found. For example:
[{'requirements': [{'name': 'galaxy_sequence_utils',
'specs': [],
'type': 'package',
'version': '1.1.4'},
{'name': 'bx-python',
'specs': [],
'type': 'package',
'version': '0.8.6'}],
'status': [{'cacheable': False,
'dependency_type': None,
'exact': True,
'model_class': 'NullDependency',
'name': 'galaxy_sequence_utils',
'version': '1.1.4'},
{'cacheable': False,
'dependency_type': None,
'exact': True,
'model_class': 'NullDependency',
'name': 'bx-python',
'version': '0.8.6'}],
'tool_ids': ['vcf_to_maf_customtrack1']}]
NOTE:
This method works only on Galaxy 20.01 or later and if the user is a Galaxy admin. It
relies on an experimental API particularly tied to the GUI and therefore is subject to
breaking changes.
unused_dependency_paths() -> List[str]
List unused dependencies
----
ToolShed
----
Users
----
Visual
----
Workflows
Contains possible interactions with the Galaxy Workflows
class bioblend.galaxy.workflows.WorkflowClient(galaxy_instance: GalaxyInstance)
A generic Client interface defining the common fields.
All clients must define the following field (which will be used as part of the URL composition
(e.g., http://<galaxy_instance>/api/libraries): self.module = 'workflows' | 'libraries' |
'histories' | ...
cancel_invocation(workflow_id: str, invocation_id: str) -> Dict[str, Any]
Cancel the scheduling of a workflow.
Parameters
• workflow_id (str) -- Encoded workflow ID
• invocation_id (str) -- Encoded workflow invocation ID
Return type
dict
Returns
The workflow invocation being cancelled
delete_workflow(workflow_id: str) -> None
Delete a workflow identified by workflow_id.
Parameters
workflow_id (str) -- Encoded workflow ID
WARNING:
Deleting a workflow is irreversible in Galaxy versions < 23.01 - all workflow data will
be permanently deleted.
export_workflow_dict(workflow_id: str, version: int | None = None) -> Dict[str, Any]
Exports a workflow.
Parameters
• workflow_id (str) -- Encoded workflow ID
• version (int) -- Workflow version to export
Return type
dict
Returns
Dictionary representing the requested workflow
export_workflow_to_local_path(workflow_id: str, file_local_path: str, use_default_filename: bool =
True) -> None
Exports a workflow in JSON format to a given local path.
Parameters
• workflow_id (str) -- Encoded workflow ID
• file_local_path (str) -- Local path to which the exported file will be saved.
(Should not contain filename if use_default_name=True)
• use_default_filename (bool) -- If the use_default_name parameter is True, the
exported file will be saved as file_local_path/Galaxy-Workflow-%s.ga, where %s is
the workflow name. If use_default_name is False, file_local_path is assumed to
contain the full file path including filename.
Return type
None
Returns
None
extract_workflow_from_history(history_id: str, workflow_name: str, job_ids: List[str] | None =
None, dataset_hids: List[str] | None = None, dataset_collection_hids: List[str] | None = None) ->
Dict[str, Any]
Extract a workflow from a history.
Parameters
• history_id (str) -- Encoded history ID
• workflow_name (str) -- Name of the workflow to create
• job_ids (list) -- Optional list of job IDs to filter the jobs to extract from the
history
• dataset_hids (list) -- Optional list of dataset hids corresponding to workflow
inputs when extracting a workflow from history
• dataset_collection_hids (list) -- Optional list of dataset collection hids
corresponding to workflow inputs when extracting a workflow from history
Return type
dict
Returns
A description of the created workflow
get_invocations(workflow_id: str) -> List[Dict[str, Any]]
Get a list containing all the workflow invocations corresponding to the specified workflow.
For more advanced filtering use InvocationClient.get_invocations().
Parameters
workflow_id (str) -- Encoded workflow ID
Return type
list
Returns
A list of workflow invocations. For example:
[{'history_id': '2f94e8ae9edff68a',
'id': 'df7a1f0c02a5b08e',
'model_class': 'WorkflowInvocation',
'state': 'new',
'update_time': '2015-10-31T22:00:22',
'uuid': 'c8aa2b1c-801a-11e5-a9e5-8ca98228593c',
'workflow_id': '03501d7626bd192f'}]
get_workflow_inputs(workflow_id: str, label: str) -> List[str]
Get a list of workflow input IDs that match the given label. If no input matches the given
label, an empty list is returned.
Parameters
• workflow_id (str) -- Encoded workflow ID
• label (str) -- label to filter workflow inputs on
Return type
list
Returns
list of workflow inputs matching the label query
get_workflows(workflow_id: str | None = None, name: str | None = None, published: bool = False) ->
List[Dict[str, Any]]
Get all workflows, or select a subset by specifying optional arguments for filtering (e.g.
a workflow name).
Parameters
• name (str) -- Workflow name to filter on.
• published (bool) -- if True, return also published workflows
Return type
list
Returns
A list of workflow dicts. For example:
[{'id': '92c56938c2f9b315',
'name': 'Simple',
'url': '/api/workflows/92c56938c2f9b315'}]
Changed in version 1.1.1: Using the deprecated workflow_id parameter now raises a
ValueError exception.
import_shared_workflow(workflow_id: str) -> Dict[str, Any]
Imports a new workflow from the shared published workflows.
Parameters
workflow_id (str) -- Encoded workflow ID
Return type
dict
Returns
A description of the workflow. For example:
{'id': 'ee0e2b4b696d9092',
'model_class': 'StoredWorkflow',
'name': 'Super workflow that solves everything!',
'published': False,
'tags': [],
'url': '/api/workflows/ee0e2b4b696d9092'}
import_workflow_dict(workflow_dict: Dict[str, Any], publish: bool = False) -> Dict[str, Any]
Imports a new workflow given a dictionary representing a previously exported workflow.
Parameters
• workflow_dict (dict) -- dictionary representing the workflow to be imported
• publish (bool) -- if True the uploaded workflow will be published; otherwise it
will be visible only by the user which uploads it (default)
Return type
dict
Returns
Information about the imported workflow. For example:
{'name': 'Training: 16S rRNA sequencing with mothur: main tutorial',
'tags': [],
'deleted': false,
'latest_workflow_uuid': '368c6165-ccbe-4945-8a3c-d27982206d66',
'url': '/api/workflows/94bac0a90086bdcf',
'number_of_steps': 44,
'published': false,
'owner': 'jane-doe',
'model_class': 'StoredWorkflow',
'id': '94bac0a90086bdcf'}
import_workflow_from_local_path(file_local_path: str, publish: bool = False) -> Dict[str, Any]
Imports a new workflow given the path to a file containing a previously exported workflow.
Parameters
• file_local_path (str) -- File to upload to the server for new workflow
• publish (bool) -- if True the uploaded workflow will be published; otherwise it
will be visible only by the user which uploads it (default)
Return type
dict
Returns
Information about the imported workflow. For example:
{'name': 'Training: 16S rRNA sequencing with mothur: main tutorial',
'tags': [],
'deleted': false,
'latest_workflow_uuid': '368c6165-ccbe-4945-8a3c-d27982206d66',
'url': '/api/workflows/94bac0a90086bdcf',
'number_of_steps': 44,
'published': false,
'owner': 'jane-doe',
'model_class': 'StoredWorkflow',
'id': '94bac0a90086bdcf'}
invoke_workflow(workflow_id: str, inputs: dict | None = None, params: dict | None = None,
history_id: str | None = None, history_name: str | None = None, import_inputs_to_history: bool =
False, replacement_params: dict | None = None, allow_tool_state_corrections: bool = False,
inputs_by: Literal['step_index|step_uuid', 'step_index', 'step_id', 'step_uuid', 'name'] | None =
None, parameters_normalized: bool = False, require_exact_tool_versions: bool = True) -> Dict[str,
Any]
Invoke the workflow identified by workflow_id. This will cause a workflow to be scheduled
and return an object describing the workflow invocation.
Parameters
• workflow_id (str) -- Encoded workflow ID
• inputs (dict) --
A mapping of workflow inputs to datasets and dataset collections. The datasets
source can be a LibraryDatasetDatasetAssociation (ldda), LibraryDataset (ld),
HistoryDatasetAssociation (hda), or HistoryDatasetCollectionAssociation (hdca).
The map must be in the following format: {'<input_index>': {'id': <encoded dataset
ID>, 'src': '[ldda, ld, hda, hdca]'}} (e.g. {'2': {'id': '29beef4fadeed09f',
'src': 'hda'}})
This map may also be indexed by the UUIDs of the workflow steps, as indicated by
the uuid property of steps returned from the Galaxy API. Alternatively workflow
steps may be addressed by the label that can be set in the workflow editor. If
using uuid or label you need to also set the inputs_by parameter to step_uuid or
name.
• params (dict) -- A mapping of non-datasets tool parameters (see below)
• history_id (str) -- The encoded history ID where to store the workflow output.
Alternatively, history_name may be specified to create a new history.
• history_name (str) -- Create a new history with the given name to store the
workflow output. If both history_id and history_name are provided, history_name is
ignored. If neither is specified, a new 'Unnamed history' is created.
• import_inputs_to_history (bool) -- If True, used workflow inputs will be imported
into the history. If False, only workflow outputs will be visible in the given
history.
• allow_tool_state_corrections (bool) -- If True, allow Galaxy to fill in missing
tool state when running workflows. This may be useful for workflows using tools
that have changed over time or for workflows built outside of Galaxy with only a
subset of inputs defined.
• replacement_params (dict) -- pattern-based replacements for post-job actions (see
below)
• inputs_by (str) -- Determines how inputs are referenced. Can be
"step_index|step_uuid" (default), "step_index", "step_id", "step_uuid", or "name".
• parameters_normalized (bool) -- Whether Galaxy should normalize params to ensure
everything is referenced by a numeric step ID. Default is False, but when setting
params for a subworkflow, True is required.
• require_exact_tool_versions (bool) -- Whether invocation should fail if Galaxy
does not have the exact tool versions. Default is True. Parameter does not any
effect for Galaxy versions < 22.05.
Return type
dict
Returns
A dict containing the workflow invocation describing the scheduling of the workflow.
For example:
{'history_id': '2f94e8ae9edff68a',
'id': 'df7a1f0c02a5b08e',
'inputs': {'0': {'id': 'a7db2fac67043c7e',
'src': 'hda',
'uuid': '7932ffe0-2340-4952-8857-dbaa50f1f46a'}},
'model_class': 'WorkflowInvocation',
'state': 'ready',
'steps': [{'action': None,
'id': 'd413a19dec13d11e',
'job_id': None,
'model_class': 'WorkflowInvocationStep',
'order_index': 0,
'state': None,
'update_time': '2015-10-31T22:00:26',
'workflow_step_id': 'cbbbf59e8f08c98c',
'workflow_step_label': None,
'workflow_step_uuid': 'b81250fd-3278-4e6a-b269-56a1f01ef485'},
{'action': None,
'id': '2f94e8ae9edff68a',
'job_id': 'e89067bb68bee7a0',
'model_class': 'WorkflowInvocationStep',
'order_index': 1,
'state': 'new',
'update_time': '2015-10-31T22:00:26',
'workflow_step_id': '964b37715ec9bd22',
'workflow_step_label': None,
'workflow_step_uuid': 'e62440b8-e911-408b-b124-e05435d3125e'}],
'update_time': '2015-10-31T22:00:26',
'uuid': 'c8aa2b1c-801a-11e5-a9e5-8ca98228593c',
'workflow_id': '03501d7626bd192f'}
The params dict should be specified as follows:
{STEP_ID: PARAM_DICT, ...}
where PARAM_DICT is:
{PARAM_NAME: VALUE, ...}
For backwards compatibility, the following (deprecated) format is also supported for
params:
{TOOL_ID: PARAM_DICT, ...}
in which case PARAM_DICT affects all steps with the given tool id. If both by-tool-id and
by-step-id specifications are used, the latter takes precedence.
Finally (again, for backwards compatibility), PARAM_DICT can also be specified as:
{'param': PARAM_NAME, 'value': VALUE}
Note that this format allows only one parameter to be set per step.
For a repeat parameter, the names of the contained parameters needs to be specified as
<repeat name>_<repeat index>|<param name>, with the repeat index starting at 0. For
example, if the tool XML contains:
<repeat name="cutoff" title="Parameters used to filter cells" min="1">
<param name="name" type="text" value="n_genes" label="Name of param...">
<option value="n_genes">n_genes</option>
<option value="n_counts">n_counts</option>
</param>
<param name="min" type="float" min="0" value="0" label="Min value"/>
</repeat>
then the PARAM_DICT should be something like:
{...
"cutoff_0|name": "n_genes",
"cutoff_0|min": "2",
"cutoff_1|name": "n_counts",
"cutoff_1|min": "4",
...}
At the time of this writing, it is not possible to change the number of times the contained
parameters are repeated. Therefore, the parameter indexes can go from 0 to n-1, where n is
the number of times the repeated element was added when the workflow was saved in the
Galaxy UI.
The replacement_params dict should map parameter names in post-job actions (PJAs) to their
runtime values. For instance, if the final step has a PJA like the following:
{'RenameDatasetActionout_file1': {'action_arguments': {'newname': '${output}'},
'action_type': 'RenameDatasetAction',
'output_name': 'out_file1'}}
then the following renames the output dataset to 'foo':
replacement_params = {'output': 'foo'}
see also this email thread.
WARNING:
Historically, workflow invocation consumed a dataset_map data structure that was indexed
by unencoded workflow step IDs. These IDs would not be stable across Galaxy instances.
The new inputs property is instead indexed by either the order_index property (which is
stable across workflow imports) or the step UUID which is also stable.
module: str = 'workflows'
refactor_workflow(workflow_id: str, actions: List[Dict[str, Any]], dry_run: bool = False) ->
Dict[str, Any]
Refactor workflow with given actions.
Parameters
• workflow_id (str) -- Encoded workflow ID
• actions (list of dicts) -- .INDENT 2.0
Actions to use for refactoring the workflow. The following
actions are supported: update_step_label, update_step_position,
update_output_label, update_name, update_annotation, update_license,
update_creator, update_report, add_step, add_input, disconnect, connect,
fill_defaults, fill_step_defaults, extract_input, extract_legacy_parameter,
remove_unlabeled_workflow_outputs, upgrade_all_steps, upgrade_subworkflow,
upgrade_tool.
An example value for the actions argument might be:
actions = [
{"action_type": "add_input", "type": "data", "label": "foo"},
{"action_type": "update_step_label", "label": "bar", "step": {"label": "foo"}},
]
• dry_run (bool) -- When true, perform a dry run where the existing workflow is preserved.
The refactored workflow is returned in the output of the method, but not saved on the
Galaxy server.
Return type
dict
Returns
Dictionary containing logged messages for the executed actions and the refactored workflow.
run_invocation_step_action(workflow_id: str, invocation_id: str, step_id: str, action: Any) ->
Dict[str, Any]
Execute an action for an active workflow invocation step. The nature of this action and
what is expected will vary based on the the type of workflow step (the only currently valid
action is True/False for pause steps).
Parameters
• workflow_id (str) -- Encoded workflow ID
• 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(workflow_id: str, invocation_id: str) -> Dict[str, Any]
Get a workflow invocation object representing the scheduling of a workflow. This object may
be sparse at first (missing inputs and invocation steps) and will become more populated as
the workflow is actually scheduled.
Parameters
• workflow_id (str) -- Encoded workflow ID
• 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(workflow_id: str, invocation_id: str, step_id: str) -> Dict[str, Any]
See the details of a particular workflow invocation step.
Parameters
• workflow_id (str) -- Encoded workflow ID
• 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'}
show_versions(workflow_id: str) -> List[Dict[str, Any]]
Get versions for a workflow.
Parameters
workflow_id (str) -- Encoded workflow ID
Return type
list of dicts
Returns
Ordered list of version descriptions for this workflow
show_workflow(workflow_id: str, version: int | None = None) -> Dict[str, Any]
Display information needed to run a workflow.
Parameters
• workflow_id (str) -- Encoded workflow ID
• version (int) -- Workflow version to show
Return type
dict
Returns
A description of the workflow and its inputs. For example:
{'id': '92c56938c2f9b315',
'inputs': {'23': {'label': 'Input Dataset', 'value': ''}},
'name': 'Simple',
'url': '/api/workflows/92c56938c2f9b315'}
update_workflow(workflow_id: str, **kwargs: Any) -> Dict[str, Any]
Update a given workflow.
Parameters
• workflow_id (str) -- Encoded workflow ID
• workflow (dict) -- dictionary representing the workflow to be updated
• name (str) -- New name of the workflow
• annotation (str) -- New annotation for the workflow
• menu_entry (bool) -- Whether the workflow should appear in the user's menu
• tags (list of str) -- Replace workflow tags with the given list
• published (bool) -- Whether the workflow should be published or unpublished
Return type
dict
Returns
Dictionary representing the updated workflow
Object-oriented Galaxy API
Client
Wrappers
Usage documentation
This page describes some sample use cases for the Galaxy API and provides examples for these API calls.
In addition to this page, there are functional examples of complete scripts in the docs/examples
directory of the BioBlend source code repository.
Connect to a Galaxy server
To connect to a running Galaxy server, you will need an account on that Galaxy instance and an API key
for the account. Instructions on getting an API key can be found at
https://galaxyproject.org/develop/api/ .
To open a connection call:
from bioblend.galaxy import GalaxyInstance
gi = GalaxyInstance(url='http://example.galaxy.url', key='your-API-key')
We now have a GalaxyInstance object which allows us to interact with the Galaxy server under our account,
and access our data. If the account is a Galaxy admin account we also will be able to use this connection
to carry out admin actions.
View Histories and Datasets
Methods for accessing histories and datasets are grouped under GalaxyInstance.histories.* and
GalaxyInstance.datasets.* respectively.
To get information on the Histories currently in your account, call:
>>> gi.histories.get_histories()
[{'id': 'f3c2b0f3ecac9f02',
'name': 'RNAseq_DGE_BASIC_Prep',
'url': '/api/histories/f3c2b0f3ecac9f02'},
{'id': '8a91dcf1866a80c2',
'name': 'June demo',
'url': '/api/histories/8a91dcf1866a80c2'}]
This returns a list of dictionaries containing basic metadata, including the id and name of each History.
In this case, we have two existing Histories in our account, 'RNAseq_DGE_BASIC_Prep' and 'June demo'. To
get more detailed information about a History we can pass its id to the show_history method:
>>> gi.histories.show_history('f3c2b0f3ecac9f02', contents=False)
{'annotation': '',
'contents_url': '/api/histories/f3c2b0f3ecac9f02/contents',
'id': 'f3c2b0f3ecac9f02',
'name': 'RNAseq_DGE_BASIC_Prep',
'nice_size': '93.5 MB',
'state': 'ok',
'state_details': {'discarded': 0,
'empty': 0,
'error': 0,
'failed_metadata': 0,
'new': 0,
'ok': 7,
'paused': 0,
'queued': 0,
'running': 0,
'setting_metadata': 0,
'upload': 0},
'state_ids': {'discarded': [],
'empty': [],
'error': [],
'failed_metadata': [],
'new': [],
'ok': ['d6842fb08a76e351',
'10a4b652da44e82a',
'81c601a2549966a0',
'a154f05e3bcee26b',
'1352fe19ddce0400',
'06d549c52d753e53',
'9ec54455d6279cc7'],
'paused': [],
'queued': [],
'running': [],
'setting_metadata': [],
'upload': []}}
This gives us a dictionary containing the History's metadata. With contents=False (the default), we only
get a list of ids of the datasets contained within the History; with contents=True we would get metadata
on each dataset. We can also directly access more detailed information on a particular dataset by passing
its id to the show_dataset method:
>>> gi.datasets.show_dataset('10a4b652da44e82a')
{'data_type': 'fastqsanger',
'deleted': False,
'file_size': 16527060,
'genome_build': 'dm3',
'id': 17499,
'metadata_data_lines': None,
'metadata_dbkey': 'dm3',
'metadata_sequences': None,
'misc_blurb': '15.8 MB',
'misc_info': 'Noneuploaded fastqsanger file',
'model_class': 'HistoryDatasetAssociation',
'name': 'C1_R2_1.chr4.fq',
'purged': False,
'state': 'ok',
'visible': True}
Uploading Datasets to a History
To upload a local file to a Galaxy server, you can run the upload_file method, supplying the path to a
local file:
>>> gi.tools.upload_file('test.txt', 'f3c2b0f3ecac9f02')
{'implicit_collections': [],
'jobs': [{'create_time': '2015-07-28T17:52:39.756488',
'exit_code': None,
'id': '9752b387803d3e1e',
'model_class': 'Job',
'state': 'new',
'tool_id': 'upload1',
'update_time': '2015-07-28T17:52:39.987509'}],
'output_collections': [],
'outputs': [{'create_time': '2015-07-28T17:52:39.331176',
'data_type': 'galaxy.datatypes.data.Text',
'deleted': False,
'file_ext': 'auto',
'file_size': 0,
'genome_build': '?',
'hda_ldda': 'hda',
'hid': 16,
'history_content_type': 'dataset',
'history_id': 'f3c2b0f3ecac9f02',
'id': '59c76a119581e190',
'metadata_data_lines': None,
'metadata_dbkey': '?',
'misc_blurb': None,
'misc_info': None,
'model_class': 'HistoryDatasetAssociation',
'name': 'test.txt',
'output_name': 'output0',
'peek': '<table cellspacing="0" cellpadding="3"></table>',
'purged': False,
'state': 'queued',
'tags': [],
'update_time': '2015-07-28T17:52:39.611887',
'uuid': 'ff0ee99b-7542-4125-802d-7a193f388e7e',
'visible': True}]}
If files are greater than 2GB in size, they will need to be uploaded via FTP. Importing files from the
user's FTP folder can be done via running the upload tool again:
>>> gi.tools.upload_from_ftp('test.txt', 'f3c2b0f3ecac9f02')
{'implicit_collections': [],
'jobs': [{'create_time': '2015-07-28T17:57:43.704394',
'exit_code': None,
'id': '82b264d8c3d11790',
'model_class': 'Job',
'state': 'new',
'tool_id': 'upload1',
'update_time': '2015-07-28T17:57:43.910958'}],
'output_collections': [],
'outputs': [{'create_time': '2015-07-28T17:57:43.209041',
'data_type': 'galaxy.datatypes.data.Text',
'deleted': False,
'file_ext': 'auto',
'file_size': 0,
'genome_build': '?',
'hda_ldda': 'hda',
'hid': 17,
'history_content_type': 'dataset',
'history_id': 'f3c2b0f3ecac9f02',
'id': 'a676e8f07209a3be',
'metadata_data_lines': None,
'metadata_dbkey': '?',
'misc_blurb': None,
'misc_info': None,
'model_class': 'HistoryDatasetAssociation',
'name': 'test.txt',
'output_name': 'output0',
'peek': '<table cellspacing="0" cellpadding="3"></table>',
'purged': False,
'state': 'queued',
'tags': [],
'update_time': '2015-07-28T17:57:43.544407',
'uuid': '2cbe8f0a-4019-47c4-87e2-005ce35b8449',
'visible': True}]}
View Data Libraries
Methods for accessing Data Libraries are grouped under GalaxyInstance.libraries.*. Most Data Library
methods are available to all users, but as only administrators can create new Data Libraries within
Galaxy, the create_folder and create_library methods can only be called using an API key belonging to an
admin account.
We can view the Data Libraries available to our account using:
>>> gi.libraries.get_libraries()
[{'id': '8e6f930d00d123ea',
'name': 'RNA-seq workshop data',
'url': '/api/libraries/8e6f930d00d123ea'},
{'id': 'f740ab636b360a70',
'name': '1000 genomes',
'url': '/api/libraries/f740ab636b360a70'}]
This gives a list of metadata dictionaries with basic information on each library. We can get more
information on a particular Data Library by passing its id to the show_library method:
>>> gi.libraries.show_library('8e6f930d00d123ea')
{'contents_url': '/api/libraries/8e6f930d00d123ea/contents',
'description': 'RNA-Seq workshop data',
'name': 'RNA-Seq',
'synopsis': 'Data for the RNA-Seq tutorial'}
Upload files to a Data Library
We can get files into Data Libraries in several ways: by uploading from our local machine, by retrieving
from a URL, by passing the new file content directly into the method, or by importing a file from the
filesystem on the Galaxy server.
For instance, to upload a file from our machine we might call:
>>> gi.libraries.upload_file_from_local_path('8e6f930d00d123ea', '/local/path/to/mydata.fastq', file_type='fastqsanger')
Note that we have provided the id of the destination Data Library, and in this case we have specified the
type that Galaxy should assign to the new dataset. The default value for file_type is 'auto', in which
case Galaxy will attempt to guess the dataset type.
View Workflows
Methods for accessing workflows are grouped under GalaxyInstance.workflows.*.
To get information on the Workflows currently in your account, use:
>>> gi.workflows.get_workflows()
[{'id': 'e8b85ad72aefca86',
'name': 'TopHat + cufflinks part 1',
'url': '/api/workflows/e8b85ad72aefca86'},
{'id': 'b0631c44aa74526d',
'name': 'CuffDiff',
'url': '/api/workflows/b0631c44aa74526d'}]
This returns a list of metadata dictionaries. We can get the details of a particular Workflow, including
its steps, by passing its id to the show_workflow method:
>>> gi.workflows.show_workflow('e8b85ad72aefca86')
{'id': 'e8b85ad72aefca86',
'inputs': {'252': {'label': 'Input RNA-seq fastq', 'value': ''}},
'name': 'TopHat + cufflinks part 1',
'steps': {'250': {'id': 250,
'input_steps': {'input1': {'source_step': 252,
'step_output': 'output'}},
'tool_id': 'tophat',
'type': 'tool'},
'251': {'id': 251,
'input_steps': {'input': {'source_step': 250,
'step_output': 'accepted_hits'}},
'tool_id': 'cufflinks',
'type': 'tool'},
'252': {'id': 252,
'input_steps': {},
'tool_id': None,
'type': 'data_input'}},
'url': '/api/workflows/e8b85ad72aefca86'}
Export or import a workflow
Workflows can be exported from or imported into Galaxy. This makes it possible to archive workflows, or
to move them between Galaxy instances.
To export a workflow, we can call:
>>> workflow_dict = gi.workflows.export_workflow_dict('e8b85ad72aefca86')
This gives us a complex dictionary representing the workflow. We can import this dictionary as a new
workflow with:
>>> gi.workflows.import_workflow_dict(workflow_dict)
{'id': 'c0bacafdfe211f9a',
'name': 'TopHat + cufflinks part 1 (imported from API)',
'url': '/api/workflows/c0bacafdfe211f9a'}
This call returns a dictionary containing basic metadata on the new workflow. Since in this case we have
imported the dictionary into the original Galaxy instance, we now have a duplicate of the original
workflow in our account:
>>> gi.workflows.get_workflows()
[{'id': 'c0bacafdfe211f9a',
'name': 'TopHat + cufflinks part 1 (imported from API)',
'url': '/api/workflows/c0bacafdfe211f9a'},
{'id': 'e8b85ad72aefca86',
'name': 'TopHat + cufflinks part 1',
'url': '/api/workflows/e8b85ad72aefca86'},
{'id': 'b0631c44aa74526d',
'name': 'CuffDiff',
'url': '/api/workflows/b0631c44aa74526d'}]
Instead of using dictionaries directly, workflows can be exported to or imported from files on the local
disk using the export_workflow_to_local_path and import_workflow_from_local_path methods. See the API
reference for details.
NOTE:
If we export a workflow from one Galaxy instance and import it into another, Galaxy will only run it
without modification if it has the same versions of the tool wrappers installed. This is to ensure
reproducibility. Otherwise, we will need to manually update the workflow to use the new tool versions.
Invoke a workflow
To invoke a workflow, we need to tell Galaxy which datasets to use for which workflow inputs. We can use
datasets from histories or data libraries.
Examine the workflow above. We can see that it takes only one input file. That is:
>>> wf = gi.workflows.show_workflow('e8b85ad72aefca86')
>>> wf['inputs']
{'252': {'label': 'Input RNA-seq fastq', 'value': ''}}
There is one input, labelled 'Input RNA-seq fastq'. This input is passed to the Tophat tool and should be
a fastq file. We will use the dataset we examined above, under View Histories and Datasets, which had
name 'C1_R2_1.chr4.fq' and id '10a4b652da44e82a'.
To specify the inputs, we build a data map and pass this to the invoke_workflow method. This data map is
a nested dictionary object which maps inputs to datasets. We call:
>>> datamap = {'252': {'src':'hda', 'id':'10a4b652da44e82a'}}
>>> gi.workflows.invoke_workflow('e8b85ad72aefca86', inputs=datamap, history_name='New output history')
{'history': '0a7b7992a7cabaec',
'outputs': ['33be8ad9917d9207',
'fbee1c2dc793c114',
'85866441984f9e28',
'1c51aa78d3742386',
'a68e8770e52d03b4',
'c54baf809e3036ac',
'ba0db8ce6cd1fe8f',
'c019e4cf08b2ac94']}
In this case the only input id is '252' and the corresponding dataset id is '10a4b652da44e82a'. We have
specified the dataset source to be 'hda' (HistoryDatasetAssociation) since the dataset is stored in a
History. See the API reference for allowed dataset specifications. We have also requested that a new
History be created and used to store the results of the run, by setting history_name='New output
history'.
The invoke_workflow call submits all the jobs which need to be run to the Galaxy workflow engine, with
the appropriate dependencies so that they will run in order. The call returns immediately, so we can
continue to submit new jobs while waiting for this workflow to execute. invoke_workflow returns the a
dictionary describing the workflow invocation.
If we view the output History immediately after calling invoke_workflow, we will see something like:
>>> gi.histories.show_history('0a7b7992a7cabaec')
{'annotation': '',
'contents_url': '/api/histories/0a7b7992a7cabaec/contents',
'id': '0a7b7992a7cabaec',
'name': 'New output history',
'nice_size': '0 bytes',
'state': 'queued',
'state_details': {'discarded': 0,
'empty': 0,
'error': 0,
'failed_metadata': 0,
'new': 0,
'ok': 0,
'paused': 0,
'queued': 8,
'running': 0,
'setting_metadata': 0,
'upload': 0},
'state_ids': {'discarded': [],
'empty': [],
'error': [],
'failed_metadata': [],
'new': [],
'ok': [],
'paused': [],
'queued': ['33be8ad9917d9207',
'fbee1c2dc793c114',
'85866441984f9e28',
'1c51aa78d3742386',
'a68e8770e52d03b4',
'c54baf809e3036ac',
'ba0db8ce6cd1fe8f',
'c019e4cf08b2ac94'],
'running': [],
'setting_metadata': [],
'upload': []}}
In this case, because the submitted jobs have not had time to run, the output History contains 8 datasets
in the 'queued' state and has a total size of 0 bytes. If we make this call again later we should instead
see completed output files.
View Users
Methods for managing users are grouped under GalaxyInstance.users.*. User management is only available to
Galaxy administrators, that is, the API key used to connect to Galaxy must be that of an admin account.
To get a list of users, call:
>>> gi.users.get_users()
[{'email': 'userA@example.org',
'id': '975a9ce09b49502a',
'quota_percent': None,
'url': '/api/users/975a9ce09b49502a'},
{'email': 'userB@example.org',
'id': '0193a95acf427d2c',
'quota_percent': None,
'url': '/api/users/0193a95acf427d2c'}]
Using BioBlend for raw API calls
BioBlend can be used to make HTTP requests to the Galaxy API in a more convenient way than using e.g. the
requests Python library. There are 5 available methods corresponding to the most common HTTP methods:
make_get_request, make_post_request, make_put_request, make_delete_request and make_patch_request. One
advantage of using these methods is that the API keys stored in the GalaxyInstance object is
automatically added to the request.
To make a GET request to the Galaxy API with BioBlend, call:
>>> gi.make_get_request(gi.base_url + "/api/version").json()
{'version_major': '19.05',
'extra': {}}
To make a POST request to the Galaxy API with BioBlend, call:
>>> gi.make_post_request(gi.base_url + "/api/histories", payload={"name": "test history"})
{'importable': False,
'create_time': '2019-07-05T20:10:04.823716',
'contents_url': '/api/histories/a77b3f95070d689a/contents',
'id': 'a77b3f95070d689a',
'size': 0, 'user_id': '5b732999121d4593',
'username_and_slug': None,
'annotation': None,
'state_details': {'discarded': 0,
'ok': 0,
'failed_metadata': 0,
'upload': 0,
'paused': 0,
'running': 0,
'setting_metadata': 0,
'error': 0,
'new': 0,
'queued': 0,
'empty': 0},
'state': 'new',
'empty': True,
'update_time': '2019-07-05T20:10:04.823742',
'tags': [],
'deleted': False,
'genome_build': None,
'slug': None,
'name': 'test history',
'url': '/api/histories/a77b3f95070d689a',
'state_ids': {'discarded': [],
'ok': [],
'failed_metadata': [],
'upload': [],
'paused': [],
'running': [],
'setting_metadata': [],
'error': [],
'new': [],
'queued': [],
'empty': []},
'published': False,
'model_class': 'History',
'purged': False}
Toolshed API
API used to interact with the Galaxy Toolshed, including repository management.
API documentation for interacting with the Galaxy Toolshed
ToolShedInstance
Categories
Repositories
Tools
CONFIGURATION
BioBlend allows library-wide configuration to be set in external files. These configuration files can be
used to specify access keys, for example.
Configuration documents for BioBlend
BioBlend
exception bioblend.ConnectionError(message: str, body: bytes | str | None = None, status_code: int | None
= None)
An exception class that is raised when unexpected HTTP responses come back.
Should make it easier to debug when strange HTTP things happen such as a proxy server getting in
the way of the request etc. @see: body attribute to see the content of the http response
class bioblend.NullHandler(level=0)
Initializes the instance - basically setting the formatter to None and the filter list to empty.
emit(record: LogRecord) -> None
Do whatever it takes to actually log the specified logging record.
This version is intended to be implemented by subclasses and so raises a
NotImplementedError.
exception bioblend.TimeoutException
bioblend.get_version() -> str
Returns a string with the current version of the library (e.g., "0.2.0")
bioblend.init_logging() -> None
Initialize BioBlend's logging from a configuration file.
bioblend.set_file_logger(name: str, filepath: str, level: int | str = 20, format_string: str | None =
None) -> None
bioblend.set_stream_logger(name: str, level: int | str = 10, format_string: str | None = None) -> None
Config
class bioblend.config.Config(path: str | None = None, fp: IO[str] | None = None, do_load: bool = True)
BioBlend allows library-wide configuration to be set in external files. These configuration files
can be used to specify access keys, for example. By default we use two locations for the BioBlend
configurations:
• System wide: /etc/bioblend.cfg
• Individual user: ~/.bioblend (which works on both Windows and Unix)
TESTING
If you would like to do more than just a mock test, you need to point BioBlend to an instance of Galaxy.
Do so by exporting the following two variables:
$ export BIOBLEND_GALAXY_URL=http://127.0.0.1:8080
$ export BIOBLEND_GALAXY_API_KEY=<API key>
The unit tests, stored in the tests folder, can be run using pytest. From the project root:
$ pytest
GETTING HELP
If you have run into issues, found a bug, or can't seem to find an answer to your question regarding the
use and functionality of BioBlend, please use the Github Issues page to ask your question.
RELATED DOCUMENTATION
Links to other documentation and libraries relevant to this library:
• Galaxy API documentation
• Blend4j: Galaxy API wrapper for Java
• clj-blend: Galaxy API wrapper for Clojure
INDICES AND TABLES
• Index
• Module Index
• Search Page
AUTHOR
Galaxy Project
COPYRIGHT
2012-2024, Galaxy Project
1.2.0 Jan 06, 2024 BIOBLEND(1)