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