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BIOBLEND(1) BioBlend BIOBLEND(1)

NAME

bioblend - BioBlend Documentation

ABOUT

BioBlend is a Python library for interacting with Galaxy and CloudMan APIs.

BioBlend is supported and tested on:

  • Python 3.7, 3.8, 3.9 and 3.10
  • Galaxy release 17.09 and later.

BioBlend's goal is to make it easier to script and automate the running of Galaxy analyses, administering of a Galaxy server, and cloud infrastructure provisioning and scaling via CloudMan. 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)


•
Create a CloudMan compute cluster, via an API and directly from your local machine:

from bioblend.cloudman import CloudManConfig
from bioblend.cloudman import CloudManInstance
cfg = CloudManConfig('<your cloud access key>', '<your cloud secret key>', 'My CloudMan',  'ami-<ID>', 'm1.small', '<password>')
cmi = CloudManInstance.launch_instance(cfg)
cmi.get_status()


•
Reconnect to an existing CloudMan instance and manipulate it:

from bioblend.cloudman import CloudManInstance
cmi = CloudManInstance("<instance IP>", "<password>")
cmi.add_nodes(3)
cluster_status = cmi.get_status()
cmi.remove_nodes(2)



NOTE:

Although this library allows you to blend these two services into a cohesive unit, the library itself can be used with either service irrespective of the other. For example, you can use it to just manipulate CloudMan clusters or to script the interactions with an instance of Galaxy running on your laptop.


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.

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 a list of attributes about the Galaxy instance. More attributes will be present if the user is an admin.
list
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 the current version of the Galaxy instance.
dict
Version of the Galaxy instance For example:

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





Return information about the current authenticated user.
dict
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

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

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


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

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

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

  • 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.

list
A list of datasets



Make a dataset publicly available or private. For more fine-grained control (assigning different permissions to specific roles), use the update_permissions() method.
  • dataset_id (str) -- dataset ID
  • published (bool) -- Whether to make the dataset published (True) or private (False).

dict
Current roles for all available permission types.

NOTE:

This method works only on Galaxy 19.05 or later.



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

dict
Information about the HDA or LDDA


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

dict
Current roles for all available permission types.

NOTE:

This method works only on Galaxy 19.05 or later.



Wait until a dataset is in a terminal state.
  • 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'.

dict
Details of the given dataset.







----



Dataset collections





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 a history dataset collection as an archive.
  • dataset_collection_id (str) -- Encoded dataset collection ID
  • file_path (str) -- The path to which the archive will be downloaded

dict
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. This method works only on Galaxy 18.01 or later.




Get details of a given dataset collection of the current user
  • dataset_collection_id (str) -- dataset collection ID
  • instance_type (str) -- instance type of the collection - 'history' or 'library'

dict
element view of the dataset collection


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

dict
Details of the given dataset collection.







----



Datatypes

Contains possible interactions with the Galaxy Datatype

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 the list of all installed datatypes.
  • extension_only (bool) -- Return only the extension rather than the datatype name
  • upload_only (bool) -- Whether to return only datatypes which can be uploaded

list
A list of datatype names. For example:

['snpmatrix',


 'snptest',


 'tabular',


 'taxonomy',


 'twobit',


 'txt',


 'vcf',


 'wig',


 'xgmml',


 'xml']




Get the list of all installed sniffers.
list
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']







----



Folders

Contains possible interactions with the Galaxy library folders

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 a folder.
  • parent_folder_id (str) -- Folder's description
  • name (str) -- name of the new folder
  • description (str) -- folder's description

dict
details of the updated folder


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

detailed folder information
dict


Get the permissions of a folder.
  • folder_id (str) -- the folder's encoded id, prefixed by 'F'
  • scope (str) -- scope of permissions, either 'current' or 'available'

dict
dictionary including details of the folder



Set the permissions of a folder.
  • 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

dict
dictionary including details of the folder


Display information about a folder.
  • 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.

dict
dictionary including details of the folder


Update folder information.
  • folder_id (str) -- the folder's encoded id, prefixed by 'F'
  • name (str) -- name of the new folder
  • description (str) -- folder's description

dict
details of the updated folder




----



Forms

Contains possible interactions with the Galaxy Forms

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 a new form.
form_xml_text (str) -- Form xml to create a form on galaxy instance
str
Unique URL of newly created form with encoded id


Get the list of all forms.
list
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'}]





Get details of a given form.
form_id (str) -- Encoded form ID
dict
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

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 a list of local files.
deleted (bool) -- Whether to include deleted files
list
A list of dicts with details on individual files on FTP





----



Genomes

Contains possible interactions with the Galaxy Histories

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' | ...

Returns a list of installed genomes
list
List of installed genomes


Download and/or index a genome.
  • 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
  • source (str) -- Data source for this build. Can be: UCSC, Ensembl, NCBI, URL
  • indexers (list) -- POST array of indexers to run after downloading (indexers[] = first, indexers[] = second, ...)
  • func (str) -- Allowed values: 'download', Download and index; 'index', Index only

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



Returns information about build <id>
  • id (str) -- Genome build ID to use
  • num (str) -- num
  • chrom (str) -- chrom
  • low (str) -- low
  • high (str) -- high

dict
Information about the genome build



Groups

Contains possible interactions with the Galaxy Groups

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 a role to the given group.
  • group_id (str) -- Encoded group ID
  • role_id (str) -- Encoded role ID to add to the group

dict
Added group role's info


Add a user to the given group.
  • group_id (str) -- Encoded group ID
  • user_id (str) -- Encoded user ID to add to the group

dict
Added group user's info


Create a new group.
  • 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

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

[{'id': '7c9636938c3e83bf',


  'model_class': 'Group',


  'name': 'My Group Name',


  'url': '/api/groups/7c9636938c3e83bf'}]




Remove a role from the given group.
  • group_id (str) -- Encoded group ID
  • role_id (str) -- Encoded role ID to remove from the group

dict
The role which was removed


Remove a user from the given group.
  • group_id (str) -- Encoded group ID
  • user_id (str) -- Encoded user ID to remove from the group

dict
The user which was removed


Get the list of roles associated to the given group.
group_id (str) -- Encoded group ID
list of dicts
List of group roles' info


Get the list of users associated to the given group.
group_id (str) -- Encoded group ID
list of dicts
List of group users' info


Get all (not deleted) groups.
list
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'}]





Get details of a given group.
group_id (str) -- Encoded group ID
dict
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 a group.
  • 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)

None
None




----



Histories

Contains possible interactions with the Galaxy Histories

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

dict
Information about the copied content


Copy a dataset to a history.
  • 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'

dict
Information about the copied dataset


Create a new dataset collection
  • 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'}



dict
Information about the new HDCA


Create a new history, optionally setting the name.
name (str) -- Optional name for new history
dict
Dictionary containing information about newly created history


Create history tag
  • history_id (str) -- Encoded history ID
  • tag (str) -- Add tag to history

dict
A dictionary with information regarding the tag. For example:

{'id': 'f792763bee8d277a',


 'model_class': 'HistoryTagAssociation',


 'user_tname': 'NGS_PE_RUN',


 'user_value': None}




Mark corresponding dataset as deleted.
  • history_id (str) -- Encoded history ID
  • dataset_id (str) -- Encoded dataset ID
  • purge (bool) -- if True, also purge (permanently delete) the dataset

None
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.



Mark corresponding dataset collection as deleted.
  • history_id (str) -- Encoded history ID
  • dataset_collection_id (str) -- Encoded dataset collection ID

None
None


Delete a history.
  • history_id (str) -- Encoded history ID
  • purge (bool) -- if True, also purge (permanently delete) the history

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

None
None


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

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


Get extra files associated with a composite dataset, or an empty list if there are none.
  • history_id (str) -- history ID
  • dataset_id (str) -- dataset ID

list
List of extra files

NOTE:

This method works only on Galaxy 19.01 or later.



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

list
List of history dicts.

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


Returns the current user's most recently used history (not deleted).
dict
History representation


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

list
List of history dicts.


Returns the state of this history
history_id (str) -- Encoded history ID
dict
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.


Import a history from an archive on disk or a URL.
  • file_path (str) -- Path to exported history archive on disk.
  • url (str) -- URL for an exported history archive




Open Galaxy in a new tab of the default web browser and switch to the specified history.
history_id (str) -- ID of the history to switch to
NoneType
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.



Get details about a given history dataset.
  • history_id (str) -- Encoded history ID
  • dataset_id (str) -- Encoded dataset ID

dict
Information about the dataset


Get details about a given history dataset collection.
  • history_id (str) -- Encoded history ID
  • dataset_collection_id (str) -- Encoded dataset collection ID

dict
Information about the dataset collection


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

dict
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'}




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

dict or list of dicts
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.



Get dataset details for matching datasets within a history.
  • 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

list
List of dictionaries


Undelete a history
history_id (str) -- Encoded history ID
str
'OK' if it was deleted


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

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

dict
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 metadata information. Some of the attributes that can be modified are documented below.
  • 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

dict
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 a dataset into the history from a library. Requires the library dataset ID, which can be obtained from the library contents.
  • history_id (str) -- Encoded history ID
  • lib_dataset_id (str) -- Encoded library dataset ID

dict
Information about the newly created HDA




----



Invocations

Contains possible interactions with the Galaxy workflow invocations

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 the scheduling of a workflow.
invocation_id (str) -- Encoded workflow invocation ID
dict
The workflow invocation being cancelled


Get a BioCompute object for an invocation.
invocation_id (str) -- Encoded workflow invocation ID
dict
The BioCompute object


Get a Markdown report for an invocation.
invocation_id (str) -- Encoded workflow invocation ID
dict
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 a PDF report for an invocation.
  • invocation_id (str) -- Encoded workflow invocation ID
  • file_path (str) -- Path to save the report



Get a detailed summary of an invocation, listing all jobs with their job IDs and current states.
invocation_id (str) -- Encoded workflow invocation ID
list of dicts
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 a summary of an invocation, stating the number of jobs which succeed, which are paused and which have errored.
invocation_id (str) -- Encoded workflow invocation ID
dict
The invocation summary. For example:

{'states': {'paused': 4, 'error': 2, 'ok': 2},


 'model': 'WorkflowInvocation',


 'id': 'a799d38679e985db',


 'populated_state': 'ok'}




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

list
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'}]





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

dict
A dict describing the new workflow invocation.

NOTE:

This method works only on Galaxy 21.01 or later.



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).
  • 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.

dict
Representation of the workflow invocation step


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.
invocation_id (str) -- Encoded workflow invocation ID
dict
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'}




See the details of a particular workflow invocation step.
  • invocation_id (str) -- Encoded workflow invocation ID
  • step_id (str) -- Encoded workflow invocation step ID

dict
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 until an invocation is in a terminal state.
  • 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'.

dict
Details of the workflow invocation.




----



Jobs

Contains possible interactions with the Galaxy Jobs

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 a job, deleting output datasets.
job_id (str) -- job ID
bool
True if the job was successfully cancelled, False if it was already in a terminal state before the cancellation.


Query inputs and jobs for common potential problems that might have resulted in job failure.
job_id (str) -- job ID
dict
dict containing potential problems

NOTE:

This method works only on Galaxy 19.05 or later.



Get destination parameters for a job, describing the environment and location where the job is run.
job_id (str) -- job ID
dict
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 dataset inputs used by a job.
job_id (str) -- job ID
list of dicts
Inputs for the given job


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.

  • 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).

list of dict
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 options work only on Galaxy 21.05 or later: user_id, limit, offset, workflow_id, invocation_id.



Return job metrics for a given job.
job_id (str) -- job ID
list
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 dataset outputs produced by a job.
job_id (str) -- job ID
list of dicts
Outputs of the given job


Display the current state for a given job of the current user.
job_id (str) -- job ID
str
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.



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

dict
dict containing job error reply

NOTE:

This method works only on Galaxy 20.01 or later.



Rerun a job.
  • 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.

dict
Information about outputs and the rerun job

NOTE:

This method works only on Galaxy 21.01 or later.



Resume a job if it is paused.
job_id (str) -- job ID
dict
dict containing output dataset associations

NOTE:

This method works only on Galaxy 18.09 or later.



Return jobs matching input 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

list of dicts
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.

NOTE:

This method works only on Galaxy 18.01 or later.



Get details of a given job of the current user.
  • job_id (str) -- job ID
  • full_details (bool) -- when True, the complete list of details for the given job.

dict
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 whether the job lock is active or not. If it is active, no jobs will dispatch on the Galaxy server.
bool
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 the job lock status by setting active to either True or False. If True, all job dispatching will be blocked.
bool
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 until a job is in a terminal state.
  • 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'.

dict
Details of the given job.




----



Libraries

Contains possible interactions with the Galaxy Data Libraries

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 a Galaxy dataset into a library.
  • 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

dict
LDDA information


Create a folder in a library.
  • 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

list
List with a single dictionary containing information about the new folder


Create a data library with the properties defined in the arguments.
  • name (str) -- Name of the new data library
  • description (str) -- Optional data library description
  • synopsis (str) -- Optional data library synopsis

dict
Details of the created library. For example:

{'id': 'f740ab636b360a70',


 'name': 'Library from bioblend',


 'url': '/api/libraries/f740ab636b360a70'}




Delete a data library.
library_id (str) -- Encoded data library ID identifying the library to be deleted
dict
Information about the deleted library

WARNING:

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



Delete a library dataset in a data library.
  • 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)

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

{'deleted': True,


 'id': '60e680a037f41974'}




Get the permissions for a dataset.
dataset_id (str) -- id of the dataset
dict
dictionary with all applicable permissions' values


Get all the folders in a library, or select a subset by specifying a folder name for filtering.
  • library_id (str) -- library id to use
  • folder_id (str) --

    filter for folder by folder id

    Deprecated since version 0.16.0: To get details of a folder for which you know the ID, use the much more efficient show_folder() instead.

  • 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.

list
list of dicts each containing basic information about a folder


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

    filter for library by library id

    Deprecated since version 0.16.0: To get details of a library for which you know the ID, use the much more efficient show_library() instead.

  • 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.

list
list of dicts each containing basic information about a library


Get the permissions for a library.
library_id (str) -- id of the library
dict
dictionary with all applicable permissions' values



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

dict
dictionary with all applicable permissions' values


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

dict
General information about the library


Get details about a given library dataset. The required library_id can be obtained from the datasets's library content details.
  • library_id (str) -- library id where dataset is found in
  • dataset_id (str) -- id of the dataset to be inspected

dict
A dictionary containing information about the dataset in the library


Get details about a given folder. The required folder_id can be obtained from the folder's library content details.
  • library_id (str) -- library id to inspect folders in
  • folder_id (str) -- id of the folder to be inspected

dict
Information about the folder


Get information about a library.
  • library_id (str) -- filter for library by library id
  • contents (bool) -- whether to get contents of the library (rather than just the library details)

dict
details of the given library


Update library dataset metadata. Some of the attributes that can be modified are documented below.
  • dataset_id (str) -- id of the dataset to be deleted
  • 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

dict
details of the updated dataset


Upload pasted_content to a data library as a new file.
  • 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

list
List with a single dictionary containing information about the LDDA


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

list
List with a single dictionary containing information about the LDDA


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

list
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 a file to a library from a URL.
  • 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

list
List with a single dictionary containing information about the LDDA


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

list
List with a single dictionary containing information about the 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 until the library dataset state is terminal ('ok', 'empty', 'error', 'discarded' or 'failed_metadata').
  • 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.

dict
A dictionary containing information about the dataset in the library




----



Quotas

Contains possible interactions with the Galaxy Quota

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 a new quota
  • 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.

dict
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 a quota

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

quota_id (str) -- Encoded quota ID.
str
A description of the changes, mentioning the deleted quota. For example:

"Deleted 1 quotas: Testing-B"




Get a list of quotas
deleted (bool) -- Only return quota(s) that have been deleted
list
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'}]





Display information on a quota
  • quota_id (str) -- Encoded quota ID
  • deleted (bool) -- Search for quota in list of ones already marked as deleted

dict
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 a quota
quota_id (str) -- Encoded quota ID.
str
A description of the changes, mentioning the undeleted quota. For example:

"Undeleted 1 quotas: Testing-B"




Update an existing quota
  • 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.

str
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

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 a new role.
  • 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

dict
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.


Displays a collection (list) of roles.
list
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"}]





Display information on a single role
role_id (str) -- Encoded role ID
dict
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

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 an item from a data table.
  • 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

dict
Remaining contents of the given data table


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

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


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





Reload a data table.
data_table_id (str) -- ID of the data table
dict
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'}




Get details of a given data table.
data_table_id (str) -- ID of the data table
dict
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.

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' | ...


Summarize requirements across toolbox (for Tool Management grid).
  • 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.

list of dicts
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.





----



ToolShed


----



Users


----



Visual


----



Workflows

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

CloudMan API

API used to manipulate the instantiated infrastructure. For example, scale the size of the compute cluster, get infrastructure status, get service status.

API documentation for interacting with CloudMan

CloudManLauncher

CloudManInstance

Usage documentation

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

Setting up custom cloud properties

CloudMan supports Amazon, OpenStack, OpenNebula, and Eucalyptus based clouds and BioBlend can be used to programatically manipulate CloudMan on any of those clouds. Once launched, the API calls to CloudMan are the same irrespective of the cloud. In order to launch an instance on a given cloud, cloud properties need to be provided to CloudManLauncher. If cloud properties are not specified, CloudManLauncher will default to Amazon cloud properties.

If we want to use a different cloud provider, we need to specify additional cloud properties when creating an instance of the CloudManLauncher class. For example, if we wanted to create a connection to NeCTAR, Australia's national research cloud, we would use the following properties:

from bioblend.util import Bunch
nectar = Bunch(


    name='NeCTAR',


    cloud_type='openstack',


    bucket_default='cloudman-os',


    region_name='NeCTAR',


    region_endpoint='nova.rc.nectar.org.au',


    ec2_port=8773,


    ec2_conn_path='/services/Cloud',


    cidr_range='115.146.92.0/22',


    is_secure=True,


    s3_host='swift.rc.nectar.org.au',


    s3_port=8888,


    s3_conn_path='/')


NOTE:

These properties are cloud-specific and need to be obtained from a given cloud provider.


Launching a new cluster instance

In order to launch a CloudMan cluster on a chosen cloud, we do the following (continuing from the previous example):

from bioblend.cloudman import CloudManConfig
from bioblend.cloudman import CloudManInstance
cmc = CloudManConfig('<your AWS access key', 'your AWS secret key', 'Cluster name',


     'ami-<ID>', 'm1.medium', 'choose_a_password_here', nectar)
cmi = CloudManInstance.launch_instance(cmc)


NOTE:

If you already have an existing instance of CloudMan, just create an instance of the CloudManInstance object directly by calling its constructor and connecting to it (the password you provide must match the password you provided as part of user data when launching this instance). For example: 

cmi = CloudManInstance('http://115.146.92.174', 'your_UD_password')




We now have a CloudManInstance object that allows us to manage created CloudMan instance via the API. Once launched, it will take a few minutes for the instance to boot and CloudMan start. To check on the status of the machine, (repeatedly) run the following command:

>>> cmi.get_machine_status()
{'error': '',


 'instance_state': 'pending',


 'placement': '',


 'public_ip': ''}
>>> cmi.get_machine_status()
{'error': '',


 'instance_state': 'running',


 'placement': 'melbourne-qh2',


 'public_ip': '115.146.86.29'}


Once the instance is ready, although it may still take a few moments for CloudMan to start, it is possible to start interacting with the application.

NOTE:

The CloudManInstance object (e.g., cmi) is a local representation of the actual CloudMan instance. As a result, the local object can get out of sync with the remote instance. To update the state of the local object, call the update method on the cmi object: 

>>> cmi.update()




Manipulating an existing cluster

Having a reference to a CloudManInstance object, we can manage it via the available CloudManInstance API:

>>> cmi.initialized
False
>>> cmi.initialize('SGE')
>>> cmi.get_status()
{'all_fs': [],


 'app_status': 'yellow',


 'autoscaling': {'as_max': 'N/A',


 'as_min': 'N/A',


 'use_autoscaling': False},


 'cluster_status': 'STARTING',


 'data_status': 'green',


 'disk_usage': {'pct': '0%', 'total': '0', 'used': '0'},


 'dns': '#',


 'instance_status': {'available': '0', 'idle': '0', 'requested': '0'},


 'snapshot': {'progress': 'None', 'status': 'None'}}
>>> cmi.get_cluster_size()
1
>>> cmi.get_nodes()
[{'id': 'i-00006016',


  'instance_type': 'm1.medium',


  'ld': '0.0 0.025 0.065',


  'public_ip': '115.146.86.29',


  'time_in_state': '2268'}]
>>> cmi.add_nodes(2)
{'all_fs': [],


 'app_status': 'green',


 'autoscaling': {'as_max': 'N/A',


  'as_min': 'N/A',


  'use_autoscaling': False},


 'cluster_status': 'READY',


 'data_status': 'green',


 'disk_usage': {'pct': '0%', 'total': '0', 'used': '0'},


 'dns': '#',


 'instance_status': {'available': '0', 'idle': '0', 'requested': '2'},


 'snapshot': {'progress': 'None', 'status': 'None'}}
>>> cmi.get_cluster_size()
3


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

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


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




Returns a string with the current version of the library (e.g., "0.2.0")

Initialize BioBlend's logging from a configuration file.



Config

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

Enis Afgan

COPYRIGHT

2012-2022, Enis Afgan

August 25, 2022 0.18.0