README.rst

Welcome to pyBIS!
=================

pyBIS is a Python module for interacting with openBIS, designed to be
used in Jupyter. It offers some sort of IDE for openBIS, supporting TAB
completition and input checks, making the life of a researcher hopefully
easier.

Dependencies and Requirements
-----------------------------

-  pyBIS relies the openBIS API v3
-  openBIS version 16.05.2 or newer is required
-  18.06.2 or later is recommended
-  pyBIS uses Python 3.3 and pandas

Installation
------------

::

    pip install pybis

That command will download install pybis and all its dependencies.

If you haven't done yet, install Jupyter Notebook:

::

    pip install jupyter

Usage
=====

Tab completition and other hints
--------------------------------

Used in a Jupyter Notebook environment, pybis helps you to enter the
commands. After every dot ``.`` you might hit the ``TAB`` key in order
to look at the available commands.

If you are unsure what parameters to add to a , add a question mark
right after the method and hit ``SHIFT+ENTER``. Jupyter will then look
up the signature of the method and show some helpful docstring.

When working with properties of entities, they might use a **controlled
vocabulary** or are of a specific **property type**. Add an underscore
``_`` character right after the property and hit ``SHIFT+ENTER`` to show
the valid values. When a property only acceps a controlled vocabulary,
you will be shown the valid terms in a nicely formatted table.

connect to from OpenBIS
-----------------------

::

    from pybis import Openbis
    o = Openbis('https://example.com', verify_certificates=False)

    import getpass
    password = getpass.getpass()

    o.login('username', password, save_token=True)   # save the session token in ~/.pybis/example.com.token

Check whether the session token is still valid and log out:

::

    o.token
    o.is_session_active()
    o.logout()

browsing masterdata
-------------------

::

    o.get_experiment_types()
    et = o.get_experiment_type('TEST')
    et.get_propertyAssignments()

    o.get_sample_types()
    st = o.get_sample_type('YEAST')
    st.get_propertyAssignments()

    o.get_material_types()
    mt = o.get_material_type('GENE')
    mt.get_propertyAssignments()

    o.get_dataset_types()
    dst = o.get_dataset_types()[0]
    dst = o.get_dataset_type('RAW_DATA')
    dst.get_propertyAssignments()
    dst.get_propertyAssignments(with_vocabulary=True)

    o.get_vocabularies()
    o.get_vocabulary('BACTERIAL_ANTIBIOTIC_RESISTANCE')
    o.get_terms(vocabulary='STORAGE')
    o.get_tags()

Users, Groups and RoleAssignments
---------------------------------

::

    o.get_groups()
    group = o.new_group(code='group_name', description='...')
    group = o.get_group('group_name')
    group.save()
    group.assign_role(role='ADMIN', space='DEFAULT')
    group.get_roles() 
    group.revoke_role(role='ADMIN', space='DEFAULT')

    group.add_persons(['admin'])
    group.get_persons()
    group.del_persons(['admin'])
    group.delete()

    o.get_persons()
    person = o.new_person(userId='username')
    person.space = 'USER_SPACE'
    person.save()

    person.assign_role(role='ADMIN', space='MY_SPACE')
    person.assign_role(role='OBSERVER')
    person.get_roles()
    person.revoke_role(role='ADMIN', space='MY_SPACE')
    person.revoke_role(role='OBSERVER')

    o.get_role_assignments()
    o.get_role_assignments(space='MY_SPACE')
    o.get_role_assignments(group='MY_GROUP')
    ra = o.get_role_assignment(techId)
    ra.delete()

Spaces
------

::

    space = o.new_space(code='space_name', description='')
    space.save()
    space.delete('reason for deletion')
    o.get_spaces(
        start_with = 1,                   # start_with and count
        count = 7,                        # enable paging
    )
    o.get_space('MY_SPACE')

Projects
--------

::

    project = o.new_project(
        space=space, 
        code='project_name',
        description='some project description'
    )
    project = space.new_project( code='project_code', description='project description')
    project.save()

    o.get_projects(
        space = 'MY_SPACE',               # show only projects in MY_SPACE
        start_with = 1,                   # start_with and count
        count = 7,                        # enable paging
    )
    o.get_projects(space='MY_SPACE')
    space.get_projects()

    project.get_experiments()
    project.get_attachments()
    p.add_attachment(fileName='testfile', description= 'another file', title= 'one more attachment')
    project.download_attachments()

Samples
-------

Samples are nowadays called **Objects** in openBIS. pyBIS is not yet
thoroughly supporting this term in all methods where «sample» occurs.

NOTE: In openBIS, ``samples`` entities have recently been renamed to
``objects``. All methods have synonyms using the term ``object``, e.g.
``get_object``, ``new_object``, ``get_object_types``.

::

    sample = o.new_sample(
        type     = 'YEAST', 
        space    = 'MY_SPACE',
        experiment = '/MY_SPACE/MY_PROJECT/EXPERIMENT_1',
        parents  = [parent_sample, '/MY_SPACE/YEA66'], 
        children = [child_sample],
        props    = {"name": "some name", "description": "something interesting"}
    )
    sample = space.new_sample( type='YEAST' )
    sample.save()

    sample = o.get_sample('/MY_SPACE/MY_SAMPLE_CODE')
    sample = o.get_sample('20170518112808649-52')

    sample.space
    sample.code
    sample.permId
    sample.identifier
    sample.type  # once the sample type is defined, you cannot modify it

    sample.space
    sample.space = 'MY_OTHER_SPACE'

    sample.experiment    # a sample can belong to one experiment only
    sample.experiment = '/MY_SPACE/MY_PROJECT/MY_EXPERIMENT'

    sample.project
    sample.project = '/MY_SPACE/MY_PROJECT'  # only works if project samples are
    enabled

    sample.tags
    sample.tags = ['guten_tag', 'zahl_tag' ]

    sample.get_parents()
    sample.set_parents(['/MY_SPACE/PARENT_SAMPLE_NAME')
    sample.add_parents('/MY_SPACE/PARENT_SAMPLE_NAME')
    sample.del_parents('/MY_SPACE/PARENT_SAMPLE_NAME')

    sample.get_children()
    sample.set_children('/MY_SPACE/CHILD_SAMPLE_NAME')
    sample.add_children('/MY_SPACE/CHILD_SAMPLE_NAME')
    sample.del_children('/MY_SPACE/CHILD_SAMPLE_NAME')

    # A Sample may belong to another Sample, which acts as a container.
    # As opposed to DataSets, a Sample may only belong to one container.
    sample.container    # returns a sample object
    sample.container = '/MY_SPACE/CONTAINER_SAMPLE_NAME'   # watch out, this will change the identifier of the sample to:
                                                           # /MY_SPACE/CONTAINER_SAMPLE_NAME:SAMPLE_NAME
    sample.container = ''                                  # this will remove the container. 

    # A Sample may contain other Samples, in order to act like a container (see above)
    # The Sample-objects inside that Sample are called «components» or «contained Samples»
    # You may also use the xxx_contained() functions, which are just aliases.
    sample.get_components()
    sample.set_components('/MY_SPACE/COMPONENT_NAME')
    sample.add_components('/MY_SPACE/COMPONENT_NAME')
    sample.del_components('/MY_SPACE/COMPONENT_NAME')

    sample.get_tags()
    sample.set_tags('tag1')
    sample.add_tags(['tag2','tag3'])
    sample.del_tags('tag1')

    sample.set_props({ ... })
    sample.p                              # same thing as .props
    sample.p.my_property = "some value"   # set the value of a property (value is checked)
    sample.p + TAB                        # in IPython or Jupyter: show list of available properties
    sample.p.my_property_ + TAB           # in IPython or Jupyter: show datatype or controlled vocabulary
    sample.p['my-weird.property-name']    # accessing properties containing a dash or a dot

    sample.get_attachments()
    sample.download_attachments()
    sample.add_attachment('testfile.xls')

    samples = o.get_samples(
        space ='MY_SPACE',
        type  ='YEAST',
        tags  =['*'],                     # only sample with existing tags
        start_with = 1,                   # start_with and count
        count = 7,                        # enable paging
        NAME  = 'some name',              # properties are always uppercase 
                                          # to distinguish them from attributes
        **{ "SOME.WEIRD:PROP": "value"}   # property name contains a dot or a
                                          # colon: cannot be passed as an argument 
        props=['NAME', 'MATING_TYPE']     # show these properties in the result
    )
    samples.df                            # returns a pandas DataFrame object
    samples.get_datasets(type='ANALYZED_DATA')

Experiments
-----------

NOTE: In openBIS, ``experiment`` entities have recently been renamed to
``collection``. All methods have synonyms using the term ``collection``,
e.g. ``get_collections``, ``new_collection``, ``get_collection_types``.

::

    o.new_experiment
        type='DEFAULT_EXPERIMENT',
        space='MY_SPACE',
        project='YEASTS'
    )

    o.get_experiments(
        project='YEASTS',
        space='MY_SPACE', 
        type='DEFAULT_EXPERIMENT',
        tags='*', 
        finished_flag=False,
        props=['name', 'finished_flag']
    )
    project.get_experiments()
    exp = o.get_experiment('/MY_SPACE/MY_PROJECT/MY_EXPERIMENT')

    exp.set_props({ key: value})
    exp.props
    exp.p                              # same thing as .props
    exp.p.finished_flag=True
    exp.p.my_property = "some value"   # set the value of a property (value is checked)
    exp.p + TAB                        # in IPython or Jupyter: show list of available properties
    exp.p.my_property_ + TAB           # in IPython or Jupyter: show datatype or controlled vocabulary
    exp.p['my-weird.property-name']    # accessing properties containing a dash or a dot

    exp.attrs
    exp.a                              # same thing
    exp.attrs.tags = ['some', 'tags']
    exp.tags = ['some', 'tags']        # same thing
    exp.save()

Datasets
--------

::

    sample.get_datasets()
    ds = o.get_dataset('20160719143426517-259')
    ds.get_parents()
    ds.get_children()
    ds.sample
    ds.experiment
    ds.physicalData
    ds.status              # AVAILABLE LOCKED ARCHIVED 
                           # UNARCHIVE_PENDING ARCHIVE_PENDING BACKUP_PENDING
    ds.archive()
    ds.unarchive()

    ds.get_files(start_folder="/")
    ds.file_list
    ds.add_attachment()
    ds.get_attachments()
    ds.download_attachments()
    ds.download(destination='/tmp', wait_until_finished=False)

    ds_new = o.new_dataset(
        type       = 'ANALYZED_DATA', 
        experiment = '/SPACE/PROJECT/EXP1', 
        sample     = '/SPACE/SAMP1',
        files      = ['my_analyzed_data.dat'], 
        props      = {'name': 'some good name', 'description': '...' }
    )

    # DataSet CONTAINER (contains other DataSets, but no files)
    ds_new = o.new_dataset(
        type       = 'ANALYZED_DATA', 
        experiment = '/SPACE/PROJECT/EXP1', 
        sample     = '/SPACE/SAMP1',
        kind       = 'CONTAINER',
        props      = {'name': 'some good name', 'description': '...' }
    )
    ds_new.save()

    # get, set, add and remove parent datasets
    dataset.get_parents()
    dataset.set_parents(['20170115220259155-412'])
    dataset.add_parents(['20170115220259155-412'])
    dataset.del_parents(['20170115220259155-412'])

    # get, set, add and remove child datasets
    dataset.get_children()
    dataset.set_children(['20170115220259155-412'])
    dataset.add_children(['20170115220259155-412'])
    dataset.del_children(['20170115220259155-412'])

    # A DataSet may belong to other DataSets, which must be of kind=CONTAINER
    # As opposed to Samples, DataSets may belong (contained) to more than one DataSet-container
    dataset.get_containers()
    dataset.set_containers(['20170115220259155-412'])
    dataset.add_containers(['20170115220259155-412'])
    dataset.del_containers(['20170115220259155-412'])

    # A DataSet of kind=CONTAINER may contain other DataSets, to act like a folder (see above)
    # The DataSet-objects inside that DataSet are called components or contained DataSets
    # You may also use the xxx_contained() functions, which are just aliases.
    dataset.get_components()
    dataset.set_components(['20170115220259155-412'])
    dataset.add_components(['20170115220259155-412'])
    dataset.del_components(['20170115220259155-412'])

    ds.set_props({ key: value})
    ds.props
    ds.p                              # same thing as .props
    ds.p.my_property = "some value"   # set the value of a property
    ds.p + TAB                        # show list of available properties
    ds.p.my_property_ + TAB           # show datatype or controlled vocabulary
    ds.p['my-weird.property-name']    # accessing properties containing a dash or a dot

    # complex query with chaining.
    # properties must be in UPPERCASE
    datasets = o.get_experiments(project='YEASTS').get_samples(type='FLY').get_datasets(type='ANALYZED_DATA', props=['MY_PROPERTY'],MY_PROPERTY='some analyzed data')

    # another example
    datasets = o.get_experiment('/MY_NEW_SPACE/VERMEUL_PROJECT/MY_EXPERIMENT4').get_samples(type='UNKNOWN').get_parents().get_datasets(type='RAW_DATA')

    datasets.df                       # get a pandas dataFrame object

    # use it in a for-loop:
    for dataset in datasets:
        print(dataset.permID)
        dataset.delete('give me a reason')

Semantic Annotations
--------------------

::

    # create semantic annotation for sample type 'UNKNOWN'
    sa = o.new_semantic_annotation(
        entityType = 'UNKNOWN',
        predicateOntologyId = 'po_id',
        predicateOntologyVersion = 'po_version',
        predicateAccessionId = 'pa_id',
        descriptorOntologyId = 'do_id',
        descriptorOntologyVersion = 'do_version',
        descriptorAccessionId = 'da_id'
    )
    sa.save()

    # create semantic annotation for property type 
    # (predicate and descriptor values omitted for brevity)
    sa = o.new_semantic_annotation(propertyType = 'DESCRIPTION', ...)
    sa.save()

    # create semantic annotation for sample property assignment (predicate and descriptor values omitted for brevity)
    sa = o.new_semantic_annotation(entityType = 'UNKNOWN', propertyType = 'DESCRIPTION', ...)
    sa.save()

    # create a semantic annotation directly from a sample type
    # will also create sample property assignment annotations when propertyType is given
    st = o.get_sample_type("ORDER")
    st.new_semantic_annotation(...)

    # get all semantic annotations
    o.get_semantic_annotations()

    # get semantic annotation by perm id
    sa = o.get_semantic_annotation("20171015135637955-30")

    # update semantic annotation
    sa.predicateOntologyId = 'new_po_id'
    sa.descriptorOntologyId = 'new_do_id'
    sa.save()

    # delete semantic annotation
    sa.delete('reason')

Tags
----

::

    new_tag = o.new_tag(
        code        = 'my_tag', 
        description = 'some descriptive text'
    )
    new_tag.description = 'some new description'
    new_tag.save()
    o.get_tags()
    o.get_tag('/username/TAG_Name')
    o.get_tag('TAG_Name')

    tag.get_experiments()
    tag.get_samples()
    tag.delete()

Vocabulary and VocabularyTerms
------------------------------

An entity such as Sample (Object), Experiment (Collection), Material or
DataSet can be of a specific *entity type*:

-  Sample Type
-  Experiment Type
-  DataSet Type
-  Material Type

Every type defines which **Properties** may be defined. Properties act
like **Attributes**, but they are type-specific. Properties can contain
all sorts of information, such as free text, XML, Hyperlink, Boolean and
also **Controlled Vocabulary**. Such a Controlled Vocabulary consists of
many **VocabularyTerms**. These terms are used to only allow certain
values entered in a Property field.

So for example, you want to add a property called **Animal** to a Sample
and you want to control which terms are entered in this Property field.
For this you need to do a couple of steps:

1. create a new vocabulary *AnimalVocabulary*
2. add terms to that vocabulary: *Cat, Dog, Mouse*
3. create a new PropertyType (e.g. *Animal*) of DataType
   *CONTROLLEDVOCABULARY* and assign the *AnimalVocabulary* to it
4. create a new SampleType (e.g. *Pet*) and *assign* the created
   PropertyType to that Sample type.
5. If you now create a new Sample of type *Pet* you will be able to add
   a property *Animal* to it which only accepts the terms *Cat, Dog* or
   *Mouse*.

**create new Vocabulary with three VocabularyTerms**

::

    voc = o.new_vocabulary(
        code = 'BBB',
        description = 'description of vocabulary aaa',
        urlTemplate = 'https://ethz.ch',
        terms = [
            { "code": 'term_code1', "label": "term_label1", "description": "term_description1"},
            { "code": 'term_code2', "label": "term_label2", "description": "term_description2"},
            { "code": 'term_code3', "label": "term_label3", "description": "term_description3"}
        ]   
    )
    voc.save()

**create additional VocabularyTerms**

::

    term = o.new_term(
        code='TERM_CODE_XXX', 
        vocabularyCode='BBB', 
        label='here comes a label',
        description='here might appear a meaningful description'
    )
    term.save()