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
)
space = o.get_space('MY_SPACE')
space.code
space.description
space.registrator
space.registrationDate
space.modifier
space.modificationDate
space.attrs.all()                     # returns a dict containing all attributes

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()

project.code
project.description
project.registrator
project.registrationDate
project.modifier
project.modificationDate
project.attrs.all()                   # returns a dict containing all attributes

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.attrs.all()         # returns a dict of all attributes

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.attrs.all()                    # returns all attributes as a dict
sample.props.all()                    # returns all properties as a dict

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.all()                    # returns all attributes as a dict
exp.props.all()                    # returns all properties as a dict

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

exp.code
exp.description
exp.registrator
exp.registrationDate
exp.modifier
exp.modificationDate

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.attrs.all()                    # returns all attributes as a dict
ds.props.all()                    # returns all properties as a dict

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

ds.attrs.all()                    # returns all attributes as a dict
ds.props.all()                    # returns all properties as a dict

# 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()