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()