README.rst

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

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

SYNOPSIS
========

connecting to OpenBIS
---------------------

::

   from pybis import Openbis
   o = Openbis('https://example.com:8443', verify_certificates=False)
   o.login('username', 'password', save_token=True)   # saves the session token in ~/.pybis/example.com.token
   o.token
   o.is_session_active()
   o.get_datastores()
   o.logout()

Masterdata
----------

::

   o.get_experiment_types()
   o.get_sample_types()
   o.get_sample_type('YEAST')
   o.get_material_types()
   o.get_dataset_types()
   o.get_dataset_types()[0]
   o.get_dataset_type('RAW_DATA')
   o.get_terms()
   o.get_terms('MATING_TYPE')
   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()
   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()
   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.

::

   sample = o.new_sample(
       type     = 'YEAST', 
       space    = 'MY_SPACE', 
       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.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
       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
-----------

::

   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']
   )
   exp = o.get_experiment('/MY_SPACE/MY_PROJECT/MY_EXPERIMENT')

   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.attrs
   exp.a     # same as exp.attrs
   exp.attrs.tags = ['some', 'extra', 'tags']
   exp.tags = ['some', 'extra', '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()

   dataset.get_parents()
   dataset.set_parents(['20170115220259155-412'])
   dataset.add_parents(['20170115220259155-412'])
   dataset.del_parents(['20170115220259155-412'])

   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_properties({...})
   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

   # 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(ds.permID)

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()

Vocabualry and VocabularyTerms
------------------------------

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

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

Every type defines which Properties may be defined. Properties are 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. They are used to check the terms 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 is a meandingful description'
   )
   term.save()

**fetching Vocabulary and VocabularyTerms**

Requirements and organization
=============================

Dependencies and Requirements
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

-  pyBIS relies the openBIS API v3; openBIS version 16.05.2 or newer
-  pyBIS uses Python 3.3 and pandas
-  pyBIS needs the jupyter-api to be installed, in order to register new
   datasets

Installation
~~~~~~~~~~~~

-  locate the ``jupyter-api`` folder found in ``pybis/src/coreplugins``
-  copy this folder to ``openbis/servers/core-plugins`` in your openBIS
   installation
-  register the plugin by editing
   ``openbis/servers/core-plugins/core-plugins.properties`` :
-  ``enabled-modules = jupyter-api`` (separate multiple plugins with
   comma)
-  restart your DSS to activate the plugin

Project Organization
~~~~~~~~~~~~~~~~~~~~

This project is devided in several parts:

-  src/python/\ **PyBis** Python module which holds all the method to
   interact with OpenBIS
-  src/python/\ **OBis** a command-line tool to register large datasets
   in OpenBIS without actually copying the data. Uses git annex for
   version control and OpenBIS linkedDataSet objects to register the
   metadata.
-  src/python/\ **JupyterBis** a JupyterHub authenticator module which
   uses pyBIS for authenticating against openBIS, validating and storing
   the session token
-  src/core-plugins/\ **jupyter-api**, an ingestion plug-in for openBIS,
   allowing people to upload new datasets
-  src/vagrant/\ **jupyter-bis/Vagrantfile** to set up JupyterHub on a
   virtual machine (CentOS 7), which uses the JupyterBis authenticator
   module
-  src/vagrant/\ **obis/Vagrantfile** to set up a complete OpenBIS
   instance on a virtual machine (CentOS 7)
-