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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()
Spaces and Projects
-------------------
::
o.get_spaces()
o.get_space('MY_SPACE')
o.get_projects(space='MY_SPACE')
space.get_projects()
project.get_experiments()
project.get_attachments()
project.download_attachments()
p.add_attachment(fileName='testfile', description= 'another file', title= 'one more attachment')
p.save()
Samples
-------
::
sample = o.new_sample(
piotr.kupczyk@id.ethz.ch
committed
type='YEAST',
space='MY_SPACE',
parents=[parent_sample, '/MY_SPACE/YEA66'],
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children=[child_sample]
)
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.tags
sample.tags = ['guten_tag', 'zahl_tag' ]
sample.get_parents()
sample.parents = ['/MY_SPACE/PARENT_SAMPLE_NAME']
sample.add_parents('/MY_SPACE/PARENT_SAMPLE_NAME')
sample.del_parents('/MY_SPACE/PARENT_SAMPLE_NAME')
sample.get_childeren()
sample.children = ['/MY_SPACE/CHILD_SAMPLE_NAME']
sample.add_children('/MY_SPACE/CHILD_SAMPLE_NAME')
sample.del_children('/MY_SPACE/CHILD_SAMPLE_NAME')
sample.get_childeren()
sample.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=['*'] # tags must be present
NAME = 'some name' # properties are always uppercase to distinguish them from attributes
props=['NAME', 'MATING_TYPE','SHOW_IN_PROJECT_OVERVIEW'] # show these properties in the results
)
samples.df # returns a pandas dataframe object
samples.get_datasets(type='ANALYZED_DATA')
Note: Project samples are not implemented yet.
Experiments
-----------
::
o.new_experiment
type='DEFAULT_EXPERIMENT',
space='MY_SPACE',
project='YEASTS'
)
o.get_experiments(
project='YEASTS',
piotr.kupczyk@id.ethz.ch
committed
space='MY_SPACE',
type='DEFAULT_EXPERIMENT',
piotr.kupczyk@id.ethz.ch
committed
tags='*',
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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()
sample = ds.sample
experiment = 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(
piotr.kupczyk@id.ethz.ch
committed
type='ANALYZED_DATA',
experiment=exp,
sample= samp,
parents=[my_dataset, '20160713173002405-212'],
piotr.kupczyk@id.ethz.ch
committed
files = ['my_analyzed_data.dat'],
props={'name': 'we give this dataset a name', 'notes': 'and we might need some notes, too'})
)
ds_new.save()
ds.props
ds.p # same thing as .props
ds.p.my_property = "some value" # set the value of a property (value is checked)
ds.p + TAB # in IPython or Jupyter: show list of available properties
ds.p.my_property_ + TAB # in IPython or Jupyter: show datatype or controlled vocabulary
# complex query with chaining. props adds a "name" column. To filter for some property, the name of the property 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')
# get a pandas dataFrame object
datasets.df
# use it in a for-loop:
for dataset in datasets:
print(ds.permID)
piotr.kupczyk@id.ethz.ch
committed
Semantic Annotations
--------------------
::
piotr.kupczyk@id.ethz.ch
committed
# create semantic annotation for sample type (predicate and descriptor values omitted for brevity)
piotr.kupczyk@id.ethz.ch
committed
sa = o.new_semantic_annotation(entityType = 'UNKNOWN')
sa.save()
piotr.kupczyk@id.ethz.ch
committed
piotr.kupczyk@id.ethz.ch
committed
# create semantic annotation for property type (predicate and descriptor values omitted for brevity)
piotr.kupczyk@id.ethz.ch
committed
sa = o.new_semantic_annotation(propertyType = 'DESCRIPTION')
sa.save()
piotr.kupczyk@id.ethz.ch
committed
piotr.kupczyk@id.ethz.ch
committed
# create semantic annotation for sample property assignment (predicate and descriptor values omitted for brevity)
piotr.kupczyk@id.ethz.ch
committed
sa = o.new_semantic_annotation(entityType = 'UNKNOWN', propertyType = 'DESCRIPTION')
sa.save()
piotr.kupczyk@id.ethz.ch
committed
# create semantic annotation with additional fields
piotr.kupczyk@id.ethz.ch
committed
sa = o.new_semantic_annotation(entityType = 'UNKNOWN',
piotr.kupczyk@id.ethz.ch
committed
predicateOntologyId = 'po_id',
predicateOntologyVersion = 'po_version',
predicateAccessionId = 'pa_id',
descriptorOntologyId = 'do_id',
descriptorOntologyVersion = 'do_version',
descriptorAccessionId = 'da_id')
piotr.kupczyk@id.ethz.ch
committed
sa.save()
piotr.kupczyk@id.ethz.ch
committed
# 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')
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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)