{
    "info": {
        "author": "Swen Vermeul \u2022 ID SIS \u2022 ETH Z\u00fcrich",
        "author_email": "swen@ethz.ch",
        "bugtrack_url": null,
        "classifiers": [
            "License :: OSI Approved :: Apache Software License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3"
        ],
        "description": "# Welcome to pyBIS!\npyBIS 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.\n\n## Dependencies and Requirements\n- pyBIS relies the openBIS API v3\n- openBIS version 16.05.2 or newer is required\n- 18.06.2 or later is recommended\n- pyBIS uses Python 3.3 and pandas\n\n## Installation\n\n```\npip install pybis\n```\nThat command will download install pybis and all its dependencies.\n\nIf you haven't done yet, install Jupyter Notebook:\n\n```\npip install jupyter\n```\n\n# Usage\n\n## Tab completition and other hints\nUsed 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.\n\nIf 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.\n\nWhen 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.\n\n## connect to from OpenBIS\n\n```\nfrom pybis import Openbis\no = Openbis('https://example.com', verify_certificates=False)\n\nimport getpass\npassword = getpass.getpass()\n\no.login('username', password, save_token=True)   # save the session token in ~/.pybis/example.com.token\n```\n\nCheck whether the session token is still valid and log out:\n\n```\no.token\no.is_session_active()\no.logout()\n```\n\n## browsing masterdata\n```\nsample_types = o.get_sample_types()  # get a list of sample types \nsample_types.df                      # DataFrame object\nst = o.get_sample_types()[3]         # get 4th element of that list\nst = o.get_sample_type('YEAST')\nst.code\nst.generatedCodePrefix\nst.attrs.all()                       # get all attributs as a dict\nst.validationPlugin                  # returns a plugin object\n\nst.get_property_assignments()\nst.assign_property(\n\tprop='diff_time',\n\tsection = '',\n\tordinal = 5,\n\tmandatory = True,\n\tinitialValueForExistingEntities = 'initial value'\n\tshowInEditView = True,\n\tshowRawValueInForms = True\n)\n\no.get_material_types()\n# etc. \u2014 see above\n\no.get_dataset_types()\n# etc. \u2014 see above\n\no.get_experiment_types()\n# etc. \u2014 see above\n\no.get_property_types()\npt = o.get_property_type('BARCODE_COMPLEXITY_CHECKER')\npt.attrs.all()\n\no.get_plugins()\npl = o.get_plugin('Diff_time')\npl.script  # the Jython script that processes this property\n\no.get_vocabularies()\no.get_vocabulary('BACTERIAL_ANTIBIOTIC_RESISTANCE')\no.get_terms(vocabulary='STORAGE')\no.get_tags()\n```\n\n## create plugins and property types\n```\npl = o.new_plugin(\n\tname       ='my_new_entry_validation_plugin',\n\tpluginType ='ENTITY_VALIDATION',               # or 'DYNAMIC_PROPERTY' or 'MANAGED_PROPERTY',\n\tentityKind = None,                             # or 'SAMPLE', 'MATERIAL', 'EXPERIMENT', 'DATA_SET'\n\tscript     = 'def calculate(): pass'           # a JYTHON script\n)\npl.save()\n\npt = o.new_property_type(\n\tcode='MY_NEW_PROPERTY_TYPE', \n\tlabel='yet another property type', \n   description='my first property',\n   dataType='VARCHAR'\n)\n# dataType can be any of ['INTEGER', 'VARCHAR', 'MULTILINE_VARCHAR', 'REAL', 'TIMESTAMP', 'BOOLEAN', 'CONTROLLEDVOCABULARY', 'MATERIAL', 'HYPERLINK', 'XML']\n\n\n```\n\n## Users, Groups and RoleAssignments\n\n```\no.get_groups()\ngroup = o.new_group(code='group_name', description='...')\ngroup = o.get_group('group_name')\ngroup.save()\ngroup.assign_role(role='ADMIN', space='DEFAULT')\ngroup.get_roles() \ngroup.revoke_role(role='ADMIN', space='DEFAULT')\n\ngroup.add_members(['admin'])\ngroup.get_members()\ngroup.del_members(['admin'])\ngroup.delete()\n\no.get_persons()\nperson = o.new_person(userId='username')\nperson.space = 'USER_SPACE'\nperson.save()\n\nperson.assign_role(role='ADMIN', space='MY_SPACE')\nperson.assign_role(role='OBSERVER')\nperson.get_roles()\nperson.revoke_role(role='ADMIN', space='MY_SPACE')\nperson.revoke_role(role='OBSERVER')\n\no.get_role_assignments()\no.get_role_assignments(space='MY_SPACE')\no.get_role_assignments(group='MY_GROUP')\nra = o.get_role_assignment(techId)\nra.delete()\n```\n\n## Spaces\n\n```\nspace = o.new_space(code='space_name', description='')\nspace.save()\nspace.delete('reason for deletion')\no.get_spaces(\n    start_with = 1,                   # start_with and count\n    count = 7,                        # enable paging\n)\nspace = o.get_space('MY_SPACE')\nspace.code\nspace.description\nspace.registrator\nspace.registrationDate\nspace.modifier\nspace.modificationDate\nspace.attrs.all()                     # returns a dict containing all attributes\n```\n\n## Projects\n```\nproject = o.new_project(\n    space=space, \n    code='project_name',\n    description='some project description'\n)\nproject = space.new_project( code='project_code', description='project description')\nproject.save()\n\no.get_projects(\n    space = 'MY_SPACE',               # show only projects in MY_SPACE\n    start_with = 1,                   # start_with and count\n    count = 7,                        # enable paging\n)\no.get_projects(space='MY_SPACE')\nspace.get_projects()\n\nproject.get_experiments()\nproject.get_attachments()\np.add_attachment(fileName='testfile', description= 'another file', title= 'one more attachment')\nproject.download_attachments()\n\nproject.code\nproject.description\n# ... and many more\nproject.attrs.all()                   # returns a dict containing all attributes\n\nproject.freeze = True\nproject.freezeForExperiments = True\nproject.freezeForSamples = True\n\n```\n\n## Samples\nSamples are nowadays called **Objects** in openBIS. pyBIS is not yet thoroughly supporting this term in all methods where \u00absample\u00bb occurs.\n\nNOTE: 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`.\n\n```\nsample = o.new_sample(\n    type     = 'YEAST', \n    space    = 'MY_SPACE',\n    experiment = '/MY_SPACE/MY_PROJECT/EXPERIMENT_1',\n    parents  = [parent_sample, '/MY_SPACE/YEA66'], \n    children = [child_sample],\n    props    = {\"name\": \"some name\", \"description\": \"something interesting\"}\n)\nsample = space.new_sample( type='YEAST' )\nsample.save()\n\nsample = o.get_sample('/MY_SPACE/MY_SAMPLE_CODE')\nsample = o.get_sample('20170518112808649-52')\n\nsample.space\nsample.code\nsample.permId\nsample.identifier\nsample.type  # once the sample type is defined, you cannot modify it\n\nsample.space\nsample.space = 'MY_OTHER_SPACE'\n\nsample.experiment    # a sample can belong to one experiment only\nsample.experiment = '/MY_SPACE/MY_PROJECT/MY_EXPERIMENT'\n\nsample.project\nsample.project = '/MY_SPACE/MY_PROJECT'  # only works if project samples are\nenabled\n\nsample.tags\nsample.tags = ['guten_tag', 'zahl_tag' ]\n\nsample.attrs.all()         # returns a dict of all attributes\n\nsample.get_parents()\nsample.set_parents(['/MY_SPACE/PARENT_SAMPLE_NAME')\nsample.add_parents('/MY_SPACE/PARENT_SAMPLE_NAME')\nsample.del_parents('/MY_SPACE/PARENT_SAMPLE_NAME')\n\nsample.get_children()\nsample.set_children('/MY_SPACE/CHILD_SAMPLE_NAME')\nsample.add_children('/MY_SPACE/CHILD_SAMPLE_NAME')\nsample.del_children('/MY_SPACE/CHILD_SAMPLE_NAME')\n\n# A Sample may belong to another Sample, which acts as a container.\n# As opposed to DataSets, a Sample may only belong to one container.\nsample.container    # returns a sample object\nsample.container = '/MY_SPACE/CONTAINER_SAMPLE_NAME'   # watch out, this will change the identifier of the sample to:\n                                                       # /MY_SPACE/CONTAINER_SAMPLE_NAME:SAMPLE_NAME\nsample.container = ''                                  # this will remove the container. \n\n# A Sample may contain other Samples, in order to act like a container (see above)\n# The Sample-objects inside that Sample are called \u00abcomponents\u00bb or \u00abcontained Samples\u00bb\n# You may also use the xxx_contained() functions, which are just aliases.\nsample.get_components()\nsample.set_components('/MY_SPACE/COMPONENT_NAME')\nsample.add_components('/MY_SPACE/COMPONENT_NAME')\nsample.del_components('/MY_SPACE/COMPONENT_NAME')\n\nsample.get_tags()\nsample.set_tags('tag1')\nsample.add_tags(['tag2','tag3'])\nsample.del_tags('tag1')\n\nsample.set_props({ ... })\nsample.p                              # same thing as .props\nsample.p.my_property = \"some value\"   # set the value of a property (value is checked)\nsample.p + TAB                        # in IPython or Jupyter: show list of available properties\nsample.p.my_property_ + TAB           # in IPython or Jupyter: show datatype or controlled vocabulary\nsample.p['my-weird.property-name']    # accessing properties containing a dash or a dot\n\nsample.attrs.all()                    # returns all attributes as a dict\nsample.props.all()                    # returns all properties as a dict\n\nsample.get_attachments()\nsample.download_attachments()\nsample.add_attachment('testfile.xls')\n\nsamples = o.get_samples(\n    space ='MY_SPACE',\n    type  ='YEAST',\n    tags  =['*'],                     # only sample with existing tags\n    start_with = 1,                   # start_with and count\n    count = 7,                        # enable paging\n    NAME  = 'some name',              # properties are always uppercase \n                                      # to distinguish them from attributes\n    **{ \"SOME.WEIRD:PROP\": \"value\"}   # property name contains a dot or a\n                                      # colon: cannot be passed as an argument \n    props=['NAME', 'MATING_TYPE']     # show these properties in the result\n)\nsamples.df                            # returns a pandas DataFrame object\nsamples.get_datasets(type='ANALYZED_DATA')\n\nsample.freeze = True\nsample.freezeForComponents = True\nsample.freezeForChildren = True\nsample.freezeForParents = True\nsample.freezeForDataSets = True\n```\n\n## Experiments\n\nNOTE: 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`.\n\n```\no.new_experiment\n    type='DEFAULT_EXPERIMENT',\n    space='MY_SPACE',\n    project='YEASTS'\n)\n\no.get_experiments(\n    project='YEASTS',\n    space='MY_SPACE', \n    type='DEFAULT_EXPERIMENT',\n    tags='*', \n    finished_flag=False,\n    props=['name', 'finished_flag']\n)\nproject.get_experiments()\nexp = o.get_experiment('/MY_SPACE/MY_PROJECT/MY_EXPERIMENT')\n\nexp.set_props({ key: value})\nexp.props\nexp.p                              # same thing as .props\nexp.p.finished_flag=True\nexp.p.my_property = \"some value\"   # set the value of a property (value is checked)\nexp.p + TAB                        # in IPython or Jupyter: show list of available properties\nexp.p.my_property_ + TAB           # in IPython or Jupyter: show datatype or controlled vocabulary\nexp.p['my-weird.property-name']    # accessing properties containing a dash or a dot\n\nexp.attrs.all()                    # returns all attributes as a dict\nexp.props.all()                    # returns all properties as a dict\n\nexp.attrs.tags = ['some', 'tags']\nexp.tags = ['some', 'tags']        # same thing\nexp.save()\n\nexp.code\nexp.description\nexp.registrator\nexp.registrationDate\nexp.modifier\nexp.modificationDate\n\nexp.freeze = True\nexp.freezeForDataSets = True\nexp.freezeForSamples = True\n```\n\n## Datasets\n\n```\nsample.get_datasets()\nds = o.get_dataset('20160719143426517-259')\nds.get_parents()\nds.get_children()\nds.sample\nds.experiment\nds.physicalData\nds.status              # AVAILABLE LOCKED ARCHIVED \n                       # UNARCHIVE_PENDING ARCHIVE_PENDING BACKUP_PENDING\nds.archive()\nds.unarchive()\n\nds.attrs.all()                    # returns all attributes as a dict\nds.props.all()                    # returns all properties as a dict\n\nds.get_files(start_folder=\"/\")\nds.file_list\nds.add_attachment()\nds.get_attachments()\nds.download_attachments()\nds.download(destination='/tmp', wait_until_finished=False)\n\nds_new = o.new_dataset(\n    type       = 'ANALYZED_DATA', \n    experiment = '/SPACE/PROJECT/EXP1', \n    sample     = '/SPACE/SAMP1',\n    files      = ['my_analyzed_data.dat'], \n    props      = {'name': 'some good name', 'description': '...' }\n)\n\n# DataSet CONTAINER (contains other DataSets, but no files)\nds_new = o.new_dataset(\n    type       = 'ANALYZED_DATA', \n    experiment = '/SPACE/PROJECT/EXP1', \n    sample     = '/SPACE/SAMP1',\n    kind       = 'CONTAINER',\n    props      = {'name': 'some good name', 'description': '...' }\n)\nds_new.save()\n\n# get, set, add and remove parent datasets\ndataset.get_parents()\ndataset.set_parents(['20170115220259155-412'])\ndataset.add_parents(['20170115220259155-412'])\ndataset.del_parents(['20170115220259155-412'])\n\n# get, set, add and remove child datasets\ndataset.get_children()\ndataset.set_children(['20170115220259155-412'])\ndataset.add_children(['20170115220259155-412'])\ndataset.del_children(['20170115220259155-412'])\n\n# A DataSet may belong to other DataSets, which must be of kind=CONTAINER\n# As opposed to Samples, DataSets may belong (contained) to more than one DataSet-container\ndataset.get_containers()\ndataset.set_containers(['20170115220259155-412'])\ndataset.add_containers(['20170115220259155-412'])\ndataset.del_containers(['20170115220259155-412'])\n\n# A DataSet of kind=CONTAINER may contain other DataSets, to act like a folder (see above)\n# The DataSet-objects inside that DataSet are called components or contained DataSets\n# You may also use the xxx_contained() functions, which are just aliases.\ndataset.get_components()\ndataset.set_components(['20170115220259155-412'])\ndataset.add_components(['20170115220259155-412'])\ndataset.del_components(['20170115220259155-412'])\n\nds.set_props({ key: value})\nds.props\nds.p                              # same thing as .props\nds.p.my_property = \"some value\"   # set the value of a property\nds.p + TAB                        # show list of available properties\nds.p.my_property_ + TAB           # show datatype or controlled vocabulary\nds.p['my-weird.property-name']    # accessing properties containing a dash or a dot\n\nds.attrs.all()                    # returns all attributes as a dict\nds.props.all()                    # returns all properties as a dict\n\n# complex query with chaining.\n# properties must be in UPPERCASE\ndatasets = o.get_experiments(project='YEASTS').get_samples(type='FLY').get_datasets(type='ANALYZED_DATA', props=['MY_PROPERTY'],MY_PROPERTY='some analyzed data')\n\n# another example\ndatasets = o.get_experiment('/MY_NEW_SPACE/VERMEUL_PROJECT/MY_EXPERIMENT4').get_samples(type='UNKNOWN').get_parents().get_datasets(type='RAW_DATA')\n\ndatasets.df                       # get a pandas dataFrame object\n\n# use it in a for-loop:\nfor dataset in datasets:\n    print(dataset.permID)\n    dataset.delete('give me a reason')\n\nds.freeze = True\nds.freezeForChildren = True\nds.freezeForParents = True\nds.freezeForComponents = True\nds.freezeForContainers = True\n```\n\n## Semantic Annotations\n```\n# create semantic annotation for sample type 'UNKNOWN'\nsa = o.new_semantic_annotation(\n\tentityType = 'UNKNOWN',\n\tpredicateOntologyId = 'po_id',\n\tpredicateOntologyVersion = 'po_version',\n\tpredicateAccessionId = 'pa_id',\n\tdescriptorOntologyId = 'do_id',\n\tdescriptorOntologyVersion = 'do_version',\n\tdescriptorAccessionId = 'da_id'\n)\nsa.save()\n\n# create semantic annotation for property type \n# (predicate and descriptor values omitted for brevity)\nsa = o.new_semantic_annotation(propertyType = 'DESCRIPTION', ...)\nsa.save()\n\n# create semantic annotation for sample property assignment (predicate and descriptor values omitted for brevity)\nsa = o.new_semantic_annotation(entityType = 'UNKNOWN', propertyType = 'DESCRIPTION', ...)\nsa.save()\n\n# create a semantic annotation directly from a sample type\n# will also create sample property assignment annotations when propertyType is given\nst = o.get_sample_type(\"ORDER\")\nst.new_semantic_annotation(...)\n\n# get all semantic annotations\no.get_semantic_annotations()\n\n# get semantic annotation by perm id\nsa = o.get_semantic_annotation(\"20171015135637955-30\")\n\n# update semantic annotation\nsa.predicateOntologyId = 'new_po_id'\nsa.descriptorOntologyId = 'new_do_id'\nsa.save()\n\n# delete semantic annotation\nsa.delete('reason')\n```\n\n## Tags\n```\nnew_tag = o.new_tag(\n\tcode        = 'my_tag', \n\tdescription = 'some descriptive text'\n)\nnew_tag.description = 'some new description'\nnew_tag.save()\no.get_tags()\no.get_tag('/username/TAG_Name')\no.get_tag('TAG_Name')\n\ntag.get_experiments()\ntag.get_samples()\ntag.get_owner()   # returns a person object\ntag.delete('why?')\n```\n\n## Vocabulary and VocabularyTerms\n\nAn entity such as Sample (Object), Experiment (Collection), Material or DataSet can be of a specific *entity type*:\n\n* Sample Type\n* Experiment Type\n* DataSet Type\n* Material Type\n\nEvery 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.\n\nSo 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:\n\n1. create a new vocabulary *AnimalVocabulary*\n2. add terms to that vocabulary: *Cat, Dog, Mouse*\n3. create a new PropertyType (e.g. *Animal*) of DataType *CONTROLLEDVOCABULARY* and assign the *AnimalVocabulary* to it\n4. create a new SampleType (e.g. *Pet*) and *assign* the created PropertyType to that Sample type.\n5. 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*.\n\n\n**create new Vocabulary with three VocabularyTerms**\n\n```\nvoc = o.new_vocabulary(\n    code = 'BBB',\n    description = 'description of vocabulary aaa',\n    urlTemplate = 'https://ethz.ch',\n    terms = [\n        { \"code\": 'term_code1', \"label\": \"term_label1\", \"description\": \"term_description1\"},\n        { \"code\": 'term_code2', \"label\": \"term_label2\", \"description\": \"term_description2\"},\n        { \"code\": 'term_code3', \"label\": \"term_label3\", \"description\": \"term_description3\"}\n    ]   \n)\nvoc.save()\n```\n\n**create additional VocabularyTerms**\n\n```\nterm = o.new_term(\n\tcode='TERM_CODE_XXX', \n\tvocabularyCode='BBB', \n\tlabel='here comes a label',\n\tdescription='here might appear a meaningful description'\n)\nterm.save()\n```\n\n**update VocabularyTerms**\n\nTo change the ordinal of a term, it has to be moved either to the top with the `.move_to_top()` method or after another term using the `.move_after_term('TERM_BEFORE')` method.\n\n```\nvoc = o.get_vocabulary('STORAGE')\nterm = voc.get_terms()['RT']\nterm.label = \"Room Temperature\"\nterm.official = True\nterm.move_to_top()\nterm.move_after_term('-40')\nterm.save()\nterm.delete()\n```",
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