An example of using the ISA model classes to create an ISA-JSON file.

#!/usr/bin/env python

from isatools.model import *

def create_descriptor():
    """Returns a simple but complete ISA-JSON 1.0 descriptor for illustration."""

    # Create an empty Investigation object and set some values to the instance variables.

    investigation = Investigation()
    investigation.identifier = "i1"
    investigation.title = "My Simple ISA Investigation"
    investigation.description = "We could alternatively use the class constructor's parameters to set some default " \
                                "values at the time of creation, however we want to demonstrate how to use the " \
                                "object's instance variables to set values."
    investigation.submission_date = "2016-11-03"
    investigation.public_release_date = "2016-11-03"

    # Create an empty Study object and set some values. The Study must have a filename, otherwise when we serialize it
    # to ISA-Tab we would not know where to write it. We must also attach the study to the investigation by adding it
    # to the 'investigation' object's list of studies.

    study = Study(filename="s_study.txt")
    study.identifier = "s1"
    study.title = "My ISA Study"
    study.description = "Like with the Investigation, we could use the class constructor to set some default values, " \
                        "but have chosen to demonstrate in this example the use of instance variables to set initial " \
    study.submission_date = "2016-11-03"
    study.public_release_date = "2016-11-03"

    # Some instance variables are typed with different objects and lists of objects. For example, a Study can have a
    # list of design descriptors. A design descriptor is an Ontology Annotation describing the kind of study at hand.
    # Ontology Annotations should typically reference an Ontology Source. We demonstrate a mix of using the class
    # constructors and setting values with instance variables. Note that the OntologyAnnotation object
    # 'intervention_design' links its 'term_source' directly to the 'obi' object instance. To ensure the OntologySource
    # is encapsulated in the descriptor, it is added to a list of 'ontology_source_references' in the Investigation
    # object. The 'intervention_design' object is then added to the list of 'design_descriptors' held by the Study
    # object.

    obi = OntologySource(name='OBI', description="Ontology for Biomedical Investigations")
    intervention_design = OntologyAnnotation(term_source=obi)
    intervention_design.term = "intervention design"
    intervention_design.term_accession = ""

    # Other instance variables common to both Investigation and Study objects include 'contacts' and 'publications',
    # each with lists of corresponding Person and Publication objects.

    contact = Person(first_name="Alice", last_name="Robertson", affiliation="University of Life", roles=[OntologyAnnotation(term='submitter')])
    publication = Publication(title="Experiments with Elephants", author_list="A. Robertson, B. Robertson")
    publication.pubmed_id = "12345678"
    publication.status = OntologyAnnotation(term="published")

    # To create the study graph that corresponds to the contents of the study table file (the s_*.txt file), we need
    # to create a process sequence. To do this we use the Process class and attach it to the Study object's
    # 'process_sequence' list instance variable. Each process must be linked with a Protocol object that is attached to
    # a Study object's 'protocols' list instance variable. The sample collection Process object usually has as input
    # a Source material and as output a Sample material.

    # Here we create one Source material object and attach it to our study.

    source = Source(name='source_material')

    # Then we create three Sample objects, with organism as Homo Sapiens, and attach them to the study. We use the utility function
    # batch_create_material() to clone a prototype material object. The function automatically appends
    # an index to the material name. In this case, three samples will be created, with the names
    # 'sample_material-0', 'sample_material-1' and 'sample_material-2'.

    prototype_sample = Sample(name='sample_material', derives_from=[source])
    ncbitaxon = OntologySource(name='NCBITaxon', description="NCBI Taxonomy")
    characteristic_organism = Characteristic(category=OntologyAnnotation(term="Organism"),
                                     value=OntologyAnnotation(term="Homo Sapiens", term_source=ncbitaxon,

    study.samples = batch_create_materials(prototype_sample, n=3)  # creates a batch of 3 samples

    # Now we create a single Protocol object that represents our sample collection protocol, and attach it to the
    # study object. Protocols must be declared before we describe Processes, as a processing event of some sort
    # must execute some defined protocol. In the case of the class model, Protocols should therefore be declared
    # before Processes in order for the Process to be linked to one.

    sample_collection_protocol = Protocol(name="sample collection",
                                          protocol_type=OntologyAnnotation(term="sample collection"))
    sample_collection_process = Process(executes_protocol=sample_collection_protocol)

    # Next, we link our materials to the Process. In this particular case, we are describing a sample collection
    # process that takes one source material, and produces three different samples.
    # (source_material)->(sample collection)->[(sample_material-0), (sample_material-1), (sample_material-2)]

    for src in study.sources:
    for sam in study.samples:

    # Finally, attach the finished Process object to the study process_sequence. This can be done many times to
    # describe multiple sample collection events.


    # Next, we build n Assay object and attach two protocols, extraction and sequencing.

    assay = Assay(filename="a_assay.txt")
    extraction_protocol = Protocol(name='extraction', protocol_type=OntologyAnnotation(term="material extraction"))
    sequencing_protocol = Protocol(name='sequencing', protocol_type=OntologyAnnotation(term="material sequencing"))

    # To build out assay graphs, we enumereate the samples from the study-level, and for each sample we create an
    # extraction process and a sequencing process. The extraction process takes as input a sample material, and produces
    # an extract material. The sequencing process takes the extract material and produces a data file. This will
    # produce three graphs, from sample material through to data, as follows:
    # (sample_material-0)->(extraction)->(extract-0)->(sequencing)->(sequenced-data-0)
    # (sample_material-1)->(extraction)->(extract-1)->(sequencing)->(sequenced-data-1)
    # (sample_material-2)->(extraction)->(extract-2)->(sequencing)->(sequenced-data-2)
    # Note that the extraction processes and sequencing processes are distinctly separate instances, where the three
    # graphs are NOT interconnected.

    for i, sample in enumerate(study.samples):

        # create an extraction process that executes the extraction protocol

        extraction_process = Process(executes_protocol=extraction_protocol)

        # extraction process takes as input a sample, and produces an extract material as output

        material = Material(name="extract-{}".format(i))
        material.type = "Extract Name"

        # create a sequencing process that executes the sequencing protocol

        sequencing_process = Process(executes_protocol=sequencing_protocol) = "assay-name-{}".format(i)

        # Sequencing process usually has an output data file

        datafile = DataFile(filename="sequenced-data-{}".format(i), label="Raw Data File", generated_from=[sample])

        # Ensure Processes are linked forward and backward. plink(from_process, to_process) is a function to set
        # these links for you. It is found in the isatools.model package

        plink(extraction_process, sequencing_process)

        # make sure the extract, data file, and the processes are attached to the assay

        assay.measurement_type = OntologyAnnotation(term="gene sequencing")
        assay.technology_type = OntologyAnnotation(term="nucleotide sequencing")

    # attach the assay to the study


    import json
    from isatools.isajson import ISAJSONEncoder

    # To write JSON out, use the ISAJSONEncoder class with the json package and use dump() or dumps()
    # Note that the extra parameters sort_keys, indent and separators are to make the output more human-readable.

    return json.dumps(investigation, cls=ISAJSONEncoder, sort_keys=True, indent=4, separators=(',', ': '))

if __name__ == '__main__':
    print(create_descriptor())  # print the result to stdout