def test_processed_samples_only(self): """ Don't return unprocessed samples """ experiment = Experiment() experiment.accession_code = "GSX12345" experiment.is_public = True experiment.save() sample = Sample() sample.title = "I am unprocessed" sample.accession_code = "GSXUnprocessed" sample.is_processed = False sample.save() experiment_sample_association = ExperimentSampleAssociation() experiment_sample_association.sample = sample experiment_sample_association.experiment = experiment experiment_sample_association.save() # we return all experiments response = self.client.get( reverse("search", kwargs={"version": API_VERSION}), {"search": "GSX12345"}) self.assertEqual(response.json()["count"], 1) # check requesting only experiments with processed samples response = self.client.get( reverse("search", kwargs={"version": API_VERSION}), { "search": "GSX12345", "num_processed_samples__gt": 0 }, ) self.assertEqual(response.json()["count"], 0) sample2 = Sample() sample2.title = "I am processed" sample2.accession_code = "GSXProcessed" sample2.is_processed = True sample2.save() experiment_sample2_association = ExperimentSampleAssociation() experiment_sample2_association.sample = sample2 experiment_sample2_association.experiment = experiment experiment_sample2_association.save() # update cached values experiment.num_total_samples = 2 experiment.num_processed_samples = 1 experiment.save() response = self.client.get( reverse("search", kwargs={"version": API_VERSION}), {"search": "GSX12345"}) self.assertEqual(response.json()["count"], 1) self.assertEqual(len(experiment.processed_samples), 1) experiment.delete() sample.delete() sample2.delete()
def prepare_computed_files(): # MICROARRAY TECH experiment = Experiment() experiment.accession_code = "GSE1487313" experiment.num_processed_samples = 1 experiment.save() result = ComputationalResult() result.save() gallus_gallus = Organism.get_object_for_name("GALLUS_GALLUS", taxonomy_id=1001) sample = Sample() sample.accession_code = "GSM1487313" sample.title = "GSM1487313" sample.organism = gallus_gallus sample.technology = "MICROARRAY" sample.is_processed = True sample.save() sra = SampleResultAssociation() sra.sample = sample sra.result = result sra.save() esa = ExperimentSampleAssociation() esa.experiment = experiment esa.sample = sample esa.save() computed_file = ComputedFile() computed_file.filename = "GSM1487313_liver.PCL" computed_file.result = result computed_file.size_in_bytes = 123 computed_file.is_smashable = True computed_file.s3_key = "GSM1487313_liver.PCL" computed_file.s3_bucket = TEST_DATA_BUCKET computed_file.save() assoc = SampleComputedFileAssociation() assoc.sample = sample assoc.computed_file = computed_file assoc.save() # RNASEQ TECH experiment2 = Experiment() experiment2.accession_code = "SRP332914" experiment2.num_processed_samples = 1 experiment2.save() result2 = ComputationalResult() result2.save() sample2 = Sample() sample2.accession_code = "SRR332914" sample2.title = "SRR332914" sample2.organism = gallus_gallus sample2.technology = "RNA-SEQ" sample2.is_processed = True sample2.save() sra2 = SampleResultAssociation() sra2.sample = sample2 sra2.result = result2 sra2.save() esa2 = ExperimentSampleAssociation() esa2.experiment = experiment2 esa2.sample = sample2 esa2.save() computed_file2 = ComputedFile() computed_file2.filename = "SRP149598_gene_lengthScaledTPM.tsv" computed_file2.result = result2 computed_file2.size_in_bytes = 234 computed_file2.is_smashable = True computed_file2.s3_key = "SRP149598_gene_lengthScaledTPM.tsv" computed_file2.s3_bucket = TEST_DATA_BUCKET computed_file2.save() assoc2 = SampleComputedFileAssociation() assoc2.sample = sample2 assoc2.computed_file = computed_file2 assoc2.save()
def setUp(self): # Saving this for if we have protected endpoints # self.superuser = User.objects.create_superuser('john', '*****@*****.**', 'johnpassword') # self.client.login(username='******', password='******') # self.user = User.objects.create(username="******") experiment = Experiment() experiment.accession_code = "GSE000" experiment.alternate_accession_code = "E-GEOD-000" experiment.title = "NONONONO" experiment.description = "Boooooourns. Wasabi." experiment.technology = "RNA-SEQ" experiment.save() experiment = Experiment() experiment.accession_code = "GSE123" experiment.title = "Hey Ho Let's Go" experiment.description = ( "This is a very exciting test experiment. Faygo soda. Blah blah blah." ) experiment.technology = "MICROARRAY" experiment.save() self.experiment = experiment experiment_annotation = ExperimentAnnotation() experiment_annotation.data = {"hello": "world", "123": 456} experiment_annotation.experiment = experiment experiment_annotation.save() # Create 26 test organisms numbered 0-25 for pagination test, so there should be 29 organisms total (with the 3 others below) for i in range(26): Organism(name=("TEST_ORGANISM_{}".format(i)), taxonomy_id=(1234 + i)).save() ailuropoda = Organism(name="AILUROPODA_MELANOLEUCA", taxonomy_id=9646, is_scientific_name=True) ailuropoda.save() self.homo_sapiens = Organism(name="HOMO_SAPIENS", taxonomy_id=9606, is_scientific_name=True) self.homo_sapiens.save() self.danio_rerio = Organism(name="DANIO_RERIO", taxonomy_id=1337, is_scientific_name=True) self.danio_rerio.save() sample = Sample() sample.title = "123" sample.accession_code = "123" sample.is_processed = True sample.organism = ailuropoda sample.save() sample = Sample() sample.title = "789" sample.accession_code = "789" sample.is_processed = True sample.organism = ailuropoda sample.save() self.sample = sample # add qn target for sample organism result = ComputationalResult() result.commands.append("create_qn_target.py") result.is_ccdl = True result.is_public = True result.processor = None result.save() cra = ComputationalResultAnnotation() cra.result = result cra.data = {"organism_id": ailuropoda.id, "is_qn": True} cra.save() ailuropoda.qn_target = result ailuropoda.save() sample_annotation = SampleAnnotation() sample_annotation.data = {"goodbye": "world", "789": 123} sample_annotation.sample = sample sample_annotation.save() original_file = OriginalFile() original_file.save() original_file_sample_association = OriginalFileSampleAssociation() original_file_sample_association.sample = sample original_file_sample_association.original_file = original_file original_file_sample_association.save() downloader_job = DownloaderJob() downloader_job.save() download_assoc = DownloaderJobOriginalFileAssociation() download_assoc.original_file = original_file download_assoc.downloader_job = downloader_job download_assoc.save() processor_job = ProcessorJob() processor_job.save() processor_assoc = ProcessorJobOriginalFileAssociation() processor_assoc.original_file = original_file processor_assoc.processor_job = processor_job processor_assoc.save() experiment_sample_association = ExperimentSampleAssociation() experiment_sample_association.sample = sample experiment_sample_association.experiment = experiment experiment_sample_association.save() experiment.num_total_samples = 1 experiment.num_processed_samples = 1 experiment.save() result = ComputationalResult() result.save() sra = SampleResultAssociation() sra.sample = sample sra.result = result sra.save() result = ComputationalResult() result.save() sra = SampleResultAssociation() sra.sample = sample sra.result = result sra.save() processor = Processor() processor.name = "Salmon Quant" processor.version = "v9.9.9" processor.docker_image = "dr_salmon" processor.environment = '{"some": "environment"}' processor.save() computational_result_short = ComputationalResult(processor=processor) computational_result_short.save() organism_index = OrganismIndex() organism_index.index_type = "TRANSCRIPTOME_SHORT" organism_index.organism = self.danio_rerio organism_index.result = computational_result_short organism_index.absolute_directory_path = ( "/home/user/data_store/salmon_tests/TRANSCRIPTOME_INDEX/SHORT") organism_index.is_public = True organism_index.s3_url = "not_blank" organism_index.save() return
def setUpClass(cls): super(ESTestCases, cls).setUpClass() # ref https://stackoverflow.com/a/29655301/763705 """Set up class.""" experiment = Experiment() experiment.accession_code = "GSE000-X" experiment.title = "NONONONO" experiment.description = "Boooooourns. Wasabi." experiment.technology = "RNA-SEQ" experiment.save() experiment = Experiment() experiment.accession_code = "GSE123-X" experiment.title = "Hey Ho Let's Go" experiment.description = ( "This is a very exciting test experiment. Faygo soda. Blah blah blah." ) experiment.technology = "MICROARRAY" experiment.num_processed_samples = 1 # added below experiment.num_total_samples = 1 experiment.num_downloadable_samples = 1 experiment.save() experiment_annotation = ExperimentAnnotation() experiment_annotation.data = {"hello": "world", "123": 456} experiment_annotation.experiment = experiment experiment_annotation.save() sample = Sample() sample.title = "123" sample.accession_code = "123" sample.save() organism = Organism( name="AILUROPODA_MELANOLEUCA", taxonomy_id=9646, is_scientific_name=True ) organism.save() sample = Sample() sample.title = "789" sample.accession_code = "789" sample.is_processed = True sample.organism = organism sample.save() sample_annotation = SampleAnnotation() sample_annotation.data = {"goodbye": "world", "789": 123} sample_annotation.sample = sample sample_annotation.save() original_file = OriginalFile() original_file.save() original_file_sample_association = OriginalFileSampleAssociation() original_file_sample_association.sample = sample original_file_sample_association.original_file = original_file original_file_sample_association.save() downloader_job = DownloaderJob() downloader_job.save() download_assoc = DownloaderJobOriginalFileAssociation() download_assoc.original_file = original_file download_assoc.downloader_job = downloader_job download_assoc.save() processor_job = ProcessorJob() processor_job.save() processor_assoc = ProcessorJobOriginalFileAssociation() processor_assoc.original_file = original_file processor_assoc.processor_job = processor_job processor_assoc.save() # associate the experiment with the sample experiment_sample_association = ExperimentSampleAssociation() experiment_sample_association.sample = sample experiment_sample_association.experiment = experiment experiment_sample_association.save() result = ComputationalResult() result.save() # and create a qn tarjet for the sample computational_result = ComputationalResultAnnotation() computational_result.result = result computational_result.data = {"is_qn": True, "organism_id": sample.organism.id} computational_result.save() # and associate it with the sample organism sample.organism.qn_target = result sample.organism.save() sra = SampleResultAssociation() sra.sample = sample sra.result = result sra.save() result = ComputationalResult() result.save() sra = SampleResultAssociation() sra.sample = sample sra.result = result sra.save() # clear default cache and reindex # otherwise the organisms with qn_targes will be cached. cache.clear() call_command("search_index", "--rebuild", "-f")