def test_custom_job_no_data(self): FASTA_DATA1 = """>someseq\nAAACCCGGGGTT""" db = create_db_connection(TestWithPostgres.config.dbconfig) # upload FASTA file sequence_list = SequenceList.create_with_content_and_title(db, FASTA_DATA1, "somelist") # create a job to determine predictions for a sequence_list job_uuid = CustomJob.create_job(db, DataType.PREDICTION, sequence_list, model_name='E2f1').uuid # mark job as running CustomJob.set_job_running(db, job_uuid) # upload file BED_DATA = '' result_uuid = CustomResultData.new_uuid() result = CustomResultData(db, result_uuid, job_uuid, model_name='E2f1', bed_data=BED_DATA) result.save() predictions = CustomResultData.get_predictions(db, result_uuid, sort_max_value=False, limit=None, offset=None) self.assertEqual(1, len(predictions)) first = predictions[0] self.assertEqual('someseq', first['name']) self.assertEqual('None', first['max']) self.assertEqual([], first['values']) self.assertEqual('AAACCCGGGGTT', first['sequence']) # Make sure we can convert predictions to JSON json_version = json.dumps({'data': predictions}) self.assertEqual('{"data', json_version[:6])
def test_custom_job_normal_workflow(self): SHORT_SEQUENCE = 'AAACCCGGGGTT' LONG_SEQUENCE = 'AAACCCGGGGTTAAACCCGGGGTTAAACCCGGGGTTAAACCCGGGGTTAAACCCGGGGTT' \ 'AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA' FASTA_DATA1 = '>someseq\n' + SHORT_SEQUENCE + '\n' \ '>someseq2\n' + LONG_SEQUENCE db = create_db_connection(TestWithPostgres.config.dbconfig) # upload FASTA file sequence_list = SequenceList.create_with_content_and_title(db, FASTA_DATA1, "sometitle") # create a job to determine predictions for a sequence_list job_uuid = CustomJob.create_job(db, DataType.PREDICTION, sequence_list, model_name="E2f1").uuid # mark job as running CustomJob.set_job_running(db, job_uuid) # upload file BED_DATA = """ someseq\t0\t10\t12.5\tAAACCCGGGG someseq2\t20\t30\t4.5\tGGTTAAACCC someseq2\t60\t75\t15.5\tAAAAAAAAAAAAAAA """.strip() result_uuid = CustomResultData.new_uuid() result = CustomResultData(db, result_uuid, job_uuid, model_name='E2f1', bed_data=BED_DATA) result.save() self.assertEqual(BED_DATA, CustomResultData.bed_file_contents(db, result_uuid).strip()) predictions = CustomResultData.get_predictions(db, result_uuid, sort_max_value=False, limit=None, offset=None) self.assertEqual(2, len(predictions)) first = predictions[0] self.assertEqual('someseq', first['name']) self.assertEqual(12.5, float(first['max'])) self.assertEqual([{u'start': 0, u'end': 10, u'value': 12.5}], first['values']) self.assertEqual(SHORT_SEQUENCE, first['sequence']) second = predictions[1] self.assertEqual('someseq2', second['name']) self.assertEqual(15.5, float(second['max'])) self.assertEqual(LONG_SEQUENCE, second['sequence']) predictions = CustomResultData.get_predictions(db, result_uuid, sort_max_value=True, limit=None, offset=None) self.assertEqual(2, len(predictions)) self.assertEqual(15.5, float(predictions[0]['max'])) self.assertEqual(12.5, float(predictions[1]['max'])) predictions = CustomResultData.get_predictions(db, result_uuid, sort_max_value=True, limit=1, offset=1) self.assertEqual(1, len(predictions)) self.assertEqual(12.5, float(predictions[0]['max'])) # Make sure we can convert predictions to JSON json_version = json.dumps({'data': predictions}) self.assertEqual('{"data', json_version[:6])
def post_custom_result(): """ Save custom prediction/preferences results. Secured via apache config: production/tfpredictions.conf. request['job_id'] - str: uuid of the job associated with these results request['bed_data'] - str: data that makes up the results request['model_name'] - str: name of the model used to build these results :return: json response with uuid of result stored in 'id' field """ required_prop_names = ["job_id", "model_name"] (job_id, model_name) = get_required_json_props(request, required_prop_names) bed_data = request.get_json().get('bed_data') decoded_bed_data = base64.b64decode(bed_data) result_uuid = CustomResultData.new_uuid() result_data = CustomResultData(get_db(), result_uuid, job_id, model_name, decoded_bed_data) result_data.save() return make_json_response({'result': 'ok', 'id': result_uuid})