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})
