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 test_custom_job_normal_workflow(self):
FASTA_DATA1 = """>someseq\nAAACCCGGGGTT\n>someseq2\nAAACCCGGGGTTAAACCCGGGGTTAAACCCGGGGTTAAACCCGGGGTTAAACCCGGGGTT"""
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
someseq2\t20\t30\t4.5
someseq2\t60\t75\t15.5
""".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('AAACCCGGGGTT', first['sequence'])
second = predictions[1]
self.assertEqual('someseq2', second['name'])
self.assertEqual(15.5, float(second['max']))
self.assertEqual('AAACCCGGGGTTAAACCCGGGGTTAAACCCGGGGTTAAACCCGGGGTTAAACCCGGGGTT', 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 search_custom_results(result_id):
"""
Search a result for predictions.
request['maxPredictionSort'] - when true sort by max prediction
request['all'] - include values in download
request['page'] - which page of results to show
request['perPage'] - items per page to show
:param result_id: str: uuid of the custom_predictions/custom_preferences we want to search
:return: json response with 'result' property containing an array of predictions
"""
args = request.args
format = args.get('format')
sort_by_max = args.get('maxPredictionSort')
if sort_by_max == 'false':
sort_by_max = None
all_values = args.get('all')
page = get_optional_int(args, 'page')
per_page = get_optional_int(args, 'per_page')
offset = None
if page and per_page:
offset = (page - 1) * per_page
predictions = CustomResultData.get_predictions(get_db(), result_id, sort_by_max, per_page, offset)
if format == 'tsv' or format == 'csv':
filename = "custom_result.{}".format(format)
separator = ','
if format == 'tsv':
separator = '\t'
return download_file_response(filename, make_download_custom_result(separator, all_values, predictions))
else:
return make_ok_json_response({
'result': predictions})
def search_custom_results(result_id):
"""
Search a result for predictions.
request['maxPredictionSort'] - when true sort by max prediction
request['all'] - include values in download
request['page'] - which page of results to show
request['perPage'] - items per page to show
:param result_id: str: uuid of the custom_predictions/custom_preferences we want to search
:return: json response with 'result' property containing an array of predictions
"""
args = request.args
format = args.get('format')
sort_by_max = args.get('maxPredictionSort')
if sort_by_max == 'false':
sort_by_max = None
all_values = args.get('all')
page = get_optional_int(args, 'page')
per_page = get_optional_int(args, 'per_page')
offset = None
if page and per_page:
offset = (page - 1) * per_page
predictions = CustomResultData.get_predictions(get_db(), result_id,
sort_by_max, per_page,
offset)
if format == 'tsv' or format == 'csv':
filename = "custom_result.{}".format(format)
separator = ','
if format == 'tsv':
separator = '\t'
return download_file_response(
filename,
make_download_custom_result(separator, all_values, predictions))
else:
return make_ok_json_response({'result': predictions})
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_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])
