def parameters(cls):
cls.job_uuid = ParameterFactory.job_uuid(PA_PROCESS_COLLECTION)
parameters = [
cls.job_uuid,
]
return parameters
def parameters(cls):
cls.job_uuid_param = ParameterFactory.job_uuid(SA_ASSAY_CALLER_COLLECTION)
cls.job_name_param = ParameterFactory.lc_string(JOB_NAME, JOB_NAME_DESC)
cls.exp_defs_param = ParameterFactory.experiment_definition()
cls.req_drops_param = ParameterFactory.integer(REQUIRED_DROPS,
REQ_DROPS_DESCRIPTION,
required=True, default=0,
minimum=0)
parameters = [
cls.job_uuid_param,
cls.job_name_param,
cls.exp_defs_param,
cls.req_drops_param,
]
return parameters
def parameters(cls):
cls.job_uuid_param = ParameterFactory.job_uuid(SA_IDENTITY_COLLECTION)
cls.job_name_param = ParameterFactory.lc_string(JOB_NAME, 'Unique '\
'name to give this '
'job.')
cls.exp_defs_param = ParameterFactory.experiment_definition()
cls.training_param = ParameterFactory.integer(
TRAINING_FACTOR,
TRAINING_FACTOR_DESCRIPTION,
default=AC_TRAINING_FACTOR,
minimum=1,
required=True)
cls.ctrl_thresh = ParameterFactory.float(CTRL_THRESH,
CTRL_THRESH_DESCRIPTION,
default=AC_CTRL_THRESHOLD,
minimum=0.0,
maximum=100.0)
cls.ctrl_filter = ParameterFactory.boolean(CTRL_FILTER,
CTRL_FILTER_DESCRIPTION,
default_value=False,
required=True)
cls.ac_method = ParameterFactory.ac_method(AC_METHOD,
AC_METHOD_DESCRIPTION)
cls.ac_model = ParameterFactory.cs_string(
AC_MODEL,
AC_MODEL_DESCRIPTION,
required=False,
enum=[
m for model_dict in MODEL_FILES.values() for m in model_dict
])
parameters = [
cls.job_uuid_param,
cls.job_name_param,
cls.exp_defs_param,
cls.training_param,
cls.ctrl_thresh,
cls.ctrl_filter,
cls.ac_method,
cls.ac_model,
]
return parameters
def process_request(cls, params_dict, del_file_keys=(RESULT,)):
response = {}
http_status_code = 200
uuids = params_dict[ParameterFactory.job_uuid(cls.get_collection())]
criteria = {UUID: {"$in": uuids}}
APP_LOGGER.info("Deleting the following jobs: %s" % ",".join(uuids))
records = cls._DB_CONNECTOR.find(cls.get_collection(), criteria,
{ID:0})
response["deleted"] = {}
if len(records) > 0:
# Record records
for record in records:
response["deleted"][record[UUID]] = record
# Delete records from database
result = cls._DB_CONNECTOR.remove(cls.get_collection(),
criteria)
# Delete files from disk only if removal from DB was successful
if result and result['n'] == len(response["deleted"]):
for _,record in response["deleted"].iteritems():
for key in del_file_keys:
file_path = record.get(key, None)
if file_path is not None and os.path.isfile(file_path):
os.remove(file_path)
else:
del response["deleted"]
raise Exception("Error deleting records from the " \
"database: %s" % result)
APP_LOGGER.info("Successfully deleted the following jobs: %s" \
% ",".join(uuids))
else:
http_status_code = 404
return response, http_status_code
def parameters(cls):
cls.job_uuid_param = ParameterFactory.job_uuid(PA_PROCESS_COLLECTION)
cls.job_name_param = ParameterFactory.lc_string(JOB_NAME, "Unique "\
"name to give this "
"job.")
cls.pico1_dye_param = ParameterFactory.dye(PICO1_DYE,
"Picoinjection 1 dye.")
cls.pico2_dye_param = ParameterFactory.dye(PICO2_DYE,
"Picoinjection 2 dye.")
cls.assay_dye_param = ParameterFactory.dye(ASSAY_DYE, "Assay dye.")
cls.n_probes_param = ParameterFactory.integer(NUM_PROBES,
NUM_PROBES_DESCRIPTION,
default=0, minimum=0)
cls.training_param = ParameterFactory.integer(TRAINING_FACTOR,
TRAINING_FACTOR_DESCRIPTION,
default=DEFAULT_ID_TRAINING_FACTOR,
minimum=1)
cls.dye_levels_param = ParameterFactory.dye_levels()
cls.ignored_dyes_param = ParameterFactory.dyes(name=IGNORED_DYES,
required=False)
cls.filtered_dyes_param = ParameterFactory.dyes(name=FILTERED_DYES,
required=False)
cls.ui_threshold_param = ParameterFactory.float(UI_THRESHOLD,
UI_THRESHOLD_DESCRIPTION,
default=UNINJECTED_THRESHOLD,
minimum=0.0)
cls.continuous_phase_param = ParameterFactory.boolean(CONTINUOUS_PHASE,
CONTINUOUS_PHASE_DESCRIPTION,
default_value=False,
required=False)
cls.dev_mode_param = ParameterFactory.boolean(DEV_MODE,
'Use development mode (more forgiving of mistakes).',
default_value=DEFAULT_DEV_MODE,
required=False)
cls.drift_compensate_param = ParameterFactory.boolean(DRIFT_COMPENSATE,
'Compensate for data drift.',
default_value=DEFAULT_DRIFT_COMPENSATE,
required=False)
cls.max_ui_ratio_param = ParameterFactory.float(MAX_UNINJECTED_RATIO,
MAX_UI_RATIO_DESCRIPTION,
default=UNINJECTED_RATIO,
minimum=0.0)
cls.ignore_lowest_barcode = ParameterFactory.boolean(IGNORE_LOWEST_BARCODE,
IGNORE_LOWEST_BARCODE_DESCRIPTION,
default_value=DEFAULT_IGNORE_LOWEST_BARCODE,
required=False)
parameters = [
cls.job_uuid_param,
cls.job_name_param,
cls.pico1_dye_param,
cls.pico2_dye_param,
cls.assay_dye_param,
cls.n_probes_param,
cls.training_param,
cls.dye_levels_param,
cls.ignored_dyes_param,
cls.filtered_dyes_param,
cls.ui_threshold_param,
cls.continuous_phase_param,
cls.max_ui_ratio_param,
cls.ignore_lowest_barcode,
cls.dev_mode_param,
cls.drift_compensate_param,
]
return parameters
def parameters(cls):
parameters = [
ParameterFactory.job_uuid(cls.get_collection()),
]
return parameters
