def parameters(cls): sid_enum = cls._DB_CONNECTOR.distinct(PROBE_EXPERIMENTS_COLLECTION, SAMPLE_ID) rid_enum = cls._DB_CONNECTOR.distinct(PROBE_EXPERIMENTS_COLLECTION, RUN_ID) pid_enum = cls._DB_CONNECTOR.distinct(PROBE_EXPERIMENTS_COLLECTION, PROBE_ID) date_enum = cls._DB_CONNECTOR.distinct(PROBE_EXPERIMENTS_COLLECTION, DATE) date_enum = map(lambda x: x.strftime("%Y_%m_%d"), date_enum) parameters = [ ParameterFactory.format(), ParameterFactory.lc_string(SAMPLE_ID, "Genomic DNA Sample ID(s).", required=False, allow_multiple=True, enum=sid_enum), ParameterFactory.lc_string(RUN_ID, "Run ID(s).", required=False, allow_multiple=True, enum=rid_enum), ParameterFactory.lc_string(PROBE_ID, "Probe ID(s).", required=False, allow_multiple=True, enum=pid_enum), ParameterFactory.date(required=False, enum=date_enum) ] return parameters
def parameters(cls): cls._file_param = ParameterFactory.file("Experiment results file.") cls._sid_param = ParameterFactory.lc_string(SAMPLE_ID, "Genomic DNA Sample ID.") cls._rid_param = ParameterFactory.lc_string(RUN_ID, "Run ID.") cls._date_param = ParameterFactory.date() parameters = [ cls._file_param, cls._sid_param, cls._rid_param, cls._date_param, ] return parameters
def parameters(cls): cls.pa_data_src_param = ParameterFactory.pa_data_source() cls.dyes_param = ParameterFactory.dyes() cls.device_param = ParameterFactory.device() cls.major_param = ParameterFactory.integer(MAJOR, "Major dye " \ "profile version", minimum=0) cls.minor_param = ParameterFactory.integer(MINOR, "Minor dye " \ "profile version", minimum=0) cls.job_name_param = ParameterFactory.lc_string(JOB_NAME, "Unique "\ "name to give this " "job.") cls.offset = ParameterFactory.integer(OFFSETS, "Offset used " \ "to infer a dye model. The inference will offset the dye profiles " \ "in a range of (-<offset>,<offset>] to determine the optimal " \ "offset.", default=abs(DEFAULT_OFFSETS[0]), minimum=1) cls.use_iid_param = ParameterFactory.boolean(USE_IID, "Use IID Peak " \ "Detection.", default_value=False) parameters = [ cls.pa_data_src_param, cls.dyes_param, cls.device_param, cls.major_param, cls.minor_param, cls.job_name_param, cls.offset, cls.use_iid_param, ] return parameters
def parameters(cls): cls._file_param = ParameterFactory.file("Probe metadata file.") cls._app_param = ParameterFactory.lc_string( APPLICATION, "Case-insensitive application (e.g. Oncopanel).") parameters = [ cls._file_param, cls._app_param, ] return parameters
def parameters(cls): cls.archives_param = ParameterFactory.archive() cls.job_name_param = ParameterFactory.lc_string(JOB_NAME, "Unique "\ "name to give this " "job.") parameters = [ cls.archives_param, cls.job_name_param, ] return parameters
def parameters(cls): pid_enum = cls._DB_CONNECTOR.distinct(PROBE_METADATA_COLLECTION, PROBE_ID) cls._pid_param = ParameterFactory.lc_string(PROBE_ID, "Probe ID(s).", required=True, allow_multiple=True, enum=pid_enum) parameters = [ cls._pid_param, ] return parameters
def parameters(cls): rid_enum = cls._DB_CONNECTOR.distinct(PROBE_EXPERIMENTS_COLLECTION, RUN_ID) cls._rid_param = ParameterFactory.lc_string(RUN_ID, "Run ID.", required=True, allow_multiple=True, enum=rid_enum) parameters = [ cls._rid_param, ] return parameters
def parameters(cls): parameters = [ ParameterFactory.file_uuid(PROBES, PROBES_COLLECTION), ParameterFactory.file_uuid(TARGETS, TARGETS_COLLECTION), ParameterFactory.boolean("absorb", "Check for absorbed probes."), ParameterFactory.integer("num", "Minimum number of probes for a target.", default=3, minimum=1), ParameterFactory.lc_string(JOB_NAME, "Unique name to give this job.") ] return parameters
def process_request(cls, params_dict): probes_file_uuid = params_dict[ParameterFactory.file_uuid( "probes", PROBES_COLLECTION)][0] targets_file_uuid = params_dict[ParameterFactory.file_uuid( "targets", TARGETS_COLLECTION)][0] absorb = params_dict[ParameterFactory.boolean( "absorb", "Check for absorbed probes.")][0] num = params_dict[ParameterFactory.integer( "num", "Minimum number of probes for a target.", default=3, minimum=1)][0] job_name = params_dict[ParameterFactory.lc_string( JOB_NAME, "Unique name to give this job.")][0] json_response = { PROBES: probes_file_uuid, TARGETS: targets_file_uuid, ABSORB: absorb, NUM: num, UUID: str(uuid4()), STATUS: JOB_STATUS.submitted, # @UndefinedVariable JOB_NAME: job_name, DATESTAMP: datetime.today(), } http_status_code = 200 if job_name in cls._DB_CONNECTOR.distinct(VALIDATION_COLLECTION, JOB_NAME): http_status_code = 403 else: try: probes_path = cls._DB_CONNECTOR.find_one( PROBES_COLLECTION, UUID, probes_file_uuid)[FILEPATH] targets_path = cls._DB_CONNECTOR.find_one( TARGETS_COLLECTION, UUID, targets_file_uuid)[FILEPATH] #ADD VALIDATOR JOB TO QUEUE cls._DB_CONNECTOR.insert(VALIDATION_COLLECTION, [json_response]) del json_response[ID] except: APP_LOGGER.exception(traceback.format_exc()) json_response[ERROR] = str(sys.exc_info()[1]) http_status_code = 500 return make_clean_response(json_response, http_status_code)
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 parameters(cls): cls._users_param = ParameterFactory.lc_string(USERS, "Username(s) of individual(s) that performed the run (e.g. ddicara)", allow_multiple=True) cls._date_param = ParameterFactory.date() cls._archive_param = ParameterFactory.archive() cls._beta_param = ParameterFactory.integer(BETA, "Beta (e.g. 17)", required=True, minimum=1, maximum=100) cls._device_param = ParameterFactory.cs_string(DEVICE, "Device description (e.g. PDMS bonded on COP)") cls._dye_profile_metrics_param = ParameterFactory.dye_profile_metrics() cls._surfactant_param = ParameterFactory.cs_string(SURFACTANT, "Surfactant (e.g. RAN 002-105).") parameters = [ cls._users_param, cls._date_param, cls._archive_param, cls._beta_param, cls._device_param, cls._dye_profile_metrics_param, cls._surfactant_param, ] return parameters
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): # required parameters cls.job_name_param = ParameterFactory.lc_string( JOB_NAME, 'Unique name for this job.', required=True) cls.exp_def_param = ParameterFactory.experiment_definition() cls.mask_param = ParameterFactory.cs_string( VARIANT_MASK, 'Mask code for variant selection.', required=False) # primary analysis parameters cls.pa_data_src_param = ParameterFactory.cs_string( PA_DATA_SOURCE, "Primary analysis data source (HDF5 or image stack).", required=True) cls.dyes_param = ParameterFactory.dyes(required=False) cls.device_param = ParameterFactory.device(required=False, default='katahdin') cls.major_param = ParameterFactory.integer(MAJOR, 'Major dye version', minimum=0, required=False, default=1) cls.minor_param = ParameterFactory.integer(MINOR, 'Minor dye version', minimum=0, required=False, default=0) cls.offset = ParameterFactory.integer( OFFSETS, 'Offset used to infer a dye model.', default=abs(DEFAULT_OFFSETS[0]), minimum=1, required=False) cls.use_iid_param = ParameterFactory.boolean(USE_IID, 'Use IID Peak Detection.', default_value=False, required=False) # identity parameters cls.dye_levels_param = ParameterFactory.dye_levels(required=False) cls.ignored_dyes_param = ParameterFactory.dyes(name=IGNORED_DYES, required=False) cls.filtered_dyes_param = ParameterFactory.dyes(name=FILTERED_DYES, required=False) cls.pico1_dye_param = ParameterFactory.dye(PICO1_DYE, 'picoinjection 1 dye.', required=False, default=None) cls.pico2_dye_param = ParameterFactory.dye(PICO2_DYE, 'picoinjection 2 dye.', required=False, default=DEFAULT_PICO2_DYE) cls.assay_dye_param = ParameterFactory.dye(ASSAY_DYE, 'Assay dye.', required=False, default=DEFAULT_ASSAY_DYE) cls.n_probes_param = ParameterFactory.integer(NUM_PROBES, NUM_PROBES_DESCRIPTION, minimum=4, required=False) cls.id_training_param = ParameterFactory.integer( ID_TRAINING_FACTOR, TRAINING_FACTOR_DESCRIPTION, minimum=1, required=False, default=DEFAULT_ID_TRAINING_FACTOR, ) cls.ui_threshold_param = ParameterFactory.float( UI_THRESHOLD, UI_THRESHOLD_DESCRIPTION, minimum=0.0, required=False, default=DEFAULT_UNINJECTED_THRESHOLD) cls.continuous_phase_param = ParameterFactory.boolean( CONTINUOUS_PHASE, CONTINUOUS_PHASE_DESCRIPTION, default_value=False, required=False) cls.max_ui_ratio_param = ParameterFactory.float( MAX_UNINJECTED_RATIO, MAX_UI_RATIO_DESCRIPTION, default=DEFAULT_UNINJECTED_RATIO, minimum=0.0) cls.ignore_lowest_barcode = ParameterFactory.boolean( IGNORE_LOWEST_BARCODE, IGNORE_LOWEST_BARCODE_DESCRIPTION, default_value=True, required=False) cls.use_pico1_filter = ParameterFactory.boolean( USE_PICO1_FILTER, USE_PICO1_FILTER_DESCRIPTION, default_value=True, required=False) cls.use_pico2_filter = ParameterFactory.boolean( USE_PICO2_FILTER, USE_PICO2_FILTER_DESCRIPTION, default_value=True, 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) # assay caller params cls.ac_training_param = ParameterFactory.integer( AC_TRAINING_FACTOR, TRAINING_FACTOR_DESCRIPTION, minimum=1, required=False, default=DEFAULT_AC_TRAINING_FACTOR) cls.ctrl_thresh = ParameterFactory.float( CTRL_THRESH, CTRL_THRESH_DESCRIPTION, default=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 ]) # genotyper params cls.req_drops_param = ParameterFactory.integer(REQUIRED_DROPS, REQ_DROPS_DESCRIPTION, required=False, minimum=0, default=0) parameters = [ cls.pa_data_src_param, cls.dyes_param, cls.device_param, cls.major_param, cls.minor_param, cls.job_name_param, cls.offset, cls.use_iid_param, cls.pico1_dye_param, cls.pico2_dye_param, cls.assay_dye_param, cls.n_probes_param, cls.id_training_param, cls.dye_levels_param, cls.ignored_dyes_param, cls.dev_mode_param, cls.drift_compensate_param, cls.use_pico1_filter, cls.use_pico2_filter, cls.filtered_dyes_param, cls.ui_threshold_param, cls.continuous_phase_param, cls.max_ui_ratio_param, cls.ignore_lowest_barcode, cls.ac_training_param, cls.ctrl_thresh, cls.ctrl_filter, cls.ac_method, cls.ac_model, cls.req_drops_param, cls.exp_def_param, cls.mask_param, ] return parameters