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