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.dataset_parameter = ParameterFactory.pa_data_source()
cls.report_uuid_parameter = ParameterFactory.uuid(allow_multiple=False)
parameters = [
cls.dataset_parameter,
cls.report_uuid_parameter,
ParameterFactory.format(),
]
return parameters