def parameters(cls):
cls.ac_method = ParameterFactory.ac_method(AC_METHOD,
AC_METHOD_DESCRIPTION)
parameters = [
cls.ac_method,
ParameterFactory.format(),
]
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):
# 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