SBART.Base_Models.RV_routine

Classes

RV_routine

Base class for the all the RV extraction routines.

class RV_routine

Bases: BASE

Base class for the all the RV extraction routines.

User parameters:

Parameter name	Mandatory	Default Value	Valid Values	Comment
uncertainty_prop_type	False	interpolation	interpolation / propagation	How to propagate uncertainties when interpolation the stellar template
order_removal_mode	False	per_subInstrument	per_subInstrument / global	How to combine the bad orders of the different sub-Instruments [1]
sigma_outliers_tolerance	False	6	Integer >= 0	Tolerance to flag pixels as outliers (when compared with the template)
min_block_size	False	50	Integer >= 0	If we have less than this number of consecutive valid pixels, reject that region
output_fmt	False	[2]	[3]	Control over the outputs that SBART will write to disk [4]
MEMORY_SAVE_MODE	False	False	boolean	Save RAM at the expense of more disk operations
CONTINUUM_FIT_POLY_DEGREE	False	1	Integer >= 0	Degree of the polynomial fit to the continuum.
CONTINUUM_FIT_TYPE	False	“paper”	“paper”	How to model the continuum

• [1] The valid options represent:

  • per_subInstrument: each sub-Instrument is assumes to be independent, no ensurance that we are always using the same spectral orders

  • global: We compute a global set of bad orders which is applied for all sub-Instruments

• [2] The default output format is: “BJD”,”RVc”,”RVc_ERR”,”SA”,”DRIFT”,”DRIFT_ERR”,”filename”,”frameIDs”,

• [3] The valid options are:

  • BJD :

  • MJD :

  • RVc : RV corrected from SA and drift

  • RVc_ERR : RV uncertainty

  • OBJ : Object name

  • SA : SA correction value

  • DRIFT : Drift value

  • DRIFT_ERR : Drift uncertainty

  • full_path : Full path to S2D file

  • filename : Only the filename

  • frameIDs : Internal ID of the observation

• [4] This User parameter is a list where the entries can be options specified in [3]. The list must start with

  a “time-related” key (BJD/MJD), followed by RVc and RVc_ERR.

Note: Also check the User parameters of the parent classes for further customization options of SBART

sampler_name = ''

__init__(N_jobs, RV_configs, sampler, target, valid_samplers, extra_folders_needed=None)

load_previous_RVoutputs()

find_subInstruments_to_use(dataClass, check_metadata)

Check to see which subInstruments should be used! By default only compare the previous MetaData (if it exists) with the current one

TODO: also check for the validity of stellar template in here!

Parameters:

• dataClass ([type]) – [description]

• storage_path (str) – [description]

• check_metadata (bool) – [description]

Raises:

NoDataError – If all all sub-Instruments were rejected

Return type:

None

run_routine(dataClass, storage_path, orders_to_skip=(), store_data=True, check_metadata=False, store_cube_to_disk=True)

Trigger the RV extraction for all sub-Instruments

Parameters:

• check_metadata (bool) – If True, the TM check the Metadata if it already exists on disk (if it is the same: does nothing). By default False

• store_data (bool) – If True, saves the data to disk. By default True

• storage_path (Union[pathlib.Path, str]) – Path in which the outputs of the run will be stored

• dataClass (DataClass) – [description]

• orders_to_skip (Union[list,tuple,str,dict], optional) – Orders to skip for the RV calculation, in each subInstrument. If list/tuple remove for all subInstrument the same orders. If dict, the keys should be the subInstrument and the values a list to skip (if the key does not exist, assume that there are None to skip). If str, load a previous RV cube from disk and use the orders that the previous run used!. By default ()

Return type:

None

apply_routine_to_subInst(dataClass, subInst)

Return type:

RV_cube

create_extra_plots(cube)

Return type:

NoReturn

process_workers_output(empty_cube, worker_outputs)

Return type:

RV_cube

build_target_configs()

Create a dict with extra information to be passed inside the target functions, as a kwarg

Returns:

[description]

Return type:

dict

generate_worker_configs(dataClassProxy)

Generate the dictionary that will be passed to the launching of the workers!

Parameters:

dataClassProxy –

Return type:

Dict[str, Any]

launch_workers(dataClassProxy)

Return type:

None

apply_orderskip_method()

Computing the orders that will be rejected for each subInstrument

Return type:

None

complement_orders_to_skip(dataClass)

Search for bad orders in the stellar template of all subInstruments.

Do not search the individual frames, as they might not be opened when we reach here

Parameters:

dataClass ([type]) – [description]

Return type:

None

process_orders_to_skip_from_user(to_skip)

Evaluate the input orders to skip and put them in the proper format

Parameters:

• dataClass ([type]) – DataClass

• to_skip ([type]) – Orders to skip

Returns:

Keys will be the subinstruments, values will be a set with the orders to skip

Return type:

dict

Raises:

NotImplementedError – [description]

generate_valid_orders(subInst, dataClass)

Return type:

list

trigger_data_storage(dataClassProxy, store_data=True)

Return type:

NoReturn

property subInstruments_to_use

close_multiprocessing()

Return type:

None

close_shared_mem_arrays()

Close any array that might exist in shared memory

Return type:

None

kill_workers()

open_queues()

Return type:

None

close_queues()

Return type:

None

select_wavelength_regions(dataClass)

launch_wavelength_selection(DataClassProxy)

Currently not 100% implemented!

Parameters:

DataClassProxy (DataClass) –