MCCS Calibration Architecture

The diagram below attempts to describes the calibration architecture.

Calibration architecture as currently implemented 0.17.0

observational_context: Currently consists of station_id and channel
solutions: Currently gain solutions.
observational_name: Currently the path of the hdf5 file dumped
field_conditions: Currently the outsideTemperature as reported by FieldStation

Application of Solutions

The calibration solutions are applied to each allocated station during the Subarray` Configure command. If no solution is found a unit calibration will be applied as a default.

Below is the current sequence diagram:

Storing of Solutions

The MccsStationCalibrator is responsible for orchestrating the retrieval and storage of calibration solutions.

See sequence diagram for an overview of the internals:

$@startuml participant TangoUser as TangoUser participant MccsStationCalibrator as StationCalibrator participant MccsStation as Station participant MccsCalibrationStore as MccsCalibrationStore participant StationCalibrationSolverDevice as solver participant postgresql as postgresql participant SpsStation as SpsStation participant MccsTile as MccsTile participant MccsDaq as MccsDaq participant ArtefactRepository as ArtefactRepository TangoUser -> StationCalibrator: StartCalibrationLoop loop channels StationCalibrator -> Station: AcquireDataForCalibration(channel) Station -> SpsStation: AcquireDataForCalibration(channel) SpsStation -> MccsDaq: Configure() SpsStation -> MccsDaq: Start() SpsStation -> MccsTile: SetLmcDownload() SpsStation -> MccsTile: SendDataSamples() MccsDaq -> ArtefactRepository: dump_hdf5() MccsDaq -> SpsStation: hdf5_data_path SpsStation -> Station: hdf5_data_path Station -> StationCalibrator: hdf5_data_path StationCalibrator -> solver: Solve(\n\thdf5_data_path,\n\tsolution_path) solver -> ArtefactRepository: write(gain_solution, metadata) StationCalibrator->MccsCalibrationStore: StoreSolution(\n\tsolution_path,\n\tmetadata) MccsCalibrationStore->postgresql: Store(gain_solution, metadata) end @enduml$

Database schema

The following schema is used by mccs.

$@startuml ' --- Configuration --- hide circle skinparam linetype ortho skinparam packageStyle rectangle ' --- Table: frequency_sweep --- entity "frequency_sweep" as e01 { *sweep_id : SERIAL PRIMARY KEY -- *station_id : SMALLINT *channel_start : SMALLINT *channel_stop : SMALLINT path_to_data : TEXT <<unique>> start_time : TIMESTAMP correlator_samples : TIMESTAMP nof_samples : INTEGER initial_static_delays : FLOAT8[] } ' --- Table: calibration_job --- entity "calibration_job" as e02 { *id : SERIAL <<generated>> -- *sweep_id : INTEGER <<FK>> *initial_mask : FLOAT8[] *calibration_parameters : JSON *user_friendly_name : TEXT <<unique>> preferred : BOOLEAN } ' --- Table: calibration_per_channel --- entity "calibration_per_channel" as e04 { *id : SERIAL <<generated>> -- *creation_time : TIMESTAMP *frequency_channel : SMALLINT *station_id : SMALLINT preferred : BOOLEAN calibration_path : TEXT acquisition_time : FLOAT8 solution : FLOAT8[] corrcoeff : FLOAT8[] residual_max : FLOAT8[] residual_std : FLOAT8[] xy_phase : FLOAT8 n_masked_initial : INTEGER n_masked_final : INTEGER lst : FLOAT8 galactic_centre_elevation : FLOAT8 sun_elevation : FLOAT8 sun_adjustment_factor : FLOAT8 masked_antennas : TEXT[] job_id : INTEGER <<FK>> solution_type : TEXT } ' --- Table: calibration_per_sweep --- entity "calibration_per_sweep" as e03 { *id : SERIAL <<generated>> -- *job_id : INTEGER <<FK, unique>> *final_mask : TEXT[] mean_amplitude : DOUBLE PRECISION[256] *phase : DOUBLE PRECISION *phase_gradient : DOUBLE PRECISION *amplitude_fit_cost : DOUBLE PRECISION phase_fit_cost : DOUBLE PRECISION phase_fit_covariance : DOUBLE PRECISION } ' --- Relationships (Information Engineering notation) --- e01 "0..*" -- "1" e02 e02 "0..*" -- "0..1" e04 e02 "1" -- "1" e03 @enduml$