Introduction

pyDARM serves as the primary method for quantifying the DARM control loop and computing the needed values in order to calculate h(t) and estimate the error and associated uncertainty.

Inputs and outputs

DARM model parameters are provided via a configuration file (usually a .ini file). From this the loop transfer functions can be modelled.

pyDARM can also estimate DARM loop parameters by also accepting DTT measurement files as additional input. Together with the configuration file, the measurement parameters are estimated via MCMC algorithm. Posterior samples are saved in HDF5 files

Collections of DTT measurement files can be provided in order to search for remaining systematic errors using Gaussian Process Regression that not accounted for elsewhere. Posterior samples are saved in HDF5 files

These saved HDF5 files can by supplied so that the systematic error and associated uncertainty can be estimated at any time during an observing run.

Aside from the above inputs and outputs, pyDARM can calculate other transfer functions and parameters useful for online and offline calibration, calibration diagnostics, detector commissioning and characterization.