orca.transform.gainscaling
==========================

.. py:module:: orca.transform.gainscaling

.. autoapi-nested-parse::

   Transforms that relate to amplitude scaling.

   It uses autocorrelation to figure out the scaling factor between two snapshots on a per-antenna per-channel per-pol
   basis.

   NOTE: It uses the autocorrelation  flags to figure out which antennas are flagged and does not solve
   for those antennas.



Attributes
----------

.. autoapisummary::

   orca.transform.gainscaling.log


Functions
---------

.. autoapisummary::

   orca.transform.gainscaling.auto_corr_data_and_flag
   orca.transform.gainscaling.calculate_gain_scale
   orca.transform.gainscaling.apply_gain_scale_in_place
   orca.transform.gainscaling.correct_scaling


Module Contents
---------------

.. py:data:: log

.. py:function:: auto_corr_data_and_flag(t: casacore.tables.table, data_column: str) -> Tuple[numpy.ndarray, numpy.ndarray]

   Extract autocorrelation data and flags from a measurement set table.

   :param t: Open casacore table object.
   :param data_column: Name of the data column to read.

   :returns: Tuple of (data, flag) arrays for autocorrelation baselines.


.. py:function:: calculate_gain_scale(to_scale_data: numpy.ndarray, to_scale_flag: numpy.ndarray, target_data: numpy.ndarray, target_flag: numpy.ndarray) -> numpy.ndarray

   Calculate the gain scaling factor to match autocorrelation levels.

   Computes per-antenna per-channel per-polarization scaling factors
   required to scale one dataset's autocorrelations to match another's.

   :param to_scale_data: Autocorrelation data to be scaled.
   :param to_scale_flag: Flags for to_scale_data.
   :param target_data: Target autocorrelation data to match.
   :param target_flag: Flags for target_data.

   :returns: Scaling factors array; flagged values are set to 1.0.


.. py:function:: apply_gain_scale_in_place(data: numpy.ndarray, scale_spectrum: numpy.ndarray) -> None

   Apply single pol scaling factor per antenna to cross-correlated data.
   This is similar to applycal in CASA. It multiples a cross-correlation by the scaling factor that corresponds to
   the two antennas (each of which has 2 polarizations) involved.
   Warning: this mutates data in place while returning a reference to it.

   :param data: Cross-correlated data with shape (N_vis, N_chan, 4), ordered by antennas
   :param scale_spectrum: Single polarization scaling factors; shape (N_ant, N_chan, 2), ordered by antennas

   Returns: data multiplied by scale_spectrum



.. py:function:: correct_scaling(to_scale_ms: str, target_ms: str, data_column: str = 'CORRECTED_DATA')

   Correct for per-antenna per-pol per-channel scaling between two measurement sets.

   Scales data in to_scale_ms such that its autocorrelation matches target_ms.
   Uses autocorrelation ratios to derive per-antenna scaling factors.

   :param to_scale_ms: Measurement set to scale (modified in-place).
   :param target_ms: Reference measurement set whose autocorrelation levels to match.
   :param data_column: The data column to apply this operation to.

   :returns: None. Modifies to_scale_ms in place.