JonesMatrices
This is a repository of Jones Matrices compatible with the Siamese simulations framework. All these modules obey the Siamese Standard Contracts: StandardContracts. Any external module obeying the standard contracts can be straightforwardly plugged into Siamese to create a new simulator or extend an existing one.
A simple cos^3 approximation to the WSRT beam: [[wsrt_beams.py|http://timba.svn.sourceforge.net/viewvc/checkout/timba/trunk/Timba/WH/contrib/OMS/Siamese/wsrt_beams]]. Checked in under Timba/WH/contrib/OMS/Siamese.
[[sarod_cs1_beams.py|http://timba.svn.sourceforge.net/viewvc/checkout/timba/trunk/Timba/WH/contrib/OMS/Siamese/sarod_cs1_beams.py]]. Checked in under Timba/WH/contrib/OMS/Siamese. Also requires this directory to be downloaded and built (with a simple make): cs1beams, so it's probably easier to directly check out and build Timba/WH/contrib/OMS/Siamese/cs1beams.
embrace_beams.py. An idealised Aperture Array beam, implemented as a sinc function, but also taking into account foreshortening of the array due to observing sources away from the zenith. Should be used in conjunction with a pointing model to simulate the quantised pointing steps of the AA.
Random and/or evolving (sinusoidal in time) pointing offsets: [[oms_pointing_errors.py|http://timba.svn.sourceforge.net/viewvc/checkout/timba/trunk/Timba/WH/contrib/OMS/Siamese/oms_pointing_errors.py]]. Uses [[ErrorGens.py|http://timba.svn.sourceforge.net/viewvc/checkout/timba/trunk/Timba/WH/contrib/OMS/Siamese/ErrorGens.py]]. Checked in under Timba/WH/contrib/OMS/Siamese.
Implements sinusoidal TID's and an evolving wedge. Uses a "flat world" geometry: [[oms_ionosphere.py|http://timba.svn.sourceforge.net/viewvc/checkout/timba/trunk/Timba/WH/contrib/OMS/Siamese/oms_ionosphere.py]]. Also requires this module: [[iono_geometry.py|http://timba.svn.sourceforge.net/viewvc/checkout/timba/trunk/Timba/WH/contrib/OMS/Siamese/iono_geometry.py]], which can probably be replaced by another geometry.
Checked in under Timba/WH/contrib/OMS/Siamese.
[[oms_gain_models.py|http://timba.svn.sourceforge.net/viewvc/checkout/timba/trunk/Timba/WH/contrib/OMS/Siamese/oms_gain_models.py]]. Implements random and/or evolving (sinusoidal in time) complex gain errors. Uses [[ErrorGens.py|http://timba.svn.sourceforge.net/viewvc/checkout/timba/trunk/Timba/WH/contrib/OMS/Siamese/ErrorGens.py]]. Checked in under Timba/WH/contrib/OMS/Siamese.
Apply this Jones to your sources to introduce a conjugate w-term, which will make them image correctly without the w-projection. [[oms_n_inverse.py|http://timba.svn.sourceforge.net/viewvc/checkout/timba/trunk/Timba/WH/contrib/OMS/Siamese/oms_n_inverse.py]]. Checked in under Timba/WH/contrib/OMS/Siamese.
If you have data in a text file that you want to use as input for your tree, you can use PyNode to write your own import node. The file contains such a PyNode. It reads a file that contains one x column and a number of y columns