.. _intro_prod_editing:

Data Editing
============

Your solutions will be corrupted if you include data that are statistical outliers.
For a time series from independent h-files, an error in one component of one site's coordinates will not affect any other site (and may have minimal effect on the other components), but as soon as you combine h-files with common parameters, whether multiple networks on a single day or the results from more than one day, the different estimates of the common parameter will clash, raising chi-square and distorting the solution.
There are two ways you can remove outliers:
using the :content:`rename` command of the :content:`eq_file` and using the :content:`sig_neu` command directly in the :program:`globk` command file.
In the :content:`rename` command, if you designate the extent as :content:`_XCL` the site will always be excluded;
with :content:`_XPS` the site will be excluded from any solution of more than one day but will still appear in daily time series;
e.g.

.. code-block:: text

   rename  MIT  algo_gps  algo_xcl  1997  5 14  0  0  1997  5 17 24  0

will remove from the solution the observations of ALGO in any h-files containing the characters :content:`MIT` between 14 and 17 May 1997.
Note that the date span specified must completely encompass the start time of the first h-file and the end time of the last h-file.
The h-file selector entry and/or the dates may be omitted for a more expansive exclusion.
With the :content:`sig_neu` command you can effectively remove the effect of an error in a particular component by adding sufficient noise that it has negligible weight, e.g.

.. code-block:: text

   sig_neu  MIT  algo_gps  0  0  .5  1997  5 14  0  0  1997  5 18  0  0

would reduce the effect of an error in height by adding :math:`0.25 \text{m}^2` in quadrature to the variance of the height component as estimated from the h-files specified.

Which of these approaches you choose is a matter of taste and could depend on the tools you use for editing.
For continuous data, when you can afford to remove many outliers without weakening the solution, using the :content:`nsigma` option of the :program:`tsfit` program (see Chapter 4 of the `GLOBK Reference Manual <http://geoweb.mit.edu/gg/docs/GLOBK_Ref.pdf>`_) to automatically generate :content:`_XPS` commands, or the MATLAB-based interactive program `tsview <http://geoweb.mit.edu/~tah/GGMatlab/>`_ to generate :content:`_XPS` commands with the click of a mouse, are quite convenient.
For survey-mode date, when every observation counts and you often want to down-weight heights but not horizontal components or only one horizontal component, :content:`sig_neu` commands are more convenient.
The GAMIT utility :program:`grw` can be used to generate :content:`sig_neu` commands with only a few key-strokes.