flystar.stitch_method2
======================

.. py:module:: flystar.stitch_method2


Functions
---------

.. autoapisummary::

   flystar.stitch_method2.align_starlists
   flystar.stitch_method2.weighted_mean
   flystar.stitch_method2.normal_mean
   flystar.stitch_method2.stitch


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

.. py:function:: align_starlists(starlist, ref, transModel=transforms.PolyTransform, order=2, N_loop=2, dr_tol=1.0, briteN=None, weights='both')

   Transforming a starlist(label.dat) into a reference frame.

   Parameters:
   -----------
   starlist: Table
       Starlist we would like to transform into the reference frame, eg:label.dat

   ref: Table
       Starlist that defines the reference frame.

   transModel: transformation class (default: transforms.polyTransform)
       Defines which transformation model to use. Both the four-parameter and
       polynomial transformations are supported

   order: int (default=1)
       Order of the polynomial transformation. Only used for polynomial transform

   N_loop: int (default=2)
       How many times to iterate on the transformation calculation. Ideally,
       each iteration adds more stars and thus a better transform, to some
       limit.

   dr_tol: float(default=1.0)
       the distance tolerance for matching two stars in align.transform_and_match

   briteN: int (default=100)
       the number of stars used in blind matching

   weights: string (default='both')
       if weights=='both', we use both position error in transformed starlist and
          reference starlist as uncertanty. And weights is the reciprocal of this uncertanty.
       if weights=='starlist', we only use postion error in transformed starlist.
       if weights=='reference', we only use position error in reference starlist.
       if weights==None, we don't use weights.



.. py:function:: weighted_mean(df, x, xe, frames_in_use)

.. py:function:: normal_mean(df, x, frames_in_use)

.. py:function:: stitch(all_starlists, name_initial_ref, N_iter=5, corr_thresh=0.8, outMaster='./master.lis')