:py:mod:`aces.utilities.emsim.readmat`
======================================

.. py:module:: aces.utilities.emsim.readmat


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


Functions
~~~~~~~~~

.. autoapisummary::

   aces.utilities.emsim.readmat.grid_from_file
   aces.utilities.emsim.readmat.field_from_file
   aces.utilities.emsim.readmat.interpolate_sub_slow
   aces.utilities.emsim.readmat.interp_weights
   aces.utilities.emsim.readmat.interpolate
   aces.utilities.emsim.readmat.interpolate_sub



Attributes
~~~~~~~~~~

.. autoapisummary::

   aces.utilities.emsim.readmat.N_REGIONS
   aces.utilities.emsim.readmat.POINTS_PER_REGION
   aces.utilities.emsim.readmat.N_PORTS
   aces.utilities.emsim.readmat.portmapx
   aces.utilities.emsim.readmat.portmapy
   aces.utilities.emsim.readmat.portmap
   aces.utilities.emsim.readmat.portmap


.. py:data:: N_REGIONS
   :value: 54

   

.. py:data:: POINTS_PER_REGION
   :value: 961

   

.. py:data:: N_PORTS
   :value: 188

   

.. py:data:: portmapx

   

.. py:data:: portmapy

   

.. py:data:: portmap

   

.. py:data:: portmap

   

.. py:function:: grid_from_file(regions_fname, n_regions=N_REGIONS)

   Load cartesian grid and differential solid angle from .mat files made by Stuart's simulations of ASKAP patterns

   Args:
       regions_fname (str): 
       n_regions (int): 

   Returns:
       xyz (`numpy.ndarray`): with (x,y,z) cartesian coordinates defining unit vector describing angle 
           one row per coordinate with each coordinate in a separate column, real valued.
       domega (`numpy.ndarray`): vector of differential solid angle for each pattern point, real valued.


.. py:function:: field_from_file(field_fname, n_regions=N_REGIONS, debug=False)

   Load far-field electric field (far-field pattern) from Stuart's simulations of ASKAP patterns

   Args:
       field_fname (str): template name of field file
       n_regions (int): number of regions (each in individual .mat file)
       debug (bool): print debug info if True
       
   Returns:
       e_field (`numpy.ndarray`): Ex,Ey,Ez field components in columns and one row per simulation point (direction)
       complex valued    


.. py:function:: interpolate_sub_slow(e_field, xyz, xyz_target)

   Interpolate function at finite list of coordinates that are not necessarily on a grid

   Interpolation is performed in the Sin projection, with tangent at the positive x direction that is the forward 
   hemisphere of the antenna pattern.  This is probably okay near the boresight.

   Args:
       e_field (`numpy.ndarray`): Complex valued function of direction in 3-space
       xyz (`numpy.ndarray`): Grid points representing directions in 3-space at which e_field is evaluated
       xyz_target (`numpy.ndarray`): List of specific directions in 3-space to interpolate at

   Returns:
       e_interp (`numpy.ndarray`): Values of e_field at directions xyz_target



.. py:function:: interp_weights(xy, uv, d=2)

   Setup function for reimplementation of scipy.interpolate.griddata to avoid repeating setup for repeated 
   interpolation for common input and output grids.

   1. Call sp.spatial.qhull.Dealunay is made to triangulate the irregular grid coordinates.
   2. For each point in the new grid, the triangulation is searched to find in which triangle (actually, in which 
   simplex, which in your 3D case will be in which tetrahedron) does it lay.
   3. The barycentric coordinates of each new grid point with respect to the vertices of the enclosing simplex are 
   computed.

   From http://stackoverflow.com/questions/20915502

   Args:
       xy: existing coordinates for available data
       uv: points to interpolate to
       d: dimension 

   Returns:
       vertices:
       weights: 



.. py:function:: interpolate(values, vtx, wts)

   Reimplementation of scipy.interpolate.griddata to avoid repeating setup for repeated interpolation for common input
   and output grids.

   4. An interpolated values is computed for that grid point, using the barycentric coordinates, and the values of the 
   function at the vertices of the enclosing simplex.

   http://stackoverflow.com/questions/20915502


   Args:
       values: available data
       vtx: vertices from interp_weights()
       wts: weights from interp_weights()

   Returns:
       values interpolated to points determined by setup function interp_weights()



.. py:function:: interpolate_sub(e_field, xyz, xyz_target)

   Interpolate function at finite list of coordinates that are not necessarily on a grid

   Interpolation is performed in the Sin projection, with tangent at the positive x direction that is the forward 
   hemisphere of the antenna pattern.  This is probably okay near the boresight.

   Args:
       e_field (`numpy.ndarray`): Complex valued function of direction in 3-space
       xyz (`numpy.ndarray`): Grid points representing directions in 3-space at which e_field is evaluated
       xyz_target (`numpy.ndarray`): List of specific directions in 3-space to interpolate at

   Returns:
       e_interp (`numpy.ndarray`): Values of e_field at directions xyz_target