astrohack.utils.ray_tracing_general
===================================

.. py:module:: astrohack.utils.ray_tracing_general


Attributes
----------

.. autoapisummary::

   astrohack.utils.ray_tracing_general.nanvec3d
   astrohack.utils.ray_tracing_general.return_line


Classes
-------

.. autoapisummary::

   astrohack.utils.ray_tracing_general.GlobalQPS
   astrohack.utils.ray_tracing_general.LocalQPS


Functions
---------

.. autoapisummary::

   astrohack.utils.ray_tracing_general.generalized_dot
   astrohack.utils.ray_tracing_general.generalized_norm
   astrohack.utils.ray_tracing_general.generalized_dist
   astrohack.utils.ray_tracing_general.normalize_vector_map
   astrohack.utils.ray_tracing_general.reflect_light
   astrohack.utils.ray_tracing_general.simple_axis
   astrohack.utils.ray_tracing_general.np_qps_fitting
   astrohack.utils.ray_tracing_general.degrade_pcd
   astrohack.utils.ray_tracing_general.qps_compute_point_and_normal
   astrohack.utils.ray_tracing_general.qps_compute_point_and_normal_jit
   astrohack.utils.ray_tracing_general.qps_compute_normal_jit
   astrohack.utils.ray_tracing_general.local_qps_image_jit
   astrohack.utils.ray_tracing_general.global_qps_image_jit
   astrohack.utils.ray_tracing_general.global_qps_normal_image_jit


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

.. py:data:: nanvec3d

.. py:data:: return_line
   :value: '\x1b[F'


.. py:function:: generalized_dot(vec_map_a, vec_map_b)

.. py:function:: generalized_norm(vecmap)

.. py:function:: generalized_dist(vec_map_a, vec_map_b)

.. py:function:: normalize_vector_map(vector_map)

.. py:function:: reflect_light(light, normals)

.. py:function:: simple_axis(minmax, resolution, margin=0.05)

   Creates an array spaning from min to max (may go over max if resolution is not an integer division) spaced by     resolution
   :param minmax: the minimum and maximum of the axis
   :param resolution: The spacing between array elements
   :param margin: Add a margin at the edge of the array beyonf min and max

   :returns: A numpy array representation of a linear axis.


.. py:function:: np_qps_fitting(pcd)

.. py:function:: degrade_pcd(pcd, degrading_factor)

   Degrades de number of points in a pcd by an integer factor
   :param pcd: pcd data, assumes [:, 3]
   :param degrading_factor: integer pcd degrading factor

   Returns: degraded pcd


.. py:function:: qps_compute_point_and_normal(pnt, qps_coeffs, pcd)

.. py:function:: qps_compute_point_and_normal_jit(pnt, qps_coeffs, pcd)

.. py:function:: qps_compute_normal_jit(pnt, qps_coeffs, pcd)

.. py:function:: local_qps_image_jit(global_pcd, local_qps_coeffs, local_pcds, points)

.. py:function:: global_qps_image_jit(pcd, qps_coeffs, points)

.. py:function:: global_qps_normal_image_jit(pcd, qps_coeffs, points)

.. py:class:: GlobalQPS

   .. py:attribute:: n_qps_extra_vars
      :value: 6



   .. py:attribute:: n_points
      :value: None



   .. py:attribute:: point_cloud
      :value: None



   .. py:attribute:: qps_coefficients
      :value: None



   .. py:method:: from_point_cloud(pcd_data, degradation_factor=None, displacement=(0, 0, 0))
      :classmethod:



   .. py:method:: from_point_cloud_and_coefficients(point_cloud, qps_coefficients)
      :classmethod:



   .. py:method:: from_pickle(filename)
      :classmethod:



   .. py:method:: to_pickle(filename)


   .. py:method:: from_zarr(filepath)
      :classmethod:



   .. py:method:: to_zarr(filepath)


   .. py:method:: compute_gridded_z_cos(u_axis, v_axis, mask, light=(0, 0, 1))


   .. py:method:: compute_gridded_z_val_and_z_cos(u_axis, v_axis, mask, light=(0, 0, 1))


.. py:class:: LocalQPS

   .. py:attribute:: n_qps_extra_vars
      :value: 6



   .. py:attribute:: npnt
      :value: -1



   .. py:attribute:: local_qps_n_pnt
      :value: -1



   .. py:attribute:: global_pcd
      :value: None



   .. py:attribute:: local_qps_coeffs
      :value: None



   .. py:attribute:: local_pcds
      :value: None



   .. py:attribute:: current_z_val
      :value: None



   .. py:attribute:: current_z_cos
      :value: None



   .. py:attribute:: current_u_axis
      :value: None



   .. py:attribute:: current_v_axis
      :value: None



   .. py:attribute:: high_res_z_val
      :value: None



   .. py:attribute:: high_res_z_cos
      :value: None



   .. py:attribute:: high_res_u_axis
      :value: None



   .. py:attribute:: high_res_v_axis
      :value: None



   .. py:method:: from_pcd(pcd_data, local_qps_n_pnt=20, displacement=(0, 0, 0))
      :classmethod:



   .. py:method:: export_to_xr_data_variables()

      Idea is to return pcd values and qpd coeffs as Xarray data variables for storage.
      Returns: xarray data variables




   .. py:method:: from_xr_data_variable()
      :classmethod:


      Idea is to init an object from a few xarray Data variables from storage
      Returns: initialized obj




   .. py:method:: compute_z_val_and_z_cos(point)

      Idea is to compute value of QPS and angle with boresight
      :param point:

      Returns:




   .. py:method:: vectorized_z_val_and_z_cos(point_arr)


   .. py:method:: plot_z_val_and_z_cos(colormap='viridis', zlim=None, dpi=300, display=False)


   .. py:method:: compute_gridded_z_val_and_z_cos(u_axis, v_axis, mask, gridding_engine='2D regrid', light=(0, 0, -1), vectorized=True)


   .. py:method:: downgrid_high_resolution_z_val_and_z_cos(u_axis, v_axis, gridding_engine)


   .. py:method:: compute_high_resolution_z_val_and_z_cos(x_resolution, y_resolution)


   .. py:method:: from_pickle(filename)
      :classmethod:



   .. py:method:: get_local_qps(ipnt)


   .. py:method:: to_pickle(filename)