:py:mod:`hypso.classification.WaterDetection.waterdetect.Glint`
===============================================================

.. py:module:: hypso.classification.WaterDetection.waterdetect.Glint


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

Classes
~~~~~~~

.. autoapisummary::

   hypso.classification.WaterDetection.waterdetect.Glint.DWGlintProcessor




.. py:class:: DWGlintProcessor(image, limit_angle=30)

   .. py:attribute:: supported_products
      :value: ['S2_S2COR', 'S2_THEIA', 'S2_PLANETARY']

      

   .. py:method:: create(image, limit_angle=30)
      :classmethod:


   .. py:method:: get_grid_values_from_xml(tree_node, xpath_str)
      :staticmethod:

      Receives a XML tree node and a XPath parsing string and search for children matching the string.
      Then, extract the VALUES in <values> v1 v2 v3 </values> <values> v4 v5 v6 </values> format as numpy array
      Loop through the arrays to compute the mean.


   .. py:method:: create_glint_array(xml_file, product)
      :staticmethod:


   .. py:method:: create_annotated_heatmap(hm, img=None, cmap='magma', vmin=0.7, vmax=0.9)
      :staticmethod:

      Create an annotated heatmap. Parameter img is an optional background img to be blended


   .. py:method:: nn_interpolate(arr, new_size)
      :staticmethod:

      Vectorized Nearest Neighbor Interpolation
      From post: https://gist.github.com/KeremTurgutlu/68feb119c9dd148285be2e247267a203


   .. py:method:: create_glint_heatmap(rgb, glint_arr, limit_angle)
      :staticmethod:


   .. py:method:: save_heatmap(folder, filename='glint_heatmap.pdf', dpi=50, brightness=5.0)


   .. py:method:: create_multiplication_coefs(min_glint_multiplier=0.5)


   .. py:method:: show_multiplication_coefs()


   .. py:method:: glint_adjusted_threshold(band, value, thresh_type, mask=None, min_glint_multiplier=0.5)

      Create an array with the image resolution, with the threshold adjusted for the GLINT
      thresh_type can be SUP or INF 


   .. py:method:: __repr__()

      Return repr(self).