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

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


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

Classes
~~~~~~~

.. autoapisummary::

   hypso.classification.WaterDetection.waterdetect.WaterDetect.DWWaterDetect




.. py:class:: DWWaterDetect(input_folder, output_folder, shape_file, product, config_file, pekel=None, single_mode=False, *args, **kwargs)

   .. py:property:: water_mask


   .. py:property:: cluster_matrix


   .. py:method:: __repr__()

      Return repr(self).


   .. py:method:: necessary_bands(include_rgb)

      Return all the necessary bands, based on the bands used for the graphics and the clustering

      :param include_rgb: Specifies if RGB bands are necessary for creating composite, for example

      :return: All necessary bands in a list


   .. py:method:: calc_nd_index(index_name, band1, band2, save_index=False)

      Calculates a normalized difference index, adds it to the bands dict and update the mask in loader

      :param save_index: Indicate if we should save this index to a raster image
      :param index_name: name of index to be used as key in the dictionary
      :param band1: first band to be used in the normalized difference
      :param band2: second band to be used in the normalized difference

      :return: index array


   .. py:method:: calc_m_nd_index(index_name, band1, band2, band3, band4, save_index=False)

      Calculates a modified normalized difference index, adds it to the bands dict and update the mask in loader.
      Proposed by Dhalton. It uses the maximum of visible and the minimum of Nir/Swir

      :param save_index: Inidicate if we should save this index to a raster image
      :param index_name: name of index to be used as key in the dictionary
      :param band1: first band to be used in the normalized difference
      :param band2: second option for the first band to be used in the normalized difference
      :param band3: second band to be used in the normalized difference
      :param band4: second option for the second band to be used in the normalized difference

      :return: index array


   .. py:method:: calc_mbwi(bands, factor=3, save_index=False)

      Calculates the Multi band Water Index and adds it to the bands dictionary

      :param save_index: Inform if the index should be saved as array in the output folder
      :param bands: Bands dictionary with the raster bands
      :param factor: Factor to multiply the Green band as proposed in the original paper

      :return: mbwi array


   .. py:method:: calc_awei(bands, save_index=False)

      Calculates the AWEI Water Index and adds it to the bands dictionary

      :param save_index: Inform if the index should be saved as array in the output folder
      :param bands: Bands dictionary with the raster bands

      :return: mbwi array


   .. py:method:: run_water_detect(input_folder, output_folder, single_mode, shape_file=None, product='S2_THEIA', config_file=None, pekel=None, post_callback=None, **kwargs)
      :classmethod:

      Main function to launch the water detect algorithm processing. This is the function called from the script.

      :param input_folder: If single_mode=True, this is the uncompressed image product. If single_mode=False, this
          is the folder that contains all uncompressed images.
      :param output_folder: Output directory
      :param single_mode: For batch processing (multiple images at a time), single_mode should be set to False
      :param shape_file: Shape file to clip the image (optional).
      :param product: The product to be processed (S2_THEIA, L8_USGS, S2_L1C or S2_S2COR)
      :param config_file: Configuration .ini file. If not specified WaterDetect.ini from current dir and used as
          default
      :param pekel: Optional path for an occurrence base map like Pekel
      :param post_callback: Used for the WaterQuality add-on package
      :param kwargs: Additional parameters.

      :return: DWWaterDetect instance with the generated mask.


   .. py:method:: _detect_water(post_callback=None)

      Run batch is intended for multiprocessing of various images and bands combinations.
          It Loops through all unzipped images in input folder, extract water pixels and save results to output folder
          ATT: The input folder is not a satellite image itself. It should be the parent folder containing all images.
          For single detection, use run() method.

      :param post_callback: None

      :return: None


   .. py:method:: test_pekel(image, dw_image, pdf_merger_image)

      Test Pekel Function

      :param image:
      :param dw_image:
      :param pdf_merger_image:

      :return:


   .. py:method:: save_report(report_name, pdf_merger, folder)
      :staticmethod:


   .. py:method:: save_graphs(dw_image, pdf_merger_image)


   .. py:method:: create_water_mask(band_combination, pdf_merger_image, glint_processor=None)


   .. py:method:: create_colorbar_pdf(param_name, colormap, min_value, max_value, units='')


   .. py:method:: create_rgb_burn_in_pdf(product_name, burn_in_arrays, colors=None, min_value=None, max_value=None, fade=None, opt_relative_path=None, colormap='viridis', uniform_distribution=False, no_data_value=0, valid_value=1, transps=None, bright=5.0)


   .. py:method:: calc_indexes(image, indexes_list, save_index=False)