demcompare.metric.matrix_2d_metrics
===================================

.. py:module:: demcompare.metric.matrix_2d_metrics

.. autoapi-nested-parse::

   Mainly contains different matrix metric classes



Classes
-------

.. autoapisummary::

   demcompare.metric.matrix_2d_metrics.DemHillShade
   demcompare.metric.matrix_2d_metrics.DemSkyViewFactor


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

.. py:class:: DemHillShade(parameters: Dict = None)

   Bases: :py:obj:`demcompare.metric.metric_template.MetricTemplate`


   Compute the hill shade and optionnally save plots from a dem


   .. py:attribute:: type
      :value: 'matrix2D'



   .. py:attribute:: fig_title
      :value: 'DEM hill shade'



   .. py:attribute:: colorbar_title
      :value: 'Hill shade'



   .. py:attribute:: azimuth
      :type:  float
      :value: 315



   .. py:attribute:: angle_altitude
      :type:  float
      :value: 45



   .. py:attribute:: cmap
      :type:  str
      :value: 'Greys_r'



   .. py:attribute:: cmap_nodata
      :type:  str
      :value: 'royalblue'



   .. py:attribute:: plot_path
      :type:  str
      :value: None



   .. py:attribute:: no_data_location
      :type:  numpy.ndarray
      :value: None



   .. py:attribute:: bounds
      :type:  rasterio.coords.BoundingBox
      :value: None



   .. py:method:: compute_hillshade(data: numpy.ndarray, azimuth: float, angle_altitude: float) -> numpy.ndarray

      Compute the hillshade view a of a dem.

      :param data: input data to compute the metric
      :type data: np.array
      :param azimuth: angular direction of the sun
      :type azimuth: float
      :param angle_altitude: angle of the illumination source
       above the horizon
      :type angle_altitude: float
      :return: np.ndarray



   .. py:method:: compute_metric(data: numpy.ndarray) -> xarray.Dataset

      Compute and optionnally save plots the hillshade view
      a of a dem using pyplot img_show.

      :param data: input data to compute the metric
      :type data: xr.Dataset
      :return: None



.. py:class:: DemSkyViewFactor(parameters: Dict = None)

   Bases: :py:obj:`demcompare.metric.metric_template.MetricTemplate`


   Compute the sky vuew factor and optionnally save plots from a dem


   .. py:attribute:: type
      :value: 'matrix2D'



   .. py:attribute:: fig_title
      :value: 'DEM sky view factor'



   .. py:attribute:: colorbar_title
      :value: 'Sky view factor'



   .. py:attribute:: filter_intensity
      :type:  float
      :value: 0.9



   .. py:attribute:: replication
      :type:  bool
      :value: True



   .. py:attribute:: quantiles
      :value: [0.09, 0.91]



   .. py:attribute:: cmap
      :type:  str
      :value: 'Greys_r'



   .. py:attribute:: cmap_nodata
      :type:  str
      :value: 'royalblue'



   .. py:attribute:: plot_path
      :type:  str
      :value: None



   .. py:attribute:: no_data_location
      :type:  numpy.ndarray
      :value: None



   .. py:attribute:: bounds
      :type:  rasterio.coords.BoundingBox
      :value: None



   .. py:method:: compute_svf(data: numpy.ndarray) -> numpy.ndarray

      Return the sky view factor of the input DEM.
      First, compute the FFT of the input dem: F(y) = FFT(DEM).
      Then, apply a filter y^filter_intensity
      with s=0.9: F(y) = F(y)* y^filter_intensity.
      Finally, apply the inverse FFT: IFFT(F(y)).
      We keep the real part (imaginary part = digital noise).

      :param data: input data to compute the metric
      :type data: np.array
      :return: curvature np.array containing :
      :rtype: np.ndarray



   .. py:method:: compute_metric(data: numpy.ndarray) -> xarray.Dataset

      Compute and optionnally save plots the sky view factor
      a of a dem using pyplot img_show.

      :param data: input data to compute the metric
      :type data: np.ndarray
      :return: xr.Dataset