.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples/batch_mode/05-seahorse_shaded_colored.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_examples_batch_mode_05-seahorse_shaded_colored.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_examples_batch_mode_05-seahorse_shaded_colored.py:


========================================
05 - Seahorse shaded and colored example
========================================

This example shows how to create a normal map layer, and link it to a base
color layer to enable scene lighting.
Here a:
    
    - A colored background based on the continuous iteration number is used, and
      the normal map layer is based on "potential estimator".
    - The normal map itself is also output (OpenGL normal map format)

The location is a shallow one in the main Seahorse valley.

Reference:
`fractalshades.models.Mandelbrot`

.. GENERATED FROM PYTHON SOURCE LINES 20-130



.. image-sg:: /examples/batch_mode/images/sphx_glr_05-seahorse_shaded_colored_001.png
   :alt: 05 seahorse shaded colored
   :srcset: /examples/batch_mode/images/sphx_glr_05-seahorse_shaded_colored_001.png
   :class: sphx-glr-single-img





.. code-block:: default


    import os
    import numpy as np

    import fractalshades as fs
    import fractalshades.models as fsm
    import fractalshades.colors as fscolors
    from fractalshades.postproc import (
        Postproc_batch,
        Continuous_iter_pp,
        DEM_normal_pp,
        Raw_pp,
    )
    from fractalshades.colors.layers import (
        Color_layer,
        Bool_layer,
        Normal_map_layer,
        Blinn_lighting
    )

    def plot(plot_dir):
        """
        Using lighting : a shallow zoom in the Seahorses valley
        Coloring based on continuous iteration + lighting with a normal maps from
        distance estimation method
        """
        fs.settings.enable_multithreading = True
        # Define the parameters for this calculation
        x = -0.746223962861
        y = -0.0959468433527
        dx = 0.00745
        nx = 2400

        calc_name="mandelbrot"
        colormap = fscolors.cmap_register["legacy"]

        # Run the calculation
        f = fsm.Mandelbrot(plot_dir)
        f.zoom(x=x, y=y, dx=dx, nx=nx, xy_ratio=1.0,
               theta_deg=0., projection=fs.projection.Cartesian())
        f.calc_std_div(
            calc_name=calc_name,
            subset=None,
            max_iter=25000,
            M_divergence=100.,
            epsilon_stationnary= 0.01,
        )

        # Plot the image
        pp = Postproc_batch(f, calc_name)
        pp.add_postproc("cont_iter", Continuous_iter_pp())
        pp.add_postproc("interior", Raw_pp("stop_reason", func="x != 1."))
        pp.add_postproc("DEM_map", DEM_normal_pp(kind="potential"))

        plotter = fs.Fractal_plotter(pp)   
        plotter.add_layer(Bool_layer("interior", output=False))
        plotter.add_layer(Normal_map_layer("DEM_map", max_slope=45, output=False))
        plotter.add_layer(Color_layer(
                "cont_iter",
                func="np.log(x)",
                colormap=colormap,
                probes_z=[1., 2.],
                output=True
        ))

        plotter["cont_iter"].set_mask(plotter["interior"], mask_color=(0., 0., 0.))
        plotter["DEM_map"].set_mask(plotter["interior"], mask_color=(0., 0., 0.))

        # This is where we define the lighting (here 3 ccolored light sources)
        # and apply the shading
        light = Blinn_lighting(0.35, np.array([1., 1., 1.]))
        light.add_light_source(
            k_diffuse=0.2,
            k_specular=25.,
            shininess=400.,
            polar_angle=-135.,
            azimuth_angle=0.,
            color=np.array([0.05, 0.05, 1.0])
        )
        light.add_light_source(
            k_diffuse=0.2,
            k_specular=10.,
            shininess=400.,
            polar_angle=135.,
            azimuth_angle=0.,
            color=np.array([0.5, 0.5, .4])
        )
        light.add_light_source(
            k_diffuse=1.3,
            k_specular=0.,
            shininess=0.,
            polar_angle=90.,
            azimuth_angle=10.,
            color=np.array([1.0, 1.0, 1.0])
        )
        plotter["cont_iter"].shade(plotter["DEM_map"], light)
        plotter.plot()


    if __name__ == "__main__":
        # Some magic to get the directory for plotting: with a name that matches
        # the file or a temporary dir if we are building the documentation
        try:
            realpath = os.path.realpath(__file__)
            plot_dir = os.path.splitext(realpath)[0]
            plot(plot_dir)
        except NameError:
            import tempfile
            with tempfile.TemporaryDirectory() as plot_dir:
                fs.utils.exec_no_output(plot, plot_dir)


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 1 minutes  4.919 seconds)


.. _sphx_glr_download_examples_batch_mode_05-seahorse_shaded_colored.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example


    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: 05-seahorse_shaded_colored.py <05-seahorse_shaded_colored.py>`

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: 05-seahorse_shaded_colored.ipynb <05-seahorse_shaded_colored.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_