.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples/batch_mode/13-burning_ship.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        Click :ref:`here <sphx_glr_download_examples_batch_mode_13-burning_ship.py>`
        to download the full example code

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

.. _sphx_glr_examples_batch_mode_13-burning_ship.py:


=====================
13 - Burning ship DEM
=====================

Plotting of a distance estimation for the Burning ship (power-2).
This zoom is on the structure which gave the fractal its name. We use an
arbitrary-precision model, even if this is obviously not needed here.

Reference:
`fractalshades.models.Perturbation_burning_ship`

.. GENERATED FROM PYTHON SOURCE LINES 14-157



.. image-sg:: /examples/batch_mode/images/sphx_glr_13-burning_ship_001.png
   :alt: 13 burning ship
   :srcset: /examples/batch_mode/images/sphx_glr_13-burning_ship_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,
        DEM_pp,
        Raw_pp,
    )
    from fractalshades.colors.layers import (
        Color_layer,
        Bool_layer,
        Normal_map_layer,
        Virtual_layer,
        Blinn_lighting,
    )


    def plot(plot_dir):
        fs.settings.enable_multithreading = True
        fs.settings.inspect_calc = True

        # A simple showcase using perturbation technique
        x = '-1.7579317963'
        y = '0.052705991307'
        dx = '0.181287312180757'
        precision = 30
        nx = 2400
        xy_ratio = 1.0
    
        sign = 1.0
        DEM_min = 1.e-4
        zmin = -9.21034049987793
        zmax = -0.3999025523662567
    
        # As this formula is non-analytic, we will 'unskew' based on the 
        # influencing miniship "size estimate" matrix.
        has_skew = False
        skew_00 = 1.0
        skew_01 = 0.0
        skew_10 = -0.1
        skew_11 = 1.1

        calc_name="Burning_ship"
        colormap = fscolors.cmap_register["classic"]

        # Run the calculation
        f = fsm.Perturbation_burning_ship(plot_dir)

        f.zoom(
            precision=precision,
            x=x,
            y=y,
            dx=dx,
            nx=nx,
            xy_ratio=xy_ratio,
            theta_deg=0., 
            projection="cartesian",
            has_skew=has_skew,
            skew_00=skew_00,
            skew_01=skew_01,
            skew_10=skew_10,
            skew_11=skew_11
        )

        f.calc_std_div(
            calc_name=calc_name,
            subset=None,
            max_iter=1500,
            M_divergence=1.e3,
            BLA_eps= 1.e-6,
        )


        # Plot the image
        pp = Postproc_batch(f, calc_name)
        pp.add_postproc("continuous_iter", Continuous_iter_pp())
        pp.add_postproc("distance_estimation", DEM_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=30, output=False))
        plotter.add_layer(
            Virtual_layer("continuous_iter", func=None, output=False)
        )
    
        cmap_func = lambda x: sign * np.where(
           np.isinf(x),
           np.log(DEM_min),
           np.log(np.clip(x, DEM_min, None))
        )
        plotter.add_layer(Color_layer(
                "distance_estimation",
                func=cmap_func,
                colormap=colormap,
                probes_z=[zmin, zmax],
                output=True
        ))

        plotter["distance_estimation"].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 2 ccolored light sources)
        # and apply the shading
        light = Blinn_lighting(0.4, np.array([1., 1., 1.]))
        light.add_light_source(
            k_diffuse=0.2,
            k_specular=300.,
            shininess=1400.,
            polar_angle=45.,
            azimuth_angle=10.,
            color=np.array([1.0, 1.0, 0.98]))
        light.add_light_source(
            k_diffuse=0.8,
            k_specular=2.,
            shininess=400.,
            polar_angle=45.,
            azimuth_angle=10.,
            color=np.array([1., 1., 1.]))
        plotter["distance_estimation"].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:** ( 0 minutes  15.219 seconds)


.. _sphx_glr_download_examples_batch_mode_13-burning_ship.py:

.. only:: html

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


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

      :download:`Download Python source code: 13-burning_ship.py <13-burning_ship.py>`

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

      :download:`Download Jupyter notebook: 13-burning_ship.ipynb <13-burning_ship.ipynb>`


.. only:: html

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

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