.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples/batch_mode/09-run_flake_DEM.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_examples_batch_mode_09-run_flake_DEM.py>`
        to download the full example code

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

.. _sphx_glr_examples_batch_mode_09-run_flake_DEM.py:


=========================
09 - A deeper DEM example
=========================

This example shows how to create a color layer which displays a 
distance estimation from the Mandelbrot (power 2) fractal.

The location, at 1.8e-157, is well below the separation power of double,
pertubation theory must be used. This location, "Dinkidau flake", is a
common test for numerical reliability.

Reference:
`fractalshades.models.Perturbation_mandelbrot`

.. GENERATED FROM PYTHON SOURCE LINES 17-136



.. image-sg:: /examples/batch_mode/images/sphx_glr_09-run_flake_DEM_001.png
   :alt: 09 run flake DEM
   :srcset: /examples/batch_mode/images/sphx_glr_09-run_flake_DEM_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.settings as settings
    import fractalshades.colors as fscolors
    import fractalshades.projection


    from fractalshades.postproc import (
        Postproc_batch,
        DEM_pp,
        Continuous_iter_pp,
        Raw_pp,
        DEM_normal_pp,
    )
    from fractalshades.colors.layers import (
        Color_layer,
        Bool_layer,
        Normal_map_layer,
        Virtual_layer,
        Blinn_lighting
    )

    def plot(directory):
        """
        Example plot of distance estimation method
        """
        settings.enable_multithreading = True
        # A simple showcase using perturbation technique
        precision = 165
        nx = 2400
        x = '-1.99996619445037030418434688506350579675531241540724851511761922944801584242342684381376129778868913812287046406560949864353810575744772166485672496092803920095332'
        y = '-0.00000000000000000000000000000000030013824367909383240724973039775924987346831190773335270174257280120474975614823581185647299288414075519224186504978181625478529'
        dx = '1.7e-157'

        colormap = fscolors.cmap_register["valensole"]

        f = fsm.Perturbation_mandelbrot(directory)
        f.zoom(precision=precision,
                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="div",
                subset=None,
                max_iter=1000000,
                M_divergence=1.e3,
                epsilon_stationnary=1.e-3,
                BLA_eps=1.e-8,
                interior_detect=False
        )


        # Plot the image
        pp = Postproc_batch(f, "div")
        pp.add_postproc("potential", Continuous_iter_pp())
        pp.add_postproc("DEM", 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, final_render=False, supersampling="2x2")   
        plotter.add_layer(Bool_layer("interior", output=False))
        plotter.add_layer(Normal_map_layer("DEM_map", max_slope=35, output=False))
        plotter.add_layer(Virtual_layer("potential", func=None, output=False))
        plotter.add_layer(Color_layer(
                "DEM",
                func="np.log(x)",
                colormap=colormap,
                probes_z=[0., 5.0],
                output=True
        ))
        plotter["DEM"].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 light sources)
        # and apply the shading
        light = Blinn_lighting(0.35, np.array([1., 1., 1.]))
        light.add_light_source(
            k_diffuse=0.0,
            k_specular=600.,
            shininess=200.,
            polar_angle=75.,
            azimuth_angle=5.,
            color=np.array([0.9, 0.9, 0.2]))
        light.add_light_source(
            k_diffuse=1.9,
            k_specular=0.,
            shininess=400.,
            polar_angle=75.,
            azimuth_angle=30.,
            color=np.array([1., 1., 1.]))
        plotter["DEM"].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  51.742 seconds)


.. _sphx_glr_download_examples_batch_mode_09-run_flake_DEM.py:

.. only:: html

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




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

      :download:`Download Python source code: 09-run_flake_DEM.py <09-run_flake_DEM.py>`

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

      :download:`Download Jupyter notebook: 09-run_flake_DEM.ipynb <09-run_flake_DEM.ipynb>`


.. only:: html

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

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