.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/plot_3D-plot.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_auto_examples_plot_3D-plot.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_plot_3D-plot.py:


3D Slip Figure of a Stochastic generation
============================================================================================================
    This example makes an 3d plot of Slip distribution
    :param X_array: Longitude grid
    :param Y_array: Latitude grid
    :param depth: Depth grid
    :param Slip: Slip grid
    :param filename: Optional, filename if you wanna save fig
    :return: 3D Figure of Slip distribution
    

.. GENERATED FROM PYTHON SOURCE LINES 13-102



.. image-sg:: /auto_examples/images/sphx_glr_plot_3D-plot_001.png
   :alt: Slip_0004_Simulation
   :srcset: /auto_examples/images/sphx_glr_plot_3D-plot_001.png
   :class: sphx-glr-single-img





.. code-block:: Python

    import numpy as np
    import matplotlib.pyplot as plt
    import scipy.io
    from matplotlib import cm
    from matplotlib.ticker import LinearLocator
    from matplotlib.colors import ListedColormap, Normalize
    import geostochpy
    def plot_3d(X_array,Y_array,depth,Slip,filename=None):
    
        fig = plt.figure()
        ax = fig.add_subplot(111, projection='3d')
        # Plot the surface with face colors taken from the array we made.
        surf = ax.plot_surface(X_array,Y_array, depth, facecolors=cm.rainbow(Slip/np.max(Slip.flatten())), linewidth=0)
        # Customize the z axis.
        # Agregar barra de colores con el rango correcto
        norm = plt.Normalize(np.min(Slip.flat), np.max(Slip.flat))
        sm = plt.cm.ScalarMappable(cmap=plt.cm.rainbow, norm=norm)
        sm.set_array([])  # este paso es necesario para que la barra de colores refleje correctamente los valores
        plt.title('Slip_0004_Simulation')
        # Configurar etiquetas
        ax.set_xlabel('Longitude')
        ax.set_ylabel('Latitude')
        ax.set_zlabel('Depth')
        cbar = fig.colorbar(sm, ax=ax, pad=0.1)
        cbar.set_label('Slip[m]')
        # Configurar posición de la barra de colores
        cbar.ax.yaxis.set_label_position('right')  # Puedes ajustar la posición según tus necesidades
        if filename!=None:
            fig.savefig(filename)
        return plt.show()


    nx=18
    ny=50
    width=180
    length=500
    dx=width/nx
    dy=length/ny

    # Primero, se cargarán lo datos del trench de Chile, para realizar una geometría paralela a ella
    # 
    # También, se cargarán los archivos de Slab2, que contienen los parámetros geométricos de la zona de subducción de Sudamérica

    # In[3]:


    route_trench = geostochpy.get_data('trench-chile.txt') # route to trench file
    lons_fosa, lats_fosa  = geostochpy.load_trench(route_trench)
    # load slab files
    slabdep,slabdip,slabstrike,slabrake=geostochpy.load_files_slab2(zone='south_america',rake=True)


    # Se realiza la falla a lo largo del trench, y se le da el valor más al norte de la falla. 
    # 
    # Luego, se deben tener las profundidades en cada subfalla, para ello se interpolan los datos de Slab2 con geostochpy.interp_slabtofault

    # In[4]:


    north=-29.5
    lons,lons_ep,lats,lats_ep=geostochpy.make_fault_alongtrench(lons_fosa,lats_fosa,north, nx,ny,width,length)
    [X_grid,Y_grid,dep,dip,strike,rake]=geostochpy.interp_slabtofault(lons,lats,nx,ny,slabdep,slabdip,slabstrike,slabrake)


    # ##### Se crea la matriz de slips medios con geostochpy.matriz_media(mean,dep)

    # In[15]:
    #
    ## Creation slip models
    # mean matrix
    #
    Mw=9.0
    media,rigidez=geostochpy.media_slip(Mw,dx*1000,dy*1000,dep)
    leveque_taper=geostochpy.taper_LeVeque(dep,55000)
    # leveque_taper=leveque_taper/np.max(leveque_taper)
    villarroel_taper=geostochpy.taper_except_trench_tukey(dep,alpha_dip=0.3,alpha_strike=0.3)
    taper=leveque_taper*villarroel_taper
    # taper=geostochpy.taper_except_trench_tukey(dep,alpha_dip=0.6,alpha_strike=0.4,dip_taperfunc=geostochpy.taper_LeVeque,strike_taperfunc=geostochpy.tukey_window_equal)

    mu = geostochpy.matriz_medias_villarroel(media,taper)
    # matriz de covarianza
    C    = geostochpy.matriz_covarianza_optimized(dip, dep, X_grid, Y_grid,length*1000,width*1000)
    # for comcot simulation
    Slip=geostochpy.distribucion_slip(C, mu, 20)
    Slip,rigidez,Mo_original,Mo_deseado=geostochpy.escalar_magnitud_momento(Mw, Slip, dep, dy*1000, dx*1000,prem=True) # se escala el Slip a la magnitud deseada <--------- Slip final
    # Hypocenter=geostochpy.hypocenter(X_grid,Y_grid,dep,length,width) se tiene en cuenta la rigidez con el modelo PREM incluido @fetched with Rockhound

    plot_3d(X_grid,Y_grid,-1*dep,Slip)



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

   **Total running time of the script:** (0 minutes 13.056 seconds)


.. _sphx_glr_download_auto_examples_plot_3D-plot.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: plot_3D-plot.ipynb <plot_3D-plot.ipynb>`

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

      :download:`Download Python source code: plot_3D-plot.py <plot_3D-plot.py>`


.. only:: html

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

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