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

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_gallery_examples_pervious_surround_optimization_make_pervious_surround.py>`
        to download the full example code.

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

.. _sphx_glr_gallery_examples_pervious_surround_optimization_make_pervious_surround.py:


3D pervious surround
====================

.. GENERATED FROM PYTHON SOURCE LINES 5-57

.. code-block:: Python


    import numpy as np

    from HydrOpTop.Functions import p_Weighted_Head_Gradient, Volume_Percentage
    from HydrOpTop.Materials import Log_SIMP
    from HydrOpTop.Filters import Density_Filter
    from HydrOpTop.Crafter import Steady_State_Crafter
    from HydrOpTop.Solvers import PFLOTRAN


    if __name__ == "__main__":
      #1. Create a timer to time the optimization
      t = time.time()
  
      #2. create PFLOTRAN simulation object
      pflotranin = "pflotran.in"
      sim = PFLOTRAN(pflotranin)
  
      #3. Parametrize hydraulic conductivity
      #get cell ids in the region to optimize and parametrize permeability
      #same name than in pflotran input file
      pit_ids = sim.get_region_ids("Pit")
      perm = Log_SIMP(cell_ids_to_parametrize=pit_ids, property_name="PERMEABILITY", bounds=[5e-14,1e-10], power=3)
  
      #4. Define the cost function and constraint
      #define cost function as sum of the head in the pit
      cf = p_Weighted_Head_Gradient(pit_ids, invert_weighting=True)
      #define maximum volume constrains
      max_vol = Volume_Percentage(pit_ids)
      max_vol.constraint_tol = 0.2 #max volume percentage of 20%
  
      #5. Define filter
      filter = Density_Filter(6) #search neighbors in a 6 m radius
  
      #6. Craft optimization problem
      #i.e. create function to optimize, initiate IO array in classes...
      crafted_problem = Steady_State_Crafter(cf, sim, [perm], [max_vol], [filter])
  
      #7. Output
      #Define the output behavior (output density parameter every 2 iterations in vtu format)
      crafted_problem.IO.output_every_iteration(5)
      crafted_problem.IO.define_output_format("vtu")
  
      #8. Degine initial guess (homogeneous) and optimize
      p_ini = np.zeros(crafted_problem.get_problem_size(),dtype='f8') + 0.12
      out = crafted_problem.optimize(optimizer="nlopt-mma", action="minimize", max_it=200, ftol=1e-8, initial_guess=p_ini)
  
      #9. Output the final optimized and filtered density parameter in a out.vtu file
      crafted_problem.IO.write_fields_to_file([out.p_opt_filtered], "./out.vtu", ["Filtered_density"], at_ids=pit_ids-1)
      print(f"Elapsed time: {time.time()-t} seconds")
      crafted_problem.IO.plot_convergence_history()
  


.. _sphx_glr_download_gallery_examples_pervious_surround_optimization_make_pervious_surround.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: make_pervious_surround.ipynb <make_pervious_surround.ipynb>`

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

      :download:`Download Python source code: make_pervious_surround.py <make_pervious_surround.py>`


.. only:: html

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

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