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.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "_examples/02_generative_design_ex/04-project_geometries.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download__examples_02_generative_design_ex_04-project_geometries.py>`
        to download the full example code.

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

.. _sphx_glr__examples_02_generative_design_ex_04-project_geometries.py:

.. _ref_project_geometries:

Projecting Geometries onto a Model
==================================

This example demonstrates how to project a geometry onto a trained GeomAI model
and retrieve its latent parameters.

Before you begin
----------------

- Complete ":ref:`ref_build_model`" to train a GeomAI model.
- Ensure that the model training has completed successfully and that its workspace is available.
- Prepare a geometry file in ``VTP`` or ``STL`` format.

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Import necessary libraries
---------------------------

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.. code-block:: Python


    import ansys.simai.core as asc
    from ansys.simai.core.data.geomai.predictions import GeomAIPredictionConfiguration


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Configure your settings
-----------------------
Update these variables with your specific settings:

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.. code-block:: Python


    ORGANIZATION = "my_organization"  # Replace with your organization name
    WORKSPACE_NAME = "new-bracket-project #1"  # Replace with your trained model workspace
    GEOMETRY_FILE = "./td/bracket_264.vtp"  # VTP or STL file to project


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Initialize the client and get the workspace
-------------------------------------------

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.. code-block:: Python


    simai_client = asc.SimAIClient(organization=ORGANIZATION)
    geomai_client = simai_client.geomai
    workspace = geomai_client.workspaces.get(name=WORKSPACE_NAME)
    print(f"Using workspace: {workspace.name}")


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Upload a geometry
-----------------

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.. code-block:: Python


    print("Uploading geometry...")
    geometry = geomai_client.geometries.upload(GEOMETRY_FILE)
    geometry.wait()
    print(f"Uploaded {geometry.name} with id {geometry.id}")


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Project the geometry onto the workspace model
----------------------------------------------
The projection operation is idempotent: if this geometry has already been
projected onto the model, the existing projection is returned.

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.. code-block:: Python


    print("Running projection...")
    projection = geometry.run_projection(workspace)
    projection.wait()

    latent_parameters = projection.get_latent_parameters()
    print(f"Projection {projection.id} on model {projection.workspace_id}")
    print(f"Retrieved {len(latent_parameters)} latent parameters")


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Reconstruct the geometry from its latent parameters
---------------------------------------------------
The latent parameters can be passed to a prediction configuration to
regenerate the geometry from the model.

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.. code-block:: Python


    config = GeomAIPredictionConfiguration(
        latent_params=latent_parameters,
    )
    prediction = geomai_client.predictions.run(config, workspace)
    print(f"Prediction {prediction.id} started...")
    prediction.wait()

    output_path = f"reconstructed_{prediction.id}.vtp"
    geomai_client.predictions.download(prediction.id, output_path)
    print(f"Saved reconstructed geometry to {output_path}")



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Look up an existing projection
-------------------------------------
Retrieve a projection without starting a new computation. The result is
``None`` if the geometry has not been projected onto this workspace's model.

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.. code-block:: Python


    print("\nLooking up an existing projection...")
    existing_projection = geometry.get_projection(workspace)
    if existing_projection is None:
        print("Geometry has not yet been projected onto this workspace's model.")
    else:
        print(f"Existing projection: {existing_projection.id}")
        print(f"First three parameters: {existing_projection.get_latent_parameters(n=3)}")


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List projections in the workspace
----------------------------------------

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.. code-block:: Python


    projections = workspace.list_projections()
    print(f"\n{len(projections)} projections in {workspace.name}")
    for proj in projections:
        print(f"- {proj.id}")


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Next steps
----------
To explore the model further, you can:

- Generate geometries from latent parameters.
- Interpolate between geometries: :ref:`ref_interpolate_geometries`.


.. _sphx_glr_download__examples_02_generative_design_ex_04-project_geometries.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: 04-project_geometries.ipynb <04-project_geometries.ipynb>`

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

      :download:`Download Python source code: 04-project_geometries.py <04-project_geometries.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: 04-project_geometries.zip <04-project_geometries.zip>`


.. only:: html

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

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