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<article id="content">





  
  

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<header>





  
  

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<h1 class="title">Module <code>pymskt.mesh.meshRegistration</code></h1>





  
  

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</header>





  
  

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<section id="section-intro">





  
  

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<details class="source">





  
  

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<summary>





  
  

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<span>Expand source code</span>





  
  

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</summary>





  
  

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<pre><code class="python">import sys





  
  

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import vtk 





  
  

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try:





  
  

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     import pyfocusr





  
  

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except ModuleNotFoundError:





  
  

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    print(&#39;pyfocusr not found&#39;)





  
  

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    print(&#39;If you are not using the registration tools, you can ignore this message.&#39;)





  
  

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    print(&#39;install pyfocusr as described in the README: https://github.com/gattia/pymskt&#39;)





  
  

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    print(&#39;or visit the pyfocusr github repo: https://github.com/gattia/pyfocusr&#39;)





  
  

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import numpy as np





  
  

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def get_icp_transform(source, target, max_n_iter=1000, n_landmarks=1000, reg_mode=&#39;similarity&#39;):





  
  

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    &#34;&#34;&#34;





  
  

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    Get the Interative Closest Point (ICP) transformation from the `source` mesh to the





  
  

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    `target` mesh. 





  
  

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    Parameters





  
  

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    ----------





  
  

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    source : vtk.vtkPolyData





  
  

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        Source mesh that we want to transform onto the target mesh. 





  
  

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    target : vtk.vtkPolyData





  
  

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        Target mesh that we want to transform the source mesh onto. 





  
  

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    max_n_iter : int, optional





  
  

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        Max number of iterations for the registration algorithm to perform, by default 1000





  
  

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    n_landmarks : int, optional





  
  

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        How many landmarks to sample when determining distance between meshes &amp; 





  
  

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        solving for the optimal transformation, by default 1000





  
  

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    reg_mode : str, optional





  
  

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        The type of registration to perform. The options are: 





  
  

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            - &#39;rigid&#39;: true rigid, translation only 





  
  

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            - &#39;similarity&#39;: rigid + equal scale 





  
  

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        by default &#39;similarity&#39;





  
  

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    Returns





  
  

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    -------





  
  

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    vtk.vtkIterativeClosestPointTransform





  
  

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        The actual transform object after running the registration. 





  
  

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    &#34;&#34;&#34;    





  
  

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    icp = vtk.vtkIterativeClosestPointTransform()





  
  

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    icp.SetSource(source)





  
  

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    icp.SetTarget(target)





  
  

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    if reg_mode == &#39;rigid&#39;:





  
  

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        icp.GetLandmarkTransform().SetModeToRigidBody()





  
  

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    elif reg_mode == &#39;similarity&#39;:





  
  

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        icp.GetLandmarkTransform().SetModeToSimilarity()





  
  

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    icp.SetMaximumNumberOfIterations(max_n_iter)





  
  

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    icp.StartByMatchingCentroidsOn()





  
  

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    icp.Modified()





  
  

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    icp.Update()





  
  

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    icp.SetMaximumNumberOfLandmarks(n_landmarks)





  
  

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    return icp





  
  

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def non_rigidly_register(





  
  

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    target_mesh=None,





  
  

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    source_mesh=None,





  
  

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    final_pt_location=&#39;weighted_average&#39;,   # &#39;weighted_average&#39; or &#39;nearest_neighbour&#39;





  
  

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    icp_register_first=True,                # Get bones/objects into roughly the same alignment first





  
  

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    icp_registration_mode=&#39;similarity&#39;,     # similarity = rigid + scaling (isotropic), (&#34;rigid&#34;, &#34;similarity&#34;, &#34;affine&#34;)





  
  

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    icp_reg_target_to_source=True,          # For shape models, the source is usually the reference so we want target in its space (true)





  
  

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    n_spectral_features=3,





  
  

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    n_extra_spectral=3,                     # For ensuring we have the right spec coords - determined using wasserstein distances. 





  
  

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    target_eigenmap_as_reference=True,





  
  

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    get_weighted_spectral_coords=False,





  
  

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    list_features_to_calc=[&#39;curvature&#39;],    # &#39;curvature&#39;, min_curvature&#39; &#39;max_curvature&#39; (other features for registration)





  
  

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    use_features_as_coords=True,            # During registraiton - do we want to use curvature etc. 





  
  

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    rigid_reg_max_iterations=100,





  
  

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    non_rigid_alpha=0.01,





  
  

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    non_rigid_beta=50,





  
  

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    non_rigid_n_eigens=100,                 # number of eigens for low rank CPD registration





  
  

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    non_rigid_max_iterations=500,





  
  

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    rigid_before_non_rigid_reg=False,       # This is of the spectral coordinates - not the x/y/z used in icp_register_first





  
  

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    projection_smooth_iterations=30,        # Used for distributing registered points onto target surface - helps preserve diffeomorphism





  
  

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    graph_smoothing_iterations=300,         # For smoothing the target mesh before final point correspondence





  
  

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    feature_smoothing_iterations=30,        # how much should features (curvature) be smoothed before registration 





  
  

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    include_points_as_features=False,       # Do we want to incldue x/y/z positions in registration? 





  
  

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    norm_physical_and_spectral=True,        # set standardized mean and variance for each feature





  
  

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    feature_weights=np.diag([.1,.1]),       # should we weight the extra features (curvature) more/less than spectral





  
  

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    n_coords_spectral_ordering=20000,       # How many points on mesh to use for ordering spectral coordinates ()





  
  

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    n_coords_spectral_registration=1000,    # How many points to use for spectral registrtaion (usually random subsample)





  
  

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    initial_correspondence_type=&#39;kd&#39;,       # kd = nearest neightbor, hungarian = minimum cost of assigning between graphs (more compute heavy)





  
  

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    final_correspondence_type=&#39;kd&#39;          # kd = nearest neightbor, hungarian = minimum cost of assigning between graphs (more compute heavy)





  
  

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):





  
  

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    if &#39;pyfocusr&#39; not in sys.modules:





  
  

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        raise ModuleNotFoundError(&#39;pyfocusr is not installed &amp; is necessary for non-rigid registration.&#39;)





  
  

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    if final_pt_location not in [&#39;weighted_average&#39;, &#39;nearest_neighbour&#39;]:





  
  

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        raise Exception(&#39;Did not specify appropriate final_pt_location, must be either &#34;weighted_average&#34;, or &#34;nearest_neighbour&#34;&#39;)





  
  

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    # Test if mesh is a vtk mesh, or a pymsky.Mesh object. 





  
  

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    if isinstance(target_mesh, vtk.vtkPolyData):





  
  

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        vtk_mesh_target = target_mesh





  
  

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    else:





  
  

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        try:





  
  

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            vtk_mesh_target = target_mesh.mesh





  
  

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        except:





  
  

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            raise Exception(f&#39;expected type vtk.vtkPolyData or pymskt.mesh.Mesh, got: {type(target_mesh)}&#39;)





  
  

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    if isinstance(source_mesh, vtk.vtkPolyData):





  
  

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        vtk_mesh_source = source_mesh





  
  

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    else:





  
  

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        try:





  
  

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            vtk_mesh_source = source_mesh.mesh





  
  

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        except:





  
  

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            raise Exception(f&#39;expected type vtk.vtkPolyData or pymskt.mesh.Mesh, got: {type(target_mesh)}&#39;)





  
  

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    reg = pyfocusr.Focusr(





  
  

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        vtk_mesh_target=vtk_mesh_target, 





  
  

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        vtk_mesh_source=vtk_mesh_source,  





  
  

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        icp_register_first=icp_register_first,





  
  

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        icp_registration_mode=icp_registration_mode,





  
  

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        icp_reg_target_to_source=icp_reg_target_to_source,





  
  

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        n_spectral_features=n_spectral_features,





  
  

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        n_extra_spectral=n_extra_spectral,





  
  

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        target_eigenmap_as_reference=target_eigenmap_as_reference,





  
  

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        get_weighted_spectral_coords=get_weighted_spectral_coords,





  
  

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        list_features_to_calc=list_features_to_calc,





  
  

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        use_features_as_coords=use_features_as_coords,





  
  

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        rigid_reg_max_iterations=rigid_reg_max_iterations,





  
  

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        non_rigid_alpha=non_rigid_alpha,





  
  

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        non_rigid_beta=non_rigid_beta,





  
  

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        non_rigid_n_eigens=non_rigid_n_eigens,





  
  

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        non_rigid_max_iterations=non_rigid_max_iterations,





  
  

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        rigid_before_non_rigid_reg=rigid_before_non_rigid_reg,





  
  

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        projection_smooth_iterations=projection_smooth_iterations,





  
  

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        graph_smoothing_iterations=graph_smoothing_iterations,





  
  

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        feature_smoothing_iterations=feature_smoothing_iterations,





  
  

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        include_points_as_features=include_points_as_features,





  
  

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        norm_physical_and_spectral=norm_physical_and_spectral,





  
  

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        feature_weights=feature_weights,





  
  

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        n_coords_spectral_ordering=n_coords_spectral_ordering,





  
  

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        n_coords_spectral_registration=n_coords_spectral_registration,





  
  

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        initial_correspondence_type=initial_correspondence_type,





  
  

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        final_correspondence_type=final_correspondence_type





  
  

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    ) 





  
  

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    reg.align_maps()





  
  

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    if final_pt_location == &#39;weighted_average&#39;:





  
  

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        reg.get_source_mesh_transformed_weighted_avg()





  
  

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        mesh_transformed_to_target = reg.weighted_avg_transformed_mesh





  
  

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    elif final_pt_location == &#39;nearest_neighbour&#39;:





  
  

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        reg.get_source_mesh_transformed_nearest_neighbour()





  
  

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        mesh_transformed_to_target = reg.nearest_neighbour_transformed_mesh





  
  

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    return mesh_transformed_to_target    </code></pre>





  
  

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</details>





  
  

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</section>





  
  

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<section>





  
  

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</section>





  
  

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<section>





  
  

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</section>





  
  

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<section>





  
  

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<h2 class="section-title" id="header-functions">Functions</h2>





  
  

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<dl>





  
  

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<dt id="pymskt.mesh.meshRegistration.get_icp_transform"><code class="name flex">





  
  

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<span>def <span class="ident">get_icp_transform</span></span>(<span>source, target, max_n_iter=1000, n_landmarks=1000, reg_mode='similarity')</span>





  
  

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</code></dt>





  
  

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<dd>





  
  

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<div class="desc"><p>Get the Interative Closest Point (ICP) transformation from the <code>source</code> mesh to the





  
  

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<code>target</code> mesh. </p>





  
  

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<h2 id="parameters">Parameters</h2>





  
  

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<dl>





  
  

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<dt><strong><code>source</code></strong> :&ensp;<code>vtk.vtkPolyData</code></dt>





  
  

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<dd>Source mesh that we want to transform onto the target mesh.</dd>





  
  

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<dt><strong><code>target</code></strong> :&ensp;<code>vtk.vtkPolyData</code></dt>





  
  

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<dd>Target mesh that we want to transform the source mesh onto.</dd>





  
  

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<dt><strong><code>max_n_iter</code></strong> :&ensp;<code>int</code>, optional</dt>





  
  

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<dd>Max number of iterations for the registration algorithm to perform, by default 1000</dd>





  
  

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<dt><strong><code>n_landmarks</code></strong> :&ensp;<code>int</code>, optional</dt>





  
  

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<dd>How many landmarks to sample when determining distance between meshes &amp;





  
  

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solving for the optimal transformation, by default 1000</dd>





  
  

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<dt><strong><code>reg_mode</code></strong> :&ensp;<code>str</code>, optional</dt>





  
  

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<dd>The type of registration to perform. The options are:





  
  

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- 'rigid': true rigid, translation only





  
  

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- 'similarity': rigid + equal scale





  
  

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by default 'similarity'</dd>





  
  

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</dl>





  
  

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<h2 id="returns">Returns</h2>





  
  

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<dl>





  
  

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<dt><code>vtk.vtkIterativeClosestPointTransform</code></dt>





  
  

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<dd>The actual transform object after running the registration.</dd>





  
  

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</dl></div>





  
  

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<details class="source">





  
  

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<summary>





  
  

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<span>Expand source code</span>





  
  

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</summary>





  
  

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<pre><code class="python">def get_icp_transform(source, target, max_n_iter=1000, n_landmarks=1000, reg_mode=&#39;similarity&#39;):





  
  

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    &#34;&#34;&#34;





  
  

  219
  
    Get the Interative Closest Point (ICP) transformation from the `source` mesh to the





  
  

  220
  
    `target` mesh. 





  
  

  221
  





  
  

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    Parameters





  
  

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    ----------





  
  

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    source : vtk.vtkPolyData





  
  

  225
  
        Source mesh that we want to transform onto the target mesh. 





  
  

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    target : vtk.vtkPolyData





  
  

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        Target mesh that we want to transform the source mesh onto. 





  
  

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    max_n_iter : int, optional





  
  

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        Max number of iterations for the registration algorithm to perform, by default 1000





  
  

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    n_landmarks : int, optional





  
  

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        How many landmarks to sample when determining distance between meshes &amp; 





  
  

  232
  
        solving for the optimal transformation, by default 1000





  
  

  233
  
    reg_mode : str, optional





  
  

  234
  
        The type of registration to perform. The options are: 





  
  

  235
  
            - &#39;rigid&#39;: true rigid, translation only 





  
  

  236
  
            - &#39;similarity&#39;: rigid + equal scale 





  
  

  237
  
        by default &#39;similarity&#39;





  
  

  238
  





  
  

  239
  
    Returns





  
  

  240
  
    -------





  
  

  241
  
    vtk.vtkIterativeClosestPointTransform





  
  

  242
  
        The actual transform object after running the registration. 





  
  

  243
  
    &#34;&#34;&#34;    





  
  

  244
  





  
  

  245
  
    icp = vtk.vtkIterativeClosestPointTransform()





  
  

  246
  
    icp.SetSource(source)





  
  

  247
  
    icp.SetTarget(target)





  
  

  248
  
    if reg_mode == &#39;rigid&#39;:





  
  

  249
  
        icp.GetLandmarkTransform().SetModeToRigidBody()





  
  

  250
  
    elif reg_mode == &#39;similarity&#39;:





  
  

  251
  
        icp.GetLandmarkTransform().SetModeToSimilarity()





  
  

  252
  
    icp.SetMaximumNumberOfIterations(max_n_iter)





  
  

  253
  
    icp.StartByMatchingCentroidsOn()





  
  

  254
  
    icp.Modified()





  
  

  255
  
    icp.Update()





  
  

  256
  
    icp.SetMaximumNumberOfLandmarks(n_landmarks)





  
  

  257
  
    return icp</code></pre>





  
  

  258
  
</details>





  
  

  259
  
</dd>





  
  

  260
  
<dt id="pymskt.mesh.meshRegistration.non_rigidly_register"><code class="name flex">





  
  

  261
  
<span>def <span class="ident">non_rigidly_register</span></span>(<span>target_mesh=None, source_mesh=None, final_pt_location='weighted_average', icp_register_first=True, icp_registration_mode='similarity', icp_reg_target_to_source=True, n_spectral_features=3, n_extra_spectral=3, target_eigenmap_as_reference=True, get_weighted_spectral_coords=False, list_features_to_calc=['curvature'], use_features_as_coords=True, rigid_reg_max_iterations=100, non_rigid_alpha=0.01, non_rigid_beta=50, non_rigid_n_eigens=100, non_rigid_max_iterations=500, rigid_before_non_rigid_reg=False, projection_smooth_iterations=30, graph_smoothing_iterations=300, feature_smoothing_iterations=30, include_points_as_features=False, norm_physical_and_spectral=True, feature_weights=array([[0.1, 0. ],





  
  

  262
  
[0. , 0.1]]), n_coords_spectral_ordering=20000, n_coords_spectral_registration=1000, initial_correspondence_type='kd', final_correspondence_type='kd')</span>





  
  

  263
  
</code></dt>





  
  

  264
  
<dd>





  
  

  265
  
<div class="desc"></div>





  
  

  266
  
<details class="source">





  
  

  267
  
<summary>





  
  

  268
  
<span>Expand source code</span>





  
  

  269
  
</summary>





  
  

  270
  
<pre><code class="python">def non_rigidly_register(





  
  

  271
  
    target_mesh=None,





  
  

  272
  
    source_mesh=None,





  
  

  273
  
    final_pt_location=&#39;weighted_average&#39;,   # &#39;weighted_average&#39; or &#39;nearest_neighbour&#39;





  
  

  274
  
    icp_register_first=True,                # Get bones/objects into roughly the same alignment first





  
  

  275
  
    icp_registration_mode=&#39;similarity&#39;,     # similarity = rigid + scaling (isotropic), (&#34;rigid&#34;, &#34;similarity&#34;, &#34;affine&#34;)





  
  

  276
  
    icp_reg_target_to_source=True,          # For shape models, the source is usually the reference so we want target in its space (true)





  
  

  277
  
    n_spectral_features=3,





  
  

  278
  
    n_extra_spectral=3,                     # For ensuring we have the right spec coords - determined using wasserstein distances. 





  
  

  279
  
    target_eigenmap_as_reference=True,





  
  

  280
  
    get_weighted_spectral_coords=False,





  
  

  281
  
    list_features_to_calc=[&#39;curvature&#39;],    # &#39;curvature&#39;, min_curvature&#39; &#39;max_curvature&#39; (other features for registration)





  
  

  282
  
    use_features_as_coords=True,            # During registraiton - do we want to use curvature etc. 





  
  

  283
  
    rigid_reg_max_iterations=100,





  
  

  284
  
    non_rigid_alpha=0.01,





  
  

  285
  
    non_rigid_beta=50,





  
  

  286
  
    non_rigid_n_eigens=100,                 # number of eigens for low rank CPD registration





  
  

  287
  
    non_rigid_max_iterations=500,





  
  

  288
  
    rigid_before_non_rigid_reg=False,       # This is of the spectral coordinates - not the x/y/z used in icp_register_first





  
  

  289
  
    projection_smooth_iterations=30,        # Used for distributing registered points onto target surface - helps preserve diffeomorphism





  
  

  290
  
    graph_smoothing_iterations=300,         # For smoothing the target mesh before final point correspondence





  
  

  291
  
    feature_smoothing_iterations=30,        # how much should features (curvature) be smoothed before registration 





  
  

  292
  
    include_points_as_features=False,       # Do we want to incldue x/y/z positions in registration? 





  
  

  293
  
    norm_physical_and_spectral=True,        # set standardized mean and variance for each feature





  
  

  294
  
    feature_weights=np.diag([.1,.1]),       # should we weight the extra features (curvature) more/less than spectral





  
  

  295
  
    n_coords_spectral_ordering=20000,       # How many points on mesh to use for ordering spectral coordinates ()





  
  

  296
  
    n_coords_spectral_registration=1000,    # How many points to use for spectral registrtaion (usually random subsample)





  
  

  297
  
    initial_correspondence_type=&#39;kd&#39;,       # kd = nearest neightbor, hungarian = minimum cost of assigning between graphs (more compute heavy)





  
  

  298
  
    final_correspondence_type=&#39;kd&#39;          # kd = nearest neightbor, hungarian = minimum cost of assigning between graphs (more compute heavy)





  
  

  299
  
):





  
  

  300
  
    





  
  

  301
  
    if &#39;pyfocusr&#39; not in sys.modules:





  
  

  302
  
        raise ModuleNotFoundError(&#39;pyfocusr is not installed &amp; is necessary for non-rigid registration.&#39;)





  
  

  303
  





  
  

  304
  
    if final_pt_location not in [&#39;weighted_average&#39;, &#39;nearest_neighbour&#39;]:





  
  

  305
  
        raise Exception(&#39;Did not specify appropriate final_pt_location, must be either &#34;weighted_average&#34;, or &#34;nearest_neighbour&#34;&#39;)





  
  

  306
  





  
  

  307
  
    # Test if mesh is a vtk mesh, or a pymsky.Mesh object. 





  
  

  308
  
    if isinstance(target_mesh, vtk.vtkPolyData):





  
  

  309
  
        vtk_mesh_target = target_mesh





  
  

  310
  
    else:





  
  

  311
  
        try:





  
  

  312
  
            vtk_mesh_target = target_mesh.mesh





  
  

  313
  
        except:





  
  

  314
  
            raise Exception(f&#39;expected type vtk.vtkPolyData or pymskt.mesh.Mesh, got: {type(target_mesh)}&#39;)





  
  

  315
  
    





  
  

  316
  
    if isinstance(source_mesh, vtk.vtkPolyData):





  
  

  317
  
        vtk_mesh_source = source_mesh





  
  

  318
  
    else:





  
  

  319
  
        try:





  
  

  320
  
            vtk_mesh_source = source_mesh.mesh





  
  

  321
  
        except:





  
  

  322
  
            raise Exception(f&#39;expected type vtk.vtkPolyData or pymskt.mesh.Mesh, got: {type(target_mesh)}&#39;)





  
  

  323
  
    





  
  

  324
  
    reg = pyfocusr.Focusr(





  
  

  325
  
        vtk_mesh_target=vtk_mesh_target, 





  
  

  326
  
        vtk_mesh_source=vtk_mesh_source,  





  
  

  327
  
        icp_register_first=icp_register_first,





  
  

  328
  
        icp_registration_mode=icp_registration_mode,





  
  

  329
  
        icp_reg_target_to_source=icp_reg_target_to_source,





  
  

  330
  
        n_spectral_features=n_spectral_features,





  
  

  331
  
        n_extra_spectral=n_extra_spectral,





  
  

  332
  
        target_eigenmap_as_reference=target_eigenmap_as_reference,





  
  

  333
  
        get_weighted_spectral_coords=get_weighted_spectral_coords,





  
  

  334
  
        list_features_to_calc=list_features_to_calc,





  
  

  335
  
        use_features_as_coords=use_features_as_coords,





  
  

  336
  
        rigid_reg_max_iterations=rigid_reg_max_iterations,





  
  

  337
  
        non_rigid_alpha=non_rigid_alpha,





  
  

  338
  
        non_rigid_beta=non_rigid_beta,





  
  

  339
  
        non_rigid_n_eigens=non_rigid_n_eigens,





  
  

  340
  
        non_rigid_max_iterations=non_rigid_max_iterations,





  
  

  341
  
        rigid_before_non_rigid_reg=rigid_before_non_rigid_reg,





  
  

  342
  
        projection_smooth_iterations=projection_smooth_iterations,





  
  

  343
  
        graph_smoothing_iterations=graph_smoothing_iterations,





  
  

  344
  
        feature_smoothing_iterations=feature_smoothing_iterations,





  
  

  345
  
        include_points_as_features=include_points_as_features,





  
  

  346
  
        norm_physical_and_spectral=norm_physical_and_spectral,





  
  

  347
  
        feature_weights=feature_weights,





  
  

  348
  
        n_coords_spectral_ordering=n_coords_spectral_ordering,





  
  

  349
  
        n_coords_spectral_registration=n_coords_spectral_registration,





  
  

  350
  
        initial_correspondence_type=initial_correspondence_type,





  
  

  351
  
        final_correspondence_type=final_correspondence_type





  
  

  352
  
    ) 





  
  

  353
  
    reg.align_maps()





  
  

  354
  





  
  

  355
  
    if final_pt_location == &#39;weighted_average&#39;:





  
  

  356
  
        reg.get_source_mesh_transformed_weighted_avg()





  
  

  357
  
        mesh_transformed_to_target = reg.weighted_avg_transformed_mesh





  
  

  358
  
    elif final_pt_location == &#39;nearest_neighbour&#39;:





  
  

  359
  
        reg.get_source_mesh_transformed_nearest_neighbour()





  
  

  360
  
        mesh_transformed_to_target = reg.nearest_neighbour_transformed_mesh





  
  

  361
  
        





  
  

  362
  
    return mesh_transformed_to_target    </code></pre>





  
  

  363
  
</details>





  
  

  364
  
</dd>





  
  

  365
  
</dl>





  
  

  366
  
</section>





  
  

  367
  
<section>





  
  

  368
  
</section>





  
  

  369
  
</article>





  
  

  370
  
<nav id="sidebar">





  
  

  371
  
<h1>Index</h1>





  
  

  372
  
<div class="toc">





  
  

  373
  
<ul></ul>





  
  

  374
  
</div>





  
  

  375
  
<ul id="index">





  
  

  376
  
<li><h3>Super-module</h3>





  
  

  377
  
<ul>





  
  

  378
  
<li><code><a title="pymskt.mesh" href="index.html">pymskt.mesh</a></code></li>





  
  

  379
  
</ul>





  
  

  380
  
</li>





  
  

  381
  
<li><h3><a href="#header-functions">Functions</a></h3>





  
  

  382
  
<ul class="">





  
  

  383
  
<li><code><a title="pymskt.mesh.meshRegistration.get_icp_transform" href="#pymskt.mesh.meshRegistration.get_icp_transform">get_icp_transform</a></code></li>





  
  

  384
  
<li><code><a title="pymskt.mesh.meshRegistration.non_rigidly_register" href="#pymskt.mesh.meshRegistration.non_rigidly_register">non_rigidly_register</a></code></li>





  
  

  385
  
</ul>





  
  

  386
  
</li>





  
  

  387
  
</ul>





  
  

  388
  
</nav>





  
  

  389
  
</main>





  
  

  390
  
<footer id="footer">





  
  

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<p>Generated by <a href="https://pdoc3.github.io/pdoc" title="pdoc: Python API documentation generator"><cite>pdoc</cite> 0.10.0</a>.</p>





  
  

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