""" Transitioning from VTK to PyVista ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ VTK is primarily developed in C++ and uses chained setter and getter commands to access data. Instead, PyVista wraps the VTK data types into numpy arrays so that users can benefit from its bracket syntax and fancy indexing. This section demonstrates the difference between the two approaches in a series of examples. For example, to hard-code values for a :vtk:`vtkImageData` data structure using VTK Python's bindings, one would write the following: """ from math import cos, sin import vtk # %% # Create values for a 300x300 image dataset # # In our example, we want to have values from the function # # .. code-block:: python # # 127.5 + (1.0 + sin(x/25.0)*cos(y/25.0)) values = vtk.vtkDoubleArray() values.SetName("values") values.SetNumberOfComponents(1) values.SetNumberOfTuples(300 * 300) for x in range(300): for y in range(300): values.SetValue(x * 300 + y, 127.5 + (1.0 + sin(x / 25.0) * cos(y / 25.0))) # %% # Create the image structure image_data = vtk.vtkImageData() image_data.SetOrigin(0, 0, 0) image_data.SetSpacing(1, 1, 1) image_data.SetDimensions(300, 300, 1) # %% # Assign the values to the image image_data.GetPointData().SetScalars(values) # %% # As you can see, there is quite a bit of boilerplate that goes into # the creation of a simple :vtk:`vtkImageData` dataset. PyVista provides # much more concise syntax that is more "Pythonic". The equivalent code in # PyVista is: import numpy as np import pyvista as pv # %% # Use the meshgrid function to create 2D "grids" of the x and y values. # This section effectively replaces the vtkDoubleArray. xi = np.arange(300) x, y = np.meshgrid(xi, xi) values = 127.5 + (1.0 + np.sin(x / 25.0) * np.cos(y / 25.0)) # %% # Create the grid. Note how the values must use Fortran ordering. grid = pv.ImageData(dimensions=(300, 300, 1)) grid.point_data["values"] = values.flatten(order="F") # %% # Here, PyVista has done several things for us: # # #. PyVista combines the dimensionality of the data (in the shape of # the :class:`numpy.ndarray`) with the values of the data in one line. VTK uses # "tuples" to describe the shape of the data (where it sits in space) # and "components" to describe the type of data (1 = scalars/scalar # fields, 2 = vectors/vector fields, n = tensors/tensor # fields). Here, shape and values are stored concretely in one # variable. # # #. :class:`pyvista.ImageData` wraps :vtk:`vtkImageData`, just with a # different name; they are both containers of evenly spaced points. Your # data does not have to be an "image" to use it with # :vtk:`vtkImageData`; rather, like images, values in the dataset are # evenly spaced apart like pixels in an image. # # Furthermore, since we know the container is for uniformly spaced data, # pyvista sets the origin and spacing by default to ``(0, 0, 0)`` and # ``(1, 1, 1)``. This is another great thing about PyVista and Python! # Rather than having to know everything about the VTK library up front, # you can get started very easily! Once you get more familiar with it # and need to do something more complex, you can dive deeper. For # example, changing the origin and spacing is as simple as: # # .. code:: python # # grid.origin = (10, 20, 10) # grid.spacing = (2, 3, 5) # # #. The name for the :attr:`point_array ` is given # directly in dictionary-style fashion. Also, since VTK stores data # on the heap (linear segments of RAM; a C++ concept), the # data must be flattened and put in Fortran ordering (which controls # how multidimensional data is laid out in physically 1d memory; numpy # uses "C"-style memory layout by default). This is why in our earlier # example, the first argument to ``SetValue()`` was written as # ``x*300 + y``. Here, numpy takes care of this for us quite nicely # and it's made more explicit in the code, following the Python best # practice of "Explicit is better than implicit". # # Finally, with PyVista, each geometry class contains methods that allow # you to immediately plot the mesh without also setting up the plot. # For example, in VTK you would have to do: # # .. code:: python # # actor = vtk.vtkImageActor() # actor.GetMapper().SetInputData(image_data) # ren = vtk.vtkRenderer() # renWin = vtk.vtkRenderWindow() # renWin.AddRenderer(ren) # renWin.SetWindowName('ReadSTL') # iren = vtk.vtkRenderWindowInteractor() # iren.SetRenderWindow(renWin) # ren.AddActor(actor) # iren.Initialize() # renWin.Render() # iren.Start() # %% # However, with PyVista you only need: grid.plot(cpos="xy", show_scalar_bar=False, cmap="coolwarm") # %% # PointSet Construction # ^^^^^^^^^^^^^^^^^^^^^ # PyVista heavily relies on NumPy to efficiently allocate and access # VTK's C arrays. For example, to create an array of points within VTK # one would normally loop through all the points of a list and supply # that to a :vtk:`vtkPoints` class. For example: vtk_array = vtk.vtkDoubleArray() vtk_array.SetNumberOfComponents(3) vtk_array.SetNumberOfValues(9) vtk_array.SetValue(0, 0) vtk_array.SetValue(1, 0) vtk_array.SetValue(2, 0) vtk_array.SetValue(3, 1) vtk_array.SetValue(4, 0) vtk_array.SetValue(5, 0) vtk_array.SetValue(6, 0.5) vtk_array.SetValue(7, 0.667) vtk_array.SetValue(8, 0) vtk_points = vtk.vtkPoints() vtk_points.SetData(vtk_array) # %% # To do the same within PyVista, you simply need to create a NumPy array: np_points = np.array([[0, 0, 0], [1, 0, 0], [0.5, 0.667, 0]]) # %% # .. note:: # You can use :func:`pyvista.vtk_points` to construct a :vtk:`vtkPoints` # object, but this is unnecessary in almost all situations. # # Since the end goal is to construct a :class:`pyvista.DataSet # `, you would simply pass the # ``np_points`` array to the :class:`pyvista.PolyData` constructor: poly_data = pv.PolyData(np_points) # %% # Whereas in VTK you would have to do: vtk_poly_data = vtk.vtkPolyData() vtk_poly_data.SetPoints(vtk_points) # %% # The same goes with assigning face or cell connectivity/topology. With # VTK you would normally have to loop using :func:`InsertNextCell` and # :func:`InsertCellPoint`. For example, to create a single cell # (triangle) and then assign it to :vtk:`vtkPolyData`: cell_arr = vtk.vtkCellArray() cell_arr.InsertNextCell(3) cell_arr.InsertCellPoint(0) cell_arr.InsertCellPoint(1) cell_arr.InsertCellPoint(2) vtk_poly_data.SetPolys(cell_arr) # %% # In PyVista, we can assign this directly in the constructor and then # access it (or change it) from the :attr:`faces # ` attribute. faces = np.array([3, 0, 1, 2]) poly_data = pv.PolyData(np_points, faces) poly_data.faces # %% # PyVista Tradeoffs # ~~~~~~~~~~~~~~~~~ # While most features can, not everything can be simplified in PyVista without # losing functionality or performance. # # In the :class:`collision ` filter, # we demonstrate how to calculate the collision between two meshes. For # example: # create a default sphere and a shifted sphere mesh_a = pv.Sphere() mesh_b = pv.Sphere(center=(-0.4, 0, 0)) out, n_coll = mesh_a.collision(mesh_b, generate_scalars=True, contact_mode=2) # %% pl = pv.Plotter() pl.add_mesh(out) pl.add_mesh(mesh_b, style="wireframe", color="k") pl.camera_position = "xy" pl.show() # %% # Under the hood, the collision filter detects mesh collisions using # oriented bounding box (OBB) trees. For a single collision, this filter # is as performant as the VTK counterpart, but when computing multiple # collisions with the same meshes, as in the `Collision Example `_ # example, it is more efficient to use the `vtkCollisionDetectionFilter # `_, # as the OBB tree is computed once for each mesh. In most cases, pure # PyVista is sufficient for most data science, but there are times when # you may want to use VTK classes directly. # %% # .. raw:: html # #
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