deploy: 8bd8d57

_downloads/005c92a6df37721135f765060b0e02ee/c_edl.ipynb

@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Eye Dome Lighting\n=================\n\nEye-Dome Lighting (EDL) is a non-photorealistic, image-based shading\ntechnique designed to improve depth perception in scientific\nvisualization images. To learn more, please see [this blog\npost](https://blog.kitware.com/eye-dome-lighting-a-non-photorealistic-shading-technique/).\n"
+        "# Eye Dome Lighting\n\nEye-Dome Lighting (EDL) is a non-photorealistic, image-based shading\ntechnique designed to improve depth perception in scientific\nvisualization images. To learn more, please see [this blog\npost](https://blog.kitware.com/eye-dome-lighting-a-non-photorealistic-shading-technique/).\n"
       ]
     },
     {
@@ -22,7 +22,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Point Cloud\n===========\n\nWhen plotting a simple point cloud, it can be difficult to perceive\ndepth. Take this Lidar point cloud for example:\n"
+        "# Point Cloud\n\nWhen plotting a simple point cloud, it can be difficult to perceive\ndepth. Take this Lidar point cloud for example:\n"
       ]
     },
     {

_downloads/019b44a64197e32b38dfb67b0f502d46/b_clipping.ipynb

@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Clipping with Planes & Boxes {#clip_with_plane_box_example}\n============================\n\nClip/cut any dataset using using planes or boxes.\n"
+        "# Clipping with Planes & Boxes {#clip_with_plane_box_example}\n\nClip/cut any dataset using using planes or boxes.\n"
       ]
     },
     {
@@ -22,7 +22,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Clip with Plane\n===============\n\nClip any dataset by a user defined plane using the\n`pyvista.DataSetFilters.clip`{.interpreted-text role=\"func\"} filter\n"
+        "# Clip with Plane\n\nClip any dataset by a user defined plane using the\n`pyvista.DataSetFilters.clip`{.interpreted-text role=\"func\"} filter\n"
       ]
     },
     {
@@ -87,7 +87,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Clip with Bounds\n================\n\nClip any dataset by a set of XYZ bounds using the\n`pyvista.DataSetFilters.clip_box`{.interpreted-text role=\"func\"} filter.\n\nFirst, download an example dataset.\n"
+        "# Clip with Bounds\n\nClip any dataset by a set of XYZ bounds using the\n`pyvista.DataSetFilters.clip_box`{.interpreted-text role=\"func\"} filter.\n\nFirst, download an example dataset.\n"
       ]
     },
     {
@@ -152,7 +152,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Clip with Rotated Box\n=====================\n\nClip any dataset by an arbitrarily rotated solid box using the\n`pyvista.DataSetFilters.clip_box`{.interpreted-text role=\"func\"} filter.\n"
+        "# Clip with Rotated Box\n\nClip any dataset by an arbitrarily rotated solid box using the\n`pyvista.DataSetFilters.clip_box`{.interpreted-text role=\"func\"} filter.\n"
       ]
     },
     {
@@ -188,7 +188,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Crinkled Clipping\n=================\n\nCrinkled clipping is useful if you don\\'t want the clip filter to truly\nclip cells on the boundary, but want to preserve the input cell\nstructure and to pass the entire cell on through the boundary.\n\nThis option is available for\n`pyvista.DataSetFilters.clip`{.interpreted-text role=\"func\"},\n`pyvista.DataSetFilters.clip_box`{.interpreted-text role=\"func\"}, and\n`pyvista.DataSetFilters.clip_sruface`{.interpreted-text role=\"func\"},\nbut not available when clipping by scalar in\n`pyvista.DataSetFilters.clip_scalar`{.interpreted-text role=\"func\"}.\n"
+        "# Crinkled Clipping\n\nCrinkled clipping is useful if you don\\'t want the clip filter to truly\nclip cells on the boundary, but want to preserve the input cell\nstructure and to pass the entire cell on through the boundary.\n\nThis option is available for\n`pyvista.DataSetFilters.clip`{.interpreted-text role=\"func\"},\n`pyvista.DataSetFilters.clip_box`{.interpreted-text role=\"func\"}, and\n`pyvista.DataSetFilters.clip_sruface`{.interpreted-text role=\"func\"},\nbut not available when clipping by scalar in\n`pyvista.DataSetFilters.clip_scalar`{.interpreted-text role=\"func\"}.\n"
       ]
     },
     {

_downloads/0685150a3a04df2073c5706f2c8e12ca/a_lesson_mesh.ipynb

@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Lesson Overview\n===============\n\nThis exercise provides an overview of the example in the initial lesson\nfor you to try out!\n"
+        "# Lesson Overview\n\nThis exercise provides an overview of the example in the initial lesson\nfor you to try out!\n"
       ]
     },
     {
@@ -22,7 +22,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "What is a Point?\n================\n\nLet\\'s start with a point cloud - this is a mesh type that only has\nvertices. You can create one by defining a 2D array of Cartesian\ncoordinates like so:\n"
+        "# What is a Point?\n\nLet\\'s start with a point cloud - this is a mesh type that only has\nvertices. You can create one by defining a 2D array of Cartesian\ncoordinates like so:\n"
       ]
     },
     {
@@ -98,7 +98,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "What is a Cell?\n===============\n\nA cell is the geometry between points that defines the connectivity or\ntopology of a mesh. In the examples above, cells are defined by the\nlines (edges colored in black) connecting points (colored in red). For\nexample, a cell in the beam example is a voxel defined by the region\nbetween eight points in that mesh:\n"
+        "# What is a Cell?\n\nA cell is the geometry between points that defines the connectivity or\ntopology of a mesh. In the examples above, cells are defined by the\nlines (edges colored in black) connecting points (colored in red). For\nexample, a cell in the beam example is a voxel defined by the region\nbetween eight points in that mesh:\n"
       ]
     },
     {
@@ -123,14 +123,14 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "What are attributes?\n====================\n\nAttributes are data values that live on either the points or cells of a\nmesh. In PyVista, we work with both point data and cell data and allow\neasy access to data dictionaries to hold arrays for attributes that live\neither on all points or on all cells of a mesh. These attributes can be\naccessed in a dictionary-like attribute attached to any PyVista mesh\naccessible as one of the following:\n"
+        "# What are attributes?\n\nAttributes are data values that live on either the points or cells of a\nmesh. In PyVista, we work with both point data and cell data and allow\neasy access to data dictionaries to hold arrays for attributes that live\neither on all points or on all cells of a mesh. These attributes can be\naccessed in a dictionary-like attribute attached to any PyVista mesh\naccessible as one of the following:\n"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Point Data\n==========\n\nPoint data refers to arrays of values (scalars, vectors, etc.) that live\non each point of the mesh. Each element in an attribute array\ncorresponds to a point in the mesh. Let\\'s create some point data for\nthe beam mesh. When plotting, the values between points are interpolated\nacross the cells.\n"
+        "# Point Data\n\nPoint data refers to arrays of values (scalars, vectors, etc.) that live\non each point of the mesh. Each element in an attribute array\ncorresponds to a point in the mesh. Let\\'s create some point data for\nthe beam mesh. When plotting, the values between points are interpolated\nacross the cells.\n"
       ]
     },
     {
@@ -148,7 +148,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Cell Data\n=========\n\nCell data refers to arrays of values (scalars, vectors, etc.) that live\nthroughout each cell of the mesh. That is the entire cell (2D face or 3D\nvolume) is assigned the value of that attribute.\n"
+        "# Cell Data\n\nCell data refers to arrays of values (scalars, vectors, etc.) that live\nthroughout each cell of the mesh. That is the entire cell (2D face or 3D\nvolume) is assigned the value of that attribute.\n"
       ]
     },
     {
@@ -184,7 +184,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Field Data\n==========\n\nField data is not directly associated with either the points or cells\nbut still should be attached to the mesh. This may be a string array\nstoring notes.\n"
+        "# Field Data\n\nField data is not directly associated with either the points or cells\nbut still should be attached to the mesh. This may be a string array\nstoring notes.\n"
       ]
     },
     {
@@ -202,7 +202,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Assigning Scalars to a Mesh\n===========================\n\nHere\\'s how we assign values to cell attributes and plot it. Here, we\ngenerate cube containing 6 faces and assign each face an integer from\n`range(6)` and then have it plotted.\n\nNote how this varies from assigning scalars to each point\n"
+        "# Assigning Scalars to a Mesh\n\nHere\\'s how we assign values to cell attributes and plot it. Here, we\ngenerate cube containing 6 faces and assign each face an integer from\n`range(6)` and then have it plotted.\n\nNote how this varies from assigning scalars to each point\n"
       ]
     },
     {

_downloads/0688f908cba271a553ec73b76c7d993e/e_vtk_next.ipynb

@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "VTK\\'s Next Generation API\n==========================\n\nThis requires a pre-release version of VTK:\n\n``` {.bash}\npip install --extra-index-url https://wheels.vtk.org vtk==9.3.20240629.dev0\n```\n"
+        "# VTK\\'s Next Generation API\n\nThis requires a pre-release version of VTK:\n\n``` bash\npip install --extra-index-url https://wheels.vtk.org vtk==9.3.20240629.dev0\n```\n"
       ]
     },
     {

_downloads/167b8aac43549c9132bdf165de9e1e7c/c_create-uniform-grid.ipynb

@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Creating a Uniform Grid {#create_uniform_grid_exercise}\n=======================\n\nCreate a simple uniform grid from a 3D NumPy array of values.\n"
+        "# Creating a Uniform Grid {#create_uniform_grid_exercise}\n\nCreate a simple uniform grid from a 3D NumPy array of values.\n"
       ]
     },
     {
@@ -209,7 +209,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Exercise\n========\n\nNow create your own `pyvista.ImageData`{.interpreted-text role=\"class\"}\nfrom a 3D NumPy array!\n"
+        "# Exercise\n\nNow create your own `pyvista.ImageData`{.interpreted-text role=\"class\"}\nfrom a 3D NumPy array!\n"
       ]
     },
     {
@@ -245,7 +245,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Create the `pyvista.ImageData`{.interpreted-text role=\"class\"}.\n\n::: {.note}\n::: {.title}\nNote\n:::\n\nYou will likely need to `ravel` the array with Fortran-ordering:\n`arr.ravel(order=\"F\")`\n:::\n"
+        "Create the `pyvista.ImageData`{.interpreted-text role=\"class\"}.\n\n::: note\n::: title\nNote\n:::\n\nYou will likely need to `ravel` the array with Fortran-ordering:\n`arr.ravel(order=\"F\")`\n:::\n"
       ]
     },
     {
@@ -281,7 +281,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Example\n=======\n\nPyVista has several examples that use `ImageData`.\n\nSee the PyVista documentation for further details on [Volume\nRendering](https://docs.pyvista.org/examples/02-plot/volume.html)\n\nHere\\'s one of these example datasets:\n"
+        "# Example\n\nPyVista has several examples that use `ImageData`.\n\nSee the PyVista documentation for further details on [Volume\nRendering](https://docs.pyvista.org/examples/02-plot/volume.html)\n\nHere\\'s one of these example datasets:\n"
       ]
     },
     {

_downloads/1798dd69b37935a46283f09a1000af9f/a_display_options.ipynb

@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Display Options\n===============\n\nTake a look at the different display options offered by the `add_mesh`\nmethod.\n"
+        "# Display Options\n\nTake a look at the different display options offered by the `add_mesh`\nmethod.\n"
       ]
     },
     {

_downloads/1a4ac159ffc3cbadf04d90b7b0fe051f/g_orbit.ipynb

@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Orbiting {#orbiting_example}\n========\n\nOrbit around a scene.\n\n::: {.note}\n::: {.title}\nNote\n:::\n\nThe quality of the movie will be better when using\n`p.open_movie('orbit.mp4')` instead of `p.open_gif('orbit.gif')`\n:::\n\nFor orbiting to work you first have to show the scene and leave the\nplotter open with `.show(auto_close=False)`. You may also have to set\n`pv.Plotter(off_screen=True)`\n\n::: {.note}\n::: {.title}\nNote\n:::\n\nUse `lighting=False` to reduce the size of the color space to avoid\n\\\"jittery\\\" GIFs when showing the scalar bar.\n:::\n"
+        "# Orbiting {#orbiting_example}\n\nOrbit around a scene.\n\n::: note\n::: title\nNote\n:::\n\nThe quality of the movie will be better when using\n`p.open_movie('orbit.mp4')` instead of `p.open_gif('orbit.gif')`\n:::\n\nFor orbiting to work you first have to show the scene and leave the\nplotter open with `.show(auto_close=False)`. You may also have to set\n`pv.Plotter(off_screen=True)`\n\n::: note\n::: title\nNote\n:::\n\nUse `lighting=False` to reduce the size of the color space to avoid\n\\\"jittery\\\" GIFs when showing the scalar bar.\n:::\n"
       ]
     },
     {

_downloads/1cde720fa16205503d1bd3aa65933a9c/a_getting_started.ipynb

@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Getting started\n===============\n\nGetting started with PyVista and Trame\n"
+        "# Getting started\n\nGetting started with PyVista and Trame\n"
       ]
     },
     {

_downloads/25ea3d3ddf3b865b4ecd54d3386ebc2d/c_vtk_algorithms.ipynb

@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Using VTK Algorithms\n====================\n\nIn this exercise, you will use a VTK Algorithm directly to filter a\nPyVista mesh.\n\nVTK algorithms (filters) follow a standard flow for most cases:\n\n1.  Instantiate the algorithm\n2.  Set the input data object or connection: `.SetInputDataObject(mesh)`\n3.  Adjust algorithm parameters with the setter methods, e.g.,\n    `SetParameterName(value)`\n4.  Call `.Update()` to run the algorithm\n5.  Retrieve the output of the algorithm: `output = alg.GetOutput()`\n\nLet\\'s see if we can try a few VTK algorithms with that standard\nworkflow.\n"
+        "# Using VTK Algorithms\n\nIn this exercise, you will use a VTK Algorithm directly to filter a\nPyVista mesh.\n\nVTK algorithms (filters) follow a standard flow for most cases:\n\n1.  Instantiate the algorithm\n2.  Set the input data object or connection: `.SetInputDataObject(mesh)`\n3.  Adjust algorithm parameters with the setter methods, e.g.,\n    `SetParameterName(value)`\n4.  Call `.Update()` to run the algorithm\n5.  Retrieve the output of the algorithm: `output = alg.GetOutput()`\n\nLet\\'s see if we can try a few VTK algorithms with that standard\nworkflow.\n"
       ]
     },
     {
@@ -51,7 +51,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Simple Filter\n=============\n\nLet\\'s start out with a simple VTK filter: `vtkOutlineFilter`\n"
+        "# Simple Filter\n\nLet\\'s start out with a simple VTK filter: `vtkOutlineFilter`\n"
       ]
     },
     {
@@ -98,7 +98,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "::: {.note}\n::: {.title}\nNote\n:::\n\nNote that the about filter can be replaced with a `.outline()` filter in\nPyVista\n:::\n"
+        "::: note\n::: title\nNote\n:::\n\nNote that the about filter can be replaced with a `.outline()` filter in\nPyVista\n:::\n"
       ]
     },
     {
@@ -116,7 +116,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Find your own filter\n====================\n\nTake a look at VTK\\'s examples and documentation to find a filter you\\'d\nlike to apply to your mesh. The instructors will be around to help you\nimplement.\n\nSee <https://kitware.github.io/vtk-examples/site/Python/>\n"
+        "# Find your own filter\n\nTake a look at VTK\\'s examples and documentation to find a filter you\\'d\nlike to apply to your mesh. The instructors will be around to help you\nimplement.\n\nSee <https://kitware.github.io/vtk-examples/site/Python/>\n"
       ]
     },
     {