{
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Resampling {#resampling_example}\n\nThere are two main methods of interpolating or sampling data from a\ntarget mesh in PyVista.\n`pyvista.DataSetFilters.interpolate`{.interpreted-text role=\"func\"} uses\na distance weighting kernel to interpolate point data from nearby points\nof the target mesh onto the desired points.\n`pyvista.DataSetFilters.sample`{.interpreted-text role=\"func\"}\ninterpolates data using the interpolation scheme of the enclosing cell\nfrom the target mesh.\n\nIf the target mesh is a point cloud, i.e. there is no connectivity in\nthe cell structure, then\n`pyvista.DataSetFilters.interpolate`{.interpreted-text role=\"func\"} is\ntypically preferred. If interpolation is desired within the cells of the\ntarget mesh, then `pyvista.DataSetFilters.sample`{.interpreted-text\nrole=\"func\"} is typically desired.\n\nThis example uses `pyvista.DataSetFilters.sample`{.interpreted-text\nrole=\"func\"}. For `pyvista.DataSetFilters.interpolate`{.interpreted-text\nrole=\"func\"}, see `interpolate_example`{.interpreted-text role=\"ref\"}.\n\nResample one mesh\\'s point/cell arrays onto another mesh\\'s nodes.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "This example will resample a volumetric mesh\\'s scalar data onto the\nsurface of a sphere contained in that volume.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import pyvista as pv\nfrom pyvista import examples"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Simple Resample\n\nQuery a grid\\'s points onto a sphere\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "mesh = pv.Sphere(center=(4.5, 4.5, 4.5), radius=4.5)\ndata_to_probe = examples.load_uniform()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Plot the two datasets\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "p = pv.Plotter()\np.add_mesh(mesh, color=True)\np.add_mesh(data_to_probe, opacity=0.5)\np.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Run the algorithm and plot the result\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "result = mesh.sample(data_to_probe)\n\n# Plot result\nname = \"Spatial Point Data\"\nresult.plot(scalars=name, clim=data_to_probe.get_data_range(name))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Complex Resample\n\nTake a volume of data and create a grid of lower resolution to resample\non\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "data_to_probe = examples.download_embryo()\nmesh = pv.create_grid(data_to_probe, dimensions=(75, 75, 75))\n\nresult = mesh.sample(data_to_probe)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "threshold = lambda m: m.threshold(75.0, scalars='SLCImage')\ncpos = [\n    (468.9075585873713, -152.8280322856109, 152.13046602188035),\n    (121.65121514580106, 140.29327609542105, 112.28137570357188),\n    (-0.10881224951051659, 0.006229357618166009, 0.9940428006178236),\n]\ndargs = dict(clim=[0, 200], cmap='rainbow')\n\np = pv.Plotter(shape=(1, 2))\np.add_mesh(threshold(data_to_probe), **dargs)\np.subplot(0, 1)\np.add_mesh(threshold(result), **dargs)\np.link_views()\np.view_isometric()\np.show(cpos=cpos)"
      ]
    }
  ],
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