{
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    {
      "cell_type": "code",
      "execution_count": null,
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      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Warping by Vectors {#warp_by_vectors_example}\n\nThis example applies the `warp_by_vector` filter to a sphere mesh that\nhas 3D displacement vectors defined at each node.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We first compare the unwarped sphere to the warped sphere.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
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      "source": [
        "from itertools import product\n\nimport pyvista as pv\nfrom pyvista import examples\n\nsphere = examples.load_sphere_vectors()\nwarped = sphere.warp_by_vector()\n\np = pv.Plotter(shape=(1, 2))\np.subplot(0, 0)\np.add_text(\"Before warp\")\np.add_mesh(sphere, color='white')\np.subplot(0, 1)\np.add_text(\"After warp\")\np.add_mesh(warped, color='white')\np.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We then use several values for the scale factor applied to the warp\noperation. Applying a warping factor that is too high can often lead to\nunrealistic results.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "warp_factors = [0, 1.5, 3.5, 5.5]\np = pv.Plotter(shape=(2, 2))\nfor i, j in product(range(2), repeat=2):\n    idx = 2 * i + j\n    p.subplot(i, j)\n    p.add_mesh(sphere.warp_by_vector(factor=warp_factors[idx]))\n    p.add_text(f'factor={warp_factors[idx]}')\np.show()"
      ]
    }
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