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    {
      "cell_type": "code",
      "execution_count": null,
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      "outputs": [],
      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Creating a Uniform Grid\n\nCreate a simple uniform grid from a 3D NumPy array of values. This\nexample uses `pyvista.ImageData`{.interpreted-text role=\"class\"}.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import numpy as np\n\nimport pyvista as pv"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Take a 3D NumPy array of data values that holds some spatial data where\neach axis corresponds to the XYZ cartesian axes. This example will\ncreate a `pyvista.ImageData`{.interpreted-text role=\"class\"} object that\nwill hold the spatial reference for a 3D grid which a 3D NumPy array of\nvalues can be plotted against.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Create the 3D NumPy array of spatially referenced data. This is\nspatially referenced such that the grid is 20 by 5 by 10 (nx by ny by\nnz)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "values = np.linspace(0, 10, 1000).reshape((20, 5, 10))\nvalues.shape\n\n# Create the spatial reference\ngrid = pv.ImageData()\n\n# Set the grid dimensions: shape + 1 because we want to inject our values on\n#   the CELL data\ngrid.dimensions = np.array(values.shape) + 1\n\n# Edit the spatial reference\ngrid.origin = (100, 33, 55.6)  # The bottom left corner of the data set\ngrid.spacing = (1, 5, 2)  # These are the cell sizes along each axis\n\n# Add the data values to the cell data\ngrid.cell_data[\"values\"] = values.flatten(order=\"F\")  # Flatten the array\n\n# Now plot the grid\ngrid.plot(show_edges=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Don\\'t like cell data? You could also add the NumPy array to the point\ndata of a `pyvista.ImageData`{.interpreted-text role=\"class\"}. Take note\nof the subtle difference when setting the grid dimensions upon\ninitialization.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "# Create the 3D NumPy array of spatially referenced data\n# This is spatially referenced such that the grid is 20 by 5 by 10\n#   (nx by ny by nz)\nvalues = np.linspace(0, 10, 1000).reshape((20, 5, 10))\nvalues.shape\n\n# Create the spatial reference\ngrid = pv.ImageData()\n\n# Set the grid dimensions: shape because we want to inject our values on the\n#   POINT data\ngrid.dimensions = values.shape\n\n# Edit the spatial reference\ngrid.origin = (100, 33, 55.6)  # The bottom left corner of the data set\ngrid.spacing = (1, 5, 2)  # These are the cell sizes along each axis\n\n# Add the data values to the cell data\ngrid.point_data[\"values\"] = values.flatten(order=\"F\")  # Flatten the array\n\n# Now plot the grid\ngrid.plot(show_edges=True)"
      ]
    }
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