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              "value": "The interpolant is constructed by triangulating the input data with Qhull "
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              "label": "1"
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            {
              "__type": "Text",
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              "value": ", and on each triangle performing linear barycentric interpolation."
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                  "value": "note For data on a regular grid use `interpn` instead."
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              "value": "Piecewise linear interpolator in N > 1 dimensions."
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                      "value": "2-D array of data point coordinates, or a precomputed Delaunay triangulation."
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                      "value": "N-D array of data values at "
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                      "value": ".  The length of "
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                      "value": ". Unlike some interpolators, the interpolation axis cannot be changed."
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                      "value": "Rescale points to unit cube before performing interpolation. This is useful if some of the input dimensions have incommensurable units and differ by many orders of magnitude."
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        "value": "from scipy.interpolate import LinearNDInterpolator\nimport numpy as np\nimport matplotlib.pyplot as plt\nrng = np.random.default_rng()\nx = rng.random(10) - 0.5\ny = rng.random(10) - 0.5\nz = np.hypot(x, y)\nX = np.linspace(min(x), max(x))\nY = np.linspace(min(y), max(y))\nX, Y = np.meshgrid(X, Y)  # 2D grid for interpolation\ninterp = LinearNDInterpolator(list(zip(x, y)), z)\nZ = interp(X, Y)\n",
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        "value": "plt.pcolormesh(X, Y, Z, shading='auto')\nplt.plot(x, y, \"ok\", label=\"input point\")\nplt.legend()\nplt.colorbar()\nplt.axis(\"equal\")\n",
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              "value": "interpn",
              "domain": null,
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              "value": " wraps this class)."
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