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                      "__type": "Text",
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                      "value": " kernel."
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              "name": "epsilon",
              "annotation": "float, optional",
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                  "__type": "Paragraph",
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                      "value": "Shape parameter that scales the input to the RBF. If "
                    },
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                      "name": "kernel"
                    },
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                      "__type": "Text",
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                      "value": " is 'linear', 'thin_plate_spline', 'cubic', or 'quintic', this defaults to 1 and can be ignored because it has the same effect as scaling the smoothing parameter. Otherwise, this must be specified."
                    }
                  ]
                }
              ]
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              "name": "degree",
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                  "__type": "Paragraph",
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                  "children": [
                    {
                      "__type": "Text",
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                      "value": "Degree of the added polynomial. For some RBFs the interpolant may not be well-posed if the polynomial degree is too small. Those RBFs and their corresponding minimum degrees are"
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                  ]
                },
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    "Parameters",
    "Attributes",
    "Methods",
    "Returns",
    "Yields",
    "Receives",
    "Other Parameters",
    "Raises",
    "Warns",
    "Warnings",
    "Notes"
  ],
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  "item_line": 66,
  "item_type": "class",
  "aliases": [
    "scipy.interpolate.RBFInterpolator"
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    "__tag": 4015,
    "children": [
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        "__type": "Text",
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        "value": "Demonstrate interpolating scattered data to a grid in 2-D.\n\n"
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        "__type": "Code",
        "__tag": 4050,
        "value": "import numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.interpolate import RBFInterpolator\nfrom scipy.stats.qmc import Halton\n",
        "execution_status": "success",
        "out": ""
      },
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        "__type": "Text",
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        "value": "\n"
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        "value": "rng = np.random.default_rng()\nxobs = 2*Halton(2, seed=rng).random(100) - 1\nyobs = np.sum(xobs, axis=1)*np.exp(-6*np.sum(xobs**2, axis=1))\n",
        "execution_status": "success",
        "out": ""
      },
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        "__type": "Text",
        "__tag": 4046,
        "value": "\n"
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        "__type": "Code",
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        "value": "x1 = np.linspace(-1, 1, 50)\nxgrid = np.asarray(np.meshgrid(x1, x1, indexing='ij'))\nxflat = xgrid.reshape(2, -1).T     # make it a 2-D array\nyflat = RBFInterpolator(xobs, yobs)(xflat)\nygrid = yflat.reshape(50, 50)\n",
        "execution_status": "success",
        "out": ""
      },
      {
        "__type": "Text",
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        "value": "\n"
      },
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        "value": "fig, ax = plt.subplots()\n",
        "execution_status": "success",
        "out": ""
      },
      {
        "__type": "Code",
        "__tag": 4050,
        "value": "ax.pcolormesh(*xgrid, ygrid, vmin=-0.25, vmax=0.25, shading='gouraud')\n",
        "execution_status": "failure",
        "out": "<matplotlib.collections.QuadMesh object at 0x7f3f60269d10>\n"
      },
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        "__type": "Code",
        "__tag": 4050,
        "value": "p = ax.scatter(*xobs.T, c=yobs, s=50, ec='k', vmin=-0.25, vmax=0.25)\n",
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        "out": ""
      },
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        "value": "fig.colorbar(p)\n",
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        "out": "<matplotlib.colorbar.Colorbar object at 0x7f3f60ccc2f0>\n"
      },
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        "value": "plt.show()\n",
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          "version": "2.0.0.dev0+git20260907.41eeb59",
          "kind": "assets",
          "path": "fig-20a073cb329c1451.png"
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    "target": null
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      "__type": "SeeAlsoItem",
      "__tag": 4028,
      "name": {
        "__type": "CrossRef",
        "__tag": 4002,
        "value": "CloughTocher2DInterpolator",
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          "path": "scipy.interpolate._interpnd:CloughTocher2DInterpolator"
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          "path": "scipy.interpolate._ndgriddata:NearestNDInterpolator"
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        "default": "thin_plate_spline"
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        "name": "epsilon",
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    "return_annotation": {
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    "target_name": "RBFInterpolator"
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  "references": [
    ".. [1] Fasshauer, G., 2007. Meshfree Approximation Methods with Matlab.",
    "    World Scientific Publishing Co.",
    "",
    ".. [2] http://amadeus.math.iit.edu/~fass/603_ch3.pdf",
    "",
    ".. [3] Wahba, G., 1990. Spline Models for Observational Data. SIAM.",
    "",
    ".. [4] http://pages.stat.wisc.edu/~wahba/stat860public/lect/lect8/lect8.pdf"
  ],
  "qa": "scipy.interpolate._rbfinterp:RBFInterpolator",
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}