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Linear, nearest-neighbor, spline interpolations are supported. After setting up the interpolator object, the interpolation method may be chosen at each evaluation." } ] } ], "title": [], "level": 0, "target": null }, "Other Parameters": { "__type": "Section", "__tag": 4015, "children": [], "title": [], "level": 0, "target": null } }, "_ordered_sections": [ "Summary", "Extended Summary", "Parameters", "Attributes", "Methods", "Returns", "Yields", "Receives", "Other Parameters", "Raises", "Warns", "Warnings", "Notes" ], "item_file": "/scipy/interpolate/_rgi.py", "item_line": 59, "item_type": "class", "aliases": [ "scipy.interpolate.RegularGridInterpolator" ], "example_section_data": { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Text", "__tag": 4046, "value": "**Evaluate a function on the points of a 3-D grid**\n\nAs a first example, we evaluate a simple example function on the points of\na 3-D grid:\n\n" }, { "__type": "Code", "__tag": 4050, "value": "from scipy.interpolate import RegularGridInterpolator\nimport numpy as np\ndef f(x, y, z):\n return 2 * x**3 + 3 * y**2 - z\nx = np.linspace(1, 4, 11)\ny = np.linspace(4, 7, 22)\nz = np.linspace(7, 9, 33)\nxg, yg ,zg = np.meshgrid(x, y, z, indexing='ij', sparse=True)\ndata = f(xg, yg, zg)\n", "execution_status": "success" }, { "__type": "Text", "__tag": 4046, "value": "\n``data`` is now a 3-D array with ``data[i, j, k] = f(x[i], y[j], z[k])``.\nNext, define an interpolating function from this data:\n\n" }, { "__type": "Code", "__tag": 4050, "value": "interp = RegularGridInterpolator((x, y, z), data)\n", "execution_status": "success" }, { "__type": "Text", "__tag": 4046, "value": "\nEvaluate the interpolating function at the two points\n``(x,y,z) = (2.1, 6.2, 8.3)`` and ``(3.3, 5.2, 7.1)``:\n\n" }, { "__type": "Code", "__tag": 4050, "value": "pts = np.array([[2.1, 6.2, 8.3],\n [3.3, 5.2, 7.1]])\n", "execution_status": "success" }, { "__type": "Code", "__tag": 4050, "value": "interp(pts)\n", "execution_status": "failure" }, { "__type": "Text", "__tag": 4046, "value": "\nwhich is indeed a close approximation to\n\n" }, { "__type": "Code", "__tag": 4050, "value": "f(2.1, 6.2, 8.3), f(3.3, 5.2, 7.1)\n", "execution_status": "success" }, { "__type": "Text", "__tag": 4046, "value": "\n**Interpolate and extrapolate a 2D dataset**\n\nAs a second example, we interpolate and extrapolate a 2D data set:\n\n" }, { "__type": "Code", "__tag": 4050, "value": "x, y = np.array([-2, 0, 4]), np.array([-2, 0, 2, 5])\ndef ff(x, y):\n return x**2 + y**2\n", "execution_status": "success" }, { "__type": "Text", "__tag": 4046, "value": "\n" }, { "__type": "Code", "__tag": 4050, "value": "xg, yg = np.meshgrid(x, y, indexing='ij')\ndata = ff(xg, yg)\ninterp = RegularGridInterpolator((x, y), data,\n bounds_error=False, fill_value=None)\n", "execution_status": "success" }, { "__type": "Text", "__tag": 4046, "value": "\n" }, { "__type": "Code", "__tag": 4050, "value": "import matplotlib.pyplot as plt\nfig = plt.figure()\nax = fig.add_subplot(projection='3d')\n", "execution_status": "success" }, { "__type": "Code", "__tag": 4050, "value": "ax.scatter(xg.ravel(), yg.ravel(), data.ravel(),\n s=60, c='k', label='data')\n", "execution_status": "failure" }, { "__type": "Text", "__tag": 4046, "value": "\nEvaluate and plot the interpolator on a finer grid\n\n" }, { "__type": "Code", "__tag": 4050, "value": "xx = np.linspace(-4, 9, 31)\nyy = np.linspace(-4, 9, 31)\nX, Y = np.meshgrid(xx, yy, indexing='ij')\n", "execution_status": "success" }, { "__type": "Text", "__tag": 4046, "value": "\n" }, { "__type": "Code", "__tag": 4050, "value": "ax.plot_wireframe(X, Y, interp((X, Y)), rstride=3, cstride=3,\n alpha=0.4, color='m', label='linear interp')\n", "execution_status": "failure" }, { "__type": "Text", "__tag": 4046, "value": "\n" }, { "__type": "Code", "__tag": 4050, "value": "ax.plot_wireframe(X, Y, ff(X, Y), rstride=3, cstride=3,\n alpha=0.4, label='ground truth')\nplt.legend()\n", "execution_status": "failure" }, { "__type": "Code", "__tag": 4050, "value": "plt.show()\n", "execution_status": "success" }, { "__type": "Figure", "__tag": 4024, "value": { "__type": "RefInfo", "__tag": 4000, "module": "scipy", "version": "1.17.1", "kind": "assets", "path": "fig-a84658cee1927fca.png" } }, { "__type": "Text", "__tag": 4046, "value": "\nOther examples are given\n:ref:`in the tutorial `." } ], "title": [], "level": 0, "target": null }, "see_also": [ { "__type": "SeeAlsoItem", "__tag": 4028, "name": { "__type": "CrossRef", "__tag": 4002, "value": "LinearNDInterpolator", "reference": { "__type": "LocalRef", "__tag": 4022, "kind": "module", "path": "scipy.interpolate._interpnd:LinearNDInterpolator" }, "kind": "module" }, "descriptions": [ { "__type": "Paragraph", "__tag": 4045, "children": [ { "__type": "Text", "__tag": 4046, "value": "Piecewise linear interpolator on " }, { "__type": "Emphasis", "__tag": 4047, "children": [ { "__type": "Text", "__tag": 4046, "value": "unstructured" } ] }, { "__type": "Text", "__tag": 4046, "value": " data 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[1] Python package *regulargrid* by Johannes Buchner, see", " https://pypi.python.org/pypi/regulargrid/", ".. [2] Wikipedia, \"Trilinear interpolation\",", " https://en.wikipedia.org/wiki/Trilinear_interpolation", ".. [3] Weiser, Alan, and Sergio E. Zarantonello. \"A note on piecewise linear", " and multilinear table interpolation in many dimensions.\" MATH.", " COMPUT. 50.181 (1988): 189-196.", " https://www.ams.org/journals/mcom/1988-50-181/S0025-5718-1988-0917826-0/S0025-5718-1988-0917826-0.pdf", " :doi:`10.1090/S0025-5718-1988-0917826-0`" ], "qa": "scipy.interpolate._rgi:RegularGridInterpolator", "arbitrary": [], "local_refs": [ "__call__", "bounds_error", "fill_value", "grid", "method", "optional", "points", "solver", "solver_args: dict", "values" ] }