# encoding: utf-8 # module scipy.interpolate._interpnd # from C:\Programs\Python\Python313\Lib\site-packages\scipy\interpolate\_interpnd.cp313-win_amd64.pyd # by generator 1.147 """ Simple N-D interpolation .. versionadded:: 0.9 """ # imports import builtins as __builtins__ # import numpy as np # C:\Programs\Python\Python313\Lib\site-packages\numpy\__init__.py import scipy.spatial._qhull as qhull # C:\Programs\Python\Python313\Lib\site-packages\scipy\spatial\_qhull.cp313-win_amd64.pyd import warnings as warnings # C:\Programs\Python\Python313\Lib\warnings.py # functions def estimate_gradients_2d_global(*args, **kwargs): # real signature unknown pass def _ndim_coords_from_arrays(*args, **kwargs): # real signature unknown """ Convert a tuple of coordinate arrays to a (..., ndim)-shaped array. """ pass def __pyx_unpickle_Enum(*args, **kwargs): # real signature unknown pass # classes class NDInterpolatorBase(object): """ Common routines for interpolators. .. versionadded:: 0.9 """ def _calculate_triangulation(self, *args, **kwargs): # real signature unknown pass def _check_call_shape(self, *args, **kwargs): # real signature unknown pass def _preprocess_xi(self, *args, **kwargs): # real signature unknown pass def _scale_x(self, *args, **kwargs): # real signature unknown pass def _set_values(self, *args, **kwargs): # real signature unknown pass def __call__(self, *args, **kwargs): # real signature unknown """ interpolator(xi) Evaluate interpolator at given points. Parameters ---------- x1, x2, ... xn: array-like of float Points where to interpolate data at. x1, x2, ... xn can be array-like of float with broadcastable shape. or x1 can be array-like of float with shape ``(..., ndim)`` """ pass def __init__(self, *args, **kwargs): # real signature unknown """ Check shape of points and values arrays, and reshape values to (npoints, nvalues). Ensure the `points` and values arrays are C-contiguous, and of correct type. """ pass __weakref__ = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """list of weak references to the object""" __dict__ = None # (!) real value is "mappingproxy({'__module__': 'scipy.interpolate._interpnd', '__doc__': '\\n Common routines for interpolators.\\n\\n .. versionadded:: 0.9\\n\\n ', '__init__': , '_calculate_triangulation': , '_set_values': , '_check_call_shape': , '_scale_x': , '_preprocess_xi': , '__call__': , '__dict__': , '__weakref__': })" class CloughTocher2DInterpolator(NDInterpolatorBase): """ CloughTocher2DInterpolator(points, values, tol=1e-6). Piecewise cubic, C1 smooth, curvature-minimizing interpolator in 2D. .. versionadded:: 0.9 Methods ------- __call__ Parameters ---------- points : ndarray of floats, shape (npoints, ndims); or Delaunay 2-D array of data point coordinates, or a precomputed Delaunay triangulation. values : ndarray of float or complex, shape (npoints, ...) N-D array of data values at `points`. The length of `values` along the first axis must be equal to the length of `points`. Unlike some interpolators, the interpolation axis cannot be changed. fill_value : float, optional Value used to fill in for requested points outside of the convex hull of the input points. If not provided, then the default is ``nan``. tol : float, optional Absolute/relative tolerance for gradient estimation. maxiter : int, optional Maximum number of iterations in gradient estimation. rescale : bool, optional 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. Notes ----- The interpolant is constructed by triangulating the input data with Qhull [1]_, and constructing a piecewise cubic interpolating Bezier polynomial on each triangle, using a Clough-Tocher scheme [CT]_. The interpolant is guaranteed to be continuously differentiable. The gradients of the interpolant are chosen so that the curvature of the interpolating surface is approximatively minimized. The gradients necessary for this are estimated using the global algorithm described in [Nielson83]_ and [Renka84]_. .. note:: For data on a regular grid use `interpn` instead. Examples -------- We can interpolate values on a 2D plane: >>> from scipy.interpolate import CloughTocher2DInterpolator >>> import numpy as np >>> import matplotlib.pyplot as plt >>> rng = np.random.default_rng() >>> x = rng.random(10) - 0.5 >>> y = rng.random(10) - 0.5 >>> z = np.hypot(x, y) >>> X = np.linspace(min(x), max(x)) >>> Y = np.linspace(min(y), max(y)) >>> X, Y = np.meshgrid(X, Y) # 2D grid for interpolation >>> interp = CloughTocher2DInterpolator(list(zip(x, y)), z) >>> Z = interp(X, Y) >>> plt.pcolormesh(X, Y, Z, shading='auto') >>> plt.plot(x, y, "ok", label="input point") >>> plt.legend() >>> plt.colorbar() >>> plt.axis("equal") >>> plt.show() See also -------- griddata : Interpolate unstructured D-D data. LinearNDInterpolator : Piecewise linear interpolator in N > 1 dimensions. NearestNDInterpolator : Nearest-neighbor interpolator in N > 1 dimensions. interpn : Interpolation on a regular grid or rectilinear grid. RegularGridInterpolator : Interpolator on a regular or rectilinear grid in arbitrary dimensions (`interpn` wraps this class). References ---------- .. [1] http://www.qhull.org/ .. [CT] See, for example, P. Alfeld, ''A trivariate Clough-Tocher scheme for tetrahedral data''. Computer Aided Geometric Design, 1, 169 (1984); G. Farin, ''Triangular Bernstein-Bezier patches''. Computer Aided Geometric Design, 3, 83 (1986). .. [Nielson83] G. Nielson, ''A method for interpolating scattered data based upon a minimum norm network''. Math. Comp., 40, 253 (1983). .. [Renka84] R. J. Renka and A. K. Cline. ''A Triangle-based C1 interpolation method.'', Rocky Mountain J. Math., 14, 223 (1984). """ def _calculate_triangulation(self, *args, **kwargs): # real signature unknown pass def _do_evaluate(self, *args, **kwargs): # real signature unknown pass def _evaluate_complex(self, *args, **kwargs): # real signature unknown pass def _evaluate_double(self, *args, **kwargs): # real signature unknown pass def _set_values(self, *args, **kwargs): # real signature unknown """ Sets the values of the interpolation points. Parameters ---------- values : ndarray of float or complex, shape (npoints, ...) Data values. """ pass def __init__(self, *args, **kwargs): # real signature unknown pass class GradientEstimationWarning(Warning): # no doc def __init__(self, *args, **kwargs): # real signature unknown pass __weakref__ = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """list of weak references to the object""" class LinearNDInterpolator(NDInterpolatorBase): """ LinearNDInterpolator(points, values, fill_value=np.nan, rescale=False) Piecewise linear interpolator in N > 1 dimensions. .. versionadded:: 0.9 Methods ------- __call__ Parameters ---------- points : ndarray of floats, shape (npoints, ndims); or Delaunay 2-D array of data point coordinates, or a precomputed Delaunay triangulation. values : ndarray of float or complex, shape (npoints, ...), optional N-D array of data values at `points`. The length of `values` along the first axis must be equal to the length of `points`. Unlike some interpolators, the interpolation axis cannot be changed. fill_value : float, optional Value used to fill in for requested points outside of the convex hull of the input points. If not provided, then the default is ``nan``. rescale : bool, optional 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. Notes ----- The interpolant is constructed by triangulating the input data with Qhull [1]_, and on each triangle performing linear barycentric interpolation. .. note:: For data on a regular grid use `interpn` instead. Examples -------- We can interpolate values on a 2D plane: >>> from scipy.interpolate import LinearNDInterpolator >>> import numpy as np >>> import matplotlib.pyplot as plt >>> rng = np.random.default_rng() >>> x = rng.random(10) - 0.5 >>> y = rng.random(10) - 0.5 >>> z = np.hypot(x, y) >>> X = np.linspace(min(x), max(x)) >>> Y = np.linspace(min(y), max(y)) >>> X, Y = np.meshgrid(X, Y) # 2D grid for interpolation >>> interp = LinearNDInterpolator(list(zip(x, y)), z) >>> Z = interp(X, Y) >>> plt.pcolormesh(X, Y, Z, shading='auto') >>> plt.plot(x, y, "ok", label="input point") >>> plt.legend() >>> plt.colorbar() >>> plt.axis("equal") >>> plt.show() See also -------- griddata : Interpolate unstructured D-D data. NearestNDInterpolator : Nearest-neighbor interpolator in N dimensions. CloughTocher2DInterpolator : Piecewise cubic, C1 smooth, curvature-minimizing interpolator in 2D. interpn : Interpolation on a regular grid or rectilinear grid. RegularGridInterpolator : Interpolator on a regular or rectilinear grid in arbitrary dimensions (`interpn` wraps this class). References ---------- .. [1] http://www.qhull.org/ """ def _calculate_triangulation(self, *args, **kwargs): # real signature unknown pass def _do_evaluate(self, *args, **kwargs): # real signature unknown pass def _evaluate_complex(self, *args, **kwargs): # real signature unknown pass def _evaluate_double(self, *args, **kwargs): # real signature unknown pass def __init__(self, *args, **kwargs): # real signature unknown pass # variables with complex values __loader__ = None # (!) real value is '<_frozen_importlib_external.ExtensionFileLoader object at 0x000002C47A1EB590>' __spec__ = None # (!) real value is "ModuleSpec(name='scipy.interpolate._interpnd', loader=<_frozen_importlib_external.ExtensionFileLoader object at 0x000002C47A1EB590>, origin='C:\\\\Programs\\\\Python\\\\Python313\\\\Lib\\\\site-packages\\\\scipy\\\\interpolate\\\\_interpnd.cp313-win_amd64.pyd')" __test__ = {}