o X۷i2@sHdZddlmZddlZddlmZmZddlmZGdddeZ dS)z, Convenience interface to N-D interpolation ) annotationsN)NDInterpolatorBase_ndim_coords_from_arrays)KDTreec@s"eZdZdZdddZddZdS) NearestNDInterpolatoraNearestNDInterpolator(x, y). Nearest-neighbor interpolator in N > 1 dimensions. Parameters ---------- x : (npoints, ndims) 2-D ndarray of floats Data point coordinates. y : (npoints, ) 1-D ndarray of float or complex Data values. rescale : boolean, 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. tree_options : dict, optional Options passed to the underlying ``cKDTree``. See Also -------- griddata : Interpolate unstructured D-D data. LinearNDInterpolator : Piecewise linear 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). Notes ----- Uses ``cupyx.scipy.spatial.KDTree`` .. note:: For data on a regular grid use `interpn` instead. Examples -------- We can interpolate values on a 2D plane: >>> from scipy.interpolate import NearestNDInterpolator >>> import numpy as np >>> import matplotlib.pyplot as plt >>> rng = cupy.random.default_rng() >>> x = rng.random(10) - 0.5 >>> y = rng.random(10) - 0.5 >>> z = cupy.hypot(x, y) >>> X = cupy.linspace(min(x), max(x)) >>> Y = cupy.linspace(min(y), max(y)) >>> X, Y = cupy.meshgrid(X, Y) # 2D grid for interpolation >>> interp = NearestNDInterpolator(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() FNcCsHtj||||ddd|durt}t|jfi||_t||_dS)NF)rescaleneed_contiguous need_values) r__init__dictrpointstreecupyasarrayvalues)selfxyr tree_optionsrY/home/ubuntu/vllm_env/lib/python3.10/site-packages/cupyx/scipy/interpolate/_ndgriddata.pyr Ps zNearestNDInterpolator.__init__c Os0t||jjdd}||}||}|d|jd}|j}|j}|jj|fi|\}}t |} |j j dkrI|dd|j jdd} n|dd} t |j j tjrdtj| tj|j j d} nt| tj} |j || df| | <|j j dkr|dd|j jdd} n|dd} | | } | S)a 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)`` **query_options This allows ``eps``, ``p`` and ``distance_upper_bound`` being passed to the KDTree's query function to be explicitly set. See `cupyx.scipy.spatial.KDTree.query` for an overview of the different options. .. versionadded:: 1.12.0 )ndimN)dtype.)rr shape_check_call_shape_scale_xreshaper queryrisfiniterr issubdtypercomplexfloatingfullnan) rargs query_optionsxixi_flatoriginal_shapeflattened_shapedisti valid_mask interp_shape interp_values new_shaperrr__call__Ys,          zNearestNDInterpolator.__call__)FN)__name__ __module__ __qualname____doc__r r1rrrrrs = r) r5 __future__rr!cupyx.scipy.interpolate._interpndrrcupyx.scipy.spatialrrrrrrs