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Editing: _ndgriddata.cpython-311.pyc
� d�c# � �r � d Z ddlZddlmZmZmZmZ ddlm Z g d�Z G d� de� � Z d ej d fd �Z dS ) zD Convenience interface to N-D interpolation .. versionadded:: 0.9 � N� )�LinearNDInterpolator�NDInterpolatorBase�CloughTocher2DInterpolator�_ndim_coords_from_arrays)�cKDTree)�griddata�NearestNDInterpolatorr r c � � e Zd ZdZdd�Zd� ZdS )r a NearestNDInterpolator(x, y). Nearest-neighbor interpolation in N > 1 dimensions. .. versionadded:: 0.9 Methods ------- __call__ Parameters ---------- x : (Npoints, Ndims) ndarray of floats Data point coordinates. y : (Npoints,) 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. .. versionadded:: 0.14.0 tree_options : dict, optional Options passed to the underlying ``cKDTree``. .. versionadded:: 0.17.0 Notes ----- Uses ``scipy.spatial.cKDTree`` Examples -------- We can interpolate values on a 2D plane: >>> from scipy.interpolate import NearestNDInterpolator >>> 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 = 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() See also -------- griddata : Interpolate unstructured D-D data. LinearNDInterpolator : Piecewise linear interpolant in N dimensions. CloughTocher2DInterpolator : Piecewise cubic, C1 smooth, curvature-minimizing interpolant in 2D. FNc � � t j | |||dd�� � |�t � � }t | j fi |��| _ t j |� � | _ d S )NF)�rescale�need_contiguous� need_values) r �__init__�dictr �points�tree�np�asarray�values)�self�x�yr � tree_optionss �?/usr/lib/python3/dist-packages/scipy/interpolate/_ndgriddata.pyr z NearestNDInterpolator.__init__W sg � ��#�D�!�Q��49�05� 7� 7� 7� 7� � � �6�6�L��D�K�8�8�<�8�8�� ��j��m�m�� � � � c �� � t || j j d �� � }| � |� � }| � |� � }| j � |� � \ }}| j | S )aV 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)`` r )�ndim)r r �shape�_check_call_shape�_scale_xr �queryr )r �args�xi�dist�is r �__call__z NearestNDInterpolator.__call__` sg � � &�d���1B�1�1E� F� F� F�� � #� #�B� '� '�� �]�]�2� � ���)�/�/�"�%�%���a��{�1�~� r )FN)�__name__� __module__� __qualname__�__doc__r r' � r r r r sD � � � � � �@� @�D$� $� $� $� � � � � r r �linearFc � � t | � � } | j dk r| j }n | j d }|dk r�|dv r�ddlm} | � � � } t |t � � r&t |� � dk rt d� � �|\ }t j | � � }| | } || }|dk rd} || ||d d |� � � } | |� � S |dk r t | ||� � � } | |� � S |d k r t | |||�� � } | |� � S |dk r$|dk r t | |||�� � } | |� � S t d||fz � � �)aL Interpolate unstructured D-D data. Parameters ---------- points : 2-D ndarray of floats with shape (n, D), or length D tuple of 1-D ndarrays with shape (n,). Data point coordinates. values : ndarray of float or complex, shape (n,) Data values. xi : 2-D ndarray of floats with shape (m, D), or length D tuple of ndarrays broadcastable to the same shape. Points at which to interpolate data. method : {'linear', 'nearest', 'cubic'}, optional Method of interpolation. One of ``nearest`` return the value at the data point closest to the point of interpolation. See `NearestNDInterpolator` for more details. ``linear`` tessellate the input point set to N-D simplices, and interpolate linearly on each simplex. See `LinearNDInterpolator` for more details. ``cubic`` (1-D) return the value determined from a cubic spline. ``cubic`` (2-D) return the value determined from a piecewise cubic, continuously differentiable (C1), and approximately curvature-minimizing polynomial surface. See `CloughTocher2DInterpolator` for more details. 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``. This option has no effect for the 'nearest' method. 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. .. versionadded:: 0.14.0 Returns ------- ndarray Array of interpolated values. Notes ----- .. versionadded:: 0.9 For data on a regular grid use `interpn` instead. Examples -------- Suppose we want to interpolate the 2-D function >>> import numpy as np >>> def func(x, y): ... return x*(1-x)*np.cos(4*np.pi*x) * np.sin(4*np.pi*y**2)**2 on a grid in [0, 1]x[0, 1] >>> grid_x, grid_y = np.mgrid[0:1:100j, 0:1:200j] but we only know its values at 1000 data points: >>> rng = np.random.default_rng() >>> points = rng.random((1000, 2)) >>> values = func(points[:,0], points[:,1]) This can be done with `griddata` -- below we try out all of the interpolation methods: >>> from scipy.interpolate import griddata >>> grid_z0 = griddata(points, values, (grid_x, grid_y), method='nearest') >>> grid_z1 = griddata(points, values, (grid_x, grid_y), method='linear') >>> grid_z2 = griddata(points, values, (grid_x, grid_y), method='cubic') One can see that the exact result is reproduced by all of the methods to some degree, but for this smooth function the piecewise cubic interpolant gives the best results: >>> import matplotlib.pyplot as plt >>> plt.subplot(221) >>> plt.imshow(func(grid_x, grid_y).T, extent=(0,1,0,1), origin='lower') >>> plt.plot(points[:,0], points[:,1], 'k.', ms=1) >>> plt.title('Original') >>> plt.subplot(222) >>> plt.imshow(grid_z0.T, extent=(0,1,0,1), origin='lower') >>> plt.title('Nearest') >>> plt.subplot(223) >>> plt.imshow(grid_z1.T, extent=(0,1,0,1), origin='lower') >>> plt.title('Linear') >>> plt.subplot(224) >>> plt.imshow(grid_z2.T, extent=(0,1,0,1), origin='lower') >>> plt.title('Cubic') >>> plt.gcf().set_size_inches(6, 6) >>> plt.show() See Also -------- LinearNDInterpolator : Piecewise linear interpolant in N dimensions. NearestNDInterpolator : Nearest-neighbor interpolation in N dimensions. 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