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AD$`Ml$LxJ?Lt I\$|$@ HTMAD$hHN<@(H|$@APH A/ :At$`A$HD$@t$ f/<$#At$hf/t$8 f(f($\\$\$xd$0cfcd$0\$x$Y$f(D$ff.zt<$f/|$\\f(f(c$$Yf/D$0YHD$@A/AD$ f(l$x\\$@$\@cf7c\$@l$x$f(Yff.zt<$f/\d$\T$@f(f(b$$Yf/\D$0L$8T$@\T$XT$ Xf(fAD$`HD$PHL$XMHT$pHt$hL@0H|$`ID$pHID$p<H@HI$MT$MD$IC@AyfAD$`MnHJ f(JD5M|HHHMlHIfA'3)|$ IB,3A\/Hf(f(d$0\\l$t$\$afat$d$0\$l$$\\f(f(Ua$f(|$ YYffXA|$`M9KyfAD$`MtHD$J f(HHHLILtHLlHff.A&3)|$ IB,;A\.Hf(f(d$0\\l$t$\$`fw`t$d$0\$l$$\\f(f(E`$f(|$ YYffXA|$`M9KwfDff.QfM|$ f6HD$0gLH$LD$hLT$Pl$`d$X)$$ALD$hLT$P5=l$`d$Xqf(LT$p^LD$hL\$`d$Xl$P]LT$pLD$h5BL\$`d$X=7l$Pff/ f.-fIt$IL$H@aL95I$H6H2Ml$I\$HHIt$pMt$0HIT-H @ AD$hLAWAVAUATUSHHo H_0H|$ Lw8HD-Ht$8HD$0Hf/4f(f(f(#HD$ fH$HDŽ$H$)$M HL9"LOAfHnJ,0HflH$)$I&"Ht$L@HD$fH$L$HDŽ$)$H) HH9"H@fHnH,HflH$)$H!Ht$H@fH$HDŽ$`)$Pf@fffH$PHL$H$`H$XXX @0H@HP@`Hp 0@P`pL;$D$@H$hHDŽ$0H$HDŽ$8Y$(f($fo)$@ML$fLhHfJ,L$1f(~ I/7f.YYHHfXfX)$0I9CEDAH]LLmH]H[]A\A]A^A_LLHHuL)LÐHuL)I|H==f.fHHShared Cython type %.200s is not a type objectShared Cython type %.200s has the wrong size, try recompiling__int__ returned non-int (type %.200s). The ability to return an instance of a strict subclass of int is deprecated, and may be removed in a future version of Python.__int__ returned non-int (type %.200s)Interpreter change detected - this module can only be loaded into one interpreter per process.base class '%.200s' is not a heap typeextension type '%.200s' has no __dict__ slot, but base type '%.200s' has: either add 'cdef dict __dict__' to the extension type or add '__slots__ = [...]' to the base typevalue too large to convert to intunbound method %.200S() needs an argumenttoo many values to unpack (expected %zd)__annotations__ must be set to a dict object__kwdefaults__ must be set to a dict objectchanges to cyfunction.__kwdefaults__ will not currently affect the values used in function calls__defaults__ must be set to a tuple objectchanges to cyfunction.__defaults__ will not currently affect the values used in function calls__qualname__ must be set to a string object__name__ must be set to a string objectFailed to import '%.20s.decompress' - cannot initialise module strings. String compression was configured with the C macro 'CYTHON_COMPRESS_STRINGS=%d'.%.200s.%.200s is not a type object%.200s.%.200s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObject%.200s does not export expected C %.8s %.200sC %.8s %.200s.%.200s has wrong signature (expected %.500s, got %.500s)Acquisition count is %d (line %d)%.200s() takes %.8s %zd positional argument%.1s (%zd given)invalid vtable found for imported typemultiple bases have vtable conflict: '%.200s' and '%.200s'Cannot convert %.200s to %.200s while calling a Python objectNULL result without error in PyObject_Callscipy.spatial._ckdtree.coo_entries.__init__scipy.spatial._ckdtree.ordered_pairs.__init__scipy.spatial._ckdtree.ordered_pairs.set'%.200s' object is unsliceablescipy.spatial._ckdtree.cKDTreeNode.indices.__get__scipy.spatial._ckdtree.cKDTree._post_initscipy.spatial._ckdtree.cKDTree._post_init_traversescipy.spatial._ckdtree.cKDTree.size.__get__scipy.spatial._ckdtree.cKDTree.leafsize.__get__scipy.spatial._ckdtree.cKDTree.m.__get__scipy.spatial._ckdtree.cKDTree.n.__get__scipy.spatial._ckdtree.cKDTreeNode.split.__get__scipy.spatial._ckdtree.cKDTreeNode.end_idx.__get__scipy.spatial._ckdtree.cKDTreeNode.start_idx.__get__scipy.spatial._ckdtree.cKDTreeNode.children.__get__scipy.spatial._ckdtree.cKDTreeNode.split_dim.__get__scipy.spatial._ckdtree.cKDTreeNode.level.__get__scipy.spatial._ckdtree.coo_entries.dictscipy.spatial._ckdtree.cKDTree.__setstate_cython__scipy.spatial._ckdtree.cKDTree.__reduce_cython__scipy.spatial._ckdtree.ordered_pairs.__setstate_cython__scipy.spatial._ckdtree.ordered_pairs.__reduce_cython__scipy.spatial._ckdtree.coo_entries.__setstate_cython__scipy.spatial._ckdtree.coo_entries.__reduce_cython__scipy.spatial._ckdtree.coo_entries.dok_matrixcannot fit '%.200s' into an index-sized integerscipy.spatial._ckdtree.cKDTree.tree.__get__free variable '%s' referenced before assignment in enclosing scopescipy.spatial._ckdtree.cKDTree.query._thread_funcscipy.spatial._ckdtree.cKDTreeNode._setupscipy.spatial._ckdtree.num_pointsOut of bounds on buffer access (axis %d)scipy.spatial._ckdtree.cKDTree._build_weightsscipy.spatial._ckdtree._run_threadsneed more than %zd value%.1s to unpackscipy.spatial._ckdtree.cKDTreeNode.__reduce_cython__scipy.spatial._ckdtree.__pyx_unpickle_cKDTreeNode__set_statecannot pass None into a C function argument that is declared 'not None'scipy.spatial._ckdtree.cKDTreeNode.__setstate_cython__scipy.spatial._ckdtree.broadcast_contiguousscipy.spatial._ckdtree.coo_entries.ndarrayscipy.spatial._ckdtree.cKDTree.sparse_distance_matrixscipy.spatial._ckdtree.cKDTree.query_pairsscipy.spatial._ckdtree.__pyx_unpickle_cKDTreeNodeIncompatible checksums (0x%x vs (0x%x, 0x%x, 0x%x) = (%s))scipy.spatial._ckdtree.coo_entries.coo_matrixCannot create cython.array from NULL pointerscipy.spatial._ckdtree.cKDTree.__getstate__'%.200s' object is not subscriptablescipy.spatial._ckdtree.cKDTree.count_neighborsscipy.spatial._ckdtree.cKDTreeNode.data_points.__get__scipy.spatial._ckdtree.get_num_workersscipy.spatial._ckdtree.cKDTree.query_ball_pointscipy.spatial._ckdtree.cKDTree.query'%.200s' object does not support slice %.10sscipy.spatial._ckdtree.cKDTree.__init__scipy.spatial._ckdtree.ordered_pairs.ndarrayUnable to initialize pickling for %.200sModule '_ckdtree' has already been imported. Re-initialisation is not supported.compile time Python version %d.%d of module '%.100s' %s runtime version %d.%d_ARRAY_API is not PyCapsule objectmodule compiled against ABI version 0x%x but this version of numpy is 0x%xmodule was compiled against NumPy C-API version 0x%x (NumPy 1.23) but the running NumPy has C-API version 0x%x. Check the section C-API incompatibility at the Troubleshooting ImportError section at https://numpy.org/devdocs/user/troubleshooting-importerror.html#c-api-incompatibility for indications on how to solve this problem.FATAL: module compiled as unknown endianFATAL: module compiled as little endian, but detected different endianness at runtime../../tmp/build-env-vm5wde73/lib/python3.11/site-packages/numpy/__init__.cython-30.pxdscipy.spatial._ckdtree.cKDTree.query_ball_tree'NoneType' object is not subscriptablescipy.spatial._ckdtree.cKDTree.query_ball_point._thread_funcscipy.spatial._ckdtree.cKDTree.__setstate___cython_3_2_4.cython_function_or_method_cython_3_2_4._common_types_metatypescipy.spatial._ckdtree.__pyx_scope_struct_1_query_ball_pointscipy.spatial._ckdtree.__pyx_scope_struct__queryscipy.spatial._ckdtree.cKDTree cKDTree(data, leafsize=16, compact_nodes=True, copy_data=False, balanced_tree=True, boxsize=None) kd-tree for quick nearest-neighbor lookup This class provides an index into a set of k-dimensional points which can be used to rapidly look up the nearest neighbors of any point. .. note:: `cKDTree` is functionally identical to `KDTree`. Prior to SciPy v1.6.0, `cKDTree` had better performance and slightly different functionality but now the two names exist only for backward-compatibility reasons. If compatibility with SciPy < 1.6 is not a concern, prefer `KDTree`. Parameters ---------- data : array_like, shape (n,m) The n data points of dimension m to be indexed. This array is not copied unless this is necessary to produce a contiguous array of doubles, and so modifying this data will result in bogus results. The data are also copied if the kd-tree is built with copy_data=True. leafsize : positive int, optional The number of points at which the algorithm switches over to brute-force. Default: 16. compact_nodes : bool, optional If True, the kd-tree is built to shrink the hyperrectangles to the actual data range. This usually gives a more compact tree that is robust against degenerated input data and gives faster queries at the expense of longer build time. Default: True. copy_data : bool, optional If True the data is always copied to protect the kd-tree against data corruption. Default: False. balanced_tree : bool, optional If True, the median is used to split the hyperrectangles instead of the midpoint. This usually gives a more compact tree and faster queries at the expense of longer build time. Default: True. boxsize : array_like or scalar, optional Apply an m-d toroidal topology to the KDTree. The topology is generated by :math:`x_i + n_i L_i` where :math:`n_i` are integers and :math:`L_i` is the boxsize along i-th dimension. The input data shall be wrapped into :math:`[0, L_i)`. A ValueError is raised if any of the data is outside of this bound. Notes ----- The algorithm used is described in [1]_. The general idea is that the kd-tree is a binary tree, each of whose nodes represents an axis-aligned hyperrectangle. Each node specifies an axis and splits the set of points based on whether their coordinate along that axis is greater than or less than a particular value. During construction, the axis and splitting point are chosen by the "sliding midpoint" rule, which ensures that the cells do not all become long and thin. The tree can be queried for the r closest neighbors of any given point (optionally returning only those within some maximum distance of the point). It can also be queried, with a substantial gain in efficiency, for the r approximate closest neighbors. For large dimensions (20 is already large) do not expect this to run significantly faster than brute force. High-dimensional nearest-neighbor queries are a substantial open problem in computer science. References ---------- .. [1] S. Maneewongvatana and D.E. Mount, "Analysis of approximate nearest neighbor searching with clustered point sets," Arxiv e-print, 1999, https://arxiv.org/pdf/cs.CG/9901013 Attributes ---------- data : ndarray, shape (n,m) The n data points of dimension m to be indexed. This array is not copied unless this is necessary to produce a contiguous array of doubles. The data are also copied if the kd-tree is built with ``copy_data=True``. leafsize : positive int The number of points at which the algorithm switches over to brute-force. m : int The dimension of a single data-point. n : int The number of data points. maxes : ndarray, shape (m,) The maximum value in each dimension of the n data points. mins : ndarray, shape (m,) The minimum value in each dimension of the n data points. tree : object, class cKDTreeNode This attribute exposes a Python view of the root node in the cKDTree object. A full Python view of the kd-tree is created dynamically on the first access. This attribute allows you to create your own query functions in Python. size : int The number of nodes in the tree. scipy.spatial._ckdtree.cKDTreeNode class cKDTreeNode This class exposes a Python view of a node in the cKDTree object. All attributes are read-only. Attributes ---------- level : int The depth of the node. 0 is the level of the root node. split_dim : int The dimension along which this node is split. If this value is -1 the node is a leafnode in the kd-tree. Leafnodes are not split further and scanned by brute force. split : float The value used to separate split this node. Points with value >= split in the split_dim dimension are sorted to the 'greater' subnode whereas those with value < split are sorted to the 'lesser' subnode. children : int The number of data points sorted to this node. data_points : ndarray of float64 An array with the data points sorted to this node. indices : ndarray of intp An array with the indices of the data points sorted to this node. The indices refer to the position in the data set used to construct the kd-tree. lesser : cKDTreeNode or None Subnode with the 'lesser' data points. This attribute is None for leafnodes. greater : cKDTreeNode or None Subnode with the 'greater' data points. This attribute is None for leafnodes. scipy.spatial._ckdtree.ordered_pairsscipy.spatial._ckdtree.coo_entriescannot create std::vector larger than max_size()Bad stack size. This error should never occur.rect1 and rect2 have different dimensionsEncountering floating point overflow. 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Parameters ---------- other : cKDTree max_distance : positive float p : float, 1<=p<=infinity Which Minkowski p-norm to use. A finite large p may cause a ValueError if overflow can occur. output_type : string, optional Which container to use for output data. Options: 'dok_matrix', 'coo_matrix', 'dict', or 'ndarray'. Default: 'dok_matrix'. Returns ------- result : dok_matrix, coo_matrix, dict or ndarray Sparse matrix representing the results in "dictionary of keys" format. If a dict is returned the keys are (i,j) tuples of indices. If output_type is 'ndarray' a record array with fields 'i', 'j', and 'v' is returned, Examples -------- You can compute a sparse distance matrix between two kd-trees: >>> import numpy as np >>> from scipy.spatial import cKDTree >>> rng = np.random.default_rng() >>> points1 = rng.random((5, 2)) >>> points2 = rng.random((5, 2)) >>> kd_tree1 = cKDTree(points1) >>> kd_tree2 = cKDTree(points2) >>> sdm = kd_tree1.sparse_distance_matrix(kd_tree2, 0.3) >>> sdm.toarray() array([[0. , 0. , 0.12295571, 0. , 0. ], [0. , 0. , 0. , 0. , 0. ], [0.28942611, 0. , 0. , 0.2333084 , 0. ], [0. , 0. , 0. , 0. , 0. ], [0.24617575, 0.29571802, 0.26836782, 0. , 0. ]]) You can check distances above the `max_distance` are zeros: >>> from scipy.spatial import distance_matrix >>> distance_matrix(points1, points2) array([[0.56906522, 0.39923701, 0.12295571, 0.8658745 , 0.79428925], [0.37327919, 0.7225693 , 0.87665969, 0.32580855, 0.75679479], [0.28942611, 0.30088013, 0.6395831 , 0.2333084 , 0.33630734], [0.31994999, 0.72658602, 0.71124834, 0.55396483, 0.90785663], [0.24617575, 0.29571802, 0.26836782, 0.57714465, 0.6473269 ]]) count_neighbors(self, other, r, p=2.0, weights=None, cumulative=True) Count how many nearby pairs can be formed. Count the number of pairs ``(x1,x2)`` can be formed, with ``x1`` drawn from ``self`` and ``x2`` drawn from ``other``, and where ``distance(x1, x2, p) <= r``. Data points on ``self`` and ``other`` are optionally weighted by the ``weights`` argument. (See below) This is adapted from the "two-point correlation" algorithm described by Gray and Moore [1]_. See notes for further discussion. Parameters ---------- other : cKDTree instance The other tree to draw points from, can be the same tree as self. r : float or one-dimensional array of floats The radius to produce a count for. Multiple radii are searched with a single tree traversal. If the count is non-cumulative (``cumulative=False``), ``r`` defines the edges of the bins, and must be non-decreasing. p : float, optional 1<=p<=infinity. Which Minkowski p-norm to use. Default 2.0. A finite large p may cause a ValueError if overflow can occur. weights : tuple, array_like, or None, optional If None, the pair-counting is unweighted. If given as a tuple, weights[0] is the weights of points in ``self``, and weights[1] is the weights of points in ``other``; either can be None to indicate the points are unweighted. If given as an array_like, weights is the weights of points in ``self`` and ``other``. For this to make sense, ``self`` and ``other`` must be the same tree. If ``self`` and ``other`` are two different trees, a ``ValueError`` is raised. Default: None cumulative : bool, optional Whether the returned counts are cumulative. When cumulative is set to ``False`` the algorithm is optimized to work with a large number of bins (>10) specified by ``r``. When ``cumulative`` is set to True, the algorithm is optimized to work with a small number of ``r``. Default: True Returns ------- result : scalar or 1-D array The number of pairs. For unweighted counts, the result is integer. For weighted counts, the result is float. If cumulative is False, ``result[i]`` contains the counts with ``(-inf if i == 0 else r[i-1]) < R <= r[i]`` Notes ----- Pair-counting is the basic operation used to calculate the two point correlation functions from a data set composed of position of objects. Two point correlation function measures the clustering of objects and is widely used in cosmology to quantify the large scale structure in our Universe, but it may be useful for data analysis in other fields where self-similar assembly of objects also occur. The Landy-Szalay estimator for the two point correlation function of ``D`` measures the clustering signal in ``D``. [2]_ For example, given the position of two sets of objects, - objects ``D`` (data) contains the clustering signal, and - objects ``R`` (random) that contains no signal, .. math:: \xi(r) = \frac{ - 2 f + f^2}{f^2}, where the brackets represents counting pairs between two data sets in a finite bin around ``r`` (distance), corresponding to setting ``cumulative=False``, and ``f = float(len(D)) / float(len(R))`` is the ratio between number of objects from data and random. The algorithm implemented here is loosely based on the dual-tree algorithm described in [1]_. We switch between two different pair-cumulation scheme depending on the setting of ``cumulative``. The computing time of the method we use when for ``cumulative == False`` does not scale with the total number of bins. The algorithm for ``cumulative == True`` scales linearly with the number of bins, though it is slightly faster when only 1 or 2 bins are used. [5]_. As an extension to the naive pair-counting, weighted pair-counting counts the product of weights instead of number of pairs. Weighted pair-counting is used to estimate marked correlation functions ([3]_, section 2.2), or to properly calculate the average of data per distance bin (e.g. [4]_, section 2.1 on redshift). .. [1] Gray and Moore, "N-body problems in statistical learning", Mining the sky, 2000, :arxiv:`astro-ph/0012333` .. [2] Landy and Szalay, "Bias and variance of angular correlation functions", The Astrophysical Journal, 1993, :doi:`10.1086/172900` .. [3] Sheth, Connolly and Skibba, "Marked correlations in galaxy formation models", 2005, :arxiv:`astro-ph/0511773` .. [4] Hawkins, et al., "The 2dF Galaxy Redshift Survey: correlation functions, peculiar velocities and the matter density of the Universe", Monthly Notices of the Royal Astronomical Society, 2002, :doi:`10.1046/j.1365-2966.2003.07063.x` .. [5] https://github.com/scipy/scipy/pull/5647#issuecomment-168474926 Examples -------- You can count neighbors number between two kd-trees within a distance: >>> import numpy as np >>> from scipy.spatial import cKDTree >>> rng = np.random.default_rng() >>> points1 = rng.random((5, 2)) >>> points2 = rng.random((5, 2)) >>> kd_tree1 = cKDTree(points1) >>> kd_tree2 = cKDTree(points2) >>> kd_tree1.count_neighbors(kd_tree2, 0.2) 1 This number is same as the total pair number calculated by `query_ball_tree`: >>> indexes = kd_tree1.query_ball_tree(kd_tree2, r=0.2) >>> sum([len(i) for i in indexes]) 1 _build_weights(weights) Compute weights of nodes from weights of data points. This will sum up the total weight per node. This function is used internally. Parameters ---------- weights : array_like weights of data points; must be the same length as the data points. currently only scalar weights are supported. Therefore the weights array must be 1 dimensional. Returns ------- node_weights : array_like total weight for each KD-Tree node. query_pairs(self, r, p=2.0, eps=0.0, output_type='set') Find all pairs of points in `self` whose distance is at most r. Parameters ---------- r : positive float The maximum distance. p : float, optional Which Minkowski norm to use. ``p`` has to meet the condition ``1 <= p <= infinity``. A finite large p may cause a ValueError if overflow can occur. eps : float, optional Approximate search. Branches of the tree are not explored if their nearest points are further than ``r/(1+eps)``, and branches are added in bulk if their furthest points are nearer than ``r * (1+eps)``. `eps` has to be non-negative. output_type : string, optional Choose the output container, 'set' or 'ndarray'. Default: 'set' Returns ------- results : set or ndarray Set of pairs ``(i,j)``, with ``i < j``, for which the corresponding positions are close. If output_type is 'ndarray', an ndarray is returned instead of a set. Examples -------- You can search all pairs of points in a kd-tree within a distance: >>> import matplotlib.pyplot as plt >>> import numpy as np >>> from scipy.spatial import cKDTree >>> rng = np.random.default_rng() >>> points = rng.random((20, 2)) >>> plt.figure(figsize=(6, 6)) >>> plt.plot(points[:, 0], points[:, 1], "xk", markersize=14) >>> kd_tree = cKDTree(points) >>> pairs = kd_tree.query_pairs(r=0.2) >>> for (i, j) in pairs: ... plt.plot([points[i, 0], points[j, 0]], ... [points[i, 1], points[j, 1]], "-r") >>> plt.show() query_ball_tree(self, other, r, p=2.0, eps=0.0) Find all pairs of points between `self` and `other` whose distance is at most r Parameters ---------- other : cKDTree instance The tree containing points to search against. r : float The maximum distance, has to be positive. p : float, optional Which Minkowski norm to use. `p` has to meet the condition ``1 <= p <= infinity``. A finite large p may cause a ValueError if overflow can occur. eps : float, optional Approximate search. Branches of the tree are not explored if their nearest points are further than ``r/(1+eps)``, and branches are added in bulk if their furthest points are nearer than ``r * (1+eps)``. `eps` has to be non-negative. Returns ------- results : list of lists For each element ``self.data[i]`` of this tree, ``results[i]`` is a list of the indices of its neighbors in ``other.data``. Examples -------- You can search all pairs of points between two kd-trees within a distance: >>> import matplotlib.pyplot as plt >>> import numpy as np >>> from scipy.spatial import cKDTree >>> rng = np.random.default_rng() >>> points1 = rng.random((15, 2)) >>> points2 = rng.random((15, 2)) >>> plt.figure(figsize=(6, 6)) >>> plt.plot(points1[:, 0], points1[:, 1], "xk", markersize=14) >>> plt.plot(points2[:, 0], points2[:, 1], "og", markersize=14) >>> kd_tree1 = cKDTree(points1) >>> kd_tree2 = cKDTree(points2) >>> indexes = kd_tree1.query_ball_tree(kd_tree2, r=0.2) >>> for i in range(len(indexes)): ... for j in indexes[i]: ... plt.plot([points1[i, 0], points2[j, 0]], ... [points1[i, 1], points2[j, 1]], "-r") >>> plt.show() query_ball_point(self, x, r, p=2.0, eps=0.0, workers=1, return_sorted=None, return_length=False) Find all points within distance r of point(s) x. Parameters ---------- x : array_like, shape tuple + (self.m,) The point or points to search for neighbors of. r : array_like, float The radius of points to return, shall broadcast to the length of x. p : float, optional Which Minkowski p-norm to use. Should be in the range [1, inf]. A finite large p may cause a ValueError if overflow can occur. eps : nonnegative float, optional Approximate search. Branches of the tree are not explored if their nearest points are further than ``r / (1 + eps)``, and branches are added in bulk if their furthest points are nearer than ``r * (1 + eps)``. workers : int, optional Number of jobs to schedule for parallel processing. If -1 is given all processors are used. Default: 1. .. versionchanged:: 1.9.0 The "n_jobs" argument was renamed "workers". The old name "n_jobs" was deprecated in SciPy 1.6.0 and was removed in SciPy 1.9.0. return_sorted : bool, optional Sorts returned indices if True and does not sort them if False. If None, does not sort single point queries, but does sort multi-point queries which was the behavior before this option was added. .. versionadded:: 1.2.0 return_length: bool, optional Return the number of points inside the radius instead of a list of the indices. .. versionadded:: 1.3.0 Returns ------- results : list or array of lists If `x` is a single point, returns a list of the indices of the neighbors of `x`. If `x` is an array of points, returns an object array of shape tuple containing lists of neighbors. Notes ----- If you have many points whose neighbors you want to find, you may save substantial amounts of time by putting them in a cKDTree and using query_ball_tree. Examples -------- >>> import numpy as np >>> from scipy import spatial >>> x, y = np.mgrid[0:4, 0:4] >>> points = np.c_[x.ravel(), y.ravel()] >>> tree = spatial.cKDTree(points) >>> tree.query_ball_point([2, 0], 1) [4, 8, 9, 12] Query multiple points and plot the results: >>> import matplotlib.pyplot as plt >>> points = np.asarray(points) >>> plt.plot(points[:,0], points[:,1], '.') >>> for results in tree.query_ball_point(([2, 0], [3, 3]), 1): ... nearby_points = points[results] ... plt.plot(nearby_points[:,0], nearby_points[:,1], 'o') >>> plt.margins(0.1, 0.1) >>> plt.show() query(self, x, k=1, eps=0.0, p=2.0, distance_upper_bound=np.inf, workers=1) Query the kd-tree for nearest neighbors Parameters ---------- x : array_like, last dimension self.m An array of points to query. k : list of integer or integer The list of k-th nearest neighbors to return. If k is an integer it is treated as a list of [1, ... k] (range(1, k+1)). Note that the counting starts from 1. eps : non-negative float Return approximate nearest neighbors; the k-th returned value is guaranteed to be no further than (1+eps) times the distance to the real k-th nearest neighbor. p : float, 1<=p<=infinity Which Minkowski p-norm to use. 1 is the sum-of-absolute-values "Manhattan" distance 2 is the usual Euclidean distance infinity is the maximum-coordinate-difference distance A finite large p may cause a ValueError if overflow can occur. distance_upper_bound : nonnegative float Return only neighbors within this distance. This is used to prune tree searches, so if you are doing a series of nearest-neighbor queries, it may help to supply the distance to the nearest neighbor of the most recent point. workers : int, optional Number of workers to use for parallel processing. If -1 is given all CPU threads are used. Default: 1. .. versionchanged:: 1.9.0 The "n_jobs" argument was renamed "workers". The old name "n_jobs" was deprecated in SciPy 1.6.0 and was removed in SciPy 1.9.0. Returns ------- d : array of floats The distances to the nearest neighbors. If ``x`` has shape ``tuple+(self.m,)``, then ``d`` has shape ``tuple+(k,)``. When k == 1, the last dimension of the output is squeezed. Missing neighbors are indicated with infinite distances. i : ndarray of ints The index of each neighbor in ``self.data``. If ``x`` has shape ``tuple+(self.m,)``, then ``i`` has shape ``tuple+(k,)``. When k == 1, the last dimension of the output is squeezed. Missing neighbors are indicated with ``self.n``. Notes ----- If the KD-Tree is periodic, the position ``x`` is wrapped into the box. When the input k is a list, a query for arange(max(k)) is performed, but only columns that store the requested values of k are preserved. This is implemented in a manner that reduces memory usage. Examples -------- >>> import numpy as np >>> from scipy.spatial import cKDTree >>> x, y = np.mgrid[0:5, 2:8] >>> tree = cKDTree(np.c_[x.ravel(), y.ravel()]) To query the nearest neighbours and return squeezed result, use >>> dd, ii = tree.query([[0, 0], [2.2, 2.9]], k=1) >>> print(dd, ii, sep='\n') [2. 0.2236068] [ 0 13] To query the nearest neighbours and return unsqueezed result, use >>> dd, ii = tree.query([[0, 0], [2.2, 2.9]], k=[1]) >>> print(dd, ii, sep='\n') [[2. ] [0.2236068]] [[ 0] [13]] To query the second nearest neighbours and return unsqueezed result, use >>> dd, ii = tree.query([[0, 0], [2.2, 2.9]], k=[2]) >>> print(dd, ii, sep='\n') [[2.23606798] [0.80622577]] [[ 6] [19]] To query the first and second nearest neighbours, use >>> dd, ii = tree.query([[0, 0], [2.2, 2.9]], k=2) >>> print(dd, ii, sep='\n') [[2. 2.23606798] [0.2236068 0.80622577]] [[ 0 6] [13 19]] or, be more specific >>> dd, ii = tree.query([[0, 0], [2.2, 2.9]], k=[1, 2]) >>> print(dd, ii, sep='\n') [[2. 2.23606798] [0.2236068 0.80622577]] [[ 0 6] [13 19]] _data, _indices, children, end_idx, greater, lesser, level, split, split_dim, start_idx@@! ??0C?@?; O0[ f hf hh@~jjak4lmUp@JX+oρބ$"Jx##I4%()0+@/0133M`45f6F F^>8?\C`D0E0@G@II J0QPS0VpXx0YY@\P]]^P_p@___`@``0`Pap@a@dfPPhij@ kp Pm n!`q|!s!v!x8"yp"p|"Ј##0$h$@$X%%&L&P&@'' ' 4((-)6H*?*KX+P+PP+`,0,-0--8.@K.0Sp//йt00d30$45p,345P5X6/6/6`0C0D0161702872X72x7P373747p47485<8P6`8787888P98:9:89@=9F:Ol:W:\(;d;k0<p<Px===D>0>0??`@X@0@`AApB@BB`\CC LDPLEl@FF`GGpH IFDFDHdEII@KIPM`JPVKZK _0L`cLgpMplNpN}4OO0J0JP@PPP0 Q Q`@RR S TT/XU eUВVVWXЯXY0$ZZD[ :[g`Vh\0r]`{]0^|^^l__\`CP\D`zRx $P FJ w?;*3$"D X l       |  x  t 4p Hl \h pt p @\ cYXAV  Dt H H H \ jAG n AI  zPLRx $$YcTAT E Xl lh A} B  A D @x AGG0g GAJ d DAG Z CAJ   EQc D L$ 8,/Q]T@ hL|Ay F g I (yAAD T DAA @AAG  AAB P AAF LAA A J s4 YxBED D(A0_(C ABBHX|BEE B(D0D8DP 8D0A(B BBBB 4zAAD ~ AAJ D DAG 4jADD  CAE I CAK 4@ADD  CAH I CAK L;lNd;lN |8jA C H H )ActXBEE I(K0J8D`hEpIxKODAADAABL`m8D0A(B BBB$4YjAIC ZAA8\ZyBEE G(A0[(C BBBAdN(%ZRBAD GAB4eBDD N ABE AAB4BDD { GBB AAB4LhBDD  GBG AAB4ZBDA _ ABE AB4AD M AE G AH V AA |@BBB B(D0D8GZ 8F0A(B BBBH Z 8G0A(B BBBN  8D0A(B BBBD tp7Aj E }DE G (ADD O AAF (|ADD O AAF  hDo E o$ PhDo E oHD YBNB B(A0A8G 8A0A(B BBB( 9\AAGAA jGD`BD jBHE D(D08I@T8A0W(A BBBT kDBEE L(D0A8B@HIPKHB@m8A0A(B BBB4t pyAN0Z8K@X8A0I DF G DE  AG@ lAAD0 DAB K8\@Y8A0K DAG $ k`AAA ZAAH4 l?BBB B(D0C8D`!8A0A(B BBB@ BAA G@D  AABE  AAB4  AAG@ AAK FAA4 h AAG@ AAK FAA4 @! 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